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0000000000000000000000000000000000000000..0960fe90aa30759ac35af17fb7eb6d956a50ad3d --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/RESULTS.md @@ -0,0 +1,45 @@ +[INFO] /home/stan/Desktop/espnet/egs2/ml_superb/asr1/../../../tools/activate_python.sh is not present + +# RESULTS +## Environments +- date: `Tue Jan 16 22:21:56 CST 2024` +- python version: `3.8.10 (default, Jun 4 2021, 15:09:15) [GCC 7.5.0]` +- espnet version: `espnet 202310` +- pytorch version: `pytorch 1.12.0+cu113` +- Git hash: `aa855dffb81937a097ee03089926a0d5256426e2` + - Commit date: `Tue Jan 16 19:36:29 2024 +0800` + +## test_pr/asr_train_asr_s3prl_houlsby_deu1_10min +### WER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|decode_asr_asr_model_valid.loss.ave/test_10min_deu1|661|8099|11.1|65.4|23.5|4.0|92.9|100.0| + +### CER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|decode_asr_asr_model_valid.loss.ave/test_10min_deu1|661|54401|72.6|10.5|16.9|6.3|33.7|100.0| + +### TER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +## test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave +### WER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|org/dev_10min_deu1|207|1298|13.8|68.3|17.9|6.3|92.5|100.0| + +### CER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|org/dev_10min_deu1|207|9248|76.0|9.2|14.8|6.0|30.0|100.0| + +### TER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/att_ws/swc_deu_001201/encoder.encoders.0.self_attn.10ep.png b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/att_ws/swc_deu_001201/encoder.encoders.0.self_attn.10ep.png new file mode 100644 index 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+multiprocessing_distributed: false +unused_parameters: true +sharded_ddp: false +cudnn_enabled: true +cudnn_benchmark: false +cudnn_deterministic: true +collect_stats: false +write_collected_feats: false +max_epoch: 30 +patience: null +val_scheduler_criterion: +- valid +- loss +early_stopping_criterion: +- valid +- loss +- min +best_model_criterion: +- - valid + - loss + - min +keep_nbest_models: 5 +nbest_averaging_interval: 0 +grad_clip: 5.0 +grad_clip_type: 2.0 +grad_noise: false +accum_grad: 4 +no_forward_run: false +resume: true +train_dtype: float32 +use_amp: false +log_interval: null +use_matplotlib: true +use_tensorboard: true +create_graph_in_tensorboard: false +use_wandb: false +wandb_project: null +wandb_id: null +wandb_entity: null +wandb_name: null +wandb_model_log_interval: -1 +detect_anomaly: false +use_adapter: true +adapter: houlsby +save_adapter_only: true +adapter_conf: + bottleneck: 32 +pretrain_path: null +init_param: [] +ignore_init_mismatch: false +freeze_param: +- frontend.upstream +num_iters_per_epoch: 800 +batch_size: 8 +valid_batch_size: null +batch_bins: 1000000 +valid_batch_bins: null +train_shape_file: +- test_pr/asr_stats_deu1_10min/train/speech_shape +- test_pr/asr_stats_deu1_10min/train/text_shape.char +valid_shape_file: +- test_pr/asr_stats_deu1_10min/valid/speech_shape +- test_pr/asr_stats_deu1_10min/valid/text_shape.char +batch_type: sorted +valid_batch_type: null +fold_length: +- 80000 +- 150 +sort_in_batch: descending +shuffle_within_batch: false +sort_batch: descending +multiple_iterator: false +chunk_length: 500 +chunk_shift_ratio: 0.5 +num_cache_chunks: 1024 +chunk_excluded_key_prefixes: [] +chunk_default_fs: null +train_data_path_and_name_and_type: +- - dump/raw/train_10min_deu1/wav.scp + - speech + - sound +- - dump/raw/train_10min_deu1/text + - text + - text +valid_data_path_and_name_and_type: +- - dump/raw/dev_10min_deu1/wav.scp + - speech + - sound +- - dump/raw/dev_10min_deu1/text + - text + - text +allow_variable_data_keys: false +max_cache_size: 0.0 +max_cache_fd: 32 +allow_multi_rates: false +valid_max_cache_size: null +exclude_weight_decay: false +exclude_weight_decay_conf: {} +optim: adam +optim_conf: + lr: 0.0001 + weight_decay: 1.0e-06 +scheduler: null +scheduler_conf: {} +token_list: +- +- +- E +- +- N +- I +- R +- S +- T +- A +- D +- H +- U +- L +- G +- C +- O +- M +- B +- F +- Z +- W +- K +- P +- V +- Ü +- Ä +- Ö +- J +- Y +- X +- Q +- – +- É +- Ã +- Ū +- Ō +- Š +- Ć +- '4' +- '1' +- '2' +- '3' +- +init: null +input_size: null +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: null + zero_infinity: true + brctc_risk_strategy: exp + brctc_group_strategy: end + brctc_risk_factor: 0.0 +joint_net_conf: null +use_preprocessor: true +use_lang_prompt: false +use_nlp_prompt: false +token_type: char +bpemodel: null +non_linguistic_symbols: null +cleaner: null +g2p: null +speech_volume_normalize: null +rir_scp: null +rir_apply_prob: 1.0 +noise_scp: null +noise_apply_prob: 1.0 +noise_db_range: '13_15' +short_noise_thres: 0.5 +aux_ctc_tasks: [] +frontend: s3prl +frontend_conf: + frontend_conf: + upstream: hubert_base + download_dir: ./hub + multilayer_feature: true + fs: 16k +specaug: specaug +specaug_conf: + apply_time_warp: true + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 27 + num_freq_mask: 2 + apply_time_mask: true + time_mask_width_ratio_range: + - 0.0 + - 0.05 + num_time_mask: 10 +normalize: utterance_mvn +normalize_conf: {} +model: espnet +model_conf: + ctc_weight: 1.0 + extract_feats_in_collect_stats: false +preencoder: linear +preencoder_conf: + input_size: 768 + output_size: 80 +encoder: transformer +encoder_conf: + output_size: 256 + attention_heads: 8 + linear_units: 1024 + num_blocks: 2 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: conv2d2 + normalize_before: true +postencoder: null +postencoder_conf: {} +decoder: null +decoder_conf: {} +preprocessor: default +preprocessor_conf: {} +required: +- output_dir +- token_list +version: '202310' +distributed: false diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.1.log b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.1.log new file mode 100644 index 0000000000000000000000000000000000000000..6a980198bca1e44ce686292fdaa38e433e4a14fe --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.1.log @@ -0,0 +1,591 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1 --config conf/decode_asr.yaml +# Started at Tue Jan 16 22:14:54 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1 --config conf/decode_asr.yaml +2024-01-16 22:14:55,945 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml +2024-01-16 22:14:55,963 (asr:523) INFO: Vocabulary size: 44 +2024-01-16 22:14:56,025 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 22:14:56,025 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 22:14:56,135 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 22:14:57,430 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 22:14:58,695 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 22:14:58,695 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 22:14:58,695 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 22:14:58,728 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-16 22:14:58,804 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 22:14:58,916 (asr_inference:494) INFO: speech length: 62080 +2024-01-16 22:15:00,130 (beam_search:428) INFO: decoder input length: 94 +2024-01-16 22:15:00,130 (beam_search:429) INFO: max output length: 94 +2024-01-16 22:15:00,130 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:00,309 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:00,309 (beam_search:476) INFO: -21.46 * 1.0 = -21.46 for ctc +2024-01-16 22:15:00,309 (beam_search:479) INFO: total log probability: -21.46 +2024-01-16 22:15:00,309 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:15:00,309 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:00,310 (beam_search:483) INFO: best hypo: DRERVELIEBTEUNGEHEARZOGKDERANSCHLEGESEINSFATESNICHBERACHTDITHA + +2024-01-16 22:15:00,334 (asr_inference:494) INFO: speech length: 26240 +2024-01-16 22:15:00,342 (beam_search:428) INFO: decoder input length: 38 +2024-01-16 22:15:00,342 (beam_search:429) INFO: max output length: 38 +2024-01-16 22:15:00,342 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:00,379 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:00,379 (beam_search:476) INFO: -5.41 * 1.0 = -5.41 for ctc +2024-01-16 22:15:00,379 (beam_search:479) INFO: total log probability: -5.41 +2024-01-16 22:15:00,379 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:15:00,379 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:00,379 (beam_search:483) INFO: best hypo: DIEINDEHANZESTEÄTENALS + +2024-01-16 22:15:00,381 (asr_inference:494) INFO: speech length: 22080 +2024-01-16 22:15:00,388 (beam_search:428) INFO: decoder input length: 32 +2024-01-16 22:15:00,388 (beam_search:429) INFO: max output length: 32 +2024-01-16 22:15:00,388 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:00,413 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:00,413 (beam_search:476) INFO: -6.71 * 1.0 = -6.71 for ctc +2024-01-16 22:15:00,413 (beam_search:479) INFO: total log probability: -6.71 +2024-01-16 22:15:00,413 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:15:00,413 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:00,413 (beam_search:483) INFO: best hypo: ARKEINGROSEEFRELK + +2024-01-16 22:15:00,415 (asr_inference:494) INFO: speech length: 29440 +2024-01-16 22:15:00,422 (beam_search:428) INFO: decoder input length: 43 +2024-01-16 22:15:00,422 (beam_search:429) INFO: max output length: 43 +2024-01-16 22:15:00,422 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:00,467 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:00,467 (beam_search:476) INFO: -5.53 * 1.0 = -5.53 for ctc +2024-01-16 22:15:00,467 (beam_search:479) INFO: total log probability: -5.53 +2024-01-16 22:15:00,467 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:00,467 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:00,467 (beam_search:483) INFO: best hypo: GOSENSCHEHMISCHINVERBRIKTE + +2024-01-16 22:15:00,468 (asr_inference:494) INFO: speech length: 45120 +2024-01-16 22:15:00,477 (beam_search:428) INFO: decoder input length: 68 +2024-01-16 22:15:00,477 (beam_search:429) INFO: max output length: 68 +2024-01-16 22:15:00,477 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:00,569 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:00,569 (beam_search:476) INFO: -9.17 * 1.0 = -9.17 for ctc +2024-01-16 22:15:00,569 (beam_search:479) INFO: total log probability: -9.17 +2024-01-16 22:15:00,569 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:00,569 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:00,569 (beam_search:483) INFO: best hypo: WODENACHMEREREALOEUTRUNGSBÜSCHEVERFNTLIH + +2024-01-16 22:15:00,570 (asr_inference:494) INFO: speech length: 35680 +2024-01-16 22:15:00,578 (beam_search:428) INFO: decoder input length: 53 +2024-01-16 22:15:00,578 (beam_search:429) INFO: max output length: 53 +2024-01-16 22:15:00,578 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:00,638 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:00,638 (beam_search:476) INFO: -3.41 * 1.0 = -3.41 for ctc +2024-01-16 22:15:00,638 (beam_search:479) INFO: total log probability: -3.41 +2024-01-16 22:15:00,638 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 22:15:00,638 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:00,638 (beam_search:483) INFO: best hypo: VORBEREITETENBIERTEICGGETUNKT + +2024-01-16 22:15:00,639 (asr_inference:494) INFO: speech length: 32160 +2024-01-16 22:15:00,647 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 22:15:00,647 (beam_search:429) INFO: max output length: 48 +2024-01-16 22:15:00,647 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:00,686 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:00,686 (beam_search:476) INFO: -7.02 * 1.0 = -7.02 for ctc +2024-01-16 22:15:00,686 (beam_search:479) INFO: total log probability: -7.02 +2024-01-16 22:15:00,686 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:15:00,686 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:00,686 (beam_search:483) INFO: best hypo: DOKOMNTESHIESIGINI + +2024-01-16 22:15:00,688 (asr_inference:494) INFO: speech length: 53120 +2024-01-16 22:15:00,696 (beam_search:428) INFO: decoder input length: 80 +2024-01-16 22:15:00,697 (beam_search:429) INFO: max output length: 80 +2024-01-16 22:15:00,697 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:00,794 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:00,794 (beam_search:476) INFO: -10.61 * 1.0 = -10.61 for ctc +2024-01-16 22:15:00,794 (beam_search:479) INFO: total log probability: -10.61 +2024-01-16 22:15:00,794 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:15:00,794 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:00,795 (beam_search:483) INFO: best hypo: TAURTAGVÜRDENTUTVONKNICHVRECHLE + +2024-01-16 22:15:00,796 (asr_inference:494) INFO: speech length: 49280 +2024-01-16 22:15:00,804 (beam_search:428) INFO: decoder input length: 74 +2024-01-16 22:15:00,804 (beam_search:429) INFO: max output length: 74 +2024-01-16 22:15:00,804 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:00,914 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:00,914 (beam_search:476) INFO: -14.00 * 1.0 = -14.00 for ctc +2024-01-16 22:15:00,914 (beam_search:479) INFO: total log probability: -14.00 +2024-01-16 22:15:00,914 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:15:00,915 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:00,915 (beam_search:483) INFO: best hypo: DARUNDERSINMATÜLDEARSENDISWECHTERDSKROLTZIS + +2024-01-16 22:15:00,916 (asr_inference:494) INFO: speech length: 51040 +2024-01-16 22:15:00,924 (beam_search:428) INFO: decoder input length: 77 +2024-01-16 22:15:00,924 (beam_search:429) INFO: max output length: 77 +2024-01-16 22:15:00,924 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:01,053 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:01,053 (beam_search:476) INFO: -14.67 * 1.0 = -14.67 for ctc +2024-01-16 22:15:01,053 (beam_search:479) INFO: total log probability: -14.67 +2024-01-16 22:15:01,053 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:15:01,053 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:01,053 (beam_search:483) INFO: best hypo: ININENSHETENMEHRUNDMHRDEROLEDERTHADTITZSNERLNFIS + +2024-01-16 22:15:01,055 (asr_inference:494) INFO: speech length: 42240 +2024-01-16 22:15:01,063 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 22:15:01,063 (beam_search:429) INFO: max output length: 63 +2024-01-16 22:15:01,063 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:01,118 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:01,118 (beam_search:476) INFO: -6.21 * 1.0 = -6.21 for ctc +2024-01-16 22:15:01,119 (beam_search:479) INFO: total log probability: -6.21 +2024-01-16 22:15:01,119 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:01,119 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:01,119 (beam_search:483) INFO: best hypo: ZUDENENMETLOUVIGKEIT + +2024-01-16 22:15:01,120 (asr_inference:494) INFO: speech length: 56160 +2024-01-16 22:15:01,129 (beam_search:428) INFO: decoder input length: 85 +2024-01-16 22:15:01,129 (beam_search:429) INFO: max output length: 85 +2024-01-16 22:15:01,129 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:01,242 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:01,242 (beam_search:476) INFO: -9.21 * 1.0 = -9.21 for ctc +2024-01-16 22:15:01,242 (beam_search:479) INFO: total log probability: -9.21 +2024-01-16 22:15:01,242 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:15:01,242 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:01,242 (beam_search:483) INFO: best hypo: DRACHEDISHOFESSUNDESADETZFÜRNFRFEL + +2024-01-16 22:15:01,243 (asr_inference:494) INFO: speech length: 21120 +2024-01-16 22:15:01,250 (beam_search:428) INFO: decoder input length: 30 +2024-01-16 22:15:01,250 (beam_search:429) INFO: max output length: 30 +2024-01-16 22:15:01,250 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:01,278 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:01,278 (beam_search:476) INFO: -6.09 * 1.0 = -6.09 for ctc +2024-01-16 22:15:01,278 (beam_search:479) INFO: total log probability: -6.09 +2024-01-16 22:15:01,278 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:01,278 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:01,278 (beam_search:483) INFO: best hypo: ZSEITANGABEMVERSICHTID + +2024-01-16 22:15:01,279 (asr_inference:494) INFO: speech length: 70080 +2024-01-16 22:15:01,289 (beam_search:428) INFO: decoder input length: 107 +2024-01-16 22:15:01,289 (beam_search:429) INFO: max output length: 107 +2024-01-16 22:15:01,289 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:01,466 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:01,466 (beam_search:476) INFO: -15.17 * 1.0 = -15.17 for ctc +2024-01-16 22:15:01,466 (beam_search:479) INFO: total log probability: -15.17 +2024-01-16 22:15:01,466 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:15:01,466 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:01,467 (beam_search:483) INFO: best hypo: ALLSACHTINUNDETACHRTZIGHMITODTUOBRAMSAUFSATZS + +2024-01-16 22:15:01,468 (asr_inference:494) INFO: speech length: 48960 +2024-01-16 22:15:01,476 (beam_search:428) INFO: decoder input length: 74 +2024-01-16 22:15:01,476 (beam_search:429) INFO: max output length: 74 +2024-01-16 22:15:01,476 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:01,559 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:01,559 (beam_search:476) INFO: -11.76 * 1.0 = -11.76 for ctc +2024-01-16 22:15:01,559 (beam_search:479) INFO: total log probability: -11.76 +2024-01-16 22:15:01,559 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 22:15:01,559 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:01,559 (beam_search:483) INFO: best hypo: MÜLENWESENIETZINUNERDACHUNWANI + +2024-01-16 22:15:01,560 (asr_inference:494) INFO: speech length: 17280 +2024-01-16 22:15:01,567 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 22:15:01,567 (beam_search:429) INFO: max output length: 24 +2024-01-16 22:15:01,567 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:01,584 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:01,584 (beam_search:476) INFO: -3.86 * 1.0 = -3.86 for ctc +2024-01-16 22:15:01,584 (beam_search:479) INFO: total log probability: -3.86 +2024-01-16 22:15:01,584 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:15:01,584 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:01,584 (beam_search:483) INFO: best hypo: ASDEFISCHRIS + +2024-01-16 22:15:01,585 (asr_inference:494) INFO: speech length: 93920 +2024-01-16 22:15:01,596 (beam_search:428) INFO: decoder input length: 144 +2024-01-16 22:15:01,596 (beam_search:429) INFO: max output length: 144 +2024-01-16 22:15:01,596 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:01,961 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:01,961 (beam_search:476) INFO: -21.77 * 1.0 = -21.77 for ctc +2024-01-16 22:15:01,961 (beam_search:479) INFO: total log probability: -21.77 +2024-01-16 22:15:01,961 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:15:01,961 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:01,962 (beam_search:483) INFO: best hypo: SIDIMABSHLOSIMJARENEUNZENHNDERZWALUNACHTZICHUNDRNAMEREINEERSTELENGEREREISENACSPBANEN + +2024-01-16 22:15:01,963 (asr_inference:494) INFO: speech length: 35040 +2024-01-16 22:15:01,971 (beam_search:428) INFO: decoder input length: 52 +2024-01-16 22:15:01,971 (beam_search:429) INFO: max output length: 52 +2024-01-16 22:15:01,971 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:02,021 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:02,021 (beam_search:476) INFO: -9.19 * 1.0 = -9.19 for ctc +2024-01-16 22:15:02,021 (beam_search:479) INFO: total log probability: -9.19 +2024-01-16 22:15:02,021 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:15:02,021 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:02,021 (beam_search:483) INFO: best hypo: VERNSCATSOVURGETZEICHNET + +2024-01-16 22:15:02,022 (asr_inference:494) INFO: speech length: 68640 +2024-01-16 22:15:02,032 (beam_search:428) INFO: decoder input length: 105 +2024-01-16 22:15:02,032 (beam_search:429) INFO: max output length: 105 +2024-01-16 22:15:02,032 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:02,248 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:02,248 (beam_search:476) INFO: -14.17 * 1.0 = -14.17 for ctc +2024-01-16 22:15:02,248 (beam_search:479) INFO: total log probability: -14.17 +2024-01-16 22:15:02,248 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:02,248 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:02,248 (beam_search:483) INFO: best hypo: FEITENSTEINSVELSTENDIGGECHICHTENUNDTDIEAUKSBURGERSTAGSCHIHEDESELTRE + +2024-01-16 22:15:02,249 (asr_inference:494) INFO: speech length: 58240 +2024-01-16 22:15:02,258 (beam_search:428) INFO: decoder input length: 88 +2024-01-16 22:15:02,258 (beam_search:429) INFO: max output length: 88 +2024-01-16 22:15:02,258 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:02,391 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:02,391 (beam_search:476) INFO: -9.66 * 1.0 = -9.66 for ctc +2024-01-16 22:15:02,391 (beam_search:479) INFO: total log probability: -9.66 +2024-01-16 22:15:02,391 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:15:02,391 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:02,391 (beam_search:483) INFO: best hypo: NACHDIENZERSTÖRONMENWURDEDERASCHWIDERAUFPLÜN + +2024-01-16 22:15:02,392 (asr_inference:494) INFO: speech length: 117280 +2024-01-16 22:15:02,405 (beam_search:428) INFO: decoder input length: 181 +2024-01-16 22:15:02,405 (beam_search:429) INFO: max output length: 181 +2024-01-16 22:15:02,405 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:02,887 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:02,887 (beam_search:476) INFO: -15.33 * 1.0 = -15.33 for ctc +2024-01-16 22:15:02,887 (beam_search:479) INFO: total log probability: -15.33 +2024-01-16 22:15:02,887 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:15:02,888 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:02,888 (beam_search:483) INFO: best hypo: ACHTENEINFLUSREICHENHANIEATENBEIMKOMIESARESCHEINGESETZNBÜÖRGEMEISTERMAKERTIERAUFAHTUN + +2024-01-16 22:15:02,889 (asr_inference:494) INFO: speech length: 28480 +2024-01-16 22:15:02,897 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 22:15:02,897 (beam_search:429) INFO: max output length: 42 +2024-01-16 22:15:02,897 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:02,938 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:02,938 (beam_search:476) INFO: -5.76 * 1.0 = -5.76 for ctc +2024-01-16 22:15:02,938 (beam_search:479) INFO: total log probability: -5.76 +2024-01-16 22:15:02,938 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:02,938 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:02,938 (beam_search:483) INFO: best hypo: AILSENTRALDESHANDESKONTOR + +2024-01-16 22:15:02,939 (asr_inference:494) INFO: speech length: 38080 +2024-01-16 22:15:02,947 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 22:15:02,947 (beam_search:429) INFO: max output length: 57 +2024-01-16 22:15:02,947 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:03,014 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:03,015 (beam_search:476) INFO: -6.83 * 1.0 = -6.83 for ctc +2024-01-16 22:15:03,015 (beam_search:479) INFO: total log probability: -6.83 +2024-01-16 22:15:03,015 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:03,015 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:03,015 (beam_search:483) INFO: best hypo: SANDERSTELUNGINERHEIBDECSTATKREFE + +2024-01-16 22:15:03,016 (asr_inference:494) INFO: speech length: 29440 +2024-01-16 22:15:03,023 (beam_search:428) INFO: decoder input length: 43 +2024-01-16 22:15:03,023 (beam_search:429) INFO: max output length: 43 +2024-01-16 22:15:03,023 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:03,065 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:03,065 (beam_search:476) INFO: -5.31 * 1.0 = -5.31 for ctc +2024-01-16 22:15:03,065 (beam_search:479) INFO: total log probability: -5.31 +2024-01-16 22:15:03,065 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:03,065 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:03,065 (beam_search:483) INFO: best hypo: VFINEZSICHINHALOBAKTERIEN + +2024-01-16 22:15:03,066 (asr_inference:494) INFO: speech length: 67360 +2024-01-16 22:15:03,076 (beam_search:428) INFO: decoder input length: 103 +2024-01-16 22:15:03,076 (beam_search:429) INFO: max output length: 103 +2024-01-16 22:15:03,076 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:03,253 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:03,253 (beam_search:476) INFO: -10.72 * 1.0 = -10.72 for ctc +2024-01-16 22:15:03,254 (beam_search:479) INFO: total log probability: -10.72 +2024-01-16 22:15:03,254 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:03,254 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:03,254 (beam_search:483) INFO: best hypo: AUFDERBESEITEFINDEZSIHTASEBEMFALSVONMEIKELKOMPUNIET + +2024-01-16 22:15:03,255 (asr_inference:494) INFO: speech length: 81760 +2024-01-16 22:15:03,265 (beam_search:428) INFO: decoder input length: 125 +2024-01-16 22:15:03,265 (beam_search:429) INFO: max output length: 125 +2024-01-16 22:15:03,265 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:03,539 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:03,539 (beam_search:476) INFO: -18.18 * 1.0 = -18.18 for ctc +2024-01-16 22:15:03,539 (beam_search:479) INFO: total log probability: -18.18 +2024-01-16 22:15:03,539 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:15:03,539 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:03,539 (beam_search:483) INFO: best hypo: INHNDEATICHERZEITHATDIDIZERKEGESERSCHFTKEINENAUSCHLAGEBENTENEINFLOSMHR + +2024-01-16 22:15:03,541 (asr_inference:494) INFO: speech length: 107360 +2024-01-16 22:15:03,553 (beam_search:428) INFO: decoder input length: 165 +2024-01-16 22:15:03,553 (beam_search:429) INFO: max output length: 165 +2024-01-16 22:15:03,553 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:03,977 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:03,977 (beam_search:476) INFO: -14.35 * 1.0 = -14.35 for ctc +2024-01-16 22:15:03,977 (beam_search:479) INFO: total log probability: -14.35 +2024-01-16 22:15:03,977 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 22:15:03,977 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:03,978 (beam_search:483) INFO: best hypo: DASTDERCHVRWENDUNVONAUFTRIEBSKAPANODERHALZSEINERGERINGEREMITTERERDICHTDEALSWASERHAT + +2024-01-16 22:15:03,979 (asr_inference:494) INFO: speech length: 19520 +2024-01-16 22:15:03,986 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 22:15:03,986 (beam_search:429) INFO: max output length: 28 +2024-01-16 22:15:03,986 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:04,005 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:04,005 (beam_search:476) INFO: -3.55 * 1.0 = -3.55 for ctc +2024-01-16 22:15:04,005 (beam_search:479) INFO: total log probability: -3.55 +2024-01-16 22:15:04,005 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:15:04,005 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:04,005 (beam_search:483) INFO: best hypo: DAMATESIERUNGEN + +2024-01-16 22:15:04,006 (asr_inference:494) INFO: speech length: 24480 +2024-01-16 22:15:04,013 (beam_search:428) INFO: decoder input length: 36 +2024-01-16 22:15:04,013 (beam_search:429) INFO: max output length: 36 +2024-01-16 22:15:04,013 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:04,041 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:04,041 (beam_search:476) INFO: -4.58 * 1.0 = -4.58 for ctc +2024-01-16 22:15:04,041 (beam_search:479) INFO: total log probability: -4.58 +2024-01-16 22:15:04,041 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:15:04,041 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:04,041 (beam_search:483) INFO: best hypo: UMNSIEBENURFÜNVO + +2024-01-16 22:15:04,042 (asr_inference:494) INFO: speech length: 33120 +2024-01-16 22:15:04,050 (beam_search:428) INFO: decoder input length: 49 +2024-01-16 22:15:04,050 (beam_search:429) INFO: max output length: 49 +2024-01-16 22:15:04,050 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:04,101 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:04,101 (beam_search:476) INFO: -5.17 * 1.0 = -5.17 for ctc +2024-01-16 22:15:04,101 (beam_search:479) INFO: total log probability: -5.17 +2024-01-16 22:15:04,101 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:15:04,101 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:04,101 (beam_search:483) INFO: best hypo: DESELBREICHTDIEBADESTOCHTE + +2024-01-16 22:15:04,102 (asr_inference:494) INFO: speech length: 33440 +2024-01-16 22:15:04,110 (beam_search:428) INFO: decoder input length: 50 +2024-01-16 22:15:04,110 (beam_search:429) INFO: max output length: 50 +2024-01-16 22:15:04,110 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:04,157 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:04,157 (beam_search:476) INFO: -6.64 * 1.0 = -6.64 for ctc +2024-01-16 22:15:04,157 (beam_search:479) INFO: total log probability: -6.64 +2024-01-16 22:15:04,157 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:04,157 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:04,157 (beam_search:483) INFO: best hypo: TARTBABMBARERSCHESTDATZR + +2024-01-16 22:15:04,158 (asr_inference:494) INFO: speech length: 23840 +2024-01-16 22:15:04,165 (beam_search:428) INFO: decoder input length: 35 +2024-01-16 22:15:04,165 (beam_search:429) INFO: max output length: 35 +2024-01-16 22:15:04,165 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:04,195 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:04,196 (beam_search:476) INFO: -5.45 * 1.0 = -5.45 for ctc +2024-01-16 22:15:04,196 (beam_search:479) INFO: total log probability: -5.45 +2024-01-16 22:15:04,196 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:15:04,196 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:04,196 (beam_search:483) INFO: best hypo: ERSLITBESONDERSLIEBTE + +2024-01-16 22:15:04,197 (asr_inference:494) INFO: speech length: 90240 +2024-01-16 22:15:04,207 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 22:15:04,207 (beam_search:429) INFO: max output length: 138 +2024-01-16 22:15:04,207 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:04,542 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:04,542 (beam_search:476) INFO: -16.86 * 1.0 = -16.86 for ctc +2024-01-16 22:15:04,542 (beam_search:479) INFO: total log probability: -16.86 +2024-01-16 22:15:04,542 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:15:04,542 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:04,542 (beam_search:483) INFO: best hypo: AUFKUNDDERSWACSENDENPUPLIKUMSINTRESSESWURDEDERAUFTRITSORTÜDIPRIMAVISTALESUNGE + +2024-01-16 22:15:04,543 (asr_inference:494) INFO: speech length: 21120 +2024-01-16 22:15:04,550 (beam_search:428) INFO: decoder input length: 30 +2024-01-16 22:15:04,550 (beam_search:429) INFO: max output length: 30 +2024-01-16 22:15:04,550 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:04,572 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:04,572 (beam_search:476) INFO: -4.56 * 1.0 = -4.56 for ctc +2024-01-16 22:15:04,572 (beam_search:479) INFO: total log probability: -4.56 +2024-01-16 22:15:04,572 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:15:04,572 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:04,572 (beam_search:483) INFO: best hypo: NDFREITECHTSBIE + +2024-01-16 22:15:04,573 (asr_inference:494) INFO: speech length: 55840 +2024-01-16 22:15:04,582 (beam_search:428) INFO: decoder input length: 85 +2024-01-16 22:15:04,582 (beam_search:429) INFO: max output length: 85 +2024-01-16 22:15:04,582 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:04,723 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:04,723 (beam_search:476) INFO: -15.17 * 1.0 = -15.17 for ctc +2024-01-16 22:15:04,723 (beam_search:479) INFO: total log probability: -15.17 +2024-01-16 22:15:04,724 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:15:04,724 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:04,724 (beam_search:483) INFO: best hypo: DASDIEREIDENSTÖRZSRLTIUNBESCHARTETBERSTANDTENHATE + +2024-01-16 22:15:04,725 (asr_inference:494) INFO: speech length: 29440 +2024-01-16 22:15:04,732 (beam_search:428) INFO: decoder input length: 43 +2024-01-16 22:15:04,732 (beam_search:429) INFO: max output length: 43 +2024-01-16 22:15:04,732 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:04,771 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:04,771 (beam_search:476) INFO: -6.97 * 1.0 = -6.97 for ctc +2024-01-16 22:15:04,771 (beam_search:479) INFO: total log probability: -6.97 +2024-01-16 22:15:04,771 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:15:04,771 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:04,771 (beam_search:483) INFO: best hypo: EARENERSCHNENZWEIIMRL + +2024-01-16 22:15:04,772 (asr_inference:494) INFO: speech length: 70080 +2024-01-16 22:15:04,782 (beam_search:428) INFO: decoder input length: 107 +2024-01-16 22:15:04,782 (beam_search:429) INFO: max output length: 107 +2024-01-16 22:15:04,782 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:04,948 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:04,948 (beam_search:476) INFO: -14.33 * 1.0 = -14.33 for ctc +2024-01-16 22:15:04,948 (beam_search:479) INFO: total log probability: -14.33 +2024-01-16 22:15:04,948 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:15:04,948 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:04,948 (beam_search:483) INFO: best hypo: DERGRABMANUNDGRABGAPELENODEROLTATENNACHALT + +2024-01-16 22:15:04,949 (asr_inference:494) INFO: speech length: 53440 +2024-01-16 22:15:04,958 (beam_search:428) INFO: decoder input length: 81 +2024-01-16 22:15:04,958 (beam_search:429) INFO: max output length: 81 +2024-01-16 22:15:04,958 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:05,092 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:05,093 (beam_search:476) INFO: -22.66 * 1.0 = -22.66 for ctc +2024-01-16 22:15:05,093 (beam_search:479) INFO: total log probability: -22.66 +2024-01-16 22:15:05,093 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-16 22:15:05,093 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:05,093 (beam_search:483) INFO: best hypo: IUNENENZENHUNDARSEXSUNEUINZIHKNDIKDERRSEINEBEITENSOBPS + +2024-01-16 22:15:05,094 (asr_inference:494) INFO: speech length: 20160 +2024-01-16 22:15:05,101 (beam_search:428) INFO: decoder input length: 29 +2024-01-16 22:15:05,101 (beam_search:429) INFO: max output length: 29 +2024-01-16 22:15:05,101 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:05,124 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:05,124 (beam_search:476) INFO: -5.12 * 1.0 = -5.12 for ctc +2024-01-16 22:15:05,124 (beam_search:479) INFO: total log probability: -5.12 +2024-01-16 22:15:05,124 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:15:05,124 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:05,124 (beam_search:483) INFO: best hypo: INGEPOTIENEKOPEL + +2024-01-16 22:15:05,125 (asr_inference:494) INFO: speech length: 58880 +2024-01-16 22:15:05,134 (beam_search:428) INFO: decoder input length: 89 +2024-01-16 22:15:05,134 (beam_search:429) INFO: max output length: 89 +2024-01-16 22:15:05,134 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:05,272 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:05,272 (beam_search:476) INFO: -10.70 * 1.0 = -10.70 for ctc +2024-01-16 22:15:05,272 (beam_search:479) INFO: total log probability: -10.70 +2024-01-16 22:15:05,272 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:05,272 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:05,273 (beam_search:483) INFO: best hypo: NEUNUNSECHTZIGDERMEDIERKONTWOLALBUMSCHATZEIN + +2024-01-16 22:15:05,274 (asr_inference:494) INFO: speech length: 21440 +2024-01-16 22:15:05,281 (beam_search:428) INFO: decoder input length: 31 +2024-01-16 22:15:05,281 (beam_search:429) INFO: max output length: 31 +2024-01-16 22:15:05,281 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:05,298 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:05,299 (beam_search:476) INFO: -3.27 * 1.0 = -3.27 for ctc +2024-01-16 22:15:05,299 (beam_search:479) INFO: total log probability: -3.27 +2024-01-16 22:15:05,299 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:15:05,299 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:05,299 (beam_search:483) INFO: best hypo: MTARUSCKOM + +2024-01-16 22:15:05,300 (asr_inference:494) INFO: speech length: 72800 +2024-01-16 22:15:05,309 (beam_search:428) INFO: decoder input length: 111 +2024-01-16 22:15:05,309 (beam_search:429) INFO: max output length: 111 +2024-01-16 22:15:05,310 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:05,533 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:05,533 (beam_search:476) INFO: -15.69 * 1.0 = -15.69 for ctc +2024-01-16 22:15:05,533 (beam_search:479) INFO: total log probability: -15.69 +2024-01-16 22:15:05,533 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:15:05,533 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:05,534 (beam_search:483) INFO: best hypo: UONEHENICHTDENKROSSHANDEKAUFLEUTENGESERSHAFTLICGKLEICHGESTERTWARE + +2024-01-16 22:15:05,535 (asr_inference:494) INFO: speech length: 25600 +2024-01-16 22:15:05,542 (beam_search:428) INFO: decoder input length: 37 +2024-01-16 22:15:05,542 (beam_search:429) INFO: max output length: 37 +2024-01-16 22:15:05,542 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:05,577 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:05,577 (beam_search:476) INFO: -5.98 * 1.0 = -5.98 for ctc +2024-01-16 22:15:05,577 (beam_search:479) INFO: total log probability: -5.98 +2024-01-16 22:15:05,577 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:15:05,577 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:05,577 (beam_search:483) INFO: best hypo: VRNDERNARUNGUNDVOMKIEMA + +2024-01-16 22:15:05,579 (asr_inference:494) INFO: speech length: 18400 +2024-01-16 22:15:05,585 (beam_search:428) INFO: decoder input length: 26 +2024-01-16 22:15:05,585 (beam_search:429) INFO: max output length: 26 +2024-01-16 22:15:05,585 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:05,601 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:05,602 (beam_search:476) INFO: -3.18 * 1.0 = -3.18 for ctc +2024-01-16 22:15:05,602 (beam_search:479) INFO: total log probability: -3.18 +2024-01-16 22:15:05,602 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:15:05,602 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:05,602 (beam_search:483) INFO: best hypo: APRLOEEIENZS + +2024-01-16 22:15:05,603 (asr_inference:494) INFO: speech length: 28480 +2024-01-16 22:15:05,610 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 22:15:05,610 (beam_search:429) INFO: max output length: 42 +2024-01-16 22:15:05,610 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:05,648 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:05,648 (beam_search:476) INFO: -7.84 * 1.0 = -7.84 for ctc +2024-01-16 22:15:05,648 (beam_search:479) INFO: total log probability: -7.84 +2024-01-16 22:15:05,648 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:15:05,648 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:05,648 (beam_search:483) INFO: best hypo: BRÜÖÜLUNDHÖERTNACKELE + +2024-01-16 22:15:05,649 (asr_inference:494) INFO: speech length: 20000 +2024-01-16 22:15:05,655 (beam_search:428) INFO: decoder input length: 29 +2024-01-16 22:15:05,655 (beam_search:429) INFO: max output length: 29 +2024-01-16 22:15:05,655 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:05,677 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:05,677 (beam_search:476) INFO: -2.62 * 1.0 = -2.62 for ctc +2024-01-16 22:15:05,677 (beam_search:479) INFO: total log probability: -2.62 +2024-01-16 22:15:05,677 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 22:15:05,677 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:05,677 (beam_search:483) INFO: best hypo: TWERINENKLOSTE + +2024-01-16 22:15:05,678 (asr_inference:494) INFO: speech length: 132480 +2024-01-16 22:15:05,692 (beam_search:428) INFO: decoder input length: 204 +2024-01-16 22:15:05,692 (beam_search:429) INFO: max output length: 204 +2024-01-16 22:15:05,692 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:06,363 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:06,363 (beam_search:476) INFO: -29.51 * 1.0 = -29.51 for ctc +2024-01-16 22:15:06,363 (beam_search:479) INFO: total log probability: -29.51 +2024-01-16 22:15:06,363 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:15:06,363 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:06,364 (beam_search:483) INFO: best hypo: DIEVIRZSMAUFIZEHRENKANEWALENSTANTUNTREUTEINEMISCHNGUSKÖRSCHENKANDEWALUNDBPLIDSCHEKABRETMTKOMDELEMENTENDASTELTU + +2024-01-16 22:15:06,365 (asr_inference:494) INFO: speech length: 25280 +2024-01-16 22:15:06,372 (beam_search:428) INFO: decoder input length: 37 +2024-01-16 22:15:06,372 (beam_search:429) INFO: max output length: 37 +2024-01-16 22:15:06,372 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:06,411 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:06,411 (beam_search:476) INFO: -9.98 * 1.0 = -9.98 for ctc +2024-01-16 22:15:06,411 (beam_search:479) INFO: total log probability: -9.98 +2024-01-16 22:15:06,411 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:15:06,411 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:06,411 (beam_search:483) INFO: best hypo: DIEWUNSTIENRESLEDESFÜIRTE + +2024-01-16 22:15:06,412 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 22:15:06,420 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 22:15:06,421 (beam_search:429) INFO: max output length: 69 +2024-01-16 22:15:06,421 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:06,505 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:06,505 (beam_search:476) INFO: -7.85 * 1.0 = -7.85 for ctc +2024-01-16 22:15:06,505 (beam_search:479) INFO: total log probability: -7.85 +2024-01-16 22:15:06,505 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:06,505 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:06,505 (beam_search:483) INFO: best hypo: NANTITZIEGLERDIEARMORDUMDERBANAURN + +2024-01-16 22:15:06,506 (asr_inference:494) INFO: speech length: 20480 +2024-01-16 22:15:06,513 (beam_search:428) INFO: decoder input length: 29 +2024-01-16 22:15:06,513 (beam_search:429) INFO: max output length: 29 +2024-01-16 22:15:06,513 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:06,534 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:06,534 (beam_search:476) INFO: -3.92 * 1.0 = -3.92 for ctc +2024-01-16 22:15:06,534 (beam_search:479) INFO: total log probability: -3.92 +2024-01-16 22:15:06,534 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:15:06,534 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:06,535 (beam_search:483) INFO: best hypo: INTEROHRISTVOEL + +2024-01-16 22:15:06,536 (asr_inference:494) INFO: speech length: 157120 +2024-01-16 22:15:06,551 (beam_search:428) INFO: decoder input length: 243 +2024-01-16 22:15:06,551 (beam_search:429) INFO: max output length: 243 +2024-01-16 22:15:06,551 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:07,418 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:07,418 (beam_search:476) INFO: -24.17 * 1.0 = -24.17 for ctc +2024-01-16 22:15:07,418 (beam_search:479) INFO: total log probability: -24.17 +2024-01-16 22:15:07,418 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:07,418 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:07,419 (beam_search:483) INFO: best hypo: DIESTRENGDERVORGENGERLEITUNGWURDENZWISHEEUNZHNHUNDERTNEUNUENDZWANZIGUNDNEUNZHNHUNDERDREINDFÜNFZIGARCHELOGESCHERKRABEN + +2024-01-16 22:15:07,420 (asr_inference:494) INFO: speech length: 19840 +2024-01-16 22:15:07,427 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 22:15:07,427 (beam_search:429) INFO: max output length: 28 +2024-01-16 22:15:07,427 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:07,443 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:07,443 (beam_search:476) INFO: -3.05 * 1.0 = -3.05 for ctc +2024-01-16 22:15:07,443 (beam_search:479) INFO: total log probability: -3.05 +2024-01-16 22:15:07,443 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:15:07,443 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:07,443 (beam_search:483) INFO: best hypo: INGEGESAT + +# Accounting: time=14 threads=1 +# Ended (code 0) at Tue Jan 16 22:15:08 CST 2024, elapsed time 14 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.2.log b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.2.log new file mode 100644 index 0000000000000000000000000000000000000000..0c0f1ae7c6b1a92bf206bd6006cc36e2ec997684 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.2.log @@ -0,0 +1,591 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2 --config conf/decode_asr.yaml +# Started at Tue Jan 16 22:15:08 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2 --config conf/decode_asr.yaml +2024-01-16 22:15:09,263 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml +2024-01-16 22:15:09,281 (asr:523) INFO: Vocabulary size: 44 +2024-01-16 22:15:09,343 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 22:15:09,343 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 22:15:09,453 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 22:15:10,741 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 22:15:11,975 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 22:15:11,975 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 22:15:11,975 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 22:15:12,008 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-16 22:15:12,083 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 22:15:12,194 (asr_inference:494) INFO: speech length: 30720 +2024-01-16 22:15:13,393 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 22:15:13,393 (beam_search:429) INFO: max output length: 45 +2024-01-16 22:15:13,393 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:13,442 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:13,443 (beam_search:476) INFO: -7.22 * 1.0 = -7.22 for ctc +2024-01-16 22:15:13,443 (beam_search:479) INFO: total log probability: -7.22 +2024-01-16 22:15:13,443 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:15:13,443 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:13,443 (beam_search:483) INFO: best hypo: FABEVERNERDIGENSENLAUNDHORT + +2024-01-16 22:15:13,467 (asr_inference:494) INFO: speech length: 24000 +2024-01-16 22:15:13,475 (beam_search:428) INFO: decoder input length: 35 +2024-01-16 22:15:13,475 (beam_search:429) INFO: max output length: 35 +2024-01-16 22:15:13,475 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:13,503 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:13,503 (beam_search:476) INFO: -2.52 * 1.0 = -2.52 for ctc +2024-01-16 22:15:13,503 (beam_search:479) INFO: total log probability: -2.52 +2024-01-16 22:15:13,503 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 22:15:13,503 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:13,503 (beam_search:483) INFO: best hypo: LEIFVERANSTALTUMEN + +2024-01-16 22:15:13,504 (asr_inference:494) INFO: speech length: 44800 +2024-01-16 22:15:13,513 (beam_search:428) INFO: decoder input length: 67 +2024-01-16 22:15:13,513 (beam_search:429) INFO: max output length: 67 +2024-01-16 22:15:13,513 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:13,608 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:13,608 (beam_search:476) INFO: -6.66 * 1.0 = -6.66 for ctc +2024-01-16 22:15:13,608 (beam_search:479) INFO: total log probability: -6.66 +2024-01-16 22:15:13,608 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 22:15:13,608 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:13,608 (beam_search:483) INFO: best hypo: ZOWERENHUTEINDEREGELALLEDORTLEBENDENBRAUN + +2024-01-16 22:15:13,609 (asr_inference:494) INFO: speech length: 19520 +2024-01-16 22:15:13,617 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 22:15:13,617 (beam_search:429) INFO: max output length: 28 +2024-01-16 22:15:13,617 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:13,642 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:13,642 (beam_search:476) INFO: -6.95 * 1.0 = -6.95 for ctc +2024-01-16 22:15:13,642 (beam_search:479) INFO: total log probability: -6.95 +2024-01-16 22:15:13,642 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:15:13,642 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:13,642 (beam_search:483) INFO: best hypo: IEDAFÜREUSCSELMER + +2024-01-16 22:15:13,643 (asr_inference:494) INFO: speech length: 21760 +2024-01-16 22:15:13,651 (beam_search:428) INFO: decoder input length: 31 +2024-01-16 22:15:13,651 (beam_search:429) INFO: max output length: 31 +2024-01-16 22:15:13,651 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:13,677 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:13,678 (beam_search:476) INFO: -6.35 * 1.0 = -6.35 for ctc +2024-01-16 22:15:13,678 (beam_search:479) INFO: total log probability: -6.35 +2024-01-16 22:15:13,678 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:15:13,678 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:13,678 (beam_search:483) INFO: best hypo: DESHANDIEATENFÜÖRE + +2024-01-16 22:15:13,679 (asr_inference:494) INFO: speech length: 24160 +2024-01-16 22:15:13,686 (beam_search:428) INFO: decoder input length: 35 +2024-01-16 22:15:13,686 (beam_search:429) INFO: max output length: 35 +2024-01-16 22:15:13,686 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:13,715 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:13,715 (beam_search:476) INFO: -7.18 * 1.0 = -7.18 for ctc +2024-01-16 22:15:13,715 (beam_search:479) INFO: total log probability: -7.18 +2024-01-16 22:15:13,715 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:15:13,715 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:13,715 (beam_search:483) INFO: best hypo: HEBELTSARGNESBANAU + +2024-01-16 22:15:13,716 (asr_inference:494) INFO: speech length: 23360 +2024-01-16 22:15:13,723 (beam_search:428) INFO: decoder input length: 34 +2024-01-16 22:15:13,723 (beam_search:429) INFO: max output length: 34 +2024-01-16 22:15:13,723 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:13,752 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:13,752 (beam_search:476) INFO: -4.16 * 1.0 = -4.16 for ctc +2024-01-16 22:15:13,752 (beam_search:479) INFO: total log probability: -4.16 +2024-01-16 22:15:13,752 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:13,752 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:13,752 (beam_search:483) INFO: best hypo: LIEBENSWEISEVERKRBPR + +2024-01-16 22:15:13,753 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 22:15:13,761 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 22:15:13,761 (beam_search:429) INFO: max output length: 69 +2024-01-16 22:15:13,761 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:13,860 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:13,860 (beam_search:476) INFO: -12.51 * 1.0 = -12.51 for ctc +2024-01-16 22:15:13,860 (beam_search:479) INFO: total log probability: -12.51 +2024-01-16 22:15:13,860 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:15:13,860 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:13,860 (beam_search:483) INFO: best hypo: EDEFEILDESFIERENHAMBURGANZUKATORLESVRAMMEN + +2024-01-16 22:15:13,861 (asr_inference:494) INFO: speech length: 35360 +2024-01-16 22:15:13,869 (beam_search:428) INFO: decoder input length: 53 +2024-01-16 22:15:13,869 (beam_search:429) INFO: max output length: 53 +2024-01-16 22:15:13,869 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:13,924 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:13,924 (beam_search:476) INFO: -6.84 * 1.0 = -6.84 for ctc +2024-01-16 22:15:13,924 (beam_search:479) INFO: total log probability: -6.84 +2024-01-16 22:15:13,924 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:15:13,924 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:13,924 (beam_search:483) INFO: best hypo: KERLTULNDIERTHRFTAUSTAUSCHE + +2024-01-16 22:15:13,925 (asr_inference:494) INFO: speech length: 27840 +2024-01-16 22:15:13,932 (beam_search:428) INFO: decoder input length: 41 +2024-01-16 22:15:13,932 (beam_search:429) INFO: max output length: 41 +2024-01-16 22:15:13,932 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:13,972 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:13,972 (beam_search:476) INFO: -4.70 * 1.0 = -4.70 for ctc +2024-01-16 22:15:13,972 (beam_search:479) INFO: total log probability: -4.70 +2024-01-16 22:15:13,972 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:15:13,972 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:13,973 (beam_search:483) INFO: best hypo: MIJARZWEITAUSENDVERTONDTE + +2024-01-16 22:15:13,974 (asr_inference:494) INFO: speech length: 80480 +2024-01-16 22:15:13,984 (beam_search:428) INFO: decoder input length: 123 +2024-01-16 22:15:13,984 (beam_search:429) INFO: max output length: 123 +2024-01-16 22:15:13,984 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:14,232 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:14,232 (beam_search:476) INFO: -11.72 * 1.0 = -11.72 for ctc +2024-01-16 22:15:14,232 (beam_search:479) INFO: total log probability: -11.72 +2024-01-16 22:15:14,232 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:15:14,232 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:14,233 (beam_search:483) INFO: best hypo: DASEDIESELEITUNGSCNALERVOLENTENKÜNEALSDERBAUMEISTERDENKÖNERDOUM + +2024-01-16 22:15:14,234 (asr_inference:494) INFO: speech length: 44160 +2024-01-16 22:15:14,242 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 22:15:14,242 (beam_search:429) INFO: max output length: 66 +2024-01-16 22:15:14,242 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:14,329 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:14,329 (beam_search:476) INFO: -10.89 * 1.0 = -10.89 for ctc +2024-01-16 22:15:14,329 (beam_search:479) INFO: total log probability: -10.89 +2024-01-16 22:15:14,329 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:15:14,329 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:14,330 (beam_search:483) INFO: best hypo: IDEHNRIHTUNGDEBANAUARINHABESICSRLIHTUM + +2024-01-16 22:15:14,331 (asr_inference:494) INFO: speech length: 16960 +2024-01-16 22:15:14,337 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 22:15:14,337 (beam_search:429) INFO: max output length: 24 +2024-01-16 22:15:14,337 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:14,348 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:14,348 (beam_search:476) INFO: -3.92 * 1.0 = -3.92 for ctc +2024-01-16 22:15:14,348 (beam_search:479) INFO: total log probability: -3.92 +2024-01-16 22:15:14,348 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-16 22:15:14,348 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:14,348 (beam_search:483) INFO: best hypo: LORDWEI + +2024-01-16 22:15:14,349 (asr_inference:494) INFO: speech length: 41920 +2024-01-16 22:15:14,357 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 22:15:14,357 (beam_search:429) INFO: max output length: 63 +2024-01-16 22:15:14,357 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:14,435 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:14,435 (beam_search:476) INFO: -3.87 * 1.0 = -3.87 for ctc +2024-01-16 22:15:14,435 (beam_search:479) INFO: total log probability: -3.87 +2024-01-16 22:15:14,435 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 22:15:14,435 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:14,435 (beam_search:483) INFO: best hypo: DERZEITDERBSTIGKHENERDEREIFELEITUN + +2024-01-16 22:15:14,436 (asr_inference:494) INFO: speech length: 45920 +2024-01-16 22:15:14,444 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 22:15:14,444 (beam_search:429) INFO: max output length: 69 +2024-01-16 22:15:14,444 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:14,528 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:14,528 (beam_search:476) INFO: -7.12 * 1.0 = -7.12 for ctc +2024-01-16 22:15:14,528 (beam_search:479) INFO: total log probability: -7.12 +2024-01-16 22:15:14,528 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:14,528 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:14,528 (beam_search:483) INFO: best hypo: VOKUSBESWISENSHAFTLICHENINDTARESES + +2024-01-16 22:15:14,530 (asr_inference:494) INFO: speech length: 16160 +2024-01-16 22:15:14,536 (beam_search:428) INFO: decoder input length: 23 +2024-01-16 22:15:14,536 (beam_search:429) INFO: max output length: 23 +2024-01-16 22:15:14,536 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:14,555 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:14,555 (beam_search:476) INFO: -3.20 * 1.0 = -3.20 for ctc +2024-01-16 22:15:14,555 (beam_search:479) INFO: total log probability: -3.20 +2024-01-16 22:15:14,555 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:15:14,555 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:14,555 (beam_search:483) INFO: best hypo: TEMERZUBEGEISTE + +2024-01-16 22:15:14,556 (asr_inference:494) INFO: speech length: 61280 +2024-01-16 22:15:14,565 (beam_search:428) INFO: decoder input length: 93 +2024-01-16 22:15:14,565 (beam_search:429) INFO: max output length: 93 +2024-01-16 22:15:14,565 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:14,705 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:14,705 (beam_search:476) INFO: -4.99 * 1.0 = -4.99 for ctc +2024-01-16 22:15:14,705 (beam_search:479) INFO: total log probability: -4.99 +2024-01-16 22:15:14,705 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 22:15:14,705 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:14,706 (beam_search:483) INFO: best hypo: METEUNDKONTEDAMITAUFONINENBERGANGENWERDEN + +2024-01-16 22:15:14,707 (asr_inference:494) INFO: speech length: 28160 +2024-01-16 22:15:14,714 (beam_search:428) INFO: decoder input length: 41 +2024-01-16 22:15:14,714 (beam_search:429) INFO: max output length: 41 +2024-01-16 22:15:14,714 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:14,758 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:14,758 (beam_search:476) INFO: -9.15 * 1.0 = -9.15 for ctc +2024-01-16 22:15:14,758 (beam_search:479) INFO: total log probability: -9.15 +2024-01-16 22:15:14,758 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:15:14,758 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:14,758 (beam_search:483) INFO: best hypo: HAHTKABERBESSELLALISTEUDENH + +2024-01-16 22:15:14,760 (asr_inference:494) INFO: speech length: 17920 +2024-01-16 22:15:14,766 (beam_search:428) INFO: decoder input length: 25 +2024-01-16 22:15:14,766 (beam_search:429) INFO: max output length: 25 +2024-01-16 22:15:14,766 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:14,785 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:14,785 (beam_search:476) INFO: -2.82 * 1.0 = -2.82 for ctc +2024-01-16 22:15:14,786 (beam_search:479) INFO: total log probability: -2.82 +2024-01-16 22:15:14,786 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 22:15:14,786 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:14,786 (beam_search:483) INFO: best hypo: DARREINENZIKLOP + +2024-01-16 22:15:14,787 (asr_inference:494) INFO: speech length: 35200 +2024-01-16 22:15:14,794 (beam_search:428) INFO: decoder input length: 52 +2024-01-16 22:15:14,794 (beam_search:429) INFO: max output length: 52 +2024-01-16 22:15:14,794 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:14,856 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:14,856 (beam_search:476) INFO: -6.57 * 1.0 = -6.57 for ctc +2024-01-16 22:15:14,856 (beam_search:479) INFO: total log probability: -6.57 +2024-01-16 22:15:14,856 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:15:14,856 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:14,856 (beam_search:483) INFO: best hypo: DNGRESLIEWIDERAUFDILESETZUSETZEN + +2024-01-16 22:15:14,858 (asr_inference:494) INFO: speech length: 56320 +2024-01-16 22:15:14,866 (beam_search:428) INFO: decoder input length: 85 +2024-01-16 22:15:14,866 (beam_search:429) INFO: max output length: 85 +2024-01-16 22:15:14,866 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:14,992 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:14,992 (beam_search:476) INFO: -6.68 * 1.0 = -6.68 for ctc +2024-01-16 22:15:14,992 (beam_search:479) INFO: total log probability: -6.68 +2024-01-16 22:15:14,992 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 22:15:14,992 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:14,992 (beam_search:483) INFO: best hypo: WIELANGDIESEKAPLANSTELEAUFRICHTERHETENWRD + +2024-01-16 22:15:14,994 (asr_inference:494) INFO: speech length: 64960 +2024-01-16 22:15:15,003 (beam_search:428) INFO: decoder input length: 99 +2024-01-16 22:15:15,003 (beam_search:429) INFO: max output length: 99 +2024-01-16 22:15:15,003 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:15,196 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:15,196 (beam_search:476) INFO: -17.07 * 1.0 = -17.07 for ctc +2024-01-16 22:15:15,196 (beam_search:479) INFO: total log probability: -17.07 +2024-01-16 22:15:15,196 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:15,196 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:15,196 (beam_search:483) INFO: best hypo: SIEWARNMARSCHEINLIGHBEREITZDREISZIGSIKUNDTENNECHAUSPRCHTESVORER + +2024-01-16 22:15:15,197 (asr_inference:494) INFO: speech length: 36640 +2024-01-16 22:15:15,205 (beam_search:428) INFO: decoder input length: 55 +2024-01-16 22:15:15,205 (beam_search:429) INFO: max output length: 55 +2024-01-16 22:15:15,205 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:15,276 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:15,276 (beam_search:476) INFO: -7.96 * 1.0 = -7.96 for ctc +2024-01-16 22:15:15,276 (beam_search:479) INFO: total log probability: -7.96 +2024-01-16 22:15:15,276 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:15:15,276 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:15,276 (beam_search:483) INFO: best hypo: METERGESAMTLINGEUNDBISTUTSEHNMIETER + +2024-01-16 22:15:15,278 (asr_inference:494) INFO: speech length: 24320 +2024-01-16 22:15:15,285 (beam_search:428) INFO: decoder input length: 35 +2024-01-16 22:15:15,285 (beam_search:429) INFO: max output length: 35 +2024-01-16 22:15:15,285 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:15,314 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:15,314 (beam_search:476) INFO: -4.39 * 1.0 = -4.39 for ctc +2024-01-16 22:15:15,314 (beam_search:479) INFO: total log probability: -4.39 +2024-01-16 22:15:15,314 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:15:15,314 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:15,314 (beam_search:483) INFO: best hypo: VEINERITZENUNDSPALT + +2024-01-16 22:15:15,316 (asr_inference:494) INFO: speech length: 48960 +2024-01-16 22:15:15,324 (beam_search:428) INFO: decoder input length: 74 +2024-01-16 22:15:15,324 (beam_search:429) INFO: max output length: 74 +2024-01-16 22:15:15,324 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:15,424 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:15,424 (beam_search:476) INFO: -10.82 * 1.0 = -10.82 for ctc +2024-01-16 22:15:15,424 (beam_search:479) INFO: total log probability: -10.82 +2024-01-16 22:15:15,424 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:15,424 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:15,424 (beam_search:483) INFO: best hypo: DINMENVENAUSSNDIEKOLERHINABPFLIESENSIET + +2024-01-16 22:15:15,425 (asr_inference:494) INFO: speech length: 21440 +2024-01-16 22:15:15,432 (beam_search:428) INFO: decoder input length: 31 +2024-01-16 22:15:15,432 (beam_search:429) INFO: max output length: 31 +2024-01-16 22:15:15,432 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:15,458 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:15,458 (beam_search:476) INFO: -5.60 * 1.0 = -5.60 for ctc +2024-01-16 22:15:15,458 (beam_search:479) INFO: total log probability: -5.60 +2024-01-16 22:15:15,458 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:15:15,458 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:15,458 (beam_search:483) INFO: best hypo: NEITERIOSAKTEBRAUN + +2024-01-16 22:15:15,459 (asr_inference:494) INFO: speech length: 20960 +2024-01-16 22:15:15,466 (beam_search:428) INFO: decoder input length: 30 +2024-01-16 22:15:15,466 (beam_search:429) INFO: max output length: 30 +2024-01-16 22:15:15,466 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:15,490 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:15,490 (beam_search:476) INFO: -4.98 * 1.0 = -4.98 for ctc +2024-01-16 22:15:15,490 (beam_search:479) INFO: total log probability: -4.98 +2024-01-16 22:15:15,490 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:15,490 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:15,490 (beam_search:483) INFO: best hypo: DASFÜNFTENGERIUM + +2024-01-16 22:15:15,491 (asr_inference:494) INFO: speech length: 41280 +2024-01-16 22:15:15,499 (beam_search:428) INFO: decoder input length: 62 +2024-01-16 22:15:15,499 (beam_search:429) INFO: max output length: 62 +2024-01-16 22:15:15,499 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:15,573 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:15,573 (beam_search:476) INFO: -5.03 * 1.0 = -5.03 for ctc +2024-01-16 22:15:15,573 (beam_search:479) INFO: total log probability: -5.03 +2024-01-16 22:15:15,573 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 22:15:15,573 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:15,573 (beam_search:483) INFO: best hypo: REISENSIMANCHMALWEIDETIEREISCHAFE + +2024-01-16 22:15:15,574 (asr_inference:494) INFO: speech length: 50560 +2024-01-16 22:15:15,583 (beam_search:428) INFO: decoder input length: 76 +2024-01-16 22:15:15,583 (beam_search:429) INFO: max output length: 76 +2024-01-16 22:15:15,583 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:15,669 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:15,669 (beam_search:476) INFO: -7.18 * 1.0 = -7.18 for ctc +2024-01-16 22:15:15,669 (beam_search:479) INFO: total log probability: -7.18 +2024-01-16 22:15:15,669 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:15:15,669 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:15,670 (beam_search:483) INFO: best hypo: SIHÖRENDENARTIKELDESEINRÜFIU + +2024-01-16 22:15:15,671 (asr_inference:494) INFO: speech length: 24480 +2024-01-16 22:15:15,678 (beam_search:428) INFO: decoder input length: 36 +2024-01-16 22:15:15,678 (beam_search:429) INFO: max output length: 36 +2024-01-16 22:15:15,678 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:15,708 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:15,708 (beam_search:476) INFO: -4.58 * 1.0 = -4.58 for ctc +2024-01-16 22:15:15,708 (beam_search:479) INFO: total log probability: -4.58 +2024-01-16 22:15:15,708 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:15,708 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:15,708 (beam_search:483) INFO: best hypo: KUSERISGLENTERKOCH + +2024-01-16 22:15:15,709 (asr_inference:494) INFO: speech length: 53440 +2024-01-16 22:15:15,718 (beam_search:428) INFO: decoder input length: 81 +2024-01-16 22:15:15,718 (beam_search:429) INFO: max output length: 81 +2024-01-16 22:15:15,718 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:15,780 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:15,780 (beam_search:476) INFO: -6.65 * 1.0 = -6.65 for ctc +2024-01-16 22:15:15,780 (beam_search:479) INFO: total log probability: -6.65 +2024-01-16 22:15:15,780 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:15:15,780 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:15,781 (beam_search:483) INFO: best hypo: SHANZEWENTSTIFTDUN + +2024-01-16 22:15:15,782 (asr_inference:494) INFO: speech length: 56320 +2024-01-16 22:15:15,790 (beam_search:428) INFO: decoder input length: 85 +2024-01-16 22:15:15,790 (beam_search:429) INFO: max output length: 85 +2024-01-16 22:15:15,790 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:15,919 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:15,919 (beam_search:476) INFO: -10.06 * 1.0 = -10.06 for ctc +2024-01-16 22:15:15,919 (beam_search:479) INFO: total log probability: -10.06 +2024-01-16 22:15:15,919 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:15,919 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:15,919 (beam_search:483) INFO: best hypo: NEUNZHNHNDERTACHTZIENALTSHANIEARTENANGESIE + +2024-01-16 22:15:15,920 (asr_inference:494) INFO: speech length: 29280 +2024-01-16 22:15:15,928 (beam_search:428) INFO: decoder input length: 43 +2024-01-16 22:15:15,928 (beam_search:429) INFO: max output length: 43 +2024-01-16 22:15:15,928 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:15,962 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:15,962 (beam_search:476) INFO: -5.41 * 1.0 = -5.41 for ctc +2024-01-16 22:15:15,962 (beam_search:479) INFO: total log probability: -5.41 +2024-01-16 22:15:15,962 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:15:15,962 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:15,962 (beam_search:483) INFO: best hypo: MERERESNACHIMTUN + +2024-01-16 22:15:15,963 (asr_inference:494) INFO: speech length: 69920 +2024-01-16 22:15:15,973 (beam_search:428) INFO: decoder input length: 107 +2024-01-16 22:15:15,973 (beam_search:429) INFO: max output length: 107 +2024-01-16 22:15:15,973 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:16,203 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:16,203 (beam_search:476) INFO: -20.57 * 1.0 = -20.57 for ctc +2024-01-16 22:15:16,203 (beam_search:479) INFO: total log probability: -20.57 +2024-01-16 22:15:16,203 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:15:16,203 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:16,204 (beam_search:483) INFO: best hypo: AUCSHIHDESGERICHSZUOLANDESWEITENBERLIEBENKOULINARISCHERSPÄTZELTETARMÜG + +2024-01-16 22:15:16,205 (asr_inference:494) INFO: speech length: 59040 +2024-01-16 22:15:16,214 (beam_search:428) INFO: decoder input length: 90 +2024-01-16 22:15:16,214 (beam_search:429) INFO: max output length: 90 +2024-01-16 22:15:16,214 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:16,371 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:16,371 (beam_search:476) INFO: -12.54 * 1.0 = -12.54 for ctc +2024-01-16 22:15:16,371 (beam_search:479) INFO: total log probability: -12.54 +2024-01-16 22:15:16,371 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:16,371 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:16,372 (beam_search:483) INFO: best hypo: KOLLETSCHUNDEINZWEITCOBALTSPBANSHLIERERINHEMNVORLSEN + +2024-01-16 22:15:16,373 (asr_inference:494) INFO: speech length: 41760 +2024-01-16 22:15:16,381 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 22:15:16,381 (beam_search:429) INFO: max output length: 63 +2024-01-16 22:15:16,381 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:16,453 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:16,453 (beam_search:476) INFO: -9.17 * 1.0 = -9.17 for ctc +2024-01-16 22:15:16,453 (beam_search:479) INFO: total log probability: -9.17 +2024-01-16 22:15:16,453 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:15:16,453 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:16,453 (beam_search:483) INFO: best hypo: BURDENEINISWEGSAELLEGEBÜIROTIGE + +2024-01-16 22:15:16,455 (asr_inference:494) INFO: speech length: 30560 +2024-01-16 22:15:16,462 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 22:15:16,462 (beam_search:429) INFO: max output length: 45 +2024-01-16 22:15:16,462 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:16,503 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:16,503 (beam_search:476) INFO: -4.93 * 1.0 = -4.93 for ctc +2024-01-16 22:15:16,503 (beam_search:479) INFO: total log probability: -4.93 +2024-01-16 22:15:16,503 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:16,503 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:16,503 (beam_search:483) INFO: best hypo: ISTIERKERBEBAUGREFTIGKH + +2024-01-16 22:15:16,504 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 22:15:16,512 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 22:15:16,512 (beam_search:429) INFO: max output length: 54 +2024-01-16 22:15:16,512 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:16,569 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:16,569 (beam_search:476) INFO: -7.40 * 1.0 = -7.40 for ctc +2024-01-16 22:15:16,569 (beam_search:479) INFO: total log probability: -7.40 +2024-01-16 22:15:16,569 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:15:16,569 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:16,570 (beam_search:483) INFO: best hypo: ANLESLICHTERNOUASANSPRACREKH + +2024-01-16 22:15:16,571 (asr_inference:494) INFO: speech length: 28480 +2024-01-16 22:15:16,578 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 22:15:16,578 (beam_search:429) INFO: max output length: 42 +2024-01-16 22:15:16,578 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:16,617 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:16,617 (beam_search:476) INFO: -7.07 * 1.0 = -7.07 for ctc +2024-01-16 22:15:16,617 (beam_search:479) INFO: total log probability: -7.07 +2024-01-16 22:15:16,617 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:15:16,617 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:16,617 (beam_search:483) INFO: best hypo: MITWINVONDSCREÄGHINTEN + +2024-01-16 22:15:16,618 (asr_inference:494) INFO: speech length: 53760 +2024-01-16 22:15:16,626 (beam_search:428) INFO: decoder input length: 81 +2024-01-16 22:15:16,626 (beam_search:429) INFO: max output length: 81 +2024-01-16 22:15:16,626 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:16,755 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:16,755 (beam_search:476) INFO: -17.54 * 1.0 = -17.54 for ctc +2024-01-16 22:15:16,755 (beam_search:479) INFO: total log probability: -17.54 +2024-01-16 22:15:16,755 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:15:16,755 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:16,755 (beam_search:483) INFO: best hypo: DINREÖCSTENTEALDEBITTZIÖGSFORTRIETUNGÜRDINGENAUS + +2024-01-16 22:15:16,756 (asr_inference:494) INFO: speech length: 20480 +2024-01-16 22:15:16,763 (beam_search:428) INFO: decoder input length: 29 +2024-01-16 22:15:16,763 (beam_search:429) INFO: max output length: 29 +2024-01-16 22:15:16,763 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:16,791 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:16,791 (beam_search:476) INFO: -4.34 * 1.0 = -4.34 for ctc +2024-01-16 22:15:16,791 (beam_search:479) INFO: total log probability: -4.34 +2024-01-16 22:15:16,791 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:15:16,791 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:16,791 (beam_search:483) INFO: best hypo: ACHTINHUNERTEILUNZWANZI + +2024-01-16 22:15:16,793 (asr_inference:494) INFO: speech length: 39520 +2024-01-16 22:15:16,800 (beam_search:428) INFO: decoder input length: 59 +2024-01-16 22:15:16,800 (beam_search:429) INFO: max output length: 59 +2024-01-16 22:15:16,800 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:16,873 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:16,873 (beam_search:476) INFO: -7.86 * 1.0 = -7.86 for ctc +2024-01-16 22:15:16,873 (beam_search:479) INFO: total log probability: -7.86 +2024-01-16 22:15:16,873 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:16,873 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:16,873 (beam_search:483) INFO: best hypo: DSKROSENATELTSANGESAMITENREICHTUMS + +2024-01-16 22:15:16,874 (asr_inference:494) INFO: speech length: 29600 +2024-01-16 22:15:16,881 (beam_search:428) INFO: decoder input length: 44 +2024-01-16 22:15:16,881 (beam_search:429) INFO: max output length: 44 +2024-01-16 22:15:16,881 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:16,931 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:16,931 (beam_search:476) INFO: -12.04 * 1.0 = -12.04 for ctc +2024-01-16 22:15:16,931 (beam_search:479) INFO: total log probability: -12.04 +2024-01-16 22:15:16,931 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 22:15:16,931 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:16,931 (beam_search:483) INFO: best hypo: DESONNIHTALSEHZWOLPROROKATIU + +2024-01-16 22:15:16,932 (asr_inference:494) INFO: speech length: 36320 +2024-01-16 22:15:16,940 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 22:15:16,940 (beam_search:429) INFO: max output length: 54 +2024-01-16 22:15:16,940 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:17,002 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:17,002 (beam_search:476) INFO: -7.45 * 1.0 = -7.45 for ctc +2024-01-16 22:15:17,002 (beam_search:479) INFO: total log probability: -7.45 +2024-01-16 22:15:17,002 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:15:17,002 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:17,003 (beam_search:483) INFO: best hypo: TEILHBEDEVRMERGOSMANUNDIORGENZ + +2024-01-16 22:15:17,004 (asr_inference:494) INFO: speech length: 32160 +2024-01-16 22:15:17,011 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 22:15:17,011 (beam_search:429) INFO: max output length: 48 +2024-01-16 22:15:17,011 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:17,059 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:17,059 (beam_search:476) INFO: -6.17 * 1.0 = -6.17 for ctc +2024-01-16 22:15:17,059 (beam_search:479) INFO: total log probability: -6.17 +2024-01-16 22:15:17,059 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:15:17,059 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:17,059 (beam_search:483) INFO: best hypo: INEMITEFRAUNTERKAUTINSELT + +2024-01-16 22:15:17,061 (asr_inference:494) INFO: speech length: 59360 +2024-01-16 22:15:17,069 (beam_search:428) INFO: decoder input length: 90 +2024-01-16 22:15:17,069 (beam_search:429) INFO: max output length: 90 +2024-01-16 22:15:17,069 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:17,218 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:17,218 (beam_search:476) INFO: -12.29 * 1.0 = -12.29 for ctc +2024-01-16 22:15:17,218 (beam_search:479) INFO: total log probability: -12.29 +2024-01-16 22:15:17,218 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:15:17,218 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:17,219 (beam_search:483) INFO: best hypo: AURDIEOISTEANDUTCHERHEBOFVARLARGMITSITZINMEÜNCHEN + +2024-01-16 22:15:17,220 (asr_inference:494) INFO: speech length: 55520 +2024-01-16 22:15:17,229 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 22:15:17,229 (beam_search:429) INFO: max output length: 84 +2024-01-16 22:15:17,229 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:17,353 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:17,353 (beam_search:476) INFO: -11.83 * 1.0 = -11.83 for ctc +2024-01-16 22:15:17,353 (beam_search:479) INFO: total log probability: -11.83 +2024-01-16 22:15:17,353 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:17,353 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:17,354 (beam_search:483) INFO: best hypo: FAPEIKMENTEUNDSCHMICHEVORPREDUKTERHERSTELT + +2024-01-16 22:15:17,355 (asr_inference:494) INFO: speech length: 172480 +2024-01-16 22:15:17,372 (beam_search:428) INFO: decoder input length: 267 +2024-01-16 22:15:17,372 (beam_search:429) INFO: max output length: 267 +2024-01-16 22:15:17,372 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:18,292 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:18,292 (beam_search:476) INFO: -23.39 * 1.0 = -23.39 for ctc +2024-01-16 22:15:18,292 (beam_search:479) INFO: total log probability: -23.39 +2024-01-16 22:15:18,292 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:18,292 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:18,293 (beam_search:483) INFO: best hypo: ARPLICHENPRESISCHENFREIHERENSTANDINDERZOLANSCHLOSVRAGEINTSCHIEBDENGGEGENGDENSINARTAUFDIESEITEBISMAGSGESTELT + +2024-01-16 22:15:18,294 (asr_inference:494) INFO: speech length: 67040 +2024-01-16 22:15:18,304 (beam_search:428) INFO: decoder input length: 102 +2024-01-16 22:15:18,304 (beam_search:429) INFO: max output length: 102 +2024-01-16 22:15:18,304 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:18,472 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:18,472 (beam_search:476) INFO: -12.31 * 1.0 = -12.31 for ctc +2024-01-16 22:15:18,472 (beam_search:479) INFO: total log probability: -12.31 +2024-01-16 22:15:18,472 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:15:18,472 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:18,472 (beam_search:483) INFO: best hypo: WENDIKWELENVONSESTERVORGWELENUNDAUFENZUTAGELIGEN + +2024-01-16 22:15:18,474 (asr_inference:494) INFO: speech length: 47200 +2024-01-16 22:15:18,482 (beam_search:428) INFO: decoder input length: 71 +2024-01-16 22:15:18,482 (beam_search:429) INFO: max output length: 71 +2024-01-16 22:15:18,482 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:18,578 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:18,578 (beam_search:476) INFO: -8.82 * 1.0 = -8.82 for ctc +2024-01-16 22:15:18,578 (beam_search:479) INFO: total log probability: -8.82 +2024-01-16 22:15:18,578 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:18,578 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:18,578 (beam_search:483) INFO: best hypo: DSVOUNNACHBARBAUTRIUBBELLEITZBEGONWOT + +2024-01-16 22:15:18,579 (asr_inference:494) INFO: speech length: 73600 +2024-01-16 22:15:18,589 (beam_search:428) INFO: decoder input length: 112 +2024-01-16 22:15:18,589 (beam_search:429) INFO: max output length: 112 +2024-01-16 22:15:18,589 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:18,794 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:18,794 (beam_search:476) INFO: -9.81 * 1.0 = -9.81 for ctc +2024-01-16 22:15:18,794 (beam_search:479) INFO: total log probability: -9.81 +2024-01-16 22:15:18,794 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:15:18,794 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:18,794 (beam_search:483) INFO: best hypo: WEHRDENPRÄRGENDEELEMENTEDESHANSEATENTUMSZUOSAMMENGEFAST + +2024-01-16 22:15:18,795 (asr_inference:494) INFO: speech length: 39360 +2024-01-16 22:15:18,803 (beam_search:428) INFO: decoder input length: 59 +2024-01-16 22:15:18,803 (beam_search:429) INFO: max output length: 59 +2024-01-16 22:15:18,803 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:18,876 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:18,876 (beam_search:476) INFO: -10.17 * 1.0 = -10.17 for ctc +2024-01-16 22:15:18,876 (beam_search:479) INFO: total log probability: -10.17 +2024-01-16 22:15:18,876 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:15:18,876 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:18,876 (beam_search:483) INFO: best hypo: DEASSLIEZWRDERTSVOLCGSTLIETANGESIEN + +# Accounting: time=11 threads=1 +# Ended (code 0) at Tue Jan 16 22:15:19 CST 2024, elapsed time 11 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.3.log b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.3.log new file mode 100644 index 0000000000000000000000000000000000000000..8449fe1cd7d6bfb7b7e94648f362b0c676cb8055 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.3.log @@ -0,0 +1,591 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3 --config conf/decode_asr.yaml +# Started at Tue Jan 16 22:15:19 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3 --config conf/decode_asr.yaml +2024-01-16 22:15:20,692 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml +2024-01-16 22:15:20,711 (asr:523) INFO: Vocabulary size: 44 +2024-01-16 22:15:20,773 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 22:15:20,773 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 22:15:20,885 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 22:15:22,179 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 22:15:23,411 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 22:15:23,411 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 22:15:23,411 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 22:15:23,444 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-16 22:15:23,520 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 22:15:23,630 (asr_inference:494) INFO: speech length: 32800 +2024-01-16 22:15:24,842 (beam_search:428) INFO: decoder input length: 49 +2024-01-16 22:15:24,842 (beam_search:429) INFO: max output length: 49 +2024-01-16 22:15:24,842 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:24,900 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:24,900 (beam_search:476) INFO: -11.45 * 1.0 = -11.45 for ctc +2024-01-16 22:15:24,900 (beam_search:479) INFO: total log probability: -11.45 +2024-01-16 22:15:24,900 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:15:24,900 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:24,901 (beam_search:483) INFO: best hypo: DERZONNENDEMHUSVRLAGSKGOBIGEHÖRT + +2024-01-16 22:15:24,925 (asr_inference:494) INFO: speech length: 47680 +2024-01-16 22:15:24,934 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 22:15:24,934 (beam_search:429) INFO: max output length: 72 +2024-01-16 22:15:24,935 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:25,046 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:25,046 (beam_search:476) INFO: -15.36 * 1.0 = -15.36 for ctc +2024-01-16 22:15:25,046 (beam_search:479) INFO: total log probability: -15.36 +2024-01-16 22:15:25,046 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:15:25,046 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:25,047 (beam_search:483) INFO: best hypo: FRDEKNFDIGENBARDPBÜSCHERINDWIKETEDIPAPIEFERPR + +2024-01-16 22:15:25,048 (asr_inference:494) INFO: speech length: 16960 +2024-01-16 22:15:25,055 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 22:15:25,055 (beam_search:429) INFO: max output length: 24 +2024-01-16 22:15:25,055 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:25,069 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:25,069 (beam_search:476) INFO: -5.67 * 1.0 = -5.67 for ctc +2024-01-16 22:15:25,069 (beam_search:479) INFO: total log probability: -5.67 +2024-01-16 22:15:25,069 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-16 22:15:25,069 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:25,069 (beam_search:483) INFO: best hypo: AMBREWUOKS + +2024-01-16 22:15:25,070 (asr_inference:494) INFO: speech length: 79040 +2024-01-16 22:15:25,081 (beam_search:428) INFO: decoder input length: 121 +2024-01-16 22:15:25,081 (beam_search:429) INFO: max output length: 121 +2024-01-16 22:15:25,081 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:25,302 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:25,302 (beam_search:476) INFO: -14.23 * 1.0 = -14.23 for ctc +2024-01-16 22:15:25,302 (beam_search:479) INFO: total log probability: -14.23 +2024-01-16 22:15:25,302 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:15:25,302 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:25,302 (beam_search:483) INFO: best hypo: VDIEKWARARSIEARDLIGENLANZITZEBETRIEBENAUFANDSEISBEMBAU + +2024-01-16 22:15:25,303 (asr_inference:494) INFO: speech length: 69120 +2024-01-16 22:15:25,313 (beam_search:428) INFO: decoder input length: 105 +2024-01-16 22:15:25,313 (beam_search:429) INFO: max output length: 105 +2024-01-16 22:15:25,313 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:25,508 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:25,508 (beam_search:476) INFO: -15.46 * 1.0 = -15.46 for ctc +2024-01-16 22:15:25,508 (beam_search:479) INFO: total log probability: -15.46 +2024-01-16 22:15:25,508 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:15:25,508 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:25,508 (beam_search:483) INFO: best hypo: JAZWEITAUSENZWÖLFINDNBELIENERKLUOPSOSESHNDREISICHVELLIGT + +2024-01-16 22:15:25,510 (asr_inference:494) INFO: speech length: 47360 +2024-01-16 22:15:25,518 (beam_search:428) INFO: decoder input length: 71 +2024-01-16 22:15:25,518 (beam_search:429) INFO: max output length: 71 +2024-01-16 22:15:25,518 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:25,598 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:25,598 (beam_search:476) INFO: -11.19 * 1.0 = -11.19 for ctc +2024-01-16 22:15:25,598 (beam_search:479) INFO: total log probability: -11.19 +2024-01-16 22:15:25,598 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:15:25,598 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:25,598 (beam_search:483) INFO: best hypo: SECHTINHNENFÜNFTIGHEIDBÜNDNIS + +2024-01-16 22:15:25,599 (asr_inference:494) INFO: speech length: 44000 +2024-01-16 22:15:25,607 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 22:15:25,607 (beam_search:429) INFO: max output length: 66 +2024-01-16 22:15:25,607 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:25,678 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:25,678 (beam_search:476) INFO: -11.29 * 1.0 = -11.29 for ctc +2024-01-16 22:15:25,678 (beam_search:479) INFO: total log probability: -11.29 +2024-01-16 22:15:25,678 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:15:25,678 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:25,679 (beam_search:483) INFO: best hypo: DASPROBLENBIDENPRADTACHSONIST + +2024-01-16 22:15:25,680 (asr_inference:494) INFO: speech length: 75520 +2024-01-16 22:15:25,690 (beam_search:428) INFO: decoder input length: 115 +2024-01-16 22:15:25,690 (beam_search:429) INFO: max output length: 115 +2024-01-16 22:15:25,690 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:25,812 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:25,812 (beam_search:476) INFO: -11.48 * 1.0 = -11.48 for ctc +2024-01-16 22:15:25,812 (beam_search:479) INFO: total log probability: -11.48 +2024-01-16 22:15:25,812 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:15:25,812 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:25,812 (beam_search:483) INFO: best hypo: AMINWESENTETIAMALIESIEVEKEIN + +2024-01-16 22:15:25,813 (asr_inference:494) INFO: speech length: 116960 +2024-01-16 22:15:25,826 (beam_search:428) INFO: decoder input length: 180 +2024-01-16 22:15:25,826 (beam_search:429) INFO: max output length: 180 +2024-01-16 22:15:25,826 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:26,306 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:26,306 (beam_search:476) INFO: -23.91 * 1.0 = -23.91 for ctc +2024-01-16 22:15:26,306 (beam_search:479) INFO: total log probability: -23.91 +2024-01-16 22:15:26,306 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:26,306 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:26,307 (beam_search:483) INFO: best hypo: ICHEIMALEINEANSATZWEISEUNTESOCHRUNGZUIEREMVERHALKTENINDERZEITTDESNAZUNALSUTZELISMUS + +2024-01-16 22:15:26,308 (asr_inference:494) INFO: speech length: 37760 +2024-01-16 22:15:26,316 (beam_search:428) INFO: decoder input length: 56 +2024-01-16 22:15:26,316 (beam_search:429) INFO: max output length: 56 +2024-01-16 22:15:26,316 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:26,378 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:26,378 (beam_search:476) INFO: -8.03 * 1.0 = -8.03 for ctc +2024-01-16 22:15:26,378 (beam_search:479) INFO: total log probability: -8.03 +2024-01-16 22:15:26,378 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:15:26,378 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:26,379 (beam_search:483) INFO: best hypo: LIETZSEINSFÜRFREIEDOKOMENTATION + +2024-01-16 22:15:26,380 (asr_inference:494) INFO: speech length: 46560 +2024-01-16 22:15:26,388 (beam_search:428) INFO: decoder input length: 70 +2024-01-16 22:15:26,388 (beam_search:429) INFO: max output length: 70 +2024-01-16 22:15:26,388 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:26,485 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:26,486 (beam_search:476) INFO: -13.82 * 1.0 = -13.82 for ctc +2024-01-16 22:15:26,486 (beam_search:479) INFO: total log probability: -13.82 +2024-01-16 22:15:26,486 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:15:26,486 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:26,486 (beam_search:483) INFO: best hypo: DIMCHZENNERHUNDERDIGATENHOLSEVORENTORE + +2024-01-16 22:15:26,487 (asr_inference:494) INFO: speech length: 27040 +2024-01-16 22:15:26,494 (beam_search:428) INFO: decoder input length: 40 +2024-01-16 22:15:26,494 (beam_search:429) INFO: max output length: 40 +2024-01-16 22:15:26,494 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:26,528 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:26,528 (beam_search:476) INFO: -2.32 * 1.0 = -2.32 for ctc +2024-01-16 22:15:26,528 (beam_search:479) INFO: total log probability: -2.32 +2024-01-16 22:15:26,528 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 22:15:26,528 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:26,528 (beam_search:483) INFO: best hypo: GANZSIMSTIELDERZEIT + +2024-01-16 22:15:26,529 (asr_inference:494) INFO: speech length: 58720 +2024-01-16 22:15:26,538 (beam_search:428) INFO: decoder input length: 89 +2024-01-16 22:15:26,538 (beam_search:429) INFO: max output length: 89 +2024-01-16 22:15:26,538 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:26,666 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:26,666 (beam_search:476) INFO: -9.75 * 1.0 = -9.75 for ctc +2024-01-16 22:15:26,666 (beam_search:479) INFO: total log probability: -9.75 +2024-01-16 22:15:26,666 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:26,666 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:26,666 (beam_search:483) INFO: best hypo: ERBRÜLUNDHÜÖRTAREICHEDILEITUNSCHISLIKEN + +2024-01-16 22:15:26,668 (asr_inference:494) INFO: speech length: 35520 +2024-01-16 22:15:26,675 (beam_search:428) INFO: decoder input length: 53 +2024-01-16 22:15:26,675 (beam_search:429) INFO: max output length: 53 +2024-01-16 22:15:26,675 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:26,729 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:26,729 (beam_search:476) INFO: -4.36 * 1.0 = -4.36 for ctc +2024-01-16 22:15:26,729 (beam_search:479) INFO: total log probability: -4.36 +2024-01-16 22:15:26,729 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 22:15:26,729 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:26,729 (beam_search:483) INFO: best hypo: AUSSTZEICHNUMENREMDERHEREN + +2024-01-16 22:15:26,730 (asr_inference:494) INFO: speech length: 28000 +2024-01-16 22:15:26,737 (beam_search:428) INFO: decoder input length: 41 +2024-01-16 22:15:26,737 (beam_search:429) INFO: max output length: 41 +2024-01-16 22:15:26,737 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:26,777 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:26,777 (beam_search:476) INFO: -4.26 * 1.0 = -4.26 for ctc +2024-01-16 22:15:26,777 (beam_search:479) INFO: total log probability: -4.26 +2024-01-16 22:15:26,777 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 22:15:26,777 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:26,778 (beam_search:483) INFO: best hypo: DIESCHEFTELLEREIAUFTZUGEBE + +2024-01-16 22:15:26,779 (asr_inference:494) INFO: speech length: 49760 +2024-01-16 22:15:26,787 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 22:15:26,787 (beam_search:429) INFO: max output length: 75 +2024-01-16 22:15:26,787 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:26,887 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:26,887 (beam_search:476) INFO: -12.63 * 1.0 = -12.63 for ctc +2024-01-16 22:15:26,887 (beam_search:479) INFO: total log probability: -12.63 +2024-01-16 22:15:26,887 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:15:26,887 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:26,888 (beam_search:483) INFO: best hypo: DAZUTZIHRNDEBEGEGNUNMITVERLETZTENTIERE + +2024-01-16 22:15:26,889 (asr_inference:494) INFO: speech length: 25920 +2024-01-16 22:15:26,896 (beam_search:428) INFO: decoder input length: 38 +2024-01-16 22:15:26,896 (beam_search:429) INFO: max output length: 38 +2024-01-16 22:15:26,896 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:26,919 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:26,919 (beam_search:476) INFO: -2.58 * 1.0 = -2.58 for ctc +2024-01-16 22:15:26,919 (beam_search:479) INFO: total log probability: -2.58 +2024-01-16 22:15:26,919 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:15:26,919 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:26,919 (beam_search:483) INFO: best hypo: IENESCHSTDIFT + +2024-01-16 22:15:26,920 (asr_inference:494) INFO: speech length: 17280 +2024-01-16 22:15:26,927 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 22:15:26,927 (beam_search:429) INFO: max output length: 24 +2024-01-16 22:15:26,927 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:26,947 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:26,947 (beam_search:476) INFO: -2.73 * 1.0 = -2.73 for ctc +2024-01-16 22:15:26,947 (beam_search:479) INFO: total log probability: -2.73 +2024-01-16 22:15:26,947 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 22:15:26,947 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:26,947 (beam_search:483) INFO: best hypo: WESTLICHVONKERLEN + +2024-01-16 22:15:26,948 (asr_inference:494) INFO: speech length: 23360 +2024-01-16 22:15:26,955 (beam_search:428) INFO: decoder input length: 34 +2024-01-16 22:15:26,955 (beam_search:429) INFO: max output length: 34 +2024-01-16 22:15:26,955 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:26,986 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:26,986 (beam_search:476) INFO: -4.43 * 1.0 = -4.43 for ctc +2024-01-16 22:15:26,986 (beam_search:479) INFO: total log probability: -4.43 +2024-01-16 22:15:26,986 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:26,986 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:26,986 (beam_search:483) INFO: best hypo: DIESTENDINBETRIPWARE + +2024-01-16 22:15:26,987 (asr_inference:494) INFO: speech length: 25600 +2024-01-16 22:15:26,994 (beam_search:428) INFO: decoder input length: 37 +2024-01-16 22:15:26,995 (beam_search:429) INFO: max output length: 37 +2024-01-16 22:15:26,995 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:27,033 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:27,033 (beam_search:476) INFO: -6.28 * 1.0 = -6.28 for ctc +2024-01-16 22:15:27,033 (beam_search:479) INFO: total log probability: -6.28 +2024-01-16 22:15:27,033 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:27,033 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:27,033 (beam_search:483) INFO: best hypo: DIVOMBABIJERHASIERTWEHRDE + +2024-01-16 22:15:27,034 (asr_inference:494) INFO: speech length: 43680 +2024-01-16 22:15:27,042 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 22:15:27,042 (beam_search:429) INFO: max output length: 66 +2024-01-16 22:15:27,042 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:27,131 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:27,131 (beam_search:476) INFO: -12.89 * 1.0 = -12.89 for ctc +2024-01-16 22:15:27,131 (beam_search:479) INFO: total log probability: -12.89 +2024-01-16 22:15:27,131 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:15:27,131 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:27,131 (beam_search:483) INFO: best hypo: ERSCHENNOREINEITRAUFSEATZFONGRISTERNMEIAL + +2024-01-16 22:15:27,132 (asr_inference:494) INFO: speech length: 79680 +2024-01-16 22:15:27,143 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 22:15:27,143 (beam_search:429) INFO: max output length: 122 +2024-01-16 22:15:27,143 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:27,368 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:27,368 (beam_search:476) INFO: -15.63 * 1.0 = -15.63 for ctc +2024-01-16 22:15:27,368 (beam_search:479) INFO: total log probability: -15.63 +2024-01-16 22:15:27,368 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:27,368 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:27,368 (beam_search:483) INFO: best hypo: WAEILSEBSTEXSTRMEREICHTUMKEINESWEHSTINUNMITLEBARENSZUGAN + +2024-01-16 22:15:27,370 (asr_inference:494) INFO: speech length: 25440 +2024-01-16 22:15:27,377 (beam_search:428) INFO: decoder input length: 37 +2024-01-16 22:15:27,377 (beam_search:429) INFO: max output length: 37 +2024-01-16 22:15:27,377 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:27,411 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:27,411 (beam_search:476) INFO: -3.89 * 1.0 = -3.89 for ctc +2024-01-16 22:15:27,411 (beam_search:479) INFO: total log probability: -3.89 +2024-01-16 22:15:27,411 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 22:15:27,411 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:27,411 (beam_search:483) INFO: best hypo: GEBTEUSCHEICHSELBERAUF + +2024-01-16 22:15:27,412 (asr_inference:494) INFO: speech length: 67680 +2024-01-16 22:15:27,422 (beam_search:428) INFO: decoder input length: 103 +2024-01-16 22:15:27,422 (beam_search:429) INFO: max output length: 103 +2024-01-16 22:15:27,422 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:27,603 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:27,603 (beam_search:476) INFO: -15.62 * 1.0 = -15.62 for ctc +2024-01-16 22:15:27,603 (beam_search:479) INFO: total log probability: -15.62 +2024-01-16 22:15:27,603 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:15:27,603 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:27,603 (beam_search:483) INFO: best hypo: ERHAETDIESENBRAUCHNEUNEHINHNDERWEIUNDFÜNFTIGHGIGNÜB + +2024-01-16 22:15:27,604 (asr_inference:494) INFO: speech length: 54400 +2024-01-16 22:15:27,613 (beam_search:428) INFO: decoder input length: 82 +2024-01-16 22:15:27,613 (beam_search:429) INFO: max output length: 82 +2024-01-16 22:15:27,613 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:27,740 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:27,740 (beam_search:476) INFO: -9.98 * 1.0 = -9.98 for ctc +2024-01-16 22:15:27,740 (beam_search:479) INFO: total log probability: -9.98 +2024-01-16 22:15:27,740 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:27,740 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:27,740 (beam_search:483) INFO: best hypo: ODELEITUNGÜBEDIEALTEHÜÖRTEARLEITUNGEFIERTWURDE + +2024-01-16 22:15:27,741 (asr_inference:494) INFO: speech length: 73600 +2024-01-16 22:15:27,751 (beam_search:428) INFO: decoder input length: 112 +2024-01-16 22:15:27,751 (beam_search:429) INFO: max output length: 112 +2024-01-16 22:15:27,751 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:27,925 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:27,925 (beam_search:476) INFO: -12.48 * 1.0 = -12.48 for ctc +2024-01-16 22:15:27,925 (beam_search:479) INFO: total log probability: -12.48 +2024-01-16 22:15:27,925 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:27,925 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:27,926 (beam_search:483) INFO: best hypo: INEBLIEBTEKLSCHOKTROPEASTEKENERUMNANDTDIEHÖNE + +2024-01-16 22:15:27,927 (asr_inference:494) INFO: speech length: 49120 +2024-01-16 22:15:27,935 (beam_search:428) INFO: decoder input length: 74 +2024-01-16 22:15:27,935 (beam_search:429) INFO: max output length: 74 +2024-01-16 22:15:27,935 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:28,014 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:28,014 (beam_search:476) INFO: -6.37 * 1.0 = -6.37 for ctc +2024-01-16 22:15:28,014 (beam_search:479) INFO: total log probability: -6.37 +2024-01-16 22:15:28,014 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:15:28,014 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:28,014 (beam_search:483) INFO: best hypo: GEWARDENSEIOUNDALBRECHTDICH + +2024-01-16 22:15:28,016 (asr_inference:494) INFO: speech length: 42560 +2024-01-16 22:15:28,024 (beam_search:428) INFO: decoder input length: 64 +2024-01-16 22:15:28,024 (beam_search:429) INFO: max output length: 64 +2024-01-16 22:15:28,024 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:28,105 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:28,105 (beam_search:476) INFO: -7.46 * 1.0 = -7.46 for ctc +2024-01-16 22:15:28,105 (beam_search:479) INFO: total log probability: -7.46 +2024-01-16 22:15:28,105 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:28,105 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:28,105 (beam_search:483) INFO: best hypo: DETAGESBEDAFEINESWAGSTENENANITEMINAR + +2024-01-16 22:15:28,107 (asr_inference:494) INFO: speech length: 30560 +2024-01-16 22:15:28,114 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 22:15:28,114 (beam_search:429) INFO: max output length: 45 +2024-01-16 22:15:28,114 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:28,157 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:28,157 (beam_search:476) INFO: -6.35 * 1.0 = -6.35 for ctc +2024-01-16 22:15:28,157 (beam_search:479) INFO: total log probability: -6.35 +2024-01-16 22:15:28,157 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:15:28,157 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:28,157 (beam_search:483) INFO: best hypo: SIBTINULERZIHENOBARALTE + +2024-01-16 22:15:28,158 (asr_inference:494) INFO: speech length: 35840 +2024-01-16 22:15:28,166 (beam_search:428) INFO: decoder input length: 53 +2024-01-16 22:15:28,166 (beam_search:429) INFO: max output length: 53 +2024-01-16 22:15:28,166 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:28,218 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:28,218 (beam_search:476) INFO: -4.62 * 1.0 = -4.62 for ctc +2024-01-16 22:15:28,218 (beam_search:479) INFO: total log probability: -4.62 +2024-01-16 22:15:28,218 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:15:28,218 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:28,218 (beam_search:483) INFO: best hypo: WEITERHENLIESICHNACRWEISEN + +2024-01-16 22:15:28,219 (asr_inference:494) INFO: speech length: 46560 +2024-01-16 22:15:28,227 (beam_search:428) INFO: decoder input length: 70 +2024-01-16 22:15:28,228 (beam_search:429) INFO: max output length: 70 +2024-01-16 22:15:28,228 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:28,311 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:28,311 (beam_search:476) INFO: -10.47 * 1.0 = -10.47 for ctc +2024-01-16 22:15:28,311 (beam_search:479) INFO: total log probability: -10.47 +2024-01-16 22:15:28,311 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:15:28,311 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:28,311 (beam_search:483) INFO: best hypo: ZUNGRÜNUNGSTDARTUMKONTBAMBEREIT + +2024-01-16 22:15:28,312 (asr_inference:494) INFO: speech length: 44160 +2024-01-16 22:15:28,320 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 22:15:28,320 (beam_search:429) INFO: max output length: 66 +2024-01-16 22:15:28,320 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:28,394 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:28,394 (beam_search:476) INFO: -7.73 * 1.0 = -7.73 for ctc +2024-01-16 22:15:28,394 (beam_search:479) INFO: total log probability: -7.73 +2024-01-16 22:15:28,394 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:15:28,394 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:28,395 (beam_search:483) INFO: best hypo: KEINLECSCHLARGMÜKLICHNACHTEILE + +2024-01-16 22:15:28,396 (asr_inference:494) INFO: speech length: 57280 +2024-01-16 22:15:28,405 (beam_search:428) INFO: decoder input length: 87 +2024-01-16 22:15:28,405 (beam_search:429) INFO: max output length: 87 +2024-01-16 22:15:28,405 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:28,538 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:28,539 (beam_search:476) INFO: -9.33 * 1.0 = -9.33 for ctc +2024-01-16 22:15:28,539 (beam_search:479) INFO: total log probability: -9.33 +2024-01-16 22:15:28,539 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:28,539 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:28,539 (beam_search:483) INFO: best hypo: RTDIKATOLICHEKÜRSCHESANPÄTERANDERSTELLEDEALT + +2024-01-16 22:15:28,540 (asr_inference:494) INFO: speech length: 64800 +2024-01-16 22:15:28,550 (beam_search:428) INFO: decoder input length: 99 +2024-01-16 22:15:28,550 (beam_search:429) INFO: max output length: 99 +2024-01-16 22:15:28,550 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:28,735 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:28,736 (beam_search:476) INFO: -18.55 * 1.0 = -18.55 for ctc +2024-01-16 22:15:28,736 (beam_search:479) INFO: total log probability: -18.55 +2024-01-16 22:15:28,736 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:15:28,736 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:28,736 (beam_search:483) INFO: best hypo: ERFEKNARPBUNDESBROTWEITZENTRATABSCHNBAITDIKERTOFELEISERSAT + +2024-01-16 22:15:28,737 (asr_inference:494) INFO: speech length: 26560 +2024-01-16 22:15:28,745 (beam_search:428) INFO: decoder input length: 39 +2024-01-16 22:15:28,745 (beam_search:429) INFO: max output length: 39 +2024-01-16 22:15:28,745 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:28,788 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:28,788 (beam_search:476) INFO: -8.03 * 1.0 = -8.03 for ctc +2024-01-16 22:15:28,788 (beam_search:479) INFO: total log probability: -8.03 +2024-01-16 22:15:28,788 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:15:28,788 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:28,788 (beam_search:483) INFO: best hypo: KREMTDIESENNACHKOMNZOEUGE + +2024-01-16 22:15:28,789 (asr_inference:494) INFO: speech length: 33600 +2024-01-16 22:15:28,797 (beam_search:428) INFO: decoder input length: 50 +2024-01-16 22:15:28,797 (beam_search:429) INFO: max output length: 50 +2024-01-16 22:15:28,797 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:28,853 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:28,854 (beam_search:476) INFO: -5.99 * 1.0 = -5.99 for ctc +2024-01-16 22:15:28,854 (beam_search:479) INFO: total log probability: -5.99 +2024-01-16 22:15:28,854 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:28,854 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:28,854 (beam_search:483) INFO: best hypo: ALLENEINFOLGENDERHÖRSIEREIE + +2024-01-16 22:15:28,855 (asr_inference:494) INFO: speech length: 24640 +2024-01-16 22:15:28,862 (beam_search:428) INFO: decoder input length: 36 +2024-01-16 22:15:28,862 (beam_search:429) INFO: max output length: 36 +2024-01-16 22:15:28,862 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:28,893 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:28,893 (beam_search:476) INFO: -8.51 * 1.0 = -8.51 for ctc +2024-01-16 22:15:28,893 (beam_search:479) INFO: total log probability: -8.51 +2024-01-16 22:15:28,893 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-16 22:15:28,893 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:28,893 (beam_search:483) INFO: best hypo: SCHIBPSMIEBERACTENSO + +2024-01-16 22:15:28,894 (asr_inference:494) INFO: speech length: 60640 +2024-01-16 22:15:28,903 (beam_search:428) INFO: decoder input length: 92 +2024-01-16 22:15:28,903 (beam_search:429) INFO: max output length: 92 +2024-01-16 22:15:28,903 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:29,060 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:29,060 (beam_search:476) INFO: -13.72 * 1.0 = -13.72 for ctc +2024-01-16 22:15:29,060 (beam_search:479) INFO: total log probability: -13.72 +2024-01-16 22:15:29,060 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:29,060 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:29,060 (beam_search:483) INFO: best hypo: KLIGEANRERSBUOCHNEVERZICHTEAUFENEBERSÖNICHEBEWERTUN + +2024-01-16 22:15:29,061 (asr_inference:494) INFO: speech length: 21120 +2024-01-16 22:15:29,068 (beam_search:428) INFO: decoder input length: 30 +2024-01-16 22:15:29,068 (beam_search:429) INFO: max output length: 30 +2024-01-16 22:15:29,068 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:29,092 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:29,092 (beam_search:476) INFO: -2.19 * 1.0 = -2.19 for ctc +2024-01-16 22:15:29,092 (beam_search:479) INFO: total log probability: -2.19 +2024-01-16 22:15:29,092 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 22:15:29,092 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:29,092 (beam_search:483) INFO: best hypo: BENIGERINTRUSSTE + +2024-01-16 22:15:29,093 (asr_inference:494) INFO: speech length: 39840 +2024-01-16 22:15:29,101 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 22:15:29,101 (beam_search:429) INFO: max output length: 60 +2024-01-16 22:15:29,101 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:29,177 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:29,177 (beam_search:476) INFO: -5.70 * 1.0 = -5.70 for ctc +2024-01-16 22:15:29,177 (beam_search:479) INFO: total log probability: -5.70 +2024-01-16 22:15:29,177 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 22:15:29,177 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:29,177 (beam_search:483) INFO: best hypo: WEITERHENVERSORKDDIELEITUNGTERMEN + +2024-01-16 22:15:29,178 (asr_inference:494) INFO: speech length: 33760 +2024-01-16 22:15:29,186 (beam_search:428) INFO: decoder input length: 50 +2024-01-16 22:15:29,186 (beam_search:429) INFO: max output length: 50 +2024-01-16 22:15:29,186 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:29,238 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:29,238 (beam_search:476) INFO: -8.49 * 1.0 = -8.49 for ctc +2024-01-16 22:15:29,238 (beam_search:479) INFO: total log probability: -8.49 +2024-01-16 22:15:29,238 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:15:29,238 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:29,238 (beam_search:483) INFO: best hypo: WARTETENDAFÜHRABEMITEINIG + +2024-01-16 22:15:29,240 (asr_inference:494) INFO: speech length: 57440 +2024-01-16 22:15:29,248 (beam_search:428) INFO: decoder input length: 87 +2024-01-16 22:15:29,248 (beam_search:429) INFO: max output length: 87 +2024-01-16 22:15:29,248 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:29,391 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:29,391 (beam_search:476) INFO: -12.95 * 1.0 = -12.95 for ctc +2024-01-16 22:15:29,391 (beam_search:479) INFO: total log probability: -12.95 +2024-01-16 22:15:29,391 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:15:29,391 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:29,391 (beam_search:483) INFO: best hypo: DEDIKLICHANTRÜNDFONKLEINMUNIERTIINSEINARETZENSOND + +2024-01-16 22:15:29,392 (asr_inference:494) INFO: speech length: 21600 +2024-01-16 22:15:29,399 (beam_search:428) INFO: decoder input length: 31 +2024-01-16 22:15:29,399 (beam_search:429) INFO: max output length: 31 +2024-01-16 22:15:29,399 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:29,424 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:29,424 (beam_search:476) INFO: -6.58 * 1.0 = -6.58 for ctc +2024-01-16 22:15:29,424 (beam_search:479) INFO: total log probability: -6.58 +2024-01-16 22:15:29,424 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:15:29,424 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:29,424 (beam_search:483) INFO: best hypo: EMJABRNEUZENERTFÜM + +2024-01-16 22:15:29,425 (asr_inference:494) INFO: speech length: 160480 +2024-01-16 22:15:29,441 (beam_search:428) INFO: decoder input length: 248 +2024-01-16 22:15:29,441 (beam_search:429) INFO: max output length: 248 +2024-01-16 22:15:29,441 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:30,374 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:30,374 (beam_search:476) INFO: -34.46 * 1.0 = -34.46 for ctc +2024-01-16 22:15:30,374 (beam_search:479) INFO: total log probability: -34.46 +2024-01-16 22:15:30,374 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:30,374 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:30,375 (beam_search:483) INFO: best hypo: IERSITEMFRTFALDESBÜURGERECHTUNDEREINFÜRUNDEREITZÜGICHKEITIMZBANSISNERNDERTWANDTETDESICHDIESEANSCHAUNGANSERTSWEIESEINDARIEN + +2024-01-16 22:15:30,377 (asr_inference:494) INFO: speech length: 60800 +2024-01-16 22:15:30,386 (beam_search:428) INFO: decoder input length: 92 +2024-01-16 22:15:30,386 (beam_search:429) INFO: max output length: 92 +2024-01-16 22:15:30,386 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:30,523 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:30,523 (beam_search:476) INFO: -5.59 * 1.0 = -5.59 for ctc +2024-01-16 22:15:30,523 (beam_search:479) INFO: total log probability: -5.59 +2024-01-16 22:15:30,523 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 22:15:30,523 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:30,523 (beam_search:483) INFO: best hypo: DESSZWISTBACRESBALREINBACHEINEBOGENBRÜCKEV + +2024-01-16 22:15:30,524 (asr_inference:494) INFO: speech length: 34080 +2024-01-16 22:15:30,532 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 22:15:30,532 (beam_search:429) INFO: max output length: 51 +2024-01-16 22:15:30,532 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:30,591 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:30,591 (beam_search:476) INFO: -10.53 * 1.0 = -10.53 for ctc +2024-01-16 22:15:30,591 (beam_search:479) INFO: total log probability: -10.53 +2024-01-16 22:15:30,591 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:15:30,591 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:30,592 (beam_search:483) INFO: best hypo: ACTZINULETESNDREISIGBWORDEEHMBUG + +2024-01-16 22:15:30,593 (asr_inference:494) INFO: speech length: 17440 +2024-01-16 22:15:30,599 (beam_search:428) INFO: decoder input length: 25 +2024-01-16 22:15:30,600 (beam_search:429) INFO: max output length: 25 +2024-01-16 22:15:30,600 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:30,608 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:30,608 (beam_search:476) INFO: -4.13 * 1.0 = -4.13 for ctc +2024-01-16 22:15:30,608 (beam_search:479) INFO: total log probability: -4.13 +2024-01-16 22:15:30,608 (beam_search:480) INFO: normalized log probability: -0.52 +2024-01-16 22:15:30,608 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:30,608 (beam_search:483) INFO: best hypo: DGAEM + +2024-01-16 22:15:30,609 (asr_inference:494) INFO: speech length: 76000 +2024-01-16 22:15:30,619 (beam_search:428) INFO: decoder input length: 116 +2024-01-16 22:15:30,619 (beam_search:429) INFO: max output length: 116 +2024-01-16 22:15:30,619 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:30,817 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:30,818 (beam_search:476) INFO: -12.65 * 1.0 = -12.65 for ctc +2024-01-16 22:15:30,818 (beam_search:479) INFO: total log probability: -12.65 +2024-01-16 22:15:30,818 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:15:30,818 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:30,818 (beam_search:483) INFO: best hypo: AUFGRUNDDEKONTINENTEALSPBAREACHTZEHNHUNERELFBANKOT + +2024-01-16 22:15:30,819 (asr_inference:494) INFO: speech length: 75200 +2024-01-16 22:15:30,829 (beam_search:428) INFO: decoder input length: 115 +2024-01-16 22:15:30,829 (beam_search:429) INFO: max output length: 115 +2024-01-16 22:15:30,829 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:31,039 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:31,039 (beam_search:476) INFO: -19.68 * 1.0 = -19.68 for ctc +2024-01-16 22:15:31,039 (beam_search:479) INFO: total log probability: -19.68 +2024-01-16 22:15:31,039 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:15:31,039 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:31,040 (beam_search:483) INFO: best hypo: EITERESMEIMUSTDENUNPLEIPTBRAUNDIWARBUNGFÜREASBOHSEBTWANEM + +2024-01-16 22:15:31,041 (asr_inference:494) INFO: speech length: 132800 +2024-01-16 22:15:31,054 (beam_search:428) INFO: decoder input length: 205 +2024-01-16 22:15:31,054 (beam_search:429) INFO: max output length: 205 +2024-01-16 22:15:31,054 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:31,753 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:31,753 (beam_search:476) INFO: -39.43 * 1.0 = -39.43 for ctc +2024-01-16 22:15:31,753 (beam_search:479) INFO: total log probability: -39.43 +2024-01-16 22:15:31,753 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:15:31,753 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:31,754 (beam_search:483) INFO: best hypo: DINERIHVMSIEGTDERBÜRGELICHMUGRATISCHENFIEPOARERLUTZIONVENACHZEINHNDETACHTNVIRZICHNRANKREICHVWRDENHAMBURGMITIOBEAUFGENAME + +2024-01-16 22:15:31,755 (asr_inference:494) INFO: speech length: 34240 +2024-01-16 22:15:31,763 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 22:15:31,763 (beam_search:429) INFO: max output length: 51 +2024-01-16 22:15:31,763 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:31,815 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:31,815 (beam_search:476) INFO: -10.01 * 1.0 = -10.01 for ctc +2024-01-16 22:15:31,815 (beam_search:479) INFO: total log probability: -10.01 +2024-01-16 22:15:31,815 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:15:31,815 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:31,815 (beam_search:483) INFO: best hypo: OUMBLIETZWEIAREONEDERBRESCHN + +2024-01-16 22:15:31,816 (asr_inference:494) INFO: speech length: 29280 +2024-01-16 22:15:31,824 (beam_search:428) INFO: decoder input length: 43 +2024-01-16 22:15:31,824 (beam_search:429) INFO: max output length: 43 +2024-01-16 22:15:31,824 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:31,870 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:31,871 (beam_search:476) INFO: -9.83 * 1.0 = -9.83 for ctc +2024-01-16 22:15:31,871 (beam_search:479) INFO: total log probability: -9.83 +2024-01-16 22:15:31,871 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:15:31,871 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:31,871 (beam_search:483) INFO: best hypo: ZOALREICHEGASTSPILNUNTERWEGHS + +# Accounting: time=13 threads=1 +# Ended (code 0) at Tue Jan 16 22:15:32 CST 2024, elapsed time 13 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.4.log b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.4.log new file mode 100644 index 0000000000000000000000000000000000000000..73878421c93f559c427d8aa3ec1fb3902c43f2f2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.4.log @@ -0,0 +1,580 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4 --config conf/decode_asr.yaml +# Started at Tue Jan 16 22:15:32 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4 --config conf/decode_asr.yaml +2024-01-16 22:15:33,689 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml +2024-01-16 22:15:33,707 (asr:523) INFO: Vocabulary size: 44 +2024-01-16 22:15:33,769 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 22:15:33,769 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 22:15:33,880 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 22:15:35,172 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 22:15:36,410 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 22:15:36,410 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 22:15:36,410 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 22:15:36,443 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-16 22:15:36,518 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 22:15:36,630 (asr_inference:494) INFO: speech length: 23680 +2024-01-16 22:15:37,827 (beam_search:428) INFO: decoder input length: 34 +2024-01-16 22:15:37,827 (beam_search:429) INFO: max output length: 34 +2024-01-16 22:15:37,827 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:37,853 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:37,853 (beam_search:476) INFO: -5.08 * 1.0 = -5.08 for ctc +2024-01-16 22:15:37,853 (beam_search:479) INFO: total log probability: -5.08 +2024-01-16 22:15:37,853 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:15:37,853 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:37,853 (beam_search:483) INFO: best hypo: KRANZIETETGENÜTEN + +2024-01-16 22:15:37,877 (asr_inference:494) INFO: speech length: 20160 +2024-01-16 22:15:37,885 (beam_search:428) INFO: decoder input length: 29 +2024-01-16 22:15:37,885 (beam_search:429) INFO: max output length: 29 +2024-01-16 22:15:37,885 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:37,907 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:37,908 (beam_search:476) INFO: -3.01 * 1.0 = -3.01 for ctc +2024-01-16 22:15:37,908 (beam_search:479) INFO: total log probability: -3.01 +2024-01-16 22:15:37,908 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 22:15:37,908 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:37,908 (beam_search:483) INFO: best hypo: UNBEROKERAUSTATO + +2024-01-16 22:15:37,909 (asr_inference:494) INFO: speech length: 101920 +2024-01-16 22:15:37,922 (beam_search:428) INFO: decoder input length: 157 +2024-01-16 22:15:37,922 (beam_search:429) INFO: max output length: 157 +2024-01-16 22:15:37,922 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:38,332 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:38,332 (beam_search:476) INFO: -21.96 * 1.0 = -21.96 for ctc +2024-01-16 22:15:38,332 (beam_search:479) INFO: total log probability: -21.96 +2024-01-16 22:15:38,332 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:15:38,332 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:38,333 (beam_search:483) INFO: best hypo: DAGERICHTVOUMBEIVWARGENSANGESMOTORADISAUSINDIEZUDESERZEITNOUENSTENDENABETASIETLOMENZU + +2024-01-16 22:15:38,334 (asr_inference:494) INFO: speech length: 29760 +2024-01-16 22:15:38,341 (beam_search:428) INFO: decoder input length: 44 +2024-01-16 22:15:38,342 (beam_search:429) INFO: max output length: 44 +2024-01-16 22:15:38,342 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:38,384 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:38,384 (beam_search:476) INFO: -5.03 * 1.0 = -5.03 for ctc +2024-01-16 22:15:38,384 (beam_search:479) INFO: total log probability: -5.03 +2024-01-16 22:15:38,384 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:15:38,384 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:38,384 (beam_search:483) INFO: best hypo: DIMITZSAMIEHERECHENSTOBE + +2024-01-16 22:15:38,385 (asr_inference:494) INFO: speech length: 16320 +2024-01-16 22:15:38,392 (beam_search:428) INFO: decoder input length: 23 +2024-01-16 22:15:38,392 (beam_search:429) INFO: max output length: 23 +2024-01-16 22:15:38,392 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:38,408 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:38,408 (beam_search:476) INFO: -2.93 * 1.0 = -2.93 for ctc +2024-01-16 22:15:38,408 (beam_search:479) INFO: total log probability: -2.93 +2024-01-16 22:15:38,408 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:38,408 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:38,408 (beam_search:483) INFO: best hypo: KEISERFERDINAN + +2024-01-16 22:15:38,409 (asr_inference:494) INFO: speech length: 76640 +2024-01-16 22:15:38,419 (beam_search:428) INFO: decoder input length: 117 +2024-01-16 22:15:38,419 (beam_search:429) INFO: max output length: 117 +2024-01-16 22:15:38,419 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:38,647 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:38,647 (beam_search:476) INFO: -15.32 * 1.0 = -15.32 for ctc +2024-01-16 22:15:38,647 (beam_search:479) INFO: total log probability: -15.32 +2024-01-16 22:15:38,647 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:15:38,647 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:38,647 (beam_search:483) INFO: best hypo: VOMFERNSERESCHSUHRERFEANZSAVERBUGNERINDEMFERNSEFILENDASWIGELIET + +2024-01-16 22:15:38,648 (asr_inference:494) INFO: speech length: 29280 +2024-01-16 22:15:38,655 (beam_search:428) INFO: decoder input length: 43 +2024-01-16 22:15:38,655 (beam_search:429) INFO: max output length: 43 +2024-01-16 22:15:38,656 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:38,697 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:38,697 (beam_search:476) INFO: -5.75 * 1.0 = -5.75 for ctc +2024-01-16 22:15:38,697 (beam_search:479) INFO: total log probability: -5.75 +2024-01-16 22:15:38,697 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:38,697 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:38,697 (beam_search:483) INFO: best hypo: URINSEINBESTENZEITENDE + +2024-01-16 22:15:38,699 (asr_inference:494) INFO: speech length: 37120 +2024-01-16 22:15:38,706 (beam_search:428) INFO: decoder input length: 55 +2024-01-16 22:15:38,707 (beam_search:429) INFO: max output length: 55 +2024-01-16 22:15:38,707 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:38,765 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:38,765 (beam_search:476) INFO: -7.45 * 1.0 = -7.45 for ctc +2024-01-16 22:15:38,765 (beam_search:479) INFO: total log probability: -7.45 +2024-01-16 22:15:38,765 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:15:38,765 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:38,765 (beam_search:483) INFO: best hypo: SEHÖREDENARTIKELFISCHENTSCIS + +2024-01-16 22:15:38,766 (asr_inference:494) INFO: speech length: 20000 +2024-01-16 22:15:38,772 (beam_search:428) INFO: decoder input length: 29 +2024-01-16 22:15:38,772 (beam_search:429) INFO: max output length: 29 +2024-01-16 22:15:38,772 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:38,792 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:38,792 (beam_search:476) INFO: -5.08 * 1.0 = -5.08 for ctc +2024-01-16 22:15:38,792 (beam_search:479) INFO: total log probability: -5.08 +2024-01-16 22:15:38,792 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:15:38,792 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:38,792 (beam_search:483) INFO: best hypo: UNDTEFARMOFWI + +2024-01-16 22:15:38,793 (asr_inference:494) INFO: speech length: 47520 +2024-01-16 22:15:38,801 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 22:15:38,801 (beam_search:429) INFO: max output length: 72 +2024-01-16 22:15:38,801 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:38,890 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:38,890 (beam_search:476) INFO: -11.88 * 1.0 = -11.88 for ctc +2024-01-16 22:15:38,890 (beam_search:479) INFO: total log probability: -11.88 +2024-01-16 22:15:38,890 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:15:38,890 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:38,890 (beam_search:483) INFO: best hypo: REAZSIBZIONDERHEHSENIMATIGVONKRESTA + +2024-01-16 22:15:38,891 (asr_inference:494) INFO: speech length: 77760 +2024-01-16 22:15:38,901 (beam_search:428) INFO: decoder input length: 119 +2024-01-16 22:15:38,901 (beam_search:429) INFO: max output length: 119 +2024-01-16 22:15:38,901 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:39,149 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:39,149 (beam_search:476) INFO: -9.87 * 1.0 = -9.87 for ctc +2024-01-16 22:15:39,149 (beam_search:479) INFO: total log probability: -9.87 +2024-01-16 22:15:39,149 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 22:15:39,149 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:39,149 (beam_search:483) INFO: best hypo: DIGESAMTERANLARGEWABISETVERTZWEIHUNDERSECHTZIGNACHKRISTUSINBEDRIE + +2024-01-16 22:15:39,151 (asr_inference:494) INFO: speech length: 41440 +2024-01-16 22:15:39,159 (beam_search:428) INFO: decoder input length: 62 +2024-01-16 22:15:39,159 (beam_search:429) INFO: max output length: 62 +2024-01-16 22:15:39,159 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:39,235 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:39,235 (beam_search:476) INFO: -10.12 * 1.0 = -10.12 for ctc +2024-01-16 22:15:39,235 (beam_search:479) INFO: total log probability: -10.12 +2024-01-16 22:15:39,235 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:15:39,235 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:39,235 (beam_search:483) INFO: best hypo: DERISDEFASTFUTLIERSOARWISWAGEBUN + +2024-01-16 22:15:39,236 (asr_inference:494) INFO: speech length: 19840 +2024-01-16 22:15:39,243 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 22:15:39,243 (beam_search:429) INFO: max output length: 28 +2024-01-16 22:15:39,243 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:39,263 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:39,263 (beam_search:476) INFO: -1.51 * 1.0 = -1.51 for ctc +2024-01-16 22:15:39,263 (beam_search:479) INFO: total log probability: -1.51 +2024-01-16 22:15:39,263 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 22:15:39,263 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:39,264 (beam_search:483) INFO: best hypo: EINDEMKABELBAUN + +2024-01-16 22:15:39,265 (asr_inference:494) INFO: speech length: 43040 +2024-01-16 22:15:39,273 (beam_search:428) INFO: decoder input length: 65 +2024-01-16 22:15:39,273 (beam_search:429) INFO: max output length: 65 +2024-01-16 22:15:39,273 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:39,354 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:39,354 (beam_search:476) INFO: -7.32 * 1.0 = -7.32 for ctc +2024-01-16 22:15:39,354 (beam_search:479) INFO: total log probability: -7.32 +2024-01-16 22:15:39,354 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:39,354 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:39,354 (beam_search:483) INFO: best hypo: MORLEDUNGMITDEEFAFVERBUNDENGEWES + +2024-01-16 22:15:39,355 (asr_inference:494) INFO: speech length: 28320 +2024-01-16 22:15:39,363 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 22:15:39,363 (beam_search:429) INFO: max output length: 42 +2024-01-16 22:15:39,363 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:39,407 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:39,407 (beam_search:476) INFO: -10.50 * 1.0 = -10.50 for ctc +2024-01-16 22:15:39,407 (beam_search:479) INFO: total log probability: -10.50 +2024-01-16 22:15:39,407 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:15:39,407 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:39,407 (beam_search:483) INFO: best hypo: EELTZISTNPRIERERENINAUSRHEP + +2024-01-16 22:15:39,408 (asr_inference:494) INFO: speech length: 67360 +2024-01-16 22:15:39,418 (beam_search:428) INFO: decoder input length: 103 +2024-01-16 22:15:39,418 (beam_search:429) INFO: max output length: 103 +2024-01-16 22:15:39,418 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:39,621 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:39,621 (beam_search:476) INFO: -14.85 * 1.0 = -14.85 for ctc +2024-01-16 22:15:39,621 (beam_search:479) INFO: total log probability: -14.85 +2024-01-16 22:15:39,621 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:39,621 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:39,621 (beam_search:483) INFO: best hypo: SONENACHDERNAZUNNALISUTSERDISTISCHENKUNZTAUFFASONGERECHTWEHREN + +2024-01-16 22:15:39,622 (asr_inference:494) INFO: speech length: 30400 +2024-01-16 22:15:39,630 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 22:15:39,630 (beam_search:429) INFO: max output length: 45 +2024-01-16 22:15:39,630 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:39,678 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:39,678 (beam_search:476) INFO: -5.99 * 1.0 = -5.99 for ctc +2024-01-16 22:15:39,678 (beam_search:479) INFO: total log probability: -5.99 +2024-01-16 22:15:39,678 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:15:39,678 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:39,678 (beam_search:483) INFO: best hypo: DIEWELTZSICHTDESHANSEAHRTENI + +2024-01-16 22:15:39,679 (asr_inference:494) INFO: speech length: 35520 +2024-01-16 22:15:39,687 (beam_search:428) INFO: decoder input length: 53 +2024-01-16 22:15:39,687 (beam_search:429) INFO: max output length: 53 +2024-01-16 22:15:39,687 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:39,742 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:39,742 (beam_search:476) INFO: -7.14 * 1.0 = -7.14 for ctc +2024-01-16 22:15:39,742 (beam_search:479) INFO: total log probability: -7.14 +2024-01-16 22:15:39,742 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:15:39,742 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:39,742 (beam_search:483) INFO: best hypo: AUCHNACRKOMMENSINNICHBEKANT + +2024-01-16 22:15:39,743 (asr_inference:494) INFO: speech length: 32960 +2024-01-16 22:15:39,750 (beam_search:428) INFO: decoder input length: 49 +2024-01-16 22:15:39,751 (beam_search:429) INFO: max output length: 49 +2024-01-16 22:15:39,751 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:39,807 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:39,807 (beam_search:476) INFO: -6.37 * 1.0 = -6.37 for ctc +2024-01-16 22:15:39,807 (beam_search:479) INFO: total log probability: -6.37 +2024-01-16 22:15:39,807 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:15:39,807 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:39,808 (beam_search:483) INFO: best hypo: ERENTEKSTEDINEINDRUGKTZEVERMITE + +2024-01-16 22:15:39,809 (asr_inference:494) INFO: speech length: 31360 +2024-01-16 22:15:39,816 (beam_search:428) INFO: decoder input length: 46 +2024-01-16 22:15:39,816 (beam_search:429) INFO: max output length: 46 +2024-01-16 22:15:39,816 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:39,867 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:39,867 (beam_search:476) INFO: -5.12 * 1.0 = -5.12 for ctc +2024-01-16 22:15:39,867 (beam_search:479) INFO: total log probability: -5.12 +2024-01-16 22:15:39,867 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:15:39,867 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:39,867 (beam_search:483) INFO: best hypo: VERDINSTUMDASKELNALIETVERLIEN + +2024-01-16 22:15:39,868 (asr_inference:494) INFO: speech length: 107520 +2024-01-16 22:15:39,880 (beam_search:428) INFO: decoder input length: 165 +2024-01-16 22:15:39,880 (beam_search:429) INFO: max output length: 165 +2024-01-16 22:15:39,880 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:40,270 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:40,270 (beam_search:476) INFO: -29.68 * 1.0 = -29.68 for ctc +2024-01-16 22:15:40,270 (beam_search:479) INFO: total log probability: -29.68 +2024-01-16 22:15:40,270 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:15:40,270 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:40,270 (beam_search:483) INFO: best hypo: ABPVOLHOFMEINVNHAFMEINWELDAUSWERRKGOSENEINPLIUSAUSPÄRTERERDICHTERAUSCÜBP + +2024-01-16 22:15:40,272 (asr_inference:494) INFO: speech length: 37120 +2024-01-16 22:15:40,280 (beam_search:428) INFO: decoder input length: 55 +2024-01-16 22:15:40,280 (beam_search:429) INFO: max output length: 55 +2024-01-16 22:15:40,280 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:40,339 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:40,339 (beam_search:476) INFO: -9.22 * 1.0 = -9.22 for ctc +2024-01-16 22:15:40,339 (beam_search:479) INFO: total log probability: -9.22 +2024-01-16 22:15:40,339 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:15:40,339 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:40,339 (beam_search:483) INFO: best hypo: OMSOREANZTALSTATSBERHAUPFVE + +2024-01-16 22:15:40,340 (asr_inference:494) INFO: speech length: 23200 +2024-01-16 22:15:40,347 (beam_search:428) INFO: decoder input length: 34 +2024-01-16 22:15:40,347 (beam_search:429) INFO: max output length: 34 +2024-01-16 22:15:40,347 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:40,375 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:40,375 (beam_search:476) INFO: -4.33 * 1.0 = -4.33 for ctc +2024-01-16 22:15:40,375 (beam_search:479) INFO: total log probability: -4.33 +2024-01-16 22:15:40,375 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:40,375 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:40,375 (beam_search:483) INFO: best hypo: EFREIEDOKMENTATZION + +2024-01-16 22:15:40,376 (asr_inference:494) INFO: speech length: 34400 +2024-01-16 22:15:40,384 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 22:15:40,384 (beam_search:429) INFO: max output length: 51 +2024-01-16 22:15:40,384 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:40,445 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:40,445 (beam_search:476) INFO: -5.37 * 1.0 = -5.37 for ctc +2024-01-16 22:15:40,445 (beam_search:479) INFO: total log probability: -5.37 +2024-01-16 22:15:40,445 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 22:15:40,445 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:40,445 (beam_search:483) INFO: best hypo: GESTALTUMBESKAVERSWIEDERSPBIEGELT + +2024-01-16 22:15:40,447 (asr_inference:494) INFO: speech length: 27200 +2024-01-16 22:15:40,454 (beam_search:428) INFO: decoder input length: 40 +2024-01-16 22:15:40,454 (beam_search:429) INFO: max output length: 40 +2024-01-16 22:15:40,454 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:40,493 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:40,493 (beam_search:476) INFO: -2.54 * 1.0 = -2.54 for ctc +2024-01-16 22:15:40,493 (beam_search:479) INFO: total log probability: -2.54 +2024-01-16 22:15:40,493 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 22:15:40,493 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:40,493 (beam_search:483) INFO: best hypo: DEGESAMTERAUFWANTWITAUF + +2024-01-16 22:15:40,494 (asr_inference:494) INFO: speech length: 96000 +2024-01-16 22:15:40,505 (beam_search:428) INFO: decoder input length: 147 +2024-01-16 22:15:40,505 (beam_search:429) INFO: max output length: 147 +2024-01-16 22:15:40,505 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:40,851 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:40,851 (beam_search:476) INFO: -16.88 * 1.0 = -16.88 for ctc +2024-01-16 22:15:40,851 (beam_search:479) INFO: total log probability: -16.88 +2024-01-16 22:15:40,851 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:40,851 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:40,852 (beam_search:483) INFO: best hypo: AUBGLEICHMBURKDIESIEMANGHRTUUNDEINENOBLITIERUNDRICHENKEISERDAMITKEINEDRESC + +2024-01-16 22:15:40,853 (asr_inference:494) INFO: speech length: 71520 +2024-01-16 22:15:40,863 (beam_search:428) INFO: decoder input length: 109 +2024-01-16 22:15:40,863 (beam_search:429) INFO: max output length: 109 +2024-01-16 22:15:40,863 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:41,084 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:41,084 (beam_search:476) INFO: -11.76 * 1.0 = -11.76 for ctc +2024-01-16 22:15:41,084 (beam_search:479) INFO: total log probability: -11.76 +2024-01-16 22:15:41,084 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:15:41,084 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:41,085 (beam_search:483) INFO: best hypo: DERASTURCHTDENSICHAUSWEITENTENWELTHANDELARBEITUNDWOLSTANVERSPRACH + +2024-01-16 22:15:41,086 (asr_inference:494) INFO: speech length: 80800 +2024-01-16 22:15:41,096 (beam_search:428) INFO: decoder input length: 124 +2024-01-16 22:15:41,096 (beam_search:429) INFO: max output length: 124 +2024-01-16 22:15:41,096 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:41,355 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:41,355 (beam_search:476) INFO: -18.38 * 1.0 = -18.38 for ctc +2024-01-16 22:15:41,355 (beam_search:479) INFO: total log probability: -18.38 +2024-01-16 22:15:41,355 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:15:41,355 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:41,356 (beam_search:483) INFO: best hypo: FÖÖERDIEZEITMITEDESNEUNIHNDEJERHUNDARSBEKLAKDERCHITEKTMATINHALE + +2024-01-16 22:15:41,357 (asr_inference:494) INFO: speech length: 42080 +2024-01-16 22:15:41,365 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 22:15:41,365 (beam_search:429) INFO: max output length: 63 +2024-01-16 22:15:41,365 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:41,433 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:41,433 (beam_search:476) INFO: -6.55 * 1.0 = -6.55 for ctc +2024-01-16 22:15:41,433 (beam_search:479) INFO: total log probability: -6.55 +2024-01-16 22:15:41,433 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:41,433 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:41,433 (beam_search:483) INFO: best hypo: EITBNDEKANZERHRMUTSMITLENTE + +2024-01-16 22:15:41,434 (asr_inference:494) INFO: speech length: 56480 +2024-01-16 22:15:41,443 (beam_search:428) INFO: decoder input length: 86 +2024-01-16 22:15:41,443 (beam_search:429) INFO: max output length: 86 +2024-01-16 22:15:41,443 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:41,551 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:41,551 (beam_search:476) INFO: -9.87 * 1.0 = -9.87 for ctc +2024-01-16 22:15:41,551 (beam_search:479) INFO: total log probability: -9.87 +2024-01-16 22:15:41,551 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:15:41,551 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:41,551 (beam_search:483) INFO: best hypo: DINAMENGUODEFREIIMSTAZSHANTBUOCHTZ + +2024-01-16 22:15:41,552 (asr_inference:494) INFO: speech length: 27200 +2024-01-16 22:15:41,559 (beam_search:428) INFO: decoder input length: 40 +2024-01-16 22:15:41,559 (beam_search:429) INFO: max output length: 40 +2024-01-16 22:15:41,559 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:41,602 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:41,602 (beam_search:476) INFO: -7.24 * 1.0 = -7.24 for ctc +2024-01-16 22:15:41,602 (beam_search:479) INFO: total log probability: -7.24 +2024-01-16 22:15:41,602 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:15:41,602 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:41,602 (beam_search:483) INFO: best hypo: WENAUCHMTENERGEWSENLETAGIE + +2024-01-16 22:15:41,603 (asr_inference:494) INFO: speech length: 18400 +2024-01-16 22:15:41,610 (beam_search:428) INFO: decoder input length: 26 +2024-01-16 22:15:41,610 (beam_search:429) INFO: max output length: 26 +2024-01-16 22:15:41,610 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:41,627 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:41,627 (beam_search:476) INFO: -3.24 * 1.0 = -3.24 for ctc +2024-01-16 22:15:41,627 (beam_search:479) INFO: total log probability: -3.24 +2024-01-16 22:15:41,627 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:41,627 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:41,628 (beam_search:483) INFO: best hypo: KAKOULIERBAN + +2024-01-16 22:15:41,629 (asr_inference:494) INFO: speech length: 52000 +2024-01-16 22:15:41,637 (beam_search:428) INFO: decoder input length: 79 +2024-01-16 22:15:41,637 (beam_search:429) INFO: max output length: 79 +2024-01-16 22:15:41,637 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:41,750 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:41,750 (beam_search:476) INFO: -14.71 * 1.0 = -14.71 for ctc +2024-01-16 22:15:41,750 (beam_search:479) INFO: total log probability: -14.71 +2024-01-16 22:15:41,750 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:15:41,750 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:41,750 (beam_search:483) INFO: best hypo: ANGEFANGDETZSOANDEFMISCHULEREINENDIEGJEPEN + +2024-01-16 22:15:41,752 (asr_inference:494) INFO: speech length: 90080 +2024-01-16 22:15:41,762 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 22:15:41,762 (beam_search:429) INFO: max output length: 138 +2024-01-16 22:15:41,762 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:42,041 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:42,041 (beam_search:476) INFO: -17.70 * 1.0 = -17.70 for ctc +2024-01-16 22:15:42,041 (beam_search:479) INFO: total log probability: -17.70 +2024-01-16 22:15:42,041 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:15:42,041 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:42,041 (beam_search:483) INFO: best hypo: FIELMENSCHESEINEINGRESLILSNARUNGSKONGURENTENUNDELSPUTENELLGEFA + +2024-01-16 22:15:42,043 (asr_inference:494) INFO: speech length: 16480 +2024-01-16 22:15:42,049 (beam_search:428) INFO: decoder input length: 23 +2024-01-16 22:15:42,049 (beam_search:429) INFO: max output length: 23 +2024-01-16 22:15:42,049 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:42,068 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:42,068 (beam_search:476) INFO: -4.56 * 1.0 = -4.56 for ctc +2024-01-16 22:15:42,068 (beam_search:479) INFO: total log probability: -4.56 +2024-01-16 22:15:42,068 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:42,068 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:42,068 (beam_search:483) INFO: best hypo: DINAUFTITVERKÖRE + +2024-01-16 22:15:42,069 (asr_inference:494) INFO: speech length: 182560 +2024-01-16 22:15:42,086 (beam_search:428) INFO: decoder input length: 283 +2024-01-16 22:15:42,086 (beam_search:429) INFO: max output length: 283 +2024-01-16 22:15:42,086 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:43,261 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:43,261 (beam_search:476) INFO: -43.28 * 1.0 = -43.28 for ctc +2024-01-16 22:15:43,261 (beam_search:479) INFO: total log probability: -43.28 +2024-01-16 22:15:43,261 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:15:43,261 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:43,262 (beam_search:483) INFO: best hypo: MIDEMSTANVOMDREIZEHNNIULIEZWEITAUSENSWERFDERINERSTITUNDEDERIEZENZKEHRTZUEKOMMONSEITZEIEWUSCHENSCHERELEITREIPUNKNOLEANPRTETUNDUNTERDE + +2024-01-16 22:15:43,263 (asr_inference:494) INFO: speech length: 24480 +2024-01-16 22:15:43,271 (beam_search:428) INFO: decoder input length: 36 +2024-01-16 22:15:43,271 (beam_search:429) INFO: max output length: 36 +2024-01-16 22:15:43,271 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:43,304 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:43,304 (beam_search:476) INFO: -4.94 * 1.0 = -4.94 for ctc +2024-01-16 22:15:43,304 (beam_search:479) INFO: total log probability: -4.94 +2024-01-16 22:15:43,304 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:15:43,304 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:43,304 (beam_search:483) INFO: best hypo: EINIKLEINEREBOGENPRÜKE + +2024-01-16 22:15:43,305 (asr_inference:494) INFO: speech length: 22080 +2024-01-16 22:15:43,312 (beam_search:428) INFO: decoder input length: 32 +2024-01-16 22:15:43,312 (beam_search:429) INFO: max output length: 32 +2024-01-16 22:15:43,312 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:43,340 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:43,340 (beam_search:476) INFO: -4.48 * 1.0 = -4.48 for ctc +2024-01-16 22:15:43,340 (beam_search:479) INFO: total log probability: -4.48 +2024-01-16 22:15:43,340 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:15:43,341 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:43,341 (beam_search:483) INFO: best hypo: SICHNUNVERSENBITERKOM + +2024-01-16 22:15:43,342 (asr_inference:494) INFO: speech length: 36800 +2024-01-16 22:15:43,350 (beam_search:428) INFO: decoder input length: 55 +2024-01-16 22:15:43,350 (beam_search:429) INFO: max output length: 55 +2024-01-16 22:15:43,350 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:43,409 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:43,409 (beam_search:476) INFO: -8.63 * 1.0 = -8.63 for ctc +2024-01-16 22:15:43,409 (beam_search:479) INFO: total log probability: -8.63 +2024-01-16 22:15:43,409 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:15:43,409 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:43,409 (beam_search:483) INFO: best hypo: AUSSDEMGEMELDETZUENTFERINEN + +2024-01-16 22:15:43,410 (asr_inference:494) INFO: speech length: 64320 +2024-01-16 22:15:43,420 (beam_search:428) INFO: decoder input length: 98 +2024-01-16 22:15:43,420 (beam_search:429) INFO: max output length: 98 +2024-01-16 22:15:43,420 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:43,592 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:43,592 (beam_search:476) INFO: -18.65 * 1.0 = -18.65 for ctc +2024-01-16 22:15:43,592 (beam_search:479) INFO: total log probability: -18.65 +2024-01-16 22:15:43,592 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:15:43,592 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:43,592 (beam_search:483) INFO: best hypo: ISDNEHAREPESCHERICSTINENEUNZICGFIEORNERTEENEUNZICHEWI + +2024-01-16 22:15:43,594 (asr_inference:494) INFO: speech length: 17600 +2024-01-16 22:15:43,600 (beam_search:428) INFO: decoder input length: 25 +2024-01-16 22:15:43,600 (beam_search:429) INFO: max output length: 25 +2024-01-16 22:15:43,600 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:43,619 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:43,619 (beam_search:476) INFO: -3.96 * 1.0 = -3.96 for ctc +2024-01-16 22:15:43,619 (beam_search:479) INFO: total log probability: -3.96 +2024-01-16 22:15:43,619 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:15:43,619 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:43,619 (beam_search:483) INFO: best hypo: ARIEMUNSELTAUF + +2024-01-16 22:15:43,620 (asr_inference:494) INFO: speech length: 55200 +2024-01-16 22:15:43,629 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 22:15:43,629 (beam_search:429) INFO: max output length: 84 +2024-01-16 22:15:43,629 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:43,731 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:43,731 (beam_search:476) INFO: -8.24 * 1.0 = -8.24 for ctc +2024-01-16 22:15:43,731 (beam_search:479) INFO: total log probability: -8.24 +2024-01-16 22:15:43,731 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:15:43,731 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:43,731 (beam_search:483) INFO: best hypo: ONDSIESEIAURCHDIEEINEFÜLRSTEN + +2024-01-16 22:15:43,733 (asr_inference:494) INFO: speech length: 26080 +2024-01-16 22:15:43,740 (beam_search:428) INFO: decoder input length: 38 +2024-01-16 22:15:43,740 (beam_search:429) INFO: max output length: 38 +2024-01-16 22:15:43,740 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:43,777 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:43,777 (beam_search:476) INFO: -6.18 * 1.0 = -6.18 for ctc +2024-01-16 22:15:43,777 (beam_search:479) INFO: total log probability: -6.18 +2024-01-16 22:15:43,777 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:15:43,777 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:43,778 (beam_search:483) INFO: best hypo: NEITZIHNHUNDERTNOEINSIEE + +2024-01-16 22:15:43,779 (asr_inference:494) INFO: speech length: 53920 +2024-01-16 22:15:43,787 (beam_search:428) INFO: decoder input length: 82 +2024-01-16 22:15:43,787 (beam_search:429) INFO: max output length: 82 +2024-01-16 22:15:43,787 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:43,895 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:43,895 (beam_search:476) INFO: -11.35 * 1.0 = -11.35 for ctc +2024-01-16 22:15:43,895 (beam_search:479) INFO: total log probability: -11.35 +2024-01-16 22:15:43,895 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:15:43,895 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:43,895 (beam_search:483) INFO: best hypo: STARTESENHABMDERÖÜMICHENINSCHENJIÖRE + +2024-01-16 22:15:43,896 (asr_inference:494) INFO: speech length: 26400 +2024-01-16 22:15:43,903 (beam_search:428) INFO: decoder input length: 39 +2024-01-16 22:15:43,903 (beam_search:429) INFO: max output length: 39 +2024-01-16 22:15:43,903 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:43,940 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:43,940 (beam_search:476) INFO: -5.88 * 1.0 = -5.88 for ctc +2024-01-16 22:15:43,940 (beam_search:479) INFO: total log probability: -5.88 +2024-01-16 22:15:43,940 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:15:43,940 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:43,940 (beam_search:483) INFO: best hypo: DELKRISIEBEHRUNDMENSCH + +2024-01-16 22:15:43,941 (asr_inference:494) INFO: speech length: 21120 +2024-01-16 22:15:43,948 (beam_search:428) INFO: decoder input length: 30 +2024-01-16 22:15:43,948 (beam_search:429) INFO: max output length: 30 +2024-01-16 22:15:43,948 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:43,975 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:43,975 (beam_search:476) INFO: -4.88 * 1.0 = -4.88 for ctc +2024-01-16 22:15:43,975 (beam_search:479) INFO: total log probability: -4.88 +2024-01-16 22:15:43,975 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:43,975 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:43,975 (beam_search:483) INFO: best hypo: MIESISCHENZKOALISCHE + +2024-01-16 22:15:43,976 (asr_inference:494) INFO: speech length: 37120 +2024-01-16 22:15:43,984 (beam_search:428) INFO: decoder input length: 55 +2024-01-16 22:15:43,984 (beam_search:429) INFO: max output length: 55 +2024-01-16 22:15:43,984 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:44,053 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:44,053 (beam_search:476) INFO: -9.92 * 1.0 = -9.92 for ctc +2024-01-16 22:15:44,053 (beam_search:479) INFO: total log probability: -9.92 +2024-01-16 22:15:44,053 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:15:44,053 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:44,053 (beam_search:483) INFO: best hypo: BECTURSCHMLGEBDESSDREIBARERTIONENI + +2024-01-16 22:15:44,054 (asr_inference:494) INFO: speech length: 79680 +2024-01-16 22:15:44,065 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 22:15:44,065 (beam_search:429) INFO: max output length: 122 +2024-01-16 22:15:44,065 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:44,336 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:44,336 (beam_search:476) INFO: -19.96 * 1.0 = -19.96 for ctc +2024-01-16 22:15:44,336 (beam_search:479) INFO: total log probability: -19.96 +2024-01-16 22:15:44,336 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:44,336 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:44,337 (beam_search:483) INFO: best hypo: KZUKSKGEBIETAERWESIHTERACHZEHNHUNDARZWEIUNSIEBZICGERÜNDETEALUSDEUNERINALPAR + +2024-01-16 22:15:44,338 (asr_inference:494) INFO: speech length: 17120 +2024-01-16 22:15:44,345 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 22:15:44,345 (beam_search:429) INFO: max output length: 24 +2024-01-16 22:15:44,345 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:44,359 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:44,359 (beam_search:476) INFO: -2.81 * 1.0 = -2.81 for ctc +2024-01-16 22:15:44,359 (beam_search:479) INFO: total log probability: -2.81 +2024-01-16 22:15:44,359 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:44,359 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:44,359 (beam_search:483) INFO: best hypo: DEVFINITZION + +2024-01-16 22:15:44,360 (asr_inference:494) INFO: speech length: 66240 +2024-01-16 22:15:44,370 (beam_search:428) INFO: decoder input length: 101 +2024-01-16 22:15:44,370 (beam_search:429) INFO: max output length: 101 +2024-01-16 22:15:44,370 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:44,549 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:44,549 (beam_search:476) INFO: -12.63 * 1.0 = -12.63 for ctc +2024-01-16 22:15:44,549 (beam_search:479) INFO: total log probability: -12.63 +2024-01-16 22:15:44,549 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:15:44,549 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:44,549 (beam_search:483) INFO: best hypo: UMINEUNVWESITETZIEWILERZWEISEMESTERKUNSGSCHCHTZUSTUDIEN + +2024-01-16 22:15:44,550 (asr_inference:494) INFO: speech length: 22880 +2024-01-16 22:15:44,557 (beam_search:428) INFO: decoder input length: 33 +2024-01-16 22:15:44,558 (beam_search:429) INFO: max output length: 33 +2024-01-16 22:15:44,558 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:44,584 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:44,584 (beam_search:476) INFO: -5.13 * 1.0 = -5.13 for ctc +2024-01-16 22:15:44,584 (beam_search:479) INFO: total log probability: -5.13 +2024-01-16 22:15:44,584 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:15:44,584 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:44,584 (beam_search:483) INFO: best hypo: DIETROTZERERGRINE + +# Accounting: time=13 threads=1 +# Ended (code 0) at Tue Jan 16 22:15:45 CST 2024, elapsed time 13 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/calculate_rtf.log b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/calculate_rtf.log new file mode 100644 index 0000000000000000000000000000000000000000..8dc50e0cc08c96b79dea7a7827b5dea58a23d712 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/calculate_rtf.log @@ -0,0 +1,9 @@ +# pyscripts/utils/calculate_rtf.py --log-dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir --log-name asr_inference --input-shift 0.0625 --start-times-marker "speech length" --end-times-marker "best hypo" --inf-num 1 +# Started at Tue Jan 16 22:15:45 CST 2024 +# +Total audio duration: 601.310 [sec] +Total decoding time: 31.078 [sec] +RTF: 0.052 +Latency: 150.135 [ms/sentence] +# Accounting: time=0 threads=1 +# Ended (code 0) at Tue Jan 16 22:15:45 CST 2024, elapsed time 0 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.1.scp b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.1.scp new file mode 100644 index 0000000000000000000000000000000000000000..3be02abae20d1a8f79ae6b79e939cf252d56adda --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.1.scp @@ -0,0 +1,52 @@ +swc_deu_001201 dump/raw/org/dev_10min_deu1/data/format.1/swc_deu_001201.flac +swc_deu_001202 dump/raw/org/dev_10min_deu1/data/format.1/swc_deu_001202.flac +swc_deu_001203 dump/raw/org/dev_10min_deu1/data/format.1/swc_deu_001203.flac +swc_deu_001204 dump/raw/org/dev_10min_deu1/data/format.1/swc_deu_001204.flac +swc_deu_001205 dump/raw/org/dev_10min_deu1/data/format.1/swc_deu_001205.flac +swc_deu_001206 dump/raw/org/dev_10min_deu1/data/format.1/swc_deu_001206.flac +swc_deu_001207 dump/raw/org/dev_10min_deu1/data/format.1/swc_deu_001207.flac +swc_deu_001208 dump/raw/org/dev_10min_deu1/data/format.2/swc_deu_001208.flac +swc_deu_001209 dump/raw/org/dev_10min_deu1/data/format.2/swc_deu_001209.flac +swc_deu_001210 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dump/raw/org/dev_10min_deu1/data/format.5/swc_deu_001235.flac +swc_deu_001236 dump/raw/org/dev_10min_deu1/data/format.6/swc_deu_001236.flac +swc_deu_001237 dump/raw/org/dev_10min_deu1/data/format.6/swc_deu_001237.flac +swc_deu_001238 dump/raw/org/dev_10min_deu1/data/format.6/swc_deu_001238.flac +swc_deu_001239 dump/raw/org/dev_10min_deu1/data/format.6/swc_deu_001239.flac +swc_deu_001240 dump/raw/org/dev_10min_deu1/data/format.6/swc_deu_001240.flac +swc_deu_001241 dump/raw/org/dev_10min_deu1/data/format.6/swc_deu_001241.flac +swc_deu_001242 dump/raw/org/dev_10min_deu1/data/format.6/swc_deu_001242.flac +swc_deu_001243 dump/raw/org/dev_10min_deu1/data/format.7/swc_deu_001243.flac +swc_deu_001244 dump/raw/org/dev_10min_deu1/data/format.7/swc_deu_001244.flac +swc_deu_001245 dump/raw/org/dev_10min_deu1/data/format.7/swc_deu_001245.flac +swc_deu_001246 dump/raw/org/dev_10min_deu1/data/format.7/swc_deu_001246.flac +swc_deu_001247 dump/raw/org/dev_10min_deu1/data/format.7/swc_deu_001247.flac +swc_deu_001248 dump/raw/org/dev_10min_deu1/data/format.7/swc_deu_001248.flac +swc_deu_001249 dump/raw/org/dev_10min_deu1/data/format.7/swc_deu_001249.flac +swc_deu_001250 dump/raw/org/dev_10min_deu1/data/format.8/swc_deu_001250.flac +swc_deu_001251 dump/raw/org/dev_10min_deu1/data/format.8/swc_deu_001251.flac +swc_deu_001252 dump/raw/org/dev_10min_deu1/data/format.8/swc_deu_001252.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.2.scp b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.2.scp new file mode 100644 index 0000000000000000000000000000000000000000..43b17cbdeb83612084a25bf37809aa1749e07484 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.2.scp @@ -0,0 +1,52 @@ +swc_deu_001253 dump/raw/org/dev_10min_deu1/data/format.8/swc_deu_001253.flac +swc_deu_001254 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dump/raw/org/dev_10min_deu1/data/format.13/swc_deu_001291.flac +swc_deu_001292 dump/raw/org/dev_10min_deu1/data/format.14/swc_deu_001292.flac +swc_deu_001293 dump/raw/org/dev_10min_deu1/data/format.14/swc_deu_001293.flac +swc_deu_001294 dump/raw/org/dev_10min_deu1/data/format.14/swc_deu_001294.flac +swc_deu_001295 dump/raw/org/dev_10min_deu1/data/format.14/swc_deu_001295.flac +swc_deu_001296 dump/raw/org/dev_10min_deu1/data/format.14/swc_deu_001296.flac +swc_deu_001297 dump/raw/org/dev_10min_deu1/data/format.14/swc_deu_001297.flac +swc_deu_001298 dump/raw/org/dev_10min_deu1/data/format.14/swc_deu_001298.flac +swc_deu_001299 dump/raw/org/dev_10min_deu1/data/format.15/swc_deu_001299.flac +swc_deu_001300 dump/raw/org/dev_10min_deu1/data/format.15/swc_deu_001300.flac +swc_deu_001301 dump/raw/org/dev_10min_deu1/data/format.15/swc_deu_001301.flac +swc_deu_001302 dump/raw/org/dev_10min_deu1/data/format.15/swc_deu_001302.flac +swc_deu_001303 dump/raw/org/dev_10min_deu1/data/format.15/swc_deu_001303.flac +swc_deu_001304 dump/raw/org/dev_10min_deu1/data/format.15/swc_deu_001304.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.3.scp b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.3.scp new file mode 100644 index 0000000000000000000000000000000000000000..a000807a239b76afd9643fb1256cb64557e5cf48 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.3.scp @@ -0,0 +1,52 @@ +swc_deu_001305 dump/raw/org/dev_10min_deu1/data/format.15/swc_deu_001305.flac +swc_deu_001306 dump/raw/org/dev_10min_deu1/data/format.16/swc_deu_001306.flac +swc_deu_001307 dump/raw/org/dev_10min_deu1/data/format.16/swc_deu_001307.flac +swc_deu_001308 dump/raw/org/dev_10min_deu1/data/format.16/swc_deu_001308.flac +swc_deu_001309 dump/raw/org/dev_10min_deu1/data/format.16/swc_deu_001309.flac +swc_deu_001310 dump/raw/org/dev_10min_deu1/data/format.16/swc_deu_001310.flac +swc_deu_001311 dump/raw/org/dev_10min_deu1/data/format.16/swc_deu_001311.flac +swc_deu_001312 dump/raw/org/dev_10min_deu1/data/format.17/swc_deu_001312.flac +swc_deu_001313 dump/raw/org/dev_10min_deu1/data/format.17/swc_deu_001313.flac +swc_deu_001314 dump/raw/org/dev_10min_deu1/data/format.17/swc_deu_001314.flac +swc_deu_001315 dump/raw/org/dev_10min_deu1/data/format.17/swc_deu_001315.flac +swc_deu_001316 dump/raw/org/dev_10min_deu1/data/format.17/swc_deu_001316.flac +swc_deu_001317 dump/raw/org/dev_10min_deu1/data/format.17/swc_deu_001317.flac +swc_deu_001318 dump/raw/org/dev_10min_deu1/data/format.18/swc_deu_001318.flac +swc_deu_001319 dump/raw/org/dev_10min_deu1/data/format.18/swc_deu_001319.flac +swc_deu_001320 dump/raw/org/dev_10min_deu1/data/format.18/swc_deu_001320.flac +swc_deu_001321 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dump/raw/org/dev_10min_deu1/data/format.22/swc_deu_001345.flac +swc_deu_001346 dump/raw/org/dev_10min_deu1/data/format.22/swc_deu_001346.flac +swc_deu_001347 dump/raw/org/dev_10min_deu1/data/format.22/swc_deu_001347.flac +swc_deu_001348 dump/raw/org/dev_10min_deu1/data/format.23/swc_deu_001348.flac +swc_deu_001349 dump/raw/org/dev_10min_deu1/data/format.23/swc_deu_001349.flac +swc_deu_001350 dump/raw/org/dev_10min_deu1/data/format.23/swc_deu_001350.flac +swc_deu_001351 dump/raw/org/dev_10min_deu1/data/format.23/swc_deu_001351.flac +swc_deu_001352 dump/raw/org/dev_10min_deu1/data/format.23/swc_deu_001352.flac +swc_deu_001353 dump/raw/org/dev_10min_deu1/data/format.23/swc_deu_001353.flac +swc_deu_001354 dump/raw/org/dev_10min_deu1/data/format.24/swc_deu_001354.flac +swc_deu_001355 dump/raw/org/dev_10min_deu1/data/format.24/swc_deu_001355.flac +swc_deu_001356 dump/raw/org/dev_10min_deu1/data/format.24/swc_deu_001356.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.4.scp b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.4.scp new file mode 100644 index 0000000000000000000000000000000000000000..0dfdfcf1c657e4dbd7f486bca9e463558fa851a5 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.4.scp @@ -0,0 +1,51 @@ +swc_deu_001357 dump/raw/org/dev_10min_deu1/data/format.24/swc_deu_001357.flac +swc_deu_001358 dump/raw/org/dev_10min_deu1/data/format.24/swc_deu_001358.flac +swc_deu_001359 dump/raw/org/dev_10min_deu1/data/format.24/swc_deu_001359.flac +swc_deu_001360 dump/raw/org/dev_10min_deu1/data/format.25/swc_deu_001360.flac +swc_deu_001361 dump/raw/org/dev_10min_deu1/data/format.25/swc_deu_001361.flac +swc_deu_001362 dump/raw/org/dev_10min_deu1/data/format.25/swc_deu_001362.flac +swc_deu_001363 dump/raw/org/dev_10min_deu1/data/format.25/swc_deu_001363.flac +swc_deu_001364 dump/raw/org/dev_10min_deu1/data/format.25/swc_deu_001364.flac +swc_deu_001365 dump/raw/org/dev_10min_deu1/data/format.25/swc_deu_001365.flac +swc_deu_001366 dump/raw/org/dev_10min_deu1/data/format.26/swc_deu_001366.flac +swc_deu_001367 dump/raw/org/dev_10min_deu1/data/format.26/swc_deu_001367.flac +swc_deu_001368 dump/raw/org/dev_10min_deu1/data/format.26/swc_deu_001368.flac +swc_deu_001369 dump/raw/org/dev_10min_deu1/data/format.26/swc_deu_001369.flac +swc_deu_001370 dump/raw/org/dev_10min_deu1/data/format.26/swc_deu_001370.flac +swc_deu_001371 dump/raw/org/dev_10min_deu1/data/format.26/swc_deu_001371.flac +swc_deu_001372 dump/raw/org/dev_10min_deu1/data/format.27/swc_deu_001372.flac +swc_deu_001373 dump/raw/org/dev_10min_deu1/data/format.27/swc_deu_001373.flac +swc_deu_001374 dump/raw/org/dev_10min_deu1/data/format.27/swc_deu_001374.flac +swc_deu_001375 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dump/raw/org/dev_10min_deu1/data/format.31/swc_deu_001399.flac +swc_deu_001400 dump/raw/org/dev_10min_deu1/data/format.31/swc_deu_001400.flac +swc_deu_001401 dump/raw/org/dev_10min_deu1/data/format.31/swc_deu_001401.flac +swc_deu_001402 dump/raw/org/dev_10min_deu1/data/format.32/swc_deu_001402.flac +swc_deu_001403 dump/raw/org/dev_10min_deu1/data/format.32/swc_deu_001403.flac +swc_deu_001404 dump/raw/org/dev_10min_deu1/data/format.32/swc_deu_001404.flac +swc_deu_001405 dump/raw/org/dev_10min_deu1/data/format.32/swc_deu_001405.flac +swc_deu_001406 dump/raw/org/dev_10min_deu1/data/format.32/swc_deu_001406.flac +swc_deu_001407 dump/raw/org/dev_10min_deu1/data/format.32/swc_deu_001407.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..067f58524bdd25206cc6a9cd94b9ac1caade9a6b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/score @@ -0,0 +1,52 @@ +swc_deu_001201 tensor(-21.4560) +swc_deu_001202 tensor(-5.4075) +swc_deu_001203 tensor(-6.7054) +swc_deu_001204 tensor(-5.5257) +swc_deu_001205 tensor(-9.1748) +swc_deu_001206 tensor(-3.4143) +swc_deu_001207 tensor(-7.0187) +swc_deu_001208 tensor(-10.6144) +swc_deu_001209 tensor(-13.9964) +swc_deu_001210 tensor(-14.6661) +swc_deu_001211 tensor(-6.2095) +swc_deu_001212 tensor(-9.2147) +swc_deu_001213 tensor(-6.0865) +swc_deu_001214 tensor(-15.1703) +swc_deu_001215 tensor(-11.7558) +swc_deu_001216 tensor(-3.8636) +swc_deu_001217 tensor(-21.7676) +swc_deu_001218 tensor(-9.1893) +swc_deu_001219 tensor(-14.1655) +swc_deu_001220 tensor(-9.6632) +swc_deu_001221 tensor(-15.3264) +swc_deu_001222 tensor(-5.7564) +swc_deu_001223 tensor(-6.8262) +swc_deu_001224 tensor(-5.3060) +swc_deu_001225 tensor(-10.7174) +swc_deu_001226 tensor(-18.1823) +swc_deu_001227 tensor(-14.3506) +swc_deu_001228 tensor(-3.5546) +swc_deu_001229 tensor(-4.5802) +swc_deu_001230 tensor(-5.1701) +swc_deu_001231 tensor(-6.6418) +swc_deu_001232 tensor(-5.4494) +swc_deu_001233 tensor(-16.8586) +swc_deu_001234 tensor(-4.5645) +swc_deu_001235 tensor(-15.1733) +swc_deu_001236 tensor(-6.9743) +swc_deu_001237 tensor(-14.3309) +swc_deu_001238 tensor(-22.6624) +swc_deu_001239 tensor(-5.1200) +swc_deu_001240 tensor(-10.6965) +swc_deu_001241 tensor(-3.2669) +swc_deu_001242 tensor(-15.6922) +swc_deu_001243 tensor(-5.9792) +swc_deu_001244 tensor(-3.1812) +swc_deu_001245 tensor(-7.8415) +swc_deu_001246 tensor(-2.6247) +swc_deu_001247 tensor(-29.5124) +swc_deu_001248 tensor(-9.9813) +swc_deu_001249 tensor(-7.8475) +swc_deu_001250 tensor(-3.9151) +swc_deu_001251 tensor(-24.1679) +swc_deu_001252 tensor(-3.0454) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..7344d4f342f016844d1960717897b6f7d21632f4 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/text @@ -0,0 +1,52 @@ +swc_deu_001201 DRERVELIEBTE UNGEHEARZOGK DE RANSCHLE GESEINSFATESNICH BERACHTDITHA +swc_deu_001202 DIE IN DE HANZESTEÄTEN ALS +swc_deu_001203 ARKEINGROSE E FRELK +swc_deu_001204 GOSEN SCHEHMISCHIN VER BRIKTE +swc_deu_001205 WODEN ACH MERERE ALOEUTRUNGSBÜSCHEVERFNTLIH +swc_deu_001206 VORBEREITETEN BIER TEICG GE TUNKT +swc_deu_001207 DOKOMNTE SHIESIG IN I +swc_deu_001208 TAURTAG VÜRDEN TUT VON KNICH VRECHL E +swc_deu_001209 DARUNDER SIN MATÜLDE ARSENDIS WECHTER DS KROLTZIS +swc_deu_001210 IN INEN SHETEN MEHR UND MHR DE ROLEDERTHADTITZSNERLN FIS +swc_deu_001211 ZU DENEN MET LOU VIGKEIT +swc_deu_001212 DRACHE DISHOFESS UN DES ADETZ FÜR N FRFEL +swc_deu_001213 ZSEIT ANGABEMVERSICHTID +swc_deu_001214 ALLS ACHTIN UNDET ACHRT ZIGHMIT ODTUO BRAMS AUF SATZS +swc_deu_001215 MÜLENWESEN IETZIN UNERDACHUN WANI +swc_deu_001216 AS DE FISCH RIS +swc_deu_001217 SIDIM ABSHLOS IM JARENEUNZEN HNDER ZWALUN ACHTZICH UNDRNAMER EINE ERSTELENGEREREISENACSPBANEN +swc_deu_001218 VERN SCAT SO VURGETZEICHNET +swc_deu_001219 FEITEN STEINS VELSTENDIGGECHICHTEN UNDT DIE AUKSBURGERSTAGSCHIH E DES ELTRE +swc_deu_001220 NACH DIENZERSTÖRONMEN WURDE DERASCH WIDER AUFPLÜN +swc_deu_001221 ACHTEN EINFLUSREICHENHAN IE ATEN BEIM KOMIE SARESCHEINGESETZ N BÜÖRGEMEISTER MAKERT IER AUFAHTUN +swc_deu_001222 AILS ENTRALDESHANDES KONTOR +swc_deu_001223 SANDERSTELUNG INERHEIB DECSTAT KREFE +swc_deu_001224 VFINEZSICH IN HALOBAKTERIEN +swc_deu_001225 AUF DER BESEITE FINDEZSIHTAS EBEMFALS VON MEIKEL KOMPUNIET +swc_deu_001226 IN HN DEATICHER ZEITHATDI DI ZERKEGESERSCHFT KEINEN AUSCHLAGEBENTEN EINFLOSMHR +swc_deu_001227 DA STDERCHVRWENDUN VON AUFTRIEB SKAPAN ODERHALZS EINER GERINGERE MITTERERDICHTDE ALSWASER HAT +swc_deu_001228 DAMATESIERUNGEN +swc_deu_001229 UMN SIEBEN UR FÜNVO +swc_deu_001230 DES ELBREICHT DIE BADES TOCHTE +swc_deu_001231 TART BABMBARERSCHE STDATZR +swc_deu_001232 ERSLIT BESONDERSLIEBTE +swc_deu_001233 AUFKUND DERS WACSENDEN PUPLIKUMS INTRESSES WURDE DER AUFTRITS ORT Ü DI PRIMA VISTALESUNGE +swc_deu_001234 ND FREITECHTSBIE +swc_deu_001235 DAS DIE REIDEN STÖRZS RLTI UN BESCHARTET BERSTANDTEN HATE +swc_deu_001236 EAREN ERSCHNEN ZWEI IMRL +swc_deu_001237 DERGRABMAN UND GRAB GA PELEN O DER OLTATEN NACHALT +swc_deu_001238 IUNENENZENHUNDARSEXSUNEUINZIH KNDIKDER RSEINEBEITEN SOBPS +swc_deu_001239 IN GE POTIENEKOPEL +swc_deu_001240 NEUNUN SECHTZIG DER ME DIER KONTWOL ALBUMSCHATZ EIN +swc_deu_001241 M TARUSC KOM +swc_deu_001242 UONE HENICHT DEN KROSSHANDE KAUFLEUTEN GESERSHAFTLICGKLEICHGESTERT WARE +swc_deu_001243 VRNDERNARUNG UND VOMKIEMA +swc_deu_001244 APRLOE EIENZS +swc_deu_001245 BRÜÖÜL UND HÖERT NACKELE +swc_deu_001246 TWER IN EN KLOSTE +swc_deu_001247 DIEVIRZSM AUFIZEHREN KANEWALENSTANT UNTREUTE INEMISCHNG US KÖRSCHEN KANDEWAL UNDBPLIDSCHE KABRET MT KOM DELEMENTEN DASTELT U +swc_deu_001248 DIE WUNSTIEN RESLE DES FÜIRTE +swc_deu_001249 NANTIT ZIEGLER DIEARMORDUM DERBANAURN +swc_deu_001250 INTEROHR IST VOEL +swc_deu_001251 DIE STRENG DER VORGENGERLEITUNG WURDEN ZWISHE EUNZHN HUNDERT NEUNUENDZWANZIG UND NEUNZHN HUNDER DREINDFÜNFZIG ARCHE LOGESCH ERKRABEN +swc_deu_001252 IN GEGE SAT diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..a601c8a24c108686d775bd437b1182ffe57e73a2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/token @@ -0,0 +1,52 @@ +swc_deu_001201 D R E R V E L I E B T E U N G E H E A R Z O G K D E R A N S C H L E G E S E I N S F A T E S N I C H B E R A C H T D I T H A +swc_deu_001202 D I E I N D E H A N Z E S T E Ä T E N A L S +swc_deu_001203 A R K E I N G R O S E E F R E L K +swc_deu_001204 G O S E N S C H E H M I S C H I N V E R B R I K T E +swc_deu_001205 W O D E N A C H M E R E R E A L O E U T R U N G S B Ü S C H E V E R F N T L I H +swc_deu_001206 V O R B E R E I T E T E N B I E R T E I C G G E T U N K T +swc_deu_001207 D O K O M N T E S H I E S I G I N I +swc_deu_001208 T A U R T A G V Ü R D E N T U T V O N K N I C H V R E C H L E +swc_deu_001209 D A R U N D E R S I N M A T Ü L D E A R S E N D I S W E C H T E R D S K R O L T Z I S +swc_deu_001210 I N I N E N S H E T E N M E H R U N D M H R D E R O L E D E R T H A D T I T Z S N E R L N F I S +swc_deu_001211 Z U D E N E N M E T L O U V I G K E I T +swc_deu_001212 D R A C H E D I S H O F E S S U N D E S A D E T Z F Ü R N F R F E L +swc_deu_001213 Z S E I T A N G A B E M V E R S I C H T I D +swc_deu_001214 A L L S A C H T I N U N D E T A C H R T Z I G H M I T O D T U O B R A M S A U F S A T Z S +swc_deu_001215 M Ü L E N W E S E N I E T Z I N U N E R D A C H U N W A N I +swc_deu_001216 A S D E F I S C H R I S +swc_deu_001217 S I D I M A B S H L O S I M J A R E N E U N Z E N H N D E R Z W A L U N A C H T Z I C H U N D R N A M E R E I N E E R S T E L E N G E R E R E I S E N A C S P B A N E N +swc_deu_001218 V E R N S C A T S O V U R G E T Z E I C H N E T +swc_deu_001219 F E I T E N S T E I N S V E L S T E N D I G G E C H I C H T E N U N D T D I E A U K S B U R G E R S T A G S C H I H E D E S E L T R E +swc_deu_001220 N A C H D I E N Z E R S T Ö R O N M E N W U R D E D E R A S C H W I D E R A U F P L Ü N +swc_deu_001221 A C H T E N E I N F L U S R E I C H E N H A N I E A T E N B E I M K O M I E S A R E S C H E I N G E S E T Z N B Ü Ö R G E M E I S T E R M A K E R T I E R A U F A H T U N +swc_deu_001222 A I L S E N T R A L D E S H A N D E S K O N T O R +swc_deu_001223 S A N D E R S T E L U N G I N E R H E I B D E C S T A T K R E F E +swc_deu_001224 V F I N E Z S I C H I N H A L O B A K T E R I E N +swc_deu_001225 A U F D E R B E S E I T E F I N D E Z S I H T A S E B E M F A L S V O N M E I K E L K O M P U N I E T +swc_deu_001226 I N H N D E A T I C H E R Z E I T H A T D I D I Z E R K E G E S E R S C H F T K E I N E N A U S C H L A G E B E N T E N E I N F L O S M H R +swc_deu_001227 D A S T D E R C H V R W E N D U N V O N A U F T R I E B S K A P A N O D E R H A L Z S E I N E R G E R I N G E R E M I T T E R E R D I C H T D E A L S W A S E R H A T +swc_deu_001228 D A M A T E S I E R U N G E N +swc_deu_001229 U M N S I E B E N U R F Ü N V O +swc_deu_001230 D E S E L B R E I C H T D I E B A D E S T O C H T E +swc_deu_001231 T A R T B A B M B A R E R S C H E S T D A T Z R +swc_deu_001232 E R S L I T B E S O N D E R S L I E B T E +swc_deu_001233 A U F K U N D D E R S W A C S E N D E N P U P L I K U M S I N T R E S S E S W U R D E D E R A U F T R I T S O R T Ü D I P R I M A V I S T A L E S U N G E +swc_deu_001234 N D F R E I T E C H T S B I E +swc_deu_001235 D A S D I E R E I D E N S T Ö R Z S R L T I U N B E S C H A R T E T B E R S T A N D T E N H A T E +swc_deu_001236 E A R E N E R S C H N E N Z W E I I M R L +swc_deu_001237 D E R G R A B M A N U N D G R A B G A P E L E N O D E R O L T A T E N N A C H A L T +swc_deu_001238 I U N E N E N Z E N H U N D A R S E X S U N E U I N Z I H K N D I K D E R R S E I N E B E I T E N S O B P S +swc_deu_001239 I N G E P O T I E N E K O P E L +swc_deu_001240 N E U N U N S E C H T Z I G D E R M E D I E R K O N T W O L A L B U M S C H A T Z E I N +swc_deu_001241 M T A R U S C K O M +swc_deu_001242 U O N E H E N I C H T D E N K R O S S H A N D E K A U F L E U T E N G E S E R S H A F T L I C G K L E I C H G E S T E R T W A R E +swc_deu_001243 V R N D E R N A R U N G U N D V O M K I E M A +swc_deu_001244 A P R L O E E I E N Z S +swc_deu_001245 B R Ü Ö Ü L U N D H Ö E R T N A C K E L E +swc_deu_001246 T W E R I N E N K L O S T E +swc_deu_001247 D I E V I R Z S M A U F I Z E H R E N K A N E W A L E N S T A N T U N T R E U T E I N E M I S C H N G U S K Ö R S C H E N K A N D E W A L U N D B P L I D S C H E K A B R E T M T K O M D E L E M E N T E N D A S T E L T U +swc_deu_001248 D I E W U N S T I E N R E S L E D E S F Ü I R T E +swc_deu_001249 N A N T I T Z I E G L E R D I E A R M O R D U M D E R B A N A U R N +swc_deu_001250 I N T E R O H R I S T V O E L +swc_deu_001251 D I E S T R E N G D E R V O R G E N G E R L E I T U N G W U R D E N Z W I S H E E U N Z H N H U N D E R T N E U N U E N D Z W A N Z I G U N D N E U N Z H N H U N D E R D R E I N D F Ü N F Z I G A R C H E L O G E S C H E R K R A B E N +swc_deu_001252 I N G E G E S A T diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..fa169ffc89f0ae5f5c3b35abd177a81aae698652 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/token_int @@ -0,0 +1,52 @@ +swc_deu_001201 10 6 2 6 24 2 13 5 2 18 8 2 3 12 4 14 2 11 2 9 6 20 16 14 22 3 10 2 3 6 9 4 7 15 11 13 2 3 14 2 7 2 5 4 7 19 9 8 2 7 4 5 15 11 3 18 2 6 9 15 11 8 10 5 8 11 9 +swc_deu_001202 10 5 2 3 5 4 3 10 2 3 11 9 4 20 2 7 8 2 26 8 2 4 3 9 13 7 +swc_deu_001203 9 6 22 2 5 4 14 6 16 7 2 3 2 3 19 6 2 13 22 +swc_deu_001204 14 16 7 2 4 3 7 15 11 2 11 17 5 7 15 11 5 4 3 24 2 6 3 18 6 5 22 8 2 +swc_deu_001205 21 16 10 2 4 3 9 15 11 3 17 2 6 2 6 2 3 9 13 16 2 12 8 6 12 4 14 7 18 25 7 15 11 2 24 2 6 19 4 8 13 5 11 +swc_deu_001206 24 16 6 18 2 6 2 5 8 2 8 2 4 3 18 5 2 6 3 8 2 5 15 14 3 14 2 3 8 12 4 22 8 +swc_deu_001207 10 16 22 16 17 4 8 2 3 7 11 5 2 7 5 14 3 5 4 3 5 +swc_deu_001208 8 9 12 6 8 9 14 3 24 25 6 10 2 4 3 8 12 8 3 24 16 4 3 22 4 5 15 11 3 24 6 2 15 11 13 3 2 +swc_deu_001209 10 9 6 12 4 10 2 6 3 7 5 4 3 17 9 8 25 13 10 2 3 9 6 7 2 4 10 5 7 3 21 2 15 11 8 2 6 3 10 7 3 22 6 16 13 8 20 5 7 +swc_deu_001210 5 4 3 5 4 2 4 3 7 11 2 8 2 4 3 17 2 11 6 3 12 4 10 3 17 11 6 3 10 2 3 6 16 13 2 10 2 6 8 11 9 10 8 5 8 20 7 4 2 6 13 4 3 19 5 7 +swc_deu_001211 20 12 3 10 2 4 2 4 3 17 2 8 3 13 16 12 3 24 5 14 22 2 5 8 +swc_deu_001212 10 6 9 15 11 2 3 10 5 7 11 16 19 2 7 7 3 12 4 3 10 2 7 3 9 10 2 8 20 3 19 25 6 3 4 3 19 6 19 2 13 +swc_deu_001213 20 7 2 5 8 3 9 4 14 9 18 2 17 24 2 6 7 5 15 11 8 5 10 +swc_deu_001214 3 9 13 13 7 3 9 15 11 8 5 4 3 12 4 10 2 8 3 9 15 11 6 8 3 20 5 14 11 17 5 8 3 16 10 8 12 16 3 18 6 9 17 7 3 9 12 19 3 7 9 8 20 7 +swc_deu_001215 17 25 13 2 4 21 2 7 2 4 3 5 2 8 20 5 4 3 12 4 2 6 10 9 15 11 12 4 3 21 9 4 5 +swc_deu_001216 9 7 3 10 2 3 19 5 7 15 11 3 6 5 7 +swc_deu_001217 7 5 10 5 17 3 9 18 7 11 13 16 7 3 5 17 3 28 9 6 2 4 2 12 4 20 2 4 3 11 4 10 2 6 3 20 21 9 13 12 4 3 9 15 11 8 20 5 15 11 3 12 4 10 6 4 9 17 2 6 3 2 5 4 2 3 2 6 7 8 2 13 2 4 14 2 6 2 6 2 5 7 2 4 9 15 7 23 18 9 4 2 4 +swc_deu_001218 24 2 6 4 3 7 15 9 8 3 7 16 3 24 12 6 14 2 8 20 2 5 15 11 4 2 8 +swc_deu_001219 19 2 5 8 2 4 3 7 8 2 5 4 7 3 24 2 13 7 8 2 4 10 5 14 14 2 15 11 5 15 11 8 2 4 3 12 4 10 8 3 10 5 2 3 9 12 22 7 18 12 6 14 2 6 7 8 9 14 7 15 11 5 11 3 2 3 10 2 7 3 2 13 8 6 2 +swc_deu_001220 4 9 15 11 3 10 5 2 4 20 2 6 7 8 27 6 16 4 17 2 4 3 21 12 6 10 2 3 10 2 6 9 7 15 11 3 21 5 10 2 6 3 9 12 19 23 13 25 4 +swc_deu_001221 9 15 11 8 2 4 3 2 5 4 19 13 12 7 6 2 5 15 11 2 4 11 9 4 3 5 2 3 9 8 2 4 3 18 2 5 17 3 22 16 17 5 2 3 7 9 6 2 7 15 11 2 5 4 14 2 7 2 8 20 3 4 3 18 25 27 6 14 2 17 2 5 7 8 2 6 3 17 9 22 2 6 8 3 5 2 6 3 9 12 19 9 11 8 12 4 +swc_deu_001222 9 5 13 7 3 2 4 8 6 9 13 10 2 7 11 9 4 10 2 7 3 22 16 4 8 16 6 +swc_deu_001223 7 9 4 10 2 6 7 8 2 13 12 4 14 3 5 4 2 6 11 2 5 18 3 10 2 15 7 8 9 8 3 22 6 2 19 2 +swc_deu_001224 24 19 5 4 2 20 7 5 15 11 3 5 4 3 11 9 13 16 18 9 22 8 2 6 5 2 4 +swc_deu_001225 9 12 19 3 10 2 6 3 18 2 7 2 5 8 2 3 19 5 4 10 2 20 7 5 11 8 9 7 3 2 18 2 17 19 9 13 7 3 24 16 4 3 17 2 5 22 2 13 3 22 16 17 23 12 4 5 2 8 +swc_deu_001226 5 4 3 11 4 3 10 2 9 8 5 15 11 2 6 3 20 2 5 8 11 9 8 10 5 3 10 5 3 20 2 6 22 2 14 2 7 2 6 7 15 11 19 8 3 22 2 5 4 2 4 3 9 12 7 15 11 13 9 14 2 18 2 4 8 2 4 3 2 5 4 19 13 16 7 17 11 6 +swc_deu_001227 10 9 3 7 8 10 2 6 15 11 24 6 21 2 4 10 12 4 3 24 16 4 3 9 12 19 8 6 5 2 18 3 7 22 9 23 9 4 3 16 10 2 6 11 9 13 20 7 3 2 5 4 2 6 3 14 2 6 5 4 14 2 6 2 3 17 5 8 8 2 6 2 6 10 5 15 11 8 10 2 3 9 13 7 21 9 7 2 6 3 11 9 8 +swc_deu_001228 10 9 17 9 8 2 7 5 2 6 12 4 14 2 4 +swc_deu_001229 12 17 4 3 7 5 2 18 2 4 3 12 6 3 19 25 4 24 16 +swc_deu_001230 10 2 7 3 2 13 18 6 2 5 15 11 8 3 10 5 2 3 18 9 10 2 7 3 8 16 15 11 8 2 +swc_deu_001231 8 9 6 8 3 18 9 18 17 18 9 6 2 6 7 15 11 2 3 7 8 10 9 8 20 6 +swc_deu_001232 2 6 7 13 5 8 3 18 2 7 16 4 10 2 6 7 13 5 2 18 8 2 +swc_deu_001233 9 12 19 22 12 4 10 3 10 2 6 7 3 21 9 15 7 2 4 10 2 4 3 23 12 23 13 5 22 12 17 7 3 5 4 8 6 2 7 7 2 7 3 21 12 6 10 2 3 10 2 6 3 9 12 19 8 6 5 8 7 3 16 6 8 3 25 3 10 5 3 23 6 5 17 9 3 24 5 7 8 9 13 2 7 12 4 14 2 +swc_deu_001234 4 10 3 19 6 2 5 8 2 15 11 8 7 18 5 2 +swc_deu_001235 10 9 7 3 10 5 2 3 6 2 5 10 2 4 3 7 8 27 6 20 7 3 6 13 8 5 3 12 4 3 18 2 7 15 11 9 6 8 2 8 3 18 2 6 7 8 9 4 10 8 2 4 3 11 9 8 2 +swc_deu_001236 2 9 6 2 4 3 2 6 7 15 11 4 2 4 3 20 21 2 5 3 5 17 6 13 +swc_deu_001237 10 2 6 14 6 9 18 17 9 4 3 12 4 10 3 14 6 9 18 3 14 9 3 23 2 13 2 4 3 16 3 10 2 6 3 16 13 8 9 8 2 4 3 4 9 15 11 9 13 8 +swc_deu_001238 5 12 4 2 4 2 4 20 2 4 11 12 4 10 9 6 7 2 30 7 12 4 2 12 5 4 20 5 11 3 22 4 10 5 22 10 2 6 3 6 7 2 5 4 2 18 2 5 8 2 4 3 7 16 18 23 7 +swc_deu_001239 5 4 3 14 2 3 23 16 8 5 2 4 2 22 16 23 2 13 +swc_deu_001240 4 2 12 4 12 4 3 7 2 15 11 8 20 5 14 3 10 2 6 3 17 2 3 10 5 2 6 3 22 16 4 8 21 16 13 3 9 13 18 12 17 7 15 11 9 8 20 3 2 5 4 +swc_deu_001241 17 3 8 9 6 12 7 15 3 22 16 17 +swc_deu_001242 12 16 4 2 3 11 2 4 5 15 11 8 3 10 2 4 3 22 6 16 7 7 11 9 4 10 2 3 22 9 12 19 13 2 12 8 2 4 3 14 2 7 2 6 7 11 9 19 8 13 5 15 14 22 13 2 5 15 11 14 2 7 8 2 6 8 3 21 9 6 2 +swc_deu_001243 24 6 4 10 2 6 4 9 6 12 4 14 3 12 4 10 3 24 16 17 22 5 2 17 9 +swc_deu_001244 9 23 6 13 16 2 3 2 5 2 4 20 7 +swc_deu_001245 18 6 25 27 25 13 3 12 4 10 3 11 27 2 6 8 3 4 9 15 22 2 13 2 +swc_deu_001246 8 21 2 6 3 5 4 3 2 4 3 22 13 16 7 8 2 +swc_deu_001247 10 5 2 24 5 6 20 7 17 3 9 12 19 5 20 2 11 6 2 4 3 22 9 4 2 21 9 13 2 4 7 8 9 4 8 3 12 4 8 6 2 12 8 2 3 5 4 2 17 5 7 15 11 4 14 3 12 7 3 22 27 6 7 15 11 2 4 3 22 9 4 10 2 21 9 13 3 12 4 10 18 23 13 5 10 7 15 11 2 3 22 9 18 6 2 8 3 17 8 3 22 16 17 3 10 2 13 2 17 2 4 8 2 4 3 10 9 7 8 2 13 8 3 12 +swc_deu_001248 10 5 2 3 21 12 4 7 8 5 2 4 3 6 2 7 13 2 3 10 2 7 3 19 25 5 6 8 2 +swc_deu_001249 4 9 4 8 5 8 3 20 5 2 14 13 2 6 3 10 5 2 9 6 17 16 6 10 12 17 3 10 2 6 18 9 4 9 12 6 4 +swc_deu_001250 5 4 8 2 6 16 11 6 3 5 7 8 3 24 16 2 13 +swc_deu_001251 10 5 2 3 7 8 6 2 4 14 3 10 2 6 3 24 16 6 14 2 4 14 2 6 13 2 5 8 12 4 14 3 21 12 6 10 2 4 3 20 21 5 7 11 2 3 2 12 4 20 11 4 3 11 12 4 10 2 6 8 3 4 2 12 4 12 2 4 10 20 21 9 4 20 5 14 3 12 4 10 3 4 2 12 4 20 11 4 3 11 12 4 10 2 6 3 10 6 2 5 4 10 19 25 4 19 20 5 14 3 9 6 15 11 2 3 13 16 14 2 7 15 11 3 2 6 22 6 9 18 2 4 +swc_deu_001252 3 5 4 3 14 2 14 2 3 7 9 8 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..8ce08daa570f72351cd8121d2e5d7e1d46963d78 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/score @@ -0,0 +1,52 @@ +swc_deu_001253 tensor(-7.2178) +swc_deu_001254 tensor(-2.5170) +swc_deu_001255 tensor(-6.6613) +swc_deu_001256 tensor(-6.9462) +swc_deu_001257 tensor(-6.3518) +swc_deu_001258 tensor(-7.1800) +swc_deu_001259 tensor(-4.1606) +swc_deu_001260 tensor(-12.5074) +swc_deu_001261 tensor(-6.8418) +swc_deu_001262 tensor(-4.6965) +swc_deu_001263 tensor(-11.7198) +swc_deu_001264 tensor(-10.8928) +swc_deu_001265 tensor(-3.9204) +swc_deu_001266 tensor(-3.8656) +swc_deu_001267 tensor(-7.1237) +swc_deu_001268 tensor(-3.2044) +swc_deu_001269 tensor(-4.9920) +swc_deu_001270 tensor(-9.1500) +swc_deu_001271 tensor(-2.8213) +swc_deu_001272 tensor(-6.5661) +swc_deu_001273 tensor(-6.6792) +swc_deu_001274 tensor(-17.0657) +swc_deu_001275 tensor(-7.9558) +swc_deu_001276 tensor(-4.3851) +swc_deu_001277 tensor(-10.8162) +swc_deu_001278 tensor(-5.5964) +swc_deu_001279 tensor(-4.9785) +swc_deu_001280 tensor(-5.0266) +swc_deu_001281 tensor(-7.1802) +swc_deu_001282 tensor(-4.5758) +swc_deu_001283 tensor(-6.6537) +swc_deu_001284 tensor(-10.0632) +swc_deu_001285 tensor(-5.4067) +swc_deu_001286 tensor(-20.5660) +swc_deu_001287 tensor(-12.5413) +swc_deu_001288 tensor(-9.1659) +swc_deu_001289 tensor(-4.9292) +swc_deu_001290 tensor(-7.4037) +swc_deu_001291 tensor(-7.0673) +swc_deu_001292 tensor(-17.5372) +swc_deu_001293 tensor(-4.3445) +swc_deu_001294 tensor(-7.8586) +swc_deu_001295 tensor(-12.0364) +swc_deu_001296 tensor(-7.4454) +swc_deu_001297 tensor(-6.1695) +swc_deu_001298 tensor(-12.2854) +swc_deu_001299 tensor(-11.8347) +swc_deu_001300 tensor(-23.3939) +swc_deu_001301 tensor(-12.3052) +swc_deu_001302 tensor(-8.8220) +swc_deu_001303 tensor(-9.8133) +swc_deu_001304 tensor(-10.1707) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..ab5db89dadcf7f60297a8c86727c1c8668fea4cd --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/text @@ -0,0 +1,52 @@ +swc_deu_001253 FABE VERN ERDIGENSEN LAUND HORT +swc_deu_001254 LEIF VERANSTALTUMEN +swc_deu_001255 ZOWERENHUTEINDEREGEL ALLE DORT LEBENDEN BRAUN +swc_deu_001256 IE DA FÜRE USCSELMER +swc_deu_001257 DESHAN DIE ATEN FÜÖRE +swc_deu_001258 HEBELTS ARG NES BANAU +swc_deu_001259 LIEBENSWEISE VERKRBPR +swc_deu_001260 EDEFEIL DES FIEREN HAMBURGANZU KATORLES VRAMMEN +swc_deu_001261 KERLTUL ND IERTHRFT AUSTAUSCHE +swc_deu_001262 MIJARZWEI TAUSEND VERTONDTE +swc_deu_001263 DASE DIESELEITUNGSCNALERVOLENTEN KÜNE ALS DER BAUMEISTER DEN KÖNER DOUM +swc_deu_001264 IDE HNRIHTUNG DE BANAUARIN HABESICSRLIHTUM +swc_deu_001265 LORDWEI +swc_deu_001266 DERZEIT DER BSTIGKHENER DER EIFELEITUN +swc_deu_001267 VOKUS BES WISENSHAFTLICHEN INDTARESES +swc_deu_001268 TEMER ZU BEGEISTE +swc_deu_001269 METE UND KONTE DAMIT AUFON INEN BERGANGEN WERDEN +swc_deu_001270 HAHTKABER BES SELLALISTEU DENH +swc_deu_001271 DARREIN EN ZIKLOP +swc_deu_001272 DN GRESLIE WIDER AUF DILESETZUSETZEN +swc_deu_001273 WIE LANG DIESE KAPLAN STELE AUFRICHTER HETENWRD +swc_deu_001274 SIEWARN MARSCHEINLIGHBEREITZ DREISZIG SIKUNDTEN NECH AUSPRCHTES VORER +swc_deu_001275 METER GESAMTLINGE UND BISTUT SEHN MIETER +swc_deu_001276 VEINER ITZEN UNDSPALT +swc_deu_001277 DINMEN VEN AUSSNDIEKOLER HINABPFLIESEN SIET +swc_deu_001278 NE ITER IOSAKTEBRAUN +swc_deu_001279 DAS FÜNFT EN GERIUM +swc_deu_001280 REISEN SIMANCHMAL WEIDETIERE ISCHAFE +swc_deu_001281 SI HÖREN DEN ARTIKEL DESEIN RÜFIU +swc_deu_001282 KUSER IS GLENTER KOCH +swc_deu_001283 SHANZE WENT STIFTDUN +swc_deu_001284 NEUNZHN HNDERT ACHTZIEN ALT S HAN IE ARTENANGESIE +swc_deu_001285 MERER ES NACH IM TUN +swc_deu_001286 AUCSHIH DES GERICHS ZUO LANDES WEITEN BERLIEBEN KOULINA RISCHER SPÄTZELTET ARMÜG +swc_deu_001287 KOLLETSCH UND EIN ZWEITCOB ALTSPBANSHLIERER IN HEM N VORLSEN +swc_deu_001288 BURDEN EINIS WEGS AELLE GEBÜIROTIGE +swc_deu_001289 IST IER KERBEBAUGREFTIGKH +swc_deu_001290 AN LESLICHTER NOUAS ANSPRACREKH +swc_deu_001291 MIT WIN VONDSCREÄGHINTEN +swc_deu_001292 DIN REÖCSTEN TEALDE BIT TZIÖGSFORTRIETUNG ÜRDINGEN AUS +swc_deu_001293 ACHTIN HUNERT EILUNZWANZI +swc_deu_001294 DSKROSEN ATELTS AN GESAMITENREICHTUMS +swc_deu_001295 DESON NIHT AL SEH ZWOL PROROKATIU +swc_deu_001296 TEILHBEDE VRMER GOSMAN UND IORGENZ +swc_deu_001297 INEMITE FRAUNTER KAUT INSELT +swc_deu_001298 AUR DIEO ISTEANDUTCHER HEBOFVARLARG MIT SITZ INMEÜNCHEN +swc_deu_001299 FA PEIK MENTE UNDSCHMICHE VORPRED UKTER HERSTELT +swc_deu_001300 ARPLICHEN PRESISCHEN FREI HEREN STAND IN DER ZOL ANSCHLOS VRAGE INTSCHIEBDENG GEGENG DEN SINART AUF DIESEITE BISMAGS GESTELT +swc_deu_001301 WENDIKWELEN VON SEST ER VORGWELEN UND AUFEN ZUTAGELIGEN +swc_deu_001302 DS VOUN NACHBARBAUTRIUB BELLEITZ BEGON WOT +swc_deu_001303 WEHRDEN PRÄRGENDE ELEMENTE DES HANSE ATENTUMSZUOSAMMEN GEFAST +swc_deu_001304 DEASSLIEZWRDERTS VOLCGSTLIET AN GESIEN diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..d69769cc0714390eee8d70d08cad0f010ad966be --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/token @@ -0,0 +1,52 @@ +swc_deu_001253 F A B E V E R N E R D I G E N S E N L A U N D H O R T +swc_deu_001254 L E I F V E R A N S T A L T U M E N +swc_deu_001255 Z O W E R E N H U T E I N D E R E G E L A L L E D O R T L E B E N D E N B R A U N +swc_deu_001256 I E D A F Ü R E U S C S E L M E R +swc_deu_001257 D E S H A N D I E A T E N F Ü Ö R E +swc_deu_001258 H E B E L T S A R G N E S B A N A U +swc_deu_001259 L I E B E N S W E I S E V E R K R B P R +swc_deu_001260 E D E F E I L D E S F I E R E N H A M B U R G A N Z U K A T O R L E S V R A M M E N +swc_deu_001261 K E R L T U L N D I E R T H R F T A U S T A U S C H E +swc_deu_001262 M I J A R Z W E I T A U S E N D V E R T O N D T E +swc_deu_001263 D A S E D I E S E L E I T U N G S C N A L E R V O L E N T E N K Ü N E A L S D E R B A U M E I S T E R D E N K Ö N E R D O U M +swc_deu_001264 I D E H N R I H T U N G D E B A N A U A R I N H A B E S I C S R L I H T U M +swc_deu_001265 L O R D W E I +swc_deu_001266 D E R Z E I T D E R B S T I G K H E N E R D E R E I F E L E I T U N +swc_deu_001267 V O K U S B E S W I S E N S H A F T L I C H E N I N D T A R E S E S +swc_deu_001268 T E M E R Z U B E G E I S T E +swc_deu_001269 M E T E U N D K O N T E D A M I T A U F O N I N E N B E R G A N G E N W E R D E N +swc_deu_001270 H A H T K A B E R B E S S E L L A L I S T E U D E N H +swc_deu_001271 D A R R E I N E N Z I K L O P +swc_deu_001272 D N G R E S L I E W I D E R A U F D I L E S E T Z U S E T Z E N +swc_deu_001273 W I E L A N G D I E S E K A P L A N S T E L E A U F R I C H T E R H E T E N W R D +swc_deu_001274 S I E W A R N M A R S C H E I N L I G H B E R E I T Z D R E I S Z I G S I K U N D T E N N E C H A U S P R C H T E S V O R E R +swc_deu_001275 M E T E R G E S A M T L I N G E U N D B I S T U T S E H N M I E T E R +swc_deu_001276 V E I N E R I T Z E N U N D S P A L T +swc_deu_001277 D I N M E N V E N A U S S N D I E K O L E R H I N A B P F L I E S E N S I E T +swc_deu_001278 N E I T E R I O S A K T E B R A U N +swc_deu_001279 D A S F Ü N F T E N G E R I U M +swc_deu_001280 R E I S E N S I M A N C H M A L W E I D E T I E R E I S C H A F E +swc_deu_001281 S I H Ö R E N D E N A R T I K E L D E S E I N R Ü F I U +swc_deu_001282 K U S E R I S G L E N T E R K O C H +swc_deu_001283 S H A N Z E W E N T S T I F T D U N +swc_deu_001284 N E U N Z H N H N D E R T A C H T Z I E N A L T S H A N I E A R T E N A N G E S I E +swc_deu_001285 M E R E R E S N A C H I M T U N +swc_deu_001286 A U C S H I H D E S G E R I C H S Z U O L A N D E S W E I T E N B E R L I E B E N K O U L I N A R I S C H E R S P Ä T Z E L T E T A R M Ü G +swc_deu_001287 K O L L E T S C H U N D E I N Z W E I T C O B A L T S P B A N S H L I E R E R I N H E M N V O R L S E N +swc_deu_001288 B U R D E N E I N I S W E G S A E L L E G E B Ü I R O T I G E +swc_deu_001289 I S T I E R K E R B E B A U G R E F T I G K H +swc_deu_001290 A N L E S L I C H T E R N O U A S A N S P R A C R E K H +swc_deu_001291 M I T W I N V O N D S C R E Ä G H I N T E N +swc_deu_001292 D I N R E Ö C S T E N T E A L D E B I T T Z I Ö G S F O R T R I E T U N G Ü R D I N G E N A U S +swc_deu_001293 A C H T I N H U N E R T E I L U N Z W A N Z I +swc_deu_001294 D S K R O S E N A T E L T S A N G E S A M I T E N R E I C H T U M S +swc_deu_001295 D E S O N N I H T A L S E H Z W O L P R O R O K A T I U +swc_deu_001296 T E I L H B E D E V R M E R G O S M A N U N D I O R G E N Z +swc_deu_001297 I N E M I T E F R A U N T E R K A U T I N S E L T +swc_deu_001298 A U R D I E O I S T E A N D U T C H E R H E B O F V A R L A R G M I T S I T Z I N M E Ü N C H E N +swc_deu_001299 F A P E I K M E N T E U N D S C H M I C H E V O R P R E D U K T E R H E R S T E L T +swc_deu_001300 A R P L I C H E N P R E S I S C H E N F R E I H E R E N S T A N D I N D E R Z O L A N S C H L O S V R A G E I N T S C H I E B D E N G G E G E N G D E N S I N A R T A U F D I E S E I T E B I S M A G S G E S T E L T +swc_deu_001301 W E N D I K W E L E N V O N S E S T E R V O R G W E L E N U N D A U F E N Z U T A G E L I G E N +swc_deu_001302 D S V O U N N A C H B A R B A U T R I U B B E L L E I T Z B E G O N W O T +swc_deu_001303 W E H R D E N P R Ä R G E N D E E L E M E N T E D E S H A N S E A T E N T U M S Z U O S A M M E N G E F A S T +swc_deu_001304 D E A S S L I E Z W R D E R T S V O L C G S T L I E T A N G E S I E N diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..a8ccd7be36f24ef06e570b0b20ea0c7d477a9530 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/token_int @@ -0,0 +1,52 @@ +swc_deu_001253 19 9 18 2 3 24 2 6 4 3 2 6 10 5 14 2 4 7 2 4 3 13 9 12 4 10 3 11 16 6 8 +swc_deu_001254 13 2 5 19 3 24 2 6 9 4 7 8 9 13 8 12 17 2 4 +swc_deu_001255 20 16 21 2 6 2 4 11 12 8 2 5 4 10 2 6 2 14 2 13 3 9 13 13 2 3 10 16 6 8 3 13 2 18 2 4 10 2 4 3 18 6 9 12 4 +swc_deu_001256 5 2 3 10 9 3 19 25 6 2 3 12 7 15 7 2 13 17 2 6 +swc_deu_001257 10 2 7 11 9 4 3 10 5 2 3 9 8 2 4 3 19 25 27 6 2 +swc_deu_001258 11 2 18 2 13 8 7 3 9 6 14 3 4 2 7 3 18 9 4 9 12 +swc_deu_001259 13 5 2 18 2 4 7 21 2 5 7 2 3 24 2 6 22 6 18 23 6 +swc_deu_001260 2 10 2 19 2 5 13 3 10 2 7 3 19 5 2 6 2 4 3 11 9 17 18 12 6 14 9 4 20 12 3 22 9 8 16 6 13 2 7 3 24 6 9 17 17 2 4 +swc_deu_001261 22 2 6 13 8 12 13 3 4 10 3 5 2 6 8 11 6 19 8 3 9 12 7 8 9 12 7 15 11 2 +swc_deu_001262 17 5 28 9 6 20 21 2 5 3 8 9 12 7 2 4 10 3 24 2 6 8 16 4 10 8 2 +swc_deu_001263 10 9 7 2 3 10 5 2 7 2 13 2 5 8 12 4 14 7 15 4 9 13 2 6 24 16 13 2 4 8 2 4 3 22 25 4 2 3 9 13 7 3 10 2 6 3 18 9 12 17 2 5 7 8 2 6 3 10 2 4 3 22 27 4 2 6 3 10 16 12 17 +swc_deu_001264 5 10 2 3 11 4 6 5 11 8 12 4 14 3 10 2 3 18 9 4 9 12 9 6 5 4 3 11 9 18 2 7 5 15 7 6 13 5 11 8 12 17 +swc_deu_001265 3 13 16 6 10 21 2 5 +swc_deu_001266 10 2 6 20 2 5 8 3 10 2 6 3 18 7 8 5 14 22 11 2 4 2 6 3 10 2 6 3 2 5 19 2 13 2 5 8 12 4 +swc_deu_001267 24 16 22 12 7 3 18 2 7 3 21 5 7 2 4 7 11 9 19 8 13 5 15 11 2 4 3 5 4 10 8 9 6 2 7 2 7 +swc_deu_001268 8 2 17 2 6 3 20 12 3 18 2 14 2 5 7 8 2 +swc_deu_001269 17 2 8 2 3 12 4 10 3 22 16 4 8 2 3 10 9 17 5 8 3 9 12 19 16 4 3 5 4 2 4 3 18 2 6 14 9 4 14 2 4 3 21 2 6 10 2 4 +swc_deu_001270 11 9 11 8 22 9 18 2 6 3 18 2 7 3 7 2 13 13 9 13 5 7 8 2 12 3 10 2 4 11 +swc_deu_001271 10 9 6 6 2 5 4 3 2 4 3 20 5 22 13 16 23 +swc_deu_001272 10 4 3 14 6 2 7 13 5 2 3 21 5 10 2 6 3 9 12 19 3 10 5 13 2 7 2 8 20 12 7 2 8 20 2 4 +swc_deu_001273 21 5 2 3 13 9 4 14 3 10 5 2 7 2 3 22 9 23 13 9 4 3 7 8 2 13 2 3 9 12 19 6 5 15 11 8 2 6 3 11 2 8 2 4 21 6 10 +swc_deu_001274 7 5 2 21 9 6 4 3 17 9 6 7 15 11 2 5 4 13 5 14 11 18 2 6 2 5 8 20 3 10 6 2 5 7 20 5 14 3 7 5 22 12 4 10 8 2 4 3 4 2 15 11 3 9 12 7 23 6 15 11 8 2 7 3 24 16 6 2 6 +swc_deu_001275 17 2 8 2 6 3 14 2 7 9 17 8 13 5 4 14 2 3 12 4 10 3 18 5 7 8 12 8 3 7 2 11 4 3 17 5 2 8 2 6 +swc_deu_001276 24 2 5 4 2 6 3 5 8 20 2 4 3 12 4 10 7 23 9 13 8 +swc_deu_001277 10 5 4 17 2 4 3 24 2 4 3 9 12 7 7 4 10 5 2 22 16 13 2 6 3 11 5 4 9 18 23 19 13 5 2 7 2 4 3 7 5 2 8 +swc_deu_001278 4 2 3 5 8 2 6 3 5 16 7 9 22 8 2 18 6 9 12 4 +swc_deu_001279 10 9 7 3 19 25 4 19 8 3 2 4 3 14 2 6 5 12 17 +swc_deu_001280 6 2 5 7 2 4 3 7 5 17 9 4 15 11 17 9 13 3 21 2 5 10 2 8 5 2 6 2 3 5 7 15 11 9 19 2 +swc_deu_001281 7 5 3 11 27 6 2 4 3 10 2 4 3 9 6 8 5 22 2 13 3 10 2 7 2 5 4 3 6 25 19 5 12 +swc_deu_001282 22 12 7 2 6 3 5 7 3 14 13 2 4 8 2 6 3 22 16 15 11 +swc_deu_001283 3 7 11 9 4 20 2 3 21 2 4 8 3 7 8 5 19 8 10 12 4 +swc_deu_001284 4 2 12 4 20 11 4 3 11 4 10 2 6 8 3 9 15 11 8 20 5 2 4 3 9 13 8 3 7 3 11 9 4 3 5 2 3 9 6 8 2 4 9 4 14 2 7 5 2 +swc_deu_001285 17 2 6 2 6 3 2 7 3 4 9 15 11 3 5 17 3 8 12 4 +swc_deu_001286 9 12 15 7 11 5 11 3 10 2 7 3 14 2 6 5 15 11 7 3 20 12 16 3 13 9 4 10 2 7 3 21 2 5 8 2 4 3 18 2 6 13 5 2 18 2 4 3 22 16 12 13 5 4 9 3 6 5 7 15 11 2 6 3 7 23 26 8 20 2 13 8 2 8 3 9 6 17 25 14 +swc_deu_001287 22 16 13 13 2 8 7 15 11 3 12 4 10 3 2 5 4 3 20 21 2 5 8 15 16 18 3 9 13 8 7 23 18 9 4 7 11 13 5 2 6 2 6 3 5 4 3 11 2 17 3 4 3 24 16 6 13 7 2 4 +swc_deu_001288 18 12 6 10 2 4 3 2 5 4 5 7 3 21 2 14 7 3 9 2 13 13 2 3 14 2 18 25 5 6 16 8 5 14 2 +swc_deu_001289 5 7 8 3 5 2 6 3 22 2 6 18 2 18 9 12 14 6 2 19 8 5 14 22 11 +swc_deu_001290 9 4 3 13 2 7 13 5 15 11 8 2 6 3 4 16 12 9 7 3 9 4 7 23 6 9 15 6 2 22 11 +swc_deu_001291 3 17 5 8 3 21 5 4 3 24 16 4 10 7 15 6 2 26 14 11 5 4 8 2 4 +swc_deu_001292 10 5 4 3 6 2 27 15 7 8 2 4 3 8 2 9 13 10 2 3 18 5 8 3 8 20 5 27 14 7 19 16 6 8 6 5 2 8 12 4 14 3 25 6 10 5 4 14 2 4 3 9 12 7 +swc_deu_001293 9 15 11 8 5 4 3 11 12 4 2 6 8 3 2 5 13 12 4 20 21 9 4 20 5 +swc_deu_001294 10 7 22 6 16 7 2 4 3 9 8 2 13 8 7 3 9 4 3 14 2 7 9 17 5 8 2 4 6 2 5 15 11 8 12 17 7 +swc_deu_001295 10 2 7 16 4 3 4 5 11 8 3 9 13 3 7 2 11 3 20 21 16 13 3 23 6 16 6 16 22 9 8 5 12 +swc_deu_001296 8 2 5 13 11 18 2 10 2 3 24 6 17 2 6 3 14 16 7 17 9 4 3 12 4 10 3 5 16 6 14 2 4 20 +swc_deu_001297 5 4 2 17 5 8 2 3 19 6 9 12 4 8 2 6 3 22 9 12 8 3 5 4 7 2 13 8 +swc_deu_001298 9 12 6 3 10 5 2 16 3 5 7 8 2 9 4 10 12 8 15 11 2 6 3 11 2 18 16 19 24 9 6 13 9 6 14 3 17 5 8 3 7 5 8 20 3 5 4 17 2 25 4 15 11 2 4 +swc_deu_001299 19 9 3 23 2 5 22 3 17 2 4 8 2 3 12 4 10 7 15 11 17 5 15 11 2 3 24 16 6 23 6 2 10 3 12 22 8 2 6 3 11 2 6 7 8 2 13 8 +swc_deu_001300 9 6 23 13 5 15 11 2 4 3 3 23 6 2 7 5 7 15 11 2 4 3 19 6 2 5 3 11 2 6 2 4 3 7 8 9 4 10 3 5 4 3 10 2 6 3 20 16 13 3 9 4 7 15 11 13 16 7 3 24 6 9 14 2 3 5 4 8 7 15 11 5 2 18 10 2 4 14 3 14 2 14 2 4 14 3 10 2 4 3 7 5 4 9 6 8 3 9 12 19 3 10 5 2 7 2 5 8 2 3 18 5 7 17 9 14 7 3 14 2 7 8 2 13 8 +swc_deu_001301 21 2 4 10 5 22 21 2 13 2 4 3 24 16 4 3 7 2 7 8 3 2 6 3 24 16 6 14 21 2 13 2 4 3 12 4 10 3 9 12 19 2 4 3 20 12 8 9 14 2 13 5 14 2 4 +swc_deu_001302 10 7 3 24 16 12 4 3 4 9 15 11 18 9 6 18 9 12 8 6 5 12 18 3 18 2 13 13 2 5 8 20 3 18 2 14 16 4 3 21 16 8 +swc_deu_001303 21 2 11 6 10 2 4 3 23 6 26 6 14 2 4 10 2 3 2 13 2 17 2 4 8 2 3 10 2 7 3 11 9 4 7 2 3 9 8 2 4 8 12 17 7 20 12 16 7 9 17 17 2 4 3 14 2 19 9 7 8 +swc_deu_001304 10 2 9 7 7 13 5 2 20 21 6 10 2 6 8 7 3 24 16 13 15 14 7 8 13 5 2 8 3 9 4 3 14 2 7 5 2 4 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..6486471033f91027f75de0d4bb2cfe4b7607a4c4 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/score @@ -0,0 +1,52 @@ +swc_deu_001305 tensor(-11.4478) +swc_deu_001306 tensor(-15.3636) +swc_deu_001307 tensor(-5.6724) +swc_deu_001308 tensor(-14.2332) +swc_deu_001309 tensor(-15.4615) +swc_deu_001310 tensor(-11.1936) +swc_deu_001311 tensor(-11.2855) +swc_deu_001312 tensor(-11.4843) +swc_deu_001313 tensor(-23.9136) +swc_deu_001314 tensor(-8.0266) +swc_deu_001315 tensor(-13.8212) +swc_deu_001316 tensor(-2.3233) +swc_deu_001317 tensor(-9.7453) +swc_deu_001318 tensor(-4.3564) +swc_deu_001319 tensor(-4.2551) +swc_deu_001320 tensor(-12.6318) +swc_deu_001321 tensor(-2.5785) +swc_deu_001322 tensor(-2.7335) +swc_deu_001323 tensor(-4.4292) +swc_deu_001324 tensor(-6.2805) +swc_deu_001325 tensor(-12.8895) +swc_deu_001326 tensor(-15.6260) +swc_deu_001327 tensor(-3.8861) +swc_deu_001328 tensor(-15.6160) +swc_deu_001329 tensor(-9.9752) +swc_deu_001330 tensor(-12.4754) +swc_deu_001331 tensor(-6.3681) +swc_deu_001332 tensor(-7.4555) +swc_deu_001333 tensor(-6.3481) +swc_deu_001334 tensor(-4.6248) +swc_deu_001335 tensor(-10.4714) +swc_deu_001336 tensor(-7.7291) +swc_deu_001337 tensor(-9.3276) +swc_deu_001338 tensor(-18.5487) +swc_deu_001339 tensor(-8.0278) +swc_deu_001340 tensor(-5.9875) +swc_deu_001341 tensor(-8.5114) +swc_deu_001342 tensor(-13.7181) +swc_deu_001343 tensor(-2.1871) +swc_deu_001344 tensor(-5.7028) +swc_deu_001345 tensor(-8.4875) +swc_deu_001346 tensor(-12.9542) +swc_deu_001347 tensor(-6.5822) +swc_deu_001348 tensor(-34.4553) +swc_deu_001349 tensor(-5.5948) +swc_deu_001350 tensor(-10.5272) +swc_deu_001351 tensor(-4.1323) +swc_deu_001352 tensor(-12.6522) +swc_deu_001353 tensor(-19.6775) +swc_deu_001354 tensor(-39.4262) +swc_deu_001355 tensor(-10.0117) +swc_deu_001356 tensor(-9.8342) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..83ac3a172b2bef8a307b1bb2bfc3948a11929542 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/text @@ -0,0 +1,52 @@ +swc_deu_001305 DERZON NENDEM HUSVRLAG SKGOBIGE HÖRT +swc_deu_001306 FR DE KNFDIGEN BARD PBÜSCHER IND WIKETE DI PAPIEFERPR +swc_deu_001307 AMBRE WUOKS +swc_deu_001308 V DIEKWARAR SIE ARD LIGEN LANZITZE BETRIEBEN AUFAND SEISBEMBAU +swc_deu_001309 JA ZWEI TAUSEN ZWÖLF INDN BELIENER KLUOPSO SE SHNDREISICH VELLIGT +swc_deu_001310 SECHTIN HNEN FÜNFTIGH EID BÜNDNIS +swc_deu_001311 DASPROBLEN BI DEN PRADTACHSONIST +swc_deu_001312 AMINWESEN TETIAMALIE S IEVEKEIN +swc_deu_001313 ICH EI MALEINE AN SATZWEISE UNTE SOCHRUNG ZU IEREM VERHALKTEN INDER ZEITT DES NAZUNALSUTZELISMUS +swc_deu_001314 LIETZSEINS FÜRFREIE DOKOMENTATION +swc_deu_001315 DIM CHZEN NER HUNDER DIGATEN HOLSE VOR ENTORE +swc_deu_001316 GANZS IMSTIELDER ZEIT +swc_deu_001317 ER BRÜL UND HÜÖRT AREICH E DI LEITUNSCHISLIKEN +swc_deu_001318 AUSSTZEICHNUMEN REM DER HEREN +swc_deu_001319 DIESCHEFTELLEREI AUFTZUGEBE +swc_deu_001320 DAZUTZIHRN DE BEGEGNUNMIT VERLETZTENTIERE +swc_deu_001321 IENESCH STDIFT +swc_deu_001322 WESTLICH VON KERLEN +swc_deu_001323 DIE STENDI N BETRIPWARE +swc_deu_001324 DI VOM BA BIJERHASIERT WEHRDE +swc_deu_001325 ERSCHEN NOREINEITRAUFSEATZFON GRISTERNMEIAL +swc_deu_001326 WAEIL SEBST EXSTRME REICHTUMKEINES WEHST IN UNMITLEBAREN SZUGAN +swc_deu_001327 GEBT EUSCHEICH SELBER AUF +swc_deu_001328 ER HAET DIESEN BRAUCH NEUNEHIN HNDER WEIUND FÜNFTIGH GIGN ÜB +swc_deu_001329 O DELEITUNG ÜBE DIE ALTE HÜÖRTEARLEITUNGEFIERT WURDE +swc_deu_001330 INE BLIEBTE KLSCHOKTROPEASTE KENER UMNANDTDIE HÖNE +swc_deu_001331 GEWARDEN SEIO UND ALBRECHT DICH +swc_deu_001332 DETAGESBEDAF EINESWAGSTENEN ANITEMIN AR +swc_deu_001333 SIBTINULER ZIHEN OBAR ALTE +swc_deu_001334 WEITERHEN LIESICHNACRWEISEN +swc_deu_001335 ZUN GRÜNUNGSTDARTUM KONTBAM BEREIT +swc_deu_001336 KEIN LECSCHLARG MÜKLICH NACHTEILE +swc_deu_001337 RTDIKATOLICHE KÜRSCHESAN PÄTER ANDER STELLE DE ALT +swc_deu_001338 ERFEKNARPBUN DESBROT WEITZEN TRAT ABSCHNBAIT DIKERTOFEL EIS ERSAT +swc_deu_001339 KRE MT DIESEN NACHKOMN ZOEUGE +swc_deu_001340 ALLE NEIN FOLGEN DER HÖRS IEREIE +swc_deu_001341 SCHIBPSMIE BERACTENSO +swc_deu_001342 KLIGE AN RERSBUOCHNE VERZICHTE AUFENEBERSÖNICHE BEWERTUN +swc_deu_001343 BENIGER IN TRUSSTE +swc_deu_001344 WEITER HEN VERSORK D DIELEI TUNG TERMEN +swc_deu_001345 WARTETEN DA FÜHR ABEMIT EINIG +swc_deu_001346 DE DIKLICH ANTRÜNDFONKLEIN MUNIERTI INSEINARETZEN SOND +swc_deu_001347 EM JABRNEUZENERTFÜM +swc_deu_001348 IERSIT EM FRT FAL DESBÜURGERECHT UN DEREINFÜRUN DE REITZÜGICHKEIT IM ZBANSIS NERNDERT WANDTET DE SICH DIESE ANSCHAUNG ANSERT SWEIESEIN DAR IEN +swc_deu_001349 DES SZWIST BACRES BALREINBACH EINE BOGEN BRÜCKE V +swc_deu_001350 ACTZINULETE SNDREISIG BWORDE E HMBUG +swc_deu_001351 DG AEM +swc_deu_001352 AUFGRUND DE KONTINEN TEALSPBARE ACHTZEHN HUNER ELF BANKOT +swc_deu_001353 EITERESMEIMUST DEN UNPLEIPT BRAUN DIWARBUNG FÜREAS BOHSEBTWANEM +swc_deu_001354 DINERIH VM SIEGTDER BÜRGELICH MUGRATISCHEN FIEPOARERLUTZIONVEN ACHZEIN HNDET ACHT N VIRZICHN RANKREICHVWRDEN HAMBURGMITIOBE AUFGENAME +swc_deu_001355 OUMBLIETZWEIARE ONE DERBRESCHN +swc_deu_001356 ZOALREICHE GASTSPILN UNTERWEGHS diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..ecdbfe90131cef0d4cd7b4d4b9b90de7266e0e2a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/token @@ -0,0 +1,52 @@ +swc_deu_001305 D E R Z O N N E N D E M H U S V R L A G S K G O B I G E H Ö R T +swc_deu_001306 F R D E K N F D I G E N B A R D P B Ü S C H E R I N D W I K E T E D I P A P I E F E R P R +swc_deu_001307 A M B R E W U O K S +swc_deu_001308 V D I E K W A R A R S I E A R D L I G E N L A N Z I T Z E B E T R I E B E N A U F A N D S E I S B E M B A U +swc_deu_001309 J A Z W E I T A U S E N Z W Ö L F I N D N B E L I E N E R K L U O P S O S E S H N D R E I S I C H V E L L I G T +swc_deu_001310 S E C H T I N H N E N F Ü N F T I G H E I D B Ü N D N I S +swc_deu_001311 D A S P R O B L E N B I D E N P R A D T A C H S O N I S T +swc_deu_001312 A M I N W E S E N T E T I A M A L I E S I E V E K E I N +swc_deu_001313 I C H E I M A L E I N E A N S A T Z W E I S E U N T E S O C H R U N G Z U I E R E M V E R H A L K T E N I N D E R Z E I T T D E S N A Z U N A L S U T Z E L I S M U S +swc_deu_001314 L I E T Z S E I N S F Ü R F R E I E D O K O M E N T A T I O N +swc_deu_001315 D I M C H Z E N N E R H U N D E R D I G A T E N H O L S E V O R E N T O R E +swc_deu_001316 G A N Z S I M S T I E L D E R Z E I T +swc_deu_001317 E R B R Ü L U N D H Ü Ö R T A R E I C H E D I L E I T U N S C H I S L I K E N +swc_deu_001318 A U S S T Z E I C H N U M E N R E M D E R H E R E N +swc_deu_001319 D I E S C H E F T E L L E R E I A U F T Z U G E B E +swc_deu_001320 D A Z U T Z I H R N D E B E G E G N U N M I T V E R L E T Z T E N T I E R E +swc_deu_001321 I E N E S C H S T D I F T +swc_deu_001322 W E S T L I C H V O N K E R L E N +swc_deu_001323 D I E S T E N D I N B E T R I P W A R E +swc_deu_001324 D I V O M B A B I J E R H A S I E R T W E H R D E +swc_deu_001325 E R S C H E N N O R E I N E I T R A U F S E A T Z F O N G R I S T E R N M E I A L +swc_deu_001326 W A E I L S E B S T E X S T R M E R E I C H T U M K E I N E S W E H S T I N U N M I T L E B A R E N S Z U G A N +swc_deu_001327 G E B T E U S C H E I C H S E L B E R A U F +swc_deu_001328 E R H A E T D I E S E N B R A U C H N E U N E H I N H N D E R W E I U N D F Ü N F T I G H G I G N Ü B +swc_deu_001329 O D E L E I T U N G Ü B E D I E A L T E H Ü Ö R T E A R L E I T U N G E F I E R T W U R D E +swc_deu_001330 I N E B L I E B T E K L S C H O K T R O P E A S T E K E N E R U M N A N D T D I E H Ö N E +swc_deu_001331 G E W A R D E N S E I O U N D A L B R E C H T D I C H +swc_deu_001332 D E T A G E S B E D A F E I N E S W A G S T E N E N A N I T E M I N A R +swc_deu_001333 S I B T I N U L E R Z I H E N O B A R A L T E +swc_deu_001334 W E I T E R H E N L I E S I C H N A C R W E I S E N +swc_deu_001335 Z U N G R Ü N U N G S T D A R T U M K O N T B A M B E R E I T +swc_deu_001336 K E I N L E C S C H L A R G M Ü K L I C H N A C H T E I L E +swc_deu_001337 R T D I K A T O L I C H E K Ü R S C H E S A N P Ä T E R A N D E R S T E L L E D E A L T +swc_deu_001338 E R F E K N A R P B U N D E S B R O T W E I T Z E N T R A T A B S C H N B A I T D I K E R T O F E L E I S E R S A T +swc_deu_001339 K R E M T D I E S E N N A C H K O M N Z O E U G E +swc_deu_001340 A L L E N E I N F O L G E N D E R H Ö R S I E R E I E +swc_deu_001341 S C H I B P S M I E B E R A C T E N S O +swc_deu_001342 K L I G E A N R E R S B U O C H N E V E R Z I C H T E A U F E N E B E R S Ö N I C H E B E W E R T U N +swc_deu_001343 B E N I G E R I N T R U S S T E +swc_deu_001344 W E I T E R H E N V E R S O R K D D I E L E I T U N G T E R M E N +swc_deu_001345 W A R T E T E N D A F Ü H R A B E M I T E I N I G +swc_deu_001346 D E D I K L I C H A N T R Ü N D F O N K L E I N M U N I E R T I I N S E I N A R E T Z E N S O N D +swc_deu_001347 E M J A B R N E U Z E N E R T F Ü M +swc_deu_001348 I E R S I T E M F R T F A L D E S B Ü U R G E R E C H T U N D E R E I N F Ü R U N D E R E I T Z Ü G I C H K E I T I M Z B A N S I S N E R N D E R T W A N D T E T D E S I C H D I E S E A N S C H A U N G A N S E R T S W E I E S E I N D A R I E N +swc_deu_001349 D E S S Z W I S T B A C R E S B A L R E I N B A C H E I N E B O G E N B R Ü C K E V +swc_deu_001350 A C T Z I N U L E T E S N D R E I S I G B W O R D E E H M B U G +swc_deu_001351 D G A E M +swc_deu_001352 A U F G R U N D D E K O N T I N E N T E A L S P B A R E A C H T Z E H N H U N E R E L F B A N K O T +swc_deu_001353 E I T E R E S M E I M U S T D E N U N P L E I P T B R A U N D I W A R B U N G F Ü R E A S B O H S E B T W A N E M +swc_deu_001354 D I N E R I H V M S I E G T D E R B Ü R G E L I C H M U G R A T I S C H E N F I E P O A R E R L U T Z I O N V E N A C H Z E I N H N D E T A C H T N V I R Z I C H N R A N K R E I C H V W R D E N H A M B U R G M I T I O B E A U F G E N A M E +swc_deu_001355 O U M B L I E T Z W E I A R E O N E D E R B R E S C H N +swc_deu_001356 Z O A L R E I C H E G A S T S P I L N U N T E R W E G H S diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..84fc4a5f074ca2f90962dea1f82dc11f48938f7a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/token_int @@ -0,0 +1,52 @@ +swc_deu_001305 10 2 6 20 16 4 3 4 2 4 10 2 17 3 11 12 7 24 6 13 9 14 3 7 22 14 16 18 5 14 2 3 11 27 6 8 +swc_deu_001306 19 6 3 10 2 3 22 4 19 10 5 14 2 4 3 18 9 6 10 3 23 18 25 7 15 11 2 6 3 5 4 10 3 21 5 22 2 8 2 3 10 5 3 23 9 23 5 2 19 2 6 23 6 +swc_deu_001307 3 9 17 18 6 2 3 21 12 16 22 7 +swc_deu_001308 24 3 10 5 2 22 21 9 6 9 6 3 7 5 2 3 9 6 10 3 13 5 14 2 4 3 13 9 4 20 5 8 20 2 3 18 2 8 6 5 2 18 2 4 3 9 12 19 9 4 10 3 7 2 5 7 18 2 17 18 9 12 +swc_deu_001309 28 9 3 20 21 2 5 3 8 9 12 7 2 4 3 20 21 27 13 19 3 5 4 10 4 3 18 2 13 5 2 4 2 6 3 22 13 12 16 23 7 16 3 7 2 3 7 11 4 10 6 2 5 7 5 15 11 3 24 2 13 13 5 14 8 +swc_deu_001310 7 2 15 11 8 5 4 3 11 4 2 4 3 19 25 4 19 8 5 14 11 3 2 5 10 3 18 25 4 10 4 5 7 3 +swc_deu_001311 10 9 7 23 6 16 18 13 2 4 3 18 5 3 10 2 4 3 23 6 9 10 8 9 15 11 7 16 4 5 7 8 +swc_deu_001312 9 17 5 4 21 2 7 2 4 3 8 2 8 5 9 17 9 13 5 2 3 7 3 5 2 24 2 22 2 5 4 +swc_deu_001313 5 15 11 3 2 5 3 17 9 13 2 5 4 2 3 9 4 3 7 9 8 20 21 2 5 7 2 3 12 4 8 2 3 7 16 15 11 6 12 4 14 3 20 12 3 5 2 6 2 17 3 24 2 6 11 9 13 22 8 2 4 3 5 4 10 2 6 3 20 2 5 8 8 3 10 2 7 3 4 9 20 12 4 9 13 7 12 8 20 2 13 5 7 17 12 7 +swc_deu_001314 13 5 2 8 20 7 2 5 4 7 3 19 25 6 19 6 2 5 2 3 10 16 22 16 17 2 4 8 9 8 5 16 4 +swc_deu_001315 10 5 17 3 15 11 20 2 4 3 4 2 6 3 11 12 4 10 2 6 3 10 5 14 9 8 2 4 3 11 16 13 7 2 3 24 16 6 3 2 4 8 16 6 2 +swc_deu_001316 14 9 4 20 7 3 5 17 7 8 5 2 13 10 2 6 3 20 2 5 8 +swc_deu_001317 2 6 3 18 6 25 13 3 12 4 10 3 11 25 27 6 8 3 9 6 2 5 15 11 3 2 3 10 5 3 13 2 5 8 12 4 7 15 11 5 7 13 5 22 2 4 +swc_deu_001318 9 12 7 7 8 20 2 5 15 11 4 12 17 2 4 3 6 2 17 3 10 2 6 3 11 2 6 2 4 +swc_deu_001319 10 5 2 7 15 11 2 19 8 2 13 13 2 6 2 5 3 9 12 19 8 20 12 14 2 18 2 +swc_deu_001320 10 9 20 12 8 20 5 11 6 4 3 10 2 3 18 2 14 2 14 4 12 4 17 5 8 3 24 2 6 13 2 8 20 8 2 4 8 5 2 6 2 +swc_deu_001321 5 2 4 2 7 15 11 3 7 8 10 5 19 8 +swc_deu_001322 21 2 7 8 13 5 15 11 3 24 16 4 3 22 2 6 13 2 4 +swc_deu_001323 10 5 2 3 7 8 2 4 10 5 3 4 3 18 2 8 6 5 23 21 9 6 2 +swc_deu_001324 10 5 3 24 16 17 3 18 9 3 18 5 28 2 6 11 9 7 5 2 6 8 3 21 2 11 6 10 2 +swc_deu_001325 2 6 7 15 11 2 4 3 4 16 6 2 5 4 2 5 8 6 9 12 19 7 2 9 8 20 19 16 4 3 14 6 5 7 8 2 6 4 17 2 5 9 13 +swc_deu_001326 21 9 2 5 13 3 7 2 18 7 8 3 2 30 7 8 6 17 2 3 6 2 5 15 11 8 12 17 22 2 5 4 2 7 3 21 2 11 7 8 3 5 4 3 12 4 17 5 8 13 2 18 9 6 2 4 3 7 20 12 14 9 4 +swc_deu_001327 14 2 18 8 3 2 12 7 15 11 2 5 15 11 3 7 2 13 18 2 6 3 9 12 19 +swc_deu_001328 2 6 3 11 9 2 8 3 10 5 2 7 2 4 3 18 6 9 12 15 11 3 4 2 12 4 2 11 5 4 3 11 4 10 2 6 3 21 2 5 12 4 10 3 19 25 4 19 8 5 14 11 3 14 5 14 4 3 25 18 +swc_deu_001329 16 3 10 2 13 2 5 8 12 4 14 3 25 18 2 3 10 5 2 3 9 13 8 2 3 11 25 27 6 8 2 9 6 13 2 5 8 12 4 14 2 19 5 2 6 8 3 21 12 6 10 2 +swc_deu_001330 5 4 2 3 18 13 5 2 18 8 2 3 22 13 7 15 11 16 22 8 6 16 23 2 9 7 8 2 3 22 2 4 2 6 3 12 17 4 9 4 10 8 10 5 2 3 11 27 4 2 +swc_deu_001331 14 2 21 9 6 10 2 4 3 7 2 5 16 3 12 4 10 3 9 13 18 6 2 15 11 8 3 10 5 15 11 +swc_deu_001332 10 2 8 9 14 2 7 18 2 10 9 19 3 2 5 4 2 7 21 9 14 7 8 2 4 2 4 3 9 4 5 8 2 17 5 4 3 9 6 +swc_deu_001333 7 5 18 8 5 4 12 13 2 6 3 20 5 11 2 4 3 16 18 9 6 3 9 13 8 2 +swc_deu_001334 21 2 5 8 2 6 11 2 4 3 13 5 2 7 5 15 11 4 9 15 6 21 2 5 7 2 4 +swc_deu_001335 3 20 12 4 3 14 6 25 4 12 4 14 7 8 10 9 6 8 12 17 3 22 16 4 8 18 9 17 3 18 2 6 2 5 8 +swc_deu_001336 22 2 5 4 3 13 2 15 7 15 11 13 9 6 14 3 17 25 22 13 5 15 11 3 4 9 15 11 8 2 5 13 2 +swc_deu_001337 6 8 10 5 22 9 8 16 13 5 15 11 2 3 22 25 6 7 15 11 2 7 9 4 3 23 26 8 2 6 3 9 4 10 2 6 3 7 8 2 13 13 2 3 10 2 3 9 13 8 +swc_deu_001338 2 6 19 2 22 4 9 6 23 18 12 4 3 10 2 7 18 6 16 8 3 21 2 5 8 20 2 4 3 8 6 9 8 3 9 18 7 15 11 4 18 9 5 8 3 10 5 22 2 6 8 16 19 2 13 3 2 5 7 3 2 6 7 9 8 +swc_deu_001339 22 6 2 3 17 8 3 10 5 2 7 2 4 3 4 9 15 11 22 16 17 4 3 20 16 2 12 14 2 +swc_deu_001340 9 13 13 2 3 4 2 5 4 3 19 16 13 14 2 4 3 10 2 6 3 11 27 6 7 3 5 2 6 2 5 2 +swc_deu_001341 7 15 11 5 18 23 7 17 5 2 3 18 2 6 9 15 8 2 4 7 16 +swc_deu_001342 22 13 5 14 2 3 9 4 3 6 2 6 7 18 12 16 15 11 4 2 3 24 2 6 20 5 15 11 8 2 3 9 12 19 2 4 2 18 2 6 7 27 4 5 15 11 2 3 18 2 21 2 6 8 12 4 +swc_deu_001343 18 2 4 5 14 2 6 3 5 4 3 8 6 12 7 7 8 2 +swc_deu_001344 21 2 5 8 2 6 3 11 2 4 3 24 2 6 7 16 6 22 3 10 3 10 5 2 13 2 5 3 8 12 4 14 3 8 2 6 17 2 4 +swc_deu_001345 3 21 9 6 8 2 8 2 4 3 10 9 3 19 25 11 6 3 9 18 2 17 5 8 3 2 5 4 5 14 +swc_deu_001346 10 2 3 10 5 22 13 5 15 11 3 9 4 8 6 25 4 10 19 16 4 22 13 2 5 4 3 17 12 4 5 2 6 8 5 3 5 4 7 2 5 4 9 6 2 8 20 2 4 3 7 16 4 10 +swc_deu_001347 2 17 3 28 9 18 6 4 2 12 20 2 4 2 6 8 19 25 17 +swc_deu_001348 5 2 6 7 5 8 3 2 17 3 19 6 8 3 19 9 13 3 10 2 7 18 25 12 6 14 2 6 2 15 11 8 3 12 4 3 10 2 6 2 5 4 19 25 6 12 4 3 10 2 3 6 2 5 8 20 25 14 5 15 11 22 2 5 8 3 5 17 3 20 18 9 4 7 5 7 3 4 2 6 4 10 2 6 8 3 21 9 4 10 8 2 8 3 10 2 3 7 5 15 11 3 10 5 2 7 2 3 9 4 7 15 11 9 12 4 14 3 9 4 7 2 6 8 3 7 21 2 5 2 7 2 5 4 3 10 9 6 3 5 2 4 +swc_deu_001349 10 2 7 3 7 20 21 5 7 8 3 18 9 15 6 2 7 3 18 9 13 6 2 5 4 18 9 15 11 3 2 5 4 2 3 18 16 14 2 4 3 18 6 25 15 22 2 3 24 +swc_deu_001350 9 15 8 20 5 4 12 13 2 8 2 3 7 4 10 6 2 5 7 5 14 3 18 21 16 6 10 2 3 2 3 11 17 18 12 14 +swc_deu_001351 10 14 3 9 2 17 +swc_deu_001352 9 12 19 14 6 12 4 10 3 10 2 3 22 16 4 8 5 4 2 4 3 8 2 9 13 7 23 18 9 6 2 3 9 15 11 8 20 2 11 4 3 11 12 4 2 6 3 2 13 19 3 18 9 4 22 16 8 +swc_deu_001353 2 5 8 2 6 2 7 17 2 5 17 12 7 8 3 10 2 4 3 12 4 23 13 2 5 23 8 3 18 6 9 12 4 3 10 5 21 9 6 18 12 4 14 3 19 25 6 2 9 7 3 18 16 11 7 2 18 8 21 9 4 2 17 +swc_deu_001354 3 10 5 4 2 6 5 11 3 24 17 3 7 5 2 14 8 10 2 6 3 18 25 6 14 2 13 5 15 11 3 17 12 14 6 9 8 5 7 15 11 2 4 3 19 5 2 23 16 9 6 2 6 13 12 8 20 5 16 4 24 2 4 3 9 15 11 20 2 5 4 3 11 4 10 2 8 3 9 15 11 8 3 4 3 24 5 6 20 5 15 11 4 3 6 9 4 22 6 2 5 15 11 24 21 6 10 2 4 3 11 9 17 18 12 6 14 17 5 8 5 16 18 2 3 9 12 19 14 2 4 9 17 2 +swc_deu_001355 16 12 17 18 13 5 2 8 20 21 2 5 9 6 2 3 16 4 2 3 10 2 6 18 6 2 7 15 11 4 +swc_deu_001356 20 16 9 13 6 2 5 15 11 2 3 14 9 7 8 7 23 5 13 4 3 12 4 8 2 6 21 2 14 11 7 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..f826dbeb1e6cb8df20f10e3aa14948c24ea82550 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/score @@ -0,0 +1,51 @@ +swc_deu_001357 tensor(-5.0837) +swc_deu_001358 tensor(-3.0116) +swc_deu_001359 tensor(-21.9631) +swc_deu_001360 tensor(-5.0325) +swc_deu_001361 tensor(-2.9258) +swc_deu_001362 tensor(-15.3205) +swc_deu_001363 tensor(-5.7455) +swc_deu_001364 tensor(-7.4512) +swc_deu_001365 tensor(-5.0751) +swc_deu_001366 tensor(-11.8756) +swc_deu_001367 tensor(-9.8654) +swc_deu_001368 tensor(-10.1191) +swc_deu_001369 tensor(-1.5082) +swc_deu_001370 tensor(-7.3173) +swc_deu_001371 tensor(-10.4969) +swc_deu_001372 tensor(-14.8548) +swc_deu_001373 tensor(-5.9889) +swc_deu_001374 tensor(-7.1384) +swc_deu_001375 tensor(-6.3667) +swc_deu_001376 tensor(-5.1192) +swc_deu_001377 tensor(-29.6838) +swc_deu_001378 tensor(-9.2237) +swc_deu_001379 tensor(-4.3314) +swc_deu_001380 tensor(-5.3738) +swc_deu_001381 tensor(-2.5408) +swc_deu_001382 tensor(-16.8791) +swc_deu_001383 tensor(-11.7602) +swc_deu_001384 tensor(-18.3782) +swc_deu_001385 tensor(-6.5452) +swc_deu_001386 tensor(-9.8693) +swc_deu_001387 tensor(-7.2391) +swc_deu_001388 tensor(-3.2435) +swc_deu_001389 tensor(-14.7085) +swc_deu_001390 tensor(-17.7024) +swc_deu_001391 tensor(-4.5593) +swc_deu_001392 tensor(-43.2840) +swc_deu_001393 tensor(-4.9371) +swc_deu_001394 tensor(-4.4838) +swc_deu_001395 tensor(-8.6284) +swc_deu_001396 tensor(-18.6460) +swc_deu_001397 tensor(-3.9583) +swc_deu_001398 tensor(-8.2363) +swc_deu_001399 tensor(-6.1778) +swc_deu_001400 tensor(-11.3471) +swc_deu_001401 tensor(-5.8830) +swc_deu_001402 tensor(-4.8805) +swc_deu_001403 tensor(-9.9244) +swc_deu_001404 tensor(-19.9562) +swc_deu_001405 tensor(-2.8078) +swc_deu_001406 tensor(-12.6265) +swc_deu_001407 tensor(-5.1347) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..897e6b6ae001a2b880d96e168e7ab115b55dc76f --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/text @@ -0,0 +1,51 @@ +swc_deu_001357 KRANZIETET GENÜTEN +swc_deu_001358 UN BEROKER AUSTATO +swc_deu_001359 DAGERICHT VOUM BEIVWARGENSANGESMOTORADIS AUS INDIE ZU DESERZEIT NOU EN STENDEN ABET ASIET LOMENZU +swc_deu_001360 DIMITZSAM IE HERECHEN STOBE +swc_deu_001361 KEISERFERDINAN +swc_deu_001362 VOM FERNSERESCHSUHRERFEANZSAVERBUGNER INDEM FERNSEFILEN DAS WIGELIET +swc_deu_001363 UR IN SEIN BESTEN ZEITEN DE +swc_deu_001364 SEHÖRE DEN ARTIKEL FISCHENTSCIS +swc_deu_001365 UND TE FARMOFWI +swc_deu_001366 REAZSIBZIONDERHEHSEN IMATIG VON KRESTA +swc_deu_001367 DIGESAMTER ANLARGE WABIS ET VERTZWEI HUNDER SECHTZIG NACHKRISTUS IN BEDRIE +swc_deu_001368 DER IS DE FAST FUT LIERSOARWISWAGEBUN +swc_deu_001369 EINDEM KABELBAUN +swc_deu_001370 MORLE DUNG MIT DE E FAFVERBUNDEN GEWES +swc_deu_001371 E ELTZISTN PRIERERENIN AUSRHEP +swc_deu_001372 SONEN ACH DERNA ZUN NALI SUTSER DISTISCHEN KUNZT AUF FASONGERECHTWEHREN +swc_deu_001373 DIEWELTZSICHT DES HANSE AHRTENI +swc_deu_001374 AUCHNACRKOMMEN SIN NICH BEKANT +swc_deu_001375 EREN TEKSTE DIN EINDRUGKTZE VERMITE +swc_deu_001376 VERDINSTUM DASKELNALIET VERLIEN +swc_deu_001377 ABPVOL HOFMEIN VN HAFMEIN WEL DAUS WERRK GOSENEINPLIUSAU SPÄRTERER DICHTERAUSCÜBP +swc_deu_001378 OM SOR EANZTAL STATS BERHAUPFVE +swc_deu_001379 EFREIE DOKMENTATZION +swc_deu_001380 GESTALTUM BES KAVERS WIEDERSPBIEGELT +swc_deu_001381 DEGESAMTER AUFWANT WIT AUF +swc_deu_001382 AUBGLEICHMBURK DIESIEM AN GHRTU UND EINE NOBLI TIERUN DRICHENKEISERDAMIT KEINEDRESC +swc_deu_001383 DER ASTURCHTDEN SICH AUSWEITENTEN WELTHANDEL ARBEIT UND WOLSTANVERSPRACH +swc_deu_001384 FÖÖER DIE ZEIT MITE DESNEUN IHNDE JERHUNDARS BEKLAK DERCHITEKT MATIN HALE +swc_deu_001385 EIT BNDE KANZER HRMUTSMIT LENTE +swc_deu_001386 DINAMEN GUODEFREI IM STAZSHANT BUOCHTZ +swc_deu_001387 WEN AUCHMT EN ERGEWSEN LETAGIE +swc_deu_001388 KA KOULIER BAN +swc_deu_001389 ANGEFANGDETZSOANDE FM ISCHULEREINEN DIEG JEPEN +swc_deu_001390 FIELMENSCHE SEINEING RESLI LSNARUNGSKONGURENTEN UNDELSPUTEN ELLGE FA +swc_deu_001391 DIN AUFTITVERKÖRE +swc_deu_001392 MI DEM STAN VOM DREIZEHN NIULIEZWEITAUSENSWERF DER INERSTIT UNDE DER IE ZENZ KEHRTZUE KOMMONS EIT ZEIEWUSCHEN SCHER ELEITREI PUNK NOLE AN PRTET UND UNTER DE +swc_deu_001393 EINI KLEINEREBOGEN PRÜKE +swc_deu_001394 SICH NUNVERSENBITERKOM +swc_deu_001395 AUSS DEM GEMELDETZU ENT FERINEN +swc_deu_001396 ISDNEH AREPESCHERICSTINE NEUNZICG FIEORNERTE ENEUNZICH EWI +swc_deu_001397 ARIEM UNSELT AUF +swc_deu_001398 OND SIE SEI AURCH DIE EINE FÜLRSTEN +swc_deu_001399 NEITZIHN HUNDERT NOEIN SIEE +swc_deu_001400 STARTESEN HABM DERÖÜMICHEN INSCHEN JIÖRE +swc_deu_001401 DELKRISIE BEHR UND MENSCH +swc_deu_001402 MIE SISCHENZ KOALISCHE +swc_deu_001403 BECTURSCHMLGEB DES S DREIBARERTIONEN I +swc_deu_001404 KZUKSKGEBIET AERWESIHTER ACHZEHN HUNDARZWEIUNSIEBZICGE RÜNDETE ALUSDEUNER INALPAR +swc_deu_001405 DEVFINITZION +swc_deu_001406 UM INE UNVWE SITETZIEWILER ZWEISEMESTER KUNSGSCHCHT ZUSTUDIEN +swc_deu_001407 DIE TROTZ ERER GRINE diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..39e4b0dc06cbf8c6fbacdb9444538bd658424af3 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/token @@ -0,0 +1,51 @@ +swc_deu_001357 K R A N Z I E T E T G E N Ü T E N +swc_deu_001358 U N B E R O K E R A U S T A T O +swc_deu_001359 D A G E R I C H T V O U M B E I V W A R G E N S A N G E S M O T O R A D I S A U S I N D I E Z U D E S E R Z E I T N O U E N S T E N D E N A B E T A S I E T L O M E N Z U +swc_deu_001360 D I M I T Z S A M I E H E R E C H E N S T O B E +swc_deu_001361 K E I S E R F E R D I N A N +swc_deu_001362 V O M F E R N S E R E S C H S U H R E R F E A N Z S A V E R B U G N E R I N D E M F E R N S E F I L E N D A S W I G E L I E T +swc_deu_001363 U R I N S E I N B E S T E N Z E I T E N D E +swc_deu_001364 S E H Ö R E D E N A R T I K E L F I S C H E N T S C I S +swc_deu_001365 U N D T E F A R M O F W I +swc_deu_001366 R E A Z S I B Z I O N D E R H E H S E N I M A T I G V O N K R E S T A +swc_deu_001367 D I G E S A M T E R A N L A R G E W A B I S E T V E R T Z W E I H U N D E R S E C H T Z I G N A C H K R I S T U S I N B E D R I E +swc_deu_001368 D E R I S D E F A S T F U T L I E R S O A R W I S W A G E B U N +swc_deu_001369 E I N D E M K A B E L B A U N +swc_deu_001370 M O R L E D U N G M I T D E E F A F V E R B U N D E N G E W E S +swc_deu_001371 E E L T Z I S T N P R I E R E R E N I N A U S R H E P +swc_deu_001372 S O N E N A C H D E R N A Z U N N A L I S U T S E R D I S T I S C H E N K U N Z T A U F F A S O N G E R E C H T W E H R E N +swc_deu_001373 D I E W E L T Z S I C H T D E S H A N S E A H R T E N I +swc_deu_001374 A U C H N A C R K O M M E N S I N N I C H B E K A N T +swc_deu_001375 E R E N T E K S T E D I N E I N D R U G K T Z E V E R M I T E +swc_deu_001376 V E R D I N S T U M D A S K E L N A L I E T V E R L I E N +swc_deu_001377 A B P V O L H O F M E I N V N H A F M E I N W E L D A U S W E R R K G O S E N E I N P L I U S A U S P Ä R T E R E R D I C H T E R A U S C Ü B P +swc_deu_001378 O M S O R E A N Z T A L S T A T S B E R H A U P F V E +swc_deu_001379 E F R E I E D O K M E N T A T Z I O N +swc_deu_001380 G E S T A L T U M B E S K A V E R S W I E D E R S P B I E G E L T +swc_deu_001381 D E G E S A M T E R A U F W A N T W I T A U F +swc_deu_001382 A U B G L E I C H M B U R K D I E S I E M A N G H R T U U N D E I N E N O B L I T I E R U N D R I C H E N K E I S E R D A M I T K E I N E D R E S C +swc_deu_001383 D E R A S T U R C H T D E N S I C H A U S W E I T E N T E N W E L T H A N D E L A R B E I T U N D W O L S T A N V E R S P R A C H +swc_deu_001384 F Ö Ö E R D I E Z E I T M I T E D E S N E U N I H N D E J E R H U N D A R S B E K L A K D E R C H I T E K T M A T I N H A L E +swc_deu_001385 E I T B N D E K A N Z E R H R M U T S M I T L E N T E +swc_deu_001386 D I N A M E N G U O D E F R E I I M S T A Z S H A N T B U O C H T Z +swc_deu_001387 W E N A U C H M T E N E R G E W S E N L E T A G I E +swc_deu_001388 K A K O U L I E R B A N +swc_deu_001389 A N G E F A N G D E T Z S O A N D E F M I S C H U L E R E I N E N D I E G J E P E N +swc_deu_001390 F I E L M E N S C H E S E I N E I N G R E S L I L S N A R U N G S K O N G U R E N T E N U N D E L S P U T E N E L L G E F A +swc_deu_001391 D I N A U F T I T V E R K Ö R E +swc_deu_001392 M I D E M S T A N V O M D R E I Z E H N N I U L I E Z W E I T A U S E N S W E R F D E R I N E R S T I T U N D E D E R I E Z E N Z K E H R T Z U E K O M M O N S E I T Z E I E W U S C H E N S C H E R E L E I T R E I P U N K N O L E A N P R T E T U N D U N T E R D E +swc_deu_001393 E I N I K L E I N E R E B O G E N P R Ü K E +swc_deu_001394 S I C H N U N V E R S E N B I T E R K O M +swc_deu_001395 A U S S D E M G E M E L D E T Z U E N T F E R I N E N +swc_deu_001396 I S D N E H A R E P E S C H E R I C S T I N E N E U N Z I C G F I E O R N E R T E E N E U N Z I C H E W I +swc_deu_001397 A R I E M U N S E L T A U F +swc_deu_001398 O N D S I E S E I A U R C H D I E E I N E F Ü L R S T E N +swc_deu_001399 N E I T Z I H N H U N D E R T N O E I N S I E E +swc_deu_001400 S T A R T E S E N H A B M D E R Ö Ü M I C H E N I N S C H E N J I Ö R E +swc_deu_001401 D E L K R I S I E B E H R U N D M E N S C H +swc_deu_001402 M I E S I S C H E N Z K O A L I S C H E +swc_deu_001403 B E C T U R S C H M L G E B D E S S D R E I B A R E R T I O N E N I +swc_deu_001404 K Z U K S K G E B I E T A E R W E S I H T E R A C H Z E H N H U N D A R Z W E I U N S I E B Z I C G E R Ü N D E T E A L U S D E U N E R I N A L P A R +swc_deu_001405 D E V F I N I T Z I O N +swc_deu_001406 U M I N E U N V W E S I T E T Z I E W I L E R Z W E I S E M E S T E R K U N S G S C H C H T Z U S T U D I E N +swc_deu_001407 D I E T R O T Z E R E R G R I N E diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..cf08cd25f45c0939c3ed5f1c4995fcd0153ce3e2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/token_int @@ -0,0 +1,51 @@ +swc_deu_001357 22 6 9 4 20 5 2 8 2 8 3 14 2 4 25 8 2 4 +swc_deu_001358 12 4 3 18 2 6 16 22 2 6 3 9 12 7 8 9 8 16 +swc_deu_001359 10 9 14 2 6 5 15 11 8 3 24 16 12 17 3 18 2 5 24 21 9 6 14 2 4 7 9 4 14 2 7 17 16 8 16 6 9 10 5 7 3 9 12 7 3 5 4 10 5 2 3 20 12 3 10 2 7 2 6 20 2 5 8 3 4 16 12 3 2 4 3 7 8 2 4 10 2 4 3 9 18 2 8 3 9 7 5 2 8 3 13 16 17 2 4 20 12 +swc_deu_001360 10 5 17 5 8 20 7 9 17 3 5 2 3 11 2 6 2 15 11 2 4 3 7 8 16 18 2 +swc_deu_001361 22 2 5 7 2 6 19 2 6 10 5 4 9 4 +swc_deu_001362 24 16 17 3 19 2 6 4 7 2 6 2 7 15 11 7 12 11 6 2 6 19 2 9 4 20 7 9 24 2 6 18 12 14 4 2 6 3 5 4 10 2 17 3 19 2 6 4 7 2 19 5 13 2 4 3 10 9 7 3 21 5 14 2 13 5 2 8 +swc_deu_001363 12 6 3 5 4 3 7 2 5 4 3 18 2 7 8 2 4 3 20 2 5 8 2 4 3 10 2 +swc_deu_001364 7 2 11 27 6 2 3 10 2 4 3 9 6 8 5 22 2 13 3 19 5 7 15 11 2 4 8 7 15 5 7 +swc_deu_001365 12 4 10 3 8 2 3 19 9 6 17 16 19 21 5 +swc_deu_001366 6 2 9 20 7 5 18 20 5 16 4 10 2 6 11 2 11 7 2 4 3 5 17 9 8 5 14 3 24 16 4 3 22 6 2 7 8 9 +swc_deu_001367 10 5 14 2 7 9 17 8 2 6 3 9 4 13 9 6 14 2 3 21 9 18 5 7 3 2 8 3 24 2 6 8 20 21 2 5 3 11 12 4 10 2 6 3 7 2 15 11 8 20 5 14 3 4 9 15 11 22 6 5 7 8 12 7 3 5 4 3 18 2 10 6 5 2 +swc_deu_001368 10 2 6 3 5 7 3 10 2 3 19 9 7 8 3 19 12 8 3 13 5 2 6 7 16 9 6 21 5 7 21 9 14 2 18 12 4 +swc_deu_001369 2 5 4 10 2 17 3 22 9 18 2 13 18 9 12 4 +swc_deu_001370 17 16 6 13 2 3 10 12 4 14 3 17 5 8 3 10 2 3 2 3 19 9 19 24 2 6 18 12 4 10 2 4 3 14 2 21 2 7 +swc_deu_001371 2 3 2 13 8 20 5 7 8 4 3 23 6 5 2 6 2 6 2 4 5 4 3 9 12 7 6 11 2 23 +swc_deu_001372 7 16 4 2 4 3 9 15 11 3 10 2 6 4 9 3 20 12 4 3 4 9 13 5 3 7 12 8 7 2 6 3 10 5 7 8 5 7 15 11 2 4 3 22 12 4 20 8 3 9 12 19 3 19 9 7 16 4 14 2 6 2 15 11 8 21 2 11 6 2 4 +swc_deu_001373 10 5 2 21 2 13 8 20 7 5 15 11 8 3 10 2 7 3 11 9 4 7 2 3 9 11 6 8 2 4 5 +swc_deu_001374 9 12 15 11 4 9 15 6 22 16 17 17 2 4 3 7 5 4 3 4 5 15 11 3 18 2 22 9 4 8 +swc_deu_001375 2 6 2 4 3 8 2 22 7 8 2 3 10 5 4 3 2 5 4 10 6 12 14 22 8 20 2 3 24 2 6 17 5 8 2 +swc_deu_001376 24 2 6 10 5 4 7 8 12 17 3 10 9 7 22 2 13 4 9 13 5 2 8 3 24 2 6 13 5 2 4 +swc_deu_001377 9 18 23 24 16 13 3 11 16 19 17 2 5 4 3 24 4 3 11 9 19 17 2 5 4 3 21 2 13 3 10 9 12 7 3 21 2 6 6 22 3 14 16 7 2 4 2 5 4 23 13 5 12 7 9 12 3 7 23 26 6 8 2 6 2 6 3 10 5 15 11 8 2 6 9 12 7 15 25 18 23 +swc_deu_001378 16 17 3 7 16 6 3 2 9 4 20 8 9 13 3 7 8 9 8 7 3 18 2 6 11 9 12 23 19 24 2 +swc_deu_001379 2 19 6 2 5 2 3 10 16 22 17 2 4 8 9 8 20 5 16 4 +swc_deu_001380 14 2 7 8 9 13 8 12 17 3 18 2 7 3 22 9 24 2 6 7 3 21 5 2 10 2 6 7 23 18 5 2 14 2 13 8 +swc_deu_001381 10 2 14 2 7 9 17 8 2 6 3 9 12 19 21 9 4 8 3 21 5 8 3 9 12 19 +swc_deu_001382 9 12 18 14 13 2 5 15 11 17 18 12 6 22 3 10 5 2 7 5 2 17 3 9 4 3 14 11 6 8 12 3 12 4 10 3 2 5 4 2 3 4 16 18 13 5 3 8 5 2 6 12 4 3 10 6 5 15 11 2 4 22 2 5 7 2 6 10 9 17 5 8 3 22 2 5 4 2 10 6 2 7 15 +swc_deu_001383 10 2 6 3 9 7 8 12 6 15 11 8 10 2 4 3 7 5 15 11 3 9 12 7 21 2 5 8 2 4 8 2 4 3 21 2 13 8 11 9 4 10 2 13 3 9 6 18 2 5 8 3 12 4 10 3 21 16 13 7 8 9 4 24 2 6 7 23 6 9 15 11 +swc_deu_001384 19 27 27 2 6 3 10 5 2 3 20 2 5 8 3 17 5 8 2 3 10 2 7 4 2 12 4 3 5 11 4 10 2 3 28 2 6 11 12 4 10 9 6 7 3 18 2 22 13 9 22 3 10 2 6 15 11 5 8 2 22 8 3 17 9 8 5 4 3 11 9 13 2 +swc_deu_001385 2 5 8 3 18 4 10 2 3 22 9 4 20 2 6 3 11 6 17 12 8 7 17 5 8 3 13 2 4 8 2 +swc_deu_001386 10 5 4 9 17 2 4 3 14 12 16 10 2 19 6 2 5 3 5 17 3 7 8 9 20 7 11 9 4 8 3 18 12 16 15 11 8 20 +swc_deu_001387 21 2 4 3 9 12 15 11 17 8 3 2 4 3 2 6 14 2 21 7 2 4 3 13 2 8 9 14 5 2 +swc_deu_001388 22 9 3 22 16 12 13 5 2 6 3 18 9 4 +swc_deu_001389 9 4 14 2 19 9 4 14 10 2 8 20 7 16 9 4 10 2 3 19 17 3 5 7 15 11 12 13 2 6 2 5 4 2 4 3 10 5 2 14 3 28 2 23 2 4 +swc_deu_001390 19 5 2 13 17 2 4 7 15 11 2 3 7 2 5 4 2 5 4 14 3 6 2 7 13 5 3 13 7 4 9 6 12 4 14 7 22 16 4 14 12 6 2 4 8 2 4 3 12 4 10 2 13 7 23 12 8 2 4 3 2 13 13 14 2 3 19 9 +swc_deu_001391 10 5 4 3 9 12 19 8 5 8 24 2 6 22 27 6 2 +swc_deu_001392 17 5 3 10 2 17 3 7 8 9 4 3 24 16 17 3 10 6 2 5 20 2 11 4 3 4 5 12 13 5 2 20 21 2 5 8 9 12 7 2 4 7 21 2 6 19 3 10 2 6 3 5 4 2 6 7 8 5 8 3 12 4 10 2 3 10 2 6 3 5 2 3 20 2 4 20 3 22 2 11 6 8 20 12 2 3 22 16 17 17 16 4 7 3 2 5 8 3 20 2 5 2 21 12 7 15 11 2 4 3 7 15 11 2 6 3 2 13 2 5 8 6 2 5 3 23 12 4 22 3 4 16 13 2 3 9 4 3 23 6 8 2 8 3 12 4 10 3 12 4 8 2 6 3 10 2 +swc_deu_001393 2 5 4 5 3 22 13 2 5 4 2 6 2 18 16 14 2 4 3 23 6 25 22 2 +swc_deu_001394 7 5 15 11 3 4 12 4 24 2 6 7 2 4 18 5 8 2 6 22 16 17 +swc_deu_001395 9 12 7 7 3 10 2 17 3 14 2 17 2 13 10 2 8 20 12 3 2 4 8 3 19 2 6 5 4 2 4 +swc_deu_001396 5 7 10 4 2 11 3 9 6 2 23 2 7 15 11 2 6 5 15 7 8 5 4 2 3 4 2 12 4 20 5 15 14 3 3 19 5 2 16 6 4 2 6 8 2 3 2 4 2 12 4 20 5 15 11 3 2 21 5 +swc_deu_001397 9 6 5 2 17 3 12 4 7 2 13 8 3 9 12 19 +swc_deu_001398 3 16 4 10 3 7 5 2 3 7 2 5 3 9 12 6 15 11 3 10 5 2 3 2 5 4 2 3 19 25 13 6 7 8 2 4 +swc_deu_001399 4 2 5 8 20 5 11 4 3 11 12 4 10 2 6 8 3 4 16 2 5 4 3 7 5 2 2 +swc_deu_001400 7 8 9 6 8 2 7 2 4 3 11 9 18 17 3 10 2 6 27 25 17 5 15 11 2 4 3 5 4 7 15 11 2 4 3 28 5 27 6 2 +swc_deu_001401 10 2 13 22 6 5 7 5 2 3 18 2 11 6 3 12 4 10 3 17 2 4 7 15 11 +swc_deu_001402 17 5 2 3 7 5 7 15 11 2 4 20 3 22 16 9 13 5 7 15 11 2 +swc_deu_001403 18 2 15 8 12 6 7 15 11 17 13 14 2 18 3 10 2 7 3 7 3 10 6 2 5 18 9 6 2 6 8 5 16 4 2 4 3 5 +swc_deu_001404 22 20 12 22 7 22 14 2 18 5 2 8 3 9 2 6 21 2 7 5 11 8 2 6 3 9 15 11 20 2 11 4 3 11 12 4 10 9 6 20 21 2 5 12 4 7 5 2 18 20 5 15 14 2 3 6 25 4 10 2 8 2 3 9 13 12 7 10 2 12 4 2 6 3 5 4 9 13 23 9 6 +swc_deu_001405 10 2 24 19 5 4 5 8 20 5 16 4 +swc_deu_001406 12 17 3 5 4 2 3 12 4 24 21 2 3 7 5 8 2 8 20 5 2 21 5 13 2 6 3 20 21 2 5 7 2 17 2 7 8 2 6 3 22 12 4 7 14 7 15 11 15 11 8 3 20 12 7 8 12 10 5 2 4 +swc_deu_001407 10 5 2 3 8 6 16 8 20 3 2 6 2 6 3 14 6 5 4 2 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score new file mode 100644 index 0000000000000000000000000000000000000000..71e6b27b3f10af8136ef3bc4a44ed905c5d405f6 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score @@ -0,0 +1,207 @@ +swc_deu_001201 tensor(-21.4560) +swc_deu_001202 tensor(-5.4075) +swc_deu_001203 tensor(-6.7054) +swc_deu_001204 tensor(-5.5257) +swc_deu_001205 tensor(-9.1748) +swc_deu_001206 tensor(-3.4143) +swc_deu_001207 tensor(-7.0187) +swc_deu_001208 tensor(-10.6144) +swc_deu_001209 tensor(-13.9964) +swc_deu_001210 tensor(-14.6661) +swc_deu_001211 tensor(-6.2095) +swc_deu_001212 tensor(-9.2147) +swc_deu_001213 tensor(-6.0865) +swc_deu_001214 tensor(-15.1703) +swc_deu_001215 tensor(-11.7558) +swc_deu_001216 tensor(-3.8636) +swc_deu_001217 tensor(-21.7676) +swc_deu_001218 tensor(-9.1893) +swc_deu_001219 tensor(-14.1655) +swc_deu_001220 tensor(-9.6632) +swc_deu_001221 tensor(-15.3264) +swc_deu_001222 tensor(-5.7564) +swc_deu_001223 tensor(-6.8262) +swc_deu_001224 tensor(-5.3060) +swc_deu_001225 tensor(-10.7174) +swc_deu_001226 tensor(-18.1823) +swc_deu_001227 tensor(-14.3506) +swc_deu_001228 tensor(-3.5546) +swc_deu_001229 tensor(-4.5802) +swc_deu_001230 tensor(-5.1701) +swc_deu_001231 tensor(-6.6418) +swc_deu_001232 tensor(-5.4494) +swc_deu_001233 tensor(-16.8586) +swc_deu_001234 tensor(-4.5645) +swc_deu_001235 tensor(-15.1733) +swc_deu_001236 tensor(-6.9743) +swc_deu_001237 tensor(-14.3309) +swc_deu_001238 tensor(-22.6624) +swc_deu_001239 tensor(-5.1200) +swc_deu_001240 tensor(-10.6965) +swc_deu_001241 tensor(-3.2669) +swc_deu_001242 tensor(-15.6922) +swc_deu_001243 tensor(-5.9792) +swc_deu_001244 tensor(-3.1812) +swc_deu_001245 tensor(-7.8415) +swc_deu_001246 tensor(-2.6247) +swc_deu_001247 tensor(-29.5124) +swc_deu_001248 tensor(-9.9813) +swc_deu_001249 tensor(-7.8475) +swc_deu_001250 tensor(-3.9151) +swc_deu_001251 tensor(-24.1679) +swc_deu_001252 tensor(-3.0454) +swc_deu_001253 tensor(-7.2178) +swc_deu_001254 tensor(-2.5170) +swc_deu_001255 tensor(-6.6613) +swc_deu_001256 tensor(-6.9462) +swc_deu_001257 tensor(-6.3518) +swc_deu_001258 tensor(-7.1800) +swc_deu_001259 tensor(-4.1606) +swc_deu_001260 tensor(-12.5074) +swc_deu_001261 tensor(-6.8418) +swc_deu_001262 tensor(-4.6965) +swc_deu_001263 tensor(-11.7198) +swc_deu_001264 tensor(-10.8928) +swc_deu_001265 tensor(-3.9204) +swc_deu_001266 tensor(-3.8656) +swc_deu_001267 tensor(-7.1237) +swc_deu_001268 tensor(-3.2044) +swc_deu_001269 tensor(-4.9920) +swc_deu_001270 tensor(-9.1500) +swc_deu_001271 tensor(-2.8213) +swc_deu_001272 tensor(-6.5661) +swc_deu_001273 tensor(-6.6792) +swc_deu_001274 tensor(-17.0657) +swc_deu_001275 tensor(-7.9558) +swc_deu_001276 tensor(-4.3851) +swc_deu_001277 tensor(-10.8162) +swc_deu_001278 tensor(-5.5964) +swc_deu_001279 tensor(-4.9785) +swc_deu_001280 tensor(-5.0266) +swc_deu_001281 tensor(-7.1802) +swc_deu_001282 tensor(-4.5758) +swc_deu_001283 tensor(-6.6537) +swc_deu_001284 tensor(-10.0632) +swc_deu_001285 tensor(-5.4067) +swc_deu_001286 tensor(-20.5660) +swc_deu_001287 tensor(-12.5413) +swc_deu_001288 tensor(-9.1659) +swc_deu_001289 tensor(-4.9292) +swc_deu_001290 tensor(-7.4037) +swc_deu_001291 tensor(-7.0673) +swc_deu_001292 tensor(-17.5372) +swc_deu_001293 tensor(-4.3445) +swc_deu_001294 tensor(-7.8586) +swc_deu_001295 tensor(-12.0364) +swc_deu_001296 tensor(-7.4454) +swc_deu_001297 tensor(-6.1695) +swc_deu_001298 tensor(-12.2854) +swc_deu_001299 tensor(-11.8347) +swc_deu_001300 tensor(-23.3939) +swc_deu_001301 tensor(-12.3052) +swc_deu_001302 tensor(-8.8220) +swc_deu_001303 tensor(-9.8133) +swc_deu_001304 tensor(-10.1707) +swc_deu_001305 tensor(-11.4478) +swc_deu_001306 tensor(-15.3636) +swc_deu_001307 tensor(-5.6724) +swc_deu_001308 tensor(-14.2332) +swc_deu_001309 tensor(-15.4615) +swc_deu_001310 tensor(-11.1936) +swc_deu_001311 tensor(-11.2855) +swc_deu_001312 tensor(-11.4843) +swc_deu_001313 tensor(-23.9136) +swc_deu_001314 tensor(-8.0266) +swc_deu_001315 tensor(-13.8212) +swc_deu_001316 tensor(-2.3233) +swc_deu_001317 tensor(-9.7453) +swc_deu_001318 tensor(-4.3564) +swc_deu_001319 tensor(-4.2551) +swc_deu_001320 tensor(-12.6318) +swc_deu_001321 tensor(-2.5785) +swc_deu_001322 tensor(-2.7335) +swc_deu_001323 tensor(-4.4292) +swc_deu_001324 tensor(-6.2805) +swc_deu_001325 tensor(-12.8895) +swc_deu_001326 tensor(-15.6260) +swc_deu_001327 tensor(-3.8861) +swc_deu_001328 tensor(-15.6160) +swc_deu_001329 tensor(-9.9752) +swc_deu_001330 tensor(-12.4754) +swc_deu_001331 tensor(-6.3681) +swc_deu_001332 tensor(-7.4555) +swc_deu_001333 tensor(-6.3481) +swc_deu_001334 tensor(-4.6248) +swc_deu_001335 tensor(-10.4714) +swc_deu_001336 tensor(-7.7291) +swc_deu_001337 tensor(-9.3276) +swc_deu_001338 tensor(-18.5487) +swc_deu_001339 tensor(-8.0278) +swc_deu_001340 tensor(-5.9875) +swc_deu_001341 tensor(-8.5114) +swc_deu_001342 tensor(-13.7181) +swc_deu_001343 tensor(-2.1871) +swc_deu_001344 tensor(-5.7028) +swc_deu_001345 tensor(-8.4875) +swc_deu_001346 tensor(-12.9542) +swc_deu_001347 tensor(-6.5822) +swc_deu_001348 tensor(-34.4553) +swc_deu_001349 tensor(-5.5948) +swc_deu_001350 tensor(-10.5272) +swc_deu_001351 tensor(-4.1323) +swc_deu_001352 tensor(-12.6522) +swc_deu_001353 tensor(-19.6775) +swc_deu_001354 tensor(-39.4262) +swc_deu_001355 tensor(-10.0117) +swc_deu_001356 tensor(-9.8342) +swc_deu_001357 tensor(-5.0837) +swc_deu_001358 tensor(-3.0116) +swc_deu_001359 tensor(-21.9631) +swc_deu_001360 tensor(-5.0325) +swc_deu_001361 tensor(-2.9258) +swc_deu_001362 tensor(-15.3205) +swc_deu_001363 tensor(-5.7455) +swc_deu_001364 tensor(-7.4512) +swc_deu_001365 tensor(-5.0751) +swc_deu_001366 tensor(-11.8756) +swc_deu_001367 tensor(-9.8654) +swc_deu_001368 tensor(-10.1191) +swc_deu_001369 tensor(-1.5082) +swc_deu_001370 tensor(-7.3173) +swc_deu_001371 tensor(-10.4969) +swc_deu_001372 tensor(-14.8548) +swc_deu_001373 tensor(-5.9889) +swc_deu_001374 tensor(-7.1384) +swc_deu_001375 tensor(-6.3667) +swc_deu_001376 tensor(-5.1192) +swc_deu_001377 tensor(-29.6838) +swc_deu_001378 tensor(-9.2237) +swc_deu_001379 tensor(-4.3314) +swc_deu_001380 tensor(-5.3738) +swc_deu_001381 tensor(-2.5408) +swc_deu_001382 tensor(-16.8791) +swc_deu_001383 tensor(-11.7602) +swc_deu_001384 tensor(-18.3782) +swc_deu_001385 tensor(-6.5452) +swc_deu_001386 tensor(-9.8693) +swc_deu_001387 tensor(-7.2391) +swc_deu_001388 tensor(-3.2435) +swc_deu_001389 tensor(-14.7085) +swc_deu_001390 tensor(-17.7024) +swc_deu_001391 tensor(-4.5593) +swc_deu_001392 tensor(-43.2840) +swc_deu_001393 tensor(-4.9371) +swc_deu_001394 tensor(-4.4838) +swc_deu_001395 tensor(-8.6284) +swc_deu_001396 tensor(-18.6460) +swc_deu_001397 tensor(-3.9583) +swc_deu_001398 tensor(-8.2363) +swc_deu_001399 tensor(-6.1778) +swc_deu_001400 tensor(-11.3471) +swc_deu_001401 tensor(-5.8830) +swc_deu_001402 tensor(-4.8805) +swc_deu_001403 tensor(-9.9244) +swc_deu_001404 tensor(-19.9562) +swc_deu_001405 tensor(-2.8078) +swc_deu_001406 tensor(-12.6265) +swc_deu_001407 tensor(-5.1347) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..937e53859ac0bab43b0a170cef04883843f0c83f --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/hyp.trn @@ -0,0 +1,207 @@ +D R E R V E L I E B T E U N G E H E A R Z O G K D E R A N S C H L E G E S E I N S F A T E S N I C H B E R A C H T D I T H A (swc_deu_001201-swc_deu_001201) +D I E I N D E H A N Z E S T E Ä T E N A L S (swc_deu_001202-swc_deu_001202) +A R K E I N G R O S E E F R E L K (swc_deu_001203-swc_deu_001203) +G O S E N S C H E H M I S C H I N V E R B R I K T E (swc_deu_001204-swc_deu_001204) +W O D E N A C H M E R E R E A L O E U T R U N G S B Ü S C H E V E R F N T L I H (swc_deu_001205-swc_deu_001205) +V O R B E R E I T E T E N B I E R T E I C G G E T U N K T (swc_deu_001206-swc_deu_001206) +D O K O M N T E S H I E S I G I N I (swc_deu_001207-swc_deu_001207) +T A U R T A G V Ü R D E N T U T V O N K N I C H V R E C H L E (swc_deu_001208-swc_deu_001208) +D A R U N D E R S I N M A T Ü L D E A R S E N D I S W E C H T E R D S K R O L T Z I S (swc_deu_001209-swc_deu_001209) +I N I N E N S H E T E N M E H R U N D M H R D E R O L E D E R T H A D T I T Z S N E R L N F I S (swc_deu_001210-swc_deu_001210) +Z U D E N E N M E T L O U V I G K E I T (swc_deu_001211-swc_deu_001211) +D R A C H E D I S H O F E S S U N D E S A D E T Z F Ü R N F R F E L (swc_deu_001212-swc_deu_001212) +Z S E I T A N G A B E M V E R S I C H T I D (swc_deu_001213-swc_deu_001213) +A L L S A C H T I N U N D E T A C H R T Z I G H M I T O D T U O B R A M S A U F S A T Z S (swc_deu_001214-swc_deu_001214) +M Ü L E N W E S E N I E T Z I N U N E R D A C H U N W A N I (swc_deu_001215-swc_deu_001215) +A S D E F I S C H R I S (swc_deu_001216-swc_deu_001216) +S I D I M A B S H L O S I M J A R E N E U N Z E N H N D E R Z W A L U N A C H T Z I C H U N D R N A M E R E I N E E R S T E L E N G E R E R E I S E N A C S P B A N E N (swc_deu_001217-swc_deu_001217) +V E R N S C A T S O V U R G E T Z E I C H N E T (swc_deu_001218-swc_deu_001218) +F E I T E N S T E I N S V E L S T E N D I G G E C H I C H T E N U N D T D I E A U K S B U R G E R S T A G S C H I H E D E S E L T R E (swc_deu_001219-swc_deu_001219) +N A C H D I E N Z E R S T Ö R O N M E N W U R D E D E R A S C H W I D E R A U F P L Ü N (swc_deu_001220-swc_deu_001220) +A C H T E N E I N F L U S R E I C H E N H A N I E A T E N B E I M K O M I E S A R E S C H E I N G E S E T Z N B Ü Ö R G E M E I S T E R M A K E R T I E R A U F A H T U N (swc_deu_001221-swc_deu_001221) +A I L S E N T R A L D E S H A N D E S K O N T O R (swc_deu_001222-swc_deu_001222) +S A N D E R S T E L U N G I N E R H E I B D E C S T A T K R E F E (swc_deu_001223-swc_deu_001223) +V F I N E Z S I C H I N H A L O B A K T E R I E N (swc_deu_001224-swc_deu_001224) +A U F D E R B E S E I T E F I N D E Z S I H T A S E B E M F A L S V O N M E I K E L K O M P U N I E T (swc_deu_001225-swc_deu_001225) +I N H N D E A T I C H E R Z E I T H A T D I D I Z E R K E G E S E R S C H F T K E I N E N A U S C H L A G E B E N T E N E I N F L O S M H R (swc_deu_001226-swc_deu_001226) +D A S T D E R C H V R W E N D U N V O N A U F T R I E B S K A P A N O D E R H A L Z S E I N E R G E R I N G E R E M I T T E R E R D I C H T D E A L S W A S E R H A T (swc_deu_001227-swc_deu_001227) +D A M A T E S I E R U N G E N (swc_deu_001228-swc_deu_001228) +U M N S I E B E N U R F Ü N V O (swc_deu_001229-swc_deu_001229) +D E S E L B R E I C H T D I E B A D E S T O C H T E (swc_deu_001230-swc_deu_001230) +T A R T B A B M B A R E R S C H E S T D A T Z R (swc_deu_001231-swc_deu_001231) +E R S L I T B E S O N D E R S L I E B T E (swc_deu_001232-swc_deu_001232) +A U F K U N D D E R S W A C S E N D E N P U P L I K U M S I N T R E S S E S W U R D E D E R A U F T R I T S O R T Ü D I P R I M A V I S T A L E S U N G E (swc_deu_001233-swc_deu_001233) +N D F R E I T E C H T S B I E (swc_deu_001234-swc_deu_001234) +D A S D I E R E I D E N S T Ö R Z S R L T I U N B E S C H A R T E T B E R S T A N D T E N H A T E (swc_deu_001235-swc_deu_001235) +E A R E N E R S C H N E N Z W E I I M R L (swc_deu_001236-swc_deu_001236) +D E R G R A B M A N U N D G R A B G A P E L E N O D E R O L T A T E N N A C H A L T (swc_deu_001237-swc_deu_001237) +I U N E N E N Z E N H U N D A R S E X S U N E U I N Z I H K N D I K D E R R S E I N E B E I T E N S O B P S (swc_deu_001238-swc_deu_001238) +I N G E P O T I E N E K O P E L (swc_deu_001239-swc_deu_001239) +N E U N U N S E C H T Z I G D E R M E D I E R K O N T W O L A L B U M S C H A T Z E I N (swc_deu_001240-swc_deu_001240) +M T A R U S C K O M (swc_deu_001241-swc_deu_001241) +U O N E H E N I C H T D E N K R O S S H A N D E K A U F L E U T E N G E S E R S H A F T L I C G K L E I C H G E S T E R T W A R E (swc_deu_001242-swc_deu_001242) +V R N D E R N A R U N G U N D V O M K I E M A (swc_deu_001243-swc_deu_001243) +A P R L O E E I E N Z S (swc_deu_001244-swc_deu_001244) +B R Ü Ö Ü L U N D H Ö E R T N A C K E L E (swc_deu_001245-swc_deu_001245) +T W E R I N E N K L O S T E (swc_deu_001246-swc_deu_001246) +D I E V I R Z S M A U F I Z E H R E N K A N E W A L E N S T A N T U N T R E U T E I N E M I S C H N G U S K Ö R S C H E N K A N D E W A L U N D B P L I D S C H E K A B R E T M T K O M D E L E M E N T E N D A S T E L T U (swc_deu_001247-swc_deu_001247) +D I E W U N S T I E N R E S L E D E S F Ü I R T E (swc_deu_001248-swc_deu_001248) +N A N T I T Z I E G L E R D I E A R M O R D U M D E R B A N A U R N (swc_deu_001249-swc_deu_001249) +I N T E R O H R I S T V O E L (swc_deu_001250-swc_deu_001250) +D I E S T R E N G D E R V O R G E N G E R L E I T U N G W U R D E N Z W I S H E E U N Z H N H U N D E R T N E U N U E N D Z W A N Z I G U N D N E U N Z H N H U N D E R D R E I N D F Ü N F Z I G A R C H E L O G E S C H E R K R A B E N (swc_deu_001251-swc_deu_001251) +I N G E G E S A T (swc_deu_001252-swc_deu_001252) +F A B E V E R N E R D I G E N S E N L A U N D H O R T (swc_deu_001253-swc_deu_001253) +L E I F V E R A N S T A L T U M E N (swc_deu_001254-swc_deu_001254) +Z O W E R E N H U T E I N D E R E G E L A L L E D O R T L E B E N D E N B R A U N (swc_deu_001255-swc_deu_001255) +I E D A F Ü R E U S C S E L M E R (swc_deu_001256-swc_deu_001256) +D E S H A N D I E A T E N F Ü Ö R E (swc_deu_001257-swc_deu_001257) +H E B E L T S A R G N E S B A N A U (swc_deu_001258-swc_deu_001258) +L I E B E N S W E I S E V E R K R B P R (swc_deu_001259-swc_deu_001259) +E D E F E I L D E S F I E R E N H A M B U R G A N Z U K A T O R L E S V R A M M E N (swc_deu_001260-swc_deu_001260) +K E R L T U L N D I E R T H R F T A U S T A U S C H E (swc_deu_001261-swc_deu_001261) +M I J A R Z W E I T A U S E N D V E R T O N D T E (swc_deu_001262-swc_deu_001262) +D A S E D I E S E L E I T U N G S C N A L E R V O L E N T E N K Ü N E A L S D E R B A U M E I S T E R D E N K Ö N E R D O U M (swc_deu_001263-swc_deu_001263) +I D E H N R I H T U N G D E B A N A U A R I N H A B E S I C S R L I H T U M (swc_deu_001264-swc_deu_001264) +L O R D W E I (swc_deu_001265-swc_deu_001265) +D E R Z E I T D E R B S T I G K H E N E R D E R E I F E L E I T U N (swc_deu_001266-swc_deu_001266) +V O K U S B E S W I S E N S H A F T L I C H E N I N D T A R E S E S (swc_deu_001267-swc_deu_001267) +T E M E R Z U B E G E I S T E (swc_deu_001268-swc_deu_001268) +M E T E U N D K O N T E D A M I T A U F O N I N E N B E R G A N G E N W E R D E N (swc_deu_001269-swc_deu_001269) +H A H T K A B E R B E S S E L L A L I S T E U D E N H (swc_deu_001270-swc_deu_001270) +D A R R E I N E N Z I K L O P (swc_deu_001271-swc_deu_001271) +D N G R E S L I E W I D E R A U F D I L E S E T Z U S E T Z E N (swc_deu_001272-swc_deu_001272) +W I E L A N G D I E S E K A P L A N S T E L E A U F R I C H T E R H E T E N W R D (swc_deu_001273-swc_deu_001273) +S I E W A R N M A R S C H E I N L I G H B E R E I T Z D R E I S Z I G S I K U N D T E N N E C H A U S P R C H T E S V O R E R (swc_deu_001274-swc_deu_001274) +M E T E R G E S A M T L I N G E U N D B I S T U T S E H N M I E T E R (swc_deu_001275-swc_deu_001275) +V E I N E R I T Z E N U N D S P A L T (swc_deu_001276-swc_deu_001276) +D I N M E N V E N A U S S N D I E K O L E R H I N A B P F L I E S E N S I E T (swc_deu_001277-swc_deu_001277) +N E I T E R I O S A K T E B R A U N (swc_deu_001278-swc_deu_001278) +D A S F Ü N F T E N G E R I U M (swc_deu_001279-swc_deu_001279) +R E I S E N S I M A N C H M A L W E I D E T I E R E I S C H A F E (swc_deu_001280-swc_deu_001280) +S I H Ö R E N D E N A R T I K E L D E S E I N R Ü F I U (swc_deu_001281-swc_deu_001281) +K U S E R I S G L E N T E R K O C H (swc_deu_001282-swc_deu_001282) +S H A N Z E W E N T S T I F T D U N (swc_deu_001283-swc_deu_001283) +N E U N Z H N H N D E R T A C H T Z I E N A L T S H A N I E A R T E N A N G E S I E (swc_deu_001284-swc_deu_001284) +M E R E R E S N A C H I M T U N (swc_deu_001285-swc_deu_001285) +A U C S H I H D E S G E R I C H S Z U O L A N D E S W E I T E N B E R L I E B E N K O U L I N A R I S C H E R S P Ä T Z E L T E T A R M Ü G (swc_deu_001286-swc_deu_001286) +K O L L E T S C H U N D E I N Z W E I T C O B A L T S P B A N S H L I E R E R I N H E M N V O R L S E N (swc_deu_001287-swc_deu_001287) +B U R D E N E I N I S W E G S A E L L E G E B Ü I R O T I G E (swc_deu_001288-swc_deu_001288) +I S T I E R K E R B E B A U G R E F T I G K H (swc_deu_001289-swc_deu_001289) +A N L E S L I C H T E R N O U A S A N S P R A C R E K H (swc_deu_001290-swc_deu_001290) +M I T W I N V O N D S C R E Ä G H I N T E N (swc_deu_001291-swc_deu_001291) +D I N R E Ö C S T E N T E A L D E B I T T Z I Ö G S F O R T R I E T U N G Ü R D I N G E N A U S (swc_deu_001292-swc_deu_001292) +A C H T I N H U N E R T E I L U N Z W A N Z I (swc_deu_001293-swc_deu_001293) +D S K R O S E N A T E L T S A N G E S A M I T E N R E I C H T U M S (swc_deu_001294-swc_deu_001294) +D E S O N N I H T A L S E H Z W O L P R O R O K A T I U (swc_deu_001295-swc_deu_001295) +T E I L H B E D E V R M E R G O S M A N U N D I O R G E N Z (swc_deu_001296-swc_deu_001296) +I N E M I T E F R A U N T E R K A U T I N S E L T (swc_deu_001297-swc_deu_001297) +A U R D I E O I S T E A N D U T C H E R H E B O F V A R L A R G M I T S I T Z I N M E Ü N C H E N (swc_deu_001298-swc_deu_001298) +F A P E I K M E N T E U N D S C H M I C H E V O R P R E D U K T E R H E R S T E L T (swc_deu_001299-swc_deu_001299) +A R P L I C H E N P R E S I S C H E N F R E I H E R E N S T A N D I N D E R Z O L A N S C H L O S V R A G E I N T S C H I E B D E N G G E G E N G D E N S I N A R T A U F D I E S E I T E B I S M A G S G E S T E L T (swc_deu_001300-swc_deu_001300) +W E N D I K W E L E N V O N S E S T E R V O R G W E L E N U N D A U F E N Z U T A G E L I G E N (swc_deu_001301-swc_deu_001301) +D S V O U N N A C H B A R B A U T R I U B B E L L E I T Z B E G O N W O T (swc_deu_001302-swc_deu_001302) +W E H R D E N P R Ä R G E N D E E L E M E N T E D E S H A N S E A T E N T U M S Z U O S A M M E N G E F A S T (swc_deu_001303-swc_deu_001303) +D E A S S L I E Z W R D E R T S V O L C G S T L I E T A N G E S I E N (swc_deu_001304-swc_deu_001304) +D E R Z O N N E N D E M H U S V R L A G S K G O B I G E H Ö R T (swc_deu_001305-swc_deu_001305) +F R D E K N F D I G E N B A R D P B Ü S C H E R I N D W I K E T E D I P A P I E F E R P R (swc_deu_001306-swc_deu_001306) +A M B R E W U O K S (swc_deu_001307-swc_deu_001307) +V D I E K W A R A R S I E A R D L I G E N L A N Z I T Z E B E T R I E B E N A U F A N D S E I S B E M B A U (swc_deu_001308-swc_deu_001308) +J A Z W E I T A U S E N Z W Ö L F I N D N B E L I E N E R K L U O P S O S E S H N D R E I S I C H V E L L I G T (swc_deu_001309-swc_deu_001309) +S E C H T I N H N E N F Ü N F T I G H E I D B Ü N D N I S (swc_deu_001310-swc_deu_001310) +D A S P R O B L E N B I D E N P R A D T A C H S O N I S T (swc_deu_001311-swc_deu_001311) +A M I N W E S E N T E T I A M A L I E S I E V E K E I N (swc_deu_001312-swc_deu_001312) +I C H E I M A L E I N E A N S A T Z W E I S E U N T E S O C H R U N G Z U I E R E M V E R H A L K T E N I N D E R Z E I T T D E S N A Z U N A L S U T Z E L I S M U S (swc_deu_001313-swc_deu_001313) +L I E T Z S E I N S F Ü R F R E I E D O K O M E N T A T I O N (swc_deu_001314-swc_deu_001314) +D I M C H Z E N N E R H U N D E R D I G A T E N H O L S E V O R E N T O R E (swc_deu_001315-swc_deu_001315) +G A N Z S I M S T I E L D E R Z E I T (swc_deu_001316-swc_deu_001316) +E R B R Ü L U N D H Ü Ö R T A R E I C H E D I L E I T U N S C H I S L I K E N (swc_deu_001317-swc_deu_001317) +A U S S T Z E I C H N U M E N R E M D E R H E R E N (swc_deu_001318-swc_deu_001318) +D I E S C H E F T E L L E R E I A U F T Z U G E B E (swc_deu_001319-swc_deu_001319) +D A Z U T Z I H R N D E B E G E G N U N M I T V E R L E T Z T E N T I E R E (swc_deu_001320-swc_deu_001320) +I E N E S C H S T D I F T (swc_deu_001321-swc_deu_001321) +W E S T L I C H V O N K E R L E N (swc_deu_001322-swc_deu_001322) +D I E S T E N D I N B E T R I P W A R E (swc_deu_001323-swc_deu_001323) +D I V O M B A B I J E R H A S I E R T W E H R D E (swc_deu_001324-swc_deu_001324) +E R S C H E N N O R E I N E I T R A U F S E A T Z F O N G R I S T E R N M E I A L (swc_deu_001325-swc_deu_001325) +W A E I L S E B S T E X S T R M E R E I C H T U M K E I N E S W E H S T I N U N M I T L E B A R E N S Z U G A N (swc_deu_001326-swc_deu_001326) +G E B T E U S C H E I C H S E L B E R A U F (swc_deu_001327-swc_deu_001327) +E R H A E T D I E S E N B R A U C H N E U N E H I N H N D E R W E I U N D F Ü N F T I G H G I G N Ü B (swc_deu_001328-swc_deu_001328) +O D E L E I T U N G Ü B E D I E A L T E H Ü Ö R T E A R L E I T U N G E F I E R T W U R D E (swc_deu_001329-swc_deu_001329) +I N E B L I E B T E K L S C H O K T R O P E A S T E K E N E R U M N A N D T D I E H Ö N E (swc_deu_001330-swc_deu_001330) +G E W A R D E N S E I O U N D A L B R E C H T D I C H (swc_deu_001331-swc_deu_001331) +D E T A G E S B E D A F E I N E S W A G S T E N E N A N I T E M I N A R (swc_deu_001332-swc_deu_001332) +S I B T I N U L E R Z I H E N O B A R A L T E (swc_deu_001333-swc_deu_001333) +W E I T E R H E N L I E S I C H N A C R W E I S E N (swc_deu_001334-swc_deu_001334) +Z U N G R Ü N U N G S T D A R T U M K O N T B A M B E R E I T (swc_deu_001335-swc_deu_001335) +K E I N L E C S C H L A R G M Ü K L I C H N A C H T E I L E (swc_deu_001336-swc_deu_001336) +R T D I K A T O L I C H E K Ü R S C H E S A N P Ä T E R A N D E R S T E L L E D E A L T (swc_deu_001337-swc_deu_001337) +E R F E K N A R P B U N D E S B R O T W E I T Z E N T R A T A B S C H N B A I T D I K E R T O F E L E I S E R S A T (swc_deu_001338-swc_deu_001338) +K R E M T D I E S E N N A C H K O M N Z O E U G E (swc_deu_001339-swc_deu_001339) +A L L E N E I N F O L G E N D E R H Ö R S I E R E I E (swc_deu_001340-swc_deu_001340) +S C H I B P S M I E B E R A C T E N S O (swc_deu_001341-swc_deu_001341) +K L I G E A N R E R S B U O C H N E V E R Z I C H T E A U F E N E B E R S Ö N I C H E B E W E R T U N (swc_deu_001342-swc_deu_001342) +B E N I G E R I N T R U S S T E (swc_deu_001343-swc_deu_001343) +W E I T E R H E N V E R S O R K D D I E L E I T U N G T E R M E N (swc_deu_001344-swc_deu_001344) +W A R T E T E N D A F Ü H R A B E M I T E I N I G (swc_deu_001345-swc_deu_001345) +D E D I K L I C H A N T R Ü N D F O N K L E I N M U N I E R T I I N S E I N A R E T Z E N S O N D (swc_deu_001346-swc_deu_001346) +E M J A B R N E U Z E N E R T F Ü M (swc_deu_001347-swc_deu_001347) +I E R S I T E M F R T F A L D E S B Ü U R G E R E C H T U N D E R E I N F Ü R U N D E R E I T Z Ü G I C H K E I T I M Z B A N S I S N E R N D E R T W A N D T E T D E S I C H D I E S E A N S C H A U N G A N S E R T S W E I E S E I N D A R I E N (swc_deu_001348-swc_deu_001348) +D E S S Z W I S T B A C R E S B A L R E I N B A C H E I N E B O G E N B R Ü C K E V (swc_deu_001349-swc_deu_001349) +A C T Z I N U L E T E S N D R E I S I G B W O R D E E H M B U G (swc_deu_001350-swc_deu_001350) +D G A E M (swc_deu_001351-swc_deu_001351) +A U F G R U N D D E K O N T I N E N T E A L S P B A R E A C H T Z E H N H U N E R E L F B A N K O T (swc_deu_001352-swc_deu_001352) +E I T E R E S M E I M U S T D E N U N P L E I P T B R A U N D I W A R B U N G F Ü R E A S B O H S E B T W A N E M (swc_deu_001353-swc_deu_001353) +D I N E R I H V M S I E G T D E R B Ü R G E L I C H M U G R A T I S C H E N F I E P O A R E R L U T Z I O N V E N A C H Z E I N H N D E T A C H T N V I R Z I C H N R A N K R E I C H V W R D E N H A M B U R G M I T I O B E A U F G E N A M E (swc_deu_001354-swc_deu_001354) +O U M B L I E T Z W E I A R E O N E D E R B R E S C H N (swc_deu_001355-swc_deu_001355) +Z O A L R E I C H E G A S T S P I L N U N T E R W E G H S (swc_deu_001356-swc_deu_001356) +K R A N Z I E T E T G E N Ü T E N (swc_deu_001357-swc_deu_001357) +U N B E R O K E R A U S T A T O (swc_deu_001358-swc_deu_001358) +D A G E R I C H T V O U M B E I V W A R G E N S A N G E S M O T O R A D I S A U S I N D I E Z U D E S E R Z E I T N O U E N S T E N D E N A B E T A S I E T L O M E N Z U (swc_deu_001359-swc_deu_001359) +D I M I T Z S A M I E H E R E C H E N S T O B E (swc_deu_001360-swc_deu_001360) +K E I S E R F E R D I N A N (swc_deu_001361-swc_deu_001361) +V O M F E R N S E R E S C H S U H R E R F E A N Z S A V E R B U G N E R I N D E M F E R N S E F I L E N D A S W I G E L I E T (swc_deu_001362-swc_deu_001362) +U R I N S E I N B E S T E N Z E I T E N D E (swc_deu_001363-swc_deu_001363) +S E H Ö R E D E N A R T I K E L F I S C H E N T S C I S (swc_deu_001364-swc_deu_001364) +U N D T E F A R M O F W I (swc_deu_001365-swc_deu_001365) +R E A Z S I B Z I O N D E R H E H S E N I M A T I G V O N K R E S T A (swc_deu_001366-swc_deu_001366) +D I G E S A M T E R A N L A R G E W A B I S E T V E R T Z W E I H U N D E R S E C H T Z I G N A C H K R I S T U S I N B E D R I E (swc_deu_001367-swc_deu_001367) +D E R I S D E F A S T F U T L I E R S O A R W I S W A G E B U N (swc_deu_001368-swc_deu_001368) +E I N D E M K A B E L B A U N (swc_deu_001369-swc_deu_001369) +M O R L E D U N G M I T D E E F A F V E R B U N D E N G E W E S (swc_deu_001370-swc_deu_001370) +E E L T Z I S T N P R I E R E R E N I N A U S R H E P (swc_deu_001371-swc_deu_001371) +S O N E N A C H D E R N A Z U N N A L I S U T S E R D I S T I S C H E N K U N Z T A U F F A S O N G E R E C H T W E H R E N (swc_deu_001372-swc_deu_001372) +D I E W E L T Z S I C H T D E S H A N S E A H R T E N I (swc_deu_001373-swc_deu_001373) +A U C H N A C R K O M M E N S I N N I C H B E K A N T (swc_deu_001374-swc_deu_001374) +E R E N T E K S T E D I N E I N D R U G K T Z E V E R M I T E (swc_deu_001375-swc_deu_001375) +V E R D I N S T U M D A S K E L N A L I E T V E R L I E N (swc_deu_001376-swc_deu_001376) +A B P V O L H O F M E I N V N H A F M E I N W E L D A U S W E R R K G O S E N E I N P L I U S A U S P Ä R T E R E R D I C H T E R A U S C Ü B P (swc_deu_001377-swc_deu_001377) +O M S O R E A N Z T A L S T A T S B E R H A U P F V E (swc_deu_001378-swc_deu_001378) +E F R E I E D O K M E N T A T Z I O N (swc_deu_001379-swc_deu_001379) +G E S T A L T U M B E S K A V E R S W I E D E R S P B I E G E L T (swc_deu_001380-swc_deu_001380) +D E G E S A M T E R A U F W A N T W I T A U F (swc_deu_001381-swc_deu_001381) +A U B G L E I C H M B U R K D I E S I E M A N G H R T U U N D E I N E N O B L I T I E R U N D R I C H E N K E I S E R D A M I T K E I N E D R E S C (swc_deu_001382-swc_deu_001382) +D E R A S T U R C H T D E N S I C H A U S W E I T E N T E N W E L T H A N D E L A R B E I T U N D W O L S T A N V E R S P R A C H (swc_deu_001383-swc_deu_001383) +F Ö Ö E R D I E Z E I T M I T E D E S N E U N I H N D E J E R H U N D A R S B E K L A K D E R C H I T E K T M A T I N H A L E (swc_deu_001384-swc_deu_001384) +E I T B N D E K A N Z E R H R M U T S M I T L E N T E (swc_deu_001385-swc_deu_001385) +D I N A M E N G U O D E F R E I I M S T A Z S H A N T B U O C H T Z (swc_deu_001386-swc_deu_001386) +W E N A U C H M T E N E R G E W S E N L E T A G I E (swc_deu_001387-swc_deu_001387) +K A K O U L I E R B A N (swc_deu_001388-swc_deu_001388) +A N G E F A N G D E T Z S O A N D E F M I S C H U L E R E I N E N D I E G J E P E N (swc_deu_001389-swc_deu_001389) +F I E L M E N S C H E S E I N E I N G R E S L I L S N A R U N G S K O N G U R E N T E N U N D E L S P U T E N E L L G E F A (swc_deu_001390-swc_deu_001390) +D I N A U F T I T V E R K Ö R E (swc_deu_001391-swc_deu_001391) +M I D E M S T A N V O M D R E I Z E H N N I U L I E Z W E I T A U S E N S W E R F D E R I N E R S T I T U N D E D E R I E Z E N Z K E H R T Z U E K O M M O N S E I T Z E I E W U S C H E N S C H E R E L E I T R E I P U N K N O L E A N P R T E T U N D U N T E R D E (swc_deu_001392-swc_deu_001392) +E I N I K L E I N E R E B O G E N P R Ü K E (swc_deu_001393-swc_deu_001393) +S I C H N U N V E R S E N B I T E R K O M (swc_deu_001394-swc_deu_001394) +A U S S D E M G E M E L D E T Z U E N T F E R I N E N (swc_deu_001395-swc_deu_001395) +I S D N E H A R E P E S C H E R I C S T I N E N E U N Z I C G F I E O R N E R T E E N E U N Z I C H E W I (swc_deu_001396-swc_deu_001396) +A R I E M U N S E L T A U F (swc_deu_001397-swc_deu_001397) +O N D S I E S E I A U R C H D I E E I N E F Ü L R S T E N (swc_deu_001398-swc_deu_001398) +N E I T Z I H N H U N D E R T N O E I N S I E E (swc_deu_001399-swc_deu_001399) +S T A R T E S E N H A B M D E R Ö Ü M I C H E N I N S C H E N J I Ö R E (swc_deu_001400-swc_deu_001400) +D E L K R I S I E B E H R U N D M E N S C H (swc_deu_001401-swc_deu_001401) +M I E S I S C H E N Z K O A L I S C H E (swc_deu_001402-swc_deu_001402) +B E C T U R S C H M L G E B D E S S D R E I B A R E R T I O N E N I (swc_deu_001403-swc_deu_001403) +K Z U K S K G E B I E T A E R W E S I H T E R A C H Z E H N H U N D A R Z W E I U N S I E B Z I C G E R Ü N D E T E A L U S D E U N E R I N A L P A R (swc_deu_001404-swc_deu_001404) +D E V F I N I T Z I O N (swc_deu_001405-swc_deu_001405) +U M I N E U N V W E S I T E T Z I E W I L E R Z W E I S E M E S T E R K U N S G S C H C H T Z U S T U D I E N (swc_deu_001406-swc_deu_001406) +D I E T R O T Z E R E R G R I N E (swc_deu_001407-swc_deu_001407) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..33c9d64f751ef2e3b2000ed111d4cc8b6fcbc0d0 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/ref.trn @@ -0,0 +1,207 @@ +D E R V E R L I E B T E J U N G E H E R Z O G D I E R A T S C H L Ä G E S E I N E S V A T E R S N I C H T B E A C H T E T H A B E (swc_deu_001201-swc_deu_001201) +D I E I N D E N H A N S E S T Ä D T E N A L S (swc_deu_001202-swc_deu_001202) +W A R K E I N G R O S S E R E R F O L G (swc_deu_001203-swc_deu_001203) +G R O S S E N C H E M I S C H E N F A B R I K E N (swc_deu_001204-swc_deu_001204) +W U R D E N A U C H M E H R E R E E R L Ä U T E R U N G S B Ü C H E R V E R Ö F F E N T L I C H T (swc_deu_001205-swc_deu_001205) +V O R B E R E I T E T E N B I E R T E I G G E T U N K T (swc_deu_001206-swc_deu_001206) +D O K U M E N T E S C H L I E S S L I C H I N (swc_deu_001207-swc_deu_001207) +T R A U E R T A G F Ü R D E N T O D V O N K Ö N I G F R I E D R I C H W I L H E L M (swc_deu_001208-swc_deu_001208) +D A R U N T E R S I N D M A T I L D E A S E N S I S W Ä C H T E R D E S K R E U Z E S (swc_deu_001209-swc_deu_001209) +I N N E N S T Ä D T E N M E H R U N D M E H R D I E R O L L E D E R T R A D I T I O N E L L E N F I S H (swc_deu_001210-swc_deu_001210) +Z U D E N E N W E L T L Ä U F I G K E I T (swc_deu_001211-swc_deu_001211) +R A C H E D E S H O F E S U N D D E S A D E L S F Ü R D E N F R E V E L (swc_deu_001212-swc_deu_001212) +Z E I T A N G A B E N V E R Z I C H T E T E (swc_deu_001213-swc_deu_001213) +A L S A C H T Z E H N H U N D E R T A C H T Z I G M I T O T T O B R A H M S A U F S A T Z (swc_deu_001214-swc_deu_001214) +E I N T A U S E N D S I E B E N H U N D E R T A C H T U N D Z W A N Z I G – (swc_deu_001215-swc_deu_001215) +D A S S D E R F I S C H F R I S C H (swc_deu_001216-swc_deu_001216) +S E I N E M A B S C H L U S S I M J A H R E N E U N Z E H N H U N D E R T Z W E I U N D A C H T Z I G U N T E R N A H M E R E I N E E R S T E L Ä N G E R E R E I S E N A C H S P A N I E N (swc_deu_001217-swc_deu_001217) +V O N C H A S Ô T V O R G E Z E I C H N E T (swc_deu_001218-swc_deu_001218) +F A L C K E N S T E I N S V O L L S T Ä N D I G E G E S C H I C H T E N U N D D I E A U G S B U R G E R S T A D T G E S C H I C H T E D E S Ä L T E R E N (swc_deu_001219-swc_deu_001219) +N A C H D I E S E N Z E R S T Ö R U N G E N W U R D E D I E R A S C H W I E D E R A U F B L Ü H E N D E (swc_deu_001220-swc_deu_001220) +M A C H T E N E I N F L U S S R E I C H E N H A N S E A T E N B E I M K O M M I S S A R I S C H E I N G E S E T Z T E N B Ü R G E R M E I S T E R M A R K E R T I H R E A U F W A R T U N G (swc_deu_001221-swc_deu_001221) +A L S Z E N T R A L E S H A N D E L S K O N T O R (swc_deu_001222-swc_deu_001222) +S O N D E R S T E L L U N G I N N E R H A L B D E R S T A D T K R E F E L D (swc_deu_001223-swc_deu_001223) +F I N D E T S I C H I N H A L O B A K T E R I E N (swc_deu_001224-swc_deu_001224) +A U F D E R B S E I T E F I N D E T S I C H D A S E B E N F A L L S V O N M I C H A E L K O M P O N I E R T E (swc_deu_001225-swc_deu_001225) +I N H A N S E A T I S C H E R Z E I T H A T T E D I E Z I R K E L G E S E L L S C H A F T K E I N E N A U S S C H L A G G E B E N D E N E I N F L U S S M E H R (swc_deu_001226-swc_deu_001226) +D A E S D U R C H V E R W E N D U N G V O N A U F T R I E B S K Ö R P E R N O D E R H O L Z E I N E G E R I N G E R E M I T T L E R E D I C H T E A L S W A S S E R H A T (swc_deu_001227-swc_deu_001227) +D R A M A T I S I E R U N G E N (swc_deu_001228-swc_deu_001228) +U M 7 5 5 (swc_deu_001229-swc_deu_001229) +D A S S A L B R E C H T D I E B A D E R S T O C H T E R (swc_deu_001230-swc_deu_001230) +T H A T B A R B A R I S C H E R S T A A T S R A I S O N (swc_deu_001231-swc_deu_001231) +D E R D A S L I E D B E S O N D E R S L I E B T E (swc_deu_001232-swc_deu_001232) +A U F G R U N D D E S W A C H S E N D E N P U B L I K U M S I N T E R E S S E S W U R D E D E R A U F T R I T T S O R T F Ü R D I E P R I M A V I S T A L E S U N G E N (swc_deu_001233-swc_deu_001233) +U N D F R E I L I C H T S P I E L E (swc_deu_001234-swc_deu_001234) +D A S S D I E D R E I D E N S T U R Z R E L A T I V U N B E S C H A D E T Ü B E R S T A N D E N H A T T E N (swc_deu_001235-swc_deu_001235) +J A H R E N E R S C H I E N E N Z W E I I M M E R (swc_deu_001236-swc_deu_001236) +G R A B M A L E U N D G R A B K A P E L L E N O D E R W O H L T A T E N N A C H H A L T I G (swc_deu_001237-swc_deu_001237) +J U N I N E U N Z E H N H U N D E R T S E C H S U N D N E U N Z I G K Ü N D I G T E E R S E I N E B E I D E N J O B S (swc_deu_001238-swc_deu_001238) +E I N G P R O T E I N G E K O P P E L T (swc_deu_001239-swc_deu_001239) +N E U N U N D S E C H Z I G D E R M E D I A C O N T R O L A L B U M C H A R T S E I N (swc_deu_001240-swc_deu_001240) +D A D U R C H K O M M T (swc_deu_001241-swc_deu_001241) +O H N E H I N N I C H T D E N G R O S S H A N D E L S K A U F L E U T E N G E S E L L S C H A F T L I C H G L E I C H G E S T E L L T W A R E N (swc_deu_001242-swc_deu_001242) +V O N D E R N A H R U N G U N D V O M K L I M A (swc_deu_001243-swc_deu_001243) +A P O L L O E I N S (swc_deu_001244-swc_deu_001244) +B R Ü H L U N D H Ü R T H N A C H K Ö L N (swc_deu_001245-swc_deu_001245) +E T W A I N E I N K L O S T E R (swc_deu_001246-swc_deu_001246) +Z U M O F F I Z I E L L E N K A R N E V A L E N T S T A N D U N D H E U T E E I N E M I S C H U N G A U S K Ö L S C H E M K A R N E V A L U N D P O L I T I S C H E M K A B A R E T T M I T C O M E D Y E L E M E N T E N D A R S T E L L T U N D (swc_deu_001247-swc_deu_001247) +D I E Z U R E N T S T E H U N G D E S L I E D E S F Ü H R T E N (swc_deu_001248-swc_deu_001248) +N A N N T E Z I E G L E R D I E E R M O R D U N G D E R B E R N A U E R I N (swc_deu_001249-swc_deu_001249) +W I N T E R R U H E I S T V O R A L L E M (swc_deu_001250-swc_deu_001250) +D I E S T R Ä N G E D E R V O R G Ä N G E R L E I T U N G W U R D E N Z W I S C H E N N E U N Z E H N H U N D E R T N E U N U N D Z W A N Z I G U N D N E U N Z E H N H U N D E R T D R E I U N D F Ü N F Z I G A R C H Ä O L O G I S C H E R G R A B E N (swc_deu_001251-swc_deu_001251) +I M G E G E N S A T Z (swc_deu_001252-swc_deu_001252) +F A R B E N V O N U E R D I N G E N S I N D B L A U U N D R O T (swc_deu_001253-swc_deu_001253) +L I V E V E R A N S T A L T U N G E N (swc_deu_001254-swc_deu_001254) +S O W E R D E N H E U T E I N D E R R E G E L A L L E D O R T L E B E N D E N B R A U N B Ä R E N (swc_deu_001255-swc_deu_001255) +L I E D E R F Ü R R E V U E F I L M E (swc_deu_001256-swc_deu_001256) +D E S H A N S E A T E N F Ü H R E N (swc_deu_001257-swc_deu_001257) +H E B B E L S A G N E S B E R N A U E R (swc_deu_001258-swc_deu_001258) +L E B E N S W E I S E V E R K Ö R P E R N (swc_deu_001259-swc_deu_001259) +W I E D E R F A L L D E S V I E L E N H A M B U R G E R N Z U K A T H O L I S C H F R O M M E N (swc_deu_001260-swc_deu_001260) +K U L T U R U N D W I R T S C H A F T A U S T A U S C H E N (swc_deu_001261-swc_deu_001261) +J A H R Z W E I T A U S E N D V E R T O N T E (swc_deu_001262-swc_deu_001262) +D A S S E R D I E S E L E I T U N G S C H N E L L E R V O L L E N D E N K Ö N N E A L S D E R B A U M E I S T E R D E N K Ö L N E R D O M (swc_deu_001263-swc_deu_001263) +H I N R I C H T U N G D E R B E R N A U E R I N H A B E E S S I C H S C H L I C H T U M (swc_deu_001264-swc_deu_001264) +L U D W I G (swc_deu_001265-swc_deu_001265) +D E R Z E I T D E R B E S T E K E N N E R D E R E I F E L L E I T U N G (swc_deu_001266-swc_deu_001266) +F O K U S D E S W I S S E N S C H A F T L I C H E N I N T E R E S S E S (swc_deu_001267-swc_deu_001267) +T H E M A Z U B E G E I S T E R N (swc_deu_001268-swc_deu_001268) +M E T E R U N D K O N N T E D A M I T A U C H V O N I N N E N B E G A N G E N W E R D E N (swc_deu_001269-swc_deu_001269) +H A R D C O V E R B E S T S E L L E R L I S T E D E R N E W (swc_deu_001270-swc_deu_001270) +D E R F R E I E N E N Z Y K L O P Ä D I E (swc_deu_001271-swc_deu_001271) +D E N G R I Z Z L Y W I E D E R A U F D I E L I S T E Z U S E T Z E N (swc_deu_001272-swc_deu_001272) +L A N G D I E S E K A P L A N S S T E L L E A U F R E C H T E R H A L T E N W U R D E (swc_deu_001273-swc_deu_001273) +S I E W A R E N W A H R S C H E I N L I C H B E R E I T S D R E I S S I G S E K U N D E N N A C H A U S B R U C H D E S F E U E R S (swc_deu_001274-swc_deu_001274) +M E T E R N G E S A M T L Ä N G E U N D B I S Z U Z E H N M E T E R N (swc_deu_001275-swc_deu_001275) +F E I N E R I T Z E N U N D S P A L T E N (swc_deu_001276-swc_deu_001276) +D E N M A N V O N A U S S E N D I E K E H L E H I N A B F L I E S S E N S I E H T (swc_deu_001277-swc_deu_001277) +E I N E M I N T E R V I E W S A G T E B R O W N (swc_deu_001278-swc_deu_001278) +D A S F Ü N F T E E V A N G E L I U M (swc_deu_001279-swc_deu_001279) +R E I S S E N S I E M A N C H M A L W E I D E T I E R E W I E S C H A F E (swc_deu_001280-swc_deu_001280) +S I E H Ö R E N D E N A R T I K E L D E S I G N R E V I E W (swc_deu_001281-swc_deu_001281) +C H A K U Z A I S T G E L E R N T E R K O C H (swc_deu_001282-swc_deu_001282) +H A N S W E N D T S T I F T U N G (swc_deu_001283-swc_deu_001283) +N E U N Z E H N H U N D E R T A C H T Z E H N A L S H A N S E A T E N A N G E S E H E N (swc_deu_001284-swc_deu_001284) +M E H R E R E E S N A C H I H M T H U N (swc_deu_001285-swc_deu_001285) +A U F S T I E G D E S G E R I C H T S Z U R L A N D E S W E I T B E L I E B T E N K U L I N A R I S C H E N S P E Z I A L I T Ä T E R M Ö G L I C H T E (swc_deu_001286-swc_deu_001286) +C O L L E G E U N D E I N E N Z W E I T J O B A L S S P A N I S C H L E H R E R I N H A M P T O N F A L L S A N (swc_deu_001287-swc_deu_001287) +W U R D E N K E I N E S W E G S A L L E G E B Ü R T I G E N (swc_deu_001288-swc_deu_001288) +I S T I H R K Ö R P E R B A U K R Ä F T I G (swc_deu_001289-swc_deu_001289) +A N L Ä S S L I C H D E R N E U J A H R E S A N S P R A C H E K I M (swc_deu_001290-swc_deu_001290) +M I T W I N D V O N S C H R Ä G H I N T E N (swc_deu_001291-swc_deu_001291) +D E N G R Ö S S T E N T E I L D E R B E Z I R K S V E R T R E T U N G U E R D I N G E N A U S (swc_deu_001292-swc_deu_001292) +A C H T Z E H N H U N D E R T E I N U N D Z W A N Z I G (swc_deu_001293-swc_deu_001293) +D E S G R O S S E N A D E L S A N G E S A M M E L T E N R E I C H T U M S (swc_deu_001294-swc_deu_001294) +S O L L T E N N I C H T A L S S E X U E L L E P R O V O K A T I O N (swc_deu_001295-swc_deu_001295) +T E I L H A B E R D E R F I R M A G O S S M A N N U N D J Ü R G E N S (swc_deu_001296-swc_deu_001296) +D E R K R A U T I N S E L B I L D E T S I E D I E G E M E I N D E (swc_deu_001297-swc_deu_001297) +A U D I O I S T E I N D E U T S C H E R H Ö R B U C H V E R L A G M I T S I T Z I N M Ü N C H E N (swc_deu_001298-swc_deu_001298) +F A R B P I G M E N T E U N D C H E M I S C H E V O R P R O D U K T E H E R S T E L L T (swc_deu_001299-swc_deu_001299) +E R B L I C H E N P R E U S S I S C H E N F R E I H E R R E N S T A N D I N D E R Z O L L A N S C H L U S S F R A G E E N T S C H I E D E N G E G E N D E N S E N A T A U F D I E S E I T E B I S M A R C K S G E S T E L L T (swc_deu_001300-swc_deu_001300) +W E N N D I E Q U E L L E N V O N S E L B S T H E R V O R Q U E L L E N U N D O F F E N Z U T A G E L I E G E N (swc_deu_001301-swc_deu_001301) +D A S V O M N A C H B A R B A U T R U P P B E R E I T S B E G O N N E N W U R D E (swc_deu_001302-swc_deu_001302) +W E R D E N P R Ä G E N D E E L E M E N T E D E S H A N S E A T E N T U M S Z U S A M M E N G E F A S S T (swc_deu_001303-swc_deu_001303) +D A S L I E D W U R D E A L S V O L K S L I E D A N G E S E H E N (swc_deu_001304-swc_deu_001304) +D E R Z U R R A N D O M H O U S E V E R L A G S G R U P P E G E H Ö R T (swc_deu_001305-swc_deu_001305) +F Ü R D I E K Ü N F T I G E N B O R D B Ü C H E R E N T W I C K E L T E D I E P A P I E R F A B R I K (swc_deu_001306-swc_deu_001306) +H A M B U R G W U C H S (swc_deu_001307-swc_deu_001307) +F Ü R D I E Q U A S I A D L I G E N L A N D S I T Z E B E T R I E B E N E A U F W A N D – S E I E S B E I M B A U (swc_deu_001308-swc_deu_001308) +J A H R Z W E I T A U S E N D Z W Ö L F I N D E N B E R L I N E R C L U B S O S E C H S U N D D R E I S S I G V E R L E G T (swc_deu_001309-swc_deu_001309) +S E C H Z E H N H U N D E R T F Ü N F Z I G A L S B Ü N D N I S D I E (swc_deu_001310-swc_deu_001310) +P R O B L E M B E I D I E S E M P A R A D O X O N I S T (swc_deu_001311-swc_deu_001311) +A R M E N W E S E N T Ä T I G A M A L I E S I E V E K I N G (swc_deu_001312-swc_deu_001312) +N I C H T E I N M A L E I N E A N S A T Z W E I S E U N T E R S U C H U N G Z U I H R E M V E R H A L T E N I N D E R Z E I T D E S N A T I O N A L S O Z I A L I S M U S (swc_deu_001313-swc_deu_001313) +L I Z E N Z F Ü R F R E I E D O K U M E N T A T I O N (swc_deu_001314-swc_deu_001314) +I M A C H T Z E H N T E J A H R H U N D E R T D I E G A R T E N H Ä U S E R V O R D E N T O R E N (swc_deu_001315-swc_deu_001315) +G A N Z I M S T I L D E R Z E I T (swc_deu_001316-swc_deu_001316) +Ü B E R B R Ü H L U N D H Ü R T H E R R E I C H T E D I E L E I T U N G S C H L I E S S L I C H K Ö L N (swc_deu_001317-swc_deu_001317) +A U S Z E I C H N U N G E N F R E M D E R H E R R E N (swc_deu_001318-swc_deu_001318) +D I E S C H R I F T S T E L L E R E I A U F Z U G E B E N (swc_deu_001319-swc_deu_001319) +Z Ä H L E N D I E B E G E G N U N G M I T V E R L E T Z T E N T I E R E N (swc_deu_001320-swc_deu_001320) +J E N I S C H S T I F T (swc_deu_001321-swc_deu_001321) +W E S T L I C H V O N K Ö L N (swc_deu_001322-swc_deu_001322) +D I E S T Ä N D I G I N B E T R I E B W A R E N (swc_deu_001323-swc_deu_001323) +D I E V O M B A R B I E R R A S I E R T W E R D E N (swc_deu_001324-swc_deu_001324) +E R S C H I E N N O C H E I N W E I T E R E R A U F S A T Z V O N C H R I S T I A N M E Y E R (swc_deu_001325-swc_deu_001325) +W E I L S E L B S T E X T R E M E R R E I C H T U M K E I N E S W E G S D E N U N M I T T E L B A R E N Z U G A N G (swc_deu_001326-swc_deu_001326) +G E B T E U C H N I C H T S E L B E R A U F (swc_deu_001327-swc_deu_001327) +H A T D I E S E N B R A U C H N E U N Z E H N H U N D E R T Z W E I U N D F Ü N F Z I G G E G E N Ü B E R (swc_deu_001328-swc_deu_001328) +W O D I E L E I T U N G Ü B E R D I E A L T E H Ü R T H E R L E I T U N G G E F Ü H R T W U R D E (swc_deu_001329-swc_deu_001329) +E I N E B E L I E B T E K Ö L S C H R O C K T R U P P E A U S D E M K Ö L N E R U M L A N D D I E H Ö H N E R (swc_deu_001330-swc_deu_001330) +G E W O R D E N S E I U N D A L B R E C H T S I C H (swc_deu_001331-swc_deu_001331) +D E R T A G E S B E D A R F E I N E S E R W A C H S E N E N A N V I T A M I N A (swc_deu_001332-swc_deu_001332) +S I E B Z E H N H U N D E R T Z E H N O B E R A L T E R (swc_deu_001333-swc_deu_001333) +W E I T E R H I N L I E S S S I C H N A C H W E I S E N (swc_deu_001334-swc_deu_001334) +Z U M G R Ü N D U N G S D A T U M K O N N T E M A N B E R E I T S (swc_deu_001335-swc_deu_001335) +K E I N L E C K S C H L A G E N M Ö G L I C H N A C H T E I L E (swc_deu_001336-swc_deu_001336) +W I R D D I E K A T H O L I S C H E K I R C H E S T Ü C K P E T E R A N D E R S T E L L E D E R A L T E N (swc_deu_001337-swc_deu_001337) +D E R V E R K N A P P U N G D E S B R O T W E I Z E N S T R A T A B E R S C H O N B A L D D I E K A R T O F F E L A L S E R S A T Z (swc_deu_001338-swc_deu_001338) +K Ö N N E N M I T D I E S E N N A C H K O M M E N Z E U G E N (swc_deu_001339-swc_deu_001339) +A L L E N E U E N F O L G E N D E R H Ö R S P I E L R E I H E (swc_deu_001340-swc_deu_001340) +C H I P S M I T B R A T E N S O S S E (swc_deu_001341-swc_deu_001341) +K O L L E G E A N D R E A S B U C H N E R V E R Z I C H T E T E A U F E I N E P E R S Ö N L I C H E B E W E R T U N G (swc_deu_001342-swc_deu_001342) +W E N I G E R E N T R Ü S T E T (swc_deu_001343-swc_deu_001343) +W E I T E R H I N V E R S O R G T E D I E L E I T U N G T H E R M E N (swc_deu_001344-swc_deu_001344) +W A R T E T E N D A F Ü R A B E R M I T E I N I G E N (swc_deu_001345-swc_deu_001345) +L E D I G L I C H A N T O N V O N K L E I N M O N I E R T E I N S E I N E R R E Z E N S I O N D E R (swc_deu_001346-swc_deu_001346) +I M J A H R E N E U N Z E H N H U N D E R T F Ü N F (swc_deu_001347-swc_deu_001347) +E R S T M I T D E M F O R T F A L L D E S B Ü R G E R R E C H T S U N D D E R E I N F Ü H R U N G D E R F R E I Z Ü G I G K E I T I M Z W A N Z I G S T E J A H R H U N D E R T W A N D E L T E S I C H D I E S E A N S C H A U U N G A N S A T Z W E I S E D A H I N (swc_deu_001348-swc_deu_001348) +D E S S W I S T B A C H E S B E I R H E I N B A C H E I N E B O G E N B R Ü C K E V O N (swc_deu_001349-swc_deu_001349) +A C H T Z E H N H U N D E R T S E C H S U N D D R E I S S I G W U R D E D E R H A M B U R G E R (swc_deu_001350-swc_deu_001350) +A M K A R N E V A L S S O N N T A G (swc_deu_001351-swc_deu_001351) +A U F G R U N D D E R K O N T I N E N T A L S P E R R E A C H T Z E H N H U N D E R T E L F B A N K R O T T (swc_deu_001352-swc_deu_001352) +W E I T E R E S M A L M U S S T E N D A N U N D B L Y T H E B R O W N D I E W E R B U N G F Ü R D A S B U C H S E L B S T Ü B E R N E H M E N (swc_deu_001353-swc_deu_001353) +D I E N A C H R I C H T V O M S I E G D E R B Ü R G E R L I C H D E M O K R A T I S C H E N F E B R U A R R E V O L U T I O N V O N A C H T Z E H N H U N D E R T A C H T U N D V I E R Z I G I N F R A N K R E I C H W U R D E I N H A M B U R G M I T J U B E L A U F G E N O M M E N (swc_deu_001354-swc_deu_001354) +Z W E I J A H R E O H N E U N T E R B R E C H U N G (swc_deu_001355-swc_deu_001355) +Z A H L R E I C H E N G A S T S P I E L E N U N T E R W E G S (swc_deu_001356-swc_deu_001356) +Q U A N T I T Ä T G E N Ü G T E N (swc_deu_001357-swc_deu_001357) +B A R O C K E R A U S S T A T T U N G (swc_deu_001358-swc_deu_001358) +D A S G E R I C H T V O M B E I W A G E N S E I N E S M O T O R R A D E S A U S I N D I E Z U D I E S E R Z E I T N E U E N T S T E H E N D E N A R B E I T E R S I E D L U N G E N Z U (swc_deu_001359-swc_deu_001359) +D I E M I T S A M T I H R E R R E C H E N S T U B E (swc_deu_001360-swc_deu_001360) +K A I S E R F E R D I N A N D (swc_deu_001361-swc_deu_001361) +V O M F E R N S E H R E G I S S E U R F R A N Z X A V E R B O G N E R I N D E M F E R N S E H F I L M D A S E W I G E L I E D (swc_deu_001362-swc_deu_001362) +W U R D E I N S E I N E N B E S T E N Z E I T E N D E R (swc_deu_001363-swc_deu_001363) +S I E H Ö R E N D E N A R T I K E L F I S H A N D C H I P S (swc_deu_001364-swc_deu_001364) +U N D T V M O V I E (swc_deu_001365-swc_deu_001365) +R E Z E P T I O N D E R H E X E N T H E M A T I K V O N C H R I S T A (swc_deu_001366-swc_deu_001366) +D I E G E S A M T E A N L A G E W A R B I S E T W A Z W E I H U N D E R T S E C H Z I G N A C H C H R I S T U S I N B E T R I E B (swc_deu_001367-swc_deu_001367) +D E R E R S T E F A S T F O O D L I E F E R S E R V I C E W A R G E B O R E N (swc_deu_001368-swc_deu_001368) +E I N E M K A B E L B A U M (swc_deu_001369-swc_deu_001369) +E R M O R D U N G M I T D E R G E F A H R V E R B U N D E N G E W E S E N (swc_deu_001370-swc_deu_001370) +D E R Ä L T E S T E N P F E R D E R E N N E N A U S S E R H A L B (swc_deu_001371-swc_deu_001371) +S O N D E R N A U C H D E R N A T I O N A L S O Z I A L I S T I S C H E N K U N S T A U F F A S S U N G G E R E C H T W E R D E N (swc_deu_001372-swc_deu_001372) +D I E W E L T S I C H T D E S H A N S E A T E N (swc_deu_001373-swc_deu_001373) +A U C H N A C H K O M M E N S I N D N I C H T B E K A N N T (swc_deu_001374-swc_deu_001374) +I H R E N T E X T E N D E N E I N D R U C K Z U V E R M I T T E L N (swc_deu_001375-swc_deu_001375) +V E R D I E N S T E U M D A S K Ö L N E R L I E D V E R L I E H E N (swc_deu_001376-swc_deu_001376) +O B W O H L H O F F M A N N V O N H O F F M A N N S W A L D A U S W E R K G R O S S E N E I N F L U S S A U F S P Ä T E R E D I C H T E R A U S Ü B T E (swc_deu_001377-swc_deu_001377) +U M S O E R N S T A L S S T A A T S O B E R H A U P T V O N (swc_deu_001378-swc_deu_001378) +D O K U M E N T A T I O N (swc_deu_001379-swc_deu_001379) +G E S T A L T U N G D E S C O V E R S W I D E R S P I E G E L T (swc_deu_001380-swc_deu_001380) +D E R G E S A M T E A U F W A N D W I R D A U F (swc_deu_001381-swc_deu_001381) +O B G L E I C H H A M B U R G D I E S E M A N G E H Ö R T E U N D E I N E N O B I L I T I E R U N G D U R C H D E N K A I S E R D A M I T K E I N E D U R C H (swc_deu_001382-swc_deu_001382) +D A E S D U R C H D E N S I C H A U S W E I T E N D E N W E L T H A N D E L A R B E I T U N D W O H L S T A N D V E R S P R A C H (swc_deu_001383-swc_deu_001383) +F Ü R D I E Z E I T M I T T E D E S N E U N Z E H N T E J A H R H U N D E R T S B E K L A G T E D E R A R C H I T E K T M A R T I N H A L L E R (swc_deu_001384-swc_deu_001384) +A L T B U N D E S K A N Z L E R H E L M U T S C H M I D T L E H N T E (swc_deu_001385-swc_deu_001385) +D E N N A M E N G O D E F F R O Y I M S T A A T S H A N D B U C H Z U S T R E I C H E N (swc_deu_001386-swc_deu_001386) +W E N N A U C H M I T E I N E R G E W I S S E N L E T H A R G I E (swc_deu_001387-swc_deu_001387) +K A L K U L I E R B A R (swc_deu_001388-swc_deu_001388) +A N G E F A N G E N E Z W E I H U N D E R T F Ü N F Z I G S C H Ü L E R E I N E N D E L E G I E R T E N (swc_deu_001389-swc_deu_001389) +V I E L E M E N S C H E N S A H E N D E N G R I Z Z L Y A L S N A H R U N G S K O N K U R R E N T E N U N D A L S P O T E N T I E L L E G E F A H R (swc_deu_001390-swc_deu_001390) +D E N A U F T R I T T V E R K Ü R Z E N (swc_deu_001391-swc_deu_001391) +D E M S T A N D V O M D E R I N H A L T S T E H T U N T E R D E R L I Z E N Z C R E A T I V E C O M M O N S A T T R I B U T I O N S H A R E A L I K E D R E I P U N K T N U L L U N P O R T E D U N D U N T E R D E R (swc_deu_001392-swc_deu_001392) +E I N E K L E I N E R E B O G E N B R Ü C K E (swc_deu_001393-swc_deu_001393) +S I C H N U N F Ü R S E I N W E I T E R K O M M E N (swc_deu_001394-swc_deu_001394) +A U S D E M G E M Ä L D E Z U E N T F E R N E N (swc_deu_001395-swc_deu_001395) +N A C H E U R O P Ä I S C H E R R I C H T L I N I E N E U N Z I G V I E R H U N D E R T S E C H S U N D N E U N Z I G E W G (swc_deu_001396-swc_deu_001396) +E I N E M U M F E L D A U F (swc_deu_001397-swc_deu_001397) +U N D S I E S E I A U C H W I E E I N E F Ü R S T I N (swc_deu_001398-swc_deu_001398) +N E U N Z E H N H U N D E R T N E U N Z E H N (swc_deu_001399-swc_deu_001399) +S T A T T D E S S E N H A B E N D I E R Ö M I S C H E N I N G E N I E U R E (swc_deu_001400-swc_deu_001400) +G R I Z Z L Y B Ä R U N D M E N S C H (swc_deu_001401-swc_deu_001401) +M U S I C I A N S C O A L I T I O N (swc_deu_001402-swc_deu_001402) +B E R T O L D H U M M E L G I B T E S D R E I V A R I A T I O N E N M I T (swc_deu_001403-swc_deu_001403) +R Ü C K Z U G S G E B I E T E R W I E S S I C H D E R A C H T Z E H N H U N D E R T Z W E I U N D S I E B Z I G G E G R Ü N D E T E Y E L L O W S T O N E N A T I O N A L P A R K (swc_deu_001404-swc_deu_001404) +D E F I N I T I O N (swc_deu_001405-swc_deu_001405) +U M A N D E R U N I V E R S I T Ä T S E V I L L A Z W E I S E M E S T E R K U N S T G E S C H I C H T E Z U S T U D I E R E N (swc_deu_001406-swc_deu_001406) +T R O T Z I H R E R G E R I N G E N (swc_deu_001407-swc_deu_001407) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/result.txt b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..3d2d1adf01be19a44ab0f2ca9e1ddd0fb8a77df5 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/result.txt @@ -0,0 +1,2525 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,-----------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/hyp.trn| +|-----------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001201 | 1 75 | 76.0 5.3 18.7 8.0 32.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001202 | 1 27 | 88.9 3.7 7.4 3.7 14.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001203 | 1 23 | 65.2 13.0 21.7 4.3 39.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001204 | 1 27 | 77.8 11.1 11.1 18.5 40.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001205 | 1 53 | 71.7 5.7 22.6 3.8 32.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001206 | 1 30 | 100.0 0.0 0.0 10.0 10.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001207 | 1 25 | 68.0 8.0 24.0 8.0 40.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001208 | 1 49 | 61.2 12.2 26.5 2.0 40.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001209 | 1 49 | 81.6 14.3 4.1 4.1 22.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001210 | 1 60 | 76.7 10.0 13.3 6.7 30.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001211 | 1 23 | 82.6 13.0 4.3 8.7 26.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001212 | 1 44 | 79.5 9.1 11.4 4.5 25.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001213 | 1 23 | 73.9 17.4 8.7 8.7 34.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001214 | 1 52 | 84.6 5.8 9.6 11.5 26.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001215 | 1 43 | 53.5 14.0 32.6 9.3 55.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001216 | 1 21 | 71.4 0.0 28.6 0.0 28.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001217 | 1 108 | 75.9 8.3 15.7 1.9 25.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001218 | 1 24 | 79.2 12.5 8.3 20.8 41.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001219 | 1 85 | 76.5 9.4 14.1 2.4 25.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001220 | 1 59 | 78.0 5.1 16.9 0.0 22.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001221 | 1 103 | 83.5 5.8 10.7 3.9 20.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001222 | 1 27 | 88.9 0.0 11.1 11.1 22.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001223 | 1 42 | 76.2 9.5 14.3 0.0 23.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001224 | 1 28 | 89.3 3.6 7.1 3.6 14.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001225 | 1 65 | 78.5 9.2 12.3 1.5 23.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001226 | 1 89 | 78.7 7.9 13.5 1.1 22.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001227 | 1 99 | 83.8 6.1 10.1 4.0 20.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001228 | 1 16 | 87.5 6.3 6.3 0.0 12.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001229 | 1 6 | 50.0 50.0 0.0 216.7 266.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001230 | 1 31 | 83.9 6.5 9.7 6.5 22.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001231 | 1 30 | 63.3 13.3 23.3 10.0 46.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001232 | 1 29 | 72.4 3.4 24.1 0.0 27.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001233 | 1 95 | 88.4 2.1 9.5 3.2 14.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001234 | 1 19 | 68.4 15.8 15.8 0.0 31.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001235 | 1 62 | 82.3 3.2 14.5 6.5 24.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001236 | 1 28 | 78.6 3.6 17.9 3.6 25.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001237 | 1 51 | 82.4 3.9 13.7 11.8 29.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001238 | 1 67 | 67.2 13.4 19.4 4.5 37.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001239 | 1 23 | 69.6 0.0 30.4 8.7 39.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001240 | 1 48 | 87.5 10.4 2.1 8.3 20.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001241 | 1 13 | 53.8 23.1 23.1 15.4 61.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001242 | 1 78 | 80.8 6.4 12.8 3.8 23.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001243 | 1 29 | 79.3 3.4 17.2 3.4 24.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001244 | 1 11 | 81.8 9.1 9.1 27.3 45.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001245 | 1 25 | 72.0 16.0 12.0 8.0 36.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001246 | 1 19 | 78.9 5.3 15.8 5.3 26.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001247 | 1 138 | 73.2 11.6 15.2 5.1 31.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001248 | 1 37 | 64.9 8.1 27.0 5.4 40.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001249 | 1 43 | 74.4 9.3 16.3 2.3 27.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001250 | 1 24 | 58.3 12.5 29.2 0.0 41.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001251 | 1 139 | 89.9 4.3 5.8 0.7 10.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001252 | 1 12 | 75.0 16.7 8.3 0.0 25.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001253 | 1 38 | 68.4 7.9 23.7 5.3 36.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001254 | 1 20 | 80.0 10.0 10.0 5.0 25.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001255 | 1 58 | 75.9 1.7 22.4 0.0 24.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001256 | 1 21 | 57.1 23.8 19.0 14.3 57.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001257 | 1 20 | 80.0 10.0 10.0 15.0 35.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001258 | 1 22 | 77.3 4.5 18.2 13.6 36.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001259 | 1 22 | 86.4 0.0 13.6 9.1 22.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001260 | 1 56 | 67.9 14.3 17.9 1.8 33.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001261 | 1 33 | 75.8 9.1 15.2 6.1 30.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001262 | 1 26 | 92.3 0.0 7.7 11.5 19.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001263 | 1 81 | 82.7 3.7 13.6 1.2 18.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001264 | 1 51 | 68.6 5.9 25.5 7.8 39.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001265 | 1 6 | 66.7 16.7 16.7 33.3 66.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001266 | 1 41 | 85.4 4.9 9.8 2.4 17.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001267 | 1 39 | 84.6 7.7 7.7 2.6 17.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001268 | 1 19 | 78.9 5.3 15.8 5.3 26.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001269 | 1 53 | 86.8 1.9 11.3 1.9 15.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001270 | 1 33 | 63.6 24.2 12.1 3.0 39.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001271 | 1 23 | 60.9 8.7 30.4 4.3 43.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001272 | 1 42 | 71.4 11.9 16.7 2.4 31.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001273 | 1 47 | 83.0 6.4 10.6 10.6 27.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001274 | 1 75 | 76.0 14.7 9.3 1.3 25.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001275 | 1 41 | 85.4 7.3 7.3 4.9 19.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001276 | 1 24 | 79.2 4.2 16.7 4.2 25.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001277 | 1 48 | 77.1 8.3 14.6 4.2 27.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001278 | 1 27 | 55.6 18.5 25.9 0.0 44.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001279 | 1 21 | 81.0 4.8 14.3 4.8 23.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001280 | 1 42 | 85.7 0.0 14.3 0.0 14.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001281 | 1 35 | 82.9 8.6 8.6 2.9 20.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001282 | 1 26 | 69.2 7.7 23.1 3.8 34.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001283 | 1 19 | 84.2 5.3 10.5 15.8 31.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001284 | 1 49 | 83.7 4.1 12.2 12.2 28.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001285 | 1 24 | 83.3 0.0 16.7 0.0 16.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001286 | 1 84 | 75.0 11.9 13.1 8.3 33.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001287 | 1 65 | 69.2 16.9 13.8 6.2 36.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001288 | 1 33 | 87.9 6.1 6.1 12.1 24.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001289 | 1 25 | 72.0 20.0 8.0 8.0 36.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001290 | 1 37 | 64.9 13.5 21.6 5.4 40.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001291 | 1 26 | 84.6 3.8 11.5 3.8 19.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001292 | 1 53 | 75.5 17.0 7.5 9.4 34.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001293 | 1 30 | 76.7 6.7 16.7 0.0 23.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001294 | 1 41 | 78.0 7.3 14.6 4.9 26.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001295 | 1 38 | 63.2 15.8 21.1 7.9 44.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001296 | 1 40 | 70.0 12.5 17.5 2.5 32.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001297 | 1 38 | 31.6 31.6 36.8 10.5 78.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001298 | 1 57 | 78.9 8.8 12.3 8.8 29.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001299 | 1 48 | 83.3 8.3 8.3 8.3 25.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001300 | 1 125 | 87.2 7.2 5.6 4.8 17.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001301 | 1 66 | 72.7 9.1 18.2 1.5 28.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001302 | 1 46 | 71.7 13.0 15.2 6.5 34.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001303 | 1 58 | 96.6 0.0 3.4 8.6 12.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001304 | 1 38 | 71.1 18.4 10.5 10.5 39.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001305 | 1 41 | 61.0 22.0 17.1 4.9 43.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001306 | 1 57 | 73.7 12.3 14.0 7.0 33.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001307 | 1 13 | 61.5 23.1 15.4 0.0 38.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001308 | 1 68 | 75.0 7.4 17.6 8.8 33.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001309 | 1 73 | 75.3 11.0 13.7 2.7 27.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001310 | 1 40 | 62.5 17.5 20.0 2.5 40.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001311 | 1 32 | 68.8 12.5 18.8 18.8 50.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001312 | 1 33 | 81.8 6.1 12.1 6.1 24.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001313 | 1 97 | 85.6 8.2 6.2 5.2 19.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001314 | 1 30 | 90.0 6.7 3.3 13.3 23.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001315 | 1 56 | 67.9 7.1 25.0 5.4 37.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001316 | 1 21 | 90.5 0.0 9.5 9.5 19.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001317 | 1 60 | 70.0 5.0 25.0 1.7 31.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001318 | 1 29 | 86.2 3.4 10.3 10.3 24.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001319 | 1 31 | 80.6 3.2 16.1 3.2 22.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001320 | 1 42 | 81.0 4.8 14.3 11.9 31.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001321 | 1 13 | 84.6 15.4 0.0 7.7 23.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001322 | 1 17 | 94.1 5.9 0.0 11.8 17.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001323 | 1 28 | 75.0 7.1 17.9 0.0 25.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001324 | 1 30 | 83.3 6.7 10.0 6.7 23.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001325 | 1 54 | 63.0 14.8 22.2 1.9 38.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001326 | 1 65 | 84.6 4.6 10.8 7.7 23.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001327 | 1 26 | 88.5 3.8 7.7 3.8 15.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001328 | 1 59 | 84.7 3.4 11.9 13.6 28.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001329 | 1 58 | 82.8 3.4 13.8 3.4 20.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001330 | 1 63 | 71.4 7.9 20.6 0.0 28.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001331 | 1 30 | 93.3 6.7 0.0 3.3 10.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001332 | 1 46 | 76.1 4.3 19.6 4.3 28.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001333 | 1 31 | 61.3 16.1 22.6 6.5 45.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001334 | 1 31 | 80.6 6.5 12.9 0.0 19.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001335 | 1 37 | 78.4 8.1 13.5 5.4 27.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001336 | 1 35 | 85.7 5.7 8.6 2.9 17.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001337 | 1 63 | 69.8 7.9 22.2 1.6 31.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001338 | 1 77 | 71.4 9.1 19.5 3.9 32.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001339 | 1 35 | 77.1 2.9 20.0 2.9 25.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001340 | 1 35 | 85.7 5.7 8.6 0.0 14.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001341 | 1 21 | 76.2 4.8 19.0 19.0 42.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001342 | 1 66 | 77.3 6.1 16.7 1.5 24.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001343 | 1 17 | 76.5 17.6 5.9 11.8 35.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001344 | 1 39 | 84.6 10.3 5.1 5.1 20.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001345 | 1 31 | 87.1 0.0 12.9 6.5 19.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001346 | 1 58 | 72.4 13.8 13.8 6.9 34.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001347 | 1 30 | 53.3 10.0 36.7 0.0 46.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001348 | 1 150 | 79.3 5.3 15.3 10.0 30.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001349 | 1 50 | 86.0 6.0 8.0 6.0 20.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001350 | 1 53 | 58.5 7.5 34.0 1.9 43.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001351 | 1 19 | 15.8 15.8 68.4 0.0 84.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001352 | 1 60 | 88.3 3.3 8.3 3.3 15.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001353 | 1 84 | 57.1 17.9 25.0 0.0 42.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001354 | 1 159 | 71.7 10.7 17.6 1.3 29.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001355 | 1 29 | 69.0 3.4 27.6 31.0 62.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001356 | 1 33 | 87.9 0.0 12.1 6.1 18.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001357 | 1 18 | 72.2 22.2 5.6 5.6 33.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001358 | 1 20 | 65.0 10.0 25.0 15.0 50.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001359 | 1 107 | 77.6 8.4 14.0 4.7 27.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001360 | 1 29 | 75.9 3.4 20.7 13.8 37.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001361 | 1 16 | 81.3 6.3 12.5 0.0 18.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001362 | 1 73 | 76.7 12.3 11.0 4.1 27.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001363 | 1 33 | 81.8 0.0 18.2 0.0 18.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001364 | 1 36 | 75.0 8.3 16.7 2.8 27.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001365 | 1 13 | 69.2 23.1 7.7 23.1 53.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001366 | 1 39 | 66.7 23.1 10.3 7.7 41.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001367 | 1 77 | 83.1 6.5 10.4 6.5 23.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001368 | 1 45 | 60.0 17.8 22.2 4.4 44.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001369 | 1 15 | 93.3 6.7 0.0 6.7 13.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001370 | 1 42 | 78.6 2.4 19.0 9.5 31.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001371 | 1 36 | 61.1 16.7 22.2 5.6 44.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001372 | 1 71 | 73.2 14.1 12.7 12.7 39.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001373 | 1 27 | 96.3 0.0 3.7 18.5 22.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001374 | 1 34 | 85.3 2.9 11.8 0.0 14.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001375 | 1 39 | 71.8 15.4 12.8 2.6 30.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001376 | 1 39 | 71.8 7.7 20.5 0.0 28.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001377 | 1 86 | 73.3 12.8 14.0 8.1 34.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001378 | 1 35 | 68.6 17.1 14.3 2.9 34.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001379 | 1 13 | 92.3 0.0 7.7 61.5 69.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001380 | 1 35 | 85.7 11.4 2.9 5.7 20.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001381 | 1 28 | 82.1 7.1 10.7 3.6 21.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001382 | 1 91 | 79.1 4.4 16.5 7.7 28.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001383 | 1 75 | 88.0 6.7 5.3 1.3 13.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001384 | 1 83 | 77.1 8.4 14.5 2.4 25.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001385 | 1 38 | 68.4 10.5 21.1 2.6 34.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001386 | 1 50 | 56.0 18.0 26.0 2.0 46.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001387 | 1 38 | 76.3 0.0 23.7 2.6 26.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001388 | 1 12 | 83.3 16.7 0.0 16.7 33.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001389 | 1 58 | 58.6 19.0 22.4 1.7 43.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001390 | 1 84 | 66.7 9.5 23.8 4.8 38.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001391 | 1 22 | 68.2 9.1 22.7 0.0 31.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001392 | 1 127 | 70.9 19.7 9.4 32.3 61.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001393 | 1 25 | 84.0 8.0 8.0 4.0 20.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001394 | 1 30 | 63.3 10.0 26.7 0.0 36.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001395 | 1 28 | 92.9 7.1 0.0 10.7 17.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001396 | 1 71 | 60.6 11.3 28.2 9.9 49.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001397 | 1 16 | 68.8 25.0 6.3 6.3 37.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001398 | 1 33 | 90.9 9.1 0.0 6.1 15.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001399 | 1 25 | 72.0 24.0 4.0 12.0 40.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001400 | 1 42 | 73.8 9.5 16.7 11.9 38.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001401 | 1 21 | 71.4 28.6 0.0 19.0 47.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001402 | 1 19 | 52.6 47.4 0.0 15.8 63.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001403 | 1 43 | 60.5 20.9 18.6 7.0 46.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001404 | 1 98 | 69.4 9.2 21.4 4.1 34.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001405 | 1 10 | 100.0 0.0 0.0 20.0 20.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001406 | 1 72 | 72.2 11.1 16.7 1.4 29.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001407 | 1 20 | 75.0 5.0 20.0 20.0 45.0 100.0 | +|=======================================================================================================================| +| Sum/Avg | 207 9248 | 76.0 9.2 14.8 6.0 30.0 100.0 | +|=======================================================================================================================| +| Mean | 1.0 44.7 | 75.7 9.7 14.6 7.8 32.1 100.0 | +| S.D. | 0.0 27.5 | 11.2 7.3 8.5 16.2 20.6 0.0 | +| Median | 1.0 38.0 | 76.5 8.1 14.0 5.1 28.6 100.0 | +`-----------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,-----------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/hyp.trn| +|-----------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001201 | 1 75 | 57 4 14 6 24 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001202 | 1 27 | 24 1 2 1 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001203 | 1 23 | 15 3 5 1 9 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001204 | 1 27 | 21 3 3 5 11 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001205 | 1 53 | 38 3 12 2 17 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001206 | 1 30 | 30 0 0 3 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001207 | 1 25 | 17 2 6 2 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001208 | 1 49 | 30 6 13 1 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001209 | 1 49 | 40 7 2 2 11 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001210 | 1 60 | 46 6 8 4 18 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001211 | 1 23 | 19 3 1 2 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001212 | 1 44 | 35 4 5 2 11 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001213 | 1 23 | 17 4 2 2 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001214 | 1 52 | 44 3 5 6 14 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001215 | 1 43 | 23 6 14 4 24 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001216 | 1 21 | 15 0 6 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001217 | 1 108 | 82 9 17 2 28 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001218 | 1 24 | 19 3 2 5 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001219 | 1 85 | 65 8 12 2 22 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001220 | 1 59 | 46 3 10 0 13 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001221 | 1 103 | 86 6 11 4 21 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001222 | 1 27 | 24 0 3 3 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001223 | 1 42 | 32 4 6 0 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001224 | 1 28 | 25 1 2 1 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001225 | 1 65 | 51 6 8 1 15 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001226 | 1 89 | 70 7 12 1 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001227 | 1 99 | 83 6 10 4 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001228 | 1 16 | 14 1 1 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001229 | 1 6 | 3 3 0 13 16 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001230 | 1 31 | 26 2 3 2 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001231 | 1 30 | 19 4 7 3 14 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001232 | 1 29 | 21 1 7 0 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001233 | 1 95 | 84 2 9 3 14 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001234 | 1 19 | 13 3 3 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001235 | 1 62 | 51 2 9 4 15 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001236 | 1 28 | 22 1 5 1 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001237 | 1 51 | 42 2 7 6 15 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001238 | 1 67 | 45 9 13 3 25 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001239 | 1 23 | 16 0 7 2 9 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001240 | 1 48 | 42 5 1 4 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001241 | 1 13 | 7 3 3 2 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001242 | 1 78 | 63 5 10 3 18 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001243 | 1 29 | 23 1 5 1 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001244 | 1 11 | 9 1 1 3 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001245 | 1 25 | 18 4 3 2 9 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001246 | 1 19 | 15 1 3 1 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001247 | 1 138 | 101 16 21 7 44 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001248 | 1 37 | 24 3 10 2 15 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001249 | 1 43 | 32 4 7 1 12 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001250 | 1 24 | 14 3 7 0 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001251 | 1 139 | 125 6 8 1 15 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001252 | 1 12 | 9 2 1 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001253 | 1 38 | 26 3 9 2 14 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001254 | 1 20 | 16 2 2 1 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001255 | 1 58 | 44 1 13 0 14 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001256 | 1 21 | 12 5 4 3 12 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001257 | 1 20 | 16 2 2 3 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001258 | 1 22 | 17 1 4 3 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001259 | 1 22 | 19 0 3 2 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001260 | 1 56 | 38 8 10 1 19 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001261 | 1 33 | 25 3 5 2 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001262 | 1 26 | 24 0 2 3 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001263 | 1 81 | 67 3 11 1 15 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001264 | 1 51 | 35 3 13 4 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001265 | 1 6 | 4 1 1 2 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001266 | 1 41 | 35 2 4 1 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001267 | 1 39 | 33 3 3 1 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001268 | 1 19 | 15 1 3 1 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001269 | 1 53 | 46 1 6 1 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001270 | 1 33 | 21 8 4 1 13 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001271 | 1 23 | 14 2 7 1 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001272 | 1 42 | 30 5 7 1 13 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001273 | 1 47 | 39 3 5 5 13 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001274 | 1 75 | 57 11 7 1 19 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001275 | 1 41 | 35 3 3 2 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001276 | 1 24 | 19 1 4 1 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001277 | 1 48 | 37 4 7 2 13 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001278 | 1 27 | 15 5 7 0 12 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001279 | 1 21 | 17 1 3 1 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001280 | 1 42 | 36 0 6 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001281 | 1 35 | 29 3 3 1 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001282 | 1 26 | 18 2 6 1 9 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001283 | 1 19 | 16 1 2 3 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001284 | 1 49 | 41 2 6 6 14 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001285 | 1 24 | 20 0 4 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001286 | 1 84 | 63 10 11 7 28 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001287 | 1 65 | 45 11 9 4 24 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001288 | 1 33 | 29 2 2 4 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001289 | 1 25 | 18 5 2 2 9 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001290 | 1 37 | 24 5 8 2 15 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001291 | 1 26 | 22 1 3 1 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001292 | 1 53 | 40 9 4 5 18 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001293 | 1 30 | 23 2 5 0 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001294 | 1 41 | 32 3 6 2 11 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001295 | 1 38 | 24 6 8 3 17 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001296 | 1 40 | 28 5 7 1 13 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001297 | 1 38 | 12 12 14 4 30 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001298 | 1 57 | 45 5 7 5 17 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001299 | 1 48 | 40 4 4 4 12 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001300 | 1 125 | 109 9 7 6 22 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001301 | 1 66 | 48 6 12 1 19 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001302 | 1 46 | 33 6 7 3 16 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001303 | 1 58 | 56 0 2 5 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001304 | 1 38 | 27 7 4 4 15 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001305 | 1 41 | 25 9 7 2 18 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001306 | 1 57 | 42 7 8 4 19 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001307 | 1 13 | 8 3 2 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001308 | 1 68 | 51 5 12 6 23 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001309 | 1 73 | 55 8 10 2 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001310 | 1 40 | 25 7 8 1 16 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001311 | 1 32 | 22 4 6 6 16 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001312 | 1 33 | 27 2 4 2 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001313 | 1 97 | 83 8 6 5 19 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001314 | 1 30 | 27 2 1 4 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001315 | 1 56 | 38 4 14 3 21 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001316 | 1 21 | 19 0 2 2 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001317 | 1 60 | 42 3 15 1 19 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001318 | 1 29 | 25 1 3 3 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001319 | 1 31 | 25 1 5 1 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001320 | 1 42 | 34 2 6 5 13 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001321 | 1 13 | 11 2 0 1 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001322 | 1 17 | 16 1 0 2 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001323 | 1 28 | 21 2 5 0 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001324 | 1 30 | 25 2 3 2 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001325 | 1 54 | 34 8 12 1 21 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001326 | 1 65 | 55 3 7 5 15 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001327 | 1 26 | 23 1 2 1 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001328 | 1 59 | 50 2 7 8 17 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001329 | 1 58 | 48 2 8 2 12 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001330 | 1 63 | 45 5 13 0 18 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001331 | 1 30 | 28 2 0 1 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001332 | 1 46 | 35 2 9 2 13 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001333 | 1 31 | 19 5 7 2 14 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001334 | 1 31 | 25 2 4 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001335 | 1 37 | 29 3 5 2 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001336 | 1 35 | 30 2 3 1 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001337 | 1 63 | 44 5 14 1 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001338 | 1 77 | 55 7 15 3 25 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001339 | 1 35 | 27 1 7 1 9 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001340 | 1 35 | 30 2 3 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001341 | 1 21 | 16 1 4 4 9 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001342 | 1 66 | 51 4 11 1 16 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001343 | 1 17 | 13 3 1 2 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001344 | 1 39 | 33 4 2 2 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001345 | 1 31 | 27 0 4 2 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001346 | 1 58 | 42 8 8 4 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001347 | 1 30 | 16 3 11 0 14 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001348 | 1 150 | 119 8 23 15 46 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001349 | 1 50 | 43 3 4 3 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001350 | 1 53 | 31 4 18 1 23 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001351 | 1 19 | 3 3 13 0 16 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001352 | 1 60 | 53 2 5 2 9 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001353 | 1 84 | 48 15 21 0 36 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001354 | 1 159 | 114 17 28 2 47 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001355 | 1 29 | 20 1 8 9 18 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001356 | 1 33 | 29 0 4 2 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001357 | 1 18 | 13 4 1 1 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001358 | 1 20 | 13 2 5 3 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001359 | 1 107 | 83 9 15 5 29 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001360 | 1 29 | 22 1 6 4 11 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001361 | 1 16 | 13 1 2 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001362 | 1 73 | 56 9 8 3 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001363 | 1 33 | 27 0 6 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001364 | 1 36 | 27 3 6 1 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001365 | 1 13 | 9 3 1 3 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001366 | 1 39 | 26 9 4 3 16 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001367 | 1 77 | 64 5 8 5 18 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001368 | 1 45 | 27 8 10 2 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001369 | 1 15 | 14 1 0 1 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001370 | 1 42 | 33 1 8 4 13 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001371 | 1 36 | 22 6 8 2 16 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001372 | 1 71 | 52 10 9 9 28 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001373 | 1 27 | 26 0 1 5 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001374 | 1 34 | 29 1 4 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001375 | 1 39 | 28 6 5 1 12 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001376 | 1 39 | 28 3 8 0 11 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001377 | 1 86 | 63 11 12 7 30 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001378 | 1 35 | 24 6 5 1 12 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001379 | 1 13 | 12 0 1 8 9 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001380 | 1 35 | 30 4 1 2 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001381 | 1 28 | 23 2 3 1 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001382 | 1 91 | 72 4 15 7 26 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001383 | 1 75 | 66 5 4 1 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001384 | 1 83 | 64 7 12 2 21 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001385 | 1 38 | 26 4 8 1 13 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001386 | 1 50 | 28 9 13 1 23 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001387 | 1 38 | 29 0 9 1 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001388 | 1 12 | 10 2 0 2 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001389 | 1 58 | 34 11 13 1 25 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001390 | 1 84 | 56 8 20 4 32 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001391 | 1 22 | 15 2 5 0 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001392 | 1 127 | 90 25 12 41 78 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001393 | 1 25 | 21 2 2 1 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001394 | 1 30 | 19 3 8 0 11 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001395 | 1 28 | 26 2 0 3 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001396 | 1 71 | 43 8 20 7 35 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001397 | 1 16 | 11 4 1 1 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001398 | 1 33 | 30 3 0 2 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001399 | 1 25 | 18 6 1 3 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001400 | 1 42 | 31 4 7 5 16 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001401 | 1 21 | 15 6 0 4 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001402 | 1 19 | 10 9 0 3 12 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001403 | 1 43 | 26 9 8 3 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001404 | 1 98 | 68 9 21 4 34 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001405 | 1 10 | 10 0 0 2 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001406 | 1 72 | 52 8 12 1 21 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001407 | 1 20 | 15 1 4 4 9 1 | +|=======================================================================================================================| +| Sum | 207 9248 | 7031 847 1370 556 2773 207 | +|=======================================================================================================================| +| Mean | 1.0 44.7 | 34.0 4.1 6.6 2.7 13.4 1.0 | +| S.D. | 0.0 27.5 | 21.9 3.5 5.0 3.5 9.3 0.0 | +| Median | 1.0 38.0 | 28.0 3.0 6.0 2.0 11.0 1.0 | +`-----------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/hyp.trn + +Speakers: + 0: swc_deu_001201 + 1: swc_deu_001202 + 2: swc_deu_001203 + 3: swc_deu_001204 + 4: swc_deu_001205 + 5: swc_deu_001206 + 6: swc_deu_001207 + 7: swc_deu_001208 + 8: swc_deu_001209 + 9: swc_deu_001210 + 10: swc_deu_001211 + 11: swc_deu_001212 + 12: swc_deu_001213 + 13: swc_deu_001214 + 14: swc_deu_001215 + 15: swc_deu_001216 + 16: swc_deu_001217 + 17: swc_deu_001218 + 18: swc_deu_001219 + 19: swc_deu_001220 + 20: swc_deu_001221 + 21: swc_deu_001222 + 22: swc_deu_001223 + 23: swc_deu_001224 + 24: swc_deu_001225 + 25: swc_deu_001226 + 26: swc_deu_001227 + 27: swc_deu_001228 + 28: swc_deu_001229 + 29: swc_deu_001230 + 30: swc_deu_001231 + 31: swc_deu_001232 + 32: swc_deu_001233 + 33: swc_deu_001234 + 34: swc_deu_001235 + 35: swc_deu_001236 + 36: swc_deu_001237 + 37: swc_deu_001238 + 38: swc_deu_001239 + 39: swc_deu_001240 + 40: swc_deu_001241 + 41: swc_deu_001242 + 42: swc_deu_001243 + 43: swc_deu_001244 + 44: swc_deu_001245 + 45: swc_deu_001246 + 46: swc_deu_001247 + 47: swc_deu_001248 + 48: swc_deu_001249 + 49: swc_deu_001250 + 50: swc_deu_001251 + 51: swc_deu_001252 + 52: swc_deu_001253 + 53: swc_deu_001254 + 54: swc_deu_001255 + 55: swc_deu_001256 + 56: swc_deu_001257 + 57: swc_deu_001258 + 58: swc_deu_001259 + 59: swc_deu_001260 + 60: swc_deu_001261 + 61: swc_deu_001262 + 62: swc_deu_001263 + 63: swc_deu_001264 + 64: swc_deu_001265 + 65: swc_deu_001266 + 66: swc_deu_001267 + 67: swc_deu_001268 + 68: swc_deu_001269 + 69: swc_deu_001270 + 70: swc_deu_001271 + 71: swc_deu_001272 + 72: swc_deu_001273 + 73: swc_deu_001274 + 74: swc_deu_001275 + 75: swc_deu_001276 + 76: swc_deu_001277 + 77: swc_deu_001278 + 78: swc_deu_001279 + 79: swc_deu_001280 + 80: swc_deu_001281 + 81: swc_deu_001282 + 82: swc_deu_001283 + 83: swc_deu_001284 + 84: swc_deu_001285 + 85: swc_deu_001286 + 86: swc_deu_001287 + 87: swc_deu_001288 + 88: swc_deu_001289 + 89: swc_deu_001290 + 90: swc_deu_001291 + 91: swc_deu_001292 + 92: swc_deu_001293 + 93: swc_deu_001294 + 94: swc_deu_001295 + 95: swc_deu_001296 + 96: swc_deu_001297 + 97: swc_deu_001298 + 98: swc_deu_001299 + 99: swc_deu_001300 + 100: swc_deu_001301 + 101: swc_deu_001302 + 102: swc_deu_001303 + 103: swc_deu_001304 + 104: swc_deu_001305 + 105: swc_deu_001306 + 106: swc_deu_001307 + 107: swc_deu_001308 + 108: swc_deu_001309 + 109: swc_deu_001310 + 110: swc_deu_001311 + 111: swc_deu_001312 + 112: swc_deu_001313 + 113: swc_deu_001314 + 114: swc_deu_001315 + 115: swc_deu_001316 + 116: swc_deu_001317 + 117: swc_deu_001318 + 118: swc_deu_001319 + 119: swc_deu_001320 + 120: swc_deu_001321 + 121: swc_deu_001322 + 122: swc_deu_001323 + 123: swc_deu_001324 + 124: swc_deu_001325 + 125: swc_deu_001326 + 126: swc_deu_001327 + 127: swc_deu_001328 + 128: swc_deu_001329 + 129: swc_deu_001330 + 130: swc_deu_001331 + 131: swc_deu_001332 + 132: swc_deu_001333 + 133: swc_deu_001334 + 134: swc_deu_001335 + 135: swc_deu_001336 + 136: swc_deu_001337 + 137: swc_deu_001338 + 138: swc_deu_001339 + 139: swc_deu_001340 + 140: swc_deu_001341 + 141: swc_deu_001342 + 142: swc_deu_001343 + 143: swc_deu_001344 + 144: swc_deu_001345 + 145: swc_deu_001346 + 146: swc_deu_001347 + 147: swc_deu_001348 + 148: swc_deu_001349 + 149: swc_deu_001350 + 150: swc_deu_001351 + 151: swc_deu_001352 + 152: swc_deu_001353 + 153: swc_deu_001354 + 154: swc_deu_001355 + 155: swc_deu_001356 + 156: swc_deu_001357 + 157: swc_deu_001358 + 158: swc_deu_001359 + 159: swc_deu_001360 + 160: swc_deu_001361 + 161: swc_deu_001362 + 162: swc_deu_001363 + 163: swc_deu_001364 + 164: swc_deu_001365 + 165: swc_deu_001366 + 166: swc_deu_001367 + 167: swc_deu_001368 + 168: swc_deu_001369 + 169: swc_deu_001370 + 170: swc_deu_001371 + 171: swc_deu_001372 + 172: swc_deu_001373 + 173: swc_deu_001374 + 174: swc_deu_001375 + 175: swc_deu_001376 + 176: swc_deu_001377 + 177: swc_deu_001378 + 178: swc_deu_001379 + 179: swc_deu_001380 + 180: swc_deu_001381 + 181: swc_deu_001382 + 182: swc_deu_001383 + 183: swc_deu_001384 + 184: swc_deu_001385 + 185: swc_deu_001386 + 186: swc_deu_001387 + 187: swc_deu_001388 + 188: swc_deu_001389 + 189: swc_deu_001390 + 190: swc_deu_001391 + 191: swc_deu_001392 + 192: swc_deu_001393 + 193: swc_deu_001394 + 194: swc_deu_001395 + 195: swc_deu_001396 + 196: swc_deu_001397 + 197: swc_deu_001398 + 198: swc_deu_001399 + 199: swc_deu_001400 + 200: swc_deu_001401 + 201: swc_deu_001402 + 202: swc_deu_001403 + 203: swc_deu_001404 + 204: swc_deu_001405 + 205: swc_deu_001406 + 206: swc_deu_001407 + +Speaker sentences 0: swc_deu_001201 #utts: 1 +id: (swc_deu_001201-swc_deu_001201) +Scores: (#C #S #D #I) 57 4 14 6 +REF: d * e r v e R l i e b t e J u n g e h e * r z o g * d I e r a T s c h l * Ä g e s e i n E s V a t e R s n i c h T b e * a c h t * E t h a B E +HYP: d R e r ******* v e * l i e b t e * u n g e ******* h e A r z o g K d * e r a N s c h l E g e ******* s e i n * s ******* F a t e * s ******* n i c h * b e R a c h t D I t ******* h a * * +Eval: I D D D D I I D S I S D D D S D D D I I S D D D + +Speaker sentences 1: swc_deu_001202 #utts: 1 +id: (swc_deu_001202-swc_deu_001202) +Scores: (#C #S #D #I) 24 1 2 1 +REF: d i e i n d e N h a n S e s t * Ä D t e n a l s +HYP: d i e i n d e * h a n Z e s t E Ä * t e n a l s +Eval: D S I D + +Speaker sentences 2: swc_deu_001203 #utts: 1 +id: (swc_deu_001203-swc_deu_001203) +Scores: (#C #S #D #I) 15 3 5 1 +REF: W a r k e i n g r o S s e R e R f * O l G +HYP: * a r ******* k e i n ******* g r o * s e * e f R E l K +Eval: D D D D D S I S S + +Speaker sentences 3: swc_deu_001204 #utts: 1 +id: (swc_deu_001204-swc_deu_001204) +Scores: (#C #S #D #I) 21 3 3 5 +REF: g R o S s e n * c h e * m i s c h E n * * F A b r i k * e N +HYP: g * o * s e n S c h e H m i s c h I n V E R b r i k T e * +Eval: D D I I S I I S S I D + +Speaker sentences 4: swc_deu_001205 #utts: 1 +id: (swc_deu_001205-swc_deu_001205) +Scores: (#C #S #D #I) 38 3 12 2 +REF: w U R d e n a U c h m e H r e r e E R l * Ä u t E r u n g s b Ü * c h e R v e r Ö F f E n t l i C h T +HYP: w * O d e n a * c h m e * r e r e * A l O E u t * r u n g s b Ü S c h e * ******* v e r ** * f * n t l i * h * +Eval: D S D D D S I S D I D D D D D D D + +Speaker sentences 5: swc_deu_001206 #utts: 1 +id: (swc_deu_001206-swc_deu_001206) +Scores: (#C #S #D #I) 30 0 0 3 +REF: v o r b e r e i t e t e n b i e r ******* t e i * g g e ******* t u n k t +HYP: v o r b e r e i t e t e n b i e r t e i C g g e t u n k t +Eval: I I I + +Speaker sentences 6: swc_deu_001207 #utts: 1 +id: (swc_deu_001207-swc_deu_001207) +Scores: (#C #S #D #I) 17 2 6 2 +REF: d o k U m E n t e s C h L i e S s L i C H i n ******* * +HYP: d o k O m * n t e s * h * i e * s * i * G i n I +Eval: S D D D D D D S I I + +Speaker sentences 7: swc_deu_001208 #utts: 1 +id: (swc_deu_001208-swc_deu_001208) +Scores: (#C #S #D #I) 30 6 13 1 +REF: t R a u E r t a g F Ü r d e n t O D v o n k Ö n i * G F r I e D R I c h W I l H e L M +HYP: t * a u * r t a g V Ü r ******* d e n t U T v o n k ** n i C H V r * e * * * c h ******* * * l e * * +Eval: D D S D S S D I S S D D D D D D D S D D + +Speaker sentences 8: swc_deu_001209 #utts: 1 +id: (swc_deu_001209-swc_deu_001209) +Scores: (#C #S #D #I) 40 7 2 2 +REF: d a r u n T e r s i n D m a t I l d e a * s e n S i s w Ä c h t e r d E s k r * E U z E s +HYP: d a r u n D e r s i n * m a t Ü l d e a R s e n D i s w E c h t e r d * s k r O L T z I s +Eval: S D S I S S D I S S S + +Speaker sentences 9: swc_deu_001210 #utts: 1 +id: (swc_deu_001210-swc_deu_001210) +Scores: (#C #S #D #I) 46 6 8 4 +REF: i n ******* * n e n ******* s T Ä D t e n m e h r u n d m E h r d I e r o L l e d e r t R a d * i t I O n e L l E n f i s H +HYP: i n I n e n s * H E t e n m e h r u n d m * h r d * e r o * l e ******* d e r ******* t H a d T i t Z S n e R l * n f i s * +Eval: I I I D S S D D D D D S I S S S D D + +Speaker sentences 10: swc_deu_001211 #utts: 1 +id: (swc_deu_001211-swc_deu_001211) +Scores: (#C #S #D #I) 19 3 1 2 +REF: z u d e n e n W e L t ******* l Ä u ******* F i g k e i t +HYP: z u d e n e n M e * t l O u V i g k e i t +Eval: S D I S I S + +Speaker sentences 11: swc_deu_001212 #utts: 1 +id: (swc_deu_001212-swc_deu_001212) +Scores: (#C #S #D #I) 35 4 5 2 +REF: * r a c h e d E s h o f e * s u n D d e s a d e L S f Ü r D E n f r E V e l +HYP: D r a c h e d I s ******* h o f e S s u n * d e s a d e T Z f Ü r * * n f r * F e l +Eval: I S D I D S S D D D S + +Speaker sentences 12: swc_deu_001213 #utts: 1 +id: (swc_deu_001213-swc_deu_001213) +Scores: (#C #S #D #I) 17 4 2 2 +REF: z * e i t ******* a n g a b e N v e r Z i c h t E T E +HYP: z S e i t a n g a b e * M v e r S i c h t * I D +Eval: I I D S S D S S + +Speaker sentences 13: swc_deu_001214 #utts: 1 +id: (swc_deu_001214-swc_deu_001214) +Scores: (#C #S #D #I) 44 3 5 6 +REF: a * l s a c h t Z E H n H u n d e R t a c h * t ******* z i g m i t o * t T o b r a H m s a u f ******* s a t z * +HYP: a L l s a c h t * * I n * u n d e * t a c h R t z i g H m i t o D t U o b r a * m s a u f s a t z S +Eval: I D D S D D I I S I S D I I + +Speaker sentences 14: swc_deu_001215 #utts: 1 +id: (swc_deu_001215-swc_deu_001215) +Scores: (#C #S #D #I) 23 6 14 4 +REF: * ** * e I n T A U s e n D S i e * B E n H u n D e r T a c h T u n D Z w a n Z i G – +HYP: M Ü L e * n ******* * W E s e n * * i e T Z I n * u n * e r * D a c h * u n * w a n * i * ******* *** +Eval: I I I D D D S S D D I S S D D D S D D S D D D D + +Speaker sentences 15: swc_deu_001216 #utts: 1 +id: (swc_deu_001216-swc_deu_001216) +Scores: (#C #S #D #I) 15 0 6 0 +REF: D a S s d e R f i s c h F r i s C H +HYP: * a * s d e * f i s c h * r i s * * +Eval: D D D D D D + +Speaker sentences 16: swc_deu_001217 #utts: 1 +id: (swc_deu_001217-swc_deu_001217) +Scores: (#C #S #D #I) 82 9 17 2 +REF: s E i N E m a b s C h l U S s i m j a H r e n e u n z e H n h U n d e r T z w E I u n D a c h t z i * G u n T E r n a H m e r e i n e e r s t e l Ä n g e r e r e i s e n a c H s p * a n I e n +HYP: s * i D I m a b s * h l * O s i m j a * r e ******* n e u n z e * n h * n d e r * z w A L u n a c h t z i C H u n * D r n a * m ******* e r e i n e e r s t e ******* l E n g e r e ******* r e i s e ******* n a c * ******* s p B a n * e n +Eval: D S S D D S D D D D D S S S I S D S D D D S D D D D I D + +Speaker sentences 17: swc_deu_001218 #utts: 1 +id: (swc_deu_001218-swc_deu_001218) +Scores: (#C #S #D #I) 19 3 2 5 +REF: v * O n * c H a * ******* s Ô T v O r g e * z e i c h n e t +HYP: v E R n S c * a T s ** O v U r g e T z e i c h n e t +Eval: I S I D I I D S S I + +Speaker sentences 18: swc_deu_001219 #utts: 1 +id: (swc_deu_001219-swc_deu_001219) +Scores: (#C #S #D #I) 65 8 12 2 +REF: f A L C K e n ******* s t e i n s v O L l s t Ä n d i g E g e S c h i c h t e n u n d * d i e a u G s b u r g e r s t a D T g E s c h i C h T e d e s Ä l t E r e N +HYP: f * E I T e n s t e i n s v * E l s t E n d i g * ******* g e * c h i c h t e n u n d T d i e a u K s b u r g e r ******* s t a * * g * s c h i * h e d e s E l t * r e * +Eval: D S S S I D S S D D D I S D D D D D S S D D + +Speaker sentences 19: swc_deu_001220 #utts: 1 +id: (swc_deu_001220-swc_deu_001220) +Scores: (#C #S #D #I) 46 3 10 0 +REF: n a c h d i E S e n z e r s t Ö r U n G e n w u r d e d I e r a s c h w i E d e r a u f B l Ü H E n D E +HYP: n a c h d i * * e n ******* z e r s t Ö r O n M e n w u r d e d * e ******* r a s c h w i * d e r a u f P l Ü * * n * * +Eval: D D D S S D D D S D D D D + +Speaker sentences 20: swc_deu_001221 #utts: 1 +id: (swc_deu_001221-swc_deu_001221) +Scores: (#C #S #D #I) 86 6 11 4 +REF: M a c h t e n e i n f l u S s r e i c h e n h a n ******* S e ******* a t e n b e i m k o M m i * S s a r I s c h e i n g e s e t z T E n b Ü ** r g e R m e i s t e r m a R k e r t i H r E a u f W a R t u n G +HYP: * a c h t e n e i n f l u * s r e i c h e n ******* h a n I e a t e n b e i m k o * m i E s a r E s c h ******* e i n g e s e t z * n b Ü Ö r g e * m e i s t e r m a * k e r t i E r * a u f * a H t u n * +Eval: D D D I S I D I S S D D S I D D S D D S D + +Speaker sentences 21: swc_deu_001222 #utts: 1 +id: (swc_deu_001222-swc_deu_001222) +Scores: (#C #S #D #I) 24 0 3 3 +REF: a * l s Z e n t r a l * e s h a n d e L s ******* k o n t o r +HYP: a I l s * e n t r a l D e s ******* h a n d e * s k o n t o r +Eval: I D I D D I + +Speaker sentences 22: swc_deu_001223 #utts: 1 +id: (swc_deu_001223-swc_deu_001223) +Scores: (#C #S #D #I) 32 4 6 0 +REF: s O n d e r s t e L l u n g i N n e r h A L b d e R s t a D t k r e f e L D +HYP: s A n d e r s t e * l u n g i * n e r h E I b d e * C s t a * t k r e f e * * +Eval: S D D S S D S D D D + +Speaker sentences 23: swc_deu_001224 #utts: 1 +id: (swc_deu_001224-swc_deu_001224) +Scores: (#C #S #D #I) 25 1 2 1 +REF: * f i n D e T s i c h i n h a l o b a k t e r i e n +HYP: V f i n * e * Z s i c h i n h a l o b a k t e r i e n +Eval: I D D S + +Speaker sentences 24: swc_deu_001225 #utts: 1 +id: (swc_deu_001225-swc_deu_001225) +Scores: (#C #S #D #I) 51 6 8 1 +REF: a u f d e r b s e i t e f i n d e T s i C h D a s e b e N f a L l s v o n m * i C H A e l k o m p O n i e R t E +HYP: a u f d e r b E s e i t e f i n d e * Z s i * h ******* T a s e b e M f a * l s v o n m E i * * K e l k o m p U n i e * t * +Eval: S D S D D S S D I D D S S D D + +Speaker sentences 25: swc_deu_001226 #utts: 1 +id: (swc_deu_001226-swc_deu_001226) +Scores: (#C #S #D #I) 70 7 12 1 +REF: i n h A n ******* S e a t i S c h e r z e i t h a t T E d i E z I r k e L g e s e L L s c h A f t k e i n e n a u S s c h l a G g e b e n D e n e i n f l U S s m E h r +HYP: i n h * n D e a t i * c h e r z e i t ******* h a t D I d i * z E r k e * g e s e * R s c h * f t k e i n e n a u * s c h l a * g e b e n T e n e i n f l * O s ******* m * h r +Eval: D I S D D S S D S D D S D D D S D S D D + +Speaker sentences 26: swc_deu_001227 #utts: 1 +id: (swc_deu_001227-swc_deu_001227) +Scores: (#C #S #D #I) 83 6 10 4 +REF: d a E s d U r c h v E r w e n d u n G v o n a u f t r i e b ******* s k Ö R p E R n o d e r h O l z * e i n e * g e r i n g e r e m i t t L e r e d i c h t * e a l s w a S s e r h a t +HYP: d a * s T d E r c h ******* v * r w e n d u n * v o n a u f t r i e b s k ** A p * A n o d e r ******* h A l z S e i n e R g e r i n g e r e m i t t * e r e R d i c h t D e a l s ******* w a * s e r h a t +Eval: D S S D D D I D S D S D S I I D S I D D + +Speaker sentences 27: swc_deu_001228 #utts: 1 +id: (swc_deu_001228-swc_deu_001228) +Scores: (#C #S #D #I) 14 1 1 0 +REF: d R a m a t I s i e r u n g e n +HYP: d * a m a t E s i e r u n g e n +Eval: D S + +Speaker sentences 28: swc_deu_001229 #utts: 1 +id: (swc_deu_001229-swc_deu_001229) +Scores: (#C #S #D #I) 3 3 0 13 +REF: u m * ******* * * * * * * ******* * * * ** 7 5 5 +HYP: u m N S I E B E N U R F Ü N V O +Eval: I I I I I I I I I I I I I S S S + +Speaker sentences 29: swc_deu_001230 #utts: 1 +id: (swc_deu_001230-swc_deu_001230) +Scores: (#C #S #D #I) 26 2 3 2 +REF: d A S s A l b r e * c h t d i e b a d e R s ******* t o c h t e R +HYP: d * E s E l b r e I c h t d i e b a d e * s t o c h t e * +Eval: D S S I D I D + +Speaker sentences 30: swc_deu_001231 #utts: 1 +id: (swc_deu_001231-swc_deu_001231) +Scores: (#C #S #D #I) 19 4 7 3 +REF: t H a * t b a * R b a r * I s c h e R s t A a t S r A I S O N +HYP: t * a R t b a B M b a r E R s c h e * s t D a t Z r * * * * * +Eval: D I I S I S D S S D D D D D + +Speaker sentences 31: swc_deu_001232 #utts: 1 +id: (swc_deu_001232-swc_deu_001232) +Scores: (#C #S #D #I) 21 1 7 0 +REF: D e r D A s l i E D b e s o n d e r s l i e b t e +HYP: * e r ******* * * s ******* l i * T b e s o n d e r s ******* l i e b t e +Eval: D D D D D D S D + +Speaker sentences 32: swc_deu_001233 #utts: 1 +id: (swc_deu_001233-swc_deu_001233) +Scores: (#C #S #D #I) 84 2 9 3 +REF: a u f G R u n d d e * s w a c H s e n d e n p u B l i k u m s ******* i n t E r e s s e s w u r d e d e r a u f t r i T t s ******* o r t F Ü R d i E p r i m a v i s t a l e s u n g e N +HYP: a u f * K u n d d e R s w a c * s e n d e n p u P l i k u m s i n t * r e s s e s w u r d e d e r a u f t r i * t s o r t * Ü * d i * p r i m a v i s t a ******* l e s u n g e * +Eval: D S I D S I D D I D D D D D + +Speaker sentences 33: swc_deu_001234 #utts: 1 +id: (swc_deu_001234-swc_deu_001234) +Scores: (#C #S #D #I) 13 3 3 0 +REF: U n d f r e i L I c h t s P i E L e +HYP: * n d f r e i T E c h t s B i * * e +Eval: D S S S D D + +Speaker sentences 34: swc_deu_001235 #utts: 1 +id: (swc_deu_001235-swc_deu_001235) +Scores: (#C #S #D #I) 51 2 9 4 +REF: d a S s d i e D r e i d e n s t U r z * r E l A t i V u n ******* b e s c h a * D e t Ü b e r s t a n d * e n h a T t e N +HYP: d a * s d i e * r e i ******* d e n s t Ö r z S r * l * t i * u n b e s c h a R T e t ** b e r s t a n d T e n h a * t e * +Eval: D D D S I D D D I I S D I D D + +Speaker sentences 35: swc_deu_001236 #utts: 1 +id: (swc_deu_001236-swc_deu_001236) +Scores: (#C #S #D #I) 22 1 5 1 +REF: J a H r e n e r s c h I E n e n z w e i i M m E r * +HYP: E a * r e n e r s c h * * n e n z w e i i * m * r L +Eval: S D D D D D I + +Speaker sentences 36: swc_deu_001237 #utts: 1 +id: (swc_deu_001237-swc_deu_001237) +Scores: (#C #S #D #I) 42 2 7 6 +REF: * * * g r a b m a L E u n d g r a b ******* K a ******* p e L l e n o ******* d e r W o H l t a t e n n a c H h a l t I G +HYP: D E R g r a b m a * N u n d g r a b G a p e * l e n o d e r * o * l t a t e n n a c * h a l t * * +Eval: I I I D S I S I D I D D D D D + +Speaker sentences 37: swc_deu_001238 #utts: 1 +id: (swc_deu_001238-swc_deu_001238) +Scores: (#C #S #D #I) 45 9 13 3 +REF: J u n I n e U n z e H n h u n d E r T s e C H s u N D n e u * n z i G k Ü n d i G T E e r * s e i n e b e i D e n J o b * s +HYP: I u n * E n e * n z e * n ******* h u n d A r * ******* s e * X s u * * n e u I n z i H k ** n d i * * K D e r R s e i n e ******* b e i T e n S o b P s +Eval: S D S D D D S D D D S D D I S D D D S S I D S S I + +Speaker sentences 38: swc_deu_001239 #utts: 1 +id: (swc_deu_001239-swc_deu_001239) +Scores: (#C #S #D #I) 16 0 7 2 +REF: E i n g * p R o t E i * n G e k o P p e l T +HYP: * i n g E p * o t * i E n ******* * e k o * p e l * +Eval: D I D D I D D D D + +Speaker sentences 39: swc_deu_001240 #utts: 1 +id: (swc_deu_001240-swc_deu_001240) +Scores: (#C #S #D #I) 42 5 1 4 +REF: n e u n u n D s e c h * z i g d e r m e ******* d i * A C o n t R o l a l b u m * c h a R t S e i n +HYP: n e u n u n s e c h T z i g d e r m e d i E R K o n t W o l a l b u m S c h a * t Z e i n +Eval: S I I I S S S I D S + +Speaker sentences 40: swc_deu_001241 #utts: 1 +id: (swc_deu_001241-swc_deu_001241) +Scores: (#C #S #D #I) 7 3 3 2 +REF: * ******* D a D u R c H k o M m T +HYP: M T a R u S c * k o * m * +Eval: I I S S S D D D + +Speaker sentences 41: swc_deu_001242 #utts: 1 +id: (swc_deu_001242-swc_deu_001242) +Scores: (#C #S #D #I) 63 5 10 3 +REF: * o H n e ******* h I N n i c h t d e n G r o s s h a n d e L S k a u f l e u t e n g e s e L L s C h a f t l i c H g * l e i c h g e s t e L L t w a r e N +HYP: U o * n e h * * E n i c h t d e n K r o s s h a n d e * k a u f l e u t e n g e s e * R s * h a f t l i c * ******* g K l e i c h g e s t e * R t w a r e * +Eval: I D I D D S S D S D S D D D I D S D + +Speaker sentences 42: swc_deu_001243 #utts: 1 +id: (swc_deu_001243-swc_deu_001243) +Scores: (#C #S #D #I) 23 1 5 1 +REF: v O n d e r n a H r u n g u n d v o m k L i * m a +HYP: v R n ******* d e r ******* n a * r u n g u n d v o m ******* k * i E m a +Eval: S D D D D D I + +Speaker sentences 43: swc_deu_001244 #utts: 1 +id: (swc_deu_001244-swc_deu_001244) +Scores: (#C #S #D #I) 9 1 1 3 +REF: a p O L l o * e i * n * s +HYP: a p * R l o E e i E n Z s +Eval: D S I I I + +Speaker sentences 44: swc_deu_001245 #utts: 1 +id: (swc_deu_001245-swc_deu_001245) +Scores: (#C #S #D #I) 18 4 3 2 +REF: b r Ü ** H l u n d h ** Ü r t H n a c H k Ö l N +HYP: b r Ü Ö Ü l u n d h Ö E r t * n a c * ******* k E l E +Eval: I S I S D D D S S + +Speaker sentences 45: swc_deu_001246 #utts: 1 +id: (swc_deu_001246-swc_deu_001246) +Scores: (#C #S #D #I) 15 1 3 1 +REF: E t w * A i n e I n k l o s t e R +HYP: * t w E R i n e * n k l o s t e * +Eval: D I S D D + +Speaker sentences 46: swc_deu_001247 #utts: 1 +id: (swc_deu_001247-swc_deu_001247) +Scores: (#C #S #D #I) 101 16 21 7 +REF: * * * * * * z U m O F f i z I e L L e n k a R n e V a l e n T s t a n D u n D H e u t e E i n e m i s c h U n g A u s k Ö L s c h e M k a R n * e V a l u n d p O l i T I s c h e M k a b A r e T t m I t C o m E d Y e l e m e n t e n d a R s t e L l t u N D +HYP: D I E V I R z S m A U f i z * e H R e n k a * n e W a l ******* e n * s t a n T u n * T R e u t e * i n e ******* m i s c h * n g * u s k Ö R s c h e N k a * n D e W a l u n d B p * l i * D s c h e * k a b * r e * t m * t K o m d * e l e m e n t e n d a * s t e * l t u * * +Eval: I I I I I I S S S D S S D S D D S D S S D D D D S S D I S S D D S D D D D S S D D D D D + +Speaker sentences 47: swc_deu_001248 #utts: 1 +id: (swc_deu_001248-swc_deu_001248) +Scores: (#C #S #D #I) 24 3 10 2 +REF: d i e Z u R E n T s t * e H U n G D e s l I e ******* d e s f Ü H r t e N +HYP: d i e W u * ******* * n * s t I e * * n * R e s ******* l * e d e s f Ü I r t e * +Eval: S D D D D I D D D S D D I S D + +Speaker sentences 48: swc_deu_001249 #utts: 1 +id: (swc_deu_001249-swc_deu_001249) +Scores: (#C #S #D #I) 32 4 7 1 +REF: n a N n t * E z i e g l e r d i e E r m o r d u N G d e r b E R n a u E r I n +HYP: n a * n t I T z i e g l e r d i e ******* A r m o r d u * M d e r ******* b * A n a u * r * n +Eval: D I S D S D S D D S D D + +Speaker sentences 49: swc_deu_001250 #utts: 1 +id: (swc_deu_001250-swc_deu_001250) +Scores: (#C #S #D #I) 14 3 7 0 +REF: W i n t e R r U h E i s t v o R A L L e M +HYP: * i n t e * r O h R i s t v o * ******* * * * e L +Eval: D D S S D D D D D S + +Speaker sentences 50: swc_deu_001251 #utts: 1 +id: (swc_deu_001251-swc_deu_001251) +Scores: (#C #S #D #I) 125 6 8 1 +REF: d i e s t r Ä n g E d e r v o r g Ä n g e r l e i t u n g w u r d e n z w i s C h e N N e u n z E h n h u n d e r t n e u n u * n d z w a n z i g u n d n e u n z E h n h u n d e r T d r e i U n d f Ü n f z i g a r c h Ä O l o g I s c h e r G r a b e n +HYP: d i e s t r E n g * d e r v o r g E n g e r l e i t u n g w u r d e n z w i s * h e * * e u n z * h n h u n d e r t n e u n u E n d z w a n z i g u n d n e u n z * h n h u n d e r * d r e i * n d f Ü n f z i g a r c h E l o g E s c h e r K r a b e n +Eval: S D S D D D D I D D D S S S S + +Speaker sentences 51: swc_deu_001252 #utts: 1 +id: (swc_deu_001252-swc_deu_001252) +Scores: (#C #S #D #I) 9 2 1 0 +REF: i M g e g e N s a t Z +HYP: i N g e g e s a t * +Eval: S S D + +Speaker sentences 52: swc_deu_001253 #utts: 1 +id: (swc_deu_001253-swc_deu_001253) +Scores: (#C #S #D #I) 26 3 9 2 +REF: f a R b e N v * O n U e r d i N g e n s I n D B l a U u n d R o * t +HYP: f a * b e * v E R n * e r d i * g e n ******* s E n * * l a * ******* u n d H o R t +Eval: D D I S D D D S D D D D S I + +Speaker sentences 53: swc_deu_001254 #utts: 1 +id: (swc_deu_001254-swc_deu_001254) +Scores: (#C #S #D #I) 16 2 2 1 +REF: l * i V E v e r a n s t a l t u N G e n +HYP: l E i * F v e r a n s t a l t u * M e n +Eval: I D S D S + +Speaker sentences 54: swc_deu_001255 #utts: 1 +id: (swc_deu_001255-swc_deu_001255) +Scores: (#C #S #D #I) 44 1 13 0 +REF: S o w e r D e n h E u t e i n d e R r e g e l a l l e d o r t l e b e n d e n b r a u N B Ä R E n +HYP: Z o ******* w e r * e n ******* h * u t e ******* i n ******* d e * ******* r e g e l a l l e d o r t l e b e n d e n b r a u * * ** * * n +Eval: S D D D D D D D D D D D D D + +Speaker sentences 55: swc_deu_001256 #utts: 1 +id: (swc_deu_001256-swc_deu_001256) +Scores: (#C #S #D #I) 12 5 4 3 +REF: L i e ******* d E R f Ü R r e V u * E F I l m e * +HYP: * i e d * A f Ü * ******* r e u S C S E l m e R +Eval: D I D S D D S I S S S I + +Speaker sentences 56: swc_deu_001257 #utts: 1 +id: (swc_deu_001257-swc_deu_001257) +Scores: (#C #S #D #I) 16 2 2 3 +REF: d e s h a n ******* * S e ******* a t e n f Ü H r e N +HYP: d e s ******* h a n D I e a t e n f Ü Ö r e * +Eval: D I I S I S D + +Speaker sentences 57: swc_deu_001258 #utts: 1 +id: (swc_deu_001258-swc_deu_001258) +Scores: (#C #S #D #I) 17 1 4 3 +REF: h e B b e l * s a * g ******* n e s b E R n a u E R +HYP: h e * b e l T s a R g n e s b * A n a u * * +Eval: D I I I D S D D + +Speaker sentences 58: swc_deu_001259 #utts: 1 +id: (swc_deu_001259-swc_deu_001259) +Scores: (#C #S #D #I) 19 0 3 2 +REF: l * e b e n s w e i s e v e r k Ö r * p E r N +HYP: l I e b e n s w e i s e v e r k ** r B p * r * +Eval: I D I D D + +Speaker sentences 59: swc_deu_001260 #utts: 1 +id: (swc_deu_001260-swc_deu_001260) +Scores: (#C #S #D #I) 38 8 10 1 +REF: W I e d e R f A L l d e s V i e L e n h a m b u r g E R n z u k a t H o * l I s C H F r O m m e n +HYP: * * e ******* d e * ******* f E I l d e s F i e R e n h a m b u r g * A n ******* z u k a t * o R l E s * * V r A m m e n +Eval: D D D D D S S S S D S D D I S D D S S + +Speaker sentences 60: swc_deu_001261 #utts: 1 +id: (swc_deu_001261-swc_deu_001261) +Scores: (#C #S #D #I) 25 3 5 2 +REF: k * U l t u R U n d W i * r t S C h A f t a u s t a u s c h e N +HYP: k E R l t u L * n d * i E r t * * h R f t a u s t a u s c h e * +Eval: I S S D D I D D S D + +Speaker sentences 61: swc_deu_001262 #utts: 1 +id: (swc_deu_001262-swc_deu_001262) +Scores: (#C #S #D #I) 24 0 2 3 +REF: * * j a H r z w e i t a u s e n d v e r t o n * t e +HYP: M I j a * r ******* z w e i t a u s e n d v e r t o n D t e +Eval: I I D D I + +Speaker sentences 62: swc_deu_001263 #utts: 1 +id: (swc_deu_001263-swc_deu_001263) +Scores: (#C #S #D #I) 67 3 11 1 +REF: d a S s e R d i e s e l e i t u n g s c H n E L l e r v o L l e n D e n k Ö N n e a l s d e r b a u m e i s t e r d e n k Ö L n e r d o * m +HYP: d a * s ******* e * d i e s e ******* l e i t u n g ******* s c * n * A l e r ******* v o * l e n T e n k ** Ü n e a l s d e r b a u m e i s t e r d e n k Ö * n e r d o U m +Eval: D D D D D D D S D D S D S D I + +Speaker sentences 63: swc_deu_001264 #utts: 1 +id: (swc_deu_001264-swc_deu_001264) +Scores: (#C #S #D #I) 35 3 13 4 +REF: H i * * ******* * n r i C h t u n g d e R b E R n a u E r i n h a b E e S s i c H s C H l i C h t u m +HYP: * i D E H n r i * h t u n g d e * b * A n a u A r i n h a b * ******* e * ******* s i c * ******* s * R l i * h t ******* u m +Eval: D I I I I D D D S S D D D D D D D S D D + +Speaker sentences 64: swc_deu_001265 #utts: 1 +id: (swc_deu_001265-swc_deu_001265) +Scores: (#C #S #D #I) 4 1 1 2 +REF: l * U d w * i G +HYP: l O R d w E i * +Eval: I S I D + +Speaker sentences 65: swc_deu_001266 #utts: 1 +id: (swc_deu_001266-swc_deu_001266) +Scores: (#C #S #D #I) 35 2 4 1 +REF: d e r z e i t d e r b E s t E k * e N n e r d e r e i f e L l e i t u n G +HYP: d e r z e i t d e r b * s t I G k H e * n e r d e r e i f e * l e i t u n * +Eval: D S S I D D D + +Speaker sentences 66: swc_deu_001267 #utts: 1 +id: (swc_deu_001267-swc_deu_001267) +Scores: (#C #S #D #I) 33 3 3 1 +REF: F o k u s D e s w i S s e n s C h a f t l i c h e n i n * t E r e S s e s +HYP: V o k u s B e s w i * s e n s * h a f t l i c h e n i n D t A r e * s e s +Eval: S S D D I S D + +Speaker sentences 67: swc_deu_001268 #utts: 1 +id: (swc_deu_001268-swc_deu_001268) +Scores: (#C #S #D #I) 15 1 3 1 +REF: t H e m * A z u b e g e i s t e R N +HYP: t * e m E R z u b e g e i s t e * * +Eval: D I S D D + +Speaker sentences 68: swc_deu_001269 #utts: 1 +id: (swc_deu_001269-swc_deu_001269) +Scores: (#C #S #D #I) 46 1 6 1 +REF: m e t e R u n d k o N n t e d a m i t a u C H V o n i N n e n b e * g a n g e n w e r d e n +HYP: m e t e * u n d k o * n t e d a m i t a u * * ******* F o n i * n e n b e R g a n g e n w e r d e n +Eval: D D D D D S D I + +Speaker sentences 69: swc_deu_001270 #utts: 1 +id: (swc_deu_001270-swc_deu_001270) +Scores: (#C #S #D #I) 21 8 4 1 +REF: h a R D C O V e r b e s T s e l l E R l i s t e * d e R n E W +HYP: h a H T K A B e r b e s s e l l * A l i s t e U d e * ******* n * H +Eval: S S S S S S D S I D D D S + +Speaker sentences 70: swc_deu_001271 #utts: 1 +id: (swc_deu_001271-swc_deu_001271) +Scores: (#C #S #D #I) 14 2 7 1 +REF: d E r F r e i E n e n ******* z Y k l o p Ä D I E +HYP: d A r ******* * r e i * n e n z I k l o p ** * * * +Eval: S D D D I S D D D D + +Speaker sentences 71: swc_deu_001272 #utts: 1 +id: (swc_deu_001272-swc_deu_001272) +Scores: (#C #S #D #I) 30 5 7 1 +REF: d E n g r I Z Z l * Y w i E d e r a u f d i E l I s T e z u s e t z e n +HYP: d * n g r * E S l I E w i * d e r a u f d i * ******* l E s * e T z u ******* s e t z e n +Eval: D D S S I S D D D S D S D + +Speaker sentences 72: swc_deu_001273 #utts: 1 +id: (swc_deu_001273-swc_deu_001273) +Scores: (#C #S #D #I) 39 3 5 5 +REF: * * * ******* l a n g d i e s e k a p l a n S s t e L l e a u f r E c h t e r ******* h A L t e n w U r d E +HYP: W I E l a n g d i e s e k a p l a n s t e * l e a u f r I c h t e r h * E t e n ******* w * r d * +Eval: I I I I S D S I D S D D D + +Speaker sentences 73: swc_deu_001274 #utts: 1 +id: (swc_deu_001274-swc_deu_001274) +Scores: (#C #S #D #I) 57 11 7 1 +REF: s i e w a r E n W a H r s c h e i n l i C h b e r e i t S d r e i s S i g s E k u n d * e n n A c h a u s B r U c h D e s F E U e r S +HYP: s i e ******* w a r * n M a * r s c h e i n l i G h ******* b e r e i t Z d r e i s Z i g s I k u n d T e n n E c h a u s P r * c h ******* T e s V O R e r * +Eval: D D S D S D S S S I S S D D S S S S D + +Speaker sentences 74: swc_deu_001275 #utts: 1 +id: (swc_deu_001275-swc_deu_001275) +Scores: (#C #S #D #I) 35 3 3 2 +REF: m e t e r N g e s a m t l Ä n g e u n d b i s Z u * Z e h n m * e t e r N +HYP: m e t e r * g e s a m t l I n g e u n d b i s ******* T u T S e h n m I e t e r * +Eval: D S D S I S I D + +Speaker sentences 75: swc_deu_001276 #utts: 1 +id: (swc_deu_001276-swc_deu_001276) +Scores: (#C #S #D #I) 19 1 4 1 +REF: F e i n e r ******* i t z e n u n d s p a l t E N +HYP: V e i n e ******* r i t z e n u n d ******* s p a l t * * +Eval: S D I D D D + +Speaker sentences 76: swc_deu_001277 #utts: 1 +id: (swc_deu_001277-swc_deu_001277) +Scores: (#C #S #D #I) 37 4 7 2 +REF: d E n m A n v O n a u s s E n d i e k E H l e * h i n a b * f l i e S s e n s i e H t +HYP: d I n ******* m E n v E n a u s s * n ******* d i e ******* k * O l e R h i n a b P f l i e * s e n s i e * t +Eval: S D S S D D D D S I I D D + +Speaker sentences 77: swc_deu_001278 #utts: 1 +id: (swc_deu_001278-swc_deu_001278) +Scores: (#C #S #D #I) 15 5 7 0 +REF: E I n e M i N t e r V i E W s a G t e b r O W n +HYP: * * n e * i * t e r i * * O s a K t e ******* b r A U n +Eval: D D D D S D D S S D S S + +Speaker sentences 78: swc_deu_001279 #utts: 1 +id: (swc_deu_001279-swc_deu_001279) +Scores: (#C #S #D #I) 17 1 3 1 +REF: d a s f Ü n f t E e V A n ******* g e L i u m +HYP: d a s f Ü n f t * e * * n g e R i u m +Eval: D D D I S + +Speaker sentences 79: swc_deu_001280 #utts: 1 +id: (swc_deu_001280-swc_deu_001280) +Scores: (#C #S #D #I) 36 0 6 0 +REF: r e i S s e n s i E m a n c h m a l w e i d e t i e r e W i E s c h a f e +HYP: r e i * s e n s i * ******* m a n c h m a l w e i d e t i e r e * i * ******* s c h a f e +Eval: D D D D D D + +Speaker sentences 80: swc_deu_001281 #utts: 1 +id: (swc_deu_001281-swc_deu_001281) +Scores: (#C #S #D #I) 29 3 3 1 +REF: s i E h Ö r e n d e n a r t i k e l d e s * i G n r E V i E W +HYP: s i * h Ö r e n d e n a r t i k e l d e s E i * n r Ü F i * U +Eval: D I D S S D S + +Speaker sentences 81: swc_deu_001282 #utts: 1 +id: (swc_deu_001282-swc_deu_001282) +Scores: (#C #S #D #I) 18 2 6 1 +REF: C H A k u * Z A i s T g E l e R n t e r k o c h +HYP: * * * k u S E R i s * g * l e * n t e r k o c h +Eval: D D D I S S D D D + +Speaker sentences 82: swc_deu_001283 #utts: 1 +id: (swc_deu_001283-swc_deu_001283) +Scores: (#C #S #D #I) 16 1 2 3 +REF: * h a n * S w e n D t s t i f t * u n G +HYP: S h a n Z E w e n * t s t i f t D u n * +Eval: I I S D I D + +Speaker sentences 83: swc_deu_001284 #utts: 1 +id: (swc_deu_001284-swc_deu_001284) +Scores: (#C #S #D #I) 41 2 6 6 +REF: n e u n z E h n h U n d e r t a c h t z * e H n a l * ******* s h a n ******* S e ******* a * t e n a n g e s E H e N +HYP: n e u n z * h n h * n d e r t a c h t z I e * n a l T s h a n I e a R t e n ******* a n g e s * I e * +Eval: D D I D I I I S I I D D S D + +Speaker sentences 84: swc_deu_001285 #utts: 1 +id: (swc_deu_001285-swc_deu_001285) +Scores: (#C #S #D #I) 20 0 4 0 +REF: m e H r e r E e s n a c h i H m t H u n +HYP: m e * r e r * e s n a c h i * m t * u n +Eval: D D D D + +Speaker sentences 85: swc_deu_001286 #utts: 1 +id: (swc_deu_001286-swc_deu_001286) +Scores: (#C #S #D #I) 63 10 11 7 +REF: a u F s T i E G d e s g e r i c h T s z u R l a n d e s ******* w e i t * * b e * l i e b T e n k * u l i n a ******* r i s c h e N s p ** E z I A l I t Ä t E r m Ö g L I C H T E +HYP: a u C s H i * H d e s g e r i c h * s z u O l a n d e s w e i t E N b e R l i e b * e n k O u l i n a r i s c h e R s p Ä T z * E l * t E t A r m Ü g * * * * * * +Eval: S S D S D S I I I I D I I S I S D S D S S S D D D D D D + +Speaker sentences 86: swc_deu_001287 #utts: 1 +id: (swc_deu_001287-swc_deu_001287) +Scores: (#C #S #D #I) 45 11 9 4 +REF: C o l l e * * G E u n d e i N E n z w e i t J o b a l S s p * a n I s C h l * e H r e r i n h A m P T O n F A L l s A n +HYP: K o l l e T S C H u n d e i * * n z w e i t C o b a l * T s p B a n * s * h l I e * r e r i n h E m * * n V O R l s ******* E n +Eval: S I I S S D D S D S I D D I D S D D S S S S D S + +Speaker sentences 87: swc_deu_001288 #utts: 1 +id: (swc_deu_001288-swc_deu_001288) +Scores: (#C #S #D #I) 29 2 2 4 +REF: W u r d e n K e i n E s ******* w e g s a * l l e g e b Ü * r * t i g e N +HYP: B u r d e n * e i n I s w e g s a E l l e g e b Ü I r O t i g e * +Eval: S D S I I I I D + +Speaker sentences 88: swc_deu_001289 #utts: 1 +id: (swc_deu_001289-swc_deu_001289) +Scores: (#C #S #D #I) 18 5 2 2 +REF: i s t i H r k Ö r P e R b a u K r Ä f t i g * * +HYP: i s t i E r k E r B e * b a u ******* G r E f t i g K H +Eval: S S S D D S S I I + +Speaker sentences 89: swc_deu_001290 #utts: 1 +id: (swc_deu_001290-swc_deu_001290) +Scores: (#C #S #D #I) 24 5 8 2 +REF: a n ******* l Ä S s l i c h D e r n E u J a H R E s ******* a n s p r a c H e k I M +HYP: a n l ** E s l i c h ******* T e r n O u * a * * * s a n s p r a c R e ******* k * H +Eval: I D S D S S D D D D I S D D S + +Speaker sentences 90: swc_deu_001291 #utts: 1 +id: (swc_deu_001291-swc_deu_001291) +Scores: (#C #S #D #I) 22 1 3 1 +REF: m i t w i n D v o n s c H r * Ä g h i n t e n +HYP: m i t w i n * v o n D s c * r E Ä g ******* h i n t e n +Eval: D S D I D + +Speaker sentences 91: swc_deu_001292 #utts: 1 +id: (swc_deu_001292-swc_deu_001292) +Scores: (#C #S #D #I) 40 9 4 5 +REF: d E n G r * Ö S s t e n t e I l d e R b * * ******* E z i R K s V E r t r * e t u n g U E r d i n g e n a u s +HYP: d I n * r E Ö C s t e n t e A l ******* d e * b I T T z i Ö G s F O r t r I e t u n g * Ü r d i n g e n a u s +Eval: S D I S S D D I I I S S S S S I D S + +Speaker sentences 92: swc_deu_001293 #utts: 1 +id: (swc_deu_001293-swc_deu_001293) +Scores: (#C #S #D #I) 23 2 5 0 +REF: a c h t Z E H n h u n D e r t e i N u n D z w a n z i G +HYP: a c h t * * I n h u n * e r t e i L u n * z w a n z i * +Eval: D D S D S D D + +Speaker sentences 93: swc_deu_001294 #utts: 1 +id: (swc_deu_001294-swc_deu_001294) +Scores: (#C #S #D #I) 32 3 6 2 +REF: d E s G r o S s e n a D e l * s a n ******* g e s a M m E L t e n r e i c h t u m s +HYP: d * s ******* K r o * s e n a T e l T s a n g e s a * m * I t e n ******* r e i c h t u m s +Eval: D D S D S I I D D S D + +Speaker sentences 94: swc_deu_001295 #utts: 1 +id: (swc_deu_001295-swc_deu_001295) +Scores: (#C #S #D #I) 24 6 8 3 +REF: * * s o L L T E n n i C h t a l S s e * X U E L l E p r o V o k a t i O N +HYP: D E s o * * * * n n i * h t a l * s e H Z W O l * p r o R o k a t i * U +Eval: I I D D D D D D I S S S S D S D S + +Speaker sentences 95: swc_deu_001296 #utts: 1 +id: (swc_deu_001296-swc_deu_001296) +Scores: (#C #S #D #I) 28 5 7 1 +REF: t e i l h A b e R d e R F I r m * A g o S s m a N n u n d J Ü r g e n S +HYP: t e i l h * b e * ******* d e * * V r m E R g o * s m a * n u n d I O r g e n Z +Eval: D D D D D S I S D D S S S + +Speaker sentences 96: swc_deu_001297 #utts: 1 +id: (swc_deu_001297-swc_deu_001297) +Scores: (#C #S #D #I) 12 12 14 4 +REF: * D e * * * R K r a u T I n S e L B I L D E t S I E D i E G E M e I N D E +HYP: I N e M I T E F r a u * * n T e R * * K A U t ******* * * * * i * ******* * N S e * * L T +Eval: I S I I I S S D D S S D D S S S D D D D D D D D S S D D S S + +Speaker sentences 97: swc_deu_001298 #utts: 1 +id: (swc_deu_001298-swc_deu_001298) +Scores: (#C #S #D #I) 45 5 7 5 +REF: a u * ******* d i * o i s t e I n d E u t S c h e r h Ö R b U C H v E r l a * g m i t s i t z i n m * Ü n c h e n +HYP: a u R d i E o i s t ******* e A n ******* d * u t * c h e r h ** E b * O F v A r l a R g m i t s i t z i n ******* m E Ü n c h e n +Eval: I I I D S D D D D S D S S S I D I + +Speaker sentences 98: swc_deu_001299 #utts: 1 +id: (swc_deu_001299-swc_deu_001299) +Scores: (#C #S #D #I) 40 4 4 4 +REF: f a R B p * i * G m e n t e u n d c h E m i S c h e v o r p r O d ******* u k t e * h e r s t e L l t +HYP: f a * p E i K m e n t e u n d S c h * m i * c h e v o r p r E d u k t e R h e r s t e * l t +Eval: D S I I S S D D S I I D + +Speaker sentences 99: swc_deu_001300 #utts: 1 +id: (swc_deu_001300-swc_deu_001300) +Scores: (#C #S #D #I) 109 9 7 6 +REF: E r B l i c h e n p r e U S s i s c h e n f r e i ******* h e R r e n ******* s t a n d i n d e r z o l L a n s c h l U s S F r a g e E n t s c h i e * d e n * g e g e n * d e n s E n a * t a u f d i e s e i t e b i s m a R C K s g e s t e L l t +HYP: A r P l i c h e n p r e * * s i s c h e n f r e i h e * r e n s t a n d i n d e r z o l a n s c h l O s V r a g e I n t s c h i e B d e n G g e g e n G d e n s I n a R t a u f d i e ******* s e i t e b i s m a * * G s g e s t e * l t +Eval: S S D D I D I S S S S S I I I S I D D D S D + +Speaker sentences 100: swc_deu_001301 #utts: 1 +id: (swc_deu_001301-swc_deu_001301) +Scores: (#C #S #D #I) 48 6 12 1 +REF: w e N n d i E Q U e L l e n v o n s e L B s t H e r ******* v o r Q U e L l e n u n d O F f e n z u t a g e l i E g e n +HYP: w e * n ******* d i * ******* K W e * l e n v o n s e * * s t * e r v o r G W e * l e n u n d A U f e n z u ******* t a g e ******* l i * g e n +Eval: D D D D S S D D D D I S S D S S D D D + +Speaker sentences 101: swc_deu_001302 #utts: 1 +id: (swc_deu_001302-swc_deu_001302) +Scores: (#C #S #D #I) 33 6 7 3 +REF: d A s v o * M n a c h b a r b a u t r * u P P b e * R e i t S b e g o N N E n w U R D E +HYP: d * s v o U N n a c h b a r b a u t r I u * B b e L L e i t Z b e g o * * * n w * * O T +Eval: D I S I D S I S S D D D D D S S + +Speaker sentences 102: swc_deu_001303 #utts: 1 +id: (swc_deu_001303-swc_deu_001303) +Scores: (#C #S #D #I) 56 0 2 5 +REF: w e * r d e n p r Ä * g e n d e e l e m e n t e d e s h a n s e ******* a t e n t u m s z u * s a m m e n ******* g e f a S s t +HYP: w e H r d e n p r Ä R g e n d e e l e m e n t e d e s h a n s e a t e n t u m s ******* z u O s a m m e n g e f a * s t +Eval: I I I D I I D + +Speaker sentences 103: swc_deu_001304 #utts: 1 +id: (swc_deu_001304-swc_deu_001304) +Scores: (#C #S #D #I) 27 7 4 4 +REF: d * a s l i e D w U r d e A L s v o l * K s * l i e D a n ******* g e s E H e n +HYP: d E a s S l i e * Z w * r d e ******* R T s v o l C G s T l i e T a n g e s * I e n +Eval: I S D S D D S S I S I S I D S + +Speaker sentences 104: swc_deu_001305 #utts: 1 +id: (swc_deu_001305-swc_deu_001305) +Scores: (#C #S #D #I) 25 9 7 2 +REF: d e r z U R R A n d O m h O u s E v E r l a g ******* s * g R U P P e G E h Ö r t +HYP: d e r ******* z O N N E n d E m h * u s * ******* v * r l a g s K g O B I G e * * h Ö r t +Eval: D S S S S S D D D D I I S S S S D D + +Speaker sentences 105: swc_deu_001306 #utts: 1 +id: (swc_deu_001306-swc_deu_001306) +Scores: (#C #S #D #I) 42 7 8 4 +REF: f Ü r d I e k Ü n f T i g e n b O r d ******* * b Ü * c h e r E n * T w i C k e L t e d i E p a p i e R f A B r I K +HYP: f ** r d * e k ** n f D i g e n b A r d P b Ü S c h e r I n D w i * k e * t e d i * p a p i e * f * E r P R +Eval: D D D S S I I I S I S D D D D D S S S + +Speaker sentences 106: swc_deu_001307 #utts: 1 +id: (swc_deu_001307-swc_deu_001307) +Scores: (#C #S #D #I) 8 3 2 0 +REF: H a m b U r G w u C H s +HYP: * a m b * r E w u O K s +Eval: D D S S S + +Speaker sentences 107: swc_deu_001308 #utts: 1 +id: (swc_deu_001308-swc_deu_001308) +Scores: (#C #S #D #I) 51 5 12 6 +REF: F Ü R d i e * Q U a * ******* s i * a * d ******* l i g e n l a n D S i t z e b e t r i e b e n E a u f W a n d – s e i E s b e I m b a u +HYP: * ** V d i e K W A R a R s i E a R d l i g e n l a n * Z i t z e b e t r i e b e n * a u f * a n d ******* *** s e i ******* * s ******* b e * m ******* b a u +Eval: D D S I S S S I I I I I D S D D D D D D D D D + +Speaker sentences 108: swc_deu_001309 #utts: 1 +id: (swc_deu_001309-swc_deu_001309) +Scores: (#C #S #D #I) 55 8 10 2 +REF: j a H R z w e i t a u s e n D z w Ö l f i n d E n b e R l i * n e r C l u B s o s e C H s U n D d r e i S s i * G v e R l E g t +HYP: j a * * z w e i t a u s e n * z w Ö l f i n ******* d * n b e * l i E n e r K l u O P s ******* o s e * s H n * d r e i * s i C H v e L l I g t +Eval: D D D D D D I S S S D D S S D D I S S S + +Speaker sentences 109: swc_deu_001310 #utts: 1 +id: (swc_deu_001310-swc_deu_001310) +Scores: (#C #S #D #I) 25 7 8 1 +REF: s e c h Z E H n h U n D e R T f Ü n f Z i g * A L S b Ü n d n i s D I E +HYP: s e c h * T I n h * n * e * N f Ü n f T i g H E I D b Ü n d n i s ******* * * * +Eval: D S S D D D S S I S S S D D D D + +Speaker sentences 110: swc_deu_001311 #utts: 1 +id: (swc_deu_001311-swc_deu_001311) +Scores: (#C #S #D #I) 22 4 6 6 +REF: * * * p r o b l e M b E i d I E S e M p A r a d * * * O X o n i s t +HYP: D A S p r o b l e N b * i d * * * e N p * r a d T A C H S o n ******* i s t +Eval: I I I S D D D D S D I I I S S D + +Speaker sentences 111: swc_deu_001312 #utts: 1 +id: (swc_deu_001312-swc_deu_001312) +Scores: (#C #S #D #I) 27 2 4 2 +REF: a R m E n w e s e n t Ä t i G a m a l i e s ******* i e v e k * i n G +HYP: a * m I n w e s e n t E t i * ******* a m a l i e s i e v e k E i n * +Eval: D S S D D I I D + +Speaker sentences 112: swc_deu_001313 #utts: 1 +id: (swc_deu_001313-swc_deu_001313) +Scores: (#C #S #D #I) 83 8 6 5 +REF: N i c h T e i N m a l e i n e a n ******* s a t z w e i s e u n t e R s U c h * u n g z u i H r e m v e r h a l * t e n i n d e r z e i * t d e s n a T I O n a l s * O z I A l i s m u s +HYP: * i c h * e i m a l ******* e i n e a n s a t z w e i s e u n t e s O c h R u n g z u i E r e m v e r h a l K t e n i n ******* d e r z e i T t d e s n a * Z U n a l s U T z * E l i s m u s +Eval: D D S D I S S I S I D I D S S I S D S + +Speaker sentences 113: swc_deu_001314 #utts: 1 +id: (swc_deu_001314-swc_deu_001314) +Scores: (#C #S #D #I) 27 2 1 4 +REF: l i * * z * e * n Z f Ü r f r e i e d o k U m e n t a t i o n +HYP: l i E T z S e I n S f Ü r ******* f r e i e d o k O m e n t a t i o n +Eval: I I I I S D S + +Speaker sentences 114: swc_deu_001315 #utts: 1 +id: (swc_deu_001315-swc_deu_001315) +Scores: (#C #S #D #I) 38 4 14 3 +REF: * i m A c h T z e H n T E J A H r ******* h u n d e r T d i E g a R t e n ******* h Ä U s e R v o r D e n t o r e N +HYP: D i m * c h * z e * n * * * N E r h u n d e r * d i * ******* g a * t e n h O L s e * v o r * e n ******* t o r e * +Eval: I D D D D D D S S I D D D D I S S D D D D + +Speaker sentences 115: swc_deu_001316 #utts: 1 +id: (swc_deu_001316-swc_deu_001316) +Scores: (#C #S #D #I) 19 0 2 2 +REF: g a n z * i m s t i * l d e r z e i t +HYP: g a n z S i m ******* s t i E l ******* d e r z e i t +Eval: I D I D + +Speaker sentences 116: swc_deu_001317 #utts: 1 +id: (swc_deu_001317-swc_deu_001317) +Scores: (#C #S #D #I) 42 3 15 1 +REF: Ü B e r b r Ü H l u n d h Ü ** r t H E R r e i c h T e d i E l e i t u n G s c h L i E S s l i C H k Ö L n +HYP: ** * e r b r Ü * l u n d h Ü Ö r t * * A r e i c h e d i * l e i t u n * ******* s c h * i * * s l i * * ******* k ** E n +Eval: D D D I D D S S D D D D D D D D D D S + +Speaker sentences 117: swc_deu_001318 #utts: 1 +id: (swc_deu_001318-swc_deu_001318) +Scores: (#C #S #D #I) 25 1 3 3 +REF: a u * s * z e i c h n u N G e n F r e m ******* d e r h e R r e n +HYP: a u S s T z e i c h n u * M e n * r e m d e r h e * r e n +Eval: I I D S D I D + +Speaker sentences 118: swc_deu_001319 #utts: 1 +id: (swc_deu_001319-swc_deu_001319) +Scores: (#C #S #D #I) 25 1 5 1 +REF: d i e s c h R I f T S t e l l e r e i a u f * z u g e b e N +HYP: d i e ******* s c h * E f * * t e l l e r e i a u f T z u g e b e * +Eval: D D S D D I D + +Speaker sentences 119: swc_deu_001320 #utts: 1 +id: (swc_deu_001320-swc_deu_001320) +Scores: (#C #S #D #I) 34 2 6 5 +REF: * * * * * z Ä h L E n d I e b e g e g n u n G m i t v e r l e t z t e n t i e r e N +HYP: D A Z U T z I h * R n d * e b e g e g n u n * ******* m i t v e r l e t z t e n ******* t i e r e * +Eval: I I I I I S D S D D D D D + +Speaker sentences 120: swc_deu_001321 #utts: 1 +id: (swc_deu_001321-swc_deu_001321) +Scores: (#C #S #D #I) 11 2 0 1 +REF: J e n I s c h s t * i f t +HYP: I e n E s c h s t D i f t +Eval: S S I + +Speaker sentences 121: swc_deu_001322 #utts: 1 +id: (swc_deu_001322-swc_deu_001322) +Scores: (#C #S #D #I) 16 1 0 2 +REF: w e s t l i c h v o n k * Ö l * n +HYP: w e s t l i c h v o n k E R l E n +Eval: I S I + +Speaker sentences 122: swc_deu_001323 #utts: 1 +id: (swc_deu_001323-swc_deu_001323) +Scores: (#C #S #D #I) 21 2 5 0 +REF: d i e s t Ä n d i G I n b e t r i E B w a r e N +HYP: d i e s t E n d i * * n b e t r i * * P w a r e * +Eval: S D D D D S D + +Speaker sentences 123: swc_deu_001324 #utts: 1 +id: (swc_deu_001324-swc_deu_001324) +Scores: (#C #S #D #I) 25 2 3 2 +REF: d i E v o m b a R b i * e r R a s i e r t w e * r d e N +HYP: d i * v o m b a b i J e r ******* H a s i e r t w e H r d e * +Eval: D S I D S I D + +Speaker sentences 124: swc_deu_001325 #utts: 1 +id: (swc_deu_001325-swc_deu_001325) +Scores: (#C #S #D #I) 34 8 12 1 +REF: e r s c h I e n n o C H e i n W e i t E R E r a u f s * a t z V o n C H r i s t I A n m e Y E R +HYP: e r s c h * e n n o * * R e i n ******* * e i t * * * r ******* a u f s E a t z ******* F o n * G r i s t E R n ******* m e I A L +Eval: D D D S D D D D D D I D S D S S S D S S S + +Speaker sentences 125: swc_deu_001326 #utts: 1 +id: (swc_deu_001326-swc_deu_001326) +Scores: (#C #S #D #I) 55 3 7 5 +REF: w * e i l s e L b s t e x * t r E m e R r e i c h t u m k e i n e s ******* w e G s * D E n u n m i t T e L b a r e n * z u g a n G +HYP: w A e i l s e * b s t e x S t r * m e * r e i c h t u m ******* k e i n e s w e H s T * I n u n m i t L e * b a r e n S z u g a n * +Eval: I D I D D D I S I D S S D I D + +Speaker sentences 126: swc_deu_001327 #utts: 1 +id: (swc_deu_001327-swc_deu_001327) +Scores: (#C #S #D #I) 23 1 2 1 +REF: g e b t e u * c h N i c h T s e l b e r a u f +HYP: g e b t e u S c h ******* E i c h * s e l b e r a u f +Eval: I D S D + +Speaker sentences 127: swc_deu_001328 #utts: 1 +id: (swc_deu_001328-swc_deu_001328) +Scores: (#C #S #D #I) 50 2 7 8 +REF: * * ******* h a * t d i e s e n b r a u c h n e u n Z e h * n h U n d e r T Z w e i u n d ******* f Ü n f Z i g * g E g E n ******* Ü b E R +HYP: E R h a E t d i e s e n b r a u c h n e u n * e h I n h * n d e r * * w e i u n d f Ü n f T i g H g I g * n Ü b * * +Eval: I I I I D I D D D I S I S D I D D + +Speaker sentences 128: swc_deu_001329 #utts: 1 +id: (swc_deu_001329-swc_deu_001329) +Scores: (#C #S #D #I) 48 2 8 2 +REF: W o d I e l e i t u n g Ü b e R d i e a l t e h Ü ** r t H e * r l e i t u n G g e f Ü H r t w u r d e +HYP: * o d * e ******* l e i t u n g Ü b e * d i e a l t e h Ü Ö r t * e A r ******* l e i t u n * ******* g e f I E r t w u r d e +Eval: D D D D I D I D D D S S + +Speaker sentences 129: swc_deu_001330 #utts: 1 +id: (swc_deu_001330-swc_deu_001330) +Scores: (#C #S #D #I) 45 5 13 0 +REF: E i n e b E l i e b t e k Ö l s c h R o C k t r U P p e a U s D e M k Ö L n e r u m L a n d d i e h Ö H n e R +HYP: * i n e b * l i e b t e k ** l s c h * o * k t r * O p e ******* a * s ******* T e * k ** E n e r u m N a n d T d i e h Ö * n e * +Eval: D D D D D D S D D D S D D S S S D D + +Speaker sentences 130: swc_deu_001331 #utts: 1 +id: (swc_deu_001331-swc_deu_001331) +Scores: (#C #S #D #I) 28 2 0 1 +REF: g e w O r d e n s e i * u n d a l b r e c h t S i c h +HYP: g e w A r d e n s e i O u n d a l b r e c h t D i c h +Eval: S I S + +Speaker sentences 131: swc_deu_001332 #utts: 1 +id: (swc_deu_001332-swc_deu_001332) +Scores: (#C #S #D #I) 35 2 9 2 +REF: d e R t a g e s b e d a R f e i n e s E R w a C H s * e n e n a n V i t A m i n a * +HYP: d e * ******* t a g e s b e d a * f e i n e s ******* * * w a * G s T e n e n a n ******* * i t E m i n a R +Eval: D D D D D D D S I D D S I + +Speaker sentences 132: swc_deu_001333 #utts: 1 +id: (swc_deu_001333-swc_deu_001333) +Scores: (#C #S #D #I) 19 5 7 2 +REF: s i E b Z E H n H u N D e r T z E h * n o b E r ******* a l t e R +HYP: s i * b * T I n ******* * u * L e r * z I h E n o b A r a l t e * +Eval: D D S S D D D S D S I S I D + +Speaker sentences 133: swc_deu_001334 #utts: 1 +id: (swc_deu_001334-swc_deu_001334) +Scores: (#C #S #D #I) 25 2 4 0 +REF: w e i t e r h I n l i e S S s i c h n a c H w e i s e n +HYP: w e i t e r h E n l i e * * ******* s i c h ******* n a c R w e i s e n +Eval: S D D D D S + +Speaker sentences 134: swc_deu_001335 #utts: 1 +id: (swc_deu_001335-swc_deu_001335) +Scores: (#C #S #D #I) 29 3 5 2 +REF: z u M g r Ü n D u n g s * d a * t u m k o N n t E M a N b e r e i t S +HYP: z u N g r Ü n * u n g s T d a R t u m k o * n t * ******* B a M b e r e i t * +Eval: S D I I D D D S S D + +Speaker sentences 135: swc_deu_001336 #utts: 1 +id: (swc_deu_001336-swc_deu_001336) +Scores: (#C #S #D #I) 30 2 3 1 +REF: k e i n l e c K s c h l a * g E N m Ö G l i c h n a c h t e i l e +HYP: k e i n l e c * s c h l a R g * * m Ü K l i c h n a c h t e i l e +Eval: D I D D S S + +Speaker sentences 136: swc_deu_001337 #utts: 1 +id: (swc_deu_001337-swc_deu_001337) +Scores: (#C #S #D #I) 44 5 14 1 +REF: W I r D d i E k a t H o l i S c h e k I r * c h e s T Ü C K p E t e r a n d e r s t e l l e d e R a l t E N +HYP: * * r * T d i * ******* k a t * o l i * c h e k Ü r S c h e ******* s * ** A N p Ä t e r a n ******* d e r s t e l l e d e * a l t * * +Eval: D D D S D D D D S I D D D S S S D D D D + +Speaker sentences 137: swc_deu_001338 #utts: 1 +id: (swc_deu_001338-swc_deu_001338) +Scores: (#C #S #D #I) 55 7 15 3 +REF: D e r V e R k n a * p P u n G d e s b r o t ******* w e i * z e n S t r a t a b E R s c h O n b a L D d i E k A r t o F f e l A L s e r s a t Z +HYP: * e r ******* F e * k n a R p B u n * d e s ******* b r o t w e i T z e n * t r a t a b * * ******* s c h * n ******* b a I T d i * ******* k E r t o * f e l E I s e r s a t * +Eval: D D S D I S D D I I D D D D D D S S D D S D S S D + +Speaker sentences 138: swc_deu_001339 #utts: 1 +id: (swc_deu_001339-swc_deu_001339) +Scores: (#C #S #D #I) 27 1 7 1 +REF: k Ö N N e N m I t d i e s e n n a c h k o M m E n z * e u g e N +HYP: k ** * R e * m * t d i e s e n n a c h k o * m * n z O e u g e * +Eval: D D S D D D D I D + +Speaker sentences 139: swc_deu_001340 #utts: 1 +id: (swc_deu_001340-swc_deu_001340) +Scores: (#C #S #D #I) 30 2 3 0 +REF: a l l e n e U E n f o l g e n d e r h Ö r s P i e L r e i H e +HYP: a l l e n e * I n f o l g e n d e r h Ö r s i e * r e i * e +Eval: D S S D D + +Speaker sentences 140: swc_deu_001341 #utts: 1 +id: (swc_deu_001341-swc_deu_001341) +Scores: (#C #S #D #I) 16 1 4 4 +REF: * c h i * p s m i T b * r a * t e n s o S S E +HYP: S c h i B p s ******* m i E b E r a C t e n s o * * * +Eval: I I D S I I D D D + +Speaker sentences 141: swc_deu_001342 #utts: 1 +id: (swc_deu_001342-swc_deu_001342) +Scores: (#C #S #D #I) 51 4 11 1 +REF: k O L l E g e a n D r e A s b u * c h n e R v e r z i c h T E t e a u f e I n e P e r s Ö n L i c h e b e w e r t u n G +HYP: k * * l I g e a n r e R s ******* b u O c h n e * v e r z i c h * * t e a u f ******* e * n e ******* B e r s Ö n * i c h e b e w e r t u n * +Eval: D D S S S D I D D D D D D S D D + +Speaker sentences 142: swc_deu_001343 #utts: 1 +id: (swc_deu_001343-swc_deu_001343) +Scores: (#C #S #D #I) 13 3 1 2 +REF: W e n i g e r E n ******* t r * Ü s t e T +HYP: B e n i g e r I n t r U S s t e * +Eval: S S I I S D + +Speaker sentences 143: swc_deu_001344 #utts: 1 +id: (swc_deu_001344-swc_deu_001344) +Scores: (#C #S #D #I) 33 4 2 2 +REF: w e i t e r ******* h I n v e r s o r G T E d i e l e i ******* t u n g t H e r m e n +HYP: w e i t e r h E n v e r s o r K D d i e ******* l e i t u n g t * e r m e n +Eval: I S S S S D I D + +Speaker sentences 144: swc_deu_001345 #utts: 1 +id: (swc_deu_001345-swc_deu_001345) +Scores: (#C #S #D #I) 27 0 4 2 +REF: w a r t e t e n d a ******* f Ü * r a b e R m i t e i n i g E N +HYP: w a r t e t e n d a f Ü H r a b e * ******* m i t e i n i g * * +Eval: I I D D D D + +Speaker sentences 145: swc_deu_001346 #utts: 1 +id: (swc_deu_001346-swc_deu_001346) +Scores: (#C #S #D #I) 42 8 8 4 +REF: L e ******* d i G l i c h a n t * O n V o n k l e i n m O n i e r t E i n s e i n E R r e * z e n ******* s I o n d E R +HYP: D e d i K l i c h a n t R Ü n D F o n ******* k l e i n m U n i e r t I i n ******* s e i n * * A r e T z e n s * o n ******* d * * +Eval: S I S I S S S D S S D D D S I I D D D D + +Speaker sentences 146: swc_deu_001347 #utts: 1 +id: (swc_deu_001347-swc_deu_001347) +Scores: (#C #S #D #I) 16 3 11 0 +REF: I m j a H r E n e u N z e H N H U n D e r t f Ü N F +HYP: E m j a B r * ******* n e u * z e * * ******* * * n * e r t ******* f Ü * M +Eval: S S D D D D D D D D D D D S + +Speaker sentences 147: swc_deu_001348 #utts: 1 +id: (swc_deu_001348-swc_deu_001348) +Scores: (#C #S #D #I) 119 8 23 15 +REF: * e r s T M i t D e m f O r t ******* f a L l d e s b Ü * r g e R r e c h t S u n D d e r e i n f Ü H r u n G d e R F r e i * z Ü g i * G k e i t i m z W a n Z i G s T E J A H r H U n d e r t w a n d * e L t ******* * e s i c h d i e s e a n s c h a U u n g a n s * A t ******* Z w e i * s e * * d a * H i * n +HYP: I e r s * ******* * i t * e m f * r t f a * l d e s ******* b Ü U r g e * r e c h t * u n * d e r ******* e i n f Ü * r u n * d e * * r e i T z Ü g i C H k e i t i m z B a n S i * s * * * N E r * * n d e r t w a n d T e * t D e s i c h d i e s e a n s c h a * u n g a n s E R t S w e i E s e I N d a R i E n +Eval: I D D D D D I D D I D D D D D D D D I I S S S D D D D S S D D I D I I D I S I S I I I I S I + +Speaker sentences 148: swc_deu_001349 #utts: 1 +id: (swc_deu_001349-swc_deu_001349) +Scores: (#C #S #D #I) 43 3 4 3 +REF: d e s s * w i s t ******* b a c H e s b E I r H e i n b a c h e i n e b o g e n ******* b r Ü c k e v O N +HYP: d e s s Z w i s t b a c R e s b * A L r * e i n b a c h e i n e b o g e n b r Ü c k e v * * +Eval: I I S D S S D I D D + +Speaker sentences 149: swc_deu_001350 #utts: 1 +id: (swc_deu_001350-swc_deu_001350) +Scores: (#C #S #D #I) 31 4 18 1 +REF: a c H t z E H n H u N D e R t S e C H s U n D d r e i S s i g * w U r d e D e R h A m b u R g E R +HYP: a c * t z * I n ******* * u * L e * t ******* * e * s * n * d r e i * s i g B w O r d e * e * h * m b u * g * * +Eval: D D S D D D S D D D D S D D D I S D D D D D D + +Speaker sentences 150: swc_deu_001351 #utts: 1 +id: (swc_deu_001351-swc_deu_001351) +Scores: (#C #S #D #I) 3 3 13 0 +REF: A M K a R N e V A L S S O N N T A G +HYP: D G * a * * e * * * * * * * * * * M +Eval: S S D D D D D D D D D D D D D S + +Speaker sentences 151: swc_deu_001352 #utts: 1 +id: (swc_deu_001352-swc_deu_001352) +Scores: (#C #S #D #I) 53 2 5 2 +REF: a u f g r u n d d e R k o n t i n e n ******* t * a l s p E R r e a c h t z e h n h u n D e r T e l f b a n k R o T t +HYP: a u f g r u n d d e * k o n t i n e n t E a l s p B A r e a c h t z e h n h u n * e r * e l f b a n k * o * t +Eval: D I I S S D D D D + +Speaker sentences 152: swc_deu_001353 #utts: 1 +id: (swc_deu_001353-swc_deu_001353) +Scores: (#C #S #D #I) 48 15 21 0 +REF: W e i t e r e s m A L m u S s t E N d A n u n D B l Y T H E b r O W n d i E w E r b u n g f Ü r D a s b U C h s e L b S t Ü B E R n e H m E N +HYP: * e i t e r e s ******* m * E I m u * s t * * d E n u n * ******* P l E I P T b r A U n d i * ******* w A r b u n g f Ü r ******* E a s b * O h ******* s e * b * t ******* ** * W A n e * m * * +Eval: D D D S S D D D S D D S S S S S S S D D S D S D S D D D D D D S S D D D + +Speaker sentences 153: swc_deu_001354 #utts: 1 +id: (swc_deu_001354-swc_deu_001354) +Scores: (#C #S #D #I) 114 17 28 2 +REF: d i E n A C H r i C h T v O m s i e g d e r b Ü r g e R l i c h D E m O K r a t i s c h e n f * e B R U a R r e V O l u t * i o n v O n a c h T z e H n h U n d e R t a c h t U n D v i E r z i G I n F r a n k r e i c h w U r d e I n h a m b u r g m i t J U b e L a u f g e n O M m e N +HYP: d i * ******* n * * E r i * h * v * m s i e g T d e r b Ü r g e * l i c h * * m U G r a t i s c h e n f I e * P O a * r e * R l u t Z i o n ******* v E n a c h * z e I n h * n d e * t a c h t n v i * r z i * C H n * r a n k r e i c h V w * r d e ******* * n h a m b u r g ******* m i t ******* I O b e * a u f g e n * A m e * +Eval: D D D D S D D D S D D D S S I D S S D D S I D S D S D D S S D D S S D S D D D D D S S D D S D + +Speaker sentences 154: swc_deu_001355 #utts: 1 +id: (swc_deu_001355-swc_deu_001355) +Scores: (#C #S #D #I) 20 1 8 9 +REF: * * * * * * * * z w e i J a H r e o H n e U N T e r b r e * c h U n G +HYP: O U M B L I E T z w e i ******* * a * r e o * n e * * D e r b r e S c h * n * +Eval: I I I I I I I I D D D D D D S I D D + +Speaker sentences 155: swc_deu_001356 #utts: 1 +id: (swc_deu_001356-swc_deu_001356) +Scores: (#C #S #D #I) 29 0 4 2 +REF: z * a H l r e i c h e N g a s t s p i E l E n u n t e r w e g * s +HYP: z O a * l r e i c h e * g a s t s p i * l * n u n t e r w e g H s +Eval: I D D D D I + +Speaker sentences 156: swc_deu_001357 #utts: 1 +id: (swc_deu_001357-swc_deu_001357) +Scores: (#C #S #D #I) 13 4 1 1 +REF: Q U a n T i * t Ä t g e n Ü G t e n +HYP: K R a n Z i E t E t g e n Ü * t e n +Eval: S S S I S D + +Speaker sentences 157: swc_deu_001358 #utts: 1 +id: (swc_deu_001358-swc_deu_001358) +Scores: (#C #S #D #I) 13 2 5 3 +REF: * * ******* b A r o C k e r a u S s t a T t U N G +HYP: U N b E r o * k e r a u * s t a * t * * O +Eval: I I I S D D D D D S + +Speaker sentences 158: swc_deu_001359 #utts: 1 +id: (swc_deu_001359-swc_deu_001359) +Scores: (#C #S #D #I) 83 9 15 5 +REF: d a S g e r i c h t v o * m b e i * w a * g e n s E I n * e s m o t o R r a d E s a u s i n d i e z u d I e s e r z e i t n E u e n T s t E H e n d e n a R b e I t E R s i e * D l U N G e n z u +HYP: d a * ******* g e r i c h t v o U m b e i V w a R g e n ******* s * A n G e s ******* m o t o * r a d I s a u s i n ******* d i e z u d * e s e r ******* z e i t n O u e n s t * * e n d e n a * b e * t A s i e T l * O M e n ******* z u +Eval: D D I I I D D S I D D S D D D S S D D D D S S I S D S S D + +Speaker sentences 159: swc_deu_001360 #utts: 1 +id: (swc_deu_001360-swc_deu_001360) +Scores: (#C #S #D #I) 22 1 6 4 +REF: d i E m i t * s a m T i H R e R * * r e c h e n ******* s t U b e +HYP: d i * ******* m i t Z s a m * i * * e * H E r e c h e n s t O b e +Eval: D D I D D D D I I I S + +Speaker sentences 160: swc_deu_001361 #utts: 1 +id: (swc_deu_001361-swc_deu_001361) +Scores: (#C #S #D #I) 13 1 2 0 +REF: k A i s e r f e r d i n a n D +HYP: k E i s e r ******* f e r d i n a n * +Eval: S D D + +Speaker sentences 161: swc_deu_001362 #utts: 1 +id: (swc_deu_001362-swc_deu_001362) +Scores: (#C #S #D #I) 56 9 8 3 +REF: v o m f e r n s e H r e G I S s E u * r * f R a n z X a v e r b O g n e r i n d e m f e r n s e H f i l * M d a s E w i g e l i e D +HYP: v o m f e r n s e * r e S C H s * u H r E R f E a n z ******* S a v e r ******* b U g n e r i n ******* d e m f e r n s e * f i l E N d a s * w i g e ******* l i e T +Eval: D S S S D I I S S D S D S D D I S D D S + +Speaker sentences 162: swc_deu_001363 #utts: 1 +id: (swc_deu_001363-swc_deu_001363) +Scores: (#C #S #D #I) 27 0 6 0 +REF: W u r D E i n s e i N E n b e s t e n z e i t e n d e R +HYP: * u r * * i n s e i * * n b e s t e n z e i t e n d e * +Eval: D D D D D D + +Speaker sentences 163: swc_deu_001364 #utts: 1 +id: (swc_deu_001364-swc_deu_001364) +Scores: (#C #S #D #I) 27 3 6 1 +REF: s I e h Ö r e N d e n a r t i k e l f i s * h A n D c H i P s +HYP: s * e ******* h Ö r e * d e n a r t i k e l f i s C h ******* E n T S c * i * s +Eval: D D D I D S S S D D + +Speaker sentences 164: swc_deu_001365 #utts: 1 +id: (swc_deu_001365-swc_deu_001365) +Scores: (#C #S #D #I) 9 3 1 3 +REF: u n d t * * V m o * V i E +HYP: u n d t E F A R m o F W i * +Eval: I I S S I S D + +Speaker sentences 165: swc_deu_001366 #utts: 1 +id: (swc_deu_001366-swc_deu_001366) +Scores: (#C #S #D #I) 26 9 4 3 +REF: r e * z * E P T i o n d e r h e * X e n T H E m a t i K v o n C H r I s t a +HYP: r e A z S I B Z i o n ******* d e r ******* h e H S e n * I m a t i G v o n * K r E s t a +Eval: I I S S S D D I S D S S S D S S + +Speaker sentences 166: swc_deu_001367 #utts: 1 +id: (swc_deu_001367-swc_deu_001367) +Scores: (#C #S #D #I) 64 5 8 5 +REF: d i E g e s a m t e * a n l a * g e w a R b i s e t ******* * W A z w e i h u n d e r T s e c h * z i g n a c h C H r i s t u s i n b e T r i e B +HYP: d i * ******* g e s a m t e R a n l a R g e w a * ******* b i s e t V E R T z w e i h u n d e r * s e c h T z i g n a c h ******* * K r i s t u s i n b e D r i e * +Eval: D D I I D D I I S S S D I D D S S D + +Speaker sentences 167: swc_deu_001368 #utts: 1 +id: (swc_deu_001368-swc_deu_001368) +Scores: (#C #S #D #I) 27 8 10 2 +REF: d e r E R s ******* T e f a s t f O O D l i E F e r s * E r V i C E w a R g e b O R E n +HYP: d e r * I s D e f a s t f * U T l i * * e r s O A r W i * * S w a * ******* g e b * * U n +Eval: D S I S D S S D D I S S D D S D D D D S + +Speaker sentences 168: swc_deu_001369 #utts: 1 +id: (swc_deu_001369-swc_deu_001369) +Scores: (#C #S #D #I) 14 1 0 1 +REF: e i n * e m k a b e l b a u M +HYP: e i n D e m k a b e l b a u N +Eval: I S + +Speaker sentences 169: swc_deu_001370 #utts: 1 +id: (swc_deu_001370-swc_deu_001370) +Scores: (#C #S #D #I) 33 1 8 4 +REF: E R m o r * * ******* d u n g m i t d e R G e ******* f a H R v e r b u n d e n g e w e s E N +HYP: * * m o r L E d u n g m i t d e * * e f a * * F v e r b u n d e n g e w e s * * +Eval: D D I I I D D I D D S D D + +Speaker sentences 170: swc_deu_001371 #utts: 1 +id: (swc_deu_001371-swc_deu_001371) +Scores: (#C #S #D #I) 22 6 8 2 +REF: D e R Ä l t * E s t E n p * F e r D e r e N n E n a u S s E r h A L B +HYP: * e * E l t Z I s t * n p R I e r * e r e * n I n a u * s * r h * E P +Eval: D D S I S D I S D D S D D D S S + +Speaker sentences 171: swc_deu_001372 #utts: 1 +id: (swc_deu_001372-swc_deu_001372) +Scores: (#C #S #D #I) 52 10 9 9 +REF: s o n D e R n a U c h d e r n a ******* * T I O n a l * ******* s * * O Z I A L i s t i s c h e n k u n S t ******* a u f ******* f a S s U n G g e r e c h t w e * r D e n +HYP: s o n * e * n a * c h d e r ******* n a Z U N n a l I s U T S E R D i s t i s c h e n k u n Z t a u f f a * s O n * ******* g e r e c h t ******* w e H r * e n +Eval: D D D D I I S S S I I I I S S S S S S I I D S D D D I D + +Speaker sentences 172: swc_deu_001373 #utts: 1 +id: (swc_deu_001373-swc_deu_001373) +Scores: (#C #S #D #I) 26 0 1 5 +REF: d i e w e l t * s i c h t d e s h a n s e ******* a * * t e n * +HYP: d i e ******* w e l t Z s i c h t d e s h a n s e a H R t e n I +Eval: D I I I I I + +Speaker sentences 173: swc_deu_001374 #utts: 1 +id: (swc_deu_001374-swc_deu_001374) +Scores: (#C #S #D #I) 29 1 4 0 +REF: a u c h n a c H k o m m e n s i n D n i c h T b e k a N n t +HYP: a u c h ******* n a c R k o m m e n s i n * n i c h * b e k a * n t +Eval: D S D D D + +Speaker sentences 174: swc_deu_001375 #utts: 1 +id: (swc_deu_001375-swc_deu_001375) +Scores: (#C #S #D #I) 28 6 5 1 +REF: I H r e n t e * X t e N d E n e i n d r u C k z U v e r m i T t e L N +HYP: * E r e n t e K S t e * d I n e i n d r u G k T z E v e r m i * t e * * +Eval: D S I S D S S S S D D D + +Speaker sentences 175: swc_deu_001376 #utts: 1 +id: (swc_deu_001376-swc_deu_001376) +Scores: (#C #S #D #I) 28 3 8 0 +REF: v e r d i E n s t E u m d a s k Ö l n E R l i e D v e r l i E H e n +HYP: v e r d i * n s t * ******* u m d a s ******* k E l n * * A l i e T v e r l i * * e n +Eval: D D D D S D D S S D D + +Speaker sentences 176: swc_deu_001377 #utts: 1 +id: (swc_deu_001377-swc_deu_001377) +Scores: (#C #S #D #I) 63 11 12 7 +REF: O b * W o H l h o F f m A N n v O n h O F f m A N n S w A l ******* d a u s w e * r k g R o S s e n e i n F l * u S s a u F s p Ä * t e r e * d i c h t e r a u s * Ü b T E +HYP: A b P V o * l h o * f m E I n v * n h * A f m E I n w E l d a u s w e R r k g * o * s e n ******* e i n P l I u * s ******* a u * s p Ä R t e r e R d i c h t e r ******* a u s C Ü b * P +Eval: S I S D D S S D D S S S S S I I D D D S I D D D I I D I D S + +Speaker sentences 177: swc_deu_001378 #utts: 1 +id: (swc_deu_001378-swc_deu_001378) +Scores: (#C #S #D #I) 24 6 5 1 +REF: U m s o * e R n S t a l S s t A a t s O b e r h a u p T v O N +HYP: O m s o R e A n Z t ******* a l * s t * a t s b e r h a u p * F v * E +Eval: S I S S D D D S D S D S + +Speaker sentences 178: swc_deu_001379 #utts: 1 +id: (swc_deu_001379-swc_deu_001379) +Scores: (#C #S #D #I) 12 0 1 8 +REF: * * * * * * ******* d o k U m e n t a t * i o n +HYP: E F R E I E d o k * m e n t a t Z i o n +Eval: I I I I I I I D I + +Speaker sentences 179: swc_deu_001380 #utts: 1 +id: (swc_deu_001380-swc_deu_001380) +Scores: (#C #S #D #I) 30 4 1 2 +REF: g e s t a l t u N G D e s C O v e r s w i * d e r s p * i e g e l t +HYP: g e s t a l t u * M B e s K A v e r s w i E d e r s p B i e g e l t +Eval: D S S S S I I + +Speaker sentences 180: swc_deu_001381 #utts: 1 +id: (swc_deu_001381-swc_deu_001381) +Scores: (#C #S #D #I) 23 2 3 1 +REF: d e R g e s a m t e * a u f w a n D w i R D a u f +HYP: d e * ******* g e s a m t e R a u f w a n T w i * T a u f +Eval: D D I S D S + +Speaker sentences 181: swc_deu_001382 #utts: 1 +id: (swc_deu_001382-swc_deu_001382) +Scores: (#C #S #D #I) 72 4 15 7 +REF: * O b g l e i c H h A m b u r G d i e s * e m a n ******* g E h Ö r t E u n d e i n e n o b I l i ******* t i e r u n G d U r * c h D e n k A i s e r d a m i t k e i n e d U r * * c H +HYP: A U b g l e i c * ******* h * m b u r K d i e s I e m a n g * h ** r t U u n d e i n e n o b * l i t i e r u n * d * r I c h ******* * e n ******* k E i s e r ******* d a m i t k e i n e ******* d * r E S c * +Eval: I S D D D S I I D D S D I D D I D D D S D D D I I D + +Speaker sentences 182: swc_deu_001383 #utts: 1 +id: (swc_deu_001383-swc_deu_001383) +Scores: (#C #S #D #I) 66 5 4 1 +REF: d * A E s D u r c h d e n s i c h a u s w e i t e n D e n w e l t h a n d e l a r b e i t u n d w o H l s t a n D v e r s p r a c h +HYP: d E R A s ******* T u r c h T d e n s i c h a u s w e i t e n T e n w e l t h a n d e l a r b e i t u n d w o * l s t a n * ******* v e r s p r a c h +Eval: I S S D S S S D D D + +Speaker sentences 183: swc_deu_001384 #utts: 1 +id: (swc_deu_001384-swc_deu_001384) +Scores: (#C #S #D #I) 64 7 12 2 +REF: f ** ** Ü r d i e z e i t m i T t e d e s n e u n Z E h n T e j A H r h u n d E r T s b e k l a G T E d e R A r c h i t e k t m a R t i n h a L l e R +HYP: f Ö Ö E r d i e z e i t m i * t e d e s ******* n e u n I h n D e j * E r h u n d A r * s b e k l a * * K d e * ******* * r c h i t e k t m a * t i n h a * l e * +Eval: I I S D D S S S D S S D D D S D D D D D D + +Speaker sentences 184: swc_deu_001385 #utts: 1 +id: (swc_deu_001385-swc_deu_001385) +Scores: (#C #S #D #I) 26 4 8 1 +REF: A L t ******* b U n d e S k a n z L e r h E L m u t s C H m i D t l e H n t e +HYP: E I t b * n d e k a n z * e r h * R m u t ******* s * * m i * t l e * n t e +Eval: S S I D S D D S D D D D D + +Speaker sentences 185: swc_deu_001386 #utts: 1 +id: (swc_deu_001386-swc_deu_001386) +Scores: (#C #S #D #I) 28 9 13 1 +REF: d E N n a m e n g * o d e F f r O Y i m s t A a T s h a n D B U C H Z u S T R E I c h E N +HYP: d * * I n a m e n g U o d e * f r E I i m s t * a Z s h a n * * * * T B u ******* * * * * O c h T Z +Eval: D D S I D S S D S D D D D S S D D D D D S S S + +Speaker sentences 186: swc_deu_001387 #utts: 1 +id: (swc_deu_001387-swc_deu_001387) +Scores: (#C #S #D #I) 29 0 9 1 +REF: w e N n a u c h m I t e I n ******* e r g e w I S s e n l e t H a R g i e +HYP: w e * n a u c h ******* m * t e * n e r ******* g e w * * s e n l e t * a * g i e +Eval: D D D D I D D D D D + +Speaker sentences 187: swc_deu_001388 #utts: 1 +id: (swc_deu_001388-swc_deu_001388) +Scores: (#C #S #D #I) 10 2 0 2 +REF: k a L k * u l i e r ******* b a R +HYP: k a k O u l i e r b a N +Eval: S I I S + +Speaker sentences 188: swc_deu_001389 #utts: 1 +id: (swc_deu_001389-swc_deu_001389) +Scores: (#C #S #D #I) 34 11 13 1 +REF: a n g e f a n g E N e z W E I H U n d e R T f Ü N F Z i G s c h Ü l e r e i n e n d E L e g ******* I e R T e n +HYP: a n g e f a n g * D e T z * * * S O A n d e * * f ** * M i * ******* s c h U l e r ******* e i n e n d * I e g J e * P e n +Eval: D S S D D D S S S D D D D S S D D S D D S I S D S + +Speaker sentences 189: swc_deu_001390 #utts: 1 +id: (swc_deu_001390-swc_deu_001390) +Scores: (#C #S #D #I) 56 8 20 4 +REF: V i e l E m e n s c h e N s A H e * n D e * n g ******* r I Z Z l Y A l s n a H r u n g s k o n K u R r e n t e n u n d A l s p O t e n T I e l l E g e ******* f a H R +HYP: F i e l * ******* m e n s c h e * s * * e I n ******* * e I n ******* g r * E S l I * l s ******* n a * r u n g s k o n G u * r e n t e n u n d ******* E l s ******* p U t e n * e l l * ******* g e f a * * +Eval: S D D D D D I D D I D I D S S S D D D S D D S D S D S D D I D D + +Speaker sentences 190: swc_deu_001391 #utts: 1 +id: (swc_deu_001391-swc_deu_001391) +Scores: (#C #S #D #I) 15 2 5 0 +REF: d E n a u f t R i T t v e r k Ü r Z e N +HYP: d I n a u f t * i * t ******* v e r k Ö r * e * +Eval: S D D D S D D + +Speaker sentences 191: swc_deu_001392 #utts: 1 +id: (swc_deu_001392-swc_deu_001392) +Scores: (#C #S #D #I) 90 25 12 41 +REF: * * ******* d e m s t a n D v o m d E r i * * * * ******* n H A l * * * * * * t * * * * * * * * * * ******* * * * * * * * s t E H t u n T e R d e r L i * ******* z e n z C R e * A t I V e C o m m o n s * A t ******* T R i * B u * T I O n s * h A r E A l I K e D r e i p u n k T n U l L U n ******* p O r t e D u n d u n t e r d e R +HYP: M I d e m s t a n * v o m d * r E i Z E H N n I U l I E Z W E I t A U S E N S W E R F D E R I N E R s t * I t u n D e * d e r * i E z e n z * K e H R t Z U e K o m m o n s E I t Z E i E W u S C H E n s C h E r * E l * * e I T r e i p u n k * n O l E A n p * r t e T u n d u n t e r d e * +Eval: I I I D D S I I I I I S S I I I I I I I I I I I I I I I I I I I I I I I I D S S D D I I D S I S S S S I S I S S I S I S S S I S D S D D S S D S S S I D S D + +Speaker sentences 192: swc_deu_001393 #utts: 1 +id: (swc_deu_001393-swc_deu_001393) +Scores: (#C #S #D #I) 21 2 2 1 +REF: e i n E k l e i n e r e b o g e n ******* B r Ü C k e +HYP: e i n I k l e i n e r e ******* b o g e n P r Ü * k e +Eval: S D I S D + +Speaker sentences 193: swc_deu_001394 #utts: 1 +id: (swc_deu_001394-swc_deu_001394) +Scores: (#C #S #D #I) 19 3 8 0 +REF: s i c h n u n F Ü r s e I n W E i t e r k o M m E N +HYP: s i c h n u n ******* V E r ******* s e * n ******* * B i t e r k o * m * * +Eval: D S S D D D D S D D D + +Speaker sentences 194: swc_deu_001395 #utts: 1 +id: (swc_deu_001395-swc_deu_001395) +Scores: (#C #S #D #I) 26 2 0 3 +REF: a u * s d e m g e m Ä l d e z u e n t ******* f e r * n e n +HYP: a u S s d e m g e m E l d e T z u e n t f e r I n e n +Eval: I S S I I + +Speaker sentences 195: swc_deu_001396 #utts: 1 +id: (swc_deu_001396-swc_deu_001396) +Scores: (#C #S #D #I) 43 8 20 7 +REF: * * * n A C h E U r O p Ä I s c h e R r i c H t L i n I e n e u n z i * g V i e * r H U n D e r t * S e C H S U N D n e u n z i * G e w G +HYP: I S D n * E h * A r E p ** E s c h e * ******* r i c S t * i n * e n e u n z i C g F i e O r ******* * * n * e r t E * e * * * * * * n e u n z i C H e ******* w ******* I +Eval: I I I D S D S S D S D D S D D I S I D D D D I D D D D D D D I S D D S + +Speaker sentences 196: swc_deu_001397 #utts: 1 +id: (swc_deu_001397-swc_deu_001397) +Scores: (#C #S #D #I) 11 4 1 1 +REF: * E i N e m u M F e l D a u f +HYP: A R i * e m u N S e l T a u f +Eval: I S D S S S + +Speaker sentences 197: swc_deu_001398 #utts: 1 +id: (swc_deu_001398-swc_deu_001398) +Scores: (#C #S #D #I) 30 3 0 2 +REF: U n d s i e s e i a u * c h W i e e i n e f Ü * r s t I n +HYP: O n d s i e s e i a u R c h D i e e i n e f Ü L r s t E n +Eval: S I S I S + +Speaker sentences 198: swc_deu_001399 #utts: 1 +id: (swc_deu_001399-swc_deu_001399) +Scores: (#C #S #D #I) 18 6 1 3 +REF: n e U N z E h n h u n d e r t n * e U n ******* * Z e H N +HYP: n e I T z I h n h u n d e r t n O e I n S I e * E +Eval: S S S I S I I S D S + +Speaker sentences 199: swc_deu_001400 #utts: 1 +id: (swc_deu_001400-swc_deu_001400) +Scores: (#C #S #D #I) 31 4 7 5 +REF: s t a T t D e S s e n h a b E N d I e r Ö ** m i S c h e n i n * * G e n ******* * i E U r e +HYP: s t a R t * e * s e n h a b * M d * e ******* r Ö Ü m i * c h e n i n S C H e n J i * Ö r e +Eval: S D D D S D D I D I I S I I D S + +Speaker sentences 200: swc_deu_001401 #utts: 1 +id: (swc_deu_001401-swc_deu_001401) +Scores: (#C #S #D #I) 15 6 0 4 +REF: * * * G r i Z Z L Y b * Ä r u n d m e n s c h +HYP: D E L K r i S I E b E H r u n d m e n s c h +Eval: I I I S S S S S I S + +Speaker sentences 201: swc_deu_001402 #utts: 1 +id: (swc_deu_001402-swc_deu_001402) +Scores: (#C #S #D #I) 10 9 0 3 +REF: m * * U s i * c I A n S C o a l i T I O N +HYP: m I E s i S c H E n Z K o a l i S C H E +Eval: I I S I S S S S S S S S + +Speaker sentences 202: swc_deu_001403 #utts: 1 +id: (swc_deu_001403-swc_deu_001403) +Scores: (#C #S #D #I) 26 9 8 3 +REF: b e R t O L D h U M m E l g I b T * e * ******* s d r e i V a r I A t i o n e n M i T +HYP: b e C t U R S C h * * m * l ******* g E b * D e S s d r e i ******* B a r E R t i o n e n * i * +Eval: S S S S S D D D D S D I I I D S S S D D + +Speaker sentences 203: swc_deu_001404 #utts: 1 +id: (swc_deu_001404-swc_deu_001404) +Scores: (#C #S #D #I) 68 9 21 4 +REF: R Ü C k z u G s * g e b i e t * e r w I e S s i C h D e r a c h T z e h n h u n d E r T z w e i u n D s i e b z i G g e G r Ü n d e t e Y E L l O W s * T O n e * N A T i O n a l p a r K +HYP: * ** * k z u K s K g e b i e t A e r w * e * ******* s i * h ******* T e r a c h * z e h n h u n d A r * ******* z w e i u n * s i e b z i * C g e r Ü n d e t e * * A l * U s D E U n e R * * * i * n a l p a r * +Eval: D D D S I I D D D D D S D S D D D D S S D D S D S I S S I D D D D D + +Speaker sentences 204: swc_deu_001405 #utts: 1 +id: (swc_deu_001405-swc_deu_001405) +Scores: (#C #S #D #I) 10 0 0 2 +REF: d e * f i n i t * i o n +HYP: d e V f i n i t Z i o n +Eval: I I + +Speaker sentences 205: swc_deu_001406 #utts: 1 +id: (swc_deu_001406-swc_deu_001406) +Scores: (#C #S #D #I) 52 8 12 1 +REF: u m A n D e R u n I v * e R s i t Ä t S e V i l L A z w e i s e m e s t e r k u n s T g E s c h I c h t E z u s t u d i E R e n +HYP: u m I n ******* * e * u n * v W e s i t E t Z I e W i l E R z w e i ******* s e m e s t e r k u n s * g * s c h * c h t * z u ******* s t u d i * * e n +Eval: S D D D D I S S S S S S S D D D D D D D D + +Speaker sentences 206: swc_deu_001407 #utts: 1 +id: (swc_deu_001407-swc_deu_001407) +Scores: (#C #S #D #I) 15 1 4 4 +REF: * * * ******* t r o t z I H r e r g E r i n G e N +HYP: D I E t r o t z * E r e r g * r i n * e * +Eval: I I I I D S D D D + + diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..c3454e2b75279fa64e0a59dd7c55b2aa5cb02ab2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/hyp.trn @@ -0,0 +1,207 @@ +DRERVELIEBTE UNGEHEARZOGK DE RANSCHLE GESEINSFATESNICH BERACHTDITHA (swc_deu_001201-swc_deu_001201) +DIE IN DE HANZESTEÄTEN ALS (swc_deu_001202-swc_deu_001202) +ARKEINGROSE E FRELK (swc_deu_001203-swc_deu_001203) +GOSEN SCHEHMISCHIN VER BRIKTE (swc_deu_001204-swc_deu_001204) +WODEN ACH MERERE ALOEUTRUNGSBÜSCHEVERFNTLIH (swc_deu_001205-swc_deu_001205) +VORBEREITETEN BIER TEICG GE TUNKT (swc_deu_001206-swc_deu_001206) +DOKOMNTE SHIESIG IN I (swc_deu_001207-swc_deu_001207) +TAURTAG VÜRDEN TUT VON KNICH VRECHL E (swc_deu_001208-swc_deu_001208) +DARUNDER SIN MATÜLDE ARSENDIS WECHTER DS KROLTZIS (swc_deu_001209-swc_deu_001209) +IN INEN SHETEN MEHR UND MHR DE ROLEDERTHADTITZSNERLN FIS (swc_deu_001210-swc_deu_001210) +ZU DENEN MET LOU VIGKEIT (swc_deu_001211-swc_deu_001211) +DRACHE DISHOFESS UN DES ADETZ FÜR N FRFEL (swc_deu_001212-swc_deu_001212) +ZSEIT ANGABEMVERSICHTID (swc_deu_001213-swc_deu_001213) +ALLS ACHTIN UNDET ACHRT ZIGHMIT ODTUO BRAMS AUF SATZS (swc_deu_001214-swc_deu_001214) +MÜLENWESEN IETZIN UNERDACHUN WANI (swc_deu_001215-swc_deu_001215) +AS DE FISCH RIS (swc_deu_001216-swc_deu_001216) +SIDIM ABSHLOS IM JARENEUNZEN HNDER ZWALUN ACHTZICH UNDRNAMER EINE ERSTELENGEREREISENACSPBANEN (swc_deu_001217-swc_deu_001217) +VERN SCAT SO VURGETZEICHNET (swc_deu_001218-swc_deu_001218) +FEITEN STEINS VELSTENDIGGECHICHTEN UNDT DIE AUKSBURGERSTAGSCHIH E DES ELTRE (swc_deu_001219-swc_deu_001219) +NACH DIENZERSTÖRONMEN WURDE DERASCH WIDER AUFPLÜN (swc_deu_001220-swc_deu_001220) +ACHTEN EINFLUSREICHENHAN IE ATEN BEIM KOMIE SARESCHEINGESETZ N BÜÖRGEMEISTER MAKERT IER AUFAHTUN (swc_deu_001221-swc_deu_001221) +AILS ENTRALDESHANDES KONTOR (swc_deu_001222-swc_deu_001222) +SANDERSTELUNG INERHEIB DECSTAT KREFE (swc_deu_001223-swc_deu_001223) +VFINEZSICH IN HALOBAKTERIEN (swc_deu_001224-swc_deu_001224) +AUF DER BESEITE FINDEZSIHTAS EBEMFALS VON MEIKEL KOMPUNIET (swc_deu_001225-swc_deu_001225) +IN HN DEATICHER ZEITHATDI DI ZERKEGESERSCHFT KEINEN AUSCHLAGEBENTEN EINFLOSMHR (swc_deu_001226-swc_deu_001226) +DA STDERCHVRWENDUN VON AUFTRIEB SKAPAN ODERHALZS EINER GERINGERE MITTERERDICHTDE ALSWASER HAT (swc_deu_001227-swc_deu_001227) +DAMATESIERUNGEN (swc_deu_001228-swc_deu_001228) +UMN SIEBEN UR FÜNVO (swc_deu_001229-swc_deu_001229) +DES ELBREICHT DIE BADES TOCHTE (swc_deu_001230-swc_deu_001230) +TART BABMBARERSCHE STDATZR (swc_deu_001231-swc_deu_001231) +ERSLIT BESONDERSLIEBTE (swc_deu_001232-swc_deu_001232) +AUFKUND DERS WACSENDEN PUPLIKUMS INTRESSES WURDE DER AUFTRITS ORT Ü DI PRIMA VISTALESUNGE (swc_deu_001233-swc_deu_001233) +ND FREITECHTSBIE (swc_deu_001234-swc_deu_001234) +DAS DIE REIDEN STÖRZS RLTI UN BESCHARTET BERSTANDTEN HATE (swc_deu_001235-swc_deu_001235) +EAREN ERSCHNEN ZWEI IMRL (swc_deu_001236-swc_deu_001236) +DERGRABMAN UND GRAB GA PELEN O DER OLTATEN NACHALT (swc_deu_001237-swc_deu_001237) +IUNENENZENHUNDARSEXSUNEUINZIH KNDIKDER RSEINEBEITEN SOBPS (swc_deu_001238-swc_deu_001238) +IN GE POTIENEKOPEL (swc_deu_001239-swc_deu_001239) +NEUNUN SECHTZIG DER ME DIER KONTWOL ALBUMSCHATZ EIN (swc_deu_001240-swc_deu_001240) +M TARUSC KOM (swc_deu_001241-swc_deu_001241) +UONE HENICHT DEN KROSSHANDE KAUFLEUTEN GESERSHAFTLICGKLEICHGESTERT WARE (swc_deu_001242-swc_deu_001242) +VRNDERNARUNG UND VOMKIEMA (swc_deu_001243-swc_deu_001243) +APRLOE EIENZS (swc_deu_001244-swc_deu_001244) +BRÜÖÜL UND HÖERT NACKELE (swc_deu_001245-swc_deu_001245) +TWER IN EN KLOSTE (swc_deu_001246-swc_deu_001246) +DIEVIRZSM AUFIZEHREN KANEWALENSTANT UNTREUTE INEMISCHNG US KÖRSCHEN KANDEWAL UNDBPLIDSCHE KABRET MT KOM DELEMENTEN DASTELT U (swc_deu_001247-swc_deu_001247) +DIE WUNSTIEN RESLE DES FÜIRTE (swc_deu_001248-swc_deu_001248) +NANTIT ZIEGLER DIEARMORDUM DERBANAURN (swc_deu_001249-swc_deu_001249) +INTEROHR IST VOEL (swc_deu_001250-swc_deu_001250) +DIE STRENG DER VORGENGERLEITUNG WURDEN ZWISHE EUNZHN HUNDERT NEUNUENDZWANZIG UND NEUNZHN HUNDER DREINDFÜNFZIG ARCHE LOGESCH ERKRABEN (swc_deu_001251-swc_deu_001251) +IN GEGE SAT (swc_deu_001252-swc_deu_001252) +FABE VERN ERDIGENSEN LAUND HORT (swc_deu_001253-swc_deu_001253) +LEIF VERANSTALTUMEN (swc_deu_001254-swc_deu_001254) +ZOWERENHUTEINDEREGEL ALLE DORT LEBENDEN BRAUN (swc_deu_001255-swc_deu_001255) +IE DA FÜRE USCSELMER (swc_deu_001256-swc_deu_001256) +DESHAN DIE ATEN FÜÖRE (swc_deu_001257-swc_deu_001257) +HEBELTS ARG NES BANAU (swc_deu_001258-swc_deu_001258) +LIEBENSWEISE VERKRBPR (swc_deu_001259-swc_deu_001259) +EDEFEIL DES FIEREN HAMBURGANZU KATORLES VRAMMEN (swc_deu_001260-swc_deu_001260) +KERLTUL ND IERTHRFT AUSTAUSCHE (swc_deu_001261-swc_deu_001261) +MIJARZWEI TAUSEND VERTONDTE (swc_deu_001262-swc_deu_001262) +DASE DIESELEITUNGSCNALERVOLENTEN KÜNE ALS DER BAUMEISTER DEN KÖNER DOUM (swc_deu_001263-swc_deu_001263) +IDE HNRIHTUNG DE BANAUARIN HABESICSRLIHTUM (swc_deu_001264-swc_deu_001264) +LORDWEI (swc_deu_001265-swc_deu_001265) +DERZEIT DER BSTIGKHENER DER EIFELEITUN (swc_deu_001266-swc_deu_001266) +VOKUS BES WISENSHAFTLICHEN INDTARESES (swc_deu_001267-swc_deu_001267) +TEMER ZU BEGEISTE (swc_deu_001268-swc_deu_001268) +METE UND KONTE DAMIT AUFON INEN BERGANGEN WERDEN (swc_deu_001269-swc_deu_001269) +HAHTKABER BES SELLALISTEU DENH (swc_deu_001270-swc_deu_001270) +DARREIN EN ZIKLOP (swc_deu_001271-swc_deu_001271) +DN GRESLIE WIDER AUF DILESETZUSETZEN (swc_deu_001272-swc_deu_001272) +WIE LANG DIESE KAPLAN STELE AUFRICHTER HETENWRD (swc_deu_001273-swc_deu_001273) +SIEWARN MARSCHEINLIGHBEREITZ DREISZIG SIKUNDTEN NECH AUSPRCHTES VORER (swc_deu_001274-swc_deu_001274) +METER GESAMTLINGE UND BISTUT SEHN MIETER (swc_deu_001275-swc_deu_001275) +VEINER ITZEN UNDSPALT (swc_deu_001276-swc_deu_001276) +DINMEN VEN AUSSNDIEKOLER HINABPFLIESEN SIET (swc_deu_001277-swc_deu_001277) +NE ITER IOSAKTEBRAUN (swc_deu_001278-swc_deu_001278) +DAS FÜNFT EN GERIUM (swc_deu_001279-swc_deu_001279) +REISEN SIMANCHMAL WEIDETIERE ISCHAFE (swc_deu_001280-swc_deu_001280) +SI HÖREN DEN ARTIKEL DESEIN RÜFIU (swc_deu_001281-swc_deu_001281) +KUSER IS GLENTER KOCH (swc_deu_001282-swc_deu_001282) +SHANZE WENT STIFTDUN (swc_deu_001283-swc_deu_001283) +NEUNZHN HNDERT ACHTZIEN ALT S HAN IE ARTENANGESIE (swc_deu_001284-swc_deu_001284) +MERER ES NACH IM TUN (swc_deu_001285-swc_deu_001285) +AUCSHIH DES GERICHS ZUO LANDES WEITEN BERLIEBEN KOULINA RISCHER SPÄTZELTET ARMÜG (swc_deu_001286-swc_deu_001286) +KOLLETSCH UND EIN ZWEITCOB ALTSPBANSHLIERER IN HEM N VORLSEN (swc_deu_001287-swc_deu_001287) +BURDEN EINIS WEGS AELLE GEBÜIROTIGE (swc_deu_001288-swc_deu_001288) +IST IER KERBEBAUGREFTIGKH (swc_deu_001289-swc_deu_001289) +AN LESLICHTER NOUAS ANSPRACREKH (swc_deu_001290-swc_deu_001290) +MIT WIN VONDSCREÄGHINTEN (swc_deu_001291-swc_deu_001291) +DIN REÖCSTEN TEALDE BIT TZIÖGSFORTRIETUNG ÜRDINGEN AUS (swc_deu_001292-swc_deu_001292) +ACHTIN HUNERT EILUNZWANZI (swc_deu_001293-swc_deu_001293) +DSKROSEN ATELTS AN GESAMITENREICHTUMS (swc_deu_001294-swc_deu_001294) +DESON NIHT AL SEH ZWOL PROROKATIU (swc_deu_001295-swc_deu_001295) +TEILHBEDE VRMER GOSMAN UND IORGENZ (swc_deu_001296-swc_deu_001296) +INEMITE FRAUNTER KAUT INSELT (swc_deu_001297-swc_deu_001297) +AUR DIEO ISTEANDUTCHER HEBOFVARLARG MIT SITZ INMEÜNCHEN (swc_deu_001298-swc_deu_001298) +FA PEIK MENTE UNDSCHMICHE VORPRED UKTER HERSTELT (swc_deu_001299-swc_deu_001299) +ARPLICHEN PRESISCHEN FREI HEREN STAND IN DER ZOL ANSCHLOS VRAGE INTSCHIEBDENG GEGENG DEN SINART AUF DIESEITE BISMAGS GESTELT (swc_deu_001300-swc_deu_001300) +WENDIKWELEN VON SEST ER VORGWELEN UND AUFEN ZUTAGELIGEN (swc_deu_001301-swc_deu_001301) +DS VOUN NACHBARBAUTRIUB BELLEITZ BEGON WOT (swc_deu_001302-swc_deu_001302) +WEHRDEN PRÄRGENDE ELEMENTE DES HANSE ATENTUMSZUOSAMMEN GEFAST (swc_deu_001303-swc_deu_001303) +DEASSLIEZWRDERTS VOLCGSTLIET AN GESIEN (swc_deu_001304-swc_deu_001304) +DERZON NENDEM HUSVRLAG SKGOBIGE HÖRT (swc_deu_001305-swc_deu_001305) +FR DE KNFDIGEN BARD PBÜSCHER IND WIKETE DI PAPIEFERPR (swc_deu_001306-swc_deu_001306) +AMBRE WUOKS (swc_deu_001307-swc_deu_001307) +V DIEKWARAR SIE ARD LIGEN LANZITZE BETRIEBEN AUFAND SEISBEMBAU (swc_deu_001308-swc_deu_001308) +JA ZWEI TAUSEN ZWÖLF INDN BELIENER KLUOPSO SE SHNDREISICH VELLIGT (swc_deu_001309-swc_deu_001309) +SECHTIN HNEN FÜNFTIGH EID BÜNDNIS (swc_deu_001310-swc_deu_001310) +DASPROBLEN BI DEN PRADTACHSONIST (swc_deu_001311-swc_deu_001311) +AMINWESEN TETIAMALIE S IEVEKEIN (swc_deu_001312-swc_deu_001312) +ICH EI MALEINE AN SATZWEISE UNTE SOCHRUNG ZU IEREM VERHALKTEN INDER ZEITT DES NAZUNALSUTZELISMUS (swc_deu_001313-swc_deu_001313) +LIETZSEINS FÜRFREIE DOKOMENTATION (swc_deu_001314-swc_deu_001314) +DIM CHZEN NER HUNDER DIGATEN HOLSE VOR ENTORE (swc_deu_001315-swc_deu_001315) +GANZS IMSTIELDER ZEIT (swc_deu_001316-swc_deu_001316) +ER BRÜL UND HÜÖRT AREICH E DI LEITUNSCHISLIKEN (swc_deu_001317-swc_deu_001317) +AUSSTZEICHNUMEN REM DER HEREN (swc_deu_001318-swc_deu_001318) +DIESCHEFTELLEREI AUFTZUGEBE (swc_deu_001319-swc_deu_001319) +DAZUTZIHRN DE BEGEGNUNMIT VERLETZTENTIERE (swc_deu_001320-swc_deu_001320) +IENESCH STDIFT (swc_deu_001321-swc_deu_001321) +WESTLICH VON KERLEN (swc_deu_001322-swc_deu_001322) +DIE STENDI N BETRIPWARE (swc_deu_001323-swc_deu_001323) +DI VOM BA BIJERHASIERT WEHRDE (swc_deu_001324-swc_deu_001324) +ERSCHEN NOREINEITRAUFSEATZFON GRISTERNMEIAL (swc_deu_001325-swc_deu_001325) +WAEIL SEBST EXSTRME REICHTUMKEINES WEHST IN UNMITLEBAREN SZUGAN (swc_deu_001326-swc_deu_001326) +GEBT EUSCHEICH SELBER AUF (swc_deu_001327-swc_deu_001327) +ER HAET DIESEN BRAUCH NEUNEHIN HNDER WEIUND FÜNFTIGH GIGN ÜB (swc_deu_001328-swc_deu_001328) +O DELEITUNG ÜBE DIE ALTE HÜÖRTEARLEITUNGEFIERT WURDE (swc_deu_001329-swc_deu_001329) +INE BLIEBTE KLSCHOKTROPEASTE KENER UMNANDTDIE HÖNE (swc_deu_001330-swc_deu_001330) +GEWARDEN SEIO UND ALBRECHT DICH (swc_deu_001331-swc_deu_001331) +DETAGESBEDAF EINESWAGSTENEN ANITEMIN AR (swc_deu_001332-swc_deu_001332) +SIBTINULER ZIHEN OBAR ALTE (swc_deu_001333-swc_deu_001333) +WEITERHEN LIESICHNACRWEISEN (swc_deu_001334-swc_deu_001334) +ZUN GRÜNUNGSTDARTUM KONTBAM BEREIT (swc_deu_001335-swc_deu_001335) +KEIN LECSCHLARG MÜKLICH NACHTEILE (swc_deu_001336-swc_deu_001336) +RTDIKATOLICHE KÜRSCHESAN PÄTER ANDER STELLE DE ALT (swc_deu_001337-swc_deu_001337) +ERFEKNARPBUN DESBROT WEITZEN TRAT ABSCHNBAIT DIKERTOFEL EIS ERSAT (swc_deu_001338-swc_deu_001338) +KRE MT DIESEN NACHKOMN ZOEUGE (swc_deu_001339-swc_deu_001339) +ALLE NEIN FOLGEN DER HÖRS IEREIE (swc_deu_001340-swc_deu_001340) +SCHIBPSMIE BERACTENSO (swc_deu_001341-swc_deu_001341) +KLIGE AN RERSBUOCHNE VERZICHTE AUFENEBERSÖNICHE BEWERTUN (swc_deu_001342-swc_deu_001342) +BENIGER IN TRUSSTE (swc_deu_001343-swc_deu_001343) +WEITER HEN VERSORK D DIELEI TUNG TERMEN (swc_deu_001344-swc_deu_001344) +WARTETEN DA FÜHR ABEMIT EINIG (swc_deu_001345-swc_deu_001345) +DE DIKLICH ANTRÜNDFONKLEIN MUNIERTI INSEINARETZEN SOND (swc_deu_001346-swc_deu_001346) +EM JABRNEUZENERTFÜM (swc_deu_001347-swc_deu_001347) +IERSIT EM FRT FAL DESBÜURGERECHT UN DEREINFÜRUN DE REITZÜGICHKEIT IM ZBANSIS NERNDERT WANDTET DE SICH DIESE ANSCHAUNG ANSERT SWEIESEIN DAR IEN (swc_deu_001348-swc_deu_001348) +DES SZWIST BACRES BALREINBACH EINE BOGEN BRÜCKE V (swc_deu_001349-swc_deu_001349) +ACTZINULETE SNDREISIG BWORDE E HMBUG (swc_deu_001350-swc_deu_001350) +DG AEM (swc_deu_001351-swc_deu_001351) +AUFGRUND DE KONTINEN TEALSPBARE ACHTZEHN HUNER ELF BANKOT (swc_deu_001352-swc_deu_001352) +EITERESMEIMUST DEN UNPLEIPT BRAUN DIWARBUNG FÜREAS BOHSEBTWANEM (swc_deu_001353-swc_deu_001353) +DINERIH VM SIEGTDER BÜRGELICH MUGRATISCHEN FIEPOARERLUTZIONVEN ACHZEIN HNDET ACHT N VIRZICHN RANKREICHVWRDEN HAMBURGMITIOBE AUFGENAME (swc_deu_001354-swc_deu_001354) +OUMBLIETZWEIARE ONE DERBRESCHN (swc_deu_001355-swc_deu_001355) +ZOALREICHE GASTSPILN UNTERWEGHS (swc_deu_001356-swc_deu_001356) +KRANZIETET GENÜTEN (swc_deu_001357-swc_deu_001357) +UN BEROKER AUSTATO (swc_deu_001358-swc_deu_001358) +DAGERICHT VOUM BEIVWARGENSANGESMOTORADIS AUS INDIE ZU DESERZEIT NOU EN STENDEN ABET ASIET LOMENZU (swc_deu_001359-swc_deu_001359) +DIMITZSAM IE HERECHEN STOBE (swc_deu_001360-swc_deu_001360) +KEISERFERDINAN (swc_deu_001361-swc_deu_001361) +VOM FERNSERESCHSUHRERFEANZSAVERBUGNER INDEM FERNSEFILEN DAS WIGELIET (swc_deu_001362-swc_deu_001362) +UR IN SEIN BESTEN ZEITEN DE (swc_deu_001363-swc_deu_001363) +SEHÖRE DEN ARTIKEL FISCHENTSCIS (swc_deu_001364-swc_deu_001364) +UND TE FARMOFWI (swc_deu_001365-swc_deu_001365) +REAZSIBZIONDERHEHSEN IMATIG VON KRESTA (swc_deu_001366-swc_deu_001366) +DIGESAMTER ANLARGE WABIS ET VERTZWEI HUNDER SECHTZIG NACHKRISTUS IN BEDRIE (swc_deu_001367-swc_deu_001367) +DER IS DE FAST FUT LIERSOARWISWAGEBUN (swc_deu_001368-swc_deu_001368) +EINDEM KABELBAUN (swc_deu_001369-swc_deu_001369) +MORLE DUNG MIT DE E FAFVERBUNDEN GEWES (swc_deu_001370-swc_deu_001370) +E ELTZISTN PRIERERENIN AUSRHEP (swc_deu_001371-swc_deu_001371) +SONEN ACH DERNA ZUN NALI SUTSER DISTISCHEN KUNZT AUF FASONGERECHTWEHREN (swc_deu_001372-swc_deu_001372) +DIEWELTZSICHT DES HANSE AHRTENI (swc_deu_001373-swc_deu_001373) +AUCHNACRKOMMEN SIN NICH BEKANT (swc_deu_001374-swc_deu_001374) +EREN TEKSTE DIN EINDRUGKTZE VERMITE (swc_deu_001375-swc_deu_001375) +VERDINSTUM DASKELNALIET VERLIEN (swc_deu_001376-swc_deu_001376) +ABPVOL HOFMEIN VN HAFMEIN WEL DAUS WERRK GOSENEINPLIUSAU SPÄRTERER DICHTERAUSCÜBP (swc_deu_001377-swc_deu_001377) +OM SOR EANZTAL STATS BERHAUPFVE (swc_deu_001378-swc_deu_001378) +EFREIE DOKMENTATZION (swc_deu_001379-swc_deu_001379) +GESTALTUM BES KAVERS WIEDERSPBIEGELT (swc_deu_001380-swc_deu_001380) +DEGESAMTER AUFWANT WIT AUF (swc_deu_001381-swc_deu_001381) +AUBGLEICHMBURK DIESIEM AN GHRTU UND EINE NOBLI TIERUN DRICHENKEISERDAMIT KEINEDRESC (swc_deu_001382-swc_deu_001382) +DER ASTURCHTDEN SICH AUSWEITENTEN WELTHANDEL ARBEIT UND WOLSTANVERSPRACH (swc_deu_001383-swc_deu_001383) +FÖÖER DIE ZEIT MITE DESNEUN IHNDE JERHUNDARS BEKLAK DERCHITEKT MATIN HALE (swc_deu_001384-swc_deu_001384) +EIT BNDE KANZER HRMUTSMIT LENTE (swc_deu_001385-swc_deu_001385) +DINAMEN GUODEFREI IM STAZSHANT BUOCHTZ (swc_deu_001386-swc_deu_001386) +WEN AUCHMT EN ERGEWSEN LETAGIE (swc_deu_001387-swc_deu_001387) +KA KOULIER BAN (swc_deu_001388-swc_deu_001388) +ANGEFANGDETZSOANDE FM ISCHULEREINEN DIEG JEPEN (swc_deu_001389-swc_deu_001389) +FIELMENSCHE SEINEING RESLI LSNARUNGSKONGURENTEN UNDELSPUTEN ELLGE FA (swc_deu_001390-swc_deu_001390) +DIN AUFTITVERKÖRE (swc_deu_001391-swc_deu_001391) +MI DEM STAN VOM DREIZEHN NIULIEZWEITAUSENSWERF DER INERSTIT UNDE DER IE ZENZ KEHRTZUE KOMMONS EIT ZEIEWUSCHEN SCHER ELEITREI PUNK NOLE AN PRTET UND UNTER DE (swc_deu_001392-swc_deu_001392) +EINI KLEINEREBOGEN PRÜKE (swc_deu_001393-swc_deu_001393) +SICH NUNVERSENBITERKOM (swc_deu_001394-swc_deu_001394) +AUSS DEM GEMELDETZU ENT FERINEN (swc_deu_001395-swc_deu_001395) +ISDNEH AREPESCHERICSTINE NEUNZICG FIEORNERTE ENEUNZICH EWI (swc_deu_001396-swc_deu_001396) +ARIEM UNSELT AUF (swc_deu_001397-swc_deu_001397) +OND SIE SEI AURCH DIE EINE FÜLRSTEN (swc_deu_001398-swc_deu_001398) +NEITZIHN HUNDERT NOEIN SIEE (swc_deu_001399-swc_deu_001399) +STARTESEN HABM DERÖÜMICHEN INSCHEN JIÖRE (swc_deu_001400-swc_deu_001400) +DELKRISIE BEHR UND MENSCH (swc_deu_001401-swc_deu_001401) +MIE SISCHENZ KOALISCHE (swc_deu_001402-swc_deu_001402) +BECTURSCHMLGEB DES S DREIBARERTIONEN I (swc_deu_001403-swc_deu_001403) +KZUKSKGEBIET AERWESIHTER ACHZEHN HUNDARZWEIUNSIEBZICGE RÜNDETE ALUSDEUNER INALPAR (swc_deu_001404-swc_deu_001404) +DEVFINITZION (swc_deu_001405-swc_deu_001405) +UM INE UNVWE SITETZIEWILER ZWEISEMESTER KUNSGSCHCHT ZUSTUDIEN (swc_deu_001406-swc_deu_001406) +DIE TROTZ ERER GRINE (swc_deu_001407-swc_deu_001407) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..0aad4d12cd21f4608e3b2073807b9e63516b202a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/ref.trn @@ -0,0 +1,207 @@ +DER VERLIEBTE JUNGE HERZOG DIE RATSCHLÄGE SEINES VATERS NICHT BEACHTET HABE (swc_deu_001201-swc_deu_001201) +DIE IN DEN HANSESTÄDTEN ALS (swc_deu_001202-swc_deu_001202) +WAR KEIN GROSSER ERFOLG (swc_deu_001203-swc_deu_001203) +GROSSEN CHEMISCHEN FABRIKEN (swc_deu_001204-swc_deu_001204) +WURDEN AUCH MEHRERE ERLÄUTERUNGSBÜCHER VERÖFFENTLICHT (swc_deu_001205-swc_deu_001205) +VORBEREITETEN BIERTEIG GETUNKT (swc_deu_001206-swc_deu_001206) +DOKUMENTE SCHLIESSLICH IN (swc_deu_001207-swc_deu_001207) +TRAUERTAG FÜR DEN TOD VON KÖNIG FRIEDRICH WILHELM (swc_deu_001208-swc_deu_001208) +DARUNTER SIND MATILDE ASENSIS WÄCHTER DES KREUZES (swc_deu_001209-swc_deu_001209) +INNENSTÄDTEN MEHR UND MEHR DIE ROLLE DER TRADITIONELLEN FISH (swc_deu_001210-swc_deu_001210) +ZU DENEN WELTLÄUFIGKEIT (swc_deu_001211-swc_deu_001211) +RACHE DES HOFES UND DES ADELS FÜR DEN FREVEL (swc_deu_001212-swc_deu_001212) +ZEITANGABEN VERZICHTETE (swc_deu_001213-swc_deu_001213) +ALS ACHTZEHN HUNDERT ACHTZIG MIT OTTO BRAHMS AUFSATZ (swc_deu_001214-swc_deu_001214) +EIN TAUSEND SIEBEN HUNDERT ACHTUNDZWANZIG – (swc_deu_001215-swc_deu_001215) +DASS DER FISCH FRISCH (swc_deu_001216-swc_deu_001216) +SEINEM ABSCHLUSS IM JAHRE NEUNZEHN HUNDERT ZWEIUNDACHTZIG UNTERNAHM ER EINE ERSTE LÄNGERE REISE NACH SPANIEN (swc_deu_001217-swc_deu_001217) +VON CHASÔT VORGEZEICHNET (swc_deu_001218-swc_deu_001218) +FALCKENSTEINS VOLLSTÄNDIGE GESCHICHTEN UND DIE AUGSBURGER STADTGESCHICHTE DES ÄLTEREN (swc_deu_001219-swc_deu_001219) +NACH DIESEN ZERSTÖRUNGEN WURDE DIE RASCH WIEDER AUFBLÜHENDE (swc_deu_001220-swc_deu_001220) +MACHTEN EINFLUSSREICHEN HANSEATEN BEIM KOMMISSARISCH EINGESETZTEN BÜRGERMEISTER MARKERT IHRE AUFWARTUNG (swc_deu_001221-swc_deu_001221) +ALS ZENTRALES HANDELSKONTOR (swc_deu_001222-swc_deu_001222) +SONDERSTELLUNG INNERHALB DER STADT KREFELD (swc_deu_001223-swc_deu_001223) +FINDET SICH IN HALOBAKTERIEN (swc_deu_001224-swc_deu_001224) +AUF DER B SEITE FINDET SICH DAS EBENFALLS VON MICHAEL KOMPONIERTE (swc_deu_001225-swc_deu_001225) +IN HANSEATISCHER ZEIT HATTE DIE ZIRKELGESELLSCHAFT KEINEN AUSSCHLAGGEBENDEN EINFLUSS MEHR (swc_deu_001226-swc_deu_001226) +DA ES DURCH VERWENDUNG VON AUFTRIEBSKÖRPERN ODER HOLZ EINE GERINGERE MITTLERE DICHTE ALS WASSER HAT (swc_deu_001227-swc_deu_001227) +DRAMATISIERUNGEN (swc_deu_001228-swc_deu_001228) +UM 755 (swc_deu_001229-swc_deu_001229) +DASS ALBRECHT DIE BADERSTOCHTER (swc_deu_001230-swc_deu_001230) +THAT BARBARISCHER STAATSRAISON (swc_deu_001231-swc_deu_001231) +DER DAS LIED BESONDERS LIEBTE (swc_deu_001232-swc_deu_001232) +AUFGRUND DES WACHSENDEN PUBLIKUMSINTERESSES WURDE DER AUFTRITTSORT FÜR DIE PRIMA VISTA LESUNGEN (swc_deu_001233-swc_deu_001233) +UND FREILICHTSPIELE (swc_deu_001234-swc_deu_001234) +DASS DIE DREI DEN STURZ RELATIV UNBESCHADET ÜBERSTANDEN HATTEN (swc_deu_001235-swc_deu_001235) +JAHREN ERSCHIENEN ZWEI IMMER (swc_deu_001236-swc_deu_001236) +GRABMALE UND GRABKAPELLEN ODER WOHLTATEN NACHHALTIG (swc_deu_001237-swc_deu_001237) +JUNI NEUNZEHN HUNDERT SECHSUNDNEUNZIG KÜNDIGTE ER SEINE BEIDEN JOBS (swc_deu_001238-swc_deu_001238) +EIN G PROTEIN GEKOPPELT (swc_deu_001239-swc_deu_001239) +NEUNUNDSECHZIG DER MEDIA CONTROL ALBUMCHARTS EIN (swc_deu_001240-swc_deu_001240) +DADURCH KOMMT (swc_deu_001241-swc_deu_001241) +OHNEHIN NICHT DEN GROSSHANDELSKAUFLEUTEN GESELLSCHAFTLICH GLEICHGESTELLT WAREN (swc_deu_001242-swc_deu_001242) +VON DER NAHRUNG UND VOM KLIMA (swc_deu_001243-swc_deu_001243) +APOLLO EINS (swc_deu_001244-swc_deu_001244) +BRÜHL UND HÜRTH NACH KÖLN (swc_deu_001245-swc_deu_001245) +ETWA IN EIN KLOSTER (swc_deu_001246-swc_deu_001246) +ZUM OFFIZIELLEN KARNEVAL ENTSTAND UND HEUTE EINE MISCHUNG AUS KÖLSCHEM KARNEVAL UND POLITISCHEM KABARETT MIT COMEDYELEMENTEN DARSTELLT UND (swc_deu_001247-swc_deu_001247) +DIE ZUR ENTSTEHUNG DES LIEDES FÜHRTEN (swc_deu_001248-swc_deu_001248) +NANNTE ZIEGLER DIE ERMORDUNG DER BERNAUERIN (swc_deu_001249-swc_deu_001249) +WINTERRUHE IST VOR ALLEM (swc_deu_001250-swc_deu_001250) +DIE STRÄNGE DER VORGÄNGERLEITUNG WURDEN ZWISCHEN NEUNZEHN HUNDERT NEUNUNDZWANZIG UND NEUNZEHN HUNDERT DREIUNDFÜNFZIG ARCHÄOLOGISCH ERGRABEN (swc_deu_001251-swc_deu_001251) +IM GEGENSATZ (swc_deu_001252-swc_deu_001252) +FARBEN VON UERDINGEN SIND BLAU UND ROT (swc_deu_001253-swc_deu_001253) +LIVE VERANSTALTUNGEN (swc_deu_001254-swc_deu_001254) +SO WERDEN HEUTE IN DER REGEL ALLE DORT LEBENDEN BRAUNBÄREN (swc_deu_001255-swc_deu_001255) +LIEDER FÜR REVUEFILME (swc_deu_001256-swc_deu_001256) +DES HANSEATEN FÜHREN (swc_deu_001257-swc_deu_001257) +HEBBELS AGNES BERNAUER (swc_deu_001258-swc_deu_001258) +LEBENSWEISE VERKÖRPERN (swc_deu_001259-swc_deu_001259) +WIE DER FALL DES VIELEN HAMBURGERN ZU KATHOLISCH FROMMEN (swc_deu_001260-swc_deu_001260) +KULTUR UND WIRTSCHAFT AUSTAUSCHEN (swc_deu_001261-swc_deu_001261) +JAHR ZWEI TAUSEND VERTONTE (swc_deu_001262-swc_deu_001262) +DASS ER DIESE LEITUNG SCHNELLER VOLLENDEN KÖNNE ALS DER BAUMEISTER DEN KÖLNER DOM (swc_deu_001263-swc_deu_001263) +HINRICHTUNG DER BERNAUERIN HABE ES SICH SCHLICHT UM (swc_deu_001264-swc_deu_001264) +LUDWIG (swc_deu_001265-swc_deu_001265) +DERZEIT DER BESTE KENNER DER EIFELLEITUNG (swc_deu_001266-swc_deu_001266) +FOKUS DES WISSENSCHAFTLICHEN INTERESSES (swc_deu_001267-swc_deu_001267) +THEMA ZU BEGEISTERN (swc_deu_001268-swc_deu_001268) +METER UND KONNTE DAMIT AUCH VON INNEN BEGANGEN WERDEN (swc_deu_001269-swc_deu_001269) +HARDCOVER BESTSELLERLISTE DER NEW (swc_deu_001270-swc_deu_001270) +DER FREIEN ENZYKLOPÄDIE (swc_deu_001271-swc_deu_001271) +DEN GRIZZLY WIEDER AUF DIE LISTE ZU SETZEN (swc_deu_001272-swc_deu_001272) +LANG DIESE KAPLANSSTELLE AUFRECHTERHALTEN WURDE (swc_deu_001273-swc_deu_001273) +SIE WAREN WAHRSCHEINLICH BEREITS DREISSIG SEKUNDEN NACH AUSBRUCH DES FEUERS (swc_deu_001274-swc_deu_001274) +METERN GESAMTLÄNGE UND BIS ZU ZEHN METERN (swc_deu_001275-swc_deu_001275) +FEINE RITZEN UND SPALTEN (swc_deu_001276-swc_deu_001276) +DEN MAN VON AUSSEN DIE KEHLE HINABFLIESSEN SIEHT (swc_deu_001277-swc_deu_001277) +EINEM INTERVIEW SAGTE BROWN (swc_deu_001278-swc_deu_001278) +DAS FÜNFTE EVANGELIUM (swc_deu_001279-swc_deu_001279) +REISSEN SIE MANCHMAL WEIDETIERE WIE SCHAFE (swc_deu_001280-swc_deu_001280) +SIE HÖREN DEN ARTIKEL DESIGN REVIEW (swc_deu_001281-swc_deu_001281) +CHAKUZA IST GELERNTER KOCH (swc_deu_001282-swc_deu_001282) +HANS WENDT STIFTUNG (swc_deu_001283-swc_deu_001283) +NEUNZEHN HUNDERT ACHTZEHN ALS HANSEATEN ANGESEHEN (swc_deu_001284-swc_deu_001284) +MEHRERE ES NACH IHM THUN (swc_deu_001285-swc_deu_001285) +AUFSTIEG DES GERICHTS ZUR LANDESWEIT BELIEBTEN KULINARISCHEN SPEZIALITÄT ERMÖGLICHTE (swc_deu_001286-swc_deu_001286) +COLLEGE UND EINEN ZWEITJOB ALS SPANISCHLEHRER IN HAMPTON FALLS AN (swc_deu_001287-swc_deu_001287) +WURDEN KEINESWEGS ALLE GEBÜRTIGEN (swc_deu_001288-swc_deu_001288) +IST IHR KÖRPERBAU KRÄFTIG (swc_deu_001289-swc_deu_001289) +ANLÄSSLICH DER NEUJAHRESANSPRACHE KIM (swc_deu_001290-swc_deu_001290) +MIT WIND VON SCHRÄG HINTEN (swc_deu_001291-swc_deu_001291) +DEN GRÖSSTEN TEIL DER BEZIRKSVERTRETUNG UERDINGEN AUS (swc_deu_001292-swc_deu_001292) +ACHTZEHN HUNDERT EINUNDZWANZIG (swc_deu_001293-swc_deu_001293) +DES GROSSEN ADELS ANGESAMMELTEN REICHTUMS (swc_deu_001294-swc_deu_001294) +SOLLTEN NICHT ALS SEXUELLE PROVOKATION (swc_deu_001295-swc_deu_001295) +TEILHABER DER FIRMA GOSSMANN UND JÜRGENS (swc_deu_001296-swc_deu_001296) +DER KRAUTINSEL BILDET SIE DIE GEMEINDE (swc_deu_001297-swc_deu_001297) +AUDIO IST EIN DEUTSCHER HÖRBUCHVERLAG MIT SITZ IN MÜNCHEN (swc_deu_001298-swc_deu_001298) +FARBPIGMENTE UND CHEMISCHE VORPRODUKTE HERSTELLT (swc_deu_001299-swc_deu_001299) +ERBLICHEN PREUSSISCHEN FREIHERRENSTAND IN DER ZOLLANSCHLUSSFRAGE ENTSCHIEDEN GEGEN DEN SENAT AUF DIE SEITE BISMARCKS GESTELLT (swc_deu_001300-swc_deu_001300) +WENN DIE QUELLEN VON SELBST HERVORQUELLEN UND OFFEN ZU TAGE LIEGEN (swc_deu_001301-swc_deu_001301) +DAS VOM NACHBARBAUTRUPP BEREITS BEGONNEN WURDE (swc_deu_001302-swc_deu_001302) +WERDEN PRÄGENDE ELEMENTE DES HANSEATENTUMS ZUSAMMENGEFASST (swc_deu_001303-swc_deu_001303) +DAS LIED WURDE ALS VOLKSLIED ANGESEHEN (swc_deu_001304-swc_deu_001304) +DER ZUR RANDOM HOUSE VERLAGSGRUPPE GEHÖRT (swc_deu_001305-swc_deu_001305) +FÜR DIE KÜNFTIGEN BORDBÜCHER ENTWICKELTE DIE PAPIERFABRIK (swc_deu_001306-swc_deu_001306) +HAMBURG WUCHS (swc_deu_001307-swc_deu_001307) +FÜR DIE QUASI ADLIGEN LANDSITZE BETRIEBENE AUFWAND – SEI ES BEIM BAU (swc_deu_001308-swc_deu_001308) +JAHR ZWEI TAUSEND ZWÖLF IN DEN BERLINER CLUB S O SECHSUNDDREISSIG VERLEGT (swc_deu_001309-swc_deu_001309) +SECHZEHN HUNDERT FÜNFZIG ALS BÜNDNIS DIE (swc_deu_001310-swc_deu_001310) +PROBLEM BEI DIESEM PARADOXON IST (swc_deu_001311-swc_deu_001311) +ARMENWESEN TÄTIG AMALIE SIEVEKING (swc_deu_001312-swc_deu_001312) +NICHT EINMAL EINE ANSATZWEISE UNTERSUCHUNG ZU IHREM VERHALTEN IN DER ZEIT DES NATIONALSOZIALISMUS (swc_deu_001313-swc_deu_001313) +LIZENZ FÜR FREIE DOKUMENTATION (swc_deu_001314-swc_deu_001314) +IM ACHTZEHNTE JAHRHUNDERT DIE GARTENHÄUSER VOR DEN TOREN (swc_deu_001315-swc_deu_001315) +GANZ IM STIL DER ZEIT (swc_deu_001316-swc_deu_001316) +ÜBER BRÜHL UND HÜRTH ERREICHTE DIE LEITUNG SCHLIESSLICH KÖLN (swc_deu_001317-swc_deu_001317) +AUSZEICHNUNGEN FREMDER HERREN (swc_deu_001318-swc_deu_001318) +DIE SCHRIFTSTELLEREI AUFZUGEBEN (swc_deu_001319-swc_deu_001319) +ZÄHLEN DIE BEGEGNUNG MIT VERLETZTEN TIEREN (swc_deu_001320-swc_deu_001320) +JENISCH STIFT (swc_deu_001321-swc_deu_001321) +WESTLICH VON KÖLN (swc_deu_001322-swc_deu_001322) +DIE STÄNDIG IN BETRIEB WAREN (swc_deu_001323-swc_deu_001323) +DIE VOM BARBIER RASIERT WERDEN (swc_deu_001324-swc_deu_001324) +ERSCHIEN NOCH EIN WEITERER AUFSATZ VON CHRISTIAN MEYER (swc_deu_001325-swc_deu_001325) +WEIL SELBST EXTREMER REICHTUM KEINESWEGS DEN UNMITTELBAREN ZUGANG (swc_deu_001326-swc_deu_001326) +GEBT EUCH NICHT SELBER AUF (swc_deu_001327-swc_deu_001327) +HAT DIESEN BRAUCH NEUNZEHN HUNDERT ZWEIUNDFÜNFZIG GEGENÜBER (swc_deu_001328-swc_deu_001328) +WO DIE LEITUNG ÜBER DIE ALTE HÜRTHER LEITUNG GEFÜHRT WURDE (swc_deu_001329-swc_deu_001329) +EINE BELIEBTE KÖLSCHROCKTRUPPE AUS DEM KÖLNER UMLAND DIE HÖHNER (swc_deu_001330-swc_deu_001330) +GEWORDEN SEI UND ALBRECHT SICH (swc_deu_001331-swc_deu_001331) +DER TAGESBEDARF EINES ERWACHSENEN AN VITAMIN A (swc_deu_001332-swc_deu_001332) +SIEBZEHN HUNDERT ZEHN OBERALTER (swc_deu_001333-swc_deu_001333) +WEITERHIN LIESS SICH NACHWEISEN (swc_deu_001334-swc_deu_001334) +ZUM GRÜNDUNGSDATUM KONNTE MAN BEREITS (swc_deu_001335-swc_deu_001335) +KEIN LECKSCHLAGEN MÖGLICH NACHTEILE (swc_deu_001336-swc_deu_001336) +WIRD DIE KATHOLISCHE KIRCHE STÜCK PETER AN DER STELLE DER ALTEN (swc_deu_001337-swc_deu_001337) +DER VERKNAPPUNG DES BROTWEIZENS TRAT ABER SCHON BALD DIE KARTOFFEL ALS ERSATZ (swc_deu_001338-swc_deu_001338) +KÖNNEN MIT DIESEN NACHKOMMEN ZEUGEN (swc_deu_001339-swc_deu_001339) +ALLE NEUEN FOLGEN DER HÖRSPIELREIHE (swc_deu_001340-swc_deu_001340) +CHIPS MIT BRATENSOSSE (swc_deu_001341-swc_deu_001341) +KOLLEGE ANDREAS BUCHNER VERZICHTETE AUF EINE PERSÖNLICHE BEWERTUNG (swc_deu_001342-swc_deu_001342) +WENIGER ENTRÜSTET (swc_deu_001343-swc_deu_001343) +WEITERHIN VERSORGTE DIE LEITUNG THERMEN (swc_deu_001344-swc_deu_001344) +WARTETEN DAFÜR ABER MIT EINIGEN (swc_deu_001345-swc_deu_001345) +LEDIGLICH ANTON VON KLEIN MONIERTE IN SEINER REZENSION DER (swc_deu_001346-swc_deu_001346) +IM JAHRE NEUNZEHN HUNDERT FÜNF (swc_deu_001347-swc_deu_001347) +ERST MIT DEM FORTFALL DES BÜRGERRECHTS UND DER EINFÜHRUNG DER FREIZÜGIGKEIT IM ZWANZIGSTE JAHRHUNDERT WANDELTE SICH DIESE ANSCHAUUNG ANSATZWEISE DAHIN (swc_deu_001348-swc_deu_001348) +DES SWISTBACHES BEI RHEINBACH EINE BOGENBRÜCKE VON (swc_deu_001349-swc_deu_001349) +ACHTZEHN HUNDERT SECHSUNDDREISSIG WURDE DER HAMBURGER (swc_deu_001350-swc_deu_001350) +AM KARNEVALSSONNTAG (swc_deu_001351-swc_deu_001351) +AUFGRUND DER KONTINENTALSPERRE ACHTZEHN HUNDERT ELF BANKROTT (swc_deu_001352-swc_deu_001352) +WEITERES MAL MUSSTEN DAN UND BLYTHE BROWN DIE WERBUNG FÜR DAS BUCH SELBST ÜBERNEHMEN (swc_deu_001353-swc_deu_001353) +DIE NACHRICHT VOM SIEG DER BÜRGERLICH DEMOKRATISCHEN FEBRUARREVOLUTION VON ACHTZEHN HUNDERT ACHTUNDVIERZIG IN FRANKREICH WURDE IN HAMBURG MIT JUBEL AUFGENOMMEN (swc_deu_001354-swc_deu_001354) +ZWEI JAHRE OHNE UNTERBRECHUNG (swc_deu_001355-swc_deu_001355) +ZAHLREICHEN GASTSPIELEN UNTERWEGS (swc_deu_001356-swc_deu_001356) +QUANTITÄT GENÜGTEN (swc_deu_001357-swc_deu_001357) +BAROCKER AUSSTATTUNG (swc_deu_001358-swc_deu_001358) +DAS GERICHT VOM BEIWAGEN SEINES MOTORRADES AUS IN DIE ZU DIESER ZEIT NEU ENTSTEHENDEN ARBEITERSIEDLUNGEN ZU (swc_deu_001359-swc_deu_001359) +DIE MITSAMT IHRER RECHENSTUBE (swc_deu_001360-swc_deu_001360) +KAISER FERDINAND (swc_deu_001361-swc_deu_001361) +VOM FERNSEHREGISSEUR FRANZ XAVER BOGNER IN DEM FERNSEHFILM DAS EWIGE LIED (swc_deu_001362-swc_deu_001362) +WURDE IN SEINEN BESTEN ZEITEN DER (swc_deu_001363-swc_deu_001363) +SIE HÖREN DEN ARTIKEL FISH AND CHIPS (swc_deu_001364-swc_deu_001364) +UND T V MOVIE (swc_deu_001365-swc_deu_001365) +REZEPTION DER HEXENTHEMATIK VON CHRISTA (swc_deu_001366-swc_deu_001366) +DIE GESAMTE ANLAGE WAR BIS ETWA ZWEI HUNDERT SECHZIG NACH CHRISTUS IN BETRIEB (swc_deu_001367-swc_deu_001367) +DER ERSTE FAST FOOD LIEFERSERVICE WAR GEBOREN (swc_deu_001368-swc_deu_001368) +EINEM KABELBAUM (swc_deu_001369-swc_deu_001369) +ERMORDUNG MIT DER GEFAHR VERBUNDEN GEWESEN (swc_deu_001370-swc_deu_001370) +DER ÄLTESTEN PFERDERENNEN AUSSERHALB (swc_deu_001371-swc_deu_001371) +SONDERN AUCH DER NATIONALSOZIALISTISCHEN KUNSTAUFFASSUNG GERECHT WERDEN (swc_deu_001372-swc_deu_001372) +DIE WELTSICHT DES HANSEATEN (swc_deu_001373-swc_deu_001373) +AUCH NACHKOMMEN SIND NICHT BEKANNT (swc_deu_001374-swc_deu_001374) +IHREN TEXTEN DEN EINDRUCK ZU VERMITTELN (swc_deu_001375-swc_deu_001375) +VERDIENSTE UM DAS KÖLNER LIED VERLIEHEN (swc_deu_001376-swc_deu_001376) +OBWOHL HOFFMANN VON HOFFMANNSWALDAUS WERK GROSSEN EINFLUSS AUF SPÄTERE DICHTER AUSÜBTE (swc_deu_001377-swc_deu_001377) +UM SO ERNST ALS STAATSOBERHAUPT VON (swc_deu_001378-swc_deu_001378) +DOKUMENTATION (swc_deu_001379-swc_deu_001379) +GESTALTUNG DES COVERS WIDERSPIEGELT (swc_deu_001380-swc_deu_001380) +DER GESAMTE AUFWAND WIRD AUF (swc_deu_001381-swc_deu_001381) +OBGLEICH HAMBURG DIESEM ANGEHÖRTE UND EINE NOBILITIERUNG DURCH DEN KAISER DAMIT KEINE DURCH (swc_deu_001382-swc_deu_001382) +DA ES DURCH DEN SICH AUSWEITENDEN WELTHANDEL ARBEIT UND WOHLSTAND VERSPRACH (swc_deu_001383-swc_deu_001383) +FÜR DIE ZEIT MITTE DES NEUNZEHNTE JAHRHUNDERTS BEKLAGTE DER ARCHITEKT MARTIN HALLER (swc_deu_001384-swc_deu_001384) +ALTBUNDESKANZLER HELMUT SCHMIDT LEHNTE (swc_deu_001385-swc_deu_001385) +DEN NAMEN GODEFFROY IM STAATSHANDBUCH ZU STREICHEN (swc_deu_001386-swc_deu_001386) +WENN AUCH MIT EINER GEWISSEN LETHARGIE (swc_deu_001387-swc_deu_001387) +KALKULIERBAR (swc_deu_001388-swc_deu_001388) +ANGEFANGENE ZWEI HUNDERT FÜNFZIG SCHÜLER EINEN DELEGIERTEN (swc_deu_001389-swc_deu_001389) +VIELE MENSCHEN SAHEN DEN GRIZZLY ALS NAHRUNGSKONKURRENTEN UND ALS POTENTIELLE GEFAHR (swc_deu_001390-swc_deu_001390) +DEN AUFTRITT VERKÜRZEN (swc_deu_001391-swc_deu_001391) +DEM STAND VOM DER INHALT STEHT UNTER DER LIZENZ CREATIVE COMMONS ATTRIBUTION SHARE ALIKE DREI PUNKT NULL UNPORTED UND UNTER DER (swc_deu_001392-swc_deu_001392) +EINE KLEINERE BOGENBRÜCKE (swc_deu_001393-swc_deu_001393) +SICH NUN FÜR SEIN WEITERKOMMEN (swc_deu_001394-swc_deu_001394) +AUS DEM GEMÄLDE ZU ENTFERNEN (swc_deu_001395-swc_deu_001395) +NACH EUROPÄISCHER RICHTLINIE NEUNZIG VIER HUNDERT SECHSUNDNEUNZIG E W G (swc_deu_001396-swc_deu_001396) +EINEM UMFELD AUF (swc_deu_001397-swc_deu_001397) +UND SIE SEI AUCH WIE EINE FÜRSTIN (swc_deu_001398-swc_deu_001398) +NEUNZEHN HUNDERT NEUNZEHN (swc_deu_001399-swc_deu_001399) +STATTDESSEN HABEN DIE RÖMISCHEN INGENIEURE (swc_deu_001400-swc_deu_001400) +GRIZZLYBÄR UND MENSCH (swc_deu_001401-swc_deu_001401) +MUSICIANS COALITION (swc_deu_001402-swc_deu_001402) +BERTOLD HUMMEL GIBT ES DREI VARIATIONEN MIT (swc_deu_001403-swc_deu_001403) +RÜCKZUGSGEBIET ERWIES SICH DER ACHTZEHN HUNDERT ZWEIUNDSIEBZIG GEGRÜNDETE YELLOWSTONE NATIONALPARK (swc_deu_001404-swc_deu_001404) +DEFINITION (swc_deu_001405-swc_deu_001405) +UM AN DER UNIVERSITÄT SEVILLA ZWEI SEMESTER KUNSTGESCHICHTE ZU STUDIEREN (swc_deu_001406-swc_deu_001406) +TROTZ IHRER GERINGEN (swc_deu_001407-swc_deu_001407) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/result.txt b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..2aaee13135d6e0733a5944ba87134d800decd41e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/result.txt @@ -0,0 +1,2525 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,-----------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/hyp.trn| +|-----------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001201 | 1 11 | 0.0 54.5 45.5 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001202 | 1 5 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001203 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001204 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001205 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001206 | 1 3 | 33.3 66.7 0.0 66.7 133.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001207 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001208 | 1 8 | 12.5 75.0 12.5 0.0 87.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001209 | 1 7 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001210 | 1 9 | 11.1 88.9 0.0 0.0 88.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001211 | 1 3 | 66.7 33.3 0.0 66.7 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001212 | 1 9 | 22.2 66.7 11.1 0.0 77.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001213 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001214 | 1 8 | 0.0 100.0 0.0 12.5 112.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001215 | 1 6 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001216 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001217 | 1 15 | 13.3 53.3 33.3 0.0 86.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001218 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001219 | 1 9 | 22.2 77.8 0.0 0.0 77.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001220 | 1 8 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001221 | 1 10 | 10.0 90.0 0.0 20.0 110.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001222 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001223 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001224 | 1 4 | 50.0 25.0 25.0 0.0 50.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001225 | 1 11 | 27.3 45.5 27.3 0.0 72.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001226 | 1 10 | 20.0 70.0 10.0 0.0 80.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001227 | 1 15 | 26.7 46.7 26.7 0.0 73.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001228 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001229 | 1 2 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001230 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001231 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001232 | 1 5 | 0.0 40.0 60.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001233 | 1 12 | 25.0 66.7 8.3 16.7 91.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001234 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001235 | 1 9 | 11.1 88.9 0.0 0.0 88.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001236 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001237 | 1 6 | 16.7 83.3 0.0 50.0 133.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001238 | 1 9 | 0.0 44.4 55.6 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001239 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001240 | 1 6 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001241 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001242 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001243 | 1 6 | 16.7 33.3 50.0 0.0 83.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001244 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001245 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001246 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001247 | 1 18 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001248 | 1 6 | 33.3 50.0 16.7 0.0 66.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001249 | 1 6 | 16.7 50.0 33.3 0.0 83.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001250 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001251 | 1 15 | 33.3 66.7 0.0 6.7 73.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001252 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001253 | 1 7 | 0.0 71.4 28.6 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001254 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001255 | 1 10 | 30.0 20.0 50.0 0.0 70.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001256 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001257 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001258 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001259 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001260 | 1 9 | 11.1 55.6 33.3 0.0 88.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001261 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001262 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001263 | 1 13 | 30.8 38.5 30.8 0.0 69.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001264 | 1 8 | 0.0 62.5 37.5 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001265 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001266 | 1 6 | 50.0 33.3 16.7 0.0 50.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001267 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001268 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001269 | 1 9 | 33.3 55.6 11.1 0.0 66.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001270 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001271 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001272 | 1 8 | 12.5 50.0 37.5 0.0 87.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001273 | 1 5 | 40.0 60.0 0.0 40.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001274 | 1 10 | 0.0 70.0 30.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001275 | 1 7 | 14.3 71.4 14.3 0.0 85.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001276 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001277 | 1 8 | 0.0 62.5 37.5 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001278 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001279 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001280 | 1 6 | 16.7 50.0 33.3 0.0 83.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001281 | 1 6 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001282 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001283 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001284 | 1 6 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001285 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001286 | 1 9 | 11.1 88.9 0.0 22.2 111.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001287 | 1 10 | 20.0 70.0 10.0 0.0 80.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001288 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001289 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001290 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001291 | 1 5 | 20.0 40.0 40.0 0.0 80.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001292 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001293 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001294 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001295 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001296 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001297 | 1 6 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001298 | 1 9 | 22.2 55.6 22.2 0.0 77.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001299 | 1 5 | 0.0 100.0 0.0 40.0 140.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001300 | 1 15 | 26.7 66.7 6.7 26.7 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001301 | 1 11 | 18.2 45.5 36.4 9.1 90.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001302 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001303 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001304 | 1 6 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001305 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001306 | 1 7 | 0.0 100.0 0.0 28.6 128.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001307 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001308 | 1 12 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001309 | 1 12 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001310 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001311 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001312 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001313 | 1 13 | 15.4 76.9 7.7 15.4 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001314 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001315 | 1 8 | 12.5 75.0 12.5 12.5 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001316 | 1 5 | 20.0 40.0 40.0 0.0 80.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001317 | 1 9 | 11.1 77.8 11.1 0.0 88.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001318 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001319 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001320 | 1 6 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001321 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001322 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001323 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001324 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001325 | 1 8 | 0.0 37.5 62.5 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001326 | 1 8 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001327 | 1 5 | 60.0 20.0 20.0 0.0 40.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001328 | 1 7 | 28.6 71.4 0.0 42.9 114.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001329 | 1 10 | 30.0 40.0 30.0 0.0 70.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001330 | 1 9 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001331 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001332 | 1 7 | 0.0 57.1 42.9 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001333 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001334 | 1 4 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001335 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001336 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001337 | 1 11 | 9.1 54.5 36.4 0.0 90.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001338 | 1 12 | 8.3 58.3 33.3 0.0 91.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001339 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001340 | 1 5 | 60.0 40.0 0.0 20.0 60.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001341 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001342 | 1 8 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001343 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001344 | 1 5 | 0.0 100.0 0.0 40.0 140.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001345 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001346 | 1 9 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001347 | 1 5 | 0.0 40.0 60.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001348 | 1 20 | 15.0 75.0 10.0 15.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001349 | 1 7 | 28.6 71.4 0.0 14.3 85.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001350 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001351 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001352 | 1 7 | 42.9 57.1 0.0 14.3 71.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001353 | 1 14 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001354 | 1 20 | 0.0 70.0 30.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001355 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001356 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001357 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001358 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001359 | 1 16 | 12.5 62.5 25.0 6.3 93.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001360 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001361 | 1 2 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001362 | 1 11 | 18.2 36.4 45.5 0.0 81.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001363 | 1 6 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001364 | 1 7 | 28.6 28.6 42.9 0.0 71.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001365 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001366 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001367 | 1 13 | 7.7 69.2 23.1 0.0 92.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001368 | 1 7 | 28.6 42.9 28.6 14.3 85.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001369 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001370 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001371 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001372 | 1 7 | 0.0 100.0 0.0 42.9 142.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001373 | 1 4 | 25.0 50.0 25.0 25.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001374 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001375 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001376 | 1 6 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001377 | 1 11 | 0.0 90.9 9.1 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001378 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001379 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001380 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001381 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001382 | 1 13 | 15.4 61.5 23.1 0.0 84.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001383 | 1 11 | 36.4 36.4 27.3 0.0 63.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001384 | 1 12 | 16.7 75.0 8.3 0.0 83.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001385 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001386 | 1 7 | 14.3 57.1 28.6 0.0 85.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001387 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001388 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001389 | 1 7 | 0.0 71.4 28.6 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001390 | 1 11 | 0.0 63.6 36.4 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001391 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001392 | 1 21 | 28.6 66.7 4.8 23.8 95.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001393 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001394 | 1 5 | 20.0 20.0 60.0 0.0 80.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001395 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001396 | 1 10 | 0.0 60.0 40.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001397 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001398 | 1 7 | 42.9 57.1 0.0 0.0 57.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001399 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001400 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001401 | 1 3 | 66.7 33.3 0.0 33.3 66.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001402 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001403 | 1 7 | 0.0 71.4 28.6 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001404 | 1 10 | 0.0 70.0 30.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001405 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001406 | 1 10 | 10.0 60.0 30.0 0.0 90.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001407 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|=======================================================================================================================| +| Sum/Avg | 207 1298 | 13.8 68.3 17.9 6.3 92.5 100.0 | +|=======================================================================================================================| +| Mean | 1.0 6.3 | 12.9 72.6 14.5 9.2 96.3 100.0 | +| S.D. | 0.0 3.8 | 16.4 21.7 16.7 21.8 27.0 0.0 | +| Median | 1.0 5.0 | 0.0 71.4 8.3 0.0 100.0 100.0 | +`-----------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,-----------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/hyp.trn| +|-----------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001201 | 1 11 | 0 6 5 0 11 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001202 | 1 5 | 3 2 0 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001203 | 1 4 | 0 3 1 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001204 | 1 3 | 0 3 0 1 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001205 | 1 5 | 0 4 1 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001206 | 1 3 | 1 2 0 2 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001207 | 1 3 | 1 2 0 1 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001208 | 1 8 | 1 6 1 0 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001209 | 1 7 | 0 7 0 0 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001210 | 1 9 | 1 8 0 0 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001211 | 1 3 | 2 1 0 2 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001212 | 1 9 | 2 6 1 0 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001213 | 1 2 | 0 2 0 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001214 | 1 8 | 0 8 0 1 9 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001215 | 1 6 | 0 4 2 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001216 | 1 4 | 1 3 0 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001217 | 1 15 | 2 8 5 0 13 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001218 | 1 3 | 0 3 0 1 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001219 | 1 9 | 2 7 0 0 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001220 | 1 8 | 2 4 2 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001221 | 1 10 | 1 9 0 2 11 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001222 | 1 3 | 0 3 0 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001223 | 1 5 | 0 4 1 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001224 | 1 4 | 2 1 1 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001225 | 1 11 | 3 5 3 0 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001226 | 1 10 | 2 7 1 0 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001227 | 1 15 | 4 7 4 0 11 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001228 | 1 1 | 0 1 0 0 1 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001229 | 1 2 | 0 2 0 2 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001230 | 1 4 | 1 3 0 1 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001231 | 1 3 | 0 3 0 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001232 | 1 5 | 0 2 3 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001233 | 1 12 | 3 8 1 2 11 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001234 | 1 2 | 0 2 0 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001235 | 1 9 | 1 8 0 0 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001236 | 1 4 | 1 3 0 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001237 | 1 6 | 1 5 0 3 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001238 | 1 9 | 0 4 5 0 9 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001239 | 1 4 | 0 3 1 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001240 | 1 6 | 2 4 0 2 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001241 | 1 2 | 0 2 0 1 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001242 | 1 7 | 1 6 0 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001243 | 1 6 | 1 2 3 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001244 | 1 2 | 0 2 0 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001245 | 1 5 | 1 3 1 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001246 | 1 4 | 1 3 0 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001247 | 1 18 | 0 15 3 0 18 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001248 | 1 6 | 2 3 1 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001249 | 1 6 | 1 3 2 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001250 | 1 4 | 1 2 1 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001251 | 1 15 | 5 10 0 1 11 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001252 | 1 2 | 0 2 0 1 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001253 | 1 7 | 0 5 2 0 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001254 | 1 2 | 0 2 0 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001255 | 1 10 | 3 2 5 0 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001256 | 1 3 | 0 3 0 1 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001257 | 1 3 | 0 3 0 1 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001258 | 1 3 | 0 3 0 1 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001259 | 1 2 | 0 2 0 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001260 | 1 9 | 1 5 3 0 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001261 | 1 4 | 0 4 0 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001262 | 1 4 | 1 2 1 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001263 | 1 13 | 4 5 4 0 9 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001264 | 1 8 | 0 5 3 0 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001265 | 1 1 | 0 1 0 0 1 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001266 | 1 6 | 3 2 1 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001267 | 1 4 | 0 4 0 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001268 | 1 3 | 1 2 0 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001269 | 1 9 | 3 5 1 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001270 | 1 4 | 0 4 0 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001271 | 1 3 | 0 3 0 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001272 | 1 8 | 1 4 3 0 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001273 | 1 5 | 2 3 0 2 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001274 | 1 10 | 0 7 3 0 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001275 | 1 7 | 1 5 1 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001276 | 1 4 | 0 3 1 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001277 | 1 8 | 0 5 3 0 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001278 | 1 4 | 0 3 1 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001279 | 1 3 | 1 2 0 1 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001280 | 1 6 | 1 3 2 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001281 | 1 6 | 3 3 0 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001282 | 1 4 | 1 3 0 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001283 | 1 3 | 0 3 0 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001284 | 1 6 | 0 6 0 2 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001285 | 1 5 | 2 3 0 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001286 | 1 9 | 1 8 0 2 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001287 | 1 10 | 2 7 1 0 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001288 | 1 4 | 0 4 0 1 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001289 | 1 4 | 1 2 1 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001290 | 1 4 | 0 4 0 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001291 | 1 5 | 1 2 2 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001292 | 1 7 | 1 6 0 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001293 | 1 3 | 0 3 0 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001294 | 1 5 | 0 4 1 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001295 | 1 5 | 0 5 0 1 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001296 | 1 6 | 1 4 1 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001297 | 1 6 | 0 4 2 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001298 | 1 9 | 2 5 2 0 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001299 | 1 5 | 0 5 0 2 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001300 | 1 15 | 4 10 1 4 15 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001301 | 1 11 | 2 5 4 1 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001302 | 1 6 | 0 6 0 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001303 | 1 6 | 2 4 0 1 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001304 | 1 6 | 0 4 2 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001305 | 1 6 | 0 5 1 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001306 | 1 7 | 0 7 0 2 9 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001307 | 1 2 | 0 2 0 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001308 | 1 12 | 0 9 3 0 12 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001309 | 1 12 | 2 8 2 0 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001310 | 1 6 | 1 4 1 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001311 | 1 5 | 0 4 1 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001312 | 1 4 | 0 4 0 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001313 | 1 13 | 2 10 1 2 13 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001314 | 1 4 | 0 3 1 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001315 | 1 8 | 1 6 1 1 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001316 | 1 5 | 1 2 2 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001317 | 1 9 | 1 7 1 0 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001318 | 1 3 | 0 3 0 1 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001319 | 1 3 | 0 2 1 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001320 | 1 6 | 0 4 2 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001321 | 1 2 | 0 2 0 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001322 | 1 3 | 2 1 0 0 1 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001323 | 1 5 | 1 3 1 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001324 | 1 5 | 1 4 0 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001325 | 1 8 | 0 3 5 0 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001326 | 1 8 | 0 8 0 0 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001327 | 1 5 | 3 1 1 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001328 | 1 7 | 2 5 0 3 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001329 | 1 10 | 3 4 3 0 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001330 | 1 9 | 0 6 3 0 9 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001331 | 1 5 | 2 3 0 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001332 | 1 7 | 0 4 3 0 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001333 | 1 4 | 0 4 0 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001334 | 1 4 | 0 2 2 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001335 | 1 5 | 0 4 1 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001336 | 1 4 | 2 2 0 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001337 | 1 11 | 1 6 4 0 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001338 | 1 12 | 1 7 4 0 11 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001339 | 1 5 | 1 4 0 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001340 | 1 5 | 3 2 0 1 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001341 | 1 3 | 0 2 1 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001342 | 1 8 | 0 6 2 0 8 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001343 | 1 2 | 0 2 0 1 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001344 | 1 5 | 0 5 0 2 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001345 | 1 5 | 1 4 0 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001346 | 1 9 | 0 6 3 0 9 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001347 | 1 5 | 0 2 3 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001348 | 1 20 | 3 15 2 3 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001349 | 1 7 | 2 5 0 1 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001350 | 1 6 | 0 5 1 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001351 | 1 2 | 0 2 0 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001352 | 1 7 | 3 4 0 1 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001353 | 1 14 | 0 7 7 0 14 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001354 | 1 20 | 0 14 6 0 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001355 | 1 4 | 0 3 1 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001356 | 1 3 | 0 3 0 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001357 | 1 2 | 0 2 0 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001358 | 1 2 | 0 2 0 1 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001359 | 1 16 | 2 10 4 1 15 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001360 | 1 4 | 0 4 0 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001361 | 1 2 | 0 1 1 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001362 | 1 11 | 2 4 5 0 9 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001363 | 1 6 | 3 3 0 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001364 | 1 7 | 2 2 3 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001365 | 1 4 | 1 2 1 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001366 | 1 5 | 1 3 1 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001367 | 1 13 | 1 9 3 0 12 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001368 | 1 7 | 2 3 2 1 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001369 | 1 2 | 0 2 0 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001370 | 1 6 | 1 5 0 1 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001371 | 1 4 | 0 4 0 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001372 | 1 7 | 0 7 0 3 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001373 | 1 4 | 1 2 1 1 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001374 | 1 5 | 0 4 1 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001375 | 1 6 | 0 5 1 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001376 | 1 6 | 0 3 3 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001377 | 1 11 | 0 10 1 0 11 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001378 | 1 6 | 0 5 1 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001379 | 1 1 | 0 1 0 1 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001380 | 1 4 | 0 4 0 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001381 | 1 5 | 1 3 1 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001382 | 1 13 | 2 8 3 0 11 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001383 | 1 11 | 4 4 3 0 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001384 | 1 12 | 2 9 1 0 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001385 | 1 4 | 0 4 0 1 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001386 | 1 7 | 1 4 2 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001387 | 1 6 | 0 5 1 0 6 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001388 | 1 1 | 0 1 0 2 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001389 | 1 7 | 0 5 2 0 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001390 | 1 11 | 0 7 4 0 11 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001391 | 1 3 | 0 2 1 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001392 | 1 21 | 6 14 1 5 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001393 | 1 3 | 0 3 0 0 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001394 | 1 5 | 1 1 3 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001395 | 1 5 | 1 4 0 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001396 | 1 10 | 0 6 4 0 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001397 | 1 3 | 1 2 0 0 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001398 | 1 7 | 3 4 0 0 4 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001399 | 1 3 | 1 2 0 1 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001400 | 1 5 | 0 5 0 0 5 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001401 | 1 3 | 2 1 0 1 2 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001402 | 1 2 | 0 2 0 1 3 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001403 | 1 7 | 0 5 2 0 7 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001404 | 1 10 | 0 7 3 0 10 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001405 | 1 1 | 0 1 0 0 1 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001406 | 1 10 | 1 6 3 0 9 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| swc_deu_001407 | 1 3 | 1 2 0 1 3 1 | +|=======================================================================================================================| +| Sum | 207 1298 | 179 887 232 82 1201 207 | +|=======================================================================================================================| +| Mean | 1.0 6.3 | 0.9 4.3 1.1 0.4 5.8 1.0 | +| S.D. | 0.0 3.8 | 1.1 2.6 1.5 0.8 3.5 0.0 | +| Median | 1.0 5.0 | 0.0 4.0 1.0 0.0 5.0 1.0 | +`-----------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/hyp.trn + +Speakers: + 0: swc_deu_001201 + 1: swc_deu_001202 + 2: swc_deu_001203 + 3: swc_deu_001204 + 4: swc_deu_001205 + 5: swc_deu_001206 + 6: swc_deu_001207 + 7: swc_deu_001208 + 8: swc_deu_001209 + 9: swc_deu_001210 + 10: swc_deu_001211 + 11: swc_deu_001212 + 12: swc_deu_001213 + 13: swc_deu_001214 + 14: swc_deu_001215 + 15: swc_deu_001216 + 16: swc_deu_001217 + 17: swc_deu_001218 + 18: swc_deu_001219 + 19: swc_deu_001220 + 20: swc_deu_001221 + 21: swc_deu_001222 + 22: swc_deu_001223 + 23: swc_deu_001224 + 24: swc_deu_001225 + 25: swc_deu_001226 + 26: swc_deu_001227 + 27: swc_deu_001228 + 28: swc_deu_001229 + 29: swc_deu_001230 + 30: swc_deu_001231 + 31: swc_deu_001232 + 32: swc_deu_001233 + 33: swc_deu_001234 + 34: swc_deu_001235 + 35: swc_deu_001236 + 36: swc_deu_001237 + 37: swc_deu_001238 + 38: swc_deu_001239 + 39: swc_deu_001240 + 40: swc_deu_001241 + 41: swc_deu_001242 + 42: swc_deu_001243 + 43: swc_deu_001244 + 44: swc_deu_001245 + 45: swc_deu_001246 + 46: swc_deu_001247 + 47: swc_deu_001248 + 48: swc_deu_001249 + 49: swc_deu_001250 + 50: swc_deu_001251 + 51: swc_deu_001252 + 52: swc_deu_001253 + 53: swc_deu_001254 + 54: swc_deu_001255 + 55: swc_deu_001256 + 56: swc_deu_001257 + 57: swc_deu_001258 + 58: swc_deu_001259 + 59: swc_deu_001260 + 60: swc_deu_001261 + 61: swc_deu_001262 + 62: swc_deu_001263 + 63: swc_deu_001264 + 64: swc_deu_001265 + 65: swc_deu_001266 + 66: swc_deu_001267 + 67: swc_deu_001268 + 68: swc_deu_001269 + 69: swc_deu_001270 + 70: swc_deu_001271 + 71: swc_deu_001272 + 72: swc_deu_001273 + 73: swc_deu_001274 + 74: swc_deu_001275 + 75: swc_deu_001276 + 76: swc_deu_001277 + 77: swc_deu_001278 + 78: swc_deu_001279 + 79: swc_deu_001280 + 80: swc_deu_001281 + 81: swc_deu_001282 + 82: swc_deu_001283 + 83: swc_deu_001284 + 84: swc_deu_001285 + 85: swc_deu_001286 + 86: swc_deu_001287 + 87: swc_deu_001288 + 88: swc_deu_001289 + 89: swc_deu_001290 + 90: swc_deu_001291 + 91: swc_deu_001292 + 92: swc_deu_001293 + 93: swc_deu_001294 + 94: swc_deu_001295 + 95: swc_deu_001296 + 96: swc_deu_001297 + 97: swc_deu_001298 + 98: swc_deu_001299 + 99: swc_deu_001300 + 100: swc_deu_001301 + 101: swc_deu_001302 + 102: swc_deu_001303 + 103: swc_deu_001304 + 104: swc_deu_001305 + 105: swc_deu_001306 + 106: swc_deu_001307 + 107: swc_deu_001308 + 108: swc_deu_001309 + 109: swc_deu_001310 + 110: swc_deu_001311 + 111: swc_deu_001312 + 112: swc_deu_001313 + 113: swc_deu_001314 + 114: swc_deu_001315 + 115: swc_deu_001316 + 116: swc_deu_001317 + 117: swc_deu_001318 + 118: swc_deu_001319 + 119: swc_deu_001320 + 120: swc_deu_001321 + 121: swc_deu_001322 + 122: swc_deu_001323 + 123: swc_deu_001324 + 124: swc_deu_001325 + 125: swc_deu_001326 + 126: swc_deu_001327 + 127: swc_deu_001328 + 128: swc_deu_001329 + 129: swc_deu_001330 + 130: swc_deu_001331 + 131: swc_deu_001332 + 132: swc_deu_001333 + 133: swc_deu_001334 + 134: swc_deu_001335 + 135: swc_deu_001336 + 136: swc_deu_001337 + 137: swc_deu_001338 + 138: swc_deu_001339 + 139: swc_deu_001340 + 140: swc_deu_001341 + 141: swc_deu_001342 + 142: swc_deu_001343 + 143: swc_deu_001344 + 144: swc_deu_001345 + 145: swc_deu_001346 + 146: swc_deu_001347 + 147: swc_deu_001348 + 148: swc_deu_001349 + 149: swc_deu_001350 + 150: swc_deu_001351 + 151: swc_deu_001352 + 152: swc_deu_001353 + 153: swc_deu_001354 + 154: swc_deu_001355 + 155: swc_deu_001356 + 156: swc_deu_001357 + 157: swc_deu_001358 + 158: swc_deu_001359 + 159: swc_deu_001360 + 160: swc_deu_001361 + 161: swc_deu_001362 + 162: swc_deu_001363 + 163: swc_deu_001364 + 164: swc_deu_001365 + 165: swc_deu_001366 + 166: swc_deu_001367 + 167: swc_deu_001368 + 168: swc_deu_001369 + 169: swc_deu_001370 + 170: swc_deu_001371 + 171: swc_deu_001372 + 172: swc_deu_001373 + 173: swc_deu_001374 + 174: swc_deu_001375 + 175: swc_deu_001376 + 176: swc_deu_001377 + 177: swc_deu_001378 + 178: swc_deu_001379 + 179: swc_deu_001380 + 180: swc_deu_001381 + 181: swc_deu_001382 + 182: swc_deu_001383 + 183: swc_deu_001384 + 184: swc_deu_001385 + 185: swc_deu_001386 + 186: swc_deu_001387 + 187: swc_deu_001388 + 188: swc_deu_001389 + 189: swc_deu_001390 + 190: swc_deu_001391 + 191: swc_deu_001392 + 192: swc_deu_001393 + 193: swc_deu_001394 + 194: swc_deu_001395 + 195: swc_deu_001396 + 196: swc_deu_001397 + 197: swc_deu_001398 + 198: swc_deu_001399 + 199: swc_deu_001400 + 200: swc_deu_001401 + 201: swc_deu_001402 + 202: swc_deu_001403 + 203: swc_deu_001404 + 204: swc_deu_001405 + 205: swc_deu_001406 + 206: swc_deu_001407 + +Speaker sentences 0: swc_deu_001201 #utts: 1 +id: (swc_deu_001201-swc_deu_001201) +Scores: (#C #S #D #I) 0 6 5 0 +REF: DER VERLIEBTE JUNGE HERZOG DIE RATSCHLÄGE SEINES VATERS NICHT BEACHTET HABE +HYP: *** ********* ***** ****** *** DRERVELIEBTE UNGEHEARZOGK DE RANSCHLE GESEINSFATESNICH BERACHTDITHA +Eval: D D D D D S S S S S S + +Speaker sentences 1: swc_deu_001202 #utts: 1 +id: (swc_deu_001202-swc_deu_001202) +Scores: (#C #S #D #I) 3 2 0 0 +REF: die in DEN HANSESTÄDTEN als +HYP: die in DE HANZESTEÄTEN als +Eval: S S + +Speaker sentences 2: swc_deu_001203 #utts: 1 +id: (swc_deu_001203-swc_deu_001203) +Scores: (#C #S #D #I) 0 3 1 0 +REF: WAR KEIN GROSSER ERFOLG +HYP: *** ARKEINGROSE E FRELK +Eval: D S S S + +Speaker sentences 3: swc_deu_001204 #utts: 1 +id: (swc_deu_001204-swc_deu_001204) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ***** GROSSEN CHEMISCHEN FABRIKEN +HYP: GOSEN SCHEHMISCHIN VER BRIKTE +Eval: I S S S + +Speaker sentences 4: swc_deu_001205 #utts: 1 +id: (swc_deu_001205-swc_deu_001205) +Scores: (#C #S #D #I) 0 4 1 0 +REF: WURDEN AUCH MEHRERE ERLÄUTERUNGSBÜCHER VERÖFFENTLICHT +HYP: ****** WODEN ACH MERERE ALOEUTRUNGSBÜSCHEVERFNTLIH +Eval: D S S S S + +Speaker sentences 5: swc_deu_001206 #utts: 1 +id: (swc_deu_001206-swc_deu_001206) +Scores: (#C #S #D #I) 1 2 0 2 +REF: vorbereiteten **** ***** BIERTEIG GETUNKT +HYP: vorbereiteten BIER TEICG GE TUNKT +Eval: I I S S + +Speaker sentences 6: swc_deu_001207 #utts: 1 +id: (swc_deu_001207-swc_deu_001207) +Scores: (#C #S #D #I) 1 2 0 1 +REF: DOKUMENTE SCHLIESSLICH in * +HYP: DOKOMNTE SHIESIG in I +Eval: S S I + +Speaker sentences 7: swc_deu_001208 #utts: 1 +id: (swc_deu_001208-swc_deu_001208) +Scores: (#C #S #D #I) 1 6 1 0 +REF: TRAUERTAG FÜR DEN TOD von KÖNIG FRIEDRICH WILHELM +HYP: ********* TAURTAG VÜRDEN TUT von KNICH VRECHL E +Eval: D S S S S S S + +Speaker sentences 8: swc_deu_001209 #utts: 1 +id: (swc_deu_001209-swc_deu_001209) +Scores: (#C #S #D #I) 0 7 0 0 +REF: DARUNTER SIND MATILDE ASENSIS WÄCHTER DES KREUZES +HYP: DARUNDER SIN MATÜLDE ARSENDIS WECHTER DS KROLTZIS +Eval: S S S S S S S + +Speaker sentences 9: swc_deu_001210 #utts: 1 +id: (swc_deu_001210-swc_deu_001210) +Scores: (#C #S #D #I) 1 8 0 0 +REF: INNENSTÄDTEN MEHR UND mehr DIE ROLLE DER TRADITIONELLEN FISH +HYP: IN INEN SHETEN mehr UND MHR DE ROLEDERTHADTITZSNERLN FIS +Eval: S S S S S S S S + +Speaker sentences 10: swc_deu_001211 #utts: 1 +id: (swc_deu_001211-swc_deu_001211) +Scores: (#C #S #D #I) 2 1 0 2 +REF: zu denen *** *** WELTLÄUFIGKEIT +HYP: zu denen MET LOU VIGKEIT +Eval: I I S + +Speaker sentences 11: swc_deu_001212 #utts: 1 +id: (swc_deu_001212-swc_deu_001212) +Scores: (#C #S #D #I) 2 6 1 0 +REF: RACHE DES HOFES UND des ADELS fÜr DEN FREVEL +HYP: ***** DRACHE DISHOFESS UN des ADETZ fÜr N FRFEL +Eval: D S S S S S S + +Speaker sentences 12: swc_deu_001213 #utts: 1 +id: (swc_deu_001213-swc_deu_001213) +Scores: (#C #S #D #I) 0 2 0 0 +REF: ZEITANGABEN VERZICHTETE +HYP: ZSEIT ANGABEMVERSICHTID +Eval: S S + +Speaker sentences 13: swc_deu_001214 #utts: 1 +id: (swc_deu_001214-swc_deu_001214) +Scores: (#C #S #D #I) 0 8 0 1 +REF: **** ALS ACHTZEHN HUNDERT ACHTZIG MIT OTTO BRAHMS AUFSATZ +HYP: ALLS ACHTIN UNDET ACHRT ZIGHMIT ODTUO BRAMS AUF SATZS +Eval: I S S S S S S S S + +Speaker sentences 14: swc_deu_001215 #utts: 1 +id: (swc_deu_001215-swc_deu_001215) +Scores: (#C #S #D #I) 0 4 2 0 +REF: EIN TAUSEND SIEBEN HUNDERT ACHTUNDZWANZIG – +HYP: *** ******* MÜLENWESEN IETZIN UNERDACHUN WANI +Eval: D D S S S S + +Speaker sentences 15: swc_deu_001216 #utts: 1 +id: (swc_deu_001216-swc_deu_001216) +Scores: (#C #S #D #I) 1 3 0 0 +REF: DASS DER fisch FRISCH +HYP: AS DE fisch RIS +Eval: S S S + +Speaker sentences 16: swc_deu_001217 #utts: 1 +id: (swc_deu_001217-swc_deu_001217) +Scores: (#C #S #D #I) 2 8 5 0 +REF: SEINEM ABSCHLUSS im JAHRE NEUNZEHN HUNDERT ZWEIUNDACHTZIG UNTERNAHM ER eine ERSTE LÄNGERE REISE NACH SPANIEN +HYP: SIDIM ABSHLOS im ***** JARENEUNZEN HNDER ZWALUN ACHTZICH UNDRNAMER eine ***** ******** ***** **** ERSTELENGEREREISENACSPBANEN +Eval: S S D S S S S S D D D D S + +Speaker sentences 17: swc_deu_001218 #utts: 1 +id: (swc_deu_001218-swc_deu_001218) +Scores: (#C #S #D #I) 0 3 0 1 +REF: **** VON CHASÔT VORGEZEICHNET +HYP: VERN SCAT SO VURGETZEICHNET +Eval: I S S S + +Speaker sentences 18: swc_deu_001219 #utts: 1 +id: (swc_deu_001219-swc_deu_001219) +Scores: (#C #S #D #I) 2 7 0 0 +REF: FALCKENSTEINS VOLLSTÄNDIGE GESCHICHTEN UND die AUGSBURGER STADTGESCHICHTE des ÄLTEREN +HYP: FEITEN STEINS VELSTENDIGGECHICHTEN UNDT die AUKSBURGERSTAGSCHIH E des ELTRE +Eval: S S S S S S S + +Speaker sentences 19: swc_deu_001220 #utts: 1 +id: (swc_deu_001220-swc_deu_001220) +Scores: (#C #S #D #I) 2 4 2 0 +REF: nach DIESEN ZERSTÖRUNGEN wurde DIE RASCH WIEDER AUFBLÜHENDE +HYP: nach ****** DIENZERSTÖRONMEN wurde *** DERASCH WIDER AUFPLÜN +Eval: D S D S S S + +Speaker sentences 20: swc_deu_001221 #utts: 1 +id: (swc_deu_001221-swc_deu_001221) +Scores: (#C #S #D #I) 1 9 0 2 +REF: ****** MACHTEN EINFLUSSREICHEN HANSEATEN beim ***** KOMMISSARISCH EINGESETZTEN BÜRGERMEISTER MARKERT IHRE AUFWARTUNG +HYP: ACHTEN EINFLUSREICHENHAN IE ATEN beim KOMIE SARESCHEINGESETZ N BÜÖRGEMEISTER MAKERT IER AUFAHTUN +Eval: I S S S I S S S S S S + +Speaker sentences 21: swc_deu_001222 #utts: 1 +id: (swc_deu_001222-swc_deu_001222) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ALS ZENTRALES HANDELSKONTOR +HYP: AILS ENTRALDESHANDES KONTOR +Eval: S S S + +Speaker sentences 22: swc_deu_001223 #utts: 1 +id: (swc_deu_001223-swc_deu_001223) +Scores: (#C #S #D #I) 0 4 1 0 +REF: SONDERSTELLUNG INNERHALB DER STADT KREFELD +HYP: ************** SANDERSTELUNG INERHEIB DECSTAT KREFE +Eval: D S S S S + +Speaker sentences 23: swc_deu_001224 #utts: 1 +id: (swc_deu_001224-swc_deu_001224) +Scores: (#C #S #D #I) 2 1 1 0 +REF: FINDET SICH in halobakterien +HYP: ****** VFINEZSICH in halobakterien +Eval: D S + +Speaker sentences 24: swc_deu_001225 #utts: 1 +id: (swc_deu_001225-swc_deu_001225) +Scores: (#C #S #D #I) 3 5 3 0 +REF: auf der B SEITE FINDET SICH DAS EBENFALLS von MICHAEL KOMPONIERTE +HYP: auf der * ***** ****** BESEITE FINDEZSIHTAS EBEMFALS von MEIKEL KOMPUNIET +Eval: D D D S S S S S + +Speaker sentences 25: swc_deu_001226 #utts: 1 +id: (swc_deu_001226-swc_deu_001226) +Scores: (#C #S #D #I) 2 7 1 0 +REF: in HANSEATISCHER ZEIT HATTE DIE ZIRKELGESELLSCHAFT keinen AUSSCHLAGGEBENDEN EINFLUSS MEHR +HYP: in HN DEATICHER ZEITHATDI DI ZERKEGESERSCHFT keinen ***************** AUSCHLAGEBENTEN EINFLOSMHR +Eval: S S S S S D S S + +Speaker sentences 26: swc_deu_001227 #utts: 1 +id: (swc_deu_001227-swc_deu_001227) +Scores: (#C #S #D #I) 4 7 4 0 +REF: da ES DURCH VERWENDUNG von AUFTRIEBSKÖRPERN ODER HOLZ EINE geringere MITTLERE DICHTE ALS WASSER hat +HYP: da ** ***** STDERCHVRWENDUN von AUFTRIEB SKAPAN ODERHALZS EINER geringere ******** ****** MITTERERDICHTDE ALSWASER hat +Eval: D D S S S S S D D S S + +Speaker sentences 27: swc_deu_001228 #utts: 1 +id: (swc_deu_001228-swc_deu_001228) +Scores: (#C #S #D #I) 0 1 0 0 +REF: DRAMATISIERUNGEN +HYP: DAMATESIERUNGEN +Eval: S + +Speaker sentences 28: swc_deu_001229 #utts: 1 +id: (swc_deu_001229-swc_deu_001229) +Scores: (#C #S #D #I) 0 2 0 2 +REF: *** ****** UM 755 +HYP: UMN SIEBEN UR FÜNVO +Eval: I I S S + +Speaker sentences 29: swc_deu_001230 #utts: 1 +id: (swc_deu_001230-swc_deu_001230) +Scores: (#C #S #D #I) 1 3 0 1 +REF: DASS ALBRECHT die ***** BADERSTOCHTER +HYP: DES ELBREICHT die BADES TOCHTE +Eval: S S I S + +Speaker sentences 30: swc_deu_001231 #utts: 1 +id: (swc_deu_001231-swc_deu_001231) +Scores: (#C #S #D #I) 0 3 0 0 +REF: THAT BARBARISCHER STAATSRAISON +HYP: TART BABMBARERSCHE STDATZR +Eval: S S S + +Speaker sentences 31: swc_deu_001232 #utts: 1 +id: (swc_deu_001232-swc_deu_001232) +Scores: (#C #S #D #I) 0 2 3 0 +REF: DER DAS LIED BESONDERS LIEBTE +HYP: *** *** **** ERSLIT BESONDERSLIEBTE +Eval: D D D S S + +Speaker sentences 32: swc_deu_001233 #utts: 1 +id: (swc_deu_001233-swc_deu_001233) +Scores: (#C #S #D #I) 3 8 1 2 +REF: ******* AUFGRUND DES WACHSENDEN PUBLIKUMSINTERESSES wurde der ******** AUFTRITTSORT FÜR DIE prima VISTA LESUNGEN +HYP: AUFKUND DERS WACSENDEN PUPLIKUMS INTRESSES wurde der AUFTRITS ORT Ü DI prima ***** VISTALESUNGE +Eval: I S S S S I S S S D S + +Speaker sentences 33: swc_deu_001234 #utts: 1 +id: (swc_deu_001234-swc_deu_001234) +Scores: (#C #S #D #I) 0 2 0 0 +REF: UND FREILICHTSPIELE +HYP: ND FREITECHTSBIE +Eval: S S + +Speaker sentences 34: swc_deu_001235 #utts: 1 +id: (swc_deu_001235-swc_deu_001235) +Scores: (#C #S #D #I) 1 8 0 0 +REF: DASS die DREI DEN STURZ RELATIV UNBESCHADET ÜBERSTANDEN HATTEN +HYP: DAS die REIDEN STÖRZS RLTI UN BESCHARTET BERSTANDTEN HATE +Eval: S S S S S S S S + +Speaker sentences 35: swc_deu_001236 #utts: 1 +id: (swc_deu_001236-swc_deu_001236) +Scores: (#C #S #D #I) 1 3 0 0 +REF: JAHREN ERSCHIENEN zwei IMMER +HYP: EAREN ERSCHNEN zwei IMRL +Eval: S S S + +Speaker sentences 36: swc_deu_001237 #utts: 1 +id: (swc_deu_001237-swc_deu_001237) +Scores: (#C #S #D #I) 1 5 0 3 +REF: GRABMALE und **** ** ***** GRABKAPELLEN ODER WOHLTATEN NACHHALTIG +HYP: DERGRABMAN und GRAB GA PELEN O DER OLTATEN NACHALT +Eval: S I I I S S S S + +Speaker sentences 37: swc_deu_001238 #utts: 1 +id: (swc_deu_001238-swc_deu_001238) +Scores: (#C #S #D #I) 0 4 5 0 +REF: JUNI NEUNZEHN HUNDERT SECHSUNDNEUNZIG KÜNDIGTE ER SEINE BEIDEN JOBS +HYP: **** ******** ******* *************** ********* IUNENENZENHUNDARSEXSUNEUINZIH KNDIKDER RSEINEBEITEN SOBPS +Eval: D D D D D S S S S + +Speaker sentences 38: swc_deu_001239 #utts: 1 +id: (swc_deu_001239-swc_deu_001239) +Scores: (#C #S #D #I) 0 3 1 0 +REF: EIN G PROTEIN GEKOPPELT +HYP: *** IN GE POTIENEKOPEL +Eval: D S S S + +Speaker sentences 39: swc_deu_001240 #utts: 1 +id: (swc_deu_001240-swc_deu_001240) +Scores: (#C #S #D #I) 2 4 0 2 +REF: ****** NEUNUNDSECHZIG der ** MEDIA CONTROL ALBUMCHARTS ein +HYP: NEUNUN SECHTZIG der ME DIER KONTWOL ALBUMSCHATZ ein +Eval: I S I S S S + +Speaker sentences 40: swc_deu_001241 #utts: 1 +id: (swc_deu_001241-swc_deu_001241) +Scores: (#C #S #D #I) 0 2 0 1 +REF: * DADURCH KOMMT +HYP: M TARUSC KOM +Eval: I S S + +Speaker sentences 41: swc_deu_001242 #utts: 1 +id: (swc_deu_001242-swc_deu_001242) +Scores: (#C #S #D #I) 1 6 0 0 +REF: OHNEHIN NICHT den GROSSHANDELSKAUFLEUTEN GESELLSCHAFTLICH GLEICHGESTELLT WAREN +HYP: UONE HENICHT den KROSSHANDE KAUFLEUTEN GESERSHAFTLICGKLEICHGESTERT WARE +Eval: S S S S S S + +Speaker sentences 42: swc_deu_001243 #utts: 1 +id: (swc_deu_001243-swc_deu_001243) +Scores: (#C #S #D #I) 1 2 3 0 +REF: VON DER NAHRUNG und VOM KLIMA +HYP: *** *** VRNDERNARUNG und *** VOMKIEMA +Eval: D D S D S + +Speaker sentences 43: swc_deu_001244 #utts: 1 +id: (swc_deu_001244-swc_deu_001244) +Scores: (#C #S #D #I) 0 2 0 0 +REF: APOLLO EINS +HYP: APRLOE EIENZS +Eval: S S + +Speaker sentences 44: swc_deu_001245 #utts: 1 +id: (swc_deu_001245-swc_deu_001245) +Scores: (#C #S #D #I) 1 3 1 0 +REF: BRÜHL und HÜRTH NACH KÖLN +HYP: BRÜÖÜL und ****** HÖERT NACKELE +Eval: S D S S + +Speaker sentences 45: swc_deu_001246 #utts: 1 +id: (swc_deu_001246-swc_deu_001246) +Scores: (#C #S #D #I) 1 3 0 0 +REF: ETWA in EIN KLOSTER +HYP: TWER in EN KLOSTE +Eval: S S S + +Speaker sentences 46: swc_deu_001247 #utts: 1 +id: (swc_deu_001247-swc_deu_001247) +Scores: (#C #S #D #I) 0 15 3 0 +REF: ZUM OFFIZIELLEN KARNEVAL ENTSTAND UND HEUTE EINE MISCHUNG AUS KÖLSCHEM KARNEVAL UND POLITISCHEM KABARETT MIT COMEDYELEMENTEN DARSTELLT UND +HYP: *** *********** ******** DIEVIRZSM AUFIZEHREN KANEWALENSTANT UNTREUTE INEMISCHNG US KÖRSCHEN KANDEWAL UNDBPLIDSCHE KABRET MT KOM DELEMENTEN DASTELT U +Eval: D D D S S S S S S S S S S S S S S S + +Speaker sentences 47: swc_deu_001248 #utts: 1 +id: (swc_deu_001248-swc_deu_001248) +Scores: (#C #S #D #I) 2 3 1 0 +REF: die ZUR ENTSTEHUNG des LIEDES FÜHRTEN +HYP: die WUNSTIEN RESLE des ****** FÜIRTE +Eval: S S D S + +Speaker sentences 48: swc_deu_001249 #utts: 1 +id: (swc_deu_001249-swc_deu_001249) +Scores: (#C #S #D #I) 1 3 2 0 +REF: NANNTE ziegler DIE ERMORDUNG DER BERNAUERIN +HYP: NANTIT ziegler *** ********* DIEARMORDUM DERBANAURN +Eval: S D D S S + +Speaker sentences 49: swc_deu_001250 #utts: 1 +id: (swc_deu_001250-swc_deu_001250) +Scores: (#C #S #D #I) 1 2 1 0 +REF: WINTERRUHE ist VOR ALLEM +HYP: INTEROHR ist *** VOEL +Eval: S D S + +Speaker sentences 50: swc_deu_001251 #utts: 1 +id: (swc_deu_001251-swc_deu_001251) +Scores: (#C #S #D #I) 5 10 0 1 +REF: die STRÄNGE der VORGÄNGERLEITUNG wurden ZWISCHEN NEUNZEHN hundert NEUNUNDZWANZIG und ******* NEUNZEHN HUNDERT DREIUNDFÜNFZIG ARCHÄOLOGISCH ERGRABEN +HYP: die STRENG der VORGENGERLEITUNG wurden ZWISHE EUNZHN hundert NEUNUENDZWANZIG und NEUNZHN HUNDER DREINDFÜNFZIG ARCHE LOGESCH ERKRABEN +Eval: S S S S S I S S S S S + +Speaker sentences 51: swc_deu_001252 #utts: 1 +id: (swc_deu_001252-swc_deu_001252) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ** IM GEGENSATZ +HYP: IN GEGE SAT +Eval: I S S + +Speaker sentences 52: swc_deu_001253 #utts: 1 +id: (swc_deu_001253-swc_deu_001253) +Scores: (#C #S #D #I) 0 5 2 0 +REF: FARBEN VON UERDINGEN SIND BLAU UND ROT +HYP: ****** *** FABE VERN ERDIGENSEN LAUND HORT +Eval: D D S S S S S + +Speaker sentences 53: swc_deu_001254 #utts: 1 +id: (swc_deu_001254-swc_deu_001254) +Scores: (#C #S #D #I) 0 2 0 0 +REF: LIVE VERANSTALTUNGEN +HYP: LEIF VERANSTALTUMEN +Eval: S S + +Speaker sentences 54: swc_deu_001255 #utts: 1 +id: (swc_deu_001255-swc_deu_001255) +Scores: (#C #S #D #I) 3 2 5 0 +REF: SO WERDEN HEUTE IN DER REGEL alle dort lebenden BRAUNBÄREN +HYP: ** ****** ***** ** *** ZOWERENHUTEINDEREGEL alle dort lebenden BRAUN +Eval: D D D D D S S + +Speaker sentences 55: swc_deu_001256 #utts: 1 +id: (swc_deu_001256-swc_deu_001256) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** LIEDER FÜR REVUEFILME +HYP: IE DA FÜRE USCSELMER +Eval: I S S S + +Speaker sentences 56: swc_deu_001257 #utts: 1 +id: (swc_deu_001257-swc_deu_001257) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ****** DES HANSEATEN FÜHREN +HYP: DESHAN DIE ATEN FÜÖRE +Eval: I S S S + +Speaker sentences 57: swc_deu_001258 #utts: 1 +id: (swc_deu_001258-swc_deu_001258) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ******* HEBBELS AGNES BERNAUER +HYP: HEBELTS ARG NES BANAU +Eval: I S S S + +Speaker sentences 58: swc_deu_001259 #utts: 1 +id: (swc_deu_001259-swc_deu_001259) +Scores: (#C #S #D #I) 0 2 0 0 +REF: LEBENSWEISE VERKÖRPERN +HYP: LIEBENSWEISE VERKRBPR +Eval: S S + +Speaker sentences 59: swc_deu_001260 #utts: 1 +id: (swc_deu_001260-swc_deu_001260) +Scores: (#C #S #D #I) 1 5 3 0 +REF: WIE DER FALL des VIELEN HAMBURGERN ZU KATHOLISCH FROMMEN +HYP: *** *** EDEFEIL des ****** FIEREN HAMBURGANZU KATORLES VRAMMEN +Eval: D D S D S S S S + +Speaker sentences 60: swc_deu_001261 #utts: 1 +id: (swc_deu_001261-swc_deu_001261) +Scores: (#C #S #D #I) 0 4 0 0 +REF: KULTUR UND WIRTSCHAFT AUSTAUSCHEN +HYP: KERLTUL ND IERTHRFT AUSTAUSCHE +Eval: S S S S + +Speaker sentences 61: swc_deu_001262 #utts: 1 +id: (swc_deu_001262-swc_deu_001262) +Scores: (#C #S #D #I) 1 2 1 0 +REF: JAHR ZWEI tausend VERTONTE +HYP: **** MIJARZWEI tausend VERTONDTE +Eval: D S S + +Speaker sentences 62: swc_deu_001263 #utts: 1 +id: (swc_deu_001263-swc_deu_001263) +Scores: (#C #S #D #I) 4 5 4 0 +REF: DASS ER DIESE LEITUNG SCHNELLER VOLLENDEN KÖNNE als der baumeister den KÖLNER DOM +HYP: **** ** ***** ******* DASE DIESELEITUNGSCNALERVOLENTEN KÜNE als der baumeister den KÖNER DOUM +Eval: D D D D S S S S S + +Speaker sentences 63: swc_deu_001264 #utts: 1 +id: (swc_deu_001264-swc_deu_001264) +Scores: (#C #S #D #I) 0 5 3 0 +REF: HINRICHTUNG DER BERNAUERIN HABE ES SICH SCHLICHT UM +HYP: *********** *** ********** IDE HNRIHTUNG DE BANAUARIN HABESICSRLIHTUM +Eval: D D D S S S S S + +Speaker sentences 64: swc_deu_001265 #utts: 1 +id: (swc_deu_001265-swc_deu_001265) +Scores: (#C #S #D #I) 0 1 0 0 +REF: LUDWIG +HYP: LORDWEI +Eval: S + +Speaker sentences 65: swc_deu_001266 #utts: 1 +id: (swc_deu_001266-swc_deu_001266) +Scores: (#C #S #D #I) 3 2 1 0 +REF: derzeit der BESTE KENNER der EIFELLEITUNG +HYP: derzeit der ***** BSTIGKHENER der EIFELEITUN +Eval: D S S + +Speaker sentences 66: swc_deu_001267 #utts: 1 +id: (swc_deu_001267-swc_deu_001267) +Scores: (#C #S #D #I) 0 4 0 0 +REF: FOKUS DES WISSENSCHAFTLICHEN INTERESSES +HYP: VOKUS BES WISENSHAFTLICHEN INDTARESES +Eval: S S S S + +Speaker sentences 67: swc_deu_001268 #utts: 1 +id: (swc_deu_001268-swc_deu_001268) +Scores: (#C #S #D #I) 1 2 0 0 +REF: THEMA zu BEGEISTERN +HYP: TEMER zu BEGEISTE +Eval: S S + +Speaker sentences 68: swc_deu_001269 #utts: 1 +id: (swc_deu_001269-swc_deu_001269) +Scores: (#C #S #D #I) 3 5 1 0 +REF: METER und KONNTE damit AUCH VON INNEN BEGANGEN werden +HYP: METE und KONTE damit **** AUFON INEN BERGANGEN werden +Eval: S S D S S S + +Speaker sentences 69: swc_deu_001270 #utts: 1 +id: (swc_deu_001270-swc_deu_001270) +Scores: (#C #S #D #I) 0 4 0 0 +REF: HARDCOVER BESTSELLERLISTE DER NEW +HYP: HAHTKABER BES SELLALISTEU DENH +Eval: S S S S + +Speaker sentences 70: swc_deu_001271 #utts: 1 +id: (swc_deu_001271-swc_deu_001271) +Scores: (#C #S #D #I) 0 3 0 0 +REF: DER FREIEN ENZYKLOPÄDIE +HYP: DARREIN EN ZIKLOP +Eval: S S S + +Speaker sentences 71: swc_deu_001272 #utts: 1 +id: (swc_deu_001272-swc_deu_001272) +Scores: (#C #S #D #I) 1 4 3 0 +REF: DEN GRIZZLY WIEDER auf DIE LISTE ZU SETZEN +HYP: DN GRESLIE WIDER auf *** ***** ** DILESETZUSETZEN +Eval: S S S D D D S + +Speaker sentences 72: swc_deu_001273 #utts: 1 +id: (swc_deu_001273-swc_deu_001273) +Scores: (#C #S #D #I) 2 3 0 2 +REF: *** lang diese ****** KAPLANSSTELLE AUFRECHTERHALTEN WURDE +HYP: WIE lang diese KAPLAN STELE AUFRICHTER HETENWRD +Eval: I I S S S + +Speaker sentences 73: swc_deu_001274 #utts: 1 +id: (swc_deu_001274-swc_deu_001274) +Scores: (#C #S #D #I) 0 7 3 0 +REF: SIE WAREN WAHRSCHEINLICH BEREITS DREISSIG SEKUNDEN NACH AUSBRUCH DES FEUERS +HYP: *** ***** ************** SIEWARN MARSCHEINLIGHBEREITZ DREISZIG SIKUNDTEN NECH AUSPRCHTES VORER +Eval: D D D S S S S S S S + +Speaker sentences 74: swc_deu_001275 #utts: 1 +id: (swc_deu_001275-swc_deu_001275) +Scores: (#C #S #D #I) 1 5 1 0 +REF: METERN GESAMTLÄNGE und BIS ZU ZEHN METERN +HYP: METER GESAMTLINGE und *** BISTUT SEHN MIETER +Eval: S S D S S S + +Speaker sentences 75: swc_deu_001276 #utts: 1 +id: (swc_deu_001276-swc_deu_001276) +Scores: (#C #S #D #I) 0 3 1 0 +REF: FEINE RITZEN UND SPALTEN +HYP: ***** VEINER ITZEN UNDSPALT +Eval: D S S S + +Speaker sentences 76: swc_deu_001277 #utts: 1 +id: (swc_deu_001277-swc_deu_001277) +Scores: (#C #S #D #I) 0 5 3 0 +REF: DEN MAN VON AUSSEN DIE KEHLE HINABFLIESSEN SIEHT +HYP: *** *** *** DINMEN VEN AUSSNDIEKOLER HINABPFLIESEN SIET +Eval: D D D S S S S S + +Speaker sentences 77: swc_deu_001278 #utts: 1 +id: (swc_deu_001278-swc_deu_001278) +Scores: (#C #S #D #I) 0 3 1 0 +REF: EINEM INTERVIEW SAGTE BROWN +HYP: ***** NE ITER IOSAKTEBRAUN +Eval: D S S S + +Speaker sentences 78: swc_deu_001279 #utts: 1 +id: (swc_deu_001279-swc_deu_001279) +Scores: (#C #S #D #I) 1 2 0 1 +REF: das ****** FÜNFTE EVANGELIUM +HYP: das FÜNFT EN GERIUM +Eval: I S S + +Speaker sentences 79: swc_deu_001280 #utts: 1 +id: (swc_deu_001280-swc_deu_001280) +Scores: (#C #S #D #I) 1 3 2 0 +REF: REISSEN SIE MANCHMAL weidetiere WIE SCHAFE +HYP: ******* REISEN SIMANCHMAL weidetiere *** ISCHAFE +Eval: D S S D S + +Speaker sentences 80: swc_deu_001281 #utts: 1 +id: (swc_deu_001281-swc_deu_001281) +Scores: (#C #S #D #I) 3 3 0 0 +REF: SIE hÖren den artikel DESIGN REVIEW +HYP: SI hÖren den artikel DESEIN RÜFIU +Eval: S S S + +Speaker sentences 81: swc_deu_001282 #utts: 1 +id: (swc_deu_001282-swc_deu_001282) +Scores: (#C #S #D #I) 1 3 0 0 +REF: CHAKUZA IST GELERNTER koch +HYP: KUSER IS GLENTER koch +Eval: S S S + +Speaker sentences 82: swc_deu_001283 #utts: 1 +id: (swc_deu_001283-swc_deu_001283) +Scores: (#C #S #D #I) 0 3 0 0 +REF: HANS WENDT STIFTUNG +HYP: SHANZE WENT STIFTDUN +Eval: S S S + +Speaker sentences 83: swc_deu_001284 #utts: 1 +id: (swc_deu_001284-swc_deu_001284) +Scores: (#C #S #D #I) 0 6 0 2 +REF: ******* ****** NEUNZEHN HUNDERT ACHTZEHN ALS HANSEATEN ANGESEHEN +HYP: NEUNZHN HNDERT ACHTZIEN ALT S HAN IE ARTENANGESIE +Eval: I I S S S S S S + +Speaker sentences 84: swc_deu_001285 #utts: 1 +id: (swc_deu_001285-swc_deu_001285) +Scores: (#C #S #D #I) 2 3 0 0 +REF: MEHRERE es nach IHM THUN +HYP: MERER es nach IM TUN +Eval: S S S + +Speaker sentences 85: swc_deu_001286 #utts: 1 +id: (swc_deu_001286-swc_deu_001286) +Scores: (#C #S #D #I) 1 8 0 2 +REF: AUFSTIEG des ******* *** GERICHTS ZUR LANDESWEIT BELIEBTEN KULINARISCHEN SPEZIALITÄT ERMÖGLICHTE +HYP: AUCSHIH des GERICHS ZUO LANDES WEITEN BERLIEBEN KOULINA RISCHER SPÄTZELTET ARMÜG +Eval: S I I S S S S S S S + +Speaker sentences 86: swc_deu_001287 #utts: 1 +id: (swc_deu_001287-swc_deu_001287) +Scores: (#C #S #D #I) 2 7 1 0 +REF: COLLEGE und EINEN ZWEITJOB ALS SPANISCHLEHRER in HAMPTON FALLS AN +HYP: KOLLETSCH und ***** EIN ZWEITCOB ALTSPBANSHLIERER in HEM N VORLSEN +Eval: S D S S S S S S + +Speaker sentences 87: swc_deu_001288 #utts: 1 +id: (swc_deu_001288-swc_deu_001288) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ****** WURDEN KEINESWEGS ALLE GEBÜRTIGEN +HYP: BURDEN EINIS WEGS AELLE GEBÜIROTIGE +Eval: I S S S S + +Speaker sentences 88: swc_deu_001289 #utts: 1 +id: (swc_deu_001289-swc_deu_001289) +Scores: (#C #S #D #I) 1 2 1 0 +REF: ist IHR KÖRPERBAU KRÄFTIG +HYP: ist *** IER KERBEBAUGREFTIGKH +Eval: D S S + +Speaker sentences 89: swc_deu_001290 #utts: 1 +id: (swc_deu_001290-swc_deu_001290) +Scores: (#C #S #D #I) 0 4 0 0 +REF: ANLÄSSLICH DER NEUJAHRESANSPRACHE KIM +HYP: AN LESLICHTER NOUAS ANSPRACREKH +Eval: S S S S + +Speaker sentences 90: swc_deu_001291 #utts: 1 +id: (swc_deu_001291-swc_deu_001291) +Scores: (#C #S #D #I) 1 2 2 0 +REF: mit WIND VON SCHRÄG HINTEN +HYP: mit **** *** WIN VONDSCREÄGHINTEN +Eval: D D S S + +Speaker sentences 91: swc_deu_001292 #utts: 1 +id: (swc_deu_001292-swc_deu_001292) +Scores: (#C #S #D #I) 1 6 0 0 +REF: DEN GRÖSSTEN TEIL DER BEZIRKSVERTRETUNG UERDINGEN aus +HYP: DIN REÖCSTEN TEALDE BIT TZIÖGSFORTRIETUNG ÜRDINGEN aus +Eval: S S S S S S + +Speaker sentences 92: swc_deu_001293 #utts: 1 +id: (swc_deu_001293-swc_deu_001293) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ACHTZEHN HUNDERT EINUNDZWANZIG +HYP: ACHTIN HUNERT EILUNZWANZI +Eval: S S S + +Speaker sentences 93: swc_deu_001294 #utts: 1 +id: (swc_deu_001294-swc_deu_001294) +Scores: (#C #S #D #I) 0 4 1 0 +REF: DES GROSSEN ADELS ANGESAMMELTEN REICHTUMS +HYP: *** DSKROSEN ATELTS AN GESAMITENREICHTUMS +Eval: D S S S S + +Speaker sentences 94: swc_deu_001295 #utts: 1 +id: (swc_deu_001295-swc_deu_001295) +Scores: (#C #S #D #I) 0 5 0 1 +REF: ***** SOLLTEN NICHT ALS SEXUELLE PROVOKATION +HYP: DESON NIHT AL SEH ZWOL PROROKATIU +Eval: I S S S S S + +Speaker sentences 95: swc_deu_001296 #utts: 1 +id: (swc_deu_001296-swc_deu_001296) +Scores: (#C #S #D #I) 1 4 1 0 +REF: TEILHABER DER FIRMA GOSSMANN und JÜRGENS +HYP: ********* TEILHBEDE VRMER GOSMAN und IORGENZ +Eval: D S S S S + +Speaker sentences 96: swc_deu_001297 #utts: 1 +id: (swc_deu_001297-swc_deu_001297) +Scores: (#C #S #D #I) 0 4 2 0 +REF: DER KRAUTINSEL BILDET SIE DIE GEMEINDE +HYP: *** ********** INEMITE FRAUNTER KAUT INSELT +Eval: D D S S S S + +Speaker sentences 97: swc_deu_001298 #utts: 1 +id: (swc_deu_001298-swc_deu_001298) +Scores: (#C #S #D #I) 2 5 2 0 +REF: AUDIO IST EIN DEUTSCHER HÖRBUCHVERLAG mit sitz IN MÜNCHEN +HYP: ***** AUR DIEO ISTEANDUTCHER HEBOFVARLARG mit sitz ** INMEÜNCHEN +Eval: D S S S S D S + +Speaker sentences 98: swc_deu_001299 #utts: 1 +id: (swc_deu_001299-swc_deu_001299) +Scores: (#C #S #D #I) 0 5 0 2 +REF: ** **** FARBPIGMENTE UND CHEMISCHE VORPRODUKTE HERSTELLT +HYP: FA PEIK MENTE UNDSCHMICHE VORPRED UKTER HERSTELT +Eval: I I S S S S S + +Speaker sentences 99: swc_deu_001300 #utts: 1 +id: (swc_deu_001300-swc_deu_001300) +Scores: (#C #S #D #I) 4 10 1 4 +REF: ********* ********** ERBLICHEN PREUSSISCHEN FREIHERRENSTAND in der *** ******** ZOLLANSCHLUSSFRAGE ENTSCHIEDEN GEGEN den SENAT auf DIE SEITE BISMARCKS GESTELLT +HYP: ARPLICHEN PRESISCHEN FREI HEREN STAND in der ZOL ANSCHLOS VRAGE INTSCHIEBDENG GEGENG den SINART auf *** DIESEITE BISMAGS GESTELT +Eval: I I S S S I I S S S S D S S S + +Speaker sentences 100: swc_deu_001301 #utts: 1 +id: (swc_deu_001301-swc_deu_001301) +Scores: (#C #S #D #I) 2 5 4 1 +REF: WENN DIE QUELLEN von **** SELBST HERVORQUELLEN und OFFEN ZU TAGE LIEGEN +HYP: **** *** WENDIKWELEN von SEST ER VORGWELEN und ***** ** AUFEN ZUTAGELIGEN +Eval: D D S I S S D D S S + +Speaker sentences 101: swc_deu_001302 #utts: 1 +id: (swc_deu_001302-swc_deu_001302) +Scores: (#C #S #D #I) 0 6 0 0 +REF: DAS VOM NACHBARBAUTRUPP BEREITS BEGONNEN WURDE +HYP: DS VOUN NACHBARBAUTRIUB BELLEITZ BEGON WOT +Eval: S S S S S S + +Speaker sentences 102: swc_deu_001303 #utts: 1 +id: (swc_deu_001303-swc_deu_001303) +Scores: (#C #S #D #I) 2 4 0 1 +REF: WERDEN PRÄGENDE elemente des ***** HANSEATENTUMS ZUSAMMENGEFASST +HYP: WEHRDEN PRÄRGENDE elemente des HANSE ATENTUMSZUOSAMMEN GEFAST +Eval: S S I S S + +Speaker sentences 103: swc_deu_001304 #utts: 1 +id: (swc_deu_001304-swc_deu_001304) +Scores: (#C #S #D #I) 0 4 2 0 +REF: DAS LIED WURDE ALS VOLKSLIED ANGESEHEN +HYP: *** **** DEASSLIEZWRDERTS VOLCGSTLIET AN GESIEN +Eval: D D S S S S + +Speaker sentences 104: swc_deu_001305 #utts: 1 +id: (swc_deu_001305-swc_deu_001305) +Scores: (#C #S #D #I) 0 5 1 0 +REF: DER ZUR RANDOM HOUSE VERLAGSGRUPPE GEHÖRT +HYP: *** DERZON NENDEM HUSVRLAG SKGOBIGE HÖRT +Eval: D S S S S S + +Speaker sentences 105: swc_deu_001306 #utts: 1 +id: (swc_deu_001306-swc_deu_001306) +Scores: (#C #S #D #I) 0 7 0 2 +REF: ** ** FÜR DIE KÜNFTIGEN BORDBÜCHER ENTWICKELTE DIE PAPIERFABRIK +HYP: FR DE KNFDIGEN BARD PBÜSCHER IND WIKETE DI PAPIEFERPR +Eval: I I S S S S S S S + +Speaker sentences 106: swc_deu_001307 #utts: 1 +id: (swc_deu_001307-swc_deu_001307) +Scores: (#C #S #D #I) 0 2 0 0 +REF: HAMBURG WUCHS +HYP: AMBRE WUOKS +Eval: S S + +Speaker sentences 107: swc_deu_001308 #utts: 1 +id: (swc_deu_001308-swc_deu_001308) +Scores: (#C #S #D #I) 0 9 3 0 +REF: FÜR DIE QUASI ADLIGEN LANDSITZE BETRIEBENE AUFWAND – SEI ES BEIM BAU +HYP: **** *** ***** V DIEKWARAR SIE ARD LIGEN LANZITZE BETRIEBEN AUFAND SEISBEMBAU +Eval: D D D S S S S S S S S S + +Speaker sentences 108: swc_deu_001309 #utts: 1 +id: (swc_deu_001309-swc_deu_001309) +Scores: (#C #S #D #I) 2 8 2 0 +REF: JAHR zwei TAUSEND zwÖlf IN DEN BERLINER CLUB S O SECHSUNDDREISSIG VERLEGT +HYP: JA zwei TAUSEN zwÖlf ** *** INDN BELIENER KLUOPSO SE SHNDREISICH VELLIGT +Eval: S S D D S S S S S S + +Speaker sentences 109: swc_deu_001310 #utts: 1 +id: (swc_deu_001310-swc_deu_001310) +Scores: (#C #S #D #I) 1 4 1 0 +REF: SECHZEHN HUNDERT FÜNFZIG ALS bÜndnis DIE +HYP: SECHTIN HNEN FÜNFTIGH EID bÜndnis *** +Eval: S S S S D + +Speaker sentences 110: swc_deu_001311 #utts: 1 +id: (swc_deu_001311-swc_deu_001311) +Scores: (#C #S #D #I) 0 4 1 0 +REF: PROBLEM BEI DIESEM PARADOXON IST +HYP: ******* DASPROBLEN BI DEN PRADTACHSONIST +Eval: D S S S S + +Speaker sentences 111: swc_deu_001312 #utts: 1 +id: (swc_deu_001312-swc_deu_001312) +Scores: (#C #S #D #I) 0 4 0 0 +REF: ARMENWESEN TÄTIG AMALIE SIEVEKING +HYP: AMINWESEN TETIAMALIE S IEVEKEIN +Eval: S S S S + +Speaker sentences 112: swc_deu_001313 #utts: 1 +id: (swc_deu_001313-swc_deu_001313) +Scores: (#C #S #D #I) 2 10 1 2 +REF: *** ** NICHT EINMAL EINE ANSATZWEISE UNTERSUCHUNG zu IHREM VERHALTEN IN DER ZEIT des NATIONALSOZIALISMUS +HYP: ICH EI MALEINE AN SATZWEISE UNTE SOCHRUNG zu ***** IEREM VERHALKTEN INDER ZEITT des NAZUNALSUTZELISMUS +Eval: I I S S S S S D S S S S S + +Speaker sentences 113: swc_deu_001314 #utts: 1 +id: (swc_deu_001314-swc_deu_001314) +Scores: (#C #S #D #I) 0 3 1 0 +REF: LIZENZ FÜR FREIE DOKUMENTATION +HYP: ****** LIETZSEINS FÜRFREIE DOKOMENTATION +Eval: D S S S + +Speaker sentences 114: swc_deu_001315 #utts: 1 +id: (swc_deu_001315-swc_deu_001315) +Scores: (#C #S #D #I) 1 6 1 1 +REF: *** IM ACHTZEHNTE JAHRHUNDERT DIE GARTENHÄUSER vor DEN TOREN +HYP: DIM CHZEN NER HUNDER DIGATEN HOLSE vor *** ENTORE +Eval: I S S S S S D S + +Speaker sentences 115: swc_deu_001316 #utts: 1 +id: (swc_deu_001316-swc_deu_001316) +Scores: (#C #S #D #I) 1 2 2 0 +REF: GANZ IM STIL DER zeit +HYP: **** ** GANZS IMSTIELDER zeit +Eval: D D S S + +Speaker sentences 116: swc_deu_001317 #utts: 1 +id: (swc_deu_001317-swc_deu_001317) +Scores: (#C #S #D #I) 1 7 1 0 +REF: ÜBER BRÜHL und HÜRTH ERREICHTE DIE LEITUNG SCHLIESSLICH KÖLN +HYP: ER BRÜL und ****** HÜÖRT AREICH E DI LEITUNSCHISLIKEN +Eval: S S D S S S S S + +Speaker sentences 117: swc_deu_001318 #utts: 1 +id: (swc_deu_001318-swc_deu_001318) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *************** AUSZEICHNUNGEN FREMDER HERREN +HYP: AUSSTZEICHNUMEN REM DER HEREN +Eval: I S S S + +Speaker sentences 118: swc_deu_001319 #utts: 1 +id: (swc_deu_001319-swc_deu_001319) +Scores: (#C #S #D #I) 0 2 1 0 +REF: DIE SCHRIFTSTELLEREI AUFZUGEBEN +HYP: *** DIESCHEFTELLEREI AUFTZUGEBE +Eval: D S S + +Speaker sentences 119: swc_deu_001320 #utts: 1 +id: (swc_deu_001320-swc_deu_001320) +Scores: (#C #S #D #I) 0 4 2 0 +REF: ZÄHLEN DIE BEGEGNUNG MIT VERLETZTEN TIEREN +HYP: ******* *** DAZUTZIHRN DE BEGEGNUNMIT VERLETZTENTIERE +Eval: D D S S S S + +Speaker sentences 120: swc_deu_001321 #utts: 1 +id: (swc_deu_001321-swc_deu_001321) +Scores: (#C #S #D #I) 0 2 0 0 +REF: JENISCH STIFT +HYP: IENESCH STDIFT +Eval: S S + +Speaker sentences 121: swc_deu_001322 #utts: 1 +id: (swc_deu_001322-swc_deu_001322) +Scores: (#C #S #D #I) 2 1 0 0 +REF: westlich von KÖLN +HYP: westlich von KERLEN +Eval: S + +Speaker sentences 122: swc_deu_001323 #utts: 1 +id: (swc_deu_001323-swc_deu_001323) +Scores: (#C #S #D #I) 1 3 1 0 +REF: die STÄNDIG IN BETRIEB WAREN +HYP: die ******** STENDI N BETRIPWARE +Eval: D S S S + +Speaker sentences 123: swc_deu_001324 #utts: 1 +id: (swc_deu_001324-swc_deu_001324) +Scores: (#C #S #D #I) 1 4 0 0 +REF: DIE vom BARBIER RASIERT WERDEN +HYP: DI vom BA BIJERHASIERT WEHRDE +Eval: S S S S + +Speaker sentences 124: swc_deu_001325 #utts: 1 +id: (swc_deu_001325-swc_deu_001325) +Scores: (#C #S #D #I) 0 3 5 0 +REF: ERSCHIEN NOCH EIN WEITERER AUFSATZ VON CHRISTIAN MEYER +HYP: ******** **** *** ******** ******* ERSCHEN NOREINEITRAUFSEATZFON GRISTERNMEIAL +Eval: D D D D D S S S + +Speaker sentences 125: swc_deu_001326 #utts: 1 +id: (swc_deu_001326-swc_deu_001326) +Scores: (#C #S #D #I) 0 8 0 0 +REF: WEIL SELBST EXTREMER REICHTUM KEINESWEGS DEN UNMITTELBAREN ZUGANG +HYP: WAEIL SEBST EXSTRME REICHTUMKEINES WEHST IN UNMITLEBAREN SZUGAN +Eval: S S S S S S S S + +Speaker sentences 126: swc_deu_001327 #utts: 1 +id: (swc_deu_001327-swc_deu_001327) +Scores: (#C #S #D #I) 3 1 1 0 +REF: gebt EUCH NICHT selber auf +HYP: gebt **** EUSCHEICH selber auf +Eval: D S + +Speaker sentences 127: swc_deu_001328 #utts: 1 +id: (swc_deu_001328-swc_deu_001328) +Scores: (#C #S #D #I) 2 5 0 3 +REF: ** HAT diesen brauch ******** ***** NEUNZEHN HUNDERT ZWEIUNDFÜNFZIG GEGENÜBER +HYP: ER HAET diesen brauch NEUNEHIN HNDER WEIUND FÜNFTIGH GIGN ÜB +Eval: I S I I S S S S + +Speaker sentences 128: swc_deu_001329 #utts: 1 +id: (swc_deu_001329-swc_deu_001329) +Scores: (#C #S #D #I) 3 4 3 0 +REF: WO DIE LEITUNG ÜBER die alte HÜRTHER LEITUNG GEFÜHRT wurde +HYP: ** O DELEITUNG ÜBE die alte ******** ******* HÜÖRTEARLEITUNGEFIERT wurde +Eval: D S S S D D S + +Speaker sentences 129: swc_deu_001330 #utts: 1 +id: (swc_deu_001330-swc_deu_001330) +Scores: (#C #S #D #I) 0 6 3 0 +REF: EINE BELIEBTE KÖLSCHROCKTRUPPE AUS DEM KÖLNER UMLAND DIE HÖHNER +HYP: **** ******** ***************** INE BLIEBTE KLSCHOKTROPEASTE KENER UMNANDTDIE HÖNE +Eval: D D D S S S S S S + +Speaker sentences 130: swc_deu_001331 #utts: 1 +id: (swc_deu_001331-swc_deu_001331) +Scores: (#C #S #D #I) 2 3 0 0 +REF: GEWORDEN SEI und albrecht SICH +HYP: GEWARDEN SEIO und albrecht DICH +Eval: S S S + +Speaker sentences 131: swc_deu_001332 #utts: 1 +id: (swc_deu_001332-swc_deu_001332) +Scores: (#C #S #D #I) 0 4 3 0 +REF: DER TAGESBEDARF EINES ERWACHSENEN AN VITAMIN A +HYP: *** *********** ***** DETAGESBEDAF EINESWAGSTENEN ANITEMIN AR +Eval: D D D S S S S + +Speaker sentences 132: swc_deu_001333 #utts: 1 +id: (swc_deu_001333-swc_deu_001333) +Scores: (#C #S #D #I) 0 4 0 0 +REF: SIEBZEHN HUNDERT ZEHN OBERALTER +HYP: SIBTINULER ZIHEN OBAR ALTE +Eval: S S S S + +Speaker sentences 133: swc_deu_001334 #utts: 1 +id: (swc_deu_001334-swc_deu_001334) +Scores: (#C #S #D #I) 0 2 2 0 +REF: WEITERHIN LIESS SICH NACHWEISEN +HYP: ********* ***** WEITERHEN LIESICHNACRWEISEN +Eval: D D S S + +Speaker sentences 134: swc_deu_001335 #utts: 1 +id: (swc_deu_001335-swc_deu_001335) +Scores: (#C #S #D #I) 0 4 1 0 +REF: ZUM GRÜNDUNGSDATUM KONNTE MAN BEREITS +HYP: *** ZUN GRÜNUNGSTDARTUM KONTBAM BEREIT +Eval: D S S S S + +Speaker sentences 135: swc_deu_001336 #utts: 1 +id: (swc_deu_001336-swc_deu_001336) +Scores: (#C #S #D #I) 2 2 0 0 +REF: kein LECKSCHLAGEN MÖGLICH nachteile +HYP: kein LECSCHLARG MÜKLICH nachteile +Eval: S S + +Speaker sentences 136: swc_deu_001337 #utts: 1 +id: (swc_deu_001337-swc_deu_001337) +Scores: (#C #S #D #I) 1 6 4 0 +REF: WIRD DIE KATHOLISCHE KIRCHE STÜCK PETER AN DER stelle DER ALTEN +HYP: **** *** *********** ****** RTDIKATOLICHE KÜRSCHESAN PÄTER ANDER stelle DE ALT +Eval: D D D D S S S S S S + +Speaker sentences 137: swc_deu_001338 #utts: 1 +id: (swc_deu_001338-swc_deu_001338) +Scores: (#C #S #D #I) 1 7 4 0 +REF: DER VERKNAPPUNG DES BROTWEIZENS trat ABER SCHON BALD DIE KARTOFFEL ALS ERSATZ +HYP: *** ERFEKNARPBUN DESBROT WEITZEN trat **** ***** **** ABSCHNBAIT DIKERTOFEL EIS ERSAT +Eval: D S S S D D D S S S S + +Speaker sentences 138: swc_deu_001339 #utts: 1 +id: (swc_deu_001339-swc_deu_001339) +Scores: (#C #S #D #I) 1 4 0 0 +REF: KÖNNEN MIT diesen NACHKOMMEN ZEUGEN +HYP: KRE MT diesen NACHKOMN ZOEUGE +Eval: S S S S + +Speaker sentences 139: swc_deu_001340 #utts: 1 +id: (swc_deu_001340-swc_deu_001340) +Scores: (#C #S #D #I) 3 2 0 1 +REF: alle NEUEN folgen der ***** HÖRSPIELREIHE +HYP: alle NEIN folgen der HÖRS IEREIE +Eval: S I S + +Speaker sentences 140: swc_deu_001341 #utts: 1 +id: (swc_deu_001341-swc_deu_001341) +Scores: (#C #S #D #I) 0 2 1 0 +REF: CHIPS MIT BRATENSOSSE +HYP: ***** SCHIBPSMIE BERACTENSO +Eval: D S S + +Speaker sentences 141: swc_deu_001342 #utts: 1 +id: (swc_deu_001342-swc_deu_001342) +Scores: (#C #S #D #I) 0 6 2 0 +REF: KOLLEGE ANDREAS BUCHNER VERZICHTETE AUF EINE PERSÖNLICHE BEWERTUNG +HYP: ******* ******* KLIGE AN RERSBUOCHNE VERZICHTE AUFENEBERSÖNICHE BEWERTUN +Eval: D D S S S S S S + +Speaker sentences 142: swc_deu_001343 #utts: 1 +id: (swc_deu_001343-swc_deu_001343) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ******* WENIGER ENTRÜSTET +HYP: BENIGER IN TRUSSTE +Eval: I S S + +Speaker sentences 143: swc_deu_001344 #utts: 1 +id: (swc_deu_001344-swc_deu_001344) +Scores: (#C #S #D #I) 0 5 0 2 +REF: ****** *** WEITERHIN VERSORGTE DIE LEITUNG THERMEN +HYP: WEITER HEN VERSORK D DIELEI TUNG TERMEN +Eval: I I S S S S S + +Speaker sentences 144: swc_deu_001345 #utts: 1 +id: (swc_deu_001345-swc_deu_001345) +Scores: (#C #S #D #I) 1 4 0 0 +REF: warteten DAFÜR ABER MIT EINIGEN +HYP: warteten DA FÜHR ABEMIT EINIG +Eval: S S S S + +Speaker sentences 145: swc_deu_001346 #utts: 1 +id: (swc_deu_001346-swc_deu_001346) +Scores: (#C #S #D #I) 0 6 3 0 +REF: LEDIGLICH ANTON VON KLEIN MONIERTE IN SEINER REZENSION DER +HYP: ********* ***** *** DE DIKLICH ANTRÜNDFONKLEIN MUNIERTI INSEINARETZEN SOND +Eval: D D D S S S S S S + +Speaker sentences 146: swc_deu_001347 #utts: 1 +id: (swc_deu_001347-swc_deu_001347) +Scores: (#C #S #D #I) 0 2 3 0 +REF: IM JAHRE NEUNZEHN HUNDERT FÜNF +HYP: ** ***** ******** EM JABRNEUZENERTFÜM +Eval: D D D S S + +Speaker sentences 147: swc_deu_001348 #utts: 1 +id: (swc_deu_001348-swc_deu_001348) +Scores: (#C #S #D #I) 3 15 2 3 +REF: ERST MIT DEM FORTFALL DES BÜRGERRECHTS UND DER EINFÜHRUNG DER FREIZÜGIGKEIT im ******* ZWANZIGSTE JAHRHUNDERT WANDELTE sich diese ********* ****** ANSCHAUUNG ANSATZWEISE DAHIN +HYP: **** *** IERSIT EM FRT FAL DESBÜURGERECHT UN DEREINFÜRUN DE REITZÜGICHKEIT im ZBANSIS NERNDERT WANDTET DE sich diese ANSCHAUNG ANSERT SWEIESEIN DAR IEN +Eval: D D S S S S S S S S S I S S S I I S S S + +Speaker sentences 148: swc_deu_001349 #utts: 1 +id: (swc_deu_001349-swc_deu_001349) +Scores: (#C #S #D #I) 2 5 0 1 +REF: des SWISTBACHES BEI RHEINBACH eine ***** BOGENBRÜCKE VON +HYP: des SZWIST BACRES BALREINBACH eine BOGEN BRÜCKE V +Eval: S S S I S S + +Speaker sentences 149: swc_deu_001350 #utts: 1 +id: (swc_deu_001350-swc_deu_001350) +Scores: (#C #S #D #I) 0 5 1 0 +REF: ACHTZEHN HUNDERT SECHSUNDDREISSIG WURDE DER HAMBURGER +HYP: ******** ACTZINULETE SNDREISIG BWORDE E HMBUG +Eval: D S S S S S + +Speaker sentences 150: swc_deu_001351 #utts: 1 +id: (swc_deu_001351-swc_deu_001351) +Scores: (#C #S #D #I) 0 2 0 0 +REF: AM KARNEVALSSONNTAG +HYP: DG AEM +Eval: S S + +Speaker sentences 151: swc_deu_001352 #utts: 1 +id: (swc_deu_001352-swc_deu_001352) +Scores: (#C #S #D #I) 3 4 0 1 +REF: aufgrund ** DER KONTINENTALSPERRE achtzehn HUNDERT elf BANKROTT +HYP: aufgrund DE KONTINEN TEALSPBARE achtzehn HUNER elf BANKOT +Eval: I S S S S + +Speaker sentences 152: swc_deu_001353 #utts: 1 +id: (swc_deu_001353-swc_deu_001353) +Scores: (#C #S #D #I) 0 7 7 0 +REF: WEITERES MAL MUSSTEN DAN UND BLYTHE BROWN DIE WERBUNG FÜR DAS BUCH SELBST ÜBERNEHMEN +HYP: ******** *** ******* *** *** ****** ***** EITERESMEIMUST DEN UNPLEIPT BRAUN DIWARBUNG FÜREAS BOHSEBTWANEM +Eval: D D D D D D D S S S S S S S + +Speaker sentences 153: swc_deu_001354 #utts: 1 +id: (swc_deu_001354-swc_deu_001354) +Scores: (#C #S #D #I) 0 14 6 0 +REF: DIE NACHRICHT VOM SIEG DER BÜRGERLICH DEMOKRATISCHEN FEBRUARREVOLUTION VON ACHTZEHN HUNDERT ACHTUNDVIERZIG IN FRANKREICH WURDE IN HAMBURG MIT JUBEL AUFGENOMMEN +HYP: *** ********* *** **** *** *********** DINERIH VM SIEGTDER BÜRGELICH MUGRATISCHEN FIEPOARERLUTZIONVEN ACHZEIN HNDET ACHT N VIRZICHN RANKREICHVWRDEN HAMBURGMITIOBE AUFGENAME +Eval: D D D D D D S S S S S S S S S S S S S S + +Speaker sentences 154: swc_deu_001355 #utts: 1 +id: (swc_deu_001355-swc_deu_001355) +Scores: (#C #S #D #I) 0 3 1 0 +REF: ZWEI JAHRE OHNE UNTERBRECHUNG +HYP: **** OUMBLIETZWEIARE ONE DERBRESCHN +Eval: D S S S + +Speaker sentences 155: swc_deu_001356 #utts: 1 +id: (swc_deu_001356-swc_deu_001356) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ZAHLREICHEN GASTSPIELEN UNTERWEGS +HYP: ZOALREICHE GASTSPILN UNTERWEGHS +Eval: S S S + +Speaker sentences 156: swc_deu_001357 #utts: 1 +id: (swc_deu_001357-swc_deu_001357) +Scores: (#C #S #D #I) 0 2 0 0 +REF: QUANTITÄT GENÜGTEN +HYP: KRANZIETET GENÜTEN +Eval: S S + +Speaker sentences 157: swc_deu_001358 #utts: 1 +id: (swc_deu_001358-swc_deu_001358) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ** BAROCKER AUSSTATTUNG +HYP: UN BEROKER AUSTATO +Eval: I S S + +Speaker sentences 158: swc_deu_001359 #utts: 1 +id: (swc_deu_001359-swc_deu_001359) +Scores: (#C #S #D #I) 2 10 4 1 +REF: DAS GERICHT VOM BEIWAGEN SEINES MOTORRADES aus IN DIE zu ********* DIESER ZEIT NEU ENTSTEHENDEN ARBEITERSIEDLUNGEN ZU +HYP: *** ******* *** DAGERICHT VOUM BEIVWARGENSANGESMOTORADIS aus ** INDIE zu DESERZEIT NOU EN STENDEN ABET ASIET LOMENZU +Eval: D D D S S S D S I S S S S S S + +Speaker sentences 159: swc_deu_001360 #utts: 1 +id: (swc_deu_001360-swc_deu_001360) +Scores: (#C #S #D #I) 0 4 0 0 +REF: DIE MITSAMT IHRER RECHENSTUBE +HYP: DIMITZSAM IE HERECHEN STOBE +Eval: S S S S + +Speaker sentences 160: swc_deu_001361 #utts: 1 +id: (swc_deu_001361-swc_deu_001361) +Scores: (#C #S #D #I) 0 1 1 0 +REF: KAISER FERDINAND +HYP: ****** KEISERFERDINAN +Eval: D S + +Speaker sentences 161: swc_deu_001362 #utts: 1 +id: (swc_deu_001362-swc_deu_001362) +Scores: (#C #S #D #I) 2 4 5 0 +REF: vom FERNSEHREGISSEUR FRANZ XAVER BOGNER IN DEM FERNSEHFILM das EWIGE LIED +HYP: vom **************** ***** ***** ****** FERNSERESCHSUHRERFEANZSAVERBUGNER INDEM FERNSEFILEN das ***** WIGELIET +Eval: D D D D S S S D S + +Speaker sentences 162: swc_deu_001363 #utts: 1 +id: (swc_deu_001363-swc_deu_001363) +Scores: (#C #S #D #I) 3 3 0 0 +REF: WURDE in SEINEN besten zeiten DER +HYP: UR in SEIN besten zeiten DE +Eval: S S S + +Speaker sentences 163: swc_deu_001364 #utts: 1 +id: (swc_deu_001364-swc_deu_001364) +Scores: (#C #S #D #I) 2 2 3 0 +REF: SIE HÖREN den artikel FISH AND CHIPS +HYP: *** SEHÖRE den artikel **** *** FISCHENTSCIS +Eval: D S D D S + +Speaker sentences 164: swc_deu_001365 #utts: 1 +id: (swc_deu_001365-swc_deu_001365) +Scores: (#C #S #D #I) 1 2 1 0 +REF: und T V MOVIE +HYP: und * TE FARMOFWI +Eval: D S S + +Speaker sentences 165: swc_deu_001366 #utts: 1 +id: (swc_deu_001366-swc_deu_001366) +Scores: (#C #S #D #I) 1 3 1 0 +REF: REZEPTION DER HEXENTHEMATIK von CHRISTA +HYP: ********* REAZSIBZIONDERHEHSEN IMATIG von KRESTA +Eval: D S S S + +Speaker sentences 166: swc_deu_001367 #utts: 1 +id: (swc_deu_001367-swc_deu_001367) +Scores: (#C #S #D #I) 1 9 3 0 +REF: DIE GESAMTE ANLAGE WAR BIS ETWA ZWEI HUNDERT SECHZIG NACH CHRISTUS in BETRIEB +HYP: *** ******* ****** DIGESAMTER ANLARGE WABIS ET VERTZWEI HUNDER SECHTZIG NACHKRISTUS in BEDRIE +Eval: D D D S S S S S S S S S + +Speaker sentences 167: swc_deu_001368 #utts: 1 +id: (swc_deu_001368-swc_deu_001368) +Scores: (#C #S #D #I) 2 3 2 1 +REF: der ** ERSTE fast FOOD LIEFERSERVICE WAR GEBOREN +HYP: der IS DE fast **** ************* FUT LIERSOARWISWAGEBUN +Eval: I S D D S S + +Speaker sentences 168: swc_deu_001369 #utts: 1 +id: (swc_deu_001369-swc_deu_001369) +Scores: (#C #S #D #I) 0 2 0 0 +REF: EINEM KABELBAUM +HYP: EINDEM KABELBAUN +Eval: S S + +Speaker sentences 169: swc_deu_001370 #utts: 1 +id: (swc_deu_001370-swc_deu_001370) +Scores: (#C #S #D #I) 1 5 0 1 +REF: ***** ERMORDUNG mit DER GEFAHR VERBUNDEN GEWESEN +HYP: MORLE DUNG mit DE E FAFVERBUNDEN GEWES +Eval: I S S S S S + +Speaker sentences 170: swc_deu_001371 #utts: 1 +id: (swc_deu_001371-swc_deu_001371) +Scores: (#C #S #D #I) 0 4 0 0 +REF: DER ÄLTESTEN PFERDERENNEN AUSSERHALB +HYP: E ELTZISTN PRIERERENIN AUSRHEP +Eval: S S S S + +Speaker sentences 171: swc_deu_001372 #utts: 1 +id: (swc_deu_001372-swc_deu_001372) +Scores: (#C #S #D #I) 0 7 0 3 +REF: ***** *** ***** SONDERN AUCH DER NATIONALSOZIALISTISCHEN KUNSTAUFFASSUNG GERECHT WERDEN +HYP: SONEN ACH DERNA ZUN NALI SUTSER DISTISCHEN KUNZT AUF FASONGERECHTWEHREN +Eval: I I I S S S S S S S + +Speaker sentences 172: swc_deu_001373 #utts: 1 +id: (swc_deu_001373-swc_deu_001373) +Scores: (#C #S #D #I) 1 2 1 1 +REF: DIE WELTSICHT des ***** HANSEATEN +HYP: *** DIEWELTZSICHT des HANSE AHRTENI +Eval: D S I S + +Speaker sentences 173: swc_deu_001374 #utts: 1 +id: (swc_deu_001374-swc_deu_001374) +Scores: (#C #S #D #I) 0 4 1 0 +REF: AUCH NACHKOMMEN SIND NICHT BEKANNT +HYP: **** AUCHNACRKOMMEN SIN NICH BEKANT +Eval: D S S S S + +Speaker sentences 174: swc_deu_001375 #utts: 1 +id: (swc_deu_001375-swc_deu_001375) +Scores: (#C #S #D #I) 0 5 1 0 +REF: IHREN TEXTEN DEN EINDRUCK ZU VERMITTELN +HYP: ***** EREN TEKSTE DIN EINDRUGKTZE VERMITE +Eval: D S S S S S + +Speaker sentences 175: swc_deu_001376 #utts: 1 +id: (swc_deu_001376-swc_deu_001376) +Scores: (#C #S #D #I) 0 3 3 0 +REF: VERDIENSTE UM DAS KÖLNER LIED VERLIEHEN +HYP: ********** ** *** VERDINSTUM DASKELNALIET VERLIEN +Eval: D D D S S S + +Speaker sentences 176: swc_deu_001377 #utts: 1 +id: (swc_deu_001377-swc_deu_001377) +Scores: (#C #S #D #I) 0 10 1 0 +REF: OBWOHL HOFFMANN VON HOFFMANNSWALDAUS WERK GROSSEN EINFLUSS AUF SPÄTERE DICHTER AUSÜBTE +HYP: ****** ABPVOL HOFMEIN VN HAFMEIN WEL DAUS WERRK GOSENEINPLIUSAU SPÄRTERER DICHTERAUSCÜBP +Eval: D S S S S S S S S S S + +Speaker sentences 177: swc_deu_001378 #utts: 1 +id: (swc_deu_001378-swc_deu_001378) +Scores: (#C #S #D #I) 0 5 1 0 +REF: UM SO ERNST ALS STAATSOBERHAUPT VON +HYP: ** OM SOR EANZTAL STATS BERHAUPFVE +Eval: D S S S S S + +Speaker sentences 178: swc_deu_001379 #utts: 1 +id: (swc_deu_001379-swc_deu_001379) +Scores: (#C #S #D #I) 0 1 0 1 +REF: ****** DOKUMENTATION +HYP: EFREIE DOKMENTATZION +Eval: I S + +Speaker sentences 179: swc_deu_001380 #utts: 1 +id: (swc_deu_001380-swc_deu_001380) +Scores: (#C #S #D #I) 0 4 0 0 +REF: GESTALTUNG DES COVERS WIDERSPIEGELT +HYP: GESTALTUM BES KAVERS WIEDERSPBIEGELT +Eval: S S S S + +Speaker sentences 180: swc_deu_001381 #utts: 1 +id: (swc_deu_001381-swc_deu_001381) +Scores: (#C #S #D #I) 1 3 1 0 +REF: DER GESAMTE AUFWAND WIRD auf +HYP: *** DEGESAMTER AUFWANT WIT auf +Eval: D S S S + +Speaker sentences 181: swc_deu_001382 #utts: 1 +id: (swc_deu_001382-swc_deu_001382) +Scores: (#C #S #D #I) 2 8 3 0 +REF: OBGLEICH HAMBURG DIESEM ANGEHÖRTE und eine NOBILITIERUNG DURCH DEN KAISER DAMIT KEINE DURCH +HYP: AUBGLEICHMBURK DIESIEM AN GHRTU und eine ************* ***** *** NOBLI TIERUN DRICHENKEISERDAMIT KEINEDRESC +Eval: S S S S D D D S S S S + +Speaker sentences 182: swc_deu_001383 #utts: 1 +id: (swc_deu_001383-swc_deu_001383) +Scores: (#C #S #D #I) 4 4 3 0 +REF: DA ES DURCH DEN sich AUSWEITENDEN welthandel arbeit und WOHLSTAND VERSPRACH +HYP: ** ** DER ASTURCHTDEN sich AUSWEITENTEN welthandel arbeit und ********* WOLSTANVERSPRACH +Eval: D D S S S D S + +Speaker sentences 183: swc_deu_001384 #utts: 1 +id: (swc_deu_001384-swc_deu_001384) +Scores: (#C #S #D #I) 2 9 1 0 +REF: FÜR die zeit MITTE DES NEUNZEHNTE JAHRHUNDERTS BEKLAGTE DER ARCHITEKT MARTIN HALLER +HYP: FÖÖER die zeit ***** MITE DESNEUN IHNDE JERHUNDARS BEKLAK DERCHITEKT MATIN HALE +Eval: S D S S S S S S S S + +Speaker sentences 184: swc_deu_001385 #utts: 1 +id: (swc_deu_001385-swc_deu_001385) +Scores: (#C #S #D #I) 0 4 0 1 +REF: *** ALTBUNDESKANZLER HELMUT SCHMIDT LEHNTE +HYP: EIT BNDE KANZER HRMUTSMIT LENTE +Eval: I S S S S + +Speaker sentences 185: swc_deu_001386 #utts: 1 +id: (swc_deu_001386-swc_deu_001386) +Scores: (#C #S #D #I) 1 4 2 0 +REF: DEN NAMEN GODEFFROY im STAATSHANDBUCH ZU STREICHEN +HYP: *** DINAMEN GUODEFREI im ************** STAZSHANT BUOCHTZ +Eval: D S S D S S + +Speaker sentences 186: swc_deu_001387 #utts: 1 +id: (swc_deu_001387-swc_deu_001387) +Scores: (#C #S #D #I) 0 5 1 0 +REF: WENN AUCH MIT EINER GEWISSEN LETHARGIE +HYP: **** WEN AUCHMT EN ERGEWSEN LETAGIE +Eval: D S S S S S + +Speaker sentences 187: swc_deu_001388 #utts: 1 +id: (swc_deu_001388-swc_deu_001388) +Scores: (#C #S #D #I) 0 1 0 2 +REF: ** ******* KALKULIERBAR +HYP: KA KOULIER BAN +Eval: I I S + +Speaker sentences 188: swc_deu_001389 #utts: 1 +id: (swc_deu_001389-swc_deu_001389) +Scores: (#C #S #D #I) 0 5 2 0 +REF: ANGEFANGENE ZWEI HUNDERT FÜNFZIG SCHÜLER EINEN DELEGIERTEN +HYP: *********** **** ANGEFANGDETZSOANDE FM ISCHULEREINEN DIEG JEPEN +Eval: D D S S S S S + +Speaker sentences 189: swc_deu_001390 #utts: 1 +id: (swc_deu_001390-swc_deu_001390) +Scores: (#C #S #D #I) 0 7 4 0 +REF: VIELE MENSCHEN SAHEN DEN GRIZZLY ALS NAHRUNGSKONKURRENTEN UND ALS POTENTIELLE GEFAHR +HYP: ***** ******** ***** *** FIELMENSCHE SEINEING RESLI LSNARUNGSKONGURENTEN UNDELSPUTEN ELLGE FA +Eval: D D D D S S S S S S S + +Speaker sentences 190: swc_deu_001391 #utts: 1 +id: (swc_deu_001391-swc_deu_001391) +Scores: (#C #S #D #I) 0 2 1 0 +REF: DEN AUFTRITT VERKÜRZEN +HYP: *** DIN AUFTITVERKÖRE +Eval: D S S + +Speaker sentences 191: swc_deu_001392 #utts: 1 +id: (swc_deu_001392-swc_deu_001392) +Scores: (#C #S #D #I) 6 14 1 5 +REF: ** dem STAND vom ******** ********************* der INHALT STEHT UNTER der ** **** LIZENZ CREATIVE COMMONS ATTRIBUTION SHARE ALIKE DREI PUNKT NULL UNPORTED und unter DER +HYP: MI dem STAN vom DREIZEHN NIULIEZWEITAUSENSWERF der ****** INERSTIT UNDE der IE ZENZ KEHRTZUE KOMMONS EIT ZEIEWUSCHEN SCHER ELEITREI PUNK NOLE AN PRTET und unter DE +Eval: I S I I D S S I I S S S S S S S S S S S + +Speaker sentences 192: swc_deu_001393 #utts: 1 +id: (swc_deu_001393-swc_deu_001393) +Scores: (#C #S #D #I) 0 3 0 0 +REF: EINE KLEINERE BOGENBRÜCKE +HYP: EINI KLEINEREBOGEN PRÜKE +Eval: S S S + +Speaker sentences 193: swc_deu_001394 #utts: 1 +id: (swc_deu_001394-swc_deu_001394) +Scores: (#C #S #D #I) 1 1 3 0 +REF: sich NUN FÜR SEIN WEITERKOMMEN +HYP: sich *** **** **** NUNVERSENBITERKOM +Eval: D D D S + +Speaker sentences 194: swc_deu_001395 #utts: 1 +id: (swc_deu_001395-swc_deu_001395) +Scores: (#C #S #D #I) 1 4 0 0 +REF: AUS dem GEMÄLDE ZU ENTFERNEN +HYP: AUSS dem GEMELDETZU ENT FERINEN +Eval: S S S S + +Speaker sentences 195: swc_deu_001396 #utts: 1 +id: (swc_deu_001396-swc_deu_001396) +Scores: (#C #S #D #I) 0 6 4 0 +REF: NACH EUROPÄISCHER RICHTLINIE NEUNZIG VIER HUNDERT SECHSUNDNEUNZIG E W G +HYP: **** ************* ********** ******* ISDNEH AREPESCHERICSTINE NEUNZICG FIEORNERTE ENEUNZICH EWI +Eval: D D D D S S S S S S + +Speaker sentences 196: swc_deu_001397 #utts: 1 +id: (swc_deu_001397-swc_deu_001397) +Scores: (#C #S #D #I) 1 2 0 0 +REF: EINEM UMFELD auf +HYP: ARIEM UNSELT auf +Eval: S S + +Speaker sentences 197: swc_deu_001398 #utts: 1 +id: (swc_deu_001398-swc_deu_001398) +Scores: (#C #S #D #I) 3 4 0 0 +REF: UND sie sei AUCH WIE eine FÜRSTIN +HYP: OND sie sei AURCH DIE eine FÜLRSTEN +Eval: S S S S + +Speaker sentences 198: swc_deu_001399 #utts: 1 +id: (swc_deu_001399-swc_deu_001399) +Scores: (#C #S #D #I) 1 2 0 1 +REF: NEUNZEHN hundert ***** NEUNZEHN +HYP: NEITZIHN hundert NOEIN SIEE +Eval: S I S + +Speaker sentences 199: swc_deu_001400 #utts: 1 +id: (swc_deu_001400-swc_deu_001400) +Scores: (#C #S #D #I) 0 5 0 0 +REF: STATTDESSEN HABEN DIE RÖMISCHEN INGENIEURE +HYP: STARTESEN HABM DERÖÜMICHEN INSCHEN JIÖRE +Eval: S S S S S + +Speaker sentences 200: swc_deu_001401 #utts: 1 +id: (swc_deu_001401-swc_deu_001401) +Scores: (#C #S #D #I) 2 1 0 1 +REF: ********* GRIZZLYBÄR und mensch +HYP: DELKRISIE BEHR und mensch +Eval: I S + +Speaker sentences 201: swc_deu_001402 #utts: 1 +id: (swc_deu_001402-swc_deu_001402) +Scores: (#C #S #D #I) 0 2 0 1 +REF: *** MUSICIANS COALITION +HYP: MIE SISCHENZ KOALISCHE +Eval: I S S + +Speaker sentences 202: swc_deu_001403 #utts: 1 +id: (swc_deu_001403-swc_deu_001403) +Scores: (#C #S #D #I) 0 5 2 0 +REF: BERTOLD HUMMEL GIBT ES DREI VARIATIONEN MIT +HYP: ******* ****** BECTURSCHMLGEB DES S DREIBARERTIONEN I +Eval: D D S S S S S + +Speaker sentences 203: swc_deu_001404 #utts: 1 +id: (swc_deu_001404-swc_deu_001404) +Scores: (#C #S #D #I) 0 7 3 0 +REF: RÜCKZUGSGEBIET ERWIES SICH DER ACHTZEHN HUNDERT ZWEIUNDSIEBZIG GEGRÜNDETE YELLOWSTONE NATIONALPARK +HYP: *************** ****** **** KZUKSKGEBIET AERWESIHTER ACHZEHN HUNDARZWEIUNSIEBZICGE RÜNDETE ALUSDEUNER INALPAR +Eval: D D D S S S S S S S + +Speaker sentences 204: swc_deu_001405 #utts: 1 +id: (swc_deu_001405-swc_deu_001405) +Scores: (#C #S #D #I) 0 1 0 0 +REF: DEFINITION +HYP: DEVFINITZION +Eval: S + +Speaker sentences 205: swc_deu_001406 #utts: 1 +id: (swc_deu_001406-swc_deu_001406) +Scores: (#C #S #D #I) 1 6 3 0 +REF: um AN DER UNIVERSITÄT SEVILLA ZWEI SEMESTER KUNSTGESCHICHTE ZU STUDIEREN +HYP: um ** *** ************ INE UNVWE SITETZIEWILER ZWEISEMESTER KUNSGSCHCHT ZUSTUDIEN +Eval: D D D S S S S S S + +Speaker sentences 206: swc_deu_001407 #utts: 1 +id: (swc_deu_001407-swc_deu_001407) +Scores: (#C #S #D #I) 1 2 0 1 +REF: *** trotz IHRER GERINGEN +HYP: DIE trotz ERER GRINE +Eval: I S S + + diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/text b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/text new file mode 100644 index 0000000000000000000000000000000000000000..b5c36344a6d425129add7de2806a27f472dada25 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/text @@ -0,0 +1,207 @@ +swc_deu_001201 DRERVELIEBTE UNGEHEARZOGK DE RANSCHLE GESEINSFATESNICH BERACHTDITHA +swc_deu_001202 DIE IN DE HANZESTEÄTEN ALS +swc_deu_001203 ARKEINGROSE E FRELK +swc_deu_001204 GOSEN SCHEHMISCHIN VER BRIKTE +swc_deu_001205 WODEN ACH MERERE ALOEUTRUNGSBÜSCHEVERFNTLIH +swc_deu_001206 VORBEREITETEN BIER TEICG GE TUNKT +swc_deu_001207 DOKOMNTE SHIESIG IN I +swc_deu_001208 TAURTAG VÜRDEN TUT VON KNICH VRECHL E +swc_deu_001209 DARUNDER SIN MATÜLDE ARSENDIS WECHTER DS KROLTZIS +swc_deu_001210 IN INEN SHETEN MEHR UND MHR DE ROLEDERTHADTITZSNERLN FIS +swc_deu_001211 ZU DENEN MET LOU VIGKEIT +swc_deu_001212 DRACHE DISHOFESS UN DES ADETZ FÜR N FRFEL +swc_deu_001213 ZSEIT ANGABEMVERSICHTID +swc_deu_001214 ALLS ACHTIN UNDET ACHRT ZIGHMIT ODTUO BRAMS AUF SATZS +swc_deu_001215 MÜLENWESEN IETZIN UNERDACHUN WANI +swc_deu_001216 AS DE FISCH RIS +swc_deu_001217 SIDIM ABSHLOS IM JARENEUNZEN HNDER ZWALUN ACHTZICH UNDRNAMER EINE ERSTELENGEREREISENACSPBANEN +swc_deu_001218 VERN SCAT SO VURGETZEICHNET +swc_deu_001219 FEITEN STEINS VELSTENDIGGECHICHTEN UNDT DIE AUKSBURGERSTAGSCHIH E DES ELTRE +swc_deu_001220 NACH DIENZERSTÖRONMEN WURDE DERASCH WIDER AUFPLÜN +swc_deu_001221 ACHTEN EINFLUSREICHENHAN IE ATEN BEIM KOMIE SARESCHEINGESETZ N BÜÖRGEMEISTER MAKERT IER AUFAHTUN +swc_deu_001222 AILS ENTRALDESHANDES KONTOR +swc_deu_001223 SANDERSTELUNG INERHEIB DECSTAT KREFE +swc_deu_001224 VFINEZSICH IN HALOBAKTERIEN +swc_deu_001225 AUF DER BESEITE FINDEZSIHTAS EBEMFALS VON MEIKEL KOMPUNIET +swc_deu_001226 IN HN DEATICHER ZEITHATDI DI ZERKEGESERSCHFT KEINEN AUSCHLAGEBENTEN EINFLOSMHR +swc_deu_001227 DA STDERCHVRWENDUN VON AUFTRIEB SKAPAN ODERHALZS EINER GERINGERE MITTERERDICHTDE ALSWASER HAT +swc_deu_001228 DAMATESIERUNGEN +swc_deu_001229 UMN SIEBEN UR FÜNVO +swc_deu_001230 DES ELBREICHT DIE BADES TOCHTE +swc_deu_001231 TART BABMBARERSCHE STDATZR +swc_deu_001232 ERSLIT BESONDERSLIEBTE +swc_deu_001233 AUFKUND DERS WACSENDEN PUPLIKUMS INTRESSES WURDE DER AUFTRITS ORT Ü DI PRIMA VISTALESUNGE +swc_deu_001234 ND FREITECHTSBIE +swc_deu_001235 DAS DIE REIDEN STÖRZS RLTI UN BESCHARTET BERSTANDTEN HATE +swc_deu_001236 EAREN ERSCHNEN ZWEI IMRL +swc_deu_001237 DERGRABMAN UND GRAB GA PELEN O DER OLTATEN NACHALT +swc_deu_001238 IUNENENZENHUNDARSEXSUNEUINZIH KNDIKDER RSEINEBEITEN SOBPS +swc_deu_001239 IN GE POTIENEKOPEL +swc_deu_001240 NEUNUN SECHTZIG DER ME DIER KONTWOL ALBUMSCHATZ EIN +swc_deu_001241 M TARUSC KOM +swc_deu_001242 UONE HENICHT DEN KROSSHANDE KAUFLEUTEN GESERSHAFTLICGKLEICHGESTERT WARE +swc_deu_001243 VRNDERNARUNG UND VOMKIEMA +swc_deu_001244 APRLOE EIENZS +swc_deu_001245 BRÜÖÜL UND HÖERT NACKELE +swc_deu_001246 TWER IN EN KLOSTE +swc_deu_001247 DIEVIRZSM AUFIZEHREN KANEWALENSTANT UNTREUTE INEMISCHNG US KÖRSCHEN KANDEWAL UNDBPLIDSCHE KABRET MT KOM DELEMENTEN DASTELT U +swc_deu_001248 DIE WUNSTIEN RESLE DES FÜIRTE +swc_deu_001249 NANTIT ZIEGLER DIEARMORDUM DERBANAURN +swc_deu_001250 INTEROHR IST VOEL +swc_deu_001251 DIE STRENG DER VORGENGERLEITUNG WURDEN ZWISHE EUNZHN HUNDERT NEUNUENDZWANZIG UND NEUNZHN HUNDER DREINDFÜNFZIG ARCHE LOGESCH ERKRABEN +swc_deu_001252 IN GEGE SAT +swc_deu_001253 FABE VERN ERDIGENSEN LAUND HORT +swc_deu_001254 LEIF VERANSTALTUMEN +swc_deu_001255 ZOWERENHUTEINDEREGEL ALLE DORT LEBENDEN BRAUN +swc_deu_001256 IE DA FÜRE USCSELMER +swc_deu_001257 DESHAN DIE ATEN FÜÖRE +swc_deu_001258 HEBELTS ARG NES BANAU +swc_deu_001259 LIEBENSWEISE VERKRBPR +swc_deu_001260 EDEFEIL DES FIEREN HAMBURGANZU KATORLES VRAMMEN +swc_deu_001261 KERLTUL ND IERTHRFT AUSTAUSCHE +swc_deu_001262 MIJARZWEI TAUSEND VERTONDTE +swc_deu_001263 DASE DIESELEITUNGSCNALERVOLENTEN KÜNE ALS DER BAUMEISTER DEN KÖNER DOUM +swc_deu_001264 IDE HNRIHTUNG DE BANAUARIN HABESICSRLIHTUM +swc_deu_001265 LORDWEI +swc_deu_001266 DERZEIT DER BSTIGKHENER DER EIFELEITUN +swc_deu_001267 VOKUS BES WISENSHAFTLICHEN INDTARESES +swc_deu_001268 TEMER ZU BEGEISTE +swc_deu_001269 METE UND KONTE DAMIT AUFON INEN BERGANGEN WERDEN +swc_deu_001270 HAHTKABER BES SELLALISTEU DENH +swc_deu_001271 DARREIN EN ZIKLOP +swc_deu_001272 DN GRESLIE WIDER AUF DILESETZUSETZEN +swc_deu_001273 WIE LANG DIESE KAPLAN STELE AUFRICHTER HETENWRD +swc_deu_001274 SIEWARN MARSCHEINLIGHBEREITZ DREISZIG SIKUNDTEN NECH AUSPRCHTES VORER +swc_deu_001275 METER GESAMTLINGE UND BISTUT SEHN MIETER +swc_deu_001276 VEINER ITZEN UNDSPALT +swc_deu_001277 DINMEN VEN AUSSNDIEKOLER HINABPFLIESEN SIET +swc_deu_001278 NE ITER IOSAKTEBRAUN +swc_deu_001279 DAS FÜNFT EN GERIUM +swc_deu_001280 REISEN SIMANCHMAL WEIDETIERE ISCHAFE +swc_deu_001281 SI HÖREN DEN ARTIKEL DESEIN RÜFIU +swc_deu_001282 KUSER IS GLENTER KOCH +swc_deu_001283 SHANZE WENT STIFTDUN +swc_deu_001284 NEUNZHN HNDERT ACHTZIEN ALT S HAN IE ARTENANGESIE +swc_deu_001285 MERER ES NACH IM TUN +swc_deu_001286 AUCSHIH DES GERICHS ZUO LANDES WEITEN BERLIEBEN KOULINA RISCHER SPÄTZELTET ARMÜG +swc_deu_001287 KOLLETSCH UND EIN ZWEITCOB ALTSPBANSHLIERER IN HEM N VORLSEN +swc_deu_001288 BURDEN EINIS WEGS AELLE GEBÜIROTIGE +swc_deu_001289 IST IER KERBEBAUGREFTIGKH +swc_deu_001290 AN LESLICHTER NOUAS ANSPRACREKH +swc_deu_001291 MIT WIN VONDSCREÄGHINTEN +swc_deu_001292 DIN REÖCSTEN TEALDE BIT TZIÖGSFORTRIETUNG ÜRDINGEN AUS +swc_deu_001293 ACHTIN HUNERT EILUNZWANZI +swc_deu_001294 DSKROSEN ATELTS AN GESAMITENREICHTUMS +swc_deu_001295 DESON NIHT AL SEH ZWOL PROROKATIU +swc_deu_001296 TEILHBEDE VRMER GOSMAN UND IORGENZ +swc_deu_001297 INEMITE FRAUNTER KAUT INSELT +swc_deu_001298 AUR DIEO ISTEANDUTCHER HEBOFVARLARG MIT SITZ INMEÜNCHEN +swc_deu_001299 FA PEIK MENTE UNDSCHMICHE VORPRED UKTER HERSTELT +swc_deu_001300 ARPLICHEN PRESISCHEN FREI HEREN STAND IN DER ZOL ANSCHLOS VRAGE INTSCHIEBDENG GEGENG DEN SINART AUF DIESEITE BISMAGS GESTELT +swc_deu_001301 WENDIKWELEN VON SEST ER VORGWELEN UND AUFEN ZUTAGELIGEN +swc_deu_001302 DS VOUN NACHBARBAUTRIUB BELLEITZ BEGON WOT +swc_deu_001303 WEHRDEN PRÄRGENDE ELEMENTE DES HANSE ATENTUMSZUOSAMMEN GEFAST +swc_deu_001304 DEASSLIEZWRDERTS VOLCGSTLIET AN GESIEN +swc_deu_001305 DERZON NENDEM HUSVRLAG SKGOBIGE HÖRT +swc_deu_001306 FR DE KNFDIGEN BARD PBÜSCHER IND WIKETE DI PAPIEFERPR +swc_deu_001307 AMBRE WUOKS +swc_deu_001308 V DIEKWARAR SIE ARD LIGEN LANZITZE BETRIEBEN AUFAND SEISBEMBAU +swc_deu_001309 JA ZWEI TAUSEN ZWÖLF INDN BELIENER KLUOPSO SE SHNDREISICH VELLIGT +swc_deu_001310 SECHTIN HNEN FÜNFTIGH EID BÜNDNIS +swc_deu_001311 DASPROBLEN BI DEN PRADTACHSONIST +swc_deu_001312 AMINWESEN TETIAMALIE S IEVEKEIN +swc_deu_001313 ICH EI MALEINE AN SATZWEISE UNTE SOCHRUNG ZU IEREM VERHALKTEN INDER ZEITT DES NAZUNALSUTZELISMUS +swc_deu_001314 LIETZSEINS FÜRFREIE DOKOMENTATION +swc_deu_001315 DIM CHZEN NER HUNDER DIGATEN HOLSE VOR ENTORE +swc_deu_001316 GANZS IMSTIELDER ZEIT +swc_deu_001317 ER BRÜL UND HÜÖRT AREICH E DI LEITUNSCHISLIKEN +swc_deu_001318 AUSSTZEICHNUMEN REM DER HEREN +swc_deu_001319 DIESCHEFTELLEREI AUFTZUGEBE +swc_deu_001320 DAZUTZIHRN DE BEGEGNUNMIT VERLETZTENTIERE +swc_deu_001321 IENESCH STDIFT +swc_deu_001322 WESTLICH VON KERLEN +swc_deu_001323 DIE STENDI N BETRIPWARE +swc_deu_001324 DI VOM BA BIJERHASIERT WEHRDE +swc_deu_001325 ERSCHEN NOREINEITRAUFSEATZFON GRISTERNMEIAL +swc_deu_001326 WAEIL SEBST EXSTRME REICHTUMKEINES WEHST IN UNMITLEBAREN SZUGAN +swc_deu_001327 GEBT EUSCHEICH SELBER AUF +swc_deu_001328 ER HAET DIESEN BRAUCH NEUNEHIN HNDER WEIUND FÜNFTIGH GIGN ÜB +swc_deu_001329 O DELEITUNG ÜBE DIE ALTE HÜÖRTEARLEITUNGEFIERT WURDE 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DESBÜURGERECHT UN DEREINFÜRUN DE REITZÜGICHKEIT IM ZBANSIS NERNDERT WANDTET DE SICH DIESE ANSCHAUNG ANSERT SWEIESEIN DAR IEN +swc_deu_001349 DES SZWIST BACRES BALREINBACH EINE BOGEN BRÜCKE V +swc_deu_001350 ACTZINULETE SNDREISIG BWORDE E HMBUG +swc_deu_001351 DG AEM +swc_deu_001352 AUFGRUND DE KONTINEN TEALSPBARE ACHTZEHN HUNER ELF BANKOT +swc_deu_001353 EITERESMEIMUST DEN UNPLEIPT BRAUN DIWARBUNG FÜREAS BOHSEBTWANEM +swc_deu_001354 DINERIH VM SIEGTDER BÜRGELICH MUGRATISCHEN FIEPOARERLUTZIONVEN ACHZEIN HNDET ACHT N VIRZICHN RANKREICHVWRDEN HAMBURGMITIOBE AUFGENAME +swc_deu_001355 OUMBLIETZWEIARE ONE DERBRESCHN +swc_deu_001356 ZOALREICHE GASTSPILN UNTERWEGHS +swc_deu_001357 KRANZIETET GENÜTEN +swc_deu_001358 UN BEROKER AUSTATO +swc_deu_001359 DAGERICHT VOUM BEIVWARGENSANGESMOTORADIS AUS INDIE ZU DESERZEIT NOU EN STENDEN ABET ASIET LOMENZU +swc_deu_001360 DIMITZSAM IE HERECHEN STOBE +swc_deu_001361 KEISERFERDINAN +swc_deu_001362 VOM FERNSERESCHSUHRERFEANZSAVERBUGNER INDEM FERNSEFILEN DAS 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S T I N U N M I T L E B A R E N S Z U G A N +swc_deu_001327 G E B T E U S C H E I C H S E L B E R A U F +swc_deu_001328 E R H A E T D I E S E N B R A U C H N E U N E H I N H N D E R W E I U N D F Ü N F T I G H G I G N Ü B +swc_deu_001329 O D E L E I T U N G Ü B E D I E A L T E H Ü Ö R T E A R L E I T U N G E F I E R T W U R D E +swc_deu_001330 I N E B L I E B T E K L S C H O K T R O P E A S T E K E N E R U M N A N D T D I E H Ö N E +swc_deu_001331 G E W A R D E N S E I O U N D A L B R E C H T D I C H +swc_deu_001332 D E T A G E S B E D A F E I N E S W A G S T E N E N A N I T E M I N A R +swc_deu_001333 S I B T I N U L E R Z I H E N O B A R A L T E +swc_deu_001334 W E I T E R H E N L I E S I C H N A C R W E I S E N +swc_deu_001335 Z U N G R Ü N U N G S T D A R T U M K O N T B A M B E R E I T +swc_deu_001336 K E I N L E C S C H L A R G M Ü K L I C H N A C H T E I L E +swc_deu_001337 R T D I K A T O L I C H E K Ü R S C H E S A N P Ä T E R A N D E R S T E L L E D E A L T +swc_deu_001338 E R F E K N A R P B U N D E S B R O T W E I T Z E N T R A T A B S C H N B A I T D I K E R T O F E L E I S E R S A T +swc_deu_001339 K R E M T D I E S E N N A C H K O M N Z O E U G E +swc_deu_001340 A L L E N E I N F O L G E N D E R H Ö R S I E R E I E +swc_deu_001341 S C H I B P S M I E B E R A C T E N S O +swc_deu_001342 K L I G E A N R E R S B U O C H N E V E R Z I C H T E A U F E N E B E R S Ö N I C H E B E W E R T U N +swc_deu_001343 B E N I G E R I N T R U S S T E +swc_deu_001344 W E I T E R H E N V E R S O R K D D I E L E I T U N G T E R M E N +swc_deu_001345 W A R T E T E N D A F Ü H R A B E M I T E I N I G +swc_deu_001346 D E D I K L I C H A N T R Ü N D F O N K L E I N M U N I E R T I I N S E I N A R E T Z E N S O N D +swc_deu_001347 E M J A B R N E U Z E N E R T F Ü M +swc_deu_001348 I E R S I T E M F R T F A L D E S B Ü U R G E R E C H T U N D E R E I N F Ü R U N D E R E I T Z Ü G I C H K E I T I M Z B A N S I S N E R N D E R T W A N D T E T D E S I C H D I E S E A N S C H A U N G A N S E R T S W E I E S E I N D A R I E N +swc_deu_001349 D E S S Z W I S T B A C R E S B A L R E I N B A C H E I N E B O G E N B R Ü C K E V +swc_deu_001350 A C T Z I N U L E T E S N D R E I S I G B W O R D E E H M B U G +swc_deu_001351 D G A E M +swc_deu_001352 A U F G R U N D D E K O N T I N E N T E A L S P B A R E A C H T Z E H N H U N E R E L F B A N K O T +swc_deu_001353 E I T E R E S M E I M U S T D E N U N P L E I P T B R A U N D I W A R B U N G F Ü R E A S B O H S E B T W A N E M +swc_deu_001354 D I N E R I H V M S I E G T D E R B Ü R G E L I C H M U G R A T I S C H E N F I E P O A R E R L U T Z I O N V E N A C H Z E I N H N D E T A C H T N V I R Z I C H N R A N K R E I C H V W R D E N H A M B U R G M I T I O B E A U F G E N A M E +swc_deu_001355 O U M B L I E T Z W E I A R E O N E D E R B R E S C H N +swc_deu_001356 Z O A L R E I C H E G A S T S P I L N U N T E R W E G H S +swc_deu_001357 K R A N Z I E T E T G E N Ü T E N +swc_deu_001358 U N B E R O K E R A U S T A T O +swc_deu_001359 D A G E R I C H T V O U M B E I V W A R G E N S A N G E S M O T O R A D I S A U S I N D I E Z U D E S E R Z E I T N O U E N S T E N D E N A B E T A S I E T L O M E N Z U +swc_deu_001360 D I M I T Z S A M I E H E R E C H E N S T O B E +swc_deu_001361 K E I S E R F E R D I N A N +swc_deu_001362 V O M F E R N S E R E S C H S U H R E R F E A N Z S A V E R B U G N E R I N D E M F E R N S E F I L E N D A S W I G E L I E T +swc_deu_001363 U R I N S E I N B E S T E N Z E I T E N D E +swc_deu_001364 S E H Ö R E D E N A R T I K E L F I S C H E N T S C I S +swc_deu_001365 U N D T E F A R M O F W I +swc_deu_001366 R E A Z S I B Z I O N D E R H E H S E N I M A T I G V O N K R E S T A +swc_deu_001367 D I G E S A M T E R A N L A R G E W A B I S E T V E R T Z W E I H U N D E R S E C H T Z I G N A C H K R I S T U S I N B E D R I E +swc_deu_001368 D E R I S D E F A S T F U T L I E R S O A R W I S W A G E B U N +swc_deu_001369 E I N D E M K A B E L B A U N +swc_deu_001370 M O R L E D U N G M I T D E E F A F V E R B U N D E N G E W E S +swc_deu_001371 E E L T Z I S T N P R I E R E R E N I N A U S R H E P +swc_deu_001372 S O N E N A C H D E R N A Z U N N A L I S U T S E R D I S T I S C H E N K U N Z T A U F F A S O N G E R E C H T W E H R E N +swc_deu_001373 D I E W E L T Z S I C H T D E S H A N S E A H R T E N I +swc_deu_001374 A U C H N A C R K O M M E N S I N N I C H B E K A N T +swc_deu_001375 E R E N T E K S T E D I N E I N D R U G K T Z E V E R M I T E +swc_deu_001376 V E R D I N S T U M D A S K E L N A L I E T V E R L I E N +swc_deu_001377 A B P V O L H O F M E I N V N H A F M E I N W E L D A U S W E R R K G O S E N E I N P L I U S A U S P Ä R T E R E R D I C H T E R A U S C Ü B P +swc_deu_001378 O M S O R E A N Z T A L S T A T S B E R H A U P F V E +swc_deu_001379 E F R E I E D O K M E N T A T Z I O N +swc_deu_001380 G E S T A L T U M B E S K A V E R S W I E D E R S P B I E G E L T +swc_deu_001381 D E G E S A M T E R A U F W A N T W I T A U F +swc_deu_001382 A U B G L E I C H M B U R K D I E S I E M A N G H R T U U N D E I N E N O B L I T I E R U N D R I C H E N K E I S E R D A M I T K E I N E D R E S C +swc_deu_001383 D E R A S T U R C H T D E N S I C H A U S W E I T E N T E N W E L T H A N D E L A R B E I T U N D W O L S T A N V E R S P R A C H +swc_deu_001384 F Ö Ö E R D I E Z E I T M I T E D E S N E U N I H N D E J E R H U N D A R S B E K L A K D E R C H I T E K T M A T I N H A L E +swc_deu_001385 E I T B N D E K A N Z E R H R M U T S M I T L E N T E +swc_deu_001386 D I N A M E N G U O D E F R E I I M S T A Z S H A N T B U O C H T Z +swc_deu_001387 W E N A U C H M T E N E R G E W S E N L E T A G I E +swc_deu_001388 K A K O U L I E R B A N +swc_deu_001389 A N G E F A N G D E T Z S O A N D E F M I S C H U L E R E I N E N D I E G J E P E N +swc_deu_001390 F I E L M E N S C H E S E I N E I N G R E S L I L S N A R U N G S K O N G U R E N T E N U N D E L S P U T E N E L L G E F A +swc_deu_001391 D I N A U F T I T V E R K Ö R E +swc_deu_001392 M I D E M S T A N V O M D R E I Z E H N N I U L I E Z W E I T A U S E N S W E R F D E R I N E R S T I T U N D E D E R I E Z E N Z K E H R T Z U E K O M M O N S E I T Z E I E W U S C H E N S C H E R E L E I T R E I P U N K N O L E A N P R T E T U N D U N T E R D E +swc_deu_001393 E I N I K L E I N E R E B O G E N P R Ü K E +swc_deu_001394 S I C H N U N V E R S E N B I T E R K O M +swc_deu_001395 A U S S D E M G E M E L D E T Z U E N T F E R I N E N +swc_deu_001396 I S D N E H A R E P E S C H E R I C S T I N E N E U N Z I C G F I E O R N E R T E E N E U N Z I C H E W I +swc_deu_001397 A R I E M U N S E L T A U F +swc_deu_001398 O N D S I E S E I A U R C H D I E E I N E F Ü L R S T E N +swc_deu_001399 N E I T Z I H N H U N D E R T N O E I N S I E E +swc_deu_001400 S T A R T E S E N H A B M D E R Ö Ü M I C H E N I N S C H E N J I Ö R E +swc_deu_001401 D E L K R I S I E B E H R U N D M E N S C H +swc_deu_001402 M I E S I S C H E N Z K O A L I S C H E +swc_deu_001403 B E C T U R S C H M L G E B D E S S D R E I B A R E R T I O N E N I +swc_deu_001404 K Z U K S K G E B I E T A E R W E S I H T E R A C H Z E H N H U N D A R Z W E I U N S I E B Z I C G E R Ü N D E T E A L U S D E U N E R I N A L P A R +swc_deu_001405 D E V F I N I T Z I O N +swc_deu_001406 U M I N E U N V W E S I T E T Z I E W I L E R Z W E I S E M E S T E R K U N S G S C H C H T Z U S T U D I E N +swc_deu_001407 D I E T R O T Z E R E R G R I N E diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/token_int new file mode 100644 index 0000000000000000000000000000000000000000..ae85f5da155b6e8dabe507a94ef04536ee60f7a7 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/token_int @@ -0,0 +1,207 @@ +swc_deu_001201 10 6 2 6 24 2 13 5 2 18 8 2 3 12 4 14 2 11 2 9 6 20 16 14 22 3 10 2 3 6 9 4 7 15 11 13 2 3 14 2 7 2 5 4 7 19 9 8 2 7 4 5 15 11 3 18 2 6 9 15 11 8 10 5 8 11 9 +swc_deu_001202 10 5 2 3 5 4 3 10 2 3 11 9 4 20 2 7 8 2 26 8 2 4 3 9 13 7 +swc_deu_001203 9 6 22 2 5 4 14 6 16 7 2 3 2 3 19 6 2 13 22 +swc_deu_001204 14 16 7 2 4 3 7 15 11 2 11 17 5 7 15 11 5 4 3 24 2 6 3 18 6 5 22 8 2 +swc_deu_001205 21 16 10 2 4 3 9 15 11 3 17 2 6 2 6 2 3 9 13 16 2 12 8 6 12 4 14 7 18 25 7 15 11 2 24 2 6 19 4 8 13 5 11 +swc_deu_001206 24 16 6 18 2 6 2 5 8 2 8 2 4 3 18 5 2 6 3 8 2 5 15 14 3 14 2 3 8 12 4 22 8 +swc_deu_001207 10 16 22 16 17 4 8 2 3 7 11 5 2 7 5 14 3 5 4 3 5 +swc_deu_001208 8 9 12 6 8 9 14 3 24 25 6 10 2 4 3 8 12 8 3 24 16 4 3 22 4 5 15 11 3 24 6 2 15 11 13 3 2 +swc_deu_001209 10 9 6 12 4 10 2 6 3 7 5 4 3 17 9 8 25 13 10 2 3 9 6 7 2 4 10 5 7 3 21 2 15 11 8 2 6 3 10 7 3 22 6 16 13 8 20 5 7 +swc_deu_001210 5 4 3 5 4 2 4 3 7 11 2 8 2 4 3 17 2 11 6 3 12 4 10 3 17 11 6 3 10 2 3 6 16 13 2 10 2 6 8 11 9 10 8 5 8 20 7 4 2 6 13 4 3 19 5 7 +swc_deu_001211 20 12 3 10 2 4 2 4 3 17 2 8 3 13 16 12 3 24 5 14 22 2 5 8 +swc_deu_001212 10 6 9 15 11 2 3 10 5 7 11 16 19 2 7 7 3 12 4 3 10 2 7 3 9 10 2 8 20 3 19 25 6 3 4 3 19 6 19 2 13 +swc_deu_001213 20 7 2 5 8 3 9 4 14 9 18 2 17 24 2 6 7 5 15 11 8 5 10 +swc_deu_001214 3 9 13 13 7 3 9 15 11 8 5 4 3 12 4 10 2 8 3 9 15 11 6 8 3 20 5 14 11 17 5 8 3 16 10 8 12 16 3 18 6 9 17 7 3 9 12 19 3 7 9 8 20 7 +swc_deu_001215 17 25 13 2 4 21 2 7 2 4 3 5 2 8 20 5 4 3 12 4 2 6 10 9 15 11 12 4 3 21 9 4 5 +swc_deu_001216 9 7 3 10 2 3 19 5 7 15 11 3 6 5 7 +swc_deu_001217 7 5 10 5 17 3 9 18 7 11 13 16 7 3 5 17 3 28 9 6 2 4 2 12 4 20 2 4 3 11 4 10 2 6 3 20 21 9 13 12 4 3 9 15 11 8 20 5 15 11 3 12 4 10 6 4 9 17 2 6 3 2 5 4 2 3 2 6 7 8 2 13 2 4 14 2 6 2 6 2 5 7 2 4 9 15 7 23 18 9 4 2 4 +swc_deu_001218 24 2 6 4 3 7 15 9 8 3 7 16 3 24 12 6 14 2 8 20 2 5 15 11 4 2 8 +swc_deu_001219 19 2 5 8 2 4 3 7 8 2 5 4 7 3 24 2 13 7 8 2 4 10 5 14 14 2 15 11 5 15 11 8 2 4 3 12 4 10 8 3 10 5 2 3 9 12 22 7 18 12 6 14 2 6 7 8 9 14 7 15 11 5 11 3 2 3 10 2 7 3 2 13 8 6 2 +swc_deu_001220 4 9 15 11 3 10 5 2 4 20 2 6 7 8 27 6 16 4 17 2 4 3 21 12 6 10 2 3 10 2 6 9 7 15 11 3 21 5 10 2 6 3 9 12 19 23 13 25 4 +swc_deu_001221 9 15 11 8 2 4 3 2 5 4 19 13 12 7 6 2 5 15 11 2 4 11 9 4 3 5 2 3 9 8 2 4 3 18 2 5 17 3 22 16 17 5 2 3 7 9 6 2 7 15 11 2 5 4 14 2 7 2 8 20 3 4 3 18 25 27 6 14 2 17 2 5 7 8 2 6 3 17 9 22 2 6 8 3 5 2 6 3 9 12 19 9 11 8 12 4 +swc_deu_001222 9 5 13 7 3 2 4 8 6 9 13 10 2 7 11 9 4 10 2 7 3 22 16 4 8 16 6 +swc_deu_001223 7 9 4 10 2 6 7 8 2 13 12 4 14 3 5 4 2 6 11 2 5 18 3 10 2 15 7 8 9 8 3 22 6 2 19 2 +swc_deu_001224 24 19 5 4 2 20 7 5 15 11 3 5 4 3 11 9 13 16 18 9 22 8 2 6 5 2 4 +swc_deu_001225 9 12 19 3 10 2 6 3 18 2 7 2 5 8 2 3 19 5 4 10 2 20 7 5 11 8 9 7 3 2 18 2 17 19 9 13 7 3 24 16 4 3 17 2 5 22 2 13 3 22 16 17 23 12 4 5 2 8 +swc_deu_001226 5 4 3 11 4 3 10 2 9 8 5 15 11 2 6 3 20 2 5 8 11 9 8 10 5 3 10 5 3 20 2 6 22 2 14 2 7 2 6 7 15 11 19 8 3 22 2 5 4 2 4 3 9 12 7 15 11 13 9 14 2 18 2 4 8 2 4 3 2 5 4 19 13 16 7 17 11 6 +swc_deu_001227 10 9 3 7 8 10 2 6 15 11 24 6 21 2 4 10 12 4 3 24 16 4 3 9 12 19 8 6 5 2 18 3 7 22 9 23 9 4 3 16 10 2 6 11 9 13 20 7 3 2 5 4 2 6 3 14 2 6 5 4 14 2 6 2 3 17 5 8 8 2 6 2 6 10 5 15 11 8 10 2 3 9 13 7 21 9 7 2 6 3 11 9 8 +swc_deu_001228 10 9 17 9 8 2 7 5 2 6 12 4 14 2 4 +swc_deu_001229 12 17 4 3 7 5 2 18 2 4 3 12 6 3 19 25 4 24 16 +swc_deu_001230 10 2 7 3 2 13 18 6 2 5 15 11 8 3 10 5 2 3 18 9 10 2 7 3 8 16 15 11 8 2 +swc_deu_001231 8 9 6 8 3 18 9 18 17 18 9 6 2 6 7 15 11 2 3 7 8 10 9 8 20 6 +swc_deu_001232 2 6 7 13 5 8 3 18 2 7 16 4 10 2 6 7 13 5 2 18 8 2 +swc_deu_001233 9 12 19 22 12 4 10 3 10 2 6 7 3 21 9 15 7 2 4 10 2 4 3 23 12 23 13 5 22 12 17 7 3 5 4 8 6 2 7 7 2 7 3 21 12 6 10 2 3 10 2 6 3 9 12 19 8 6 5 8 7 3 16 6 8 3 25 3 10 5 3 23 6 5 17 9 3 24 5 7 8 9 13 2 7 12 4 14 2 +swc_deu_001234 4 10 3 19 6 2 5 8 2 15 11 8 7 18 5 2 +swc_deu_001235 10 9 7 3 10 5 2 3 6 2 5 10 2 4 3 7 8 27 6 20 7 3 6 13 8 5 3 12 4 3 18 2 7 15 11 9 6 8 2 8 3 18 2 6 7 8 9 4 10 8 2 4 3 11 9 8 2 +swc_deu_001236 2 9 6 2 4 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10 3 7 5 2 3 7 2 5 3 9 12 6 15 11 3 10 5 2 3 2 5 4 2 3 19 25 13 6 7 8 2 4 +swc_deu_001399 4 2 5 8 20 5 11 4 3 11 12 4 10 2 6 8 3 4 16 2 5 4 3 7 5 2 2 +swc_deu_001400 7 8 9 6 8 2 7 2 4 3 11 9 18 17 3 10 2 6 27 25 17 5 15 11 2 4 3 5 4 7 15 11 2 4 3 28 5 27 6 2 +swc_deu_001401 10 2 13 22 6 5 7 5 2 3 18 2 11 6 3 12 4 10 3 17 2 4 7 15 11 +swc_deu_001402 17 5 2 3 7 5 7 15 11 2 4 20 3 22 16 9 13 5 7 15 11 2 +swc_deu_001403 18 2 15 8 12 6 7 15 11 17 13 14 2 18 3 10 2 7 3 7 3 10 6 2 5 18 9 6 2 6 8 5 16 4 2 4 3 5 +swc_deu_001404 22 20 12 22 7 22 14 2 18 5 2 8 3 9 2 6 21 2 7 5 11 8 2 6 3 9 15 11 20 2 11 4 3 11 12 4 10 9 6 20 21 2 5 12 4 7 5 2 18 20 5 15 14 2 3 6 25 4 10 2 8 2 3 9 13 12 7 10 2 12 4 2 6 3 5 4 9 13 23 9 6 +swc_deu_001405 10 2 24 19 5 4 5 8 20 5 16 4 +swc_deu_001406 12 17 3 5 4 2 3 12 4 24 21 2 3 7 5 8 2 8 20 5 2 21 5 13 2 6 3 20 21 2 5 7 2 17 2 7 8 2 6 3 22 12 4 7 14 7 15 11 15 11 8 3 20 12 7 8 12 10 5 2 4 +swc_deu_001407 10 5 2 3 8 6 16 8 20 3 2 6 2 6 3 14 6 5 4 2 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/run.sh b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/run.sh new file mode 100755 index 0000000000000000000000000000000000000000..0b1750a6d194cc15b88e43a8b5aeb2c0c62fe8c7 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/run.sh @@ -0,0 +1 @@ +./asr.sh --ngpu 1 --stage 11 --stop_stage 13 --nj 32 --inference_nj 4 --gpu_inference true --lang deu1 --inference_asr_model valid.loss.ave.pth --local_data_opts '--duration 10min --lid false --multilingual false --single_lang deu1' --use_lm false --token_type char --feats_type raw --feats_normalize utterance_mvn --asr_config conf/tuning/train_asr_s3prl_houlsby.yaml --inference_config conf/decode_asr.yaml --train_set train_10min_deu1 --valid_set dev_10min_deu1 --test_sets 'dev_10min_deu1 test_10min_deu1' --asr_tag train_asr_s3prl_houlsby_deu1_10min --expdir test_pr --asr_stats_dir test_pr/asr_stats_deu1_10min --local_score_opts 'false false monolingual' --stage 12 "$@"; exit $? diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.1.log b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.1.log new file mode 100644 index 0000000000000000000000000000000000000000..35f536cfb6bc323aa84f6d2b595c5f323cda5317 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.1.log @@ -0,0 +1,1845 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1 --config conf/decode_asr.yaml +# Started at Tue Jan 16 22:15:45 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1 --config conf/decode_asr.yaml +2024-01-16 22:15:46,563 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml +2024-01-16 22:15:46,581 (asr:523) INFO: Vocabulary size: 44 +2024-01-16 22:15:46,643 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 22:15:46,643 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 22:15:46,753 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 22:15:48,039 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 22:15:49,270 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 22:15:49,270 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 22:15:49,270 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 22:15:49,303 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-16 22:15:49,378 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 22:15:49,491 (asr_inference:494) INFO: speech length: 140637 +2024-01-16 22:15:50,709 (beam_search:428) INFO: decoder input length: 217 +2024-01-16 22:15:50,709 (beam_search:429) INFO: max output length: 217 +2024-01-16 22:15:50,709 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:51,386 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:51,387 (beam_search:476) INFO: -30.21 * 1.0 = -30.21 for ctc +2024-01-16 22:15:51,387 (beam_search:479) INFO: total log probability: -30.21 +2024-01-16 22:15:51,387 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:15:51,387 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:51,387 (beam_search:483) INFO: best hypo: DIBEHRDIGONMACHTEINERAEUSESTWICHTIGENSEHEININDEDERPÄTITZIONNDINGOWANÜÖRFÜRDESINJANERDSOUSSBEGENADIGUN + +2024-01-16 22:15:51,412 (asr_inference:494) INFO: speech length: 83197 +2024-01-16 22:15:51,423 (beam_search:428) INFO: decoder input length: 127 +2024-01-16 22:15:51,423 (beam_search:429) INFO: max output length: 127 +2024-01-16 22:15:51,423 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:51,676 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:51,676 (beam_search:476) INFO: -11.90 * 1.0 = -11.90 for ctc +2024-01-16 22:15:51,676 (beam_search:479) INFO: total log probability: -11.90 +2024-01-16 22:15:51,676 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:15:51,676 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:51,677 (beam_search:483) INFO: best hypo: DARHABESIDIEVWOULGJEDEMHERINERINERUNKGEBLIEBENENWARTEGESPAOCHEN + +2024-01-16 22:15:51,678 (asr_inference:494) INFO: speech length: 181277 +2024-01-16 22:15:51,695 (beam_search:428) INFO: decoder input length: 281 +2024-01-16 22:15:51,695 (beam_search:429) INFO: max output length: 281 +2024-01-16 22:15:51,695 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:52,775 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:52,775 (beam_search:476) INFO: -36.01 * 1.0 = -36.01 for ctc +2024-01-16 22:15:52,775 (beam_search:479) INFO: total log probability: -36.01 +2024-01-16 22:15:52,775 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:15:52,775 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:52,776 (beam_search:483) INFO: best hypo: ERSTUMACHTUHRWARERAUFMALELBRCHTERDENGAFIDESNESCHENINZSZIMERUNDESPEHRLINGEDIEDASSAUSDENHEXESEGTENGEFALNEOTARKONAUFSPBIKTEN + +2024-01-16 22:15:52,778 (asr_inference:494) INFO: speech length: 67837 +2024-01-16 22:15:52,788 (beam_search:428) INFO: decoder input length: 103 +2024-01-16 22:15:52,788 (beam_search:429) INFO: max output length: 103 +2024-01-16 22:15:52,788 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:52,952 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:52,952 (beam_search:476) INFO: -13.82 * 1.0 = -13.82 for ctc +2024-01-16 22:15:52,952 (beam_search:479) INFO: total log probability: -13.82 +2024-01-16 22:15:52,952 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:15:52,952 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:52,952 (beam_search:483) INFO: best hypo: SICHERLICHANIRENGEBUOTSTAKETERBAIERBLEIBENKONEN + +2024-01-16 22:15:52,954 (asr_inference:494) INFO: speech length: 109868 +2024-01-16 22:15:52,966 (beam_search:428) INFO: decoder input length: 169 +2024-01-16 22:15:52,966 (beam_search:429) INFO: max output length: 169 +2024-01-16 22:15:52,966 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:53,444 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:53,444 (beam_search:476) INFO: -26.97 * 1.0 = -26.97 for ctc +2024-01-16 22:15:53,444 (beam_search:479) INFO: total log probability: -26.97 +2024-01-16 22:15:53,444 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:15:53,444 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:53,445 (beam_search:483) INFO: best hypo: NDESEALBEÖMMUSMANDAURTVOROMENTCHNSCHIERIGKETENHABENDISOUCHEINASEITSEKLIERENAINGEBUTEMACHENBEALEW + +2024-01-16 22:15:53,446 (asr_inference:494) INFO: speech length: 104637 +2024-01-16 22:15:53,458 (beam_search:428) INFO: decoder input length: 161 +2024-01-16 22:15:53,458 (beam_search:429) INFO: max output length: 161 +2024-01-16 22:15:53,458 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:53,843 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:53,843 (beam_search:476) INFO: -17.76 * 1.0 = -17.76 for ctc +2024-01-16 22:15:53,843 (beam_search:479) INFO: total log probability: -17.76 +2024-01-16 22:15:53,843 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:15:53,843 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:53,843 (beam_search:483) INFO: best hypo: ESMENNRFTDIELTKOMTUMSEBSTIDERINSONDZUOHABENDERDIEVERERHUNGKDERANENVORTETZT + +2024-01-16 22:15:53,845 (asr_inference:494) INFO: speech length: 159093 +2024-01-16 22:15:53,860 (beam_search:428) INFO: decoder input length: 246 +2024-01-16 22:15:53,860 (beam_search:429) INFO: max output length: 246 +2024-01-16 22:15:53,860 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:54,728 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:54,728 (beam_search:476) INFO: -32.80 * 1.0 = -32.80 for ctc +2024-01-16 22:15:54,728 (beam_search:479) INFO: total log probability: -32.80 +2024-01-16 22:15:54,728 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:54,728 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:54,729 (beam_search:483) INFO: best hypo: ABEÖANEUNEINLICHERSCHULEBELUNGUNTENERLICHEMÜKLICHTEITRAUHDEREITERBLUNGUNTDASDEGEHNVONGEDENKTAGNDEMICHUNAUFLSLICHTRAMD + +2024-01-16 22:15:54,731 (asr_inference:494) INFO: speech length: 156957 +2024-01-16 22:15:54,746 (beam_search:428) INFO: decoder input length: 243 +2024-01-16 22:15:54,746 (beam_search:429) INFO: max output length: 243 +2024-01-16 22:15:54,746 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:55,576 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:55,576 (beam_search:476) INFO: -31.51 * 1.0 = -31.51 for ctc +2024-01-16 22:15:55,576 (beam_search:479) INFO: total log probability: -31.51 +2024-01-16 22:15:55,576 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:55,576 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:55,577 (beam_search:483) INFO: best hypo: EINANSASHENSAKZISISTWERGERTSTWIEDERGANZSKGUTZWISCHENUNSARBEEDUNICHTALIESGESTDIESTDGIETIEERINRUNGANDASBEÖSENICHTWEG + +2024-01-16 22:15:55,578 (asr_inference:494) INFO: speech length: 46077 +2024-01-16 22:15:55,586 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 22:15:55,586 (beam_search:429) INFO: max output length: 69 +2024-01-16 22:15:55,586 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:55,649 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:55,649 (beam_search:476) INFO: -4.88 * 1.0 = -4.88 for ctc +2024-01-16 22:15:55,649 (beam_search:479) INFO: total log probability: -4.88 +2024-01-16 22:15:55,649 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:15:55,649 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:55,650 (beam_search:483) INFO: best hypo: NEINWEIBERBRAUREICHENICH + +2024-01-16 22:15:55,651 (asr_inference:494) INFO: speech length: 93277 +2024-01-16 22:15:55,662 (beam_search:428) INFO: decoder input length: 143 +2024-01-16 22:15:55,662 (beam_search:429) INFO: max output length: 143 +2024-01-16 22:15:55,662 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:55,927 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:55,927 (beam_search:476) INFO: -14.89 * 1.0 = -14.89 for ctc +2024-01-16 22:15:55,927 (beam_search:479) INFO: total log probability: -14.89 +2024-01-16 22:15:55,927 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:15:55,927 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:55,928 (beam_search:483) INFO: best hypo: ENDENGORTHATNICHTVERGEBLICHNEHMERGERUOFENSAKTEDERCHIEVER + +2024-01-16 22:15:55,929 (asr_inference:494) INFO: speech length: 197437 +2024-01-16 22:15:55,947 (beam_search:428) INFO: decoder input length: 306 +2024-01-16 22:15:55,947 (beam_search:429) INFO: max output length: 306 +2024-01-16 22:15:55,947 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:57,225 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:57,225 (beam_search:476) INFO: -34.48 * 1.0 = -34.48 for ctc +2024-01-16 22:15:57,225 (beam_search:479) INFO: total log probability: -34.48 +2024-01-16 22:15:57,225 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:15:57,225 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:57,226 (beam_search:483) INFO: best hypo: NUREINESWEISSICHDIESERVURCHTBARENFRARGEINDGEGENZUSETZHENUNDSCHLEIDERERDASWARTINDEWARSCALLEDIGLUTLEINIESLIEBESWILENZSISTSTERKERALSTRENUNG + +2024-01-16 22:15:57,227 (asr_inference:494) INFO: speech length: 85277 +2024-01-16 22:15:57,238 (beam_search:428) INFO: decoder input length: 131 +2024-01-16 22:15:57,238 (beam_search:429) INFO: max output length: 131 +2024-01-16 22:15:57,238 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:57,468 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:57,468 (beam_search:476) INFO: -14.52 * 1.0 = -14.52 for ctc +2024-01-16 22:15:57,468 (beam_search:479) INFO: total log probability: -14.52 +2024-01-16 22:15:57,468 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:57,468 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:57,469 (beam_search:483) INFO: best hypo: TOMSAMIGEANEINGROSENSIEGNEHINELLANHERDNECKIGENSLCHT + +2024-01-16 22:15:57,470 (asr_inference:494) INFO: speech length: 97597 +2024-01-16 22:15:57,481 (beam_search:428) INFO: decoder input length: 150 +2024-01-16 22:15:57,481 (beam_search:429) INFO: max output length: 150 +2024-01-16 22:15:57,481 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:57,795 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:57,795 (beam_search:476) INFO: -15.53 * 1.0 = -15.53 for ctc +2024-01-16 22:15:57,795 (beam_search:479) INFO: total log probability: -15.53 +2024-01-16 22:15:57,795 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:15:57,795 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:57,795 (beam_search:483) INFO: best hypo: SSEINNAHMEDEMSICHTITUHEBEITAKUNNCHTAFNENKANBRERSCHERUNDTWERKOEN + +2024-01-16 22:15:57,797 (asr_inference:494) INFO: speech length: 71677 +2024-01-16 22:15:57,806 (beam_search:428) INFO: decoder input length: 109 +2024-01-16 22:15:57,807 (beam_search:429) INFO: max output length: 109 +2024-01-16 22:15:57,807 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:57,955 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:57,955 (beam_search:476) INFO: -7.27 * 1.0 = -7.27 for ctc +2024-01-16 22:15:57,955 (beam_search:479) INFO: total log probability: -7.27 +2024-01-16 22:15:57,955 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:15:57,955 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:57,955 (beam_search:483) INFO: best hypo: ENABERICHFERTSEIEINENIRERUNWISENHEIT + +2024-01-16 22:15:57,956 (asr_inference:494) INFO: speech length: 117117 +2024-01-16 22:15:57,968 (beam_search:428) INFO: decoder input length: 180 +2024-01-16 22:15:57,968 (beam_search:429) INFO: max output length: 180 +2024-01-16 22:15:57,968 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:58,438 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:58,438 (beam_search:476) INFO: -16.21 * 1.0 = -16.21 for ctc +2024-01-16 22:15:58,438 (beam_search:479) INFO: total log probability: -16.21 +2024-01-16 22:15:58,438 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:15:58,438 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:58,439 (beam_search:483) INFO: best hypo: FONDERTRITTENUNTEREDUNGANSAKTEMISTERHERVISCHEMWARMERDIEPERSUNINHUHEMASEVERDECHTIH + +2024-01-16 22:15:58,440 (asr_inference:494) INFO: speech length: 130973 +2024-01-16 22:15:58,454 (beam_search:428) INFO: decoder input length: 202 +2024-01-16 22:15:58,454 (beam_search:429) INFO: max output length: 202 +2024-01-16 22:15:58,454 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:59,108 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:59,108 (beam_search:476) INFO: -30.31 * 1.0 = -30.31 for ctc +2024-01-16 22:15:59,108 (beam_search:479) INFO: total log probability: -30.31 +2024-01-16 22:15:59,108 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:15:59,108 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:59,109 (beam_search:483) INFO: best hypo: ICHENKEDEAMTMANUNSANEVERMEIEWERDENESRECHTVONDERFINDENTDASTUDIHSELPSTANGIBSTUNSEWERDENHREUNTLICHGEGENDIESEI + +2024-01-16 22:15:59,110 (asr_inference:494) INFO: speech length: 137117 +2024-01-16 22:15:59,125 (beam_search:428) INFO: decoder input length: 212 +2024-01-16 22:15:59,125 (beam_search:429) INFO: max output length: 212 +2024-01-16 22:15:59,125 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:59,719 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:59,719 (beam_search:476) INFO: -24.01 * 1.0 = -24.01 for ctc +2024-01-16 22:15:59,719 (beam_search:479) INFO: total log probability: -24.01 +2024-01-16 22:15:59,719 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:15:59,719 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:59,719 (beam_search:483) INFO: best hypo: ETZSCHLUOKTIEHELEFLAMMERAUFUNDNUNERKANTEERUNSDIEWINRIMERZSAMENGEDRENTINDEMWENKESTANDEN + +2024-01-16 22:15:59,721 (asr_inference:494) INFO: speech length: 80317 +2024-01-16 22:15:59,731 (beam_search:428) INFO: decoder input length: 123 +2024-01-16 22:15:59,731 (beam_search:429) INFO: max output length: 123 +2024-01-16 22:15:59,731 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:15:59,946 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:15:59,946 (beam_search:476) INFO: -12.86 * 1.0 = -12.86 for ctc +2024-01-16 22:15:59,946 (beam_search:479) INFO: total log probability: -12.86 +2024-01-16 22:15:59,946 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:15:59,946 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:15:59,947 (beam_search:483) INFO: best hypo: DERSEINESIELEANSPBONENDTDASERMUNTERNDEWAUTVORWEALTZS + +2024-01-16 22:15:59,948 (asr_inference:494) INFO: speech length: 70327 +2024-01-16 22:15:59,958 (beam_search:428) INFO: decoder input length: 107 +2024-01-16 22:15:59,958 (beam_search:429) INFO: max output length: 107 +2024-01-16 22:15:59,958 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:00,144 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:00,144 (beam_search:476) INFO: -21.80 * 1.0 = -21.80 for ctc +2024-01-16 22:16:00,144 (beam_search:479) INFO: total log probability: -21.80 +2024-01-16 22:16:00,144 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:16:00,144 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:00,144 (beam_search:483) INFO: best hypo: VRMICHAFTDINBSUCHTDESTUNESUSHEMMNESAPERSEDEANDTENORNT + +2024-01-16 22:16:00,145 (asr_inference:494) INFO: speech length: 90397 +2024-01-16 22:16:00,156 (beam_search:428) INFO: decoder input length: 139 +2024-01-16 22:16:00,156 (beam_search:429) INFO: max output length: 139 +2024-01-16 22:16:00,156 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:00,448 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:00,448 (beam_search:476) INFO: -18.93 * 1.0 = -18.93 for ctc +2024-01-16 22:16:00,448 (beam_search:479) INFO: total log probability: -18.93 +2024-01-16 22:16:00,448 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:16:00,448 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:00,449 (beam_search:483) INFO: best hypo: WASTFIHREHERFOLGUNGENWASFIRNARSTELUMENHABICHNICHZUEARDULENGEHABT + +2024-01-16 22:16:00,450 (asr_inference:494) INFO: speech length: 159837 +2024-01-16 22:16:00,466 (beam_search:428) INFO: decoder input length: 247 +2024-01-16 22:16:00,466 (beam_search:429) INFO: max output length: 247 +2024-01-16 22:16:00,466 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:01,245 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:01,245 (beam_search:476) INFO: -27.13 * 1.0 = -27.13 for ctc +2024-01-16 22:16:01,245 (beam_search:479) INFO: total log probability: -27.13 +2024-01-16 22:16:01,245 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:16:01,245 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:01,245 (beam_search:483) INFO: best hypo: SIKOEINARWARENESIEONAUTZUOALTVFORENEINKAUMAWAKSNDESIUNGESDINKAMTZUMIEHERANGEHÜEPFTUNBÄTKELEDENNEIN + +2024-01-16 22:16:01,247 (asr_inference:494) INFO: speech length: 139037 +2024-01-16 22:16:01,261 (beam_search:428) INFO: decoder input length: 215 +2024-01-16 22:16:01,261 (beam_search:429) INFO: max output length: 215 +2024-01-16 22:16:01,261 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:01,902 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:01,902 (beam_search:476) INFO: -27.62 * 1.0 = -27.62 for ctc +2024-01-16 22:16:01,902 (beam_search:479) INFO: total log probability: -27.62 +2024-01-16 22:16:01,902 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:16:01,902 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:01,903 (beam_search:483) INFO: best hypo: AKICHWERTIHINEBOTHINFEANDERTASBUOTERANLEGUNDITERTZOEGTUDENAESGANZSELLEINWOSDECGANICHTHUMZOKEMAN + +2024-01-16 22:16:01,904 (asr_inference:494) INFO: speech length: 129277 +2024-01-16 22:16:01,917 (beam_search:428) INFO: decoder input length: 199 +2024-01-16 22:16:01,917 (beam_search:429) INFO: max output length: 199 +2024-01-16 22:16:01,917 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:02,463 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:02,463 (beam_search:476) INFO: -20.38 * 1.0 = -20.38 for ctc +2024-01-16 22:16:02,463 (beam_search:479) INFO: total log probability: -20.38 +2024-01-16 22:16:02,463 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:16:02,463 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:02,463 (beam_search:483) INFO: best hypo: ALSNREINMALUNOCHENDRAUCHVENANEMHAUSAUSDERVERNERAUFSTEIGENZUESENUMDANBERUEKTZUSTDERBM + +2024-01-16 22:16:02,465 (asr_inference:494) INFO: speech length: 129309 +2024-01-16 22:16:02,478 (beam_search:428) INFO: decoder input length: 200 +2024-01-16 22:16:02,478 (beam_search:429) INFO: max output length: 200 +2024-01-16 22:16:02,478 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:03,031 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:03,031 (beam_search:476) INFO: -17.99 * 1.0 = -17.99 for ctc +2024-01-16 22:16:03,031 (beam_search:479) INFO: total log probability: -17.99 +2024-01-16 22:16:03,031 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:16:03,031 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:03,032 (beam_search:483) INFO: best hypo: IETENZERINABARLARKAUFENKNIENVORBRAMASBILTENISINNAHMENLOSERSENSUCHTUNDTWEINTEJAMAVOLLT + +2024-01-16 22:16:03,033 (asr_inference:494) INFO: speech length: 79357 +2024-01-16 22:16:03,043 (beam_search:428) INFO: decoder input length: 121 +2024-01-16 22:16:03,043 (beam_search:429) INFO: max output length: 121 +2024-01-16 22:16:03,043 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:03,274 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:03,274 (beam_search:476) INFO: -20.38 * 1.0 = -20.38 for ctc +2024-01-16 22:16:03,274 (beam_search:479) INFO: total log probability: -20.38 +2024-01-16 22:16:03,274 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:16:03,274 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:03,275 (beam_search:483) INFO: best hypo: ECHTFERTICHTMICHSENDEWEGLIGKEITNOCHNICHTAFTDIEHICHBEUFENGKHR + +2024-01-16 22:16:03,276 (asr_inference:494) INFO: speech length: 97917 +2024-01-16 22:16:03,287 (beam_search:428) INFO: decoder input length: 150 +2024-01-16 22:16:03,287 (beam_search:429) INFO: max output length: 150 +2024-01-16 22:16:03,287 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:03,620 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:03,620 (beam_search:476) INFO: -20.43 * 1.0 = -20.43 for ctc +2024-01-16 22:16:03,620 (beam_search:479) INFO: total log probability: -20.43 +2024-01-16 22:16:03,620 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:16:03,620 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:03,621 (beam_search:483) INFO: best hypo: ICHELRGERTEMICHTANWENICHAUHFACHTEREISWASUFWUNDERSHEÖNENGEWEESENDARSFLIEN + +2024-01-16 22:16:03,622 (asr_inference:494) INFO: speech length: 140957 +2024-01-16 22:16:03,636 (beam_search:428) INFO: decoder input length: 218 +2024-01-16 22:16:03,636 (beam_search:429) INFO: max output length: 218 +2024-01-16 22:16:03,636 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:04,326 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:04,326 (beam_search:476) INFO: -31.59 * 1.0 = -31.59 for ctc +2024-01-16 22:16:04,326 (beam_search:479) INFO: total log probability: -31.59 +2024-01-16 22:16:04,326 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:16:04,326 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:04,327 (beam_search:483) INFO: best hypo: NERHDEMESCHONDINGANZENVORMITEAGMITIMVARBRACHTKAMSDENHOBNACHTISSCHINZSKRANSCHERHAUSUMGASBALEOLTZOSRGEN + +2024-01-16 22:16:04,328 (asr_inference:494) INFO: speech length: 60778 +2024-01-16 22:16:04,337 (beam_search:428) INFO: decoder input length: 92 +2024-01-16 22:16:04,338 (beam_search:429) INFO: max output length: 92 +2024-01-16 22:16:04,338 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:04,480 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:04,480 (beam_search:476) INFO: -9.97 * 1.0 = -9.97 for ctc +2024-01-16 22:16:04,480 (beam_search:479) INFO: total log probability: -9.97 +2024-01-16 22:16:04,480 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:16:04,480 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:04,481 (beam_search:483) INFO: best hypo: HRWARAINALTERHIRHTVOLMEDIEZINESCHERGNENALITET + +2024-01-16 22:16:04,482 (asr_inference:494) INFO: speech length: 70397 +2024-01-16 22:16:04,492 (beam_search:428) INFO: decoder input length: 107 +2024-01-16 22:16:04,492 (beam_search:429) INFO: max output length: 107 +2024-01-16 22:16:04,492 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:04,664 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:04,664 (beam_search:476) INFO: -12.92 * 1.0 = -12.92 for ctc +2024-01-16 22:16:04,664 (beam_search:479) INFO: total log probability: -12.92 +2024-01-16 22:16:04,665 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:16:04,665 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:04,665 (beam_search:483) INFO: best hypo: DESVOLAUHTDERMIETAERSEINEVUNDARICHSKEITENHARBEMSE + +2024-01-16 22:16:04,666 (asr_inference:494) INFO: speech length: 162877 +2024-01-16 22:16:04,682 (beam_search:428) INFO: decoder input length: 252 +2024-01-16 22:16:04,682 (beam_search:429) INFO: max output length: 252 +2024-01-16 22:16:04,682 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:05,530 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:05,530 (beam_search:476) INFO: -23.98 * 1.0 = -23.98 for ctc +2024-01-16 22:16:05,530 (beam_search:479) INFO: total log probability: -23.98 +2024-01-16 22:16:05,530 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:16:05,530 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:05,531 (beam_search:483) INFO: best hypo: ENSIESANALEENSTLICGUNDBETRÜBTAUSUNDAUCHERARENESASCWEHRMÜTICGDARWIEDIEANDERENUNDSTÜTZTEDESHAUPTINDIEHEN + +2024-01-16 22:16:05,532 (asr_inference:494) INFO: speech length: 188157 +2024-01-16 22:16:05,549 (beam_search:428) INFO: decoder input length: 291 +2024-01-16 22:16:05,549 (beam_search:429) INFO: max output length: 291 +2024-01-16 22:16:05,549 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:06,764 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:06,764 (beam_search:476) INFO: -34.87 * 1.0 = -34.87 for ctc +2024-01-16 22:16:06,764 (beam_search:479) INFO: total log probability: -34.87 +2024-01-16 22:16:06,764 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:16:06,764 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:06,765 (beam_search:483) INFO: best hypo: UNTERENDAMENMEISTIONGEFRSCHIGESICHTERAUNTERINHERENNEBENMHUENDKLICHENSOCHMITFALTIGARSTIERNUNDBREITZSMEHRAUDARMINDERMONDUMGGLENZSTEMSCHÄHTEL + +2024-01-16 22:16:06,767 (asr_inference:494) INFO: speech length: 64125 +2024-01-16 22:16:06,776 (beam_search:428) INFO: decoder input length: 98 +2024-01-16 22:16:06,776 (beam_search:429) INFO: max output length: 98 +2024-01-16 22:16:06,776 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:06,926 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:06,926 (beam_search:476) INFO: -11.57 * 1.0 = -11.57 for ctc +2024-01-16 22:16:06,926 (beam_search:479) INFO: total log probability: -11.57 +2024-01-16 22:16:06,926 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:16:06,926 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:06,926 (beam_search:483) INFO: best hypo: SEITERENCHONATDEISBESONDEASDREIENDEKLUNGEBER + +2024-01-16 22:16:06,928 (asr_inference:494) INFO: speech length: 27357 +2024-01-16 22:16:06,935 (beam_search:428) INFO: decoder input length: 40 +2024-01-16 22:16:06,935 (beam_search:429) INFO: max output length: 40 +2024-01-16 22:16:06,935 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:06,955 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:06,955 (beam_search:476) INFO: -2.28 * 1.0 = -2.28 for ctc +2024-01-16 22:16:06,955 (beam_search:479) INFO: total log probability: -2.28 +2024-01-16 22:16:06,955 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:16:06,955 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:06,955 (beam_search:483) INFO: best hypo: SONDERBARH + +2024-01-16 22:16:06,956 (asr_inference:494) INFO: speech length: 133757 +2024-01-16 22:16:06,970 (beam_search:428) INFO: decoder input length: 206 +2024-01-16 22:16:06,970 (beam_search:429) INFO: max output length: 206 +2024-01-16 22:16:06,970 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:07,544 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:07,544 (beam_search:476) INFO: -23.87 * 1.0 = -23.87 for ctc +2024-01-16 22:16:07,544 (beam_search:479) INFO: total log probability: -23.87 +2024-01-16 22:16:07,544 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:16:07,544 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:07,545 (beam_search:483) INFO: best hypo: ERBPVONEHRBMHEMSTANDMIZENHRGATEINVOLWEMUTUNGDANKGBAKEIDANDERGROUFTUFDERINMECHTIENG + +2024-01-16 22:16:07,546 (asr_inference:494) INFO: speech length: 99677 +2024-01-16 22:16:07,557 (beam_search:428) INFO: decoder input length: 153 +2024-01-16 22:16:07,557 (beam_search:429) INFO: max output length: 153 +2024-01-16 22:16:07,557 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:07,893 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:07,893 (beam_search:476) INFO: -18.88 * 1.0 = -18.88 for ctc +2024-01-16 22:16:07,893 (beam_search:479) INFO: total log probability: -18.88 +2024-01-16 22:16:07,893 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:16:07,893 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:07,893 (beam_search:483) INFO: best hypo: IERWAIHIEDEAMENSCHEINUNDERUNDFASTALLESVESMENSCHENTALTENITASSFONDABARES + +2024-01-16 22:16:07,895 (asr_inference:494) INFO: speech length: 48637 +2024-01-16 22:16:07,903 (beam_search:428) INFO: decoder input length: 73 +2024-01-16 22:16:07,903 (beam_search:429) INFO: max output length: 73 +2024-01-16 22:16:07,903 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:07,979 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:07,979 (beam_search:476) INFO: -7.99 * 1.0 = -7.99 for ctc +2024-01-16 22:16:07,979 (beam_search:479) INFO: total log probability: -7.99 +2024-01-16 22:16:07,979 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:16:07,979 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:07,979 (beam_search:483) INFO: best hypo: WELTHEJERWESIEENDLENGSTFIER + +2024-01-16 22:16:07,980 (asr_inference:494) INFO: speech length: 101437 +2024-01-16 22:16:07,991 (beam_search:428) INFO: decoder input length: 156 +2024-01-16 22:16:07,991 (beam_search:429) INFO: max output length: 156 +2024-01-16 22:16:07,991 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:08,359 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:08,359 (beam_search:476) INFO: -18.09 * 1.0 = -18.09 for ctc +2024-01-16 22:16:08,359 (beam_search:479) INFO: total log probability: -18.09 +2024-01-16 22:16:08,359 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:16:08,359 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:08,360 (beam_search:483) INFO: best hypo: IEWERTINSASNICHTHINTEREMSCANKTISUNDKEINEERERDIENZSTLOUTEBEFANZEIHNDERSTUBE + +2024-01-16 22:16:08,361 (asr_inference:494) INFO: speech length: 155517 +2024-01-16 22:16:08,377 (beam_search:428) INFO: decoder input length: 240 +2024-01-16 22:16:08,377 (beam_search:429) INFO: max output length: 240 +2024-01-16 22:16:08,377 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:09,149 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:09,149 (beam_search:476) INFO: -29.21 * 1.0 = -29.21 for ctc +2024-01-16 22:16:09,149 (beam_search:479) INFO: total log probability: -29.21 +2024-01-16 22:16:09,149 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:16:09,149 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:09,150 (beam_search:483) INFO: best hypo: ALSDEHERSCHAFTAUSDERKIELCHETATSTANDENDIELEUITEUMHEHRUMSIEVORBEIGEHNZUSIENUNDAMKELCHUFSTORERWATETEEINMAN + +2024-01-16 22:16:09,151 (asr_inference:494) INFO: speech length: 42512 +2024-01-16 22:16:09,160 (beam_search:428) INFO: decoder input length: 64 +2024-01-16 22:16:09,160 (beam_search:429) INFO: max output length: 64 +2024-01-16 22:16:09,160 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:09,234 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:09,234 (beam_search:476) INFO: -14.68 * 1.0 = -14.68 for ctc +2024-01-16 22:16:09,234 (beam_search:479) INFO: total log probability: -14.68 +2024-01-16 22:16:09,234 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-16 22:16:09,234 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:09,235 (beam_search:483) INFO: best hypo: SMSNMELTORNOMDIETARISMUSENGNKTI + +2024-01-16 22:16:09,236 (asr_inference:494) INFO: speech length: 37670 +2024-01-16 22:16:09,244 (beam_search:428) INFO: decoder input length: 56 +2024-01-16 22:16:09,244 (beam_search:429) INFO: max output length: 56 +2024-01-16 22:16:09,244 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:09,313 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:09,313 (beam_search:476) INFO: -11.21 * 1.0 = -11.21 for ctc +2024-01-16 22:16:09,313 (beam_search:479) INFO: total log probability: -11.21 +2024-01-16 22:16:09,313 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:16:09,313 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:09,313 (beam_search:483) INFO: best hypo: GELAUBERDEASIESGUDETBERMEINENHERTACT + +2024-01-16 22:16:09,314 (asr_inference:494) INFO: speech length: 106653 +2024-01-16 22:16:09,326 (beam_search:428) INFO: decoder input length: 164 +2024-01-16 22:16:09,326 (beam_search:429) INFO: max output length: 164 +2024-01-16 22:16:09,326 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:09,684 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:09,684 (beam_search:476) INFO: -12.72 * 1.0 = -12.72 for ctc +2024-01-16 22:16:09,684 (beam_search:479) INFO: total log probability: -12.72 +2024-01-16 22:16:09,684 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:16:09,684 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:09,685 (beam_search:483) INFO: best hypo: ENTRIMANFANGGEWANEKEINEAUFMERSAMKEITVERANDEREDINGEALZFÜRDERSESEN + +2024-01-16 22:16:09,686 (asr_inference:494) INFO: speech length: 127517 +2024-01-16 22:16:09,699 (beam_search:428) INFO: decoder input length: 197 +2024-01-16 22:16:09,699 (beam_search:429) INFO: max output length: 197 +2024-01-16 22:16:09,699 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:10,223 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:10,223 (beam_search:476) INFO: -19.01 * 1.0 = -19.01 for ctc +2024-01-16 22:16:10,223 (beam_search:479) INFO: total log probability: -19.01 +2024-01-16 22:16:10,223 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:16:10,223 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:10,224 (beam_search:483) INFO: best hypo: DIESFLÄSCHENZOGERGETSTEILICHERVORWERENDJENESICHMITWASARFELTENUNDBTESDERUNKVERTZIÜSAN + +2024-01-16 22:16:10,225 (asr_inference:494) INFO: speech length: 128911 +2024-01-16 22:16:10,238 (beam_search:428) INFO: decoder input length: 199 +2024-01-16 22:16:10,238 (beam_search:429) INFO: max output length: 199 +2024-01-16 22:16:10,238 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:10,882 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:10,882 (beam_search:476) INFO: -31.46 * 1.0 = -31.46 for ctc +2024-01-16 22:16:10,882 (beam_search:479) INFO: total log probability: -31.46 +2024-01-16 22:16:10,882 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:16:10,882 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:10,883 (beam_search:483) INFO: best hypo: SERBASAURICHIHONWICHTICHDEASCHINERDRCHERTZSANSHPUSFOLLGESAKTATVERWERDENUCRHAININZEITBUNGDERIDUKTZIUNKOMMENDESDGU + +2024-01-16 22:16:10,885 (asr_inference:494) INFO: speech length: 51677 +2024-01-16 22:16:10,893 (beam_search:428) INFO: decoder input length: 78 +2024-01-16 22:16:10,893 (beam_search:429) INFO: max output length: 78 +2024-01-16 22:16:10,893 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:10,989 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:10,989 (beam_search:476) INFO: -9.02 * 1.0 = -9.02 for ctc +2024-01-16 22:16:10,989 (beam_search:479) INFO: total log probability: -9.02 +2024-01-16 22:16:10,989 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:16:10,989 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:10,989 (beam_search:483) INFO: best hypo: NICHTDAOCHMUTERWERGESIEERTZTNCHNIG + +2024-01-16 22:16:10,990 (asr_inference:494) INFO: speech length: 81569 +2024-01-16 22:16:11,001 (beam_search:428) INFO: decoder input length: 125 +2024-01-16 22:16:11,001 (beam_search:429) INFO: max output length: 125 +2024-01-16 22:16:11,001 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:11,246 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:11,246 (beam_search:476) INFO: -11.90 * 1.0 = -11.90 for ctc +2024-01-16 22:16:11,246 (beam_search:479) INFO: total log probability: -11.90 +2024-01-16 22:16:11,246 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:16:11,246 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:11,246 (beam_search:483) INFO: best hypo: ABIERHHABENINENDETZTNJANRICHTENGEBITIUNZUBASIIENAUFGEBAUTPR + +2024-01-16 22:16:11,247 (asr_inference:494) INFO: speech length: 53597 +2024-01-16 22:16:11,256 (beam_search:428) INFO: decoder input length: 81 +2024-01-16 22:16:11,256 (beam_search:429) INFO: max output length: 81 +2024-01-16 22:16:11,256 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:11,355 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:11,355 (beam_search:476) INFO: -10.02 * 1.0 = -10.02 for ctc +2024-01-16 22:16:11,355 (beam_search:479) INFO: total log probability: -10.02 +2024-01-16 22:16:11,355 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:16:11,355 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:11,355 (beam_search:483) INFO: best hypo: SSIEVIRDESICHNICHTVERANDRABPFON + +2024-01-16 22:16:11,357 (asr_inference:494) INFO: speech length: 17793 +2024-01-16 22:16:11,363 (beam_search:428) INFO: decoder input length: 25 +2024-01-16 22:16:11,363 (beam_search:429) INFO: max output length: 25 +2024-01-16 22:16:11,363 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:11,380 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:11,380 (beam_search:476) INFO: -3.74 * 1.0 = -3.74 for ctc +2024-01-16 22:16:11,380 (beam_search:479) INFO: total log probability: -3.74 +2024-01-16 22:16:11,380 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:16:11,380 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:11,380 (beam_search:483) INFO: best hypo: LECHRIFENMETZ + +2024-01-16 22:16:11,381 (asr_inference:494) INFO: speech length: 88797 +2024-01-16 22:16:11,392 (beam_search:428) INFO: decoder input length: 136 +2024-01-16 22:16:11,392 (beam_search:429) INFO: max output length: 136 +2024-01-16 22:16:11,392 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:11,630 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:11,630 (beam_search:476) INFO: -19.52 * 1.0 = -19.52 for ctc +2024-01-16 22:16:11,630 (beam_search:479) INFO: total log probability: -19.52 +2024-01-16 22:16:11,630 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:16:11,630 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:11,630 (beam_search:483) INFO: best hypo: GKOTWASIIEARZTELTERHÖRENSINNURISISEINGANZERUMARN + +2024-01-16 22:16:11,632 (asr_inference:494) INFO: speech length: 67517 +2024-01-16 22:16:11,641 (beam_search:428) INFO: decoder input length: 103 +2024-01-16 22:16:11,641 (beam_search:429) INFO: max output length: 103 +2024-01-16 22:16:11,641 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:11,796 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:11,796 (beam_search:476) INFO: -13.13 * 1.0 = -13.13 for ctc +2024-01-16 22:16:11,796 (beam_search:479) INFO: total log probability: -13.13 +2024-01-16 22:16:11,796 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:16:11,796 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:11,796 (beam_search:483) INFO: best hypo: SEINEMTERKININMOEFLUSWASERGEBENDESELBPWEINDER + +2024-01-16 22:16:11,798 (asr_inference:494) INFO: speech length: 194317 +2024-01-16 22:16:11,816 (beam_search:428) INFO: decoder input length: 301 +2024-01-16 22:16:11,816 (beam_search:429) INFO: max output length: 301 +2024-01-16 22:16:11,816 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:13,106 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:13,106 (beam_search:476) INFO: -43.49 * 1.0 = -43.49 for ctc +2024-01-16 22:16:13,106 (beam_search:479) INFO: total log probability: -43.49 +2024-01-16 22:16:13,106 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:16:13,106 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:13,107 (beam_search:483) INFO: best hypo: UNDSWOTCHASMINDESTAWERTEMENUSAMTERNETZARGENTURAMFIERTENJIUNIZUMERSTENMALPRESENDIERNWIESICHDIENETSBETREIBERUNGIKRASTDARKGEDINEUNETSTLENEVORSTERUN + +2024-01-16 22:16:13,109 (asr_inference:494) INFO: speech length: 140637 +2024-01-16 22:16:13,123 (beam_search:428) INFO: decoder input length: 217 +2024-01-16 22:16:13,123 (beam_search:429) INFO: max output length: 217 +2024-01-16 22:16:13,123 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:13,805 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:13,805 (beam_search:476) INFO: -26.91 * 1.0 = -26.91 for ctc +2024-01-16 22:16:13,805 (beam_search:479) INFO: total log probability: -26.91 +2024-01-16 22:16:13,805 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:16:13,805 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:13,805 (beam_search:483) INFO: best hypo: EBWEARATESECHTZEIETANDVORTODESFVECHERVONDEMGETABEFREITUNZUCHTETZUNDFINENBEDERSMALEGATENBUOTKEINENAUSFI + +2024-01-16 22:16:13,807 (asr_inference:494) INFO: speech length: 100477 +2024-01-16 22:16:13,818 (beam_search:428) INFO: decoder input length: 154 +2024-01-16 22:16:13,818 (beam_search:429) INFO: max output length: 154 +2024-01-16 22:16:13,818 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:14,150 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:14,150 (beam_search:476) INFO: -19.81 * 1.0 = -19.81 for ctc +2024-01-16 22:16:14,150 (beam_search:479) INFO: total log probability: -19.81 +2024-01-16 22:16:14,150 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:16:14,150 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:14,150 (beam_search:483) INFO: best hypo: BICHMEINWERKFWRUTELETENLASENNDENRHEINANLAUFNEMENUNTESROLENDNSOLTE + +2024-01-16 22:16:14,152 (asr_inference:494) INFO: speech length: 179357 +2024-01-16 22:16:14,168 (beam_search:428) INFO: decoder input length: 278 +2024-01-16 22:16:14,168 (beam_search:429) INFO: max output length: 278 +2024-01-16 22:16:14,168 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:15,205 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:15,205 (beam_search:476) INFO: -34.61 * 1.0 = -34.61 for ctc +2024-01-16 22:16:15,205 (beam_search:479) INFO: total log probability: -34.61 +2024-01-16 22:16:15,205 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:16:15,205 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:15,206 (beam_search:483) INFO: best hypo: EHRWADASKETZHENDERSTUNDDETEITEIBEAUFTRAKTEMADAMUNSCHELDIEAUCHTASTANDUNDIGEKAUFTENSEIDENSDEKETZUSAMFELTETEFÜRTSCHNDZUSORGENG + +2024-01-16 22:16:15,208 (asr_inference:494) INFO: speech length: 30717 +2024-01-16 22:16:15,215 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 22:16:15,215 (beam_search:429) INFO: max output length: 45 +2024-01-16 22:16:15,215 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:15,242 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:15,243 (beam_search:476) INFO: -6.95 * 1.0 = -6.95 for ctc +2024-01-16 22:16:15,243 (beam_search:479) INFO: total log probability: -6.95 +2024-01-16 22:16:15,243 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-16 22:16:15,243 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:15,243 (beam_search:483) INFO: best hypo: DWERENACHSIEN + +2024-01-16 22:16:15,244 (asr_inference:494) INFO: speech length: 65053 +2024-01-16 22:16:15,253 (beam_search:428) INFO: decoder input length: 99 +2024-01-16 22:16:15,253 (beam_search:429) INFO: max output length: 99 +2024-01-16 22:16:15,253 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:15,408 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:15,408 (beam_search:476) INFO: -11.15 * 1.0 = -11.15 for ctc +2024-01-16 22:16:15,408 (beam_search:479) INFO: total log probability: -11.15 +2024-01-16 22:16:15,408 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:16:15,408 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:15,408 (beam_search:483) INFO: best hypo: ABALETEBSODAFVORGABENDADMACHMENEDILIGNICHS + +2024-01-16 22:16:15,409 (asr_inference:494) INFO: speech length: 138415 +2024-01-16 22:16:15,423 (beam_search:428) INFO: decoder input length: 214 +2024-01-16 22:16:15,423 (beam_search:429) INFO: max output length: 214 +2024-01-16 22:16:15,423 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:16,081 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:16,081 (beam_search:476) INFO: -28.86 * 1.0 = -28.86 for ctc +2024-01-16 22:16:16,081 (beam_search:479) INFO: total log probability: -28.86 +2024-01-16 22:16:16,081 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:16:16,081 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:16,082 (beam_search:483) INFO: best hypo: ALSUNSREEDEBEKANTWODERWADIFÜSIOGNMEDERWEALTEASPBOGERUNGERFÄHRDIEINERSKEALBERSDASZUMEHRSTNMALTDONHÖR + +2024-01-16 22:16:16,083 (asr_inference:494) INFO: speech length: 69094 +2024-01-16 22:16:16,093 (beam_search:428) INFO: decoder input length: 105 +2024-01-16 22:16:16,093 (beam_search:429) INFO: max output length: 105 +2024-01-16 22:16:16,093 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:16,270 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:16,270 (beam_search:476) INFO: -13.07 * 1.0 = -13.07 for ctc +2024-01-16 22:16:16,270 (beam_search:479) INFO: total log probability: -13.07 +2024-01-16 22:16:16,270 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:16:16,270 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:16,271 (beam_search:483) INFO: best hypo: ITZEMCHNSGEFÄLIHTAUFUNDESKLANDIEEINAMANDERHILVER + +2024-01-16 22:16:16,272 (asr_inference:494) INFO: speech length: 105109 +2024-01-16 22:16:16,284 (beam_search:428) INFO: decoder input length: 162 +2024-01-16 22:16:16,284 (beam_search:429) INFO: max output length: 162 +2024-01-16 22:16:16,284 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:16,636 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:16,636 (beam_search:476) INFO: -20.16 * 1.0 = -20.16 for ctc +2024-01-16 22:16:16,637 (beam_search:479) INFO: total log probability: -20.16 +2024-01-16 22:16:16,637 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:16:16,637 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:16,637 (beam_search:483) INFO: best hypo: ERDAOKTORSAERINEFRAUDESHNOGRIENERDISEAFTZUINENKOMISTENGIGANIGKRAN + +2024-01-16 22:16:16,638 (asr_inference:494) INFO: speech length: 217117 +2024-01-16 22:16:16,658 (beam_search:428) INFO: decoder input length: 337 +2024-01-16 22:16:16,659 (beam_search:429) INFO: max output length: 337 +2024-01-16 22:16:16,659 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:18,333 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:18,333 (beam_search:476) INFO: -43.68 * 1.0 = -43.68 for ctc +2024-01-16 22:16:18,333 (beam_search:479) INFO: total log probability: -43.68 +2024-01-16 22:16:18,333 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:16:18,333 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:18,334 (beam_search:483) INFO: best hypo: DIEALTEERINRUNGANDINFRÜHRENTAUMTAUCHTEEBENFALZWIEDERAUFUNDUNWIELKÖRLICFASSTBAIDERBEHAUPTUNGDASDESELEENKÖRPERVELLASTENUNDTZUIEMZURÜCKERENKENESCHENESIERARDENDLI + +2024-01-16 22:16:18,335 (asr_inference:494) INFO: speech length: 119837 +2024-01-16 22:16:18,348 (beam_search:428) INFO: decoder input length: 185 +2024-01-16 22:16:18,348 (beam_search:429) INFO: max output length: 185 +2024-01-16 22:16:18,348 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:18,852 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:18,852 (beam_search:476) INFO: -27.57 * 1.0 = -27.57 for ctc +2024-01-16 22:16:18,852 (beam_search:479) INFO: total log probability: -27.57 +2024-01-16 22:16:18,852 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:16:18,852 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:18,853 (beam_search:483) INFO: best hypo: ALZSIEAFRINBALKONZUREKHRTEFANZIEIENDISEITUNGKLIESENDTDIEWERENRISVORTZEINSANGELANGKTWAH + +2024-01-16 22:16:18,854 (asr_inference:494) INFO: speech length: 178877 +2024-01-16 22:16:18,871 (beam_search:428) INFO: decoder input length: 277 +2024-01-16 22:16:18,871 (beam_search:429) INFO: max output length: 277 +2024-01-16 22:16:18,871 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:19,828 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:19,828 (beam_search:476) INFO: -28.08 * 1.0 = -28.08 for ctc +2024-01-16 22:16:19,828 (beam_search:479) INFO: total log probability: -28.08 +2024-01-16 22:16:19,828 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:16:19,828 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:19,829 (beam_search:483) INFO: best hypo: TEERWAREINKINDRSTRASEVONKLEINAUFABERINIEMLEBTEVONJEHRINRGWISESENSOCHTNACHEINEIERBARENBÜRGERLICHENEISTENS + +2024-01-16 22:16:19,830 (asr_inference:494) INFO: speech length: 89947 +2024-01-16 22:16:19,841 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 22:16:19,841 (beam_search:429) INFO: max output length: 138 +2024-01-16 22:16:19,841 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:20,186 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:20,186 (beam_search:476) INFO: -33.86 * 1.0 = -33.86 for ctc +2024-01-16 22:16:20,186 (beam_search:479) INFO: total log probability: -33.86 +2024-01-16 22:16:20,186 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:16:20,186 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:20,187 (beam_search:483) INFO: best hypo: ISUNESDGHRUNKFÜNUNEHNIGEINERGROPEFEANFORDLICHSONAWÜEFÜNENSINGMEINWUOLFRANWORDLICHN + +2024-01-16 22:16:20,188 (asr_inference:494) INFO: speech length: 47197 +2024-01-16 22:16:20,197 (beam_search:428) INFO: decoder input length: 71 +2024-01-16 22:16:20,197 (beam_search:429) INFO: max output length: 71 +2024-01-16 22:16:20,197 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:20,265 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:20,265 (beam_search:476) INFO: -5.57 * 1.0 = -5.57 for ctc +2024-01-16 22:16:20,265 (beam_search:479) INFO: total log probability: -5.57 +2024-01-16 22:16:20,265 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:16:20,265 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:20,265 (beam_search:483) INFO: best hypo: WASMEILIEBESGEINTWASGKAN + +2024-01-16 22:16:20,266 (asr_inference:494) INFO: speech length: 192157 +2024-01-16 22:16:20,284 (beam_search:428) INFO: decoder input length: 298 +2024-01-16 22:16:20,284 (beam_search:429) INFO: max output length: 298 +2024-01-16 22:16:20,284 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:21,499 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:21,499 (beam_search:476) INFO: -39.09 * 1.0 = -39.09 for ctc +2024-01-16 22:16:21,499 (beam_search:479) INFO: total log probability: -39.09 +2024-01-16 22:16:21,499 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:16:21,499 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:21,500 (beam_search:483) INFO: best hypo: UNDDANWULTEICHTINANBLIGDERANICHTMESSENDEMÄRGEBLEBEMWARENVORELEMABAWARISNIDARUMTZUTUNWEINDESYSELICHIESERBETEINIGAMASSENGETREÖSTETZUSEN + +2024-01-16 22:16:21,502 (asr_inference:494) INFO: speech length: 63071 +2024-01-16 22:16:21,512 (beam_search:428) INFO: decoder input length: 96 +2024-01-16 22:16:21,512 (beam_search:429) INFO: max output length: 96 +2024-01-16 22:16:21,512 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:21,680 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:21,680 (beam_search:476) INFO: -16.34 * 1.0 = -16.34 for ctc +2024-01-16 22:16:21,680 (beam_search:479) INFO: total log probability: -16.34 +2024-01-16 22:16:21,680 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:16:21,680 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:21,681 (beam_search:483) INFO: best hypo: ERDASAUCHWIERUNDSGNGSEIDIHMBISHNUNTARSTITZENKERNERM + +2024-01-16 22:16:21,682 (asr_inference:494) INFO: speech length: 88733 +2024-01-16 22:16:21,692 (beam_search:428) INFO: decoder input length: 136 +2024-01-16 22:16:21,692 (beam_search:429) INFO: max output length: 136 +2024-01-16 22:16:21,693 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:22,005 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:22,005 (beam_search:476) INFO: -23.23 * 1.0 = -23.23 for ctc +2024-01-16 22:16:22,005 (beam_search:479) INFO: total log probability: -23.23 +2024-01-16 22:16:22,005 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:16:22,005 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:22,006 (beam_search:483) INFO: best hypo: SEINEESCHETICHELAUFBARNHARBESDIEBENSNASKÜCHENBOINEINRMUTELFVIERTENGRADESBGON + +2024-01-16 22:16:22,007 (asr_inference:494) INFO: speech length: 133917 +2024-01-16 22:16:22,021 (beam_search:428) INFO: decoder input length: 207 +2024-01-16 22:16:22,021 (beam_search:429) INFO: max output length: 207 +2024-01-16 22:16:22,021 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:22,710 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:22,710 (beam_search:476) INFO: -30.49 * 1.0 = -30.49 for ctc +2024-01-16 22:16:22,710 (beam_search:479) INFO: total log probability: -30.49 +2024-01-16 22:16:22,710 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:16:22,710 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:22,711 (beam_search:483) INFO: best hypo: FILEICHTEENSIGUTISEANSICHTEDESBSCHOFENHUSETZUMELENZAKTEERTATSHENDEHRIMARMHREINMANDESGESCHIEBENENWURTESVIEDERTARDT + +2024-01-16 22:16:22,712 (asr_inference:494) INFO: speech length: 174845 +2024-01-16 22:16:22,728 (beam_search:428) INFO: decoder input length: 271 +2024-01-16 22:16:22,728 (beam_search:429) INFO: max output length: 271 +2024-01-16 22:16:22,728 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:23,694 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:23,694 (beam_search:476) INFO: -34.74 * 1.0 = -34.74 for ctc +2024-01-16 22:16:23,694 (beam_search:479) INFO: total log probability: -34.74 +2024-01-16 22:16:23,694 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:16:23,694 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:23,695 (beam_search:483) INFO: best hypo: AMANDANMORENERHOPERSICHSHPETSCHIKTENLARKEIENNDEBUNGVORABACHSUNTLISUMEINNTERIEDUNGBITENDEMANKAMMITERBOTSCAFTTZORKDT + +2024-01-16 22:16:23,696 (asr_inference:494) INFO: speech length: 104157 +2024-01-16 22:16:23,708 (beam_search:428) INFO: decoder input length: 160 +2024-01-16 22:16:23,708 (beam_search:429) INFO: max output length: 160 +2024-01-16 22:16:23,708 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:24,068 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:24,068 (beam_search:476) INFO: -20.17 * 1.0 = -20.17 for ctc +2024-01-16 22:16:24,068 (beam_search:479) INFO: total log probability: -20.17 +2024-01-16 22:16:24,068 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:16:24,068 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:24,069 (beam_search:483) INFO: best hypo: THNEINWENICHTHAURICHWURDERSWEINMEDEASELBERKAMENSINNEZUFRIEDENSCHIEN + +2024-01-16 22:16:24,070 (asr_inference:494) INFO: speech length: 185117 +2024-01-16 22:16:24,087 (beam_search:428) INFO: decoder input length: 287 +2024-01-16 22:16:24,087 (beam_search:429) INFO: max output length: 287 +2024-01-16 22:16:24,087 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:25,198 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:25,198 (beam_search:476) INFO: -37.70 * 1.0 = -37.70 for ctc +2024-01-16 22:16:25,198 (beam_search:479) INFO: total log probability: -37.70 +2024-01-16 22:16:25,198 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:16:25,198 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:25,198 (beam_search:483) INFO: best hypo: EINSOMAHRWAHMANOWENMBARTARKLARGMITZONGLITSANNBERDEHUTSTABTUNDUNDEDINLIENDENDRENGKTEINETAUSENKERFIGERMENSCHENMENGERAUORFVONNEDER + +2024-01-16 22:16:25,200 (asr_inference:494) INFO: speech length: 70237 +2024-01-16 22:16:25,210 (beam_search:428) INFO: decoder input length: 107 +2024-01-16 22:16:25,210 (beam_search:429) INFO: max output length: 107 +2024-01-16 22:16:25,210 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:25,357 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:25,357 (beam_search:476) INFO: -9.59 * 1.0 = -9.59 for ctc +2024-01-16 22:16:25,357 (beam_search:479) INFO: total log probability: -9.59 +2024-01-16 22:16:25,357 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:16:25,357 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:25,357 (beam_search:483) INFO: best hypo: KOMITMIHMEINSONDENICHPRAUCHERDEINELEBE + +2024-01-16 22:16:25,358 (asr_inference:494) INFO: speech length: 102717 +2024-01-16 22:16:25,370 (beam_search:428) INFO: decoder input length: 158 +2024-01-16 22:16:25,370 (beam_search:429) INFO: max output length: 158 +2024-01-16 22:16:25,370 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:25,740 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:25,740 (beam_search:476) INFO: -16.09 * 1.0 = -16.09 for ctc +2024-01-16 22:16:25,740 (beam_search:479) INFO: total log probability: -16.09 +2024-01-16 22:16:25,740 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:16:25,740 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:25,741 (beam_search:483) INFO: best hypo: NORESANKESICHTRODEINWENICHNACHDENKLICHERSOOWIEVONEINERERINERUNGKERHELT + +2024-01-16 22:16:25,742 (asr_inference:494) INFO: speech length: 77906 +2024-01-16 22:16:25,752 (beam_search:428) INFO: decoder input length: 119 +2024-01-16 22:16:25,752 (beam_search:429) INFO: max output length: 119 +2024-01-16 22:16:25,752 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:25,985 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:25,986 (beam_search:476) INFO: -19.76 * 1.0 = -19.76 for ctc +2024-01-16 22:16:25,986 (beam_search:479) INFO: total log probability: -19.76 +2024-01-16 22:16:25,986 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:16:25,986 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:25,986 (beam_search:483) INFO: best hypo: NWUTAUFIDEDENWATIUNSDROKKSTEIGENUNAZUSASESTEMERENGEFÜRTWON + +2024-01-16 22:16:25,987 (asr_inference:494) INFO: speech length: 111677 +2024-01-16 22:16:25,999 (beam_search:428) INFO: decoder input length: 172 +2024-01-16 22:16:25,999 (beam_search:429) INFO: max output length: 172 +2024-01-16 22:16:25,999 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:26,469 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:26,469 (beam_search:476) INFO: -27.36 * 1.0 = -27.36 for ctc +2024-01-16 22:16:26,469 (beam_search:479) INFO: total log probability: -27.36 +2024-01-16 22:16:26,469 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:16:26,469 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:26,470 (beam_search:483) INFO: best hypo: NETGEWARTEEAMITENSETZSNDISCHOUISLICHERTOEFLSCHERAHFENFRATZEDIEBEDISMENCHENSHULTEARSCHIELTE + +2024-01-16 22:16:26,471 (asr_inference:494) INFO: speech length: 119037 +2024-01-16 22:16:26,484 (beam_search:428) INFO: decoder input length: 183 +2024-01-16 22:16:26,484 (beam_search:429) INFO: max output length: 183 +2024-01-16 22:16:26,484 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:26,932 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:26,932 (beam_search:476) INFO: -24.95 * 1.0 = -24.95 for ctc +2024-01-16 22:16:26,932 (beam_search:479) INFO: total log probability: -24.95 +2024-01-16 22:16:26,932 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:16:26,932 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:26,932 (beam_search:483) INFO: best hypo: ERRDERWIERDNIGTEDASGÖRDEINEGEWISENWUETSCHAFBERNHATWURTSCHOUFISTETWASFACGNSEN + +2024-01-16 22:16:26,934 (asr_inference:494) INFO: speech length: 70077 +2024-01-16 22:16:26,944 (beam_search:428) INFO: decoder input length: 107 +2024-01-16 22:16:26,944 (beam_search:429) INFO: max output length: 107 +2024-01-16 22:16:26,944 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:27,115 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:27,115 (beam_search:476) INFO: -14.58 * 1.0 = -14.58 for ctc +2024-01-16 22:16:27,115 (beam_search:479) INFO: total log probability: -14.58 +2024-01-16 22:16:27,115 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:16:27,115 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:27,116 (beam_search:483) INFO: best hypo: WULTEHEINWEHEIDIEESENTÖRTENUNDTKNDIERSCHLISEN + +2024-01-16 22:16:27,117 (asr_inference:494) INFO: speech length: 140317 +2024-01-16 22:16:27,131 (beam_search:428) INFO: decoder input length: 217 +2024-01-16 22:16:27,131 (beam_search:429) INFO: max output length: 217 +2024-01-16 22:16:27,131 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:27,807 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:27,807 (beam_search:476) INFO: -21.60 * 1.0 = -21.60 for ctc +2024-01-16 22:16:27,807 (beam_search:479) INFO: total log probability: -21.60 +2024-01-16 22:16:27,807 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:16:27,807 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:27,807 (beam_search:483) INFO: best hypo: BATZEDISERESPEKTVOLUOBEIERNNUREINIGESEENVERSCHLUKTDEWASIMBEITENBELIEBTENLANGENWÖRTANDESEFTANVORKA + +2024-01-16 22:16:27,809 (asr_inference:494) INFO: speech length: 98077 +2024-01-16 22:16:27,820 (beam_search:428) INFO: decoder input length: 151 +2024-01-16 22:16:27,820 (beam_search:429) INFO: max output length: 151 +2024-01-16 22:16:27,820 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:28,119 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:28,119 (beam_search:476) INFO: -15.11 * 1.0 = -15.11 for ctc +2024-01-16 22:16:28,119 (beam_search:479) INFO: total log probability: -15.11 +2024-01-16 22:16:28,119 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:16:28,119 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:28,119 (beam_search:483) INFO: best hypo: LORTFONDLEROAIEVERTNICHTZSENDBERENDESENBINICHGEWISVERSETZTEHR + +2024-01-16 22:16:28,120 (asr_inference:494) INFO: speech length: 92157 +2024-01-16 22:16:28,131 (beam_search:428) INFO: decoder input length: 141 +2024-01-16 22:16:28,131 (beam_search:429) INFO: max output length: 141 +2024-01-16 22:16:28,131 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:28,410 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:28,410 (beam_search:476) INFO: -14.19 * 1.0 = -14.19 for ctc +2024-01-16 22:16:28,410 (beam_search:479) INFO: total log probability: -14.19 +2024-01-16 22:16:28,410 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:16:28,410 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:28,411 (beam_search:483) INFO: best hypo: KAMGLEICHFWALSINSSCHLAFTZIMERAUFEINENNARGELINDERNERDESBPETES + +2024-01-16 22:16:28,412 (asr_inference:494) INFO: speech length: 122943 +2024-01-16 22:16:28,424 (beam_search:428) INFO: decoder input length: 190 +2024-01-16 22:16:28,424 (beam_search:429) INFO: max output length: 190 +2024-01-16 22:16:28,424 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:29,004 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:29,004 (beam_search:476) INFO: -44.41 * 1.0 = -44.41 for ctc +2024-01-16 22:16:29,004 (beam_search:479) INFO: total log probability: -44.41 +2024-01-16 22:16:29,004 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-16 22:16:29,004 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:29,004 (beam_search:483) INFO: best hypo: DASDISCHANSDEHNDIESARKRISSTEGDISCHAUNGSVÜRINTERNATZEHNALEREGENISIENEMRONZFIBPTEDESETSWALNMAKLTAERINTIEN + +2024-01-16 22:16:29,006 (asr_inference:494) INFO: speech length: 96637 +2024-01-16 22:16:29,017 (beam_search:428) INFO: decoder input length: 148 +2024-01-16 22:16:29,017 (beam_search:429) INFO: max output length: 148 +2024-01-16 22:16:29,017 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:29,351 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:29,351 (beam_search:476) INFO: -13.66 * 1.0 = -13.66 for ctc +2024-01-16 22:16:29,351 (beam_search:479) INFO: total log probability: -13.66 +2024-01-16 22:16:29,351 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:16:29,351 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:29,351 (beam_search:483) INFO: best hypo: ANFANGSFIELDEREIGENSCHRÄUNDPEITSTERSIEEINERDANDIEANDERESEITEDESWAENS + +2024-01-16 22:16:29,353 (asr_inference:494) INFO: speech length: 104317 +2024-01-16 22:16:29,364 (beam_search:428) INFO: decoder input length: 160 +2024-01-16 22:16:29,364 (beam_search:429) INFO: max output length: 160 +2024-01-16 22:16:29,364 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:29,716 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:29,716 (beam_search:476) INFO: -14.37 * 1.0 = -14.37 for ctc +2024-01-16 22:16:29,716 (beam_search:479) INFO: total log probability: -14.37 +2024-01-16 22:16:29,716 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:16:29,716 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:29,716 (beam_search:483) INFO: best hypo: FASTRLEICHTZEINIGENBERMESUNGERESWERTESAUFTZUGEBEMSICHENDTSLOSSENHATE + +2024-01-16 22:16:29,718 (asr_inference:494) INFO: speech length: 74689 +2024-01-16 22:16:29,728 (beam_search:428) INFO: decoder input length: 114 +2024-01-16 22:16:29,728 (beam_search:429) INFO: max output length: 114 +2024-01-16 22:16:29,728 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:29,952 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:29,952 (beam_search:476) INFO: -18.34 * 1.0 = -18.34 for ctc +2024-01-16 22:16:29,952 (beam_search:479) INFO: total log probability: -18.34 +2024-01-16 22:16:29,952 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:16:29,952 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:29,952 (beam_search:483) INFO: best hypo: SSEISTDIEFRARGEMENCHICHENABETNDIERARGEWASKANTÄCHNSGLÜSTWEREND + +2024-01-16 22:16:29,953 (asr_inference:494) INFO: speech length: 86237 +2024-01-16 22:16:29,964 (beam_search:428) INFO: decoder input length: 132 +2024-01-16 22:16:29,964 (beam_search:429) INFO: max output length: 132 +2024-01-16 22:16:29,964 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:30,222 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:30,222 (beam_search:476) INFO: -20.25 * 1.0 = -20.25 for ctc +2024-01-16 22:16:30,222 (beam_search:479) INFO: total log probability: -20.25 +2024-01-16 22:16:30,222 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:16:30,222 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:30,222 (beam_search:483) INFO: best hypo: ISARFARIWAUFDIRIDEMEHSIGBENTZTENWASSARSTELLENIESERUTANGERIESEN + +2024-01-16 22:16:30,224 (asr_inference:494) INFO: speech length: 119229 +2024-01-16 22:16:30,236 (beam_search:428) INFO: decoder input length: 184 +2024-01-16 22:16:30,236 (beam_search:429) INFO: max output length: 184 +2024-01-16 22:16:30,236 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:30,718 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:30,718 (beam_search:476) INFO: -15.81 * 1.0 = -15.81 for ctc +2024-01-16 22:16:30,719 (beam_search:479) INFO: total log probability: -15.81 +2024-01-16 22:16:30,719 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:16:30,719 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:30,719 (beam_search:483) INFO: best hypo: DIEBEITENMISTENHIEOBEMAUFDEMGEPVELGESTANTENHABEMUNDERSPRACHDIEALTENWAURTEVOSECHEN + +2024-01-16 22:16:30,720 (asr_inference:494) INFO: speech length: 119197 +2024-01-16 22:16:30,733 (beam_search:428) INFO: decoder input length: 184 +2024-01-16 22:16:30,733 (beam_search:429) INFO: max output length: 184 +2024-01-16 22:16:30,733 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:31,132 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:31,132 (beam_search:476) INFO: -15.39 * 1.0 = -15.39 for ctc +2024-01-16 22:16:31,132 (beam_search:479) INFO: total log probability: -15.39 +2024-01-16 22:16:31,132 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:16:31,132 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:31,132 (beam_search:483) INFO: best hypo: ENTLICPIKTESEDRIGAUFHWEISENUIGALESVONDINARMENLOEITENFRACKTEER + +2024-01-16 22:16:31,133 (asr_inference:494) INFO: speech length: 103174 +2024-01-16 22:16:31,145 (beam_search:428) INFO: decoder input length: 159 +2024-01-16 22:16:31,145 (beam_search:429) INFO: max output length: 159 +2024-01-16 22:16:31,145 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:31,583 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:31,583 (beam_search:476) INFO: -29.57 * 1.0 = -29.57 for ctc +2024-01-16 22:16:31,583 (beam_search:479) INFO: total log probability: -29.57 +2024-01-16 22:16:31,583 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:16:31,583 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:31,584 (beam_search:483) INFO: best hypo: SHOLDEINEWUNDABARIRSAMABEITZUÜCHNBUNDUNDLENEANINDIESENRAGENGIEBDETSERSEINTRESANENPRIEGKTENOU + +2024-01-16 22:16:31,585 (asr_inference:494) INFO: speech length: 141597 +2024-01-16 22:16:31,600 (beam_search:428) INFO: decoder input length: 219 +2024-01-16 22:16:31,600 (beam_search:429) INFO: max output length: 219 +2024-01-16 22:16:31,600 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:32,292 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:32,292 (beam_search:476) INFO: -36.39 * 1.0 = -36.39 for ctc +2024-01-16 22:16:32,292 (beam_search:479) INFO: total log probability: -36.39 +2024-01-16 22:16:32,292 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:16:32,292 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:32,293 (beam_search:483) INFO: best hypo: KASBAFERHARTERANGENMRETZELTENSENMPLTZSZEINLEDERJASEINERAUGENBWANIEVERSTEINERTALSEITHUNZWEITENMALHINIKTE + +2024-01-16 22:16:32,295 (asr_inference:494) INFO: speech length: 125597 +2024-01-16 22:16:32,308 (beam_search:428) INFO: decoder input length: 194 +2024-01-16 22:16:32,308 (beam_search:429) INFO: max output length: 194 +2024-01-16 22:16:32,308 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:32,855 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:32,856 (beam_search:476) INFO: -17.79 * 1.0 = -17.79 for ctc +2024-01-16 22:16:32,856 (beam_search:479) INFO: total log probability: -17.79 +2024-01-16 22:16:32,856 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:16:32,856 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:32,856 (beam_search:483) INFO: best hypo: EINIGETZEITDANACHFRAKTEERMICHOPICHGKLAUBERDEASDEREISGANGDINSHLITENDESANDERENZSERSTERTABE + +2024-01-16 22:16:32,858 (asr_inference:494) INFO: speech length: 105216 +2024-01-16 22:16:32,870 (beam_search:428) INFO: decoder input length: 162 +2024-01-16 22:16:32,870 (beam_search:429) INFO: max output length: 162 +2024-01-16 22:16:32,870 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:33,101 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:33,101 (beam_search:476) INFO: -15.09 * 1.0 = -15.09 for ctc +2024-01-16 22:16:33,101 (beam_search:479) INFO: total log probability: -15.09 +2024-01-16 22:16:33,101 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:16:33,102 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:33,102 (beam_search:483) INFO: best hypo: ABENENBLÜSENICHTGENEINERSCHOSTAREEVALEN + +2024-01-16 22:16:33,103 (asr_inference:494) INFO: speech length: 50304 +2024-01-16 22:16:33,111 (beam_search:428) INFO: decoder input length: 76 +2024-01-16 22:16:33,111 (beam_search:429) INFO: max output length: 76 +2024-01-16 22:16:33,111 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:33,164 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:33,165 (beam_search:476) INFO: -7.41 * 1.0 = -7.41 for ctc +2024-01-16 22:16:33,165 (beam_search:479) INFO: total log probability: -7.41 +2024-01-16 22:16:33,165 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:16:33,165 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:33,165 (beam_search:483) INFO: best hypo: JEITSKOMIAERSCHEUN + +2024-01-16 22:16:33,166 (asr_inference:494) INFO: speech length: 77184 +2024-01-16 22:16:33,176 (beam_search:428) INFO: decoder input length: 118 +2024-01-16 22:16:33,176 (beam_search:429) INFO: max output length: 118 +2024-01-16 22:16:33,176 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:33,349 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:33,349 (beam_search:476) INFO: -13.26 * 1.0 = -13.26 for ctc +2024-01-16 22:16:33,349 (beam_search:479) INFO: total log probability: -13.26 +2024-01-16 22:16:33,349 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:16:33,349 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:33,349 (beam_search:483) INFO: best hypo: SDEMBEIEABETDDEEISAUSERELSKRAFTAOENOFHA + +2024-01-16 22:16:33,350 (asr_inference:494) INFO: speech length: 102528 +2024-01-16 22:16:33,362 (beam_search:428) INFO: decoder input length: 158 +2024-01-16 22:16:33,362 (beam_search:429) INFO: max output length: 158 +2024-01-16 22:16:33,362 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:33,710 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:33,710 (beam_search:476) INFO: -22.82 * 1.0 = -22.82 for ctc +2024-01-16 22:16:33,710 (beam_search:479) INFO: total log probability: -22.82 +2024-01-16 22:16:33,710 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:16:33,710 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:33,710 (beam_search:483) INFO: best hypo: EINTERITTORHEIGKOSELSOHOPAWIETDNICHTMITENIETAMESISKEINEREAOCHOPAREICT + +2024-01-16 22:16:33,712 (asr_inference:494) INFO: speech length: 92928 +2024-01-16 22:16:33,723 (beam_search:428) INFO: decoder input length: 143 +2024-01-16 22:16:33,723 (beam_search:429) INFO: max output length: 143 +2024-01-16 22:16:33,723 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:33,944 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:33,945 (beam_search:476) INFO: -18.99 * 1.0 = -18.99 for ctc +2024-01-16 22:16:33,945 (beam_search:479) INFO: total log probability: -18.99 +2024-01-16 22:16:33,945 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-16 22:16:33,945 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:33,945 (beam_search:483) INFO: best hypo: ESHOUNKABNDERKÜNZSLIHERBEVROCHTDUNGTZERBERT + +2024-01-16 22:16:33,946 (asr_inference:494) INFO: speech length: 94848 +2024-01-16 22:16:33,957 (beam_search:428) INFO: decoder input length: 146 +2024-01-16 22:16:33,957 (beam_search:429) INFO: max output length: 146 +2024-01-16 22:16:33,957 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:34,248 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:34,248 (beam_search:476) INFO: -21.64 * 1.0 = -21.64 for ctc +2024-01-16 22:16:34,248 (beam_search:479) INFO: total log probability: -21.64 +2024-01-16 22:16:34,248 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:16:34,248 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:34,249 (beam_search:483) INFO: best hypo: DEINNACHTERKTIEFNEFALRTERFLEGENVONMITERIOUOLDIEBESSMITEROPTOE + +2024-01-16 22:16:34,250 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 22:16:34,257 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 22:16:34,257 (beam_search:429) INFO: max output length: 48 +2024-01-16 22:16:34,257 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:34,276 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:34,276 (beam_search:476) INFO: -7.70 * 1.0 = -7.70 for ctc +2024-01-16 22:16:34,276 (beam_search:479) INFO: total log probability: -7.70 +2024-01-16 22:16:34,276 (beam_search:480) INFO: normalized log probability: -0.70 +2024-01-16 22:16:34,276 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:34,276 (beam_search:483) INFO: best hypo: ELRETHE + +2024-01-16 22:16:34,278 (asr_inference:494) INFO: speech length: 41472 +2024-01-16 22:16:34,286 (beam_search:428) INFO: decoder input length: 62 +2024-01-16 22:16:34,286 (beam_search:429) INFO: max output length: 62 +2024-01-16 22:16:34,286 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:34,314 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:34,314 (beam_search:476) INFO: -5.21 * 1.0 = -5.21 for ctc +2024-01-16 22:16:34,314 (beam_search:479) INFO: total log probability: -5.21 +2024-01-16 22:16:34,314 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-16 22:16:34,314 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:34,314 (beam_search:483) INFO: best hypo: EINDHEIREN + +2024-01-16 22:16:34,315 (asr_inference:494) INFO: speech length: 111744 +2024-01-16 22:16:34,327 (beam_search:428) INFO: decoder input length: 172 +2024-01-16 22:16:34,327 (beam_search:429) INFO: max output length: 172 +2024-01-16 22:16:34,327 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:34,715 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:34,715 (beam_search:476) INFO: -15.37 * 1.0 = -15.37 for ctc +2024-01-16 22:16:34,715 (beam_search:479) INFO: total log probability: -15.37 +2024-01-16 22:16:34,715 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:16:34,715 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:34,715 (beam_search:483) INFO: best hypo: MUOZEKNIERERLESEZEIGEONEINABSPEICHERVERWALITENUNBITANERENOTZANTEIREN + +2024-01-16 22:16:34,717 (asr_inference:494) INFO: speech length: 57984 +2024-01-16 22:16:34,726 (beam_search:428) INFO: decoder input length: 88 +2024-01-16 22:16:34,726 (beam_search:429) INFO: max output length: 88 +2024-01-16 22:16:34,726 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:34,789 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:34,790 (beam_search:476) INFO: -6.19 * 1.0 = -6.19 for ctc +2024-01-16 22:16:34,790 (beam_search:479) INFO: total log probability: -6.19 +2024-01-16 22:16:34,790 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:16:34,790 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:34,790 (beam_search:483) INFO: best hypo: DEDEMBOSGONKTERAE + +2024-01-16 22:16:34,791 (asr_inference:494) INFO: speech length: 122880 +2024-01-16 22:16:34,803 (beam_search:428) INFO: decoder input length: 189 +2024-01-16 22:16:34,804 (beam_search:429) INFO: max output length: 189 +2024-01-16 22:16:34,804 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:35,216 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:35,216 (beam_search:476) INFO: -37.07 * 1.0 = -37.07 for ctc +2024-01-16 22:16:35,216 (beam_search:479) INFO: total log probability: -37.07 +2024-01-16 22:16:35,216 (beam_search:480) INFO: normalized log probability: -0.49 +2024-01-16 22:16:35,216 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:35,216 (beam_search:483) INFO: best hypo: SAULBASTZEHLZUDENGNRTISTENDISEIDABMAUMNFMECRÜENDLSERLZERIUDN + +2024-01-16 22:16:35,218 (asr_inference:494) INFO: speech length: 80640 +2024-01-16 22:16:35,228 (beam_search:428) INFO: decoder input length: 123 +2024-01-16 22:16:35,228 (beam_search:429) INFO: max output length: 123 +2024-01-16 22:16:35,228 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:35,438 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:35,438 (beam_search:476) INFO: -16.44 * 1.0 = -16.44 for ctc +2024-01-16 22:16:35,438 (beam_search:479) INFO: total log probability: -16.44 +2024-01-16 22:16:35,438 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:16:35,438 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:35,438 (beam_search:483) INFO: best hypo: INKMÜNÜMWÖHRSEBELAENWELNBAUGKEINERSELBONDEREISCHE + +2024-01-16 22:16:35,440 (asr_inference:494) INFO: speech length: 126720 +2024-01-16 22:16:35,453 (beam_search:428) INFO: decoder input length: 195 +2024-01-16 22:16:35,453 (beam_search:429) INFO: max output length: 195 +2024-01-16 22:16:35,453 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:35,984 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:35,985 (beam_search:476) INFO: -24.26 * 1.0 = -24.26 for ctc +2024-01-16 22:16:35,985 (beam_search:479) INFO: total log probability: -24.26 +2024-01-16 22:16:35,985 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:16:35,985 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:35,985 (beam_search:483) INFO: best hypo: EITEREWICHTIGEINDESTRIZWEIGESENDEMIKRUMICHANIGGERWANOPLASTIGMITEIBAUUNTIEHELTZVERABEITUNE + +2024-01-16 22:16:35,986 (asr_inference:494) INFO: speech length: 101760 +2024-01-16 22:16:35,998 (beam_search:428) INFO: decoder input length: 156 +2024-01-16 22:16:35,998 (beam_search:429) INFO: max output length: 156 +2024-01-16 22:16:35,998 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:36,320 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:36,320 (beam_search:476) INFO: -17.28 * 1.0 = -17.28 for ctc +2024-01-16 22:16:36,320 (beam_search:479) INFO: total log probability: -17.28 +2024-01-16 22:16:36,320 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:16:36,320 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:36,321 (beam_search:483) INFO: best hypo: IBERDENAUTORISNIHZBEKANDVERMUTLIGSTAMTEHRAUSEDEITENPRAHGBIET + +2024-01-16 22:16:36,322 (asr_inference:494) INFO: speech length: 74112 +2024-01-16 22:16:36,332 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 22:16:36,332 (beam_search:429) INFO: max output length: 113 +2024-01-16 22:16:36,332 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:36,449 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:36,449 (beam_search:476) INFO: -13.47 * 1.0 = -13.47 for ctc +2024-01-16 22:16:36,449 (beam_search:479) INFO: total log probability: -13.47 +2024-01-16 22:16:36,449 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-16 22:16:36,449 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:36,449 (beam_search:483) INFO: best hypo: NDSTDEUÖERISSMIENETOPLEPATE + +2024-01-16 22:16:36,451 (asr_inference:494) INFO: speech length: 66432 +2024-01-16 22:16:36,460 (beam_search:428) INFO: decoder input length: 101 +2024-01-16 22:16:36,460 (beam_search:429) INFO: max output length: 101 +2024-01-16 22:16:36,460 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:36,579 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:36,579 (beam_search:476) INFO: -10.69 * 1.0 = -10.69 for ctc +2024-01-16 22:16:36,579 (beam_search:479) INFO: total log probability: -10.69 +2024-01-16 22:16:36,579 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:16:36,579 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:36,579 (beam_search:483) INFO: best hypo: DEHORBMENBEBLEMERPOSIESCHIGAUCHT + +2024-01-16 22:16:36,581 (asr_inference:494) INFO: speech length: 87552 +2024-01-16 22:16:36,591 (beam_search:428) INFO: decoder input length: 134 +2024-01-16 22:16:36,591 (beam_search:429) INFO: max output length: 134 +2024-01-16 22:16:36,591 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:36,806 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:36,806 (beam_search:476) INFO: -21.01 * 1.0 = -21.01 for ctc +2024-01-16 22:16:36,807 (beam_search:479) INFO: total log probability: -21.01 +2024-01-16 22:16:36,807 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-16 22:16:36,807 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:36,807 (beam_search:483) INFO: best hypo: EWISCHIENMANURABAREEMAUGTÜUREELTEITMACHBERH + +2024-01-16 22:16:36,808 (asr_inference:494) INFO: speech length: 79104 +2024-01-16 22:16:36,818 (beam_search:428) INFO: decoder input length: 121 +2024-01-16 22:16:36,818 (beam_search:429) INFO: max output length: 121 +2024-01-16 22:16:36,818 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:36,996 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:36,996 (beam_search:476) INFO: -19.45 * 1.0 = -19.45 for ctc +2024-01-16 22:16:36,996 (beam_search:479) INFO: total log probability: -19.45 +2024-01-16 22:16:36,996 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-16 22:16:36,996 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:36,996 (beam_search:483) INFO: best hypo: KEDEZALICDEESELDEARNANNANENESHEWABENG + +2024-01-16 22:16:36,998 (asr_inference:494) INFO: speech length: 110016 +2024-01-16 22:16:37,010 (beam_search:428) INFO: decoder input length: 169 +2024-01-16 22:16:37,010 (beam_search:429) INFO: max output length: 169 +2024-01-16 22:16:37,010 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:37,260 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:37,260 (beam_search:476) INFO: -15.61 * 1.0 = -15.61 for ctc +2024-01-16 22:16:37,260 (beam_search:479) INFO: total log probability: -15.61 +2024-01-16 22:16:37,260 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:16:37,260 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:37,260 (beam_search:483) INFO: best hypo: EEESEENZNAMERINMINCHENDWOEAUGSTABEBEI + +2024-01-16 22:16:37,261 (asr_inference:494) INFO: speech length: 144000 +2024-01-16 22:16:37,276 (beam_search:428) INFO: decoder input length: 222 +2024-01-16 22:16:37,276 (beam_search:429) INFO: max output length: 222 +2024-01-16 22:16:37,276 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:37,864 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:37,864 (beam_search:476) INFO: -27.01 * 1.0 = -27.01 for ctc +2024-01-16 22:16:37,864 (beam_search:479) INFO: total log probability: -27.01 +2024-01-16 22:16:37,864 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:16:37,864 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:37,865 (beam_search:483) INFO: best hypo: INERNHTUNDELSERERNARTEIGSKÖRDENALSGERENTETAEILEENDESNARTIGSAURGEMEINSAMVORKOMEN + +2024-01-16 22:16:37,866 (asr_inference:494) INFO: speech length: 73728 +2024-01-16 22:16:37,876 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 22:16:37,876 (beam_search:429) INFO: max output length: 113 +2024-01-16 22:16:37,876 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:38,046 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:38,046 (beam_search:476) INFO: -11.92 * 1.0 = -11.92 for ctc +2024-01-16 22:16:38,046 (beam_search:479) INFO: total log probability: -11.92 +2024-01-16 22:16:38,046 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:16:38,046 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:38,046 (beam_search:483) INFO: best hypo: DABEIEBELEGKTEREHRDIEPLÄTZSEFVIERUNTREIC + +2024-01-16 22:16:38,047 (asr_inference:494) INFO: speech length: 100224 +2024-01-16 22:16:38,058 (beam_search:428) INFO: decoder input length: 154 +2024-01-16 22:16:38,058 (beam_search:429) INFO: max output length: 154 +2024-01-16 22:16:38,058 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:38,338 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:38,338 (beam_search:476) INFO: -21.85 * 1.0 = -21.85 for ctc +2024-01-16 22:16:38,338 (beam_search:479) INFO: total log probability: -21.85 +2024-01-16 22:16:38,339 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:16:38,339 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:38,339 (beam_search:483) INFO: best hypo: AKEDEABIJEISDIETHOUCHTARTZWEIERBROFVESENEARTENZAN + +2024-01-16 22:16:38,340 (asr_inference:494) INFO: speech length: 65664 +2024-01-16 22:16:38,350 (beam_search:428) INFO: decoder input length: 100 +2024-01-16 22:16:38,350 (beam_search:429) INFO: max output length: 100 +2024-01-16 22:16:38,350 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:38,488 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:38,488 (beam_search:476) INFO: -12.02 * 1.0 = -12.02 for ctc +2024-01-16 22:16:38,488 (beam_search:479) INFO: total log probability: -12.02 +2024-01-16 22:16:38,488 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:16:38,488 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:38,488 (beam_search:483) INFO: best hypo: DISKLAUBETASFÜRTNISTINRISTIGERISTRUNG + +2024-01-16 22:16:38,489 (asr_inference:494) INFO: speech length: 62976 +2024-01-16 22:16:38,499 (beam_search:428) INFO: decoder input length: 96 +2024-01-16 22:16:38,499 (beam_search:429) INFO: max output length: 96 +2024-01-16 22:16:38,499 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:38,614 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:38,614 (beam_search:476) INFO: -13.65 * 1.0 = -13.65 for ctc +2024-01-16 22:16:38,614 (beam_search:479) INFO: total log probability: -13.65 +2024-01-16 22:16:38,614 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:16:38,614 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:38,614 (beam_search:483) INFO: best hypo: DASESEINEXSTRENSHLESTERISTFLIENER + +2024-01-16 22:16:38,615 (asr_inference:494) INFO: speech length: 70656 +2024-01-16 22:16:38,625 (beam_search:428) INFO: decoder input length: 108 +2024-01-16 22:16:38,625 (beam_search:429) INFO: max output length: 108 +2024-01-16 22:16:38,625 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:38,761 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:38,761 (beam_search:476) INFO: -8.22 * 1.0 = -8.22 for ctc +2024-01-16 22:16:38,761 (beam_search:479) INFO: total log probability: -8.22 +2024-01-16 22:16:38,761 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:16:38,761 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:38,761 (beam_search:483) INFO: best hypo: HERLOSCHENBLESTZSAINHAGERESGESIHT + +2024-01-16 22:16:38,762 (asr_inference:494) INFO: speech length: 54912 +2024-01-16 22:16:38,771 (beam_search:428) INFO: decoder input length: 83 +2024-01-16 22:16:38,771 (beam_search:429) INFO: max output length: 83 +2024-01-16 22:16:38,771 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:38,847 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:38,847 (beam_search:476) INFO: -8.76 * 1.0 = -8.76 for ctc +2024-01-16 22:16:38,847 (beam_search:479) INFO: total log probability: -8.76 +2024-01-16 22:16:38,847 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:16:38,847 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:38,847 (beam_search:483) INFO: best hypo: MRKALENFINDETOSSUNFER + +2024-01-16 22:16:38,849 (asr_inference:494) INFO: speech length: 65088 +2024-01-16 22:16:38,858 (beam_search:428) INFO: decoder input length: 99 +2024-01-16 22:16:38,858 (beam_search:429) INFO: max output length: 99 +2024-01-16 22:16:38,858 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:38,978 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:38,978 (beam_search:476) INFO: -6.89 * 1.0 = -6.89 for ctc +2024-01-16 22:16:38,978 (beam_search:479) INFO: total log probability: -6.89 +2024-01-16 22:16:38,978 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:16:38,978 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:38,978 (beam_search:483) INFO: best hypo: TINGEBOKABERHATDERDEIGESCHISTENT + +2024-01-16 22:16:38,979 (asr_inference:494) INFO: speech length: 93888 +2024-01-16 22:16:38,990 (beam_search:428) INFO: decoder input length: 144 +2024-01-16 22:16:38,990 (beam_search:429) INFO: max output length: 144 +2024-01-16 22:16:38,990 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:39,258 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:39,258 (beam_search:476) INFO: -19.10 * 1.0 = -19.10 for ctc +2024-01-16 22:16:39,258 (beam_search:479) INFO: total log probability: -19.10 +2024-01-16 22:16:39,258 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:16:39,258 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:39,258 (beam_search:483) INFO: best hypo: TSKOMPLITLISTABANMNDASSOLCEDATENUDIESEREBNEPASTBERDEN + +2024-01-16 22:16:39,260 (asr_inference:494) INFO: speech length: 76032 +2024-01-16 22:16:39,270 (beam_search:428) INFO: decoder input length: 116 +2024-01-16 22:16:39,270 (beam_search:429) INFO: max output length: 116 +2024-01-16 22:16:39,270 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:39,435 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:39,435 (beam_search:476) INFO: -10.31 * 1.0 = -10.31 for ctc +2024-01-16 22:16:39,435 (beam_search:479) INFO: total log probability: -10.31 +2024-01-16 22:16:39,435 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:16:39,435 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:39,435 (beam_search:483) INFO: best hypo: TRAMINHENGEGENERIBTEINHRMUNICHESPOSIEREN + +2024-01-16 22:16:39,436 (asr_inference:494) INFO: speech length: 57600 +2024-01-16 22:16:39,445 (beam_search:428) INFO: decoder input length: 87 +2024-01-16 22:16:39,445 (beam_search:429) INFO: max output length: 87 +2024-01-16 22:16:39,445 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:39,557 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:39,557 (beam_search:476) INFO: -11.93 * 1.0 = -11.93 for ctc +2024-01-16 22:16:39,557 (beam_search:479) INFO: total log probability: -11.93 +2024-01-16 22:16:39,557 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:16:39,557 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:39,557 (beam_search:483) INFO: best hypo: BENIHZUMKAFENERHPOETEGBEREICHDIGKT + +2024-01-16 22:16:39,558 (asr_inference:494) INFO: speech length: 106368 +2024-01-16 22:16:39,570 (beam_search:428) INFO: decoder input length: 164 +2024-01-16 22:16:39,570 (beam_search:429) INFO: max output length: 164 +2024-01-16 22:16:39,570 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:39,808 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:39,808 (beam_search:476) INFO: -25.52 * 1.0 = -25.52 for ctc +2024-01-16 22:16:39,808 (beam_search:479) INFO: total log probability: -25.52 +2024-01-16 22:16:39,808 (beam_search:480) INFO: normalized log probability: -0.54 +2024-01-16 22:16:39,808 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:39,809 (beam_search:483) INFO: best hypo: KCZUNEDEUNENEUNEINERSCHENENENDERNENENN + +2024-01-16 22:16:39,810 (asr_inference:494) INFO: speech length: 62208 +2024-01-16 22:16:39,819 (beam_search:428) INFO: decoder input length: 95 +2024-01-16 22:16:39,819 (beam_search:429) INFO: max output length: 95 +2024-01-16 22:16:39,819 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:39,927 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:39,927 (beam_search:476) INFO: -18.48 * 1.0 = -18.48 for ctc +2024-01-16 22:16:39,927 (beam_search:479) INFO: total log probability: -18.48 +2024-01-16 22:16:39,927 (beam_search:480) INFO: normalized log probability: -0.53 +2024-01-16 22:16:39,927 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:39,928 (beam_search:483) INFO: best hypo: HHUNEDEIRENSENDESELWDSLENDNUN + +2024-01-16 22:16:39,929 (asr_inference:494) INFO: speech length: 142848 +2024-01-16 22:16:39,943 (beam_search:428) INFO: decoder input length: 221 +2024-01-16 22:16:39,943 (beam_search:429) INFO: max output length: 221 +2024-01-16 22:16:39,943 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:40,559 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:40,559 (beam_search:476) INFO: -26.38 * 1.0 = -26.38 for ctc +2024-01-16 22:16:40,559 (beam_search:479) INFO: total log probability: -26.38 +2024-01-16 22:16:40,559 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:16:40,559 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:40,559 (beam_search:483) INFO: best hypo: ONEDEROWISENELENGTSTIZUNGDAMASSATDIRENABTALIUNWANDISEBAGENDERKONKEWENZSENDOCHUNTOLEGEN + +2024-01-16 22:16:40,561 (asr_inference:494) INFO: speech length: 90240 +2024-01-16 22:16:40,571 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 22:16:40,571 (beam_search:429) INFO: max output length: 138 +2024-01-16 22:16:40,571 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:40,814 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:40,815 (beam_search:476) INFO: -17.09 * 1.0 = -17.09 for ctc +2024-01-16 22:16:40,815 (beam_search:479) INFO: total log probability: -17.09 +2024-01-16 22:16:40,815 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:16:40,815 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:40,815 (beam_search:483) INFO: best hypo: SIIENDEUNECSTASUNEKUMFVERBELLGISCHIESATZUNGSTRUPEN + +2024-01-16 22:16:40,816 (asr_inference:494) INFO: speech length: 89862 +2024-01-16 22:16:40,827 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 22:16:40,827 (beam_search:429) INFO: max output length: 138 +2024-01-16 22:16:40,827 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:40,997 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:40,997 (beam_search:476) INFO: -14.76 * 1.0 = -14.76 for ctc +2024-01-16 22:16:40,997 (beam_search:479) INFO: total log probability: -14.76 +2024-01-16 22:16:40,997 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-16 22:16:40,997 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:40,998 (beam_search:483) INFO: best hypo: DEMISENWIESPLÄNGENMEITEERZHANATZT + +2024-01-16 22:16:40,999 (asr_inference:494) INFO: speech length: 158825 +2024-01-16 22:16:41,014 (beam_search:428) INFO: decoder input length: 246 +2024-01-16 22:16:41,014 (beam_search:429) INFO: max output length: 246 +2024-01-16 22:16:41,014 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:41,677 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:41,677 (beam_search:476) INFO: -28.51 * 1.0 = -28.51 for ctc +2024-01-16 22:16:41,677 (beam_search:479) INFO: total log probability: -28.51 +2024-01-16 22:16:41,677 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:16:41,677 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:41,678 (beam_search:483) INFO: best hypo: AUERDEMSPHIETERBERMNACHVERGETIEMNINMARKETROEDIUSSOWIEBERLIEGERKONKUERENTENLONDNEITZ + +2024-01-16 22:16:41,679 (asr_inference:494) INFO: speech length: 130752 +2024-01-16 22:16:41,692 (beam_search:428) INFO: decoder input length: 202 +2024-01-16 22:16:41,692 (beam_search:429) INFO: max output length: 202 +2024-01-16 22:16:41,692 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:42,202 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:42,202 (beam_search:476) INFO: -26.95 * 1.0 = -26.95 for ctc +2024-01-16 22:16:42,202 (beam_search:479) INFO: total log probability: -26.95 +2024-01-16 22:16:42,202 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:16:42,202 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:42,202 (beam_search:483) INFO: best hypo: IEAUPTERSENZSTENWANAHFVAUTHINGAERFÜRTIKUOUMSWALDASKONDOARECKETKTERIAOMNICHT + +2024-01-16 22:16:42,204 (asr_inference:494) INFO: speech length: 44352 +2024-01-16 22:16:42,212 (beam_search:428) INFO: decoder input length: 67 +2024-01-16 22:16:42,212 (beam_search:429) INFO: max output length: 67 +2024-01-16 22:16:42,212 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:42,273 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:42,273 (beam_search:476) INFO: -12.11 * 1.0 = -12.11 for ctc +2024-01-16 22:16:42,273 (beam_search:479) INFO: total log probability: -12.11 +2024-01-16 22:16:42,273 (beam_search:480) INFO: normalized log probability: -0.43 +2024-01-16 22:16:42,273 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:42,274 (beam_search:483) INFO: best hypo: SMETFWUOEINTCHIKARBOAUF + +2024-01-16 22:16:42,275 (asr_inference:494) INFO: speech length: 114048 +2024-01-16 22:16:42,287 (beam_search:428) INFO: decoder input length: 176 +2024-01-16 22:16:42,287 (beam_search:429) INFO: max output length: 176 +2024-01-16 22:16:42,287 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:42,405 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:42,405 (beam_search:476) INFO: -6.01 * 1.0 = -6.01 for ctc +2024-01-16 22:16:42,405 (beam_search:479) INFO: total log probability: -6.01 +2024-01-16 22:16:42,405 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:16:42,405 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:42,405 (beam_search:483) INFO: best hypo: WIEIETIEALEINEN + +2024-01-16 22:16:42,406 (asr_inference:494) INFO: speech length: 117504 +2024-01-16 22:16:42,419 (beam_search:428) INFO: decoder input length: 181 +2024-01-16 22:16:42,419 (beam_search:429) INFO: max output length: 181 +2024-01-16 22:16:42,419 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:42,773 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:42,773 (beam_search:476) INFO: -25.95 * 1.0 = -25.95 for ctc +2024-01-16 22:16:42,773 (beam_search:479) INFO: total log probability: -25.95 +2024-01-16 22:16:42,773 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-16 22:16:42,773 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:42,773 (beam_search:483) INFO: best hypo: NDBUMIESBERTWASBWEISWUERTROSTDESDESUÖRNBISENSVELCHUNIE + +2024-01-16 22:16:42,775 (asr_inference:494) INFO: speech length: 137088 +2024-01-16 22:16:42,789 (beam_search:428) INFO: decoder input length: 212 +2024-01-16 22:16:42,789 (beam_search:429) INFO: max output length: 212 +2024-01-16 22:16:42,789 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:43,231 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:43,231 (beam_search:476) INFO: -26.12 * 1.0 = -26.12 for ctc +2024-01-16 22:16:43,231 (beam_search:479) INFO: total log probability: -26.12 +2024-01-16 22:16:43,231 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-16 22:16:43,231 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:43,231 (beam_search:483) INFO: best hypo: HARBTEMAERSCHEOISTDEREWANSCVEREGÜNBICHISTREIHERENTEPFREINEN + +2024-01-16 22:16:43,233 (asr_inference:494) INFO: speech length: 92544 +2024-01-16 22:16:43,244 (beam_search:428) INFO: decoder input length: 142 +2024-01-16 22:16:43,244 (beam_search:429) INFO: max output length: 142 +2024-01-16 22:16:43,244 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:43,477 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:43,477 (beam_search:476) INFO: -12.74 * 1.0 = -12.74 for ctc +2024-01-16 22:16:43,477 (beam_search:479) INFO: total log probability: -12.74 +2024-01-16 22:16:43,477 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:16:43,477 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:43,477 (beam_search:483) INFO: best hypo: LEICHZEITIGHWUONDENSPROTDWERTENTALLWEISEVERBULHEN + +2024-01-16 22:16:43,479 (asr_inference:494) INFO: speech length: 61056 +2024-01-16 22:16:43,488 (beam_search:428) INFO: decoder input length: 93 +2024-01-16 22:16:43,488 (beam_search:429) INFO: max output length: 93 +2024-01-16 22:16:43,488 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:43,561 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:43,561 (beam_search:476) INFO: -24.55 * 1.0 = -24.55 for ctc +2024-01-16 22:16:43,561 (beam_search:479) INFO: total log probability: -24.55 +2024-01-16 22:16:43,561 (beam_search:480) INFO: normalized log probability: -1.02 +2024-01-16 22:16:43,561 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:43,561 (beam_search:483) INFO: best hypo: DASENHSFSSANAAFAAAE + +2024-01-16 22:16:43,562 (asr_inference:494) INFO: speech length: 38016 +2024-01-16 22:16:43,570 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 22:16:43,570 (beam_search:429) INFO: max output length: 57 +2024-01-16 22:16:43,570 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:43,614 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:43,614 (beam_search:476) INFO: -22.66 * 1.0 = -22.66 for ctc +2024-01-16 22:16:43,614 (beam_search:479) INFO: total log probability: -22.66 +2024-01-16 22:16:43,614 (beam_search:480) INFO: normalized log probability: -0.99 +2024-01-16 22:16:43,614 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:43,614 (beam_search:483) INFO: best hypo: OTSTIERERNNNFBENN + +2024-01-16 22:16:43,615 (asr_inference:494) INFO: speech length: 112128 +2024-01-16 22:16:43,628 (beam_search:428) INFO: decoder input length: 173 +2024-01-16 22:16:43,628 (beam_search:429) INFO: max output length: 173 +2024-01-16 22:16:43,628 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:43,992 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:43,992 (beam_search:476) INFO: -21.72 * 1.0 = -21.72 for ctc +2024-01-16 22:16:43,992 (beam_search:479) INFO: total log probability: -21.72 +2024-01-16 22:16:43,992 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:16:43,992 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:43,993 (beam_search:483) INFO: best hypo: ZUDEMFARSAEIERLEMKLOSTALNGJAREDEMTERDESENOWITZENMEISTASUNPRIOR + +2024-01-16 22:16:43,994 (asr_inference:494) INFO: speech length: 67968 +2024-01-16 22:16:44,004 (beam_search:428) INFO: decoder input length: 104 +2024-01-16 22:16:44,004 (beam_search:429) INFO: max output length: 104 +2024-01-16 22:16:44,004 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:44,145 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:44,145 (beam_search:476) INFO: -8.67 * 1.0 = -8.67 for ctc +2024-01-16 22:16:44,145 (beam_search:479) INFO: total log probability: -8.67 +2024-01-16 22:16:44,145 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:16:44,145 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:44,145 (beam_search:483) INFO: best hypo: HEIDENHEIDENENSTMTEINERERZTEVERMIEER + +2024-01-16 22:16:44,146 (asr_inference:494) INFO: speech length: 31872 +2024-01-16 22:16:44,154 (beam_search:428) INFO: decoder input length: 47 +2024-01-16 22:16:44,154 (beam_search:429) INFO: max output length: 47 +2024-01-16 22:16:44,154 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:44,187 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:44,188 (beam_search:476) INFO: -7.66 * 1.0 = -7.66 for ctc +2024-01-16 22:16:44,188 (beam_search:479) INFO: total log probability: -7.66 +2024-01-16 22:16:44,188 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-16 22:16:44,188 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:44,188 (beam_search:483) INFO: best hypo: DARERTZUEGKTENEN + +2024-01-16 22:16:44,189 (asr_inference:494) INFO: speech length: 25344 +2024-01-16 22:16:44,196 (beam_search:428) INFO: decoder input length: 37 +2024-01-16 22:16:44,196 (beam_search:429) INFO: max output length: 37 +2024-01-16 22:16:44,196 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:44,213 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:44,213 (beam_search:476) INFO: -4.32 * 1.0 = -4.32 for ctc +2024-01-16 22:16:44,213 (beam_search:479) INFO: total log probability: -4.32 +2024-01-16 22:16:44,213 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:16:44,213 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:44,214 (beam_search:483) INFO: best hypo: ZWEIELGNR + +2024-01-16 22:16:44,215 (asr_inference:494) INFO: speech length: 143424 +2024-01-16 22:16:44,229 (beam_search:428) INFO: decoder input length: 222 +2024-01-16 22:16:44,229 (beam_search:429) INFO: max output length: 222 +2024-01-16 22:16:44,229 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:44,580 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:44,580 (beam_search:476) INFO: -19.76 * 1.0 = -19.76 for ctc +2024-01-16 22:16:44,580 (beam_search:479) INFO: total log probability: -19.76 +2024-01-16 22:16:44,580 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-16 22:16:44,580 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:44,580 (beam_search:483) INFO: best hypo: EBEPFEALLEINAUGNENGEIETZENIKLTEREINDEREAT + +2024-01-16 22:16:44,581 (asr_inference:494) INFO: speech length: 82368 +2024-01-16 22:16:44,592 (beam_search:428) INFO: decoder input length: 126 +2024-01-16 22:16:44,592 (beam_search:429) INFO: max output length: 126 +2024-01-16 22:16:44,592 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:44,836 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:44,836 (beam_search:476) INFO: -22.62 * 1.0 = -22.62 for ctc +2024-01-16 22:16:44,836 (beam_search:479) INFO: total log probability: -22.62 +2024-01-16 22:16:44,836 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 22:16:44,836 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:44,836 (beam_search:483) INFO: best hypo: DIESERSDETAHFEABPELWENDENEINHRMSCHERCHOLMETARTSKNTDESENOFEM + +2024-01-16 22:16:44,838 (asr_inference:494) INFO: speech length: 34944 +2024-01-16 22:16:44,845 (beam_search:428) INFO: decoder input length: 52 +2024-01-16 22:16:44,845 (beam_search:429) INFO: max output length: 52 +2024-01-16 22:16:44,845 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:44,881 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:44,881 (beam_search:476) INFO: -5.64 * 1.0 = -5.64 for ctc +2024-01-16 22:16:44,881 (beam_search:479) INFO: total log probability: -5.64 +2024-01-16 22:16:44,881 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:16:44,881 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:44,882 (beam_search:483) INFO: best hypo: ALSOECHIORENIGS + +2024-01-16 22:16:44,883 (asr_inference:494) INFO: speech length: 36864 +2024-01-16 22:16:44,891 (beam_search:428) INFO: decoder input length: 55 +2024-01-16 22:16:44,891 (beam_search:429) INFO: max output length: 55 +2024-01-16 22:16:44,891 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:44,938 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:44,938 (beam_search:476) INFO: -5.21 * 1.0 = -5.21 for ctc +2024-01-16 22:16:44,938 (beam_search:479) INFO: total log probability: -5.21 +2024-01-16 22:16:44,938 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:16:44,938 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:44,938 (beam_search:483) INFO: best hypo: WEKONMANSISCHSCUTZEN + +2024-01-16 22:16:44,939 (asr_inference:494) INFO: speech length: 92160 +2024-01-16 22:16:44,950 (beam_search:428) INFO: decoder input length: 141 +2024-01-16 22:16:44,950 (beam_search:429) INFO: max output length: 141 +2024-01-16 22:16:44,950 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:45,257 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:45,257 (beam_search:476) INFO: -22.34 * 1.0 = -22.34 for ctc +2024-01-16 22:16:45,257 (beam_search:479) INFO: total log probability: -22.34 +2024-01-16 22:16:45,257 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:16:45,257 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:45,257 (beam_search:483) INFO: best hypo: AFÜINFMNERTNLAGEINENFINDLICHERBEPLOTEANZTDEBISTDAHEINERHLTLICHENVOR + +2024-01-16 22:16:45,259 (asr_inference:494) INFO: speech length: 96384 +2024-01-16 22:16:45,270 (beam_search:428) INFO: decoder input length: 148 +2024-01-16 22:16:45,270 (beam_search:429) INFO: max output length: 148 +2024-01-16 22:16:45,270 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:45,618 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:45,618 (beam_search:476) INFO: -33.26 * 1.0 = -33.26 for ctc +2024-01-16 22:16:45,618 (beam_search:479) INFO: total log probability: -33.26 +2024-01-16 22:16:45,618 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-16 22:16:45,618 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:45,619 (beam_search:483) INFO: best hypo: TILIETERSDIEBWINSTNMUNGINESOFTENSNSTEBZSMINTSANASCGIEZICHIKAZHOUNZUNBABPRLFMCH + +2024-01-16 22:16:45,620 (asr_inference:494) INFO: speech length: 104832 +2024-01-16 22:16:45,631 (beam_search:428) INFO: decoder input length: 161 +2024-01-16 22:16:45,631 (beam_search:429) INFO: max output length: 161 +2024-01-16 22:16:45,631 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:45,972 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:45,973 (beam_search:476) INFO: -29.90 * 1.0 = -29.90 for ctc +2024-01-16 22:16:45,973 (beam_search:479) INFO: total log probability: -29.90 +2024-01-16 22:16:45,973 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-16 22:16:45,973 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:45,973 (beam_search:483) INFO: best hypo: ATENETHEILENWAHAMENGENSHENIHENDALUSWESZISTIKELNTELLNBESORAESHEILTEN + +2024-01-16 22:16:45,974 (asr_inference:494) INFO: speech length: 120576 +2024-01-16 22:16:45,987 (beam_search:428) INFO: decoder input length: 186 +2024-01-16 22:16:45,987 (beam_search:429) INFO: max output length: 186 +2024-01-16 22:16:45,987 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:46,438 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:46,439 (beam_search:476) INFO: -28.27 * 1.0 = -28.27 for ctc +2024-01-16 22:16:46,439 (beam_search:479) INFO: total log probability: -28.27 +2024-01-16 22:16:46,439 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:16:46,439 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:46,439 (beam_search:483) INFO: best hypo: DIEANDIEWERBENSOSCHÖHRISTANRKENGELRUSCHENNDANDALERGBNBHENTELNRSSLANGELIGT + +2024-01-16 22:16:46,440 (asr_inference:494) INFO: speech length: 72576 +2024-01-16 22:16:46,450 (beam_search:428) INFO: decoder input length: 111 +2024-01-16 22:16:46,450 (beam_search:429) INFO: max output length: 111 +2024-01-16 22:16:46,450 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:46,611 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:46,611 (beam_search:476) INFO: -15.89 * 1.0 = -15.89 for ctc +2024-01-16 22:16:46,611 (beam_search:479) INFO: total log probability: -15.89 +2024-01-16 22:16:46,611 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:16:46,611 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:46,611 (beam_search:483) INFO: best hypo: IERTUARGEISTENDIESARZEIRTGUNEFLANENSC + +2024-01-16 22:16:46,612 (asr_inference:494) INFO: speech length: 97536 +2024-01-16 22:16:46,623 (beam_search:428) INFO: decoder input length: 150 +2024-01-16 22:16:46,623 (beam_search:429) INFO: max output length: 150 +2024-01-16 22:16:46,623 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:46,972 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:46,972 (beam_search:476) INFO: -32.35 * 1.0 = -32.35 for ctc +2024-01-16 22:16:46,972 (beam_search:479) INFO: total log probability: -32.35 +2024-01-16 22:16:46,972 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-16 22:16:46,972 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:46,973 (beam_search:483) INFO: best hypo: EDIESCEÄKEBEGENMSÜGENBERHUMARSINFÜLTISDIEGOBDEEICHTUMEGARSIÜTAUSSTE + +2024-01-16 22:16:46,974 (asr_inference:494) INFO: speech length: 72192 +2024-01-16 22:16:46,984 (beam_search:428) INFO: decoder input length: 110 +2024-01-16 22:16:46,984 (beam_search:429) INFO: max output length: 110 +2024-01-16 22:16:46,984 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:47,153 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:47,153 (beam_search:476) INFO: -11.17 * 1.0 = -11.17 for ctc +2024-01-16 22:16:47,153 (beam_search:479) INFO: total log probability: -11.17 +2024-01-16 22:16:47,153 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:16:47,153 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:47,154 (beam_search:483) INFO: best hypo: ERSTVONDORTKONTEERENWEGFREIEVOTZSEITZSEN + +2024-01-16 22:16:47,155 (asr_inference:494) INFO: speech length: 119808 +2024-01-16 22:16:47,167 (beam_search:428) INFO: decoder input length: 185 +2024-01-16 22:16:47,167 (beam_search:429) INFO: max output length: 185 +2024-01-16 22:16:47,167 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:47,549 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:47,549 (beam_search:476) INFO: -24.17 * 1.0 = -24.17 for ctc +2024-01-16 22:16:47,549 (beam_search:479) INFO: total log probability: -24.17 +2024-01-16 22:16:47,549 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 22:16:47,549 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:47,549 (beam_search:483) INFO: best hypo: SIERHEBZICHHEUTEIMERNOGUTERKENBERTAUSSTDEIENSCSHWEMLANTHEAUS + +2024-01-16 22:16:47,550 (asr_inference:494) INFO: speech length: 61056 +2024-01-16 22:16:47,559 (beam_search:428) INFO: decoder input length: 93 +2024-01-16 22:16:47,559 (beam_search:429) INFO: max output length: 93 +2024-01-16 22:16:47,559 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:47,668 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:47,668 (beam_search:476) INFO: -11.44 * 1.0 = -11.44 for ctc +2024-01-16 22:16:47,668 (beam_search:479) INFO: total log probability: -11.44 +2024-01-16 22:16:47,668 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:16:47,668 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:47,669 (beam_search:483) INFO: best hypo: DIEKANARESCHENINENEHANZUSPBERCHE + +2024-01-16 22:16:47,670 (asr_inference:494) INFO: speech length: 84864 +2024-01-16 22:16:47,680 (beam_search:428) INFO: decoder input length: 130 +2024-01-16 22:16:47,680 (beam_search:429) INFO: max output length: 130 +2024-01-16 22:16:47,680 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:47,931 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:47,931 (beam_search:476) INFO: -27.44 * 1.0 = -27.44 for ctc +2024-01-16 22:16:47,931 (beam_search:479) INFO: total log probability: -27.44 +2024-01-16 22:16:47,931 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-16 22:16:47,931 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:47,931 (beam_search:483) INFO: best hypo: WESNSCHRFLERHRBENDIESEMUTDTZUNESEHRENEOBEIFOARANBEREBATET + +2024-01-16 22:16:47,933 (asr_inference:494) INFO: speech length: 102528 +2024-01-16 22:16:47,944 (beam_search:428) INFO: decoder input length: 158 +2024-01-16 22:16:47,944 (beam_search:429) INFO: max output length: 158 +2024-01-16 22:16:47,944 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:48,239 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:48,239 (beam_search:476) INFO: -17.06 * 1.0 = -17.06 for ctc +2024-01-16 22:16:48,239 (beam_search:479) INFO: total log probability: -17.06 +2024-01-16 22:16:48,239 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:16:48,239 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:48,239 (beam_search:483) INFO: best hypo: SENIGISCHETZSPETZIUNENREISCSTENBISNORDAMEHRIKAENDARSIEN + +2024-01-16 22:16:48,241 (asr_inference:494) INFO: speech length: 139776 +2024-01-16 22:16:48,255 (beam_search:428) INFO: decoder input length: 216 +2024-01-16 22:16:48,255 (beam_search:429) INFO: max output length: 216 +2024-01-16 22:16:48,255 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:48,888 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:48,888 (beam_search:476) INFO: -29.06 * 1.0 = -29.06 for ctc +2024-01-16 22:16:48,888 (beam_search:479) INFO: total log probability: -29.06 +2024-01-16 22:16:48,888 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:16:48,888 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:48,888 (beam_search:483) INFO: best hypo: AREICHEVORDEREDMAETZIERUNENBEIDUTSCHENEROPARUNDWEHTLECSTESCAFTENZOBIENLÜBICHENSPILENVORLKTEN + +2024-01-16 22:16:48,890 (asr_inference:494) INFO: speech length: 76032 +2024-01-16 22:16:48,900 (beam_search:428) INFO: decoder input length: 116 +2024-01-16 22:16:48,900 (beam_search:429) INFO: max output length: 116 +2024-01-16 22:16:48,900 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:49,092 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:49,092 (beam_search:476) INFO: -18.24 * 1.0 = -18.24 for ctc +2024-01-16 22:16:49,092 (beam_search:479) INFO: total log probability: -18.24 +2024-01-16 22:16:49,092 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:16:49,092 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:49,093 (beam_search:483) INFO: best hypo: INANERTELIEDSOTUEMLETERMDSOEIGKITAUFTERPARGBANG + +2024-01-16 22:16:49,094 (asr_inference:494) INFO: speech length: 63360 +2024-01-16 22:16:49,103 (beam_search:428) INFO: decoder input length: 96 +2024-01-16 22:16:49,103 (beam_search:429) INFO: max output length: 96 +2024-01-16 22:16:49,103 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:49,254 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:49,254 (beam_search:476) INFO: -15.73 * 1.0 = -15.73 for ctc +2024-01-16 22:16:49,254 (beam_search:479) INFO: total log probability: -15.73 +2024-01-16 22:16:49,254 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:16:49,254 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:49,254 (beam_search:483) INFO: best hypo: ITIUMWAMITRENGINBAUPLSSIDIEKERLTEBESAUSHADIE + +2024-01-16 22:16:49,256 (asr_inference:494) INFO: speech length: 68352 +2024-01-16 22:16:49,265 (beam_search:428) INFO: decoder input length: 104 +2024-01-16 22:16:49,265 (beam_search:429) INFO: max output length: 104 +2024-01-16 22:16:49,265 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:49,345 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:49,346 (beam_search:476) INFO: -13.46 * 1.0 = -13.46 for ctc +2024-01-16 22:16:49,346 (beam_search:479) INFO: total log probability: -13.46 +2024-01-16 22:16:49,346 (beam_search:480) INFO: normalized log probability: -0.56 +2024-01-16 22:16:49,346 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:49,346 (beam_search:483) INFO: best hypo: WOENEENTERHEWESSI + +2024-01-16 22:16:49,347 (asr_inference:494) INFO: speech length: 82944 +2024-01-16 22:16:49,357 (beam_search:428) INFO: decoder input length: 127 +2024-01-16 22:16:49,357 (beam_search:429) INFO: max output length: 127 +2024-01-16 22:16:49,357 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:49,571 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:49,571 (beam_search:476) INFO: -16.49 * 1.0 = -16.49 for ctc +2024-01-16 22:16:49,571 (beam_search:479) INFO: total log probability: -16.49 +2024-01-16 22:16:49,571 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:16:49,571 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:49,571 (beam_search:483) INFO: best hypo: AOSTANISENEEINEBOCHENACHTENESTENUEMUNDINRLULEN + +2024-01-16 22:16:49,573 (asr_inference:494) INFO: speech length: 98304 +2024-01-16 22:16:49,584 (beam_search:428) INFO: decoder input length: 151 +2024-01-16 22:16:49,584 (beam_search:429) INFO: max output length: 151 +2024-01-16 22:16:49,584 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:49,843 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:49,843 (beam_search:476) INFO: -15.97 * 1.0 = -15.97 for ctc +2024-01-16 22:16:49,843 (beam_search:479) INFO: total log probability: -15.97 +2024-01-16 22:16:49,843 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:16:49,843 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:49,844 (beam_search:483) INFO: best hypo: ENMMITELEITERHERTENEÄKZINDEHERSCHAFTDASTDAFHINER + +2024-01-16 22:16:49,845 (asr_inference:494) INFO: speech length: 104448 +2024-01-16 22:16:49,856 (beam_search:428) INFO: decoder input length: 161 +2024-01-16 22:16:49,856 (beam_search:429) INFO: max output length: 161 +2024-01-16 22:16:49,856 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:50,133 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:50,133 (beam_search:476) INFO: -17.70 * 1.0 = -17.70 for ctc +2024-01-16 22:16:50,133 (beam_search:479) INFO: total log probability: -17.70 +2024-01-16 22:16:50,133 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:16:50,133 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:50,134 (beam_search:483) INFO: best hypo: DENNAMSCHEPLETRARNAOCHLEITEEFATSOLGEVERMASSERHATI + +2024-01-16 22:16:50,135 (asr_inference:494) INFO: speech length: 86016 +2024-01-16 22:16:50,145 (beam_search:428) INFO: decoder input length: 132 +2024-01-16 22:16:50,145 (beam_search:429) INFO: max output length: 132 +2024-01-16 22:16:50,145 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:50,326 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:50,326 (beam_search:476) INFO: -16.89 * 1.0 = -16.89 for ctc +2024-01-16 22:16:50,326 (beam_search:479) INFO: total log probability: -16.89 +2024-01-16 22:16:50,326 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-16 22:16:50,326 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:50,326 (beam_search:483) INFO: best hypo: PLUKANEMITEBUSLOCHANVONERONDEREFHOREN + +2024-01-16 22:16:50,327 (asr_inference:494) INFO: speech length: 31872 +2024-01-16 22:16:50,335 (beam_search:428) INFO: decoder input length: 47 +2024-01-16 22:16:50,335 (beam_search:429) INFO: max output length: 47 +2024-01-16 22:16:50,335 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:50,361 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:50,361 (beam_search:476) INFO: -7.42 * 1.0 = -7.42 for ctc +2024-01-16 22:16:50,361 (beam_search:479) INFO: total log probability: -7.42 +2024-01-16 22:16:50,361 (beam_search:480) INFO: normalized log probability: -0.49 +2024-01-16 22:16:50,361 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:50,361 (beam_search:483) INFO: best hypo: NEREORIEGOL + +2024-01-16 22:16:50,362 (asr_inference:494) INFO: speech length: 133632 +2024-01-16 22:16:50,376 (beam_search:428) INFO: decoder input length: 206 +2024-01-16 22:16:50,376 (beam_search:429) INFO: max output length: 206 +2024-01-16 22:16:50,376 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:50,926 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:50,926 (beam_search:476) INFO: -34.79 * 1.0 = -34.79 for ctc +2024-01-16 22:16:50,927 (beam_search:479) INFO: total log probability: -34.79 +2024-01-16 22:16:50,927 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:16:50,927 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:50,927 (beam_search:483) INFO: best hypo: ALLERTINGSERGAHBENWEITERIEPUÜNFVONGENDASESMITELFRISTIKEINPIDAFISCEUCHEAUTUOBANDEHRER + +2024-01-16 22:16:50,928 (asr_inference:494) INFO: speech length: 125568 +2024-01-16 22:16:50,942 (beam_search:428) INFO: decoder input length: 194 +2024-01-16 22:16:50,942 (beam_search:429) INFO: max output length: 194 +2024-01-16 22:16:50,942 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:51,351 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:51,351 (beam_search:476) INFO: -20.31 * 1.0 = -20.31 for ctc +2024-01-16 22:16:51,351 (beam_search:479) INFO: total log probability: -20.31 +2024-01-16 22:16:51,351 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:16:51,351 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:51,352 (beam_search:483) INFO: best hypo: UNGEKRTKANENFREIPRIEFFEINEHARAUSCHREIBUNGALZVOBELEFREIGEMEINZEIN + +2024-01-16 22:16:51,353 (asr_inference:494) INFO: speech length: 115584 +2024-01-16 22:16:51,365 (beam_search:428) INFO: decoder input length: 178 +2024-01-16 22:16:51,365 (beam_search:429) INFO: max output length: 178 +2024-01-16 22:16:51,365 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:51,738 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:51,739 (beam_search:476) INFO: -24.14 * 1.0 = -24.14 for ctc +2024-01-16 22:16:51,739 (beam_search:479) INFO: total log probability: -24.14 +2024-01-16 22:16:51,739 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:16:51,739 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:51,739 (beam_search:483) INFO: best hypo: MBIEZARKORTESGEABSCHNITERSEIENEINPFLUSEDUIGSCHOSTDARKOLNWIESCSH + +2024-01-16 22:16:51,740 (asr_inference:494) INFO: speech length: 100992 +2024-01-16 22:16:51,752 (beam_search:428) INFO: decoder input length: 155 +2024-01-16 22:16:51,752 (beam_search:429) INFO: max output length: 155 +2024-01-16 22:16:51,752 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:52,050 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:52,050 (beam_search:476) INFO: -15.19 * 1.0 = -15.19 for ctc +2024-01-16 22:16:52,050 (beam_search:479) INFO: total log probability: -15.19 +2024-01-16 22:16:52,050 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:16:52,050 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:52,050 (beam_search:483) INFO: best hypo: REREINERDERPIAERNIEREAFTDMGEBIETDERNUTZIUNGDERSONENERGEEN + +2024-01-16 22:16:52,051 (asr_inference:494) INFO: speech length: 73344 +2024-01-16 22:16:52,061 (beam_search:428) INFO: decoder input length: 112 +2024-01-16 22:16:52,061 (beam_search:429) INFO: max output length: 112 +2024-01-16 22:16:52,061 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:52,250 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:52,251 (beam_search:476) INFO: -18.63 * 1.0 = -18.63 for ctc +2024-01-16 22:16:52,251 (beam_search:479) INFO: total log probability: -18.63 +2024-01-16 22:16:52,251 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:16:52,251 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:52,251 (beam_search:483) INFO: best hypo: ARHFENMEDIEKONDENAFINERFENGENMUSICHELICKETPBEWAN + +# Accounting: time=67 threads=1 +# Ended (code 0) at Tue Jan 16 22:16:52 CST 2024, elapsed time 67 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.2.log b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.2.log new file mode 100644 index 0000000000000000000000000000000000000000..099c1019b068348bc498c913d263add745943f70 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.2.log @@ -0,0 +1,1834 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2 --config conf/decode_asr.yaml +# Started at Tue Jan 16 22:16:52 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2 --config conf/decode_asr.yaml +2024-01-16 22:16:54,063 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml +2024-01-16 22:16:54,081 (asr:523) INFO: Vocabulary size: 44 +2024-01-16 22:16:54,143 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 22:16:54,143 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 22:16:54,253 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 22:16:55,539 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 22:16:56,765 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 22:16:56,765 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 22:16:56,765 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 22:16:56,798 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-16 22:16:56,873 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 22:16:56,985 (asr_inference:494) INFO: speech length: 36096 +2024-01-16 22:16:58,189 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 22:16:58,189 (beam_search:429) INFO: max output length: 54 +2024-01-16 22:16:58,189 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:58,239 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:58,239 (beam_search:476) INFO: -7.80 * 1.0 = -7.80 for ctc +2024-01-16 22:16:58,239 (beam_search:479) INFO: total log probability: -7.80 +2024-01-16 22:16:58,239 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:16:58,239 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:58,239 (beam_search:483) INFO: best hypo: DICHBEMASCHINESTVERTISH + +2024-01-16 22:16:58,263 (asr_inference:494) INFO: speech length: 134016 +2024-01-16 22:16:58,279 (beam_search:428) INFO: decoder input length: 207 +2024-01-16 22:16:58,279 (beam_search:429) INFO: max output length: 207 +2024-01-16 22:16:58,279 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:58,737 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:58,737 (beam_search:476) INFO: -22.12 * 1.0 = -22.12 for ctc +2024-01-16 22:16:58,737 (beam_search:479) INFO: total log probability: -22.12 +2024-01-16 22:16:58,737 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:16:58,737 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:58,737 (beam_search:483) INFO: best hypo: INDEARCHERISCHENPERIONDEWURDENERSTEIEVORMENDESOCKEBASSNINTUIKELLT + +2024-01-16 22:16:58,739 (asr_inference:494) INFO: speech length: 79104 +2024-01-16 22:16:58,749 (beam_search:428) INFO: decoder input length: 121 +2024-01-16 22:16:58,749 (beam_search:429) INFO: max output length: 121 +2024-01-16 22:16:58,749 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:58,879 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:58,879 (beam_search:476) INFO: -9.93 * 1.0 = -9.93 for ctc +2024-01-16 22:16:58,879 (beam_search:479) INFO: total log probability: -9.93 +2024-01-16 22:16:58,879 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:16:58,879 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:58,879 (beam_search:483) INFO: best hypo: DIEKOMÜDESEBESELTERESTEFÜN + +2024-01-16 22:16:58,880 (asr_inference:494) INFO: speech length: 57984 +2024-01-16 22:16:58,889 (beam_search:428) INFO: decoder input length: 88 +2024-01-16 22:16:58,889 (beam_search:429) INFO: max output length: 88 +2024-01-16 22:16:58,889 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:58,981 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:58,981 (beam_search:476) INFO: -17.12 * 1.0 = -17.12 for ctc +2024-01-16 22:16:58,981 (beam_search:479) INFO: total log probability: -17.12 +2024-01-16 22:16:58,981 (beam_search:480) INFO: normalized log probability: -0.52 +2024-01-16 22:16:58,981 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:58,981 (beam_search:483) INFO: best hypo: ARTZLEREGEDVERNERMUMEMSALP + +2024-01-16 22:16:58,982 (asr_inference:494) INFO: speech length: 127104 +2024-01-16 22:16:58,996 (beam_search:428) INFO: decoder input length: 196 +2024-01-16 22:16:58,996 (beam_search:429) INFO: max output length: 196 +2024-01-16 22:16:58,996 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:16:59,556 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:16:59,556 (beam_search:476) INFO: -38.71 * 1.0 = -38.71 for ctc +2024-01-16 22:16:59,556 (beam_search:479) INFO: total log probability: -38.71 +2024-01-16 22:16:59,556 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-16 22:16:59,556 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:16:59,556 (beam_search:483) INFO: best hypo: TDERMITENDEEINEEAEWERKREISCHIEENARZUNERLIENKÄERDENSABENVORELENINMNSISCHKÜNTENAUNENENZSKAU + +2024-01-16 22:16:59,558 (asr_inference:494) INFO: speech length: 122112 +2024-01-16 22:16:59,571 (beam_search:428) INFO: decoder input length: 188 +2024-01-16 22:16:59,571 (beam_search:429) INFO: max output length: 188 +2024-01-16 22:16:59,571 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:00,024 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:00,024 (beam_search:476) INFO: -21.53 * 1.0 = -21.53 for ctc +2024-01-16 22:17:00,024 (beam_search:479) INFO: total log probability: -21.53 +2024-01-16 22:17:00,024 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:17:00,024 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:00,025 (beam_search:483) INFO: best hypo: DERSONEINESBEREGNANZBEGANEINIUSPALKERJERIWEIDENSPURTFREINDTENWANEEIKELN + +2024-01-16 22:17:00,026 (asr_inference:494) INFO: speech length: 115584 +2024-01-16 22:17:00,039 (beam_search:428) INFO: decoder input length: 178 +2024-01-16 22:17:00,039 (beam_search:429) INFO: max output length: 178 +2024-01-16 22:17:00,039 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:00,455 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:00,455 (beam_search:476) INFO: -18.26 * 1.0 = -18.26 for ctc +2024-01-16 22:17:00,455 (beam_search:479) INFO: total log probability: -18.26 +2024-01-16 22:17:00,455 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:17:00,455 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:00,455 (beam_search:483) INFO: best hypo: INDIENJARGABESSIEDENNOMEREIENSINGESUNSÄCHSUNREISZIGNOMEREIENSALEBEN + +2024-01-16 22:17:00,457 (asr_inference:494) INFO: speech length: 117888 +2024-01-16 22:17:00,469 (beam_search:428) INFO: decoder input length: 182 +2024-01-16 22:17:00,469 (beam_search:429) INFO: max output length: 182 +2024-01-16 22:17:00,469 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:00,874 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:00,875 (beam_search:476) INFO: -21.55 * 1.0 = -21.55 for ctc +2024-01-16 22:17:00,875 (beam_search:479) INFO: total log probability: -21.55 +2024-01-16 22:17:00,875 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:17:00,875 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:00,875 (beam_search:483) INFO: best hypo: NNORDWESTLICHVERNHARGHAUSENBERFINDESICHIOARTSCHAFTHAEKENBOREUICSCH + +2024-01-16 22:17:00,876 (asr_inference:494) INFO: speech length: 150528 +2024-01-16 22:17:00,892 (beam_search:428) INFO: decoder input length: 233 +2024-01-16 22:17:00,892 (beam_search:429) INFO: max output length: 233 +2024-01-16 22:17:00,892 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:01,533 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:01,534 (beam_search:476) INFO: -23.61 * 1.0 = -23.61 for ctc +2024-01-16 22:17:01,534 (beam_search:479) INFO: total log probability: -23.61 +2024-01-16 22:17:01,534 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:17:01,534 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:01,534 (beam_search:483) INFO: best hypo: IEMORTKGNNAENBURGIENENVIELESOSSIAEEINERICHTENUNVONEREMANLAMPRÄCHTNDERMARIENÜTEAUS + +2024-01-16 22:17:01,536 (asr_inference:494) INFO: speech length: 100800 +2024-01-16 22:17:01,547 (beam_search:428) INFO: decoder input length: 155 +2024-01-16 22:17:01,547 (beam_search:429) INFO: max output length: 155 +2024-01-16 22:17:01,547 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:01,891 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:01,891 (beam_search:476) INFO: -16.33 * 1.0 = -16.33 for ctc +2024-01-16 22:17:01,891 (beam_search:479) INFO: total log probability: -16.33 +2024-01-16 22:17:01,891 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:17:01,891 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:01,892 (beam_search:483) INFO: best hypo: ICHWERDEVOLKLCHDINRATBERDEMPALLMNDVORGETRAGENENBETENTENNVOMIEREN + +2024-01-16 22:17:01,893 (asr_inference:494) INFO: speech length: 90432 +2024-01-16 22:17:01,904 (beam_search:428) INFO: decoder input length: 139 +2024-01-16 22:17:01,904 (beam_search:429) INFO: max output length: 139 +2024-01-16 22:17:01,904 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:02,188 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:02,188 (beam_search:476) INFO: -20.59 * 1.0 = -20.59 for ctc +2024-01-16 22:17:02,188 (beam_search:479) INFO: total log probability: -20.59 +2024-01-16 22:17:02,188 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:17:02,188 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:02,189 (beam_search:483) INFO: best hypo: SIERETRAURSCGEWIENEINSOWICHTDIUSTHEMANICHEMKONDETHABSCHENZUKEN + +2024-01-16 22:17:02,190 (asr_inference:494) INFO: speech length: 123264 +2024-01-16 22:17:02,203 (beam_search:428) INFO: decoder input length: 190 +2024-01-16 22:17:02,203 (beam_search:429) INFO: max output length: 190 +2024-01-16 22:17:02,203 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:02,635 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:02,635 (beam_search:476) INFO: -22.77 * 1.0 = -22.77 for ctc +2024-01-16 22:17:02,635 (beam_search:479) INFO: total log probability: -22.77 +2024-01-16 22:17:02,635 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:17:02,635 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:02,636 (beam_search:483) INFO: best hypo: NOCHTIHSEINMTEOTENMKLEISCHENJAHRGAMESGUTZWISTDIEKGANANDERERDIESETZ + +2024-01-16 22:17:02,637 (asr_inference:494) INFO: speech length: 151488 +2024-01-16 22:17:02,652 (beam_search:428) INFO: decoder input length: 234 +2024-01-16 22:17:02,652 (beam_search:429) INFO: max output length: 234 +2024-01-16 22:17:02,652 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:03,241 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:03,241 (beam_search:476) INFO: -30.08 * 1.0 = -30.08 for ctc +2024-01-16 22:17:03,241 (beam_search:479) INFO: total log probability: -30.08 +2024-01-16 22:17:03,241 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:17:03,241 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:03,241 (beam_search:483) INFO: best hypo: KOTZDANERCHGABISEINENWERBERBWORDTMIDEDENMTKANKAMNDTUNDSCHEKESHOCHENBACH + +2024-01-16 22:17:03,243 (asr_inference:494) INFO: speech length: 73728 +2024-01-16 22:17:03,253 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 22:17:03,253 (beam_search:429) INFO: max output length: 113 +2024-01-16 22:17:03,253 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:03,369 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:03,369 (beam_search:476) INFO: -28.78 * 1.0 = -28.78 for ctc +2024-01-16 22:17:03,369 (beam_search:479) INFO: total log probability: -28.78 +2024-01-16 22:17:03,369 (beam_search:480) INFO: normalized log probability: -0.90 +2024-01-16 22:17:03,369 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:03,369 (beam_search:483) INFO: best hypo: IDDITBEEHTSUSFSNUNNANNAE + +2024-01-16 22:17:03,371 (asr_inference:494) INFO: speech length: 37632 +2024-01-16 22:17:03,378 (beam_search:428) INFO: decoder input length: 56 +2024-01-16 22:17:03,378 (beam_search:429) INFO: max output length: 56 +2024-01-16 22:17:03,378 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:03,432 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:03,432 (beam_search:476) INFO: -9.22 * 1.0 = -9.22 for ctc +2024-01-16 22:17:03,432 (beam_search:479) INFO: total log probability: -9.22 +2024-01-16 22:17:03,432 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:17:03,432 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:03,432 (beam_search:483) INFO: best hypo: WESIEISMNDLEITZEITAUSH + +2024-01-16 22:17:03,433 (asr_inference:494) INFO: speech length: 115200 +2024-01-16 22:17:03,446 (beam_search:428) INFO: decoder input length: 177 +2024-01-16 22:17:03,446 (beam_search:429) INFO: max output length: 177 +2024-01-16 22:17:03,446 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:03,817 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:03,818 (beam_search:476) INFO: -22.95 * 1.0 = -22.95 for ctc +2024-01-16 22:17:03,818 (beam_search:479) INFO: total log probability: -22.95 +2024-01-16 22:17:03,818 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:17:03,818 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:03,818 (beam_search:483) INFO: best hypo: NCHEDEMDOCHFBEFINDETICHARCHDERKRMKENIUNASIHNALLEBPACKGERBUOT + +2024-01-16 22:17:03,819 (asr_inference:494) INFO: speech length: 87552 +2024-01-16 22:17:03,830 (beam_search:428) INFO: decoder input length: 134 +2024-01-16 22:17:03,830 (beam_search:429) INFO: max output length: 134 +2024-01-16 22:17:03,830 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:04,035 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:04,035 (beam_search:476) INFO: -11.64 * 1.0 = -11.64 for ctc +2024-01-16 22:17:04,035 (beam_search:479) INFO: total log probability: -11.64 +2024-01-16 22:17:04,035 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:17:04,035 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:04,035 (beam_search:483) INFO: best hypo: IESERENDERKÜNDENDESDIELIEBEENTOEDBESIKTART + +2024-01-16 22:17:04,036 (asr_inference:494) INFO: speech length: 139968 +2024-01-16 22:17:04,051 (beam_search:428) INFO: decoder input length: 216 +2024-01-16 22:17:04,051 (beam_search:429) INFO: max output length: 216 +2024-01-16 22:17:04,051 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:04,537 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:04,537 (beam_search:476) INFO: -17.43 * 1.0 = -17.43 for ctc +2024-01-16 22:17:04,537 (beam_search:479) INFO: total log probability: -17.43 +2024-01-16 22:17:04,537 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:17:04,537 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:04,537 (beam_search:483) INFO: best hypo: BETECKTISTDIREBPRENSENTHERTIEFGESTALTEEWELERMITDEINENMANSARTDACH + +2024-01-16 22:17:04,539 (asr_inference:494) INFO: speech length: 111168 +2024-01-16 22:17:04,551 (beam_search:428) INFO: decoder input length: 171 +2024-01-16 22:17:04,551 (beam_search:429) INFO: max output length: 171 +2024-01-16 22:17:04,551 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:04,847 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:04,847 (beam_search:476) INFO: -12.80 * 1.0 = -12.80 for ctc +2024-01-16 22:17:04,847 (beam_search:479) INFO: total log probability: -12.80 +2024-01-16 22:17:04,847 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:17:04,847 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:04,848 (beam_search:483) INFO: best hypo: DIESESIEUNIESMITERARTSCHAFTDELECHZUSAMMNGEWAKNZEN + +2024-01-16 22:17:04,849 (asr_inference:494) INFO: speech length: 82560 +2024-01-16 22:17:04,860 (beam_search:428) INFO: decoder input length: 126 +2024-01-16 22:17:04,860 (beam_search:429) INFO: max output length: 126 +2024-01-16 22:17:04,860 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:04,993 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:04,993 (beam_search:476) INFO: -14.57 * 1.0 = -14.57 for ctc +2024-01-16 22:17:04,993 (beam_search:479) INFO: total log probability: -14.57 +2024-01-16 22:17:04,993 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-16 22:17:04,993 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:04,993 (beam_search:483) INFO: best hypo: IWARDIESHNEINALENENKLOBES + +2024-01-16 22:17:04,995 (asr_inference:494) INFO: speech length: 74496 +2024-01-16 22:17:05,004 (beam_search:428) INFO: decoder input length: 114 +2024-01-16 22:17:05,004 (beam_search:429) INFO: max output length: 114 +2024-01-16 22:17:05,004 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:05,129 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:05,129 (beam_search:476) INFO: -12.11 * 1.0 = -12.11 for ctc +2024-01-16 22:17:05,129 (beam_search:479) INFO: total log probability: -12.11 +2024-01-16 22:17:05,129 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:17:05,129 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:05,129 (beam_search:483) INFO: best hypo: BUORANGEISTISTDAUCHVERIOERET + +2024-01-16 22:17:05,131 (asr_inference:494) INFO: speech length: 135936 +2024-01-16 22:17:05,145 (beam_search:428) INFO: decoder input length: 210 +2024-01-16 22:17:05,145 (beam_search:429) INFO: max output length: 210 +2024-01-16 22:17:05,145 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:05,742 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:05,742 (beam_search:476) INFO: -36.60 * 1.0 = -36.60 for ctc +2024-01-16 22:17:05,742 (beam_search:479) INFO: total log probability: -36.60 +2024-01-16 22:17:05,742 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:17:05,742 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:05,743 (beam_search:483) INFO: best hypo: DEHEXTVONDERSTRASSEBUENSIVONALTFTETDIONLEGISEINERFWESTENSESCHOUESCHOBEBILNERENEÜSIEALTT + +2024-01-16 22:17:05,744 (asr_inference:494) INFO: speech length: 86976 +2024-01-16 22:17:05,755 (beam_search:428) INFO: decoder input length: 133 +2024-01-16 22:17:05,755 (beam_search:429) INFO: max output length: 133 +2024-01-16 22:17:05,755 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:06,013 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:06,014 (beam_search:476) INFO: -19.55 * 1.0 = -19.55 for ctc +2024-01-16 22:17:06,014 (beam_search:479) INFO: total log probability: -19.55 +2024-01-16 22:17:06,014 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:17:06,014 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:06,014 (beam_search:483) INFO: best hypo: AIHARSCPEITERESSLLTERERTZUNELFNATZSUNBERWUDELRANGEREISCHE + +2024-01-16 22:17:06,015 (asr_inference:494) INFO: speech length: 163008 +2024-01-16 22:17:06,031 (beam_search:428) INFO: decoder input length: 252 +2024-01-16 22:17:06,031 (beam_search:429) INFO: max output length: 252 +2024-01-16 22:17:06,031 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:06,500 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:06,500 (beam_search:476) INFO: -21.66 * 1.0 = -21.66 for ctc +2024-01-16 22:17:06,500 (beam_search:479) INFO: total log probability: -21.66 +2024-01-16 22:17:06,500 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:17:06,500 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:06,500 (beam_search:483) INFO: best hypo: INDERLERNVITHEKERNDERERTRARETDEUTLIEDEOTZIERTWERDEN + +2024-01-16 22:17:06,502 (asr_inference:494) INFO: speech length: 124416 +2024-01-16 22:17:06,515 (beam_search:428) INFO: decoder input length: 192 +2024-01-16 22:17:06,515 (beam_search:429) INFO: max output length: 192 +2024-01-16 22:17:06,515 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:06,854 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:06,854 (beam_search:476) INFO: -12.72 * 1.0 = -12.72 for ctc +2024-01-16 22:17:06,854 (beam_search:479) INFO: total log probability: -12.72 +2024-01-16 22:17:06,854 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:17:06,854 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:06,854 (beam_search:483) INFO: best hypo: MAINSURSPIERTEINSEINERHEIMAUTSTADTKEIEUOFIERALALE + +2024-01-16 22:17:06,855 (asr_inference:494) INFO: speech length: 135360 +2024-01-16 22:17:06,869 (beam_search:428) INFO: decoder input length: 209 +2024-01-16 22:17:06,869 (beam_search:429) INFO: max output length: 209 +2024-01-16 22:17:06,869 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:07,206 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:07,207 (beam_search:476) INFO: -33.41 * 1.0 = -33.41 for ctc +2024-01-16 22:17:07,207 (beam_search:479) INFO: total log probability: -33.41 +2024-01-16 22:17:07,207 (beam_search:480) INFO: normalized log probability: -0.63 +2024-01-16 22:17:07,207 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:07,207 (beam_search:483) INFO: best hypo: COHDERTRARDERREIMAUHALUNUNDENINTABEBEISIHAH + +2024-01-16 22:17:07,208 (asr_inference:494) INFO: speech length: 146304 +2024-01-16 22:17:07,223 (beam_search:428) INFO: decoder input length: 226 +2024-01-16 22:17:07,223 (beam_search:429) INFO: max output length: 226 +2024-01-16 22:17:07,223 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:07,770 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:07,770 (beam_search:476) INFO: -29.15 * 1.0 = -29.15 for ctc +2024-01-16 22:17:07,770 (beam_search:479) INFO: total log probability: -29.15 +2024-01-16 22:17:07,770 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:17:07,770 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:07,770 (beam_search:483) INFO: best hypo: MITFÜÖOSTWEREHERDEROWTFIADEEITSERECHTICHERALLEDEUONBEDSHEICENENEMEINT + +2024-01-16 22:17:07,772 (asr_inference:494) INFO: speech length: 254400 +2024-01-16 22:17:07,795 (beam_search:428) INFO: decoder input length: 395 +2024-01-16 22:17:07,795 (beam_search:429) INFO: max output length: 395 +2024-01-16 22:17:07,795 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:09,515 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:09,515 (beam_search:476) INFO: -54.22 * 1.0 = -54.22 for ctc +2024-01-16 22:17:09,515 (beam_search:479) INFO: total log probability: -54.22 +2024-01-16 22:17:09,515 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:17:09,515 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:09,516 (beam_search:483) INFO: best hypo: ETZTERVOCHUGABDASMETIBEKANDTDASSESHONEPELÜBERFIERNDASCHWALTEEVORFELEVONBERITZUNNTOMIRTODENVWERIDESNDERNEHNENALSNCHTSHIERIENKEEITETE + +2024-01-16 22:17:09,518 (asr_inference:494) INFO: speech length: 483840 +2024-01-16 22:17:09,563 (beam_search:428) INFO: decoder input length: 753 +2024-01-16 22:17:09,563 (beam_search:429) INFO: max output length: 753 +2024-01-16 22:17:09,563 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:14,660 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:14,660 (beam_search:476) INFO: -98.46 * 1.0 = -98.46 for ctc +2024-01-16 22:17:14,660 (beam_search:479) INFO: total log probability: -98.46 +2024-01-16 22:17:14,660 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-16 22:17:14,660 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:14,661 (beam_search:483) INFO: best hypo: SIEBEBENIJEÖESEÄESCHENENSZIGIGSUNDERSCÜZSTDEDENBIHIFDESUNÜNBISCHENKOMITISDERVEREINIGTENSTATENUNDARSIPTIERTESASAPTUTENOTWENDIGKEITDASIHIEUNLÜNBISCHEVERNMIIENFÜENSICHERESUNFWELTZFÜEALEUNSERERSPOTLEREINSEST + +2024-01-16 22:17:14,663 (asr_inference:494) INFO: speech length: 178560 +2024-01-16 22:17:14,680 (beam_search:428) INFO: decoder input length: 276 +2024-01-16 22:17:14,680 (beam_search:429) INFO: max output length: 276 +2024-01-16 22:17:14,680 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:15,798 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:15,798 (beam_search:476) INFO: -42.24 * 1.0 = -42.24 for ctc +2024-01-16 22:17:15,798 (beam_search:479) INFO: total log probability: -42.24 +2024-01-16 22:17:15,798 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:17:15,798 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:15,799 (beam_search:483) INFO: best hypo: ALICHKENEABPIETKOMPETIEBELMETACHTNATZWEIBUNDELFARACHTNRTZWEIBUNDELFBEUNDCHTNETZWEIPUNDELFGESEINVERSGESDIEBASISTATIUNVERFÜGKBERDUOALRADIE + +2024-01-16 22:17:15,801 (asr_inference:494) INFO: speech length: 98880 +2024-01-16 22:17:15,812 (beam_search:428) INFO: decoder input length: 152 +2024-01-16 22:17:15,812 (beam_search:429) INFO: max output length: 152 +2024-01-16 22:17:15,812 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:16,117 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:16,117 (beam_search:476) INFO: -23.18 * 1.0 = -23.18 for ctc +2024-01-16 22:17:16,117 (beam_search:479) INFO: total log probability: -23.18 +2024-01-16 22:17:16,117 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:17:16,117 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:16,117 (beam_search:483) INFO: best hypo: JERBIEZEISHENSDIEGERICHTERALSPLICICHESGESCHWÄTSUNDTALLBENHEITZS + +2024-01-16 22:17:16,119 (asr_inference:494) INFO: speech length: 166080 +2024-01-16 22:17:16,135 (beam_search:428) INFO: decoder input length: 257 +2024-01-16 22:17:16,135 (beam_search:429) INFO: max output length: 257 +2024-01-16 22:17:16,135 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:17,159 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:17,159 (beam_search:476) INFO: -40.05 * 1.0 = -40.05 for ctc +2024-01-16 22:17:17,159 (beam_search:479) INFO: total log probability: -40.05 +2024-01-16 22:17:17,159 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:17:17,159 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:17,160 (beam_search:483) INFO: best hypo: LTERWUCHRGABTASEMIEITHEBEKANDASISVONEBLWERFIERNDRESIEWEITRIEVOHFELEVONBERHITZUNINVOMIERTWORDENWAIEDASUNTERNEMALZNICHCHVERIGEBTZEICTETE + +2024-01-16 22:17:17,162 (asr_inference:494) INFO: speech length: 130560 +2024-01-16 22:17:17,175 (beam_search:428) INFO: decoder input length: 201 +2024-01-16 22:17:17,175 (beam_search:429) INFO: max output length: 201 +2024-01-16 22:17:17,175 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:17,813 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:17,813 (beam_search:476) INFO: -39.35 * 1.0 = -39.35 for ctc +2024-01-16 22:17:17,813 (beam_search:479) INFO: total log probability: -39.35 +2024-01-16 22:17:17,813 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:17:17,813 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:17,814 (beam_search:483) INFO: best hypo: NACHDIMDERDEMMEUNHNUNDERTREIUNSECHZICBAUTWRDENWARKAMDIARESZELIGHNBEPFLUTUNDESEDEMENTEMNPLSVERTELNZUMSHÖLSTEN + +2024-01-16 22:17:17,815 (asr_inference:494) INFO: speech length: 172800 +2024-01-16 22:17:17,832 (beam_search:428) INFO: decoder input length: 267 +2024-01-16 22:17:17,832 (beam_search:429) INFO: max output length: 267 +2024-01-16 22:17:17,832 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:18,940 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:18,940 (beam_search:476) INFO: -44.05 * 1.0 = -44.05 for ctc +2024-01-16 22:17:18,941 (beam_search:479) INFO: total log probability: -44.05 +2024-01-16 22:17:18,941 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:17:18,941 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:18,941 (beam_search:483) INFO: best hypo: ERWAUHMSTECHEVNGELSCHEINVEILEENDEBETEILICHTAKTULEBEISCHPIESANAHRBESCHLISENIBPREMJHMINISTERPRTRISAFDERVORDESERTDEKANADSCHENFÜNNUNDERTOLLENUTENEIN + +2024-01-16 22:17:18,943 (asr_inference:494) INFO: speech length: 168960 +2024-01-16 22:17:18,959 (beam_search:428) INFO: decoder input length: 261 +2024-01-16 22:17:18,959 (beam_search:429) INFO: max output length: 261 +2024-01-16 22:17:18,959 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:19,679 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:19,679 (beam_search:476) INFO: -30.84 * 1.0 = -30.84 for ctc +2024-01-16 22:17:19,679 (beam_search:479) INFO: total log probability: -30.84 +2024-01-16 22:17:19,679 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:17:19,679 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:19,680 (beam_search:483) INFO: best hypo: DIHAUPTSTVERMRDAWIENISTKHENENDIEINEIMPISPBAHEISTGUMENESCABERFIELEMENTENSRECHENEROSEH + +2024-01-16 22:17:19,681 (asr_inference:494) INFO: speech length: 519360 +2024-01-16 22:17:19,729 (beam_search:428) INFO: decoder input length: 809 +2024-01-16 22:17:19,729 (beam_search:429) INFO: max output length: 809 +2024-01-16 22:17:19,729 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:24,962 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:24,962 (beam_search:476) INFO: -98.62 * 1.0 = -98.62 for ctc +2024-01-16 22:17:24,962 (beam_search:479) INFO: total log probability: -98.62 +2024-01-16 22:17:24,962 (beam_search:480) INFO: normalized log probability: -0.43 +2024-01-16 22:17:24,962 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:24,963 (beam_search:483) INFO: best hypo: SISISEBETZWICHENDENEINZENNBÜNESTDIENHERSTENAUOCHUNBESTENDIGERZEITENGETALTEPROEWENZENDIEBEKANTDISTDEDIESEPERIOEDENWADIEEPOCHEDERDEILGÜNINGEICHEDESEÄCHTZICHIEARRELANZEIHENDERHANUNDEEENDENESTISTATVFVANT + +2024-01-16 22:17:24,965 (asr_inference:494) INFO: speech length: 232320 +2024-01-16 22:17:24,986 (beam_search:428) INFO: decoder input length: 360 +2024-01-16 22:17:24,986 (beam_search:429) INFO: max output length: 360 +2024-01-16 22:17:24,986 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:26,475 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:26,475 (beam_search:476) INFO: -46.27 * 1.0 = -46.27 for ctc +2024-01-16 22:17:26,475 (beam_search:479) INFO: total log probability: -46.27 +2024-01-16 22:17:26,475 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:17:26,475 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:26,476 (beam_search:483) INFO: best hypo: AMANDEREENEERSPEKTRUMSHRWEINEMANSICHENEINICHWIDEZUERKENDEINEWIEDEUMDSAESANDRMACHENMOSASERSTIEESGEMACTAUNDZSICHALLESUOELIGEMACHT + +2024-01-16 22:17:26,478 (asr_inference:494) INFO: speech length: 515520 +2024-01-16 22:17:26,525 (beam_search:428) INFO: decoder input length: 803 +2024-01-16 22:17:26,525 (beam_search:429) INFO: max output length: 803 +2024-01-16 22:17:26,525 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:33,987 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:33,987 (beam_search:476) INFO: -136.58 * 1.0 = -136.58 for ctc +2024-01-16 22:17:33,987 (beam_search:479) INFO: total log probability: -136.58 +2024-01-16 22:17:33,987 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-16 22:17:33,987 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:33,989 (beam_search:483) INFO: best hypo: DGIGIDEMEISTENINDERPITEATZIONENDESTECHENLOGISCHENDETEMINISENUSTAENZWEIALGEMEINEVORSCTERLUNGENEINESEITSDESDIINDICGEMDERTICHENLÜGIESEPSTEINENWEGVOLGTDERWEITGENDIENSEISUNTOWELEORDERPULICISCHINPLSNAMENDIGTUNDANDERERSEITSDASTIGHNEÖÜGEIERERSEITSAUSFWÖHRKENENAUFGESALSCAFTNARARTDIEHERINHERERNTASZUTSALBEDENZINT + +2024-01-16 22:17:33,992 (asr_inference:494) INFO: speech length: 301440 +2024-01-16 22:17:34,019 (beam_search:428) INFO: decoder input length: 468 +2024-01-16 22:17:34,019 (beam_search:429) INFO: max output length: 468 +2024-01-16 22:17:34,019 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:36,575 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:36,575 (beam_search:476) INFO: -64.16 * 1.0 = -64.16 for ctc +2024-01-16 22:17:36,575 (beam_search:479) INFO: total log probability: -64.16 +2024-01-16 22:17:36,575 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:17:36,575 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:36,576 (beam_search:483) INFO: best hypo: WÜSHEDENEINZENENDNASTIENHERSTENAURUNBESTENDIGETZEITENGETEALTERROWENZENDEEKANDESTESEPERIODENWAHDIEPOCHRELDEREKÜNIGREICHEDESECHTZICHARLANGTZWISCHENDERHANUNTERIENDINASTITTVANDT + +2024-01-16 22:17:36,578 (asr_inference:494) INFO: speech length: 234240 +2024-01-16 22:17:36,599 (beam_search:428) INFO: decoder input length: 363 +2024-01-16 22:17:36,599 (beam_search:429) INFO: max output length: 363 +2024-01-16 22:17:36,599 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:38,416 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:38,416 (beam_search:476) INFO: -52.92 * 1.0 = -52.92 for ctc +2024-01-16 22:17:38,416 (beam_search:479) INFO: total log probability: -52.92 +2024-01-16 22:17:38,416 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:17:38,416 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:38,417 (beam_search:483) INFO: best hypo: DEMICKHZUVORGIEBIEZIZICHESTOGOMENTAUFDENGEÄNSTREITINDENDIEPALISINENSEREINZURÖGSALTZENDERGENZSENINDENZUSTANTVORDEMSERSTALEGRIVORNANZESNUNDERTSEBERNUSETICVORDEN + +2024-01-16 22:17:38,419 (asr_inference:494) INFO: speech length: 185280 +2024-01-16 22:17:38,437 (beam_search:428) INFO: decoder input length: 287 +2024-01-16 22:17:38,437 (beam_search:429) INFO: max output length: 287 +2024-01-16 22:17:38,437 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:39,412 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:39,413 (beam_search:476) INFO: -41.73 * 1.0 = -41.73 for ctc +2024-01-16 22:17:39,413 (beam_search:479) INFO: total log probability: -41.73 +2024-01-16 22:17:39,413 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:17:39,413 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:39,413 (beam_search:483) INFO: best hypo: MITHEMPERLUSTGRECHEERSPACHKENEARDERECSTENVONSEIENVLESOFISCHENUNDWISENCHALICHENWOTZEINKIHENENABIESLETEN + +2024-01-16 22:17:39,415 (asr_inference:494) INFO: speech length: 233280 +2024-01-16 22:17:39,435 (beam_search:428) INFO: decoder input length: 362 +2024-01-16 22:17:39,435 (beam_search:429) INFO: max output length: 362 +2024-01-16 22:17:39,435 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:41,258 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:41,258 (beam_search:476) INFO: -68.21 * 1.0 = -68.21 for ctc +2024-01-16 22:17:41,258 (beam_search:479) INFO: total log probability: -68.21 +2024-01-16 22:17:41,258 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:17:41,259 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:41,260 (beam_search:483) INFO: best hypo: ERSTEMITERAUSSAGDESIÖRSUSIEBEINDASTDENNRESENUNSRATLEDENVEREINUNDERSPOTSBPESRGEDIENDISDBENWERNEHALBUNSRARGESATIOUNDHNVLEVERINDRUNGVORANTREIBENANSTEINERTITZSRITZTVITIERUNGVOTZN + +2024-01-16 22:17:41,261 (asr_inference:494) INFO: speech length: 201600 +2024-01-16 22:17:41,279 (beam_search:428) INFO: decoder input length: 312 +2024-01-16 22:17:41,279 (beam_search:429) INFO: max output length: 312 +2024-01-16 22:17:41,279 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:42,403 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:42,404 (beam_search:476) INFO: -46.03 * 1.0 = -46.03 for ctc +2024-01-16 22:17:42,404 (beam_search:479) INFO: total log probability: -46.03 +2024-01-16 22:17:42,404 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:17:42,404 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:42,404 (beam_search:483) INFO: best hypo: IGREZFATENAENGPIGESBOGBIETENAUCHZEITÜÖRINAUFENTALINERSTATKGREUTZFERPASRSHERESEINVNDEIEUNSLICHBEREITSIEBEDENGEN + +2024-01-16 22:17:42,406 (asr_inference:494) INFO: speech length: 297600 +2024-01-16 22:17:42,433 (beam_search:428) INFO: decoder input length: 462 +2024-01-16 22:17:42,433 (beam_search:429) INFO: max output length: 462 +2024-01-16 22:17:42,433 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:44,335 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:44,335 (beam_search:476) INFO: -54.26 * 1.0 = -54.26 for ctc +2024-01-16 22:17:44,335 (beam_search:479) INFO: total log probability: -54.26 +2024-01-16 22:17:44,335 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-16 22:17:44,335 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:44,336 (beam_search:483) INFO: best hypo: SCSCTEREISENDEVERDENRINGENDGEWANDTKAUFIEWIEDEATVONUNWENTEZUACHTENDIEEGEBIBIERIFTDADNDISSIGHAUFALEREISEPLENEAOSIERENKON + +2024-01-16 22:17:44,338 (asr_inference:494) INFO: speech length: 298560 +2024-01-16 22:17:44,365 (beam_search:428) INFO: decoder input length: 464 +2024-01-16 22:17:44,365 (beam_search:429) INFO: max output length: 464 +2024-01-16 22:17:44,365 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:46,367 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:46,367 (beam_search:476) INFO: -73.98 * 1.0 = -73.98 for ctc +2024-01-16 22:17:46,367 (beam_search:479) INFO: total log probability: -73.98 +2024-01-16 22:17:46,367 (beam_search:480) INFO: normalized log probability: -0.49 +2024-01-16 22:17:46,367 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:46,368 (beam_search:483) INFO: best hypo: SICHRTRISEBESERTDASDERGKOLTZUNGSPBUNKTDDERLENMENDEINBEEWERDIGKALEUNTHUREHONTALDRITENDEEHIGKISTDPLATSFÜITESHOUPTMNOIEISTSIEBEISCHEN + +2024-01-16 22:17:46,370 (asr_inference:494) INFO: speech length: 246720 +2024-01-16 22:17:46,393 (beam_search:428) INFO: decoder input length: 383 +2024-01-16 22:17:46,393 (beam_search:429) INFO: max output length: 383 +2024-01-16 22:17:46,393 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:48,520 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:48,520 (beam_search:476) INFO: -63.31 * 1.0 = -63.31 for ctc +2024-01-16 22:17:48,520 (beam_search:479) INFO: total log probability: -63.31 +2024-01-16 22:17:48,520 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:17:48,520 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:48,521 (beam_search:483) INFO: best hypo: EITNUNZEUNRTACHENACHTZIHMISTNWALUNDRANSBPRENZSEINDAMITWEENDBOBACTERBIETZOEUGENGKENDASWEGNDERWALEINMSCHLIGEVWANENSINDUDASKEINUMSCHLÄGEINGEVAOFENWERDENAUSEREHNEDERTOTDEUNGSMESKSELTETATRESIERTENEE + +2024-01-16 22:17:48,523 (asr_inference:494) INFO: speech length: 238080 +2024-01-16 22:17:48,544 (beam_search:428) INFO: decoder input length: 369 +2024-01-16 22:17:48,544 (beam_search:429) INFO: max output length: 369 +2024-01-16 22:17:48,544 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:50,326 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:50,326 (beam_search:476) INFO: -43.65 * 1.0 = -43.65 for ctc +2024-01-16 22:17:50,326 (beam_search:479) INFO: total log probability: -43.65 +2024-01-16 22:17:50,326 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:17:50,326 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:50,327 (beam_search:483) INFO: best hypo: OTERERISTKANERDESBETZSAOUBENDEZWEISCHEALIGEHAUPTSTATUNDZSELTENDICHICHEINEREIEUNKUNZSTDGEERIENUNDMOSEENAUSDIEKANENDERSVERGANGENHEITUNDGGENWARTPRESINTIEREN + +2024-01-16 22:17:50,329 (asr_inference:494) INFO: speech length: 71040 +2024-01-16 22:17:50,339 (beam_search:428) INFO: decoder input length: 108 +2024-01-16 22:17:50,339 (beam_search:429) INFO: max output length: 108 +2024-01-16 22:17:50,339 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:50,523 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:50,523 (beam_search:476) INFO: -12.99 * 1.0 = -12.99 for ctc +2024-01-16 22:17:50,523 (beam_search:479) INFO: total log probability: -12.99 +2024-01-16 22:17:50,523 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:17:50,523 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:50,524 (beam_search:483) INFO: best hypo: DIESEPAREKENSICHFVEINADEBTIONSPLANVERBEBEENSHEIDEN + +2024-01-16 22:17:50,525 (asr_inference:494) INFO: speech length: 120000 +2024-01-16 22:17:50,537 (beam_search:428) INFO: decoder input length: 185 +2024-01-16 22:17:50,538 (beam_search:429) INFO: max output length: 185 +2024-01-16 22:17:50,538 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:51,065 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:51,065 (beam_search:476) INFO: -29.54 * 1.0 = -29.54 for ctc +2024-01-16 22:17:51,065 (beam_search:479) INFO: total log probability: -29.54 +2024-01-16 22:17:51,065 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:17:51,065 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:51,065 (beam_search:483) INFO: best hypo: INVOLEDESENEINZWREIFISCHABENAUSGSTOLMDZWEAWEITRISENVOMAUSTERBEMBETRORTTAUNDERDERJLAZIÜFVER + +2024-01-16 22:17:51,067 (asr_inference:494) INFO: speech length: 193920 +2024-01-16 22:17:51,084 (beam_search:428) INFO: decoder input length: 300 +2024-01-16 22:17:51,084 (beam_search:429) INFO: max output length: 300 +2024-01-16 22:17:51,085 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:52,005 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:52,005 (beam_search:476) INFO: -38.03 * 1.0 = -38.03 for ctc +2024-01-16 22:17:52,005 (beam_search:479) INFO: total log probability: -38.03 +2024-01-16 22:17:52,005 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:17:52,005 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:52,005 (beam_search:483) INFO: best hypo: REANZENSENNJHERENERTÖLIHEMNGEBENAMETENAUSIERSTENSIEALSODERERSCHUNGAUCHNUREINEEMKLAENDTVERN + +2024-01-16 22:17:52,007 (asr_inference:494) INFO: speech length: 218880 +2024-01-16 22:17:52,027 (beam_search:428) INFO: decoder input length: 339 +2024-01-16 22:17:52,027 (beam_search:429) INFO: max output length: 339 +2024-01-16 22:17:52,027 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:53,126 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:53,126 (beam_search:476) INFO: -40.66 * 1.0 = -40.66 for ctc +2024-01-16 22:17:53,126 (beam_search:479) INFO: total log probability: -40.66 +2024-01-16 22:17:53,126 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-16 22:17:53,126 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:53,126 (beam_search:483) INFO: best hypo: AUFERNARSEITEKNTEISMERMRIERGEBENDDIEKROSTEDNESTISWREINVERAFDELAVERANDIOBEPLICHAUFTSTDEGENT + +2024-01-16 22:17:53,128 (asr_inference:494) INFO: speech length: 345600 +2024-01-16 22:17:53,159 (beam_search:428) INFO: decoder input length: 537 +2024-01-16 22:17:53,159 (beam_search:429) INFO: max output length: 537 +2024-01-16 22:17:53,159 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:56,016 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:56,016 (beam_search:476) INFO: -62.36 * 1.0 = -62.36 for ctc +2024-01-16 22:17:56,016 (beam_search:479) INFO: total log probability: -62.36 +2024-01-16 22:17:56,016 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:17:56,016 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:56,017 (beam_search:483) INFO: best hypo: SGREFÜGKTDECHENZUUNDASSIEDUOCHNICHTDERTZUAUFGIEVORDERTWERDENSOLLTENFERTFICHTUNGENEINDZUÜGEENDEIÜEBERIERENINTWILUNGSTANDIEREVERANTORTUNGUNDIERERFÄEKETENHENOARNSINGEN + +2024-01-16 22:17:56,019 (asr_inference:494) INFO: speech length: 344640 +2024-01-16 22:17:56,049 (beam_search:428) INFO: decoder input length: 536 +2024-01-16 22:17:56,050 (beam_search:429) INFO: max output length: 536 +2024-01-16 22:17:56,050 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:58,849 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:58,849 (beam_search:476) INFO: -65.42 * 1.0 = -65.42 for ctc +2024-01-16 22:17:58,849 (beam_search:479) INFO: total log probability: -65.42 +2024-01-16 22:17:58,849 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:17:58,849 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:58,850 (beam_search:483) INFO: best hypo: SISIEWICETUELEHIELFISTEUNGENISENTINDIESOFTWEREINGEBUTDTUNSOENABELTSCHITENDIEDERSCHYLEALLEINMÜGLICHERWEISENIHTBEVETIGENKARNHENTERFRAGENNEIELEGENUNDTDERKLEREN + +2024-01-16 22:17:58,852 (asr_inference:494) INFO: speech length: 138240 +2024-01-16 22:17:58,866 (beam_search:428) INFO: decoder input length: 213 +2024-01-16 22:17:58,866 (beam_search:429) INFO: max output length: 213 +2024-01-16 22:17:58,866 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:17:59,530 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:17:59,530 (beam_search:476) INFO: -27.65 * 1.0 = -27.65 for ctc +2024-01-16 22:17:59,530 (beam_search:479) INFO: total log probability: -27.65 +2024-01-16 22:17:59,530 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:17:59,530 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:17:59,531 (beam_search:483) INFO: best hypo: AMFÜNFZENNARGUSTNUNZHNHUDETVIRZICFELIEALIERTENNSÜTRANKRAICHEINDINWASIONWRDEAPERESCHEERGUNGENERNDT + +2024-01-16 22:17:59,532 (asr_inference:494) INFO: speech length: 178560 +2024-01-16 22:17:59,548 (beam_search:428) INFO: decoder input length: 276 +2024-01-16 22:17:59,549 (beam_search:429) INFO: max output length: 276 +2024-01-16 22:17:59,549 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:00,308 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:00,308 (beam_search:476) INFO: -25.77 * 1.0 = -25.77 for ctc +2024-01-16 22:18:00,308 (beam_search:479) INFO: total log probability: -25.77 +2024-01-16 22:18:00,308 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:18:00,308 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:00,309 (beam_search:483) INFO: best hypo: ERRIFOCHALLSANWASENWASERKARMSEBTENGROSERDENSAURIEIDERTIEWEXSWEIMICHTGEWAKEN + +2024-01-16 22:18:00,310 (asr_inference:494) INFO: speech length: 144000 +2024-01-16 22:18:00,325 (beam_search:428) INFO: decoder input length: 222 +2024-01-16 22:18:00,325 (beam_search:429) INFO: max output length: 222 +2024-01-16 22:18:00,325 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:01,069 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:01,069 (beam_search:476) INFO: -36.70 * 1.0 = -36.70 for ctc +2024-01-16 22:18:01,069 (beam_search:479) INFO: total log probability: -36.70 +2024-01-16 22:18:01,069 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:18:01,069 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:01,070 (beam_search:483) INFO: best hypo: ETERKRÜNDUNGVNASUNTZIORFINHTZENDESIENUNDREISISSPAREGEGELUNGVIELVONSEIMINDIGKENKARACTERNDSEINEIDENDITETZBEWAREN + +2024-01-16 22:18:01,071 (asr_inference:494) INFO: speech length: 560640 +2024-01-16 22:18:01,128 (beam_search:428) INFO: decoder input length: 873 +2024-01-16 22:18:01,128 (beam_search:429) INFO: max output length: 873 +2024-01-16 22:18:01,128 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:07,709 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:07,709 (beam_search:476) INFO: -109.81 * 1.0 = -109.81 for ctc +2024-01-16 22:18:07,709 (beam_search:479) INFO: total log probability: -109.81 +2024-01-16 22:18:07,709 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-16 22:18:07,709 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:07,711 (beam_search:483) INFO: best hypo: SISIBEBEDRTZSDENISDEANTELLANIEGSDEERBENDESTRIEGHTDEBINERGESANTDENGOPEDERLOLTEEMITDUGBERKULOSENOFENBARDENNOCHNGERINGSEXSDAUSENDEINZSGESANDREIHUNDEDEISIGTAUESENLOLTEDEINSÜTDAWRIKEWHEINENBISTINTENZEITPBUNGKTANGISTETISENT + +2024-01-16 22:18:07,713 (asr_inference:494) INFO: speech length: 125760 +2024-01-16 22:18:07,726 (beam_search:428) INFO: decoder input length: 194 +2024-01-16 22:18:07,726 (beam_search:429) INFO: max output length: 194 +2024-01-16 22:18:07,726 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:08,313 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:08,313 (beam_search:476) INFO: -27.14 * 1.0 = -27.14 for ctc +2024-01-16 22:18:08,313 (beam_search:479) INFO: total log probability: -27.14 +2024-01-16 22:18:08,313 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:18:08,313 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:08,313 (beam_search:483) INFO: best hypo: EHNSCHELZWEITAUSENSECKSELEUTERTASKONTINUMKONDZEBTASEINEMITORDEMURGENSATIONZSEHLFNLESTUNGSFEGEZEWEREN + +2024-01-16 22:18:08,315 (asr_inference:494) INFO: speech length: 371520 +2024-01-16 22:18:08,349 (beam_search:428) INFO: decoder input length: 578 +2024-01-16 22:18:08,349 (beam_search:429) INFO: max output length: 578 +2024-01-16 22:18:08,349 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:10,490 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:10,490 (beam_search:476) INFO: -62.58 * 1.0 = -62.58 for ctc +2024-01-16 22:18:10,490 (beam_search:479) INFO: total log probability: -62.58 +2024-01-16 22:18:10,490 (beam_search:480) INFO: normalized log probability: -0.50 +2024-01-16 22:18:10,490 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:10,491 (beam_search:483) INFO: best hypo: SGSEEINDIESEBERIOEDENDERUERROEPEHENGICHIGTEMSTDANTDERICHUNDMEÄCHTIHENEVODENERKARTONESIGIEHENAUFTENPREÖSTANET + +2024-01-16 22:18:10,492 (asr_inference:494) INFO: speech length: 220800 +2024-01-16 22:18:10,513 (beam_search:428) INFO: decoder input length: 342 +2024-01-16 22:18:10,513 (beam_search:429) INFO: max output length: 342 +2024-01-16 22:18:10,513 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:12,305 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:12,305 (beam_search:476) INFO: -64.55 * 1.0 = -64.55 for ctc +2024-01-16 22:18:12,305 (beam_search:479) INFO: total log probability: -64.55 +2024-01-16 22:18:12,305 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:18:12,305 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:12,307 (beam_search:483) INFO: best hypo: DIERSDERCHENDIEBZICHMFÄELUNGISTASEINENEUDEPLOMATSCHINITKETIPEVERENDDIENJARSEGRIFENWERDENSOLLTEMDIERAGSCENGRENZENGEGBAEREINTLIENTERETIONZUSICHERNUNDPLUMATSCHBTZEMITZSEINACHBANIEERTZSTEN + +2024-01-16 22:18:12,308 (asr_inference:494) INFO: speech length: 174720 +2024-01-16 22:18:12,325 (beam_search:428) INFO: decoder input length: 270 +2024-01-16 22:18:12,325 (beam_search:429) INFO: max output length: 270 +2024-01-16 22:18:12,325 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:13,082 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:13,082 (beam_search:476) INFO: -29.12 * 1.0 = -29.12 for ctc +2024-01-16 22:18:13,082 (beam_search:479) INFO: total log probability: -29.12 +2024-01-16 22:18:13,082 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:18:13,082 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:13,083 (beam_search:483) INFO: best hypo: DEESPBETEINEUTEGELEGENHEITDASNORTLCHZUSENDEHEMENMEHRUDEWENGERRUNDUMDIUERDUNKELEST + +2024-01-16 22:18:13,084 (asr_inference:494) INFO: speech length: 312960 +2024-01-16 22:18:13,113 (beam_search:428) INFO: decoder input length: 486 +2024-01-16 22:18:13,113 (beam_search:429) INFO: max output length: 486 +2024-01-16 22:18:13,113 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:15,402 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:15,402 (beam_search:476) INFO: -64.40 * 1.0 = -64.40 for ctc +2024-01-16 22:18:15,402 (beam_search:479) INFO: total log probability: -64.40 +2024-01-16 22:18:15,402 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-16 22:18:15,402 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:15,403 (beam_search:483) INFO: best hypo: STKTPROSORENPAMELERVERGUSSONVONDERUNEWÖOETIADANDIEMERKTANSOALISTENSCHEIENEINEGEÄERLIEGENZEUERSREITENWENDIEPOTONSEWEITEVONERDECHTIEVEENTEIHEN + +2024-01-16 22:18:15,405 (asr_inference:494) INFO: speech length: 140160 +2024-01-16 22:18:15,419 (beam_search:428) INFO: decoder input length: 216 +2024-01-16 22:18:15,419 (beam_search:429) INFO: max output length: 216 +2024-01-16 22:18:15,419 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:16,148 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:16,148 (beam_search:476) INFO: -38.56 * 1.0 = -38.56 for ctc +2024-01-16 22:18:16,148 (beam_search:479) INFO: total log probability: -38.56 +2024-01-16 22:18:16,148 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:18:16,148 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:16,148 (beam_search:483) INFO: best hypo: SKASIACHLONEINENEILGKARZUKAUFENDIETZUTRITENWDERTORSGEWELTENPAZENCHENTAFRIKARERTUOALNZHTAVRIKANCHENERZNALPAXSGEWERT + +2024-01-16 22:18:16,150 (asr_inference:494) INFO: speech length: 147840 +2024-01-16 22:18:16,165 (beam_search:428) INFO: decoder input length: 228 +2024-01-16 22:18:16,165 (beam_search:429) INFO: max output length: 228 +2024-01-16 22:18:16,165 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:16,969 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:16,969 (beam_search:476) INFO: -39.36 * 1.0 = -39.36 for ctc +2024-01-16 22:18:16,969 (beam_search:479) INFO: total log probability: -39.36 +2024-01-16 22:18:16,969 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:18:16,969 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:16,970 (beam_search:483) INFO: best hypo: DIEPRÜKESOLMSERTEMBEATZWEITELENSIEBZINVERSTENDIHTNETRIEAUFNEMISWRDARWARTEASIRASJANICHNZOLPUNTEANFEARTIGSTELTZEINWEREN + +2024-01-16 22:18:16,972 (asr_inference:494) INFO: speech length: 188160 +2024-01-16 22:18:16,990 (beam_search:428) INFO: decoder input length: 291 +2024-01-16 22:18:16,990 (beam_search:429) INFO: max output length: 291 +2024-01-16 22:18:16,990 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:18,265 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:18,265 (beam_search:476) INFO: -48.01 * 1.0 = -48.01 for ctc +2024-01-16 22:18:18,265 (beam_search:479) INFO: total log probability: -48.01 +2024-01-16 22:18:18,265 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:18:18,265 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:18,266 (beam_search:483) INFO: best hypo: WERNDENEXSPRMETELLEMSTAUINERLAGTSUSEINSCHEINTIEBULEMOTELITETZUSENUNGEBTASBSERKEINMDIKAMENTEDIALSEINDRIHZUBEHANDLUNBSTENDENVEKTIONGEREIGNETNACHKGEIESENORDEN + +2024-01-16 22:18:18,268 (asr_inference:494) INFO: speech length: 194080 +2024-01-16 22:18:18,285 (beam_search:428) INFO: decoder input length: 301 +2024-01-16 22:18:18,286 (beam_search:429) INFO: max output length: 301 +2024-01-16 22:18:18,286 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:19,699 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:19,699 (beam_search:476) INFO: -51.75 * 1.0 = -51.75 for ctc +2024-01-16 22:18:19,699 (beam_search:479) INFO: total log probability: -51.75 +2024-01-16 22:18:19,699 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:18:19,699 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:19,700 (beam_search:483) INFO: best hypo: EINEUSSERSLIEBAFERDBESCHENWEXSELANSTATMAINARUOKDIMPLANEINALGEMEINSTATENKONGRESTZUBRUFENUNDKONDESIHVOLFIGNOCNICHTBERDESVORTZLEGEDEBRGAMUNDDINORTESTZUSAMTRTSEINIGEN + +2024-01-16 22:18:19,702 (asr_inference:494) INFO: speech length: 270240 +2024-01-16 22:18:19,727 (beam_search:428) INFO: decoder input length: 420 +2024-01-16 22:18:19,727 (beam_search:429) INFO: max output length: 420 +2024-01-16 22:18:19,727 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:21,681 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:21,681 (beam_search:476) INFO: -47.51 * 1.0 = -47.51 for ctc +2024-01-16 22:18:21,681 (beam_search:479) INFO: total log probability: -47.51 +2024-01-16 22:18:21,681 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:18:21,681 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:21,682 (beam_search:483) INFO: best hypo: ERWUSSTENCHTASIMDASLEBENKOSPARESGERAUPTATESCHPANGRAFTUNDMUDTDASESINFEIGUNSCOLIGEMACHTATEUNDFÄHCHZUDENHONDINGENZUODENENUNGETRIÜPTEITRALENGHRT + +2024-01-16 22:18:21,684 (asr_inference:494) INFO: speech length: 292320 +2024-01-16 22:18:21,711 (beam_search:428) INFO: decoder input length: 454 +2024-01-16 22:18:21,711 (beam_search:429) INFO: max output length: 454 +2024-01-16 22:18:21,711 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:24,577 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:24,577 (beam_search:476) INFO: -69.20 * 1.0 = -69.20 for ctc +2024-01-16 22:18:24,577 (beam_search:479) INFO: total log probability: -69.20 +2024-01-16 22:18:24,577 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:18:24,577 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:24,578 (beam_search:483) INFO: best hypo: DIESERUNGEMANHIESKAKALITZIENUNDBEFANICGAEFDRANDESCHAFTALSINEMGENANTENKÖÜNIGREICHBEKANDMACHUNGWIGDERNZEENVELENWURDEEISAKTDESHTNLDERWENESWEITENIHTZSISTEINEIPAUCHNHNICKÜKLSTUNDUTSKÖNIGSEIDAMZUWERENDASGELSETIGALEDINGST + +2024-01-16 22:18:24,580 (asr_inference:494) INFO: speech length: 279040 +2024-01-16 22:18:24,606 (beam_search:428) INFO: decoder input length: 433 +2024-01-16 22:18:24,606 (beam_search:429) INFO: max output length: 433 +2024-01-16 22:18:24,606 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:27,092 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:27,092 (beam_search:476) INFO: -75.33 * 1.0 = -75.33 for ctc +2024-01-16 22:18:27,092 (beam_search:479) INFO: total log probability: -75.33 +2024-01-16 22:18:27,092 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 22:18:27,092 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:27,093 (beam_search:483) INFO: best hypo: SRDRNOCFÜNFMINUTENUNDDEWOLGENDEBEUSTLOSIGKALTBEGANZUSCHINDENIERSTUSESERULDASIHNMEIMEIGNENBETELAGUNDDASDIEROTNGLUTDNIHTZANDRESWAILSDASVOYRIMKAMINDERINDASTUBEESWANACHTEINIKRZEBANTEAFTEIMTISHE + +2024-01-16 22:18:27,095 (asr_inference:494) INFO: speech length: 199840 +2024-01-16 22:18:27,113 (beam_search:428) INFO: decoder input length: 310 +2024-01-16 22:18:27,113 (beam_search:429) INFO: max output length: 310 +2024-01-16 22:18:27,113 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:28,522 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:28,522 (beam_search:476) INFO: -56.30 * 1.0 = -56.30 for ctc +2024-01-16 22:18:28,522 (beam_search:479) INFO: total log probability: -56.30 +2024-01-16 22:18:28,522 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:18:28,523 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:28,523 (beam_search:483) INFO: best hypo: EICHTDIEHERTRENGUNGENWEBECHTERUNTERHEILTENKOMDANEMPRBETITZSALDEDIHOCHFLUDESEXSUEINBEDÖFTIGKEITSOFNDESENDENGENANENSLSCHERARKTIONSODERIDERSTANZSPLDONGENDEMER + +2024-01-16 22:18:28,525 (asr_inference:494) INFO: speech length: 320000 +2024-01-16 22:18:28,554 (beam_search:428) INFO: decoder input length: 497 +2024-01-16 22:18:28,554 (beam_search:429) INFO: max output length: 497 +2024-01-16 22:18:28,554 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:31,153 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:31,153 (beam_search:476) INFO: -58.93 * 1.0 = -58.93 for ctc +2024-01-16 22:18:31,153 (beam_search:479) INFO: total log probability: -58.93 +2024-01-16 22:18:31,153 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:18:31,153 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:31,154 (beam_search:483) INFO: best hypo: TABERAFENGEHRENDBEHARGENBEGANDIEKISTDENWANTNUNSZOHÖRTEICHAUOFACFEZOSEINEINKLARERSCÖNAGERDANKTENGANGDENIHTILRGENGIMITDIMBAUCHAUSGEHEKTABEMOSSDENAFENDENKENMI + +2024-01-16 22:18:31,156 (asr_inference:494) INFO: speech length: 304160 +2024-01-16 22:18:31,183 (beam_search:428) INFO: decoder input length: 473 +2024-01-16 22:18:31,183 (beam_search:429) INFO: max output length: 473 +2024-01-16 22:18:31,183 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:34,267 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:34,267 (beam_search:476) INFO: -86.96 * 1.0 = -86.96 for ctc +2024-01-16 22:18:34,267 (beam_search:479) INFO: total log probability: -86.96 +2024-01-16 22:18:34,267 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:18:34,267 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:34,269 (beam_search:483) INFO: best hypo: RISSESPOTTRIEHERNEMENSCHNDENIGKENNENFRAKTEILEISERWELCHUNBMAKTEAMICHRANGITRETENBAICHNDGEGNIETEDASISNNFANTESIKOKFSEIUNDSCHOPTTEICHEUNGEILICHNTEDIEANDONLTTERNTÜLISPRACRICHIUNBARHEITDNDESWEINSERIETOEIESPETRIEMISTEROTSCHSTES + +2024-01-16 22:18:34,271 (asr_inference:494) INFO: speech length: 233120 +2024-01-16 22:18:34,291 (beam_search:428) INFO: decoder input length: 362 +2024-01-16 22:18:34,291 (beam_search:429) INFO: max output length: 362 +2024-01-16 22:18:34,291 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:36,122 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:36,122 (beam_search:476) INFO: -65.20 * 1.0 = -65.20 for ctc +2024-01-16 22:18:36,122 (beam_search:479) INFO: total log probability: -65.20 +2024-01-16 22:18:36,122 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:18:36,122 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:36,123 (beam_search:483) INFO: best hypo: CIHWEISDASIHSERKRANGBINSAKESHNERENAWEILEVORNPAMINUTENVESCHTEICHMICHNBÄTERUENZURENUNDFÜLEDEDASICKEINGLIEDMARENIHRNKANSWEREGUTWENIHMENGEMÜTDELEICHTANKÖRNTEBEVORAISTDERRBER + +2024-01-16 22:18:36,125 (asr_inference:494) INFO: speech length: 226080 +2024-01-16 22:18:36,145 (beam_search:428) INFO: decoder input length: 351 +2024-01-16 22:18:36,145 (beam_search:429) INFO: max output length: 351 +2024-01-16 22:18:36,145 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:37,845 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:37,845 (beam_search:476) INFO: -47.49 * 1.0 = -47.49 for ctc +2024-01-16 22:18:37,845 (beam_search:479) INFO: total log probability: -47.49 +2024-01-16 22:18:37,845 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:18:37,846 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:37,846 (beam_search:483) INFO: best hypo: SOABERISZWVERUNSERWESENSKONDGORTSELVERDAHERUMHTZIHEHDECDERCHLANGENKNOULTDSALPENSATANGESHLONGENUNIÜBERDEMFÜNGKGENDEIEBEISDIEENSERNISTESHASESKELAGERTWASWUNDERDAN + +2024-01-16 22:18:37,848 (asr_inference:494) INFO: speech length: 166240 +2024-01-16 22:18:37,864 (beam_search:428) INFO: decoder input length: 257 +2024-01-16 22:18:37,864 (beam_search:429) INFO: max output length: 257 +2024-01-16 22:18:37,864 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:38,797 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:38,797 (beam_search:476) INFO: -36.19 * 1.0 = -36.19 for ctc +2024-01-16 22:18:38,797 (beam_search:479) INFO: total log probability: -36.19 +2024-01-16 22:18:38,797 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:18:38,797 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:38,798 (beam_search:483) INFO: best hypo: BESEVERELEWAGEBLIEBENAUERSEWAGETZWUNZUGENBEDIEPÜNKTIGKEITBEIDNMALZEITENEINESACHEWAAUWECHINGEHTSHÄRHOLSTRENGRGERHELTENURDE + +2024-01-16 22:18:38,800 (asr_inference:494) INFO: speech length: 261600 +2024-01-16 22:18:38,823 (beam_search:428) INFO: decoder input length: 406 +2024-01-16 22:18:38,823 (beam_search:429) INFO: max output length: 406 +2024-01-16 22:18:38,823 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:41,171 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:41,171 (beam_search:476) INFO: -78.09 * 1.0 = -78.09 for ctc +2024-01-16 22:18:41,171 (beam_search:479) INFO: total log probability: -78.09 +2024-01-16 22:18:41,171 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:18:41,171 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:41,172 (beam_search:483) INFO: best hypo: NLIKLICHFÜLHTEWIEREAMNSICHTENBEMICGIERMPFINDNGNDFÜÖRMICHNICHTUMEINATUNVERINDARTWAANNBEHUPTKRNENDERUNGFECHWANIGSEISEREMFERSTEINETNAUOGEEIHSNEMELSTUOKTRINENGENETZTNIEMASINTÄRTICKATAUFGELOEICHTETHATTEAMN + +2024-01-16 22:18:41,174 (asr_inference:494) INFO: speech length: 320000 +2024-01-16 22:18:41,203 (beam_search:428) INFO: decoder input length: 497 +2024-01-16 22:18:41,203 (beam_search:429) INFO: max output length: 497 +2024-01-16 22:18:41,203 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:44,811 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:44,811 (beam_search:476) INFO: -88.09 * 1.0 = -88.09 for ctc +2024-01-16 22:18:44,811 (beam_search:479) INFO: total log probability: -88.09 +2024-01-16 22:18:44,811 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:18:44,811 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:44,813 (beam_search:483) INFO: best hypo: SBPODERSEMISZERGUTMINDERAUKLIGNERFÄHRWRESICVREUNWEWERNSCHNELLDANACHANDENSUOULBEAUPRECHENSCNALREIDNEMITÜROFODERACHDASLAGERREICHENERSTIEGAUDITFEHRDEDIUNAUSGEROTATENUNDFLOGENGALOPTDAVONDISMALÜTENWIRUNZDERFÄERDEIDEDEREKTZEVOLIENERENGERADEAUSSNDERSPADEU + +2024-01-16 22:18:44,815 (asr_inference:494) INFO: speech length: 313600 +2024-01-16 22:18:44,843 (beam_search:428) INFO: decoder input length: 487 +2024-01-16 22:18:44,843 (beam_search:429) INFO: max output length: 487 +2024-01-16 22:18:44,843 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:48,327 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:48,327 (beam_search:476) INFO: -71.04 * 1.0 = -71.04 for ctc +2024-01-16 22:18:48,327 (beam_search:479) INFO: total log probability: -71.04 +2024-01-16 22:18:48,327 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:18:48,327 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:48,328 (beam_search:483) INFO: best hypo: WELDEBEAMITPECHPESTRICHENWAHRBIEBEINERVONDNGOLLENDENPANTOFELNFÄSTEINGENUNDINDERANGSDACHESNICHTERANINITZONEMUNDIEISDNLETZTENSHIETVONDERTRERPETATDEHATISTZWLFAUSGESCHLAGENGDARWAWAGENUNDFIÄRDEVERSHUNDENNDASCHENPUTESTANINSEINASHENKLEIDEAUFEDUNKLNSTRASE + +2024-01-16 22:18:48,330 (asr_inference:494) INFO: speech length: 183840 +2024-01-16 22:18:48,347 (beam_search:428) INFO: decoder input length: 285 +2024-01-16 22:18:48,347 (beam_search:429) INFO: max output length: 285 +2024-01-16 22:18:48,347 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:49,309 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:49,309 (beam_search:476) INFO: -43.42 * 1.0 = -43.42 for ctc +2024-01-16 22:18:49,309 (beam_search:479) INFO: total log probability: -43.42 +2024-01-16 22:18:49,309 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:18:49,309 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:49,310 (beam_search:483) INFO: best hypo: IENOMDSKGASVMARGEENFINDTERANTLARGEREESENENTZUÜKENESISCHRIKESAKTEHERIETESMENIGEGIETAUNBUTDVERGISENVERMEINEN + +2024-01-16 22:18:49,311 (asr_inference:494) INFO: speech length: 284960 +2024-01-16 22:18:49,337 (beam_search:428) INFO: decoder input length: 443 +2024-01-16 22:18:49,337 (beam_search:429) INFO: max output length: 443 +2024-01-16 22:18:49,337 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:51,618 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:51,619 (beam_search:476) INFO: -47.39 * 1.0 = -47.39 for ctc +2024-01-16 22:18:51,619 (beam_search:479) INFO: total log probability: -47.39 +2024-01-16 22:18:51,619 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:18:51,619 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:51,620 (beam_search:483) INFO: best hypo: NURDERDAOCKTORUNDEWERTERENSOLENVORSEINEAUGENKOMMENERKLERTEDIETRIENEINKROSENAMTSEIFERDAMITWADIEFTRAROBERSTGANSEINFVERSTANDENUNDPHÜÖÜKSTERFPREITKARTESIEMETIEREN + +2024-01-16 22:18:51,621 (asr_inference:494) INFO: speech length: 298240 +2024-01-16 22:18:51,648 (beam_search:428) INFO: decoder input length: 463 +2024-01-16 22:18:51,648 (beam_search:429) INFO: max output length: 463 +2024-01-16 22:18:51,648 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:54,304 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:54,304 (beam_search:476) INFO: -66.21 * 1.0 = -66.21 for ctc +2024-01-16 22:18:54,304 (beam_search:479) INFO: total log probability: -66.21 +2024-01-16 22:18:54,304 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:18:54,304 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:54,305 (beam_search:483) INFO: best hypo: KAWARUNDTRÜSTICHEBEDIELAGEDASKÜNZSLRSEBEGENUWEINENUNSCHLCHTZTDELANGEINDEVORGERHELTENENHENDERDEKNSLAWATETERBDESKASICBERUICHTHATEUNTENTCHLOSICHTDANDAERKEINANDARNAUSFIGFANTDERNOCHZUMPEITERSCREIBE + +2024-01-16 22:18:54,307 (asr_inference:494) INFO: speech length: 320000 +2024-01-16 22:18:54,336 (beam_search:428) INFO: decoder input length: 497 +2024-01-16 22:18:54,336 (beam_search:429) INFO: max output length: 497 +2024-01-16 22:18:54,336 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:57,934 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:57,934 (beam_search:476) INFO: -91.92 * 1.0 = -91.92 for ctc +2024-01-16 22:18:57,934 (beam_search:479) INFO: total log probability: -91.92 +2024-01-16 22:18:57,934 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:18:57,934 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:57,935 (beam_search:483) INFO: best hypo: ONDEMFERDEHERDNDERPATSCHENUNSAGUNZSTASIFENAPATSCHNFÄRDUNSEBESOVIEWARENUNPRENDIGEBEWÜRDENIÜFERINKEIUOWABPFIERTTDASINDUNRIGKLIGEAVODUMAPATSCHENVÄHRDTSOULENASRCHTIHGERASCHLEIMTODEIESHRGEFALENUNDANEBLUTVERGISENWEICHISUNBEVORSTANDWEISESEFEHRDEHENDLERW + +2024-01-16 22:18:57,937 (asr_inference:494) INFO: speech length: 228640 +2024-01-16 22:18:57,958 (beam_search:428) INFO: decoder input length: 355 +2024-01-16 22:18:57,958 (beam_search:429) INFO: max output length: 355 +2024-01-16 22:18:57,958 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:18:59,849 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:18:59,850 (beam_search:476) INFO: -71.22 * 1.0 = -71.22 for ctc +2024-01-16 22:18:59,850 (beam_search:479) INFO: total log probability: -71.22 +2024-01-16 22:18:59,850 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 22:18:59,850 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:18:59,851 (beam_search:483) INFO: best hypo: DASMATONENHÜTCHNVODSCWARTZAMAMINDGATZIÖRSRIERELANGNLOTNGEDRIKTDIERWANGEUMFLOSENNTBERSCHLTHNHRAPWEITENSOTRAEIDASEINVERHRERLNTLICHGEBOEUDEUNDSTÄEBPETZWSCHNREINDERHIBPGEBLNEINDOFKENRAUFENTAB + +2024-01-16 22:18:59,852 (asr_inference:494) INFO: speech length: 251040 +2024-01-16 22:18:59,875 (beam_search:428) INFO: decoder input length: 390 +2024-01-16 22:18:59,875 (beam_search:429) INFO: max output length: 390 +2024-01-16 22:18:59,875 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:01,919 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:01,919 (beam_search:476) INFO: -64.79 * 1.0 = -64.79 for ctc +2024-01-16 22:19:01,919 (beam_search:479) INFO: total log probability: -64.79 +2024-01-16 22:19:01,919 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:19:01,919 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:01,920 (beam_search:483) INFO: best hypo: RBMUSTERTINZAGENGALEMSHNTHAFTENSTREBENUNENDTIEVEREIUNDDEMUDDIEFÜÖHBIDEDERHELINGAEFLENGEGENDEUGEREELTRSTDEJEMLENGERWELCHEPFANTCHSKOSULNEGEFLONSOCHTENINAUFINSREWERKSTUNFANDTENIN + +2024-01-16 22:19:01,922 (asr_inference:494) INFO: speech length: 171520 +2024-01-16 22:19:01,938 (beam_search:428) INFO: decoder input length: 265 +2024-01-16 22:19:01,938 (beam_search:429) INFO: max output length: 265 +2024-01-16 22:19:01,938 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:02,882 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:02,882 (beam_search:476) INFO: -32.36 * 1.0 = -32.36 for ctc +2024-01-16 22:19:02,882 (beam_search:479) INFO: total log probability: -32.36 +2024-01-16 22:19:02,882 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:19:02,882 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:02,883 (beam_search:483) INFO: best hypo: ERLIESZSEINEGRETENCHTVORTSCHLEBTENAMALERWINIGSDNABERINENGROSENVOGELBAURUSIEALEINEINEMTONEFEIEFEMOUSTENIERSHITSKTE + +2024-01-16 22:19:02,884 (asr_inference:494) INFO: speech length: 268800 +2024-01-16 22:19:02,910 (beam_search:428) INFO: decoder input length: 417 +2024-01-16 22:19:02,910 (beam_search:429) INFO: max output length: 417 +2024-01-16 22:19:02,910 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:05,397 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:05,397 (beam_search:476) INFO: -77.52 * 1.0 = -77.52 for ctc +2024-01-16 22:19:05,397 (beam_search:479) INFO: total log probability: -77.52 +2024-01-16 22:19:05,397 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:19:05,397 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:05,398 (beam_search:483) INFO: best hypo: FRNTCHESKOMALTENUNHELIGEBGEISTRUNFILEBIETASELÜGENHATENABEELTKHEINNELSERERMOCHTDIEBULERISCHELBIKETDEWEIBICENGSTALTENSOBERHAFDASISTELENINDEMVNLEBEDTEMODDELENDIEKALNATIONGVODNALTENMAHMOBILENBERORMONBILUNINDNAN + +2024-01-16 22:19:05,400 (asr_inference:494) INFO: speech length: 320000 +2024-01-16 22:19:05,429 (beam_search:428) INFO: decoder input length: 497 +2024-01-16 22:19:05,429 (beam_search:429) INFO: max output length: 497 +2024-01-16 22:19:05,429 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:08,878 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:08,878 (beam_search:476) INFO: -77.27 * 1.0 = -77.27 for ctc +2024-01-16 22:19:08,878 (beam_search:479) INFO: total log probability: -77.27 +2024-01-16 22:19:08,878 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:19:08,878 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:08,879 (beam_search:483) INFO: best hypo: BEWEGUNUNTATDENSTENZUGERSTHMEDIEVOEUNANGEGEBENINGERDENZHERNMICHRÜBEHANFEEICHENUNSAURAMFANALEINDEMPFEIFENKOPFERANWESENABEINFÜNDFTNAUTSTOHARICHNIGENANDIETSTROCHUNDSCHMÄKTIGDASEHNSTICHENFILSHDERBEISEINMSEIGHPLIESTENRAUCHAUCHGEGDENHEELUNGEGNGD + +2024-01-16 22:19:08,881 (asr_inference:494) INFO: speech length: 302560 +2024-01-16 22:19:08,908 (beam_search:428) INFO: decoder input length: 470 +2024-01-16 22:19:08,908 (beam_search:429) INFO: max output length: 470 +2024-01-16 22:19:08,908 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:12,093 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:12,093 (beam_search:476) INFO: -73.17 * 1.0 = -73.17 for ctc +2024-01-16 22:19:12,093 (beam_search:479) INFO: total log probability: -73.17 +2024-01-16 22:19:12,093 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:19:12,093 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:12,095 (beam_search:483) INFO: best hypo: UNDASVOÄERSTANDAUFUNDFLACKERTUNKOCHEDASESENFÄATIGHUNDERBRATENBRUTZELTEFORTUNDERKOCHGABDEMKÜSCHENIUNGENEINERROARFEIGEUNDIMARKTRUPFTETDESHUNFERTIGHDARWARTDIEHOCHZEITVONDEMKÜNIGHSONITETDONGRÖÜSIHNGEFEIHRTUNSIENEERNÜTEBISANIERENDE + +2024-01-16 22:19:12,097 (asr_inference:494) INFO: speech length: 203520 +2024-01-16 22:19:12,115 (beam_search:428) INFO: decoder input length: 315 +2024-01-16 22:19:12,115 (beam_search:429) INFO: max output length: 315 +2024-01-16 22:19:12,115 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:13,377 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:13,377 (beam_search:476) INFO: -45.15 * 1.0 = -45.15 for ctc +2024-01-16 22:19:13,377 (beam_search:479) INFO: total log probability: -45.15 +2024-01-16 22:19:13,377 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:19:13,377 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:13,378 (beam_search:483) INFO: best hypo: UNDDASEMINICHNACHTRARGENGOLLEWENICHNEDERSHPENSTIGWARGINSEINULMEINENBRARTDERHERFARARHEDEINALLENREICHTDEHATUNICHMEANUNRECHTABERH + +2024-01-16 22:19:13,379 (asr_inference:494) INFO: speech length: 162720 +2024-01-16 22:19:13,395 (beam_search:428) INFO: decoder input length: 252 +2024-01-16 22:19:13,395 (beam_search:429) INFO: max output length: 252 +2024-01-16 22:19:13,395 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:14,254 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:14,254 (beam_search:476) INFO: -45.96 * 1.0 = -45.96 for ctc +2024-01-16 22:19:14,254 (beam_search:479) INFO: total log probability: -45.96 +2024-01-16 22:19:14,254 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:19:14,254 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:14,254 (beam_search:483) INFO: best hypo: UOGEHNEMASENUWINIGEKRAMBTUGERBREITEDSICHKHEIEFÖMIGAUSENMUSTEDEERESHEMINDGEGENFIGEDESPRENKISCHOSAUFANENTZSOUERINGEN + +2024-01-16 22:19:14,256 (asr_inference:494) INFO: speech length: 320000 +2024-01-16 22:19:14,284 (beam_search:428) INFO: decoder input length: 497 +2024-01-16 22:19:14,284 (beam_search:429) INFO: max output length: 497 +2024-01-16 22:19:14,285 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:18,030 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:18,030 (beam_search:476) INFO: -87.20 * 1.0 = -87.20 for ctc +2024-01-16 22:19:18,030 (beam_search:479) INFO: total log probability: -87.20 +2024-01-16 22:19:18,030 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:19:18,030 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:18,032 (beam_search:483) INFO: best hypo: DERVUGSREICHESEMIUNFRITICHEFRIEDENSPWEIFVERHENDERMANTATWACKASEINESEKSZÜGENSAKTEDERGROSIGEISACHTENICHAUFDIVERSCHIEDNEHAUTDERMENSCHENDENDIKRNSICHMITVABEBSCHMIHRENMINTZSTROLSCHENSONDENERSIDASHETZSANDEHETZHNDERKRLIGEVOMBERÜBENSTAMIDERKAIOWASINTAPFERUNERSCHROGMNTREDASMEINIGEHENG + +2024-01-16 22:19:18,034 (asr_inference:494) INFO: speech length: 251200 +2024-01-16 22:19:18,056 (beam_search:428) INFO: decoder input length: 390 +2024-01-16 22:19:18,056 (beam_search:429) INFO: max output length: 390 +2024-01-16 22:19:18,056 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:20,058 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:20,059 (beam_search:476) INFO: -46.26 * 1.0 = -46.26 for ctc +2024-01-16 22:19:20,059 (beam_search:479) INFO: total log probability: -46.26 +2024-01-16 22:19:20,059 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:19:20,059 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:20,060 (beam_search:483) INFO: best hypo: ALLESASWIMETIERBEGEGNETSCHEBSICHTDOESCHUNDBERINANDERBALTUNTERSCHEBEMWERINKONTAKTDEISTIERERHANDUNDEMEINIGEIERNAHRMONDERMEINIGEBEIDELRSHEEINANDERAUSBEIDEVERSCHLINGENSICH + +2024-01-16 22:19:20,061 (asr_inference:494) INFO: speech length: 281920 +2024-01-16 22:19:20,087 (beam_search:428) INFO: decoder input length: 438 +2024-01-16 22:19:20,087 (beam_search:429) INFO: max output length: 438 +2024-01-16 22:19:20,087 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:22,696 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:22,696 (beam_search:476) INFO: -70.23 * 1.0 = -70.23 for ctc +2024-01-16 22:19:22,696 (beam_search:479) INFO: total log probability: -70.23 +2024-01-16 22:19:22,696 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:19:22,696 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:22,697 (beam_search:483) INFO: best hypo: ERMÜSTEENENFERHENGRONITENKÖERALDESMALESMITALLERKLEHRMENZURECHTWESENENRIMITGRAUSENIGUNVRCHNARCKHIUNVERBREMENICHTRETENDIEPERSONDESERAUSGEIBESNDBITETICHIKÜNSTIGELISERUWOLSTIEDUWEITELISISTFOLENDISDIGITISTNERTEN + +2024-01-16 22:19:22,699 (asr_inference:494) INFO: speech length: 294560 +2024-01-16 22:19:22,726 (beam_search:428) INFO: decoder input length: 458 +2024-01-16 22:19:22,726 (beam_search:429) INFO: max output length: 458 +2024-01-16 22:19:22,726 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:25,720 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:25,720 (beam_search:476) INFO: -70.97 * 1.0 = -70.97 for ctc +2024-01-16 22:19:25,720 (beam_search:479) INFO: total log probability: -70.97 +2024-01-16 22:19:25,720 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:19:25,720 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:25,721 (beam_search:483) INFO: best hypo: DIHOFDAMENBEKAMENKREMPFEUNDIKÜNIGENUNDIERONZTESSENENDIERERALLALIEBZSENHÜNZCHENWERNDERMEILTHERAUINSCHOSGENOMHADTENERMERKTENZUIRENSCREÄKENGDASDILIELERAMARANTFABENENUNDORANSCGAHLDENSEIDENKLEIDERALEDISTDESETMEINHESLIHSTENÖFLEGENWAN + +2024-01-16 22:19:25,723 (asr_inference:494) INFO: speech length: 211200 +2024-01-16 22:19:25,743 (beam_search:428) INFO: decoder input length: 327 +2024-01-16 22:19:25,743 (beam_search:429) INFO: max output length: 327 +2024-01-16 22:19:25,743 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:27,295 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:27,296 (beam_search:476) INFO: -48.32 * 1.0 = -48.32 for ctc +2024-01-16 22:19:27,296 (beam_search:479) INFO: total log probability: -48.32 +2024-01-16 22:19:27,296 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:19:27,296 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:27,297 (beam_search:483) INFO: best hypo: VONLEDEANDIESIESINUNKLARWIERPIESENDIESISPBIELNVONGÄHRTBÖRSENDISIHEGKENVONDRANZÜESCHNBÜCHANDIESIBASRSETZENKONTEBISMANGEMÜTWERENDIHLAUSTZONNACHAMONAUFKESTCHETWURDE + +2024-01-16 22:19:27,298 (asr_inference:494) INFO: speech length: 224320 +2024-01-16 22:19:27,319 (beam_search:428) INFO: decoder input length: 348 +2024-01-16 22:19:27,319 (beam_search:429) INFO: max output length: 348 +2024-01-16 22:19:27,319 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:29,056 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:29,056 (beam_search:476) INFO: -52.05 * 1.0 = -52.05 for ctc +2024-01-16 22:19:29,056 (beam_search:479) INFO: total log probability: -52.05 +2024-01-16 22:19:29,056 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:19:29,056 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:29,057 (beam_search:483) INFO: best hypo: AMUNDNAENWANBLOSIEREINZIGERSCMUOKWANIEREASTANENBRANFLÄCHTENWEICHINWILDERUNDDATÜRLICHERANMUNDAUIRSCHUSTHENHRABPFIENIHNAMEINBOGENGFEINKATUNGSNDZEIHETMTROSERSOKVELTIOMGESER + +2024-01-16 22:19:29,059 (asr_inference:494) INFO: speech length: 264800 +2024-01-16 22:19:29,083 (beam_search:428) INFO: decoder input length: 411 +2024-01-16 22:19:29,083 (beam_search:429) INFO: max output length: 411 +2024-01-16 22:19:29,083 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:31,641 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:31,641 (beam_search:476) INFO: -61.86 * 1.0 = -61.86 for ctc +2024-01-16 22:19:31,641 (beam_search:479) INFO: total log probability: -61.86 +2024-01-16 22:19:31,641 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:19:31,641 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:31,643 (beam_search:483) INFO: best hypo: RARWIEUSTRTSCHLANNOCHASILENEINIMANDRENSTARTKONTMANEFANWASDERGENSCHEANUNDESESLTATDISUNTEREDUNGEWISENSEANVEMUTETEDSESICHUMEKLIRENGDERMATZHEÜBEERABSICHTENUNDUNDIVEMITLUNGDERMÄCHTEZUISCHNMASTATENUNGROSPETANIENHANDE + +2024-01-16 22:19:31,644 (asr_inference:494) INFO: speech length: 205120 +2024-01-16 22:19:31,663 (beam_search:428) INFO: decoder input length: 318 +2024-01-16 22:19:31,663 (beam_search:429) INFO: max output length: 318 +2024-01-16 22:19:31,663 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:33,090 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:33,090 (beam_search:476) INFO: -42.26 * 1.0 = -42.26 for ctc +2024-01-16 22:19:33,090 (beam_search:479) INFO: total log probability: -42.26 +2024-01-16 22:19:33,090 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:19:33,090 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:33,091 (beam_search:483) INFO: best hypo: LAUNWENIGSTENSEINEZEITLANGVERSUOCHENINBIEFERUNWIERAUFDIESESBEISNMTEINANDERAUSREICHENDADERSTUSAMENHNGNDEIEDESARGSTAEIGENKLIGHOEERLEMENTISERSETZTIERURT + +2024-01-16 22:19:33,093 (asr_inference:494) INFO: speech length: 270400 +2024-01-16 22:19:33,118 (beam_search:428) INFO: decoder input length: 420 +2024-01-16 22:19:33,118 (beam_search:429) INFO: max output length: 420 +2024-01-16 22:19:33,118 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:35,325 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:35,325 (beam_search:476) INFO: -58.69 * 1.0 = -58.69 for ctc +2024-01-16 22:19:35,325 (beam_search:479) INFO: total log probability: -58.69 +2024-01-16 22:19:35,325 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:19:35,325 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:35,326 (beam_search:483) INFO: best hypo: VESCHENFVORKOMSEKFÜRDENZUDEVERMUTUNGDASFRAUWIESEDIEKLEINENWIENVERBRENEISOLISFEINSUSTACHGERHITSTABENDASDIHERTPLATENZSPTANGAUSEDESOLEINFÜRCHTELICHERGEROCHOWAGENUNMNWURTENSEIN + +2024-01-16 22:19:35,328 (asr_inference:494) INFO: speech length: 161920 +2024-01-16 22:19:35,344 (beam_search:428) INFO: decoder input length: 250 +2024-01-16 22:19:35,344 (beam_search:429) INFO: max output length: 250 +2024-01-16 22:19:35,344 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:36,222 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:36,222 (beam_search:476) INFO: -24.37 * 1.0 = -24.37 for ctc +2024-01-16 22:19:36,222 (beam_search:479) INFO: total log probability: -24.37 +2024-01-16 22:19:36,222 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:19:36,222 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:36,222 (beam_search:483) INFO: best hypo: UNKGINDEMSHREIENACHSOSAERENTLICHEINHUONBAUMUNDOBENDERAUFSASENKLEINESKENDTUNDERDEMBAUMABALAREINEFRAURDIESHLIEF + +2024-01-16 22:19:36,224 (asr_inference:494) INFO: speech length: 286880 +2024-01-16 22:19:36,250 (beam_search:428) INFO: decoder input length: 446 +2024-01-16 22:19:36,250 (beam_search:429) INFO: max output length: 446 +2024-01-16 22:19:36,250 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:38,915 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:38,915 (beam_search:476) INFO: -74.71 * 1.0 = -74.71 for ctc +2024-01-16 22:19:38,915 (beam_search:479) INFO: total log probability: -74.71 +2024-01-16 22:19:38,915 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:19:38,915 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:38,916 (beam_search:483) INFO: best hypo: CITSIEHRTENSERHEBENDIFISCHEGADENEWALLSCHEDENARCHTIAUBERTAUSGERURDHFVINWADENHEREINGEZAOUGENDIESELOEITERGERHÄORTHENAUGENSHEIMLESCHVESCHETENENNOCTZIONENARNABUORLDERALOPÄHSCHERKAREKTEBEIALENAUSGETRÜKTWAL + +2024-01-16 22:19:38,918 (asr_inference:494) INFO: speech length: 173600 +2024-01-16 22:19:38,934 (beam_search:428) INFO: decoder input length: 269 +2024-01-16 22:19:38,935 (beam_search:429) INFO: max output length: 269 +2024-01-16 22:19:38,935 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:39,772 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:39,772 (beam_search:476) INFO: -37.39 * 1.0 = -37.39 for ctc +2024-01-16 22:19:39,772 (beam_search:479) INFO: total log probability: -37.39 +2024-01-16 22:19:39,772 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 22:19:39,772 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:39,772 (beam_search:483) INFO: best hypo: NEINEINICHSCÄEMEMÄICGHTLSMCHRNDEINEMBUSENMENGESICHTVERBEHRGENHERSINGTENSGRASNIEDEUNZIETSINACH + +2024-01-16 22:19:39,774 (asr_inference:494) INFO: speech length: 226240 +2024-01-16 22:19:39,794 (beam_search:428) INFO: decoder input length: 351 +2024-01-16 22:19:39,794 (beam_search:429) INFO: max output length: 351 +2024-01-16 22:19:39,794 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:41,515 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:41,515 (beam_search:476) INFO: -47.94 * 1.0 = -47.94 for ctc +2024-01-16 22:19:41,515 (beam_search:479) INFO: total log probability: -47.94 +2024-01-16 22:19:41,515 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:19:41,515 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:41,516 (beam_search:483) INFO: best hypo: DIKENDERABARSASENVERDEMWALTUNDALSIEDIEDREIGNECHTEVONWEITEMLAUFENSANSPRACHLENCHENZUNPFÜNDEVOGELVERLESTUMICHNICHTZOVELASICHTIGAUCRENICTSURSPRACHFONDEVOGELNUNUNDNEMARMER + +2024-01-16 22:19:41,518 (asr_inference:494) INFO: speech length: 180480 +2024-01-16 22:19:41,535 (beam_search:428) INFO: decoder input length: 279 +2024-01-16 22:19:41,535 (beam_search:429) INFO: max output length: 279 +2024-01-16 22:19:41,535 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:42,532 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:42,532 (beam_search:476) INFO: -24.27 * 1.0 = -24.27 for ctc +2024-01-16 22:19:42,532 (beam_search:479) INFO: total log probability: -24.27 +2024-01-16 22:19:42,532 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:19:42,532 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:42,533 (beam_search:483) INFO: best hypo: WIEDERSCHULZEINSEINERHULDIEGUNGSRIEDEHERVORHUBDELERABRACHTEAMKLARENSOMAMRADENGMITSEINSCHULKENDENEINGESANGSSTENTIE + +2024-01-16 22:19:42,534 (asr_inference:494) INFO: speech length: 23360 +2024-01-16 22:19:42,542 (beam_search:428) INFO: decoder input length: 34 +2024-01-16 22:19:42,542 (beam_search:429) INFO: max output length: 34 +2024-01-16 22:19:42,542 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:42,570 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:42,570 (beam_search:476) INFO: -3.34 * 1.0 = -3.34 for ctc +2024-01-16 22:19:42,570 (beam_search:479) INFO: total log probability: -3.34 +2024-01-16 22:19:42,570 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 22:19:42,570 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:42,570 (beam_search:483) INFO: best hypo: STERTWISIESEIENSOLN + +2024-01-16 22:19:42,571 (asr_inference:494) INFO: speech length: 66880 +2024-01-16 22:19:42,581 (beam_search:428) INFO: decoder input length: 102 +2024-01-16 22:19:42,581 (beam_search:429) INFO: max output length: 102 +2024-01-16 22:19:42,581 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:42,750 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:42,750 (beam_search:476) INFO: -9.47 * 1.0 = -9.47 for ctc +2024-01-16 22:19:42,750 (beam_search:479) INFO: total log probability: -9.47 +2024-01-16 22:19:42,750 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:19:42,750 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:42,750 (beam_search:483) INFO: best hypo: DERENTSCHINGRNGENDRCEINERZUSARSCHALTUNGSTUFENLOS + +2024-01-16 22:19:42,751 (asr_inference:494) INFO: speech length: 32800 +2024-01-16 22:19:42,759 (beam_search:428) INFO: decoder input length: 49 +2024-01-16 22:19:42,759 (beam_search:429) INFO: max output length: 49 +2024-01-16 22:19:42,759 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:42,811 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:42,811 (beam_search:476) INFO: -5.16 * 1.0 = -5.16 for ctc +2024-01-16 22:19:42,811 (beam_search:479) INFO: total log probability: -5.16 +2024-01-16 22:19:42,811 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:19:42,811 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:42,811 (beam_search:483) INFO: best hypo: DIEAUFALEBEIDERSITZVERTELUNZ + +2024-01-16 22:19:42,812 (asr_inference:494) INFO: speech length: 18560 +2024-01-16 22:19:42,819 (beam_search:428) INFO: decoder input length: 26 +2024-01-16 22:19:42,819 (beam_search:429) INFO: max output length: 26 +2024-01-16 22:19:42,819 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:42,838 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:42,838 (beam_search:476) INFO: -4.84 * 1.0 = -4.84 for ctc +2024-01-16 22:19:42,838 (beam_search:479) INFO: total log probability: -4.84 +2024-01-16 22:19:42,838 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:19:42,838 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:42,838 (beam_search:483) INFO: best hypo: UMDENÜERLEMEND + +2024-01-16 22:19:42,839 (asr_inference:494) INFO: speech length: 68800 +2024-01-16 22:19:42,849 (beam_search:428) INFO: decoder input length: 105 +2024-01-16 22:19:42,849 (beam_search:429) INFO: max output length: 105 +2024-01-16 22:19:42,849 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:43,034 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:43,034 (beam_search:476) INFO: -9.19 * 1.0 = -9.19 for ctc +2024-01-16 22:19:43,034 (beam_search:479) INFO: total log probability: -9.19 +2024-01-16 22:19:43,034 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 22:19:43,034 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:43,034 (beam_search:483) INFO: best hypo: SPBETERURDENTEILWEISESUGARACHTPARLILLOSTREIFENENGESETZT + +2024-01-16 22:19:43,035 (asr_inference:494) INFO: speech length: 34720 +2024-01-16 22:19:43,043 (beam_search:428) INFO: decoder input length: 52 +2024-01-16 22:19:43,043 (beam_search:429) INFO: max output length: 52 +2024-01-16 22:19:43,043 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:43,091 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:43,091 (beam_search:476) INFO: -5.76 * 1.0 = -5.76 for ctc +2024-01-16 22:19:43,091 (beam_search:479) INFO: total log probability: -5.76 +2024-01-16 22:19:43,091 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:19:43,091 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:43,092 (beam_search:483) INFO: best hypo: MAORDEBEKANTUNDVELLANGKT + +2024-01-16 22:19:43,093 (asr_inference:494) INFO: speech length: 35040 +2024-01-16 22:19:43,100 (beam_search:428) INFO: decoder input length: 52 +2024-01-16 22:19:43,100 (beam_search:429) INFO: max output length: 52 +2024-01-16 22:19:43,100 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:43,158 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:43,158 (beam_search:476) INFO: -7.00 * 1.0 = -7.00 for ctc +2024-01-16 22:19:43,158 (beam_search:479) INFO: total log probability: -7.00 +2024-01-16 22:19:43,158 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:19:43,158 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:43,158 (beam_search:483) INFO: best hypo: BUNDESWAIGESETSDIESTMVONWIELEN + +2024-01-16 22:19:43,159 (asr_inference:494) INFO: speech length: 42720 +2024-01-16 22:19:43,167 (beam_search:428) INFO: decoder input length: 64 +2024-01-16 22:19:43,167 (beam_search:429) INFO: max output length: 64 +2024-01-16 22:19:43,167 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:43,205 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:43,205 (beam_search:476) INFO: -7.32 * 1.0 = -7.32 for ctc +2024-01-16 22:19:43,205 (beam_search:479) INFO: total log probability: -7.32 +2024-01-16 22:19:43,205 (beam_search:480) INFO: normalized log probability: -0.43 +2024-01-16 22:19:43,205 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:43,205 (beam_search:483) INFO: best hypo: SFNAGESCEICHTE + +2024-01-16 22:19:43,206 (asr_inference:494) INFO: speech length: 17120 +2024-01-16 22:19:43,213 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 22:19:43,213 (beam_search:429) INFO: max output length: 24 +2024-01-16 22:19:43,213 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:43,228 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:43,228 (beam_search:476) INFO: -3.31 * 1.0 = -3.31 for ctc +2024-01-16 22:19:43,228 (beam_search:479) INFO: total log probability: -3.31 +2024-01-16 22:19:43,228 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:19:43,228 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:43,228 (beam_search:483) INFO: best hypo: SBALTUNGFEEC + +2024-01-16 22:19:43,229 (asr_inference:494) INFO: speech length: 37120 +2024-01-16 22:19:43,238 (beam_search:428) INFO: decoder input length: 55 +2024-01-16 22:19:43,238 (beam_search:429) INFO: max output length: 55 +2024-01-16 22:19:43,238 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:43,297 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:43,297 (beam_search:476) INFO: -5.82 * 1.0 = -5.82 for ctc +2024-01-16 22:19:43,297 (beam_search:479) INFO: total log probability: -5.82 +2024-01-16 22:19:43,297 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:19:43,297 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:43,298 (beam_search:483) INFO: best hypo: STPALEBORNDIEUSERENDFERANDES + +2024-01-16 22:19:43,299 (asr_inference:494) INFO: speech length: 35200 +2024-01-16 22:19:43,306 (beam_search:428) INFO: decoder input length: 52 +2024-01-16 22:19:43,306 (beam_search:429) INFO: max output length: 52 +2024-01-16 22:19:43,306 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:43,361 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:43,362 (beam_search:476) INFO: -5.58 * 1.0 = -5.58 for ctc +2024-01-16 22:19:43,362 (beam_search:479) INFO: total log probability: -5.58 +2024-01-16 22:19:43,362 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:19:43,362 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:43,362 (beam_search:483) INFO: best hypo: UMWEITERINHUMANITERERHLFIETZ + +2024-01-16 22:19:43,363 (asr_inference:494) INFO: speech length: 56480 +2024-01-16 22:19:43,372 (beam_search:428) INFO: decoder input length: 86 +2024-01-16 22:19:43,372 (beam_search:429) INFO: max output length: 86 +2024-01-16 22:19:43,372 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:43,503 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:43,503 (beam_search:476) INFO: -11.43 * 1.0 = -11.43 for ctc +2024-01-16 22:19:43,503 (beam_search:479) INFO: total log probability: -11.43 +2024-01-16 22:19:43,503 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:19:43,503 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:43,503 (beam_search:483) INFO: best hypo: SIERKAMTENDIENNOUERICHINESISHEREGIOUNGNICHTAN + +2024-01-16 22:19:43,504 (asr_inference:494) INFO: speech length: 48480 +2024-01-16 22:19:43,513 (beam_search:428) INFO: decoder input length: 73 +2024-01-16 22:19:43,513 (beam_search:429) INFO: max output length: 73 +2024-01-16 22:19:43,513 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:43,629 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:43,629 (beam_search:476) INFO: -10.89 * 1.0 = -10.89 for ctc +2024-01-16 22:19:43,629 (beam_search:479) INFO: total log probability: -10.89 +2024-01-16 22:19:43,629 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:19:43,629 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:43,629 (beam_search:483) INFO: best hypo: DIEURAUFÜHGENVONANDEINZWANSENSETEMERZWERDENACHTI + +2024-01-16 22:19:43,630 (asr_inference:494) INFO: speech length: 54240 +2024-01-16 22:19:43,639 (beam_search:428) INFO: decoder input length: 82 +2024-01-16 22:19:43,639 (beam_search:429) INFO: max output length: 82 +2024-01-16 22:19:43,639 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:43,776 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:43,776 (beam_search:476) INFO: -13.79 * 1.0 = -13.79 for ctc +2024-01-16 22:19:43,776 (beam_search:479) INFO: total log probability: -13.79 +2024-01-16 22:19:43,776 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:19:43,776 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:43,776 (beam_search:483) INFO: best hypo: EREICHNIHTSCHOLICUREMITSCHOLIHMACKENANTODERNSMITGESE + +2024-01-16 22:19:43,778 (asr_inference:494) INFO: speech length: 24480 +2024-01-16 22:19:43,785 (beam_search:428) INFO: decoder input length: 36 +2024-01-16 22:19:43,785 (beam_search:429) INFO: max output length: 36 +2024-01-16 22:19:43,785 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:43,813 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:43,814 (beam_search:476) INFO: -6.06 * 1.0 = -6.06 for ctc +2024-01-16 22:19:43,814 (beam_search:479) INFO: total log probability: -6.06 +2024-01-16 22:19:43,814 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:19:43,814 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:43,814 (beam_search:483) INFO: best hypo: DIEDERSZSTMEREINEN + +2024-01-16 22:19:43,815 (asr_inference:494) INFO: speech length: 19520 +2024-01-16 22:19:43,821 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 22:19:43,822 (beam_search:429) INFO: max output length: 28 +2024-01-16 22:19:43,822 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:43,845 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:43,845 (beam_search:476) INFO: -2.07 * 1.0 = -2.07 for ctc +2024-01-16 22:19:43,845 (beam_search:479) INFO: total log probability: -2.07 +2024-01-16 22:19:43,845 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 22:19:43,845 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:43,845 (beam_search:483) INFO: best hypo: UNTEIFENTISENBEIDE + +2024-01-16 22:19:43,846 (asr_inference:494) INFO: speech length: 51520 +2024-01-16 22:19:43,855 (beam_search:428) INFO: decoder input length: 78 +2024-01-16 22:19:43,855 (beam_search:429) INFO: max output length: 78 +2024-01-16 22:19:43,855 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:43,975 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:43,975 (beam_search:476) INFO: -8.78 * 1.0 = -8.78 for ctc +2024-01-16 22:19:43,975 (beam_search:479) INFO: total log probability: -8.78 +2024-01-16 22:19:43,975 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:19:43,975 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:43,975 (beam_search:483) INFO: best hypo: KREISWALFVORSCHLAGUNDEINELANDESLISTERNDERZEITEN + +2024-01-16 22:19:43,976 (asr_inference:494) INFO: speech length: 133760 +2024-01-16 22:19:43,990 (beam_search:428) INFO: decoder input length: 206 +2024-01-16 22:19:43,990 (beam_search:429) INFO: max output length: 206 +2024-01-16 22:19:43,990 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:44,662 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:44,662 (beam_search:476) INFO: -30.51 * 1.0 = -30.51 for ctc +2024-01-16 22:19:44,662 (beam_search:479) INFO: total log probability: -30.51 +2024-01-16 22:19:44,662 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:19:44,662 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:44,662 (beam_search:483) INFO: best hypo: ANUMSERZUNGDERSAGEINVOMANESFNFZEINTEILGENLIEERTZIKLSZWETENACTWURDEPRESLASKABERTINNEBERABETUNGVONHOSTHABEMAN + +2024-01-16 22:19:44,664 (asr_inference:494) INFO: speech length: 31520 +2024-01-16 22:19:44,671 (beam_search:428) INFO: decoder input length: 47 +2024-01-16 22:19:44,671 (beam_search:429) INFO: max output length: 47 +2024-01-16 22:19:44,671 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:44,717 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:44,717 (beam_search:476) INFO: -6.26 * 1.0 = -6.26 for ctc +2024-01-16 22:19:44,717 (beam_search:479) INFO: total log probability: -6.26 +2024-01-16 22:19:44,717 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:19:44,717 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:44,717 (beam_search:483) INFO: best hypo: IEDIEVOLEDERTABELEDASTET + +2024-01-16 22:19:44,718 (asr_inference:494) INFO: speech length: 16320 +2024-01-16 22:19:44,725 (beam_search:428) INFO: decoder input length: 23 +2024-01-16 22:19:44,725 (beam_search:429) INFO: max output length: 23 +2024-01-16 22:19:44,725 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:44,741 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:44,741 (beam_search:476) INFO: -3.57 * 1.0 = -3.57 for ctc +2024-01-16 22:19:44,741 (beam_search:479) INFO: total log probability: -3.57 +2024-01-16 22:19:44,741 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:19:44,741 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:44,741 (beam_search:483) INFO: best hypo: ZUMSTRUNFLSBAE + +2024-01-16 22:19:44,742 (asr_inference:494) INFO: speech length: 53760 +2024-01-16 22:19:44,751 (beam_search:428) INFO: decoder input length: 81 +2024-01-16 22:19:44,751 (beam_search:429) INFO: max output length: 81 +2024-01-16 22:19:44,751 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:44,859 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:44,859 (beam_search:476) INFO: -8.93 * 1.0 = -8.93 for ctc +2024-01-16 22:19:44,859 (beam_search:479) INFO: total log probability: -8.93 +2024-01-16 22:19:44,859 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:19:44,859 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:44,859 (beam_search:483) INFO: best hypo: DEBUNDESWALEITERBISTZUMSIENZIGSTENTAR + +2024-01-16 22:19:44,860 (asr_inference:494) INFO: speech length: 41600 +2024-01-16 22:19:44,868 (beam_search:428) INFO: decoder input length: 62 +2024-01-16 22:19:44,868 (beam_search:429) INFO: max output length: 62 +2024-01-16 22:19:44,868 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:44,940 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:44,940 (beam_search:476) INFO: -8.67 * 1.0 = -8.67 for ctc +2024-01-16 22:19:44,940 (beam_search:479) INFO: total log probability: -8.67 +2024-01-16 22:19:44,940 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:19:44,940 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:44,940 (beam_search:483) INFO: best hypo: ORERICHKGEWRDENDUSHENICHTMITWEEN + +2024-01-16 22:19:44,942 (asr_inference:494) INFO: speech length: 47360 +2024-01-16 22:19:44,950 (beam_search:428) INFO: decoder input length: 71 +2024-01-16 22:19:44,950 (beam_search:429) INFO: max output length: 71 +2024-01-16 22:19:44,950 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:45,024 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:45,025 (beam_search:476) INFO: -9.90 * 1.0 = -9.90 for ctc +2024-01-16 22:19:45,025 (beam_search:479) INFO: total log probability: -9.90 +2024-01-16 22:19:45,025 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:19:45,025 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:45,025 (beam_search:483) INFO: best hypo: AUSFIONMUSTENGUSERKOTEBEGIN + +2024-01-16 22:19:45,026 (asr_inference:494) INFO: speech length: 37280 +2024-01-16 22:19:45,034 (beam_search:428) INFO: decoder input length: 56 +2024-01-16 22:19:45,034 (beam_search:429) INFO: max output length: 56 +2024-01-16 22:19:45,034 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:45,098 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:45,098 (beam_search:476) INFO: -8.00 * 1.0 = -8.00 for ctc +2024-01-16 22:19:45,098 (beam_search:479) INFO: total log probability: -8.00 +2024-01-16 22:19:45,098 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:19:45,098 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:45,098 (beam_search:483) INFO: best hypo: VERKIEICHPBANZEALNWERTUMNGEANDE + +2024-01-16 22:19:45,099 (asr_inference:494) INFO: speech length: 20480 +2024-01-16 22:19:45,106 (beam_search:428) INFO: decoder input length: 29 +2024-01-16 22:19:45,106 (beam_search:429) INFO: max output length: 29 +2024-01-16 22:19:45,106 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:45,133 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:45,133 (beam_search:476) INFO: -4.52 * 1.0 = -4.52 for ctc +2024-01-16 22:19:45,133 (beam_search:479) INFO: total log probability: -4.52 +2024-01-16 22:19:45,133 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:19:45,133 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:45,133 (beam_search:483) INFO: best hypo: BETRAHTEDEELGEMEINHEI + +2024-01-16 22:19:45,134 (asr_inference:494) INFO: speech length: 46560 +2024-01-16 22:19:45,142 (beam_search:428) INFO: decoder input length: 70 +2024-01-16 22:19:45,142 (beam_search:429) INFO: max output length: 70 +2024-01-16 22:19:45,142 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:45,238 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:45,238 (beam_search:476) INFO: -7.81 * 1.0 = -7.81 for ctc +2024-01-16 22:19:45,238 (beam_search:479) INFO: total log probability: -7.81 +2024-01-16 22:19:45,238 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:19:45,238 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:45,239 (beam_search:483) INFO: best hypo: UNTERSHITLICHEAUFASINGENGABESNURDARIEBER + +2024-01-16 22:19:45,240 (asr_inference:494) INFO: speech length: 116320 +2024-01-16 22:19:45,252 (beam_search:428) INFO: decoder input length: 179 +2024-01-16 22:19:45,252 (beam_search:429) INFO: max output length: 179 +2024-01-16 22:19:45,252 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:45,765 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:45,766 (beam_search:476) INFO: -21.06 * 1.0 = -21.06 for ctc +2024-01-16 22:19:45,766 (beam_search:479) INFO: total log probability: -21.06 +2024-01-16 22:19:45,766 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:19:45,766 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:45,766 (beam_search:483) INFO: best hypo: DOLLBEIMBUNESLIGISTENBRSERTORTMUNDTNACHVOLGERDESUNMITLBARZOVORTZURÜCKETRETENRENASSJIRENRÜBER + +2024-01-16 22:19:45,768 (asr_inference:494) INFO: speech length: 20000 +2024-01-16 22:19:45,774 (beam_search:428) INFO: decoder input length: 29 +2024-01-16 22:19:45,774 (beam_search:429) INFO: max output length: 29 +2024-01-16 22:19:45,775 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:45,799 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:45,799 (beam_search:476) INFO: -6.77 * 1.0 = -6.77 for ctc +2024-01-16 22:19:45,799 (beam_search:479) INFO: total log probability: -6.77 +2024-01-16 22:19:45,799 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:19:45,799 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:45,799 (beam_search:483) INFO: best hypo: NUNZHNADCHTUNAHTZIG + +2024-01-16 22:19:45,800 (asr_inference:494) INFO: speech length: 22400 +2024-01-16 22:19:45,807 (beam_search:428) INFO: decoder input length: 32 +2024-01-16 22:19:45,808 (beam_search:429) INFO: max output length: 32 +2024-01-16 22:19:45,808 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:45,832 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:45,833 (beam_search:476) INFO: -4.63 * 1.0 = -4.63 for ctc +2024-01-16 22:19:45,833 (beam_search:479) INFO: total log probability: -4.63 +2024-01-16 22:19:45,833 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:19:45,833 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:45,833 (beam_search:483) INFO: best hypo: REINENZIGKLPETDIE + +2024-01-16 22:19:45,834 (asr_inference:494) INFO: speech length: 61760 +2024-01-16 22:19:45,843 (beam_search:428) INFO: decoder input length: 94 +2024-01-16 22:19:45,843 (beam_search:429) INFO: max output length: 94 +2024-01-16 22:19:45,843 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:46,008 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:46,008 (beam_search:476) INFO: -11.66 * 1.0 = -11.66 for ctc +2024-01-16 22:19:46,008 (beam_search:479) INFO: total log probability: -11.66 +2024-01-16 22:19:46,008 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:19:46,008 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:46,008 (beam_search:483) INFO: best hypo: DERVOTSTROMISÜBERFIELEGRUSSENORNUNENLENARTZUMLICHTENVAL + +2024-01-16 22:19:46,009 (asr_inference:494) INFO: speech length: 37920 +2024-01-16 22:19:46,017 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 22:19:46,017 (beam_search:429) INFO: max output length: 57 +2024-01-16 22:19:46,017 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:46,086 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:46,086 (beam_search:476) INFO: -9.45 * 1.0 = -9.45 for ctc +2024-01-16 22:19:46,086 (beam_search:479) INFO: total log probability: -9.45 +2024-01-16 22:19:46,086 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:19:46,086 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:46,087 (beam_search:483) INFO: best hypo: DASHTDVFÜKLEINEPRTEINGROSEAUSWIRKUN + +2024-01-16 22:19:46,088 (asr_inference:494) INFO: speech length: 33440 +2024-01-16 22:19:46,095 (beam_search:428) INFO: decoder input length: 50 +2024-01-16 22:19:46,095 (beam_search:429) INFO: max output length: 50 +2024-01-16 22:19:46,095 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:46,142 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:46,142 (beam_search:476) INFO: -5.70 * 1.0 = -5.70 for ctc +2024-01-16 22:19:46,142 (beam_search:479) INFO: total log probability: -5.70 +2024-01-16 22:19:46,142 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:19:46,142 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:46,142 (beam_search:483) INFO: best hypo: ISDEITERATIEFERTIENSUGC + +2024-01-16 22:19:46,143 (asr_inference:494) INFO: speech length: 56000 +2024-01-16 22:19:46,152 (beam_search:428) INFO: decoder input length: 85 +2024-01-16 22:19:46,152 (beam_search:429) INFO: max output length: 85 +2024-01-16 22:19:46,152 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:46,265 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:46,265 (beam_search:476) INFO: -12.11 * 1.0 = -12.11 for ctc +2024-01-16 22:19:46,265 (beam_search:479) INFO: total log probability: -12.11 +2024-01-16 22:19:46,265 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:19:46,265 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:46,266 (beam_search:483) INFO: best hypo: DIESKRENZUMBEISHPBILKONDENSAERTHORENSEIN + +2024-01-16 22:19:46,267 (asr_inference:494) INFO: speech length: 64160 +2024-01-16 22:19:46,276 (beam_search:428) INFO: decoder input length: 98 +2024-01-16 22:19:46,276 (beam_search:429) INFO: max output length: 98 +2024-01-16 22:19:46,276 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:46,454 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:46,454 (beam_search:476) INFO: -16.45 * 1.0 = -16.45 for ctc +2024-01-16 22:19:46,454 (beam_search:479) INFO: total log probability: -16.45 +2024-01-16 22:19:46,454 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:19:46,454 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:46,454 (beam_search:483) INFO: best hypo: ALDTDIEKOURSAUFKUBERHALKGENDENSORIDTISCHNSCHERABPTRETEN + +2024-01-16 22:19:46,455 (asr_inference:494) INFO: speech length: 88800 +2024-01-16 22:19:46,466 (beam_search:428) INFO: decoder input length: 136 +2024-01-16 22:19:46,466 (beam_search:429) INFO: max output length: 136 +2024-01-16 22:19:46,466 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:46,784 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:46,784 (beam_search:476) INFO: -20.64 * 1.0 = -20.64 for ctc +2024-01-16 22:19:46,784 (beam_search:479) INFO: total log probability: -20.64 +2024-01-16 22:19:46,784 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:19:46,784 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:46,785 (beam_search:483) INFO: best hypo: BUNDESTAGWANTZUNDERDREIUNDFMFZIVRERSMALSNCHEINEMVOMBUNDESTAIGSESTALSENGESET + +2024-01-16 22:19:46,786 (asr_inference:494) INFO: speech length: 34240 +2024-01-16 22:19:46,794 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 22:19:46,794 (beam_search:429) INFO: max output length: 51 +2024-01-16 22:19:46,794 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:46,851 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:46,851 (beam_search:476) INFO: -7.28 * 1.0 = -7.28 for ctc +2024-01-16 22:19:46,851 (beam_search:479) INFO: total log probability: -7.28 +2024-01-16 22:19:46,851 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:19:46,851 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:46,851 (beam_search:483) INFO: best hypo: BUNDEWEIGEZFIEFCHGEINERTWORDE + +2024-01-16 22:19:46,852 (asr_inference:494) INFO: speech length: 37280 +2024-01-16 22:19:46,860 (beam_search:428) INFO: decoder input length: 56 +2024-01-16 22:19:46,860 (beam_search:429) INFO: max output length: 56 +2024-01-16 22:19:46,860 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:46,925 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:46,925 (beam_search:476) INFO: -6.43 * 1.0 = -6.43 for ctc +2024-01-16 22:19:46,925 (beam_search:479) INFO: total log probability: -6.43 +2024-01-16 22:19:46,925 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:19:46,925 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:46,925 (beam_search:483) INFO: best hypo: ERIBERLAGERTENVOTUSTOMEUNDTREIK + +2024-01-16 22:19:46,926 (asr_inference:494) INFO: speech length: 45920 +2024-01-16 22:19:46,934 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 22:19:46,934 (beam_search:429) INFO: max output length: 69 +2024-01-16 22:19:46,934 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:47,028 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:47,028 (beam_search:476) INFO: -10.46 * 1.0 = -10.46 for ctc +2024-01-16 22:19:47,028 (beam_search:479) INFO: total log probability: -10.46 +2024-01-16 22:19:47,028 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:19:47,028 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:47,029 (beam_search:483) INFO: best hypo: DRTSINTIEGRATIOUMDERBEIDENDEUTCENSTATEN + +2024-01-16 22:19:47,030 (asr_inference:494) INFO: speech length: 25920 +2024-01-16 22:19:47,037 (beam_search:428) INFO: decoder input length: 38 +2024-01-16 22:19:47,037 (beam_search:429) INFO: max output length: 38 +2024-01-16 22:19:47,037 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:47,067 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:47,067 (beam_search:476) INFO: -3.49 * 1.0 = -3.49 for ctc +2024-01-16 22:19:47,067 (beam_search:479) INFO: total log probability: -3.49 +2024-01-16 22:19:47,067 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:19:47,067 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:47,067 (beam_search:483) INFO: best hypo: BERIENEÜLMESENSTAT + +2024-01-16 22:19:47,068 (asr_inference:494) INFO: speech length: 17920 +2024-01-16 22:19:47,075 (beam_search:428) INFO: decoder input length: 25 +2024-01-16 22:19:47,075 (beam_search:429) INFO: max output length: 25 +2024-01-16 22:19:47,075 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:47,092 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:47,092 (beam_search:476) INFO: -6.65 * 1.0 = -6.65 for ctc +2024-01-16 22:19:47,092 (beam_search:479) INFO: total log probability: -6.65 +2024-01-16 22:19:47,092 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-16 22:19:47,092 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:47,093 (beam_search:483) INFO: best hypo: OAFITZEEFÜLRN + +2024-01-16 22:19:47,094 (asr_inference:494) INFO: speech length: 51840 +2024-01-16 22:19:47,102 (beam_search:428) INFO: decoder input length: 78 +2024-01-16 22:19:47,102 (beam_search:429) INFO: max output length: 78 +2024-01-16 22:19:47,102 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:47,217 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:47,217 (beam_search:476) INFO: -8.37 * 1.0 = -8.37 for ctc +2024-01-16 22:19:47,217 (beam_search:479) INFO: total log probability: -8.37 +2024-01-16 22:19:47,217 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:19:47,217 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:47,217 (beam_search:483) INFO: best hypo: BIDERVERHENSWALWITZUSERTLICHDIEEINHALTUNGDER + +2024-01-16 22:19:47,219 (asr_inference:494) INFO: speech length: 35520 +2024-01-16 22:19:47,226 (beam_search:428) INFO: decoder input length: 53 +2024-01-16 22:19:47,226 (beam_search:429) INFO: max output length: 53 +2024-01-16 22:19:47,226 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:47,292 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:47,292 (beam_search:476) INFO: -9.95 * 1.0 = -9.95 for ctc +2024-01-16 22:19:47,292 (beam_search:479) INFO: total log probability: -9.95 +2024-01-16 22:19:47,292 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:19:47,292 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:47,292 (beam_search:483) INFO: best hypo: WIEWENICTINOLANERNOCHMPELTSDERZEIT + +2024-01-16 22:19:47,294 (asr_inference:494) INFO: speech length: 58720 +2024-01-16 22:19:47,303 (beam_search:428) INFO: decoder input length: 89 +2024-01-16 22:19:47,303 (beam_search:429) INFO: max output length: 89 +2024-01-16 22:19:47,303 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:47,458 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:47,459 (beam_search:476) INFO: -14.56 * 1.0 = -14.56 for ctc +2024-01-16 22:19:47,459 (beam_search:479) INFO: total log probability: -14.56 +2024-01-16 22:19:47,459 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:19:47,459 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:47,459 (beam_search:483) INFO: best hypo: IEDCETWVRDIEDUCHFIUGENVONWALWERBUNGAFKOSTENDESTATES + +2024-01-16 22:19:47,460 (asr_inference:494) INFO: speech length: 18720 +2024-01-16 22:19:47,467 (beam_search:428) INFO: decoder input length: 27 +2024-01-16 22:19:47,467 (beam_search:429) INFO: max output length: 27 +2024-01-16 22:19:47,467 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:47,489 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:47,489 (beam_search:476) INFO: -4.87 * 1.0 = -4.87 for ctc +2024-01-16 22:19:47,489 (beam_search:479) INFO: total log probability: -4.87 +2024-01-16 22:19:47,489 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:19:47,489 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:47,489 (beam_search:483) INFO: best hypo: DASNIHMRUNGESETZ + +2024-01-16 22:19:47,490 (asr_inference:494) INFO: speech length: 42080 +2024-01-16 22:19:47,498 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 22:19:47,498 (beam_search:429) INFO: max output length: 63 +2024-01-16 22:19:47,498 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:47,574 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:47,574 (beam_search:476) INFO: -6.09 * 1.0 = -6.09 for ctc +2024-01-16 22:19:47,574 (beam_search:479) INFO: total log probability: -6.09 +2024-01-16 22:19:47,574 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:19:47,574 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:47,574 (beam_search:483) INFO: best hypo: HEIMATVERTRIEBENUNDRUSTICSIGGEWAL + +2024-01-16 22:19:47,576 (asr_inference:494) INFO: speech length: 42240 +2024-01-16 22:19:47,584 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 22:19:47,584 (beam_search:429) INFO: max output length: 63 +2024-01-16 22:19:47,584 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:47,665 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:47,665 (beam_search:476) INFO: -7.56 * 1.0 = -7.56 for ctc +2024-01-16 22:19:47,665 (beam_search:479) INFO: total log probability: -7.56 +2024-01-16 22:19:47,665 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:19:47,665 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:47,666 (beam_search:483) INFO: best hypo: UNDSPBEICHERIENININEWACKTESHLANGEABP + +2024-01-16 22:19:47,667 (asr_inference:494) INFO: speech length: 145760 +2024-01-16 22:19:47,682 (beam_search:428) INFO: decoder input length: 225 +2024-01-16 22:19:47,682 (beam_search:429) INFO: max output length: 225 +2024-01-16 22:19:47,682 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:48,387 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:48,387 (beam_search:476) INFO: -27.82 * 1.0 = -27.82 for ctc +2024-01-16 22:19:48,387 (beam_search:479) INFO: total log probability: -27.82 +2024-01-16 22:19:48,387 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:19:48,387 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:48,388 (beam_search:483) INFO: best hypo: OUREGENALTHONBENDERUNDDIDOKMENTARTIONDESTURDIOSURDENEINZEHNHUNDERTZWUOUNSEBTZIGINDERSIMENSECHIÜERSTELT + +2024-01-16 22:19:48,389 (asr_inference:494) INFO: speech length: 79520 +2024-01-16 22:19:48,399 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 22:19:48,399 (beam_search:429) INFO: max output length: 122 +2024-01-16 22:19:48,399 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:48,576 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:48,576 (beam_search:476) INFO: -13.99 * 1.0 = -13.99 for ctc +2024-01-16 22:19:48,576 (beam_search:479) INFO: total log probability: -13.99 +2024-01-16 22:19:48,576 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:19:48,576 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:48,576 (beam_search:483) INFO: best hypo: SOMISENAUEINEMSTATEGESHENRERKTEENUOBOOT + +2024-01-16 22:19:48,577 (asr_inference:494) INFO: speech length: 27680 +2024-01-16 22:19:48,584 (beam_search:428) INFO: decoder input length: 41 +2024-01-16 22:19:48,584 (beam_search:429) INFO: max output length: 41 +2024-01-16 22:19:48,584 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:48,619 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:48,620 (beam_search:476) INFO: -4.38 * 1.0 = -4.38 for ctc +2024-01-16 22:19:48,620 (beam_search:479) INFO: total log probability: -4.38 +2024-01-16 22:19:48,620 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:19:48,620 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:48,620 (beam_search:483) INFO: best hypo: FLÜTENSPBILEEDLICHE + +2024-01-16 22:19:48,621 (asr_inference:494) INFO: speech length: 52160 +2024-01-16 22:19:48,629 (beam_search:428) INFO: decoder input length: 79 +2024-01-16 22:19:48,629 (beam_search:429) INFO: max output length: 79 +2024-01-16 22:19:48,629 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:48,743 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:48,743 (beam_search:476) INFO: -7.10 * 1.0 = -7.10 for ctc +2024-01-16 22:19:48,743 (beam_search:479) INFO: total log probability: -7.10 +2024-01-16 22:19:48,743 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 22:19:48,743 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:48,743 (beam_search:483) INFO: best hypo: DRASTDISHMORDERANELIGKTRONISCHERKLANGESHALTUN + +2024-01-16 22:19:48,744 (asr_inference:494) INFO: speech length: 177600 +2024-01-16 22:19:48,761 (beam_search:428) INFO: decoder input length: 275 +2024-01-16 22:19:48,761 (beam_search:429) INFO: max output length: 275 +2024-01-16 22:19:48,761 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:49,916 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:49,916 (beam_search:476) INFO: -44.91 * 1.0 = -44.91 for ctc +2024-01-16 22:19:49,916 (beam_search:479) INFO: total log probability: -44.91 +2024-01-16 22:19:49,916 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:19:49,916 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:49,917 (beam_search:483) INFO: best hypo: ANCHISENWODEDIESOAMITETEMANDATZTSALIEDERPARTEINTDIMSEBMVERFANENSPECHENTERANZALIRARZWEITSTEIEMPROPRTZINALAUFDIELANESLISTEDEPARTEIUNTERVETEIERT + +2024-01-16 22:19:49,918 (asr_inference:494) INFO: speech length: 44000 +2024-01-16 22:19:49,927 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 22:19:49,927 (beam_search:429) INFO: max output length: 66 +2024-01-16 22:19:49,927 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:50,005 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:50,005 (beam_search:476) INFO: -9.86 * 1.0 = -9.86 for ctc +2024-01-16 22:19:50,005 (beam_search:479) INFO: total log probability: -9.86 +2024-01-16 22:19:50,005 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:19:50,005 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:50,005 (beam_search:483) INFO: best hypo: AUBPFANDERNATSEBOMADIUUNDTERKNFTE + +2024-01-16 22:19:50,006 (asr_inference:494) INFO: speech length: 21920 +2024-01-16 22:19:50,013 (beam_search:428) INFO: decoder input length: 32 +2024-01-16 22:19:50,013 (beam_search:429) INFO: max output length: 32 +2024-01-16 22:19:50,013 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:50,037 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:50,037 (beam_search:476) INFO: -4.59 * 1.0 = -4.59 for ctc +2024-01-16 22:19:50,037 (beam_search:479) INFO: total log probability: -4.59 +2024-01-16 22:19:50,038 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:19:50,038 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:50,038 (beam_search:483) INFO: best hypo: DERREINENZUKLOPE + +2024-01-16 22:19:50,039 (asr_inference:494) INFO: speech length: 16960 +2024-01-16 22:19:50,045 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 22:19:50,045 (beam_search:429) INFO: max output length: 24 +2024-01-16 22:19:50,045 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:50,062 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:50,062 (beam_search:476) INFO: -4.80 * 1.0 = -4.80 for ctc +2024-01-16 22:19:50,062 (beam_search:479) INFO: total log probability: -4.80 +2024-01-16 22:19:50,062 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:19:50,062 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:50,062 (beam_search:483) INFO: best hypo: MIERBEILSCHNN + +2024-01-16 22:19:50,063 (asr_inference:494) INFO: speech length: 68000 +2024-01-16 22:19:50,072 (beam_search:428) INFO: decoder input length: 104 +2024-01-16 22:19:50,072 (beam_search:429) INFO: max output length: 104 +2024-01-16 22:19:50,072 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:50,279 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:50,279 (beam_search:476) INFO: -15.91 * 1.0 = -15.91 for ctc +2024-01-16 22:19:50,279 (beam_search:479) INFO: total log probability: -15.91 +2024-01-16 22:19:50,279 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:19:50,279 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:50,279 (beam_search:483) INFO: best hypo: WERWIGENEINEVERBRECHENSRECHSGÖEFTICHZUINEREITSTRAEVONMNDESENSEINE + +2024-01-16 22:19:50,280 (asr_inference:494) INFO: speech length: 54880 +2024-01-16 22:19:50,289 (beam_search:428) INFO: decoder input length: 83 +2024-01-16 22:19:50,289 (beam_search:429) INFO: max output length: 83 +2024-01-16 22:19:50,289 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:50,423 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:50,423 (beam_search:476) INFO: -9.26 * 1.0 = -9.26 for ctc +2024-01-16 22:19:50,423 (beam_search:479) INFO: total log probability: -9.26 +2024-01-16 22:19:50,423 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:19:50,423 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:50,423 (beam_search:483) INFO: best hypo: DERESCWNDICKEITZWERTUNGERAGENDREIEBEEINHULERTACHT + +2024-01-16 22:19:50,424 (asr_inference:494) INFO: speech length: 31520 +2024-01-16 22:19:50,432 (beam_search:428) INFO: decoder input length: 47 +2024-01-16 22:19:50,432 (beam_search:429) INFO: max output length: 47 +2024-01-16 22:19:50,432 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:50,484 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:50,484 (beam_search:476) INFO: -7.44 * 1.0 = -7.44 for ctc +2024-01-16 22:19:50,484 (beam_search:479) INFO: total log probability: -7.44 +2024-01-16 22:19:50,484 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:19:50,484 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:50,485 (beam_search:483) INFO: best hypo: EBORUIUSAMERSTENGEBORIESAMFTE + +2024-01-16 22:19:50,486 (asr_inference:494) INFO: speech length: 49920 +2024-01-16 22:19:50,494 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 22:19:50,494 (beam_search:429) INFO: max output length: 75 +2024-01-16 22:19:50,494 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:50,599 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:50,599 (beam_search:476) INFO: -12.26 * 1.0 = -12.26 for ctc +2024-01-16 22:19:50,600 (beam_search:479) INFO: total log probability: -12.26 +2024-01-16 22:19:50,600 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:19:50,600 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:50,600 (beam_search:483) INFO: best hypo: NTHIMSONAERGÜFVEFARENAUDELENERVERTEIRT + +2024-01-16 22:19:50,601 (asr_inference:494) INFO: speech length: 50080 +2024-01-16 22:19:50,609 (beam_search:428) INFO: decoder input length: 76 +2024-01-16 22:19:50,609 (beam_search:429) INFO: max output length: 76 +2024-01-16 22:19:50,609 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:50,707 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:50,707 (beam_search:476) INFO: -10.46 * 1.0 = -10.46 for ctc +2024-01-16 22:19:50,708 (beam_search:479) INFO: total log probability: -10.46 +2024-01-16 22:19:50,708 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:19:50,708 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:50,708 (beam_search:483) INFO: best hypo: REFORMINGOABERSCHAFSUNDABPRSTUNSCHLT + +2024-01-16 22:19:50,709 (asr_inference:494) INFO: speech length: 39840 +2024-01-16 22:19:50,717 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 22:19:50,717 (beam_search:429) INFO: max output length: 60 +2024-01-16 22:19:50,717 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:50,777 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:50,777 (beam_search:476) INFO: -8.20 * 1.0 = -8.20 for ctc +2024-01-16 22:19:50,777 (beam_search:479) INFO: total log probability: -8.20 +2024-01-16 22:19:50,777 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:19:50,777 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:50,777 (beam_search:483) INFO: best hypo: SIERENANPHRTETUNTUNDERDE + +2024-01-16 22:19:50,778 (asr_inference:494) INFO: speech length: 49600 +2024-01-16 22:19:50,787 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 22:19:50,787 (beam_search:429) INFO: max output length: 75 +2024-01-16 22:19:50,787 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:50,885 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:50,885 (beam_search:476) INFO: -11.36 * 1.0 = -11.36 for ctc +2024-01-16 22:19:50,885 (beam_search:479) INFO: total log probability: -11.36 +2024-01-16 22:19:50,885 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:19:50,885 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:50,885 (beam_search:483) INFO: best hypo: ANDEMBESTLICHEGREFTEAUFGEDENRULTZUN + +# Accounting: time=179 threads=1 +# Ended (code 0) at Tue Jan 16 22:19:51 CST 2024, elapsed time 179 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.3.log b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.3.log new file mode 100644 index 0000000000000000000000000000000000000000..f357c1eacd44bc14d8e73b72f0aca33eba753c7c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.3.log @@ -0,0 +1,1834 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.3 --config conf/decode_asr.yaml +# Started at Tue Jan 16 22:19:51 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.3 --config conf/decode_asr.yaml +2024-01-16 22:19:52,708 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml +2024-01-16 22:19:52,726 (asr:523) INFO: Vocabulary size: 44 +2024-01-16 22:19:52,789 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 22:19:52,789 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 22:19:52,899 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 22:19:54,189 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 22:19:55,411 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 22:19:55,411 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 22:19:55,412 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 22:19:55,446 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-16 22:19:55,521 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 22:19:55,633 (asr_inference:494) INFO: speech length: 22880 +2024-01-16 22:19:56,827 (beam_search:428) INFO: decoder input length: 33 +2024-01-16 22:19:56,827 (beam_search:429) INFO: max output length: 33 +2024-01-16 22:19:56,827 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:56,859 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:56,859 (beam_search:476) INFO: -4.01 * 1.0 = -4.01 for ctc +2024-01-16 22:19:56,859 (beam_search:479) INFO: total log probability: -4.01 +2024-01-16 22:19:56,859 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 22:19:56,860 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:56,860 (beam_search:483) INFO: best hypo: ITUNTERANDERUMVERWENDE + +2024-01-16 22:19:56,884 (asr_inference:494) INFO: speech length: 16800 +2024-01-16 22:19:56,892 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 22:19:56,892 (beam_search:429) INFO: max output length: 24 +2024-01-16 22:19:56,892 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:56,908 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:56,908 (beam_search:476) INFO: -6.36 * 1.0 = -6.36 for ctc +2024-01-16 22:19:56,908 (beam_search:479) INFO: total log probability: -6.36 +2024-01-16 22:19:56,908 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-16 22:19:56,908 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:56,908 (beam_search:483) INFO: best hypo: USWÜCKIPÄNDER + +2024-01-16 22:19:56,909 (asr_inference:494) INFO: speech length: 19680 +2024-01-16 22:19:56,917 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 22:19:56,917 (beam_search:429) INFO: max output length: 28 +2024-01-16 22:19:56,917 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:56,937 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:56,937 (beam_search:476) INFO: -3.23 * 1.0 = -3.23 for ctc +2024-01-16 22:19:56,937 (beam_search:479) INFO: total log probability: -3.23 +2024-01-16 22:19:56,937 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:19:56,937 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:56,937 (beam_search:483) INFO: best hypo: UNDKOUBARGRIESE + +2024-01-16 22:19:56,938 (asr_inference:494) INFO: speech length: 83360 +2024-01-16 22:19:56,950 (beam_search:428) INFO: decoder input length: 128 +2024-01-16 22:19:56,950 (beam_search:429) INFO: max output length: 128 +2024-01-16 22:19:56,950 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:57,254 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:57,254 (beam_search:476) INFO: -20.93 * 1.0 = -20.93 for ctc +2024-01-16 22:19:57,254 (beam_search:479) INFO: total log probability: -20.93 +2024-01-16 22:19:57,254 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:19:57,254 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:57,255 (beam_search:483) INFO: best hypo: ENLTZSDAWALAUFGRUNDEIGNERWEIRVOSHLIGENETERRCHENMTMINDESENSFMFABGONENVERTIEENSIN + +2024-01-16 22:19:57,256 (asr_inference:494) INFO: speech length: 60000 +2024-01-16 22:19:57,265 (beam_search:428) INFO: decoder input length: 91 +2024-01-16 22:19:57,265 (beam_search:429) INFO: max output length: 91 +2024-01-16 22:19:57,265 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:57,417 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:57,417 (beam_search:476) INFO: -9.51 * 1.0 = -9.51 for ctc +2024-01-16 22:19:57,417 (beam_search:479) INFO: total log probability: -9.51 +2024-01-16 22:19:57,417 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:19:57,417 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:57,418 (beam_search:483) INFO: best hypo: VERPREITUNGIEDIOLOGESCHAPROPAGANDERDERSUPERMECHTEUND + +2024-01-16 22:19:57,419 (asr_inference:494) INFO: speech length: 35360 +2024-01-16 22:19:57,426 (beam_search:428) INFO: decoder input length: 53 +2024-01-16 22:19:57,426 (beam_search:429) INFO: max output length: 53 +2024-01-16 22:19:57,426 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:57,482 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:57,482 (beam_search:476) INFO: -4.90 * 1.0 = -4.90 for ctc +2024-01-16 22:19:57,482 (beam_search:479) INFO: total log probability: -4.90 +2024-01-16 22:19:57,482 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 22:19:57,482 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:57,483 (beam_search:483) INFO: best hypo: KOMIGSAUFDERLITHETBERTHAGE + +2024-01-16 22:19:57,484 (asr_inference:494) INFO: speech length: 43680 +2024-01-16 22:19:57,492 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 22:19:57,492 (beam_search:429) INFO: max output length: 66 +2024-01-16 22:19:57,492 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:57,583 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:57,583 (beam_search:476) INFO: -7.78 * 1.0 = -7.78 for ctc +2024-01-16 22:19:57,583 (beam_search:479) INFO: total log probability: -7.78 +2024-01-16 22:19:57,583 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:19:57,583 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:57,583 (beam_search:483) INFO: best hypo: ALDERKALTIGKLIEGSICHVORTWERENZUSPITZSTE + +2024-01-16 22:19:57,584 (asr_inference:494) INFO: speech length: 68000 +2024-01-16 22:19:57,594 (beam_search:428) INFO: decoder input length: 104 +2024-01-16 22:19:57,594 (beam_search:429) INFO: max output length: 104 +2024-01-16 22:19:57,594 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:57,761 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:57,761 (beam_search:476) INFO: -12.72 * 1.0 = -12.72 for ctc +2024-01-16 22:19:57,761 (beam_search:479) INFO: total log probability: -12.72 +2024-01-16 22:19:57,761 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:19:57,761 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:57,761 (beam_search:483) INFO: best hypo: SICHEIZPERSNALODERECHUNENNSERSCHWIERIGBETETENERN + +2024-01-16 22:19:57,763 (asr_inference:494) INFO: speech length: 25600 +2024-01-16 22:19:57,770 (beam_search:428) INFO: decoder input length: 37 +2024-01-16 22:19:57,770 (beam_search:429) INFO: max output length: 37 +2024-01-16 22:19:57,770 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:57,805 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:57,805 (beam_search:476) INFO: -3.56 * 1.0 = -3.56 for ctc +2024-01-16 22:19:57,805 (beam_search:479) INFO: total log probability: -3.56 +2024-01-16 22:19:57,805 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 22:19:57,805 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:57,805 (beam_search:483) INFO: best hypo: DARURHAFESBLEIBERICHTUN + +2024-01-16 22:19:57,806 (asr_inference:494) INFO: speech length: 27040 +2024-01-16 22:19:57,813 (beam_search:428) INFO: decoder input length: 40 +2024-01-16 22:19:57,813 (beam_search:429) INFO: max output length: 40 +2024-01-16 22:19:57,813 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:57,857 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:57,857 (beam_search:476) INFO: -10.06 * 1.0 = -10.06 for ctc +2024-01-16 22:19:57,857 (beam_search:479) INFO: total log probability: -10.06 +2024-01-16 22:19:57,857 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:19:57,857 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:57,857 (beam_search:483) INFO: best hypo: IBESERWIEDERSMUTIFTERLESUNGBIC + +2024-01-16 22:19:57,859 (asr_inference:494) INFO: speech length: 32800 +2024-01-16 22:19:57,866 (beam_search:428) INFO: decoder input length: 49 +2024-01-16 22:19:57,866 (beam_search:429) INFO: max output length: 49 +2024-01-16 22:19:57,866 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:57,912 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:57,912 (beam_search:476) INFO: -6.09 * 1.0 = -6.09 for ctc +2024-01-16 22:19:57,912 (beam_search:479) INFO: total log probability: -6.09 +2024-01-16 22:19:57,912 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:19:57,912 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:57,912 (beam_search:483) INFO: best hypo: WENFÜNIEMANNACHPÜCSBEIST + +2024-01-16 22:19:57,913 (asr_inference:494) INFO: speech length: 40640 +2024-01-16 22:19:57,921 (beam_search:428) INFO: decoder input length: 61 +2024-01-16 22:19:57,921 (beam_search:429) INFO: max output length: 61 +2024-01-16 22:19:57,921 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:58,001 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:58,001 (beam_search:476) INFO: -10.49 * 1.0 = -10.49 for ctc +2024-01-16 22:19:58,001 (beam_search:479) INFO: total log probability: -10.49 +2024-01-16 22:19:58,001 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:19:58,001 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:58,002 (beam_search:483) INFO: best hypo: ISDIEKLEWARTEARVORSCHNVONEINRICHTUNMEM + +2024-01-16 22:19:58,003 (asr_inference:494) INFO: speech length: 68960 +2024-01-16 22:19:58,012 (beam_search:428) INFO: decoder input length: 105 +2024-01-16 22:19:58,012 (beam_search:429) INFO: max output length: 105 +2024-01-16 22:19:58,012 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:58,201 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:58,201 (beam_search:476) INFO: -12.72 * 1.0 = -12.72 for ctc +2024-01-16 22:19:58,201 (beam_search:479) INFO: total log probability: -12.72 +2024-01-16 22:19:58,201 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:19:58,201 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:58,201 (beam_search:483) INFO: best hypo: GESENDAVONBIRDENSEBSDANOCHIEINSPECHENDENPALKOUTSFIEEN + +2024-01-16 22:19:58,202 (asr_inference:494) INFO: speech length: 42720 +2024-01-16 22:19:58,211 (beam_search:428) INFO: decoder input length: 64 +2024-01-16 22:19:58,211 (beam_search:429) INFO: max output length: 64 +2024-01-16 22:19:58,211 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:58,286 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:58,286 (beam_search:476) INFO: -8.08 * 1.0 = -8.08 for ctc +2024-01-16 22:19:58,286 (beam_search:479) INFO: total log probability: -8.08 +2024-01-16 22:19:58,286 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:19:58,286 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:58,286 (beam_search:483) INFO: best hypo: SPECHENBNÜTICHTDEAEMLUFTKLIEFVERT + +2024-01-16 22:19:58,287 (asr_inference:494) INFO: speech length: 36320 +2024-01-16 22:19:58,295 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 22:19:58,295 (beam_search:429) INFO: max output length: 54 +2024-01-16 22:19:58,295 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:58,361 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:58,361 (beam_search:476) INFO: -9.94 * 1.0 = -9.94 for ctc +2024-01-16 22:19:58,361 (beam_search:479) INFO: total log probability: -9.94 +2024-01-16 22:19:58,361 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:19:58,361 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:58,361 (beam_search:483) INFO: best hypo: EMUCLICHSCUTZIMVORNGENKANGREITE + +2024-01-16 22:19:58,362 (asr_inference:494) INFO: speech length: 23680 +2024-01-16 22:19:58,369 (beam_search:428) INFO: decoder input length: 34 +2024-01-16 22:19:58,369 (beam_search:429) INFO: max output length: 34 +2024-01-16 22:19:58,369 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:58,403 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:58,403 (beam_search:476) INFO: -4.36 * 1.0 = -4.36 for ctc +2024-01-16 22:19:58,403 (beam_search:479) INFO: total log probability: -4.36 +2024-01-16 22:19:58,403 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:19:58,403 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:58,403 (beam_search:483) INFO: best hypo: SHNEINENLICHENVERSUCHGAR + +2024-01-16 22:19:58,404 (asr_inference:494) INFO: speech length: 119520 +2024-01-16 22:19:58,417 (beam_search:428) INFO: decoder input length: 184 +2024-01-16 22:19:58,417 (beam_search:429) INFO: max output length: 184 +2024-01-16 22:19:58,417 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:58,966 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:58,966 (beam_search:476) INFO: -24.50 * 1.0 = -24.50 for ctc +2024-01-16 22:19:58,966 (beam_search:479) INFO: total log probability: -24.50 +2024-01-16 22:19:58,966 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:19:58,966 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:58,967 (beam_search:483) INFO: best hypo: RUNKGENSTRANANINEMPHENÜBERGANODERPEEENIEBERGANDESTHNINRUNVOTUERVEKTINEINELEKRISCHNSTROMUMWANDE + +2024-01-16 22:19:58,968 (asr_inference:494) INFO: speech length: 31520 +2024-01-16 22:19:58,976 (beam_search:428) INFO: decoder input length: 47 +2024-01-16 22:19:58,976 (beam_search:429) INFO: max output length: 47 +2024-01-16 22:19:58,976 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:59,024 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:59,024 (beam_search:476) INFO: -8.06 * 1.0 = -8.06 for ctc +2024-01-16 22:19:59,024 (beam_search:479) INFO: total log probability: -8.06 +2024-01-16 22:19:59,024 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:19:59,024 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:59,024 (beam_search:483) INFO: best hypo: BRMASEINDESEBESALNOCHTFREIDET + +2024-01-16 22:19:59,026 (asr_inference:494) INFO: speech length: 17280 +2024-01-16 22:19:59,032 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 22:19:59,032 (beam_search:429) INFO: max output length: 24 +2024-01-16 22:19:59,032 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:59,049 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:59,049 (beam_search:476) INFO: -4.13 * 1.0 = -4.13 for ctc +2024-01-16 22:19:59,049 (beam_search:479) INFO: total log probability: -4.13 +2024-01-16 22:19:59,049 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:19:59,049 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:59,050 (beam_search:483) INFO: best hypo: ELAENDERBIGIF + +2024-01-16 22:19:59,051 (asr_inference:494) INFO: speech length: 55520 +2024-01-16 22:19:59,059 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 22:19:59,059 (beam_search:429) INFO: max output length: 84 +2024-01-16 22:19:59,059 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:59,190 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:59,190 (beam_search:476) INFO: -8.57 * 1.0 = -8.57 for ctc +2024-01-16 22:19:59,190 (beam_search:479) INFO: total log probability: -8.57 +2024-01-16 22:19:59,190 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:19:59,190 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:59,190 (beam_search:483) INFO: best hypo: ANKFEILNESUNTERDINSTEIMNOCHEINELOGESCHABPVOERG + +2024-01-16 22:19:59,192 (asr_inference:494) INFO: speech length: 41120 +2024-01-16 22:19:59,199 (beam_search:428) INFO: decoder input length: 62 +2024-01-16 22:19:59,199 (beam_search:429) INFO: max output length: 62 +2024-01-16 22:19:59,199 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:59,288 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:59,289 (beam_search:476) INFO: -10.50 * 1.0 = -10.50 for ctc +2024-01-16 22:19:59,289 (beam_search:479) INFO: total log probability: -10.50 +2024-01-16 22:19:59,289 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:19:59,289 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:59,289 (beam_search:483) INFO: best hypo: KABERTLENDIESISANGEBODIEDOMITENSCIETENEITA + +2024-01-16 22:19:59,290 (asr_inference:494) INFO: speech length: 78560 +2024-01-16 22:19:59,300 (beam_search:428) INFO: decoder input length: 120 +2024-01-16 22:19:59,300 (beam_search:429) INFO: max output length: 120 +2024-01-16 22:19:59,300 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:59,518 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:59,518 (beam_search:476) INFO: -11.12 * 1.0 = -11.12 for ctc +2024-01-16 22:19:59,518 (beam_search:479) INFO: total log probability: -11.12 +2024-01-16 22:19:59,518 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:19:59,518 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:59,518 (beam_search:483) INFO: best hypo: STANDVOMZWELFTENMARTZSZWEITAUSENZWELFDERINHEISTHIUT + +2024-01-16 22:19:59,519 (asr_inference:494) INFO: speech length: 31680 +2024-01-16 22:19:59,527 (beam_search:428) INFO: decoder input length: 47 +2024-01-16 22:19:59,527 (beam_search:429) INFO: max output length: 47 +2024-01-16 22:19:59,527 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:59,578 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:59,578 (beam_search:476) INFO: -5.86 * 1.0 = -5.86 for ctc +2024-01-16 22:19:59,578 (beam_search:479) INFO: total log probability: -5.86 +2024-01-16 22:19:59,578 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:19:59,578 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:59,579 (beam_search:483) INFO: best hypo: OGENISERTIONUNTERBRACHTERFIND + +2024-01-16 22:19:59,580 (asr_inference:494) INFO: speech length: 49920 +2024-01-16 22:19:59,588 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 22:19:59,588 (beam_search:429) INFO: max output length: 75 +2024-01-16 22:19:59,588 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:59,681 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:59,681 (beam_search:476) INFO: -5.26 * 1.0 = -5.26 for ctc +2024-01-16 22:19:59,681 (beam_search:479) INFO: total log probability: -5.26 +2024-01-16 22:19:59,681 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 22:19:59,681 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:59,681 (beam_search:483) INFO: best hypo: VERPÜNDITZENDTODERGAFÜSIEARBEITEN + +2024-01-16 22:19:59,683 (asr_inference:494) INFO: speech length: 24000 +2024-01-16 22:19:59,690 (beam_search:428) INFO: decoder input length: 35 +2024-01-16 22:19:59,690 (beam_search:429) INFO: max output length: 35 +2024-01-16 22:19:59,690 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:59,725 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:59,725 (beam_search:476) INFO: -9.74 * 1.0 = -9.74 for ctc +2024-01-16 22:19:59,725 (beam_search:479) INFO: total log probability: -9.74 +2024-01-16 22:19:59,725 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 22:19:59,725 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:59,725 (beam_search:483) INFO: best hypo: VRSGLIETEVORHRICHKTEILDE + +2024-01-16 22:19:59,727 (asr_inference:494) INFO: speech length: 37760 +2024-01-16 22:19:59,734 (beam_search:428) INFO: decoder input length: 56 +2024-01-16 22:19:59,734 (beam_search:429) INFO: max output length: 56 +2024-01-16 22:19:59,734 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:59,803 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:59,803 (beam_search:476) INFO: -4.04 * 1.0 = -4.04 for ctc +2024-01-16 22:19:59,803 (beam_search:479) INFO: total log probability: -4.04 +2024-01-16 22:19:59,803 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 22:19:59,803 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:59,803 (beam_search:483) INFO: best hypo: IARICSTUNGDERBELIENEMAURAMÜNDETEN + +2024-01-16 22:19:59,804 (asr_inference:494) INFO: speech length: 27040 +2024-01-16 22:19:59,811 (beam_search:428) INFO: decoder input length: 40 +2024-01-16 22:19:59,811 (beam_search:429) INFO: max output length: 40 +2024-01-16 22:19:59,811 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:59,851 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:59,851 (beam_search:476) INFO: -3.44 * 1.0 = -3.44 for ctc +2024-01-16 22:19:59,851 (beam_search:479) INFO: total log probability: -3.44 +2024-01-16 22:19:59,851 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 22:19:59,851 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:59,851 (beam_search:483) INFO: best hypo: ERICHTUNVONKLIERANLAGEN + +2024-01-16 22:19:59,852 (asr_inference:494) INFO: speech length: 56160 +2024-01-16 22:19:59,861 (beam_search:428) INFO: decoder input length: 85 +2024-01-16 22:19:59,861 (beam_search:429) INFO: max output length: 85 +2024-01-16 22:19:59,861 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:19:59,986 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:19:59,986 (beam_search:476) INFO: -10.21 * 1.0 = -10.21 for ctc +2024-01-16 22:19:59,986 (beam_search:479) INFO: total log probability: -10.21 +2024-01-16 22:19:59,986 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:19:59,986 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:19:59,987 (beam_search:483) INFO: best hypo: AFGANDESTANZUNDIMIERARGHERZIESEITIMEINMARST + +2024-01-16 22:19:59,988 (asr_inference:494) INFO: speech length: 65440 +2024-01-16 22:19:59,997 (beam_search:428) INFO: decoder input length: 100 +2024-01-16 22:19:59,997 (beam_search:429) INFO: max output length: 100 +2024-01-16 22:19:59,997 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:00,161 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:00,162 (beam_search:476) INFO: -9.92 * 1.0 = -9.92 for ctc +2024-01-16 22:20:00,162 (beam_search:479) INFO: total log probability: -9.92 +2024-01-16 22:20:00,162 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:20:00,162 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:00,162 (beam_search:483) INFO: best hypo: DERVONARZIUNDSTOMVONDENLUNGENIERDEBRONCHENBIS + +2024-01-16 22:20:00,163 (asr_inference:494) INFO: speech length: 70720 +2024-01-16 22:20:00,173 (beam_search:428) INFO: decoder input length: 108 +2024-01-16 22:20:00,173 (beam_search:429) INFO: max output length: 108 +2024-01-16 22:20:00,173 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:00,349 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:00,349 (beam_search:476) INFO: -13.07 * 1.0 = -13.07 for ctc +2024-01-16 22:20:00,349 (beam_search:479) INFO: total log probability: -13.07 +2024-01-16 22:20:00,349 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:00,349 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:00,349 (beam_search:483) INFO: best hypo: AUSEDEMNNAMENSENDERHÜRSPBILEMITVERRMDETRSPBRAHR + +2024-01-16 22:20:00,350 (asr_inference:494) INFO: speech length: 25920 +2024-01-16 22:20:00,358 (beam_search:428) INFO: decoder input length: 38 +2024-01-16 22:20:00,358 (beam_search:429) INFO: max output length: 38 +2024-01-16 22:20:00,358 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:00,392 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:00,392 (beam_search:476) INFO: -4.58 * 1.0 = -4.58 for ctc +2024-01-16 22:20:00,392 (beam_search:479) INFO: total log probability: -4.58 +2024-01-16 22:20:00,392 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:20:00,392 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:00,392 (beam_search:483) INFO: best hypo: UNDIEGRNDMANDARZKLAUSE + +2024-01-16 22:20:00,393 (asr_inference:494) INFO: speech length: 59040 +2024-01-16 22:20:00,402 (beam_search:428) INFO: decoder input length: 90 +2024-01-16 22:20:00,402 (beam_search:429) INFO: max output length: 90 +2024-01-16 22:20:00,402 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:00,547 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:00,547 (beam_search:476) INFO: -11.32 * 1.0 = -11.32 for ctc +2024-01-16 22:20:00,547 (beam_search:479) INFO: total log probability: -11.32 +2024-01-16 22:20:00,547 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:20:00,547 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:00,547 (beam_search:483) INFO: best hypo: KEINEABKHRVONENGRUNDPLAGENDESTOTZELISMUSEINSHLISE + +2024-01-16 22:20:00,549 (asr_inference:494) INFO: speech length: 69600 +2024-01-16 22:20:00,558 (beam_search:428) INFO: decoder input length: 106 +2024-01-16 22:20:00,558 (beam_search:429) INFO: max output length: 106 +2024-01-16 22:20:00,558 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:00,711 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:00,711 (beam_search:476) INFO: -7.81 * 1.0 = -7.81 for ctc +2024-01-16 22:20:00,711 (beam_search:479) INFO: total log probability: -7.81 +2024-01-16 22:20:00,711 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:20:00,711 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:00,712 (beam_search:483) INFO: best hypo: ITKOMPRNENTENSOHOLANALSAUCHTIEFINDERWAFE + +2024-01-16 22:20:00,713 (asr_inference:494) INFO: speech length: 20640 +2024-01-16 22:20:00,719 (beam_search:428) INFO: decoder input length: 30 +2024-01-16 22:20:00,719 (beam_search:429) INFO: max output length: 30 +2024-01-16 22:20:00,719 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:00,743 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:00,743 (beam_search:476) INFO: -4.37 * 1.0 = -4.37 for ctc +2024-01-16 22:20:00,743 (beam_search:479) INFO: total log probability: -4.37 +2024-01-16 22:20:00,743 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:20:00,743 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:00,743 (beam_search:483) INFO: best hypo: BEDEUCTUNGVORLEWAR + +2024-01-16 22:20:00,744 (asr_inference:494) INFO: speech length: 31200 +2024-01-16 22:20:00,752 (beam_search:428) INFO: decoder input length: 46 +2024-01-16 22:20:00,752 (beam_search:429) INFO: max output length: 46 +2024-01-16 22:20:00,752 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:00,802 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:00,802 (beam_search:476) INFO: -11.09 * 1.0 = -11.09 for ctc +2024-01-16 22:20:00,802 (beam_search:479) INFO: total log probability: -11.09 +2024-01-16 22:20:00,802 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:20:00,802 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:00,803 (beam_search:483) INFO: best hypo: REIFITIGEHENVERTEROUGANISTZIUN + +2024-01-16 22:20:00,804 (asr_inference:494) INFO: speech length: 43840 +2024-01-16 22:20:00,812 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 22:20:00,812 (beam_search:429) INFO: max output length: 66 +2024-01-16 22:20:00,812 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:00,902 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:00,902 (beam_search:476) INFO: -8.79 * 1.0 = -8.79 for ctc +2024-01-16 22:20:00,902 (beam_search:479) INFO: total log probability: -8.79 +2024-01-16 22:20:00,902 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:20:00,902 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:00,902 (beam_search:483) INFO: best hypo: UMELEKTRUNVOMWALENSBANDENSLEITUNSBAN + +2024-01-16 22:20:00,903 (asr_inference:494) INFO: speech length: 35840 +2024-01-16 22:20:00,911 (beam_search:428) INFO: decoder input length: 53 +2024-01-16 22:20:00,911 (beam_search:429) INFO: max output length: 53 +2024-01-16 22:20:00,911 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:00,980 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:00,980 (beam_search:476) INFO: -10.51 * 1.0 = -10.51 for ctc +2024-01-16 22:20:00,980 (beam_search:479) INFO: total log probability: -10.51 +2024-01-16 22:20:00,980 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:20:00,980 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:00,980 (beam_search:483) INFO: best hypo: ALLEDINGSUNVERLEICHBARIVEKTEMUKLICH + +2024-01-16 22:20:00,982 (asr_inference:494) INFO: speech length: 120160 +2024-01-16 22:20:00,994 (beam_search:428) INFO: decoder input length: 185 +2024-01-16 22:20:00,994 (beam_search:429) INFO: max output length: 185 +2024-01-16 22:20:00,994 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:01,484 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:01,484 (beam_search:476) INFO: -16.69 * 1.0 = -16.69 for ctc +2024-01-16 22:20:01,484 (beam_search:479) INFO: total log probability: -16.69 +2024-01-16 22:20:01,484 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:20:01,484 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:01,484 (beam_search:483) INFO: best hypo: DIESEKONTENABERLSEINGABELINEINENFRICGWENZSUMSETZSERDIENUDERSTEUERTENZUNGOHNMUTURN + +2024-01-16 22:20:01,486 (asr_inference:494) INFO: speech length: 58240 +2024-01-16 22:20:01,495 (beam_search:428) INFO: decoder input length: 88 +2024-01-16 22:20:01,495 (beam_search:429) INFO: max output length: 88 +2024-01-16 22:20:01,495 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:01,633 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:01,633 (beam_search:476) INFO: -9.44 * 1.0 = -9.44 for ctc +2024-01-16 22:20:01,633 (beam_search:479) INFO: total log probability: -9.44 +2024-01-16 22:20:01,633 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:20:01,633 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:01,634 (beam_search:483) INFO: best hypo: TOUMASHRMANSPRDTZIERTIZWEITAUSENZWEIMITKGREEBE + +2024-01-16 22:20:01,635 (asr_inference:494) INFO: speech length: 21920 +2024-01-16 22:20:01,642 (beam_search:428) INFO: decoder input length: 32 +2024-01-16 22:20:01,642 (beam_search:429) INFO: max output length: 32 +2024-01-16 22:20:01,642 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:01,668 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:01,668 (beam_search:476) INFO: -6.52 * 1.0 = -6.52 for ctc +2024-01-16 22:20:01,668 (beam_search:479) INFO: total log probability: -6.52 +2024-01-16 22:20:01,668 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:20:01,668 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:01,668 (beam_search:483) INFO: best hypo: PIENUBERGANTRIFEN + +2024-01-16 22:20:01,669 (asr_inference:494) INFO: speech length: 22560 +2024-01-16 22:20:01,676 (beam_search:428) INFO: decoder input length: 33 +2024-01-16 22:20:01,676 (beam_search:429) INFO: max output length: 33 +2024-01-16 22:20:01,676 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:01,705 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:01,705 (beam_search:476) INFO: -7.99 * 1.0 = -7.99 for ctc +2024-01-16 22:20:01,705 (beam_search:479) INFO: total log probability: -7.99 +2024-01-16 22:20:01,705 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:20:01,705 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:01,706 (beam_search:483) INFO: best hypo: DIEFEILKTENHOSTSCAOUN + +2024-01-16 22:20:01,707 (asr_inference:494) INFO: speech length: 60160 +2024-01-16 22:20:01,715 (beam_search:428) INFO: decoder input length: 91 +2024-01-16 22:20:01,715 (beam_search:429) INFO: max output length: 91 +2024-01-16 22:20:01,715 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:01,867 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:01,867 (beam_search:476) INFO: -13.94 * 1.0 = -13.94 for ctc +2024-01-16 22:20:01,867 (beam_search:479) INFO: total log probability: -13.94 +2024-01-16 22:20:01,867 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:01,867 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:01,867 (beam_search:483) INFO: best hypo: ANIESOUJETCHDEMNSTRATSIONENWURDENPLUTIGNEDERGSHLAG + +2024-01-16 22:20:01,869 (asr_inference:494) INFO: speech length: 44960 +2024-01-16 22:20:01,877 (beam_search:428) INFO: decoder input length: 68 +2024-01-16 22:20:01,877 (beam_search:429) INFO: max output length: 68 +2024-01-16 22:20:01,877 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:01,962 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:01,962 (beam_search:476) INFO: -6.98 * 1.0 = -6.98 for ctc +2024-01-16 22:20:01,962 (beam_search:479) INFO: total log probability: -6.98 +2024-01-16 22:20:01,962 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:20:01,962 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:01,963 (beam_search:483) INFO: best hypo: EIENFIERKANALMISPBULTDENTEVEKLEINER + +2024-01-16 22:20:01,964 (asr_inference:494) INFO: speech length: 86720 +2024-01-16 22:20:01,974 (beam_search:428) INFO: decoder input length: 133 +2024-01-16 22:20:01,974 (beam_search:429) INFO: max output length: 133 +2024-01-16 22:20:01,975 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:02,264 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:02,264 (beam_search:476) INFO: -15.80 * 1.0 = -15.80 for ctc +2024-01-16 22:20:02,264 (beam_search:479) INFO: total log probability: -15.80 +2024-01-16 22:20:02,264 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:20:02,264 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:02,264 (beam_search:483) INFO: best hypo: DIESEHTNDIEVORWANZEITENVÜEREINENANGRIFAUFDIEUÖSAREXSTREMHRARPGESETST + +2024-01-16 22:20:02,266 (asr_inference:494) INFO: speech length: 39200 +2024-01-16 22:20:02,273 (beam_search:428) INFO: decoder input length: 59 +2024-01-16 22:20:02,273 (beam_search:429) INFO: max output length: 59 +2024-01-16 22:20:02,273 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:02,350 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:02,350 (beam_search:476) INFO: -5.83 * 1.0 = -5.83 for ctc +2024-01-16 22:20:02,350 (beam_search:479) INFO: total log probability: -5.83 +2024-01-16 22:20:02,350 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 22:20:02,350 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:02,350 (beam_search:483) INFO: best hypo: WERCHESAMNEGHSTENZUMSTATKENODENLIEKT + +2024-01-16 22:20:02,352 (asr_inference:494) INFO: speech length: 68960 +2024-01-16 22:20:02,361 (beam_search:428) INFO: decoder input length: 105 +2024-01-16 22:20:02,361 (beam_search:429) INFO: max output length: 105 +2024-01-16 22:20:02,361 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:02,549 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:02,549 (beam_search:476) INFO: -13.49 * 1.0 = -13.49 for ctc +2024-01-16 22:20:02,549 (beam_search:479) INFO: total log probability: -13.49 +2024-01-16 22:20:02,549 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:20:02,549 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:02,549 (beam_search:483) INFO: best hypo: AIOGINDOLZERÜCINDEBUNDESLIEGERUNDWEXSELDEZUEINRCHT + +2024-01-16 22:20:02,550 (asr_inference:494) INFO: speech length: 19200 +2024-01-16 22:20:02,557 (beam_search:428) INFO: decoder input length: 27 +2024-01-16 22:20:02,557 (beam_search:429) INFO: max output length: 27 +2024-01-16 22:20:02,557 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:02,578 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:02,579 (beam_search:476) INFO: -3.05 * 1.0 = -3.05 for ctc +2024-01-16 22:20:02,579 (beam_search:479) INFO: total log probability: -3.05 +2024-01-16 22:20:02,579 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 22:20:02,579 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:02,579 (beam_search:483) INFO: best hypo: ÜBERISEKANGKHEIT + +2024-01-16 22:20:02,580 (asr_inference:494) INFO: speech length: 30880 +2024-01-16 22:20:02,587 (beam_search:428) INFO: decoder input length: 46 +2024-01-16 22:20:02,587 (beam_search:429) INFO: max output length: 46 +2024-01-16 22:20:02,587 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:02,636 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:02,636 (beam_search:476) INFO: -9.66 * 1.0 = -9.66 for ctc +2024-01-16 22:20:02,636 (beam_search:479) INFO: total log probability: -9.66 +2024-01-16 22:20:02,636 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:20:02,636 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:02,636 (beam_search:483) INFO: best hypo: ARZWEITAUSENDTFÜNFEKÜIDESERT + +2024-01-16 22:20:02,637 (asr_inference:494) INFO: speech length: 41440 +2024-01-16 22:20:02,645 (beam_search:428) INFO: decoder input length: 62 +2024-01-16 22:20:02,645 (beam_search:429) INFO: max output length: 62 +2024-01-16 22:20:02,645 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:02,726 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:02,726 (beam_search:476) INFO: -8.89 * 1.0 = -8.89 for ctc +2024-01-16 22:20:02,726 (beam_search:479) INFO: total log probability: -8.89 +2024-01-16 22:20:02,726 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:20:02,726 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:02,727 (beam_search:483) INFO: best hypo: DIESEAUFSSUNGZUNEUTRARITETUNTERSCHEIDE + +2024-01-16 22:20:02,728 (asr_inference:494) INFO: speech length: 65920 +2024-01-16 22:20:02,737 (beam_search:428) INFO: decoder input length: 100 +2024-01-16 22:20:02,737 (beam_search:429) INFO: max output length: 100 +2024-01-16 22:20:02,737 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:02,915 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:02,915 (beam_search:476) INFO: -14.26 * 1.0 = -14.26 for ctc +2024-01-16 22:20:02,915 (beam_search:479) INFO: total log probability: -14.26 +2024-01-16 22:20:02,915 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:02,915 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:02,916 (beam_search:483) INFO: best hypo: REDELWURDEALSKÜNZTALICHERLEITERDERIEMENSTHUDIESBESTEL + +2024-01-16 22:20:02,917 (asr_inference:494) INFO: speech length: 37920 +2024-01-16 22:20:02,924 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 22:20:02,925 (beam_search:429) INFO: max output length: 57 +2024-01-16 22:20:02,925 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:02,987 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:02,987 (beam_search:476) INFO: -7.51 * 1.0 = -7.51 for ctc +2024-01-16 22:20:02,987 (beam_search:479) INFO: total log probability: -7.51 +2024-01-16 22:20:02,987 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:20:02,987 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:02,987 (beam_search:483) INFO: best hypo: WENMENDIEWÄLTALGANZESPERACHT + +2024-01-16 22:20:02,988 (asr_inference:494) INFO: speech length: 93760 +2024-01-16 22:20:02,999 (beam_search:428) INFO: decoder input length: 144 +2024-01-16 22:20:02,999 (beam_search:429) INFO: max output length: 144 +2024-01-16 22:20:02,999 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:03,336 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:03,336 (beam_search:476) INFO: -18.94 * 1.0 = -18.94 for ctc +2024-01-16 22:20:03,336 (beam_search:479) INFO: total log probability: -18.94 +2024-01-16 22:20:03,336 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:20:03,336 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:03,337 (beam_search:483) INFO: best hypo: SENDKITISCHEKOMPBONENENDESDETUNERDIOUNZSISTEMSABSICHTLICHSCHWACHENDWURHFEN + +2024-01-16 22:20:03,338 (asr_inference:494) INFO: speech length: 19360 +2024-01-16 22:20:03,344 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 22:20:03,344 (beam_search:429) INFO: max output length: 28 +2024-01-16 22:20:03,344 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:03,368 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:03,369 (beam_search:476) INFO: -5.06 * 1.0 = -5.06 for ctc +2024-01-16 22:20:03,369 (beam_search:479) INFO: total log probability: -5.06 +2024-01-16 22:20:03,369 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:20:03,369 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:03,369 (beam_search:483) INFO: best hypo: NICHTWERBEIISTEDOCH + +2024-01-16 22:20:03,370 (asr_inference:494) INFO: speech length: 35680 +2024-01-16 22:20:03,377 (beam_search:428) INFO: decoder input length: 53 +2024-01-16 22:20:03,377 (beam_search:429) INFO: max output length: 53 +2024-01-16 22:20:03,377 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:03,441 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:03,441 (beam_search:476) INFO: -7.00 * 1.0 = -7.00 for ctc +2024-01-16 22:20:03,441 (beam_search:479) INFO: total log probability: -7.00 +2024-01-16 22:20:03,441 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:20:03,441 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:03,442 (beam_search:483) INFO: best hypo: ERBUOTEINEFEREINIGUNGDETSCLANSAN + +2024-01-16 22:20:03,443 (asr_inference:494) INFO: speech length: 54240 +2024-01-16 22:20:03,451 (beam_search:428) INFO: decoder input length: 82 +2024-01-16 22:20:03,451 (beam_search:429) INFO: max output length: 82 +2024-01-16 22:20:03,451 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:03,521 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:03,521 (beam_search:476) INFO: -6.40 * 1.0 = -6.40 for ctc +2024-01-16 22:20:03,521 (beam_search:479) INFO: total log probability: -6.40 +2024-01-16 22:20:03,521 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:20:03,521 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:03,521 (beam_search:483) INFO: best hypo: PELIENZWEITAUSENFÜNF + +2024-01-16 22:20:03,523 (asr_inference:494) INFO: speech length: 68000 +2024-01-16 22:20:03,532 (beam_search:428) INFO: decoder input length: 104 +2024-01-16 22:20:03,532 (beam_search:429) INFO: max output length: 104 +2024-01-16 22:20:03,533 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:03,686 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:03,687 (beam_search:476) INFO: -6.02 * 1.0 = -6.02 for ctc +2024-01-16 22:20:03,687 (beam_search:479) INFO: total log probability: -6.02 +2024-01-16 22:20:03,687 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 22:20:03,687 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:03,687 (beam_search:483) INFO: best hypo: KERNABGESTEIMTUNDTUMHLENDIESENENSPRECHENT + +2024-01-16 22:20:03,688 (asr_inference:494) INFO: speech length: 34080 +2024-01-16 22:20:03,696 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 22:20:03,696 (beam_search:429) INFO: max output length: 51 +2024-01-16 22:20:03,696 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:03,742 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:03,742 (beam_search:476) INFO: -7.45 * 1.0 = -7.45 for ctc +2024-01-16 22:20:03,742 (beam_search:479) INFO: total log probability: -7.45 +2024-01-16 22:20:03,742 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:20:03,742 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:03,742 (beam_search:483) INFO: best hypo: ARZOELGUNGVONDNAMIGAUS + +2024-01-16 22:20:03,743 (asr_inference:494) INFO: speech length: 22560 +2024-01-16 22:20:03,750 (beam_search:428) INFO: decoder input length: 33 +2024-01-16 22:20:03,750 (beam_search:429) INFO: max output length: 33 +2024-01-16 22:20:03,750 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:03,772 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:03,772 (beam_search:476) INFO: -4.41 * 1.0 = -4.41 for ctc +2024-01-16 22:20:03,772 (beam_search:479) INFO: total log probability: -4.41 +2024-01-16 22:20:03,772 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:20:03,772 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:03,772 (beam_search:483) INFO: best hypo: SEMTUNDENGDER + +2024-01-16 22:20:03,773 (asr_inference:494) INFO: speech length: 26880 +2024-01-16 22:20:03,780 (beam_search:428) INFO: decoder input length: 39 +2024-01-16 22:20:03,780 (beam_search:429) INFO: max output length: 39 +2024-01-16 22:20:03,780 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:03,818 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:03,818 (beam_search:476) INFO: -6.75 * 1.0 = -6.75 for ctc +2024-01-16 22:20:03,818 (beam_search:479) INFO: total log probability: -6.75 +2024-01-16 22:20:03,818 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:03,818 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:03,818 (beam_search:483) INFO: best hypo: UNGVONDSCHIEHRUNTERNERUN + +2024-01-16 22:20:03,820 (asr_inference:494) INFO: speech length: 26080 +2024-01-16 22:20:03,826 (beam_search:428) INFO: decoder input length: 38 +2024-01-16 22:20:03,827 (beam_search:429) INFO: max output length: 38 +2024-01-16 22:20:03,827 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:03,858 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:03,858 (beam_search:476) INFO: -5.47 * 1.0 = -5.47 for ctc +2024-01-16 22:20:03,858 (beam_search:479) INFO: total log probability: -5.47 +2024-01-16 22:20:03,858 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:03,858 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:03,858 (beam_search:483) INFO: best hypo: DNESCHENUNGERWRTCEN + +2024-01-16 22:20:03,859 (asr_inference:494) INFO: speech length: 24320 +2024-01-16 22:20:03,866 (beam_search:428) INFO: decoder input length: 35 +2024-01-16 22:20:03,866 (beam_search:429) INFO: max output length: 35 +2024-01-16 22:20:03,866 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:03,889 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:03,889 (beam_search:476) INFO: -3.76 * 1.0 = -3.76 for ctc +2024-01-16 22:20:03,890 (beam_search:479) INFO: total log probability: -3.76 +2024-01-16 22:20:03,890 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:20:03,890 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:03,890 (beam_search:483) INFO: best hypo: ROBETERFKENEDI + +2024-01-16 22:20:03,891 (asr_inference:494) INFO: speech length: 19200 +2024-01-16 22:20:03,897 (beam_search:428) INFO: decoder input length: 27 +2024-01-16 22:20:03,897 (beam_search:429) INFO: max output length: 27 +2024-01-16 22:20:03,897 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:03,917 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:03,917 (beam_search:476) INFO: -4.07 * 1.0 = -4.07 for ctc +2024-01-16 22:20:03,917 (beam_search:479) INFO: total log probability: -4.07 +2024-01-16 22:20:03,917 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:20:03,917 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:03,917 (beam_search:483) INFO: best hypo: KAMSHLISELICHZUM + +2024-01-16 22:20:03,918 (asr_inference:494) INFO: speech length: 33280 +2024-01-16 22:20:03,926 (beam_search:428) INFO: decoder input length: 49 +2024-01-16 22:20:03,926 (beam_search:429) INFO: max output length: 49 +2024-01-16 22:20:03,926 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:03,947 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:03,947 (beam_search:476) INFO: -4.19 * 1.0 = -4.19 for ctc +2024-01-16 22:20:03,947 (beam_search:479) INFO: total log probability: -4.19 +2024-01-16 22:20:03,947 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:20:03,947 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:03,947 (beam_search:483) INFO: best hypo: VOLSTENI + +2024-01-16 22:20:03,948 (asr_inference:494) INFO: speech length: 38560 +2024-01-16 22:20:03,956 (beam_search:428) INFO: decoder input length: 58 +2024-01-16 22:20:03,956 (beam_search:429) INFO: max output length: 58 +2024-01-16 22:20:03,956 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:04,014 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:04,014 (beam_search:476) INFO: -5.10 * 1.0 = -5.10 for ctc +2024-01-16 22:20:04,014 (beam_search:479) INFO: total log probability: -5.10 +2024-01-16 22:20:04,014 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:20:04,014 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:04,014 (beam_search:483) INFO: best hypo: STANTENSICHVONDENUERESAR + +2024-01-16 22:20:04,016 (asr_inference:494) INFO: speech length: 39680 +2024-01-16 22:20:04,023 (beam_search:428) INFO: decoder input length: 59 +2024-01-16 22:20:04,023 (beam_search:429) INFO: max output length: 59 +2024-01-16 22:20:04,023 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:04,094 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:04,094 (beam_search:476) INFO: -8.86 * 1.0 = -8.86 for ctc +2024-01-16 22:20:04,094 (beam_search:479) INFO: total log probability: -8.86 +2024-01-16 22:20:04,094 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:04,094 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:04,094 (beam_search:483) INFO: best hypo: AFRIKARSTDIHTESERHAHRERGEORTET + +2024-01-16 22:20:04,095 (asr_inference:494) INFO: speech length: 19200 +2024-01-16 22:20:04,102 (beam_search:428) INFO: decoder input length: 27 +2024-01-16 22:20:04,102 (beam_search:429) INFO: max output length: 27 +2024-01-16 22:20:04,102 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:04,122 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:04,122 (beam_search:476) INFO: -3.59 * 1.0 = -3.59 for ctc +2024-01-16 22:20:04,122 (beam_search:479) INFO: total log probability: -3.59 +2024-01-16 22:20:04,123 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:20:04,123 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:04,123 (beam_search:483) INFO: best hypo: DIEARMEMUITETEL + +2024-01-16 22:20:04,124 (asr_inference:494) INFO: speech length: 19520 +2024-01-16 22:20:04,130 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 22:20:04,130 (beam_search:429) INFO: max output length: 28 +2024-01-16 22:20:04,130 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:04,151 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:04,152 (beam_search:476) INFO: -3.15 * 1.0 = -3.15 for ctc +2024-01-16 22:20:04,152 (beam_search:479) INFO: total log probability: -3.15 +2024-01-16 22:20:04,152 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:20:04,152 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:04,152 (beam_search:483) INFO: best hypo: STALIENSERTZTEM + +2024-01-16 22:20:04,153 (asr_inference:494) INFO: speech length: 18560 +2024-01-16 22:20:04,159 (beam_search:428) INFO: decoder input length: 26 +2024-01-16 22:20:04,159 (beam_search:429) INFO: max output length: 26 +2024-01-16 22:20:04,159 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:04,182 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:04,182 (beam_search:476) INFO: -4.23 * 1.0 = -4.23 for ctc +2024-01-16 22:20:04,182 (beam_search:479) INFO: total log probability: -4.23 +2024-01-16 22:20:04,182 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:20:04,182 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:04,182 (beam_search:483) INFO: best hypo: VEIHTENESAUSTLEICG + +2024-01-16 22:20:04,183 (asr_inference:494) INFO: speech length: 42400 +2024-01-16 22:20:04,191 (beam_search:428) INFO: decoder input length: 64 +2024-01-16 22:20:04,191 (beam_search:429) INFO: max output length: 64 +2024-01-16 22:20:04,191 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:04,249 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:04,249 (beam_search:476) INFO: -6.17 * 1.0 = -6.17 for ctc +2024-01-16 22:20:04,249 (beam_search:479) INFO: total log probability: -6.17 +2024-01-16 22:20:04,249 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:04,249 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:04,249 (beam_search:483) INFO: best hypo: KLMEAUFBROSSCREIBTLEIH + +2024-01-16 22:20:04,250 (asr_inference:494) INFO: speech length: 43520 +2024-01-16 22:20:04,258 (beam_search:428) INFO: decoder input length: 65 +2024-01-16 22:20:04,258 (beam_search:429) INFO: max output length: 65 +2024-01-16 22:20:04,258 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:04,337 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:04,337 (beam_search:476) INFO: -7.20 * 1.0 = -7.20 for ctc +2024-01-16 22:20:04,337 (beam_search:479) INFO: total log probability: -7.20 +2024-01-16 22:20:04,337 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:20:04,337 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:04,337 (beam_search:483) INFO: best hypo: AMZWEITENJUNEZWETAUSENDFIERBURDE + +2024-01-16 22:20:04,338 (asr_inference:494) INFO: speech length: 23040 +2024-01-16 22:20:04,345 (beam_search:428) INFO: decoder input length: 33 +2024-01-16 22:20:04,345 (beam_search:429) INFO: max output length: 33 +2024-01-16 22:20:04,345 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:04,375 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:04,375 (beam_search:476) INFO: -6.90 * 1.0 = -6.90 for ctc +2024-01-16 22:20:04,375 (beam_search:479) INFO: total log probability: -6.90 +2024-01-16 22:20:04,375 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:20:04,375 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:04,375 (beam_search:483) INFO: best hypo: INEBNDESTERNACHRIUGT + +2024-01-16 22:20:04,376 (asr_inference:494) INFO: speech length: 65600 +2024-01-16 22:20:04,386 (beam_search:428) INFO: decoder input length: 100 +2024-01-16 22:20:04,386 (beam_search:429) INFO: max output length: 100 +2024-01-16 22:20:04,386 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:04,561 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:04,561 (beam_search:476) INFO: -7.77 * 1.0 = -7.77 for ctc +2024-01-16 22:20:04,561 (beam_search:479) INFO: total log probability: -7.77 +2024-01-16 22:20:04,561 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 22:20:04,561 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:04,561 (beam_search:483) INFO: best hypo: DINARTOOSTERWEITERUNGUNDDIEEINSEITIGERAUFKÜNDIGUNGDE + +2024-01-16 22:20:04,563 (asr_inference:494) INFO: speech length: 20960 +2024-01-16 22:20:04,569 (beam_search:428) INFO: decoder input length: 30 +2024-01-16 22:20:04,569 (beam_search:429) INFO: max output length: 30 +2024-01-16 22:20:04,570 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:04,585 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:04,586 (beam_search:476) INFO: -1.57 * 1.0 = -1.57 for ctc +2024-01-16 22:20:04,586 (beam_search:479) INFO: total log probability: -1.57 +2024-01-16 22:20:04,586 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 22:20:04,586 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:04,586 (beam_search:483) INFO: best hypo: THERBEIIS + +2024-01-16 22:20:04,587 (asr_inference:494) INFO: speech length: 47680 +2024-01-16 22:20:04,595 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 22:20:04,595 (beam_search:429) INFO: max output length: 72 +2024-01-16 22:20:04,595 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:04,697 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:04,697 (beam_search:476) INFO: -10.98 * 1.0 = -10.98 for ctc +2024-01-16 22:20:04,697 (beam_search:479) INFO: total log probability: -10.98 +2024-01-16 22:20:04,697 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:04,697 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:04,698 (beam_search:483) INFO: best hypo: DIESRSTELLEKAMMNSANMTICHEMITIEDERDERKAPLET + +2024-01-16 22:20:04,699 (asr_inference:494) INFO: speech length: 184320 +2024-01-16 22:20:04,716 (beam_search:428) INFO: decoder input length: 285 +2024-01-16 22:20:04,716 (beam_search:429) INFO: max output length: 285 +2024-01-16 22:20:04,716 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:05,865 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:05,865 (beam_search:476) INFO: -36.06 * 1.0 = -36.06 for ctc +2024-01-16 22:20:05,865 (beam_search:479) INFO: total log probability: -36.06 +2024-01-16 22:20:05,865 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:05,865 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:05,866 (beam_search:483) INFO: best hypo: POTZSTAMABPKOMMENENTHIELTZWARALGEMEINEVEREINBAUNENÜBEDIEKÜNFTIGEGEMEINSAMEVERWALTHNDERSEGERMICHTEUNDVOMULIERTEGRUNDSETZEBDEMLITRISIERUN + +2024-01-16 22:20:05,867 (asr_inference:494) INFO: speech length: 59360 +2024-01-16 22:20:05,876 (beam_search:428) INFO: decoder input length: 90 +2024-01-16 22:20:05,876 (beam_search:429) INFO: max output length: 90 +2024-01-16 22:20:05,876 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:06,016 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:06,017 (beam_search:476) INFO: -13.17 * 1.0 = -13.17 for ctc +2024-01-16 22:20:06,017 (beam_search:479) INFO: total log probability: -13.17 +2024-01-16 22:20:06,017 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:06,017 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:06,017 (beam_search:483) INFO: best hypo: DANACHRUNDRSCIEBEINENVARARKGBEIMWIEFTZSIDYNAMO + +2024-01-16 22:20:06,018 (asr_inference:494) INFO: speech length: 22560 +2024-01-16 22:20:06,025 (beam_search:428) INFO: decoder input length: 33 +2024-01-16 22:20:06,025 (beam_search:429) INFO: max output length: 33 +2024-01-16 22:20:06,025 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:06,053 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:06,053 (beam_search:476) INFO: -6.35 * 1.0 = -6.35 for ctc +2024-01-16 22:20:06,053 (beam_search:479) INFO: total log probability: -6.35 +2024-01-16 22:20:06,053 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:20:06,053 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:06,054 (beam_search:483) INFO: best hypo: EINWEITEROWARIJANTMA + +2024-01-16 22:20:06,055 (asr_inference:494) INFO: speech length: 77600 +2024-01-16 22:20:06,065 (beam_search:428) INFO: decoder input length: 119 +2024-01-16 22:20:06,065 (beam_search:429) INFO: max output length: 119 +2024-01-16 22:20:06,065 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:06,293 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:06,293 (beam_search:476) INFO: -12.25 * 1.0 = -12.25 for ctc +2024-01-16 22:20:06,293 (beam_search:479) INFO: total log probability: -12.25 +2024-01-16 22:20:06,293 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:20:06,293 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:06,293 (beam_search:483) INFO: best hypo: SIEWURDENMODOLARNTURCHELOCHSTREIFENGESTEYERTUNDDIKLINGEKONTE + +2024-01-16 22:20:06,294 (asr_inference:494) INFO: speech length: 62080 +2024-01-16 22:20:06,304 (beam_search:428) INFO: decoder input length: 94 +2024-01-16 22:20:06,304 (beam_search:429) INFO: max output length: 94 +2024-01-16 22:20:06,304 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:06,475 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:06,475 (beam_search:476) INFO: -13.67 * 1.0 = -13.67 for ctc +2024-01-16 22:20:06,475 (beam_search:479) INFO: total log probability: -13.67 +2024-01-16 22:20:06,475 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:20:06,475 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:06,475 (beam_search:483) INFO: best hypo: DIEGRUNMADATKLAUSELBERVORTZUGTUNDEDINKLEINERNPARTEINJIENE + +2024-01-16 22:20:06,476 (asr_inference:494) INFO: speech length: 41920 +2024-01-16 22:20:06,484 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 22:20:06,484 (beam_search:429) INFO: max output length: 63 +2024-01-16 22:20:06,484 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:06,568 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:06,568 (beam_search:476) INFO: -9.25 * 1.0 = -9.25 for ctc +2024-01-16 22:20:06,569 (beam_search:479) INFO: total log probability: -9.25 +2024-01-16 22:20:06,569 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:20:06,569 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:06,569 (beam_search:483) INFO: best hypo: ABERTOTZDINKEINEWÜKLICHERHUNGESNODTHEAST + +2024-01-16 22:20:06,570 (asr_inference:494) INFO: speech length: 17600 +2024-01-16 22:20:06,577 (beam_search:428) INFO: decoder input length: 25 +2024-01-16 22:20:06,577 (beam_search:429) INFO: max output length: 25 +2024-01-16 22:20:06,577 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:06,597 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:06,597 (beam_search:476) INFO: -4.47 * 1.0 = -4.47 for ctc +2024-01-16 22:20:06,597 (beam_search:479) INFO: total log probability: -4.47 +2024-01-16 22:20:06,597 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:20:06,597 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:06,597 (beam_search:483) INFO: best hypo: NKOGMALTERZIOND + +2024-01-16 22:20:06,598 (asr_inference:494) INFO: speech length: 41600 +2024-01-16 22:20:06,606 (beam_search:428) INFO: decoder input length: 62 +2024-01-16 22:20:06,606 (beam_search:429) INFO: max output length: 62 +2024-01-16 22:20:06,606 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:06,688 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:06,688 (beam_search:476) INFO: -12.24 * 1.0 = -12.24 for ctc +2024-01-16 22:20:06,688 (beam_search:479) INFO: total log probability: -12.24 +2024-01-16 22:20:06,688 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:20:06,688 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:06,688 (beam_search:483) INFO: best hypo: ZUOFORBEDIGUNGKONGRIETERAPRÜSTENSCHLETE + +2024-01-16 22:20:06,689 (asr_inference:494) INFO: speech length: 32320 +2024-01-16 22:20:06,696 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 22:20:06,696 (beam_search:429) INFO: max output length: 48 +2024-01-16 22:20:06,696 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:06,735 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:06,735 (beam_search:476) INFO: -6.67 * 1.0 = -6.67 for ctc +2024-01-16 22:20:06,735 (beam_search:479) INFO: total log probability: -6.67 +2024-01-16 22:20:06,735 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:20:06,735 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:06,735 (beam_search:483) INFO: best hypo: BUNDESTARGEWALRECHT + +2024-01-16 22:20:06,736 (asr_inference:494) INFO: speech length: 35040 +2024-01-16 22:20:06,743 (beam_search:428) INFO: decoder input length: 52 +2024-01-16 22:20:06,743 (beam_search:429) INFO: max output length: 52 +2024-01-16 22:20:06,743 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:06,808 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:06,808 (beam_search:476) INFO: -6.81 * 1.0 = -6.81 for ctc +2024-01-16 22:20:06,808 (beam_search:479) INFO: total log probability: -6.81 +2024-01-16 22:20:06,808 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:20:06,808 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:06,808 (beam_search:483) INFO: best hypo: ISMUSTIMGREISWALEITARVORGELIGTWERN + +2024-01-16 22:20:06,809 (asr_inference:494) INFO: speech length: 143360 +2024-01-16 22:20:06,824 (beam_search:428) INFO: decoder input length: 221 +2024-01-16 22:20:06,824 (beam_search:429) INFO: max output length: 221 +2024-01-16 22:20:06,824 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:07,602 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:07,602 (beam_search:476) INFO: -41.40 * 1.0 = -41.40 for ctc +2024-01-16 22:20:07,602 (beam_search:479) INFO: total log probability: -41.40 +2024-01-16 22:20:07,602 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:20:07,602 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:07,603 (beam_search:483) INFO: best hypo: HATMANINEIMPIERESCHEBASESFÜBSZUCHOSOTIALEPROGEAMMEZUORSENKUNGDERSEBSTMUTERATHEUNDZERSTDARKUMGDESIHGERHITZSGEFÜSINDEBEFEKERUN + +2024-01-16 22:20:07,604 (asr_inference:494) INFO: speech length: 99680 +2024-01-16 22:20:07,616 (beam_search:428) INFO: decoder input length: 153 +2024-01-16 22:20:07,616 (beam_search:429) INFO: max output length: 153 +2024-01-16 22:20:07,616 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:07,975 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:07,975 (beam_search:476) INFO: -18.64 * 1.0 = -18.64 for ctc +2024-01-16 22:20:07,975 (beam_search:479) INFO: total log probability: -18.64 +2024-01-16 22:20:07,975 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:20:07,975 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:07,976 (beam_search:483) INFO: best hypo: BEIDENERSTENWREINPALEMENZWALNURLEIELIEHRSGUIMMEINUNZEINHNDERTNEUNZIGINSEINE + +2024-01-16 22:20:07,977 (asr_inference:494) INFO: speech length: 44320 +2024-01-16 22:20:07,985 (beam_search:428) INFO: decoder input length: 67 +2024-01-16 22:20:07,986 (beam_search:429) INFO: max output length: 67 +2024-01-16 22:20:07,986 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:08,078 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:08,078 (beam_search:476) INFO: -7.86 * 1.0 = -7.86 for ctc +2024-01-16 22:20:08,078 (beam_search:479) INFO: total log probability: -7.86 +2024-01-16 22:20:08,078 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:20:08,078 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:08,079 (beam_search:483) INFO: best hypo: DAMITLASENSICHBESTRALUNGSTERENSERGENOUMESE + +2024-01-16 22:20:08,080 (asr_inference:494) INFO: speech length: 40640 +2024-01-16 22:20:08,087 (beam_search:428) INFO: decoder input length: 61 +2024-01-16 22:20:08,087 (beam_search:429) INFO: max output length: 61 +2024-01-16 22:20:08,087 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:08,164 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:08,164 (beam_search:476) INFO: -9.86 * 1.0 = -9.86 for ctc +2024-01-16 22:20:08,164 (beam_search:479) INFO: total log probability: -9.86 +2024-01-16 22:20:08,164 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:08,164 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:08,164 (beam_search:483) INFO: best hypo: WINGRSPIÄTERKAMSTZUEINEWEITERENKRNDN + +2024-01-16 22:20:08,166 (asr_inference:494) INFO: speech length: 26880 +2024-01-16 22:20:08,172 (beam_search:428) INFO: decoder input length: 39 +2024-01-16 22:20:08,173 (beam_search:429) INFO: max output length: 39 +2024-01-16 22:20:08,173 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:08,205 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:08,205 (beam_search:476) INFO: -4.65 * 1.0 = -4.65 for ctc +2024-01-16 22:20:08,205 (beam_search:479) INFO: total log probability: -4.65 +2024-01-16 22:20:08,205 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:20:08,205 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:08,206 (beam_search:483) INFO: best hypo: WRARDIOKABERERTPEILS + +2024-01-16 22:20:08,207 (asr_inference:494) INFO: speech length: 38880 +2024-01-16 22:20:08,214 (beam_search:428) INFO: decoder input length: 58 +2024-01-16 22:20:08,214 (beam_search:429) INFO: max output length: 58 +2024-01-16 22:20:08,214 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:08,284 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:08,284 (beam_search:476) INFO: -7.09 * 1.0 = -7.09 for ctc +2024-01-16 22:20:08,284 (beam_search:479) INFO: total log probability: -7.09 +2024-01-16 22:20:08,284 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:20:08,284 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:08,284 (beam_search:483) INFO: best hypo: STÜKTEBOUMBERAUFISTARTBANENROLEN + +2024-01-16 22:20:08,285 (asr_inference:494) INFO: speech length: 75040 +2024-01-16 22:20:08,295 (beam_search:428) INFO: decoder input length: 115 +2024-01-16 22:20:08,295 (beam_search:429) INFO: max output length: 115 +2024-01-16 22:20:08,295 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:08,529 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:08,530 (beam_search:476) INFO: -15.96 * 1.0 = -15.96 for ctc +2024-01-16 22:20:08,530 (beam_search:479) INFO: total log probability: -15.96 +2024-01-16 22:20:08,530 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:20:08,530 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:08,530 (beam_search:483) INFO: best hypo: MITDIESERIGELUNGSOLINERFAKTUISCHTZWEIFERCHINFLUSTNAERDISERWELERAUF + +2024-01-16 22:20:08,531 (asr_inference:494) INFO: speech length: 19200 +2024-01-16 22:20:08,538 (beam_search:428) INFO: decoder input length: 27 +2024-01-16 22:20:08,538 (beam_search:429) INFO: max output length: 27 +2024-01-16 22:20:08,538 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:08,558 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:08,558 (beam_search:476) INFO: -4.15 * 1.0 = -4.15 for ctc +2024-01-16 22:20:08,558 (beam_search:479) INFO: total log probability: -4.15 +2024-01-16 22:20:08,558 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:20:08,558 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:08,558 (beam_search:483) INFO: best hypo: BARKKÜRSCHENBAU + +2024-01-16 22:20:08,559 (asr_inference:494) INFO: speech length: 76320 +2024-01-16 22:20:08,569 (beam_search:428) INFO: decoder input length: 117 +2024-01-16 22:20:08,569 (beam_search:429) INFO: max output length: 117 +2024-01-16 22:20:08,569 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:08,838 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:08,838 (beam_search:476) INFO: -12.93 * 1.0 = -12.93 for ctc +2024-01-16 22:20:08,838 (beam_search:479) INFO: total log probability: -12.93 +2024-01-16 22:20:08,838 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 22:20:08,839 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:08,839 (beam_search:483) INFO: best hypo: DERHEVORAGENTZWICGENDENLANDEKLABENWIDERUMHERVORAGENERLANGSMFLUGEIGENSCHAFTE + +2024-01-16 22:20:08,840 (asr_inference:494) INFO: speech length: 88320 +2024-01-16 22:20:08,851 (beam_search:428) INFO: decoder input length: 135 +2024-01-16 22:20:08,851 (beam_search:429) INFO: max output length: 135 +2024-01-16 22:20:08,851 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:09,170 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:09,170 (beam_search:476) INFO: -19.81 * 1.0 = -19.81 for ctc +2024-01-16 22:20:09,170 (beam_search:479) INFO: total log probability: -19.81 +2024-01-16 22:20:09,170 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:09,170 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:09,170 (beam_search:483) INFO: best hypo: MITERSCHVERBINDUNGSFLUKZAGEOUDERUMSCHULMASCHIENVÜRDIEBEEEINHNDERDNEUNVERWENDET + +2024-01-16 22:20:09,172 (asr_inference:494) INFO: speech length: 41440 +2024-01-16 22:20:09,180 (beam_search:428) INFO: decoder input length: 62 +2024-01-16 22:20:09,180 (beam_search:429) INFO: max output length: 62 +2024-01-16 22:20:09,180 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:09,258 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:09,259 (beam_search:476) INFO: -14.32 * 1.0 = -14.32 for ctc +2024-01-16 22:20:09,259 (beam_search:479) INFO: total log probability: -14.32 +2024-01-16 22:20:09,259 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:20:09,259 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:09,259 (beam_search:483) INFO: best hypo: LEISTETEMEIZINESHONBSICHELOGESCEHEILEF + +2024-01-16 22:20:09,260 (asr_inference:494) INFO: speech length: 36160 +2024-01-16 22:20:09,267 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 22:20:09,267 (beam_search:429) INFO: max output length: 54 +2024-01-16 22:20:09,267 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:09,324 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:09,324 (beam_search:476) INFO: -7.36 * 1.0 = -7.36 for ctc +2024-01-16 22:20:09,324 (beam_search:479) INFO: total log probability: -7.36 +2024-01-16 22:20:09,324 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:09,324 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:09,325 (beam_search:483) INFO: best hypo: KAMANDECHIMFOMRENVORBEUIGEN + +2024-01-16 22:20:09,326 (asr_inference:494) INFO: speech length: 66880 +2024-01-16 22:20:09,335 (beam_search:428) INFO: decoder input length: 102 +2024-01-16 22:20:09,335 (beam_search:429) INFO: max output length: 102 +2024-01-16 22:20:09,335 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:09,531 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:09,531 (beam_search:476) INFO: -15.95 * 1.0 = -15.95 for ctc +2024-01-16 22:20:09,531 (beam_search:479) INFO: total log probability: -15.95 +2024-01-16 22:20:09,531 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:09,531 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:09,532 (beam_search:483) INFO: best hypo: MERDINAUSBUCHTISERKANKEITENEREFLUKTEINFRKTIONVELLANGSMMENKAN + +2024-01-16 22:20:09,533 (asr_inference:494) INFO: speech length: 55040 +2024-01-16 22:20:09,542 (beam_search:428) INFO: decoder input length: 83 +2024-01-16 22:20:09,542 (beam_search:429) INFO: max output length: 83 +2024-01-16 22:20:09,542 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:09,659 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:09,660 (beam_search:476) INFO: -8.14 * 1.0 = -8.14 for ctc +2024-01-16 22:20:09,660 (beam_search:479) INFO: total log probability: -8.14 +2024-01-16 22:20:09,660 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:20:09,660 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:09,660 (beam_search:483) INFO: best hypo: DIEINENEUTRDIETETUNTERALENUMSTENNVORSAR + +2024-01-16 22:20:09,661 (asr_inference:494) INFO: speech length: 19520 +2024-01-16 22:20:09,668 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 22:20:09,668 (beam_search:429) INFO: max output length: 28 +2024-01-16 22:20:09,668 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:09,687 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:09,687 (beam_search:476) INFO: -3.03 * 1.0 = -3.03 for ctc +2024-01-16 22:20:09,687 (beam_search:479) INFO: total log probability: -3.03 +2024-01-16 22:20:09,687 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:20:09,687 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:09,687 (beam_search:483) INFO: best hypo: UNZSIEGENHERT + +2024-01-16 22:20:09,688 (asr_inference:494) INFO: speech length: 119360 +2024-01-16 22:20:09,701 (beam_search:428) INFO: decoder input length: 184 +2024-01-16 22:20:09,701 (beam_search:429) INFO: max output length: 184 +2024-01-16 22:20:09,701 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:10,188 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:10,189 (beam_search:476) INFO: -19.66 * 1.0 = -19.66 for ctc +2024-01-16 22:20:10,189 (beam_search:479) INFO: total log probability: -19.66 +2024-01-16 22:20:10,189 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:20:10,189 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:10,189 (beam_search:483) INFO: best hypo: DASNEUINZHNHUNDERACHTENDREISIGGRÜNDETEKOMITIFVIRUNAMERIEKANSCHEUMTRIEBEWURDEDAFERNRU + +2024-01-16 22:20:10,191 (asr_inference:494) INFO: speech length: 87040 +2024-01-16 22:20:10,201 (beam_search:428) INFO: decoder input length: 133 +2024-01-16 22:20:10,201 (beam_search:429) INFO: max output length: 133 +2024-01-16 22:20:10,201 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:10,485 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:10,485 (beam_search:476) INFO: -20.04 * 1.0 = -20.04 for ctc +2024-01-16 22:20:10,485 (beam_search:479) INFO: total log probability: -20.04 +2024-01-16 22:20:10,485 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:20:10,485 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:10,486 (beam_search:483) INFO: best hypo: ZEINDRALEDEPROKRESIENUNDHRTDISINHENJÖRGESTITSTENKUNZSTDENGKENS + +2024-01-16 22:20:10,487 (asr_inference:494) INFO: speech length: 42720 +2024-01-16 22:20:10,495 (beam_search:428) INFO: decoder input length: 64 +2024-01-16 22:20:10,495 (beam_search:429) INFO: max output length: 64 +2024-01-16 22:20:10,495 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:10,568 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:10,568 (beam_search:476) INFO: -5.37 * 1.0 = -5.37 for ctc +2024-01-16 22:20:10,568 (beam_search:479) INFO: total log probability: -5.37 +2024-01-16 22:20:10,568 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 22:20:10,568 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:10,569 (beam_search:483) INFO: best hypo: INDEREUESPRESEDENTANKÜNDIGKTE + +2024-01-16 22:20:10,570 (asr_inference:494) INFO: speech length: 30080 +2024-01-16 22:20:10,577 (beam_search:428) INFO: decoder input length: 44 +2024-01-16 22:20:10,577 (beam_search:429) INFO: max output length: 44 +2024-01-16 22:20:10,577 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:10,616 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:10,616 (beam_search:476) INFO: -6.37 * 1.0 = -6.37 for ctc +2024-01-16 22:20:10,616 (beam_search:479) INFO: total log probability: -6.37 +2024-01-16 22:20:10,616 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:20:10,616 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:10,616 (beam_search:483) INFO: best hypo: SNEKZSTUNDVORSHPEISEN + +2024-01-16 22:20:10,617 (asr_inference:494) INFO: speech length: 70240 +2024-01-16 22:20:10,627 (beam_search:428) INFO: decoder input length: 107 +2024-01-16 22:20:10,627 (beam_search:429) INFO: max output length: 107 +2024-01-16 22:20:10,627 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:10,836 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:10,836 (beam_search:476) INFO: -15.33 * 1.0 = -15.33 for ctc +2024-01-16 22:20:10,836 (beam_search:479) INFO: total log probability: -15.33 +2024-01-16 22:20:10,836 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:20:10,836 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:10,837 (beam_search:483) INFO: best hypo: DESBUNDESWAIGESETZESBISTZUNDREISIGSTENCHUNIEZWEITOSENODEAUFGEM + +2024-01-16 22:20:10,838 (asr_inference:494) INFO: speech length: 17120 +2024-01-16 22:20:10,844 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 22:20:10,844 (beam_search:429) INFO: max output length: 24 +2024-01-16 22:20:10,844 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:10,859 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:10,859 (beam_search:476) INFO: -6.12 * 1.0 = -6.12 for ctc +2024-01-16 22:20:10,859 (beam_search:479) INFO: total log probability: -6.12 +2024-01-16 22:20:10,859 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-16 22:20:10,859 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:10,859 (beam_search:483) INFO: best hypo: ARDEPUSSEÖL + +2024-01-16 22:20:10,860 (asr_inference:494) INFO: speech length: 76800 +2024-01-16 22:20:10,870 (beam_search:428) INFO: decoder input length: 117 +2024-01-16 22:20:10,870 (beam_search:429) INFO: max output length: 117 +2024-01-16 22:20:10,870 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:11,092 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:11,092 (beam_search:476) INFO: -11.43 * 1.0 = -11.43 for ctc +2024-01-16 22:20:11,092 (beam_search:479) INFO: total log probability: -11.43 +2024-01-16 22:20:11,092 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:20:11,092 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:11,092 (beam_search:483) INFO: best hypo: FLÜCFTLINGENVONDERIETNUSHENMINDERHEITDESOMALESHENBANTUN + +2024-01-16 22:20:11,094 (asr_inference:494) INFO: speech length: 40640 +2024-01-16 22:20:11,101 (beam_search:428) INFO: decoder input length: 61 +2024-01-16 22:20:11,101 (beam_search:429) INFO: max output length: 61 +2024-01-16 22:20:11,101 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:11,176 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:11,176 (beam_search:476) INFO: -6.58 * 1.0 = -6.58 for ctc +2024-01-16 22:20:11,176 (beam_search:479) INFO: total log probability: -6.58 +2024-01-16 22:20:11,176 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:20:11,176 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:11,176 (beam_search:483) INFO: best hypo: DIEBIEPOLAREWELTORTENUNSEMINTIERT + +2024-01-16 22:20:11,178 (asr_inference:494) INFO: speech length: 116640 +2024-01-16 22:20:11,190 (beam_search:428) INFO: decoder input length: 180 +2024-01-16 22:20:11,190 (beam_search:429) INFO: max output length: 180 +2024-01-16 22:20:11,190 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:11,647 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:11,647 (beam_search:476) INFO: -21.95 * 1.0 = -21.95 for ctc +2024-01-16 22:20:11,647 (beam_search:479) INFO: total log probability: -21.95 +2024-01-16 22:20:11,647 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:11,647 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:11,647 (beam_search:483) INFO: best hypo: TARANFANGEININTEKRIERTEUDEREXSTERNANGEBRCHTVORICHTUNANEINEMNUKLEARENWAFENSSTEM + +2024-01-16 22:20:11,649 (asr_inference:494) INFO: speech length: 39680 +2024-01-16 22:20:11,657 (beam_search:428) INFO: decoder input length: 59 +2024-01-16 22:20:11,657 (beam_search:429) INFO: max output length: 59 +2024-01-16 22:20:11,657 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:11,734 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:11,734 (beam_search:476) INFO: -11.14 * 1.0 = -11.14 for ctc +2024-01-16 22:20:11,734 (beam_search:479) INFO: total log probability: -11.14 +2024-01-16 22:20:11,734 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:20:11,734 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:11,734 (beam_search:483) INFO: best hypo: STARTERTDIHILFSOGENISITZUNLANKPVRESTIG + +2024-01-16 22:20:11,735 (asr_inference:494) INFO: speech length: 116320 +2024-01-16 22:20:11,748 (beam_search:428) INFO: decoder input length: 179 +2024-01-16 22:20:11,748 (beam_search:429) INFO: max output length: 179 +2024-01-16 22:20:11,748 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:12,260 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:12,260 (beam_search:476) INFO: -21.21 * 1.0 = -21.21 for ctc +2024-01-16 22:20:12,260 (beam_search:479) INFO: total log probability: -21.21 +2024-01-16 22:20:12,260 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:20:12,260 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:12,261 (beam_search:483) INFO: best hypo: WENDIESEECSTERENEFEKTEINDERICHTIGENREINVOLGEAUFTRETENUNDSICHINERHALBSPEZIFISCERPAREMETERBEWIGEN + +2024-01-16 22:20:12,262 (asr_inference:494) INFO: speech length: 138080 +2024-01-16 22:20:12,276 (beam_search:428) INFO: decoder input length: 213 +2024-01-16 22:20:12,276 (beam_search:429) INFO: max output length: 213 +2024-01-16 22:20:12,276 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:12,977 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:12,977 (beam_search:476) INFO: -29.18 * 1.0 = -29.18 for ctc +2024-01-16 22:20:12,978 (beam_search:479) INFO: total log probability: -29.18 +2024-01-16 22:20:12,978 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:12,978 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:12,978 (beam_search:483) INFO: best hypo: ZUOKDESEWERTUNIONAUCHBEIDEWAERSTAOFPBAUMBEMUNDTNENINFLUGKZOEUGEMITINTERKONTINENTALLAREICHWEITEMITDENURSARGLEICH + +2024-01-16 22:20:12,980 (asr_inference:494) INFO: speech length: 38560 +2024-01-16 22:20:12,987 (beam_search:428) INFO: decoder input length: 58 +2024-01-16 22:20:12,987 (beam_search:429) INFO: max output length: 58 +2024-01-16 22:20:12,987 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:13,039 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:13,039 (beam_search:476) INFO: -8.00 * 1.0 = -8.00 for ctc +2024-01-16 22:20:13,039 (beam_search:479) INFO: total log probability: -8.00 +2024-01-16 22:20:13,039 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:20:13,039 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:13,039 (beam_search:483) INFO: best hypo: PENDESTATHEEWABENTIEOM + +2024-01-16 22:20:13,040 (asr_inference:494) INFO: speech length: 55680 +2024-01-16 22:20:13,049 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 22:20:13,049 (beam_search:429) INFO: max output length: 84 +2024-01-16 22:20:13,049 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:13,172 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:13,172 (beam_search:476) INFO: -10.09 * 1.0 = -10.09 for ctc +2024-01-16 22:20:13,172 (beam_search:479) INFO: total log probability: -10.09 +2024-01-16 22:20:13,173 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:20:13,173 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:13,173 (beam_search:483) INFO: best hypo: DIESERANSATZSGELDALGEMEINALSAUSCGEBUORGUNDE + +2024-01-16 22:20:13,174 (asr_inference:494) INFO: speech length: 19840 +2024-01-16 22:20:13,180 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 22:20:13,180 (beam_search:429) INFO: max output length: 28 +2024-01-16 22:20:13,180 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:13,203 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:13,203 (beam_search:476) INFO: -3.75 * 1.0 = -3.75 for ctc +2024-01-16 22:20:13,203 (beam_search:479) INFO: total log probability: -3.75 +2024-01-16 22:20:13,203 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:20:13,203 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:13,203 (beam_search:483) INFO: best hypo: NACHINZUSAMBROHTE + +2024-01-16 22:20:13,205 (asr_inference:494) INFO: speech length: 28000 +2024-01-16 22:20:13,212 (beam_search:428) INFO: decoder input length: 41 +2024-01-16 22:20:13,212 (beam_search:429) INFO: max output length: 41 +2024-01-16 22:20:13,212 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:13,256 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:13,256 (beam_search:476) INFO: -12.15 * 1.0 = -12.15 for ctc +2024-01-16 22:20:13,256 (beam_search:479) INFO: total log probability: -12.15 +2024-01-16 22:20:13,256 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:20:13,256 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:13,257 (beam_search:483) INFO: best hypo: DEUBERLAUSETZZFICHENHEIERWERD + +2024-01-16 22:20:13,258 (asr_inference:494) INFO: speech length: 32160 +2024-01-16 22:20:13,265 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 22:20:13,265 (beam_search:429) INFO: max output length: 48 +2024-01-16 22:20:13,265 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:13,318 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:13,318 (beam_search:476) INFO: -4.06 * 1.0 = -4.06 for ctc +2024-01-16 22:20:13,318 (beam_search:479) INFO: total log probability: -4.06 +2024-01-16 22:20:13,318 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 22:20:13,318 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:13,318 (beam_search:483) INFO: best hypo: DABEIENZWEIFHASENUNTERTEIELT + +2024-01-16 22:20:13,319 (asr_inference:494) INFO: speech length: 68320 +2024-01-16 22:20:13,328 (beam_search:428) INFO: decoder input length: 104 +2024-01-16 22:20:13,328 (beam_search:429) INFO: max output length: 104 +2024-01-16 22:20:13,328 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:13,516 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:13,516 (beam_search:476) INFO: -10.46 * 1.0 = -10.46 for ctc +2024-01-16 22:20:13,516 (beam_search:479) INFO: total log probability: -10.46 +2024-01-16 22:20:13,516 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:20:13,516 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:13,516 (beam_search:483) INFO: best hypo: SCHIETENANDEREROPRMISTERSCHAFTEILUNDWURDEMTERDIRBEELF + +2024-01-16 22:20:13,517 (asr_inference:494) INFO: speech length: 43840 +2024-01-16 22:20:13,525 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 22:20:13,525 (beam_search:429) INFO: max output length: 66 +2024-01-16 22:20:13,525 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:13,618 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:13,618 (beam_search:476) INFO: -13.38 * 1.0 = -13.38 for ctc +2024-01-16 22:20:13,618 (beam_search:479) INFO: total log probability: -13.38 +2024-01-16 22:20:13,618 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:20:13,618 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:13,619 (beam_search:483) INFO: best hypo: MASTERERLFNEKABARSCHISIENWEITERGEMÜKLIHKEIT + +2024-01-16 22:20:13,620 (asr_inference:494) INFO: speech length: 47200 +2024-01-16 22:20:13,628 (beam_search:428) INFO: decoder input length: 71 +2024-01-16 22:20:13,628 (beam_search:429) INFO: max output length: 71 +2024-01-16 22:20:13,628 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:13,722 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:13,723 (beam_search:476) INFO: -6.83 * 1.0 = -6.83 for ctc +2024-01-16 22:20:13,723 (beam_search:479) INFO: total log probability: -6.83 +2024-01-16 22:20:13,723 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:20:13,723 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:13,723 (beam_search:483) INFO: best hypo: EINEMAUSWERTZSAEVOLÜGINWOLSBURGELAN + +2024-01-16 22:20:13,724 (asr_inference:494) INFO: speech length: 73760 +2024-01-16 22:20:13,734 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 22:20:13,734 (beam_search:429) INFO: max output length: 113 +2024-01-16 22:20:13,734 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:13,924 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:13,924 (beam_search:476) INFO: -8.77 * 1.0 = -8.77 for ctc +2024-01-16 22:20:13,924 (beam_search:479) INFO: total log probability: -8.77 +2024-01-16 22:20:13,924 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:20:13,924 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:13,925 (beam_search:483) INFO: best hypo: MITSCHEWEBUNGSSUMANKONTENKLIESANDIERZRELKTWERDEN + +2024-01-16 22:20:13,926 (asr_inference:494) INFO: speech length: 19520 +2024-01-16 22:20:13,932 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 22:20:13,932 (beam_search:429) INFO: max output length: 28 +2024-01-16 22:20:13,932 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:13,957 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:13,957 (beam_search:476) INFO: -5.49 * 1.0 = -5.49 for ctc +2024-01-16 22:20:13,957 (beam_search:479) INFO: total log probability: -5.49 +2024-01-16 22:20:13,957 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:13,957 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:13,957 (beam_search:483) INFO: best hypo: DERBALEDIGLIHTZEIKTE + +2024-01-16 22:20:13,958 (asr_inference:494) INFO: speech length: 55680 +2024-01-16 22:20:13,967 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 22:20:13,967 (beam_search:429) INFO: max output length: 84 +2024-01-16 22:20:13,967 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:14,080 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:14,080 (beam_search:476) INFO: -8.72 * 1.0 = -8.72 for ctc +2024-01-16 22:20:14,080 (beam_search:479) INFO: total log probability: -8.72 +2024-01-16 22:20:14,080 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:20:14,080 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:14,080 (beam_search:483) INFO: best hypo: KOSPRETANDIENEINEESTEWICHTIGEVEINBAUN + +2024-01-16 22:20:14,081 (asr_inference:494) INFO: speech length: 22880 +2024-01-16 22:20:14,088 (beam_search:428) INFO: decoder input length: 33 +2024-01-16 22:20:14,088 (beam_search:429) INFO: max output length: 33 +2024-01-16 22:20:14,088 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:14,115 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:14,115 (beam_search:476) INFO: -8.54 * 1.0 = -8.54 for ctc +2024-01-16 22:20:14,115 (beam_search:479) INFO: total log probability: -8.54 +2024-01-16 22:20:14,115 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-16 22:20:14,115 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:14,116 (beam_search:483) INFO: best hypo: IDAUHTESHUTDNESIN + +2024-01-16 22:20:14,117 (asr_inference:494) INFO: speech length: 82560 +2024-01-16 22:20:14,127 (beam_search:428) INFO: decoder input length: 126 +2024-01-16 22:20:14,127 (beam_search:429) INFO: max output length: 126 +2024-01-16 22:20:14,127 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:14,414 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:14,414 (beam_search:476) INFO: -22.13 * 1.0 = -22.13 for ctc +2024-01-16 22:20:14,414 (beam_search:479) INFO: total log probability: -22.13 +2024-01-16 22:20:14,414 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:20:14,414 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:14,414 (beam_search:483) INFO: best hypo: WURDEMITDEBUNDESWAIGESETZVONENZHNASICHSUNFMFTIGAINEDAURHFTERELUNGENGEFÜHRT + +2024-01-16 22:20:14,415 (asr_inference:494) INFO: speech length: 31200 +2024-01-16 22:20:14,423 (beam_search:428) INFO: decoder input length: 46 +2024-01-16 22:20:14,423 (beam_search:429) INFO: max output length: 46 +2024-01-16 22:20:14,423 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:14,472 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:14,472 (beam_search:476) INFO: -7.84 * 1.0 = -7.84 for ctc +2024-01-16 22:20:14,472 (beam_search:479) INFO: total log probability: -7.84 +2024-01-16 22:20:14,472 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:14,472 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:14,473 (beam_search:483) INFO: best hypo: DIEANZEALDRIBEHNGMENDATEKAN + +2024-01-16 22:20:14,474 (asr_inference:494) INFO: speech length: 61920 +2024-01-16 22:20:14,483 (beam_search:428) INFO: decoder input length: 94 +2024-01-16 22:20:14,483 (beam_search:429) INFO: max output length: 94 +2024-01-16 22:20:14,483 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:14,640 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:14,640 (beam_search:476) INFO: -8.76 * 1.0 = -8.76 for ctc +2024-01-16 22:20:14,640 (beam_search:479) INFO: total log probability: -8.76 +2024-01-16 22:20:14,640 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 22:20:14,640 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:14,640 (beam_search:483) INFO: best hypo: BSCHLSDIESEREINMLITERSCHSEINGREIFENINDENKORARKRIK + +2024-01-16 22:20:14,642 (asr_inference:494) INFO: speech length: 19840 +2024-01-16 22:20:14,648 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 22:20:14,648 (beam_search:429) INFO: max output length: 28 +2024-01-16 22:20:14,648 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:14,668 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:14,668 (beam_search:476) INFO: -4.37 * 1.0 = -4.37 for ctc +2024-01-16 22:20:14,668 (beam_search:479) INFO: total log probability: -4.37 +2024-01-16 22:20:14,669 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:14,669 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:14,669 (beam_search:483) INFO: best hypo: NARTUOVEBNTLIC + +2024-01-16 22:20:14,670 (asr_inference:494) INFO: speech length: 23680 +2024-01-16 22:20:14,677 (beam_search:428) INFO: decoder input length: 34 +2024-01-16 22:20:14,677 (beam_search:429) INFO: max output length: 34 +2024-01-16 22:20:14,677 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:14,703 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:14,703 (beam_search:476) INFO: -2.71 * 1.0 = -2.71 for ctc +2024-01-16 22:20:14,703 (beam_search:479) INFO: total log probability: -2.71 +2024-01-16 22:20:14,703 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 22:20:14,703 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:14,703 (beam_search:483) INFO: best hypo: KALZIGRIEGBEINDET + +2024-01-16 22:20:14,704 (asr_inference:494) INFO: speech length: 82880 +2024-01-16 22:20:14,714 (beam_search:428) INFO: decoder input length: 127 +2024-01-16 22:20:14,715 (beam_search:429) INFO: max output length: 127 +2024-01-16 22:20:14,715 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:14,971 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:14,971 (beam_search:476) INFO: -21.55 * 1.0 = -21.55 for ctc +2024-01-16 22:20:14,971 (beam_search:479) INFO: total log probability: -21.55 +2024-01-16 22:20:14,971 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:20:14,971 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:14,971 (beam_search:483) INFO: best hypo: AUNNZENETEIAEMDNEUNZIGUNUSTRALIENSWIDERÜSTEREICHSCHABPLIGER + +2024-01-16 22:20:14,972 (asr_inference:494) INFO: speech length: 155360 +2024-01-16 22:20:14,987 (beam_search:428) INFO: decoder input length: 240 +2024-01-16 22:20:14,987 (beam_search:429) INFO: max output length: 240 +2024-01-16 22:20:14,987 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:15,890 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:15,890 (beam_search:476) INFO: -33.60 * 1.0 = -33.60 for ctc +2024-01-16 22:20:15,890 (beam_search:479) INFO: total log probability: -33.60 +2024-01-16 22:20:15,890 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:15,890 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:15,891 (beam_search:483) INFO: best hypo: DADIESEITANFANGNEUNZHNUNDERTNEUNUNFÜNFZIDRTHERSHENDERERULOTIOUNZRIGJIONGUNDERVIEDELKASTRUEINDENSOSELISTISCHENKURSEINGESHLAGENHAT + +2024-01-16 22:20:15,892 (asr_inference:494) INFO: speech length: 193440 +2024-01-16 22:20:15,910 (beam_search:428) INFO: decoder input length: 300 +2024-01-16 22:20:15,910 (beam_search:429) INFO: max output length: 300 +2024-01-16 22:20:15,910 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:17,274 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:17,274 (beam_search:476) INFO: -42.09 * 1.0 = -42.09 for ctc +2024-01-16 22:20:17,274 (beam_search:479) INFO: total log probability: -42.09 +2024-01-16 22:20:17,274 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:17,274 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:17,275 (beam_search:483) INFO: best hypo: DACHRWEITERENVELUSTRECHENKEMFENUNENENZWÖRTEVOLGEBEIDEGRIGSPATEINURDERUNTDREARENERBEGINDEAUSANDANDERSETZUNGEINBSREITEGÜLTIGESWASFENENSTILSTANSABKOMMNAPGESCLSSEN + +2024-01-16 22:20:17,276 (asr_inference:494) INFO: speech length: 38795 +2024-01-16 22:20:17,284 (beam_search:428) INFO: decoder input length: 58 +2024-01-16 22:20:17,284 (beam_search:429) INFO: max output length: 58 +2024-01-16 22:20:17,284 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:17,341 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:17,341 (beam_search:476) INFO: -4.03 * 1.0 = -4.03 for ctc +2024-01-16 22:20:17,341 (beam_search:479) INFO: total log probability: -4.03 +2024-01-16 22:20:17,341 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 22:20:17,341 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:17,341 (beam_search:483) INFO: best hypo: MANISTERBEISERVOSICHTICH + +2024-01-16 22:20:17,342 (asr_inference:494) INFO: speech length: 94000 +2024-01-16 22:20:17,353 (beam_search:428) INFO: decoder input length: 144 +2024-01-16 22:20:17,353 (beam_search:429) INFO: max output length: 144 +2024-01-16 22:20:17,353 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:17,633 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:17,633 (beam_search:476) INFO: -11.14 * 1.0 = -11.14 for ctc +2024-01-16 22:20:17,633 (beam_search:479) INFO: total log probability: -11.14 +2024-01-16 22:20:17,633 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:20:17,633 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:17,633 (beam_search:483) INFO: best hypo: DIEWERFLICHTSOLINDEUTSCHLANDLEIERNOCHNICHTABGESCHAFTWERN + +2024-01-16 22:20:17,635 (asr_inference:494) INFO: speech length: 52464 +2024-01-16 22:20:17,643 (beam_search:428) INFO: decoder input length: 79 +2024-01-16 22:20:17,643 (beam_search:429) INFO: max output length: 79 +2024-01-16 22:20:17,643 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:17,745 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:17,745 (beam_search:476) INFO: -7.70 * 1.0 = -7.70 for ctc +2024-01-16 22:20:17,745 (beam_search:479) INFO: total log probability: -7.70 +2024-01-16 22:20:17,745 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:20:17,745 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:17,745 (beam_search:483) INFO: best hypo: ESGEBTAUCHMISPRAUCHTUCHABERTGEBER + +2024-01-16 22:20:17,746 (asr_inference:494) INFO: speech length: 48816 +2024-01-16 22:20:17,755 (beam_search:428) INFO: decoder input length: 74 +2024-01-16 22:20:17,755 (beam_search:429) INFO: max output length: 74 +2024-01-16 22:20:17,755 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:17,827 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:17,827 (beam_search:476) INFO: -6.36 * 1.0 = -6.36 for ctc +2024-01-16 22:20:17,827 (beam_search:479) INFO: total log probability: -6.36 +2024-01-16 22:20:17,827 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:20:17,827 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:17,827 (beam_search:483) INFO: best hypo: DIEKINDESINDANHANKEBONEN + +2024-01-16 22:20:17,828 (asr_inference:494) INFO: speech length: 66352 +2024-01-16 22:20:17,838 (beam_search:428) INFO: decoder input length: 101 +2024-01-16 22:20:17,838 (beam_search:429) INFO: max output length: 101 +2024-01-16 22:20:17,838 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:17,996 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:17,996 (beam_search:476) INFO: -9.06 * 1.0 = -9.06 for ctc +2024-01-16 22:20:17,996 (beam_search:479) INFO: total log probability: -9.06 +2024-01-16 22:20:17,996 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:20:17,996 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:17,996 (beam_search:483) INFO: best hypo: DERACKWEITEDERKADASTOFESOLLVERDEUTLICHTWERDN + +2024-01-16 22:20:17,997 (asr_inference:494) INFO: speech length: 30098 +2024-01-16 22:20:18,004 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 22:20:18,004 (beam_search:429) INFO: max output length: 45 +2024-01-16 22:20:18,004 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:18,028 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:18,028 (beam_search:476) INFO: -8.43 * 1.0 = -8.43 for ctc +2024-01-16 22:20:18,028 (beam_search:479) INFO: total log probability: -8.43 +2024-01-16 22:20:18,028 (beam_search:480) INFO: normalized log probability: -0.60 +2024-01-16 22:20:18,028 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:18,028 (beam_search:483) INFO: best hypo: SENRLLNDET + +2024-01-16 22:20:18,029 (asr_inference:494) INFO: speech length: 52000 +2024-01-16 22:20:18,037 (beam_search:428) INFO: decoder input length: 79 +2024-01-16 22:20:18,037 (beam_search:429) INFO: max output length: 79 +2024-01-16 22:20:18,037 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:18,151 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:18,151 (beam_search:476) INFO: -8.48 * 1.0 = -8.48 for ctc +2024-01-16 22:20:18,151 (beam_search:479) INFO: total log probability: -8.48 +2024-01-16 22:20:18,151 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:20:18,151 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:18,151 (beam_search:483) INFO: best hypo: BEIMOGANDSTREITSTREITENBERDVEFASSUNGSOGANE + +2024-01-16 22:20:18,152 (asr_inference:494) INFO: speech length: 41365 +2024-01-16 22:20:18,160 (beam_search:428) INFO: decoder input length: 62 +2024-01-16 22:20:18,160 (beam_search:429) INFO: max output length: 62 +2024-01-16 22:20:18,160 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:18,214 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:18,214 (beam_search:476) INFO: -6.20 * 1.0 = -6.20 for ctc +2024-01-16 22:20:18,214 (beam_search:479) INFO: total log probability: -6.20 +2024-01-16 22:20:18,214 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:18,214 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:18,214 (beam_search:483) INFO: best hypo: DAWAGECHARZUBTZWEIFEN + +2024-01-16 22:20:18,215 (asr_inference:494) INFO: speech length: 49275 +2024-01-16 22:20:18,223 (beam_search:428) INFO: decoder input length: 74 +2024-01-16 22:20:18,223 (beam_search:429) INFO: max output length: 74 +2024-01-16 22:20:18,223 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:18,310 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:18,310 (beam_search:476) INFO: -7.93 * 1.0 = -7.93 for ctc +2024-01-16 22:20:18,310 (beam_search:479) INFO: total log probability: -7.93 +2024-01-16 22:20:18,310 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:20:18,310 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:18,310 (beam_search:483) INFO: best hypo: MANSOTEDENAUFGARGHEINVFALTRAUN + +2024-01-16 22:20:18,311 (asr_inference:494) INFO: speech length: 60075 +2024-01-16 22:20:18,320 (beam_search:428) INFO: decoder input length: 91 +2024-01-16 22:20:18,320 (beam_search:429) INFO: max output length: 91 +2024-01-16 22:20:18,320 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:18,447 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:18,448 (beam_search:476) INFO: -8.13 * 1.0 = -8.13 for ctc +2024-01-16 22:20:18,448 (beam_search:479) INFO: total log probability: -8.13 +2024-01-16 22:20:18,448 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:20:18,448 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:18,448 (beam_search:483) INFO: best hypo: DEEFNLICHESCHULENWERNNICHGETELKTWEREN + +2024-01-16 22:20:18,449 (asr_inference:494) INFO: speech length: 75094 +2024-01-16 22:20:18,459 (beam_search:428) INFO: decoder input length: 115 +2024-01-16 22:20:18,459 (beam_search:429) INFO: max output length: 115 +2024-01-16 22:20:18,459 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:18,584 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:18,584 (beam_search:476) INFO: -11.28 * 1.0 = -11.28 for ctc +2024-01-16 22:20:18,584 (beam_search:479) INFO: total log probability: -11.28 +2024-01-16 22:20:18,584 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:20:18,584 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:18,584 (beam_search:483) INFO: best hypo: BAEGELTISTAUSKETZHLTWORDEN + +2024-01-16 22:20:18,585 (asr_inference:494) INFO: speech length: 80336 +2024-01-16 22:20:18,595 (beam_search:428) INFO: decoder input length: 123 +2024-01-16 22:20:18,595 (beam_search:429) INFO: max output length: 123 +2024-01-16 22:20:18,595 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:18,783 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:18,783 (beam_search:476) INFO: -8.90 * 1.0 = -8.90 for ctc +2024-01-16 22:20:18,783 (beam_search:479) INFO: total log probability: -8.90 +2024-01-16 22:20:18,783 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:20:18,783 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:18,784 (beam_search:483) INFO: best hypo: ESOLENREIHUNDERDTAUSENDNOEABESPLÄTEINSTIEN + +2024-01-16 22:20:18,785 (asr_inference:494) INFO: speech length: 82000 +2024-01-16 22:20:18,795 (beam_search:428) INFO: decoder input length: 126 +2024-01-16 22:20:18,795 (beam_search:429) INFO: max output length: 126 +2024-01-16 22:20:18,795 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:18,981 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:18,981 (beam_search:476) INFO: -12.01 * 1.0 = -12.01 for ctc +2024-01-16 22:20:18,981 (beam_search:479) INFO: total log probability: -12.01 +2024-01-16 22:20:18,981 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:20:18,981 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:18,981 (beam_search:483) INFO: best hypo: DIEKERBERVELETZUNGKANALSBEISPILENDWERDENT + +2024-01-16 22:20:18,983 (asr_inference:494) INFO: speech length: 46069 +2024-01-16 22:20:18,991 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 22:20:18,991 (beam_search:429) INFO: max output length: 69 +2024-01-16 22:20:18,991 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:19,056 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:19,056 (beam_search:476) INFO: -8.06 * 1.0 = -8.06 for ctc +2024-01-16 22:20:19,056 (beam_search:479) INFO: total log probability: -8.06 +2024-01-16 22:20:19,056 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:20:19,056 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:19,056 (beam_search:483) INFO: best hypo: DIERENZEITWERSCHTENBODEN + +2024-01-16 22:20:19,057 (asr_inference:494) INFO: speech length: 68000 +2024-01-16 22:20:19,067 (beam_search:428) INFO: decoder input length: 104 +2024-01-16 22:20:19,067 (beam_search:429) INFO: max output length: 104 +2024-01-16 22:20:19,067 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:19,212 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:19,212 (beam_search:476) INFO: -13.70 * 1.0 = -13.70 for ctc +2024-01-16 22:20:19,212 (beam_search:479) INFO: total log probability: -13.70 +2024-01-16 22:20:19,212 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:20:19,212 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:19,212 (beam_search:483) INFO: best hypo: DSTDABEFELUGSBEÜRDENKEIENZULIERESKELHABEN + +2024-01-16 22:20:19,214 (asr_inference:494) INFO: speech length: 53125 +2024-01-16 22:20:19,222 (beam_search:428) INFO: decoder input length: 81 +2024-01-16 22:20:19,222 (beam_search:429) INFO: max output length: 81 +2024-01-16 22:20:19,222 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:19,303 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:19,303 (beam_search:476) INFO: -4.70 * 1.0 = -4.70 for ctc +2024-01-16 22:20:19,303 (beam_search:479) INFO: total log probability: -4.70 +2024-01-16 22:20:19,303 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 22:20:19,303 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:19,303 (beam_search:483) INFO: best hypo: DIINERESENFINDENKEINEHÖR + +2024-01-16 22:20:19,304 (asr_inference:494) INFO: speech length: 223575 +2024-01-16 22:20:19,324 (beam_search:428) INFO: decoder input length: 347 +2024-01-16 22:20:19,324 (beam_search:429) INFO: max output length: 347 +2024-01-16 22:20:19,324 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:20,167 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:20,167 (beam_search:476) INFO: -37.63 * 1.0 = -37.63 for ctc +2024-01-16 22:20:20,167 (beam_search:479) INFO: total log probability: -37.63 +2024-01-16 22:20:20,167 (beam_search:480) INFO: normalized log probability: -0.47 +2024-01-16 22:20:20,167 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:20,168 (beam_search:483) INFO: best hypo: IFWEILTASSTDETABLARTOHERRÜGSCHIEDTASSTDENRÜGGTASSTDERÜGEGIERSTASSTDEA + +2024-01-16 22:20:20,169 (asr_inference:494) INFO: speech length: 82000 +2024-01-16 22:20:20,179 (beam_search:428) INFO: decoder input length: 126 +2024-01-16 22:20:20,179 (beam_search:429) INFO: max output length: 126 +2024-01-16 22:20:20,179 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:20,384 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:20,384 (beam_search:476) INFO: -17.84 * 1.0 = -17.84 for ctc +2024-01-16 22:20:20,384 (beam_search:479) INFO: total log probability: -17.84 +2024-01-16 22:20:20,384 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:20:20,384 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:20,384 (beam_search:483) INFO: best hypo: DEBETROTCENENUNANBERECHTIGDEENHERIGELTENMACHEN + +2024-01-16 22:20:20,386 (asr_inference:494) INFO: speech length: 116566 +2024-01-16 22:20:20,398 (beam_search:428) INFO: decoder input length: 180 +2024-01-16 22:20:20,398 (beam_search:429) INFO: max output length: 180 +2024-01-16 22:20:20,398 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:20,767 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:20,767 (beam_search:476) INFO: -16.90 * 1.0 = -16.90 for ctc +2024-01-16 22:20:20,767 (beam_search:479) INFO: total log probability: -16.90 +2024-01-16 22:20:20,767 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:20,767 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:20,768 (beam_search:483) INFO: best hypo: EINDRITERHARDDEMSCHEIDIGKTENVREIWELIGKLEISTDONGENZUKOMENLAEN + +2024-01-16 22:20:20,769 (asr_inference:494) INFO: speech length: 60000 +2024-01-16 22:20:20,778 (beam_search:428) INFO: decoder input length: 91 +2024-01-16 22:20:20,778 (beam_search:429) INFO: max output length: 91 +2024-01-16 22:20:20,778 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:20,859 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:20,859 (beam_search:476) INFO: -7.49 * 1.0 = -7.49 for ctc +2024-01-16 22:20:20,859 (beam_search:479) INFO: total log probability: -7.49 +2024-01-16 22:20:20,859 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:20:20,859 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:20,859 (beam_search:483) INFO: best hypo: SONENAURECHZNEBEDEBILT + +2024-01-16 22:20:20,860 (asr_inference:494) INFO: speech length: 70000 +2024-01-16 22:20:20,870 (beam_search:428) INFO: decoder input length: 107 +2024-01-16 22:20:20,870 (beam_search:429) INFO: max output length: 107 +2024-01-16 22:20:20,870 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:21,029 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:21,030 (beam_search:476) INFO: -14.20 * 1.0 = -14.20 for ctc +2024-01-16 22:20:21,030 (beam_search:479) INFO: total log probability: -14.20 +2024-01-16 22:20:21,030 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:20:21,030 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:21,030 (beam_search:483) INFO: best hypo: IEREINENICHTARZLSCHEMEITEBÜLSERKLEUNABPKREN + +2024-01-16 22:20:21,031 (asr_inference:494) INFO: speech length: 84822 +2024-01-16 22:20:21,041 (beam_search:428) INFO: decoder input length: 130 +2024-01-16 22:20:21,042 (beam_search:429) INFO: max output length: 130 +2024-01-16 22:20:21,042 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:21,201 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:21,201 (beam_search:476) INFO: -10.68 * 1.0 = -10.68 for ctc +2024-01-16 22:20:21,201 (beam_search:479) INFO: total log probability: -10.68 +2024-01-16 22:20:21,201 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:20:21,201 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:21,201 (beam_search:483) INFO: best hypo: DAMUSTEJAHRAUFIEENFALSOKOMEN + +2024-01-16 22:20:21,202 (asr_inference:494) INFO: speech length: 80000 +2024-01-16 22:20:21,212 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 22:20:21,212 (beam_search:429) INFO: max output length: 122 +2024-01-16 22:20:21,212 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:21,389 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:21,389 (beam_search:476) INFO: -11.57 * 1.0 = -11.57 for ctc +2024-01-16 22:20:21,389 (beam_search:479) INFO: total log probability: -11.57 +2024-01-16 22:20:21,389 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:21,389 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:21,389 (beam_search:483) INFO: best hypo: MEREREKLEINSKONSICGEINEIPIERRESETEINT + +2024-01-16 22:20:21,390 (asr_inference:494) INFO: speech length: 131838 +2024-01-16 22:20:21,404 (beam_search:428) INFO: decoder input length: 203 +2024-01-16 22:20:21,404 (beam_search:429) INFO: max output length: 203 +2024-01-16 22:20:21,404 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:21,768 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:21,768 (beam_search:476) INFO: -20.96 * 1.0 = -20.96 for ctc +2024-01-16 22:20:21,768 (beam_search:479) INFO: total log probability: -20.96 +2024-01-16 22:20:21,768 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:20:21,768 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:21,768 (beam_search:483) INFO: best hypo: WADIEGINZTIGERVEISHIESALSORSIHZUSAMENENMENANSTATZUN + +2024-01-16 22:20:21,770 (asr_inference:494) INFO: speech length: 71680 +2024-01-16 22:20:21,780 (beam_search:428) INFO: decoder input length: 109 +2024-01-16 22:20:21,780 (beam_search:429) INFO: max output length: 109 +2024-01-16 22:20:21,780 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:21,920 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:21,921 (beam_search:476) INFO: -8.70 * 1.0 = -8.70 for ctc +2024-01-16 22:20:21,921 (beam_search:479) INFO: total log probability: -8.70 +2024-01-16 22:20:21,921 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:20:21,921 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:21,921 (beam_search:483) INFO: best hypo: DERCHOLEHERZEINELEISTUNGANGEBORTEN + +2024-01-16 22:20:21,922 (asr_inference:494) INFO: speech length: 33267 +2024-01-16 22:20:21,930 (beam_search:428) INFO: decoder input length: 49 +2024-01-16 22:20:21,930 (beam_search:429) INFO: max output length: 49 +2024-01-16 22:20:21,930 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:21,951 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:21,951 (beam_search:476) INFO: -6.00 * 1.0 = -6.00 for ctc +2024-01-16 22:20:21,951 (beam_search:479) INFO: total log probability: -6.00 +2024-01-16 22:20:21,951 (beam_search:480) INFO: normalized log probability: -0.50 +2024-01-16 22:20:21,951 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:21,951 (beam_search:483) INFO: best hypo: SODASEISF + +2024-01-16 22:20:21,952 (asr_inference:494) INFO: speech length: 79531 +2024-01-16 22:20:21,962 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 22:20:21,962 (beam_search:429) INFO: max output length: 122 +2024-01-16 22:20:21,962 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:22,098 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:22,098 (beam_search:476) INFO: -6.75 * 1.0 = -6.75 for ctc +2024-01-16 22:20:22,098 (beam_search:479) INFO: total log probability: -6.75 +2024-01-16 22:20:22,098 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:20:22,098 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:22,098 (beam_search:483) INFO: best hypo: DEBATRIENWARNERSTAVERALTET + +2024-01-16 22:20:22,100 (asr_inference:494) INFO: speech length: 86699 +2024-01-16 22:20:22,110 (beam_search:428) INFO: decoder input length: 133 +2024-01-16 22:20:22,110 (beam_search:429) INFO: max output length: 133 +2024-01-16 22:20:22,110 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:22,267 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:22,267 (beam_search:476) INFO: -14.37 * 1.0 = -14.37 for ctc +2024-01-16 22:20:22,267 (beam_search:479) INFO: total log probability: -14.37 +2024-01-16 22:20:22,267 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-16 22:20:22,267 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:22,267 (beam_search:483) INFO: best hypo: DESESZHIEVORDENORTALWALSEREICHT + +2024-01-16 22:20:22,268 (asr_inference:494) INFO: speech length: 46731 +2024-01-16 22:20:22,277 (beam_search:428) INFO: decoder input length: 71 +2024-01-16 22:20:22,277 (beam_search:429) INFO: max output length: 71 +2024-01-16 22:20:22,277 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:22,352 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:22,352 (beam_search:476) INFO: -8.56 * 1.0 = -8.56 for ctc +2024-01-16 22:20:22,352 (beam_search:479) INFO: total log probability: -8.56 +2024-01-16 22:20:22,352 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:20:22,352 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:22,352 (beam_search:483) INFO: best hypo: TIESEWERUNGWIRTSELRLANGELEBEN + +2024-01-16 22:20:22,353 (asr_inference:494) INFO: speech length: 73728 +2024-01-16 22:20:22,363 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 22:20:22,363 (beam_search:429) INFO: max output length: 113 +2024-01-16 22:20:22,363 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:22,471 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:22,471 (beam_search:476) INFO: -11.42 * 1.0 = -11.42 for ctc +2024-01-16 22:20:22,471 (beam_search:479) INFO: total log probability: -11.42 +2024-01-16 22:20:22,471 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-16 22:20:22,471 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:22,471 (beam_search:483) INFO: best hypo: DRDZEIEANAFENBASHONVIEE + +2024-01-16 22:20:22,473 (asr_inference:494) INFO: speech length: 140686 +2024-01-16 22:20:22,487 (beam_search:428) INFO: decoder input length: 217 +2024-01-16 22:20:22,487 (beam_search:429) INFO: max output length: 217 +2024-01-16 22:20:22,487 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:22,912 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:22,912 (beam_search:476) INFO: -23.89 * 1.0 = -23.89 for ctc +2024-01-16 22:20:22,912 (beam_search:479) INFO: total log probability: -23.89 +2024-01-16 22:20:22,912 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:20:22,912 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:22,913 (beam_search:483) INFO: best hypo: ALSIGEENENNEIGKTEMÄAGIIERNORGANSFLCHTIGKZIUOUBUNDERFARTEAR + +2024-01-16 22:20:22,914 (asr_inference:494) INFO: speech length: 54085 +2024-01-16 22:20:22,922 (beam_search:428) INFO: decoder input length: 82 +2024-01-16 22:20:22,922 (beam_search:429) INFO: max output length: 82 +2024-01-16 22:20:22,922 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:22,989 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:22,990 (beam_search:476) INFO: -9.70 * 1.0 = -9.70 for ctc +2024-01-16 22:20:22,990 (beam_search:479) INFO: total log probability: -9.70 +2024-01-16 22:20:22,990 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-16 22:20:22,990 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:22,990 (beam_search:483) INFO: best hypo: TIERTEMEMIEBERISTIAN + +2024-01-16 22:20:22,991 (asr_inference:494) INFO: speech length: 89600 +2024-01-16 22:20:23,002 (beam_search:428) INFO: decoder input length: 137 +2024-01-16 22:20:23,002 (beam_search:429) INFO: max output length: 137 +2024-01-16 22:20:23,002 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:23,208 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:23,208 (beam_search:476) INFO: -16.82 * 1.0 = -16.82 for ctc +2024-01-16 22:20:23,208 (beam_search:479) INFO: total log probability: -16.82 +2024-01-16 22:20:23,208 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 22:20:23,208 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:23,209 (beam_search:483) INFO: best hypo: DEMSTDEHENADTÜÖRLICHAUCHFAMMÖHENGEGENÜEBER + +2024-01-16 22:20:23,210 (asr_inference:494) INFO: speech length: 69093 +2024-01-16 22:20:23,219 (beam_search:428) INFO: decoder input length: 105 +2024-01-16 22:20:23,219 (beam_search:429) INFO: max output length: 105 +2024-01-16 22:20:23,219 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:23,373 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:23,373 (beam_search:476) INFO: -9.98 * 1.0 = -9.98 for ctc +2024-01-16 22:20:23,373 (beam_search:479) INFO: total log probability: -9.98 +2024-01-16 22:20:23,373 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:20:23,373 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:23,373 (beam_search:483) INFO: best hypo: DIEEREALELANGEWIETNICHTVOUSTENDICHABPGEBELET + +# Accounting: time=32 threads=1 +# Ended (code 0) at Tue Jan 16 22:20:23 CST 2024, elapsed time 32 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.4.log b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.4.log new file mode 100644 index 0000000000000000000000000000000000000000..aeebf01653598709319bdc15868369cb5ee259cc --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.4.log @@ -0,0 +1,1834 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.4 --config conf/decode_asr.yaml +# Started at Tue Jan 16 22:20:23 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.4 --config conf/decode_asr.yaml +2024-01-16 22:20:25,197 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml +2024-01-16 22:20:25,215 (asr:523) INFO: Vocabulary size: 44 +2024-01-16 22:20:25,277 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 22:20:25,277 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 22:20:25,388 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 22:20:26,683 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 22:20:27,911 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 22:20:27,911 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 22:20:27,911 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 22:20:27,944 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-16 22:20:28,018 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 22:20:28,130 (asr_inference:494) INFO: speech length: 62416 +2024-01-16 22:20:29,344 (beam_search:428) INFO: decoder input length: 95 +2024-01-16 22:20:29,344 (beam_search:429) INFO: max output length: 95 +2024-01-16 22:20:29,344 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:29,449 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:29,449 (beam_search:476) INFO: -7.88 * 1.0 = -7.88 for ctc +2024-01-16 22:20:29,449 (beam_search:479) INFO: total log probability: -7.88 +2024-01-16 22:20:29,449 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:29,449 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:29,450 (beam_search:483) INFO: best hypo: ESKANAUCHNOCHFVIESCLMAWERDEN + +2024-01-16 22:20:29,474 (asr_inference:494) INFO: speech length: 43477 +2024-01-16 22:20:29,483 (beam_search:428) INFO: decoder input length: 65 +2024-01-16 22:20:29,483 (beam_search:429) INFO: max output length: 65 +2024-01-16 22:20:29,483 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:29,556 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:29,556 (beam_search:476) INFO: -9.71 * 1.0 = -9.71 for ctc +2024-01-16 22:20:29,556 (beam_search:479) INFO: total log probability: -9.71 +2024-01-16 22:20:29,556 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:20:29,556 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:29,556 (beam_search:483) INFO: best hypo: DIEBPLITIGINRESIERZSICHNICHTEMER + +2024-01-16 22:20:29,557 (asr_inference:494) INFO: speech length: 112000 +2024-01-16 22:20:29,570 (beam_search:428) INFO: decoder input length: 172 +2024-01-16 22:20:29,570 (beam_search:429) INFO: max output length: 172 +2024-01-16 22:20:29,570 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:29,929 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:29,929 (beam_search:476) INFO: -16.42 * 1.0 = -16.42 for ctc +2024-01-16 22:20:29,929 (beam_search:479) INFO: total log probability: -16.42 +2024-01-16 22:20:29,929 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:29,929 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:29,929 (beam_search:483) INFO: best hypo: INHALTZFREHERDBEDEITEDTDASEINHALDDEVERTRACKLIHNVEEINBARUNGENU + +2024-01-16 22:20:29,931 (asr_inference:494) INFO: speech length: 108000 +2024-01-16 22:20:29,943 (beam_search:428) INFO: decoder input length: 166 +2024-01-16 22:20:29,943 (beam_search:429) INFO: max output length: 166 +2024-01-16 22:20:29,943 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:30,200 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:30,200 (beam_search:476) INFO: -20.96 * 1.0 = -20.96 for ctc +2024-01-16 22:20:30,200 (beam_search:479) INFO: total log probability: -20.96 +2024-01-16 22:20:30,200 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-16 22:20:30,200 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:30,200 (beam_search:483) INFO: best hypo: DERCUNERVELITZDESEIESUOGKVERSPLIENSCHULTEAFT + +2024-01-16 22:20:30,202 (asr_inference:494) INFO: speech length: 101888 +2024-01-16 22:20:30,213 (beam_search:428) INFO: decoder input length: 157 +2024-01-16 22:20:30,213 (beam_search:429) INFO: max output length: 157 +2024-01-16 22:20:30,213 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:30,452 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:30,452 (beam_search:476) INFO: -14.94 * 1.0 = -14.94 for ctc +2024-01-16 22:20:30,452 (beam_search:479) INFO: total log probability: -14.94 +2024-01-16 22:20:30,452 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:20:30,452 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:30,452 (beam_search:483) INFO: best hypo: DIESEGETREIDEDENDINZSPESONDEREALSFVIVOTTA + +2024-01-16 22:20:30,453 (asr_inference:494) INFO: speech length: 86000 +2024-01-16 22:20:30,464 (beam_search:428) INFO: decoder input length: 132 +2024-01-16 22:20:30,464 (beam_search:429) INFO: max output length: 132 +2024-01-16 22:20:30,464 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:30,711 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:30,711 (beam_search:476) INFO: -17.00 * 1.0 = -17.00 for ctc +2024-01-16 22:20:30,711 (beam_search:479) INFO: total log probability: -17.00 +2024-01-16 22:20:30,711 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:20:30,711 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:30,712 (beam_search:483) INFO: best hypo: ZTÜPBESCHRWEISEWERDSTDATICHEEIPIRRESENVONDSORVENEINGETZT + +2024-01-16 22:20:30,713 (asr_inference:494) INFO: speech length: 55568 +2024-01-16 22:20:30,722 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 22:20:30,722 (beam_search:429) INFO: max output length: 84 +2024-01-16 22:20:30,722 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:30,803 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:30,803 (beam_search:476) INFO: -12.71 * 1.0 = -12.71 for ctc +2024-01-16 22:20:30,803 (beam_search:479) INFO: total log probability: -12.71 +2024-01-16 22:20:30,803 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-16 22:20:30,803 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:30,803 (beam_search:483) INFO: best hypo: JEZTWRZOLANEAMGEGLAUBPT + +2024-01-16 22:20:30,804 (asr_inference:494) INFO: speech length: 94891 +2024-01-16 22:20:30,815 (beam_search:428) INFO: decoder input length: 146 +2024-01-16 22:20:30,815 (beam_search:429) INFO: max output length: 146 +2024-01-16 22:20:30,815 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:31,047 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:31,047 (beam_search:476) INFO: -11.69 * 1.0 = -11.69 for ctc +2024-01-16 22:20:31,047 (beam_search:479) INFO: total log probability: -11.69 +2024-01-16 22:20:31,047 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:20:31,047 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:31,047 (beam_search:483) INFO: best hypo: UNDARSCHITLICHEERGEBNESEHABEMSECHEREIGNERDT + +2024-01-16 22:20:31,049 (asr_inference:494) INFO: speech length: 94550 +2024-01-16 22:20:31,060 (beam_search:428) INFO: decoder input length: 145 +2024-01-16 22:20:31,060 (beam_search:429) INFO: max output length: 145 +2024-01-16 22:20:31,060 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:31,316 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:31,316 (beam_search:476) INFO: -9.45 * 1.0 = -9.45 for ctc +2024-01-16 22:20:31,316 (beam_search:479) INFO: total log probability: -9.45 +2024-01-16 22:20:31,316 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:20:31,316 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:31,317 (beam_search:483) INFO: best hypo: TERHFARDECHTIGEWUORDENNEÄCHTVOREINGERECHTGESTÄLLT + +2024-01-16 22:20:31,318 (asr_inference:494) INFO: speech length: 94346 +2024-01-16 22:20:31,329 (beam_search:428) INFO: decoder input length: 145 +2024-01-16 22:20:31,329 (beam_search:429) INFO: max output length: 145 +2024-01-16 22:20:31,329 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:31,585 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:31,585 (beam_search:476) INFO: -17.63 * 1.0 = -17.63 for ctc +2024-01-16 22:20:31,585 (beam_search:479) INFO: total log probability: -17.63 +2024-01-16 22:20:31,585 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:20:31,585 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:31,586 (beam_search:483) INFO: best hypo: AUFMACHENDISTDEFÜNICHTAUSTZIEHNUNWEISEGERDWASNOCALES + +2024-01-16 22:20:31,587 (asr_inference:494) INFO: speech length: 120000 +2024-01-16 22:20:31,600 (beam_search:428) INFO: decoder input length: 185 +2024-01-16 22:20:31,600 (beam_search:429) INFO: max output length: 185 +2024-01-16 22:20:31,600 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:31,976 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:31,976 (beam_search:476) INFO: -11.78 * 1.0 = -11.78 for ctc +2024-01-16 22:20:31,976 (beam_search:479) INFO: total log probability: -11.78 +2024-01-16 22:20:31,976 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:20:31,976 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:31,976 (beam_search:483) INFO: best hypo: INKESAMDREIUNDZWANZICPERSONENAUSVERSCHIEDENPAEMENTENNEMENTEI + +2024-01-16 22:20:31,978 (asr_inference:494) INFO: speech length: 73728 +2024-01-16 22:20:31,988 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 22:20:31,988 (beam_search:429) INFO: max output length: 113 +2024-01-16 22:20:31,988 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:32,179 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:32,179 (beam_search:476) INFO: -18.59 * 1.0 = -18.59 for ctc +2024-01-16 22:20:32,179 (beam_search:479) INFO: total log probability: -18.59 +2024-01-16 22:20:32,179 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:20:32,179 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:32,179 (beam_search:483) INFO: best hypo: VORDRUNGSEFTEWERENEMGLEIBIGEAUSCLIESIEZOGEORORTENET + +2024-01-16 22:20:32,180 (asr_inference:494) INFO: speech length: 74240 +2024-01-16 22:20:32,190 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 22:20:32,190 (beam_search:429) INFO: max output length: 113 +2024-01-16 22:20:32,190 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:32,302 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:32,302 (beam_search:476) INFO: -9.31 * 1.0 = -9.31 for ctc +2024-01-16 22:20:32,302 (beam_search:479) INFO: total log probability: -9.31 +2024-01-16 22:20:32,302 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:20:32,302 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:32,302 (beam_search:483) INFO: best hypo: DAPOBLEEMHWVWRDEBEHOBEN + +2024-01-16 22:20:32,303 (asr_inference:494) INFO: speech length: 74949 +2024-01-16 22:20:32,313 (beam_search:428) INFO: decoder input length: 115 +2024-01-16 22:20:32,313 (beam_search:429) INFO: max output length: 115 +2024-01-16 22:20:32,313 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:32,492 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:32,492 (beam_search:476) INFO: -12.09 * 1.0 = -12.09 for ctc +2024-01-16 22:20:32,492 (beam_search:479) INFO: total log probability: -12.09 +2024-01-16 22:20:32,492 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:32,492 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:32,492 (beam_search:483) INFO: best hypo: FRDIERHEINUNVNUNTERROCHENERDESKRITERSPACHE + +2024-01-16 22:20:32,494 (asr_inference:494) INFO: speech length: 56197 +2024-01-16 22:20:32,502 (beam_search:428) INFO: decoder input length: 85 +2024-01-16 22:20:32,502 (beam_search:429) INFO: max output length: 85 +2024-01-16 22:20:32,503 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:32,609 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:32,610 (beam_search:476) INFO: -8.09 * 1.0 = -8.09 for ctc +2024-01-16 22:20:32,610 (beam_search:479) INFO: total log probability: -8.09 +2024-01-16 22:20:32,610 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:20:32,610 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:32,610 (beam_search:483) INFO: best hypo: DICHINSENKÖÜNDTENSERFLWICHTIGAWERDN + +2024-01-16 22:20:32,611 (asr_inference:494) INFO: speech length: 43691 +2024-01-16 22:20:32,619 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 22:20:32,619 (beam_search:429) INFO: max output length: 66 +2024-01-16 22:20:32,619 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:32,708 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:32,708 (beam_search:476) INFO: -9.10 * 1.0 = -9.10 for ctc +2024-01-16 22:20:32,708 (beam_search:479) INFO: total log probability: -9.10 +2024-01-16 22:20:32,708 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:20:32,708 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:32,708 (beam_search:483) INFO: best hypo: DIERSTUSLIEDEDIGLIHREINZIGEMALVERWENDET + +2024-01-16 22:20:32,709 (asr_inference:494) INFO: speech length: 104960 +2024-01-16 22:20:32,721 (beam_search:428) INFO: decoder input length: 161 +2024-01-16 22:20:32,721 (beam_search:429) INFO: max output length: 161 +2024-01-16 22:20:32,721 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:33,023 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:33,023 (beam_search:476) INFO: -10.00 * 1.0 = -10.00 for ctc +2024-01-16 22:20:33,023 (beam_search:479) INFO: total log probability: -10.00 +2024-01-16 22:20:33,024 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:20:33,024 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:33,024 (beam_search:483) INFO: best hypo: DASLANDENWEGKELTESECHZUEINERMEIETERESCHENGROSSMACHT + +2024-01-16 22:20:33,025 (asr_inference:494) INFO: speech length: 60016 +2024-01-16 22:20:33,034 (beam_search:428) INFO: decoder input length: 91 +2024-01-16 22:20:33,034 (beam_search:429) INFO: max output length: 91 +2024-01-16 22:20:33,034 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:33,144 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:33,144 (beam_search:476) INFO: -7.52 * 1.0 = -7.52 for ctc +2024-01-16 22:20:33,144 (beam_search:479) INFO: total log probability: -7.52 +2024-01-16 22:20:33,144 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:20:33,144 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:33,144 (beam_search:483) INFO: best hypo: ESINDTUNDBLEIBEVERBREICHERBANDEN + +2024-01-16 22:20:33,145 (asr_inference:494) INFO: speech length: 42715 +2024-01-16 22:20:33,154 (beam_search:428) INFO: decoder input length: 64 +2024-01-16 22:20:33,154 (beam_search:429) INFO: max output length: 64 +2024-01-16 22:20:33,154 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:33,216 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:33,217 (beam_search:476) INFO: -3.76 * 1.0 = -3.76 for ctc +2024-01-16 22:20:33,217 (beam_search:479) INFO: total log probability: -3.76 +2024-01-16 22:20:33,217 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 22:20:33,217 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:33,217 (beam_search:483) INFO: best hypo: DIEZEITENWERENSICHENDAN + +2024-01-16 22:20:33,218 (asr_inference:494) INFO: speech length: 108000 +2024-01-16 22:20:33,230 (beam_search:428) INFO: decoder input length: 166 +2024-01-16 22:20:33,230 (beam_search:429) INFO: max output length: 166 +2024-01-16 22:20:33,230 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:33,509 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:33,509 (beam_search:476) INFO: -11.21 * 1.0 = -11.21 for ctc +2024-01-16 22:20:33,509 (beam_search:479) INFO: total log probability: -11.21 +2024-01-16 22:20:33,509 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:20:33,509 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:33,509 (beam_search:483) INFO: best hypo: DENSTEFTINIEAKSBORUNGEINSCHIEBENWISTZUMANSCLAG + +2024-01-16 22:20:33,510 (asr_inference:494) INFO: speech length: 108000 +2024-01-16 22:20:33,522 (beam_search:428) INFO: decoder input length: 166 +2024-01-16 22:20:33,522 (beam_search:429) INFO: max output length: 166 +2024-01-16 22:20:33,522 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:33,874 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:33,874 (beam_search:476) INFO: -24.55 * 1.0 = -24.55 for ctc +2024-01-16 22:20:33,874 (beam_search:479) INFO: total log probability: -24.55 +2024-01-16 22:20:33,874 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:20:33,874 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:33,875 (beam_search:483) INFO: best hypo: DIEAUCHTBEIMBOAURSERWVIERSAMWIERTBALSPBILTSWEISSBELVEIERHFOGSN + +2024-01-16 22:20:33,876 (asr_inference:494) INFO: speech length: 48485 +2024-01-16 22:20:33,884 (beam_search:428) INFO: decoder input length: 73 +2024-01-16 22:20:33,884 (beam_search:429) INFO: max output length: 73 +2024-01-16 22:20:33,884 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:33,941 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:33,941 (beam_search:476) INFO: -5.99 * 1.0 = -5.99 for ctc +2024-01-16 22:20:33,941 (beam_search:479) INFO: total log probability: -5.99 +2024-01-16 22:20:33,941 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:20:33,941 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:33,941 (beam_search:483) INFO: best hypo: DAWANOCGAHEINIKLIE + +2024-01-16 22:20:33,942 (asr_inference:494) INFO: speech length: 47808 +2024-01-16 22:20:33,951 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 22:20:33,951 (beam_search:429) INFO: max output length: 72 +2024-01-16 22:20:33,951 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:34,031 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:34,031 (beam_search:476) INFO: -11.50 * 1.0 = -11.50 for ctc +2024-01-16 22:20:34,031 (beam_search:479) INFO: total log probability: -11.50 +2024-01-16 22:20:34,031 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:20:34,031 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:34,031 (beam_search:483) INFO: best hypo: DIEHABEMAUFMBARZHMNICHKOSEANGST + +2024-01-16 22:20:34,033 (asr_inference:494) INFO: speech length: 48059 +2024-01-16 22:20:34,041 (beam_search:428) INFO: decoder input length: 73 +2024-01-16 22:20:34,041 (beam_search:429) INFO: max output length: 73 +2024-01-16 22:20:34,041 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:34,122 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:34,123 (beam_search:476) INFO: -6.12 * 1.0 = -6.12 for ctc +2024-01-16 22:20:34,123 (beam_search:479) INFO: total log probability: -6.12 +2024-01-16 22:20:34,123 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:20:34,123 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:34,123 (beam_search:483) INFO: best hypo: FIELEVERLIERENERENABEITSPLATZS + +2024-01-16 22:20:34,124 (asr_inference:494) INFO: speech length: 80214 +2024-01-16 22:20:34,134 (beam_search:428) INFO: decoder input length: 123 +2024-01-16 22:20:34,134 (beam_search:429) INFO: max output length: 123 +2024-01-16 22:20:34,134 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:34,292 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:34,292 (beam_search:476) INFO: -9.99 * 1.0 = -9.99 for ctc +2024-01-16 22:20:34,292 (beam_search:479) INFO: total log probability: -9.99 +2024-01-16 22:20:34,292 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:34,293 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:34,293 (beam_search:483) INFO: best hypo: DARFÜHERGEBTESEINENPUNKTABPTZIOGE + +2024-01-16 22:20:34,294 (asr_inference:494) INFO: speech length: 82000 +2024-01-16 22:20:34,304 (beam_search:428) INFO: decoder input length: 126 +2024-01-16 22:20:34,304 (beam_search:429) INFO: max output length: 126 +2024-01-16 22:20:34,304 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:34,545 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:34,545 (beam_search:476) INFO: -14.42 * 1.0 = -14.42 for ctc +2024-01-16 22:20:34,545 (beam_search:479) INFO: total log probability: -14.42 +2024-01-16 22:20:34,545 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:20:34,546 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:34,546 (beam_search:483) INFO: best hypo: CDIEBEIDENSENDTWEINEUNSICHEVERBENUNGMUTDANANDERNKONTAKT + +2024-01-16 22:20:34,547 (asr_inference:494) INFO: speech length: 54053 +2024-01-16 22:20:34,556 (beam_search:428) INFO: decoder input length: 82 +2024-01-16 22:20:34,556 (beam_search:429) INFO: max output length: 82 +2024-01-16 22:20:34,556 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:34,648 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:34,648 (beam_search:476) INFO: -4.22 * 1.0 = -4.22 for ctc +2024-01-16 22:20:34,648 (beam_search:479) INFO: total log probability: -4.22 +2024-01-16 22:20:34,648 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 22:20:34,648 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:34,648 (beam_search:483) INFO: best hypo: BEIDESTÄKENTIEFINROTENZALN + +2024-01-16 22:20:34,650 (asr_inference:494) INFO: speech length: 79216 +2024-01-16 22:20:34,659 (beam_search:428) INFO: decoder input length: 121 +2024-01-16 22:20:34,660 (beam_search:429) INFO: max output length: 121 +2024-01-16 22:20:34,660 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:34,810 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:34,810 (beam_search:476) INFO: -13.34 * 1.0 = -13.34 for ctc +2024-01-16 22:20:34,810 (beam_search:479) INFO: total log probability: -13.34 +2024-01-16 22:20:34,810 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 22:20:34,810 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:34,810 (beam_search:483) INFO: best hypo: FÜNDFTZEHNOEFÜNFTZENDOFONENGOLF + +2024-01-16 22:20:34,811 (asr_inference:494) INFO: speech length: 131853 +2024-01-16 22:20:34,825 (beam_search:428) INFO: decoder input length: 204 +2024-01-16 22:20:34,825 (beam_search:429) INFO: max output length: 204 +2024-01-16 22:20:34,825 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:35,205 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:35,205 (beam_search:476) INFO: -19.49 * 1.0 = -19.49 for ctc +2024-01-16 22:20:35,205 (beam_search:479) INFO: total log probability: -19.49 +2024-01-16 22:20:35,205 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:20:35,205 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:35,205 (beam_search:483) INFO: best hypo: WIEMEINSCHENASEINEANDERENWÄLTSCHERDENZIERRETEUNDTDOCH + +2024-01-16 22:20:35,207 (asr_inference:494) INFO: speech length: 53697 +2024-01-16 22:20:35,215 (beam_search:428) INFO: decoder input length: 81 +2024-01-16 22:20:35,215 (beam_search:429) INFO: max output length: 81 +2024-01-16 22:20:35,215 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:35,307 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:35,307 (beam_search:476) INFO: -10.57 * 1.0 = -10.57 for ctc +2024-01-16 22:20:35,307 (beam_search:479) INFO: total log probability: -10.57 +2024-01-16 22:20:35,307 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:20:35,307 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:35,307 (beam_search:483) INFO: best hypo: BNDHMITEMHINTERNDESKEMILSAUFERT + +2024-01-16 22:20:35,308 (asr_inference:494) INFO: speech length: 131822 +2024-01-16 22:20:35,322 (beam_search:428) INFO: decoder input length: 203 +2024-01-16 22:20:35,322 (beam_search:429) INFO: max output length: 203 +2024-01-16 22:20:35,322 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:35,683 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:35,684 (beam_search:476) INFO: -18.31 * 1.0 = -18.31 for ctc +2024-01-16 22:20:35,684 (beam_search:479) INFO: total log probability: -18.31 +2024-01-16 22:20:35,684 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:20:35,684 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:35,684 (beam_search:483) INFO: best hypo: ADERUBERFÖRSTDEAZUNGDDEMITENCHIEFENGAUNBGAUENEIN + +2024-01-16 22:20:35,685 (asr_inference:494) INFO: speech length: 71525 +2024-01-16 22:20:35,695 (beam_search:428) INFO: decoder input length: 109 +2024-01-16 22:20:35,695 (beam_search:429) INFO: max output length: 109 +2024-01-16 22:20:35,695 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:35,856 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:35,856 (beam_search:476) INFO: -7.25 * 1.0 = -7.25 for ctc +2024-01-16 22:20:35,856 (beam_search:479) INFO: total log probability: -7.25 +2024-01-16 22:20:35,856 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 22:20:35,856 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:35,856 (beam_search:483) INFO: best hypo: ICHWUNDEREMIGIMARWIEDERÜBERIESEERKLERUNGEN + +2024-01-16 22:20:35,857 (asr_inference:494) INFO: speech length: 62806 +2024-01-16 22:20:35,867 (beam_search:428) INFO: decoder input length: 96 +2024-01-16 22:20:35,867 (beam_search:429) INFO: max output length: 96 +2024-01-16 22:20:35,867 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:36,012 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:36,012 (beam_search:476) INFO: -14.55 * 1.0 = -14.55 for ctc +2024-01-16 22:20:36,012 (beam_search:479) INFO: total log probability: -14.55 +2024-01-16 22:20:36,012 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:20:36,012 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:36,013 (beam_search:483) INFO: best hypo: BAINEMSMETRISCHNKRIOPTUSTEMBETANDESVORGEGAN + +2024-01-16 22:20:36,014 (asr_inference:494) INFO: speech length: 74070 +2024-01-16 22:20:36,024 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 22:20:36,024 (beam_search:429) INFO: max output length: 113 +2024-01-16 22:20:36,024 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:36,125 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:36,125 (beam_search:476) INFO: -11.07 * 1.0 = -11.07 for ctc +2024-01-16 22:20:36,125 (beam_search:479) INFO: total log probability: -11.07 +2024-01-16 22:20:36,125 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-16 22:20:36,125 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:36,126 (beam_search:483) INFO: best hypo: DAISDORDVERZEICHETE + +2024-01-16 22:20:36,127 (asr_inference:494) INFO: speech length: 69120 +2024-01-16 22:20:36,136 (beam_search:428) INFO: decoder input length: 105 +2024-01-16 22:20:36,136 (beam_search:429) INFO: max output length: 105 +2024-01-16 22:20:36,136 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:36,240 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:36,240 (beam_search:476) INFO: -7.25 * 1.0 = -7.25 for ctc +2024-01-16 22:20:36,240 (beam_search:479) INFO: total log probability: -7.25 +2024-01-16 22:20:36,240 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:36,240 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:36,241 (beam_search:483) INFO: best hypo: GELTISANERUTESTAUSCMITE + +2024-01-16 22:20:36,242 (asr_inference:494) INFO: speech length: 81067 +2024-01-16 22:20:36,252 (beam_search:428) INFO: decoder input length: 124 +2024-01-16 22:20:36,252 (beam_search:429) INFO: max output length: 124 +2024-01-16 22:20:36,252 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:36,406 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:36,406 (beam_search:476) INFO: -9.61 * 1.0 = -9.61 for ctc +2024-01-16 22:20:36,406 (beam_search:479) INFO: total log probability: -9.61 +2024-01-16 22:20:36,406 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:36,406 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:36,406 (beam_search:483) INFO: best hypo: DAWEHREWISENCHAFTLICHNODWENDEIGKG + +2024-01-16 22:20:36,407 (asr_inference:494) INFO: speech length: 81920 +2024-01-16 22:20:36,417 (beam_search:428) INFO: decoder input length: 125 +2024-01-16 22:20:36,417 (beam_search:429) INFO: max output length: 125 +2024-01-16 22:20:36,417 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:36,583 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:36,583 (beam_search:476) INFO: -12.45 * 1.0 = -12.45 for ctc +2024-01-16 22:20:36,583 (beam_search:479) INFO: total log probability: -12.45 +2024-01-16 22:20:36,583 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:20:36,583 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:36,584 (beam_search:483) INFO: best hypo: NBESDINTISSTRAPTATENKOMENENBETHACHT + +2024-01-16 22:20:36,585 (asr_inference:494) INFO: speech length: 95403 +2024-01-16 22:20:36,596 (beam_search:428) INFO: decoder input length: 147 +2024-01-16 22:20:36,596 (beam_search:429) INFO: max output length: 147 +2024-01-16 22:20:36,596 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:36,814 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:36,814 (beam_search:476) INFO: -11.53 * 1.0 = -11.53 for ctc +2024-01-16 22:20:36,814 (beam_search:479) INFO: total log probability: -11.53 +2024-01-16 22:20:36,814 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:36,814 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:36,815 (beam_search:483) INFO: best hypo: DAMITKANMANBARSCHEINDICHLÄCHTEIENKAUFEN + +2024-01-16 22:20:36,816 (asr_inference:494) INFO: speech length: 55077 +2024-01-16 22:20:36,824 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 22:20:36,824 (beam_search:429) INFO: max output length: 84 +2024-01-16 22:20:36,825 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:36,884 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:36,884 (beam_search:476) INFO: -5.57 * 1.0 = -5.57 for ctc +2024-01-16 22:20:36,884 (beam_search:479) INFO: total log probability: -5.57 +2024-01-16 22:20:36,884 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:20:36,884 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:36,884 (beam_search:483) INFO: best hypo: DAFIEWODEGESOKT + +2024-01-16 22:20:36,885 (asr_inference:494) INFO: speech length: 58053 +2024-01-16 22:20:36,894 (beam_search:428) INFO: decoder input length: 88 +2024-01-16 22:20:36,894 (beam_search:429) INFO: max output length: 88 +2024-01-16 22:20:36,894 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:36,978 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:36,978 (beam_search:476) INFO: -10.63 * 1.0 = -10.63 for ctc +2024-01-16 22:20:36,978 (beam_search:479) INFO: total log probability: -10.63 +2024-01-16 22:20:36,978 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-16 22:20:36,978 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:36,978 (beam_search:483) INFO: best hypo: CAMAINADEREUTVEKAUFENN + +2024-01-16 22:20:36,980 (asr_inference:494) INFO: speech length: 42000 +2024-01-16 22:20:36,988 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 22:20:36,988 (beam_search:429) INFO: max output length: 63 +2024-01-16 22:20:36,988 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:37,048 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:37,048 (beam_search:476) INFO: -10.49 * 1.0 = -10.49 for ctc +2024-01-16 22:20:37,048 (beam_search:479) INFO: total log probability: -10.49 +2024-01-16 22:20:37,048 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:20:37,048 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:37,049 (beam_search:483) INFO: best hypo: ZNENAUCHNDESTELEERTIOUNG + +2024-01-16 22:20:37,050 (asr_inference:494) INFO: speech length: 62667 +2024-01-16 22:20:37,059 (beam_search:428) INFO: decoder input length: 95 +2024-01-16 22:20:37,059 (beam_search:429) INFO: max output length: 95 +2024-01-16 22:20:37,059 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:37,188 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:37,188 (beam_search:476) INFO: -8.45 * 1.0 = -8.45 for ctc +2024-01-16 22:20:37,188 (beam_search:479) INFO: total log probability: -8.45 +2024-01-16 22:20:37,188 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:20:37,188 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:37,188 (beam_search:483) INFO: best hypo: DARIBERRIEDEDIEPASTOGRINENUNDTREDET + +2024-01-16 22:20:37,189 (asr_inference:494) INFO: speech length: 76000 +2024-01-16 22:20:37,199 (beam_search:428) INFO: decoder input length: 116 +2024-01-16 22:20:37,199 (beam_search:429) INFO: max output length: 116 +2024-01-16 22:20:37,199 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:37,301 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:37,301 (beam_search:476) INFO: -9.59 * 1.0 = -9.59 for ctc +2024-01-16 22:20:37,302 (beam_search:479) INFO: total log probability: -9.59 +2024-01-16 22:20:37,302 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:20:37,302 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:37,302 (beam_search:483) INFO: best hypo: DEICHLENENEEDANSDEEN + +2024-01-16 22:20:37,303 (asr_inference:494) INFO: speech length: 96256 +2024-01-16 22:20:37,314 (beam_search:428) INFO: decoder input length: 148 +2024-01-16 22:20:37,314 (beam_search:429) INFO: max output length: 148 +2024-01-16 22:20:37,314 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:37,534 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:37,534 (beam_search:476) INFO: -14.83 * 1.0 = -14.83 for ctc +2024-01-16 22:20:37,534 (beam_search:479) INFO: total log probability: -14.83 +2024-01-16 22:20:37,534 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:20:37,534 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:37,534 (beam_search:483) INFO: best hypo: AUFDENERSTENBLEGSCHEINDASUNGEVÖÜRNLEICH + +2024-01-16 22:20:37,536 (asr_inference:494) INFO: speech length: 69627 +2024-01-16 22:20:37,545 (beam_search:428) INFO: decoder input length: 106 +2024-01-16 22:20:37,545 (beam_search:429) INFO: max output length: 106 +2024-01-16 22:20:37,545 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:37,717 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:37,717 (beam_search:476) INFO: -9.12 * 1.0 = -9.12 for ctc +2024-01-16 22:20:37,717 (beam_search:479) INFO: total log probability: -9.12 +2024-01-16 22:20:37,717 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:20:37,717 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:37,717 (beam_search:483) INFO: best hypo: DERDOLAWIERTENICHTMEHRISWERUNGAKZIEPTIERTWERDEN + +2024-01-16 22:20:37,718 (asr_inference:494) INFO: speech length: 119037 +2024-01-16 22:20:37,731 (beam_search:428) INFO: decoder input length: 183 +2024-01-16 22:20:37,731 (beam_search:429) INFO: max output length: 183 +2024-01-16 22:20:37,731 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:38,095 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:38,095 (beam_search:476) INFO: -20.54 * 1.0 = -20.54 for ctc +2024-01-16 22:20:38,095 (beam_search:479) INFO: total log probability: -20.54 +2024-01-16 22:20:38,095 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:20:38,095 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:38,096 (beam_search:483) INFO: best hypo: ILENKOUTFFESTGEGENDENHALSTERINGERENDANMKÜSTDISSIEDINVATER + +2024-01-16 22:20:38,097 (asr_inference:494) INFO: speech length: 62635 +2024-01-16 22:20:38,107 (beam_search:428) INFO: decoder input length: 95 +2024-01-16 22:20:38,107 (beam_search:429) INFO: max output length: 95 +2024-01-16 22:20:38,107 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:38,192 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:38,192 (beam_search:476) INFO: -7.57 * 1.0 = -7.57 for ctc +2024-01-16 22:20:38,192 (beam_search:479) INFO: total log probability: -7.57 +2024-01-16 22:20:38,192 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:20:38,192 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:38,192 (beam_search:483) INFO: best hypo: DAWORDENICHTWAGENOMMEN + +2024-01-16 22:20:38,194 (asr_inference:494) INFO: speech length: 48640 +2024-01-16 22:20:38,202 (beam_search:428) INFO: decoder input length: 73 +2024-01-16 22:20:38,202 (beam_search:429) INFO: max output length: 73 +2024-01-16 22:20:38,202 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:38,271 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:38,272 (beam_search:476) INFO: -7.05 * 1.0 = -7.05 for ctc +2024-01-16 22:20:38,272 (beam_search:479) INFO: total log probability: -7.05 +2024-01-16 22:20:38,272 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:38,272 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:38,272 (beam_search:483) INFO: best hypo: MANHTTERDAMEITSVORGELEN + +2024-01-16 22:20:38,273 (asr_inference:494) INFO: speech length: 82000 +2024-01-16 22:20:38,283 (beam_search:428) INFO: decoder input length: 126 +2024-01-16 22:20:38,283 (beam_search:429) INFO: max output length: 126 +2024-01-16 22:20:38,283 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:38,505 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:38,505 (beam_search:476) INFO: -13.74 * 1.0 = -13.74 for ctc +2024-01-16 22:20:38,505 (beam_search:479) INFO: total log probability: -13.74 +2024-01-16 22:20:38,505 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:38,505 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:38,505 (beam_search:483) INFO: best hypo: BEIBESONDERWERTWONSACHUNGISDIKENZEMITRIGEISDERWANMERT + +2024-01-16 22:20:38,506 (asr_inference:494) INFO: speech length: 66560 +2024-01-16 22:20:38,516 (beam_search:428) INFO: decoder input length: 101 +2024-01-16 22:20:38,516 (beam_search:429) INFO: max output length: 101 +2024-01-16 22:20:38,516 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:38,610 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:38,610 (beam_search:476) INFO: -8.03 * 1.0 = -8.03 for ctc +2024-01-16 22:20:38,610 (beam_search:479) INFO: total log probability: -8.03 +2024-01-16 22:20:38,610 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:20:38,610 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:38,610 (beam_search:483) INFO: best hypo: NDSMOTZREIKETZELTWEHRDEN + +2024-01-16 22:20:38,611 (asr_inference:494) INFO: speech length: 64000 +2024-01-16 22:20:38,621 (beam_search:428) INFO: decoder input length: 97 +2024-01-16 22:20:38,621 (beam_search:429) INFO: max output length: 97 +2024-01-16 22:20:38,621 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:38,752 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:38,752 (beam_search:476) INFO: -8.29 * 1.0 = -8.29 for ctc +2024-01-16 22:20:38,752 (beam_search:479) INFO: total log probability: -8.29 +2024-01-16 22:20:38,752 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:20:38,752 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:38,752 (beam_search:483) INFO: best hypo: ZWISCHENLOLBIGERUNDSCHOLDENEHERELEITET + +2024-01-16 22:20:38,754 (asr_inference:494) INFO: speech length: 58710 +2024-01-16 22:20:38,762 (beam_search:428) INFO: decoder input length: 89 +2024-01-16 22:20:38,762 (beam_search:429) INFO: max output length: 89 +2024-01-16 22:20:38,762 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:38,878 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:38,878 (beam_search:476) INFO: -10.92 * 1.0 = -10.92 for ctc +2024-01-16 22:20:38,878 (beam_search:479) INFO: total log probability: -10.92 +2024-01-16 22:20:38,878 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:20:38,878 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:38,879 (beam_search:483) INFO: best hypo: EINABZUNMUNTESRECHTIEECHTZIERIVLLET + +2024-01-16 22:20:38,880 (asr_inference:494) INFO: speech length: 91648 +2024-01-16 22:20:38,891 (beam_search:428) INFO: decoder input length: 141 +2024-01-16 22:20:38,891 (beam_search:429) INFO: max output length: 141 +2024-01-16 22:20:38,891 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:39,092 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:39,092 (beam_search:476) INFO: -13.33 * 1.0 = -13.33 for ctc +2024-01-16 22:20:39,092 (beam_search:479) INFO: total log probability: -13.33 +2024-01-16 22:20:39,092 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:20:39,092 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:39,092 (beam_search:483) INFO: best hypo: MANBERAUCHTDNECHTANDENZUOFLZUKLAUBEM + +2024-01-16 22:20:39,093 (asr_inference:494) INFO: speech length: 108923 +2024-01-16 22:20:39,105 (beam_search:428) INFO: decoder input length: 168 +2024-01-16 22:20:39,105 (beam_search:429) INFO: max output length: 168 +2024-01-16 22:20:39,105 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:39,414 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:39,414 (beam_search:476) INFO: -14.54 * 1.0 = -14.54 for ctc +2024-01-16 22:20:39,414 (beam_search:479) INFO: total log probability: -14.54 +2024-01-16 22:20:39,414 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:39,414 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:39,414 (beam_search:483) INFO: best hypo: ZERLICHENWESENNURINTFWALETENWOMANIELIEBEBOTVORHARTEN + +2024-01-16 22:20:39,415 (asr_inference:494) INFO: speech length: 82000 +2024-01-16 22:20:39,425 (beam_search:428) INFO: decoder input length: 126 +2024-01-16 22:20:39,425 (beam_search:429) INFO: max output length: 126 +2024-01-16 22:20:39,425 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:39,660 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:39,660 (beam_search:476) INFO: -14.38 * 1.0 = -14.38 for ctc +2024-01-16 22:20:39,660 (beam_search:479) INFO: total log probability: -14.38 +2024-01-16 22:20:39,660 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:20:39,660 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:39,660 (beam_search:483) INFO: best hypo: DERTZIÜKLICDEBERWEISCLASTUNDEHAFTUNGFIERHELFPERSCHONEN + +2024-01-16 22:20:39,662 (asr_inference:494) INFO: speech length: 76000 +2024-01-16 22:20:39,671 (beam_search:428) INFO: decoder input length: 116 +2024-01-16 22:20:39,671 (beam_search:429) INFO: max output length: 116 +2024-01-16 22:20:39,671 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:39,831 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:39,831 (beam_search:476) INFO: -12.40 * 1.0 = -12.40 for ctc +2024-01-16 22:20:39,831 (beam_search:479) INFO: total log probability: -12.40 +2024-01-16 22:20:39,831 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:20:39,831 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:39,831 (beam_search:483) INFO: best hypo: BEIDEINOMAELNOTZUNGEISIEVOLEBANDREITE + +2024-01-16 22:20:39,832 (asr_inference:494) INFO: speech length: 80965 +2024-01-16 22:20:39,842 (beam_search:428) INFO: decoder input length: 124 +2024-01-16 22:20:39,842 (beam_search:429) INFO: max output length: 124 +2024-01-16 22:20:39,842 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:40,030 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:40,030 (beam_search:476) INFO: -9.40 * 1.0 = -9.40 for ctc +2024-01-16 22:20:40,030 (beam_search:479) INFO: total log probability: -9.40 +2024-01-16 22:20:40,030 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:20:40,030 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:40,030 (beam_search:483) INFO: best hypo: ABERWIEISDIESPROBLEMIMKLOBALMASTAPTZOLEÖEN + +2024-01-16 22:20:40,032 (asr_inference:494) INFO: speech length: 54000 +2024-01-16 22:20:40,040 (beam_search:428) INFO: decoder input length: 82 +2024-01-16 22:20:40,040 (beam_search:429) INFO: max output length: 82 +2024-01-16 22:20:40,040 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:40,146 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:40,146 (beam_search:476) INFO: -13.63 * 1.0 = -13.63 for ctc +2024-01-16 22:20:40,146 (beam_search:479) INFO: total log probability: -13.63 +2024-01-16 22:20:40,146 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:20:40,146 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:40,146 (beam_search:483) INFO: best hypo: DASEIGENWERPLOKGERHTPDENZERMERLESERT + +2024-01-16 22:20:40,147 (asr_inference:494) INFO: speech length: 84000 +2024-01-16 22:20:40,158 (beam_search:428) INFO: decoder input length: 129 +2024-01-16 22:20:40,158 (beam_search:429) INFO: max output length: 129 +2024-01-16 22:20:40,158 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:40,314 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:40,314 (beam_search:476) INFO: -13.91 * 1.0 = -13.91 for ctc +2024-01-16 22:20:40,314 (beam_search:479) INFO: total log probability: -13.91 +2024-01-16 22:20:40,314 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:20:40,314 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:40,315 (beam_search:483) INFO: best hypo: DASFREMDEVERBLOKGITNOCHBIEBTEROS + +2024-01-16 22:20:40,316 (asr_inference:494) INFO: speech length: 48688 +2024-01-16 22:20:40,324 (beam_search:428) INFO: decoder input length: 74 +2024-01-16 22:20:40,324 (beam_search:429) INFO: max output length: 74 +2024-01-16 22:20:40,324 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:40,413 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:40,413 (beam_search:476) INFO: -10.40 * 1.0 = -10.40 for ctc +2024-01-16 22:20:40,413 (beam_search:479) INFO: total log probability: -10.40 +2024-01-16 22:20:40,413 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:20:40,413 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:40,414 (beam_search:483) INFO: best hypo: EINENEEBESTHIMUNGISTDELAENBURTEN + +2024-01-16 22:20:40,415 (asr_inference:494) INFO: speech length: 81408 +2024-01-16 22:20:40,425 (beam_search:428) INFO: decoder input length: 125 +2024-01-16 22:20:40,425 (beam_search:429) INFO: max output length: 125 +2024-01-16 22:20:40,425 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:40,551 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:40,551 (beam_search:476) INFO: -11.22 * 1.0 = -11.22 for ctc +2024-01-16 22:20:40,551 (beam_search:479) INFO: total log probability: -11.22 +2024-01-16 22:20:40,551 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:20:40,551 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:40,552 (beam_search:483) INFO: best hypo: DAROUAUFESTENGEWIENWORDEN + +2024-01-16 22:20:40,553 (asr_inference:494) INFO: speech length: 57611 +2024-01-16 22:20:40,561 (beam_search:428) INFO: decoder input length: 88 +2024-01-16 22:20:40,561 (beam_search:429) INFO: max output length: 88 +2024-01-16 22:20:40,561 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:40,663 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:40,663 (beam_search:476) INFO: -8.74 * 1.0 = -8.74 for ctc +2024-01-16 22:20:40,663 (beam_search:479) INFO: total log probability: -8.74 +2024-01-16 22:20:40,664 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:40,664 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:40,664 (beam_search:483) INFO: best hypo: DEBEFÜRKRUNGISTGANZMASSIVERAMT + +2024-01-16 22:20:40,665 (asr_inference:494) INFO: speech length: 55387 +2024-01-16 22:20:40,673 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 22:20:40,673 (beam_search:429) INFO: max output length: 84 +2024-01-16 22:20:40,673 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:40,776 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:40,776 (beam_search:476) INFO: -9.86 * 1.0 = -9.86 for ctc +2024-01-16 22:20:40,776 (beam_search:479) INFO: total log probability: -9.86 +2024-01-16 22:20:40,776 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:20:40,776 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:40,776 (beam_search:483) INFO: best hypo: DIEWERENDASGANZPESHINNICHTMACHEN + +2024-01-16 22:20:40,777 (asr_inference:494) INFO: speech length: 90000 +2024-01-16 22:20:40,788 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 22:20:40,788 (beam_search:429) INFO: max output length: 138 +2024-01-16 22:20:40,788 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:41,010 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:41,010 (beam_search:476) INFO: -12.63 * 1.0 = -12.63 for ctc +2024-01-16 22:20:41,010 (beam_search:479) INFO: total log probability: -12.63 +2024-01-16 22:20:41,010 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:41,010 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:41,011 (beam_search:483) INFO: best hypo: DEDARTENENENDIGESENDEIERETISTEHEBICHGERINER + +2024-01-16 22:20:41,012 (asr_inference:494) INFO: speech length: 46528 +2024-01-16 22:20:41,021 (beam_search:428) INFO: decoder input length: 70 +2024-01-16 22:20:41,021 (beam_search:429) INFO: max output length: 70 +2024-01-16 22:20:41,021 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:41,087 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:41,087 (beam_search:476) INFO: -9.70 * 1.0 = -9.70 for ctc +2024-01-16 22:20:41,087 (beam_search:479) INFO: total log probability: -9.70 +2024-01-16 22:20:41,087 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:20:41,087 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:41,087 (beam_search:483) INFO: best hypo: DASEGEBNISISTVEFELSTWODEN + +2024-01-16 22:20:41,088 (asr_inference:494) INFO: speech length: 105131 +2024-01-16 22:20:41,100 (beam_search:428) INFO: decoder input length: 162 +2024-01-16 22:20:41,100 (beam_search:429) INFO: max output length: 162 +2024-01-16 22:20:41,100 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:41,407 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:41,407 (beam_search:476) INFO: -14.11 * 1.0 = -14.11 for ctc +2024-01-16 22:20:41,407 (beam_search:479) INFO: total log probability: -14.11 +2024-01-16 22:20:41,407 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:20:41,407 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:41,407 (beam_search:483) INFO: best hypo: EINBESCHRENKUNGKTRITERSTBEIBESONDERSINTENSIEVERNOZUNGAUF + +2024-01-16 22:20:41,409 (asr_inference:494) INFO: speech length: 116000 +2024-01-16 22:20:41,421 (beam_search:428) INFO: decoder input length: 179 +2024-01-16 22:20:41,421 (beam_search:429) INFO: max output length: 179 +2024-01-16 22:20:41,421 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:41,823 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:41,823 (beam_search:476) INFO: -16.66 * 1.0 = -16.66 for ctc +2024-01-16 22:20:41,823 (beam_search:479) INFO: total log probability: -16.66 +2024-01-16 22:20:41,823 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:20:41,823 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:41,824 (beam_search:483) INFO: best hypo: DERENTBENOTZERHATEINEHÖHÖREGERSCHWINDIGKEITFÜRENDAUNLOTZUOFERFÜLGUN + +2024-01-16 22:20:41,825 (asr_inference:494) INFO: speech length: 59125 +2024-01-16 22:20:41,834 (beam_search:428) INFO: decoder input length: 90 +2024-01-16 22:20:41,834 (beam_search:429) INFO: max output length: 90 +2024-01-16 22:20:41,834 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:41,952 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:41,952 (beam_search:476) INFO: -13.03 * 1.0 = -13.03 for ctc +2024-01-16 22:20:41,952 (beam_search:479) INFO: total log probability: -13.03 +2024-01-16 22:20:41,952 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:20:41,952 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:41,953 (beam_search:483) INFO: best hypo: DERSEMANTISCHETHEILEWUDESGEPTISPTRACHTET + +2024-01-16 22:20:41,954 (asr_inference:494) INFO: speech length: 63541 +2024-01-16 22:20:41,963 (beam_search:428) INFO: decoder input length: 97 +2024-01-16 22:20:41,963 (beam_search:429) INFO: max output length: 97 +2024-01-16 22:20:41,963 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:42,071 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:42,072 (beam_search:476) INFO: -9.20 * 1.0 = -9.20 for ctc +2024-01-16 22:20:42,072 (beam_search:479) INFO: total log probability: -9.20 +2024-01-16 22:20:42,072 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:20:42,072 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:42,072 (beam_search:483) INFO: best hypo: DOTWIRTSEFILMEHRGELTVERDIENT + +2024-01-16 22:20:42,073 (asr_inference:494) INFO: speech length: 122573 +2024-01-16 22:20:42,086 (beam_search:428) INFO: decoder input length: 189 +2024-01-16 22:20:42,086 (beam_search:429) INFO: max output length: 189 +2024-01-16 22:20:42,086 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:42,444 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:42,445 (beam_search:476) INFO: -19.09 * 1.0 = -19.09 for ctc +2024-01-16 22:20:42,445 (beam_search:479) INFO: total log probability: -19.09 +2024-01-16 22:20:42,445 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:20:42,445 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:42,445 (beam_search:483) INFO: best hypo: FVERSTENNISVFIERDASSVERANWURDLICHKEIZSGIFILEINERMUNTER + +2024-01-16 22:20:42,446 (asr_inference:494) INFO: speech length: 84480 +2024-01-16 22:20:42,457 (beam_search:428) INFO: decoder input length: 129 +2024-01-16 22:20:42,457 (beam_search:429) INFO: max output length: 129 +2024-01-16 22:20:42,457 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:42,587 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:42,587 (beam_search:476) INFO: -12.74 * 1.0 = -12.74 for ctc +2024-01-16 22:20:42,587 (beam_search:479) INFO: total log probability: -12.74 +2024-01-16 22:20:42,587 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-16 22:20:42,587 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:42,587 (beam_search:483) INFO: best hypo: DAWRTVERDEMEENGEMACHTT + +2024-01-16 22:20:42,588 (asr_inference:494) INFO: speech length: 89600 +2024-01-16 22:20:42,599 (beam_search:428) INFO: decoder input length: 137 +2024-01-16 22:20:42,599 (beam_search:429) INFO: max output length: 137 +2024-01-16 22:20:42,599 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:42,802 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:42,802 (beam_search:476) INFO: -16.01 * 1.0 = -16.01 for ctc +2024-01-16 22:20:42,802 (beam_search:479) INFO: total log probability: -16.01 +2024-01-16 22:20:42,802 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:20:42,802 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:42,803 (beam_search:483) INFO: best hypo: SEKOANEINEGANKLAREKOFMPFÄELONGAUSPRECHEN + +2024-01-16 22:20:42,804 (asr_inference:494) INFO: speech length: 82432 +2024-01-16 22:20:42,814 (beam_search:428) INFO: decoder input length: 126 +2024-01-16 22:20:42,814 (beam_search:429) INFO: max output length: 126 +2024-01-16 22:20:42,814 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:42,974 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:42,974 (beam_search:476) INFO: -9.25 * 1.0 = -9.25 for ctc +2024-01-16 22:20:42,974 (beam_search:479) INFO: total log probability: -9.25 +2024-01-16 22:20:42,974 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:42,974 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:42,975 (beam_search:483) INFO: best hypo: ZALREICHEPOTESTEWERDENARTIKOLIERDT + +2024-01-16 22:20:42,976 (asr_inference:494) INFO: speech length: 79531 +2024-01-16 22:20:42,986 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 22:20:42,986 (beam_search:429) INFO: max output length: 122 +2024-01-16 22:20:42,986 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:43,113 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:43,113 (beam_search:476) INFO: -13.04 * 1.0 = -13.04 for ctc +2024-01-16 22:20:43,113 (beam_search:479) INFO: total log probability: -13.04 +2024-01-16 22:20:43,113 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-16 22:20:43,113 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:43,113 (beam_search:483) INFO: best hypo: DEDECHFÜÖRONGEWANEICHTZIHER + +2024-01-16 22:20:43,114 (asr_inference:494) INFO: speech length: 57280 +2024-01-16 22:20:43,123 (beam_search:428) INFO: decoder input length: 87 +2024-01-16 22:20:43,123 (beam_search:429) INFO: max output length: 87 +2024-01-16 22:20:43,123 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:43,228 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:43,229 (beam_search:476) INFO: -8.96 * 1.0 = -8.96 for ctc +2024-01-16 22:20:43,229 (beam_search:479) INFO: total log probability: -8.96 +2024-01-16 22:20:43,229 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:43,229 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:43,229 (beam_search:483) INFO: best hypo: DEWERUNENGHARTÜBERHOPTKEINEDIKUN + +2024-01-16 22:20:43,230 (asr_inference:494) INFO: speech length: 109356 +2024-01-16 22:20:43,242 (beam_search:428) INFO: decoder input length: 168 +2024-01-16 22:20:43,242 (beam_search:429) INFO: max output length: 168 +2024-01-16 22:20:43,242 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:43,607 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:43,607 (beam_search:476) INFO: -19.44 * 1.0 = -19.44 for ctc +2024-01-16 22:20:43,607 (beam_search:479) INFO: total log probability: -19.44 +2024-01-16 22:20:43,607 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:20:43,607 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:43,608 (beam_search:483) INFO: best hypo: AOUBPIEBRIGENSSEERSTAURFDEREINENDECHAUSTIELBERWUSTENLEBEMSKLOGEN + +2024-01-16 22:20:43,609 (asr_inference:494) INFO: speech length: 92000 +2024-01-16 22:20:43,620 (beam_search:428) INFO: decoder input length: 141 +2024-01-16 22:20:43,620 (beam_search:429) INFO: max output length: 141 +2024-01-16 22:20:43,620 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:43,839 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:43,839 (beam_search:476) INFO: -9.57 * 1.0 = -9.57 for ctc +2024-01-16 22:20:43,839 (beam_search:479) INFO: total log probability: -9.57 +2024-01-16 22:20:43,839 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:20:43,839 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:43,839 (beam_search:483) INFO: best hypo: MAIRSPRECHTENDESEMFEILVONKONTRERHERUNGSTZWANG + +2024-01-16 22:20:43,840 (asr_inference:494) INFO: speech length: 74240 +2024-01-16 22:20:43,850 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 22:20:43,850 (beam_search:429) INFO: max output length: 113 +2024-01-16 22:20:43,850 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:43,972 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:43,972 (beam_search:476) INFO: -12.69 * 1.0 = -12.69 for ctc +2024-01-16 22:20:43,972 (beam_search:479) INFO: total log probability: -12.69 +2024-01-16 22:20:43,972 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-16 22:20:43,972 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:43,972 (beam_search:483) INFO: best hypo: GLOEBEGERUNSCHLNESENZIEINIG + +2024-01-16 22:20:43,973 (asr_inference:494) INFO: speech length: 50123 +2024-01-16 22:20:43,982 (beam_search:428) INFO: decoder input length: 76 +2024-01-16 22:20:43,982 (beam_search:429) INFO: max output length: 76 +2024-01-16 22:20:43,982 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:44,066 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:44,066 (beam_search:476) INFO: -5.84 * 1.0 = -5.84 for ctc +2024-01-16 22:20:44,066 (beam_search:479) INFO: total log probability: -5.84 +2024-01-16 22:20:44,066 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:20:44,066 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:44,066 (beam_search:483) INFO: best hypo: DASWIRTENICHTMERLANESOBLEIBEN + +2024-01-16 22:20:44,067 (asr_inference:494) INFO: speech length: 72000 +2024-01-16 22:20:44,078 (beam_search:428) INFO: decoder input length: 110 +2024-01-16 22:20:44,078 (beam_search:429) INFO: max output length: 110 +2024-01-16 22:20:44,078 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:44,254 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:44,254 (beam_search:476) INFO: -16.06 * 1.0 = -16.06 for ctc +2024-01-16 22:20:44,254 (beam_search:479) INFO: total log probability: -16.06 +2024-01-16 22:20:44,254 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:20:44,254 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:44,254 (beam_search:483) INFO: best hypo: ISGABUNTERCHITLICHSHIHEEVOMENDERRALHETSTAFER + +2024-01-16 22:20:44,255 (asr_inference:494) INFO: speech length: 80000 +2024-01-16 22:20:44,265 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 22:20:44,265 (beam_search:429) INFO: max output length: 122 +2024-01-16 22:20:44,265 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:44,385 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:44,385 (beam_search:476) INFO: -8.15 * 1.0 = -8.15 for ctc +2024-01-16 22:20:44,385 (beam_search:479) INFO: total log probability: -8.15 +2024-01-16 22:20:44,385 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:20:44,385 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:44,386 (beam_search:483) INFO: best hypo: EHNDEEEMEINEFREIESOFTWER + +2024-01-16 22:20:44,387 (asr_inference:494) INFO: speech length: 90059 +2024-01-16 22:20:44,397 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 22:20:44,397 (beam_search:429) INFO: max output length: 138 +2024-01-16 22:20:44,397 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:44,671 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:44,671 (beam_search:476) INFO: -17.94 * 1.0 = -17.94 for ctc +2024-01-16 22:20:44,671 (beam_search:479) INFO: total log probability: -17.94 +2024-01-16 22:20:44,671 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:20:44,671 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:44,671 (beam_search:483) INFO: best hypo: ORGANSTREITVEFANKNAUCHAUSCISLICGAUFTELNDESEBENISTATFINTEN + +2024-01-16 22:20:44,672 (asr_inference:494) INFO: speech length: 77083 +2024-01-16 22:20:44,682 (beam_search:428) INFO: decoder input length: 118 +2024-01-16 22:20:44,682 (beam_search:429) INFO: max output length: 118 +2024-01-16 22:20:44,682 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:44,832 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:44,832 (beam_search:476) INFO: -10.68 * 1.0 = -10.68 for ctc +2024-01-16 22:20:44,832 (beam_search:479) INFO: total log probability: -10.68 +2024-01-16 22:20:44,832 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:20:44,832 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:44,832 (beam_search:483) INFO: best hypo: WEGENOZHLOSAUFGEWENETERULEBSEITKAN + +2024-01-16 22:20:44,833 (asr_inference:494) INFO: speech length: 59285 +2024-01-16 22:20:44,842 (beam_search:428) INFO: decoder input length: 90 +2024-01-16 22:20:44,842 (beam_search:429) INFO: max output length: 90 +2024-01-16 22:20:44,842 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:44,946 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:44,947 (beam_search:476) INFO: -9.62 * 1.0 = -9.62 for ctc +2024-01-16 22:20:44,947 (beam_search:479) INFO: total log probability: -9.62 +2024-01-16 22:20:44,947 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:20:44,947 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:44,947 (beam_search:483) INFO: best hypo: DAWIRTNICHIMAPERVEKTVUNTZEINEN + +2024-01-16 22:20:44,948 (asr_inference:494) INFO: speech length: 55973 +2024-01-16 22:20:44,956 (beam_search:428) INFO: decoder input length: 85 +2024-01-16 22:20:44,956 (beam_search:429) INFO: max output length: 85 +2024-01-16 22:20:44,956 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:45,062 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:45,062 (beam_search:476) INFO: -9.56 * 1.0 = -9.56 for ctc +2024-01-16 22:20:45,062 (beam_search:479) INFO: total log probability: -9.56 +2024-01-16 22:20:45,062 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:45,062 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:45,062 (beam_search:483) INFO: best hypo: MANMUSICHANGERCHIENDASWAKSTUMSWENGN + +2024-01-16 22:20:45,063 (asr_inference:494) INFO: speech length: 75606 +2024-01-16 22:20:45,073 (beam_search:428) INFO: decoder input length: 116 +2024-01-16 22:20:45,073 (beam_search:429) INFO: max output length: 116 +2024-01-16 22:20:45,073 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:45,227 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:45,227 (beam_search:476) INFO: -8.10 * 1.0 = -8.10 for ctc +2024-01-16 22:20:45,227 (beam_search:479) INFO: total log probability: -8.10 +2024-01-16 22:20:45,227 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 22:20:45,227 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:45,228 (beam_search:483) INFO: best hypo: WELICHERWEGESOLENEINESCLAGENWEARDEN + +2024-01-16 22:20:45,229 (asr_inference:494) INFO: speech length: 79019 +2024-01-16 22:20:45,239 (beam_search:428) INFO: decoder input length: 121 +2024-01-16 22:20:45,239 (beam_search:429) INFO: max output length: 121 +2024-01-16 22:20:45,239 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:45,347 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:45,347 (beam_search:476) INFO: -10.15 * 1.0 = -10.15 for ctc +2024-01-16 22:20:45,347 (beam_search:479) INFO: total log probability: -10.15 +2024-01-16 22:20:45,347 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:20:45,347 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:45,347 (beam_search:483) INFO: best hypo: DAWERTENDIEPEISEGEEN + +2024-01-16 22:20:45,348 (asr_inference:494) INFO: speech length: 66485 +2024-01-16 22:20:45,358 (beam_search:428) INFO: decoder input length: 101 +2024-01-16 22:20:45,358 (beam_search:429) INFO: max output length: 101 +2024-01-16 22:20:45,358 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:45,466 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:45,466 (beam_search:476) INFO: -12.37 * 1.0 = -12.37 for ctc +2024-01-16 22:20:45,466 (beam_search:479) INFO: total log probability: -12.37 +2024-01-16 22:20:45,466 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 22:20:45,466 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:45,466 (beam_search:483) INFO: best hypo: SDIEBERNDAMEERVOLKTEWERTLICH + +2024-01-16 22:20:45,468 (asr_inference:494) INFO: speech length: 80896 +2024-01-16 22:20:45,478 (beam_search:428) INFO: decoder input length: 124 +2024-01-16 22:20:45,478 (beam_search:429) INFO: max output length: 124 +2024-01-16 22:20:45,478 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:45,635 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:45,635 (beam_search:476) INFO: -9.88 * 1.0 = -9.88 for ctc +2024-01-16 22:20:45,635 (beam_search:479) INFO: total log probability: -9.88 +2024-01-16 22:20:45,635 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:45,635 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:45,635 (beam_search:483) INFO: best hypo: DEEINTWEKLONGEISTWEITVORANGESHIETEN + +2024-01-16 22:20:45,637 (asr_inference:494) INFO: speech length: 93696 +2024-01-16 22:20:45,648 (beam_search:428) INFO: decoder input length: 144 +2024-01-16 22:20:45,648 (beam_search:429) INFO: max output length: 144 +2024-01-16 22:20:45,648 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:45,884 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:45,884 (beam_search:476) INFO: -12.74 * 1.0 = -12.74 for ctc +2024-01-16 22:20:45,884 (beam_search:479) INFO: total log probability: -12.74 +2024-01-16 22:20:45,884 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:45,884 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:45,884 (beam_search:483) INFO: best hypo: DIESMTORMELTRIEDENDANSCHONNACHWEHNEGESTONTENARF + +2024-01-16 22:20:45,886 (asr_inference:494) INFO: speech length: 65493 +2024-01-16 22:20:45,895 (beam_search:428) INFO: decoder input length: 100 +2024-01-16 22:20:45,895 (beam_search:429) INFO: max output length: 100 +2024-01-16 22:20:45,895 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:46,011 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:46,011 (beam_search:476) INFO: -8.47 * 1.0 = -8.47 for ctc +2024-01-16 22:20:46,011 (beam_search:479) INFO: total log probability: -8.47 +2024-01-16 22:20:46,011 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:46,011 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:46,011 (beam_search:483) INFO: best hypo: SGEBTEINEGROSEWÄLLEVONPROTZEEN + +2024-01-16 22:20:46,012 (asr_inference:494) INFO: speech length: 83286 +2024-01-16 22:20:46,022 (beam_search:428) INFO: decoder input length: 128 +2024-01-16 22:20:46,022 (beam_search:429) INFO: max output length: 128 +2024-01-16 22:20:46,022 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:46,187 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:46,187 (beam_search:476) INFO: -6.02 * 1.0 = -6.02 for ctc +2024-01-16 22:20:46,187 (beam_search:479) INFO: total log probability: -6.02 +2024-01-16 22:20:46,187 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 22:20:46,187 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:46,187 (beam_search:483) INFO: best hypo: ESESTBEREITZMEINZWEITERAUTOMARDT + +2024-01-16 22:20:46,189 (asr_inference:494) INFO: speech length: 151359 +2024-01-16 22:20:46,204 (beam_search:428) INFO: decoder input length: 234 +2024-01-16 22:20:46,204 (beam_search:429) INFO: max output length: 234 +2024-01-16 22:20:46,204 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:46,941 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:46,941 (beam_search:476) INFO: -25.36 * 1.0 = -25.36 for ctc +2024-01-16 22:20:46,941 (beam_search:479) INFO: total log probability: -25.36 +2024-01-16 22:20:46,941 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:20:46,941 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:46,942 (beam_search:483) INFO: best hypo: NPLMENTIERUNGVONHÖHRINSTANDATSTUSCUTZSPERSENLIGERTATENEBENFWEISGENAREUNSHREIUTETZUSAMMENABEITALEICHTEN + +2024-01-16 22:20:46,943 (asr_inference:494) INFO: speech length: 78400 +2024-01-16 22:20:46,953 (beam_search:428) INFO: decoder input length: 120 +2024-01-16 22:20:46,953 (beam_search:429) INFO: max output length: 120 +2024-01-16 22:20:46,954 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:47,211 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:47,211 (beam_search:476) INFO: -20.55 * 1.0 = -20.55 for ctc +2024-01-16 22:20:47,211 (beam_search:479) INFO: total log probability: -20.55 +2024-01-16 22:20:47,211 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:20:47,211 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:47,212 (beam_search:483) INFO: best hypo: ERAMTEABNDERSCLIMSTEVERINDERDARMELEBENGLÄDTISINSEBERVERLTSTVORDNICGLAB + +2024-01-16 22:20:47,213 (asr_inference:494) INFO: speech length: 41920 +2024-01-16 22:20:47,221 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 22:20:47,221 (beam_search:429) INFO: max output length: 63 +2024-01-16 22:20:47,221 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:47,292 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:47,292 (beam_search:476) INFO: -8.94 * 1.0 = -8.94 for ctc +2024-01-16 22:20:47,292 (beam_search:479) INFO: total log probability: -8.94 +2024-01-16 22:20:47,292 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:20:47,292 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:47,293 (beam_search:483) INFO: best hypo: ICGMEÜBRIEDASDEROMIESANICTIES + +2024-01-16 22:20:47,294 (asr_inference:494) INFO: speech length: 55680 +2024-01-16 22:20:47,302 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 22:20:47,302 (beam_search:429) INFO: max output length: 84 +2024-01-16 22:20:47,302 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:47,432 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:47,432 (beam_search:476) INFO: -19.85 * 1.0 = -19.85 for ctc +2024-01-16 22:20:47,432 (beam_search:479) INFO: total log probability: -19.85 +2024-01-16 22:20:47,432 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-16 22:20:47,432 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:47,433 (beam_search:483) INFO: best hypo: MITKLEDUNDECHOVEDASWERNECHSEIEAHRÜRERSWANTI + +2024-01-16 22:20:47,434 (asr_inference:494) INFO: speech length: 106551 +2024-01-16 22:20:47,446 (beam_search:428) INFO: decoder input length: 164 +2024-01-16 22:20:47,446 (beam_search:429) INFO: max output length: 164 +2024-01-16 22:20:47,446 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:47,878 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:47,878 (beam_search:476) INFO: -30.20 * 1.0 = -30.20 for ctc +2024-01-16 22:20:47,878 (beam_search:479) INFO: total log probability: -30.20 +2024-01-16 22:20:47,878 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:20:47,878 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:47,879 (beam_search:483) INFO: best hypo: NDISDAFNCHTDERSEHNWERNDSIMERHINBERIFÜMZIGUTSENTDERBEFELKÖNGDAREBISCHNUNUONEMLENTICHNDRAMLI + +2024-01-16 22:20:47,880 (asr_inference:494) INFO: speech length: 108799 +2024-01-16 22:20:47,892 (beam_search:428) INFO: decoder input length: 167 +2024-01-16 22:20:47,892 (beam_search:429) INFO: max output length: 167 +2024-01-16 22:20:47,892 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:48,326 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:48,326 (beam_search:476) INFO: -25.80 * 1.0 = -25.80 for ctc +2024-01-16 22:20:48,326 (beam_search:479) INFO: total log probability: -25.80 +2024-01-16 22:20:48,326 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:20:48,326 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:48,327 (beam_search:483) INFO: best hypo: SODASDEBÜUGERSHELNEAUSKUNBEKOMDOBSEINEBESCHÄEREBEHAUTINGENOMENWIRTOSIEBERECHTIHTEST + +2024-01-16 22:20:48,328 (asr_inference:494) INFO: speech length: 120639 +2024-01-16 22:20:48,341 (beam_search:428) INFO: decoder input length: 186 +2024-01-16 22:20:48,341 (beam_search:429) INFO: max output length: 186 +2024-01-16 22:20:48,341 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:48,799 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:48,799 (beam_search:476) INFO: -26.65 * 1.0 = -26.65 for ctc +2024-01-16 22:20:48,799 (beam_search:479) INFO: total log probability: -26.65 +2024-01-16 22:20:48,799 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:20:48,799 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:48,800 (beam_search:483) INFO: best hypo: NERHRESETUONZERERBITZIONGENISTNICHTVONEUTENABERARBURUEKONTINNERLICHESFEINTIONEN + +2024-01-16 22:20:48,801 (asr_inference:494) INFO: speech length: 51193 +2024-01-16 22:20:48,809 (beam_search:428) INFO: decoder input length: 77 +2024-01-16 22:20:48,809 (beam_search:429) INFO: max output length: 77 +2024-01-16 22:20:48,809 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:48,926 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:48,926 (beam_search:476) INFO: -12.56 * 1.0 = -12.56 for ctc +2024-01-16 22:20:48,926 (beam_search:479) INFO: total log probability: -12.56 +2024-01-16 22:20:48,926 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:20:48,926 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:48,927 (beam_search:483) INFO: best hypo: LDIEGANSTOLSGESARGJARBDBESCHETIUNGSTEIKTIERAN + +2024-01-16 22:20:48,928 (asr_inference:494) INFO: speech length: 159338 +2024-01-16 22:20:48,943 (beam_search:428) INFO: decoder input length: 246 +2024-01-16 22:20:48,943 (beam_search:429) INFO: max output length: 246 +2024-01-16 22:20:48,943 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:49,830 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:49,830 (beam_search:476) INFO: -50.35 * 1.0 = -50.35 for ctc +2024-01-16 22:20:49,830 (beam_search:479) INFO: total log probability: -50.35 +2024-01-16 22:20:49,830 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-16 22:20:49,830 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:49,831 (beam_search:483) INFO: best hypo: IEDASESFERUNSISTEURHRUNTEREVERLTETWIEREXSPORTIERUNZUFIEELZUONBELIHAUNWERINPADTIERUNZSEWENIGNERVARSCHENKENWOLSTANT + +2024-01-16 22:20:49,832 (asr_inference:494) INFO: speech length: 59834 +2024-01-16 22:20:49,841 (beam_search:428) INFO: decoder input length: 91 +2024-01-16 22:20:49,841 (beam_search:429) INFO: max output length: 91 +2024-01-16 22:20:49,841 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:49,998 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:49,998 (beam_search:476) INFO: -13.10 * 1.0 = -13.10 for ctc +2024-01-16 22:20:49,998 (beam_search:479) INFO: total log probability: -13.10 +2024-01-16 22:20:49,998 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:20:49,998 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:49,998 (beam_search:483) INFO: best hypo: SIEHOULDERABENDIERANWESENSINISTENPROSSITIEVERSIGNAL + +2024-01-16 22:20:49,999 (asr_inference:494) INFO: speech length: 161280 +2024-01-16 22:20:50,015 (beam_search:428) INFO: decoder input length: 249 +2024-01-16 22:20:50,015 (beam_search:429) INFO: max output length: 249 +2024-01-16 22:20:50,015 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:50,880 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:50,880 (beam_search:476) INFO: -34.12 * 1.0 = -34.12 for ctc +2024-01-16 22:20:50,881 (beam_search:479) INFO: total log probability: -34.12 +2024-01-16 22:20:50,881 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:20:50,881 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:50,881 (beam_search:483) INFO: best hypo: NEUNZSICHPRETZENTALLERAROBPÄECHENFLMEDIEAUSEHELTIRESEINMATLANDESGITZEICHTWERDENSINTVOAMMEDIERPOGAMGEERDERTWURTEN + +2024-01-16 22:20:50,883 (asr_inference:494) INFO: speech length: 92800 +2024-01-16 22:20:50,894 (beam_search:428) INFO: decoder input length: 142 +2024-01-16 22:20:50,894 (beam_search:429) INFO: max output length: 142 +2024-01-16 22:20:50,894 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:51,193 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:51,193 (beam_search:476) INFO: -18.24 * 1.0 = -18.24 for ctc +2024-01-16 22:20:51,193 (beam_search:479) INFO: total log probability: -18.24 +2024-01-16 22:20:51,193 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:20:51,193 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:51,193 (beam_search:483) INFO: best hypo: BISOKALIHEMERGEBPLDISTERAERAUSHUSABTDEMUNGINDIESEVORMNICHZUSTEN + +2024-01-16 22:20:51,194 (asr_inference:494) INFO: speech length: 95994 +2024-01-16 22:20:51,205 (beam_search:428) INFO: decoder input length: 147 +2024-01-16 22:20:51,206 (beam_search:429) INFO: max output length: 147 +2024-01-16 22:20:51,206 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:51,586 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:51,586 (beam_search:476) INFO: -23.15 * 1.0 = -23.15 for ctc +2024-01-16 22:20:51,586 (beam_search:479) INFO: total log probability: -23.15 +2024-01-16 22:20:51,586 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:20:51,586 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:51,587 (beam_search:483) INFO: best hypo: BIERBURTEVERHNDRNDASSICHCHEHEHINDERDIENGEISIGENEIGENTUMDIEAUSGUMSFLICHTEVERSTECKENKONTE + +2024-01-16 22:20:51,588 (asr_inference:494) INFO: speech length: 105253 +2024-01-16 22:20:51,600 (beam_search:428) INFO: decoder input length: 162 +2024-01-16 22:20:51,600 (beam_search:429) INFO: max output length: 162 +2024-01-16 22:20:51,600 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:52,009 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:52,009 (beam_search:476) INFO: -16.78 * 1.0 = -16.78 for ctc +2024-01-16 22:20:52,009 (beam_search:479) INFO: total log probability: -16.78 +2024-01-16 22:20:52,009 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 22:20:52,009 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:52,010 (beam_search:483) INFO: best hypo: ISGEBDETZHNMZUSAMANGDERVERSTERKTENZUSAMARBEITEINENERSSTENGANGVONEIIGENMITITSTARTENNAC + +2024-01-16 22:20:52,011 (asr_inference:494) INFO: speech length: 223985 +2024-01-16 22:20:52,031 (beam_search:428) INFO: decoder input length: 347 +2024-01-16 22:20:52,031 (beam_search:429) INFO: max output length: 347 +2024-01-16 22:20:52,031 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:53,794 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:53,794 (beam_search:476) INFO: -54.74 * 1.0 = -54.74 for ctc +2024-01-16 22:20:53,794 (beam_search:479) INFO: total log probability: -54.74 +2024-01-16 22:20:53,794 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:20:53,794 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:53,795 (beam_search:483) INFO: best hypo: WASDIGREINZHBERCHREITENDETZUSAMENABEITANBELANTUNDTWASTIEERVERPREITUNGINTRIGKLENDERETRIFTNDTEMECHICHEINBEISPBELENEINENDESENERVOLGSBEISPILVÜEMICHISUNTZWARELSLAMDAUGMLIERNJAREIDES + +2024-01-16 22:20:53,797 (asr_inference:494) INFO: speech length: 128959 +2024-01-16 22:20:53,810 (beam_search:428) INFO: decoder input length: 199 +2024-01-16 22:20:53,810 (beam_search:429) INFO: max output length: 199 +2024-01-16 22:20:53,810 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:54,437 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:54,438 (beam_search:476) INFO: -34.75 * 1.0 = -34.75 for ctc +2024-01-16 22:20:54,438 (beam_search:479) INFO: total log probability: -34.75 +2024-01-16 22:20:54,438 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:20:54,438 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:54,438 (beam_search:483) INFO: best hypo: DASNICHTNURINPORTUGALDERGLICHENLANTSNENAURUENSEUVERMEINTLICGEICHENMITWISSTARTENWIEDEUTSCSLANDDERUSPETANJEN + +2024-01-16 22:20:54,440 (asr_inference:494) INFO: speech length: 25279 +2024-01-16 22:20:54,447 (beam_search:428) INFO: decoder input length: 37 +2024-01-16 22:20:54,447 (beam_search:429) INFO: max output length: 37 +2024-01-16 22:20:54,447 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:54,482 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:54,482 (beam_search:476) INFO: -3.68 * 1.0 = -3.68 for ctc +2024-01-16 22:20:54,482 (beam_search:479) INFO: total log probability: -3.68 +2024-01-16 22:20:54,482 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 22:20:54,482 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:54,482 (beam_search:483) INFO: best hypo: TVERAUSWEGENISVORBEIDA + +2024-01-16 22:20:54,483 (asr_inference:494) INFO: speech length: 112000 +2024-01-16 22:20:54,495 (beam_search:428) INFO: decoder input length: 172 +2024-01-16 22:20:54,495 (beam_search:429) INFO: max output length: 172 +2024-01-16 22:20:54,495 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:54,942 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:54,942 (beam_search:476) INFO: -24.99 * 1.0 = -24.99 for ctc +2024-01-16 22:20:54,942 (beam_search:479) INFO: total log probability: -24.99 +2024-01-16 22:20:54,942 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:20:54,942 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:54,943 (beam_search:483) INFO: best hypo: ALLELFLIENGATFMIGKDIDADESISHAUSESVARSCHEINHEDORDTLICHOLFIGERALSTEIEUNDUSCNITZBÜÖRGET + +2024-01-16 22:20:54,944 (asr_inference:494) INFO: speech length: 72948 +2024-01-16 22:20:54,954 (beam_search:428) INFO: decoder input length: 111 +2024-01-16 22:20:54,954 (beam_search:429) INFO: max output length: 111 +2024-01-16 22:20:54,954 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:55,168 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:55,168 (beam_search:476) INFO: -18.86 * 1.0 = -18.86 for ctc +2024-01-16 22:20:55,168 (beam_search:479) INFO: total log probability: -18.86 +2024-01-16 22:20:55,168 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:20:55,168 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:55,169 (beam_search:483) INFO: best hypo: ENSICHEHRDASEREBEDOEUITCUNGINARAZSFUKOMFTUGANOCHTZUNEMWIERT + +2024-01-16 22:20:55,170 (asr_inference:494) INFO: speech length: 184000 +2024-01-16 22:20:55,187 (beam_search:428) INFO: decoder input length: 285 +2024-01-16 22:20:55,187 (beam_search:429) INFO: max output length: 285 +2024-01-16 22:20:55,187 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:56,349 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:56,349 (beam_search:476) INFO: -41.94 * 1.0 = -41.94 for ctc +2024-01-16 22:20:56,349 (beam_search:479) INFO: total log probability: -41.94 +2024-01-16 22:20:56,349 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:20:56,349 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:56,350 (beam_search:483) INFO: best hypo: TASKEDIERUNDIRICHTLIENDERDISARDESTEFISTEGNGUNDLÄEGDASISARITSNORMENFÜRDENSCHUTZVERDENGEFARENEINEREXSPOSITSOUNGGIEBERIONISIERENDARSTRALUNG + +2024-01-16 22:20:56,351 (asr_inference:494) INFO: speech length: 37759 +2024-01-16 22:20:56,359 (beam_search:428) INFO: decoder input length: 56 +2024-01-16 22:20:56,359 (beam_search:429) INFO: max output length: 56 +2024-01-16 22:20:56,359 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:56,410 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:56,410 (beam_search:476) INFO: -4.60 * 1.0 = -4.60 for ctc +2024-01-16 22:20:56,410 (beam_search:479) INFO: total log probability: -4.60 +2024-01-16 22:20:56,410 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:20:56,410 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:56,410 (beam_search:483) INFO: best hypo: DASKGILTESIDERHERSCUSTEN + +2024-01-16 22:20:56,411 (asr_inference:494) INFO: speech length: 63018 +2024-01-16 22:20:56,421 (beam_search:428) INFO: decoder input length: 96 +2024-01-16 22:20:56,421 (beam_search:429) INFO: max output length: 96 +2024-01-16 22:20:56,421 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:56,568 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:56,568 (beam_search:476) INFO: -11.34 * 1.0 = -11.34 for ctc +2024-01-16 22:20:56,568 (beam_search:479) INFO: total log probability: -11.34 +2024-01-16 22:20:56,568 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:20:56,568 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:56,568 (beam_search:483) INFO: best hypo: DENEINENEINZIGENSITZKEBTESLENGSDASISTASPURG + +2024-01-16 22:20:56,570 (asr_inference:494) INFO: speech length: 282859 +2024-01-16 22:20:56,596 (beam_search:428) INFO: decoder input length: 439 +2024-01-16 22:20:56,596 (beam_search:429) INFO: max output length: 439 +2024-01-16 22:20:56,596 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:59,558 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:59,558 (beam_search:476) INFO: -86.83 * 1.0 = -86.83 for ctc +2024-01-16 22:20:59,558 (beam_search:479) INFO: total log probability: -86.83 +2024-01-16 22:20:59,558 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:20:59,558 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:59,559 (beam_search:483) INFO: best hypo: EDASASPASIERINMALTERDIEBSONLISTEINDIEKOBTSUNDFLEAUFGEDEKTERDISVERWHEGENBCHNERMADEBAWEDERWERENUSDEMARISIKOBSOUNDFELEEBEUNDERSUCHNOCHITDERMOARSERBEARERGETILTEUNDERSUTEANATVFASSINANDUGASWENALSIERUNTERDEMANDELISCHWEINTZUGEDEKTWENSEOS + +2024-01-16 22:20:59,561 (asr_inference:494) INFO: speech length: 96311 +2024-01-16 22:20:59,572 (beam_search:428) INFO: decoder input length: 148 +2024-01-16 22:20:59,572 (beam_search:429) INFO: max output length: 148 +2024-01-16 22:20:59,572 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:20:59,942 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:20:59,942 (beam_search:476) INFO: -23.24 * 1.0 = -23.24 for ctc +2024-01-16 22:20:59,942 (beam_search:479) INFO: total log probability: -23.24 +2024-01-16 22:20:59,942 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:20:59,942 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:20:59,943 (beam_search:483) INFO: best hypo: LITLANGEKÜSTEDIEWANSTEINEDIAUFDIROSENKATERSVOFENITZUNAMIESEDERVRGANGNERTINWEISEN + +2024-01-16 22:20:59,944 (asr_inference:494) INFO: speech length: 214332 +2024-01-16 22:20:59,964 (beam_search:428) INFO: decoder input length: 332 +2024-01-16 22:20:59,964 (beam_search:429) INFO: max output length: 332 +2024-01-16 22:20:59,964 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:01,554 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:01,554 (beam_search:476) INFO: -63.08 * 1.0 = -63.08 for ctc +2024-01-16 22:21:01,554 (beam_search:479) INFO: total log probability: -63.08 +2024-01-16 22:21:01,554 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 22:21:01,554 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:01,555 (beam_search:483) INFO: best hypo: DENTICHARBEBENZIEPÜRDEBERCHTGISTEIMENTOAUOLEINSWEHRNVELERINTELTESWIRDNEMITAUEAUFGEFADERTDSAURBEHESHEPALAMENDAUFDEMWÄEGKTSEIDIMEINZIGENGSITSTZUONDESTELTZEN + +2024-01-16 22:21:01,557 (asr_inference:494) INFO: speech length: 149440 +2024-01-16 22:21:01,572 (beam_search:428) INFO: decoder input length: 231 +2024-01-16 22:21:01,572 (beam_search:429) INFO: max output length: 231 +2024-01-16 22:21:01,572 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:02,348 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:02,348 (beam_search:476) INFO: -32.85 * 1.0 = -32.85 for ctc +2024-01-16 22:21:02,348 (beam_search:479) INFO: total log probability: -32.85 +2024-01-16 22:21:02,348 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:21:02,348 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:02,348 (beam_search:483) INFO: best hypo: INDIESEMRIFENWHUOTDENGEMEISMIBPLITDESCHEVERABREDUNGENINKREISDERSIEBENUNZWANZSIGETROFVERNUNDAUCHUBLIKGEMAHTT + +2024-01-16 22:21:02,350 (asr_inference:494) INFO: speech length: 367025 +2024-01-16 22:21:02,383 (beam_search:428) INFO: decoder input length: 571 +2024-01-16 22:21:02,383 (beam_search:429) INFO: max output length: 571 +2024-01-16 22:21:02,383 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:07,053 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:07,053 (beam_search:476) INFO: -122.21 * 1.0 = -122.21 for ctc +2024-01-16 22:21:07,053 (beam_search:479) INFO: total log probability: -122.21 +2024-01-16 22:21:07,053 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-16 22:21:07,053 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:07,054 (beam_search:483) INFO: best hypo: IEBENERBERTZOUGENDSWERSHOEUTHMIDINVORSCHARGSIMUMALGAUSCHSGECHAFTAMINCHTWEITEAUKMASIGPERFEKTAURBECHEATZSAGEHEDENÜERHOCHRISIGOBRTUGKTDANEDSENDALIZULASENHAMMSSENDASHAICHEICHGESHAFTARMIDEMERSODAHEMTICHLIGPLAUBEICHDASWIRTROTEMEINENGORSENSCRILTFLEIGKEINMEINSTEINEINGROSENCHITZUMERATEDENSEATAEN + +2024-01-16 22:21:07,056 (asr_inference:494) INFO: speech length: 54080 +2024-01-16 22:21:07,065 (beam_search:428) INFO: decoder input length: 82 +2024-01-16 22:21:07,065 (beam_search:429) INFO: max output length: 82 +2024-01-16 22:21:07,065 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:07,186 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:07,186 (beam_search:476) INFO: -16.02 * 1.0 = -16.02 for ctc +2024-01-16 22:21:07,186 (beam_search:479) INFO: total log probability: -16.02 +2024-01-16 22:21:07,186 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 22:21:07,186 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:07,187 (beam_search:483) INFO: best hypo: PEHLEDANGESFVERLKTFÜÖRTSWEIENHEIBMINONTENERG + +2024-01-16 22:21:07,188 (asr_inference:494) INFO: speech length: 123185 +2024-01-16 22:21:07,200 (beam_search:428) INFO: decoder input length: 190 +2024-01-16 22:21:07,200 (beam_search:429) INFO: max output length: 190 +2024-01-16 22:21:07,200 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:07,811 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:07,811 (beam_search:476) INFO: -32.96 * 1.0 = -32.96 for ctc +2024-01-16 22:21:07,811 (beam_search:479) INFO: total log probability: -32.96 +2024-01-16 22:21:07,811 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 22:21:07,811 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:07,812 (beam_search:483) INFO: best hypo: ZUMAKTUELENICHKLABISKANKEINEVENUNSANNEMEDASWEWERTLICGIARSTFEITDISENWUCHNGENDEWISHENDASENSDITALUNSUNFEICHKEITDROT + +2024-01-16 22:21:07,813 (asr_inference:494) INFO: speech length: 51521 +2024-01-16 22:21:07,822 (beam_search:428) INFO: decoder input length: 78 +2024-01-16 22:21:07,822 (beam_search:429) INFO: max output length: 78 +2024-01-16 22:21:07,822 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:07,941 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:07,941 (beam_search:476) INFO: -8.49 * 1.0 = -8.49 for ctc +2024-01-16 22:21:07,941 (beam_search:479) INFO: total log probability: -8.49 +2024-01-16 22:21:07,941 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 22:21:07,941 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:07,941 (beam_search:483) INFO: best hypo: DSNDEINFCHPEDINGUNGENDINIGAKTZEPTABESENMANK + +2024-01-16 22:21:07,942 (asr_inference:494) INFO: speech length: 204139 +2024-01-16 22:21:07,960 (beam_search:428) INFO: decoder input length: 316 +2024-01-16 22:21:07,960 (beam_search:429) INFO: max output length: 316 +2024-01-16 22:21:07,960 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:09,387 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:09,387 (beam_search:476) INFO: -44.06 * 1.0 = -44.06 for ctc +2024-01-16 22:21:09,387 (beam_search:479) INFO: total log probability: -44.06 +2024-01-16 22:21:09,387 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:21:09,387 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:09,388 (beam_search:483) INFO: best hypo: INDEZWISCHENSEISINDIRETUNGSARGENISARIONENDEGRÖSTENSCHLPERHWEISIEDIEMIEGANTENZWANZICHGHLMETERVERDERLIEBISCHENKÜSDAUBGREIFENUNDALLNERHITALIENPRASPRTIEREN + +2024-01-16 22:21:09,390 (asr_inference:494) INFO: speech length: 36157 +2024-01-16 22:21:09,398 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 22:21:09,398 (beam_search:429) INFO: max output length: 54 +2024-01-16 22:21:09,398 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:09,461 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:09,461 (beam_search:476) INFO: -9.76 * 1.0 = -9.76 for ctc +2024-01-16 22:21:09,461 (beam_search:479) INFO: total log probability: -9.76 +2024-01-16 22:21:09,461 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:21:09,461 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:09,462 (beam_search:483) INFO: best hypo: DASSEIKTDERFALJULIERTDEMSCHÄNKOU + +2024-01-16 22:21:09,463 (asr_inference:494) INFO: speech length: 38399 +2024-01-16 22:21:09,470 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 22:21:09,470 (beam_search:429) INFO: max output length: 57 +2024-01-16 22:21:09,470 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:09,532 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:09,532 (beam_search:476) INFO: -5.68 * 1.0 = -5.68 for ctc +2024-01-16 22:21:09,532 (beam_search:479) INFO: total log probability: -5.68 +2024-01-16 22:21:09,532 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 22:21:09,532 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:09,532 (beam_search:483) INFO: best hypo: IWASERPREDIGENONDWEINTRINEN + +2024-01-16 22:21:09,534 (asr_inference:494) INFO: speech length: 77120 +2024-01-16 22:21:09,543 (beam_search:428) INFO: decoder input length: 118 +2024-01-16 22:21:09,543 (beam_search:429) INFO: max output length: 118 +2024-01-16 22:21:09,543 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:09,767 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:09,767 (beam_search:476) INFO: -18.29 * 1.0 = -18.29 for ctc +2024-01-16 22:21:09,767 (beam_search:479) INFO: total log probability: -18.29 +2024-01-16 22:21:09,767 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:21:09,767 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:09,767 (beam_search:483) INFO: best hypo: WIRDIEINSCHEIDUNGRARHENWIERFILEPATNRNECHTZULETZIESTÄTTE + +2024-01-16 22:21:09,769 (asr_inference:494) INFO: speech length: 167360 +2024-01-16 22:21:09,785 (beam_search:428) INFO: decoder input length: 259 +2024-01-16 22:21:09,785 (beam_search:429) INFO: max output length: 259 +2024-01-16 22:21:09,785 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:10,732 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:10,732 (beam_search:476) INFO: -41.80 * 1.0 = -41.80 for ctc +2024-01-16 22:21:10,732 (beam_search:479) INFO: total log probability: -41.80 +2024-01-16 22:21:10,732 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:21:10,732 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:10,732 (beam_search:483) INFO: best hypo: DIEVOLGEISEINHÖRNFLUGKVOMPROPLISTNEXSTRLMISTENEINIGMIGISTATENERENBUMFUMPAROULENSETZENIERKONGKRIETERVERINDERUNGENGEGEN + +2024-01-16 22:21:10,734 (asr_inference:494) INFO: speech length: 261119 +2024-01-16 22:21:10,758 (beam_search:428) INFO: decoder input length: 405 +2024-01-16 22:21:10,758 (beam_search:429) INFO: max output length: 405 +2024-01-16 22:21:10,758 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:12,923 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:12,923 (beam_search:476) INFO: -51.30 * 1.0 = -51.30 for ctc +2024-01-16 22:21:12,923 (beam_search:479) INFO: total log probability: -51.30 +2024-01-16 22:21:12,923 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:21:12,923 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:12,924 (beam_search:483) INFO: best hypo: WAILDIEINWESTIZIONENVRANTÖRSISCHAUNDDELUTSCHERBANGENGERETETWVERDENMUSTDENDÖRHTDERICHENLANTZWEITAUSENZEHNNICHTDBPEITEGENUNDHLUTERUSESEINENRIESIGENSCHOTDENBERKVARSICHEHERTDRIUK + +2024-01-16 22:21:12,926 (asr_inference:494) INFO: speech length: 166079 +2024-01-16 22:21:12,942 (beam_search:428) INFO: decoder input length: 257 +2024-01-16 22:21:12,942 (beam_search:429) INFO: max output length: 257 +2024-01-16 22:21:12,942 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:13,992 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:13,992 (beam_search:476) INFO: -47.42 * 1.0 = -47.42 for ctc +2024-01-16 22:21:13,992 (beam_search:479) INFO: total log probability: -47.42 +2024-01-16 22:21:13,992 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:21:13,992 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:13,993 (beam_search:483) INFO: best hypo: DEMITIGITSTATENDÖROFENNICHIEMÜKICHKEITHABMDERENENAURBPESHENSTASAMBALDERANZERHNDERNENIERNAREGIONGGANZSGETZIELUNSTDEMATISKORULTONFERNACRZEIGENESIEN + +2024-01-16 22:21:13,994 (asr_inference:494) INFO: speech length: 48640 +2024-01-16 22:21:14,002 (beam_search:428) INFO: decoder input length: 73 +2024-01-16 22:21:14,002 (beam_search:429) INFO: max output length: 73 +2024-01-16 22:21:14,002 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:14,106 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:14,106 (beam_search:476) INFO: -9.54 * 1.0 = -9.54 for ctc +2024-01-16 22:21:14,106 (beam_search:479) INFO: total log probability: -9.54 +2024-01-16 22:21:14,106 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:21:14,106 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:14,106 (beam_search:483) INFO: best hypo: EIMILIONMENSCHENSINABPENGIHVONUNSERHELVER + +2024-01-16 22:21:14,107 (asr_inference:494) INFO: speech length: 89920 +2024-01-16 22:21:14,118 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 22:21:14,118 (beam_search:429) INFO: max output length: 138 +2024-01-16 22:21:14,118 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:14,455 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:14,455 (beam_search:476) INFO: -28.18 * 1.0 = -28.18 for ctc +2024-01-16 22:21:14,455 (beam_search:479) INFO: total log probability: -28.18 +2024-01-16 22:21:14,455 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:21:14,455 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:14,455 (beam_search:483) INFO: best hypo: EINFETHINRGERJUNGEWETINHERKADIVONEINPLISISTENAINDESONDEREISATSKOMANDUSENKOMAGSCHAEN + +2024-01-16 22:21:14,457 (asr_inference:494) INFO: speech length: 98559 +2024-01-16 22:21:14,468 (beam_search:428) INFO: decoder input length: 151 +2024-01-16 22:21:14,468 (beam_search:429) INFO: max output length: 151 +2024-01-16 22:21:14,468 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:14,805 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:14,805 (beam_search:476) INFO: -20.39 * 1.0 = -20.39 for ctc +2024-01-16 22:21:14,805 (beam_search:479) INFO: total log probability: -20.39 +2024-01-16 22:21:14,805 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:21:14,805 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:14,805 (beam_search:483) INFO: best hypo: DIEINDERHEILIGKUEITMANVORSICHERETRANENDASAUPTAUTUSUDERALUNSHENTENWEK + +2024-01-16 22:21:14,807 (asr_inference:494) INFO: speech length: 63360 +2024-01-16 22:21:14,816 (beam_search:428) INFO: decoder input length: 96 +2024-01-16 22:21:14,817 (beam_search:429) INFO: max output length: 96 +2024-01-16 22:21:14,817 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:14,970 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:14,970 (beam_search:476) INFO: -11.44 * 1.0 = -11.44 for ctc +2024-01-16 22:21:14,970 (beam_search:479) INFO: total log probability: -11.44 +2024-01-16 22:21:14,970 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 22:21:14,970 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:14,971 (beam_search:483) INFO: best hypo: REIDERATRIGETEREFFENHABENINZISCHENSTADGEFUNDE + +2024-01-16 22:21:14,972 (asr_inference:494) INFO: speech length: 26238 +2024-01-16 22:21:14,979 (beam_search:428) INFO: decoder input length: 38 +2024-01-16 22:21:14,979 (beam_search:429) INFO: max output length: 38 +2024-01-16 22:21:14,979 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:15,014 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:15,014 (beam_search:476) INFO: -3.75 * 1.0 = -3.75 for ctc +2024-01-16 22:21:15,014 (beam_search:479) INFO: total log probability: -3.75 +2024-01-16 22:21:15,014 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 22:21:15,014 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:15,014 (beam_search:483) INFO: best hypo: RDICHIETENEINMONERDEBPT + +2024-01-16 22:21:15,015 (asr_inference:494) INFO: speech length: 325708 +2024-01-16 22:21:15,044 (beam_search:428) INFO: decoder input length: 506 +2024-01-16 22:21:15,044 (beam_search:429) INFO: max output length: 506 +2024-01-16 22:21:15,044 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:18,503 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:18,503 (beam_search:476) INFO: -76.40 * 1.0 = -76.40 for ctc +2024-01-16 22:21:18,503 (beam_search:479) INFO: total log probability: -76.40 +2024-01-16 22:21:18,503 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:21:18,503 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:18,505 (beam_search:483) INFO: best hypo: DASWEGEEINEWICHTIGEFHAHGEANDIKOMITIONENEINLANDDIGRANZKONDTCOLLEWIEDEREINFÜÖONNTDACHEMSCHNGENIONBLEIDENMITZUGANGKZSUORIMATIONZUSTEMETSETERARORDERISDRSEINENWERERODEARDIERAGEISWESTICFÜRDIEDENISCHERIEPATENDISPÄTEUMEINEKLAEANWORTDA + +2024-01-16 22:21:18,507 (asr_inference:494) INFO: speech length: 148799 +2024-01-16 22:21:18,522 (beam_search:428) INFO: decoder input length: 230 +2024-01-16 22:21:18,522 (beam_search:429) INFO: max output length: 230 +2024-01-16 22:21:18,522 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:19,328 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:19,328 (beam_search:476) INFO: -32.69 * 1.0 = -32.69 for ctc +2024-01-16 22:21:19,328 (beam_search:479) INFO: total log probability: -32.69 +2024-01-16 22:21:19,328 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:21:19,328 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:19,329 (beam_search:483) INFO: best hypo: DESCHONAUSSCGIEFÜRTWURDELAGESNICHBARANDASESHEROBEFFELEGIGEBENHETDISNENDSGABENHREIHEVONDKLEIENUNGEREINMTEITENBITIENSWEI + +2024-01-16 22:21:19,331 (asr_inference:494) INFO: speech length: 92788 +2024-01-16 22:21:19,342 (beam_search:428) INFO: decoder input length: 142 +2024-01-16 22:21:19,342 (beam_search:429) INFO: max output length: 142 +2024-01-16 22:21:19,342 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:19,639 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:19,639 (beam_search:476) INFO: -17.56 * 1.0 = -17.56 for ctc +2024-01-16 22:21:19,639 (beam_search:479) INFO: total log probability: -17.56 +2024-01-16 22:21:19,639 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:21:19,639 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:19,639 (beam_search:483) INFO: best hypo: NVERGEMEINCHRFTENDEAUSNONSIERITSPALITIGASGOSISTZIELDIESERUNJON + +2024-01-16 22:21:19,641 (asr_inference:494) INFO: speech length: 91839 +2024-01-16 22:21:19,651 (beam_search:428) INFO: decoder input length: 141 +2024-01-16 22:21:19,651 (beam_search:429) INFO: max output length: 141 +2024-01-16 22:21:19,651 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:19,985 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:19,985 (beam_search:476) INFO: -17.48 * 1.0 = -17.48 for ctc +2024-01-16 22:21:19,985 (beam_search:479) INFO: total log probability: -17.48 +2024-01-16 22:21:19,985 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:21:19,985 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:19,986 (beam_search:483) INFO: best hypo: DENICHERHEITISANISZWERIEGERUNDITEILWEICHERARBREITNICHTNUHRIMTECHNISHNBAREICH + +2024-01-16 22:21:19,987 (asr_inference:494) INFO: speech length: 155840 +2024-01-16 22:21:20,002 (beam_search:428) INFO: decoder input length: 241 +2024-01-16 22:21:20,002 (beam_search:429) INFO: max output length: 241 +2024-01-16 22:21:20,002 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:20,812 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:20,812 (beam_search:476) INFO: -32.80 * 1.0 = -32.80 for ctc +2024-01-16 22:21:20,812 (beam_search:479) INFO: total log probability: -32.80 +2024-01-16 22:21:20,812 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:21:20,812 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:20,813 (beam_search:483) INFO: best hypo: DIKSELTENGENDIENTERESENVONBÜÖRGENUNPOLIETIGENSOWEIAUSNANDERBEREMBÜRERNENGANZEROBERSHIDSTEMARKIENTGANZSOBEN + +2024-01-16 22:21:20,814 (asr_inference:494) INFO: speech length: 17279 +2024-01-16 22:21:20,821 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 22:21:20,821 (beam_search:429) INFO: max output length: 24 +2024-01-16 22:21:20,821 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:20,836 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:20,836 (beam_search:476) INFO: -2.81 * 1.0 = -2.81 for ctc +2024-01-16 22:21:20,836 (beam_search:479) INFO: total log probability: -2.81 +2024-01-16 22:21:20,836 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 22:21:20,836 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:20,836 (beam_search:483) INFO: best hypo: HERPRSIDENT + +2024-01-16 22:21:20,837 (asr_inference:494) INFO: speech length: 144949 +2024-01-16 22:21:20,852 (beam_search:428) INFO: decoder input length: 224 +2024-01-16 22:21:20,852 (beam_search:429) INFO: max output length: 224 +2024-01-16 22:21:20,852 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:21,618 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:21,618 (beam_search:476) INFO: -34.61 * 1.0 = -34.61 for ctc +2024-01-16 22:21:21,618 (beam_search:479) INFO: total log probability: -34.61 +2024-01-16 22:21:21,618 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 22:21:21,618 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:21,618 (beam_search:483) INFO: best hypo: EFÜRENESPRÄECHEMITRESEDENKASEITZAREICHENREGJERUNGSERTRIEENVFRAUNMENSCHENRECHTOGAMISRTIONENUNDIEWANDDRCHAUSEMUTIGENT + +2024-01-16 22:21:21,620 (asr_inference:494) INFO: speech length: 101108 +2024-01-16 22:21:21,631 (beam_search:428) INFO: decoder input length: 155 +2024-01-16 22:21:21,631 (beam_search:429) INFO: max output length: 155 +2024-01-16 22:21:21,631 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:22,000 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:22,000 (beam_search:476) INFO: -26.82 * 1.0 = -26.82 for ctc +2024-01-16 22:21:22,000 (beam_search:479) INFO: total log probability: -26.82 +2024-01-16 22:21:22,000 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 22:21:22,000 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:22,001 (beam_search:483) INFO: best hypo: NGSACHEINEURSACHEFÜRDINEWACKSNNATZUNALISNUSDELLIGSEIDEFOLICHPERSPEKTIEFLOSSIS + +2024-01-16 22:21:22,002 (asr_inference:494) INFO: speech length: 48314 +2024-01-16 22:21:22,010 (beam_search:428) INFO: decoder input length: 73 +2024-01-16 22:21:22,010 (beam_search:429) INFO: max output length: 73 +2024-01-16 22:21:22,010 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:22,109 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:22,109 (beam_search:476) INFO: -13.17 * 1.0 = -13.17 for ctc +2024-01-16 22:21:22,109 (beam_search:479) INFO: total log probability: -13.17 +2024-01-16 22:21:22,109 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 22:21:22,109 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:22,109 (beam_search:483) INFO: best hypo: OUIDEINEIMANAOSORWEITVNDENZIEENFERNS + +2024-01-16 22:21:22,110 (asr_inference:494) INFO: speech length: 365439 +2024-01-16 22:21:22,142 (beam_search:428) INFO: decoder input length: 568 +2024-01-16 22:21:22,142 (beam_search:429) INFO: max output length: 568 +2024-01-16 22:21:22,142 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:26,411 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:26,411 (beam_search:476) INFO: -96.57 * 1.0 = -96.57 for ctc +2024-01-16 22:21:26,411 (beam_search:479) INFO: total log probability: -96.57 +2024-01-16 22:21:26,411 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:21:26,411 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:26,413 (beam_search:483) INFO: best hypo: DWERDERSWIANZMINISTEAUCHENEINENLANDZIEDENTAGGDAMITKONFVONDTIETDASNDTÜLICHAUCHTUSBUSTZSINGEGEBENSENMUSSDASSTASHUSHALTEVONDENSTLERSOALERENEUNSTELERSOLLENDINERZIETZINTUNDDASIERTDAMITAUFHTDIEANTUERTUNGAGENINENENTSCHEIDUNGENDEIERHIENISENRAMENDREFFNMETMMUNTERN + +2024-01-16 22:21:26,415 (asr_inference:494) INFO: speech length: 80623 +2024-01-16 22:21:26,425 (beam_search:428) INFO: decoder input length: 123 +2024-01-16 22:21:26,425 (beam_search:429) INFO: max output length: 123 +2024-01-16 22:21:26,425 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:26,646 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:26,646 (beam_search:476) INFO: -13.75 * 1.0 = -13.75 for ctc +2024-01-16 22:21:26,646 (beam_search:479) INFO: total log probability: -13.75 +2024-01-16 22:21:26,646 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:21:26,646 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:26,646 (beam_search:483) INFO: best hypo: AUDEMOUUROBEISCHNAUTEBEBILMARGKTINSGESAMTDRMATDISCHISS + +2024-01-16 22:21:26,648 (asr_inference:494) INFO: speech length: 173416 +2024-01-16 22:21:26,664 (beam_search:428) INFO: decoder input length: 268 +2024-01-16 22:21:26,664 (beam_search:429) INFO: max output length: 268 +2024-01-16 22:21:26,664 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:27,730 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:27,730 (beam_search:476) INFO: -46.65 * 1.0 = -46.65 for ctc +2024-01-16 22:21:27,730 (beam_search:479) INFO: total log probability: -46.65 +2024-01-16 22:21:27,730 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:21:27,730 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:27,731 (beam_search:483) INFO: best hypo: EBPEHSCHUNJONHARTMIDISEINSTRUMENZSDISCHONSEEINEAKTIVEROLLENERNACKTPAEGIONZUSPIENUMDEMOUGRADSCHERDEVORMENNAENACHALIGINIKTUNVRANZITREIE + +2024-01-16 22:21:27,732 (asr_inference:494) INFO: speech length: 95360 +2024-01-16 22:21:27,743 (beam_search:428) INFO: decoder input length: 146 +2024-01-16 22:21:27,743 (beam_search:429) INFO: max output length: 146 +2024-01-16 22:21:27,743 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:28,040 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:28,040 (beam_search:476) INFO: -20.07 * 1.0 = -20.07 for ctc +2024-01-16 22:21:28,040 (beam_search:479) INFO: total log probability: -20.07 +2024-01-16 22:21:28,040 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:21:28,040 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:28,041 (beam_search:483) INFO: best hypo: STULTALITERERSCHIEMEVONAUSENUDAUVNINENISRESCHTUNDOSCHIETLEG + +2024-01-16 22:21:28,042 (asr_inference:494) INFO: speech length: 157103 +2024-01-16 22:21:28,057 (beam_search:428) INFO: decoder input length: 243 +2024-01-16 22:21:28,057 (beam_search:429) INFO: max output length: 243 +2024-01-16 22:21:28,057 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:28,975 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:28,975 (beam_search:476) INFO: -39.92 * 1.0 = -39.92 for ctc +2024-01-16 22:21:28,975 (beam_search:479) INFO: total log probability: -39.92 +2024-01-16 22:21:28,975 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:21:28,975 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:28,976 (beam_search:483) INFO: best hypo: EHHAMIMERGESARKEINÜBEREILTUSTATZUNIERUNGSENCHEIDUNGISUNSINISGWEITZUMJERTZIGENZEITFUNESKEINEBEDRONGBEISCSPIEASWESAUSEMIERANGET + +2024-01-16 22:21:28,978 (asr_inference:494) INFO: speech length: 340122 +2024-01-16 22:21:29,008 (beam_search:428) INFO: decoder input length: 529 +2024-01-16 22:21:29,008 (beam_search:429) INFO: max output length: 529 +2024-01-16 22:21:29,008 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:31,793 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:31,793 (beam_search:476) INFO: -117.45 * 1.0 = -117.45 for ctc +2024-01-16 22:21:31,793 (beam_search:479) INFO: total log probability: -117.45 +2024-01-16 22:21:31,793 (beam_search:480) INFO: normalized log probability: -0.63 +2024-01-16 22:21:31,793 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:31,794 (beam_search:483) INFO: best hypo: DEERVARKLEICHISTEINETZUÜENESHEMISEATDNDERAUBRHRAVORNMENZCHENREITZWRLEIDDNELAABELSZSSFHFHFGFAGSRFDARSTSTSSTSBODSSAAAIEONGANDANANANEINESOEUSCHEUNENKLAUPLICHERANWOROF + +2024-01-16 22:21:31,796 (asr_inference:494) INFO: speech length: 82231 +2024-01-16 22:21:31,806 (beam_search:428) INFO: decoder input length: 126 +2024-01-16 22:21:31,806 (beam_search:429) INFO: max output length: 126 +2024-01-16 22:21:31,806 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:32,062 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:32,062 (beam_search:476) INFO: -15.95 * 1.0 = -15.95 for ctc +2024-01-16 22:21:32,062 (beam_search:479) INFO: total log probability: -15.95 +2024-01-16 22:21:32,062 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 22:21:32,062 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:32,063 (beam_search:483) INFO: best hypo: DIESPEERHRTDIESEUNFASENDERHUTZUNTALERICHLINDEWÜBEFÜÖBOTETWENGIE + +2024-01-16 22:21:32,064 (asr_inference:494) INFO: speech length: 270710 +2024-01-16 22:21:32,089 (beam_search:428) INFO: decoder input length: 420 +2024-01-16 22:21:32,089 (beam_search:429) INFO: max output length: 420 +2024-01-16 22:21:32,089 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:34,522 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:34,522 (beam_search:476) INFO: -84.87 * 1.0 = -84.87 for ctc +2024-01-16 22:21:34,522 (beam_search:479) INFO: total log probability: -84.87 +2024-01-16 22:21:34,522 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-16 22:21:34,522 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:34,524 (beam_search:483) INFO: best hypo: GICIWERKISKSLICHINANDUNDEWRSARSTGHGDEVLANTVOUNDEEINEINMALNMEHRIJETSTDERVERANTZWOCFTDUNGFÜRINEUTUPTIMALENWRALMGRASIGKALIVITZTIERUNGUNRERABEITNEHMEUDARBEITNEMERRINENDANSPERSONDERETSTRESHUNZITAGEN + +2024-01-16 22:21:34,525 (asr_inference:494) INFO: speech length: 171493 +2024-01-16 22:21:34,542 (beam_search:428) INFO: decoder input length: 265 +2024-01-16 22:21:34,542 (beam_search:429) INFO: max output length: 265 +2024-01-16 22:21:34,542 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:35,586 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:35,586 (beam_search:476) INFO: -44.26 * 1.0 = -44.26 for ctc +2024-01-16 22:21:35,586 (beam_search:479) INFO: total log probability: -44.26 +2024-01-16 22:21:35,586 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 22:21:35,587 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:35,587 (beam_search:483) INFO: best hypo: ANDRDAUCHONLENDIESERKUDERGEBESERZIELNAISANDEREGIESISHWÄHRTUNDIMITELABPTZIUOFNTWACRIGJIONWIEKALARBRIHNZITZILENODAUKRICHLARDRAUCHOMENEN + +2024-01-16 22:21:35,589 (asr_inference:494) INFO: speech length: 176315 +2024-01-16 22:21:35,605 (beam_search:428) INFO: decoder input length: 273 +2024-01-16 22:21:35,605 (beam_search:429) INFO: max output length: 273 +2024-01-16 22:21:35,605 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:36,667 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:36,667 (beam_search:476) INFO: -37.00 * 1.0 = -37.00 for ctc +2024-01-16 22:21:36,667 (beam_search:479) INFO: total log probability: -37.00 +2024-01-16 22:21:36,667 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:21:36,667 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:36,668 (beam_search:483) INFO: best hypo: DEBERICHKOESESVORDERZUREICHTDSESRETINGSTATLICHERSCHULTTIEEISERFENLICHERAUFGABEBEGRIFENUNDDARHERONEFENICHEAKTÜHRNVORGENAMWERENMUSS + +2024-01-16 22:21:36,669 (asr_inference:494) INFO: speech length: 103359 +2024-01-16 22:21:36,680 (beam_search:428) INFO: decoder input length: 159 +2024-01-16 22:21:36,681 (beam_search:429) INFO: max output length: 159 +2024-01-16 22:21:36,681 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:37,096 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:37,096 (beam_search:476) INFO: -22.75 * 1.0 = -22.75 for ctc +2024-01-16 22:21:37,096 (beam_search:479) INFO: total log probability: -22.75 +2024-01-16 22:21:37,096 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:21:37,096 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:37,097 (beam_search:483) INFO: best hypo: DABISABELNUNMITEINEMUTSCARPOGAMTUTUNHABEMMSWILDAFÜHEINENSPECHENDERECHIGEKONTLAGESCHAEN + +2024-01-16 22:21:37,098 (asr_inference:494) INFO: speech length: 23995 +2024-01-16 22:21:37,105 (beam_search:428) INFO: decoder input length: 35 +2024-01-16 22:21:37,105 (beam_search:429) INFO: max output length: 35 +2024-01-16 22:21:37,105 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:37,135 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:37,135 (beam_search:476) INFO: -6.62 * 1.0 = -6.62 for ctc +2024-01-16 22:21:37,135 (beam_search:479) INFO: total log probability: -6.62 +2024-01-16 22:21:37,135 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 22:21:37,135 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:37,136 (beam_search:483) INFO: best hypo: SIERNOHANALISIERNWOR + +2024-01-16 22:21:37,137 (asr_inference:494) INFO: speech length: 100774 +2024-01-16 22:21:37,148 (beam_search:428) INFO: decoder input length: 155 +2024-01-16 22:21:37,148 (beam_search:429) INFO: max output length: 155 +2024-01-16 22:21:37,148 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:37,543 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:37,543 (beam_search:476) INFO: -23.33 * 1.0 = -23.33 for ctc +2024-01-16 22:21:37,543 (beam_search:479) INFO: total log probability: -23.33 +2024-01-16 22:21:37,543 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:21:37,543 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:37,544 (beam_search:483) INFO: best hypo: DMAKENENETULIEVERLANGENGEBENLIEMERGARTFHRNDIKUNSHIHVERAUSDIEAMENOEITEWRAUHENDASABE + +2024-01-16 22:21:37,545 (asr_inference:494) INFO: speech length: 152959 +2024-01-16 22:21:37,560 (beam_search:428) INFO: decoder input length: 236 +2024-01-16 22:21:37,560 (beam_search:429) INFO: max output length: 236 +2024-01-16 22:21:37,560 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:38,408 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:38,409 (beam_search:476) INFO: -47.66 * 1.0 = -47.66 for ctc +2024-01-16 22:21:38,409 (beam_search:479) INFO: total log probability: -47.66 +2024-01-16 22:21:38,409 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 22:21:38,409 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:38,409 (beam_search:483) INFO: best hypo: GERARDIÜEKLEINEPORJÄECKDEISDASÜNHBERMEHESICBEROGATESHRAUFANDRECHTISDASDASIERSAWBEINZEITAUMVONREIARENGESENTWERENSORUNUMNT + +2024-01-16 22:21:38,411 (asr_inference:494) INFO: speech length: 166708 +2024-01-16 22:21:38,427 (beam_search:428) INFO: decoder input length: 258 +2024-01-16 22:21:38,427 (beam_search:429) INFO: max output length: 258 +2024-01-16 22:21:38,427 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:39,244 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:39,244 (beam_search:476) INFO: -39.29 * 1.0 = -39.29 for ctc +2024-01-16 22:21:39,244 (beam_search:479) INFO: total log probability: -39.29 +2024-01-16 22:21:39,244 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 22:21:39,244 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:39,244 (beam_search:483) INFO: best hypo: IKANDERVERSICHERNDIARLOBPESCHEKOMISIONIESSTERKOMITITZUMAHRARARUBTSALROBECHENERSPIGKIEVEDISKOSSERUSNT + +2024-01-16 22:21:39,246 (asr_inference:494) INFO: speech length: 40959 +2024-01-16 22:21:39,254 (beam_search:428) INFO: decoder input length: 61 +2024-01-16 22:21:39,254 (beam_search:429) INFO: max output length: 61 +2024-01-16 22:21:39,254 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:39,305 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:39,305 (beam_search:476) INFO: -11.09 * 1.0 = -11.09 for ctc +2024-01-16 22:21:39,305 (beam_search:479) INFO: total log probability: -11.09 +2024-01-16 22:21:39,305 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-16 22:21:39,305 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:39,306 (beam_search:483) INFO: best hypo: EIEDANZEHEAUFTAUGHSON + +2024-01-16 22:21:39,307 (asr_inference:494) INFO: speech length: 85432 +2024-01-16 22:21:39,317 (beam_search:428) INFO: decoder input length: 131 +2024-01-16 22:21:39,317 (beam_search:429) INFO: max output length: 131 +2024-01-16 22:21:39,317 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:39,609 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:39,609 (beam_search:476) INFO: -19.45 * 1.0 = -19.45 for ctc +2024-01-16 22:21:39,610 (beam_search:479) INFO: total log probability: -19.45 +2024-01-16 22:21:39,610 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:21:39,610 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:39,610 (beam_search:483) INFO: best hypo: DTDIESMEHAUSHELKAANDIENGÖRGERINUNDBURGERNICHTIBERTZOELITENNBEGEISTEAN + +2024-01-16 22:21:39,611 (asr_inference:494) INFO: speech length: 176953 +2024-01-16 22:21:39,627 (beam_search:428) INFO: decoder input length: 274 +2024-01-16 22:21:39,627 (beam_search:429) INFO: max output length: 274 +2024-01-16 22:21:39,627 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:40,684 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:40,685 (beam_search:476) INFO: -35.62 * 1.0 = -35.62 for ctc +2024-01-16 22:21:40,685 (beam_search:479) INFO: total log probability: -35.62 +2024-01-16 22:21:40,685 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 22:21:40,685 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:40,685 (beam_search:483) INFO: best hypo: TIALEMUKRARENEMITGROSAFHROUDEZORKENNESDASDIGDEIERVORIETRARGENHABENEBZSICHAUHIMZUSAMENARITVERENDRUNGNEDENWEICHSTATENUMSETSN + +2024-01-16 22:21:40,687 (asr_inference:494) INFO: speech length: 171509 +2024-01-16 22:21:40,703 (beam_search:428) INFO: decoder input length: 265 +2024-01-16 22:21:40,703 (beam_search:429) INFO: max output length: 265 +2024-01-16 22:21:40,703 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:41,718 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:41,718 (beam_search:476) INFO: -33.97 * 1.0 = -33.97 for ctc +2024-01-16 22:21:41,718 (beam_search:479) INFO: total log probability: -33.97 +2024-01-16 22:21:41,718 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 22:21:41,718 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:41,718 (beam_search:483) INFO: best hypo: DEAHABESCHURSDIEELDARORBPESCHESEMESTERHERHERTZUNEMENUNTIKOROBTUNZSIKLASIUNERIMRAMDERLINERBRECHTDETZUVEREÜFNIGENISTNIGAUSEIGENT + +2024-01-16 22:21:41,720 (asr_inference:494) INFO: speech length: 298215 +2024-01-16 22:21:41,747 (beam_search:428) INFO: decoder input length: 463 +2024-01-16 22:21:41,747 (beam_search:429) INFO: max output length: 463 +2024-01-16 22:21:41,747 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:44,569 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:44,569 (beam_search:476) INFO: -76.99 * 1.0 = -76.99 for ctc +2024-01-16 22:21:44,569 (beam_search:479) INFO: total log probability: -76.99 +2024-01-16 22:21:44,569 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 22:21:44,569 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:44,570 (beam_search:483) INFO: best hypo: NDMEINMEINEBITEODAMDASWASICHMERVORSTDENISDASMAHRGENGWIECKTLICGINDERTAHATEINGGROSEEINEBREITEMEHRHEITFÜRDIESIKOLSIONSPLITIGHSORLGEPLITIGSTSTDEMTSÜRDIEMENSCHENVORORTDAITIUNSADESWEHENIEAUCHBESCREÄNKENKEINEDAS + +2024-01-16 22:21:44,572 (asr_inference:494) INFO: speech length: 325439 +2024-01-16 22:21:44,601 (beam_search:428) INFO: decoder input length: 506 +2024-01-16 22:21:44,601 (beam_search:429) INFO: max output length: 506 +2024-01-16 22:21:44,601 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:47,201 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:47,201 (beam_search:476) INFO: -71.11 * 1.0 = -71.11 for ctc +2024-01-16 22:21:47,201 (beam_search:479) INFO: total log probability: -71.11 +2024-01-16 22:21:47,201 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-16 22:21:47,201 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:47,202 (beam_search:483) INFO: best hypo: WENWIERARHOLTEDIEEVORARDNUNGVRABSCIEDENOAOFERICHDASSEWIERNACHEIMLANGNKARUSELELSUEIMBUDNABCHUSKOMUNTITMAÜCHTERMÄCHEBEIERKOMISIONBEDANGENDIEGONZTOTIESACHARBEITHAT + +2024-01-16 22:21:47,204 (asr_inference:494) INFO: speech length: 73280 +2024-01-16 22:21:47,214 (beam_search:428) INFO: decoder input length: 112 +2024-01-16 22:21:47,214 (beam_search:429) INFO: max output length: 112 +2024-01-16 22:21:47,214 (beam_search:430) INFO: min output length: 0 +2024-01-16 22:21:47,416 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 22:21:47,416 (beam_search:476) INFO: -15.90 * 1.0 = -15.90 for ctc +2024-01-16 22:21:47,416 (beam_search:479) INFO: total log probability: -15.90 +2024-01-16 22:21:47,416 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 22:21:47,416 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 22:21:47,416 (beam_search:483) INFO: best hypo: UNZERERESCHÄARSCHNUNZIEKONTRORLNHABENKEINENPELEGERPRAFT + +# Accounting: time=85 threads=1 +# Ended (code 0) at Tue Jan 16 22:21:48 CST 2024, elapsed time 85 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/calculate_rtf.log b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/calculate_rtf.log new file mode 100644 index 0000000000000000000000000000000000000000..131bd507d147df4fbf76cd3164e5c54ac0f5544d --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/calculate_rtf.log @@ -0,0 +1,9 @@ +# pyscripts/utils/calculate_rtf.py --log-dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir --log-name asr_inference --input-shift 0.0625 --start-times-marker "speech length" --end-times-marker "best hypo" --inf-num 1 +# Started at Tue Jan 16 22:21:48 CST 2024 +# +Total audio duration: 4186.603 [sec] +Total decoding time: 342.727 [sec] +RTF: 0.082 +Latency: 518.498 [ms/sentence] +# Accounting: time=0 threads=1 +# Ended (code 0) at Tue Jan 16 22:21:48 CST 2024, elapsed time 0 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.1.scp b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.1.scp new file mode 100644 index 0000000000000000000000000000000000000000..607708d66ff795d9377b71f61af658c17e32d014 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.1.scp @@ -0,0 +1,166 @@ +M-AILABS_deu_000165 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000165.flac +M-AILABS_deu_000166 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000166.flac +M-AILABS_deu_000167 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000167.flac +M-AILABS_deu_000168 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000168.flac +M-AILABS_deu_000169 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000169.flac +M-AILABS_deu_000170 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000170.flac +M-AILABS_deu_000171 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000171.flac +M-AILABS_deu_000172 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000172.flac +M-AILABS_deu_000173 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000173.flac 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dump/raw/test_10min_deu1/data/format.32/voxpopuli_deu_000376.flac +voxpopuli_deu_000377 dump/raw/test_10min_deu1/data/format.32/voxpopuli_deu_000377.flac +voxpopuli_deu_000378 dump/raw/test_10min_deu1/data/format.32/voxpopuli_deu_000378.flac +voxpopuli_deu_000379 dump/raw/test_10min_deu1/data/format.32/voxpopuli_deu_000379.flac +voxpopuli_deu_000380 dump/raw/test_10min_deu1/data/format.32/voxpopuli_deu_000380.flac +voxpopuli_deu_000381 dump/raw/test_10min_deu1/data/format.32/voxpopuli_deu_000381.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..1123b0e5938ac5e6b59af238552f73ac2035210b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/score @@ -0,0 +1,166 @@ +M-AILABS_deu_000165 tensor(-30.2070) +M-AILABS_deu_000166 tensor(-11.9022) +M-AILABS_deu_000167 tensor(-36.0084) +M-AILABS_deu_000168 tensor(-13.8163) +M-AILABS_deu_000169 tensor(-26.9687) +M-AILABS_deu_000170 tensor(-17.7643) +M-AILABS_deu_000171 tensor(-32.8022) +M-AILABS_deu_000172 tensor(-31.5102) +M-AILABS_deu_000173 tensor(-4.8772) +M-AILABS_deu_000174 tensor(-14.8937) +M-AILABS_deu_000175 tensor(-34.4802) +M-AILABS_deu_000176 tensor(-14.5163) +M-AILABS_deu_000177 tensor(-15.5303) +M-AILABS_deu_000178 tensor(-7.2729) +M-AILABS_deu_000179 tensor(-16.2150) +M-AILABS_deu_000180 tensor(-30.3143) +M-AILABS_deu_000181 tensor(-24.0067) +M-AILABS_deu_000182 tensor(-12.8639) +M-AILABS_deu_000183 tensor(-21.7973) +M-AILABS_deu_000184 tensor(-18.9314) +M-AILABS_deu_000185 tensor(-27.1297) +M-AILABS_deu_000186 tensor(-27.6226) 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tensor(-17.2786) +cv_deu_000712 tensor(-13.4727) +cv_deu_000713 tensor(-10.6918) +cv_deu_000714 tensor(-21.0052) +cv_deu_000715 tensor(-19.4453) +cv_deu_000716 tensor(-15.6074) +cv_deu_000717 tensor(-27.0091) +cv_deu_000718 tensor(-11.9231) +cv_deu_000719 tensor(-21.8510) +cv_deu_000720 tensor(-12.0153) +cv_deu_000721 tensor(-13.6461) +cv_deu_000722 tensor(-8.2199) +cv_deu_000723 tensor(-8.7622) +cv_deu_000724 tensor(-6.8944) +cv_deu_000725 tensor(-19.0965) +cv_deu_000726 tensor(-10.3085) +cv_deu_000727 tensor(-11.9307) +cv_deu_000728 tensor(-25.5181) +cv_deu_000729 tensor(-18.4820) +cv_deu_000730 tensor(-26.3846) +cv_deu_000731 tensor(-17.0945) +cv_deu_000732 tensor(-14.7601) +cv_deu_000733 tensor(-28.5110) +cv_deu_000734 tensor(-26.9470) +cv_deu_000735 tensor(-12.1146) +cv_deu_000736 tensor(-6.0099) +cv_deu_000737 tensor(-25.9486) +cv_deu_000738 tensor(-26.1209) +cv_deu_000739 tensor(-12.7422) +cv_deu_000740 tensor(-24.5539) +cv_deu_000741 tensor(-22.6639) +cv_deu_000742 tensor(-21.7190) +cv_deu_000743 tensor(-8.6668) +cv_deu_000744 tensor(-7.6556) +cv_deu_000745 tensor(-4.3242) +cv_deu_000746 tensor(-19.7583) +cv_deu_000747 tensor(-22.6231) +cv_deu_000748 tensor(-5.6405) +cv_deu_000749 tensor(-5.2133) +cv_deu_000750 tensor(-22.3424) +cv_deu_000751 tensor(-33.2573) +cv_deu_000752 tensor(-29.8971) +cv_deu_000753 tensor(-28.2741) +cv_deu_000754 tensor(-15.8900) +cv_deu_000755 tensor(-32.3537) +cv_deu_000756 tensor(-11.1731) +cv_deu_000757 tensor(-24.1737) +cv_deu_000758 tensor(-11.4368) +cv_deu_000759 tensor(-27.4393) +cv_deu_000760 tensor(-17.0587) +cv_deu_000761 tensor(-29.0633) +cv_deu_000762 tensor(-18.2389) +cv_deu_000763 tensor(-15.7265) +cv_deu_000764 tensor(-13.4605) +cv_deu_000765 tensor(-16.4862) +cv_deu_000766 tensor(-15.9654) +cv_deu_000767 tensor(-17.6974) +cv_deu_000768 tensor(-16.8860) +cv_deu_000769 tensor(-7.4160) +cv_deu_000770 tensor(-34.7886) +cv_deu_000771 tensor(-20.3054) +cv_deu_000772 tensor(-24.1436) +cv_deu_000773 tensor(-15.1916) +cv_deu_000774 tensor(-18.6282) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..1d7ceff75ab7f21a954d437bb9002a6dbbfe00d7 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/text @@ -0,0 +1,166 @@ +M-AILABS_deu_000165 DIBE HRDIGON MACHTEINER AEUSESTWICHTIGEN SEHEIN INDE DER PÄTIT ZION N DINGOWANÜÖR FÜRDES IN JANERDSOUSSBEGENA DIGUN +M-AILABS_deu_000166 DARHABESI DIEVWOULGJEDEM HER INER INERUNKGEBLIEBENEN WARTE GESPAOCHEN +M-AILABS_deu_000167 ERST UM ACHT UHR WAR ER AUF MALEL BRCHTERDEN GAFI DESNESCHEN INZSZIMER UNDESPEHRLINGE DIE DASSAUS DEN HEXE SEGTEN GEFALNE OTARKON AUFSPBIKTEN 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E S C S H +cv_deu_000773 R E R E I N E R D E R P I A E R N I E R E A F T D M G E B I E T D E R N U T Z I U N G D E R S O N E N E R G E E N +cv_deu_000774 A R H F E N M E D I E K O N D E N A F I N E R F E N G E N M U S I C H E L I C K E T P B E W A N diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..0b9863d7553fd4ef634d99e0b2c9ca81103600aa --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/token_int @@ -0,0 +1,166 @@ +M-AILABS_deu_000165 10 5 18 2 3 11 6 10 5 14 16 4 3 17 9 15 11 8 2 5 4 2 6 3 9 2 12 7 2 7 8 21 5 15 11 8 5 14 2 4 3 7 2 11 2 5 4 3 5 4 10 2 3 10 2 6 3 23 26 8 5 8 3 20 5 16 4 3 4 3 10 5 4 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a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..7e6e9a9229b44e479965fb134dea3eda3d488b1a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/text @@ -0,0 +1,165 @@ +cv_deu_000775 DICHBEMASCHINE ST VERTISH +cv_deu_000776 IN DE ARCHERISCHEN PERIONDE WURDEN ERSTEIE VORMEN DESOCKEBASSNIN TUIKELLT +cv_deu_000777 DIE KOMÜDE SE BESE LTER ESTE FÜN +cv_deu_000778 ARTZLERE GED VER NERMUMEMS ALP +cv_deu_000779 TDERMIT ENDE EINE EAE WERKREISCH IE ENAR ZUNERLIENKÄERDENS ABEN VORELEN INMNSISCHKÜNTEN AUNENENZSKAU +cv_deu_000780 DERSON EINES BEREGNANZ BEGAN EINI USPALKER JERI WEI DENSPURT FREINDTEN WANE EIKELN +cv_deu_000781 IN DIEN 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T A D T K E I E U O F I E R A L A L E +cv_deu_000800 C O H D E R T R A R D E R R E I M A U H A L U N U N D E N I N T A B E B E I S I H A H +cv_deu_000801 M I T F Ü Ö O S T W E R E H E R D E R O W T F I A D E E I T S E R E C H T I C H E R A L L E D E U O N B E D S H E I C E N E N E M E I N T +fleurs_deu_000378 E T Z T E R V O C H U G A B D A S M E T I B E K A N D T D A S S E S H O N E P E L Ü B E R F I E R N D A S C H W A L T E E V O R F E L E V O N B E R I T Z U N N T O M I R T O D E N V W E R I D E S N D E R N E H N E N A L S N C H T S H I E R I E N K E E I T E T E +fleurs_deu_000379 S I E B E B E N I J E Ö E S E Ä E S C H E N E N S Z I G I G S U N D E R S C Ü Z S T D E D E N B I H I F D E S U N Ü N B I S C H E N K O M I T I S D E R V E R E I N I G T E N S T A T E N U N D A R S I P T I E R T E S A S A P T U T E N O T W E N D I G K E I T D A S I H I E U N L Ü N B I S C H E V E R N M I I E N F Ü E N S I C H E R E S U N F W E L T Z F Ü E A L E U N S E R E R S P O T L E R E I N S E S T +fleurs_deu_000380 A L I C H K E N E A B P I E T K O M P E T I E B E L M E T A C H T N A T Z W E I B U N D E L F A R A C H T N R T Z W E I B U N D E L F B E U N D C H T N E T Z W E I P U N D E L F G E S E I N V E R S G E S D I E B A S I S T A T I U N V E R F Ü G K B E R D U O A L R A D I E +fleurs_deu_000381 J E R B I E Z E I S H E N S D I E G E R I C H T E R A L S P L I C I C H E S G E S C H W Ä T S U N D T A L L B E N H E I T Z S +fleurs_deu_000382 L T E R W U C H R G A B T A S E M I E I T H E B E K A N D A S I S V O N E B L W E R F I E R N D R E S I E W E I T R I E V O H F E L E V O N B E R H I T Z U N I N V O M I E R T W O R D E N W A I E D A S U N T E R N E M A L Z N I C H C H V E R I G E B T Z E I C T E T E +fleurs_deu_000383 N A C H D I M D E R D E M M E U N H N U N D E R T R E I U N S E C H Z I C B A U T W R D E N W A R K A M D I A R E S Z E L I G H N B E P F L U T U N D E S E D E M E N T E M N P L S V E R T E L N Z U M S H Ö L S T E N +fleurs_deu_000384 E R W A U H M S T E C H E V N G E L S C H E I N V E I L E E N D E B E T E I L I C H T A K T U L E B E I S C H P I E S A N A H R B E S C H L I S E N I B P R E M J H M I N I S T E R P R T R I S A F D E R V O R D E S E R T D E K A N A D S C H E N F Ü N N U N D E R T O L L E N U T E N E I N +fleurs_deu_000385 D I H A U P T S T V E R M R D A W I E N I S T K H E N E N D I E I N E I M P I S P B A H E I S T G U M E N E S C A B E R F I E L E M E N T E N S R E C H E N E R O S E H +fleurs_deu_000386 S I S I S E B E T Z W I C H E N D E N E I N Z E N N B Ü N E S T D I E N H E R S T E N A U O C H U N B E S T E N D I G E R Z E I T E N G E T A L T E P R O E W E N Z E N D I E B E K A N T D I S T D E D I E S E P E R I O E D E N W A D I E E P O C H E D E R D E I L G Ü N I N G E I C H E D E S E Ä C H T Z I C H I E A R R E L A N Z E I H E N D E R H A N U N D E E E N D E N E S T I S T A T V F V A N T +fleurs_deu_000387 A M A N D E R E E N E E R S P E K T R U M S H R W E I N E M A N S I C H E N E I N I C H W I D E Z U E R K E N D E I N E W I E D E U M D S A E S A N D R M A C H E N M O S A S E R S T I E E S G E M A C T A U N D Z S I C H A L L E S U O E L I G E M A C H T +fleurs_deu_000388 D G I G I D E M E I S T E N I N D E R P I T E A T Z I O N E N D E S T E C H E N L O G I S C H E N D E T E M I N I S E N U S T A E N Z W E I A L G E M E I N E V O R S C T E R L U N G E N E I N E S E I T S D E S D I I N D I C G E M D E R T I C H E N L Ü G I E S E P S T E I N E N W E G V O L G T D E R W E I T G E N D I E N S E I S U N T O W E L E O R D E R P U L I C I S C H I N P L S N A M E N D I G T U N D A N D E R E R S E I T S D A S T I G H N E Ö Ü G E I E R E R S E I T S A U S F W Ö H R K E N E N A U F G E S A L S C A F T N A R A R T D I E H E R I N H E R E R N T A S Z U T S A L B E D E N Z I N T +fleurs_deu_000389 W Ü S H E D E N E I N Z E N E N D N A S T I E N H E R S T E N A U R U N B E S T E N D I G E T Z E I T E N G E T E A L T E R R O W E N Z E N D E E K A N D E S T E S E P E R I O D E N W A H D I E P O C H R E L D E R E K Ü N I G R E I C H E D E S E C H T Z I C H A R L A N G T Z W I S C H E N D E R H A N U N T E R I E N D I N A S T I T T V A N D T +fleurs_deu_000390 D E M I C K H Z U V O R G I E B I E Z I Z I C H E S T O G O M E N T A U F D E N G E Ä N S T R E I T I N D E N D I E P A L I S I N E N S E R E I N Z U R Ö G S A L T Z E N D E R G E N Z S E N I N D E N Z U S T A N T V O R D E M S E R S T A L E G R I V O R N A N Z E S N U N D E R T S E B E R N U S E T I C V O R D E N +fleurs_deu_000391 M I T H E M P E R L U S T G R E C H E E R S P A C H K E N E A R D E R E C S T E N V O N S E I E N V L E S O F I S C H E N U N D W I S E N C H A L I C H E N W O T Z E I N K I H E N E N A B I E S L E T E N +fleurs_deu_000392 E R S T E M I T E R A U S S A G D E S I Ö R S U S I E B E I N D A S T D E N N R E S E N U N S R A T L E D E N V E R E I N U N D E R S P O T S B P E S R G E D I E N D I S D B E N W E R N E H A L B U N S R A R G E S A T I O U N D H N V L E V E R I N D R U N G V O R A N T R E I B E N A N S T E I N E R T I T Z S R I T Z T V I T I E R U N G V O T Z N +fleurs_deu_000393 I G R E Z F A T E N A E N G P I G E S B O G B I E T E N A U C H Z E I T Ü Ö R I N A U F E N T A L I N E R S T A T K G R E U T Z F E R P A S R S H E R E S E I N V N D E I E U N S L I C H B E R E I T S I E B E D E N G E N +fleurs_deu_000394 S C S C T E R E I S E N D E V E R D E N R I N G E N D G E W A N D T K A U F I E W I E D E A T V O N U N W E N T E Z U A C H T E N D I E E G E B I B I E R I F T D A D N D I S S I G H A U F A L E R E I S E P L E N E A O S I E R E N K O N +fleurs_deu_000395 S I C H R T R I S E B E S E R T D A S D E R G K O L T Z U N G S P B U N K T D D E R L E N M E N D E I N B E E W E R D I G K A L E U N T H U R E H O N T A L D R I T E N D E E H I G K I S T D P L A T S F Ü I T E S H O U P T M N O I E I S T S I E B E I S C H E N +fleurs_deu_000396 E I T N U N Z E U N R T A C H E N A C H T Z I H M I S T N W A L U N D R A N S B P R E N Z S E I N D A M I T W E E N D B O B A C T E R B I E T Z O E U G E N G K E N D A S W E G N D E R W A L E I N M S C H L I G E V W A N E N S I N D U D A S K E I N U M S C H L Ä G E I N G E V A O F E N W E R D E N A U S E R E H N E D E R T O T D E U N G S M E S K S E L T E T A T R E S I E R T E N E E +fleurs_deu_000397 O T E R E R I S T K A N E R D E S B E T Z S A O U B E N D E Z W E I S C H E A L I G E H A U P T S T A T U N D Z S E L T E N D I C H I C H E I N E R E I E U N K U N Z S T D G E E R I E N U N D M O S E E N A U S D I E K A N E N D E R S V E R G A N G E N H E I T U N D G G E N W A R T P R E S I N T I E R E N +fleurs_deu_000398 D I E S E P A R E K E N S I C H F V E I N A D E B T I O N S P L A N V E R B E B E E N S H E I D E N +fleurs_deu_000399 I N V O L E D E S E N E I N Z W R E I F I S C H A B E N A U S G S T O L M D Z W E A W E I T R I S E N V O M A U S T E R B E M B E T R O R T T A U N D E R D E R J L A Z I Ü F V E R +fleurs_deu_000400 R E A N Z E N S E N N J H E R E N E R T Ö L I H E M N G E B E N A M E T E N A U S I E R S T E N S I E A L S O D E R E R S C H U N G A U C H N U R E I N E E M K L A E N D T V E R N +fleurs_deu_000401 A U F E R N A R S E I T E K N T E I S M E R M R I E R G E B E N D D I E K R O S T E D N E S T I S W R E I N V E R A F D E L A V E R A N D I O B E P L I C H A U F T S T D E G E N T +fleurs_deu_000402 S G R E F Ü G K T D E C H E N Z U U N D A S S I E D U O C H N I C H T D E R T Z U A U F G I E V O R D E R T W E R D E N S O L L T E N F E R T F I C H T U N G E N E I N D Z U Ü G E E N D E I Ü E B E R I E R E N I N T W I L U N G S T A N D I E R E V E R A N T O R T U N G U N D I E R E R F Ä E K E T E N H E N O A R N S I N G E N +fleurs_deu_000403 S I S I E W I C E T U E L E H I E L F I S T E U N G E N I S E N T I N D I E S O F T W E R E I N G E B U T D T U N S O E N A B E L T S C H I T E N D I E D E R S C H Y L E A L L E I N M Ü G L I C H E R W E I S E N I H T B E V E T I G E N K A R N H E N T E R F R A G E N N E I E L E G E N U N D T D E R K L E R E N +fleurs_deu_000404 A M F Ü N F Z E N N A R G U S T N U N Z H N H U D E T V I R Z I C F E L I E A L I E R T E N N S Ü T R A N K R A I C H E I N D I N W A S I O N W R D E A P E R E S C H E E R G U N G E N E R N D T +fleurs_deu_000405 E R R I F O C H A L L S A N W A S E N W A S E R K A R M S E B T E N G R O S E R D E N S A U R I E I D E R T I E W E X S W E I M I C H T G E W A K E N +fleurs_deu_000406 E T E R K R Ü N D U N G V N A S U N T Z I O R F I N H T Z E N D E S I E N U N D R E I S I S S P A R E G E G E L U N G V I E L V O N S E I M I N D I G K E N K A R A C T E R N D S E I N E I D E N D I T E T Z B E W A R E N +fleurs_deu_000407 S I S I B E B E D R T Z S D E N I S D E A N T E L L A N I E G S D E E R B E N D E S T R I E G H T D E B I N E R G E S A N T D E N G O P E D E R L O L T E E M I T D U G B E R K U L O S E N O F E N B A R D E N N O C H N G E R I N G S E X S D A U S E N D E I N Z S G E S A N D R E I H U N D E D E I S I G T A U E S E N L O L T E D E I N S Ü T D A W R I K E W H E I N E N B I S T I N T E N Z E I T P B U N G K T A N G I S T E T I S E N T +fleurs_deu_000408 E H N S C H E L Z W E I T A U S E N S E C K S E L E U T E R T A S K O N T I N U M K O N D Z E B T A S E I N E M I T O R D E M U R G E N S A T I O N Z S E H L F N L E S T U N G S F E G E Z E W E R E N +fleurs_deu_000409 S G S E E I N D I E S E B E R I O E D E N D E R U E R R O E P E H E N G I C H I G T E M S T D A N T D E R I C H U N D M E Ä C H T I H E N E V O D E N E R K A R T O N E S I G I E H E N A U F T E N P R E Ö S T A N E T +fleurs_deu_000410 D I E R S D E R C H E N D I E B Z I C H M F Ä E L U N G I S T A S E I N E N E U D E P L O M A T S C H I N I T K E T I P E V E R E N D D I E N J A R S E G R I F E N W E R D E N S O L L T E M D I E R A G S C E N G R E N Z E N G E G B A E R E I N T L I E N T E R E T I O N Z U S I C H E R N U N D P L U M A T S C H B T Z E M I T Z S E I N A C H B A N I E E R T Z S T E N +fleurs_deu_000411 D E E S P B E T E I N E U T E G E L E G E N H E I T D A S N O R T L C H Z U S E N D E H E M E N M E H R U D E W E N G E R R U N D U M D I U E R D U N K E L E S T +fleurs_deu_000412 S T K T P R O S O R E N P A M E L E R V E R G U S S O N V O N D E R U N E W Ö O E T I A D A N D I E M E R K T A N S O A L I S T E N S C H E I E N E I N E G E Ä E R L I E G E N Z E U E R S R E I T E N W E N D I E P O T O N S E W E I T E V O N E R D E C H T I E V E E N T E I H E N +fleurs_deu_000413 S K A S I A C H L O N E I N E N E I L G K A R Z U K A U F E N D I E T Z U T R I T E N W D E R T O R S G E W E L T E N P A Z E N C H E N T A F R I K A R E R T U O A L N Z H T A V R I K A N C H E N E R Z N A L P A X S G E W E R T +fleurs_deu_000414 D I E P R Ü K E S O L M S E R T E M B E A T Z W E I T E L E N S I E B Z I N V E R S T E N D I H T N E T R I E A U F N E M I S W R D A R W A R T E A S I R A S J A N I C H N Z O L P U N T E A N F E A R T I G S T E L T Z E I N W E R E N +fleurs_deu_000415 W E R N D E N E X S P R M E T E L L E M S T A U I N E R L A G T S U S E I N S C H E I N T I E B U L E M O T E L I T E T Z U S E N U N G E B T A S B S E R K E I N M D I K A M E N T E D I A L S E I N D R I H Z U B E H A N D L U N B S T E N D E N V E K T I O N G E R E I G N E T N A C H K G E I E S E N O R D E N +mls_deu_000281 E I N E U S S E R S L I E B A F E R D B E S C H E N W E X S E L A N S T A T M A I N A R U O K D I M P L A N E I N A L G E M E I N S T A T E N K O N G R E S T Z U B R U F E N U N D K O N D E S I H V O L F I G N O C N I C H T B E R D E S V O R T Z L E G E D E B R G A M U N D D I N O R T E S T Z U S A M T R T S E I N I G E N +mls_deu_000282 E R W U S S T E N C H T A S I M D A S L E B E N K O S P A R E S G E R A U P T A T E S C H P A N G R A F T U N D M U D T D A S E S I N F E I G U N S C O L I G E M A C H T A T E U N D F Ä H C H Z U D E N H O N D I N G E N Z U O D E N E N U N G E T R I Ü P T E I T R A L E N G H R T +mls_deu_000283 D I E S E R U N G E M A N H I E S K A K A L I T Z I E N U N D B E F A N I C G A E F D R A N D E S C H A F T A L S I N E M G E N A N T E N K Ö Ü N I G R E I C H B E K A N D M 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A F E N G E H R E N D B E H A R G E N B E G A N D I E K I S T D E N W A N T N U N S Z O H Ö R T E I C H A U O F A C F E Z O S E I N E I N K L A R E R S C Ö N A G E R D A N K T E N G A N G D E N I H T I L R G E N G I M I T D I M B A U C H A U S G E H E K T A B E M O S S D E N A F E N D E N K E N M I +mls_deu_000287 R I S S E S P O T T R I E H E R N E M E N S C H N D E N I G K E N N E N F R A K T E I L E I S E R W E L C H U N B M A K T E A M I C H R A N G I T R E T E N B A I C H N D G E G N I E T E D A S I S N N F A N T E S I K O K F S E I U N D S C H O P T T E I C H E U N G E I L I C H N T E D I E A N D O N L T T E R N T Ü L I S P R A C R I C H I U N B A R H E I T D N D E S W E I N S E R I E T O E I E S P E T R I E M I S T E R O T S C H S T E S +mls_deu_000288 C I H W E I S D A S I H S E R K R A N G B I N S A K E S H N E R E N A W E I L E V O R N P A M I N U T E N V E S C H T E I C H M I C H N B Ä T E R U E N Z U R E N U N D F Ü L E D E D A S I C K E I N G L I E D M A R E N I H R N K A 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A N D A E R K E I N A N D A R N A U S F I G F A N T D E R N O C H Z U M P E I T E R S C R E I B E +mls_deu_000297 O N D E M F E R D E H E R D N D E R P A T S C H E N U N S A G U N Z S T A S I F E N A P A T S C H N F Ä R D U N S E B E S O V I E W A R E N U N P R E N D I G E B E W Ü R D E N I Ü F E R I N K E I U O W A B P F I E R T T D A S I N D U N R I G K L I G E A V O D U M A P A T S C H E N V Ä H R D T S O U L E N A S R C H T I H G E R A S C H L E I M T O D E I E S H R G E F A L E N U N D A N E B L U T V E R G I S E N W E I C H I S U N B E V O R S T A N D W E I S E S E F E H R D E H E N D L E R W +mls_deu_000298 D A S M A T O N E N H Ü T C H N V O D S C W A R T Z A M A M I N D G A T Z I Ö R S R I E R E L A N G N L O T N G E D R I K T D I E R W A N G E U M F L O S E N N T B E R S C H L T H N H R A P W E I T E N S O T R A E I D A S E I N V E R H R E R L N T L I C H G E B O E U D E U N D S T Ä E B P E T Z W S C H N R E I N D E R H I B P G E B L N E I N D O F K E N R A U F E N T A B +mls_deu_000299 R B M U S T E R T I N Z A G E N G A L E M S H N T H A F T E N S T R E B E N U N E N D T I E V E R E I U N D D E M U D D I E F Ü Ö H B I D E D E R H E L I N G A E F L E N G E G E N D E U G E R E E L T R S T D E J E M L E N G E R W E L C H E P F A N T C H S K O S U L N E G E F L O N S O C H T E N I N A U F I N S R E W E R K S T U N F A N D T E N I N +mls_deu_000300 E R L I E S Z S E I N E G R E T E N C H T V O R T S C H L E B T E N A M A L E R W I N I G S D N A B E R I N E N G R O S E N V O G E L B A U R U S I E A L E I N E I N E M T O N E F E I E F E M O U S T E N I E R S H I T S K T E +mls_deu_000301 F R N T C H E S K O M A L T E N U N H E L I G E B G E I S T R U N F I L E B I E T A S E L Ü G E N H A T E N A B E E L T K H E I N N E L S E R E R M O C H T D I E B U L E R I S C H E L B I K E T D E W E I B I C E N G S T A L T E N S O B E R H A F D A S I S T E L E N I N D E M V N L E B E D T E M O D D E L E N D I E K A L N A T I O N G V O D N A L T E N M A H M O B I L E N B E R O R M O N B I L U N I N D N A N +mls_deu_000302 B E W E G U N U N T A T D E N S T E N Z U G E R S T H M E D I E V O E U N A N G E G E B E N I N G E R D E N Z H E R N M I C H R Ü B E H A N F E E I C H E N U N S A U R A M F A N A L E I N D E M P F E I F E N K O P F E R A N W E S E N A B E I N F Ü N D F T N A U T S T O H A R I C H N I G E N A N D I E T S T R O C H U N D S C H M Ä K T I G D A S E H N S T I C H E N F I L S H D E R B E I S E I N M S E I G H P L I E S T E N R A U C H A U C H G E G D E N H E E L U N G E G N G D +mls_deu_000303 U N D A S V O Ä E R S T A N D A U F U N D F L A C K E R T U N K O C H E D A S E S E N F Ä A T I G H U N D E R B R A T E N B R U T Z E L T E F O R T U N D E R K O C H G A B D E M K Ü S C H E N I U N G E N E I N E R R O A R F E I G E U N D I M A R K T R U P F T E T D E S H U N F E R T I G H D A R W A R T D I E H O C H Z E I T V O N D E M K Ü N I G H S O N I T E T D O N G R Ö Ü S I H N G E F E I H R T U N S I E N E E R N Ü T E B I S A N I E R E N D E +mls_deu_000304 U N D D A S E M I N I C H N A C H T R A R G E N G O L L E W E N I C H N E D E R S H P E N S T I G W A R G I N S E I N U L M E I N E N B R A R T D E R H E R F A R A R H E D E I N A L L E N R E I C H T D E H A T U N I C H M E A N U N R E C H T A B E R H +mls_deu_000305 U O G E H N E M A S E N U W I N I G E K R A M B T U G E R B R E I T E D S I C H K H E I E F Ö M I G A U S E N M U S T E D E E R E S H E M I N D G E G E N F I G E D E S P R E N K I S C H O S A U F A N E N T Z S O U E R I N G E N +mls_deu_000306 D E R V U G S R E I C H E S E M I U N F R I T I C H E F R I E D E N S P W E I F V E R H E N D E R M A N T A T W A C K A S E I N E S E K S Z Ü G E N S A K T E D E R G R O S I G E I S A C H T E N I C H A U F D I V E R S C H I E D N E H A U T D E R M E N S C H E N D E N D I K R N S I C H M I T V A B E B S C H M I H R E N M I N T Z S T R O L S C H E N S O N D E N E R S I D A S H E T Z S A N D E H E T Z H N D E R K R L I G E V O M B E R Ü B E N S T A M I D E R K A I O W A S I N T A P F E R U N E R S C H R O G M N T R E D A S M E I N I G E H E N G +mls_deu_000307 A L L E S A S W I M E T I E R B E G E G N E T S C H E B S I C H T D O E S C H U N D B E R I N A N D E R B A L T U N T E R S C H E B E M W E R I N K O N T A K T D E I S T I E R E R H A N D U N D E M E I N I G E I E R N A H R M O N D E R M E I N I G E B E I D E L R S H E E I N A N D E R A U S B E I D E V E R S C H L I N G E N S I C H +mls_deu_000308 E R M Ü S T E E N E N F E R H E N G R O N I T E N K Ö E R A L D E S M A L E S M I T A L L E R K L E H R M E N Z U R E C H T W E S E N E N R I M I T G R A U S E N I G U N V R C H N A R C K H I U N V E R B R E M E N I C H T R E T E N D I E P E R S O N D E S E R A U S G E I B E S N D B I T E T I C H I K Ü N S T I G E L I S E R U W O L S T I E D U W E I T E L I S I S T F O L E N D I S D I G I T I S T N E R T E N +mls_deu_000309 D I H O F D A M E N B E K A M E N K R E M P F E U N D I K Ü N I G E N U N D I E R O N Z T E S S E N E N D I E R E R A L L A L I E B Z S E N H Ü N Z C H E N W E R N D E R M E I L T H E R A U I N S C H O S G E N O M H A D T E N E R M E R K T E N Z U I R E N S C R E Ä K E N G D A S D I L I E L E R A M A R A N T F A B E N E N U N D O R A N S C G A H L D E N S E I D E N K L E I D E R A L E D I S T D E S E T M E I N H E S L I H S T E N Ö F L E G E N W A N +mls_deu_000310 V O N L E D E A N D I E S I E S I N U N K L A R W I E R P I E S E N D I E S I S P B I E L N V O N G Ä H R T B Ö R S E N D I S I H E G K E N V O N D R A N Z Ü E S C H N B Ü C H A N D I E S I B A S R S E T Z E N K O N T E B I S M A N G E M Ü T W E R E N D I H L A U S T Z O N N A C H A M O N A U F K E S T C H E T W U R D E +mls_deu_000311 A M U N D N A E N W A N B L O S I E R E I N Z I G E R S C M U O K W A N I E R E A S T A N E N B R A N F L Ä C H T E N W E I C H I N W I L D E R U N D D A T Ü R L I C H E R A N M U N D A U I R S C H U S T H E N H R A B P F I E N I H N A M E I N B O G E N G F E I N K A T U N G S N D Z E I H E T M T R O S E R S O K V E L T I O M G E S E R +mls_deu_000312 R A R W I E U S T R T S C H L A N N O C H A S I L E N E I N I M A N D R E N S T A R T K O N T M A N E F A N W A S D E R G E N S C H E A N U N D E S E S L T A T D I S U N T E R E D U N G E W I S E N S E A N V E M U T E T E D S E S I C H U M E K L I R E N G D E R M A T Z H E Ü B E E R A B S I C H T E N U N D U N D I V E M I T L U N G D E R M Ä C H T E Z U I S C H N M A S T A T E N U N G R O S P E T A N I E N H A N D E +mls_deu_000313 L A U N W E N I G S T E N S E I N E Z E I T L A N G V E R S U O C H E N I N B I E F E R U N W I E R A U F D I E S E S B E I S N M T E I N A N D E R A U S R E I C H E N D A D E R S T U S A M E N H N G N D E I E D E S A R G S T A E I G E N K L I G H O E E R L E M E N T I S E R S E T Z T I E R U R T +mls_deu_000314 V E S C H E N F V O R K O M S E K F Ü R D E N Z U D E V E R M U T U N G D A S F R A U W I E S E D I E K L E I N E N W I E N V E R B R E N E I S O L I S F E I N S U S T A C H G E R H I T S T A B E N D A S D I H E R T P L A T E N Z S P T A N G A U S E D E S O L E I N F Ü R C H T E L I C H E R G E R O C H O W A G E N U N M N W U R T E N S E I N +mls_deu_000315 U N K G I N D E M S H R E I E N A C H S O S A E R E N T L I C H E I N H U O N B A U M U N D O B E N D E R A U F S A S E N K L E I N E S K E N D T U N D E R D E M B A U M A B A L A R E I N E F R A U R D I E S H L I E F +mls_deu_000316 C I T S I E H R T E N S E R H E B E N D I F I S C H E G A D E N E W A L L S C H E D E N A R C H T I A U B E R T A U S G E R U R D H F V I N W A D E N H E R E I N G E Z A O U G E N D I E S E L O E I T E R G E R H Ä O R T H E N A U G E N S H E I M L E S C H V E S C H E T E N E N N O C T Z I O N E N A R N A B U O R L D E R A L O P Ä H S C H E R K A R E K T E B E I A L E N A U S G E T R Ü K T W A L +mls_deu_000317 N E I N E I N I C H S C Ä E M E M Ä I C G H T L S M C H R N D E I N E M B U S E N M E N G E S I C H T V E R B E H R G E N H E R S I N G T E N S G R A S N I E D E U N Z I E T S I N A C H +mls_deu_000318 D I K E N D E R A B A R S A S E N V E R D E M W A L T U N D A L S I E D I E D R E I G N E C H T E V O N W E I T E M L A U F E N S A N S P R A C H L E N C H E N Z U N P F Ü N D E V O G E L V E R L E S T U M I C H N I C H T Z O V E L A S I C H T I G A U C R E N I C T S U R S P R A C H F O N D E V O G E L N U N U N D N E M A R M E R +mls_deu_000319 W I E D E R S C H U L Z E I N S E I N E R H U L D I E G U N G S R I E D E H E R V O R H U B D E L E R A B R A C H T E A M K L A R E N S O M A M R A D E N G M I T S E I N S C H U L K E N D E N E I N G E S A N G S S T E N T I E +swc_deu_001408 S T E R T W I S I E S E I E N S O L N +swc_deu_001409 D E R E N T S C H I N G R N G E N D R C E I N E R Z U S A R S C H A L T U N G S T U F E N L O S +swc_deu_001410 D I E A U F A L E B E I D E R S I T Z V E R T E L U N Z +swc_deu_001411 U M D E N Ü E R L E M E N D +swc_deu_001412 S P B E T E R U R D E N T E I L W E I S E S U G A R A C H T P A R L I L L O S T R E I F E N E N G E S E T Z T +swc_deu_001413 M A O R D E B E K A N T U N D V E L L A N G K T +swc_deu_001414 B U N D E S W A I G E S E T S D I E S T M V O N W I E L E N +swc_deu_001415 S F N A G E S C E I C H T E +swc_deu_001416 S B A L T U N G F E E C +swc_deu_001417 S T P A L E B O R N D I E U S E R E N D F E R A N D E S +swc_deu_001418 U M W E I T E R I N H U M A N I T E R E R H L F I E T Z +swc_deu_001419 S I E R K A M T E N D I E N N O U E R I C H I N E S I S H E R E G I O U N G N I C H T A N +swc_deu_001420 D I E U R A U F Ü H G E N V O N A N D E I N Z W A N S E N S E T E M E R Z W E R D E N A C H T I +swc_deu_001421 E R E I C H N I H T S C H O L I C U R E M I T S C H O L I H M A C K E N A N T O D E R N S M I T G E S E +swc_deu_001422 D I E D E R S Z S T M E R E I N E N +swc_deu_001423 U N T E I F E N T I S E N B E I D E +swc_deu_001424 K R E I S W A L F V O R S C H L A G U N D E I N E L A N D E S L I S T E R N D E R Z E I T E N +swc_deu_001425 A N U M S E R Z U N G D E R S A G E I N V O M A N E S F N F Z E I N T E I L G E N L I E E R T Z I K L S Z W E T E N A C T W U R D E P R E S L A S K A B E R T I N N E B E R A B E T U N G V O N H O S T H A B E M A N +swc_deu_001426 I E D I E V O L E D E R T A B E L E D A S T E T +swc_deu_001427 Z U M S T R U N F L S B A E +swc_deu_001428 D E B U N D E S W A L E I T E R B I S T Z U M S I E N Z I G S T E N T A R +swc_deu_001429 O R E R I C H K G E W R D E N D U S H E N I C H T M I T W E E N +swc_deu_001430 A U S F I O N M U S T E N G U S E R K O T E B E G I N +swc_deu_001431 V E R K I E I C H P B A N Z E A L N W E R T U M N G E A N D E +swc_deu_001432 B E T R A H T E D E E L G E M E I N H E I +swc_deu_001433 U N T E R S H I T L I C H E A U F A S I N G E N G A B E S N U R D A R I E B E R +swc_deu_001434 D O L L B E I M B U N E S L I G I S T E N B R S E R T O R T M U N D T N A C H V O L G E R D E S U N M I T L B A R Z O V O R T Z U R Ü C K E T R E T E N R E N A S S J I R E N R Ü B E R +swc_deu_001435 N U N Z H N A D C H T U N A H T Z I G +swc_deu_001436 R E I N E N Z I G K L P E T D I E +swc_deu_001437 D E R V O T S T R O M I S Ü B E R F I E L E G R U S S E N O R N U N E N L E N A R T Z U M L I C H T E N V A L +swc_deu_001438 D A S H T D V F Ü K L E I N E P R T E I N G R O S E A U S W I R K U N +swc_deu_001439 I S D E I T E R A T I E F E R T I E N S U G C +swc_deu_001440 D I E S K R E N Z U M B E I S H P B I L K O N D E N S A E R T H O R E N S E I N +swc_deu_001441 A L D T D I E K O U R S A U F K U B E R H A L K G E N D E N S O R I D T I S C H N S C H E R A B P T R E T E N +swc_deu_001442 B U N D E S T A G W A N T Z U N D E R D R E I U N D F M F Z I V R E R S M A L S N C H E I N E M V O M B U N D E S T A I G S E S T A L S E N G E S E T +swc_deu_001443 B U N D E W E I G E Z F I E F C H G E I N E R T W O R D E +swc_deu_001444 E R I B E R L A G E R T E N V O T U S T O M E U N D T R E I K +swc_deu_001445 D R T S I N T I E G R A T I O U M D E R B E I D E N D E U T C E N S T A T E N +swc_deu_001446 B E R I E N E Ü L M E S E N S T A T +swc_deu_001447 O A F I T Z E E F Ü L R N +swc_deu_001448 B I D E R V E R H E N S W A L W I T Z U S E R T L I C H D I E E I N H A L T U N G D E R +swc_deu_001449 W I E W E N I C T I N O L A N E R N O C H M P E L T S D E R Z E I T +swc_deu_001450 I E D C E T W V R D I E D U C H F I U G E N V O N W A L W E R B U N G A F K O S T E N D E S T A T E S +swc_deu_001451 D A S N I H M R U N G E S E T Z +swc_deu_001452 H E I M A T V E R T R I E B E N U N D R U S T I C S I G G E W A L +swc_deu_001453 U N D S P B E I C H E R I E N I N I N E W A C K T E S H L A N G E A B P +swc_deu_001454 O U R E G E N A L T H O N B E N D E R U N D D I D O K M E N T A R T I O N D E S T U R D I O S U R D E N E I N Z E H N H U N D E R T Z W U O U N S E B T Z I G I N D E R S I M E N S E C H I Ü E R S T E L T +swc_deu_001455 S O M I S E N A U E I N E M S T A T E G E S H E N R E R K T E E N U O B O O T +swc_deu_001456 F L Ü T E N S P B I L E E D L I C H E +swc_deu_001457 D R A S T D I S H M O R D E R A N E L I G K T R O N I S C H E R K L A N G E S H A L T U N +swc_deu_001458 A N C H I S E N W O D E D I E S O A M I T E T E M A N D A T Z T S A L I E D E R P A R T E I N T D I M S E B M V E R F A N E N S P E C H E N T E R A N Z A L I R A R Z W E I T S T E I E M P R O P R T Z I N A L A U F D I E L A N E S L I S T E D E P A R T E I U N T E R V E T E I E R T +swc_deu_001459 A U B P F A N D E R N A T S E B O M A D I U U N D T E R K N F T E +swc_deu_001460 D E R R E I N E N Z U K L O P E +swc_deu_001461 M I E R B E I L S C H N N +swc_deu_001462 W E R W I G E N E I N E V E R B R E C H E N S R E C H S G Ö E F T I C H Z U I N E R E I T S T R A E V O N M N D E S E N S E I N E +swc_deu_001463 D E R E S C W N D I C K E I T Z W E R T U N G E R A G E N D R E I E B E E I N H U L E R T A C H T +swc_deu_001464 E B O R U I U S A M E R S T E N G E B O R I E S A M F T E +swc_deu_001465 N T H I M S O N A E R G Ü F V E F A R E N A U D E L E N E R V E R T E I R T +swc_deu_001466 R E F O R M I N G O A B E R S C H A F S U N D A B P R S T U N S C H L T +swc_deu_001467 S I E R E N A N P H R T E T U N T U N D E R D E +swc_deu_001468 A N D E M B E S T L I C H E G R E F T E A U F G E D E N R U L T Z U N diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..ed3b06314b805278c121206e6ba0f270cc45286a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/token_int @@ -0,0 +1,165 @@ +cv_deu_000775 10 5 15 11 18 2 17 9 7 15 11 5 4 2 3 7 8 3 24 2 6 8 5 7 11 +cv_deu_000776 3 5 4 3 10 2 3 9 6 15 11 2 6 5 7 15 11 2 4 3 23 2 6 5 16 4 10 2 3 21 12 6 10 2 4 3 2 6 7 8 2 5 2 3 24 16 6 17 2 4 3 10 2 7 16 15 22 2 18 9 7 7 4 5 4 3 8 12 5 22 2 13 13 8 +cv_deu_000777 10 5 2 3 22 16 17 25 10 2 3 7 2 3 18 2 7 2 3 13 8 2 6 3 2 7 8 2 3 19 25 4 +cv_deu_000778 3 9 6 8 20 13 2 6 2 3 14 2 10 3 24 2 6 3 4 2 6 17 12 17 2 17 7 3 9 13 23 +cv_deu_000779 8 10 2 6 17 5 8 3 2 4 10 2 3 2 5 4 2 3 2 9 2 3 21 2 6 22 6 2 5 7 15 11 3 5 2 3 2 4 9 6 3 20 12 4 2 6 13 5 2 4 22 26 2 6 10 2 4 7 3 9 18 2 4 3 24 16 6 2 13 2 4 3 5 4 17 4 7 5 7 15 11 22 25 4 8 2 4 3 9 12 4 2 4 2 4 20 7 22 9 12 +cv_deu_000780 10 2 6 7 16 4 3 2 5 4 2 7 3 18 2 6 2 14 4 9 4 20 3 18 2 14 9 4 3 2 5 4 5 3 12 7 23 9 13 22 2 6 3 28 2 6 5 3 21 2 5 3 10 2 4 7 23 12 6 8 3 19 6 2 5 4 10 8 2 4 3 21 9 4 2 3 2 5 22 2 13 4 +cv_deu_000781 3 5 4 3 10 5 2 4 3 28 9 6 14 9 18 2 7 3 7 5 2 10 2 4 3 4 16 17 2 6 3 2 5 2 4 3 7 5 4 14 2 7 3 12 4 3 7 26 15 11 7 12 4 3 6 2 5 7 20 5 14 3 4 16 17 2 6 3 2 5 2 4 7 3 9 13 2 18 2 4 +cv_deu_000782 3 4 3 4 16 6 10 3 21 2 7 8 13 5 15 11 3 24 2 6 4 3 11 9 6 14 11 9 12 7 2 4 3 18 2 6 19 5 4 10 2 3 7 5 15 11 3 5 16 3 9 6 8 7 15 11 9 19 8 11 9 2 22 2 4 3 18 16 6 2 12 5 15 7 15 11 +cv_deu_000783 3 5 2 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S T A M A B P K O M M E N E N T H I E L T Z W A R A L G E M E I N E V E R E I N B A U N E N Ü B E D I E K Ü N F T I G E G E M E I N S A M E V E R W A L T H N D E R S E G E R M I C H T E U N D V O M U L I E R T E G R U N D S E T Z E B D E M L I T R I S I E R U N +swc_deu_001544 D A N A C H R U N D R S C I E B E I N E N V A R A R K G B E I M W I E F T Z S I D Y N A M O +swc_deu_001545 E I N W E I T E R O W A R I J A N T M A +swc_deu_001546 S I E W U R D E N M O D O L A R N T U R C H E L O C H S T R E I F E N G E S T E Y E R T U N D D I K L I N G E K O N T E +swc_deu_001547 D I E G R U N M A D A T K L A U S E L B E R V O R T Z U G T U N D E D I N K L E I N E R N P A R T E I N J I E N E +swc_deu_001548 A B E R T O T Z D I N K E I N E W Ü K L I C H E R H U N G E S N O D T H E A S T +swc_deu_001549 N K O G M A L T E R Z I O N D +swc_deu_001550 Z U O F O R B E D I G U N G K O N G R I E T E R A P R Ü S T E N S C H L E T E +swc_deu_001551 B U N D E S T A R G E W A L R E C H T +swc_deu_001552 I S M U S T I M G R E I S W A L E I T A R V O R G E L I G T W E R N +swc_deu_001553 H A T M A N I N E I M P I E R E S C H E B A S E S F Ü B S Z U C H O S O T I A L E P R O G E A M M E Z U O R S E N K U N G D E R S E B S T M U T E R A T H E U N D Z E R S T D A R K U M G D E S I H G E R H I T Z S G E F Ü S I N D E B E F E K E R U N +swc_deu_001554 B E I D E N E R S T E N W R E I N P A L E M E N Z W A L N U R L E I E L I E H R S G U I M M E I N U N Z E I N H N D E R T N E U N Z I G I N S E I N E +swc_deu_001555 D A M I T L A S E N S I C H B E S T R A L U N G S T E R E N S E R G E N O U M E S E +swc_deu_001556 W I N G R S P I Ä T E R K A M S T Z U E I N E W E I T E R E N K R N D N +swc_deu_001557 W R A R D I O K A B E R E R T P E I L S +swc_deu_001558 S T Ü K T E B O U M B E R A U F I S T A R T B A N E N R O L E N +swc_deu_001559 M I T D I E S E R I G E L U N G S O L I N E R F A K T U I S C H T Z W E I F E R C H I N F L U S T N A E R D I S E R W E L E R A U F +swc_deu_001560 B A R K K Ü R S C H E N B A U +swc_deu_001561 D E R H E V O R A G E N T Z W I C G E N D E N L A N D E K L A B E N W I D E R U M H E R V O R A G E N E R L A N G S M F L U G E I G E N S C H A F T E +swc_deu_001562 M I T E R S C H V E R B I N D U N G S F L U K Z A G E O U D E R U M S C H U L M A S C H I E N V Ü R D I E B E E E I N H N D E R D N E U N V E R W E N D E T +swc_deu_001563 L E I S T E T E M E I Z I N E S H O N B S I C H E L O G E S C E H E I L E F +swc_deu_001564 K A M A N D E C H I M F O M R E N V O R B E U I G E N +swc_deu_001565 M E R D I N A U S B U C H T I S E R K A N K E I T E N E R E F L U K T E I N F R K T I O N V E L L A N G S M M E N K A N +swc_deu_001566 D I E I N E N E U T R D I E T E T U N T E R A L E N U M S T E N N V O R S A R +swc_deu_001567 U N Z S I E G E N H E R T +swc_deu_001568 D A S N E U I N Z H N H U N D E R A C H T E N D R E I S I G G R Ü N D E T E K O M I T I F V I R U N A M E R I E K A N S C H E U M T R I E B E W U R D E D A F E R N R U +swc_deu_001569 Z E I N D R A L E D E P R O K R E S I E N U N D H R T D I S I N H E N J Ö R G E S T I T S T E N K U N Z S T D E N G K E N S +swc_deu_001570 I N D E R E U E S P R E S E D E N T A N K Ü N D I G K T E +swc_deu_001571 S N E K Z S T U N D V O R S H P E I S E N +swc_deu_001572 D E S B U N D E S W A I G E S E T Z E S B I S T Z U N D R E I S I G S T E N C H U N I E Z W E I T O S E N O D E A U F G E M +swc_deu_001573 A R D E P U S S E Ö L +swc_deu_001574 F L Ü C F T L I N G E N V O N D E R I E T N U S H E N M I N D E R H E I T D E S O M A L E S H E N B A N T U N +swc_deu_001575 D I E B I E P O L A R E W E L T O R T E N U N S E M I N T I E R T +swc_deu_001576 T A R A N F A N G E I N I N T E K R I E R T E U D E R E X S T E R N A N G E B R C H T V O R I C H T U N A N E I N E M N U K L E A R E N W A F E N S S T E M +swc_deu_001577 S T A R T E R T D I H I L F S O G E N I S I T Z U N L A N K P V R E S T I G +swc_deu_001578 W E N D I E S E E C S T E R E N E F E K T E I N D E R I C H T I G E N R E I N V O L G E A U F T R E T E N U N D S I C H I N E R H A L B S P E Z I F I S C E R P A R E M E T E R B E W I G E N +swc_deu_001579 Z U O K D E S E W E R T U N I O N A U C H B E I D E W A E R S T A O F P B A U M B E M U N D T N E N I N F L U G K Z O E U G E M I T I N T E R K O N T I N E N T A L L A R E I C H W E I T E M I T D E N U R S A R G L E I C H +swc_deu_001580 P E N D E S T A T H E E W A B E N T I E O M +swc_deu_001581 D I E S E R A N S A T Z S G E L D A L G E M E I N A L S A U S C G E B U O R G U N D E +swc_deu_001582 N A C H I N Z U S A M B R O H T E +swc_deu_001583 D E U B E R L A U S E T Z Z F I C H E N H E I E R W E R D +swc_deu_001584 D A B E I E N Z W E I F H A S E N U N T E R T E I E L T +swc_deu_001585 S C H I E T E N A N D E R E R O P R M I S T E R S C H A F T E I L U N D W U R D E M T E R D I R B E E L F +swc_deu_001586 M A S T E R E R L F N E K A B A R S C H I S I E N W E I T E R G E M Ü K L I H K E I T +swc_deu_001587 E I N E M A U S W E R T Z S A E V O L Ü G I N W O L S B U R G E L A N +swc_deu_001588 M I T S C H E W E B U N G S S U M A N K O N T E N K L I E S A N D I E R Z R E L K T W E R D E N +swc_deu_001589 D E R B A L E D I G L I H T Z E I K T E +swc_deu_001590 K O S P R E T A N D I E N E I N E E S T E W I C H T I G E V E I N B A U N +swc_deu_001591 I D A U H T E S H U T D N E S I N +swc_deu_001592 W U R D E M I T D E B U N D E S W A I G E S E T Z V O N E N Z H N A S I C H S U N F M F T I G A I N E D A U R H F T E R E L U N G E N G E F Ü H R T +swc_deu_001593 D I E A N Z E A L D R I B E H N G M E N D A T E K A N +swc_deu_001594 B S C H L S D I E S E R E I N M L I T E R S C H S E I N G R E I F E N I N D E N K O R A R K R I K +swc_deu_001595 N A R T U O V E B N T L I C +swc_deu_001596 K A L Z I G R I E G B E I N D E T +swc_deu_001597 A U N N Z E N E T E I A E M D N E U N Z I G U N U S T R A L I E N S W I D E R Ü S T E R E I C H S C H A B P L I G E R +swc_deu_001598 D A D I E S E I T A N F A N G N E U N Z H N U N D E R T N E U N U N F Ü N F Z I D R T H E R S H E N D E R E R U L O T I O U N Z R I G J I O N G U N D E R V I E D E L K A S T R U E I N D E N S O S E L I S T I S C H E N K U R S E I N G E S H L A G E N H A T +swc_deu_001599 D A C H R W E I T E R E N V E L U S T R E C H E N K E M F E N U N E N E N Z W Ö R T E V O L G E B E I D E G R I G S P A T E I N U R D E R U N T D R E A R E N E R B E G I N D E A U S A N D A N D E R S E T Z U N G E I N B S R E I T E G Ü L T I G E S W A S F E N E N S T I L S T A N S A B K O M M N A P G E S C L S S E N +voxforge_deu_000891 M A N I S T E R B E I S E R V O S I C H T I C H +voxforge_deu_000892 D I E W E R F L I C H T S O L I N D E U T S C H L A N D L E I E R N O C H N I C H T A B G E S C H A F T W E R N +voxforge_deu_000893 E S G E B T A U C H M I S P R A U C H T U C H A B E R T G E B E R +voxforge_deu_000894 D I E K I N D E S I N D A N H A N K E B O N E N +voxforge_deu_000895 D E R A C K W E I T E D E R K A D A S T O F E S O L L V E R D E U T L I C H T W E R D N +voxforge_deu_000897 S E N R L L N D E T +voxforge_deu_000898 B E I M O G A N D S T R E I T S T R E I T E N B E R D V E F A S S U N G S O G A N E +voxforge_deu_000899 D A W A G E C H A R Z U B T Z W E I F E N +voxforge_deu_000900 M A N S O T E D E N A U F G A R G H E I N V F A L T R A U N +voxforge_deu_000901 D E E F N L I C H E S C H U L E N W E R N N I C H G E T E L K T W E R E N +voxforge_deu_000902 B A E G E L T I S T A U S K E T Z H L T W O R D E N +voxforge_deu_000903 E S O L E N R E I H U N D E R D T A U S E N D N O E A B E S P L Ä T E I N S T I E N +voxforge_deu_000904 D I E K E R B E R V E L E T Z U N G K A N A L S B E I S P I L E N D W E R D E N T +voxforge_deu_000905 D I E R E N Z E I T W E R S C H T E N B O D E N +voxforge_deu_000906 D S T D A B E F E L U G S B E Ü R D E N K E I E N Z U L I E R E S K E L H A B E N +voxforge_deu_000907 D I I N E R E S E N F I N D E N K E I N E H Ö R +voxforge_deu_000908 I F W E I L T A S S T D E T A B L A R T O H E R R Ü G S C H I E D T A S S T D E N R Ü G G T A S S T D E R Ü G E G I E R S T A S S T D E A +voxforge_deu_000909 D E B E T R O T C E N E N U N A N B E R E C H T I G D E E N H E R I G E L T E N M A C H E N +voxforge_deu_000910 E I N D R I T E R H A R D D E M S C H E I D I G K T E N V R E I W E L I G K L E I S T D O N G E N Z U K O M E N L A E N +voxforge_deu_000911 S O N E N A U R E C H Z N E B E D E B I L T +voxforge_deu_000912 I E R E I N E N I C H T A R Z L S C H E M E I T E B Ü L S E R K L E U N A B P K R E N +voxforge_deu_000913 D A M U S T E J A H R A U F I E E N F A L S O K O M E N +voxforge_deu_000914 M E R E R E K L E I N S K O N S I C G E I N E I P I E R R E S E T E I N T +voxforge_deu_000915 W A D I E G I N Z T I G E R V E I S H I E S A L S O R S I H Z U S A M E N E N M E N A N S T A T Z U N +voxforge_deu_000917 D E R C H O L E H E R Z E I N E L E I S T U N G A N G E B O R T E N +voxforge_deu_000918 S O D A S E I S F +voxforge_deu_000919 D E B A T R I E N W A R N E R S T A V E R A L T E T +voxforge_deu_000920 D E S E S Z H I E V O R D E N O R T A L W A L S E R E I C H T +voxforge_deu_000921 T I E S E W E R U N G W I R T S E L R L A N G E L E B E N +voxforge_deu_000922 D R D Z E I E A N A F E N B A S H O N V I E E +voxforge_deu_000923 A L S I G E E N E N N E I G K T E M Ä A G I I E R N O R G A N S F L C H T I G K Z I U O U B U N D E R F A R T E A R +voxforge_deu_000924 T I E R T E M E M I E B E R I S T I A N +voxforge_deu_000925 D E M S T D E H E N A D T Ü Ö R L I C H A U C H F A M M Ö H E N G E G E N Ü E B E R +voxforge_deu_000926 D I E E R E A L E L A N G E W I E T N I C H T V O U S T E N D I C H A B P G E B E L E T diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.3/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.3/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..cd17fb8f0e432356b3b6f63bc28819d85a17e9c8 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.3/1best_recog/token_int @@ -0,0 +1,165 @@ +swc_deu_001469 5 8 3 12 4 8 2 6 3 9 4 10 2 6 12 17 3 24 2 6 21 2 4 10 2 +swc_deu_001470 12 7 21 25 15 22 5 23 26 4 3 10 2 6 +swc_deu_001471 12 4 10 3 22 16 12 18 9 6 14 6 5 2 7 2 +swc_deu_001472 2 4 13 8 20 7 3 10 9 21 9 13 9 12 19 14 6 12 4 10 3 2 5 14 4 2 6 21 2 5 6 24 16 7 11 13 5 14 2 3 4 2 8 2 6 3 6 15 11 2 4 17 8 3 17 5 4 10 2 7 2 4 7 3 19 17 19 3 9 18 14 16 4 2 3 4 3 24 2 6 8 5 2 3 2 4 7 5 4 +swc_deu_001473 24 2 6 23 6 2 5 8 12 4 14 3 5 2 10 5 16 13 16 14 2 7 15 11 9 3 23 6 16 23 9 14 9 4 10 2 6 3 10 2 6 3 7 12 23 2 6 17 2 15 11 8 2 3 12 4 10 +swc_deu_001474 22 16 17 5 14 7 3 9 12 19 3 10 2 6 13 5 8 11 2 8 3 18 2 6 8 3 11 9 14 2 +swc_deu_001475 9 13 3 10 2 6 3 22 9 13 8 5 14 22 13 5 2 14 7 5 15 11 3 24 16 6 8 3 21 2 6 2 4 3 20 12 7 23 5 8 20 7 8 2 +swc_deu_001476 7 5 15 11 2 5 20 23 2 6 7 4 9 13 3 16 10 2 6 2 15 11 12 4 2 4 3 4 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9 4 3 10 2 7 8 11 4 3 5 4 6 12 4 3 24 16 8 12 3 2 6 24 2 22 8 3 5 4 2 5 4 3 2 13 2 22 6 5 7 15 11 4 3 7 8 6 16 17 3 12 17 21 9 4 10 2 +swc_deu_001486 18 6 17 9 7 2 5 4 10 2 7 2 18 2 7 9 13 4 16 15 11 8 19 6 2 5 10 2 8 +swc_deu_001487 2 13 9 3 2 4 3 10 2 6 18 5 14 5 19 +swc_deu_001488 9 4 22 19 2 5 13 3 4 2 7 3 12 4 8 2 6 10 5 4 7 8 2 5 17 3 4 16 15 11 2 5 4 2 3 13 16 14 2 7 15 11 3 9 18 23 3 24 16 2 6 14 +swc_deu_001489 22 9 18 2 6 8 3 13 2 4 10 5 2 7 5 7 3 9 4 14 2 18 16 10 5 2 3 10 16 3 17 5 8 3 2 4 7 15 5 2 8 2 4 2 5 8 3 9 +swc_deu_001490 7 8 9 4 10 3 24 16 17 20 21 2 13 19 8 2 4 3 17 9 6 8 20 7 3 20 21 2 5 3 8 9 12 7 2 4 3 20 21 2 13 3 19 10 2 6 3 5 4 3 11 2 5 7 8 11 5 3 12 8 +swc_deu_001491 16 14 2 4 5 7 2 6 8 3 5 16 4 12 4 8 2 6 3 18 6 9 15 11 8 2 6 19 5 4 10 +swc_deu_001492 24 2 6 3 23 25 4 10 5 8 20 2 4 10 8 3 16 10 2 6 3 14 9 19 25 7 5 2 3 9 6 18 2 5 8 2 4 +swc_deu_001493 24 6 7 14 13 5 2 3 8 2 3 24 16 6 11 6 5 15 11 22 8 3 2 5 13 10 2 +swc_deu_001494 5 3 9 6 5 15 7 8 12 4 14 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9 13 2 13 9 4 14 2 3 21 5 2 8 4 5 15 11 8 24 16 12 7 8 2 4 10 5 15 11 3 9 18 23 14 2 18 2 13 2 8 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.4/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.4/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..1e89c1b38665ddb78795ea39ab04fbc6ca6d477d --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.4/1best_recog/score @@ -0,0 +1,165 @@ +voxforge_deu_000927 tensor(-7.8822) +voxforge_deu_000928 tensor(-9.7097) +voxforge_deu_000929 tensor(-16.4236) +voxforge_deu_000930 tensor(-20.9614) +voxforge_deu_000931 tensor(-14.9439) +voxforge_deu_000932 tensor(-17.0038) +voxforge_deu_000933 tensor(-12.7142) +voxforge_deu_000934 tensor(-11.6948) +voxforge_deu_000935 tensor(-9.4473) 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N D I E P E I S E G E E N +voxforge_deu_001016 S D I E B E R N D A M E E R V O L K T E W E R T L I C H +voxforge_deu_001017 D E E I N T W E K L O N G E I S T W E I T V O R A N G E S H I E T E N +voxforge_deu_001018 D I E S M T O R M E L T R I E D E N D A N S C H O N N A C H W E H N E G E S T O N T E N A R F +voxforge_deu_001019 S G E B T E I N E G R O S E W Ä L L E V O N P R O T Z E E N +voxforge_deu_001020 E S E S T B E R E I T Z M E I N Z W E I T E R A U T O M A R D T +voxpopuli_deu_000309 N P L M E N T I E R U N G V O N H Ö H R I N S T A N D A T S T U S C U T Z S P E R S E N L I G E R T A T E N E B E N F W E I S G E N A R E U N S H R E I U T E T Z U S A M M E N A B E I T A L E I C H T E N +voxpopuli_deu_000310 E R A M T E A B N D E R S C L I M S T E V E R I N D E R D A R M E L E B E N G L Ä D T I S I N S E B E R V E R L T S T V O R D N I C G L A B +voxpopuli_deu_000311 I C G M E Ü B R I E D A S D E R O M I E S A N I C T I E S +voxpopuli_deu_000312 M I T K L E D U N D E C H O V E D A S W E R N E C H S E I E A H R Ü R E R S W A N T I +voxpopuli_deu_000313 N D I S D A F N C H T D E R S E H N W E R N D S I M E R H I N B E R I F Ü M Z I G U T S E N T D E R B E F E L K Ö N G D A R E B I S C H N U N U O N E M L E N T I C H N D R A M L I +voxpopuli_deu_000314 S O D A S D E B Ü U G E R S H E L N E A U S K U N B E K O M D O B S E I N E B E S C H Ä E R E B E H A U T I N G E N O M E N W I R T O S I E B E R E C H T I H T E S T +voxpopuli_deu_000315 N E R H R E S E T U O N Z E R E R B I T Z I O N G E N I S T N I C H T V O N E U T E N A B E R A R B U R U E K O N T I N N E R L I C H E S F E I N T I O N E N +voxpopuli_deu_000316 L D I E G A N S T O L S G E S A R G J A R B D B E S C H E T I U N G S T E I K T I E R A N +voxpopuli_deu_000317 I E D A S E S F E R U N S I S T E U R H R U N T E R E V E R L T E T W I E R E X S P O R T I E R U N Z U F I E E L Z U O N B E L I H A U N W E R I N P A D T I E R U N Z S E W E N I G N E R V A R S C H E N K E N W O L S T A N T +voxpopuli_deu_000318 S I E H O U L D E R A B E N D I E R A N W E S E N S I N I S T E N P R O S S I T I E V E R S I G N A L +voxpopuli_deu_000319 N E U N Z S I C H P R E T Z E N T A L L E R A R O B P Ä E C H E N F L M E D I E A U S E H E L T I R E S E I N M A T L A N D E S G I T Z E I C H T W E R D E N S I N T V O A M M E D I E R P O G A M G E E R D E R T W U R T E N +voxpopuli_deu_000320 B I S O K A L I H E M E R G E B P L D I S T E R A E R A U S H U S A B T D E M U N G I N D I E S E V O R M N I C H Z U S T E N +voxpopuli_deu_000321 B I E R B U R T E V E R H N D R N D A S S I C H C H E H E H I N D E R D I E N G E I S I G E N E I G E N T U M D I E A U S G U M S F L I C H T E V E R S T E C K E N K O N T E +voxpopuli_deu_000322 I S G E B D E T Z H N M Z U S A M A N G D E R V E R S T E R K T E N Z U S A M A R B E I T E I N E N E R S S T E N G A N G V O N E I I G E N M I T I T S T A R T E N N A C +voxpopuli_deu_000323 W A S D I G R E I N Z H B E R C H R E I T E N D E T Z U S A M E N A B E I T A N B E L A N T U N D T W A S T I E E R V E R P R E I T U N G I N T R I G K L E N D E R E T R I F T N D T E M E C H I C H E I N B E I S P B E L E N E I N E N D E S E N E R V O L G S B E I S P I L V Ü E M I C H I S U N T Z W A R E L S L A M D A U G M L I E R N J A R E I D E S +voxpopuli_deu_000324 D A S N I C H T N U R I N P O R T U G A L D E R G L I C H E N L A N T S N E N A U R U E N S E U V E R M E I N T L I C G E I C H E N M I T W I S S T A R T E N W I E D E U T S C S L A N D D E R U S P E T A N J E N +voxpopuli_deu_000325 T V E R A U S W E G E N I S V O R B E I D A +voxpopuli_deu_000326 A L L E L F L I E N G A T F M I G K D I D A D E S I S H A U S E S V A R S C H E I N H E D O R D T L I C H O L F I G E R A L S T E I E U N D U S C N I T Z B Ü Ö R G E T +voxpopuli_deu_000327 E N S I C H E H R D A S E R E B E D O E U I T C U N G I N A R A Z S F U K O M F T U G A N O C H T Z U N E M W I E R T +voxpopuli_deu_000328 T A S K E D I E R U N D I R I C H T L I E N D E R D I S A R D E S T E F I S T E G N G U N D L Ä E G D A S I S A R I T S N O R M E N F Ü R D E N S C H U T Z V E R D E N G E F A R E N E I N E R E X S P O S I T S O U N G G I E B E R I O N I S I E R E N D A R S T R A L U N G +voxpopuli_deu_000329 D A S K G I L T E S I D E R H E R S C U S T E N +voxpopuli_deu_000330 D E N E I N E N E I N Z I G E N S I T Z K E B T E S L E N G S D A S I S T A S P U R G +voxpopuli_deu_000331 E D A S A S P A S I E R I N M A L T E R D I E B S O N L I S T E I N D I E K O B T S U N D F L E A U F G E D E K T E R D I S V E R W H E G E N B C H N E R M A D E B A W E D E R W E R E N U S D E M A R I S I K O B S O U N D F E L E E B E U N D E R S U C H N O C H I T D E R M O A R S E R B E A R E R G E T I L T E U N D E R S U T E A N A T V F A S S I N A N D U G A S W E N A L S I E R U N T E R D E M A N D E L I S C H W E I N T Z U G E D E K T W E N S E O S +voxpopuli_deu_000332 L I T L A N G E K Ü S T E D I E W A N S T E I N E D I A U F D I R O S E N K A T E R S V O F E N I T Z U N A M I E S E D E R V R G A N G N E R T I N W E I S E N +voxpopuli_deu_000333 D E N T I C H A R B E B E N Z I E P Ü R D E B E R C H T G I S T E I M E N T O A U O L E I N S W E H R N V E L E R I N T E L T E S W I R D N E M I T A U E A U F G E F A D E R T D S A U R B E H E S H E P A L A M E N D A U F D E M W Ä E G K T S E I D I M E I N Z I G E N G S I T S T Z U O N D E S T E L T Z E N +voxpopuli_deu_000334 I N D I E S E M R I F E N W H U O T D E N G E M E I S M I B P L I T D E S C H E V E R A B R E D U N G E N I N K R E I S D E R S I E B E N U N Z W A N Z S I G E T R O F V E R N U N D A U C H U B L I K G E M A H T T +voxpopuli_deu_000335 I E B E N E R B E R T Z O U G E N D S W E R S H O E U T H M I D I N V O R S C H A R G S I M U M A L G A U S C H S G E C H A F T A M I N C H T W E I T E A U K M A S I G P E R F E K T A U R B E C H E A T Z S A G E H E D E N Ü E R H O C H R I S I G O B R T U G K T D A N E D S E N D A L I Z U L A S E N H A M M S S E N D A S H A I C H E I C H G E S H A F T A R M I D E M E R S O D A H E M T I C H L I G P L A U B E I C H D A S W I R T R O T E M E I N E N G O R S E N S C R I L T F L E I G K E I N M E I N S T E I N E I N G R O S E N C H I T Z U M E R A T E D E N S E A T A E N +voxpopuli_deu_000336 P E H L E D A N G E S F V E R L K T F Ü Ö R T S W E I E N H E I B M I N O N T E N E R G +voxpopuli_deu_000337 Z U M A K T U E L E N I C H K L A B I S K A N K E I N E V E N U N S A N N E M E D A S W E W E R T L I C G I A R S T F E I T D I S E N W U C H N G E N D E W I S H E N D A S E N S D I T A L U N S U N F E I C H K E I T D R O T +voxpopuli_deu_000338 D S N D E I N F C H P E D I N G U N G E N D I N I G A K T Z E P T A B E S E N M A N K +voxpopuli_deu_000339 I N D E Z W I S C H E N S E I S I N D I R E T U N G S A R G E N I S A R I O N E N D E G R Ö S T E N S C H L P E R H W E I S I E D I E M I E G A N T E N Z W A N Z I C H G H L M E T E R V E R D E R L I E B I S C H E N K Ü S D A U B G R E I F E N U N D A L L N E R H I T A L I E N P R A S P R T I E R E N +voxpopuli_deu_000340 D A S S E I K T D E R F A L J U L I E R T D E M S C H Ä N K O U +voxpopuli_deu_000341 I W A S E R P R E D I G E N O N D W E I N T R I N E N +voxpopuli_deu_000342 W I R D I E I N S C H E I D U N G R A R H E N W I E R F I L E P A T N R N E C H T Z U L E T Z I E S T Ä T T E +voxpopuli_deu_000343 D I E V O L G E I S E I N H Ö R N F L U G K V O M P R O P L I S T N E X S T R L M I S T E N E I N I G M I G I S T A T E N E R E N B U M F U M P A R O U L E N S E T Z E N I E R K O N G K R I E T E R V E R I N D E R U N G E N G E G E N +voxpopuli_deu_000344 W A I L D I E I N W E S T I Z I O N E N V R A N T Ö R S I S C H A U N D D E L U T S C H E R B A N G E N G E R E T E T W V E R D E N M U S T D E N D Ö R H T D E R I C H E N L A N T Z W E I T A U S E N Z E H N N I C H T D B P E I T E G E N U N D H L U T E R U S E S E I N E N R I E S I G E N S C H O T D E N B E R K V A R S I C H E H E R T D R I U K +voxpopuli_deu_000345 D E M I T I G I T S T A T E N D Ö R O F E N N I C H I E M Ü K I C H K E I T H A B M D E R E N E N A U R B P E S H E N S T A S A M B A L D E R A N Z E R H N D E R N E N I E R N A R E G I O N G G A N Z S G E T Z I E L U N S T D E M A T I S K O R U L T O N F E R N A C R Z E I G E N E S I E N +voxpopuli_deu_000346 E I M I L I O N M E N S C H E N S I N A B P E N G I H V O N U N S E R H E L V E R +voxpopuli_deu_000347 E I N F E T H I N R G E R J U N G E W E T I N H E R K A D I V O N E I N P L I S I S T E N A I N D E S O N D E R E I S A T S K O M A N D U S E N K O M A G S C H A E N +voxpopuli_deu_000348 D I E I N D E R H E I L I G K U E I T M A N V O R S I C H E R E T R A N E N D A S A U P T A U T U S U D E R A L U N S H E N T E N W E K +voxpopuli_deu_000349 R E I D E R A T R I G E T E R E F F E N H A B E N I N Z I S C H E N S T A D G E F U N D E +voxpopuli_deu_000350 R D I C H I E T E N E I N M O N E R D E B P T +voxpopuli_deu_000351 D A S W E G E E I N E W I C H T I G E F H A H G E A N D I K O M I T I O N E N E I N L A N D D I G R A N Z K O N D T C O L L E W I E D E R E I N F Ü Ö O N N T D A C H E M S C H N G E N I O N B L E I D E N M I T Z U G A N G K Z S U O R I M A T I O N Z U S T E M E T S E T E R A R O R D E R I S D R S E I N E N W E R E R O D E A R D I E R A G E I S W E S T I C F Ü R D I E D E N I S C H E R I E P A T E N D I S P Ä T E U M E I N E K L A E A N W O R T D A +voxpopuli_deu_000352 D E S C H O N A U S S C G I E F Ü R T W U R D E L A G E S N I C H B A R A N D A S E S H E R O B E F F E L E G I G E B E N H E T D I S N E N D S G A B E N H R E I H E V O N D K L E I E N U N G E R E I N M T E I T E N B I T I E N S W E I +voxpopuli_deu_000353 N V E R G E M E I N C H R F T E N D E A U S N O N S I E R I T S P A L I T I G A S G O S I S T Z I E L D I E S E R U N J O N +voxpopuli_deu_000354 D E N I C H E R H E I T I S A N I S Z W E R I E G E R U N D I T E I L W E I C H E R A R B R E I T N I C H T N U H R I M T E C H N I S H N B A R E I C H +voxpopuli_deu_000355 D I K S E L T E N G E N D I E N T E R E S E N V O N B Ü Ö R G E N U N P O L I E T I G E N S O W E I A U S N A N D E R B E R E M B Ü R E R N E N G A N Z E R O B E R S H I D S T E M A R K I E N T G A N Z S O B E N +voxpopuli_deu_000356 H E R P R S I D E N T +voxpopuli_deu_000357 E F Ü R E N E S P R Ä E C H E M I T R E S E D E N K A S E I T Z A R E I C H E N R E G J E R U N G S E R T R I E E N V F R A U N M E N S C H E N R E C H T O G A M I S R T I O N E N U N D I E W A N D D R C H A U S E M U T I G E N T +voxpopuli_deu_000358 N G S A C H E I N E U R S A C H E F Ü R D I N E W A C K S N N A T Z U N A L I S N U S D E L L I G S E I D E F O L I C H P E R S P E K T I E F L O S S I S +voxpopuli_deu_000359 O U I D E I N E I M A N A O S O R W E I T V N D E N Z I E E N F E R N S +voxpopuli_deu_000360 D W E R D E R S W I A N Z M I N I S T E A U C H E N E I N E N L A N D Z I E D E N T A G G D A M I T K O N F V O N D T I E T D A S N D T Ü L I C H A U C H T U S B U S T Z S I N G E G E B E N S E N M U S S D A S S T A S H U S H A L T E V O N D E N S T L E R S O A L E R E N E U N S T E L E R S O L L E N D I N E R Z I E T Z I N T U N D D A S I E R T D A M I T A U F H T D I E A N T U E R T U N G A G E N I N E N E N T S C H E I D U N G E N D E I E R H I E N I S E N R A M E N D R E F F N M E T M M U N T E R N +voxpopuli_deu_000361 A U D E M O U U R O B E I S C H N A U T E B E B I L M A R G K T I N S G E S A M T D R M A T D I S C H I S S +voxpopuli_deu_000362 E B P E H S C H U N J O N H A R T M I D I S E I N S T R U M E N Z S D I S C H O N S E E I N E A K T I V E R O L L E N E R N A C K T P A E G I O N Z U S P I E N U M D E M O U G R A D S C H E R D E V O R M E N N A E N A C H A L I G I N I K T U N V R A N Z I T R E I E +voxpopuli_deu_000363 S T U L T A L I T E R E R S C H I E M E V O N A U S E N U D A U V N I N E N I S R E S C H T U N D O S C H I E T L E G +voxpopuli_deu_000364 E H H A M I M E R G E S A R K E I N Ü B E R E I L T U S T A T Z U N I E R U N G S E N C H E I D U N G I S U N S I N I S G W E I T Z U M J E R T Z I G E N Z E I T F U N E S K E I N E B E D R O N G B E I S C S P I E A S W E S A U S E M I E R A N G E T +voxpopuli_deu_000365 D E E R V A R K L E I C H I S T E I N E T Z U Ü E N E S H E M I S E A T D N D E R A U B R H R A V O R N M E N Z C H E N R E I T Z W R L E I D D N E L A A B E L S Z S S F H F H F G F A G S R F D A R S T S T S S T S B O D S S A A A I E O N G A N D A N A N A N E I N E S O E U S C H E U N E N K L A U P L I C H E R A N W O R O F +voxpopuli_deu_000366 D I E S P E E R H R T D I E S E U N F A S E N D E R H U T Z U N T A L E R I C H L I N D E W Ü B E F Ü Ö B O T E T W E N G I E +voxpopuli_deu_000367 G I C I W E R K I S K S L I C H I N A N D U N D E W R S A R S T G H G D E V L A N T V O U N D E E I N E I N M A L N M E H R I J E T S T D E R V E R A N T Z W O C F T D U N G F Ü R I N E U T U P T I M A L E N W R A L M G R A S I G K A L I V I T Z T I E R U N G U N R E R A B E I T N E H M E U D A R B E I T N E M E R R I N E N D A N S P E R S O N D E R E T S T R E S H U N Z I T A G E N +voxpopuli_deu_000368 A N D R D A U C H O N L E N D I E S E R K U D E R G E B E S E R Z I E L N A I S A N D E R E G I E S I S H W Ä H R T U N D I M I T E L A B P T Z I U O F N T W A C R I G J I O N W I E K A L A R B R I H N Z I T Z I L E N O D A U K R I C H L A R D R A U C H O M E N E N +voxpopuli_deu_000369 D E B E R I C H K O E S E S V O R D E R Z U R E I C H T D S E S R E T I N G S T A T L I C H E R S C H U L T T I E E I S E R F E N L I C H E R A U F G A B E B E G R I F E N U N D D A R H E R O N E F E N I C H E A K T Ü H R N V O R G E N A M W E R E N M U S S +voxpopuli_deu_000370 D A B I S A B E L N U N M I T E I N E M U T S C A R P O G A M T U T U N H A B E M M S W I L D A F Ü H E I N E N S P E C H E N D E R E C H I G E K O N T L A G E S C H A E N +voxpopuli_deu_000371 S I E R N O H A N A L I S I E R N W O R +voxpopuli_deu_000372 D M A K E N E N E T U L I E V E R L A N G E N G E B E N L I E M E R G A R T F H R N D I K U N S H I H V E R A U S D I E A M E N O E I T E W R A U H E N D A S A B E +voxpopuli_deu_000373 G E R A R D I Ü E K L E I N E P O R J Ä E C K D E I S D A S Ü N H B E R M E H E S I C B E R O G A T E S H R A U F A N D R E C H T I S D A S D A S I E R S A W B E I N Z E I T A U M V O N R E I A R E N G E S E N T W E R E N S O R U N U M N T +voxpopuli_deu_000374 I K A N D E R V E R S I C H E R N D I A R L O B P E S C H E K O M I S I O N I E S S T E R K O M I T I T Z U M A H R A R A R U B T S A L R O B E C H E N E R S P I G K I E V E D I S K O S S E R U S N T +voxpopuli_deu_000375 E I E D A N Z E H E A U F T A U G H S O N +voxpopuli_deu_000376 D T D I E S M E H A U S H E L K A A N D I E N G Ö R G E R I N U N D B U R G E R N I C H T I B E R T Z O E L I T E N N B E G E I S T E A N +voxpopuli_deu_000377 T I A L E M U K R A R E N E M I T G R O S A F H R O U D E Z O R K E N N E S D A S D I G D E I E R V O R I E T R A R G E N H A B E N E B Z S I C H A U H I M Z U S A M E N A R I T V E R E N D R U N G N E D E N W E I C H S T A T E N U M S E T S N +voxpopuli_deu_000378 D E A H A B E S C H U R S D I E E L D A R O R B P E S C H E S E M E S T E R H E R H E R T Z U N E M E N U N T I K O R O B T U N Z S I K L A S I U N E R I M R A M D E R L I N E R B R E C H T D E T Z U V E R E Ü F N I G E N I S T N I G A U S E I G E N T +voxpopuli_deu_000379 N D M E I N M E I N E B I T E O D A M D A S W A S I C H M E R V O R S T D E N I S D A S M A H R G E N G W I E C K T L I C G I N D E R T A H A T E I N G G R O S E E I N E B R E I T E M E H R H E I T F Ü R D I E S I K O L S I O N S P L I T I G H S O R L G E P L I T I G S T S T D E M T S Ü R D I E M E N S C H E N V O R O R T D A I T I U N S A D E S W E H E N I E A U C H B E S C R E Ä N K E N K E I N E D A S +voxpopuli_deu_000380 W E N W I E R A R H O L T E D I E E V O R A R D N U N G V R A B S C I E D E N O A O F E R I C H D A S S E W I E R N A C H E I M L A N G N K A R U S E L E L S U E I M B U D N A B C H U S K O M U N T I T M A Ü C H T E R M Ä C H E B E I E R K O M I S I O N B E D A N G E N D I E G O N Z T O T I E S A C H A R B E I T H A T +voxpopuli_deu_000381 U N Z E R E R E S C H Ä A R S C H N U N Z I E K O N T R O R L N H A B E N K E I N E N P E L E G E R P R A F T diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.4/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.4/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..eb2e6dc8a259114f162ccc8c46061fde675ce73a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.4/1best_recog/token_int @@ -0,0 +1,165 @@ +voxforge_deu_000927 3 2 7 22 9 4 3 9 12 15 11 3 4 16 15 11 3 19 24 5 2 3 7 15 13 17 9 21 2 6 10 2 4 +voxforge_deu_000928 10 5 2 18 23 13 5 8 5 14 3 5 4 6 2 7 5 2 6 20 7 5 15 11 4 5 15 11 8 2 17 2 6 +voxforge_deu_000929 3 5 4 11 9 13 8 20 19 6 2 11 2 6 10 3 18 2 10 2 5 8 2 10 8 3 10 9 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+voxpopuli_deu_000361 tensor(-13.7536) +voxpopuli_deu_000362 tensor(-46.6465) +voxpopuli_deu_000363 tensor(-20.0672) +voxpopuli_deu_000364 tensor(-39.9235) +voxpopuli_deu_000365 tensor(-117.4481) +voxpopuli_deu_000366 tensor(-15.9464) +voxpopuli_deu_000367 tensor(-84.8658) +voxpopuli_deu_000368 tensor(-44.2582) +voxpopuli_deu_000369 tensor(-37.0009) +voxpopuli_deu_000370 tensor(-22.7452) +voxpopuli_deu_000371 tensor(-6.6161) +voxpopuli_deu_000372 tensor(-23.3269) +voxpopuli_deu_000373 tensor(-47.6635) +voxpopuli_deu_000374 tensor(-39.2948) +voxpopuli_deu_000375 tensor(-11.0928) +voxpopuli_deu_000376 tensor(-19.4465) +voxpopuli_deu_000377 tensor(-35.6220) +voxpopuli_deu_000378 tensor(-33.9686) +voxpopuli_deu_000379 tensor(-76.9896) +voxpopuli_deu_000380 tensor(-71.1145) +voxpopuli_deu_000381 tensor(-15.9042) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..ddb83ca13a692bd1b067bd449752ce5ccdcaa04a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/hyp.trn @@ -0,0 +1,661 @@ +D I B E H R D I G O N M A C H T E I N E R A E U S E S T W I C H T I G E N S E H E I N I N D E D E R P Ä T I T Z I O N N D I N G O W A N Ü Ö R F Ü R D E S I N J A N E R D S O U S S B E G E N A D I G U N (M-AILABS_deu_000165-M-AILABS_deu_000165) +D A R H A B E S I D I E V W O U L G J E D E M H E R I N E R I N E R U N K G E B L I E B E N E N W A R T E G E S P A O C H E N (M-AILABS_deu_000166-M-AILABS_deu_000166) +E R S T U M A C H T U H R W A R E R A U F M A L E L B R C H T E R D E N G A F I D E S N E S C H E N I N Z S Z I M E R U N D E S P E H R L I N G E D I E D A S S A U S D E N H E X E S E G T E N G E F A L N E O T A R K O N A U F S P B I K T E N (M-AILABS_deu_000167-M-AILABS_deu_000167) +S I C H E R L I C H A N I R E N G E B U O T S T A K E T E R B A I E R B L E I B E N K O N E N (M-AILABS_deu_000168-M-AILABS_deu_000168) +N D E S E A L B E Ö M M U S M A N D A U R T V O R O M E N T C H N S C H I E R I G K E T E N H A B E N D I S O U C H E I N A S E I T S E K L I E R E N A I N G E B U T E M A C H E N B E A L E W (M-AILABS_deu_000169-M-AILABS_deu_000169) +E S M E N N R F T D I E L T K O M T U M S E B S T I D E R I N S O N D Z U O H A B E N D E R D I E V E R E R H U N G K D E R A N E N V O R T E T Z T (M-AILABS_deu_000170-M-AILABS_deu_000170) +A B E Ö A N E U N E I N L I C H E R S C H U L E B E L U N G U N T E N E R L I C H E M Ü K L I C H T E I T R A U H D E R E I T E R B L U N G U N T D A S D E G E H N V O N G E D E N K T A G N D E M I C H U N A U F L S L I C H T R A M D (M-AILABS_deu_000171-M-AILABS_deu_000171) +E I N A N S A S H E N S A K Z I S I S T W E R G E R T S T W I E D E R G A N Z S K G U T Z W I S C H E N U N S A R B E E D U N I C H T A L I E S G E S T D I E S T D G I E T I E E R I N R U N G A N D A S B E Ö S E N I C H T W E G (M-AILABS_deu_000172-M-AILABS_deu_000172) +N E I N W E I B E R B R A U R E I C H E N I C H (M-AILABS_deu_000173-M-AILABS_deu_000173) +E N D E N G O R T H A T N I C H T V E R G E B L I C H N E H M E R G E R U O F E N S A K T E D E R C H I E V E R (M-AILABS_deu_000174-M-AILABS_deu_000174) +N U R E I N E S W E I S S I C H D I E S E R V U R C H T B A R E N F R A R G E I N D G E G E N Z U S E T Z H E N U N D S C H L E I D E R E R D A S W A R T I N D E W A R S C A L L E D I G L U T L E I N I E S L I E B E S W I L E N Z S I S T S T E R K E R A L S T R E N U N G (M-AILABS_deu_000175-M-AILABS_deu_000175) +T O M S A M I G E A N E I N G R O S E N S I E G N E H I N E L L A N H E R D N E C K I G E N S L C H T (M-AILABS_deu_000176-M-AILABS_deu_000176) +S S E I N N A H M E D E M S I C H T I T U H E B E I T A K U N N C H T A F N E N K A N B R E R S C H E R U N D T W E R K O E N (M-AILABS_deu_000177-M-AILABS_deu_000177) +E N A B E R I C H F E R T S E I E I N E N I R E R U N W I S E N H E I T (M-AILABS_deu_000178-M-AILABS_deu_000178) +F O N D E R T R I T T E N U N T E R E D U N G A N S A K T E M I S T E R H E R V I S C H E M W A R M E R D I E P E R S U N I N H U H E M A S E V E R D E C H T I H (M-AILABS_deu_000179-M-AILABS_deu_000179) +I C H E N K E D E A M T M A N U N S A N E V E R M E I E W E R D E N E S R E C H T V O N D E R F I N D E N T D A S T U D I H S E L P S T A N G I B S T U N S E W E R D E N H R E U N T L I C H G E G E N D I E S E I (M-AILABS_deu_000180-M-AILABS_deu_000180) +E T Z S C H L U O K T I E H E L E F L A M M E R A U F U N D N U N E R K A N T E E R U N S D I E W I N R I M E R Z S A M E N G E D R E N T I N D E M W E N K E S T A N D E N (M-AILABS_deu_000181-M-AILABS_deu_000181) +D E R S E I N E S I E L E A N S P B O N E N D T D A S E R M U N T E R N D E W A U T V O R W E A L T Z S (M-AILABS_deu_000182-M-AILABS_deu_000182) +V R M I C H A F T D I N B S U C H T D E S T U N E S U S H E M M N E S A P E R S E D E A N D T E N O R N T (M-AILABS_deu_000183-M-AILABS_deu_000183) +W A S T F I H R E H E R F O L G U N G E N W A S F I R N A R S T E L U M E N H A B I C H N I C H Z U E A R D U L E N G E H A B T (M-AILABS_deu_000184-M-AILABS_deu_000184) +S I K O E I N A R W A R E N E S I E O N A U T Z U O A L T V F O R E N E I N K A U M A W A K S N D E S I U N G E S D I N K A M T Z U M I E H E R A N G E H Ü E P F T U N B Ä T K E L E D E N N E I N (M-AILABS_deu_000185-M-AILABS_deu_000185) +A K I C H W E R T I H I N E B O T H I N F E A N D E R T A S B U O T E R A N L E G U N D I T E R T Z O E G T U D E N A E S G A N Z S E L L E I N W O S D E C G A N I C H T H U M Z O K E M A N (M-AILABS_deu_000186-M-AILABS_deu_000186) +A L S N R E I N M A L U N O C H E N D R A U C H V E N A N E M H A U S A U S D E R V E R N E R A U F S T E I G E N Z U E S E N U M D A N B E R U E K T Z U S T D E R B M (M-AILABS_deu_000187-M-AILABS_deu_000187) +I E T E N Z E R I N A B A R L A R K A U F E N K N I E N V O R B R A M A S B I L T E N I S I N N A H M E N L O S E R S E N S U C H T U N D T W E I N T E J A M A V O L L T (M-AILABS_deu_000188-M-AILABS_deu_000188) +E C H T F E R T I C H T M I C H S E N D E W E G L I G K E I T N O C H N I C H T A F T D I E H I C H B E U F E N G K H R (M-AILABS_deu_000189-M-AILABS_deu_000189) +I C H E L R G E R T E M I C H T A N W E N I C H A U H F A C H T E R E I S W A S U F W U N D E R S H E Ö N E N G E W E E S E N D A R S F L I E N (M-AILABS_deu_000190-M-AILABS_deu_000190) +N E R H D E M E S C H O N D I N G A N Z E N V O R M I T E A G M I T I M V A R B R A C H T K A M S D E N H O B N A C H T I S S C H I N Z S K R A N S C H E R H A U S U M G A S B A L E O L T Z O S R G E N (M-AILABS_deu_000191-M-AILABS_deu_000191) +H R W A R A I N A L T E R H I R H T V O L M E D I E Z I N E S C H E R G N E N A L I T E T (M-AILABS_deu_000192-M-AILABS_deu_000192) +D E S V O L A U H T D E R M I E T A E R S E I N E V U N D A R I C H S K E I T E N H A R B E M S E (M-AILABS_deu_000193-M-AILABS_deu_000193) +E N S I E S A N A L E E N S T L I C G U N D B E T R Ü B T A U S U N D A U C H E R A R E N E S A S C W E H R M Ü T I C G D A R W I E D I E A N D E R E N U N D S T Ü T Z T E D E S H A U P T I N D I E H E N (M-AILABS_deu_000194-M-AILABS_deu_000194) +U N T E R E N D A M E N M E I S T I O N G E F R S C H I G E S I C H T E R A U N T E R I N H E R E N N E B E N M H U E N D K L I C H E N S O C H M I T F A L T I G A R S T I E R N U N D B R E I T Z S M E H R A U D A R M I N D E R M O N D U M G G L E N Z S T E M S C H Ä H T E L (M-AILABS_deu_000195-M-AILABS_deu_000195) +S E I T E R E N C H O N A T D E I S B E S O N D E A S D R E I E N D E K L U N G E B E R (M-AILABS_deu_000196-M-AILABS_deu_000196) +S O N D E R B A R H (M-AILABS_deu_000197-M-AILABS_deu_000197) +E R B P V O N E H R B M H E M S T A N D M I Z E N H R G A T E I N V O L W E M U T U N G D A N K G B A K E I D A N D E R G R O U F T U F D E R I N M E C H T I E N G (M-AILABS_deu_000198-M-AILABS_deu_000198) +I E R W A I H I E D E A M E N S C H E I N U N D E R U N D F A S T A L L E S V E S M E N S C H E N T A L T E N I T A S S F O N D A B A R E S (M-AILABS_deu_000199-M-AILABS_deu_000199) +W E L T H E J E R W E S I E E N D L E N G S T F I E R (M-AILABS_deu_000200-M-AILABS_deu_000200) +I E W E R T I N S A S N I C H T H I N T E R E M S C A N K T I S U N D K E I N E E R E R D I E N Z S T L O U T E B E F A N Z E I H N D E R S T U B E (M-AILABS_deu_000201-M-AILABS_deu_000201) +A L S D E H E R S C H A F T A U S D E R K I E L C H E T A T S T A N D E N D I E L E U I T E U M H E H R U M S I E V O R B E I G E H N Z U S I E N U N D A M K E L C H U F S T O R E R W A T E T E E I N M A N (M-AILABS_deu_000202-M-AILABS_deu_000202) +S M S N M E L T O R N O M D I E T A R I S M U S E N G N K T I (M-AILABS_deu_000203-M-AILABS_deu_000203) +G E L A U B E R D E A S I E S G U D E T B E R M E I N E N H E R T A C T (M-AILABS_deu_000204-M-AILABS_deu_000204) +E N T R I M A N F A N G G E W A N E K E I N E A U F M E R S A M K E I T V E R A N D E R E D I N G E A L Z F Ü R D E R S E S E N (M-AILABS_deu_000205-M-AILABS_deu_000205) +D I E S F L Ä S C H E N Z O G E R G E T S T E I L I C H E R V O R W E R E N D J E N E S I C H M I T W A S A R F E L T E N U N D B T E S D E R U N K V E R T Z I Ü S A N (M-AILABS_deu_000206-M-AILABS_deu_000206) +S E R B A S A U R I C H I H O N W I C H T I C H D E A S C H I N E R D R C H E R T Z S A N S H P U S F O L L G E S A K T A T V E R W E R D E N U C R H A I N I N Z E I T B U N G D E R I D U K T Z I U N K O M M E N D E S D G U (M-AILABS_deu_000207-M-AILABS_deu_000207) +N I C H T D A O C H M U T E R W E R G E S I E E R T Z T N C H N I G (M-AILABS_deu_000208-M-AILABS_deu_000208) +A B I E R H H A B E N I N E N D E T Z T N J A N R I C H T E N G E B I T I U N Z U B A S I I E N A U F G E B A U T P R (M-AILABS_deu_000209-M-AILABS_deu_000209) +S S I E V I R D E S I C H N I C H T V E R A N D R A B P F O N (M-AILABS_deu_000210-M-AILABS_deu_000210) +L E C H R I F E N M E T Z (M-AILABS_deu_000211-M-AILABS_deu_000211) +G K O T W A S I I E A R Z T E L T E R H Ö R E N S I N N U R I S I S E I N G A N Z E R U M A R N (M-AILABS_deu_000212-M-AILABS_deu_000212) +S E I N E M T E R K I N I N M O E F L U S W A S E R G E B E N D E S E L B P W E I N D E R (M-AILABS_deu_000213-M-AILABS_deu_000213) +U N D S W O T C H A S M I N D E S T A W E R T E M E N U S A M T E R N E T Z A R G E N T U R A M F I E R T E N J I U N I Z U M E R S T E N M A L P R E S E N D I E R N W I E S I C H D I E N E T S B E T R E I B E R U N G I K R A S T D A R K G E D I N E U N E T S T L E N E V O R S T E R U N (M-AILABS_deu_000214-M-AILABS_deu_000214) +E B W E A R A T E S E C H T Z E I E T A N D V O R T O D E S F V E C H E R V O N D E M G E T A B E F R E I T U N Z U C H T E T Z U N D F I N E N B E D E R S M A L E G A T E N B U O T K E I N E N A U S F I (M-AILABS_deu_000215-M-AILABS_deu_000215) +B I C H M E I N W E R K F W R U T E L E T E N L A S E N N D E N R H E I N A N L A U F N E M E N U N T E S R O L E N D N S O L T E (M-AILABS_deu_000216-M-AILABS_deu_000216) +E H R W A D A S K E T Z H E N D E R S T U N D D E T E I T E I B E A U F T R A K T E M A D A M U N S C H E L D I E A U C H T A S T A N D U N D I G E K A U F T E N S E I D E N S D E K E T Z U S A M F E L T E T E F Ü R T S C H N D Z U S O R G E N G (M-AILABS_deu_000217-M-AILABS_deu_000217) +D W E R E N A C H S I E N (M-AILABS_deu_000218-M-AILABS_deu_000218) +A B A L E T E B S O D A F V O R G A B E N D A D M A C H M E N E D I L I G N I C H S (M-AILABS_deu_000219-M-AILABS_deu_000219) +A L S U N S R E E D E B E K A N T W O D E R W A D I F Ü S I O G N M E D E R W E A L T E A S P B O G E R U N G E R F Ä H R D I E I N E R S K E A L B E R S D A S Z U M E H R S T N M A L T D O N H Ö R (M-AILABS_deu_000220-M-AILABS_deu_000220) +I T Z E M C H N S G E F Ä L I H T A U F U N D E S K L A N D I E E I N A M A N D E R H I L V E R (M-AILABS_deu_000221-M-AILABS_deu_000221) +E R D A O K T O R S A E R I N E F R A U D E S H N O G R I E N E R D I S E A F T Z U I N E N K O M I S T E N G I G A N I G K R A N (M-AILABS_deu_000222-M-AILABS_deu_000222) +D I E A L T E E R I N R U N G A N D I N F R Ü H R E N T A U M T A U C H T E E B E N F A L Z W I E D E R A U F U N D U N W I E L K Ö R L I C F A S S T B A I D E R B E H A U P T U N G D A S D E S E L E E N K Ö R P E R V E L L A S T E N U N D T Z U I E M Z U R Ü C K E R E N K E N E S C H E N E S I E R A R D E N D L I (M-AILABS_deu_000223-M-AILABS_deu_000223) +A L Z S I E A F R I N B A L K O N Z U R E K H R T E F A N Z I E I E N D I S E I T U N G K L I E S E N D T D I E W E R E N R I S V O R T Z E I N S A N G E L A N G K T W A H (M-AILABS_deu_000224-M-AILABS_deu_000224) +T E E R W A R E I N K I N D R S T R A S E V O N K L E I N A U F A B E R I N I E M L E B T E V O N J E H R I N R G W I S E S E N S O C H T N A C H E I N E I E R B A R E N B Ü R G E R L I C H E N E I S T E N S (M-AILABS_deu_000225-M-AILABS_deu_000225) +I S U N E S D G H R U N K F Ü N U N E H N I G E I N E R G R O P E F E A N F O R D L I C H S O N A W Ü E F Ü N E N S I N G M E I N W U O L F R A N W O R D L I C H N (M-AILABS_deu_000226-M-AILABS_deu_000226) +W A S M E I L I E B E S G E I N T W A S G K A N (M-AILABS_deu_000227-M-AILABS_deu_000227) +U N D D A N W U L T E I C H T I N A N B L I G D E R A N I C H T M E S S E N D E M Ä R G E B L E B E M W A R E N V O R E L E M A B A W A R I S N I D A R U M T Z U T U N W E I N D E S Y S E L I C H I E S E R B E T E I N I G A M A S S E N G E T R E Ö S T E T Z U S E N (M-AILABS_deu_000228-M-AILABS_deu_000228) +E R D A S A U C H W I E R U N D S G N G S E I D I H M B I S H N U N T A R S T I T Z E N K E R N E R M (M-AILABS_deu_000229-M-AILABS_deu_000229) +S E I N E E S C H E T I C H E L A U F B A R N H A R B E S D I E B E N S N A S K Ü C H E N B O I N E I N R M U T E L F V I E R T E N G R A D E S B G O N (M-AILABS_deu_000230-M-AILABS_deu_000230) +F I L E I C H T E E N S I G U T I S E A N S I C H T E D E S B S C H O F E N H U S E T Z U M E L E N Z A K T E E R T A T S H E N D E H R I M A R M H R E I N M A N D E S G E S C H I E B E N E N W U R T E S V I E D E R T A R D T (M-AILABS_deu_000231-M-AILABS_deu_000231) +A M A N D A N M O R E N E R H O P E R S I C H S H P E T S C H I K T E N L A R K E I E N N D E B U N G V O R A B A C H S U N T L I S U M E I N N T E R I E D U N G B I T E N D E M A N K A M M I T E R B O T S C A F T T Z O R K D T (M-AILABS_deu_000232-M-AILABS_deu_000232) +T H N E I N W E N I C H T H A U R I C H W U R D E R S W E I N M E D E A S E L B E R K A M E N S I N N E Z U F R I E D E N S C H I E N (M-AILABS_deu_000233-M-AILABS_deu_000233) +E I N S O M A H R W A H M A N O W E N M B A R T A R K L A R G M I T Z O N G L I T S A N N B E R D E H U T S T A B T U N D U N D E D I N L I E N D E N D R E N G K T E I N E T A U S E N K E R F I G E R M E N S C H E N M E N G E R A U O R F V O N N E D E R (M-AILABS_deu_000234-M-AILABS_deu_000234) +K O M I T M I H M E I N S O N D E N I C H P R A U C H E R D E I N E L E B E (M-AILABS_deu_000235-M-AILABS_deu_000235) +N O R E S A N K E S I C H T R O D E I N W E N I C H N A C H D E N K L I C H E R S O O W I E V O N E I N E R E R I N E R U N G K E R H E L T (M-AILABS_deu_000236-M-AILABS_deu_000236) +N W U T A U F I D E D E N W A T I U N S D R O K K S T E I G E N U N A Z U S A S E S T E M E R E N G E F Ü R T W O N (M-AILABS_deu_000237-M-AILABS_deu_000237) +N E T G E W A R T E E A M I T E N S E T Z S N D I S C H O U I S L I C H E R T O E F L S C H E R A H F E N F R A T Z E D I E B E D I S M E N C H E N S H U L T E A R S C H I E L T E (M-AILABS_deu_000238-M-AILABS_deu_000238) +E R R D E R W I E R D N I G T E D A S G Ö R D E I N E G E W I S E N W U E T S C H A F B E R N H A T W U R T S C H O U F I S T E T W A S F A C G N S E N (M-AILABS_deu_000239-M-AILABS_deu_000239) +W U L T E H E I N W E H E I D I E E S E N T Ö R T E N U N D T K N D I E R S C H L I S E N (M-AILABS_deu_000240-M-AILABS_deu_000240) +B A T Z E D I S E R E S P E K T V O L U O B E I E R N N U R E I N I G E S E E N V E R S C H L U K T D E W A S I M B E I T E N B E L I E B T E N L A N G E N W Ö R T A N D E S E F T A N V O R K A (M-AILABS_deu_000241-M-AILABS_deu_000241) +L O R T F O N D L E R O A I E V E R T N I C H T Z S E N D B E R E N D E S E N B I N I C H G E W I S V E R S E T Z T E H R (M-AILABS_deu_000242-M-AILABS_deu_000242) +K A M G L E I C H F W A L S I N S S C H L A F T Z I M E R A U F E I N E N N A R G E L I N D E R N E R D E S B P E T E S (M-AILABS_deu_000243-M-AILABS_deu_000243) +D A S D I S C H A N S D E H N D I E S A R K R I S S T E G D I S C H A U N G S V Ü R I N T E R N A T Z E H N A L E R E G E N I S I E N E M R O N Z F I B P T E D E S E T S W A L N M A K L T A E R I N T I E N (M-AILABS_deu_000244-M-AILABS_deu_000244) +A N F A N G S F I E L D E R E I G E N S C H R Ä U N D P E I T S T E R S I E E I N E R D A N D I E A N D E R E S E I T E D E S W A E N S (M-AILABS_deu_000245-M-AILABS_deu_000245) +F A S T R L E I C H T Z E I N I G E N B E R M E S U N G E R E S W E R T E S A U F T Z U G E B E M S I C H E N D T S L O S S E N H A T E (M-AILABS_deu_000246-M-AILABS_deu_000246) +S S E I S T D I E F R A R G E M E N C H I C H E N A B E T N D I E R A R G E W A S K A N T Ä C H N S G L Ü S T W E R E N D (M-AILABS_deu_000247-M-AILABS_deu_000247) +I S A R F A R I W A U F D I R I D E M E H S I G B E N T Z T E N W A S S A R S T E L L E N I E S E R U T A N G E R I E S E N (M-AILABS_deu_000248-M-AILABS_deu_000248) +D I E B E I T E N M I S T E N H I E O B E M A U F D E M G E P V E L G E S T A N T E N H A B E M U N D E R S P R A C H D I E A L T E N W A U R T E V O S E C H E N (M-AILABS_deu_000249-M-AILABS_deu_000249) +E N T L I C P I K T E S E D R I G A U F H W E I S E N U I G A L E S V O N D I N A R M E N L O E I T E N F R A C K T E E R (M-AILABS_deu_000250-M-AILABS_deu_000250) +S H O L D E I N E W U N D A B A R I R S A M A B E I T Z U Ü C H N B U N D U N D L E N E A N I N D I E S E N R A G E N G I E B D E T S E R S E I N T R E S A N E N P R I E G K T E N O U (M-AILABS_deu_000251-M-AILABS_deu_000251) +K A S B A F E R H A R T E R A N G E N M R E T Z E L T E N S E N M P L T Z S Z E I N L E D E R J A S E I N E R A U G E N B W A N I E V E R S T E I N E R T A L S E I T H U N Z W E I T E N M A L H I N I K T E (M-AILABS_deu_000252-M-AILABS_deu_000252) +E I N I G E T Z E I T D A N A C H F R A K T E E R M I C H O P I C H G K L A U B E R D E A S D E R E I S G A N G D I N S H L I T E N D E S A N D E R E N Z S E R S T E R T A B E (M-AILABS_deu_000253-M-AILABS_deu_000253) +A B E N E N B L Ü S E N I C H T G E N E I N E R S C H O S T A R E E V A L E N (cv_deu_000698-cv_deu_000698) +J E I T S K O M I A E R S C H E U N (cv_deu_000699-cv_deu_000699) +S D E M B E I E A B E T D D E E I S A U S E R E L S K R A F T A O E N O F H A (cv_deu_000700-cv_deu_000700) +E I N T E R I T T O R H E I G K O S E L S O H O P A W I E T D N I C H T M I T E N I E T A M E S I S K E I N E R E A O C H O P A R E I C T (cv_deu_000701-cv_deu_000701) +E S H O U N K A B N D E R K Ü N Z S L I H E R B E V R O C H T D U N G T Z E R B E R T (cv_deu_000702-cv_deu_000702) +D E I N N A C H T E R K T I E F N E F A L R T E R F L E G E N V O N M I T E R I O U O L D I E B E S S M I T E R O P T O E (cv_deu_000703-cv_deu_000703) +E L R E T H E (cv_deu_000704-cv_deu_000704) +E I N D H E I R E N (cv_deu_000705-cv_deu_000705) +M U O Z E K N I E R E R L E S E Z E I G E O N E I N A B S P E I C H E R V E R W A L I T E N U N B I T A N E R E N O T Z A N T E I R E N (cv_deu_000706-cv_deu_000706) +D E D E M B O S G O N K T E R A E (cv_deu_000707-cv_deu_000707) +S A U L B A S T Z E H L Z U D E N G N R T I S T E N D I S E I D A B M A U M N F M E C R Ü E N D L S E R L Z E R I U D N (cv_deu_000708-cv_deu_000708) +I N K M Ü N Ü M W Ö H R S E B E L A E N W E L N B A U G K E I N E R S E L B O N D E R E I S C H E (cv_deu_000709-cv_deu_000709) +E I T E R E W I C H T I G E I N D E S T R I Z W E I G E S E N D E M I K R U M I C H A N I G G E R W A N O P L A S T I G M I T E I B A U U N T I E H E L T Z V E R A B E I T U N E (cv_deu_000710-cv_deu_000710) +I B E R D E N A U T O R I S N I H Z B E K A N D V E R M U T L I G S T A M T E H R A U S E D E I T E N P R A H G B I E T (cv_deu_000711-cv_deu_000711) +N D S T D E U Ö E R I S S M I E N E T O P L E P A T E (cv_deu_000712-cv_deu_000712) +D E H O R B M E N B E B L E M E R P O S I E S C H I G A U C H T (cv_deu_000713-cv_deu_000713) +E W I S C H I E N M A N U R A B A R E E M A U G T Ü U R E E L T E I T M A C H B E R H (cv_deu_000714-cv_deu_000714) +K E D E Z A L I C D E E S E L D E A R N A N N A N E N E S H E W A B E N G (cv_deu_000715-cv_deu_000715) +E E E S E E N Z N A M E R I N M I N C H E N D W O E A U G S T A B E B E I (cv_deu_000716-cv_deu_000716) +I N E R N H T U N D E L S E R E R N A R T E I G S K Ö R D E N A L S G E R E N T E T A E I L E E N D E S N A R T I G S A U R G E M E I N S A M V O R K O M E N (cv_deu_000717-cv_deu_000717) +D A B E I E B E L E G K T E R E H R D I E P L Ä T Z S E F V I E R U N T R E I C (cv_deu_000718-cv_deu_000718) +A K E D E A B I J E I S D I E T H O U C H T A R T Z W E I E R B R O F V E S E N E A R T E N Z A N (cv_deu_000719-cv_deu_000719) +D I S K L A U B E T A S F Ü R T N I S T I N R I S T I G E R I S T R U N G (cv_deu_000720-cv_deu_000720) +D A S E S E I N E X S T R E N S H L E S T E R I S T F L I E N E R (cv_deu_000721-cv_deu_000721) +H E R L O S C H E N B L E S T Z S A I N H A G E R E S G E S I H T (cv_deu_000722-cv_deu_000722) +M R K A L E N F I N D E T O S S U N F E R (cv_deu_000723-cv_deu_000723) +T I N G E B O K A B E R H A T D E R D E I G E S C H I S T E N T (cv_deu_000724-cv_deu_000724) +T S K O M P L I T L I S T A B A N M N D A S S O L C E D A T E N U D I E S E R E B N E P A S T B E R D E N (cv_deu_000725-cv_deu_000725) +T R A M I N H E N G E G E N E R I B T E I N H R M U N I C H E S P O S I E R E N (cv_deu_000726-cv_deu_000726) +B E N I H Z U M K A F E N E R H P O E T E G B E R E I C H D I G K T (cv_deu_000727-cv_deu_000727) +K C Z U N E D E U N E N E U N E I N E R S C H E N E N E N D E R N E N E N N (cv_deu_000728-cv_deu_000728) +H H U N E D E I R E N S E N D E S E L W D S L E N D N U N (cv_deu_000729-cv_deu_000729) +O N E D E R O W I S E N E L E N G T S T I Z U N G D A M A S S A T D I R E N A B T A L I U N W A N D I S E B A G E N D E R K O N K E W E N Z S E N D O C H U N T O L E G E N (cv_deu_000730-cv_deu_000730) +S I I E N D E U N E C S T A S U N E K U M F V E R B E L L G I S C H I E S A T Z U N G S T R U P E N (cv_deu_000731-cv_deu_000731) +D E M I S E N W I E S P L Ä N G E N M E I T E E R Z H A N A T Z T (cv_deu_000732-cv_deu_000732) +A U E R D E M S P H I E T E R B E R M N A C H V E R G E T I E M N I N M A R K E T R O E D I U S S O W I E B E R L I E G E R K O N K U E R E N T E N L O N D N E I T Z (cv_deu_000733-cv_deu_000733) +I E A U P T E R S E N Z S T E N W A N A H F V A U T H I N G A E R F Ü R T I K U O U M S W A L D A S K O N D O A R E C K E T K T E R I A O M N I C H T (cv_deu_000734-cv_deu_000734) +S M E T F W U O E I N T C H I K A R B O A U F (cv_deu_000735-cv_deu_000735) +W I E I E T I E A L E I N E N (cv_deu_000736-cv_deu_000736) +N D B U M I E S B E R T W A S B W E I S W U E R T R O S T D E S D E S U Ö R N B I S E N S V E L C H U N I E (cv_deu_000737-cv_deu_000737) +H A R B T E M A E R S C H E O I S T D E R E W A N S C V E R E G Ü N B I C H I S T R E I H E R E N T E P F R E I N E N (cv_deu_000738-cv_deu_000738) +L E I C H Z E I T I G H W U O N D E N S P R O T D W E R T E N T A L L W E I S E V E R B U L H E N (cv_deu_000739-cv_deu_000739) +D A S E N H S F S S A N A A F A A A E (cv_deu_000740-cv_deu_000740) +O T S T I E R E R N N N F B E N N (cv_deu_000741-cv_deu_000741) +Z U D E M F A R S A E I E R L E M K L O S T A L N G J A R E D E M T E R D E S E N O W I T Z E N M E I S T A S U N P R I O R (cv_deu_000742-cv_deu_000742) +H E I D E N H E I D E N E N S T M T E I N E R E R Z T E V E R M I E E R (cv_deu_000743-cv_deu_000743) +D A R E R T Z U E G K T E N E N (cv_deu_000744-cv_deu_000744) +Z W E I E L G N R (cv_deu_000745-cv_deu_000745) +E B E P F E A L L E I N A U G N E N G E I E T Z E N I K L T E R E I N D E R E A T (cv_deu_000746-cv_deu_000746) +D I E S E R S D E T A H F E A B P E L W E N D E N E I N H R M S C H E R C H O L M E T A R T S K N T D E S E N O F E M (cv_deu_000747-cv_deu_000747) +A L S O E C H I O R E N I G S (cv_deu_000748-cv_deu_000748) +W E K O N M A N S I S C H S C U T Z E N (cv_deu_000749-cv_deu_000749) +A F Ü I N F M N E R T N L A G E I N E N F I N D L I C H E R B E P L O T E A N Z T D E B I S T D A H E I N E R H L T L I C H E N V O R (cv_deu_000750-cv_deu_000750) +T I L I E T E R S D I E B W I N S T N M U N G I N E S O F T E N S N S T E B Z S M I N T S A N A S C G I E Z I C H I K A Z H O U N Z U N B A B P R L F M C H (cv_deu_000751-cv_deu_000751) +A T E N E T H E I L E N W A H A M E N G E N S H E N I H E N D A L U S W E S Z I S T I K E L N T E L L N B E S O R A E S H E I L T E N (cv_deu_000752-cv_deu_000752) +D I E A N D I E W E R B E N S O S C H Ö H R I S T A N R K E N G E L R U S C H E N N D A N D A L E R G B N B H E N T E L N R S S L A N G E L I G T (cv_deu_000753-cv_deu_000753) +I E R T U A R G E I S T E N D I E S A R Z E I R T G U N E F L A N E N S C (cv_deu_000754-cv_deu_000754) +E D I E S C E Ä K E B E G E N M S Ü G E N B E R H U M A R S I N F Ü L T I S D I E G O B D E E I C H T U M E G A R S I Ü T A U S S T E (cv_deu_000755-cv_deu_000755) +E R S T V O N D O R T K O N T E E R E N W E G F R E I E V O T Z S E I T Z S E N (cv_deu_000756-cv_deu_000756) +S I E R H E B Z I C H H E U T E I M E R N O G U T E R K E N B E R T A U S S T D E I E N S C S H W E M L A N T H E A U S (cv_deu_000757-cv_deu_000757) +D I E K A N A R E S C H E N I N E N E H A N Z U S P B E R C H E (cv_deu_000758-cv_deu_000758) +W E S N S C H R F L E R H R B E N D I E S E M U T D T Z U N E S E H R E N E O B E I F O A R A N B E R E B A T E T (cv_deu_000759-cv_deu_000759) +S E N I G I S C H E T Z S P E T Z I U N E N R E I S C S T E N B I S N O R D A M E H R I K A E N D A R S I E N (cv_deu_000760-cv_deu_000760) +A R E I C H E V O R D E R E D M A E T Z I E R U N E N B E I D U T S C H E N E R O P A R U N D W E H T L E C S T E S C A F T E N Z O B I E N L Ü B I C H E N S P I L E N V O R L K T E N (cv_deu_000761-cv_deu_000761) +I N A N E R T E L I E D S O T U E M L E T E R M D S O E I G K I T A U F T E R P A R G B A N G (cv_deu_000762-cv_deu_000762) +I T I U M W A M I T R E N G I N B A U P L S S I D I E K E R L T E B E S A U S H A D I E (cv_deu_000763-cv_deu_000763) +W O E N E E N T E R H E W E S S I (cv_deu_000764-cv_deu_000764) +A O S T A N I S E N E E I N E B O C H E N A C H T E N E S T E N U E M U N D I N R L U L E N (cv_deu_000765-cv_deu_000765) +E N M M I T E L E I T E R H E R T E N E Ä K Z I N D E H E R S C H A F T D A S T D A F H I N E R (cv_deu_000766-cv_deu_000766) +D E N N A M S C H E P L E T R A R N A O C H L E I T E E F A T S O L G E V E R M A S S E R H A T I (cv_deu_000767-cv_deu_000767) +P L U K A N E M I T E B U S L O C H A N V O N E R O N D E R E F H O R E N (cv_deu_000768-cv_deu_000768) +N E R E O R I E G O L (cv_deu_000769-cv_deu_000769) +A L L E R T I N G S E R G A H B E N W E I T E R I E P U Ü N F V O N G E N D A S E S M I T E L F R I S T I K E I N P I D A F I S C E U C H E A U T U O B A N D E H R E R (cv_deu_000770-cv_deu_000770) +U N G E K R T K A N E N F R E I P R I E F F E I N E H A R A U S C H R E I B U N G A L Z V O B E L E F R E I G E M E I N Z E I N (cv_deu_000771-cv_deu_000771) +M B I E Z A R K O R T E S G E A B S C H N I T E R S E I E N E I N P F L U S E D U I G S C H O S T D A R K O L N W I E S C S H (cv_deu_000772-cv_deu_000772) +R E R E I N E R D E R P I A E R N I E R E A F T D M G E B I E T D E R N U T Z I U N G D E R S O N E N E R G E E N (cv_deu_000773-cv_deu_000773) +A R H F E N M E D I E K O N D E N A F I N E R F E N G E N M U S I C H E L I C K E T P B E W A N (cv_deu_000774-cv_deu_000774) +D I C H B E M A S C H I N E S T V E R T I S H (cv_deu_000775-cv_deu_000775) +I N D E A R C H E R I S C H E N P E R I O N D E W U R D E N E R S T E I E V O R M E N D E S O C K E B A S S N I N T U I K E L L T (cv_deu_000776-cv_deu_000776) +D I E K O M Ü D E S E B E S E L T E R E S T E F Ü N (cv_deu_000777-cv_deu_000777) +A R T Z L E R E G E D V E R N E R M U M E M S A L P (cv_deu_000778-cv_deu_000778) +T D E R M I T E N D E E I N E E A E W E R K R E I S C H I E E N A R Z U N E R L I E N K Ä E R D E N S A B E N V O R E L E N I N M N S I S C H K Ü N T E N A U N E N E N Z S K A U (cv_deu_000779-cv_deu_000779) +D E R S O N E I N E S B E R E G N A N Z B E G A N E I N I U S P A L K E R J E R I W E I D E N S P U R T F R E I N D T E N W A N E E I K E L N (cv_deu_000780-cv_deu_000780) +I N D I E N J A R G A B E S S I E D E N N O M E R E I E N S I N G E S U N S Ä C H S U N R E I S Z I G N O M E R E I E N S A L E B E N (cv_deu_000781-cv_deu_000781) +N N O R D W E S T L I C H V E R N H A R G H A U S E N B E R F I N D E S I C H I O A R T S C H A F T H A E K E N B O R E U I C S C H (cv_deu_000782-cv_deu_000782) +I E M O R T K G N N A E N B U R G I E N E N V I E L E S O S S I A E E I N E R I C H T E N U N V O N E R E M A N L A M P R Ä C H T N D E R M A R I E N Ü T E A U S (cv_deu_000783-cv_deu_000783) +I C H W E R D E V O L K L C H D I N R A T B E R D E M P A L L M N D V O R G E T R A G E N E N B E T E N T E N N V O M I E R E N (cv_deu_000784-cv_deu_000784) +S I E R E T R A U R S C G E W I E N E I N S O W I C H T D I U S T H E M A N I C H E M K O N D E T H A B S C H E N Z U K E N (cv_deu_000785-cv_deu_000785) +N O C H T I H S E I N M T E O T E N M K L E I S C H E N J A H R G A M E S G U T Z W I S T D I E K G A N A N D E R E R D I E S E T Z (cv_deu_000786-cv_deu_000786) +K O T Z D A N E R C H G A B I S E I N E N W E R B E R B W O R D T M I D E D E N M T K A N K A M N D T U N D S C H E K E S H O C H E N B A C H (cv_deu_000787-cv_deu_000787) +I D D I T B E E H T S U S F S N U N N A N N A E (cv_deu_000788-cv_deu_000788) +W E S I E I S M N D L E I T Z E I T A U S H (cv_deu_000789-cv_deu_000789) +N C H E D E M D O C H F B E F I N D E T I C H A R C H D E R K R M K E N I U N A S I H N A L L E B P A C K G E R B U O T (cv_deu_000790-cv_deu_000790) +I E S E R E N D E R K Ü N D E N D E S D I E L I E B E E N T O E D B E S I K T A R T (cv_deu_000791-cv_deu_000791) +B E T E C K T I S T D I R E B P R E N S E N T H E R T I E F G E S T A L T E E W E L E R M I T D E I N E N M A N S A R T D A C H (cv_deu_000792-cv_deu_000792) +D I E S E S I E U N I E S M I T E R A R T S C H A F T D E L E C H Z U S A M M N G E W A K N Z E N (cv_deu_000793-cv_deu_000793) +I W A R D I E S H N E I N A L E N E N K L O B E S (cv_deu_000794-cv_deu_000794) +B U O R A N G E I S T I S T D A U C H V E R I O E R E T (cv_deu_000795-cv_deu_000795) +D E H E X T V O N D E R S T R A S S E B U E N S I V O N A L T F T E T D I O N L E G I S E I N E R F W E S T E N S E S C H O U E S C H O B E B I L N E R E N E Ü S I E A L T T (cv_deu_000796-cv_deu_000796) +A I H A R S C P E I T E R E S S L L T E R E R T Z U N E L F N A T Z S U N B E R W U D E L R A N G E R E I S C H E (cv_deu_000797-cv_deu_000797) +I N D E R L E R N V I T H E K E R N D E R E R T R A R E T D E U T L I E D E O T Z I E R T W E R D E N (cv_deu_000798-cv_deu_000798) +M A I N S U R S P I E R T E I N S E I N E R H E I M A U T S T A D T K E I E U O F I E R A L A L E (cv_deu_000799-cv_deu_000799) +C O H D E R T R A R D E R R E I M A U H A L U N U N D E N I N T A B E B E I S I H A H (cv_deu_000800-cv_deu_000800) +M I T F Ü Ö O S T W E R E H E R D E R O W T F I A D E E I T S E R E C H T I C H E R A L L E D E U O N B E D S H E I C E N E N E M E I N T (cv_deu_000801-cv_deu_000801) +E T Z T E R V O C H U G A B D A S M E T I B E K A N D T D A S S E S H O N E P E L Ü B E R F I E R N D A S C H W A L T E E V O R F E L E V O N B E R I T Z U N N T O M I R T O D E N V W E R I D E S N D E R N E H N E N A L S N C H T S H I E R I E N K E E I T E T E (fleurs_deu_000378-fleurs_deu_000378) +S I E B E B E N I J E Ö E S E Ä E S C H E N E N S Z I G I G S U N D E R S C Ü Z S T D E D E N B I H I F D E S U N Ü N B I S C H E N K O M I T I S D E R V E R E I N I G T E N S T A T E N U N D A R S I P T I E R T E S A S A P T U T E N O T W E N D I G K E I T D A S I H I E U N L Ü N B I S C H E V E R N M I I E N F Ü E N S I C H E R E S U N F W E L T Z F Ü E A L E U N S E R E R S P O T L E R E I N S E S T (fleurs_deu_000379-fleurs_deu_000379) +A L I C H K E N E A B P I E T K O M P E T I E B E L M E T A C H T N A T Z W E I B U N D E L F A R A C H T N R T Z W E I B U N D E L F B E U N D C H T N E T Z W E I P U N D E L F G E S E I N V E R S G E S D I E B A S I S T A T I U N V E R F Ü G K B E R D U O A L R A D I E (fleurs_deu_000380-fleurs_deu_000380) +J E R B I E Z E I S H E N S D I E G E R I C H T E R A L S P L I C I C H E S G E S C H W Ä T S U N D T A L L B E N H E I T Z S (fleurs_deu_000381-fleurs_deu_000381) +L T E R W U C H R G A B T A S E M I E I T H E B E K A N D A S I S V O N E B L W E R F I E R N D R E S I E W E I T R I E V O H F E L E V O N B E R H I T Z U N I N V O M I E R T W O R D E N W A I E D A S U N T E R N E M A L Z N I C H C H V E R I G E B T Z E I C T E T E (fleurs_deu_000382-fleurs_deu_000382) +N A C H D I M D E R D E M M E U N H N U N D E R T R E I U N S E C H Z I C B A U T W R D E N W A R K A M D I A R E S Z E L I G H N B E P F L U T U N D E S E D E M E N T E M N P L S V E R T E L N Z U M S H Ö L S T E N (fleurs_deu_000383-fleurs_deu_000383) +E R W A U H M S T E C H E V N G E L S C H E I N V E I L E E N D E B E T E I L I C H T A K T U L E B E I S C H P I E S A N A H R B E S C H L I S E N I B P R E M J H M I N I S T E R P R T R I S A F D E R V O R D E S E R T D E K A N A D S C H E N F Ü N N U N D E R T O L L E N U T E N E I N (fleurs_deu_000384-fleurs_deu_000384) +D I H A U P T S T V E R M R D A W I E N I S T K H E N E N D I E I N E I M P I S P B A H E I S T G U M E N E S C A B E R F I E L E M E N T E N S R E C H E N E R O S E H (fleurs_deu_000385-fleurs_deu_000385) +S I S I S E B E T Z W I C H E N D E N E I N Z E N N B Ü N E S T D I E N H E R S T E N A U O C H U N B E S T E N D I G E R Z E I T E N G E T A L T E P R O E W E N Z E N D I E B E K A N T D I S T D E D I E S E P E R I O E D E N W A D I E E P O C H E D E R D E I L G Ü N I N G E I C H E D E S E Ä C H T Z I C H I E A R R E L A N Z E I H E N D E R H A N U N D E E E N D E N E S T I S T A T V F V A N T (fleurs_deu_000386-fleurs_deu_000386) +A M A N D E R E E N E E R S P E K T R U M S H R W E I N E M A N S I C H E N E I N I C H W I D E Z U E R K E N D E I N E W I E D E U M D S A E S A N D R M A C H E N M O S A S E R S T I E E S G E M A C T A U N D Z S I C H A L L E S U O E L I G E M A C H T (fleurs_deu_000387-fleurs_deu_000387) +D G I G I D E M E I S T E N I N D E R P I T E A T Z I O N E N D E S T E C H E N L O G I S C H E N D E T E M I N I S E N U S T A E N Z W E I A L G E M E I N E V O R S C T E R L U N G E N E I N E S E I T S D E S D I I N D I C G E M D E R T I C H E N L Ü G I E S E P S T E I N E N W E G V O L G T D E R W E I T G E N D I E N S E I S U N T O W E L E O R D E R P U L I C I S C H I N P L S N A M E N D I G T U N D A N D E R E R S E I T S D A S T I G H N E Ö Ü G E I E R E R S E I T S A U S F W Ö H R K E N E N A U F G E S A L S C A F T N A R A R T D I E H E R I N H E R E R N T A S Z U T S A L B E D E N Z I N T (fleurs_deu_000388-fleurs_deu_000388) +W Ü S H E D E N E I N Z E N E N D N A S T I E N H E R S T E N A U R U N B E S T E N D I G E T Z E I T E N G E T E A L T E R R O W E N Z E N D E E K A N D E S T E S E P E R I O D E N W A H D I E P O C H R E L D E R E K Ü N I G R E I C H E D E S E C H T Z I C H A R L A N G T Z W I S C H E N D E R H A N U N T E R I E N D I N A S T I T T V A N D T (fleurs_deu_000389-fleurs_deu_000389) +D E M I C K H Z U V O R G I E B I E Z I Z I C H E S T O G O M E N T A U F D E N G E Ä N S T R E I T I N D E N D I E P A L I S I N E N S E R E I N Z U R Ö G S A L T Z E N D E R G E N Z S E N I N D E N Z U S T A N T V O R D E M S E R S T A L E G R I V O R N A N Z E S N U N D E R T S E B E R N U S E T I C V O R D E N (fleurs_deu_000390-fleurs_deu_000390) +M I T H E M P E R L U S T G R E C H E E R S P A C H K E N E A R D E R E C S T E N V O N S E I E N V L E S O F I S C H E N U N D W I S E N C H A L I C H E N W O T Z E I N K I H E N E N A B I E S L E T E N (fleurs_deu_000391-fleurs_deu_000391) +E R S T E M I T E R A U S S A G D E S I Ö R S U S I E B E I N D A S T D E N N R E S E N U N S R A T L E D E N V E R E I N U N D E R S P O T S B P E S R G E D I E N D I S D B E N W E R N E H A L B U N S R A R G E S A T I O U N D H N V L E V E R I N D R U N G V O R A N T R E I B E N A N S T E I N E R T I T Z S R I T Z T V I T I E R U N G V O T Z N (fleurs_deu_000392-fleurs_deu_000392) +I G R E Z F A T E N A E N G P I G E S B O G B I E T E N A U C H Z E I T Ü Ö R I N A U F E N T A L I N E R S T A T K G R E U T Z F E R P A S R S H E R E S E I N V N D E I E U N S L I C H B E R E I T S I E B E D E N G E N (fleurs_deu_000393-fleurs_deu_000393) +S C S C T E R E I S E N D E V E R D E N R I N G E N D G E W A N D T K A U F I E W I E D E A T V O N U N W E N T E Z U A C H T E N D I E E G E B I B I E R I F T D A D N D I S S I G H A U F A L E R E I S E P L E N E A O S I E R E N K O N (fleurs_deu_000394-fleurs_deu_000394) +S I C H R T R I S E B E S E R T D A S D E R G K O L T Z U N G S P B U N K T D D E R L E N M E N D E I N B E E W E R D I G K A L E U N T H U R E H O N T A L D R I T E N D E E H I G K I S T D P L A T S F Ü I T E S H O U P T M N O I E I S T S I E B E I S C H E N (fleurs_deu_000395-fleurs_deu_000395) +E I T N U N Z E U N R T A C H E N A C H T Z I H M I S T N W A L U N D R A N S B P R E N Z S E I N D A M I T W E E N D B O B A C T E R B I E T Z O E U G E N G K E N D A S W E G N D E R W A L E I N M S C H L I G E V W A N E N S I N D U D A S K E I N U M S C H L Ä G E I N G E V A O F E N W E R D E N A U S E R E H N E D E R T O T D E U N G S M E S K S E L T E T A T R E S I E R T E N E E (fleurs_deu_000396-fleurs_deu_000396) +O T E R E R I S T K A N E R D E S B E T Z S A O U B E N D E Z W E I S C H E A L I G E H A U P T S T A T U N D Z S E L T E N D I C H I C H E I N E R E I E U N K U N Z S T D G E E R I E N U N D M O S E E N A U S D I E K A N E N D E R S V E R G A N G E N H E I T U N D G G E N W A R T P R E S I N T I E R E N (fleurs_deu_000397-fleurs_deu_000397) +D I E S E P A R E K E N S I C H F V E I N A D E B T I O N S P L A N V E R B E B E E N S H E I D E N (fleurs_deu_000398-fleurs_deu_000398) +I N V O L E D E S E N E I N Z W R E I F I S C H A B E N A U S G S T O L M D Z W E A W E I T R I S E N V O M A U S T E R B E M B E T R O R T T A U N D E R D E R J L A Z I Ü F V E R (fleurs_deu_000399-fleurs_deu_000399) +R E A N Z E N S E N N J H E R E N E R T Ö L I H E M N G E B E N A M E T E N A U S I E R S T E N S I E A L S O D E R E R S C H U N G A U C H N U R E I N E E M K L A E N D T V E R N (fleurs_deu_000400-fleurs_deu_000400) +A U F E R N A R S E I T E K N T E I S M E R M R I E R G E B E N D D I E K R O S T E D N E S T I S W R E I N V E R A F D E L A V E R A N D I O B E P L I C H A U F T S T D E G E N T (fleurs_deu_000401-fleurs_deu_000401) +S G R E F Ü G K T D E C H E N Z U U N D A S S I E D U O C H N I C H T D E R T Z U A U F G I E V O R D E R T W E R D E N S O L L T E N F E R T F I C H T U N G E N E I N D Z U Ü G E E N D E I Ü E B E R I E R E N I N T W I L U N G S T A N D I E R E V E R A N T O R T U N G U N D I E R E R F Ä E K E T E N H E N O A R N S I N G E N (fleurs_deu_000402-fleurs_deu_000402) +S I S I E W I C E T U E L E H I E L F I S T E U N G E N I S E N T I N D I E S O F T W E R E I N G E B U T D T U N S O E N A B E L T S C H I T E N D I E D E R S C H Y L E A L L E I N M Ü G L I C H E R W E I S E N I H T B E V E T I G E N K A R N H E N T E R F R A G E N N E I E L E G E N U N D T D E R K L E R E N (fleurs_deu_000403-fleurs_deu_000403) +A M F Ü N F Z E N N A R G U S T N U N Z H N H U D E T V I R Z I C F E L I E A L I E R T E N N S Ü T R A N K R A I C H E I N D I N W A S I O N W R D E A P E R E S C H E E R G U N G E N E R N D T (fleurs_deu_000404-fleurs_deu_000404) +E R R I F O C H A L L S A N W A S E N W A S E R K A R M S E B T E N G R O S E R D E N S A U R I E I D E R T I E W E X S W E I M I C H T G E W A K E N (fleurs_deu_000405-fleurs_deu_000405) +E T E R K R Ü N D U N G V N A S U N T Z I O R F I N H T Z E N D E S I E N U N D R E I S I S S P A R E G E G E L U N G V I E L V O N S E I M I N D I G K E N K A R A C T E R N D S E I N E I D E N D I T E T Z B E W A R E N (fleurs_deu_000406-fleurs_deu_000406) +S I S I B E B E D R T Z S D E N I S D E A N T E L L A N I E G S D E E R B E N D E S T R I E G H T D E B I N E R G E S A N T D E N G O P E D E R L O L T E E M I T D U G B E R K U L O S E N O F E N B A R D E N N O C H N G E R I N G S E X S D A U S E N D E I N Z S G E S A N D R E I H U N D E D E I S I G T A U E S E N L O L T E D E I N S Ü T D A W R I K E W H E I N E N B I S T I N T E N Z E I T P B U N G K T A N G I S T E T I S E N T (fleurs_deu_000407-fleurs_deu_000407) +E H N S C H E L Z W E I T A U S E N S E C K S E L E U T E R T A S K O N T I N U M K O N D Z E B T A S E I N E M I T O R D E M U R G E N S A T I O N Z S E H L F N L E S T U N G S F E G E Z E W E R E N (fleurs_deu_000408-fleurs_deu_000408) +S G S E E I N D I E S E B E R I O E D E N D E R U E R R O E P E H E N G I C H I G T E M S T D A N T D E R I C H U N D M E Ä C H T I H E N E V O D E N E R K A R T O N E S I G I E H E N A U F T E N P R E Ö S T A N E T (fleurs_deu_000409-fleurs_deu_000409) +D I E R S D E R C H E N D I E B Z I C H M F Ä E L U N G I S T A S E I N E N E U D E P L O M A T S C H I N I T K E T I P E V E R E N D D I E N J A R S E G R I F E N W E R D E N S O L L T E M D I E R A G S C E N G R E N Z E N G E G B A E R E I N T L I E N T E R E T I O N Z U S I C H E R N U N D P L U M A T S C H B T Z E M I T Z S E I N A C H B A N I E E R T Z S T E N (fleurs_deu_000410-fleurs_deu_000410) +D E E S P B E T E I N E U T E G E L E G E N H E I T D A S N O R T L C H Z U S E N D E H E M E N M E H R U D E W E N G E R R U N D U M D I U E R D U N K E L E S T (fleurs_deu_000411-fleurs_deu_000411) +S T K T P R O S O R E N P A M E L E R V E R G U S S O N V O N D E R U N E W Ö O E T I A D A N D I E M E R K T A N S O A L I S T E N S C H E I E N E I N E G E Ä E R L I E G E N Z E U E R S R E I T E N W E N D I E P O T O N S E W E I T E V O N E R D E C H T I E V E E N T E I H E N (fleurs_deu_000412-fleurs_deu_000412) +S K A S I A C H L O N E I N E N E I L G K A R Z U K A U F E N D I E T Z U T R I T E N W D E R T O R S G E W E L T E N P A Z E N C H E N T A F R I K A R E R T U O A L N Z H T A V R I K A N C H E N E R Z N A L P A X S G E W E R T (fleurs_deu_000413-fleurs_deu_000413) +D I E P R Ü K E S O L M S E R T E M B E A T Z W E I T E L E N S I E B Z I N V E R S T E N D I H T N E T R I E A U F N E M I S W R D A R W A R T E A S I R A S J A N I C H N Z O L P U N T E A N F E A R T I G S T E L T Z E I N W E R E N (fleurs_deu_000414-fleurs_deu_000414) +W E R N D E N E X S P R M E T E L L E M S T A U I N E R L A G T S U S E I N S C H E I N T I E B U L E M O T E L I T E T Z U S E N U N G E B T A S B S E R K E I N M D I K A M E N T E D I A L S E I N D R I H Z U B E H A N D L U N B S T E N D E N V E K T I O N G E R E I G N E T N A C H K G E I E S E N O R D E N (fleurs_deu_000415-fleurs_deu_000415) +E I N E U S S E R S L I E B A F E R D B E S C H E N W E X S E L A N S T A T M A I N A R U O K D I M P L A N E I N A L G E M E I N S T A T E N K O N G R E S T Z U B R U F E N U N D K O N D E S I H V O L F I G N O C N I C H T B E R D E S V O R T Z L E G E D E B R G A M U N D D I N O R T E S T Z U S A M T R T S E I N I G E N (mls_deu_000281-mls_deu_000281) +E R W U S S T E N C H T A S I M D A S L E B E N K O S P A R E S G E R A U P T A T E S C H P A N G R A F T U N D M U D T D A S E S I N F E I G U N S C O L I G E M A C H T A T E U N D F Ä H C H Z U D E N H O N D I N G E N Z U O D E N E N U N G E T R I Ü P T E I T R A L E N G H R T (mls_deu_000282-mls_deu_000282) +D I E S E R U N G E M A N H I E S K A K A L I T Z I E N U N D B E F A N I C G A E F D R A N D E S C H A F T A L S I N E M G E N A N T E N K Ö Ü N I G R E I C H B E K A N D M A C H U N G W I G D E R N Z E E N V E L E N W U R D E E I S A K T D E S H T N L D E R W E N E S W E I T E N I H T Z S I S T E I N E I P A U C H N H N I C K Ü K L S T U N D U T S K Ö N I G S E I D A M Z U W E R E N D A S G E L S E T I G A L E D I N G S T (mls_deu_000283-mls_deu_000283) +S R D R N O C F Ü N F M I N U T E N U N D D E W O L G E N D E B E U S T L O S I G K A L T B E G A N Z U S C H I N D E N I E R S T U S E S E R U L D A S I H N M E I M E I G N E N B E T E L A G U N D D A S D I E R O T N G L U T D N I H T Z A N D R E S W A I L S D A S V O Y R I M K A M I N D E R I N D A S T U B E E S W A N A C H T E I N I K R Z E B A N T E A F T E I M T I S H E (mls_deu_000284-mls_deu_000284) +E I C H T D I E H E R T R E N G U N G E N W E B E C H T E R U N T E R H E I L T E N K O M D A N E M P R B E T I T Z S A L D E D I H O C H F L U D E S E X S U E I N B E D Ö F T I G K E I T S O F N D E S E N D E N G E N A N E N S L S C H E R A R K T I O N S O D E R I D E R S T A N Z S P L D O N G E N D E M E R (mls_deu_000285-mls_deu_000285) +T A B E R A F E N G E H R E N D B E H A R G E N B E G A N D I E K I S T D E N W A N T N U N S Z O H Ö R T E I C H A U O F A C F E Z O S E I N E I N K L A R E R S C Ö N A G E R D A N K T E N G A N G D E N I H T I L R G E N G I M I T D I M B A U C H A U S G E H E K T A B E M O S S D E N A F E N D E N K E N M I (mls_deu_000286-mls_deu_000286) +R I S S E S P O T T R I E H E R N E M E N S C H N D E N I G K E N N E N F R A K T E I L E I S E R W E L C H U N B M A K T E A M I C H R A N G I T R E T E N B A I C H N D G E G N I E T E D A S I S N N F A N T E S I K O K F S E I U N D S C H O P T T E I C H E U N G E I L I C H N T E D I E A N D O N L T T E R N T Ü L I S P R A C R I C H I U N B A R H E I T D N D E S W E I N S E R I E T O E I E S P E T R I E M I S T E R O T S C H S T E S (mls_deu_000287-mls_deu_000287) +C I H W E I S D A S I H S E R K R A N G B I N S A K E S H N E R E N A W E I L E V O R N P A M I N U T E N V E S C H T E I C H M I C H N B Ä T E R U E N Z U R E N U N D F Ü L E D E D A S I C K E I N G L I E D M A R E N I H R N K A N S W E R E G U T W E N I H M E N G E M Ü T D E L E I C H T A N K Ö R N T E B E V O R A I S T D E R R B E R (mls_deu_000288-mls_deu_000288) +S O A B E R I S Z W V E R U N S E R W E S E N S K O N D G O R T S E L V E R D A H E R U M H T Z I H E H D E C D E R C H L A N G E N K N O U L T D S A L P E N S A T A N G E S H L O N G E N U N I Ü B E R D E M F Ü N G K G E N D E I E B E I S D I E E N S E R N I S T E S H A S E S K E L A G E R T W A S W U N D E R D A N (mls_deu_000289-mls_deu_000289) +B E S E V E R E L E W A G E B L I E B E N A U E R S E W A G E T Z W U N Z U G E N B E D I E P Ü N K T I G K E I T B E I D N M A L Z E I T E N E I N E S A C H E W A A U W E C H I N G E H T S H Ä R H O L S T R E N G R G E R H E L T E N U R D E (mls_deu_000290-mls_deu_000290) +N L I K L I C H F Ü L H T E W I E R E A M N S I C H T E N B E M I C G I E R M P F I N D N G N D F Ü Ö R M I C H N I C H T U M E I N A T U N V E R I N D A R T W A A N N B E H U P T K R N E N D E R U N G F E C H W A N I G S E I S E R E M F E R S T E I N E T N A U O G E E I H S N E M E L S T U O K T R I N E N G E N E T Z T N I E M A S I N T Ä R T I C K A T A U F G E L O E I C H T E T H A T T E A M N (mls_deu_000291-mls_deu_000291) +S B P O D E R S E M I S Z E R G U T M I N D E R A U K L I G N E R F Ä H R W R E S I C V R E U N W E W E R N S C H N E L L D A N A C H A N D E N S U O U L B E A U P R E C H E N S C N A L R E I D N E M I T Ü R O F O D E R A C H D A S L A G E R R E I C H E N E R S T I E G A U D I T F E H R D E D I U N A U S G E R O T A T E N U N D F L O G E N G A L O P T D A V O N D I S M A L Ü T E N W I R U N Z D E R F Ä E R D E I D E D E R E K T Z E V O L I E N E R E N G E R A D E A U S S N D E R S P A D E U (mls_deu_000292-mls_deu_000292) +W E L D E B E A M I T P E C H P E S T R I C H E N W A H R B I E B E I N E R V O N D N G O L L E N D E N P A N T O F E L N F Ä S T E I N G E N U N D I N D E R A N G S D A C H E S N I C H T E R A N I N I T Z O N E M U N D I E I S D N L E T Z T E N S H I E T V O N D E R T R E R P E T A T D E H A T I S T Z W L F A U S G E S C H L A G E N G D A R W A W A G E N U N D F I Ä R D E V E R S H U N D E N N D A S C H E N P U T E S T A N I N S E I N A S H E N K L E I D E A U F E D U N K L N S T R A S E (mls_deu_000293-mls_deu_000293) +I E N O M D S K G A S V M A R G E E N F I N D T E R A N T L A R G E R E E S E N E N T Z U Ü K E N E S I S C H R I K E S A K T E H E R I E T E S M E N I G E G I E T A U N B U T D V E R G I S E N V E R M E I N E N (mls_deu_000294-mls_deu_000294) +N U R D E R D A O C K T O R U N D E W E R T E R E N S O L E N V O R S E I N E A U G E N K O M M E N E R K L E R T E D I E T R I E N E I N K R O S E N A M T S E I F E R D A M I T W A D I E F T R A R O B E R S T G A N S E I N F V E R S T A N D E N U N D P H Ü Ö Ü K S T E R F P R E I T K A R T E S I E M E T I E R E N (mls_deu_000295-mls_deu_000295) +K A W A R U N D T R Ü S T I C H E B E D I E L A G E D A S K Ü N Z S L R S E B E G E N U W E I N E N U N S C H L C H T Z T D E L A N G E I N D E V O R G E R H E L T E N E N H E N D E R D E K N S L A W A T E T E R B D E S K A S I C B E R U I C H T H A T E U N T E N T C H L O S I C H T D A N D A E R K E I N A N D A R N A U S F I G F A N T D E R N O C H Z U M P E I T E R S C R E I B E (mls_deu_000296-mls_deu_000296) +O N D E M F E R D E H E R D N D E R P A T S C H E N U N S A G U N Z S T A S I F E N A P A T S C H N F Ä R D U N S E B E S O V I E W A R E N U N P R E N D I G E B E W Ü R D E N I Ü F E R I N K E I U O W A B P F I E R T T D A S I N D U N R I G K L I G E A V O D U M A P A T S C H E N V Ä H R D T S O U L E N A S R C H T I H G E R A S C H L E I M T O D E I E S H R G E F A L E N U N D A N E B L U T V E R G I S E N W E I C H I S U N B E V O R S T A N D W E I S E S E F E H R D E H E N D L E R W (mls_deu_000297-mls_deu_000297) +D A S M A T O N E N H Ü T C H N V O D S C W A R T Z A M A M I N D G A T Z I Ö R S R I E R E L A N G N L O T N G E D R I K T D I E R W A N G E U M F L O S E N N T B E R S C H L T H N H R A P W E I T E N S O T R A E I D A S E I N V E R H R E R L N T L I C H G E B O E U D E U N D S T Ä E B P E T Z W S C H N R E I N D E R H I B P G E B L N E I N D O F K E N R A U F E N T A B (mls_deu_000298-mls_deu_000298) +R B M U S T E R T I N Z A G E N G A L E M S H N T H A F T E N S T R E B E N U N E N D T I E V E R E I U N D D E M U D D I E F Ü Ö H B I D E D E R H E L I N G A E F L E N G E G E N D E U G E R E E L T R S T D E J E M L E N G E R W E L C H E P F A N T C H S K O S U L N E G E F L O N S O C H T E N I N A U F I N S R E W E R K S T U N F A N D T E N I N (mls_deu_000299-mls_deu_000299) +E R L I E S Z S E I N E G R E T E N C H T V O R T S C H L E B T E N A M A L E R W I N I G S D N A B E R I N E N G R O S E N V O G E L B A U R U S I E A L E I N E I N E M T O N E F E I E F E M O U S T E N I E R S H I T S K T E (mls_deu_000300-mls_deu_000300) +F R N T C H E S K O M A L T E N U N H E L I G E B G E I S T R U N F I L E B I E T A S E L Ü G E N H A T E N A B E E L T K H E I N N E L S E R E R M O C H T D I E B U L E R I S C H E L B I K E T D E W E I B I C E N G S T A L T E N S O B E R H A F D A S I S T E L E N I N D E M V N L E B E D T E M O D D E L E N D I E K A L N A T I O N G V O D N A L T E N M A H M O B I L E N B E R O R M O N B I L U N I N D N A N (mls_deu_000301-mls_deu_000301) +B E W E G U N U N T A T D E N S T E N Z U G E R S T H M E D I E V O E U N A N G E G E B E N I N G E R D E N Z H E R N M I C H R Ü B E H A N F E E I C H E N U N S A U R A M F A N A L E I N D E M P F E I F E N K O P F E R A N W E S E N A B E I N F Ü N D F T N A U T S T O H A R I C H N I G E N A N D I E T S T R O C H U N D S C H M Ä K T I G D A S E H N S T I C H E N F I L S H D E R B E I S E I N M S E I G H P L I E S T E N R A U C H A U C H G E G D E N H E E L U N G E G N G D (mls_deu_000302-mls_deu_000302) +U N D A S V O Ä E R S T A N D A U F U N D F L A C K E R T U N K O C H E D A S E S E N F Ä A T I G H U N D E R B R A T E N B R U T Z E L T E F O R T U N D E R K O C H G A B D E M K Ü S C H E N I U N G E N E I N E R R O A R F E I G E U N D I M A R K T R U P F T E T D E S H U N F E R T I G H D A R W A R T D I E H O C H Z E I T V O N D E M K Ü N I G H S O N I T E T D O N G R Ö Ü S I H N G E F E I H R T U N S I E N E E R N Ü T E B I S A N I E R E N D E (mls_deu_000303-mls_deu_000303) +U N D D A S E M I N I C H N A C H T R A R G E N G O L L E W E N I C H N E D E R S H P E N S T I G W A R G I N S E I N U L M E I N E N B R A R T D E R H E R F A R A R H E D E I N A L L E N R E I C H T D E H A T U N I C H M E A N U N R E C H T A B E R H (mls_deu_000304-mls_deu_000304) +U O G E H N E M A S E N U W I N I G E K R A M B T U G E R B R E I T E D S I C H K H E I E F Ö M I G A U S E N M U S T E D E E R E S H E M I N D G E G E N F I G E D E S P R E N K I S C H O S A U F A N E N T Z S O U E R I N G E N (mls_deu_000305-mls_deu_000305) +D E R V U G S R E I C H E S E M I U N F R I T I C H E F R I E D E N S P W E I F V E R H E N D E R M A N T A T W A C K A S E I N E S E K S Z Ü G E N S A K T E D E R G R O S I G E I S A C H T E N I C H A U F D I V E R S C H I E D N E H A U T D E R M E N S C H E N D E N D I K R N S I C H M I T V A B E B S C H M I H R E N M I N T Z S T R O L S C H E N S O N D E N E R S I D A S H E T Z S A N D E H E T Z H N D E R K R L I G E V O M B E R Ü B E N S T A M I D E R K A I O W A S I N T A P F E R U N E R S C H R O G M N T R E D A S M E I N I G E H E N G (mls_deu_000306-mls_deu_000306) +A L L E S A S W I M E T I E R B E G E G N E T S C H E B S I C H T D O E S C H U N D B E R I N A N D E R B A L T U N T E R S C H E B E M W E R I N K O N T A K T D E I S T I E R E R H A N D U N D E M E I N I G E I E R N A H R M O N D E R M E I N I G E B E I D E L R S H E E I N A N D E R A U S B E I D E V E R S C H L I N G E N S I C H (mls_deu_000307-mls_deu_000307) +E R M Ü S T E E N E N F E R H E N G R O N I T E N K Ö E R A L D E S M A L E S M I T A L L E R K L E H R M E N Z U R E C H T W E S E N E N R I M I T G R A U S E N I G U N V R C H N A R C K H I U N V E R B R E M E N I C H T R E T E N D I E P E R S O N D E S E R A U S G E I B E S N D B I T E T I C H I K Ü N S T I G E L I S E R U W O L S T I E D U W E I T E L I S I S T F O L E N D I S D I G I T I S T N E R T E N (mls_deu_000308-mls_deu_000308) +D I H O F D A M E N B E K A M E N K R E M P F E U N D I K Ü N I G E N U N D I E R O N Z T E S S E N E N D I E R E R A L L A L I E B Z S E N H Ü N Z C H E N W E R N D E R M E I L T H E R A U I N S C H O S G E N O M H A D T E N E R M E R K T E N Z U I R E N S C R E Ä K E N G D A S D I L I E L E R A M A R A N T F A B E N E N U N D O R A N S C G A H L D E N S E I D E N K L E I D E R A L E D I S T D E S E T M E I N H E S L I H S T E N Ö F L E G E N W A N (mls_deu_000309-mls_deu_000309) +V O N L E D E A N D I E S I E S I N U N K L A R W I E R P I E S E N D I E S I S P B I E L N V O N G Ä H R T B Ö R S E N D I S I H E G K E N V O N D R A N Z Ü E S C H N B Ü C H A N D I E S I B A S R S E T Z E N K O N T E B I S M A N G E M Ü T W E R E N D I H L A U S T Z O N N A C H A M O N A U F K E S T C H E T W U R D E (mls_deu_000310-mls_deu_000310) +A M U N D N A E N W A N B L O S I E R E I N Z I G E R S C M U O K W A N I E R E A S T A N E N B R A N F L Ä C H T E N W E I C H I N W I L D E R U N D D A T Ü R L I C H E R A N M U N D A U I R S C H U S T H E N H R A B P F I E N I H N A M E I N B O G E N G F E I N K A T U N G S N D Z E I H E T M T R O S E R S O K V E L T I O M G E S E R (mls_deu_000311-mls_deu_000311) +R A R W I E U S T R T S C H L A N N O C H A S I L E N E I N I M A N D R E N S T A R T K O N T M A N E F A N W A S D E R G E N S C H E A N U N D E S E S L T A T D I S U N T E R E D U N G E W I S E N S E A N V E M U T E T E D S E S I C H U M E K L I R E N G D E R M A T Z H E Ü B E E R A B S I C H T E N U N D U N D I V E M I T L U N G D E R M Ä C H T E Z U I S C H N M A S T A T E N U N G R O S P E T A N I E N H A N D E (mls_deu_000312-mls_deu_000312) +L A U N W E N I G S T E N S E I N E Z E I T L A N G V E R S U O C H E N I N B I E F E R U N W I E R A U F D I E S E S B E I S N M T E I N A N D E R A U S R E I C H E N D A D E R S T U S A M E N H N G N D E I E D E S A R G S T A E I G E N K L I G H O E E R L E M E N T I S E R S E T Z T I E R U R T (mls_deu_000313-mls_deu_000313) +V E S C H E N F V O R K O M S E K F Ü R D E N Z U D E V E R M U T U N G D A S F R A U W I E S E D I E K L E I N E N W I E N V E R B R E N E I S O L I S F E I N S U S T A C H G E R H I T S T A B E N D A S D I H E R T P L A T E N Z S P T A N G A U S E D E S O L E I N F Ü R C H T E L I C H E R G E R O C H O W A G E N U N M N W U R T E N S E I N (mls_deu_000314-mls_deu_000314) +U N K G I N D E M S H R E I E N A C H S O S A E R E N T L I C H E I N H U O N B A U M U N D O B E N D E R A U F S A S E N K L E I N E S K E N D T U N D E R D E M B A U M A B A L A R E I N E F R A U R D I E S H L I E F (mls_deu_000315-mls_deu_000315) +C I T S I E H R T E N S E R H E B E N D I F I S C H E G A D E N E W A L L S C H E D E N A R C H T I A U B E R T A U S G E R U R D H F V I N W A D E N H E R E I N G E Z A O U G E N D I E S E L O E I T E R G E R H Ä O R T H E N A U G E N S H E I M L E S C H V E S C H E T E N E N N O C T Z I O N E N A R N A B U O R L D E R A L O P Ä H S C H E R K A R E K T E B E I A L E N A U S G E T R Ü K T W A L (mls_deu_000316-mls_deu_000316) +N E I N E I N I C H S C Ä E M E M Ä I C G H T L S M C H R N D E I N E M B U S E N M E N G E S I C H T V E R B E H R G E N H E R S I N G T E N S G R A S N I E D E U N Z I E T S I N A C H (mls_deu_000317-mls_deu_000317) +D I K E N D E R A B A R S A S E N V E R D E M W A L T U N D A L S I E D I E D R E I G N E C H T E V O N W E I T E M L A U F E N S A N S P R A C H L E N C H E N Z U N P F Ü N D E V O G E L V E R L E S T U M I C H N I C H T Z O V E L A S I C H T I G A U C R E N I C T S U R S P R A C H F O N D E V O G E L N U N U N D N E M A R M E R (mls_deu_000318-mls_deu_000318) +W I E D E R S C H U L Z E I N S E I N E R H U L D I E G U N G S R I E D E H E R V O R H U B D E L E R A B R A C H T E A M K L A R E N S O M A M R A D E N G M I T S E I N S C H U L K E N D E N E I N G E S A N G S S T E N T I E (mls_deu_000319-mls_deu_000319) +S T E R T W I S I E S E I E N S O L N (swc_deu_001408-swc_deu_001408) +D E R E N T S C H I N G R N G E N D R C E I N E R Z U S A R S C H A L T U N G S T U F E N L O S (swc_deu_001409-swc_deu_001409) +D I E A U F A L E B E I D E R S I T Z V E R T E L U N Z (swc_deu_001410-swc_deu_001410) +U M D E N Ü E R L E M E N D (swc_deu_001411-swc_deu_001411) +S P B E T E R U R D E N T E I L W E I S E S U G A R A C H T P A R L I L L O S T R E I F E N E N G E S E T Z T (swc_deu_001412-swc_deu_001412) +M A O R D E B E K A N T U N D V E L L A N G K T (swc_deu_001413-swc_deu_001413) +B U N D E S W A I G E S E T S D I E S T M V O N W I E L E N (swc_deu_001414-swc_deu_001414) +S F N A G E S C E I C H T E (swc_deu_001415-swc_deu_001415) +S B A L T U N G F E E C (swc_deu_001416-swc_deu_001416) +S T P A L E B O R N D I E U S E R E N D F E R A N D E S (swc_deu_001417-swc_deu_001417) +U M W E I T E R I N H U M A N I T E R E R H L F I E T Z (swc_deu_001418-swc_deu_001418) +S I E R K A M T E N D I E N N O U E R I C H I N E S I S H E R E G I O U N G N I C H T A N (swc_deu_001419-swc_deu_001419) +D I E U R A U F Ü H G E N V O N A N D E I N Z W A N S E N S E T E M E R Z W E R D E N A C H T I (swc_deu_001420-swc_deu_001420) +E R E I C H N I H T S C H O L I C U R E M I T S C H O L I H M A C K E N A N T O D E R N S M I T G E S E (swc_deu_001421-swc_deu_001421) +D I E D E R S Z S T M E R E I N E N (swc_deu_001422-swc_deu_001422) +U N T E I F E N T I S E N B E I D E (swc_deu_001423-swc_deu_001423) +K R E I S W A L F V O R S C H L A G U N D E I N E L A N D E S L I S T E R N D E R Z E I T E N (swc_deu_001424-swc_deu_001424) +A N U M S E R Z U N G D E R S A G E I N V O M A N E S F N F Z E I N T E I L G E N L I E E R T Z I K L S Z W E T E N A C T W U R D E P R E S L A S K A B E R T I N N E B E R A B E T U N G V O N H O S T H A B E M A N (swc_deu_001425-swc_deu_001425) +I E D I E V O L E D E R T A B E L E D A S T E T (swc_deu_001426-swc_deu_001426) +Z U M S T R U N F L S B A E (swc_deu_001427-swc_deu_001427) +D E B U N D E S W A L E I T E R B I S T Z U M S I E N Z I G S T E N T A R (swc_deu_001428-swc_deu_001428) +O R E R I C H K G E W R D E N D U S H E N I C H T M I T W E E N (swc_deu_001429-swc_deu_001429) +A U S F I O N M U S T E N G U S E R K O T E B E G I N (swc_deu_001430-swc_deu_001430) +V E R K I E I C H P B A N Z E A L N W E R T U M N G E A N D E (swc_deu_001431-swc_deu_001431) +B E T R A H T E D E E L G E M E I N H E I (swc_deu_001432-swc_deu_001432) +U N T E R S H I T L I C H E A U F A S I N G E N G A B E S N U R D A R I E B E R (swc_deu_001433-swc_deu_001433) +D O L L B E I M B U N E S L I G I S T E N B R S E R T O R T M U N D T N A C H V O L G E R D E S U N M I T L B A R Z O V O R T Z U R Ü C K E T R E T E N R E N A S S J I R E N R Ü B E R (swc_deu_001434-swc_deu_001434) +N U N Z H N A D C H T U N A H T Z I G (swc_deu_001435-swc_deu_001435) +R E I N E N Z I G K L P E T D I E (swc_deu_001436-swc_deu_001436) +D E R V O T S T R O M I S Ü B E R F I E L E G R U S S E N O R N U N E N L E N A R T Z U M L I C H T E N V A L (swc_deu_001437-swc_deu_001437) +D A S H T D V F Ü K L E I N E P R T E I N G R O S E A U S W I R K U N (swc_deu_001438-swc_deu_001438) +I S D E I T E R A T I E F E R T I E N S U G C (swc_deu_001439-swc_deu_001439) +D I E S K R E N Z U M B E I S H P B I L K O N D E N S A E R T H O R E N S E I N (swc_deu_001440-swc_deu_001440) +A L D T D I E K O U R S A U F K U B E R H A L K G E N D E N S O R I D T I S C H N S C H E R A B P T R E T E N (swc_deu_001441-swc_deu_001441) +B U N D E S T A G W A N T Z U N D E R D R E I U N D F M F Z I V R E R S M A L S N C H E I N E M V O M B U N D E S T A I G S E S T A L S E N G E S E T (swc_deu_001442-swc_deu_001442) +B U N D E W E I G E Z F I E F C H G E I N E R T W O R D E (swc_deu_001443-swc_deu_001443) +E R I B E R L A G E R T E N V O T U S T O M E U N D T R E I K (swc_deu_001444-swc_deu_001444) +D R T S I N T I E G R A T I O U M D E R B E I D E N D E U T C E N S T A T E N (swc_deu_001445-swc_deu_001445) +B E R I E N E Ü L M E S E N S T A T (swc_deu_001446-swc_deu_001446) +O A F I T Z E E F Ü L R N (swc_deu_001447-swc_deu_001447) +B I D E R V E R H E N S W A L W I T Z U S E R T L I C H D I E E I N H A L T U N G D E R (swc_deu_001448-swc_deu_001448) +W I E W E N I C T I N O L A N E R N O C H M P E L T S D E R Z E I T (swc_deu_001449-swc_deu_001449) +I E D C E T W V R D I E D U C H F I U G E N V O N W A L W E R B U N G A F K O S T E N D E S T A T E S (swc_deu_001450-swc_deu_001450) +D A S N I H M R U N G E S E T Z (swc_deu_001451-swc_deu_001451) +H E I M A T V E R T R I E B E N U N D R U S T I C S I G G E W A L (swc_deu_001452-swc_deu_001452) +U N D S P B E I C H E R I E N I N I N E W A C K T E S H L A N G E A B P (swc_deu_001453-swc_deu_001453) +O U R E G E N A L T H O N B E N D E R U N D D I D O K M E N T A R T I O N D E S T U R D I O S U R D E N E I N Z E H N H U N D E R T Z W U O U N S E B T Z I G I N D E R S I M E N S E C H I Ü E R S T E L T (swc_deu_001454-swc_deu_001454) +S O M I S E N A U E I N E M S T A T E G E S H E N R E R K T E E N U O B O O T (swc_deu_001455-swc_deu_001455) +F L Ü T E N S P B I L E E D L I C H E (swc_deu_001456-swc_deu_001456) +D R A S T D I S H M O R D E R A N E L I G K T R O N I S C H E R K L A N G E S H A L T U N (swc_deu_001457-swc_deu_001457) +A N C H I S E N W O D E D I E S O A M I T E T E M A N D A T Z T S A L I E D E R P A R T E I N T D I M S E B M V E R F A N E N S P E C H E N T E R A N Z A L I R A R Z W E I T S T E I E M P R O P R T Z I N A L A U F D I E L A N E S L I S T E D E P A R T E I U N T E R V E T E I E R T (swc_deu_001458-swc_deu_001458) +A U B P F A N D E R N A T S E B O M A D I U U N D T E R K N F T E (swc_deu_001459-swc_deu_001459) +D E R R E I N E N Z U K L O P E (swc_deu_001460-swc_deu_001460) +M I E R B E I L S C H N N (swc_deu_001461-swc_deu_001461) +W E R W I G E N E I N E V E R B R E C H E N S R E C H S G Ö E F T I C H Z U I N E R E I T S T R A E V O N M N D E S E N S E I N E (swc_deu_001462-swc_deu_001462) +D E R E S C W N D I C K E I T Z W E R T U N G E R A G E N D R E I E B E E I N H U L E R T A C H T (swc_deu_001463-swc_deu_001463) +E B O R U I U S A M E R S T E N G E B O R I E S A M F T E (swc_deu_001464-swc_deu_001464) +N T H I M S O N A E R G Ü F V E F A R E N A U D E L E N E R V E R T E I R T (swc_deu_001465-swc_deu_001465) +R E F O R M I N G O A B E R S C H A F S U N D A B P R S T U N S C H L T (swc_deu_001466-swc_deu_001466) +S I E R E N A N P H R T E T U N T U N D E R D E (swc_deu_001467-swc_deu_001467) +A N D E M B E S T L I C H E G R E F T E A U F G E D E N R U L T Z U N (swc_deu_001468-swc_deu_001468) +I T U N T E R A N D E R U M V E R W E N D E (swc_deu_001469-swc_deu_001469) +U S W Ü C K I P Ä N D E R (swc_deu_001470-swc_deu_001470) +U N D K O U B A R G R I E S E (swc_deu_001471-swc_deu_001471) +E N L T Z S D A W A L A U F G R U N D E I G N E R W E I R V O S H L I G E N E T E R R C H E N M T M I N D E S E N S F M F A B G O N E N V E R T I E E N S I N (swc_deu_001472-swc_deu_001472) +V E R P R E I T U N G I E D I O L O G E S C H A P R O P A G A N D E R D E R S U P E R M E C H T E U N D (swc_deu_001473-swc_deu_001473) +K O M I G S A U F D E R L I T H E T B E R T H A G E (swc_deu_001474-swc_deu_001474) +A L D E R K A L T I G K L I E G S I C H V O R T W E R E N Z U S P I T Z S T E (swc_deu_001475-swc_deu_001475) +S I C H E I Z P E R S N A L O D E R E C H U N E N N S E R S C H W I E R I G B E T E T E N E R N (swc_deu_001476-swc_deu_001476) +D A R U R H A F E S B L E I B E R I C H T U N (swc_deu_001477-swc_deu_001477) +I B E S E R W I E D E R S M U T I F T E R L E S U N G B I C (swc_deu_001478-swc_deu_001478) +W E N F Ü N I E M A N N A C H P Ü C S B E I S T (swc_deu_001479-swc_deu_001479) +I S D I E K L E W A R T E A R V O R S C H N V O N E I N R I C H T U N M E M (swc_deu_001480-swc_deu_001480) +G E S E N D A V O N B I R D E N S E B S D A N O C H I E I N S P E C H E N D E N P A L K O U T S F I E E N (swc_deu_001481-swc_deu_001481) +S P E C H E N B N Ü T I C H T D E A E M L U F T K L I E F V E R T (swc_deu_001482-swc_deu_001482) +E M U C L I C H S C U T Z I M V O R N G E N K A N G R E I T E (swc_deu_001483-swc_deu_001483) +S H N E I N E N L I C H E N V E R S U C H G A R (swc_deu_001484-swc_deu_001484) +R U N K G E N S T R A N A N I N E M P H E N Ü B E R G A N O D E R P E E E N I E B E R G A N D E S T H N I N R U N V O T U E R V E K T I N E I N E L E K R I S C H N S T R O M U M W A N D E (swc_deu_001485-swc_deu_001485) +B R M A S E I N D E S E B E S A L N O C H T F R E I D E T (swc_deu_001486-swc_deu_001486) +E L A E N D E R B I G I F (swc_deu_001487-swc_deu_001487) +A N K F E I L N E S U N T E R D I N S T E I M N O C H E I N E L O G E S C H A B P V O E R G (swc_deu_001488-swc_deu_001488) +K A B E R T L E N D I E S I S A N G E B O D I E D O M I T E N S C I E T E N E I T A (swc_deu_001489-swc_deu_001489) +S T A N D V O M Z W E L F T E N M A R T Z S Z W E I T A U S E N Z W E L F D E R I N H E I S T H I U T (swc_deu_001490-swc_deu_001490) +O G E N I S E R T I O N U N T E R B R A C H T E R F I N D (swc_deu_001491-swc_deu_001491) +V E R P Ü N D I T Z E N D T O D E R G A F Ü S I E A R B E I T E N (swc_deu_001492-swc_deu_001492) +V R S G L I E T E V O R H R I C H K T E I L D E (swc_deu_001493-swc_deu_001493) +I A R I C S T U N G D E R B E L I E N E M A U R A M Ü N D E T E N (swc_deu_001494-swc_deu_001494) +E R I C H T U N V O N K L I E R A N L A G E N (swc_deu_001495-swc_deu_001495) +A F G A N D E S T A N Z U N D I M I E R A R G H E R Z I E S E I T I M E I N M A R S T (swc_deu_001496-swc_deu_001496) +D E R V O N A R Z I U N D S T O M V O N D E N L U N G E N I E R D E B R O N C H E N B I S (swc_deu_001497-swc_deu_001497) +A U S E D E M N N A M E N S E N D E R H Ü R S P B I L E M I T V E R R M D E T R S P B R A H R (swc_deu_001498-swc_deu_001498) +U N D I E G R N D M A N D A R Z K L A U S E (swc_deu_001499-swc_deu_001499) +K E I N E A B K H R V O N E N G R U N D P L A G E N D E S T O T Z E L I S M U S E I N S H L I S E (swc_deu_001500-swc_deu_001500) +I T K O M P R N E N T E N S O H O L A N A L S A U C H T I E F I N D E R W A F E (swc_deu_001501-swc_deu_001501) +B E D E U C T U N G V O R L E W A R (swc_deu_001502-swc_deu_001502) +R E I F I T I G E H E N V E R T E R O U G A N I S T Z I U N (swc_deu_001503-swc_deu_001503) +U M E L E K T R U N V O M W A L E N S B A N D E N S L E I T U N S B A N (swc_deu_001504-swc_deu_001504) +A L L E D I N G S U N V E R L E I C H B A R I V E K T E M U K L I C H (swc_deu_001505-swc_deu_001505) +D I E S E K O N T E N A B E R L S E I N G A B E L I N E I N E N F R I C G W E N Z S U M S E T Z S E R D I E N U D E R S T E U E R T E N Z U N G O H N M U T U R N (swc_deu_001506-swc_deu_001506) +T O U M A S H R M A N S P R D T Z I E R T I Z W E I T A U S E N Z W E I M I T K G R E E B E (swc_deu_001507-swc_deu_001507) +P I E N U B E R G A N T R I F E N (swc_deu_001508-swc_deu_001508) +D I E F E I L K T E N H O S T S C A O U N (swc_deu_001509-swc_deu_001509) +A N I E S O U J E T C H D E M N S T R A T S I O N E N W U R D E N P L U T I G N E D E R G S H L A G (swc_deu_001510-swc_deu_001510) +E I E N F I E R K A N A L M I S P B U L T D E N T E V E K L E I N E R (swc_deu_001511-swc_deu_001511) +D I E S E H T N D I E V O R W A N Z E I T E N V Ü E R E I N E N A N G R I F A U F D I E U Ö S A R E X S T R E M H R A R P G E S E T S T (swc_deu_001512-swc_deu_001512) +W E R C H E S A M N E G H S T E N Z U M S T A T K E N O D E N L I E K T (swc_deu_001513-swc_deu_001513) +A I O G I N D O L Z E R Ü C I N D E B U N D E S L I E G E R U N D W E X S E L D E Z U E I N R C H T (swc_deu_001514-swc_deu_001514) +Ü B E R I S E K A N G K H E I T (swc_deu_001515-swc_deu_001515) +A R Z W E I T A U S E N D T F Ü N F E K Ü I D E S E R T (swc_deu_001516-swc_deu_001516) +D I E S E A U F S S U N G Z U N E U T R A R I T E T U N T E R S C H E I D E (swc_deu_001517-swc_deu_001517) +R E D E L W U R D E A L S K Ü N Z T A L I C H E R L E I T E R D E R I E M E N S T H U D I E S B E S T E L (swc_deu_001518-swc_deu_001518) +W E N M E N D I E W Ä L T A L G A N Z E S P E R A C H T (swc_deu_001519-swc_deu_001519) +S E N D K I T I S C H E K O M P B O N E N E N D E S D E T U N E R D I O U N Z S I S T E M S A B S I C H T L I C H S C H W A C H E N D W U R H F E N (swc_deu_001520-swc_deu_001520) +N I C H T W E R B E I I S T E D O C H (swc_deu_001521-swc_deu_001521) +E R B U O T E I N E F E R E I N I G U N G D E T S C L A N S A N (swc_deu_001522-swc_deu_001522) +P E L I E N Z W E I T A U S E N F Ü N F (swc_deu_001523-swc_deu_001523) +K E R N A B G E S T E I M T U N D T U M H L E N D I E S E N E N S P R E C H E N T (swc_deu_001524-swc_deu_001524) +A R Z O E L G U N G V O N D N A M I G A U S (swc_deu_001525-swc_deu_001525) +S E M T U N D E N G D E R (swc_deu_001526-swc_deu_001526) +U N G V O N D S C H I E H R U N T E R N E R U N (swc_deu_001527-swc_deu_001527) +D N E S C H E N U N G E R W R T C E N (swc_deu_001528-swc_deu_001528) +R O B E T E R F K E N E D I (swc_deu_001529-swc_deu_001529) +K A M S H L I S E L I C H Z U M (swc_deu_001530-swc_deu_001530) +V O L S T E N I (swc_deu_001531-swc_deu_001531) +S T A N T E N S I C H V O N D E N U E R E S A R (swc_deu_001532-swc_deu_001532) +A F R I K A R S T D I H T E S E R H A H R E R G E O R T E T (swc_deu_001533-swc_deu_001533) +D I E A R M E M U I T E T E L (swc_deu_001534-swc_deu_001534) +S T A L I E N S E R T Z T E M (swc_deu_001535-swc_deu_001535) +V E I H T E N E S A U S T L E I C G (swc_deu_001536-swc_deu_001536) +K L M E A U F B R O S S C R E I B T L E I H (swc_deu_001537-swc_deu_001537) +A M Z W E I T E N J U N E Z W E T A U S E N D F I E R B U R D E (swc_deu_001538-swc_deu_001538) +I N E B N D E S T E R N A C H R I U G T (swc_deu_001539-swc_deu_001539) +D I N A R T O O S T E R W E I T E R U N G U N D D I E E I N S E I T I G E R A U F K Ü N D I G U N G D E (swc_deu_001540-swc_deu_001540) +T H E R B E I I S (swc_deu_001541-swc_deu_001541) +D I E S R S T E L L E K A M M N S A N M T I C H E M I T I E D E R D E R K A P L E T (swc_deu_001542-swc_deu_001542) +P O T Z S T A M A B P K O M M E N E N T H I E L T Z W A R A L G E M E I N E V E R E I N B A U N E N Ü B E D I E K Ü N F T I G E G E M E I N S A M E V E R W A L T H N D E R S E G E R M I C H T E U N D V O M U L I E R T E G R U N D S E T Z E B D E M L I T R I S I E R U N (swc_deu_001543-swc_deu_001543) +D A N A C H R U N D R S C I E B E I N E N V A R A R K G B E I M W I E F T Z S I D Y N A M O (swc_deu_001544-swc_deu_001544) +E I N W E I T E R O W A R I J A N T M A (swc_deu_001545-swc_deu_001545) +S I E W U R D E N M O D O L A R N T U R C H E L O C H S T R E I F E N G E S T E Y E R T U N D D I K L I N G E K O N T E (swc_deu_001546-swc_deu_001546) +D I E G R U N M A D A T K L A U S E L B E R V O R T Z U G T U N D E D I N K L E I N E R N P A R T E I N J I E N E (swc_deu_001547-swc_deu_001547) +A B E R T O T Z D I N K E I N E W Ü K L I C H E R H U N G E S N O D T H E A S T (swc_deu_001548-swc_deu_001548) +N K O G M A L T E R Z I O N D (swc_deu_001549-swc_deu_001549) +Z U O F O R B E D I G U N G K O N G R I E T E R A P R Ü S T E N S C H L E T E (swc_deu_001550-swc_deu_001550) +B U N D E S T A R G E W A L R E C H T (swc_deu_001551-swc_deu_001551) +I S M U S T I M G R E I S W A L E I T A R V O R G E L I G T W E R N (swc_deu_001552-swc_deu_001552) +H A T M A N I N E I M P I E R E S C H E B A S E S F Ü B S Z U C H O S O T I A L E P R O G E A M M E Z U O R S E N K U N G D E R S E B S T M U T E R A T H E U N D Z E R S T D A R K U M G D E S I H G E R H I T Z S G E F Ü S I N D E B E F E K E R U N (swc_deu_001553-swc_deu_001553) +B E I D E N E R S T E N W R E I N P A L E M E N Z W A L N U R L E I E L I E H R S G U I M M E I N U N Z E I N H N D E R T N E U N Z I G I N S E I N E (swc_deu_001554-swc_deu_001554) +D A M I T L A S E N S I C H B E S T R A L U N G S T E R E N S E R G E N O U M E S E (swc_deu_001555-swc_deu_001555) +W I N G R S P I Ä T E R K A M S T Z U E I N E W E I T E R E N K R N D N (swc_deu_001556-swc_deu_001556) +W R A R D I O K A B E R E R T P E I L S (swc_deu_001557-swc_deu_001557) +S T Ü K T E B O U M B E R A U F I S T A R T B A N E N R O L E N (swc_deu_001558-swc_deu_001558) +M I T D I E S E R I G E L U N G S O L I N E R F A K T U I S C H T Z W E I F E R C H I N F L U S T N A E R D I S E R W E L E R A U F (swc_deu_001559-swc_deu_001559) +B A R K K Ü R S C H E N B A U (swc_deu_001560-swc_deu_001560) +D E R H E V O R A G E N T Z W I C G E N D E N L A N D E K L A B E N W I D E R U M H E R V O R A G E N E R L A N G S M F L U G E I G E N S C H A F T E (swc_deu_001561-swc_deu_001561) +M I T E R S C H V E R B I N D U N G S F L U K Z A G E O U D E R U M S C H U L M A S C H I E N V Ü R D I E B E E E I N H N D E R D N E U N V E R W E N D E T (swc_deu_001562-swc_deu_001562) +L E I S T E T E M E I Z I N E S H O N B S I C H E L O G E S C E H E I L E F (swc_deu_001563-swc_deu_001563) +K A M A N D E C H I M F O M R E N V O R B E U I G E N (swc_deu_001564-swc_deu_001564) +M E R D I N A U S B U C H T I S E R K A N K E I T E N E R E F L U K T E I N F R K T I O N V E L L A N G S M M E N K A N (swc_deu_001565-swc_deu_001565) +D I E I N E N E U T R D I E T E T U N T E R A L E N U M S T E N N V O R S A R (swc_deu_001566-swc_deu_001566) +U N Z S I E G E N H E R T (swc_deu_001567-swc_deu_001567) +D A S N E U I N Z H N H U N D E R A C H T E N D R E I S I G G R Ü N D E T E K O M I T I F V I R U N A M E R I E K A N S C H E U M T R I E B E W U R D E D A F E R N R U (swc_deu_001568-swc_deu_001568) +Z E I N D R A L E D E P R O K R E S I E N U N D H R T D I S I N H E N J Ö R G E S T I T S T E N K U N Z S T D E N G K E N S (swc_deu_001569-swc_deu_001569) +I N D E R E U E S P R E S E D E N T A N K Ü N D I G K T E (swc_deu_001570-swc_deu_001570) +S N E K Z S T U N D V O R S H P E I S E N (swc_deu_001571-swc_deu_001571) +D E S B U N D E S W A I G E S E T Z E S B I S T Z U N D R E I S I G S T E N C H U N I E Z W E I T O S E N O D E A U F G E M (swc_deu_001572-swc_deu_001572) +A R D E P U S S E Ö L (swc_deu_001573-swc_deu_001573) +F L Ü C F T L I N G E N V O N D E R I E T N U S H E N M I N D E R H E I T D E S O M A L E S H E N B A N T U N (swc_deu_001574-swc_deu_001574) +D I E B I E P O L A R E W E L T O R T E N U N S E M I N T I E R T (swc_deu_001575-swc_deu_001575) +T A R A N F A N G E I N I N T E K R I E R T E U D E R E X S T E R N A N G E B R C H T V O R I C H T U N A N E I N E M N U K L E A R E N W A F E N S S T E M (swc_deu_001576-swc_deu_001576) +S T A R T E R T D I H I L F S O G E N I S I T Z U N L A N K P V R E S T I G (swc_deu_001577-swc_deu_001577) +W E N D I E S E E C S T E R E N E F E K T E I N D E R I C H T I G E N R E I N V O L G E A U F T R E T E N U N D S I C H I N E R H A L B S P E Z I F I S C E R P A R E M E T E R B E W I G E N (swc_deu_001578-swc_deu_001578) +Z U O K D E S E W E R T U N I O N A U C H B E I D E W A E R S T A O F P B A U M B E M U N D T N E N I N F L U G K Z O E U G E M I T I N T E R K O N T I N E N T A L L A R E I C H W E I T E M I T D E N U R S A R G L E I C H (swc_deu_001579-swc_deu_001579) +P E N D E S T A T H E E W A B E N T I E O M (swc_deu_001580-swc_deu_001580) +D I E S E R A N S A T Z S G E L D A L G E M E I N A L S A U S C G E B U O R G U N D E (swc_deu_001581-swc_deu_001581) +N A C H I N Z U S A M B R O H T E (swc_deu_001582-swc_deu_001582) +D E U B E R L A U S E T Z Z F I C H E N H E I E R W E R D (swc_deu_001583-swc_deu_001583) +D A B E I E N Z W E I F H A S E N U N T E R T E I E L T (swc_deu_001584-swc_deu_001584) +S C H I E T E N A N D E R E R O P R M I S T E R S C H A F T E I L U N D W U R D E M T E R D I R B E E L F (swc_deu_001585-swc_deu_001585) +M A S T E R E R L F N E K A B A R S C H I S I E N W E I T E R G E M Ü K L I H K E I T (swc_deu_001586-swc_deu_001586) +E I N E M A U S W E R T Z S A E V O L Ü G I N W O L S B U R G E L A N (swc_deu_001587-swc_deu_001587) +M I T S C H E W E B U N G S S U M A N K O N T E N K L I E S A N D I E R Z R E L K T W E R D E N (swc_deu_001588-swc_deu_001588) +D E R B A L E D I G L I H T Z E I K T E (swc_deu_001589-swc_deu_001589) +K O S P R E T A N D I E N E I N E E S T E W I C H T I G E V E I N B A U N (swc_deu_001590-swc_deu_001590) +I D A U H T E S H U T D N E S I N (swc_deu_001591-swc_deu_001591) +W U R D E M I T D E B U N D E S W A I G E S E T Z V O N E N Z H N A S I C H S U N F M F T I G A I N E D A U R H F T E R E L U N G E N G E F Ü H R T (swc_deu_001592-swc_deu_001592) +D I E A N Z E A L D R I B E H N G M E N D A T E K A N (swc_deu_001593-swc_deu_001593) +B S C H L S D I E S E R E I N M L I T E R S C H S E I N G R E I F E N I N D E N K O R A R K R I K (swc_deu_001594-swc_deu_001594) +N A R T U O V E B N T L I C (swc_deu_001595-swc_deu_001595) +K A L Z I G R I E G B E I N D E T (swc_deu_001596-swc_deu_001596) +A U N N Z E N E T E I A E M D N E U N Z I G U N U S T R A L I E N S W I D E R Ü S T E R E I C H S C H A B P L I G E R (swc_deu_001597-swc_deu_001597) +D A D I E S E I T A N F A N G N E U N Z H N U N D E R T N E U N U N F Ü N F Z I D R T H E R S H E N D E R E R U L O T I O U N Z R I G J I O N G U N D E R V I E D E L K A S T R U E I N D E N S O S E L I S T I S C H E N K U R S E I N G E S H L A G E N H A T (swc_deu_001598-swc_deu_001598) +D A C H R W E I T E R E N V E L U S T R E C H E N K E M F E N U N E N E N Z W Ö R T E V O L G E B E I D E G R I G S P A T E I N U R D E R U N T D R E A R E N E R B E G I N D E A U S A N D A N D E R S E T Z U N G E I N B S R E I T E G Ü L T I G E S W A S F E N E N S T I L S T A N S A B K O M M N A P G E S C L S S E N (swc_deu_001599-swc_deu_001599) +M A N I S T E R B E I S E R V O S I C H T I C H (voxforge_deu_000891-voxforge_deu_000891) +D I E W E R F L I C H T S O L I N D E U T S C H L A N D L E I E R N O C H N I C H T A B G E S C H A F T W E R N (voxforge_deu_000892-voxforge_deu_000892) +E S G E B T A U C H M I S P R A U C H T U C H A B E R T G E B E R (voxforge_deu_000893-voxforge_deu_000893) +D I E K I N D E S I N D A N H A N K E B O N E N (voxforge_deu_000894-voxforge_deu_000894) +D E R A C K W E I T E D E R K A D A S T O F E S O L L V E R D E U T L I C H T W E R D N (voxforge_deu_000895-voxforge_deu_000895) +S E N R L L N D E T (voxforge_deu_000897-voxforge_deu_000897) +B E I M O G A N D S T R E I T S T R E I T E N B E R D V E F A S S U N G S O G A N E (voxforge_deu_000898-voxforge_deu_000898) +D A W A G E C H A R Z U B T Z W E I F E N (voxforge_deu_000899-voxforge_deu_000899) +M A N S O T E D E N A U F G A R G H E I N V F A L T R A U N (voxforge_deu_000900-voxforge_deu_000900) +D E E F N L I C H E S C H U L E N W E R N N I C H G E T E L K T W E R E N (voxforge_deu_000901-voxforge_deu_000901) +B A E G E L T I S T A U S K E T Z H L T W O R D E N (voxforge_deu_000902-voxforge_deu_000902) +E S O L E N R E I H U N D E R D T A U S E N D N O E A B E S P L Ä T E I N S T I E N (voxforge_deu_000903-voxforge_deu_000903) +D I E K E R B E R V E L E T Z U N G K A N A L S B E I S P I L E N D W E R D E N T (voxforge_deu_000904-voxforge_deu_000904) +D I E R E N Z E I T W E R S C H T E N B O D E N (voxforge_deu_000905-voxforge_deu_000905) +D S T D A B E F E L U G S B E Ü R D E N K E I E N Z U L I E R E S K E L H A B E N (voxforge_deu_000906-voxforge_deu_000906) +D I I N E R E S E N F I N D E N K E I N E H Ö R (voxforge_deu_000907-voxforge_deu_000907) +I F W E I L T A S S T D E T A B L A R T O H E R R Ü G S C H I E D T A S S T D E N R Ü G G T A S S T D E R Ü G E G I E R S T A S S T D E A (voxforge_deu_000908-voxforge_deu_000908) +D E B E T R O T C E N E N U N A N B E R E C H T I G D E E N H E R I G E L T E N M A C H E N 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(voxpopuli_deu_000329-voxpopuli_deu_000329) +D E N E I N E N E I N Z I G E N S I T Z K E B T E S L E N G S D A S I S T A S P U R G (voxpopuli_deu_000330-voxpopuli_deu_000330) +E D A S A S P A S I E R I N M A L T E R D I E B S O N L I S T E I N D I E K O B T S U N D F L E A U F G E D E K T E R D I S V E R W H E G E N B C H N E R M A D E B A W E D E R W E R E N U S D E M A R I S I K O B S O U N D F E L E E B E U N D E R S U C H N O C H I T D E R M O A R S E R B E A R E R G E T I L T E U N D E R S U T E A N A T V F A S S I N A N D U G A S W E N A L S I E R U N T E R D E M A N D E L I S C H W E I N T Z U G E D E K T W E N S E O S (voxpopuli_deu_000331-voxpopuli_deu_000331) +L I T L A N G E K Ü S T E D I E W A N S T E I N E D I A U F D I R O S E N K A T E R S V O F E N I T Z U N A M I E S E D E R V R G A N G N E R T I N W E I S E N (voxpopuli_deu_000332-voxpopuli_deu_000332) +D E N T I C H A R B E B E N Z I E P Ü R D E B E R C H T G I S T E I M E N T O A U O L E I N S W E H R N V E L E R I N T E L T E S W I R D N E M I T A U E A U F G E F A D E R T D S A U R B E H E S H E P A L A M E N D A U F D E M W Ä E G K T S E I D I M E I N Z I G E N G S I T S T Z U O N D E S T E L T Z E N (voxpopuli_deu_000333-voxpopuli_deu_000333) +I N D I E S E M R I F E N W H U O T D E N G E M E I S M I B P L I T D E S C H E V E R A B R E D U N G E N I N K R E I S D E R S I E B E N U N Z W A N Z S I G E T R O F V E R N U N D A U C H U B L I K G E M A H T T (voxpopuli_deu_000334-voxpopuli_deu_000334) +I E B E N E R B E R T Z O U G E N D S W E R S H O E U T H M I D I N V O R S C H A R G S I M U M A L G A U S C H S G E C H A F T A M I N C H T W E I T E A U K M A S I G P E R F E K T A U R B E C H E A T Z S A G E H E D E N Ü E R H O C H R I S I G O B R T U G K T D A N E D S E N D A L I Z U L A S E N H A M M S S E N D A S H A I C H E I C H G E S H A F T A R M I D E M E R S O D A H E M T I C H L I G P L A U B E I C H D A S W I R T R O T E M E I N E N G O R S E N S C R I L T F L E I G K E I N M E I N S T E I N E I N G R O S E N C H I T Z U M E R A T E D E N S E A T A E N (voxpopuli_deu_000335-voxpopuli_deu_000335) +P E H L E D A N G E S F V E R L K T F Ü Ö R T S W E I E N H E I B M I N O N T E N E R G (voxpopuli_deu_000336-voxpopuli_deu_000336) +Z U M A K T U E L E N I C H K L A B I S K A N K E I N E V E N U N S A N N E M E D A S W E W E R T L I C G I A R S T F E I T D I S E N W U C H N G E N D E W I S H E N D A S E N S D I T A L U N S U N F E I C H K E I T D R O T (voxpopuli_deu_000337-voxpopuli_deu_000337) +D S N D E I N F C H P E D I N G U N G E N D I N I G A K T Z E P T A B E S E N M A N K (voxpopuli_deu_000338-voxpopuli_deu_000338) +I N D E Z W I S C H E N S E I S I N D I R E T U N G S A R G E N I S A R I O N E N D E G R Ö S T E N S C H L P E R H W E I S I E D I E M I E G A N T E N Z W A N Z I C H G H L M E T E R V E R D E R L I E B I S C H E N K Ü S D A U B G R E I F E N U N D A L L N E R H I T A L I E N P R A S P R T I E R E N (voxpopuli_deu_000339-voxpopuli_deu_000339) +D A S S E I K T D E R F A L J U L I E R T D E M S C H Ä N K O U (voxpopuli_deu_000340-voxpopuli_deu_000340) +I W A S E R P R E D I G E N O N D W E I N T R I N E N (voxpopuli_deu_000341-voxpopuli_deu_000341) +W I R D I E I N S C H E I D U N G R A R H E N W I E R F I L E P A T N R N E C H T Z U L E T Z I E S T Ä T T E (voxpopuli_deu_000342-voxpopuli_deu_000342) +D I E V O L G E I S E I N H Ö R N F L U G K V O M P R O P L I S T N E X S T R L M I S T E N E I N I G M I G I S T A T E N E R E N B U M F U M P A R O U L E N S E T Z E N I E R K O N G K R I E T E R V E R I N D E R U N G E N G E G E N (voxpopuli_deu_000343-voxpopuli_deu_000343) +W A I L D I E I N W E S T I Z I O N E N V R A N T Ö R S I S C H A U N D D E L U T S C H E R B A N G E N G E R E T E T W V E R D E N M U S T D E N D Ö R H T D E R I C H E N L A N T Z W E I T A U S E N Z E H N N I C H T D B P E I T E G E N U N D H L U T E R U S E S E I N E N R I E S I G E N S C H O T D E N B E R K V A R S I C H E H E R T D R I U K (voxpopuli_deu_000344-voxpopuli_deu_000344) +D E M I T I G I T S T A T E N D Ö R O F E N N I C H I E M Ü K I C H K E I T H A B M D E R E N E N A U R B P E S H E N S T A S A M B A L D E R A N Z E R H N D E R N E N I E R N A R E G I O N G G A N Z S G E T Z I E L U N S T D E M A T I S K O R U L T O N F E R N A C R Z E I G E N E S I E N (voxpopuli_deu_000345-voxpopuli_deu_000345) +E I M I L I O N M E N S C H E N S I N A B P E N G I H V O N U N S E R H E L V E R (voxpopuli_deu_000346-voxpopuli_deu_000346) +E I N F E T H I N R G E R J U N G E W E T I N H E R K A D I V O N E I N P L I S I S T E N A I N D E S O N D E R E I S A T S K O M A N D U S E N K O M A G S C H A E N (voxpopuli_deu_000347-voxpopuli_deu_000347) +D I E I N D E R H E I L I G K U E I T M A N V O R S I C H E R E T R A N E N D A S A U P T A U T U S U D E R A L U N S H E N T E N W E K (voxpopuli_deu_000348-voxpopuli_deu_000348) +R E I D E R A T R I G E T E R E F F E N H A B E N I N Z I S C H E N S T A D G E F U N D E (voxpopuli_deu_000349-voxpopuli_deu_000349) +R D I C H I E T E N E I N M O N E R D E B P T (voxpopuli_deu_000350-voxpopuli_deu_000350) +D A S W E G E E I N E W I C H T I G E F H A H G E A N D I K O M I T I O N E N E I N L A N D D I G R A N Z K O N D T C O L L E W I E D E R E I N F Ü Ö O N N T D A C H E M S C H N G E N I O N B L E I D E N M I T Z U G A N G K Z S U O R I M A T I O N Z U S T E M E T S E T E R A R O R D E R I S D R S E I N E N W E R E R O D E A R D I E R A G E I S W E S T I C F Ü R D I E D E N I S C H E R I E P A T E N D I S P Ä T E U M E I N E K L A E A N W O R T D A (voxpopuli_deu_000351-voxpopuli_deu_000351) +D E S C H O N A U S S C G I E F Ü R T W U R D E L A G E S N I C H B A R A N D A S E S H E R O B E F F E L E G I G E B E N H E T D I S N E N D S G A B E N H R E I H E V O N D K L E I E N U N G E R E I N M T E I T E N B I T I E N S W E I (voxpopuli_deu_000352-voxpopuli_deu_000352) +N V E R G E M E I N C H R F T E N D E A U S N O N S I E R I T S P A L I T I G A S G O S I S T Z I E L D I E S E R U N J O N (voxpopuli_deu_000353-voxpopuli_deu_000353) +D E N I C H E R H E I T I S A N I S Z W E R I E G E R U N D I T E I L W E I C H E R A R B R E I T N I C H T N U H R I M T E C H N I S H N B A R E I C H (voxpopuli_deu_000354-voxpopuli_deu_000354) +D I K S E L T E N G E N D I E N T E R E S E N V O N B Ü Ö R G E N U N P O L I E T I G E N S O W E I A U S N A N D E R B E R E M B Ü R E R N E N G A N Z E R O B E R S H I D S T E M A R K I E N T G A N Z S O B E N (voxpopuli_deu_000355-voxpopuli_deu_000355) +H E R P R S I D E N T (voxpopuli_deu_000356-voxpopuli_deu_000356) +E F Ü R E N E S P R Ä E C H E M I T R E S E D E N K A S E I T Z A R E I C H E N R E G J E R U N G S E R T R I E E N V F R A U N M E N S C H E N R E C H T O G A M I S R T I O N E N U N D I E W A N D D R C H A U S E M U T I G E N T (voxpopuli_deu_000357-voxpopuli_deu_000357) +N G S A C H E I N E U R S A C H E F Ü R D I N E W A C K S N N A T Z U N A L I S N U S D E L L I G S E I D E F O L I C H P E R S P E K T I E F L O S S I S (voxpopuli_deu_000358-voxpopuli_deu_000358) +O U I D E I N E I M A N A O S O R W E I T V N D E N Z I E E N F E R N S (voxpopuli_deu_000359-voxpopuli_deu_000359) +D W E R D E R S W I A N Z M I N I S T E A U C H E N E I N E N L A N D Z I E D E N T A G G D A M I T K O N F V O N D T I E T D A S N D T Ü L I C H A U C H T U S B U S T Z S I N G E G E B E N S E N M U S S D A S S T A S H U S H A L T E V O N D E N S T L E R S O A L E R E N E U N S T E L E R S O L L E N D I N E R Z I E T Z I N T U N D D A S I E R T D A M I T A U F H T D I E A N T U E R T U N G A G E N I N E N E N T S C H E I D U N G E N D E I E R H I E N I S E N R A M E N D R E F F N M E T M M U N T E R N (voxpopuli_deu_000360-voxpopuli_deu_000360) +A U D E M O U U R O B E I S C H N A U T E B E B I L M A R G K T I N S G E S A M T D R M A T D I S C H I S S (voxpopuli_deu_000361-voxpopuli_deu_000361) +E B P E H S C H U N J O N H A R T M I D I S E I N S T R U M E N Z S D I S C H O N S E E I N E A K T I V E R O L L E N E R N A C K T P A E G I O N Z U S P I E N U M D E M O U G R A D S C H E R D E V O R M E N N A E N A C H A L I G I N I K T U N V R A N Z I T R E I E (voxpopuli_deu_000362-voxpopuli_deu_000362) +S T U L T A L I T E R E R S C H I E M E V O N A U S E N U D A U V N I N E N I S R E S C H T U N D O S C H I E T L E G (voxpopuli_deu_000363-voxpopuli_deu_000363) +E H H A M I M E R G E S A R K E I N Ü B E R E I L T U S T A T Z U N I E R U N G S E N C H E I D U N G I S U N S I N I S G W E I T Z U M J E R T Z I G E N Z E I T F U N E S K E I N E B E D R O N G B E I S C S P I E A S W E S A U S E M I E R A N G E T (voxpopuli_deu_000364-voxpopuli_deu_000364) +D E E R V A R K L E I C H I S T E I N E T Z U Ü E N E S H E M I S E A T D N D E R A U B R H R A V O R N M E N Z C H E N R E I T Z W R L E I D D N E L A A B E L S Z S S F H F H F G F A G S R F D A R S T S T S S T S B O D S S A A A I E O N G A N D A N A N A N E I N E S O E U S C H E U N E N K L A U P L I C H E R A N W O R O F (voxpopuli_deu_000365-voxpopuli_deu_000365) +D I E S P E E R H R T D I E S E U N F A S E N D E R H U T Z U N T A L E R I C H L I N D E W Ü B E F Ü Ö B O T E T W E N G I E (voxpopuli_deu_000366-voxpopuli_deu_000366) +G I C I W E R K I S K S L I C H I N A N D U N D E W R S A R S T G H G D E V L A N T V O U N D E E I N E I N M A L N M E H R I J E T S T D E R V E R A N T Z W O C F T D U N G F Ü R I N E U T U P T I M A L E N W R A L M G R A S I G K A L I V I T Z T I E R U N G U N R E R A B E I T N E H M E U D A R B E I T N E M E R R I N E N D A N S P E R S O N D E R E T S T R E S H U N Z I T A G E N (voxpopuli_deu_000367-voxpopuli_deu_000367) +A N D R D A U C H O N L E N D I E S E R K U D E R G E B E S E R Z I E L N A I S A N D E R E G I E S I S H W Ä H R T U N D I M I T E L A B P T Z I U O F N T W A C R I G J I O N W I E K A L A R B R I H N Z I T Z I L E N O D A U K R I C H L A R D R A U C H O M E N E N (voxpopuli_deu_000368-voxpopuli_deu_000368) +D E B E R I C H K O E S E S V O R D E R Z U R E I C H T D S E S R E T I N G S T A T L I C H E R S C H U L T T I E E I S E R F E N L I C H E R A U F G A B E B E G R I F E N U N D D A R H E R O N E F E N I C H E A K T Ü H R N V O R G E N A M W E R E N M U S S (voxpopuli_deu_000369-voxpopuli_deu_000369) +D A B I S A B E L N U N M I T E I N E M U T S C A R P O G A M T U T U N H A B E M M S W I L D A F Ü H E I N E N S P E C H E N D E R E C H I G E K O N T L A G E S C H A E N (voxpopuli_deu_000370-voxpopuli_deu_000370) +S I E R N O H A N A L I S I E R N W O R (voxpopuli_deu_000371-voxpopuli_deu_000371) +D M A K E N E N E T U L I E V E R L A N G E N G E B E N L I E M E R G A R T F H R N D I K U N S H I H V E R A U S D I E A M E N O E I T E W R A U H E N D A S A B E (voxpopuli_deu_000372-voxpopuli_deu_000372) +G E R A R D I Ü E K L E I N E P O R J Ä E C K D E I S D A S Ü N H B E R M E H E S I C B E R O G A T E S H R A U F A N D R E C H T I S D A S D A S I E R S A W B E I N Z E I T A U M V O N R E I A R E N G E S E N T W E R E N S O R U N U M N T (voxpopuli_deu_000373-voxpopuli_deu_000373) +I K A N D E R V E R S I C H E R N D I A R L O B P E S C H E K O M I S I O N I E S S T E R K O M I T I T Z U M A H R A R A R U B T S A L R O B E C H E N E R S P I G K I E V E D I S K O S S E R U S N T (voxpopuli_deu_000374-voxpopuli_deu_000374) +E I E D A N Z E H E A U F T A U G H S O N (voxpopuli_deu_000375-voxpopuli_deu_000375) +D T D I E S M E H A U S H E L K A A N D I E N G Ö R G E R I N U N D B U R G E R N I C H T I B E R T Z O E L I T E N N B E G E I S T E A N (voxpopuli_deu_000376-voxpopuli_deu_000376) +T I A L E M U K R A R E N E M I T G R O S A F H R O U D E Z O R K E N N E S D A S D I G D E I E R V O R I E T R A R G E N H A B E N E B Z S I C H A U H I M Z U S A M E N A R I T V E R E N D R U N G N E D E N W E I C H S T A T E N U M S E T S N (voxpopuli_deu_000377-voxpopuli_deu_000377) +D E A H A B E S C H U R S D I E E L D A R O R B P E S C H E S E M E S T E R H E R H E R T Z U N E M E N U N T I K O R O B T U N Z S I K L A S I U N E R I M R A M D E R L I N E R B R E C H T D E T Z U V E R E Ü F N I G E N I S T N I G A U S E I G E N T (voxpopuli_deu_000378-voxpopuli_deu_000378) +N D M E I N M E I N E B I T E O D A M D A S W A S I C H M E R V O R S T D E N I S D A S M A H R G E N G W I E C K T L I C G I N D E R T A H A T E I N G G R O S E E I N E B R E I T E M E H R H E I T F Ü R D I E S I K O L S I O N S P L I T I G H S O R L G E P L I T I G S T S T D E M T S Ü R D I E M E N S C H E N V O R O R T D A I T I U N S A D E S W E H E N I E A U C H B E S C R E Ä N K E N K E I N E D A S (voxpopuli_deu_000379-voxpopuli_deu_000379) +W E N W I E R A R H O L T E D I E E V O R A R D N U N G V R A B S C I E D E N O A O F E R I C H D A S S E W I E R N A C H E I M L A N G N K A R U S E L E L S U E I M B U D N A B C H U S K O M U N T I T M A Ü C H T E R M Ä C H E B E I E R K O M I S I O N B E D A N G E N D I E G O N Z T O T I E S A C H A R B E I T H A T (voxpopuli_deu_000380-voxpopuli_deu_000380) +U N Z E R E R E S C H Ä A R S C H N U N Z I E K O N T R O R L N H A B E N K E I N E N P E L E G E R P R A F T (voxpopuli_deu_000381-voxpopuli_deu_000381) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..7ba21c36a0fa3f69bb39cbcf3cf0a08f112cf5e4 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/ref.trn @@ -0,0 +1,661 @@ +D I E B E E R D I G U N G M A C H T E E I N E R Ä U S S E R S T W I C H T I G E N S A C H E E I N E N D E D E R P E T I T I O N A N D E N G O U V E R N E U R F Ü R D E S I N D I A N E R J O E S B E G N A D I G U N G (M-AILABS_deu_000165-M-AILABS_deu_000165) +D A H A B E S I E D I E W O H L J E D E M H I E R I N D E R E R I N N E R U N G G E B L I E B E N E N W O R T E G E S P R O C H E N (M-AILABS_deu_000166-M-AILABS_deu_000166) +E R S T U M A C H T U H R W A R E R A U F M A L E B R A C H T E D E N K A F F E E D I E S O N N E S C H I E N I N S Z I M M E R U N D D I E S P E R L I N G E D I E D A S A U S D E N H Ä C K S E L S Ä C K E N G E F A L L E N E F U T T E R K O R N A U F P I C K T E N (M-AILABS_deu_000167-M-AILABS_deu_000167) +S I C H E R L I C H A N I H R E M G E B U R T S T A G H Ä T T E E R B E I I H R B L E I B E N K Ö N N E N (M-AILABS_deu_000168-M-AILABS_deu_000168) +U N D D E S H A L B M U S S M A N D O R T W O M E N S C H E N S C H W I E R I G K E I T E N H A B E N D I E S A U C H E I N E R S E I T S E R K L Ä R E N A N G E B O T E M A C H E N (M-AILABS_deu_000169-M-AILABS_deu_000169) +D A S S M A N N U R A U F D I E W E L T K O M M T U M S E L B S T W I E D E R E I N E N S O H N Z U H A B E N D E R D I E V E R E H R U N G D E R A H N E N F O R T S E T Z T (M-AILABS_deu_000170-M-AILABS_deu_000170) +D E S H A L B G E H Ö R E N K O N T I N U I E R L I C H E S C H U L B I L D U N G A U C H K O N T I N U I E R L I C H E M Ö G L I C H K E I T E N D E R W E I T E R B I L D U N G U N D D A S B E G E H E N V O N G E D E N K T A G E N F Ü R M I C H U N A U F L Ö S L I C H Z U S A M M E N (M-AILABS_deu_000171-M-AILABS_deu_000171) +M E I N A N S A S C H E N S A G T S I E E S I S T J A J E T Z T W I E D E R G A N Z G U T Z W I S C H E N U N S A B E R E H E D U N I C H T A L L E S G E S T E H S T G E H T D I E E R I N N E R U N G A N D A S B Ö S E N I C H T W E G (M-AILABS_deu_000172-M-AILABS_deu_000172) +N E I N W E I B E R B R A U C H E I C H N I C H T (M-AILABS_deu_000173-M-AILABS_deu_000173) +G O T T H A T N I C H T V E R G E B L I C H N A C H M I R G E R U F E N S A G T E D E R S C H I F F E R (M-AILABS_deu_000174-M-AILABS_deu_000174) +N U R E I N E S W E I S S I C H D I E S E R F U R C H T B A R E N F R A G E E N T G E G E N Z U S E T Z E N U N D S C H L E U D E R E D A S W O R T I N D I E W A A G S C H A L E D I E G L U T M E I N E S L I E B E S W I L L E N S I S T S T Ä R K E R A L S T R E N N U N G (M-AILABS_deu_000175-M-AILABS_deu_000175) +T O M S A R M E E G E W A N N E I N E N G R O S S E N S I E G N A C H E I N E R L A N G E N H A R T N Ä C K I G E N S C H L A C H T (M-AILABS_deu_000176-M-AILABS_deu_000176) +E S I S T E I N N A M E D E M S I C H D I E T Ü R B E I T A G U N D N A C H T Ö F F N E N K A N N B U R S C H E U N D W I L L K O M M E N (M-AILABS_deu_000177-M-AILABS_deu_000177) +A B E R I C H V E R Z E I H E I H N E N I H R E U N W I S S E N H E I T (M-AILABS_deu_000178-M-AILABS_deu_000178) +V O N D E R D R I T T E N U N T E R R E D U N G A N S A G T E M I S T E R H A V I S H A M W A R M I R D I E P E R S O N I N H O H E M M A S S E V E R D Ä C H T I G (M-AILABS_deu_000179-M-AILABS_deu_000179) +I C H D E N K E D E R A M T M A N N U N D S E I N E F A M I L I E W E R D E N E S R E C H T V O N D I R F I N D E N D A S S D U D I C H S E L B S T A N G I B S T U N D S I E W E R D E N F R E U N D L I C H G E G E N D I C H S E I N (M-AILABS_deu_000180-M-AILABS_deu_000180) +J E T Z T S C H L U G D I E H E L L E F L A M M E A U F U N D N U N E R K A N N T E E R U N S D I E W I R N O C H I M M E R Z U S A M M E N G E D R Ä N G T I N D E M W I N K E L S T A N D E N (M-AILABS_deu_000181-M-AILABS_deu_000181) +D E R S E I N E R S E E L E A N S P O R N E N D D A S E R M U N T E R N D E W O R T V O R W Ä R T S (M-AILABS_deu_000182-M-AILABS_deu_000182) +I C H F R E U E M I C H A U F D E N B E S U C H D E S T U N E S I S C H E N M I N I S T E R P R Ä S I D E N T E N (M-AILABS_deu_000183-M-AILABS_deu_000183) +W A S F Ü R V E R F O L G U N G E N W A S F Ü R N A C H S T E L L U N G E N H A B E I C H N I C H T Z U E R D U L D E N G E H A B T (M-AILABS_deu_000184-M-AILABS_deu_000184) +Z I G E U N E R W A R E N E S D I E V O N O R T Z U O R T F U H R E N E I N K A U M E R W A C H S E N E S J U N G E S D I N G K A M Z U M I R H E R A N G E H Ü P F T U N D B E T T E L T E N E I N (M-AILABS_deu_000185-M-AILABS_deu_000185) +H U C K I C H W E R D E D I C H I N E N E M B O O T H I N F A H R E N W E R D E D A S B O O T D A A N L E G E N U N D E S W I E D E R Z U R Ü C K R U D E R N A L L E S G A N Z A L L E I N B R A U C H S T D I C H G A R N I C H T D R U M Z U K Ü M M E R N (M-AILABS_deu_000186-M-AILABS_deu_000186) +A L S N U R E I N M A L N O C H D E N R A U C H V O N S E I N E M H A U S E A U S D E R F E R N E A U F S T E I G E N Z U S E H E N U M D A N N B E R U H I G T Z U S T E R B E N (M-AILABS_deu_000187-M-AILABS_deu_000187) +D I E T Ä N Z E R I N A B E R L A G A U F D E N K N I E E N V O R B R A H M A S B I L D N I S I N N A M E N L O S E R S E H N S U C H T U N D W E I N T E J A M M E R V O L L (M-AILABS_deu_000188-M-AILABS_deu_000188) +R E C H T F E R T I G T M I C H D E N N D I E W I R K L I C H K E I T N O C H N I C H T A U F D I E I C H M I C H B E R U F E N K A N N (M-AILABS_deu_000189-M-AILABS_deu_000189) +I C H Ä R G E R T E M I C H D A N N W E N N I C H A U F W A C H T E E S W A R S O W U N D E R S C H Ö N G E W E S E N D A S F L I E G E N (M-AILABS_deu_000190-M-AILABS_deu_000190) +N A C H D E M E R S C H O N D E N G A N Z E N V O R M I T T A G M I T I H M V E R B R A C H T K A M S T A N H O P E N A C H T I S C H I N S Q U A N D T S C H E H A U S U M C A S P A R L E B E W O H L Z U S A G E N (M-AILABS_deu_000191-M-AILABS_deu_000191) +E R W A R E I N A L T E R H I R T V O L L M E D I Z I N I S C H E R G E N I N A L I T Ä T (M-AILABS_deu_000192-M-AILABS_deu_000192) +D A S S W O H L A U C H D E R M I E T E R S E I N E W U N D E R L I C H K E I T E N H A B E N M Ü S S E (M-AILABS_deu_000193-M-AILABS_deu_000193) +S I E S A H E N A L L E Ä N G S T L I C H U N D B E T R Ü B T A U S U N D A U C H H E R R A R N E S A S S S C H W E R M Ü T I G D A W I E D I E A N D E R E N U N D S T Ü T Z T E D A S H A U P T I N D I E H A N D (M-AILABS_deu_000194-M-AILABS_deu_000194) +U N T E R D E N D A M E N M E I S T J U N G E F R I S C H E G E S I C H T E R U N T E R D E N H E R R E N N E B E N J U G E N D L I C H E N S O L C H E M I T F A L T I G E R S T I R N U N D B E R E I T S M E H R O D E R M I N D E R M O N D U M G L Ä N Z T E M S C H Ä D E L (M-AILABS_deu_000195-M-AILABS_deu_000195) +S E I T T A G E N S C H O N H A T T E E S B E S O N D E R S D R Ä U E N D G E K L U N G E N (M-AILABS_deu_000196-M-AILABS_deu_000196) +S O N D E R B A R (M-AILABS_deu_000197-M-AILABS_deu_000197) +E R B V O N E R B E N H E I M S T A N D M I T S E I N E R G A T T I N V O L L W E H M U T U N D D A N K B A R K E I T A N D E R G R U F T A U F D E R E R E I N E N M Ä C H T I G E N (M-AILABS_deu_000198-M-AILABS_deu_000198) +I H R W A R J E D E R M E N S C H E I N W U N D E R U N D F A S T A L L E S W A S M E N S C H E N T A T E N E T W A S W U N D E R B A R E S (M-AILABS_deu_000199-M-AILABS_deu_000199) +W E L C H E I H R W E G S I E E N T L Ä N G S T F Ü H R T E (M-AILABS_deu_000200-M-AILABS_deu_000200) +D I E W I R T I N S A S S N I C H T H I N T E R I H R E M S C H A N K T I S C H U N D K E I N E R I H R E R D I E N S T L E U T E B E F A N D S I C H I N D E R S T U B E (M-AILABS_deu_000201-M-AILABS_deu_000201) +A L S D I E H E R R S C H A F T A U S D E R K I R C H E T R A T S T A N D E N D I E L E U T E U M H E R U M S I E V O R B E I G E H E N Z U S E H E N U N D A M K I R C H H O F T H O R E W A R T E T E E I N M A N N (M-AILABS_deu_000202-M-AILABS_deu_000202) +W A S M Ü S S E N W I R T U N U M D E M T E R R O R I S M U S E N T G E G E N Z U T R E T E N (M-AILABS_deu_000203-M-AILABS_deu_000203) +I C H G L A U B E D A S S S I E E S G U T M I T M I R M E I N E N H E R R D O K T O R (M-AILABS_deu_000204-M-AILABS_deu_000204) +D O C H I M A N F A N G G E W A N N E R K E I N E A U F M E R K S A M K E I T F Ü R A N D E R E D I N G E A L S F Ü R D A S E S S E N (M-AILABS_deu_000205-M-AILABS_deu_000205) +D I E S F L Ä S C H C H E N Z O G E R J E T Z T E I L I G H E R V O R W Ä H R E N D J E N E S I C H M I T W A S S E R F Ü L L T E N U N D B O T E S D E R J U N G F E R Z Ü S A N (M-AILABS_deu_000206-M-AILABS_deu_000206) +D E S H A L B W A R E S A U C H R I C H T I G U N D W I C H T I G D A S S C H I N A D O C H J E T Z T A N S P R U C H S V O L L G E S A G T H A T W I R W E R D E N A U C H A N D E N Z E I T P U N K T D E R R E D U K T I O N K O M M E N (M-AILABS_deu_000207-M-AILABS_deu_000207) +N I C H T D O C H M U T T E R W E C K E S I E J E T Z T N O C H N I C H T (M-AILABS_deu_000208-M-AILABS_deu_000208) +J A W I R H A B E N I N D E N L E T Z T E N J A H R E N R E C H T E N G E B E Z I E H U N G E N Z U B R A S I L I E N A U F G E B A U T (M-AILABS_deu_000209-M-AILABS_deu_000209) +S I E W Ü R D E S I C H N I C H T F Ü R A N D E R E O P F E R N (M-AILABS_deu_000210-M-AILABS_deu_000210) +R I E F E N S I E M I R Z U (M-AILABS_deu_000211-M-AILABS_deu_000211) +G O T T W A S S I E I H R E R Z Ä H L T E H Ö R E N S I E N U R E S I S T E I N G A N Z E R R O M A N (M-AILABS_deu_000212-M-AILABS_deu_000212) +S E I N E M U T T E R K A N N I H M N U R F L U S S W A S S E R G E B E N D E S H A L B W E I N T E R (M-AILABS_deu_000213-M-AILABS_deu_000213) +D E R B U N D E W I R T S C H A F T S M I N I S T E R W I R D Z U S A M M E N M I T D E R N E T Z A G E N T U R A M V I E R T E J U N I Z U M E R S T E N M A L P R Ä S E N T I E R E N W I E S I C H D I E N E T Z B E T R E I B E R U N D D I E K R A F T W E R K E D I E N E U E N N E T Z P L Ä N E V O R S T E L L E N (M-AILABS_deu_000214-M-AILABS_deu_000214) +E V A H A T T E S I C H Z I T T E R N D V O R T O D E S S C H W Ä C H E V O N D E M G I T T E R B E F R E I T U N D S U C H T E Z U E N T F L I E H E N A B E R D E R S C H M A L E G A R T E N B O T K E I N E N A U S W E G (M-AILABS_deu_000215-M-AILABS_deu_000215) +O B I C H M E I N W E R K F Ü R H E U T E L I E G E N L A S S E N O D E R N O C H E I N E N A N L A U F N E H M E N U N D E S V O L L E N D E N S O L L T E (M-AILABS_deu_000216-M-AILABS_deu_000216) +E R W A R D A S G Ö T Z C H E N D E R S T U N D E D I E T A I T A I B E A U F T R A G T E M A D A M E A N G E L E D I E A U C H D A S T A N D U N D D I E G E K A U F T E N S E I D E N S T Ü C K E Z U S A M M E N F A L T E T E F Ü R T S C H U N Z U S O R G E N (M-AILABS_deu_000217-M-AILABS_deu_000217) +I C H W E R D E N A C H S E H E N (M-AILABS_deu_000218-M-AILABS_deu_000218) +A B E R T I P P S O D E R V O R G A B E N D A S M A C H E N W I R N A T Ü R L I C H N I C H T (M-AILABS_deu_000219-M-AILABS_deu_000219) +A L S U N S E R E I D E E B E K A N N T W U R D E W A R D I E P H Y S I O G N O M I E D E R W A L T E R S B U R G E R U N G E F Ä H R D I E E I N E S K A L B E S D A S Z U M E R S T E N M A L E D O N N E R N H Ö R T (M-AILABS_deu_000220-M-AILABS_deu_000220) +B I T T E M A C H E N S I E G E F Ä L L I G S T A U F U N D E S K L A N G W I E E I N J A M M E R N D E R H I L F E R U F (M-AILABS_deu_000221-M-AILABS_deu_000221) +H E R R D O K T O R S A G T E E I N E F R A U D I E S C H N U R R G R I N E D I E S O O F T Z U I H N E N K O M M T I S T E I G E N T L I C H G A R N I C H T K R A N K (M-AILABS_deu_000222-M-AILABS_deu_000222) +D I E A L T E E R I N N E R U N G A N D E N F R Ü H E R E N T R A U M T A U C H T E E B E N F A L L S W I E D E R A U F U N D U N W I L L K Ü R L I C H F A S T B E I D E R B E H A U P T U N G D A S S D I E S E E L E D E N K Ö R P E R V E R L A S S E N U N D Z U I H M Z U R Ü C K K E H R E N K Ö N N E S C H I E N E S I H R O R D E N T L I C H (M-AILABS_deu_000223-M-AILABS_deu_000223) +A L S S I E A U F D E N B A L K O N Z U R Ü C K K E H R T E F A N D S I E I H N D I E Z E I T U N G L E S E N D D I E W Ä H R E N D I H R E S F O R T S E I N S A N G E L A N G T W A R (M-AILABS_deu_000224-M-AILABS_deu_000224) +E R W A R E I N K I N D D E R S T R A S S E V O N K L E I N A U F A B E R I N I H M L E B T E V O N J E H E R E I N E G E W I S S E S E H N S U C H T N A C H E I N E R E H R B A R E N B Ü R G E R L I C H E N E X I S T E N Z (M-AILABS_deu_000225-M-AILABS_deu_000225) +U N D W I R A L S B U N D E S R E G I E R U N G F Ü H L E N U N S H I E R N I C H T E I N E R G R U P P E V E R A N T W O R T L I C H S O N D E R N W I R F Ü H L E N U N S D E M G E M E I N W O H L V E R A N T W O R T L I C H (M-AILABS_deu_000226-M-AILABS_deu_000226) +W A S M E I N L I E B E S K I N D W A S K A N N (M-AILABS_deu_000227-M-AILABS_deu_000227) +U N D D A N N W O L L T E I C H D E N A N B L I C K D E R E R N I C H T M I S S E N D I E M I R G E B L I E B E N W A R E N V O R A L L E M A B E R W A R E S M I R D A R U M Z U T U N M E I N E S Ü S S E E L I S A B E T H E I N I G E R M A S S E N G E T R Ö S T E T Z U S E H E N (M-AILABS_deu_000228-M-AILABS_deu_000228) +A B E R I C H G L A U B E D A S S W I R U N S A U C H G E G E N S E I T I G E I N B I S S C H E N U N T E R S T Ü T Z E N K Ö N N E N (M-AILABS_deu_000229-M-AILABS_deu_000229) +S E I N E G E S C H Ä F T L I C H E L A U F B A H N H A B E S T E F E N S O N A L S K Ü C H E N B O Y I N E I N E M H O T E L V I E R T E N G R A D E S B E G O N N E N (M-AILABS_deu_000230-M-AILABS_deu_000230) +V I E L L E I C H T T Ä T E N S I E G U T D I E S E A N S I C H T E N D E S B I S C H O F S N A C H H A U S E Z U M E L D E N S A G T E D E R T A J E N D E R I M M E R M E H R E I N M A N N D E S G E S C H R I E B E N E N W O R T E S W I E D E R T A T (M-AILABS_deu_000231-M-AILABS_deu_000231) +A M A N D E R N M O R G E N E R H O B E R S I C H S P Ä T S C H I C K T E D E N L A K A I E N I N D I E W O H N U N G F E U E R B A C H S U N D L I E S S U M E I N E U N T E R R E D U N G B I T T E N D E R M A N N K A M M I T D E R B O T S C H A F T Z U R Ü C K (M-AILABS_deu_000232-M-AILABS_deu_000232) +N U R E I N W E N I G T R A U R I G W U R D E E S W E N N I M M E R D A S S E L B E K A M W E N N S I E N I E Z U F R I E D E N S C H I E N E N (M-AILABS_deu_000233-M-AILABS_deu_000233) +E I N S O M M E R W A R M E R N O V E M B E R T A G L A G M I T S O N N E N G L I T Z E R N Ü B E R D E R H A U P T S T A D T U N D U N T E R D E N L I N D E N D R Ä N G T E E I N E T A U S E N D K Ö P F I G E M E N S C H E N M E N G E A U F U N D N I E D E R (M-AILABS_deu_000234-M-AILABS_deu_000234) +K O M M M I T M I R M E I N S O H N D E N N I C H B R A U C H E D E I N E L I E B E (M-AILABS_deu_000235-M-AILABS_deu_000235) +N U R S E I N G E S I C H T W U R D E E I N W E N I G N A C H D E N K L I C H E R S O W I E V O N E I N E R E R I N N E R U N G E R H E L L T (M-AILABS_deu_000236-M-AILABS_deu_000236) +D A N N W I R D A U C H W I E D E R D E R I N N O V A T I O N S D R U C K S T E I G E N U N D D A Z U I S T D A S S Y S T E M J A E I N G E F Ü H R T W O R D E N (M-AILABS_deu_000237-M-AILABS_deu_000237) +J E T Z T G E W A H R T E E R M I T E N T S E T Z E N D I E S C H E U S S L I C H E T E U F L I S C H E A F F E N F R A T Z E D I E Ü B E R D E S M Ä D C H E N S S C H U L T E R S C H I E L T E (M-AILABS_deu_000238-M-AILABS_deu_000238) +J A D E R W I R T N I C K T E D A S G E H Ö R T E I N E M G E W I S S E N W U T S C H O W B E R N H A R D W U T S C H O W I S T E T W A S V E R R U F E N (M-AILABS_deu_000239-M-AILABS_deu_000239) +W O L L T I H R I N W A H R H E I T D I E L Ö W E N T Ö T E N U N D K Ö N N T I H R S C H I E S S E N (M-AILABS_deu_000240-M-AILABS_deu_000240) +B A T C E D D I E S E H R R E S P E K T V O L L W O B E I E R N U R E I N I G E S I L B E N V E R S C H L U C K T E W A S I H M B E I D E N B E L I E B T E N L A N G E N W Ö R T E R N D E S Ö F T E R N V O R K A M (M-AILABS_deu_000241-M-AILABS_deu_000241) +L O R D F A U N T L E R O Y W I R D N I C H T S E N T B E H R E N D E S S E N B I N I C H G E W I S S V E R S E T Z T E E R (M-AILABS_deu_000242-M-AILABS_deu_000242) +K A M G L E I C H F A L L S I N S S C H L A F Z I M M E R A U F E I N E N N A G E L I N D E R N Ä H E D E S B E T T E S (M-AILABS_deu_000243-M-AILABS_deu_000243) +U N D D A S I S T D I E C H A N C E D I E I N D I E S E R K R I S E S T E C K T D I E C H A N C E F Ü R I N T E R N A T I O N A L E R E G E L N D I E S I C H A N D E N P R I N Z I P I E N D E R S O Z I A L E N M A R K T W I R T S C H A F T O R I E N T I E R E N (M-AILABS_deu_000244-M-AILABS_deu_000244) +A N F A N G S F I E L D E R R E G E N S C H R Ä G U N D P E I T S C H T E E R S T D I E E I N E U N D D A N N D I E A N D E R E S E I T E D E S W A G E N S (M-AILABS_deu_000245-M-AILABS_deu_000245) +F A S T L E I C H T S I N N I G E N B E M E S S U N G I H R E S W E R T E S A U F Z U G E B E N S I C H E N T S C H L O S S E N H A T T E (M-AILABS_deu_000246-M-AILABS_deu_000246) +D A S H E I S S T D I E F R A G E D E R M E N S C H L I C H E N A R B E I T U N D D I E F R A G E W A S K A N N T E C H N I S C H G E L Ö S T W E R D E N (M-AILABS_deu_000247-M-AILABS_deu_000247) +D I E S A F A R I W A R A U F D I E R E G E L M Ä S S I G B E N U T Z T E N W A S S E R S T E L L E N D I E S E R R O U T E A N G E W I E S E N (M-AILABS_deu_000248-M-AILABS_deu_000248) +D I E B E I D E N M Ü S S T E N H I E R O B E N A U F D E M G I P F E L G E S T A N D E N H A B E N U N D E R S P R A C H D I E A L T E N W O R T E V O R S I C H H I N (M-AILABS_deu_000249-M-AILABS_deu_000249) +E N D L I C H B L I C K T E C E D R I K A U F W E I S S N E W I C K A L L E S V O N D E N A R M E N L E U T E N F R A G T E E R (M-AILABS_deu_000250-M-AILABS_deu_000250) +D A S S E S H E U T E E I N E W U N D E R B A R E Z U S A M M E N A R B E I T Z W I S C H E N B U N D U N D L Ä N D E R N I N D I E S E N F R A G E N G I B T M I T S E H R S E H R I N T E R E S S A N T E N P R O J E K T E N (M-AILABS_deu_000251-M-AILABS_deu_000251) +C A S P A R V E R H A R R T E A N G E W U R Z E L T A N S E I N E M P L A T Z S E I N E G L I E D E R J A S E I N E A U G E N W A R E N W I E V E R S T E I N E R T A L S E R Z U M Z W E I T E N M A L H I N B L I C K T E (M-AILABS_deu_000252-M-AILABS_deu_000252) +E I N I G E Z E I T D A N A C H F R A G T E E R M I C H O B I C H G L A U B E D A S S D E R E I S G A N G D E N S C H L I T T E N D E S A N D E R E N Z E R S T Ö R T H A B E (M-AILABS_deu_000253-M-AILABS_deu_000253) +A B E R N U N B L O S S N I C H T I N E I N E S C H O C K S T A R R E V E R F A L L E N (cv_deu_000698-cv_deu_000698) +J A I C H K O M M E J A S C H O N (cv_deu_000699-cv_deu_000699) +N E B E N B E I A R B E I T E T E E R A L S A U S H I L F S K R A F T A U F E I N E R F A R M (cv_deu_000700-cv_deu_000700) +E I N T E R R I T O R I A L G R Ö S S E R E S E U R O P A W I R D N I C H T M I T E I N E M E T A T M Ä S S I G K L E I N E R E N E U R O P A E R R E I C H T (cv_deu_000701-cv_deu_000701) +I H R S O H N K A M D U R C H K Ü N S T L I C H E B E F R U C H T U N G Z U R W E L T (cv_deu_000702-cv_deu_000702) +D I E N A C H T A K T I V E N F A L T E R F L I E G E N V O N M I T T E J U L I B I S M I T T E O K T O B E R (cv_deu_000703-cv_deu_000703) +A C H T (cv_deu_000704-cv_deu_000704) +F Ü N F (cv_deu_000705-cv_deu_000705) +N U T Z E R K Ö N N E N I H R E L E S E Z E I C H E N O N L I N E A B S P E I C H E R N V E R W A L T E N U N D M I T A N D E R E N N U T Z E R N T E I L E N (cv_deu_000706-cv_deu_000706) +D I E D O N B O S C O K A T H (cv_deu_000707-cv_deu_000707) +S A U L B A S S Z Ä H L T Z U D E N I N N O V A T I V S T E N D E S I G N E R N U N D F I L M E M A C H E R N S E I N E R Z E I T (cv_deu_000708-cv_deu_000708) +I N G R Ü N Ü B E R S I L B E R N E M W E L L E N B A L K E N E I N E S I L B E R N E E I C H E (cv_deu_000709-cv_deu_000709) +W E I T E R E W I C H T I G E I N D U S T R I E Z W E I G E S I N D D I E M I K R O M E C H A N I K G A L V A N O P L A S T I K M E T A L L B A U U N D D I E H O L Z V E R A R B E I T U N G (cv_deu_000710-cv_deu_000710) +Ü B E R D E N A U T O R I S T N I C H T S B E K A N N T V E R M U T L I C H S T A M M T E E R A U S D E M D E U T S C H E N S P R A C H G E B I E T (cv_deu_000711-cv_deu_000711) +M A N S T E U E R T E S M I T E I N E M D O P P E L P A D D E L (cv_deu_000712-cv_deu_000712) +W I R H A B E N E I N P R O B L E M A U F O S I S C H I C H T A C H T (cv_deu_000713-cv_deu_000713) +W I R S P I E L E N I M M E R N O C H A B E R D A S L E B E N A U F T O U R I S T D E R Z E I T N I C H T M A C H B A R (cv_deu_000714-cv_deu_000714) +H E U T E Z E I G T S I C H D E R G R Ö S S T E T E I L D E R A N L A G E A L S E N G L I S C H E R G A R T E N (cv_deu_000715-cv_deu_000715) +S E I N E R E S I D E N Z N A H M E R I N M Ü N C H E N W O E R A U C H S T A R B (cv_deu_000716-cv_deu_000716) +I N N E R E R U N D Ä U S S E R E R N A R T H E X K Ö N N E N A L S G E T R E N N T E T E I L E E I N E S N A R T H E X A U C H G E M E I N S A M V O R K O M M E N (cv_deu_000717-cv_deu_000717) +D A B E I B E L E G T E E R D I E P L Ä T Z E V I E R U N D D R E I (cv_deu_000718-cv_deu_000718) +K I M D A R B Y I S T D I E T O C H T E R Z W E I E R P R O F E S S I O N E L L E R T Ä N Z E R (cv_deu_000719-cv_deu_000719) +I C H G L A U B E D A S F Ü H R T N I C H T I N D I E R I C H T I G E R I C H T U N G (cv_deu_000720-cv_deu_000720) +D A S I S T E I N E E X T R E M S C H L E C H T E R I C H T L I N I E (cv_deu_000721-cv_deu_000721) +H E R R L U R C H E N T B L Ö S S T E S E I N H A G E R E S G E S I C H T (cv_deu_000722-cv_deu_000722) +N U R C A R M E N F I N D E T D A S U N F A I R (cv_deu_000723-cv_deu_000723) +I N G E B O R G K R A B B E H A T T E D R E I G E S C H W I S T E R (cv_deu_000724-cv_deu_000724) +E S K O M M T W I R K L I C H D A R A U F A N D A S S S O L C H E D A T E N A U F D I E S E R E B E N E E R F A S S T W E R D E N (cv_deu_000725-cv_deu_000725) +S T R U M M I N G H I N G E G E N E R G I B T E I N H A R M O N I S C H E S P U L S I E R E N (cv_deu_000726-cv_deu_000726) +B I N I C H Z U M K A U F E I N E R H Y P O T H E K B E R E C H T I G T (cv_deu_000727-cv_deu_000727) +T E H E R A N I S T D I E H A U P T S T A D T V O M I R A N (cv_deu_000728-cv_deu_000728) +K O H L E N H Y D R A T E S I N D B E S S E R A L S I H R R U F (cv_deu_000729-cv_deu_000729) +O H N E D I E P R O F E S S I O N E L L E U N T E R S T Ü T Z U N G D E R M A S E R A T I R E N N A B T E I L U N G W A R E N D I E S E W A G E N D E R K O N K U R R E N Z N U N D O C H U N T E R L E G E N (cv_deu_000730-cv_deu_000730) +S I E D I E N T E Z U N Ä C H S T A L S U N T E R K U N F T F Ü R B E L G I S C H E B E S A T Z U N G S T R U P P E N (cv_deu_000731-cv_deu_000731) +D A M Ü S S E N W I R S P R E N G E N M E I N T E D E R Z A H N A R Z T (cv_deu_000732-cv_deu_000732) +A U S S E R D E M S P I E L T E E R B E I M N A C H F O L G E T E A M N E W M A R K E T R O Y A L S S O W I E B E I M L I G A K O N K U R R E N T E N L O N D O N K N I G H T S (cv_deu_000733-cv_deu_000733) +W I E A U C H D A S I N S T A N T R U N O F F V O T I N G E R F Ü L L T D I E C O O M B S W A H L D A S C O N D O R C E T K R I T E R I U M N I C H T (cv_deu_000734-cv_deu_000734) +S M I T H W U C H S I N C H I C A G O A U F (cv_deu_000735-cv_deu_000735) +W I R S I N D H I E R A L L E I N (cv_deu_000736-cv_deu_000736) +D U M M I S T W E R E T W A S W E I S S A B E R T R O T Z D E S B E S S E R E N W I S S E N S F A L S C H H A N D E L T (cv_deu_000737-cv_deu_000737) +H A U P T T H E M A D E R S H O W I S T D I E R E V A N C H E F Ü R Ü B L E S T R E I C H E U N T E R F R E U N D E N (cv_deu_000738-cv_deu_000738) +G L E I C H Z E I T I G W U R D E N S P O R T W E T T E N T E I L W E I S E V E R B O T E N (cv_deu_000739-cv_deu_000739) +S I E B E N (cv_deu_000740-cv_deu_000740) +J A (cv_deu_000741-cv_deu_000741) +Z U D E M V E R S A H E R I M K L O S T E R L A N G E J A H R E D I E Ä M T E R D E S N O V I Z E N M E I S T E R S U N D P R I O R (cv_deu_000742-cv_deu_000742) +H E I D E N H A I N E N T S T A M M T E E I N E R Ä R Z T E F A M I L I E (cv_deu_000743-cv_deu_000743) +A C H T (cv_deu_000744-cv_deu_000744) +Z W E I (cv_deu_000745-cv_deu_000745) +E B E N F A L L S I N A U G G E N A N G E S I E D E L T S I N D D I E K E L T E R E I D E R F A (cv_deu_000746-cv_deu_000746) +D I E S E S T E H T A U C H F Ü R A B S O L V E N T E N E I N H E I M I S C H E R S C H U L E N M I T D E U T S C H K E N N T N I S S E N O F F E N (cv_deu_000747-cv_deu_000747) +A L S O I C H H Ö R E N I C H T S (cv_deu_000748-cv_deu_000748) +W I E K A N N M A N S I C H S C H Ü T Z E N (cv_deu_000749-cv_deu_000749) +N A C H F Ü N F M O N A T E N L A G E I N E E M P F I N D L I C H E R E P L A T T E A L S D I E B I S D A H I N E R H Ä L T L I C H E N V O R (cv_deu_000750-cv_deu_000750) +Z I E L I S T E S D I E Ü B E R E I N S T I M M U N G E I N E S S O F T W A R E S Y S T E M S M I T S E I N E R S P E Z I F I K A T I O N Z U Ü B E R P R Ü F E N (cv_deu_000751-cv_deu_000751) +M I T E I N E M W A R M E N G E T R Ä N K I M B A U C H L Ä S S T S I C H D I E K Ä L T E B E S S E R A U S H A L T E N (cv_deu_000752-cv_deu_000752) +D I E A N T I V I R E N S O F T W A R E I S T A M O K G E L A U F E N U N D H A T A L L E C O M P U T E R I M H A U S L A H M G E L E G T (cv_deu_000753-cv_deu_000753) +I H R E K L O A K E I S T I N D I E S E R Z E I T K U G E L F Ö R M I G (cv_deu_000754-cv_deu_000754) +D I E S T R E C K E B E G I N N T I M S Ü D E N V E R O N A S U N D F Ü H R T D U R C H D I E P O E B E N E R I C H T U N G S Ü D O S T E N (cv_deu_000755-cv_deu_000755) +E R S T V O N D O R T K O N N T E E R S E I N E N W E G F R E I F O R T S E T Z E N (cv_deu_000756-cv_deu_000756) +S I E E R H E B T S I C H H E U T E I M M E R N O C H G U T E R K E N N B A R A U S D E M S C H W E M M L A N D H E R A U S (cv_deu_000757-cv_deu_000757) +D I E K A N A R I S C H E N I N S E L N G E H Ö R E N Z U S P A N I E N (cv_deu_000758-cv_deu_000758) +W I S S E N S C H A F T L E R H A B E N D I E S E M U T A T I O N B I S H E R N U R B E I F R A U E N B E O B A C H T E T (cv_deu_000759-cv_deu_000759) +S E I N E G E S C H Ä F T S B E Z I E H U N G E N R E I C H T E N B I S N O R D A M E R I K A U N D A S I E N (cv_deu_000760-cv_deu_000760) +Z A H L R E I C H E V O R D E R E P L A T Z I E R U N G E N B E I D E U T S C H E N E U R O P A U N D W E L T M E I S T E R S C H A F T E N S O W I E O L Y M P I S C H E N S P I E L E N F O L G T E N (cv_deu_000761-cv_deu_000761) +I N E I N E R T A G E S Z E I T U N G B L Ä T T E R N D S I T Z T S I E G F R I E D A U F E I N E R P A R K B A N K (cv_deu_000762-cv_deu_000762) +M I T E I N E M W A R M E N G E T R Ä N K I M B A U C H L Ä S S T S I C H D I E K Ä L T E B E S S E R A U S H A L T E N (cv_deu_000763-cv_deu_000763) +F O L G E D E M Q U E R V E R W E I S (cv_deu_000764-cv_deu_000764) +O S T E R N I S T I M M E R E I N E W O C H E N A C H D E M E R S T E N V O L L M O N D I M F R Ü H L I N G (cv_deu_000765-cv_deu_000765) +I M M I T T E L A L T E R H A T T E N W E C H S E L N D E H E R R S C H A F T E N D A S D O R F I N N E (cv_deu_000766-cv_deu_000766) +D E N N A M E N G H I B L I T R A G E N A U C H W E I T E R E F A H R Z E U G E V O N M A S E R A T I (cv_deu_000767-cv_deu_000767) +D U K A N N S T M I T D E M B U S N A C H F R A N K F U R T A N D E R O D E R F A H R E N (cv_deu_000768-cv_deu_000768) +M I R D O C H E G A L (cv_deu_000769-cv_deu_000769) +A L L E R D I N G S E R G A B E N W E I T E R E P R Ü F U N G E N D A S S E S M I T T E L F R I S T I G K E I N E N B E D A R F F Ü R E I N E S O L C H E A U T O B A H N G Ä B E (cv_deu_000770-cv_deu_000770) +U M G E K E H R T K A N N E I N F R E I B R I E F E I N E A U S S C H R E I B U N G A L S V O G E L F R E I G E M E I N T S E I N (cv_deu_000771-cv_deu_000771) +B I Z A R R G R O T E S K E A B S C H N I T T E Z E I G E N E I N F L Ü S S E D U R C H S C H O S T A K O W I T S C H (cv_deu_000772-cv_deu_000772) +E R W A R E I N E R D E R P I O N I E R E A U F D E M G E B I E T D E R N U T Z U N G D E R S O N N E N E N E R G I E (cv_deu_000773-cv_deu_000773) +A U C H W E N N M I R D I E K U N D E N A U F D I E N E R V E N G E H E N M U S S I C H H Ö F L I C H K E I T B E W A H R E N (cv_deu_000774-cv_deu_000774) +D I E S P Ü L M A S C H I N E I S T F E R T I G (cv_deu_000775-cv_deu_000775) +I N D E R A R C H A I S C H E N P E R I O D E W U R D E N E R S T E F O R M E N D E S A C K E R B A U S E N T W I C K E L T (cv_deu_000776-cv_deu_000776) +D I E K O M Ö D I E S E I B E S S E R A L S D E R E R S T E F I L M (cv_deu_000777-cv_deu_000777) +A K T U E L L G I L T F O L G E N D E R M O D U S (cv_deu_000778-cv_deu_000778) +D A M I T E N D E T E I N E E R F O L G R E I C H E I N T E R N A T I O N A L E B I L D U N G S A R B E I T V O R A L L E M I M M U S I S C H K U L T U R E L L E N B E R E I C H (cv_deu_000779-cv_deu_000779) +D E R S O H N E I N E S B E R G M A N N S B E G A N N S E I N E F U S S B A L L K A R R I E R E B E I D E N S P O R T F R E U N D E N W A N N E E I C K E L (cv_deu_000780-cv_deu_000780) +I N D I E S E M J A H R G A B E S S I E B E N N U M M E R E I N S S I N G L E S U N D S E C H S U N D D R E I S S I G N U M M E R E I N S A L B E N (cv_deu_000781-cv_deu_000781) +N O R D W E S T L I C H V O N H A C K H A U S E N B E F I N D E T S I C H D I E O R T S C H A F T H A C K E N B R O I C H (cv_deu_000782-cv_deu_000782) +I M O R T G N A R R E N B U R G G I N G E N V I E L E S O Z I A L E E I N R I C H T U N G E N V O N H E R M A N N L A M P R E C H T U N D D E R M A R I E N H Ü T T E A U S (cv_deu_000783-cv_deu_000783) +I C H W E R D E F O L G L I C H D E N R A T Ü B E R D I E I M P A R L A M E N T V O R G E T R A G E N E N B E D E N K E N I N F O R M I E R E N (cv_deu_000784-cv_deu_000784) +E S W Ä R E T R A U R I G G E W E S E N E I N S O W I C H T I G E S T H E M A N I C H T I M K O N S E N S V E R A B S C H I E D E N Z U K Ö N N E N (cv_deu_000785-cv_deu_000785) +N A C H D E S S E N T O D I M G L E I C H E N J A H R K A M E S K U R Z F R I S T I G A N A N D E R E B E S I T Z E R (cv_deu_000786-cv_deu_000786) +K U R Z D A N A C H G A B E S E I N E N W E R B E S P O T M I T D E M C A N C A N V O N J A C Q U E S O F F E N B A C H (cv_deu_000787-cv_deu_000787) +D A S I S T B E S S E R (cv_deu_000788-cv_deu_000788) +W I E S I E H T E S M I T G L E I T Z E I T A U S (cv_deu_000789-cv_deu_000789) +N A H E D E M D O R F B E F I N D E T S I C H A U C H D E R G R A N D C A N Y O N N A T I O N A L P A R K A I R P O R T (cv_deu_000790-cv_deu_000790) +S I E S O L L E N V E R K Ü N D E N D A S S D I E L I E B E D E N T O D B E S I E G T H A T (cv_deu_000791-cv_deu_000791) +B E D E C K T I S T D I E R E P R Ä S E N T A T I V G E S T A L T E T E V I L L A M I T E I N E M M A N S A R D D A C H (cv_deu_000792-cv_deu_000792) +D I E S E S I E D L U N G I S T M I T D E R O R T S C H A F T D E L L A C H Z U S A M M E N G E W A C H S E N (cv_deu_000793-cv_deu_000793) +W A R T I H R S C H O N E I N M A L I N D E M C L U B (cv_deu_000794-cv_deu_000794) +W O R A U C H I S T I S T A U C H F E U E R (cv_deu_000795-cv_deu_000795) +D I R E K T V O N D E R S T R A S S E W U R D E N S I E V O N A L F R E D B I O L E K F Ü R S E I N E F E R N S E H S H O W S H O W B Ü H N E E N G A G I E R T (cv_deu_000796-cv_deu_000796) +E I N J A H R S P Ä T E R W E C H S E L T E E R Z U H E A L T H N E T U N D E R W U R D E E R F O L G R E I C H E R (cv_deu_000797-cv_deu_000797) +I N D E R L A N D W I R T S C H A F T K A N N D E R E R T R A G D E U T L I C H R E D U Z I E R T W E R D E N (cv_deu_000798-cv_deu_000798) +M A N S O U R S P I E L T E I N S E I N E R H E I M A T S T A D T K A I R O F Ü R A L A H L Y (cv_deu_000799-cv_deu_000799) +E R T R A T D E R F R E I M A U R E R L O G E L A U T A R O B E I (cv_deu_000800-cv_deu_000800) +M I T „ F Ü R S T “ W A R E H E R D I E S O Z I A L E A L S D I E R E C H T L I C H E R O L L E D E S S O B E Z E I C H N E T E N G E M E I N T (cv_deu_000801-cv_deu_000801) +L E T Z T E W O C H E G A B D A S M E T I B E K A N N T D A S S E S V O N A P P L E Ü B E R 3 4 W E I T E R E V O R F Ä L L E V O N Ü B E R H I T Z U N G I N F O R M I E R T W O R D E N W A R D I E D A S U N T E R N E H M E N A L S N I C H T S C H W E R W I E G E N D B E Z E I C H N E T E (fleurs_deu_000378-fleurs_deu_000378) +U S A G Y M N A S T I C S U N T E R S T Ü T Z T D E N B R I E F D E S O L Y M P I S C H E N K O M I T E E S D E R V E R E I N I G T E N S T A A T E N U N D A K Z E P T I E R T E S A L S A B S O L U T E N O T W E N D I G K E I T D A S S S I C H D I E O L Y M P I S C H E F A M I L I E F Ü R E I N S I C H E R E S U M F E L D F Ü R A L L E U N S E R E S P O R T L E R E I N S E T Z T (fleurs_deu_000379-fleurs_deu_000379) +D A D U R C H K A N N E R A B W Ä R T S K O M P A T I B E L M I T 8 0 2 1 1 A 8 0 2 1 1 B U N D 8 0 2 1 1 G S E I N V O R A U S G E S E T Z T D I E B A S I S S T A T I O N V E R F Ü G T Ü B E R D U A L R A D I O (fleurs_deu_000380-fleurs_deu_000380) +E R B E Z E I C H N E T E D I E G E R Ü C H T E A L S P O L I T I S C H E S G E S C H W Ä T Z U N D A L B E R N H E I T (fleurs_deu_000381-fleurs_deu_000381) +L E T Z T E W O C H E G A B D A S M E T I B E K A N N T D A S S E S V O N A P P L E Ü B E R 3 4 W E I T E R E V O R F Ä L L E V O N Ü B E R H I T Z U N G I N F O R M I E R T W O R D E N W A R D I E D A S U N T E R N E H M E N A L S N I C H T S C H W E R W I E G E N D B E Z E I C H N E T E (fleurs_deu_000382-fleurs_deu_000382) +N A C H D E M D E R D A M M 1 9 6 3 E R B A U T W O R D E N W A R K A M E N D I E J A H R E S Z E I T L I C H E N Ü B E R F L U T U N G E N D I E S E D I M E N T E I M F L U S S V E R T E I L E N Z U M S T I L L S T A N D (fleurs_deu_000383-fleurs_deu_000383) +E R W A R A U C H A M S T E C H E N V O N G E L D S C H E I N E N F Ü R V I E L E L Ä N D E R B E T E I L I G T A K T U E L L E B E I S P I E L E S E I N E R A R B E I T S C H L I E S S E N D I E P R E M I E R M I N I S T E R P O R T R A I T S A U F D E R V O R D E R S E I T E D E R K A N A D I S C H E N 5 U N D 1 0 0 D O L L A R N O T E N E I N (fleurs_deu_000384-fleurs_deu_000384) +D I E H A U P T S T A D T V O N M O L D A W I E N I S T K I S C H I N A U D I E E I N H E I M I S C H E S P R A C H E I S T R U M Ä N I S C H A B E R V I E L E M E N S C H E N S P R E C H E N A U C H R U S S I S C H (fleurs_deu_000385-fleurs_deu_000385) +Z W I S C H E N D E N E I N Z E L N E N D Y N A S T I E N H E R R S C H T E N A U C H U N B E S T Ä N D I G E Z E I T E N G E T E I L T E R P R O V I N Z E N D I E B E K A N N T E S T E D I E S E R P E R I O D E N W A R D I E E P O C H E D E R D R E I K Ö N I G R E I C H E D I E 6 0 J A H R E L A N G Z W I S C H E N D E R H A N U N D D E R J I N D Y N A S T I E S T A T T F A N D (fleurs_deu_000386-fleurs_deu_000386) +A M A N D E R E N E N D E D E S S P E K T R U M S V E R W A N D E L T M A N S I C H I N E I N N I C H T W I E D E R Z U E R K E N N E N D E S I N D I V I D U U M D A S A L L E S A N D E R S M A C H E N M U S S A L S D A S T E A M E S G E M A C H T H A T U N D S I C H A L L E S Z U E I G E N M A C H T (fleurs_deu_000387-fleurs_deu_000387) +D I E M E I S T E N I N T E R P R E T A T I O N E N D E S T E C H N O L O G I S C H E N D E T E R M I N I S M U S T E I L E N Z W E I A L L G E M E I N E V O R S T E L L U N G E N E I N E R S E I T S D A S S D I E E N T W I C K L U N G D E R T E C H N O L O G I E S E L B S T E I N E M W E G F O L G T D E R W E I T G E H E N D J E N S E I T S K U L T U R E L L E R O D E R P O L I T I S C H E R E I N F L U S S N A H M E L I E G T U N D A N D E R E R S E I T S D A S S T E C H N O L O G I E I H R E R S E I T S A U S W I R K U N G E N A U F G E S E L L S C H A F T E N H A T D I E E H E R I N H Ä R E N T A L S S O Z I A L B E D I N G T S I N D (fleurs_deu_000388-fleurs_deu_000388) +Z W I S C H E N D E N E I N Z E L N E N D Y N A S T I E N H E R R S C H T E N A U C H U N B E S T Ä N D I G E Z E I T E N G E T E I L T E R P R O V I N Z E N D I E B E K A N N T E S T E D I E S E R P E R I O D E N W A R D I E E P O C H E D E R D R E I K Ö N I G R E I C H E D I E 6 0 J A H R E L A N G Z W I S C H E N D E R H A N U N D D E R J I N D Y N A S T I E S T A T T F A N D (fleurs_deu_000389-fleurs_deu_000389) +D E M L E A K Z U F O L G E B E Z I E H T S I C H D A S D O K U M E N T A U F D E N G R E N Z S T R E I T I N D E M D I E P A L Ä S T I N E N S E R E I N Z U R Ü C K S E T Z E N D E R G R E N Z E N I N D E N Z U S T A N D V O R D E M S E C H S T A G E K R I E G V O N 1 9 6 7 F O R D E R N (fleurs_deu_000390-fleurs_deu_000390) +M I T D E M V E R L U S T G R I E C H I S C H E R S P R A C H K E N N T N I S S E W A R D E R W E S T E N V O N S E I N E N P H I L O S O P H I S C H E N U N D W I S S E N S C H A F T L I C H E N W U R Z E L N I N G R I E C H E N L A N D A B G E S C H N I T T E N (fleurs_deu_000391-fleurs_deu_000391) +W I R S T I M M E N M I T D E R A U S S A G E D E S U S O C Ü B E R E I N D A S S D E N I N T E R E S S E N U N S E R E R A T H L E T E N U N D V E R E I N E U N D I H R E S S P O R T S B E S S E R G E D I E N T I S T W E N N W I R I N N E R H A L B U N S E R E R O R G A N I S A T I O N S I N N V O L L E V E R Ä N D E R U N G E N V O R A N T R E I B E N A N S T A T T E I N E D E Z E R T I F I Z I E R U N G V O R Z U N E H M E N (fleurs_deu_000392-fleurs_deu_000392) +D I E K R E U Z F A H R T E N N A C H S A N K T P E T E R S B U R G B I E T E N A U C H Z E I T F Ü R E I N E N A U F E N T H A L T I N D E R S T A D T K R E U Z F A H R T P A S S A G I E R E S I N D V O N D E R V I S U M P F L I C H T B E F R E I T S I E H E B E D I N G U N G E N (fleurs_deu_000393-fleurs_deu_000393) +R E I S E N D E W E R D E N D R I N G E N D G E W A R N T A U F J E D W E D E A R T V O N U N W E T T E R Z U A C H T E N D I E I H R G E B I E T B E T R I F F T D A D I E S S I C H A U F A L L E R E I S E P L Ä N E A U S W I R K E N K A N N (fleurs_deu_000394-fleurs_deu_000394) +S I E B E S A G T D A S S D E R K R E U Z U N G S P U N K T D E R L I N I E N D I E E I N B I L D V E R T I K A L U N D H O R I Z O N T A L D R I T T E L N D E R E F F E K T I V S T E P L A T Z F Ü R D A S H A U P T M O T I V I S T S I E H E B E I S P I E L (fleurs_deu_000395-fleurs_deu_000395) +S E I T 1 9 8 8 M Ü S S E N W A H L U R N E N T R A N S P A R E N T S E I N D A M I T W Ä H L E R U N D B E O B A C H T E R B E Z E U G E N K Ö N N E N D A S S Z U B E G I N N D E R W A H L K E I N E U M S C H L Ä G E V O R H A N D E N S I N D U N D D A S S K E I N E U M S C H L Ä G E E I N G E W O R F E N W E R D E N A U S S E R J E N E D E R O R D N U N G S G E M Ä S S G E Z Ä H L T E N U N D A U T O R I S I E R T E N W Ä H L E R (fleurs_deu_000396-fleurs_deu_000396) +O T T A W A I S T K A N A D A S B E Z A U B E R N D E Z W E I S P R A C H I G E H A U P T S T A D T U N D Z E I C H N E T S I C H D U R C H E I N E R E I H E V O N K U N S T G A L E R I E N U N D M U S E E N A U S D I E K A N A D A S V E R G A N G E N H E I T U N D G E G E N W A R T P R Ä S E N T I E R E N (fleurs_deu_000397-fleurs_deu_000397) +D I E S E P A A R E K Ö N N E N S I C H F Ü R E I N E N A D O P T I O N S P L A N F Ü R I H R B A B Y E N T S C H E I D E N (fleurs_deu_000398-fleurs_deu_000398) +I N F O L G E D E S S E N S I N D Z W E I F I S C H A R T E N A U S G E S T O R B E N U N D Z W E I W E I T E R E S I N D V O M A U S S T E R B E N B E D R O H T D A R U N T E R D E R G I L A C Y P H A (fleurs_deu_000399-fleurs_deu_000399) +P F L A N Z E N S E H E N I N I H R E R N A T Ü R L I C H E N U M G E B U N G A M B E S T E N A U S W I D E R S T E H E N S I E A L S O D E R V E R S U C H U N G A U C H N U R E I N E X E M P L A R Z U E N T F E R N E N (fleurs_deu_000400-fleurs_deu_000400) +A U F D E R N A H S E I T E K Ö N N T E E S M E H R M A R I A G E B E N D A D I E K R U S T E D Ü N N E R I S T E S W A R E I N F A C H E R F Ü R D I E L A V A A N D I E O B E R F L Ä C H E A U F Z U S T E I G E N (fleurs_deu_000401-fleurs_deu_000401) +E R F Ü G T E H I N Z U D A S S S I E J E D O C H N I C H T D A Z U A U F G E F O R D E R T W E R D E N S O L L T E N V E R P F L I C H T U N G E N E I N Z U G E H E N D I E Ü B E R I H R E N E N T W I C K L U N G S S T A N D I H R E V E R A N T W O R T U N G U N D I H R E F Ä H I G K E I T E N H I N A U S G E H E N (fleurs_deu_000402-fleurs_deu_000402) +V I R T U E L L E H I L F E S T E L L U N G E N S I N D I N D I E S O F T W A R E E I N G E B A U T U N D S O L L E N A R B E I T S S C H R I T T E D I E D E R S C H Ü L E R A L L E I N M Ö G L I C H E R W E I S E N I C H T B E W Ä L T I G E N K A N N H I N T E R F R A G E N N A H E L E G E N U N D E R K L Ä R E N (fleurs_deu_000403-fleurs_deu_000403) +A M 1 5 A U G U S T 1 9 4 0 F I E L E N D I E A L L I I E R T E N I N S Ü D F R A N K R E I C H E I N D I E I N V A S I O N W U R D E O P E R A T I O N D R A G O O N G E N A N N T (fleurs_deu_000404-fleurs_deu_000404) +E R G R I F F A U C H A L L E S A N W A S I N S W A S S E R K A M S E L B S T E I N G R O S S E R D I N O S A U R I E R W I E D E R T R E X W A R I H M N I C H T G E W A C H S E N (fleurs_deu_000405-fleurs_deu_000405) +S E I T D E R G R Ü N D U N G V O N A S U N C I Ó N 1 5 3 7 I S T E S P A R A G U A Y G E L U N G E N V I E L V O N S E I N E M I N D I G E N E N C H A R A K T E R U N D S E I N E R I D E N T I T Ä T Z U B E W A H R E N (fleurs_deu_000406-fleurs_deu_000406) +T R O T Z D E M I S T D E R A N T E I L A N X D R T B I N D E R G E S A M T E N G R U P P E D E R L E U T E M I T T U B E R K U L O S E O F F E N B A R D E N N O C H G E R I N G 6 0 0 0 D E R I N S G E S A M T 3 3 0 0 0 0 L E U T E D I E I N S Ü D A F R I K A Z U E I N E M B E S T I M M T E N Z E I T P U N K T A N G E S T E C K T S I N D (fleurs_deu_000407-fleurs_deu_000407) +A N G E L 2 0 0 6 E R L Ä U T E R T D A S K O N T I N U U M K O N Z E P T A L S E I N E M E T H O D E U M O R G A N I S A T I O N E N Z U H E L F E N L E I S T U N G S F Ä H I G E R Z U W E R D E N (fleurs_deu_000408-fleurs_deu_000408) +I N D I E S E R P E R I O D E D E R E U R O P Ä I S C H E N G E S C H I C H T E S T A N D D I E R E I C H U N D M Ä C H T I G G E W O R D E N E K A T H O L I S C H E K I R C H E A U F D E M P R Ü F S T A N D (fleurs_deu_000409-fleurs_deu_000409) +D I E E R S T E D E R 7 8 E M P F E H L U N G E N I S T D A S S E I N E N E U E D I P L O M A T I S C H E I N I T I A T I V E V O R E N D E D I E S E S J A H R E S E R G R I F F E N W E R D E N S O L L T E U M D I E I R A K I S C H E N G R E N Z E N G E G E N Ü B E R F E I N D L I C H E N I N T E R V E N T I O N E N Z U S I C H E R N U N D D I P L O M A T I S C H E B E Z I E H U N G E N M I T S E I N E N N A C H B A R N W I E D E R H E R Z U S T E L L E N (fleurs_deu_000410-fleurs_deu_000410) +D I E S B I E T E T E I N E G U T E G E L E G E N H E I T D A S N O R D L I C H T Z U S E H E N D A D E R H I M M E L M E H R O D E R W E N I G E R R U N D U M D I E U H R D U N K E L I S T (fleurs_deu_000411-fleurs_deu_000411) +P R O F E S S O R I N P A M E L A F E R G U S O N V O N D E R U N I V E R S I T Y O F D U N D E E M E R K T A N J O U R N A L I S T E N S C H E I N E N E I N E G E F Ä H R L I C H E G R E N Z E Z U Ü B E R S C H R E I T E N W E N N S I E F O T O S U S W V O N V E R D Ä C H T I G E N V E R Ö F F E N T L I C H E N (fleurs_deu_000412-fleurs_deu_000412) +E S K A N N S I C H A U C H L O H N E N E I N E W I L D C A R D Z U K A U F E N D I E Z U T R I T T E N T W E D E R Z U A U S G E W Ä H L T E N P A R K S I N S Ü D A F R I K A O D E R Z U A L L E N S Ü D A F R I K A N I S C H E N N A T I O N A L P A R K S G E W Ä H R T (fleurs_deu_000413-fleurs_deu_000413) +D I E B R Ü C K E S O L L I M S E P T E M B E R 2 0 1 7 V O L L S T Ä N D I G D E N B E T R I E B A U F N E H M E N E S W I R D E R W A R T E T D A S S D I E B R A S I L I A N I S C H E N Z O L L K O N T R O L L P U N K T E D A N N F E R T I G G E S T E L L T S E I N W E R D E N (fleurs_deu_000414-fleurs_deu_000414) +W Ä H R E N D E I N E X P E R I M E N T E L L E R I M P F S T O F F I N D E R L A G E Z U S E I N S C H E I N T D I E E B O L A M O R T A L I T Ä T Z U S E N K E N G I B T E S B I S H E R K E I N E M E D I K A M E N T E D I E A L S E I N D E U T I G Z U R B E H A N D L U N G B E S T E H E N D E R I N F E K T I O N E N G E E I G N E T N A C H G E W I E S E N W U R D E N (fleurs_deu_000415-fleurs_deu_000415) +E I N Ä U S S E R S T L E B H A F T E R D E P E S C H E N W E C H S E L F A N D S T A T T M A N E R W O G D E N P L A N E I N E N A L L G E M E I N E N S T A A T E N K O N G R E S S Z U B E R U F E N U N D K O N N T E S I C H V O R L Ä U F I G N U R N O C H N I C H T Ü B E R D A S V O R Z U L E G E N D E P R O G R A M M U N D D E N O R T D E S Z U S A M M E N T R I T T S E I N I G E N (mls_deu_000281-mls_deu_000281) +E R W U S S T E N I C H T W A S I H M D A S L E B E N K O S T B A R E S G E R A U B T H A T T E S P A N N K R A F T U N D M U T D A S S E S I H N F E I G U N D S C H E U G E M A C H T H A T T E U N F Ä H I G Z U D E N H O H E N D I N G E N Z U D E N E N U N G E T R Ü B T E M I T F R E U D E G E H Ö R T (mls_deu_000282-mls_deu_000282) +D I E S E R J U N G E M A N N H I E S S K A C K E R L I T Z C H E N U N D B E F A N D S I C H G E R A D E A U F D E R W A N D E R S C H A F T A L S I N D E M G E N A N N T E N K Ö N I G R E I C H D I E B E K A N N T M A C H U N G W E G E N D E R P R I N Z E S S I N V E R L E S E N W U R D E E I S A G T E D E R S C H N E I D E R W E N N E S W E I T E R N I C H T S I S T E I N W E I B H A B I C H N O C H N I C H T G E K Ü S S T U N D D E S K Ö N I G S E I D A M Z U W E R D E N D A S G E L Ü S T E T M I C H A L L E R D I N G S (mls_deu_000283-mls_deu_000283) +N O C H F Ü N F M I N U T E N U N D D I E W O L K E N D E R B E W U S S T L O S I G K E I T B E G A N N E N Z U S C H W I N D E N J E T Z T W U S S T E I C H S E H R W O H L D A S S I C H I N M E I N E M E I G E N E N B E T T E L A G U N D D A S S D I E R O T E G L U T N I C H T S A N D E R E S W A R A L S D A S F E U E R I M K A M I N D E R K I N D E R S T U B E E S W A R N A C H T E I N E K E R Z E B R A N N T E A U F D E M T I S C H E (mls_deu_000284-mls_deu_000284) +W E L C H E D I E S E V E R D R Ä N G U N G E N W I E W Ä C H T E R U N T E R H A L T E N K O M M T D A N N I M P U B E R T Ä T S A L T E R D I E H O C H F L U T D E R S E X U E L L E N B E D Ü R F T I G K E I T S O F I N D E T S I E A N D E N G E N A N N T E N S E E L I S C H E N R E A K T I O N S O D E R W I D E R S T A N D S B I L D U N G E N D Ä M M E (mls_deu_000285-mls_deu_000285) +A B E R A F F E N G E H Ö R E N B E I H A G E N B E C K A N D I E K I S T E N W A N D N U N S O H Ö R T E I C H A U F A F F E Z U S E I N E I N K L A R E R S C H Ö N E R G E D A N K E N G A N G D E N I C H I R G E N D W I E M I T D E M B A U C H A U S G E H E C K T H A B E N M U S S D E N N A F F E N D E N K E N M I T (mls_deu_000286-mls_deu_000286) +I S T E S D A S P O R T R Ä T E I N E S M E N S C H E N D E N S I E K E N N E N F R A G T E E L I Z A W E L C H E U N B E M E R K T A N M I C H H E R A N G E T R E T E N W A R I C H E N T G E G N E T E D A S S E S N U R E I N P H A N T A S I E K O P F S E I U N D S C H O B D I E Z E I C H N U N G E I L I G U N T E R D I E A N D E R N B L Ä T T E R N A T Ü R L I C H S P R A C H I C H D I E U N W A H R H E I T D E N N E S W A R E I N S E H R G E T R E U E S P O R T R Ä T M R R O C H E S T E R S (mls_deu_000287-mls_deu_000287) +I C H W E I S S D A S S I C H S E H R K R A N K B I N S A G T E S I E N A C H E I N E R W E I L E V O R E I N P A A R M I N U T E N V E R S U C H T E I C H M I C H I M B E T T E U M Z U D R E H E N U N D F Ü H L T E D A S S I C H K E I N G L I E D M E H R R Ü H R E N K A N N E S W Ä R E G U T W E N N I C H M E I N G E M Ü T E R L E I C H T E R N K Ö N N T E B E V O R I C H S T E R B E (mls_deu_000288-mls_deu_000288) +S O A B E R I S T Z W A R U N S E R W E S E N S G R U N D G O T T S E L B E R D A H E R U M H A T S I C H J E D O C H D E R S C H L A N G E N K N Ä U E L D E S A L T E N S A T A N G E S C H L U N G E N U N D Ü B E R D E M F Ü N K C H E N D E R L I E B E I S T D I E F I N S T E R N I S D E S H A S S E S G E L A G E R T W A S W U N D E R D A N N (mls_deu_000289-mls_deu_000289) +B E S S I E W Ä R E L I E B E R G E B L I E B E N A B E R S I E W A R G E Z W U N G E N Z U G E H E N W E I L D I E P Ü N K T L I C H K E I T B E I D E N M A H L Z E I T E N E I N E S A C H E W A R A U F W E L C H E I N G A T E S H E A D H A L L S T R E N G E G E H A L T E N W U R D E (mls_deu_000290-mls_deu_000290) +A U G E N B L I C K L I C H F Ü H L T E W I E I H R E A N S I C H T E N Ü B E R M I C H I H R E E M P F I N D U N G E N F Ü R M I C H N I C H T U M E I N A T O M V E R Ä N D E R T W A R E N Ü B E R H A U P T K E I N E R Ä N D E R U N G F Ä H I G W A R E N I C H S A H E S I H R E M V E R S T E I N E R T E N A U G E W E L C H E S N I E M A L S D U R C H T R Ä N E N G E N E T Z T N I E M A L S I N Z Ä R T L I C H K E I T A U F G E L E U C H T E T H A T T E A N (mls_deu_000291-mls_deu_000291) +B R U D E R S A M I S T S E H R G U T W E N N D E R H Ä U P T L I N G I H N E R F Ä H R T W I R D E R S I C H F R E U E N U N D W I R W E R D E N S C H N E L L D A N A C H H A N D E L N S O W O L L E N W I R A U F B R E C H E N U N D S C H N E L L R E I T E N D A M I T W I R N O C H V O R N A C H T D A S L A G E R E R R E I C H E N W I R S T I E G E N A U F D I E P F E R D E D I E N U N A U S G E R U H T H A T T E N U N D F L O G E N I M G A L O P P D A V O N D I E S M A L H Ü T E T E N W I R U N S D E R F Ä H R T E W I E D E R D I R E K T Z U F O L G E N W I R R I T T E N G E R A D E A U S U N D E R S P A R T E N U N S (mls_deu_000292-mls_deu_000292) +W E I L D I E A B E R M I T P E C H B E S T R I C H E N W A R B L I E B E I N E R V O N D E N G O L D E N E N P A N T O F F E L N F E S T H Ä N G E N U N D I N D E R A N G S T D A C H T E S N I C H T D A R A N I H N M I T Z U N E H M E N U N D W I E E S D E N L E T Z T E N S C H R I T T V O N D E R T R E P P E T A T D A H A T T E E S Z W Ö L F A U S G E S C H L A G E N D A W A R W A G E N U N D P F E R D E V E R S C H W U N D E N U N D A S C H E N P U T T E L S T A N D I N S E I N E N A S C H E N K L E I D E R N A U F D E R D U N K E L N S T R A S S E (mls_deu_000293-mls_deu_000293) +I L L N A H M D A S G L A S V O M A U G E E I N F I N S T E R E R E R N S T L A G E R T E Ü B E R S E I N E N Z Ü G E N E S I S T S C H R E C K L I C H S A G T E E R I C H H A B D A S M E I N I G E G E T A N U M B L U T V E R G I E S S E N Z U V E R M E I D E N (mls_deu_000294-mls_deu_000294) +N U R D E R D O K T O R U N D D I E W Ä R T E R I N S O L L E N V O R S E I N E A U G E N K O M M E N E R K L Ä R T E D I E T R I N E I N G R O S S E M A M T S E I F E R D A M I T W A R D I E F R A U O B E R S T G A N Z E I N V E R S T A N D E N U N D H Ö C H S T E R F R E U T K E H R T E S I E M I T I H R E N (mls_deu_000295-mls_deu_000295) +K W A R U N T R Ö S T L I C H Ü B E R D I E L A G E D E S K Ü N S T L E R S E R B E G A N N Z U W E I N E N U N D S C H L U C H Z T E L A N G E I N D I E V O R G E H A L T E N E N H Ä N D E D E R K Ü N S T L E R W A R T E T E B I S K S I C H B E R U H I G T H A T T E U N D E N T S C H L O S S S I C H D A N N D A E R K E I N E N A N D E R E N A U S W E G F A N D D E N N O C H Z U M W E I T E R S C H R E I B E N (mls_deu_000296-mls_deu_000296) +V O N D E N P F E R D E H E R D E N D E R A P A C H E N U N D S A G T E N U N S D A S S S I E F Ü R E I N A P A C H E N P F E R D U N S E B E N S O V I E L E W A R E N U N D B R A N D Y G E B E N W Ü R D E N W I E F Ü R E I N K I O W A P F E R D D A S I N D U N S E R E K R I E G E R F O R T U M A P A C H E N P F E R D E Z U H O L E N A L S O R I C H T I G W E R W A R S C H U L D A N D E M T O D E D E R B I S H E R G E F A L L E N E N U N D A N D E M B L U T V E R G I E S S E N W E L C H E S N U N B E V O R S T A N D W E I S S E P F E R D E H Ä N D L E R (mls_deu_000297-mls_deu_000297) +D A S A M A Z O N E N H Ü T C H E N V O N S C H W A R Z E M S A M M E T G R A Z I Ö S A U F I H R E L A N G E N L O C K E N G E D R Ü C K T D I E I H R E W A N G E N U M F L O S S E N U N D Ü B E R I H R E S C H U L T E R N H E R A B W A L L T E N S O T R A T S I E I N D A S E I N F A C H E L Ä N D L I C H E G E B Ä U D E U N D S C H W E B T E Z W I S C H E N D E N R E I H E N D E R H A L B G E B L E N D E T E N D O R F K I N D E R A U F U N D A B (mls_deu_000298-mls_deu_000298) +D U M U S S T E R S T E N T S A G E N A L L E M S Ü N D H A F T E N S T R E B E N U N D I N T I E F E R R E U E U N D D E M U T D I E F Ü R B I T T E D E R H E I L I G E N E R F L E H E N G E G E N D I E D U G E F R E V E L T H A S T D I E J Ü N G L I N G E W E L C H E F R A N C E S K O S O L A N G E G E F L O H E N S U C H T E N I H N A U F I N S E I N E R W E R K S T A T T U N D F A N D E N I H N (mls_deu_000299-mls_deu_000299) +E R L I E S S S E I N E G R E T E L N I C H T F O R T S C H L E P P E N A M A L L E R W E N I G S T E N A B E R I N D E N G R O S S E N V O G E L B A U E R W O S I E A L L E I N E I N E M T O N E P F E I F E N M U S S T E N W I E E R S T E T S S A G T E (mls_deu_000300-mls_deu_000300) +F R A N C E S K O M A L T E I N U N H E I L I G E R B E G E I S T E R U N G V I E L E B I L D E R A U S D E R L Ü G E N H A F T E N F A B E L W E L T K E I N E R A L S E R V E R M O C H T E D I E B U H L E R I S C H E Ü P P I G K E I T D E R W E I B L I C H E N G E S T A L T E N S O W A H R H A F T D A R Z U S T E L L E N I N D E M E R V O N L E B E N D E N M O D E L L E N D I E K A R N A T I O N V O N D E N A L T E N M A R M O R B I L D E R N A B E R F O R M U N D B I L D U N G E N T N A H M (mls_deu_000301-mls_deu_000301) +B E W E G U N G U N D T A T D E N E R S T E N Z U G J A E S S T I M M T E D I E V O R H I N A N G E G E B E N E N I N G R E D I E N Z I E N N Ä M L I C H R Ü B E N H A N F E I C H E L N U N D S A U E R A M P F E R W A R E N A L L E I N D E M P F E I F E N K O P F E A N W E S E N D A B E R E I N E N F Ü N F T E N H A U P T S T O F F H A T T E I C H N I C H T G E N A N N T J E T Z T R O C H U N D S C H M E C K T E I C H D A S S A U C H E I N S T Ü C K C H E N F I L Z S C H U H D A B E I S E I N M Ü S S E I C H B L I E S D E N R A U C H A U C H G E G E N D E N H I M M E L U N D G E G E N D I E (mls_deu_000302-mls_deu_000302) +U N D D A S F E U E R S T A N D A U F U N D F L A C K E R T E U N D K O C H T E D A S E S S E N F E R T I G U N D D E R B R A T E N B R U T Z E L T E F O R T U N D D E R K O C H G A B D E M K Ü C H E N J U N G E N E I N E O H R F E I G E U N D D I E M A G D R U P F T E D A S H U H N F E R T I G D A W A R D D I E H O C H Z E I T V O N D E M K Ö N I G S S O H N M I T D O R N R Ö S C H E N G E F E I E R T U N D S I E L E B T E N V E R G N Ü G T B I S A N I H R E N D E (mls_deu_000303-mls_deu_000303) +U N D D A S S E R M I R N I C H T N A C H T R A G E N W O L L E W E N N I C H W I D E R S P E N S T I G W A R G E G E N S E I N E N W O H L M E I N E N D E N R A T D E R H E R R P F A R R E R H A T J A I N A L L E M R E C H T G E H A B T U N D I C H W A R I M U N R E C H T A B E R (mls_deu_000304-mls_deu_000304) +O B G L E I C H S E I N E M A S S E N U R W E N I G E G R A M M B E T R U G E R B R E I T E T E S I C H K E G E L F Ö R M I G A U S U N D M U S S T E D A H E R D A S I H M E N T G E G E N F L I E G E N D E S P R E N G G E S C H O S S A U F F A N G E N U N D Z U R R U H E B R I N G E N (mls_deu_000305-mls_deu_000305) +D E R F U C H S R E I C H T E S A M D I E U N F R I E D L I C H E F R I E D E N S P F E I F E H I N D E R M A N N T A T W A C K E R S E I N E S E C H S Z Ü G E U N D S A G T E D E R G R O S S E G E I S T A C H T E T N I C H T A U F D I E V E R S C H I E D E N E H A U T D E R M E N S C H E N D E N N D I E K Ö N N E N S I C H M I T F A R B E B E S C H M I E R E N U M I H N Z U T Ä U S C H E N S O N D E R N E R S I E H T D A S H E R Z A N D I E H E R Z E N D E R K R I E G E R V O M B E R Ü H M T E N S T A M M E D E R K I O W A S S I N D T A P F E R U N E R S C H R O C K E N U N D T R E U D A S M E I N I G E H Ä N G T (mls_deu_000306-mls_deu_000306) +A L L E S W A S W I R M I T I H R B E G E G N E T S C H I E B T S I C H D U R C H U N D Ü B E R E I N A N D E R B A L D U N T E R S C H R E I B E N W I R E I N E N K O N T R A K T D A I S T I H R E H A N D U N D D I E M E I N I G E I H R N A M E U N D D E R M E I N I G E B E I D E L Ö S C H E N E I N A N D E R A U S B E I D E V E R S C H L I N G E N S I C H (mls_deu_000307-mls_deu_000307) +E R M Ü S S T E D E N E I N F A C H E N C H R O N I K E N C H O R A L D E S M A L E R S M I T A L L E R L E I E R K L Ä R U N G E N U N D Z U R E C H T W E I S U N G E N W I E M I T K R A U S E N F I G U R E N V E R S C H N Ö R K E L N U N D V E R B R Ä M E N I C H T R E T E I N D I E P E R S O N D E S H E R A U S G E B E R S U N D B I T T E D I C H G Ü N S T I G E R L E S E R D U W O L L E S T E H E D U W E I T E R L I E S E S T F O L G E N D E S D I R G Ü T I G S T M E R K E N (mls_deu_000308-mls_deu_000308) +D I E H O F D A M E N B E K A M E N K R Ä M P F E U N D D I E K Ö N I G I N U N D D I E P R I N Z E S S I N N E N D I E I H R E A L L E R L I E B S T E N H Ü N D C H E N W Ä H R E N D D E R M A H L Z E I T A U F D E N S C H O S S G E N O M M E N H A T T E N B E M E R K T E N Z U I H R E M S C H R E C K E N D A S S D I E L I L A A M A R A N T F A R B E N E N U N D O R A N G E G E L B E N S E I D E N K L E I D E R A L L E D I C H T B E S Ä T M I T D E N H Ä S S L I C H S T E N Ö L F L E C K E N W A R E N (mls_deu_000309-mls_deu_000309) +V O N L I E D E R N D I E S I E S I N G E N U N D K L A V I E R P I E C E N D I E S I E S P I E L E N V O N G E L D B Ö R S E N D I E S I E H Ä K E L N V O N F R A N Z Ö S I S C H E N B Ü C H E R N D I E S I E Ü B E R S E T Z E N K O N N T E B I S M E I N G E M Ü T W Ä H R E N D I C H L A U S C H T E Z U R N A C H A H M U N G A U F G E S T A C H E L T W U R D E (mls_deu_000310-mls_deu_000310) +A R M E U N D N A C K E N W A R E N B L O S S I H R E I N Z I G E R S C H M U C K W A R E N I H R E K A S T A N I E N B R A U N E N F L E C H T E N W E L C H E I N W I L D E R U N D N A T Ü R L I C H E R A N M U T A U F I H R E S C H U L T E R N H E R A B F I E L E N I C H N A H M E I N E N B O G E N F E I N E N K A R T O N S U N D Z E I C H N E T E M I T G R O S S E R S O R G F A L T D I E U M R I S S E (mls_deu_000311-mls_deu_000311) +A B E R W E D E R A U S D E U T S C H L A N D N O C H A U S I R G E N D E I N E M A N D E R E N S T A A T K O N N T E M A N E R F A H R E N W A S D E R G E G E N S T A N D U N D D A S R E S U L T A T D I E S E R U N T E R R E D U N G E N G E W E S E N S E I M A N V E R M U T E T E D A S S E S S I C H U M E R K L Ä R U N G E N D E R M A R T I E R Ü B E R I H R E A B S I C H T E N U N D U M D I E V E R M I T T L U N G D E R M Ä C H T E Z W I S C H E N D E N M A R S S T A A T E N U N D G R O S S B R I T A N N I E N H A N D L E (mls_deu_000312-mls_deu_000312) +L A S S U N S W E N I G S T E N S E I N E Z E I T L A N G V E R S U C H E N I N W I E F E R N W I R A U F D I E S E W E I S E M I T E I N A N D E R A U S R E I C H E N D A D A S Z U S A M M E N H Ä N G E N D E W I E D U S A G S T E I G E N T L I C H E U E R E L E M E N T I S T V E R S E T Z T E (mls_deu_000313-mls_deu_000313) +V E R S C H I E D E N E V O R K O M M N I S S E F Ü H R T E N Z U D E R V E R M U T U N G D A S S F R A U W I E S E D I E K L E I N E N W E S E N V E R B R E N N E S I E S O L L B I S W E I L E N S O S T A R K G E H E I Z T H A B E N D A S S D I E H E R D P L A T T E N Z E R S P R A N G E N A U S S E R D E M S O L L E I N F Ü R C H T E R L I C H E R G E R U C H W A H R G E N O M M E N W O R D E N S E I N (mls_deu_000314-mls_deu_000314) +U N D G I N G D E M S C H R E I E N N A C H S O S A H E R E N D L I C H E I N E N H O H E N B A U M U N D O B E N D A R A U F S A S S E I N K L E I N E S K I N D U N T E R D E M B A U M A B E R L A G E I N E F R A U D I E S C H L I E F (mls_deu_000315-mls_deu_000315) +S I E H A T T E N S O E B E N D I E F I S C H E R G A R N E W E L C H E D I E N A C H T Ü B E R A U S G E W O R F E N W A R E N H E R E I N G E Z O G E N D I E S E E L E U T E G E H Ö R T E N A U G E N S C H E I N L I C H V E R S C H I E D E N E N N A T I O N E N A N O B W O H L D E R E U R O P Ä I S C H E C H A R A K T E R B E I A L L E N A U S G E D R Ü C K T W A R (mls_deu_000316-mls_deu_000316) +N E I N N E I N I C H S C H Ä M E M I C H L A S S M I C H A N D E I N E M B U S E N M E I N G E S I C H T V E R B E R G E N E R S I N K T I N S G R A S N I E D E R U N D Z I E H T S I E N A C H (mls_deu_000317-mls_deu_000317) +D I E K I N D E R A B E R S A S S E N V O R D E M W A L D U N D A L S S I E D I E D R E I K N E C H T E V O N W E I T E M L A U F E N S A H E N S P R A C H L E H N C H E N Z U M F U N D E V O G E L V E R L Ä S S T D U M I C H N I C H T S O V E R L A S S I C H D I C H A U C H N I C H T S O S P R A C H F U N D E V O G E L N U N U N D N I M M E R M E H R (mls_deu_000318-mls_deu_000318) +W I E D E R S C H U L Z E I N S E I N E R H U L D I G U N G S R E D E H E R V O R H O B D E R L E H R E R B R A C H T E A M K L A R E N S O M M E R M O R G E N M I T S E I N E N S C H U L K I N D E R N E I N G E S A N G S S T Ä N D C H E N (mls_deu_000319-mls_deu_000319) +W I E S I E S E I N S O L L T E N (swc_deu_001408-swc_deu_001408) +D E R E N S C H W I N G U N G E N D U R C H E I N E Z U S A T Z S C H A L T U N G S T U F E N L O S (swc_deu_001409-swc_deu_001409) +D I E A U F A L L E B E I D E R S I T Z V E R T E I L U N G Z U (swc_deu_001410-swc_deu_001410) +U M D E N Ü B E R L E B E N D E N D E R (swc_deu_001411-swc_deu_001411) +S P Ä T E R W U R D E N T E I L W E I S E S O G A R A C H T P A R A L L E L E L O C H S T R E I F E N E I N G E S E T Z T (swc_deu_001412-swc_deu_001412) +M O R D E B E K A N N T U N D V E R L A N G T E (swc_deu_001413-swc_deu_001413) +B W A H L G D I E S T I M M E N V O N W Ä H L E R N (swc_deu_001414-swc_deu_001414) +G E S C H I C H T E (swc_deu_001415-swc_deu_001415) +S P A L T U N G F Ä H I G (swc_deu_001416-swc_deu_001416) +S T A D T P A D E R B O R N D I E Ä U S S E R E N F E I E R N D E S (swc_deu_001417-swc_deu_001417) +W E I T E R H I N H U M A N I T Ä R E H I L F E Z U (swc_deu_001418-swc_deu_001418) +S I E E R K A N N T E N D I E N E U E C H I N E S I S C H E R E G I E R U N G N I C H T A N (swc_deu_001419-swc_deu_001419) +D I E U R A U F F Ü H R U N G F A N D A M D R E I U N D Z W A N Z I G S T E S E P T E M B E R Z W E I T A U S E N D A C H T I N (swc_deu_001420-swc_deu_001420) +E R W I L L S I C H N I C H T S C H U L D I G O D E R M I T S C H U L D I G M A C H E N A M T O D E E I N E S M I T G E S E L L E N (swc_deu_001421-swc_deu_001421) +D I E M I T D E R E R S T S T I M M E E I N E N (swc_deu_001422-swc_deu_001422) +U N D H A L F E N D I E S E N B E I D E R (swc_deu_001423-swc_deu_001423) +K R E I S W A H L V O R S C H L A G U N D E I N E L A N D E S L I S T E U N T E R Z E I C H N E N (swc_deu_001424-swc_deu_001424) +E I N E U M S E T Z U N G D E R S A G E I N F O R M E I N E S F Ü N F Z E H N T E I L I G E N L I E D E R Z Y K L U S Z W E I T A U S E N D A C H T W U R D E P R E U S S L E R S K R A B A T I N E I N E R B E A R B E I T U N G V O N H O R S T H A W E M A N N (swc_deu_001425-swc_deu_001425) +W I E D I E F O L G E N D E T A B E L L E D A R S T E L L T (swc_deu_001426-swc_deu_001426) +Z U M S T R O M F L U S S B E I (swc_deu_001427-swc_deu_001427) +D E M B U N D E S W A H L L E I T E R B I S Z U M S I E B E N U N D N E U N Z I G S T E T A G (swc_deu_001428-swc_deu_001428) +V O L L J Ä H R I G G E W O R D E N E D E U T S C H E N I C H T M I T W Ä H L E N (swc_deu_001429-swc_deu_001429) +A U S F Ü H R U N G M U S S E I N G U T E R Q U A R T E R B A C K I N (swc_deu_001430-swc_deu_001430) +V E R G L E I C H B A R E N Z A H L E N W E R T U M G E W A N D E L T (swc_deu_001431-swc_deu_001431) +B E T R A C H T E T E A L L G E M E I N H E I T (swc_deu_001432-swc_deu_001432) +U N T E R S C H I E D L I C H E A U F F A S S U N G E N G A B E S N U R D A R Ü B E R (swc_deu_001433-swc_deu_001433) +D O L L B E I M B U N D E S L I G I S T E N B O R U S S I A D O R T M U N D N A C H F O L G E R D E S U N M I T T E L B A R Z U V O R Z U R Ü C K G E T R E T E N E N T R A I N E R S J Ü R G E N R Ö B E R (swc_deu_001434-swc_deu_001434) +N E U N Z E H N H U N D E R T A C H T U N D A C H T Z I G (swc_deu_001435-swc_deu_001435) +F R E I E N E N Z Y K L O P Ä D I E (swc_deu_001436-swc_deu_001436) +D E R P H O T O S T R O M I S T Ü B E R V I E L E G R Ö S S E N O R D N U N G E N L I N E A R Z U M L I C H T E I N F A L L (swc_deu_001437-swc_deu_001437) +D A S H A T T E F Ü R K L E I N E P A R T E I E N G R O S S E A U S W I R K U N G E N (swc_deu_001438-swc_deu_001438) +I S T D I E I T E R A T I V E T I E F E N S U C H E (swc_deu_001439-swc_deu_001439) +D I E S K Ö N N E N Z U M B E I S P I E L K O N D E N S A T O R E N S E I N (swc_deu_001440-swc_deu_001440) +A L S D I E K U R S A U F K U B A H A L T E N D E N S O W J E T I S C H E N S C H I F F E A B D R E H T E N (swc_deu_001441-swc_deu_001441) +B U N D E S T A G S W A H L N E U N Z E H N H U N D E R T D R E I U N D F Ü N F Z I G W U R D E E R S T M A L S N A C H E I N E M V O M B U N D E S T A G S E L B S T E R L A S S E N E N G E S E T Z (swc_deu_001442-swc_deu_001442) +B U N D E S W A H L G E S E T Z V I E L F A C H G E Ä N D E R T W O R D E N (swc_deu_001443-swc_deu_001443) +E R Ü B E R L A G E R T D E N P H O T O S T R O M U N D T R Ä G T (swc_deu_001444-swc_deu_001444) +T R O T Z I N T E G R A T I O N D E R B E I D E N D E U T S C H E N S T A A T E N (swc_deu_001445-swc_deu_001445) +B E R L I N E R W Ü H L M Ä U S E N S T A T T (swc_deu_001446-swc_deu_001446) +O F F I Z I E L L E F Ü H R U N G E N (swc_deu_001447-swc_deu_001447) +B E I D E R V E R H Ä L T N I S W A H L W I R D Z U S Ä T Z L I C H D I E E I N H A L T U N G D E R (swc_deu_001448-swc_deu_001448) +W I E W E N I G D I E I N S U L A N E R N O C H A M P U L S D E R Z E I T (swc_deu_001449-swc_deu_001449) +J E D O C H E T W A D I E D U R C H F Ü H R U N G V O N W A H L W E R B U N G A U F K O S T E N D E S S T A A T E S (swc_deu_001450-swc_deu_001450) +D A S N I C H T I M G R U N D G E S E T Z (swc_deu_001451-swc_deu_001451) +H E I M A T V E R T R I E B E N U N D H Ä U S L I C H E G E W A L T (swc_deu_001452-swc_deu_001452) +U N D S P E I C H E R E I H N I N E I N E R W A R T E S C H L A N G E A B (swc_deu_001453-swc_deu_001453) +O R I G I N A L T O N B Ä N D E R U N D D I E D O K U M E N T A T I O N D E S S T U D I O S W U R D E N N E U N Z E H N H U N D E R T Z W E I U N D S I E B Z I G I N D A S S I E M E N S A R C H I V Ü B E R S T E L L T (swc_deu_001454-swc_deu_001454) +S O M Ü S S E N A U F E I N E M S T R A T E G I S C H E N R A K E T E N U B O O T (swc_deu_001455-swc_deu_001455) +F L Ö T E N S P I E L Ä H N L I C H E (swc_deu_001456-swc_deu_001456) +D R A S T I S C H M O D E R N E E L E K T R O N I S C H E K L A N G G E S T A L T U N G (swc_deu_001457-swc_deu_001457) +A N S C H L I E S S E N D W U R D E N D I E S O E R M I T T E L T E M A N D A T S Z A H L J E D E R P A R T E I N A C H D E M S E L B E N V E R F A H R E N E N T S P R E C H E N D D E R A N Z A H L I H R E R Z W E I T S T I M M E N P R O P O R T I O N A L A U F D I E L A N D E S L I S T E N D E R P A R T E I U N T E R V E R T E I L T (swc_deu_001458-swc_deu_001458) +O P F E R N D E R N A T O B O M B A R D I E R U N G U N T E R K Ü N F T E (swc_deu_001459-swc_deu_001459) +D E R F R E I E N E N Z Y K L O P Ä D I E (swc_deu_001460-swc_deu_001460) +M I T T L E R W E I L E F I N D E N (swc_deu_001461-swc_deu_001461) +W E R W E G E N E I N E S V E R B R E C H E N S R E C H T S K R Ä F T I G Z U E I N E R F R E I H E I T S S T R A F E V O N M I N D E S T E N S E I N E M (swc_deu_001462-swc_deu_001462) +D E R G E S C H W I N D I G K E I T S W E R T U N G E R R A N G E N D R E I B F E I N H U N D E R T A C H T (swc_deu_001463-swc_deu_001463) +L I B O R I U S A M E R S T E N L I B O R I S A M S T A G (swc_deu_001464-swc_deu_001464) +N A C H D E M S A I N T E L A G U Ë V E R F A H R E N A U F D I E L Ä N D E R V E R T E I L T (swc_deu_001465-swc_deu_001465) +R E F O R M E N G O R B A T S C H O W S U N D A B R Ü S T U N G S S C H R I T T E (swc_deu_001466-swc_deu_001466) +N U L L U N P O R T E D U N D U N T E R D E R (swc_deu_001467-swc_deu_001467) +A N D E M W E S T L I C H E K R Ä F T E A U F G E G E N R E V O L U T I O N Ä R E R (swc_deu_001468-swc_deu_001468) +W I R D U N T E R A N D E R E M V E R W E N D E T (swc_deu_001469-swc_deu_001469) +A U S W I K I P E D I A (swc_deu_001470-swc_deu_001470) +U N D K U B A K R I S E (swc_deu_001471-swc_deu_001471) +L E T Z T E R W A H L A U F G R U N D E I G E N E R W A H L V O R S C H L Ä G E U N U N T E R B R O C H E N M I T M I N D E S T E N S F Ü N F A B G E O R D N E T E N V E R T R E T E N S I N D (swc_deu_001472-swc_deu_001472) +V E R B R E I T U N G I D E O L O G I S C H E R P R O P A G A N D A D E R S U P E R M Ä C H T E U N D (swc_deu_001473-swc_deu_001473) +W E B C O M I C S A U F D I E R E A L I T Ä T Ü B E R T R A G E N (swc_deu_001474-swc_deu_001474) +A L S D E R K A L T E K R I E G S I C H F O R T W Ä H R E N D Z U S P I T Z E (swc_deu_001475-swc_deu_001475) +S I C H E R H E I T S P E R S O N A L O D E R W A C H H U N D E N N U R S E H R S C H W I E R I G B E T R E T E N W E R D E N (swc_deu_001476-swc_deu_001476) +D A U E R H A F T E S B L E I B E R E C H T U N D (swc_deu_001477-swc_deu_001477) +E B E N S O W I E D A S M O T I V D E R E R L Ö S U N G D U R C H (swc_deu_001478-swc_deu_001478) +W E N N F Ü R N I E M A N D E N N A C H P R Ü F B A R I S T (swc_deu_001479-swc_deu_001479) +P R I V A T E E R F O R S C H U N G V O N E I N R I C H T U N G E N (swc_deu_001480-swc_deu_001480) +A B G E S E H E N D A V O N W Ü R D E N S E L B S T D A N N N O C H D I E E N T S P R E C H E N D E N P A L C O D E S F E H L E N (swc_deu_001481-swc_deu_001481) +S P R E C H E N B E N Ö T I G T E A T E M L U F T L I E F E R T (swc_deu_001482-swc_deu_001482) +M Ö G L I C H E N S C H U T Z I M P F U N G E N G E G E N K R A N K H E I T E N (swc_deu_001483-swc_deu_001483) +S C H O N E I N E N Ä H N L I C H E N V E R S U C H G A B (swc_deu_001484-swc_deu_001484) +A N E I N E M P N Ü B E R G A N G O D E R P I N Ü B E R G A N G D U R C H D E N I N N E R E N P H O T O E F F E K T I N E I N E N E L E K T R I S C H E N S T R O M U M W A N D E L T (swc_deu_001485-swc_deu_001485) +B E I M M E I S T E R I N D E R S I L V E S T E R N A C H T F R E I B I T T E N (swc_deu_001486-swc_deu_001486) +J A H R E N D E R B E G R I F F V A D D I N G (swc_deu_001487-swc_deu_001487) +R A N G V E R H Ä L T N I S U N T E R D E N S T I M M E N N O C H E I N E L O G I S C H E A B F O L G E (swc_deu_001488-swc_deu_001488) +K R A B A T L E H N T D I E S E S A N G E B O T J E D O C H M I T E N T S C H I E D E N H E I T A B (swc_deu_001489-swc_deu_001489) +S T A N D V O M D E R I N H A L T S T E H T U N T E R (swc_deu_001490-swc_deu_001490) +O R G A N I S A T I O N U N T E R B R A C H D A R A U F H I N D I E (swc_deu_001491-swc_deu_001491) +V E R B Ü N D E T S I N D O D E R G A R F Ü R S I E A R B E I T E N (swc_deu_001492-swc_deu_001492) +F E S T G E L E G T E V O L L J Ä H R I G K E I T S A L T E R (swc_deu_001493-swc_deu_001493) +D I E E R R I C H T U N G D E R B E R L I N E R M A U E R M Ü N D E T E N (swc_deu_001494-swc_deu_001494) +E R R I C H T U N G V O N K L Ä R A N L A G E N (swc_deu_001495-swc_deu_001495) +A F G H A N I S T A N S U N D I M I R A K H A T S I C H S E I T D E M E I N M A R S C H (swc_deu_001496-swc_deu_001496) +D E R P H O N A T I O N S S T R O M V O N D E N L U N G E N Ü B E R D I E B R O N C H I E N B I S (swc_deu_001497-swc_deu_001497) +A U S S E R D E M N A H M E N S E N D E R H Ö R S P I E L E M I T V E R F R E M D E T E R S P R A C H E (swc_deu_001498-swc_deu_001498) +U N D D I E G R U N D M A N D A T S K L A U S E L (swc_deu_001499-swc_deu_001499) +K E I N E A B K E H R V O N D E N G R U N D L A G E N D E S S O Z I A L I S M U S E I N S C H L I E S S E (swc_deu_001500-swc_deu_001500) +M I T K O M P O N E N T E N S O W O H L A N A L S A U C H T I E F I N D E R W A F F E (swc_deu_001501-swc_deu_001501) +B E D E U T U N G S V O L L W A R (swc_deu_001502-swc_deu_001502) +F R E I W I L L I G E H E L F E R D E R (swc_deu_001503-swc_deu_001503) +U M E L E K T R O N E N V O M V A L E N Z B A N D I N S L E I T U N G S B A N D (swc_deu_001504-swc_deu_001504) +A L L E R D I N G S S I N D V E R G L E I C H B A R E E F F E K T E M Ö G L I C H (swc_deu_001505-swc_deu_001505) +D I E S E K O N N T E N A B E R A L S E I N G A B E I N E I N E N F R E Q U E N Z U M S E T Z E R D I E N E N O D E R S T E U E R T E N S Y N C H R O N M O T O R E N (swc_deu_001506-swc_deu_001506) +T H O M A S H E R M A N N S P R O D U Z I E R T E Z W E I T A U S E N D Z W E I M I T G R E B E (swc_deu_001507-swc_deu_001507) +P N Ü B E R G A N G T R E F F E N (swc_deu_001508-swc_deu_001508) +D I E F A L K E N H O R S T S H O W (swc_deu_001509-swc_deu_001509) +A N T I S O W J E T I S C H E D E M O N S T R A T I O N E N W U R D E N B L U T I G N I E D E R G E S C H L A G E N (swc_deu_001510-swc_deu_001510) +E I N V I E R K A N A L M I S C H P U L T D I E N T E F Ü R K L E I N E R E (swc_deu_001511-swc_deu_001511) +D I E S E H Ä T T E N D I E V O R W A R N Z E I T E N F Ü R E I N E N A N G R I F F A U F D I E U S A E X T R E M H E R A B G E S E T Z T (swc_deu_001512-swc_deu_001512) +W E L C H E S A M N Ä C H S T E N Z U M S T A R T K N O T E N L I E G T (swc_deu_001513-swc_deu_001513) +L A Z I O G I N G D O L L Z U R Ü C K I N D I E B U N D E S L I G A U N D W E C H S E L T E Z U E I N T R A C H T (swc_deu_001514-swc_deu_001514) +Ü B E R D I E S E K R A N K H E I T (swc_deu_001515-swc_deu_001515) +J A H R Z W E I T A U S E N D F Ü N F K R I T I S I E R T E (swc_deu_001516-swc_deu_001516) +D I E S E A U F F A S S U N G Z U R N E U T R A L I T Ä T U N T E R S C H E I D E T (swc_deu_001517-swc_deu_001517) +R I E D L W U R D E A L S K Ü N S T L E R I S C H E R L E I T E R D E S S I E M E N S S T U D I O S B E S T E L L T (swc_deu_001518-swc_deu_001518) +W E N N M A N D I E W E L T A L S G A N Z E S B E T R A C H T E T (swc_deu_001519-swc_deu_001519) +S I N D K R I T I S C H E K O M P O N E N T E N D E S D E T O N A T I O N S S Y S T E M S A B S I C H T L I C H S C H W A C H E N T W O R F E N (swc_deu_001520-swc_deu_001520) +N I C H T W Ä H L B A R I S T J E D O C H (swc_deu_001521-swc_deu_001521) +E R B O T E I N E V E R E I N I G U N G D E U T S C H L A N D S A N (swc_deu_001522-swc_deu_001522) +B E R L I N Z W E I T A U S E N D F Ü N F (swc_deu_001523-swc_deu_001523) +K E R N A B G E S T I M M T U N D U M H Ü L L E N D I E S E N E N T S P R E C H E N D (swc_deu_001524-swc_deu_001524) +E R Z E U G U N G V O N D Y N A M I K A U S (swc_deu_001525-swc_deu_001525) +Z I M T U N D I N G W E R (swc_deu_001526-swc_deu_001526) +V O N S C H W E R E R U N T E R E R N Ä H R U N G (swc_deu_001527-swc_deu_001527) +N Ü S S E N U N D G E W Ü R Z E N (swc_deu_001528-swc_deu_001528) +R O B E R T F K E N N E D Y (swc_deu_001529-swc_deu_001529) +K A M S C H L I E S S L I C H Z U M (swc_deu_001530-swc_deu_001530) +V O L L S T Ä N D I G K E I T (swc_deu_001531-swc_deu_001531) +S T A N D E N S I C H V O N D E N U S A (swc_deu_001532-swc_deu_001532) +A F R I K A S Ü D L I C H D E R S A H A R A G E O R T E T (swc_deu_001533-swc_deu_001533) +D I E A R M E E M E U T E R T E (swc_deu_001534-swc_deu_001534) +S T A L I N S E T Z T E I M (swc_deu_001535-swc_deu_001535) +V E R H Ä L T N I S A U S G L E I C H (swc_deu_001536-swc_deu_001536) +P R O S C R I B E D G L E I C H (swc_deu_001537-swc_deu_001537) +A M Z W E I T E J U N I Z W E I T A U S E N D V I E R W U R D E N (swc_deu_001538-swc_deu_001538) +I N D E N B U N D E S T A G N A C H R Ü C K T (swc_deu_001539-swc_deu_001539) +D I E N A T O O S T E R W E I T E R U N G U N D D I E E I N S E I T I G E A U F K Ü N D I G U N G D E S (swc_deu_001540-swc_deu_001540) +H I E R B E I I S T (swc_deu_001541-swc_deu_001541) +D I E S E R S T E L L E K A M E N S Ä M T L I C H E M I T G L I E D E R D E R K A P E L L E D E R (swc_deu_001542-swc_deu_001542) +P O T S D A M E R A B K O M M E N E N T H I E L T Z W A R A L L G E M E I N E V E R E I N B A R U N G E N Ü B E R D I E K Ü N F T I G E G E M E I N S A M E V E R W A L T U N G D E R S I E G E R M Ä C H T E U N D F O R M U L I E R T E G R U N D S Ä T Z E W I E D E M I L I T A R I S I E R U N G (swc_deu_001543-swc_deu_001543) +D A N A C H U N T E R S C H R I E B E R E I N E N V E R T R A G B E I M B F C D Y N A M O (swc_deu_001544-swc_deu_001544) +E I N E W E I T E R E V A R I A N T E M A G (swc_deu_001545-swc_deu_001545) +S I E W U R D E N M O D U L A R D U R C H L O C H S T R E I F E N G E S T E U E R T U N D D I E K L Ä N G E K O N N T E N (swc_deu_001546-swc_deu_001546) +D I E G R U N D M A N D A T S K L A U S E L B E V O R Z U G T U N T E R D E N K L E I N E N P A R T E I E N J E N E (swc_deu_001547-swc_deu_001547) +A B E R T R O T Z D E M K E I N E W I R K L I C H E H U N G E R S N O T H E R R S C H T (swc_deu_001548-swc_deu_001548) +U N D D O K U M E N T A T I O N D E R O B J E K T E (swc_deu_001549-swc_deu_001549) +Z U R V O R B E D I N G U N G K O N K R E T E R A B R Ü S T U N G S S C H R I T T E (swc_deu_001550-swc_deu_001550) +B U N D E S T A G S W A H L R E C H T (swc_deu_001551-swc_deu_001551) +E S M U S S D E M K R E I S W A H L L E I T E R V O R G E L E G T W E R D E N (swc_deu_001552-swc_deu_001552) +H A T M A N E I N E E M P I R I S C H E B A S I S F Ü R P S Y C H O S O Z I A L E P R O G R A M M E Z U R S E N K U N G D E R S E L B S T M O R D R A T E U N D Z U R S T Ä R K U N G D E S S I C H E R H E I T S G E F Ü H L S I N D E R B E V Ö L K E R U N G (swc_deu_001553-swc_deu_001553) +B E I D E N E R S T E N F R E I E N P A R L A M E N T S W A H L E N W U R D E I L I E S C U I M M A I N E U N Z E H N H U N D E R T N E U N Z I G I N S E I N E M (swc_deu_001554-swc_deu_001554) +D A M I T L A S S E N S I C H B E S T R A H L U N G S S T Ä R K E N S E H R G E N A U M E S S E N (swc_deu_001555-swc_deu_001555) +W E N I G E J A H R E S P Ä T E R K A M E S Z U E I N E R W E I T E R E N G R Ü N D U N G (swc_deu_001556-swc_deu_001556) +R A D I O K A B A R E T T P R E I S (swc_deu_001557-swc_deu_001557) +B E S T Ü C K T E B O M B E R A U F D I E S T A R T B A H N E N R O L L E N (swc_deu_001558-swc_deu_001558) +M I T D I E S E R R E G E L U N G S O L L E I N E F A K T I S C H Z W E I F A C H E E I N F L U S S N A H M E D I E S E R W Ä H L E R A U F (swc_deu_001559-swc_deu_001559) +B A R O C K E R K I R C H E N B A U (swc_deu_001560-swc_deu_001560) +D E R H E R V O R R A G E N D W I R K E N D E N L A N D E K L A P P E N W I E D E R U M H E R V O R R A G E N D E L A N G S A M F L U G E I G E N S C H A F T E N (swc_deu_001561-swc_deu_001561) +M I L I T Ä R I S C H E V E R B I N D U N G S F L U G Z E U G E O D E R U M S C H U L M A S C H I N E N F Ü R D I E B F E I N H U N D E R T N E U N V E R W E N D E T (swc_deu_001562-swc_deu_001562) +L E I S T E T E M E D I Z I N I S C H E U N D P S Y C H O L O G I S C H E H I L F E (swc_deu_001563-swc_deu_001563) +K A N N M A N D U R C H I M P F U N G E N V O R B E U G E N (swc_deu_001564-swc_deu_001564) +M A N D E N A U S B R U C H D I E S E R K R A N K H E I T N A C H E R F O L G T E R I N F E K T I O N V E R L A N G S A M E N K A N N (swc_deu_001565-swc_deu_001565) +D I E E I N E N E U T R A L I T Ä T U N T E R A L L E N U M S T Ä N D E N V O R S A H (swc_deu_001566-swc_deu_001566) +U N D Z I E G E N H I R T E N (swc_deu_001567-swc_deu_001567) +D A S N E U N Z E H N H U N D E R T A C H T U N D D R E I S S I G G E G R Ü N D E T E K O M I T E E F Ü R U N A M E R I K A N I S C H E U M T R I E B E W U R D E D A F Ü R N U N (swc_deu_001568-swc_deu_001568) +Z E N T R A L E D E R P R O G R E S S I V E N U N D H O R T D E S I N G E N I E U R G E S T Ü T Z T E N K U N S T D E N K E N S (swc_deu_001569-swc_deu_001569) +I N D E R D E R U S P R Ä S I D E N T A N K Ü N D I G T E (swc_deu_001570-swc_deu_001570) +S N A C K S U N D V O R S P E I S E N 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(voxpopuli_deu_000314-voxpopuli_deu_000314) +E I N „ R E S E T U N S E R E R B E Z I E H U N G E N I S T N I C H T V O N N Ö T E N A B E R S E H R W O H L K O N T I N U I E R L I C H E S F E I N T U N I N G (voxpopuli_deu_000315-voxpopuli_deu_000315) +U N D D A W I R D G A N Z S T O L Z G E S A G T D I E B E S C H Ä F T I G U N G S T E I G T J A A N (voxpopuli_deu_000316-voxpopuli_deu_000316) +I C H W I L L S A G E N W I E E S I S T F Ü R U N S I S T D E R E U R O U N T E R B E W E R T E T W I R E X P O R T I E R E N Z U V I E L Z U B I L L I G U N D W I R I M P O R T I E R E N Z U W E N I G W I R V E R S C H E N K E N W O H L S T A N D (voxpopuli_deu_000317-voxpopuli_deu_000317) +D A S S S I E H E U T E A B E N D H I E R A N W E S E N D S I N D I S T E I N P O S I T I V E S S I G N A L (voxpopuli_deu_000318-voxpopuli_deu_000318) +9 0 P R O Z E N T A L L E R E U R O P Ä I S C H E N F I L M E D I E A U S S E R H A L B I H R E S H E I M A T L A N D E S G E Z E I G T W E R D E N S I N D V 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E R F O L G S B E I S P I E L F Ü R M I C H I S T U N D Z W A R S L U M D O G M I L L I O N Ä R (voxpopuli_deu_000323-voxpopuli_deu_000323) +U N D D A S N I C H T N U R I N P O R T U G A L O D E R G R I E C H E N L A N D S O N D E R N A U C H I N S O V E R M E I N T L I C H R E I C H E N M I T G L I E D S T A A T E N W I E D E U T S C H L A N D O D E R G R O S S B R I T A N N I E N (voxpopuli_deu_000324-voxpopuli_deu_000324) +D I E Z E I T F Ü R A U S R E D E N I S T V O R B E I (voxpopuli_deu_000325-voxpopuli_deu_000325) +S I E A L L E F L I E G E N A L S M I T G L I E D E R D I E S E S H A U S E S W A H R S C H E I N L I C H D E U T L I C H H Ä U F I G E R A L S D E R E U D U R C H S C H N I T T S B Ü R G E R (voxpopuli_deu_000326-voxpopuli_deu_000326) +U N D I C H B I N S I C H E R D A S S I H R E B E D E U T U N G I N N A H E R Z U K U N F T S O G A R N O C H Z U N E H M E N W I R D (voxpopuli_deu_000327-voxpopuli_deu_000327) +E S G E H T H I E R U M D I E R I C H T L I N I E D E S 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I E R A L L E S U N T E R D E M M A N T E L D E S S C H W E I G E N S Z U G E D E C K T W E R D E N S O L L (voxpopuli_deu_000331-voxpopuli_deu_000331) +D O R T S T E H E N Ü B E R A L L E N T L A N G D E R K Ü S T E D I E W A R N S T E I N E D I E A U F D I E G R O S S E N K A T A S T R O P H E N M I T T S U N A M I S I N D E R V E R G A N G E N H E I T H I N W E I S E N (voxpopuli_deu_000332-voxpopuli_deu_000332) +H E R R P R Ä S I D E N T I C H H A B E I M P R I N Z I P F Ü R D E N B E R I C H T G E S T I M M T O B W O H L E R E I N E N S C H W E R E N F E H L E R E N T H Ä L T E S W I R D N Ä M L I C H D A Z U A U F G E F O R D E R T D A S E U R O P Ä I S C H E P A R L A M E N T A U F D E M W E G Z U E I N E M E I N Z I G E N S I T Z Z U U N T E R S T Ü T Z E N (voxpopuli_deu_000333-voxpopuli_deu_000333) +I N D I E S E N T R E F F E N W U R D E N G E M E I N S A M E P O L I T I S C H E V E R A B R E D U N G E N I M K R E I S D E R 2 7 G E T R O F F E N U N D A U C H P U B L I K G E M A C H T (voxpopuli_deu_000334-voxpopuli_deu_000334) +I C H B I N D E R Ü B E R Z E U G U N G D A S S W I R E S H E U T E M I T D E M V O R S C H L A G A U S D E M U M W E L T A U S S C H U S S G E S C H A F F T H A B E N E I N E N S C H R I T T W E I T E R Z U K O M M E N E S I S T N I C H T P E R F E K T E U R O P Ä I S C H E Ä R Z T E S A G E N W I R H Ä T T E N F Ü R H O C H R I S I K O P R O D U K T E E I N E Z E N T R A L E Z U L A S S U N G H A B E N M Ü S S E N D A S H A B E I C H N I C H T G E S C H A F F T A B E R M I T D E M W A S H E U T E A U F D E M T I S C H L I E G T S C H A F F E N W I R W O H L T R O T Z D E M E I N E N G R O S S E N S C H R I T T V I E L L E I C H T K E I N E N M E I L E N S T E I N A B E R E I N E N G R O S S E N S C H R I T T H I N Z U M E H R P A T I E N T E N S I C H E R H E I T (voxpopuli_deu_000335-voxpopuli_deu_000335) +F R A U P R Ä S I D E N T I N F R A U K O M M I S S A R I N L I E B E K O L L E G E N (voxpopuli_deu_000336-voxpopuli_deu_000336) +Z U M A K T U E L L E N I C H G L A U B E E S K A N N K E I N E R V O N U N S A N N E H M E N D A S S W I R W I R K L I C H E R S T S E I T D I E S E M W O C H E N E N D E W I S S E N D A S S U N S D I E Z A H L U N G S U N F Ä H I G K E I T D R O H T (voxpopuli_deu_000337-voxpopuli_deu_000337) +D A S S I N D E I N F A C H B E D I N G U N G E N D I E N I C H T A K Z E P T A B E L S I N D (voxpopuli_deu_000338-voxpopuli_deu_000338) +I N D E R Z W I S C H E N Z E I T S I N D D I E R E T T U N G S O R G A N I S A T I O N E N D I E G R Ö S S T E N S C H L E P P E R W E I L S I E D I E M I G R A N T E N 2 0 K I L O M E T E R V O R D E R L I B Y S C H E N K Ü S T E A U F G R E I F E N U N D A L L E N A C H I T A L I E N T R A N S P O R T I E R E N (voxpopuli_deu_000339-voxpopuli_deu_000339) +D A S Z E I G T D E R F A L L J U L I A T I M O S C H E N K O (voxpopuli_deu_000340-voxpopuli_deu_000340) +W I R D Ü R F E N N I C H T W A S S E R P R E D I G E N U N D W E I N T R I N K E N (voxpopuli_deu_000341-voxpopuli_deu_000341) +F Ü R D I E S E E N T S C H E I D U N G B R A U C H E N W I R V I E L E P A R T N E R N I C H T Z U L E T Z T D I E S T Ä D T E (voxpopuli_deu_000342-voxpopuli_deu_000342) +D I E F O L G E I S T E I N H Ö H E N F L U G V O N P O P U L I S T E N U N D E X T R E M I S T E N I N E I N I G E N M I T G L I E D S T A A T E N I H R E N D U M P F E N P A R O L E N S E T Z E N W I R K O N K R E T E V E R Ä N D E R U N G E N T G E G E N (voxpopuli_deu_000343-voxpopuli_deu_000343) +W E I L D I E I N V E S T I T I O N E N F R A N Z Ö S I S C H E R U N D D E U T S C H E R B A N K E N G E R E T T E T W E R D E N M U S S T E N D U R F T E G R I E C H E N L A N D 2 0 1 0 N I C H T P L E I T E G E H E N U N D H E U T E M U S S E S E I N E N R I E S I G E N S C H U L D E N B E R G V O R S I C H H E R S C H I E B E N (voxpopuli_deu_000344-voxpopuli_deu_000344) +D I E M I T G L I E D S T A A T E N D Ü R F E N N I C H T D I E M Ö G L I C H K E I T H A B E N D E N E U R O P Ä I S C H E N S T A A T S A N W A L T D A R A N Z U H I N D E R N I N I H R E N R E G I O N E N G A N Z G E Z I E L T U N D S Y S T E M A T I S C H K O R R U P T I O N S F Ä L L E N N A C H Z U G E H E N (voxpopuli_deu_000345-voxpopuli_deu_000345) +D R E I M I L L I O N E N M E N S C H E N S I N D A B H Ä N G I G V O N U N S E R E R H I L F E (voxpopuli_deu_000346-voxpopuli_deu_000346) +E I N V I E R Z E H N J Ä H R I G E R J U N G E W I R D I N H A K K A R I V O N E I N E M P O L I Z I S T E N E I N E S S O N D E R E I N S A T Z K O M M A N D O S I N S K O M A G E S C H L A G E N (voxpopuli_deu_000347-voxpopuli_deu_000347) +W I E E I N E H E I L I G E K U H H A T M A N V O R S I C H H E R G E T R A G E N D A S O P T O U T M Ü S S E U N T E R A L L E N U M S T Ä N D E N W E G (voxpopuli_deu_000348-voxpopuli_deu_000348) +D R E I D E R A R T I G E T R E F F E N H A B E N I N Z W I S C H E N S T A T T G E F U N D E N (voxpopuli_deu_000349-voxpopuli_deu_000349) +I C H H O F F E E S D A U E R T N I C H T W I E D E R N E U N M O N A T E (voxpopuli_deu_000350-voxpopuli_deu_000350) +D E S W E G E N E I N E W I C H T I G E F R A G E A N D I E K O M M I S S I O N K A N N E I N L A N D D I E G R E N Z K O N T R O L L E W I E D E R E I N F Ü H R E N U N D G L E I C H Z E I T I G I N D E R S C H E N G E N U N I O N B L E I B E N M I T Z U G A N G Z U M I N F O R M A T I O N S S Y S T E M E T C O D E R I S T D A S E I N E N T W E D E R O D E R D I E F R A G E I S T W I C H T I G F Ü R D I E D Ä N I S C H E D E B A T T E U N D I C H B I T T E U M E I N E K L A R E A N T W O R T (voxpopuli_deu_000351-voxpopuli_deu_000351) +W I E H E U T E S C H O N A U S G E F Ü H R T W U R D E L A G E S N I C H T D A R A N D A S S E S H I E R G R O B E F E H L E R G E G E B E N H Ä T T E S O N D E R N E S G A B E I N E R E I H E V O N K L E I N E N U N G E R E I M T H E I T E N B Z W (voxpopuli_deu_000352-voxpopuli_deu_000352) +E I N E V E R G E M E I N S C H A F T U N G D E R A U S S E N U N D S I C H E R H E I T S P O L I T I K A L S G R O S S E S Z I E L D I E S E R U N I O N (voxpopuli_deu_000353-voxpopuli_deu_000353) +D E N N S I C H E R H E I T I S T E I N E S C H W I E R I G E U N D D E T A I L R E I C H E A R B E I T N I C H T N U R I M T E C H N I S C H E N B E R E I C H (voxpopuli_deu_000354-voxpopuli_deu_000354) +K I N D E R U N D P O L I T I K S E L T E N L I E G E N D I E I N T E R E S S E N V O N B Ü R G E R N U N D P O L I T I K E R N S O W E I T A U S E I N A N D E R B E I D E N B Ü R G E R N I N G A N Z E U R O P A S T E H T D A S T H E M A K I N D G A N Z O B E N (voxpopuli_deu_000355-voxpopuli_deu_000355) +H E R R P R Ä S I D E N T (voxpopuli_deu_000356-voxpopuli_deu_000356) +W I R F Ü H R T E N G E S P R Ä C H E M I T P R Ä S I D E N T K A R Z A I Z A H L R E I C H E N R E G I E R U N G S V E R T R E T E R N F R A U E N U N D M E N S C H E N R E C H T S O R G A N I S A T I O N E N U N D D I E W A R E N D U R C H A U S E R M U T I G E N D (voxpopuli_deu_000357-voxpopuli_deu_000357) +D A S I S T Ü B R I G E N S A U C H E I N E U R S A C H E F Ü R D E N W A C H S E N D E N N A T I O N A L I S M U S D E R A L L E R D I N G S L E I D E R V Ö L L I G P E R S P E K T I V L O S I S T (voxpopuli_deu_000358-voxpopuli_deu_000358) +H E U T E S I N D W I R I M M E R N O C H S O W E I T V O N D I E S E M Z I E L E N T F E R N T (voxpopuli_deu_000359-voxpopuli_deu_000359) +I C H W E R D E A L S F I N A N Z M I N I S T E R A U C H I N M E I N E M L A N D J E D E N T A G D A M I T K O N F R O N T I E R T D A S S N A T Ü R L I C H A U C H D A S B E W U S S T S E I N G E G E B E N S E I N M U S S D A S S S T A A T S H A U S H A L T E V O N D E N S T E U E R Z A H L E R I N N E N U N D S T E U E R Z A H L E R N F I N A N Z I E R T S I N D U N D D A S S W I R D A M I T A U C H D I E V E R A N T W O R T U N G T R A G E N B E I D E N E N T S C H E I D U N G E N D I E W I R H I E R I N D I E S E M R A H M E N T R E F F E N M E I N E D A M E N U N D H E R R E N (voxpopuli_deu_000360-voxpopuli_deu_000360) +A U F D E M E U R O P Ä I S C H E N A U T O M O B I L M A R K T I N S G E S A M T D R A M A T I S C H I S T (voxpopuli_deu_000361-voxpopuli_deu_000361) +D I E E U R O P Ä I S C H E U N I O N H A T M I T D I E S E M I N S T R U M E N T D I E C H A N C E E I N E A K T I V E R O L L E I N I H R E R N A C H B A R R E G I O N Z U S P I E L E N U M D E M O K R A T I S C H E R E F O R M E N U N D E I N E N A C H H A L T I G E E N T W I C K L U N G V O R A N Z U T R E I B E N (voxpopuli_deu_000362-voxpopuli_deu_000362) +D I E S I C H T A U F T O T A L I T Ä R E R E G I M E V O N A U S S E N O D E R V O N I N N E N I S T R E C H T U N T E R S C H I E D L I C H (voxpopuli_deu_000363-voxpopuli_deu_000363) +W I R H A B E N I M M E R G E S A G T D A S S E I N E Ü B E R E I L T E S T A T I O N I E R U N G S E N T S C H E I D U N G U N S I N N I G I S T W E I L E S Z U M J E T Z I G E N Z E I T P U N K T K E I N E B E D R O H U N G B E I S P I E L S W E I S E A U S D E M I R A N G I B T (voxpopuli_deu_000364-voxpopuli_deu_000364) +D I E S E R V E R G L E I C H I S T E I N E Z Y N I S C H E M I S S A C H T U N G D E R O P F E R V O N M E N S C H E N R E C H T S V E R L E T Z U N G E N I N A L L E R W E L T E R I S T Z U M A N D E R E N E I N S O L C H U N G L A U B L I C H E R A N W U R F (voxpopuli_deu_000365-voxpopuli_deu_000365) +D I E S P E H A T D I E S E U M F A S S E N D E H O R I Z O N T A L E R I C H T L I N I E B E F Ü R W O R T E T (voxpopuli_deu_000366-voxpopuli_deu_000366) +D E N N E I N E S I S T W I R K L I C H K L A R D I E F I N A N Z U N D W I R T S C H A F T S K R I S E V E R L A N G T V O N U N S A L L E N E I N M A L M E H R D E R V E R A N T W O R T U N G F Ü R E I N E O P T I M A L E U N D V O R A L L E M R A S C H E Q U A L I F I Z I E R U N G U N S E R E R A R B E I T N E H M E R U N D A R B E I T N E H M E R I N N E N G A N Z B E S O N D E R S J E T Z T R E C H N U N G Z U T R A G E N (voxpopuli_deu_000367-voxpopuli_deu_000367) +E S T L A N D O D E R A U C H P O L E N D I E S E H R G U T E E R G E B N I S S E E R Z I E L E N A L S A N D E R E D I E S I C H S C H W E R T U N D I E M I T T E L A B Z U R U F E N E T W A R E G I O N E N W I E K A L A B R I E N S I Z I L I E N O D E R A U C H G R I E C H E N L A N D O D E R R U M Ä N I E N (voxpopuli_deu_000368-voxpopuli_deu_000368) +D E R B E R I C H T G A U Z È S F O R D E R T Z U R E C H T D A S S D A S R A T I N G S T A A T L I C H E R S C H U L D T I T E L A L S Ö F F E N T L I C H E A U F G A B E B E G R I F F E N U N D D A H E R V O N Ö F F E N T L I C H E N A K T E U R E N V O R G E N O M M E N W E R D E N M U S S (voxpopuli_deu_000369-voxpopuli_deu_000369) +D A W I R E S A B E R N U N M I T E I N E M S O Z I A L P R O G R A M M Z U T U N H A B E N M Ü S S E N W I R D A F Ü R E I N E E N T S P R E C H E N D E R E C H T L I C H E G R U N D L A G E S C H A F F E N (voxpopuli_deu_000370-voxpopuli_deu_000370) +A B E R D A S M Ü S S E N W I R N O C H A N A L Y S I E R E N (voxpopuli_deu_000371-voxpopuli_deu_000371) +M A N K A N N N A T Ü R L I C H V E R L A N G E N G E B E N W I R M E H R G E L D F Ü R E N T W I C K L U N G S H I L F E A U S D I E A R M E N L E U T E B R A U C H E N D A S (voxpopuli_deu_000372-voxpopuli_deu_000372) +G E R A D E F Ü R K L E I N E R E P R O J E K T E I S T D A S E I N Ü B E R M Ä S S I G E R B Ü R O K R A T I S C H E R A U F W A N D R I C H T I G D A S S D A S J E T Z T A U F E I N E N Z E I T R A U M V O N D R E I J A H R E N G E S E N K T W E R D E N S O L L (voxpopuli_deu_000373-voxpopuli_deu_000373) +I C H K A N N N U R V E R S I C H E R N D I E E U R O P Ä I S C H E K O M M I S S I O N I S T C O M M I T T E D Z U R E U R O P Ä I S C H E N P E R S P E K T I V E D E S K O S O V O (voxpopuli_deu_000374-voxpopuli_deu_000374) +A B E R H I E R I M H A U S E I S T E S S E H R O F T A U C H S O (voxpopuli_deu_000375-voxpopuli_deu_000375) +M I T D I E S E M H A U S H A L T K A N N M A N D I E E U B Ü R G E R I N N E N U N D B Ü R G E R N I C H T Ü B E R Z E U G E N U N D B E G E I S T E R N (voxpopuli_deu_000376-voxpopuli_deu_000376) +W I R A L S S O Z I A L D E M O K R A T E N N E H M E N M I T G R O S S E R F R E U D E Z U R K E N N T N I S D A S S D I N G E D I E W I R V O R G E T R A G E N H A B E N J E T Z T I M Z U S A M M E N H A N G M I T D E N V E R Ä N D E R U N G E N I N D E N V E R E I N I G T E N S T A A T E N U M G E S E T Z T W E R D E N (voxpopuli_deu_000377-voxpopuli_deu_000377) +D E R B E S C H L U S S D A S E U R O P Ä I S C H E S E M E S T E R H E R Z U N E H M E N U N D D I E K O R R U P T I O N S S I T U A T I O N I M R A H M E N D E R L Ä N D E R B E R I C H T E Z U V E R Ö F F E N T L I C H E N I S T N I C H T A U S R E I C H E N D (voxpopuli_deu_000378-voxpopuli_deu_000378) +U N D M E I N E B I T T E O D E R D A S W A S I C H M I R V O R S T E L L E I S T D A S S M O R G E N W I R K L I C H I N D E R T A T E I N E G R O S S E E I N E B R E I T E M E H R H E I T F Ü R D I E S E K O H Ä S I O N S P O L I T I K F Ü R U N S E R E P O L I T I K S T I M M T F Ü R D I E M E N S C H E N V O R O R T D A M I T W I R U N S A U F D A S W E S E N T L I C H E B E S C H R Ä N K E N K Ö N N E N (voxpopuli_deu_000379-voxpopuli_deu_000379) +W E N N W I R H E U T E D I E S E V E R O R D N U N G V E R A B S C H I E D E N H O F F E I C H D A S S W I R N A C H E I N E M L A N G E N W E G Z U E I N E M G U T E N A B S C H L U S S K O M M E N U N D I C H M Ö C H T E M I C H B E I D E R K O M M I S S I O N B E D A N K E N D I E U N S D U R C H K O N S T R U K T I V E S A C H A R B E I T (voxpopuli_deu_000380-voxpopuli_deu_000380) +U N S E R E K O N T R O L L E N H A B E N K E I N E N B E L E G E R B R A C H T I C H K A N N (voxpopuli_deu_000381-voxpopuli_deu_000381) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/result.txt b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..5eef559fb98d25ba4dbe04588d3c741b75700b8b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/result.txt @@ -0,0 +1,7481 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,--------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/hyp.trn| +|--------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------------+---------------------+-------------------------------------------------------------------| +| m | 89 8584 | 74.1 9.4 16.5 4.8 30.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000698 | 1 51 | 70.6 15.7 13.7 2.0 31.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000699 | 1 21 | 52.4 28.6 19.0 9.5 57.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000700 | 1 54 | 55.6 20.4 24.1 11.1 55.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000701 | 1 90 | 62.2 16.7 21.1 5.6 43.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000702 | 1 50 | 66.0 22.0 12.0 12.0 46.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000703 | 1 64 | 76.6 10.9 12.5 18.8 42.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000704 | 1 4 | 25.0 75.0 0.0 100.0 175.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000705 | 1 4 | 25.0 75.0 0.0 175.0 250.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000706 | 1 90 | 72.2 11.1 16.7 2.2 30.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000707 | 1 18 | 66.7 22.2 11.1 22.2 55.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000708 | 1 75 | 61.3 21.3 17.3 13.3 52.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000709 | 1 55 | 63.6 27.3 9.1 10.9 47.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000710 | 1 105 | 77.1 14.3 8.6 1.9 24.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000711 | 1 86 | 74.4 7.0 18.6 0.0 25.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000712 | 1 37 | 59.5 18.9 21.6 5.4 45.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000713 | 1 41 | 56.1 17.1 26.8 12.2 56.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000714 | 1 72 | 47.2 18.1 34.7 4.2 56.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000715 | 1 66 | 50.0 18.2 31.8 3.0 53.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000716 | 1 50 | 72.0 10.0 18.0 8.0 36.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000717 | 1 94 | 77.7 11.7 10.6 6.4 28.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000718 | 1 41 | 92.7 2.4 4.9 17.1 24.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000719 | 1 55 | 69.1 18.2 12.7 18.2 49.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000720 | 1 51 | 66.7 11.8 21.6 3.9 37.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000721 | 1 40 | 67.5 10.0 22.5 12.5 45.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000722 | 1 42 | 73.8 11.9 14.3 2.4 28.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000723 | 1 28 | 64.3 21.4 14.3 3.6 39.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000724 | 1 38 | 73.7 7.9 18.4 15.8 42.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000725 | 1 77 | 64.9 11.7 23.4 3.9 39.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000726 | 1 52 | 75.0 9.6 15.4 1.9 26.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000727 | 1 42 | 78.6 7.1 14.3 7.1 28.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000728 | 1 35 | 34.3 54.3 11.4 40.0 105.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000729 | 1 37 | 35.1 51.4 13.5 2.7 67.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000730 | 1 116 | 66.4 13.8 19.8 2.6 36.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000731 | 1 66 | 72.7 10.6 16.7 3.0 30.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000732 | 1 42 | 71.4 14.3 14.3 4.8 33.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000733 | 1 99 | 71.7 14.1 14.1 8.1 36.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000734 | 1 84 | 66.7 21.4 11.9 15.5 48.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000735 | 1 26 | 61.5 23.1 15.4 15.4 53.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000736 | 1 20 | 65.0 15.0 20.0 10.0 45.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000737 | 1 71 | 64.8 15.5 19.7 8.5 43.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000738 | 1 69 | 69.6 17.4 13.0 11.6 42.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000739 | 1 50 | 84.0 12.0 4.0 10.0 26.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000740 | 1 6 | 50.0 33.3 16.7 283.3 333.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000741 | 1 2 | 0.0 100.0 0.0 950.0 1050.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000742 | 1 78 | 73.1 9.0 17.9 7.7 34.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000743 | 1 40 | 75.0 15.0 10.0 15.0 40.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000744 | 1 4 | 50.0 50.0 0.0 350.0 400.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000745 | 1 4 | 100.0 0.0 0.0 150.0 150.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000746 | 1 56 | 66.1 10.7 23.2 7.1 41.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000747 | 1 83 | 59.0 18.1 22.9 1.2 42.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000748 | 1 20 | 60.0 20.0 20.0 5.0 45.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000749 | 1 26 | 76.9 7.7 15.4 3.8 26.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000750 | 1 83 | 73.5 10.8 15.7 6.0 32.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000751 | 1 92 | 56.5 23.9 19.6 8.7 52.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000752 | 1 71 | 52.1 35.2 12.7 15.5 63.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000753 | 1 80 | 56.3 32.5 11.3 16.3 60.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000754 | 1 42 | 61.9 35.7 2.4 9.5 47.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000755 | 1 82 | 62.2 23.2 14.6 15.9 53.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000756 | 1 50 | 86.0 2.0 12.0 10.0 24.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000757 | 1 73 | 75.3 8.2 16.4 9.6 34.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000758 | 1 41 | 68.3 12.2 19.5 7.3 39.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000759 | 1 69 | 66.7 20.3 13.0 8.7 42.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000760 | 1 61 | 80.3 9.8 9.8 9.8 29.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000761 | 1 111 | 79.3 11.7 9.0 4.5 25.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000762 | 1 66 | 51.5 27.3 21.2 3.0 51.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000763 | 1 71 | 57.7 12.7 29.6 1.4 43.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000764 | 1 21 | 52.4 38.1 9.5 9.5 57.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000765 | 1 64 | 59.4 21.9 18.8 3.1 43.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000766 | 1 59 | 66.1 18.6 15.3 8.5 42.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000767 | 1 59 | 62.7 27.1 10.2 3.4 40.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000768 | 1 55 | 58.2 10.9 30.9 7.3 49.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000769 | 1 13 | 46.2 46.2 7.7 0.0 53.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000770 | 1 102 | 73.5 9.8 16.7 8.8 35.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000771 | 1 74 | 82.4 8.1 9.5 5.4 23.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000772 | 1 64 | 79.7 12.5 7.8 17.2 37.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000773 | 1 70 | 78.6 7.1 14.3 5.7 27.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000774 | 1 75 | 62.7 8.0 29.3 2.7 40.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000775 | 1 27 | 66.7 22.2 11.1 3.7 37.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000776 | 1 71 | 84.5 9.9 5.6 8.5 23.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000777 | 1 41 | 68.3 9.8 22.0 0.0 31.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000778 | 1 28 | 39.3 46.4 14.3 21.4 82.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000779 | 1 99 | 56.6 29.3 14.1 15.2 58.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000780 | 1 88 | 75.0 12.5 12.5 5.7 30.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000781 | 1 83 | 78.3 9.6 12.0 7.2 28.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000782 | 1 68 | 85.3 10.3 4.4 14.7 29.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000783 | 1 99 | 79.8 8.1 12.1 8.1 28.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000784 | 1 83 | 79.5 9.6 10.8 1.2 21.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000785 | 1 87 | 65.5 11.5 23.0 1.1 35.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000786 | 1 70 | 72.9 21.4 5.7 12.9 40.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000787 | 1 71 | 71.8 23.9 4.2 19.7 47.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000788 | 1 14 | 71.4 14.3 14.3 128.6 157.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000789 | 1 30 | 70.0 10.0 20.0 6.7 36.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000790 | 1 71 | 69.0 21.1 9.9 8.5 39.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000791 | 1 55 | 76.4 9.1 14.5 3.6 27.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000792 | 1 68 | 80.9 16.2 2.9 13.2 32.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000793 | 1 62 | 75.8 9.7 14.5 3.2 27.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000794 | 1 33 | 60.6 18.2 21.2 15.2 54.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000795 | 1 27 | 74.1 22.2 3.7 22.2 48.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000796 | 1 93 | 71.0 21.5 7.5 17.2 46.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000797 | 1 69 | 66.7 17.4 15.9 13.0 46.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000798 | 1 63 | 73.0 12.7 14.3 7.9 34.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000799 | 1 55 | 81.8 9.1 9.1 10.9 29.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000800 | 1 38 | 63.2 26.3 10.5 44.7 81.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| cv_deu_000801 | 1 85 | 71.8 15.3 12.9 7.1 35.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000378 | 1 170 | 71.2 13.5 15.3 6.5 35.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000379 | 1 221 | 76.0 14.5 9.5 16.7 40.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000380 | 1 121 | 62.8 24.8 12.4 39.7 76.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000381 | 1 68 | 79.4 10.3 10.3 10.3 30.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000382 | 1 170 | 69.4 12.4 18.2 8.8 39.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000383 | 1 128 | 66.4 11.7 21.9 14.8 48.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000384 | 1 200 | 69.0 7.0 24.0 5.0 36.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000385 | 1 124 | 62.1 12.9 25.0 0.8 38.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000386 | 1 221 | 77.8 10.9 11.3 13.1 35.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000387 | 1 180 | 71.7 6.7 21.7 3.3 31.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000388 | 1 366 | 74.3 13.9 11.7 6.3 32.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000389 | 1 221 | 76.0 10.0 14.0 3.6 27.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000390 | 1 171 | 75.4 16.4 8.2 17.5 42.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000391 | 1 148 | 66.9 10.8 22.3 0.7 33.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000392 | 1 248 | 61.3 11.7 27.0 4.4 43.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000393 | 1 162 | 66.7 8.6 24.7 3.1 36.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000394 | 1 143 | 80.4 7.7 11.9 11.2 30.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000395 | 1 151 | 69.5 19.2 11.3 10.6 41.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000396 | 1 252 | 66.3 9.1 24.6 10.7 44.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000397 | 1 176 | 80.1 11.9 8.0 9.7 29.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000398 | 1 72 | 68.1 9.7 22.2 0.0 31.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000399 | 1 116 | 64.7 19.8 15.5 3.4 38.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000400 | 1 129 | 69.8 7.8 22.5 3.1 33.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000401 | 1 129 | 70.5 8.5 20.9 5.4 34.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000402 | 1 181 | 81.8 10.5 7.7 12.2 30.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000403 | 1 179 | 81.6 8.9 9.5 10.6 29.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000404 | 1 105 | 61.9 29.5 8.6 15.2 53.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000405 | 1 110 | 70.9 7.3 21.8 2.7 31.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000406 | 1 129 | 69.0 13.2 17.8 13.2 44.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000407 | 1 200 | 77.5 15.5 7.0 38.0 60.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000408 | 1 113 | 69.0 15.0 15.9 15.9 46.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000409 | 1 120 | 69.2 15.8 15.0 16.7 47.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000410 | 1 263 | 66.5 7.6 25.9 3.8 37.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000411 | 1 115 | 73.9 7.8 18.3 0.9 27.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000412 | 1 180 | 71.7 11.7 16.7 7.2 35.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000413 | 1 158 | 60.8 17.1 22.2 3.2 42.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000414 | 1 161 | 62.1 9.9 28.0 9.9 47.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| fleurs_deu_000415 | 1 214 | 67.3 9.3 23.4 1.9 34.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000281 | 1 225 | 73.3 6.7 20.0 1.3 28.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000282 | 1 182 | 79.1 8.2 12.6 4.4 25.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000283 | 1 314 | 70.4 4.8 24.8 1.9 31.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000284 | 1 267 | 72.7 6.7 20.6 2.2 29.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000285 | 1 204 | 73.5 8.8 17.6 3.4 29.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000286 | 1 192 | 84.4 5.7 9.9 6.3 21.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000287 | 1 296 | 67.9 12.5 19.6 4.1 36.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000288 | 1 236 | 69.5 8.5 22.0 3.8 34.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000289 | 1 206 | 78.2 7.3 14.6 3.4 25.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000290 | 1 169 | 69.8 7.7 22.5 2.4 32.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000291 | 1 269 | 69.5 9.7 20.8 4.1 34.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000292 | 1 378 | 64.8 9.0 26.2 2.4 37.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000293 | 1 333 | 79.0 4.8 16.2 1.5 22.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000294 | 1 157 | 61.1 11.5 27.4 3.2 42.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000295 | 1 186 | 88.2 7.0 4.8 4.8 16.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000296 | 1 246 | 74.4 8.1 17.5 3.7 29.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000297 | 1 333 | 69.7 9.9 20.4 6.6 36.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000298 | 1 264 | 64.0 12.1 23.9 3.0 39.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000299 | 1 239 | 68.6 10.9 20.5 2.9 34.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000300 | 1 151 | 76.8 7.3 15.9 2.6 25.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000301 | 1 288 | 71.9 5.2 22.9 3.8 31.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000302 | 1 355 | 68.5 6.5 25.1 1.4 33.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000303 | 1 284 | 81.3 8.1 10.6 3.5 22.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000304 | 1 170 | 73.5 8.8 17.6 4.1 30.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000305 | 1 166 | 61.4 9.6 28.9 4.2 42.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000306 | 1 372 | 73.1 7.5 19.4 1.3 28.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000307 | 1 214 | 80.8 7.0 12.1 2.3 21.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000308 | 1 283 | 69.6 9.5 20.8 1.4 31.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000309 | 1 294 | 74.5 9.5 16.0 3.1 28.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000310 | 1 213 | 74.2 8.0 17.8 3.8 29.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000311 | 1 238 | 71.4 6.3 22.3 2.9 31.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000312 | 1 310 | 71.3 6.8 21.9 1.3 30.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000313 | 1 171 | 84.2 4.7 11.1 11.1 26.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000314 | 1 235 | 73.6 7.2 19.1 1.7 28.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000315 | 1 145 | 82.1 4.8 13.1 2.1 20.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000316 | 1 212 | 80.2 12.3 7.5 15.1 34.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000317 | 1 118 | 81.4 2.5 16.1 7.6 26.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000318 | 1 213 | 77.5 8.5 14.1 1.4 23.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| mls_deu_000319 | 1 137 | 83.2 5.8 10.9 5.1 21.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001408 | 1 20 | 70.0 10.0 20.0 25.0 55.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001409 | 1 55 | 87.3 7.3 5.5 3.6 16.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001410 | 1 38 | 78.9 0.0 21.1 0.0 21.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001411 | 1 23 | 60.9 8.7 30.4 0.0 39.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001412 | 1 68 | 82.4 4.4 13.2 1.5 19.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001413 | 1 27 | 85.2 3.7 11.1 7.4 22.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001414 | 1 30 | 66.7 10.0 23.3 33.3 66.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001415 | 1 10 | 90.0 10.0 0.0 50.0 60.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001416 | 1 14 | 64.3 28.6 7.1 0.0 35.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001417 | 1 39 | 71.8 2.6 25.6 5.1 33.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001418 | 1 29 | 79.3 10.3 10.3 17.2 37.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001419 | 1 53 | 81.1 7.5 11.3 5.7 24.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001420 | 1 73 | 63.0 11.0 26.0 1.4 38.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001421 | 1 77 | 61.0 11.7 27.3 2.6 41.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001422 | 1 28 | 64.3 3.6 32.1 3.6 39.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001423 | 1 25 | 64.0 16.0 20.0 0.0 36.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001424 | 1 53 | 86.8 3.8 9.4 7.5 20.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001425 | 1 149 | 72.5 8.1 19.5 2.7 30.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001426 | 1 34 | 70.6 8.8 20.6 0.0 29.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001427 | 1 18 | 66.7 11.1 22.2 5.6 38.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001428 | 1 52 | 75.0 1.9 23.1 3.8 28.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001429 | 1 45 | 64.4 11.1 24.4 2.2 37.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001430 | 1 40 | 57.5 20.0 22.5 0.0 42.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001431 | 1 37 | 75.7 8.1 16.2 8.1 32.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001432 | 1 25 | 80.0 8.0 12.0 4.0 24.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001433 | 1 48 | 81.3 6.3 12.5 2.1 20.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001434 | 1 114 | 78.9 9.6 11.4 5.3 26.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001435 | 1 31 | 61.3 6.5 32.3 0.0 38.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001436 | 1 19 | 73.7 10.5 15.8 15.8 42.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001437 | 1 70 | 77.1 10.0 12.9 0.0 22.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001438 | 1 49 | 71.4 6.1 22.4 0.0 28.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001439 | 1 29 | 75.9 3.4 20.7 10.3 34.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001440 | 1 43 | 83.7 2.3 14.0 11.6 27.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001441 | 1 62 | 79.0 12.9 8.1 9.7 30.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001442 | 1 111 | 70.3 4.5 25.2 0.9 30.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001443 | 1 41 | 65.9 12.2 22.0 2.4 36.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001444 | 1 38 | 71.1 18.4 10.5 0.0 28.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001445 | 1 46 | 82.6 6.5 10.9 6.5 23.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001446 | 1 25 | 72.0 4.0 24.0 4.0 32.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001447 | 1 20 | 55.0 15.0 30.0 5.0 50.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001448 | 1 57 | 77.2 5.3 17.5 3.5 26.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001449 | 1 45 | 71.1 11.1 17.8 2.2 31.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001450 | 1 67 | 79.1 4.5 16.4 7.5 28.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001451 | 1 24 | 75.0 4.2 20.8 0.0 25.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001452 | 1 38 | 84.2 10.5 5.3 2.6 18.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001453 | 1 43 | 83.7 4.7 11.6 7.0 23.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001454 | 1 124 | 79.0 7.3 13.7 6.5 27.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001455 | 1 48 | 81.3 8.3 10.4 4.2 22.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001456 | 1 19 | 73.7 26.3 0.0 15.8 42.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001457 | 1 47 | 83.0 6.4 10.6 10.6 27.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001458 | 1 190 | 76.3 5.3 18.4 3.7 27.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001459 | 1 41 | 70.7 9.8 19.5 7.3 36.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001460 | 1 23 | 73.9 4.3 21.7 0.0 26.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001461 | 1 19 | 47.4 26.3 26.3 0.0 52.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001462 | 1 87 | 74.7 8.0 17.2 0.0 25.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001463 | 1 62 | 80.6 6.5 12.9 1.6 21.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001464 | 1 33 | 72.7 24.2 3.0 3.0 30.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001465 | 1 55 | 61.8 16.4 21.8 3.6 41.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001466 | 1 44 | 70.5 15.9 13.6 4.5 34.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001467 | 1 27 | 59.3 33.3 7.4 11.1 51.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001468 | 1 47 | 68.1 14.9 17.0 4.3 36.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001469 | 1 28 | 82.1 7.1 10.7 0.0 17.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001470 | 1 13 | 53.8 30.8 15.4 23.1 69.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001471 | 1 13 | 92.3 7.7 0.0 23.1 30.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001472 | 1 107 | 68.2 13.1 18.7 1.9 33.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001473 | 1 56 | 87.5 10.7 1.8 3.6 16.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001474 | 1 37 | 64.9 10.8 24.3 5.4 40.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001475 | 1 45 | 80.0 11.1 8.9 6.7 26.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001476 | 1 70 | 72.9 2.9 24.3 0.0 27.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001477 | 1 27 | 85.2 7.4 7.4 3.7 18.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001478 | 1 39 | 53.8 25.6 20.5 2.6 48.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001479 | 1 34 | 67.6 5.9 26.5 2.9 35.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001480 | 1 37 | 73.0 18.9 8.1 18.9 45.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001481 | 1 75 | 70.7 8.0 21.3 2.7 32.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001482 | 1 35 | 82.9 8.6 8.6 11.4 28.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001483 | 1 43 | 60.5 20.9 18.6 4.7 44.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001484 | 1 33 | 72.7 6.1 21.2 0.0 27.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001485 | 1 107 | 75.7 10.3 14.0 18.7 43.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001486 | 1 45 | 44.4 20.0 35.6 0.0 55.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001487 | 1 26 | 42.3 11.5 46.2 3.8 61.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001488 | 1 59 | 66.1 15.3 18.6 6.8 40.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001489 | 1 57 | 71.9 8.8 19.3 3.5 31.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001490 | 1 32 | 62.5 37.5 0.0 87.5 125.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001491 | 1 37 | 64.9 10.8 24.3 8.1 43.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001492 | 1 40 | 77.5 10.0 12.5 5.0 27.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001493 | 1 32 | 53.1 18.8 28.1 12.5 59.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001494 | 1 42 | 78.6 7.1 14.3 2.4 23.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001495 | 1 26 | 88.5 3.8 7.7 11.5 23.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001496 | 1 52 | 65.4 17.3 17.3 9.6 44.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001497 | 1 57 | 80.7 8.8 10.5 5.3 24.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001498 | 1 58 | 81.0 6.9 12.1 5.2 24.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001499 | 1 27 | 74.1 7.4 18.5 7.4 33.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001500 | 1 60 | 83.3 3.3 13.3 3.3 20.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001501 | 1 52 | 82.7 3.8 13.5 1.9 19.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001502 | 1 18 | 83.3 16.7 0.0 5.6 22.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001503 | 1 22 | 63.6 22.7 13.6 59.1 95.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001504 | 1 45 | 80.0 8.9 11.1 0.0 20.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001505 | 1 45 | 73.3 13.3 13.3 0.0 26.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001506 | 1 94 | 81.9 9.6 8.5 6.4 24.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001507 | 1 55 | 83.6 3.6 12.7 5.5 21.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001508 | 1 20 | 75.0 10.0 15.0 10.0 35.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001509 | 1 20 | 75.0 15.0 10.0 25.0 50.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001510 | 1 62 | 80.6 6.5 12.9 4.8 24.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001511 | 1 44 | 77.3 6.8 15.9 4.5 27.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001512 | 1 82 | 85.4 4.9 9.8 4.9 19.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001513 | 1 41 | 85.4 12.2 2.4 4.9 19.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001514 | 1 67 | 77.6 9.0 13.4 4.5 26.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001515 | 1 20 | 85.0 0.0 15.0 5.0 20.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001516 | 1 34 | 73.5 11.8 14.7 2.9 29.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001517 | 1 46 | 82.6 4.3 13.0 2.2 19.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001518 | 1 66 | 78.8 9.1 12.1 3.0 24.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001519 | 1 39 | 74.4 7.7 17.9 0.0 25.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001520 | 1 79 | 87.3 10.1 2.5 7.6 20.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001521 | 1 24 | 66.7 16.7 16.7 4.2 37.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001522 | 1 39 | 89.7 2.6 7.7 5.1 15.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001523 | 1 24 | 87.5 4.2 8.3 8.3 20.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001524 | 1 48 | 89.6 2.1 8.3 8.3 18.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001525 | 1 25 | 80.0 12.0 8.0 8.0 28.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001526 | 1 15 | 73.3 26.7 0.0 0.0 26.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001527 | 1 27 | 66.7 18.5 14.8 14.8 48.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001528 | 1 19 | 73.7 15.8 10.5 21.1 47.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001529 | 1 16 | 75.0 6.3 18.8 18.8 43.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001530 | 1 20 | 80.0 5.0 15.0 0.0 20.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001531 | 1 15 | 46.7 13.3 40.0 0.0 53.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001532 | 1 26 | 92.3 7.7 0.0 11.5 19.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001533 | 1 33 | 78.8 12.1 9.1 21.2 42.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001534 | 1 18 | 83.3 0.0 16.7 11.1 27.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001535 | 1 16 | 93.8 0.0 6.3 12.5 18.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001536 | 1 19 | 68.4 21.1 10.5 15.8 47.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001537 | 1 17 | 70.6 11.8 17.6 64.7 94.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001538 | 1 39 | 82.1 7.7 10.3 2.6 20.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001539 | 1 26 | 65.4 19.2 15.4 3.8 38.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001540 | 1 59 | 94.9 0.0 5.1 5.1 10.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001541 | 1 11 | 81.8 0.0 18.2 18.2 36.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001542 | 1 56 | 73.2 5.4 21.4 1.8 28.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001543 | 1 164 | 86.6 3.7 9.8 2.4 15.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001544 | 1 54 | 72.2 16.7 11.1 7.4 35.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001545 | 1 25 | 76.0 8.0 16.0 4.0 28.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001546 | 1 70 | 84.3 5.7 10.0 4.3 20.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001547 | 1 65 | 86.2 7.7 6.2 6.2 20.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001548 | 1 49 | 73.5 10.2 16.3 4.1 30.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001549 | 1 29 | 34.5 20.7 44.8 6.9 72.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001550 | 1 45 | 75.6 15.6 8.9 2.2 26.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001551 | 1 19 | 89.5 5.3 5.3 15.8 26.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001552 | 1 44 | 72.7 13.6 13.6 2.3 29.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001553 | 1 147 | 74.1 10.2 15.6 7.5 33.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001554 | 1 94 | 78.7 7.4 13.8 3.2 24.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001555 | 1 55 | 78.2 3.6 18.2 0.0 21.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001556 | 1 53 | 66.0 5.7 28.3 1.9 35.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001557 | 1 19 | 84.2 10.5 5.3 21.1 36.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001558 | 1 43 | 79.1 0.0 20.9 7.0 27.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001559 | 1 80 | 80.0 6.3 13.8 5.0 25.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001560 | 1 19 | 73.7 5.3 21.1 10.5 36.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001561 | 1 87 | 85.1 8.0 6.9 5.7 20.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001562 | 1 94 | 81.9 7.4 10.6 2.1 20.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001563 | 1 46 | 65.2 13.0 21.7 6.5 41.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001564 | 1 34 | 70.6 14.7 14.7 2.9 32.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001565 | 1 76 | 71.1 15.8 13.2 2.6 31.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001566 | 1 49 | 77.6 10.2 12.2 4.1 26.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001567 | 1 16 | 75.0 6.3 18.8 12.5 37.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001568 | 1 100 | 84.0 5.0 11.0 5.0 21.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001569 | 1 71 | 81.7 12.7 5.6 9.9 28.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001570 | 1 35 | 82.9 5.7 11.4 8.6 25.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001571 | 1 21 | 90.5 4.8 4.8 14.3 23.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001572 | 1 75 | 77.3 8.0 14.7 5.3 28.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001573 | 1 14 | 50.0 28.6 21.4 7.1 57.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001574 | 1 64 | 87.5 6.3 6.3 6.3 18.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001575 | 1 35 | 85.7 8.6 5.7 11.4 25.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001576 | 1 84 | 86.9 2.4 10.7 16.7 29.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001577 | 1 43 | 74.4 16.3 9.3 7.0 32.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001578 | 1 116 | 85.3 4.3 10.3 2.6 17.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001579 | 1 126 | 82.5 8.7 8.7 7.9 25.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001580 | 1 28 | 57.1 14.3 28.6 17.9 60.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001581 | 1 45 | 86.7 8.9 4.4 11.1 24.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001582 | 1 26 | 57.7 15.4 26.9 0.0 42.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001583 | 1 36 | 72.2 16.7 11.1 0.0 27.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001584 | 1 31 | 93.5 6.5 0.0 3.2 9.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001585 | 1 68 | 82.4 5.9 11.8 2.9 20.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001586 | 1 61 | 65.6 8.2 26.2 1.6 36.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001587 | 1 40 | 85.0 7.5 7.5 10.0 25.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001588 | 1 54 | 83.3 9.3 7.4 7.4 24.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001589 | 1 25 | 72.0 8.0 20.0 4.0 32.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001590 | 1 48 | 72.9 10.4 16.7 2.1 29.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001591 | 1 25 | 52.0 20.0 28.0 8.0 56.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001592 | 1 103 | 72.8 6.8 20.4 0.0 27.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001593 | 1 35 | 80.0 5.7 14.3 2.9 22.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001594 | 1 63 | 82.5 3.2 14.3 1.6 19.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001595 | 1 17 | 70.6 11.8 17.6 11.8 41.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001596 | 1 19 | 73.7 21.1 5.3 0.0 26.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001597 | 1 82 | 70.7 9.8 19.5 3.7 32.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001598 | 1 154 | 80.5 8.4 11.0 4.5 24.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| swc_deu_001599 | 1 204 | 76.0 8.3 15.7 2.9 27.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000891 | 1 29 | 75.9 10.3 13.8 6.9 31.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000892 | 1 72 | 88.9 2.8 8.3 0.0 11.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000893 | 1 41 | 82.9 9.8 7.3 2.4 19.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000894 | 1 35 | 68.6 8.6 22.9 0.0 31.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000895 | 1 54 | 83.3 7.4 9.3 1.9 18.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000897 | 1 2 | 0.0 100.0 0.0 450.0 550.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000898 | 1 51 | 84.3 2.0 13.7 5.9 21.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000899 | 1 29 | 75.9 3.4 20.7 3.4 27.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000900 | 1 43 | 72.1 4.7 23.3 2.3 30.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000901 | 1 53 | 73.6 9.4 17.0 0.0 26.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000902 | 1 30 | 83.3 13.3 3.3 3.3 20.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000903 | 1 57 | 73.7 7.0 19.3 3.5 29.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000904 | 1 53 | 75.5 5.7 18.9 3.8 28.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000905 | 1 37 | 67.6 5.4 27.0 0.0 32.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000906 | 1 63 | 55.6 14.3 30.2 0.0 44.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000907 | 1 32 | 87.5 0.0 12.5 3.1 15.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000908 | 1 62 | 74.2 21.0 4.8 29.0 54.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000909 | 1 61 | 65.6 13.1 21.3 6.6 41.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000910 | 1 70 | 82.9 7.1 10.0 7.1 24.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000911 | 1 34 | 70.6 5.9 23.5 0.0 29.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000912 | 1 64 | 57.8 17.2 25.0 0.0 42.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000913 | 1 38 | 84.2 5.3 10.5 5.3 21.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000914 | 1 50 | 68.0 12.0 20.0 10.0 42.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000915 | 1 63 | 79.4 6.3 14.3 4.8 25.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000917 | 1 42 | 76.2 11.9 11.9 4.8 28.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000918 | 1 10 | 70.0 0.0 30.0 20.0 50.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000919 | 1 39 | 76.9 0.0 23.1 2.6 25.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000920 | 1 40 | 67.5 15.0 17.5 5.0 37.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000921 | 1 35 | 77.1 11.4 11.4 0.0 22.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000922 | 1 33 | 66.7 9.1 24.2 6.1 39.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000923 | 1 67 | 71.6 10.4 17.9 13.4 41.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000924 | 1 30 | 53.3 10.0 36.7 13.3 60.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000925 | 1 44 | 88.6 9.1 2.3 11.4 22.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000926 | 1 48 | 77.1 12.5 10.4 8.3 31.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000927 | 1 39 | 76.9 2.6 20.5 2.6 25.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000928 | 1 35 | 71.4 14.3 14.3 8.6 37.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000929 | 1 73 | 80.8 9.6 9.6 4.1 23.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000930 | 1 59 | 61.0 16.9 22.0 3.4 42.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000931 | 1 49 | 77.6 14.3 8.2 4.1 26.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000932 | 1 66 | 77.3 9.1 13.6 9.1 31.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000933 | 1 33 | 69.7 6.1 24.2 3.0 33.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000934 | 1 47 | 78.7 14.9 6.4 8.5 29.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000935 | 1 55 | 83.6 12.7 3.6 3.6 20.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000936 | 1 67 | 76.1 9.0 14.9 1.5 25.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000937 | 1 63 | 79.4 7.9 12.7 19.0 39.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000938 | 1 64 | 70.3 12.5 17.2 4.7 34.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000939 | 1 25 | 88.0 4.0 8.0 16.0 28.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000940 | 1 54 | 79.6 9.3 11.1 1.9 22.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000941 | 1 47 | 74.5 6.4 19.1 2.1 27.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000942 | 1 58 | 62.1 8.6 29.3 1.7 39.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000943 | 1 59 | 86.4 13.6 0.0 1.7 15.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000944 | 1 36 | 91.7 0.0 8.3 5.6 13.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000945 | 1 29 | 86.2 6.9 6.9 0.0 13.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000946 | 1 57 | 87.7 5.3 7.0 1.8 14.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000947 | 1 62 | 77.4 16.1 6.5 22.6 45.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000948 | 1 28 | 64.3 10.7 25.0 0.0 35.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000950 | 1 40 | 65.0 17.5 17.5 0.0 35.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000951 | 1 34 | 88.2 5.9 5.9 2.9 14.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000952 | 1 30 | 93.3 6.7 0.0 30.0 36.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000953 | 1 69 | 78.3 8.7 13.0 5.8 27.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000954 | 1 34 | 88.2 2.9 8.8 2.9 14.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000955 | 1 34 | 85.3 5.9 8.8 14.7 29.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000956 | 1 68 | 73.5 8.8 17.6 4.4 30.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000957 | 1 41 | 73.2 9.8 17.1 0.0 26.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000958 | 1 61 | 78.7 9.8 11.5 4.9 26.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000959 | 1 52 | 84.6 5.8 9.6 0.0 15.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000960 | 1 60 | 70.0 10.0 20.0 1.7 31.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000961 | 1 24 | 83.3 4.2 12.5 8.3 25.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000962 | 1 36 | 72.2 5.6 22.2 0.0 27.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000963 | 1 35 | 82.9 8.6 8.6 14.3 31.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000964 | 1 43 | 72.1 14.0 14.0 7.0 34.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000965 | 1 48 | 81.3 6.3 12.5 6.3 25.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000966 | 1 19 | 68.4 21.1 10.5 5.3 36.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000967 | 1 31 | 64.5 16.1 19.4 6.5 41.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000968 | 1 39 | 56.4 12.8 30.8 0.0 43.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000969 | 1 36 | 94.4 2.8 2.8 16.7 22.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000970 | 1 40 | 50.0 12.5 37.5 0.0 50.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000971 | 1 45 | 84.4 8.9 6.7 6.7 22.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000972 | 1 56 | 82.1 7.1 10.7 3.6 21.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000973 | 1 69 | 79.7 11.6 8.7 2.9 23.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000974 | 1 28 | 82.1 3.6 14.3 0.0 17.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000975 | 1 29 | 69.0 17.2 13.8 3.4 34.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000976 | 1 74 | 67.6 9.5 23.0 2.7 35.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000977 | 1 29 | 58.6 17.2 24.1 13.8 55.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000978 | 1 44 | 84.1 9.1 6.8 2.3 18.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000979 | 1 47 | 66.0 14.9 19.1 4.3 38.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000980 | 1 42 | 85.7 9.5 4.8 4.8 19.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000981 | 1 62 | 85.5 4.8 9.7 4.8 19.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000982 | 1 58 | 84.5 8.6 6.9 15.5 31.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000983 | 1 53 | 69.8 7.5 22.6 1.9 32.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000984 | 1 57 | 75.4 7.0 17.5 1.8 26.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000985 | 1 46 | 69.6 8.7 21.7 8.7 39.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000986 | 1 41 | 73.2 7.3 19.5 7.3 34.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000987 | 1 40 | 75.0 10.0 15.0 5.0 30.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000988 | 1 29 | 79.3 10.3 10.3 10.3 31.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000989 | 1 39 | 79.5 7.7 12.8 0.0 20.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000990 | 1 41 | 80.5 7.3 12.2 0.0 19.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000991 | 1 55 | 80.0 5.5 14.5 7.3 27.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000992 | 1 34 | 73.5 5.9 20.6 0.0 26.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000993 | 1 65 | 87.7 4.6 7.7 3.1 15.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000994 | 1 78 | 83.3 10.3 6.4 5.1 21.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000995 | 1 47 | 78.7 6.4 14.9 2.1 23.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000996 | 1 38 | 76.3 7.9 15.8 0.0 23.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000997 | 1 58 | 79.3 17.2 3.4 8.6 29.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000998 | 1 31 | 71.0 12.9 16.1 6.5 35.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_000999 | 1 51 | 80.4 5.9 13.7 3.9 23.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001000 | 1 38 | 92.1 2.6 5.3 2.6 10.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001001 | 1 33 | 72.7 12.1 15.2 6.1 33.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001002 | 1 39 | 79.5 7.7 12.8 7.7 28.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001003 | 1 69 | 84.1 11.6 4.3 11.6 27.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001004 | 1 50 | 80.0 14.0 6.0 4.0 24.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001006 | 1 39 | 64.1 15.4 20.5 0.0 35.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001007 | 1 36 | 83.3 8.3 8.3 0.0 16.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001008 | 1 57 | 71.9 14.0 14.0 3.5 31.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001009 | 1 38 | 63.2 7.9 28.9 2.6 39.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001010 | 1 80 | 72.5 7.5 20.0 3.8 31.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001011 | 1 44 | 79.5 4.5 15.9 2.3 22.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001012 | 1 42 | 61.9 19.0 19.0 0.0 38.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001013 | 1 44 | 68.2 6.8 25.0 9.1 40.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001014 | 1 39 | 92.3 2.6 5.1 7.7 15.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001015 | 1 28 | 75.0 10.7 14.3 0.0 25.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001016 | 1 31 | 83.9 9.7 6.5 9.7 25.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001017 | 1 41 | 78.0 9.8 12.2 4.9 26.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001018 | 1 55 | 78.2 10.9 10.9 7.3 29.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001019 | 1 39 | 82.1 5.1 12.8 2.6 20.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxforge_deu_001020 | 1 35 | 94.3 5.7 0.0 8.6 14.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000309 | 1 137 | 70.8 9.5 19.7 2.2 31.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000310 | 1 102 | 56.9 11.8 31.4 7.8 51.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000311 | 1 55 | 49.1 10.9 40.0 1.8 52.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000312 | 1 56 | 66.1 14.3 19.6 10.7 44.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000313 | 1 119 | 58.0 16.0 26.1 7.6 49.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000314 | 1 131 | 66.4 5.3 28.2 0.0 33.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000315 | 1 92 | 69.6 18.5 12.0 5.4 35.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000316 | 1 60 | 61.7 20.0 18.3 3.3 41.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000317 | 1 149 | 67.1 14.1 18.8 8.1 40.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000318 | 1 64 | 76.6 6.3 17.2 9.4 32.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000319 | 1 125 | 77.6 12.0 10.4 12.0 34.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000320 | 1 82 | 69.5 8.5 22.0 8.5 39.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000321 | 1 102 | 74.5 13.7 11.8 5.9 31.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000322 | 1 110 | 70.9 8.2 20.9 4.5 33.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000323 | 1 197 | 75.6 12.7 11.7 11.7 36.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000324 | 1 140 | 71.4 11.4 17.1 2.1 30.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000325 | 1 32 | 56.3 12.5 31.3 9.4 53.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000326 | 1 109 | 62.4 15.6 22.0 5.5 43.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000327 | 1 80 | 62.5 10.0 27.5 10.0 47.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000328 | 1 170 | 74.1 10.6 15.3 5.3 31.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000329 | 1 31 | 71.0 6.5 22.6 3.2 32.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000330 | 1 60 | 76.7 6.7 16.7 0.0 23.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000331 | 1 339 | 58.4 13.9 27.7 6.2 47.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000332 | 1 129 | 61.2 7.0 31.8 1.6 40.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000333 | 1 210 | 64.3 14.3 21.4 7.6 43.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000334 | 1 110 | 85.5 8.2 6.4 19.1 33.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000335 | 1 454 | 55.9 11.9 32.2 3.1 47.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000336 | 1 48 | 27.1 60.4 12.5 12.5 85.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000337 | 1 148 | 68.9 10.8 20.3 2.7 33.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000338 | 1 54 | 72.2 11.1 16.7 9.3 37.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000339 | 1 182 | 78.0 7.7 14.3 6.6 28.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000340 | 1 36 | 75.0 16.7 8.3 5.6 30.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000341 | 1 49 | 61.2 2.0 36.7 0.0 38.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000342 | 1 74 | 74.3 9.5 16.2 2.7 28.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000343 | 1 148 | 77.7 6.8 15.5 6.1 28.4 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000344 | 1 191 | 78.5 13.1 8.4 12.6 34.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000345 | 1 179 | 66.5 15.6 17.9 7.3 40.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000346 | 1 55 | 70.9 10.9 18.2 1.8 30.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000347 | 1 112 | 67.0 12.5 20.5 1.8 34.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000348 | 1 93 | 66.7 15.1 18.3 2.2 35.5 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000349 | 1 53 | 86.8 3.8 9.4 9.4 22.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000350 | 1 44 | 34.1 11.4 54.5 9.1 75.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000351 | 1 295 | 72.9 9.8 17.3 7.1 34.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000352 | 1 149 | 71.8 6.7 21.5 8.7 36.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000353 | 1 87 | 70.1 10.3 19.5 2.3 32.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000354 | 1 92 | 78.3 8.7 13.0 6.5 28.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000355 | 1 156 | 69.2 5.8 25.0 3.8 34.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000356 | 1 14 | 78.6 0.0 21.4 7.1 28.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000357 | 1 149 | 73.8 8.1 18.1 4.7 30.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000358 | 1 114 | 61.4 10.5 28.1 2.6 41.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000359 | 1 58 | 56.9 12.1 31.0 5.2 48.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000360 | 1 345 | 71.3 9.3 19.4 3.5 32.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000361 | 1 60 | 81.7 11.7 6.7 6.7 25.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000362 | 1 184 | 66.8 10.3 22.8 4.9 38.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000363 | 1 83 | 63.9 14.5 21.7 4.8 41.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000364 | 1 162 | 69.8 10.5 19.8 6.8 37.0 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000365 | 1 151 | 64.2 28.5 7.3 29.1 64.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000366 | 1 63 | 77.8 14.3 7.9 17.5 39.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000367 | 1 250 | 66.0 12.4 21.6 9.6 43.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000368 | 1 183 | 63.9 8.7 27.3 7.7 43.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000369 | 1 169 | 72.2 11.2 16.6 3.0 30.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000370 | 1 122 | 64.8 13.1 22.1 0.8 36.1 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000371 | 1 36 | 44.4 5.6 50.0 11.1 66.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000372 | 1 103 | 67.0 18.4 14.6 5.8 38.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000373 | 1 154 | 67.5 12.3 20.1 8.4 40.9 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000374 | 1 104 | 67.3 17.3 15.4 23.1 55.8 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000375 | 1 42 | 35.7 16.7 47.6 2.4 66.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000376 | 1 90 | 73.3 10.0 16.7 5.6 32.2 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000377 | 1 188 | 60.6 10.6 28.7 5.3 44.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000378 | 1 149 | 68.5 16.1 15.4 12.1 43.6 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000379 | 1 246 | 74.4 12.2 13.4 7.7 33.3 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000380 | 1 204 | 68.1 11.8 20.1 8.8 40.7 100.0 | +|--------------------------+---------------------+-------------------------------------------------------------------| +| voxpopuli_deu_000381 | 1 54 | 68.5 9.3 22.2 35.2 66.7 100.0 | +|====================================================================================================================| +| Sum/Avg | 661 54401 | 72.6 10.5 16.9 6.3 33.7 100.0 | +|====================================================================================================================| +| Mean | 1.2 94.9 | 72.2 12.0 15.8 11.6 39.3 100.0 | +| S.D. | 3.7 362.2 | 11.6 9.9 8.1 49.1 54.5 0.0 | +| Median | 1.0 55.0 | 73.3 10.0 15.0 5.0 32.0 100.0 | +`--------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,--------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/hyp.trn| +|--------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------------+----------------------+------------------------------------------------------------------| +| m | 89 8584 | 6365 803 1416 408 2627 89 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000698 | 1 51 | 36 8 7 1 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000699 | 1 21 | 11 6 4 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000700 | 1 54 | 30 11 13 6 30 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000701 | 1 90 | 56 15 19 5 39 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000702 | 1 50 | 33 11 6 6 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000703 | 1 64 | 49 7 8 12 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000704 | 1 4 | 1 3 0 4 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000705 | 1 4 | 1 3 0 7 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000706 | 1 90 | 65 10 15 2 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000707 | 1 18 | 12 4 2 4 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000708 | 1 75 | 46 16 13 10 39 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000709 | 1 55 | 35 15 5 6 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000710 | 1 105 | 81 15 9 2 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000711 | 1 86 | 64 6 16 0 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000712 | 1 37 | 22 7 8 2 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000713 | 1 41 | 23 7 11 5 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000714 | 1 72 | 34 13 25 3 41 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000715 | 1 66 | 33 12 21 2 35 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000716 | 1 50 | 36 5 9 4 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000717 | 1 94 | 73 11 10 6 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000718 | 1 41 | 38 1 2 7 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000719 | 1 55 | 38 10 7 10 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000720 | 1 51 | 34 6 11 2 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000721 | 1 40 | 27 4 9 5 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000722 | 1 42 | 31 5 6 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000723 | 1 28 | 18 6 4 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000724 | 1 38 | 28 3 7 6 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000725 | 1 77 | 50 9 18 3 30 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000726 | 1 52 | 39 5 8 1 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000727 | 1 42 | 33 3 6 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000728 | 1 35 | 12 19 4 14 37 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000729 | 1 37 | 13 19 5 1 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000730 | 1 116 | 77 16 23 3 42 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000731 | 1 66 | 48 7 11 2 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000732 | 1 42 | 30 6 6 2 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000733 | 1 99 | 71 14 14 8 36 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000734 | 1 84 | 56 18 10 13 41 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000735 | 1 26 | 16 6 4 4 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000736 | 1 20 | 13 3 4 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000737 | 1 71 | 46 11 14 6 31 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000738 | 1 69 | 48 12 9 8 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000739 | 1 50 | 42 6 2 5 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000740 | 1 6 | 3 2 1 17 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000741 | 1 2 | 0 2 0 19 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000742 | 1 78 | 57 7 14 6 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000743 | 1 40 | 30 6 4 6 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000744 | 1 4 | 2 2 0 14 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000745 | 1 4 | 4 0 0 6 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000746 | 1 56 | 37 6 13 4 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000747 | 1 83 | 49 15 19 1 35 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000748 | 1 20 | 12 4 4 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000749 | 1 26 | 20 2 4 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000750 | 1 83 | 61 9 13 5 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000751 | 1 92 | 52 22 18 8 48 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000752 | 1 71 | 37 25 9 11 45 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000753 | 1 80 | 45 26 9 13 48 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000754 | 1 42 | 26 15 1 4 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000755 | 1 82 | 51 19 12 13 44 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000756 | 1 50 | 43 1 6 5 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000757 | 1 73 | 55 6 12 7 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000758 | 1 41 | 28 5 8 3 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000759 | 1 69 | 46 14 9 6 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000760 | 1 61 | 49 6 6 6 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000761 | 1 111 | 88 13 10 5 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000762 | 1 66 | 34 18 14 2 34 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000763 | 1 71 | 41 9 21 1 31 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000764 | 1 21 | 11 8 2 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000765 | 1 64 | 38 14 12 2 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000766 | 1 59 | 39 11 9 5 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000767 | 1 59 | 37 16 6 2 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000768 | 1 55 | 32 6 17 4 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000769 | 1 13 | 6 6 1 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000770 | 1 102 | 75 10 17 9 36 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000771 | 1 74 | 61 6 7 4 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000772 | 1 64 | 51 8 5 11 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000773 | 1 70 | 55 5 10 4 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000774 | 1 75 | 47 6 22 2 30 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000775 | 1 27 | 18 6 3 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000776 | 1 71 | 60 7 4 6 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000777 | 1 41 | 28 4 9 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000778 | 1 28 | 11 13 4 6 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000779 | 1 99 | 56 29 14 15 58 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000780 | 1 88 | 66 11 11 5 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000781 | 1 83 | 65 8 10 6 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000782 | 1 68 | 58 7 3 10 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000783 | 1 99 | 79 8 12 8 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000784 | 1 83 | 66 8 9 1 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000785 | 1 87 | 57 10 20 1 31 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000786 | 1 70 | 51 15 4 9 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000787 | 1 71 | 51 17 3 14 34 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000788 | 1 14 | 10 2 2 18 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000789 | 1 30 | 21 3 6 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000790 | 1 71 | 49 15 7 6 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000791 | 1 55 | 42 5 8 2 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000792 | 1 68 | 55 11 2 9 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000793 | 1 62 | 47 6 9 2 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000794 | 1 33 | 20 6 7 5 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000795 | 1 27 | 20 6 1 6 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000796 | 1 93 | 66 20 7 16 43 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000797 | 1 69 | 46 12 11 9 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000798 | 1 63 | 46 8 9 5 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000799 | 1 55 | 45 5 5 6 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000800 | 1 38 | 24 10 4 17 31 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000801 | 1 85 | 61 13 11 6 30 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000378 | 1 170 | 121 23 26 11 60 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000379 | 1 221 | 168 32 21 37 90 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000380 | 1 121 | 76 30 15 48 93 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000381 | 1 68 | 54 7 7 7 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000382 | 1 170 | 118 21 31 15 67 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000383 | 1 128 | 85 15 28 19 62 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000384 | 1 200 | 138 14 48 10 72 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000385 | 1 124 | 77 16 31 1 48 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000386 | 1 221 | 172 24 25 29 78 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000387 | 1 180 | 129 12 39 6 57 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000388 | 1 366 | 272 51 43 23 117 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000389 | 1 221 | 168 22 31 8 61 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000390 | 1 171 | 129 28 14 30 72 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000391 | 1 148 | 99 16 33 1 50 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000392 | 1 248 | 152 29 67 11 107 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000393 | 1 162 | 108 14 40 5 59 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000394 | 1 143 | 115 11 17 16 44 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000395 | 1 151 | 105 29 17 16 62 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000396 | 1 252 | 167 23 62 27 112 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000397 | 1 176 | 141 21 14 17 52 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000398 | 1 72 | 49 7 16 0 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000399 | 1 116 | 75 23 18 4 45 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000400 | 1 129 | 90 10 29 4 43 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000401 | 1 129 | 91 11 27 7 45 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000402 | 1 181 | 148 19 14 22 55 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000403 | 1 179 | 146 16 17 19 52 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000404 | 1 105 | 65 31 9 16 56 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000405 | 1 110 | 78 8 24 3 35 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000406 | 1 129 | 89 17 23 17 57 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000407 | 1 200 | 155 31 14 76 121 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000408 | 1 113 | 78 17 18 18 53 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000409 | 1 120 | 83 19 18 20 57 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000410 | 1 263 | 175 20 68 10 98 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000411 | 1 115 | 85 9 21 1 31 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000412 | 1 180 | 129 21 30 13 64 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000413 | 1 158 | 96 27 35 5 67 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000414 | 1 161 | 100 16 45 16 77 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000415 | 1 214 | 144 20 50 4 74 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000281 | 1 225 | 165 15 45 3 63 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000282 | 1 182 | 144 15 23 8 46 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000283 | 1 314 | 221 15 78 6 99 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000284 | 1 267 | 194 18 55 6 79 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000285 | 1 204 | 150 18 36 7 61 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000286 | 1 192 | 162 11 19 12 42 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000287 | 1 296 | 201 37 58 12 107 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000288 | 1 236 | 164 20 52 9 81 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000289 | 1 206 | 161 15 30 7 52 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000290 | 1 169 | 118 13 38 4 55 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000291 | 1 269 | 187 26 56 11 93 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000292 | 1 378 | 245 34 99 9 142 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000293 | 1 333 | 263 16 54 5 75 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000294 | 1 157 | 96 18 43 5 66 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000295 | 1 186 | 164 13 9 9 31 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000296 | 1 246 | 183 20 43 9 72 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000297 | 1 333 | 232 33 68 22 123 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000298 | 1 264 | 169 32 63 8 103 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000299 | 1 239 | 164 26 49 7 82 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000300 | 1 151 | 116 11 24 4 39 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000301 | 1 288 | 207 15 66 11 92 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000302 | 1 355 | 243 23 89 5 117 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000303 | 1 284 | 231 23 30 10 63 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000304 | 1 170 | 125 15 30 7 52 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000305 | 1 166 | 102 16 48 7 71 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000306 | 1 372 | 272 28 72 5 105 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000307 | 1 214 | 173 15 26 5 46 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000308 | 1 283 | 197 27 59 4 90 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000309 | 1 294 | 219 28 47 9 84 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000310 | 1 213 | 158 17 38 8 63 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000311 | 1 238 | 170 15 53 7 75 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000312 | 1 310 | 221 21 68 4 93 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000313 | 1 171 | 144 8 19 19 46 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000314 | 1 235 | 173 17 45 4 66 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000315 | 1 145 | 119 7 19 3 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000316 | 1 212 | 170 26 16 32 74 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000317 | 1 118 | 96 3 19 9 31 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000318 | 1 213 | 165 18 30 3 51 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000319 | 1 137 | 114 8 15 7 30 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001408 | 1 20 | 14 2 4 5 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001409 | 1 55 | 48 4 3 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001410 | 1 38 | 30 0 8 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001411 | 1 23 | 14 2 7 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001412 | 1 68 | 56 3 9 1 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001413 | 1 27 | 23 1 3 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001414 | 1 30 | 20 3 7 10 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001415 | 1 10 | 9 1 0 5 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001416 | 1 14 | 9 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001417 | 1 39 | 28 1 10 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001418 | 1 29 | 23 3 3 5 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001419 | 1 53 | 43 4 6 3 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001420 | 1 73 | 46 8 19 1 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001421 | 1 77 | 47 9 21 2 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001422 | 1 28 | 18 1 9 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001423 | 1 25 | 16 4 5 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001424 | 1 53 | 46 2 5 4 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001425 | 1 149 | 108 12 29 4 45 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001426 | 1 34 | 24 3 7 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001427 | 1 18 | 12 2 4 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001428 | 1 52 | 39 1 12 2 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001429 | 1 45 | 29 5 11 1 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001430 | 1 40 | 23 8 9 0 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001431 | 1 37 | 28 3 6 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001432 | 1 25 | 20 2 3 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001433 | 1 48 | 39 3 6 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001434 | 1 114 | 90 11 13 6 30 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001435 | 1 31 | 19 2 10 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001436 | 1 19 | 14 2 3 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001437 | 1 70 | 54 7 9 0 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001438 | 1 49 | 35 3 11 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001439 | 1 29 | 22 1 6 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001440 | 1 43 | 36 1 6 5 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001441 | 1 62 | 49 8 5 6 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001442 | 1 111 | 78 5 28 1 34 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001443 | 1 41 | 27 5 9 1 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001444 | 1 38 | 27 7 4 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001445 | 1 46 | 38 3 5 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001446 | 1 25 | 18 1 6 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001447 | 1 20 | 11 3 6 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001448 | 1 57 | 44 3 10 2 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001449 | 1 45 | 32 5 8 1 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001450 | 1 67 | 53 3 11 5 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001451 | 1 24 | 18 1 5 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001452 | 1 38 | 32 4 2 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001453 | 1 43 | 36 2 5 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001454 | 1 124 | 98 9 17 8 34 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001455 | 1 48 | 39 4 5 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001456 | 1 19 | 14 5 0 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001457 | 1 47 | 39 3 5 5 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001458 | 1 190 | 145 10 35 7 52 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001459 | 1 41 | 29 4 8 3 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001460 | 1 23 | 17 1 5 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001461 | 1 19 | 9 5 5 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001462 | 1 87 | 65 7 15 0 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001463 | 1 62 | 50 4 8 1 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001464 | 1 33 | 24 8 1 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001465 | 1 55 | 34 9 12 2 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001466 | 1 44 | 31 7 6 2 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001467 | 1 27 | 16 9 2 3 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001468 | 1 47 | 32 7 8 2 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001469 | 1 28 | 23 2 3 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001470 | 1 13 | 7 4 2 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001471 | 1 13 | 12 1 0 3 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001472 | 1 107 | 73 14 20 2 36 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001473 | 1 56 | 49 6 1 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001474 | 1 37 | 24 4 9 2 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001475 | 1 45 | 36 5 4 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001476 | 1 70 | 51 2 17 0 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001477 | 1 27 | 23 2 2 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001478 | 1 39 | 21 10 8 1 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001479 | 1 34 | 23 2 9 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001480 | 1 37 | 27 7 3 7 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001481 | 1 75 | 53 6 16 2 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001482 | 1 35 | 29 3 3 4 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001483 | 1 43 | 26 9 8 2 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001484 | 1 33 | 24 2 7 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001485 | 1 107 | 81 11 15 20 46 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001486 | 1 45 | 20 9 16 0 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001487 | 1 26 | 11 3 12 1 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001488 | 1 59 | 39 9 11 4 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001489 | 1 57 | 41 5 11 2 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001490 | 1 32 | 20 12 0 28 40 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001491 | 1 37 | 24 4 9 3 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001492 | 1 40 | 31 4 5 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001493 | 1 32 | 17 6 9 4 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001494 | 1 42 | 33 3 6 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001495 | 1 26 | 23 1 2 3 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001496 | 1 52 | 34 9 9 5 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001497 | 1 57 | 46 5 6 3 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001498 | 1 58 | 47 4 7 3 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001499 | 1 27 | 20 2 5 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001500 | 1 60 | 50 2 8 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001501 | 1 52 | 43 2 7 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001502 | 1 18 | 15 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001503 | 1 22 | 14 5 3 13 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001504 | 1 45 | 36 4 5 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001505 | 1 45 | 33 6 6 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001506 | 1 94 | 77 9 8 6 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001507 | 1 55 | 46 2 7 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001508 | 1 20 | 15 2 3 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001509 | 1 20 | 15 3 2 5 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001510 | 1 62 | 50 4 8 3 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001511 | 1 44 | 34 3 7 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001512 | 1 82 | 70 4 8 4 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001513 | 1 41 | 35 5 1 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001514 | 1 67 | 52 6 9 3 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001515 | 1 20 | 17 0 3 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001516 | 1 34 | 25 4 5 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001517 | 1 46 | 38 2 6 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001518 | 1 66 | 52 6 8 2 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001519 | 1 39 | 29 3 7 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001520 | 1 79 | 69 8 2 6 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001521 | 1 24 | 16 4 4 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001522 | 1 39 | 35 1 3 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001523 | 1 24 | 21 1 2 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001524 | 1 48 | 43 1 4 4 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001525 | 1 25 | 20 3 2 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001526 | 1 15 | 11 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001527 | 1 27 | 18 5 4 4 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001528 | 1 19 | 14 3 2 4 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001529 | 1 16 | 12 1 3 3 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001530 | 1 20 | 16 1 3 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001531 | 1 15 | 7 2 6 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001532 | 1 26 | 24 2 0 3 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001533 | 1 33 | 26 4 3 7 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001534 | 1 18 | 15 0 3 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001535 | 1 16 | 15 0 1 2 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001536 | 1 19 | 13 4 2 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001537 | 1 17 | 12 2 3 11 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001538 | 1 39 | 32 3 4 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001539 | 1 26 | 17 5 4 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001540 | 1 59 | 56 0 3 3 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001541 | 1 11 | 9 0 2 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001542 | 1 56 | 41 3 12 1 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001543 | 1 164 | 142 6 16 4 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001544 | 1 54 | 39 9 6 4 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001545 | 1 25 | 19 2 4 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001546 | 1 70 | 59 4 7 3 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001547 | 1 65 | 56 5 4 4 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001548 | 1 49 | 36 5 8 2 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001549 | 1 29 | 10 6 13 2 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001550 | 1 45 | 34 7 4 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001551 | 1 19 | 17 1 1 3 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001552 | 1 44 | 32 6 6 1 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001553 | 1 147 | 109 15 23 11 49 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001554 | 1 94 | 74 7 13 3 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001555 | 1 55 | 43 2 10 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001556 | 1 53 | 35 3 15 1 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001557 | 1 19 | 16 2 1 4 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001558 | 1 43 | 34 0 9 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001559 | 1 80 | 64 5 11 4 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001560 | 1 19 | 14 1 4 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001561 | 1 87 | 74 7 6 5 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001562 | 1 94 | 77 7 10 2 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001563 | 1 46 | 30 6 10 3 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001564 | 1 34 | 24 5 5 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001565 | 1 76 | 54 12 10 2 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001566 | 1 49 | 38 5 6 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001567 | 1 16 | 12 1 3 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001568 | 1 100 | 84 5 11 5 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001569 | 1 71 | 58 9 4 7 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001570 | 1 35 | 29 2 4 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001571 | 1 21 | 19 1 1 3 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001572 | 1 75 | 58 6 11 4 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001573 | 1 14 | 7 4 3 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001574 | 1 64 | 56 4 4 4 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001575 | 1 35 | 30 3 2 4 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001576 | 1 84 | 73 2 9 14 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001577 | 1 43 | 32 7 4 3 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001578 | 1 116 | 99 5 12 3 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001579 | 1 126 | 104 11 11 10 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001580 | 1 28 | 16 4 8 5 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001581 | 1 45 | 39 4 2 5 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001582 | 1 26 | 15 4 7 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001583 | 1 36 | 26 6 4 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001584 | 1 31 | 29 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001585 | 1 68 | 56 4 8 2 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001586 | 1 61 | 40 5 16 1 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001587 | 1 40 | 34 3 3 4 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001588 | 1 54 | 45 5 4 4 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001589 | 1 25 | 18 2 5 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001590 | 1 48 | 35 5 8 1 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001591 | 1 25 | 13 5 7 2 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001592 | 1 103 | 75 7 21 0 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001593 | 1 35 | 28 2 5 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001594 | 1 63 | 52 2 9 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001595 | 1 17 | 12 2 3 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001596 | 1 19 | 14 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001597 | 1 82 | 58 8 16 3 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001598 | 1 154 | 124 13 17 7 37 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001599 | 1 204 | 155 17 32 6 55 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000891 | 1 29 | 22 3 4 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000892 | 1 72 | 64 2 6 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000893 | 1 41 | 34 4 3 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000894 | 1 35 | 24 3 8 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000895 | 1 54 | 45 4 5 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000897 | 1 2 | 0 2 0 9 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000898 | 1 51 | 43 1 7 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000899 | 1 29 | 22 1 6 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000900 | 1 43 | 31 2 10 1 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000901 | 1 53 | 39 5 9 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000902 | 1 30 | 25 4 1 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000903 | 1 57 | 42 4 11 2 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000904 | 1 53 | 40 3 10 2 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000905 | 1 37 | 25 2 10 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000906 | 1 63 | 35 9 19 0 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000907 | 1 32 | 28 0 4 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000908 | 1 62 | 46 13 3 18 34 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000909 | 1 61 | 40 8 13 4 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000910 | 1 70 | 58 5 7 5 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000911 | 1 34 | 24 2 8 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000912 | 1 64 | 37 11 16 0 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000913 | 1 38 | 32 2 4 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000914 | 1 50 | 34 6 10 5 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000915 | 1 63 | 50 4 9 3 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000917 | 1 42 | 32 5 5 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000918 | 1 10 | 7 0 3 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000919 | 1 39 | 30 0 9 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000920 | 1 40 | 27 6 7 2 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000921 | 1 35 | 27 4 4 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000922 | 1 33 | 22 3 8 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000923 | 1 67 | 48 7 12 9 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000924 | 1 30 | 16 3 11 4 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000925 | 1 44 | 39 4 1 5 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000926 | 1 48 | 37 6 5 4 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000927 | 1 39 | 30 1 8 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000928 | 1 35 | 25 5 5 3 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000929 | 1 73 | 59 7 7 3 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000930 | 1 59 | 36 10 13 2 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000931 | 1 49 | 38 7 4 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000932 | 1 66 | 51 6 9 6 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000933 | 1 33 | 23 2 8 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000934 | 1 47 | 37 7 3 4 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000935 | 1 55 | 46 7 2 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000936 | 1 67 | 51 6 10 1 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000937 | 1 63 | 50 5 8 12 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000938 | 1 64 | 45 8 11 3 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000939 | 1 25 | 22 1 2 4 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000940 | 1 54 | 43 5 6 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000941 | 1 47 | 35 3 9 1 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000942 | 1 58 | 36 5 17 1 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000943 | 1 59 | 51 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000944 | 1 36 | 33 0 3 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000945 | 1 29 | 25 2 2 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000946 | 1 57 | 50 3 4 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000947 | 1 62 | 48 10 4 14 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000948 | 1 28 | 18 3 7 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000950 | 1 40 | 26 7 7 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000951 | 1 34 | 30 2 2 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000952 | 1 30 | 28 2 0 9 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000953 | 1 69 | 54 6 9 4 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000954 | 1 34 | 30 1 3 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000955 | 1 34 | 29 2 3 5 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000956 | 1 68 | 50 6 12 3 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000957 | 1 41 | 30 4 7 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000958 | 1 61 | 48 6 7 3 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000959 | 1 52 | 44 3 5 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000960 | 1 60 | 42 6 12 1 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000961 | 1 24 | 20 1 3 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000962 | 1 36 | 26 2 8 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000963 | 1 35 | 29 3 3 5 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000964 | 1 43 | 31 6 6 3 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000965 | 1 48 | 39 3 6 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000966 | 1 19 | 13 4 2 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000967 | 1 31 | 20 5 6 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000968 | 1 39 | 22 5 12 0 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000969 | 1 36 | 34 1 1 6 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000970 | 1 40 | 20 5 15 0 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000971 | 1 45 | 38 4 3 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000972 | 1 56 | 46 4 6 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000973 | 1 69 | 55 8 6 2 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000974 | 1 28 | 23 1 4 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000975 | 1 29 | 20 5 4 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000976 | 1 74 | 50 7 17 2 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000977 | 1 29 | 17 5 7 4 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000978 | 1 44 | 37 4 3 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000979 | 1 47 | 31 7 9 2 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000980 | 1 42 | 36 4 2 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000981 | 1 62 | 53 3 6 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000982 | 1 58 | 49 5 4 9 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000983 | 1 53 | 37 4 12 1 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000984 | 1 57 | 43 4 10 1 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000985 | 1 46 | 32 4 10 4 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000986 | 1 41 | 30 3 8 3 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000987 | 1 40 | 30 4 6 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000988 | 1 29 | 23 3 3 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000989 | 1 39 | 31 3 5 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000990 | 1 41 | 33 3 5 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000991 | 1 55 | 44 3 8 4 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000992 | 1 34 | 25 2 7 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000993 | 1 65 | 57 3 5 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000994 | 1 78 | 65 8 5 4 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000995 | 1 47 | 37 3 7 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000996 | 1 38 | 29 3 6 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000997 | 1 58 | 46 10 2 5 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000998 | 1 31 | 22 4 5 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000999 | 1 51 | 41 3 7 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001000 | 1 38 | 35 1 2 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001001 | 1 33 | 24 4 5 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001002 | 1 39 | 31 3 5 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001003 | 1 69 | 58 8 3 8 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001004 | 1 50 | 40 7 3 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001006 | 1 39 | 25 6 8 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001007 | 1 36 | 30 3 3 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001008 | 1 57 | 41 8 8 2 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001009 | 1 38 | 24 3 11 1 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001010 | 1 80 | 58 6 16 3 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001011 | 1 44 | 35 2 7 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001012 | 1 42 | 26 8 8 0 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001013 | 1 44 | 30 3 11 4 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001014 | 1 39 | 36 1 2 3 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001015 | 1 28 | 21 3 4 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001016 | 1 31 | 26 3 2 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001017 | 1 41 | 32 4 5 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001018 | 1 55 | 43 6 6 4 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001019 | 1 39 | 32 2 5 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001020 | 1 35 | 33 2 0 3 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000309 | 1 137 | 97 13 27 3 43 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000310 | 1 102 | 58 12 32 8 52 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000311 | 1 55 | 27 6 22 1 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000312 | 1 56 | 37 8 11 6 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000313 | 1 119 | 69 19 31 9 59 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000314 | 1 131 | 87 7 37 0 44 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000315 | 1 92 | 64 17 11 5 33 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000316 | 1 60 | 37 12 11 2 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000317 | 1 149 | 100 21 28 12 61 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000318 | 1 64 | 49 4 11 6 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000319 | 1 125 | 97 15 13 15 43 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000320 | 1 82 | 57 7 18 7 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000321 | 1 102 | 76 14 12 6 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000322 | 1 110 | 78 9 23 5 37 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000323 | 1 197 | 149 25 23 23 71 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000324 | 1 140 | 100 16 24 3 43 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000325 | 1 32 | 18 4 10 3 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000326 | 1 109 | 68 17 24 6 47 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000327 | 1 80 | 50 8 22 8 38 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000328 | 1 170 | 126 18 26 9 53 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000329 | 1 31 | 22 2 7 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000330 | 1 60 | 46 4 10 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000331 | 1 339 | 198 47 94 21 162 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000332 | 1 129 | 79 9 41 2 52 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000333 | 1 210 | 135 30 45 16 91 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000334 | 1 110 | 94 9 7 21 37 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000335 | 1 454 | 254 54 146 14 214 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000336 | 1 48 | 13 29 6 6 41 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000337 | 1 148 | 102 16 30 4 50 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000338 | 1 54 | 39 6 9 5 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000339 | 1 182 | 142 14 26 12 52 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000340 | 1 36 | 27 6 3 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000341 | 1 49 | 30 1 18 0 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000342 | 1 74 | 55 7 12 2 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000343 | 1 148 | 115 10 23 9 42 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000344 | 1 191 | 150 25 16 24 65 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000345 | 1 179 | 119 28 32 13 73 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000346 | 1 55 | 39 6 10 1 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000347 | 1 112 | 75 14 23 2 39 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000348 | 1 93 | 62 14 17 2 33 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000349 | 1 53 | 46 2 5 5 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000350 | 1 44 | 15 5 24 4 33 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000351 | 1 295 | 215 29 51 21 101 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000352 | 1 149 | 107 10 32 13 55 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000353 | 1 87 | 61 9 17 2 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000354 | 1 92 | 72 8 12 6 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000355 | 1 156 | 108 9 39 6 54 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000356 | 1 14 | 11 0 3 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000357 | 1 149 | 110 12 27 7 46 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000358 | 1 114 | 70 12 32 3 47 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000359 | 1 58 | 33 7 18 3 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000360 | 1 345 | 246 32 67 12 111 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000361 | 1 60 | 49 7 4 4 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000362 | 1 184 | 123 19 42 9 70 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000363 | 1 83 | 53 12 18 4 34 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000364 | 1 162 | 113 17 32 11 60 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000365 | 1 151 | 97 43 11 44 98 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000366 | 1 63 | 49 9 5 11 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000367 | 1 250 | 165 31 54 24 109 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000368 | 1 183 | 117 16 50 14 80 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000369 | 1 169 | 122 19 28 5 52 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000370 | 1 122 | 79 16 27 1 44 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000371 | 1 36 | 16 2 18 4 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000372 | 1 103 | 69 19 15 6 40 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000373 | 1 154 | 104 19 31 13 63 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000374 | 1 104 | 70 18 16 24 58 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000375 | 1 42 | 15 7 20 1 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000376 | 1 90 | 66 9 15 5 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000377 | 1 188 | 114 20 54 10 84 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000378 | 1 149 | 102 24 23 18 65 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000379 | 1 246 | 183 30 33 19 82 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000380 | 1 204 | 139 24 41 18 83 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000381 | 1 54 | 37 5 12 19 36 1 | +|====================================================================================================================| +| Sum | 661 54401 | 39478 5738 9185 3417 18340 661 | +|====================================================================================================================| +| Mean | 1.2 94.9 | 68.9 10.0 16.0 6.0 32.0 1.2 | +| S.D. | 3.7 362.2 | 268.2 34.1 60.7 18.1 111.7 3.7 | +| Median | 1.0 55.0 | 40.0 6.0 8.0 3.0 18.0 1.0 | +`--------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/hyp.trn + +Speakers: + 0: m + 1: cv_deu_000698 + 2: cv_deu_000699 + 3: cv_deu_000700 + 4: cv_deu_000701 + 5: cv_deu_000702 + 6: cv_deu_000703 + 7: cv_deu_000704 + 8: cv_deu_000705 + 9: cv_deu_000706 + 10: 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voxforge_deu_001006 + 486: voxforge_deu_001007 + 487: voxforge_deu_001008 + 488: voxforge_deu_001009 + 489: voxforge_deu_001010 + 490: voxforge_deu_001011 + 491: voxforge_deu_001012 + 492: voxforge_deu_001013 + 493: voxforge_deu_001014 + 494: voxforge_deu_001015 + 495: voxforge_deu_001016 + 496: voxforge_deu_001017 + 497: voxforge_deu_001018 + 498: voxforge_deu_001019 + 499: voxforge_deu_001020 + 500: voxpopuli_deu_000309 + 501: voxpopuli_deu_000310 + 502: voxpopuli_deu_000311 + 503: voxpopuli_deu_000312 + 504: voxpopuli_deu_000313 + 505: voxpopuli_deu_000314 + 506: voxpopuli_deu_000315 + 507: voxpopuli_deu_000316 + 508: voxpopuli_deu_000317 + 509: voxpopuli_deu_000318 + 510: voxpopuli_deu_000319 + 511: voxpopuli_deu_000320 + 512: voxpopuli_deu_000321 + 513: voxpopuli_deu_000322 + 514: voxpopuli_deu_000323 + 515: voxpopuli_deu_000324 + 516: voxpopuli_deu_000325 + 517: voxpopuli_deu_000326 + 518: voxpopuli_deu_000327 + 519: voxpopuli_deu_000328 + 520: voxpopuli_deu_000329 + 521: voxpopuli_deu_000330 + 522: voxpopuli_deu_000331 + 523: voxpopuli_deu_000332 + 524: voxpopuli_deu_000333 + 525: voxpopuli_deu_000334 + 526: voxpopuli_deu_000335 + 527: voxpopuli_deu_000336 + 528: voxpopuli_deu_000337 + 529: voxpopuli_deu_000338 + 530: voxpopuli_deu_000339 + 531: voxpopuli_deu_000340 + 532: voxpopuli_deu_000341 + 533: voxpopuli_deu_000342 + 534: voxpopuli_deu_000343 + 535: voxpopuli_deu_000344 + 536: voxpopuli_deu_000345 + 537: voxpopuli_deu_000346 + 538: voxpopuli_deu_000347 + 539: voxpopuli_deu_000348 + 540: voxpopuli_deu_000349 + 541: voxpopuli_deu_000350 + 542: voxpopuli_deu_000351 + 543: voxpopuli_deu_000352 + 544: voxpopuli_deu_000353 + 545: voxpopuli_deu_000354 + 546: voxpopuli_deu_000355 + 547: voxpopuli_deu_000356 + 548: voxpopuli_deu_000357 + 549: voxpopuli_deu_000358 + 550: voxpopuli_deu_000359 + 551: voxpopuli_deu_000360 + 552: voxpopuli_deu_000361 + 553: voxpopuli_deu_000362 + 554: voxpopuli_deu_000363 + 555: voxpopuli_deu_000364 + 556: voxpopuli_deu_000365 + 557: voxpopuli_deu_000366 + 558: voxpopuli_deu_000367 + 559: voxpopuli_deu_000368 + 560: voxpopuli_deu_000369 + 561: voxpopuli_deu_000370 + 562: voxpopuli_deu_000371 + 563: voxpopuli_deu_000372 + 564: voxpopuli_deu_000373 + 565: voxpopuli_deu_000374 + 566: voxpopuli_deu_000375 + 567: voxpopuli_deu_000376 + 568: voxpopuli_deu_000377 + 569: voxpopuli_deu_000378 + 570: voxpopuli_deu_000379 + 571: voxpopuli_deu_000380 + 572: voxpopuli_deu_000381 + +Speaker sentences 0: m #utts: 89 +id: (m-ailabs_deu_000165-m-ailabs_deu_000165) +Scores: (#C #S #D #I) 92 16 17 7 +REF: d i E b e ******* E r d i g U n G m a c h t E e i n e r * Ä u S s e R s t w i c h t i g e n s A C H e e i n E n d e d e r p E t i t ******* * i o n A n d E n g o U V E R n E U r f Ü r d e s i n D I a n e r * J o E s b e g * n a ******* d i g u n G +HYP: d i * ******* b e H r d i g O n * m a c h t * ******* e i n e r A E u * s e * s t ******* w i c h t i g e n s * * * e H e i n I n d e d e r p Ä t i t Z i o n * n d I n ******* g o * * W A n Ü Ö r f Ü r ******* d e s i n J a n e r D S o U s S b e g E n a d i g u n * +Eval: D D I S S D D D I S D D D D D D S S S I I D S D D D S S S S D S S I S S S I I D + +id: (m-ailabs_deu_000166-m-ailabs_deu_000166) +Scores: (#C #S #D #I) 62 7 9 0 +REF: d a h a b e s i E d i e w o H l j e d e m h I e r i n D e r E R i N n e r u n G g e b l i e b e n e n w O r t e g e s p R o c h e n +HYP: d a R h a b e ******* s i * d i e V w o U l G j e d e m h * e r i n ******* * e r * * i * n e r u n * K g e b l i e b e n e n w A r t e g e s p A o c h e n +Eval: S D D S S S D D D D D D D S S S + +id: (m-ailabs_deu_000167-m-ailabs_deu_000167) +Scores: (#C #S #D #I) 124 12 23 5 +REF: e r s t u m a c h t u h r w a r e r a u f m a l e * b r A c h t e d e n K a F f E E d I e s O N n e s c h I e n i n * s z i M m e r u n D d I e s p e * r l i n g e d i e d a s a u s d e n h Ä C K S e L s Ä C K e n g e f a L l E n e F U T t E r k o R n a u f * p * i C k t e n +HYP: e r s t u m a c h t u h r w a r e r a u f m a l e L b r * c h t e R d e n G a * f * I d * e ******* s * * n e ******* s c h * e n i n Z s ******* z i * m e r u n * ******* d * e ******* s p e H r l i n g e d i e d a s S a u s d e n h ** * E X e s E G T e n g e f a * l * n e * * O t A r k o * n a u f S p B i * k t e n +Eval: I D S S D D S D D D D D D I D D D D D D I S D D S S S S S S D D D D S S D I I D + +id: (m-ailabs_deu_000168-m-ailabs_deu_000168) +Scores: (#C #S #D #I) 45 7 10 0 +REF: s i c h e r l i c h a n i H r e M g e b u R t s t a G H Ä T t E e r b E i I H r b l e i b e n k Ö N n e n +HYP: s i c h e r l i c h a n i * r e * N g e b u O t s t a * ******* * K E t * e r ******* b A i * E r ******* b l e i b e n k ** O n e n +Eval: D D S S D D D S S D D S D S D D S + +id: (m-ailabs_deu_000169-m-ailabs_deu_000169) +Scores: (#C #S #D #I) 82 10 13 14 +REF: U n D d e s H a l b * ** * m u S s m a n d * O r t W o * m e n S c h E n s c h W i e r i g k e I t e n h a b e n d i E s A u c h e i n E R s e i t s e R k l * Ä r e n a * n g e b O t e m a c h e n * * * * * ******* * +HYP: * n * ******* d e s E a l b E Ö M m u * s ******* m a n d A U r t V o R O m e n T c h * n ******* s c h * i e r i g k e * t e n h a b e n d i * s ******* O u c h e i n A s e i t s e * k l I E r e n a I n g e b U t e ******* m a c h e n B E A L E W +Eval: D D D S I I I D D I S S I S S D D D D D D S S S D I S I S D I I I I I I I + +id: (m-ailabs_deu_000170-m-ailabs_deu_000170) +Scores: (#C #S #D #I) 75 6 25 3 +REF: D A S s m A n n U r A U f d i E W e l t k o M m t u m s e L b s t W i E d e r E i N E n s o H n z u h a b e n d e r d i e v e r ******* e H r * u n g * d e r a H n e n F o r t S e t z t +HYP: * * E s ******* m E n n * r ******* * * f T d i * ******* * e l t k o * m t u m ******* s e * b s t ******* * i * d e r * i * * n s o * n D z u O h a b e n d e r ******* d i e ******* v e r e * r H u n g K d e r a * n e n V o r t * e t z t +Eval: D D S D S D D D D S D D D D D D D D D D D D D S S D D I D I I D S D + +id: (m-ailabs_deu_000171-m-ailabs_deu_000171) +Scores: (#C #S #D #I) 106 21 33 5 +REF: D E S H a L B G e H Ö R E n K O n T i n U I E R l i c h e * s c h u l * b I l D u n g A u C H K O n t I n U I e r l i c h e m Ö G l i c h K e i t * * E N d e r W e i t e r b I l D u n g u n D d a s B e g e h E n v o n g e d e n k t a g E n F Ü R m i c h u n ******* a u f l Ö s l i c h Z U S a M m E N +HYP: * * * * a * * B e * Ö * A n E U n E i n * * * l i c h e R s c h u l E b E l * u n g * u * * ******* * * n t E n * e r l i c h e m Ü K l i c h T e i t R A U H d e r ******* * e i t e r b * l * u n g u n T d a s D e g e h * n v o n ******* g e d e n k t a g * n * ** D E m i c h u n a u f l ** s l i c h ******* * T R a * m * D +Eval: D D D D D D S D D S S S S D D D S I I S D D D D D D D S D S S S S I I S S D D D D S S D D D D D S S I D D D S S D D S + +id: (m-ailabs_deu_000172-m-ailabs_deu_000172) +Scores: (#C #S #D #I) 109 9 25 9 +REF: M e i n a n s a s C h e n s a G T S i E E s i s t J A J e * t Z t w i e d e r g a n z * g u t z w i s c h e n u n s a * b e R E H e d u n i c h t a l L e s g e s t * * e H s t * g * e H t D i e e r ******* i N n E r u n g a n d a s b * Ö s e n i c h t w e g +HYP: * e i n a n s a s * h e n s a * * K Z i * * s ******* i s t ******* W E R G e R t S t ******* w i e d e r g a n z S K g u t ******* z w i s c h e n u n s a R b e * * * e d u ******* n i c h t a l I e s ******* g e s t D I e * s t D g I e * t ******* * i e e r i * n * r u n g ******* a n ******* d a s b E Ö s e ******* n i c h t ******* w e g +Eval: D D D D S S D D D D S S S S I S D I S D I D D D D S D I I D I I D D D I D D D D I D D + +id: (m-ailabs_deu_000173-m-ailabs_deu_000173) +Scores: (#C #S #D #I) 23 2 4 0 +REF: n e i n w e i b e r b r a u C H e i c h n i c h T +HYP: n e i n w e i b e r ******* b r a u * R e ******* i c h E n i c h * +Eval: D D S D S D + +id: (m-ailabs_deu_000174-m-ailabs_deu_000174) +Scores: (#C #S #D #I) 49 6 6 7 +REF: * * * * * g o T t h a t n i c h t v e r ******* g e b l i c h n A C h m I r g e r u * f e n s a G t e d e r S c h i F F e r +HYP: E N D E N g o R t h a t n i c h t ******* v e r g e b l i c h ******* n * E h ******* m E r g e r u O f e n s a K t e d e r ******* * c h i E V e r +Eval: I I I I I S D I D D S D S I S D D S S + +id: (m-ailabs_deu_000175-m-ailabs_deu_000175) +Scores: (#C #S #D #I) 139 9 10 8 +REF: n u r e i n e s w e i s s i c h d i e s e r F u r c h t b a r e n f r a * g e E n T g e g e n ******* z u s e t z * e n u n d s c h ******* l e U d e r e d a s w O r t i n d I e w A a G s c H a * l e d i E g l u t M e i n * e s l i e b e s ******* w i L l e n * s i s t s t Ä r k e r a l s t r e N n u n g +HYP: n u r e i n e s w e i s s ******* i c h d i e s e r V u r c h t b a r e n f r a R g e ******* I n D g e g e n z u s e t z H e n u n d s c h l e I d e r e R d a s ******* w A r t i n d * e w * a R s c * a L l e ******* d i * g l u t L e i n I e s l i e b e s w i * l e n Z s i s t s t E r k e r a l s t r e * n u n g +Eval: D S I D S S I I I S S D S D D S D I D D S I I D I S D + +id: (m-ailabs_deu_000176-m-ailabs_deu_000176) +Scores: (#C #S #D #I) 51 7 18 1 +REF: t o m s a R m E E g e W a N n e i N E n g r o S s e n s i e g n A C h E i n e R l a N G E n h A r * T n Ä c k i g e n s C H l A c h t +HYP: t o m s ******* a * m * I g e a * n ******* e i * * n g r o * s e n s i e g n * E h ******* * i n e * L l a * * * n h E r D n E c k i g e n s * * l * c h t +Eval: D D D S S D D D D D D S D D D S D D D S I S S D D D + +id: (m-ailabs_deu_000177-m-ailabs_deu_000177) +Scores: (#C #S #D #I) 58 10 17 4 +REF: E s I s T e i n n a * m e d e m s i c h D i E t Ü R b e i t a G u n D n A c h t Ö F f n e n k a N n b * U r s c h e * u n d * w I L L k o M M e n +HYP: * s ******* * s * ******* e i n n a H m e d e m s i c h ******* T i * t U H E b e i ******* t a * K u n * n * c h t ******* ** A f n e n k a * n ******* b R E r s c h e R u n d T w E R k o * * e n +Eval: D D D D D I D S D S S S D D S D D D D S D D I S I I S S S D D + +id: (m-ailabs_deu_000178-m-ailabs_deu_000178) +Scores: (#C #S #D #I) 34 2 5 6 +REF: * * a b e r i c h V e r * ******* Z e i H e i H n e n i H r e * u n w i S s e n ******* h e i t +HYP: E N a b e r i c h ******* F e r T S e i * e i * n e n i * r e R u n w i * s e n h e i t +Eval: I I D S I I S D D D I D I + +id: (m-ailabs_deu_000179-m-ailabs_deu_000179) +Scores: (#C #S #D #I) 80 10 7 2 +REF: V o n d e r D r i t t e n u n t e R r e d u n g a n s a G t e m i s t e r h * A v i s * h A m w a r m I r d i e p e r s O n i n h O h e M m a S s e v e r d Ä c h t i G +HYP: F o n d e r ******* T r i t t e n u n t e * r e d u n g a n s a K t e m i s t e r ******* h E R v i s C h E m w a r ******* m E r d i e p e r s U n i n h U h e * ******* m a * s e v e r d E c h t i H +Eval: S D S D S D I S I S D S S S D D D S S + +id: (m-ailabs_deu_000180-m-ailabs_deu_000180) +Scores: (#C #S #D #I) 107 10 23 3 +REF: i c h D e n k e d e R a m t ******* m a N n u n D s E I n e * F A m I L i e w e r d e n e s r e c h t v o n d I r f i n d e n * d a S s D u d i C h s e l B s t a n g i b s t u n D s I e w e r d e n F r e u n D l i c h g e g e n d i C H s e i N +HYP: i c h * e n k e d e * a m t m a * n ******* u n * ******* s * A n e V E R m * E i e ******* w e r d e n ******* e s ******* r e c h t v o n d E r ******* f i n d e n T d a * s ******* T u d i * h ******* s e l P s t ******* a n g i b s t u n * s * e ******* w e r d e n H r e u n T l i c h g e g e n d i * * E s e i * +Eval: D D I D D D D D S I S S D S D D D S D I D D S D D S D D D D S S D D S D + +id: (m-ailabs_deu_000181-m-ailabs_deu_000181) +Scores: (#C #S #D #I) 91 5 19 4 +REF: J e t z T s c h l u * G D i e h e L l e f l a m m e * a u f u n d n u n e r ******* k a N n t e e r u n s d i e w i R n O C H i M m e r z U s a M m e n ******* g e d r Ä n G t i n d e m w I n k e L s t a n d e n +HYP: * e t z * ******* s c h l u O K T i e h e * l e f l a m m e R a u f u n d n u n e r k a * n t e e r u n s d i e ******* w i * ******* n * * R i * m e r ******* z * s a * m e n g e d r E n * t i n ******* d e m ******* w E n k e * ******* s t a n d e n +Eval: D D D I S S D I I D D D D D D S D D D D I S D D D S D D + +id: (m-ailabs_deu_000182-m-ailabs_deu_000182) +Scores: (#C #S #D #I) 49 5 3 4 +REF: d e r s e i n e R s E e l e a n s p * o R n e n d * d a s e r m u n t e r n d e w O R t v o r w * Ä R t * s +HYP: d e r s e i n e * s I e l e a n s p B o * n e n d T d a s e r m u n t e r n d e ******* w A U t v o r w E A L t Z s +Eval: D S I D I D S S I S S I + +id: (m-ailabs_deu_000183-m-ailabs_deu_000183) +Scores: (#C #S #D #I) 40 9 16 9 +REF: I C H F r E U E m i c h a U f d E n b E s u c h d e s t u n e s I s C h e N m I n I s T E R p * r Ä s * I d e * n * t e * ******* * * n * +HYP: * * * ******* V r * * * ******* m i c h ******* a * f T d I n b * s u c h T d e s t u n e s U s * h e M m * n E s * * A p E r ** s E d e A n D t e N O R n T +Eval: D D D D S D D D D D D S S D S S D S D S D D S I D I S I I I I I I I + +id: (m-ailabs_deu_000184-m-ailabs_deu_000184) +Scores: (#C #S #D #I) 61 6 10 5 +REF: w a s f * Ü r * V e r ******* f o l g u n g e n w a s f Ü r n a C H s t e L l u N G e n h a b E i c h n i c h T z u e * r d u l D e n g e ******* h a b t +HYP: w a s T f I H r E H e r f o l g u n g e n w a s ******* f I r ******* n a * R s t e * l u * M e n h a b * ******* i c h n i c h * ******* z u e A r d u l * e n g e h a b t +Eval: S I S I S I D S D D S D D S D D D D I D I + +id: (m-ailabs_deu_000185-m-ailabs_deu_000185) +Scores: (#C #S #D #I) 88 16 14 10 +REF: Z i * G e U n E r w a r e n e s D i e V o n O R t z u o * R t * f U H r e n e i n k a u m E R w a C H s E n * e s J u n g e s d i n G k a m z u m i R h e r a n ******* g e ******* h Ü * p f t u n D b E t T e l * T e ******* * n e i n +HYP: S i K O e I n A r ******* w a r e n e s * i e * o n A U t ******* z u ******* o A L t V f * O r e n e i n k a u m * A w a * K s * n D e s I u n g e s d i n * k a m T z u ******* m i * E h e r a n g e h Ü E p f t ******* u n * b Ä t K e l E D e N n e i n +Eval: S I S S S D D D S S D D I S I D S D S D S D I S D S D D S I I I D D S S I S I I + +id: (m-ailabs_deu_000186-m-ailabs_deu_000186) +Scores: (#C #S #D #I) 83 22 48 3 +REF: H U C k i c h w e r D E D i C h i N E n e M b O o t h i n ******* f A H R e * n W E R d e D a s b O o t D A a n ******* l e g E N u n d E S W i E D e r z U R Ü C K R u d e R n a L L e s g a n z A l l e i n B R A U C H s T d I c H g a R n i c h t D R u m z U k Ü M m E R n +HYP: * * A k i c h ******* w e r * * ******* T i * h i * ******* * n e * b * o t ******* h i n f * * * e A n * * * d e R T a s ******* b U o t ******* E R a n l e g * * ******* u n d ******* * * ******* * i * T e r T z * * O E G T u d e * n a * * e s g a n z S E l l e i n * * * * W O s * d E c * g a * ******* n i c h t ******* * H u m z * O k ** E m * A n +Eval: D D S D D D D S D D D D D D D I D D D I D D D S S D S D S S I D D D D D D D D D S S D D S S S S D D D S S D D D D S S D S D D D D D S D S D S D S + +id: (m-ailabs_deu_000187-m-ailabs_deu_000187) +Scores: (#C #S #D #I) 86 8 14 4 +REF: a l s n U r e i n m a l n o c h D e n * r a u c h v O n S E I n e m h a u s E a u s d e r F e r n e * a u f s t e i g e n z u S e H e n u m d a N n b e ******* r u H I G t z u s t * e r b E N +HYP: a l s n * r e i n m a l U n o c h * e n D r a u c h v E n * * A n e m h a u s * ******* a u s d e r V e r n e R a u f s t e i g e n ******* z u ******* * e S e n u m d a * n ******* b e r u * E K t ******* z u s t D e r b * M +Eval: D S D I S D D S D D S I D D D S D D I D S S D I D S + +id: (m-ailabs_deu_000188-m-ailabs_deu_000188) +Scores: (#C #S #D #I) 86 7 9 6 +REF: D i e t Ä n z e r i n a b E r l a G a u f D e n k n i E e n v o r b r a H m a s b i l * D n i s i n n a * m e n ******* l o s e r s e H n ******* s u c h t u n d * w e i n t e j a M m E R v o l l * +HYP: * i e t E n z e r i n a b A r ******* l a R K a u f * e n k n i * e n v o r b r a * m a s ******* b i l T E n i s i n n a H m e n l o s e r ******* s e * n s u c h t u n d T w e i n t e j a * m A v o l l T +Eval: D S S D S S D D D D I S I I D D I I D S S I + +id: (m-ailabs_deu_000189-m-ailabs_deu_000189) +Scores: (#C #S #D #I) 53 11 16 2 +REF: R e c h t ******* f e r t i * G t m i c h D e N n d I e w I R K l i C H k e i t n o c h n i c h t a U f d i e I C h M i c h b e R u f e n k A N N +HYP: * e c h t f e r t i C H t m i c h ******* S e * n ******* d * e w * E G l i * G k e i t ******* n o c h ******* n i c h t a f T d i e ******* * * h ******* * i c h ******* b e u f e n G k * H R +Eval: D I I S D S D D D D S S D S D D S S D D D D D D S S D S S + +id: (m-ailabs_deu_000190-m-ailabs_deu_000190) +Scores: (#C #S #D #I) 64 4 14 10 +REF: i c h Ä r g e r t e m i c h D a N n w e N n i c h a u * ******* f W a c h t e * e * s w a R s * O w u n d e r s C h * Ö * * n g e w * e s e n d a * s f l i E G e n +HYP: i c h E L r g e r t e ******* m i c h ******* T a * n ******* w e * n ******* i c h a u H f * a c h t e R e I s ******* w a * ******* s U F w u n d e r s * h E Ö N E n g e w E e s e n ******* d a R s f l i * * e n +Eval: S S D D S D D D D I I D I I D D D I S D I I I I D I D D + +id: (m-ailabs_deu_000191-m-ailabs_deu_000191) +Scores: (#C #S #D #I) 93 17 17 7 +REF: n A C h ******* d e m e R s c h o n d E n g a n z e n v o r m i t T a g m i t i H m v E r b r a c h t k a m s ******* T A n ******* h o P E n a c h t i * s c h i n S * * Q U a n D T s c h e h a u s u m C a s P a R l E B e W o H l z U s ******* A g e n +HYP: n E R h d e m ******* e * ******* s c h o n ******* d I n g a n z e n v o r m i t E a g m i t i * m v A r b r a c h t k a m ******* s D E n h o * B n a c h ******* t i S s c h i n * Z S K R a n * * s c h e R h a u s u m G a s B a * ******* l * * e o * l T z * O s R g e n +Eval: S S I D D D D S S D S D I S S I D S D I D I I S S D D S S S D D D D S D S D S I S + +id: (m-ailabs_deu_000192-m-ailabs_deu_000192) +Scores: (#C #S #D #I) 43 5 4 3 +REF: E r w a r E i n a l t e r h i r * t v o L l m e d i * ******* z i n I s c h e r g E n I n a l i t Ä t +HYP: H r ******* w a r A i n a l t e r h i r H t v o * l ******* m e d i E z i n E s c h e r g * n E n a l i t E t +Eval: S D S I D D I I S D S S + +id: (m-ailabs_deu_000193-m-ailabs_deu_000193) +Scores: (#C #S #D #I) 44 5 11 3 +REF: d A S s W o H l a u C h d e r m i e t * e r s e i n e W u n d E r L i c h * k e i t e n h a * b e N m Ü S s e +HYP: d * E s ******* V o * l a u * h T d e r ******* m i e t A e r ******* s e i n e V u n d A r * i c h S k e i t e n h a R b e * ******* m ** * s e +Eval: D S D S D D S D I D S S D I I D D D D + +id: (m-ailabs_deu_000194-m-ailabs_deu_000194) +Scores: (#C #S #D #I) 108 4 17 8 +REF: * * ******* s i e s a H E n a L l e Ä n G s t l i c H u n d b e t r Ü b t a u s u n d a u c H h e R r a r * n e s a S S s c H w e * r ******* m Ü t i * g d a * w i e d i e a n d e r e n u n d s t Ü t z t e d A s h a u p t i n d i e h A n D +HYP: E N s i e ******* s a * * n a * l e E n * s t l i c G u n d b e t r Ü b t ******* a u s u n d a u c * ******* h e * r a r E n e s a * * ******* s c * w e H r m Ü t i C g d a R w i e d i e a n d e r e n u n d ******* s t Ü t z t e d E s ******* h a u p t i n ******* d i e h E n * +Eval: I I I D D D D S D S D D D D I D D D D I I I I D S D D S D + +id: (m-ailabs_deu_000195-m-ailabs_deu_000195) +Scores: (#C #S #D #I) 133 11 15 11 +REF: u n t e R D e n d a m e n m e i s t J U n g e f r I s c h E g e s i c h t e r u n t e r D E n h e R r e n n e b e n * J u G e n d * l i c h e n s o L c h E m i t f a l t i g E r s t i * r n u n d b E r e i t * s m e h r * * O d E r m i n d e r m o n d ******* u m * ******* g l Ä n z * t e m s c h Ä * D e l +HYP: u n t e * R e n d a m e n m e i s t ******* I O n g e f r * s c h * I g e s i c h t e r A u n t e r * I n h e * r e n n e b e n M H u * e n d K l i c h e n s o * c h * ******* m i t f a l t i g A r ******* s t i E r n u n d b * r e i t Z s ******* m e h r A U * d A r ******* m i n d e r m o n d u m G g l E n z S t e m s c h Ä H T e l +Eval: D S D S S D D S S D S D I S D I D D D S D I D I D I I D S D I I I S I I S + +id: (m-ailabs_deu_000196-m-ailabs_deu_000196) +Scores: (#C #S #D #I) 39 8 6 2 +REF: s e i T t A G e n S c h o n H a t T e E s b e s o n d e R s d r Ä U e n d G e k l u n g * * e N +HYP: s e i * t E R e n * c h o n ******* * a t D e ******* I s b e s o n d e A s d r E I e n d * e k l u n g E B e R +Eval: D S S D D D S D S S S S D I I S + +id: (m-ailabs_deu_000197-m-ailabs_deu_000197) +Scores: (#C #S #D #I) 9 0 0 1 +REF: s o n d e r b a r * +HYP: s o n d e r b a r H +Eval: I + +id: (m-ailabs_deu_000198-m-ailabs_deu_000198) +Scores: (#C #S #D #I) 84 7 18 7 +REF: e r b * v o n e * r b E N h e I m s t a n d m i T S e I n ******* E r g a t T i n v o L l w e H m u t u n D d a n k * b a R k e i T * a n d e r g r * u f t A u f d E R e r E i N E n m Ä c h t i G e n * +HYP: e r b P v o n e H r b * M h e * m s t a n d m i * ******* Z e * n H r g a t E i n v o * l w e m u t ******* u n G d a n k G b a * k e i * D a n ******* d e r g r O u f t * u f d * * ******* e r * i * * n m E c h t i * e n G +Eval: I I D S D D D S D I S S D S D S I D D I D I D D D D D D D S D I + +id: (m-ailabs_deu_000199-m-ailabs_deu_000199) +Scores: (#C #S #D #I) 62 13 8 1 +REF: i H r w a R J e d e R m e n s c h e i n W u n d e r u n d f a s t a l l e s W A s m e n s c h e n t a * t e n E t W a s W U n d E R b a r e s +HYP: i E r ******* w a I H I e d e * A m e n s c h ******* e i n * u n d e r u n d f a s t ******* a l l e s V E s ******* m e n s c h e n ******* t a L t e n I t a s S F O n d * A b a r e s +Eval: S D S S S D S D D D S S D D I S S S S S D S + +id: (m-ailabs_deu_000200-m-ailabs_deu_000200) +Scores: (#C #S #D #I) 23 7 5 1 +REF: w e l C h e I H r w e G s i e e n * T l Ä n g s t f Ü H r T E +HYP: w e l T h e J E r ******* w e * s i e e n D l E n g s t ******* f I E r * * +Eval: S S S D D I S S D S S D D + +id: (m-ailabs_deu_000201-m-ailabs_deu_000201) +Scores: (#C #S #D #I) 76 6 18 2 +REF: D i e w I r t i n s a S s n i c h t h i n t e R I H r e m s c H a n k t i s C H u n d k e i n e R I H r e r d i e n * s t ******* l E u t e b e f a n D S i C H I n d e r s t u b e +HYP: * i e ******* w E r t i n s a * s ******* n i c h t ******* h i n t e * * * r e m s c * a n k t i s * * u n d k e i n e * * E r e r d i e n Z s t l O u t e b e f a n * Z E i * * H n ******* d e r ******* s t u b e +Eval: D D S D D D D D D D D D D D S I I S D S S D D S D D + +id: (m-ailabs_deu_000202-m-ailabs_deu_000202) +Scores: (#C #S #D #I) 105 6 17 6 +REF: a l s d I e h e R r s c h a f t a u s d e r k i * R c h e t R a t s t a n d e n d i e l e u * t e u m ******* h e * r u m s i e v o r b e i g e h E n z u s E H e n u n d a m k I R c H h O f * t H o r e w a R t e ******* t e e i n m a N n +HYP: a l s d * e h e * r s c h a f t a u s ******* d e r ******* k i E L c h e ******* t * a t s t a n d e n d i e ******* l e u I t e u m h e H r u m ******* s i e v o r b e i g e h * n ******* z u ******* s * I e n u n d a m ******* k E L c * h U f S t * o r e R w a * t e t e e i n m a * n +Eval: D D D D I S D D D I I I D D D D D S D S S D S I D S D I D + +id: (m-ailabs_deu_000203-m-ailabs_deu_000203) +Scores: (#C #S #D #I) 25 9 20 2 +REF: W A s m Ü S s E n W I R t * U n U m d * e M t E R R O r i s m u s e n T G E g E n Z U T R E t E N +HYP: * * s ******* m ** * s * n ******* M E L t O R n ******* O m d I e * t * * * A r i s m u s e n * * g * n * * * * K t * I +Eval: D D D D D D D S S S I S D S I D D D D S D D S D D D D D S D S + +id: (m-ailabs_deu_000204-m-ailabs_deu_000204) +Scores: (#C #S #D #I) 28 8 17 2 +REF: I C H g * l a u b e d * a S S s i E e s g u T M I t M I r m e i n e n h e R r D O K t O R +HYP: * * * ******* g E l a u b e R d E a * * ******* s i * ******* e s g u * ******* D E t B E r ******* m e i n e n ******* h e * r ******* T A C t * * +Eval: D D D D I S I D D D D D D D S S S S D D D D S S S D D + +id: (m-ailabs_deu_000205-m-ailabs_deu_000205) +Scores: (#C #S #D #I) 64 9 7 2 +REF: D O C H i m a n f a n g g e w a N n e R k e i n e a u f m e r K s a m ******* k e i t F Ü r a n d e r e d i n g e a l S f Ü r d * A s e S s e n +HYP: * E N T R i m a n f a n g g e w a * n ******* e * k e i n e a u f m e r s a m k e i t V E r a n d e r e d i n g e a l * Z f Ü r ******* d E R s e * s e n +Eval: D S S S S D D D S I S S D S D I S D + +id: (m-ailabs_deu_000206-m-ailabs_deu_000206) +Scores: (#C #S #D #I) 84 9 15 2 +REF: d i e s f l Ä s C H c h e n z o g e r J e t Z t e i l i G h e r ******* v o r w Ä H r e n d j e n e s i c h m i t w a S s E r f Ü L l t e n u n d b O t e s d e r J u n G F e r z * Ü s a n +HYP: d i e s f l Ä s * * c h e n z o g ******* e r ******* G e t S t e i l i * C h e r v o r w ** E r e n d ******* j e n e ******* s i c h ******* m i t w a * s A r ******* f ** E l t e n u n d b * t ******* e s d e r * u n K V e r T z I Ü s a n +Eval: D D D D S S D S I D S D D D D S D D S D D D S S S I + +id: (m-ailabs_deu_000207-m-ailabs_deu_000207) +Scores: (#C #S #D #I) 91 18 31 15 +REF: D E s H A L B W a R E s a u C H r i c h T i G U n D w i c h t i G d * a S s c h i n * A d O c h J e * t z T a n s * p R u C H s V o l l g e s a G t H a t W I r w e r d e n A u c * h a * n D E n z e i t ******* P u n K T d e r * R E d u k t * i O n k o m m e n * * * * * * +HYP: * * s * * E R B a * ******* * s ******* a u * * ******* r i c h * i * H O n * w i c h t i C H d E a * s ******* c h i n E R d R c h ******* * e R t z S a n s H p * u * * s F o l l g e s a K t ******* * a t V E r ******* w e r d e n * u c R h ******* a I n ******* * I n z e i t B u n * G d e r I * * d u k t Z i U n k o m m e n D E S D G U +Eval: D D D D S S S D D D D D D D D D S S D S S I D D I S S D D I S I D D D S S D D S S D D I D I D D S I S D S I D D I S I I I I I I + +id: (m-ailabs_deu_000208-m-ailabs_deu_000208) +Scores: (#C #S #D #I) 32 3 9 2 +REF: n i c h t d * o c h m u T t e r w e C K e s i e J e * t z t n O c h n i C H T +HYP: n i c h t ******* d A o c h ******* m u * t e r w e R G e ******* s i e * e R t z t n * c h ******* n i * * G +Eval: D I D D S S D D I D D D D S + +id: (m-ailabs_deu_000209-m-ailabs_deu_000209) +Scores: (#C #S #D #I) 57 5 18 5 +REF: J a W i * r ******* * h a b e n i n D e n L e t z t E n j a H R E n r E c h t e n g e b E Z i E H u N G E n z u b R a s i L i e n a u f g e b a u t * * +HYP: * a B i E r H h a b e n i n ******* * e n ******* D e t z t * n ******* j a * * * n ******* r I c h t e n g e b * * i T I u * * * n z u ******* b * a s i * i e n a u f g e b a u t P R +Eval: D S I I I D D D S D D D D D D S D D S S D D D D D D I I + +id: (m-ailabs_deu_000210-m-ailabs_deu_000210) +Scores: (#C #S #D #I) 28 6 4 4 +REF: * ******* s i e W Ü r ******* d e s i c h n i c h t F Ü r a n d E r E * O p f E R n +HYP: S s i e V I r d e ******* s i c h n i c h t V E r a n d * r * A B p f * O n +Eval: I I S S I D S S D D I S D S + +id: (m-ailabs_deu_000211-m-ailabs_deu_000211) +Scores: (#C #S #D #I) 8 2 7 4 +REF: * * * * r i E f e n S I E m I R z U +HYP: L E C H r i * f e n ******* * * * m * E T z * +Eval: I I I I D D D D D D S S D + +id: (m-ailabs_deu_000212-m-ailabs_deu_000212) +Scores: (#C #S #D #I) 48 6 9 4 +REF: g * o T t w a s * S i e I H r E R z Ä H l t e * h Ö r e n s i E n u r E s i s T e i n g a n z e R r O m a * n +HYP: g K o * t w a s I * i e * A r * * z T E l t e R h Ö r e n s i N n u r I s i s * ******* e i n g a n z e * ******* r U m a R n +Eval: I D I D D S D D S S I S S D D D D S I + +id: (m-ailabs_deu_000213-m-ailabs_deu_000213) +Scores: (#C #S #D #I) 42 6 12 1 +REF: s e i n e m U T t e r k A n * n I H m N U R f l u S s w a S s e r g e b e n d e s H A l b w e i n T e r +HYP: s e i n e ******* m * * t e r k I n I n * * m ******* * * O E f l u * s w a * s e r ******* g e b e n d e s * E l b P w e i n D e r +Eval: D D D S I D D D D D S S D D D D S S S + +id: (m-ailabs_deu_000214-m-ailabs_deu_000214) +Scores: (#C #S #D #I) 131 21 30 11 +REF: D E R B u n d E w I R t S c h a F T s m i n * I s t E R w I r D Z U S A M m e n * * * m I t D e r n e t z ******* a * g e n t u r a m V i e r t e * j * u n i z u m e r s t e n m a l p r Ä s e n * T i e r E n w i e s i c h d i e n e t * Z b e t r e i b e r u n D D i E k r a F t W E r k * e d i E n e u E N n e t Z P l Ä n e v o r s t e L L E n +HYP: * * * ******* * u n d S w * O t * c h a * * s m i n D E s t * A w E r T * * * * E m e n U S A m * t ******* * e r ******* n e t z a R g e n t u r a m ******* F i e r t e N j I u n i z u m e r s t e n ******* m a l ******* p r E s e n D i e r * n w i e ******* s i c h d i e n e t S b e t r e i b e r u n G * i * k r a S t D A r k G e d i * ******* n e u * * ******* n e t S T l E n e v o r s t e R U n +Eval: D D D D D S D S D D D I S D S S S D D D D S I I I D D D D I I D S I I D D S I S D D I S S D D S S S I D D D D D S S S S S S + +id: (m-ailabs_deu_000215-m-ailabs_deu_000215) +Scores: (#C #S #D #I) 92 16 23 6 +REF: e * * V a H a T t e s I c h * z * i T t E R n d v o r t o d e S s C H W Ä c h e * v o n d e m g I T T e R b e f r e i t u n D S u c h t e z u E n T f L i E H e n A b e R d e r s C H m a l e g a R t e n b * o t k e i n e n a u s W E G +HYP: e B W E a ******* R a * t e ******* s E c h T z E i E t * A n d v o r ******* t o d e * s * F V E c h e R v o n ******* d e m g * * * e T A b e f r e i t ******* u n * Z u c h t e T z u * n D f * i * N e n * b e * d e r s * * m a l e ******* g a * t e n b U o t k e i n e n a u s * F I +Eval: I I S D S D D S I I S D S D D D S S S I D D D D S S D D S S D S D D S D D D D D D I D S S + +id: (m-ailabs_deu_000216-m-ailabs_deu_000216) +Scores: (#C #S #D #I) 66 6 22 1 +REF: O b i c h m e i n w e r k f Ü r H E u t e l I e G e n l a S s e n O d e R n O C h e i N E n a n ******* l a u f n e H m e n u n D e s V o L l e n d E n s o L l t e +HYP: * b ******* i c h ******* m e i n w e r k ******* f W r ******* * * u t e l * e T e n l a * s e n N d e * n * R h ******* e i * * n a n l a u f ******* n e * m e n ******* u n * T e s ******* R o * l e n d * n s o * l t e +Eval: D D D D S D D D D S D S D D S D D D I D D D D S D S D D D + +id: (m-ailabs_deu_000217-m-ailabs_deu_000217) +Scores: (#C #S #D #I) 115 16 21 6 +REF: e * r w a R d a s G Ö t z C h e n d e r s t u n d E d I e t A i t A i b e ******* a u f t r a G t e m a d a m E A n * * G e l E d i e a u c h D a s t a n d u n D d i E g e k a u f t e n s e i d e n s ******* T Ü C k e z u s a M m E N f A l t e t e f Ü r t s c h U n z u s o r g e n * +HYP: e H r w a * d a s ******* K E t z h e n d e r s t u n d * d * e ******* t E i t E i ******* b e a u f t r a K t e ******* m a d a m * U n S C H e l * d i e ******* a u c h ******* T a s t a n d u n * ******* d i * ******* g e k a u f t e n ******* s e i d e n s D E k e T z u s a * m * f E l t e t e f Ü r ******* t s c h * n D z u ******* s o r g e n G +Eval: I D D S S S D D D S S D I S D D S I I S D D D S D D D D D I S S S S D D S S D D S D I + +id: (m-ailabs_deu_000218-m-ailabs_deu_000218) +Scores: (#C #S #D #I) 11 3 5 1 +REF: I C H w e r D e n ******* a c h s E H e n +HYP: * * * D w e r e ******* n a c h s * I e n +Eval: D D D S S D I D S + +id: (m-ailabs_deu_000219-m-ailabs_deu_000219) +Scores: (#C #S #D #I) 34 12 9 5 +REF: a b * * ******* e R t I P P s o d E R v o r ******* g a b e n d a S m a c h E N W I R n A T Ü R l i C H n i c h ******* T +HYP: a b A L e * t * E B s o d A F v o r g a b e n d a D m a c h * M * * * E n * E D I l i * * G n i c h S +Eval: I I I D D S S S S I S D S D D D S D S S S D D S I S + +id: (m-ailabs_deu_000220-m-ailabs_deu_000220) +Scores: (#C #S #D #I) 96 7 24 10 +REF: a l s u n s E r e * I d E e b e k a N n t w U R d e * w a R d i E P H Y s i o g ******* n O m I e d e r w * a l t e R s * b U R g e r u n g e * f Ä h r d i E e i n e * s k * a l b e * s d a s z u m e * r s t E n m a l E d o N N E R n h Ö r T +HYP: a l s u n s * r e E d * e ******* b e k a * n t w * O d e R w a * ******* d i * * F Ü s i o g n * m * e d e r ******* w E a l t e A s P b * O g e r u n g e R f Ä h r d i * e i n e R s k E a l b e R s d a s ******* z u m e H r s t * n ******* m a l * T d o * * * * n ******* h Ö r * +Eval: D I S D D D D S I D D D D S S I D D D I S I D S I D I I I D I D D D S D D D D D D + +id: (m-ailabs_deu_000221-m-ailabs_deu_000221) +Scores: (#C #S #D #I) 51 5 16 1 +REF: B i t T e m A c h E n s I E g e ******* f Ä L l i G S t a u f u n d e s k l a n G W i e e i n J a M m E R n d e r h i l F e r U F +HYP: * i t Z e m * c h * n ******* s * * g e f Ä * l i * H t a u f u n d ******* e s ******* k l a n * D i e e i n * a * m * A n d e r h i l V e r * * +Eval: D S D D D D D I D D S D D D S D D D S S D D + +id: (m-ailabs_deu_000222-m-ailabs_deu_000222) +Scores: (#C #S #D #I) 65 5 31 4 +REF: H e R r d * o k t o r s a G T e * E i n e f r a u d I e s C h n U R R g r i * n e * d i E s O O f t z u i H n e n k o M m T i s t E I G e n T L i C H g a R n i C H T k r a n K +HYP: * e * r d A o k t o r ******* s a * * e R * i n e ******* f r a u d * e ******* s * h n * * O g r i E n e R d i * ******* s E A f t ******* z u i * n e n k o * m * i s t * * * e n * G i * * ******* g a * ******* n i * * * G k r a n * +Eval: D D I D D D I D D D D D D D S I I D D S S D D D D D D D D S D D D D D D D D S D + +id: (m-ailabs_deu_000223-m-ailabs_deu_000223) +Scores: (#C #S #D #I) 171 13 19 5 +REF: d i e a l t e e r ******* i N n E r u n g a n d E n f r Ü h E r e n t R a u m t a u c h t e e b e n ******* f a L l S w i e d e r a u f u n d u n w i L l k Ü r l i c H f a * s t b E i d e r b e ******* h a u p t u n g d a S s d I e s E e l e D e n k Ö r p e r v e R l a s S e n u n d * z u i H m z u r Ü c K k e H r e n k Ö N n e s c h I e n e s i H r O r d e n T l i C H +HYP: d i e a l t e e r i * n r u n g ******* a n d I n f r Ü h * r e n t * a u m t a u c h t e e b e n f a * l * Z w i e d e r a u f u n d u n w i E l k Ö r l i c * f a S s t b A i d e r ******* b e h a u p t u n g d a * s d * e s * e l e * e n k Ö r p e r v e L l a s T e n u n d T z u i E m z u r Ü c * k e * r e n k ** E n e s c h * e n ******* e s i E r A r d e n D l i * * +Eval: I D S D S D D I D D S S S D I S D I D D D D S S I S D D D S D D S S S D D + +id: (m-ailabs_deu_000224-m-ailabs_deu_000224) +Scores: (#C #S #D #I) 81 14 14 3 +REF: a l S s i e a U f D E n b a l k o n z u r Ü C K k E h r t e f a n D S i e i H n d i E Z e i t u n g l * e s e n d * d i e w Ä H r e n D I H r E s F o r t S e i n s a n g e l a n g * t w a R +HYP: a l Z s i e a * f ******* R I n b a l k o n z u r ** E k * h r t e f a n * ******* Z i e i E n d i * S e i t u n g K l I e s e n d T d i e w ** E r e n * * * r I s ******* V o r t Z e i n s ******* a n g e l a n g K t ******* w a H +Eval: S D D S S D S S D D D S S D S S I I D S D D D S D S S D I D S + +id: (m-ailabs_deu_000225-m-ailabs_deu_000225) +Scores: (#C #S #D #I) 115 6 13 4 +REF: * e R * * w a r e i n k i n D d E r s t r a S s e v o n k l e i n a u f a b e r i n i H m l e b t e v o n j e h E r E i n E g E w i S s e s e H n ******* s U c h t n a c h e i n e R e H r b a r e n b Ü r g e r l i c h e n e X i s t e n Z +HYP: T e * E R w a r e i n k i n * d * r ******* s t r a * s e v o n k l e i n a u f a b e r i n i E m l e b t e v o n j e h * r * i n * R g * w i * s e s e * n s O c h t n a c h ******* e i n e I e * r b a r e n b Ü r g e r l i c h e n e i s t e n S +Eval: I D I I D D D D S D D D S D D D I S D S D S S + +id: (m-ailabs_deu_000226-m-ailabs_deu_000226) +Scores: (#C #S #D #I) 71 20 39 4 +REF: U N D W i R A L s B u n D e s R E g I E r u n G f Ü H L E n u n S H I e R n i C H T e i n e r g r U P p e V e R a n T W o r T l i c h s o n D E R N w I R f Ü H L E n U n s D E M g E m e i n w * o H l V E r ******* a n T w o r T l i c h ******* * +HYP: * * * ******* * i * ******* * * s * u n * e s D g H r u n * K f Ü * * * n ******* u n * * * e * H n i * * G e i n e r g r * O p e ******* F e a n * F o r D l i c h s o n * * * A w * Ü E f Ü * * * n ******* E n s * I N g * m e i n w U o * l ******* * F r a n * w o r D l i c h N +Eval: D D D D D D D D D D D S S S S D S D D D D D D D D S D D S D S D S S D S S D D D S D S S D D D D S D S S D I D D D S I D S I I + +id: (m-ailabs_deu_000227-m-ailabs_deu_000227) +Scores: (#C #S #D #I) 23 3 3 1 +REF: w a s m e i N l i e b e s * K i n D w a s k a N n +HYP: w a s m e i * ******* l i e b e s G E i n T w a s G k a * n +Eval: D D I S S S D + +id: (m-ailabs_deu_000228-m-ailabs_deu_000228) +Scores: (#C #S #D #I) 125 18 25 7 +REF: u n d d a N n w O L l t e i c h D E n a n b l i C K d e R E r n i c h t m I s s e n d I e m I r g e b l I e b e N w a r e n v o r A L l e m a b E R w a r E s M i R d a r u m z u t u n M e i n * e s Ü S s e E l * * * i * s * A b e t H e i n i g E R m a s s e n g e t r * Ö s t e t z u s E H e n +HYP: u n d d a * n ******* w * U l t e ******* i c h ******* T I n a n b l i * G d e * r A n i c h t ******* m E s s e n d * e ******* m Ä r g e b l * e b e M w a r e n v o r * E l e m a b * * A w a r I s ******* N i * d a r u m T z u ******* t u n W e i n D e ******* s ** Y s e * l I C H i E s E R b e t * e i n i g * A m a s s e n g e t r E Ö s t e t z u ******* s * * e n +Eval: D D D S D D S S D S D S S D S D D S D S D S D D S S D S D S D S I D D S D I I I I I S D D S I D D D + +id: (m-ailabs_deu_000229-m-ailabs_deu_000229) +Scores: (#C #S #D #I) 41 13 24 5 +REF: A B e r I C H G L a u B E D A S S w i * r u n * s A U C H g E g E N s e I T i G E i * N b i S s C h E n u n t E r s t Ü t z e n k * Ö n N e * N +HYP: * * e r ******* D A S * * a u * * ******* * * C H w i E r u n D s ******* * * * * g N g * * s e * * i * D i H M b i * s * h * n u n t A r s t I t z e n ******* k E R n e R M +Eval: D D D S S S D D D D D D D S S I I D D D D D S D D D D D S I S D D D S S D I S S I S + +id: (m-ailabs_deu_000230-m-ailabs_deu_000230) +Scores: (#C #S #D #I) 72 8 16 4 +REF: s e i n e G e s c h Ä F t L i c h e l a u f ******* b a H n h a * b e * S T e F e n s O n a L s k Ü c h e n ******* b o Y i n e i n E m H O t e l v i e r t e n g r a d e s b E g o N N E n +HYP: s e i n e * e s c h ** E t * i c h e ******* l a u f b a R n ******* h a R b e S D I e B e n s * n a * s ******* k Ü c h e n b o * i n e i n R m ******* * U t e l F v i e r t e n g r a d e s ******* b * g o * * * n +Eval: D D S D D I S D I I S S S D D D I D S D D S S D D D D D + +id: (m-ailabs_deu_000231-m-ailabs_deu_000231) +Scores: (#C #S #D #I) 113 9 29 6 +REF: V i E L l e i c h t * T Ä T e n s i E g u t D i E s e a n s i c h t e N d e s b I s c h o f S n A C H h A u s e z u m e l D e n S a G t e D e r t a * * J e n d e * r i M m E r m E h r e i n m a N n d e s g e s c h R i e b e n e n w O r t e s W i e d e r t a * * t +HYP: F i * * l e i c h t E * ** * e n s i * ******* g u t ******* * i * s e a n s i c h t e * ******* d e s b * s c h o f * E n * * * h * u s e T z u ******* m e l * e n Z a K t e * e r ******* t a T S H e n d e H r i * m A r m * h r e i n m a * n ******* d e s g e s c h * i e b e n e n ******* w U r t e s ******* V i e d e r t a R D t +Eval: S D D I D D D D D D D D D D D D S D D D D S D D S S D D I I S I D S D D D D D S D S I I + +id: (m-ailabs_deu_000232-m-ailabs_deu_000232) +Scores: (#C #S #D #I) 111 16 30 4 +REF: a m a n d E R n m o r G e n e r h o B e r s i c h s * p Ä t s c h i C k t e D E n l a * k A i e n I n d I e W O H N u n g F E U E R b a c h s u n D l i E S s u m e i n E U n t e r R e d u n g b i T t e n d e R m a N n k a m m i t D e r b o t s c H a f * t z U r * Ü C K +HYP: a m ******* a n d * A n ******* m o r * e n e r h o * P e r ******* s i c h ******* s H p E t s c h i * k t e * * n l a R k E i e n * n d * e * * * B u n g V O R A b a c h s ******* u n * T l i * * s ******* u m e i n * * n t e r I e d u n g b i * t e n ******* d e * ******* m a * n k a m m i t ******* * e r b o t s c * a f T t z O r K D T +Eval: D D S D D D S D D I S D D D I S D D D D D S S S S S S D D S D D D D D S D D D D D D D D I S I S S S + +id: (m-ailabs_deu_000233-m-ailabs_deu_000233) +Scores: (#C #S #D #I) 62 12 12 5 +REF: * N U R e i n w e n i G t R a u r i * G w u r d e E s w e N n I M m e R d * a S s e l ******* b e k a m W e N n s i E n I e z u ******* f r i e d e n s c h i E N e n +HYP: T H N e i n ******* w e n i C H t H a u r i C H w u r d e ******* R s w e I n * * m e * d E a s e l b e R k a m ******* * e * n ******* s i N n * e z u f r i e d e n s c h i * * e n +Eval: I S S S D S S S I S D S S D D D I S I S D D D D S D I D D + +id: (m-ailabs_deu_000234-m-ailabs_deu_000234) +Scores: (#C #S #D #I) 110 24 15 10 +REF: e i n s o m M E r w a R m E R n o V e * m b E r t a G l a * g m i t S o N n E N g l i t ******* Z E R n Ü b e r d e R h A u P t s t a D t u n d u n T e R d E n l i * n d e n d r Ä n g * t e E i n e t a u s e n D k * Ö P f i g e m e n s c h e n ******* m e n g e * a u * * f U n D n I e d e r +HYP: e i n s o m A H r w a H m * * A n o W e N m b A r t a R K l a R g ******* m i t ******* Z o * n * g l i t S A N n ** b e r ******* d e * h * u * t s t a B t u n d u n D e * d I n l i E n d e n ******* d r E n g K t e * i n e t a u s e n k E R f i g e R m e n s c h e n m e n g e R a u O R f V O n * n e d e r +Eval: S S S D D S S I S S S I D D S D D S I S S S D D D D D S S D S I D S I D S I S S S I I I I S S D S + +id: (m-ailabs_deu_000235-m-ailabs_deu_000235) +Scores: (#C #S #D #I) 40 2 9 1 +REF: k o M M m i t m i R m e i n s o H n d e N n i c h B r a u c h e * d e i n e l I e b e +HYP: k o * * ******* m i t m i H m e i n ******* s o * n d e * n i c h ******* P r a u c h e R d e i n e ******* l * e b e +Eval: D D D S D D D D S I D D + +id: (m-ailabs_deu_000236-m-ailabs_deu_000236) +Scores: (#C #S #D #I) 70 5 8 7 +REF: n U r * s E I n G e s i c h t W U r * d E e i n w e n i G n a c h ******* d e n k l i c h e r s * o w i e v o n e i n e r e r ******* i N n e r u n g * e r ******* h e L l t +HYP: n O r E s * A n K e s i c h t * * r O d * ******* e i n ******* w e n i C H n a c h d e n k l i c h e r s O o w i e v o n e i n e r e r i * n e r u n g K e r h e * l t +Eval: S I D S S D D I D D D S S I I I D I I D + +id: (m-ailabs_deu_000237-m-ailabs_deu_000237) +Scores: (#C #S #D #I) 58 10 27 1 +REF: D A N n w I R D a u C H W i E d e R d e R I N n O V a t i O n s ******* d r U C k s t e i g e n u n D D a z u I s T D a S s Y s t e m J A e I n g e f Ü H r t w o R D E n +HYP: * * * n w * U T a u * * ******* F i * d e * d e * * * n * W a t i U n s d r O K k s t e i g e n u n * * a z u * s * ******* * a * ******* s E s t e m ******* E R e * n g e f Ü * r t w o * * * n +Eval: D D D D S S D D D S D D D D D D S S I S S D D D D D D D D S D S S D D D D D + +id: (m-ailabs_deu_000238-m-ailabs_deu_000238) +Scores: (#C #S #D #I) 86 12 13 4 +REF: J e T Z t g e w a H r t e e R m i t e n T s e t z E n d i E s c h E u S s l i c h e t * e U f l I s c h e * a F f e n ******* f r a t z e d i e Ü b e R d E s m Ä D c h e n S s C h u l t e * r s c h i e l t e +HYP: N e * * t g e w a * r t e e * A m i t e n s e t z S n d i * s c h O u I s l i c h e R t O e f l * s c h e R a H f e n f r a t z e d i e ** b e * d I s ******* m E N c h e n * ******* s * h u l t e A r ******* s c h i e l t e +Eval: S D D D D S S S D S S S I S D I S I D D S D S S D D D I D + +id: (m-ailabs_deu_000239-m-ailabs_deu_000239) +Scores: (#C #S #D #I) 64 15 11 6 +REF: * J A d e r w i * r T n i C K t e d a s g E H Ö r T e i n e M g e w i S s e n w u * t s c h O W b e r n h a R D w u * t s c h o * W i s t e ******* t w a s V E R R U F e n +HYP: E R R d e r ******* w i E r * D n i * G t e d a s ******* g * * Ö r D e i n e * g e w i * s e n w u E t s c h A F b e r n h a * T w u R t s c h o U F i s t ******* e t w a s ******* F A C G N S e n +Eval: I S S D I D S D S D D D S D D I S S D S I I S D I D S S S S S S + +id: (m-ailabs_deu_000240-m-ailabs_deu_000240) +Scores: (#C #S #D #I) 42 8 11 3 +REF: w O L l t I h R i n w A H R h e i T d i e L Ö W e n t Ö * t e n u n d * k Ö N n T i H r s c h * i E S s e n +HYP: w * U l t E h E i n w * * E h e i * ******* d i e * E S e n t Ö R t e n u n d T k ** * n D i E r ******* s c h L i * * s e n +Eval: D S S S D D S D D D S S I I D D S S D I D D + +id: (m-ailabs_deu_000241-m-ailabs_deu_000241) +Scores: (#C #S #D #I) 105 6 15 4 +REF: b a t C e D d i E s e H R r e s p e k t v o L l W o b e i e r * n u r e i n i g e s I L B e n v e r s c h l u C k t * e w a s i H m b e i * D e n b e l i e b t e n l a n ******* g e n w Ö r t E R n d e s Ö f t E R n v o r k a M +HYP: b a t Z e * d i * s e * * ******* r e s p e k t v o * l U o b e i e r N n u r e i n i g e ******* s * * E e n v e r s c h l u * k t D e w a s i * m b e i T * e n b e l i e b t e n l a n g e n w Ö r t * A n d e s E f t * A n v o r k a * +Eval: S D D D D D D S I D D D S D I D I D I D S S D S D + +id: (m-ailabs_deu_000242-m-ailabs_deu_000242) +Scores: (#C #S #D #I) 53 10 9 3 +REF: l o r D f A U n T l e r o * Y W I r D n i c h t * s e n * T b e H r e n d e S s e n b i n i c h g e w i S s v e r s e t z t e E r +HYP: l o r T f * O n D l e r o A I E V E r * T n i c h t Z s ******* e n D b e * r e n d e * s e n b i n ******* i c h ******* g e w i * s v e r s e t z t e ******* H r +Eval: S D S S I S S S S D S I D I S D D D D D D S + +id: (m-ailabs_deu_000243-m-ailabs_deu_000243) +Scores: (#C #S #D #I) 63 0 8 5 +REF: k a m g l e i c h f * a L l s i n s s c h l a f * z i M m e r a u f e i n e n n a * g e l i n d e r n Ä H e * d e s b * e T t e s +HYP: k a m g l e i c h f W a * l s i n s s c h l a f T z i * m e r a u f e i n e n n a R g e l i n ******* d e r ******* n ** * e R d e s ******* b P e * t e s +Eval: I D I D I D D D D I D I D + +id: (m-ailabs_deu_000244-m-ailabs_deu_000244) +Scores: (#C #S #D #I) 89 19 46 7 +REF: U N D d a S I s T d i E c h a n C E d I e I n d i e s E r k r i s E s t e C K T d i E c h a * n C E F Ü r i n t e r n a t * I O n a l e r e g e L n D i E s i C H A n D e N P r I n ******* z * i * p I e N d e * R * s O Z I a l E n m a R k T W I R t S C H a F T O r i E n t i E R e n +HYP: * * * ******* d a * ******* * s * d i * S c h a n * S d * e ******* H n ******* d i e s A r ******* k r i s * ******* s t e * * G d i * S c h a U n G S V Ü r i n t e r n a t Z E H n a l e ******* r e g e * n * i * ******* s i * * ******* E n * e M * r O n z F i B p T e * d e S E T s * * W a l * n ******* m a * k * * * L t * * * a * * E r i * n t i * * e n +Eval: D D D D D D D D D S D S D D S D S D D D D D S D S I S S S I S S D D D D D D D D S D S D S I I I S D I S I D D S D D D D D D S D D D D D S D D D + +id: (m-ailabs_deu_000245-m-ailabs_deu_000245) +Scores: (#C #S #D #I) 75 1 18 1 +REF: a n f a n g s f i e l d e R r e * g e n s c h r Ä G u n d p e i t s C H t E e r s T D i e e i n e U N D d a N n d i e a n d e r e s e i t e d e s w a G e n s +HYP: a n f a n g s ******* f i e l ******* d e * ******* r e I g e n s c h r Ä * u n d p e i t s * * t * e r s * * i e e i n e ******* * * R d a * n d i e ******* a n d e r e ******* s e i t e d e s ******* w a * e n s +Eval: D D D D I D D D D D D D D D S D D D D D + +id: (m-ailabs_deu_000246-m-ailabs_deu_000246) +Scores: (#C #S #D #I) 64 3 10 6 +REF: f a s t * l e i c h t * S i N n i g e n b e * ******* m e S s u n g I H r e s w e r t e s a u f * z u g e b e N s i c h e n * t s C H l o s s e n h a T t e +HYP: f a s t R l e i c h t Z E i * n i g e n ******* b e R m e * s u n g * E r e s ******* w e r t e s a u f T z u g e b e * M s i c h e n D t s * * l o s s e n ******* h a * t e +Eval: I I S D D I I D D S D I D S I D D D D + +id: (m-ailabs_deu_000247-m-ailabs_deu_000247) +Scores: (#C #S #D #I) 62 3 26 4 +REF: D A s H e i S s t d i e f r a * g E D e R m e n S c h L i c h e n a R b e I t U n D d i e F r a * g e w a s k a N n t E c h n I s C H g E l Ö s t w e r D e n ******* * +HYP: * * s ******* S e i * s t d i e f r a R g * ******* * e * ******* m e n * c h * i c h e n a * b e * t ******* * n * d i e ******* * r a R g e ******* w a s k a * n t Ä c h n * s * * g * l Ü s t ******* w e r * e n D +Eval: D D D S D I D D D D D D D D D D D D D D I D D S D D D D S D D I I + +id: (m-ailabs_deu_000248-m-ailabs_deu_000248) +Scores: (#C #S #D #I) 59 6 17 2 +REF: D i E s a * ******* f a r i w A R a u f d i E r E G e L m Ä S s i g b e n U t z t e n w a s s E r s t e l l e n D i e s e R r O u t E a n g e W i e s e n +HYP: * i * ******* s a R f a r i w * * ******* a u f ******* d i * ******* r I D e * m E H s i g b e n * t z t e n w a s s A r s t e l l e n * i e s e * ******* r * u t * ******* a n g e R i e s e n +Eval: D D D I I D D D D D D S S D S S D S D D D D D D S + +id: (m-ailabs_deu_000249-m-ailabs_deu_000249) +Scores: (#C #S #D #I) 83 10 9 1 +REF: d i e b e i D e n m Ü S s t e n h i e R o b e N a u f d e m g I p F e l g e s t a n D e n h a b e N u n d e r s p r a c h d i e a l t e n w * O r t e v o R s I c H h I n +HYP: d i e ******* b e i T e n m ** I s t e n h i e * o b e M a u f d e m g E p V e l g e s t a n T e n h a b e M u n d ******* e r ******* s p r a c h d i e a l t e n w A U r t e v o * ******* s E c * ******* h E n +Eval: D S D S D S S S S S D D I S D D S D D S + +id: (m-ailabs_deu_000250-m-ailabs_deu_000250) +Scores: (#C #S #D #I) 57 11 8 3 +REF: e n D l i c H B L i C k t e C e d r i K a u f * w e i S s n E W i C K a L l e s v o n d E n a r m e n l * e U t e n f r a * G t e e r +HYP: e n T l i c * * P i * k t e ******* S e d r i G a u f H w e i * s E n U i * G a * l e s v o n ******* d I n a r m e n l O e I t e n f r a C K t e e r +Eval: S D D S D D S S I D S S S D S D D S I S I S + +id: (m-ailabs_deu_000251-m-ailabs_deu_000251) +Scores: (#C #S #D #I) 87 12 31 5 +REF: D A S S E s h E U T e E i n e w u n d E R b a r * E Z U s a M m E N a R b e i t z W I S c h E n b u n d u n d l Ä n D e R n i n d i e s e n F r a g e n g i * b T M I t s e H r s e H R i n t E r e S s a n T e n p r O J e * k t e n * * +HYP: * * * * ******* * s ******* h O L D e * i n e w u n d * A b a r I R * * s a * m * * a * b e i t ******* z * U Ü c h * n b u n d u n d l E n * e A n i n ******* d i e s e n * r a g e n ******* g i E b * D E t s e * r ******* s e * * i n t * r e * s a n * e n p r * I e G k t e n O U +Eval: D D D D D D D S S S D D S I S D D D D D D D D S S D S D S D D D I D S S D D D D D D D D S I I I + +id: (m-ailabs_deu_000252-m-ailabs_deu_000252) +Scores: (#C #S #D #I) 97 12 19 9 +REF: C a s P a R V e r h a R r t e * a n ******* g e W U r * * z e l t A n s e I n E m p l A t z * S e i n E G l I e ******* d e r j a s e i n e * a u g e n * w a R E n W i e v e r s t e i n e r t a l s e R Z u M z w e i t e n ******* m a l h i n B L i C k t e +HYP: K a s B a * F e r h a * r t e R a n g e N M r E T z e l t ******* E n ******* s e * n * m ******* p l * t z S Z e i n * * l * e d e r j a s e i n e R a u g e n B w a * * n ******* * i e v e r s t e i n e r t a l s ******* e I T H u N z w e i t e n m a l ******* h i n * i * k t e +Eval: S S D S D I I S S I I D S D D D D D I S D D D I I I D D D D D S S S S I D D S D + +id: (m-ailabs_deu_000253-m-ailabs_deu_000253) +Scores: (#C #S #D #I) 92 4 8 7 +REF: e i n i g e * z e i t d a n a c h f r a G t e e r m i c h o B i c h g * l a u b e * d * a S s d e r e i s ******* g a n g d E n s C h l i T t e n d e s a n d e r e n z * e r ******* s t Ö r t H a b e +HYP: e i n i g e T z e i t d a n a c h f r a K t e e r m i c h o * P i c h ******* g K l a u b e R d E a * s d e r e i s g a n g d I n s * h l i * t e n ******* d e s a n d e r e n z S e r s t E r t ******* * a b e +Eval: I S D S D I I I D I S D D D I I S D D + +Speaker sentences 1: cv_deu_000698 #utts: 1 +id: (cv_deu_000698-cv_deu_000698) +Scores: (#C #S #D #I) 36 8 7 1 +REF: a b e R n U n b l O s S n i c h t I n e i n e * s c h o C K s t a r R e V e R F a L l e n +HYP: a b e * ******* n E n b l Ü s E n i c h t G E n ******* e i n e R s c h o * * s t a r e * e V a * l e n +Eval: D D S S S S S D I D D S D S S D + +Speaker sentences 2: cv_deu_000699 #utts: 1 +id: (cv_deu_000699-cv_deu_000699) +Scores: (#C #S #D #I) 11 6 4 2 +REF: j A i C H k o M m E J a * s c h * O n +HYP: j * E i T S k o * m * ******* I a E R s c h E U n +Eval: D S S S D D D S I S I S + +Speaker sentences 3: cv_deu_000700 #utts: 1 +id: (cv_deu_000700-cv_deu_000700) +Scores: (#C #S #D #I) 30 11 13 6 +REF: N E B e * N b e i * a R b E I T e t E * e R A L s a u s * H I l F s ******* k r a f t a U F e I n E R f * a R M +HYP: S D e M b e i E a * b * * * e t D D e * E I s a u s E R E l * s k r a f t ******* a * O e * n * * O f H a * * +Eval: S S S I S I D D D D S I D S S I S S D I D D S D D D S I D D + +Speaker sentences 4: cv_deu_000701 #utts: 1 +id: (cv_deu_000701-cv_deu_000701) +Scores: (#C #S #D #I) 56 15 19 5 +REF: e i n t e R r i * ******* t o r I A L g R Ö S s e R E s E U R o p a w i * R d n i c h t m i t e I n E M * e t a T m Ä S s i G k L e i n e r e N * E U R o p a E R r e i c H t +HYP: e i n ******* t e * r i T t o r * H E I g * K O s e * L s O H o p a ******* w i E T d n i c h t ******* m i t e * n * * I e t a * m ** E s i * S k * e i n e r e * A O C H o p a ******* * * r e i c * t +Eval: D D I I D S S S D S S D S S S S D I S D D D D I D D S D S D D I S S S D D D D + +Speaker sentences 5: cv_deu_000702 #utts: 1 +id: (cv_deu_000702-cv_deu_000702) +Scores: (#C #S #D #I) 33 11 6 6 +REF: I H R s * o H n k a M d U r C H k Ü n * s T l i C h e * b e ******* F r U c h t * u n g * z U r W e L t +HYP: * * E s H o U n k a B N d E r * * k Ü n Z s * l i h e R b e V r O c h t D u n g T z E r ******* B e R t +Eval: D D S I S S S S D D I D S I I S S I I S D S S + +Speaker sentences 6: cv_deu_000703 #utts: 1 +id: (cv_deu_000703-cv_deu_000703) +Scores: (#C #S #D #I) 49 7 8 12 +REF: d * i E n a c h t ******* * A k t i V e * ******* n * f a l * t e r f l I e g e n v o n m i T t e * * J u * l * i b I s m i T t e * o K t o B e R +HYP: d E i N n a c h t E R k t i * e F n E f a l R t e r ******* f l * e g e n v o n ******* m i * t e R I O u O l D i E b E s S m i * t e R o P t o * e * +Eval: I S I I S D I I I I D D D D I I S I I S S S D I S D D + +Speaker sentences 7: cv_deu_000704 #utts: 1 +id: (cv_deu_000704-cv_deu_000704) +Scores: (#C #S #D #I) 1 3 0 4 +REF: * * * * A C h T +HYP: E L R E T h E +Eval: I I I I S S S + +Speaker sentences 8: cv_deu_000705 #utts: 1 +id: (cv_deu_000705-cv_deu_000705) +Scores: (#C #S #D #I) 1 3 0 7 +REF: F Ü n * ******* * * * * * F +HYP: E I n D H E I R E N +Eval: S S I I I I I I I S + +Speaker sentences 9: cv_deu_000706 #utts: 1 +id: (cv_deu_000706-cv_deu_000706) +Scores: (#C #S #D #I) 65 10 15 2 +REF: N u T z e R k Ö N N E n i H r e l e s e ******* z e i C H e N o n L i n E a b s p e i c h e r N v e r w a l * t e n u n D M i t a n D e r e N n U t z E R n t e i L e n +HYP: M u O z e * k ** * * * n i E r e R l e s e z e i * G e * o n E i n * a b s p e i c h e r * v e r w a l I t e n u n * B i t ******* a n * e r e * ******* n O t z * A n t e i R e n +Eval: S S D D D D D S S I D S D S D D I D S D D D D S D S S + +Speaker sentences 10: cv_deu_000707 #utts: 1 +id: (cv_deu_000707-cv_deu_000707) +Scores: (#C #S #D #I) 12 4 2 4 +REF: d I e d O N b o s C o * k A t * * * H +HYP: d * e d E M b o s G o N k * t E R A E +Eval: D S S S I D I I I S + +Speaker sentences 11: cv_deu_000708 #utts: 1 +id: (cv_deu_000708-cv_deu_000708) +Scores: (#C #S #D #I) 46 16 13 10 +REF: s a u l b a S s * z Ä h l T z u d e n I N n O V A t i V s t e n d E s * i G N E R N u * n D f I L m e M A c H E r N S e I n * * ******* * e r z e * i * * T +HYP: s a u l b a * s T z E h l * z u d e n G n * R t i * s t e n d I s E i D A B M A u M n * ******* f * m e * * c * * r * Ü e * n D L S e r L z e R i U D N +Eval: D I S D S S D S S D S I S S S S S S I D D D S D D D D D S D I I I I S I I I S + +Speaker sentences 12: cv_deu_000709 #utts: 1 +id: (cv_deu_000709-cv_deu_000709) +Scores: (#C #S #D #I) 35 15 5 6 +REF: i n G R Ü n Ü * * B E r s I L b e R N e M w e L l E n b a L K E N e i n e * s I l ******* b E R n * e e i * c h e +HYP: i n K M Ü n ******* Ü M W Ö H r s * E b e L A e N w e * l * n b a U G K e i n e R s E l b * O n D e R e i S c h e +Eval: S S D I I S S D S S S S D D S S S S I S I D S I S I + +Speaker sentences 13: cv_deu_000710 #utts: 1 +id: (cv_deu_000710-cv_deu_000710) +Scores: (#C #S #D #I) 81 15 9 2 +REF: W e i t e r e w i c h t i g e i n d U s t r i E z w e i g e s I n D d I e m i k r O m E c h a n i K g A L V a n o p l a s t i K m E t A L L b a u u n D D i e h O l * z v e r ******* a R b e i t u n G +HYP: * e i t e r e w i c h t i g e i n d E s t r i * z w e i g e ******* s E n * d * e m i k r U m I c h a n i G g E R W a n o p l a s t i G m I t * E I b a u u n * ******* T i e h E l T z v e r a * b e i t u n E +Eval: D S D D S D D S S S S S S S S D S S D D S S I I D S + +Speaker sentences 14: cv_deu_000711 #utts: 1 +id: (cv_deu_000711-cv_deu_000711) +Scores: (#C #S #D #I) 64 6 16 0 +REF: Ü b e r d e n a u t o r i s T n i C h T S b e k a N n T v e r m u t l i C H s t a M m t e E r a u s D e M d e U t S C H e n S p r a C h g E b i e t +HYP: I b e r d e n a u t o r i s * n i * h * Z b e k a * n D v e r m u t l i * G s t a * m t e ******* H r a u s ******* * e * d e I t * * * e n * p r a * h g * b i e t +Eval: S D D D S D S D S D D S D D D S D D D D D D + +Speaker sentences 15: cv_deu_000712 #utts: 1 +id: (cv_deu_000712-cv_deu_000712) +Scores: (#C #S #D #I) 22 7 8 2 +REF: M A n s t * e u ** e r T E s m i T e I n e M D o p P e L p a D D e L +HYP: * * n D s t D e u Ö e r * I s S m i * e * n e * T o p L e p a * T e * +Eval: D D S I I D S S D D D S S S D S D + +Speaker sentences 16: cv_deu_000713 #utts: 1 +id: (cv_deu_000713-cv_deu_000713) +Scores: (#C #S #D #I) 23 7 11 5 +REF: W I R h * A b * e n e I N P R O b l e m A U F * o s i * s c h i C H T a * c h t +HYP: * D E h O R b M e n B e * * ******* * * * b l e m ******* * E R P o s i E s c h i * * G a U c h t +Eval: D S S I S I S D D D D D D D D S S I I D D S I + +Speaker sentences 17: cv_deu_000714 #utts: 1 +id: (cv_deu_000714-cv_deu_000714) +Scores: (#C #S #D #I) 34 13 25 3 +REF: * w i R s * P i E L e n I M m E R n O C H a b E R D a S L e B e N a u F t O u r I S T D e R Z e I T N i C H t m a c h b A r * +HYP: E w i * ******* s C H i * * e n * * m * * A n * U R a b * * ******* * a * ******* R e e M a u * G t Ü u r ******* * * E * e L T e * * * i * * t m a c h b E r H +Eval: I D D I S D D D D D D S D S S D D D D D D S S S D S S D D D S D S S D D D D D S I + +Speaker sentences 18: cv_deu_000715 #utts: 1 +id: (cv_deu_000715-cv_deu_000715) +Scores: (#C #S #D #I) 33 12 21 2 +REF: H e U T e z E I G T S i c H d e R G R Ö S s T e T E I l d e R a * n L a G E a L S e n G L I s C h e R G a R T e n * +HYP: K e * D e z * * A L * i c * d e * * * ** E s * e * * * l d e * a R n * a N N a * N e n * E s * h e * ******* W a * B e n G +Eval: S D S D D S S D D D D D D S D D D D D I D S S D S D S S D D D S D S I + +Speaker sentences 19: cv_deu_000716 #utts: 1 +id: (cv_deu_000716-cv_deu_000716) +Scores: (#C #S #D #I) 36 5 9 4 +REF: S e I N e R e s I D e n z n a H m e r i n m Ü n c h e n * w o e R a u C H s t a * R b * * +HYP: * e * * e * e s E e n z n a * m ******* e r i n m I n c h e n D w o e * a u * * G s t a B E b E I +Eval: D D D D S S D D S I D D D S I S I I + +Speaker sentences 20: cv_deu_000717 #utts: 1 +id: (cv_deu_000717-cv_deu_000717) +Scores: (#C #S #D #I) 73 11 10 6 +REF: i N n e r * * E R u n d Ä U S s e r e r n a r t H e * * X k Ö N N e n a l s g e T r e N n t e t * e i l e e I n * e s n a r t H E X a u C H g e m e i n s a m v o r k o M m e n +HYP: i * n e r N H T u n d ** E L s e r e r n a r t * e I G S k Ö R D e n a l s ******* g e * r e * n t e ******* t A e i l e e * n D e s n a r t I G S a u * R g e m e i n s a m v o r k o * m e n +Eval: D I I S S D S S D I I S S S D D D D I D I S S S D S D + +Speaker sentences 21: cv_deu_000718 #utts: 1 +id: (cv_deu_000718-cv_deu_000718) +Scores: (#C #S #D #I) 38 1 2 7 +REF: d a b e i * b e l e g * t e * e * r d i e p l Ä t z * e * v i e r u n D D r e i * +HYP: d a b e i E b e l e g K t e R e H r d i e ******* p l Ä t z S e F v i e r u n * T r e i C +Eval: I I I I D I I D S I + +Speaker sentences 22: cv_deu_000719 #utts: 1 +id: (cv_deu_000719-cv_deu_000719) +Scores: (#C #S #D #I) 38 10 7 10 +REF: * ******* k I M d * a R b * * Y i s T d i e t * o * c h t E r * z w e i e r * P r o f * e S s I O n e L L E r t Ä n z E R +HYP: A k * E d E a * b I J E i s * d i e t H o U c h t A r T z w e i e r B r o f V e * s * E n e * A r ******* t E n z A N +Eval: I I D S I D I I S D I I S I I S I D D S D S S D S S S + +Speaker sentences 23: cv_deu_000720 #utts: 1 +id: (cv_deu_000720-cv_deu_000720) +Scores: (#C #S #D #I) 34 6 11 2 +REF: * i C H G l a u b e D a s f Ü H r t n i C H t i n D I E r i C H t i g e r i C H t * u n g +HYP: D i * S K l a u b e ******* T a s f Ü * r t n i * S t i n ******* * * * r i * S t i g e ******* r i * S t R u n g +Eval: I D S S D S D D S D D D D D S D D S I + +Speaker sentences 24: cv_deu_000721 #utts: 1 +id: (cv_deu_000721-cv_deu_000721) +Scores: (#C #S #D #I) 27 4 9 5 +REF: d a s I s T e i n e E x ******* * t r e M s C h l e C H t e r i C H t * l i * n I e * +HYP: d a s ******* E s * e i n e * x S t r e * N s * h l e * S t e ******* r i * S t F l i E n * e R +Eval: D S D D I I D S D D S D D S I I D I + +Speaker sentences 25: cv_deu_000722 #utts: 1 +id: (cv_deu_000722-cv_deu_000722) +Scores: (#C #S #D #I) 31 5 6 1 +REF: h e R r l U R c h e n T b l Ö S s t E * s E i n h a g e r e s g e s i C h t +HYP: h e * r ******* l O S c h e n b l ** E s t * Z s A i n h a g e r e s ******* g e s i * h t +Eval: D D S S S D S D I S D D + +Speaker sentences 26: cv_deu_000723 #utts: 1 +id: (cv_deu_000723-cv_deu_000723) +Scores: (#C #S #D #I) 18 6 4 1 +REF: N U r C a R M e n f i n d e t D A s u n ******* f A I r +HYP: * M r K a * L e n f i n d e t ******* O S s u n f * E r +Eval: D S S D S D S S I D S + +Speaker sentences 27: cv_deu_000724 #utts: 1 +id: (cv_deu_000724-cv_deu_000724) +Scores: (#C #S #D #I) 28 3 7 6 +REF: * ******* i n g e b o R G k R a B b e h a t ******* T e * d R e i g e s c h W i s t e ******* * R +HYP: T i n g e b o * * k * a * b e R h a t D e R d * e i ******* g e s c h * i s t e N T +Eval: I I D D D D S I S I D D D I I S + +Speaker sentences 28: cv_deu_000725 #utts: 1 +id: (cv_deu_000725-cv_deu_000725) +Scores: (#C #S #D #I) 50 9 18 3 +REF: E S * k o M m T W i R K l i C H D A R a U F a * * n d a S s s o l c H e d a t e n A u F d i e s e r e b E n e E R F a S s t W e r d e n +HYP: * T S k o * m * P L i * T l i * * ******* * S T a * B a N M n d a * s ******* s o l c * e d a t e n * u * d i e s e r e b * n e * * P a * s t B e r d e n +Eval: D S I D D S S D S D D D D S S D S I I D D D D D D D D S D S + +Speaker sentences 29: cv_deu_000726 #utts: 1 +id: (cv_deu_000726-cv_deu_000726) +Scores: (#C #S #D #I) 39 5 8 1 +REF: S t r U M m i n G h I n ******* g e g e n e r G i b t e i n h A r m O n i S c h e s p U L s i e r e n +HYP: * t r * A m i n * h E n g e g e n ******* e r i b t e i n h * r m U n i * c h e s ******* p * O s i e r e n +Eval: D D S D S I D S D S D D D S + +Speaker sentences 30: cv_deu_000727 #utts: 1 +id: (cv_deu_000727-cv_deu_000727) +Scores: (#C #S #D #I) 33 3 6 3 +REF: b I n i C h z u m k a U f e I n e r h Y p o * t H e K b e r e * c h T i g * t +HYP: b E n ******* i * h z u m k a * f e * n e r h * p o E t * e G b e r e I c h D i g K t +Eval: S D D D D D I D S I S I + +Speaker sentences 31: cv_deu_000728 #utts: 1 +id: (cv_deu_000728-cv_deu_000728) +Scores: (#C #S #D #I) 12 19 4 14 +REF: T E H E R A n I S T d * * I e * H A u * * * * P T s T A D T * V O M * I r * ******* * * * A n +HYP: * K C Z U n * * E d E U N e N * E u N E I N E R s C H E N E N E N D E r N E N E N n +Eval: D S S S S S D D S I I S I D S I I I I S S S S S S I S S S I S I I I I I S + +Speaker sentences 32: cv_deu_000729 #utts: 1 +id: (cv_deu_000729-cv_deu_000729) +Scores: (#C #S #D #I) 13 19 5 1 +REF: K O h L E n H Y d R A T e * s I n D B e S s e R A L s I H R R u F +HYP: H h * U n E d E I R e N s E n * D e * s e * L W D s ******* L E N D N u N +Eval: S S D S S S S S S I S D S D D S S S D S S S S S S + +Speaker sentences 33: cv_deu_000730 #utts: 1 +id: (cv_deu_000730-cv_deu_000730) +Scores: (#C #S #D #I) 77 16 23 3 +REF: o H n e d I e P r o F E S s I O n e L l e U n T E R s t Ü T z u n g d E R m a s E R a t * i r e N n a b t E I l * u n G w a R E n d i E s e W a g e n d e r k o n k U R R e n z * N U n d o c h u n t E R l e g e n +HYP: o * n e d * e * r o * W I s * E n e * l e ******* * n G T s t ** I z u n g d * * A m a s S a t D i r e * n a b t * A l I u n * w a * * n ******* d i * s e ******* B a g e n d e r k o n k * E W e n z S * E n d o c h ******* u n t * O l e g e n +Eval: D D D D S S D S D D D S S S D S D D S S S I D D S I D D D D D D S D S S I D S D D S + +Speaker sentences 34: cv_deu_000731 #utts: 1 +id: (cv_deu_000731-cv_deu_000731) +Scores: (#C #S #D #I) 48 7 11 2 +REF: s i E D i e n ******* T e Z u n Ä c H s t a L s u n T e R k u N f T F Ü r b e * l g i s c h E B e s a t z u n g s t r u P p e n +HYP: s i * * i e n D e * u n E c * s t a * s ******* u n e * k u M f * V E r ******* b e L l g i s c h * I e s a t z u n g s t r u * p e n +Eval: D D I S D S D D D S D S D S S D I D S D + +Speaker sentences 35: cv_deu_000732 #utts: 1 +id: (cv_deu_000732-cv_deu_000732) +Scores: (#C #S #D #I) 30 6 6 2 +REF: d A m Ü S s e n w i R s p R E n g e n m e i N t e D e r z A h * n ******* a R z t +HYP: d * E m ** I s e n w i * E s p L Ä n g e n m e i * t e * e r z * h A n a T z t +Eval: D S D S D S S S D D D I I S + +Speaker sentences 36: cv_deu_000733 #utts: 1 +id: (cv_deu_000733-cv_deu_000733) +Scores: (#C #S #D #I) 71 14 14 8 +REF: a u S S e r ******* d e m s p * i e L t e E r b e I m n a c h ******* F O L g e t * e A m n E W m a r k e t r o * Y A L s s o w i e b e I M l i * g * * A k o n k u R r e n t e n l o n d O n K N i G H t S +HYP: a u * * e r d e m s p H i e * t e * r ******* b e R m ******* n a c h V E R g e t I e * m n I N m a r k e t r o E D I U s s o w i e ******* b e * R l i E g E R k o n k u E r e n t e n l o n d * n ******* * E i * * t Z +Eval: D D I I D D D S D I S S S I D S S I S S S D D S I I I S S D D D S D D S + +Speaker sentences 37: cv_deu_000734 #utts: 1 +id: (cv_deu_000734-cv_deu_000734) +Scores: (#C #S #D #I) 56 18 10 13 +REF: W i e a u C H D A s I n * s t A n T R U n ******* O F f v * O t * i n g * e r ******* f Ü L L t D i E * C o O m B s ******* w a H l d a s C o n ******* d o * r * c * e t k R I t e r i * U m n i c h t +HYP: * i e a u * P T E R s E n Z s t E n W A n A H f v A U t H i n g A e r f Ü * R t ******* * i * K U o U m * s w a * l d a s K o n d o A r E c K e t k * t e r i A O m ******* n i c h t +Eval: D D S S S S S I S S S S I S S I S I I I D S D D D I S S D I D S I I I I D S I S D + +Speaker sentences 38: cv_deu_000735 #utts: 1 +id: (cv_deu_000735-cv_deu_000735) +Scores: (#C #S #D #I) 16 6 4 4 +REF: s m I t H * w u C H S i n c h i ******* C a G O * * a u f +HYP: s m E t * F w u * * O E i n T c h i K a * R B O a u f +Eval: S D I D D S S S I S D S I I + +Speaker sentences 39: cv_deu_000736 #utts: 1 +id: (cv_deu_000736-cv_deu_000736) +Scores: (#C #S #D #I) 13 3 4 2 +REF: w i R S i N D H i e R a L l e i * * n +HYP: w i E * i E T * i e * a * l e i N E n +Eval: S D S S D D D I I + +Speaker sentences 40: cv_deu_000737 #utts: 1 +id: (cv_deu_000737-cv_deu_000737) +Scores: (#C #S #D #I) 46 11 14 6 +REF: * ******* d * u M m i * s T W e r E t w a s * w e i S s A B e r t r o * t Z d e s B e s S E r E n W i S s e n s F A l S c H h A n D e L T +HYP: N d B u * m i E s * B e r ******* * t w a s B w e i * s W U e r ******* t r o S t * d e s D e s U Ö r * n B i * s e n s V E l * c * ******* h U n I e * * +Eval: I I I D I D S D D I D S S D I D S S S D S D S S D D D S S D D + +Speaker sentences 41: cv_deu_000738 #utts: 1 +id: (cv_deu_000738-cv_deu_000738) +Scores: (#C #S #D #I) 48 12 9 8 +REF: h a U P T t H e m a D e r s * h * o W i s t d I e r e V a n * c H E F Ü r * Ü * b L E * * s t r e i C h e U n t e R * f r e U n D e n +HYP: h a * R B t * e m a * e r s C h E o * i s t d * e r e W a n S c * * V E r E G Ü N b I C H I s t r e i h e R E n t e * P f r e I n * e n +Eval: D S S D D I I D D S I D D S S I S I S S I I S S S D I S D + +Speaker sentences 42: cv_deu_000739 #utts: 1 +id: (cv_deu_000739-cv_deu_000739) +Scores: (#C #S #D #I) 42 6 2 5 +REF: G l e i c h z e i t i g * w u * R d e n s p O r * t * w e T t e n t E I l w e i s e v e r b * O T e n +HYP: * l e i c h z e i t i g H w u O N d e n s p * r O t D w e R t e n t A L l w e i s e v e r b U L H e n +Eval: D I I S D I I S S S I S S + +Speaker sentences 43: cv_deu_000740 #utts: 1 +id: (cv_deu_000740-cv_deu_000740) +Scores: (#C #S #D #I) 3 2 1 17 +REF: * ******* * ******* s I e * ******* * * * * B E n * * * * * * * +HYP: D A s * e N H S F S S A n A A F A A A E +Eval: I I I I D I I I I I I S S I I I I I I I + +Speaker sentences 44: cv_deu_000741 #utts: 1 +id: (cv_deu_000741-cv_deu_000741) +Scores: (#C #S #D #I) 0 2 0 19 +REF: * * * * ******* * * * * * ******* * ******* * ******* * * * * J A +HYP: O T S T I E R E R N N N F B E N N +Eval: I I I I I I I I I I I I I I I I I I I S S + +Speaker sentences 45: cv_deu_000742 #utts: 1 +id: (cv_deu_000742-cv_deu_000742) +Scores: (#C #S #D #I) 57 7 14 6 +REF: z u ******* d e m V E r s a H * * e r I m k l o s t E R l A n g E j a H r e d I e Ä m t e r d e s * n o V i * z e n ******* m e i s t E R s u n D p r i o r +HYP: z u d e m F A r s a * E I e r L E m k l o s t * * A l * n g * j a * r e d * e ******* ** m t e r ******* d e s E n o W i T z e n m e i s t * A s ******* u n * ******* p r i o r +Eval: I S S D I I S S D D S D D D D D D D I S I I D S D D D + +Speaker sentences 46: cv_deu_000743 #utts: 1 +id: (cv_deu_000743-cv_deu_000743) +Scores: (#C #S #D #I) 30 6 4 6 +REF: h e i d e n ******* h A i * * n e n T s t A M m t E e i n e r Ä r z t e ******* * F A m i L I e * +HYP: h e i d e n h E i D E n e n s t * * m t * e i n e r E r z t e V E R m i * E e R +Eval: I S I I S D D D S I I S S D S I + +Speaker sentences 47: cv_deu_000744 #utts: 1 +id: (cv_deu_000744-cv_deu_000744) +Scores: (#C #S #D #I) 2 2 0 14 +REF: * ******* a * * * ******* * * * * C H t * * * * +HYP: D a R E R T Z U E G K t E N E N +Eval: I I I I I I I I I I S S I I I I + +Speaker sentences 48: cv_deu_000745 #utts: 1 +id: (cv_deu_000745-cv_deu_000745) +Scores: (#C #S #D #I) 4 0 0 6 +REF: z w e i * * ******* * * * +HYP: z w e i E L G N R +Eval: I I I I I I + +Speaker sentences 49: cv_deu_000746 #utts: 1 +id: (cv_deu_000746-cv_deu_000746) +Scores: (#C #S #D #I) 37 6 13 4 +REF: e b e N f * a l l S i n a u G g E n A n g e S i E D e L t S I n D D i E k E l t e r e i * d e r * F a * +HYP: e b e P f E a l l * E i n a u * g * n E n g e * i * * e * t ******* Z E n * * i * ******* k * l t e r e i N d e r E a T +Eval: S I D S D D S D D D D D S S D D D D D I I S I + +Speaker sentences 50: cv_deu_000747 #utts: 1 +id: (cv_deu_000747-cv_deu_000747) +Scores: (#C #S #D #I) 49 15 19 1 +REF: d i e s e s ******* T e H t a U C h f Ü R a b S O l V e n T e n e i n h E I m I s c h e r S c h U l E N m I t D E U t s C H k E N n t N I S s e n o F f e N +HYP: d i e s e R s D e * t a * * h ******* f ** E a b P E l W e n D e n e i n h * R m * s c h e r * c h O l * * ******* m E t ******* * A R t s * * k * * n t * D E s e n o * f e M +Eval: S I S D D D D D S S S S S D S D D S D D D S D D S S D D D D D S S D S + +Speaker sentences 51: cv_deu_000748 #utts: 1 +id: (cv_deu_000748-cv_deu_000748) +Scores: (#C #S #D #I) 12 4 4 1 +REF: a l ******* s o I c h H Ö r e n i C H T s +HYP: a l s o E c h ******* I O r e ******* n i * * G s +Eval: I S D S S D D D S + +Speaker sentences 52: cv_deu_000749 #utts: 1 +id: (cv_deu_000749-cv_deu_000749) +Scores: (#C #S #D #I) 20 2 4 1 +REF: w I e k A N n m a n s i * c h s c H Ü t z e n +HYP: w * e ******* k * O n m a n s i S c h s c * U t z e n +Eval: D D D S I D S + +Speaker sentences 53: cv_deu_000750 #utts: 1 +id: (cv_deu_000750-cv_deu_000750) +Scores: (#C #S #D #I) 61 9 13 5 +REF: N a C H f Ü * n f m O n * A t E n l a g e i n e E M P f i n d l i c h e r * e p l A T t e a L S d I e b i s d a h * i n e r ******* h Ä l t l i c h e n v o r +HYP: * a * * ******* f Ü I n f ******* m * n E R t n l a g e i n e * N f i n d l i c h e r B e ******* p l * O t e a N Z T d * e ******* b i s T d a h E i n ******* e r h ** l t l i c h e n v o r +Eval: D D D D I D D I S S D S S I D D S S S S D D S I D I D + +Speaker sentences 54: cv_deu_000751 #utts: 1 +id: (cv_deu_000751-cv_deu_000751) +Scores: (#C #S #D #I) 52 22 18 8 +REF: Z i E l i S T * e * s d i e Ü b E R E i n s t I M m u n g E i n e S s o f t W A R e * s Y s t e * M s m i * t s E I n E R S P e z i * F i k a T I o * n z u Ü b E R p r Ü f * E N +HYP: T i * l ******* i * E T e R s d i e ******* ** b * W i n s t * N m u n g * i n e * ******* s o f t * * * e N s N s t e B Z s ******* m i N t ******* s * A n A S C G I e z i C H i k a Z H o U n ******* z u ******* N b A B p r L f M C H +Eval: S D D D S I I D D D S S D S D D D D D D I S I S D I D D S S S S S S I S S S I D D S S S S I S S + +Speaker sentences 55: cv_deu_000752 #utts: 1 +id: (cv_deu_000752-cv_deu_000752) +Scores: (#C #S #D #I) 37 25 9 11 +REF: * * * M I t e i N e M w a * R m e n g e * ******* T R Ä n K i * M B a * u C H L Ä s * * s t S i C H D I e K Ä l T E b e S s E r a U s h * A l t e n +HYP: A T E N E t H e i L e N w a H A m e n g e N S H E n * i H E N D a L u * S W E s Z I s t ******* * i K E L N T e ******* * ** l L N b e * s O r ******* a E s h E I l t e n +Eval: I I I S S S S S I S I I S S S D I S S S I D S S S I I D D S S S S S D D D S S D S D S I S + +Speaker sentences 56: cv_deu_000753 #utts: 1 +id: (cv_deu_000753-cv_deu_000753) +Scores: (#C #S #D #I) 45 26 9 13 +REF: d i e a n T i * ******* V I r * e n ******* s o * F T W A r E i s t a * M O k * * g e l ******* A u * * F e n U n d H a * T a L l e * C O M P U T e R I M H A U s l a H M g e l E g t +HYP: d i e ******* a n D i E W E r B e n s o S C H Ö H r * i s t a N R k E N g e l R u S C H e n * n d * a N D a * l e R * G B N B H e * ******* N T E L N R s S l a * N g e l I g t +Eval: D S I I S S I I I S S S S D I S S I I I S I I S D D I S D I D S S S S S D D S S S S S S S D S S + +Speaker sentences 57: cv_deu_000754 #utts: 1 +id: (cv_deu_000754-cv_deu_000754) +Scores: (#C #S #D #I) 26 15 1 4 +REF: i H r E K L O a * K e i s t I n d i e s E r z e i ******* * t K u G e * l F Ö R M I G +HYP: i E r * T U a R G e i s t E n d i e s A r z e i R t G u N e F l A N E N S C +Eval: S D S S S I S S S I I S S I S S S S S S + +Speaker sentences 58: cv_deu_000755 #utts: 1 +id: (cv_deu_000755-cv_deu_000755) +Scores: (#C #S #D #I) 51 19 12 13 +REF: * ******* d i e s T R e C k e b e g I N n T I m s Ü D e n V e r * O N a * s U n D f Ü H R t D U R C H d i e P o E b E N e R i c h t ******* u * N g ******* * * s * Ü * ******* * D O s t e N +HYP: E d i e s * C e Ä k e b e g * E n * * m ******* s Ü G e n B e r H U M a R s I n * f Ü * L t ******* * * * I S d i e G o b D e E i c h t u M E g A R s I Ü T A U S s t e * +Eval: I I D S S D S D D D S S I S S I S D D S D D D D S S S S S S S I I S I I I I I I I S S D + +Speaker sentences 59: cv_deu_000756 #utts: 1 +id: (cv_deu_000756-cv_deu_000756) +Scores: (#C #S #D #I) 43 1 6 5 +REF: e r s t v o n d o r t k o N n t e e r S E I N e n w e g f r e i ******* * F o R t * s e * t z * e n +HYP: e r s t v o n d o r t k o * n t e e r * * * * e n w e g f r e i E V o * t Z s e I t z S e n +Eval: D D D D D I I S D I I I + +Speaker sentences 60: cv_deu_000757 #utts: 1 +id: (cv_deu_000757-cv_deu_000757) +Scores: (#C #S #D #I) 55 6 12 7 +REF: s i E e r h e b T S i c h h e u t e i M m e r n o C H g u t e r k e N n b A r ******* * a u * s d * * e M s c * h ******* w e m M l a n D h e R a u s +HYP: s i * ******* e r h e b * ******* Z i c h h e u t e i * m e r ******* n o * * ******* g u t ******* e r k e * n b E r T a u S s T d E I e N s c S h w e m l a n T h e * a u s +Eval: D D D D S D D D D D D D S I I I S I I S I I S S D + +Speaker sentences 61: cv_deu_000758 #utts: 1 +id: (cv_deu_000758-cv_deu_000758) +Scores: (#C #S #D #I) 28 5 8 3 +REF: d i e k a n a r I s c h e n i n S e L n G e ******* h Ö R E n z u s p * * A N I e N +HYP: d i e ******* k a n a r E s c h e n i n * e * n * e h ** * A n ******* z u s p B E R C H e * +Eval: D S D D D I D D S D I I S S S D + +Speaker sentences 62: cv_deu_000759 #utts: 1 +id: (cv_deu_000759-cv_deu_000759) +Scores: (#C #S #D #I) 46 14 9 6 +REF: w I S s E n ******* s c h * A f T l e r h A b e n d i e s e m u ******* t A t * I O n B I s H e * r n U R b e i f R a U E n b e * O b a C H t e t +HYP: w * E s * n s c h R f * l e r ******* h R b e n d i e s e m u t D t Z U n * E s * e H r E n * E O b e i f O a R A n b e R E b a * * t e t +Eval: D S D I I S D D S I S I S S D S D I S D S S S S S I S D D + +Speaker sentences 63: cv_deu_000760 #utts: 1 +id: (cv_deu_000760-cv_deu_000760) +Scores: (#C #S #D #I) 49 6 6 6 +REF: s e I n E g E s c h Ä F t * s B e * z i E H u n G e n r e i * c H t e n b i s n o r ******* d a m e * r i k a U n d a * s i e n +HYP: s e * n * I g I s c h ** E t Z s P e T z i * * u n * e n r e i S c S t e n b i s n o r d a m e H r i k a E n d a R s i e n +Eval: D D S S D S I S I D D D I S I I S I + +Speaker sentences 64: cv_deu_000761 #utts: 1 +id: (cv_deu_000761-cv_deu_000761) +Scores: (#C #S #D #I) 88 13 10 5 +REF: Z a H L r e i c h e v o r d e r e * P L a * t z i e r u n G e n b e i d E u t s c h e n e U r o p a * u n d w e L t M e I s t e R s c H a f t e n S o ******* W i e O l Y M P i S c h e n s p i E l e n F o * l G t e n +HYP: * a * * r e i c h e v o r d e r e D * M a E t z i e r u n e n b e i d * u t s c h e n e * r o p a R u n d w e H t L e C s t e s c * a f t e n Z o B i e N l * Ü B i * c h e n s p i * l e n V o R l K t e n +Eval: D D D I D S I S D D I S S S S D S I S S D S S D D S I S + +Speaker sentences 65: cv_deu_000762 #utts: 1 +id: (cv_deu_000762-cv_deu_000762) +Scores: (#C #S #D #I) 34 18 14 2 +REF: i n E I n ******* e r t * A G e S Z E I t u N G B l Ä T t e r N d s I T Z T S i E g F R i E D a u f E I N e r p a r K b a n K +HYP: i n * A n e r ******* t E L I e D S O t u E M * l ** E t e r M d s * * O E * i * g * K i * T a u f ******* * * T e r ******* p a r G b a n G +Eval: D S I D I S S S S S S S S D D S S D D S S D D D S D S D D D S D S S + +Speaker sentences 66: cv_deu_000763 #utts: 1 +id: (cv_deu_000763-cv_deu_000763) +Scores: (#C #S #D #I) 41 9 21 1 +REF: M i t E i N E m w a R m E N G E t r Ä n K i M b a u C H l Ä S s T s i C H d i e k * Ä l t e b e S s E R a u s h a L T e N +HYP: * i t * i * U m w a * m * * ******* * I t r E n G i N b a u * * P l ** * s * ******* s i * * d i e k E R l t e b e * s * * ******* a u s h a D I e * +Eval: D D D S D D D D D S S S S D D S D D D D D D I S D D D D S S D + +Speaker sentences 67: cv_deu_000764 #utts: 1 +id: (cv_deu_000764-cv_deu_000764) +Scores: (#C #S #D #I) 11 8 2 2 +REF: F o L G e D e M Q U e r ******* V e R w e I s * +HYP: W o E N e * e N T e r H e * w e S s I +Eval: S S S D S S S I S D S I + +Speaker sentences 68: cv_deu_000765 #utts: 1 +id: (cv_deu_000765-cv_deu_000765) +Scores: (#C #S #D #I) 38 14 12 2 +REF: * o s t E R n i s T I M M e R e i n e W o c h e n a c h D e M e R s t e n V O L L m O n D * i M F r Ü H l I n G +HYP: A o s t * A n ******* i s * * E N e * e i n e B o c h e ******* n a c h ******* T e N e * s t e n * U E m U n * D i N * r L U l E n * +Eval: I D S D D D S S D S D D S S D D S S S S D I S D S S S D + +Speaker sentences 69: cv_deu_000766 #utts: 1 +id: (cv_deu_000766-cv_deu_000766) +Scores: (#C #S #D #I) 39 11 9 5 +REF: * I m m i T t e l ******* A L t e r h A T t e n W e C H S E L n ******* d e h e R r s c h a f t E N d a s d O R f * i N n e * +HYP: E N m m i * t e l E I t e r ******* h E R t e n * e * Ä K Z I n d e h e * r s c h a f t * * d a s T d * A f H i * n e R +Eval: I S D I S S D S S D D S S S S I D D D S D S I D I + +Speaker sentences 70: cv_deu_000767 #utts: 1 +id: (cv_deu_000767-cv_deu_000767) +Scores: (#C #S #D #I) 37 16 6 2 +REF: d e n n a m E N G h I B l I t r a G E n a U c h W e i t e R e f a H R Z E U g e v O N m a * s e r * a t i +HYP: d e n n a m * * S C h E P l E t r a R n ******* a O c h ******* L e i t e e ******* f a * T S O L g e v E R m a S s e r H a t i +Eval: D D S S S S S S S D S D S S D D S S S S S S I I + +Speaker sentences 71: cv_deu_000768 #utts: 1 +id: (cv_deu_000768-cv_deu_000768) +Scores: (#C #S #D #I) 32 6 17 4 +REF: * D u k a N n S T m i t D e M b u s N A c h F R a n K F U R T * A n D e r o * d e r f A h * r e n +HYP: P L u ******* k a * n * * E m i t * e * b u s ******* L O c h ******* * * a n * * * * * V O n * e r o N d e r E f * h O r e n +Eval: I S D D D D S D D D S S D D D D D D D D I S D I S D I + +Speaker sentences 72: cv_deu_000769 #utts: 1 +id: (cv_deu_000769-cv_deu_000769) +Scores: (#C #S #D #I) 6 6 1 0 +REF: M I r D o C H e g A l +HYP: N E r E o * R I e g O l +Eval: S S S D S S S + +Speaker sentences 73: cv_deu_000770 #utts: 1 +id: (cv_deu_000770-cv_deu_000770) +Scores: (#C #S #D #I) 75 10 17 9 +REF: a l l e r D i n g s e r ******* g a * b e n w e i t e r * e p R Ü * f * U n g e n d a S s e s m i T t e l f r i s t i G k e i N E n B E d a R F f Ü R E i N E s * O L c h e a u t * o b a H n * G Ä B e * +HYP: a l l e r T i n g s e r g a H b e n w e i t e r I e p U Ü N f V O n g e n d a * s e s m i * t e l f r i s t i * ******* k e i * * n P I d a * * ******* f ** * ******* * i * * ******* s C E U c h e a u t U o b a * n D E H R e R +Eval: S I I I S I I S D D D D D D S S D D D D D D D D D D I S S I D I S S S I + +Speaker sentences 74: cv_deu_000771 #utts: 1 +id: (cv_deu_000771-cv_deu_000771) +Scores: (#C #S #D #I) 61 6 7 4 +REF: u M g e k E H r t k a N n e I n f r e i B r i e * f e i n e * * a u S s c h r e i b u n g a l S v o G e l * f r e i g e m e i n T S e i n +HYP: u N g e k * * r t k a * n e * n f r e i P r i e F f e i n e H A R a u * s c h r e i b u n g a l Z v o B e l E f r e i g e m e i n * ******* Z e i n +Eval: S D D D D S I I I S D S S I D D S + +Speaker sentences 75: cv_deu_000772 #utts: 1 +id: (cv_deu_000772-cv_deu_000772) +Scores: (#C #S #D #I) 51 8 5 11 +REF: * b i * z a R r G R o * t e s ******* K e a b s c h n i T t e * Z e i G e n e i n * f l Ü S s e d u R C H s c h o s t * a * k o * * w i T s c * h +HYP: M b i E z a * r K o R t e s G e a b s c h n i * t e R S e i * e n e i n P f l ** U s e d u * I G s c h o s t D a R k o L N w i E s c S h +Eval: I I D S S I I S D I S D I D S D S S I I I I S I + +Speaker sentences 76: cv_deu_000773 #utts: 1 +id: (cv_deu_000773-cv_deu_000773) +Scores: (#C #S #D #I) 55 5 10 4 +REF: E r W A r e i n e r d e r p i * * O n i e r e a U f d E m g e b i e t d e r n u t z * u n g d e r s o N n e N E n e r g I e * +HYP: * r ******* * E r e i n e r ******* d e r ******* p i A E R n i e r e a * f T d * m g e b i e t d e r ******* n u t z I u n g d e r s o * n e * n e r g E e N +Eval: D D D S D D I I S D S D D I D D S S I + +Speaker sentences 77: cv_deu_000774 #utts: 1 +id: (cv_deu_000774-cv_deu_000774) +Scores: (#C #S #D #I) 47 6 22 2 +REF: a U C h W e N n m I R d i e k U n d e n a U f D i E n e ******* r V e n g E H e n m u S s i c H h Ö F l i c H k e I t * b e w a H R E n +HYP: a * R h ******* F e * n m * * E d i e k O n d e n a * f * i * ******* n e r F e n ******* g * * e n m u * s ******* i c * ******* h ** E l i c * k e * t P b e w a * * * n +Eval: D S D S D D D S S D D D D I S D D D D D D D D S D D I D D D + +Speaker sentences 78: cv_deu_000775 #utts: 1 +id: (cv_deu_000775-cv_deu_000775) +Scores: (#C #S #D #I) 18 6 3 1 +REF: d i E S P Ü L m a s c h i n e I s t F e r t i * G +HYP: d i * ******* C H B E m a s c h i n e * s t V e r t i S H +Eval: D D S S S S D S I S + +Speaker sentences 79: cv_deu_000776 #utts: 1 +id: (cv_deu_000776-cv_deu_000776) +Scores: (#C #S #D #I) 60 7 4 6 +REF: i n d e R a r c h * A i s c h e n p e r i o * d e w u r d e n e r s t * * e F o r m e n d e s A c k e R b a U s E n ******* t W i C k e * l t +HYP: i n d e * a r c h E R i s c h e n p e r i o N d e w u r d e n e r s t E I e V o r m e n d e s ******* O c k e * b a S s N I n t U i * k e L l t +Eval: D I S I I I S D S D S S S I S D I + +Speaker sentences 80: cv_deu_000777 #utts: 1 +id: (cv_deu_000777-cv_deu_000777) +Scores: (#C #S #D #I) 28 4 9 0 +REF: d i e k o m Ö d I e s e I b e S s e R A l S D e r e R s t e f I L M +HYP: d i e k o m Ü d * e s e * b e * s e * * l * ******* T e r e * s t e f * Ü N +Eval: S D D D D D D D S D D S S + +Speaker sentences 81: cv_deu_000778 #utts: 1 +id: (cv_deu_000778-cv_deu_000778) +Scores: (#C #S #D #I) 11 13 4 6 +REF: a K t * U e L L g I L T F O L G E n D e r m * O D U s ******* * * * +HYP: a R t Z L e R E g * E D * V E R n * e r ******* m U M E M s A L P +Eval: S I S S S D S S D S S S S D D I S S S I I I I + +Speaker sentences 82: cv_deu_000779 #utts: 1 +id: (cv_deu_000779-cv_deu_000779) +Scores: (#C #S #D #I) 56 29 14 15 +REF: * d * A m i t e n d e T e i n e * * e R F O L G r e i * c h E i N T e R n a * T I O n * A l * e * B I L d U n G s ******* a R b e I T v o r A L l e M i M m U s i s c h k U L t U R E L L e n * * B e R e * * * I C H +HYP: T d E R m i t e n d e * e i n e E A e W E R K r e i S c h * i E e * n a R Z U n E R l I e N K Ä E R d E n * s a * b e * N v o r ******* * E l e N i * N m N s i s c h k Ü N t * * * * * e n A U N e N e N Z S K A U +Eval: I I S D I I S S S S S I D S S D I S S S I S I I S S S S S D I D D S D D S S D S S S S D D D D D I I S S I I I S S S + +Speaker sentences 83: cv_deu_000780 #utts: 1 +id: (cv_deu_000780-cv_deu_000780) +Scores: (#C #S #D #I) 66 11 11 5 +REF: d e r s o H n e i n e s b e r * g M a N n S b e g a N n S e i n E F u S s B a L l k A r R I e r E B e i d e n s p O r t ******* f r e U n d * e n w a N n e ******* e i C k e l * +HYP: d e r ******* s o * n e i n e s b e r E g N a * n Z b e g a * n * e i n I * u * s P a * l k E r J e r I W e i d e n ******* s p U r t f r e I n d T e n w a * n e e i * k e l N +Eval: D D I S D S D D S D D S D S S S S S D S I S I D I D I + +Speaker sentences 84: cv_deu_000781 #utts: 1 +id: (cv_deu_000781-cv_deu_000781) +Scores: (#C #S #D #I) 65 8 10 6 +REF: i n d i E S e M j a H r g a b e s s i e B e n n U M m e r ******* e i * n S s i n g L e s u n D s E c h s u n D D r e i s S i g n U M m e r ******* e i * n s ******* a l * b e n +HYP: i n d i * * e N j a * r ******* g a b ******* e s s i e D e n n * O m e r e i E n s i n g * e s u n * s Ä c h s u n * r e i s Z i g n * O m e r e i E n s a l E b e n +Eval: D D S D D D S D S I I S D D S D S S D S I I I I + +Speaker sentences 85: cv_deu_000782 #utts: 1 +id: (cv_deu_000782-cv_deu_000782) +Scores: (#C #S #D #I) 58 7 3 10 +REF: * ******* n o r d ******* w e s t l i c h v * O n h a C K h a u s e n b e * f i n d e T s i c h D i E O r t s c h a f t h a C k e n ******* b * r * O i * * c h +HYP: N n o r d w e s t l i c h v E R n h a R G h a u s e n b e R f i n d e * s i c h * i O A r t s c h a f t ******* h a E k e n b O r E U i C S c h +Eval: I I I I S S S I D D S S D S I I I S I I + +Speaker sentences 86: cv_deu_000783 #utts: 1 +id: (cv_deu_000783-cv_deu_000783) +Scores: (#C #S #D #I) 79 8 12 8 +REF: i * m o r t * g n A R R e n ******* b u r g G i * n G e n v i e l e s o * ******* Z i a L e e i n * r i c h t U n G E n v o n H e r * m a N n l a m p r E c h t U n D d e r m a r i e n H Ü T t e a u s +HYP: i E m o r t K g n N A e n b u r g * i E n * e n v i e l e ******* s o S S i a * e e i n E r i c h t E n * U n v o n * e r E m a * n l a m p r Ä c h t * n * ******* d e r ******* m a r i e n Ü * t e a u s +Eval: I I S S S I D I D D I I S D I S D S D I D S D D D D S D + +Speaker sentences 87: cv_deu_000784 #utts: 1 +id: (cv_deu_000784-cv_deu_000784) +Scores: (#C #S #D #I) 66 8 9 1 +REF: i c h w e r d e F o l G l I c h d E n r a t Ü b e r d I e I m p a R l A m E n T v o r g e t r a g e n e n b e D e n K e n I n ******* F o R m i e r e n +HYP: i c h w e r d e V o l K l * c h d I n r a t ** b e r d * e ******* * m p a * l L m * n D v o r g e t r a g e n e n b e T e n T e n * n V o * m i e r e n +Eval: S S D S D D D D D S D S S S D I S D + +Speaker sentences 88: cv_deu_000785 #utts: 1 +id: (cv_deu_000785-cv_deu_000785) +Scores: (#C #S #D #I) 57 10 20 1 +REF: E s W Ä r e t r a u r I G g e w E S e n e i n s o w i c h t * i G E s t h e m a n i c h T I m k o n S e N S V E R a b s c h I E D e n z u k Ö N N e n +HYP: * s I E r e ******* t r a u r * S C g e w * I e n e i n ******* s o ******* w i c h t D i * U s t h e m a ******* n i c h * E m k o n D e * T * * H a b s c h * * * e n ******* z u ******* k ** * * e n +Eval: D S S D D S S D S D D I D S D D S S D S D D S D D D D D D D D + +Speaker sentences 89: cv_deu_000786 #utts: 1 +id: (cv_deu_000786-cv_deu_000786) +Scores: (#C #S #D #I) 51 15 4 9 +REF: n A c h D E S s e * n t * o D * I m G l e i * c h e n j a h r K a m e s K u R z F R i s t * i * * g a n a n d e r e * * B e s I t z E R +HYP: n O c h ******* T I H s e I n M t E o T E N m K l e i S c h e n j a h r G a m ******* e s G u T z W i s t D i E K g a n a n d e r e R D I e s E t z * * +Eval: S D S S S I S I S I S S I S D S S S S I I I I I S S D D + +Speaker sentences 90: cv_deu_000787 #utts: 1 +id: (cv_deu_000787-cv_deu_000787) +Scores: (#C #S #D #I) 51 17 3 14 +REF: k U R z d a n * A c h g a b E s e i n e n w e r b e * ******* S P o * * t m i T d e * m ******* * C a n ******* C a * n * * V O n * J A c * Q U e s o F F e n b a c h +HYP: k O T z d a n E R c h g a b ******* I s e i n e n w e r b e R B W o R D t m i D E d e N m T K a n K a M n D T * U n D * S c H E K e s H o C H e n b a c h +Eval: S S I S D S I I S S I I S S I I I S I S I I I D S I D S I S S S S S + +Speaker sentences 91: cv_deu_000788 #utts: 1 +id: (cv_deu_000788-cv_deu_000788) +Scores: (#C #S #D #I) 10 2 2 18 +REF: * d A S i S t b * e ******* * * * * s * s * * * ******* * * * * ******* * e R +HYP: I d D i * t b E e H T S U s F s N U N N A N N A e * +Eval: I S S D I I I I I I I I I I I I I I I I I D + +Speaker sentences 92: cv_deu_000789 #utts: 1 +id: (cv_deu_000789-cv_deu_000789) +Scores: (#C #S #D #I) 21 3 6 2 +REF: w I e s i e H T E s m I T G l e i t z e i t a u s ******* * +HYP: w * e ******* s i e * * I s ******* m N D * l e i t z e i t a u s H +Eval: D D D D S D S S D I I + +Speaker sentences 93: cv_deu_000790 #utts: 1 +id: (cv_deu_000790-cv_deu_000790) +Scores: (#C #S #D #I) 49 15 7 6 +REF: n A h e d e m d o * R f b e f i n ******* d e t S i c h a U c h d e r G r A N D C A n Y O N n a T i O n a * l * p a R k * A I r * P o R t +HYP: n C h e d e m d o C H f b e f i n d e t * i c h a R c h d e r K r * * M K E n * * I U n a S i H n a L l E B p a C k G * E r B U o * t +Eval: S I S I D S S D D S S S D D S S S S I I S S I D S I S D + +Speaker sentences 94: cv_deu_000791 #utts: 1 +id: (cv_deu_000791-cv_deu_000791) +Scores: (#C #S #D #I) 42 5 8 2 +REF: S i e s O L L e n V e r k Ü n d e n d A S s d i e l i e b e D e n t o * d b e s i E G t H a * t +HYP: * i e ******* s * E R e n D e r k Ü n d e n d * E s d i e l i e b e * e n ******* t o E d b e s i * K t * a R t +Eval: D D D S S S D S D D I D S D I + +Speaker sentences 95: cv_deu_000792 #utts: 1 +id: (cv_deu_000792-cv_deu_000792) +Scores: (#C #S #D #I) 55 11 2 9 +REF: b e D e c k t i s t d i E r e * ******* p r * Ä s e n t * * A t i * V g e s t a l t e T e V I l L A m i t * e i n e M m a n ******* s a r * D d a c h +HYP: b e T e c k t i s t ******* d i * r e B p r E N s e n t H E R t i E F g e s t a l t e e W E l E R m i t D e i n e N m a n s a r T d a c h +Eval: S D D I I I S I I S I S S S S S S I S I I S + +Speaker sentences 96: cv_deu_000793 #utts: 1 +id: (cv_deu_000793-cv_deu_000793) +Scores: (#C #S #D #I) 47 6 9 2 +REF: d i e s e s i e D L u n G i * s T m i t D e r O r t s c h a f t d e L l A c h z u s a m m E n ******* g e w a C H S e n +HYP: d i e s e ******* s i e * u n * i E s * ******* m i t ******* * e r A r t s c h a f t d e * l E c h z u s a m m * n g e w a K N Z e n +Eval: D D S D I D D D D S D S D I S S S + +Speaker sentences 97: cv_deu_000794 #utts: 1 +id: (cv_deu_000794-cv_deu_000794) +Scores: (#C #S #D #I) 20 6 7 5 +REF: * ******* w a r T i H R s C h O n e i n M a l I n D e M C l U b * ******* * +HYP: I w a r * D i * E s * h * n e i n * a l ******* E n * e N K l O b E S +Eval: I I D S D S D D D D S D S S S I I I + +Speaker sentences 98: cv_deu_000795 #utts: 1 +id: (cv_deu_000795-cv_deu_000795) +Scores: (#C #S #D #I) 20 6 1 6 +REF: * W o r a U C H i s t i s t * a u c h F e * * U e r * * +HYP: B U o ******* r a N G E i s t i s t D a u c h V e R I O e r E T +Eval: I S D S S S I S I I S I I + +Speaker sentences 99: cv_deu_000796 #utts: 1 +id: (cv_deu_000796-cv_deu_000796) +Scores: (#C #S #D #I) 66 20 7 16 +REF: d I R e K t v o n d e r s t r a s s e W u R D e n s i E v o n a l * f R e D B i o * l e K F Ü R s e i n e * f * e * * R n ******* s e H s * h o ******* * W s * h o * W b Ü H n e * e n ******* G A G i e * * R t +HYP: d E H e X t v o n ******* d e r s t r a s s e B u * e n ******* s i * v o n a l T f T e T D i o N l e G * ** * I s e i n e R f W e S T E n s e s C h o U E s C h o B E b I L n e R e n E Ü S i e A L T t +Eval: S S S D S D S D D I S S S I S D D D S I I I I S I S I I I S I I S S S I I S S S I I S + +Speaker sentences 100: cv_deu_000797 #utts: 1 +id: (cv_deu_000797-cv_deu_000797) +Scores: (#C #S #D #I) 46 12 11 9 +REF: E i N J a H r s * p * Ä t e r W e C H s E l t e * e r z u * H e A l T H n E t * * u n D * e r w u R d e E r F O L g * r e i * c h e R +HYP: A i * H a * r ******* s C p E I t e r * e * S s L l t e R e r T z u N * e * l * F n A t Z S u n * B e r w u * d e L r A N g E r e i S c h e * +Eval: S D S D D I I S D D S S I S I D D D S S I I D I D S S S S I I D + +Speaker sentences 101: cv_deu_000798 #utts: 1 +id: (cv_deu_000798-cv_deu_000798) +Scores: (#C #S #D #I) 46 8 9 5 +REF: i n d e r l * A n D W i R t S C h A F T k A N n d e r e r t r a * * G d e u t l i C H R e d * * U z i e r t w e r d e n +HYP: i n d e r ******* l E R n V i * t * * h * * E k E R n d e r e r t r a R E T d e u t l i * * * e d E O T z i e r t w e r d e n +Eval: D I S S S D D D D D S S S I I S D D D I I S + +Speaker sentences 102: cv_deu_000799 #utts: 1 +id: (cv_deu_000799-cv_deu_000799) +Scores: (#C #S #D #I) 45 5 5 6 +REF: m a * n ******* s O u r s p i e L t e i n s e i n e r h e i m a * t s t a d t k A i R O * * f * Ü r a l a H l Y +HYP: m a I n s * u r ******* s p i e R t e i n s e i n e r ******* h e i m a U t s t a d t k E i * E U O f I E r a l a * l E +Eval: I I D D S D I S D S I I I S D S + +Speaker sentences 103: cv_deu_000800 #utts: 1 +id: (cv_deu_000800-cv_deu_000800) +Scores: (#C #S #D #I) 24 10 4 17 +REF: * * * ******* * e r t r a T d e r F r e i m a u R E R l * * * O G e * * L A U t a R O b e * ******* * i ******* * * * +HYP: C O H D e r ******* t r a R d e r * r e i m a u * H A l U N U N D e N I N t a * B E b e I S i H A H +Eval: I I I I I D S D D S S I I I S S I I S S S D S S I I I I I I I + +Speaker sentences 104: cv_deu_000801 #utts: 1 +id: (cv_deu_000801-cv_deu_000801) +Scores: (#C #S #D #I) 61 13 11 6 +REF: m i t „ f Ü ** R s t “ w A r e h e r d I e S o * * Z i a L e * A L s D I e r e c h t L i c h e r O l l e d e S S o b e * * Z e i c H N e T e n G e m e i n t +HYP: m i t *** f Ü Ö O s t *** w E r e h e r d * e R o W T F i a D e E I T s * * e ******* r e c h t i c h e ******* r A l l e d e * U o N b e D S H e i c * * e N e n * e m e i n t +Eval: D I S D S D S I I S S I S S D D D S D S D S S I I S D D S D + +Speaker sentences 105: fleurs_deu_000378 #utts: 1 +id: (fleurs_deu_000378-fleurs_deu_000378) +Scores: (#C #S #D #I) 121 23 26 11 +REF: L e t z t ******* e W o c h E g a b d a s m e t i b e k a n N t d a s s e s V o n A p P l E Ü b * * ******* * * e r * * * * 3 4 w E I t e R e v o r f Ä L l e v o n Ü b e r H i t z u n G I n F o R m i E r t W o R d e n w A r D i E d A s U n T e r ******* n e h M e n a l s n I c h t s C h W e r W i E G e n D B e Z e i C H N e t e +HYP: * e t z t e R V o c h U g a b ******* d a s m e t i b e k a n D t d a s s ******* e s H o n E p E l * Ü b E R F I e r N D A S C H w A L t e e v o r f ** E l e v o n ** b e r i t z u n * * n T o * m i * r t * o * d e n V w E r * i * ******* d E s * n D e r n e h N e n a l s n * c h t s * h I e r * i * * e n * ******* K e * e i * * T e t e +Eval: D I S S S D S D S S S D I I I I I I I I I S S S S S D S D S D D S D D D D S S D D D S D S I S D D S D D D D D S D D D S + +Speaker sentences 106: fleurs_deu_000379 #utts: 1 +id: (fleurs_deu_000379-fleurs_deu_000379) +Scores: (#C #S #D #I) 168 32 21 37 +REF: * * * * * * * ******* * * * * ** U s * ** A * * G Y M n * A s * * T i C s u n T e r s T Ü T z * t * * d e n b R i * E f d e s * O L Y M P i s c h e n k o m i t E E s d e r v e r ******* e i n i g t e n s t A a t e n u n D * a * K Z E p t i e r t e s a L s a B S O L u t e n o t w e n d i g k e i t d a S S s i C h D i e * * O l Y M P i s c h e * * F A m i L i e * f Ü R e I n s i c h e r e s u M f ******* * e l D * f Ü R a L l e u n s e r e * s p o R t l e r e i n s e T Z t +HYP: S I E B E B E N I J E Ö E s E Ä E S C H E n E N s Z I G i G s u n D e r s C Ü * z S t D E d e n b * i H I f d e s U N Ü N B i s c h e n k o m i t * I s d e r v e r e i n i g t e n s t * a t e n u n * D a R S I p t i e r t e s ******* a * s a * * P T u t e ******* n o t w e n d i g k e i t d a * * ******* s i * h * i e U N l Ü N B i s c h e V E R N m i * i e N f Ü * e * n s i c h e r e s u N f W e l T Z f Ü E a * l e u n s e r e R s p o * t l e r e i n s e * S t +Eval: I I I I I I I I I I I I I S I I S I I S S S I S I I S S S S D I I I D I S I S S S S S D S I D D I I S S S D D D D S S D D D D D D I I S S S S I I S S D I D D S I I S I S D I D D S + +Speaker sentences 107: fleurs_deu_000380 #utts: 1 +id: (fleurs_deu_000380-fleurs_deu_000380) +Scores: (#C #S #D #I) 76 30 15 48 +REF: D a D U R c h k A N n e R a b W Ä R t S k o m p A t i * b e l * * * * * * * * * * * * M i ******* * * * T * * 8 0 2 1 1 a * * * * * * * * * * * * * 8 0 2 1 1 b * u n d ******* * * * * * * * * * * * * * 8 0 2 1 1 g s e i n v O r A U s g e s E T Z T d i e b a s i S s t a t i O n v e r f Ü g T Ü b e r d u * a l r a d i O +HYP: * a * L I c h ******* k * E n ******* e * a b P I E t k o m p E t i E b e l M E T A C H T N A T Z W E i B U N D E L F A R a C H T N R T Z W E I B U N D E L F b E u n d C H T N E T Z W E I P U N D E L F g E s e i n v E r * * s g e s * * * * d i e ******* b a s i * s t a t i U n v e r f Ü g K ** b e r d u O a l r a d i E +Eval: D D S S D D S D D S S S S S I I I I I I I I I I I I I S I I I I S I I S S S S S I I I I I I I I I I I I I S S S S S I I I I I I I I I I I I I I I S S S S S S S D D D D D D D D S S D I S + +Speaker sentences 108: fleurs_deu_000381 #utts: 1 +id: (fleurs_deu_000381-fleurs_deu_000381) +Scores: (#C #S #D #I) 54 7 7 7 +REF: * e r b * e z e i C h N e T E d i e g e r Ü c h t e a l s p O l i T i S c h e s g e s c h w Ä t Z u n d * a * l ******* b e R n h e i t * * +HYP: J e r ******* b I e z e i S h * e N S d i e g e r I c h t e R a l s ******* p * l i C i * c h e s ******* g e s c h w Ä t S u n d T a L l b e * n h e i t Z S +Eval: I D I S D S S S S D D S D D S I I I D I I + +Speaker sentences 109: fleurs_deu_000382 #utts: 1 +id: (fleurs_deu_000382-fleurs_deu_000382) +Scores: (#C #S #D #I) 118 21 31 15 +REF: l E t Z T e w O c h E g a b * D a s * m * e * t * I b e k a N n T d a S s E s v o n A P P l ******* * e Ü B e * * * r * 3 4 w e i t E r * e v o R f Ä L l e v o n Ü b e r h i t z u n G i n ******* F o R m i e r t w o r d e n w a R D i e d a s u n t e r ******* n e H m E N a l S n i c h T S c h ******* W e r W i E g e N D b E z e i c H N e t e +HYP: l * t * e R w U c h * R g a b T * a s E m I e I t H E b e k a * n * ******* d a * s ******* I s v o n * E B l W e R F I e R N D r E S I E w e i t * r I e v o H f ** E l e ******* v o n ** b e r h i t z u n * i n V o * m i e r t w o r d e n ******* w a * * i e ******* d a s u n t e r n e * m * * a l Z n i c h * ******* * c h V e r * i * g e * * b T z e i c * T e t e +Eval: D D S S S D S I D I I I I S D D D D D S D S S I I S S S I I I I S S S D I S D S D D D I S D D D D D I D D D S D D D I S D D D D S D S + +Speaker sentences 110: fleurs_deu_000383 #utts: 1 +id: (fleurs_deu_000383-fleurs_deu_000383) +Scores: (#C #S #D #I) 85 15 28 19 +REF: n a c h ******* d E m d e r d A m m * * * 1 9 6 3 e * * r * * * * * * * * * * * ******* b a u t w O r d e n w a r k a m E N d i E J a H r e s z e I T l i C h E n Ü b e R f l u t u N G E n d I e s e ******* d I m e n t e I m F l U S s v e r t e I l E n z u m s T I L l s t A n D +HYP: n a c h d I m d e r ******* d E m m E U N H N U N D e R T r E I U N S E C H Z I C b a u t ******* w * r d e n ******* w a r k a m * * d i * ******* * a * r e s z e * * l i G h * n ** b e P f l u t u * * * n d * e ******* s e d E m e n t e * m N P l * * s ******* v e r t e * l * n ******* z u m s * H Ö l s t E n * +Eval: I S D S I I I S S S S S I I I I I I I I I I I I I I D D D D D D D D D D D S D D S D D D D D I S D S S D D D D D D D S S S D + +Speaker sentences 111: fleurs_deu_000384 #utts: 1 +id: (fleurs_deu_000384-fleurs_deu_000384) +Scores: (#C #S #D #I) 138 14 48 10 +REF: e r w A R a u C h A m s t e c h e N v O n g e l D s c h e i n * e N F Ü R V i E l e L Ä n d e R b e t e i l i * G t a k t u E L l e b e i s * * p i E L e s E I n E R a * r b e I T s c h l i E S s e n D i E p r e m I E R m i n i s t e r ******* p O r t r A i T s a U f d e r v o r d e R s e I t E d e R k a n a d I s c h e * ******* * ** n 5 u n d 1 0 0 D o l l A R n O t e n e i n +HYP: e r ******* w * * ******* a u * h * m ******* s t e c h e * v * n g e l * s c h e i n V e * ******* * ** * * i * l e * E n d e * b e t e i l i C H t a k t u * l e ******* b e i s C H p i * * e ******* s * A n * * a H r b e * * ******* s c h l i * * s e n ******* * i * B p r e m * J H m i n i s t e r p * r t r * i * s ******* a * f d e r v o r d e * s e R t * d e * k a n a d * s c h e N F Ü n N u n d ******* * E R T o l l * E n U t e n e i n +Eval: D D D D D D D D D D I D D D D D D D D S D I S D S D I I D D D D S D D I D D D D D D D D S D S S I D D D D D D S D D D I I I I S D D S S S D S S + +Speaker sentences 112: fleurs_deu_000385 #utts: 1 +id: (fleurs_deu_000385-fleurs_deu_000385) +Scores: (#C #S #D #I) 77 16 31 1 +REF: d i E h a u p t s T A D t v O N m O L d a w i e n i s t k I S C h I n A U d i e E i n H e i m * i S C H E s p R a C h e i s t R u m Ä n I s c H a b e r V i e l e m e n S C H e n s P r e c h e n A U C H r U S S I s C h +HYP: d i * h a u p t s * * * t v * E R m R d a w i e n i s t k * * * h E n E N d i e * i n * e i m P i * * * * ******* s p B a * h e i s t ******* G u m E n E s c * a b e r ******* F i e l e ******* m e n * * T e n ******* s * r e c h e n * * * * E r * * * O s E h +Eval: D D D D D S S S S D D D S S S D D I D D D D D S D D S S S D D S D D D S D D D D D D S D D D S S + +Speaker sentences 113: fleurs_deu_000386 #utts: 1 +id: (fleurs_deu_000386-fleurs_deu_000386) +Scores: (#C #S #D #I) 172 24 25 29 +REF: * * * * * * * * * z w i S c h e n d e n e i n z e L n E n D Y n A s t * i e n h e R r s C H t e n a u * c h u n ******* b e s t Ä n d i g e * z e i t e n g e t E I l t e R p r o * V I n z e n d i e b e k a N n t * E s t * e d i e s e R p e r i o * d e n w a R d i e e p o c h e d e r d R e i * K Ö n i * g R e i c h e d * I e ** * * * * 6 0 * J a H r e l a n G z W i S C h e n d e r h a n u n D d e R J I n ******* d Y n A s t i E s t a t ******* T f * a n D +HYP: S I S I S E B E T z w i * c h e n d e n e i n z e * n n B Ü n E s t D i e n h e * r s * * t e n a u O c h u n b e s t E n d i g e R z e i t e n g e t * A l t e * p r o E W E n z e n d i e b e k a * n t D I s t D e ******* d i e s e * p e r i o E d e n w a * ******* d i e ******* e p o c h e d e r d * e i L G Ü n i N g * e i c h e d E S e Ä C H T Z I C H I E a R r e ******* l a n * ******* z E i * * h e n d e r h a n u n * d e * ******* E E n d E n E s t i * ******* s t a t V f V a n T +Eval: I I I I I I I I I D D S S S S I D D D I I S I D S D I S S D I S I D D I D D D D I S S I D I S I I I I I S S S I S S D D D S D D D D D S S I S S D D I S I S + +Speaker sentences 114: fleurs_deu_000387 #utts: 1 +id: (fleurs_deu_000387-fleurs_deu_000387) +Scores: (#C #S #D #I) 129 12 39 6 +REF: a m a n d e r e N e n D e D e S s p e k t r u m s V E r w * A n D e L T m a n s i c h I n e i N n i c h T w i E d e R z u e r k E N N e n d e S i n * D i V I d * U u m d A s a L L e s a n d E r S m a c h e n m U S s a L s D A s t * e A M e s g e m a c H t H a T u n d s i c h a l l e s Z u * e * i g e N m a c h t +HYP: a m a n d e r e * e n * e * e * R s p e k t r u m s * H r w E I n * e * * m a n s i c h ******* E n e i * ******* n i c h * w i * d e z u e r k * * * e n d e * i n E W i * E d E u m d * s a * * e s a n d * r * m a c h e n ******* m * O s a * s ******* E R s ******* t I e * * ******* e s g e m a c * t ******* * a * u n d Z s i c h a l l e s ******* * u O e L i g e * ******* m a c h t +Eval: D D D D S D S I S D D D D S D D D D S D D D D I S D S I S D D D D D D D S D D S S D I D D D D D D D S D D I I D D + +Speaker sentences 115: fleurs_deu_000388 #utts: 1 +id: (fleurs_deu_000388-fleurs_deu_000388) +Scores: (#C #S #D #I) 272 51 43 23 +REF: d * * * i ******* * e m e i s t e n i n ******* T e r ******* p R E t * a t * i o n e n d e s t e c h * n O l o g i s c h e n d e t e R m i n i s * M u s t E I L e n z w e i a l L g e m e i n e v o r s * t e L l u n g e n e i n e R s e i t s d A S s d i E E n T W i c K L U N G d e r t E c h * n O l O g i e s e L B s t e i n e M w e g F o l g t d e r w e i t ******* g E H e n d J e n s e i T s K u * L t U R e L l e R o * d e r p O l i T i s c h E R E i n F l U S s n a H m e L i E g t u n d a n d e r e r s e i t s d a S s t E C h n * O L O g I e i H r e r s e i t s a u s * w ** I r k U n G e n a u f g e s E L l s c H a f t E n H a * t +HYP: d G I G i D e m e i s t e n i n D e r p * I t E a t Z i o n e n d e s ******* t e c h E n l o g i s c h e n d e t e m i n i s E N u s ******* t * A e n z w e i a l g e m e i n e v o r s C t e R l u n g e n e i n e * s e i t s d * E s d i * I n D i c * * G E M d e r ******* t I c h E n l Ü g i e ******* s e * P s t ******* e i n e N w e g ******* V o l g t d e r ******* w e i t g * * e n d I e n s e i * s * u N t O W e * l e * ******* o R d e r ******* p U l i C i s c h * * * i n P l * * s n a * m e N D i * g t u n d a n d e r e r s e i t s d a * s ******* t I G h n E Ö Ü g * e i E r e r s e i t s a u s F w Ö H r k E n e n a u f ******* g e s * A l s c * a f t * n A R a R t +Eval: I I I I I I S I D S I I D I S S I S D D S S S I S D D S D S S S D D S S S D S I S S D D S D S D S D I D D S D D I S S S D D D I D S S D D D S D D D S S D D D S S I S S S D S I I S S S D D S D D S S I + +>> REF: d i E e h e r i n ******* h Ä r e * n t a L s S O Z I a l b e ******* d I n G T S i n D +>> HYP: d i * e h e r i n h E r e R n t a * s ******* Z U T S a l b e d E n * * ******* Z i n T +>> Eval: D I S I D D S S S S I S D D D S S + +Speaker sentences 116: fleurs_deu_000389 #utts: 1 +id: (fleurs_deu_000389-fleurs_deu_000389) +Scores: (#C #S #D #I) 168 22 31 8 +REF: Z w I s C h e N d e n e i n z e L n e n d Y n a s t i e n h e R r s C H t e n a u C H u n b e s t Ä n d i g e z e i t e n g e t e I l t e r P r o V I n z e n d I e B e k a N n T e s t e D I E s e R p e r i o d e n w a R d i E e ******* p o c h * e * d e r D R e I k Ö n i g r e i c h e d * I e 6 0 J A h * r E l a n g z ******* w i s c h e n d e r h a n u n D D e r J i * n d Y n a s t i E S t A t T F a n d * +HYP: * w Ü s * h e * d e n e i n z e * n e n d * n a s t i e n h e * r s * * t e n a u * R u n b e s t E n d i g e T z e i t e n g e t e A l t e r * r o W E n z e n d * e * e k a * n D e s t e * * * s e * p e r i o d e n w a * H d i * e p o c h R e L d e r * * e * k Ü n i g r e i c h e d E S e C H T Z I C h A r * ******* l a n g T z w i s c h e n d e r h a n u n * ******* T e r * i E n d I n a s t i * ******* * t * t V a n d T +Eval: D S D D D D D D D D S S S S D S S D D D S D D D D D S D I I I D D D S I S S S S S S S I D D S I D D S D I S D D D D S S I + +Speaker sentences 117: fleurs_deu_000390 #utts: 1 +id: (fleurs_deu_000390-fleurs_deu_000390) +Scores: (#C #S #D #I) 129 28 14 30 +REF: d e m L E A k * z u ******* F o L g * e b * e z i E H T S i c h D A s D o ******* K U m e n t a u f d e n g R e ** n Z s t r e i t i n d e M d i e p a l Ä s T i n e n s e r e i n z u r Ü C K s * E t z e n d e r g R e n z * e n i n d e n z u s t a n D v o r d e m s e C H s t a G e K r i ******* * * * ******* * * * * * * * ******* * * * e * G * * * V O n * * 1 9 6 7 F o r d e R n +HYP: d e m ******* * I C k H z u V o R g I e ******* b I e z i * * * ******* Z i c h * E s T o G O m e n t a u f d e n g * e Ä n * s t r e i t i n d e N d i e p a l I s i n e n s e r e i n z u r ** Ö G s A L t z e n d e r g * e n z S e n i n d e n z u s t a n T v o r ******* d e m s e R s t a L e G r i V O R N A N Z E S N U N D e R T S E B E R n U S E T I C V o r d e * n +Eval: D D S S I I S S I D I D D D D S D S S I S S D I D S S S D S S I S D I S D S S S S I I I I I I I I I I I I I I I I I S I I I S S I I S S S S S D + +Speaker sentences 118: fleurs_deu_000391 #utts: 1 +id: (fleurs_deu_000391-fleurs_deu_000391) +Scores: (#C #S #D #I) 99 16 33 1 +REF: m i t D e m V e r l u s t g r I e c h I S C H e r s p R a c h k e N N T n I S S e W a r d e r W e * s t e n v o n s e i N e n P H I l O s o P H i s c h e n u n d w i S s e n S c h a F T l i c h e n w U R z e L N i n G R i E C h e n L A n D a b G e s C H N I T t e n +HYP: m i t H e m P e r l u s t ******* g r * e c h * * E e r ******* s p * a c h k e * * * n * * * e * a r ******* d e r * e C s t e n v o n s e i * e n * * V l E s o * F i s c h e n u n d ******* w i * s e n c h a * l i c h e n w O T z e * * i n ******* * K i * * h e n * E n * a b I e s * * * L E t e n +Eval: S S D D D D S S D D D D D D D D D D D I D D D S S D S D D S D S S S D D D D S D D D S D S D D D S S + +Speaker sentences 119: fleurs_deu_000392 #utts: 1 +id: (fleurs_deu_000392-fleurs_deu_000392) +Scores: (#C #S #D #I) 152 29 67 11 +REF: W I r s t I M M e N m i t D e r a u s s a g * e * * D E s u s O C Ü b E R e i n d a S s d e n I n T E r e S s e n u n ******* s E R E r a t H l e T e N U n D v e r e i n E u n d I H R e S s p o R t s b * e S s E r g e ******* d i e n T i s T W e N n w I r I N n e R h a l b u n s E R E r O r g A N I s a t i o * n S I n N v O L l e v e r Ä n d E r u n g E N v o r a n t r e i b e n a n s T A T t e i n e * D E z E r * t ******* * I F i Z i e r u n g v o R z U N E H M E n +HYP: * E r ******* s t * * * e * ******* m i t ******* * e r ******* a u s s a g D e S I Ö R s u s I E ** b * * e i n ******* d a * s T d e n * n * r e * s e n ******* u n s * * * r ******* a t * l e D e * ******* * n * v e r e i n * ******* u n d ******* * * * e * R s p o * t s ******* b P e * s * r ******* g e d i e n * D i s D B e * n ******* w E r * * n e * h a l b ******* u n s * * r ******* A r g * E s a t i o U n ******* D H n v * * l e v e r I n d * r u n g * * v o r a n t r e i b e n a n s * * * t e i n e R T I T z S r I t Z T V i T i e r u n g ******* v o T z * * * * * * n +Eval: D S D D D D D D D D D I I I S S S S D D D D D S D D S D D I D D D D D S D D D D D D D D D D D S D D I D D D I D S S S D D S D D D D D D S D S D S S I D S S S D D S D D D D D D I S S S S I I I S S S D S D D D D D D + +Speaker sentences 120: fleurs_deu_000393 #utts: 1 +id: (fleurs_deu_000393-fleurs_deu_000393) +Scores: (#C #S #D #I) 108 14 40 5 +REF: D i E K r e U z f a H R t e N n a C H S A n K T p E T e R s b U R g b i e t e n a u c h z e i t F Ü ** r E i N E n a u f e n t H a l T i n D e r s t a D t k * r e u * z f A H r T p a s * s A G I e r e s * i n D v O n d e R V i S u M P F l i c h T b e F r e i t s i E H e b e d I N G U n g e n +HYP: * i * ******* G r e * z f a * * t e * ******* n a * * ******* * E n * G p I G e * s b * O g b i e t e n a u c h z e i t * Ü Ö r * i * * n a u f e n t * a l * i n ******* * e r ******* s t a * t k G r e u T z f * E r p a s R s * * H e r e s E i n * v n ******* d e * * i E u * N S l i c h * b e * r e i t s i * * e b e d * * * E n g e n +Eval: D D D S D D D D D D D D D S D S S S D D S D I D D D D D D D D D I I D S S I D D S I D S D D D S D S S D D D D D D D S + +Speaker sentences 121: fleurs_deu_000394 #utts: 1 +id: (fleurs_deu_000394-fleurs_deu_000394) +Scores: (#C #S #D #I) 115 11 17 16 +REF: * * * * * ******* * r e i ******* s e n ******* d e W e r d e n D r i n g e n d g e w a R n * t a u f J e D w * e ******* d e a R t v o n u n ******* w e T t e R z u a c h t e n d i e I H R g e b i E T b * e T r i F f t d a * d i E s s i C h a u f a L l e r e i s e p l Ä n e a U s W i * r K e n k A N n +HYP: S C S C T E r e i s e n d e V e r d e n * r i n g e n d g e w a * n D t K a u f I e w I e d e a * t v o n u n w e N t e * ******* z u a c h t e n d i e * * E g e b i * * b I e * r i * f t d a D N d i * s s i G h a u f a * l e ******* r e i s e p l E n e a O s * i E r * e n k * O n +Eval: I I I I I I I I I S D D I S S S I I D I S D D D D S D D I D D I S D S D D S S D I D D S + +Speaker sentences 122: fleurs_deu_000395 #utts: 1 +id: (fleurs_deu_000395-fleurs_deu_000395) +Scores: (#C #S #D #I) 105 29 17 16 +REF: s * * * * * * ******* i * e b e s A G t d a S s d e r * k R E U z u n g s p * u n k t * d e r l I n I e n d I e E i n b I L D V e r T i * k a l * u n D h O r * I Z o n t a l d r i T t e L n d e R e F F E k T i V s t E p l a t Z f Ü R D A s h A u p t ******* m * o T i V i s t s i E H e b e i s P I e L +HYP: s I C H R T R i S e b e s E R t d a * s d e r G k O L T z u n g s p B u n k t D d e r ******* l E n M e n d * e * i n b * E E W e r D i G k a l E u n * T h U r E H o n t a l d r i * t e * n d e * e H I G k i * s t * D p l a t S f Ü * I T E s ******* h O u p t m N o * i E i s t ******* s i * * e b e i s C H e N +Eval: I I I I I I I I S S D I S S S I I D S S D D D S S S S I I D S S I S S D D D S S S S D D S S D S S S D S I I D S D D D S S S + +Speaker sentences 123: fleurs_deu_000396 #utts: 1 +id: (fleurs_deu_000396-fleurs_deu_000396) +Scores: (#C #S #D #I) 167 23 62 27 +REF: S e i * ******* * * * * * * * * t * 1 9 8 8 * * * * * * * m Ü S s E n w a H l ******* u R n E N T r a n s * p A r e n T s e i n d a m i t w Ä H L e R U n d b E o b a c H t e r b * e * z * e u g e n k Ö N N e n d a S s Z U B e g I N n d e r w a H l K e i n E U m s c h l Ä g e v O R H a n D e n s i n d u N D d a S s k e i n E u m s c h l Ä g e E i n g e * W o R f e n w e r d e n a u S s e r J e * n e d e r * o R d N u n g s G E m Ä s ******* S G e Z Ä H l t e N U N D a U t O r I s i e r t e n W Ä H L e R +HYP: * e i T N U N Z E U N R t A C H E N A C H T Z I H m ** I s T n w a * l u * n * D * r a n s B p * r e n * Z s e i n ******* d a m i t w ** * * e E * n d ******* b * o b a c * t e r ******* b I e T z O e u g e n G k ** * * e n d a * s ******* * * W e g * * n ******* d e r ******* w a * l * e i n * * m s c h l I g e ******* v * * W a n * e n ******* s i n d u * * ******* d a * s ******* k e i n * ******* u m s c h l Ä g e * i n g e V A o * f e n w e r d e n a u * s e r * e H n e d e r T o T d E u n g s * m E s K S e * ** * l t e * ******* * * T a * t * r E s i e r t e n * ** * * e E +Eval: D I I I I I I I I I I I S S S S I I I I I I I D S S D I D D S D I D D S D D D D S D D D D D I I I S D D D D D D D S D D D D D D D D S D D D S D D D D D D D D D D I S D D D I I S S D S S I S S D D D D D D D S D D S D D D D S + +Speaker sentences 124: fleurs_deu_000397 #utts: 1 +id: (fleurs_deu_000397-fleurs_deu_000397) +Scores: (#C #S #D #I) 141 21 14 17 +REF: o t T A W A i s t k a n * A d A s b e * ******* z * a * u b e R n ******* d e z w e i s * P R a C H i ******* g e h a u p t s t a D t u n d z * e I C H N e T S i c h D U R c h e i n e r e i H e V O n k u n * s t * g A L e r i e n u n d m U s e e n a u s d i e k a n * A d * A s v e r ******* g a n ******* g e n ******* h e i t u n d g E g e n ******* w a r t p r Ä s E n t i e r e n +HYP: o t E R E R i s t k a n E R d E s b e T z S a O u b e * n d e z w e i s C H E a * L i g e h a u p t s t a * t u n d ******* z S e * * L T e N D i c h * * I c h e i n e ******* r e i * e * U n k u n Z s t D g * E e r i e n u n d m O s e e n a u s d i e ******* k a n E N d E R s v e r g a n g e n h e i t u n d g * g e n w a r t p r E s I n t i e r e n +Eval: S S S S I S S I I I I D I I S S D S I D D I D D S S S S D D S D D D S I I D S S D I S I S I I I D I S S + +Speaker sentences 125: fleurs_deu_000398 #utts: 1 +id: (fleurs_deu_000398-fleurs_deu_000398) +Scores: (#C #S #D #I) 49 7 16 0 +REF: d i e s e p A a r e k Ö N N e n s i c h f Ü R e i N E n a d O P t i o n s p l a n F Ü R I H r b A b Y e n T s C h e i d e n +HYP: d i e s e p * a r e k ** * * e n ******* s i c h f ** * V e i * * n ******* a d E B t i o n s p l a n * ** V * E r ******* b E b E e n * s * h e i d e n +Eval: D D D D D D D S D D D S S D D S D S D S S D D + +Speaker sentences 126: fleurs_deu_000399 #utts: 1 +id: (fleurs_deu_000399-fleurs_deu_000399) +Scores: (#C #S #D #I) 75 23 18 4 +REF: i n ******* F o l G e d e S s e n S i n D Z w * e i f i s c h ******* a R T e n a u s g E s t o R B E N U N d z w e I w e i t E r E s I n D v o m a u S s t e r b e N b e D r o H t D a R u n T e r d e r G I l a * C Y P H A +HYP: i n V o l e d e * s e n ******* E i n Z * w R e i f i s c h a * B e n a u s g * s t o * * L M * * d ******* z w e * A w e i t r * I s E n * v o m a u * s t e r b e * M b e T r o R t T a * u n D e r ******* d e r * J l a Z I Ü F V E R +Eval: I S S D D S S D I I D S D D D S S D D D D S S D S S D D D S S S S D S D D S I S S S S S S + +Speaker sentences 127: fleurs_deu_000400 #utts: 1 +id: (fleurs_deu_000400-fleurs_deu_000400) +Scores: (#C #S #D #I) 90 10 29 4 +REF: P F L a n z e n s E H e n I n I h R e r n * A t Ü R l i C h e N U m * g e b U n G a m B e S t e n a u s W i D e r s t E H e n s i e a l s o d e r V e r s U c h u n g a u c h n u r e i n e X e m P l a R Z U e n * t ******* F e r N E n +HYP: * R E a n z e n s * * e n * n ******* J h * e r E n E R t ** Ö l i * h e * * m N g e b E n * a m * e * t e n ******* a u s * i * e r s t * * e n s i e ******* a l s o d e r ******* * e r s * c h u n g a u c h ******* n u r e i n e e m K l a * ******* * * e n D t V e r * * n +Eval: D S S D D D D S D S I S D S D D D I S D D D D D D D D D D D D D S S D D D D I I S D D + +Speaker sentences 128: fleurs_deu_000401 #utts: 1 +id: (fleurs_deu_000401-fleurs_deu_000401) +Scores: (#C #S #D #I) 91 11 27 7 +REF: a u f D e r n a H s e i t e k Ö N n t e E s m e H r m A r i * A g e b e n d A d i e k r U s t e d Ü N n e R I s t E s w A r e i n F A C H e r * f Ü R d I e l a ******* v * A a n d i E o b e R F l Ä c h E a u f Z U s t * e I g e n ******* * +HYP: a u f * e r n a R s e i t e k ** * n t e I s m e * r ******* m * r i E R g e b e n d * ******* d i e ******* k r O s t e d ** * n e * ******* * s t I s ******* w * r e i n * * V e r A f ** * d * e l a v E R a n ******* d i * o b e * P l I c h * a u f * T s t D e * g e n T +Eval: D S D D S D D D I S D D D S D D D D D S D D D D S S I D D D I I S D D D S S D D S I D I I + +Speaker sentences 129: fleurs_deu_000402 #utts: 1 +id: (fleurs_deu_000402-fleurs_deu_000402) +Scores: (#C #S #D #I) 148 19 14 22 +REF: * E r ******* * f Ü g * t * e h I n ******* z * u * d a S s s i e J E d * o c h n i c h t d * * A z u a u f g * e ******* F o r d e r t w e r d e n s o l l t e n V e r * P f L i c h t u n g e n e i n * z u ** g e H e n d I e * Ü * b e r i H r e n E n t w i C K l u n g S s t a n d i H r e v e r ******* a n t W o r t u n g u n d i H r e f Ä H I G k e I t e n h I n * a * U s ******* G E H e n +HYP: S G r E f Ü g K t D e C h E n z U u N d a * s ******* s i e * * d U o c h n i c h t ******* d E R T z u a u f g I e V o r d e r t w e r d e n s o l l t e n F e r T f * i c h t u n g e n e i n D z u Ü g e * e n d * e I Ü E b e r i E r e n I n t w i * * l u n g * s t a n d i E r e v e r a n t * o r t u n g u n d i E r e R f Ä * E k e * t e n h E n O a R N s I N G e n +Eval: I S I I I I S S I I I D D D D I D I I S I I S S I S D I I D D I I S S D D D S I D S S D S S D S I I S I S S S + +Speaker sentences 130: fleurs_deu_000403 #utts: 1 +id: (fleurs_deu_000403-fleurs_deu_000403) +Scores: (#C #S #D #I) 146 16 17 19 +REF: * * * * ******* * V i * R t u ******* e L l e h i * l ******* f E s t e L L u n g e n * s I n D i n d i e s o f t ******* w A r E e i n g e ******* b A u * * t u n D s o L L e n a R b e I t S s c h R i T t e * d i e d e r s c h Ü l e R a l l e i n m Ö g l i c h e r w e i s e n i C h t b e W Ä L t i g e n k a N n h I n t e r ******* f r a g e n n A H e l e g e n u n d * * e r k l Ä r e n +HYP: S I S I E W i C E t u e * l e h i E l f I s t e * * u n g e n I s E n T i n d i e ******* s o f t w E r * e i n g e b * u T D t u n * s o * * e n a * b e L t * s c h * i * t e N d i e d e r s c h Y l e * a l l e i n m Ü g l i c h e r w e i s e ******* n i * h t b e * V E t i g e n k a R n h E n t e r f r a g e n n E I e l e g e n u n d T D e r k l E r e n +Eval: I I I I I I S I S I D I I S D D I S S D I S D I D I I D D D D S D D D I S D S D D D S S S S I S S I I S + +Speaker sentences 131: fleurs_deu_000404 #utts: 1 +id: (fleurs_deu_000404-fleurs_deu_000404) +Scores: (#C #S #D #I) 65 31 9 16 +REF: a m * ** * * * 1 5 * a * U g u s t * * * * * * 1 9 4 0 F i E L E N * * D i e a L l i I e r t e n I n s Ü D F r a n k r E i c h e i n d i E I N V a s i o n w U r d e O p e r A T I O N D r A g O O n g e n * A n N t +HYP: a m F Ü N F Z E N N a R g u s t N U N Z H N H U D E T V i R Z I C F E L i e ******* a * l i e r t e n * n ******* s Ü T r a n k r A i c h e i n d i N * * W a s i o n w * r d e A p e r E S C H E E r g * U n ******* g e n E R n D t +Eval: I I I I I S S I I S I I I I I I S S S S S S S S S S I I S D D S D D S S S S D D S D S S S S S S S S D S D I S S + +Speaker sentences 132: fleurs_deu_000405 #utts: 1 +id: (fleurs_deu_000405-fleurs_deu_000405) +Scores: (#C #S #D #I) 78 8 24 3 +REF: e r G r i F f A U c h a l l E s a n w a s I n S w a S s e r k a * m s e L b S t e I n g r o S s e r d I n O s a u r i e R W i E d e r t * R e x * w A R i H m N i c h t g e w a C H S e n +HYP: e r ******* * r i * f * O c h a l l * s a n w a s ******* E n * w a * s e r ******* k a R m s e * b * t e * n g r o * s e r d E n s a u r i e * * i * ******* d e r t I E W e x S w * * E i * m ******* * i c h t g e w a * * K e n +Eval: D D D D S D D S D D D I D D D D S S D D D D I S S I D D S D D D D D S + +Speaker sentences 133: fleurs_deu_000406 #utts: 1 +id: (fleurs_deu_000406-fleurs_deu_000406) +Scores: (#C #S #D #I) 89 17 23 17 +REF: S e I t D e r G r Ü n d u n g v O n a s u n * C i * * ******* * Ó n 1 5 3 7 * * * i * * * S T e * * * s * p a r A g U A Y * g e l u n g E N v i e l v o n s e i N E m i n ******* d i g E N e n C H a r a K t e r U n d s e i n e R i d e n T i t Ä t z U b e w a H r e n +HYP: * e * t ******* * e r ******* K r Ü n d u n g v * n ******* a s u n T Z i O R F I n H T Z E N D E S i E N U N D R e I S I s S p a r E g * * * E g e l u n g * * v i e l v o n s e i * * m i n d i g * K e n * K a r a C t e r * n d ******* s e i n e * ******* i d e n D i t E t ******* z * b e w a * r e n +Eval: D D D D D S D D I S I I I I S S S S S S I I I I I I S S S I I I I S D D D I D D D D I D S D S S D D D D S S D D D + +Speaker sentences 134: fleurs_deu_000407 #utts: 1 +id: (fleurs_deu_000407-fleurs_deu_000407) +Scores: (#C #S #D #I) 155 31 14 76 +REF: * * * * * * * * ******* T r O t z * ******* d e M i s T d e R a n t e I l a n ******* * * * * ******* * * ******* * * * * X d * * * r * * * * ******* t * * ******* b i n D e r g e s a M t * e n g R U P p e d e r l E U t * e m i t * T u * ******* b e r ******* k u l o s e * o F f e n ******* b a r d e n ******* n o c h * g e r i n g 6 0 0 0 d * * * e * ******* * R i n * s g e s a M T * * * * 3 3 0 0 0 0 L e * * * * ******* * * u * * * * ******* * * * t e d I e i n s Ü * d a * F r i k A Z U e i n e M b E s t i M M t e n z e i t p * u n * k t a n g E s t e C K t * s I n D +HYP: S I S I B E B E D r * t z S d e N i s * d e * a n t e L l a n I E G S D E E R B E N d E S T r I E G H t D E b i n * e r g e s a N t D e n g * * O p e d e r ******* l O L t E e m i t D * u G b e r k u l o s e N o * f e n b a r d e n n o c h N g e r i n g S E X S d A U S e N D E i n Z s g e s a * N D R E I H U N D E D e I S I G T A u E S E N L O L t e d * e i n s Ü T d a W r i k E W H e i n e N b I s t i * N t e n z e i t p B u n G k t a n g I s t e * * t I s E n T +Eval: I I I I I I I I I S D I I S D D S I I I I I I I I I I I I I S I I I I I I I I I I I D S I D D S D S S I I D I I I I D I I I S S S S I I I I I I S I D S I I I I S S S S S S S I I I I I I I I I I I I I I I D I I S S S S S S D S I I S D D I S S + +Speaker sentences 135: fleurs_deu_000408 #utts: 1 +id: (fleurs_deu_000408-fleurs_deu_000408) +Scores: (#C #S #D #I) 78 17 18 18 +REF: * A n * * G e l * * * * * * 2 0 0 6 * e * * * ******* R l Ä u t e r t D a s k o n ******* t i n U u m ******* k o n * z e P t a L s e i n e m E t H o * d e U m O r g A n I s a t i o n E N Z U h E l f E n l e I s t u n g s f Ä H I g e R z U w e r D e n +HYP: E H n S C H e l Z W E I T A U S E N S e C K S E l E u t e r t * a s k o n t i n * u m k o n D z e B t ******* a * s ******* e i n e ******* m I t * o R d e * m U r g E n * s a t i o n Z S * E h * l f * n l e * s t u n g s f ** * E g e * z * E w e r * e n +Eval: I S I I S I I I I I I S S S S I I I I I S S D I D I I S D D D D S D I D S S D S S D S D D D D D S D D S D + +Speaker sentences 136: fleurs_deu_000409 #utts: 1 +id: (fleurs_deu_000409-fleurs_deu_000409) +Scores: (#C #S #D #I) 83 19 18 20 +REF: * * * * * ******* i n d i e s e R P e r i o * d e * d e r * e * U r o * p Ä I S C h e n g E S c h i C H t e * s t * a n D d I e r E i c h u n d m * Ä c h t i * * G G e ******* W o R d e n e * k a * t H o L I s C H E K i R C h e a u f D e M p r Ü F s t a n * D +HYP: S G S E E i n d i e s e * B e r i o E d e N d e r U e R r o E p ** * * E h e n ******* g * I c h i * G t e M s t D a n T d * e ******* r * i c h u n d m E Ä c h t i H E N * e V o * d e n e R k a R t * o N E s * * * I G i * E h e N a u f ******* T e N p r E Ö s t a n E T +Eval: I I I I I I D S I I I I S I D D D S D D S D S I I S D D D I I I S D I S D I I D S S D D D S S D S S D S S S S I S + +Speaker sentences 137: fleurs_deu_000410 #utts: 1 +id: (fleurs_deu_000410-fleurs_deu_000410) +Scores: (#C #S #D #I) 175 20 68 10 +REF: d i E e r s T E d e r * * 7 8 e * * * * * ******* m P f ** e H l u n g E N i s t D a S s e i n e n e u E d I p l o m a t I s c h E i n i t I A t i V e v O r e n d E d i E S e S j a H r E s e R g r i F f e n w e r d e n s o l l t e U m d i e I r a K I s c H e n g r e n z e n g e g E N Ü b * e r F e i n D l i C H e N I n t e r V e N t i o N E n z u s i c h e r n u n d D I p l O m a t I s c h E b E z I E H U N G e N m i t s e i N E N n a c h b a R n W i e D e r H E R z U s t E L L e n +HYP: d i * e r s * * d e r C H E N D I e B Z I C H m f Ä e * l u n g * * i s t ******* * a * s ******* e i n e ******* n e u * ******* d E p l o m a t * s c h * ******* i n i t K E t i P e v E r ******* e n d * d i * * e N j a * r * s ******* e g r i * f e n w e r d e n ******* s o l l t e * m d i e * r a * G s c * e n g r e n z e n ******* g e g * * b A e r ******* * e i n T l i * * e * * n t e r * e * t i o * * n ******* z u ******* s i c h e r n u n d * * p l U m a t * s c h * b T z * * * * * * e * m i t Z s e i * * * ******* n a c h b a * n ******* * i e e r * * T z * s t * * * e n +Eval: D D D I I S S S S I I I I I I S I D D D D D D D D D D S D D D S S S S D D D D S D D D S D D D D D S D D D D S I D D S D D D D D D D D D D D D S D D S D D D D D D D S D D D D D D D S D D S D D D D + +Speaker sentences 138: fleurs_deu_000411 #utts: 1 +id: (fleurs_deu_000411-fleurs_deu_000411) +Scores: (#C #S #D #I) 85 9 21 1 +REF: d I e s b I E T e t e i n e G u t e g e l e g e n ******* h e i t d a s n o r D l I c h T z u s E H e n D A d e R h I M m e L m e h r O d e R w e n I g e r r u n d u m d i E u H r d u n k e l I s t +HYP: d E e s P b * * * e t e i n e * u t e g e l e g e n h e i t d a s ******* n o r T l * c h * ******* z u ******* s * * e n ******* * * d e * h * E m e N m e h r ******* U d e * w e n g e r r u n d u m d i * u E r ******* d u n k e l ******* E s t +Eval: S S D D D D I D S D D D D D D D D D D D S S D S D S D S D D S + +Speaker sentences 139: fleurs_deu_000412 #utts: 1 +id: (fleurs_deu_000412-fleurs_deu_000412) +Scores: (#C #S #D #I) 129 21 30 13 +REF: * * * * p r o F E S s o r I n p a m e l * A F e r ******* g u * s o n v o n d e r u n I V E R S I t Y O F d U n d E e m e r k t a n J o U R N a l i s t e n s c h e i N e n e i n e g e F Ä H r l i C H e g R e n z e Z u Ü B e r s C H r e i t e n w e N n S i e F o t o S * U s * w * * * * v o n V e r d Ä c h t i G e N v e R Ö F F e n t L i C h e n +HYP: S T K T p r o * * * s o r E n p a m e l E R V e r g u S s o n v o n ******* d e r u n E W Ö O E t I * A d A n d I e m e r k t a n S o * * * a l i s t e n s c h e i * e n e i n e g e * Ä E r l i * * e ******* g * e n z e ******* * u ** * e r s * * r e i t e n w e * n D i e P o t o * N s E w E I T E v o n ******* * e r d E c h t i * e * v e * ** * e n t E i * h e n +Eval: I I I I D D D S I S S I I D S S S S S S S D S S S S D D D D D S D D D D D D D D D D D S S D I S I I I I I D D S D D D D D S S D + +Speaker sentences 140: fleurs_deu_000413 #utts: 1 +id: (fleurs_deu_000413-fleurs_deu_000413) +Scores: (#C #S #D #I) 96 27 35 5 +REF: E s k a N N s i C H a U c h l o H N E n e i n e * W i l D * C a r D z u k a u f e n d i e z u t r i T t e n T w E d e r Z U A U s g e w Ä H l t e n p a * R K S I n * S Ü D a f r i k a O D e r Z u a L l E n S Ü D a F r i k a n I S c h e N n A T I O n a l ******* p a R K s g e w Ä H r t +HYP: * s ******* k a * * ******* s i * * a * c h ******* l o * * * n e i n e N E i l * G K a r * z u k a u f e n ******* d i e T z u t r i * t e n w * d e r ******* * * T O R s g e w ** E l t e n p a Z E N C H E n T * ** * a f r i k a ******* * R e r T u O a * l * n Z H T a V r i k a n * * c h e * ******* n E R Z n a l p a * X s ******* g e w ** E r t +Eval: D D D D D D D D D D D D I S D I S D D S D S D D D D S S S D S I S S S S S I D D D D D S S S D D S S S S S D D D D S S S S I D S D D S + +Speaker sentences 141: fleurs_deu_000414 #utts: 1 +id: (fleurs_deu_000414-fleurs_deu_000414) +Scores: (#C #S #D #I) 100 16 45 16 +REF: d i e B r Ü C k e s o L l I m s e * P t e m b * * * * * * * * * * e R * * * 2 0 1 7 v O L L s t Ä n d i * G D E n B e t r i e B a u f n e H m E N E s w I r d E r w a r t e T D a S s D i E B r a s I L I a n i S c h E n z O L L K O N T R o L l p u n K t e D a N n f e * r t i g G E s t e L l t S e i n w e r D e n +HYP: d i e ******* P r Ü * k e ******* s o * l ******* * m ******* s e R t e m b E A T Z W E I T E L e N S I E B Z I N v * E R s t E n d i H T * * n * e t r i e * a u f n e * m * * I s ******* w * r d ******* A r w a r t e * * a * s * i * * r a s * J a n i * c h * n z * * * * * * * * o * l p u n * t e * a * n f e A r t i g * * s t e * l t ******* Z e i n ******* w e r * e n +Eval: D S D D D D D D I S I I I I I I I I I I S I I I S S S S D S S S I S D D D D D D D S D D D S D D D D D D D S S D D D D D D D D D D D D D D I D D D D S D D + +Speaker sentences 142: fleurs_deu_000415 #utts: 1 +id: (fleurs_deu_000415-fleurs_deu_000415) +Scores: (#C #S #D #I) 144 20 50 4 +REF: w Ä H r E n d e I n e x ******* * p E r I m e N t e l l e R I m P F s t O F F i n D e r l a g E Z u s e i n s c h e i n t D i E e b O l A m o R t A l i t Ä t z u s e n K E n g I b t E s b I s H e r k e i n E m E d i k a m e n t e d i E a l s e i n d E U T i G z u R b e h a n d l u n G b E s t E H e n d e R I n F e k t i o n * e N G E e i g n e t n a c h * g e W i e s e n W U r d e n +HYP: w ** E r * n d ******* e * n ******* e x S p * r * m e * t e l l e * * m * * s t * A U i n ******* * e r ******* l a g * T S u ******* s e i n ******* s c h e i n t ******* * i * e b U l E m o * t E l i t E t ******* z u ******* s e n U n g E b t ******* A s ******* b * s * e r ******* k e i n * ******* m d i k a m e n t e d i * ******* a l s ******* e i n d * R i * H z u * ******* b e h a n d l u n * b * s t * * e n d e * * n V e k t i o n G e R * * e i g n e t ******* n a c h K g e * i e s e n * O r d e n +Eval: D S D D D D I I D D D D D D D D S S D D D D S S D D D D D S S D S S D D S S S D S D D D D D D S D D D D S S D S D D D D D D D D S I S D D D I D D S + +Speaker sentences 143: mls_deu_000281 #utts: 1 +id: (mls_deu_000281-mls_deu_000281) +Scores: (#C #S #D #I) 165 15 45 3 +REF: e i n Ä u s s e r s T l * e b H a f T e r d E P e s c h e n w e C H s e l F a n D s t a T t m a * n E r W o G d E N p l a n e i N E n a L l g e m e i N E n s t A a t e n k o n g r e S s z u b E r u f e n u n d k o N n T e s i C h v o R l Ä U f i g N U R n o c H n i c h t Ü b e r d A s v o r * z U l e g e N d e P r O g R a M m u n d d E n o r t D e s z u s a M m E N t r I T t s e i n i g e n +HYP: e i n E u s s e r s * l I e b * a f * e r ******* d * B e s c h e n w e * X s e l * a n * ******* s t a * t m a I n A r U o K d * I M p l a n e i * * n a * l g e m e i * * n s t * a t e n k o n g r e * s T z u ******* b * r u f e n u n d k o * n D e ******* s i * h v o * l ** * f i g ******* * * * n o c * n i c h t ** b e r ******* d E s v o r T z l e g e * d e ******* B r * g * a * m u n d d I n o r t ******* * e s T z u s a * m * * t r * * t s e i n i g e n +Eval: S D I D D D D S D S D D D D I S S S D S S D D D D D D D S D D D S D D D D D D D D D D D D S I S D D S D D D S D D S D D D D D + +Speaker sentences 144: mls_deu_000282 #utts: 1 +id: (mls_deu_000282-mls_deu_000282) +Scores: (#C #S #D #I) 144 15 23 8 +REF: e r w u s s t e n I c h t W a s i H m d a s l e b e n k o s T B a r e s g e r a u B t H a T t e s * * p a n N K r a f t u n d m u * t d a S s e s i H n f e i g u n D s c H E U g e m a c h t H a T t e u n * ******* f Ä h I G z u d e n h o H E n d i n g e n z u * d e n e n u n ******* g e t r * Ü B t e M i t F r E U D e g E h Ö r t +HYP: e r ******* w u s s t e ******* n * c h t * a s i * m d a s l e b e n k o s * P a r e s ******* g e r a u P t ******* * a * t e s C H p a n G r a f t ******* u n d m u D t d a * s e s i * n f e i g u n * s c * O L I g e m a c h t ******* * a * t e u n D f Ä h C H z u d e n h o * * n d i n g e n z u O d e n e n u n g e t r I Ü P t e * i t r * A L e N g h ** r t +Eval: D D D D D D S D S D D D I I S S D I D D D D S S S D D D I I S S D D I I I S D S D S S S S D + +Speaker sentences 145: mls_deu_000283 #utts: 1 +id: (mls_deu_000283-mls_deu_000283) +Scores: (#C #S #D #I) 221 15 78 6 +REF: d i e s e r J u n g e m a N n h i e S s k a C k E R l i t z C H e n u n d b e f a n D S i c H g E R a D e A U f d E r W a n d e R s c h a f t a l s i n D e m g e n a N n t e n k Ö ** n i g r e i c h D I E b e k a n N T m a c h u n g w E g E N d e R P r I n z e S S I n v e R l E S e n w u r d e e i s a G t E d e R s C h * n E I d e r w e N n e s w e i t e R n i C h t * s i s t e i n W e i B H a B I c h n O C h n i c H T G E k Ü * S s t u n d D E s k Ö n i g s e i ******* d a m z u w e r D e n d a s g e l Ü s T e t M i C H a L l e R d i n g s * +HYP: d i e s e r * u n g e ******* m a * n h i e * s k a * k * A l i t z * I e n u n d ******* b e f a n * * i c * ******* g * * a * e * * f d * r ******* * a n d e * s c h a f t a l s i n ******* * e m g e n a * n t e n k Ö Ü n i g r e i c h ******* * * * b e k a n D m a c h u n g ******* w I g * * d e * * r * n z e * * E n v e * l * * e n w u r d e ******* e i s a K t * d e * ******* s * h T n L d e r w e * n ******* e s ******* w e i t e * ******* n i * h t Z s ******* i s t ******* e i n * e i * P a * ******* U c h ******* n * * h ******* n i c * * ******* * * k Ü K L s t ******* u n d U T s ******* k Ö n i g s ******* e i d a m z u ******* w e r * e n d a s ******* g e l ** s * e t * i * * G a * l e * d i n g s T +Eval: D D D D D D S D S D D D D D D D D D D D D D D D D D I D D D D S S D S D D D D D D D S D D D D S D D D D I S S D D D D D D I D D D D S D D S D D D D D D D D D I S D S S D D I D D D D D D D D S D D I + +Speaker sentences 146: mls_deu_000284 #utts: 1 +id: (mls_deu_000284-mls_deu_000284) +Scores: (#C #S #D #I) 194 18 55 6 +REF: * * * * ******* n o c H f Ü n f m i n u t e n u n d d I e w o l K e n d e R b e W u S s t l o s i g k E I t b e g a N N E n z u s c h W i n d e n J e T Z t W u S s T e I C H s e H r W O H l d a S s i C h I n m e i N E m e i g E n e n b e T t e l a g u n d d a S s d i e r o t E g l u t n i C h t S a n d E r e s w a R A l s d a s F E U E r i m k a m i n d e r K i n d E R s t u b e e s w a R n a c h t e i n E k E r z e b R a N n t e a U f D e * m t i s C h e +HYP: S R D R n o c * ******* f Ü n f ******* m i n u t e n u n d d * e w o l G e n d e * b e u * s t l o s i g k A L t b e g a * * * n ******* z u ******* s c h * i n d e n I e R S t * u * s * e ******* * * * s e * r * * U l ******* d a * s ******* i * h ******* * n m e i * * m e i g * n e n b e * t e ******* l a g u n d d a * s d i e ******* r o t N g l u t D n i * h t Z a n d * r e s ******* w a * I l s d a s * V O Y r i m k a m i n ******* d e r * i n d * A s t u b e e s ******* w a * ******* n a c h t e i n * I k * r z e ******* b * a * n t e a * f ******* T e I m ******* t i s * h e +Eval: I I I I I D D D D S D S D S S D D D D D D S S S D D D D D D D D D D S D D D D D D D D D D D D D S S D S D D D S D S S S D D D S D D D D S D D D D D D S I D D + +Speaker sentences 147: mls_deu_000285 #utts: 1 +id: (mls_deu_000285-mls_deu_000285) +Scores: (#C #S #D #I) 150 18 36 7 +REF: W e L c h E d i E S e V e r D r Ä n g u n g e n w I e W Ä c h t e r u n t e r h * A l t e n k o M m T d a N n I m p U b e R t Ä t * s ******* a l T e R d i E h o c h f l u T d e R s e x * u ******* e L L E n b e d Ü R f t i g k e i t s o f I n d e T s I e A n d e n g e n a N n T e n s E E l I s c h e N r E a * k t i o n s o d e r W i d e r s t a n D s B I l d U n g e n d Ä M m e * +HYP: * e I c h T d i * * e H e r T r E n g u n g e n w * e B E c h t e r ******* u n t e r h E I l t e n k o * m * d a * n ******* E m ******* p R b e * t I t Z s a l D e * d i * h o c h f l u * ******* d e * ******* s e x S u e * * I n b e d ** Ö f t i g k e i t s o f * n d e * s * e * n ******* d e n g e n a * n * e n s * * l * s c h e * r * a R k t i o n s ******* o d e r * i d e r s t a n Z s * P l d O n g e n d ** E m e R +Eval: D S S D D S S S D S S D I S D D D D S D S D S I I S D D D D D D I I D D S D S D D D D D D D D D D D D I D D S D S S D S I + +Speaker sentences 148: mls_deu_000286 #utts: 1 +id: (mls_deu_000286-mls_deu_000286) +Scores: (#C #S #D #I) 162 11 19 12 +REF: * a b e r a F f e n g e h Ö r e n b e I h a * g e n ******* b e C K a n d i e k i s t * e n ******* w a n D n u n s * o h Ö r t e i c h a u * f a F f e z U s e i n e i n k l a r e r s c H Ö n E R g e * d a n k * e n g a n g d e n i C h i * r g e n D W i E m i t d ******* E m b a u c h a u s ******* g e h e C k t H a b e N m U s s d e N n a F f e n d e n k e n m i T +HYP: T a b e r a * f e n g e h ** r e n D b e * h a R g e n b e * G a n d i e k i s t D e n w a n T n u n s Z o h Ö r t e i c h a u O f a C f e z * O s e i n e i n k l a r e r s c * Ö n * A g e R d a n k T e n g a n g d e n ******* i * h T i L r g e n G i * m i t ******* d I m b a u c h a u s g e h e * k t ******* * a b e * ******* m O s s d e * n a * f e n d e n k e n m i * +Eval: I D D S D I I D S I I S I I S D S D D S I I D D S I S S D D I S I D D D D D S D D D + +Speaker sentences 149: mls_deu_000287 #utts: 1 +id: (mls_deu_000287-mls_deu_000287) +Scores: (#C #S #D #I) 201 37 58 12 +REF: * ******* i s T E s D A s p o R t r Ä T e I n e S m e n s c h E n d e n S i E k e n n e n f r a G t e E l * i * Z A w e l c h E u n ******* b E m E R k t a N m i c H h E r a n g E t r e t e n W a R i c h E n * T g e g n * e t e d a S s E s n U R E I n P H a n t A s i E k o P f s e i u n d s c h o B D I E Z e i c h N u n g e i l i * G U n t e R d i e a n d E R n B l Ä t t e r n A t Ü R l i C H s p r a c H i c h D i E u n W a H r h e i t d E N n e s w A R e i n s e H r G e t R e U e s p O R t r Ä T m * * R r o * * c h E s t e R s +HYP: R i s * * s * E s ******* p o T t r I E H e R n e * m e n s c h * n d e n * i * G k e n n e n f r a K t e I l E i S E R w e l c h * u n b * m * A k t E a * ******* m i c * ******* h * r a n g I t r e t e n B a * ******* i c h ******* * n D g e g n I e t e d a * s I s n * * * * n * F a n t E s i * k o K f ******* s e i u n d ******* s c h o * ******* * P T T e i c h E u n g e i l i C H * n t e * d i e ******* a n d * O n * l ** t t e r n * t Ü * l i * * ******* s p r a c * R i c h * i * u n B a * r h e i t d * * n D e s ******* w * * ******* e i n s e * r I e t O e I e s ******* p * E t r I E m I S T E r o T S c h * s t e * s +Eval: I I D D D S D S S S S S D D D D S S S I I S S D I D D S S D D D D D S S D D D D I S I D S D D D D D S S D S D D D D D S S S S I S D D D D S D D D D D D D D S D D S D D D S D D D D D S S S D D S S S I I S S I I D D + +Speaker sentences 150: mls_deu_000288 #utts: 1 +id: (mls_deu_000288-mls_deu_000288) +Scores: (#C #S #D #I) 164 20 52 9 +REF: I c H * * w e i S s d a S s i C h s e H r k r a n K b i n s a G T E S I e N A C h e I N e R w e i l e v o r E I n p A a R m i n u t e n v e R s U c h t e i c h m i c h I M b E T t e * u * M z u D r E H e n u n d f Ü H l * T e d a S s i c H k e i n g l i e d m E H r R Ü h r E n k a N n E s w Ä r e g u t w e N n i C h m e I n g e m Ü t e R l e i c h t E R n k Ö N n t e b e v o r i C H s t * e * ******* r b e * +HYP: * c * I H w e i * s d a * s ******* i * h s e * r k r a n * G b i n s a * * K * * e ******* * * S h N e * R e N A w e i l e v o r ******* * * n p * a * ******* m i n u t e n v e * s * c h t e i c h ******* m i c h ******* * N b * Ä t e R u E N z u * r * * e n u n d f Ü * l E D e d a * s ******* i c * k e i n ******* g l i e d m * A r E N I h r * n k a * n * s w E r e ******* g u t w e * n ******* i * h ******* m e * n ******* g e m Ü t D e * l e i c h t * A n k Ö R n t e b e v o r A i * * ******* s t D e R r b e R +Eval: D D I I D D D D D D S D D S D D D D D S S D S S S D D D D D D D D D D D S D S I I S D D D D I S D D D D D S S S S D D D S D D D D D D D S D D S S S D D D I I I I + +Speaker sentences 151: mls_deu_000289 #utts: 1 +id: (mls_deu_000289-mls_deu_000289) +Scores: (#C #S #D #I) 161 15 30 7 +REF: s o a b e r i s T z w * A r u n ******* s e r w e s e n s G R U n d g o T t s e l B e r d a h e r u m h A t S i C h J e * d O c H d e r S c h l a n g e n ******* k n Ä u E l * d E s a l T e n s a t a n g e s C h l U n g e n u n D * Ü b e r d e m f Ü n * k C H e n d e R L i e b e i s T d i e F I n s T e r n i s D e s h a S s e s G e l a g e r t w a s w u n d e r d a N n +HYP: s o a b e r i s * ******* z w V E r u n s e r ******* w e s e n s K O n d g o R t s e l V e r d a h e r u m h * t ******* Z i * h ******* * e H d E c * d e r ******* * c h l a n g e n k n O u * l T d * s a l P e n ******* s a t a n ******* g e s * h l O n g e n u n * I Ü b e r d e m f Ü n G k * G e n ******* d e * * i e b e i s * d i e * E n s * e r n i s ******* T e s ******* h a * s e s ******* K e l a g e r t w a s w u n d e r ******* d a * n +Eval: D D I S I D S S S S S D D S D D D I S D D D I S D I D S D D D S D I I D S D D D D D S D D S D D D S D D + +Speaker sentences 152: mls_deu_000290 #utts: 1 +id: (mls_deu_000290-mls_deu_000290) +Scores: (#C #S #D #I) 118 13 38 4 +REF: b e S s I e W Ä r e l I E B e R g e b l i e b e n a B e r s I e w a R g e * z w u N G E n z u g E H e n W e I L d i e p Ü n k t L i C H k e i t b e i d E n m a H l z e i t e n e i n e s a c h e w a R a u F w e L c h E i n g * A t E s ******* h E A D h A L l s t r e n g E g e * h A l t e n W u r d e +HYP: b e * s * e ******* V E r e ******* l * * * e W A g e b l i e b e n a U e r ******* s * e ******* w a * ******* g e T z w u * * * n ******* z u ******* g * * e n B e * * d i e ******* p Ü n k t * i * G k e i t b e i d * n ******* m a * l z e i t e n e i n e s a c h e ******* w a * a u * ******* w e * c h * i n g E H t * s h * Ä R h * O l s t r e n g * R g e R h E l t e n * u r d e +Eval: D D D S S D D D D S S S D D D D D I D D D D D D D S D D D D D S D D D D D D D D D I S D I D S S D S D S I S D + +Speaker sentences 153: mls_deu_000291 #utts: 1 +id: (mls_deu_000291-mls_deu_000291) +Scores: (#C #S #D #I) 187 26 56 11 +REF: A U G E n B l i C k l i c h f Ü H l * t e w i e I H r e a * n s i c h t e n Ü b e R m i c H i H r E E m p f i n d U n g E n * f Ü ** r m i c h n i c h t u m e i n a t O M v e r ******* Ä n ******* d E r t w a R E n Ü b e R h A u p t k E I n e R Ä n d e r u n g f * Ä h I G w a R E n i C H s A H e * s I H r e m V e r s t e i n e R t E n a u * g e W e L C h E s n I e m A l s D u R C H t r Ä n e n g e n e t z t n i e m a L s i n Z Ä r t L i c H k E I t a u f g e l * e U c h t e t h a t t e a * n +HYP: * * * * n l i * k l i c h ******* f Ü * l H t e ******* w i e ******* * * r e a M n s i c h t e n ******* ** b e * ******* m i c G i E r * * m p f i n d * n g * n D f Ü Ö r ******* m i c h n i c h t ******* u m ******* e i n a t * U N v e r I n d A r t ******* w a * A n N b e * h * u p t k * R n e * ** n d e r u n g f E C h * * ******* w a * * n i * * G s * * ******* e I s * E r e m ******* F e r s t e i n e * t * n a u O g e * e * I h * s n e m E l s ******* T u * * O K t r I n e n ******* g e n e t z t n i e m a * s ******* i n T Ä r t i c k * A t a u f g e l O e I c h t e t ******* h a t t e ******* a M n +Eval: D D D D S D D D I D D D D I D D D D S S D D D D I I D D D D S S I S I S D D S S D D D S D D I S D D D D D D D S D D D I D S D S D D I D D S D S S D S D D S S S D D D S S S D S I S D D I + +Speaker sentences 154: mls_deu_000292 #utts: 1 +id: (mls_deu_000292-mls_deu_000292) +Scores: (#C #S #D #I) 245 34 99 9 +REF: * b * R U d e r s A m i s T S e H r g u t W E N n d e r H Ä u P T l i N g I H n e r ******* f Ä h r T w I r D e R s i c H F r E U e N u n D w I R w e r D E n s c h n e l l d a n a c H h a n d e L n s O W o L l E N W I R a u F B r e c h e N U n D s c H n E L l r e i T E n D A m i t W I r N o C H V o * * r N a c h T d a s l a g e r E R r e i c h e n W I r s t i e g E N a u F d i E P f e * r d e d i E N u n a u s g e r U H t H a T t e n u n d f l o g e n I M g a l o P p d a ******* v o n d i E s m a l H Ü T E t e n w i r u n S d e r f Ä H r T e +HYP: S b P O d e r s E m ******* i s * Z e * r ******* g u t * M I n ******* d e r * A u * K l i * g ******* * * n ******* e r f Ä h r * w * r * ******* e * s i c * V r * * e * ******* u n * ******* w * * E w e r * * n ******* s c h n e l l ******* d a n a c * ******* h a n d e * n s * ******* U o U l * * * B E a u * P r e c h e * ******* * n * ******* s c * n * A l ******* r e i * D n ******* * E m i t * Ü r * o * * F o D E r * a c h * d a s l a g e r * * r e i c h e n * E r ******* s t i e g * * a u * d i T * f e H r d e d i * * u n a u s g e r * O t ******* * a * t e n ******* u n d f l o g e n ******* * * g a l o * p T d a v o n d i * s m a l ******* * Ü * * t e n w i r ******* u n Z d e r f Ä E r D e +Eval: I I S S S D D S D D D S S D D S D S D D D D D I D D D D D D S D D D D D D D D S D D D D D D D D D S S D D D S S D S D D D D D D D S D D S D D S D S D D D S I I D D D D D S D D D D S D I D D D S D D D D D D D D S I D D D D D D S S S + +>> REF: W i E d e R d I r e k t z U F o l G e n W I R r I T T e n g e r a d e ******* a u s U n d e r ******* s p a R T e N u N S +>> HYP: ******* * i * d e * d E r e k t ******* z E V o l I e n * * * E r * * * e n g e r a d e a u s ******* S n d ******* e r s p a * D e * u * * +>> Eval: D D D D S D S S S D D D S D D D I D S D I D S D D D + +Speaker sentences 155: mls_deu_000293 #utts: 1 +id: (mls_deu_000293-mls_deu_000293) +Scores: (#C #S #D #I) 263 16 54 5 +REF: w e I l d I e A b e R m i t p e c h B e s t r i c h e n w a * r b L i e b e i n e r v o n d E n g o l D e n * e n p a n t o F f e l n f E s t H Ä n g e n u n d i n d e r a n g s T d a c h T e s n i c h t D A r a n i H n M i t z U n e H m E N u n d W i e E s d E n l e t z t e n s C h R i T t v o n d e r t r e P p e t a t d A h a T t E E s z w Ö l f a u s g e s c h l a g e n * d a w a R w a g e n u n d P f * E r d e v e r s C h W u n d e n U n d a s c h e n ******* p u T t e L s t a n D i n s e i N E n a s C h e n k l e i d e R N a u f D e R d u n k E l n +HYP: w e * l d * e ******* * b e * A m i t p e c h ******* P e s t r i c h e n w a H r b * i e b e i n e r v o n ******* d * n g o l L e n D e n p a n t o * f e l n f Ä s t E I n g e n u n d i n d e r a n g s * d a c h * e s n i c h t ******* * E r a n i * n * i t z O n e * m * * u n d * i e I s d * n l e t z t e n ******* s * h * i E t v o n ******* d e r ******* t r e R p e ******* t a t d * E h a * t * ******* I s T z w ** l f a u s g e s c h l a g e n G d a R w a * ******* w a g e n u n d * f I Ä r d e ******* v e r s * h * u n d e n * n d a s c h e n p u * t e * ******* s t a n * i n ******* s e i * * n a s * h e n k l e i d e * * a u f * e * d u n k * l n +Eval: D D D D D S D S I D D D S I D S S S D D D D S D D S D D D D S D D D D S D D S D D S D D D S S D I S D D D I S D D D D I D D D D D D D D D D D D D + +>> REF: s t r a S s e +>> HYP: ******* s t r a * s e +>> Eval: D D + +Speaker sentences 156: mls_deu_000294 #utts: 1 +id: (mls_deu_000294-mls_deu_000294) +Scores: (#C #S #D #I) 96 18 43 5 +REF: i L L n A H m d A s g L a s v O m a U g e e I n f i n S t E R e r E R n S t l a * ******* g e r T e Ü B e R s e I n e n z * Ü G e n e s i S T s c h r E C K L i C H s a G t e e r i C H H A B D A s m e I n i g e g * e t a N u M b L u t * v e r g i E S s e n Z U v e r m e i D e n +HYP: i * E n * O m d * s K g * a s ******* v * m ******* a R g e e * n f i n D t * * e r * A n t ******* l a R g e r * e ** * e * ******* s e * n e n T z U Ü K e n ******* e s ******* i * * ******* s c h r * * * * i K E s a K t e H e r i * * ******* * * * E T E s m e * n i g e ******* g I e t a * ******* u N b * u t D v e r g i * * s e n ******* * * v e r m e i N e n +Eval: D S D S D S D D D D S D S D D D S S D I I D D D D D D S I S D D D D D D D D D S S S S D D D D D D S S S D D I D D S D I D D D D D S + +Speaker sentences 157: mls_deu_000295 #utts: 1 +id: (mls_deu_000295-mls_deu_000295) +Scores: (#C #S #D #I) 164 13 9 9 +REF: n u r d e r d * o * k t o r u n D d I e w Ä r t e r I n s o L l e n v o r s e i n e a u g e n k o m m e n e r k l Ä r t e d i e t r i * n e i n G r o S s e M a m t ******* s e i f e r d a m i t w a R d i e f * r a U o b e r s t g a n Z e i n * v e r s t a n d e n u n d * h ** Ö C H s t e r f * r e U t k E H r t e s i e m I t i H r e n +HYP: n u r ******* d e r d A o C k t o r u n * d * e w E r t e r E n s o * l e n v o r s e i n e a u g e n k o m m e n e r k l E r t e d i e ******* t r i E n e i n K r o * s e N a m t s e i f e r d a m i t w a * d i e f T r a R o b e r s t g a n S e i n F v e r s t a n d e n u n d P h Ü Ö Ü K s t e r f P r e I t k * A r t e ******* s i e m E t i E r e n +Eval: D I I D D S S D S D I S D S I D I S S I I I S S I S D S D S S + +Speaker sentences 158: mls_deu_000296 #utts: 1 +id: (mls_deu_000296-mls_deu_000296) +Scores: (#C #S #D #I) 183 20 43 9 +REF: k w a r u n * t r Ö s t L i c h Ü b e R d i e l a g e d E s k Ü n * s T l E r s e R b e g A N n Z u w e i n e n u n D s c h l U c h * z t E * * l a n g e i n d I e v o r g e * h A l t e n e n h Ä n d e d e R k Ü n s T l E R w a R t e t e * b * I s k s i c H b e r u H i * G t h a T t e u n D e n t S c h l o S S s i c h d a N n d a e r k e i N E n a n d E r E n a u s W E g f a n D d e N n o c h z u m W e i t e r s c H r e i b e N +HYP: k A w a r u n D t r Ü s t * i c h ******* E b e * d i e ******* l a g e ******* d A s k Ü n Z s * l * r s e * ******* b e g * E n * u ******* w e i n e n u n * ******* s c h l * c h T z t * D E l a n g e i n ******* d * e ******* v o r g e R h E l t e n e n h E n d e R d e * k ** n s * l * * A w a * t e t e R b D E s ******* k A s i c * b e r u * i C H t ******* h a * t e u n T e n t * c h l o * * ******* s i c h T d a * n d a e r k e i * * n a n d A r * n a u s F I g f a n * T d e R n o c h ******* z u m ******* P e i t e r s c * r e i b e * +Eval: S I S D D S D D D S I D D D D D S D D D D D I D I I D D D I S S S D D D D D S D I I S D S D D I S D D S D D D D S D D D S D S S D S S D D S D D + +Speaker sentences 159: mls_deu_000297 #utts: 1 +id: (mls_deu_000297-mls_deu_000297) +Scores: (#C #S #D #I) 232 33 68 22 +REF: V o n d e N P f e r d e h e r d E n d e ******* r A p a * * c h e n u n D s a g T E N u n * s D a S S s i E f Ü R e I n a ******* p a * * c h E n P f E r d u n s e b e N s o v i E L e w a r e n u n D B r A n d Y g e b e N w Ü r d e n W i E f Ü r E i n k * i * o w a * p f * e r * D d a s i n d u n S E r E k R i E g e R F o R T u m a ******* p a * * c h e n ******* P F E r d E Z U H O l e n a L s O r I c h t i G W e r W a R s c h U l D A N D E m t o d e D e R B I s h E r g e ******* f a L l E N e n u n d a n D e M b l u t v e r g i E S s e n w e L c h E s N u n +HYP: * o n ******* d e M * f e r d e h e r d * n d e r * p a T S c h e n u n * s a g * * * u n Z s ******* T a * * ******* s i * ******* f ** * e * n a p a T S c h * n f Ä r d ******* u n s e b e s o v i * * e w a r e n ******* u n * P r E n d * I g e b e * w Ü r d e n * i * Ü f E r ******* * i n k E i U o w a B p f I e r T T d a ******* s i n d ******* u n * r I G k L i * g e A V o * * D u m a p a T S c h e n V Ä H r d T S O * U l e n ******* a * s * ******* r * c h t i H G e r ******* * a * ******* s c h * l * ******* * E * I m t o d e I e * ******* * * s h * r ******* g e f a * l * * e n u n d ******* a n ******* * e * b l u t v e r g i * * s e n w e I c h I s * u n +Eval: D D S D D I D I I D D D D I D S D D D D D D D D I I I D S S D S D D D D S S D S D D D S S D D I I I I I S D D D S S S S D S S D D S I I I I S S S S S S D S D D D D D S S D D D D D D D D S D S S D D D D D D I D D D D D D D D D S S D + +>> REF: b e ******* v o r s t a n d w e i s * s e P f e * r d e ******* h Ä n d l e r * +>> HYP: b e v o r s t a n d w e i s E s e * f e H r d e h E n d l e r W +>> Eval: I I D I I S I + +Speaker sentences 160: mls_deu_000298 #utts: 1 +id: (mls_deu_000298-mls_deu_000298) +Scores: (#C #S #D #I) 169 32 63 8 +REF: d a s A m a Z o n e n ******* h Ü t c h E n v o N s c H w a r * z E m S a m M E T g R a * z i Ö * s A U F i H r e l a n g E n l o C K E n g e d r Ü C k t d i e I H r E w a n g e N u m ******* f l o S s e n U n D Ü b e R I H r E s c h U l t E R n h E r a B w A L L t e n s o t r a T S I e i N d a s e i n F A C h * e l Ä n D l i c h E g e b * Ä u d e u n d s C H W e b T e z w I s c h E N D E n r e i H E n d e r h A L b * g e b l E n D e T E n d o R f k I n D E r a u f U n D a b +HYP: d a s ******* * m a T o n e n h Ü t c h * n v o * D s c * w a r T z A m * a m I N D g * a T z i Ö R s ******* * * R i E r e ******* l a n g * n l o * * T n ******* g e d r ** I k t d i e ******* * * r * ******* w a n g e * u m f l o * s e n * n T ** b e * ******* * * r * ******* s c h * l t H n ******* h * r a P w * E I t e n ******* s o ******* t r a * ******* * * e i * d a s e i n V E R h R e R l ** n T l i c h * g e b O E u d e u n d ******* s * T Ä e b P e T z w * s c h * * ******* * * n r e i * * n d e r ******* h * I b P g e b l * n * e I n ******* d o * f k E n * * r a u f ******* E n T a b +Eval: D D S I D D S D I S D S S S D I I D D D S S D D D D S D D S D D D D D D I D D S D D D D D D D D S S D D S D S S D D D D D D D S S S I S D S D I S D D S S S S D D D D D D D D D D S I D D S S D D S D D D S S + +Speaker sentences 161: mls_deu_000299 #utts: 1 +id: (mls_deu_000299-mls_deu_000299) +Scores: (#C #S #D #I) 164 26 49 7 +REF: D U m u S s t e r S t E n T S a g e n * a L l e m s Ü n D h a f t e n s t r e b e n u n D I n * t i E F e R R e U e u n d d e m u T d i e f Ü ** * R b i T T e d e r h e I l i G E n * e R f l E H e n g e g e n d I e D u g e F r e V e l t H A s t d I e j Ü N G l I n g e w e l c h e f R a n * c E s k o s O l A n G e g e f l o H E n s U c h t e n i H n a u f i n s E I N e R w e r k s T A T t u n D f a n d * e n i H n +HYP: * R B m u * s t e r * t I n Z a g e n G a * l e m s H n T h a f t e n ******* s t r e b e n u n * ******* E n D t i * * e * V e R e I u n d d e m u D d i e f Ü Ö H b i * D e ******* d e r h e * l i * * n G A e * f l * * e n g e g e n d * e * u g e r e * e l t ******* * R s t ******* d * e ******* j ** E M l E n g e R w e l c h e P f * a n T c H s k o ******* s * U l * n * e g e f l o * * n ******* s O c h t e n ******* i * n a u f ******* i n ******* s * * R e * w e r k s * * * t ******* u n * f a n d T e n ******* i * n +Eval: D S S D D S S S I D S S D D D S I D D D S S S S I I S D S D D D D I S D D D D D S D D D S D D D D S S S S S D I S D D S D D D D D S D D D D D D S D D D D D D I D D + +Speaker sentences 162: mls_deu_000300 #utts: 1 +id: (mls_deu_000300-mls_deu_000300) +Scores: (#C #S #D #I) 116 11 24 4 +REF: e r l i e S s s e i n e g ******* r e t e L n I c h t F o r t s c h l e P P e n a m a L l e r ******* w E n i g s T E n a b e r i n D e n g r o S s e n v o g e l b a u E r W O s i e a L l e i n e i n e m t o n e P f e i * f e N m * u S s t e n W i E e r s T E t S s A G t e +HYP: e r ******* l i e * s Z s e i n e g r e t e * ******* n * c h t V o r t s c h l e B T e n a m a * l e r w I n i g s D n ******* a b e r i n * e n ******* g r o * s e n v o g e l b a u * r * * U s i e ******* a * l e i n e i n e m t o n e * f e i E f e * ******* m O u * s t e n * i * ******* e r s H I t * s * K t e +Eval: D D S I D D D S S S D I S S S D D D D D D D S D D D I D D I D D D D S S D D S + +Speaker sentences 163: mls_deu_000301 #utts: 1 +id: (mls_deu_000301-mls_deu_000301) +Scores: (#C #S #D #I) 207 15 66 11 +REF: f r A n * c * e s k o m a l t e I n u n h e I l i g e R b E g e i s t E r u n G V i E l e b i L D e R a U s D e R l Ü g e n h a F t e n F a b e L W e l t k * e i * n e R A l s e r V e r m o c h t E d i e b u H l e r i s c h e * Ü P P i G k e I t d e R w e i b L i c H e n g E s t a l t e n s o W A H r h a f T d a R Z U s t e L l e n i n ******* d e m E R v O n l e b e N d * e N m o * ******* d e L l e n d i e k a R n a t i o n * v o N d E n a l t e n m a R m o R b i l D e R n A b e r F o r m U n D b i l D u n G E n * T n a H M +HYP: f r * n T c H e s k o ******* m a l t e ******* * n u n h e * l i g e * b * g e i s t * r u n * F i * l e ******* b i * * e T a * s * e * l Ü g e n h a * t e n ******* * a b e * * e l t ******* k H e i N n e * ******* * l s e r ******* * e r m o c h t * d i e b u * l e r i s c h e L ** * B i * k e * t ******* d e * ******* w e i b * i c * e n ******* g * s t a l t e n s o ******* * B E r h a f * d a * S I s t e * l e n i n d e m ******* * * v n l e b e * d T e * ******* m o D d e * l e n ******* d i e ******* k a L n a t i o n G v o * ******* d * n a l t e n m a H m o b i l * e * n * b e r * o r m ******* O n * b i l * u n * I n D n a * N +Eval: D I I D D D D D D D D S D D D D S D D D D D D D D D I I D D D D D D D I D D S D D D D D D D D D D D S S D D S S D I D D D S D I D D I I D D D S I D D D S S D D D D D S D D D S I S D S + +Speaker sentences 164: mls_deu_000302 #utts: 1 +id: (mls_deu_000302-mls_deu_000302) +Scores: (#C #S #D #I) 243 23 89 5 +REF: b e w e g u n G u n D t a t d e n E R s t e n z u g J A e S s t I M m T e d i e v o R H I n a n g e g e b E N e n i n g * r E d I e n z I e N n Ä m L i c h r Ü b e N h a n f * e i c h e L n u n D s a u E r ******* a m P f E R W a R E n a L l e i n d e m p f e i f e n k o p f e a n w e s e n D a b E R e i N E n f Ü n * f t E n H a u P t s t o F F h a T T E i c h n i C H T g e n a N n T J e t Z t r o c h u n d s c h m E C k t E i C H d a s S A U C h E I n s t Ü C K c h e n f i l Z s C H U h d * A b e i s e i n m Ü S s e i C h B l i e s D e n r a u c h a u c h g +HYP: b e w e g u n * u n * t a t d e n * * s t e n z u g ******* * * ******* e * R s t * H m e d i e v o * E U n a n g e g e b * * e n i n g E r d * e n z H e R n ** m * i c h r Ü b e * ******* h a n f E e i c h e * n ******* u n * s a u * r a m * f * * ******* * a * * n a * l e i n d e m p f e i f e n k o p f e R a n w e s e n * a b * * ******* e i * * n f Ü n D f t * n * a u * t s t o * * ******* h a * * * R i c h ******* n i * * * ******* g e n a * n * D I e t S t ******* r o c h ******* u n d ******* s c h m * Ä k t * ******* i * G d a s E * * * h ******* * * n ******* s t ** * I c h e n f i l * s * * * h d E R b e i ******* s e i n m ** * s e ******* i G h ******* P l i e s ******* T e n r a u c h ******* a u c h ******* g +Eval: D D D D D D D D D S D S S D S S D D I S D S S D D D D I D D D D I D D D D D D D D S D D D D D D I D D D D D D D D D S D D D D D D D S S S D D D D S D D D S S D D D D D D D D D S D D D D I S D D D D S D S D S D D + +>> REF: e g E N d e n h I M M e l u n D g e g E n d I E +>> HYP: e g * * d e n h * * E e l u n * ******* g e g * n G d * * +>> Eval: D D D D S D D D S D D + +Speaker sentences 165: mls_deu_000303 #utts: 1 +id: (mls_deu_000303-mls_deu_000303) +Scores: (#C #S #D #I) 231 23 30 10 +REF: u n D d a s F E U e r s t a n d a u f u n d f l a c k e r t E u n D k o c h T e d a s e S s e n f E R t i g * u n D d e r b r a t e n b r u t z e l t e f o r t u n D d e r k o c h g a b d e m k Ü * c h e n ******* J u n g e n e i n e * * o H r ******* f e i g e u n D d i E m a * G D r u p f t e d A s h u H n f e r t i g d a w a r D d i e h o c h z e i t v o n d e m k Ö n i g S s o H n M i t * d o R n * r Ö ** s C h E n g e f e i E r t u n D s i e L E B T E n V e r G n Ü G t b i s a n i H r e n d e +HYP: u n * ******* d a s V O Ä e r s t a n d a u f u n d f l a c k e r t * u n * k o c h * e d a s e * s e n f Ä A t i g H u n * d e r b r a t e n b r u t z e l t e f o r t u n * ******* d e r k o c h g a b d e m k Ü S c h e n I u n g e n e i n e R R o A r f e i g e u n * ******* d i * m a R K T r u p f t e T d E s ******* h u * n f e r t i g H d a R w a r * T d i e h o c h z e i t v o n ******* d e m k Ü n i g H s o * n ******* * i t E T d o * n G r Ö Ü s I h * n g e f e i H r t u n * ******* s i e * * * * * n ******* E e r n Ü * t E b i s a n i E r e n d e +Eval: D D S S S D D D D S S I D D D I I S I I S I D D D I S S S S D D S S D S D S S D D D I S D I I S D S D D D D D D D D S S D S S + +Speaker sentences 166: mls_deu_000304 #utts: 1 +id: (mls_deu_000304-mls_deu_000304) +Scores: (#C #S #D #I) 125 15 30 7 +REF: u n d d a S s e R m i R n i c h T n a c h t r a * g e n W o l l e w e N n i c h W I d e r s * p e n s t i g w a r G E g E n s e i N E n W O H l m e i N E n D e n * r a * t d e r h e R r P f a R r E r h A T J A i n a l l e M r e * c h t G e h a B t u n D i c h W A R I M u n ******* r e c h t a b e r * +HYP: u n d d a * s ******* e * m i * ******* n i c h * n a c h t r a R g e n ******* G o l l e w e * n ******* i c h ******* N E d e r s H p e n s t i g w a r ******* * * g I n s e i * * n * * U l m e i * * n * e n B r a R t d e r h e * r * f a * r A r ******* h * * E D E i n a l l e N r e I c h t D e h a * t u n * i c h ******* * M E A N u n r e c h t a b e r H +Eval: D D D D D D I D S D D D S S I D D D S D D D D S D D D I I D D D S D D D S S S S I S D D D D S S S S I I + +Speaker sentences 167: mls_deu_000305 #utts: 1 +id: (mls_deu_000305-mls_deu_000305) +Scores: (#C #S #D #I) 102 16 48 7 +REF: * o B g L e I C h S E I n e m a S s e n U R * w E n i g e G r a M m b E t R u g e r b r e i T E t e * s i c h k * e G e L f Ö R m i g a u s U n D m u S s t e d A H e r D A s I h * m E n * T g e g e n ******* f L i E g e N d e s p r e n G G E s c h o S s a u F f a n G e n U N D z U R R u H e B r i n g e n +HYP: U o * g * e * * h ******* * * * n e ******* m a * s e ******* n * * U w I n i g e ******* K r a * m ******* b * t * u g ******* e r ******* b r e i * * t e D s i c h ******* k H e I e f Ö * m i g ******* a u s ******* E n * ******* m u * s t e ******* d E e r ******* * E s ******* * h E m I n D g e g e n f * i * g e * d e ******* s p r e n * K I s c h o * s a u f a n * e n * * * T z S O * u * e ******* * r i n g e n +Eval: I D D D D D D D D D D D D D I S D S D D D D D D D D I D I S S D D D S D D D D S S D D S D D I S I S I D D D D D S S D S D D D D S S S D D D D + +Speaker sentences 168: mls_deu_000306 #utts: 1 +id: (mls_deu_000306-mls_deu_000306) +Scores: (#C #S #D #I) 272 28 72 5 +REF: d e r F u C H s r e i c h T e s A m D i E u n f r i E D L i c h e f r i e d e n s p F e i f * e * h I n d e r m a N n t a t w a c k E R s e i n e s e C H s z Ü g e U n D s a G t e d e r g r o S s E g e i s T a c h t e T n i c h T a u f d i E v e r s c h i e d E n e h a u t d e r m e n s c h e n d e N n d i E k Ö N N E n s i c h m i t F a R b e b E s c h m i E r e n U m i H n z U t * Ä U s c h e n s o n d e R n e r s i E H T d a s h e R z a n d I e h e R z E n d e r k r * i E g e R v o m b e r Ü H M T e n s t a M m E d e r k * i o w a S s +HYP: d e r ******* V u * G s ******* r e i c h e s E m * i * u n f r i * * T i c h e ******* f r i e d e n s p W e i f V e R h E n d e r m a * n ******* t a t w a c k * * A s e i n e ******* s e K s z Ü g e ******* * n * s a K t e d e r ******* g r o * s * I g e i s * a c h t e * n i c h * a u f ******* d i * ******* v e r s c h i e d * n e h a u t ******* d e r ******* m e n s c h e n d e * n ******* d i * ******* k ** * * R n ******* s i c h ******* m i t V a * b e ******* b * s c h m i H r e n ******* * m ******* i * n T z S t R O L s c h e n s o n d e * n e r ******* s i * * * ******* d a s ******* h e T z S a n d * e h e T z H n d e r ******* k r L i * g e * v o m b e r Ü * * B e n s t a * m * I d e r k A i o w a * ******* s +Eval: D S D S D S S D D D D S D S I I S D D D D S D S S D D D S D D D S D D D D D D D D D D D D D D D D S D D S D D D S D D D D S S I S S D D D D D D D S S D S S D I D D D D S D D S I D D + +>> REF: i n D t a p f e r u n e r s c h r o C K E N U n D t r e U d a s m e i n i g e h Ä n g T +>> HYP: i n * t a p f e r u n e r s c h r o * * * * G M n * ******* t r e * ******* d a s m e i n i g e ******* h E n g * +>> Eval: D D D D D S S D D D D D S D + +Speaker sentences 169: mls_deu_000307 #utts: 1 +id: (mls_deu_000307-mls_deu_000307) +Scores: (#C #S #D #I) 173 15 26 5 +REF: a l l e s W a s w i R m I t i H r b e g e g n e t s c h I e b T s i c h d * U R c h u n d Ü b e r E i n a n d e r b a l D u n t e r s c h R e I b e N w I r E i N E n k o n t R a k t d A i s t i H r e h a n d u n D d I e m e i n i g e i H r n a * * m E U n D d e r m e i n i g e b e i ******* d e l Ö s C h e N e i n a n d e r a u s b e i ******* d e v e r s c h l i n g e n s i c h +HYP: a l l e s * a s w i * m E t i E r ******* b e g e g n e t s c h * e b * s i c h T d O E S c h u n d ** b e r i n a n d e r ******* b a l T u n t e r s c h * e * b e * M w E r * i * * n k o n t * a k t d * E i s t i E r e R h a n d ******* u n * ******* d * e m e i n i g e i E r n a H R m * O n * ******* d e r m e i n i g e b e i d e ******* l R s * h e * e i n a n d e r a u s b e i d e v e r s c h l i n g e n ******* s i c h +Eval: D D S S D D D S I S S D S D S D D D S S D D D D D S S S D D D D S I I D S D D I D S D D I D + +Speaker sentences 170: mls_deu_000308 #utts: 1 +id: (mls_deu_000308-mls_deu_000308) +Scores: (#C #S #D #I) 197 27 59 4 +REF: e r m Ü S s t e D e n e I n ******* f A C h e n C H r o n i K e n C H O r a l d e s m a l e R s m i t a L l E R l e I E r k l * Ä r U N G e N U n D z u r e c h t w e I s U n G e n W i E m i t K r a u s e n F i g u R E n v E r S c h n Ö r * k E L N u n D v e r b r Ä m e n i c h t r e t e I n d i e p e r s o n d e s H e r a u s g e * b e R s U n d b i T t e D i c h G Ü n s t i g e R l E s e r D u w o L l E s t E H e d u w e i t e R l i E s E s t f o l G e n d E s d i R g Ü t i G s t M e r K e n +HYP: e r m Ü * s t e * e n e * n f E R h e n ******* * G r o n i T e n ******* K Ö E r a l d e s ******* m a l e * s m i t a * l * * l e * * r k l E H r * * M e * ******* * n * z u r e c h t w e * s E n e n R i * ******* m i t ******* G r a u s e n * i g u * * n ******* v * r * c h n A r C k * * H I u n * v e r b r E m e n ******* i c h ******* t r e t e * n d i e ******* p e r s o n ******* d e s ******* * e r a u s g e I b e * s ******* * n d b i * t e ******* T i c h I K Ü n s t i g e * ******* l I s e r ******* * u ******* w o * l * s t * I e d u ******* w e i t e * ******* l i * s I s t f o l * e n d I s d i * g I t i * s t N e r T e n +Eval: D D D I S S D D S S D S S S D D D D D D D I S D D S D D D D D S S S D D D S D D D D D D S I D D S S D S D D D D D D D I D D D D D S S S D D S D D D D D D S D D D D S D S D S D S S + +Speaker sentences 171: mls_deu_000309 #utts: 1 +id: (mls_deu_000309-mls_deu_000309) +Scores: (#C #S #D #I) 219 28 47 9 +REF: d i E h o f d a m e n b e k a m e n k r Ä m p f e u n D d i E k Ö n i g I n u n D d i e P r I n z * e s s I N n e n d i e I H r e * a l l E R l i e b * s T e n h Ü n D c h e n w Ä H r E n D d e r m A H l * Z e I T a u F D E n s c h o S s g e n o M m E N h a T t e n B e * m e r k t e n z u i H r e M s c H r e C k e n * d a S s d i E l i * l * A a m a r a n t f a R b e n e n u n d o r a n G E g * E l B e n s e i d e n k l e i d e r a L l e d i C H t B e s Ä t m I T D E n h Ä S s l i C h s t e n Ö L f l e C K e n w a R E n +HYP: d i * ******* h o f d a m e n b e k a m e n k r E m p f e u n * ******* d i * k Ü n i g E n u n * ******* d i e * r O n z T e s s * E n e n d i e ******* * * r e R a l l * A l i e b Z s * e n ******* h Ü n Z c h e n w ** E r * n * d e r ******* m E I l T H e * R a u * * I n ******* s c h o * s ******* g e n o * m * * h a D t e n * e R m e r k t e n z u i * r e * N s c * r e Ä k e n G d a * s d i * ******* l i E l E R a m a r a n t f a * b e n e n u n d ******* o r a n S C g A H l D e n s e i d e n k l e i d e r a * l e d i * S t D e s E t m * E * I n h ** E s l i * h s t e n Ö * f l e * G e n w a * * n +Eval: D D S D D D S S D D D S I D S D D D I D S I D D S D S D D D S S I S D S D D S D D D D D D S D I D D S D S I D D D I I S D D S S I S S D D S S S D S D S D S D D D S D D + +Speaker sentences 172: mls_deu_000310 #utts: 1 +id: (mls_deu_000310-mls_deu_000310) +Scores: (#C #S #D #I) 158 17 38 8 +REF: v o n l I e d e R n d i e s i e s i N G E n u n D k l a * V i e r p i e C e n d i e s i E s p * i e l E n v o n g ** * E L D b Ö r s e n d i E s i E h * Ä k e L n v o n * F r a n z Ö S I s c h E n b Ü c h E R n d i e s i E Ü b * E r s e t z e n k o N n t e b i s m E I n g e m Ü t w Ä H r e n d i C h l a u s C H t E z U R n a c h ******* a H m U n G a u f G e s t A c h e L t w u r d e +HYP: v o n l * e d e A n d i e ******* s i e ******* s i * * * n u n * k l a R W i e r p i e S e n d i e ******* s i * ******* s p B i e l * n v o n g Ä H R T b Ö r s e n d i * ******* s i * h E G k e * n v o n D * r a n z ** Ü E s c h * n b Ü c h * A n d i e ******* s i * ** b A S r s e t z e n ******* k o * n t e b i s ******* m * A n ******* g e m Ü t w ** E r e n d ******* i * h ******* l a u s * * t * z O N n a c h a * m O n * a u f K e s t * c h e * t w u r d e +Eval: D S D D D D D D I S S D D D I D I I S S S D D D I S D I D D S S D D S D D D I S D D D D S D D S D D D D D D S S I D S D S D D + +Speaker sentences 173: mls_deu_000311 #utts: 1 +id: (mls_deu_000311-mls_deu_000311) +Scores: (#C #S #D #I) 170 15 53 7 +REF: a R m E u n d n a C K e n w a R E n b l o S s i H r e i n z i g e r s c H m u C k w a R E n i H r e K a s t a n I e n ******* b r a U N E n f l E c h t e n w e L c h E i n w i l d e r u n d N a t Ü r l i c h e r a n m u * T a u F i H r E s c h u L t * e R n h E r a b * f i E L e n i C h n a H m e i N E n b o g e n * f e i N E n k a R t O n * s U n d z e i C h N e t E m I t G r o S s e r s o R G F A l t D i E U m R I S s e * +HYP: a * m * ******* u n d n a * * e n w a * * n ******* b l o * s i E r ******* e i n z i g e r ******* s c * m u O k w a * * n i E r e * a s t a n * e n b r a * * * n ******* f l Ä c h t e n w e I c h * i n w i l d e r u n d D a t Ü r l i c h e r a n m u N D a u * i * r * ******* s c h u S t H e * n h * r a b P f i * * e n ******* i * h ******* n a * m e i * * n b o g e n G f e i * * n k a * t U n G s * n d ******* z e i * h * e t * m * t * r o * s e r ******* s o * K V E l t ******* * i * ******* O m * G E s e R +Eval: D D D D D D D D D S D D D S D D S D D I D D D D S S D S I S D D D D S I D D I D D D D D D D D I D D D S I D D D D D D D D D D S S S D D D D S D S S I + +Speaker sentences 174: mls_deu_000312 #utts: 1 +id: (mls_deu_000312-mls_deu_000312) +Scores: (#C #S #D #I) 221 21 68 4 +REF: A B E r w E D e R A u s D E U t s c h l a n D n o c h a U s i R G e n D e i n E m a n d E r e n s t a A t k o N n t E m a n e R f a H R E n w a s d e r G E g e n s * * T a n D u n D d A s R e s U l t a t d i E s E R u n t e R r e ******* d u N G E n g e w E s e n s e I M a n v e R m u t e t e d A S s e S s i c h u m e R k l Ä r U n g E N d e r m a R t * I e R Ü b e R I H R e a b s i c h t e n u n d u M d i E v e R m i T t l u n g d e r m Ä c h t e z W i s c h E N D E n m a R S s t A a t e n u n D g r o S s B R I t a N n i e n h a n d L e +HYP: R A r w * I e * * u s ******* * T R t s c h l a n * n o c h ******* a * s i * L e n e i n I m a n d * r e n s t a R t k o * n t * m a n e * f a * * * n w a s d e r * * g e n s C H E a n * u n * ******* d E s * e s * l t a t d i * s * * u n t e * r e d u * * * n ******* g e w I s e n ******* s e * * a n v e * m u t e t e d * * s ******* e * ******* s i c h u m e * k l I r E n g * * d e r m a * t Z H e * Ü b e * * * * e R a b s i c h t e n u n d u N d i * ******* v e * m i * t l u n g d e r m Ä c h t e ******* z U i s c h * * ******* * * n m a * * s t * a t e n u n * ******* g r o * s * P E t a * n i e n h a n d * e +Eval: S S S D S D D D D S S D D D D S S S D S D D D D D D D D I I S D D D S D D D D D D I D D D D S D D D D D D D D D D S S D D D I S D D D D D S S D D D D D S D D D D D D D D D D D D S S D D + +Speaker sentences 175: mls_deu_000313 #utts: 1 +id: (mls_deu_000313-mls_deu_000313) +Scores: (#C #S #D #I) 144 8 19 19 +REF: l a S S u n S w e n i g s t e n s e i n e z e i t l a n g v e r s u * c h e n i n ******* W i e ******* f e r * n w i * r a u f d i e s e * W e i s E m I t ******* e i n a n d e r a u s r e i c h e n d a d * A s Z u s a M m e n ******* h Ä n g E n d e W i e d U s a * g s t * e i g e n T l i C h * e U e r E l e m e n t i s T V e r s e t z t ******* * e ******* * * * * +HYP: l a * * ******* u n * w e n i g s t e n s ******* e i n e z e i t l a n g v e r s u O c h e n i n B i e f e r U n w i E r a u f d i e s e S B e i s * N m * t e i n a n d e r a u s r e i c h e n ******* d a ******* d E R s ******* T u s a * m e n h ** n g * n d e * i e d * E s a R g s t A e i g e n K l i G h O e * e r * l e m e n t i s * * e r s e t z t I e R U R T +Eval: D D D D D I I S I I I I S D S D I D D I S D S D I D D D D S I I S S I D D D D I I I I I I I + +Speaker sentences 176: mls_deu_000314 #utts: 1 +id: (mls_deu_000314-mls_deu_000314) +Scores: (#C #S #D #I) 173 17 45 4 +REF: v e R s c h I E D e n E * v o r k o M m N I S s e * f Ü H r T e n z u d e R v e r m u t u n g d a S s f r a u w i e s e d i e k l e i n e n w E S e n v e r ******* b r e N n e S i E s o L l B i s W e i L E n s O s t a R K g e * h E i Z t H a b e n d a S s d i E h e r D p l a T t e n z E R s p R a n g E N a u S s e R d e M s o L l e i n f Ü r c h t e R l i c h e r g e r U c h w a H R g e n O M m E n w O r D e n s e i n +HYP: v e * s c h * * * e n * F v o r k o * m * * * s e K f Ü * r D e n z u d e * v e r m u t u n g d a * s f r a u w i e s e d i e ******* k l e i n e n w * I e n v e r b r e * n e ******* * i * ******* s o * l * i s F e i * * n s U s t a C H g e R h * i * t S T a b e n ******* d a * s d i * h e r T p l a * t e n ******* z * * s p T a n g * * ******* a u * s e d e * s o * l ******* e i n f Ü r c h t e * l i c h e r g e r O c h O w a * * g e n U N m * n ******* w U r T e n ******* s e i n +Eval: D D D D D I D D D D I D S D D D D S I D D D D D D D S D D S S S I D D S S D D D S D D D D S D D D D S D D D D S S D D S S D D S S D + +Speaker sentences 177: mls_deu_000315 #utts: 1 +id: (mls_deu_000315-mls_deu_000315) +Scores: (#C #S #D #I) 119 7 19 3 +REF: u n D * g i n G d e m s C h r e i e N n a c h s o s a H e r e n D l i c h e i N E n h * o H E n b a u m u n d o b e n d A r a u f s a S s e I n k l e i n e s k I n d * u n T e r d e m b a u m a b E R l a G e i n e f r a u d i e s C h l i e f +HYP: u n * K g i n * d e m s * h r e i e * n a c h s o ******* s a * e r e n T l i c h e i * * n h U o * * n b a u m u n d o b e n d E r a u f ******* s a * s e * n ******* k l e i n e s ******* k E n d T u n D e r d e m b a u m a b * * A l a R e i n e f r a u R d i e ******* s * h l i e f +Eval: D I D D D D D S D D I D D S D D D D D S I S D D S S S D D + +Speaker sentences 178: mls_deu_000316 #utts: 1 +id: (mls_deu_000316-mls_deu_000316) +Scores: (#C #S #D #I) 170 26 16 32 +REF: * * * ******* s i e h A T t e n s * * O e b e n d i E f i s c h e R g a * R n e w * E l * c h e d I e n a * c h t * * Ü b e r * a u s g e W O r * * f * E n w a R e n h e r e i n g e ******* z * o * g e n d i e s E e l * e U t e * g e * ******* h ** Ö r t * e n a u g e n s C h e i N l * I c h v e R s c h I e D e n e n n * A t * i o n e n a * n O b W o H l d e r E U R o p Ä I s c h e * C H a r A k t e R b e i a L l e n a u s ******* g e * D r Ü C k t w a R +HYP: C I T s i e ******* h * R t e n s E R H e b e n d i * f i s c h e g a D E n e w A L l S c h e d * e n a R c h t I A U b e r T a u s g e R U r D H f V I n w a D e n h e r e i n g e z A o U g e n d i e ******* s * e l O e I t e R g e R h Ä O r t H e n a u g e n s * h e i M l E S c h v e * s c h * e T e n e n n O C t Z i o n e n ******* a R n A b U o R l d e r * A L o p Ä H s c h e R * K a r E k t e * b e i a * l e n a u s g e T r Ü * k t ******* w a L +Eval: I I I I D D S I I S D S I S I S I D I I I S I S S I I I S S I I I D D I S I I I I S I D S I S D D S I S I D I S S S D S S S I D S S D D I I S D D S + +Speaker sentences 179: mls_deu_000317 #utts: 1 +id: (mls_deu_000317-mls_deu_000317) +Scores: (#C #S #D #I) 96 3 19 9 +REF: n e i N n e i n i c h s c H Ä * m e m ** i c * h * l A S s m I c h A n d e i n e m b u s e n m e I n g e ******* s i c h t v e r ******* b e * r g e n * e r s i n K t I n ******* s g r a s n i e d e R u n D z i e H t s i E n a c h +HYP: n e i * ******* n e i n ******* i c h s c * Ä E m e ******* m Ä i c G h T l * * s ******* m * c h R n d e i n e m b u s e n m e * n ******* g e s i c h t v e r b e H r g e n H e r s i n G t ******* E n s g r a s ******* n i e d e * u n * ******* z i e * t s i * ******* n a c h +Eval: D D D D I D I I I D D D D S D D I I I I S D S I D D D D D D D + +Speaker sentences 180: mls_deu_000318 #utts: 1 +id: (mls_deu_000318-mls_deu_000318) +Scores: (#C #S #D #I) 165 18 30 3 +REF: d i E k I n d e r a b E r s a S s e n v O r d e m w a l D u n d a l S s i e d i e d r e i K n e c h t e v o n w e i t e m l a u f e n s a H E n s p r a c h l e H n c h e n z u * M f U n d e ******* v o g e l v e r l Ä S s t D u m i c h n i c h t S o v e R l a S s i c h D i C H a u c H n i c H t s O s p r a c h f U n d e ******* v o g e l n u n u n d n I M m E r m e H r +HYP: d i * ******* k E n d e r a b A r ******* s a * s e n v E r ******* d e m w a l T u n d a l * s i e d i e ******* d r e i ******* G n e c h t e v o n ******* w e i t e m l a u f e n s a * * n ******* s p r a c h ******* l e * n c h e n ******* z u N P f Ü n d e v o g e l v e r l ** E s t ******* * u ******* m i c h ******* n i c h t ******* Z o ******* v e * l a * s ******* i c h ******* T i * G a u c R E n i c * t s U R s p r a c h f O n d e v o g e l n u n u n d n * E m A r m e * r +Eval: D D S S D D S D S D D D S D D D D D D D I S S I D S D D D D D S D D D D D S D S S S D S S S I D S S D + +Speaker sentences 181: mls_deu_000319 #utts: 1 +id: (mls_deu_000319-mls_deu_000319) +Scores: (#C #S #D #I) 114 8 15 7 +REF: w i e d e r s c h u l z e i n s e i n e r h u l ******* d i * g u n g s r * e d e h e r ******* v o r h O b d e R l E H R e r b r a c h t e a m k l a r e n s o m * m E r M O R G e n * m i t s e i N E n s c h u l k I n d e R n e i n g e s a n g s ******* s t Ä n D C H e N +HYP: w i e ******* d e r s c h u l z e i n s e i n e r h u l d i E g u n g s r I e d e h e r v o r h U b d e * ******* l * * * e r A b r a c h t e a m k l a r e n s o m A m * r * * A D e n G m i t s e i * * n ******* s c h u l k E n d e * n e i n g e s a n g s s t E n * T I e * +Eval: D I I I I S D D D D D S I D D D S S I D D D S D I S D S S D + +Speaker sentences 182: swc_deu_001408 #utts: 1 +id: (swc_deu_001408-swc_deu_001408) +Scores: (#C #S #D #I) 14 2 4 5 +REF: W I e * * * * s i e s e i * n s o L l T E n +HYP: S T e R T W I s i e ******* s e i E n s o * l * * n +Eval: S S I I I I D I D D D + +Speaker sentences 183: swc_deu_001409 #utts: 1 +id: (swc_deu_001409-swc_deu_001409) +Scores: (#C #S #D #I) 48 4 3 2 +REF: d e r e n s c h W i n g U n g e n d U r c H e i n e * z u s a T Z s c h a l t u n g s t u f e n ******* l o s +HYP: d e r e n T s c h * i n g R n g e n d * r c * e i n e R z u s a R s c h a l t u n g s t u f e n l o s +Eval: S D S D D I S S I + +Speaker sentences 184: swc_deu_001410 #utts: 1 +id: (swc_deu_001410-swc_deu_001410) +Scores: (#C #S #D #I) 30 0 8 0 +REF: d i e a u f a L l e b e i d e r s i t z v e r t e I l u n G z U +HYP: d i e ******* a u f a * l e ******* b e i ******* d e r s i t z v e r t e * l u n * ******* z * +Eval: D D D D D D D D + +Speaker sentences 185: swc_deu_001411 #utts: 1 +id: (swc_deu_001411-swc_deu_001411) +Scores: (#C #S #D #I) 14 2 7 0 +REF: u m d e n Ü B e r l E B e N D e n d E R +HYP: u m ******* d e n Ü * e r l * * e M e n ******* d * * +Eval: D D D D S S D D D + +Speaker sentences 186: swc_deu_001412 #utts: 1 +id: (swc_deu_001412-swc_deu_001412) +Scores: (#C #S #D #I) 56 3 9 1 +REF: s p * Ä t e r W u r d e n t e i l w e i s e s O g a r a c h t p a r A L l E l E l o C H s t r e i f e n e I n g e s e t z t +HYP: s p B E t e r * u r d e n ******* t e i l w e i s e ******* s U g a r a c h t p a r * * l I l * l o * * s t r e i f e n e * n g e s e t z t +Eval: I S D D D S D D S D D D D + +Speaker sentences 187: swc_deu_001413 #utts: 1 +id: (swc_deu_001413-swc_deu_001413) +Scores: (#C #S #D #I) 23 1 3 2 +REF: m * o r d e b e k a N n t u n d v e R l a n g * t E +HYP: m A o r d e ******* b e k a * n t u n d v e L l a n g K t * +Eval: I D D S I D + +Speaker sentences 188: swc_deu_001414 #utts: 1 +id: (swc_deu_001414-swc_deu_001414) +Scores: (#C #S #D #I) 20 3 7 10 +REF: b * * * * * w a H L g * * * * * d i e s t I M m E N v o n w Ä H l e R n +HYP: b U N D E S w a * I g E S E T S d i e ******* s t * * m * * v o n w I E l e * n +Eval: I I I I I D S I I I I I D D D D D S S D + +Speaker sentences 189: swc_deu_001415 #utts: 1 +id: (swc_deu_001415-swc_deu_001415) +Scores: (#C #S #D #I) 9 1 0 5 +REF: * * * * ******* g e s c H i c h t e +HYP: S F N A g e s c E i c h t e +Eval: I I I I I S + +Speaker sentences 190: swc_deu_001416 #utts: 1 +id: (swc_deu_001416-swc_deu_001416) +Scores: (#C #S #D #I) 9 4 1 0 +REF: s P a l t u n g f Ä H I G +HYP: s B a l t u n g f ** E E C +Eval: S D S S S + +Speaker sentences 191: swc_deu_001417 #utts: 1 +id: (swc_deu_001417-swc_deu_001417) +Scores: (#C #S #D #I) 28 1 10 2 +REF: s T A D t p a D e R b o r n d i e Ä u S s e r e n * f E I e r * n d e s +HYP: s * * * t p a L e * b o r n ******* d i e ******* ** u * s e r e n D f * * e r A n d e s +Eval: D D D S D D D D D I D D I + +Speaker sentences 192: swc_deu_001418 #utts: 1 +id: (swc_deu_001418-swc_deu_001418) +Scores: (#C #S #D #I) 23 3 3 5 +REF: * * w e i t e r H i n h u m a n i ******* t Ä r e * h I l f * e z U +HYP: U M w e i t e r i n ******* h u m a n i t E r e R h * l f I e T z * +Eval: I I S D I S I D I S D + +Speaker sentences 193: swc_deu_001419 #utts: 1 +id: (swc_deu_001419-swc_deu_001419) +Scores: (#C #S #D #I) 43 4 6 3 +REF: s i E e r ******* k a N N t e n d i e * n E u e * c h i n e s i s C h e r e g i E R u n g n i c h t a n +HYP: s i * ******* e r k a * M t e n d i e N n O u e R I c h i n e s i s * h e ******* r e g i * O u n g n i c h t a n +Eval: D D I D S I S I S D D D S + +Speaker sentences 194: swc_deu_001420 #utts: 1 +id: (swc_deu_001420-swc_deu_001420) +Scores: (#C #S #D #I) 46 8 19 1 +REF: d i e u r a u F f Ü h R U n G F A n D a M d R e i U n D z w a n Z I G s T e * s e P t e m B e r z w e I T A U S e n D a c h t i N +HYP: d i e u r a u * f Ü h G E n * V O n * a N d * e i * n * z w a n * * * s e N s e * t e m * e r ******* z w e * ******* * * R D e n * a c h t ******* i * +Eval: D S S D S S D S D D D D D D S I D D D D D D D S S D D D + +Speaker sentences 195: swc_deu_001421 #utts: 1 +id: (swc_deu_001421-swc_deu_001421) +Scores: (#C #S #D #I) 47 9 21 2 +REF: e r W I L L S i c h n i C h t s c h U l D i G O D E r m i t s c h U l D i G m a c H e n a M t o d e * E I n E s m i t ******* g e s E L L e N +HYP: e r ******* * * * E * i c h ******* n i * h t ******* s c h O l * i C * * U r E m i t s c h O l * i H m a c K e n a * N t o d e R * * n * s ******* m i t g e s * * * e * +Eval: D D D D S D D D D S D S D D S S S D S S D S I D D D D I D D D D + +Speaker sentences 196: swc_deu_001422 #utts: 1 +id: (swc_deu_001422-swc_deu_001422) +Scores: (#C #S #D #I) 18 1 9 1 +REF: d i e M I T d e R E r s T s t I M m e * e i n e n +HYP: d i e ******* * * * ******* d e * * r s Z s t * * m e R e i n e n +Eval: D D D D D D D S D D I + +Speaker sentences 197: swc_deu_001423 #utts: 1 +id: (swc_deu_001423-swc_deu_001423) +Scores: (#C #S #D #I) 16 4 5 0 +REF: u n D H A L f e n D i E s e n b e i d e R +HYP: u n * ******* T E I f e n T i * s e n b e i ******* d e * +Eval: D D S S S S D D D + +Speaker sentences 198: swc_deu_001424 #utts: 1 +id: (swc_deu_001424-swc_deu_001424) +Scores: (#C #S #D #I) 46 2 5 4 +REF: k r e i s ******* w a H l ******* * v o r s c h l a g u n d e i n e l a n d e s l i s t e * U n T e r z e i C H N e n +HYP: k r e i s w a * l F v o r s c h l a g u n d e i n e ******* l a n d e s l i s t e R * n D e r z e i * * T e n +Eval: I D I I D I D S D D S + +Speaker sentences 199: swc_deu_001425 #utts: 1 +id: (swc_deu_001425-swc_deu_001425) +Scores: (#C #S #D #I) 108 12 29 4 +REF: E I n E u m s e * T z u n g d e r s a g e i n F o R m E I n e s f Ü n f z e H n t e i l I g e n l i e D e r * z Y k l U s z w e I t A U S e n D a c H t w u r d e p r e U S s l E R s k R a b * A t i n * e I N e r B E a R b e I t u n g v o n h o R s t h a W e m a N n +HYP: * A n * ******* u m s e R z u n g d e r ******* s a g e i n V o * m * A n e s f ** n f z e I n t e i l * g e n l i e e r T z I k l * s ******* z w e * ******* t * * e n * a c * t w u r d e ******* p r e * * s l * A s k * a b E R t i n N e * B e r * * a * b e * t u n g v o n h o * s t ******* h a B e m a * n +Eval: D S D D I S D S D D S D S D S I S D D D D D D S D D D D D D S D I S I D S D D D D D D S D + +Speaker sentences 200: swc_deu_001426 #utts: 1 +id: (swc_deu_001426-swc_deu_001426) +Scores: (#C #S #D #I) 24 3 7 0 +REF: W i e d i e F o l G e N d e t a b e L l e d a R s t e L L t +HYP: * i e d i e V o l * e d e R t a b e * l e ******* d a * s t e * * t +Eval: D S D S S D D D D D + +Speaker sentences 201: swc_deu_001427 #utts: 1 +id: (swc_deu_001427-swc_deu_001427) +Scores: (#C #S #D #I) 12 2 4 1 +REF: z u m s t r O M f l U S s b * e I +HYP: z u m s t r U N f l * * s ******* b A e * +Eval: S S D D D I D + +Speaker sentences 202: swc_deu_001428 #utts: 1 +id: (swc_deu_001428-swc_deu_001428) +Scores: (#C #S #D #I) 39 1 12 2 +REF: d e M b u n d e s w a H L l e i t e r b i s * z u m s i E B E N U N D N e U n z i g s t e * t a G +HYP: d e * b u n d e s w a * * l e i t e r b i s T z u m s i * * * * * * * * e * n z i g s t e N t a R +Eval: D D D I D D D D D D D D D I S + +Speaker sentences 203: swc_deu_001429 #utts: 1 +id: (swc_deu_001429-swc_deu_001429) +Scores: (#C #S #D #I) 29 5 11 1 +REF: V o L L J Ä H r i * G g e w O r d e n E d E u T s C h e n i c h t m i t w Ä H L e n +HYP: * o * * * R E r i C H K g e w * r d e n * d * u * s * h e n i c h t m i t w ** * E e n +Eval: D D D D S S I S S D D D D D D D S + +Speaker sentences 204: swc_deu_001430 #utts: 1 +id: (swc_deu_001430-swc_deu_001430) +Scores: (#C #S #D #I) 23 8 9 0 +REF: a u s f Ü H R U n G m u s S e I n g u T e r Q U A R t e R b A C K i n +HYP: a u s f ** * I O n * m u s T e * n ******* g u S e r * * K O t e * b * E G i n +Eval: D D S S D S D D S D D S S D D S S + +Speaker sentences 205: swc_deu_001431 #utts: 1 +id: (swc_deu_001431-swc_deu_001431) +Scores: (#C #S #D #I) 28 3 6 3 +REF: v e r G L e i c h * b a R E n z * a H l E n w e r t u m * g e W a n d e L T +HYP: v e r K I e i c h P b a * * n z E a * l * n w e r t u m N g e a n d e * * +Eval: S S I D D I D D I S D D + +Speaker sentences 206: swc_deu_001432 #utts: 1 +id: (swc_deu_001432-swc_deu_001432) +Scores: (#C #S #D #I) 20 2 3 1 +REF: b e t r a C h t e ******* T e A L l g e m e i n h e i T +HYP: b e t r a * h t e D e * E l g e m e i n h e i * +Eval: D I S D S D + +Speaker sentences 207: swc_deu_001433 #utts: 1 +id: (swc_deu_001433-swc_deu_001433) +Scores: (#C #S #D #I) 39 3 6 1 +REF: u n t e r s C h i E D l i c h e a u F f a S s U n g e n g a b e s n u r d a r * Ü b e r +HYP: u n t e r s * h i * T l i c h e a u * f a * s I n g e n g a b ******* e s ******* n u r d a r I E b e r +Eval: D D S D D S D D I S + +Speaker sentences 208: swc_deu_001434 #utts: 1 +id: (swc_deu_001434-swc_deu_001434) +Scores: (#C #S #D #I) 90 11 13 6 +REF: d o l l b e i m b u n D e s ******* l i g i s t e n b O r U S s I A D o r t m u n d * n a c h ******* F o l g e r d e s u n m i T t E l ******* b a r z * U v o r z u r Ü c k G e t ******* r e t E N e n T r A I n E R s j Ü r G e n r Ö b e r +HYP: d o l l b e i m b u n * e s l i g i s t e n b * r * * s * E R T o r t m u n d T n a c h V o l g e r d e s u n m i * t * l b a r z O v o r T z u r Ü c k * e t r e t * * e n * r * E n A S s j I r * e n r Ü b e r +Eval: D I D D D D S S S I I S D D I I S S D I D D D D S S S S D S + +Speaker sentences 209: swc_deu_001435 #utts: 1 +id: (swc_deu_001435-swc_deu_001435) +Scores: (#C #S #D #I) 19 2 10 0 +REF: n E u n z E h n H U N d E R T A c h t u n D a C h t z i g +HYP: n * u n z * h n ******* * * A d * * * * c h t u n a * h t z i g +Eval: D D D D D S D D D D S D + +Speaker sentences 210: swc_deu_001436 #utts: 1 +id: (swc_deu_001436-swc_deu_001436) +Scores: (#C #S #D #I) 14 2 3 3 +REF: F r e i E n e n ******* z * Y k l O p * Ä d i e +HYP: * r e i * n e n z I G k l * p E T d i e +Eval: D D I I S D I S + +Speaker sentences 211: swc_deu_001437 #utts: 1 +id: (swc_deu_001437-swc_deu_001437) +Scores: (#C #S #D #I) 54 7 9 0 +REF: d e r P H o t O s t r o m i s T Ü b e r V i e l e g r Ö s s e n o r D n u n G e n l I n E a r z u m l i c h t e I n F a L l +HYP: d e r ******* * V o t * s t r o m i s * Ü b e r F i e l e ******* g r U s s e n o r n u n * e n l E n * a r T z u m l i c h t e * n V a * l +Eval: D D S D D S D S S D S D S D S D + +Speaker sentences 212: swc_deu_001438 #utts: 1 +id: (swc_deu_001438-swc_deu_001438) +Scores: (#C #S #D #I) 35 3 11 0 +REF: d a s h A t T E f Ü R k l e i n e p A r t e i E n g r o S s e a u s w i r k u N G E n +HYP: d a s ******* h * t D V f Ü * ******* k l e i n e p * r t e i * n g r o * s e ******* a u s w i r k u * * * n +Eval: D D S S S D D D D D D D D D + +Speaker sentences 213: swc_deu_001439 #utts: 1 +id: (swc_deu_001439-swc_deu_001439) +Scores: (#C #S #D #I) 22 1 6 3 +REF: i s T d I e i t e r a t i * V e * t i E F e n s u * c H E +HYP: i s * d * e i t e r a t i E F e R t i * * e n s u G c * * +Eval: D D I S I D D I D D + +Speaker sentences 214: swc_deu_001440 #utts: 1 +id: (swc_deu_001440-swc_deu_001440) +Scores: (#C #S #D #I) 36 1 6 5 +REF: d i e s k Ö N N e n z u m b e i s * p * i E l k o n d e n s a * * t * o r e n s e i n +HYP: d i e s ******* k ** * R e n ******* z u m ******* b e i s H p B i * l k o n d e n s a E R t H o r e n s e i n +Eval: D D D S D D I I D I I I + +Speaker sentences 215: swc_deu_001441 #utts: 1 +id: (swc_deu_001441-swc_deu_001441) +Scores: (#C #S #D #I) 49 8 5 6 +REF: a l * S d i e k * u r s a u f k u b A h a l * T e n d e n s o * W J E t i s c h E n s c h I F F e * a b * D r e H t e n +HYP: a l D T d i e k O u r s a u f k u b E R h a l K G e n d e n s o R I D t i s c h * n s c h * * * e R a b P T r e * t e n +Eval: I S I S S I S I S S S D D D D I I S D + +Speaker sentences 216: swc_deu_001442 #utts: 1 +id: (swc_deu_001442-swc_deu_001442) +Scores: (#C #S #D #I) 78 5 28 1 +REF: b u n d e s t a g S w a H L n E U N z E H N H u n d e r T d r e i u n d f Ü N f z i G W U r D e E r s T m a l s n A c h e i n e m v o m b u n d e s t a * g s e L B s t E R L a S s E N e n g e s e t Z +HYP: b u n d e s t a g w a * * ******* n * * T z * * * ******* * u n d e r * ******* d r e i u n d f ** M f z i * * V r * e * r s * m a l s n * c h e i n e m v o m ******* b u n d e s t a I g s e * * s t * * * a L s * * e n g e s e t * +Eval: S D D D D D S D D D D D D D D S D D S D D D D D I D D D D D S D D D + +Speaker sentences 217: swc_deu_001443 #utts: 1 +id: (swc_deu_001443-swc_deu_001443) +Scores: (#C #S #D #I) 27 5 9 1 +REF: b u n d e S w A H L g E S e T z V i e L f A c h g e ******* Ä n D e r t w o r d e N +HYP: b u n d e w * E I g * * e * z F i e * f * c h ******* g e I n * e r t w o r d e * +Eval: S D S S D D D S D D D I S D D + +Speaker sentences 218: swc_deu_001444 #utts: 1 +id: (swc_deu_001444-swc_deu_001444) +Scores: (#C #S #D #I) 27 7 4 0 +REF: e r Ü b e r l a g e r t D e n P H o t O s t R o m u n d t r Ä G T +HYP: e r I b e r l a g e r t * e n * V o t U s t * o m E u n d ******* t r E I K +Eval: S D D S S D S D S S S + +Speaker sentences 219: swc_deu_001445 #utts: 1 +id: (swc_deu_001445-swc_deu_001445) +Scores: (#C #S #D #I) 38 3 5 3 +REF: T r O t Z i n t * e g r a t ******* i o * N d e r b e i d e n d e u t S c H e n s t A a t e n +HYP: D r * t S i n t I e g r a t i o U M d e r b e i d e n ******* d e u t * c * e n s t * a t e n +Eval: S D S I I I S D D D D + +Speaker sentences 220: swc_deu_001446 #utts: 1 +id: (swc_deu_001446-swc_deu_001446) +Scores: (#C #S #D #I) 18 1 6 1 +REF: b e r L i * n e R W Ü H l m Ä U s e n s t a T t +HYP: b e r * i E n e * * Ü * l m ** E s e n s t a * t +Eval: D I D D D D S D + +Speaker sentences 221: swc_deu_001447 #utts: 1 +id: (swc_deu_001447-swc_deu_001447) +Scores: (#C #S #D #I) 11 3 6 1 +REF: o F f i * z I e L L e f Ü H r U N G E n +HYP: o A f i T z * e * e f Ü L r * * * * n +Eval: S I D D S S D D D D + +Speaker sentences 222: swc_deu_001448 #utts: 1 +id: (swc_deu_001448-swc_deu_001448) +Scores: (#C #S #D #I) 44 3 10 2 +REF: b E i d e r v e r h Ä L T n I s ******* w a H l w i R D z u s * Ä t Z l i c h d i e e i n h a l t u n g d e r +HYP: b * i ******* d e r ******* v e r h ** * E n * s w a * l ******* w i * T z u s E R t * l i c h d i e e i n h a l t u n g d e r +Eval: D D D D D S D I D D D S I S D + +Speaker sentences 223: swc_deu_001449 #utts: 1 +id: (swc_deu_001449-swc_deu_001449) +Scores: (#C #S #D #I) 32 5 8 1 +REF: w i e w e n i G D I E i n S U l a n e r n o c h A m p U l * s d e r z e i t +HYP: w i e ******* w e n i * ******* * C T i n O l a n e r ******* n o c h * m ******* p E l T s d e r ******* z e i t +Eval: D D D D S S S S D D D S I D + +Speaker sentences 224: swc_deu_001450 #utts: 1 +id: (swc_deu_001450-swc_deu_001450) +Scores: (#C #S #D #I) 53 3 11 5 +REF: J e ******* d O c H e t w * A d i e d u R c h f Ü H R u N g * * v o n w a H l ******* w e r b u n g a U f k o s t e n d e S s t A a t e s +HYP: I e d * c * e t w V R d i e d u * c h f ** * I u * g E N v o n w a * l w e r b u n g a * f ******* k o s t e n d e * s t * a t e s +Eval: S I D D I S D D D S D I I D I D D D D + +Speaker sentences 225: swc_deu_001451 #utts: 1 +id: (swc_deu_001451-swc_deu_001451) +Scores: (#C #S #D #I) 18 1 5 0 +REF: d a s n i C h T I m G r u n D g e s e t z +HYP: d a s ******* n i * h * * m * r u n g e s e t z +Eval: D D D D D S + +Speaker sentences 226: swc_deu_001452 #utts: 1 +id: (swc_deu_001452-swc_deu_001452) +Scores: (#C #S #D #I) 32 4 2 1 +REF: h e i m a t v e r t r i e b e n u n d H Ä u s L i c * H E g e w a l T +HYP: h e i m a t v e r t r i e b e n u n d * R u s T i c S I G g e w a l * +Eval: D S S I S S D + +Speaker sentences 227: swc_deu_001453 #utts: 1 +id: (swc_deu_001453-swc_deu_001453) +Scores: (#C #S #D #I) 36 2 5 3 +REF: u n d s p * e i c h e r E i H n i n E i n e R w a * R t e s C h l a n g e a b * +HYP: u n d s p B e i c h e r * i E n i n * i n e * ******* w a C K t e s * h l a n g e a b P +Eval: I D S D D D I S D I + +Speaker sentences 228: swc_deu_001454 #utts: 1 +id: (swc_deu_001454-swc_deu_001454) +Scores: (#C #S #D #I) 98 9 17 8 +REF: o * r I g I n a l t * o n ******* b Ä n d e r u n d d i E d o k U m e n t a * t ******* i o n d e S s t u * d i o s W u r d e N n e U n z e h n h u n d e r t z ******* w E I u n D s I e b * z i g i n d A S s i E m e n s A R c h i V Ü B e r s t e L l t +HYP: o U r E g E n a l t H o n b E n d e r u n d d i * d o k * m e n t a R t i o n ******* d e * ******* s t u R d i o s * u r d e * ******* n e I n z e h n h u n d e r t ******* z w U O u n * s * e b T z i g i n ******* d E R s i * m e n s * E c h i * Ü * e r s t e * l t +Eval: I S S I I S D D I I D D D I D D D S D I S S D D I D S S D D S D D D + +Speaker sentences 229: swc_deu_001455 #utts: 1 +id: (swc_deu_001455-swc_deu_001455) +Scores: (#C #S #D #I) 39 4 5 2 +REF: s o m Ü S s e n a u F e i n e m s t R a t e g I s C h e n r * A k E t * e n u b o o t +HYP: s o m ** I s e n a u * e i n e m s t * a t e g E s * h e n r E R k * t E e n u O b o o t +Eval: D S D D S D I S D I S + +Speaker sentences 230: swc_deu_001456 #utts: 1 +id: (swc_deu_001456-swc_deu_001456) +Scores: (#C #S #D #I) 14 5 0 3 +REF: f l Ö t e n ******* s p * i * e L Ä H N l i c h e +HYP: f l Ü t e n s p B i L e E D l i c h e +Eval: S I I I S S S S + +Speaker sentences 231: swc_deu_001457 #utts: 1 +id: (swc_deu_001457-swc_deu_001457) +Scores: (#C #S #D #I) 39 3 5 5 +REF: d r a s t * i s C h m o * ******* d e r * n e E l * E k t r o n i s c h e k l a n G g e s T a l t u n G +HYP: d r a s t D i s * h ******* m o R d e r A n e * l I G k t r o n i s c h e R k l a n * g e s H a l t u n * +Eval: I D D I I I D I S S D S D + +Speaker sentences 232: swc_deu_001458 #utts: 1 +id: (swc_deu_001458-swc_deu_001458) +Scores: (#C #S #D #I) 145 10 35 7 +REF: a n S c h L i E S s e n D w U R d e N d i e s o E R m i T t e L t e m a n d a t S z * * a H l J e d e r p a r t e i n A C H d E m ******* s e L b E N v e r ******* f a H R E n e n T s p R e c h e n D D e r a n z a H l i H r E r z w e i t s t * i M M e N p r o p O r t * i O n a l a u f d i e l a n D e s l i s t e N d e R p a r t e i u n t e r v e R t e i * L t +HYP: a n * c h * i * * s e n * w * O d e * d i e ******* s o * A m i * t e * t e m a n d a t * z T S a * l I e d e r p a r t e i n * * T d I m s e * b * M v e r f a * * * n e n * s p * e c h e n * ******* T e r a n z a * l ******* i * r A r z w e i t s t E i * * e M p r o p * r t Z i * n a l a u f d i e l a n * e s l i s t e * d e * p a r t e i u n t e r v e * t e i E R t +Eval: D D D D D D S D D D S D D D I I D S D D S S I D D S I D D D D D D D S D D D S I D D S D I D D D D D I S + +Speaker sentences 233: swc_deu_001459 #utts: 1 +id: (swc_deu_001459-swc_deu_001459) +Scores: (#C #S #D #I) 29 4 8 3 +REF: * * O p f E R n d e r n a t O b o m B a R d i E R u N G u n * t e r k Ü n f t e +HYP: A U B p f * A n d e r n a t S E b o m * a * d i * * u * * u n D t e r k ** n f t e +Eval: I I S D S S S D D D D D D I D + +Speaker sentences 234: swc_deu_001460 #utts: 1 +id: (swc_deu_001460-swc_deu_001460) +Scores: (#C #S #D #I) 17 1 5 0 +REF: d e r F r e i E n e n z Y k l o p Ä D I e +HYP: d e r * r e i * n e n z U k l o p ** * * e +Eval: D D S D D D + +Speaker sentences 235: swc_deu_001461 #utts: 1 +id: (swc_deu_001461-swc_deu_001461) +Scores: (#C #S #D #I) 9 5 5 0 +REF: m i T T L e r W e i l E F I n D E n +HYP: m i * * * e r B e i l * S C H n * n +Eval: D D D S D S S S D S + +Speaker sentences 236: swc_deu_001462 #utts: 1 +id: (swc_deu_001462-swc_deu_001462) +Scores: (#C #S #D #I) 65 7 15 0 +REF: w e r w E g e n e i n e S v e r b r e c h e n s r e c h T s K R Ä f t i G z u E i n e R F r E I H e i t S s t r a F e v o n m I n d e s T e n s e i n e M +HYP: w e r w I g e n e i n e * v e r b r e c h e n s ******* r e c h s G Ö E f t i C H z u * i n e * ******* * r * * * e i t * s t r a * e v o n ******* m * n d e s * e n s e i n e * +Eval: S D D S S S S S S D D D D D D D D D D D D D + +Speaker sentences 237: swc_deu_001463 #utts: 1 +id: (swc_deu_001463-swc_deu_001463) +Scores: (#C #S #D #I) 50 4 8 1 +REF: d e r G e s c H w I n d i G k e i t S w e r t u n g e R r a N g e n d r e i * b F e i n h u N D e r t a c h t +HYP: d e r * e s c * w * n d i C k e i t Z w e r t u n g e * r a * g e n d r e i E b E e i n ******* h u * L e r t ******* a c h t +Eval: D D D S S D D I S D D S D + +Speaker sentences 238: swc_deu_001464 #utts: 1 +id: (swc_deu_001464-swc_deu_001464) +Scores: (#C #S #D #I) 24 8 1 1 +REF: L I b o r * i u s a m e r s t e n L I b o r i s a m S t A G +HYP: E b o r U i u s a m e r s t e n G E b o r i E s a m F t * E +Eval: S S I S S S S S D S + +Speaker sentences 239: swc_deu_001465 #utts: 1 +id: (swc_deu_001465-swc_deu_001465) +Scores: (#C #S #D #I) 34 9 12 2 +REF: n A C h D E m s A I n ******* T e * L A g U Ë v e R f a H r e n a u F d I e l Ä n D e r v e r t e i L t +HYP: n * T h * I m s * O n A e R * * g * Ü F v e f a * r e n a u * d * e ******* l E n * e r ******* v e r t e i R t +Eval: D S D S D S I S I D D D S S S D D D D S D D S + +Speaker sentences 240: swc_deu_001466 #utts: 1 +id: (swc_deu_001466-swc_deu_001466) +Scores: (#C #S #D #I) 31 7 6 2 +REF: r e ******* f o r m E n g o R b A T s c h O W s u n d a b * r Ü s t u n G S s c h R I T t E +HYP: r e f o r m I n g o A b E R s c h A F s u n d a b P r ** s t u n * * s c h * * L t * +Eval: I S S S S S S I D D D D D S D + +Speaker sentences 241: swc_deu_001467 #utts: 1 +id: (swc_deu_001467-swc_deu_001467) +Scores: (#C #S #D #I) 16 9 2 3 +REF: * * N U L L U n ******* p O r t e D u n D u n T e r d e R +HYP: S I E R E N A n p H r t e T u n T u n D e r ******* d e * +Eval: I I S S S S S I S S S S D D + +Speaker sentences 242: swc_deu_001468 #utts: 1 +id: (swc_deu_001468-swc_deu_001468) +Scores: (#C #S #D #I) 32 7 8 2 +REF: a n d e m W e s t l i c h E * K r Ä f t e a u f g e G e n ******* r E V O l U t I O n Ä R E R +HYP: a n d e m B e s t l i c h * E G r E f t e a u f g e D e n r * * U l * t Z U n ** * * * +Eval: S D I S S S I D D S D S S D D D D + +Speaker sentences 243: swc_deu_001469 #utts: 1 +id: (swc_deu_001469-swc_deu_001469) +Scores: (#C #S #D #I) 23 2 3 0 +REF: W i R D u n t e r a n d e r E m v e r w e n d e T +HYP: * i * T u n t e r a n d e r U m v e r w e n d e * +Eval: D D S S D + +Speaker sentences 244: swc_deu_001470 #utts: 1 +id: (swc_deu_001470-swc_deu_001470) +Scores: (#C #S #D #I) 7 4 2 3 +REF: A u s w ** I k i p ** * E d I A +HYP: * u s ******* w Ü C k i p Ä N d E R +Eval: D D I S I I S S S + +Speaker sentences 245: swc_deu_001471 #utts: 1 +id: (swc_deu_001471-swc_deu_001471) +Scores: (#C #S #D #I) 12 1 0 3 +REF: u n d k * u b a * K r i * s e +HYP: u n d k O u b a R G r i E s e +Eval: I I S I + +Speaker sentences 246: swc_deu_001472 #utts: 1 +id: (swc_deu_001472-swc_deu_001472) +Scores: (#C #S #D #I) 73 14 20 2 +REF: L e * * t z T E R w a H l a u f g r u n d e i g E n e r w A H L v o R s C h l Ä g e U n U N t e r B r O c h e n m I t m i n d e s T e n s f Ü N f a b g E o R D n e T E n v e r t R e T e n s i n D +HYP: * e N L t z S D A w a * l ******* a u f g r u n d e i g * n e r ******* w E I R v o * s * h l I g e * n * E t e r r * c h e n ******* m * t m i n d e s * e n s f ** M f a b g * o * * n e * n v e r t I e e n ******* s i n * +Eval: D I I S S S S D D D D S S S D D S D D S S D D D D D S D D D D S S S D D + +Speaker sentences 247: swc_deu_001473 #utts: 1 +id: (swc_deu_001473-swc_deu_001473) +Scores: (#C #S #D #I) 49 6 1 2 +REF: v e r B r e i t u n g i * d E o l o g I s c h E R p r o p a g a n d * A d e r s u p e r m Ä c h t e u n d +HYP: v e r P r e i t u n g i E d I o l o g E s c h * A p r o p a g a n d E R d e r s u p e r m E c h t e u n d +Eval: S I S S D S I S S + +Speaker sentences 248: swc_deu_001474 #utts: 1 +id: (swc_deu_001474-swc_deu_001474) +Scores: (#C #S #D #I) 24 4 9 2 +REF: W E B C o m i C s a u f d I e r E A l i t * Ä t Ü b e r t ******* R a g e N +HYP: * * * K o m i G s a u f d * e ******* r * * l i t H E t ** b e r t H a g e * +Eval: D D D S S D D D D I S D I S D + +Speaker sentences 249: swc_deu_001475 #utts: 1 +id: (swc_deu_001475-swc_deu_001475) +Scores: (#C #S #D #I) 36 5 4 3 +REF: a l S d e r k a l t E k R i e g s i c h F o r t ******* w Ä H r e n D z u s p i t z * * e +HYP: a l * d e r k a l t I G k L i e g ******* s i c h V o r t w ** E r e n * z u s p i t z S T e +Eval: D S S S D S I D S D I I + +Speaker sentences 250: swc_deu_001476 #utts: 1 +id: (swc_deu_001476-swc_deu_001476) +Scores: (#C #S #D #I) 51 2 17 0 +REF: s i c H E R h e i T S p e r s O n a l o d e r W A c H h u n D e n n U R s e H r s c h w i e r i g b e t R e t e n W e r D E n +HYP: s i c * * * h e i * Z p e r s * n a l o d e r ******* * E c * h u n * e n n * * ******* s e * r s c h w i e r i g b e t * e t e n * e r * * n +Eval: D D D D S D D D S D D D D D D D D D D + +Speaker sentences 251: swc_deu_001477 #utts: 1 +id: (swc_deu_001477-swc_deu_001477) +Scores: (#C #S #D #I) 23 2 2 1 +REF: d a * u E r h a f T e s b l e i b e r E c h t u n D +HYP: d a R u * r h a f e s b l e i b e r I c h t u n * +Eval: I D S S D + +Speaker sentences 252: swc_deu_001478 #utts: 1 +id: (swc_deu_001478-swc_deu_001478) +Scores: (#C #S #D #I) 21 10 8 1 +REF: E b e N s O w i e d * A s m O t i V D e r E R l Ö s u n g D U R c H +HYP: I b e * s E R w i e ******* d E R s ******* m U t i F T e r * * l E s u n g ******* * B I c * +Eval: S D S S D I S D S S S D D S D D S S D + +Speaker sentences 253: swc_deu_001479 #utts: 1 +id: (swc_deu_001479-swc_deu_001479) +Scores: (#C #S #D #I) 23 2 9 1 +REF: w e N n f Ü R n i e m a N D E n n a c h p R Ü * F b A R i s t +HYP: w e * n f Ü * ******* n i e m a * * * n n a c h p * Ü C S b * * E i s t +Eval: D D D D D D D I S D D S + +Speaker sentences 254: swc_deu_001480 #utts: 1 +id: (swc_deu_001480-swc_deu_001480) +Scores: (#C #S #D #I) 27 7 3 7 +REF: * * P R i * * * * V a * t e E r F o r s c h U n G v o n e i n r i c h t u n G e N +HYP: I S D i E K L E W a R t e ******* A r V o r s c h * n * v o n e i n r i c h t u n M e M +Eval: I I S S I I I I S I D S S D D S S + +Speaker sentences 255: swc_deu_001481 #utts: 1 +id: (swc_deu_001481-swc_deu_001481) +Scores: (#C #S #D #I) 53 6 16 2 +REF: A B g e s E H e n d a ******* v o n W Ü r d e n s e L b s T d a N N n o c h D i e E n T s p R e c h e n d e n p a l C o D E s f * e H L e n +HYP: * * g e s * * e n d a v o n B I r d e n s e * b s * d a * * ******* n o c h * i e ******* I n * s p * e c h e n d e n p a l ******* K o U T s f I e * * e n +Eval: D D D D I S S D D D D D D D S D D D S S S I D D + +Speaker sentences 256: swc_deu_001482 #utts: 1 +id: (swc_deu_001482-swc_deu_001482) +Scores: (#C #S #D #I) 29 3 3 4 +REF: s p R e c h e n b E n Ö t i * G t * e a T e m ******* l u f t l i e f * e r t +HYP: s p * e c h e n b * n Ü t i C H t D e a * e m l u f t K l i e f V e r t +Eval: D D S I S I D I S I + +Speaker sentences 257: swc_deu_001483 #utts: 1 +id: (swc_deu_001483-swc_deu_001483) +Scores: (#C #S #D #I) 26 9 8 2 +REF: * m Ö G l i c h E N s c H u t z ******* i m P F U N G E n G E g e n k R a n K H e i t e N +HYP: E m U C l i c h * * s c * u t z i m * * V O R n * * g e n k a n G R e i t e * +Eval: I S S D D D I D D S S S S D D S S S D + +Speaker sentences 258: swc_deu_001484 #utts: 1 +id: (swc_deu_001484-swc_deu_001484) +Scores: (#C #S #D #I) 24 2 7 0 +REF: s C h O n e i N E n Ä H n l i c h e n v e r s u c h g a B +HYP: s * h * n ******* e i * * n ** E n l i c h e n v e r s u c h ******* g a R +Eval: D D D D D D S D S + +Speaker sentences 259: swc_deu_001485 #utts: 1 +id: (swc_deu_001485-swc_deu_001485) +Scores: (#C #S #D #I) 81 11 15 20 +REF: * * * ******* * * * * * * * a n * * E i n e m p * * n Ü b e r g a n G o d e r p * * ******* I n * Ü b e r g a n G d U R C h D E n i N n E r E n P H o t * O e F F e k t i n e i N E n e l e k T r i s c h E n s t r o m u m w a n d e L T +HYP: R U N K G E N S T R a n A N i n e m p H E n Ü b e r g a n * o d e r p E E E n I E b e r g a n * d E S T h ******* * * n i * n * r U n * V o t U e R V e k t i n ******* e i * * n e l e k * r i s c h * n s t r o m u m w a n d e * * +Eval: I I I I I I I I I I I I I S I I D I I I S I S D S S S D D D D D S D S I S S S D D D D D D D + +Speaker sentences 260: swc_deu_001486 #utts: 1 +id: (swc_deu_001486-swc_deu_001486) +Scores: (#C #S #D #I) 20 9 16 0 +REF: b E I M m E I s T e R i n d e R s I L V e s T E R n A c h t f r e I B i T T e N +HYP: b * * * R m * A s * e * ******* i n ******* d e * ******* s * E B e s * A L n O c h t ******* f r e * * i * D e T +Eval: D D D S D S D D D D D D D S S D S S S D D D D S S + +Speaker sentences 261: swc_deu_001487 #utts: 1 +id: (swc_deu_001487-swc_deu_001487) +Scores: (#C #S #D #I) 11 3 12 1 +REF: * J a H R e n d e r b E g R i F f V A D D I N G +HYP: E L a * e n d e r ******* b I g * i * f ******* * * * * * * * +Eval: I S D S D S D D D D D D D D D D + +Speaker sentences 262: swc_deu_001488 #utts: 1 +id: (swc_deu_001488-swc_deu_001488) +Scores: (#C #S #D #I) 39 9 11 4 +REF: R a n G V e R H Ä l T n I s u n t e r d E n s t * i M m E N n o c h e i n e l o g I s c h E a b * ******* F o * L g E +HYP: * a n K F e * * I l n E s u n t e r ******* d I n ******* s t E i * m * * n o c h ******* e i n e l o g E s c h * a b P V o E R g * +Eval: D S S D D S S S D S D I D D D D S D I I S I S D + +Speaker sentences 263: swc_deu_001489 #utts: 1 +id: (swc_deu_001489-swc_deu_001489) +Scores: (#C #S #D #I) 41 5 11 2 +REF: k R a b * A t l e H n T d i e s E s a n g e b o T J e ******* d o C H m i t e n T s c H i e D e n H e i t a B +HYP: k * a b E R t l e * n * ******* d i e s I s a n g e b o * D I e d o * * m i t e n * s c * i e T e n * e i t a * +Eval: D I S D D D S D S S I D D D D S D D + +Speaker sentences 264: swc_deu_001490 #utts: 1 +id: (swc_deu_001490-swc_deu_001490) +Scores: (#C #S #D #I) 20 12 0 28 +REF: s t a n d v o m D e * R I n ******* H a L t * * S T e * H t u * * n ******* * * * * ******* * T e r ******* * * ******* * * * * * * * ******* * * +HYP: s t a n d v o m Z W e L F T E n M a R t Z S Z W e I t A u S E n Z W E L F D e r I N H E I S T H I U T +Eval: S S I S S S I S S I I S S I S S I I I I I I I I I S I I I I I I I I I I I I I I + +Speaker sentences 265: swc_deu_001491 #utts: 1 +id: (swc_deu_001491-swc_deu_001491) +Scores: (#C #S #D #I) 24 4 9 3 +REF: o R g A n i s * A t ******* i o n u n t e r ******* b r a c h D A r A U f H i n d I E +HYP: o * g E n i s E R t i o n ******* u n t e r b r a c h ******* T E r * * f * i n ******* d * * +Eval: D S I S I D I D S S D D D D D D + +Speaker sentences 266: swc_deu_001492 #utts: 1 +id: (swc_deu_001492-swc_deu_001492) +Scores: (#C #S #D #I) 31 4 5 2 +REF: v e r ******* B Ü n d E t S I n d * o d e r g a R f Ü R s i e a r b e i t e n +HYP: v e r P Ü n d I t ******* Z E n d T o d e r g a * ******* f Ü * ******* s i e a r b e i t e n +Eval: I S S D S S I D D D D + +Speaker sentences 267: swc_deu_001493 #utts: 1 +id: (swc_deu_001493-swc_deu_001493) +Scores: (#C #S #D #I) 17 6 9 4 +REF: F E s T g E l * e G t e v o L L J Ä h r i * G k * ******* e i T S A l T e R +HYP: V R s * g * l I e t e v o * * * R h r i C H k T e i * * * l D e * +Eval: S S D D I S D D D S I S I I D D D S D + +Speaker sentences 268: swc_deu_001494 #utts: 1 +id: (swc_deu_001494-swc_deu_001494) +Scores: (#C #S #D #I) 33 3 6 1 +REF: D i E E R r i c H t u n g d e r b e R l i * n e R m a u E r m Ü n d e t e n +HYP: * i * * A r i c S t u n g d e r b e * l i E n e * m a u * r A m Ü n d e t e n +Eval: D D D S S D I D D S + +Speaker sentences 269: swc_deu_001495 #utts: 1 +id: (swc_deu_001495-swc_deu_001495) +Scores: (#C #S #D #I) 23 1 2 3 +REF: e R r i c h t u n G v o n k l * Ä r ******* a n ******* l a g e n +HYP: e * r i c h t u n * v o n k l I E r a n l a g e n +Eval: D D I S I I + +Speaker sentences 270: swc_deu_001496 #utts: 1 +id: (swc_deu_001496-swc_deu_001496) +Scores: (#C #S #D #I) 34 9 9 5 +REF: a f ******* g H a n * I s t a n S u n d i m i * ******* r a * K h A T S i C H s e i t D E m e i n m a r s C H +HYP: a f g * a n D E s t a n * Z u n d ******* i m ******* i E r a R G h * E R Z i * E s e i t ******* * I m e i n m a r s * T +Eval: I D I S D S D D I I I S D S S S D S D D S D S + +Speaker sentences 271: swc_deu_001497 #utts: 1 +id: (swc_deu_001497-swc_deu_001497) +Scores: (#C #S #D #I) 46 5 6 3 +REF: d e r P H o n a * ******* T i O n * S s t R o m v o n d e n l u n g e n Ü B e r d I e b r o n c h I e n b i s +HYP: d e r * V o n a R Z i U n D s t * o m v o n d e n l u n g e n ** I e r d * e ******* b r o n c h * e n b i s +Eval: D S I I S S I S D D S D D D + +Speaker sentences 272: swc_deu_001498 #utts: 1 +id: (swc_deu_001498-swc_deu_001498) +Scores: (#C #S #D #I) 47 4 7 3 +REF: a u S s e R d e m * n a H m e n s e n d e r h Ö r s p * i E l e m i t v e r F r E m d e t E r s p * r a C h E +HYP: a u * s e d e m N n a * m e n s e n d e r h Ü r s p B i * l e m i t v e r r * m d e t * r ******* s p B r a * h R +Eval: D S I D S I D S D D D I D S + +Speaker sentences 273: swc_deu_001499 #utts: 1 +id: (swc_deu_001499-swc_deu_001499) +Scores: (#C #S #D #I) 20 2 5 2 +REF: u n D d i e g r U n d ******* m a n d a * T S k l a u s e L +HYP: u n * ******* d i e ******* g r * n d m a n d a R Z k l a u s e * +Eval: D D D D I I S S D + +Speaker sentences 274: swc_deu_001500 #utts: 1 +id: (swc_deu_001500-swc_deu_001500) +Scores: (#C #S #D #I) 50 2 8 2 +REF: k e i n e a b k E h r v o n D e n g r u n d * l a g e n d e s S o * z I A l i s m u s e i n s C h l i E S s e +HYP: k e i n e a b k * h r v o n * e n ******* g r u n d P l a g e n d e s ******* T o T z * E l i s m u s e i n s * h l i * * s e +Eval: D D D I D S I D S D D D + +Speaker sentences 275: swc_deu_001501 #utts: 1 +id: (swc_deu_001501-swc_deu_001501) +Scores: (#C #S #D #I) 43 2 7 1 +REF: M i t k o m p O n e n t e n s o W O h * l a n a l s a u c h t i e f i n d e r w a F f e +HYP: * i t k o m p R n e n t e n s o * h O l a n ******* a l s ******* a u c h ******* t i e f i n d e r ******* w a * f e +Eval: D S D S I D D D D D + +Speaker sentences 276: swc_deu_001502 #utts: 1 +id: (swc_deu_001502-swc_deu_001502) +Scores: (#C #S #D #I) 15 3 0 1 +REF: b e d e u * t u n g S v o L l w a r +HYP: b e d e u C t u n g v o R l E w a r +Eval: I S S S + +Speaker sentences 277: swc_deu_001503 #utts: 1 +id: (swc_deu_001503-swc_deu_001503) +Scores: (#C #S #D #I) 14 5 3 13 +REF: F r e i W i L L i g e h e * L F e r D e r * * * * * * * * * * * * +HYP: * r e i F i * T i g e h e N V e r ******* T e r O U G A N I S T Z I U N +Eval: D S D S I S S D S I I I I I I I I I I I I + +Speaker sentences 278: swc_deu_001504 #utts: 1 +id: (swc_deu_001504-swc_deu_001504) +Scores: (#C #S #D #I) 36 4 5 0 +REF: u m e l e k t r O N E n v o m V a l e n Z b a n d I n s l e i t u n G s b a n D +HYP: u m e l e k t r * * U n v o m ******* W a l e n S b a n d E n s l e i t u n * s b a n * +Eval: D D S D S S S D D + +Speaker sentences 279: swc_deu_001505 #utts: 1 +id: (swc_deu_001505-swc_deu_001505) +Scores: (#C #S #D #I) 33 6 6 0 +REF: a l l e R d i n g S s I n D v e r G l e i c h b a r E E F F e k t e m Ö G l i c h +HYP: a l l e * d i n g * s U n * v e r l e i c h b a r * * I V e k t e ******* m U K l i c h +Eval: D D S D S D D S S D S S + +Speaker sentences 280: swc_deu_001506 #utts: 1 +id: (swc_deu_001506-swc_deu_001506) +Scores: (#C #S #D #I) 77 9 8 6 +REF: d i e s e k o N n t e n a b e r A l s e i n g a b e * i n e i n e n f r * E Q U e n z * ******* u m s e t z * e r d i E N e n O d e r s t e u e r t e n S Y n C H R o * n m O t O r E n +HYP: d i e s e k o * n t e n a b e r * l s e i n g a b e L i n e i n e n f r I C G W e n z S u m s e t z S e r d i * * e n ******* U d e r s t e u e r t e n Z U n * * G o H n m U t U r * n +Eval: D D I I S S S I I I D D D S S S D D S I S S D + +Speaker sentences 281: swc_deu_001507 #utts: 1 +id: (swc_deu_001507-swc_deu_001507) +Scores: (#C #S #D #I) 46 2 7 3 +REF: t H o * m a s h E r m a N n s p r O d U z i e r t E z w e i t a u s e n D z w e i m i t * g r * e b e +HYP: t * o U m a s h * r m a * n s p r * d T z i e r t I z w e i ******* t a u s e n * z w e i ******* m i t K g r E e b e +Eval: D I D D D S S D D D I I + +Speaker sentences 282: swc_deu_001508 #utts: 1 +id: (swc_deu_001508-swc_deu_001508) +Scores: (#C #S #D #I) 15 2 3 2 +REF: p * * n Ü b e r g a n G t r E F f e n +HYP: p I E n U b e r g a n * ******* t r * I f e n +Eval: I I S D D D S + +Speaker sentences 283: swc_deu_001509 #utts: 1 +id: (swc_deu_001509-swc_deu_001509) +Scores: (#C #S #D #I) 15 3 2 5 +REF: d i e f * A l k * e n ******* h o R s t s * H o * W +HYP: d i e f E I l k T e n h o * s t ******* s C A o U N +Eval: I S I I D D I S I S + +Speaker sentences 284: swc_deu_001510 #utts: 1 +id: (swc_deu_001510-swc_deu_001510) +Scores: (#C #S #D #I) 50 4 8 3 +REF: a n T i * s o * W j e t I S c h E d e m O n s t r a t * i o n e n w u r d e n B l u t i g n I e d e r g E s C h l a g E N +HYP: a n i E s o U j e t * c h * d e m * n s t r a t S i o n e n w u r d e n P l u t i g n * e d e r g * s * h l a g * * +Eval: S I I S D S D D I S D D D D D + +Speaker sentences 285: swc_deu_001511 #utts: 1 +id: (swc_deu_001511-swc_deu_001511) +Scores: (#C #S #D #I) 34 3 7 2 +REF: e i * n V i e r k a n a l m i s C H p * u l t d I e n t e F Ü R k l e i n e r E +HYP: e i E n F i e r k a n a l ******* m i s * * p B u l t d * e n t e * ** V E k l e i n e r * +Eval: I S D D D I D D D S S D + +Speaker sentences 286: swc_deu_001512 #utts: 1 +id: (swc_deu_001512-swc_deu_001512) +Scores: (#C #S #D #I) 70 4 8 4 +REF: d i e s e h Ä T t E n d i e v o r w a R n z e i t e n F Ü * r e i n e n a n g r i F f a u f d i e u s a * e x * t r e m h E r a * B g e s e t Z t +HYP: d i e s e h ** * t * n d i e v o r w a * n z e i t e n V Ü E r ******* e i n e n a n g r i * f a u f d i e u Ö s ******* a R e x S t r e m h * r a R P g e s e t S t +Eval: D D D D S I D D S D I I D I S S + +Speaker sentences 287: swc_deu_001513 #utts: 1 +id: (swc_deu_001513-swc_deu_001513) +Scores: (#C #S #D #I) 35 5 1 2 +REF: w e L c h e s a m n Ä C h s t e n z u m s t a R t ******* k * n o T e n l i e G t +HYP: w e R c h e s a m n E G h s t e n z u m s t a * t k E n o D e n l i e K t +Eval: S S S D I I S S + +Speaker sentences 288: swc_deu_001514 #utts: 1 +id: (swc_deu_001514-swc_deu_001514) +Scores: (#C #S #D #I) 52 6 9 3 +REF: L a Z i o g i n G d o L l z U r Ü c K i n d I e b u n d e s ******* l i * g * A u n d w e C H s e l T e z u e i n T r A c h t +HYP: * a i o g i n * d o * l z E r Ü c * i n d * e ******* b u n d e s l i E g E R u n d ******* w e * X s e l D e z u e i n r * c h t +Eval: D S D D S D D D I I I S D D S S S D + +Speaker sentences 289: swc_deu_001515 #utts: 1 +id: (swc_deu_001515-swc_deu_001515) +Scores: (#C #S #D #I) 17 0 3 1 +REF: Ü b e r D i E s e k R a n * k h e i t +HYP: Ü b e r * i * s e k * a n G k h e i t +Eval: D D D I + +Speaker sentences 290: swc_deu_001516 #utts: 1 +id: (swc_deu_001516-swc_deu_001516) +Scores: (#C #S #D #I) 25 4 5 1 +REF: J a H r z w e i t a u s e n d f Ü n f * k R i T I s I e r t E +HYP: * a * r z w e i ******* t a u s e n d T f Ü n f E k Ü i D E s * e r t * +Eval: D D D S I S S S D D + +Speaker sentences 291: swc_deu_001517 #utts: 1 +id: (swc_deu_001517-swc_deu_001517) +Scores: (#C #S #D #I) 38 2 6 1 +REF: d i e s e a u F f A s s u n g z u R n e u t r a L i t Ä t u n t e r ******* s c h e i d e T +HYP: d i e s e a u * f * s s u n g ******* z u * ******* n e u t r a R i t E t u n t e r s c h e i d e * +Eval: D D D D D S S I D + +Speaker sentences 292: swc_deu_001518 #utts: 1 +id: (swc_deu_001518-swc_deu_001518) +Scores: (#C #S #D #I) 52 6 8 2 +REF: r I e d * l w u r d e a l s k Ü n S t L E R i S c h e r l e i t e r d e S S i e m e n S s t * u d i O s b e s t e L l T +HYP: r * e d E l w u r d e a l s k Ü n Z t A L i * c h e r ******* l e i t e r d e R * i e m e n * ******* s t H u d i E s b e s t e * l * +Eval: D I S S S S D D S D D D I S D D + +Speaker sentences 293: swc_deu_001519 #utts: 1 +id: (swc_deu_001519-swc_deu_001519) +Scores: (#C #S #D #I) 29 3 7 0 +REF: w e N n m A n d i e w E l t a l S g a n z e s B e T r a c h T E t +HYP: w e * n ******* m E n ******* d i e w Ä l t a l * g a n z e s P e * r a c h * * t +Eval: D D S D S D S D D D + +Speaker sentences 294: swc_deu_001520 #utts: 1 +id: (swc_deu_001520-swc_deu_001520) +Scores: (#C #S #D #I) 69 8 2 6 +REF: s I n d k R i t i s c h e k o m p * o n e n T e n d e s d e t O n * * A T i o * n S s Y s t e m s a b s i c h t l i c h s c h w a c h e n * T w O r * f e n +HYP: s E n d k * i t i s c h e k o m p B o n e n * e n d e s d e t U n E R D i o U n Z s I s t e m s a b s i c h t l i c h s c h w a c h e n D w U r H f e n +Eval: S D I D S I I S S I S S I S S I + +Speaker sentences 295: swc_deu_001521 #utts: 1 +id: (swc_deu_001521-swc_deu_001521) +Scores: (#C #S #D #I) 16 4 4 1 +REF: n i c h t w Ä H L b A R i s t J e ******* d o c h +HYP: n i c h t ******* w ** E R b E I i s t ******* * e d o c h +Eval: D D S S S S D D I + +Speaker sentences 296: swc_deu_001522 #utts: 1 +id: (swc_deu_001522-swc_deu_001522) +Scores: (#C #S #D #I) 35 1 3 2 +REF: e r b * o t e i n e V e r ******* e i n i g u n g d e U t s c H l a n D s a n +HYP: e r b U o t e i n e F e r e i n i g u n g d e * t s c * l a n * s a n +Eval: I S I D D D + +Speaker sentences 297: swc_deu_001523 #utts: 1 +id: (swc_deu_001523-swc_deu_001523) +Scores: (#C #S #D #I) 21 1 2 2 +REF: B e R l i * n z w e i t a u s e n D f Ü n ******* f +HYP: P e * l i E n z w e i t a u s e n * f Ü n f +Eval: S D I D I + +Speaker sentences 298: swc_deu_001524 #utts: 1 +id: (swc_deu_001524-swc_deu_001524) +Scores: (#C #S #D #I) 43 1 4 4 +REF: k e r n a b g e s t ******* * i M m t u n d * u m ******* h Ü L l e n d i e s e n e n T s p r e c h e n D +HYP: k e r n a b g e s t E i * m t u n d T u m h ** * l e n d i e s e n e n * s p r e c h e n T +Eval: I I D I I D D D S + +Speaker sentences 299: swc_deu_001525 #utts: 1 +id: (swc_deu_001525-swc_deu_001525) +Scores: (#C #S #D #I) 20 3 2 2 +REF: E r ******* z * e U g u n g v o n d Y n a m i K a u s +HYP: A r z O e L g u n g v o n d * n a m i * G a u s +Eval: S I I S D D S + +Speaker sentences 300: swc_deu_001526 #utts: 1 +id: (swc_deu_001526-swc_deu_001526) +Scores: (#C #S #D #I) 11 4 0 0 +REF: Z I m t u n d I n g W e r +HYP: S E m t u n d E n g D e r +Eval: S S S S + +Speaker sentences 301: swc_deu_001527 #utts: 1 +id: (swc_deu_001527-swc_deu_001527) +Scores: (#C #S #D #I) 18 5 4 4 +REF: * * * ******* v o n s c h W e R E r u n t E R e r n Ä H r u n G +HYP: U N G v o n D s c h I e H r u n t * * e r n ** E r u n * +Eval: I I I I S S S S D D D S D + +Speaker sentences 302: swc_deu_001528 #utts: 1 +id: (swc_deu_001528-swc_deu_001528) +Scores: (#C #S #D #I) 14 3 2 4 +REF: * n Ü s * S e n u n D g e * w Ü r * Z e n +HYP: D n E s C H e n u n * g e R w ** r T C e n +Eval: I S I S D I D I S + +Speaker sentences 303: swc_deu_001529 #utts: 1 +id: (swc_deu_001529-swc_deu_001529) +Scores: (#C #S #D #I) 12 1 3 3 +REF: r o b e R t * * f k e N n e ******* d Y +HYP: r o b e * t E R f ******* k e * n e d I +Eval: D I I D D I S + +Speaker sentences 304: swc_deu_001530 #utts: 1 +id: (swc_deu_001530-swc_deu_001530) +Scores: (#C #S #D #I) 16 1 3 0 +REF: k a m s C h l i E s S l i c h z u m +HYP: k a m ******* s * h l i * s E l i c h z u m +Eval: D D D S + +Speaker sentences 305: swc_deu_001531 #utts: 1 +id: (swc_deu_001531-swc_deu_001531) +Scores: (#C #S #D #I) 7 2 6 0 +REF: v o L l s t Ä n D I G K E i T +HYP: v o * l s t E n * * * * i * +Eval: D S D D D D S D + +Speaker sentences 306: swc_deu_001532 #utts: 1 +id: (swc_deu_001532-swc_deu_001532) +Scores: (#C #S #D #I) 24 2 0 3 +REF: s t a n D e n s i c h v o n d e n u * * s a * +HYP: s t a n T e n s i c h v o n d e n u E R E s a R +Eval: S I I S I + +Speaker sentences 307: swc_deu_001533 #utts: 1 +id: (swc_deu_001533-swc_deu_001533) +Scores: (#C #S #D #I) 26 4 3 7 +REF: a f r i k a * s * Ü d L i C h D e R s * * A h a * r * A g e ******* o r t e t +HYP: a f r i k a R s T d * i * h T e * s E R h a H r E R g e o r t e t +Eval: I I S D D S D I I S I I S I + +Speaker sentences 308: swc_deu_001534 #utts: 1 +id: (swc_deu_001534-swc_deu_001534) +Scores: (#C #S #D #I) 15 0 3 2 +REF: d i e a r m E e m E u * t e R t e * +HYP: d i e a r m * e m * u I t e * t e L +Eval: D D I D I + +Speaker sentences 309: swc_deu_001535 #utts: 1 +id: (swc_deu_001535-swc_deu_001535) +Scores: (#C #S #D #I) 15 0 1 2 +REF: s t a l i * n s e * t z t e I m +HYP: s t a l i E n s e R t z t e * m +Eval: I I D + +Speaker sentences 310: swc_deu_001536 #utts: 1 +id: (swc_deu_001536-swc_deu_001536) +Scores: (#C #S #D #I) 13 4 2 3 +REF: v e R h Ä L t * n I s ******* a u s * G l e i c H +HYP: v e I h ** * t E n E s a u s T l e i c G +Eval: S D D I S I I S S + +Speaker sentences 311: swc_deu_001537 #utts: 1 +id: (swc_deu_001537-swc_deu_001537) +Scores: (#C #S #D #I) 12 2 3 11 +REF: * * * * ******* * * * ******* P r o * s c r * i b E D G l e i C h +HYP: K L M E A U F B r o S s c r E i b * T * l e i * h +Eval: I I I I I I I I I S I I D S D D + +Speaker sentences 312: swc_deu_001538 #utts: 1 +id: (swc_deu_001538-swc_deu_001538) +Scores: (#C #S #D #I) 32 3 4 1 +REF: a m z w e i t e * j u n I z w e I t a u s e n d V i e r W u r d e N +HYP: a m z w e i t e N j u n * E z w e * ******* t a u s e n d F i e r B u r d e * +Eval: I D S D D S S D + +Speaker sentences 313: swc_deu_001539 #utts: 1 +id: (swc_deu_001539-swc_deu_001539) +Scores: (#C #S #D #I) 17 5 4 1 +REF: i n D e N b U n d e s ******* t A G n a c h r Ü C K t +HYP: i n ******* * e * b * n d e s t E R n a c h r I U G t +Eval: D D D D I S S S S S + +Speaker sentences 314: swc_deu_001540 #utts: 1 +id: (swc_deu_001540-swc_deu_001540) +Scores: (#C #S #D #I) 56 0 3 3 +REF: d i E n a * t o o s t e r w e i t e r u n g u n d d i e e i n ******* s e i t i g e * a u f k Ü n d i g u n g d e S +HYP: d i * ******* n a R t o o s t e r w e i t e r u n g u n d d i e e i n s e i t i g e R a u f k Ü n d i g u n g d e * +Eval: D D I I I D + +Speaker sentences 315: swc_deu_001541 #utts: 1 +id: (swc_deu_001541-swc_deu_001541) +Scores: (#C #S #D #I) 9 0 2 2 +REF: * ******* h I e r b e i i s T +HYP: T h * e r b e i i s * +Eval: I I D D + +Speaker sentences 316: swc_deu_001542 #utts: 1 +id: (swc_deu_001542-swc_deu_001542) +Scores: (#C #S #D #I) 41 3 12 1 +REF: d i e s E r s t e l l e k a m E n s * Ä m t L i c h e m i t G L i e d e r d e r k a p E L l E D e R +HYP: d i e s * r ******* s t e l l e k a m M n s A N m t * i c h e ******* m i t * * i e d e r ******* d e r k a p * * l * ******* * e T +Eval: D D S I S D D D D D D D D D D S + +Speaker sentences 317: swc_deu_001543 #utts: 1 +id: (swc_deu_001543-swc_deu_001543) +Scores: (#C #S #D #I) 142 6 16 4 +REF: p o t * s D a m E R a b * k o m m e n e n t ******* h i e l t z w a r a L l g e m e i n e v e r ******* e i n b a R u n G e n Ü b e R d i e k Ü n f t i g e g e m e i n s a m e v e r w a l t U n G d e r s I e g e r m Ä c h t e u n d F o R m u l i e r t e g r u n d s Ä t z e W I E d e m I l i t A r i s i e r u n G +HYP: p o t Z s T a m * * a b P k o m m e n e n t h i e l t z w a r a * l g e m e i n e ******* v e r e i n b a * u n * e n Ü b e * d i e k Ü n f t i g e g e m e i n s a m e v e r w a l t H n * d e r s * e g e r m I c h t e u n d V o * m u l i e r t e ******* g r u n d s E t z e * * B d e m * l i t * r i s i e r u n * +Eval: I S D D I I D D I D D D S D D S S D D S D D S D D D + +Speaker sentences 318: swc_deu_001544 #utts: 1 +id: (swc_deu_001544-swc_deu_001544) +Scores: (#C #S #D #I) 39 9 6 4 +REF: d a n a c h u n T E r s c H R i e b e R E i n e n v E r T r A g b e i m * B f * * C d y ******* n a m o +HYP: d a n a c h R u n * D r s c * * i e b ******* e * * i n e n v A r A r K g b e i m W I E f T Z S I d y n a m o +Eval: S D S D D D D D S S S I S S I I S S I + +Speaker sentences 319: swc_deu_001545 #utts: 1 +id: (swc_deu_001545-swc_deu_001545) +Scores: (#C #S #D #I) 19 2 4 1 +REF: e i n E w e i t e r E V a r i * a n t E m a G +HYP: e i n * w e i t e r * O W a r i J a n t * m a * +Eval: D D S S I D D + +Speaker sentences 320: swc_deu_001546 #utts: 1 +id: (swc_deu_001546-swc_deu_001546) +Scores: (#C #S #D #I) 59 4 7 3 +REF: s i e w u r d e n m o d U l a r * D u r c h * l o c h s t r e i ******* f e n g e s t e U e r t u n d d i E k l Ä n g e k o N n t e N +HYP: s i e ******* w u r d e n ******* m o d O l a r N T u r c h E l o c h s t r e i f e n ******* g e s t e Y e r t u n d d i * ******* k l I n g e k o * n t e * +Eval: D D S I S I I D S D D S D D + +Speaker sentences 321: swc_deu_001547 #utts: 1 +id: (swc_deu_001547-swc_deu_001547) +Scores: (#C #S #D #I) 56 5 4 4 +REF: d i e g r u n D m a N d a t S k l a u s e l b e * v o r * z u g t u n T e R d E n k l e i n e * n p a r t e i E n j * e n e +HYP: d i e ******* g r u n m a d a t k l a u s e l b e R v o r T z u g t u n D e * d I n ******* k l e i n e R n p a r t e i * n j I e n e +Eval: D S S S I I S D S D I D I + +Speaker sentences 322: swc_deu_001548 #utts: 1 +id: (swc_deu_001548-swc_deu_001548) +Scores: (#C #S #D #I) 36 5 8 2 +REF: a b e r t R o t z ******* d E M k e i n e w I R k l i c h e h u n g e R s n o * t h e R R s C H t +HYP: a b e r ******* t * o t z d I N k e i n e w * Ü k l i c h e R h u n g e * s n o D t ******* h e * A s * * t +Eval: D D I S S D S S D I D D S D D + +Speaker sentences 323: swc_deu_001549 #utts: 1 +id: (swc_deu_001549-swc_deu_001549) +Scores: (#C #S #D #I) 10 6 13 2 +REF: U n D D o K U m E N t * * A T i o n d E R O B J E K T E +HYP: * n * K o * G m A L t E R Z i o n d * * ******* * * * * * * * +Eval: D D S D S S S I I S S D D D D D D D D D D + +Speaker sentences 324: swc_deu_001550 #utts: 1 +id: (swc_deu_001550-swc_deu_001550) +Scores: (#C #S #D #I) 34 7 4 1 +REF: z u R V o r b e d i N g u n g k o n K r * e t e r a B r Ü s t U n G S s c h R I T t e +HYP: z u O F o r b e d i * g u n g k o n G r I e t e r a P r Ü s t E n * * s c h * L E t e +Eval: S S D S I S S D D D S S + +Speaker sentences 325: swc_deu_001551 #utts: 1 +id: (swc_deu_001551-swc_deu_001551) +Scores: (#C #S #D #I) 17 1 1 3 +REF: b u n d e s ******* t a * g * S w a H l r e c h t +HYP: b u n d e s t a R g E w a * l r e c h t +Eval: I I I S D + +Speaker sentences 326: swc_deu_001552 #utts: 1 +id: (swc_deu_001552-swc_deu_001552) +Scores: (#C #S #D #I) 32 6 6 1 +REF: E s m u s S D E m K r e i s ******* w a H L l e i t E r v o r g e l E g t w e r D E n +HYP: I s ******* m u s T * I m G r e i s w a * * l e i t A r v o r g e l I g t w e r * * n +Eval: S D S D S S I D D S S D D + +Speaker sentences 327: swc_deu_001553 #utts: 1 +id: (swc_deu_001553-swc_deu_001553) +Scores: (#C #S #D #I) 109 15 23 11 +REF: h a t m a n E i n e E m ******* p i * r I s c h e b a s I s f Ü R P s * Y c h o s o * Z i a l e p r o g R a m m e z u * r s e n k u n g d e r s e L b s t ******* m O R D r a t * e u n d z U r s t * Ä r ******* k u N g d e S s i C h * e r h E i t * s g e f Ü H L s i n d e R b e V Ö L k e r u n G +HYP: h a t m a n * i n e I m p i E r E s c h e ******* b a s E s f Ü * ******* B s Z U c h o s o T i a l e p r o g E a m m e ******* z u O r ******* s e n k u n g ******* d e r ******* s e * b s t m U T E r a t H e u n d ******* z E r ******* s t D A r k u M g ******* d e * ******* s i * h G e r h * i t Z s g e f Ü * * s i n ******* d e * ******* b e * F E k e r u n * +Eval: D S I I S D S D D S I S I S S D I D D D D I S S S I D S D I S I S D D D D I D I D D D D D D S S D + +Speaker sentences 328: swc_deu_001554 #utts: 1 +id: (swc_deu_001554-swc_deu_001554) +Scores: (#C #S #D #I) 74 7 13 3 +REF: b e i d e n e r s t e n F r e i E n p a R l A m e n T S w a H l E n W u r D e i * l i e * * s C u i m m A i n E u n z e H n h U n d e r t n e u n z i g i n s e i n e M +HYP: b e i ******* d e n e r s t e n W r e i * n ******* p a * l E m e n * Z w a * l * n * u r L e i E l i e H R s G u i m m E i ******* n * u n z e I n h * n d e r t n e u n z i g i n ******* s e i n e * +Eval: D S D D D S D S D D D S I I I S S D D S D D D + +Speaker sentences 329: swc_deu_001555 #utts: 1 +id: (swc_deu_001555-swc_deu_001555) +Scores: (#C #S #D #I) 43 2 10 0 +REF: d a m i t l a S s e n s i c h b e s t r a H l u n g S s t Ä r K e n s e H r g e n A u m e S s e N +HYP: d a m i t l a * s e n s i c h ******* b e s t r a * l u n g * s t E r * e n s e * r ******* g e n O u ******* m e * s e * +Eval: D D D D S D D D S D D D + +Speaker sentences 330: swc_deu_001556 #utts: 1 +id: (swc_deu_001556-swc_deu_001556) +Scores: (#C #S #D #I) 35 3 15 1 +REF: w E n I g E J A H r E s p * Ä t e r k a m E s z u e i n e R w e i t e r e n G r Ü n d U n G +HYP: w I n * g * ******* * * * r * ******* s p I Ä t e r k a m ******* * s T z u e i n e * w e i t e r e n ******* K r ** n d * n * +Eval: S D D D D D D D D I D D S D D S D D D + +Speaker sentences 331: swc_deu_001557 #utts: 1 +id: (swc_deu_001557-swc_deu_001557) +Scores: (#C #S #D #I) 16 2 1 4 +REF: * r a * d i o k a b A r e * t T p R e i * s +HYP: W r a R d i o k a b E r e R t p * e i L s +Eval: I I S I S D I + +Speaker sentences 332: swc_deu_001558 #utts: 1 +id: (swc_deu_001558-swc_deu_001558) +Scores: (#C #S #D #I) 34 0 9 3 +REF: B E s t Ü C k ******* t e b o * m b e r a u f D i E s t a r t ******* b a H n e n r o L l e n +HYP: * * s t Ü * k t e ******* b o U m b e r a u f * i * ******* s t a r t b a * n e n r o * l e n +Eval: D D D I D I D D D I D D + +Speaker sentences 333: swc_deu_001559 #utts: 1 +id: (swc_deu_001559-swc_deu_001559) +Scores: (#C #S #D #I) 64 5 11 4 +REF: m i t d i e s e R r E g e l u n g s o L l E i n e * f a k t * i s c h z w e i f * A c h E E i n f l u s S n a H M e * d i E s e r w Ä H l e r a u f +HYP: m i t d i e s e * ******* r I g e l u n g s o * l * i n e R f a k t U i s c h T z w e i f E R c h * * i n f l u s T n a * * e R d i * s e r ******* w ** E l e r a u f +Eval: D D S D D I I S I S D D S D D I D D D S + +Speaker sentences 334: swc_deu_001560 #utts: 1 +id: (swc_deu_001560-swc_deu_001560) +Scores: (#C #S #D #I) 14 1 4 2 +REF: b a r O C k E R k I r * c h e n ******* b a u +HYP: b a r * * k * * k Ü r S c h e n b a u +Eval: D D D D S I I + +Speaker sentences 335: swc_deu_001561 #utts: 1 +id: (swc_deu_001561-swc_deu_001561) +Scores: (#C #S #D #I) 74 7 6 5 +REF: d e r h e R v o R r a g e n D w i R K e n ******* d e n l a n d e k l a P P e n w i E d e r u m h e r ******* v o R r a g e n D e * l a n g s A m ******* f l u g ******* e i g e n s c h a f t e N +HYP: d e r h e v o * r a g e n T Z w i C G e n d e n l a n d e k l a * B e n w i * d e r u m h e r v o * r a g e n e R l a n g s * m f l u g e i g e n s c h a f t e * +Eval: S D S S S S I D S D I D S I D I I D + +Speaker sentences 336: swc_deu_001562 #utts: 1 +id: (swc_deu_001562-swc_deu_001562) +Scores: (#C #S #D #I) 77 7 10 2 +REF: m i L I t Ä r I s c h E v e r b i n d u n g s f l u G z E U g e o * d e r u m s c h u l m a s c h i N e n F Ü r d i e b * F e i n h U n d e r T n e u n v e r w e n d e t +HYP: m i * t E r * s c h * v e r b i n d u n g s f l u K z * A g e ******* o U d e r u m s c h u l m a s c h i * e n V Ü r ******* d i e b E E e i n ******* h * n d e r D n e u n ******* v e r w e n d e t +Eval: D S S D D S D S D I D S D I S D D S D + +Speaker sentences 337: swc_deu_001563 #utts: 1 +id: (swc_deu_001563-swc_deu_001563) +Scores: (#C #S #D #I) 30 6 10 3 +REF: l e i s t e t e m e D i ******* z i n I s C h E U n D P s Y c h O l o g I s c H e h * i l F e * +HYP: l e i s t e t e ******* m e * i z i n E s * h * ******* O n * ******* B s I c h E l o g E s c * e ******* h E i l * e F +Eval: D D I S D D D S D D S S S S D D I D I + +Speaker sentences 338: swc_deu_001564 #utts: 1 +id: (swc_deu_001564-swc_deu_001564) +Scores: (#C #S #D #I) 24 5 5 1 +REF: k a N N m a n d U R c h i m P f U N G e n v o r b e u * g e n +HYP: k a * * ******* m a n ******* d * E c h i m f O M R e n v o r b e u I g e n +Eval: D D D D D S S S S S I + +Speaker sentences 339: swc_deu_001565 #utts: 1 +id: (swc_deu_001565-swc_deu_001565) +Scores: (#C #S #D #I) 54 12 10 2 +REF: m A N d E n a u s b R u c h D i E s e r k R a n k H e i t n A C H e R f O l * G t e R i n f E k t i o n v e R l a n g ******* s A m e n k a N n +HYP: m * E R d I n a u s b * u c h ******* T i * s e r k * a n k * e i t E n * E R e f * l U K t e * i n f R k t i o n v e L l a n g s M m e n k a * n +Eval: D S S S D D S D D D S D S S S D I S D S S I S D + +Speaker sentences 340: swc_deu_001566 #utts: 1 +id: (swc_deu_001566-swc_deu_001566) +Scores: (#C #S #D #I) 38 5 6 2 +REF: d i E e i n e n e u t r A L i * ******* t Ä t u n t e r a L l e n u m s t Ä n D E n v o r s a H +HYP: d i * ******* e i n e ******* n e u t r * D i E t E t u n t e r a * l e n u m s t E n * n v o r s a R +Eval: D D D D S I I S D S D S S + +Speaker sentences 341: swc_deu_001567 #utts: 1 +id: (swc_deu_001567-swc_deu_001567) +Scores: (#C #S #D #I) 12 1 3 2 +REF: u n D z * i e g e n ******* h I r t E N +HYP: u n * z S i e g e n h E r t * * +Eval: D I I S D D + +Speaker sentences 342: swc_deu_001568 #utts: 1 +id: (swc_deu_001568-swc_deu_001568) +Scores: (#C #S #D #I) 84 5 11 5 +REF: d a s n e u * n z E h n h u n d e r T a c h t U n D d r e i S s i g G E g r Ü n d e t e k o m i t E E f * Ü r u n ******* a m e r i * k a n I s c h e u m t r i e b e w u r d e d a f Ü r * N u N +HYP: d a s ******* n e u I n z * h n h u n d e r * a c h t E n * d r e i * s i g * * g r Ü n d e t e k o m i t * * I f V I r u n a m e r i E k a n * s c h e u m t r i e b e w u r d e d a f E r N R u * +Eval: D I D D S D D D D D D S I S I I D S I S D + +Speaker sentences 343: swc_deu_001569 #utts: 1 +id: (swc_deu_001569-swc_deu_001569) +Scores: (#C #S #D #I) 58 9 4 7 +REF: z e * n * T r a l e d e R p r o G r e S s i V e n u n d h O r t d E s i n ******* G e n I E U r g e s t Ü t Z t ******* e n k u n * s t ******* d e n * k e n s +HYP: z e I n D r a l e d e * p r o K r e * s i * e n u n d h * r t d I s i n H e n J Ö r g e s t I t S t e n k u n Z s t d e n G k e n s +Eval: I I S D S D D D S I S S S S S S I I I I + +Speaker sentences 344: swc_deu_001570 #utts: 1 +id: (swc_deu_001570-swc_deu_001570) +Scores: (#C #S #D #I) 29 2 4 3 +REF: i n d e r D e R u * s p r Ä s I d e n t a n ******* k Ü n d i g * t e +HYP: i n d e r ******* * e * u E s ******* p r E s E d e n t a n k Ü n d i g K t e +Eval: D D D I D S S I I + +Speaker sentences 345: swc_deu_001571 #utts: 1 +id: (swc_deu_001571-swc_deu_001571) +Scores: (#C #S #D #I) 19 1 1 3 +REF: s n A C k * s * u n d v o r s * p e i s e n +HYP: s n * E k Z s T u n d v o r s H p e i s e n +Eval: D S I I I + +Speaker sentences 346: swc_deu_001572 #utts: 1 +id: (swc_deu_001572-swc_deu_001572) +Scores: (#C #S #D #I) 58 6 11 4 +REF: d e s b u n d e s w a H L g e s e t z e s b i s z u M d r e i S s i g s t e * * J u n i * z w e i t A U s e n * d e L F a u f G E g E B e N +HYP: d e s ******* b u n d e s w a * I g e s e t z e s b i s T z u N d r e i * s i g s t e N C H u n i E z w e i ******* t * O s e n O d e * * a u f * * g * * e M +Eval: D D S S S D I I S I D D S I D D D D D D S + +Speaker sentences 347: swc_deu_001573 #utts: 1 +id: (swc_deu_001573-swc_deu_001573) +Scores: (#C #S #D #I) 7 4 3 1 +REF: H E N r * I p O u s s e U R +HYP: * * A r D E p * u s s e Ö L +Eval: D D S I S D S S + +Speaker sentences 348: swc_deu_001574 #utts: 1 +id: (swc_deu_001574-swc_deu_001574) +Scores: (#C #S #D #I) 56 4 4 4 +REF: f l Ü c H t l i n g e n v o n d e r * e t H n I s C h e n m i n d e r ******* h e i t d e R s o m a l I s C h e n b a n ******* t u * +HYP: f l Ü c F t l i n g e n v o n d e r I e t n U s * h e n m i n d e r h e i t d e * ******* s o m a l E s * h e n b a n t u N +Eval: S I S S D I D D S D I I + +Speaker sentences 349: swc_deu_001575 #utts: 1 +id: (swc_deu_001575-swc_deu_001575) +Scores: (#C #S #D #I) 30 3 2 4 +REF: d i e b i * ******* p o l a r e w e l t ******* o r * D n u n G Z e m E n t i e r t +HYP: d i e b i E p o l a r e ******* w e l t o r T E n u n * S e m I n t i e r t +Eval: I I D I I S D S S + +Speaker sentences 350: swc_deu_001576 #utts: 1 +id: (swc_deu_001576-swc_deu_001576) +Scores: (#C #S #D #I) 73 2 9 14 +REF: * * * ******* * * * * * * e i n E i n t e G r i e r t e O d e r e x * t e r n a n ******* g e b r A c h t E v o R r i c h t u n G a n e i n e m n u k l e ******* a r e n w a F f e n ******* s Y s t e m +HYP: T A R A N F A N G e i n * i n t e K r i e r t e U d e r e x S t e r n a n g e b r * c h t * v o * r i c h t u n * a n ******* e i n e m n u k l e a r e n ******* w a * f e n s * s t e m +Eval: I I I I I I I I I I D S S I I D D D D D I D D I D + +Speaker sentences 351: swc_deu_001577 #utts: 1 +id: (swc_deu_001577-swc_deu_001577) +Scores: (#C #S #D #I) 32 7 4 3 +REF: s t a r t e * t E d i E h i l f s ******* o R g A n i s A t I O n l a n * G F r I s t i g E +HYP: s t a r t e R t * d i * h i l f s o * g E n i s I t Z U n l a n K P V r E s t i g * +Eval: I D D I D S S S S I S S S D + +Speaker sentences 352: swc_deu_001578 #utts: 1 +id: (swc_deu_001578-swc_deu_001578) +Scores: (#C #S #D #I) 99 5 12 3 +REF: w e N n d i e s e e * X t e r N e n e F f e k t e i n d e R r i c h t i g e n r e i H E n ******* F o l g e a u f t r e t e n u n d s i c h i N n e r h a l b s p e ******* z i f i s c H e r p a r A m e t e r b e w E g e n +HYP: w e * n ******* d i e s e e C S t e r * e n e f e k t e i n d e * ******* r i c h t i g e n r e i * * n V o l g e a u f t r e t e n u n d ******* s i c h i * n e r h a l b ******* s p e z i f i s c * e r ******* p a r E m e t e r b e w I g e n +Eval: D D I S D S D D D D I S D D D I D D S S + +Speaker sentences 353: swc_deu_001579 #utts: 1 +id: (swc_deu_001579-swc_deu_001579) +Scores: (#C #S #D #I) 104 11 11 10 +REF: z * o G d I e s * O w J e * t u n ******* i o n a u c h b e i d e N w a S S e r s t * o f F b * O m b e N u n d * n e U E n f l u g * z * e u g e N m i t i n t e r ******* k o n t i n e n t a l E R r e i c h w e i t e m i t d e n u s a g l e i c h +HYP: z U o K d * e ******* s E w * e R t u n i o n ******* a u c h ******* b e i d e * w a * * e r s t A o f P b A U m b e M u n d T n e N I n f l u g K z O e u g e * ******* m i t i n t e r k o n t i n e n t a l * L A r e i c h w e i t e m i t d e n u R s a R g l e i c h +Eval: I S D D I S D I I D D D D D I S I S S I S S I I D D I D S S S S + +Speaker sentences 354: swc_deu_001580 #utts: 1 +id: (swc_deu_001580-swc_deu_001580) +Scores: (#C #S #D #I) 16 4 8 5 +REF: * * * ******* d I e s t a D t h A T I H R w a P P e n t i e * R +HYP: P E N d * e s t a * t ******* h * E * * * E w a * B e n t i e O M +Eval: I I I I D D D D S D D D S D S I S + +Speaker sentences 355: swc_deu_001581 #utts: 1 +id: (swc_deu_001581-swc_deu_001581) +Scores: (#C #S #D #I) 39 4 2 5 +REF: d i e s e r a n s a t z * g I l T a L l g e m e i n a l s a u s * g e * W o * g E n * e R +HYP: d i e s e r a n s a t z S g E l D a * l g e m e i n a l s a u s C g e B U o R g U n D e * +Eval: I S S D I I S I S I D + +Speaker sentences 356: swc_deu_001582 #utts: 1 +id: (swc_deu_001582-swc_deu_001582) +Scores: (#C #S #D #I) 15 4 7 0 +REF: n a c h D E M z u s a M m E N b r U C h D e R +HYP: n a c h * I N z u s a * m * * b r * O h ******* T e * +Eval: D S S D D D D S D S D + +Speaker sentences 357: swc_deu_001583 #utts: 1 +id: (swc_deu_001583-swc_deu_001583) +Scores: (#C #S #D #I) 26 6 4 0 +REF: d e R O b e r l a u s I t z z W i S c h e n h O Y e r S w e r d A +HYP: d e * U b e r l a u s E t z z F i * c h e n ******* h E I e r w e r d * +Eval: D S S S D D S S S D + +Speaker sentences 358: swc_deu_001584 #utts: 1 +id: (swc_deu_001584-swc_deu_001584) +Scores: (#C #S #D #I) 29 2 0 1 +REF: d a b e i I n z w e i P h a s e n u n t e r t e i * l t +HYP: d a b e i E n z w e i F h a s e n u n t e r t e i E l t +Eval: S S I + +Speaker sentences 359: swc_deu_001585 #utts: 1 +id: (swc_deu_001585-swc_deu_001585) +Scores: (#C #S #D #I) 56 4 8 2 +REF: s c h W e D e n a n d e r e U r o p A m E i s t e r s c h a f T t e i l u n d w u r d e m I t D e r d * F b * e l f +HYP: s c h I e T e n a n d e r e * r o p R m * i s t e r s c h a f * ******* t e i l u n d w u r d e ******* m * t ******* * e r d I R b E e l f +Eval: S S D S D D D D D D D I S I + +Speaker sentences 360: swc_deu_001586 #utts: 1 +id: (swc_deu_001586-swc_deu_001586) +Scores: (#C #S #D #I) 40 5 16 1 +REF: m E I s t e r e r Ö F f n e T k R a b a T s c h L i E S s L i C H e I n E w e i t e r * e m Ö G l i C h k e i t +HYP: m * A s t e r e r ** L f n e * k * a b a * R s c h * i * * s * i * * e * n * ******* w e i t e r G e ******* m Ü K l i * h k e i t +Eval: D S D S D D D S D D D D D D D D D I D S S D + +Speaker sentences 361: swc_deu_001587 #utts: 1 +id: (swc_deu_001587-swc_deu_001587) +Scores: (#C #S #D #I) 34 3 3 4 +REF: e i n e m a u s w Ä r t * s ******* * e R F o l ** g i n w o l F s b u r G g e l a n G +HYP: e i n e m a u s w E r t Z s A e V o l Ü g i n w o l * s b u r * g e l a n * +Eval: S I I I S S I D D D + +Speaker sentences 362: swc_deu_001588 #utts: 1 +id: (swc_deu_001588-swc_deu_001588) +Scores: (#C #S #D #I) 45 5 4 4 +REF: m i t s c h * w e b u n g s ******* s u M m E R n k o N n t e n G l i S s a n ******* d i e r z * e U G t w e r d e n +HYP: m i t s c h E w e b u n g s s u * m * A n k o * n t e n K l i E s a n d i e r z R e L K t ******* w e r d e n +Eval: I I D D S D S S I I S S D + +Speaker sentences 363: swc_deu_001589 #utts: 1 +id: (swc_deu_001589-swc_deu_001589) +Scores: (#C #S #D #I) 18 2 5 1 +REF: d e r A b E R l e d i g l i C h * z e i G t e +HYP: d e r ******* * b * * A l e d i g l i * h T z e i K t e +Eval: D D D D S D I S + +Speaker sentences 364: swc_deu_001590 #utts: 1 +id: (swc_deu_001590-swc_deu_001590) +Scores: (#C #S #D #I) 35 5 8 1 +REF: G R o S s B r I t a n N i e n e i n e e R s t e w i c h t i g e v e R E i n ******* b a R u n G +HYP: * K o * s P r E t a n D i e n ******* e i n e e * s t e w i c h t i g e ******* v e * i n b a * u n * +Eval: D S D S S S D D D D S I D D + +Speaker sentences 365: swc_deu_001591 #utts: 1 +id: (swc_deu_001591-swc_deu_001591) +Scores: (#C #S #D #I) 13 5 7 2 +REF: S i E H T a u C h D A s * W I t * n e S s i n G +HYP: * i * * D a u * h ******* T E s H U t D n e * s i n * +Eval: D D D S D D S S I S S I D D + +Speaker sentences 366: swc_deu_001592 #utts: 1 +id: (swc_deu_001592-swc_deu_001592) +Scores: (#C #S #D #I) 75 7 21 0 +REF: w u r d e m i t d e M b u n d e s w a H L g e s e t z v o N n e U n z E H N h U n D E R T s E c h s u n D f Ü N f Z i g E i n e d a u E r h A f t e r E G e l u n g e I n g e f Ü h r t +HYP: w u r d e ******* m i t d e * b u n d e s w a * I g e s e t z v o * ******* n e * n z * * * ******* h * n * * * A s I c h s u n f ** M f T i g A i n e d a u * r h * f t e ******* r * * e l u n g e * n g e f Ü h r t +Eval: D D D S D D D D D D D D D D D S S S D S S S D D D D D D + +Speaker sentences 367: swc_deu_001593 #utts: 1 +id: (swc_deu_001593-swc_deu_001593) +Scores: (#C #S #D #I) 28 2 5 1 +REF: d i e a n z * a H l d E r Ü b e R h A n g m A n d a t e k a N n +HYP: d i e a n z E a * l d * r I b e * h * n g m E n d a t e k a * n +Eval: I D D S D D S D + +Speaker sentences 368: swc_deu_001594 #utts: 1 +id: (swc_deu_001594-swc_deu_001594) +Scores: (#C #S #D #I) 52 2 9 1 +REF: b E s c h l O S s d i e s e r e i n m I l i t Ä r I s c h E s e i n g r e i f e n i n d e n k o r E a * k r i E G +HYP: b * s c h l * * s d i e s e r e i n m * l i t E r * s c h * s e i n g r e i f e n i n ******* d e n k o r * a R k r i * K +Eval: D D D D S D D D D I D S + +Speaker sentences 369: swc_deu_001595 #utts: 1 +id: (swc_deu_001595-swc_deu_001595) +Scores: (#C #S #D #I) 12 2 3 2 +REF: n a * t * o v e R b I n D l i c H E +HYP: n a R t U o v e b * n T l i c * * +Eval: I I S D S D D + +Speaker sentences 370: swc_deu_001596 #utts: 1 +id: (swc_deu_001596-swc_deu_001596) +Scores: (#C #S #D #I) 14 4 1 0 +REF: k a l T E K r i e g b e E n d e t +HYP: k a l * Z I G r i e g b e I n d e t +Eval: D S S S S + +Speaker sentences 371: swc_deu_001597 #utts: 1 +id: (swc_deu_001597-swc_deu_001597) +Scores: (#C #S #D #I) 58 8 16 3 +REF: V n E U n z e H N H U n D e R t D R e i * U N d ******* n e u n z i g u n D A u s t r a l i e n s O w i E d e r Ö s t e R r e i c h I s c h E a b * l E g e r +HYP: A U n * n z e * * * * n * e t * * e i A E M d n e u n z i g u n * * u s t r a l i e n s * w i * ******* d e r Ü s t e * r e i c h * s c h * a b P l I g e r +Eval: S S D S D D D D D S D D I S S I D D D D D S D D D I S + +Speaker sentences 372: swc_deu_001598 #utts: 1 +id: (swc_deu_001598-swc_deu_001598) +Scores: (#C #S #D #I) 124 13 17 7 +REF: d a d i e s e i t a n ******* f a n g n e u n z E h n H u n d e r t n e u n u n D f Ü n f z i G d O r t h e R r s C h e n ******* d e r e V O l U t i o * n S r E g * i E R U n g u n T e r F i * ******* d e l C a s t r O e i n * e n s o Z I A l i s t i s c h e n k u r s e i n g e s C h l a g e n h a T t E +HYP: d a d i e ******* s e i t a n f a n g ******* n e u n z * h n * u n d e r t ******* n e u n u n f Ü n f z i * d * r t h e * r s * h e n d e ******* r e R U l O t i o U n Z r I g J i * * O n g u n D e r V i E d e l K a s t r U e i n D e n s o * S E l i s t i s c h e n k u r s e i n g e s * h l a g e n ******* h a * t * +Eval: D I D D D D S D D D D I D S S S I S S I D D S S S I I S S I D S S D D D D + +Speaker sentences 373: swc_deu_001599 #utts: 1 +id: (swc_deu_001599-swc_deu_001599) +Scores: (#C #S #D #I) 155 17 32 6 +REF: N a c h w e i t e r e n v e R l u s t r e I c h e n k Ä m P f e n O H N E n e N n e n S w E r t E e R F o l g e b e i d e R K r i E g s p a R t e i E n W u r d e r u n D d r e I J a H r e n A C H b e g i N n d e R a u s E I n * a n d e r s e t z u n g e i n b I s H e U t e g Ü l t i g e s w a * f * F e n ******* s t i L l s t a n D s ******* a b ******* k o m m E n a B g e s c H l O s s e n +HYP: D a c h R w e i t e r e n v e * l u s t r e * c h e n k E m * f e n * * * * U n e * n e n Z w Ö r t * e V o l g e b e i d e * G r i * g s p a * t e i * n * u r d e ******* r u n T d r e * ******* * a * r e ******* n * E R b e g i * n ******* d e * a u s * A n D a n d e r s e t z u n g ******* e i n b * s R e I t e g Ü l t i g e s w a S f E N e n s t i * l s t a n * s a b k o m m * n a P g e s c * l * s s e n +Eval: S S D D S D D D D D S D S S D S S D S D D D D D S D D D D D D S S D D D D S I D D S S I I S I D D I I D S D D + +Speaker sentences 374: voxforge_deu_000891 #utts: 1 +id: (voxforge_deu_000891-voxforge_deu_000891) +Scores: (#C #S #D #I) 22 3 4 2 +REF: m a n i s t D A b e i s ******* e H r v o R s i c h t i * G +HYP: m a n i s t ******* E R b e i ******* s e * r v o * s i c h t i C H +Eval: D S S D I D D I S + +Speaker sentences 375: voxforge_deu_000892 #utts: 1 +id: (voxforge_deu_000892-voxforge_deu_000892) +Scores: (#C #S #D #I) 64 2 6 0 +REF: d i e w e H r P f l i c h t s o L l i n d e u t s c h l a n d l e i D e r n o c h n i c h t a b g e s c h a F f t w e r D E n +HYP: d i e w e * r f l i c h t s o * l i n d e u t s c h l a n d l e i e r n o c h n i c h t a b g e s c h a * f t ******* w e r * * n +Eval: D S D S D D D D + +Speaker sentences 376: voxforge_deu_000893 #utts: 1 +id: (voxforge_deu_000893-voxforge_deu_000893) +Scores: (#C #S #D #I) 34 4 3 1 +REF: e s g I b t a u c h m i S s B r a u c h D u R c h a R b e I t ******* g e b e r +HYP: e s g E b t a u c h m i * s P r a u c h T u * c h a * b e R t g e b e r +Eval: S D S S D D S I + +Speaker sentences 377: voxforge_deu_000894 #utts: 1 +id: (voxforge_deu_000894-voxforge_deu_000894) +Scores: (#C #S #D #I) 24 3 8 0 +REF: d i e k i n d e R s i n D d a N n K R a n k G e W o R D e n +HYP: d i e ******* k i n d e * s i n * d a * n * H a n k ******* * e B o * N e n +Eval: D D D D D S D D S D S + +Speaker sentences 378: voxforge_deu_000895 #utts: 1 +id: (voxforge_deu_000895-voxforge_deu_000895) +Scores: (#C #S #D #I) 45 4 5 1 +REF: d I e T r a * G w e i t e d e r k a T a s t R o P H e s o l l v e r d e u t l i c h t w e r d E n +HYP: d * e * r a C K w e i t e d e r k a D a s t o * F e s o l l v e r d e u t l i c h t ******* w e r d * n +Eval: D D I S S S D S D D + +Speaker sentences 379: voxforge_deu_000897 #utts: 1 +id: (voxforge_deu_000897-voxforge_deu_000897) +Scores: (#C #S #D #I) 0 2 0 9 +REF: * * * ******* * * * * * Ä H +HYP: S E N R L L N D E T +Eval: I I I I I I I I I S S + +Speaker sentences 380: voxforge_deu_000898 #utts: 1 +id: (voxforge_deu_000898-voxforge_deu_000898) +Scores: (#C #S #D #I) 43 1 7 3 +REF: b e i M * o R g a n * s t r e i t s t r e i t e n O b e r S T E * v e R f a s s u n g s o R g a n e +HYP: b e i * M o * g a n D s t r e i t s t r e i t e n * b e r * * * D v e f a s s u n g s o * g a n e +Eval: D I D I D D D D I S D + +Speaker sentences 381: voxforge_deu_000899 #utts: 1 +id: (voxforge_deu_000899-voxforge_deu_000899) +Scores: (#C #S #D #I) 22 1 6 1 +REF: d a S w a g e I c h J a * z u b E z w e i f e L n +HYP: d a * ******* w a g e * c h ******* * a R z u b T z w e i f e * n +Eval: D D D D D I S D + +Speaker sentences 382: voxforge_deu_000900 #utts: 1 +id: (voxforge_deu_000900-voxforge_deu_000900) +Scores: (#C #S #D #I) 31 2 10 1 +REF: m a n s o L L t e d E N e n a u f g a r K e i N E n * f a L l t r a u E n +HYP: m a n ******* s o * * t e d * * e n a u f g a r G H e i * * n V f a * l ******* t r a u * n +Eval: D D D D D S S D D I D D D + +Speaker sentences 383: voxforge_deu_000901 #utts: 1 +id: (voxforge_deu_000901-voxforge_deu_000901) +Scores: (#C #S #D #I) 39 5 9 0 +REF: d I e Ö F f E n T l i c h e N s c h u l D e n w e r D E n n i c h T g e t I l G t w e r D e n +HYP: d * e ** E f * n l i c h e * s c h u l * e n w e r * n n i c h * g e t E l K t ******* w e r * e n +Eval: D D S D S D D D S D S S D D + +Speaker sentences 384: voxforge_deu_000902 #utts: 1 +id: (voxforge_deu_000902-voxforge_deu_000902) +Scores: (#C #S #D #I) 25 4 1 1 +REF: D a S g e l D i s t a u s G e * z A h l t w o r d e n +HYP: B a E g e l T i s t a u s K e T z * h l t w o r d e n +Eval: S S S S I D + +Speaker sentences 385: voxforge_deu_000903 #utts: 1 +id: (voxforge_deu_000903-voxforge_deu_000903) +Scores: (#C #S #D #I) 42 4 11 2 +REF: e S s o L l e n D r e i ******* h u n d e r * T t a u s e n d n E U e a R b e I T s p l Ä t Z e E n T s t E H e n +HYP: e * ******* s o * l e n * r e i h u n d e r D t a u s e n d n * O e a * b e * * s p l Ä t * e I n * s t * I e n +Eval: D D D D I I S D S D D D D S D D S + +Speaker sentences 386: voxforge_deu_000904 #utts: 1 +id: (voxforge_deu_000904-voxforge_deu_000904) +Scores: (#C #S #D #I) 40 3 10 2 +REF: d i e k Ö r P e r ******* v e R l e t z u n g k a N n a l s b e i s p i E l G e N A N n T w e r d e n * +HYP: d i e ******* k E r B e r v e * l e t z u n g k a * n a l s ******* b e i s p i * l ******* * e * * * n D w e r d e n T +Eval: D S S I D D D D D D D D D S I + +Speaker sentences 387: voxforge_deu_000905 #utts: 1 +id: (voxforge_deu_000905-voxforge_deu_000905) +Scores: (#C #S #D #I) 25 2 10 0 +REF: d i E S e G r e n z e i S t Ü B e r s c h R I T t e n W o R d e n +HYP: d i * * e * r e n z e i * t ** W e r s c h * * * t e n ******* B o * d e n +Eval: D D D D D S D D D D S D + +Speaker sentences 388: voxforge_deu_000906 #utts: 1 +id: (voxforge_deu_000906-voxforge_deu_000906) +Scores: (#C #S #D #I) 35 9 19 0 +REF: d A S S s t R a F V e R f O l G u N g s b e H Ö r d e n k e i N e n z u G R i F F A U F D A s G e l D h a b e n +HYP: d * * * ******* s t D a * B e * f E l * u * g s b e * Ü r d e n k e i * e n ******* z u * L i * * * * * E R E s ******* K e l * h a b e n +Eval: D D D D S D S D S D D D S D D D S D D D D D S S S D S D + +Speaker sentences 389: voxforge_deu_000907 #utts: 1 +id: (voxforge_deu_000907-voxforge_deu_000907) +Scores: (#C #S #D #I) 28 0 4 1 +REF: d i E i n T e r e S s e n f i n d e n k e i n G e ******* h Ö r +HYP: d i * i n * e r e * s e n f i n d e n k e i n * e h Ö r +Eval: D D D D I + +Speaker sentences 390: voxforge_deu_000908 #utts: 1 +id: (voxforge_deu_000908-voxforge_deu_000908) +Scores: (#C #S #D #I) 46 13 3 18 +REF: * P f * e i l t a * s t * e t a b U l a * t o * * r r Ü C K s c h R i * T T t a * s t * e * r Ü * C K t a * s t * e r Ü C K L Ö S C H t a * s t * e ******* * +HYP: I f W e i l t a S s t D e t a b * l a R t o H E r r Ü * G s c h * i E D t a S s t D e N r Ü G G t a S s t D e r Ü G E G I E R S t a S s t D e A +Eval: I S I I I D I I I D S D I S S I I I I S S I I S S S S S S S I I I I + +Speaker sentences 391: voxforge_deu_000909 #utts: 1 +id: (voxforge_deu_000909-voxforge_deu_000909) +Scores: (#C #S #D #I) 40 8 13 4 +REF: d e R b e ******* t r o F F e n e M u S S E I n b e r e c h t i g T E S I N T e R e * S S e * * g e l t e n D m a c h e n +HYP: d e * ******* b e t r o T C e n e ******* N u * N * A n ******* b e r e c h t i g * * * * * D e * e N H e R I g e l t e n * m a c h e n +Eval: D D I S S D S D S D S D D D D D D S D I S S I I D + +Speaker sentences 392: voxforge_deu_000910 #utts: 1 +id: (voxforge_deu_000910-voxforge_deu_000910) +Scores: (#C #S #D #I) 58 5 7 5 +REF: e i n d r i T t e r h a * T d e m G E s c h * Ä d i g * t e n F r e i w I L l i g * l e i s t * U n g e n z u k o M m e n l a S S e n +HYP: e i n d r i * t e r h a R D d e m * * s c h E I d i g K t e n V r e i w * E l i g K l e i s t D O n g e n z u k o * m e n l a * * e n +Eval: D I S D D I S I S D S I I S D D D + +Speaker sentences 393: voxforge_deu_000911 #utts: 1 +id: (voxforge_deu_000911-voxforge_deu_000911) +Scores: (#C #S #D #I) 24 2 8 0 +REF: s o n D e R n a u C H r e c h T S n e b e N d e M b i l D +HYP: s o n * e * n a u * * ******* r e c h * Z n e b e * d e * b i l T +Eval: D D D D D D S D D S + +Speaker sentences 394: voxforge_deu_000912 #utts: 1 +id: (voxforge_deu_000912-voxforge_deu_000912) +Scores: (#C #S #D #I) 37 11 16 0 +REF: S i e H A T e i n e n i c h t E r N S T l I c h G e m e i N t e W I L l E N s e r k l Ä R u n G a b G E G E B e n +HYP: * i e ******* * * R e i n e ******* n i c h t A r * * Z l S c h ******* * e m e i * t e * B Ü l * s e r k l ** E u n * a b * * P K R e n +Eval: D D D D S D S D D S S D D D D S S D S D S D D D S S S + +Speaker sentences 395: voxforge_deu_000913 #utts: 1 +id: (voxforge_deu_000913-voxforge_deu_000913) +Scores: (#C #S #D #I) 32 2 4 2 +REF: d a S m u S s t e j a * * a u f J e D e n f a L l s o k o M m e n +HYP: d a * m u * s t e j a H R a u f I e e n f a * l s o k o * m e n +Eval: D D I I S S D D + +Speaker sentences 396: voxforge_deu_000914 #utts: 1 +id: (voxforge_deu_000914-voxforge_deu_000914) +Scores: (#C #S #D #I) 34 6 10 5 +REF: m e H r e r e C l I e * n T s k Ö N N E n s i c H e i n ******* e * * i P A D r e S s e t e i L E n * +HYP: m e * r e r e K l * e I n * s k ** * * O n s i c G e i n e I P i E R r e * s e ******* t e i * * n T +Eval: D S D I D D D D S S I I I S S S D D D D I + +Speaker sentences 397: voxforge_deu_000915 #utts: 1 +id: (voxforge_deu_000915-voxforge_deu_000915) +Scores: (#C #S #D #I) 50 4 9 3 +REF: w a R d i e g Ü n S t i g e r * e E s h i e S s a l s o * s i C h z u s a M m e N n e H m e n a n s t a T t z u * +HYP: w a * d i e ******* g I n Z t i g e r V e ******* I s h i e * s a l s o R s i * h ******* z u s a * m e * n e N m e n a n s t a * t z u N +Eval: D D S S I D S D I D D D D S D I + +Speaker sentences 398: voxforge_deu_000917 #utts: 1 +id: (voxforge_deu_000917-voxforge_deu_000917) +Scores: (#C #S #D #I) 32 5 5 2 +REF: d e r S c h U l D N e R h A T S e i n e l e i s t u n g a n ******* g e b o * t e n +HYP: d e r * c h O l * e * h * E R Z e i n e ******* l e i s t u n g a n g e b o R t e n +Eval: D S D S D D S S S D I I + +Speaker sentences 399: voxforge_deu_000918 #utts: 1 +id: (voxforge_deu_000918-voxforge_deu_000918) +Scores: (#C #S #D #I) 7 0 3 2 +REF: s o d a S s e * s * +HYP: s o ******* d a * s ******* e I s F +Eval: D D D I I + +Speaker sentences 400: voxforge_deu_000919 #utts: 1 +id: (voxforge_deu_000919-voxforge_deu_000919) +Scores: (#C #S #D #I) 30 0 9 1 +REF: d I e b a T t E r i e n w a r E n S e H r s t a R K v e r ******* a l t e t +HYP: d * e ******* b a * t * r i e n w a r * n * e * r s t a * * v e r a l t e t +Eval: D D D D D D D D D I + +Speaker sentences 401: voxforge_deu_000920 #utts: 1 +id: (voxforge_deu_000920-voxforge_deu_000920) +Scores: (#C #S #D #I) 27 6 7 2 +REF: d I e s e s z * i e L W U r d e n U r t E I l ******* w E I s e E R r e i c h t +HYP: d * e s e s z H i e * V O r d e ******* n O r ******* t * A l w A L s e * * r e i c h t +Eval: D I D S S D S D D S I S S D D + +Speaker sentences 402: voxforge_deu_000921 #utts: 1 +id: (voxforge_deu_000921-voxforge_deu_000921) +Scores: (#C #S #D #I) 27 4 4 0 +REF: D i e s e w Ä H r u n g w i r D s e H r l a n g e l e b e n +HYP: T i e s e ******* w ** E r u n g w i r T s e L r ******* l a n g e ******* l e b e n +Eval: S D D S S S D D + +Speaker sentences 403: voxforge_deu_000922 #utts: 1 +id: (voxforge_deu_000922-voxforge_deu_000922) +Scores: (#C #S #D #I) 22 3 8 2 +REF: d O r T z * i T T e R n O F f e n ******* b a R s C h o n v i e L e +HYP: d * r D z E i * * e A n * A f e n b a * ******* s * h o n v i e * e +Eval: D S I D D S D S I D D D D + +Speaker sentences 404: voxforge_deu_000923 #utts: 1 +id: (voxforge_deu_000923-voxforge_deu_000923) +Scores: (#C #S #D #I) 48 7 12 9 +REF: a l S s i E g * I n G e n n * i C k t e m ** a G g i E i H r n U r g a n Z f l Ü c h t i g z * * * u * u n D d e r V a * t e * r +HYP: a l * ******* s i * ******* g E E n * e n n E i G k t e ******* m Ä a * g i * i E r n O r g a n * S f l ** c h t i g K z I U O u B u n * ******* d e r F a R t e A r +Eval: D D D D I S D I S D I D D S S D S D S I I I I D D S I I + +Speaker sentences 405: voxforge_deu_000924 #utts: 1 +id: (voxforge_deu_000924-voxforge_deu_000924) +Scores: (#C #S #D #I) 16 3 11 4 +REF: E R Z Ä H L M i * r * * m e * H R Ü b e R C H r i s t i a n +HYP: * * * ** * T * i E r T E m e M I E ** b e * ******* * * r i s t i a n +Eval: D D D D D S D I I I I S S D D D D D + +Speaker sentences 406: voxforge_deu_000925 #utts: 1 +id: (voxforge_deu_000925-voxforge_deu_000925) +Scores: (#C #S #D #I) 39 4 1 5 +REF: d e m s t * e h e N n a * t Ü ** r l i c h a u c h V E R m Ö G e n g e g e n ******* Ü * b e r +HYP: d e m s t D e h e * n a D t Ü Ö r l i c h a u c h F A M m Ö H e n g e g e n Ü E b e r +Eval: I D I I S S S S I I + +Speaker sentences 407: voxforge_deu_000926 #utts: 1 +id: (voxforge_deu_000926-voxforge_deu_000926) +Scores: (#C #S #D #I) 37 6 5 4 +REF: d i * e r e a l e l a * g e w i R D n i c h t v o L L s t Ä n d i * G a b * g e b I l D e t +HYP: d i E e r e a l e ******* l a N g e w i * E T n i c h t ******* v o * U s t E n d i C H a b P g e b E l * e t +Eval: I D I D S S D D S S I S I S D + +Speaker sentences 408: voxforge_deu_000927 #utts: 1 +id: (voxforge_deu_000927-voxforge_deu_000927) +Scores: (#C #S #D #I) 30 1 8 1 +REF: e s k a N n a u c h n o c h * v i e L s c H l I M m E R w e r d e n +HYP: e s ******* k a * n a u c h n o c h F v i e * s c * l * * m * * A w e r d e n +Eval: D D I D D D D D D S + +Speaker sentences 409: voxforge_deu_000928 #utts: 1 +id: (voxforge_deu_000928-voxforge_deu_000928) +Scores: (#C #S #D #I) 25 5 5 3 +REF: d i e p O l i t i K i n T E r e S s i e r * * * T n i c h t m e H r +HYP: d i e B p * l i t i G i n * * r e * s i e r Z S I C H n i c h t E m e * r +Eval: S D S D D D I I I S S S D + +Speaker sentences 410: voxforge_deu_000929 #utts: 1 +id: (voxforge_deu_000929-voxforge_deu_000929) +Scores: (#C #S #D #I) 59 7 7 3 +REF: i n h a l t S f r e I h e I T b e d e U t e * t d a S s D e R i n h a l T d e R v e r t r a * G l i C h E n v e R e i n b a r u n g e n * +HYP: i n h a l t Z f r e * h e R D b e d e I t e D t d a * s * e * i n h a l D d e * v e r t r a C K l i * h * n v e e i n b a r u n g e n U +Eval: S D S S S I D D D S D I S D D S I + +Speaker sentences 411: voxforge_deu_000930 #utts: 1 +id: (voxforge_deu_000930-voxforge_deu_000930) +Scores: (#C #S #D #I) 36 10 13 2 +REF: d e r S c H u L D n e r v e R l E t z ******* T e s e i N e s * o R g F A L T s p F l i C H T e n s c h u l D H a f t +HYP: d e r ******* * c * u * n e r v e * l I t z D e ******* s e i * e ******* s U o * g K V E R s p * l i * * e n ******* s c h u l T E a f t +Eval: D D D D S D S I S D D D I D S S S S D D D S D S S + +Speaker sentences 412: voxforge_deu_000931 #utts: 1 +id: (voxforge_deu_000931-voxforge_deu_000931) +Scores: (#C #S #D #I) 38 7 4 2 +REF: d i e s e S g e t r e i d e d I e n T i n * s B e s o n d e r e a l ******* s v i E H F U t t E R +HYP: d i e s e * g e t r e i d e d * e n D i n Z s P e s o n d e r e a l s F v i * V O t t * A +Eval: D D S I S I S D S S S D S + +Speaker sentences 413: voxforge_deu_000932 #utts: 1 +id: (voxforge_deu_000932-voxforge_deu_000932) +Scores: (#C #S #D #I) 51 6 9 6 +REF: * t Y p * I s c h E r w e i s e w e r d E N s t * a t i S c h e * i ******* p * A D r e S s e n v o n s E r v e R n e i n g E S e t z t +HYP: Z t Ü p B E s c h * r w e i s e w e r d * * ******* s t D a t i * c h e E i p I R r e * s e n v o n D s O r v e * n e i n g * * e t z t +Eval: I S I S D D D D I D I I I S S D S S D D D + +Speaker sentences 414: voxforge_deu_000933 #utts: 1 +id: (voxforge_deu_000933-voxforge_deu_000933) +Scores: (#C #S #D #I) 23 2 8 1 +REF: j e T z t w I r D E S S o l a n G S a m g e g l a u b * t +HYP: j e * z t ******* w * r * ******* * * Z o l a n * E a m g e g l a u b P t +Eval: D D D D D D D S D S I + +Speaker sentences 415: voxforge_deu_000934 #utts: 1 +id: (voxforge_deu_000934-voxforge_deu_000934) +Scores: (#C #S #D #I) 37 7 3 4 +REF: u n T E r s c h i E D l i c h e e r * e I G n I S s e h a b e N s I c h e r ******* e i g n e * * t +HYP: u n D A r s c h i * T l i c h e e r G e * B n * E s e h a b e M s E c h e r e i g n e R D t +Eval: S S D S I D S D S S S I I I + +Speaker sentences 416: voxforge_deu_000935 #utts: 1 +id: (voxforge_deu_000935-voxforge_deu_000935) +Scores: (#C #S #D #I) 46 7 2 2 +REF: t e R r O R V E r d Ä c h t i g e w u * r d e n n * I c h t v o r e i n g e r I c h t g e s t E l l t +HYP: t e * r * H F A r d E c h t i g e w u O r d e n n E Ä c h t v o r e i n g e r E c h t g e s t Ä l l t +Eval: D D S S S S I I S S S + +Speaker sentences 417: voxforge_deu_000936 #utts: 1 +id: (voxforge_deu_000936-voxforge_deu_000936) +Scores: (#C #S #D #I) 51 6 10 1 +REF: a u f m a c h e n d i E s t I e f E L n i c h t a u s * z i e h E n u n D w e i S s g O T T w a s n o c H a L l e s +HYP: a u f m a c h e n d i * ******* s t D e f * Ü n i c h t a u s T z i e h * n u n * w e i * s E g * E R D w a s ******* n o c * a * l e s +Eval: D D S D S I D D D S D S S S D D D + +Speaker sentences 418: voxforge_deu_000937 #utts: 1 +id: (voxforge_deu_000937-voxforge_deu_000937) +Scores: (#C #S #D #I) 50 5 8 12 +REF: i n S G e s a m T * * * * * * * * * * * * 2 3 p e r s o n e n a u s v e r s c h i e d E N e n p a R L A m e n t e n n e H m e n t e i L +HYP: i n K e s a m * D R E I U N D Z W A N Z I C p e r s o n e n a u s ******* v e r s c h i e d * * e n p a * * E m e n t e n n e * m e n t e i * +Eval: S S D I I I I I I I I I I I I S S D D D D D S D D + +Speaker sentences 419: voxforge_deu_000938 #utts: 1 +id: (voxforge_deu_000938-voxforge_deu_000938) +Scores: (#C #S #D #I) 45 8 11 3 +REF: F o r d E r u n g s R e C H t e w e r D e n D e m g l Ä U b i g e R a u S s c H l i e S s L i C H z U g e * * o r * D n e t +HYP: V o r d * r u n g s e * F t e w e r * e n ******* * e m g l E I b i g e * a u * s c * l i e * s * i * E z O g e O R o r T E n e t +Eval: S D S D S D D D S S D D D D D D S S I I I S + +Speaker sentences 420: voxforge_deu_000939 #utts: 1 +id: (voxforge_deu_000939-voxforge_deu_000939) +Scores: (#C #S #D #I) 22 1 2 4 +REF: d a S p R o b l * e m * w * U r d e b e ******* h o b e n +HYP: d a * p * o b l E e m H w V W r d e b e h o b e n +Eval: D D I I I S I + +Speaker sentences 421: voxforge_deu_000940 #utts: 1 +id: (voxforge_deu_000940-voxforge_deu_000940) +Scores: (#C #S #D #I) 43 5 6 1 +REF: f Ü r d i E e r ******* K e N n u n G v O n u n t e r B r o c h e n e r d I s k r E t e r s p R a c h e +HYP: f ** r d i * ******* e r H e I n u n * v * n u n t e r r o c h e n e r d E s k r I t e r s p * a c h e +Eval: D D D I S S D D S S S D + +Speaker sentences 422: voxforge_deu_000941 #utts: 1 +id: (voxforge_deu_000941-voxforge_deu_000941) +Scores: (#C #S #D #I) 35 3 9 1 +REF: d i E c h i n E s e n k Ö ** n N t e n s e H r V I E l w i c h t i g E R w e r d E n +HYP: d i * ******* c h i n * s e n k Ö Ü n D t e n s e * r * * F l w i c h t i g * * A w e r d * n +Eval: D D D I S D D D S D D S D + +Speaker sentences 423: voxforge_deu_000942 #utts: 1 +id: (voxforge_deu_000942-voxforge_deu_000942) +Scores: (#C #S #D #I) 36 5 17 1 +REF: d i E S e r s C H L Ü S s E l W i R D L e ******* d i g l i C h E I N e i n z i g e S m a l v e r w e n d e t +HYP: d i * * e r ******* s * * * T U s * l ******* * i * E D e d i g l i * h ******* * * R e i n z i g e * ******* m a l ******* v e r w e n d e t +Eval: D D D D D D S S D D D D S S I D D D D S D D D + +Speaker sentences 424: voxforge_deu_000943 #utts: 1 +id: (voxforge_deu_000943-voxforge_deu_000943) +Scores: (#C #S #D #I) 51 8 0 1 +REF: d a s l a n d e n T w I C k e l t e s I c h z u e i n e r m * i L I t Ä r I s c h e n g r o s s m a c h t +HYP: d a s l a n d e n w E G k e l t e s E c h z u e i n e r m E i E t E r E s c h e n g r o s s m a c h t +Eval: S S S S I S S S S + +Speaker sentences 425: voxforge_deu_000944 #utts: 1 +id: (voxforge_deu_000944-voxforge_deu_000944) +Scores: (#C #S #D #I) 33 0 3 2 +REF: e S s i n d * u n d b l e i b e N v e r b r e * c h e r b a n d e n +HYP: e * ******* s i n d T u n d b l e i b e * v e r b r e I c h e r b a n d e n +Eval: D D I D I + +Speaker sentences 426: voxforge_deu_000945 #utts: 1 +id: (voxforge_deu_000945-voxforge_deu_000945) +Scores: (#C #S #D #I) 25 2 2 0 +REF: d i e z e i t e n w e r D e n s i c h Ä n d E R n +HYP: d i e z e i t e n w e r * e n s i c h E n d * A n +Eval: D S D S + +Speaker sentences 427: voxforge_deu_000946 #utts: 1 +id: (voxforge_deu_000946-voxforge_deu_000946) +Scores: (#C #S #D #I) 50 3 4 1 +REF: d e n s t I f t i n D i e a C H s b o H r u n g e i n s c h i e b e n B i s * z u m a n s c H l a g +HYP: d e n s t E f t i n * i e a * K s b o * r u n g e i n s c h i e b e n W i s T z u m a n s c * l a g +Eval: S D D S D S I D + +Speaker sentences 428: voxforge_deu_000947 #utts: 1 +id: (voxforge_deu_000947-voxforge_deu_000947) +Scores: (#C #S #D #I) 48 10 4 14 +REF: d i e a u c h * b e i m b R o * * W s e r w * i * r K s a m w i * r D b E I s p * i E l * s ******* w e i s ******* E b e I M * F i * r E f o * * X +HYP: d i e a u c h T b e i m b * o A U R s e r w V i E r s a m ******* w i E r T b A L s p B i * l T s w e i s S b e * L V E i E r H f o G S N +Eval: I D I I S I I S D I S S S I D I I I S D S I S I S I I S + +Speaker sentences 429: voxforge_deu_000948 #utts: 1 +id: (voxforge_deu_000948-voxforge_deu_000948) +Scores: (#C #S #D #I) 18 3 7 0 +REF: d a S w a R n o c H g a R K e i n E k R i S e +HYP: d a * ******* w a * ******* n o c * g a * H e i n I k L i * e +Eval: D D D D D D S S S D + +Speaker sentences 430: voxforge_deu_000950 #utts: 1 +id: (voxforge_deu_000950-voxforge_deu_000950) +Scores: (#C #S #D #I) 26 7 7 0 +REF: d i e h a b e N O F f E N b a r z I E m L i c h G R o S s e a n g s t +HYP: d i e h a b e M A U f * M b a r ******* z * H m N i c h ******* * K o * s e ******* a n g s t +Eval: S S S D S D D S S D D S D D + +Speaker sentences 431: voxforge_deu_000951 #utts: 1 +id: (voxforge_deu_000951-voxforge_deu_000951) +Scores: (#C #S #D #I) 30 2 2 1 +REF: V i e l e v e r l i e r e n I H r e n a R b e i t s p l a t z * +HYP: F i e l e v e r l i e r e n * E r e n a * b e i t s p l a t z S +Eval: S D S D I + +Speaker sentences 432: voxforge_deu_000952 #utts: 1 +id: (voxforge_deu_000952-voxforge_deu_000952) +Scores: (#C #S #D #I) 28 2 0 9 +REF: d a * ******* f Ü * * r g I b t e s e i n e n p u n k t ******* a b * * z * U g * +HYP: d a R f Ü H E r g E b t e s e i n e n p u n k t a b P T z I O g E +Eval: I I I I S I I I I S I + +Speaker sentences 433: voxforge_deu_000953 #utts: 1 +id: (voxforge_deu_000953-voxforge_deu_000953) +Scores: (#C #S #D #I) 54 6 9 4 +REF: * ******* d i e b e i d e n s I n d * Ü B E R e i n e u n ******* s i c h E R e v e r b I n D u n g m I t E I n a n d e r I n k o n t a k t +HYP: C d i e ******* b e i d e n s E n d T ** * * * W e i n e u n s i c h * * e v e r b E n * u n g m U t D A n a n d e r * n k o n t a k t +Eval: I I D S I D D D D S I D D S D S S S D + +Speaker sentences 434: voxforge_deu_000954 #utts: 1 +id: (voxforge_deu_000954-voxforge_deu_000954) +Scores: (#C #S #D #I) 30 1 3 1 +REF: b e i ******* d e s t E C k e n t i e f i n r o t e n z a H l E n +HYP: b e i d e s t * Ä k e n t i e f i n r o t e n z a * l * n +Eval: I D S D D + +Speaker sentences 435: voxforge_deu_000955 #utts: 1 +id: (voxforge_deu_000955-voxforge_deu_000955) +Scores: (#C #S #D #I) 29 2 3 5 +REF: f Ü n * f * z e h n U H R f Ü n f * z e H n d o R f o * * n g o l f +HYP: f Ü n D f T z e h n * O E f Ü n f T z e * n d o * f o N E n g o l f +Eval: I I D S S I D D I I + +Speaker sentences 436: voxforge_deu_000956 #utts: 1 +id: (voxforge_deu_000956-voxforge_deu_000956) +Scores: (#C #S #D #I) 50 6 12 3 +REF: w i e m e * n s c h e n a U s e i n e R a n d e r * n w E l t E R s c h I e * N e n S i E I H r H e U t e u n d d o c h +HYP: w i e ******* m e I n s c h e n a * s ******* e i n e * a n d e r E n ******* w Ä l t * * s c h * e R D e n ******* Z i * * E r R e * t e u n d T d o c h +Eval: D I D D D I D S D D D I S D S D D S S D S + +Speaker sentences 437: voxforge_deu_000957 #utts: 1 +id: (voxforge_deu_000957-voxforge_deu_000957) +Scores: (#C #S #D #I) 30 4 7 0 +REF: b Ü n d I G m i t D e m h i n t e r n d e s k A m E l s a u f H Ö r t +HYP: b ** n d * * H m i t * e m h i n t e r n ******* d e s k E m I l s ******* a u f * E r t +Eval: D D D S D D S S D D S + +Speaker sentences 438: voxforge_deu_000958 #utts: 1 +id: (voxforge_deu_000958-voxforge_deu_000958) +Scores: (#C #S #D #I) 48 6 7 3 +REF: a C H d e r O b e r f Ö r s t * e R z u * * C K T e m i t D e n S c h i e f e n g R a u E n b R a u e n e i n +HYP: a * * ******* d e r U b e r f Ö r s t D e A z u N G D D e m i t * e n * c h i e f e n g * a u * n b G a u e n e i n +Eval: D D D S I S I I S S S D D D D S + +Speaker sentences 439: voxforge_deu_000959 #utts: 1 +id: (voxforge_deu_000959-voxforge_deu_000959) +Scores: (#C #S #D #I) 44 3 5 0 +REF: i c h w u n d e r e m i C H i M m E r w i e d e r Ü b e r D i e s e e r k l Ä r u n g e n +HYP: i c h ******* w u n d e r e ******* m i * G i * m A r w i e d e r Ü b e r * i e s e e r k l E r u n g e n +Eval: D D D S D S D S + +Speaker sentences 440: voxforge_deu_000960 #utts: 1 +id: (voxforge_deu_000960-voxforge_deu_000960) +Scores: (#C #S #D #I) 42 6 12 1 +REF: b E I E i n e m s Y M m e t r i s c h E n k r * Y p t O S Y s t e m W I R D a n d e R s v o r g e g a N G E n +HYP: b * A * i n e m s * * m e t r i s c h * n k r I O p t * * U s t e m * B E T a n d e * s v o r g e g a * * * n +Eval: D S D D D D I S D D S D S S S D D D D + +Speaker sentences 441: voxforge_deu_000961 #utts: 1 +id: (voxforge_deu_000961-voxforge_deu_000961) +Scores: (#C #S #D #I) 20 1 3 2 +REF: d a S i s T d o r T v e r ******* z e i c h N e t * +HYP: d a * i s * d o r D v e r z e i c h * e t E +Eval: D D S I D I + +Speaker sentences 442: voxforge_deu_000962 #utts: 1 +id: (voxforge_deu_000962-voxforge_deu_000962) +Scores: (#C #S #D #I) 26 2 8 0 +REF: g e l D i s T E I n S e H r G u t e s t a u s c H m i T t e L +HYP: g e l T i s * * A n * e * r * u t e s t a u s c * m i * t e * +Eval: S D D S D D D D D D + +Speaker sentences 443: voxforge_deu_000963 #utts: 1 +id: (voxforge_deu_000963-voxforge_deu_000963) +Scores: (#C #S #D #I) 29 3 3 5 +REF: d a S w * Ä r e w i S s e n S c h a f t l i c h n o * T w e n d * i * * g +HYP: d a * w E H r e ******* w i * s e n c h a f t l i c h n o D w e n d E i G K g +Eval: D I S D D S I S I I I + +Speaker sentences 444: voxforge_deu_000964 #utts: 1 +id: (voxforge_deu_000964-voxforge_deu_000964) +Scores: (#C #S #D #I) 31 6 6 3 +REF: n U R b e s ******* T i M M t * E s t r a * F t a t e n k o M m e n I n b e t R a c h t +HYP: n * * ******* b e s D i * N t I S s t r a P t a t e n k o * m e n ******* E n b e t H a c h t +Eval: D D D I S D S I S I S D D S S + +Speaker sentences 445: voxforge_deu_000965 #utts: 1 +id: (voxforge_deu_000965-voxforge_deu_000965) +Scores: (#C #S #D #I) 39 3 6 3 +REF: d a m i t k a N n m a n W a H r s c h e i n ******* L i C H S c h l E c h t e i * n ******* k a u f e n +HYP: d a m i t k a * n ******* m a n B a * r s c h e i n D i * * * c h l Ä c h t e i E n k a u f e n +Eval: D D S D I S D D D S I I + +Speaker sentences 446: voxforge_deu_000966 #utts: 1 +id: (voxforge_deu_000966-voxforge_deu_000966) +Scores: (#C #S #D #I) 13 4 2 1 +REF: d a ******* f Ü R w U R d e g e s o R G t +HYP: d a f I E w * O d e g e s o * K t +Eval: I S S D S D S + +Speaker sentences 447: voxforge_deu_000967 #utts: 1 +id: (voxforge_deu_000967-voxforge_deu_000967) +Scores: (#C #S #D #I) 20 5 6 2 +REF: M a N K a N n D a S S e H r * G u t v e R k a u f e * n +HYP: C a * M a I n * a * ******* D e * r E u t v e * k a u f e N n +Eval: S D S S D D D S D I S D I + +Speaker sentences 448: voxforge_deu_000968 #utts: 1 +id: (voxforge_deu_000968-voxforge_deu_000968) +Scores: (#C #S #D #I) 22 5 12 0 +REF: S O n D e R n a u c h I n d e R s t e U e R H I N T e r Z i E H u n g +HYP: * Z n * e * n a u c h ******* * n ******* d e * s t e L e * * * * e r T i * O u n g +Eval: D S D D D D D D S D D D D S S D S + +Speaker sentences 449: voxforge_deu_000969 #utts: 1 +id: (voxforge_deu_000969-voxforge_deu_000969) +Scores: (#C #S #D #I) 34 1 1 6 +REF: d a r Ü b e r r * e ******* d e T d i e p a s t o * r i * * n u n d * r e d e t +HYP: d a r I b e r r I e d e * d i e p a s t o G r i N E n u n d T r e d e t +Eval: S I I D I I I I + +Speaker sentences 450: voxforge_deu_000970 #utts: 1 +id: (voxforge_deu_000970-voxforge_deu_000970) +Scores: (#C #S #D #I) 20 5 15 0 +REF: d e N S c h A l T e R I n D e n D R I T T e N G a n G s T e L L e n +HYP: d e I * c h * l * e * ******* * n * e n * * * E e * D a n * ******* s D e * * e n +Eval: S D D D D D D D D D D S S D S D D S D D + +Speaker sentences 451: voxforge_deu_000971 #utts: 1 +id: (voxforge_deu_000971-voxforge_deu_000971) +Scores: (#C #S #D #I) 38 4 3 3 +REF: a u f d e n e r s t e n b l I C K s c h e i n T d a s u n ******* g e W Ö ** H n l * i c h +HYP: a u f d e n e r s t e n b l * E G s c h e i n * ******* d a s u n g e V Ö Ü R n l E i c h +Eval: D S S D D I S I S I + +Speaker sentences 452: voxforge_deu_000972 #utts: 1 +id: (voxforge_deu_000972-voxforge_deu_000972) +Scores: (#C #S #D #I) 46 4 6 2 +REF: d e r d o L l a R w i * r D n i c h t m e h r A L s w Ä H r u n g a k z * e p t i e r t w e r d e n +HYP: d e r d o * l a * w i E r T E n i c h t ******* m e h r * I s w ** E r u n g a k z I e p t i e r t ******* w e r d e n +Eval: D D I S S D D S D S I D + +Speaker sentences 453: voxforge_deu_000973 #utts: 1 +id: (voxforge_deu_000973-voxforge_deu_000973) +Scores: (#C #S #D #I) 55 8 6 2 +REF: i H R e n k o * P f f e s t g e g e n d e n h a l s D e r J Ü n g e r e n d a n N k Ü S s t * E s i e d E n v a t e r +HYP: i * L e n k o U T f f e s t g e g e n d e n h a l s ******* T e r ******* * I n g e r e n ******* d a n M k Ü * s t D I S s i e d I n v a t e r +Eval: D S I S D S D D S D S D I S S S + +Speaker sentences 454: voxforge_deu_000974 #utts: 1 +id: (voxforge_deu_000974-voxforge_deu_000974) +Scores: (#C #S #D #I) 23 1 4 0 +REF: d a S w U r d e n i c h t w a H R g e n o m m e n +HYP: d a * w O r d e ******* n i c h t w a * * g e n o m m e n +Eval: D S D D D + +Speaker sentences 455: voxforge_deu_000975 #utts: 1 +id: (voxforge_deu_000975-voxforge_deu_000975) +Scores: (#C #S #D #I) 20 5 4 1 +REF: m a n h A t D A S d a m * A L s v o r g e l E S e n +HYP: m a n h * t ******* T E R d a m E I T s v o r g e l * * e n +Eval: D D S S S I S S D D + +Speaker sentences 456: voxforge_deu_000976 #utts: 1 +id: (voxforge_deu_000976-voxforge_deu_000976) +Scores: (#C #S #D #I) 50 7 17 2 +REF: b e i b e s o n d e r S w e r t ******* V o L L E n s a c h E n * i s T d i E G R e n z e N i E D r i g e R A L s d e r w a R E n W e r t +HYP: b e i ******* b e s o n d e r * w e r t W o * * * n ******* s a c h U n G i s * d i * ******* * K e n z e ******* M i * T r i g e * * I s d e r ******* w a * * n M e r t +Eval: D D I S D D D D S I D D D D S D S D S D D S D D D S + +Speaker sentences 457: voxforge_deu_000977 #utts: 1 +id: (voxforge_deu_000977-voxforge_deu_000977) +Scores: (#C #S #D #I) 17 5 7 4 +REF: * d A s m U S S z U r Ü C k G e * ******* z A H l t w e * r d e n +HYP: N d * s ******* m * * O T z * r E I k * e T z * E l t w e H r d e n +Eval: I D D D D S S D S S D I I D S I + +Speaker sentences 458: voxforge_deu_000978 #utts: 1 +id: (voxforge_deu_000978-voxforge_deu_000978) +Scores: (#C #S #D #I) 37 4 3 1 +REF: z w i s c h e n G l Ä U b i g e r u n d s c h U l d * n e R h e r G e l e i t e t +HYP: z w i s c h e n * l O L b i g e r u n d ******* s c h O l d E n e * h e r e l e i t e t +Eval: D S S D S I D S + +Speaker sentences 459: voxforge_deu_000979 #utts: 1 +id: (voxforge_deu_000979-voxforge_deu_000979) +Scores: (#C #S #D #I) 31 7 9 2 +REF: e i n a b * S O L u * t e s r e c h t W U R D e R e c h t S W i D r i G v E R l e T Z t +HYP: e i n a b Z U N M u N t e s r e c h t * * * I e * e c h t * Z i E r i * v * L l e * * t +Eval: I S S S I D D D S D D S S D D S D D + +Speaker sentences 460: voxforge_deu_000980 #utts: 1 +id: (voxforge_deu_000980-voxforge_deu_000980) +Scores: (#C #S #D #I) 36 4 2 2 +REF: m a n b * r a u c h t n I c h t a n d e n z u * f A L l z u G l a u b e N +HYP: m a n b E r a u c h t D n E c h t a n d e n z u O f * * l z u K l a u b e M +Eval: I S S I D D S S + +Speaker sentences 461: voxforge_deu_000981 #utts: 1 +id: (voxforge_deu_000981-voxforge_deu_000981) +Scores: (#C #S #D #I) 53 3 6 3 +REF: z Ä r T l i c h e n w e s e n n u r E n t ******* f * a l * t e n w o m a n i H R l i e b e b o t v o r h a r t e n +HYP: z E r * l i c h e n w e s e n n u r I n t f W a l E t e n w o ******* m a n i * * E l i e b e ******* b o t v o r ******* h a r t e n +Eval: S D S I I I D D D S D D + +Speaker sentences 462: voxforge_deu_000982 #utts: 1 +id: (voxforge_deu_000982-voxforge_deu_000982) +Scores: (#C #S #D #I) 49 5 4 9 +REF: B e * * ******* z * Ü G l i c H d e R b e * w e i s * l a s t u n D d e R h a f t u n g f * Ü r h I l f S p e r s * * o n e n +HYP: D e R T z I Ü K l i c * d e * b e R w e i s C l a s t u n * d e * h a f t u n g f I E r h E l f p e r s C H o n e n +Eval: S I I I I S D D I I D D I S S S I I + +Speaker sentences 463: voxforge_deu_000983 #utts: 1 +id: (voxforge_deu_000983-voxforge_deu_000983) +Scores: (#C #S #D #I) 37 4 12 1 +REF: b e i d e R n o R m a L e N n U t z u n g * G i B T E s D i e v o L l e b a n d B r e i t e +HYP: b e i d e * I n o * m a * e * L n O t z u n g E * i * * ******* * s * i e v o * l e ******* b a n d r e i t e +Eval: D S D D D S S I D D D D D D D D S + +Speaker sentences 464: voxforge_deu_000984 #utts: 1 +id: (voxforge_deu_000984-voxforge_deu_000984) +Scores: (#C #S #D #I) 43 4 10 1 +REF: a b e r w i e i s T d i E S e s p r o b l e m i m G l o b a l E N m a S S s t a B z U l * Ö S e n +HYP: a b e r w i e i s * d i * * e s ******* p r o b l e m i m K l o b a l * * m a * * s t a P T z * O l E Ö * e n +Eval: D D D D S D D D D S S D S I D + +Speaker sentences 465: voxforge_deu_000985 #utts: 1 +id: (voxforge_deu_000985-voxforge_deu_000985) +Scores: (#C #S #D #I) 32 4 10 4 +REF: d a s e i g e n E w e * ******* B l o * g e r h Ä L t p O T e n T I e L L m e H r l e s e r * +HYP: d a s e i g e n * ******* w e R P l o K g ******* e r h ** * t p * D e n * Z e * R m e * r ******* l e s e r T +Eval: D D I I S I D D D D S D S D S D D I + +Speaker sentences 466: voxforge_deu_000986 #utts: 1 +id: (voxforge_deu_000986-voxforge_deu_000986) +Scores: (#C #S #D #I) 30 3 8 3 +REF: d a s f r e m ******* d e W e * b l o * g S i E H t n o c h b E L e b t e r A U s +HYP: d a s ******* f r e m d e ******* V e R b l o K g * i * * t n o c h ******* b * I e b t e r * O s +Eval: D I D S I I D D D D D S D S + +Speaker sentences 467: voxforge_deu_000987 #utts: 1 +id: (voxforge_deu_000987-voxforge_deu_000987) +Scores: (#C #S #D #I) 30 4 6 2 +REF: e i ******* n e n e U e b e s t * i M m u n g i s t e R l a S S e n W O r D e n +HYP: e i n e ******* n e * e b e s t H i * m u n g i s t D e * l a * * e n B U r T e n +Eval: I D D I D S D D D S S S + +Speaker sentences 468: voxforge_deu_000988 #utts: 1 +id: (voxforge_deu_000988-voxforge_deu_000988) +Scores: (#C #S #D #I) 23 3 3 3 +REF: d a r * * a u f I s T H I n ******* g e w i E S e n w o r d e n +HYP: d a r O U a u f E s * T E n g e w i * * e n w o r d e n +Eval: I I S D S S I D D + +Speaker sentences 469: voxforge_deu_000989 #utts: 1 +id: (voxforge_deu_000989-voxforge_deu_000989) +Scores: (#C #S #D #I) 31 3 5 0 +REF: d I e b e V Ö L k E r u n g i s t g a n z m a s s i V v e r a R m t +HYP: d * e b e F Ü R k * r u n g i s t g a n z ******* m a s s i * v e r a * m t +Eval: D S S S D D D D + +Speaker sentences 470: voxforge_deu_000990 #utts: 1 +id: (voxforge_deu_000990-voxforge_deu_000990) +Scores: (#C #S #D #I) 33 3 5 0 +REF: d i e w e r D e n d a s g a n z B e s T i M M T n i c h t m a c h e n +HYP: d i e ******* w e r * e n d a s g a n z ******* P e s H i * * N n i c h t m a c h e n +Eval: D D D S S D D S + +Speaker sentences 471: voxforge_deu_000991 #utts: 1 +id: (voxforge_deu_000991-voxforge_deu_000991) +Scores: (#C #S #D #I) 44 3 8 4 +REF: d I e d a * t e n M e n G e * d i E g e s e n d e T W i * r * D i s t e R h e b L i c h g e r i n G e r +HYP: d * e d a R t e n e n * e N d i * ******* g e s e n d e * * i E r E T i s t e h e b * i c h g e r i n * e r +Eval: D I S D I D D D D I I S S D D + +Speaker sentences 472: voxforge_deu_000992 #utts: 1 +id: (voxforge_deu_000992-voxforge_deu_000992) +Scores: (#C #S #D #I) 25 2 7 0 +REF: d a s e R g e b n i s i s t v e R f Ä l s C H t w o R d e n +HYP: d a s ******* e * g e b n i s i s t ******* v e f E l s * * t ******* w o * d e n +Eval: D D D S S D D D D + +Speaker sentences 473: voxforge_deu_000993 #utts: 1 +id: (voxforge_deu_000993-voxforge_deu_000993) +Scores: (#C #S #D #I) 57 3 5 2 +REF: e i n E b e s c h r Ä n k u n g t r i T t e r s t b e i b e s o n d e r ******* s i n t e n s i * v e r n U T z u n g a u f +HYP: e i n * ******* b e s c h r E n k u n g K t r i * t e r s t b e i b e s o n d e r s i n t e n s i E v e r ******* n * O z u n g a u f +Eval: D D S S D I I D D S + +Speaker sentences 474: voxforge_deu_000994 #utts: 1 +id: (voxforge_deu_000994-voxforge_deu_000994) +Scores: (#C #S #D #I) 65 8 5 4 +REF: d e r e n * D b e n U t z e r h a t e i n e h Ö h E r e g e * s c h w i n d i g k e i t f Ü r D e n d O W n ******* l o A D z u R V e r f Ü * g u n G +HYP: d e r e n T b e n O t z e r h a t e i n e h Ö h Ö r e g e R s c h w i n d i g k e i t ******* f Ü r * e n d A U n l o * T z u * O F e r f Ü L g u n * +Eval: I S S S I D D S S I D S D S S I D + +Speaker sentences 475: voxforge_deu_000995 #utts: 1 +id: (voxforge_deu_000995-voxforge_deu_000995) +Scores: (#C #S #D #I) 37 3 7 1 +REF: d e r s e m a n t i s c h e t * e i l w u R d e s K e p t i s C H B E t r a c h t e t +HYP: d e r s e m a n t i s c h e ******* t H e i l E w u * d e ******* s G e p t i s * * ******* * P t r a c h t e t +Eval: D I S D D S D D D D S + +Speaker sentences 476: voxforge_deu_000996 #utts: 1 +id: (voxforge_deu_000996-voxforge_deu_000996) +Scores: (#C #S #D #I) 29 3 6 0 +REF: d o R t w i r D s e H R V i E l m e h r g e l D v e r d i e n t +HYP: d o * t ******* w i r T s e * * F i * l ******* m e h r g e l T v e r d i e n t +Eval: D D S D D S D D S + +Speaker sentences 477: voxforge_deu_000997 #utts: 1 +id: (voxforge_deu_000997-voxforge_deu_000997) +Scores: (#C #S #D #I) 46 10 2 5 +REF: * v e r s t Ä n D n i s * f * Ü r d a * s v e r ******* a n T w O r T l i c h k e i T s g E f Ü H l e i n e r m u T t e r +HYP: F v e r s t E n n i s V f I E r d a S s v e r a n w U r D l i c h k e i Z s g I f ** I l e i n e r ******* m u N t e r +Eval: I S S I I S I I S S S S S D S D S + +Speaker sentences 478: voxforge_deu_000998 #utts: 1 +id: (voxforge_deu_000998-voxforge_deu_000998) +Scores: (#C #S #D #I) 22 4 5 2 +REF: d a S w I r D F Ü r d I e m e D I e n g e m a c h * ******* t +HYP: d a * ******* w * r T V E r d * e m e * e n g e m a c h T t +Eval: D D D S S S D D S I I + +Speaker sentences 479: voxforge_deu_000999 #utts: 1 +id: (voxforge_deu_000999-voxforge_deu_000999) +Scores: (#C #S #D #I) 41 3 7 2 +REF: s I e k * a N n e i n e g a n Z k l a r e k A U f E m p f ** e H l U n g a u S s p r e c h e n +HYP: s * e k O a * n e i n e ******* g a n * k l a r e k * O f m p f Ä e * l O n g a u * s p r e c h e n +Eval: D I D D D D S S I D S D + +Speaker sentences 480: voxforge_deu_001000 #utts: 1 +id: (voxforge_deu_001000-voxforge_deu_001000) +Scores: (#C #S #D #I) 35 1 2 1 +REF: z a H l r e i c h e p R o t e s t e w e r d e n a r t i k U l i e r * t +HYP: z a * l r e i c h e p * o t e s t e w e r d e n a r t i k O l i e r D t +Eval: D D S I + +Speaker sentences 481: voxforge_deu_001001 #utts: 1 +id: (voxforge_deu_001001-voxforge_deu_001001) +Scores: (#C #S #D #I) 24 4 5 2 +REF: d I e d U R c h f Ü H r U n g * w a R n * i c h t S i C h e r +HYP: d * e d * E c h f Ü Ö r O n g E w a * ******* n E i c h t Z i * h e r +Eval: D D S S S I D D I S D + +Speaker sentences 482: voxforge_deu_001002 #utts: 1 +id: (voxforge_deu_001002-voxforge_deu_001002) +Scores: (#C #S #D #I) 31 3 5 3 +REF: d I e w Ä H r u * * n g h a * t Ü b e r h A U p t k e i n e d E C k u n G +HYP: d * e w ** E r u N E n g h a R t Ü b e r h * O p t k e i n e d * I k u n * +Eval: D D S I I I D S D S D + +Speaker sentences 483: voxforge_deu_001003 #utts: 1 +id: (voxforge_deu_001003-voxforge_deu_001003) +Scores: (#C #S #D #I) 58 8 3 8 +REF: * o * b * * Ü b r i g e n s s e C K e r s * D O r f d e r e i n e n d U R c h ******* a u s Z i e l ******* b e * w u S s t e n l e b e N s k l U g e n +HYP: A o U b P I E b r i g e n s s e * e r s T A U r f d e r e i n e n d * E c h a u s T i e l b e R w u * s t e n l e b e M s k l O g e n +Eval: I I I I S D S I S S D S I S I I D S S + +Speaker sentences 484: voxforge_deu_001004 #utts: 1 +id: (voxforge_deu_001004-voxforge_deu_001004) +Scores: (#C #S #D #I) 40 7 3 2 +REF: m a N s p r I c h t I n d I e s e m f A L l v o n k o n t r * A h I e r u n g s * z w a n g +HYP: m a I R s p r E c h t ******* E n d * e s e m f E I l v o n k o n t r E R h * e r u n g s T z w a n g +Eval: S S S D S D S S I S D I + +Speaker sentences 485: voxforge_deu_001006 #utts: 1 +id: (voxforge_deu_001006-voxforge_deu_001006) +Scores: (#C #S #D #I) 25 6 8 0 +REF: g l Ä U b I g e r u n D s c h U l D n e R s I n D S i C H e i n i g +HYP: g l O E b E g e r u n * s c h * l n e * ******* s E n * ******* Z i * * e i n i g +Eval: S S S D D S D D S D D S D D + +Speaker sentences 486: voxforge_deu_001007 #utts: 1 +id: (voxforge_deu_001007-voxforge_deu_001007) +Scores: (#C #S #D #I) 30 3 3 0 +REF: d a s w i r D n i c h t m e H r l a n G e s o b l e i b e n +HYP: d a s ******* w i r T E n i c h t m e * r l a n e s o ******* b l e i b e n +Eval: D S S D S D + +Speaker sentences 487: voxforge_deu_001008 #utts: 1 +id: (voxforge_deu_001008-voxforge_deu_001008) +Scores: (#C #S #D #I) 41 8 8 2 +REF: E s g a b u n t e r S c h i E D l i c h s C h * W e R e F o R m e n d e r F r E I h e I t S s t R a f e * +HYP: I s g a b u n t e r * c h i T l i c h ******* s * h I H e e V o * m e n d e r * r A L h e * t * s t * a f e R +Eval: S D S S D D I S S S D D S S D D D I + +Speaker sentences 488: voxforge_deu_001009 #utts: 1 +id: (voxforge_deu_001009-voxforge_deu_001009) +Scores: (#C #S #D #I) 24 3 11 1 +REF: e S h A n d e L T S I C H U m e i n e f r e i e s o f t ******* w A r E +HYP: e * ******* h * n d e * * * * * E E m ******* e i n e ******* f r e i e s o f t w E r * +Eval: D D D D D D D D S S D D I S D + +Speaker sentences 489: voxforge_deu_001010 #utts: 1 +id: (voxforge_deu_001010-voxforge_deu_001010) +Scores: (#C #S #D #I) 58 6 16 3 +REF: o r g a n ******* s t r e i t ******* v e R f a H R E n k Ö N N E n a u c h a u S s c H L i E S s l i c H a u f D e R l A n d e s ******* e b e n E s t a T t f i n D e n +HYP: o r g a n s t r e i t v e f a * * * n k ** * * * n a u c h a u * s c * i * * s l i c G a u f ******* T e * l * n d e s e b e n * I s t a * t f i n T e n +Eval: I I S D D D D D D D D D S D D S D S D D I D S D S + +Speaker sentences 490: voxforge_deu_001011 #utts: 1 +id: (voxforge_deu_001011-voxforge_deu_001011) +Scores: (#C #S #D #I) 35 2 7 1 +REF: w e g e N n U T z * l o s a u f g e w e n D e t e r u R l A U b s Z e i t k a N n +HYP: w e g e * n * O z H l o s a u f g e w e n * e t e r u * l * E b s * e i t k a * n +Eval: D D S I D D D S D D + +Speaker sentences 491: voxforge_deu_001012 #utts: 1 +id: (voxforge_deu_001012-voxforge_deu_001012) +Scores: (#C #S #D #I) 26 8 8 0 +REF: d a S w i r D n i c h T i M m E R p e r F e k t F u n K t I O N i E R e n +HYP: d a * ******* w i r * T n i c h * i * m A p e r V e k t V u n * t * Z E i * N e n +Eval: D D D S D D S S S S D D S S D S + +Speaker sentences 492: voxforge_deu_001013 #utts: 1 +id: (voxforge_deu_001013-voxforge_deu_001013) +Scores: (#C #S #D #I) 30 3 11 4 +REF: m a n m u S S s i c h E n G A g I e r * * * e n d E s w a C H s t u m s w e * g E n +HYP: m a n ******* m u * * ******* s i c h A n * * g * e r C H I e n d A s ******* w a * K s t u m s ******* w e N g * n +Eval: D D D D S D D D I I I S D D S D I D + +Speaker sentences 493: voxforge_deu_001014 #utts: 1 +id: (voxforge_deu_001014-voxforge_deu_001014) +Scores: (#C #S #D #I) 36 1 2 3 +REF: w e l * c h e * w e g e s o L l e n e i n G e s c H l a g e n w e * r d e n +HYP: w e l I c h e R w e g e s o * l e n e i n e s c * l a g e n w e A r d e n +Eval: I I D S D I + +Speaker sentences 494: voxforge_deu_001015 #utts: 1 +id: (voxforge_deu_001015-voxforge_deu_001015) +Scores: (#C #S #D #I) 21 3 4 0 +REF: d a S w I r D I n d i e p R e i s e g e H e n +HYP: d a * w E r T E n d i e p * e i s e ******* g e * e n +Eval: D S S S D D D + +Speaker sentences 495: voxforge_deu_001016 #utts: 1 +id: (voxforge_deu_001016-voxforge_deu_001016) +Scores: (#C #S #D #I) 26 3 2 3 +REF: * ******* d i e Ü b e r n * a H m e e r F o l G t e w Ö r t l i c h +HYP: S d i e ** b e r n D a * m e e r V o l K t e w E r t l i c h +Eval: I I D I D S S S + +Speaker sentences 496: voxforge_deu_001017 #utts: 1 +id: (voxforge_deu_001017-voxforge_deu_001017) +Scores: (#C #S #D #I) 32 4 5 2 +REF: d I e e * n t ******* w I C k l U n g i s t w e i t v o r a n g e s C h R i T t e n +HYP: d * e ******* e I n t w * E k l O n g E i s t w e i t v o r a n g e s * h * i E t e n +Eval: D D I I D S S S D D S + +Speaker sentences 497: voxforge_deu_001018 #utts: 1 +id: (voxforge_deu_001018-voxforge_deu_001018) +Scores: (#C #S #D #I) 43 6 6 4 +REF: d i e s Y m P t o * m e * t r * e T e n d a N n s c h o n n a c h w e * n I g e N s t U n D e n a U f +HYP: d i e ******* s * m t o R m e L t r I e D e n d a * n ******* s c h o n n a c h ******* w e H n E g e * s t O n T e n a R f +Eval: D D S I I I S D D D I S D S S S + +Speaker sentences 498: voxforge_deu_001019 #utts: 1 +id: (voxforge_deu_001019-voxforge_deu_001019) +Scores: (#C #S #D #I) 32 2 5 1 +REF: E s g I b t e i n e g r o S s e w E l l e v o n p r o * z e S S e n +HYP: * s ******* g E b t e i n e g r o * s e w Ä l l e v o n p r o T z e * * e n +Eval: D D S D S I D D + +Speaker sentences 499: voxforge_deu_001020 #utts: 1 +id: (voxforge_deu_001020-voxforge_deu_001020) +Scores: (#C #S #D #I) 33 2 0 3 +REF: e s I s t b e r e i t S m e i n z ******* w e i t e r a u t o m a * * t +HYP: e s E s t b e r e i t Z m e i n z w e i t e r a u t o m a R D t +Eval: S S I I I + +Speaker sentences 500: voxpopuli_deu_000309 #utts: 1 +id: (voxpopuli_deu_000309-voxpopuli_deu_000309) +Scores: (#C #S #D #I) 97 13 27 3 +REF: B R U S S L A n D S I M p l E m e n t i e r u n g v o n h Ö h E r E n s t a n d a R D s Z u M s c H u t z * p e r s Ö n l i C H e r D a t e n e b e n ******* f A L L s g e n E r e L L u n s E r e * G u t e z u s a m m e n a R b e i t E R l e i c h t e R n +HYP: * ******* * * * * * * n * * * * p l * m e n t i e r u n g v o n h Ö h * r I n s t a n d a * T s ******* T u * ******* s c * u t z S p e r s E n l i * G e r ******* T a t e n e b e n f W E I s g e n A r e * * u n s H r e I * u t e T z u s a m m e n a * b e i t * A l e i c h t e * n +Eval: D D D D D D D D D D D D D D S D S D S D D D I S D S D S I S S S S D D S I D S D D S D + +Speaker sentences 501: voxpopuli_deu_000310 #utts: 1 +id: (voxpopuli_deu_000310-voxpopuli_deu_000310) +Scores: (#C #S #D #I) 58 12 32 8 +REF: P O L I Z e I B E a m t e H a b E n d A S s c H l i M m s ******* t e v e r H i n d e r T H a B E N I H r * l e b e n g E R E T T E t U N D s i n D s e L b e r v e r l E t Z t W o r d E n * * * * * * +HYP: * * * * * e * R a m t e ******* * a b * n ******* d * E R s c * l i * m s t e v e r * i n d e r * D a * * * ******* * * r M E l e b e n g * * * L Ä D t ******* * * I s i n * s e * b e r v e r l * t S t ******* V o r d * n I C G L A B +Eval: D D D D D D S S D D D D D S S D D I D D S D D D D D D I S D D D S S S D D D S D D D S D S D I I I I I I + +Speaker sentences 502: voxpopuli_deu_000311 #utts: 1 +id: (voxpopuli_deu_000311-voxpopuli_deu_000311) +Scores: (#C #S #D #I) 27 6 22 1 +REF: D A S I S T N i c H T m * Ö G L i C H d a S s d e r K o M m i S s a R n i c H t H i e R I s T +HYP: * * * ******* * * * ******* * i c * G m E Ü B R i * E d a * s d e r * o * m i E s a * ******* n i c * t * i e * ******* * s * +Eval: D D D D D D D D D D S I S S S D S D D D S D D D D D D D D + +Speaker sentences 503: voxpopuli_deu_000312 #utts: 1 +id: (voxpopuli_deu_000312-voxpopuli_deu_000312) +Scores: (#C #S #D #I) 37 8 11 6 +REF: 1 9 m i t G l I e d u n d * * h o F F e d a S s w I r I M n Ä c h s T e N * J a h r Ü B e r * D a * * S +HYP: * * ******* m i t K l * e d u n d E C h o * V e d a * s w E r ******* * * n E c h s * e * I E a h r Ü R e r S W a N T I +Eval: D D D S D I I D S D S D D D S D D I S S I S I I S + +Speaker sentences 504: voxpopuli_deu_000313 #utts: 1 +id: (voxpopuli_deu_000313-voxpopuli_deu_000313) +Scores: (#C #S #D #I) 69 19 31 9 +REF: * * E s d a R f n I c h t Ü B e r s e h E n w e r D E n d A S s i M m e r h i n M e H r * * ** * * * A L s 5 0 d e r b e V Ö l k E R U n g d E r e U R O P Ä i s c h E n u n I o n I m l Ä n D L i c h E n * r a U m l E B T +HYP: N D I s d a * f n * c h t ******* ** D e r s e h * n w e r * * n ******* d * * s i * m e r h i n B e * r I F Ü M Z I G U T s ******* E N T d e r ******* b e F E l k * * Ö n g d A r ******* e * * * * B i s c h * n ******* u n U o n ******* E m ******* l E n * T i c h * n D r a * m ******* l * * I +Eval: I I S D D D D S D D D D D D D S D I I I I I I S S S D S S S D S S D D S S D D D D D S D D S D S D S D S D I D D D D S + +Speaker sentences 505: voxpopuli_deu_000314 #utts: 1 +id: (voxpopuli_deu_000314-voxpopuli_deu_000314) +Scores: (#C #S #D #I) 87 7 37 0 +REF: W I R W O L L E N A L s o d a S s d e R b Ü R g e r s C h N e L l E R E I n e a u s k u n F T b e k o M m T o b s e i n e b e s c h W e r D e Ü b e R h a u P t A n g e n o M m e n w i r D o B s i e b e r e c h t i G t I s t +HYP: * * * ******* * * * * * * ******* * * s o ******* d a * s d e * b Ü U g e r s * h * e * l * * ******* * * n e a u s k u n * * b e k o * m D o b ******* s e i n e b e s c h Ä e r * e ** b e * h a u * t I n g e n o * m e n ******* w i r T o * ******* s i e b e r e c h t i H t ******* E s t +Eval: D D D D D D D D D D D D D D D D S D D D D D D D D D D D S D S D D D D S D D S D D S D S + +Speaker sentences 506: voxpopuli_deu_000315 #utts: 1 +id: (voxpopuli_deu_000315-voxpopuli_deu_000315) +Scores: (#C #S #D #I) 64 17 11 5 +REF: E I n * „ r e s e t u * n S e r e r b * E z i E H U n g e n i s t n i c h t v o n N Ö t e n a b e r S E H r W O H L k o n t i n ******* U I e r l i c h e s f e i n t * U n I n G +HYP: * * n E R H r e s e t u O n Z e r e r ******* b I T z i * * O n g e n i s t ******* n i c h t ******* v o n E U t e n a b e r * * A r B U R U E k o n t i n N e r l i c h e s ******* f e i n t I O n E n * +Eval: D D I S S I S D I S D D S D D S S D D S S S S S S I S S D I S S D + +Speaker sentences 507: voxpopuli_deu_000316 #utts: 1 +id: (voxpopuli_deu_000316-voxpopuli_deu_000316) +Scores: (#C #S #D #I) 37 12 11 2 +REF: U N D d A W i R D g a n Z s t o l Z g e s a * g T D I E b e s c h Ä F t i G u n g s t e i G t * J A a n +HYP: * * L d * ******* * i * E g a n * ******* s t o l * S g e s a R g J A R B D b e s c h ** E t i * u n g s t e i K t I E R a n +Eval: D D S D D D D S D D D S I S S S S S D S D S I S S + +Speaker sentences 508: voxpopuli_deu_000317 #utts: 1 +id: (voxpopuli_deu_000317-voxpopuli_deu_000317) +Scores: (#C #S #D #I) 100 21 28 12 +REF: I C H W I L L S A G E N W i e E s I s T f Ü r u n S * i s t D E R e u r * O u n t e r B e ******* W e r * t e t w i * r e x * p o r t i e r E n z u V i * e l z u * * b I L l i G * u n D w I r i * M p O R t i e r E n z U w e n i g W I r v E r s c h e n ******* k e n w o H l s t a n D +HYP: * * * ******* * * * * ******* * * * * * ******* * i e D A s ******* E s * f E r u n * S i s t ******* * * * e u r H R u n t e r * e V e r L t e t w i E r e x S p o r t i e r U n z u F i E e l z u O N b * E l i H A u n * w E r i N p A D t i e r U n ******* z S E w e n i g N E r v A r s c h e n k e n w o * l s t a n T +Eval: D D D D D D D D D D D D D D D D S S D S D S D I D D D D I S D I S I I I S S I I I D S S I D S I S S S S D S S S S S I D S + +Speaker sentences 509: voxpopuli_deu_000318 #utts: 1 +id: (voxpopuli_deu_000318-voxpopuli_deu_000318) +Scores: (#C #S #D #I) 49 4 11 6 +REF: D A S S s i e h E u * ******* T e * a b e n D H i e r a n w e s e n D s i n D i s t e I n p * o * s i t i * v e S s i g n a l +HYP: * * * * ******* s i e h O u L D e R a b e n * D i e r a n w e s e n * ******* s i n * i s t ******* e * n p R o S s i t i E v e R s i g n a l +Eval: D D D D D S I I S I D S D D D D D I I I S + +Speaker sentences 510: voxpopuli_deu_000319 #utts: 1 +id: (voxpopuli_deu_000319-voxpopuli_deu_000319) +Scores: (#C #S #D #I) 97 15 13 15 +REF: * * * * * * 9 0 p r * O z e n t a l l e r E U r o * p Ä I S c h e n f I l m e d i e a u S s e R h A l B i H r e s H e i * m a t l a n d e s g * E z e i * G t w e r d e n s i n D v o * m m e ******* d i * A p R o g R a M m g e F Ö r ******* d e r t w O r D e n +HYP: N E U N Z S I C H p r E T z e n t a l l e r ******* * A r o B p Ä * E c h e n f * l m e d i e a u * s e * h E l T i * r e s ******* * e i N m a t l a n d e s g I T z e i C H t w e r d e n s i n T v o A m m e d i E R p * o g * a * m g e * E r d e r t w U r T e n +Eval: I I I I I I S S S I S D D S I D S D D D S S D D D I I S I S S I I I S D D D D S I S S + +Speaker sentences 511: voxpopuli_deu_000320 #utts: 1 +id: (voxpopuli_deu_000320-voxpopuli_deu_000320) +Scores: (#C #S #D #I) 57 7 18 7 +REF: W i E s o k a N N i C h D e m e r ******* g e b * * N i s ******* * * * D e r a u S s C h u S s a b S t I M m u n g i n d i e s e R F o r m n i c h T z u s t I M M e n +HYP: B i * s o ******* k a * * L i * h ******* * e m ******* e r g e b P L D i s T E R A e r a u * s * h u * s a b * t D E m u n g i n d i e s e * V o r m n i c h * ******* z u s t * * * e n +Eval: S D D D D S D D D D I I I S I I I I S D D D D S S D S D D D D D + +Speaker sentences 512: voxpopuli_deu_000321 #utts: 1 +id: (voxpopuli_deu_000321-voxpopuli_deu_000321) +Scores: (#C #S #D #I) 76 14 12 6 +REF: W i * r W O L L t e N v e r h I n d E r n d a s s S I c * * h * h i n T e r d i E S e M g e i s T i g e n e i g e n t u m d i e a u s K u N F T s P f l i c h t * v e r s t e c k e n k A n * N +HYP: B i E r ******* * B U R t e * v e r h * n d * r n d a s s I C H c H E h E h i n D e r ******* d i * * e N g e i s * i g e n e i g e n t u m d i e ******* a u s G u * M s * f l i c h t E v e r s t e c k e n k O n T E +Eval: S I D D S S S D D D S S S I I I S D D D S D D S D S S D I S I S + +Speaker sentences 513: voxpopuli_deu_000322 #utts: 1 +id: (voxpopuli_deu_000322-voxpopuli_deu_000322) +Scores: (#C #S #D #I) 78 9 23 5 +REF: E s g I b T J e t z T I m z u s a M m E N H a n g M I T d e r v e r s t Ä r k t e n z u s a M m E N a r b e i t e i n e n e r * s t e n g a n g v o n e i N i g e n m i t G L i E D s t a A t e * ******* n * * +HYP: I s ******* g E b * ******* D e t z * H N m z u s a * m * * * a n g ******* * * * ******* d e r ******* v e r s t E r k t e n ******* z u s a * m * a r b e i t e i n e n e r S s t e n g a n g ******* v o n e i * i g e n ******* m i t * * i * T s t a R t e N n A C +Eval: S D S D D S D S S D D D D D D D D D D S D D D S I D D D D D D S S I I I I + +Speaker sentences 514: voxpopuli_deu_000323 #utts: 1 +id: (voxpopuli_deu_000323-voxpopuli_deu_000323) +Scores: (#C #S #D #I) 149 25 23 23 +REF: w a s d i E g r e * n z Ü b e r S c h r e i t e n ******* d e z u s a M m e n a R b e i t a n b e l a n G t u n d * * * D i * ******* e * v e r B r e i t u n g i n D r i * T T l Ä n d e r B e t r i F f t H I e R m Ö c h T E i c h e i n b e i s p I e l n e N n e n d A s e I n e r ******* F o l g s b e i s p i E l F Ü R m i c h i s T u n D z w a r * * s l U m ******* d * O g m I L l i * * O n * Ä r * * ******* * * * +HYP: w a s d i * ******* g r e I n z H b e r * c h r e i t e n d e T z u s a * m e n a * b e i t a n b e l a n * t u n d T W A S T i E e R v e r P r e i t u n g i n ******* T r i G K l E n d e r * e t r i * f t N D T e * ******* m E c h * * ******* i c h e i n ******* b e i s p B e l E n e I n e n ******* d E s e * n e r V o l g s b e i s p i * l ******* V Ü * E m i c h i s * u n T z w a r E L s l A m d A U g m * * l i E R n J A r E I D E S +Eval: D D I S D I S D D D I I I S S I I I S D S I S S S D D S S S D D S D D D D S S S D S D I S D D S D S D S I I S I I S D D I I S I S I I I I I I + +Speaker sentences 515: voxpopuli_deu_000324 #utts: 1 +id: (voxpopuli_deu_000324-voxpopuli_deu_000324) +Scores: (#C #S #D #I) 100 16 24 3 +REF: U N D d a s n i c h t n u r i n p o r t u ******* g a l O d e r g R i E c h e n ******* l a n D s O n D e R n a u C H I n s * O v e r m e i n t l i c H R e i c h e n m i t G L i E D s t a A t e n w i e d e u t s c H l a n d O d e r G R O S s B R I t a N n I e n +HYP: * * * ******* d a s ******* n i c h t ******* n u r i n p o r t u g a l * d e r ******* g L i * c h e n l a n T s * n * e * n a u * R U E n ******* s E U v e r m e i n t l i c G * e i c h e n ******* m i t * W i S s t a R t e n w i e ******* d e u t s c S l a n d * d e r * * * U s * P E t a * n J e n +Eval: D D D D D D I D D S D I S D D D D S S S D I S S D D D S S S S D S D D D D S D S S D S + +Speaker sentences 516: voxpopuli_deu_000325 #utts: 1 +id: (voxpopuli_deu_000325-voxpopuli_deu_000325) +Scores: (#C #S #D #I) 18 4 10 3 +REF: D I E Z E I t F Ü r a u s R e D e n i s T v o r b e i ******* * * +HYP: * * * ******* * * * t ******* V E r a u s W e G e n ******* i s * v o r b e i D A +Eval: D D D D D D D D S S S S D D I I I + +Speaker sentences 517: voxpopuli_deu_000326 #utts: 1 +id: (voxpopuli_deu_000326-voxpopuli_deu_000326) +Scores: (#C #S #D #I) 68 17 24 6 +REF: S I E a l l e f l i E G e n a L S m i T g * L i E d E R d I e s E s h a u s e s W a H r s c h e i n L I C h * d * E U t l i c H h Ä U f i g e r a l s D e R * e ******* u d u R C H s c H n i T t S b Ü ** r g e R +HYP: * * * ******* a l l e L f l i * * e n G a * T F m i * g K D i d * A d * e s I s ******* h a u s e s V a * r s c h e i n * * * h E d O R D t l i c * ******* h O L f i g e r a l s ******* T e * I e u N d u * * * s c * n i * t Z b Ü Ö r g e T +Eval: D D D D S D D S D S S D I S S D S D S D S D D D D I I S S D D S S D S D I I S D D D D D S I S + +Speaker sentences 518: voxpopuli_deu_000327 #utts: 1 +id: (voxpopuli_deu_000327-voxpopuli_deu_000327) +Scores: (#C #S #D #I) 50 8 22 8 +REF: U N D I C H B I n s i c h e * r d a S s I H r e b e ******* d * e u * t * u n g i N n a H E r z * * u k U N f t S O g a R n o c h z u n e H m E N w i * r D +HYP: * * * ******* * * * ******* * E n s i c h e H r d a * s * E r e b e d O e u I t C u n g i * ******* n a * * r A z S F u k O M f t ******* * U g a * ******* n o c h T z u n e * m * * w i E r T +Eval: D D D D D D D D D S I D D S I I I I D D D D S I I S S D D S D D S D D D I S + +Speaker sentences 519: voxpopuli_deu_000328 #utts: 1 +id: (voxpopuli_deu_000328-voxpopuli_deu_000328) +Scores: (#C #S #D #I) 126 18 26 9 +REF: * E s G e H T H i e r u M d i E r i c h t l i * n I e * d E s R a * T e s Z U R f E s t L e g U n G g R u n d ******* l ** e g E N d E R s i C H E r H E i t s n o r m e n f Ü r d e n s c h u t z v O r d e n g e f a H r e n e i n e r e x * p o s i t I o * n * g E G e N Ü b e r i o n i s i e r e n d E r s t r a H l u n g +HYP: T A s ******* K e * * D i e r ******* u N d i * ******* r i c h t l i E n D e R d I s * a R D e s * T E f I s t * e g * n * ******* g * u n d l Ä e g * d * * A s i * S A r * * i t s n o r m e n f Ü r d e n s c h u t z v E r ******* d e n g e f a * r e n e i n e r e x S p o s i t S o U n G g * I e * ** b e r i o n i s i e r e n d A r ******* s t r a * l u n g +Eval: I S D S D D S D S D D I S I S D I S D S S S D D D D D I I D S D D S D S S D D S D D I S I I D S D D S D D + +Speaker sentences 520: voxpopuli_deu_000329 #utts: 1 +id: (voxpopuli_deu_000329-voxpopuli_deu_000329) +Scores: (#C #S #D #I) 22 2 7 1 +REF: d a s g i l t e s W i E d e r h e r * Z u s t E L L e n +HYP: d a s K g i l t ******* e s * i * d e r ******* h e r S C u s t * * * e n +Eval: S D D D D I S D D D + +Speaker sentences 521: voxpopuli_deu_000330 #utts: 1 +id: (voxpopuli_deu_000330-voxpopuli_deu_000330) +Scores: (#C #S #D #I) 46 4 10 0 +REF: d I E S e n e i n e n e i n z i g e n s i t z G I b t e s l Ä n g s T d a s i S T s t R a S s B u r g +HYP: d * * * e n e i n e n e i n z i g e n s i t z K E b t e s l E n g s * d a s ******* i * * ******* s t * a * s P u r g +Eval: D D D S S S D D D D D D D S + +Speaker sentences 522: voxpopuli_deu_000331 #utts: 1 +id: (voxpopuli_deu_000331-voxpopuli_deu_000331) +Scores: (#C #S #D #I) 198 47 94 21 +REF: W I R S E H E N J A G E R A D e d a S s D a s p a S s i e r T i n m a l t A d i e J O U R n A l i s t * i n d i e k o R R U P t I O n S f Ä L l e a u f g e ******* d e C k t H A T * i s T v O r w * e N I g e n W O c h E n e r m O R d e T W O R D E N w e ******* d e r w e r D e n * s Y S T e m a T i s C H D i E k o R R U P T I o * n * S f Ä l * ******* * ******* L e u n T e r s u c h T n o c h W i R D d e r m o * r D s e L b * * * ******* e r g e Z i E l t ******* * u n T e r s u C H t M a n H a t f a s T D E n E I n d R u C K a L s O B H I e R a l * ******* L e S u n t e r d e M m a n T E L +HYP: * * * ******* * * * * * ******* * * ******* * * * * * e d a * s * a s ******* p a * s i e r * i n m a l t E R d i e B S O n * l i s t E i n d i e ******* k o * * * B t S U n D f ** * l e a u f g e d e * k t ******* * E R D i s * v E r ******* w H e * * g e n * B c h * n ******* e r m * A d e * * * * * B A w e d e r w e r * e n U s * D e m a R i s * * ******* * i * ******* k o * * * B S o U n D f E l E E B e u n D e r s u c h * n o c h * i * * T d e r m o A r * s e R b E A R e r g e T i * l t E u n D e r s u * * t ******* E a n ******* * a t V f a s * ******* S I n * A n d * u * * G a * s ******* * * ******* * W e N a l S I e R u n t e r d e * ******* m a n * * * ******* +Eval: D D D D D D D D D D D D D D D D D D D D D D D S S S S S S D I D D D D S S S S D D I D D D S S I D S D I D D D S D D D S D D D D D S S I D I D S S S D D D D D D D D D S S S I I S S I I I I S S D D D D S I D S I I I I S D I I S D D D S D D S D D S S D S D D D S D D D D D D S S I I S S D D D D D D + +>> REF: d e S s c h w e i G E n S z u g e d e C k t w e R D e N s O L L +>> HYP: d e L I s c h w e i * * n * T z u g e d e * k t ******* w e N S e O s * * * +>> Eval: S S D D D S D D S S S D D D + +Speaker sentences 523: voxpopuli_deu_000332 #utts: 1 +id: (voxpopuli_deu_000332-voxpopuli_deu_000332) +Scores: (#C #S #D #I) 79 9 41 2 +REF: D O R T S t E H E N Ü B E R A L L E N T l a n g D e R k Ü s t e d i e w a R n s t e i n e d i E a u f d i E G r o S s e n k a t * A s T R o P H e n M i T t S u n a m i * s I N d e r v E r g a n g E n H e I t H i n w e i s e n +HYP: * * * * L I t * * * * ******* ** * * * * * * * * * l a n g * e * k Ü s t e d i e ******* w a * n s t e i n e d i * a u f d i * * r o * s e n ******* k a t E R s V o * F e n ******* * i * ******* t Z u n a m i E s ******* * E d e r ******* v * r g a n g * n * e R t ******* * i n w e i s e n +Eval: D D D D S S D D D D D D D D D D D D D D D D D D D D D D D D I S S S D S D D D D S I D D S D D D D S D D + +Speaker sentences 524: voxpopuli_deu_000333 #utts: 1 +id: (voxpopuli_deu_000333-voxpopuli_deu_000333) +Scores: (#C #S #D #I) 135 30 45 16 +REF: H E R R P R Ä S I d e n t i c H h a * b e I M P R I n z i * p F Ü r d e N b e r I c h t g E s t * i m * M t o B W o H l E R e i N E n s C H w e * r E n F e H l e r E n t H Ä l t e s w i r d n * Ä m L i C H D a Z u * a u f ******* g e ******* f O R d e r t d A s E u r ******* * O P Ä I s C h e p a R l a m e n T a u f d e m w ** e g Z U e ******* i ******* N E m e i n z i g e n s i t Z z u * U n T e R s t * Ü t z e n +HYP: * * * * ******* * * ** * * d e n t i c * ******* h a R b e ******* * * * B E n z i E p ******* * Ü r d e * b e r * c h t g I s t E i m E N t o A U o * l ******* * * ******* e i * * n ******* s * w e H r * n V e * l e r I n t * E l t ******* e s w i r d n E m * i * * ******* T a u E a u f g e f * A d e r t d * s A u r B E H E s * h e p a * l a m e n * D a u f d e m w Ä e g ******* K T S e i D I m e i n z i g e n G s i t S T z u O * n D e * s t E L t z e n +Eval: D D D D D D D D D D D D I D D D D S S I D D D D S I I S S S D D D D D D D D D S I D S D S D S D I S D D D D S S I I I D S D S I I S S S S D D D S I D S S S I I S S S S S I D S D I S + +Speaker sentences 525: voxpopuli_deu_000334 #utts: 1 +id: (voxpopuli_deu_000334-voxpopuli_deu_000334) +Scores: (#C #S #D #I) 94 9 7 21 +REF: i n d i e s e N T r E F f e n w * u * R d e n g e m e i N s A m E * * p O l i t * I s c h e v e r ******* a b r e d u n g e n i M k r e i s d e r * * * * 2 7 * * * * * * * * * g e t r o f F e * n u n d a u c h P u b l i k g e m a C h * t +HYP: i n d i e s e M * r * I f e n w H u O T d e n g e m e i s * m * I B p * l i t D E s c h e v e r a b r e d u n g e n i N k r e i s d e r S I E B E N U N Z W A N Z S I g e t r o f V e R n u n d a u c h * u b l i k g e m a * h T t +Eval: S D D S I I S S D D I I D I S I S I I I I S S I I I I I I I I I S I D D I + +Speaker sentences 526: voxpopuli_deu_000335 #utts: 1 +id: (voxpopuli_deu_000335-voxpopuli_deu_000335) +Scores: (#C #S #D #I) 254 54 146 14 +REF: i C H b I n D e r Ü b e r * z E u g U n G d A S s w I r E s h * e u t E m i T d E M v o r s c h L a * g A U s D E m u m W E l * T a u S s c h U S s g e S c h a F f t H a B E N E i N E n S c h R I T t w e i t e R Z u k O M m E N E S I s T N i C H T p e r f e k t E u r O P Ä I S c h e Ä R Z t E s a g E N W I R h Ä T T e n F Ü * r h o c h r i s i K o P r O D u * k t E E I n e Z e n T R a l E z u ******* l a S s U n G h a B E N m Ü s s e n d a s h a B E i c h N i c h T g e s C h a F f t a B E r m i T d e m W A s * H E U T E a U F D e m t i S c h l i E g T S C H a F F e * * +HYP: i * * E b E n ******* * e r ******* ** b e r T z O u g E n * d * * s w E r ******* * s h O e u t H m i * d I N v o r s c h * a R g ******* * * s * I m u m * A l G a u * s c h * * s g e * c h a * f t ******* * a * * M * i * * n ******* * c h * * * t ******* w e i t e A u k * * m * * ******* * A * s * ******* * i * * G p e r f e k t A u r * * ** B E c h e ** * A t * Z s a g * * * * * E h ** E D e n * Ü E r h o c h r i s i G o B r * T u G k t * ******* D A n e D S e n D a l * I z u l a * s E n * h a * * M m ** s s e n ******* d a s ******* h a * * ******* i c h ******* E i c h * g e s * h a * f t a * * r ******* m i * d e m ******* E R s O * * * * * D a * H * e m ******* t i * c h ******* l i * g * ******* * P L a U B e I C +Eval: D D S S D D D D I S S D D D S D D I S D S S D I D D D D S D S I S D D D D D D D D D S D D D D D D D D D S S D D D D D D S D D D D D D S S D D D S S D D S D S D D D D D S D S S D I S S D S I D D S S S S S S D S I D S D D D S D D D D D D D S D D D D D D D D S S I D D D D D S D S D D D D D D D D S S S S I I + +>> REF: N * * * w i r W O H L t r o t Z D e m e i n e n g R o S s e n s c H r i T t V I E L l e i C H T k e i N E n m e i L E n s t e i n A B E R e i N E n g r o S s e n S c h R i T t H I N z u m e H r P a t I e N T e n s I C H e * R H e I T +>> HYP: H D A S w i r ******* * * * * ******* t r o t * * e m ******* e i n e n ******* g * o R s e n s c * r i L t * * * F l e i * * G k e i * * n m e i * * n s t e i n ******* * * * * ******* e i * * n ******* g r o * s e n ******* * c h * i * t ******* * * * ******* z u m e * r * a t * e * D e n s * * * e A T A e * N +>> Eval: S I I I D D D D D D D D D D D S D S D D D S D D S D D D D D D D D D D D D D D D D D D D D D D D D D D D S D D D I S S D S + +Speaker sentences 527: voxpopuli_deu_000336 #utts: 1 +id: (voxpopuli_deu_000336-voxpopuli_deu_000336) +Scores: (#C #S #D #I) 13 29 6 6 +REF: F R A U p * R Ä S I d E n T I N f * * r A U K O M M I S s A R i * n L i E b E K o L L e * G e * N +HYP: * * * * ******* p E H L E d A n G E S f V E r L K T F Ü Ö R T s W E i E n H E i * b M I N o N T e N e R G +Eval: D D D D D I S S S S S S S S S I I S S S S S S S S S S S I S S D S S S S S I S I S + +Speaker sentences 528: voxpopuli_deu_000337 #utts: 1 +id: (voxpopuli_deu_000337-voxpopuli_deu_000337) +Scores: (#C #S #D #I) 102 16 30 4 +REF: z u m a k t u e L l e n i c h G l a U b E E s k a N n k e i n e R v O n u n s a n ******* n e H m e N d a S s w I R w I r K l i c H * E r s t S e i t d i E s e M w O c h E n * e n d e w i s S e n d a S s U n s d i E Z a H l u n G s u n f Ä H i * G k e i t d r o H t +HYP: z u m a k t u e * l e n i c h ******* K l a * b * ******* I s k a * n ******* k e i n e * v E n ******* u n s ******* a n n e * m e * d a * s w * * E w E r T l i c G I A r s t ******* F e i t ******* d i * s e N w U c h * n G e n d e ******* w i s H e n ******* d a * s ******* E n s d i * ******* T a * l u n * s u n f ** E i C H k e i t ******* d r o * t +Eval: D D S D D D S D D D S D D I D D D D D S S S S I S D S D D S S D I D S D D D S D D S D D D S I S D D + +Speaker sentences 529: voxpopuli_deu_000338 #utts: 1 +id: (voxpopuli_deu_000338-voxpopuli_deu_000338) +Scores: (#C #S #D #I) 39 6 9 5 +REF: d A S s I n d e i n f A c h B e ******* d i n g u n g e n d i E n i C H T a k * z e p t a b * * e L S I n ******* D +HYP: d * * ******* s * n d e i n f * c h P e d i n g u n g e n d i * ******* n i * * G a k T z e p t a b E S e N M A n K +Eval: D D D D D S I D D D D S I I I S S S I S + +Speaker sentences 530: voxpopuli_deu_000339 #utts: 1 +id: (voxpopuli_deu_000339-voxpopuli_deu_000339) +Scores: (#C #S #D #I) 142 14 26 12 +REF: i n d e R z w i s c h e n ******* Z e i T s i n D d i E r e T t u n g s O r g A n i s a * T i o n e n d I e g r Ö S s t e n s c h l E P p e r * w e i L s i e d i e m i * g R a n t e n * 2 0 K i * * * * l O m e t e r v O r d e r l i * b Y s c h e n k Ü s T E * a u * F g r e i f e n u n d a l l E n A C h i t a l i e n T r a N s p O r t i e r e n +HYP: i n ******* d e * ******* z w i s c h e n S e i * s i n * ******* d i * r e * t u n g s A r g E n i s a R i o n e n d * e ******* g r Ö * s t e n ******* s c h l * * p e r H w e i * ******* s i e d i e m i E g * a n t e n Z W A N Z i C H G H l * m e t e r v E r d e r ******* l i E b I s c h e n k Ü s * * D a u B g r e i f e n u n d a l l * ******* n E R h ******* i t a l i e n P r a * s p * r t i e r e n +Eval: D D D I S D D D D D S S I S D D D D D D I D D I D I S S S S I I I I D S D I S D D I I S D D S S D S D D + +Speaker sentences 531: voxpopuli_deu_000340 #utts: 1 +id: (voxpopuli_deu_000340-voxpopuli_deu_000340) +Scores: (#C #S #D #I) 27 6 3 2 +REF: d a s Z e i G t d e r f a L l j u l i A t * I m O s c h E n k o * +HYP: d a s S e i K t ******* d e r f a * l j u l i E R t D E m * s c h Ä n k o U +Eval: S S D D S S I S D S I + +Speaker sentences 532: voxpopuli_deu_000341 #utts: 1 +id: (voxpopuli_deu_000341-voxpopuli_deu_000341) +Scores: (#C #S #D #I) 30 1 18 0 +REF: W I R D Ü R F E N N i C H T w a S s e r p r e d i g e n U n d w e i n t r i n K e n +HYP: * * * ******* * ** * * * * ******* * i * * * w a * s e r p r e d i g e n O n d w e i n ******* t r i n * e n +Eval: D D D D D D D D D D D D D D D D S D D + +Speaker sentences 533: voxpopuli_deu_000342 #utts: 1 +id: (voxpopuli_deu_000342-voxpopuli_deu_000342) +Scores: (#C #S #D #I) 55 7 12 2 +REF: F Ü r d i E S e E n T s c h e i ******* d u n g B r a U C h e n w i * r V i E l e p a R t n E r n I c h t z u l e t z T D i e s t Ä D t e +HYP: W I r d i * * e I n * s c h e i d u n g * r a * R h e n w i E r F i * l e ******* p a * t n * r n E c h t ******* z u l e t z * * i e s t Ä T t e +Eval: S S D D S D I D D S I S D D D D S D D D S + +Speaker sentences 534: voxpopuli_deu_000343 #utts: 1 +id: (voxpopuli_deu_000343-voxpopuli_deu_000343) +Scores: (#C #S #D #I) 115 10 23 9 +REF: d i e F o l g e i s T e i n h Ö H E n ******* f l u g * v o N p * o p U l i s t E N U n D e x * t r E m i s t e N I n e i n i g E N m i T g L i E D s t A a t e n I H r e n D u m P f E N p a r o * l e n s e t z e n W i * r k o n * k r * e t e * v e r Ä n d e r u n g e n T g e g e n +HYP: d i e ******* V o l g e i s * e i n h Ö * R n f l u g K v o M p R o p * l i s t * * * n * e x S t r L m i s t e * ******* * n e i n i g * * ******* m i * g * i * * s t * a t e n ******* * E r e n B u m * f U M p a r o U l e n s e t z e n * i E r k o n G k r I e t e R v e r I n d e r u n g e n g e g e n +Eval: D S D D S I I S I D D D D D I S D D D D D D D D D D D D D S S D S S I D I I I I S S + +Speaker sentences 535: voxpopuli_deu_000344 #utts: 1 +id: (voxpopuli_deu_000344-voxpopuli_deu_000344) +Scores: (#C #S #D #I) 150 25 16 24 +REF: w E i l d i e i n V e s t i T i o n e n F r a n Z Ö * s i s c h E R u n d d e * u t s c h e r b a n K e n g e r e T t e t w * e r d e n m u S s t ******* * e n d U r F t * e G r ******* i E c h e n ******* l a n * ******* * * * * * * * * * D 2 0 1 0 n i c h t * p L e i t e g E H e n u n d h E u t e * M u S s e s e i n e n r i e s i g e n s c h U L d e n ******* b e r G v O r s i c h * h e r * S C H i E B E N +HYP: w A i l d i e i n W e s t i Z i o n e n V r a n T Ö R s i s c h * A u n d d e L u t s c h e r ******* b a n G e n g e r e * t e t w V e r d e n m u * s t D e n d Ö r H t D e ******* * r i * c h e n l a n T Z W E I T A U S E N Z E H N n i c h t D B p * e i t e g * * e n u n d h L u t e R * u * s ******* e s ******* e i n e n r i e s i g e n s c h O T d e n b e r K v A r s i c h E h e r T D R i * * U K +Eval: S S S S S I D S I D S D I D I I S S I D D I D I I I I I I I I I I I I S S S S S I S D D D S I D D D D S S I S S I I S S S D D S S + +Speaker sentences 536: voxpopuli_deu_000345 #utts: 1 +id: (voxpopuli_deu_000345-voxpopuli_deu_000345) +Scores: (#C #S #D #I) 119 28 32 13 +REF: d I e m i t * g L i E D s t A a t e n d Ü r * f e n n i c h T D i e m Ö G L i c h ******* k e i t h a b * * e N D e n E u r O p Ä I s C h e n s t A a T s a N W a l T d A r a n z U h I n d e r n I n i H r E n r e g ******* i o n E N g a n z g e * ******* z i e l T u n D s Y S T e m a t i s C H k o R r u P t I o n S f Ä L L e N n a c H z * U g * * ******* e * H e n +HYP: d * e ******* m i t I g * i * T s t * a t e n d Ö r O f e n n i c h * * i e ******* m ** Ü K i c h k e i t h a b M D e R E N e n A u r B p ** E s * h e n s t * a * s a M B a l * d E r a n ******* z E R h * n d e r n ******* E n i E r * n A r e g i o n * G g a n z S g e T z i e l * u n * ******* s T D e m a t i s * * ******* k o * r u L t * o n * f ** * * e * R n a c R z E I g E N e S I e n +Eval: D D I D D S D S I D D D D S S I I I S S S S S D S D D D S S D S D S S D D S S D S I D S S I I D D D S S S D D D D S D D D D D D S S I S I I I I S + +Speaker sentences 537: voxpopuli_deu_000346 #utts: 1 +id: (voxpopuli_deu_000346-voxpopuli_deu_000346) +Scores: (#C #S #D #I) 39 6 10 1 +REF: D R e i m i l L i o N E n m e n s c h e n s i n D a b H Ä n g i G v o n u n s E R e r h I l F e * +HYP: * * e i ******* m i l i o * * n m e n s c h e n s i n * a b P E n g i H v o n ******* u n s * * e r ******* h E l V e R +Eval: D D D S D D D S S S D D D D S S I + +Speaker sentences 538: voxpopuli_deu_000347 #utts: 1 +id: (voxpopuli_deu_000347-voxpopuli_deu_000347) +Scores: (#C #S #D #I) 75 14 23 2 +REF: e i n V I e R Z E h * n J Ä H r I g e r j u n g e w I R D i n h A K k a R i v o n e i n E M p O l i Z i s t e n E i n * e S s o n d e r e i N s a t Z k o M m a n d O s I n S k o m a g E s c h L a G e n +HYP: e i n ******* * F e * * T h I n * ** * r g e r j u n g e w * E T i n ******* h E R k a D i ******* v o n e i n * * ******* p * l i S i s t e n A i n D e * ******* s o n d e r e i s a t S k o * m a n d U s ******* E n * k o m a ******* g * s c h * a * e n +Eval: D D S D D S I D D D S D S S D S S S D D D D D S S I D D S S D S D S D D D D D + +Speaker sentences 539: voxpopuli_deu_000348 #utts: 1 +id: (voxpopuli_deu_000348-voxpopuli_deu_000348) +Scores: (#C #S #D #I) 62 14 17 2 +REF: W i e E i n * e h e i l i g E k u H H A t m a n v o r s i c H h e r G e t r a G e n d a s * O p t O u t M Ü S s E u N T e r a L l E N u M s T Ä n D e n w e G +HYP: D i e * i n D e R h e i l i g * k u * ******* E I t ******* m a n ******* v o r ******* s i c * ******* h e r * e t r a N e n d a s A U p t A u t * ** U s * u * D e r a * l * * u N s H E n T e n w e K +Eval: S D I S D D D S S D D D D D D S I S S D D S D D S D D D S S S S S + +Speaker sentences 540: voxpopuli_deu_000349 #utts: 1 +id: (voxpopuli_deu_000349-voxpopuli_deu_000349) +Scores: (#C #S #D #I) 46 2 5 5 +REF: D r e i d e r ******* a R t * i ******* g e t * r e f f e n h a b e n i n z W i s c h e n s t a T T g e f u n ******* d e N +HYP: * r e i d e r a * t R i g e ******* t E r e f f e n h a b e n i n z * i s c h e n s t a D g e f u n d e * +Eval: D I D I I D I D S S I D + +Speaker sentences 541: voxpopuli_deu_000350 #utts: 1 +id: (voxpopuli_deu_000350-voxpopuli_deu_000350) +Scores: (#C #S #D #I) 15 5 24 4 +REF: I C H H O F F E E S D A U E r T N i c h T W i e D e R n e U n m o n * A T e * * * +HYP: * * * ******* * * * * * ******* * * ******* * * * * r D * i c h * ******* * i e T e * ******* n e I n ******* m o n E R D e B P T +Eval: D D D D D D D D D D D D D D D D D S D D D D S D D S D I S S I I I + +Speaker sentences 542: voxpopuli_deu_000351 #utts: 1 +id: (voxpopuli_deu_000351-voxpopuli_deu_000351) +Scores: (#C #S #D #I) 215 29 51 21 +REF: d E s w e g e N e i n e w i c h t i ******* g e f R a * g e a n d i E k o M m i S S i o n K A N n e i n l a n d d i E g r E n z ******* k o n * t R o l l e w i e d e r e i n ******* f Ü H R E n U n * d G L E I c h Z E I T I G I N D e R s c h E n g e N U n i o n b l e i B e n m i t z u g a n g * z * u * M i N F O R m a t i o n S S Y s t e m * * * e t * * * C o * d e r i s T d A s e i n e n T w e D e r o d e * r d i e F r a g e i s T w I C H t i G f Ü r d i e d Ä n i s c h e * D e B a T t e U n d i C H B I T t e u m e i n e k l a R e a n T w o r t ******* * * +HYP: d A s w e g e * e i n e ******* w i c h t i g e f H a H g e ******* a n ******* d i * k o * m i * T i o n ******* * * E n e i n l a n d d i * g r A n z k o n D t C o l l e w i e d e r e i n f Ü * Ö O n ******* * n T d ******* * * * A c h * * * * * * ******* * * * e M s c h * n g e * * n i o n b l e i D e n m i t ******* z u g a n g K z S u O R i * * * m a t i o n * Z U s t e m E T S e t E R A R o R d e r ******* i s * ******* d R s e i n e n w e R e r o d e A r d i e ******* * r a g e i s * w * E S t i C f Ü r d i e ******* d E n i s c h e R I e P a * t e * n d ******* i * * ******* S P Ä t e u m e i n e k l a * e a n w o r t D A +Eval: S D D I S I D D D D D S D D D S D S I I S I D S S D D I D D D D S D D D D D D D D D D S D D D S D I I I S D D D S D S S I I I I I I S I D D D S S S I D D D D S S S D S I S S D D D D D D S S S D S I I I + +Speaker sentences 543: voxpopuli_deu_000352 #utts: 1 +id: (voxpopuli_deu_000352-voxpopuli_deu_000352) +Scores: (#C #S #D #I) 107 10 32 13 +REF: W I E H E U T e s c h o n a u * s * g * e f Ü H r t w u r d e l a g e s n i c h T D a r a n d a S s e s h I e R G r o b e f e H l e R g E g e b e n h Ä T t E s O n D e R n E s g a b e I n E r e i h e v o n * k l e i N e n u n ******* g e r e i * m t H e i t e n b * * * * * Z w * * +HYP: * * * ******* * * * D e s c h o n ******* a u S s C g I e f Ü * r t w u r d e l a g ******* e s ******* n i c h * B a r a n d a * s ******* e s ******* h * e * * r o b e F f e * l e * ******* g I g e b e n h ** E t D I s * n * e * n ******* D s ******* g a b ******* e * n * H r e i h e v o n D k l e i * e n u n g e r e i N m t * e i t e n b I T I E N S w E I +Eval: D D D D D D D S D I I I D D D D S D D D D D D S D D D S D S S S D D D D S D D D D S I D I I D I I I I I S I I + +Speaker sentences 544: voxpopuli_deu_000353 #utts: 1 +id: (voxpopuli_deu_000353-voxpopuli_deu_000353) +Scores: (#C #S #D #I) 61 9 17 2 +REF: E I n E v e r ******* g e m e i n S c h A f t U n G d e R a u S s E n U n D s i C H e r H E i t s p O l i t i K a L s g R o S s E s z i e l d i e s e r u n ******* I o n +HYP: * * n * ******* v e r g e m e i n * c h R f t E n * d e * a u * s n ******* O n * s i * * e r * * i t s p A l i t i G a * s g * o * s I s T z i e l d i e s e r u n J o n +Eval: D D D D I D S S D D D S D S D D D D D S S D D D S S I S + +Speaker sentences 545: voxpopuli_deu_000354 #utts: 1 +id: (voxpopuli_deu_000354-voxpopuli_deu_000354) +Scores: (#C #S #D #I) 72 8 12 6 +REF: d e N n S i c h e r h e i t i s T E I n E s C H w I e r i * g e * u n D d E t A i l ******* R e i c h e * a r b * e i t n i c h t n u * r i m t e c h n i s C h E n b E r e i c h +HYP: d e * n * i c h e r h e i t i s * * A n * I s Z w * e r i E g e R u n * d I t E i l W e i c h e R a r b R e i t n i c h t ******* n u H r i m ******* t e c h n i s * h * n ******* b A r e i c h +Eval: D D D D S D S S S D I I D S S I S I I D I D D D D S + +Speaker sentences 546: voxpopuli_deu_000355 #utts: 1 +id: (voxpopuli_deu_000355-voxpopuli_deu_000355) +Scores: (#C #S #D #I) 108 9 39 6 +REF: K I N D E R U N d P O L I T i k s e l t e n L I E g e n d i e I n t e r e S s e n v o n b Ü ** r g e R n u n D p o l i * t i K e R n s o w e i T a u s E I n a n d e r b e I D e N b Ü r G e r n I n g a n z e U r ******* o P A s T E h T d A s t H e m a * k i * n D g a n z * o b e n +HYP: * * * * * * ******* * * d ******* * * * * * i k s e l t e n * * * g e n d i e ******* * n t e r e * s e n v o n b Ü Ö r g e * n ******* u n * p o l i E t i G e * n s o ******* w e i * a u s * * n a n d e r b e R * e M b Ü r * e r n ******* E n ******* g a n z ******* e * r o B E R s * * h I d * s t * e m a R k i E n T g a n z S o b e n +Eval: D D D D D D D D D D D D D D D D D D D D D I D D D I S D D D D D S D S D D S D D D I S S S D D S D D I I S I + +Speaker sentences 547: voxpopuli_deu_000356 #utts: 1 +id: (voxpopuli_deu_000356-voxpopuli_deu_000356) +Scores: (#C #S #D #I) 11 0 3 1 +REF: h e R r p r Ä s i ******* d e n t +HYP: h e * r ******* p r ** s i d e n t +Eval: D D D I + +Speaker sentences 548: voxpopuli_deu_000357 #utts: 1 +id: (voxpopuli_deu_000357-voxpopuli_deu_000357) +Scores: (#C #S #D #I) 110 12 27 7 +REF: W I R f Ü H r T e n G e s p r Ä * c h e m i t P r Ä s * I d e n T k a R Z A i * z a H L r e i c h e n r e g I e r u n g s V e r t r * e T e R n * f r a U E N u n D m e n s c h e n r e c h t S o R g a N i s A t ******* i o n e n u n D d i e w a R E n * d U r c h a u s e R m u t i g e n D +HYP: * * * E f Ü * r * e n * e s p r Ä E c h e ******* m i t * r E s E d e n * k a * S E i T z a * * r e i c h e n ******* r e g J e r u n g s e r t r I e e * n V f r a * * * ******* u n * m e n s c h e n r e c h t o * g a M i s R t i o n e n u n * d i e ******* w a * * n D d * r c h a u s ******* e * m u t i g e n T +Eval: D D D S D D D I D D S I S D D S S I D D D S S I S D I D D D D D S D S S I D D D D I D D D S + +Speaker sentences 549: voxpopuli_deu_000358 #utts: 1 +id: (voxpopuli_deu_000358-voxpopuli_deu_000358) +Scores: (#C #S #D #I) 70 12 32 3 +REF: D A S I S T Ü B R I g E N s a U c h e i n e u r s a c h e f Ü r d E n ******* * w a c H s E N D E n n a t I O n a l i s M u s d e R A l l E R D i N g s L e i d e R V Ö L l i G p e r s p e k t i * V l o s i s T +HYP: * * * ******* * * * ******* ** * * N g * * s ******* a * c h e i n e u r s a c h e ******* f Ü r ******* d I n E w a c K s * * * * n n a t Z U n a l i s N u s d e * * l l * * * i * g s ******* * e i d e * * F O l i C H p e r s p e k t i E F l o s S i s * +Eval: D D D D D D D D D D D S D D D D D D S I I S D D D D S S S D D D D D D D D D D S S S S I S S D + +Speaker sentences 550: voxpopuli_deu_000359 #utts: 1 +id: (voxpopuli_deu_000359-voxpopuli_deu_000359) +Scores: (#C #S #D #I) 33 7 18 3 +REF: H E u * T e S i n D W I R i M m E R n * o C H s o w e i t v O n d I E S e M z i e L e n T f e r n ******* T +HYP: * O u I D e * i n * * * E i * m * * A n A o * * s o R w e i t v * n ******* d * * * e * N z i e * e n * f e r n S +Eval: D S I S D D D D S D D D S I D D S D D D D D D S D D I S + +Speaker sentences 551: voxpopuli_deu_000360 #utts: 1 +id: (voxpopuli_deu_000360-voxpopuli_deu_000360) +Scores: (#C #S #D #I) 246 32 67 12 +REF: I C H w e r ******* d e A L s F i N a n z m i n i s t e R a u c h I n M e i n e M l a n d J e d e n t a * g d a m i t k o n f R o n * t i e R t d a S s n A t Ü R l i c h a u c h D A s b E W u S s t * s E i n g e g e b e n s e I n m u s s d a S s s t A a T s h A u s h a l t e v o n d e n s t E U e r ******* * Z a H l e r I N n e N u n D s t e U e r ******* Z A H l e R n * F i n A N z i e R t S i n D u n d d a S s W i * r d a m i t a u C h * d i E V e R a n t W O r t u n g T R a g e n B E i * D e n e n t s c h e i d u n g e n d I e W i * r h i e R I n D i E s e M r a H m e n T r e f f E n m e I N E +HYP: * * * D w e r d e ******* * R s W i a n z m i n i s t e * a u c h ******* E n * e i n e N l a n d Z I e d e n t a G g d a m i t k o n f V o n D t i e * t ******* d a * s n D t Ü * l i c h a u c h ******* T U s b * * u * s t Z s * i n ******* g e g e b e n ******* s e * n ******* m u s s d a * s s t * a * s h * u s h a l t e v o n ******* d e n s t * L e r S O a * l e r * E n e * u n * s t e L e r S O L l e * n D * i n E R z i e * t ******* Z i n T u n d d a * s * i E r T d a m i t a u F h T d i * ******* * e * a n t U E r t u n g ******* * * a g e n * * i N * e n e n t s c h e i d u n g e n d * e ******* * i E r h i e * ******* * n * i * s e N r a * m e n D r e f f * n m e * * T +Eval: D D D S I D D S S S D D S D S S S I S I D D D S D D S S D D D I D D D D D D D D D D D S I I S D D S D D S I S S S D I D S S D D S S D D I S S I D D D D S S D D D D D I D D D D I D D D D D S D S D D D S + +>> REF: D A m E N u n D H e R r E n +>> HYP: * * m * * M u n * ******* T e * r * n +>> Eval: D D D D S D D S D D + +Speaker sentences 552: voxpopuli_deu_000361 #utts: 1 +id: (voxpopuli_deu_000361-voxpopuli_deu_000361) +Scores: (#C #S #D #I) 49 7 4 4 +REF: a u F d e m * E u r o P Ä i s c h E n a u t O M O b i l m a r * k t i n s ******* g e s a m t d r A m a t * i s c h i s T +HYP: a u * d e m O U u r o B E i s c h * n a u t E B E b i l m a r G k t i n s g e s a m t d r * m a t D i s c h ******* i s S +Eval: D I S S S D S S S I I D I D S + +Speaker sentences 553: voxpopuli_deu_000362 #utts: 1 +id: (voxpopuli_deu_000362-voxpopuli_deu_000362) +Scores: (#C #S #D #I) 123 19 42 9 +REF: D I E e U R O p Ä I s c h E u n ******* I o n h a * t m i T d i E s e M i n s t r u m e n * T d i E c h A n C e e i n e a k t i ******* v e r o l l e I n I H R e R * n a c * H B a R R e g i o n z u s p i E L e n u m d e m o * K r a T I s c h e r * e ******* F o r m e n U n D E I N e n a c H h a l T i g E E n T W i C k L u n G v O r a n z U t r e i B e N +HYP: * * * ******* e * * B p E H s c h * ******* u n J o n h a R t ******* m i * ******* d i * s e * i n s t r u m e n Z S d i * S c h O n S e e i n e a k t i v e ******* r o l l e ******* * n ******* * * * e * R n a c K T P a * e g i o n ******* z u s p i * * e n u m d e m o U G r a * D s c h e ******* r D e V o r m e n * n A * * * e ******* n a c * h a l * i g * I n * i * k T u n * v * r a n z I t r e i * e * +Eval: D D D D D D S S S D D I S I D D D D D I S D S S S I D D D D D D D D I I S S D S D D D I S D S D I I S D S D D D D D D D S D S D S D D S D D + +Speaker sentences 554: voxpopuli_deu_000363 #utts: 1 +id: (voxpopuli_deu_000363-voxpopuli_deu_000363) +Scores: (#C #S #D #I) 53 12 18 4 +REF: D I E s I C H t A u F T O t a l i t Ä r e r * E G i * m e v o n a u S s e n O d E R v O n i N n e n i s T r e * c h t u n T E R s c h i e * D l I C H +HYP: * * * ******* s * * * t ******* * u L * * t a l i t E r e ******* r S C H i E m e v o n a u * s e n U d A U v * n i * n e n i s * r e S c h t u n * D O s c h i e T l * E G +Eval: D D D D D D D D D S D D S D I S S I D S S S D D D I D S S I S D S S + +Speaker sentences 555: voxpopuli_deu_000364 #utts: 1 +id: (voxpopuli_deu_000364-voxpopuli_deu_000364) +Scores: (#C #S #D #I) 113 17 32 11 +REF: W I R h a B E N i M m e r g e s A G T D a S S e i n E Ü b e r ******* e i l t E s t a t I O n ******* i e r u n g s ******* e n T S c h e i d u n g * * u n ******* s i N n I G i s T w e i L E S z u m j e * t z i g e n z e i t P u n K T k e i n e b e ******* d r o H U n g b e i * * s p i e L s w e I s E a u s D e m i * r a n g I B t +HYP: * E H h a * * M i * m e r g e s * * * ******* * a R K e i n * Ü b e r e i l t * U s t a t Z U n i e r u n g s e n * c h e i d u n g I S u n s i * n * * ******* i s * G w e i * ******* * * T z u m j e R t z i g e n ******* z e i t F u n E S k e i n e ******* b e d r o * * n g ******* b e i S C s p i e A s w e * s * ******* a u s * e m i E r a n ******* g * E t +Eval: D S S D D S D D D D D D S S D I D S S S I I D S I I I D D D D D S D D D D S I D S S S S D I D D D I I S D D D D I D D S + +Speaker sentences 556: voxpopuli_deu_000365 #utts: 1 +id: (voxpopuli_deu_000365-voxpopuli_deu_000365) +Scores: (#C #S #D #I) 97 43 11 44 +REF: d I e S e r v E r G l e i c h i s t e i n e * z * ** Y n I s C h e m i s * S a C H t ******* U n G d e r * O P F E r v o * n m e n S c h e n r e C H t S V E r l e * T Z U n G e N * * * * * * * * * * * * * I N a * * * L L E r * * W E L t * * * * * * E R i S T Z U M a n d * * ******* E R E n e i n * s o * * L c h * u * * n ******* G l a u B l i c h e r a n ******* w U r * f +HYP: d * e * e r v A r K l e i c h i s t e i n e T z U Ü E n E s * h e m i s E a * * t D n * d e r A U B R H r A v o R n ******* m e n Z c h e n r e * I t * Z W r l e I D D n * e L A A B E L S Z S S F H F H F G F a G S R F D A r S T S T S S t S B O D S S A A A i * E O N G a n d A N A N A n e i n E s o E U S c h E u N E n K l a u P l i c h e r a n w O r O f +Eval: D D S S I I I S S D I S D D I S D I S S S S S I D S D S D S S I S S S D S I I I I I I I I I I I I I S S S I I I S S S I I S S S S I I I I I I S S S D S S S S I I I S S S I I I S I I I I S S I S I + +Speaker sentences 557: voxpopuli_deu_000366 #utts: 1 +id: (voxpopuli_deu_000366-voxpopuli_deu_000366) +Scores: (#C #S #D #I) 49 9 5 11 +REF: d i e s p e * * h A t d i e s e u M f a S s e n d e h O R I z O n t a l e r i c h T l i n I e * ** b e f Ü R W o R t e t ******* * * * * * * +HYP: d i e ******* s p e E R h R t d i e s e u N f a * s e n d e R h * U T z U n t a l e r i c h * l i n D e W Ü b e f Ü Ö B o * t e t W E N G I E +Eval: D I I S S D S D S S S D S I I S S D I I I I I I I + +Speaker sentences 558: voxpopuli_deu_000367 #utts: 1 +id: (voxpopuli_deu_000367-voxpopuli_deu_000367) +Scores: (#C #S #D #I) 165 31 54 24 +REF: D E N N E i N E S i S T w I r k L i C H k * l A R D i E F i n a n Z u n d ******* * w I r T s C H a F T s K R I S E V e R l a n G t v o N u n * S A L L e * n e i n m a l m e h r ******* * * * * * * d e r v e r a n t * w o * R t * u n g f Ü r E i n e * * O p t i ******* m a l e U n D V O r a L l E m r a s C H E Q U a l i F i * z * i e r u n g u n S E r e r a R b e i t ******* n e h m e R u N d a r b e i t n e H m e * ******* r i N n e n G a n Z B e * s o n d e r S J e t Z t r e C h N u n G z U t R a g e n +HYP: * * * * ******* G i * * * C i * * w E r k * i * * S k S l * * * i * C H i n a n D u n d E w * r * s * * a * R s * T G H G D e V l a n * t ******* v o * u n D E * * * e I n e i n m a l N m e h r I J E T S T d e r v e r a n t Z w o C F t D u n g ******* f Ü r * i n e U T U p t i m a l e * n * * W r ******* a * l * m G r a s * * * I G K a l i V i T z T i e r u n g u n * r e r a * b e i t n e h m e * u * d a r b e i t n e * m e R r i * n e n D a n S P e R s o n d e r * * e t S t ******* r e S h * u n * ******* z * I t * a g e n +Eval: D D D D D S D D D S D D S D D D S I D D D D S S S I I D D D D D S D S S S S S S D D D I S D D D I S I I I I I I I I I S I D D I I S I D D D S D D D S D D D S S S S I I D S D I D D D I I D S S S I D D S D S D D D D S D + +Speaker sentences 559: voxpopuli_deu_000368 #utts: 1 +id: (voxpopuli_deu_000368-voxpopuli_deu_000368) +Scores: (#C #S #D #I) 117 16 50 14 +REF: E S T L a n d O d E R a u c h P o * l e n d i e s e H r G u T E e r ******* g e b N I S S e e r ******* z i e l E n a L s a n d e r e D i e s i C H s C h w ** E r t u n d i E m i T t e l a b * * z * u R U f E n E t w a r E g ******* * i o N E n w i e k a l a * b r i E n S i * z i l I e n o d E R a u C H G r i E c h E N l a N D O d E r R u * * * m Ä n I e n +HYP: * * * * a n d ******* R d * * a u c h ******* * o N l e n d i e ******* s e * r ******* K u * * D e r g e b * * * * e S e r z i e l * n ******* a I s ******* a n d e r e G i e ******* s i * * s * h w Ä H r ******* t u n d i * ******* m i * t e l a b P T z I u * O f * n * t w a C r I g J i o * * n ******* w i e ******* k a l a R b r i H n ******* Z i T z i l * e n ******* o d * * a u * * K r i * c h * * l a * R * d * r A u C H O m E n * e n +Eval: D D D D D S D D D D I D D D S D D S I D D D D S I D D S D S D D D D I S D D D D I I I D S D D S S I I D D D D I S D S I D D D D D D S D D D D S D D S I I I S D + +Speaker sentences 560: voxpopuli_deu_000369 #utts: 1 +id: (voxpopuli_deu_000369-voxpopuli_deu_000369) +Scores: (#C #S #D #I) 122 19 28 5 +REF: d e R b e r i c h T G A U Z È s F o r d e r T z u r e * c h t d A S s D A s r A t i n g s t A a t l i c h e r s c h u l D t I t * e L A L s Ö F f e n T l i c h e * a u f g a b e b e g r i F f e n u n d d a * ******* h e r V o n Ö F f e n T L i c h e N a k t E U r E n v o r g e n O M m E N w e r D e n m u s s +HYP: d e * ******* b e r i c h * ******* K O E S E s V o r d e r * z u ******* r e I c h t ******* d * * s * E s r E t i n g s t * a t l i c h e r ******* s c h u l * t t I e * ******* E I s E R f e n l i c h e R a u f g a b e ******* b e g r i * f e n u n d d a R h e r * o n ** E f e n * i c h e * a k t Ü H r * n v o r g e n * A m * * ******* w e r * e n ******* m u s s +Eval: D D D D S S S S S S D D I D D D D S S D D D S I D D S S S S S I D D I I D D S D S D S S D D S D D D D D + +Speaker sentences 561: voxpopuli_deu_000370 #utts: 1 +id: (voxpopuli_deu_000370-voxpopuli_deu_000370) +Scores: (#C #S #D #I) 79 16 27 1 +REF: d a W i R E s a b e R n u n m i t e i n e m S O Z I a * L p R o g R a M m Z u t u n h a b e N m Ü S s E N w i R d a f Ü R e i n E e n T s p R e c h e n d e r e c h T L i C H e G R U n D l a g e s c h a F F e n +HYP: d a ******* B i * ******* * s a b e * L n u n m i t ******* e i n e m U T S C a R p * o g * a * m ******* T u ******* t u n h a b e M m ** * s * * ******* w i L d a f Ü H e i n * e n s p * e c h e n d e ******* r e c h * * i * G e ******* * K O n T l a g e ******* s c h a * * e n +Eval: D S D D D D S D S S S S I S D D D D S D S D D D D D S S D S D D D D D S D D S S S D D D + +Speaker sentences 562: voxpopuli_deu_000371 #utts: 1 +id: (voxpopuli_deu_000371-voxpopuli_deu_000371) +Scores: (#C #S #D #I) 16 2 18 4 +REF: A B E R D A S M Ü s S e N W I r n o C h a n a l Y s i e r E n ******* * * * +HYP: * * * * ******* * * * ******* * ** s I e * ******* * * r n o * h ******* a n a l I s i e r * n W O R +Eval: D D D D D D D D D D D S D D D D D D S D I I I I + +Speaker sentences 563: voxpopuli_deu_000372 #utts: 1 +id: (voxpopuli_deu_000372-voxpopuli_deu_000372) +Scores: (#C #S #D #I) 69 19 15 6 +REF: * m a N k A N n n A t Ü R l i C H v e r l a n g e n g e b e n W i R m e H r g E L D f Ü r E n T W i C k L u n G s h i * L F e a u s d i e a R m e n L e U t e B r a u C h e n d a s ******* * * * +HYP: D m a * ******* k * E n E n E t ** U l i * E v e r l a n g e n g e b e n ******* L i E m e * r g * A R T f H r ******* * n D i * k * u n * s h i H V e R a u s d i e a * m e n O e I t e W r a u * h e n d a s A B E +Eval: I D D D S S S D S D S D S S D D S S S S D D S S D D D I S S S D S S S D I I I I + +Speaker sentences 564: voxpopuli_deu_000373 #utts: 1 +id: (voxpopuli_deu_000373-voxpopuli_deu_000373) +Scores: (#C #S #D #I) 104 19 31 13 +REF: g e r a * d E F Ü R k l e i n E R e p R o * j ** e * k ******* T e i s T d a s E I n Ü b e r m * Ä S s i G E R b Ü r o K R a t I s C h E r a u f W a n d r I c h t i G d a S s d a s J e T Z T * a U F e i N E n z e i t R a u m v o n D r e i J a H r e n g e s e n K t w e r D e n s o * ******* * * * * L L +HYP: g e r a R d * ******* I Ü * E k l e i n * * e p * o R j Ä e C k D e i s * d a s * Ü n ******* H b e r m E H E s i * * C b E r o * G a t E s * h * r a u f * a n d r E c h t i * S d a * s d a s I e * * R S a * W B e i * * n z e i t * a u m v o n * r e i ******* * a * r e n g e s e n * t ******* w e r * e n ******* s o R U N U M N T +Eval: I D D S D S D D D I I I I S D D S D S I S S D D S S D S S D D D S D S D S D D S I D S S D D D D D D D D D D D I I I I I I S S + +Speaker sentences 565: voxpopuli_deu_000374 #utts: 1 +id: (voxpopuli_deu_000374-voxpopuli_deu_000374) +Scores: (#C #S #D #I) 70 18 16 24 +REF: i C H k a N n N U r v e r s i c h e r n d i E E U r * o * p Ä I s c h e k o M m i S s i o n i * * ******* s t * * C o M m i t * t * E D * * * Z U r * E u * * * * * r o P Ä I S c h e n P e r s p * E k T i * v e d E s k o * s * * * O V O +HYP: i * * ******* k a * n ******* D E r v e r s i c h e r n d i * ******* * A r L o B p ** E s c h e k o * m i * s i o n i E S s t E R K o * m i t I t Z U M A H R A r A R u B T S A L r o * ** B E c h e n * e r s p I G k i E v e d I s ******* k o S s E R U S N T +Eval: D D D D D S S D D D S I I D S D D I I I I I S D I I S S I I I S S I S I I I I I D D S S D I S S I S D I I I I S S S + +Speaker sentences 566: voxpopuli_deu_000375 #utts: 1 +id: (voxpopuli_deu_000375-voxpopuli_deu_000375) +Scores: (#C #S #D #I) 15 7 20 1 +REF: A B e R H i e R I M H a U S e I S T e S S E H R O f t a u C h s o * +HYP: * * e * ******* * i e * ******* * * ******* D a N Z e ******* * * * H e * * * * * A U f t a u G h ******* s o N +Eval: D D D D D D D D D D S S S D D D D S D D D D D S S S D I + +Speaker sentences 567: voxpopuli_deu_000376 #utts: 1 +id: (voxpopuli_deu_000376-voxpopuli_deu_000376) +Scores: (#C #S #D #I) 66 9 15 5 +REF: M I t d i e s E m * h a u s h A l T k a N N M a n d i E e U * B Ü r g e r i N N E n u n d b Ü r g e r n i c h t Ü b e r * z * e * U G e n U n D b e g e i s t e R n +HYP: * D t d i e s * m E h a u s h E l * k a * * ******* * a n ******* d i * e * N G Ö r g e r i * * * n u n d b U r g e r n i c h t I b e r T z O e L I T e n * n * b e g e i s t e A n +Eval: D S D I S D D D D D D D D I S S D D D S S I I I S S D D S + +Speaker sentences 568: voxpopuli_deu_000377 #utts: 1 +id: (voxpopuli_deu_000377-voxpopuli_deu_000377) +Scores: (#C #S #D #I) 114 20 54 10 +REF: W i R a l S S O Z I A L D e m O k r a T e N n E H M e N m i t g r o s S E R F r E u d e z U r k e N n T n I s d a S s d i N g E d I e W i * r v o r G e t r a * g e n h a b e n J e T z ******* * * * * ******* * * T i m z u s a M m e n H a N G M i t D E N v e r Ä n d E r u n g E n I N d e n V E R e I N i G T E N s t A a t e n u m G E s e T Z t W E R D E n +HYP: T i * ******* a l * * * * * * * * e m U k r a R e * n * * * e * ******* m i t g r o s A F H * r O u d e z O r ******* k e * n n E s d a * s d i * g * d * e * i E r v o r I e t r a R g e n h a b e n * e B z S I C H A U H i m z u s a * m e n * a * R * i t ******* * * * v e r E n d * r u n g * n ******* * E d e n ******* * * W e * * i * * C H s t * a t e n u m * * s e * * t ******* * * * * S n +Eval: S D D D D D D D D D D S S D D D D D D S S S D S S D D S S D D D D D I S I D S I I I I I I I I S D D D S D D D D D S D D D D S D D D S D D D D S S D D D D D D D D D D S + +Speaker sentences 569: voxpopuli_deu_000378 #utts: 1 +id: (voxpopuli_deu_000378-voxpopuli_deu_000378) +Scores: (#C #S #D #I) 102 24 23 18 +REF: d e * * R b e s c h L u S s d A S e * ******* * U r o * * p Ä I s c h e s e m e s t e r * * * h e r * z u n e H m e n u n D D i E k o R r U P t I O n * S s i T U a ******* T i O n ******* * * i m r a H m E N d e r l Ä n D e r ******* b E r I c h t * e z u v e r Ö F f E n T L i C H e n i s t n i C H T a u s R e i C H e n D +HYP: d e A H A b e s c h * u R s d I E e L D A r o R B p ** E s c h e s e m e s t e r H E R h e r T z u n e * m e n u n * ******* T i * k o * r O B t * U n Z s i K L a S i U n E R i m r a * m * * d e r l I n * e r b * r E c h t D e T z u ******* v e r E Ü f * n * * i * G e n i s t ******* n i * * G a u s * e i * G e n T +Eval: I I S D S S S I I I S I I D S I I I I D D D S D D S S D S I S S S I S S I I I D D D S D I D S I S D S S D D D D S D D D S D D S S + +Speaker sentences 570: voxpopuli_deu_000379 #utts: 1 +id: (voxpopuli_deu_000379-voxpopuli_deu_000379) +Scores: (#C #S #D #I) 183 30 33 19 +REF: U n d ******* * * * * m e i n e b i T t e o d E R d a s w a s i c h m I r v o r s t * e L L E i s T d a S s m * O r g e n w i * R k * l i c H i n d e r t * * a t e i n E g r o S s e e i n e b r e i t e m e h r h e i t f Ü r d i e ******* s E k o H Ä s i o n s p O l i t i K F Ü r U N S E R e p O l i t i * K s t I M m t F Ü r d i e m e n s c h e n v o r o r t d a M i t W i R u n s a U F d A s w e S e n * * T L I c h E b e s c H r * Ä n k e n k Ö N n e ******* * * N +HYP: * n d M E I N m e i n e b i * t e o d A M d a s w a s i c h ******* m E r v o r s t D e * * N i s * d a * s ******* m A H r g e n G w i E C k T l i c G i n ******* d e r ******* t A H a t e i n G g r o * s e e i n e b r e i t e m e h r h e i t f Ü r d i e s I k o * L s i o n s p * l i t i G H S O r ******* * * * L G e ******* p * l i t i G S T s t D E m t S Ü r d i e m e n s c h e n v o r ******* o r t ******* d a * i t ******* * i * ******* u n s ******* a * * d E s ******* w e H e n I E A U c h * b e s c * r E Ä n k e n ******* k E I n e D A S +Eval: D I I I I I D S S D S I D D S D D D I S S I S I S D D I I S D I S D S D S S S S D D D D S S D D I S S S S S D D D D D D D D D D S D S I I S S S D D I D S S I I I S + +Speaker sentences 571: voxpopuli_deu_000380 #utts: 1 +id: (voxpopuli_deu_000380-voxpopuli_deu_000380) +Scores: (#C #S #D #I) 139 24 41 18 +REF: w e N n w i * r * * h E U t e d i e S e v E r O r d n u n g v E r a b s c H i e d e n H o * F f e * i c h d a s ******* s * w i * r n a c h e i N E m l a n g E n * * * W e * G Z u e i N E m G u T E n a b S c h L u S s k o M m E N u n D i C H m * Ö c h t e m I c h * b e i D e r k o M m i S s i o n b e d a n K e n d i e U N S D U R C H K o n S t R U K t i V e s a c h a r b e i * ******* * * t +HYP: w e * n ******* w i E r A R h O L t e d i e e ******* v O r A r d n u n g v * r a b s c * i e d e n * o A O f e R i c h d a s s E w i E r n a c h e i * * m l a n g * n K A R U S e L E L S u e i * * m B u D n ******* a b * c h * u * s ******* k o * m * * ******* u n * T i * * T m A Ü c h t e R m Ä c h E b e i ******* * e r ******* k o * m i * s i o n b e d a n G e n d i e ******* * * * ******* * * * * * ******* G o n Z t * O t i * e s a c h a r b e i T H A t +Eval: D D I I I S S S D S S D D D I S I I I I D D D I I I S S I S S S D D S S S D D D D D D D D D D S D D S I S S S I D D D D D S D D D D D D D D D D D S S D S S D I I I I + +Speaker sentences 572: voxpopuli_deu_000381 #utts: 1 +id: (voxpopuli_deu_000381-voxpopuli_deu_000381) +Scores: (#C #S #D #I) 37 5 12 19 +REF: u n * * * * * * s * * ** E r * * * * ******* * * * * e k o n t r o L l E n h a b e n k e i n e n B e l e G * e r B r a C H t I C H K A N N +HYP: u n Z E R E R E s C H Ä A r S C H N U N Z I e k o n t r o R l * n h a b e n k e i n e n P e l e * G e r P r a * F t ******* * * * ******* * * * * +Eval: I I I I I I I I I S I I I I I I I I I S D S D I S D S D D D D D D D D D + + diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..eeb0c5ffefa397addc7c52d57bb503589096e9f5 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/hyp.trn @@ -0,0 +1,661 @@ +DIBE HRDIGON MACHTEINER AEUSESTWICHTIGEN SEHEIN INDE DER PÄTIT ZION N DINGOWANÜÖR FÜRDES IN JANERDSOUSSBEGENA DIGUN (M-AILABS_deu_000165-M-AILABS_deu_000165) +DARHABESI DIEVWOULGJEDEM HER INER INERUNKGEBLIEBENEN WARTE GESPAOCHEN (M-AILABS_deu_000166-M-AILABS_deu_000166) +ERST UM ACHT UHR WAR ER AUF MALEL BRCHTERDEN GAFI DESNESCHEN INZSZIMER UNDESPEHRLINGE DIE DASSAUS DEN HEXE SEGTEN GEFALNE OTARKON AUFSPBIKTEN (M-AILABS_deu_000167-M-AILABS_deu_000167) +SICHERLICH AN IRENGEBUOTSTAKET ERBAI ERBLEIBEN KONEN (M-AILABS_deu_000168-M-AILABS_deu_000168) +NDESEALBEÖM MUSMAN DAURT VOROMENTCHNSCHIERIGKETEN HABEN DISOUCH EINA SEITS EKLIEREN AINGEBUTEMACHENBEALE W (M-AILABS_deu_000169-M-AILABS_deu_000169) +ESMEN NRFTDIELT KOMT UMSEBSTIDER IN SONDZUOHABEN DERDIEVER ERHUNGK DER ANEN VORTETZT (M-AILABS_deu_000170-M-AILABS_deu_000170) +A BEÖAN EUNEIN LICHER SCHULEBELUNG UNTEN ERLICHE MÜKLICHTEITRAUH DEREITERBLUNG UNT DAS DEGEHN VONGEDENKTAGN DEMICH UN AUFLSLICHTRAMD (M-AILABS_deu_000171-M-AILABS_deu_000171) +EIN ANSASHEN SAKZI SISTWERGERTSTWIEDER GANZSKGUTZWISCHEN UNS ARBE E DUNICHT ALIESGESTDIESTD GIETIE ER INRUNGANDAS BEÖSENICHTWEG (M-AILABS_deu_000172-M-AILABS_deu_000172) +NEIN WEIBERBRAUREICHENICH (M-AILABS_deu_000173-M-AILABS_deu_000173) +ENDENGORT HAT NICHTVER GEBLICHNEHMER GERUOFEN SAKTE DERCHIEVER (M-AILABS_deu_000174-M-AILABS_deu_000174) +NUR EINES WEISSICH DIESER VURCHTBAREN FRARGEINDGEGEN ZUSETZHEN UND SCH LEIDERERDASWART IN DE WARSCALLEDI GLUT LEINIES LIEBES WILENZS IST STERKER ALS TRENUNG (M-AILABS_deu_000175-M-AILABS_deu_000175) +TOMSAMI GE ANEIN GROSEN SIEG NEHINELLAN HERD NECKIGEN SLCHT (M-AILABS_deu_000176-M-AILABS_deu_000176) +SSEIN NAHME DEM SICHTI TUHEBEITAKUN NCHTAFNEN KANBRERSCHER UNDT WER KOEN (M-AILABS_deu_000177-M-AILABS_deu_000177) +ENABER ICHFERT SEIE INEN IRER UNWISEN HEIT (M-AILABS_deu_000178-M-AILABS_deu_000178) +FON DERTRITTEN UNTEREDUNG AN SAKTE MISTERHERVISCHEM WARMER DIE PERSUN IN HUHEMASE VERDECHTIH (M-AILABS_deu_000179-M-AILABS_deu_000179) +ICH ENKE DE AMT MANUNSANE VERMEIEWERDENESRECHT VON DERFINDEN TDASTU DIHSELPSTANGIBST UN SEWERDEN HREUNTLICH GEGEN DIESEI (M-AILABS_deu_000180-M-AILABS_deu_000180) +ETZSCHLUOK TIE HELE FLAMMER AUF UND NUN ER KANTE ER UNS DIEWINR IMERZSAMEN GEDRENT INDEMWENKESTANDEN (M-AILABS_deu_000181-M-AILABS_deu_000181) +DER SEINE SIELE ANSPBONENDT DAS ERMUNTERNDEWAUT VORWEALTZS (M-AILABS_deu_000182-M-AILABS_deu_000182) +VRMICHAFTDIN BSUCHTDES TUNESUSHEM MNESAPERSE DEANDTEN ORNT (M-AILABS_deu_000183-M-AILABS_deu_000183) +WASTFIHRE HER FOLGUNGEN WASFIRNARSTELUMEN HABICH NICHZU EARDULEN GE HABT (M-AILABS_deu_000184-M-AILABS_deu_000184) +SIKOEINARWAREN ES IE ON AUTZUOALT VFOREN EIN KAUM AWAKSNDES IUNGES DIN KAMTZUMIEHERAN GE HÜEPFTUN BÄTKELEDE N NEIN (M-AILABS_deu_000185-M-AILABS_deu_000185) +AK ICHWERTIH INE BOTHIN FEAN DERTASBUOTER AN LEGUNDITERTZOEGTUDEN AES GANZSELLEIN WOS DEC GANICHTHUM ZOKEMAN (M-AILABS_deu_000186-M-AILABS_deu_000186) +ALS NR EINMALUNOCH EN DRAUCH VEN ANEM HAUSAUS DER VERNER AUFSTEIGENZUESEN UM DANBE RUEKTZU STDERBM (M-AILABS_deu_000187-M-AILABS_deu_000187) +IE TENZERIN ABARLARKAUF EN KNIEN VOR BRAMASBILTENIS IN NAHMEN LOSERSEN SUCHT UNDT WEINTE JAMA VOLLT (M-AILABS_deu_000188-M-AILABS_deu_000188) +ECHT FERTICHT MICHSENDE WEGLIGKEITNOCHNICHT A FTDIEHICHBE UFENGKHR (M-AILABS_deu_000189-M-AILABS_deu_000189) +ICHELRGERTEMICHTANWENICH AUH FACHTER EISWASUF WUNDERSHEÖNEN GEWEESENDARS FLIEN (M-AILABS_deu_000190-M-AILABS_deu_000190) +NERH DEMESCHONDIN GANZEN VORMITEAG MIT IM VARBRACHT KAMS DEN HOB NACHTISSCH IN ZSKRANSCHERHAUS UM GASBALE OLTZOS RGEN (M-AILABS_deu_000191-M-AILABS_deu_000191) +HRWAR AIN ALTER HIRHT VOLMEDIE ZINESCHER GNENALITET (M-AILABS_deu_000192-M-AILABS_deu_000192) +DESVOL AUHTDERMIETAERSEINE VUNDARICHSKEITEN HARBEMSE (M-AILABS_deu_000193-M-AILABS_deu_000193) +EN SIESAN ALE ENSTLICG UND BETRÜBTAUS UND AUCHER ARENE SASCWEHR MÜTICG DAR WIE DIE ANDEREN UNDSTÜTZTE DESHAUPT INDIE HEN (M-AILABS_deu_000194-M-AILABS_deu_000194) +UNTE REN DAMEN MEISTIONGE FRSCHIGESICHTERAUNTER IN HEREN NEBENM HUENDKLICHEN SOCHMIT FALTIGARSTIERN UND BREITZSMEHRAU DARMINDER MOND UMG GLENZSTEM SCHÄHTEL (M-AILABS_deu_000195-M-AILABS_deu_000195) +SEI TEREN CHONATDEIS BESONDEAS DREIEND EKLUNGEBER (M-AILABS_deu_000196-M-AILABS_deu_000196) +SONDERBARH (M-AILABS_deu_000197-M-AILABS_deu_000197) +ERBP VON EHRBMHEM STAND MIZEN HR GATEIN VOL WE MUTUNG DANKGBAKEI DANDER GROUFT UF DER IN MECHTIENG (M-AILABS_deu_000198-M-AILABS_deu_000198) +IERWAIHIEDEAMENSCHEIN UNDER UND FASTALLES VESMENSCHENTALTEN IT ASSFONDABARES (M-AILABS_deu_000199-M-AILABS_deu_000199) +WELTHE JERWE SIE END LENGSTFIER (M-AILABS_deu_000200-M-AILABS_deu_000200) +IEWERTIN SASNICHTHINTE REM SCANKTIS UND KEINE ERER DIENZST LOUTE BEFANZEI HNDERSTUBE (M-AILABS_deu_000201-M-AILABS_deu_000201) +ALS DE HERSCHAFT AUSDERKIELCHETAT STANDEN DIELEUITE UM HEHR UMSIE VORBEIGEHNZUSIEN UND AMKELCHUFSTORERWATE TE EIN MAN (M-AILABS_deu_000202-M-AILABS_deu_000202) +SMSNMEL TORNOM DIE TARISMUS EN GNKTI (M-AILABS_deu_000203-M-AILABS_deu_000203) +GELAUBERDEASIES GUDET BERMEINENHERTACT (M-AILABS_deu_000204-M-AILABS_deu_000204) +ENTRIM ANFANG GEWANE KEINE AUFMER SAM KEIT VER ANDERE DINGE ALZFÜRDERS ESEN (M-AILABS_deu_000205-M-AILABS_deu_000205) +DIES FLÄSCHEN ZOGERGETST EILICHER VOR WERENDJENESICHMIT WASARFELTEN UND BTES DER UNKVERTZIÜS AN (M-AILABS_deu_000206-M-AILABS_deu_000206) +SER BASAURICHIHON WICHTICHDEASCHINER DRCHERTZS ANSHPUSFOLL GESAKTAT VERWERDEN UCRHAININ ZEIT BUNG DERI DUKTZIUN KOMMENDESDGU (M-AILABS_deu_000207-M-AILABS_deu_000207) +NICHTDAOCHMUTER WERGESIE ERTZT NCHNIG (M-AILABS_deu_000208-M-AILABS_deu_000208) +A BIER H HABEN INENDETZTNJANRICHT ENGE BITIUN ZUBASIIEN AUFGEBAUTPR (M-AILABS_deu_000209-M-AILABS_deu_000209) +S SIE VIR DESICH NICHT VER ANDR ABPFON (M-AILABS_deu_000210-M-AILABS_deu_000210) +LECHRIFEN METZ (M-AILABS_deu_000211-M-AILABS_deu_000211) +GKOT WASI IE AR ZTELTER HÖREN SIN NUR IS ISEIN GANZERUMARN (M-AILABS_deu_000212-M-AILABS_deu_000212) +SEINEMTER KININ MOEFLUSWASERGEBEN DESELBPWEIND ER (M-AILABS_deu_000213-M-AILABS_deu_000213) +UNDSWOTCHASMINDESTA WERT EMEN USAMTERNETZ ARGENTUR AMFIERTEN JIUNI ZUM ERSTENMALPRESEND IERN WIESICH DIE NETS BETREIBER UNG I KRASTDARKGE DINEUNETSTLENE VORSTER UN (M-AILABS_deu_000214-M-AILABS_deu_000214) +EBWEARATESECHT ZEIETAND VORTODESFVECHER VONDEM GETABEFREITUN ZUCHTETZU NDFINEN BE DER SMALEGATEN BUOT KEINEN AUSFI (M-AILABS_deu_000215-M-AILABS_deu_000215) +BICHMEIN WERKFWRUTE LETEN LASEN NDE NRHEIN AN LAUFNEMENUNTESROLENDN SOLTE (M-AILABS_deu_000216-M-AILABS_deu_000216) +EHR WA DASKETZ HEN DER STUND DETEITEIBE AUFTRAKTEMADAM UNSCHEL DIEAUCHTASTAND UNDIGEKAUFTENSEIDENS DE KETZUSAM FELTETE FÜRTSCHNDZUSORGENG (M-AILABS_deu_000217-M-AILABS_deu_000217) +DWER EN ACHSIEN (M-AILABS_deu_000218-M-AILABS_deu_000218) +ABAL E TEBS ODAF VOR GABEN DAD MACHM ENEDILIGNICH S (M-AILABS_deu_000219-M-AILABS_deu_000219) +ALS UNSRE E DEBEKANT WODER WADI FÜSIOG NME DERWEALTEASPBOGER UNGERFÄHR DI EINERS KEALBERS DASZUM EHRSTNMALTDONHÖR (M-AILABS_deu_000220-M-AILABS_deu_000220) +ITZE MCHNS GE FÄLIHT AUF UNDESKLAN DIE EIN AMANDER HILVER (M-AILABS_deu_000221-M-AILABS_deu_000221) +ER DAOKTORSAER INEFRAU DESHNOGRIENER DISE AFTZU INEN KOM IST ENGIGANIGKRAN (M-AILABS_deu_000222-M-AILABS_deu_000222) +DIE ALTE ER IN RUNGAN DIN FRÜHREN TAUM TAUCHTE EBEN FALZWIEDER AUF UND UNWIELKÖRLIC FASST BAI DERBE HAUPTUNG DAS DE SELE EN KÖRPER VELLASTEN UND TZU IEM ZURÜCKEREN KENE SCHENES IER ARDENDLI (M-AILABS_deu_000223-M-AILABS_deu_000223) +ALZ SIE AFRIN BALKON ZURE KHRTE FANZIE IEN DI SEITUNGKLIESEND TDIE WEREN RISVORTZEINSANGELANGKTWAH (M-AILABS_deu_000224-M-AILABS_deu_000224) +TE ERWAR EIN KIN DRSTRASE VON KLEIN AUF ABER IN IEM LEBTE VON JEHR INRGWISE SEN SOCHT NACHEINEI ERBAREN BÜRGERLICHEN E ISTENS (M-AILABS_deu_000225-M-AILABS_deu_000225) +IS UNES DG HRUNKFÜNUN EHNIG EINER GROPEFE ANFORDLICH SONA WÜEFÜNENS IN GMEINWUOLFR ANWORDLICH N (M-AILABS_deu_000226-M-AILABS_deu_000226) +WAS MEILIEBES GEINT WASGKAN (M-AILABS_deu_000227-M-AILABS_deu_000227) +UND DANWULTEICHTIN ANBLIG DE RANICHTMESSEN DEMÄR GEBLEBEM WAREN VOR ELEM ABAWAR ISNI DARUMTZUTUN WEINDESYSE LICHIESERBET EINIGAMASSEN GETREÖSTET ZUSEN (M-AILABS_deu_000228-M-AILABS_deu_000228) +ERDAS AUCH WIER UNDS GNGSEI DIHM BISHN UNTARSTITZENKERN ERM (M-AILABS_deu_000229-M-AILABS_deu_000229) +SEINE ESCHETICHELAUF BARNHARBES DIEBENSN ASKÜCHEN BO IN EINRMUTELFVIERTEN GRADESBGON (M-AILABS_deu_000230-M-AILABS_deu_000230) +FILEICHTE EN SIGUTISE ANSICHTEDES BSCHOFEN HUSETZUMELEN ZAKTE ERTATSHEN DEHR IMAR MHR EIN MANDES GESCHIEBENENWURTESVIE DER TARDT (M-AILABS_deu_000231-M-AILABS_deu_000231) +AMANDANMOREN ERHOPERSICHSHPET SCHIKTE N LARKEIEN N DE BUNG VOR ABACHSUNTLISUM EIN NTERIEDUNG BITENDEMAN KAM MITER BOTSCAFTT ZORK DT (M-AILABS_deu_000232-M-AILABS_deu_000232) +TH N EINWENICHTHAURICH WURDERS WEIN ME DEA SEL BERKAMENSIN NE ZU FRIEDEN SCHIEN (M-AILABS_deu_000233-M-AILABS_deu_000233) +EIN SOMAHRWAHMANOWENMBARTARKLARGMITZON GLIT SANN BERDE HUTSTABT UND UNDE DIN LIENDENDRENGKTE INE TAUSEN KER FIGERMENSCHEN MENGER AUORFVON N EDER (M-AILABS_deu_000234-M-AILABS_deu_000234) +KOMIT MIH MEINSON DEN ICHPRAUCHER DEINELEBE (M-AILABS_deu_000235-M-AILABS_deu_000235) +NORE SAN KESICHT RODEINWENICHNACH DENKLICHER SOO WIE VON EINER ER INERUNGK ER HELT (M-AILABS_deu_000236-M-AILABS_deu_000236) +N WUT AUFIDE DE NWATIUNS DROKK STEIGEN UN AZU SASESTEMER ENGEFÜRT WON (M-AILABS_deu_000237-M-AILABS_deu_000237) +NET GEWARTE EAMIT EN SETZSN DI SCHOUISLICHERTOE FLSCHER AHFEN FRATZE DIE BE DISMENCHENSHULTEARSCHIELTE (M-AILABS_deu_000238-M-AILABS_deu_000238) +ERR DERWIERDNIGTE DASGÖRD EINE GEWISEN WUETSCHAF BERNHAT WURTSCHOUF ISTE TWASFACGNSEN (M-AILABS_deu_000239-M-AILABS_deu_000239) +WULT EHE IN WEHEIDIE ESEN TÖRTEN UNDT KND IERSCHLISEN (M-AILABS_deu_000240-M-AILABS_deu_000240) +BAT ZEDI SERESPEKTVOL UOBEI ERN NUR EINIGESEEN VERSCHLUKTDE WAS IM BEIT EN BELIEBTEN LAN GEN WÖRTAN DES EFTAN VORKA (M-AILABS_deu_000241-M-AILABS_deu_000241) +LORT FONDLEROAIEVERTNICHTZSEND BEREN DESEN BINICHGEWIS VERSETZTEHR (M-AILABS_deu_000242-M-AILABS_deu_000242) +KAM GLEICHFWALS INS SCHLAFTZIMER AUF EINEN NARGEL INDERNER DESBPETES (M-AILABS_deu_000243-M-AILABS_deu_000243) +DAS DISCHANS DEHNDIESARKRISSTEG DISCHAUNGS VÜR INTERNATZEHNALEREGEN ISIEN EM RON ZFIBPTE DESE TSWALNMAKLTA ERINTIEN (M-AILABS_deu_000244-M-AILABS_deu_000244) +ANFANGSFIELDEREIGEN SCHRÄ UND PEITST ERS IE EINER DAN DIEANDERESEITE DESWAENS (M-AILABS_deu_000245-M-AILABS_deu_000245) +FASTR LEICHTZEINIGENBER MESUNG ERESWERTES AUFTZUGEBEMSICH ENDTSLOSSENHATE (M-AILABS_deu_000246-M-AILABS_deu_000246) +SSEIST DIE FRARGEMENCHICHEN ABETN DIERARGEWAS KAN TÄCHNS GLÜSTWEREN D (M-AILABS_deu_000247-M-AILABS_deu_000247) +ISAR FARI WAUFDIRIDEMEHSIG BENTZTEN WASSARSTELLEN IESERUTANGERIESEN (M-AILABS_deu_000248-M-AILABS_deu_000248) +DIEBEITEN MISTEN HIE OBEM AUF DEM GEPVEL GESTANTEN HABEM UNDERSPRACH DIE ALTEN WAURTE VOSECHEN (M-AILABS_deu_000249-M-AILABS_deu_000249) +ENTLIC PIKTESEDRIG AUFH WEISEN UIG ALES VONDIN ARMEN LOEITEN FRACKTE ER (M-AILABS_deu_000250-M-AILABS_deu_000250) +SHOLDE INE WUNDABARIR SAMABEITZUÜCHN BUND UND LENEAN INDIESEN RAGENGIEB DET SERSE INTRESANEN PRIEGKTENOU (M-AILABS_deu_000251-M-AILABS_deu_000251) +KASBA FERHARTER AN GENMRETZELTENSENMPLTZS ZEIN LE DER JA SEINER AUGEN BWANIE VERSTEINERT ALSEITHUN ZWEITEN MALHIN IKTE (M-AILABS_deu_000252-M-AILABS_deu_000252) +EINIGET ZEIT DANACH FRAKTE ER MICH OPICHGKLAUBER DEAS DER EIS GANG DIN SHLITENDES ANDEREN ZSER STERTABE (M-AILABS_deu_000253-M-AILABS_deu_000253) +ABENEN BLÜSE NICHTGENEINER SCHOSTAR E E VALEN (cv_deu_000698-cv_deu_000698) +JEITS KOMIAERSCHEUN (cv_deu_000699-cv_deu_000699) +S DEM BEIE ABETD DE EIS AUSERELS KRAFTAO ENOFHA (cv_deu_000700-cv_deu_000700) +EINTERIT TORHEIGKOSELS O HOPAWIETD NICHTMIT EN IETAMESISKEINERE AOCHOPAREICT (cv_deu_000701-cv_deu_000701) +E SHOUN KABNDER KÜNZSLI HER BE VROCHTDUNG TZERBERT (cv_deu_000702-cv_deu_000702) +DEIN NACHT ERKTIEF NE FALRTERFLEGEN VONMITER IOUOLDIEBESSMITER OPTOE (cv_deu_000703-cv_deu_000703) +ELRE THE (cv_deu_000704-cv_deu_000704) +EIND HEIREN (cv_deu_000705-cv_deu_000705) +MUOZE KN IERERLESE ZEIGE ONEIN ABSPEICHER VERWALITEN UN BITANERENOTZAN TEIREN (cv_deu_000706-cv_deu_000706) +DE DEM BOSGON KTERAE (cv_deu_000707-cv_deu_000707) +SAUL BAS TZEHL ZU DEN G N RTISTEN DISEI DABMAUMNF MECR ÜENDL SERLZERIUDN (cv_deu_000708-cv_deu_000708) +IN KMÜNÜMWÖHR SEBELAEN WELNBAUG K EINER SEL BONDEREISCHE (cv_deu_000709-cv_deu_000709) +EITERE WICHTIGE INDESTRIZWEIGESEN DE MIKRUMICHANIG GERWANOPLASTIG MITEIBAU UNTIE HELTZVER ABEITUNE (cv_deu_000710-cv_deu_000710) +IBER DEN AUTOR IS NIHZ BEKAND VERMUTLIG STAMTEHR AUSE DEITEN PRAHGBIET (cv_deu_000711-cv_deu_000711) +NDSTDEUÖER ISSMI ENE TOPLE PATE (cv_deu_000712-cv_deu_000712) +DE HORBMENBEBLEMER POSIESCHIG AUCHT (cv_deu_000713-cv_deu_000713) +EWISCHIEN MANUR ABARE EM AUGTÜURE ELTE IT MACHBERH (cv_deu_000714-cv_deu_000714) +KEDE ZAL IC DE ESE L DE ARNANN AN EN ESHEWABENG (cv_deu_000715-cv_deu_000715) +EE ESE ENZ NAMER IN MINCHEND WO E AUGSTABEBEI (cv_deu_000716-cv_deu_000716) +INERNH T UND ELSERER NARTEIGS KÖRDEN ALSGERENTETAEILE ENDES NARTIGS AUR GEMEINSAM VORKOMEN (cv_deu_000717-cv_deu_000717) +DABEIE BELEGKTER EHR DIEPLÄTZSE FVIER UN TREIC (cv_deu_000718-cv_deu_000718) +A KE DEABIJE IS DIE THOUCHTART ZWEIER B ROFVESENE ARTENZAN (cv_deu_000719-cv_deu_000719) +DIS KLAUBETAS FÜRT NIST IN RISTIGERISTRUNG (cv_deu_000720-cv_deu_000720) +DASES EINE X STRENSHLESTERISTFLIENER (cv_deu_000721-cv_deu_000721) +HERLOSCH EN BLEST ZSAIN HAGERESGESIHT (cv_deu_000722-cv_deu_000722) +MR KALEN FINDETOSS UN FER (cv_deu_000723-cv_deu_000723) +T INGEBO KABERHAT DER DEIGESCHISTE NT (cv_deu_000724-cv_deu_000724) +T SKOMPLITLISTAB ANMN DASSOLCE DATEN U DIESER EBNE PAST BERDEN (cv_deu_000725-cv_deu_000725) +TRAMIN HEN GEGENER IBT EIN HRMUNICHESPOSIEREN (cv_deu_000726-cv_deu_000726) +BENIH ZUM KAF ENER HPOETEG BEREICHDIGKT (cv_deu_000727-cv_deu_000727) +KC ZUN E DEUNEN EUNEINERSCHEN ENEN DERN ENENN (cv_deu_000728-cv_deu_000728) +H HUNE DEIREN SEN DESELWDSLENDNUN (cv_deu_000729-cv_deu_000729) +ONE DE ROWISENELENG TSTIZUNG DAMASS ATDIRENABTALIUN WANDISEBAGEN DER KONKEWENZS EN DOCHUNTOLEGEN (cv_deu_000730-cv_deu_000730) +SI IEN DE UNECST ASUN EKUMF VERBELLGISCH IESATZUNGSTRUPEN (cv_deu_000731-cv_deu_000731) +DEMISEN WIESPLÄNGEN MEITE ER ZHAN ATZT (cv_deu_000732-cv_deu_000732) +AUER DEM SPHIETE RBERMNACH VERGETIEM NINMARKET ROEDIUS SOWIEBER LIEGER KONKUERENTEN LONDNEITZ (cv_deu_000733-cv_deu_000733) +IE AUPTERS ENZSTEN WAN AHFVAUTHING AER FÜRTI KUOUMS WAL DAS KON DOARECKETK TERIAOMNICHT (cv_deu_000734-cv_deu_000734) +SMET FWUOEINTCHI KAR BOAUF (cv_deu_000735-cv_deu_000735) +WIE IET IE ALEINEN (cv_deu_000736-cv_deu_000736) +N DBUM IES BERTWAS BWEIS WUERTROST DES DESUÖRN BISENS VELCHUNIE (cv_deu_000737-cv_deu_000737) +HARBTEMA ER SCHEO IST DE REWANSC VEREGÜNBIC HISTREI HERENTE PFREINEN (cv_deu_000738-cv_deu_000738) +LEICHZEITIGH WUONDEN SPROTDWERTEN TALLWEISE VERBULHEN (cv_deu_000739-cv_deu_000739) +D A SEN HSFSSANAAFAAAE (cv_deu_000740-cv_deu_000740) +OTST IERER N N NFBENN (cv_deu_000741-cv_deu_000741) +ZU DEM FARSA EIERLEM KLOSTALNG JARE DEMTERDESE NOWITZEN MEISTASUNPRIOR (cv_deu_000742-cv_deu_000742) +HEIDEN HEIDEN EN STMT EINER ERZTE VERMIEER (cv_deu_000743-cv_deu_000743) +D ARER TZUEGKTENEN (cv_deu_000744-cv_deu_000744) +ZWEIEL GNR (cv_deu_000745-cv_deu_000745) +EBEPFEALLEIN AUGN ENGEIETZEN IKLTEREIN DER E AT (cv_deu_000746-cv_deu_000746) +DIESERS DET AHFE ABPELWENDEN EINHRMSCHER CHOLMETARTSKNTDESEN OFEM (cv_deu_000747-cv_deu_000747) +AL SO ECHIORENIGS (cv_deu_000748-cv_deu_000748) +WEKON MAN SISCH SCUTZEN (cv_deu_000749-cv_deu_000749) +AFÜINFMNERT N LAG EINE N FINDLICHERBEPLOTE ANZTDEBISTDAHEINER HLTLICHEN VOR (cv_deu_000750-cv_deu_000750) +TILIE TERS DIEBW INSTNMUNG INESOFTENSNSTEBZSMINTSANASCGIEZICHIKAZHOUNZUNBABPRLFMCH (cv_deu_000751-cv_deu_000751) +ATENETHEILEN WAHAMEN GEN SHEN IHENDALUS WESZISTIKELNTELLN BESORAESHEILTEN (cv_deu_000752-cv_deu_000752) +DIEANDIE WERBEN SOSCHÖHR IST ANR KEN GEL RUSCHEN ND AND ALER GBNBHENTELNRSSLANGELIGT (cv_deu_000753-cv_deu_000753) +IER TU ARGE IST EN DIESAR ZEI RT GUNEFLANENSC (cv_deu_000754-cv_deu_000754) +E DIE SCEÄKE BEGEN MSÜGEN BERHUMARS IN FÜLTIS DIE GO B DE EICHT UMEG AR SIÜT AUSSTE (cv_deu_000755-cv_deu_000755) +ERST VON DORT KONTE ER EN WEG FREI E VOTZSEITZSEN (cv_deu_000756-cv_deu_000756) +SIERHEBZICH HEUTE IMERNOGUTERKENBER T AUSSTDEIEN SCSH WEM LANT HEAUS (cv_deu_000757-cv_deu_000757) +DIEKANARESCHEN INEN E HANZU SPBERCHE (cv_deu_000758-cv_deu_000758) +WESN SCHR FLERHRBEN DIESE MU TDTZ UN ESEHRENEOBEI FOARAN BEREBATET (cv_deu_000759-cv_deu_000759) +SENIGISCHETZSPETZIUNEN REISCSTEN BIS NOR DAMEHRIKA END ARSIEN (cv_deu_000760-cv_deu_000760) +AREICHE VORDERED MAETZIERUN EN BEI DUTSCHEN EROPAR UND WEHTLECSTE SCAFTEN ZO BIE NLÜBICHEN SPILEN VORLKTEN (cv_deu_000761-cv_deu_000761) +IN AN ERTELIED SOTUEM LETERMD SOE IGKIT AUFTERPARGBANG (cv_deu_000762-cv_deu_000762) +IT IUM WAMITRENG IN BAUPLSSI DIE KERLTE BESAUSHADIE (cv_deu_000763-cv_deu_000763) +WOENE EN T ER HEWESSI (cv_deu_000764-cv_deu_000764) +AOSTANIS ENE EINE BOCHENACHTEN ESTEN UE MUN DIN RLULEN (cv_deu_000765-cv_deu_000765) +ENM MITEL EITERHERTEN EÄKZIN DE HERSCHAFT DASTDAFH INER (cv_deu_000766-cv_deu_000766) +DEN NAMSCHEPLE TRAR NAOCHLEITE EFATSOLGE VER MASSERHATI (cv_deu_000767-cv_deu_000767) +PLUKANEMIT E BUSLOCHAN VON ER ONDEREFHOREN (cv_deu_000768-cv_deu_000768) +NER EORIEGOL (cv_deu_000769-cv_deu_000769) +ALLERTINGS ER GAHBEN WEITERIE PUÜNFVONGEN DAS ES MITELFRISTIKEIN PIDAFISCEUCHE AUTUOBAN DEHRER (cv_deu_000770-cv_deu_000770) +UNGEKRT KAN EN FREIPRIEFF EINEHARAUSCHREIBUNG ALZ VOBELEFREI GEMEINZEIN (cv_deu_000771-cv_deu_000771) +MBIEZAR KORTES GE ABSCHNITER SEIEN EINPFLUSE DUIG SCHOSTDARKOLNWIESCSH (cv_deu_000772-cv_deu_000772) +RER EINERDERPIAERNIERE AFTDM GEBIET DERNUTZIUNG DER SONE NERGEEN (cv_deu_000773-cv_deu_000773) +ARHFEN MEDIE KONDEN AF INE RFENGEN MUSICHELICKET PBEWAN (cv_deu_000774-cv_deu_000774) +DICHBEMASCHINE ST VERTISH (cv_deu_000775-cv_deu_000775) +IN DE ARCHERISCHEN PERIONDE WURDEN ERSTEIE VORMEN DESOCKEBASSNIN TUIKELLT (cv_deu_000776-cv_deu_000776) +DIE KOMÜDE SE BESE LTER ESTE FÜN (cv_deu_000777-cv_deu_000777) +ARTZLERE GED VER NERMUMEMS ALP (cv_deu_000778-cv_deu_000778) +TDERMIT ENDE EINE EAE WERKREISCH IE ENAR ZUNERLIENKÄERDENS ABEN VORELEN INMNSISCHKÜNTEN AUNENENZSKAU (cv_deu_000779-cv_deu_000779) +DERSON EINES BEREGNANZ BEGAN EINI USPALKER JERI WEI DENSPURT FREINDTEN WANE EIKELN (cv_deu_000780-cv_deu_000780) +IN DIEN JARGABES SIEDEN NOMER EIEN SINGES UN SÄCHSUN REISZIG NOMER EIENS ALEBEN (cv_deu_000781-cv_deu_000781) +N NORD WESTLICH VERN HARGHAUSEN BERFINDE SICH IO ARTSCHAFTHAEKEN BOREUICSCH (cv_deu_000782-cv_deu_000782) +IEM ORT KGN NAEN BURG IENEN VIELESOS SIAE EINERICHTENUN VON EREMAN LAMPRÄCHT NDERMARIEN ÜTE AUS (cv_deu_000783-cv_deu_000783) +ICH WERDE VOLKLCH DIN RAT BER DEM PALLMND VORGETRAGENEN BETENTEN N VOMIEREN (cv_deu_000784-cv_deu_000784) +S IERETRAURSCGEWIEN EINSOWICHTDIUS THEMANICH EM KONDET HABSCHENZUKEN (cv_deu_000785-cv_deu_000785) +NOCHTIHSEINMTEOT ENM KLEISCHEN JAHR GAMES GUTZ WISTDIEKG AN ANDERER DIESETZ (cv_deu_000786-cv_deu_000786) +KOTZ DANERCH GABIS EINEN WERBER BWORDT MIDEDENM T KAN KAMNDT UND SCHEKESHOCHENBACH (cv_deu_000787-cv_deu_000787) +ID D IT BEE HTSUSFSNUN NANN AE (cv_deu_000788-cv_deu_000788) +WESIE ISMND LEITZEIT AUS H (cv_deu_000789-cv_deu_000789) +NCHE DEM DOCHF BEFIN DET ICH ARCH DER KRM KENIUNASIHNALLEBPACKG ERBUOT (cv_deu_000790-cv_deu_000790) +IESEREN DERKÜNDEN DES DIE LIEBE ENTOED BESIKT ART (cv_deu_000791-cv_deu_000791) +BETECKT ISTDI REB PRENSENTHERTIEF GESTALTE E WELER MITD EINEN MAN SART DACH (cv_deu_000792-cv_deu_000792) +DIESESIE UN IESMITER ARTSCHAFT DELECH ZUSAMMN GEWAKNZEN (cv_deu_000793-cv_deu_000793) +I WARDIE SHN EINALEN EN KLOBE S (cv_deu_000794-cv_deu_000794) +BUORANGE IST ISTD AUCH VERIOERET (cv_deu_000795-cv_deu_000795) +DEHEXT VONDER STRASSE BU ENSI VON ALTFTET DIONLEG ISEINER FWESTEN SE SCHO UE SCHOBEBILNER EN EÜSIEALTT (cv_deu_000796-cv_deu_000796) +AI HARSCPEITER ESSLLTER ERTZUN ELF NATZS UN BER WUDE LR ANGEREISCHE (cv_deu_000797-cv_deu_000797) +IN DERLERN VITHE KERN DER ERTRARET DEUTLI EDEOTZIERT WERDEN (cv_deu_000798-cv_deu_000798) +MAIN SURSPIERTE IN SEINERHEIMAUTSTADT KEIE UOFIER AL ALE (cv_deu_000799-cv_deu_000799) +COH DERTRAR DER REIMAUHALUNUNDEN I N TABEBEI SI HAH (cv_deu_000800-cv_deu_000800) +MIT FÜÖOST WER EHER DE ROWTFIADE EITS ERECHT ICHERALLE DE UONBEDSHEICENEN EMEINT (cv_deu_000801-cv_deu_000801) +ETZT ERVOCHU GABDAS METI BEKANDT DASSES HON EPEL ÜBER FIERND ASCH WALTE E VORFELE VON BER ITZUN NTOMIRT ODENVWER IDES NDER NEHNEN ALS NCHT SHIERIENKEEITETE (fleurs_deu_000378-fleurs_deu_000378) +SIEBEBE NIJEÖESEÄE SCHE NENSZIGIGS UNDERSCÜZSTDE DEN BIHIF DES UN ÜNBISCHEN KOMITIS DER VER EINIGTEN STATEN UN DAR SIPTIERT ESAS APTUTENOTWENDIGKEIT DASIH IE UN LÜNBISCHE VERNMIIEN FÜ EN SICHERES UNF WELT ZFÜE ALE UNSERER SPOTLER EINSEST (fleurs_deu_000379-fleurs_deu_000379) +ALICHKENE ABPIET KOMPETIEBELMET ACHTNATZWEI BUND ELF AR ACHTNRTZWEI BUND ELFBE UND CHTNETZWEI PUND ELFGESEIN VERSGES DIEBASISTATIUN VERFÜGK BER DUOALRADIE (fleurs_deu_000380-fleurs_deu_000380) +JERBIEZEISHENS DIE GERICHTERALSPLICICHESGESCHWÄTS UNDT ALL BENHEITZS (fleurs_deu_000381-fleurs_deu_000381) +LT ERWUCHRGABT AS EMIEITHE BEKANDASIS VON EBL WERFIERNDRESIE WEITRIE VOHFELEVON BERHITZUN IN VOMIERT WORDENWA IEDAS UNTER NEM ALZ NICHCH VERIGE BTZEICTETE (fleurs_deu_000382-fleurs_deu_000382) +NACH DIM DERDEMM EUNHNUNDERTREIUNSECHZIC BAUTWRDENWAR KAM DIARESZELIGHN BEPFLUTUN DESE DEMENTE MNPLSVERTELNZUM SHÖLSTEN (fleurs_deu_000383-fleurs_deu_000383) +ERWAUH MSTECHE VN GELSCHEINVE ILE ENDE BETEILICHT AKTU LEBEISCHPIESAN AHRBESCHLISENIBPREMJHMINISTER PRTRISAF DER VORDESERT DE KANADSCHEN FÜN N UNDERTOLLENUTEN EIN (fleurs_deu_000384-fleurs_deu_000384) +DI HAUPTST VERMR DAWIEN IST KHENEN DIE INEIMPISPBAHE ISTGUMENESC ABERFIELEMENTENSRECHEN EROSEH (fleurs_deu_000385-fleurs_deu_000385) +SISISEBETZWICHEN DEN EINZEN N BÜNESTDIEN HERSTEN AUOCH UN BESTENDIGER ZEITEN GETALTE PROEWENZEN DIE BEKANTDISTDEDIESE PERIOEDEN WADIEEPOCHE DER DEIL GÜNINGEICHE DESEÄCHTZICH IEARRELANZEIHEN DER HAN UN DEEEN DENESTISTAT VFVANT (fleurs_deu_000386-fleurs_deu_000386) +AM ANDERE ENE ERSPEKTRUMS HRWEINE MAN SICHEN EINICH WIDE ZUERKENDE INEWIEDE UM DS AES ANDR MACHENMOS ASERSTIEES GEMACTA UNDZSICH ALLESUO ELIGEMACHT (fleurs_deu_000387-fleurs_deu_000387) +DGIGI DE MEISTEN IN DER PITEATZIONEN DESTECHEN LOGISCHEN DETE MINISENUSTA EN ZWEI AL GEMEINE VORSCTERLUNGEN EINESEITS DES DI IND ICGEM DERTICHEN LÜGIESEPSTEINEN WEGVOLGT DERWEIT GEND IENSEIS UN TOWELEORDERPULICISCH INPLSNAMENDIGT UND ANDERERSEITS DASTIGHNE ÖÜGE IERERSEITS AUSFWÖHRKEN EN AUFGESALSCAFTNARART DI EHER IN HERERNT ASZUTSAL BE DENZINT (fleurs_deu_000388-fleurs_deu_000388) +WÜSHE DEN EINZENEN DNASTIEN HERSTEN AUR UNBESTENDIGETZEITEN GETEALTER ROWENZEN DE EKANDESTE SE PERIODEN WAHDI E POCHREL DER E KÜNIGREICHE DESECHTZICHARLANGTZ WISCHEN DER HAN UNTER IENDINASTITT VANDT (fleurs_deu_000389-fleurs_deu_000389) +DEMICKH ZU VORGIEBIEZIZICH ES TO GOMENT AUF DEN GEÄNSTREIT IN DEN DIE PALIS INENSER EIN ZURÖGSALTZEN DER GENZSEN IN DEN ZUSTANT VORDEM SER STALEGRI VOR NANZESN UNDERT SEBERNU SETIC VORDEN (fleurs_deu_000390-fleurs_deu_000390) +MIT HEM PERLUSTGRECHE ERSPACHKENE ARDER ECSTEN VON SEIEN VLESOFISCHEN UNDWISEN CHA LICHEN WOTZE INKIHENEN ABIESLETEN (fleurs_deu_000391-fleurs_deu_000391) +ERSTEMITERAUSSAGDES IÖRS USIE BEINDASTDEN N RESENUN SRATLEDEN VEREINUNDERSPOTSBPESRGE DIENDISD BENWER NEHALBUNS RARGE SATIOUNDHN VLE VERINDRUNG VORANTREIBEN ANST EINERTITZSRIT ZTVITIERUNGVOTZN (fleurs_deu_000392-fleurs_deu_000392) +IGREZFATENAENG PIGESBOG BIETEN AUCH ZEIT ÜÖR IN AUFENTAL INERSTAT KGREUTZFER PASRSHERE SEIN V NDE IEUNSLICH BEREIT SIE BEDENGEN (fleurs_deu_000393-fleurs_deu_000393) +SCSCT EREI SEN DE VERDEN RINGEND GEWANDTKAUF IE WIE DE AT VON UN WENTEZU ACHTEN DIE E GEBI BIERIFT DADNDIS SIGH AUF ALEREISEPLENE AOSIEREN KON (fleurs_deu_000394-fleurs_deu_000394) +SICHRTR ISE BESERT DAS DER GKOLTZUNGSPBUNKTD DERLENMEN DE IN BEE WERDIGKALE UNTHURE HONTAL DRITEN DE EHIGK ISTDPLATS FÜITESHOUPT MNOIE ISTSIE BEISCHEN (fleurs_deu_000395-fleurs_deu_000395) +EIT NUNZEUNRT ACHEN ACHTZIHMISTN WAL UND RANSBPRENZSEINDAMIT WEE NDBOBACTERBIETZOEUGENGKEN DAS WEGNDERWAL EIN MSCHLIGEVWANENSIND UDASKEINUMSCHLÄGE INGEVAOFEN WERDEN AUSER EHNE DERT OTDEUNGS MES K SELTET ATRESIERTEN EE (fleurs_deu_000396-fleurs_deu_000396) +OTERER IST KANERDES BET ZSAOUBEN DE ZWEISCHEALI GE HAUPTSTAT UNDZSELTEN DICH ICH EINEREIE UN KUNZSTDGEERIEN UND MOSEEN AUS DIEKANENDERS VER GAN GEN HEIT UND GGEN WART PRESINTIEREN (fleurs_deu_000397-fleurs_deu_000397) +DIESE PARE KENSICH FVEINADEBTIONSPLAN V ERBEBE ENSHEIDEN (fleurs_deu_000398-fleurs_deu_000398) +IN VOL EDESENEINZ WREI FISCH ABEN AUSGSTOLM DZWEAWEIT RISEN VOM AUSTERBEMBETRORT TAUNDERDER JLAZIÜFVER (fleurs_deu_000399-fleurs_deu_000399) +REANZEN SEN NJHERENERTÖLIHE MNGEBEN AM ETENAUS IERSTEN SIEALSO DERERSCHUNG AUCHNUR EIN E EMKLA ENDT VERN (fleurs_deu_000400-fleurs_deu_000400) +AUF ER NARSEITE KNTE IS MERMRIER GEBEN DDIEKROSTE DNEST ISWR EIN VERA F DE LA VER ANDI OBEPLICH AUFTSTDEGEN T (fleurs_deu_000401-fleurs_deu_000401) +SGR E FÜGKTDECHEN ZUUN DASSIE DUOCH NICHTDERTZU AUFGIE VORDERT WERDEN SOLLTEN FERT FICHTUNGEN EINDZUÜGEEN DE IÜEBER IEREN INTWILUNGSTAND IERE VER ANTORTUNG UND IERERFÄE KETEN HENOARNS INGEN (fleurs_deu_000402-fleurs_deu_000402) +SISI EWICETU ELE HIEL FISTEUNGEN ISENT IN DIESOFT WER EINGE BUTDT UN SOEN ABELTSCHITE NDIE DER SCHYLE ALLEIN MÜGLICHERWEISENIHT BEVETIGEN KARN HENTER FRAGEN NEIELEGEN UNDT DERKLEREN (fleurs_deu_000403-fleurs_deu_000403) +AM FÜNFZEN NAR GUSTNUNZHNHUDET VIRZIC FELIEALI ERTEN NSÜT RANKRAICH EIN DIN WASION WRDE APERESCHE ER GUNGENERNDT (fleurs_deu_000404-fleurs_deu_000404) +ERRIF OCH ALLS AN WASEN WASERKARM SEBT EN GROSER DEN SAURIE IDER TIEWEXS WEIMICHT GEWAKEN (fleurs_deu_000405-fleurs_deu_000405) +ETERKRÜNDUNG VNASUNTZIOR FINHTZEN DESIENUNDREISIS SPAREG EGELUNG VIEL VON SEIM IN DIGKEN KARACTER NDSEINEIDENDITETZ BEWAREN (fleurs_deu_000406-fleurs_deu_000406) +SISIBEBE 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KOSPARESGERAUPTATE SCHPAN GRAFTUND MUDT DAS ES IN FEIG UN SCOLIGEMACHTATE UND FÄHCH ZU DEN HON DINGEN ZUO DENEN UN GETRIÜPTE IT RALENG HRT (mls_deu_000282-mls_deu_000282) +DIESER UNGEMAN HIES KAKALITZIEN UNDBEFAN ICGAE F DRANDESCHAFT ALS INEM GENANTEN KÖÜNIGREICH BEKAND MACHUNGWIG DE RNZEEN VELEN WURDEEI SAKT DESHTN LDER WENESWEITENIHTZSISTEIN EI PAUCHNHNICKÜKLSTUND UTSKÖNIGSEI DAM ZUWEREN DASGELSET IGALEDINGST (mls_deu_000283-mls_deu_000283) +SRDR NOCFÜNFMINUTEN UND DE WOLGEN DE BE USTLOSIGKALT BEGANZUSCHINDEN IERST USE SER ULDASIHN MEIM EIGNEN BETELAG UND DAS DIEROTN GLUTDNIHTZ ANDRESWA ILS DAS VOYR IM KAMINDER INDASTUBE ESWANACHT EINIKRZEBANTE AFTEIMTISHE (mls_deu_000284-mls_deu_000284) +EICHT DIE HERTRENGUNGEN WE BECHTERUNTERHEILTEN KOM DANEMPRBETITZS ALDE DI HOCHFLUDESEXSU EIN BEDÖFTIGKEIT SO FNDE SE NDEN GENANEN SLSCHE RARKTIONSODER IDERSTANZSPLDONGEN DEMER (mls_deu_000285-mls_deu_000285) +TABER AFEN GEHRENDBE HARGEN BEG AN DIE KISTDEN WANT NUN SZO HÖRTE ICH AUOF ACFE ZOSEIN EIN KLARER SCÖNA GERDANKTENGANG DENIHTILRGENG I MITD IM BAUCH AUS GEHEKTABEMOSS DEN AFEN DENKEN MI (mls_deu_000286-mls_deu_000286) +R IS S ESPOTTRIEHERNE MENSCHN DEN IGKENNEN FRAKTE ILEISER WELCH UN BMAKTEAMICHRANGITRETEN BAICHND GEGNIETE DAS IS N N FANTESIKOKFSEI UNDSCHOPT TEICHEUNG EILICH NTE DIEANDON LTTER NTÜLISPRACRICH I UNBARHEIT DNDESWEIN SER IETOEIESPETRIE MISTEROTSCHSTES (mls_deu_000287-mls_deu_000287) +C IHWEIS DASIH SER KRANGBIN SAK ESHNERENAWEILE VORN PAMINUTEN VESCHTE ICHMICHN BÄTER UENZUREN UND FÜLEDE DASIC KEINGLIED MARENIHRN KAN S WEREGUT WENIHMENGEMÜTDELEICHTAN KÖRNTE BEVORAISTDER RBER (mls_deu_000288-mls_deu_000288) +SO ABER ISZWVER UN SERWESENS KOND GORT SELVER DA HERUM HTZIHEHDEC DERCHLANGEN KNOULT DS ALPENSATANGESHLONGEN UN IÜBER DEM FÜNGKGENDE IEBE IS DIE ENSERNISTESHASESKELAGERT WAS WUNDERDAN (mls_deu_000289-mls_deu_000289) +BESEVERELEWAGEBLIEBEN AUERSEWAGETZWUNZUGEN BE DIEPÜNKTIGKEIT BEI DNMALZEITEN EINE SACHEWA AUWECH IN GEHTS HÄR HOL STRENGRGERHELTEN URDE (mls_deu_000290-mls_deu_000290) +N LIKLICHFÜLHTEWIERE AMNSICHTENBEMICG IER MPFINDNGND FÜÖRMICH NICHTUMEIN ATUNVER IN DARTWAAN NBEHUPT KRNE NDERUNG FECHWAN IGSEIS EREMFERSTEINETN AUOGE EIHS N EMELSTUOKTRINENGENETZT NIEMASIN TÄRT IC KAT AUFGELOEICHTETHATTEAMN (mls_deu_000291-mls_deu_000291) +SBPO DER SEMIS ZERGUT MINDER AUKLIGNER FÄHR WRE SIC VREUNWEWERNSCHNELLDANACHANDEN SUOUL BE AUPRECHENSCNALREIDNEMIT ÜR O FODER ACH DAS LAGER REICHEN ERSTIEG AU DIT FEHRDE DI UN AUSGEROTATENUND FLOGEN GALOPTDA VON DISMALÜTEN WIRUNZ DER FÄERDEIDE DEREKTZE VOLIEN EREN GERADE AUSSNDER SPADE U (mls_deu_000292-mls_deu_000292) +WEL DEBEAMIT PECHPESTRICHEN WAHR BIEB EINER VONDN GOLLENDEN PANTOFELN FÄSTEINGEN UND IN DER ANGS DACH ES NICHTERAN IN ITZONEM UND IE IS DN LETZTENSHIET VONDERTRERPETAT DEHATISTZWLF AUSGESCHLAGENG DARWAWAGEN UND FIÄRDEVERSHUNDEN ND ASCHEN PUTESTAN INSEIN ASHENKLEIDE AUF E DUNKLNSTRASE (mls_deu_000293-mls_deu_000293) +IE NOM DSKGASVMARGE EN FINDTER AN TLAR GERE ESENENTZUÜKENESISCHRIKE SAKTEHER IETES MENIGEGIETAUN BUTDVERGISEN VERMEINEN (mls_deu_000294-mls_deu_000294) +NURDER DAOCKTOR UN DE WERTEREN SOLEN VOR SEINE AUGEN KOMMEN ERKLERTE DIETRIENE IN KROSEN AMT SEIFER DAMIT WA DIE FTRAR OBERST GANS EINFVERSTANDEN UND PHÜÖÜKST ERFPREIT KARTESIE MET IEREN (mls_deu_000295-mls_deu_000295) +KAWAR UNDTRÜSTICHEBE DIELAGEDAS KÜNZSLRS EBEGEN UWEINEN UNSCHLCHTZT DELANGE INDEVORGERHELTENEN HENDERDE KNSLAWATETER BDESKASIC BERUICHTHATE UNT ENTCHLOSICHTDAN DA ER KEIN ANDARN AUSFIG FANTDERNOCHZUMPEITERSCREIBE (mls_deu_000296-mls_deu_000296) +ONDEM FERDEHERDN DE R PATSCHEN UN SAG UNZSTASIF EN A PATSCHN FÄRDUNS EBE SO VIE WARENUN PRENDIGEBE WÜRDEN IÜFERIN KEIUOWABPFIERTT DASINDUN RIGKLIGEA VODUM A PATSCHEN VÄHRDT SO ULENASRCHTIH GERASCHLE IM TODE IESHRGE FALEN UNDANE BLUTVERGISEN WEICHIS UN BE VORSTAND WEISESE FEHRDE HENDLERW (mls_deu_000297-mls_deu_000297) +DASMATONEN HÜTCHN VODSCWARTZAM AMIND GATZIÖRSR IERELANGN LOTNGEDRIKT DIERWANGE UM FLOSEN NT BERSCHLTH NHRAPWEITENSOTRAE I DAS EINVERHRERLNTLICH GEBOEUDE UNDSTÄEBPETZWSCHN REIN DERHIBPGEBLNE INDOFKENR AUFENT AB (mls_deu_000298-mls_deu_000298) +RBMUST ERT IN ZAGENG ALEM SHNTHAFTENSTREBEN UNEND TIE VEREIUND DEMUD DIE FÜÖH BIDEDER HELINGAEFLEN GEGEN DE U GE REELTRSTDEJEMLENGERWELCHEPFANTCHSKOSULNE GEFLONSOCHTENIN AUFINSRE WERKSTUN FANDTENIN (mls_deu_000299-mls_deu_000299) +ERLIESZSEINE G RETENCHT VORTSCHLEBTEN AM ALER WINIGS DNABER IN ENGROSEN VOGELBAUR USIEALE IN EINEM TONE FEIEFEMOUSTEN IER SHIT SKTE (mls_deu_000300-mls_deu_000300) +FRNTCHESKOMALTEN UNHELIGE BGEISTRUN FILEBIET AS E LÜGENHATENABEELTKHEINNELS ERERMOCHT DIE BULERISCHEL BIKETDEWEIBICENGSTALTEN SOBERHAF DASISTELEN IN DEM V N LEBEDTEMOD DELENDIEKALNATIONG VODN ALTEN MAHMO BILEN BER ORMON BILUN IND NAN (mls_deu_000301-mls_deu_000301) +BEWEGUN UN TAT DEN STEN ZUGERSTHM E DIE VOEUN ANGEGEBEN INGER DENZHER NMICH RÜBEHANFE EICHENUN SAUR AMFAN ALE IN DEM PFEIFENKOPFERANWESEN ABEIN FÜNDFTN AUTSTOHARICHNIGENANDIETSTROCHUNDSCHMÄKTIG DASE HNSTICHEN FILSH DERBEISEIN MSEIGHPLIESTEN RAUCHAUCHGEG DEN HEEL UNGEGNGD (mls_deu_000302-mls_deu_000302) +UNDAS VOÄER STAND AUF UND FLACKERT UN KOCHE DAS ESEN FÄATIGH UN DER BRATEN BRUTZELTE FORT UNDER KOCH GAB DEM KÜSCHEN IUNGEN EINER ROAR FEIGE UNDI MARKT RUPFTETDESHUN FERTIGHDARWARTDIE HOCHZEIT VONDEM KÜNIGHSONITETDONGRÖÜSIHN GEFEIHRT UNSIE NEER NÜTEBIS AN IER ENDE (mls_deu_000303-mls_deu_000303) +UND DASE MINICH NACHTRARGENGOLLE WENICHNEDERSHPENSTIG WARGIN SEIN ULMEINEN BRART DER HER FARARHEDE IN ALLEN REICHT DEHAT UN ICHME AN UN RECHT ABERH (mls_deu_000304-mls_deu_000304) +UOGEHNEMASEN UWINIGEKRAMBTUGERBREITE DSICHKHEIE FÖMIGAUSENMUSTEDE ERESHEM IND GEGEN FIGEDESPRENKISCHOS AU FANEN TZSO UERINGEN (mls_deu_000305-mls_deu_000305) +DERVUGSREICH E SEM I UNFRITICHEFRIEDENSPWEIFVER HEN DER MANTAT WACKASEINESE KS ZÜGEN SAKTE DERGROSIGEIS ACHTE NICH AUFDIVERSCHIEDNE HAUTDERMENSCHEN DENDIKRNSICHMIT VABEBSCHMIHRENMINTZS TROLSCHEN SONDEN ERSIDASHETZSAN DE HETZHN DERKRLIGE VOM BERÜBEN STAMIDER KAIOWASIN TAPFER UNERSCHROGMNTREDAS MEINIGEHENG (mls_deu_000306-mls_deu_000306) +ALLES AS WI MET IERBEGEGNET SCHEB SICHTDOESCH UND BER INANDERBALT UNTERSCHEBEMWER IN KONTAKT DEIST IERERHANDUNDE MEINIGE IER NAHRM ONDER MEINIGE BEI DELRSHE EINANDER AUS BEI DE VERSCHLINGENSICH (mls_deu_000307-mls_deu_000307) +ER MÜSTE EN EN FERHENGRONITENKÖERAL DESMALES MIT ALLE RKLEHRMEN ZURECHTWESEN EN RIMITGRAUSEN IGUNVRCHNARCKHIUN VERBREMENICHTRETE N DIEPERSONDESERAUSGEIBESND BITETICHIKÜNSTIGELISERUWOLST IE DUWEITELISIST FOLENDIS DI GITIST NERTEN (mls_deu_000308-mls_deu_000308) +DIHOFDAMEN BEKAMEN KREMPFE UNDI KÜNIGEN UNDIE RONZTESSENEN DIERER ALLALIEBZSENHÜNZCHEN WERN DERMEILTHER AU INSCHOSGENOM HADTEN ERMERKTEN ZU IRENSCREÄKENG DAS DILIELER AMARANTFABENEN UNDORANSCGAHLDEN SEIDENKLEIDER ALE DIST DESET ME IN HESLIHSTEN ÖFLEGEN WAN (mls_deu_000309-mls_deu_000309) +VON LEDEAN DIESIESIN UN KLARWIERPIESEN DIESISPBIELN VON GÄHRT BÖRSEN DISI HEGKEN VOND RANZÜESCHN BÜCHAN DIESI BASRSETZENKONTE BISMANGEMÜT WERENDIHLAUST ZON NACH AMON AUFKESTCHET WURDE (mls_deu_000310-mls_deu_000310) +AMUND NAEN WANBLOS IEREINZIGERSCMUOK WAN IERE ASTANEN BRANFLÄCHTEN WEICH IN WILDER UND DATÜRLICHER ANMUND AU IRSCHUSTHEN HRABPFIENIHNAM EIN BOGENG FEIN KATUNGS NDZEIHET MT ROSERSOKVELTIOMGESER (mls_deu_000311-mls_deu_000311) +R AR WIE USTRTSCHLAN NOCHAS ILEN EINIM ANDREN START KONT MAN EFAN WAS DER GENSCHEAN UNDES ESLTAT DIS UNTERE DUNGEWISENSE AN VEMUTETE DSESICH UM EKLIRENG DER MATZHE ÜBE ERABSICHTEN UND UN DIVEMITLUNG DER MÄCHTEZUISCHN MASTATEN UNGROSPETANIEN HANDE (mls_deu_000312-mls_deu_000312) +LAUN WENIGSTENSEINE ZEITLANG VERSUOCHEN IN BIE FERUN WIER AUF DIESES BEISNMT EINANDER AUSREICHENDADERSTUSAMEN HNGNDE IE DESARGST AEIGENKLIGH OEER LEMENT IS ERSETZT IE RURT (mls_deu_000313-mls_deu_000313) +VESCHEN FVORKOMSE KFÜRDEN ZU DE VERMUTUNG DAS FRAU WIESE DIEKLEINEN WIEN VER BRENEISOL ISFEIN SU STACH GERHITSTABENDAS DI HERTPLATENZSPTANGAUSE DE SOLEIN FÜRCHTELICHER GEROCHOWAGENUNMNWURTENSEIN 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AURBECHE ATZSAG EHEDEN ÜER HOCHRISIGOBRTUGKTDANEDSEN DALIZU LASEN HAM MSSENDASHAICHEICH GESHAFT ARMI DEMERSO DAH EMTICHLIGPLAUBEICH DASWIRTROTEMEINENGORSEN SCRILT FLEIG KEIN MEINSTEINEINGROSENCHITZU MER ATEDENSEATAEN (voxpopuli_deu_000335-voxpopuli_deu_000335) +PEHLE DANG ESFVERLKTFÜÖRT SWEIENHEIBMINONTEN ERG (voxpopuli_deu_000336-voxpopuli_deu_000336) +ZUM AKTUELEN ICHKLABIS KANKEINE VENUNSAN NEME DAS WEWERTLICG IARSTFEITDISEN WUCHNGENDEWISHENDASENS DITALUNSUNFEICHKEITDROT (voxpopuli_deu_000337-voxpopuli_deu_000337) +DSND EINFCH PE DINGUNGEN DINIG AKTZEPTABESEN MAN K (voxpopuli_deu_000338-voxpopuli_deu_000338) +INDEZWISCHEN SEI SINDI RETUNGSARGENISAR IONEN DEGRÖSTENSCHLPERH WEISIE DIE MIEGANTEN ZWANZICHGHLMETER VER DERLIEBISCHEN KÜS DAUB GREIFEN UND ALLNERHITALIEN PRASPRTIEREN (voxpopuli_deu_000339-voxpopuli_deu_000339) +DAS SEIKTDER FAL JULIERTDEMSCHÄNKOU (voxpopuli_deu_000340-voxpopuli_deu_000340) +I WASER PREDIGEN OND WEINTRINEN (voxpopuli_deu_000341-voxpopuli_deu_000341) +WIR DIE INSCHEI DUNG RARHEN WIER FILEPATNR NECHTZULETZ IE STÄTTE (voxpopuli_deu_000342-voxpopuli_deu_000342) +DIEVOLGE IS EIN HÖRN FLUGK VOM PROPLIST N EXSTRLMISTEN EINIGMIGISTATENEREN BUMFUM PAROULEN SETZEN IER KONGKRIETER VERINDERUNG EN GEGEN (voxpopuli_deu_000343-voxpopuli_deu_000343) +WAIL DIE INWESTIZIONEN VRANTÖRSISCHA UND DELUTSCHERBANGEN GERETET WVERDEN MUST DEN DÖRHTDER ICHEN LANT ZWEITAUSEN ZEHN NICHTDBPEITEGEN UND HLUTER USESEINEN RIESIGEN SCHOTDEN BERK VAR SICHE HERT DRIUK (voxpopuli_deu_000344-voxpopuli_deu_000344) +DEMITIGITSTATEN DÖROFEN NICH IEMÜKICH KEIT HABMDERENEN AURBPESHEN STASAMBAL DERANZERHNDERNEN IERNAREG IONG GANZSGET ZIEL UNST DEMATISKORULTONFERNACRZEIGEN ESIEN (voxpopuli_deu_000345-voxpopuli_deu_000345) +EIMIL ION MENSCHEN SIN ABPENGIH VONUNSERHELVER (voxpopuli_deu_000346-voxpopuli_deu_000346) +EINFETHINR GER JUNGE WET INHERKADIVON EINPLISISTEN AINDESONDEREI SATSKOMANDUSEN KOMAGSCHAEN (voxpopuli_deu_000347-voxpopuli_deu_000347) +DIE INDERHEILIG KUEITMANVORSICHERETRANEN DAS AUPT AUT US UDER AL UNSHENTEN WEK (voxpopuli_deu_000348-voxpopuli_deu_000348) +REI DER ATRI GETEREFFEN HABEN INZISCHEN STAD GEFUN DE (voxpopuli_deu_000349-voxpopuli_deu_000349) +RD ICHIETENEINMONERDEBPT (voxpopuli_deu_000350-voxpopuli_deu_000350) +DASWEGE EINEWICHTI GE FHAHGEANDI KOMITIONEN EIN LAND DI GRANZ KONDTCOLLE WIEDER EIN FÜÖONNTDACH EM SCHNGE NION BLEIDEN MITZUGANGK ZSUOR I MATIONZUSTEME TSETERAR ORDERISDRS EIN EN WERER ODEAR DIERAGE IS WESTIC FÜR DIEDENISCHER IEPATE NDISPÄTE UM EINE KLAE AN WORT DA (voxpopuli_deu_000351-voxpopuli_deu_000351) +DE SCHONAUSSCGIEFÜRT WURDE LAGESNICH BARAN DASESHE ROBEFFELEGIGEBEN HETDISNENDSGABENHREIHE VOND KLEIEN UN GEREINMTEITEN BITIENSWEI (voxpopuli_deu_000352-voxpopuli_deu_000352) +NVER GEMEINCHRFTEN DE AUS NON SIERITSPALITIG AS GOSISTZIEL DIESER UN JON (voxpopuli_deu_000353-voxpopuli_deu_000353) +DEN ICHERHEIT IS ANISZ WERIEGER UN DITEIL WEICHER ARBREIT NICHTNUHR IMTECHNISHNBAREICH (voxpopuli_deu_000354-voxpopuli_deu_000354) +DIK SELTEN GEN DIENTERESEN VON BÜÖRGENUN POLIETIGEN SOWEI AUSNANDER BER EM BÜRERNENGANZER OBERSHI DS TEMAR KIENT GANZS OBEN (voxpopuli_deu_000355-voxpopuli_deu_000355) +HERPRSI DENT (voxpopuli_deu_000356-voxpopuli_deu_000356) +EFÜREN ESPRÄECHEMIT RESE DEN KASEIT ZAREICHENREGJERUNGS ERTRIE EN VFRAUN MENSCHENRECHT OGAMISRT IONEN UN DIEWAND DRCHAUSEMUTIGENT (voxpopuli_deu_000357-voxpopuli_deu_000357) +NGSACH EINE URSACHEFÜRDIN E WACKSN NATZUNALISNUS DE LLIGSEIDE FOLICHPERSPEKTIEFLOSSIS (voxpopuli_deu_000358-voxpopuli_deu_000358) +OUIDE IN E IMANAO SORWEIT VNDENZIE ENFERN S (voxpopuli_deu_000359-voxpopuli_deu_000359) +DWER DERS WI ANZMINISTE AUCHEN EINEN LANDZIEDEN TAGG DAMIT KONFVONDTIETDAS NDTÜLICH AUCHTUS BUSTZSINGEGEBENSENMUSS DAS STASHUSHALTE VONDEN STLER SOALERENE UN STELER SOLLEND INERZIETZINT UND DAS IERTDAMIT AUFHT DIEANTUERTUNGAGEN IN EN ENTSCHEIDUNGEN DEIER HIEN ISEN RAMEN DREFFN MET MMUNTERN (voxpopuli_deu_000360-voxpopuli_deu_000360) +AU DEM OUUROBEISCHN AUTEBEBILMARGKT INS GESAMT DRMATDISCHISS (voxpopuli_deu_000361-voxpopuli_deu_000361) +EBPEHSCHUN JON HARTMIDISE INSTRUMENZS DISCHONSE EINE AKTI VEROLLENE RNACKTPA EGIONZU SPIEN UM DEMOUGRADSCHERDE VORMEN NA ENACHALIG IN IKTUN VRANZITREIE (voxpopuli_deu_000362-voxpopuli_deu_000362) +STUL TALITERERSCHIEME VON AUSEN UDAU VN INEN IS RESCHT UNDOSCHIET LEG (voxpopuli_deu_000363-voxpopuli_deu_000363) +EH HAM IMER GESARK EIN ÜBER EILTUSTATZUN IERUNGS EN CHEIDUNGIS UN SINISGWEITZUM JERTZIGENZEITFUN ESKEINEBE DRONGBEISCSPIEASWESAUS EM IERANGET (voxpopuli_deu_000364-voxpopuli_deu_000364) +DEER VARKLEICH IST EINET ZUÜENESHE MISE AT DN DER AUBRHRAVORNMENZCHENREITZWRLEID DNEL AABELSZSSFHFHFGFAGSRFDARSTSTSSTSBODSSAAA IE ONG ANDAN ANAN EINE SOEUSCH EUNEN KLAUPLICHER AN WOROF (voxpopuli_deu_000365-voxpopuli_deu_000365) +DIESPE ERHRT DIESE UNFASENDERHUTZUNTALE RICHLINDE WÜBEFÜÖBOTET WENGIE (voxpopuli_deu_000366-voxpopuli_deu_000366) +GICI WERKISKSL ICHINAND UND E WRSARSTGHG DEVLANTVO UNDE EIN EINMALNMEHR IJETST DER VERANTZWOCFTDUNGFÜR INE UTUPTI MALE N WRALMGRASIGKALIVITZTIERUNG UN RER ABEIT NEHME UD ARBEITNEMER RINEN DANS PERSONDER ETSTRESHUNZITAGEN (voxpopuli_deu_000367-voxpopuli_deu_000367) +ANDRD AUCHONLEN DIESERKUDER GEBESER ZIELNAISANDERE GIESI SHWÄHRTUN DIMITEL ABPTZIUOFN TWACRIG JIONWIEKALARBRIHNZITZILENOD AU KRICHLAR DR AUCHOMENEN (voxpopuli_deu_000368-voxpopuli_deu_000368) +DEBERICHKOESES VORDER ZUREICHTDS ES RETING STATLICHERSCHULT TIEEIS ERFEN LICHER AUFGABEBEGRIFEN UND DAR HER ON EFEN ICHE AKTÜHRN VORGENAMWERENMUSS (voxpopuli_deu_000369-voxpopuli_deu_000369) +DABIS ABELNUN MITEINEM UTSCAR POGAMTUTUN HABEM MSWIL DAFÜH EIN EN SPECHENDERECHIGEKONTLAGESCHAEN (voxpopuli_deu_000370-voxpopuli_deu_000370) +SIER NOHANALISIERN WOR (voxpopuli_deu_000371-voxpopuli_deu_000371) +DMAKENENETULIE VERLANGEN GEBENLIE MER GARTFHRND IKUNSHIH VERAUS DIE AMEN OEITE WRAUHEN DAS ABE (voxpopuli_deu_000372-voxpopuli_deu_000372) +GERARDIÜEKLEINE PORJÄECK DE IS DAS ÜNHBERMEHESIC BEROGATESHR AUFAND RECHTISDAS DAS IER SAWBEIN ZEITAUM VON REIAREN GESENTWERENSOR UNUMNT (voxpopuli_deu_000373-voxpopuli_deu_000373) +IKANDER VERSICHERN DIARLOBPESCHE KOMISION IES STER KOMITITZUM AHR AR ARUBTSALROBECHEN ERSPIGK IEVE DISKOSSERUSNT (voxpopuli_deu_000374-voxpopuli_deu_000374) +EIEDANZEHE AUFT AUGHSON (voxpopuli_deu_000375-voxpopuli_deu_000375) +DT DIESME HAUSHEL KAANDI E NGÖRGERIN UND BURGER NICHT IBERTZOELITEN N BEGEISTEAN (voxpopuli_deu_000376-voxpopuli_deu_000376) +TIAL EMUKRARE NEMIT GROSAFH ROUDE ZORKEN NES DAS DIG DE IER VORIETRARGEN HABEN EBZ SICH AUH IM ZUSAMENAR IT VERENDRUNGNE DENWEICH STATEN UMSETSN (voxpopuli_deu_000377-voxpopuli_deu_000377) +DEAHA BESCHURS DIE EL DARORBPESCHE SEMESTER HERHERTZUNEMEN UNTI KOROBTUNZ SIKLA SIUN ER IM RAM DER LINER BRECHTDETZUVEREÜFNIGEN ISTNIG AUSEIGENT (voxpopuli_deu_000378-voxpopuli_deu_000378) +ND MEIN MEINE BITE ODAM DAS WAS ICHMER VORSTDEN IS DASMAHRGENGWIECKTLICG INDERTAHAT EING GROSE EINE BREITE MEHRHEIT FÜR DIE SI KOLSIONSPLITIGHSORLGEPLITIGSTSTDEMT SÜR DIE MENSCHEN VORORTDAITIUNSA DESWEHENIE AUCH BESCREÄNKENKEINE DAS (voxpopuli_deu_000379-voxpopuli_deu_000379) +WENWIER ARHOLTE DIE EVORARDNUNG VRABSCIEDEN OAOFER ICH DAS SE WIER NACH EIM LANGNKARUSELELSU EIM BUD NABCHUSKOMUNTITMAÜCHTERMÄCHE BEIERKOMISION BEDANGEN DIEGONZTO TIE SACHARBEIT HAT (voxpopuli_deu_000380-voxpopuli_deu_000380) +UNZERERESCHÄARSCHN UNZIE KONTRORLN HABEN KEINEN PELE GERPRAFT (voxpopuli_deu_000381-voxpopuli_deu_000381) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..f48bbb8bf0fb07a038de1b6657debe20427aeef9 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/ref.trn @@ -0,0 +1,661 @@ +DIE BEERDIGUNG MACHTE EINER ÄUSSERST WICHTIGEN SACHE EIN ENDE DER PETITION AN DEN GOUVERNEUR FÜR DES INDIANERJOES BEGNADIGUNG (M-AILABS_deu_000165-M-AILABS_deu_000165) +DA HABE SIE DIE WOHL JEDEM HIER IN DER ERINNERUNG GEBLIEBENEN WORTE GESPROCHEN (M-AILABS_deu_000166-M-AILABS_deu_000166) +ERST UM ACHT UHR WAR ER AUF MALE BRACHTE DEN KAFFEE DIE SONNE SCHIEN INS ZIMMER UND DIE SPERLINGE DIE DAS AUS DEN HÄCKSELSÄCKEN GEFALLENE FUTTERKORN AUFPICKTEN (M-AILABS_deu_000167-M-AILABS_deu_000167) +SICHERLICH AN IHREM GEBURTSTAG HÄTTE ER BEI IHR BLEIBEN KÖNNEN (M-AILABS_deu_000168-M-AILABS_deu_000168) +UND DESHALB MUSS MAN DORT WO MENSCHEN SCHWIERIGKEITEN HABEN DIES AUCH EINERSEITS ERKLÄREN ANGEBOTE MACHEN (M-AILABS_deu_000169-M-AILABS_deu_000169) +DASS MAN NUR AUF DIE WELT KOMMT UM SELBST WIEDER EINEN SOHN ZU HABEN DER DIE VEREHRUNG DER AHNEN FORTSETZT (M-AILABS_deu_000170-M-AILABS_deu_000170) +DESHALB GEHÖREN KONTINUIERLICHE SCHULBILDUNG AUCH KONTINUIERLICHE MÖGLICHKEITEN DER WEITERBILDUNG UND DAS BEGEHEN VON GEDENKTAGEN FÜR MICH UNAUFLÖSLICH ZUSAMMEN (M-AILABS_deu_000171-M-AILABS_deu_000171) +MEIN ANSASCHEN SAGT SIE ES IST JA JETZT WIEDER GANZ GUT ZWISCHEN UNS ABER EHE DU NICHT ALLES GESTEHST GEHT DIE ERINNERUNG AN DAS BÖSE NICHT WEG (M-AILABS_deu_000172-M-AILABS_deu_000172) +NEIN WEIBER BRAUCHE ICH NICHT (M-AILABS_deu_000173-M-AILABS_deu_000173) +GOTT HAT NICHT VERGEBLICH NACH MIR GERUFEN SAGTE DER SCHIFFER (M-AILABS_deu_000174-M-AILABS_deu_000174) +NUR EINES WEISS ICH DIESER FURCHTBAREN FRAGE ENTGEGENZUSETZEN UND SCHLEUDERE DAS WORT IN DIE WAAGSCHALE DIE GLUT MEINES LIEBESWILLENS IST STÄRKER ALS TRENNUNG (M-AILABS_deu_000175-M-AILABS_deu_000175) +TOMS ARMEE GEWANN EINEN GROSSEN SIEG NACH EINER LANGEN HARTNÄCKIGEN SCHLACHT (M-AILABS_deu_000176-M-AILABS_deu_000176) +ES IST EIN NAME DEM SICH DIE TÜR BEI TAG UND NACHT ÖFFNEN KANN BURSCHE UND WILLKOMMEN (M-AILABS_deu_000177-M-AILABS_deu_000177) +ABER ICH VERZEIHE IHNEN IHRE UNWISSENHEIT (M-AILABS_deu_000178-M-AILABS_deu_000178) +VON DER DRITTEN UNTERREDUNG AN SAGTE MISTER HAVISHAM WAR MIR DIE PERSON IN HOHEM MASSE VERDÄCHTIG (M-AILABS_deu_000179-M-AILABS_deu_000179) +ICH DENKE DER AMTMANN UND SEINE FAMILIE WERDEN ES RECHT VON DIR FINDEN DASS DU DICH SELBST ANGIBST UND SIE WERDEN FREUNDLICH GEGEN DICH SEIN (M-AILABS_deu_000180-M-AILABS_deu_000180) +JETZT SCHLUG DIE HELLE FLAMME AUF UND NUN ERKANNTE ER UNS DIE WIR NOCH IMMER ZUSAMMENGEDRÄNGT IN DEM WINKEL STANDEN (M-AILABS_deu_000181-M-AILABS_deu_000181) +DER SEINER SEELE ANSPORNEND DAS ERMUNTERNDE WORT VORWÄRTS (M-AILABS_deu_000182-M-AILABS_deu_000182) +ICH FREUE MICH AUF DEN BESUCH DES TUNESISCHEN MINISTERPRÄSIDENTEN (M-AILABS_deu_000183-M-AILABS_deu_000183) +WAS FÜR VERFOLGUNGEN WAS FÜR NACHSTELLUNGEN HABE ICH NICHT ZU ERDULDEN GEHABT (M-AILABS_deu_000184-M-AILABS_deu_000184) +ZIGEUNER WAREN ES DIE VON ORT ZU ORT FUHREN EIN KAUM ERWACHSENES JUNGES DING KAM ZU MIR HERANGEHÜPFT UND BETTELTE NEIN (M-AILABS_deu_000185-M-AILABS_deu_000185) +HUCK ICH WERDE DICH IN ENEM BOOT HINFAHREN WERDE DAS BOOT DA ANLEGEN UND ES WIEDER ZURÜCKRUDERN ALLES GANZ ALLEIN BRAUCHST DICH GAR NICHT DRUM ZU KÜMMERN (M-AILABS_deu_000186-M-AILABS_deu_000186) +ALS NUR EINMAL NOCH DEN RAUCH VON SEINEM HAUSE AUS DER FERNE AUFSTEIGEN ZU SEHEN UM DANN BERUHIGT ZU STERBEN (M-AILABS_deu_000187-M-AILABS_deu_000187) +DIE TÄNZERIN ABER LAG AUF DEN KNIEEN VOR BRAHMAS BILDNIS IN NAMENLOSER SEHNSUCHT UND WEINTE JAMMERVOLL (M-AILABS_deu_000188-M-AILABS_deu_000188) +RECHTFERTIGT MICH DENN DIE WIRKLICHKEIT NOCH NICHT AUF DIE ICH MICH BERUFEN KANN (M-AILABS_deu_000189-M-AILABS_deu_000189) +ICH ÄRGERTE MICH DANN WENN ICH AUFWACHTE ES WAR SO WUNDERSCHÖN GEWESEN DAS FLIEGEN (M-AILABS_deu_000190-M-AILABS_deu_000190) +NACHDEM ER SCHON DEN GANZEN VORMITTAG MIT IHM VERBRACHT KAM STANHOPE NACH TISCH INS QUANDTSCHE HAUS UM CASPAR LEBEWOHL ZU SAGEN (M-AILABS_deu_000191-M-AILABS_deu_000191) +ER WAR EIN ALTER HIRT VOLL MEDIZINISCHER GENINALITÄT (M-AILABS_deu_000192-M-AILABS_deu_000192) +DASS WOHL AUCH DER MIETER SEINE WUNDERLICHKEITEN HABEN MÜSSE (M-AILABS_deu_000193-M-AILABS_deu_000193) +SIE SAHEN ALLE ÄNGSTLICH UND BETRÜBT AUS UND AUCH HERR ARNE SASS SCHWERMÜTIG DA WIE DIE ANDEREN UND STÜTZTE DAS HAUPT IN DIE HAND (M-AILABS_deu_000194-M-AILABS_deu_000194) +UNTER DEN DAMEN MEIST JUNGE FRISCHE GESICHTER UNTER DEN HERREN NEBEN JUGENDLICHEN SOLCHE MIT FALTIGER STIRN UND BEREITS MEHR ODER MINDER MONDUMGLÄNZTEM SCHÄDEL (M-AILABS_deu_000195-M-AILABS_deu_000195) +SEIT TAGEN SCHON HATTE ES BESONDERS DRÄUEND GEKLUNGEN (M-AILABS_deu_000196-M-AILABS_deu_000196) +SONDERBAR (M-AILABS_deu_000197-M-AILABS_deu_000197) +ERB VON ERBENHEIM STAND MIT SEINER GATTIN VOLL WEHMUT UND DANKBARKEIT AN DER GRUFT AUF DER ER EINEN MÄCHTIGEN (M-AILABS_deu_000198-M-AILABS_deu_000198) +IHR WAR JEDER MENSCH EIN WUNDER UND FAST ALLES WAS MENSCHEN TATEN ETWAS WUNDERBARES (M-AILABS_deu_000199-M-AILABS_deu_000199) +WELCHE IHR WEG SIE ENTLÄNGST FÜHRTE (M-AILABS_deu_000200-M-AILABS_deu_000200) +DIE WIRTIN SASS NICHT HINTER IHREM SCHANKTISCH UND KEINER IHRER DIENSTLEUTE BEFAND SICH IN DER STUBE (M-AILABS_deu_000201-M-AILABS_deu_000201) +ALS DIE HERRSCHAFT AUS DER KIRCHE TRAT STANDEN DIE LEUTE UMHER UM SIE VORBEIGEHEN ZU SEHEN UND AM KIRCHHOFTHORE WARTETE EIN MANN (M-AILABS_deu_000202-M-AILABS_deu_000202) +WAS MÜSSEN WIR TUN UM DEM TERRORISMUS ENTGEGENZUTRETEN (M-AILABS_deu_000203-M-AILABS_deu_000203) +ICH GLAUBE DASS SIE ES GUT MIT MIR MEINEN HERR DOKTOR (M-AILABS_deu_000204-M-AILABS_deu_000204) +DOCH IM ANFANG GEWANN ER KEINE AUFMERKSAMKEIT FÜR ANDERE DINGE ALS FÜR DAS ESSEN (M-AILABS_deu_000205-M-AILABS_deu_000205) +DIES FLÄSCHCHEN ZOG ER JETZT EILIG HERVOR WÄHREND JENE SICH MIT WASSER FÜLLTEN UND BOT ES DER JUNGFER ZÜS AN (M-AILABS_deu_000206-M-AILABS_deu_000206) +DESHALB WAR ES AUCH RICHTIG UND WICHTIG DASS CHINA DOCH JETZT ANSPRUCHSVOLL GESAGT HAT WIR WERDEN AUCH AN DEN ZEITPUNKT DER REDUKTION KOMMEN (M-AILABS_deu_000207-M-AILABS_deu_000207) +NICHT DOCH MUTTER WECKE SIE JETZT NOCH NICHT (M-AILABS_deu_000208-M-AILABS_deu_000208) +JA WIR HABEN IN DEN LETZTEN JAHREN RECHT ENGE BEZIEHUNGEN ZU BRASILIEN AUFGEBAUT (M-AILABS_deu_000209-M-AILABS_deu_000209) +SIE WÜRDE SICH NICHT FÜR ANDERE OPFERN (M-AILABS_deu_000210-M-AILABS_deu_000210) +RIEFEN SIE MIR ZU (M-AILABS_deu_000211-M-AILABS_deu_000211) +GOTT WAS SIE IHR ERZÄHLTE HÖREN SIE NUR ES IST EIN GANZER ROMAN (M-AILABS_deu_000212-M-AILABS_deu_000212) +SEINE MUTTER KANN IHM NUR FLUSSWASSER GEBEN DESHALB WEINT ER (M-AILABS_deu_000213-M-AILABS_deu_000213) +DER BUNDEWIRTSCHAFTSMINISTER WIRD ZUSAMMEN MIT DER NETZAGENTUR AM VIERTE JUNI ZUM ERSTEN MAL PRÄSENTIEREN WIE SICH DIE NETZBETREIBER UND DIE KRAFTWERKE DIE NEUEN NETZPLÄNE VORSTELLEN (M-AILABS_deu_000214-M-AILABS_deu_000214) +EVA HATTE SICH ZITTERND VOR TODESSCHWÄCHE VON DEM GITTER BEFREIT UND SUCHTE ZU ENTFLIEHEN ABER DER SCHMALE GARTEN BOT KEINEN AUSWEG (M-AILABS_deu_000215-M-AILABS_deu_000215) +OB ICH MEIN WERK FÜR HEUTE LIEGEN LASSEN ODER NOCH EINEN ANLAUF NEHMEN UND ES VOLLENDEN SOLLTE (M-AILABS_deu_000216-M-AILABS_deu_000216) +ER WAR DAS GÖTZCHEN DER STUNDE DIE TAITAI BEAUFTRAGTE MADAME ANGELE DIE AUCH DASTAND UND DIE GEKAUFTEN SEIDENSTÜCKE ZUSAMMENFALTETE FÜR TSCHUN ZU SORGEN (M-AILABS_deu_000217-M-AILABS_deu_000217) +ICH WERDE NACHSEHEN (M-AILABS_deu_000218-M-AILABS_deu_000218) +ABER TIPPS ODER VORGABEN DAS MACHEN WIR NATÜRLICH NICHT (M-AILABS_deu_000219-M-AILABS_deu_000219) +ALS UNSERE IDEE BEKANNT WURDE WAR DIE PHYSIOGNOMIE DER WALTERSBURGER UNGEFÄHR DIE EINES KALBES DAS ZUM ERSTEN MALE DONNERN HÖRT (M-AILABS_deu_000220-M-AILABS_deu_000220) +BITTE MACHEN SIE GEFÄLLIGST AUF UND ES KLANG WIE EIN JAMMERNDER HILFERUF (M-AILABS_deu_000221-M-AILABS_deu_000221) +HERR DOKTOR SAGTE EINE FRAU DIE SCHNURRGRINE DIE SO OFT ZU IHNEN KOMMT IST EIGENTLICH GAR NICHT KRANK (M-AILABS_deu_000222-M-AILABS_deu_000222) +DIE ALTE ERINNERUNG AN DEN FRÜHEREN TRAUM TAUCHTE EBENFALLS WIEDER AUF UND UNWILLKÜRLICH FAST BEI DER BEHAUPTUNG DASS DIE SEELE DEN KÖRPER VERLASSEN UND ZU IHM ZURÜCKKEHREN KÖNNE SCHIEN ES IHR ORDENTLICH (M-AILABS_deu_000223-M-AILABS_deu_000223) +ALS SIE AUF DEN BALKON ZURÜCKKEHRTE FAND SIE IHN DIE ZEITUNG LESEND DIE WÄHREND IHRES FORTSEINS ANGELANGT WAR (M-AILABS_deu_000224-M-AILABS_deu_000224) +ER WAR EIN KIND DER STRASSE VON KLEIN AUF ABER IN IHM LEBTE VON JEHER EINE GEWISSE SEHNSUCHT NACH EINER EHRBAREN BÜRGERLICHEN EXISTENZ (M-AILABS_deu_000225-M-AILABS_deu_000225) +UND WIR ALS BUNDESREGIERUNG FÜHLEN UNS HIER NICHT EINER GRUPPE VERANTWORTLICH SONDERN WIR FÜHLEN UNS DEM GEMEINWOHL VERANTWORTLICH (M-AILABS_deu_000226-M-AILABS_deu_000226) +WAS MEIN LIEBES KIND WAS KANN (M-AILABS_deu_000227-M-AILABS_deu_000227) +UND DANN WOLLTE ICH DEN ANBLICK DERER NICHT MISSEN DIE MIR GEBLIEBEN WAREN VOR ALLEM ABER WAR ES MIR DARUM ZU TUN MEINE SÜSSE ELISABETH EINIGERMASSEN GETRÖSTET ZU SEHEN (M-AILABS_deu_000228-M-AILABS_deu_000228) +ABER ICH GLAUBE DASS WIR UNS AUCH GEGENSEITIG EIN BISSCHEN UNTERSTÜTZEN KÖNNEN (M-AILABS_deu_000229-M-AILABS_deu_000229) +SEINE GESCHÄFTLICHE LAUFBAHN HABE STEFENSON ALS KÜCHENBOY IN EINEM HOTEL VIERTEN GRADES BEGONNEN (M-AILABS_deu_000230-M-AILABS_deu_000230) +VIELLEICHT TÄTEN SIE GUT DIESE ANSICHTEN DES BISCHOFS NACH HAUSE ZU MELDEN SAGTE DER TAJEN DER IMMER MEHR EIN MANN DES GESCHRIEBENEN WORTES WIE DER TAT (M-AILABS_deu_000231-M-AILABS_deu_000231) +AM ANDERN MORGEN ERHOB ER SICH SPÄT SCHICKTE DEN LAKAIEN IN DIE WOHNUNG FEUERBACHS UND LIESS UM EINE UNTERREDUNG BITTEN DER MANN KAM MIT DER BOTSCHAFT ZURÜCK (M-AILABS_deu_000232-M-AILABS_deu_000232) +NUR EIN WENIG TRAURIG WURDE ES WENN IMMER DASSELBE KAM WENN SIE NIE ZUFRIEDEN SCHIENEN (M-AILABS_deu_000233-M-AILABS_deu_000233) +EIN SOMMERWARMER NOVEMBERTAG LAG MIT SONNENGLITZERN ÜBER DER HAUPTSTADT UND UNTER DEN LINDEN DRÄNGTE EINE TAUSENDKÖPFIGE MENSCHENMENGE AUF UND NIEDER (M-AILABS_deu_000234-M-AILABS_deu_000234) +KOMM MIT MIR MEIN SOHN DENN ICH BRAUCHE DEINE LIEBE (M-AILABS_deu_000235-M-AILABS_deu_000235) +NUR SEIN GESICHT WURDE EIN WENIG NACHDENKLICHER SO WIE VON EINER ERINNERUNG ERHELLT (M-AILABS_deu_000236-M-AILABS_deu_000236) +DANN WIRD AUCH WIEDER DER INNOVATIONSDRUCK STEIGEN UND DAZU IST DAS SYSTEM JA EINGEFÜHRT WORDEN (M-AILABS_deu_000237-M-AILABS_deu_000237) +JETZT GEWAHRTE ER MIT ENTSETZEN DIE SCHEUSSLICHE TEUFLISCHE AFFENFRATZE DIE ÜBER DES MÄDCHENS SCHULTER SCHIELTE (M-AILABS_deu_000238-M-AILABS_deu_000238) +JA DER WIRT NICKTE DAS GEHÖRT EINEM GEWISSEN WUTSCHOW BERNHARD WUTSCHOW IST ETWAS VERRUFEN (M-AILABS_deu_000239-M-AILABS_deu_000239) +WOLLT IHR IN WAHRHEIT DIE LÖWEN TÖTEN UND KÖNNT IHR SCHIESSEN (M-AILABS_deu_000240-M-AILABS_deu_000240) +BAT CEDDIE SEHR RESPEKTVOLL WOBEI ER NUR EINIGE SILBEN VERSCHLUCKTE WAS IHM BEI DEN BELIEBTEN LANGEN WÖRTERN DES ÖFTERN VORKAM (M-AILABS_deu_000241-M-AILABS_deu_000241) +LORD FAUNTLEROY WIRD NICHTS ENTBEHREN DESSEN BIN ICH GEWISS VERSETZTE ER (M-AILABS_deu_000242-M-AILABS_deu_000242) +KAM GLEICHFALLS INS SCHLAFZIMMER AUF EINEN NAGEL IN DER NÄHE DES BETTES (M-AILABS_deu_000243-M-AILABS_deu_000243) +UND DAS IST DIE CHANCE DIE IN DIESER KRISE STECKT DIE CHANCE FÜR INTERNATIONALE REGELN DIE SICH AN DEN PRINZIPIEN DER SOZIALEN MARKTWIRTSCHAFT ORIENTIEREN (M-AILABS_deu_000244-M-AILABS_deu_000244) +ANFANGS FIEL DER REGEN SCHRÄG UND PEITSCHTE ERST DIE EINE UND DANN DIE ANDERE SEITE DES WAGENS (M-AILABS_deu_000245-M-AILABS_deu_000245) +FAST LEICHTSINNIGEN BEMESSUNG IHRES WERTES AUFZUGEBEN SICH ENTSCHLOSSEN HATTE (M-AILABS_deu_000246-M-AILABS_deu_000246) +DAS HEISST DIE FRAGE DER MENSCHLICHEN ARBEIT UND DIE FRAGE WAS KANN TECHNISCH GELÖST WERDEN (M-AILABS_deu_000247-M-AILABS_deu_000247) +DIE SAFARI WAR AUF DIE REGELMÄSSIG BENUTZTEN WASSERSTELLEN DIESER ROUTE ANGEWIESEN (M-AILABS_deu_000248-M-AILABS_deu_000248) +DIE BEIDEN MÜSSTEN HIER OBEN AUF DEM GIPFEL GESTANDEN HABEN UND ER SPRACH DIE ALTEN WORTE VOR SICH HIN (M-AILABS_deu_000249-M-AILABS_deu_000249) +ENDLICH BLICKTE CEDRIK AUF WEISS NEWICK ALLES VON DEN ARMEN LEUTEN FRAGTE ER (M-AILABS_deu_000250-M-AILABS_deu_000250) +DASS ES HEUTE EINE WUNDERBARE ZUSAMMENARBEIT ZWISCHEN BUND UND LÄNDERN IN DIESEN FRAGEN GIBT MIT SEHR SEHR INTERESSANTEN PROJEKTEN (M-AILABS_deu_000251-M-AILABS_deu_000251) +CASPAR VERHARRTE ANGEWURZELT AN SEINEM PLATZ SEINE GLIEDER JA SEINE AUGEN WAREN WIE VERSTEINERT ALS ER ZUM ZWEITENMAL HINBLICKTE (M-AILABS_deu_000252-M-AILABS_deu_000252) +EINIGE ZEIT DANACH FRAGTE ER MICH OB ICH GLAUBE DASS DER EISGANG DEN SCHLITTEN DES ANDEREN ZERSTÖRT HABE (M-AILABS_deu_000253-M-AILABS_deu_000253) +ABER NUN BLOSS NICHT IN EINE SCHOCKSTARRE VERFALLEN (cv_deu_000698-cv_deu_000698) +JA ICH KOMME JA SCHON (cv_deu_000699-cv_deu_000699) +NEBENBEI ARBEITETE ER ALS AUSHILFSKRAFT AUF EINER FARM (cv_deu_000700-cv_deu_000700) +EIN TERRITORIAL GRÖSSERES EUROPA WIRD NICHT MIT EINEM ETATMÄSSIG KLEINEREN EUROPA ERREICHT (cv_deu_000701-cv_deu_000701) +IHR SOHN KAM DURCH KÜNSTLICHE BEFRUCHTUNG ZUR WELT (cv_deu_000702-cv_deu_000702) +DIE NACHTAKTIVEN FALTER FLIEGEN VON MITTE JULI BIS MITTE OKTOBER (cv_deu_000703-cv_deu_000703) +ACHT (cv_deu_000704-cv_deu_000704) +FÜNF (cv_deu_000705-cv_deu_000705) +NUTZER KÖNNEN IHRE LESEZEICHEN ONLINE ABSPEICHERN VERWALTEN UND MIT ANDEREN NUTZERN TEILEN (cv_deu_000706-cv_deu_000706) +DIE DON BOSCO KATH (cv_deu_000707-cv_deu_000707) +SAUL BASS ZÄHLT ZU DEN INNOVATIVSTEN DESIGNERN UND FILMEMACHERN SEINER ZEIT (cv_deu_000708-cv_deu_000708) +IN GRÜN ÜBER SILBERNEM WELLENBALKEN EINE SILBERNE EICHE (cv_deu_000709-cv_deu_000709) +WEITERE WICHTIGE INDUSTRIEZWEIGE SIND DIE MIKROMECHANIK GALVANOPLASTIK METALLBAU UND DIE HOLZVERARBEITUNG (cv_deu_000710-cv_deu_000710) +ÜBER DEN AUTOR IST NICHTS BEKANNT VERMUTLICH STAMMTE ER AUS DEM DEUTSCHEN SPRACHGEBIET (cv_deu_000711-cv_deu_000711) +MAN STEUERT ES MIT EINEM DOPPELPADDEL (cv_deu_000712-cv_deu_000712) +WIR HABEN EIN PROBLEM AUF OSISCHICHT ACHT (cv_deu_000713-cv_deu_000713) +WIR SPIELEN IMMER NOCH ABER DAS LEBEN AUF TOUR IST DERZEIT NICHT MACHBAR (cv_deu_000714-cv_deu_000714) +HEUTE ZEIGT SICH DER GRÖSSTE TEIL DER ANLAGE ALS ENGLISCHER GARTEN (cv_deu_000715-cv_deu_000715) +SEINE RESIDENZ NAHM ER IN MÜNCHEN WO ER AUCH STARB (cv_deu_000716-cv_deu_000716) +INNERER UND ÄUSSERER NARTHEX KÖNNEN ALS GETRENNTE TEILE EINES NARTHEX AUCH GEMEINSAM VORKOMMEN (cv_deu_000717-cv_deu_000717) +DABEI BELEGTE ER DIE PLÄTZE VIER UND DREI (cv_deu_000718-cv_deu_000718) +KIM DARBY IST DIE TOCHTER ZWEIER PROFESSIONELLER TÄNZER (cv_deu_000719-cv_deu_000719) +ICH GLAUBE DAS FÜHRT NICHT IN DIE RICHTIGE RICHTUNG (cv_deu_000720-cv_deu_000720) +DAS IST EINE EXTREM SCHLECHTE RICHTLINIE (cv_deu_000721-cv_deu_000721) +HERR LURCH ENTBLÖSSTE SEIN HAGERES GESICHT (cv_deu_000722-cv_deu_000722) +NUR CARMEN FINDET DAS UNFAIR (cv_deu_000723-cv_deu_000723) +INGEBORG KRABBE HATTE DREI GESCHWISTER (cv_deu_000724-cv_deu_000724) +ES KOMMT WIRKLICH DARAUF AN DASS SOLCHE DATEN AUF DIESER EBENE ERFASST WERDEN (cv_deu_000725-cv_deu_000725) +STRUMMING HINGEGEN ERGIBT EIN HARMONISCHES PULSIEREN (cv_deu_000726-cv_deu_000726) +BIN ICH ZUM KAUF EINER HYPOTHEK BERECHTIGT (cv_deu_000727-cv_deu_000727) +TEHERAN IST DIE HAUPTSTADT VOM IRAN (cv_deu_000728-cv_deu_000728) +KOHLENHYDRATE SIND BESSER ALS IHR RUF (cv_deu_000729-cv_deu_000729) +OHNE DIE PROFESSIONELLE UNTERSTÜTZUNG DER MASERATIRENNABTEILUNG WAREN DIESE WAGEN DER KONKURRENZ NUN DOCH UNTERLEGEN (cv_deu_000730-cv_deu_000730) +SIE DIENTE ZUNÄCHST ALS UNTERKUNFT FÜR BELGISCHE BESATZUNGSTRUPPEN (cv_deu_000731-cv_deu_000731) +DA MÜSSEN WIR SPRENGEN MEINTE DER ZAHNARZT (cv_deu_000732-cv_deu_000732) +AUSSERDEM SPIELTE ER BEIM NACHFOLGETEAM NEWMARKET ROYALS SOWIE BEIM LIGAKONKURRENTEN LONDON KNIGHTS (cv_deu_000733-cv_deu_000733) +WIE AUCH DAS INSTANTRUNOFFVOTING ERFÜLLT DIE COOMBSWAHL DAS CONDORCETKRITERIUM NICHT (cv_deu_000734-cv_deu_000734) +SMITH WUCHS IN CHICAGO AUF (cv_deu_000735-cv_deu_000735) +WIR SIND HIER ALLEIN (cv_deu_000736-cv_deu_000736) +DUMM IST WER ETWAS WEISS ABER TROTZ DES BESSEREN WISSENS FALSCH HANDELT (cv_deu_000737-cv_deu_000737) +HAUPTTHEMA DER SHOW IST DIE REVANCHE FÜR ÜBLE STREICHE UNTER FREUNDEN (cv_deu_000738-cv_deu_000738) +GLEICHZEITIG WURDEN SPORTWETTEN TEILWEISE VERBOTEN (cv_deu_000739-cv_deu_000739) +SIEBEN (cv_deu_000740-cv_deu_000740) +JA (cv_deu_000741-cv_deu_000741) +ZUDEM VERSAH ER IM KLOSTER LANGE JAHRE DIE ÄMTER DES NOVIZENMEISTERS UND PRIOR (cv_deu_000742-cv_deu_000742) +HEIDENHAIN ENTSTAMMTE EINER ÄRZTEFAMILIE (cv_deu_000743-cv_deu_000743) +ACHT (cv_deu_000744-cv_deu_000744) +ZWEI (cv_deu_000745-cv_deu_000745) +EBENFALLS IN AUGGEN ANGESIEDELT SIND DIE KELTEREI DER FA (cv_deu_000746-cv_deu_000746) +DIESE STEHT AUCH FÜR ABSOLVENTEN EINHEIMISCHER SCHULEN MIT DEUTSCHKENNTNISSEN OFFEN (cv_deu_000747-cv_deu_000747) +ALSO ICH HÖRE NICHTS (cv_deu_000748-cv_deu_000748) +WIE KANN MAN SICH SCHÜTZEN (cv_deu_000749-cv_deu_000749) +NACH FÜNF MONATEN LAG EINE EMPFINDLICHERE PLATTE ALS DIE BIS DAHIN ERHÄLTLICHEN VOR (cv_deu_000750-cv_deu_000750) +ZIEL IST ES DIE ÜBEREINSTIMMUNG EINES SOFTWARESYSTEMS MIT SEINER SPEZIFIKATION ZU ÜBERPRÜFEN (cv_deu_000751-cv_deu_000751) +MIT EINEM WARMEN GETRÄNK IM BAUCH LÄSST SICH DIE KÄLTE BESSER AUSHALTEN (cv_deu_000752-cv_deu_000752) +DIE ANTIVIRENSOFTWARE IST AMOK GELAUFEN UND HAT ALLE COMPUTER IM HAUS LAHMGELEGT (cv_deu_000753-cv_deu_000753) +IHRE KLOAKE IST IN DIESER ZEIT KUGELFÖRMIG (cv_deu_000754-cv_deu_000754) +DIE STRECKE BEGINNT IM SÜDEN VERONAS UND FÜHRT DURCH DIE POEBENE RICHTUNG SÜDOSTEN (cv_deu_000755-cv_deu_000755) +ERST VON DORT KONNTE ER SEINEN WEG FREI FORTSETZEN (cv_deu_000756-cv_deu_000756) +SIE ERHEBT SICH HEUTE IMMER NOCH GUT ERKENNBAR AUS DEM SCHWEMMLAND HERAUS (cv_deu_000757-cv_deu_000757) +DIE KANARISCHEN INSELN GEHÖREN ZU SPANIEN (cv_deu_000758-cv_deu_000758) +WISSENSCHAFTLER HABEN DIESE MUTATION BISHER NUR BEI FRAUEN BEOBACHTET (cv_deu_000759-cv_deu_000759) +SEINE GESCHÄFTSBEZIEHUNGEN REICHTEN BIS NORDAMERIKA UND ASIEN (cv_deu_000760-cv_deu_000760) +ZAHLREICHE VORDERE PLATZIERUNGEN BEI DEUTSCHEN EUROPA UND WELTMEISTERSCHAFTEN SOWIE OLYMPISCHEN SPIELEN FOLGTEN (cv_deu_000761-cv_deu_000761) +IN EINER TAGESZEITUNG BLÄTTERND SITZT SIEGFRIED AUF EINER PARKBANK (cv_deu_000762-cv_deu_000762) +MIT EINEM WARMEN GETRÄNK IM BAUCH LÄSST SICH DIE KÄLTE BESSER AUSHALTEN (cv_deu_000763-cv_deu_000763) +FOLGE DEM QUERVERWEIS (cv_deu_000764-cv_deu_000764) +OSTERN IST IMMER EINE WOCHE NACH DEM ERSTEN VOLLMOND IM FRÜHLING (cv_deu_000765-cv_deu_000765) +IM MITTELALTER HATTEN WECHSELNDE HERRSCHAFTEN DAS DORF INNE (cv_deu_000766-cv_deu_000766) +DEN NAMEN GHIBLI TRAGEN AUCH WEITERE FAHRZEUGE VON MASERATI (cv_deu_000767-cv_deu_000767) +DU KANNST MIT DEM BUS NACH FRANKFURT AN DER ODER FAHREN (cv_deu_000768-cv_deu_000768) +MIR DOCH EGAL (cv_deu_000769-cv_deu_000769) +ALLERDINGS ERGABEN WEITERE PRÜFUNGEN DASS ES MITTELFRISTIG KEINEN BEDARF FÜR EINE SOLCHE AUTOBAHN GÄBE (cv_deu_000770-cv_deu_000770) +UMGEKEHRT KANN EIN FREIBRIEF EINE AUSSCHREIBUNG ALS VOGELFREI GEMEINT SEIN (cv_deu_000771-cv_deu_000771) +BIZARRGROTESKE ABSCHNITTE ZEIGEN EINFLÜSSE DURCH SCHOSTAKOWITSCH (cv_deu_000772-cv_deu_000772) +ER WAR EINER DER PIONIERE AUF DEM GEBIET DER NUTZUNG DER SONNENENERGIE (cv_deu_000773-cv_deu_000773) +AUCH WENN MIR DIE KUNDEN AUF DIE NERVEN GEHEN MUSS ICH HÖFLICHKEIT BEWAHREN (cv_deu_000774-cv_deu_000774) +DIE SPÜLMASCHINE IST FERTIG (cv_deu_000775-cv_deu_000775) +IN DER ARCHAISCHEN PERIODE WURDEN ERSTE FORMEN DES ACKERBAUS ENTWICKELT (cv_deu_000776-cv_deu_000776) +DIE KOMÖDIE SEI BESSER ALS DER ERSTE FILM (cv_deu_000777-cv_deu_000777) +AKTUELL GILT FOLGENDER MODUS (cv_deu_000778-cv_deu_000778) +DAMIT ENDET EINE ERFOLGREICHE INTERNATIONALE BILDUNGSARBEIT VOR ALLEM IM MUSISCHKULTURELLEN BEREICH (cv_deu_000779-cv_deu_000779) +DER SOHN EINES BERGMANNS BEGANN SEINE FUSSBALLKARRIERE BEI DEN SPORTFREUNDEN WANNEEICKEL (cv_deu_000780-cv_deu_000780) +IN DIESEM JAHR GAB ES SIEBEN NUMMEREINSSINGLES UND SECHSUNDDREISSIG NUMMEREINSALBEN (cv_deu_000781-cv_deu_000781) +NORDWESTLICH VON HACKHAUSEN BEFINDET SICH DIE ORTSCHAFT HACKENBROICH (cv_deu_000782-cv_deu_000782) +IM ORT GNARRENBURG GINGEN VIELE SOZIALE EINRICHTUNGEN VON HERMANN LAMPRECHT UND DER MARIENHÜTTE AUS (cv_deu_000783-cv_deu_000783) +ICH WERDE FOLGLICH DEN RAT ÜBER DIE IM PARLAMENT VORGETRAGENEN BEDENKEN INFORMIEREN (cv_deu_000784-cv_deu_000784) +ES WÄRE TRAURIG GEWESEN EIN SO WICHTIGES THEMA NICHT IM KONSENS VERABSCHIEDEN ZU KÖNNEN (cv_deu_000785-cv_deu_000785) +NACH DESSEN TOD IM GLEICHEN JAHR KAM ES KURZFRISTIG AN ANDERE BESITZER (cv_deu_000786-cv_deu_000786) +KURZ DANACH GAB ES EINEN WERBESPOT MIT DEM CANCAN VON JACQUES OFFENBACH (cv_deu_000787-cv_deu_000787) +DAS IST BESSER (cv_deu_000788-cv_deu_000788) +WIE SIEHT ES MIT GLEITZEIT AUS (cv_deu_000789-cv_deu_000789) +NAHE DEM DORF BEFINDET SICH AUCH DER GRAND CANYON NATIONAL PARK AIRPORT (cv_deu_000790-cv_deu_000790) +SIE SOLLEN VERKÜNDEN DASS DIE LIEBE DEN TOD BESIEGT HAT (cv_deu_000791-cv_deu_000791) +BEDECKT IST DIE REPRÄSENTATIV GESTALTETE VILLA MIT EINEM MANSARDDACH (cv_deu_000792-cv_deu_000792) +DIESE SIEDLUNG IST MIT DER ORTSCHAFT DELLACH ZUSAMMENGEWACHSEN (cv_deu_000793-cv_deu_000793) +WART IHR SCHON EINMAL IN DEM CLUB (cv_deu_000794-cv_deu_000794) +WO RAUCH IST IST AUCH FEUER (cv_deu_000795-cv_deu_000795) +DIREKT VON DER STRASSE WURDEN SIE VON ALFRED BIOLEK FÜR SEINE FERNSEHSHOW SHOWBÜHNE ENGAGIERT (cv_deu_000796-cv_deu_000796) +EIN JAHR SPÄTER WECHSELTE ER ZU HEALTH NET UND ER WURDE ERFOLGREICHER (cv_deu_000797-cv_deu_000797) +IN DER LANDWIRTSCHAFT KANN DER ERTRAG DEUTLICH REDUZIERT WERDEN (cv_deu_000798-cv_deu_000798) +MANSOUR SPIELTE IN SEINER HEIMATSTADT KAIRO FÜR AL AHLY (cv_deu_000799-cv_deu_000799) +ER TRAT DER FREIMAURERLOGE LAUTARO BEI (cv_deu_000800-cv_deu_000800) +MIT „FÜRST“ WAR EHER DIE SOZIALE ALS DIE RECHTLICHE ROLLE DES SO BEZEICHNETEN GEMEINT (cv_deu_000801-cv_deu_000801) +LETZTE WOCHE GAB DAS METI BEKANNT DASS ES VON APPLE ÜBER 34 WEITERE VORFÄLLE VON ÜBERHITZUNG INFORMIERT WORDEN WAR DIE DAS UNTERNEHMEN ALS NICHT SCHWERWIEGEND BEZEICHNETE (fleurs_deu_000378-fleurs_deu_000378) +USA GYMNASTICS UNTERSTÜTZT DEN BRIEF DES OLYMPISCHEN KOMITEES DER VEREINIGTEN STAATEN UND AKZEPTIERT ES ALS ABSOLUTE NOTWENDIGKEIT DASS SICH DIE OLYMPISCHE FAMILIE FÜR EIN SICHERES UMFELD FÜR ALLE UNSERE SPORTLER EINSETZT (fleurs_deu_000379-fleurs_deu_000379) +DADURCH KANN ER ABWÄRTSKOMPATIBEL MIT 80211A 80211B UND 80211G SEIN VORAUSGESETZT DIE BASISSTATION VERFÜGT ÜBER DUALRADIO (fleurs_deu_000380-fleurs_deu_000380) +ER BEZEICHNETE DIE GERÜCHTE ALS POLITISCHES GESCHWÄTZ UND ALBERNHEIT (fleurs_deu_000381-fleurs_deu_000381) +LETZTE WOCHE GAB DAS METI BEKANNT DASS ES VON APPLE ÜBER 34 WEITERE VORFÄLLE VON ÜBERHITZUNG INFORMIERT WORDEN WAR DIE DAS UNTERNEHMEN ALS NICHT SCHWERWIEGEND BEZEICHNETE (fleurs_deu_000382-fleurs_deu_000382) +NACHDEM DER DAMM 1963 ERBAUT WORDEN WAR KAMEN DIE JAHRESZEITLICHEN ÜBERFLUTUNGEN DIE SEDIMENTE IM FLUSS VERTEILEN ZUM STILLSTAND (fleurs_deu_000383-fleurs_deu_000383) +ER WAR AUCH AM STECHEN VON GELDSCHEINEN FÜR VIELE LÄNDER BETEILIGT AKTUELLE BEISPIELE SEINER ARBEIT SCHLIESSEN DIE PREMIERMINISTERPORTRAITS AUF DER VORDERSEITE DER KANADISCHEN 5 UND 100DOLLARNOTEN EIN (fleurs_deu_000384-fleurs_deu_000384) +DIE HAUPTSTADT VON MOLDAWIEN IST KISCHINAU DIE EINHEIMISCHE SPRACHE IST RUMÄNISCH ABER VIELE MENSCHEN SPRECHEN AUCH RUSSISCH (fleurs_deu_000385-fleurs_deu_000385) +ZWISCHEN DEN EINZELNEN DYNASTIEN HERRSCHTEN AUCH UNBESTÄNDIGE ZEITEN GETEILTER PROVINZEN DIE BEKANNTESTE DIESER PERIODEN WAR DIE EPOCHE DER DREI KÖNIGREICHE DIE 60 JAHRE LANG ZWISCHEN DER HAN UND DER JINDYNASTIE STATTFAND (fleurs_deu_000386-fleurs_deu_000386) +AM ANDEREN ENDE DES SPEKTRUMS VERWANDELT MAN SICH IN EIN NICHT WIEDERZUERKENNENDES INDIVIDUUM DAS ALLES ANDERS MACHEN MUSS ALS DAS TEAM ES GEMACHT HAT UND SICH ALLES ZU EIGEN MACHT (fleurs_deu_000387-fleurs_deu_000387) +DIE MEISTEN INTERPRETATIONEN DES TECHNOLOGISCHEN DETERMINISMUS TEILEN ZWEI ALLGEMEINE VORSTELLUNGEN EINERSEITS DASS DIE ENTWICKLUNG DER TECHNOLOGIE SELBST EINEM WEG FOLGT DER WEITGEHEND JENSEITS KULTURELLER ODER POLITISCHER EINFLUSSNAHME LIEGT UND ANDERERSEITS DASS TECHNOLOGIE IHRERSEITS AUSWIRKUNGEN AUF GESELLSCHAFTEN HAT DIE EHER INHÄRENT ALS SOZIAL BEDINGT SIND (fleurs_deu_000388-fleurs_deu_000388) +ZWISCHEN DEN EINZELNEN DYNASTIEN HERRSCHTEN AUCH UNBESTÄNDIGE ZEITEN GETEILTER PROVINZEN DIE BEKANNTESTE DIESER PERIODEN WAR DIE EPOCHE DER DREI KÖNIGREICHE DIE 60 JAHRE LANG ZWISCHEN DER HAN UND DER JINDYNASTIE STATTFAND (fleurs_deu_000389-fleurs_deu_000389) +DEM LEAK ZUFOLGE BEZIEHT SICH DAS DOKUMENT AUF DEN GRENZSTREIT IN DEM DIE PALÄSTINENSER EIN ZURÜCKSETZEN DER GRENZEN IN DEN ZUSTAND VOR DEM SECHSTAGEKRIEG VON 1967 FORDERN (fleurs_deu_000390-fleurs_deu_000390) +MIT DEM VERLUST GRIECHISCHER SPRACHKENNTNISSE WAR DER WESTEN VON SEINEN PHILOSOPHISCHEN UND WISSENSCHAFTLICHEN WURZELN IN GRIECHENLAND ABGESCHNITTEN (fleurs_deu_000391-fleurs_deu_000391) +WIR STIMMEN MIT DER AUSSAGE DES USOC ÜBEREIN DASS DEN INTERESSEN UNSERER ATHLETEN UND VEREINE UND IHRES SPORTS BESSER GEDIENT IST WENN WIR INNERHALB UNSERER ORGANISATION SINNVOLLE VERÄNDERUNGEN VORANTREIBEN ANSTATT EINE DEZERTIFIZIERUNG VORZUNEHMEN (fleurs_deu_000392-fleurs_deu_000392) +DIE KREUZFAHRTEN NACH SANKT PETERSBURG BIETEN AUCH ZEIT FÜR EINEN AUFENTHALT IN DER STADT KREUZFAHRTPASSAGIERE SIND VON DER VISUMPFLICHT BEFREIT SIEHE BEDINGUNGEN (fleurs_deu_000393-fleurs_deu_000393) +REISENDE WERDEN DRINGEND GEWARNT AUF JEDWEDE ART VON UNWETTER ZU ACHTEN DIE IHR GEBIET BETRIFFT DA DIES SICH AUF ALLE REISEPLÄNE AUSWIRKEN KANN (fleurs_deu_000394-fleurs_deu_000394) +SIE BESAGT DASS DER KREUZUNGSPUNKT DER LINIEN DIE EIN BILD VERTIKAL UND HORIZONTAL DRITTELN DER EFFEKTIVSTE PLATZ FÜR DAS HAUPTMOTIV IST SIEHE BEISPIEL (fleurs_deu_000395-fleurs_deu_000395) +SEIT 1988 MÜSSEN WAHLURNEN TRANSPARENT SEIN DAMIT WÄHLER UND BEOBACHTER BEZEUGEN KÖNNEN DASS ZU BEGINN DER WAHL KEINE UMSCHLÄGE VORHANDEN SIND UND DASS KEINE UMSCHLÄGE EINGEWORFEN WERDEN AUSSER JENE DER ORDNUNGSGEMÄSS GEZÄHLTEN UND AUTORISIERTEN WÄHLER (fleurs_deu_000396-fleurs_deu_000396) +OTTAWA IST KANADAS BEZAUBERNDE ZWEISPRACHIGE HAUPTSTADT UND ZEICHNET SICH DURCH EINE REIHE VON KUNSTGALERIEN UND MUSEEN AUS DIE KANADAS VERGANGENHEIT UND GEGENWART PRÄSENTIEREN (fleurs_deu_000397-fleurs_deu_000397) +DIESE PAARE KÖNNEN SICH FÜR EINEN ADOPTIONSPLAN FÜR IHR BABY ENTSCHEIDEN (fleurs_deu_000398-fleurs_deu_000398) +INFOLGEDESSEN SIND ZWEI FISCHARTEN AUSGESTORBEN UND ZWEI WEITERE SIND VOM AUSSTERBEN BEDROHT DARUNTER DER GILA CYPHA (fleurs_deu_000399-fleurs_deu_000399) +PFLANZEN SEHEN IN IHRER NATÜRLICHEN UMGEBUNG AM BESTEN AUS WIDERSTEHEN SIE ALSO DER VERSUCHUNG AUCH NUR EIN EXEMPLAR ZU ENTFERNEN (fleurs_deu_000400-fleurs_deu_000400) +AUF DER NAHSEITE KÖNNTE ES MEHR MARIA GEBEN DA DIE KRUSTE DÜNNER IST ES WAR EINFACHER FÜR DIE LAVA AN DIE OBERFLÄCHE AUFZUSTEIGEN (fleurs_deu_000401-fleurs_deu_000401) +ER FÜGTE HINZU DASS SIE JEDOCH NICHT DAZU AUFGEFORDERT WERDEN SOLLTEN VERPFLICHTUNGEN EINZUGEHEN DIE ÜBER IHREN ENTWICKLUNGSSTAND IHRE VERANTWORTUNG UND IHRE FÄHIGKEITEN HINAUSGEHEN (fleurs_deu_000402-fleurs_deu_000402) +VIRTUELLE HILFESTELLUNGEN SIND IN DIE SOFTWARE EINGEBAUT UND SOLLEN ARBEITSSCHRITTE DIE DER SCHÜLER ALLEIN MÖGLICHERWEISE NICHT BEWÄLTIGEN KANN HINTERFRAGEN NAHELEGEN UND ERKLÄREN (fleurs_deu_000403-fleurs_deu_000403) +AM 15 AUGUST 1940 FIELEN DIE ALLIIERTEN IN SÜDFRANKREICH EIN DIE INVASION WURDE OPERATION DRAGOON GENANNT (fleurs_deu_000404-fleurs_deu_000404) +ER GRIFF AUCH ALLES AN WAS INS WASSER KAM SELBST EIN GROSSER DINOSAURIER WIE DER T REX WAR IHM NICHT GEWACHSEN (fleurs_deu_000405-fleurs_deu_000405) +SEIT DER GRÜNDUNG VON ASUNCIÓN 1537 IST ES PARAGUAY GELUNGEN VIEL VON SEINEM INDIGENEN CHARAKTER UND SEINER IDENTITÄT ZU BEWAHREN (fleurs_deu_000406-fleurs_deu_000406) +TROTZDEM IST DER ANTEIL AN XDRTB IN DER GESAMTEN GRUPPE DER LEUTE MIT TUBERKULOSE OFFENBAR DENNOCH GERING 6000 DER INSGESAMT 330000 LEUTE DIE IN SÜDAFRIKA ZU EINEM BESTIMMTEN ZEITPUNKT ANGESTECKT SIND (fleurs_deu_000407-fleurs_deu_000407) +ANGEL 2006 ERLÄUTERT DAS KONTINUUMKONZEPT ALS EINE METHODE UM ORGANISATIONEN ZU HELFEN LEISTUNGSFÄHIGER ZU WERDEN (fleurs_deu_000408-fleurs_deu_000408) +IN DIESER PERIODE DER EUROPÄISCHEN GESCHICHTE STAND DIE REICH UND MÄCHTIG GEWORDENE KATHOLISCHE KIRCHE AUF DEM PRÜFSTAND (fleurs_deu_000409-fleurs_deu_000409) +DIE ERSTE DER 78 EMPFEHLUNGEN IST DASS EINE NEUE DIPLOMATISCHE INITIATIVE VOR ENDE DIESES JAHRES ERGRIFFEN WERDEN SOLLTE UM DIE IRAKISCHEN GRENZEN GEGENÜBER FEINDLICHEN INTERVENTIONEN ZU SICHERN UND DIPLOMATISCHE BEZIEHUNGEN MIT SEINEN NACHBARN WIEDERHERZUSTELLEN (fleurs_deu_000410-fleurs_deu_000410) +DIES BIETET EINE GUTE GELEGENHEIT DAS NORDLICHT ZU SEHEN DA DER HIMMEL MEHR ODER WENIGER RUND UM DIE UHR DUNKEL IST (fleurs_deu_000411-fleurs_deu_000411) +PROFESSORIN PAMELA FERGUSON VON DER UNIVERSITY OF DUNDEE MERKT AN JOURNALISTEN SCHEINEN EINE GEFÄHRLICHE GRENZE ZU ÜBERSCHREITEN WENN SIE FOTOS USW VON VERDÄCHTIGEN VERÖFFENTLICHEN (fleurs_deu_000412-fleurs_deu_000412) +ES KANN SICH AUCH LOHNEN EINE WILD CARD ZU KAUFEN DIE ZUTRITT ENTWEDER ZU AUSGEWÄHLTEN PARKS IN SÜDAFRIKA ODER ZU ALLEN SÜDAFRIKANISCHEN NATIONALPARKS GEWÄHRT (fleurs_deu_000413-fleurs_deu_000413) +DIE BRÜCKE SOLL IM SEPTEMBER 2017 VOLLSTÄNDIG DEN BETRIEB AUFNEHMEN ES WIRD ERWARTET DASS DIE BRASILIANISCHEN ZOLLKONTROLLPUNKTE DANN FERTIG GESTELLT SEIN WERDEN (fleurs_deu_000414-fleurs_deu_000414) +WÄHREND EIN EXPERIMENTELLER IMPFSTOFF IN DER LAGE ZU SEIN SCHEINT DIE EBOLAMORTALITÄT ZU SENKEN GIBT ES BISHER KEINE MEDIKAMENTE DIE ALS EINDEUTIG ZUR BEHANDLUNG BESTEHENDER INFEKTIONEN GEEIGNET NACHGEWIESEN WURDEN (fleurs_deu_000415-fleurs_deu_000415) +EIN ÄUSSERST LEBHAFTER DEPESCHENWECHSEL FAND STATT MAN ERWOG DEN PLAN EINEN ALLGEMEINEN STAATENKONGRESS ZU BERUFEN UND KONNTE SICH VORLÄUFIG NUR NOCH NICHT ÜBER DAS VORZULEGENDE PROGRAMM UND DEN ORT DES ZUSAMMENTRITTS EINIGEN (mls_deu_000281-mls_deu_000281) +ER WUSSTE NICHT WAS IHM DAS LEBEN KOSTBARES GERAUBT HATTE SPANNKRAFT UND MUT DASS ES IHN FEIG UND SCHEU GEMACHT HATTE UNFÄHIG ZU DEN HOHEN DINGEN ZU DENEN UNGETRÜBTE MITFREUDE GEHÖRT (mls_deu_000282-mls_deu_000282) +DIESER JUNGE MANN HIESS KACKERLITZCHEN UND BEFAND SICH GERADE AUF DER WANDERSCHAFT ALS IN DEM GENANNTEN KÖNIGREICH DIE BEKANNTMACHUNG WEGEN DER PRINZESSIN VERLESEN WURDE EI SAGTE DER SCHNEIDER WENN ES WEITER NICHTS IST EIN WEIB HAB ICH NOCH NICHT GEKÜSST UND DES KÖNIGS EIDAM ZU WERDEN DAS GELÜSTET MICH ALLERDINGS (mls_deu_000283-mls_deu_000283) +NOCH FÜNF MINUTEN UND DIE WOLKEN DER BEWUSSTLOSIGKEIT BEGANNEN ZU SCHWINDEN JETZT WUSSTE ICH SEHR WOHL DASS ICH IN MEINEM EIGENEN BETTE LAG UND DASS DIE ROTE GLUT NICHTS ANDERES WAR ALS DAS FEUER IM KAMIN DER KINDERSTUBE ES WAR NACHT EINE KERZE BRANNTE AUF DEM TISCHE (mls_deu_000284-mls_deu_000284) +WELCHE DIESE VERDRÄNGUNGEN WIE WÄCHTER UNTERHALTEN KOMMT DANN IM PUBERTÄTSALTER DIE HOCHFLUT DER SEXUELLEN BEDÜRFTIGKEIT SO FINDET SIE AN DEN GENANNTEN SEELISCHEN REAKTIONS ODER WIDERSTANDSBILDUNGEN DÄMME (mls_deu_000285-mls_deu_000285) +ABER AFFEN GEHÖREN BEI HAGENBECK AN DIE KISTENWAND NUN SO HÖRTE ICH AUF AFFE ZU SEIN EIN KLARER SCHÖNER GEDANKENGANG DEN ICH IRGENDWIE MIT DEM BAUCH AUSGEHECKT HABEN MUSS DENN AFFEN DENKEN MIT (mls_deu_000286-mls_deu_000286) +IST ES DAS PORTRÄT EINES MENSCHEN DEN SIE KENNEN FRAGTE ELIZA WELCHE UNBEMERKT AN MICH HERANGETRETEN WAR ICH ENTGEGNETE DASS ES NUR EIN PHANTASIEKOPF SEI UND SCHOB DIE ZEICHNUNG EILIG UNTER DIE ANDERN BLÄTTER NATÜRLICH SPRACH ICH DIE UNWAHRHEIT DENN ES WAR EIN SEHR GETREUES PORTRÄT MR ROCHESTERS (mls_deu_000287-mls_deu_000287) +ICH WEISS DASS ICH SEHR KRANK BIN SAGTE SIE NACH EINER WEILE VOR EIN PAAR MINUTEN VERSUCHTE ICH MICH IM BETTE UMZUDREHEN UND FÜHLTE DASS ICH KEIN GLIED MEHR RÜHREN KANN ES WÄRE GUT WENN ICH MEIN GEMÜT ERLEICHTERN KÖNNTE BEVOR ICH STERBE (mls_deu_000288-mls_deu_000288) +SO ABER IST ZWAR UNSER WESENSGRUND GOTT SELBER DA HERUM HAT SICH JEDOCH DER SCHLANGENKNÄUEL DES ALTEN SATAN GESCHLUNGEN UND ÜBER DEM FÜNKCHEN DER LIEBE IST DIE FINSTERNIS DES HASSES GELAGERT WAS WUNDER DANN (mls_deu_000289-mls_deu_000289) +BESSIE WÄRE LIEBER GEBLIEBEN ABER SIE WAR GEZWUNGEN ZU GEHEN WEIL DIE PÜNKTLICHKEIT BEI DEN MAHLZEITEN EINE SACHE WAR AUF WELCHE IN GATESHEAD HALL STRENGE GEHALTEN WURDE (mls_deu_000290-mls_deu_000290) +AUGENBLICKLICH FÜHLTE WIE IHRE ANSICHTEN ÜBER MICH IHRE EMPFINDUNGEN FÜR MICH NICHT UM EIN ATOM VERÄNDERT WAREN ÜBERHAUPT KEINER ÄNDERUNG FÄHIG WAREN ICH SAH ES IHREM VERSTEINERTEN AUGE WELCHES NIEMALS DURCH TRÄNEN GENETZT NIEMALS IN ZÄRTLICHKEIT AUFGELEUCHTET HATTE AN (mls_deu_000291-mls_deu_000291) +BRUDER SAM IST SEHR GUT WENN DER HÄUPTLING IHN ERFÄHRT WIRD ER SICH FREUEN UND WIR WERDEN SCHNELL DANACH HANDELN SO WOLLEN WIR AUFBRECHEN UND SCHNELL REITEN DAMIT WIR NOCH VOR NACHT DAS LAGER ERREICHEN WIR STIEGEN AUF DIE PFERDE DIE NUN AUSGERUHT HATTEN UND FLOGEN IM GALOPP DAVON DIESMAL HÜTETEN WIR UNS DER FÄHRTE WIEDER DIREKT ZU FOLGEN WIR RITTEN GERADEAUS UND ERSPARTEN UNS (mls_deu_000292-mls_deu_000292) +WEIL DIE ABER MIT PECH BESTRICHEN WAR BLIEB EINER VON DEN GOLDENEN PANTOFFELN FESTHÄNGEN UND IN DER ANGST DACHT ES NICHT DARAN IHN MITZUNEHMEN UND WIE ES DEN LETZTEN SCHRITT VON DER TREPPE TAT DA HATTE ES ZWÖLF AUSGESCHLAGEN DA WAR WAGEN UND PFERDE VERSCHWUNDEN UND ASCHENPUTTEL STAND IN SEINEN ASCHENKLEIDERN AUF DER DUNKELN STRASSE (mls_deu_000293-mls_deu_000293) +ILL NAHM DAS GLAS VOM AUGE EIN FINSTERER ERNST LAGERTE ÜBER SEINEN ZÜGEN ES IST SCHRECKLICH SAGTE ER ICH HAB DAS MEINIGE GETAN UM BLUTVERGIESSEN ZU VERMEIDEN (mls_deu_000294-mls_deu_000294) +NUR DER DOKTOR UND DIE WÄRTERIN SOLLEN VOR SEINE AUGEN KOMMEN ERKLÄRTE DIE TRINE IN GROSSEM AMTSEIFER DAMIT WAR DIE FRAU OBERST GANZ EINVERSTANDEN UND HÖCHST ERFREUT KEHRTE SIE MIT IHREN (mls_deu_000295-mls_deu_000295) +K WAR UNTRÖSTLICH ÜBER DIE LAGE DES KÜNSTLERS ER BEGANN ZU WEINEN UND SCHLUCHZTE LANGE IN DIE VORGEHALTENEN HÄNDE DER KÜNSTLER WARTETE BIS K SICH BERUHIGT HATTE UND ENTSCHLOSS SICH DANN DA ER KEINEN ANDEREN AUSWEG FAND DENNOCH ZUM WEITERSCHREIBEN (mls_deu_000296-mls_deu_000296) +VON DEN PFERDEHERDEN DER APACHEN UND SAGTEN UNS DASS SIE FÜR EIN APACHENPFERD UNS EBENSO VIELE WAREN UND BRANDY GEBEN WÜRDEN WIE FÜR EIN KIOWAPFERD DA SIND UNSERE KRIEGER FORT UM APACHENPFERDE ZU HOLEN ALSO RICHTIG WER WAR SCHULD AN DEM TODE DER BISHER GEFALLENEN UND AN DEM BLUTVERGIESSEN WELCHES NUN BEVORSTAND WEISSE PFERDEHÄNDLER (mls_deu_000297-mls_deu_000297) +DAS AMAZONENHÜTCHEN VON SCHWARZEM SAMMET GRAZIÖS AUF IHRE LANGEN LOCKEN GEDRÜCKT DIE IHRE WANGEN UMFLOSSEN UND ÜBER IHRE SCHULTERN HERABWALLTEN SO TRAT SIE IN DAS EINFACHE LÄNDLICHE GEBÄUDE UND SCHWEBTE ZWISCHEN DEN REIHEN DER HALBGEBLENDETEN DORFKINDER AUF UND AB (mls_deu_000298-mls_deu_000298) +DU MUSST ERST ENTSAGEN ALLEM SÜNDHAFTEN STREBEN UND IN TIEFER REUE UND DEMUT DIE FÜRBITTE DER HEILIGEN ERFLEHEN GEGEN DIE DU GEFREVELT HAST DIE JÜNGLINGE WELCHE FRANCESKO SO LANGE GEFLOHEN SUCHTEN IHN AUF IN SEINER WERKSTATT UND FANDEN IHN (mls_deu_000299-mls_deu_000299) +ER LIESS SEINE GRETEL NICHT FORTSCHLEPPEN AM ALLERWENIGSTEN ABER IN DEN GROSSEN VOGELBAUER WO SIE ALLE IN EINEM TONE PFEIFEN MUSSTEN WIE ER STETS SAGTE (mls_deu_000300-mls_deu_000300) +FRANCESKO MALTE IN UNHEILIGER BEGEISTERUNG VIELE BILDER AUS DER LÜGENHAFTEN FABELWELT KEINER ALS ER VERMOCHTE DIE BUHLERISCHE ÜPPIGKEIT DER WEIBLICHEN GESTALTEN SO WAHRHAFT DARZUSTELLEN INDEM ER VON LEBENDEN MODELLEN DIE KARNATION VON DEN ALTEN MARMORBILDERN ABER FORM UND BILDUNG ENTNAHM (mls_deu_000301-mls_deu_000301) +BEWEGUNG UND TAT DEN ERSTEN ZUG JA ES STIMMTE DIE VORHIN ANGEGEBENEN INGREDIENZIEN NÄMLICH RÜBEN HANF EICHELN UND SAUERAMPFER WAREN ALLE IN DEM PFEIFENKOPFE ANWESEND ABER EINEN FÜNFTEN 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GIBT JETZT IM ZUSAMMENHANG MIT DER VERSTÄRKTEN ZUSAMMENARBEIT EINEN ERSTEN GANG VON EINIGEN MITGLIEDSTAATEN (voxpopuli_deu_000322-voxpopuli_deu_000322) +WAS DIE GRENZÜBERSCHREITENDE ZUSAMMENARBEIT ANBELANGT UND DIE VERBREITUNG IN DRITTLÄNDER BETRIFFT HIER MÖCHTE ICH EIN BEISPIEL NENNEN DAS EIN ERFOLGSBEISPIEL FÜR MICH IST UND ZWAR SLUMDOG MILLIONÄR (voxpopuli_deu_000323-voxpopuli_deu_000323) +UND DAS NICHT NUR IN PORTUGAL ODER GRIECHENLAND SONDERN AUCH IN SO VERMEINTLICH REICHEN MITGLIEDSTAATEN WIE DEUTSCHLAND ODER GROSSBRITANNIEN (voxpopuli_deu_000324-voxpopuli_deu_000324) +DIE ZEIT FÜR AUSREDEN IST VORBEI (voxpopuli_deu_000325-voxpopuli_deu_000325) +SIE ALLE FLIEGEN ALS MITGLIEDER DIESES HAUSES WAHRSCHEINLICH DEUTLICH HÄUFIGER ALS DER EU DURCHSCHNITTSBÜRGER (voxpopuli_deu_000326-voxpopuli_deu_000326) +UND ICH BIN SICHER DASS IHRE BEDEUTUNG IN NAHER ZUKUNFT SOGAR NOCH ZUNEHMEN WIRD (voxpopuli_deu_000327-voxpopuli_deu_000327) +ES GEHT HIER UM DIE RICHTLINIE DES RATES ZUR FESTLEGUNG GRUNDLEGENDER SICHERHEITSNORMEN FÜR DEN SCHUTZ VOR DEN GEFAHREN EINER EXPOSITION GEGENÜBER IONISIERENDER STRAHLUNG (voxpopuli_deu_000328-voxpopuli_deu_000328) +DAS GILT ES WIEDER HERZUSTELLEN (voxpopuli_deu_000329-voxpopuli_deu_000329) +DIESEN EINEN EINZIGEN SITZ GIBT ES LÄNGST DAS IST STRASSBURG (voxpopuli_deu_000330-voxpopuli_deu_000330) +WIR SEHEN JA GERADE DASS DAS PASSIERT IN MALTA DIE JOURNALISTIN DIE KORRUPTIONSFÄLLE AUFGEDECKT HAT IST VOR WENIGEN WOCHEN ERMORDET WORDEN WEDER WERDEN SYSTEMATISCH DIE KORRUPTIONSFÄLLE UNTERSUCHT NOCH WIRD DER MORD SELBER GEZIELT UNTERSUCHT MAN HAT FAST DEN EINDRUCK ALS OB HIER ALLES UNTER DEM MANTEL DES SCHWEIGENS ZUGEDECKT WERDEN SOLL (voxpopuli_deu_000331-voxpopuli_deu_000331) +DORT STEHEN ÜBERALL ENTLANG DER KÜSTE DIE WARNSTEINE DIE AUF DIE GROSSEN KATASTROPHEN MIT TSUNAMIS IN DER VERGANGENHEIT HINWEISEN (voxpopuli_deu_000332-voxpopuli_deu_000332) +HERR PRÄSIDENT ICH HABE IM PRINZIP FÜR DEN BERICHT GESTIMMT OBWOHL ER EINEN SCHWEREN FEHLER ENTHÄLT ES WIRD NÄMLICH DAZU AUFGEFORDERT DAS EUROPÄISCHE PARLAMENT AUF DEM WEG ZU EINEM EINZIGEN SITZ ZU UNTERSTÜTZEN (voxpopuli_deu_000333-voxpopuli_deu_000333) +IN DIESEN TREFFEN WURDEN GEMEINSAME POLITISCHE VERABREDUNGEN IM KREIS DER 27 GETROFFEN UND AUCH PUBLIK GEMACHT (voxpopuli_deu_000334-voxpopuli_deu_000334) +ICH BIN DER ÜBERZEUGUNG DASS WIR ES HEUTE MIT DEM VORSCHLAG AUS DEM UMWELTAUSSCHUSS GESCHAFFT HABEN EINEN SCHRITT WEITERZUKOMMEN ES IST NICHT PERFEKT EUROPÄISCHE ÄRZTE SAGEN WIR HÄTTEN FÜR HOCHRISIKOPRODUKTE EINE ZENTRALE ZULASSUNG HABEN MÜSSEN DAS HABE ICH NICHT GESCHAFFT ABER MIT DEM WAS HEUTE AUF DEM TISCH LIEGT SCHAFFEN WIR WOHL TROTZDEM EINEN GROSSEN SCHRITT VIELLEICHT KEINEN MEILENSTEIN ABER EINEN GROSSEN SCHRITT HIN ZU MEHR PATIENTENSICHERHEIT (voxpopuli_deu_000335-voxpopuli_deu_000335) +FRAU PRÄSIDENTIN FRAU KOMMISSARIN LIEBE KOLLEGEN (voxpopuli_deu_000336-voxpopuli_deu_000336) +ZUM AKTUELLEN ICH GLAUBE ES KANN KEINER VON UNS ANNEHMEN DASS WIR WIRKLICH ERST SEIT DIESEM WOCHENENDE WISSEN DASS UNS DIE ZAHLUNGSUNFÄHIGKEIT DROHT (voxpopuli_deu_000337-voxpopuli_deu_000337) +DAS SIND EINFACH BEDINGUNGEN DIE NICHT AKZEPTABEL SIND (voxpopuli_deu_000338-voxpopuli_deu_000338) +IN DER ZWISCHENZEIT SIND DIE RETTUNGSORGANISATIONEN DIE GRÖSSTEN SCHLEPPER WEIL SIE DIE MIGRANTEN 20 KILOMETER VOR DER LIBYSCHEN KÜSTE AUFGREIFEN UND ALLE NACH ITALIEN TRANSPORTIEREN (voxpopuli_deu_000339-voxpopuli_deu_000339) +DAS ZEIGT DER FALL JULIA TIMOSCHENKO (voxpopuli_deu_000340-voxpopuli_deu_000340) +WIR DÜRFEN NICHT WASSER PREDIGEN UND WEIN TRINKEN (voxpopuli_deu_000341-voxpopuli_deu_000341) +FÜR DIESE ENTSCHEIDUNG BRAUCHEN WIR VIELE PARTNER NICHT ZULETZT DIE STÄDTE (voxpopuli_deu_000342-voxpopuli_deu_000342) +DIE FOLGE IST EIN HÖHENFLUG VON POPULISTEN UND EXTREMISTEN IN EINIGEN MITGLIEDSTAATEN IHREN DUMPFEN PAROLEN SETZEN WIR KONKRETE VERÄNDERUNG ENTGEGEN (voxpopuli_deu_000343-voxpopuli_deu_000343) +WEIL DIE INVESTITIONEN FRANZÖSISCHER UND DEUTSCHER BANKEN GERETTET WERDEN MUSSTEN DURFTE GRIECHENLAND 2010 NICHT PLEITEGEHEN UND HEUTE MUSS ES EINEN RIESIGEN SCHULDENBERG VOR SICH HERSCHIEBEN (voxpopuli_deu_000344-voxpopuli_deu_000344) +DIE MITGLIEDSTAATEN DÜRFEN NICHT DIE MÖGLICHKEIT HABEN DEN EUROPÄISCHEN STAATSANWALT DARAN ZU HINDERN IN IHREN REGIONEN GANZ GEZIELT UND SYSTEMATISCH KORRUPTIONSFÄLLEN NACHZUGEHEN (voxpopuli_deu_000345-voxpopuli_deu_000345) +DREI MILLIONEN MENSCHEN SIND ABHÄNGIG VON UNSERER HILFE (voxpopuli_deu_000346-voxpopuli_deu_000346) +EIN VIERZEHNJÄHRIGER JUNGE WIRD IN HAKKARI VON EINEM POLIZISTEN EINES SONDEREINSATZKOMMANDOS INS KOMA GESCHLAGEN (voxpopuli_deu_000347-voxpopuli_deu_000347) +WIE EINE HEILIGE KUH HAT MAN VOR SICH HERGETRAGEN DAS OPT OUT MÜSSE UNTER ALLEN UMSTÄNDEN WEG (voxpopuli_deu_000348-voxpopuli_deu_000348) +DREI DERARTIGE TREFFEN HABEN INZWISCHEN STATTGEFUNDEN (voxpopuli_deu_000349-voxpopuli_deu_000349) +ICH HOFFE ES DAUERT NICHT WIEDER NEUN MONATE (voxpopuli_deu_000350-voxpopuli_deu_000350) +DESWEGEN EINE WICHTIGE FRAGE AN DIE KOMMISSION KANN EIN LAND DIE GRENZKONTROLLE WIEDER EINFÜHREN UND GLEICHZEITIG IN DER SCHENGEN UNION BLEIBEN MIT ZUGANG ZUM INFORMATIONSSYSTEM ETC ODER IST DAS EIN ENTWEDER ODER DIE FRAGE IST WICHTIG FÜR DIE DÄNISCHE DEBATTE UND ICH BITTE UM EINE KLARE ANTWORT (voxpopuli_deu_000351-voxpopuli_deu_000351) +WIE HEUTE SCHON AUSGEFÜHRT WURDE LAG ES NICHT DARAN DASS ES HIER GROBE FEHLER GEGEBEN HÄTTE SONDERN ES GAB EINE REIHE VON KLEINEN UNGEREIMTHEITEN BZW (voxpopuli_deu_000352-voxpopuli_deu_000352) +EINE VERGEMEINSCHAFTUNG DER AUSSEN UND SICHERHEITSPOLITIK ALS GROSSES ZIEL DIESER UNION (voxpopuli_deu_000353-voxpopuli_deu_000353) +DENN SICHERHEIT IST EINE SCHWIERIGE UND DETAILREICHE ARBEIT NICHT NUR IM TECHNISCHEN BEREICH (voxpopuli_deu_000354-voxpopuli_deu_000354) +KINDER UND POLITIK SELTEN LIEGEN DIE INTERESSEN VON BÜRGERN UND POLITIKERN SO WEIT AUSEINANDER BEI DEN BÜRGERN IN GANZ EUROPA STEHT DAS THEMA KIND GANZ OBEN (voxpopuli_deu_000355-voxpopuli_deu_000355) +HERR PRÄSIDENT (voxpopuli_deu_000356-voxpopuli_deu_000356) +WIR FÜHRTEN GESPRÄCHE MIT PRÄSIDENT KARZAI ZAHLREICHEN REGIERUNGSVERTRETERN FRAUEN UND MENSCHENRECHTSORGANISATIONEN UND DIE WAREN DURCHAUS ERMUTIGEND (voxpopuli_deu_000357-voxpopuli_deu_000357) +DAS IST ÜBRIGENS AUCH EINE URSACHE FÜR DEN WACHSENDEN NATIONALISMUS DER ALLERDINGS LEIDER VÖLLIG PERSPEKTIVLOS IST (voxpopuli_deu_000358-voxpopuli_deu_000358) +HEUTE SIND WIR IMMER NOCH SO WEIT VON DIESEM ZIEL ENTFERNT (voxpopuli_deu_000359-voxpopuli_deu_000359) +ICH WERDE ALS FINANZMINISTER AUCH IN MEINEM LAND JEDEN TAG DAMIT KONFRONTIERT DASS NATÜRLICH AUCH DAS BEWUSSTSEIN GEGEBEN SEIN MUSS DASS STAATSHAUSHALTE VON DEN STEUERZAHLERINNEN UND STEUERZAHLERN FINANZIERT SIND UND DASS WIR DAMIT AUCH DIE VERANTWORTUNG TRAGEN BEI DEN ENTSCHEIDUNGEN DIE WIR HIER IN DIESEM RAHMEN TREFFEN MEINE DAMEN UND HERREN (voxpopuli_deu_000360-voxpopuli_deu_000360) +AUF DEM EUROPÄISCHEN AUTOMOBILMARKT INSGESAMT DRAMATISCH IST (voxpopuli_deu_000361-voxpopuli_deu_000361) +DIE EUROPÄISCHE UNION HAT MIT DIESEM INSTRUMENT DIE CHANCE EINE AKTIVE ROLLE IN IHRER NACHBARREGION ZU SPIELEN UM DEMOKRATISCHE REFORMEN UND EINE NACHHALTIGE ENTWICKLUNG VORANZUTREIBEN (voxpopuli_deu_000362-voxpopuli_deu_000362) +DIE SICHT AUF TOTALITÄRE REGIME VON AUSSEN ODER VON INNEN IST RECHT UNTERSCHIEDLICH (voxpopuli_deu_000363-voxpopuli_deu_000363) +WIR HABEN IMMER GESAGT DASS EINE ÜBEREILTE STATIONIERUNGSENTSCHEIDUNG UNSINNIG IST WEIL ES ZUM JETZIGEN ZEITPUNKT KEINE BEDROHUNG BEISPIELSWEISE AUS DEM IRAN GIBT (voxpopuli_deu_000364-voxpopuli_deu_000364) +DIESER VERGLEICH IST EINE ZYNISCHE MISSACHTUNG DER OPFER VON MENSCHENRECHTSVERLETZUNGEN IN ALLER WELT ER IST ZUM ANDEREN EIN SOLCH UNGLAUBLICHER ANWURF (voxpopuli_deu_000365-voxpopuli_deu_000365) +DIE SPE HAT DIESE UMFASSENDE HORIZONTALE RICHTLINIE BEFÜRWORTET (voxpopuli_deu_000366-voxpopuli_deu_000366) +DENN EINES IST WIRKLICH KLAR DIE FINANZ UND WIRTSCHAFTSKRISE VERLANGT VON UNS ALLEN EINMAL MEHR DER VERANTWORTUNG FÜR EINE OPTIMALE UND VOR ALLEM RASCHE QUALIFIZIERUNG UNSERER ARBEITNEHMER UND ARBEITNEHMERINNEN GANZ BESONDERS JETZT RECHNUNG ZU TRAGEN (voxpopuli_deu_000367-voxpopuli_deu_000367) +ESTLAND ODER AUCH POLEN DIE SEHR GUTE ERGEBNISSE ERZIELEN ALS ANDERE DIE SICH SCHWER TUN DIE MITTEL ABZURUFEN ETWA REGIONEN WIE KALABRIEN SIZILIEN ODER AUCH GRIECHENLAND ODER RUMÄNIEN (voxpopuli_deu_000368-voxpopuli_deu_000368) +DER BERICHT GAUZÈS FORDERT ZU RECHT DASS DAS RATING STAATLICHER SCHULDTITEL ALS ÖFFENTLICHE AUFGABE BEGRIFFEN UND DAHER VON ÖFFENTLICHEN AKTEUREN VORGENOMMEN WERDEN MUSS (voxpopuli_deu_000369-voxpopuli_deu_000369) +DA WIR ES ABER NUN MIT EINEM SOZIALPROGRAMM ZU TUN HABEN MÜSSEN WIR DAFÜR EINE ENTSPRECHENDE RECHTLICHE GRUNDLAGE SCHAFFEN (voxpopuli_deu_000370-voxpopuli_deu_000370) +ABER DAS MÜSSEN WIR NOCH ANALYSIEREN (voxpopuli_deu_000371-voxpopuli_deu_000371) +MAN KANN NATÜRLICH VERLANGEN GEBEN WIR MEHR GELD FÜR ENTWICKLUNGSHILFE AUS DIE ARMEN LEUTE BRAUCHEN DAS (voxpopuli_deu_000372-voxpopuli_deu_000372) +GERADE FÜR KLEINERE PROJEKTE IST DAS EIN ÜBERMÄSSIGER BÜROKRATISCHER AUFWAND RICHTIG DASS DAS JETZT AUF EINEN ZEITRAUM VON DREI JAHREN GESENKT WERDEN SOLL (voxpopuli_deu_000373-voxpopuli_deu_000373) +ICH KANN NUR VERSICHERN DIE EUROPÄISCHE KOMMISSION IST COMMITTED ZUR EUROPÄISCHEN PERSPEKTIVE DES KOSOVO (voxpopuli_deu_000374-voxpopuli_deu_000374) +ABER HIER IM HAUSE IST ES SEHR OFT AUCH SO (voxpopuli_deu_000375-voxpopuli_deu_000375) +MIT DIESEM HAUSHALT KANN MAN DIE EU BÜRGERINNEN UND BÜRGER NICHT ÜBERZEUGEN UND BEGEISTERN (voxpopuli_deu_000376-voxpopuli_deu_000376) +WIR ALS SOZIALDEMOKRATEN NEHMEN MIT GROSSER FREUDE ZUR KENNTNIS DASS DINGE DIE WIR VORGETRAGEN HABEN JETZT IM ZUSAMMENHANG MIT DEN VERÄNDERUNGEN IN DEN VEREINIGTEN STAATEN UMGESETZT WERDEN (voxpopuli_deu_000377-voxpopuli_deu_000377) +DER BESCHLUSS DAS EUROPÄISCHE SEMESTER HERZUNEHMEN UND DIE KORRUPTIONSSITUATION IM RAHMEN DER LÄNDERBERICHTE ZU VERÖFFENTLICHEN IST NICHT AUSREICHEND (voxpopuli_deu_000378-voxpopuli_deu_000378) +UND MEINE BITTE ODER DAS WAS ICH MIR VORSTELLE IST DASS MORGEN WIRKLICH IN DER TAT EINE GROSSE EINE BREITE MEHRHEIT FÜR DIESE KOHÄSIONSPOLITIK FÜR UNSERE POLITIK STIMMT FÜR DIE MENSCHEN VOR ORT DAMIT WIR UNS AUF DAS WESENTLICHE BESCHRÄNKEN KÖNNEN (voxpopuli_deu_000379-voxpopuli_deu_000379) +WENN WIR HEUTE DIESE VERORDNUNG VERABSCHIEDEN HOFFE ICH DASS WIR NACH EINEM LANGEN WEG ZU EINEM GUTEN ABSCHLUSS KOMMEN UND ICH MÖCHTE MICH BEI DER KOMMISSION BEDANKEN DIE UNS DURCH KONSTRUKTIVE SACHARBEIT (voxpopuli_deu_000380-voxpopuli_deu_000380) +UNSERE KONTROLLEN HABEN KEINEN BELEG ERBRACHT ICH KANN (voxpopuli_deu_000381-voxpopuli_deu_000381) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/result.txt b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..a2d4a85e680f2a8583e7e9c99c296978b4368284 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/result.txt @@ -0,0 +1,7445 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,--------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/hyp.trn| +|--------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------------+----------------------+------------------------------------------------------------------| +| m | 89 1395 | 11.7 59.4 28.9 1.3 89.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000698 | 1 8 | 0.0 87.5 12.5 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000699 | 1 5 | 0.0 40.0 60.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000700 | 1 8 | 0.0 100.0 0.0 12.5 112.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000701 | 1 12 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000702 | 1 8 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000703 | 1 10 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000704 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000705 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000706 | 1 12 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000707 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000708 | 1 11 | 27.3 72.7 0.0 18.2 90.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000709 | 1 8 | 12.5 87.5 0.0 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000710 | 1 11 | 9.1 81.8 9.1 0.0 90.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000711 | 1 13 | 15.4 69.2 15.4 0.0 84.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000712 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000713 | 1 7 | 0.0 57.1 42.9 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000714 | 1 13 | 0.0 61.5 38.5 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000715 | 1 11 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000716 | 1 10 | 20.0 70.0 10.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000717 | 1 13 | 15.4 69.2 15.4 7.7 92.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000718 | 1 8 | 0.0 87.5 12.5 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000719 | 1 8 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000720 | 1 9 | 11.1 55.6 33.3 0.0 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000721 | 1 6 | 16.7 50.0 33.3 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000722 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000723 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000724 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000725 | 1 13 | 15.4 61.5 23.1 0.0 84.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000726 | 1 6 | 16.7 66.7 16.7 16.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000727 | 1 7 | 14.3 71.4 14.3 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000728 | 1 6 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000729 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000730 | 1 14 | 7.1 71.4 21.4 0.0 92.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000731 | 1 8 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000732 | 1 7 | 0.0 85.7 14.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000733 | 1 12 | 0.0 91.7 8.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000734 | 1 10 | 10.0 90.0 0.0 30.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000735 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000736 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000737 | 1 12 | 8.3 75.0 16.7 0.0 91.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000738 | 1 11 | 9.1 81.8 9.1 0.0 90.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000739 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000740 | 1 1 | 0.0 100.0 0.0 300.0 400.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000741 | 1 1 | 0.0 100.0 0.0 400.0 500.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000742 | 1 13 | 0.0 69.2 30.8 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000743 | 1 4 | 25.0 75.0 0.0 75.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000744 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000745 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000746 | 1 9 | 11.1 55.6 33.3 11.1 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000747 | 1 10 | 0.0 70.0 30.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000748 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000749 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000750 | 1 13 | 23.1 46.2 30.8 0.0 76.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000751 | 1 12 | 0.0 41.7 58.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000752 | 1 12 | 0.0 58.3 41.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000753 | 1 12 | 8.3 83.3 8.3 8.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000754 | 1 7 | 14.3 85.7 0.0 28.6 114.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000755 | 1 13 | 7.7 92.3 0.0 30.8 123.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000756 | 1 9 | 66.7 33.3 0.0 11.1 44.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000757 | 1 12 | 8.3 66.7 25.0 0.0 91.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000758 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000759 | 1 9 | 11.1 88.9 0.0 11.1 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000760 | 1 7 | 14.3 71.4 14.3 14.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000761 | 1 12 | 16.7 83.3 0.0 25.0 108.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000762 | 1 9 | 11.1 77.8 11.1 0.0 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000763 | 1 12 | 8.3 58.3 33.3 0.0 91.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000764 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000765 | 1 11 | 9.1 72.7 18.2 0.0 90.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000766 | 1 8 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000767 | 1 9 | 11.1 66.7 22.2 0.0 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000768 | 1 11 | 0.0 54.5 45.5 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000769 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000770 | 1 14 | 7.1 64.3 28.6 7.1 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000771 | 1 10 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000772 | 1 6 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000773 | 1 12 | 16.7 41.7 41.7 8.3 91.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000774 | 1 13 | 0.0 61.5 38.5 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000775 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000776 | 1 10 | 20.0 70.0 10.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000777 | 1 8 | 12.5 75.0 12.5 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000778 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000779 | 1 11 | 9.1 90.9 0.0 9.1 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000780 | 1 11 | 9.1 81.8 9.1 18.2 109.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000781 | 1 10 | 10.0 90.0 0.0 30.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000782 | 1 8 | 12.5 87.5 0.0 25.0 112.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000783 | 1 14 | 21.4 71.4 7.1 14.3 92.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000784 | 1 12 | 33.3 58.3 8.3 8.3 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000785 | 1 14 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000786 | 1 12 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000787 | 1 12 | 8.3 83.3 8.3 8.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000788 | 1 3 | 0.0 100.0 0.0 133.3 233.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000789 | 1 6 | 16.7 50.0 33.3 16.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000790 | 1 12 | 16.7 66.7 16.7 8.3 91.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000791 | 1 10 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000792 | 1 9 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000793 | 1 8 | 0.0 87.5 12.5 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000794 | 1 7 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000795 | 1 6 | 33.3 50.0 16.7 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000796 | 1 14 | 14.3 78.6 7.1 21.4 107.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000797 | 1 12 | 0.0 91.7 8.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000798 | 1 9 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000799 | 1 9 | 22.2 66.7 11.1 0.0 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000800 | 1 6 | 16.7 83.3 0.0 50.0 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000801 | 1 14 | 14.3 71.4 14.3 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000378 | 1 26 | 15.4 73.1 11.5 7.7 92.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000379 | 1 31 | 12.9 87.1 0.0 12.9 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000380 | 1 16 | 6.3 93.8 0.0 18.8 112.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000381 | 1 9 | 11.1 55.6 33.3 0.0 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000382 | 1 26 | 3.8 76.9 19.2 0.0 96.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000383 | 1 18 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000384 | 1 27 | 7.4 63.0 29.6 0.0 92.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000385 | 1 17 | 11.8 52.9 35.3 0.0 88.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000386 | 1 31 | 19.4 61.3 19.4 6.5 87.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000387 | 1 30 | 6.7 63.3 30.0 0.0 93.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000388 | 1 44 | 9.1 88.6 2.3 2.3 93.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000389 | 1 31 | 16.1 67.7 16.1 0.0 83.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000390 | 1 27 | 33.3 63.0 3.7 14.8 81.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000391 | 1 17 | 11.8 76.5 11.8 0.0 88.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000392 | 1 33 | 3.0 54.5 42.4 0.0 97.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000393 | 1 22 | 18.2 59.1 22.7 4.5 86.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000394 | 1 23 | 17.4 73.9 8.7 17.4 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000395 | 1 23 | 4.3 87.0 8.7 0.0 95.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000396 | 1 35 | 5.7 62.9 31.4 2.9 97.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000397 | 1 23 | 17.4 82.6 0.0 17.4 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000398 | 1 11 | 9.1 54.5 36.4 0.0 90.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000399 | 1 16 | 6.3 75.0 18.8 0.0 93.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000400 | 1 20 | 10.0 60.0 30.0 5.0 95.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000401 | 1 23 | 8.7 78.3 13.0 0.0 91.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000402 | 1 23 | 13.0 87.0 0.0 13.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000403 | 1 22 | 13.6 86.4 0.0 18.2 104.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000404 | 1 16 | 12.5 87.5 0.0 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000405 | 1 21 | 4.8 66.7 28.6 0.0 95.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000406 | 1 20 | 10.0 60.0 30.0 0.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000407 | 1 31 | 9.7 90.3 0.0 48.4 138.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000408 | 1 15 | 0.0 93.3 6.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000409 | 1 17 | 17.6 64.7 17.6 5.9 88.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000410 | 1 34 | 5.9 58.8 35.3 0.0 94.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000411 | 1 21 | 14.3 57.1 28.6 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000412 | 1 24 | 12.5 83.3 4.2 4.2 91.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000413 | 1 24 | 4.2 58.3 37.5 0.0 95.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000414 | 1 22 | 0.0 72.7 27.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000415 | 1 29 | 0.0 48.3 51.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000281 | 1 32 | 15.6 53.1 31.3 0.0 84.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000282 | 1 31 | 25.8 58.1 16.1 6.5 80.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000283 | 1 50 | 4.0 54.0 42.0 0.0 96.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000284 | 1 47 | 8.5 55.3 36.2 0.0 91.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000285 | 1 26 | 3.8 76.9 19.2 0.0 96.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000286 | 1 33 | 27.3 63.6 9.1 3.0 75.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000287 | 1 48 | 2.1 64.6 33.3 0.0 97.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000288 | 1 43 | 2.3 55.8 41.9 0.0 97.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000289 | 1 34 | 20.6 52.9 26.5 0.0 79.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000290 | 1 27 | 11.1 44.4 44.4 0.0 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000291 | 1 39 | 0.0 64.1 35.9 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000292 | 1 65 | 7.7 55.4 36.9 0.0 92.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000293 | 1 55 | 14.5 54.5 30.9 0.0 85.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000294 | 1 27 | 0.0 51.9 48.1 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000295 | 1 31 | 29.0 61.3 9.7 3.2 74.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000296 | 1 40 | 5.0 47.5 47.5 0.0 95.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000297 | 1 54 | 3.7 74.1 22.2 0.0 96.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000298 | 1 39 | 5.1 53.8 41.0 0.0 94.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000299 | 1 39 | 5.1 53.8 41.0 0.0 94.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000300 | 1 25 | 20.0 52.0 28.0 4.0 84.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000301 | 1 40 | 5.0 62.5 32.5 2.5 97.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000302 | 1 57 | 10.5 47.4 42.1 0.0 89.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000303 | 1 50 | 28.0 50.0 22.0 0.0 72.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000304 | 1 30 | 10.0 63.3 26.7 0.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000305 | 1 24 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000306 | 1 62 | 4.8 46.8 48.4 0.0 95.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000307 | 1 35 | 17.1 60.0 22.9 0.0 82.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000308 | 1 42 | 4.8 50.0 45.2 0.0 95.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000309 | 1 41 | 7.3 65.9 26.8 0.0 92.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000310 | 1 32 | 9.4 62.5 28.1 0.0 90.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000311 | 1 35 | 8.6 60.0 31.4 0.0 91.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000312 | 1 46 | 15.2 65.2 19.6 0.0 84.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000313 | 1 24 | 4.2 91.7 4.2 0.0 95.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000314 | 1 32 | 12.5 59.4 28.1 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000315 | 1 28 | 32.1 42.9 25.0 0.0 67.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000316 | 1 28 | 10.7 89.3 0.0 7.1 96.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000317 | 1 22 | 13.6 54.5 31.8 4.5 90.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000318 | 1 37 | 13.5 43.2 43.2 0.0 86.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000319 | 1 18 | 38.9 50.0 11.1 5.6 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001408 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001409 | 1 6 | 0.0 100.0 0.0 16.7 116.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001410 | 1 7 | 0.0 42.9 57.1 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001411 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001412 | 1 8 | 12.5 62.5 25.0 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001413 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001414 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001415 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001416 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001417 | 1 6 | 16.7 50.0 33.3 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001418 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001419 | 1 8 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001420 | 1 10 | 10.0 70.0 20.0 0.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001421 | 1 12 | 0.0 58.3 41.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001422 | 1 5 | 20.0 40.0 40.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001423 | 1 5 | 0.0 60.0 40.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001424 | 1 5 | 20.0 60.0 20.0 40.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001425 | 1 21 | 14.3 71.4 14.3 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001426 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001427 | 1 3 | 33.3 33.3 33.3 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001428 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001429 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001430 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001431 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001432 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001433 | 1 6 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001434 | 1 13 | 23.1 76.9 0.0 23.1 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001435 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001436 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001437 | 1 9 | 11.1 66.7 22.2 0.0 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001438 | 1 7 | 0.0 57.1 42.9 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001439 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001440 | 1 6 | 16.7 33.3 50.0 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001441 | 1 9 | 22.2 77.8 0.0 0.0 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001442 | 1 13 | 7.7 69.2 23.1 0.0 92.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001443 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001444 | 1 6 | 16.7 50.0 33.3 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001445 | 1 6 | 16.7 66.7 16.7 16.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001446 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001447 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001448 | 1 8 | 37.5 37.5 25.0 0.0 62.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001449 | 1 9 | 0.0 55.6 44.4 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001450 | 1 10 | 20.0 80.0 0.0 10.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001451 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001452 | 1 5 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001453 | 1 7 | 28.6 57.1 14.3 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001454 | 1 16 | 6.3 75.0 18.8 6.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001455 | 1 8 | 25.0 62.5 12.5 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001456 | 1 1 | 0.0 100.0 0.0 300.0 400.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001457 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001458 | 1 23 | 17.4 78.3 4.3 0.0 82.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001459 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001460 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001461 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001462 | 1 11 | 9.1 63.6 27.3 0.0 90.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001463 | 1 9 | 22.2 55.6 22.2 0.0 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001464 | 1 5 | 20.0 60.0 20.0 20.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001465 | 1 9 | 0.0 88.9 11.1 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001466 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001467 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001468 | 1 6 | 50.0 50.0 0.0 16.7 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001469 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001470 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001471 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001472 | 1 12 | 0.0 91.7 8.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001473 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001474 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001475 | 1 7 | 14.3 71.4 14.3 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001476 | 1 8 | 12.5 62.5 25.0 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001477 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001478 | 1 7 | 0.0 42.9 57.1 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001479 | 1 5 | 0.0 60.0 40.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001480 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001481 | 1 11 | 0.0 81.8 18.2 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001482 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001483 | 1 4 | 0.0 100.0 0.0 75.0 175.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001484 | 1 5 | 0.0 60.0 40.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001485 | 1 17 | 17.6 82.4 0.0 11.8 94.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001486 | 1 6 | 0.0 16.7 83.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001487 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001488 | 1 8 | 0.0 87.5 12.5 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001489 | 1 8 | 12.5 75.0 12.5 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001490 | 1 6 | 16.7 83.3 0.0 66.7 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001491 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001492 | 1 7 | 28.6 42.9 28.6 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001493 | 1 2 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001494 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001495 | 1 3 | 33.3 66.7 0.0 66.7 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001496 | 1 9 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001497 | 1 9 | 55.6 33.3 11.1 22.2 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001498 | 1 7 | 28.6 71.4 0.0 14.3 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001499 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001500 | 1 8 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001501 | 1 10 | 10.0 60.0 30.0 0.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001502 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001503 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001504 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001505 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001506 | 1 12 | 41.7 58.3 0.0 0.0 58.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001507 | 1 8 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001508 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001509 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001510 | 1 5 | 20.0 80.0 0.0 60.0 140.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001511 | 1 7 | 0.0 71.4 28.6 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001512 | 1 14 | 28.6 50.0 21.4 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001513 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001514 | 1 11 | 18.2 72.7 9.1 18.2 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001515 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001516 | 1 5 | 0.0 60.0 40.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001517 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001518 | 1 9 | 22.2 66.7 11.1 0.0 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001519 | 1 7 | 14.3 57.1 28.6 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001520 | 1 8 | 37.5 62.5 0.0 25.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001521 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001522 | 1 6 | 50.0 50.0 0.0 16.7 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001523 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001524 | 1 6 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001525 | 1 4 | 25.0 50.0 25.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001526 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001527 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001528 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001529 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001530 | 1 3 | 33.3 33.3 33.3 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001531 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001532 | 1 7 | 42.9 42.9 14.3 0.0 57.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001533 | 1 5 | 0.0 100.0 0.0 60.0 160.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001534 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001535 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001536 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001537 | 1 2 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001538 | 1 7 | 14.3 57.1 28.6 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001539 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001540 | 1 8 | 50.0 37.5 12.5 12.5 62.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001541 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001542 | 1 8 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001543 | 1 18 | 33.3 61.1 5.6 5.6 72.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001544 | 1 10 | 10.0 60.0 30.0 0.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001545 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001546 | 1 10 | 10.0 60.0 30.0 0.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001547 | 1 8 | 0.0 100.0 0.0 12.5 112.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001548 | 1 6 | 16.7 50.0 33.3 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001549 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001550 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001551 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001552 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001553 | 1 20 | 10.0 50.0 40.0 0.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001554 | 1 14 | 21.4 50.0 28.6 0.0 78.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001555 | 1 7 | 14.3 42.9 42.9 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001556 | 1 9 | 0.0 44.4 55.6 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001557 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001558 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001559 | 1 11 | 18.2 54.5 27.3 0.0 81.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001560 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001561 | 1 7 | 14.3 85.7 0.0 71.4 157.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001562 | 1 12 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001563 | 1 5 | 0.0 40.0 60.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001564 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001565 | 1 10 | 0.0 90.0 10.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001566 | 1 7 | 14.3 71.4 14.3 14.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001567 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001568 | 1 12 | 16.7 75.0 8.3 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001569 | 1 8 | 12.5 87.5 0.0 62.5 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001570 | 1 7 | 28.6 57.1 14.3 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001571 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001572 | 1 10 | 0.0 70.0 30.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001573 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001574 | 1 8 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001575 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001576 | 1 10 | 10.0 80.0 10.0 30.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001577 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001578 | 1 15 | 13.3 73.3 13.3 0.0 86.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001579 | 1 19 | 10.5 68.4 21.1 0.0 89.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001580 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001581 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001582 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001583 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001584 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001585 | 1 13 | 30.8 46.2 23.1 0.0 69.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001586 | 1 7 | 0.0 57.1 42.9 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001587 | 1 5 | 40.0 60.0 0.0 40.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001588 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001589 | 1 4 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001590 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001591 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001592 | 1 12 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001593 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001594 | 1 8 | 37.5 50.0 12.5 0.0 62.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001595 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001596 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001597 | 1 10 | 0.0 100.0 0.0 10.0 110.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001598 | 1 18 | 11.1 83.3 5.6 0.0 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001599 | 1 23 | 0.0 87.0 13.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000891 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000892 | 1 10 | 50.0 40.0 10.0 20.0 70.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000893 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000894 | 1 6 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000895 | 1 7 | 28.6 57.1 14.3 14.3 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000897 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000898 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000899 | 1 6 | 16.7 50.0 33.3 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000900 | 1 8 | 12.5 50.0 37.5 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000901 | 1 7 | 0.0 100.0 0.0 14.3 114.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000902 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000903 | 1 6 | 0.0 100.0 0.0 16.7 116.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000904 | 1 7 | 0.0 71.4 28.6 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000905 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000906 | 1 8 | 12.5 37.5 50.0 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000907 | 1 5 | 40.0 60.0 0.0 20.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000908 | 1 5 | 0.0 100.0 0.0 80.0 180.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000909 | 1 8 | 12.5 75.0 12.5 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000910 | 1 9 | 22.2 77.8 0.0 0.0 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000911 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000912 | 1 8 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000913 | 1 8 | 25.0 75.0 0.0 12.5 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000914 | 1 8 | 0.0 100.0 0.0 12.5 112.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000915 | 1 10 | 0.0 70.0 30.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000917 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000918 | 1 3 | 0.0 33.3 66.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000919 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000920 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000921 | 1 6 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000922 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000923 | 1 13 | 0.0 53.8 46.2 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000924 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000925 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000926 | 1 7 | 0.0 57.1 42.9 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000927 | 1 7 | 28.6 42.9 28.6 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000928 | 1 5 | 0.0 40.0 60.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000929 | 1 8 | 0.0 100.0 0.0 12.5 112.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000930 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000931 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000932 | 1 8 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000933 | 1 6 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000934 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000935 | 1 7 | 28.6 71.4 0.0 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000936 | 1 11 | 18.2 45.5 36.4 0.0 81.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000937 | 1 8 | 12.5 75.0 12.5 12.5 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000938 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000939 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000940 | 1 7 | 0.0 100.0 0.0 14.3 114.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000941 | 1 7 | 0.0 71.4 28.6 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000942 | 1 8 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000943 | 1 8 | 62.5 37.5 0.0 25.0 62.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000944 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000945 | 1 5 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000946 | 1 9 | 44.4 55.6 0.0 0.0 55.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000947 | 1 9 | 22.2 77.8 0.0 22.2 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000948 | 1 6 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000950 | 1 6 | 16.7 33.3 50.0 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000951 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000952 | 1 5 | 40.0 60.0 0.0 40.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000953 | 1 10 | 10.0 90.0 0.0 0.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000954 | 1 6 | 50.0 50.0 0.0 16.7 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000955 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000956 | 1 12 | 0.0 58.3 41.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000957 | 1 7 | 0.0 57.1 42.9 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000958 | 1 10 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000959 | 1 8 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000960 | 1 7 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000961 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000962 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000963 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000964 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000965 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000966 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000967 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000968 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000969 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000970 | 1 7 | 0.0 85.7 14.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000971 | 1 7 | 42.9 57.1 0.0 0.0 57.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000972 | 1 9 | 11.1 55.6 33.3 0.0 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000973 | 1 13 | 30.8 38.5 30.8 0.0 69.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000974 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000975 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000976 | 1 11 | 0.0 63.6 36.4 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000977 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000978 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000979 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000980 | 1 8 | 50.0 37.5 12.5 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000981 | 1 11 | 18.2 54.5 27.3 0.0 81.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000982 | 1 8 | 12.5 87.5 0.0 25.0 112.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000983 | 1 9 | 11.1 55.6 33.3 0.0 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000984 | 1 10 | 30.0 40.0 30.0 0.0 70.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000985 | 1 7 | 14.3 57.1 28.6 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000986 | 1 7 | 0.0 71.4 28.6 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000987 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000988 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000989 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000990 | 1 7 | 42.9 28.6 28.6 0.0 57.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000991 | 1 8 | 12.5 87.5 0.0 12.5 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000992 | 1 5 | 0.0 60.0 40.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000993 | 1 9 | 33.3 44.4 22.2 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000994 | 1 11 | 27.3 63.6 9.1 9.1 81.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000995 | 1 6 | 16.7 16.7 66.7 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000996 | 1 7 | 14.3 57.1 28.6 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000997 | 1 6 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000998 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000999 | 1 7 | 14.3 71.4 14.3 14.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001000 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001001 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001002 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001003 | 1 8 | 25.0 75.0 0.0 37.5 112.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001004 | 1 7 | 14.3 57.1 28.6 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001006 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001007 | 1 7 | 0.0 71.4 28.6 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001008 | 1 7 | 28.6 71.4 0.0 14.3 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001009 | 1 7 | 0.0 71.4 28.6 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001010 | 1 8 | 12.5 87.5 0.0 37.5 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001011 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001012 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001013 | 1 7 | 0.0 42.9 57.1 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001014 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001015 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001016 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001017 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001018 | 1 9 | 0.0 77.8 22.2 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001019 | 1 7 | 28.6 57.1 14.3 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001020 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000309 | 1 16 | 6.3 68.8 25.0 0.0 93.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000310 | 1 14 | 0.0 64.3 35.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000311 | 1 10 | 10.0 50.0 40.0 0.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000312 | 1 11 | 0.0 81.8 18.2 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000313 | 1 19 | 0.0 42.1 57.9 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000314 | 1 20 | 0.0 60.0 40.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000315 | 1 12 | 8.3 58.3 33.3 0.0 91.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000316 | 1 11 | 18.2 36.4 45.5 9.1 90.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000317 | 1 26 | 3.8 76.9 19.2 0.0 96.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000318 | 1 11 | 18.2 63.6 18.2 0.0 81.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000319 | 1 17 | 11.8 70.6 17.6 11.8 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000320 | 1 12 | 8.3 66.7 25.0 0.0 91.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000321 | 1 13 | 15.4 61.5 23.1 0.0 84.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000322 | 1 15 | 6.7 46.7 46.7 0.0 93.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000323 | 1 27 | 3.7 77.8 18.5 0.0 96.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000324 | 1 19 | 5.3 68.4 26.3 0.0 94.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000325 | 1 6 | 16.7 33.3 50.0 16.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000326 | 1 14 | 0.0 57.1 42.9 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000327 | 1 14 | 0.0 57.1 42.9 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000328 | 1 23 | 17.4 60.9 21.7 0.0 82.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000329 | 1 5 | 0.0 40.0 60.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000330 | 1 10 | 40.0 40.0 20.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000331 | 1 51 | 9.8 62.7 27.5 2.0 92.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000332 | 1 19 | 10.5 47.4 42.1 0.0 89.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000333 | 1 33 | 3.0 81.8 15.2 0.0 97.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000334 | 1 16 | 31.3 68.8 0.0 12.5 81.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000335 | 1 67 | 1.5 52.2 46.3 0.0 98.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000336 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000337 | 1 23 | 4.3 43.5 52.2 0.0 95.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000338 | 1 8 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000339 | 1 25 | 8.0 64.0 28.0 0.0 92.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000340 | 1 6 | 16.7 50.0 33.3 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000341 | 1 8 | 12.5 50.0 37.5 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000342 | 1 11 | 0.0 90.9 9.1 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000343 | 1 20 | 10.0 75.0 15.0 5.0 95.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000344 | 1 25 | 12.0 88.0 0.0 4.0 92.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000345 | 1 22 | 0.0 72.7 27.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000346 | 1 8 | 12.5 62.5 25.0 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000347 | 1 14 | 7.1 57.1 35.7 0.0 92.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000348 | 1 17 | 5.9 58.8 35.3 0.0 94.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000349 | 1 6 | 16.7 83.3 0.0 50.0 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000350 | 1 8 | 0.0 25.0 75.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000351 | 1 47 | 14.9 63.8 21.3 6.4 91.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000352 | 1 25 | 4.0 48.0 48.0 0.0 96.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000353 | 1 11 | 9.1 81.8 9.1 9.1 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000354 | 1 13 | 0.0 84.6 15.4 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000355 | 1 26 | 11.5 57.7 30.8 0.0 88.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000356 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000357 | 1 16 | 0.0 93.8 6.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000358 | 1 16 | 6.3 50.0 43.8 0.0 93.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000359 | 1 11 | 0.0 72.7 27.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000360 | 1 51 | 7.8 64.7 27.5 0.0 92.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000361 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000362 | 1 25 | 8.0 68.0 24.0 0.0 92.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000363 | 1 13 | 7.7 69.2 23.1 7.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000364 | 1 22 | 0.0 77.3 22.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000365 | 1 21 | 9.5 85.7 4.8 9.5 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000366 | 1 8 | 12.5 75.0 12.5 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000367 | 1 35 | 5.7 74.3 20.0 0.0 94.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000368 | 1 28 | 0.0 53.6 46.4 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000369 | 1 23 | 4.3 73.9 21.7 0.0 95.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000370 | 1 19 | 0.0 57.9 42.1 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000371 | 1 6 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000372 | 1 16 | 18.8 56.3 25.0 6.3 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000373 | 1 23 | 13.0 60.9 26.1 0.0 87.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000374 | 1 14 | 7.1 78.6 14.3 7.1 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000375 | 1 10 | 0.0 30.0 70.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000376 | 1 14 | 14.3 71.4 14.3 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000377 | 1 27 | 3.7 81.5 14.8 0.0 96.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000378 | 1 18 | 16.7 72.2 11.1 16.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000379 | 1 41 | 24.4 43.9 31.7 2.4 78.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000380 | 1 32 | 9.4 53.1 37.5 6.3 96.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000381 | 1 8 | 25.0 50.0 25.0 12.5 87.5 100.0 | +|====================================================================================================================| +| Sum/Avg | 661 8099 | 11.1 65.4 23.5 4.0 92.9 100.0 | +|====================================================================================================================| +| Mean | 1.2 14.1 | 11.4 71.1 17.5 10.8 99.4 100.0 | +| S.D. | 3.7 58.8 | 12.8 18.7 16.9 32.9 36.2 0.0 | +| Median | 1.0 8.0 | 9.1 70.0 16.7 0.0 100.0 100.0 | +`--------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,--------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/hyp.trn| +|--------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------------+----------------------+------------------------------------------------------------------| +| m | 89 1395 | 163 829 403 18 1250 89 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000698 | 1 8 | 0 7 1 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000699 | 1 5 | 0 2 3 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000700 | 1 8 | 0 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000701 | 1 12 | 0 8 4 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000702 | 1 8 | 0 8 0 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000703 | 1 10 | 0 8 2 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000704 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000705 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000706 | 1 12 | 0 10 2 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000707 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000708 | 1 11 | 3 8 0 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000709 | 1 8 | 1 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000710 | 1 11 | 1 9 1 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000711 | 1 13 | 2 9 2 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000712 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000713 | 1 7 | 0 4 3 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000714 | 1 13 | 0 8 5 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000715 | 1 11 | 0 11 0 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000716 | 1 10 | 2 7 1 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000717 | 1 13 | 2 9 2 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000718 | 1 8 | 0 7 1 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000719 | 1 8 | 2 6 0 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000720 | 1 9 | 1 5 3 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000721 | 1 6 | 1 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000722 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000723 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000724 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000725 | 1 13 | 2 8 3 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000726 | 1 6 | 1 4 1 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000727 | 1 7 | 1 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000728 | 1 6 | 0 6 0 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000729 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000730 | 1 14 | 1 10 3 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000731 | 1 8 | 0 8 0 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000732 | 1 7 | 0 6 1 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000733 | 1 12 | 0 11 1 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000734 | 1 10 | 1 9 0 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000735 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000736 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000737 | 1 12 | 1 9 2 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000738 | 1 11 | 1 9 1 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000739 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000740 | 1 1 | 0 1 0 3 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000741 | 1 1 | 0 1 0 4 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000742 | 1 13 | 0 9 4 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000743 | 1 4 | 1 3 0 3 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000744 | 1 1 | 0 1 0 2 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000745 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000746 | 1 9 | 1 5 3 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000747 | 1 10 | 0 7 3 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000748 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000749 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000750 | 1 13 | 3 6 4 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000751 | 1 12 | 0 5 7 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000752 | 1 12 | 0 7 5 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000753 | 1 12 | 1 10 1 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000754 | 1 7 | 1 6 0 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000755 | 1 13 | 1 12 0 4 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000756 | 1 9 | 6 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000757 | 1 12 | 1 8 3 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000758 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000759 | 1 9 | 1 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000760 | 1 7 | 1 5 1 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000761 | 1 12 | 2 10 0 3 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000762 | 1 9 | 1 7 1 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000763 | 1 12 | 1 7 4 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000764 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000765 | 1 11 | 1 8 2 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000766 | 1 8 | 0 8 0 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000767 | 1 9 | 1 6 2 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000768 | 1 11 | 0 6 5 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000769 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000770 | 1 14 | 1 9 4 1 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000771 | 1 10 | 0 8 2 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000772 | 1 6 | 0 6 0 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000773 | 1 12 | 2 5 5 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000774 | 1 13 | 0 8 5 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000775 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000776 | 1 10 | 2 7 1 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000777 | 1 8 | 1 6 1 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000778 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000779 | 1 11 | 1 10 0 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000780 | 1 11 | 1 9 1 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000781 | 1 10 | 1 9 0 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000782 | 1 8 | 1 7 0 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000783 | 1 14 | 3 10 1 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000784 | 1 12 | 4 7 1 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000785 | 1 14 | 0 7 7 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000786 | 1 12 | 2 8 2 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000787 | 1 12 | 1 10 1 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000788 | 1 3 | 0 3 0 4 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000789 | 1 6 | 1 3 2 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000790 | 1 12 | 2 8 2 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000791 | 1 10 | 2 6 2 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000792 | 1 9 | 0 9 0 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000793 | 1 8 | 0 7 1 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000794 | 1 7 | 0 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000795 | 1 6 | 2 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000796 | 1 14 | 2 11 1 3 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000797 | 1 12 | 0 11 1 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000798 | 1 9 | 3 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000799 | 1 9 | 2 6 1 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000800 | 1 6 | 1 5 0 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_deu_000801 | 1 14 | 2 10 2 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000378 | 1 26 | 4 19 3 2 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000379 | 1 31 | 4 27 0 4 31 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000380 | 1 16 | 1 15 0 3 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000381 | 1 9 | 1 5 3 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000382 | 1 26 | 1 20 5 0 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000383 | 1 18 | 0 12 6 0 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000384 | 1 27 | 2 17 8 0 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000385 | 1 17 | 2 9 6 0 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000386 | 1 31 | 6 19 6 2 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000387 | 1 30 | 2 19 9 0 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000388 | 1 44 | 4 39 1 1 41 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000389 | 1 31 | 5 21 5 0 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000390 | 1 27 | 9 17 1 4 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000391 | 1 17 | 2 13 2 0 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000392 | 1 33 | 1 18 14 0 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000393 | 1 22 | 4 13 5 1 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000394 | 1 23 | 4 17 2 4 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000395 | 1 23 | 1 20 2 0 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000396 | 1 35 | 2 22 11 1 34 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000397 | 1 23 | 4 19 0 4 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000398 | 1 11 | 1 6 4 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000399 | 1 16 | 1 12 3 0 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000400 | 1 20 | 2 12 6 1 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000401 | 1 23 | 2 18 3 0 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000402 | 1 23 | 3 20 0 3 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000403 | 1 22 | 3 19 0 4 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000404 | 1 16 | 2 14 0 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000405 | 1 21 | 1 14 6 0 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000406 | 1 20 | 2 12 6 0 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000407 | 1 31 | 3 28 0 15 43 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000408 | 1 15 | 0 14 1 0 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000409 | 1 17 | 3 11 3 1 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000410 | 1 34 | 2 20 12 0 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000411 | 1 21 | 3 12 6 0 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000412 | 1 24 | 3 20 1 1 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000413 | 1 24 | 1 14 9 0 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000414 | 1 22 | 0 16 6 0 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_deu_000415 | 1 29 | 0 14 15 0 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000281 | 1 32 | 5 17 10 0 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000282 | 1 31 | 8 18 5 2 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000283 | 1 50 | 2 27 21 0 48 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000284 | 1 47 | 4 26 17 0 43 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000285 | 1 26 | 1 20 5 0 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000286 | 1 33 | 9 21 3 1 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000287 | 1 48 | 1 31 16 0 47 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000288 | 1 43 | 1 24 18 0 42 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000289 | 1 34 | 7 18 9 0 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000290 | 1 27 | 3 12 12 0 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000291 | 1 39 | 0 25 14 0 39 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000292 | 1 65 | 5 36 24 0 60 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000293 | 1 55 | 8 30 17 0 47 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000294 | 1 27 | 0 14 13 0 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000295 | 1 31 | 9 19 3 1 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000296 | 1 40 | 2 19 19 0 38 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000297 | 1 54 | 2 40 12 0 52 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000298 | 1 39 | 2 21 16 0 37 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000299 | 1 39 | 2 21 16 0 37 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000300 | 1 25 | 5 13 7 1 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000301 | 1 40 | 2 25 13 1 39 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000302 | 1 57 | 6 27 24 0 51 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000303 | 1 50 | 14 25 11 0 36 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000304 | 1 30 | 3 19 8 0 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000305 | 1 24 | 0 12 12 0 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000306 | 1 62 | 3 29 30 0 59 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000307 | 1 35 | 6 21 8 0 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000308 | 1 42 | 2 21 19 0 40 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000309 | 1 41 | 3 27 11 0 38 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000310 | 1 32 | 3 20 9 0 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000311 | 1 35 | 3 21 11 0 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000312 | 1 46 | 7 30 9 0 39 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000313 | 1 24 | 1 22 1 0 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000314 | 1 32 | 4 19 9 0 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000315 | 1 28 | 9 12 7 0 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000316 | 1 28 | 3 25 0 2 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000317 | 1 22 | 3 12 7 1 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000318 | 1 37 | 5 16 16 0 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_deu_000319 | 1 18 | 7 9 2 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001408 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001409 | 1 6 | 0 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001410 | 1 7 | 0 3 4 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001411 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001412 | 1 8 | 1 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001413 | 1 4 | 1 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001414 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001415 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001416 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001417 | 1 6 | 1 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001418 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001419 | 1 8 | 2 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001420 | 1 10 | 1 7 2 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001421 | 1 12 | 0 7 5 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001422 | 1 5 | 1 2 2 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001423 | 1 5 | 0 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001424 | 1 5 | 1 3 1 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001425 | 1 21 | 3 15 3 0 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001426 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001427 | 1 3 | 1 1 1 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001428 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001429 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001430 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001431 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001432 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001433 | 1 6 | 0 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001434 | 1 13 | 3 10 0 3 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001435 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001436 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001437 | 1 9 | 1 6 2 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001438 | 1 7 | 0 4 3 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001439 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001440 | 1 6 | 1 2 3 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001441 | 1 9 | 2 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001442 | 1 13 | 1 9 3 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001443 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001444 | 1 6 | 1 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001445 | 1 6 | 1 4 1 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001446 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001447 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001448 | 1 8 | 3 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001449 | 1 9 | 0 5 4 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001450 | 1 10 | 2 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001451 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001452 | 1 5 | 3 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001453 | 1 7 | 2 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001454 | 1 16 | 1 12 3 1 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001455 | 1 8 | 2 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001456 | 1 1 | 0 1 0 3 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001457 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001458 | 1 23 | 4 18 1 0 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001459 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001460 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001461 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001462 | 1 11 | 1 7 3 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001463 | 1 9 | 2 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001464 | 1 5 | 1 3 1 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001465 | 1 9 | 0 8 1 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001466 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001467 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001468 | 1 6 | 3 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001469 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001470 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001471 | 1 2 | 1 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001472 | 1 12 | 0 11 1 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001473 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001474 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001475 | 1 7 | 1 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001476 | 1 8 | 1 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001477 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001478 | 1 7 | 0 3 4 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001479 | 1 5 | 0 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001480 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001481 | 1 11 | 0 9 2 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001482 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001483 | 1 4 | 0 4 0 3 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001484 | 1 5 | 0 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001485 | 1 17 | 3 14 0 2 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001486 | 1 6 | 0 1 5 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001487 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001488 | 1 8 | 0 7 1 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001489 | 1 8 | 1 6 1 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001490 | 1 6 | 1 5 0 4 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001491 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001492 | 1 7 | 2 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001493 | 1 2 | 0 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001494 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001495 | 1 3 | 1 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001496 | 1 9 | 0 6 3 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001497 | 1 9 | 5 3 1 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001498 | 1 7 | 2 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001499 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001500 | 1 8 | 2 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001501 | 1 10 | 1 6 3 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001502 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001503 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001504 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001505 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001506 | 1 12 | 5 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001507 | 1 8 | 0 6 2 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001508 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001509 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001510 | 1 5 | 1 4 0 3 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001511 | 1 7 | 0 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001512 | 1 14 | 4 7 3 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001513 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001514 | 1 11 | 2 8 1 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001515 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001516 | 1 5 | 0 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001517 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001518 | 1 9 | 2 6 1 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001519 | 1 7 | 1 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001520 | 1 8 | 3 5 0 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001521 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001522 | 1 6 | 3 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001523 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001524 | 1 6 | 2 4 0 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001525 | 1 4 | 1 2 1 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001526 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001527 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001528 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001529 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001530 | 1 3 | 1 1 1 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001531 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001532 | 1 7 | 3 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001533 | 1 5 | 0 5 0 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001534 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001535 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001536 | 1 1 | 0 1 0 2 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001537 | 1 2 | 0 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001538 | 1 7 | 1 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001539 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001540 | 1 8 | 4 3 1 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001541 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001542 | 1 8 | 0 4 4 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001543 | 1 18 | 6 11 1 1 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001544 | 1 10 | 1 6 3 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001545 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001546 | 1 10 | 1 6 3 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001547 | 1 8 | 0 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001548 | 1 6 | 1 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001549 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001550 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001551 | 1 1 | 0 1 0 2 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001552 | 1 6 | 0 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001553 | 1 20 | 2 10 8 0 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001554 | 1 14 | 3 7 4 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001555 | 1 7 | 1 3 3 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001556 | 1 9 | 0 4 5 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001557 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001558 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001559 | 1 11 | 2 6 3 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001560 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001561 | 1 7 | 1 6 0 5 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001562 | 1 12 | 0 9 3 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001563 | 1 5 | 0 2 3 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001564 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001565 | 1 10 | 0 9 1 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001566 | 1 7 | 1 5 1 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001567 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001568 | 1 12 | 2 9 1 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001569 | 1 8 | 1 7 0 5 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001570 | 1 7 | 2 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001571 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001572 | 1 10 | 0 7 3 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001573 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001574 | 1 8 | 2 6 0 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001575 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001576 | 1 10 | 1 8 1 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001577 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001578 | 1 15 | 2 11 2 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001579 | 1 19 | 2 13 4 0 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001580 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001581 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001582 | 1 4 | 1 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001583 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001584 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001585 | 1 13 | 4 6 3 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001586 | 1 7 | 0 4 3 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001587 | 1 5 | 2 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001588 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001589 | 1 4 | 0 2 2 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001590 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001591 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001592 | 1 12 | 0 9 3 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001593 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001594 | 1 8 | 3 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001595 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001596 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001597 | 1 10 | 0 10 0 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001598 | 1 18 | 2 15 1 0 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_deu_001599 | 1 23 | 0 20 3 0 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000891 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000892 | 1 10 | 5 4 1 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000893 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000894 | 1 6 | 0 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000895 | 1 7 | 2 4 1 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000897 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000898 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000899 | 1 6 | 1 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000900 | 1 8 | 1 4 3 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000901 | 1 7 | 0 7 0 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000902 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000903 | 1 6 | 0 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000904 | 1 7 | 0 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000905 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000906 | 1 8 | 1 3 4 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000907 | 1 5 | 2 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000908 | 1 5 | 0 5 0 4 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000909 | 1 8 | 1 6 1 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000910 | 1 9 | 2 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000911 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000912 | 1 8 | 0 6 2 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000913 | 1 8 | 2 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000914 | 1 8 | 0 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000915 | 1 10 | 0 7 3 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000917 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000918 | 1 3 | 0 1 2 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000919 | 1 6 | 0 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000920 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000921 | 1 6 | 0 3 3 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000922 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000923 | 1 13 | 0 7 6 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000924 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000925 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000926 | 1 7 | 0 4 3 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000927 | 1 7 | 2 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000928 | 1 5 | 0 2 3 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000929 | 1 8 | 0 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000930 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000931 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000932 | 1 8 | 0 8 0 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000933 | 1 6 | 0 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000934 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000935 | 1 7 | 2 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000936 | 1 11 | 2 5 4 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000937 | 1 8 | 1 6 1 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000938 | 1 6 | 0 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000939 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000940 | 1 7 | 0 7 0 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000941 | 1 7 | 0 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000942 | 1 8 | 0 4 4 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000943 | 1 8 | 5 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000944 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000945 | 1 5 | 3 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000946 | 1 9 | 4 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000947 | 1 9 | 2 7 0 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000948 | 1 6 | 0 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000950 | 1 6 | 1 2 3 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000951 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000952 | 1 5 | 2 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000953 | 1 10 | 1 9 0 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000954 | 1 6 | 3 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000955 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000956 | 1 12 | 0 7 5 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000957 | 1 7 | 0 4 3 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000958 | 1 10 | 2 8 0 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000959 | 1 8 | 2 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000960 | 1 7 | 0 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000961 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000962 | 1 6 | 0 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000963 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000964 | 1 6 | 0 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000965 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000966 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000967 | 1 6 | 0 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000968 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000969 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000970 | 1 7 | 0 6 1 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000971 | 1 7 | 3 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000972 | 1 9 | 1 5 3 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000973 | 1 13 | 4 5 4 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000974 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000975 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000976 | 1 11 | 0 7 4 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000977 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000978 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000979 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000980 | 1 8 | 4 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000981 | 1 11 | 2 6 3 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000982 | 1 8 | 1 7 0 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000983 | 1 9 | 1 5 3 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000984 | 1 10 | 3 4 3 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000985 | 1 7 | 1 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000986 | 1 7 | 0 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000987 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000988 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000989 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000990 | 1 7 | 3 2 2 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000991 | 1 8 | 1 7 0 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000992 | 1 5 | 0 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000993 | 1 9 | 3 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000994 | 1 11 | 3 7 1 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000995 | 1 6 | 1 1 4 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000996 | 1 7 | 1 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000997 | 1 6 | 0 6 0 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000998 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_000999 | 1 7 | 1 5 1 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001000 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001001 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001002 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001003 | 1 8 | 2 6 0 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001004 | 1 7 | 1 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001006 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001007 | 1 7 | 0 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001008 | 1 7 | 2 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001009 | 1 7 | 0 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001010 | 1 8 | 1 7 0 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001011 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001012 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001013 | 1 7 | 0 3 4 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001014 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001015 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001016 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001017 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001018 | 1 9 | 0 7 2 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001019 | 1 7 | 2 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_deu_001020 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000309 | 1 16 | 1 11 4 0 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000310 | 1 14 | 0 9 5 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000311 | 1 10 | 1 5 4 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000312 | 1 11 | 0 9 2 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000313 | 1 19 | 0 8 11 0 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000314 | 1 20 | 0 12 8 0 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000315 | 1 12 | 1 7 4 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000316 | 1 11 | 2 4 5 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000317 | 1 26 | 1 20 5 0 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000318 | 1 11 | 2 7 2 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000319 | 1 17 | 2 12 3 2 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000320 | 1 12 | 1 8 3 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000321 | 1 13 | 2 8 3 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000322 | 1 15 | 1 7 7 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000323 | 1 27 | 1 21 5 0 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000324 | 1 19 | 1 13 5 0 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000325 | 1 6 | 1 2 3 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000326 | 1 14 | 0 8 6 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000327 | 1 14 | 0 8 6 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000328 | 1 23 | 4 14 5 0 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000329 | 1 5 | 0 2 3 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000330 | 1 10 | 4 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000331 | 1 51 | 5 32 14 1 47 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000332 | 1 19 | 2 9 8 0 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000333 | 1 33 | 1 27 5 0 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000334 | 1 16 | 5 11 0 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000335 | 1 67 | 1 35 31 0 66 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000336 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000337 | 1 23 | 1 10 12 0 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000338 | 1 8 | 0 8 0 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000339 | 1 25 | 2 16 7 0 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000340 | 1 6 | 1 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000341 | 1 8 | 1 4 3 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000342 | 1 11 | 0 10 1 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000343 | 1 20 | 2 15 3 1 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000344 | 1 25 | 3 22 0 1 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000345 | 1 22 | 0 16 6 0 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000346 | 1 8 | 1 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000347 | 1 14 | 1 8 5 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000348 | 1 17 | 1 10 6 0 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000349 | 1 6 | 1 5 0 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000350 | 1 8 | 0 2 6 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000351 | 1 47 | 7 30 10 3 43 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000352 | 1 25 | 1 12 12 0 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000353 | 1 11 | 1 9 1 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000354 | 1 13 | 0 11 2 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000355 | 1 26 | 3 15 8 0 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000356 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000357 | 1 16 | 0 15 1 0 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000358 | 1 16 | 1 8 7 0 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000359 | 1 11 | 0 8 3 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000360 | 1 51 | 4 33 14 0 47 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000361 | 1 7 | 1 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000362 | 1 25 | 2 17 6 0 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000363 | 1 13 | 1 9 3 1 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000364 | 1 22 | 0 17 5 0 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000365 | 1 21 | 2 18 1 2 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000366 | 1 8 | 1 6 1 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000367 | 1 35 | 2 26 7 0 33 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000368 | 1 28 | 0 15 13 0 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000369 | 1 23 | 1 17 5 0 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000370 | 1 19 | 0 11 8 0 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000371 | 1 6 | 0 3 3 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000372 | 1 16 | 3 9 4 1 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000373 | 1 23 | 3 14 6 0 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000374 | 1 14 | 1 11 2 1 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000375 | 1 10 | 0 3 7 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000376 | 1 14 | 2 10 2 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000377 | 1 27 | 1 22 4 0 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000378 | 1 18 | 3 13 2 3 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000379 | 1 41 | 10 18 13 1 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000380 | 1 32 | 3 17 12 2 31 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_deu_000381 | 1 8 | 2 4 2 1 7 1 | +|====================================================================================================================| +| Sum | 661 8099 | 903 5295 1901 328 7524 661 | +|====================================================================================================================| +| Mean | 1.2 14.1 | 1.6 9.2 3.3 0.6 13.1 1.2 | +| S.D. | 3.7 58.8 | 7.0 34.9 17.2 1.3 52.7 3.7 | +| Median | 1.0 8.0 | 1.0 5.0 1.0 0.0 7.0 1.0 | +`--------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/hyp.trn + +Speakers: + 0: m + 1: cv_deu_000698 + 2: cv_deu_000699 + 3: cv_deu_000700 + 4: cv_deu_000701 + 5: cv_deu_000702 + 6: cv_deu_000703 + 7: cv_deu_000704 + 8: cv_deu_000705 + 9: cv_deu_000706 + 10: cv_deu_000707 + 11: cv_deu_000708 + 12: cv_deu_000709 + 13: cv_deu_000710 + 14: cv_deu_000711 + 15: cv_deu_000712 + 16: cv_deu_000713 + 17: cv_deu_000714 + 18: cv_deu_000715 + 19: cv_deu_000716 + 20: cv_deu_000717 + 21: cv_deu_000718 + 22: cv_deu_000719 + 23: cv_deu_000720 + 24: cv_deu_000721 + 25: cv_deu_000722 + 26: cv_deu_000723 + 27: cv_deu_000724 + 28: cv_deu_000725 + 29: cv_deu_000726 + 30: cv_deu_000727 + 31: cv_deu_000728 + 32: cv_deu_000729 + 33: cv_deu_000730 + 34: cv_deu_000731 + 35: cv_deu_000732 + 36: cv_deu_000733 + 37: cv_deu_000734 + 38: cv_deu_000735 + 39: cv_deu_000736 + 40: cv_deu_000737 + 41: cv_deu_000738 + 42: cv_deu_000739 + 43: cv_deu_000740 + 44: cv_deu_000741 + 45: cv_deu_000742 + 46: cv_deu_000743 + 47: cv_deu_000744 + 48: cv_deu_000745 + 49: cv_deu_000746 + 50: cv_deu_000747 + 51: cv_deu_000748 + 52: cv_deu_000749 + 53: cv_deu_000750 + 54: cv_deu_000751 + 55: cv_deu_000752 + 56: cv_deu_000753 + 57: cv_deu_000754 + 58: cv_deu_000755 + 59: cv_deu_000756 + 60: cv_deu_000757 + 61: cv_deu_000758 + 62: cv_deu_000759 + 63: cv_deu_000760 + 64: cv_deu_000761 + 65: cv_deu_000762 + 66: cv_deu_000763 + 67: cv_deu_000764 + 68: cv_deu_000765 + 69: cv_deu_000766 + 70: cv_deu_000767 + 71: cv_deu_000768 + 72: cv_deu_000769 + 73: cv_deu_000770 + 74: cv_deu_000771 + 75: cv_deu_000772 + 76: cv_deu_000773 + 77: cv_deu_000774 + 78: cv_deu_000775 + 79: cv_deu_000776 + 80: cv_deu_000777 + 81: cv_deu_000778 + 82: cv_deu_000779 + 83: cv_deu_000780 + 84: cv_deu_000781 + 85: cv_deu_000782 + 86: cv_deu_000783 + 87: cv_deu_000784 + 88: cv_deu_000785 + 89: cv_deu_000786 + 90: cv_deu_000787 + 91: cv_deu_000788 + 92: cv_deu_000789 + 93: cv_deu_000790 + 94: cv_deu_000791 + 95: cv_deu_000792 + 96: cv_deu_000793 + 97: cv_deu_000794 + 98: cv_deu_000795 + 99: cv_deu_000796 + 100: cv_deu_000797 + 101: cv_deu_000798 + 102: cv_deu_000799 + 103: cv_deu_000800 + 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voxforge_deu_001001 + 482: voxforge_deu_001002 + 483: voxforge_deu_001003 + 484: voxforge_deu_001004 + 485: voxforge_deu_001006 + 486: voxforge_deu_001007 + 487: voxforge_deu_001008 + 488: voxforge_deu_001009 + 489: voxforge_deu_001010 + 490: voxforge_deu_001011 + 491: voxforge_deu_001012 + 492: voxforge_deu_001013 + 493: voxforge_deu_001014 + 494: voxforge_deu_001015 + 495: voxforge_deu_001016 + 496: voxforge_deu_001017 + 497: voxforge_deu_001018 + 498: voxforge_deu_001019 + 499: voxforge_deu_001020 + 500: voxpopuli_deu_000309 + 501: voxpopuli_deu_000310 + 502: voxpopuli_deu_000311 + 503: voxpopuli_deu_000312 + 504: voxpopuli_deu_000313 + 505: voxpopuli_deu_000314 + 506: voxpopuli_deu_000315 + 507: voxpopuli_deu_000316 + 508: voxpopuli_deu_000317 + 509: voxpopuli_deu_000318 + 510: voxpopuli_deu_000319 + 511: voxpopuli_deu_000320 + 512: voxpopuli_deu_000321 + 513: voxpopuli_deu_000322 + 514: voxpopuli_deu_000323 + 515: voxpopuli_deu_000324 + 516: voxpopuli_deu_000325 + 517: voxpopuli_deu_000326 + 518: voxpopuli_deu_000327 + 519: voxpopuli_deu_000328 + 520: voxpopuli_deu_000329 + 521: voxpopuli_deu_000330 + 522: voxpopuli_deu_000331 + 523: voxpopuli_deu_000332 + 524: voxpopuli_deu_000333 + 525: voxpopuli_deu_000334 + 526: voxpopuli_deu_000335 + 527: voxpopuli_deu_000336 + 528: voxpopuli_deu_000337 + 529: voxpopuli_deu_000338 + 530: voxpopuli_deu_000339 + 531: voxpopuli_deu_000340 + 532: voxpopuli_deu_000341 + 533: voxpopuli_deu_000342 + 534: voxpopuli_deu_000343 + 535: voxpopuli_deu_000344 + 536: voxpopuli_deu_000345 + 537: voxpopuli_deu_000346 + 538: voxpopuli_deu_000347 + 539: voxpopuli_deu_000348 + 540: voxpopuli_deu_000349 + 541: voxpopuli_deu_000350 + 542: voxpopuli_deu_000351 + 543: voxpopuli_deu_000352 + 544: voxpopuli_deu_000353 + 545: voxpopuli_deu_000354 + 546: voxpopuli_deu_000355 + 547: voxpopuli_deu_000356 + 548: voxpopuli_deu_000357 + 549: voxpopuli_deu_000358 + 550: voxpopuli_deu_000359 + 551: voxpopuli_deu_000360 + 552: voxpopuli_deu_000361 + 553: voxpopuli_deu_000362 + 554: voxpopuli_deu_000363 + 555: voxpopuli_deu_000364 + 556: voxpopuli_deu_000365 + 557: voxpopuli_deu_000366 + 558: voxpopuli_deu_000367 + 559: voxpopuli_deu_000368 + 560: voxpopuli_deu_000369 + 561: voxpopuli_deu_000370 + 562: voxpopuli_deu_000371 + 563: voxpopuli_deu_000372 + 564: voxpopuli_deu_000373 + 565: voxpopuli_deu_000374 + 566: voxpopuli_deu_000375 + 567: voxpopuli_deu_000376 + 568: voxpopuli_deu_000377 + 569: voxpopuli_deu_000378 + 570: voxpopuli_deu_000379 + 571: voxpopuli_deu_000380 + 572: voxpopuli_deu_000381 + +Speaker sentences 0: m #utts: 89 +id: (m-ailabs_deu_000165-m-ailabs_deu_000165) +Scores: (#C #S #D #I) 1 14 3 0 +REF: DIE BEERDIGUNG MACHTE EINER ÄUSSERST WICHTIGEN SACHE EIN ENDE der PETITION AN DEN GOUVERNEUR FÜR DES INDIANERJOES BEGNADIGUNG +HYP: *** ********** ****** DIBE HRDIGON MACHTEINER AEUSESTWICHTIGEN SEHEIN INDE der PÄTIT ZION N DINGOWANÜÖR FÜRDES IN JANERDSOUSSBEGENA DIGUN +Eval: D D D S S S S S S S S S S S S S S + +id: (m-ailabs_deu_000166-m-ailabs_deu_000166) +Scores: (#C #S #D #I) 0 7 6 0 +REF: DA HABE SIE DIE WOHL JEDEM HIER IN DER ERINNERUNG GEBLIEBENEN WORTE GESPROCHEN +HYP: ** **** *** *** **** ***** DARHABESI DIEVWOULGJEDEM HER INER INERUNKGEBLIEBENEN WARTE GESPAOCHEN +Eval: D D D D D D S S S S S S S + +id: (m-ailabs_deu_000167-m-ailabs_deu_000167) +Scores: (#C #S #D #I) 9 11 7 1 +REF: erst um acht uhr war er auf MALE BRACHTE DEN KAFFEE DIE SONNE SCHIEN INS ZIMMER UND DIE SPERLINGE die DAS AUS den **** HÄCKSELSÄCKEN GEFALLENE FUTTERKORN AUFPICKTEN +HYP: erst um acht uhr war er auf **** ******* *** ****** *** ***** MALEL BRCHTERDEN GAFI DESNESCHEN INZSZIMER UNDESPEHRLINGE die *** DASSAUS den HEXE SEGTEN GEFALNE OTARKON AUFSPBIKTEN +Eval: D D D D D D S S S S S S D S I S S S S + +id: (m-ailabs_deu_000168-m-ailabs_deu_000168) +Scores: (#C #S #D #I) 2 4 4 0 +REF: sicherlich an IHREM GEBURTSTAG HÄTTE ER BEI IHR BLEIBEN KÖNNEN +HYP: sicherlich an ***** ********** ****** ** IRENGEBUOTSTAKET ERBAI ERBLEIBEN KONEN +Eval: D D D D S S S S + +id: (m-ailabs_deu_000169-m-ailabs_deu_000169) +Scores: (#C #S #D #I) 1 10 4 0 +REF: UND DESHALB MUSS MAN DORT WO MENSCHEN SCHWIERIGKEITEN haben DIES AUCH EINERSEITS ERKLÄREN ANGEBOTE MACHEN +HYP: *** ******* **** *** NDESEALBEÖM MUSMAN DAURT VOROMENTCHNSCHIERIGKETEN haben DISOUCH EINA SEITS EKLIEREN AINGEBUTEMACHENBEALE W +Eval: D D D D S S S S S S S S S S + +id: (m-ailabs_deu_000170-m-ailabs_deu_000170) +Scores: (#C #S #D #I) 1 10 9 0 +REF: DASS MAN NUR AUF DIE WELT KOMMT UM SELBST WIEDER EINEN SOHN ZU HABEN DER DIE VEREHRUNG der AHNEN FORTSETZT +HYP: **** *** *** *** *** **** ***** ** ****** ESMEN NRFTDIELT KOMT UMSEBSTIDER IN SONDZUOHABEN DERDIEVER ERHUNGK der ANEN VORTETZT +Eval: D D D D D D D D D S S S S S S S S S S + +id: (m-ailabs_deu_000171-m-ailabs_deu_000171) +Scores: (#C #S #D #I) 1 15 2 0 +REF: DESHALB GEHÖREN KONTINUIERLICHE SCHULBILDUNG AUCH KONTINUIERLICHE MÖGLICHKEITEN DER WEITERBILDUNG UND das BEGEHEN VON GEDENKTAGEN FÜR MICH UNAUFLÖSLICH ZUSAMMEN +HYP: A BEÖAN EUNEIN LICHER SCHULEBELUNG UNTEN ERLICHE MÜKLICHTEITRAUH DEREITERBLUNG UNT das ******* *** DEGEHN VONGEDENKTAGN DEMICH UN AUFLSLICHTRAMD +Eval: S S S S S S S S S S D D S S S S S + +id: (m-ailabs_deu_000172-m-ailabs_deu_000172) +Scores: (#C #S #D #I) 1 13 13 0 +REF: MEIN ANSASCHEN SAGT SIE ES IST JA JETZT WIEDER GANZ GUT ZWISCHEN uns ABER EHE DU NICHT ALLES GESTEHST GEHT DIE ERINNERUNG AN DAS BÖSE NICHT WEG +HYP: **** ********* **** *** ** *** ** EIN ANSASHEN SAKZI SISTWERGERTSTWIEDER GANZSKGUTZWISCHEN uns **** *** ** ***** ***** ******** ARBE E DUNICHT ALIESGESTDIESTD GIETIE ER INRUNGANDAS BEÖSENICHTWEG +Eval: D D D D D D D S S S S S D D D D D D S S S S S S S S + +id: (m-ailabs_deu_000173-m-ailabs_deu_000173) +Scores: (#C #S #D #I) 1 1 3 0 +REF: nein WEIBER BRAUCHE ICH NICHT +HYP: nein ****** ******* *** WEIBERBRAUREICHENICH +Eval: D D D S + +id: (m-ailabs_deu_000174-m-ailabs_deu_000174) +Scores: (#C #S #D #I) 1 6 3 0 +REF: GOTT hat NICHT VERGEBLICH NACH MIR GERUFEN SAGTE DER SCHIFFER +HYP: ENDENGORT hat ***** ********** **** NICHTVER GEBLICHNEHMER GERUOFEN SAKTE DERCHIEVER +Eval: S D D D S S S S S + +id: (m-ailabs_deu_000175-m-ailabs_deu_000175) +Scores: (#C #S #D #I) 8 12 3 1 +REF: nur eines WEISS ICH dieser FURCHTBAREN FRAGE ENTGEGENZUSETZEN und SCHLEUDERE DAS WORT in DIE WAAGSCHALE DIE glut ******* MEINES LIEBESWILLENS ist STÄRKER als TRENNUNG +HYP: nur eines ***** WEISSICH dieser VURCHTBAREN FRARGEINDGEGEN ZUSETZHEN und ********** SCH LEIDERERDASWART in *** DE WARSCALLEDI glut LEINIES LIEBES WILENZS ist STERKER als TRENUNG +Eval: D S S S S D S S D S S I S S S S + +id: (m-ailabs_deu_000176-m-ailabs_deu_000176) +Scores: (#C #S #D #I) 1 8 2 0 +REF: TOMS ARMEE GEWANN EINEN GROSSEN sieg NACH EINER LANGEN HARTNÄCKIGEN SCHLACHT +HYP: **** TOMSAMI GE ANEIN GROSEN sieg **** NEHINELLAN HERD NECKIGEN SLCHT +Eval: D S S S S D S S S S + +id: (m-ailabs_deu_000177-m-ailabs_deu_000177) +Scores: (#C #S #D #I) 1 9 7 0 +REF: ES IST EIN NAME dem SICH DIE TÜR BEI TAG UND NACHT ÖFFNEN KANN BURSCHE UND WILLKOMMEN +HYP: ** *** SSEIN NAHME dem **** *** **** *** *** SICHTI TUHEBEITAKUN NCHTAFNEN KANBRERSCHER UNDT WER KOEN +Eval: D D S S D D D D D S S S S S S S + +id: (m-ailabs_deu_000178-m-ailabs_deu_000178) +Scores: (#C #S #D #I) 0 6 0 1 +REF: ****** ABER ICH VERZEIHE IHNEN IHRE UNWISSENHEIT +HYP: ENABER ICHFERT SEIE INEN IRER UNWISEN HEIT +Eval: I S S S S S S + +id: (m-ailabs_deu_000179-m-ailabs_deu_000179) +Scores: (#C #S #D #I) 3 9 4 0 +REF: VON DER DRITTEN UNTERREDUNG an SAGTE MISTER HAVISHAM WAR MIR die PERSON in HOHEM MASSE VERDÄCHTIG +HYP: *** FON DERTRITTEN UNTEREDUNG an ***** ****** SAKTE MISTERHERVISCHEM WARMER die PERSUN in ***** HUHEMASE VERDECHTIH +Eval: D S S S D D S S S S D S S + +id: (m-ailabs_deu_000180-m-ailabs_deu_000180) +Scores: (#C #S #D #I) 3 12 10 0 +REF: ich DENKE DER AMTMANN UND SEINE FAMILIE WERDEN ES RECHT von DIR FINDEN DASS DU DICH SELBST ANGIBST UND SIE WERDEN FREUNDLICH gegen DICH SEIN +HYP: ich ***** *** ******* *** ENKE DE AMT MANUNSANE VERMEIEWERDENESRECHT von *** ****** **** ** **** DERFINDEN TDASTU DIHSELPSTANGIBST UN SEWERDEN HREUNTLICH gegen **** DIESEI +Eval: D D D D S S S S S D D D D D S S S S S S D S + +id: (m-ailabs_deu_000181-m-ailabs_deu_000181) +Scores: (#C #S #D #I) 5 9 6 1 +REF: JETZT SCHLUG DIE HELLE FLAMME auf und nun ** ERKANNTE er uns DIE WIR NOCH IMMER ZUSAMMENGEDRÄNGT IN DEM WINKEL STANDEN +HYP: ***** ETZSCHLUOK TIE HELE FLAMMER auf und nun ER KANTE er uns *** *** **** ***** ***************** DIEWINR IMERZSAMEN GEDRENT INDEMWENKESTANDEN +Eval: D S S S S I S D D D D D S S S S + +id: (m-ailabs_deu_000182-m-ailabs_deu_000182) +Scores: (#C #S #D #I) 2 5 1 0 +REF: der SEINER SEELE ANSPORNEND das ERMUNTERNDE WORT VORWÄRTS +HYP: der SEINE SIELE ANSPBONENDT das *********** ERMUNTERNDEWAUT VORWEALTZS +Eval: S S S D S S + +id: (m-ailabs_deu_000183-m-ailabs_deu_000183) +Scores: (#C #S #D #I) 0 6 3 0 +REF: ICH FREUE MICH AUF DEN BESUCH DES TUNESISCHEN MINISTERPRÄSIDENTEN +HYP: *** ***** **** VRMICHAFTDIN BSUCHTDES TUNESUSHEM MNESAPERSE DEANDTEN ORNT +Eval: D D D S S S S S S + +id: (m-ailabs_deu_000184-m-ailabs_deu_000184) +Scores: (#C #S #D #I) 0 9 3 0 +REF: WAS FÜR VERFOLGUNGEN WAS FÜR NACHSTELLUNGEN HABE ICH NICHT ZU ERDULDEN GEHABT +HYP: *** **** ************ WASTFIHRE HER FOLGUNGEN WASFIRNARSTELUMEN HABICH NICHZU EARDULEN GE HABT +Eval: D D D S S S S S S S S S + +id: (m-ailabs_deu_000185-m-ailabs_deu_000185) +Scores: (#C #S #D #I) 4 13 4 0 +REF: ZIGEUNER WAREN es DIE VON ORT ZU ORT FUHREN ein kaum ERWACHSENES JUNGES DING KAM ZU MIR HERANGEHÜPFT UND BETTELTE nein +HYP: ******** SIKOEINARWAREN es *** *** IE ON AUTZUOALT VFOREN ein kaum *********** AWAKSNDES IUNGES DIN KAMTZUMIEHERAN GE HÜEPFTUN BÄTKELEDE N nein +Eval: D S D D S S S S D S S S S S S S S + +id: (m-ailabs_deu_000186-m-ailabs_deu_000186) +Scores: (#C #S #D #I) 0 14 13 0 +REF: HUCK ICH WERDE DICH IN ENEM BOOT HINFAHREN WERDE DAS BOOT DA ANLEGEN UND ES WIEDER ZURÜCKRUDERN ALLES GANZ ALLEIN BRAUCHST DICH GAR NICHT DRUM ZU KÜMMERN +HYP: **** *** ***** **** ** **** **** ********* ***** *** **** ** ******* AK ICHWERTIH INE BOTHIN FEAN DERTASBUOTER AN LEGUNDITERTZOEGTUDEN AES GANZSELLEIN WOS DEC GANICHTHUM ZOKEMAN +Eval: D D D D D D D D D D D D D S S S S S S S S S S S S S S + +id: (m-ailabs_deu_000187-m-ailabs_deu_000187) +Scores: (#C #S #D #I) 3 12 5 0 +REF: als NUR EINMAL NOCH DEN RAUCH VON SEINEM HAUSE AUS der FERNE AUFSTEIGEN ZU SEHEN um DANN BERUHIGT ZU STERBEN +HYP: als *** ****** NR EINMALUNOCH EN DRAUCH VEN ANEM HAUSAUS der ***** ********** VERNER AUFSTEIGENZUESEN um **** DANBE RUEKTZU STDERBM +Eval: D D S S S S S S S D D S S D S S S + +id: (m-ailabs_deu_000188-m-ailabs_deu_000188) +Scores: (#C #S #D #I) 3 10 3 2 +REF: DIE TÄNZERIN ABER LAG AUF DEN KNIEEN vor BRAHMAS BILDNIS in ****** NAMENLOSER SEHNSUCHT UND weinte **** JAMMERVOLL +HYP: *** ********* IE TENZERIN ABARLARKAUF EN KNIEN vor ******* BRAMASBILTENIS in NAHMEN LOSERSEN SUCHT UNDT weinte JAMA VOLLT +Eval: D D S S S S S D S I S S S I S + +id: (m-ailabs_deu_000189-m-ailabs_deu_000189) +Scores: (#C #S #D #I) 0 7 6 0 +REF: RECHTFERTIGT MICH DENN DIE WIRKLICHKEIT NOCH NICHT AUF DIE ICH MICH BERUFEN KANN +HYP: ************ **** **** *** ************ **** ECHT FERTICHT MICHSENDE WEGLIGKEITNOCHNICHT A FTDIEHICHBE UFENGKHR +Eval: D D D D D D S S S S S S S + +id: (m-ailabs_deu_000190-m-ailabs_deu_000190) +Scores: (#C #S #D #I) 0 7 7 0 +REF: ICH ÄRGERTE MICH DANN WENN ICH AUFWACHTE ES WAR SO WUNDERSCHÖN GEWESEN DAS FLIEGEN +HYP: *** ******** **** **** **** *** ********* ICHELRGERTEMICHTANWENICH AUH FACHTER EISWASUF WUNDERSHEÖNEN GEWEESENDARS FLIEN +Eval: D D D D D D D S S S S S S S + +id: (m-ailabs_deu_000191-m-ailabs_deu_000191) +Scores: (#C #S #D #I) 3 14 4 0 +REF: NACHDEM ER SCHON DEN ganzen VORMITTAG mit IHM VERBRACHT KAM STANHOPE NACH TISCH INS QUANDTSCHE HAUS um CASPAR LEBEWOHL ZU SAGEN +HYP: ******* ** NERH DEMESCHONDIN ganzen VORMITEAG mit *** IM VARBRACHT KAMS DEN HOB NACHTISSCH IN ZSKRANSCHERHAUS um ****** GASBALE OLTZOS RGEN +Eval: D D S S S D S S S S S S S S D S S S + +id: (m-ailabs_deu_000192-m-ailabs_deu_000192) +Scores: (#C #S #D #I) 1 6 1 0 +REF: ER WAR EIN alter HIRT VOLL MEDIZINISCHER GENINALITÄT +HYP: ** HRWAR AIN alter HIRHT VOLMEDIE ZINESCHER GNENALITET +Eval: D S S S S S S + +id: (m-ailabs_deu_000193-m-ailabs_deu_000193) +Scores: (#C #S #D #I) 0 4 5 0 +REF: DASS WOHL AUCH DER MIETER SEINE WUNDERLICHKEITEN HABEN MÜSSE +HYP: **** **** **** *** ****** DESVOL AUHTDERMIETAERSEINE VUNDARICHSKEITEN HARBEMSE +Eval: D D D D D S S S S + +id: (m-ailabs_deu_000194-m-ailabs_deu_000194) +Scores: (#C #S #D #I) 5 14 5 0 +REF: SIE SAHEN ALLE ÄNGSTLICH und BETRÜBT AUS und AUCH HERR ARNE SASS SCHWERMÜTIG DA wie die anderen UND STÜTZTE DAS HAUPT IN DIE HAND +HYP: EN SIESAN ALE ENSTLICG und ******** BETRÜBTAUS und **** AUCHER ARENE SASCWEHR MÜTICG DAR wie die anderen *** ******** *** UNDSTÜTZTE DESHAUPT INDIE HEN +Eval: S S S S D S D S S S S S D D D S S S S + +id: (m-ailabs_deu_000195-m-ailabs_deu_000195) +Scores: (#C #S #D #I) 2 16 5 0 +REF: UNTER DEN damen MEIST JUNGE FRISCHE GESICHTER UNTER DEN HERREN NEBEN JUGENDLICHEN SOLCHE MIT FALTIGER STIRN und BEREITS MEHR ODER MINDER MONDUMGLÄNZTEM SCHÄDEL +HYP: UNTE REN damen ***** ***** ******* ********* ***** MEISTIONGE FRSCHIGESICHTERAUNTER IN HEREN NEBENM HUENDKLICHEN SOCHMIT FALTIGARSTIERN und BREITZSMEHRAU DARMINDER MOND UMG GLENZSTEM SCHÄHTEL +Eval: S S D D D D D S S S S S S S S S S S S S S + +id: (m-ailabs_deu_000196-m-ailabs_deu_000196) +Scores: (#C #S #D #I) 0 6 2 0 +REF: SEIT TAGEN SCHON HATTE ES BESONDERS DRÄUEND GEKLUNGEN +HYP: **** ***** SEI TEREN CHONATDEIS BESONDEAS DREIEND EKLUNGEBER +Eval: D D S S S S S S + +id: (m-ailabs_deu_000197-m-ailabs_deu_000197) +Scores: (#C #S #D #I) 0 1 0 0 +REF: SONDERBAR +HYP: SONDERBARH +Eval: S + +id: (m-ailabs_deu_000198-m-ailabs_deu_000198) +Scores: (#C #S #D #I) 3 14 2 0 +REF: ERB von ERBENHEIM stand MIT SEINER GATTIN VOLL WEHMUT UND DANKBARKEIT AN DER GRUFT AUF der ER EINEN MÄCHTIGEN +HYP: ERBP von EHRBMHEM stand *** MIZEN HR GATEIN VOL WE MUTUNG DANKGBAKEI DANDER GROUFT UF der ** IN MECHTIENG +Eval: S S D S S S S S S S S S S D S S + +id: (m-ailabs_deu_000199-m-ailabs_deu_000199) +Scores: (#C #S #D #I) 1 6 7 0 +REF: IHR WAR JEDER MENSCH EIN WUNDER und FAST ALLES WAS MENSCHEN TATEN ETWAS WUNDERBARES +HYP: *** *** ***** ****** IERWAIHIEDEAMENSCHEIN UNDER und **** ***** *** FASTALLES VESMENSCHENTALTEN IT ASSFONDABARES +Eval: D D D D S S D D D S S S S + +id: (m-ailabs_deu_000200-m-ailabs_deu_000200) +Scores: (#C #S #D #I) 1 4 1 0 +REF: WELCHE IHR WEG sie ENTLÄNGST FÜHRTE +HYP: ****** WELTHE JERWE sie END LENGSTFIER +Eval: D S S S S + +id: (m-ailabs_deu_000201-m-ailabs_deu_000201) +Scores: (#C #S #D #I) 1 10 5 0 +REF: DIE WIRTIN SASS NICHT HINTER IHREM SCHANKTISCH und KEINER IHRER DIENSTLEUTE BEFAND SICH IN DER STUBE +HYP: *** ****** **** IEWERTIN SASNICHTHINTE REM SCANKTIS und ****** ***** KEINE ERER DIENZST LOUTE BEFANZEI HNDERSTUBE +Eval: D D D S S S S D D S S S S S S + +id: (m-ailabs_deu_000202-m-ailabs_deu_000202) +Scores: (#C #S #D #I) 5 10 7 0 +REF: als DIE HERRSCHAFT AUS DER KIRCHE TRAT standen DIE LEUTE UMHER um SIE VORBEIGEHEN ZU SEHEN und AM KIRCHHOFTHORE WARTETE ein MANN +HYP: als *** ********** *** DE HERSCHAFT AUSDERKIELCHETAT standen *** ***** DIELEUITE um *** HEHR UMSIE VORBEIGEHNZUSIEN und ** AMKELCHUFSTORERWATE TE ein MAN +Eval: D D D S S S D D S D S S S D S S S + +id: (m-ailabs_deu_000203-m-ailabs_deu_000203) +Scores: (#C #S #D #I) 0 6 2 0 +REF: WAS MÜSSEN WIR TUN UM DEM TERRORISMUS ENTGEGENZUTRETEN +HYP: *** ******* SMSNMEL TORNOM DIE TARISMUS EN GNKTI +Eval: D D S S S S S S + +id: (m-ailabs_deu_000204-m-ailabs_deu_000204) +Scores: (#C #S #D #I) 0 3 8 0 +REF: ICH GLAUBE DASS SIE ES GUT MIT MIR MEINEN HERR DOKTOR +HYP: *** ****** **** *** ** *** *** *** GELAUBERDEASIES GUDET BERMEINENHERTACT +Eval: D D D D D D D D S S S + +id: (m-ailabs_deu_000205-m-ailabs_deu_000205) +Scores: (#C #S #D #I) 4 6 4 2 +REF: DOCH IM anfang GEWANN ER keine ****** *** AUFMERKSAMKEIT FÜR andere dinge ALS FÜR DAS ESSEN +HYP: **** ENTRIM anfang ****** GEWANE keine AUFMER SAM KEIT VER andere dinge *** **** ALZFÜRDERS ESEN +Eval: D S D S I I S S D D S S + +id: (m-ailabs_deu_000206-m-ailabs_deu_000206) +Scores: (#C #S #D #I) 4 8 8 0 +REF: dies FLÄSCHCHEN ZOG ER JETZT EILIG HERVOR WÄHREND JENE SICH MIT WASSER FÜLLTEN und BOT ES der JUNGFER ZÜS an +HYP: dies *********** *** ** ***** ***** ****** FLÄSCHEN ZOGERGETST EILICHER VOR WERENDJENESICHMIT WASARFELTEN und *** BTES der ******* UNKVERTZIÜS an +Eval: D D D D D D S S S S S S D S D S + +id: (m-ailabs_deu_000207-m-ailabs_deu_000207) +Scores: (#C #S #D #I) 0 13 10 0 +REF: DESHALB WAR ES AUCH RICHTIG UND WICHTIG DASS CHINA DOCH JETZT ANSPRUCHSVOLL GESAGT HAT WIR WERDEN AUCH AN DEN ZEITPUNKT DER REDUKTION KOMMEN +HYP: ******* *** ** **** ******* *** ******* **** ***** **** SER BASAURICHIHON WICHTICHDEASCHINER DRCHERTZS ANSHPUSFOLL GESAKTAT VERWERDEN UCRHAININ ZEIT BUNG DERI DUKTZIUN KOMMENDESDGU +Eval: D D D D D D D D D D S S S S S S S S S S S S S + +id: (m-ailabs_deu_000208-m-ailabs_deu_000208) +Scores: (#C #S #D #I) 0 4 4 0 +REF: NICHT DOCH MUTTER WECKE SIE JETZT NOCH NICHT +HYP: ***** **** ****** ***** NICHTDAOCHMUTER WERGESIE ERTZT NCHNIG +Eval: D D D D S S S S + +id: (m-ailabs_deu_000209-m-ailabs_deu_000209) +Scores: (#C #S #D #I) 2 6 5 1 +REF: * JA WIR haben IN DEN LETZTEN JAHREN RECHT enge BEZIEHUNGEN ZU BRASILIEN AUFGEBAUT +HYP: A BIER H haben ** *** ******* ****** INENDETZTNJANRICHT enge *********** BITIUN ZUBASIIEN AUFGEBAUTPR +Eval: I S S D D D D S D S S S + +id: (m-ailabs_deu_000210-m-ailabs_deu_000210) +Scores: (#C #S #D #I) 2 5 0 1 +REF: * sie WÜRDE SICH nicht FÜR ANDERE OPFERN +HYP: S sie VIR DESICH nicht VER ANDR ABPFON +Eval: I S S S S S + +id: (m-ailabs_deu_000211-m-ailabs_deu_000211) +Scores: (#C #S #D #I) 0 2 2 0 +REF: RIEFEN SIE MIR ZU +HYP: ****** *** LECHRIFEN METZ +Eval: D D S S + +id: (m-ailabs_deu_000212-m-ailabs_deu_000212) +Scores: (#C #S #D #I) 2 9 2 0 +REF: GOTT WAS SIE IHR ERZÄHLTE hÖren SIE nur ES IST EIN GANZER ROMAN +HYP: GKOT WASI IE AR ZTELTER hÖren SIN nur ** *** IS ISEIN GANZERUMARN +Eval: S S S S S S D D S S S + +id: (m-ailabs_deu_000213-m-ailabs_deu_000213) +Scores: (#C #S #D #I) 1 4 5 0 +REF: SEINE MUTTER KANN IHM NUR FLUSSWASSER GEBEN DESHALB WEINT er +HYP: ***** ****** **** *** *** SEINEMTER KININ MOEFLUSWASERGEBEN DESELBPWEIND er +Eval: D D D D D S S S S + +id: (m-ailabs_deu_000214-m-ailabs_deu_000214) +Scores: (#C #S #D #I) 2 18 5 0 +REF: DER BUNDEWIRTSCHAFTSMINISTER WIRD ZUSAMMEN MIT DER NETZAGENTUR AM VIERTE JUNI zum ERSTEN MAL PRÄSENTIEREN WIE SICH die NETZBETREIBER UND DIE KRAFTWERKE DIE NEUEN NETZPLÄNE VORSTELLEN +HYP: *** ************************ **** UNDSWOTCHASMINDESTA WERT EMEN USAMTERNETZ ARGENTUR AMFIERTEN JIUNI zum ****** *** ERSTENMALPRESEND IERN WIESICH die NETS BETREIBER UNG I KRASTDARKGE DINEUNETSTLENE VORSTER UN +Eval: D D D S S S S S S S D D S S S S S S S S S S S + +id: (m-ailabs_deu_000215-m-ailabs_deu_000215) +Scores: (#C #S #D #I) 2 11 8 0 +REF: EVA HATTE SICH ZITTERND VOR TODESSCHWÄCHE VON DEM GITTER BEFREIT UND SUCHTE ZU ENTFLIEHEN ABER der SCHMALE GARTEN BOT keinen AUSWEG +HYP: *** ***** **** ******** *** ************** *** EBWEARATESECHT ZEIETAND VORTODESFVECHER VONDEM GETABEFREITUN ZUCHTETZU NDFINEN BE der ******* SMALEGATEN BUOT keinen AUSFI +Eval: D D D D D D D S S S S S S S S D S S S + +id: (m-ailabs_deu_000216-m-ailabs_deu_000216) +Scores: (#C #S #D #I) 0 9 8 0 +REF: OB ICH MEIN WERK FÜR HEUTE LIEGEN LASSEN ODER NOCH EINEN ANLAUF NEHMEN UND ES VOLLENDEN SOLLTE +HYP: ** *** **** **** **** ***** ****** ****** BICHMEIN WERKFWRUTE LETEN LASEN NDE NRHEIN AN LAUFNEMENUNTESROLENDN SOLTE +Eval: D D D D D D D D S S S S S S S S S + +id: (m-ailabs_deu_000217-m-ailabs_deu_000217) +Scores: (#C #S #D #I) 1 14 8 0 +REF: ER WAR DAS GÖTZCHEN der STUNDE DIE TAITAI BEAUFTRAGTE MADAME ANGELE DIE AUCH DASTAND UND DIE GEKAUFTEN SEIDENSTÜCKE ZUSAMMENFALTETE FÜR TSCHUN ZU SORGEN +HYP: EHR WA DASKETZ HEN der ****** *** ****** *********** ****** ****** *** **** STUND DETEITEIBE AUFTRAKTEMADAM UNSCHEL DIEAUCHTASTAND UNDIGEKAUFTENSEIDENS DE KETZUSAM FELTETE FÜRTSCHNDZUSORGENG +Eval: S S S S D D D D D D D D S S S S S S S S S S + +id: (m-ailabs_deu_000218-m-ailabs_deu_000218) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ICH WERDE NACHSEHEN +HYP: DWER EN ACHSIEN +Eval: S S S + +id: (m-ailabs_deu_000219-m-ailabs_deu_000219) +Scores: (#C #S #D #I) 0 9 0 1 +REF: **** ABER TIPPS ODER VORGABEN DAS MACHEN WIR NATÜRLICH NICHT +HYP: ABAL E TEBS ODAF VOR GABEN DAD MACHM ENEDILIGNICH S +Eval: I S S S S S S S S S + +id: (m-ailabs_deu_000220-m-ailabs_deu_000220) +Scores: (#C #S #D #I) 1 14 5 0 +REF: als UNSERE IDEE BEKANNT WURDE WAR DIE PHYSIOGNOMIE DER WALTERSBURGER UNGEFÄHR DIE EINES KALBES DAS ZUM ERSTEN MALE DONNERN HÖRT +HYP: als ****** **** ******* ***** *** UNSRE E DEBEKANT WODER WADI FÜSIOG NME DERWEALTEASPBOGER UNGERFÄHR DI EINERS KEALBERS DASZUM EHRSTNMALTDONHÖR +Eval: D D D D D S S S S S S S S S S S S S S + +id: (m-ailabs_deu_000221-m-ailabs_deu_000221) +Scores: (#C #S #D #I) 2 8 2 0 +REF: BITTE MACHEN SIE GEFÄLLIGST auf UND ES KLANG WIE ein JAMMERNDER HILFERUF +HYP: ITZE MCHNS GE FÄLIHT auf *** ** UNDESKLAN DIE ein AMANDER HILVER +Eval: S S S S D D S S S S + +id: (m-ailabs_deu_000222-m-ailabs_deu_000222) +Scores: (#C #S #D #I) 1 9 8 0 +REF: HERR DOKTOR SAGTE EINE FRAU DIE SCHNURRGRINE DIE SO OFT ZU IHNEN KOMMT ist EIGENTLICH GAR NICHT KRANK +HYP: **** ****** ***** **** **** ER DAOKTORSAER INEFRAU DESHNOGRIENER DISE AFTZU INEN KOM ist ********** *** ***** ENGIGANIGKRAN +Eval: D D D D D S S S S S S S S D D D S + +id: (m-ailabs_deu_000223-m-ailabs_deu_000223) +Scores: (#C #S #D #I) 7 24 1 1 +REF: die alte ** ERINNERUNG AN DEN FRÜHEREN TRAUM tauchte EBENFALLS WIEDER auf und UNWILLKÜRLICH FAST BEI DER BEHAUPTUNG DASS DIE SEELE DEN kÖrper VERLASSEN und ZU IHM ZURÜCKKEHREN KÖNNE SCHIEN ES IHR ORDENTLICH +HYP: die alte ER IN RUNGAN DIN FRÜHREN TAUM tauchte EBEN FALZWIEDER auf und UNWIELKÖRLIC FASST BAI DERBE HAUPTUNG DAS DE SELE EN kÖrper VELLASTEN und ** TZU IEM ZURÜCKEREN KENE SCHENES IER ARDENDLI +Eval: I S S S S S S S S S S S S S S S S S D S S S S S S S + +id: (m-ailabs_deu_000224-m-ailabs_deu_000224) +Scores: (#C #S #D #I) 2 11 5 0 +REF: ALS sie AUF DEN balkon ZURÜCKKEHRTE FAND SIE IHN DIE ZEITUNG LESEND DIE WÄHREND IHRES FORTSEINS ANGELANGT WAR +HYP: ALZ sie *** AFRIN balkon ************* **** *** *** ZURE KHRTE FANZIE IEN DI SEITUNGKLIESEND TDIE WEREN RISVORTZEINSANGELANGKTWAH +Eval: S D S D D D D S S S S S S S S S + +id: (m-ailabs_deu_000225-m-ailabs_deu_000225) +Scores: (#C #S #D #I) 9 12 2 1 +REF: ER WAR ein KIND DER STRASSE von klein auf aber in IHM lebte von JEHER EINE GEWISSE SEHNSUCHT NACH EINER EHRBAREN bÜrgerlichen * EXISTENZ +HYP: TE ERWAR ein **** KIN DRSTRASE von klein auf aber in IEM lebte von ***** JEHR INRGWISE SEN SOCHT NACHEINEI ERBAREN bÜrgerlichen E ISTENS +Eval: S S D S S S D S S S S S S I S + +id: (m-ailabs_deu_000226-m-ailabs_deu_000226) +Scores: (#C #S #D #I) 1 13 4 0 +REF: UND WIR ALS BUNDESREGIERUNG FÜHLEN UNS HIER NICHT einer GRUPPE VERANTWORTLICH SONDERN WIR FÜHLEN UNS DEM GEMEINWOHL VERANTWORTLICH +HYP: *** *** *** IS UNES DG HRUNKFÜNUN EHNIG einer ****** GROPEFE ANFORDLICH SONA WÜEFÜNENS IN GMEINWUOLFR ANWORDLICH N +Eval: D D D S S S S S D S S S S S S S S + +id: (m-ailabs_deu_000227-m-ailabs_deu_000227) +Scores: (#C #S #D #I) 1 3 2 0 +REF: was MEIN LIEBES KIND WAS KANN +HYP: was **** ****** MEILIEBES GEINT WASGKAN +Eval: D D S S S + +id: (m-ailabs_deu_000228-m-ailabs_deu_000228) +Scores: (#C #S #D #I) 3 15 11 0 +REF: und DANN WOLLTE ICH DEN ANBLICK DERER NICHT MISSEN DIE MIR GEBLIEBEN waren vor ALLEM ABER WAR ES MIR DARUM ZU TUN MEINE SÜSSE ELISABETH EINIGERMASSEN GETRÖSTET ZU SEHEN +HYP: und **** ****** *** *** ******* DANWULTEICHTIN ANBLIG DE RANICHTMESSEN DEMÄR GEBLEBEM waren vor ***** **** *** ** *** ***** ELEM ABAWAR ISNI DARUMTZUTUN WEINDESYSE LICHIESERBET EINIGAMASSEN GETREÖSTET ZUSEN +Eval: D D D D D S S S S S S D D D D D D S S S S S S S S S + +id: (m-ailabs_deu_000229-m-ailabs_deu_000229) +Scores: (#C #S #D #I) 0 9 3 0 +REF: ABER ICH GLAUBE DASS WIR UNS AUCH GEGENSEITIG EIN BISSCHEN UNTERSTÜTZEN KÖNNEN +HYP: **** *** ****** ERDAS AUCH WIER UNDS GNGSEI DIHM BISHN UNTARSTITZENKERN ERM +Eval: D D D S S S S S S S S S + +id: (m-ailabs_deu_000230-m-ailabs_deu_000230) +Scores: (#C #S #D #I) 2 7 4 0 +REF: seine GESCHÄFTLICHE LAUFBAHN HABE STEFENSON ALS KÜCHENBOY in EINEM HOTEL VIERTEN GRADES BEGONNEN +HYP: seine ************** ESCHETICHELAUF BARNHARBES DIEBENSN ASKÜCHEN BO in ***** ***** ******* EINRMUTELFVIERTEN GRADESBGON +Eval: D S S S S S D D D S S + +id: (m-ailabs_deu_000231-m-ailabs_deu_000231) +Scores: (#C #S #D #I) 2 14 10 0 +REF: VIELLEICHT TÄTEN SIE GUT DIESE ANSICHTEN DES BISCHOFS NACH HAUSE ZU MELDEN SAGTE DER TAJEN DER IMMER MEHR ein MANN DES GESCHRIEBENEN WORTES WIE der TAT +HYP: ********** ****** *** *** ***** ********* *** FILEICHTE EN SIGUTISE ANSICHTEDES BSCHOFEN HUSETZUMELEN ZAKTE ERTATSHEN DEHR IMAR MHR ein **** *** ************* MANDES GESCHIEBENENWURTESVIE der TARDT +Eval: D D D D D D D S S S S S S S S S S S D D D S S S + +id: (m-ailabs_deu_000232-m-ailabs_deu_000232) +Scores: (#C #S #D #I) 1 17 9 0 +REF: AM ANDERN MORGEN ERHOB ER SICH SPÄT SCHICKTE DEN LAKAIEN IN DIE WOHNUNG FEUERBACHS UND LIESS UM EINE UNTERREDUNG BITTEN DER MANN kam MIT DER BOTSCHAFT ZURÜCK +HYP: ** ****** ****** ***** ** **** ***** ******** *** AMANDANMOREN ERHOPERSICHSHPET SCHIKTE N LARKEIEN N DE BUNG VOR ABACHSUNTLISUM EIN NTERIEDUNG BITENDEMAN kam MITER BOTSCAFTT ZORK DT +Eval: D D D D D D D D D S S S S S S S S S S S S S S S S S + +id: (m-ailabs_deu_000233-m-ailabs_deu_000233) +Scores: (#C #S #D #I) 0 13 2 0 +REF: NUR EIN WENIG TRAURIG WURDE ES WENN IMMER DASSELBE KAM WENN SIE NIE ZUFRIEDEN SCHIENEN +HYP: *** *** TH N EINWENICHTHAURICH WURDERS WEIN ME DEA SEL BERKAMENSIN NE ZU FRIEDEN SCHIEN +Eval: D D S S S S S S S S S S S S S + +id: (m-ailabs_deu_000234-m-ailabs_deu_000234) +Scores: (#C #S #D #I) 2 15 3 1 +REF: ein SOMMERWARMER NOVEMBERTAG LAG MIT SONNENGLITZERN ÜBER DER HAUPTSTADT und **** UNTER DEN LINDEN DRÄNGTE EINE TAUSENDKÖPFIGE MENSCHENMENGE AUF UND NIEDER +HYP: ein ************ *********** *** SOMAHRWAHMANOWENMBARTARKLARGMITZON GLIT SANN BERDE HUTSTABT und UNDE DIN LIENDENDRENGKTE INE TAUSEN KER FIGERMENSCHEN MENGER AUORFVON N EDER +Eval: D D D S S S S S I S S S S S S S S S S + +id: (m-ailabs_deu_000235-m-ailabs_deu_000235) +Scores: (#C #S #D #I) 0 6 4 0 +REF: KOMM MIT MIR MEIN SOHN DENN ICH BRAUCHE DEINE LIEBE +HYP: **** *** *** **** KOMIT MIH MEINSON DEN ICHPRAUCHER DEINELEBE +Eval: D D D D S S S S S S + +id: (m-ailabs_deu_000236-m-ailabs_deu_000236) +Scores: (#C #S #D #I) 3 8 2 2 +REF: NUR SEIN GESICHT WURDE EIN WENIG NACHDENKLICHER SO wie von einer ** ******** ERINNERUNG ERHELLT +HYP: *** **** NORE SAN KESICHT RODEINWENICHNACH DENKLICHER SOO wie von einer ER INERUNGK ER HELT +Eval: D D S S S S S S I I S S + +id: (m-ailabs_deu_000237-m-ailabs_deu_000237) +Scores: (#C #S #D #I) 1 11 3 0 +REF: DANN WIRD AUCH WIEDER DER INNOVATIONSDRUCK steigen UND DAZU IST DAS SYSTEM JA EINGEFÜHRT WORDEN +HYP: N WUT AUFIDE DE NWATIUNS DROKK steigen *** **** *** UN AZU SASESTEMER ENGEFÜRT WON +Eval: S S S S S S D D D S S S S S + +id: (m-ailabs_deu_000238-m-ailabs_deu_000238) +Scores: (#C #S #D #I) 1 11 3 1 +REF: *** JETZT GEWAHRTE ER MIT ENTSETZEN DIE SCHEUSSLICHE TEUFLISCHE AFFENFRATZE die ÜBER DES MÄDCHENS SCHULTER SCHIELTE +HYP: NET GEWARTE EAMIT EN SETZSN DI SCHOUISLICHERTOE FLSCHER AHFEN FRATZE die ***** *** ********* BE DISMENCHENSHULTEARSCHIELTE +Eval: I S S S S S S S S S D D D S S + +id: (m-ailabs_deu_000239-m-ailabs_deu_000239) +Scores: (#C #S #D #I) 0 10 4 0 +REF: JA DER WIRT NICKTE DAS GEHÖRT EINEM GEWISSEN WUTSCHOW BERNHARD WUTSCHOW IST ETWAS VERRUFEN +HYP: ** *** **** ****** ERR DERWIERDNIGTE DASGÖRD EINE GEWISEN WUETSCHAF BERNHAT WURTSCHOUF ISTE TWASFACGNSEN +Eval: D D D D S S S S S S S S S S + +id: (m-ailabs_deu_000240-m-ailabs_deu_000240) +Scores: (#C #S #D #I) 1 8 2 0 +REF: WOLLT IHR in WAHRHEIT DIE LÖWEN TÖTEN UND KÖNNT IHR SCHIESSEN +HYP: WULT EHE in ******** *** WEHEIDIE ESEN TÖRTEN UNDT KND IERSCHLISEN +Eval: S S D D S S S S S S + +id: (m-ailabs_deu_000241-m-ailabs_deu_000241) +Scores: (#C #S #D #I) 5 13 2 1 +REF: bat CEDDIE SEHR RESPEKTVOLL WOBEI ER nur EINIGE SILBEN VERSCHLUCKTE was IHM BEI DEN beliebten *** LANGEN WÖRTERN des ÖFTERN VORKAM +HYP: bat ****** ZEDI SERESPEKTVOL UOBEI ERN nur ****** EINIGESEEN VERSCHLUKTDE was IM BEIT EN beliebten LAN GEN WÖRTAN des EFTAN VORKA +Eval: D S S S S D S S S S S I S S S S + +id: (m-ailabs_deu_000242-m-ailabs_deu_000242) +Scores: (#C #S #D #I) 0 6 5 0 +REF: LORD FAUNTLEROY WIRD NICHTS ENTBEHREN DESSEN BIN ICH GEWISS VERSETZTE ER +HYP: **** ********** **** ****** ********* LORT FONDLEROAIEVERTNICHTZSEND BEREN DESEN BINICHGEWIS VERSETZTEHR +Eval: D D D D D S S S S S S + +id: (m-ailabs_deu_000243-m-ailabs_deu_000243) +Scores: (#C #S #D #I) 4 5 3 0 +REF: kam GLEICHFALLS ins SCHLAFZIMMER auf einen NAGEL IN DER NÄHE DES BETTES +HYP: kam GLEICHFWALS ins SCHLAFTZIMER auf einen ***** ** *** NARGEL INDERNER DESBPETES +Eval: S S D D D S S S + +id: (m-ailabs_deu_000244-m-ailabs_deu_000244) +Scores: (#C #S #D #I) 1 12 11 0 +REF: UND das IST DIE CHANCE DIE IN DIESER KRISE STECKT DIE CHANCE FÜR INTERNATIONALE REGELN DIE SICH AN DEN PRINZIPIEN DER SOZIALEN MARKTWIRTSCHAFT ORIENTIEREN +HYP: *** das *** *** ****** *** ** ****** ***** ****** *** ****** DISCHANS DEHNDIESARKRISSTEG DISCHAUNGS VÜR INTERNATZEHNALEREGEN ISIEN EM RON ZFIBPTE DESE TSWALNMAKLTA ERINTIEN +Eval: D D D D D D D D D D D S S S S S S S S S S S S + +id: (m-ailabs_deu_000245-m-ailabs_deu_000245) +Scores: (#C #S #D #I) 1 9 7 0 +REF: ANFANGS FIEL DER REGEN SCHRÄG und PEITSCHTE ERST DIE EINE UND DANN DIE ANDERE SEITE DES WAGENS +HYP: ******* **** *** ANFANGSFIELDEREIGEN SCHRÄ und ********* **** *** **** PEITST ERS IE EINER DAN DIEANDERESEITE DESWAENS +Eval: D D D S S D D D D S S S S S S S + +id: (m-ailabs_deu_000246-m-ailabs_deu_000246) +Scores: (#C #S #D #I) 0 6 3 0 +REF: FAST LEICHTSINNIGEN BEMESSUNG IHRES WERTES AUFZUGEBEN SICH ENTSCHLOSSEN HATTE +HYP: **** ************** ********* FASTR LEICHTZEINIGENBER MESUNG ERESWERTES AUFTZUGEBEMSICH ENDTSLOSSENHATE +Eval: D D D S S S S S S + +id: (m-ailabs_deu_000247-m-ailabs_deu_000247) +Scores: (#C #S #D #I) 1 8 6 0 +REF: DAS HEISST die FRAGE DER MENSCHLICHEN ARBEIT UND DIE FRAGE WAS KANN TECHNISCH GELÖST WERDEN +HYP: *** SSEIST die ***** *** ************ ****** *** FRARGEMENCHICHEN ABETN DIERARGEWAS KAN TÄCHNS GLÜSTWEREN D +Eval: D S D D D D D S S S S S S S + +id: (m-ailabs_deu_000248-m-ailabs_deu_000248) +Scores: (#C #S #D #I) 0 6 5 0 +REF: DIE SAFARI WAR AUF DIE REGELMÄSSIG BENUTZTEN WASSERSTELLEN DIESER ROUTE ANGEWIESEN +HYP: *** ****** *** *** *** ISAR FARI WAUFDIRIDEMEHSIG BENTZTEN WASSARSTELLEN IESERUTANGERIESEN +Eval: D D D D D S S S S S S + +id: (m-ailabs_deu_000249-m-ailabs_deu_000249) +Scores: (#C #S #D #I) 4 10 5 0 +REF: DIE BEIDEN MÜSSTEN HIER OBEN auf dem GIPFEL GESTANDEN HABEN UND ER SPRACH die alten WORTE VOR SICH HIN +HYP: *** DIEBEITEN MISTEN HIE OBEM auf dem ****** ********* GEPVEL GESTANTEN HABEM UNDERSPRACH die alten ***** *** WAURTE VOSECHEN +Eval: D S S S S D D S S S S D D S S + +id: (m-ailabs_deu_000250-m-ailabs_deu_000250) +Scores: (#C #S #D #I) 2 9 2 0 +REF: ENDLICH BLICKTE CEDRIK AUF WEISS NEWICK ALLES VON DEN armen LEUTEN FRAGTE er +HYP: ******* ******* ENTLIC PIKTESEDRIG AUFH WEISEN UIG ALES VONDIN armen LOEITEN FRACKTE er +Eval: D D S S S S S S S S S + +id: (m-ailabs_deu_000251-m-ailabs_deu_000251) +Scores: (#C #S #D #I) 2 11 6 0 +REF: DASS ES HEUTE EINE WUNDERBARE ZUSAMMENARBEIT ZWISCHEN bund und LÄNDERN IN DIESEN FRAGEN GIBT MIT SEHR SEHR INTERESSANTEN PROJEKTEN +HYP: **** ** ***** SHOLDE INE WUNDABARIR SAMABEITZUÜCHN bund und ******** ** ****** LENEAN INDIESEN RAGENGIEB DET SERSE INTRESANEN PRIEGKTENOU +Eval: D D D S S S S D D D S S S S S S S + +id: (m-ailabs_deu_000252-m-ailabs_deu_000252) +Scores: (#C #S #D #I) 4 12 3 0 +REF: CASPAR VERHARRTE ANGEWURZELT an SEINEM PLATZ SEINE GLIEDER ja SEINE augen WAREN WIE versteinert ALS ER ZUM ZWEITENMAL HINBLICKTE +HYP: ****** KASBA FERHARTER an GENMRETZELTENSENMPLTZS ZEIN LE DER ja SEINER augen ***** BWANIE versteinert *** ALSEITHUN ZWEITEN MALHIN IKTE +Eval: D S S S S S S S D S D S S S S + +id: (m-ailabs_deu_000253-m-ailabs_deu_000253) +Scores: (#C #S #D #I) 6 10 2 0 +REF: EINIGE zeit danach FRAGTE er mich OB ICH GLAUBE DASS der EISGANG DEN SCHLITTEN DES anderen ZERSTÖRT HABE +HYP: EINIGET zeit danach FRAKTE er mich ** *** OPICHGKLAUBER DEAS der EIS GANG DIN SHLITENDES anderen ZSER STERTABE +Eval: S S D D S S S S S S S S + +Speaker sentences 1: cv_deu_000698 #utts: 1 +id: (cv_deu_000698-cv_deu_000698) +Scores: (#C #S #D #I) 0 7 1 0 +REF: ABER NUN BLOSS NICHT IN EINE SCHOCKSTARRE VERFALLEN +HYP: **** ABENEN BLÜSE NICHTGENEINER SCHOSTAR E E VALEN +Eval: D S S S S S S S + +Speaker sentences 2: cv_deu_000699 #utts: 1 +id: (cv_deu_000699-cv_deu_000699) +Scores: (#C #S #D #I) 0 2 3 0 +REF: JA ICH KOMME JA SCHON +HYP: ** *** ***** JEITS KOMIAERSCHEUN +Eval: D D D S S + +Speaker sentences 3: cv_deu_000700 #utts: 1 +id: (cv_deu_000700-cv_deu_000700) +Scores: (#C #S #D #I) 0 8 0 1 +REF: * NEBENBEI ARBEITETE ER ALS AUSHILFSKRAFT AUF EINER FARM +HYP: S DEM BEIE ABETD DE EIS AUSERELS KRAFTAO ENOFHA +Eval: I S S S S S S S S + +Speaker sentences 4: cv_deu_000701 #utts: 1 +id: (cv_deu_000701-cv_deu_000701) +Scores: (#C #S #D #I) 0 8 4 0 +REF: EIN TERRITORIAL GRÖSSERES EUROPA WIRD NICHT MIT EINEM ETATMÄSSIG KLEINEREN EUROPA ERREICHT +HYP: *** *********** ********** ****** EINTERIT TORHEIGKOSELS O HOPAWIETD NICHTMIT EN IETAMESISKEINERE AOCHOPAREICT +Eval: D D D D S S S S S S S S + +Speaker sentences 5: cv_deu_000702 #utts: 1 +id: (cv_deu_000702-cv_deu_000702) +Scores: (#C #S #D #I) 0 8 0 0 +REF: IHR SOHN KAM DURCH KÜNSTLICHE BEFRUCHTUNG ZUR WELT +HYP: E SHOUN KABNDER KÜNZSLI HER BE VROCHTDUNG TZERBERT +Eval: S S S S S S S S + +Speaker sentences 6: cv_deu_000703 #utts: 1 +id: (cv_deu_000703-cv_deu_000703) +Scores: (#C #S #D #I) 0 8 2 0 +REF: DIE NACHTAKTIVEN FALTER FLIEGEN VON MITTE JULI BIS MITTE OKTOBER +HYP: *** ************ DEIN NACHT ERKTIEF NE FALRTERFLEGEN VONMITER IOUOLDIEBESSMITER OPTOE +Eval: D D S S S S S S S S + +Speaker sentences 7: cv_deu_000704 #utts: 1 +id: (cv_deu_000704-cv_deu_000704) +Scores: (#C #S #D #I) 0 1 0 1 +REF: **** ACHT +HYP: ELRE THE +Eval: I S + +Speaker sentences 8: cv_deu_000705 #utts: 1 +id: (cv_deu_000705-cv_deu_000705) +Scores: (#C #S #D #I) 0 1 0 1 +REF: **** FÜNF +HYP: EIND HEIREN +Eval: I S + +Speaker sentences 9: cv_deu_000706 #utts: 1 +id: (cv_deu_000706-cv_deu_000706) +Scores: (#C #S #D #I) 0 10 2 0 +REF: NUTZER KÖNNEN IHRE LESEZEICHEN ONLINE ABSPEICHERN VERWALTEN UND MIT ANDEREN NUTZERN TEILEN +HYP: ****** ******* MUOZE KN IERERLESE ZEIGE ONEIN ABSPEICHER VERWALITEN UN BITANERENOTZAN TEIREN +Eval: D D S S S S S S S S S S + +Speaker sentences 10: cv_deu_000707 #utts: 1 +id: (cv_deu_000707-cv_deu_000707) +Scores: (#C #S #D #I) 0 4 0 0 +REF: DIE DON BOSCO KATH +HYP: DE DEM BOSGON KTERAE +Eval: S S S S + +Speaker sentences 11: cv_deu_000708 #utts: 1 +id: (cv_deu_000708-cv_deu_000708) +Scores: (#C #S #D #I) 3 8 0 2 +REF: saul BASS ZÄHLT zu den * * INNOVATIVSTEN DESIGNERN UND FILMEMACHERN SEINER ZEIT +HYP: saul BAS TZEHL zu den G N RTISTEN DISEI DABMAUMNF MECR ÜENDL SERLZERIUDN +Eval: S S I I S S S S S S + +Speaker sentences 12: cv_deu_000709 #utts: 1 +id: (cv_deu_000709-cv_deu_000709) +Scores: (#C #S #D #I) 1 7 0 0 +REF: in GRÜN ÜBER SILBERNEM WELLENBALKEN EINE SILBERNE EICHE +HYP: in KMÜNÜMWÖHR SEBELAEN WELNBAUG K EINER SEL BONDEREISCHE +Eval: S S S S S S S + +Speaker sentences 13: cv_deu_000710 #utts: 1 +id: (cv_deu_000710-cv_deu_000710) +Scores: (#C #S #D #I) 1 9 1 0 +REF: WEITERE wichtige INDUSTRIEZWEIGE SIND DIE MIKROMECHANIK GALVANOPLASTIK METALLBAU UND DIE HOLZVERARBEITUNG +HYP: EITERE wichtige *************** INDESTRIZWEIGESEN DE MIKRUMICHANIG GERWANOPLASTIG MITEIBAU UNTIE HELTZVER ABEITUNE +Eval: S D S S S S S S S S + +Speaker sentences 14: cv_deu_000711 #utts: 1 +id: (cv_deu_000711-cv_deu_000711) +Scores: (#C #S #D #I) 2 9 2 0 +REF: ÜBER den autor IST NICHTS BEKANNT VERMUTLICH STAMMTE ER AUS DEM DEUTSCHEN SPRACHGEBIET +HYP: IBER den autor *** ****** IS NIHZ BEKAND VERMUTLIG STAMTEHR AUSE DEITEN PRAHGBIET +Eval: S D D S S S S S S S S + +Speaker sentences 15: cv_deu_000712 #utts: 1 +id: (cv_deu_000712-cv_deu_000712) +Scores: (#C #S #D #I) 0 5 1 0 +REF: MAN STEUERT ES MIT EINEM DOPPELPADDEL +HYP: *** NDSTDEUÖER ISSMI ENE TOPLE PATE +Eval: D S S S S S + +Speaker sentences 16: cv_deu_000713 #utts: 1 +id: (cv_deu_000713-cv_deu_000713) +Scores: (#C #S #D #I) 0 4 3 0 +REF: WIR HABEN EIN PROBLEM AUF OSISCHICHT ACHT +HYP: *** ***** *** DE HORBMENBEBLEMER POSIESCHIG AUCHT +Eval: D D D S S S S + +Speaker sentences 17: cv_deu_000714 #utts: 1 +id: (cv_deu_000714-cv_deu_000714) +Scores: (#C #S #D #I) 0 8 5 0 +REF: WIR SPIELEN IMMER NOCH ABER DAS LEBEN AUF TOUR IST DERZEIT NICHT MACHBAR +HYP: *** ******* ***** **** **** EWISCHIEN MANUR ABARE EM AUGTÜURE ELTE IT MACHBERH +Eval: D D D D D S S S S S S S S + +Speaker sentences 18: cv_deu_000715 #utts: 1 +id: (cv_deu_000715-cv_deu_000715) +Scores: (#C #S #D #I) 0 11 0 0 +REF: HEUTE ZEIGT SICH DER GRÖSSTE TEIL DER ANLAGE ALS ENGLISCHER GARTEN +HYP: KEDE ZAL IC DE ESE L DE ARNANN AN EN ESHEWABENG +Eval: S S S S S S S S S S S + +Speaker sentences 19: cv_deu_000716 #utts: 1 +id: (cv_deu_000716-cv_deu_000716) +Scores: (#C #S #D #I) 2 7 1 0 +REF: SEINE RESIDENZ NAHM ER in MÜNCHEN wo ER AUCH STARB +HYP: EE ESE ENZ NAMER in MINCHEND wo ** E AUGSTABEBEI +Eval: S S S S S D S S + +Speaker sentences 20: cv_deu_000717 #utts: 1 +id: (cv_deu_000717-cv_deu_000717) +Scores: (#C #S #D #I) 2 9 2 1 +REF: ****** INNERER und ÄUSSERER NARTHEX KÖNNEN ALS GETRENNTE TEILE EINES NARTHEX AUCH gemeinsam VORKOMMEN +HYP: INERNH T und ********* ******* ELSERER NARTEIGS KÖRDEN ALSGERENTETAEILE ENDES NARTIGS AUR gemeinsam VORKOMEN +Eval: I S D D S S S S S S S S + +Speaker sentences 21: cv_deu_000718 #utts: 1 +id: (cv_deu_000718-cv_deu_000718) +Scores: (#C #S #D #I) 0 7 1 0 +REF: DABEI BELEGTE ER DIE PLÄTZE VIER UND DREI +HYP: ***** DABEIE BELEGKTER EHR DIEPLÄTZSE FVIER UN TREIC +Eval: D S S S S S S S + +Speaker sentences 22: cv_deu_000719 #utts: 1 +id: (cv_deu_000719-cv_deu_000719) +Scores: (#C #S #D #I) 2 6 0 2 +REF: * KIM DARBY IST die TOCHTER zweier * PROFESSIONELLER TÄNZER +HYP: A KE DEABIJE IS die THOUCHTART zweier B ROFVESENE ARTENZAN +Eval: I S S S S I S S + +Speaker sentences 23: cv_deu_000720 #utts: 1 +id: (cv_deu_000720-cv_deu_000720) +Scores: (#C #S #D #I) 1 5 3 0 +REF: ICH GLAUBE DAS FÜHRT NICHT in DIE RICHTIGE RICHTUNG +HYP: *** DIS KLAUBETAS FÜRT NIST in *** ******** RISTIGERISTRUNG +Eval: D S S S S D D S + +Speaker sentences 24: cv_deu_000721 #utts: 1 +id: (cv_deu_000721-cv_deu_000721) +Scores: (#C #S #D #I) 1 3 2 0 +REF: DAS IST eine EXTREM SCHLECHTE RICHTLINIE +HYP: *** DASES eine ****** X STRENSHLESTERISTFLIENER +Eval: D S D S S + +Speaker sentences 25: cv_deu_000722 #utts: 1 +id: (cv_deu_000722-cv_deu_000722) +Scores: (#C #S #D #I) 0 5 1 0 +REF: HERR LURCH ENTBLÖSSTE SEIN HAGERES GESICHT +HYP: **** HERLOSCH EN BLEST ZSAIN HAGERESGESIHT +Eval: D S S S S S + +Speaker sentences 26: cv_deu_000723 #utts: 1 +id: (cv_deu_000723-cv_deu_000723) +Scores: (#C #S #D #I) 0 5 0 0 +REF: NUR CARMEN FINDET DAS UNFAIR +HYP: MR KALEN FINDETOSS UN FER +Eval: S S S S S + +Speaker sentences 27: cv_deu_000724 #utts: 1 +id: (cv_deu_000724-cv_deu_000724) +Scores: (#C #S #D #I) 0 5 0 1 +REF: * INGEBORG KRABBE HATTE DREI GESCHWISTER +HYP: T INGEBO KABERHAT DER DEIGESCHISTE NT +Eval: I S S S S S + +Speaker sentences 28: cv_deu_000725 #utts: 1 +id: (cv_deu_000725-cv_deu_000725) +Scores: (#C #S #D #I) 2 8 3 0 +REF: ES KOMMT WIRKLICH DARAUF AN DASS SOLCHE daten AUF dieser EBENE ERFASST WERDEN +HYP: ** ***** ******** T SKOMPLITLISTAB ANMN DASSOLCE daten U dieser EBNE PAST BERDEN +Eval: D D D S S S S S S S S + +Speaker sentences 29: cv_deu_000726 #utts: 1 +id: (cv_deu_000726-cv_deu_000726) +Scores: (#C #S #D #I) 1 4 1 1 +REF: ****** STRUMMING HINGEGEN ERGIBT ein HARMONISCHES PULSIEREN +HYP: TRAMIN HEN GEGENER IBT ein ************ HRMUNICHESPOSIEREN +Eval: I S S S D S + +Speaker sentences 30: cv_deu_000727 #utts: 1 +id: (cv_deu_000727-cv_deu_000727) +Scores: (#C #S #D #I) 1 5 1 0 +REF: BIN ICH zum KAUF EINER HYPOTHEK BERECHTIGT +HYP: *** BENIH zum KAF ENER HPOETEG BEREICHDIGKT +Eval: D S S S S S + +Speaker sentences 31: cv_deu_000728 #utts: 1 +id: (cv_deu_000728-cv_deu_000728) +Scores: (#C #S #D #I) 0 6 0 2 +REF: ** *** TEHERAN IST DIE HAUPTSTADT VOM IRAN +HYP: KC ZUN E DEUNEN EUNEINERSCHEN ENEN DERN ENENN +Eval: I I S S S S S S + +Speaker sentences 32: cv_deu_000729 #utts: 1 +id: (cv_deu_000729-cv_deu_000729) +Scores: (#C #S #D #I) 0 5 1 0 +REF: KOHLENHYDRATE SIND BESSER ALS IHR RUF +HYP: ************* H HUNE DEIREN SEN DESELWDSLENDNUN +Eval: D S S S S S + +Speaker sentences 33: cv_deu_000730 #utts: 1 +id: (cv_deu_000730-cv_deu_000730) +Scores: (#C #S #D #I) 1 10 3 0 +REF: OHNE DIE PROFESSIONELLE UNTERSTÜTZUNG DER MASERATIRENNABTEILUNG WAREN DIESE WAGEN der KONKURRENZ NUN DOCH UNTERLEGEN +HYP: **** *** ONE DE ROWISENELENG TSTIZUNG DAMASS ATDIRENABTALIUN WANDISEBAGEN der ********** KONKEWENZS EN DOCHUNTOLEGEN +Eval: D D S S S S S S S D S S S + +Speaker sentences 34: cv_deu_000731 #utts: 1 +id: (cv_deu_000731-cv_deu_000731) +Scores: (#C #S #D #I) 0 8 0 0 +REF: SIE DIENTE ZUNÄCHST ALS UNTERKUNFT FÜR BELGISCHE BESATZUNGSTRUPPEN +HYP: SI IEN DE UNECST ASUN EKUMF VERBELLGISCH IESATZUNGSTRUPEN +Eval: S S S S S S S S + +Speaker sentences 35: cv_deu_000732 #utts: 1 +id: (cv_deu_000732-cv_deu_000732) +Scores: (#C #S #D #I) 0 6 1 0 +REF: DA MÜSSEN WIR SPRENGEN MEINTE DER ZAHNARZT +HYP: ** DEMISEN WIESPLÄNGEN MEITE ER ZHAN ATZT +Eval: D S S S S S S + +Speaker sentences 36: cv_deu_000733 #utts: 1 +id: (cv_deu_000733-cv_deu_000733) +Scores: (#C #S #D #I) 0 11 1 0 +REF: AUSSERDEM SPIELTE ER BEIM NACHFOLGETEAM NEWMARKET ROYALS SOWIE BEIM LIGAKONKURRENTEN LONDON KNIGHTS +HYP: ********* AUER DEM SPHIETE RBERMNACH VERGETIEM NINMARKET ROEDIUS SOWIEBER LIEGER KONKUERENTEN LONDNEITZ +Eval: D S S S S S S S S S S S + +Speaker sentences 37: cv_deu_000734 #utts: 1 +id: (cv_deu_000734-cv_deu_000734) +Scores: (#C #S #D #I) 1 9 0 3 +REF: ** ******* WIE AUCH DAS INSTANTRUNOFFVOTING ERFÜLLT DIE COOMBSWAHL das *** CONDORCETKRITERIUM NICHT +HYP: IE AUPTERS ENZSTEN WAN AHFVAUTHING AER FÜRTI KUOUMS WAL das KON DOARECKETK TERIAOMNICHT +Eval: I I S S S S S S S I S S + +Speaker sentences 38: cv_deu_000735 #utts: 1 +id: (cv_deu_000735-cv_deu_000735) +Scores: (#C #S #D #I) 0 4 1 0 +REF: SMITH WUCHS IN CHICAGO AUF +HYP: ***** SMET FWUOEINTCHI KAR BOAUF +Eval: D S S S S + +Speaker sentences 39: cv_deu_000736 #utts: 1 +id: (cv_deu_000736-cv_deu_000736) +Scores: (#C #S #D #I) 0 4 0 0 +REF: WIR SIND HIER ALLEIN +HYP: WIE IET IE ALEINEN +Eval: S S S S + +Speaker sentences 40: cv_deu_000737 #utts: 1 +id: (cv_deu_000737-cv_deu_000737) +Scores: (#C #S #D #I) 1 9 2 0 +REF: DUMM IST WER ETWAS WEISS ABER TROTZ des BESSEREN WISSENS FALSCH HANDELT +HYP: **** N DBUM IES BERTWAS BWEIS WUERTROST des ******** DESUÖRN BISENS VELCHUNIE +Eval: D S S S S S S D S S S + +Speaker sentences 41: cv_deu_000738 #utts: 1 +id: (cv_deu_000738-cv_deu_000738) +Scores: (#C #S #D #I) 1 9 1 0 +REF: HAUPTTHEMA DER SHOW ist DIE REVANCHE FÜR ÜBLE STREICHE UNTER FREUNDEN +HYP: HARBTEMA ER SCHEO ist *** DE REWANSC VEREGÜNBIC HISTREI HERENTE PFREINEN +Eval: S S S D S S S S S S + +Speaker sentences 42: cv_deu_000739 #utts: 1 +id: (cv_deu_000739-cv_deu_000739) +Scores: (#C #S #D #I) 0 5 0 0 +REF: GLEICHZEITIG WURDEN SPORTWETTEN TEILWEISE VERBOTEN +HYP: LEICHZEITIGH WUONDEN SPROTDWERTEN TALLWEISE VERBULHEN +Eval: S S S S S + +Speaker sentences 43: cv_deu_000740 #utts: 1 +id: (cv_deu_000740-cv_deu_000740) +Scores: (#C #S #D #I) 0 1 0 3 +REF: * * *** SIEBEN +HYP: D A SEN HSFSSANAAFAAAE +Eval: I I I S + +Speaker sentences 44: cv_deu_000741 #utts: 1 +id: (cv_deu_000741-cv_deu_000741) +Scores: (#C #S #D #I) 0 1 0 4 +REF: **** ***** * * JA +HYP: OTST IERER N N NFBENN +Eval: I I I I S + +Speaker sentences 45: cv_deu_000742 #utts: 1 +id: (cv_deu_000742-cv_deu_000742) +Scores: (#C #S #D #I) 0 9 4 0 +REF: ZUDEM VERSAH ER IM KLOSTER LANGE JAHRE DIE ÄMTER DES NOVIZENMEISTERS UND PRIOR +HYP: ***** ****** ** ** ZU DEM FARSA EIERLEM KLOSTALNG JARE DEMTERDESE NOWITZEN MEISTASUNPRIOR +Eval: D D D D S S S S S S S S S + +Speaker sentences 46: cv_deu_000743 #utts: 1 +id: (cv_deu_000743-cv_deu_000743) +Scores: (#C #S #D #I) 1 3 0 3 +REF: ****** ****** HEIDENHAIN ENTSTAMMTE einer ***** ÄRZTEFAMILIE +HYP: HEIDEN HEIDEN EN STMT einer ERZTE VERMIEER +Eval: I I S S I S + +Speaker sentences 47: cv_deu_000744 #utts: 1 +id: (cv_deu_000744-cv_deu_000744) +Scores: (#C #S #D #I) 0 1 0 2 +REF: * **** ACHT +HYP: D ARER TZUEGKTENEN +Eval: I I S + +Speaker sentences 48: cv_deu_000745 #utts: 1 +id: (cv_deu_000745-cv_deu_000745) +Scores: (#C #S #D #I) 0 1 0 1 +REF: ****** ZWEI +HYP: ZWEIEL GNR +Eval: I S + +Speaker sentences 49: cv_deu_000746 #utts: 1 +id: (cv_deu_000746-cv_deu_000746) +Scores: (#C #S #D #I) 1 5 3 1 +REF: EBENFALLS IN AUGGEN ANGESIEDELT SIND DIE KELTEREI der * FA +HYP: ********* ** ****** EBEPFEALLEIN AUGN ENGEIETZEN IKLTEREIN der E AT +Eval: D D D S S S S I S + +Speaker sentences 50: cv_deu_000747 #utts: 1 +id: (cv_deu_000747-cv_deu_000747) +Scores: (#C #S #D #I) 0 7 3 0 +REF: DIESE STEHT AUCH FÜR ABSOLVENTEN EINHEIMISCHER SCHULEN MIT DEUTSCHKENNTNISSEN OFFEN +HYP: ***** ***** **** DIESERS DET AHFE ABPELWENDEN EINHRMSCHER CHOLMETARTSKNTDESEN OFEM +Eval: D D D S S S S S S S + +Speaker sentences 51: cv_deu_000748 #utts: 1 +id: (cv_deu_000748-cv_deu_000748) +Scores: (#C #S #D #I) 0 3 1 0 +REF: ALSO ICH HÖRE NICHTS +HYP: **** AL SO ECHIORENIGS +Eval: D S S S + +Speaker sentences 52: cv_deu_000749 #utts: 1 +id: (cv_deu_000749-cv_deu_000749) +Scores: (#C #S #D #I) 1 3 1 0 +REF: WIE KANN man SICH SCHÜTZEN +HYP: *** WEKON man SISCH SCUTZEN +Eval: D S S S + +Speaker sentences 53: cv_deu_000750 #utts: 1 +id: (cv_deu_000750-cv_deu_000750) +Scores: (#C #S #D #I) 3 6 4 0 +REF: NACH FÜNF MONATEN lag eine EMPFINDLICHERE PLATTE ALS DIE BIS DAHIN ERHÄLTLICHEN vor +HYP: **** AFÜINFMNERT N lag eine ************** ****** *** N FINDLICHERBEPLOTE ANZTDEBISTDAHEINER HLTLICHEN vor +Eval: D S S D D D S S S S + +Speaker sentences 54: cv_deu_000751 #utts: 1 +id: (cv_deu_000751-cv_deu_000751) +Scores: (#C #S #D #I) 0 5 7 0 +REF: ZIEL IST ES DIE ÜBEREINSTIMMUNG EINES SOFTWARESYSTEMS MIT SEINER SPEZIFIKATION ZU ÜBERPRÜFEN +HYP: **** *** ** *** **************** ***** *************** TILIE TERS DIEBW INSTNMUNG INESOFTENSNSTEBZSMINTSANASCGIEZICHIKAZHOUNZUNBABPRLFMCH +Eval: D D D D D D D S S S S S + +Speaker sentences 55: cv_deu_000752 #utts: 1 +id: (cv_deu_000752-cv_deu_000752) +Scores: (#C #S #D #I) 0 7 5 0 +REF: MIT EINEM WARMEN GETRÄNK IM BAUCH LÄSST SICH DIE KÄLTE BESSER AUSHALTEN +HYP: *** ***** ****** ******** ** ATENETHEILEN WAHAMEN GEN SHEN IHENDALUS WESZISTIKELNTELLN BESORAESHEILTEN +Eval: D D D D D S S S S S S S + +Speaker sentences 56: cv_deu_000753 #utts: 1 +id: (cv_deu_000753-cv_deu_000753) +Scores: (#C #S #D #I) 1 10 1 1 +REF: ******** DIE ANTIVIRENSOFTWARE ist AMOK GELAUFEN UND HAT ALLE COMPUTER IM HAUS LAHMGELEGT +HYP: DIEANDIE WERBEN SOSCHÖHR ist **** ANR KEN GEL RUSCHEN ND AND ALER GBNBHENTELNRSSLANGELIGT +Eval: I S S D S S S S S S S S + +Speaker sentences 57: cv_deu_000754 #utts: 1 +id: (cv_deu_000754-cv_deu_000754) +Scores: (#C #S #D #I) 1 6 0 2 +REF: *** IHRE KLOAKE ist ** IN DIESER ZEIT KUGELFÖRMIG +HYP: IER TU ARGE ist EN DIESAR ZEI RT GUNEFLANENSC +Eval: I S S I S S S S + +Speaker sentences 58: cv_deu_000755 #utts: 1 +id: (cv_deu_000755-cv_deu_000755) +Scores: (#C #S #D #I) 1 12 0 4 +REF: * die ******* ***** ******* STRECKE BEGINNT IM SÜDEN VERONAS UND FÜHRT DURCH DIE POEBENE RICHTUNG SÜDOSTEN +HYP: E die SCEÄKE BEGEN MSÜGEN BERHUMARS IN FÜLTIS DIE GO B DE EICHT UMEG AR SIÜT AUSSTE +Eval: I I I I S S S S S S S S S S S S + +Speaker sentences 59: cv_deu_000756 #utts: 1 +id: (cv_deu_000756-cv_deu_000756) +Scores: (#C #S #D #I) 6 3 0 1 +REF: erst von dort KONNTE er SEINEN weg frei * FORTSETZEN +HYP: erst von dort KONTE er EN weg frei E VOTZSEITZSEN +Eval: S S I S + +Speaker sentences 60: cv_deu_000757 #utts: 1 +id: (cv_deu_000757-cv_deu_000757) +Scores: (#C #S #D #I) 1 8 3 0 +REF: SIE ERHEBT SICH heute IMMER NOCH GUT ERKENNBAR AUS DEM SCHWEMMLAND HERAUS +HYP: *** ****** SIERHEBZICH heute ***** IMERNOGUTERKENBER T AUSSTDEIEN SCSH WEM LANT HEAUS +Eval: D D S D S S S S S S S + +Speaker sentences 61: cv_deu_000758 #utts: 1 +id: (cv_deu_000758-cv_deu_000758) +Scores: (#C #S #D #I) 0 5 1 0 +REF: DIE KANARISCHEN INSELN GEHÖREN ZU SPANIEN +HYP: *** DIEKANARESCHEN INEN E HANZU SPBERCHE +Eval: D S S S S S + +Speaker sentences 62: cv_deu_000759 #utts: 1 +id: (cv_deu_000759-cv_deu_000759) +Scores: (#C #S #D #I) 1 8 0 1 +REF: **** WISSENSCHAFTLER HABEN diese MUTATION BISHER NUR BEI FRAUEN BEOBACHTET +HYP: WESN SCHR FLERHRBEN diese MU TDTZ UN ESEHRENEOBEI FOARAN BEREBATET +Eval: I S S S S S S S S + +Speaker sentences 63: cv_deu_000760 #utts: 1 +id: (cv_deu_000760-cv_deu_000760) +Scores: (#C #S #D #I) 1 5 1 1 +REF: SEINE GESCHÄFTSBEZIEHUNGEN REICHTEN bis *** NORDAMERIKA UND ASIEN +HYP: ***** SENIGISCHETZSPETZIUNEN REISCSTEN bis NOR DAMEHRIKA END ARSIEN +Eval: D S S I S S S + +Speaker sentences 64: cv_deu_000761 #utts: 1 +id: (cv_deu_000761-cv_deu_000761) +Scores: (#C #S #D #I) 2 10 0 3 +REF: ******* ZAHLREICHE VORDERE PLATZIERUNGEN bei DEUTSCHEN EUROPA und ********** ******* WELTMEISTERSCHAFTEN SOWIE OLYMPISCHEN SPIELEN FOLGTEN +HYP: AREICHE VORDERED MAETZIERUN EN bei DUTSCHEN EROPAR und WEHTLECSTE SCAFTEN ZO BIE NLÜBICHEN SPILEN VORLKTEN +Eval: I S S S S S I I S S S S S + +Speaker sentences 65: cv_deu_000762 #utts: 1 +id: (cv_deu_000762-cv_deu_000762) +Scores: (#C #S #D #I) 1 7 1 0 +REF: in EINER TAGESZEITUNG BLÄTTERND SITZT SIEGFRIED AUF EINER PARKBANK +HYP: in ***** AN ERTELIED SOTUEM LETERMD SOE IGKIT AUFTERPARGBANG +Eval: D S S S S S S S + +Speaker sentences 66: cv_deu_000763 #utts: 1 +id: (cv_deu_000763-cv_deu_000763) +Scores: (#C #S #D #I) 1 7 4 0 +REF: MIT EINEM WARMEN GETRÄNK IM BAUCH LÄSST SICH die KÄLTE BESSER AUSHALTEN +HYP: *** ***** ****** IT IUM WAMITRENG IN BAUPLSSI die ****** KERLTE BESAUSHADIE +Eval: D D D S S S S S D S S + +Speaker sentences 67: cv_deu_000764 #utts: 1 +id: (cv_deu_000764-cv_deu_000764) +Scores: (#C #S #D #I) 0 3 0 2 +REF: ***** ** FOLGE DEM QUERVERWEIS +HYP: WOENE EN T ER HEWESSI +Eval: I I S S S + +Speaker sentences 68: cv_deu_000765 #utts: 1 +id: (cv_deu_000765-cv_deu_000765) +Scores: (#C #S #D #I) 1 8 2 0 +REF: OSTERN IST IMMER eine WOCHE NACH DEM ERSTEN VOLLMOND IM FRÜHLING +HYP: ****** AOSTANIS ENE eine ***** BOCHENACHTEN ESTEN UE MUN DIN RLULEN +Eval: D S S D S S S S S S + +Speaker sentences 69: cv_deu_000766 #utts: 1 +id: (cv_deu_000766-cv_deu_000766) +Scores: (#C #S #D #I) 0 8 0 0 +REF: IM MITTELALTER HATTEN WECHSELNDE HERRSCHAFTEN DAS DORF INNE +HYP: ENM MITEL EITERHERTEN EÄKZIN DE HERSCHAFT DASTDAFH INER +Eval: S S S S S S S S + +Speaker sentences 70: cv_deu_000767 #utts: 1 +id: (cv_deu_000767-cv_deu_000767) +Scores: (#C #S #D #I) 1 6 2 0 +REF: den NAMEN GHIBLI TRAGEN AUCH WEITERE FAHRZEUGE VON MASERATI +HYP: den ***** ****** NAMSCHEPLE TRAR NAOCHLEITE EFATSOLGE VER MASSERHATI +Eval: D D S S S S S S + +Speaker sentences 71: cv_deu_000768 #utts: 1 +id: (cv_deu_000768-cv_deu_000768) +Scores: (#C #S #D #I) 0 6 5 0 +REF: DU KANNST MIT DEM BUS NACH FRANKFURT AN DER ODER FAHREN +HYP: ** ****** *** *** *** PLUKANEMIT E BUSLOCHAN VON ER ONDEREFHOREN +Eval: D D D D D S S S S S S + +Speaker sentences 72: cv_deu_000769 #utts: 1 +id: (cv_deu_000769-cv_deu_000769) +Scores: (#C #S #D #I) 0 2 1 0 +REF: MIR DOCH EGAL +HYP: *** NER EORIEGOL +Eval: D S S + +Speaker sentences 73: cv_deu_000770 #utts: 1 +id: (cv_deu_000770-cv_deu_000770) +Scores: (#C #S #D #I) 1 9 4 1 +REF: ********** ALLERDINGS ERGABEN WEITERE PRÜFUNGEN DASS es MITTELFRISTIG KEINEN BEDARF FÜR EINE SOLCHE AUTOBAHN GÄBE +HYP: ALLERTINGS ER GAHBEN WEITERIE PUÜNFVONGEN DAS es ************* ****** ****** **** MITELFRISTIKEIN PIDAFISCEUCHE AUTUOBAN DEHRER +Eval: I S S S S S D D D D S S S S + +Speaker sentences 74: cv_deu_000771 #utts: 1 +id: (cv_deu_000771-cv_deu_000771) +Scores: (#C #S #D #I) 0 8 2 0 +REF: UMGEKEHRT KANN EIN FREIBRIEF EINE AUSSCHREIBUNG ALS VOGELFREI GEMEINT SEIN +HYP: ********* **** UNGEKRT KAN EN FREIPRIEFF EINEHARAUSCHREIBUNG ALZ VOBELEFREI GEMEINZEIN +Eval: D D S S S S S S S S + +Speaker sentences 75: cv_deu_000772 #utts: 1 +id: (cv_deu_000772-cv_deu_000772) +Scores: (#C #S #D #I) 0 6 0 2 +REF: ******* ****** BIZARRGROTESKE ABSCHNITTE ZEIGEN EINFLÜSSE DURCH SCHOSTAKOWITSCH +HYP: MBIEZAR KORTES GE ABSCHNITER SEIEN EINPFLUSE DUIG SCHOSTDARKOLNWIESCSH +Eval: I I S S S S S S + +Speaker sentences 76: cv_deu_000773 #utts: 1 +id: (cv_deu_000773-cv_deu_000773) +Scores: (#C #S #D #I) 2 5 5 1 +REF: ER WAR EINER DER PIONIERE AUF DEM gebiet *********** der NUTZUNG DER SONNENENERGIE +HYP: ** *** ***** *** RER EINERDERPIAERNIERE AFTDM gebiet DERNUTZIUNG der ******* SONE NERGEEN +Eval: D D D D S S S I D S S + +Speaker sentences 77: cv_deu_000774 #utts: 1 +id: (cv_deu_000774-cv_deu_000774) +Scores: (#C #S #D #I) 0 8 5 0 +REF: AUCH WENN MIR DIE KUNDEN AUF DIE NERVEN GEHEN MUSS ICH HÖFLICHKEIT BEWAHREN +HYP: **** **** *** *** ****** ARHFEN MEDIE KONDEN AF INE RFENGEN MUSICHELICKET PBEWAN +Eval: D D D D D S S S S S S S S + +Speaker sentences 78: cv_deu_000775 #utts: 1 +id: (cv_deu_000775-cv_deu_000775) +Scores: (#C #S #D #I) 0 3 1 0 +REF: DIE SPÜLMASCHINE IST FERTIG +HYP: *** DICHBEMASCHINE ST VERTISH +Eval: D S S S + +Speaker sentences 79: cv_deu_000776 #utts: 1 +id: (cv_deu_000776-cv_deu_000776) +Scores: (#C #S #D #I) 2 7 1 0 +REF: in DER ARCHAISCHEN PERIODE wurden ERSTE FORMEN DES ACKERBAUS ENTWICKELT +HYP: in DE ARCHERISCHEN PERIONDE wurden ***** ERSTEIE VORMEN DESOCKEBASSNIN TUIKELLT +Eval: S S S D S S S S + +Speaker sentences 80: cv_deu_000777 #utts: 1 +id: (cv_deu_000777-cv_deu_000777) +Scores: (#C #S #D #I) 1 6 1 0 +REF: die KOMÖDIE SEI BESSER ALS DER ERSTE FILM +HYP: die ******** KOMÜDE SE BESE LTER ESTE FÜN +Eval: D S S S S S S + +Speaker sentences 81: cv_deu_000778 #utts: 1 +id: (cv_deu_000778-cv_deu_000778) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ******** AKTUELL GILT FOLGENDER MODUS +HYP: ARTZLERE GED VER NERMUMEMS ALP +Eval: I S S S S + +Speaker sentences 82: cv_deu_000779 #utts: 1 +id: (cv_deu_000779-cv_deu_000779) +Scores: (#C #S #D #I) 1 10 0 1 +REF: DAMIT ENDET eine *** ERFOLGREICHE INTERNATIONALE BILDUNGSARBEIT VOR ALLEM IM MUSISCHKULTURELLEN BEREICH +HYP: TDERMIT ENDE eine EAE WERKREISCH IE ENAR ZUNERLIENKÄERDENS ABEN VORELEN INMNSISCHKÜNTEN AUNENENZSKAU +Eval: S S I S S S S S S S S + +Speaker sentences 83: cv_deu_000780 #utts: 1 +id: (cv_deu_000780-cv_deu_000780) +Scores: (#C #S #D #I) 1 9 1 2 +REF: DER SOHN eines ********* ***** BERGMANNS BEGANN SEINE FUSSBALLKARRIERE BEI DEN SPORTFREUNDEN WANNEEICKEL +HYP: *** DERSON eines BEREGNANZ BEGAN EINI USPALKER JERI WEI DENSPURT FREINDTEN WANE EIKELN +Eval: D S I I S S S S S S S S + +Speaker sentences 84: cv_deu_000781 #utts: 1 +id: (cv_deu_000781-cv_deu_000781) +Scores: (#C #S #D #I) 1 9 0 3 +REF: in **** ******** ****** DIESEM JAHR GAB ES SIEBEN NUMMEREINSSINGLES UND SECHSUNDDREISSIG NUMMEREINSALBEN +HYP: in DIEN JARGABES SIEDEN NOMER EIEN SINGES UN SÄCHSUN REISZIG NOMER EIENS ALEBEN +Eval: I I I S S S S S S S S S + +Speaker sentences 85: cv_deu_000782 #utts: 1 +id: (cv_deu_000782-cv_deu_000782) +Scores: (#C #S #D #I) 1 7 0 2 +REF: * **** NORDWESTLICH VON HACKHAUSEN BEFINDET sich DIE ORTSCHAFT HACKENBROICH +HYP: N NORD WESTLICH VERN HARGHAUSEN BERFINDE sich IO ARTSCHAFTHAEKEN BOREUICSCH +Eval: I I S S S S S S S + +Speaker sentences 86: cv_deu_000783 #utts: 1 +id: (cv_deu_000783-cv_deu_000783) +Scores: (#C #S #D #I) 3 10 1 2 +REF: IM ort *** **** GNARRENBURG GINGEN VIELE SOZIALE EINRICHTUNGEN von HERMANN LAMPRECHT UND DER MARIENHÜTTE aus +HYP: IEM ort KGN NAEN BURG IENEN VIELESOS SIAE EINERICHTENUN von ******* EREMAN LAMPRÄCHT NDERMARIEN ÜTE aus +Eval: S I I S S S S S D S S S S + +Speaker sentences 87: cv_deu_000784 #utts: 1 +id: (cv_deu_000784-cv_deu_000784) +Scores: (#C #S #D #I) 4 7 1 1 +REF: ich werde FOLGLICH DEN rat ÜBER DIE IM PARLAMENT vorgetragenen ******** BEDENKEN INFORMIEREN +HYP: ich werde VOLKLCH DIN rat ***** BER DEM PALLMND vorgetragenen BETENTEN N VOMIEREN +Eval: S S D S S S I S S + +Speaker sentences 88: cv_deu_000785 #utts: 1 +id: (cv_deu_000785-cv_deu_000785) +Scores: (#C #S #D #I) 0 7 7 0 +REF: ES WÄRE TRAURIG GEWESEN EIN SO WICHTIGES THEMA NICHT IM KONSENS VERABSCHIEDEN ZU KÖNNEN +HYP: ** ***** ******* ******* *** ** ********* S IERETRAURSCGEWIEN EINSOWICHTDIUS THEMANICH EM KONDET HABSCHENZUKEN +Eval: D D D D D D D S S S S S S S + +Speaker sentences 89: cv_deu_000786 #utts: 1 +id: (cv_deu_000786-cv_deu_000786) +Scores: (#C #S #D #I) 2 8 2 0 +REF: NACH DESSEN TOD IM GLEICHEN jahr KAM ES KURZFRISTIG an ANDERE BESITZER +HYP: **** ****** NOCHTIHSEINMTEOT ENM KLEISCHEN jahr GAMES GUTZ WISTDIEKG an ANDERER DIESETZ +Eval: D D S S S S S S S S + +Speaker sentences 90: cv_deu_000787 #utts: 1 +id: (cv_deu_000787-cv_deu_000787) +Scores: (#C #S #D #I) 1 10 1 1 +REF: KURZ DANACH GAB ES einen ****** WERBESPOT MIT DEM CANCAN VON JACQUES OFFENBACH +HYP: **** KOTZ DANERCH GABIS einen WERBER BWORDT MIDEDENM T KAN KAMNDT UND SCHEKESHOCHENBACH +Eval: D S S S I S S S S S S S + +Speaker sentences 91: cv_deu_000788 #utts: 1 +id: (cv_deu_000788-cv_deu_000788) +Scores: (#C #S #D #I) 0 3 0 4 +REF: ** * ** *** DAS IST BESSER +HYP: ID D IT BEE HTSUSFSNUN NANN AE +Eval: I I I I S S S + +Speaker sentences 92: cv_deu_000789 #utts: 1 +id: (cv_deu_000789-cv_deu_000789) +Scores: (#C #S #D #I) 1 3 2 1 +REF: WIE SIEHT ES MIT GLEITZEIT aus * +HYP: *** ***** WESIE ISMND LEITZEIT aus H +Eval: D D S S S I + +Speaker sentences 93: cv_deu_000790 #utts: 1 +id: (cv_deu_000790-cv_deu_000790) +Scores: (#C #S #D #I) 2 8 2 1 +REF: NAHE dem ***** DORF BEFINDET SICH AUCH der GRAND CANYON NATIONAL PARK AIRPORT +HYP: NCHE dem DOCHF BEFIN DET ICH ARCH der ***** ****** KRM KENIUNASIHNALLEBPACKG ERBUOT +Eval: S I S S S S D D S S S + +Speaker sentences 94: cv_deu_000791 #utts: 1 +id: (cv_deu_000791-cv_deu_000791) +Scores: (#C #S #D #I) 2 6 2 0 +REF: SIE SOLLEN VERKÜNDEN DASS die liebe DEN TOD BESIEGT HAT +HYP: *** IESEREN DERKÜNDEN DES die liebe *** ENTOED BESIKT ART +Eval: D S S S D S S S + +Speaker sentences 95: cv_deu_000792 #utts: 1 +id: (cv_deu_000792-cv_deu_000792) +Scores: (#C #S #D #I) 0 9 0 3 +REF: ******* ***** *** BEDECKT IST DIE REPRÄSENTATIV GESTALTETE VILLA MIT EINEM MANSARDDACH +HYP: BETECKT ISTDI REB PRENSENTHERTIEF GESTALTE E WELER MITD EINEN MAN SART DACH +Eval: I I I S S S S S S S S S + +Speaker sentences 96: cv_deu_000793 #utts: 1 +id: (cv_deu_000793-cv_deu_000793) +Scores: (#C #S #D #I) 0 7 1 0 +REF: DIESE SIEDLUNG IST MIT DER ORTSCHAFT DELLACH ZUSAMMENGEWACHSEN +HYP: ***** DIESESIE UN IESMITER ARTSCHAFT DELECH ZUSAMMN GEWAKNZEN +Eval: D S S S S S S S + +Speaker sentences 97: cv_deu_000794 #utts: 1 +id: (cv_deu_000794-cv_deu_000794) +Scores: (#C #S #D #I) 0 7 0 0 +REF: WART IHR SCHON EINMAL IN DEM CLUB +HYP: I WARDIE SHN EINALEN EN KLOBE S +Eval: S S S S S S S + +Speaker sentences 98: cv_deu_000795 #utts: 1 +id: (cv_deu_000795-cv_deu_000795) +Scores: (#C #S #D #I) 2 3 1 0 +REF: WO RAUCH ist IST auch FEUER +HYP: ** BUORANGE ist ISTD auch VERIOERET +Eval: D S S S + +Speaker sentences 99: cv_deu_000796 #utts: 1 +id: (cv_deu_000796-cv_deu_000796) +Scores: (#C #S #D #I) 2 11 1 3 +REF: DIREKT VON DER strasse WURDEN SIE von ******* ******* ******* ALFRED BIOLEK FÜR SEINE FERNSEHSHOW SHOWBÜHNE ENGAGIERT +HYP: ****** DEHEXT VONDER strasse BU ENSI von ALTFTET DIONLEG ISEINER FWESTEN SE SCHO UE SCHOBEBILNER EN EÜSIEALTT +Eval: D S S S S I I I S S S S S S S + +Speaker sentences 100: cv_deu_000797 #utts: 1 +id: (cv_deu_000797-cv_deu_000797) +Scores: (#C #S #D #I) 0 11 1 0 +REF: EIN JAHR SPÄTER WECHSELTE ER ZU HEALTH NET UND ER WURDE ERFOLGREICHER +HYP: *** AI HARSCPEITER ESSLLTER ERTZUN ELF NATZS UN BER WUDE LR ANGEREISCHE +Eval: D S S S S S S S S S S S + +Speaker sentences 101: cv_deu_000798 #utts: 1 +id: (cv_deu_000798-cv_deu_000798) +Scores: (#C #S #D #I) 3 6 0 0 +REF: in DER LANDWIRTSCHAFT KANN der ERTRAG DEUTLICH REDUZIERT werden +HYP: in DERLERN VITHE KERN der ERTRARET DEUTLI EDEOTZIERT werden +Eval: S S S S S S + +Speaker sentences 102: cv_deu_000799 #utts: 1 +id: (cv_deu_000799-cv_deu_000799) +Scores: (#C #S #D #I) 2 6 1 0 +REF: MANSOUR SPIELTE in SEINER HEIMATSTADT KAIRO FÜR al AHLY +HYP: MAIN SURSPIERTE in ****** SEINERHEIMAUTSTADT KEIE UOFIER al ALE +Eval: S S D S S S S + +Speaker sentences 103: cv_deu_000800 #utts: 1 +id: (cv_deu_000800-cv_deu_000800) +Scores: (#C #S #D #I) 1 5 0 3 +REF: ER TRAT der **************** * * FREIMAURERLOGE LAUTARO BEI +HYP: COH DERTRAR der REIMAUHALUNUNDEN I N TABEBEI SI HAH +Eval: S S I I I S S S + +Speaker sentences 104: cv_deu_000801 #utts: 1 +id: (cv_deu_000801-cv_deu_000801) +Scores: (#C #S #D #I) 2 10 2 0 +REF: mit „FÜRST“ WAR eher DIE SOZIALE ALS DIE RECHTLICHE ROLLE DES SO BEZEICHNETEN GEMEINT +HYP: mit FÜÖOST WER eher *** ******* DE ROWTFIADE EITS ERECHT ICHERALLE DE UONBEDSHEICENEN EMEINT +Eval: S S D D S S S S S S S S + +Speaker sentences 105: fleurs_deu_000378 #utts: 1 +id: (fleurs_deu_000378-fleurs_deu_000378) +Scores: (#C #S #D #I) 4 19 3 2 +REF: LETZTE WOCHE GAB DAS meti BEKANNT DASS ES VON APPLE Über ****** **** 34 WEITERE VORFÄLLE von ÜBERHITZUNG INFORMIERT WORDEN WAR DIE DAS UNTERNEHMEN als NICHT SCHWERWIEGEND BEZEICHNETE +HYP: ****** ETZT ERVOCHU GABDAS meti ******* BEKANDT DASSES HON EPEL Über FIERND ASCH WALTE E VORFELE von BER ITZUN NTOMIRT ODENVWER IDES NDER NEHNEN als ***** NCHT SHIERIENKEEITETE +Eval: D S S S D S S S S I I S S S S S S S S S S D S S + +Speaker sentences 106: fleurs_deu_000379 #utts: 1 +id: (fleurs_deu_000379-fleurs_deu_000379) +Scores: (#C #S #D #I) 4 27 0 4 +REF: ******* ************ USA GYMNASTICS UNTERSTÜTZT den BRIEF des ** OLYMPISCHEN KOMITEES der VEREINIGTEN STAATEN UND AKZEPTIERT ES ALS ABSOLUTE NOTWENDIGKEIT DASS SICH DIE OLYMPISCHE FAMILIE FÜR EIN sicheres *** UMFELD FÜR ALLE UNSERE SPORTLER EINSETZT +HYP: SIEBEBE NIJEÖESEÄE SCHE NENSZIGIGS UNDERSCÜZSTDE den BIHIF des UN ÜNBISCHEN KOMITIS der VER EINIGTEN STATEN UN DAR SIPTIERT ESAS APTUTENOTWENDIGKEIT DASIH IE UN LÜNBISCHE VERNMIIEN FÜ EN sicheres UNF WELT ZFÜE ALE UNSERER SPOTLER EINSEST +Eval: I I S S S S I S S S S S S S S S S S S S S S S S I S S S S S S + +Speaker sentences 107: fleurs_deu_000380 #utts: 1 +id: (fleurs_deu_000380-fleurs_deu_000380) +Scores: (#C #S #D #I) 1 15 0 3 +REF: ********* ****** ************** DADURCH KANN ER ABWÄRTSKOMPATIBEL MIT 80211A 80211B und 80211G SEIN VORAUSGESETZT DIE BASISSTATION VERFÜGT ÜBER DUALRADIO +HYP: ALICHKENE ABPIET KOMPETIEBELMET ACHTNATZWEI BUND ELF AR ACHTNRTZWEI BUND ELFBE und CHTNETZWEI PUND ELFGESEIN VERSGES DIEBASISTATIUN VERFÜGK BER DUOALRADIE +Eval: I I I S S S S S S S S S S S S S S S + +Speaker sentences 108: fleurs_deu_000381 #utts: 1 +id: (fleurs_deu_000381-fleurs_deu_000381) +Scores: (#C #S #D #I) 1 5 3 0 +REF: ER BEZEICHNETE die GERÜCHTE ALS POLITISCHES GESCHWÄTZ UND ALBERNHEIT +HYP: ** JERBIEZEISHENS die ********* *** GERICHTERALSPLICICHESGESCHWÄTS UNDT ALL BENHEITZS +Eval: D S D D S S S S + +Speaker sentences 109: fleurs_deu_000382 #utts: 1 +id: (fleurs_deu_000382-fleurs_deu_000382) +Scores: (#C #S #D #I) 1 20 5 0 +REF: LETZTE WOCHE GAB DAS METI BEKANNT DASS ES von APPLE ÜBER 34 WEITERE VORFÄLLE VON ÜBERHITZUNG INFORMIERT WORDEN WAR DIE DAS UNTERNEHMEN ALS NICHT SCHWERWIEGEND BEZEICHNETE +HYP: ****** ***** *** LT ERWUCHRGABT AS EMIEITHE BEKANDASIS von ***** ***** EBL WERFIERNDRESIE WEITRIE VOHFELEVON BERHITZUN IN VOMIERT WORDENWA IEDAS UNTER NEM ALZ NICHCH VERIGE BTZEICTETE +Eval: D D D S S S S S D D S S S S S S S S S S S S S S S + +Speaker sentences 110: fleurs_deu_000383 #utts: 1 +id: (fleurs_deu_000383-fleurs_deu_000383) +Scores: (#C #S #D #I) 0 12 6 0 +REF: NACHDEM DER DAMM 1963 ERBAUT WORDEN WAR KAMEN DIE JAHRESZEITLICHEN ÜBERFLUTUNGEN DIE SEDIMENTE IM FLUSS VERTEILEN ZUM STILLSTAND +HYP: ******* *** **** **** ****** ****** NACH DIM DERDEMM EUNHNUNDERTREIUNSECHZIC BAUTWRDENWAR KAM DIARESZELIGHN BEPFLUTUN DESE DEMENTE MNPLSVERTELNZUM SHÖLSTEN +Eval: D D D D D D S S S S S S S S S S S S + +Speaker sentences 111: fleurs_deu_000384 #utts: 1 +id: (fleurs_deu_000384-fleurs_deu_000384) +Scores: (#C #S #D #I) 2 17 8 0 +REF: ER WAR AUCH AM STECHEN VON GELDSCHEINEN FÜR VIELE LÄNDER BETEILIGT AKTUELLE BEISPIELE SEINER ARBEIT SCHLIESSEN DIE PREMIERMINISTERPORTRAITS AUF der VORDERSEITE DER KANADISCHEN 5 UND 100DOLLARNOTEN ein +HYP: ** *** **** ** ******* *** ************ **** ERWAUH MSTECHE VN GELSCHEINVE ILE ENDE BETEILICHT AKTU LEBEISCHPIESAN AHRBESCHLISENIBPREMJHMINISTER PRTRISAF der VORDESERT DE KANADSCHEN FÜN N UNDERTOLLENUTEN ein +Eval: D D D D D D D D S S S S S S S S S S S S S S S S S + +Speaker sentences 112: fleurs_deu_000385 #utts: 1 +id: (fleurs_deu_000385-fleurs_deu_000385) +Scores: (#C #S #D #I) 2 9 6 0 +REF: DIE HAUPTSTADT VON MOLDAWIEN ist KISCHINAU die EINHEIMISCHE SPRACHE IST RUMÄNISCH ABER VIELE MENSCHEN SPRECHEN AUCH RUSSISCH +HYP: DI HAUPTST VERMR DAWIEN ist KHENEN die ************ ******* *** ********** **** ***** INEIMPISPBAHE ISTGUMENESC ABERFIELEMENTENSRECHEN EROSEH +Eval: S S S S S D D D D D D S S S S + +Speaker sentences 113: fleurs_deu_000386 #utts: 1 +id: (fleurs_deu_000386-fleurs_deu_000386) +Scores: (#C #S #D #I) 6 19 6 2 +REF: ZWISCHEN den ****** * EINZELNEN DYNASTIEN HERRSCHTEN AUCH UNBESTÄNDIGE zeiten GETEILTER PROVINZEN die BEKANNTESTE DIESER PERIODEN WAR DIE EPOCHE der DREI KÖNIGREICHE DIE 60 JAHRE LANG ZWISCHEN der han UND DER JINDYNASTIE STATTFAND +HYP: SISISEBETZWICHEN den EINZEN N BÜNESTDIEN HERSTEN AUOCH UN BESTENDIGER zeiten GETALTE PROEWENZEN die *********** ****** ******** BEKANTDISTDEDIESE PERIOEDEN WADIEEPOCHE der **** ************ *** DEIL GÜNINGEICHE DESEÄCHTZICH IEARRELANZEIHEN der han UN DEEEN DENESTISTAT VFVANT +Eval: S I I S S S S S S S D D D S S S D D D S S S S S S S S + +Speaker sentences 114: fleurs_deu_000387 #utts: 1 +id: (fleurs_deu_000387-fleurs_deu_000387) +Scores: (#C #S #D #I) 2 19 9 0 +REF: am ANDEREN ENDE DES SPEKTRUMS VERWANDELT man SICH IN EIN NICHT WIEDERZUERKENNENDES INDIVIDUUM DAS ALLES ANDERS MACHEN MUSS ALS DAS TEAM ES GEMACHT HAT UND SICH ALLES ZU EIGEN MACHT +HYP: am ******* ANDERE ENE ERSPEKTRUMS HRWEINE man **** ** *** ***** ******************* ********** *** ***** SICHEN EINICH WIDE ZUERKENDE INEWIEDE UM DS AES ANDR MACHENMOS ASERSTIEES GEMACTA UNDZSICH ALLESUO ELIGEMACHT +Eval: D S S S S D D D D D D D D S S S S S S S S S S S S S S S + +Speaker sentences 115: fleurs_deu_000388 #utts: 1 +id: (fleurs_deu_000388-fleurs_deu_000388) +Scores: (#C #S #D #I) 4 39 1 1 +REF: ***** DIE meisten INTERPRETATIONEN DES TECHNOLOGISCHEN DETERMINISMUS TEILEN ZWEI ALLGEMEINE VORSTELLUNGEN EINERSEITS DASS DIE ENTWICKLUNG DER TECHNOLOGIE SELBST EINEM WEG FOLGT DER WEITGEHEND JENSEITS KULTURELLER ODER POLITISCHER EINFLUSSNAHME LIEGT und andererseits DASS TECHNOLOGIE IHRERSEITS AUSWIRKUNGEN AUF GESELLSCHAFTEN HAT DIE eher INHÄRENT ALS SOZIAL BEDINGT SIND +HYP: DGIGI DE meisten IN DER PITEATZIONEN DESTECHEN LOGISCHEN DETE MINISENUSTA EN ZWEI AL GEMEINE VORSCTERLUNGEN EINESEITS DES DI IND ICGEM DERTICHEN LÜGIESEPSTEINEN WEGVOLGT DERWEIT GEND IENSEIS UN TOWELEORDERPULICISCH INPLSNAMENDIGT und andererseits **** DASTIGHNE ÖÜGE IERERSEITS AUSFWÖHRKEN EN AUFGESALSCAFTNARART DI eher IN HERERNT ASZUTSAL BE DENZINT +Eval: I S S S S S S S S S S S S S S S S S S S S S S S S S S S D S S S S S S S S S S S S + +Speaker sentences 116: fleurs_deu_000389 #utts: 1 +id: (fleurs_deu_000389-fleurs_deu_000389) +Scores: (#C #S #D #I) 5 21 5 0 +REF: ZWISCHEN den EINZELNEN DYNASTIEN HERRSCHTEN AUCH UNBESTÄNDIGE ZEITEN GETEILTER PROVINZEN DIE BEKANNTESTE DIESER perioden WAR DIE EPOCHE der DREI KÖNIGREICHE DIE 60 JAHRE LANG ZWISCHEN der han UND DER JINDYNASTIE STATTFAND +HYP: WÜSHE den ********* EINZENEN DNASTIEN HERSTEN AUR UNBESTENDIGETZEITEN GETEALTER ROWENZEN DE EKANDESTE SE perioden WAHDI E POCHREL der **** ************ *** E KÜNIGREICHE DESECHTZICHARLANGTZ WISCHEN der han *** UNTER IENDINASTITT VANDT +Eval: S D S S S S S S S S S S S S S D D D S S S S D S S S + +Speaker sentences 117: fleurs_deu_000390 #utts: 1 +id: (fleurs_deu_000390-fleurs_deu_000390) +Scores: (#C #S #D #I) 9 17 1 4 +REF: DEM LEAK ZUFOLGE BEZIEHT SICH DAS DOKUMENT auf den GRENZSTREIT in DEM die ***** PALÄSTINENSER ein ZURÜCKSETZEN der GRENZEN in den ******* ****** *** ZUSTAND vor DEM SECHSTAGEKRIEG VON 1967 FORDERN +HYP: *** DEMICKH ZU VORGIEBIEZIZICH ES TO GOMENT auf den GEÄNSTREIT in DEN die PALIS INENSER ein ZURÖGSALTZEN der GENZSEN in den ZUSTANT VORDEM SER STALEGRI vor NANZESN UNDERT SEBERNU SETIC VORDEN +Eval: D S S S S S S S S I S S S I I I S S S S S S + +Speaker sentences 118: fleurs_deu_000391 #utts: 1 +id: (fleurs_deu_000391-fleurs_deu_000391) +Scores: (#C #S #D #I) 2 13 2 0 +REF: mit DEM VERLUST GRIECHISCHER SPRACHKENNTNISSE WAR DER WESTEN von SEINEN PHILOSOPHISCHEN UND WISSENSCHAFTLICHEN WURZELN IN GRIECHENLAND ABGESCHNITTEN +HYP: mit *** ******* HEM PERLUSTGRECHE ERSPACHKENE ARDER ECSTEN von SEIEN VLESOFISCHEN UNDWISEN CHA LICHEN WOTZE INKIHENEN ABIESLETEN +Eval: D D S S S S S S S S S S S S S + +Speaker sentences 119: fleurs_deu_000392 #utts: 1 +id: (fleurs_deu_000392-fleurs_deu_000392) +Scores: (#C #S #D #I) 1 18 14 0 +REF: WIR STIMMEN MIT DER AUSSAGE DES USOC ÜBEREIN DASS DEN INTERESSEN UNSERER ATHLETEN UND VEREINE UND IHRES SPORTS BESSER GEDIENT IST WENN WIR INNERHALB UNSERER ORGANISATION SINNVOLLE VERÄNDERUNGEN vorantreiben ANSTATT EINE DEZERTIFIZIERUNG VORZUNEHMEN +HYP: *** ******* *** *** ******* *** **** ******** **** *** ********** ******* ******** ERSTEMITERAUSSAGDES IÖRS USIE BEINDASTDEN N RESENUN SRATLEDEN VEREINUNDERSPOTSBPESRGE DIENDISD BENWER NEHALBUNS RARGE SATIOUNDHN VLE VERINDRUNG vorantreiben ******* ANST EINERTITZSRIT ZTVITIERUNGVOTZN +Eval: D D D D D D D D D D D D D S S S S S S S S S S S S S S S D S S S + +Speaker sentences 120: fleurs_deu_000393 #utts: 1 +id: (fleurs_deu_000393-fleurs_deu_000393) +Scores: (#C #S #D #I) 4 13 5 1 +REF: DIE KREUZFAHRTEN NACH SANKT PETERSBURG bieten auch zeit FÜR EINEN AUFENTHALT in ******** DER STADT KREUZFAHRTPASSAGIERE SIND VON DER VISUMPFLICHT BEFREIT SIEHE BEDINGUNGEN +HYP: *** ************ **** IGREZFATENAENG PIGESBOG bieten auch zeit **** ***** ÜÖR in AUFENTAL INERSTAT KGREUTZFER PASRSHERE SEIN V NDE IEUNSLICH BEREIT SIE BEDENGEN +Eval: D D D S S D D S I S S S S S S S S S S + +Speaker sentences 121: fleurs_deu_000394 #utts: 1 +id: (fleurs_deu_000394-fleurs_deu_000394) +Scores: (#C #S #D #I) 4 17 2 4 +REF: ***** **** *** ** REISENDE WERDEN DRINGEND GEWARNT AUF JEDWEDE ART von UNWETTER ZU achten die IHR GEBIET BETRIFFT DA DIES SICH auf ALLE REISEPLÄNE AUSWIRKEN KANN +HYP: SCSCT EREI SEN DE VERDEN RINGEND GEWANDTKAUF IE WIE DE AT von UN WENTEZU achten die *** E GEBI BIERIFT DADNDIS SIGH auf **** ALEREISEPLENE AOSIEREN KON +Eval: I I I I S S S S S S S S S D S S S S S D S S S + +Speaker sentences 122: fleurs_deu_000395 #utts: 1 +id: (fleurs_deu_000395-fleurs_deu_000395) +Scores: (#C #S #D #I) 1 20 2 0 +REF: SIE BESAGT DASS DER KREUZUNGSPUNKT der LINIEN DIE EIN BILD VERTIKAL UND HORIZONTAL DRITTELN DER EFFEKTIVSTE PLATZ FÜR DAS HAUPTMOTIV IST SIEHE BEISPIEL +HYP: *** SICHRTR ISE BESERT DAS der ****** GKOLTZUNGSPBUNKTD DERLENMEN DE IN BEE WERDIGKALE UNTHURE HONTAL DRITEN DE EHIGK ISTDPLATS FÜITESHOUPT MNOIE ISTSIE BEISCHEN +Eval: D S S S S D S S S S S S S S S S S S S S S S + +Speaker sentences 123: fleurs_deu_000396 #utts: 1 +id: (fleurs_deu_000396-fleurs_deu_000396) +Scores: (#C #S #D #I) 2 22 11 1 +REF: SEIT 1988 MÜSSEN WAHLURNEN TRANSPARENT SEIN DAMIT WÄHLER und BEOBACHTER BEZEUGEN KÖNNEN DASS ZU BEGINN DER WAHL KEINE UMSCHLÄGE VORHANDEN SIND UND DASS KEINE UMSCHLÄGE EINGEWORFEN werden ***** AUSSER JENE DER ORDNUNGSGEMÄSS GEZÄHLTEN UND AUTORISIERTEN WÄHLER +HYP: **** **** ******* EIT NUNZEUNRT ACHEN ACHTZIHMISTN WAL und ********** ******** ******* **** ** ****** *** **** RANSBPRENZSEINDAMIT WEE NDBOBACTERBIETZOEUGENGKEN DAS WEGNDERWAL EIN MSCHLIGEVWANENSIND UDASKEINUMSCHLÄGE INGEVAOFEN werden AUSER EHNE DERT OTDEUNGS MES K SELTET ATRESIERTEN EE +Eval: D D D S S S S S D D D D D D D D S S S S S S S S S I S S S S S S S S + +Speaker sentences 124: fleurs_deu_000397 #utts: 1 +id: (fleurs_deu_000397-fleurs_deu_000397) +Scores: (#C #S #D #I) 4 19 0 4 +REF: OTTAWA ist ******** KANADAS BEZAUBERNDE ZWEISPRACHIGE HAUPTSTADT UND ZEICHNET SICH DURCH EINE REIHE VON KUNSTGALERIEN und MUSEEN aus ************ *** DIE KANADAS VERGANGENHEIT und **** GEGENWART PRÄSENTIEREN +HYP: OTERER ist KANERDES BET ZSAOUBEN DE ZWEISCHEALI GE HAUPTSTAT UNDZSELTEN DICH ICH EINEREIE UN KUNZSTDGEERIEN und MOSEEN aus DIEKANENDERS VER GAN GEN HEIT und GGEN WART PRESINTIEREN +Eval: S I S S S S S S S S S S S S S I I S S S I S S + +Speaker sentences 125: fleurs_deu_000398 #utts: 1 +id: (fleurs_deu_000398-fleurs_deu_000398) +Scores: (#C #S #D #I) 1 6 4 0 +REF: diese PAARE KÖNNEN SICH FÜR EINEN ADOPTIONSPLAN FÜR IHR BABY ENTSCHEIDEN +HYP: diese ***** ******* **** **** PARE KENSICH FVEINADEBTIONSPLAN V ERBEBE ENSHEIDEN +Eval: D D D D S S S S S S + +Speaker sentences 126: fleurs_deu_000399 #utts: 1 +id: (fleurs_deu_000399-fleurs_deu_000399) +Scores: (#C #S #D #I) 1 12 3 0 +REF: INFOLGEDESSEN SIND ZWEI FISCHARTEN AUSGESTORBEN UND ZWEI WEITERE SIND vom AUSSTERBEN BEDROHT DARUNTER DER GILA CYPHA +HYP: IN VOL EDESENEINZ WREI FISCH ABEN AUSGSTOLM DZWEAWEIT RISEN vom ********** ******* ******** AUSTERBEMBETRORT TAUNDERDER JLAZIÜFVER +Eval: S S S S S S S S S D D D S S S + +Speaker sentences 127: fleurs_deu_000400 #utts: 1 +id: (fleurs_deu_000400-fleurs_deu_000400) +Scores: (#C #S #D #I) 2 12 6 1 +REF: PFLANZEN SEHEN IN IHRER NATÜRLICHEN UMGEBUNG am BESTEN AUS WIDERSTEHEN SIE ALSO DER VERSUCHUNG AUCH NUR ein * EXEMPLAR ZU ENTFERNEN +HYP: ******** ***** REANZEN SEN NJHERENERTÖLIHE MNGEBEN am ****** *** *********** *** ETENAUS IERSTEN SIEALSO DERERSCHUNG AUCHNUR ein E EMKLA ENDT VERN +Eval: D D S S S S D D D D S S S S S I S S S + +Speaker sentences 128: fleurs_deu_000401 #utts: 1 +id: (fleurs_deu_000401-fleurs_deu_000401) +Scores: (#C #S #D #I) 2 18 3 0 +REF: auf DER NAHSEITE KÖNNTE ES MEHR MARIA geben DA DIE KRUSTE DÜNNER IST ES WAR EINFACHER FÜR DIE LAVA AN DIE OBERFLÄCHE AUFZUSTEIGEN +HYP: auf *** ER NARSEITE KNTE IS MERMRIER geben ** *** DDIEKROSTE DNEST ISWR EIN VERA F DE LA VER ANDI OBEPLICH AUFTSTDEGEN T +Eval: D S S S S S D D S S S S S S S S S S S S S + +Speaker sentences 129: fleurs_deu_000402 #utts: 1 +id: (fleurs_deu_000402-fleurs_deu_000402) +Scores: (#C #S #D #I) 3 20 0 3 +REF: ER FÜGTE HINZU DASS SIE JEDOCH NICHT DAZU AUFGEFORDERT werden sollten **** ********** VERPFLICHTUNGEN EINZUGEHEN DIE ÜBER IHREN ENTWICKLUNGSSTAND IHRE VERANTWORTUNG und ********* IHRE FÄHIGKEITEN HINAUSGEHEN +HYP: SGR E FÜGKTDECHEN ZUUN DASSIE DUOCH NICHTDERTZU AUFGIE VORDERT werden sollten FERT FICHTUNGEN EINDZUÜGEEN DE IÜEBER IEREN INTWILUNGSTAND IERE VER ANTORTUNG und IERERFÄE KETEN HENOARNS INGEN +Eval: S S S S S S S S S I I S S S S S S S S I S S S + +Speaker sentences 130: fleurs_deu_000403 #utts: 1 +id: (fleurs_deu_000403-fleurs_deu_000403) +Scores: (#C #S #D #I) 3 19 0 4 +REF: **** ******* *** VIRTUELLE HILFESTELLUNGEN SIND in ******* DIE SOFTWARE EINGEBAUT UND SOLLEN ARBEITSSCHRITTE DIE der SCHÜLER allein MÖGLICHERWEISE NICHT BEWÄLTIGEN KANN HINTERFRAGEN NAHELEGEN UND ERKLÄREN +HYP: SISI EWICETU ELE HIEL FISTEUNGEN ISENT in DIESOFT WER EINGE BUTDT UN SOEN ABELTSCHITE NDIE der SCHYLE allein MÜGLICHERWEISENIHT BEVETIGEN KARN HENTER FRAGEN NEIELEGEN UNDT DERKLEREN +Eval: I I I S S S I S S S S S S S S S S S S S S S S + +Speaker sentences 131: fleurs_deu_000404 #utts: 1 +id: (fleurs_deu_000404-fleurs_deu_000404) +Scores: (#C #S #D #I) 2 14 0 0 +REF: am 15 AUGUST 1940 FIELEN DIE ALLIIERTEN IN SÜDFRANKREICH ein DIE INVASION WURDE OPERATION DRAGOON GENANNT +HYP: am FÜNFZEN NAR GUSTNUNZHNHUDET VIRZIC FELIEALI ERTEN NSÜT RANKRAICH ein DIN WASION WRDE APERESCHE ER GUNGENERNDT +Eval: S S S S S S S S S S S S S S + +Speaker sentences 132: fleurs_deu_000405 #utts: 1 +id: (fleurs_deu_000405-fleurs_deu_000405) +Scores: (#C #S #D #I) 1 14 6 0 +REF: ER GRIFF AUCH ALLES an WAS INS WASSER KAM SELBST EIN GROSSER DINOSAURIER WIE DER T REX WAR IHM NICHT GEWACHSEN +HYP: ** ERRIF OCH ALLS an *** *** ****** *** ****** WASEN WASERKARM SEBT EN GROSER DEN SAURIE IDER TIEWEXS WEIMICHT GEWAKEN +Eval: D S S S D D D D D S S S S S S S S S S S + +Speaker sentences 133: fleurs_deu_000406 #utts: 1 +id: (fleurs_deu_000406-fleurs_deu_000406) +Scores: (#C #S #D #I) 2 12 6 0 +REF: SEIT DER GRÜNDUNG VON ASUNCIÓN 1537 IST ES PARAGUAY GELUNGEN viel von SEINEM INDIGENEN CHARAKTER UND SEINER IDENTITÄT ZU BEWAHREN +HYP: **** *** ********* *** ETERKRÜNDUNG VNASUNTZIOR FINHTZEN DESIENUNDREISIS SPAREG EGELUNG viel von ****** ********* SEIM IN DIGKEN KARACTER NDSEINEIDENDITETZ BEWAREN +Eval: D D D D S S S S S S D D S S S S S S + +Speaker sentences 134: fleurs_deu_000407 #utts: 1 +id: (fleurs_deu_000407-fleurs_deu_000407) +Scores: (#C #S #D #I) 3 28 0 15 +REF: ******** ***** TROTZDEM IST DER ANTEIL an **** ** ** ************ *** XDRTB in ** ********* **** ********* **** ** *** DER GESAMTEN GRUPPE DER LEUTE MIT TUBERKULOSE OFFENBAR DENNOCH GERING 6000 DER INSGESAMT 330000 LEUTE DIE in ******* SÜDAFRIKA ZU EINEM BESTIMMTEN ZEITPUNKT ANGESTECKT SIND +HYP: SISIBEBE DRTZS DEN IS DE ANTELL an IEGS DE ER BENDESTRIEGH TDE B in ER GESANTDEN GOPE DERLOLTEE MITD UG BER KULOSEN OFEN BAR DEN NOCH NGERING SEXS DAUSEN DE INZSGESAN DREI HUNDE DEISIG TAUESEN LOLTE DE in SÜTDAW RIKE WH EINEN BISTINTEN ZEITPBUNGKT ANGISTET ISENT +Eval: I I S S S S I I I I I S I I I I I I I S S S S S S S S S S S S S S S S I S S S S S S S + +Speaker sentences 135: fleurs_deu_000408 #utts: 1 +id: (fleurs_deu_000408-fleurs_deu_000408) +Scores: (#C #S #D #I) 0 14 1 0 +REF: ANGEL 2006 ERLÄUTERT DAS KONTINUUMKONZEPT ALS EINE METHODE UM ORGANISATIONEN ZU HELFEN LEISTUNGSFÄHIGER ZU WERDEN +HYP: ***** EHNSCHEL ZWEITAUSEN SECKS ELEUTERT AS KON TINUM KONDZEBTASEINEMITORDE M URGENSATIONZS E HLFN LESTUNGSFEGE ZEWEREN +Eval: D S S S S S S S S S S S S S S + +Speaker sentences 136: fleurs_deu_000409 #utts: 1 +id: (fleurs_deu_000409-fleurs_deu_000409) +Scores: (#C #S #D #I) 3 11 3 1 +REF: ***** in DIESER PERIODE der EUROPÄISCHEN GESCHICHTE STAND DIE REICH und MÄCHTIG GEWORDENE KATHOLISCHE KIRCHE AUF DEM PRÜFSTAND +HYP: SGSEE in DIESE BERIOEDEN der ************* UER ROEPEHENGICHIGTE MSTDANT DERICH und ******** ********* MEÄCHTIHEN E VODENER KARTONESIGIEHENAUFTEN PREÖSTANET +Eval: I S S D S S S S D D S S S S S + +Speaker sentences 137: fleurs_deu_000410 #utts: 1 +id: (fleurs_deu_000410-fleurs_deu_000410) +Scores: (#C #S #D #I) 2 20 12 0 +REF: DIE ERSTE DER 78 EMPFEHLUNGEN IST DASS EINE NEUE DIPLOMATISCHE INITIATIVE VOR ENDE DIESES JAHRES ERGRIFFEN WERDEN SOLLTE UM die IRAKISCHEN GRENZEN GEGENÜBER FEINDLICHEN INTERVENTIONEN ZU SICHERN und DIPLOMATISCHE BEZIEHUNGEN MIT SEINEN NACHBARN WIEDERHERZUSTELLEN +HYP: *** ***** *** ** ************ *** **** DI ERS DERCHENDIEBZICH M FÄELUNG ISTASEINENEUDEPLOMATSCHINITKETIPE VEREND DIEN JARSE GRIFEN WERDENSOLLTE M die ********** ******* ********** RAGSCEN GRENZENGEG BAEREINTLIE NTERETIONZUSICHERN und ************* *********** PLUMATSCH BTZE MITZSEINACHBANIE ERTZSTEN +Eval: D D D D D D D S S S S S S S S S S S S D D D S S S S D D S S S S + +Speaker sentences 138: fleurs_deu_000411 #utts: 1 +id: (fleurs_deu_000411-fleurs_deu_000411) +Scores: (#C #S #D #I) 3 12 6 0 +REF: DIES BIETET eine GUTE GELEGENHEIT DAS NORDLICHT ZU SEHEN DA DER HIMMEL MEHR ODER WENIGER rund um DIE UHR DUNKEL IST +HYP: **** DEESPBET eine **** *********** *** UTE GELEGEN HEIT DASNORTLCHZUSEN DE HEMEN MEHRUDE WEN GER rund um *** *** DI UERDUNKELEST +Eval: D S D D D S S S S S S S S S D D S S + +Speaker sentences 139: fleurs_deu_000412 #utts: 1 +id: (fleurs_deu_000412-fleurs_deu_000412) +Scores: (#C #S #D #I) 3 20 1 1 +REF: ************ PROFESSORIN PAMELA FERGUSON VON DER UNIVERSITY OF DUNDEE merkt an JOURNALISTEN SCHEINEN eine GEFÄHRLICHE GRENZE ZU ÜBERSCHREITEN WENN SIE FOTOS USW VON VERDÄCHTIGEN VERÖFFENTLICHEN +HYP: STKTPROSOREN PAMELER VER GUSSON VONDER UNE WÖOETI A DANDIE merkt an SOALISTEN SCHEIEN eine ************ GEÄERLIEGENZEU ERSREITEN WEN DIE POTO N SEWEITE VONERDECHTIE VE ENTEIHEN +Eval: I S S S S S S S S S S D S S S S S S S S S S + +Speaker sentences 140: fleurs_deu_000413 #utts: 1 +id: (fleurs_deu_000413-fleurs_deu_000413) +Scores: (#C #S #D #I) 1 14 9 0 +REF: ES KANN SICH AUCH LOHNEN EINE WILD CARD ZU KAUFEN DIE ZUTRITT ENTWEDER zu AUSGEWÄHLTEN PARKS IN SÜDAFRIKA ODER ZU ALLEN SÜDAFRIKANISCHEN NATIONALPARKS GEWÄHRT +HYP: ** **** **** **** ****** **** **** **** SKASI ACHLON EINEN EIL GKAR zu ************* KAUFENDIETZUTRIT EN WDERTORSGEWELTEN PAZENCHENT AFRIKARER TUOALNZHT AVRIKANCHENER ZNAL PAXSGEWERT +Eval: D D D D D D D D S S S S S D S S S S S S S S S + +Speaker sentences 141: fleurs_deu_000414 #utts: 1 +id: (fleurs_deu_000414-fleurs_deu_000414) +Scores: (#C #S #D #I) 0 16 6 0 +REF: DIE BRÜCKE SOLL IM SEPTEMBER 2017 VOLLSTÄNDIG DEN BETRIEB AUFNEHMEN ES WIRD ERWARTET DASS DIE BRASILIANISCHEN ZOLLKONTROLLPUNKTE DANN FERTIG GESTELLT SEIN WERDEN +HYP: *** ******* **** ** ********* **** DIEPRÜKESOLMSER TEMBEATZWEITELEN SIEBZIN VERSTENDIHT N ETRIE AUFNEM ISWRDARWARTE AS I RAS JANICHN ZOLPUNTE AN FEARTIG STELTZEINWEREN +Eval: D D D D D D S S S S S S S S S S S S S S S S + +Speaker sentences 142: fleurs_deu_000415 #utts: 1 +id: (fleurs_deu_000415-fleurs_deu_000415) +Scores: (#C #S #D #I) 0 14 15 0 +REF: WÄHREND EIN EXPERIMENTELLER IMPFSTOFF IN DER LAGE ZU SEIN SCHEINT DIE EBOLAMORTALITÄT ZU SENKEN GIBT ES BISHER KEINE MEDIKAMENTE DIE ALS EINDEUTIG ZUR BEHANDLUNG BESTEHENDER INFEKTIONEN GEEIGNET NACHGEWIESEN WURDEN +HYP: ******** *** *************** ********* ** *** **** ** **** ******* *** **************** ** ****** **** WERNDENEX SPRMETELLE MSTAU INERLAGTSUSEINSCHEINTI EBULEMOTELITETZUSEN UN GEBTASBSERKEINM DIKAMENTE DIALSEINDR IHZUBEHANDLUN BSTENDE NVEKTIONGER EIGNETNACHKGEIESEN ORDEN +Eval: D D D D D D D D D D D D D D D S S S S S S S S S S S S S S + +Speaker sentences 143: mls_deu_000281 #utts: 1 +id: (mls_deu_000281-mls_deu_000281) +Scores: (#C #S #D #I) 5 17 10 0 +REF: ein ÄUSSERST LEBHAFTER DEPESCHENWECHSEL FAND STATT MAN ERWOG DEN PLAN EINEN ALLGEMEINEN STAATENKONGRESS ZU BERUFEN und KONNTE SICH VORLÄUFIG NUR NOCH nicht ÜBER DAS VORZULEGENDE PROGRAMM und DEN ORT DES ZUSAMMENTRITTS einigen +HYP: ein ********* ********* **************** **** ***** EUSSERS LIEBAFERDBESCHENWEXSEL ANSTAT MAIN ARUOK DIMPLAN EIN ALGEMEIN STATENKONGRESTZUBRUFEN und ****** **** KONDESIH VOLFIG NOC nicht ***** BERDES VORTZ LEGEDEBRGAM und *** *** DIN ORTESTZUSAMTRTS einigen +Eval: D D D D D S S S S S S S S S D D S S S D S S S D D S S + +Speaker sentences 144: mls_deu_000282 #utts: 1 +id: (mls_deu_000282-mls_deu_000282) +Scores: (#C #S #D #I) 8 18 5 2 +REF: ER WUSSTE NICHT WAS IHM das leben KOSTBARES GERAUBT HATTE SPANNKRAFT UND MUT DASS es IHN feig UND SCHEU GEMACHT HATTE UNFÄHIG zu den HOHEN dingen ZU denen ** ********** UNGETRÜBTE MITFREUDE GEHÖRT +HYP: ** ****** ERWUSSTENCHT AS IM das leben ********* ******* KOSPARESGERAUPTATE SCHPAN GRAFTUND MUDT DAS es IN feig *** UN SCOLIGEMACHTATE UND FÄHCH zu den HON dingen ZUO denen UN GETRIÜPTE IT RALENG HRT +Eval: D D S S S D D S S S S S S D S S S S S S I I S S S + +Speaker sentences 145: mls_deu_000283 #utts: 1 +id: (mls_deu_000283-mls_deu_000283) +Scores: (#C #S #D #I) 2 27 21 0 +REF: dieser JUNGE MANN HIESS KACKERLITZCHEN UND BEFAND SICH GERADE AUF DER WANDERSCHAFT als IN DEM GENANNTEN KÖNIGREICH DIE BEKANNTMACHUNG WEGEN DER PRINZESSIN VERLESEN WURDE EI SAGTE DER SCHNEIDER WENN ES WEITER NICHTS IST EIN WEIB HAB ICH NOCH NICHT GEKÜSST UND DES KÖNIGS EIDAM ZU WERDEN DAS GELÜSTET MICH ALLERDINGS +HYP: dieser ***** **** ***** ************** UNGEMAN HIES KAKALITZIEN UNDBEFAN ICGAE F DRANDESCHAFT als ** *** ********* *********** *** ************** ***** *** ********** ******** ***** ** ***** *** ********* **** ** INEM GENANTEN KÖÜNIGREICH BEKAND MACHUNGWIG DE RNZEEN VELEN WURDEEI SAKT DESHTN LDER WENESWEITENIHTZSISTEIN EI PAUCHNHNICKÜKLSTUND UTSKÖNIGSEI DAM ZUWEREN DASGELSET IGALEDINGST +Eval: D D D D S S S S S S S D D D D D D D D D D D D D D D D D S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 146: mls_deu_000284 #utts: 1 +id: (mls_deu_000284-mls_deu_000284) +Scores: (#C #S #D #I) 4 26 17 0 +REF: NOCH FÜNF MINUTEN und DIE WOLKEN DER BEWUSSTLOSIGKEIT BEGANNEN ZU SCHWINDEN JETZT WUSSTE ICH SEHR WOHL DASS ICH IN MEINEM EIGENEN BETTE LAG und DASS DIE ROTE GLUT NICHTS ANDERES WAR ALS das FEUER im KAMIN DER KINDERSTUBE ES WAR NACHT EINE KERZE BRANNTE AUF DEM TISCHE +HYP: **** SRDR NOCFÜNFMINUTEN und *** ****** *** **************** ******** ** DE WOLGEN DE BE USTLOSIGKALT BEGANZUSCHINDEN IERST USE SER ULDASIHN MEIM EIGNEN BETELAG und **** *** **** DAS DIEROTN GLUTDNIHTZ ANDRESWA ILS das VOYR im ***** *** *********** ** *** ***** **** KAMINDER INDASTUBE ESWANACHT EINIKRZEBANTE AFTEIMTISHE +Eval: D S S D D D D D D S S S S S S S S S S S S S D D D S S S S S S D D D D D D D S S S S S + +Speaker sentences 147: mls_deu_000285 #utts: 1 +id: (mls_deu_000285-mls_deu_000285) +Scores: (#C #S #D #I) 1 20 5 0 +REF: WELCHE DIESE VERDRÄNGUNGEN WIE WÄCHTER UNTERHALTEN KOMMT DANN IM PUBERTÄTSALTER DIE HOCHFLUT DER SEXUELLEN BEDÜRFTIGKEIT so FINDET SIE AN DEN GENANNTEN SEELISCHEN REAKTIONS ODER WIDERSTANDSBILDUNGEN DÄMME +HYP: ****** ***** ************** EICHT DIE HERTRENGUNGEN WE BECHTERUNTERHEILTEN KOM DANEMPRBETITZS ALDE DI HOCHFLUDESEXSU EIN BEDÖFTIGKEIT so ****** *** FNDE SE NDEN GENANEN SLSCHE RARKTIONSODER IDERSTANZSPLDONGEN DEMER +Eval: D D D S S S S S S S S S S S S D D S S S S S S S S + +Speaker sentences 148: mls_deu_000286 #utts: 1 +id: (mls_deu_000286-mls_deu_000286) +Scores: (#C #S #D #I) 9 21 3 1 +REF: ABER AFFEN GEHÖREN BEI HAGENBECK an die ******* KISTENWAND nun SO hÖrte ich AUF AFFE ZU SEIN ein klarer SCHÖNER GEDANKENGANG DEN ICH IRGENDWIE MIT DEM bauch AUSGEHECKT HABEN MUSS DENN AFFEN denken MIT +HYP: TABER AFEN GEHRENDBE HARGEN BEG an die KISTDEN WANT nun SZO hÖrte ich *** AUOF ACFE ZOSEIN ein klarer ******** SCÖNA GERDANKTENGANG DENIHTILRGENG I MITD IM bauch ********** AUS GEHEKTABEMOSS DEN AFEN denken MI +Eval: S S S S S I S S D S S S D S S S S S S D S S S S S + +Speaker sentences 149: mls_deu_000287 #utts: 1 +id: (mls_deu_000287-mls_deu_000287) +Scores: (#C #S #D #I) 1 31 16 0 +REF: IST ES DAS PORTRÄT EINES MENSCHEN den SIE KENNEN FRAGTE ELIZA WELCHE UNBEMERKT AN MICH HERANGETRETEN WAR ICH ENTGEGNETE DASS ES NUR EIN PHANTASIEKOPF SEI UND SCHOB DIE ZEICHNUNG EILIG UNTER DIE ANDERN BLÄTTER NATÜRLICH SPRACH ICH DIE UNWAHRHEIT DENN ES WAR EIN SEHR GETREUES PORTRÄT MR ROCHESTERS +HYP: *** R IS S ESPOTTRIEHERNE MENSCHN den *** ****** ****** ***** ****** ********* ** **** ************* *** *** ********** **** ** *** IGKENNEN FRAKTE ILEISER WELCH UN BMAKTEAMICHRANGITRETEN BAICHND GEGNIETE DAS IS N N FANTESIKOKFSEI UNDSCHOPT TEICHEUNG EILICH NTE DIEANDON LTTER NTÜLISPRACRICH I UNBARHEIT DNDESWEIN SER IETOEIESPETRIE MISTEROTSCHSTES +Eval: D S S S S S D D D D D D D D D D D D D D D S S S S S S S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 150: mls_deu_000288 #utts: 1 +id: (mls_deu_000288-mls_deu_000288) +Scores: (#C #S #D #I) 1 24 18 0 +REF: ICH WEISS DASS ICH SEHR KRANK BIN SAGTE SIE NACH EINER WEILE VOR EIN PAAR MINUTEN VERSUCHTE ICH MICH IM BETTE UMZUDREHEN und FÜHLTE DASS ICH KEIN GLIED MEHR RÜHREN KANN ES WÄRE GUT WENN ICH MEIN GEMÜT ERLEICHTERN KÖNNTE BEVOR ICH STERBE +HYP: *** ***** **** *** **** ***** *** ***** *** C IHWEIS DASIH SER KRANGBIN SAK ESHNERENAWEILE VORN PAMINUTEN VESCHTE ICHMICHN BÄTER UENZUREN und ******* **** *** **** ***** **** ******* **** ** FÜLEDE DASIC KEINGLIED MARENIHRN KAN S WEREGUT WENIHMENGEMÜTDELEICHTAN KÖRNTE BEVORAISTDER RBER +Eval: D D D D D D D D D S S S S S S S S S S S S S D D D D D D D D D S S S S S S S S S S S + +Speaker sentences 151: mls_deu_000289 #utts: 1 +id: (mls_deu_000289-mls_deu_000289) +Scores: (#C #S #D #I) 7 18 9 0 +REF: so aber IST ZWAR UNSER WESENSGRUND GOTT SELBER da herum HAT SICH JEDOCH DER SCHLANGENKNÄUEL DES ALTEN SATAN GESCHLUNGEN UND ÜBER dem FÜNKCHEN DER LIEBE IST die FINSTERNIS DES HASSES GELAGERT was WUNDER DANN +HYP: so aber ISZWVER UN SERWESENS KOND GORT SELVER da herum *** **** ****** *** HTZIHEHDEC DERCHLANGEN KNOULT DS ALPENSATANGESHLONGEN UN IÜBER dem ********* FÜNGKGENDE IEBE IS die ********** *** ****** ENSERNISTESHASESKELAGERT was ****** WUNDERDAN +Eval: S S S S S S D D D D S S S S S S S D S S S D D D S D S + +Speaker sentences 152: mls_deu_000290 #utts: 1 +id: (mls_deu_000290-mls_deu_000290) +Scores: (#C #S #D #I) 3 12 12 0 +REF: BESSIE WÄRE LIEBER GEBLIEBEN ABER SIE WAR GEZWUNGEN ZU GEHEN WEIL DIE PÜNKTLICHKEIT bei DEN MAHLZEITEN eine SACHE WAR AUF WELCHE in GATESHEAD HALL STRENGE GEHALTEN WURDE +HYP: ****** ***** ****** ********* **** *** *** ********* ** BESEVERELEWAGEBLIEBEN AUERSEWAGETZWUNZUGEN BE DIEPÜNKTIGKEIT bei *** DNMALZEITEN eine ***** *** SACHEWA AUWECH in GEHTS HÄR HOL STRENGRGERHELTEN URDE +Eval: D D D D D D D D D S S S S D S D D S S S S S S S + +Speaker sentences 153: mls_deu_000291 #utts: 1 +id: (mls_deu_000291-mls_deu_000291) +Scores: (#C #S #D #I) 0 25 14 0 +REF: AUGENBLICKLICH FÜHLTE WIE IHRE ANSICHTEN ÜBER MICH IHRE EMPFINDUNGEN FÜR MICH NICHT UM EIN ATOM VERÄNDERT WAREN ÜBERHAUPT KEINER ÄNDERUNG FÄHIG WAREN ICH SAH ES IHREM VERSTEINERTEN AUGE WELCHES NIEMALS DURCH TRÄNEN GENETZT NIEMALS IN ZÄRTLICHKEIT AUFGELEUCHTET HATTE AN +HYP: ************** ******* *** **** ********* ***** **** **** ************ **** **** ***** ** *** N LIKLICHFÜLHTEWIERE AMNSICHTENBEMICG IER MPFINDNGND FÜÖRMICH NICHTUMEIN ATUNVER IN DARTWAAN NBEHUPT KRNE NDERUNG FECHWAN IGSEIS EREMFERSTEINETN AUOGE EIHS N EMELSTUOKTRINENGENETZT NIEMASIN TÄRT IC KAT AUFGELOEICHTETHATTEAMN +Eval: D D D D D D D D D D D D D D S S S S S S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 154: mls_deu_000292 #utts: 1 +id: (mls_deu_000292-mls_deu_000292) +Scores: (#C #S #D #I) 5 36 24 0 +REF: BRUDER SAM IST SEHR GUT WENN der HÄUPTLING IHN ERFÄHRT WIRD ER SICH FREUEN UND WIR WERDEN SCHNELL DANACH HANDELN SO WOLLEN WIR AUFBRECHEN UND SCHNELL REITEN DAMIT WIR NOCH VOR NACHT das lager ERREICHEN WIR STIEGEN AUF DIE PFERDE DIE NUN AUSGERUHT HATTEN UND flogen IM GALOPP DAVON DIESMAL HÜTETEN WIR UNS der FÄHRTE WIEDER DIREKT ZU FOLGEN WIR RITTEN GERADEAUS UND ERSPARTEN UNS +HYP: ****** *** *** **** *** SBPO der ********** *** ******** **** ** **** ****** *** *** ****** SEMIS ZERGUT MINDER AUKLIGNER FÄHR WRE SIC VREUNWEWERNSCHNELLDANACHANDEN SUOUL BE AUPRECHENSCNALREIDNEMIT ÜR O FODER ACH das lager ********* *** ******* REICHEN ERSTIEG AU DIT FEHRDE DI UN AUSGEROTATENUND flogen ** ****** ***** GALOPTDA VON DISMALÜTEN WIRUNZ der ******* ****** ****** FÄERDEIDE DEREKTZE VOLIEN EREN GERADE AUSSNDER SPADE U +Eval: D D D D D S D D D D D D D D D D S S S S S S S S S S S S S S S D D D S S S S S S S S D D D S S S S D D D S S S S S S S S + +Speaker sentences 155: mls_deu_000293 #utts: 1 +id: (mls_deu_000293-mls_deu_000293) +Scores: (#C #S #D #I) 8 30 17 0 +REF: WEIL DIE ABER MIT PECH BESTRICHEN WAR BLIEB einer VON DEN GOLDENEN PANTOFFELN FESTHÄNGEN und in der ANGST DACHT es NICHT DARAN IHN MITZUNEHMEN und WIE ES DEN LETZTEN SCHRITT VON DER TREPPE TAT DA HATTE ES ZWÖLF AUSGESCHLAGEN DA WAR WAGEN und PFERDE VERSCHWUNDEN UND ASCHENPUTTEL STAND IN SEINEN ASCHENKLEIDERN auf DER DUNKELN STRASSE +HYP: **** *** **** WEL DEBEAMIT PECHPESTRICHEN WAHR BIEB einer *** VONDN GOLLENDEN PANTOFELN FÄSTEINGEN und in der ANGS DACH es ***** NICHTERAN IN ITZONEM und *** ** *** ******* ******* *** *** ****** *** IE IS DN LETZTENSHIET VONDERTRERPETAT DEHATISTZWLF AUSGESCHLAGENG DARWAWAGEN und ****** ************ FIÄRDEVERSHUNDEN ND ASCHEN PUTESTAN INSEIN ASHENKLEIDE auf *** E DUNKLNSTRASE +Eval: D D D S S S S S D S S S S S S D S S S D D D D D D D D D S S S S S S S S D D S S S S S S D S S + +Speaker sentences 156: mls_deu_000294 #utts: 1 +id: (mls_deu_000294-mls_deu_000294) +Scores: (#C #S #D #I) 0 14 13 0 +REF: ILL NAHM DAS GLAS VOM AUGE EIN FINSTERER ERNST LAGERTE ÜBER SEINEN ZÜGEN ES IST SCHRECKLICH SAGTE ER ICH HAB DAS MEINIGE GETAN UM BLUTVERGIESSEN ZU VERMEIDEN +HYP: *** **** *** **** *** **** *** ********* ***** ******* ***** ****** ****** IE NOM DSKGASVMARGE EN FINDTER AN TLAR GERE ESENENTZUÜKENESISCHRIKE SAKTEHER IETES MENIGEGIETAUN BUTDVERGISEN VERMEINEN +Eval: D D D D D D D D D D D D D S S S S S S S S S S S S S S + +Speaker sentences 157: mls_deu_000295 #utts: 1 +id: (mls_deu_000295-mls_deu_000295) +Scores: (#C #S #D #I) 9 19 3 1 +REF: NUR DER DOKTOR UND DIE WÄRTERIN SOLLEN vor seine augen kommen ERKLÄRTE DIE TRINE in ****** GROSSEM AMTSEIFER damit WAR die FRAU oberst GANZ EINVERSTANDEN und HÖCHST ERFREUT KEHRTE SIE MIT IHREN +HYP: *** NURDER DAOCKTOR UN DE WERTEREN SOLEN vor seine augen kommen ********* ERKLERTE DIETRIENE in KROSEN AMT SEIFER damit WA die FTRAR oberst GANS EINFVERSTANDEN und ******* PHÜÖÜKST ERFPREIT KARTESIE MET IEREN +Eval: D S S S S S S D S S I S S S S S S D S S S S S + +Speaker sentences 158: mls_deu_000296 #utts: 1 +id: (mls_deu_000296-mls_deu_000296) +Scores: (#C #S #D #I) 2 19 19 0 +REF: K WAR UNTRÖSTLICH ÜBER DIE LAGE DES KÜNSTLERS ER BEGANN ZU WEINEN UND SCHLUCHZTE LANGE IN DIE VORGEHALTENEN HÄNDE DER KÜNSTLER WARTETE BIS K SICH BERUHIGT HATTE UND ENTSCHLOSS SICH DANN da er KEINEN ANDEREN AUSWEG FAND DENNOCH ZUM WEITERSCHREIBEN +HYP: * *** ************ ***** *** **** *** ********** ** ****** ** ****** *** ********** ***** ** KAWAR UNDTRÜSTICHEBE DIELAGEDAS KÜNZSLRS EBEGEN UWEINEN UNSCHLCHTZT DELANGE INDEVORGERHELTENEN HENDERDE KNSLAWATETER BDESKASIC BERUICHTHATE UNT ENTCHLOSICHTDAN da er ****** ******* ****** KEIN ANDARN AUSFIG FANTDERNOCHZUMPEITERSCREIBE +Eval: D D D D D D D D D D D D D D D D S S S S S S S S S S S S S S S D D D S S S S + +Speaker sentences 159: mls_deu_000297 #utts: 1 +id: (mls_deu_000297-mls_deu_000297) +Scores: (#C #S #D #I) 2 40 12 0 +REF: VON DEN PFERDEHERDEN DER APACHEN UND SAGTEN UNS DASS SIE FÜR EIN APACHENPFERD UNS EBENSO VIELE WAREN UND BRANDY GEBEN wÜrden WIE FÜR EIN KIOWAPFERD DA SIND UNSERE KRIEGER FORT UM APACHENPFERDE ZU HOLEN ALSO RICHTIG WER WAR SCHULD AN DEM tode DER BISHER GEFALLENEN UND AN DEM BLUTVERGIESSEN WELCHES NUN BEVORSTAND WEISSE PFERDEHÄNDLER +HYP: *** *** ************ ONDEM FERDEHERDN DE R PATSCHEN UN SAG UNZSTASIF EN A PATSCHN FÄRDUNS EBE SO VIE WARENUN PRENDIGEBE wÜrden *** **** *** ********** ** **** ****** ******* IÜFERIN KEIUOWABPFIERTT DASINDUN RIGKLIGEA VODUM A PATSCHEN VÄHRDT SO ULENASRCHTIH GERASCHLE IM tode *** IESHRGE FALEN UNDANE BLUTVERGISEN WEICHIS UN BE VORSTAND WEISESE FEHRDE HENDLERW +Eval: D D D S S S S S S S S S S S S S S S S S D D D D D D D D S S S S S S S S S S S S D S S S S S S S S S S S + +Speaker sentences 160: mls_deu_000298 #utts: 1 +id: (mls_deu_000298-mls_deu_000298) +Scores: (#C #S #D #I) 2 21 16 0 +REF: DAS AMAZONENHÜTCHEN VON SCHWARZEM SAMMET GRAZIÖS AUF IHRE LANGEN LOCKEN GEDRÜCKT DIE IHRE WANGEN UMFLOSSEN UND ÜBER IHRE SCHULTERN HERABWALLTEN SO TRAT SIE IN das EINFACHE LÄNDLICHE GEBÄUDE UND SCHWEBTE ZWISCHEN DEN REIHEN DER HALBGEBLENDETEN DORFKINDER AUF UND ab +HYP: *** **************** *** ********* ****** ******** *** **** ****** ****** DASMATONEN HÜTCHN VODSCWARTZAM AMIND GATZIÖRSR IERELANGN LOTNGEDRIKT DIERWANGE UM FLOSEN NT BERSCHLTH NHRAPWEITENSOTRAE I das ******** ********** ******** *** ******** ******** EINVERHRERLNTLICH GEBOEUDE UNDSTÄEBPETZWSCHN REIN DERHIBPGEBLNE INDOFKENR AUFENT ab +Eval: D D D D D D D D D D S S S S S S S S S S S S S S D D D D D D S S S S S S S + +Speaker sentences 161: mls_deu_000299 #utts: 1 +id: (mls_deu_000299-mls_deu_000299) +Scores: (#C #S #D #I) 2 21 16 0 +REF: DU MUSST ERST ENTSAGEN ALLEM SÜNDHAFTEN STREBEN UND in TIEFER REUE UND DEMUT DIE FÜRBITTE DER HEILIGEN ERFLEHEN GEGEN DIE DU GEFREVELT HAST die JÜNGLINGE WELCHE FRANCESKO SO LANGE GEFLOHEN SUCHTEN IHN AUF IN SEINER WERKSTATT UND FANDEN IHN +HYP: ** ***** **** ******** ***** *********** RBMUST ERT in ****** **** *** ***** *** ********* *** ZAGENG ALEM SHNTHAFTENSTREBEN UNEND TIE VEREIUND DEMUD die ********** ****** ********* FÜÖH BIDEDER HELINGAEFLEN GEGEN DE U GE REELTRSTDEJEMLENGERWELCHEPFANTCHSKOSULNE GEFLONSOCHTENIN AUFINSRE WERKSTUN FANDTENIN +Eval: D D D D D D S S D D D D D D D S S S S S S S D D D S S S S S S S S S S S S + +Speaker sentences 162: mls_deu_000300 #utts: 1 +id: (mls_deu_000300-mls_deu_000300) +Scores: (#C #S #D #I) 5 13 7 1 +REF: ER LIESS SEINE GRETEL NICHT FORTSCHLEPPEN am **** ALLERWENIGSTEN ABER in DEN GROSSEN VOGELBAUER WO SIE ALLE in einem tone PFEIFEN MUSSTEN WIE ER STETS SAGTE +HYP: ** ***** ERLIESZSEINE G RETENCHT VORTSCHLEBTEN am ALER WINIGS DNABER in *** ******* ********** ENGROSEN VOGELBAUR USIEALE in einem tone ******* ******* FEIEFEMOUSTEN IER SHIT SKTE +Eval: D D S S S S I S S D D D S S S D D S S S S + +Speaker sentences 163: mls_deu_000301 #utts: 1 +id: (mls_deu_000301-mls_deu_000301) +Scores: (#C #S #D #I) 2 25 13 1 +REF: FRANCESKO MALTE IN UNHEILIGER BEGEISTERUNG VIELE BILDER AUS DER LÜGENHAFTEN FABELWELT KEINER ALS ER VERMOCHTE die BUHLERISCHE ÜPPIGKEIT DER WEIBLICHEN GESTALTEN SO WAHRHAFT DARZUSTELLEN INDEM ER VON LEBENDEN MODELLEN DIE KARNATION VON DEN alten ***** MARMORBILDERN ABER FORM UND BILDUNG ENTNAHM +HYP: ********* ***** ** ********** ************ ***** ****** FRNTCHESKOMALTEN UNHELIGE BGEISTRUN FILEBIET AS E LÜGENHATENABEELTKHEINNELS ERERMOCHT die *********** ********** *** ********** ********* ** BULERISCHEL BIKETDEWEIBICENGSTALTEN SOBERHAF DASISTELEN IN DEM V N LEBEDTEMOD DELENDIEKALNATIONG VODN alten MAHMO BILEN BER ORMON BILUN IND NAN +Eval: D D D D D D D S S S S S S S S D D D D D D S S S S S S S S S S S I S S S S S S + +Speaker sentences 164: mls_deu_000302 #utts: 1 +id: (mls_deu_000302-mls_deu_000302) +Scores: (#C #S #D #I) 6 27 24 0 +REF: BEWEGUNG UND tat den ERSTEN ZUG JA ES STIMMTE die VORHIN ANGEGEBENEN INGREDIENZIEN NÄMLICH RÜBEN HANF EICHELN UND SAUERAMPFER WAREN ALLE in dem PFEIFENKOPFE ANWESEND ABER EINEN FÜNFTEN HAUPTSTOFF HATTE ICH NICHT GENANNT JETZT ROCH UND SCHMECKTE ICH DASS AUCH EIN STÜCKCHEN FILZSCHUH DABEI SEIN MÜSSE ICH BLIES DEN RAUCH AUCH GEGEN den HIMMEL UND GEGEN DIE +HYP: BEWEGUN UN tat den ****** *** STEN ZUGERSTHM E die ****** VOEUN ANGEGEBEN INGER DENZHER NMICH RÜBEHANFE EICHENUN SAUR AMFAN ALE in dem ************ ******** **** ***** ******** ********** ***** *** ***** ******* ***** **** *** ********* *** **** **** *** ********** PFEIFENKOPFERANWESEN ABEIN FÜNDFTN AUTSTOHARICHNIGENANDIETSTROCHUNDSCHMÄKTIG DASE HNSTICHEN FILSH DERBEISEIN MSEIGHPLIESTEN RAUCHAUCHGEG den ****** *** HEEL UNGEGNGD +Eval: S S D D S S S D S S S S S S S S S S D D D D D D D D D D D D D D D D D D D S S S S S S S S S S D D S S + +Speaker sentences 165: mls_deu_000303 #utts: 1 +id: (mls_deu_000303-mls_deu_000303) +Scores: (#C #S #D #I) 14 25 11 0 +REF: UND DAS FEUER stand auf und FLACKERTE UND KOCHTE das ESSEN FERTIG UND der braten brutzelte fort UND DER koch gab dem KÜCHENJUNGEN EINE OHRFEIGE UND DIE MAGD RUPFTE DAS HUHN FERTIG DA WARD DIE hochzeit VON DEM KÖNIGSSOHN MIT DORNRÖSCHEN GEFEIERT UND SIE LEBTEN VERGNÜGT BIS an IHR ende +HYP: *** UNDAS VOÄER stand auf und FLACKERT UN KOCHE das ESEN FÄATIGH UN der braten brutzelte fort *** UNDER koch gab dem ************* **** ******** *** KÜSCHEN IUNGEN EINER ROAR FEIGE UNDI MARKT RUPFTETDESHUN FERTIGHDARWARTDIE hochzeit *** *** *********** *** ************ VONDEM KÜNIGHSONITETDONGRÖÜSIHN GEFEIHRT UNSIE NEER NÜTEBIS an IER ende +Eval: D S S S S S S S S D S D D D D S S S S S S S S S D D D D D S S S S S S S + +Speaker sentences 166: mls_deu_000304 #utts: 1 +id: (mls_deu_000304-mls_deu_000304) +Scores: (#C #S #D #I) 3 19 8 0 +REF: und DASS ER MIR NICHT NACHTRAGEN WOLLE WENN ICH WIDERSPENSTIG WAR GEGEN SEINEN WOHLMEINENDEN RAT der HERR PFARRER HAT JA in ALLEM RECHT GEHABT UND ICH WAR IM UNRECHT ABER +HYP: und **** ** *** ***** ********** ***** DASE MINICH NACHTRARGENGOLLE WENICHNEDERSHPENSTIG WARGIN SEIN ULMEINEN BRART der **** ******* HER FARARHEDE in ALLEN REICHT DEHAT UN ICHME AN UN RECHT ABERH +Eval: D D D D D D S S S S S S S S D D S S S S S S S S S S S + +Speaker sentences 167: mls_deu_000305 #utts: 1 +id: (mls_deu_000305-mls_deu_000305) +Scores: (#C #S #D #I) 0 12 12 0 +REF: OBGLEICH SEINE MASSE NUR WENIGE GRAMM BETRUG ER BREITETE SICH KEGELFÖRMIG AUS UND MUSSTE DAHER DAS IHM ENTGEGENFLIEGENDE SPRENGGESCHOSS AUFFANGEN UND ZUR RUHE BRINGEN +HYP: ******** ***** ***** *** ****** ***** ****** ** ******** **** ************ *** UOGEHNEMASEN UWINIGEKRAMBTUGERBREITE DSICHKHEIE FÖMIGAUSENMUSTEDE ERESHEM IND GEGEN FIGEDESPRENKISCHOS AU FANEN TZSO UERINGEN +Eval: D D D D D D D D D D D D S S S S S S S S S S S S + +Speaker sentences 168: mls_deu_000306 #utts: 1 +id: (mls_deu_000306-mls_deu_000306) +Scores: (#C #S #D #I) 3 29 30 0 +REF: DER FUCHS REICHTE SAM DIE UNFRIEDLICHE FRIEDENSPFEIFE HIN DER MANN TAT WACKER SEINE SECHS ZÜGE UND SAGTE DER GROSSE GEIST ACHTET NICHT AUF DIE VERSCHIEDENE HAUT der MENSCHEN DENN DIE KÖNNEN SICH MIT FARBE BESCHMIEREN UM IHN ZU TÄUSCHEN SONDERN ER SIEHT DAS HERZ AN DIE HERZEN DER KRIEGER vom BERÜHMTEN STAMME DER KIOWAS SIND tapfer UNERSCHROCKEN UND TREU DAS MEINIGE HÄNGT +HYP: *** ***** ******* *** *** ************ ************** *** *** **** *** ****** ***** ***** ***** *** ***** *** ****** ***** DERVUGSREICH E SEM I UNFRITICHEFRIEDENSPWEIFVER HEN der ******** **** *** ******* MANTAT WACKASEINESE KS ZÜGEN SAKTE DERGROSIGEIS ACHTE NICH AUFDIVERSCHIEDNE HAUTDERMENSCHEN DENDIKRNSICHMIT VABEBSCHMIHRENMINTZS TROLSCHEN SONDEN ERSIDASHETZSAN DE HETZHN DERKRLIGE vom ********** ****** BERÜBEN STAMIDER KAIOWASIN tapfer ************* *** **** *** UNERSCHROGMNTREDAS MEINIGEHENG +Eval: D D D D D D D D D D D D D D D D D D D D S S S S S S D D D D S S S S S S S S S S S S S S S S S S D D S S S D D D D S S + +Speaker sentences 169: mls_deu_000307 #utts: 1 +id: (mls_deu_000307-mls_deu_000307) +Scores: (#C #S #D #I) 6 21 8 0 +REF: alles WAS WIR MIT IHR BEGEGNET SCHIEBT SICH DURCH und ÜBEREINANDER BALD UNTERSCHREIBEN WIR EINEN KONTRAKT DA IST IHRE HAND UND DIE meinige IHR NAME UND DER meinige BEIDE LÖSCHEN einander aus BEIDE VERSCHLINGEN SICH +HYP: alles *** *** AS WI MET IERBEGEGNET SCHEB SICHTDOESCH und ************* **** ************** *** ***** BER INANDERBALT UNTERSCHEBEMWER IN KONTAKT DEIST IERERHANDUNDE meinige *** IER NAHRM ONDER meinige BEI DELRSHE einander aus BEI DE VERSCHLINGENSICH +Eval: D D S S S S S S D D D D D S S S S S S S D S S S S S S S S + +Speaker sentences 170: mls_deu_000308 #utts: 1 +id: (mls_deu_000308-mls_deu_000308) +Scores: (#C #S #D #I) 2 21 19 0 +REF: er MÜSSTE DEN EINFACHEN CHRONIKEN CHORAL DES MALERS MIT ALLERLEI ERKLÄRUNGEN UND ZURECHTWEISUNGEN WIE mit KRAUSEN FIGUREN VERSCHNÖRKELN UND VERBRÄMEN ICH TRETE IN DIE PERSON DES HERAUSGEBERS UND BITTE DICH GÜNSTIGER LESER DU WOLLEST EHE DU WEITER LIESEST FOLGENDES DIR GÜTIGST MERKEN +HYP: er ******* *** ********* ********* ****** *** ****** *** MÜSTE EN EN FERHENGRONITENKÖERAL DESMALES mit ******* ******* ************** *** ********** *** ***** ** *** ****** *** ALLE RKLEHRMEN ZURECHTWESEN EN RIMITGRAUSEN IGUNVRCHNARCKHIUN VERBREMENICHTRETE N DIEPERSONDESERAUSGEIBESND BITETICHIKÜNSTIGELISERUWOLST IE DUWEITELISIST FOLENDIS DI GITIST NERTEN +Eval: D D D D D D D D S S S S S D D D D D D D D D D D S S S S S S S S S S S S S S S S + +Speaker sentences 171: mls_deu_000309 #utts: 1 +id: (mls_deu_000309-mls_deu_000309) +Scores: (#C #S #D #I) 3 27 11 0 +REF: DIE HOFDAMEN bekamen KRÄMPFE UND DIE KÖNIGIN UND DIE PRINZESSINNEN DIE IHRE ALLERLIEBSTEN HÜNDCHEN WÄHREND DER MAHLZEIT AUF DEN SCHOSS GENOMMEN HATTEN BEMERKTEN zu IHREM SCHRECKEN DASS DIE LILA AMARANTFARBENEN UND ORANGEGELBEN seidenkleider ALLE DICHT BESÄT MIT DEN HÄSSLICHSTEN ÖLFLECKEN WAREN +HYP: *** DIHOFDAMEN bekamen ******** *** *** ******** *** *** ************* KREMPFE UNDI KÜNIGEN UNDIE RONZTESSENEN DIERER ALLALIEBZSENHÜNZCHEN WERN DERMEILTHER AU INSCHOSGENOM HADTEN ERMERKTEN zu ***** ********* **** IRENSCREÄKENG DAS DILIELER AMARANTFABENEN UNDORANSCGAHLDEN seidenkleider ALE DIST DESET ME IN HESLIHSTEN ÖFLEGEN WAN +Eval: D S D D D D D D D S S S S S S S S S S S S S D D D S S S S S S S S S S S S S + +Speaker sentences 172: mls_deu_000310 #utts: 1 +id: (mls_deu_000310-mls_deu_000310) +Scores: (#C #S #D #I) 3 20 9 0 +REF: von LIEDERN DIE SIE SINGEN UND KLAVIERPIECEN DIE SIE SPIELEN VON GELDBÖRSEN DIE SIE HÄKELN von FRANZÖSISCHEN BÜCHERN DIE SIE ÜBERSETZEN KONNTE BIS MEIN GEMÜT WÄHREND ICH LAUSCHTE ZUR NACHAHMUNG AUFGESTACHELT wurde +HYP: von ******* *** *** ****** *** ************* *** *** ******* LEDEAN DIESIESIN UN KLARWIERPIESEN DIESISPBIELN von GÄHRT BÖRSEN DISI HEGKEN VOND RANZÜESCHN BÜCHAN DIESI BASRSETZENKONTE BISMANGEMÜT WERENDIHLAUST ZON NACH AMON AUFKESTCHET wurde +Eval: D D D D D D D D D S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 173: mls_deu_000311 #utts: 1 +id: (mls_deu_000311-mls_deu_000311) +Scores: (#C #S #D #I) 3 21 11 0 +REF: ARME UND NACKEN WAREN BLOSS IHR EINZIGER SCHMUCK WAREN IHRE KASTANIENBRAUNEN FLECHTEN WELCHE in wilder und NATÜRLICHER ANMUT AUF IHRE SCHULTERN HERABFIELEN ICH NAHM EINEN BOGEN FEINEN KARTONS UND ZEICHNETE MIT GROSSER SORGFALT DIE UMRISSE +HYP: **** *** ****** ***** AMUND NAEN WANBLOS IEREINZIGERSCMUOK WAN IERE ASTANEN BRANFLÄCHTEN WEICH in wilder und ************ ***** *** **** ********* *********** *** DATÜRLICHER ANMUND AU IRSCHUSTHEN HRABPFIENIHNAM EIN BOGENG FEIN KATUNGS NDZEIHET MT ROSERSOKVELTIOMGESER +Eval: D D D D S S S S S S S S S D D D D D D D S S S S S S S S S S S S + +Speaker sentences 174: mls_deu_000312 #utts: 1 +id: (mls_deu_000312-mls_deu_000312) +Scores: (#C #S #D #I) 7 30 9 0 +REF: ABER WEDER AUS DEUTSCHLAND NOCH AUS IRGENDEINEM ANDEREN STAAT KONNTE man ERFAHREN was der GEGENSTAND UND DAS RESULTAT DIESER UNTERREDUNGEN GEWESEN SEI MAN VERMUTETE DASS ES SICH um ERKLÄRUNGEN der MARTIER ÜBER IHRE ABSICHTEN und UM DIE VERMITTLUNG der MÄCHTE ZWISCHEN DEN MARSSTAATEN UND GROSSBRITANNIEN HANDLE +HYP: R AR WIE USTRTSCHLAN NOCHAS ILEN EINIM ANDREN START KONT man EFAN was der ********** *** *** ******** GENSCHEAN UNDES ESLTAT DIS UNTERE DUNGEWISENSE AN VEMUTETE DSESICH um EKLIRENG der ******* MATZHE ÜBE ERABSICHTEN und ** UN DIVEMITLUNG der ******* ******** *** MÄCHTEZUISCHN MASTATEN UNGROSPETANIEN HANDE +Eval: S S S S S S S S S S S D D D D S S S S S S S S S S D S S S D S S D D D S S S S + +Speaker sentences 175: mls_deu_000313 #utts: 1 +id: (mls_deu_000313-mls_deu_000313) +Scores: (#C #S #D #I) 1 22 1 0 +REF: LASS UNS WENIGSTENS EINE ZEITLANG VERSUCHEN INWIEFERN WIR auf DIESE WEISE MITEINANDER AUSREICHEN DA DAS ZUSAMMENHÄNGENDE WIE DU SAGST EIGENTLICH EUER ELEMENT IST VERSETZTE +HYP: LAUN WENIGSTENSEINE ZEITLANG VERSUOCHEN IN BIE FERUN WIER auf ***** DIESES BEISNMT EINANDER AUSREICHENDADERSTUSAMEN HNGNDE IE DESARGST AEIGENKLIGH OEER LEMENT IS ERSETZT IE RURT +Eval: S S S S S S S S D S S S S S S S S S S S S S S + +Speaker sentences 176: mls_deu_000314 #utts: 1 +id: (mls_deu_000314-mls_deu_000314) +Scores: (#C #S #D #I) 4 19 9 0 +REF: VERSCHIEDENE VORKOMMNISSE FÜHRTEN zu DER vermutung DASS frau wiese DIE KLEINEN WESEN VERBRENNE SIE SOLL BISWEILEN SO STARK GEHEIZT HABEN DASS DIE HERDPLATTEN ZERSPRANGEN AUSSERDEM SOLL EIN FÜRCHTERLICHER GERUCH WAHRGENOMMEN WORDEN SEIN +HYP: VESCHEN FVORKOMSE KFÜRDEN zu DE vermutung DAS frau wiese *** ******* ***** ********* *** **** ********* ** ***** DIEKLEINEN WIEN VER BRENEISOL ISFEIN SU STACH GERHITSTABENDAS DI HERTPLATENZSPTANGAUSE DE SOLEIN FÜRCHTELICHER GEROCHOWAGENUNMNWURTENSEIN +Eval: S S S S S D D D D D D D D D S S S S S S S S S S S S S S + +Speaker sentences 177: mls_deu_000315 #utts: 1 +id: (mls_deu_000315-mls_deu_000315) +Scores: (#C #S #D #I) 9 12 7 0 +REF: UND GING dem SCHREIEN nach SO SAH er ENDLICH EINEN HOHEN baum und oben DARAUF SASS EIN KLEINES KIND UNTER dem baum ABER LAG eine FRAU DIE SCHLIEF +HYP: UN KGIN dem SHREIE nach ** SOSA er ENTLICH EIN HUON baum und oben ****** **** *** DERAUFSAS ENKLEINESKENDT UNDER dem baum **** ABALAR eine **** *** FRAURDIESHLIEF +Eval: S S S D S S S S D D D S S S D S D D S + +Speaker sentences 178: mls_deu_000316 #utts: 1 +id: (mls_deu_000316-mls_deu_000316) +Scores: (#C #S #D #I) 3 25 0 2 +REF: *** SIE HATTEN SOEBEN DIE FISCHERGARNE WELCHE DIE NACHT ÜBER AUSGEWORFEN WAREN herein GEZOGEN DIE SEELEUTE GEHÖRTEN AUGENSCHEINLICH VERSCHIEDENEN NATIONEN AN OBWOHL der EUROPÄISCHE CHARAKTER bei **** ALLEN AUSGEDRÜCKT WAR +HYP: CIT SIEHRTEN SERHEBEN DI FISCHE GADENE WALLSCHE DE NARCHT IAUBERT AUSGERURDHFVIN WADEN herein GE ZAOUGEN DIESELOEITER GER HÄORTHEN AUGENSHEIMLESCH VESCHETENEN NOCTZIONENARN ABUORL der ALOPÄHSCHER KAREKTE bei ALEN AUS GET RÜKTWAL +Eval: I S S S S S S S S S S S S S S S S S S S S S S I S S S + +Speaker sentences 179: mls_deu_000317 #utts: 1 +id: (mls_deu_000317-mls_deu_000317) +Scores: (#C #S #D #I) 3 12 7 1 +REF: NEIN NEIN ICH SCHÄME MICH LASS MICH AN deinem busen ***** MEIN GESICHT VERBERGEN er SINKT INS GRAS NIEDER UND ZIEHT SIE NACH +HYP: **** **** *** ******* NEINEINICH SCÄEMEMÄICGHT LSMCH RN deinem busen MENGE SICHT VER BEHRGENH er ***** *** **** SINGTEN S GRASNIEDE UNZIET SINACH +Eval: D D D D S S S S I S S S D D D S S S S S + +Speaker sentences 180: mls_deu_000318 #utts: 1 +id: (mls_deu_000318-mls_deu_000318) +Scores: (#C #S #D #I) 5 16 16 0 +REF: DIE KINDER ABER SASSEN VOR DEM WALD und ALS sie DIE DREI KNECHTE VON WEITEM laufen SAHEN SPRACH LEHNCHEN ZUM FUNDEVOGEL VERLÄSST DU MICH NICHT SO VERLASS ICH DICH AUCH NICHT SO SPRACH FUNDEVOGEL nun und NIMMERMEHR +HYP: *** ****** **** DIKENDER ABARSASEN VERDEM WALT und AL sie *** **** ******* DIEDREIGNECHTE VONWEITEM laufen ***** ****** ******** *** ********** ********* ** **** ***** ** SANSPRACHLENCHENZUNP FÜNDE VOGEL VERLESTUMICHNICHTZOVELASICHTIG AUCRENICT SURSPRACH FONDE VOGEL nun und NEMARMER +Eval: D D D S S S S S D D D S S D D D D D D D D D D S S S S S S S S S + +Speaker sentences 181: mls_deu_000319 #utts: 1 +id: (mls_deu_000319-mls_deu_000319) +Scores: (#C #S #D #I) 7 9 2 1 +REF: WIE DER schulze in seiner HULDIGUNGSREDE HERVORHOB DER LEHRER BRACHTE am klaren SOMMERMORGEN mit SEINEN SCHULKINDERN ein ******* GESANGSSTÄNDCHEN +HYP: *** WIEDER schulze in seiner HUL DIEGUNGSRIEDE HER VORHUB DELERABRACHTE am klaren SOMAMRADENG mit ****** SEINSCHULKENDEN ein GESANGS STENTIE +Eval: D S S S S S S S D S I S + +Speaker sentences 182: swc_deu_001408 #utts: 1 +id: (swc_deu_001408-swc_deu_001408) +Scores: (#C #S #D #I) 0 3 1 0 +REF: WIE SIE SEIN SOLLTEN +HYP: *** STERT WISIESEIEN SOLN +Eval: D S S S + +Speaker sentences 183: swc_deu_001409 #utts: 1 +id: (swc_deu_001409-swc_deu_001409) +Scores: (#C #S #D #I) 0 6 0 1 +REF: ***************** DEREN SCHWINGUNGEN DURCH EINE ZUSATZSCHALTUNG STUFENLOS +HYP: DERENTSCHINGRNGEN DRC EINER ZUSAR SCHALTUNG STUFEN LOS +Eval: I S S S S S S + +Speaker sentences 184: swc_deu_001410 #utts: 1 +id: (swc_deu_001410-swc_deu_001410) +Scores: (#C #S #D #I) 0 3 4 0 +REF: DIE AUF ALLE BEI DER SITZVERTEILUNG ZU +HYP: *** *** **** *** DIEAUF ALEBEIDER SITZVERTELUNZ +Eval: D D D D S S S + +Speaker sentences 185: swc_deu_001411 #utts: 1 +id: (swc_deu_001411-swc_deu_001411) +Scores: (#C #S #D #I) 0 3 1 0 +REF: UM DEN ÜBERLEBENDEN DER +HYP: ** UMDEN ÜERLEM END +Eval: D S S S + +Speaker sentences 186: swc_deu_001412 #utts: 1 +id: (swc_deu_001412-swc_deu_001412) +Scores: (#C #S #D #I) 1 5 2 0 +REF: SPÄTER WURDEN TEILWEISE SOGAR acht PARALLELE LOCHSTREIFEN EINGESETZT +HYP: ******* ****** SPBETER URDENTEILWEISESUGAR acht PARLIL LOSTREIFEN ENGESETZT +Eval: D D S S S S S + +Speaker sentences 187: swc_deu_001413 #utts: 1 +id: (swc_deu_001413-swc_deu_001413) +Scores: (#C #S #D #I) 1 2 1 0 +REF: MORDE BEKANNT und VERLANGTE +HYP: ***** MAORDEBEKANT und VELLANGKT +Eval: D S S + +Speaker sentences 188: swc_deu_001414 #utts: 1 +id: (swc_deu_001414-swc_deu_001414) +Scores: (#C #S #D #I) 1 3 1 0 +REF: BWAHLG DIE STIMMEN von WÄHLERN +HYP: ****** BUNDESWAIGESETS DIESTM von WIELEN +Eval: D S S S + +Speaker sentences 189: swc_deu_001415 #utts: 1 +id: (swc_deu_001415-swc_deu_001415) +Scores: (#C #S #D #I) 0 1 0 1 +REF: **** GESCHICHTE +HYP: SFNA GESCEICHTE +Eval: I S + +Speaker sentences 190: swc_deu_001416 #utts: 1 +id: (swc_deu_001416-swc_deu_001416) +Scores: (#C #S #D #I) 0 2 0 0 +REF: SPALTUNG FÄHIG +HYP: SBALTUNG FEEC +Eval: S S + +Speaker sentences 191: swc_deu_001417 #utts: 1 +id: (swc_deu_001417-swc_deu_001417) +Scores: (#C #S #D #I) 1 3 2 0 +REF: STADT PADERBORN DIE ÄUSSEREN FEIERN des +HYP: ***** ********* ST PALEBORNDIEUSEREND FERAN des +Eval: D D S S S + +Speaker sentences 192: swc_deu_001418 #utts: 1 +id: (swc_deu_001418-swc_deu_001418) +Scores: (#C #S #D #I) 0 4 0 0 +REF: WEITERHIN HUMANITÄRE HILFE ZU +HYP: UMWEITER INHUMANI TERER HLFIETZ +Eval: S S S S + +Speaker sentences 193: swc_deu_001419 #utts: 1 +id: (swc_deu_001419-swc_deu_001419) +Scores: (#C #S #D #I) 2 4 2 0 +REF: SIE ERKANNTEN DIE NEUE CHINESISCHE REGIERUNG nicht an +HYP: *** ********* SIER KAMTEN DIEN NOUERICHINESISHEREGIOUNG nicht an +Eval: D D S S S S + +Speaker sentences 194: swc_deu_001420 #utts: 1 +id: (swc_deu_001420-swc_deu_001420) +Scores: (#C #S #D #I) 1 7 2 0 +REF: die URAUFFÜHRUNG FAND AM DREIUNDZWANZIGSTE SEPTEMBER ZWEI TAUSEND ACHT IN +HYP: die ************* **** URAUFÜHGEN VON AN DEINZWANS EN SETEMERZWERDEN ACHTI +Eval: D D S S S S S S S + +Speaker sentences 195: swc_deu_001421 #utts: 1 +id: (swc_deu_001421-swc_deu_001421) +Scores: (#C #S #D #I) 0 7 5 0 +REF: ER WILL SICH NICHT SCHULDIG ODER MITSCHULDIG MACHEN AM TODE EINES MITGESELLEN +HYP: ** **** **** ***** ******** ERE ICHNIHTSCHOLIC UREMITSCHOLIH MACKEN ANTODER NSMIT GESE +Eval: D D D D D S S S S S S S + +Speaker sentences 196: swc_deu_001422 #utts: 1 +id: (swc_deu_001422-swc_deu_001422) +Scores: (#C #S #D #I) 1 2 2 0 +REF: DIE MIT DER ERSTSTIMME einen +HYP: *** *** DIEDE RSZSTMER einen +Eval: D D S S + +Speaker sentences 197: swc_deu_001423 #utts: 1 +id: (swc_deu_001423-swc_deu_001423) +Scores: (#C #S #D #I) 0 3 2 0 +REF: UND HALFEN DIESEN BEI DER +HYP: *** ****** UNTEIFEN TISEN BEIDE +Eval: D D S S S + +Speaker sentences 198: swc_deu_001424 #utts: 1 +id: (swc_deu_001424-swc_deu_001424) +Scores: (#C #S #D #I) 1 3 1 2 +REF: ***** *** KREISWAHLVORSCHLAG und EINE LANDESLISTE UNTERZEICHNEN +HYP: KREIS WAL FVORSCHLAG und **** EINELANDESLISTER NDERZEITEN +Eval: I I S D S S + +Speaker sentences 199: swc_deu_001425 #utts: 1 +id: (swc_deu_001425-swc_deu_001425) +Scores: (#C #S #D #I) 3 15 3 0 +REF: EINE UMSETZUNG DER SAGE in FORM EINES FÜNFZEHNTEILIGEN LIEDERZYKLUS ZWEI TAUSEND ACHT WURDE PREUSSLERS KRABAT in EINER BEARBEITUNG von HORST HAWEMANN +HYP: **** ANUMSER ZUNG DERSAGE in **** VOM ANES FNFZEINTEILGEN LIE ERTZIKLSZWET EN ACT WURDEPRESLAS KABERT in NEBER ABETUNG von ***** HOSTHABEMAN +Eval: D S S S D S S S S S S S S S S S D S + +Speaker sentences 200: swc_deu_001426 #utts: 1 +id: (swc_deu_001426-swc_deu_001426) +Scores: (#C #S #D #I) 1 3 1 0 +REF: WIE die FOLGENDE TABELLE DARSTELLT +HYP: IE die ******** VOLE DERTABELEDASTET +Eval: S D S S + +Speaker sentences 201: swc_deu_001427 #utts: 1 +id: (swc_deu_001427-swc_deu_001427) +Scores: (#C #S #D #I) 1 1 1 0 +REF: zum STROMFLUSS BEI +HYP: zum ********** STRUNFLSBAE +Eval: D S + +Speaker sentences 202: swc_deu_001428 #utts: 1 +id: (swc_deu_001428-swc_deu_001428) +Scores: (#C #S #D #I) 1 5 0 0 +REF: DEM BUNDESWAHLLEITER BIS zum SIEBENUNDNEUNZIGSTE TAG +HYP: DE BUNDESWALEITER BIST zum SIENZIGSTEN TAR +Eval: S S S S S + +Speaker sentences 203: swc_deu_001429 #utts: 1 +id: (swc_deu_001429-swc_deu_001429) +Scores: (#C #S #D #I) 1 3 1 0 +REF: VOLLJÄHRIG GEWORDENE DEUTSCHE nicht MITWÄHLEN +HYP: *********** ORERICHKGEWRDEN DUSHE nicht MITWEEN +Eval: D S S S + +Speaker sentences 204: swc_deu_001430 #utts: 1 +id: (swc_deu_001430-swc_deu_001430) +Scores: (#C #S #D #I) 1 4 1 0 +REF: AUSFÜHRUNG MUSS EIN GUTER QUARTERBACK in +HYP: *********** AUSFION MUST ENGUSER KOTEBEG in +Eval: D S S S S + +Speaker sentences 205: swc_deu_001431 #utts: 1 +id: (swc_deu_001431-swc_deu_001431) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ************* VERGLEICHBAREN ZAHLENWERT UMGEWANDELT +HYP: VERKIEICHPBAN ZEALNWERT UMNGE ANDE +Eval: I S S S + +Speaker sentences 206: swc_deu_001432 #utts: 1 +id: (swc_deu_001432-swc_deu_001432) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ******** BETRACHTETE ALLGEMEINHEIT +HYP: BETRAHTE DE ELGEMEINHEI +Eval: I S S + +Speaker sentences 207: swc_deu_001433 #utts: 1 +id: (swc_deu_001433-swc_deu_001433) +Scores: (#C #S #D #I) 0 4 2 0 +REF: UNTERSCHIEDLICHE AUFFASSUNGEN GAB ES NUR DARÜBER +HYP: **************** ************ UNTERSHITLICHE AUFASINGEN GABESNUR DARIEBER +Eval: D D S S S S + +Speaker sentences 208: swc_deu_001434 #utts: 1 +id: (swc_deu_001434-swc_deu_001434) +Scores: (#C #S #D #I) 3 10 0 3 +REF: doll beim ***** BUNDESLIGISTEN BORUSSIA DORTMUND NACHFOLGER des ****** *** UNMITTELBAR ZUVOR ZURÜCKGETRETENEN TRAINERS JÜRGEN RÖBER +HYP: doll beim BUNES LIGISTEN BRSERTORTMUNDT NACH VOLGER des UNMITL BAR ZO VORTZURÜCKET RETEN RENASS JIREN RÜBER +Eval: I S S S S I I S S S S S S + +Speaker sentences 209: swc_deu_001435 #utts: 1 +id: (swc_deu_001435-swc_deu_001435) +Scores: (#C #S #D #I) 0 3 0 0 +REF: NEUNZEHN HUNDERT ACHTUNDACHTZIG +HYP: NUNZHNAD CHTUN AHTZIG +Eval: S S S + +Speaker sentences 210: swc_deu_001436 #utts: 1 +id: (swc_deu_001436-swc_deu_001436) +Scores: (#C #S #D #I) 0 2 0 1 +REF: **** FREIEN ENZYKLOPÄDIE +HYP: REIN EN ZIGKLPETDIE +Eval: I S S + +Speaker sentences 211: swc_deu_001437 #utts: 1 +id: (swc_deu_001437-swc_deu_001437) +Scores: (#C #S #D #I) 1 6 2 0 +REF: DER PHOTOSTROM IST Über VIELE GRÖSSENORDNUNGEN LINEAR ZUM LICHTEINFALL +HYP: *** DERVOTSTROM IS Über ***** FIELEGRUSSENOR NUNEN LENARTZUM LICHTENVAL +Eval: D S S D S S S S + +Speaker sentences 212: swc_deu_001438 #utts: 1 +id: (swc_deu_001438-swc_deu_001438) +Scores: (#C #S #D #I) 0 4 3 0 +REF: DAS HATTE FÜR KLEINE PARTEIEN GROSSE AUSWIRKUNGEN +HYP: *** ***** **** DASHT DVFÜKLEINE PRTEIN GROSEAUSWIRKUN +Eval: D D D S S S S + +Speaker sentences 213: swc_deu_001439 #utts: 1 +id: (swc_deu_001439-swc_deu_001439) +Scores: (#C #S #D #I) 0 4 0 0 +REF: IST DIE ITERATIVE TIEFENSUCHE +HYP: IS DE ITERATIEFER TIENSUGC +Eval: S S S S + +Speaker sentences 214: swc_deu_001440 #utts: 1 +id: (swc_deu_001440-swc_deu_001440) +Scores: (#C #S #D #I) 1 2 3 0 +REF: DIES KÖNNEN ZUM BEISPIEL KONDENSATOREN sein +HYP: **** ******* *** DIESKRENZUMBEISHPBIL KONDENSAERTHOREN sein +Eval: D D D S S + +Speaker sentences 215: swc_deu_001441 #utts: 1 +id: (swc_deu_001441-swc_deu_001441) +Scores: (#C #S #D #I) 2 7 0 0 +REF: ALS die KURS auf KUBA HALTENDEN SOWJETISCHEN SCHIFFE ABDREHTEN +HYP: ALDT die KOURS auf KUBERHALKGENDEN SOR IDTISCHN SCHER ABPTRETEN +Eval: S S S S S S S + +Speaker sentences 216: swc_deu_001442 #utts: 1 +id: (swc_deu_001442-swc_deu_001442) +Scores: (#C #S #D #I) 1 9 3 0 +REF: BUNDESTAGSWAHL NEUNZEHN HUNDERT DREIUNDFÜNFZIG WURDE ERSTMALS NACH einem VOM BUNDESTAG SELBST ERLASSENEN GESETZ +HYP: ************** ******** BUNDESTAG WANTZUNDERDREIUNDFMFZI VRE RSMALS NCH einem *** VOMBUNDESTAIG SEST ALSEN GESET +Eval: D D S S S S S D S S S S + +Speaker sentences 217: swc_deu_001443 #utts: 1 +id: (swc_deu_001443-swc_deu_001443) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ***** BUNDESWAHLGESETZ VIELFACH GEÄNDERT WORDEN +HYP: BUNDE WEIGEZ FIEFCHGE INERT WORDE +Eval: I S S S S + +Speaker sentences 218: swc_deu_001444 #utts: 1 +id: (swc_deu_001444-swc_deu_001444) +Scores: (#C #S #D #I) 1 3 2 0 +REF: er ÜBERLAGERT DEN PHOTOSTROM UND TRÄGT +HYP: er *********** *** IBERLAGERT EN VOTUSTOMEUNDTREIK +Eval: D D S S S + +Speaker sentences 219: swc_deu_001445 #utts: 1 +id: (swc_deu_001445-swc_deu_001445) +Scores: (#C #S #D #I) 1 4 1 1 +REF: **** TROTZ INTEGRATION der BEIDEN DEUTSCHEN STAATEN +HYP: DRTS INTIEGRAT IOUM der ****** BEIDENDEUTCEN STATEN +Eval: I S S D S S + +Speaker sentences 220: swc_deu_001446 #utts: 1 +id: (swc_deu_001446-swc_deu_001446) +Scores: (#C #S #D #I) 0 3 0 0 +REF: BERLINER WÜHLMÄUSEN STATT +HYP: BERIENE ÜLMESEN STAT +Eval: S S S + +Speaker sentences 221: swc_deu_001447 #utts: 1 +id: (swc_deu_001447-swc_deu_001447) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ******* OFFIZIELLE FÜHRUNGEN +HYP: OAFITZE E FÜLRN +Eval: I S S + +Speaker sentences 222: swc_deu_001448 #utts: 1 +id: (swc_deu_001448-swc_deu_001448) +Scores: (#C #S #D #I) 3 3 2 0 +REF: BEI DER VERHÄLTNISWAHL WIRD ZUSÄTZLICH die einhaltung der +HYP: *** *** BIDERVERHENS WALWIT ZUSERTLICH die einhaltung der +Eval: D D S S S + +Speaker sentences 223: swc_deu_001449 #utts: 1 +id: (swc_deu_001449-swc_deu_001449) +Scores: (#C #S #D #I) 0 5 4 0 +REF: WIE WENIG DIE INSULANER NOCH AM PULS DER ZEIT +HYP: *** ***** *** ********* WIEWENICT IN OLANERNOCH MPELTS DERZEIT +Eval: D D D D S S S S S + +Speaker sentences 224: swc_deu_001450 #utts: 1 +id: (swc_deu_001450-swc_deu_001450) +Scores: (#C #S #D #I) 2 8 0 1 +REF: ** JEDOCH ETWA die DURCHFÜHRUNG von WAHLWERBUNG AUF KOSTEN DES STAATES +HYP: IE DC ETWVR die DUCHFIUGEN von WAL WERBUNG AFKOSTEN DE STATES +Eval: I S S S S S S S S + +Speaker sentences 225: swc_deu_001451 #utts: 1 +id: (swc_deu_001451-swc_deu_001451) +Scores: (#C #S #D #I) 0 4 0 0 +REF: DAS NICHT IM GRUNDGESETZ +HYP: DASNIH M RUN GESETZ +Eval: S S S S + +Speaker sentences 226: swc_deu_001452 #utts: 1 +id: (swc_deu_001452-swc_deu_001452) +Scores: (#C #S #D #I) 3 2 0 0 +REF: heimat vertrieben und HÄUSLICHE GEWALT +HYP: heimat vertrieben und RUSTICSIG GEWAL +Eval: S S + +Speaker sentences 227: swc_deu_001453 #utts: 1 +id: (swc_deu_001453-swc_deu_001453) +Scores: (#C #S #D #I) 2 4 1 0 +REF: und SPEICHERE IHN in EINER WARTESCHLANGE AB +HYP: und SPBEICHER IEN in ***** INEWACKTESHLANGE ABP +Eval: S S D S S + +Speaker sentences 228: swc_deu_001454 #utts: 1 +id: (swc_deu_001454-swc_deu_001454) +Scores: (#C #S #D #I) 1 12 3 1 +REF: ********* ORIGINAL TONBÄNDER und DIE DOKUMENTATION DES STUDIOS WURDEN NEUNZEHN HUNDERT ZWEIUNDSIEBZIG IN DAS SIEMENS ARCHIV ÜBERSTELLT +HYP: OUREGENAL THON BENDER und *** ************* *** DI DOKMENTART IONDESTURDIOS URDENEINZEHN HUNDERTZ WUOUNSEBTZIG INDER SIMENS ECHI ÜERSTELT +Eval: I S S D D D S S S S S S S S S S + +Speaker sentences 229: swc_deu_001455 #utts: 1 +id: (swc_deu_001455-swc_deu_001455) +Scores: (#C #S #D #I) 2 5 1 0 +REF: so MÜSSEN AUF einem STRATEGISCHEN RAKETEN U BOOT +HYP: so MISEN AU einem ************* STATEGESHEN RERKTEEN UOBOOT +Eval: S S D S S S + +Speaker sentences 230: swc_deu_001456 #utts: 1 +id: (swc_deu_001456-swc_deu_001456) +Scores: (#C #S #D #I) 0 1 0 3 +REF: ******* ****** ** FLÖTENSPIELÄHNLICHE +HYP: FLÜTEN SPBILE ED LICHE +Eval: I I I S + +Speaker sentences 231: swc_deu_001457 #utts: 1 +id: (swc_deu_001457-swc_deu_001457) +Scores: (#C #S #D #I) 0 3 1 0 +REF: DRASTISCH MODERNE ELEKTRONISCHE KLANGGESTALTUNG +HYP: ********* DRASTDISHMOR DERANE LIGKTRONISCHERKLANGESHALTUN +Eval: D S S S + +Speaker sentences 232: swc_deu_001458 #utts: 1 +id: (swc_deu_001458-swc_deu_001458) +Scores: (#C #S #D #I) 4 18 1 0 +REF: ANSCHLIESSEND WURDEN DIE SO ERMITTELTE MANDATSZAHL JEDER partei NACH DEMSELBEN VERFAHREN ENTSPRECHEND DER ANZAHL IHRER ZWEITSTIMMEN PROPORTIONAL auf die LANDESLISTEN DER partei UNTERVERTEILT +HYP: ************* ANCHISEN WODE DIESO AMITETE MANDATZTSAL IEDER partei NT DIM SEBM VER FAN ENSPECHENTER ANZALIRAR ZWEITSTEIEM PROPRTZINAL auf die LANESLISTE DE partei UNTERVETEIERT +Eval: D S S S S S S S S S S S S S S S S S S + +Speaker sentences 233: swc_deu_001459 #utts: 1 +id: (swc_deu_001459-swc_deu_001459) +Scores: (#C #S #D #I) 1 3 1 0 +REF: OPFERN der NATO BOMBARDIERUNG UNTERKÜNFTE +HYP: AUBPFAN der **** NATSEBOMADIU UNDTERKNFTE +Eval: S D S S + +Speaker sentences 234: swc_deu_001460 #utts: 1 +id: (swc_deu_001460-swc_deu_001460) +Scores: (#C #S #D #I) 1 2 0 0 +REF: der FREIEN ENZYKLOPÄDIE +HYP: der REIN ENZUKLOPE +Eval: S S + +Speaker sentences 235: swc_deu_001461 #utts: 1 +id: (swc_deu_001461-swc_deu_001461) +Scores: (#C #S #D #I) 0 2 0 0 +REF: MITTLERWEILE FINDEN +HYP: MIERBEILSCHN N +Eval: S S + +Speaker sentences 236: swc_deu_001462 #utts: 1 +id: (swc_deu_001462-swc_deu_001462) +Scores: (#C #S #D #I) 1 7 3 0 +REF: wer WEGEN EINES VERBRECHENS RECHTSKRÄFTIG ZU EINER FREIHEITSSTRAFE VON MINDESTENS EINEM +HYP: wer ***** ***** *********** WIGEN EINE VERBRECHENSRECH SGÖEFTICHZU INEREITSTRAE VONMNDESENS EINE +Eval: D D D S S S S S S S + +Speaker sentences 237: swc_deu_001463 #utts: 1 +id: (swc_deu_001463-swc_deu_001463) +Scores: (#C #S #D #I) 2 5 2 0 +REF: der GESCHWINDIGKEITSWERTUNG ERRANGEN DREI b F EIN HUNDERT ACHT +HYP: der ESCWNDICKEITZWERTUNG ERAGEN DREIE b * *** E EINHULERTACHT +Eval: S S S D D S S + +Speaker sentences 238: swc_deu_001464 #utts: 1 +id: (swc_deu_001464-swc_deu_001464) +Scores: (#C #S #D #I) 1 3 1 1 +REF: * LIBORIUS am ERSTEN LIBORI SAMSTAG +HYP: E BORUIUS am ****** ERSTENGE BORIESAMFTE +Eval: I S D S S + +Speaker sentences 239: swc_deu_001465 #utts: 1 +id: (swc_deu_001465-swc_deu_001465) +Scores: (#C #S #D #I) 0 8 1 0 +REF: NACH DEM SAINTE LAGUË VERFAHREN AUF DIE LÄNDER VERTEILT +HYP: **** NTH IM SON AER GÜFVE FAREN AU DELENERVERTEIRT +Eval: D S S S S S S S S + +Speaker sentences 240: swc_deu_001466 #utts: 1 +id: (swc_deu_001466-swc_deu_001466) +Scores: (#C #S #D #I) 1 3 0 1 +REF: ** REFORMEN GORBATSCHOWS und ABRÜSTUNGSSCHRITTE +HYP: RE FORMIN GOABERSCHAFS und ABPRSTUNSCHLT +Eval: I S S S + +Speaker sentences 241: swc_deu_001467 #utts: 1 +id: (swc_deu_001467-swc_deu_001467) +Scores: (#C #S #D #I) 0 5 0 0 +REF: NULL UNPORTED UND UNTER DER +HYP: SIEREN AN PHRTET UNT UNDERDE +Eval: S S S S S + +Speaker sentences 242: swc_deu_001468 #utts: 1 +id: (swc_deu_001468-swc_deu_001468) +Scores: (#C #S #D #I) 3 3 0 1 +REF: an dem WESTLICHE KRÄFTE auf ***** GEGENREVOLUTIONÄRER +HYP: an dem BESTLICH EGREFTE auf GEDEN RULTZUN +Eval: S S I S + +Speaker sentences 243: swc_deu_001469 #utts: 1 +id: (swc_deu_001469-swc_deu_001469) +Scores: (#C #S #D #I) 1 3 0 0 +REF: WIRD unter ANDEREM VERWENDET +HYP: IT unter ANDERUM VERWENDE +Eval: S S S + +Speaker sentences 244: swc_deu_001470 #utts: 1 +id: (swc_deu_001470-swc_deu_001470) +Scores: (#C #S #D #I) 0 2 0 0 +REF: AUS WIKIPEDIA +HYP: USWÜCKIPÄN DER +Eval: S S + +Speaker sentences 245: swc_deu_001471 #utts: 1 +id: (swc_deu_001471-swc_deu_001471) +Scores: (#C #S #D #I) 1 1 0 0 +REF: und KUBAKRISE +HYP: und KOUBARGRIESE +Eval: S + +Speaker sentences 246: swc_deu_001472 #utts: 1 +id: (swc_deu_001472-swc_deu_001472) +Scores: (#C #S #D #I) 0 11 1 0 +REF: LETZTER WAHL AUFGRUND EIGENER WAHLVORSCHLÄGE UNUNTERBROCHEN MIT MINDESTENS FÜNF ABGEORDNETEN VERTRETEN SIND +HYP: ******* ENLTZS DAWALAUFGRUND EIGNERWEIRVOSHLIGE NETER RCHENMT MINDESENS FMF ABGONE N VERTIE ENSIN +Eval: D S S S S S S S S S S S + +Speaker sentences 247: swc_deu_001473 #utts: 1 +id: (swc_deu_001473-swc_deu_001473) +Scores: (#C #S #D #I) 2 4 0 0 +REF: VERBREITUNG IDEOLOGISCHER PROPAGANDA der SUPERMÄCHTE und +HYP: VERPREITUNG IEDIOLOGESCHA PROPAGANDER der SUPERMECHTE und +Eval: S S S S + +Speaker sentences 248: swc_deu_001474 #utts: 1 +id: (swc_deu_001474-swc_deu_001474) +Scores: (#C #S #D #I) 1 4 0 0 +REF: WEBCOMICS auf DIE REALITÄT ÜBERTRAGEN +HYP: KOMIGS auf DERLITHET BERT HAGE +Eval: S S S S + +Speaker sentences 249: swc_deu_001475 #utts: 1 +id: (swc_deu_001475-swc_deu_001475) +Scores: (#C #S #D #I) 1 5 1 0 +REF: ALS der KALTE KRIEG SICH FORTWÄHREND ZUSPITZE +HYP: AL der ***** KALTIGKLIEGSICH VORT WEREN ZUSPITZSTE +Eval: S D S S S S + +Speaker sentences 250: swc_deu_001476 #utts: 1 +id: (swc_deu_001476-swc_deu_001476) +Scores: (#C #S #D #I) 1 5 2 0 +REF: SICHERHEITSPERSONAL ODER WACHHUNDEN NUR SEHR schwierig BETRETEN WERDEN +HYP: ******************* **** SICHEIZPERSNAL ODERECHUNEN NSER schwierig BETETEN ERN +Eval: D D S S S S S + +Speaker sentences 251: swc_deu_001477 #utts: 1 +id: (swc_deu_001477-swc_deu_001477) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ******** DAUERHAFTES BLEIBERECHT UND +HYP: DARURHAF ES BLEIBERICHT UN +Eval: I S S S + +Speaker sentences 252: swc_deu_001478 #utts: 1 +id: (swc_deu_001478-swc_deu_001478) +Scores: (#C #S #D #I) 0 3 4 0 +REF: EBENSO WIE DAS MOTIV DER ERLÖSUNG DURCH +HYP: ****** *** *** ***** IBESERWIEDERSMUTIF TER LESUNGBIC +Eval: D D D D S S S + +Speaker sentences 253: swc_deu_001479 #utts: 1 +id: (swc_deu_001479-swc_deu_001479) +Scores: (#C #S #D #I) 0 3 2 0 +REF: WENN FÜR NIEMANDEN NACHPRÜFBAR IST +HYP: **** **** WEN FÜNIEMAN NACHPÜCSBEIST +Eval: D D S S S + +Speaker sentences 254: swc_deu_001480 #utts: 1 +id: (swc_deu_001480-swc_deu_001480) +Scores: (#C #S #D #I) 1 3 0 0 +REF: PRIVATE ERFORSCHUNG von EINRICHTUNGEN +HYP: IS DIEKLEWARTEARVORSCHN von EINRICHTUNMEM +Eval: S S S + +Speaker sentences 255: swc_deu_001481 #utts: 1 +id: (swc_deu_001481-swc_deu_001481) +Scores: (#C #S #D #I) 0 9 2 0 +REF: ABGESEHEN DAVON WÜRDEN SELBST DANN NOCH DIE ENTSPRECHENDEN PAL CODES FEHLEN +HYP: ********* ***** GESEN DA VON BIRDEN SEBS DANOCH IEINSPECHENDEN PALKOUTS FIEEN +Eval: D D S S S S S S S S S + +Speaker sentences 256: swc_deu_001482 #utts: 1 +id: (swc_deu_001482-swc_deu_001482) +Scores: (#C #S #D #I) 0 4 0 0 +REF: SPRECHEN BENÖTIGTE ATEMLUFT LIEFERT +HYP: SPECHEN BNÜTICHTDE AEM LUFTKLIEFVERT +Eval: S S S S + +Speaker sentences 257: swc_deu_001483 #utts: 1 +id: (swc_deu_001483-swc_deu_001483) +Scores: (#C #S #D #I) 0 4 0 3 +REF: ******** ***** ** MÖGLICHEN SCHUTZIMPFUNGEN GEGEN KRANKHEITEN +HYP: EMUCLICH SCUTZ IM VORN GEN K ANGREITE +Eval: I I I S S S S + +Speaker sentences 258: swc_deu_001484 #utts: 1 +id: (swc_deu_001484-swc_deu_001484) +Scores: (#C #S #D #I) 0 3 2 0 +REF: SCHON EINEN ÄHNLICHEN VERSUCH GAB +HYP: ***** ***** SHNEIN ENLICHEN VERSUCHGAR +Eval: D D S S S + +Speaker sentences 259: swc_deu_001485 #utts: 1 +id: (swc_deu_001485-swc_deu_001485) +Scores: (#C #S #D #I) 3 14 0 2 +REF: *** ********* an EINEM P N ÜBERGANG oder PIN ÜBERGANG DURCH DEN INNEREN PHOTOEFFEKT IN EINEN ELEKTRISCHEN strom UMWANDELT +HYP: RUN KGENSTRAN an INEM PH EN ÜBERGAN oder PEE EN IEBERGAN DESTHN INRUN VOTU ERVEKT INEIN ELEKRISCHN strom UMWANDE +Eval: I I S S S S S S S S S S S S S S + +Speaker sentences 260: swc_deu_001486 #utts: 1 +id: (swc_deu_001486-swc_deu_001486) +Scores: (#C #S #D #I) 0 1 5 0 +REF: BEIM MEISTER IN DER SILVESTERNACHT FREIBITTEN +HYP: **** ******* ** *** ************** BRMASEINDESEBESALNOCHTFREIDET +Eval: D D D D D S + +Speaker sentences 261: swc_deu_001487 #utts: 1 +id: (swc_deu_001487-swc_deu_001487) +Scores: (#C #S #D #I) 0 3 1 0 +REF: JAHREN DER BEGRIFF VADDING +HYP: ****** ELA EN DERBIGIF +Eval: D S S S + +Speaker sentences 262: swc_deu_001488 #utts: 1 +id: (swc_deu_001488-swc_deu_001488) +Scores: (#C #S #D #I) 0 7 1 0 +REF: RANGVERHÄLTNIS UNTER DEN STIMMEN NOCH EINE LOGISCHE ABFOLGE +HYP: *************** ANKFEIL NES UNTERDINSTEIM NOCHEINE LOGESCH ABP VOERG +Eval: D S S S S S S S + +Speaker sentences 263: swc_deu_001489 #utts: 1 +id: (swc_deu_001489-swc_deu_001489) +Scores: (#C #S #D #I) 1 6 1 0 +REF: KRABAT LEHNT DIESES ANGEBOT JEDOCH mit ENTSCHIEDENHEIT AB +HYP: ****** KABERT LENDIESIS ANGEBODIE DO mit ENSCIETENEIT A +Eval: D S S S S S S + +Speaker sentences 264: swc_deu_001490 #utts: 1 +id: (swc_deu_001490-swc_deu_001490) +Scores: (#C #S #D #I) 1 5 0 4 +REF: stand *********** ****** **** ****** VOM DER INHALT STEHT UNTER +HYP: stand VOMZWELFTEN MARTZS ZWEI TAUSEN ZWEL FDER IN HEISTHI UT +Eval: I I I I S S S S S + +Speaker sentences 265: swc_deu_001491 #utts: 1 +id: (swc_deu_001491-swc_deu_001491) +Scores: (#C #S #D #I) 0 3 1 0 +REF: ORGANISATION UNTERBRACH DARAUFHIN DIE +HYP: ************ OGENISERT IONUNTER BRACHTERFIND +Eval: D S S S + +Speaker sentences 266: swc_deu_001492 #utts: 1 +id: (swc_deu_001492-swc_deu_001492) +Scores: (#C #S #D #I) 2 3 2 0 +REF: VERBÜNDET SIND oder GAR FÜR SIE arbeiten +HYP: VER PÜNDITZENDT oder *** **** GAFÜSIE arbeiten +Eval: S S D D S + +Speaker sentences 267: swc_deu_001493 #utts: 1 +id: (swc_deu_001493-swc_deu_001493) +Scores: (#C #S #D #I) 0 2 0 2 +REF: ******* ** FESTGELEGTE VOLLJÄHRIGKEITSALTER +HYP: VRSGLIE TE VORHRICHKT EILDE +Eval: I I S S + +Speaker sentences 268: swc_deu_001494 #utts: 1 +id: (swc_deu_001494-swc_deu_001494) +Scores: (#C #S #D #I) 1 4 1 0 +REF: DIE ERRICHTUNG der BERLINER MAUER MÜNDETEN +HYP: I ARICSTUNG der ******** BELIENE MAURAMÜNDETEN +Eval: S S D S S + +Speaker sentences 269: swc_deu_001495 #utts: 1 +id: (swc_deu_001495-swc_deu_001495) +Scores: (#C #S #D #I) 1 2 0 2 +REF: ERRICHTUNG von ***** ** KLÄRANLAGEN +HYP: ERICHTUN von KLIER AN LAGEN +Eval: S I I S + +Speaker sentences 270: swc_deu_001496 #utts: 1 +id: (swc_deu_001496-swc_deu_001496) +Scores: (#C #S #D #I) 0 6 3 0 +REF: AFGHANISTANS UND IM IRAK HAT SICH SEIT DEM EINMARSCH +HYP: ************ *** ** AF GANDESTANZUNDIMIE RARG HERZIE SEITIM EINMARST +Eval: D D D S S S S S S + +Speaker sentences 271: swc_deu_001497 #utts: 1 +id: (swc_deu_001497-swc_deu_001497) +Scores: (#C #S #D #I) 5 3 1 2 +REF: der ***** ***** PHONATIONSSTROM von den lungen ÜBER DIE BRONCHIEN bis +HYP: der VONAR ZIUND STOM von den lungen ***** IER DEBRONCHEN bis +Eval: I I S D S S + +Speaker sentences 272: swc_deu_001498 #utts: 1 +id: (swc_deu_001498-swc_deu_001498) +Scores: (#C #S #D #I) 2 5 0 1 +REF: **** AUSSERDEM NAHMEN sender HÖRSPIELE mit VERFREMDETER SPRACHE +HYP: AUSE DEMN NAMEN sender HÜRSPBILE mit VER RMDETRSPBRAHR +Eval: I S S S S S + +Speaker sentences 273: swc_deu_001499 #utts: 1 +id: (swc_deu_001499-swc_deu_001499) +Scores: (#C #S #D #I) 0 3 0 0 +REF: UND DIE GRUNDMANDATSKLAUSEL +HYP: UNDIEGRND MANDARZ KLAUSE +Eval: S S S + +Speaker sentences 274: swc_deu_001500 #utts: 1 +id: (swc_deu_001500-swc_deu_001500) +Scores: (#C #S #D #I) 2 4 2 0 +REF: keine ABKEHR von DEN GRUNDLAGEN DES SOZIALISMUS EINSCHLIESSE +HYP: keine ABKHR von *** ********** ENGRUNDPLAGEN DESTOTZELISMUS EINSHLISE +Eval: S D D S S S + +Speaker sentences 275: swc_deu_001501 #utts: 1 +id: (swc_deu_001501-swc_deu_001501) +Scores: (#C #S #D #I) 1 6 3 0 +REF: MIT KOMPONENTEN SOWOHL AN ALS AUCH TIEF in DER WAFFE +HYP: *** *********** IT KOMPRNENTEN SO HOL ANALSAUCHTIEF in *** DERWAFE +Eval: D D S S S S S D S + +Speaker sentences 276: swc_deu_001502 #utts: 1 +id: (swc_deu_001502-swc_deu_001502) +Scores: (#C #S #D #I) 0 2 0 0 +REF: BEDEUTUNGSVOLL WAR +HYP: BEDEUCTUNG VORLEWAR +Eval: S S + +Speaker sentences 277: swc_deu_001503 #utts: 1 +id: (swc_deu_001503-swc_deu_001503) +Scores: (#C #S #D #I) 0 3 0 0 +REF: FREIWILLIGE HELFER DER +HYP: REIFITIGE HEN VERTEROUGANISTZIUN +Eval: S S S + +Speaker sentences 278: swc_deu_001504 #utts: 1 +id: (swc_deu_001504-swc_deu_001504) +Scores: (#C #S #D #I) 1 4 1 0 +REF: um ELEKTRONEN VOM VALENZBAND INS LEITUNGSBAND +HYP: um ********** ELEKTRUN VOMWALENSBAND ENS LEITUNSBAN +Eval: D S S S S + +Speaker sentences 279: swc_deu_001505 #utts: 1 +id: (swc_deu_001505-swc_deu_001505) +Scores: (#C #S #D #I) 0 5 0 0 +REF: ALLERDINGS SIND VERGLEICHBARE EFFEKTE MÖGLICH +HYP: ALLEDING SUN VER LEICHBAR IVEKTEMUKLICH +Eval: S S S S S + +Speaker sentences 280: swc_deu_001506 #utts: 1 +id: (swc_deu_001506-swc_deu_001506) +Scores: (#C #S #D #I) 5 7 0 0 +REF: diese KONNTEN aber ALS EINGABE in einen FREQUENZUMSETZER DIENEN ODER steuerten SYNCHRONMOTOREN +HYP: diese KONTEN aber LS EINGABEL in einen FRICGWENZS UMSETZSER DIENUDER steuerten ZUNGOHNMUTURN +Eval: S S S S S S S + +Speaker sentences 281: swc_deu_001507 #utts: 1 +id: (swc_deu_001507-swc_deu_001507) +Scores: (#C #S #D #I) 0 6 2 0 +REF: THOMAS HERMANNS PRODUZIERTE ZWEI TAUSEND ZWEI MIT GREBE +HYP: ****** ******** TOUMAS HRMANS PRDTZIERTI ZWEITAUSEN ZWEIMIT KGREEBE +Eval: D D S S S S S S + +Speaker sentences 282: swc_deu_001508 #utts: 1 +id: (swc_deu_001508-swc_deu_001508) +Scores: (#C #S #D #I) 0 3 1 0 +REF: P N ÜBERGANG TREFFEN +HYP: * PI EN UBERGANTRIFEN +Eval: D S S S + +Speaker sentences 283: swc_deu_001509 #utts: 1 +id: (swc_deu_001509-swc_deu_001509) +Scores: (#C #S #D #I) 1 2 0 0 +REF: die FALKENHORST SHOW +HYP: die FEILKTEN HOSTSCAOUN +Eval: S S + +Speaker sentences 284: swc_deu_001510 #utts: 1 +id: (swc_deu_001510-swc_deu_001510) +Scores: (#C #S #D #I) 1 4 0 3 +REF: ** ***** *** ANTISOWJETISCHE DEMONSTRATIONEN wurden BLUTIG NIEDERGESCHLAGEN +HYP: AN IESOU JET CH DEMNSTRATSIONEN wurden PLUTIG NEDERGSHLAG +Eval: I I I S S S S + +Speaker sentences 285: swc_deu_001511 #utts: 1 +id: (swc_deu_001511-swc_deu_001511) +Scores: (#C #S #D #I) 0 5 2 0 +REF: EIN VIER KANAL MISCHPULT DIENTE FÜR KLEINERE +HYP: *** **** EIEN FIER KANALMISPBULT DENTE VEKLEINER +Eval: D D S S S S S + +Speaker sentences 286: swc_deu_001512 #utts: 1 +id: (swc_deu_001512-swc_deu_001512) +Scores: (#C #S #D #I) 4 7 3 0 +REF: diese HÄTTEN die VORWARNZEITEN FÜR EINEN ANGRIFF auf die U S A EXTREM HERABGESETZT +HYP: diese HTN die ************* VORWANZEITEN VÜEREINEN ANGRIF auf die * * UÖSAR EXSTREM HRARPGESETST +Eval: S D S S S D D S S S + +Speaker sentences 287: swc_deu_001513 #utts: 1 +id: (swc_deu_001513-swc_deu_001513) +Scores: (#C #S #D #I) 2 4 0 1 +REF: WELCHES am NÄCHSTEN zum **** STARTKNOTEN LIEGT +HYP: WERCHES am NEGHSTEN zum STAT KENODEN LIEKT +Eval: S S I S S + +Speaker sentences 288: swc_deu_001514 #utts: 1 +id: (swc_deu_001514-swc_deu_001514) +Scores: (#C #S #D #I) 2 8 1 2 +REF: * LAZIO GING DOLL ZURÜCK in DIE BUNDESLIGA UND WECHSELTE zu *** EINTRACHT +HYP: A IO GIN DOL ZERÜC in *** DEBUNDES LIEGER UNDWEXSELDE zu EIN RCHT +Eval: I S S S S D S S S I S + +Speaker sentences 289: swc_deu_001515 #utts: 1 +id: (swc_deu_001515-swc_deu_001515) +Scores: (#C #S #D #I) 1 2 0 0 +REF: Über DIESE KRANKHEIT +HYP: Über ISE KANGKHEIT +Eval: S S + +Speaker sentences 290: swc_deu_001516 #utts: 1 +id: (swc_deu_001516-swc_deu_001516) +Scores: (#C #S #D #I) 0 3 2 0 +REF: JAHR ZWEI TAUSEND FÜNF KRITISIERTE +HYP: **** **** AR ZWEITAUSENDTFÜNFE KÜIDESERT +Eval: D D S S S + +Speaker sentences 291: swc_deu_001517 #utts: 1 +id: (swc_deu_001517-swc_deu_001517) +Scores: (#C #S #D #I) 1 3 1 0 +REF: diese AUFFASSUNG ZUR NEUTRALITÄT UNTERSCHEIDET +HYP: diese ********** AUFSSUNGZUNEUTRARITET UNTER SCHEIDE +Eval: D S S S + +Speaker sentences 292: swc_deu_001518 #utts: 1 +id: (swc_deu_001518-swc_deu_001518) +Scores: (#C #S #D #I) 2 6 1 0 +REF: RIEDL wurde als KÜNSTLERISCHER LEITER DES SIEMENS STUDIOS BESTELLT +HYP: REDEL wurde als *************** KÜNZT ALICHERLEITER DER IEMENSTHUDIES BESTEL +Eval: S D S S S S S + +Speaker sentences 293: swc_deu_001519 #utts: 1 +id: (swc_deu_001519-swc_deu_001519) +Scores: (#C #S #D #I) 1 4 2 0 +REF: WENN MAN DIE WELT ALS ganzes BETRACHTET +HYP: **** *** WENMENDIE WÄLT AL ganzes PERACHT +Eval: D D S S S S + +Speaker sentences 294: swc_deu_001520 #utts: 1 +id: (swc_deu_001520-swc_deu_001520) +Scores: (#C #S #D #I) 3 5 0 2 +REF: SIND KRITISCHE KOMPONENTEN des ******* DETONATIONSSYSTEMS absichtlich schwach *** ENTWORFEN +HYP: SEND KITISCHE KOMPBONENEN des DETUNER DIOUNZSISTEMS absichtlich schwach END WURHFEN +Eval: S S S I S I S + +Speaker sentences 295: swc_deu_001521 #utts: 1 +id: (swc_deu_001521-swc_deu_001521) +Scores: (#C #S #D #I) 0 3 1 0 +REF: NICHT WÄHLBAR IST JEDOCH +HYP: ***** NICHTWERBEI ISTE DOCH +Eval: D S S S + +Speaker sentences 296: swc_deu_001522 #utts: 1 +id: (swc_deu_001522-swc_deu_001522) +Scores: (#C #S #D #I) 3 3 0 1 +REF: er BOT eine *** VEREINIGUNG DEUTSCHLANDS an +HYP: er BUOT eine FER EINIGUNG DETSCLANS an +Eval: S I S S + +Speaker sentences 297: swc_deu_001523 #utts: 1 +id: (swc_deu_001523-swc_deu_001523) +Scores: (#C #S #D #I) 1 3 0 1 +REF: BERLIN zwei ****** TAUSEND FÜNF +HYP: PELIEN zwei TAUSEN FÜN F +Eval: S I S S + +Speaker sentences 298: swc_deu_001524 #utts: 1 +id: (swc_deu_001524-swc_deu_001524) +Scores: (#C #S #D #I) 2 4 0 2 +REF: kern ****** **** ABGESTIMMT UND UMHÜLLEN diesen ENTSPRECHEND +HYP: kern ABGEST EIMT UNDT UM HLEN diesen ENSPRECHENT +Eval: I I S S S S + +Speaker sentences 299: swc_deu_001525 #utts: 1 +id: (swc_deu_001525-swc_deu_001525) +Scores: (#C #S #D #I) 1 2 1 1 +REF: ** ERZEUGUNG von DYNAMIK AUS +HYP: AR ZOELGUNG von ******* DNAMIGAUS +Eval: I S D S + +Speaker sentences 300: swc_deu_001526 #utts: 1 +id: (swc_deu_001526-swc_deu_001526) +Scores: (#C #S #D #I) 1 2 0 0 +REF: ZIMT und INGWER +HYP: SEMT und ENGDER +Eval: S S + +Speaker sentences 301: swc_deu_001527 #utts: 1 +id: (swc_deu_001527-swc_deu_001527) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** VON SCHWERER UNTERERNÄHRUNG +HYP: UNG VONDSCHIE HR UNTERNERUN +Eval: I S S S + +Speaker sentences 302: swc_deu_001528 #utts: 1 +id: (swc_deu_001528-swc_deu_001528) +Scores: (#C #S #D #I) 0 3 0 0 +REF: NÜSSEN UND GEWÜRZEN +HYP: DNESCHEN UN GERWRTCEN +Eval: S S S + +Speaker sentences 303: swc_deu_001529 #utts: 1 +id: (swc_deu_001529-swc_deu_001529) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ROBERT F KENNEDY +HYP: ROBET ERFKENE DI +Eval: S S S + +Speaker sentences 304: swc_deu_001530 #utts: 1 +id: (swc_deu_001530-swc_deu_001530) +Scores: (#C #S #D #I) 1 1 1 0 +REF: KAM SCHLIESSLICH zum +HYP: *** KAMSHLISELICH zum +Eval: D S + +Speaker sentences 305: swc_deu_001531 #utts: 1 +id: (swc_deu_001531-swc_deu_001531) +Scores: (#C #S #D #I) 0 1 0 1 +REF: ******* VOLLSTÄNDIGKEIT +HYP: VOLSTEN I +Eval: I S + +Speaker sentences 306: swc_deu_001532 #utts: 1 +id: (swc_deu_001532-swc_deu_001532) +Scores: (#C #S #D #I) 3 3 1 0 +REF: STANDEN sich von den U S A +HYP: STANTEN sich von den * UERES AR +Eval: S D S S + +Speaker sentences 307: swc_deu_001533 #utts: 1 +id: (swc_deu_001533-swc_deu_001533) +Scores: (#C #S #D #I) 0 5 0 3 +REF: ******* ** *** AFRIKA SÜDLICH DER SAHARA GEORTET +HYP: AFRIKAR ST DIH TE SER HAHRER GE ORTET +Eval: I I I S S S S S + +Speaker sentences 308: swc_deu_001534 #utts: 1 +id: (swc_deu_001534-swc_deu_001534) +Scores: (#C #S #D #I) 1 2 0 0 +REF: die ARMEE MEUTERTE +HYP: die ARME MUITETEL +Eval: S S + +Speaker sentences 309: swc_deu_001535 #utts: 1 +id: (swc_deu_001535-swc_deu_001535) +Scores: (#C #S #D #I) 0 3 0 0 +REF: STALIN SETZTE IM +HYP: STALIEN SERTZTE M +Eval: S S S + +Speaker sentences 310: swc_deu_001536 #utts: 1 +id: (swc_deu_001536-swc_deu_001536) +Scores: (#C #S #D #I) 0 1 0 2 +REF: ********* **** VERHÄLTNISAUSGLEICH +HYP: VEIHTENES AUST LEICG +Eval: I I S + +Speaker sentences 311: swc_deu_001537 #utts: 1 +id: (swc_deu_001537-swc_deu_001537) +Scores: (#C #S #D #I) 0 2 0 2 +REF: **** *** PROSCRIBED GLEICH +HYP: KLME AUF BROSSCREIBT LEIH +Eval: I I S S + +Speaker sentences 312: swc_deu_001538 #utts: 1 +id: (swc_deu_001538-swc_deu_001538) +Scores: (#C #S #D #I) 1 4 2 0 +REF: am ZWEITE JUNI ZWEI TAUSEND VIER WURDEN +HYP: am ****** **** ZWEITEN JUNEZWETAUSEND FIER BURDE +Eval: D D S S S S + +Speaker sentences 313: swc_deu_001539 #utts: 1 +id: (swc_deu_001539-swc_deu_001539) +Scores: (#C #S #D #I) 0 4 0 0 +REF: IN DEN BUNDESTAG NACHRÜCKT +HYP: INE BNDES TER NACHRIUGT +Eval: S S S S + +Speaker sentences 314: swc_deu_001540 #utts: 1 +id: (swc_deu_001540-swc_deu_001540) +Scores: (#C #S #D #I) 4 3 1 1 +REF: DIE NATO osterweiterung und die *** EINSEITIGE aufkÜndigung DES +HYP: *** DINARTO osterweiterung und die EIN SEITIGER aufkÜndigung DE +Eval: D S I S S + +Speaker sentences 315: swc_deu_001541 #utts: 1 +id: (swc_deu_001541-swc_deu_001541) +Scores: (#C #S #D #I) 0 2 0 1 +REF: * HIERBEI IST +HYP: T HERBEI IS +Eval: I S S + +Speaker sentences 316: swc_deu_001542 #utts: 1 +id: (swc_deu_001542-swc_deu_001542) +Scores: (#C #S #D #I) 0 4 4 0 +REF: DIESER STELLE KAMEN SÄMTLICHE MITGLIEDER DER KAPELLE DER +HYP: ****** ****** ***** ********** DIESRSTELLE KAMMN SANMTICHEMITIEDERDER KAPLET +Eval: D D D D S S S S + +Speaker sentences 317: swc_deu_001543 #utts: 1 +id: (swc_deu_001543-swc_deu_001543) +Scores: (#C #S #D #I) 6 11 1 1 +REF: ******** POTSDAMER ABKOMMEN ENTHIELT zwar ALLGEMEINE VEREINBARUNGEN ÜBER die kÜnftige gemeinsame VERWALTUNG der SIEGERMÄCHTE und FORMULIERTE GRUNDSÄTZE WIE DEMILITARISIERUNG +HYP: POTZSTAM ABPKOMMEN ENT HIELT zwar ALGEMEINEVER EINBAUNEN ÜBE die kÜnftige gemeinsame VERWALTHN der SEGERMICHTE und *********** VOMULIERTEGRUNDSETZE B DEMLITRISIERUN +Eval: I S S S S S S S S D S S S + +Speaker sentences 318: swc_deu_001544 #utts: 1 +id: (swc_deu_001544-swc_deu_001544) +Scores: (#C #S #D #I) 1 6 3 0 +REF: DANACH UNTERSCHRIEB ER EINEN VERTRAG beim B F C DYNAMO +HYP: ****** ************ DANACHRUNDRSCIEBE INEN VARARKG beim * WIEFTZSI DY NAMO +Eval: D D S S S D S S S + +Speaker sentences 319: swc_deu_001545 #utts: 1 +id: (swc_deu_001545-swc_deu_001545) +Scores: (#C #S #D #I) 0 3 1 0 +REF: EINE WEITERE VARIANTE MAG +HYP: **** EIN WEITEROWARIJANT MA +Eval: D S S S + +Speaker sentences 320: swc_deu_001546 #utts: 1 +id: (swc_deu_001546-swc_deu_001546) +Scores: (#C #S #D #I) 1 6 3 0 +REF: SIE WURDEN MODULAR DURCH LOCHSTREIFEN GESTEUERT und DIE KLÄNGE KONNTEN +HYP: *** ****** SIEWURDENMODOLARN TURCHE LOCHSTREI FENGESTEYERT und *** DIKLINGE KONTE +Eval: D D S S S S D S S + +Speaker sentences 321: swc_deu_001547 #utts: 1 +id: (swc_deu_001547-swc_deu_001547) +Scores: (#C #S #D #I) 0 8 0 1 +REF: ******* DIE GRUNDMANDATSKLAUSEL BEVORZUGT UNTER DEN KLEINEN PARTEIEN JENE +HYP: DIEGRUN MA DAT KLAUSEL BERVORTZUGT UNDE DINKLEINERN PARTEIN JIENE +Eval: I S S S S S S S S + +Speaker sentences 322: swc_deu_001548 #utts: 1 +id: (swc_deu_001548-swc_deu_001548) +Scores: (#C #S #D #I) 1 3 2 0 +REF: ABER TROTZDEM keine WIRKLICHE HUNGERSNOT HERRSCHT +HYP: ABERTOTZ DIN keine ********* ********** WÜKLICHERHUNGESNODTHEAST +Eval: S S D D S + +Speaker sentences 323: swc_deu_001549 #utts: 1 +id: (swc_deu_001549-swc_deu_001549) +Scores: (#C #S #D #I) 0 4 0 0 +REF: UND DOKUMENTATION DER OBJEKTE +HYP: N KOGMALTER ZION D +Eval: S S S S + +Speaker sentences 324: swc_deu_001550 #utts: 1 +id: (swc_deu_001550-swc_deu_001550) +Scores: (#C #S #D #I) 0 4 0 0 +REF: ZUR VORBEDINGUNG KONKRETER ABRÜSTUNGSSCHRITTE +HYP: ZUO FORBEDIGUNG KONGRIETER APRÜSTENSCHLETE +Eval: S S S S + +Speaker sentences 325: swc_deu_001551 #utts: 1 +id: (swc_deu_001551-swc_deu_001551) +Scores: (#C #S #D #I) 0 1 0 2 +REF: ****** ***** BUNDESTAGSWAHLRECHT +HYP: BUNDES TARGE WALRECHT +Eval: I I S + +Speaker sentences 326: swc_deu_001552 #utts: 1 +id: (swc_deu_001552-swc_deu_001552) +Scores: (#C #S #D #I) 0 6 0 0 +REF: ES MUSS DEM KREISWAHLLEITER VORGELEGT WERDEN +HYP: ISMUST IM GREIS WALEITAR VORGELIGT WERN +Eval: S S S S S S + +Speaker sentences 327: swc_deu_001553 #utts: 1 +id: (swc_deu_001553-swc_deu_001553) +Scores: (#C #S #D #I) 2 10 8 0 +REF: hat man EINE EMPIRISCHE BASIS FÜR PSYCHOSOZIALE PROGRAMME ZUR SENKUNG DER SELBSTMORDRATE UND ZUR STÄRKUNG DES SICHERHEITSGEFÜHLS IN DER BEVÖLKERUNG +HYP: hat man **** ********** ***** **** ************* ********* *** ******* INE IM PIERESCHEBASES FÜBSZUCHOSOT IALE PROGEAMMEZUORSENKUNGDERSEBST MUTERATHE UNDZERSTDAR KUMGDESIHGERHITZSGEFÜS INDEBEFEKERUN +Eval: D D D D D D D D S S S S S S S S S S + +Speaker sentences 328: swc_deu_001554 #utts: 1 +id: (swc_deu_001554-swc_deu_001554) +Scores: (#C #S #D #I) 3 7 4 0 +REF: BEI DEN ersten FREIEN PARLAMENTSWAHLEN WURDE ILIESCU im MAI NEUNZEHN HUNDERT neunzig IN SEINEM +HYP: *** BEIDEN ersten ****** WREINPALEMENZWALN URLE IELIEHRSGU im *** MEINUNZEIN HNDERT neunzig ** INSEINE +Eval: D S D S S S D S S D S + +Speaker sentences 329: swc_deu_001555 #utts: 1 +id: (swc_deu_001555-swc_deu_001555) +Scores: (#C #S #D #I) 1 3 3 0 +REF: damit LASSEN SICH BESTRAHLUNGSSTÄRKEN SEHR GENAU MESSEN +HYP: damit ****** **** ******************** LASEN SICHBESTRALUNGSTEREN SERGENOUMESE +Eval: D D D S S S + +Speaker sentences 330: swc_deu_001556 #utts: 1 +id: (swc_deu_001556-swc_deu_001556) +Scores: (#C #S #D #I) 0 4 5 0 +REF: WENIGE JAHRE SPÄTER KAM ES ZU EINER WEITEREN GRÜNDUNG +HYP: ****** ***** ******* *** ** WINGRSPIÄTER KAMSTZU EINE WEITERENKRNDN +Eval: D D D D D S S S S + +Speaker sentences 331: swc_deu_001557 #utts: 1 +id: (swc_deu_001557-swc_deu_001557) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ******* RADIO KABARETTPREIS +HYP: WRARDIO KABERERT PEILS +Eval: I S S + +Speaker sentences 332: swc_deu_001558 #utts: 1 +id: (swc_deu_001558-swc_deu_001558) +Scores: (#C #S #D #I) 1 5 0 0 +REF: BESTÜCKTE BOMBER auf DIE STARTBAHNEN ROLLEN +HYP: STÜK TEBOUMBER auf ISTART BANEN ROLEN +Eval: S S S S S + +Speaker sentences 333: swc_deu_001559 #utts: 1 +id: (swc_deu_001559-swc_deu_001559) +Scores: (#C #S #D #I) 2 6 3 0 +REF: mit DIESER REGELUNG SOLL EINE FAKTISCH ZWEIFACHE EINFLUSSNAHME DIESER WÄHLER auf +HYP: mit ****** ******** **** DIESERIGELUNG SOL INER FAKTUISCHTZWEIFERCH INFLUSTNAER DISERWELER auf +Eval: D D D S S S S S S + +Speaker sentences 334: swc_deu_001560 #utts: 1 +id: (swc_deu_001560-swc_deu_001560) +Scores: (#C #S #D #I) 0 2 0 1 +REF: **** BAROCKER KIRCHENBAU +HYP: BARK KÜRSCHEN BAU +Eval: I S S + +Speaker sentences 335: swc_deu_001561 #utts: 1 +id: (swc_deu_001561-swc_deu_001561) +Scores: (#C #S #D #I) 1 6 0 5 +REF: der ** *************** *** *********** ******* HERVORRAGEND WIRKENDEN LANDEKLAPPEN WIEDERUM HERVORRAGENDE LANGSAMFLUGEIGENSCHAFTEN +HYP: der HE VORAGENTZWICGEN DEN LANDEKLABEN WIDERUM HER VORAGEN ER LANGSM FLUG EIGENSCHAFTE +Eval: I I I I I S S S S S S + +Speaker sentences 336: swc_deu_001562 #utts: 1 +id: (swc_deu_001562-swc_deu_001562) +Scores: (#C #S #D #I) 0 9 3 0 +REF: MILITÄRISCHE VERBINDUNGSFLUGZEUGE ODER UMSCHULMASCHINEN FÜR DIE B F EIN HUNDERT NEUN VERWENDET +HYP: ************* ******************** **** MI TERSCH VERBINDUNGSFLUKZAGEOUDER UMSCHULMASCHIEN VÜRDIE BE E EINHNDERD NEUNVERWENDET +Eval: D D D S S S S S S S S S + +Speaker sentences 337: swc_deu_001563 #utts: 1 +id: (swc_deu_001563-swc_deu_001563) +Scores: (#C #S #D #I) 0 2 3 0 +REF: LEISTETE MEDIZINISCHE UND PSYCHOLOGISCHE HILFE +HYP: ******** ************ *** LEISTETEMEI ZINESHONBSICHELOGESCEHEILEF +Eval: D D D S S + +Speaker sentences 338: swc_deu_001564 #utts: 1 +id: (swc_deu_001564-swc_deu_001564) +Scores: (#C #S #D #I) 0 4 1 0 +REF: KANN MAN DURCH IMPFUNGEN VORBEUGEN +HYP: **** KAMANDECH IM FOMREN VORBEUIGEN +Eval: D S S S S + +Speaker sentences 339: swc_deu_001565 #utts: 1 +id: (swc_deu_001565-swc_deu_001565) +Scores: (#C #S #D #I) 0 9 1 0 +REF: MAN DEN AUSBRUCH DIESER KRANKHEIT NACH ERFOLGTER INFEKTION VERLANGSAMEN KANN +HYP: *** MERDIN AUSBUCHTISER KANKEITENER E FLUKTE INFRKTION VELLANG SMMEN KAN +Eval: D S S S S S S S S S + +Speaker sentences 340: swc_deu_001566 #utts: 1 +id: (swc_deu_001566-swc_deu_001566) +Scores: (#C #S #D #I) 1 5 1 1 +REF: DIE EINE NEUTRALITÄT unter **** ALLEN UMSTÄNDEN VORSAH +HYP: *** DIEINENEUTRDIE TET unter ALEN UMSTEN N VORSAR +Eval: D S S I S S S + +Speaker sentences 341: swc_deu_001567 #utts: 1 +id: (swc_deu_001567-swc_deu_001567) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ** UND ZIEGENHIRTEN +HYP: UN ZSIEGEN HERT +Eval: I S S + +Speaker sentences 342: swc_deu_001568 #utts: 1 +id: (swc_deu_001568-swc_deu_001568) +Scores: (#C #S #D #I) 2 9 1 0 +REF: DAS NEUNZEHN HUNDERT ACHTUNDDREISSIG GEGRÜNDETE KOMITEE FÜR UNAMERIKANISCHE umtriebe wurde DAFÜR NUN +HYP: *** DASNEUINZHN HUNDER ACHTENDREISIG GRÜNDETE KOMITIFVIR UN AMERIEKANSCHE umtriebe wurde DAFERN RU +Eval: D S S S S S S S S S + +Speaker sentences 343: swc_deu_001569 #utts: 1 +id: (swc_deu_001569-swc_deu_001569) +Scores: (#C #S #D #I) 1 7 0 5 +REF: ***** ZENTRALE DER PROGRESSIVEN und *** *** ** *** HORT DES INGENIEURGESTÜTZTEN KUNSTDENKENS +HYP: ZEIND RALE DE PROKRESIEN und HRT DIS IN HEN JÖRGESTITST EN KUNZST DENGKENS +Eval: I S S S I I I I S S S S + +Speaker sentences 344: swc_deu_001570 #utts: 1 +id: (swc_deu_001570-swc_deu_001570) +Scores: (#C #S #D #I) 2 4 1 0 +REF: in DER DER u S PRÄSIDENT ANKÜNDIGTE +HYP: in *** DERE u ESPRESEDENT AN KÜNDIGKTE +Eval: D S S S S + +Speaker sentences 345: swc_deu_001571 #utts: 1 +id: (swc_deu_001571-swc_deu_001571) +Scores: (#C #S #D #I) 1 2 0 0 +REF: SNACKS und VORSPEISEN +HYP: SNEKZST und VORSHPEISEN +Eval: S S + +Speaker sentences 346: swc_deu_001572 #utts: 1 +id: (swc_deu_001572-swc_deu_001572) +Scores: (#C #S #D #I) 0 7 3 0 +REF: DES BUNDESWAHLGESETZES BIS ZUM DREISSIGSTE JUNI ZWEI TAUSEND ELF AUFGEGEBEN +HYP: *** ****************** *** DESBUNDESWAIGESETZES BISTZUN DREISIGSTEN CHUNIE ZWEITOSENOD E AUFGEM +Eval: D D D S S S S S S S + +Speaker sentences 347: swc_deu_001573 #utts: 1 +id: (swc_deu_001573-swc_deu_001573) +Scores: (#C #S #D #I) 0 2 0 0 +REF: HENRI POUSSEUR +HYP: ARDE PUSSEÖL +Eval: S S + +Speaker sentences 348: swc_deu_001574 #utts: 1 +id: (swc_deu_001574-swc_deu_001574) +Scores: (#C #S #D #I) 2 6 0 2 +REF: FLÜCHTLINGEN von der *** ****** ETHNISCHEN MINDERHEIT DER SOMALISCHEN BANTU +HYP: FLÜCFTLINGEN von der IET NUSHEN MINDER HEIT DESOMALESHEN BAN TUN +Eval: S I I S S S S S + +Speaker sentences 349: swc_deu_001575 #utts: 1 +id: (swc_deu_001575-swc_deu_001575) +Scores: (#C #S #D #I) 1 3 0 1 +REF: die *** BIPOLARE WELTORDNUNG ZEMENTIERT +HYP: die BIE POLAREWELT ORTENUN SEMINTIERT +Eval: I S S S + +Speaker sentences 350: swc_deu_001576 #utts: 1 +id: (swc_deu_001576-swc_deu_001576) +Scores: (#C #S #D #I) 1 8 1 3 +REF: EINE INTEGRIERTE ODER EXTERN ANGEBRACHTE VORRICHTUNG an ******* ********* ******* EINEM NUKLEAREN WAFFENSYSTEM +HYP: **** TAR ANFANGEIN INTEKRIERTE UDER EXSTERN an GEBRCHT VORICHTUN ANEINEM NUKLE ARENWAFEN SSTEM +Eval: D S S S S S I I I S S S + +Speaker sentences 351: swc_deu_001577 #utts: 1 +id: (swc_deu_001577-swc_deu_001577) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ******** STARTETE DIE HILFSORGANISATION LANGFRISTIGE +HYP: STARTERT DI HILFS OGENISITZUN LANKPVRESTIG +Eval: I S S S S + +Speaker sentences 352: swc_deu_001578 #utts: 1 +id: (swc_deu_001578-swc_deu_001578) +Scores: (#C #S #D #I) 2 11 2 0 +REF: WENN DIESE EXTERNEN EFFEKTE in DER RICHTIGEN REIHENFOLGE auftreten UND SICH INNERHALB SPEZIFISCHER PARAMETER BEWEGEN +HYP: WENDIESE ECSTEREN E FEKTE in DERICHTIGEN REIN VOLGE auftreten *** **** UNDSICH INERHALBSPE ZIFISCERPAREMETER BEWIGEN +Eval: S S S S S S S D D S S S S + +Speaker sentences 353: swc_deu_001579 #utts: 1 +id: (swc_deu_001579-swc_deu_001579) +Scores: (#C #S #D #I) 2 13 4 0 +REF: ZOG DIE SOWJETUNION AUCH BEI DEN WASSERSTOFFBOMBEN UND NEUEN FLUGZEUGEN MIT INTERKONTINENTALER REICHWEITE mit den U S A GLEICH +HYP: *** *** ZUOK DESE WERTUN IONAUCHBEI DE WAERSTAOFPBAUMBEM UNDT NENIN FLUGKZOEUGEMIT INTER KONTINENTALLAREICHWEITE mit den * * URS ARGLEICH +Eval: D D S S S S S S S S S S S D D S S + +Speaker sentences 354: swc_deu_001580 #utts: 1 +id: (swc_deu_001580-swc_deu_001580) +Scores: (#C #S #D #I) 0 4 1 0 +REF: DIE STADT HAT IHR WAPPENTIER +HYP: *** PEN DE STATHE EWABENTIEOM +Eval: D S S S S + +Speaker sentences 355: swc_deu_001581 #utts: 1 +id: (swc_deu_001581-swc_deu_001581) +Scores: (#C #S #D #I) 2 4 0 0 +REF: dieser ANSATZ GILT ALLGEMEIN als AUSGEWOGENER +HYP: dieser ANSATZS GELD ALGEMEIN als AUSCGEBUORGUNDE +Eval: S S S S + +Speaker sentences 356: swc_deu_001582 #utts: 1 +id: (swc_deu_001582-swc_deu_001582) +Scores: (#C #S #D #I) 1 2 1 0 +REF: nach DEM ZUSAMMENBRUCH DER +HYP: nach *** IN ZUSAMBROHTE +Eval: D S S + +Speaker sentences 357: swc_deu_001583 #utts: 1 +id: (swc_deu_001583-swc_deu_001583) +Scores: (#C #S #D #I) 0 4 0 0 +REF: DER OBERLAUSITZ ZWISCHEN HOYERSWERDA +HYP: DE UBERLAUSETZ ZFICHENHEIER WERD +Eval: S S S S + +Speaker sentences 358: swc_deu_001584 #utts: 1 +id: (swc_deu_001584-swc_deu_001584) +Scores: (#C #S #D #I) 2 3 0 0 +REF: dabei IN zwei PHASEN UNTERTEILT +HYP: dabei EN zwei FHASEN UNTERTEIELT +Eval: S S S + +Speaker sentences 359: swc_deu_001585 #utts: 1 +id: (swc_deu_001585-swc_deu_001585) +Scores: (#C #S #D #I) 4 6 3 0 +REF: SCHWEDEN an der EUROPAMEISTERSCHAFT TEIL und WURDE MIT DER D F B elf +HYP: SCHIETEN an der ******************* EROPRMISTERSCHAFTEIL und ***** *** WURDEMTER DI R BE elf +Eval: S D S D D S S S S + +Speaker sentences 360: swc_deu_001586 #utts: 1 +id: (swc_deu_001586-swc_deu_001586) +Scores: (#C #S #D #I) 0 4 3 0 +REF: MEISTER ERÖFFNET KRABAT SCHLIESSLICH EINE WEITERE MÖGLICHKEIT +HYP: ******* ********* ****** MASTER ERLFNE KABARSCHISI ENWEITERGEMÜKLIHKEIT +Eval: D D D S S S S + +Speaker sentences 361: swc_deu_001587 #utts: 1 +id: (swc_deu_001587-swc_deu_001587) +Scores: (#C #S #D #I) 2 3 0 2 +REF: einem ********* ** AUSWÄRTSERFOLG in WOLFSBURG GELANG +HYP: einem AUSWERTZS AE VOLÜG in WOLSBUR GELAN +Eval: I I S S S + +Speaker sentences 362: swc_deu_001588 #utts: 1 +id: (swc_deu_001588-swc_deu_001588) +Scores: (#C #S #D #I) 1 5 0 1 +REF: mit *********** SCHWEBUNGSSUMMERN KONNTEN GLISSANDI ERZEUGT WERDEN +HYP: mit SCHEWEBUNGS SUMAN KONTEN KLIESAN DI ERZRELKTWERDEN +Eval: I S S S S S + +Speaker sentences 363: swc_deu_001589 #utts: 1 +id: (swc_deu_001589-swc_deu_001589) +Scores: (#C #S #D #I) 0 2 2 0 +REF: DER ABER LEDIGLICH ZEIGTE +HYP: *** **** DERBALEDIGLIHT ZEIKTE +Eval: D D S S + +Speaker sentences 364: swc_deu_001590 #utts: 1 +id: (swc_deu_001590-swc_deu_001590) +Scores: (#C #S #D #I) 0 5 0 0 +REF: GROSSBRITANNIEN EINE ERSTE WICHTIGE VEREINBARUNG +HYP: KOSPRETANDIENEINE ESTE WICHTIGEVE IN BAUN +Eval: S S S S S + +Speaker sentences 365: swc_deu_001591 #utts: 1 +id: (swc_deu_001591-swc_deu_001591) +Scores: (#C #S #D #I) 0 4 0 0 +REF: SIEHT AUCH DAS WITNESSING +HYP: ID AUHTES H UTDNESIN +Eval: S S S S + +Speaker sentences 366: swc_deu_001592 #utts: 1 +id: (swc_deu_001592-swc_deu_001592) +Scores: (#C #S #D #I) 0 9 3 0 +REF: WURDE MIT DEM BUNDESWAHLGESETZ VON NEUNZEHN HUNDERT SECHSUNDFÜNFZIG EINE DAUERHAFTE REGELUNG EINGEFÜHRT +HYP: ***** *** *** WURDEMIT DE BUNDESWAIGESETZ VONENZHNA SICHSUN FMFTIG AINE DAURHFTERELUNG ENGEFÜHRT +Eval: D D D S S S S S S S S S + +Speaker sentences 367: swc_deu_001593 #utts: 1 +id: (swc_deu_001593-swc_deu_001593) +Scores: (#C #S #D #I) 1 4 0 0 +REF: die ANZAHL DER ÜBERHANGMANDATE KANN +HYP: die ANZEAL DR IBEHNGMENDATE KAN +Eval: S S S S + +Speaker sentences 368: swc_deu_001594 #utts: 1 +id: (swc_deu_001594-swc_deu_001594) +Scores: (#C #S #D #I) 3 4 1 0 +REF: BESCHLOSS dieser ein MILITÄRISCHES eingreifen IN DEN KOREAKRIEG +HYP: BSCHLS dieser ein MLITERSCHS eingreifen ** INDEN KORARKRIK +Eval: S S D S S + +Speaker sentences 369: swc_deu_001595 #utts: 1 +id: (swc_deu_001595-swc_deu_001595) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ****** NATO VERBINDLICHE +HYP: NARTUO VE BNTLIC +Eval: I S S + +Speaker sentences 370: swc_deu_001596 #utts: 1 +id: (swc_deu_001596-swc_deu_001596) +Scores: (#C #S #D #I) 0 2 1 0 +REF: KALTE KRIEG BEENDET +HYP: ***** KALZIGRIEG BEINDET +Eval: D S S + +Speaker sentences 371: swc_deu_001597 #utts: 1 +id: (swc_deu_001597-swc_deu_001597) +Scores: (#C #S #D #I) 0 10 0 1 +REF: *** V NEUNZEHN HUNDERT DREIUNDNEUNZIG UND AUSTRALIEN SOWIE DER ÖSTERREICHISCHE ABLEGER +HYP: AUN NZE NE T EIAEMD NEUNZIG UN USTRALIEN SWIDER ÜSTEREICHSCH ABPLIGER +Eval: I S S S S S S S S S S + +Speaker sentences 372: swc_deu_001598 #utts: 1 +id: (swc_deu_001598-swc_deu_001598) +Scores: (#C #S #D #I) 2 15 1 0 +REF: da DIE SEIT ANFANG NEUNZEHN HUNDERT NEUNUNDFÜNFZIG DORT HERRSCHENDE REVOLUTIONSREGIERUNG UNTER FIDEL CASTRO EINEN SOZIALISTISCHEN kurs EINGESCHLAGEN HATTE +HYP: da DIESEIT AN FANGNEUNZHN UNDERTNEUNUN FÜNFZI DRT HERSHEN DERERULOTIOUNZRIGJIONG UNDER VIE DEL KASTRU EINDEN SOSELISTISCHEN kurs ************* EINGESHLAGENHAT +Eval: S S S S S S S S S S S S S S D S + +Speaker sentences 373: swc_deu_001599 #utts: 1 +id: (swc_deu_001599-swc_deu_001599) +Scores: (#C #S #D #I) 0 20 3 0 +REF: NACH WEITEREN VERLUSTREICHEN KÄMPFEN OHNE NENNENSWERTE ERFOLGE BEIDER KRIEGSPARTEIEN WURDE RUND DREI JAHRE NACH BEGINN DER AUSEINANDERSETZUNG EIN BIS HEUTE GÜLTIGES WAFFENSTILLSTANDSABKOMMEN ABGESCHLOSSEN +HYP: **** ******** ************** DACHRWEITEREN VELUSTRECHEN KEMFEN UNENENZWÖRT E VOLGE BEIDE GRIGSPATEIN URDERUNT DREARENER BEGINDE AUSANDANDERSETZUNGEIN BS REITE GÜLTIGES WASFENEN STILSTANS AB KOMMN APGESCLSSEN +Eval: D D D S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 374: voxforge_deu_000891 #utts: 1 +id: (voxforge_deu_000891-voxforge_deu_000891) +Scores: (#C #S #D #I) 1 3 1 0 +REF: man IST DABEI SEHR VORSICHTIG +HYP: man *** ISTERBEIS ER VOSICHTICH +Eval: D S S S + +Speaker sentences 375: voxforge_deu_000892 #utts: 1 +id: (voxforge_deu_000892-voxforge_deu_000892) +Scores: (#C #S #D #I) 5 4 1 2 +REF: die *** WEHRPFLICHT SOLL in deutschland *** LEIDER noch nicht ABGESCHAFFT WERDEN +HYP: die WER FLICHT SOL in deutschland LEI ER noch nicht *********** ABGESCHAFTWERN +Eval: I S S I S D S + +Speaker sentences 376: voxforge_deu_000893 #utts: 1 +id: (voxforge_deu_000893-voxforge_deu_000893) +Scores: (#C #S #D #I) 2 4 0 1 +REF: es GIBT auch ********* MISSBRAUCH DURCH ARBEITGEBER +HYP: es GEBT auch MISPRAUCH TUCH ABERT GEBER +Eval: S I S S S + +Speaker sentences 377: voxforge_deu_000894 #utts: 1 +id: (voxforge_deu_000894-voxforge_deu_000894) +Scores: (#C #S #D #I) 0 4 2 0 +REF: DIE KINDER SIND DANN KRANK GEWORDEN +HYP: *** ****** DIEKINDE SIN DAN HANKEBONEN +Eval: D D S S S S + +Speaker sentences 378: voxforge_deu_000895 #utts: 1 +id: (voxforge_deu_000895-voxforge_deu_000895) +Scores: (#C #S #D #I) 2 4 1 1 +REF: DIE TRAGWEITE der ****** KATASTROPHE soll VERDEUTLICHT WERDEN +HYP: DE RACKWEITE der KADAST OFE soll ************ VERDEUTLICHTWERDN +Eval: S S I S D S + +Speaker sentences 379: voxforge_deu_000897 #utts: 1 +id: (voxforge_deu_000897-voxforge_deu_000897) +Scores: (#C #S #D #I) 0 1 0 1 +REF: *** ÄH +HYP: SEN RLLNDET +Eval: I S + +Speaker sentences 380: voxforge_deu_000898 #utts: 1 +id: (voxforge_deu_000898-voxforge_deu_000898) +Scores: (#C #S #D #I) 1 4 0 1 +REF: BEIM ORGANSTREIT streiten *** OBERSTE VERFASSUNGSORGANE +HYP: BEI MOGANDSTREIT streiten BER DVE FASSUNGSOGANE +Eval: S S I S S + +Speaker sentences 381: voxforge_deu_000899 #utts: 1 +id: (voxforge_deu_000899-voxforge_deu_000899) +Scores: (#C #S #D #I) 1 3 2 0 +REF: DAS WAGE ICH JA zu BEZWEIFELN +HYP: *** **** DAWAGE CHAR zu BTZWEIFEN +Eval: D D S S S + +Speaker sentences 382: voxforge_deu_000900 #utts: 1 +id: (voxforge_deu_000900-voxforge_deu_000900) +Scores: (#C #S #D #I) 1 4 3 0 +REF: MAN SOLLTE DENEN auf GAR KEINEN FALL TRAUEN +HYP: *** MANSOTE DEN auf *** ****** GARGHEIN VFALTRAUN +Eval: D S S D D S S + +Speaker sentences 383: voxforge_deu_000901 #utts: 1 +id: (voxforge_deu_000901-voxforge_deu_000901) +Scores: (#C #S #D #I) 0 7 0 1 +REF: ** DIE ÖFFENTLICHEN SCHULDEN WERDEN NICHT GETILGT WERDEN +HYP: DE EFN LICHE SCHULEN WER N NICH GETELKTWEREN +Eval: I S S S S S S S + +Speaker sentences 384: voxforge_deu_000902 #utts: 1 +id: (voxforge_deu_000902-voxforge_deu_000902) +Scores: (#C #S #D #I) 2 3 0 0 +REF: DAS GELD ist AUSGEZAHLT worden +HYP: BAE GELT ist AUSKETZHLT worden +Eval: S S S + +Speaker sentences 385: voxforge_deu_000903 #utts: 1 +id: (voxforge_deu_000903-voxforge_deu_000903) +Scores: (#C #S #D #I) 0 6 0 1 +REF: ****** ES SOLLEN DREIHUNDERTTAUSEND NEUE ARBEITSPLÄTZE ENTSTEHEN +HYP: ESOLEN REI HUNDERD TAUSEND NOE ABESPLÄTE INSTIEN +Eval: I S S S S S S + +Speaker sentences 386: voxforge_deu_000904 #utts: 1 +id: (voxforge_deu_000904-voxforge_deu_000904) +Scores: (#C #S #D #I) 0 5 2 0 +REF: DIE KÖRPERVERLETZUNG KANN ALS BEISPIEL GENANNT WERDEN +HYP: *** ***************** DIEKERBER VELETZUNG KAN ALSBEISPILEND WERDENT +Eval: D D S S S S S + +Speaker sentences 387: voxforge_deu_000905 #utts: 1 +id: (voxforge_deu_000905-voxforge_deu_000905) +Scores: (#C #S #D #I) 0 4 1 0 +REF: DIESE GRENZE IST ÜBERSCHRITTEN WORDEN +HYP: ***** DIE RENZE IT WERSCHTENBODEN +Eval: D S S S S + +Speaker sentences 388: voxforge_deu_000906 #utts: 1 +id: (voxforge_deu_000906-voxforge_deu_000906) +Scores: (#C #S #D #I) 1 3 4 0 +REF: DASS STRAFVERFOLGUNGSBEHÖRDEN KEINEN ZUGRIFF AUF DAS GELD haben +HYP: **** ************************* ****** ******* DSTDABEFELUGSBEÜRDEN KEIENZULI ERESKEL haben +Eval: D D D D S S S + +Speaker sentences 389: voxforge_deu_000907 #utts: 1 +id: (voxforge_deu_000907-voxforge_deu_000907) +Scores: (#C #S #D #I) 2 3 0 1 +REF: DIE INTERESSEN finden kein * GEHÖR +HYP: DI INERESEN finden kein E HÖR +Eval: S S I S + +Speaker sentences 390: voxforge_deu_000908 #utts: 1 +id: (voxforge_deu_000908-voxforge_deu_000908) +Scores: (#C #S #D #I) 0 5 0 4 +REF: * ************ *********** ********** PFEILTASTE TABULATOR RÜCKSCHRITTTASTE RÜCKTASTE RÜCKLÖSCHTASTE +HYP: I FWEILTASSTDE TABLARTOHER RÜGSCHIED TASSTDEN RÜGG TASSTDE RÜGEGIERSTASSTDE A +Eval: I I I I S S S S S + +Speaker sentences 391: voxforge_deu_000909 #utts: 1 +id: (voxforge_deu_000909-voxforge_deu_000909) +Scores: (#C #S #D #I) 1 6 1 0 +REF: DER BETROFFENE MUSS EIN BERECHTIGTES INTERESSE GELTEND machen +HYP: *** DEBE TROTCENENUN ANBERECHTIG DEEN HER IGELTEN machen +Eval: D S S S S S S + +Speaker sentences 392: voxforge_deu_000910 #utts: 1 +id: (voxforge_deu_000910-voxforge_deu_000910) +Scores: (#C #S #D #I) 2 7 0 0 +REF: ein DRITTER HAT dem GESCHÄDIGTEN FREIWILLIG LEISTUNGEN ZUKOMMEN LASSEN +HYP: ein DRITER HARD dem SCHEIDIGKTEN VREIWELIGK LEISTDONGEN ZUKOMEN LAEN +Eval: S S S S S S S + +Speaker sentences 393: voxforge_deu_000911 #utts: 1 +id: (voxforge_deu_000911-voxforge_deu_000911) +Scores: (#C #S #D #I) 0 5 1 0 +REF: SONDERN AUCH RECHTS NEBEN DEM BILD +HYP: ******* SONEN AURECHZ NEBE DE BILT +Eval: D S S S S S + +Speaker sentences 394: voxforge_deu_000912 #utts: 1 +id: (voxforge_deu_000912-voxforge_deu_000912) +Scores: (#C #S #D #I) 0 6 2 0 +REF: SIE HAT EINE NICHT ERNSTLICH GEMEINTE WILLENSERKLÄRUNG ABGEGEBEN +HYP: *** *** IER EINENICHT ARZLSCHEMEITE BÜL SERKLEUN ABPKREN +Eval: D D S S S S S S + +Speaker sentences 395: voxforge_deu_000913 #utts: 1 +id: (voxforge_deu_000913-voxforge_deu_000913) +Scores: (#C #S #D #I) 2 6 0 1 +REF: DAS MUSSTE JA auf ** JEDEN FALL so KOMMEN +HYP: DA MUSTE JAHR auf IE EN FAL so KOMEN +Eval: S S S I S S S + +Speaker sentences 396: voxforge_deu_000914 #utts: 1 +id: (voxforge_deu_000914-voxforge_deu_000914) +Scores: (#C #S #D #I) 0 8 0 1 +REF: ****** MEHRERE CLIENTS KÖNNEN SICH EINE IP ADRESSE TEILEN +HYP: MERERE KLEINS KON SICG EIN EI PIE R RESETEINT +Eval: I S S S S S S S S + +Speaker sentences 397: voxforge_deu_000915 #utts: 1 +id: (voxforge_deu_000915-voxforge_deu_000915) +Scores: (#C #S #D #I) 0 7 3 0 +REF: WAR DIE GÜNSTIGERE ES HIESS ALSO SICH ZUSAMMENNEHMEN ANSTATT ZU +HYP: *** *** *********** WA DIEGINZTIGERVEIS HIES ALSOR SIHZUSAMENENMEN ANSTAT ZUN +Eval: D D D S S S S S S S + +Speaker sentences 398: voxforge_deu_000917 #utts: 1 +id: (voxforge_deu_000917-voxforge_deu_000917) +Scores: (#C #S #D #I) 1 5 0 0 +REF: der SCHULDNER HAT SEINE LEISTUNG ANGEBOTEN +HYP: der CHOL E HERZEINELEISTUNG AN GEBORTEN +Eval: S S S S S + +Speaker sentences 399: voxforge_deu_000918 #utts: 1 +id: (voxforge_deu_000918-voxforge_deu_000918) +Scores: (#C #S #D #I) 0 1 2 0 +REF: SO DASS ES +HYP: ** **** SODASEISF +Eval: D D S + +Speaker sentences 400: voxforge_deu_000919 #utts: 1 +id: (voxforge_deu_000919-voxforge_deu_000919) +Scores: (#C #S #D #I) 0 6 0 0 +REF: DIE BATTERIEN WAREN SEHR STARK VERALTET +HYP: DEBATRIEN WARN ER STA VER ALTET +Eval: S S S S S S + +Speaker sentences 401: voxforge_deu_000920 #utts: 1 +id: (voxforge_deu_000920-voxforge_deu_000920) +Scores: (#C #S #D #I) 0 5 1 0 +REF: DIESES ZIEL WURDE NUR TEILWEISE ERREICHT +HYP: ****** DESES ZHIE VORDENORTAL WALSE REICHT +Eval: D S S S S S + +Speaker sentences 402: voxforge_deu_000921 #utts: 1 +id: (voxforge_deu_000921-voxforge_deu_000921) +Scores: (#C #S #D #I) 0 3 3 0 +REF: DIESE WÄHRUNG WIRD SEHR LANGE LEBEN +HYP: ***** ******** **** TIESEWERUNG WIRT SELRLANGELEBEN +Eval: D D D S S S + +Speaker sentences 403: voxforge_deu_000922 #utts: 1 +id: (voxforge_deu_000922-voxforge_deu_000922) +Scores: (#C #S #D #I) 0 5 0 0 +REF: DORT ZITTERN OFFENBAR SCHON VIELE +HYP: DRD ZEIEAN AFEN BASHON VIEE +Eval: S S S S S + +Speaker sentences 404: voxforge_deu_000923 #utts: 1 +id: (voxforge_deu_000923-voxforge_deu_000923) +Scores: (#C #S #D #I) 0 7 6 0 +REF: ALS SIE GINGEN NICKTE MAGGIE IHR NUR GANZ FLÜCHTIG ZU UND DER VATER +HYP: *** *** ****** ****** ****** *** ALSIGEENEN NEIGKTEMÄAGI IER NOR GANSFLCHTIGKZIUOU BUNDER FARTEAR +Eval: D D D D D D S S S S S S S + +Speaker sentences 405: voxforge_deu_000924 #utts: 1 +id: (voxforge_deu_000924-voxforge_deu_000924) +Scores: (#C #S #D #I) 0 4 1 0 +REF: ERZÄHL MIR MEHR ÜBER CHRISTIAN +HYP: ******* T IERTE MEMIE BERISTIAN +Eval: D S S S S + +Speaker sentences 406: voxforge_deu_000925 #utts: 1 +id: (voxforge_deu_000925-voxforge_deu_000925) +Scores: (#C #S #D #I) 2 4 0 1 +REF: dem STEHEN NATÜRLICH auch ********* VERMÖGEN GEGENÜBER +HYP: dem STDEHE NADTÜÖRLICH auch FAMMÖHEN GEGEN ÜEBER +Eval: S S I S S + +Speaker sentences 407: voxforge_deu_000926 #utts: 1 +id: (voxforge_deu_000926-voxforge_deu_000926) +Scores: (#C #S #D #I) 0 4 3 0 +REF: DIE REALE LAGE WIRD NICHT VOLLSTÄNDIG ABGEBILDET +HYP: *** ***** **** DIEE REALELANGE WIETNICHTVOUSTENDICH ABPGEBELET +Eval: D D D S S S S + +Speaker sentences 408: voxforge_deu_000927 #utts: 1 +id: (voxforge_deu_000927-voxforge_deu_000927) +Scores: (#C #S #D #I) 2 3 2 0 +REF: ES KANN auch noch VIEL SCHLIMMER WERDEN +HYP: ** ESKAN auch noch **** FVIE SCLMAWERDEN +Eval: D S D S S + +Speaker sentences 409: voxforge_deu_000928 #utts: 1 +id: (voxforge_deu_000928-voxforge_deu_000928) +Scores: (#C #S #D #I) 0 2 3 0 +REF: DIE POLITIK INTERESSIERT NICHT MEHR +HYP: *** ******* ************ DIEBPLITIG INRESIERZSICHNICHTEMER +Eval: D D D S S + +Speaker sentences 410: voxforge_deu_000929 #utts: 1 +id: (voxforge_deu_000929-voxforge_deu_000929) +Scores: (#C #S #D #I) 0 8 0 1 +REF: ************** INHALTSFREIHEIT BEDEUTET DASS DER INHALT DER VERTRAGLICHEN VEREINBARUNGEN +HYP: INHALTZFREHERD BEDEITEDT DAS E INHALD DE VERTRACKLIHN VE EINBARUNGENU +Eval: I S S S S S S S S + +Speaker sentences 411: voxforge_deu_000930 #utts: 1 +id: (voxforge_deu_000930-voxforge_deu_000930) +Scores: (#C #S #D #I) 0 5 1 0 +REF: DER SCHULDNER VERLETZTE SEINE SORGFALTSPFLICHTEN SCHULDHAFT +HYP: *** DERCU NER VELITZ DESEIESUOGKVERSPLI ENSCHULTEAFT +Eval: D S S S S S + +Speaker sentences 412: voxforge_deu_000931 #utts: 1 +id: (voxforge_deu_000931-voxforge_deu_000931) +Scores: (#C #S #D #I) 1 5 0 1 +REF: DIESES getreide **** DIENT INSBESONDERE ALS VIEHFUTTER +HYP: DIESE getreide DEND INZSPESONDERE AL SFVI VOTTA +Eval: S I S S S S + +Speaker sentences 413: voxforge_deu_000932 #utts: 1 +id: (voxforge_deu_000932-voxforge_deu_000932) +Scores: (#C #S #D #I) 0 8 0 0 +REF: TYPISCHERWEISE WERDEN STATISCHE IP ADRESSEN VON SERVERN EINGESETZT +HYP: ZTÜPBESCHRWEISE WERDSTDATICHE EI PI R RESEN VONDSORVEN EINGETZT +Eval: S S S S S S S S + +Speaker sentences 414: voxforge_deu_000933 #utts: 1 +id: (voxforge_deu_000933-voxforge_deu_000933) +Scores: (#C #S #D #I) 0 4 2 0 +REF: JETZT WIRD ES SO LANGSAM GEGLAUBT +HYP: ***** **** JEZTWR ZO LANEAM GEGLAUBPT +Eval: D D S S S S + +Speaker sentences 415: voxforge_deu_000934 #utts: 1 +id: (voxforge_deu_000934-voxforge_deu_000934) +Scores: (#C #S #D #I) 0 5 0 1 +REF: *************** UNTERSCHIEDLICHE EREIGNISSE HABEN SICH EREIGNET +HYP: UNDARSCHITLICHE ERGEBNESE HABEM SECH ER EIGNERDT +Eval: I S S S S S + +Speaker sentences 416: voxforge_deu_000935 #utts: 1 +id: (voxforge_deu_000935-voxforge_deu_000935) +Scores: (#C #S #D #I) 2 5 0 0 +REF: TERRORVERDÄCHTIGE WURDEN NICHT vor ein GERICHT GESTELLT +HYP: TERHFARDECHTIGE WUORDEN NEÄCHT vor ein GERECHT GESTÄLLT +Eval: S S S S S + +Speaker sentences 417: voxforge_deu_000936 #utts: 1 +id: (voxforge_deu_000936-voxforge_deu_000936) +Scores: (#C #S #D #I) 2 5 4 0 +REF: aufmachen DIE STIEFEL nicht AUSZIEHEN UND WEISS GOTT WAS NOCH ALLES +HYP: aufmachen *** DISTDEFÜ nicht ********* *** ***** AUSTZIEHN UN WEISEGERDWASNOC ALES +Eval: D S D D D S S S S + +Speaker sentences 418: voxforge_deu_000937 #utts: 1 +id: (voxforge_deu_000937-voxforge_deu_000937) +Scores: (#C #S #D #I) 1 6 1 1 +REF: ** INSGESAMT 23 personen AUS VERSCHIEDENEN PARLAMENTEN NEHMEN TEIL +HYP: IN KESAM DREIUNDZWANZIC personen *** AUSVERSCHIEDEN PAEMENTEN NEMEN TEI +Eval: I S S D S S S S + +Speaker sentences 419: voxforge_deu_000938 #utts: 1 +id: (voxforge_deu_000938-voxforge_deu_000938) +Scores: (#C #S #D #I) 0 6 0 0 +REF: FORDERUNGSRECHTE WERDEN DEM GLÄUBIGER AUSSCHLIESSLICH ZUGEORDNET +HYP: VORDRUNGS EFTE WERENEM GLEIBIGE AUSCLIESIE ZOGEORORTENET +Eval: S S S S S S + +Speaker sentences 420: voxforge_deu_000939 #utts: 1 +id: (voxforge_deu_000939-voxforge_deu_000939) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ** DAS PROBLEM WURDE BEHOBEN +HYP: DA POBLEEM HWVWRDE BE HOBEN +Eval: I S S S S + +Speaker sentences 421: voxforge_deu_000940 #utts: 1 +id: (voxforge_deu_000940-voxforge_deu_000940) +Scores: (#C #S #D #I) 0 7 0 1 +REF: ** FÜR DIE ERKENNUNG VON UNTERBROCHENER DISKRETER SPRACHE +HYP: FR DIER HEINUN VN UNTER ROCHENER DESKRITER SPACHE +Eval: I S S S S S S S + +Speaker sentences 422: voxforge_deu_000941 #utts: 1 +id: (voxforge_deu_000941-voxforge_deu_000941) +Scores: (#C #S #D #I) 0 5 2 0 +REF: DIE CHINESEN KÖNNTEN SEHR VIEL WICHTIGER WERDEN +HYP: *** ******** DICHINSEN KÖÜNDTEN SER FL WICHTIGAWERDN +Eval: D D S S S S S + +Speaker sentences 423: voxforge_deu_000942 #utts: 1 +id: (voxforge_deu_000942-voxforge_deu_000942) +Scores: (#C #S #D #I) 0 4 4 0 +REF: DIESER SCHLÜSSEL WIRD LEDIGLICH EIN EINZIGES MAL VERWENDET +HYP: ****** ********** **** ********* DIERSTUSLIE DE DIGLIHR EINZIGEMALVERWENDET +Eval: D D D D S S S S + +Speaker sentences 424: voxforge_deu_000943 #utts: 1 +id: (voxforge_deu_000943-voxforge_deu_000943) +Scores: (#C #S #D #I) 5 3 0 2 +REF: das land ** ENTWICKELTE SICH zu einer **** MILITÄRISCHEN grossmacht +HYP: das land EN WEGKELTE SECH zu einer MEIE TERESCHEN grossmacht +Eval: I S S I S + +Speaker sentences 425: voxforge_deu_000944 #utts: 1 +id: (voxforge_deu_000944-voxforge_deu_000944) +Scores: (#C #S #D #I) 1 3 1 0 +REF: ES SIND und BLEIBEN VERBRECHERBANDEN +HYP: ** ESINDT und BLEIBE VERBREICHERBANDEN +Eval: D S S S + +Speaker sentences 426: voxforge_deu_000945 #utts: 1 +id: (voxforge_deu_000945-voxforge_deu_000945) +Scores: (#C #S #D #I) 3 2 0 0 +REF: die zeiten WERDEN sich ÄNDERN +HYP: die zeiten WEREN sich ENDAN +Eval: S S + +Speaker sentences 427: voxforge_deu_000946 #utts: 1 +id: (voxforge_deu_000946-voxforge_deu_000946) +Scores: (#C #S #D #I) 4 5 0 0 +REF: den STIFT in DIE ACHSBOHRUNG einschieben BIS zum ANSCHLAG +HYP: den STEFT in IE AKSBORUNG einschieben WIST zum ANSCLAG +Eval: S S S S S + +Speaker sentences 428: voxforge_deu_000947 #utts: 1 +id: (voxforge_deu_000947-voxforge_deu_000947) +Scores: (#C #S #D #I) 2 7 0 2 +REF: die AUCH beim ******** ***** BROWSER WIRKSAM WIRD BEISPIELSWEISE BEIM FIREFOX +HYP: die AUCHT beim BOAURSER WVIER SAMWIERT BALSPBILTS WEIS S BEL VEIERHFOGSN +Eval: S I I S S S S S S + +Speaker sentences 429: voxforge_deu_000948 #utts: 1 +id: (voxforge_deu_000948-voxforge_deu_000948) +Scores: (#C #S #D #I) 0 4 2 0 +REF: DAS WAR NOCH GAR KEINE KRISE +HYP: *** *** DAWANOC GA HEINI KLIE +Eval: D D S S S S + +Speaker sentences 430: voxforge_deu_000950 #utts: 1 +id: (voxforge_deu_000950-voxforge_deu_000950) +Scores: (#C #S #D #I) 1 2 3 0 +REF: die HABEN OFFENBAR ZIEMLICH GROSSE ANGST +HYP: die ***** ******** ******** HABEM AUFMBARZHMNICHKOSEANGST +Eval: D D D S S + +Speaker sentences 431: voxforge_deu_000951 #utts: 1 +id: (voxforge_deu_000951-voxforge_deu_000951) +Scores: (#C #S #D #I) 1 3 0 0 +REF: VIELE verlieren IHREN ARBEITSPLATZ +HYP: FIELE verlieren EREN ABEITSPLATZS +Eval: S S S + +Speaker sentences 432: voxforge_deu_000952 #utts: 1 +id: (voxforge_deu_000952-voxforge_deu_000952) +Scores: (#C #S #D #I) 2 3 0 2 +REF: *** DAFÜR GIBT es einen ***** PUNKTABZUG +HYP: DAR FÜHER GEBT es einen PUNKT ABPTZIOGE +Eval: I S S I S + +Speaker sentences 433: voxforge_deu_000953 #utts: 1 +id: (voxforge_deu_000953-voxforge_deu_000953) +Scores: (#C #S #D #I) 1 9 0 0 +REF: DIE BEIDEN SIND ÜBER EINE UNSICHERE VERBINDUNG MITEINANDER IN kontakt +HYP: C DIEBEIDEN SENDT WEINE UN SICHE VERBENUNG MUTDANANDER N kontakt +Eval: S S S S S S S S S + +Speaker sentences 434: voxforge_deu_000954 #utts: 1 +id: (voxforge_deu_000954-voxforge_deu_000954) +Scores: (#C #S #D #I) 3 3 0 1 +REF: *** BEIDE STECKEN tief in roten ZAHLEN +HYP: BEI DE STÄKEN tief in roten ZALN +Eval: I S S S + +Speaker sentences 435: voxforge_deu_000955 #utts: 1 +id: (voxforge_deu_000955-voxforge_deu_000955) +Scores: (#C #S #D #I) 1 5 0 0 +REF: FÜNFZEHN UHR FÜNFZEHN DORF ON golf +HYP: FÜNDFTZEHN OE FÜNFTZEN DOF ONEN golf +Eval: S S S S S + +Speaker sentences 436: voxforge_deu_000956 #utts: 1 +id: (voxforge_deu_000956-voxforge_deu_000956) +Scores: (#C #S #D #I) 0 7 5 0 +REF: WIE MENSCHEN AUS EINER ANDERN WELT ERSCHIENEN SIE IHR HEUTE UND DOCH +HYP: *** ******** *** ***** ****** WIEMEINSCHEN ASEINE ANDERENWÄLT SCHERDENZI ER RETE UNDTDOCH +Eval: D D D D D S S S S S S S + +Speaker sentences 437: voxforge_deu_000957 #utts: 1 +id: (voxforge_deu_000957-voxforge_deu_000957) +Scores: (#C #S #D #I) 0 4 3 0 +REF: BÜNDIG MIT DEM HINTERN DES KAMELS AUFHÖRT +HYP: ******* *** *** BNDHMIT EM HINTERNDES KEMILSAUFERT +Eval: D D D S S S S + +Speaker sentences 438: voxforge_deu_000958 #utts: 1 +id: (voxforge_deu_000958-voxforge_deu_000958) +Scores: (#C #S #D #I) 2 8 0 0 +REF: ACH DER OBERFÖRSTER ZUCKTE mit DEN SCHIEFEN GRAUEN BRAUEN ein +HYP: ADER UBERFÖRSTDEA ZUNGD DE mit EN CHIEFEN GAUN BGAUEN ein +Eval: S S S S S S S S + +Speaker sentences 439: voxforge_deu_000959 #utts: 1 +id: (voxforge_deu_000959-voxforge_deu_000959) +Scores: (#C #S #D #I) 2 4 2 0 +REF: ICH WUNDERE MICH IMMER wieder Über DIESE ERKLÄRUNGEN +HYP: *** ******* ICHWUNDEREMIG IMAR wieder Über IESE ERKLERUNGEN +Eval: D D S S S S + +Speaker sentences 440: voxforge_deu_000960 #utts: 1 +id: (voxforge_deu_000960-voxforge_deu_000960) +Scores: (#C #S #D #I) 0 7 0 0 +REF: BEI EINEM SYMMETRISCHEN KRYPTOSYSTEM WIRD ANDERS VORGEGANGEN +HYP: BA INEM SMETRISCHN KRIOPTUSTEM BET ANDES VORGEGAN +Eval: S S S S S S S + +Speaker sentences 441: voxforge_deu_000961 #utts: 1 +id: (voxforge_deu_000961-voxforge_deu_000961) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ** DAS IST DORT VERZEICHNET +HYP: DA IS DORD VER ZEICHETE +Eval: I S S S S + +Speaker sentences 442: voxforge_deu_000962 #utts: 1 +id: (voxforge_deu_000962-voxforge_deu_000962) +Scores: (#C #S #D #I) 0 6 0 0 +REF: GELD IST EIN SEHR GUTES TAUSCHMITTEL +HYP: GELT IS AN ER UTES TAUSCMITE +Eval: S S S S S S + +Speaker sentences 443: voxforge_deu_000963 #utts: 1 +id: (voxforge_deu_000963-voxforge_deu_000963) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ** DAS WÄRE WISSENSCHAFTLICH NOTWENDIG +HYP: DA WEHREWISEN CHAFTLICH NOD WENDEIGKG +Eval: I S S S S + +Speaker sentences 444: voxforge_deu_000964 #utts: 1 +id: (voxforge_deu_000964-voxforge_deu_000964) +Scores: (#C #S #D #I) 0 6 0 0 +REF: NUR BESTIMMTE STRAFTATEN KOMMEN IN BETRACHT +HYP: NBES DINTIS STRAP TATEN KOMENEN BETHACHT +Eval: S S S S S S + +Speaker sentences 445: voxforge_deu_000965 #utts: 1 +id: (voxforge_deu_000965-voxforge_deu_000965) +Scores: (#C #S #D #I) 1 5 0 1 +REF: damit ****** KANN MAN WAHRSCHEINLICH SCHLECHT EINKAUFEN +HYP: damit KANMAN BARSCHEIN DI CHLÄCHT EIEN KAUFEN +Eval: I S S S S S + +Speaker sentences 446: voxforge_deu_000966 #utts: 1 +id: (voxforge_deu_000966-voxforge_deu_000966) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** DAFÜR WURDE GESORGT +HYP: DA FIE WODE GESOKT +Eval: I S S S + +Speaker sentences 447: voxforge_deu_000967 #utts: 1 +id: (voxforge_deu_000967-voxforge_deu_000967) +Scores: (#C #S #D #I) 0 6 0 0 +REF: MAN KANN DAS SEHR GUT VERKAUFEN +HYP: CA MAIN ADER E UT VEKAUFENN +Eval: S S S S S S + +Speaker sentences 448: voxforge_deu_000968 #utts: 1 +id: (voxforge_deu_000968-voxforge_deu_000968) +Scores: (#C #S #D #I) 0 4 1 0 +REF: SONDERN AUCH IN DER STEUERHINTERZIEHUNG +HYP: ******* ZNEN AUCHNDE STELE ERTIOUNG +Eval: D S S S S + +Speaker sentences 449: voxforge_deu_000969 #utts: 1 +id: (voxforge_deu_000969-voxforge_deu_000969) +Scores: (#C #S #D #I) 2 4 0 1 +REF: ******* DARÜBER REDET die PASTORIN UND redet +HYP: DARIBER RIE DE die PASTOGRINEN UNDT redet +Eval: I S S S S + +Speaker sentences 450: voxforge_deu_000970 #utts: 1 +id: (voxforge_deu_000970-voxforge_deu_000970) +Scores: (#C #S #D #I) 0 6 1 0 +REF: DEN SCHALTER IN DEN DRITTEN GANG STELLEN +HYP: *** DEI CHLEN EN E E DANSDEEN +Eval: D S S S S S S + +Speaker sentences 451: voxforge_deu_000971 #utts: 1 +id: (voxforge_deu_000971-voxforge_deu_000971) +Scores: (#C #S #D #I) 3 4 0 0 +REF: auf den ersten BLICK SCHEINT DAS UNGEWÖHNLICH +HYP: auf den ersten BLEG SCHEINDAS UN GEVÖÜRNLEICH +Eval: S S S S + +Speaker sentences 452: voxforge_deu_000972 #utts: 1 +id: (voxforge_deu_000972-voxforge_deu_000972) +Scores: (#C #S #D #I) 1 5 3 0 +REF: der DOLLAR WIRD NICHT MEHR ALS WÄHRUNG AKZEPTIERT WERDEN +HYP: der ****** **** ***** DOLA WIERTENICHTMEHR IS WERUNG AKZIEPTIERTWERDEN +Eval: D D D S S S S S + +Speaker sentences 453: voxforge_deu_000973 #utts: 1 +id: (voxforge_deu_000973-voxforge_deu_000973) +Scores: (#C #S #D #I) 4 5 4 0 +REF: IHREN KOPF fest gegen den HALS DER JÜNGEREN DANN KÜSSTE SIE DEN vater +HYP: ILEN KOUTF fest gegen den **** *** ********* **** HALSTERINGERENDANM KÜSTDISSIE DIN vater +Eval: S S D D D D S S S + +Speaker sentences 454: voxforge_deu_000974 #utts: 1 +id: (voxforge_deu_000974-voxforge_deu_000974) +Scores: (#C #S #D #I) 0 3 1 0 +REF: DAS WURDE NICHT WAHRGENOMMEN +HYP: *** DA WORDENICHT WAGENOMMEN +Eval: D S S S + +Speaker sentences 455: voxforge_deu_000975 #utts: 1 +id: (voxforge_deu_000975-voxforge_deu_000975) +Scores: (#C #S #D #I) 1 3 1 0 +REF: man HAT DAS DAMALS VORGELESEN +HYP: man *** HTTER DAMEITS VORGELEN +Eval: D S S S + +Speaker sentences 456: voxforge_deu_000976 #utts: 1 +id: (voxforge_deu_000976-voxforge_deu_000976) +Scores: (#C #S #D #I) 0 7 4 0 +REF: BEI BESONDERS WERTVOLLEN SACHEN IST DIE GRENZE NIEDRIGER ALS DER WARENWERT +HYP: *** ********* ********** ****** BEIBESONDER WERT WONSACHUNG IS DIKENZEMITRIGE IS DERWANMERT +Eval: D D D D S S S S S S S + +Speaker sentences 457: voxforge_deu_000977 #utts: 1 +id: (voxforge_deu_000977-voxforge_deu_000977) +Scores: (#C #S #D #I) 0 3 1 0 +REF: DAS MUSS ZURÜCKGEZAHLT WERDEN +HYP: *** NDSMOTZREIKET ZELT WEHRDEN +Eval: D S S S + +Speaker sentences 458: voxforge_deu_000978 #utts: 1 +id: (voxforge_deu_000978-voxforge_deu_000978) +Scores: (#C #S #D #I) 1 4 0 0 +REF: zwischen GLÄUBIGER UND SCHULDNER HERGELEITET +HYP: zwischen LOLBIGER UNDSCHOLDENE HER ELEITET +Eval: S S S S + +Speaker sentences 459: voxforge_deu_000979 #utts: 1 +id: (voxforge_deu_000979-voxforge_deu_000979) +Scores: (#C #S #D #I) 2 4 0 0 +REF: ein ABSOLUTES recht WURDE RECHTSWIDRIG VERLETZT +HYP: ein ABZUNMUNTES recht IE ECHTZIERI VLLET +Eval: S S S S + +Speaker sentences 460: voxforge_deu_000980 #utts: 1 +id: (voxforge_deu_000980-voxforge_deu_000980) +Scores: (#C #S #D #I) 4 3 1 0 +REF: man BRAUCHT NICHT an den ZUFALL zu GLAUBEN +HYP: man ******* BERAUCHTDNECHT an den ZUOFL zu KLAUBEM +Eval: D S S S + +Speaker sentences 461: voxforge_deu_000981 #utts: 1 +id: (voxforge_deu_000981-voxforge_deu_000981) +Scores: (#C #S #D #I) 2 6 3 0 +REF: ZÄRTLICHEN wesen nur ENTFALTEN WO MAN IHR LIEBE BOT VOR HARTEN +HYP: ZERLICHEN wesen nur ********* ** *** INT FWALETEN WOMAN IELIEBEBOT VORHARTEN +Eval: S D D D S S S S S + +Speaker sentences 462: voxforge_deu_000982 #utts: 1 +id: (voxforge_deu_000982-voxforge_deu_000982) +Scores: (#C #S #D #I) 1 7 0 2 +REF: **** BEZÜGLICH DER BEWEISLAST UND DER haftung **** FÜR HILFSPERSONEN +HYP: DERT ZIÜKLIC DE BERWEISCLAST UN DE haftung FIER HELF PERSCHONEN +Eval: I S S S S S I S S + +Speaker sentences 463: voxforge_deu_000983 #utts: 1 +id: (voxforge_deu_000983-voxforge_deu_000983) +Scores: (#C #S #D #I) 1 5 3 0 +REF: bei DER NORMALEN NUTZUNG GIBT ES DIE VOLLE BANDBREITE +HYP: bei *** ******** ******* DEINOMAELNOTZUNGE IS IE VOLEBAND REITE +Eval: D D D S S S S S + +Speaker sentences 464: voxforge_deu_000984 #utts: 1 +id: (voxforge_deu_000984-voxforge_deu_000984) +Scores: (#C #S #D #I) 3 4 3 0 +REF: aber wie IST DIESES PROBLEM im GLOBALEN MASSSTAB ZU LÖSEN +HYP: aber wie *** IS DIESPROBLEM im ******** ******** KLOBAL MASTAPTZOLEÖEN +Eval: D S S D D S S + +Speaker sentences 465: voxforge_deu_000985 #utts: 1 +id: (voxforge_deu_000985-voxforge_deu_000985) +Scores: (#C #S #D #I) 1 4 2 0 +REF: das EIGENE WEBLOG ERHÄLT POTENTIELL MEHR LESER +HYP: das ****** ****** EIGENWER PLOKGERHT PDENZER MERLESERT +Eval: D D S S S S + +Speaker sentences 466: voxforge_deu_000986 #utts: 1 +id: (voxforge_deu_000986-voxforge_deu_000986) +Scores: (#C #S #D #I) 0 5 2 0 +REF: DAS FREMDE WEBLOG SIEHT NOCH BELEBTER AUS +HYP: *** ****** DASFREM DEVERBLOKG IT NOCHBIEBTER OS +Eval: D D S S S S S + +Speaker sentences 467: voxforge_deu_000987 #utts: 1 +id: (voxforge_deu_000987-voxforge_deu_000987) +Scores: (#C #S #D #I) 0 5 1 0 +REF: EINE NEUE BESTIMMUNG IST ERLASSEN WORDEN +HYP: **** EI NENEE BESTHIMUNG ISTDELAEN BURTEN +Eval: D S S S S S + +Speaker sentences 468: voxforge_deu_000988 #utts: 1 +id: (voxforge_deu_000988-voxforge_deu_000988) +Scores: (#C #S #D #I) 1 3 0 1 +REF: ******** DARAUF IST HINGEWIESEN worden +HYP: DAROUAUF ES TEN GEWIEN worden +Eval: I S S S + +Speaker sentences 469: voxforge_deu_000989 #utts: 1 +id: (voxforge_deu_000989-voxforge_deu_000989) +Scores: (#C #S #D #I) 1 4 1 0 +REF: DIE BEVÖLKERUNG ist GANZ MASSIV VERARMT +HYP: DE BEFÜRKRUNG ist **** GANZMASSI VERAMT +Eval: S S D S S + +Speaker sentences 470: voxforge_deu_000990 #utts: 1 +id: (voxforge_deu_000990-voxforge_deu_000990) +Scores: (#C #S #D #I) 3 2 2 0 +REF: DIE WERDEN das GANZ BESTIMMT nicht machen +HYP: *** DIEWEREN das **** GANZPESHIN nicht machen +Eval: D S D S + +Speaker sentences 471: voxforge_deu_000991 #utts: 1 +id: (voxforge_deu_000991-voxforge_deu_000991) +Scores: (#C #S #D #I) 1 7 0 1 +REF: DIE DATENMENGE DIE GESENDET WIRD ist * ERHEBLICH GERINGER +HYP: DE DARTEN ENEN DIGESENDE IERET ist E HEBICH GERINER +Eval: S S S S S I S S + +Speaker sentences 472: voxforge_deu_000992 #utts: 1 +id: (voxforge_deu_000992-voxforge_deu_000992) +Scores: (#C #S #D #I) 0 3 2 0 +REF: DAS ERGEBNIS IST VERFÄLSCHT WORDEN +HYP: *** ******** DASEGEBNIS ISTVE FELSTWODEN +Eval: D D S S S + +Speaker sentences 473: voxforge_deu_000993 #utts: 1 +id: (voxforge_deu_000993-voxforge_deu_000993) +Scores: (#C #S #D #I) 3 4 2 0 +REF: EINE BESCHRÄNKUNG TRITT erst bei BESONDERS INTENSIVER NUTZUNG auf +HYP: **** ************* EINBESCHRENKUNGKTRIT erst bei BESONDER S INTENSIEVERNOZUNG auf +Eval: D D S S S S + +Speaker sentences 474: voxforge_deu_000994 #utts: 1 +id: (voxforge_deu_000994-voxforge_deu_000994) +Scores: (#C #S #D #I) 3 7 1 1 +REF: der *** ENDBENUTZER hat eine HÖHERE GESCHWINDIGKEIT FÜR DEN DOWNLOAD ZUR VERFÜGUNG +HYP: der ENT BENOTZER hat eine ******* HÖHÖRE GERSCHWINDIGKEITFÜR EN DAUN LOT ZUOFERFÜLGUN +Eval: I S D S S S S S S + +Speaker sentences 475: voxforge_deu_000995 #utts: 1 +id: (voxforge_deu_000995-voxforge_deu_000995) +Scores: (#C #S #D #I) 1 1 4 0 +REF: der SEMANTISCHE TEIL WURDE SKEPTISCH BETRACHTET +HYP: der *********** **** ***** ********* SEMANTISCHETHEILEWUDESGEPTISPTRACHTET +Eval: D D D D S + +Speaker sentences 476: voxforge_deu_000996 #utts: 1 +id: (voxforge_deu_000996-voxforge_deu_000996) +Scores: (#C #S #D #I) 1 4 2 0 +REF: DORT WIRD SEHR VIEL MEHR GELD verdient +HYP: **** **** DOTWIRT SE FILMEHR GELT verdient +Eval: D D S S S S + +Speaker sentences 477: voxforge_deu_000997 #utts: 1 +id: (voxforge_deu_000997-voxforge_deu_000997) +Scores: (#C #S #D #I) 0 6 0 2 +REF: ******** *** VERSTÄNDNIS FÜR DAS VERANTWORTLICHKEITSGEFÜHL EINER MUTTER +HYP: FVERSTEN NIS VFIER DASS VER AN WURDLICHKEIZSGIFIL EINERMUNTER +Eval: I I S S S S S S + +Speaker sentences 478: voxforge_deu_000998 #utts: 1 +id: (voxforge_deu_000998-voxforge_deu_000998) +Scores: (#C #S #D #I) 1 5 0 1 +REF: DAS WIRD FÜR DIE MEDIEN gemacht * +HYP: DAWRT VER DE ME EN gemacht T +Eval: S S S S S I + +Speaker sentences 479: voxforge_deu_000999 #utts: 1 +id: (voxforge_deu_000999-voxforge_deu_000999) +Scores: (#C #S #D #I) 1 5 1 1 +REF: SIE KANN EINE GANZ klare *** KAUFEMPFEHLUNG AUSSPRECHEN +HYP: *** SE KOAN EINEGAN klare KOF MPFÄELONG AUSPRECHEN +Eval: D S S S I S S + +Speaker sentences 480: voxforge_deu_001000 #utts: 1 +id: (voxforge_deu_001000-voxforge_deu_001000) +Scores: (#C #S #D #I) 1 3 0 0 +REF: ZAHLREICHE PROTESTE werden ARTIKULIERT +HYP: ZALREICHE POTESTE werden ARTIKOLIERDT +Eval: S S S + +Speaker sentences 481: voxforge_deu_001001 #utts: 1 +id: (voxforge_deu_001001-voxforge_deu_001001) +Scores: (#C #S #D #I) 0 4 1 0 +REF: DIE DURCHFÜHRUNG WAR NICHT SICHER +HYP: *** DE DECHFÜÖRONGE WANEICHT ZIHER +Eval: D S S S S + +Speaker sentences 482: voxforge_deu_001002 #utts: 1 +id: (voxforge_deu_001002-voxforge_deu_001002) +Scores: (#C #S #D #I) 1 5 0 0 +REF: DIE WÄHRUNG HAT ÜBERHAUPT keine DECKUNG +HYP: DE WERUNENG HART ÜBERHOPT keine DIKUN +Eval: S S S S S + +Speaker sentences 483: voxforge_deu_001003 #utts: 1 +id: (voxforge_deu_001003-voxforge_deu_001003) +Scores: (#C #S #D #I) 2 6 0 3 +REF: ***** OB ÜBRIGENS SECKERSDORF der einen **** *** DURCHAUS ZIELBEWUSSTEN LEBENSKLUGEN +HYP: AOUBP IEBRIGENS SE ERSTAURF der einen DECH AUS TIEL BERWUSTEN LEBEMSKLOGEN +Eval: I S S S I I S S S + +Speaker sentences 484: voxforge_deu_001004 #utts: 1 +id: (voxforge_deu_001004-voxforge_deu_001004) +Scores: (#C #S #D #I) 1 4 2 0 +REF: MAN SPRICHT IN DIESEM FALL von KONTRAHIERUNGSZWANG +HYP: *** ******* MAIRSPRECHTEN DESEM FEIL von KONTRERHERUNGSTZWANG +Eval: D D S S S S + +Speaker sentences 485: voxforge_deu_001006 #utts: 1 +id: (voxforge_deu_001006-voxforge_deu_001006) +Scores: (#C #S #D #I) 1 4 1 0 +REF: GLÄUBIGER UND SCHULDNER SIND SICH einig +HYP: ********** GLOEBEGER UN SCHL NESENZI einig +Eval: D S S S S + +Speaker sentences 486: voxforge_deu_001007 #utts: 1 +id: (voxforge_deu_001007-voxforge_deu_001007) +Scores: (#C #S #D #I) 0 5 2 0 +REF: DAS WIRD NICHT MEHR LANGE SO BLEIBEN +HYP: *** **** DASWIRTENICHT MER LAN E SOBLEIBEN +Eval: D D S S S S S + +Speaker sentences 487: voxforge_deu_001008 #utts: 1 +id: (voxforge_deu_001008-voxforge_deu_001008) +Scores: (#C #S #D #I) 2 5 0 1 +REF: ES gab ********* UNTERSCHIEDLICH SCHWERE FORMEN der FREIHEITSSTRAFE +HYP: IS gab UNTERCHIT LICHSHIHE E VOMEN der RALHETSTAFER +Eval: S I S S S S + +Speaker sentences 488: voxforge_deu_001009 #utts: 1 +id: (voxforge_deu_001009-voxforge_deu_001009) +Scores: (#C #S #D #I) 0 5 2 0 +REF: ES HANDELT SICH UM EINE FREIE SOFTWARE +HYP: ** ******* EHNDE E EMEINEFREIE SOFT WER +Eval: D D S S S S S + +Speaker sentences 489: voxforge_deu_001010 #utts: 1 +id: (voxforge_deu_001010-voxforge_deu_001010) +Scores: (#C #S #D #I) 1 7 0 3 +REF: ***** ****** ** ORGANSTREITVERFAHREN KÖNNEN auch AUSSCHLIESSLICH AUF DER LANDESEBENE STATTFINDEN +HYP: ORGAN STREIT VE FAN KN auch AUSC ISLICG AUFTE LNDES EBENISTATFINTEN +Eval: I I I S S S S S S S + +Speaker sentences 490: voxforge_deu_001011 #utts: 1 +id: (voxforge_deu_001011-voxforge_deu_001011) +Scores: (#C #S #D #I) 0 5 0 0 +REF: WEGEN NUTZLOS AUFGEWENDETER URLAUBSZEIT KANN +HYP: WEGE NOZHLOS AUFGEWENETER ULEBSEIT KAN +Eval: S S S S S + +Speaker sentences 491: voxforge_deu_001012 #utts: 1 +id: (voxforge_deu_001012-voxforge_deu_001012) +Scores: (#C #S #D #I) 0 5 1 0 +REF: DAS WIRD NICHT IMMER PERFEKT FUNKTIONIEREN +HYP: *** DAWIRTNICH IM A PERVEKT VUNTZEINEN +Eval: D S S S S S + +Speaker sentences 492: voxforge_deu_001013 #utts: 1 +id: (voxforge_deu_001013-voxforge_deu_001013) +Scores: (#C #S #D #I) 0 3 4 0 +REF: MAN MUSS SICH ENGAGIEREN DES WACHSTUMS WEGEN +HYP: *** **** **** ********** MANMUSICH ANGERCHIEN DASWAKSTUMSWENGN +Eval: D D D D S S S + +Speaker sentences 493: voxforge_deu_001014 #utts: 1 +id: (voxforge_deu_001014-voxforge_deu_001014) +Scores: (#C #S #D #I) 1 4 0 1 +REF: WELCHE wege ***** SOLLEN EINGESCHLAGEN WERDEN +HYP: WELICHER wege SOLEN EIN ESCLAGEN WEARDEN +Eval: S I S S S + +Speaker sentences 494: voxforge_deu_001015 #utts: 1 +id: (voxforge_deu_001015-voxforge_deu_001015) +Scores: (#C #S #D #I) 1 4 1 0 +REF: DAS WIRD IN die PREISE GEHEN +HYP: DA WERT EN die ****** PEISEGEEN +Eval: S S S D S + +Speaker sentences 495: voxforge_deu_001016 #utts: 1 +id: (voxforge_deu_001016-voxforge_deu_001016) +Scores: (#C #S #D #I) 1 3 0 1 +REF: * die ÜBERNAHME ERFOLGTE WÖRTLICH +HYP: S die BERNDAME ERVOLKTE WERTLICH +Eval: I S S S + +Speaker sentences 496: voxforge_deu_001017 #utts: 1 +id: (voxforge_deu_001017-voxforge_deu_001017) +Scores: (#C #S #D #I) 1 3 1 0 +REF: DIE ENTWICKLUNG IST weit VORANGESCHRITTEN +HYP: *** DEEINT WEKLONGEIST weit VORANGESHIETEN +Eval: D S S S + +Speaker sentences 497: voxforge_deu_001018 #utts: 1 +id: (voxforge_deu_001018-voxforge_deu_001018) +Scores: (#C #S #D #I) 0 7 2 0 +REF: DIE SYMPTOME TRETEN DANN SCHON NACH WENIGEN STUNDEN AUF +HYP: *** ******** DIESM TORMEL TRIEDEN DANSCHON NACHWEHNEGE STONTEN ARF +Eval: D D S S S S S S S + +Speaker sentences 498: voxforge_deu_001019 #utts: 1 +id: (voxforge_deu_001019-voxforge_deu_001019) +Scores: (#C #S #D #I) 2 4 1 0 +REF: ES GIBT eine GROSSE WELLE von PROZESSEN +HYP: ** SGEBT eine GROSE WÄLLE von PROTZEEN +Eval: D S S S S + +Speaker sentences 499: voxforge_deu_001020 #utts: 1 +id: (voxforge_deu_001020-voxforge_deu_001020) +Scores: (#C #S #D #I) 2 4 0 1 +REF: es IST BEREITS mein * ZWEITER AUTOMAT +HYP: es EST BEREITZ mein Z WEITER AUTOMARDT +Eval: S S I S S + +Speaker sentences 500: voxpopuli_deu_000309 #utts: 1 +id: (voxpopuli_deu_000309-voxpopuli_deu_000309) +Scores: (#C #S #D #I) 1 11 4 0 +REF: B RUSSLANDS IMPLEMENTIERUNG von HÖHEREN STANDARDS ZUM SCHUTZ PERSÖNLICHER DATEN EBENFALLS GENERELL UNSERE GUTE ZUSAMMENARBEIT ERLEICHTERN +HYP: * N PLMENTIERUNG von ******** ********* *** HÖHRIN STANDATSTUSCUTZS PERSENLIGERTATEN EBEN FWEIS GENARE UNSHREI UTETZUSAMMENABEIT ALEICHTEN +Eval: D S S D D D S S S S S S S S S + +Speaker sentences 501: voxpopuli_deu_000310 #utts: 1 +id: (voxpopuli_deu_000310-voxpopuli_deu_000310) +Scores: (#C #S #D #I) 0 9 5 0 +REF: POLIZEIBEAMTE HABEN DAS SCHLIMMSTE VERHINDERT HABEN IHR LEBEN GERETTET UND SIND SELBER VERLETZT WORDEN +HYP: ************* ***** *** ********** ********** ER AMTEABNDERSCLIMS TE VERINDER DARMELEBEN GLÄDTI SIN SEBER VERLTSTVORDNICGLAB +Eval: D D D D D S S S S S S S S S + +Speaker sentences 502: voxpopuli_deu_000311 #utts: 1 +id: (voxpopuli_deu_000311-voxpopuli_deu_000311) +Scores: (#C #S #D #I) 1 5 4 0 +REF: DAS IST NICHT MÖGLICH DASS der KOMMISSAR NICHT HIER IST +HYP: *** *** ICG MEÜBRIE DAS der ********* ***** OMIESANICT IES +Eval: D D S S S D D S S + +Speaker sentences 503: voxpopuli_deu_000312 #utts: 1 +id: (voxpopuli_deu_000312-voxpopuli_deu_000312) +Scores: (#C #S #D #I) 0 9 2 0 +REF: 19 MITGLIED UND HOFFE DASS WIR IM NÄCHSTEN JAHR ÜBER DAS +HYP: ** ******** MITKLED UNDE CHOVE DAS WER NECHSE IEAHR ÜRERS WANTI +Eval: D D S S S S S S S S S + +Speaker sentences 504: voxpopuli_deu_000313 #utts: 1 +id: (voxpopuli_deu_000313-voxpopuli_deu_000313) +Scores: (#C #S #D #I) 0 8 11 0 +REF: ES DARF NICHT ÜBERSEHEN WERDEN DASS IMMERHIN MEHR ALS 50 DER BEVÖLKERUNG DER EUROPÄISCHEN UNION IM LÄNDLICHEN RAUM LEBT +HYP: ** **** ***** ********** ****** **** ******** **** *** ** *** NDIS DAF NCHTDERSEHN WERNDS IMERHIN BERIFÜMZIGUTSENTDERBEFELKÖNG DAREBISCHNUNUONEMLENTICHN DRAMLI +Eval: D D D D D D D D D D D S S S S S S S S + +Speaker sentences 505: voxpopuli_deu_000314 #utts: 1 +id: (voxpopuli_deu_000314-voxpopuli_deu_000314) +Scores: (#C #S #D #I) 0 12 8 0 +REF: WIR WOLLEN ALSO DASS DER BÜRGER SCHNELLER EINE AUSKUNFT BEKOMMT OB SEINE BESCHWERDE ÜBERHAUPT ANGENOMMEN WIRD OB SIE BERECHTIGT IST +HYP: *** ****** **** **** *** ******* ********* **** SODAS DE BÜUGER SHELNE AUSKUN BEKOMD OBSEINE BESCHÄERE BEHAUT INGENOMENWIRT OSIE BERECHTIHTEST +Eval: D D D D D D D D S S S S S S S S S S S S + +Speaker sentences 506: voxpopuli_deu_000315 #utts: 1 +id: (voxpopuli_deu_000315-voxpopuli_deu_000315) +Scores: (#C #S #D #I) 1 7 4 0 +REF: EIN „RESET UNSERER BEZIEHUNGEN IST NICHT VONNÖTEN aber SEHR WOHL KONTINUIERLICHES FEINTUNING +HYP: *** ******** ******* *********** NERHRESET UONZERERBITZIONGEN ISTNICHTVONEUTEN aber ARBURUE KONTIN N ERLICHESFEINTIONEN +Eval: D D D D S S S S S S S + +Speaker sentences 507: voxpopuli_deu_000316 #utts: 1 +id: (voxpopuli_deu_000316-voxpopuli_deu_000316) +Scores: (#C #S #D #I) 2 4 5 1 +REF: UND DA WIRD GANZ STOLZ GESAGT die ******************* BESCHÄFTIGUNG STEIGT JA an +HYP: *** ** **** **** ***** L die GANSTOLSGESARGJARBD BESCHETIUNG STEIKT IER an +Eval: D D D D D S I S S S + +Speaker sentences 508: voxpopuli_deu_000317 #utts: 1 +id: (voxpopuli_deu_000317-voxpopuli_deu_000317) +Scores: (#C #S #D #I) 1 20 5 0 +REF: ICH WILL SAGEN WIE ES IST FÜR UNS IST DER EURO UNTERBEWERTET WIR EXPORTIEREN zu VIEL ZU BILLIG UND WIR IMPORTIEREN ZU WENIG WIR VERSCHENKEN WOHLSTAND +HYP: *** **** ***** *** ** IEDASES FER UN SIST EURHR UNTERE VERLTET WIER EXSPORTIERUN zu FIEEL ZUON BELIH AUN WER IN PADTIERUNZSEWENIG NER VARSCHEN KEN WOLSTANT +Eval: D D D D D S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 509: voxpopuli_deu_000318 #utts: 1 +id: (voxpopuli_deu_000318-voxpopuli_deu_000318) +Scores: (#C #S #D #I) 2 7 2 0 +REF: DASS sie HEUTE ABEND HIER ANWESEND SIND IST EIN POSITIVES signal +HYP: **** sie ***** HOUL DER ABEN DIER ANWESENSIN ISTEN PROSSITIEVER signal +Eval: D D S S S S S S S + +Speaker sentences 510: voxpopuli_deu_000319 #utts: 1 +id: (voxpopuli_deu_000319-voxpopuli_deu_000319) +Scores: (#C #S #D #I) 2 12 3 2 +REF: 90 PROZENT ALLER EUROPÄISCHEN FILME die AUSSERHALB IHRES HEIMATLANDES GEZEIGT werden **** **** SIND VOM MEDIA PROGRAMM GEFÖRDERT WORDEN +HYP: ** ******* NEUNZSICHPRETZENT ALLERAROBPÄECHEN FLME die ********** AUSEHELT IRESEINMATLANDES GITZEICHT werden SINT VOAM ME DIER POGAM GEER DERT WURTEN +Eval: D D S S S D S S S I I S S S S S S + +Speaker sentences 511: voxpopuli_deu_000320 #utts: 1 +id: (voxpopuli_deu_000320-voxpopuli_deu_000320) +Scores: (#C #S #D #I) 1 8 3 0 +REF: WIESO KANN ICH DEM ERGEBNIS DER AUSSCHUSSABSTIMMUNG in DIESER FORM NICHT ZUSTIMMEN +HYP: ***** **** BISOKALIHEMER GEBPLDIS TER AER AUSHUSABTDEMUNG in ****** DIESE VORM NICHZUSTEN +Eval: D D S S S S S D S S S + +Speaker sentences 512: voxpopuli_deu_000321 #utts: 1 +id: (voxpopuli_deu_000321-voxpopuli_deu_000321) +Scores: (#C #S #D #I) 2 8 3 0 +REF: WIR WOLLTEN VERHINDERN DASS SICH HINTER DIESEM GEISTIGEN eigentum DIE AUSKUNFTSPFLICHT verstecken KANN +HYP: *** ******* ********** BIERBURTE VERHNDRN DASSICHCHEHE HINDERDIEN GEISIGEN eigentum DIEAUSGUM SFLICHTE verstecken KONTE +Eval: D D D S S S S S S S S + +Speaker sentences 513: voxpopuli_deu_000322 #utts: 1 +id: (voxpopuli_deu_000322-voxpopuli_deu_000322) +Scores: (#C #S #D #I) 1 7 7 0 +REF: ES GIBT JETZT IM ZUSAMMENHANG MIT DER VERSTÄRKTEN ZUSAMMENARBEIT einen ERSTEN GANG VON EINIGEN MITGLIEDSTAATEN +HYP: ** **** ***** ** ************ *** ISGEBDETZHNM ZUSAMANGDERVERSTERKTENZUSAM ARBEIT einen ****** ERSSTEN GANGVON EIIGENMITITSTARTEN NAC +Eval: D D D D D D S S S D S S S S + +Speaker sentences 514: voxpopuli_deu_000323 #utts: 1 +id: (voxpopuli_deu_000323-voxpopuli_deu_000323) +Scores: (#C #S #D #I) 1 21 5 0 +REF: was DIE GRENZÜBERSCHREITENDE ZUSAMMENARBEIT ANBELANGT UND DIE VERBREITUNG IN DRITTLÄNDER BETRIFFT HIER MÖCHTE ICH EIN BEISPIEL NENNEN DAS EIN ERFOLGSBEISPIEL FÜR MICH IST UND ZWAR SLUMDOG MILLIONÄR +HYP: was *** ********************* ************** ********* *** DIGREINZHBERCHREITEN DETZUSAMENABEIT ANBELANT UNDTWASTIE ER VERPREITUNG INTRIGK LENDER ETRIFTNDTEMECHICH EINBEISPBELENEINENDES EN ER VOLGSBEISPILVÜEMICH IS UNT ZWAR ELSLAM DAUG MLIER NJAREI DES +Eval: D D D D D S S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 515: voxpopuli_deu_000324 #utts: 1 +id: (voxpopuli_deu_000324-voxpopuli_deu_000324) +Scores: (#C #S #D #I) 1 13 5 0 +REF: UND DAS NICHT NUR in PORTUGAL ODER GRIECHENLAND SONDERN AUCH IN SO VERMEINTLICH REICHEN MITGLIEDSTAATEN WIE DEUTSCHLAND ODER GROSSBRITANNIEN +HYP: *** *** ***** DASNICHTNUR in ******** **** PORTU GAL DERGLICHEN LANT SNEN AURUENSEU VERMEINTLICG EICHENMITWIS STARTEN WIEDEUTSCSLAND DER USPETANJEN +Eval: D D D S D D S S S S S S S S S S S S + +Speaker sentences 516: voxpopuli_deu_000325 #utts: 1 +id: (voxpopuli_deu_000325-voxpopuli_deu_000325) +Scores: (#C #S #D #I) 1 2 3 1 +REF: DIE ZEIT FÜR AUSREDEN IST vorbei ** +HYP: *** **** **** TVER AUSWEGENIS vorbei DA +Eval: D D D S S I + +Speaker sentences 517: voxpopuli_deu_000326 #utts: 1 +id: (voxpopuli_deu_000326-voxpopuli_deu_000326) +Scores: (#C #S #D #I) 0 8 6 0 +REF: SIE ALLE FLIEGEN ALS MITGLIEDER DIESES HAUSES WAHRSCHEINLICH DEUTLICH HÄUFIGER ALS DER EU DURCHSCHNITTSBÜRGER +HYP: *** **** ******* *** ********** ****** ALLELFLIENGATFMIGKDI DA DESISHAUSES VARSCHEINHE DORDTLICHOLFIGER ALSTE IE UNDUSCNITZBÜÖRGET +Eval: D D D D D D S S S S S S S S + +Speaker sentences 518: voxpopuli_deu_000327 #utts: 1 +id: (voxpopuli_deu_000327-voxpopuli_deu_000327) +Scores: (#C #S #D #I) 0 8 6 0 +REF: UND ICH BIN SICHER DASS IHRE BEDEUTUNG IN NAHER ZUKUNFT SOGAR NOCH ZUNEHMEN WIRD +HYP: *** *** *** ****** **** **** EN SICHEHR DAS ERE BE DOEUITCUNG INARAZSFUKOMFTUGANOCHTZUNEM WIERT +Eval: D D D D D D S S S S S S S S + +Speaker sentences 519: voxpopuli_deu_000328 #utts: 1 +id: (voxpopuli_deu_000328-voxpopuli_deu_000328) +Scores: (#C #S #D #I) 4 14 5 0 +REF: ES GEHT HIER UM DIE RICHTLINIE DES RATES ZUR FESTLEGUNG GRUNDLEGENDER SICHERHEITSNORMEN fÜr den schutz VOR DEN GEFAHREN einer EXPOSITION GEGENÜBER IONISIERENDER STRAHLUNG +HYP: ** **** **** TASKE DIERUN DIRICHTLIENDER DIS ARDES TE FISTEGNGUND LÄEG DASISARITSNORMEN fÜr den schutz *** VERDEN GEFAREN einer ********** EXSPOSITSOUNG GIEBER IONISIERENDARSTRALUNG +Eval: D D D S S S S S S S S S D S S D S S S + +Speaker sentences 520: voxpopuli_deu_000329 #utts: 1 +id: (voxpopuli_deu_000329-voxpopuli_deu_000329) +Scores: (#C #S #D #I) 0 2 3 0 +REF: DAS GILT ES WIEDER HERZUSTELLEN +HYP: *** **** ** DASKGILTES IDERHERSCUSTEN +Eval: D D D S S + +Speaker sentences 521: voxpopuli_deu_000330 #utts: 1 +id: (voxpopuli_deu_000330-voxpopuli_deu_000330) +Scores: (#C #S #D #I) 4 4 2 0 +REF: DIESEN einen einzigen sitz GIBT es LÄNGST DAS IST STRASSBURG +HYP: DEN einen einzigen sitz KEBT es ******* *** LENGS DASISTASPURG +Eval: S S D D S S + +Speaker sentences 522: voxpopuli_deu_000331 #utts: 1 +id: (voxpopuli_deu_000331-voxpopuli_deu_000331) +Scores: (#C #S #D #I) 5 32 14 1 +REF: WIR SEHEN JA GERADE DASS das PASSIERT in ********* MALTA DIE JOURNALISTIN DIE KORRUPTIONSFÄLLE AUFGEDECKT HAT IST VOR WENIGEN WOCHEN ERMORDET WORDEN WEDER WERDEN SYSTEMATISCH DIE KORRUPTIONSFÄLLE UNTERSUCHT noch WIRD DER MORD SELBER GEZIELT UNTERSUCHT MAN HAT FAST DEN EINDRUCK als OB HIER ALLES unter DEM MANTEL DES SCHWEIGENS ZUGEDECKT WERDEN SOLL +HYP: *** ***** ** ****** E das ASPASIER in MALTERDIE B SONLISTEIN DIEKOBTSUNDFLE AUFGE DEKTER DIS VERWHEGEN BCHNERMADE BA WE DER WEREN US DEMARISIKOB SOUND FELE E BE UNDERSUCH noch **** *** **** ITDER MOAR SERBEAR ER GETILT E UNDERSUTEANATVFASSIN ANDUGASWEN als ** **** IER unter *** ****** *** ********** ********* DEMANDELISCHWEINTZUGEDEKTWENSEO S +Eval: D D D D S S I S S S S S S S S S S S S S S S S S S S D D D S S S S S S S S D D S D D D D D S S + +Speaker sentences 523: voxpopuli_deu_000332 #utts: 1 +id: (voxpopuli_deu_000332-voxpopuli_deu_000332) +Scores: (#C #S #D #I) 2 9 8 0 +REF: DORT STEHEN ÜBERALL ENTLANG DER kÜste DIE WARNSTEINE DIE auf DIE GROSSEN KATASTROPHEN MIT TSUNAMIS IN DER VERGANGENHEIT HINWEISEN +HYP: **** ****** LIT LANG E kÜste *** DIEWANSTEINE DI auf *** ******* ************ *** ******** DI ROSENKATERS VOFENITZUNAMIESE DERVRGANGNERTINWEISEN +Eval: D D S S S D S S D D D D D S S S S + +Speaker sentences 524: voxpopuli_deu_000333 #utts: 1 +id: (voxpopuli_deu_000333-voxpopuli_deu_000333) +Scores: (#C #S #D #I) 1 27 5 0 +REF: HERR PRÄSIDENT ICH HABE IM PRINZIP FÜR DEN BERICHT GESTIMMT OBWOHL ER EINEN SCHWEREN FEHLER ENTHÄLT ES WIRD NÄMLICH DAZU AUFGEFORDERT DAS EUROPÄISCHE PARLAMENT AUF dem WEG ZU EINEM EINZIGEN SITZ ZU UNTERSTÜTZEN +HYP: **** ********** *** DENT ICHARBE BENZIEPÜR DE BERCHT GISTEIMENT OAUOLEINS WEHRN VELER INTELTES WIRD NE MITA UE AUF GE FADERT DS AUR BEHE SHE PALAMENDAUF dem *** ** WÄEGKTSE I DIM EINZIGENGSITSTZUO NDESTELTZEN +Eval: D D D S S S S S S S S S S S S S S S S S S S S S S D D S S S S S + +Speaker sentences 525: voxpopuli_deu_000334 #utts: 1 +id: (voxpopuli_deu_000334-voxpopuli_deu_000334) +Scores: (#C #S #D #I) 5 11 0 2 +REF: in ****** ***** DIESEN TREFFEN WURDEN GEMEINSAME POLITISCHE VERABREDUNGEN IM kreis der 27 GETROFFEN und auch PUBLIK GEMACHT +HYP: in DIESEM RIFEN WHUOTDEN GEMEI SM IBPLITDESCHE VER ABREDUNGEN IN kreis der SIEBEN UNZWANZSIGETROFVERN und auch UBLIK GEMAHTT +Eval: I I S S S S S S S S S S S + +Speaker sentences 526: voxpopuli_deu_000335 #utts: 1 +id: (voxpopuli_deu_000335-voxpopuli_deu_000335) +Scores: (#C #S #D #I) 1 35 31 0 +REF: ICH BIN DER ÜBERZEUGUNG DASS WIR ES HEUTE MIT DEM VORSCHLAG AUS DEM UMWELTAUSSCHUSS GESCHAFFT HABEN EINEN SCHRITT WEITERZUKOMMEN ES IST NICHT perfekt EUROPÄISCHE ÄRZTE SAGEN WIR HÄTTEN FÜR HOCHRISIKOPRODUKTE EINE ZENTRALE ZULASSUNG HABEN MÜSSEN DAS HABE ICH NICHT GESCHAFFT ABER MIT DEM WAS HEUTE AUF DEM TISCH LIEGT SCHAFFEN WIR WOHL TROTZDEM EINEN GROSSEN SCHRITT VIELLEICHT KEINEN MEILENSTEIN ABER EINEN GROSSEN SCHRITT HIN ZU MEHR PATIENTENSICHERHEIT +HYP: *** *** *** ************ **** *** ** ***** IEBENERBERTZOUGEN DS WERS HOEUTH MI DIN VORSCHARGS IM UMALG AUSCHS GECHAFTAM INCHTWEITEA UKMA SIG perfekt ************ ****** ***** *** ******* **** ****************** **** ******** ********* ***** ******* *** **** *** ***** ********* **** *** *** *** ***** *** AURBECHE ATZSAG EHEDEN ÜER HOCHRISIGOBRTUGKTDANEDSEN DALIZU LASEN HAM MSSENDASHAICHEICH GESHAFT ARMI DEMERSO DAH EMTICHLIGPLAUBEICH DASWIRTROTEMEINENGORSEN SCRILT FLEIG KEIN MEINSTEINEINGROSENCHITZU MER ATEDENSEATAEN +Eval: D D D D D D D D S S S S S S S S S S S S S S D D D D D D D D D D D D D D D D D D D D D D D S S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 527: voxpopuli_deu_000336 #utts: 1 +id: (voxpopuli_deu_000336-voxpopuli_deu_000336) +Scores: (#C #S #D #I) 0 5 1 0 +REF: FRAU PRÄSIDENTIN FRAU KOMMISSARIN LIEBE KOLLEGEN +HYP: **** PEHLE DANG ESFVERLKTFÜÖRT SWEIENHEIBMINONTEN ERG +Eval: D S S S S S + +Speaker sentences 528: voxpopuli_deu_000337 #utts: 1 +id: (voxpopuli_deu_000337-voxpopuli_deu_000337) +Scores: (#C #S #D #I) 1 10 12 0 +REF: zum AKTUELLEN ICH GLAUBE ES KANN KEINER VON UNS ANNEHMEN DASS WIR WIRKLICH ERST SEIT DIESEM WOCHENENDE WISSEN DASS UNS DIE ZAHLUNGSUNFÄHIGKEIT DROHT +HYP: zum ********* *** ****** ** **** ****** *** *** ******** **** *** ******** AKTUELEN ICHKLABIS KANKEINE VENUNSAN NEME DAS WEWERTLICG IARSTFEITDISEN WUCHNGENDEWISHENDASENS DITALUNSUNFEICHKEITDROT +Eval: D D D D D D D D D D D D S S S S S S S S S S + +Speaker sentences 529: voxpopuli_deu_000338 #utts: 1 +id: (voxpopuli_deu_000338-voxpopuli_deu_000338) +Scores: (#C #S #D #I) 0 8 0 0 +REF: DAS SIND EINFACH BEDINGUNGEN DIE NICHT AKZEPTABEL SIND +HYP: DSND EINFCH PE DINGUNGEN DINIG AKTZEPTABESEN MAN K +Eval: S S S S S S S S + +Speaker sentences 530: voxpopuli_deu_000339 #utts: 1 +id: (voxpopuli_deu_000339-voxpopuli_deu_000339) +Scores: (#C #S #D #I) 2 16 7 0 +REF: IN DER ZWISCHENZEIT SIND DIE RETTUNGSORGANISATIONEN DIE GRÖSSTEN SCHLEPPER WEIL SIE die MIGRANTEN 20 KILOMETER VOR DER LIBYSCHEN KÜSTE AUFGREIFEN und ALLE NACH ITALIEN TRANSPORTIEREN +HYP: ** *** ************ **** INDEZWISCHEN SEI SINDI RETUNGSARGENISAR IONEN DEGRÖSTENSCHLPERH WEISIE die ********* MIEGANTEN ZWANZICHGHLMETER VER DERLIEBISCHEN KÜS DAUB GREIFEN und **** **** ALLNERHITALIEN PRASPRTIEREN +Eval: D D D D S S S S S S S D S S S S S S S D D S S + +Speaker sentences 531: voxpopuli_deu_000340 #utts: 1 +id: (voxpopuli_deu_000340-voxpopuli_deu_000340) +Scores: (#C #S #D #I) 1 3 2 0 +REF: das ZEIGT DER FALL JULIA TIMOSCHENKO +HYP: das ***** *** SEIKTDER FAL JULIERTDEMSCHÄNKOU +Eval: D D S S S + +Speaker sentences 532: voxpopuli_deu_000341 #utts: 1 +id: (voxpopuli_deu_000341-voxpopuli_deu_000341) +Scores: (#C #S #D #I) 1 4 3 0 +REF: WIR DÜRFEN NICHT WASSER predigen UND WEIN TRINKEN +HYP: *** ******* I WASER predigen *** OND WEINTRINEN +Eval: D D S S D S S + +Speaker sentences 533: voxpopuli_deu_000342 #utts: 1 +id: (voxpopuli_deu_000342-voxpopuli_deu_000342) +Scores: (#C #S #D #I) 0 10 1 0 +REF: FÜR DIESE ENTSCHEIDUNG BRAUCHEN WIR VIELE PARTNER NICHT ZULETZT DIE STÄDTE +HYP: **** WIR DIE INSCHEI DUNG RARHEN WIER FILEPATNR NECHTZULETZ IE STÄTTE +Eval: D S S S S S S S S S S + +Speaker sentences 534: voxpopuli_deu_000343 #utts: 1 +id: (voxpopuli_deu_000343-voxpopuli_deu_000343) +Scores: (#C #S #D #I) 2 15 3 1 +REF: DIE FOLGE IST ein HÖHENFLUG VON POPULISTEN UND EXTREMISTEN IN EINIGEN MITGLIEDSTAATEN IHREN DUMPFEN PAROLEN setzen *** WIR KONKRETE VERÄNDERUNG ENTGEGEN +HYP: *** DIEVOLGE IS ein ********** *** HÖRN FLUGK VOM PROPLIST N EXSTRLMISTEN EINIGMIGISTATENEREN BUMFUM PAROULEN setzen IER KONGKRIETER VERINDERUNG EN GEGEN +Eval: D S S D D S S S S S S S S S I S S S S + +Speaker sentences 535: voxpopuli_deu_000344 #utts: 1 +id: (voxpopuli_deu_000344-voxpopuli_deu_000344) +Scores: (#C #S #D #I) 3 22 0 1 +REF: WEIL die INVESTITIONEN FRANZÖSISCHER und **************** DEUTSCHER BANKEN GERETTET WERDEN MUSSTEN DURFTE GRIECHENLAND 2010 NICHT PLEITEGEHEN und HEUTE MUSS ES EINEN RIESIGEN SCHULDENBERG VOR SICH HERSCHIEBEN +HYP: WAIL die INWESTIZIONEN VRANTÖRSISCHA und DELUTSCHERBANGEN GERETET WVERDEN MUST DEN DÖRHTDER ICHEN LANT ZWEITAUSEN ZEHN NICHTDBPEITEGEN und HLUTER USESEINEN RIESIGEN SCHOTDEN BERK VAR SICHE HERT DRIUK +Eval: S S S I S S S S S S S S S S S S S S S S S S S + +Speaker sentences 536: voxpopuli_deu_000345 #utts: 1 +id: (voxpopuli_deu_000345-voxpopuli_deu_000345) +Scores: (#C #S #D #I) 0 16 6 0 +REF: DIE MITGLIEDSTAATEN DÜRFEN NICHT DIE MÖGLICHKEIT HABEN DEN EUROPÄISCHEN STAATSANWALT DARAN ZU HINDERN IN IHREN REGIONEN GANZ GEZIELT UND SYSTEMATISCH KORRUPTIONSFÄLLEN NACHZUGEHEN +HYP: *** *************** ******* ***** *** ************ DEMITIGITSTATEN DÖROFEN NICH IEMÜKICH KEIT HABMDERENEN AURBPESHEN STASAMBAL DERANZERHNDERNEN IERNAREG IONG GANZSGET ZIEL UNST DEMATISKORULTONFERNACRZEIGEN ESIEN +Eval: D D D D D D S S S S S S S S S S S S S S S S + +Speaker sentences 537: voxpopuli_deu_000346 #utts: 1 +id: (voxpopuli_deu_000346-voxpopuli_deu_000346) +Scores: (#C #S #D #I) 1 5 2 0 +REF: DREI MILLIONEN menschen SIND ABHÄNGIG VON UNSERER HILFE +HYP: EIMIL ION menschen **** ********* SIN ABPENGIH VONUNSERHELVER +Eval: S S D D S S S + +Speaker sentences 538: voxpopuli_deu_000347 #utts: 1 +id: (voxpopuli_deu_000347-voxpopuli_deu_000347) +Scores: (#C #S #D #I) 1 8 5 0 +REF: EIN VIERZEHNJÄHRIGER junge WIRD IN HAKKARI VON EINEM POLIZISTEN EINES SONDEREINSATZKOMMANDOS INS KOMA GESCHLAGEN +HYP: EINFETHINR GER junge **** ** ******* *** ***** WET INHERKADIVON EINPLISISTEN AINDESONDEREI SATSKOMANDUSEN KOMAGSCHAEN +Eval: S S D D D D D S S S S S S + +Speaker sentences 539: voxpopuli_deu_000348 #utts: 1 +id: (voxpopuli_deu_000348-voxpopuli_deu_000348) +Scores: (#C #S #D #I) 1 10 6 0 +REF: WIE EINE HEILIGE KUH HAT MAN VOR SICH HERGETRAGEN das OPT OUT MÜSSE UNTER ALLEN UMSTÄNDEN WEG +HYP: *** **** ******* *** *** *** DIE INDERHEILIG KUEITMANVORSICHERETRANEN das AUPT AUT US UDER AL UNSHENTEN WEK +Eval: D D D D D D S S S S S S S S S S + +Speaker sentences 540: voxpopuli_deu_000349 #utts: 1 +id: (voxpopuli_deu_000349-voxpopuli_deu_000349) +Scores: (#C #S #D #I) 1 5 0 3 +REF: *** DREI DERARTIGE TREFFEN haben ********* **** INZWISCHEN STATTGEFUNDEN +HYP: REI DER ATRI GETEREFFEN haben INZISCHEN STAD GEFUN DE +Eval: I S S S I I S S + +Speaker sentences 541: voxpopuli_deu_000350 #utts: 1 +id: (voxpopuli_deu_000350-voxpopuli_deu_000350) +Scores: (#C #S #D #I) 0 2 6 0 +REF: ICH HOFFE ES DAUERT NICHT WIEDER NEUN MONATE +HYP: *** ***** ** ****** ***** ****** RD ICHIETENEINMONERDEBPT +Eval: D D D D D D S S + +Speaker sentences 542: voxpopuli_deu_000351 #utts: 1 +id: (voxpopuli_deu_000351-voxpopuli_deu_000351) +Scores: (#C #S #D #I) 7 30 10 3 +REF: DESWEGEN EINE WICHTIGE FRAGE AN DIE KOMMISSION KANN ein land ** DIE GRENZKONTROLLE wieder EINFÜHREN UND GLEICHZEITIG IN DER SCHENGEN UNION BLEIBEN MIT ZUGANG ZUM INFORMATIONSSYSTEM ETC ODER IST DAS ein ENTWEDER ODER DIE FRAGE IST WICHTIG fÜr DIE DÄNISCHE DEBATTE UND ICH BITTE um eine **** ** KLARE ANTWORT +HYP: ******** **** ******** DASWEGE EINEWICHTI GE FHAHGEANDI KOMITIONEN ein land DI GRANZ KONDTCOLLE wieder ********** *** ************ ** EIN FÜÖONNTDACH EM SCHNGE NION BLEIDEN MITZUGANGK ZSUOR I MATIONZUSTEME TSETERAR ORDERISDRS ein EN WERER ODEAR DIERAGE IS WESTIC fÜr *** ********* ******* DIEDENISCHER IEPATE NDISPÄTE um eine KLAE AN WORT DA +Eval: D D D S S S S S I S S D D D D S S S S S S S S S S S S S S S S S S D D D S S S I I S S + +Speaker sentences 543: voxpopuli_deu_000352 #utts: 1 +id: (voxpopuli_deu_000352-voxpopuli_deu_000352) +Scores: (#C #S #D #I) 1 12 12 0 +REF: WIE HEUTE SCHON AUSGEFÜHRT wurde LAG ES NICHT DARAN DASS ES HIER GROBE FEHLER GEGEBEN HÄTTE SONDERN ES GAB EINE REIHE VON KLEINEN UNGEREIMTHEITEN BZW +HYP: *** ***** DE SCHONAUSSCGIEFÜRT wurde *** ** ***** ***** **** ** **** ***** ****** ******* LAGESNICH BARAN DASESHE ROBEFFELEGIGEBEN HETDISNENDSGABENHREIHE VOND KLEIEN UN GEREINMTEITEN BITIENSWEI +Eval: D D S S D D D D D D D D D D S S S S S S S S S S + +Speaker sentences 544: voxpopuli_deu_000353 #utts: 1 +id: (voxpopuli_deu_000353-voxpopuli_deu_000353) +Scores: (#C #S #D #I) 1 9 1 1 +REF: EINE VERGEMEINSCHAFTUNG DER AUSSEN UND SICHERHEITSPOLITIK ALS GROSSES ZIEL dieser ** UNION +HYP: **** NVER GEMEINCHRFTEN DE AUS NON SIERITSPALITIG AS GOSISTZIEL dieser UN JON +Eval: D S S S S S S S S I S + +Speaker sentences 545: voxpopuli_deu_000354 #utts: 1 +id: (voxpopuli_deu_000354-voxpopuli_deu_000354) +Scores: (#C #S #D #I) 0 11 2 0 +REF: DENN SICHERHEIT IST EINE SCHWIERIGE UND DETAILREICHE ARBEIT NICHT NUR IM TECHNISCHEN BEREICH +HYP: **** ********** DEN ICHERHEIT IS ANISZ WERIEGER UN DITEIL WEICHER ARBREIT NICHTNUHR IMTECHNISHNBAREICH +Eval: D D S S S S S S S S S S S + +Speaker sentences 546: voxpopuli_deu_000355 #utts: 1 +id: (voxpopuli_deu_000355-voxpopuli_deu_000355) +Scores: (#C #S #D #I) 3 15 8 0 +REF: KINDER UND POLITIK selten LIEGEN DIE INTERESSEN von BÜRGERN UND POLITIKERN SO WEIT AUSEINANDER BEI DEN BÜRGERN IN GANZ EUROPA STEHT DAS THEMA KIND GANZ oben +HYP: ****** *** DIK selten ****** GEN DIENTERESEN von ******** *** ********** ** **** BÜÖRGENUN POLIETIGEN SOWEI AUSNANDER BER EM BÜRERNENGANZER OBERSHI DS TEMAR KIENT GANZS oben +Eval: D D S D S S D D D D D S S S S S S S S S S S S + +Speaker sentences 547: voxpopuli_deu_000356 #utts: 1 +id: (voxpopuli_deu_000356-voxpopuli_deu_000356) +Scores: (#C #S #D #I) 0 2 0 0 +REF: HERR PRÄSIDENT +HYP: HERPRSI DENT +Eval: S S + +Speaker sentences 548: voxpopuli_deu_000357 #utts: 1 +id: (voxpopuli_deu_000357-voxpopuli_deu_000357) +Scores: (#C #S #D #I) 0 15 1 0 +REF: WIR FÜHRTEN GESPRÄCHE MIT PRÄSIDENT KARZAI ZAHLREICHEN REGIERUNGSVERTRETERN FRAUEN UND MENSCHENRECHTSORGANISATIONEN UND DIE WAREN DURCHAUS ERMUTIGEND +HYP: *** EFÜREN ESPRÄECHEMIT RESE DEN KASEIT ZAREICHENREGJERUNGS ERTRIE EN VFRAUN MENSCHENRECHT OGAMISRT IONEN UN DIEWAND DRCHAUSEMUTIGENT +Eval: D S S S S S S S S S S S S S S S + +Speaker sentences 549: voxpopuli_deu_000358 #utts: 1 +id: (voxpopuli_deu_000358-voxpopuli_deu_000358) +Scores: (#C #S #D #I) 1 8 7 0 +REF: DAS IST ÜBRIGENS AUCH eine URSACHE FÜR DEN WACHSENDEN NATIONALISMUS DER ALLERDINGS LEIDER VÖLLIG PERSPEKTIVLOS IST +HYP: *** *** ********* NGSACH eine ******* **** *** ********** URSACHEFÜRDIN E WACKSN NATZUNALISNUS DE LLIGSEIDE FOLICHPERSPEKTIEFLOSSIS +Eval: D D D S D D D D S S S S S S S + +Speaker sentences 550: voxpopuli_deu_000359 #utts: 1 +id: (voxpopuli_deu_000359-voxpopuli_deu_000359) +Scores: (#C #S #D #I) 0 8 3 0 +REF: HEUTE SIND WIR IMMER NOCH SO WEIT VON DIESEM ZIEL ENTFERNT +HYP: ***** **** *** OUIDE IN E IMANAO SORWEIT VNDENZIE ENFERN S +Eval: D D D S S S S S S S S + +Speaker sentences 551: voxpopuli_deu_000360 #utts: 1 +id: (voxpopuli_deu_000360-voxpopuli_deu_000360) +Scores: (#C #S #D #I) 4 33 14 0 +REF: ICH WERDE ALS FINANZMINISTER AUCH IN MEINEM LAND JEDEN TAG damit KONFRONTIERT DASS NATÜRLICH AUCH das BEWUSSTSEIN GEGEBEN SEIN MUSS DASS STAATSHAUSHALTE VON DEN STEUERZAHLERINNEN UND STEUERZAHLERN FINANZIERT SIND und DASS WIR DAMIT AUCH DIE VERANTWORTUNG TRAGEN BEI DEN entscheidungen DIE WIR HIER IN DIESEM RAHMEN TREFFEN MEINE DAMEN UND HERREN +HYP: *** ***** DWER DERS WI ANZMINISTE AUCHEN EINEN LANDZIEDEN TAGG damit KONFVONDTIETDAS NDTÜLICH AUCHTUS BUSTZSINGEGEBENSENMUSS das *********** ******* **** **** **** STASHUSHALTE VONDEN STLER SOALERENE UN STELER SOLLEND INERZIETZINT und **** *** ***** DAS IERTDAMIT AUFHT DIEANTUERTUNGAGEN IN EN entscheidungen *** *** **** ** DEIER HIEN ISEN RAMEN DREFFN MET MMUNTERN +Eval: D D S S S S S S S S S S S S D D D D D S S S S S S S S D D D S S S S S S D D D D S S S S S S S + +Speaker sentences 552: voxpopuli_deu_000361 #utts: 1 +id: (voxpopuli_deu_000361-voxpopuli_deu_000361) +Scores: (#C #S #D #I) 1 6 0 0 +REF: AUF dem EUROPÄISCHEN AUTOMOBILMARKT INSGESAMT DRAMATISCH IST +HYP: AU dem OUUROBEISCHN AUTEBEBILMARGKT INS GESAMT DRMATDISCHISS +Eval: S S S S S S + +Speaker sentences 553: voxpopuli_deu_000362 #utts: 1 +id: (voxpopuli_deu_000362-voxpopuli_deu_000362) +Scores: (#C #S #D #I) 2 17 6 0 +REF: DIE EUROPÄISCHE UNION HAT MIT DIESEM INSTRUMENT DIE CHANCE eine AKTIVE ROLLE IN IHRER NACHBARREGION ZU SPIELEN um DEMOKRATISCHE REFORMEN UND EINE NACHHALTIGE ENTWICKLUNG VORANZUTREIBEN +HYP: *** ************ ***** *** EBPEHSCHUN JON HARTMIDISE INSTRUMENZS DISCHONSE eine ****** ***** AKTI VEROLLENE RNACKTPA EGIONZU SPIEN um DEMOUGRADSCHERDE VORMEN NA ENACHALIG IN IKTUN VRANZITREIE +Eval: D D D D S S S S S D D S S S S S S S S S S S S + +Speaker sentences 554: voxpopuli_deu_000363 #utts: 1 +id: (voxpopuli_deu_000363-voxpopuli_deu_000363) +Scores: (#C #S #D #I) 1 9 3 1 +REF: DIE SICHT AUF TOTALITÄRE REGIME von ***** AUSSEN ODER VON INNEN IST RECHT UNTERSCHIEDLICH +HYP: *** ***** *** STUL TALITERERSCHIEME von AUSEN UDAU VN INEN IS RESCHT UNDOSCHIET LEG +Eval: D D D S S I S S S S S S S + +Speaker sentences 555: voxpopuli_deu_000364 #utts: 1 +id: (voxpopuli_deu_000364-voxpopuli_deu_000364) +Scores: (#C #S #D #I) 0 17 5 0 +REF: WIR HABEN IMMER GESAGT DASS EINE ÜBEREILTE STATIONIERUNGSENTSCHEIDUNG UNSINNIG IST WEIL ES ZUM JETZIGEN ZEITPUNKT KEINE BEDROHUNG BEISPIELSWEISE AUS DEM IRAN GIBT +HYP: *** ***** ***** ****** **** EH HAM IMER GESARK EIN ÜBER EILTUSTATZUN IERUNGS EN CHEIDUNGIS UN SINISGWEITZUM JERTZIGENZEITFUN ESKEINEBE DRONGBEISCSPIEASWESAUS EM IERANGET +Eval: D D D D D S S S S S S S S S S S S S S S S S + +Speaker sentences 556: voxpopuli_deu_000365 #utts: 1 +id: (voxpopuli_deu_000365-voxpopuli_deu_000365) +Scores: (#C #S #D #I) 2 18 1 2 +REF: DIESER VERGLEICH ist ***** ********** EINE ZYNISCHE MISSACHTUNG der OPFER VON MENSCHENRECHTSVERLETZUNGEN IN ALLER WELT ER IST ZUM ANDEREN EIN SOLCH UNGLAUBLICHER ANWURF +HYP: DEER VARKLEICH ist EINET ZUÜENESHE MISE AT DN der ***** AUBRHRAVORNMENZCHENREITZWRLEID DNEL AABELSZSSFHFHFGFAGSRFDARSTSTSSTSBODSSAAA IE ONG ANDAN ANAN EINE SOEUSCH EUNEN KLAUPLICHER AN WOROF +Eval: S S I I S S S D S S S S S S S S S S S S S + +Speaker sentences 557: voxpopuli_deu_000366 #utts: 1 +id: (voxpopuli_deu_000366-voxpopuli_deu_000366) +Scores: (#C #S #D #I) 1 6 1 0 +REF: DIE SPE HAT diese UMFASSENDE HORIZONTALE RICHTLINIE BEFÜRWORTET +HYP: *** DIESPE ERHRT diese UNFASENDERHUTZUNTALE RICHLINDE WÜBEFÜÖBOTET WENGIE +Eval: D S S S S S S + +Speaker sentences 558: voxpopuli_deu_000367 #utts: 1 +id: (voxpopuli_deu_000367-voxpopuli_deu_000367) +Scores: (#C #S #D #I) 2 26 7 0 +REF: DENN EINES IST WIRKLICH KLAR DIE FINANZ und WIRTSCHAFTSKRISE VERLANGT VON UNS ALLEN EINMAL MEHR der VERANTWORTUNG FÜR EINE OPTIMALE UND VOR ALLEM RASCHE QUALIFIZIERUNG UNSERER ARBEITNEHMER UND ARBEITNEHMERINNEN GANZ BESONDERS JETZT RECHNUNG ZU TRAGEN +HYP: **** ***** *** ******** GICI WERKISKSL ICHINAND und E WRSARSTGHG DEVLANTVO UNDE EIN EINMALNMEHR IJETST der ************* **** **** VERANTZWOCFTDUNGFÜR INE UTUPTI MALE N WRALMGRASIGKALIVITZTIERUNG UN RER ABEIT NEHME UD ARBEITNEMER RINEN DANS PERSONDER ETSTRESHUNZITAGEN +Eval: D D D D S S S S S S S S S S D D D S S S S S S S S S S S S S S S S + +Speaker sentences 559: voxpopuli_deu_000368 #utts: 1 +id: (voxpopuli_deu_000368-voxpopuli_deu_000368) +Scores: (#C #S #D #I) 0 15 13 0 +REF: ESTLAND ODER AUCH POLEN DIE SEHR GUTE ERGEBNISSE ERZIELEN ALS ANDERE DIE SICH SCHWER TUN DIE MITTEL ABZURUFEN ETWA REGIONEN WIE KALABRIEN SIZILIEN ODER AUCH GRIECHENLAND ODER RUMÄNIEN +HYP: ******* **** **** ***** *** **** **** ********** ******** *** ****** *** **** ANDRD AUCHONLEN DIESERKUDER GEBESER ZIELNAISANDERE GIESI SHWÄHRTUN DIMITEL ABPTZIUOFN TWACRIG JIONWIEKALARBRIHNZITZILENOD AU KRICHLAR DR AUCHOMENEN +Eval: D D D D D D D D D D D D D S S S S S S S S S S S S S S S + +Speaker sentences 560: voxpopuli_deu_000369 #utts: 1 +id: (voxpopuli_deu_000369-voxpopuli_deu_000369) +Scores: (#C #S #D #I) 1 17 5 0 +REF: DER BERICHT GAUZÈS FORDERT ZU RECHT DASS DAS RATING STAATLICHER SCHULDTITEL ALS ÖFFENTLICHE AUFGABE BEGRIFFEN und DAHER VON ÖFFENTLICHEN AKTEUREN VORGENOMMEN WERDEN MUSS +HYP: *** ******* ******* ******* ** DEBERICHKOESES VORDER ZUREICHTDS ES RETING STATLICHERSCHULT TIEEIS ERFEN LICHER AUFGABEBEGRIFEN und DAR HER ON EFEN ICHE AKTÜHRN VORGENAMWERENMUSS +Eval: D D D D D S S S S S S S S S S S S S S S S S + +Speaker sentences 561: voxpopuli_deu_000370 #utts: 1 +id: (voxpopuli_deu_000370-voxpopuli_deu_000370) +Scores: (#C #S #D #I) 0 11 8 0 +REF: DA WIR ES ABER NUN MIT EINEM SOZIALPROGRAMM ZU TUN HABEN MÜSSEN WIR DAFÜR EINE ENTSPRECHENDE RECHTLICHE GRUNDLAGE SCHAFFEN +HYP: ** *** ** **** *** *** ***** ************** DABIS ABELNUN MITEINEM UTSCAR POGAMTUTUN HABEM MSWIL DAFÜH EIN EN SPECHENDERECHIGEKONTLAGESCHAEN +Eval: D D D D D D D D S S S S S S S S S S S + +Speaker sentences 562: voxpopuli_deu_000371 #utts: 1 +id: (voxpopuli_deu_000371-voxpopuli_deu_000371) +Scores: (#C #S #D #I) 0 3 3 0 +REF: ABER DAS MÜSSEN WIR NOCH ANALYSIEREN +HYP: **** *** ******* SIER NOHANALISIERN WOR +Eval: D D D S S S + +Speaker sentences 563: voxpopuli_deu_000372 #utts: 1 +id: (voxpopuli_deu_000372-voxpopuli_deu_000372) +Scores: (#C #S #D #I) 3 9 4 1 +REF: MAN KANN NATÜRLICH verlangen GEBEN WIR MEHR GELD FÜR ENTWICKLUNGSHILFE AUS die ARMEN LEUTE BRAUCHEN das *** +HYP: *** **** DMAKENENETULIE verlangen ***** *** GEBENLIE MER GARTFHRND IKUNSHIH VERAUS die AMEN OEITE WRAUHEN das ABE +Eval: D D S D D S S S S S S S S I + +Speaker sentences 564: voxpopuli_deu_000373 #utts: 1 +id: (voxpopuli_deu_000373-voxpopuli_deu_000373) +Scores: (#C #S #D #I) 3 14 6 0 +REF: GERADE FÜR KLEINERE PROJEKTE IST das EIN ÜBERMÄSSIGER BÜROKRATISCHER AUFWAND RICHTIG DASS das JETZT AUF EINEN ZEITRAUM von DREI JAHREN GESENKT WERDEN SOLL +HYP: ****** GERARDIÜEKLEINE PORJÄECK DE IS das *** ************** ÜNHBERMEHESIC BEROGATESHR AUFAND RECHTISDAS das ***** IER SAWBEIN ZEITAUM von **** ****** REIAREN GESENTWERENSOR UNUMNT +Eval: D S S S S D D S S S S D S S S D D S S S + +Speaker sentences 565: voxpopuli_deu_000374 #utts: 1 +id: (voxpopuli_deu_000374-voxpopuli_deu_000374) +Scores: (#C #S #D #I) 1 11 2 1 +REF: ICH KANN NUR versichern ************* DIE EUROPÄISCHE KOMMISSION IST COMMITTED ZUR EUROPÄISCHEN PERSPEKTIVE DES KOSOVO +HYP: *** **** IKANDER versichern DIARLOBPESCHE KOMISION IES STER KOMITITZUM AHR AR ARUBTSALROBECHEN ERSPIGK IEVE DISKOSSERUSNT +Eval: D D S I S S S S S S S S S S + +Speaker sentences 566: voxpopuli_deu_000375 #utts: 1 +id: (voxpopuli_deu_000375-voxpopuli_deu_000375) +Scores: (#C #S #D #I) 0 3 7 0 +REF: ABER HIER IM HAUSE IST ES SEHR OFT AUCH SO +HYP: **** **** ** ***** *** ** **** EIEDANZEHE AUFT AUGHSON +Eval: D D D D D D D S S S + +Speaker sentences 567: voxpopuli_deu_000376 #utts: 1 +id: (voxpopuli_deu_000376-voxpopuli_deu_000376) +Scores: (#C #S #D #I) 2 10 2 0 +REF: MIT DIESEM HAUSHALT KANN MAN DIE EU BÜRGERINNEN und BÜRGER nicht ÜBERZEUGEN UND BEGEISTERN +HYP: *** ****** DT DIESME HAUSHEL KAANDI E NGÖRGERIN und BURGER nicht IBERTZOELITEN N BEGEISTEAN +Eval: D D S S S S S S S S S S + +Speaker sentences 568: voxpopuli_deu_000377 #utts: 1 +id: (voxpopuli_deu_000377-voxpopuli_deu_000377) +Scores: (#C #S #D #I) 1 22 4 0 +REF: WIR ALS SOZIALDEMOKRATEN NEHMEN MIT GROSSER FREUDE ZUR KENNTNIS DASS DINGE DIE WIR VORGETRAGEN haben JETZT IM ZUSAMMENHANG MIT DEN VERÄNDERUNGEN IN DEN VEREINIGTEN STAATEN UMGESETZT WERDEN +HYP: *** *** TIAL EMUKRARE NEMIT GROSAFH ROUDE ZORKEN NES DAS DIG DE IER VORIETRARGEN haben ***** ** EBZ SICH AUH IM ZUSAMENAR IT VERENDRUNGNE DENWEICH STATEN UMSETSN +Eval: D D S S S S S S S S S S S S D D S S S S S S S S S S + +Speaker sentences 569: voxpopuli_deu_000378 #utts: 1 +id: (voxpopuli_deu_000378-voxpopuli_deu_000378) +Scores: (#C #S #D #I) 3 13 2 3 +REF: ***** DER BESCHLUSS DAS EUROPÄISCHE semester ************** **** HERZUNEHMEN UND DIE KORRUPTIONSSITUATION im RAHMEN der LÄNDERBERICHTE ZU VERÖFFENTLICHEN IST NICHT AUSREICHEND +HYP: DEAHA BESCHURS DIE EL DARORBPESCHE semester HERHERTZUNEMEN UNTI KOROBTUNZ SIKLA SIUN ER im RAM der *************** ** LINER BRECHTDETZUVEREÜFNIGEN ISTNIG AUSEIGENT +Eval: I S S S S I I S S S S S D D S S S S + +Speaker sentences 570: voxpopuli_deu_000379 #utts: 1 +id: (voxpopuli_deu_000379-voxpopuli_deu_000379) +Scores: (#C #S #D #I) 10 18 13 1 +REF: ** UND meine BITTE ODER das was ICH MIR VORSTELLE IST DASS MORGEN WIRKLICH IN DER TAT EINE GROSSE eine breite mehrheit FÜR DIESE KOHÄSIONSPOLITIK fÜr UNSERE POLITIK STIMMT FÜR die menschen VOR ORT DAMIT WIR UNS AUF das WESENTLICHE BESCHRÄNKEN KÖNNEN +HYP: ND MEIN meine BITE ODAM das was *** *** ********* *** **** ICHMER VORSTDEN IS DASMAHRGENGWIECKTLICG INDERTAHAT EING GROSE eine breite mehrheit **** ***** ***************** fÜr DIE SI KOLSIONSPLITIGHSORLGEPLITIGSTSTDEMT SÜR die menschen *** *** VORORTDAITIUNSA DESWEHENIE AUCH BESCREÄNKENKEINE das *********** ************ ******* +Eval: I S S S D D D D D S S S S S S S D D D S S S S D D S S S S D D D + +Speaker sentences 571: voxpopuli_deu_000380 #utts: 1 +id: (voxpopuli_deu_000380-voxpopuli_deu_000380) +Scores: (#C #S #D #I) 3 17 12 2 +REF: WENN WIR HEUTE DIESE VERORDNUNG VERABSCHIEDEN HOFFE ich *** DASS WIR nach EINEM LANGEN WEG ZU EINEM GUTEN ABSCHLUSS KOMMEN UND ICH MÖCHTE MICH BEI DER KOMMISSION BEDANKEN DIE UNS DURCH KONSTRUKTIVE sacharbeit *** +HYP: **** WENWIER ARHOLTE DIE EVORARDNUNG VRABSCIEDEN OAOFER ich DAS SE WIER nach ***** ****** *** ** ***** ***** ********* ****** *** *** ******* EIM LANGNKARUSELELSU EIM BUD NABCHUSKOMUNTITMAÜCHTERMÄCHE BEIERKOMISION BEDANGEN DIEGONZTO TIE sacharbeit HAT +Eval: D S S S S S S I S S D D D D D D D D D D D S S S S S S S S S I + +Speaker sentences 572: 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S A S A P T U T E N O T W E N D I G K E I T D A S I H I E U N L Ü N B I S C H E V E R N M I I E N F Ü E N S I C H E R E S U N F W E L T Z F Ü E A L E U N S E R E R S P O T L E R E I N S E S T +fleurs_deu_000380 A L I C H K E N E A B P I E T K O M P E T I E B E L M E T A C H T N A T Z W E I B U N D E L F A R A C H T N R T Z W E I B U N D E L F B E U N D C H T N E T Z W E I P U N D E L F G E S E I N V E R S G E S D I E B A S I S T A T I U N V E R F Ü G K B E R D U O A L R A D I E +fleurs_deu_000381 J E R B I E Z E I S H E N S D I E G E R I C H T E R A L S P L I C I C H E S G E S C H W Ä T S U N D T A L L B E N H E I T Z S +fleurs_deu_000382 L T E R W U C H R G A B T A S E M I E I T H E B E K A N D A S I S V O N E B L W E R F I E R N D R E S I E W E I T R I E V O H F E L E V O N B E R H I T Z U N I N V O M I E R T W O R D E N W A I E D A S U N T E R N E M A L Z N I C H C H V E R I G E B T Z E I C T E T E +fleurs_deu_000383 N A C H D I M D E R D E M M E U N H N U N D E R T R E I U N S E C H Z I C B A U T W R D E N W A R K A M D I A R E S Z E L I G H N B E P F L U T U N D E S E D E M E N T E M N P L S V E R T E L N Z U M S H Ö L S T E N +fleurs_deu_000384 E R W A U H M S T E C H E V N G E L S C H E I N V E I L E E N D E B E T E I L I C H T A K T U L E B E I S C H P I E S A N A H R B E S C H L I S E N I B P R E M J H M I N I S T E R P R T R I S A F D E R V O R D E S E R T D E K A N A D S C H E N F Ü N N U N D E R T O L L E N U T E N E I N +fleurs_deu_000385 D I H A U P T S T V E R M R D A W I E N I S T K H E N E N D I E I N E I M P I S P B A H E I S T G U M E N E S C A B E R F I E L E M E N T E N S R E C H E N E R O S E H +fleurs_deu_000386 S I S I S E B E T Z W I C H E N D E N E I N Z E N N B Ü N E S T D I E N H E R S T E N A U O C H U N B E S T E N D I G E R Z E I T E N G E T A L T E P R O E W E N Z E N D I E B E K A N T D I S T D E D I E S E P E R I O E D E N W A D I E E P O C H E D E R D E I L G Ü N I N G E I C H E D E S E Ä C H T Z I C H I E A R R E L A N Z E I H E N D E R H A N U N D E E E N D E N E S T I S T A T V F V A N T +fleurs_deu_000387 A M A N D E R E E N E E R S P E K T R U M S H R W E I N E M A N S I C H E N E I N I C H W I D E Z U E R K E N D E I N E W I E D E U M D S A E S A N D R M A C H E N M O S A S E R S T I E E S G E M A C T A U N D Z S I C H A L L E S U O E L I G E M A C H T +fleurs_deu_000388 D G I G I D E M E I S T E N I N D E R P I T E A T Z I O N E N D E S T E C H E N L O G I S C H E N D E T E M I N I S E N U S T A E N Z W E I A L G E M E I N E V O R S C T E R L U N G E N E I N E S E I T S D E S D I I N D I C G E M D E R T I C H E N L Ü G I E S E P S T E I N E N W E G V O L G T D E R W E I T G E N D I E N S E I S U N T O W E L E O R D E R P U L I C I S C H I N P L S N A M E N D I G T U N D A N D E R E R S E I T S D A S T I G H N E Ö Ü G E I E R E R S E I T S A U S F W Ö H R K E N E N A U F G E S A L S C A F T N A R A R T D I E H E R I N H E R E R N T A S Z U T S A L B E D E N Z I N T +fleurs_deu_000389 W Ü S H E D E N E I N Z E N E N D N A S T I E N H E R S T E N A U R U N B E S T E N D I G E T Z E I T E N G E T E A L T E R R O W E N Z E N D E E K A N D E S T E S E P E R I O D E N W A H D I E P O C H R E L D E R E K Ü N I G R E I C H E D E S E C H T Z I C H A R L A N G T Z W I S C H E N D E R H A N U N T E R I E N D I N A S T I T T V A N D T +fleurs_deu_000390 D E M I C K H Z U V O R G I E B I E Z I Z I C H E S T O G O M E N T A U F D E N G E Ä N S T R E I T I N D E N D I E P A L I S I N E N S E R E I N Z U R Ö G S A L T Z E N D E R G E N Z S E N I N D E N Z U S T A N T V O R D E M S E R S T A L E G R I V O R N A N Z E S N U N D E R T S E B E R N U S E T I C V O R D E N +fleurs_deu_000391 M I T H E M P E R L U S T G R E C H E E R S P A C H K E N E A R D E R E C S T E N V O N S E I E N V L E S O F I S C H E N U N D W I S E N C H A L I C H E N W O T Z E I N K I H E N E N A B I E S L E T E N +fleurs_deu_000392 E R S T E M I T E R A U S S A G D E S I Ö R S U S I E B E I N D A S T D E N N R E S E N U N S R A T L E D E N V E R E I N U N D E R S P O T S B P E S R G E D I E N D I S D B E N W E R N E H A L B U N S R A R G E S A T I O U N D H N V L E V E R I N D R U N G V O R A N T R E I B E N A N S T E I N E R T I T Z S R I T Z T V I T I E R U N G V O T Z N +fleurs_deu_000393 I G R E Z F A T E N A E N G P I G E S B O G B I E T E N A U C H Z E I T Ü Ö R I N A U F E N T A L I N E R S T A T K G R E U T Z F E R P A S R S H E R E S E I N V N D E I E U N S L I C H B E R E I T S I E B E D E N G E N +fleurs_deu_000394 S C S C T E R E I S E N D E V E R D E N R I N G E N D G E W A N D T K A U F I E W I E D E A T V O N U N W E N T E Z U A C H T E N D I E E G E B I B I E R I F T D A D N D I S S I G H A U F A L E R E I S E P L E N E A O S I E R E N K O N +fleurs_deu_000395 S I C H R T R I S E B E S E R T D A S D E R G K O L T Z U N G S P B U N K T D D E R L E N M E N D E I N B E E W E R D I G K A L E U N T H U R E H O N T A L D R I T E N D E E H I G K I S T D P L A T S F Ü I T E S H O U P T M N O I E I S T S I E B E I S C H E N +fleurs_deu_000396 E I T N U N Z E U N R T A C H E N A C H T Z I H M I S T N W A L U N D R A N S B P R E N Z S E I N D A M I T W E E N D B O B A C T E R B I E T Z O E U G E N G K E N D A S W E G N D E R W A L E I N M S C H L I G E V W A N E N S I N D U D A S K E I N U M S C H L Ä G E I N G E V A O F E N W E R D E N A U S E R E H N E D E R T O T D E U N G S M E S K S E L T E T A T R E S I E R T E N E E +fleurs_deu_000397 O T E R E R I S T K A N E R D E S B E T Z S A O U B E N D E Z W E I S C H E A L I G E H A U P T S T A T U N D Z S E L T E N D I C H I C H E I N E R E I E U N K U N Z S T D G E E R I E N U N D M O S E E N A U S D I E K A N E N D E R S V E R G A N G E N H E I T U N D G G E N W A R T P R E S I N T I E R E N +fleurs_deu_000398 D I E S E P A R E K E N S I C H F V E I N A D E B T I O N S P L A N V E R B E B E E N S H E I D E N +fleurs_deu_000399 I N V O L E D E S E N E I N Z W R E I F I S C H A B E N A U S G S T O L M D Z W E A W E I T R I S E N V O M A U S T E R B E M B E T R O R T T A U N D E R D E R J L A Z I Ü F V E R +fleurs_deu_000400 R E A N Z E N S E N N J H E R E N E R T Ö L I H E M N G E B E N A M E T E N A U S I E R S T E N S I E A L S O D E R E R S C H U N G A U C H N U R E I N E E M K L A E N D T V E R N +fleurs_deu_000401 A U F E R N A R S E I T E K N T E I S M E R M R I E R G E B E N D D I E K R O S T E D N E S T I S W R E I N V E R A F D E L A V E R A N D I O B E P L I C H A U F T S T D E G E N T +fleurs_deu_000402 S G R E F Ü G K T D E C H E N Z U U N D A S S I E D U O C H N I C H T D E R T Z U A U F G I E V O R D E R T W E R D E N S O L L T E N F E R T F I C H T U N G E N E I N D Z U Ü G E E N D E I Ü E B E R I E R E N I N T W I L U N G S T A N D I E R E V E R A N T O R T U N G U N D I E R E R F Ä E K E T E N H E N O A R N S I N G E N +fleurs_deu_000403 S I S I E W I C E T U E L E H I E L F I S T E U N G E N I S E N T I N D I E S O F T W E R E I N G E B U T D T U N S O E N A B E L T S C H I T E N D I E D E R S C H Y L E A L L E I N M Ü G L I C H E R W E I S E N I H T B E V E T I G E N K A R N H E N T E R F R A G E N N E I E L E G E N U N D T D E R K L E R E N +fleurs_deu_000404 A M F Ü N F Z E N N A R G U S T N U N Z H N H U D E T V I R Z I C F E L I E A L I E R T E N N S Ü T R A N K R A I C H E I N D I N W A S I O N W R D E A P E R E S C H E E R G U N G E N E R N D T +fleurs_deu_000405 E R R I F O C H A L L S A N W A S E N W A S E R K A R M S E B T E N G R O S E R D E N S A U R I E I D E R T I E W E X S W E I M I C H T G E W A K E N +fleurs_deu_000406 E T E R K R Ü N D U N G V N A S U N T Z I O R F I N H T Z E N D E S I E N U N D R E I S I S S P A R E G E G E L U N G V I E L V O N S E I M I N D I G K E N K A R A C T E R N D S E I N E I D E N D I T E T Z B E W A R E N +fleurs_deu_000407 S I S I B E B E D R T Z S D E N I S D E A N T E L L A N I E G S D E E R B E N D E S T R I E G H T D E B I N E R G E S A N T D E N G O P E D E R L O L T E E M I T D U G B E R K U L O S E N O F E N B A R D E N N O C H N G E R I N G S E X S D A U S E N D E I N Z S G E S A N D R E I H U N D E D E I S I G T A U E S E N L O L T E D E I N S Ü T D A W R I K E W H E I N E N B I S T I N T E N Z E I T P B U N G K T A N G I S T E T I S E N T +fleurs_deu_000408 E H N S C H E L Z W E I T A U S E N S E C K S E L E U T E R T A S K O N T I N U M K O N D Z E B T A S E I N E M I T O R D E M U R G E N S A T I O N Z S E H L F N L E S T U N G S F E G E Z E W E R E N +fleurs_deu_000409 S G S E E I N D I E S E B E R I O E D E N D E R U E R R O E P E H E N G I C H I G T E M S T D A N T D E R I C H U N D M E Ä C H T I H E N E V O D E N E R K A R T O N E S I G I E H E N A U F T E N P R E Ö S T A N E T +fleurs_deu_000410 D I E R S D E R C H E N D I E B Z I C H M F Ä E L U N G I S T A S E I N E N E U D E P L O M A T S C H I N I T K E T I P E V E R E N D D I E N J A R S E G R I F E N W E R D E N S O L L T E M D I E R A G S C E N G R E N Z E N G E G B A E R E I N T L I E N T E R E T I O N Z U S I C H E R N U N D P L U M A T S C H B T Z E M I T Z S E I N A C H B A N I E E R T Z S T E N +fleurs_deu_000411 D E E S P B E T E I N E U T E G E L E G E N H E I T D A S N O R T L C H Z U S E N D E H E M E N M E H R U D E W E N G E R R U N D U M D I U E R D U N K E L E S T +fleurs_deu_000412 S T K T P R O S O R E N P A M E L E R V E R G U S S O N V O N D E R U N E W Ö O E T I A D A N D I E M E R K T A N S O A L I S T E N S C H E I E N E I N E G E Ä E R L I E G E N Z E U E R S R E I T E N W E N D I E P O T O N S E W E I T E V O N E R D E C H T I E V E E N T E I H E N +fleurs_deu_000413 S K A S I A C H L O N E I N E N E I L G K A R Z U K A U F E N D I E T Z U T R I T E N W D E R T O R S G E W E L T E N P A Z E N C H E N T A F R I K A R E R T U O A L N Z H T A V R I K A N C H E N E R Z N A L P A X S G E W E R T +fleurs_deu_000414 D I E P R Ü K E S O L M S E R T E M B E A T Z W E I T E L E N S I E B Z I N V E R S T E N D I H T N E T R I E A U F N E M I S W R D A R W A R T E A S I R A S J A N I C H N Z O L P U N T E A N F E A R T I G S T E L T Z E I N W E R E N +fleurs_deu_000415 W E R N D E N E X S P R M E T E L L E M S T A U I N E R L A G T S U S E I N S C H E I N T I E B U L E M O T E L I T E T Z U S E N U N G E B T A S B S E R K E I N M D I K A M E N T E D I A L S E I N D R I H Z U B E H A N D L U N B S T E N D E N V E K T I O N G E R E I G N E T N A C H K G E I E S E N O R D E N +mls_deu_000281 E I N E U S S E R S L I E B A F E R D B E S C H E N W E X S E L A N S T A T M A I N A R U O K D I M P L A N E I N A L G E M E I N S T A T E N K O N G R E S T Z U B R U F E N U N D K O N D E S I H V O L F I G N O C N I C H T B E R D E S V O R T Z L E G E D E B R G A M U N D D I N O R T E S T Z U S A M T R T S E I N I G E N +mls_deu_000282 E R W U S S T E N C H T A S I M D A S L E B E N K O S P A R E S G E R A U P T A T E S C H P A N G R A F T U N D M U D T D A S E S I N F E I G U N S C O L I G E M A C H T A T E U N D F Ä H C H Z U D E N H O N D I N G E N Z U O D E N E N U N G E T R I Ü P T E I T R A L E N G H R T +mls_deu_000283 D I E S E R U N G E M A N H I E S K A K A L I T Z I E N U N D B E F A N I C G A E F D R A N D E S C H A F T A L S I N E M G E N A N T E N K Ö Ü N I G R E I C H B E K A N D M A C H U N G W I G D E R N Z E E N V E L E N W U R D E E I S A K T D E S H T N L D E R W E N E S W E I T E N I H T Z S I S T E I N E I P A U C H N H N I C K Ü K L S T U N D U T S K Ö N I G S E I D A M Z U W E R E N D A S G E L S E T I G A L E D I N G S T +mls_deu_000284 S R D R N O C F Ü N F M I N U T E N U N D D E W O L G E N D E B E U S T L O S I G K A L T B E G A N Z U S C H I N D E N I E R S T U S E S E R U L D A S I H N M E I M E I G N E N B E T E L A G U N D D A S D I E R O T N G L U T D N I H T Z A N D R E S W A I L S D A S V O Y R I M K A M I N D E R I N D A S T U B E E S W A N A C H T E I N I K R Z E B A N T E A F T E I M T I S H E +mls_deu_000285 E I C H T D I E H E R T R E N G U N G E N W E B E C H T E R U N T E R H E I L T E N K O M D A N E M P R B E T I T Z S A L D E D I H O C H F L U D E S E X S U E I N B E D Ö F T I G K E I T S O F N D E S E N D E N G E N A N E N S L S C H E R A R K T I O N S O D E R I D E R S T A N Z S P L D O N G E N D E M E R +mls_deu_000286 T A B E R A F E N G E H R E N D B E H A R G E N B E G A N D I E K I S T D E N W A N T N U N S Z O H Ö R T E I C H A U O F A C F E Z O S E I N E I N K L A R E R S C Ö N A G E R D A N K T E N G A N G D E N I H T I L R G E N G I M I T D I M B A U C H A U S G E H E K T A B E M O S S D E N A F E N D E N K E N M I +mls_deu_000287 R I S S E S P O T T R I E H E R N E M E N S C H N D E N I G K E N N E N F R A K T E I L E I S E R W E L C H U N B M A K T E A M I C H R A N G I T R E T E N B A I C H N D G E G N I E T E D A S I S N N F A N T E S I K O K F S E I U N D S C H O P T T E I C H E U N G E I L I C H N T E D I E A N D O N L T T E R N T Ü L I S P R A C R I C H I U N B A R H E I T D N D E S W E I N S E R I E T O E I E S P E T R I E M I S T E R O T S C H S T E S +mls_deu_000288 C I H W E I S D A S I H S E R K R A N G B I N S A K E S H N E R E N A W E I L E V O R N P A M I N U T E N V E S C H T E I C H M I C H N B Ä T E R U E N Z U R E N U N D F Ü L E D E D A S I C K E I N G L I E D M A R E N I H R N K A N S W E R E G U T W E N I H M E N G E M Ü T D E L E I C H T A N K Ö R N T E B E V O R A I S T D E R R B E R +mls_deu_000289 S O A B E R I S Z W V E R U N S E R W E S E N S K O N D G O R T S E L V E R D A H E R U M H T Z I H E H D E C D E R C H L A N G E N K N O U L T D S A L P E N S A T A N G E S H L O N G E N U N I Ü B E R D E M F Ü N G K G E N D E I E B E I S D I E E N S E R N I S T E S H A S E S K E L A G E R T W A S W U N D E R D A N +mls_deu_000290 B E S E V E R E L E W A G E B L I E B E N A U E R S E W A G E T Z W U N Z U G E N B E D I E P Ü N K T I G K E I T B E I D N M A L Z E I T E N E I N E S A C H E W A A U W E C H I N G E H T S H Ä R H O L S T R E N G R G E R H E L T E N U R D E +mls_deu_000291 N L I K L I C H F Ü L H T E W I E R E A M N S I C H T E N B E M I C G I E R M P F I N D N G N D F Ü Ö R M I C H N I C H T U M E I N A T U N V E R I N D A R T W A A N N B E H U P T K R N E N D E R U N G F E C H W A N I G S E I S E R E M F E R S T E I N E T N A U O G E E I H S N E M E L S T U O K T R I N E N G E N E T Z T N I E M A S I N T Ä R T I C K A T A U F G E L O E I C H T E T H A T T E A M N +mls_deu_000292 S B P O D E R S E M I S Z E R G U T M I N D E R A U K L I G N E R F Ä H R W R E S I C V R E U N W E W E R N S C H N E L L D A N A C H A N D E N S U O U L B E A U P R E C H E N S C N A L R E I D N E M I T Ü R O F O D E R A C H D A S L A G E R R E I C H E N E R S T I E G A U D I T F E H R D E D I U N A U S G E R O T A T E N U N D F L O G E N G A L O P T D A V O N D I S M A L Ü T E N W I R U N Z D E R F Ä E R D E I D E D E R E K T Z E V O L I E N E R E N G E R A D E A U S S N D E R S P A D E U +mls_deu_000293 W E L D E B E A M I T P E C H P E S T R I C H E N W A H R B I E B E I N E R V O N D N G O L L E N D E N P A N T O F E L N F Ä S T E I N G E N U N D I N D E R A N G S D A C H E S N I C H T E R A N I N I T Z O N E M U N D I E I S D N L E T Z T E N S H I E T V O N D E R T R E R P E T A T D E H A T I S T Z W L F A U S G E S C H L A G E N G D A R W A W A G E N U N D F I Ä R D E V E R S H U N D E N N D A S C H E N P U T E S T A N I N S E I N A S H E N K L E I D E A U F E D U N K L N S T R A S E +mls_deu_000294 I E N O M D S K G A S V M A R G E E N F I N D T E R A N T L A R G E R E E S E N E N T Z U Ü K E N E S I S C H R I K E S A K T E H E R I E T E S M E N I G E G I E T A U N B U T D V E R G I S E N V E R M E I N E N +mls_deu_000295 N U R D E R D A O C K T O R U N D E W E R T E R E N S O L E N V O R S E I N E A U G E N K O M M E N E R K L E R T E D I E T R I E N E I N K R O S E N A M T S E I F E R D A M I T W A D I E F T R A R O B E R S T G A N S E I N F V E R S T A N D E N U N D P H Ü Ö Ü K S T E R F P R E I T K A R T E S I E M E T I E R E N +mls_deu_000296 K A W A R U N D T R Ü S T I C H E B E D I E L A G E D A S K Ü N Z S L R S E B E G E N U W E I N E N U N S C H L C H T Z T D E L A N G E I N D E V O R G E R H E L T E N E N H E N D E R D E K N S L A W A T E T E R B D E S K A S I C B E R U I C H T H A T E U N T E N T C H L O S I C H T D A N D A E R K E I N A N D A R N A U S F I G F A N T D E R N O C H Z U M P E I T E R S C R E I B E +mls_deu_000297 O N D E M F E R D E H E R D N D E R P A T S C H E N U N S A G U N Z S T A S I F E N A P A T S C H N F Ä R D U N S E B E S O V I E W A R E N U N P R E N D I G E B E W Ü R D E N I Ü F E R I N K E I U O W A B P F I E R T T D A S I N D U N R I G K L I G E A V O D U M A P A T S C H E N V Ä H R D T S O U L E N A S R C H T I H G E R A S C H L E I M T O D E I E S H R G E F A L E N U N D A N E B L U T V E R G I S E N W E I C H I S U N B E V O R S T A N D W E I S E S E F E H R D E H E N D L E R W +mls_deu_000298 D A S M A T O N E N H Ü T C H N V O D S C W A R T Z A M A M I N D G A T Z I Ö R S R I E R E L A N G N L O T N G E D R I K T D I E R W A N G E U M F L O S E N N T B E R S C H L T H N H R A P W E I T E N S O T R A E I D A S E I N V E R H R E R L N T L I C H G E B O E U D E U N D S T Ä E B P E T Z W S C H N R E I N D E R H I B P G E B L N E I N D O F K E N R A U F E N T A B +mls_deu_000299 R B M U S T E R T I N Z A G E N G A L E M S H N T H A F T E N S T R E B E N U N E N D T I E V E R E I U N D D E M U D D I E F Ü Ö H B I D E D E R H E L I N G A E F L E N G E G E N D E U G E R E E L T R S T D E J E M L E N G E R W E L C H E P F A N T C H S K O S U L N E G E F L O N S O C H T E N I N A U F I N S R E W E R K S T U N F A N D T E N I N +mls_deu_000300 E R L I E S Z S E I N E G R E T E N C H T V O R T S C H L E B T E N A M A L E R W I N I G S D N A B E R I N E N G R O S E N V O G E L B A U R U S I E A L E I N E I N E M T O N E F E I E F E M O U S T E N I E R S H I T S K T E +mls_deu_000301 F R N T C H E S K O M A L T E N U N H E L I G E B G E I S T R U N F I L E B I E T A S E L Ü G E N H A T E N A B E E L T K H E I N N E L S E R E R M O C H T D I E B U L E R I S C H E L B I K E T D E W E I B I C E N G S T A L T E N S O B E R H A F D A S I S T E L E N I N D E M V N L E B E D T E M O D D E L E N D I E K A L N A T I O N G V O D N A L T E N M A H M O B I L E N B E R O R M O N B I L U N I N D N A N +mls_deu_000302 B E W E G U N U N T A T D E N S T E N Z U G E R S T H M E D I E V O E U N A N G E G E B E N I N G E R D E N Z H E R N M I C H R Ü B E H A N F E E I C H E N U N S A U R A M F A N A L E I N D E M P F E I F E N K O P F E R A N W E S E N A B E I N F Ü N D F T N A U T S T O H A R I C H N I G E N A N D I E T S T R O C H U N D S C H M Ä K T I G D A S E H N S T I C H E N F I L S H D E R B E I S E I N M S E I G H P L I E S T E N R A U C H A U C H G E G D E N H E E L U N G E G N G D +mls_deu_000303 U N D A S V O Ä E R S T A N D A U F U N D F L A C K E R T U N K O C H E D A S E S E N F Ä A T I G H U N D E R B R A T E N B R U T Z E L T E F O R T U N D E R K O C H G A B D E M K Ü S C H E N I U N G E N E I N E R R O A R F E I G E U N D I M A R K T R U P F T E T D E S H U N F E R T I G H D A R W A R T D I E H O C H Z E I T V O N D E M K Ü N I G H S O N I T E T D O N G R Ö Ü S I H N G E F E I H R T U N S I E N E E R N Ü T E B I S A N I E R E N D E +mls_deu_000304 U N D D A S E M I N I C H N A C H T R A R G E N G O L L E W E N I C H N E D E R S H P E N S T I G W A R G I N S E I N U L M E I N E N B R A R T D E R H E R F A R A R H E D E I N A L L E N R E I C H T D E H A T U N I C H M E A N U N R E C H T A B E R H +mls_deu_000305 U O G E H N E M A S E N U W I N I G E K R A M B T U G E R B R E I T E D S I C H K H E I E F Ö M I G A U S E N M U S T E D E E R E S H E M I N D G E G E N F I G E D E S P R E N K I S C H O S A U F A N E N T Z S O U E R I N G E N +mls_deu_000306 D E R V U G S R E I C H E S E M I U N F R I T I C H E F R I E D E N S P W E I F V E R H E N D E R M A N T A T W A C K A S E I N E S E K S Z Ü G E N S A K T E D E R G R O S I G E I S A C H T E N I C H A U F D I V E R S C H I E D N E H A U T D E R M E N S C H E N D E N D I K R N S I C H M I T V A B E B S C H M I H R E N M I N T Z S T R O L S C H E N S O N D E N E R S I D A S H E T Z S A N D E H E T Z H N D E R K R L I G E V O M B E R Ü B E N S T A M I D E R K A I O W A S I N T A P F E R U N E R S C H R O G M N T R E D A S M E I N I G E H E N G +mls_deu_000307 A L L E S A S W I M E T I E R B E G E G N E T S C H E B S I C H T D O E S C H U N D B E R I N A N D E R B A L T U N T E R S C H E B E M W E R I N K O N T A K T D E I S T I E R E R H A N D U N D E M E I N I G E I E R N A H R M O N D E R M E I N I G E B E I D E L R S H E E I N A N D E R A U S B E I D E V E R S C H L I N G E N S I C H +mls_deu_000308 E R M Ü S T E E N E N F E R H E N G R O N I T E N K Ö E R A L D E S M A L E S M I T A L L E R K L E H R M E N Z U R E C H T W E S E N E N R I M I T G R A U S E N I G U N V R C H N A R C K H I U N V E R B R E M E N I C H T R E T E N D I E P E R S O N D E S E R A U S G E I B E S N D B I T E T I C H I K Ü N S T I G E L I S E R U W O L S T I E D U W E I T E L I S I S T F O L E N D I S D I G I T I S T N E R T E N +mls_deu_000309 D I H O F D A M E N B E K A M E N K R E M P F E U N D I K Ü N I G E N U N D I E R O N Z T E S S E N E N D I E R E R A L L A L I E B Z S E N H Ü N Z C H E N W E R N D E R M E I L T H E R A U I N S C H O S G E N O M H A D T E N E R M E R K T E N Z U I R E N S C R E Ä K E N G D A S D I L I E L E R A M A R A N T F A B E N E N U N D O R A N S C G A H L D E N S E I D E N K L E I D E R A L E D I S T D E S E T M E I N H E S L I H S T E N Ö F L E G E N W A N +mls_deu_000310 V O N L E D E A N D I E S I E S I N U N K L A R W I E R P I E S E N D I E S I S P B I E L N V O N G Ä H R T B Ö R S E N D I S I H E G K E N V O N D R A N Z Ü E S C H N B Ü C H A N D I E S I B A S R S E T Z E N K O N T E B I S M A N G E M Ü T W E R E N D I H L A U S T Z O N N A C H A M O N A U F K E S T C H E T W U R D E +mls_deu_000311 A M U N D N A E N W A N B L O S I E R E I N Z I G E R S C M U O K W A N I E R E A S T A N E N B R A N F L Ä C H T E N W E I C H I N W I L D E R U N D D A T Ü R L I C H E R A N M U N D A U I R S C H U S T H E N H R A B P F I E N I H N A M E I N B O G E N G F E I N K A T U N G S N D Z E I H E T M T R O S E R S O K V E L T I O M G E S E R +mls_deu_000312 R A R W I E U S T R T S C H L A N N O C H A S I L E N E I N I M A N D R E N S T A R T K O N T M A N E F A N W A S D E R G E N S C H E A N U N D E S E S L T A T D I S U N T E R E D U N G E W I S E N S E A N V E M U T E T E D S E S I C H U M E K L I R E N G D E R M A T Z H E Ü B E E R A B S I C H T E N U N D U N D I V E M I T L U N G D E R M Ä C H T E Z U I S C H N M A S T A T E N U N G R O S P E T A N I E N H A N D E +mls_deu_000313 L A U N W E N I G S T E N S E I N E Z E I T L A N G V E R S U O C H E N I N B I E F E R U N W I E R A U F D I E S E S B E I S N M T E I N A N D E R A U S R E I C H E N D A D E R S T U S A M E N H N G N D E I E D E S A R G S T A E I G E N K L I G H O E E R L E M E N T I S E R S E T Z T I E R U R T +mls_deu_000314 V E S C H E N F V O R K O M S E K F Ü R D E N Z U D E V E R M U T U N G D A S F R A U W I E S E D I E K L E I N E N W I E N V E R B R E N E I S O L I S F E I N S U S T A C H G E R H I T S T A B E N D A S D I H E R T P L A T E N Z S P T A N G A U S E D E S O L E I N F Ü R C H T E L I C H E R G E R O C H O W A G E N U N M N W U R T E N S E I N +mls_deu_000315 U N K G I N D E M S H R E I E N A C H S O S A E R E N T L I C H E I N H U O N B A U M U N D O B E N D E R A U F S A S E N K L E I N E S K E N D T U N D E R D E M B A U M A B A L A R E I N E F R A U R D I E S H L I E F +mls_deu_000316 C I T S I E H R T E N S E R H E B E N D I F I S C H E G A D E N E W A L L S C H E D E N A R C H T I A U B E R T A U S G E R U R D H F V I N W A D E N H E R E I N G E Z A O U G E N D I E S E L O E I T E R G E R H Ä O R T H E N A U G E N S H E I M L E S C H V E S C H E T E N E N N O C T Z I O N E N A R N A B U O R L D E R A L O P Ä H S C H E R K A R E K T E B E I A L E N A U S G E T R Ü K T W A L +mls_deu_000317 N E I N E I N I C H S C Ä E M E M Ä I C G H T L S M C H R N D E I N E M B U S E N M E N G E S I C H T V E R B E H R G E N H E R S I N G T E N S G R A S N I E D E U N Z I E T S I N A C H +mls_deu_000318 D I K E N D E R A B A R S A S E N V E R D E M W A L T U N D A L S I E D I E D R E I G N E C H T E V O N W E I T E M L A U F E N S A N S P R A C H L E N C H E N Z U N P F Ü N D E V O G E L V E R L E S T U M I C H N I C H T Z O V E L A S I C H T I G A U C R E N I C T S U R S P R A C H F O N D E V O G E L N U N U N D N E M A R M E R +mls_deu_000319 W I E D E R S C H U L Z E I N S E I N E R H U L D I E G U N G S R I E D E H E R V O R H U B D E L E R A B R A C H T E A M K L A R E N S O M A M R A D E N G M I T S E I N S C H U L K E N D E N E I N G E S A N G S S T E N T I E +swc_deu_001408 S T E R T W I S I E S E I E N S O L N +swc_deu_001409 D E R E N T S C H I N G R N G E N D R C E I N E R Z U S A R S C H A L T U N G S T U F E N L O S +swc_deu_001410 D I E A U F A L E B E I D E R S I T Z V E R T E L U N Z +swc_deu_001411 U M D E N Ü E R L E M E N D +swc_deu_001412 S P B E T E R U R D E N T E I L W E I S E S U G A R A C H T P A R L I L L O S T R E I F E N E N G E S E T Z T +swc_deu_001413 M A O R D E B E K A N T U N D V E L L A N G K T +swc_deu_001414 B U N D E S W A I G E S E T S D I E S T M V O N W I E L E N +swc_deu_001415 S F N A G E S C E I C H T E +swc_deu_001416 S B A L T U N G F E E C +swc_deu_001417 S T P A L E B O R N D I E U S E R E N D F E R A N D E S +swc_deu_001418 U M W E I T E R I N H U M A N I T E R E R H L F I E T Z +swc_deu_001419 S I E R K A M T E N D I E N N O U E R I C H I N E S I S H E R E G I O U N G N I C H T A N +swc_deu_001420 D I E U R A U F Ü H G E N V O N A N D E I N Z W A N S E N S E T E M E R Z W E R D E N A C H T I +swc_deu_001421 E R E I C H N I H T S C H O L I C U R E M I T S C H O L I H M A C K E N A N T O D E R N S M I T G E S E +swc_deu_001422 D I E D E R S Z S T M E R E I N E N +swc_deu_001423 U N T E I F E N T I S E N B E I D E +swc_deu_001424 K R E I S W A L F V O R S C H L A G U N D E I N E L A N D E S L I S T E R N D E R Z E I T E N +swc_deu_001425 A N U M S E R Z U N G D E R S A G E I N V O M A N E S F N F Z E I N T E I L G E N L I E E R T Z I K L S Z W E T E N A C T W U R D E P R E S L A S K A B E R T I N N E B E R A B E T U N G V O N H O S T H A B E M A N +swc_deu_001426 I E D I E V O L E D E R T A B E L E D A S T E T +swc_deu_001427 Z U M S T R U N F L S B A E +swc_deu_001428 D E B U N D E S W A L E I T E R B I S T Z U M S I E N Z I G S T E N T A R +swc_deu_001429 O R E R I C H K G E W R D E N D U S H E N I C H T M I T W E E N +swc_deu_001430 A U S F I O N M U S T E N G U S E R K O T E B E G I N +swc_deu_001431 V E R K I E I C H P B A N Z E A L N W E R T U M N G E A N D E +swc_deu_001432 B E T R A H T E D E E L G E M E I N H E I +swc_deu_001433 U N T E R S H I T L I C H E A U F A S I N G E N G A B E S N U R D A R I E B E R +swc_deu_001434 D O L L B E I M B U N E S L I G I S T E N B R S E R T O R T M U N D T N A C H V O L G E R D E S U N M I T L B A R Z O V O R T Z U R Ü C K E T R E T E N R E N A S S J I R E N R Ü B E R +swc_deu_001435 N U N Z H N A D C H T U N A H T Z I G +swc_deu_001436 R E I N E N Z I G K L P E T D I E +swc_deu_001437 D E R V O T S T R O M I S Ü B E R F I E L E G R U S S E N O R N U N E N L E N A R T Z U M L I C H T E N V A L +swc_deu_001438 D A S H T D V F Ü K L E I N E P R T E I N G R O S E A U S W I R K U N +swc_deu_001439 I S D E I T E R A T I E F E R T I E N S U G C +swc_deu_001440 D I E S K R E N Z U M B E I S H P B I L K O N D E N S A E R T H O R E N S E I N +swc_deu_001441 A L D T D I E K O U R S A U F K U B E R H A L K G E N D E N S O R I D T I S C H N S C H E R A B P T R E T E N +swc_deu_001442 B U N D E S T A G W A N T Z U N D E R D R E I U N D F M F Z I V R E R S M A L S N C H E I N E M V O M B U N D E S T A I G S E S T A L S E N G E S E T +swc_deu_001443 B U N D E W E I G E Z F I E F C H G E I N E R T W O R D E +swc_deu_001444 E R I B E R L A G E R T E N V O T U S T O M E U N D T R E I K +swc_deu_001445 D R T S I N T I E G R A T I O U M D E R B E I D E N D E U T C E N S T A T E N +swc_deu_001446 B E R I E N E Ü L M E S E N S T A T +swc_deu_001447 O A F I T Z E E F Ü L R N +swc_deu_001448 B I D E R V E R H E N S W A L W I T Z U S E R T L I C H D I E E I N H A L T U N G D E R +swc_deu_001449 W I E W E N I C T I N O L A N E R N O C H M P E L T S D E R Z E I T +swc_deu_001450 I E D C E T W V R D I E D U C H F I U G E N V O N W A L W E R B U N G A F K O S T E N D E S T A T E S +swc_deu_001451 D A S N I H M R U N G E S E T Z +swc_deu_001452 H E I M A T V E R T R I E B E N U N D R U S T I C S I G G E W A L +swc_deu_001453 U N D S P B E I C H E R I E N I N I N E W A C K T E S H L A N G E A B P +swc_deu_001454 O U R E G E N A L T H O N B E N D E R U N D D I D O K M E N T A R T I O N D E S T U R D I O S U R D E N E I N Z E H N H U N D E R T Z W U O U N S E B T Z I G I N D E R S I M E N S E C H I Ü E R S T E L T +swc_deu_001455 S O M I S E N A U E I N E M S T A T E G E S H E N R E R K T E E N U O B O O T +swc_deu_001456 F L Ü T E N S P B I L E E D L I C H E +swc_deu_001457 D R A S T D I S H M O R D E R A N E L I G K T R O N I S C H E R K L A N G E S H A L T U N +swc_deu_001458 A N C H I S E N W O D E D I E S O A M I T E T E M A N D A T Z T S A L I E D E R P A R T E I N T D I M S E B M V E R F A N E N S P E C H E N T E R A N Z A L I R A R Z W E I T S T E I E M P R O P R T Z I N A L A U F D I E L A N E S L I S T E D E P A R T E I U N T E R V E T E I E R T +swc_deu_001459 A U B P F A N D E R N A T S E B O M A D I U U N D T E R K N F T E +swc_deu_001460 D E R R E I N E N Z U K L O P E +swc_deu_001461 M I E R B E I L S C H N N +swc_deu_001462 W E R W I G E N E I N E V E R B R E C H E N S R E C H S G Ö E F T I C H Z U I N E R E I T S T R A E V O N M N D E S E N S E I N E +swc_deu_001463 D E R E S C W N D I C K E I T Z W E R T U N G E R A G E N D R E I E B E E I N H U L E R T A C H T +swc_deu_001464 E B O R U I U S A M E R S T E N G E B O R I E S A M F T E +swc_deu_001465 N T H I M S O N A E R G Ü F V E F A R E N A U D E L E N E R V E R T E I R T +swc_deu_001466 R E F O R M I N G O A B E R S C H A F S U N D A B P R S T U N S C H L T +swc_deu_001467 S I E R E N A N P H R T E T U N T U N D E R D E +swc_deu_001468 A N D E M B E S T L I C H E G R E F T E A U F G E D E N R U L T Z U N +swc_deu_001469 I T U N T E R A N D E R U M V E R W E N D E +swc_deu_001470 U S W Ü C K I P Ä N D E R +swc_deu_001471 U N D K O U B A R G R I E S E +swc_deu_001472 E N L T Z S D A W A L A U F G R U N D E I G N E R W E I R V O S H L I G E N E T E R R C H E N M T M I N D E S E N S F M F A B G O N E N V E R T I E E N S I N +swc_deu_001473 V E R P R E I T U N G I E D I O L O G E S C H A P R O P A G A N D E R D E R S U P E R M E C H T E U N D +swc_deu_001474 K O M I G S A U F D E R L I T H E T B E R T H A G E +swc_deu_001475 A L D E R K A L T I G K L I E G S I C H V O R T W E R E N Z U S P I T Z S T E +swc_deu_001476 S I C H E I Z P E R S N A L O D E R E C H U N E N N S E R S C H W I E R I G B E T E T E N E R N +swc_deu_001477 D A R U R H A F E S B L E I B E R I C H T U N +swc_deu_001478 I B E S E R W I E D E R S M U T I F T E R L E S U N G B I C +swc_deu_001479 W E N F Ü N I E M A N N A C H P Ü C S B E I S T +swc_deu_001480 I S D I E K L E W A R T E A R V O R S C H N V O N E I N R I C H T U N M E M +swc_deu_001481 G E S E N D A V O N B I R D E N S E B S D A N O C H I E I N S P E C H E N D E N P A L K O U T S F I E E N +swc_deu_001482 S P E C H E N B N Ü T I C H T D E A E M L U F T K L I E F V E R T +swc_deu_001483 E M U C L I C H S C U T Z I M V O R N G E N K A N G R E I T E +swc_deu_001484 S H N E I N E N L I C H E N V E R S U C H G A R +swc_deu_001485 R U N K G E N S T R A N A N I N E M P H E N Ü B E R G A N O D E R P E E E N I E B E R G A N D E S T H N I N R U N V O T U E R V E K T I N E I N E L E K R I S C H N S T R O M U M W A N D E +swc_deu_001486 B R M A S E I N D E S E B E S A L N O C H T F R E I D E T +swc_deu_001487 E L A E N D E R B I G I F +swc_deu_001488 A N K F E I L N E S U N T E R D I N S T E I M N O C H E I N E L O G E S C H A B P V O E R G +swc_deu_001489 K A B E R T L E N D I E S I S A N G E B O D I E D O M I T E N S C I E T E N E I T A +swc_deu_001490 S T A N D V O M Z W E L F T E N M A R T Z S Z W E I T A U S E N Z W E L F D E R I N H E I S T H I U T +swc_deu_001491 O G E N I S E R T I O N U N T E R B R A C H T E R F I N D +swc_deu_001492 V E R P Ü N D I T Z E N D T O D E R G A F Ü S I E A R B E I T E N +swc_deu_001493 V R S G L I E T E V O R H R I C H K T E I L D E +swc_deu_001494 I A R I C S T U N G D E R B E L I E N E M A U R A M Ü N D E T E N +swc_deu_001495 E R I C H T U N V O N K L I E R A N L A G E N +swc_deu_001496 A F G A N D E S T A N Z U N D I M I E R A R G H E R Z I E S E I T I M E I N M A R S T +swc_deu_001497 D E R V O N A R Z I U N D S T O M V O N D E N L U N G E N I E R D E B R O N C H E N B I S +swc_deu_001498 A U S E D E M N N A M E N S E N D E R H Ü R S P B I L E M I T V E R R M D E T R S P B R A H R +swc_deu_001499 U N D I E G R N D M A N D A R Z K L A U S E +swc_deu_001500 K E I N E A B K H R V O N E N G R U N D P L A G E N D E S T O T Z E L I S M U S E I N S H L I S E +swc_deu_001501 I T K O M P R N E N T E N S O H O L A N A L S A U C H T I E F I N D E R W A F E +swc_deu_001502 B E D E U C T U N G V O R L E W A R +swc_deu_001503 R E I F I T I G E H E N V E R T E R O U G A N I S T Z I U N +swc_deu_001504 U M E L E K T R U N V O M W A L E N S B A N D E N S L E I T U N S B A N +swc_deu_001505 A L L E D I N G S U N V E R L E I C H B A R I V E K T E M U K L I C H +swc_deu_001506 D I E S E K O N T E N A B E R L S E I N G A B E L I N E I N E N F R I C G W E N Z S U M S E T Z S E R D I E N U D E R S T E U E R T E N Z U N G O H N M U T U R N +swc_deu_001507 T O U M A S H R M A N S P R D T Z I E R T I Z W E I T A U S E N Z W E I M I T K G R E E B E +swc_deu_001508 P I E N U B E R G A N T R I F E N +swc_deu_001509 D I E F E I L K T E N H O S T S C A O U N +swc_deu_001510 A N I E S O U J E T C H D E M N S T R A T S I O N E N W U R D E N P L U T I G N E D E R G S H L A G +swc_deu_001511 E I E N F I E R K A N A L M I S P B U L T D E N T E V E K L E I N E R +swc_deu_001512 D I E S E H T N D I E V O R W A N Z E I T E N V Ü E R E I N E N A N G R I F A U F D I E U Ö S A R E X S T R E M H R A R P G E S E T S T +swc_deu_001513 W E R C H E S A M N E G H S T E N Z U M S T A T K E N O D E N L I E K T +swc_deu_001514 A I O G I N D O L Z E R Ü C I N D E B U N D E S L I E G E R U N D W E X S E L D E Z U E I N R C H T +swc_deu_001515 Ü B E R I S E K A N G K H E I T +swc_deu_001516 A R Z W E I T A U S E N D T F Ü N F E K Ü I D E S E R T +swc_deu_001517 D I E S E A U F S S U N G Z U N E U T R A R I T E T U N T E R S C H E I D E +swc_deu_001518 R E D E L W U R D E A L S K Ü N Z T A L I C H E R L E I T E R D E R I E M E N S T H U D I E S B E S T E L +swc_deu_001519 W E N M E N D I E W Ä L T A L G A N Z E S P E R A C H T +swc_deu_001520 S E N D K I T I S C H E K O M P B O N E N E N D E S D E T U N E R D I O U N Z S I S T E M S A B S I C H T L I C H S C H W A C H E N D W U R H F E N +swc_deu_001521 N I C H T W E R B E I I S T E D O C H +swc_deu_001522 E R B U O T E I N E F E R E I N I G U N G D E T S C L A N S A N +swc_deu_001523 P E L I E N Z W E I T A U S E N F Ü N F +swc_deu_001524 K E R N A B G E S T E I M T U N D T U M H L E N D I E S E N E N S P R E C H E N T +swc_deu_001525 A R Z O E L G U N G V O N D N A M I G A U S +swc_deu_001526 S E M T U N D E N G D E R +swc_deu_001527 U N G V O N D S C H I E H R U N T E R N E R U N +swc_deu_001528 D N E S C H E N U N G E R W R T C E N +swc_deu_001529 R O B E T E R F K E N E D I +swc_deu_001530 K A M S H L I S E L I C H Z U M +swc_deu_001531 V O L S T E N I +swc_deu_001532 S T A N T E N S I C H V O N D E N U E R E S A R +swc_deu_001533 A F R I K A R S T D I H T E S E R H A H R E R G E O R T E T +swc_deu_001534 D I E A R M E M U I T E T E L +swc_deu_001535 S T A L I E N S E R T Z T E M +swc_deu_001536 V E I H T E N E S A U S T L E I C G +swc_deu_001537 K L M E A U F B R O S S C R E I B T L E I H +swc_deu_001538 A M Z W E I T E N J U N E Z W E T A U S E N D F I E R B U R D E +swc_deu_001539 I N E B N D E S T E R N A C H R I U G T +swc_deu_001540 D I N A R T O O S T E R W E I T E R U N G U N D D I E E I N S E I T I G E R A U F K Ü N D I G U N G D E +swc_deu_001541 T H E R B E I I S +swc_deu_001542 D I E S R S T E L L E K A M M N S A N M T I C H E M I T I E D E R D E R K A P L E T +swc_deu_001543 P O T Z S T A M A B P K O M M E N E N T H I E L T Z W A R A L G E M E I N E V E R E I N B A U N E N Ü B E D I E K Ü N F T I G E G E M E I N S A M E V E R W A L T H N D E R S E G E R M I C H T E U N D V O M U L I E R T E G R U N D S E T Z E B D E M L I T R I S I E R U N +swc_deu_001544 D A N A C H R U N D R S C I E B E I N E N V A R A R K G B E I M W I E F T Z S I D Y N A M O +swc_deu_001545 E I N W E I T E R O W A R I J A N T M A +swc_deu_001546 S I E W U R D E N M O D O L A R N T U R C H E L O C H S T R E I F E N G E S T E Y E R T U N D D I K L I N G E K O N T E +swc_deu_001547 D I E G R U N M A D A T K L A U S E L B E R V O R T Z U G T U N D E D I N K L E I N E R N P A R T E I N J I E N E +swc_deu_001548 A B E R T O T Z D I N K E I N E W Ü K L I C H E R H U N G E S N O D T H E A S T +swc_deu_001549 N K O G M A L T E R Z I O N D +swc_deu_001550 Z U O F O R B E D I G U N G K O N G R I E T E R A P R Ü S T E N S C H L E T E +swc_deu_001551 B U N D E S T A R G E W A L R E C H T +swc_deu_001552 I S M U S T I M G R E I S W A L E I T A R V O R G E L I G T W E R N +swc_deu_001553 H A T M A N I N E I M P I E R E S C H E B A S E S F Ü B S Z U C H O S O T I A L E P R O G E A M M E Z U O R S E N K U N G D E R S E B S T M U T E R A T H E U N D Z E R S T D A R K U M G D E S I H G E R H I T Z S G E F Ü S I N D E B E F E K E R U N +swc_deu_001554 B E I D E N E R S T E N W R E I N P A L E M E N Z W A L N U R L E I E L I E H R S G U I M M E I N U N Z E I N H N D E R T N E U N Z I G I N S E I N E +swc_deu_001555 D A M I T L A S E N S I C H B E S T R A L U N G S T E R E N S E R G E N O U M E S E +swc_deu_001556 W I N G R S P I Ä T E R K A M S T Z U E I N E W E I T E R E N K R N D N +swc_deu_001557 W R A R D I O K A B E R E R T P E I L S +swc_deu_001558 S T Ü K T E B O U M B E R A U F I S T A R T B A N E N R O L E N +swc_deu_001559 M I T D I E S E R I G E L U N G S O L I N E R F A K T U I S C H T Z W E I F E R C H I N F L U S T N A E R D I S E R W E L E R A U F +swc_deu_001560 B A R K K Ü R S C H E N B A U +swc_deu_001561 D E R H E V O R A G E N T Z W I C G E N D E N L A N D E K L A B E N W I D E R U M H E R V O R A G E N E R L A N G S M F L U G E I G E N S C H A F T E +swc_deu_001562 M I T E R S C H V E R B I N D U N G S F L U K Z A G E O U D E R U M S C H U L M A S C H I E N V Ü R D I E B E E E I N H N D E R D N E U N V E R W E N D E T +swc_deu_001563 L E I S T E T E M E I Z I N E S H O N B S I C H E L O G E S C E H E I L E F +swc_deu_001564 K A M A N D E C H I M F O M R E N V O R B E U I G E N +swc_deu_001565 M E R D I N A U S B U C H T I S E R K A N K E I T E N E R E F L U K T E I N F R K T I O N V E L L A N G S M M E N K A N +swc_deu_001566 D I E I N E N E U T R D I E T E T U N T E R A L E N U M S T E N N V O R S A R +swc_deu_001567 U N Z S I E G E N H E R T +swc_deu_001568 D A S N E U I N Z H N H U N D E R A C H T E N D R E I S I G G R Ü N D E T E K O M I T I F V I R U N A M E R I E K A N S C H E U M T R I E B E W U R D E D A F E R N R U +swc_deu_001569 Z E I N D R A L E D E P R O K R E S I E N U N D H R T D I S I N H E N J Ö R G E S T I T S T E N K U N Z S T D E N G K E N S +swc_deu_001570 I N D E R E U E S P R E S E D E N T A N K Ü N D I G K T E +swc_deu_001571 S N E K Z S T U N D V O R S H P E I S E N +swc_deu_001572 D E S B U N D E S W A I G E S E T Z E S B I S T Z U N D R E I S I G S T E N C H U N I E Z W E I T O S E N O D E A U F G E M +swc_deu_001573 A R D E P U S S E Ö L +swc_deu_001574 F L Ü C F T L I N G E N V O N D E R I E T N U S H E N M I N D E R H E I T D E S O M A L E S H E N B A N T U N +swc_deu_001575 D I E B I E P O L A R E W E L T O R T E N U N S E M I N T I E R T +swc_deu_001576 T A R A N F A N G E I N I N T E K R I E R T E U D E R E X S T E R N A N G E B R C H T V O R I C H T U N A N E I N E M N U K L E A R E N W A F E N S S T E M +swc_deu_001577 S T A R T E R T D I H I L F S O G E N I S I T Z U N L A N K P V R E S T I G +swc_deu_001578 W E N D I E S E E C S T E R E N E F E K T E I N D E R I C H T I G E N R E I N V O L G E A U F T R E T E N U N D S I C H I N E R H A L B S P E Z I F I S C E R P A R E M E T E R B E W I G E N +swc_deu_001579 Z U O K D E S E W E R T U N I O N A U C H B E I D E W A E R S T A O F P B A U M B E M U N D T N E N I N F L U G K Z O E U G E M I T I N T E R K O N T I N E N T A L L A R E I C H W E I T E M I T D E N U R S A R G L E I C H +swc_deu_001580 P E N D E S T A T H E E W A B E N T I E O M +swc_deu_001581 D I E S E R A N S A T Z S G E L D A L G E M E I N A L S A U S C G E B U O R G U N D E +swc_deu_001582 N A C H I N Z U S A M B R O H T E +swc_deu_001583 D E U B E R L A U S E T Z Z F I C H E N H E I E R W E R D +swc_deu_001584 D A B E I E N Z W E I F H A S E N U N T E R T E I E L T +swc_deu_001585 S C H I E T E N A N D E R E R O P R M I S T E R S C H A F T E I L U N D W U R D E M T E R D I R B E E L F +swc_deu_001586 M A S T E R E R L F N E K A B A R S C H I S I E N W E I T E R G E M Ü K L I H K E I T +swc_deu_001587 E I N E M A U S W E R T Z S A E V O L Ü G I N W O L S B U R G E L A N +swc_deu_001588 M I T S C H E W E B U N G S S U M A N K O N T E N K L I E S A N D I E R Z R E L K T W E R D E N +swc_deu_001589 D E R B A L E D I G L I H T Z E I K T E +swc_deu_001590 K O S P R E T A N D I E N E I N E E S T E W I C H T I G E V E I N B A U N +swc_deu_001591 I D A U H T E S H U T D N E S I N +swc_deu_001592 W U R D E M I T D E B U N D E S W A I G E S E T Z V O N E N Z H N A S I C H S U N F M F T I G A I N E D A U R H F T E R E L U N G E N G E F Ü H R T +swc_deu_001593 D I E A N Z E A L D R I B E H N G M E N D A T E K A N +swc_deu_001594 B S C H L S D I E S E R E I N M L I T E R S C H S E I N G R E I F E N I N D E N K O R A R K R I K +swc_deu_001595 N A R T U O V E B N T L I C +swc_deu_001596 K A L Z I G R I E G B E I N D E T +swc_deu_001597 A U N N Z E N E T E I A E M D N E U N Z I G U N U S T R A L I E N S W I D E R Ü S T E R E I C H S C H A B P L I G E R +swc_deu_001598 D A D I E S E I T A N F A N G N E U N Z H N U N D E R T N E U N U N F Ü N F Z I D R T H E R S H E N D E R E R U L O T I O U N Z R I G J I O N G U N D E R V I E D E L K A S T R U E I N D E N S O S E L I S T I S C H E N K U R S E I N G E S H L A G E N H A T +swc_deu_001599 D A C H R W E I T E R E N V E L U S T R E C H E N K E M F E N U N E N E N Z W Ö R T E V O L G E B E I D E G R I G S P A T E I N U R D E R U N T D R E A R E N E R B E G I N D E A U S A N D A N D E R S E T Z U N G E I N B S R E I T E G Ü L T I G E S W A S F E N E N S T I L S T A N S A B K O M M N A P G E S C L S S E N +voxforge_deu_000891 M A N I S T E R B E I S E R V O S I C H T I C H +voxforge_deu_000892 D I E W E R F L I C H T S O L I N D E U T S C H L A N D L E I E R N O C H N I C H T A B G E S C H A F T W E R N +voxforge_deu_000893 E S G E B T A U C H M I S P R A U C H T U C H A B E R T G E B E R +voxforge_deu_000894 D I E K I N D E S I N D A N H A N K E B O N E N +voxforge_deu_000895 D E R A C K W E I T E D E R K A D A S T O F E S O L L V E R D E U T L I C H T W E R D N +voxforge_deu_000897 S E N R L L N D E T +voxforge_deu_000898 B E I M O G A N D S T R E I T S T R E I T E N B E R D V E F A S S U N G S O G A N E +voxforge_deu_000899 D A W A G E C H A R Z U B T Z W E I F E N +voxforge_deu_000900 M A N S O T E D E N A U F G A R G H E I N V F A L T R A U N +voxforge_deu_000901 D E E F N L I C H E S C H U L E N W E R N N I C H G E T E L K T W E R E N +voxforge_deu_000902 B A E G E L T I S T A U S K E T Z H L T W O R D E N +voxforge_deu_000903 E S O L E N R E I H U N D E R D T A U S E N D N O E A B E S P L Ä T E I N S T I E N +voxforge_deu_000904 D I E K E R B E R V E L E T Z U N G K A N A L S B E I S P I L E N D W E R D E N T +voxforge_deu_000905 D I E R E N Z E I T W E R S C H T E N B O D E N +voxforge_deu_000906 D S T D A B E F E L U G S B E Ü R D E N K E I E N Z U L I E R E S K E L H A B E N +voxforge_deu_000907 D I I N E R E S E N F I N D E N K E I N E H Ö R +voxforge_deu_000908 I F W E I L T A S S T D E T A B L A R T O H E R R Ü G S C H I E D T A S S T D E N R Ü G G T A S S T D E R Ü G E G I E R S T A S S T D E A +voxforge_deu_000909 D E B E T R O T C E N E N U N A N B E R E C H T I G D E E N H E R I G E L T E N M A C H E N +voxforge_deu_000910 E I N D R I T E R H A R D D E M S C H E I D I G K T E N V R E I W E L I G K L E I S T D O N G E N Z U K O M E N L A E N +voxforge_deu_000911 S O N E N A U R E C H Z N E B E D E B I L T +voxforge_deu_000912 I E R E I N E N I C H T A R Z L S C H E M E I T E B Ü L S E R K L E U N A B P K R E N +voxforge_deu_000913 D A M U S T E J A H R A U F I E E N F A L S O K O M E N +voxforge_deu_000914 M E R E R E K L E I N S K O N S I C G E I N E I P I E R R E S E T E I N T +voxforge_deu_000915 W A D I E G I N Z T I G E R V E I S H I E S A L S O R S I H Z U S A M E N E N M E N A N S T A T Z U N +voxforge_deu_000917 D E R C H O L E H E R Z E I N E L E I S T U N G A N G E B O R T E N +voxforge_deu_000918 S O D A S E I S F +voxforge_deu_000919 D E B A T R I E N W A R N E R S T A V E R A L T E T +voxforge_deu_000920 D E S E S Z H I E V O R D E N O R T A L W A L S E R E I C H T +voxforge_deu_000921 T I E S E W E R U N G W I R T S E L R L A N G E L E B E N +voxforge_deu_000922 D R D Z E I E A N A F E N B A S H O N V I E E +voxforge_deu_000923 A L S I G E E N E N N E I G K T E M Ä A G I I E R N O R G A N S F L C H T I G K Z I U O U B U N D E R F A R T E A R +voxforge_deu_000924 T I E R T E M E M I E B E R I S T I A N +voxforge_deu_000925 D E M S T D E H E N A D T Ü Ö R L I C H A U C H F A M M Ö H E N G E G E N Ü E B E R +voxforge_deu_000926 D I E E R E A L E L A N G E W I E T N I C H T V O U S T E N D I C H A B P G E B E L E T +voxforge_deu_000927 E S K A N A U C H N O C H F V I E S C L M A W E R D E N +voxforge_deu_000928 D I E B P L I T I G I N R E S I E R Z S I C H N I C H T E M E R +voxforge_deu_000929 I N H A L T Z F R E H E R D B E D E I T E D T D A S E I N H A L D D E V E R T R A C K L I H N V E E I N B A R U N G E N U +voxforge_deu_000930 D E R C U N E R V E L I T Z D E S E I E S U O G K V E R S P L I E N S C H U L T E A F T +voxforge_deu_000931 D I E S E G E T R E I D E D E N D I N Z S P E S O N D E R E A L S F V I V O T T A +voxforge_deu_000932 Z T Ü P B E S C H R W E I S E W E R D S T D A T I C H E E I P I R R E S E N V O N D S O R V E N E I N G E T Z T +voxforge_deu_000933 J E Z T W R Z O L A N E A M G E G L A U B P T +voxforge_deu_000934 U N D A R S C H I T L I C H E E R G E B N E S E H A B E M S E C H E R E I G N E R D T +voxforge_deu_000935 T E R H F A R D E C H T I G E W U O R D E N N E Ä C H T V O R E I N G E R E C H T G E S T Ä L L T +voxforge_deu_000936 A U F M A C H E N D I S T D E F Ü N I C H T A U S T Z I E H N U N W E I S E G E R D W A S N O C A L E S +voxforge_deu_000937 I N K E S A M D R E I U N D Z W A N Z I C P E R S O N E N A U S V E R S C H I E D E N P A E M E N T E N N E M E N T E I +voxforge_deu_000938 V O R D R U N G S E F T E W E R E N E M G L E I B I G E A U S C L I E S I E Z O G E O R O R T E N E T +voxforge_deu_000939 D A P O B L E E M H W V W R D E B E H O B E N +voxforge_deu_000940 F R D I E R H E I N U N V N U N T E R R O C H E N E R D E S K R I T E R S P A C H E +voxforge_deu_000941 D I C H I N S E N K Ö Ü N D T E N S E R F L W I C H T I G A W E R D N +voxforge_deu_000942 D I E R S T U S L I E D E D I G L I H R E I N Z I G E M A L V E R W E N D E T +voxforge_deu_000943 D A S L A N D E N W E G K E L T E S E C H Z U E I N E R M E I E T E R E S C H E N G R O S S M A C H T +voxforge_deu_000944 E S I N D T U N D B L E I B E V E R B R E I C H E R B A N D E N +voxforge_deu_000945 D I E Z E I T E N W E R E N S I C H E N D A N +voxforge_deu_000946 D E N S T E F T I N I E A K S B O R U N G E I N S C H I E B E N W I S T Z U M A N S C L A G +voxforge_deu_000947 D I E A U C H T B E I M B O A U R S E R W V I E R S A M W I E R T B A L S P B I L T S W E I S S B E L V E I E R H F O G S N +voxforge_deu_000948 D A W A N O C G A H E I N I K L I E +voxforge_deu_000950 D I E H A B E M A U F M B A R Z H M N I C H K O S E A N G S T +voxforge_deu_000951 F I E L E V E R L I E R E N E R E N A B E I T S P L A T Z S +voxforge_deu_000952 D A R F Ü H E R G E B T E S E I N E N P U N K T A B P T Z I O G E +voxforge_deu_000953 C D I E B E I D E N S E N D T W E I N E U N S I C H E V E R B E N U N G M U T D A N A N D E R N K O N T A K T +voxforge_deu_000954 B E I D E S T Ä K E N T I E F I N R O T E N Z A L N +voxforge_deu_000955 F Ü N D F T Z E H N O E F Ü N F T Z E N D O F O N E N G O L F +voxforge_deu_000956 W I E M E I N S C H E N A S E I N E A N D E R E N W Ä L T S C H E R D E N Z I E R R E T E U N D T D O C H +voxforge_deu_000957 B N D H M I T E M H I N T E R N D E S K E M I L S A U F E R T +voxforge_deu_000958 A D E R U B E R F Ö R S T D E A Z U N G D D E M I T E N C H I E F E N G A U N B G A U E N E I N +voxforge_deu_000959 I C H W U N D E R E M I G I M A R W I E D E R Ü B E R I E S E E R K L E R U N G E N +voxforge_deu_000960 B A I N E M S M E T R I S C H N K R I O P T U S T E M B E T A N D E S V O R G E G A N +voxforge_deu_000961 D A I S D O R D V E R Z E I C H E T E +voxforge_deu_000962 G E L T I S A N E R U T E S T A U S C M I T E +voxforge_deu_000963 D A W E H R E W I S E N C H A F T L I C H N O D W E N D E I G K G +voxforge_deu_000964 N B E S D I N T I S S T R A P T A T E N K O M E N E N B E T H A C H T +voxforge_deu_000965 D A M I T K A N M A N B A R S C H E I N D I C H L Ä C H T E I E N K A U F E N +voxforge_deu_000966 D A F I E W O D E G E S O K T +voxforge_deu_000967 C A M A I N A D E R E U T V E K A U F E N N +voxforge_deu_000968 Z N E N A U C H N D E S T E L E E R T I O U N G +voxforge_deu_000969 D A R I B E R R I E D E D I E P A S T O G R I N E N U N D T R E D E T +voxforge_deu_000970 D E I C H L E N E N E E D A N S D E E N +voxforge_deu_000971 A U F D E N E R S T E N B L E G S C H E I N D A S U N G E V Ö Ü R N L E I C H +voxforge_deu_000972 D E R D O L A W I E R T E N I C H T M E H R I S W E R U N G A K Z I E P T I E R T W E R D E N +voxforge_deu_000973 I L E N K O U T F F E S T G E G E N D E N H A L S T E R I N G E R E N D A N M K Ü S T D I S S I E D I N V A T E R +voxforge_deu_000974 D A W O R D E N I C H T W A G E N O M M E N +voxforge_deu_000975 M A N H T T E R D A M E I T S V O R G E L E N +voxforge_deu_000976 B E I B E S O N D E R W E R T W O N S A C H U N G I S D I K E N Z E M I T R I G E I S D E R W A N M E R T +voxforge_deu_000977 N D S M O T Z R E I K E T Z E L T W E H R D E N +voxforge_deu_000978 Z W I S C H E N L O L B I G E R U N D S C H O L D E N E H E R E L E I T E T +voxforge_deu_000979 E I N A B Z U N M U N T E S R E C H T I E E C H T Z I E R I V L L E T +voxforge_deu_000980 M A N B E R A U C H T D N E C H T A N D E N Z U O F L Z U K L A U B E M +voxforge_deu_000981 Z E R L I C H E N W E S E N N U R I N T F W A L E T E N W O M A N I E L I E B E B O T V O R H A R T E N +voxforge_deu_000982 D E R T Z I Ü K L I C D E B E R W E I S C L A S T U N D E H A F T U N G F I E R H E L F P E R S C H O N E N +voxforge_deu_000983 B E I D E I N O M A E L N O T Z U N G E I S I E V O L E B A N D R E I T E +voxforge_deu_000984 A B E R W I E I S D I E S P R O B L E M I M K L O B A L M A S T A P T Z O L E Ö E N +voxforge_deu_000985 D A S E I G E N W E R P L O K G E R H T P D E N Z E R M E R L E S E R T +voxforge_deu_000986 D A S F R E M D E V E R B L O K G I T N O C H B I E B T E R O S +voxforge_deu_000987 E I N E N E E B E S T H I M U N G I S T D E L A E N B U R T E N +voxforge_deu_000988 D A R O U A U F E S T E N G E W I E N W O R D E N +voxforge_deu_000989 D E B E F Ü R K R U N G I S T G A N Z M A S S I V E R A M T +voxforge_deu_000990 D I E W E R E N D A S G A N Z P E S H I N N I C H T M A C H E N +voxforge_deu_000991 D E D A R T E N E N E N D I G E S E N D E I E R E T I S T E H E B I C H G E R I N E R +voxforge_deu_000992 D A S E G E B N I S I S T V E F E L S T W O D E N +voxforge_deu_000993 E I N B E S C H R E N K U N G K T R I T E R S T B E I B E S O N D E R S I N T E N S I E V E R N O Z U N G A U F +voxforge_deu_000994 D E R E N T B E N O T Z E R H A T E I N E H Ö H Ö R E G E R S C H W I N D I G K E I T F Ü R E N D A U N L O T Z U O F E R F Ü L G U N +voxforge_deu_000995 D E R S E M A N T I S C H E T H E I L E W U D E S G E P T I S P T R A C H T E T +voxforge_deu_000996 D O T W I R T S E F I L M E H R G E L T V E R D I E N T +voxforge_deu_000997 F V E R S T E N N I S V F I E R D A S S V E R A N W U R D L I C H K E I Z S G I F I L E I N E R M U N T E R +voxforge_deu_000998 D A W R T V E R D E M E E N G E M A C H T T +voxforge_deu_000999 S E K O A N E I N E G A N K L A R E K O F M P F Ä E L O N G A U S P R E C H E N +voxforge_deu_001000 Z A L R E I C H E P O T E S T E W E R D E N A R T I K O L I E R D T +voxforge_deu_001001 D E D E C H F Ü Ö R O N G E W A N E I C H T Z I H E R +voxforge_deu_001002 D E W E R U N E N G H A R T Ü B E R H O P T K E I N E D I K U N +voxforge_deu_001003 A O U B P I E B R I G E N S S E E R S T A U R F D E R E I N E N D E C H A U S T I E L B E R W U S T E N L E B E M S K L O G E N +voxforge_deu_001004 M A I R S P R E C H T E N D E S E M F E I L V O N K O N T R E R H E R U N G S T Z W A N G +voxforge_deu_001006 G L O E B E G E R U N S C H L N E S E N Z I E I N I G +voxforge_deu_001007 D A S W I R T E N I C H T M E R L A N E S O B L E I B E N +voxforge_deu_001008 I S G A B U N T E R C H I T L I C H S H I H E E V O M E N D E R R A L H E T S T A F E R +voxforge_deu_001009 E H N D E E E M E I N E F R E I E S O F T W E R +voxforge_deu_001010 O R G A N S T R E I T V E F A N K N A U C H A U S C I S L I C G A U F T E L N D E S E B E N I S T A T F I N T E N +voxforge_deu_001011 W E G E N O Z H L O S A U F G E W E N E T E R U L E B S E I T K A N +voxforge_deu_001012 D A W I R T N I C H I M A P E R V E K T V U N T Z E I N E N +voxforge_deu_001013 M A N M U S I C H A N G E R C H I E N D A S W A K S T U M S W E N G N +voxforge_deu_001014 W E L I C H E R W E G E S O L E N E I N E S C L A G E N W E A R D E N +voxforge_deu_001015 D A W E R T E N D I E P E I S E G E E N +voxforge_deu_001016 S D I E B E R N D A M E E R V O L K T E W E R T L I C H +voxforge_deu_001017 D E E I N T W E K L O N G E I S T W E I T V O R A N G E S H I E T E N +voxforge_deu_001018 D I E S M T O R M E L T R I E D E N D A N S C H O N N A C H W E H N E G E S T O N T E N A R F +voxforge_deu_001019 S G E B T E I N E G R O S E W Ä L L E V O N P R O T Z E E N +voxforge_deu_001020 E S E S T B E R E I T Z M E I N Z W E I T E R A U T O M A R D T +voxpopuli_deu_000309 N P L M E N T I E R U N G V O N H Ö H R I N S T A N D A T S T U S C U T Z S P E R S E N L I G E R T A T E N E B E N F W E I S G E N A R E U N S H R E I U T E T Z U S A M M E N A B E I T A L E I C H T E N +voxpopuli_deu_000310 E R A M T E A B N D E R S C L I M S T E V E R I N D E R D A R M E L E B E N G L Ä D T I S I N S E B E R V E R L T S T V O R D N I C G L A B +voxpopuli_deu_000311 I C G M E Ü B R I E D A S D E R O M I E S A N I C T I E S +voxpopuli_deu_000312 M I T K L E D U N D E C H O V E D A S W E R N E C H S E I E A H R Ü R E R S W A N T I +voxpopuli_deu_000313 N D I S D A F N C H T D E R S E H N W E R N D S I M E R H I N B E R I F Ü M Z I G U T S E N T D E R B E F E L K Ö N G D A R E B I S C H N U N U O N E M L E N T I C H N D R A M L I +voxpopuli_deu_000314 S O D A S D E B Ü U G E R S H E L N E A U S K U N B E K O M D O B S E I N E B E S C H Ä E R E B E H A U T I N G E N O M E N W I R T O S I E B E R E C H T I H T E S T +voxpopuli_deu_000315 N E R H R E S E T U O N Z E R E R B I T Z I O N G E N I S T N I C H T V O N E U T E N A B E R A R B U R U E K O N T I N N E R L I C H E S F E I N T I O N E N +voxpopuli_deu_000316 L D I E G A N S T O L S G E S A R G J A R B D B E S C H E T I U N G S T E I K T I E R A N +voxpopuli_deu_000317 I E D A S E S F E R U N S I S T E U R H R U N T E R E V E R L T E T W I E R E X S P O R T I E R U N Z U F I E E L Z U O N B E L I H A U N W E R I N P A D T I E R U N Z S E W E N I G N E R V A R S C H E N K E N W O L S T A N T +voxpopuli_deu_000318 S I E H O U L D E R A B E N D I E R A N W E S E N S I N I S T E N P R O S S I T I E V E R S I G N A L +voxpopuli_deu_000319 N E U N Z S I C H P R E T Z E N T A L L E R A R O B P Ä E C H E N F L M E D I E A U S E H E L T I R E S E I N M A T L A N D E S G I T Z E I C H T W E R D E N S I N T V O A M M E D I E R P O G A M G E E R D E R T W U R T E N +voxpopuli_deu_000320 B I S O K A L I H E M E R G E B P L D I S T E R A E R A U S H U S A B T D E M U N G I N D I E S E V O R M N I C H Z U S T E N +voxpopuli_deu_000321 B I E R B U R T E V E R H N D R N D A S S I C H C H E H E H I N D E R D I E N G E I S I G E N E I G E N T U M D I E A U S G U M S F L I C H T E V E R S T E C K E N K O N T E +voxpopuli_deu_000322 I S G E B D E T Z H N M Z U S A M A N G D E R V E R S T E R K T E N Z U S A M A R B E I T E I N E N E R S S T E N G A N G V O N E I I G E N M I T I T S T A R T E N N A C +voxpopuli_deu_000323 W A S D I G R E I N Z H B E R C H R E I T E N D E T Z U S A M E N A B E I T A N B E L A N T U N D T W A S T I E E R V E R P R E I T U N G I N T R I G K L E N D E R E T R I F T N D T E M E C H I C H E I N B E I S P B E L E N E I N E N D E S E N E R V O L G S B E I S P I L V Ü E M I C H I S U N T Z W A R E L S L A M D A U G M L I E R N J A R E I D E S +voxpopuli_deu_000324 D A S N I C H T N U R I N P O R T U G A L D E R G L I C H E N L A N T S N E N A U R U E N S E U V E R M E I N T L I C G E I C H E N M I T W I S S T A R T E N W I E D E U T S C S L A N D D E R U S P E T A N J E N +voxpopuli_deu_000325 T V E R A U S W E G E N I S V O R B E I D A +voxpopuli_deu_000326 A L L E L F L I E N G A T F M I G K D I D A D E S I S H A U S E S V A R S C H E I N H E D O R D T L I C H O L F I G E R A L S T E I E U N D U S C N I T Z B Ü Ö R G E T +voxpopuli_deu_000327 E N S I C H E H R D A S E R E B E D O E U I T C U N G I N A R A Z S F U K O M F T U G A N O C H T Z U N E M W I E R T +voxpopuli_deu_000328 T A S K E D I E R U N D I R I C H T L I E N D E R D I S A R D E S T E F I S T E G N G U N D L Ä E G D A S I S A R I T S N O R M E N F Ü R D E N S C H U T Z V E R D E N G E F A R E N E I N E R E X S P O S I T S O U N G G I E B E R I O N I S I E R E N D A R S T R A L U N G +voxpopuli_deu_000329 D A S K G I L T E S I D E R H E R S C U S T E N +voxpopuli_deu_000330 D E N E I N E N E I N Z I G E N S I T Z K E B T E S L E N G S D A S I S T A S P U R G +voxpopuli_deu_000331 E D A S A S P A S I E R I N M A L T E R D I E B S O N L I S T E I N D I E K O B T S U N D F L E A U F G E D E K T E R D I S V E R W H E G E N B C H N E R M A D E B A W E D E R W E R E N U S D E M A R I S I K O B S O U N D F E L E E B E U N D E R S U C H N O C H I T D E R M O A R S E R B E A R E R G E T I L T E U N D E R S U T E A N A T V F A S S I N A N D U G A S W E N A L S I E R U N T E R D E M A N D E L I S C H W E I N T Z U G E D E K T W E N S E O S +voxpopuli_deu_000332 L I T L A N G E K Ü S T E D I E W A N S T E I N E D I A U F D I R O S E N K A T E R S V O F E N I T Z U N A M I E S E D E R V R G A N G N E R T I N W E I S E N +voxpopuli_deu_000333 D E N T I C H A R B E B E N Z I E P Ü R D E B E R C H T G I S T E I M E N T O A U O L E I N S W E H R N V E L E R I N T E L T E S W I R D N E M I T A U E A U F G E F A D E R T D S A U R B E H E S H E P A L A M E N D A U F D E M W Ä E G K T S E I D I M E I N Z I G E N G S I T S T Z U O N D E S T E L T Z E N +voxpopuli_deu_000334 I N D I E S E M R I F E N W H U O T D E N G E M E I S M I B P L I T D E S C H E V E R A B R E D U N G E N I N K R E I S D E R S I E B E N U N Z W A N Z S I G E T R O F V E R N U N D A U C H U B L I K G E M A H T T +voxpopuli_deu_000335 I E B E N E R B E R T Z O U G E N D S W E R S H O E U T H M I D I N V O R S C H A R G S I M U M A L G A U S C H S G E C H A F T A M I N C H T W E I T E A U K M A S I G P E R F E K T A U R B E C H E A T Z S A G E H E D E N Ü E R H O C H R I S I G O B R T U G K T D A N E D S E N D A L I Z U L A S E N H A M M S S E N D A S H A I C H E I C H G E S H A F T A R M I D E M E R S O D A H E M T I C H L I G P L A U B E I C H D A S W I R T R O T E M E I N E N G O R S E N S C R I L T F L E I G K E I N M E I N S T E I N E I N G R O S E N C H I T Z U M E R A T E D E N S E A T A E N +voxpopuli_deu_000336 P E H L E D A N G E S F V E R L K T F Ü Ö R T S W E I E N H E I B M I N O N T E N E R G +voxpopuli_deu_000337 Z U M A K T U E L E N I C H K L A B I S K A N K E I N E V E N U N S A N N E M E D A S W E W E R T L I C G I A R S T F E I T D I S E N W U C H N G E N D E W I S H E N D A S E N S D I T A L U N S U N F E I C H K E I T D R O T +voxpopuli_deu_000338 D S N D E I N F C H P E D I N G U N G E N D I N I G A K T Z E P T A B E S E N M A N K +voxpopuli_deu_000339 I N D E Z W I S C H E N S E I S I N D I R E T U N G S A R G E N I S A R I O N E N D E G R Ö S T E N S C H L P E R H W E I S I E D I E M I E G A N T E N Z W A N Z I C H G H L M E T E R V E R D E R L I E B I S C H E N K Ü S D A U B G R E I F E N U N D A L L N E R H I T A L I E N P R A S P R T I E R E N +voxpopuli_deu_000340 D A S S E I K T D E R F A L J U L I E R T D E M S C H Ä N K O U +voxpopuli_deu_000341 I W A S E R P R E D I G E N O N D W E I N T R I N E N +voxpopuli_deu_000342 W I R D I E I N S C H E I D U N G R A R H E N W I E R F I L E P A T N R N E C H T Z U L E T Z I E S T Ä T T E +voxpopuli_deu_000343 D I E V O L G E I S E I N H Ö R N F L U G K V O M P R O P L I S T N E X S T R L M I S T E N E I N I G M I G I S T A T E N E R E N B U M F U M P A R O U L E N S E T Z E N I E R K O N G K R I E T E R V E R I N D E R U N G E N G E G E N +voxpopuli_deu_000344 W A I L D I E I N W E S T I Z I O N E N V R A N T Ö R S I S C H A U N D D E L U T S C H E R B A N G E N G E R E T E T W V E R D E N M U S T D E N D Ö R H T D E R I C H E N L A N T Z W E I T A U S E N Z E H N N I C H T D B P E I T E G E N U N D H L U T E R U S E S E I N E N R I E S I G E N S C H O T D E N B E R K V A R S I C H E H E R T D R I U K +voxpopuli_deu_000345 D E M I T I G I T S T A T E N D Ö R O F E N N I C H I E M Ü K I C H K E I T H A B M D E R E N E N A U R B P E S H E N S T A S A M B A L D E R A N Z E R H N D E R N E N I E R N A R E G I O N G G A N Z S G E T Z I E L U N S T D E M A T I S K O R U L T O N F E R N A C R Z E I G E N E S I E N +voxpopuli_deu_000346 E I M I L I O N M E N S C H E N S I N A B P E N G I H V O N U N S E R H E L V E R +voxpopuli_deu_000347 E I N F E T H I N R G E R J U N G E W E T I N H E R K A D I V O N E I N P L I S I S T E N A I N D E S O N D E R E I S A T S K O M A N D U S E N K O M A G S C H A E N +voxpopuli_deu_000348 D I E I N D E R H E I L I G K U E I T M A N V O R S I C H E R E T R A N E N D A S A U P T A U T U S U D E R A L U N S H E N T E N W E K +voxpopuli_deu_000349 R E I D E R A T R I G E T E R E F F E N H A B E N I N Z I S C H E N S T A D G E F U N D E +voxpopuli_deu_000350 R D I C H I E T E N E I N M O N E R D E B P T +voxpopuli_deu_000351 D A S W E G E E I N E W I C H T I G E F H A H G E A N D I K O M I T I O N E N E I N L A N D D I G R A N Z K O N D T C O L L E W I E D E R E I N F Ü Ö O N N T D A C H E M S C H N G E N I O N B L E I D E N M I T Z U G A N G K Z S U O R I M A T I O N Z U S T E M E T S E T E R A R O R D E R I S D R S E I N E N W E R E R O D E A R D I E R A G E I S W E S T I C F Ü R D I E D E N I S C H E R I E P A T E N D I S P Ä T E U M E I N E K L A E A N W O R T D A +voxpopuli_deu_000352 D E S C H O N A U S S C G I E F Ü R T W U R D E L A G E S N I C H B A R A N D A S E S H E R O B E F F E L E G I G E B E N H E T D I S N E N D S G A B E N H R E I H E V O N D K L E I E N U N G E R E I N M T E I T E N B I T I E N S W E I +voxpopuli_deu_000353 N V E R G E M E I N C H R F T E N D E A U S N O N S I E R I T S P A L I T I G A S G O S I S T Z I E L D I E S E R U N J O N +voxpopuli_deu_000354 D E N I C H E R H E I T I S A N I S Z W E R I E G E R U N D I T E I L W E I C H E R A R B R E I T N I C H T N U H R I M T E C H N I S H N B A R E I C H +voxpopuli_deu_000355 D I K S E L T E N G E N D I E N T E R E S E N V O N B Ü Ö R G E N U N P O L I E T I G E N S O W E I A U S N A N D E R B E R E M B Ü R E R N E N G A N Z E R O B E R S H I D S T E M A R K I E N T G A N Z S O B E N +voxpopuli_deu_000356 H E R P R S I D E N T +voxpopuli_deu_000357 E F Ü R E N E S P R Ä E C H E M I T R E S E D E N K A S E I T Z A R E I C H E N R E G J E R U N G S E R T R I E E N V F R A U N M E N S C H E N R E C H T O G A M I S R T I O N E N U N D I E W A N D D R C H A U S E M U T I G E N T +voxpopuli_deu_000358 N G S A C H E I N E U R S A C H E F Ü R D I N E W A C K S N N A T Z U N A L I S N U S D E L L I G S E I D E F O L I C H P E R S P E K T I E F L O S S I S +voxpopuli_deu_000359 O U I D E I N E I M A N A O S O R W E I T V N D E N Z I E E N F E R N S +voxpopuli_deu_000360 D W E R D E R S W I A N Z M I N I S T E A U C H E N E I N E N L A N D Z I E D E N T A G G D A M I T K O N F V O N D T I E T D A S N D T Ü L I C H A U C H T U S B U S T Z S I N G E G E B E N S E N M U S S D A S S T A S H U S H A L T E V O N D E N S T L E R S O A L E R E N E U N S T E L E R S O L L E N D I N E R Z I E T Z I N T U N D D A S I E R T D A M I T A U F H T D I E A N T U E R T U N G A G E N I N E N E N T S C H E I D U N G E N D E I E R H I E N I S E N R A M E N D R E F F N M E T M M U N T E R N +voxpopuli_deu_000361 A U D E M O U U R O B E I S C H N A U T E B E B I L M A R G K T I N S G E S A M T D R M A T D I S C H I S S +voxpopuli_deu_000362 E B P E H S C H U N J O N H A R T M I D I S E I N S T R U M E N Z S D I S C H O N S E E I N E A K T I V E R O L L E N E R N A C K T P A E G I O N Z U S P I E N U M D E M O U G R A D S C H E R D E V O R M E N N A E N A C H A L I G I N I K T U N V R A N Z I T R E I E +voxpopuli_deu_000363 S T U L T A L I T E R E R S C H I E M E V O N A U S E N U D A U V N I N E N I S R E S C H T U N D O S C H I E T L E G +voxpopuli_deu_000364 E H H A M I M E R G E S A R K E I N Ü B E R E I L T U S T A T Z U N I E R U N G S E N C H E I D U N G I S U N S I N I S G W E I T Z U M J E R T Z I G E N Z E I T F U N E S K E I N E B E D R O N G B E I S C S P I E A S W E S A U S E M I E R A N G E T +voxpopuli_deu_000365 D E E R V A R K L E I C H I S T E I N E T Z U Ü E N E S H E M I S E A T D N D E R A U B R H R A V O R N M E N Z C H E N R E I T Z W R L E I D D N E L A A B E L S Z S S F H F H F G F A G S R F D A R S T S T S S T S B O D S S A A A I E O N G A N D A N A N A N E I N E S O E U S C H E U N E N K L A U P L I C H E R A N W O R O F +voxpopuli_deu_000366 D I E S P E E R H R T D I E S E U N F A S E N D E R H U T Z U N T A L E R I C H L I N D E W Ü B E F Ü Ö B O T E T W E N G I E +voxpopuli_deu_000367 G I C I W E R K I S K S L I C H I N A N D U N D E W R S A R S T G H G D E V L A N T V O U N D E E I N E I N M A L N M E H R I J E T S T D E R V E R A N T Z W O C F T D U N G F Ü R I N E U T U P T I M A L E N W R A L M G R A S I G K A L I V I T Z T I E R U N G U N R E R A B E I T N E H M E U D A R B E I T N E M E R R I N E N D A N S P E R S O N D E R E T S T R E S H U N Z I T A G E N +voxpopuli_deu_000368 A N D R D A U C H O N L E N D I E S E R K U D E R G E B E S E R Z I E L N A I S A N D E R E G I E S I S H W Ä H R T U N D I M I T E L A B P T Z I U O F N T W A C R I G J I O N W I E K A L A R B R I H N Z I T Z I L E N O D A U K R I C H L A R D R A U C H O M E N E N +voxpopuli_deu_000369 D E B E R I C H K O E S E S V O R D E R Z U R E I C H T D S E S R E T I N G S T A T L I C H E R S C H U L T T I E E I S E R F E N L I C H E R A U F G A B E B E G R I F E N U N D D A R H E R O N E F E N I C H E A K T Ü H R N V O R G E N A M W E R E N M U S S +voxpopuli_deu_000370 D A B I S A B E L N U N M I T E I N E M U T S C A R P O G A M T U T U N H A B E M M S W I L D A F Ü H E I N E N S P E C H E N D E R E C H I G E K O N T L A G E S C H A E N +voxpopuli_deu_000371 S I E R N O H A N A L I S I E R N W O R +voxpopuli_deu_000372 D M A K E N E N E T U L I E V E R L A N G E N G E B E N L I E M E R G A R T F H R N D I K U N S H I H V E R A U S D I E A M E N O E I T E W R A U H E N D A S A B E +voxpopuli_deu_000373 G E R A R D I Ü E K L E I N E P O R J Ä E C K D E I S D A S Ü N H B E R M E H E S I C B E R O G A T E S H R A U F A N D R E C H T I S D A S D A S I E R S A W B E I N Z E I T A U M V O N R E I A R E N G E S E N T W E R E N S O R U N U M N T +voxpopuli_deu_000374 I K A N D E R V E R S I C H E R N D I A R L O B P E S C H E K O M I S I O N I E S S T E R K O M I T I T Z U M A H R A R A R U B T S A L R O B E C H E N E R S P I G K I E V E D I S K O S S E R U S N T +voxpopuli_deu_000375 E I E D A N Z E H E A U F T A U G H S O N +voxpopuli_deu_000376 D T D I E S M E H A U S H E L K A A N D I E N G Ö R G E R I N U N D B U R G E R N I C H T I B E R T Z O E L I T E N N B E G E I S T E A N +voxpopuli_deu_000377 T I A L E M U K R A R E N E M I T G R O S A F H R O U D E Z O R K E N N E S D A S D I G D E I E R V O R I E T R A R G E N H A B E N E B Z S I C H A U H I M Z U S A M E N A R I T V E R E N D R U N G N E D E N W E I C H S T A T E N U M S E T S N +voxpopuli_deu_000378 D E A H A B E S C H U R S D I E E L D A R O R B P E S C H E S E M E S T E R H E R H E R T Z U N E M E N U N T I K O R O B T U N Z S I K L A S I U N E R I M R A M D E R L I N E R B R E C H T D E T Z U V E R E Ü F N I G E N I S T N I G A U S E I G E N T +voxpopuli_deu_000379 N D M E I N M E I N E B I T E O D A M D A S W A S I C H M E R V O R S T D E N I S D A S M A H R G E N G W I E C K T L I C G I N D E R T A H A T E I N G G R O S E E I N E B R E I T E M E H R H E I T F Ü R D I E S I K O L S I O N S P L I T I G H S O R L G E P L I T I G S T S T D E M T S Ü R D I E M E N S C H E N V O R O R T D A I T I U N S A D E S W E H E N I E A U C H B E S C R E Ä N K E N K E I N E D A S +voxpopuli_deu_000380 W E N W I E R A R H O L T E D I E E V O R A R D N U N G V R A B S C I E D E N O A O F E R I C H D A S S E W I E R N A C H E I M L A N G N K A R U S E L E L S U E I M B U D N A B C H U S K O M U N T I T M A Ü C H T E R M Ä C H E B E I E R K O M I S I O N B E D A N G E N D I E G O N Z T O T I E S A C H A R B E I T H A T +voxpopuli_deu_000381 U N Z E R E R E S C H Ä A R S C H N U N Z I E K O N T R O R L N H A B E N K E I N E N P E L E G E R P R A F T diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/token_int new file mode 100644 index 0000000000000000000000000000000000000000..3e568f5f87d5ba3ea644c4e800bcd52c6835c7fc --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/token_int @@ -0,0 +1,661 @@ +M-AILABS_deu_000165 10 5 18 2 3 11 6 10 5 14 16 4 3 17 9 15 11 8 2 5 4 2 6 3 9 2 12 7 2 7 8 21 5 15 11 8 5 14 2 4 3 7 2 11 2 5 4 3 5 4 10 2 3 10 2 6 3 23 26 8 5 8 3 20 5 16 4 3 4 3 10 5 4 14 16 21 9 4 25 27 6 3 19 25 6 10 2 7 3 5 4 3 28 9 4 2 6 10 7 16 12 7 7 18 2 14 2 4 9 3 10 5 14 12 4 +M-AILABS_deu_000166 10 9 6 11 9 18 2 7 5 3 10 5 2 24 21 16 12 13 14 28 2 10 2 17 3 11 2 6 3 5 4 2 6 3 5 4 2 6 12 4 22 14 2 18 13 5 2 18 2 4 2 4 3 21 9 6 8 2 3 14 2 7 23 9 16 15 11 2 4 +M-AILABS_deu_000167 2 6 7 8 3 12 17 3 9 15 11 8 3 12 11 6 3 21 9 6 3 2 6 3 9 12 19 3 17 9 13 2 13 3 18 6 15 11 8 2 6 10 2 4 3 14 9 19 5 3 10 2 7 4 2 7 15 11 2 4 3 5 4 20 7 20 5 17 2 6 3 12 4 10 2 7 23 2 11 6 13 5 4 14 2 3 10 5 2 3 10 9 7 7 9 12 7 3 10 2 4 3 11 2 30 2 3 7 2 14 8 2 4 3 14 2 19 9 13 4 2 3 16 8 9 6 22 16 4 3 9 12 19 7 23 18 5 22 8 2 4 +M-AILABS_deu_000168 7 5 15 11 2 6 13 5 15 11 3 9 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12 16 8 2 6 3 9 4 3 13 2 14 12 4 10 5 8 2 6 8 20 16 2 14 8 12 10 2 4 3 9 2 7 3 14 9 4 20 7 2 13 13 2 5 4 3 21 16 7 3 10 2 15 3 14 9 4 5 15 11 8 11 12 17 3 20 16 22 2 17 9 4 +M-AILABS_deu_000187 9 13 7 3 4 6 3 2 5 4 17 9 13 12 4 16 15 11 3 2 4 3 10 6 9 12 15 11 3 24 2 4 3 9 4 2 17 3 11 9 12 7 9 12 7 3 10 2 6 3 24 2 6 4 2 6 3 9 12 19 7 8 2 5 14 2 4 20 12 2 7 2 4 3 12 17 3 10 9 4 18 2 3 6 12 2 22 8 20 12 3 7 8 10 2 6 18 17 +M-AILABS_deu_000188 5 2 3 8 2 4 20 2 6 5 4 3 9 18 9 6 13 9 6 22 9 12 19 3 2 4 3 22 4 5 2 4 3 24 16 6 3 18 6 9 17 9 7 18 5 13 8 2 4 5 7 3 5 4 3 4 9 11 17 2 4 3 13 16 7 2 6 7 2 4 3 7 12 15 11 8 3 12 4 10 8 3 21 2 5 4 8 2 3 28 9 17 9 3 24 16 13 13 8 +M-AILABS_deu_000189 2 15 11 8 3 19 2 6 8 5 15 11 8 3 17 5 15 11 7 2 4 10 2 3 21 2 14 13 5 14 22 2 5 8 4 16 15 11 4 5 15 11 8 3 9 3 19 8 10 5 2 11 5 15 11 18 2 3 12 19 2 4 14 22 11 6 +M-AILABS_deu_000190 5 15 11 2 13 6 14 2 6 8 2 17 5 15 11 8 9 4 21 2 4 5 15 11 3 9 12 11 3 19 9 15 11 8 2 6 3 2 5 7 21 9 7 12 19 3 21 12 4 10 2 6 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b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/images/wer.png differ diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/latest.pth b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/latest.pth new file mode 120000 index 0000000000000000000000000000000000000000..d52af6357e1c6c465ad7c4b26911c5298cdbde12 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/latest.pth @@ -0,0 +1 @@ +30epoch.pth \ No newline at end of file diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/run.sh b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/run.sh new file mode 100755 index 0000000000000000000000000000000000000000..5c2e811deafa06203352a1d3aa836fef50fd7620 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/run.sh @@ -0,0 +1 @@ +./asr.sh --ngpu 1 --stage 11 --stop_stage 13 --nj 32 --inference_nj 4 --gpu_inference true --lang deu1 --inference_asr_model valid.loss.ave.pth --local_data_opts '--duration 10min --lid false --multilingual false --single_lang deu1' --use_lm false --token_type char --feats_type raw --feats_normalize utterance_mvn --asr_config conf/tuning/train_asr_s3prl_houlsby.yaml --inference_config conf/decode_asr.yaml --train_set train_10min_deu1 --valid_set dev_10min_deu1 --test_sets 'dev_10min_deu1 test_10min_deu1' --asr_tag train_asr_s3prl_houlsby_deu1_10min --expdir test_pr --asr_stats_dir test_pr/asr_stats_deu1_10min --local_score_opts 'false false monolingual' --stage 11 "$@"; exit $? diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/tensorboard/train/events.out.tfevents.1705232854.stan.247691.0 b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/tensorboard/train/events.out.tfevents.1705232854.stan.247691.0 new file mode 100644 index 0000000000000000000000000000000000000000..ec13580ef56d1559dcd0e6a4be9605f4dfe77cc1 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/tensorboard/train/events.out.tfevents.1705232854.stan.247691.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:48807af377cda1af69ef9d444a1d82a2964f90c0d17df59835d134ca9a923d48 +size 27120411 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/tensorboard/train/events.out.tfevents.1705412728.stan.837791.0 b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/tensorboard/train/events.out.tfevents.1705412728.stan.837791.0 new file mode 100644 index 0000000000000000000000000000000000000000..7148d612442e02d2ff2366467be433a3cebc3385 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/tensorboard/train/events.out.tfevents.1705412728.stan.837791.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:48ab91f27d87d5860daaae181b23a86c2a7e59bb2c4a81eab5b84a4c7ea80dac +size 27249713 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/tensorboard/valid/events.out.tfevents.1705232854.stan.247691.1 b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/tensorboard/valid/events.out.tfevents.1705232854.stan.247691.1 new file mode 100644 index 0000000000000000000000000000000000000000..8526941c2cf1b0e6c0d52e6c915a0b375cd65208 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/tensorboard/valid/events.out.tfevents.1705232854.stan.247691.1 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:99b2b210bc9da74f65d75351c5dcd664b67642d5a71528e776516a9b9570555c +size 11190 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/tensorboard/valid/events.out.tfevents.1705412728.stan.837791.1 b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/tensorboard/valid/events.out.tfevents.1705412728.stan.837791.1 new file mode 100644 index 0000000000000000000000000000000000000000..e7588cb3b5f39ad48cda90981ed8f1da73e26baa --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/tensorboard/valid/events.out.tfevents.1705412728.stan.837791.1 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:98af67ca2912e46a45cb7599f330ef1a7c25d17beccae0f3ac2fdc1f20930b2d +size 11190 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/train.1.log b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/train.1.log new file mode 100644 index 0000000000000000000000000000000000000000..d58be9028f0b6dd9336b1b2cb33c57e7d0e4e1dd --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/train.1.log @@ -0,0 +1,1406 @@ +# python3 -m espnet2.bin.asr_train --use_preprocessor true --bpemodel none --token_type char --token_list data/deu1_token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_deu1/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_deu1_10min/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_10min_deu1/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_deu1_10min/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_10min_deu1/text,text,text --train_shape_file test_pr/asr_stats_deu1_10min/train/text_shape.char --valid_data_path_and_name_and_type dump/raw/dev_10min_deu1/text,text,text --valid_shape_file test_pr/asr_stats_deu1_10min/valid/text_shape.char --ngpu 1 --multiprocessing_distributed True +# Started at Sun Jan 14 19:47:30 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_train.py --use_preprocessor true --bpemodel none --token_type char --token_list data/deu1_token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_deu1/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_deu1_10min/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_10min_deu1/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_deu1_10min/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_10min_deu1/text,text,text --train_shape_file test_pr/asr_stats_deu1_10min/train/text_shape.char --valid_data_path_and_name_and_type dump/raw/dev_10min_deu1/text,text,text --valid_shape_file test_pr/asr_stats_deu1_10min/valid/text_shape.char --ngpu 1 --multiprocessing_distributed True +[stan] 2024-01-14 19:47:31,348 (asr:523) INFO: Vocabulary size: 44 +[stan] 2024-01-14 19:47:31,411 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +[stan] 2024-01-14 19:47:31,411 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +[stan] 2024-01-14 19:47:31,524 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +[stan] 2024-01-14 19:47:32,830 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.mask_emb.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.label_embs_concat.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.0.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.1.0.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.2.0.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.3.0.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.4.0.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.5.0.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.6.0.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_g.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_v.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,667 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,668 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.bias.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.weight.requires_grad = False +[stan] 2024-01-14 19:47:33,669 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.bias.requires_grad = False +[stan] 2024-01-14 19:47:34,071 (abs_task:1275) INFO: pytorch.version=1.12.0+cu113, cuda.available=True, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True +[stan] 2024-01-14 19:47:34,074 (abs_task:1276) INFO: Model structure: +ESPnetASRModel( + (frontend): S3prlFrontend( + (upstream): S3PRLUpstream( + (upstream): UpstreamExpert( + (model): HubertModel( + (feature_extractor): ConvFeatureExtractionModel( + (conv_layers): ModuleList( + (0): Sequential( + (0): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): Fp32GroupNorm(512, 512, eps=1e-05, affine=True) + (3): GELU(approximate=none) + ) + (1): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (2): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (3): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (4): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (5): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (6): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + ) + ) + (post_extract_proj): Linear(in_features=512, out_features=768, bias=True) + (dropout_input): Dropout(p=0.1, inplace=False) + (dropout_features): Dropout(p=0.1, inplace=False) + (encoder): TransformerEncoder( + (pos_conv): Sequential( + (0): Conv1d(768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16) + (1): SamePad() + (2): GELU(approximate=none) + ) + (layers): ModuleList( + (0): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (1): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (2): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (3): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (4): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (5): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (6): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (7): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (8): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (9): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (10): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (11): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + ) + (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (final_proj): Linear(in_features=768, out_features=256, bias=True) + ) + ) + ) + (featurizer): Featurizer() + ) + (specaug): SpecAug( + (time_warp): TimeWarp(window=5, mode=bicubic) + (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq) + (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time) + ) + (normalize): UtteranceMVN(norm_means=True, norm_vars=False) + (preencoder): LinearProjection( + (linear_out): Linear(in_features=768, out_features=80, bias=True) + (dropout): Dropout(p=0.0, inplace=False) + ) + (encoder): TransformerEncoder( + (embed): Conv2dSubsampling2( + (conv): Sequential( + (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU() + (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) + (3): ReLU() + ) + (out): Sequential( + (0): Linear(in_features=9472, out_features=256, bias=True) + (1): PositionalEncoding( + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + ) + (encoders): MultiSequential( + (0): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (1): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + ) + (criterion_att): LabelSmoothingLoss( + (criterion): KLDivLoss() + ) + (ctc): CTC( + (ctc_lo): Linear(in_features=256, out_features=44, bias=True) + (ctc_loss): CTCLoss() + ) +) + +Model summary: + Class Name: ESPnetASRModel + Total Number of model parameters: 99.97 M + Number of trainable parameters: 5.27 M (5.3%) + Size: 21.08 MB + Type: torch.float32 +[stan] 2024-01-14 19:47:34,074 (abs_task:1279) INFO: Optimizer: +Adam ( +Parameter Group 0 + amsgrad: False + betas: (0.9, 0.999) + capturable: False + eps: 1e-08 + foreach: None + lr: 0.0001 + maximize: False + weight_decay: 1e-06 +) +[stan] 2024-01-14 19:47:34,074 (abs_task:1280) INFO: Scheduler: None +[stan] 2024-01-14 19:47:34,074 (abs_task:1289) INFO: Saving the configuration in test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml +[stan] 2024-01-14 19:47:34,227 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-14 19:47:34,269 (abs_task:1665) INFO: [train] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/train_10min_deu1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/train_10min_deu1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-14 19:47:34,269 (abs_task:1666) INFO: [train] Batch sampler: SortedBatchSampler(N-batch=24, batch_size=8, shape_file=test_pr/asr_stats_deu1_10min/train/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-14 19:47:34,269 (abs_task:1667) INFO: [train] mini-batch sizes summary: N-batch=24, mean=8.2, min=8, max=9 +[stan] 2024-01-14 19:47:34,280 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-14 19:47:34,281 (abs_task:1665) INFO: [valid] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_deu1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_deu1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-14 19:47:34,281 (abs_task:1666) INFO: [valid] Batch sampler: SortedBatchSampler(N-batch=25, batch_size=8, shape_file=test_pr/asr_stats_deu1_10min/valid/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-14 19:47:34,281 (abs_task:1667) INFO: [valid] mini-batch sizes summary: N-batch=25, mean=8.3, min=8, max=9 +[stan] 2024-01-14 19:47:34,282 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-14 19:47:34,292 (abs_task:1665) INFO: [plot_att] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_deu1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_deu1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-14 19:47:34,292 (abs_task:1666) INFO: [plot_att] Batch sampler: UnsortedBatchSampler(N-batch=207, batch_size=1, key_file=test_pr/asr_stats_deu1_10min/valid/speech_shape, +[stan] 2024-01-14 19:47:34,292 (abs_task:1667) INFO: [plot_att] mini-batch sizes summary: N-batch=3, mean=1.0, min=1, max=1 +[stan] 2024-01-14 19:47:34,325 (trainer:300) INFO: 1/30epoch started +[stan] 2024-01-14 19:47:38,714 (trainer:763) INFO: 1epoch:train:1-40batch: iter_time=0.002, forward_time=0.067, loss_ctc=40.274, loss=40.274, backward_time=0.010, grad_norm=356.498, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 19:47:41,168 (trainer:763) INFO: 1epoch:train:41-80batch: iter_time=4.107e-05, forward_time=0.033, loss_ctc=32.923, loss=32.923, backward_time=0.007, grad_norm=120.909, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 19:47:43,881 (trainer:763) INFO: 1epoch:train:81-120batch: iter_time=4.206e-05, forward_time=0.036, loss_ctc=35.207, loss=35.207, backward_time=0.008, grad_norm=79.053, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 19:47:46,661 (trainer:763) INFO: 1epoch:train:121-160batch: iter_time=3.998e-05, forward_time=0.037, loss_ctc=35.233, loss=35.233, backward_time=0.008, grad_norm=76.088, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 19:47:49,324 (trainer:763) INFO: 1epoch:train:161-200batch: iter_time=4.008e-05, forward_time=0.035, loss_ctc=30.859, loss=30.859, backward_time=0.008, grad_norm=94.411, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-14 19:47:51,820 (trainer:763) INFO: 1epoch:train:201-240batch: iter_time=4.024e-05, forward_time=0.033, loss_ctc=26.586, loss=26.586, backward_time=0.007, grad_norm=105.083, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 19:47:54,573 (trainer:763) INFO: 1epoch:train:241-280batch: iter_time=4.088e-05, forward_time=0.036, loss_ctc=25.648, loss=25.648, backward_time=0.008, grad_norm=79.956, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 19:47:57,289 (trainer:763) INFO: 1epoch:train:281-320batch: iter_time=4.175e-05, forward_time=0.036, loss_ctc=22.795, loss=22.795, backward_time=0.008, grad_norm=85.736, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 19:47:59,757 (trainer:763) INFO: 1epoch:train:321-360batch: iter_time=4.398e-05, forward_time=0.033, loss_ctc=20.135, loss=20.135, backward_time=0.007, grad_norm=78.700, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-14 19:48:02,210 (trainer:763) INFO: 1epoch:train:361-400batch: iter_time=4.181e-05, forward_time=0.033, loss_ctc=18.588, loss=18.588, backward_time=0.007, grad_norm=78.620, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-14 19:48:05,241 (trainer:763) INFO: 1epoch:train:401-440batch: iter_time=4.203e-05, forward_time=0.040, loss_ctc=20.592, loss=20.592, backward_time=0.008, grad_norm=87.032, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.303 +[stan] 2024-01-14 19:48:07,729 (trainer:763) INFO: 1epoch:train:441-480batch: iter_time=4.100e-05, forward_time=0.033, loss_ctc=17.652, loss=17.652, backward_time=0.007, grad_norm=82.389, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 19:48:10,395 (trainer:763) INFO: 1epoch:train:481-520batch: iter_time=4.045e-05, forward_time=0.035, loss_ctc=17.419, loss=17.419, backward_time=0.007, grad_norm=76.361, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-14 19:48:13,106 (trainer:763) INFO: 1epoch:train:521-560batch: iter_time=4.140e-05, forward_time=0.036, loss_ctc=17.065, loss=17.065, backward_time=0.008, grad_norm=83.881, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 19:48:15,706 (trainer:763) INFO: 1epoch:train:561-600batch: iter_time=4.143e-05, forward_time=0.034, loss_ctc=16.533, loss=16.533, backward_time=0.007, grad_norm=80.679, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-14 19:48:18,195 (trainer:763) INFO: 1epoch:train:601-640batch: iter_time=4.134e-05, forward_time=0.033, loss_ctc=15.380, loss=15.380, backward_time=0.007, grad_norm=80.823, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 19:48:20,941 (trainer:763) INFO: 1epoch:train:641-680batch: iter_time=4.048e-05, forward_time=0.036, loss_ctc=15.966, loss=15.966, backward_time=0.008, grad_norm=83.866, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 19:48:23,714 (trainer:763) INFO: 1epoch:train:681-720batch: iter_time=4.216e-05, forward_time=0.037, loss_ctc=15.473, loss=15.473, backward_time=0.008, grad_norm=98.086, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 19:48:26,487 (trainer:763) INFO: 1epoch:train:721-760batch: iter_time=4.112e-05, forward_time=0.037, loss_ctc=14.845, loss=14.845, backward_time=0.008, grad_norm=82.925, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 19:48:29,210 (trainer:763) INFO: 1epoch:train:761-800batch: iter_time=4.006e-05, forward_time=0.036, loss_ctc=14.442, loss=14.442, backward_time=0.007, grad_norm=84.920, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-14 19:48:33,388 (trainer:354) INFO: 1epoch results: [train] iter_time=1.537e-04, forward_time=0.037, loss_ctc=22.678, loss=22.678, backward_time=0.008, grad_norm=99.801, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274, time=54.93 seconds, total_count=800, gpu_max_cached_mem_GB=9.184, [valid] loss_ctc=66.748, cer_ctc=0.332, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=66.748, time=1.13 seconds, total_count=25, gpu_max_cached_mem_GB=10.023, [att_plot] time=3 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 19:48:34,420 (trainer:417) INFO: The best model has been updated: valid.loss +[stan] 2024-01-14 19:48:34,420 (trainer:288) INFO: 2/30epoch started. Estimated time to finish: 29 minutes and 2.76 seconds +[stan] 2024-01-14 19:48:37,183 (trainer:763) INFO: 2epoch:train:1-40batch: iter_time=0.002, forward_time=0.034, loss_ctc=13.199, loss=13.199, backward_time=0.008, grad_norm=85.924, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 19:48:39,813 (trainer:763) INFO: 2epoch:train:41-80batch: iter_time=4.200e-05, forward_time=0.035, loss_ctc=12.613, loss=12.613, backward_time=0.008, grad_norm=86.036, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 19:48:42,652 (trainer:763) INFO: 2epoch:train:81-120batch: iter_time=4.136e-05, forward_time=0.037, loss_ctc=13.762, loss=13.762, backward_time=0.008, grad_norm=97.519, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.284 +[stan] 2024-01-14 19:48:45,193 (trainer:763) INFO: 2epoch:train:121-160batch: iter_time=4.007e-05, forward_time=0.034, loss_ctc=12.240, loss=12.240, backward_time=0.008, grad_norm=108.576, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-14 19:48:47,601 (trainer:763) INFO: 2epoch:train:161-200batch: iter_time=3.973e-05, forward_time=0.032, loss_ctc=11.308, loss=11.308, backward_time=0.008, grad_norm=94.281, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-14 19:48:50,421 (trainer:763) INFO: 2epoch:train:201-240batch: iter_time=4.056e-05, forward_time=0.037, loss_ctc=12.650, loss=12.650, backward_time=0.008, grad_norm=98.740, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-14 19:48:53,207 (trainer:763) INFO: 2epoch:train:241-280batch: iter_time=4.157e-05, forward_time=0.037, loss_ctc=12.121, loss=12.121, backward_time=0.008, grad_norm=108.493, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-14 19:48:56,045 (trainer:763) INFO: 2epoch:train:281-320batch: iter_time=4.084e-05, forward_time=0.037, loss_ctc=11.990, loss=11.990, backward_time=0.008, grad_norm=103.615, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.284 +[stan] 2024-01-14 19:48:58,576 (trainer:763) INFO: 2epoch:train:321-360batch: iter_time=4.394e-05, forward_time=0.034, loss_ctc=10.441, loss=10.441, backward_time=0.008, grad_norm=101.760, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-14 19:49:01,230 (trainer:763) INFO: 2epoch:train:361-400batch: iter_time=4.115e-05, forward_time=0.035, loss_ctc=10.336, loss=10.336, backward_time=0.008, grad_norm=110.113, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.265 +[stan] 2024-01-14 19:49:03,859 (trainer:763) INFO: 2epoch:train:401-440batch: iter_time=4.087e-05, forward_time=0.035, loss_ctc=10.378, loss=10.378, backward_time=0.008, grad_norm=115.794, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 19:49:06,796 (trainer:763) INFO: 2epoch:train:441-480batch: iter_time=4.218e-05, forward_time=0.039, loss_ctc=10.515, loss=10.515, backward_time=0.009, grad_norm=104.850, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.294 +[stan] 2024-01-14 19:49:09,254 (trainer:763) INFO: 2epoch:train:481-520batch: iter_time=4.037e-05, forward_time=0.033, loss_ctc=9.269, loss=9.269, backward_time=0.008, grad_norm=107.034, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 19:49:11,758 (trainer:763) INFO: 2epoch:train:521-560batch: iter_time=4.451e-05, forward_time=0.033, loss_ctc=9.028, loss=9.028, backward_time=0.008, grad_norm=102.307, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 19:49:14,439 (trainer:763) INFO: 2epoch:train:561-600batch: iter_time=4.045e-05, forward_time=0.035, loss_ctc=9.120, loss=9.120, backward_time=0.008, grad_norm=123.293, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-14 19:49:17,276 (trainer:763) INFO: 2epoch:train:601-640batch: iter_time=4.132e-05, forward_time=0.037, loss_ctc=9.553, loss=9.553, backward_time=0.008, grad_norm=127.783, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.284 +[stan] 2024-01-14 19:49:19,938 (trainer:763) INFO: 2epoch:train:641-680batch: iter_time=4.186e-05, forward_time=0.035, loss_ctc=8.447, loss=8.447, backward_time=0.008, grad_norm=120.639, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-14 19:49:22,595 (trainer:763) INFO: 2epoch:train:681-720batch: iter_time=4.087e-05, forward_time=0.035, loss_ctc=8.438, loss=8.438, backward_time=0.008, grad_norm=111.850, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-14 19:49:25,302 (trainer:763) INFO: 2epoch:train:721-760batch: iter_time=4.146e-05, forward_time=0.036, loss_ctc=8.401, loss=8.401, backward_time=0.008, grad_norm=111.089, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 19:49:28,210 (trainer:763) INFO: 2epoch:train:761-800batch: iter_time=4.521e-05, forward_time=0.038, loss_ctc=8.592, loss=8.592, backward_time=0.008, grad_norm=122.993, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.291 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-14 19:49:32,226 (trainer:354) INFO: 2epoch results: [train] iter_time=1.444e-04, forward_time=0.035, loss_ctc=10.619, loss=10.619, backward_time=0.008, grad_norm=107.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.87 seconds, total_count=1600, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=71.577, cer_ctc=0.312, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=71.577, time=1.14 seconds, total_count=50, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.79 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 19:49:33,082 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:49:33,082 (trainer:288) INFO: 3/30epoch started. Estimated time to finish: 27 minutes and 42.6 seconds +[stan] 2024-01-14 19:49:35,917 (trainer:763) INFO: 3epoch:train:1-40batch: iter_time=0.003, forward_time=0.034, loss_ctc=7.556, loss=7.556, backward_time=0.008, grad_norm=109.151, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-14 19:49:38,469 (trainer:763) INFO: 3epoch:train:41-80batch: iter_time=4.085e-05, forward_time=0.034, loss_ctc=7.379, loss=7.379, backward_time=0.008, grad_norm=112.462, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 19:49:41,254 (trainer:763) INFO: 3epoch:train:81-120batch: iter_time=4.144e-05, forward_time=0.037, loss_ctc=8.023, loss=8.023, backward_time=0.008, grad_norm=123.663, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 19:49:43,925 (trainer:763) INFO: 3epoch:train:121-160batch: iter_time=4.144e-05, forward_time=0.035, loss_ctc=7.328, loss=7.328, backward_time=0.008, grad_norm=115.720, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-14 19:49:46,502 (trainer:763) INFO: 3epoch:train:161-200batch: iter_time=4.075e-05, forward_time=0.034, loss_ctc=7.208, loss=7.208, backward_time=0.008, grad_norm=135.877, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 19:49:48,937 (trainer:763) INFO: 3epoch:train:201-240batch: iter_time=4.190e-05, forward_time=0.032, loss_ctc=6.385, loss=6.385, backward_time=0.008, grad_norm=106.218, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-14 19:49:51,797 (trainer:763) INFO: 3epoch:train:241-280batch: iter_time=4.172e-05, forward_time=0.038, loss_ctc=7.528, loss=7.528, backward_time=0.008, grad_norm=127.108, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.286 +[stan] 2024-01-14 19:49:54,543 (trainer:763) INFO: 3epoch:train:281-320batch: iter_time=4.155e-05, forward_time=0.036, loss_ctc=6.932, loss=6.932, backward_time=0.008, grad_norm=120.260, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 19:49:57,269 (trainer:763) INFO: 3epoch:train:321-360batch: iter_time=4.024e-05, forward_time=0.036, loss_ctc=6.562, loss=6.562, backward_time=0.008, grad_norm=120.147, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 19:49:59,816 (trainer:763) INFO: 3epoch:train:361-400batch: iter_time=4.283e-05, forward_time=0.034, loss_ctc=6.200, loss=6.200, backward_time=0.008, grad_norm=112.165, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 19:50:02,587 (trainer:763) INFO: 3epoch:train:401-440batch: iter_time=4.116e-05, forward_time=0.037, loss_ctc=6.732, loss=6.732, backward_time=0.008, grad_norm=118.748, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 19:50:05,147 (trainer:763) INFO: 3epoch:train:441-480batch: iter_time=4.098e-05, forward_time=0.034, loss_ctc=5.995, loss=5.995, backward_time=0.008, grad_norm=129.692, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-14 19:50:07,857 (trainer:763) INFO: 3epoch:train:481-520batch: iter_time=4.636e-05, forward_time=0.036, loss_ctc=6.121, loss=6.121, backward_time=0.008, grad_norm=125.365, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 19:50:10,628 (trainer:763) INFO: 3epoch:train:521-560batch: iter_time=4.209e-05, forward_time=0.037, loss_ctc=6.256, loss=6.256, backward_time=0.008, grad_norm=121.333, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 19:50:13,393 (trainer:763) INFO: 3epoch:train:561-600batch: iter_time=4.153e-05, forward_time=0.036, loss_ctc=6.038, loss=6.038, backward_time=0.008, grad_norm=115.337, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 19:50:15,959 (trainer:763) INFO: 3epoch:train:601-640batch: iter_time=4.255e-05, forward_time=0.034, loss_ctc=5.750, loss=5.750, backward_time=0.008, grad_norm=116.279, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 19:50:18,687 (trainer:763) INFO: 3epoch:train:641-680batch: iter_time=4.149e-05, forward_time=0.036, loss_ctc=5.697, loss=5.697, backward_time=0.008, grad_norm=117.219, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 19:50:21,420 (trainer:763) INFO: 3epoch:train:681-720batch: iter_time=4.172e-05, forward_time=0.036, loss_ctc=5.455, loss=5.455, backward_time=0.008, grad_norm=113.242, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 19:50:24,190 (trainer:763) INFO: 3epoch:train:721-760batch: iter_time=4.237e-05, forward_time=0.038, loss_ctc=5.479, loss=5.479, backward_time=0.008, grad_norm=121.568, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 19:50:26,831 (trainer:763) INFO: 3epoch:train:761-800batch: iter_time=3.890e-05, forward_time=0.035, loss_ctc=5.836, loss=5.836, backward_time=0.008, grad_norm=134.437, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-14 19:50:30,847 (trainer:354) INFO: 3epoch results: [train] iter_time=1.705e-04, forward_time=0.035, loss_ctc=6.523, loss=6.523, backward_time=0.008, grad_norm=119.800, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.83 seconds, total_count=2400, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=80.440, cer_ctc=0.319, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=80.440, time=1.14 seconds, total_count=75, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.79 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 19:50:31,717 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:50:31,717 (trainer:288) INFO: 4/30epoch started. Estimated time to finish: 26 minutes and 36.53 seconds +[stan] 2024-01-14 19:50:34,797 (trainer:763) INFO: 4epoch:train:1-40batch: iter_time=0.002, forward_time=0.037, loss_ctc=5.905, loss=5.905, backward_time=0.008, grad_norm=127.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.307 +[stan] 2024-01-14 19:50:37,523 (trainer:763) INFO: 4epoch:train:41-80batch: iter_time=4.212e-05, forward_time=0.036, loss_ctc=5.444, loss=5.444, backward_time=0.008, grad_norm=118.398, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 19:50:40,026 (trainer:763) INFO: 4epoch:train:81-120batch: iter_time=3.974e-05, forward_time=0.033, loss_ctc=4.888, loss=4.888, backward_time=0.008, grad_norm=115.653, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 19:50:42,829 (trainer:763) INFO: 4epoch:train:121-160batch: iter_time=4.206e-05, forward_time=0.037, loss_ctc=5.493, loss=5.493, backward_time=0.008, grad_norm=114.278, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 19:50:45,497 (trainer:763) INFO: 4epoch:train:161-200batch: iter_time=4.393e-05, forward_time=0.035, loss_ctc=4.986, loss=4.986, backward_time=0.008, grad_norm=115.067, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-14 19:50:48,077 (trainer:763) INFO: 4epoch:train:201-240batch: iter_time=4.030e-05, forward_time=0.034, loss_ctc=4.921, loss=4.921, backward_time=0.008, grad_norm=122.885, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 19:50:50,892 (trainer:763) INFO: 4epoch:train:241-280batch: iter_time=4.091e-05, forward_time=0.037, loss_ctc=5.336, loss=5.336, backward_time=0.008, grad_norm=132.650, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-14 19:50:53,434 (trainer:763) INFO: 4epoch:train:281-320batch: iter_time=4.052e-05, forward_time=0.034, loss_ctc=4.799, loss=4.799, backward_time=0.008, grad_norm=111.225, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-14 19:50:56,124 (trainer:763) INFO: 4epoch:train:321-360batch: iter_time=4.117e-05, forward_time=0.035, loss_ctc=4.577, loss=4.577, backward_time=0.008, grad_norm=110.314, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-14 19:50:58,864 (trainer:763) INFO: 4epoch:train:361-400batch: iter_time=4.481e-05, forward_time=0.036, loss_ctc=4.867, loss=4.867, backward_time=0.008, grad_norm=108.461, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 19:51:01,377 (trainer:763) INFO: 4epoch:train:401-440batch: iter_time=4.366e-05, forward_time=0.033, loss_ctc=4.456, loss=4.456, backward_time=0.008, grad_norm=105.944, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-14 19:51:04,177 (trainer:763) INFO: 4epoch:train:441-480batch: iter_time=4.069e-05, forward_time=0.037, loss_ctc=4.892, loss=4.892, backward_time=0.008, grad_norm=115.050, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 19:51:06,680 (trainer:763) INFO: 4epoch:train:481-520batch: iter_time=4.275e-05, forward_time=0.033, loss_ctc=4.172, loss=4.172, backward_time=0.008, grad_norm=102.214, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 19:51:09,415 (trainer:763) INFO: 4epoch:train:521-560batch: iter_time=4.479e-05, forward_time=0.036, loss_ctc=4.910, loss=4.910, backward_time=0.008, grad_norm=114.989, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 19:51:12,234 (trainer:763) INFO: 4epoch:train:561-600batch: iter_time=4.223e-05, forward_time=0.037, loss_ctc=5.167, loss=5.167, backward_time=0.008, grad_norm=123.399, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-14 19:51:14,877 (trainer:763) INFO: 4epoch:train:601-640batch: iter_time=4.104e-05, forward_time=0.035, loss_ctc=4.276, loss=4.276, backward_time=0.008, grad_norm=107.533, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-14 19:51:17,608 (trainer:763) INFO: 4epoch:train:641-680batch: iter_time=4.140e-05, forward_time=0.036, loss_ctc=4.592, loss=4.592, backward_time=0.008, grad_norm=111.886, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 19:51:20,286 (trainer:763) INFO: 4epoch:train:681-720batch: iter_time=4.210e-05, forward_time=0.035, loss_ctc=4.417, loss=4.417, backward_time=0.008, grad_norm=107.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-14 19:51:22,769 (trainer:763) INFO: 4epoch:train:721-760batch: iter_time=4.100e-05, forward_time=0.033, loss_ctc=4.142, loss=4.142, backward_time=0.008, grad_norm=107.150, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 19:51:25,786 (trainer:763) INFO: 4epoch:train:761-800batch: iter_time=3.918e-05, forward_time=0.040, loss_ctc=5.177, loss=5.177, backward_time=0.009, grad_norm=124.907, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.302 +[stan] 2024-01-14 19:51:29,812 (trainer:354) INFO: 4epoch results: [train] iter_time=1.633e-04, forward_time=0.036, loss_ctc=4.871, loss=4.871, backward_time=0.008, grad_norm=114.819, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.15 seconds, total_count=3200, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=84.751, cer_ctc=0.316, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=84.751, time=1.15 seconds, total_count=100, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.8 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 19:51:30,786 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:51:30,786 (trainer:288) INFO: 5/30epoch started. Estimated time to finish: 25 minutes and 37 seconds +[stan] 2024-01-14 19:51:33,582 (trainer:763) INFO: 5epoch:train:1-40batch: iter_time=0.002, forward_time=0.034, loss_ctc=4.270, loss=4.270, backward_time=0.008, grad_norm=110.911, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-14 19:51:36,410 (trainer:763) INFO: 5epoch:train:41-80batch: iter_time=4.243e-05, forward_time=0.037, loss_ctc=4.730, loss=4.730, backward_time=0.008, grad_norm=117.018, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-14 19:51:38,937 (trainer:763) INFO: 5epoch:train:81-120batch: iter_time=4.291e-05, forward_time=0.034, loss_ctc=4.172, loss=4.172, backward_time=0.008, grad_norm=110.225, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-14 19:51:41,629 (trainer:763) INFO: 5epoch:train:121-160batch: iter_time=4.249e-05, forward_time=0.036, loss_ctc=4.180, loss=4.180, backward_time=0.008, grad_norm=106.416, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-14 19:51:44,276 (trainer:763) INFO: 5epoch:train:161-200batch: iter_time=4.092e-05, forward_time=0.035, loss_ctc=3.771, loss=3.771, backward_time=0.008, grad_norm=105.115, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.265 +[stan] 2024-01-14 19:51:46,964 (trainer:763) INFO: 5epoch:train:201-240batch: iter_time=4.225e-05, forward_time=0.036, loss_ctc=4.107, loss=4.107, backward_time=0.008, grad_norm=103.495, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-14 19:51:49,687 (trainer:763) INFO: 5epoch:train:241-280batch: iter_time=4.418e-05, forward_time=0.036, loss_ctc=4.014, loss=4.014, backward_time=0.008, grad_norm=104.278, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 19:51:52,126 (trainer:763) INFO: 5epoch:train:281-320batch: iter_time=4.149e-05, forward_time=0.032, loss_ctc=3.482, loss=3.482, backward_time=0.008, grad_norm=97.313, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 19:51:55,059 (trainer:763) INFO: 5epoch:train:321-360batch: iter_time=4.048e-05, forward_time=0.039, loss_ctc=4.832, loss=4.832, backward_time=0.008, grad_norm=111.981, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.293 +[stan] 2024-01-14 19:51:57,736 (trainer:763) INFO: 5epoch:train:361-400batch: iter_time=4.250e-05, forward_time=0.035, loss_ctc=4.196, loss=4.196, backward_time=0.008, grad_norm=114.508, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-14 19:52:00,271 (trainer:763) INFO: 5epoch:train:401-440batch: iter_time=4.335e-05, forward_time=0.034, loss_ctc=3.844, loss=3.844, backward_time=0.008, grad_norm=100.162, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-14 19:52:03,012 (trainer:763) INFO: 5epoch:train:441-480batch: iter_time=4.157e-05, forward_time=0.036, loss_ctc=4.024, loss=4.024, backward_time=0.008, grad_norm=104.193, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 19:52:05,788 (trainer:763) INFO: 5epoch:train:481-520batch: iter_time=4.157e-05, forward_time=0.037, loss_ctc=4.277, loss=4.277, backward_time=0.008, grad_norm=111.855, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 19:52:08,515 (trainer:763) INFO: 5epoch:train:521-560batch: iter_time=4.234e-05, forward_time=0.036, loss_ctc=3.933, loss=3.933, backward_time=0.008, grad_norm=104.831, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 19:52:11,433 (trainer:763) INFO: 5epoch:train:561-600batch: iter_time=4.156e-05, forward_time=0.038, loss_ctc=4.471, loss=4.471, backward_time=0.008, grad_norm=112.720, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.292 +[stan] 2024-01-14 19:52:13,837 (trainer:763) INFO: 5epoch:train:601-640batch: iter_time=4.245e-05, forward_time=0.032, loss_ctc=3.203, loss=3.203, backward_time=0.008, grad_norm=97.705, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-14 19:52:16,519 (trainer:763) INFO: 5epoch:train:641-680batch: iter_time=4.246e-05, forward_time=0.035, loss_ctc=4.027, loss=4.027, backward_time=0.008, grad_norm=104.312, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-14 19:52:19,071 (trainer:763) INFO: 5epoch:train:681-720batch: iter_time=4.163e-05, forward_time=0.034, loss_ctc=3.686, loss=3.686, backward_time=0.008, grad_norm=100.346, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 19:52:21,880 (trainer:763) INFO: 5epoch:train:721-760batch: iter_time=4.207e-05, forward_time=0.037, loss_ctc=4.044, loss=4.044, backward_time=0.008, grad_norm=107.552, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-14 19:52:24,578 (trainer:763) INFO: 5epoch:train:761-800batch: iter_time=3.974e-05, forward_time=0.036, loss_ctc=3.720, loss=3.720, backward_time=0.008, grad_norm=92.952, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-14 19:52:28,555 (trainer:354) INFO: 5epoch results: [train] iter_time=1.564e-04, forward_time=0.035, loss_ctc=4.049, loss=4.049, backward_time=0.008, grad_norm=105.894, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.87 seconds, total_count=4000, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=88.021, cer_ctc=0.311, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=88.021, time=1.14 seconds, total_count=125, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.76 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 19:52:29,441 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:52:29,441 (trainer:288) INFO: 6/30epoch started. Estimated time to finish: 24 minutes and 35.58 seconds +[stan] 2024-01-14 19:52:32,327 (trainer:763) INFO: 6epoch:train:1-40batch: iter_time=0.003, forward_time=0.035, loss_ctc=3.427, loss=3.427, backward_time=0.008, grad_norm=100.007, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.288 +[stan] 2024-01-14 19:52:35,056 (trainer:763) INFO: 6epoch:train:41-80batch: iter_time=4.122e-05, forward_time=0.036, loss_ctc=3.781, loss=3.781, backward_time=0.008, grad_norm=116.430, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 19:52:37,788 (trainer:763) INFO: 6epoch:train:81-120batch: iter_time=4.207e-05, forward_time=0.036, loss_ctc=3.914, loss=3.914, backward_time=0.008, grad_norm=110.276, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 19:52:40,299 (trainer:763) INFO: 6epoch:train:121-160batch: iter_time=4.017e-05, forward_time=0.033, loss_ctc=3.240, loss=3.240, backward_time=0.008, grad_norm=100.653, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-14 19:52:43,100 (trainer:763) INFO: 6epoch:train:161-200batch: iter_time=4.209e-05, forward_time=0.037, loss_ctc=3.888, loss=3.888, backward_time=0.008, grad_norm=103.920, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 19:52:45,740 (trainer:763) INFO: 6epoch:train:201-240batch: iter_time=3.979e-05, forward_time=0.035, loss_ctc=3.647, loss=3.647, backward_time=0.008, grad_norm=112.375, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-14 19:52:48,690 (trainer:763) INFO: 6epoch:train:241-280batch: iter_time=4.208e-05, forward_time=0.039, loss_ctc=4.311, loss=4.311, backward_time=0.009, grad_norm=100.988, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.295 +[stan] 2024-01-14 19:52:51,141 (trainer:763) INFO: 6epoch:train:281-320batch: iter_time=4.143e-05, forward_time=0.033, loss_ctc=3.006, loss=3.006, backward_time=0.008, grad_norm=92.233, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-14 19:52:53,818 (trainer:763) INFO: 6epoch:train:321-360batch: iter_time=4.073e-05, forward_time=0.035, loss_ctc=3.538, loss=3.538, backward_time=0.008, grad_norm=99.191, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-14 19:52:56,380 (trainer:763) INFO: 6epoch:train:361-400batch: iter_time=4.242e-05, forward_time=0.034, loss_ctc=3.555, loss=3.555, backward_time=0.008, grad_norm=102.365, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-14 19:52:59,182 (trainer:763) INFO: 6epoch:train:401-440batch: iter_time=4.370e-05, forward_time=0.037, loss_ctc=4.070, loss=4.070, backward_time=0.008, grad_norm=108.275, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 19:53:01,781 (trainer:763) INFO: 6epoch:train:441-480batch: iter_time=4.360e-05, forward_time=0.034, loss_ctc=3.728, loss=3.728, backward_time=0.008, grad_norm=100.595, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-14 19:53:04,358 (trainer:763) INFO: 6epoch:train:481-520batch: iter_time=4.095e-05, forward_time=0.034, loss_ctc=3.347, loss=3.347, backward_time=0.008, grad_norm=92.503, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 19:53:07,213 (trainer:763) INFO: 6epoch:train:521-560batch: iter_time=4.252e-05, forward_time=0.038, loss_ctc=3.890, loss=3.890, backward_time=0.008, grad_norm=99.141, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-14 19:53:09,833 (trainer:763) INFO: 6epoch:train:561-600batch: iter_time=4.207e-05, forward_time=0.035, loss_ctc=3.411, loss=3.411, backward_time=0.008, grad_norm=91.747, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-14 19:53:12,413 (trainer:763) INFO: 6epoch:train:601-640batch: iter_time=4.152e-05, forward_time=0.034, loss_ctc=3.305, loss=3.305, backward_time=0.008, grad_norm=96.858, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 19:53:15,245 (trainer:763) INFO: 6epoch:train:641-680batch: iter_time=4.064e-05, forward_time=0.037, loss_ctc=3.808, loss=3.808, backward_time=0.008, grad_norm=106.436, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-14 19:53:17,760 (trainer:763) INFO: 6epoch:train:681-720batch: iter_time=3.992e-05, forward_time=0.033, loss_ctc=3.098, loss=3.098, backward_time=0.008, grad_norm=94.125, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-14 19:53:20,523 (trainer:763) INFO: 6epoch:train:721-760batch: iter_time=4.248e-05, forward_time=0.036, loss_ctc=3.600, loss=3.600, backward_time=0.008, grad_norm=98.169, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 19:53:23,282 (trainer:763) INFO: 6epoch:train:761-800batch: iter_time=4.425e-05, forward_time=0.036, loss_ctc=3.448, loss=3.448, backward_time=0.008, grad_norm=102.002, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 19:53:27,353 (trainer:354) INFO: 6epoch results: [train] iter_time=1.946e-04, forward_time=0.035, loss_ctc=3.601, loss=3.601, backward_time=0.008, grad_norm=101.414, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.92 seconds, total_count=4800, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=92.219, cer_ctc=0.317, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=92.219, time=1.15 seconds, total_count=150, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.84 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 19:53:28,258 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:53:28,258 (trainer:288) INFO: 7/30epoch started. Estimated time to finish: 23 minutes and 35.73 seconds +[stan] 2024-01-14 19:53:30,973 (trainer:763) INFO: 7epoch:train:1-40batch: iter_time=0.003, forward_time=0.033, loss_ctc=2.848, loss=2.848, backward_time=0.008, grad_norm=89.656, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 19:53:33,904 (trainer:763) INFO: 7epoch:train:41-80batch: iter_time=4.198e-05, forward_time=0.039, loss_ctc=4.054, loss=4.054, backward_time=0.009, grad_norm=99.823, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.293 +[stan] 2024-01-14 19:53:36,564 (trainer:763) INFO: 7epoch:train:81-120batch: iter_time=4.059e-05, forward_time=0.035, loss_ctc=3.249, loss=3.249, backward_time=0.008, grad_norm=98.878, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-14 19:53:39,370 (trainer:763) INFO: 7epoch:train:121-160batch: iter_time=4.183e-05, forward_time=0.037, loss_ctc=3.514, loss=3.514, backward_time=0.008, grad_norm=99.380, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 19:53:41,853 (trainer:763) INFO: 7epoch:train:161-200batch: iter_time=4.117e-05, forward_time=0.033, loss_ctc=2.813, loss=2.813, backward_time=0.008, grad_norm=87.791, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 19:53:44,602 (trainer:763) INFO: 7epoch:train:201-240batch: iter_time=4.122e-05, forward_time=0.036, loss_ctc=3.646, loss=3.646, backward_time=0.008, grad_norm=100.351, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 19:53:47,406 (trainer:763) INFO: 7epoch:train:241-280batch: iter_time=4.060e-05, forward_time=0.037, loss_ctc=3.463, loss=3.463, backward_time=0.008, grad_norm=96.063, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 19:53:49,848 (trainer:763) INFO: 7epoch:train:281-320batch: iter_time=4.032e-05, forward_time=0.032, loss_ctc=2.820, loss=2.820, backward_time=0.008, grad_norm=85.472, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 19:53:52,637 (trainer:763) INFO: 7epoch:train:321-360batch: iter_time=4.236e-05, forward_time=0.037, loss_ctc=3.707, loss=3.707, backward_time=0.008, grad_norm=113.111, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-14 19:53:55,443 (trainer:763) INFO: 7epoch:train:361-400batch: iter_time=4.119e-05, forward_time=0.037, loss_ctc=3.629, loss=3.629, backward_time=0.008, grad_norm=98.522, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 19:53:57,982 (trainer:763) INFO: 7epoch:train:401-440batch: iter_time=4.091e-05, forward_time=0.034, loss_ctc=2.827, loss=2.827, backward_time=0.008, grad_norm=89.778, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-14 19:54:00,675 (trainer:763) INFO: 7epoch:train:441-480batch: iter_time=4.241e-05, forward_time=0.036, loss_ctc=3.213, loss=3.213, backward_time=0.008, grad_norm=102.923, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-14 19:54:03,408 (trainer:763) INFO: 7epoch:train:481-520batch: iter_time=4.192e-05, forward_time=0.036, loss_ctc=3.402, loss=3.402, backward_time=0.008, grad_norm=95.314, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 19:54:05,884 (trainer:763) INFO: 7epoch:train:521-560batch: iter_time=4.402e-05, forward_time=0.033, loss_ctc=3.115, loss=3.115, backward_time=0.008, grad_norm=94.136, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 19:54:08,714 (trainer:763) INFO: 7epoch:train:561-600batch: iter_time=4.142e-05, forward_time=0.037, loss_ctc=3.384, loss=3.384, backward_time=0.008, grad_norm=98.563, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-14 19:54:11,194 (trainer:763) INFO: 7epoch:train:601-640batch: iter_time=4.125e-05, forward_time=0.033, loss_ctc=2.880, loss=2.880, backward_time=0.008, grad_norm=90.657, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 19:54:14,294 (trainer:763) INFO: 7epoch:train:641-680batch: iter_time=4.103e-05, forward_time=0.041, loss_ctc=3.876, loss=3.876, backward_time=0.009, grad_norm=106.107, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.310 +[stan] 2024-01-14 19:54:16,755 (trainer:763) INFO: 7epoch:train:681-720batch: iter_time=4.216e-05, forward_time=0.033, loss_ctc=2.657, loss=2.657, backward_time=0.008, grad_norm=85.732, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 19:54:19,529 (trainer:763) INFO: 7epoch:train:721-760batch: iter_time=4.132e-05, forward_time=0.037, loss_ctc=3.350, loss=3.350, backward_time=0.008, grad_norm=101.126, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 19:54:22,236 (trainer:763) INFO: 7epoch:train:761-800batch: iter_time=3.877e-05, forward_time=0.036, loss_ctc=3.255, loss=3.255, backward_time=0.008, grad_norm=90.041, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 19:54:26,235 (trainer:354) INFO: 7epoch results: [train] iter_time=1.701e-04, forward_time=0.035, loss_ctc=3.285, loss=3.285, backward_time=0.008, grad_norm=96.171, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.06 seconds, total_count=5600, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=93.214, cer_ctc=0.307, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=93.214, time=1.15 seconds, total_count=175, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.76 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 19:54:27,238 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:54:27,238 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/6epoch.pth +[stan] 2024-01-14 19:54:27,238 (trainer:288) INFO: 8/30epoch started. Estimated time to finish: 22 minutes and 36.72 seconds +[stan] 2024-01-14 19:54:30,045 (trainer:763) INFO: 8epoch:train:1-40batch: iter_time=0.002, forward_time=0.034, loss_ctc=3.099, loss=3.099, backward_time=0.008, grad_norm=91.668, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 19:54:32,752 (trainer:763) INFO: 8epoch:train:41-80batch: iter_time=4.189e-05, forward_time=0.036, loss_ctc=3.444, loss=3.444, backward_time=0.008, grad_norm=93.018, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 19:54:35,463 (trainer:763) INFO: 8epoch:train:81-120batch: iter_time=4.121e-05, forward_time=0.036, loss_ctc=3.098, loss=3.098, backward_time=0.008, grad_norm=91.704, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 19:54:38,096 (trainer:763) INFO: 8epoch:train:121-160batch: iter_time=4.183e-05, forward_time=0.035, loss_ctc=2.935, loss=2.935, backward_time=0.008, grad_norm=93.324, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 19:54:40,930 (trainer:763) INFO: 8epoch:train:161-200batch: iter_time=4.035e-05, forward_time=0.037, loss_ctc=3.074, loss=3.074, backward_time=0.008, grad_norm=91.408, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-14 19:54:43,366 (trainer:763) INFO: 8epoch:train:201-240batch: iter_time=4.331e-05, forward_time=0.032, loss_ctc=2.406, loss=2.406, backward_time=0.008, grad_norm=79.683, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 19:54:46,156 (trainer:763) INFO: 8epoch:train:241-280batch: iter_time=4.284e-05, forward_time=0.037, loss_ctc=3.092, loss=3.092, backward_time=0.008, grad_norm=96.280, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-14 19:54:48,707 (trainer:763) INFO: 8epoch:train:281-320batch: iter_time=4.170e-05, forward_time=0.034, loss_ctc=2.635, loss=2.635, backward_time=0.008, grad_norm=86.823, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 19:54:51,593 (trainer:763) INFO: 8epoch:train:321-360batch: iter_time=4.493e-05, forward_time=0.038, loss_ctc=3.419, loss=3.419, backward_time=0.009, grad_norm=98.856, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.289 +[stan] 2024-01-14 19:54:54,218 (trainer:763) INFO: 8epoch:train:361-400batch: iter_time=4.136e-05, forward_time=0.035, loss_ctc=2.922, loss=2.922, backward_time=0.008, grad_norm=88.131, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-14 19:54:56,938 (trainer:763) INFO: 8epoch:train:401-440batch: iter_time=4.199e-05, forward_time=0.036, loss_ctc=3.004, loss=3.004, backward_time=0.008, grad_norm=91.418, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 19:54:59,831 (trainer:763) INFO: 8epoch:train:441-480batch: iter_time=4.470e-05, forward_time=0.038, loss_ctc=3.319, loss=3.319, backward_time=0.008, grad_norm=93.101, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.289 +[stan] 2024-01-14 19:55:02,283 (trainer:763) INFO: 8epoch:train:481-520batch: iter_time=4.647e-05, forward_time=0.033, loss_ctc=2.519, loss=2.519, backward_time=0.008, grad_norm=83.660, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-14 19:55:04,725 (trainer:763) INFO: 8epoch:train:521-560batch: iter_time=4.170e-05, forward_time=0.032, loss_ctc=2.460, loss=2.460, backward_time=0.008, grad_norm=87.724, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 19:55:07,465 (trainer:763) INFO: 8epoch:train:561-600batch: iter_time=4.095e-05, forward_time=0.036, loss_ctc=2.991, loss=2.991, backward_time=0.008, grad_norm=92.887, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 19:55:10,341 (trainer:763) INFO: 8epoch:train:601-640batch: iter_time=4.164e-05, forward_time=0.038, loss_ctc=3.201, loss=3.201, backward_time=0.008, grad_norm=96.773, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.287 +[stan] 2024-01-14 19:55:12,758 (trainer:763) INFO: 8epoch:train:641-680batch: iter_time=4.433e-05, forward_time=0.032, loss_ctc=2.394, loss=2.394, backward_time=0.008, grad_norm=80.902, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-14 19:55:15,831 (trainer:763) INFO: 8epoch:train:681-720batch: iter_time=4.205e-05, forward_time=0.040, loss_ctc=3.682, loss=3.682, backward_time=0.009, grad_norm=102.648, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.307 +[stan] 2024-01-14 19:55:18,386 (trainer:763) INFO: 8epoch:train:721-760batch: iter_time=4.213e-05, forward_time=0.034, loss_ctc=2.441, loss=2.441, backward_time=0.008, grad_norm=82.029, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 19:55:21,148 (trainer:763) INFO: 8epoch:train:761-800batch: iter_time=3.935e-05, forward_time=0.036, loss_ctc=3.151, loss=3.151, backward_time=0.008, grad_norm=94.138, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 19:55:25,097 (trainer:354) INFO: 8epoch results: [train] iter_time=1.512e-04, forward_time=0.035, loss_ctc=2.964, loss=2.964, backward_time=0.008, grad_norm=90.809, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.98 seconds, total_count=6400, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=96.745, cer_ctc=0.316, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=96.745, time=1.14 seconds, total_count=200, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.73 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 19:55:26,033 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:55:26,033 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/7epoch.pth +[stan] 2024-01-14 19:55:26,034 (trainer:288) INFO: 9/30epoch started. Estimated time to finish: 21 minutes and 37.2 seconds +[stan] 2024-01-14 19:55:28,804 (trainer:763) INFO: 9epoch:train:1-40batch: iter_time=0.003, forward_time=0.033, loss_ctc=2.463, loss=2.463, backward_time=0.008, grad_norm=83.919, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 19:55:31,588 (trainer:763) INFO: 9epoch:train:41-80batch: iter_time=4.216e-05, forward_time=0.037, loss_ctc=3.249, loss=3.249, backward_time=0.008, grad_norm=94.650, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 19:55:34,061 (trainer:763) INFO: 9epoch:train:81-120batch: iter_time=4.133e-05, forward_time=0.033, loss_ctc=2.506, loss=2.506, backward_time=0.008, grad_norm=83.061, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-14 19:55:36,982 (trainer:763) INFO: 9epoch:train:121-160batch: iter_time=4.363e-05, forward_time=0.038, loss_ctc=3.330, loss=3.330, backward_time=0.008, grad_norm=92.343, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.292 +[stan] 2024-01-14 19:55:39,643 (trainer:763) INFO: 9epoch:train:161-200batch: iter_time=3.957e-05, forward_time=0.035, loss_ctc=2.808, loss=2.808, backward_time=0.008, grad_norm=84.329, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-14 19:55:42,373 (trainer:763) INFO: 9epoch:train:201-240batch: iter_time=4.135e-05, forward_time=0.036, loss_ctc=3.061, loss=3.061, backward_time=0.008, grad_norm=84.883, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 19:55:44,888 (trainer:763) INFO: 9epoch:train:241-280batch: iter_time=4.068e-05, forward_time=0.033, loss_ctc=2.638, loss=2.638, backward_time=0.008, grad_norm=83.142, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-14 19:55:47,692 (trainer:763) INFO: 9epoch:train:281-320batch: iter_time=4.242e-05, forward_time=0.037, loss_ctc=2.878, loss=2.878, backward_time=0.008, grad_norm=85.460, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 19:55:50,560 (trainer:763) INFO: 9epoch:train:321-360batch: iter_time=4.332e-05, forward_time=0.038, loss_ctc=3.051, loss=3.051, backward_time=0.008, grad_norm=89.376, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.287 +[stan] 2024-01-14 19:55:53,117 (trainer:763) INFO: 9epoch:train:361-400batch: iter_time=4.340e-05, forward_time=0.035, loss_ctc=2.470, loss=2.470, backward_time=0.008, grad_norm=76.813, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-14 19:55:55,835 (trainer:763) INFO: 9epoch:train:401-440batch: iter_time=4.142e-05, forward_time=0.036, loss_ctc=2.940, loss=2.940, backward_time=0.008, grad_norm=88.457, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 19:55:58,612 (trainer:763) INFO: 9epoch:train:441-480batch: iter_time=4.317e-05, forward_time=0.037, loss_ctc=2.936, loss=2.936, backward_time=0.008, grad_norm=86.204, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 19:56:01,328 (trainer:763) INFO: 9epoch:train:481-520batch: iter_time=4.422e-05, forward_time=0.036, loss_ctc=2.910, loss=2.910, backward_time=0.008, grad_norm=95.964, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 19:56:03,902 (trainer:763) INFO: 9epoch:train:521-560batch: iter_time=4.688e-05, forward_time=0.034, loss_ctc=2.670, loss=2.670, backward_time=0.008, grad_norm=81.043, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 19:56:06,611 (trainer:763) INFO: 9epoch:train:561-600batch: iter_time=4.175e-05, forward_time=0.036, loss_ctc=2.994, loss=2.994, backward_time=0.008, grad_norm=90.455, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 19:56:09,142 (trainer:763) INFO: 9epoch:train:601-640batch: iter_time=4.152e-05, forward_time=0.034, loss_ctc=2.577, loss=2.577, backward_time=0.008, grad_norm=79.629, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-14 19:56:11,977 (trainer:763) INFO: 9epoch:train:641-680batch: iter_time=4.241e-05, forward_time=0.037, loss_ctc=3.004, loss=3.004, backward_time=0.008, grad_norm=96.685, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-14 19:56:14,579 (trainer:763) INFO: 9epoch:train:681-720batch: iter_time=4.121e-05, forward_time=0.034, loss_ctc=2.638, loss=2.638, backward_time=0.008, grad_norm=87.889, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-14 19:56:17,308 (trainer:763) INFO: 9epoch:train:721-760batch: iter_time=4.102e-05, forward_time=0.036, loss_ctc=2.664, loss=2.664, backward_time=0.008, grad_norm=87.126, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 19:56:20,038 (trainer:763) INFO: 9epoch:train:761-800batch: iter_time=3.936e-05, forward_time=0.036, loss_ctc=2.700, loss=2.700, backward_time=0.008, grad_norm=83.572, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 19:56:24,013 (trainer:354) INFO: 9epoch results: [train] iter_time=1.849e-04, forward_time=0.036, loss_ctc=2.824, loss=2.824, backward_time=0.008, grad_norm=86.750, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.08 seconds, total_count=7200, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=97.215, cer_ctc=0.314, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=97.215, time=1.15 seconds, total_count=225, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.76 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 19:56:24,975 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:56:24,976 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/8epoch.pth +[stan] 2024-01-14 19:56:24,976 (trainer:288) INFO: 10/30epoch started. Estimated time to finish: 20 minutes and 38.19 seconds +[stan] 2024-01-14 19:56:27,747 (trainer:763) INFO: 10epoch:train:1-40batch: iter_time=0.003, forward_time=0.033, loss_ctc=2.458, loss=2.458, backward_time=0.008, grad_norm=77.961, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 19:56:30,744 (trainer:763) INFO: 10epoch:train:41-80batch: iter_time=4.065e-05, forward_time=0.039, loss_ctc=3.300, loss=3.300, backward_time=0.009, grad_norm=90.457, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.300 +[stan] 2024-01-14 19:56:33,293 (trainer:763) INFO: 10epoch:train:81-120batch: iter_time=5.081e-05, forward_time=0.034, loss_ctc=2.388, loss=2.388, backward_time=0.008, grad_norm=80.959, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 19:56:35,821 (trainer:763) INFO: 10epoch:train:121-160batch: iter_time=3.974e-05, forward_time=0.033, loss_ctc=2.371, loss=2.371, backward_time=0.008, grad_norm=85.544, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-14 19:56:38,547 (trainer:763) INFO: 10epoch:train:161-200batch: iter_time=4.118e-05, forward_time=0.036, loss_ctc=2.743, loss=2.743, backward_time=0.008, grad_norm=86.006, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 19:56:41,331 (trainer:763) INFO: 10epoch:train:201-240batch: iter_time=4.214e-05, forward_time=0.037, loss_ctc=2.889, loss=2.889, backward_time=0.008, grad_norm=89.102, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 19:56:43,867 (trainer:763) INFO: 10epoch:train:241-280batch: iter_time=4.052e-05, forward_time=0.034, loss_ctc=2.630, loss=2.630, backward_time=0.008, grad_norm=86.574, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-14 19:56:46,785 (trainer:763) INFO: 10epoch:train:281-320batch: iter_time=4.051e-05, forward_time=0.038, loss_ctc=2.950, loss=2.950, backward_time=0.008, grad_norm=87.280, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.292 +[stan] 2024-01-14 19:56:49,361 (trainer:763) INFO: 10epoch:train:321-360batch: iter_time=4.155e-05, forward_time=0.034, loss_ctc=2.455, loss=2.455, backward_time=0.008, grad_norm=80.903, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 19:56:51,948 (trainer:763) INFO: 10epoch:train:361-400batch: iter_time=4.114e-05, forward_time=0.034, loss_ctc=2.525, loss=2.525, backward_time=0.008, grad_norm=85.419, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-14 19:56:54,677 (trainer:763) INFO: 10epoch:train:401-440batch: iter_time=4.181e-05, forward_time=0.036, loss_ctc=2.660, loss=2.660, backward_time=0.008, grad_norm=84.319, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 19:56:57,400 (trainer:763) INFO: 10epoch:train:441-480batch: iter_time=4.077e-05, forward_time=0.036, loss_ctc=2.791, loss=2.791, backward_time=0.008, grad_norm=85.331, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 19:57:00,165 (trainer:763) INFO: 10epoch:train:481-520batch: iter_time=4.173e-05, forward_time=0.036, loss_ctc=2.794, loss=2.794, backward_time=0.008, grad_norm=83.584, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 19:57:02,878 (trainer:763) INFO: 10epoch:train:521-560batch: iter_time=4.070e-05, forward_time=0.036, loss_ctc=2.914, loss=2.914, backward_time=0.008, grad_norm=83.190, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 19:57:05,452 (trainer:763) INFO: 10epoch:train:561-600batch: iter_time=4.137e-05, forward_time=0.034, loss_ctc=2.429, loss=2.429, backward_time=0.008, grad_norm=78.522, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 19:57:08,332 (trainer:763) INFO: 10epoch:train:601-640batch: iter_time=4.187e-05, forward_time=0.038, loss_ctc=3.081, loss=3.081, backward_time=0.008, grad_norm=90.639, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.288 +[stan] 2024-01-14 19:57:10,951 (trainer:763) INFO: 10epoch:train:641-680batch: iter_time=4.137e-05, forward_time=0.035, loss_ctc=2.358, loss=2.358, backward_time=0.008, grad_norm=80.093, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-14 19:57:13,501 (trainer:763) INFO: 10epoch:train:681-720batch: iter_time=3.965e-05, forward_time=0.034, loss_ctc=2.378, loss=2.378, backward_time=0.008, grad_norm=80.018, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 19:57:16,048 (trainer:763) INFO: 10epoch:train:721-760batch: iter_time=4.096e-05, forward_time=0.034, loss_ctc=2.163, loss=2.163, backward_time=0.008, grad_norm=80.985, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 19:57:19,084 (trainer:763) INFO: 10epoch:train:761-800batch: iter_time=3.915e-05, forward_time=0.040, loss_ctc=3.194, loss=3.194, backward_time=0.009, grad_norm=88.979, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.303 +[stan] 2024-01-14 19:57:23,100 (trainer:354) INFO: 10epoch results: [train] iter_time=1.841e-04, forward_time=0.036, loss_ctc=2.673, loss=2.673, backward_time=0.008, grad_norm=84.293, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.19 seconds, total_count=8000, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=100.751, cer_ctc=0.316, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=100.751, time=1.15 seconds, total_count=250, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.79 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 19:57:24,160 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:57:24,160 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/9epoch.pth +[stan] 2024-01-14 19:57:24,161 (trainer:288) INFO: 11/30epoch started. Estimated time to finish: 19 minutes and 39.67 seconds +[stan] 2024-01-14 19:57:26,853 (trainer:763) INFO: 11epoch:train:1-40batch: iter_time=0.002, forward_time=0.033, loss_ctc=2.402, loss=2.402, backward_time=0.008, grad_norm=79.579, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-14 19:57:29,279 (trainer:763) INFO: 11epoch:train:41-80batch: iter_time=4.190e-05, forward_time=0.032, loss_ctc=2.234, loss=2.234, backward_time=0.008, grad_norm=82.115, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-14 19:57:32,139 (trainer:763) INFO: 11epoch:train:81-120batch: iter_time=4.296e-05, forward_time=0.038, loss_ctc=2.773, loss=2.773, backward_time=0.008, grad_norm=94.406, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.286 +[stan] 2024-01-14 19:57:34,890 (trainer:763) INFO: 11epoch:train:121-160batch: iter_time=4.116e-05, forward_time=0.036, loss_ctc=2.963, loss=2.963, backward_time=0.008, grad_norm=89.235, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 19:57:37,323 (trainer:763) INFO: 11epoch:train:161-200batch: iter_time=4.193e-05, forward_time=0.032, loss_ctc=2.316, loss=2.316, backward_time=0.008, grad_norm=86.105, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-14 19:57:40,343 (trainer:763) INFO: 11epoch:train:201-240batch: iter_time=4.151e-05, forward_time=0.040, loss_ctc=3.158, loss=3.158, backward_time=0.009, grad_norm=89.914, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.302 +[stan] 2024-01-14 19:57:42,934 (trainer:763) INFO: 11epoch:train:241-280batch: iter_time=4.127e-05, forward_time=0.034, loss_ctc=2.403, loss=2.403, backward_time=0.008, grad_norm=80.106, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-14 19:57:45,710 (trainer:763) INFO: 11epoch:train:281-320batch: iter_time=4.229e-05, forward_time=0.037, loss_ctc=2.738, loss=2.738, backward_time=0.008, grad_norm=82.829, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 19:57:48,264 (trainer:763) INFO: 11epoch:train:321-360batch: iter_time=4.188e-05, forward_time=0.034, loss_ctc=2.268, loss=2.268, backward_time=0.008, grad_norm=75.113, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 19:57:50,985 (trainer:763) INFO: 11epoch:train:361-400batch: iter_time=4.223e-05, forward_time=0.036, loss_ctc=2.452, loss=2.452, backward_time=0.008, grad_norm=80.384, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 19:57:53,535 (trainer:763) INFO: 11epoch:train:401-440batch: iter_time=4.594e-05, forward_time=0.034, loss_ctc=2.298, loss=2.298, backward_time=0.008, grad_norm=76.389, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 19:57:56,286 (trainer:763) INFO: 11epoch:train:441-480batch: iter_time=4.160e-05, forward_time=0.036, loss_ctc=2.553, loss=2.553, backward_time=0.008, grad_norm=83.042, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 19:57:59,028 (trainer:763) INFO: 11epoch:train:481-520batch: iter_time=4.178e-05, forward_time=0.036, loss_ctc=2.604, loss=2.604, backward_time=0.008, grad_norm=87.037, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 19:58:01,576 (trainer:763) INFO: 11epoch:train:521-560batch: iter_time=4.134e-05, forward_time=0.034, loss_ctc=2.265, loss=2.265, backward_time=0.008, grad_norm=78.876, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 19:58:04,636 (trainer:763) INFO: 11epoch:train:561-600batch: iter_time=4.391e-05, forward_time=0.040, loss_ctc=2.889, loss=2.889, backward_time=0.009, grad_norm=84.043, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.306 +[stan] 2024-01-14 19:58:07,076 (trainer:763) INFO: 11epoch:train:601-640batch: iter_time=4.104e-05, forward_time=0.032, loss_ctc=2.360, loss=2.360, backward_time=0.008, grad_norm=75.290, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 19:58:09,909 (trainer:763) INFO: 11epoch:train:641-680batch: iter_time=4.288e-05, forward_time=0.037, loss_ctc=2.760, loss=2.760, backward_time=0.008, grad_norm=84.167, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-14 19:58:12,394 (trainer:763) INFO: 11epoch:train:681-720batch: iter_time=4.287e-05, forward_time=0.033, loss_ctc=2.312, loss=2.312, backward_time=0.008, grad_norm=78.100, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 19:58:15,125 (trainer:763) INFO: 11epoch:train:721-760batch: iter_time=4.150e-05, forward_time=0.036, loss_ctc=2.806, loss=2.806, backward_time=0.008, grad_norm=84.930, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 19:58:17,546 (trainer:763) INFO: 11epoch:train:761-800batch: iter_time=4.087e-05, forward_time=0.032, loss_ctc=2.224, loss=2.224, backward_time=0.008, grad_norm=77.011, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-14 19:58:21,520 (trainer:354) INFO: 11epoch results: [train] iter_time=1.572e-04, forward_time=0.035, loss_ctc=2.539, loss=2.539, backward_time=0.008, grad_norm=82.434, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267, time=53.46 seconds, total_count=8800, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=100.937, cer_ctc=0.318, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=100.937, time=1.14 seconds, total_count=275, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.76 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 19:58:22,417 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:58:22,418 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/10epoch.pth +[stan] 2024-01-14 19:58:22,418 (trainer:288) INFO: 12/30epoch started. Estimated time to finish: 18 minutes and 39.43 seconds +[stan] 2024-01-14 19:58:25,550 (trainer:763) INFO: 12epoch:train:1-40batch: iter_time=0.003, forward_time=0.038, loss_ctc=2.779, loss=2.779, backward_time=0.008, grad_norm=85.068, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.313 +[stan] 2024-01-14 19:58:28,312 (trainer:763) INFO: 12epoch:train:41-80batch: iter_time=4.227e-05, forward_time=0.036, loss_ctc=2.488, loss=2.488, backward_time=0.008, grad_norm=84.601, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 19:58:31,059 (trainer:763) INFO: 12epoch:train:81-120batch: iter_time=4.248e-05, forward_time=0.036, loss_ctc=2.579, loss=2.579, backward_time=0.008, grad_norm=83.039, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 19:58:33,856 (trainer:763) INFO: 12epoch:train:121-160batch: iter_time=4.269e-05, forward_time=0.037, loss_ctc=2.630, loss=2.630, backward_time=0.008, grad_norm=81.331, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 19:58:36,347 (trainer:763) INFO: 12epoch:train:161-200batch: iter_time=4.154e-05, forward_time=0.033, loss_ctc=2.451, loss=2.451, backward_time=0.008, grad_norm=80.166, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 19:58:39,213 (trainer:763) INFO: 12epoch:train:201-240batch: iter_time=4.274e-05, forward_time=0.038, loss_ctc=2.645, loss=2.645, backward_time=0.008, grad_norm=83.149, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.286 +[stan] 2024-01-14 19:58:41,903 (trainer:763) INFO: 12epoch:train:241-280batch: iter_time=4.339e-05, forward_time=0.036, loss_ctc=2.369, loss=2.369, backward_time=0.008, grad_norm=79.546, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-14 19:58:44,388 (trainer:763) INFO: 12epoch:train:281-320batch: iter_time=4.097e-05, forward_time=0.033, loss_ctc=2.066, loss=2.066, backward_time=0.008, grad_norm=74.240, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 19:58:47,063 (trainer:763) INFO: 12epoch:train:321-360batch: iter_time=4.122e-05, forward_time=0.035, loss_ctc=2.499, loss=2.499, backward_time=0.008, grad_norm=80.626, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-14 19:58:49,599 (trainer:763) INFO: 12epoch:train:361-400batch: iter_time=4.104e-05, forward_time=0.034, loss_ctc=2.270, loss=2.270, backward_time=0.008, grad_norm=77.530, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-14 19:58:52,424 (trainer:763) INFO: 12epoch:train:401-440batch: iter_time=4.055e-05, forward_time=0.037, loss_ctc=2.913, loss=2.913, backward_time=0.008, grad_norm=88.286, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-14 19:58:54,984 (trainer:763) INFO: 12epoch:train:441-480batch: iter_time=4.290e-05, forward_time=0.034, loss_ctc=2.095, loss=2.095, backward_time=0.008, grad_norm=73.304, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-14 19:58:57,743 (trainer:763) INFO: 12epoch:train:481-520batch: iter_time=4.162e-05, forward_time=0.036, loss_ctc=2.534, loss=2.534, backward_time=0.008, grad_norm=77.674, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 19:59:00,564 (trainer:763) INFO: 12epoch:train:521-560batch: iter_time=4.187e-05, forward_time=0.038, loss_ctc=2.433, loss=2.433, backward_time=0.008, grad_norm=80.097, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-14 19:59:03,067 (trainer:763) INFO: 12epoch:train:561-600batch: iter_time=4.289e-05, forward_time=0.033, loss_ctc=2.287, loss=2.287, backward_time=0.008, grad_norm=75.220, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 19:59:06,143 (trainer:763) INFO: 12epoch:train:601-640batch: iter_time=4.147e-05, forward_time=0.040, loss_ctc=2.869, loss=2.869, backward_time=0.009, grad_norm=82.402, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.308 +[stan] 2024-01-14 19:59:08,614 (trainer:763) INFO: 12epoch:train:641-680batch: iter_time=4.399e-05, forward_time=0.033, loss_ctc=2.093, loss=2.093, backward_time=0.008, grad_norm=75.944, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-14 19:59:11,141 (trainer:763) INFO: 12epoch:train:681-720batch: iter_time=4.223e-05, forward_time=0.033, loss_ctc=2.166, loss=2.166, backward_time=0.008, grad_norm=71.934, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-14 19:59:13,889 (trainer:763) INFO: 12epoch:train:721-760batch: iter_time=4.275e-05, forward_time=0.036, loss_ctc=2.237, loss=2.237, backward_time=0.008, grad_norm=75.172, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 19:59:16,662 (trainer:763) INFO: 12epoch:train:761-800batch: iter_time=4.131e-05, forward_time=0.037, loss_ctc=2.374, loss=2.374, backward_time=0.008, grad_norm=76.327, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 19:59:20,624 (trainer:354) INFO: 12epoch results: [train] iter_time=2.042e-04, forward_time=0.036, loss_ctc=2.439, loss=2.439, backward_time=0.008, grad_norm=79.283, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271, time=54.32 seconds, total_count=9600, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=103.286, cer_ctc=0.312, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=103.286, time=1.14 seconds, total_count=300, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.75 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 19:59:21,542 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:59:21,542 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/11epoch.pth +[stan] 2024-01-14 19:59:21,542 (trainer:288) INFO: 13/30epoch started. Estimated time to finish: 17 minutes and 40.83 seconds +[stan] 2024-01-14 19:59:24,330 (trainer:763) INFO: 13epoch:train:1-40batch: iter_time=0.002, forward_time=0.034, loss_ctc=2.201, loss=2.201, backward_time=0.008, grad_norm=75.417, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 19:59:27,051 (trainer:763) INFO: 13epoch:train:41-80batch: iter_time=4.139e-05, forward_time=0.036, loss_ctc=2.142, loss=2.142, backward_time=0.008, grad_norm=73.149, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 19:59:29,837 (trainer:763) INFO: 13epoch:train:81-120batch: iter_time=4.150e-05, forward_time=0.037, loss_ctc=2.688, loss=2.688, backward_time=0.008, grad_norm=85.697, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-14 19:59:32,541 (trainer:763) INFO: 13epoch:train:121-160batch: iter_time=4.045e-05, forward_time=0.036, loss_ctc=2.276, loss=2.276, backward_time=0.008, grad_norm=77.329, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-14 19:59:35,319 (trainer:763) INFO: 13epoch:train:161-200batch: iter_time=4.408e-05, forward_time=0.037, loss_ctc=2.429, loss=2.429, backward_time=0.008, grad_norm=75.795, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 19:59:37,890 (trainer:763) INFO: 13epoch:train:201-240batch: iter_time=4.188e-05, forward_time=0.034, loss_ctc=2.091, loss=2.091, backward_time=0.008, grad_norm=74.614, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 19:59:40,664 (trainer:763) INFO: 13epoch:train:241-280batch: iter_time=4.093e-05, forward_time=0.037, loss_ctc=2.469, loss=2.469, backward_time=0.008, grad_norm=77.228, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 19:59:43,483 (trainer:763) INFO: 13epoch:train:281-320batch: iter_time=4.163e-05, forward_time=0.037, loss_ctc=2.528, loss=2.528, backward_time=0.008, grad_norm=80.951, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-14 19:59:45,929 (trainer:763) INFO: 13epoch:train:321-360batch: iter_time=4.097e-05, forward_time=0.032, loss_ctc=2.152, loss=2.152, backward_time=0.008, grad_norm=81.374, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-14 19:59:48,683 (trainer:763) INFO: 13epoch:train:361-400batch: iter_time=4.195e-05, forward_time=0.036, loss_ctc=2.279, loss=2.279, backward_time=0.008, grad_norm=76.451, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 19:59:51,168 (trainer:763) INFO: 13epoch:train:401-440batch: iter_time=4.184e-05, forward_time=0.033, loss_ctc=1.934, loss=1.934, backward_time=0.008, grad_norm=73.629, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 19:59:53,979 (trainer:763) INFO: 13epoch:train:441-480batch: iter_time=4.233e-05, forward_time=0.037, loss_ctc=2.264, loss=2.264, backward_time=0.008, grad_norm=75.932, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-14 19:59:56,868 (trainer:763) INFO: 13epoch:train:481-520batch: iter_time=4.095e-05, forward_time=0.038, loss_ctc=2.458, loss=2.458, backward_time=0.008, grad_norm=78.706, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.289 +[stan] 2024-01-14 19:59:59,276 (trainer:763) INFO: 13epoch:train:521-560batch: iter_time=4.134e-05, forward_time=0.032, loss_ctc=1.801, loss=1.801, backward_time=0.008, grad_norm=68.859, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-14 20:00:02,030 (trainer:763) INFO: 13epoch:train:561-600batch: iter_time=4.162e-05, forward_time=0.036, loss_ctc=2.324, loss=2.324, backward_time=0.008, grad_norm=75.985, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 20:00:04,829 (trainer:763) INFO: 13epoch:train:601-640batch: iter_time=4.403e-05, forward_time=0.037, loss_ctc=2.383, loss=2.383, backward_time=0.008, grad_norm=78.621, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 20:00:07,301 (trainer:763) INFO: 13epoch:train:641-680batch: iter_time=4.056e-05, forward_time=0.033, loss_ctc=2.035, loss=2.035, backward_time=0.008, grad_norm=76.389, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-14 20:00:10,081 (trainer:763) INFO: 13epoch:train:681-720batch: iter_time=4.263e-05, forward_time=0.037, loss_ctc=2.325, loss=2.325, backward_time=0.008, grad_norm=76.489, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 20:00:12,835 (trainer:763) INFO: 13epoch:train:721-760batch: iter_time=4.174e-05, forward_time=0.036, loss_ctc=2.328, loss=2.328, backward_time=0.008, grad_norm=78.948, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 20:00:15,651 (trainer:763) INFO: 13epoch:train:761-800batch: iter_time=4.081e-05, forward_time=0.037, loss_ctc=2.303, loss=2.303, backward_time=0.008, grad_norm=80.302, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-14 20:00:19,642 (trainer:354) INFO: 13epoch results: [train] iter_time=1.643e-04, forward_time=0.036, loss_ctc=2.270, loss=2.270, backward_time=0.008, grad_norm=77.093, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.18 seconds, total_count=10400, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=105.273, cer_ctc=0.316, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=105.273, time=1.14 seconds, total_count=325, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.77 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:00:20,638 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:00:20,638 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/12epoch.pth +[stan] 2024-01-14 20:00:20,638 (trainer:288) INFO: 14/30epoch started. Estimated time to finish: 16 minutes and 42.1 seconds +[stan] 2024-01-14 20:00:23,362 (trainer:763) INFO: 14epoch:train:1-40batch: iter_time=0.002, forward_time=0.033, loss_ctc=1.886, loss=1.886, backward_time=0.008, grad_norm=73.733, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 20:00:25,824 (trainer:763) INFO: 14epoch:train:41-80batch: iter_time=4.228e-05, forward_time=0.033, loss_ctc=1.928, loss=1.928, backward_time=0.008, grad_norm=70.592, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 20:00:28,673 (trainer:763) INFO: 14epoch:train:81-120batch: iter_time=4.257e-05, forward_time=0.038, loss_ctc=2.303, loss=2.303, backward_time=0.008, grad_norm=77.929, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-14 20:00:31,394 (trainer:763) INFO: 14epoch:train:121-160batch: iter_time=4.199e-05, forward_time=0.036, loss_ctc=2.260, loss=2.260, backward_time=0.008, grad_norm=73.613, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 20:00:33,845 (trainer:763) INFO: 14epoch:train:161-200batch: iter_time=4.364e-05, forward_time=0.033, loss_ctc=2.092, loss=2.092, backward_time=0.008, grad_norm=75.173, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-14 20:00:36,938 (trainer:763) INFO: 14epoch:train:201-240batch: iter_time=4.162e-05, forward_time=0.041, loss_ctc=3.038, loss=3.038, backward_time=0.009, grad_norm=84.068, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.309 +[stan] 2024-01-14 20:00:39,427 (trainer:763) INFO: 14epoch:train:241-280batch: iter_time=4.164e-05, forward_time=0.033, loss_ctc=2.190, loss=2.190, backward_time=0.008, grad_norm=74.869, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 20:00:41,857 (trainer:763) INFO: 14epoch:train:281-320batch: iter_time=4.204e-05, forward_time=0.032, loss_ctc=2.054, loss=2.054, backward_time=0.008, grad_norm=72.582, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-14 20:00:44,765 (trainer:763) INFO: 14epoch:train:321-360batch: iter_time=4.224e-05, forward_time=0.038, loss_ctc=2.547, loss=2.547, backward_time=0.008, grad_norm=80.591, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.291 +[stan] 2024-01-14 20:00:47,471 (trainer:763) INFO: 14epoch:train:361-400batch: iter_time=4.149e-05, forward_time=0.036, loss_ctc=2.245, loss=2.245, backward_time=0.008, grad_norm=81.374, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-14 20:00:50,216 (trainer:763) INFO: 14epoch:train:401-440batch: iter_time=4.075e-05, forward_time=0.036, loss_ctc=2.326, loss=2.326, backward_time=0.008, grad_norm=76.065, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 20:00:52,630 (trainer:763) INFO: 14epoch:train:441-480batch: iter_time=4.177e-05, forward_time=0.032, loss_ctc=1.837, loss=1.837, backward_time=0.008, grad_norm=70.836, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-14 20:00:55,501 (trainer:763) INFO: 14epoch:train:481-520batch: iter_time=4.147e-05, forward_time=0.038, loss_ctc=2.439, loss=2.439, backward_time=0.008, grad_norm=83.359, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.287 +[stan] 2024-01-14 20:00:58,338 (trainer:763) INFO: 14epoch:train:521-560batch: iter_time=4.130e-05, forward_time=0.037, loss_ctc=2.564, loss=2.564, backward_time=0.008, grad_norm=83.850, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.284 +[stan] 2024-01-14 20:01:00,756 (trainer:763) INFO: 14epoch:train:561-600batch: iter_time=4.103e-05, forward_time=0.032, loss_ctc=1.917, loss=1.917, backward_time=0.008, grad_norm=69.395, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-14 20:01:03,541 (trainer:763) INFO: 14epoch:train:601-640batch: iter_time=4.439e-05, forward_time=0.037, loss_ctc=2.295, loss=2.295, backward_time=0.008, grad_norm=83.510, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 20:01:06,312 (trainer:763) INFO: 14epoch:train:641-680batch: iter_time=4.163e-05, forward_time=0.037, loss_ctc=2.338, loss=2.338, backward_time=0.008, grad_norm=76.488, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 20:01:08,946 (trainer:763) INFO: 14epoch:train:681-720batch: iter_time=4.224e-05, forward_time=0.035, loss_ctc=2.159, loss=2.159, backward_time=0.008, grad_norm=77.878, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 20:01:11,577 (trainer:763) INFO: 14epoch:train:721-760batch: iter_time=4.230e-05, forward_time=0.035, loss_ctc=2.038, loss=2.038, backward_time=0.008, grad_norm=69.494, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 20:01:14,188 (trainer:763) INFO: 14epoch:train:761-800batch: iter_time=4.248e-05, forward_time=0.035, loss_ctc=2.042, loss=2.042, backward_time=0.008, grad_norm=75.924, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-14 20:01:18,161 (trainer:354) INFO: 14epoch results: [train] iter_time=1.534e-04, forward_time=0.035, loss_ctc=2.225, loss=2.225, backward_time=0.008, grad_norm=76.566, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268, time=53.63 seconds, total_count=11200, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=104.778, cer_ctc=0.319, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=104.778, time=1.15 seconds, total_count=350, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.75 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:01:19,086 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:01:19,087 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/13epoch.pth +[stan] 2024-01-14 20:01:19,087 (trainer:288) INFO: 15/30epoch started. Estimated time to finish: 15 minutes and 42.58 seconds +[stan] 2024-01-14 20:01:22,180 (trainer:763) INFO: 15epoch:train:1-40batch: iter_time=0.003, forward_time=0.037, loss_ctc=2.492, loss=2.492, backward_time=0.009, grad_norm=80.243, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.309 +[stan] 2024-01-14 20:01:24,779 (trainer:763) INFO: 15epoch:train:41-80batch: iter_time=4.345e-05, forward_time=0.034, loss_ctc=1.893, loss=1.893, backward_time=0.008, grad_norm=69.844, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-14 20:01:27,419 (trainer:763) INFO: 15epoch:train:81-120batch: iter_time=4.673e-05, forward_time=0.035, loss_ctc=1.894, loss=1.894, backward_time=0.008, grad_norm=70.352, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-14 20:01:30,018 (trainer:763) INFO: 15epoch:train:121-160batch: iter_time=4.065e-05, forward_time=0.034, loss_ctc=1.954, loss=1.954, backward_time=0.008, grad_norm=71.046, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-14 20:01:32,821 (trainer:763) INFO: 15epoch:train:161-200batch: iter_time=4.200e-05, forward_time=0.037, loss_ctc=2.313, loss=2.313, backward_time=0.008, grad_norm=77.562, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 20:01:35,679 (trainer:763) INFO: 15epoch:train:201-240batch: iter_time=4.436e-05, forward_time=0.038, loss_ctc=2.354, loss=2.354, backward_time=0.008, grad_norm=75.230, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.286 +[stan] 2024-01-14 20:01:38,184 (trainer:763) INFO: 15epoch:train:241-280batch: iter_time=4.234e-05, forward_time=0.033, loss_ctc=1.888, loss=1.888, backward_time=0.008, grad_norm=70.056, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 20:01:40,865 (trainer:763) INFO: 15epoch:train:281-320batch: iter_time=4.233e-05, forward_time=0.035, loss_ctc=2.199, loss=2.199, backward_time=0.008, grad_norm=77.620, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-14 20:01:43,591 (trainer:763) INFO: 15epoch:train:321-360batch: iter_time=4.271e-05, forward_time=0.036, loss_ctc=2.242, loss=2.242, backward_time=0.008, grad_norm=71.472, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 20:01:46,473 (trainer:763) INFO: 15epoch:train:361-400batch: iter_time=4.175e-05, forward_time=0.038, loss_ctc=2.250, loss=2.250, backward_time=0.008, grad_norm=74.725, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.288 +[stan] 2024-01-14 20:01:48,909 (trainer:763) INFO: 15epoch:train:401-440batch: iter_time=4.097e-05, forward_time=0.032, loss_ctc=1.717, loss=1.717, backward_time=0.008, grad_norm=65.927, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-14 20:01:51,490 (trainer:763) INFO: 15epoch:train:441-480batch: iter_time=4.430e-05, forward_time=0.034, loss_ctc=2.087, loss=2.087, backward_time=0.008, grad_norm=77.251, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 20:01:54,247 (trainer:763) INFO: 15epoch:train:481-520batch: iter_time=4.393e-05, forward_time=0.038, loss_ctc=2.155, loss=2.155, backward_time=0.008, grad_norm=71.979, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 20:01:57,059 (trainer:763) INFO: 15epoch:train:521-560batch: iter_time=4.166e-05, forward_time=0.037, loss_ctc=2.152, loss=2.152, backward_time=0.008, grad_norm=71.140, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-14 20:01:59,909 (trainer:763) INFO: 15epoch:train:561-600batch: iter_time=4.202e-05, forward_time=0.038, loss_ctc=2.213, loss=2.213, backward_time=0.008, grad_norm=75.972, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-14 20:02:02,391 (trainer:763) INFO: 15epoch:train:601-640batch: iter_time=4.432e-05, forward_time=0.033, loss_ctc=1.565, loss=1.565, backward_time=0.008, grad_norm=65.961, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 20:02:05,112 (trainer:763) INFO: 15epoch:train:641-680batch: iter_time=4.219e-05, forward_time=0.036, loss_ctc=2.157, loss=2.157, backward_time=0.008, grad_norm=74.524, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 20:02:07,910 (trainer:763) INFO: 15epoch:train:681-720batch: iter_time=4.190e-05, forward_time=0.037, loss_ctc=2.197, loss=2.197, backward_time=0.008, grad_norm=73.164, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 20:02:10,365 (trainer:763) INFO: 15epoch:train:721-760batch: iter_time=4.270e-05, forward_time=0.033, loss_ctc=1.700, loss=1.700, backward_time=0.008, grad_norm=67.210, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-14 20:02:13,166 (trainer:763) INFO: 15epoch:train:761-800batch: iter_time=3.943e-05, forward_time=0.037, loss_ctc=2.248, loss=2.248, backward_time=0.008, grad_norm=73.213, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 20:02:17,133 (trainer:354) INFO: 15epoch results: [train] iter_time=1.747e-04, forward_time=0.036, loss_ctc=2.083, loss=2.083, backward_time=0.008, grad_norm=72.725, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.16 seconds, total_count=12000, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=106.644, cer_ctc=0.320, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=106.644, time=1.14 seconds, total_count=375, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:02:18,062 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:02:18,063 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/14epoch.pth +[stan] 2024-01-14 20:02:18,063 (trainer:288) INFO: 16/30epoch started. Estimated time to finish: 14 minutes and 43.74 seconds +[stan] 2024-01-14 20:02:20,919 (trainer:763) INFO: 16epoch:train:1-40batch: iter_time=0.003, forward_time=0.035, loss_ctc=1.985, loss=1.985, backward_time=0.008, grad_norm=72.925, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-14 20:02:23,547 (trainer:763) INFO: 16epoch:train:41-80batch: iter_time=4.032e-05, forward_time=0.035, loss_ctc=1.843, loss=1.843, backward_time=0.008, grad_norm=71.415, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 20:02:26,377 (trainer:763) INFO: 16epoch:train:81-120batch: iter_time=4.097e-05, forward_time=0.037, loss_ctc=2.137, loss=2.137, backward_time=0.008, grad_norm=71.821, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-14 20:02:29,240 (trainer:763) INFO: 16epoch:train:121-160batch: iter_time=4.015e-05, forward_time=0.038, loss_ctc=2.123, loss=2.123, backward_time=0.008, grad_norm=72.471, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.286 +[stan] 2024-01-14 20:02:31,552 (trainer:763) INFO: 16epoch:train:161-200batch: iter_time=4.055e-05, forward_time=0.031, loss_ctc=1.689, loss=1.689, backward_time=0.008, grad_norm=64.378, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-14 20:02:34,418 (trainer:763) INFO: 16epoch:train:201-240batch: iter_time=4.356e-05, forward_time=0.038, loss_ctc=2.196, loss=2.196, backward_time=0.008, grad_norm=71.570, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.287 +[stan] 2024-01-14 20:02:36,980 (trainer:763) INFO: 16epoch:train:241-280batch: iter_time=4.380e-05, forward_time=0.034, loss_ctc=1.897, loss=1.897, backward_time=0.008, grad_norm=75.263, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-14 20:02:39,846 (trainer:763) INFO: 16epoch:train:281-320batch: iter_time=4.212e-05, forward_time=0.038, loss_ctc=2.346, loss=2.346, backward_time=0.008, grad_norm=78.385, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.287 +[stan] 2024-01-14 20:02:42,458 (trainer:763) INFO: 16epoch:train:321-360batch: iter_time=4.493e-05, forward_time=0.035, loss_ctc=1.840, loss=1.840, backward_time=0.008, grad_norm=68.151, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-14 20:02:45,243 (trainer:763) INFO: 16epoch:train:361-400batch: iter_time=4.154e-05, forward_time=0.037, loss_ctc=2.114, loss=2.114, backward_time=0.008, grad_norm=71.194, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 20:02:48,010 (trainer:763) INFO: 16epoch:train:401-440batch: iter_time=4.167e-05, forward_time=0.037, loss_ctc=2.087, loss=2.087, backward_time=0.008, grad_norm=76.055, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 20:02:50,508 (trainer:763) INFO: 16epoch:train:441-480batch: iter_time=4.164e-05, forward_time=0.033, loss_ctc=1.834, loss=1.834, backward_time=0.008, grad_norm=63.932, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 20:02:53,363 (trainer:763) INFO: 16epoch:train:481-520batch: iter_time=4.005e-05, forward_time=0.038, loss_ctc=2.251, loss=2.251, backward_time=0.008, grad_norm=75.382, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-14 20:02:55,723 (trainer:763) INFO: 16epoch:train:521-560batch: iter_time=4.037e-05, forward_time=0.031, loss_ctc=1.626, loss=1.626, backward_time=0.007, grad_norm=67.869, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-14 20:02:58,552 (trainer:763) INFO: 16epoch:train:561-600batch: iter_time=4.106e-05, forward_time=0.037, loss_ctc=2.367, loss=2.367, backward_time=0.008, grad_norm=75.924, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-14 20:03:01,090 (trainer:763) INFO: 16epoch:train:601-640batch: iter_time=4.180e-05, forward_time=0.034, loss_ctc=1.856, loss=1.856, backward_time=0.008, grad_norm=70.101, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-14 20:03:03,807 (trainer:763) INFO: 16epoch:train:641-680batch: iter_time=4.090e-05, forward_time=0.036, loss_ctc=2.174, loss=2.174, backward_time=0.008, grad_norm=73.165, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 20:03:06,595 (trainer:763) INFO: 16epoch:train:681-720batch: iter_time=4.207e-05, forward_time=0.037, loss_ctc=2.123, loss=2.123, backward_time=0.008, grad_norm=70.196, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-14 20:03:09,169 (trainer:763) INFO: 16epoch:train:721-760batch: iter_time=4.108e-05, forward_time=0.034, loss_ctc=1.998, loss=1.998, backward_time=0.008, grad_norm=75.251, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 20:03:11,770 (trainer:763) INFO: 16epoch:train:761-800batch: iter_time=3.962e-05, forward_time=0.034, loss_ctc=2.019, loss=2.019, backward_time=0.008, grad_norm=72.670, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-14 20:03:15,742 (trainer:354) INFO: 16epoch results: [train] iter_time=1.740e-04, forward_time=0.035, loss_ctc=2.025, loss=2.025, backward_time=0.008, grad_norm=71.906, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268, time=53.78 seconds, total_count=12800, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=107.359, cer_ctc=0.317, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=107.359, time=1.14 seconds, total_count=400, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.75 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:03:16,783 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:03:16,783 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/15epoch.pth +[stan] 2024-01-14 20:03:16,783 (trainer:288) INFO: 17/30epoch started. Estimated time to finish: 13 minutes and 44.65 seconds +[stan] 2024-01-14 20:03:19,895 (trainer:763) INFO: 17epoch:train:1-40batch: iter_time=0.002, forward_time=0.038, loss_ctc=2.151, loss=2.151, backward_time=0.008, grad_norm=74.148, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.311 +[stan] 2024-01-14 20:03:22,644 (trainer:763) INFO: 17epoch:train:41-80batch: iter_time=4.177e-05, forward_time=0.036, loss_ctc=1.877, loss=1.877, backward_time=0.008, grad_norm=67.275, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 20:03:25,414 (trainer:763) INFO: 17epoch:train:81-120batch: iter_time=4.361e-05, forward_time=0.036, loss_ctc=2.002, loss=2.002, backward_time=0.008, grad_norm=70.878, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 20:03:27,934 (trainer:763) INFO: 17epoch:train:121-160batch: iter_time=4.129e-05, forward_time=0.033, loss_ctc=1.847, loss=1.847, backward_time=0.008, grad_norm=70.079, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-14 20:03:30,664 (trainer:763) INFO: 17epoch:train:161-200batch: iter_time=4.079e-05, forward_time=0.036, loss_ctc=1.893, loss=1.893, backward_time=0.008, grad_norm=71.410, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 20:03:33,552 (trainer:763) INFO: 17epoch:train:201-240batch: iter_time=4.236e-05, forward_time=0.038, loss_ctc=2.382, loss=2.382, backward_time=0.008, grad_norm=73.386, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.289 +[stan] 2024-01-14 20:03:35,979 (trainer:763) INFO: 17epoch:train:241-280batch: iter_time=4.417e-05, forward_time=0.032, loss_ctc=1.566, loss=1.566, backward_time=0.008, grad_norm=67.829, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-14 20:03:38,703 (trainer:763) INFO: 17epoch:train:281-320batch: iter_time=4.118e-05, forward_time=0.036, loss_ctc=2.091, loss=2.091, backward_time=0.008, grad_norm=70.661, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 20:03:41,422 (trainer:763) INFO: 17epoch:train:321-360batch: iter_time=4.268e-05, forward_time=0.036, loss_ctc=1.941, loss=1.941, backward_time=0.008, grad_norm=71.301, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 20:03:44,026 (trainer:763) INFO: 17epoch:train:361-400batch: iter_time=4.221e-05, forward_time=0.034, loss_ctc=1.959, loss=1.959, backward_time=0.008, grad_norm=67.778, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-14 20:03:46,589 (trainer:763) INFO: 17epoch:train:401-440batch: iter_time=4.525e-05, forward_time=0.034, loss_ctc=1.806, loss=1.806, backward_time=0.008, grad_norm=67.910, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-14 20:03:49,562 (trainer:763) INFO: 17epoch:train:441-480batch: iter_time=4.236e-05, forward_time=0.039, loss_ctc=2.521, loss=2.521, backward_time=0.009, grad_norm=79.791, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.297 +[stan] 2024-01-14 20:03:52,072 (trainer:763) INFO: 17epoch:train:481-520batch: iter_time=4.217e-05, forward_time=0.033, loss_ctc=1.566, loss=1.566, backward_time=0.008, grad_norm=66.527, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-14 20:03:54,565 (trainer:763) INFO: 17epoch:train:521-560batch: iter_time=4.152e-05, forward_time=0.033, loss_ctc=1.804, loss=1.804, backward_time=0.008, grad_norm=67.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 20:03:57,526 (trainer:763) INFO: 17epoch:train:561-600batch: iter_time=4.181e-05, forward_time=0.039, loss_ctc=2.275, loss=2.275, backward_time=0.009, grad_norm=79.369, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.296 +[stan] 2024-01-14 20:04:00,108 (trainer:763) INFO: 17epoch:train:601-640batch: iter_time=4.073e-05, forward_time=0.034, loss_ctc=1.732, loss=1.732, backward_time=0.008, grad_norm=67.168, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 20:04:02,730 (trainer:763) INFO: 17epoch:train:641-680batch: iter_time=4.077e-05, forward_time=0.035, loss_ctc=2.011, loss=2.011, backward_time=0.008, grad_norm=70.492, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-14 20:04:05,446 (trainer:763) INFO: 17epoch:train:681-720batch: iter_time=4.178e-05, forward_time=0.036, loss_ctc=2.020, loss=2.020, backward_time=0.008, grad_norm=70.656, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 20:04:08,161 (trainer:763) INFO: 17epoch:train:721-760batch: iter_time=4.288e-05, forward_time=0.036, loss_ctc=1.739, loss=1.739, backward_time=0.008, grad_norm=67.890, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 20:04:10,881 (trainer:763) INFO: 17epoch:train:761-800batch: iter_time=3.957e-05, forward_time=0.036, loss_ctc=1.919, loss=1.919, backward_time=0.008, grad_norm=74.690, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 20:04:14,836 (trainer:354) INFO: 17epoch results: [train] iter_time=1.573e-04, forward_time=0.036, loss_ctc=1.955, loss=1.955, backward_time=0.008, grad_norm=70.817, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.18 seconds, total_count=13600, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=106.976, cer_ctc=0.322, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=106.976, time=1.14 seconds, total_count=425, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.73 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:04:15,782 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:04:15,783 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/16epoch.pth +[stan] 2024-01-14 20:04:15,783 (trainer:288) INFO: 18/30epoch started. Estimated time to finish: 12 minutes and 45.82 seconds +[stan] 2024-01-14 20:04:18,594 (trainer:763) INFO: 18epoch:train:1-40batch: iter_time=0.003, forward_time=0.034, loss_ctc=1.829, loss=1.829, backward_time=0.008, grad_norm=66.238, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-14 20:04:21,357 (trainer:763) INFO: 18epoch:train:41-80batch: iter_time=4.083e-05, forward_time=0.036, loss_ctc=2.195, loss=2.195, backward_time=0.008, grad_norm=77.032, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 20:04:24,071 (trainer:763) INFO: 18epoch:train:81-120batch: iter_time=4.133e-05, forward_time=0.036, loss_ctc=1.904, loss=1.904, backward_time=0.008, grad_norm=68.433, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 20:04:26,586 (trainer:763) INFO: 18epoch:train:121-160batch: iter_time=4.142e-05, forward_time=0.033, loss_ctc=1.759, loss=1.759, backward_time=0.008, grad_norm=66.617, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-14 20:04:29,408 (trainer:763) INFO: 18epoch:train:161-200batch: iter_time=4.188e-05, forward_time=0.037, loss_ctc=2.009, loss=2.009, backward_time=0.008, grad_norm=69.372, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-14 20:04:31,955 (trainer:763) INFO: 18epoch:train:201-240batch: iter_time=4.165e-05, forward_time=0.034, loss_ctc=1.658, loss=1.658, backward_time=0.008, grad_norm=65.593, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 20:04:34,688 (trainer:763) INFO: 18epoch:train:241-280batch: iter_time=4.096e-05, forward_time=0.036, loss_ctc=2.021, loss=2.021, backward_time=0.008, grad_norm=71.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 20:04:37,462 (trainer:763) INFO: 18epoch:train:281-320batch: iter_time=4.267e-05, forward_time=0.037, loss_ctc=1.970, loss=1.970, backward_time=0.008, grad_norm=68.965, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 20:04:40,226 (trainer:763) INFO: 18epoch:train:321-360batch: iter_time=4.228e-05, forward_time=0.036, loss_ctc=1.938, loss=1.938, backward_time=0.008, grad_norm=71.995, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 20:04:42,665 (trainer:763) INFO: 18epoch:train:361-400batch: iter_time=4.032e-05, forward_time=0.032, loss_ctc=1.575, loss=1.575, backward_time=0.008, grad_norm=64.571, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 20:04:45,515 (trainer:763) INFO: 18epoch:train:401-440batch: iter_time=4.225e-05, forward_time=0.038, loss_ctc=2.053, loss=2.053, backward_time=0.008, grad_norm=75.893, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-14 20:04:48,355 (trainer:763) INFO: 18epoch:train:441-480batch: iter_time=4.182e-05, forward_time=0.037, loss_ctc=1.930, loss=1.930, backward_time=0.008, grad_norm=68.648, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.284 +[stan] 2024-01-14 20:04:50,932 (trainer:763) INFO: 18epoch:train:481-520batch: iter_time=4.197e-05, forward_time=0.034, loss_ctc=1.635, loss=1.635, backward_time=0.008, grad_norm=62.616, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 20:04:53,559 (trainer:763) INFO: 18epoch:train:521-560batch: iter_time=4.031e-05, forward_time=0.035, loss_ctc=1.700, loss=1.700, backward_time=0.008, grad_norm=65.839, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 20:04:56,334 (trainer:763) INFO: 18epoch:train:561-600batch: iter_time=4.283e-05, forward_time=0.037, loss_ctc=1.803, loss=1.803, backward_time=0.008, grad_norm=70.654, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 20:04:58,873 (trainer:763) INFO: 18epoch:train:601-640batch: iter_time=4.068e-05, forward_time=0.034, loss_ctc=1.800, loss=1.800, backward_time=0.008, grad_norm=68.976, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-14 20:05:01,614 (trainer:763) INFO: 18epoch:train:641-680batch: iter_time=4.056e-05, forward_time=0.036, loss_ctc=1.959, loss=1.959, backward_time=0.008, grad_norm=73.000, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 20:05:04,364 (trainer:763) INFO: 18epoch:train:681-720batch: iter_time=4.145e-05, forward_time=0.036, loss_ctc=1.755, loss=1.755, backward_time=0.008, grad_norm=69.596, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 20:05:06,907 (trainer:763) INFO: 18epoch:train:721-760batch: iter_time=4.303e-05, forward_time=0.034, loss_ctc=1.758, loss=1.758, backward_time=0.008, grad_norm=68.770, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-14 20:05:09,674 (trainer:763) INFO: 18epoch:train:761-800batch: iter_time=4.071e-05, forward_time=0.037, loss_ctc=1.849, loss=1.849, backward_time=0.008, grad_norm=70.745, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 20:05:13,661 (trainer:354) INFO: 18epoch results: [train] iter_time=1.752e-04, forward_time=0.035, loss_ctc=1.855, loss=1.855, backward_time=0.008, grad_norm=69.234, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.97 seconds, total_count=14400, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=108.829, cer_ctc=0.317, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=108.829, time=1.14 seconds, total_count=450, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.77 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:05:14,624 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:05:14,624 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/17epoch.pth +[stan] 2024-01-14 20:05:14,624 (trainer:288) INFO: 19/30epoch started. Estimated time to finish: 11 minutes and 46.87 seconds +[stan] 2024-01-14 20:05:17,620 (trainer:763) INFO: 19epoch:train:1-40batch: iter_time=0.003, forward_time=0.036, loss_ctc=2.153, loss=2.153, backward_time=0.008, grad_norm=76.635, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.299 +[stan] 2024-01-14 20:05:20,174 (trainer:763) INFO: 19epoch:train:41-80batch: iter_time=4.014e-05, forward_time=0.034, loss_ctc=1.734, loss=1.734, backward_time=0.008, grad_norm=71.991, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 20:05:22,916 (trainer:763) INFO: 19epoch:train:81-120batch: iter_time=4.119e-05, forward_time=0.036, loss_ctc=1.883, loss=1.883, backward_time=0.008, grad_norm=70.941, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 20:05:25,579 (trainer:763) INFO: 19epoch:train:121-160batch: iter_time=4.266e-05, forward_time=0.035, loss_ctc=1.855, loss=1.855, backward_time=0.008, grad_norm=68.407, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-14 20:05:28,376 (trainer:763) INFO: 19epoch:train:161-200batch: iter_time=4.122e-05, forward_time=0.037, loss_ctc=1.890, loss=1.890, backward_time=0.008, grad_norm=71.763, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 20:05:30,968 (trainer:763) INFO: 19epoch:train:201-240batch: iter_time=4.073e-05, forward_time=0.034, loss_ctc=1.847, loss=1.847, backward_time=0.008, grad_norm=69.478, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-14 20:05:33,575 (trainer:763) INFO: 19epoch:train:241-280batch: iter_time=4.171e-05, forward_time=0.035, loss_ctc=1.786, loss=1.786, backward_time=0.008, grad_norm=68.453, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-14 20:05:36,497 (trainer:763) INFO: 19epoch:train:281-320batch: iter_time=4.229e-05, forward_time=0.038, loss_ctc=2.050, loss=2.050, backward_time=0.009, grad_norm=72.401, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.292 +[stan] 2024-01-14 20:05:39,014 (trainer:763) INFO: 19epoch:train:321-360batch: iter_time=4.088e-05, forward_time=0.033, loss_ctc=1.475, loss=1.475, backward_time=0.008, grad_norm=61.500, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-14 20:05:41,779 (trainer:763) INFO: 19epoch:train:361-400batch: iter_time=4.100e-05, forward_time=0.036, loss_ctc=1.964, loss=1.964, backward_time=0.008, grad_norm=71.075, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 20:05:44,262 (trainer:763) INFO: 19epoch:train:401-440batch: iter_time=4.166e-05, forward_time=0.033, loss_ctc=1.529, loss=1.529, backward_time=0.008, grad_norm=64.462, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 20:05:47,060 (trainer:763) INFO: 19epoch:train:441-480batch: iter_time=4.278e-05, forward_time=0.037, loss_ctc=1.869, loss=1.869, backward_time=0.008, grad_norm=71.918, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 20:05:49,796 (trainer:763) INFO: 19epoch:train:481-520batch: iter_time=4.124e-05, forward_time=0.036, loss_ctc=1.935, loss=1.935, backward_time=0.008, grad_norm=71.632, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 20:05:52,540 (trainer:763) INFO: 19epoch:train:521-560batch: iter_time=4.124e-05, forward_time=0.036, loss_ctc=1.985, loss=1.985, backward_time=0.008, grad_norm=68.039, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 20:05:55,103 (trainer:763) INFO: 19epoch:train:561-600batch: iter_time=4.186e-05, forward_time=0.034, loss_ctc=1.682, loss=1.682, backward_time=0.008, grad_norm=64.497, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-14 20:05:57,575 (trainer:763) INFO: 19epoch:train:601-640batch: iter_time=4.032e-05, forward_time=0.033, loss_ctc=1.503, loss=1.503, backward_time=0.008, grad_norm=62.743, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-14 20:06:00,358 (trainer:763) INFO: 19epoch:train:641-680batch: iter_time=4.077e-05, forward_time=0.037, loss_ctc=1.969, loss=1.969, backward_time=0.008, grad_norm=73.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 20:06:03,149 (trainer:763) INFO: 19epoch:train:681-720batch: iter_time=4.108e-05, forward_time=0.037, loss_ctc=1.864, loss=1.864, backward_time=0.008, grad_norm=65.725, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-14 20:06:05,874 (trainer:763) INFO: 19epoch:train:721-760batch: iter_time=4.184e-05, forward_time=0.036, loss_ctc=1.691, loss=1.691, backward_time=0.008, grad_norm=64.709, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 20:06:08,378 (trainer:763) INFO: 19epoch:train:761-800batch: iter_time=3.976e-05, forward_time=0.033, loss_ctc=1.730, loss=1.730, backward_time=0.008, grad_norm=66.752, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 20:06:12,369 (trainer:354) INFO: 19epoch results: [train] iter_time=1.658e-04, forward_time=0.035, loss_ctc=1.820, loss=1.820, backward_time=0.008, grad_norm=68.813, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.84 seconds, total_count=15200, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=108.440, cer_ctc=0.311, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=108.440, time=1.14 seconds, total_count=475, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.77 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:06:13,419 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:06:13,419 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/18epoch.pth +[stan] 2024-01-14 20:06:13,419 (trainer:288) INFO: 20/30epoch started. Estimated time to finish: 10 minutes and 47.9 seconds +[stan] 2024-01-14 20:06:16,479 (trainer:763) INFO: 20epoch:train:1-40batch: iter_time=0.002, forward_time=0.037, loss_ctc=1.837, loss=1.837, backward_time=0.008, grad_norm=70.354, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.305 +[stan] 2024-01-14 20:06:19,330 (trainer:763) INFO: 20epoch:train:41-80batch: iter_time=4.216e-05, forward_time=0.038, loss_ctc=1.846, loss=1.846, backward_time=0.008, grad_norm=67.127, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-14 20:06:21,622 (trainer:763) INFO: 20epoch:train:81-120batch: iter_time=4.109e-05, forward_time=0.031, loss_ctc=1.380, loss=1.380, backward_time=0.008, grad_norm=57.703, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-14 20:06:24,516 (trainer:763) INFO: 20epoch:train:121-160batch: iter_time=4.358e-05, forward_time=0.038, loss_ctc=2.008, loss=2.008, backward_time=0.008, grad_norm=69.836, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.289 +[stan] 2024-01-14 20:06:27,297 (trainer:763) INFO: 20epoch:train:161-200batch: iter_time=4.201e-05, forward_time=0.037, loss_ctc=1.885, loss=1.885, backward_time=0.008, grad_norm=66.821, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 20:06:29,715 (trainer:763) INFO: 20epoch:train:201-240batch: iter_time=4.136e-05, forward_time=0.032, loss_ctc=1.487, loss=1.487, backward_time=0.008, grad_norm=63.454, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-14 20:06:32,556 (trainer:763) INFO: 20epoch:train:241-280batch: iter_time=4.109e-05, forward_time=0.037, loss_ctc=1.916, loss=1.916, backward_time=0.008, grad_norm=69.406, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.284 +[stan] 2024-01-14 20:06:35,057 (trainer:763) INFO: 20epoch:train:281-320batch: iter_time=4.078e-05, forward_time=0.033, loss_ctc=1.581, loss=1.581, backward_time=0.008, grad_norm=64.271, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 20:06:37,753 (trainer:763) INFO: 20epoch:train:321-360batch: iter_time=4.389e-05, forward_time=0.036, loss_ctc=1.776, loss=1.776, backward_time=0.008, grad_norm=69.112, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-14 20:06:40,603 (trainer:763) INFO: 20epoch:train:361-400batch: iter_time=4.133e-05, forward_time=0.038, loss_ctc=1.948, loss=1.948, backward_time=0.008, grad_norm=70.831, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-14 20:06:43,049 (trainer:763) INFO: 20epoch:train:401-440batch: iter_time=4.136e-05, forward_time=0.032, loss_ctc=1.530, loss=1.530, backward_time=0.008, grad_norm=65.797, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-14 20:06:46,110 (trainer:763) INFO: 20epoch:train:441-480batch: iter_time=4.149e-05, forward_time=0.040, loss_ctc=2.044, loss=2.044, backward_time=0.009, grad_norm=71.389, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.306 +[stan] 2024-01-14 20:06:48,646 (trainer:763) INFO: 20epoch:train:481-520batch: iter_time=4.153e-05, forward_time=0.034, loss_ctc=1.450, loss=1.450, backward_time=0.008, grad_norm=62.615, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-14 20:06:51,138 (trainer:763) INFO: 20epoch:train:521-560batch: iter_time=4.347e-05, forward_time=0.033, loss_ctc=1.417, loss=1.417, backward_time=0.008, grad_norm=60.870, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 20:06:53,826 (trainer:763) INFO: 20epoch:train:561-600batch: iter_time=4.170e-05, forward_time=0.036, loss_ctc=1.734, loss=1.734, backward_time=0.008, grad_norm=71.526, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-14 20:06:56,697 (trainer:763) INFO: 20epoch:train:601-640batch: iter_time=4.174e-05, forward_time=0.038, loss_ctc=1.837, loss=1.837, backward_time=0.008, grad_norm=69.162, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.287 +[stan] 2024-01-14 20:06:59,336 (trainer:763) INFO: 20epoch:train:641-680batch: iter_time=4.562e-05, forward_time=0.035, loss_ctc=1.654, loss=1.654, backward_time=0.008, grad_norm=68.375, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-14 20:07:02,024 (trainer:763) INFO: 20epoch:train:681-720batch: iter_time=4.076e-05, forward_time=0.036, loss_ctc=1.797, loss=1.797, backward_time=0.008, grad_norm=68.326, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-14 20:07:04,743 (trainer:763) INFO: 20epoch:train:721-760batch: iter_time=4.119e-05, forward_time=0.036, loss_ctc=1.960, loss=1.960, backward_time=0.008, grad_norm=74.000, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 20:07:07,385 (trainer:763) INFO: 20epoch:train:761-800batch: iter_time=4.018e-05, forward_time=0.035, loss_ctc=1.627, loss=1.627, backward_time=0.008, grad_norm=61.777, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-14 20:07:11,338 (trainer:354) INFO: 20epoch results: [train] iter_time=1.556e-04, forward_time=0.035, loss_ctc=1.736, loss=1.736, backward_time=0.008, grad_norm=67.138, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.04 seconds, total_count=16000, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=111.439, cer_ctc=0.318, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=111.439, time=1.13 seconds, total_count=500, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.75 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:07:12,254 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:07:12,254 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/19epoch.pth +[stan] 2024-01-14 20:07:12,254 (trainer:288) INFO: 21/30epoch started. Estimated time to finish: 9 minutes and 48.96 seconds +[stan] 2024-01-14 20:07:15,190 (trainer:763) INFO: 21epoch:train:1-40batch: iter_time=0.003, forward_time=0.035, loss_ctc=1.883, loss=1.883, backward_time=0.008, grad_norm=69.049, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.293 +[stan] 2024-01-14 20:07:17,950 (trainer:763) INFO: 21epoch:train:41-80batch: iter_time=4.371e-05, forward_time=0.036, loss_ctc=1.935, loss=1.935, backward_time=0.008, grad_norm=72.922, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 20:07:20,621 (trainer:763) INFO: 21epoch:train:81-120batch: iter_time=4.192e-05, forward_time=0.035, loss_ctc=1.793, loss=1.793, backward_time=0.008, grad_norm=68.737, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-14 20:07:23,242 (trainer:763) INFO: 21epoch:train:121-160batch: iter_time=4.476e-05, forward_time=0.035, loss_ctc=1.773, loss=1.773, backward_time=0.008, grad_norm=64.848, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-14 20:07:25,997 (trainer:763) INFO: 21epoch:train:161-200batch: iter_time=4.118e-05, forward_time=0.036, loss_ctc=1.695, loss=1.695, backward_time=0.008, grad_norm=63.774, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 20:07:28,819 (trainer:763) INFO: 21epoch:train:201-240batch: iter_time=4.185e-05, forward_time=0.037, loss_ctc=1.981, loss=1.981, backward_time=0.008, grad_norm=71.911, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-14 20:07:31,234 (trainer:763) INFO: 21epoch:train:241-280batch: iter_time=4.044e-05, forward_time=0.032, loss_ctc=1.354, loss=1.354, backward_time=0.008, grad_norm=58.828, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-14 20:07:34,036 (trainer:763) INFO: 21epoch:train:281-320batch: iter_time=4.074e-05, forward_time=0.037, loss_ctc=1.915, loss=1.915, backward_time=0.008, grad_norm=73.065, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 20:07:36,798 (trainer:763) INFO: 21epoch:train:321-360batch: iter_time=4.689e-05, forward_time=0.036, loss_ctc=1.830, loss=1.830, backward_time=0.008, grad_norm=69.495, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 20:07:39,558 (trainer:763) INFO: 21epoch:train:361-400batch: iter_time=4.140e-05, forward_time=0.036, loss_ctc=1.532, loss=1.532, backward_time=0.008, grad_norm=63.019, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 20:07:42,087 (trainer:763) INFO: 21epoch:train:401-440batch: iter_time=4.169e-05, forward_time=0.034, loss_ctc=1.553, loss=1.553, backward_time=0.008, grad_norm=66.898, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-14 20:07:44,619 (trainer:763) INFO: 21epoch:train:441-480batch: iter_time=4.188e-05, forward_time=0.034, loss_ctc=1.479, loss=1.479, backward_time=0.008, grad_norm=62.952, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-14 20:07:47,378 (trainer:763) INFO: 21epoch:train:481-520batch: iter_time=4.129e-05, forward_time=0.036, loss_ctc=1.669, loss=1.669, backward_time=0.008, grad_norm=63.850, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 20:07:50,139 (trainer:763) INFO: 21epoch:train:521-560batch: iter_time=4.248e-05, forward_time=0.036, loss_ctc=1.738, loss=1.738, backward_time=0.008, grad_norm=68.629, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 20:07:52,956 (trainer:763) INFO: 21epoch:train:561-600batch: iter_time=4.103e-05, forward_time=0.037, loss_ctc=1.843, loss=1.843, backward_time=0.008, grad_norm=67.129, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-14 20:07:55,583 (trainer:763) INFO: 21epoch:train:601-640batch: iter_time=4.090e-05, forward_time=0.035, loss_ctc=1.648, loss=1.648, backward_time=0.008, grad_norm=64.159, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 20:07:58,187 (trainer:763) INFO: 21epoch:train:641-680batch: iter_time=4.224e-05, forward_time=0.034, loss_ctc=1.709, loss=1.709, backward_time=0.008, grad_norm=68.478, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-14 20:08:01,091 (trainer:763) INFO: 21epoch:train:681-720batch: iter_time=4.229e-05, forward_time=0.039, loss_ctc=1.797, loss=1.797, backward_time=0.008, grad_norm=67.975, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.290 +[stan] 2024-01-14 20:08:03,887 (trainer:763) INFO: 21epoch:train:721-760batch: iter_time=4.638e-05, forward_time=0.037, loss_ctc=1.843, loss=1.843, backward_time=0.008, grad_norm=68.449, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 20:08:06,328 (trainer:763) INFO: 21epoch:train:761-800batch: iter_time=3.938e-05, forward_time=0.032, loss_ctc=1.318, loss=1.318, backward_time=0.008, grad_norm=59.435, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 20:08:10,312 (trainer:354) INFO: 21epoch results: [train] iter_time=2.019e-04, forward_time=0.036, loss_ctc=1.714, loss=1.714, backward_time=0.008, grad_norm=66.680, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.15 seconds, total_count=16800, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=110.879, cer_ctc=0.320, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=110.879, time=1.14 seconds, total_count=525, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.77 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:08:11,257 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:08:11,257 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/20epoch.pth +[stan] 2024-01-14 20:08:11,257 (trainer:288) INFO: 22/30epoch started. Estimated time to finish: 8 minutes and 50.11 seconds +[stan] 2024-01-14 20:08:14,344 (trainer:763) INFO: 22epoch:train:1-40batch: iter_time=0.003, forward_time=0.037, loss_ctc=1.695, loss=1.695, backward_time=0.008, grad_norm=66.914, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.308 +[stan] 2024-01-14 20:08:16,821 (trainer:763) INFO: 22epoch:train:41-80batch: iter_time=4.080e-05, forward_time=0.033, loss_ctc=1.525, loss=1.525, backward_time=0.008, grad_norm=65.356, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 20:08:19,571 (trainer:763) INFO: 22epoch:train:81-120batch: iter_time=4.145e-05, forward_time=0.036, loss_ctc=1.730, loss=1.730, backward_time=0.008, grad_norm=64.798, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 20:08:22,268 (trainer:763) INFO: 22epoch:train:121-160batch: iter_time=4.047e-05, forward_time=0.036, loss_ctc=1.742, loss=1.742, backward_time=0.008, grad_norm=68.868, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-14 20:08:24,898 (trainer:763) INFO: 22epoch:train:161-200batch: iter_time=4.165e-05, forward_time=0.035, loss_ctc=1.438, loss=1.438, backward_time=0.008, grad_norm=63.172, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 20:08:27,612 (trainer:763) INFO: 22epoch:train:201-240batch: iter_time=4.161e-05, forward_time=0.036, loss_ctc=1.592, loss=1.592, backward_time=0.008, grad_norm=67.904, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 20:08:29,950 (trainer:763) INFO: 22epoch:train:241-280batch: iter_time=4.084e-05, forward_time=0.031, loss_ctc=1.306, loss=1.306, backward_time=0.008, grad_norm=61.241, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-14 20:08:33,153 (trainer:763) INFO: 22epoch:train:281-320batch: iter_time=4.071e-05, forward_time=0.042, loss_ctc=1.945, loss=1.945, backward_time=0.009, grad_norm=72.662, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.320 +[stan] 2024-01-14 20:08:35,667 (trainer:763) INFO: 22epoch:train:321-360batch: iter_time=4.600e-05, forward_time=0.033, loss_ctc=1.419, loss=1.419, backward_time=0.008, grad_norm=60.279, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-14 20:08:38,467 (trainer:763) INFO: 22epoch:train:361-400batch: iter_time=4.314e-05, forward_time=0.037, loss_ctc=1.787, loss=1.787, backward_time=0.008, grad_norm=71.058, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 20:08:41,079 (trainer:763) INFO: 22epoch:train:401-440batch: iter_time=4.216e-05, forward_time=0.035, loss_ctc=1.812, loss=1.812, backward_time=0.008, grad_norm=66.342, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-14 20:08:43,712 (trainer:763) INFO: 22epoch:train:441-480batch: iter_time=4.106e-05, forward_time=0.035, loss_ctc=1.669, loss=1.669, backward_time=0.008, grad_norm=66.133, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 20:08:46,198 (trainer:763) INFO: 22epoch:train:481-520batch: iter_time=4.107e-05, forward_time=0.033, loss_ctc=1.461, loss=1.461, backward_time=0.008, grad_norm=64.607, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 20:08:48,939 (trainer:763) INFO: 22epoch:train:521-560batch: iter_time=4.103e-05, forward_time=0.036, loss_ctc=1.825, loss=1.825, backward_time=0.008, grad_norm=65.074, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 20:08:51,763 (trainer:763) INFO: 22epoch:train:561-600batch: iter_time=4.182e-05, forward_time=0.037, loss_ctc=1.717, loss=1.717, backward_time=0.008, grad_norm=66.775, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-14 20:08:54,314 (trainer:763) INFO: 22epoch:train:601-640batch: iter_time=4.101e-05, forward_time=0.034, loss_ctc=1.480, loss=1.480, backward_time=0.008, grad_norm=59.600, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 20:08:57,068 (trainer:763) INFO: 22epoch:train:641-680batch: iter_time=4.230e-05, forward_time=0.036, loss_ctc=1.674, loss=1.674, backward_time=0.008, grad_norm=67.172, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 20:08:59,828 (trainer:763) INFO: 22epoch:train:681-720batch: iter_time=4.413e-05, forward_time=0.036, loss_ctc=1.699, loss=1.699, backward_time=0.008, grad_norm=63.519, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 20:09:02,473 (trainer:763) INFO: 22epoch:train:721-760batch: iter_time=4.093e-05, forward_time=0.035, loss_ctc=1.595, loss=1.595, backward_time=0.008, grad_norm=62.679, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-14 20:09:05,109 (trainer:763) INFO: 22epoch:train:761-800batch: iter_time=3.855e-05, forward_time=0.035, loss_ctc=1.511, loss=1.511, backward_time=0.008, grad_norm=60.121, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 20:09:09,101 (trainer:354) INFO: 22epoch results: [train] iter_time=1.672e-04, forward_time=0.035, loss_ctc=1.631, loss=1.631, backward_time=0.008, grad_norm=65.214, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.93 seconds, total_count=17600, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=112.831, cer_ctc=0.321, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=112.831, time=1.15 seconds, total_count=550, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.77 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:09:10,137 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:09:10,137 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/21epoch.pth +[stan] 2024-01-14 20:09:10,138 (trainer:288) INFO: 23/30epoch started. Estimated time to finish: 7 minutes and 51.2 seconds +[stan] 2024-01-14 20:09:13,164 (trainer:763) INFO: 23epoch:train:1-40batch: iter_time=0.003, forward_time=0.037, loss_ctc=1.680, loss=1.680, backward_time=0.008, grad_norm=68.036, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.302 +[stan] 2024-01-14 20:09:16,004 (trainer:763) INFO: 23epoch:train:41-80batch: iter_time=4.115e-05, forward_time=0.037, loss_ctc=1.885, loss=1.885, backward_time=0.008, grad_norm=68.265, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.284 +[stan] 2024-01-14 20:09:18,742 (trainer:763) INFO: 23epoch:train:81-120batch: iter_time=4.104e-05, forward_time=0.036, loss_ctc=1.699, loss=1.699, backward_time=0.008, grad_norm=63.571, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 20:09:21,212 (trainer:763) INFO: 23epoch:train:121-160batch: iter_time=4.118e-05, forward_time=0.033, loss_ctc=1.498, loss=1.498, backward_time=0.008, grad_norm=63.076, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-14 20:09:23,811 (trainer:763) INFO: 23epoch:train:161-200batch: iter_time=4.137e-05, forward_time=0.034, loss_ctc=1.394, loss=1.394, backward_time=0.008, grad_norm=62.234, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-14 20:09:26,557 (trainer:763) INFO: 23epoch:train:201-240batch: iter_time=4.257e-05, forward_time=0.036, loss_ctc=1.599, loss=1.599, backward_time=0.008, grad_norm=62.967, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 20:09:29,268 (trainer:763) INFO: 23epoch:train:241-280batch: iter_time=4.082e-05, forward_time=0.036, loss_ctc=1.569, loss=1.569, backward_time=0.008, grad_norm=63.284, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 20:09:31,861 (trainer:763) INFO: 23epoch:train:281-320batch: iter_time=4.208e-05, forward_time=0.034, loss_ctc=1.499, loss=1.499, backward_time=0.008, grad_norm=64.587, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-14 20:09:34,840 (trainer:763) INFO: 23epoch:train:321-360batch: iter_time=4.285e-05, forward_time=0.039, loss_ctc=1.774, loss=1.774, backward_time=0.009, grad_norm=69.108, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.298 +[stan] 2024-01-14 20:09:37,309 (trainer:763) INFO: 23epoch:train:361-400batch: iter_time=4.425e-05, forward_time=0.033, loss_ctc=1.513, loss=1.513, backward_time=0.008, grad_norm=64.890, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-14 20:09:40,108 (trainer:763) INFO: 23epoch:train:401-440batch: iter_time=4.215e-05, forward_time=0.037, loss_ctc=1.498, loss=1.498, backward_time=0.008, grad_norm=63.791, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 20:09:42,504 (trainer:763) INFO: 23epoch:train:441-480batch: iter_time=4.188e-05, forward_time=0.032, loss_ctc=1.464, loss=1.464, backward_time=0.008, grad_norm=59.257, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-14 20:09:45,353 (trainer:763) INFO: 23epoch:train:481-520batch: iter_time=4.146e-05, forward_time=0.038, loss_ctc=1.725, loss=1.725, backward_time=0.008, grad_norm=63.796, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-14 20:09:47,830 (trainer:763) INFO: 23epoch:train:521-560batch: iter_time=4.082e-05, forward_time=0.033, loss_ctc=1.347, loss=1.347, backward_time=0.008, grad_norm=58.965, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 20:09:50,578 (trainer:763) INFO: 23epoch:train:561-600batch: iter_time=4.144e-05, forward_time=0.036, loss_ctc=1.757, loss=1.757, backward_time=0.008, grad_norm=67.003, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 20:09:53,397 (trainer:763) INFO: 23epoch:train:601-640batch: iter_time=4.166e-05, forward_time=0.037, loss_ctc=1.789, loss=1.789, backward_time=0.008, grad_norm=68.504, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-14 20:09:56,197 (trainer:763) INFO: 23epoch:train:641-680batch: iter_time=4.455e-05, forward_time=0.037, loss_ctc=1.748, loss=1.748, backward_time=0.008, grad_norm=69.296, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 20:09:59,053 (trainer:763) INFO: 23epoch:train:681-720batch: iter_time=4.280e-05, forward_time=0.038, loss_ctc=1.710, loss=1.710, backward_time=0.008, grad_norm=67.994, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.286 +[stan] 2024-01-14 20:10:01,448 (trainer:763) INFO: 23epoch:train:721-760batch: iter_time=4.176e-05, forward_time=0.032, loss_ctc=1.412, loss=1.412, backward_time=0.008, grad_norm=61.718, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-14 20:10:03,998 (trainer:763) INFO: 23epoch:train:761-800batch: iter_time=3.979e-05, forward_time=0.034, loss_ctc=1.500, loss=1.500, backward_time=0.008, grad_norm=63.378, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 20:10:07,969 (trainer:354) INFO: 23epoch results: [train] iter_time=1.766e-04, forward_time=0.035, loss_ctc=1.603, loss=1.603, backward_time=0.008, grad_norm=64.686, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.94 seconds, total_count=18400, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=112.219, cer_ctc=0.321, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=112.219, time=1.14 seconds, total_count=575, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.75 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:10:08,942 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:10:08,943 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/22epoch.pth +[stan] 2024-01-14 20:10:08,943 (trainer:288) INFO: 24/30epoch started. Estimated time to finish: 6 minutes and 52.28 seconds +[stan] 2024-01-14 20:10:12,221 (trainer:763) INFO: 24epoch:train:1-40batch: iter_time=0.003, forward_time=0.039, loss_ctc=1.814, loss=1.814, backward_time=0.009, grad_norm=69.957, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.327 +[stan] 2024-01-14 20:10:14,712 (trainer:763) INFO: 24epoch:train:41-80batch: iter_time=4.320e-05, forward_time=0.033, loss_ctc=1.421, loss=1.421, backward_time=0.008, grad_norm=59.088, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 20:10:17,410 (trainer:763) INFO: 24epoch:train:81-120batch: iter_time=4.115e-05, forward_time=0.036, loss_ctc=1.452, loss=1.452, backward_time=0.008, grad_norm=66.484, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-14 20:10:19,989 (trainer:763) INFO: 24epoch:train:121-160batch: iter_time=4.109e-05, forward_time=0.034, loss_ctc=1.286, loss=1.286, backward_time=0.008, grad_norm=59.772, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 20:10:22,755 (trainer:763) INFO: 24epoch:train:161-200batch: iter_time=4.128e-05, forward_time=0.036, loss_ctc=1.498, loss=1.498, backward_time=0.008, grad_norm=58.909, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 20:10:25,531 (trainer:763) INFO: 24epoch:train:201-240batch: iter_time=4.191e-05, forward_time=0.037, loss_ctc=1.607, loss=1.607, backward_time=0.008, grad_norm=64.339, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 20:10:28,123 (trainer:763) INFO: 24epoch:train:241-280batch: iter_time=4.109e-05, forward_time=0.034, loss_ctc=1.306, loss=1.306, backward_time=0.008, grad_norm=59.300, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-14 20:10:30,796 (trainer:763) INFO: 24epoch:train:281-320batch: iter_time=4.239e-05, forward_time=0.035, loss_ctc=1.508, loss=1.508, backward_time=0.008, grad_norm=62.803, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-14 20:10:33,726 (trainer:763) INFO: 24epoch:train:321-360batch: iter_time=4.126e-05, forward_time=0.040, loss_ctc=1.799, loss=1.799, backward_time=0.008, grad_norm=63.003, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.293 +[stan] 2024-01-14 20:10:36,417 (trainer:763) INFO: 24epoch:train:361-400batch: iter_time=4.098e-05, forward_time=0.036, loss_ctc=1.697, loss=1.697, backward_time=0.008, grad_norm=66.543, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-14 20:10:38,958 (trainer:763) INFO: 24epoch:train:401-440batch: iter_time=4.143e-05, forward_time=0.034, loss_ctc=1.396, loss=1.396, backward_time=0.008, grad_norm=57.659, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-14 20:10:41,520 (trainer:763) INFO: 24epoch:train:441-480batch: iter_time=4.129e-05, forward_time=0.034, loss_ctc=1.416, loss=1.416, backward_time=0.008, grad_norm=61.609, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-14 20:10:44,521 (trainer:763) INFO: 24epoch:train:481-520batch: iter_time=4.053e-05, forward_time=0.039, loss_ctc=1.689, loss=1.689, backward_time=0.009, grad_norm=65.881, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.300 +[stan] 2024-01-14 20:10:47,004 (trainer:763) INFO: 24epoch:train:521-560batch: iter_time=4.061e-05, forward_time=0.033, loss_ctc=1.362, loss=1.362, backward_time=0.008, grad_norm=63.745, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 20:10:49,784 (trainer:763) INFO: 24epoch:train:561-600batch: iter_time=4.165e-05, forward_time=0.037, loss_ctc=1.658, loss=1.658, backward_time=0.008, grad_norm=67.890, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 20:10:52,258 (trainer:763) INFO: 24epoch:train:601-640batch: iter_time=4.073e-05, forward_time=0.033, loss_ctc=1.491, loss=1.491, backward_time=0.008, grad_norm=63.347, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-14 20:10:55,049 (trainer:763) INFO: 24epoch:train:641-680batch: iter_time=4.041e-05, forward_time=0.037, loss_ctc=1.534, loss=1.534, backward_time=0.008, grad_norm=62.250, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-14 20:10:57,804 (trainer:763) INFO: 24epoch:train:681-720batch: iter_time=4.088e-05, forward_time=0.036, loss_ctc=1.585, loss=1.585, backward_time=0.008, grad_norm=62.610, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 20:11:00,587 (trainer:763) INFO: 24epoch:train:721-760batch: iter_time=4.047e-05, forward_time=0.037, loss_ctc=1.657, loss=1.657, backward_time=0.008, grad_norm=62.009, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 20:11:03,102 (trainer:763) INFO: 24epoch:train:761-800batch: iter_time=3.948e-05, forward_time=0.033, loss_ctc=1.297, loss=1.297, backward_time=0.008, grad_norm=60.982, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-14 20:11:07,073 (trainer:354) INFO: 24epoch results: [train] iter_time=2.094e-04, forward_time=0.036, loss_ctc=1.524, loss=1.524, backward_time=0.008, grad_norm=62.909, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271, time=54.23 seconds, total_count=19200, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=112.134, cer_ctc=0.326, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=112.134, time=1.14 seconds, total_count=600, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.75 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:11:08,045 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:11:08,045 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/23epoch.pth +[stan] 2024-01-14 20:11:08,045 (trainer:288) INFO: 25/30epoch started. Estimated time to finish: 5 minutes and 53.43 seconds +[stan] 2024-01-14 20:11:10,757 (trainer:763) INFO: 25epoch:train:1-40batch: iter_time=0.003, forward_time=0.033, loss_ctc=1.416, loss=1.416, backward_time=0.008, grad_norm=57.416, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 20:11:13,813 (trainer:763) INFO: 25epoch:train:41-80batch: iter_time=4.238e-05, forward_time=0.040, loss_ctc=1.761, loss=1.761, backward_time=0.009, grad_norm=66.804, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.306 +[stan] 2024-01-14 20:11:16,336 (trainer:763) INFO: 25epoch:train:81-120batch: iter_time=4.144e-05, forward_time=0.033, loss_ctc=1.267, loss=1.267, backward_time=0.008, grad_norm=57.640, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-14 20:11:18,909 (trainer:763) INFO: 25epoch:train:121-160batch: iter_time=4.140e-05, forward_time=0.034, loss_ctc=1.469, loss=1.469, backward_time=0.008, grad_norm=60.580, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 20:11:21,852 (trainer:763) INFO: 25epoch:train:161-200batch: iter_time=4.112e-05, forward_time=0.039, loss_ctc=1.766, loss=1.766, backward_time=0.009, grad_norm=66.174, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.294 +[stan] 2024-01-14 20:11:24,372 (trainer:763) INFO: 25epoch:train:201-240batch: iter_time=4.043e-05, forward_time=0.033, loss_ctc=1.441, loss=1.441, backward_time=0.008, grad_norm=60.127, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-14 20:11:27,039 (trainer:763) INFO: 25epoch:train:241-280batch: iter_time=4.402e-05, forward_time=0.035, loss_ctc=1.464, loss=1.464, backward_time=0.008, grad_norm=63.528, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-14 20:11:29,608 (trainer:763) INFO: 25epoch:train:281-320batch: iter_time=4.165e-05, forward_time=0.034, loss_ctc=1.438, loss=1.438, backward_time=0.008, grad_norm=59.409, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 20:11:32,406 (trainer:763) INFO: 25epoch:train:321-360batch: iter_time=4.269e-05, forward_time=0.037, loss_ctc=1.522, loss=1.522, backward_time=0.008, grad_norm=62.614, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 20:11:35,024 (trainer:763) INFO: 25epoch:train:361-400batch: iter_time=4.532e-05, forward_time=0.035, loss_ctc=1.313, loss=1.313, backward_time=0.008, grad_norm=57.033, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-14 20:11:37,762 (trainer:763) INFO: 25epoch:train:401-440batch: iter_time=4.181e-05, forward_time=0.036, loss_ctc=1.517, loss=1.517, backward_time=0.008, grad_norm=62.552, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 20:11:40,451 (trainer:763) INFO: 25epoch:train:441-480batch: iter_time=4.314e-05, forward_time=0.036, loss_ctc=1.538, loss=1.538, backward_time=0.008, grad_norm=63.749, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-14 20:11:43,264 (trainer:763) INFO: 25epoch:train:481-520batch: iter_time=4.321e-05, forward_time=0.037, loss_ctc=1.529, loss=1.529, backward_time=0.008, grad_norm=62.034, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-14 20:11:45,610 (trainer:763) INFO: 25epoch:train:521-560batch: iter_time=4.203e-05, forward_time=0.031, loss_ctc=1.178, loss=1.178, backward_time=0.008, grad_norm=61.181, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-14 20:11:48,493 (trainer:763) INFO: 25epoch:train:561-600batch: iter_time=4.150e-05, forward_time=0.038, loss_ctc=1.747, loss=1.747, backward_time=0.008, grad_norm=67.214, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.288 +[stan] 2024-01-14 20:11:51,064 (trainer:763) INFO: 25epoch:train:601-640batch: iter_time=4.144e-05, forward_time=0.034, loss_ctc=1.321, loss=1.321, backward_time=0.008, grad_norm=58.728, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 20:11:54,001 (trainer:763) INFO: 25epoch:train:641-680batch: iter_time=4.343e-05, forward_time=0.039, loss_ctc=1.642, loss=1.642, backward_time=0.009, grad_norm=63.691, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.294 +[stan] 2024-01-14 20:11:56,549 (trainer:763) INFO: 25epoch:train:681-720batch: iter_time=4.257e-05, forward_time=0.034, loss_ctc=1.406, loss=1.406, backward_time=0.008, grad_norm=62.987, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 20:11:58,944 (trainer:763) INFO: 25epoch:train:721-760batch: iter_time=4.679e-05, forward_time=0.032, loss_ctc=1.240, loss=1.240, backward_time=0.008, grad_norm=56.288, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-14 20:12:01,798 (trainer:763) INFO: 25epoch:train:761-800batch: iter_time=3.969e-05, forward_time=0.038, loss_ctc=1.565, loss=1.565, backward_time=0.008, grad_norm=65.521, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-14 20:12:05,783 (trainer:354) INFO: 25epoch results: [train] iter_time=1.693e-04, forward_time=0.035, loss_ctc=1.477, loss=1.477, backward_time=0.008, grad_norm=61.764, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.83 seconds, total_count=20000, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=114.282, cer_ctc=0.323, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=114.282, time=1.15 seconds, total_count=625, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.76 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:12:06,842 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:12:06,842 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/24epoch.pth +[stan] 2024-01-14 20:12:06,842 (trainer:288) INFO: 26/30epoch started. Estimated time to finish: 4 minutes and 54.5 seconds +[stan] 2024-01-14 20:12:09,879 (trainer:763) INFO: 26epoch:train:1-40batch: iter_time=0.002, forward_time=0.037, loss_ctc=1.621, loss=1.621, backward_time=0.008, grad_norm=66.210, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.303 +[stan] 2024-01-14 20:12:12,563 (trainer:763) INFO: 26epoch:train:41-80batch: iter_time=4.250e-05, forward_time=0.035, loss_ctc=1.497, loss=1.497, backward_time=0.008, grad_norm=62.685, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-14 20:12:15,434 (trainer:763) INFO: 26epoch:train:81-120batch: iter_time=4.123e-05, forward_time=0.038, loss_ctc=1.569, loss=1.569, backward_time=0.008, grad_norm=64.624, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.287 +[stan] 2024-01-14 20:12:17,914 (trainer:763) INFO: 26epoch:train:121-160batch: iter_time=4.045e-05, forward_time=0.033, loss_ctc=1.316, loss=1.316, backward_time=0.008, grad_norm=58.200, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 20:12:20,710 (trainer:763) INFO: 26epoch:train:161-200batch: iter_time=4.182e-05, forward_time=0.037, loss_ctc=1.616, loss=1.616, backward_time=0.008, grad_norm=68.157, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 20:12:23,097 (trainer:763) INFO: 26epoch:train:201-240batch: iter_time=4.034e-05, forward_time=0.032, loss_ctc=1.266, loss=1.266, backward_time=0.008, grad_norm=55.332, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-14 20:12:25,959 (trainer:763) INFO: 26epoch:train:241-280batch: iter_time=4.119e-05, forward_time=0.038, loss_ctc=1.487, loss=1.487, backward_time=0.008, grad_norm=63.388, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.286 +[stan] 2024-01-14 20:12:28,799 (trainer:763) INFO: 26epoch:train:281-320batch: iter_time=4.192e-05, forward_time=0.037, loss_ctc=1.576, loss=1.576, backward_time=0.008, grad_norm=62.659, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.284 +[stan] 2024-01-14 20:12:31,126 (trainer:763) INFO: 26epoch:train:321-360batch: iter_time=4.114e-05, forward_time=0.031, loss_ctc=1.085, loss=1.085, backward_time=0.008, grad_norm=53.863, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-14 20:12:34,009 (trainer:763) INFO: 26epoch:train:361-400batch: iter_time=4.104e-05, forward_time=0.038, loss_ctc=1.656, loss=1.656, backward_time=0.008, grad_norm=64.927, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.288 +[stan] 2024-01-14 20:12:36,752 (trainer:763) INFO: 26epoch:train:401-440batch: iter_time=4.179e-05, forward_time=0.036, loss_ctc=1.594, loss=1.594, backward_time=0.008, grad_norm=67.159, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 20:12:39,364 (trainer:763) INFO: 26epoch:train:441-480batch: iter_time=4.226e-05, forward_time=0.035, loss_ctc=1.413, loss=1.413, backward_time=0.008, grad_norm=58.922, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-14 20:12:42,058 (trainer:763) INFO: 26epoch:train:481-520batch: iter_time=4.097e-05, forward_time=0.036, loss_ctc=1.405, loss=1.405, backward_time=0.008, grad_norm=59.682, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-14 20:12:44,756 (trainer:763) INFO: 26epoch:train:521-560batch: iter_time=4.038e-05, forward_time=0.036, loss_ctc=1.564, loss=1.564, backward_time=0.008, grad_norm=68.649, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-14 20:12:47,333 (trainer:763) INFO: 26epoch:train:561-600batch: iter_time=4.174e-05, forward_time=0.034, loss_ctc=1.446, loss=1.446, backward_time=0.008, grad_norm=59.655, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 20:12:50,118 (trainer:763) INFO: 26epoch:train:601-640batch: iter_time=4.247e-05, forward_time=0.037, loss_ctc=1.532, loss=1.532, backward_time=0.008, grad_norm=64.526, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 20:12:52,875 (trainer:763) INFO: 26epoch:train:641-680batch: iter_time=4.118e-05, forward_time=0.036, loss_ctc=1.666, loss=1.666, backward_time=0.008, grad_norm=63.216, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 20:12:55,376 (trainer:763) INFO: 26epoch:train:681-720batch: iter_time=4.109e-05, forward_time=0.033, loss_ctc=1.182, loss=1.182, backward_time=0.008, grad_norm=55.400, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 20:12:58,169 (trainer:763) INFO: 26epoch:train:721-760batch: iter_time=4.165e-05, forward_time=0.037, loss_ctc=1.540, loss=1.540, backward_time=0.008, grad_norm=66.098, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-14 20:13:00,733 (trainer:763) INFO: 26epoch:train:761-800batch: iter_time=4.088e-05, forward_time=0.034, loss_ctc=1.313, loss=1.313, backward_time=0.008, grad_norm=59.959, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-14 20:13:04,688 (trainer:354) INFO: 26epoch results: [train] iter_time=1.539e-04, forward_time=0.035, loss_ctc=1.467, loss=1.467, backward_time=0.008, grad_norm=62.166, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.97 seconds, total_count=20800, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=113.428, cer_ctc=0.322, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=113.428, time=1.14 seconds, total_count=650, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:13:05,675 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:13:05,675 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/25epoch.pth +[stan] 2024-01-14 20:13:05,676 (trainer:288) INFO: 27/30epoch started. Estimated time to finish: 3 minutes and 55.59 seconds +[stan] 2024-01-14 20:13:08,731 (trainer:763) INFO: 27epoch:train:1-40batch: iter_time=0.003, forward_time=0.037, loss_ctc=1.624, loss=1.624, backward_time=0.008, grad_norm=62.512, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.305 +[stan] 2024-01-14 20:13:11,445 (trainer:763) INFO: 27epoch:train:41-80batch: iter_time=4.324e-05, forward_time=0.036, loss_ctc=1.337, loss=1.337, backward_time=0.008, grad_norm=60.910, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 20:13:14,277 (trainer:763) INFO: 27epoch:train:81-120batch: iter_time=4.397e-05, forward_time=0.037, loss_ctc=1.486, loss=1.486, backward_time=0.008, grad_norm=66.470, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-14 20:13:16,870 (trainer:763) INFO: 27epoch:train:121-160batch: iter_time=4.321e-05, forward_time=0.034, loss_ctc=1.390, loss=1.390, backward_time=0.008, grad_norm=63.457, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-14 20:13:19,492 (trainer:763) INFO: 27epoch:train:161-200batch: iter_time=4.078e-05, forward_time=0.035, loss_ctc=1.490, loss=1.490, backward_time=0.008, grad_norm=59.697, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-14 20:13:22,122 (trainer:763) INFO: 27epoch:train:201-240batch: iter_time=4.169e-05, forward_time=0.035, loss_ctc=1.361, loss=1.361, backward_time=0.008, grad_norm=59.006, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 20:13:25,054 (trainer:763) INFO: 27epoch:train:241-280batch: iter_time=4.046e-05, forward_time=0.039, loss_ctc=1.592, loss=1.592, backward_time=0.008, grad_norm=65.211, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.293 +[stan] 2024-01-14 20:13:27,548 (trainer:763) INFO: 27epoch:train:281-320batch: iter_time=4.174e-05, forward_time=0.033, loss_ctc=1.226, loss=1.226, backward_time=0.008, grad_norm=60.810, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 20:13:30,096 (trainer:763) INFO: 27epoch:train:321-360batch: iter_time=4.174e-05, forward_time=0.034, loss_ctc=1.440, loss=1.440, backward_time=0.008, grad_norm=63.113, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 20:13:33,124 (trainer:763) INFO: 27epoch:train:361-400batch: iter_time=3.986e-05, forward_time=0.040, loss_ctc=1.809, loss=1.809, backward_time=0.009, grad_norm=66.506, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.303 +[stan] 2024-01-14 20:13:35,609 (trainer:763) INFO: 27epoch:train:401-440batch: iter_time=4.044e-05, forward_time=0.033, loss_ctc=1.193, loss=1.193, backward_time=0.008, grad_norm=57.161, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 20:13:38,149 (trainer:763) INFO: 27epoch:train:441-480batch: iter_time=4.241e-05, forward_time=0.035, loss_ctc=1.117, loss=1.117, backward_time=0.008, grad_norm=54.219, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-14 20:13:41,105 (trainer:763) INFO: 27epoch:train:481-520batch: iter_time=4.338e-05, forward_time=0.039, loss_ctc=1.784, loss=1.784, backward_time=0.008, grad_norm=68.647, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.295 +[stan] 2024-01-14 20:13:43,747 (trainer:763) INFO: 27epoch:train:521-560batch: iter_time=4.231e-05, forward_time=0.035, loss_ctc=1.419, loss=1.419, backward_time=0.008, grad_norm=59.636, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-14 20:13:46,397 (trainer:763) INFO: 27epoch:train:561-600batch: iter_time=3.987e-05, forward_time=0.035, loss_ctc=1.314, loss=1.314, backward_time=0.008, grad_norm=59.490, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.265 +[stan] 2024-01-14 20:13:49,209 (trainer:763) INFO: 27epoch:train:601-640batch: iter_time=4.055e-05, forward_time=0.037, loss_ctc=1.601, loss=1.601, backward_time=0.008, grad_norm=66.008, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-14 20:13:51,793 (trainer:763) INFO: 27epoch:train:641-680batch: iter_time=4.139e-05, forward_time=0.034, loss_ctc=1.495, loss=1.495, backward_time=0.008, grad_norm=63.176, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 20:13:54,398 (trainer:763) INFO: 27epoch:train:681-720batch: iter_time=4.450e-05, forward_time=0.034, loss_ctc=1.378, loss=1.378, backward_time=0.008, grad_norm=59.690, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-14 20:13:57,164 (trainer:763) INFO: 27epoch:train:721-760batch: iter_time=4.338e-05, forward_time=0.036, loss_ctc=1.387, loss=1.387, backward_time=0.008, grad_norm=62.246, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 20:13:59,845 (trainer:763) INFO: 27epoch:train:761-800batch: iter_time=4.114e-05, forward_time=0.035, loss_ctc=1.328, loss=1.328, backward_time=0.008, grad_norm=57.970, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-14 20:14:03,812 (trainer:354) INFO: 27epoch results: [train] iter_time=2.114e-04, forward_time=0.036, loss_ctc=1.438, loss=1.438, backward_time=0.008, grad_norm=61.797, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271, time=54.25 seconds, total_count=21600, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=114.841, cer_ctc=0.322, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=114.841, time=1.14 seconds, total_count=675, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:14:04,807 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:14:04,807 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/26epoch.pth +[stan] 2024-01-14 20:14:04,807 (trainer:288) INFO: 28/30epoch started. Estimated time to finish: 2 minutes and 56.72 seconds +[stan] 2024-01-14 20:14:07,718 (trainer:763) INFO: 28epoch:train:1-40batch: iter_time=0.003, forward_time=0.035, loss_ctc=1.431, loss=1.431, backward_time=0.008, grad_norm=60.721, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.291 +[stan] 2024-01-14 20:14:10,352 (trainer:763) INFO: 28epoch:train:41-80batch: iter_time=4.326e-05, forward_time=0.035, loss_ctc=1.405, loss=1.405, backward_time=0.008, grad_norm=59.372, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 20:14:13,138 (trainer:763) INFO: 28epoch:train:81-120batch: iter_time=4.080e-05, forward_time=0.037, loss_ctc=1.422, loss=1.422, backward_time=0.008, grad_norm=60.221, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-14 20:14:15,865 (trainer:763) INFO: 28epoch:train:121-160batch: iter_time=4.094e-05, forward_time=0.036, loss_ctc=1.254, loss=1.254, backward_time=0.008, grad_norm=57.630, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 20:14:18,375 (trainer:763) INFO: 28epoch:train:161-200batch: iter_time=4.060e-05, forward_time=0.033, loss_ctc=1.316, loss=1.316, backward_time=0.008, grad_norm=58.123, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-14 20:14:21,197 (trainer:763) INFO: 28epoch:train:201-240batch: iter_time=4.121e-05, forward_time=0.037, loss_ctc=1.559, loss=1.559, backward_time=0.008, grad_norm=65.527, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-14 20:14:23,933 (trainer:763) INFO: 28epoch:train:241-280batch: iter_time=4.294e-05, forward_time=0.036, loss_ctc=1.342, loss=1.342, backward_time=0.008, grad_norm=57.653, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 20:14:26,455 (trainer:763) INFO: 28epoch:train:281-320batch: iter_time=4.131e-05, forward_time=0.033, loss_ctc=1.294, loss=1.294, backward_time=0.008, grad_norm=62.927, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-14 20:14:29,251 (trainer:763) INFO: 28epoch:train:321-360batch: iter_time=4.116e-05, forward_time=0.037, loss_ctc=1.459, loss=1.459, backward_time=0.008, grad_norm=65.318, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-14 20:14:31,777 (trainer:763) INFO: 28epoch:train:361-400batch: iter_time=4.202e-05, forward_time=0.034, loss_ctc=1.334, loss=1.334, backward_time=0.008, grad_norm=61.014, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-14 20:14:34,609 (trainer:763) INFO: 28epoch:train:401-440batch: iter_time=4.427e-05, forward_time=0.037, loss_ctc=1.264, loss=1.264, backward_time=0.008, grad_norm=62.258, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-14 20:14:37,296 (trainer:763) INFO: 28epoch:train:441-480batch: iter_time=4.010e-05, forward_time=0.035, loss_ctc=1.483, loss=1.483, backward_time=0.008, grad_norm=60.243, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-14 20:14:39,752 (trainer:763) INFO: 28epoch:train:481-520batch: iter_time=4.286e-05, forward_time=0.033, loss_ctc=1.272, loss=1.272, backward_time=0.008, grad_norm=56.731, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 20:14:42,901 (trainer:763) INFO: 28epoch:train:521-560batch: iter_time=4.326e-05, forward_time=0.041, loss_ctc=1.772, loss=1.772, backward_time=0.009, grad_norm=66.542, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.315 +[stan] 2024-01-14 20:14:45,348 (trainer:763) INFO: 28epoch:train:561-600batch: iter_time=4.072e-05, forward_time=0.032, loss_ctc=1.156, loss=1.156, backward_time=0.008, grad_norm=55.367, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-14 20:14:47,875 (trainer:763) INFO: 28epoch:train:601-640batch: iter_time=4.068e-05, forward_time=0.033, loss_ctc=1.144, loss=1.144, backward_time=0.008, grad_norm=55.363, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-14 20:14:50,902 (trainer:763) INFO: 28epoch:train:641-680batch: iter_time=4.238e-05, forward_time=0.040, loss_ctc=1.506, loss=1.506, backward_time=0.009, grad_norm=60.496, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.303 +[stan] 2024-01-14 20:14:53,390 (trainer:763) INFO: 28epoch:train:681-720batch: iter_time=4.050e-05, forward_time=0.033, loss_ctc=1.269, loss=1.269, backward_time=0.008, grad_norm=57.104, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 20:14:56,105 (trainer:763) INFO: 28epoch:train:721-760batch: iter_time=4.479e-05, forward_time=0.036, loss_ctc=1.337, loss=1.337, backward_time=0.008, grad_norm=59.946, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 20:14:58,674 (trainer:763) INFO: 28epoch:train:761-800batch: iter_time=3.868e-05, forward_time=0.034, loss_ctc=1.316, loss=1.316, backward_time=0.008, grad_norm=56.004, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 20:15:02,707 (trainer:354) INFO: 28epoch results: [train] iter_time=1.976e-04, forward_time=0.035, loss_ctc=1.367, loss=1.367, backward_time=0.008, grad_norm=59.928, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.94 seconds, total_count=22400, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=113.770, cer_ctc=0.327, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=113.770, time=1.18 seconds, total_count=700, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.78 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:15:03,794 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:15:03,795 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/27epoch.pth +[stan] 2024-01-14 20:15:03,795 (trainer:288) INFO: 29/30epoch started. Estimated time to finish: 1 minute and 57.82 seconds +[stan] 2024-01-14 20:15:06,809 (trainer:763) INFO: 29epoch:train:1-40batch: iter_time=0.002, forward_time=0.037, loss_ctc=1.303, loss=1.303, backward_time=0.008, grad_norm=59.136, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.301 +[stan] 2024-01-14 20:15:09,484 (trainer:763) INFO: 29epoch:train:41-80batch: iter_time=4.229e-05, forward_time=0.035, loss_ctc=1.290, loss=1.290, backward_time=0.008, grad_norm=57.169, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-14 20:15:12,431 (trainer:763) INFO: 29epoch:train:81-120batch: iter_time=4.246e-05, forward_time=0.039, loss_ctc=1.560, loss=1.560, backward_time=0.008, grad_norm=63.303, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.295 +[stan] 2024-01-14 20:15:14,856 (trainer:763) INFO: 29epoch:train:121-160batch: iter_time=4.044e-05, forward_time=0.032, loss_ctc=1.192, loss=1.192, backward_time=0.008, grad_norm=56.065, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-14 20:15:17,488 (trainer:763) INFO: 29epoch:train:161-200batch: iter_time=4.260e-05, forward_time=0.035, loss_ctc=1.224, loss=1.224, backward_time=0.008, grad_norm=58.009, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 20:15:20,283 (trainer:763) INFO: 29epoch:train:201-240batch: iter_time=4.147e-05, forward_time=0.037, loss_ctc=1.367, loss=1.367, backward_time=0.008, grad_norm=62.430, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-14 20:15:22,903 (trainer:763) INFO: 29epoch:train:241-280batch: iter_time=4.127e-05, forward_time=0.035, loss_ctc=1.356, loss=1.356, backward_time=0.008, grad_norm=61.712, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-14 20:15:25,579 (trainer:763) INFO: 29epoch:train:281-320batch: iter_time=4.289e-05, forward_time=0.035, loss_ctc=1.370, loss=1.370, backward_time=0.008, grad_norm=58.448, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-14 20:15:28,155 (trainer:763) INFO: 29epoch:train:321-360batch: iter_time=4.368e-05, forward_time=0.034, loss_ctc=1.299, loss=1.299, backward_time=0.008, grad_norm=56.970, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 20:15:30,945 (trainer:763) INFO: 29epoch:train:361-400batch: iter_time=4.133e-05, forward_time=0.037, loss_ctc=1.527, loss=1.527, backward_time=0.008, grad_norm=65.117, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-14 20:15:33,662 (trainer:763) INFO: 29epoch:train:401-440batch: iter_time=4.202e-05, forward_time=0.036, loss_ctc=1.230, loss=1.230, backward_time=0.008, grad_norm=55.568, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 20:15:36,297 (trainer:763) INFO: 29epoch:train:441-480batch: iter_time=4.160e-05, forward_time=0.035, loss_ctc=1.233, loss=1.233, backward_time=0.008, grad_norm=60.592, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 20:15:38,988 (trainer:763) INFO: 29epoch:train:481-520batch: iter_time=4.113e-05, forward_time=0.036, loss_ctc=1.327, loss=1.327, backward_time=0.008, grad_norm=58.595, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-14 20:15:41,763 (trainer:763) INFO: 29epoch:train:521-560batch: iter_time=4.207e-05, forward_time=0.037, loss_ctc=1.440, loss=1.440, backward_time=0.008, grad_norm=59.396, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 20:15:44,372 (trainer:763) INFO: 29epoch:train:561-600batch: iter_time=4.124e-05, forward_time=0.035, loss_ctc=1.467, loss=1.467, backward_time=0.008, grad_norm=62.173, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-14 20:15:47,040 (trainer:763) INFO: 29epoch:train:601-640batch: iter_time=4.089e-05, forward_time=0.035, loss_ctc=1.337, loss=1.337, backward_time=0.008, grad_norm=60.549, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-14 20:15:49,772 (trainer:763) INFO: 29epoch:train:641-680batch: iter_time=4.218e-05, forward_time=0.036, loss_ctc=1.272, loss=1.272, backward_time=0.008, grad_norm=59.250, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 20:15:52,643 (trainer:763) INFO: 29epoch:train:681-720batch: iter_time=4.173e-05, forward_time=0.038, loss_ctc=1.399, loss=1.399, backward_time=0.008, grad_norm=60.923, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.287 +[stan] 2024-01-14 20:15:55,089 (trainer:763) INFO: 29epoch:train:721-760batch: iter_time=4.122e-05, forward_time=0.033, loss_ctc=1.246, loss=1.246, backward_time=0.008, grad_norm=58.416, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-14 20:15:57,774 (trainer:763) INFO: 29epoch:train:761-800batch: iter_time=3.958e-05, forward_time=0.036, loss_ctc=1.240, loss=1.240, backward_time=0.008, grad_norm=55.285, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-14 20:16:01,736 (trainer:354) INFO: 29epoch results: [train] iter_time=1.633e-04, forward_time=0.036, loss_ctc=1.334, loss=1.334, backward_time=0.008, grad_norm=59.455, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.05 seconds, total_count=23200, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=117.162, cer_ctc=0.322, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=117.162, time=1.14 seconds, total_count=725, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:16:02,695 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:16:02,695 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/28epoch.pth +[stan] 2024-01-14 20:16:02,695 (trainer:288) INFO: 30/30epoch started. Estimated time to finish: 58.91 seconds +[stan] 2024-01-14 20:16:05,643 (trainer:763) INFO: 30epoch:train:1-40batch: iter_time=0.003, forward_time=0.036, loss_ctc=1.384, loss=1.384, backward_time=0.008, grad_norm=63.243, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.294 +[stan] 2024-01-14 20:16:08,314 (trainer:763) INFO: 30epoch:train:41-80batch: iter_time=4.076e-05, forward_time=0.035, loss_ctc=1.282, loss=1.282, backward_time=0.008, grad_norm=54.636, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-14 20:16:11,071 (trainer:763) INFO: 30epoch:train:81-120batch: iter_time=4.309e-05, forward_time=0.036, loss_ctc=1.457, loss=1.457, backward_time=0.008, grad_norm=62.098, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 20:16:13,956 (trainer:763) INFO: 30epoch:train:121-160batch: iter_time=4.209e-05, forward_time=0.038, loss_ctc=1.345, loss=1.345, backward_time=0.008, grad_norm=63.557, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.288 +[stan] 2024-01-14 20:16:16,358 (trainer:763) INFO: 30epoch:train:161-200batch: iter_time=4.128e-05, forward_time=0.032, loss_ctc=1.156, loss=1.156, backward_time=0.008, grad_norm=56.742, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-14 20:16:18,928 (trainer:763) INFO: 30epoch:train:201-240batch: iter_time=4.071e-05, forward_time=0.034, loss_ctc=1.270, loss=1.270, backward_time=0.008, grad_norm=57.307, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 20:16:21,949 (trainer:763) INFO: 30epoch:train:241-280batch: iter_time=4.293e-05, forward_time=0.040, loss_ctc=1.389, loss=1.389, backward_time=0.009, grad_norm=58.199, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.302 +[stan] 2024-01-14 20:16:24,515 (trainer:763) INFO: 30epoch:train:281-320batch: iter_time=4.146e-05, forward_time=0.035, loss_ctc=1.138, loss=1.138, backward_time=0.008, grad_norm=60.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-14 20:16:27,238 (trainer:763) INFO: 30epoch:train:321-360batch: iter_time=4.248e-05, forward_time=0.036, loss_ctc=1.352, loss=1.352, backward_time=0.008, grad_norm=62.430, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 20:16:29,826 (trainer:763) INFO: 30epoch:train:361-400batch: iter_time=4.103e-05, forward_time=0.034, loss_ctc=1.104, loss=1.104, backward_time=0.008, grad_norm=56.037, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-14 20:16:32,555 (trainer:763) INFO: 30epoch:train:401-440batch: iter_time=4.173e-05, forward_time=0.036, loss_ctc=1.233, loss=1.233, backward_time=0.008, grad_norm=57.254, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 20:16:35,287 (trainer:763) INFO: 30epoch:train:441-480batch: iter_time=4.245e-05, forward_time=0.036, loss_ctc=1.402, loss=1.402, backward_time=0.008, grad_norm=59.937, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 20:16:38,027 (trainer:763) INFO: 30epoch:train:481-520batch: iter_time=4.245e-05, forward_time=0.036, loss_ctc=1.364, loss=1.364, backward_time=0.008, grad_norm=59.365, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 20:16:40,605 (trainer:763) INFO: 30epoch:train:521-560batch: iter_time=4.327e-05, forward_time=0.034, loss_ctc=1.120, loss=1.120, backward_time=0.008, grad_norm=56.054, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 20:16:43,197 (trainer:763) INFO: 30epoch:train:561-600batch: iter_time=4.277e-05, forward_time=0.034, loss_ctc=1.178, loss=1.178, backward_time=0.008, grad_norm=55.219, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-14 20:16:46,170 (trainer:763) INFO: 30epoch:train:601-640batch: iter_time=4.194e-05, forward_time=0.039, loss_ctc=1.555, loss=1.555, backward_time=0.009, grad_norm=65.990, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.297 +[stan] 2024-01-14 20:16:48,650 (trainer:763) INFO: 30epoch:train:641-680batch: iter_time=4.176e-05, forward_time=0.033, loss_ctc=1.109, loss=1.109, backward_time=0.008, grad_norm=54.515, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 20:16:51,441 (trainer:763) INFO: 30epoch:train:681-720batch: iter_time=4.463e-05, forward_time=0.037, loss_ctc=1.334, loss=1.334, backward_time=0.008, grad_norm=57.219, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-14 20:16:54,057 (trainer:763) INFO: 30epoch:train:721-760batch: iter_time=4.024e-05, forward_time=0.035, loss_ctc=1.260, loss=1.260, backward_time=0.008, grad_norm=59.945, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-14 20:16:56,705 (trainer:763) INFO: 30epoch:train:761-800batch: iter_time=4.036e-05, forward_time=0.035, loss_ctc=1.349, loss=1.349, backward_time=0.008, grad_norm=58.018, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.265 +[stan] 2024-01-14 20:17:00,681 (trainer:354) INFO: 30epoch results: [train] iter_time=1.878e-04, forward_time=0.036, loss_ctc=1.289, loss=1.289, backward_time=0.008, grad_norm=58.901, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.09 seconds, total_count=24000, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=117.222, cer_ctc=0.325, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=117.222, time=1.14 seconds, total_count=750, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.76 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-14 20:17:01,646 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:17:01,646 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/29epoch.pth +[stan] 2024-01-14 20:17:01,646 (trainer:489) INFO: The training was finished at 30 epochs +[stan] 2024-01-14 20:17:01,663 (average_nbest_models:69) INFO: Averaging 5best models: criterion="valid.loss": test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave_5best.pth +# Accounting: time=1772 threads=1 +# Ended (code 0) at Sun Jan 14 20:17:02 CST 2024, elapsed time 1772 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/train.log b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/train.log new file mode 100644 index 0000000000000000000000000000000000000000..15928cc4b9a834654e5f36a7d759a53926cfdbac --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_10min/train.log @@ -0,0 +1,1406 @@ +# python3 -m espnet2.bin.asr_train --use_preprocessor true --bpemodel none --token_type char --token_list data/deu1_token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_deu1/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_deu1_10min/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_10min_deu1/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_deu1_10min/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_10min_deu1/text,text,text --train_shape_file test_pr/asr_stats_deu1_10min/train/text_shape.char --valid_data_path_and_name_and_type dump/raw/dev_10min_deu1/text,text,text --valid_shape_file test_pr/asr_stats_deu1_10min/valid/text_shape.char --ngpu 1 --multiprocessing_distributed True +# Started at Tue Jan 16 21:45:24 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_train.py --use_preprocessor true --bpemodel none --token_type char --token_list data/deu1_token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_deu1/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_deu1_10min/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_10min --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_10min_deu1/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_deu1_10min/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_10min_deu1/text,text,text --train_shape_file test_pr/asr_stats_deu1_10min/train/text_shape.char --valid_data_path_and_name_and_type dump/raw/dev_10min_deu1/text,text,text --valid_shape_file test_pr/asr_stats_deu1_10min/valid/text_shape.char --ngpu 1 --multiprocessing_distributed True +[stan] 2024-01-16 21:45:25,328 (asr:523) INFO: Vocabulary size: 44 +[stan] 2024-01-16 21:45:25,390 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +[stan] 2024-01-16 21:45:25,390 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +[stan] 2024-01-16 21:45:25,499 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +[stan] 2024-01-16 21:45:26,788 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.mask_emb.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.label_embs_concat.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.0.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.1.0.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.2.0.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.3.0.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.4.0.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.5.0.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.6.0.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_g.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_v.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,617 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,618 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.bias.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.weight.requires_grad = False +[stan] 2024-01-16 21:45:27,619 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.bias.requires_grad = False +[stan] 2024-01-16 21:45:28,019 (abs_task:1275) INFO: pytorch.version=1.12.0+cu113, cuda.available=True, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True +[stan] 2024-01-16 21:45:28,021 (abs_task:1276) INFO: Model structure: +ESPnetASRModel( + (frontend): S3prlFrontend( + (upstream): S3PRLUpstream( + (upstream): UpstreamExpert( + (model): HubertModel( + (feature_extractor): ConvFeatureExtractionModel( + (conv_layers): ModuleList( + (0): Sequential( + (0): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): Fp32GroupNorm(512, 512, eps=1e-05, affine=True) + (3): GELU(approximate=none) + ) + (1): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (2): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (3): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (4): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (5): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (6): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + ) + ) + (post_extract_proj): Linear(in_features=512, out_features=768, bias=True) + (dropout_input): Dropout(p=0.1, inplace=False) + (dropout_features): Dropout(p=0.1, inplace=False) + (encoder): TransformerEncoder( + (pos_conv): Sequential( + (0): Conv1d(768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16) + (1): SamePad() + (2): GELU(approximate=none) + ) + (layers): ModuleList( + (0): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (1): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (2): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (3): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (4): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (5): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (6): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (7): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (8): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (9): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (10): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (11): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + ) + (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (final_proj): Linear(in_features=768, out_features=256, bias=True) + ) + ) + ) + (featurizer): Featurizer() + ) + (specaug): SpecAug( + (time_warp): TimeWarp(window=5, mode=bicubic) + (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq) + (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time) + ) + (normalize): UtteranceMVN(norm_means=True, norm_vars=False) + (preencoder): LinearProjection( + (linear_out): Linear(in_features=768, out_features=80, bias=True) + (dropout): Dropout(p=0.0, inplace=False) + ) + (encoder): TransformerEncoder( + (embed): Conv2dSubsampling2( + (conv): Sequential( + (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU() + (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) + (3): ReLU() + ) + (out): Sequential( + (0): Linear(in_features=9472, out_features=256, bias=True) + (1): PositionalEncoding( + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + ) + (encoders): MultiSequential( + (0): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (1): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + ) + (criterion_att): LabelSmoothingLoss( + (criterion): KLDivLoss() + ) + (ctc): CTC( + (ctc_lo): Linear(in_features=256, out_features=44, bias=True) + (ctc_loss): CTCLoss() + ) +) + +Model summary: + Class Name: ESPnetASRModel + Total Number of model parameters: 99.97 M + Number of trainable parameters: 5.27 M (5.3%) + Size: 21.08 MB + Type: torch.float32 +[stan] 2024-01-16 21:45:28,021 (abs_task:1279) INFO: Optimizer: +Adam ( +Parameter Group 0 + amsgrad: False + betas: (0.9, 0.999) + capturable: False + eps: 1e-08 + foreach: None + lr: 0.0001 + maximize: False + weight_decay: 1e-06 +) +[stan] 2024-01-16 21:45:28,021 (abs_task:1280) INFO: Scheduler: None +[stan] 2024-01-16 21:45:28,021 (abs_task:1289) INFO: Saving the configuration in test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/config.yaml +[stan] 2024-01-16 21:45:28,174 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-16 21:45:28,216 (abs_task:1665) INFO: [train] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/train_10min_deu1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/train_10min_deu1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-16 21:45:28,216 (abs_task:1666) INFO: [train] Batch sampler: SortedBatchSampler(N-batch=24, batch_size=8, shape_file=test_pr/asr_stats_deu1_10min/train/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-16 21:45:28,216 (abs_task:1667) INFO: [train] mini-batch sizes summary: N-batch=24, mean=8.2, min=8, max=9 +[stan] 2024-01-16 21:45:28,227 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-16 21:45:28,227 (abs_task:1665) INFO: [valid] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_deu1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_deu1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-16 21:45:28,228 (abs_task:1666) INFO: [valid] Batch sampler: SortedBatchSampler(N-batch=25, batch_size=8, shape_file=test_pr/asr_stats_deu1_10min/valid/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-16 21:45:28,228 (abs_task:1667) INFO: [valid] mini-batch sizes summary: N-batch=25, mean=8.3, min=8, max=9 +[stan] 2024-01-16 21:45:28,228 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-16 21:45:28,239 (abs_task:1665) INFO: [plot_att] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_deu1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_deu1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-16 21:45:28,239 (abs_task:1666) INFO: [plot_att] Batch sampler: UnsortedBatchSampler(N-batch=207, batch_size=1, key_file=test_pr/asr_stats_deu1_10min/valid/speech_shape, +[stan] 2024-01-16 21:45:28,239 (abs_task:1667) INFO: [plot_att] mini-batch sizes summary: N-batch=3, mean=1.0, min=1, max=1 +[stan] 2024-01-16 21:45:28,272 (trainer:303) INFO: 1/30epoch started +[stan] 2024-01-16 21:45:32,633 (trainer:762) INFO: 1epoch:train:1-40batch: iter_time=0.001, forward_time=0.067, loss_ctc=40.274, loss=40.274, backward_time=0.010, grad_norm=356.489, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 21:45:35,070 (trainer:762) INFO: 1epoch:train:41-80batch: iter_time=4.218e-05, forward_time=0.032, loss_ctc=32.923, loss=32.923, backward_time=0.007, grad_norm=120.876, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-16 21:45:37,775 (trainer:762) INFO: 1epoch:train:81-120batch: iter_time=4.167e-05, forward_time=0.036, loss_ctc=35.205, loss=35.205, backward_time=0.007, grad_norm=78.758, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-16 21:45:40,549 (trainer:762) INFO: 1epoch:train:121-160batch: iter_time=4.177e-05, forward_time=0.037, loss_ctc=35.220, loss=35.220, backward_time=0.008, grad_norm=74.550, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-16 21:45:43,212 (trainer:762) INFO: 1epoch:train:161-200batch: iter_time=4.405e-05, forward_time=0.035, loss_ctc=30.799, loss=30.799, backward_time=0.008, grad_norm=93.586, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-16 21:45:45,709 (trainer:762) INFO: 1epoch:train:201-240batch: iter_time=4.058e-05, forward_time=0.033, loss_ctc=26.578, loss=26.578, backward_time=0.007, grad_norm=107.594, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-16 21:45:48,465 (trainer:762) INFO: 1epoch:train:241-280batch: iter_time=4.097e-05, forward_time=0.036, loss_ctc=25.664, loss=25.664, backward_time=0.008, grad_norm=80.817, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 21:45:51,183 (trainer:762) INFO: 1epoch:train:281-320batch: iter_time=4.128e-05, forward_time=0.036, loss_ctc=22.813, loss=22.813, backward_time=0.008, grad_norm=85.921, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 21:45:53,655 (trainer:762) INFO: 1epoch:train:321-360batch: iter_time=4.173e-05, forward_time=0.033, loss_ctc=20.153, loss=20.153, backward_time=0.007, grad_norm=79.478, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-16 21:45:56,109 (trainer:762) INFO: 1epoch:train:361-400batch: iter_time=4.191e-05, forward_time=0.033, loss_ctc=18.605, loss=18.605, backward_time=0.007, grad_norm=78.999, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-16 21:45:59,134 (trainer:762) INFO: 1epoch:train:401-440batch: iter_time=4.245e-05, forward_time=0.040, loss_ctc=20.604, loss=20.604, backward_time=0.008, grad_norm=86.556, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.302 +[stan] 2024-01-16 21:46:01,620 (trainer:762) INFO: 1epoch:train:441-480batch: iter_time=4.059e-05, forward_time=0.033, loss_ctc=17.657, loss=17.657, backward_time=0.007, grad_norm=81.741, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-16 21:46:04,282 (trainer:762) INFO: 1epoch:train:481-520batch: iter_time=4.125e-05, forward_time=0.035, loss_ctc=17.426, loss=17.426, backward_time=0.007, grad_norm=76.124, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-16 21:46:06,990 (trainer:762) INFO: 1epoch:train:521-560batch: iter_time=4.220e-05, forward_time=0.036, loss_ctc=17.061, loss=17.061, backward_time=0.008, grad_norm=83.950, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 21:46:09,586 (trainer:762) INFO: 1epoch:train:561-600batch: iter_time=4.119e-05, forward_time=0.034, loss_ctc=16.537, loss=16.537, backward_time=0.007, grad_norm=79.606, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-16 21:46:12,064 (trainer:762) INFO: 1epoch:train:601-640batch: iter_time=4.090e-05, forward_time=0.033, loss_ctc=15.375, loss=15.375, backward_time=0.007, grad_norm=80.646, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-16 21:46:14,802 (trainer:762) INFO: 1epoch:train:641-680batch: iter_time=4.124e-05, forward_time=0.036, loss_ctc=15.942, loss=15.942, backward_time=0.008, grad_norm=83.853, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-16 21:46:17,561 (trainer:762) INFO: 1epoch:train:681-720batch: iter_time=4.405e-05, forward_time=0.036, loss_ctc=15.447, loss=15.447, backward_time=0.008, grad_norm=97.790, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 21:46:20,325 (trainer:762) INFO: 1epoch:train:721-760batch: iter_time=4.188e-05, forward_time=0.036, loss_ctc=14.819, loss=14.819, backward_time=0.008, grad_norm=82.834, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 21:46:23,032 (trainer:762) INFO: 1epoch:train:761-800batch: iter_time=4.192e-05, forward_time=0.036, loss_ctc=14.424, loss=14.424, backward_time=0.007, grad_norm=84.556, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-16 21:46:27,197 (trainer:357) INFO: 1epoch results: [train] iter_time=1.131e-04, forward_time=0.037, loss_ctc=22.674, loss=22.674, backward_time=0.008, grad_norm=99.736, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274, time=54.79 seconds, total_count=800, gpu_max_cached_mem_GB=9.184, [valid] loss_ctc=66.700, cer_ctc=0.330, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=66.700, time=1.12 seconds, total_count=25, gpu_max_cached_mem_GB=10.023, [att_plot] time=3 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 21:46:28,266 (trainer:416) INFO: The best model has been updated: valid.loss +[stan] 2024-01-16 21:46:28,266 (trainer:291) INFO: 2/30epoch started. Estimated time to finish: 28 minutes and 59.84 seconds +[stan] 2024-01-16 21:46:31,029 (trainer:762) INFO: 2epoch:train:1-40batch: iter_time=0.002, forward_time=0.034, loss_ctc=13.172, loss=13.172, backward_time=0.008, grad_norm=86.481, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 21:46:33,648 (trainer:762) INFO: 2epoch:train:41-80batch: iter_time=4.359e-05, forward_time=0.035, loss_ctc=12.599, loss=12.599, backward_time=0.008, grad_norm=89.438, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-16 21:46:36,481 (trainer:762) INFO: 2epoch:train:81-120batch: iter_time=4.161e-05, forward_time=0.037, loss_ctc=13.730, loss=13.730, backward_time=0.008, grad_norm=96.664, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-16 21:46:39,019 (trainer:762) INFO: 2epoch:train:121-160batch: iter_time=4.106e-05, forward_time=0.034, loss_ctc=12.225, loss=12.225, backward_time=0.008, grad_norm=109.800, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-16 21:46:41,430 (trainer:762) INFO: 2epoch:train:161-200batch: iter_time=4.092e-05, forward_time=0.032, loss_ctc=11.300, loss=11.300, backward_time=0.008, grad_norm=95.331, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-16 21:46:44,246 (trainer:762) INFO: 2epoch:train:201-240batch: iter_time=4.182e-05, forward_time=0.037, loss_ctc=12.633, loss=12.633, backward_time=0.008, grad_norm=99.314, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-16 21:46:47,023 (trainer:762) INFO: 2epoch:train:241-280batch: iter_time=4.282e-05, forward_time=0.037, loss_ctc=12.107, loss=12.107, backward_time=0.008, grad_norm=111.465, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-16 21:46:49,857 (trainer:762) INFO: 2epoch:train:281-320batch: iter_time=4.241e-05, forward_time=0.037, loss_ctc=11.969, loss=11.969, backward_time=0.008, grad_norm=104.970, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-16 21:46:52,379 (trainer:762) INFO: 2epoch:train:321-360batch: iter_time=4.164e-05, forward_time=0.033, loss_ctc=10.399, loss=10.399, backward_time=0.008, grad_norm=100.289, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-16 21:46:55,025 (trainer:762) INFO: 2epoch:train:361-400batch: iter_time=4.215e-05, forward_time=0.035, loss_ctc=10.303, loss=10.303, backward_time=0.008, grad_norm=110.405, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-16 21:46:57,648 (trainer:762) INFO: 2epoch:train:401-440batch: iter_time=4.143e-05, forward_time=0.035, loss_ctc=10.339, loss=10.339, backward_time=0.008, grad_norm=116.041, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-16 21:47:00,587 (trainer:762) INFO: 2epoch:train:441-480batch: iter_time=4.249e-05, forward_time=0.039, loss_ctc=10.487, loss=10.487, backward_time=0.009, grad_norm=103.810, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.294 +[stan] 2024-01-16 21:47:03,050 (trainer:762) INFO: 2epoch:train:481-520batch: iter_time=4.130e-05, forward_time=0.033, loss_ctc=9.251, loss=9.251, backward_time=0.008, grad_norm=107.886, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-16 21:47:05,563 (trainer:762) INFO: 2epoch:train:521-560batch: iter_time=4.133e-05, forward_time=0.033, loss_ctc=9.004, loss=9.004, backward_time=0.008, grad_norm=101.144, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-16 21:47:08,242 (trainer:762) INFO: 2epoch:train:561-600batch: iter_time=4.388e-05, forward_time=0.035, loss_ctc=9.152, loss=9.152, backward_time=0.008, grad_norm=136.000, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-16 21:47:11,072 (trainer:762) INFO: 2epoch:train:601-640batch: iter_time=4.323e-05, forward_time=0.037, loss_ctc=9.550, loss=9.550, backward_time=0.008, grad_norm=129.744, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-16 21:47:13,735 (trainer:762) INFO: 2epoch:train:641-680batch: iter_time=4.329e-05, forward_time=0.035, loss_ctc=8.444, loss=8.444, backward_time=0.008, grad_norm=114.822, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-16 21:47:16,389 (trainer:762) INFO: 2epoch:train:681-720batch: iter_time=4.251e-05, forward_time=0.035, loss_ctc=8.433, loss=8.433, backward_time=0.008, grad_norm=110.969, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.265 +[stan] 2024-01-16 21:47:19,093 (trainer:762) INFO: 2epoch:train:721-760batch: iter_time=4.167e-05, forward_time=0.036, loss_ctc=8.412, loss=8.412, backward_time=0.008, grad_norm=114.465, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-16 21:47:21,996 (trainer:762) INFO: 2epoch:train:761-800batch: iter_time=4.061e-05, forward_time=0.038, loss_ctc=8.631, loss=8.631, backward_time=0.008, grad_norm=121.692, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.290 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-16 21:47:25,987 (trainer:357) INFO: 2epoch results: [train] iter_time=1.505e-04, forward_time=0.035, loss_ctc=10.606, loss=10.606, backward_time=0.008, grad_norm=108.036, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.8 seconds, total_count=1600, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=71.465, cer_ctc=0.312, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=71.465, time=1.13 seconds, total_count=50, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.79 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 21:47:26,866 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:47:26,866 (trainer:291) INFO: 3/30epoch started. Estimated time to finish: 27 minutes and 40.32 seconds +[stan] 2024-01-16 21:47:29,690 (trainer:762) INFO: 3epoch:train:1-40batch: iter_time=0.003, forward_time=0.034, loss_ctc=7.540, loss=7.540, backward_time=0.008, grad_norm=106.094, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-16 21:47:32,240 (trainer:762) INFO: 3epoch:train:41-80batch: iter_time=4.175e-05, forward_time=0.034, loss_ctc=7.342, loss=7.342, backward_time=0.008, grad_norm=112.689, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-16 21:47:35,022 (trainer:762) INFO: 3epoch:train:81-120batch: iter_time=4.622e-05, forward_time=0.037, loss_ctc=8.039, loss=8.039, backward_time=0.008, grad_norm=130.354, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-16 21:47:37,688 (trainer:762) INFO: 3epoch:train:121-160batch: iter_time=4.178e-05, forward_time=0.035, loss_ctc=7.343, loss=7.343, backward_time=0.008, grad_norm=124.468, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-16 21:47:40,259 (trainer:762) INFO: 3epoch:train:161-200batch: iter_time=4.296e-05, forward_time=0.034, loss_ctc=7.206, loss=7.206, backward_time=0.008, grad_norm=142.704, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-16 21:47:42,690 (trainer:762) INFO: 3epoch:train:201-240batch: iter_time=4.063e-05, forward_time=0.032, loss_ctc=6.368, loss=6.368, backward_time=0.008, grad_norm=106.456, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-16 21:47:45,547 (trainer:762) INFO: 3epoch:train:241-280batch: iter_time=4.762e-05, forward_time=0.038, loss_ctc=7.523, loss=7.523, backward_time=0.008, grad_norm=125.210, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.286 +[stan] 2024-01-16 21:47:48,290 (trainer:762) INFO: 3epoch:train:281-320batch: iter_time=4.413e-05, forward_time=0.036, loss_ctc=6.959, loss=6.959, backward_time=0.008, grad_norm=121.114, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-16 21:47:51,014 (trainer:762) INFO: 3epoch:train:321-360batch: iter_time=4.307e-05, forward_time=0.036, loss_ctc=6.541, loss=6.541, backward_time=0.008, grad_norm=121.427, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 21:47:53,553 (trainer:762) INFO: 3epoch:train:361-400batch: iter_time=4.326e-05, forward_time=0.034, loss_ctc=6.178, loss=6.178, backward_time=0.008, grad_norm=110.012, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-16 21:47:56,322 (trainer:762) INFO: 3epoch:train:401-440batch: iter_time=4.284e-05, forward_time=0.037, loss_ctc=6.716, loss=6.716, backward_time=0.008, grad_norm=119.257, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-16 21:47:58,886 (trainer:762) INFO: 3epoch:train:441-480batch: iter_time=4.247e-05, forward_time=0.034, loss_ctc=5.947, loss=5.947, backward_time=0.008, grad_norm=123.640, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-16 21:48:01,597 (trainer:762) INFO: 3epoch:train:481-520batch: iter_time=4.313e-05, forward_time=0.036, loss_ctc=6.084, loss=6.084, backward_time=0.008, grad_norm=118.700, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 21:48:04,374 (trainer:762) INFO: 3epoch:train:521-560batch: iter_time=4.226e-05, forward_time=0.037, loss_ctc=6.229, loss=6.229, backward_time=0.008, grad_norm=122.793, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-16 21:48:07,137 (trainer:762) INFO: 3epoch:train:561-600batch: iter_time=4.920e-05, forward_time=0.036, loss_ctc=6.026, loss=6.026, backward_time=0.008, grad_norm=117.956, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 21:48:09,703 (trainer:762) INFO: 3epoch:train:601-640batch: iter_time=4.328e-05, forward_time=0.034, loss_ctc=5.734, loss=5.734, backward_time=0.008, grad_norm=117.049, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-16 21:48:12,431 (trainer:762) INFO: 3epoch:train:641-680batch: iter_time=4.224e-05, forward_time=0.036, loss_ctc=5.671, loss=5.671, backward_time=0.008, grad_norm=114.496, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 21:48:15,167 (trainer:762) INFO: 3epoch:train:681-720batch: iter_time=4.290e-05, forward_time=0.036, loss_ctc=5.437, loss=5.437, backward_time=0.008, grad_norm=117.887, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-16 21:48:17,840 (trainer:762) INFO: 3epoch:train:721-760batch: iter_time=4.319e-05, forward_time=0.035, loss_ctc=5.436, loss=5.436, backward_time=0.008, grad_norm=120.069, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-16 21:48:20,479 (trainer:762) INFO: 3epoch:train:761-800batch: iter_time=4.015e-05, forward_time=0.035, loss_ctc=5.786, loss=5.786, backward_time=0.008, grad_norm=132.465, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-16 21:48:24,559 (trainer:357) INFO: 3epoch results: [train] iter_time=1.714e-04, forward_time=0.035, loss_ctc=6.505, loss=6.505, backward_time=0.008, grad_norm=120.242, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268, time=53.69 seconds, total_count=2400, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=80.671, cer_ctc=0.318, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=80.671, time=1.15 seconds, total_count=75, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.85 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 21:48:25,459 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:48:25,459 (trainer:291) INFO: 4/30epoch started. Estimated time to finish: 26 minutes and 34.68 seconds +[stan] 2024-01-16 21:48:28,537 (trainer:762) INFO: 4epoch:train:1-40batch: iter_time=0.003, forward_time=0.037, loss_ctc=5.883, loss=5.883, backward_time=0.008, grad_norm=127.974, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.307 +[stan] 2024-01-16 21:48:31,253 (trainer:762) INFO: 4epoch:train:41-80batch: iter_time=4.216e-05, forward_time=0.036, loss_ctc=5.409, loss=5.409, backward_time=0.008, grad_norm=119.820, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 21:48:33,755 (trainer:762) INFO: 4epoch:train:81-120batch: iter_time=4.228e-05, forward_time=0.033, loss_ctc=4.821, loss=4.821, backward_time=0.008, grad_norm=112.097, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-16 21:48:36,558 (trainer:762) INFO: 4epoch:train:121-160batch: iter_time=4.321e-05, forward_time=0.037, loss_ctc=5.451, loss=5.451, backward_time=0.008, grad_norm=112.439, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-16 21:48:39,230 (trainer:762) INFO: 4epoch:train:161-200batch: iter_time=4.084e-05, forward_time=0.035, loss_ctc=4.956, loss=4.956, backward_time=0.008, grad_norm=112.542, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-16 21:48:41,811 (trainer:762) INFO: 4epoch:train:201-240batch: iter_time=4.276e-05, forward_time=0.034, loss_ctc=4.867, loss=4.867, backward_time=0.008, grad_norm=111.942, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-16 21:48:44,628 (trainer:762) INFO: 4epoch:train:241-280batch: iter_time=4.273e-05, forward_time=0.037, loss_ctc=5.313, loss=5.313, backward_time=0.008, grad_norm=133.870, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-16 21:48:47,171 (trainer:762) INFO: 4epoch:train:281-320batch: iter_time=4.201e-05, forward_time=0.034, loss_ctc=4.770, loss=4.770, backward_time=0.008, grad_norm=109.968, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-16 21:48:49,864 (trainer:762) INFO: 4epoch:train:321-360batch: iter_time=4.269e-05, forward_time=0.036, loss_ctc=4.559, loss=4.559, backward_time=0.008, grad_norm=114.228, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-16 21:48:52,601 (trainer:762) INFO: 4epoch:train:361-400batch: iter_time=4.198e-05, forward_time=0.036, loss_ctc=4.811, loss=4.811, backward_time=0.008, grad_norm=105.151, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-16 21:48:55,113 (trainer:762) INFO: 4epoch:train:401-440batch: iter_time=4.205e-05, forward_time=0.033, loss_ctc=4.431, loss=4.431, backward_time=0.008, grad_norm=106.151, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-16 21:48:57,922 (trainer:762) INFO: 4epoch:train:441-480batch: iter_time=4.276e-05, forward_time=0.037, loss_ctc=4.844, loss=4.844, backward_time=0.008, grad_norm=116.389, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-16 21:49:00,424 (trainer:762) INFO: 4epoch:train:481-520batch: iter_time=4.293e-05, forward_time=0.033, loss_ctc=4.142, loss=4.142, backward_time=0.008, grad_norm=103.577, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-16 21:49:03,165 (trainer:762) INFO: 4epoch:train:521-560batch: iter_time=4.341e-05, forward_time=0.036, loss_ctc=4.843, loss=4.843, backward_time=0.008, grad_norm=113.530, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-16 21:49:05,986 (trainer:762) INFO: 4epoch:train:561-600batch: iter_time=4.573e-05, forward_time=0.037, loss_ctc=5.129, loss=5.129, backward_time=0.008, grad_norm=118.886, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-16 21:49:08,639 (trainer:762) INFO: 4epoch:train:601-640batch: iter_time=4.214e-05, forward_time=0.035, loss_ctc=4.248, loss=4.248, backward_time=0.008, grad_norm=108.160, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.265 +[stan] 2024-01-16 21:49:11,377 (trainer:762) INFO: 4epoch:train:641-680batch: iter_time=4.243e-05, forward_time=0.036, loss_ctc=4.537, loss=4.537, backward_time=0.008, grad_norm=108.464, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-16 21:49:14,060 (trainer:762) INFO: 4epoch:train:681-720batch: iter_time=4.346e-05, forward_time=0.035, loss_ctc=4.388, loss=4.388, backward_time=0.008, grad_norm=107.531, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-16 21:49:16,548 (trainer:762) INFO: 4epoch:train:721-760batch: iter_time=4.213e-05, forward_time=0.033, loss_ctc=4.122, loss=4.122, backward_time=0.008, grad_norm=111.115, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-16 21:49:19,570 (trainer:762) INFO: 4epoch:train:761-800batch: iter_time=4.037e-05, forward_time=0.040, loss_ctc=5.155, loss=5.155, backward_time=0.009, grad_norm=121.512, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.302 +[stan] 2024-01-16 21:49:23,574 (trainer:357) INFO: 4epoch results: [train] iter_time=1.805e-04, forward_time=0.036, loss_ctc=4.834, loss=4.834, backward_time=0.008, grad_norm=113.767, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.19 seconds, total_count=3200, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=84.615, cer_ctc=0.310, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=84.615, time=1.14 seconds, total_count=100, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.78 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 21:49:24,505 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:49:24,505 (trainer:291) INFO: 5/30epoch started. Estimated time to finish: 25 minutes and 35.52 seconds +[stan] 2024-01-16 21:49:27,323 (trainer:762) INFO: 5epoch:train:1-40batch: iter_time=0.003, forward_time=0.034, loss_ctc=4.233, loss=4.233, backward_time=0.008, grad_norm=108.630, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-16 21:49:30,148 (trainer:762) INFO: 5epoch:train:41-80batch: iter_time=4.410e-05, forward_time=0.037, loss_ctc=4.707, loss=4.707, backward_time=0.008, grad_norm=117.214, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-16 21:49:32,671 (trainer:762) INFO: 5epoch:train:81-120batch: iter_time=4.558e-05, forward_time=0.034, loss_ctc=4.176, loss=4.176, backward_time=0.008, grad_norm=111.993, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-16 21:49:35,358 (trainer:762) INFO: 5epoch:train:121-160batch: iter_time=4.230e-05, forward_time=0.035, loss_ctc=4.155, loss=4.155, backward_time=0.008, grad_norm=109.685, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-16 21:49:37,999 (trainer:762) INFO: 5epoch:train:161-200batch: iter_time=4.198e-05, forward_time=0.035, loss_ctc=3.760, loss=3.760, backward_time=0.008, grad_norm=104.859, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-16 21:49:40,681 (trainer:762) INFO: 5epoch:train:201-240batch: iter_time=4.291e-05, forward_time=0.035, loss_ctc=4.069, loss=4.069, backward_time=0.008, grad_norm=101.071, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-16 21:49:43,502 (trainer:762) INFO: 5epoch:train:241-280batch: iter_time=4.238e-05, forward_time=0.038, loss_ctc=4.019, loss=4.019, backward_time=0.008, grad_norm=105.463, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-16 21:49:45,947 (trainer:762) INFO: 5epoch:train:281-320batch: iter_time=4.412e-05, forward_time=0.033, loss_ctc=3.484, loss=3.484, backward_time=0.008, grad_norm=99.618, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-16 21:49:48,882 (trainer:762) INFO: 5epoch:train:321-360batch: iter_time=4.195e-05, forward_time=0.039, loss_ctc=4.841, loss=4.841, backward_time=0.009, grad_norm=113.059, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.293 +[stan] 2024-01-16 21:49:51,553 (trainer:762) INFO: 5epoch:train:361-400batch: iter_time=4.212e-05, forward_time=0.035, loss_ctc=4.202, loss=4.202, backward_time=0.008, grad_norm=113.857, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-16 21:49:54,085 (trainer:762) INFO: 5epoch:train:401-440batch: iter_time=4.243e-05, forward_time=0.034, loss_ctc=3.831, loss=3.831, backward_time=0.008, grad_norm=99.941, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-16 21:49:56,822 (trainer:762) INFO: 5epoch:train:441-480batch: iter_time=4.381e-05, forward_time=0.036, loss_ctc=3.988, loss=3.988, backward_time=0.008, grad_norm=103.197, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-16 21:49:59,593 (trainer:762) INFO: 5epoch:train:481-520batch: iter_time=4.227e-05, forward_time=0.037, loss_ctc=4.233, loss=4.233, backward_time=0.008, grad_norm=109.567, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-16 21:50:02,314 (trainer:762) INFO: 5epoch:train:521-560batch: iter_time=4.292e-05, forward_time=0.036, loss_ctc=3.911, loss=3.911, backward_time=0.008, grad_norm=96.637, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 21:50:05,224 (trainer:762) INFO: 5epoch:train:561-600batch: iter_time=4.271e-05, forward_time=0.038, loss_ctc=4.441, loss=4.441, backward_time=0.008, grad_norm=109.087, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.291 +[stan] 2024-01-16 21:50:07,624 (trainer:762) INFO: 5epoch:train:601-640batch: iter_time=4.289e-05, forward_time=0.032, loss_ctc=3.227, loss=3.227, backward_time=0.008, grad_norm=100.247, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-16 21:50:10,305 (trainer:762) INFO: 5epoch:train:641-680batch: iter_time=4.227e-05, forward_time=0.035, loss_ctc=4.034, loss=4.034, backward_time=0.008, grad_norm=106.126, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-16 21:50:12,853 (trainer:762) INFO: 5epoch:train:681-720batch: iter_time=4.122e-05, forward_time=0.034, loss_ctc=3.674, loss=3.674, backward_time=0.008, grad_norm=99.056, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-16 21:50:15,663 (trainer:762) INFO: 5epoch:train:721-760batch: iter_time=4.302e-05, forward_time=0.037, loss_ctc=4.046, loss=4.046, backward_time=0.008, grad_norm=107.768, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-16 21:50:18,351 (trainer:762) INFO: 5epoch:train:761-800batch: iter_time=4.065e-05, forward_time=0.036, loss_ctc=3.728, loss=3.728, backward_time=0.008, grad_norm=94.408, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-16 21:50:22,332 (trainer:357) INFO: 5epoch results: [train] iter_time=1.988e-04, forward_time=0.035, loss_ctc=4.037, loss=4.037, backward_time=0.008, grad_norm=105.574, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.92 seconds, total_count=4000, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=88.443, cer_ctc=0.310, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=88.443, time=1.14 seconds, total_count=125, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.76 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 21:50:23,248 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:50:23,249 (trainer:291) INFO: 6/30epoch started. Estimated time to finish: 24 minutes and 34.88 seconds +[stan] 2024-01-16 21:50:26,125 (trainer:762) INFO: 6epoch:train:1-40batch: iter_time=0.003, forward_time=0.035, loss_ctc=3.429, loss=3.429, backward_time=0.008, grad_norm=100.994, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.287 +[stan] 2024-01-16 21:50:28,851 (trainer:762) INFO: 6epoch:train:41-80batch: iter_time=4.228e-05, forward_time=0.036, loss_ctc=3.774, loss=3.774, backward_time=0.008, grad_norm=113.189, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 21:50:31,584 (trainer:762) INFO: 6epoch:train:81-120batch: iter_time=4.381e-05, forward_time=0.036, loss_ctc=3.875, loss=3.875, backward_time=0.008, grad_norm=105.029, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 21:50:34,092 (trainer:762) INFO: 6epoch:train:121-160batch: iter_time=4.184e-05, forward_time=0.033, loss_ctc=3.167, loss=3.167, backward_time=0.008, grad_norm=96.332, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-16 21:50:36,888 (trainer:762) INFO: 6epoch:train:161-200batch: iter_time=4.355e-05, forward_time=0.037, loss_ctc=3.858, loss=3.858, backward_time=0.008, grad_norm=106.191, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-16 21:50:39,525 (trainer:762) INFO: 6epoch:train:201-240batch: iter_time=4.230e-05, forward_time=0.035, loss_ctc=3.567, loss=3.567, backward_time=0.008, grad_norm=106.333, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-16 21:50:42,467 (trainer:762) INFO: 6epoch:train:241-280batch: iter_time=4.355e-05, forward_time=0.039, loss_ctc=4.296, loss=4.296, backward_time=0.009, grad_norm=101.013, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.294 +[stan] 2024-01-16 21:50:44,916 (trainer:762) INFO: 6epoch:train:281-320batch: iter_time=4.339e-05, forward_time=0.033, loss_ctc=2.962, loss=2.962, backward_time=0.008, grad_norm=90.620, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-16 21:50:47,591 (trainer:762) INFO: 6epoch:train:321-360batch: iter_time=4.316e-05, forward_time=0.035, loss_ctc=3.514, loss=3.514, backward_time=0.008, grad_norm=100.236, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-16 21:50:50,149 (trainer:762) INFO: 6epoch:train:361-400batch: iter_time=4.336e-05, forward_time=0.034, loss_ctc=3.527, loss=3.527, backward_time=0.008, grad_norm=99.546, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-16 21:50:52,947 (trainer:762) INFO: 6epoch:train:401-440batch: iter_time=4.342e-05, forward_time=0.037, loss_ctc=4.005, loss=4.005, backward_time=0.008, grad_norm=103.096, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-16 21:50:55,541 (trainer:762) INFO: 6epoch:train:441-480batch: iter_time=4.488e-05, forward_time=0.034, loss_ctc=3.712, loss=3.712, backward_time=0.008, grad_norm=101.137, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-16 21:50:58,121 (trainer:762) INFO: 6epoch:train:481-520batch: iter_time=4.149e-05, forward_time=0.034, loss_ctc=3.330, loss=3.330, backward_time=0.008, grad_norm=92.815, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-16 21:51:00,974 (trainer:762) INFO: 6epoch:train:521-560batch: iter_time=4.428e-05, forward_time=0.038, loss_ctc=3.878, loss=3.878, backward_time=0.008, grad_norm=100.706, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-16 21:51:03,589 (trainer:762) INFO: 6epoch:train:561-600batch: iter_time=4.254e-05, forward_time=0.035, loss_ctc=3.386, loss=3.386, backward_time=0.008, grad_norm=92.777, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-16 21:51:06,165 (trainer:762) INFO: 6epoch:train:601-640batch: iter_time=4.180e-05, forward_time=0.034, loss_ctc=3.295, loss=3.295, backward_time=0.008, grad_norm=95.805, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-16 21:51:08,999 (trainer:762) INFO: 6epoch:train:641-680batch: iter_time=4.390e-05, forward_time=0.037, loss_ctc=3.773, loss=3.773, backward_time=0.008, grad_norm=109.366, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-16 21:51:11,517 (trainer:762) INFO: 6epoch:train:681-720batch: iter_time=4.281e-05, forward_time=0.033, loss_ctc=3.115, loss=3.115, backward_time=0.008, grad_norm=97.787, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-16 21:51:14,283 (trainer:762) INFO: 6epoch:train:721-760batch: iter_time=4.333e-05, forward_time=0.036, loss_ctc=3.613, loss=3.613, backward_time=0.008, grad_norm=100.306, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 21:51:17,040 (trainer:762) INFO: 6epoch:train:761-800batch: iter_time=4.110e-05, forward_time=0.036, loss_ctc=3.412, loss=3.412, backward_time=0.008, grad_norm=98.705, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 21:51:21,041 (trainer:357) INFO: 6epoch results: [train] iter_time=1.874e-04, forward_time=0.035, loss_ctc=3.575, loss=3.575, backward_time=0.008, grad_norm=100.599, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.86 seconds, total_count=4800, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=91.896, cer_ctc=0.312, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=91.896, time=1.15 seconds, total_count=150, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.78 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 21:51:22,061 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:51:22,062 (trainer:291) INFO: 7/30epoch started. Estimated time to finish: 23 minutes and 35.16 seconds +[stan] 2024-01-16 21:51:24,748 (trainer:762) INFO: 7epoch:train:1-40batch: iter_time=0.002, forward_time=0.033, loss_ctc=2.843, loss=2.843, backward_time=0.008, grad_norm=91.341, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-16 21:51:27,677 (trainer:762) INFO: 7epoch:train:41-80batch: iter_time=4.207e-05, forward_time=0.039, loss_ctc=4.048, loss=4.048, backward_time=0.009, grad_norm=99.726, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.293 +[stan] 2024-01-16 21:51:30,332 (trainer:762) INFO: 7epoch:train:81-120batch: iter_time=4.158e-05, forward_time=0.035, loss_ctc=3.223, loss=3.223, backward_time=0.008, grad_norm=101.609, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.265 +[stan] 2024-01-16 21:51:33,128 (trainer:762) INFO: 7epoch:train:121-160batch: iter_time=4.214e-05, forward_time=0.037, loss_ctc=3.510, loss=3.510, backward_time=0.008, grad_norm=99.045, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 21:51:35,612 (trainer:762) INFO: 7epoch:train:161-200batch: iter_time=4.371e-05, forward_time=0.033, loss_ctc=2.823, loss=2.823, backward_time=0.008, grad_norm=88.297, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-16 21:51:38,365 (trainer:762) INFO: 7epoch:train:201-240batch: iter_time=4.268e-05, forward_time=0.036, loss_ctc=3.642, loss=3.642, backward_time=0.008, grad_norm=101.302, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 21:51:41,172 (trainer:762) INFO: 7epoch:train:241-280batch: iter_time=4.457e-05, forward_time=0.037, loss_ctc=3.489, loss=3.489, backward_time=0.008, grad_norm=99.401, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-16 21:51:43,610 (trainer:762) INFO: 7epoch:train:281-320batch: iter_time=4.208e-05, forward_time=0.032, loss_ctc=2.831, loss=2.831, backward_time=0.008, grad_norm=86.938, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-16 21:51:46,398 (trainer:762) INFO: 7epoch:train:321-360batch: iter_time=4.363e-05, forward_time=0.037, loss_ctc=3.675, loss=3.675, backward_time=0.008, grad_norm=108.441, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 21:51:49,190 (trainer:762) INFO: 7epoch:train:361-400batch: iter_time=4.219e-05, forward_time=0.037, loss_ctc=3.598, loss=3.598, backward_time=0.008, grad_norm=98.435, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 21:51:51,722 (trainer:762) INFO: 7epoch:train:401-440batch: iter_time=4.354e-05, forward_time=0.034, loss_ctc=2.810, loss=2.810, backward_time=0.008, grad_norm=90.103, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-16 21:51:54,406 (trainer:762) INFO: 7epoch:train:441-480batch: iter_time=4.258e-05, forward_time=0.035, loss_ctc=3.169, loss=3.169, backward_time=0.008, grad_norm=98.915, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-16 21:51:57,139 (trainer:762) INFO: 7epoch:train:481-520batch: iter_time=4.297e-05, forward_time=0.036, loss_ctc=3.381, loss=3.381, backward_time=0.008, grad_norm=93.484, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 21:51:59,613 (trainer:762) INFO: 7epoch:train:521-560batch: iter_time=4.202e-05, forward_time=0.033, loss_ctc=3.117, loss=3.117, backward_time=0.008, grad_norm=98.536, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-16 21:52:02,444 (trainer:762) INFO: 7epoch:train:561-600batch: iter_time=4.194e-05, forward_time=0.037, loss_ctc=3.351, loss=3.351, backward_time=0.008, grad_norm=96.894, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-16 21:52:04,924 (trainer:762) INFO: 7epoch:train:601-640batch: iter_time=4.259e-05, forward_time=0.033, loss_ctc=2.863, loss=2.863, backward_time=0.008, grad_norm=89.395, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-16 21:52:08,026 (trainer:762) INFO: 7epoch:train:641-680batch: iter_time=4.266e-05, forward_time=0.041, loss_ctc=3.859, loss=3.859, backward_time=0.009, grad_norm=106.645, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.310 +[stan] 2024-01-16 21:52:10,489 (trainer:762) INFO: 7epoch:train:681-720batch: iter_time=4.467e-05, forward_time=0.033, loss_ctc=2.638, loss=2.638, backward_time=0.008, grad_norm=85.428, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-16 21:52:13,264 (trainer:762) INFO: 7epoch:train:721-760batch: iter_time=4.283e-05, forward_time=0.037, loss_ctc=3.337, loss=3.337, backward_time=0.008, grad_norm=103.162, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-16 21:52:15,979 (trainer:762) INFO: 7epoch:train:761-800batch: iter_time=4.209e-05, forward_time=0.036, loss_ctc=3.250, loss=3.250, backward_time=0.008, grad_norm=91.992, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 21:52:19,934 (trainer:357) INFO: 7epoch results: [train] iter_time=1.519e-04, forward_time=0.035, loss_ctc=3.273, loss=3.273, backward_time=0.008, grad_norm=96.454, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.99 seconds, total_count=5600, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=93.598, cer_ctc=0.313, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=93.598, time=1.13 seconds, total_count=175, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 21:52:20,884 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:52:20,886 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/6epoch.pth +[stan] 2024-01-16 21:52:20,886 (trainer:291) INFO: 8/30epoch started. Estimated time to finish: 22 minutes and 35.73 seconds +[stan] 2024-01-16 21:52:23,692 (trainer:762) INFO: 8epoch:train:1-40batch: iter_time=0.003, forward_time=0.034, loss_ctc=3.134, loss=3.134, backward_time=0.008, grad_norm=94.277, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-16 21:52:26,387 (trainer:762) INFO: 8epoch:train:41-80batch: iter_time=4.149e-05, forward_time=0.036, loss_ctc=3.431, loss=3.431, backward_time=0.008, grad_norm=90.942, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-16 21:52:29,090 (trainer:762) INFO: 8epoch:train:81-120batch: iter_time=4.337e-05, forward_time=0.036, loss_ctc=3.070, loss=3.070, backward_time=0.008, grad_norm=91.888, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-16 21:52:31,710 (trainer:762) INFO: 8epoch:train:121-160batch: iter_time=4.141e-05, forward_time=0.035, loss_ctc=2.914, loss=2.914, backward_time=0.008, grad_norm=90.489, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-16 21:52:34,534 (trainer:762) INFO: 8epoch:train:161-200batch: iter_time=4.231e-05, forward_time=0.037, loss_ctc=3.100, loss=3.100, backward_time=0.008, grad_norm=88.662, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-16 21:52:36,966 (trainer:762) INFO: 8epoch:train:201-240batch: iter_time=4.189e-05, forward_time=0.032, loss_ctc=2.412, loss=2.412, backward_time=0.008, grad_norm=80.242, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-16 21:52:39,742 (trainer:762) INFO: 8epoch:train:241-280batch: iter_time=4.037e-05, forward_time=0.037, loss_ctc=3.102, loss=3.102, backward_time=0.008, grad_norm=96.134, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-16 21:52:42,288 (trainer:762) INFO: 8epoch:train:281-320batch: iter_time=4.444e-05, forward_time=0.034, loss_ctc=2.653, loss=2.653, backward_time=0.008, grad_norm=88.289, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-16 21:52:45,163 (trainer:762) INFO: 8epoch:train:321-360batch: iter_time=4.289e-05, forward_time=0.038, loss_ctc=3.402, loss=3.402, backward_time=0.009, grad_norm=95.986, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.287 +[stan] 2024-01-16 21:52:47,776 (trainer:762) INFO: 8epoch:train:361-400batch: iter_time=4.495e-05, forward_time=0.035, loss_ctc=2.900, loss=2.900, backward_time=0.008, grad_norm=89.927, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-16 21:52:50,480 (trainer:762) INFO: 8epoch:train:401-440batch: iter_time=4.427e-05, forward_time=0.036, loss_ctc=3.013, loss=3.013, backward_time=0.008, grad_norm=89.369, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-16 21:52:53,364 (trainer:762) INFO: 8epoch:train:441-480batch: iter_time=4.615e-05, forward_time=0.038, loss_ctc=3.289, loss=3.289, backward_time=0.008, grad_norm=94.373, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.288 +[stan] 2024-01-16 21:52:55,802 (trainer:762) INFO: 8epoch:train:481-520batch: iter_time=4.253e-05, forward_time=0.032, loss_ctc=2.515, loss=2.515, backward_time=0.008, grad_norm=81.060, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-16 21:52:58,347 (trainer:762) INFO: 8epoch:train:521-560batch: iter_time=4.272e-05, forward_time=0.035, loss_ctc=2.429, loss=2.429, backward_time=0.008, grad_norm=87.005, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-16 21:53:01,080 (trainer:762) INFO: 8epoch:train:561-600batch: iter_time=4.194e-05, forward_time=0.036, loss_ctc=3.006, loss=3.006, backward_time=0.008, grad_norm=94.755, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 21:53:03,955 (trainer:762) INFO: 8epoch:train:601-640batch: iter_time=4.313e-05, forward_time=0.038, loss_ctc=3.212, loss=3.212, backward_time=0.008, grad_norm=98.571, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.287 +[stan] 2024-01-16 21:53:06,371 (trainer:762) INFO: 8epoch:train:641-680batch: iter_time=4.215e-05, forward_time=0.032, loss_ctc=2.388, loss=2.388, backward_time=0.008, grad_norm=78.683, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-16 21:53:09,438 (trainer:762) INFO: 8epoch:train:681-720batch: iter_time=4.322e-05, forward_time=0.040, loss_ctc=3.692, loss=3.692, backward_time=0.009, grad_norm=100.997, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.307 +[stan] 2024-01-16 21:53:11,987 (trainer:762) INFO: 8epoch:train:721-760batch: iter_time=4.279e-05, forward_time=0.034, loss_ctc=2.454, loss=2.454, backward_time=0.008, grad_norm=81.152, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-16 21:53:14,744 (trainer:762) INFO: 8epoch:train:761-800batch: iter_time=4.029e-05, forward_time=0.036, loss_ctc=3.220, loss=3.220, backward_time=0.008, grad_norm=99.388, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 21:53:18,698 (trainer:357) INFO: 8epoch results: [train] iter_time=1.724e-04, forward_time=0.036, loss_ctc=2.967, loss=2.967, backward_time=0.008, grad_norm=90.610, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.93 seconds, total_count=6400, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=96.668, cer_ctc=0.312, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=96.668, time=1.14 seconds, total_count=200, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 21:53:19,671 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:53:19,672 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/7epoch.pth +[stan] 2024-01-16 21:53:19,672 (trainer:291) INFO: 9/30epoch started. Estimated time to finish: 21 minutes and 36.35 seconds +[stan] 2024-01-16 21:53:22,435 (trainer:762) INFO: 9epoch:train:1-40batch: iter_time=0.003, forward_time=0.033, loss_ctc=2.456, loss=2.456, backward_time=0.008, grad_norm=84.827, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 21:53:25,211 (trainer:762) INFO: 9epoch:train:41-80batch: iter_time=4.372e-05, forward_time=0.037, loss_ctc=3.232, loss=3.232, backward_time=0.008, grad_norm=95.882, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-16 21:53:27,680 (trainer:762) INFO: 9epoch:train:81-120batch: iter_time=4.217e-05, forward_time=0.033, loss_ctc=2.495, loss=2.495, backward_time=0.008, grad_norm=80.700, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-16 21:53:30,604 (trainer:762) INFO: 9epoch:train:121-160batch: iter_time=4.385e-05, forward_time=0.038, loss_ctc=3.338, loss=3.338, backward_time=0.008, grad_norm=90.587, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.292 +[stan] 2024-01-16 21:53:33,250 (trainer:762) INFO: 9epoch:train:161-200batch: iter_time=4.166e-05, forward_time=0.035, loss_ctc=2.803, loss=2.803, backward_time=0.008, grad_norm=83.349, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-16 21:53:35,968 (trainer:762) INFO: 9epoch:train:201-240batch: iter_time=4.175e-05, forward_time=0.036, loss_ctc=3.090, loss=3.090, backward_time=0.008, grad_norm=84.667, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 21:53:38,476 (trainer:762) INFO: 9epoch:train:241-280batch: iter_time=4.461e-05, forward_time=0.033, loss_ctc=2.637, loss=2.637, backward_time=0.008, grad_norm=84.548, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-16 21:53:41,269 (trainer:762) INFO: 9epoch:train:281-320batch: iter_time=4.199e-05, forward_time=0.037, loss_ctc=2.911, loss=2.911, backward_time=0.008, grad_norm=85.907, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 21:53:44,133 (trainer:762) INFO: 9epoch:train:321-360batch: iter_time=4.289e-05, forward_time=0.038, loss_ctc=3.032, loss=3.032, backward_time=0.008, grad_norm=88.961, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.286 +[stan] 2024-01-16 21:53:46,596 (trainer:762) INFO: 9epoch:train:361-400batch: iter_time=4.438e-05, forward_time=0.033, loss_ctc=2.483, loss=2.483, backward_time=0.008, grad_norm=77.756, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-16 21:53:49,308 (trainer:762) INFO: 9epoch:train:401-440batch: iter_time=4.273e-05, forward_time=0.036, loss_ctc=2.945, loss=2.945, backward_time=0.008, grad_norm=90.458, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 21:53:52,082 (trainer:762) INFO: 9epoch:train:441-480batch: iter_time=4.158e-05, forward_time=0.037, loss_ctc=2.906, loss=2.906, backward_time=0.008, grad_norm=86.636, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-16 21:53:54,792 (trainer:762) INFO: 9epoch:train:481-520batch: iter_time=4.144e-05, forward_time=0.036, loss_ctc=2.917, loss=2.917, backward_time=0.008, grad_norm=94.410, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 21:53:57,345 (trainer:762) INFO: 9epoch:train:521-560batch: iter_time=4.116e-05, forward_time=0.034, loss_ctc=2.670, loss=2.670, backward_time=0.008, grad_norm=83.027, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-16 21:54:00,043 (trainer:762) INFO: 9epoch:train:561-600batch: iter_time=4.156e-05, forward_time=0.036, loss_ctc=3.015, loss=3.015, backward_time=0.008, grad_norm=87.055, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-16 21:54:02,568 (trainer:762) INFO: 9epoch:train:601-640batch: iter_time=4.295e-05, forward_time=0.034, loss_ctc=2.624, loss=2.624, backward_time=0.008, grad_norm=81.811, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-16 21:54:05,389 (trainer:762) INFO: 9epoch:train:641-680batch: iter_time=4.191e-05, forward_time=0.037, loss_ctc=2.999, loss=2.999, backward_time=0.008, grad_norm=89.965, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-16 21:54:07,981 (trainer:762) INFO: 9epoch:train:681-720batch: iter_time=4.224e-05, forward_time=0.034, loss_ctc=2.643, loss=2.643, backward_time=0.008, grad_norm=88.286, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-16 21:54:10,705 (trainer:762) INFO: 9epoch:train:721-760batch: iter_time=4.245e-05, forward_time=0.036, loss_ctc=2.647, loss=2.647, backward_time=0.008, grad_norm=85.120, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 21:54:13,430 (trainer:762) INFO: 9epoch:train:761-800batch: iter_time=4.205e-05, forward_time=0.036, loss_ctc=2.657, loss=2.657, backward_time=0.008, grad_norm=82.134, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 21:54:17,390 (trainer:357) INFO: 9epoch results: [train] iter_time=1.886e-04, forward_time=0.035, loss_ctc=2.825, loss=2.825, backward_time=0.008, grad_norm=86.304, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.83 seconds, total_count=7200, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=96.942, cer_ctc=0.312, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=96.942, time=1.14 seconds, total_count=225, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.75 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 21:54:18,469 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:54:18,470 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/8epoch.pth +[stan] 2024-01-16 21:54:18,471 (trainer:291) INFO: 10/30epoch started. Estimated time to finish: 20 minutes and 37.13 seconds +[stan] 2024-01-16 21:54:21,219 (trainer:762) INFO: 10epoch:train:1-40batch: iter_time=0.002, forward_time=0.033, loss_ctc=2.478, loss=2.478, backward_time=0.008, grad_norm=79.989, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-16 21:54:24,220 (trainer:762) INFO: 10epoch:train:41-80batch: iter_time=4.182e-05, forward_time=0.039, loss_ctc=3.274, loss=3.274, backward_time=0.009, grad_norm=90.279, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.300 +[stan] 2024-01-16 21:54:26,770 (trainer:762) INFO: 10epoch:train:81-120batch: iter_time=4.445e-05, forward_time=0.034, loss_ctc=2.381, loss=2.381, backward_time=0.008, grad_norm=82.344, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-16 21:54:29,303 (trainer:762) INFO: 10epoch:train:121-160batch: iter_time=4.156e-05, forward_time=0.034, loss_ctc=2.399, loss=2.399, backward_time=0.008, grad_norm=87.242, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-16 21:54:32,031 (trainer:762) INFO: 10epoch:train:161-200batch: iter_time=4.184e-05, forward_time=0.036, loss_ctc=2.743, loss=2.743, backward_time=0.008, grad_norm=83.180, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 21:54:34,820 (trainer:762) INFO: 10epoch:train:201-240batch: iter_time=4.489e-05, forward_time=0.037, loss_ctc=2.860, loss=2.860, backward_time=0.008, grad_norm=87.566, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 21:54:37,359 (trainer:762) INFO: 10epoch:train:241-280batch: iter_time=4.171e-05, forward_time=0.034, loss_ctc=2.623, loss=2.623, backward_time=0.008, grad_norm=84.435, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-16 21:54:40,278 (trainer:762) INFO: 10epoch:train:281-320batch: iter_time=4.184e-05, forward_time=0.038, loss_ctc=2.929, loss=2.929, backward_time=0.008, grad_norm=85.640, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.292 +[stan] 2024-01-16 21:54:42,856 (trainer:762) INFO: 10epoch:train:321-360batch: iter_time=4.408e-05, forward_time=0.034, loss_ctc=2.458, loss=2.458, backward_time=0.008, grad_norm=81.298, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-16 21:54:45,446 (trainer:762) INFO: 10epoch:train:361-400batch: iter_time=4.217e-05, forward_time=0.034, loss_ctc=2.549, loss=2.549, backward_time=0.008, grad_norm=84.727, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-16 21:54:48,178 (trainer:762) INFO: 10epoch:train:401-440batch: iter_time=4.393e-05, forward_time=0.036, loss_ctc=2.684, loss=2.684, backward_time=0.008, grad_norm=87.368, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 21:54:50,899 (trainer:762) INFO: 10epoch:train:441-480batch: iter_time=4.167e-05, forward_time=0.036, loss_ctc=2.799, loss=2.799, backward_time=0.008, grad_norm=84.275, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 21:54:53,658 (trainer:762) INFO: 10epoch:train:481-520batch: iter_time=4.321e-05, forward_time=0.036, loss_ctc=2.784, loss=2.784, backward_time=0.008, grad_norm=85.333, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 21:54:56,369 (trainer:762) INFO: 10epoch:train:521-560batch: iter_time=4.329e-05, forward_time=0.036, loss_ctc=2.858, loss=2.858, backward_time=0.008, grad_norm=79.485, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 21:54:58,940 (trainer:762) INFO: 10epoch:train:561-600batch: iter_time=4.352e-05, forward_time=0.034, loss_ctc=2.430, loss=2.430, backward_time=0.008, grad_norm=78.518, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-16 21:55:01,806 (trainer:762) INFO: 10epoch:train:601-640batch: iter_time=4.220e-05, forward_time=0.038, loss_ctc=3.051, loss=3.051, backward_time=0.008, grad_norm=86.648, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.287 +[stan] 2024-01-16 21:55:04,420 (trainer:762) INFO: 10epoch:train:641-680batch: iter_time=4.085e-05, forward_time=0.035, loss_ctc=2.338, loss=2.338, backward_time=0.008, grad_norm=80.289, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-16 21:55:06,965 (trainer:762) INFO: 10epoch:train:681-720batch: iter_time=4.403e-05, forward_time=0.034, loss_ctc=2.387, loss=2.387, backward_time=0.008, grad_norm=81.487, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-16 21:55:09,510 (trainer:762) INFO: 10epoch:train:721-760batch: iter_time=4.144e-05, forward_time=0.034, loss_ctc=2.146, loss=2.146, backward_time=0.008, grad_norm=80.704, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-16 21:55:12,543 (trainer:762) INFO: 10epoch:train:761-800batch: iter_time=4.027e-05, forward_time=0.040, loss_ctc=3.186, loss=3.186, backward_time=0.009, grad_norm=86.654, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.303 +[stan] 2024-01-16 21:55:16,500 (trainer:357) INFO: 10epoch results: [train] iter_time=1.616e-04, forward_time=0.036, loss_ctc=2.668, loss=2.668, backward_time=0.008, grad_norm=83.873, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.15 seconds, total_count=8000, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=101.529, cer_ctc=0.314, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=101.529, time=1.15 seconds, total_count=250, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 21:55:17,515 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:55:17,516 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/9epoch.pth +[stan] 2024-01-16 21:55:17,516 (trainer:291) INFO: 11/30epoch started. Estimated time to finish: 19 minutes and 38.49 seconds +[stan] 2024-01-16 21:55:20,225 (trainer:762) INFO: 11epoch:train:1-40batch: iter_time=0.003, forward_time=0.033, loss_ctc=2.413, loss=2.413, backward_time=0.008, grad_norm=81.089, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-16 21:55:22,650 (trainer:762) INFO: 11epoch:train:41-80batch: iter_time=4.417e-05, forward_time=0.032, loss_ctc=2.205, loss=2.205, backward_time=0.008, grad_norm=80.693, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-16 21:55:25,505 (trainer:762) INFO: 11epoch:train:81-120batch: iter_time=4.094e-05, forward_time=0.038, loss_ctc=2.740, loss=2.740, backward_time=0.008, grad_norm=90.155, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-16 21:55:28,244 (trainer:762) INFO: 11epoch:train:121-160batch: iter_time=4.223e-05, forward_time=0.036, loss_ctc=2.953, loss=2.953, backward_time=0.008, grad_norm=94.591, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-16 21:55:30,671 (trainer:762) INFO: 11epoch:train:161-200batch: iter_time=4.355e-05, forward_time=0.032, loss_ctc=2.354, loss=2.354, backward_time=0.008, grad_norm=85.462, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-16 21:55:33,686 (trainer:762) INFO: 11epoch:train:201-240batch: iter_time=4.227e-05, forward_time=0.040, loss_ctc=3.154, loss=3.154, backward_time=0.009, grad_norm=90.206, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.301 +[stan] 2024-01-16 21:55:36,267 (trainer:762) INFO: 11epoch:train:241-280batch: iter_time=4.218e-05, forward_time=0.034, loss_ctc=2.403, loss=2.403, backward_time=0.008, grad_norm=79.899, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-16 21:55:39,032 (trainer:762) INFO: 11epoch:train:281-320batch: iter_time=4.325e-05, forward_time=0.036, loss_ctc=2.792, loss=2.792, backward_time=0.008, grad_norm=85.811, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 21:55:41,577 (trainer:762) INFO: 11epoch:train:321-360batch: iter_time=4.324e-05, forward_time=0.034, loss_ctc=2.307, loss=2.307, backward_time=0.008, grad_norm=77.315, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-16 21:55:44,288 (trainer:762) INFO: 11epoch:train:361-400batch: iter_time=4.225e-05, forward_time=0.036, loss_ctc=2.477, loss=2.477, backward_time=0.008, grad_norm=82.358, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 21:55:46,829 (trainer:762) INFO: 11epoch:train:401-440batch: iter_time=4.181e-05, forward_time=0.034, loss_ctc=2.312, loss=2.312, backward_time=0.008, grad_norm=76.352, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-16 21:55:49,576 (trainer:762) INFO: 11epoch:train:441-480batch: iter_time=4.436e-05, forward_time=0.036, loss_ctc=2.512, loss=2.512, backward_time=0.008, grad_norm=79.066, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 21:55:52,310 (trainer:762) INFO: 11epoch:train:481-520batch: iter_time=4.227e-05, forward_time=0.036, loss_ctc=2.589, loss=2.589, backward_time=0.008, grad_norm=83.791, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 21:55:54,851 (trainer:762) INFO: 11epoch:train:521-560batch: iter_time=4.615e-05, forward_time=0.034, loss_ctc=2.214, loss=2.214, backward_time=0.008, grad_norm=74.694, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-16 21:55:58,011 (trainer:762) INFO: 11epoch:train:561-600batch: iter_time=4.434e-05, forward_time=0.043, loss_ctc=2.894, loss=2.894, backward_time=0.009, grad_norm=85.125, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.316 +[stan] 2024-01-16 21:56:00,448 (trainer:762) INFO: 11epoch:train:601-640batch: iter_time=4.094e-05, forward_time=0.032, loss_ctc=2.338, loss=2.338, backward_time=0.008, grad_norm=73.482, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-16 21:56:03,277 (trainer:762) INFO: 11epoch:train:641-680batch: iter_time=4.309e-05, forward_time=0.037, loss_ctc=2.748, loss=2.748, backward_time=0.008, grad_norm=80.444, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-16 21:56:05,756 (trainer:762) INFO: 11epoch:train:681-720batch: iter_time=4.226e-05, forward_time=0.033, loss_ctc=2.293, loss=2.293, backward_time=0.008, grad_norm=76.331, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-16 21:56:08,480 (trainer:762) INFO: 11epoch:train:721-760batch: iter_time=4.283e-05, forward_time=0.036, loss_ctc=2.796, loss=2.796, backward_time=0.008, grad_norm=85.948, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 21:56:10,898 (trainer:762) INFO: 11epoch:train:761-800batch: iter_time=4.034e-05, forward_time=0.032, loss_ctc=2.230, loss=2.230, backward_time=0.008, grad_norm=80.125, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-16 21:56:14,849 (trainer:357) INFO: 11epoch results: [train] iter_time=1.811e-04, forward_time=0.035, loss_ctc=2.537, loss=2.537, backward_time=0.008, grad_norm=82.147, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267, time=53.46 seconds, total_count=8800, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=101.364, cer_ctc=0.321, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=101.364, time=1.13 seconds, total_count=275, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.75 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 21:56:15,788 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:56:15,790 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/10epoch.pth +[stan] 2024-01-16 21:56:15,790 (trainer:291) INFO: 12/30epoch started. Estimated time to finish: 18 minutes and 38.44 seconds +[stan] 2024-01-16 21:56:18,903 (trainer:762) INFO: 12epoch:train:1-40batch: iter_time=0.003, forward_time=0.038, loss_ctc=2.738, loss=2.738, backward_time=0.008, grad_norm=83.936, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.311 +[stan] 2024-01-16 21:56:21,660 (trainer:762) INFO: 12epoch:train:41-80batch: iter_time=4.430e-05, forward_time=0.036, loss_ctc=2.493, loss=2.493, backward_time=0.008, grad_norm=87.456, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 21:56:24,402 (trainer:762) INFO: 12epoch:train:81-120batch: iter_time=4.170e-05, forward_time=0.036, loss_ctc=2.541, loss=2.541, backward_time=0.008, grad_norm=80.531, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-16 21:56:27,200 (trainer:762) INFO: 12epoch:train:121-160batch: iter_time=4.156e-05, forward_time=0.037, loss_ctc=2.607, loss=2.607, backward_time=0.008, grad_norm=79.660, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-16 21:56:29,687 (trainer:762) INFO: 12epoch:train:161-200batch: iter_time=4.134e-05, forward_time=0.033, loss_ctc=2.447, loss=2.447, backward_time=0.008, grad_norm=83.946, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-16 21:56:32,541 (trainer:762) INFO: 12epoch:train:201-240batch: iter_time=4.248e-05, forward_time=0.038, loss_ctc=2.629, loss=2.629, backward_time=0.008, grad_norm=82.844, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-16 21:56:35,230 (trainer:762) INFO: 12epoch:train:241-280batch: iter_time=4.303e-05, forward_time=0.036, loss_ctc=2.377, loss=2.377, backward_time=0.008, grad_norm=81.774, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-16 21:56:37,706 (trainer:762) INFO: 12epoch:train:281-320batch: iter_time=4.156e-05, forward_time=0.033, loss_ctc=2.072, loss=2.072, backward_time=0.008, grad_norm=75.104, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-16 21:56:40,370 (trainer:762) INFO: 12epoch:train:321-360batch: iter_time=4.246e-05, forward_time=0.035, loss_ctc=2.495, loss=2.495, backward_time=0.008, grad_norm=81.888, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-16 21:56:42,904 (trainer:762) INFO: 12epoch:train:361-400batch: iter_time=4.233e-05, forward_time=0.034, loss_ctc=2.308, loss=2.308, backward_time=0.008, grad_norm=84.078, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-16 21:56:45,720 (trainer:762) INFO: 12epoch:train:401-440batch: iter_time=4.165e-05, forward_time=0.037, loss_ctc=2.915, loss=2.915, backward_time=0.008, grad_norm=84.441, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-16 21:56:48,272 (trainer:762) INFO: 12epoch:train:441-480batch: iter_time=4.391e-05, forward_time=0.034, loss_ctc=2.083, loss=2.083, backward_time=0.008, grad_norm=71.326, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-16 21:56:51,029 (trainer:762) INFO: 12epoch:train:481-520batch: iter_time=4.432e-05, forward_time=0.036, loss_ctc=2.547, loss=2.547, backward_time=0.008, grad_norm=79.807, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 21:56:53,752 (trainer:762) INFO: 12epoch:train:521-560batch: iter_time=4.259e-05, forward_time=0.036, loss_ctc=2.403, loss=2.403, backward_time=0.008, grad_norm=81.388, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 21:56:56,250 (trainer:762) INFO: 12epoch:train:561-600batch: iter_time=4.207e-05, forward_time=0.033, loss_ctc=2.258, loss=2.258, backward_time=0.008, grad_norm=75.247, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-16 21:56:59,318 (trainer:762) INFO: 12epoch:train:601-640batch: iter_time=4.595e-05, forward_time=0.040, loss_ctc=2.947, loss=2.947, backward_time=0.009, grad_norm=84.162, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.307 +[stan] 2024-01-16 21:57:01,790 (trainer:762) INFO: 12epoch:train:641-680batch: iter_time=4.334e-05, forward_time=0.033, loss_ctc=2.138, loss=2.138, backward_time=0.008, grad_norm=80.063, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-16 21:57:04,319 (trainer:762) INFO: 12epoch:train:681-720batch: iter_time=4.382e-05, forward_time=0.034, loss_ctc=2.212, loss=2.212, backward_time=0.008, grad_norm=73.275, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-16 21:57:07,067 (trainer:762) INFO: 12epoch:train:721-760batch: iter_time=4.408e-05, forward_time=0.036, loss_ctc=2.225, loss=2.225, backward_time=0.008, grad_norm=77.419, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 21:57:09,840 (trainer:762) INFO: 12epoch:train:761-800batch: iter_time=4.135e-05, forward_time=0.037, loss_ctc=2.409, loss=2.409, backward_time=0.008, grad_norm=78.835, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-16 21:57:13,802 (trainer:357) INFO: 12epoch results: [train] iter_time=1.881e-04, forward_time=0.036, loss_ctc=2.442, loss=2.442, backward_time=0.008, grad_norm=80.359, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.12 seconds, total_count=9600, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=103.276, cer_ctc=0.316, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=103.276, time=1.15 seconds, total_count=300, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 21:57:14,843 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:57:14,844 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/11epoch.pth +[stan] 2024-01-16 21:57:14,844 (trainer:291) INFO: 13/30epoch started. Estimated time to finish: 17 minutes and 39.86 seconds +[stan] 2024-01-16 21:57:17,616 (trainer:762) INFO: 13epoch:train:1-40batch: iter_time=0.002, forward_time=0.034, loss_ctc=2.216, loss=2.216, backward_time=0.008, grad_norm=72.400, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-16 21:57:20,340 (trainer:762) INFO: 13epoch:train:41-80batch: iter_time=4.241e-05, forward_time=0.036, loss_ctc=2.188, loss=2.188, backward_time=0.008, grad_norm=74.597, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 21:57:23,123 (trainer:762) INFO: 13epoch:train:81-120batch: iter_time=4.314e-05, forward_time=0.037, loss_ctc=2.675, loss=2.675, backward_time=0.008, grad_norm=82.544, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-16 21:57:25,826 (trainer:762) INFO: 13epoch:train:121-160batch: iter_time=4.213e-05, forward_time=0.036, loss_ctc=2.268, loss=2.268, backward_time=0.008, grad_norm=76.317, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-16 21:57:28,599 (trainer:762) INFO: 13epoch:train:161-200batch: iter_time=4.207e-05, forward_time=0.037, loss_ctc=2.471, loss=2.471, backward_time=0.008, grad_norm=80.007, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-16 21:57:31,156 (trainer:762) INFO: 13epoch:train:201-240batch: iter_time=4.266e-05, forward_time=0.034, loss_ctc=2.125, loss=2.125, backward_time=0.008, grad_norm=74.027, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-16 21:57:33,930 (trainer:762) INFO: 13epoch:train:241-280batch: iter_time=4.181e-05, forward_time=0.037, loss_ctc=2.459, loss=2.459, backward_time=0.008, grad_norm=80.916, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-16 21:57:36,744 (trainer:762) INFO: 13epoch:train:281-320batch: iter_time=4.271e-05, forward_time=0.037, loss_ctc=2.531, loss=2.531, backward_time=0.008, grad_norm=80.279, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-16 21:57:39,184 (trainer:762) INFO: 13epoch:train:321-360batch: iter_time=4.533e-05, forward_time=0.032, loss_ctc=2.115, loss=2.115, backward_time=0.008, grad_norm=77.790, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-16 21:57:41,932 (trainer:762) INFO: 13epoch:train:361-400batch: iter_time=4.326e-05, forward_time=0.036, loss_ctc=2.291, loss=2.291, backward_time=0.008, grad_norm=74.908, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 21:57:44,410 (trainer:762) INFO: 13epoch:train:401-440batch: iter_time=4.235e-05, forward_time=0.033, loss_ctc=1.901, loss=1.901, backward_time=0.008, grad_norm=72.935, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-16 21:57:47,215 (trainer:762) INFO: 13epoch:train:441-480batch: iter_time=4.281e-05, forward_time=0.037, loss_ctc=2.236, loss=2.236, backward_time=0.008, grad_norm=74.678, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-16 21:57:50,098 (trainer:762) INFO: 13epoch:train:481-520batch: iter_time=4.615e-05, forward_time=0.038, loss_ctc=2.442, loss=2.442, backward_time=0.008, grad_norm=78.755, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.288 +[stan] 2024-01-16 21:57:52,505 (trainer:762) INFO: 13epoch:train:521-560batch: iter_time=4.203e-05, forward_time=0.032, loss_ctc=1.776, loss=1.776, backward_time=0.008, grad_norm=70.132, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-16 21:57:55,258 (trainer:762) INFO: 13epoch:train:561-600batch: iter_time=4.346e-05, forward_time=0.036, loss_ctc=2.312, loss=2.312, backward_time=0.008, grad_norm=74.508, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 21:57:58,048 (trainer:762) INFO: 13epoch:train:601-640batch: iter_time=4.276e-05, forward_time=0.037, loss_ctc=2.401, loss=2.401, backward_time=0.008, grad_norm=81.942, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 21:58:00,513 (trainer:762) INFO: 13epoch:train:641-680batch: iter_time=4.327e-05, forward_time=0.033, loss_ctc=2.019, loss=2.019, backward_time=0.008, grad_norm=74.893, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-16 21:58:03,289 (trainer:762) INFO: 13epoch:train:681-720batch: iter_time=4.270e-05, forward_time=0.037, loss_ctc=2.318, loss=2.318, backward_time=0.008, grad_norm=75.429, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-16 21:58:06,042 (trainer:762) INFO: 13epoch:train:721-760batch: iter_time=4.392e-05, forward_time=0.036, loss_ctc=2.344, loss=2.344, backward_time=0.008, grad_norm=74.686, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 21:58:08,857 (trainer:762) INFO: 13epoch:train:761-800batch: iter_time=4.043e-05, forward_time=0.037, loss_ctc=2.301, loss=2.301, backward_time=0.008, grad_norm=74.449, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-16 21:58:12,824 (trainer:357) INFO: 13epoch results: [train] iter_time=1.481e-04, forward_time=0.036, loss_ctc=2.269, loss=2.269, backward_time=0.008, grad_norm=76.310, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.09 seconds, total_count=10400, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=104.826, cer_ctc=0.320, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=104.826, time=1.15 seconds, total_count=325, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 21:58:13,777 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:58:13,779 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/12epoch.pth +[stan] 2024-01-16 21:58:13,779 (trainer:291) INFO: 14/30epoch started. Estimated time to finish: 16 minutes and 41.05 seconds +[stan] 2024-01-16 21:58:16,503 (trainer:762) INFO: 14epoch:train:1-40batch: iter_time=0.003, forward_time=0.033, loss_ctc=1.897, loss=1.897, backward_time=0.008, grad_norm=73.273, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 21:58:18,964 (trainer:762) INFO: 14epoch:train:41-80batch: iter_time=4.437e-05, forward_time=0.033, loss_ctc=1.913, loss=1.913, backward_time=0.008, grad_norm=71.968, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-16 21:58:21,807 (trainer:762) INFO: 14epoch:train:81-120batch: iter_time=4.214e-05, forward_time=0.038, loss_ctc=2.297, loss=2.297, backward_time=0.008, grad_norm=75.930, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.284 +[stan] 2024-01-16 21:58:24,523 (trainer:762) INFO: 14epoch:train:121-160batch: iter_time=4.284e-05, forward_time=0.036, loss_ctc=2.239, loss=2.239, backward_time=0.008, grad_norm=72.747, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 21:58:26,970 (trainer:762) INFO: 14epoch:train:161-200batch: iter_time=4.194e-05, forward_time=0.033, loss_ctc=2.103, loss=2.103, backward_time=0.008, grad_norm=74.262, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-16 21:58:30,066 (trainer:762) INFO: 14epoch:train:201-240batch: iter_time=4.404e-05, forward_time=0.041, loss_ctc=3.017, loss=3.017, backward_time=0.009, grad_norm=82.723, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.309 +[stan] 2024-01-16 21:58:32,555 (trainer:762) INFO: 14epoch:train:241-280batch: iter_time=4.550e-05, forward_time=0.033, loss_ctc=2.228, loss=2.228, backward_time=0.008, grad_norm=77.617, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-16 21:58:34,982 (trainer:762) INFO: 14epoch:train:281-320batch: iter_time=4.190e-05, forward_time=0.032, loss_ctc=2.044, loss=2.044, backward_time=0.008, grad_norm=69.445, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-16 21:58:37,884 (trainer:762) INFO: 14epoch:train:321-360batch: iter_time=4.528e-05, forward_time=0.038, loss_ctc=2.571, loss=2.571, backward_time=0.008, grad_norm=83.025, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.290 +[stan] 2024-01-16 21:58:40,591 (trainer:762) INFO: 14epoch:train:361-400batch: iter_time=4.322e-05, forward_time=0.036, loss_ctc=2.205, loss=2.205, backward_time=0.008, grad_norm=76.189, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 21:58:43,342 (trainer:762) INFO: 14epoch:train:401-440batch: iter_time=4.269e-05, forward_time=0.036, loss_ctc=2.317, loss=2.317, backward_time=0.008, grad_norm=76.454, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 21:58:45,748 (trainer:762) INFO: 14epoch:train:441-480batch: iter_time=4.150e-05, forward_time=0.032, loss_ctc=1.869, loss=1.869, backward_time=0.008, grad_norm=75.833, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-16 21:58:48,617 (trainer:762) INFO: 14epoch:train:481-520batch: iter_time=4.266e-05, forward_time=0.038, loss_ctc=2.440, loss=2.440, backward_time=0.008, grad_norm=82.095, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.287 +[stan] 2024-01-16 21:58:51,451 (trainer:762) INFO: 14epoch:train:521-560batch: iter_time=4.251e-05, forward_time=0.037, loss_ctc=2.452, loss=2.452, backward_time=0.008, grad_norm=76.739, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-16 21:58:53,864 (trainer:762) INFO: 14epoch:train:561-600batch: iter_time=4.191e-05, forward_time=0.032, loss_ctc=1.927, loss=1.927, backward_time=0.008, grad_norm=68.763, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-16 21:58:56,649 (trainer:762) INFO: 14epoch:train:601-640batch: iter_time=4.209e-05, forward_time=0.037, loss_ctc=2.284, loss=2.284, backward_time=0.008, grad_norm=80.835, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-16 21:58:59,419 (trainer:762) INFO: 14epoch:train:641-680batch: iter_time=4.546e-05, forward_time=0.037, loss_ctc=2.310, loss=2.310, backward_time=0.008, grad_norm=75.220, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-16 21:59:02,053 (trainer:762) INFO: 14epoch:train:681-720batch: iter_time=4.350e-05, forward_time=0.035, loss_ctc=2.088, loss=2.088, backward_time=0.008, grad_norm=73.968, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-16 21:59:04,686 (trainer:762) INFO: 14epoch:train:721-760batch: iter_time=4.273e-05, forward_time=0.035, loss_ctc=2.050, loss=2.050, backward_time=0.008, grad_norm=70.822, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-16 21:59:07,301 (trainer:762) INFO: 14epoch:train:761-800batch: iter_time=3.996e-05, forward_time=0.035, loss_ctc=2.028, loss=2.028, backward_time=0.008, grad_norm=73.588, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-16 21:59:11,326 (trainer:357) INFO: 14epoch results: [train] iter_time=1.736e-04, forward_time=0.035, loss_ctc=2.214, loss=2.214, backward_time=0.008, grad_norm=75.575, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267, time=53.6 seconds, total_count=11200, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=103.958, cer_ctc=0.318, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=103.958, time=1.15 seconds, total_count=350, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.8 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 21:59:12,384 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:59:12,386 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/13epoch.pth +[stan] 2024-01-16 21:59:12,386 (trainer:291) INFO: 15/30epoch started. Estimated time to finish: 15 minutes and 41.84 seconds +[stan] 2024-01-16 21:59:15,464 (trainer:762) INFO: 15epoch:train:1-40batch: iter_time=0.002, forward_time=0.038, loss_ctc=2.455, loss=2.455, backward_time=0.009, grad_norm=78.360, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.307 +[stan] 2024-01-16 21:59:18,057 (trainer:762) INFO: 15epoch:train:41-80batch: iter_time=4.353e-05, forward_time=0.034, loss_ctc=1.883, loss=1.883, backward_time=0.008, grad_norm=67.764, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-16 21:59:20,690 (trainer:762) INFO: 15epoch:train:81-120batch: iter_time=4.640e-05, forward_time=0.035, loss_ctc=1.884, loss=1.884, backward_time=0.008, grad_norm=70.878, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-16 21:59:23,289 (trainer:762) INFO: 15epoch:train:121-160batch: iter_time=4.266e-05, forward_time=0.034, loss_ctc=1.945, loss=1.945, backward_time=0.008, grad_norm=71.479, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-16 21:59:26,088 (trainer:762) INFO: 15epoch:train:161-200batch: iter_time=4.306e-05, forward_time=0.037, loss_ctc=2.293, loss=2.293, backward_time=0.008, grad_norm=79.435, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-16 21:59:28,944 (trainer:762) INFO: 15epoch:train:201-240batch: iter_time=4.415e-05, forward_time=0.038, loss_ctc=2.322, loss=2.322, backward_time=0.008, grad_norm=74.555, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-16 21:59:31,442 (trainer:762) INFO: 15epoch:train:241-280batch: iter_time=4.179e-05, forward_time=0.033, loss_ctc=1.904, loss=1.904, backward_time=0.008, grad_norm=70.356, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-16 21:59:34,119 (trainer:762) INFO: 15epoch:train:281-320batch: iter_time=4.255e-05, forward_time=0.035, loss_ctc=2.184, loss=2.184, backward_time=0.008, grad_norm=75.448, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-16 21:59:36,841 (trainer:762) INFO: 15epoch:train:321-360batch: iter_time=4.319e-05, forward_time=0.036, loss_ctc=2.257, loss=2.257, backward_time=0.008, grad_norm=72.739, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 21:59:39,717 (trainer:762) INFO: 15epoch:train:361-400batch: iter_time=4.288e-05, forward_time=0.038, loss_ctc=2.255, loss=2.255, backward_time=0.008, grad_norm=75.351, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.288 +[stan] 2024-01-16 21:59:42,143 (trainer:762) INFO: 15epoch:train:401-440batch: iter_time=4.157e-05, forward_time=0.032, loss_ctc=1.751, loss=1.751, backward_time=0.008, grad_norm=69.173, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-16 21:59:44,723 (trainer:762) INFO: 15epoch:train:441-480batch: iter_time=4.262e-05, forward_time=0.034, loss_ctc=2.110, loss=2.110, backward_time=0.008, grad_norm=76.121, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-16 21:59:47,376 (trainer:762) INFO: 15epoch:train:481-520batch: iter_time=4.250e-05, forward_time=0.035, loss_ctc=2.176, loss=2.176, backward_time=0.008, grad_norm=74.747, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.265 +[stan] 2024-01-16 21:59:50,180 (trainer:762) INFO: 15epoch:train:521-560batch: iter_time=4.293e-05, forward_time=0.037, loss_ctc=2.141, loss=2.141, backward_time=0.008, grad_norm=70.868, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-16 21:59:53,019 (trainer:762) INFO: 15epoch:train:561-600batch: iter_time=4.218e-05, forward_time=0.037, loss_ctc=2.225, loss=2.225, backward_time=0.008, grad_norm=74.215, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.284 +[stan] 2024-01-16 21:59:55,496 (trainer:762) INFO: 15epoch:train:601-640batch: iter_time=4.528e-05, forward_time=0.033, loss_ctc=1.581, loss=1.581, backward_time=0.008, grad_norm=65.835, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-16 21:59:58,214 (trainer:762) INFO: 15epoch:train:641-680batch: iter_time=4.208e-05, forward_time=0.036, loss_ctc=2.155, loss=2.155, backward_time=0.008, grad_norm=74.162, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 22:00:01,005 (trainer:762) INFO: 15epoch:train:681-720batch: iter_time=4.273e-05, forward_time=0.037, loss_ctc=2.159, loss=2.159, backward_time=0.008, grad_norm=67.883, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 22:00:03,454 (trainer:762) INFO: 15epoch:train:721-760batch: iter_time=4.260e-05, forward_time=0.033, loss_ctc=1.714, loss=1.714, backward_time=0.008, grad_norm=68.743, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-16 22:00:06,250 (trainer:762) INFO: 15epoch:train:761-800batch: iter_time=4.176e-05, forward_time=0.037, loss_ctc=2.247, loss=2.247, backward_time=0.008, grad_norm=75.062, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-16 22:00:10,208 (trainer:357) INFO: 15epoch results: [train] iter_time=1.570e-04, forward_time=0.035, loss_ctc=2.082, loss=2.082, backward_time=0.008, grad_norm=72.659, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.94 seconds, total_count=12000, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=105.998, cer_ctc=0.319, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=105.998, time=1.14 seconds, total_count=375, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.75 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 22:00:11,169 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:00:11,171 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/14epoch.pth +[stan] 2024-01-16 22:00:11,171 (trainer:291) INFO: 16/30epoch started. Estimated time to finish: 14 minutes and 42.9 seconds +[stan] 2024-01-16 22:00:14,019 (trainer:762) INFO: 16epoch:train:1-40batch: iter_time=0.003, forward_time=0.034, loss_ctc=1.991, loss=1.991, backward_time=0.008, grad_norm=71.713, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.284 +[stan] 2024-01-16 22:00:16,632 (trainer:762) INFO: 16epoch:train:41-80batch: iter_time=4.201e-05, forward_time=0.035, loss_ctc=1.824, loss=1.824, backward_time=0.008, grad_norm=71.430, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-16 22:00:19,456 (trainer:762) INFO: 16epoch:train:81-120batch: iter_time=4.325e-05, forward_time=0.037, loss_ctc=2.143, loss=2.143, backward_time=0.008, grad_norm=74.919, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-16 22:00:22,311 (trainer:762) INFO: 16epoch:train:121-160batch: iter_time=4.237e-05, forward_time=0.038, loss_ctc=2.127, loss=2.127, backward_time=0.008, grad_norm=72.827, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-16 22:00:24,620 (trainer:762) INFO: 16epoch:train:161-200batch: iter_time=4.103e-05, forward_time=0.031, loss_ctc=1.678, loss=1.678, backward_time=0.008, grad_norm=65.130, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-16 22:00:27,470 (trainer:762) INFO: 16epoch:train:201-240batch: iter_time=4.249e-05, forward_time=0.038, loss_ctc=2.209, loss=2.209, backward_time=0.008, grad_norm=74.685, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-16 22:00:30,030 (trainer:762) INFO: 16epoch:train:241-280batch: iter_time=4.248e-05, forward_time=0.034, loss_ctc=1.901, loss=1.901, backward_time=0.008, grad_norm=76.091, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-16 22:00:32,892 (trainer:762) INFO: 16epoch:train:281-320batch: iter_time=4.215e-05, forward_time=0.038, loss_ctc=2.371, loss=2.371, backward_time=0.009, grad_norm=77.268, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.286 +[stan] 2024-01-16 22:00:35,499 (trainer:762) INFO: 16epoch:train:321-360batch: iter_time=4.484e-05, forward_time=0.035, loss_ctc=1.825, loss=1.825, backward_time=0.008, grad_norm=68.693, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-16 22:00:38,273 (trainer:762) INFO: 16epoch:train:361-400batch: iter_time=4.127e-05, forward_time=0.037, loss_ctc=2.158, loss=2.158, backward_time=0.008, grad_norm=71.946, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-16 22:00:41,032 (trainer:762) INFO: 16epoch:train:401-440batch: iter_time=4.296e-05, forward_time=0.036, loss_ctc=2.112, loss=2.112, backward_time=0.008, grad_norm=72.144, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 22:00:43,522 (trainer:762) INFO: 16epoch:train:441-480batch: iter_time=4.248e-05, forward_time=0.033, loss_ctc=1.829, loss=1.829, backward_time=0.008, grad_norm=66.301, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-16 22:00:46,372 (trainer:762) INFO: 16epoch:train:481-520batch: iter_time=4.289e-05, forward_time=0.038, loss_ctc=2.254, loss=2.254, backward_time=0.008, grad_norm=75.928, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-16 22:00:48,727 (trainer:762) INFO: 16epoch:train:521-560batch: iter_time=4.245e-05, forward_time=0.031, loss_ctc=1.585, loss=1.585, backward_time=0.008, grad_norm=64.675, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-16 22:00:51,551 (trainer:762) INFO: 16epoch:train:561-600batch: iter_time=4.297e-05, forward_time=0.037, loss_ctc=2.424, loss=2.424, backward_time=0.008, grad_norm=82.412, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-16 22:00:54,084 (trainer:762) INFO: 16epoch:train:601-640batch: iter_time=4.338e-05, forward_time=0.034, loss_ctc=1.884, loss=1.884, backward_time=0.008, grad_norm=70.716, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-16 22:00:56,796 (trainer:762) INFO: 16epoch:train:641-680batch: iter_time=4.088e-05, forward_time=0.036, loss_ctc=2.127, loss=2.127, backward_time=0.008, grad_norm=72.842, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 22:00:59,575 (trainer:762) INFO: 16epoch:train:681-720batch: iter_time=4.201e-05, forward_time=0.037, loss_ctc=2.112, loss=2.112, backward_time=0.008, grad_norm=75.060, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-16 22:01:02,137 (trainer:762) INFO: 16epoch:train:721-760batch: iter_time=4.210e-05, forward_time=0.034, loss_ctc=2.004, loss=2.004, backward_time=0.008, grad_norm=75.598, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-16 22:01:04,729 (trainer:762) INFO: 16epoch:train:761-800batch: iter_time=3.844e-05, forward_time=0.034, loss_ctc=1.980, loss=1.980, backward_time=0.008, grad_norm=69.853, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-16 22:01:08,831 (trainer:357) INFO: 16epoch results: [train] iter_time=1.850e-04, forward_time=0.035, loss_ctc=2.027, loss=2.027, backward_time=0.008, grad_norm=72.512, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268, time=53.63 seconds, total_count=12800, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=106.686, cer_ctc=0.321, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=106.686, time=1.26 seconds, total_count=400, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.76 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 22:01:09,815 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:01:09,816 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/15epoch.pth +[stan] 2024-01-16 22:01:09,816 (trainer:291) INFO: 17/30epoch started. Estimated time to finish: 13 minutes and 43.85 seconds +[stan] 2024-01-16 22:01:12,927 (trainer:762) INFO: 17epoch:train:1-40batch: iter_time=0.003, forward_time=0.038, loss_ctc=2.184, loss=2.184, backward_time=0.008, grad_norm=75.954, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.310 +[stan] 2024-01-16 22:01:15,663 (trainer:762) INFO: 17epoch:train:41-80batch: iter_time=4.212e-05, forward_time=0.036, loss_ctc=1.883, loss=1.883, backward_time=0.008, grad_norm=66.326, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-16 22:01:18,424 (trainer:762) INFO: 17epoch:train:81-120batch: iter_time=4.218e-05, forward_time=0.036, loss_ctc=2.004, loss=2.004, backward_time=0.008, grad_norm=68.930, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 22:01:20,944 (trainer:762) INFO: 17epoch:train:121-160batch: iter_time=4.660e-05, forward_time=0.033, loss_ctc=1.846, loss=1.846, backward_time=0.008, grad_norm=70.185, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-16 22:01:23,674 (trainer:762) INFO: 17epoch:train:161-200batch: iter_time=4.359e-05, forward_time=0.036, loss_ctc=1.947, loss=1.947, backward_time=0.008, grad_norm=72.500, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 22:01:26,558 (trainer:762) INFO: 17epoch:train:201-240batch: iter_time=4.494e-05, forward_time=0.038, loss_ctc=2.425, loss=2.425, backward_time=0.008, grad_norm=78.068, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.288 +[stan] 2024-01-16 22:01:28,975 (trainer:762) INFO: 17epoch:train:241-280batch: iter_time=4.121e-05, forward_time=0.032, loss_ctc=1.532, loss=1.532, backward_time=0.008, grad_norm=67.590, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-16 22:01:31,691 (trainer:762) INFO: 17epoch:train:281-320batch: iter_time=4.209e-05, forward_time=0.036, loss_ctc=2.082, loss=2.082, backward_time=0.008, grad_norm=70.519, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 22:01:34,406 (trainer:762) INFO: 17epoch:train:321-360batch: iter_time=4.395e-05, forward_time=0.036, loss_ctc=1.920, loss=1.920, backward_time=0.008, grad_norm=71.327, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 22:01:37,010 (trainer:762) INFO: 17epoch:train:361-400batch: iter_time=4.275e-05, forward_time=0.034, loss_ctc=1.997, loss=1.997, backward_time=0.008, grad_norm=71.085, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-16 22:01:39,563 (trainer:762) INFO: 17epoch:train:401-440batch: iter_time=4.240e-05, forward_time=0.034, loss_ctc=1.823, loss=1.823, backward_time=0.008, grad_norm=66.431, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-16 22:01:42,533 (trainer:762) INFO: 17epoch:train:441-480batch: iter_time=4.444e-05, forward_time=0.039, loss_ctc=2.470, loss=2.470, backward_time=0.009, grad_norm=76.176, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.297 +[stan] 2024-01-16 22:01:45,035 (trainer:762) INFO: 17epoch:train:481-520batch: iter_time=4.119e-05, forward_time=0.033, loss_ctc=1.580, loss=1.580, backward_time=0.008, grad_norm=69.652, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-16 22:01:47,526 (trainer:762) INFO: 17epoch:train:521-560batch: iter_time=4.307e-05, forward_time=0.033, loss_ctc=1.812, loss=1.812, backward_time=0.008, grad_norm=66.631, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-16 22:01:50,488 (trainer:762) INFO: 17epoch:train:561-600batch: iter_time=4.250e-05, forward_time=0.039, loss_ctc=2.269, loss=2.269, backward_time=0.009, grad_norm=81.259, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.296 +[stan] 2024-01-16 22:01:53,071 (trainer:762) INFO: 17epoch:train:601-640batch: iter_time=4.155e-05, forward_time=0.034, loss_ctc=1.739, loss=1.739, backward_time=0.008, grad_norm=66.978, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-16 22:01:55,682 (trainer:762) INFO: 17epoch:train:641-680batch: iter_time=4.233e-05, forward_time=0.035, loss_ctc=1.964, loss=1.964, backward_time=0.008, grad_norm=66.857, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-16 22:01:58,391 (trainer:762) INFO: 17epoch:train:681-720batch: iter_time=4.460e-05, forward_time=0.036, loss_ctc=1.995, loss=1.995, backward_time=0.008, grad_norm=69.073, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 22:02:01,106 (trainer:762) INFO: 17epoch:train:721-760batch: iter_time=4.358e-05, forward_time=0.036, loss_ctc=1.767, loss=1.767, backward_time=0.008, grad_norm=69.049, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 22:02:03,817 (trainer:762) INFO: 17epoch:train:761-800batch: iter_time=3.960e-05, forward_time=0.036, loss_ctc=1.921, loss=1.921, backward_time=0.008, grad_norm=73.875, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 22:02:07,770 (trainer:357) INFO: 17epoch results: [train] iter_time=1.735e-04, forward_time=0.036, loss_ctc=1.958, loss=1.958, backward_time=0.008, grad_norm=70.923, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.08 seconds, total_count=13600, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=107.113, cer_ctc=0.317, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=107.113, time=1.14 seconds, total_count=425, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 22:02:08,830 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:02:08,832 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/16epoch.pth +[stan] 2024-01-16 22:02:08,832 (trainer:291) INFO: 18/30epoch started. Estimated time to finish: 12 minutes and 45.13 seconds +[stan] 2024-01-16 22:02:11,633 (trainer:762) INFO: 18epoch:train:1-40batch: iter_time=0.002, forward_time=0.034, loss_ctc=1.851, loss=1.851, backward_time=0.008, grad_norm=68.977, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-16 22:02:14,390 (trainer:762) INFO: 18epoch:train:41-80batch: iter_time=4.268e-05, forward_time=0.036, loss_ctc=2.110, loss=2.110, backward_time=0.008, grad_norm=72.274, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 22:02:17,099 (trainer:762) INFO: 18epoch:train:81-120batch: iter_time=4.273e-05, forward_time=0.036, loss_ctc=1.911, loss=1.911, backward_time=0.008, grad_norm=70.834, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 22:02:19,606 (trainer:762) INFO: 18epoch:train:121-160batch: iter_time=4.265e-05, forward_time=0.033, loss_ctc=1.722, loss=1.722, backward_time=0.008, grad_norm=67.268, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-16 22:02:22,423 (trainer:762) INFO: 18epoch:train:161-200batch: iter_time=4.284e-05, forward_time=0.037, loss_ctc=2.000, loss=2.000, backward_time=0.008, grad_norm=68.724, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-16 22:02:24,958 (trainer:762) INFO: 18epoch:train:201-240batch: iter_time=4.191e-05, forward_time=0.034, loss_ctc=1.676, loss=1.676, backward_time=0.008, grad_norm=65.251, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-16 22:02:27,685 (trainer:762) INFO: 18epoch:train:241-280batch: iter_time=4.306e-05, forward_time=0.036, loss_ctc=2.032, loss=2.032, backward_time=0.008, grad_norm=71.284, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 22:02:30,455 (trainer:762) INFO: 18epoch:train:281-320batch: iter_time=4.564e-05, forward_time=0.037, loss_ctc=1.982, loss=1.982, backward_time=0.008, grad_norm=70.351, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-16 22:02:33,220 (trainer:762) INFO: 18epoch:train:321-360batch: iter_time=4.596e-05, forward_time=0.036, loss_ctc=1.930, loss=1.930, backward_time=0.008, grad_norm=72.673, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 22:02:35,661 (trainer:762) INFO: 18epoch:train:361-400batch: iter_time=4.238e-05, forward_time=0.032, loss_ctc=1.576, loss=1.576, backward_time=0.008, grad_norm=64.875, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-16 22:02:38,508 (trainer:762) INFO: 18epoch:train:401-440batch: iter_time=4.333e-05, forward_time=0.038, loss_ctc=2.073, loss=2.073, backward_time=0.008, grad_norm=73.450, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-16 22:02:41,351 (trainer:762) INFO: 18epoch:train:441-480batch: iter_time=4.429e-05, forward_time=0.037, loss_ctc=1.939, loss=1.939, backward_time=0.008, grad_norm=71.877, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.284 +[stan] 2024-01-16 22:02:43,930 (trainer:762) INFO: 18epoch:train:481-520batch: iter_time=4.230e-05, forward_time=0.034, loss_ctc=1.641, loss=1.641, backward_time=0.008, grad_norm=62.040, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-16 22:02:46,562 (trainer:762) INFO: 18epoch:train:521-560batch: iter_time=4.153e-05, forward_time=0.035, loss_ctc=1.721, loss=1.721, backward_time=0.008, grad_norm=69.663, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-16 22:02:49,329 (trainer:762) INFO: 18epoch:train:561-600batch: iter_time=4.284e-05, forward_time=0.037, loss_ctc=1.847, loss=1.847, backward_time=0.008, grad_norm=66.718, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-16 22:02:51,863 (trainer:762) INFO: 18epoch:train:601-640batch: iter_time=4.470e-05, forward_time=0.034, loss_ctc=1.881, loss=1.881, backward_time=0.008, grad_norm=73.527, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-16 22:02:54,600 (trainer:762) INFO: 18epoch:train:641-680batch: iter_time=4.222e-05, forward_time=0.036, loss_ctc=1.951, loss=1.951, backward_time=0.008, grad_norm=72.049, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-16 22:02:57,338 (trainer:762) INFO: 18epoch:train:681-720batch: iter_time=4.205e-05, forward_time=0.036, loss_ctc=1.772, loss=1.772, backward_time=0.008, grad_norm=72.158, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-16 22:02:59,877 (trainer:762) INFO: 18epoch:train:721-760batch: iter_time=4.252e-05, forward_time=0.034, loss_ctc=1.775, loss=1.775, backward_time=0.008, grad_norm=70.029, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-16 22:03:02,639 (trainer:762) INFO: 18epoch:train:761-800batch: iter_time=4.165e-05, forward_time=0.036, loss_ctc=1.851, loss=1.851, backward_time=0.008, grad_norm=71.738, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 22:03:06,606 (trainer:357) INFO: 18epoch results: [train] iter_time=1.642e-04, forward_time=0.035, loss_ctc=1.862, loss=1.862, backward_time=0.008, grad_norm=69.788, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.88 seconds, total_count=14400, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=109.624, cer_ctc=0.318, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=109.624, time=1.15 seconds, total_count=450, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 22:03:07,596 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:03:07,598 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/17epoch.pth +[stan] 2024-01-16 22:03:07,598 (trainer:291) INFO: 19/30epoch started. Estimated time to finish: 11 minutes and 46.22 seconds +[stan] 2024-01-16 22:03:10,605 (trainer:762) INFO: 19epoch:train:1-40batch: iter_time=0.003, forward_time=0.036, loss_ctc=2.127, loss=2.127, backward_time=0.008, grad_norm=72.372, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.300 +[stan] 2024-01-16 22:03:13,162 (trainer:762) INFO: 19epoch:train:41-80batch: iter_time=4.239e-05, forward_time=0.034, loss_ctc=1.747, loss=1.747, backward_time=0.008, grad_norm=70.115, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-16 22:03:15,895 (trainer:762) INFO: 19epoch:train:81-120batch: iter_time=4.266e-05, forward_time=0.036, loss_ctc=1.865, loss=1.865, backward_time=0.008, grad_norm=67.757, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 22:03:18,553 (trainer:762) INFO: 19epoch:train:121-160batch: iter_time=4.358e-05, forward_time=0.035, loss_ctc=1.869, loss=1.869, backward_time=0.008, grad_norm=71.543, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-16 22:03:21,347 (trainer:762) INFO: 19epoch:train:161-200batch: iter_time=4.444e-05, forward_time=0.037, loss_ctc=1.877, loss=1.877, backward_time=0.008, grad_norm=69.511, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 22:03:23,931 (trainer:762) INFO: 19epoch:train:201-240batch: iter_time=4.541e-05, forward_time=0.034, loss_ctc=1.825, loss=1.825, backward_time=0.008, grad_norm=68.639, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-16 22:03:26,534 (trainer:762) INFO: 19epoch:train:241-280batch: iter_time=4.325e-05, forward_time=0.034, loss_ctc=1.782, loss=1.782, backward_time=0.008, grad_norm=70.747, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-16 22:03:29,447 (trainer:762) INFO: 19epoch:train:281-320batch: iter_time=4.456e-05, forward_time=0.038, loss_ctc=2.027, loss=2.027, backward_time=0.009, grad_norm=70.467, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.291 +[stan] 2024-01-16 22:03:31,962 (trainer:762) INFO: 19epoch:train:321-360batch: iter_time=4.296e-05, forward_time=0.033, loss_ctc=1.481, loss=1.481, backward_time=0.008, grad_norm=63.875, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-16 22:03:34,721 (trainer:762) INFO: 19epoch:train:361-400batch: iter_time=4.747e-05, forward_time=0.036, loss_ctc=1.930, loss=1.930, backward_time=0.008, grad_norm=68.703, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 22:03:37,207 (trainer:762) INFO: 19epoch:train:401-440batch: iter_time=4.264e-05, forward_time=0.033, loss_ctc=1.492, loss=1.492, backward_time=0.008, grad_norm=63.368, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-16 22:03:39,997 (trainer:762) INFO: 19epoch:train:441-480batch: iter_time=4.404e-05, forward_time=0.037, loss_ctc=1.866, loss=1.866, backward_time=0.008, grad_norm=70.838, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 22:03:42,727 (trainer:762) INFO: 19epoch:train:481-520batch: iter_time=4.347e-05, forward_time=0.036, loss_ctc=1.936, loss=1.936, backward_time=0.008, grad_norm=69.296, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 22:03:45,469 (trainer:762) INFO: 19epoch:train:521-560batch: iter_time=4.475e-05, forward_time=0.036, loss_ctc=1.994, loss=1.994, backward_time=0.008, grad_norm=67.177, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-16 22:03:48,031 (trainer:762) INFO: 19epoch:train:561-600batch: iter_time=4.435e-05, forward_time=0.034, loss_ctc=1.690, loss=1.690, backward_time=0.008, grad_norm=64.389, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-16 22:03:50,505 (trainer:762) INFO: 19epoch:train:601-640batch: iter_time=4.375e-05, forward_time=0.033, loss_ctc=1.519, loss=1.519, backward_time=0.008, grad_norm=64.022, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-16 22:03:53,398 (trainer:762) INFO: 19epoch:train:641-680batch: iter_time=4.565e-05, forward_time=0.039, loss_ctc=1.926, loss=1.926, backward_time=0.008, grad_norm=66.983, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.289 +[stan] 2024-01-16 22:03:56,189 (trainer:762) INFO: 19epoch:train:681-720batch: iter_time=4.621e-05, forward_time=0.037, loss_ctc=1.859, loss=1.859, backward_time=0.008, grad_norm=66.650, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 22:03:58,918 (trainer:762) INFO: 19epoch:train:721-760batch: iter_time=4.403e-05, forward_time=0.036, loss_ctc=1.660, loss=1.660, backward_time=0.008, grad_norm=63.777, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 22:04:01,431 (trainer:762) INFO: 19epoch:train:761-800batch: iter_time=4.156e-05, forward_time=0.033, loss_ctc=1.695, loss=1.695, backward_time=0.008, grad_norm=67.954, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-16 22:04:05,378 (trainer:357) INFO: 19epoch results: [train] iter_time=1.782e-04, forward_time=0.035, loss_ctc=1.808, loss=1.808, backward_time=0.008, grad_norm=67.909, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.9 seconds, total_count=15200, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=108.862, cer_ctc=0.324, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=108.862, time=1.13 seconds, total_count=475, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 22:04:06,385 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:04:06,387 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/18epoch.pth +[stan] 2024-01-16 22:04:06,387 (trainer:291) INFO: 20/30epoch started. Estimated time to finish: 10 minutes and 47.33 seconds +[stan] 2024-01-16 22:04:09,450 (trainer:762) INFO: 20epoch:train:1-40batch: iter_time=0.003, forward_time=0.037, loss_ctc=1.863, loss=1.863, backward_time=0.008, grad_norm=75.162, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.306 +[stan] 2024-01-16 22:04:12,301 (trainer:762) INFO: 20epoch:train:41-80batch: iter_time=4.284e-05, forward_time=0.038, loss_ctc=1.862, loss=1.862, backward_time=0.008, grad_norm=68.893, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-16 22:04:14,590 (trainer:762) INFO: 20epoch:train:81-120batch: iter_time=4.272e-05, forward_time=0.030, loss_ctc=1.404, loss=1.404, backward_time=0.008, grad_norm=60.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-16 22:04:17,476 (trainer:762) INFO: 20epoch:train:121-160batch: iter_time=4.342e-05, forward_time=0.038, loss_ctc=2.082, loss=2.082, backward_time=0.008, grad_norm=78.596, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.289 +[stan] 2024-01-16 22:04:20,263 (trainer:762) INFO: 20epoch:train:161-200batch: iter_time=4.134e-05, forward_time=0.037, loss_ctc=1.851, loss=1.851, backward_time=0.008, grad_norm=66.250, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 22:04:22,682 (trainer:762) INFO: 20epoch:train:201-240batch: iter_time=4.725e-05, forward_time=0.032, loss_ctc=1.448, loss=1.448, backward_time=0.008, grad_norm=62.566, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-16 22:04:25,514 (trainer:762) INFO: 20epoch:train:241-280batch: iter_time=4.355e-05, forward_time=0.037, loss_ctc=1.930, loss=1.930, backward_time=0.008, grad_norm=71.322, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-16 22:04:28,009 (trainer:762) INFO: 20epoch:train:281-320batch: iter_time=4.256e-05, forward_time=0.033, loss_ctc=1.590, loss=1.590, backward_time=0.008, grad_norm=64.322, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-16 22:04:30,703 (trainer:762) INFO: 20epoch:train:321-360batch: iter_time=4.184e-05, forward_time=0.036, loss_ctc=1.789, loss=1.789, backward_time=0.008, grad_norm=70.614, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-16 22:04:33,550 (trainer:762) INFO: 20epoch:train:361-400batch: iter_time=4.460e-05, forward_time=0.038, loss_ctc=1.999, loss=1.999, backward_time=0.008, grad_norm=74.670, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-16 22:04:35,993 (trainer:762) INFO: 20epoch:train:401-440batch: iter_time=4.350e-05, forward_time=0.032, loss_ctc=1.528, loss=1.528, backward_time=0.008, grad_norm=67.601, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-16 22:04:39,052 (trainer:762) INFO: 20epoch:train:441-480batch: iter_time=4.339e-05, forward_time=0.040, loss_ctc=2.095, loss=2.095, backward_time=0.009, grad_norm=71.016, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.306 +[stan] 2024-01-16 22:04:41,582 (trainer:762) INFO: 20epoch:train:481-520batch: iter_time=4.373e-05, forward_time=0.034, loss_ctc=1.413, loss=1.413, backward_time=0.008, grad_norm=61.634, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-16 22:04:44,069 (trainer:762) INFO: 20epoch:train:521-560batch: iter_time=4.449e-05, forward_time=0.033, loss_ctc=1.422, loss=1.422, backward_time=0.008, grad_norm=61.810, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-16 22:04:46,751 (trainer:762) INFO: 20epoch:train:561-600batch: iter_time=4.366e-05, forward_time=0.035, loss_ctc=1.752, loss=1.752, backward_time=0.008, grad_norm=68.387, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-16 22:04:49,619 (trainer:762) INFO: 20epoch:train:601-640batch: iter_time=4.279e-05, forward_time=0.038, loss_ctc=1.786, loss=1.786, backward_time=0.008, grad_norm=66.763, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.287 +[stan] 2024-01-16 22:04:52,255 (trainer:762) INFO: 20epoch:train:641-680batch: iter_time=4.610e-05, forward_time=0.035, loss_ctc=1.647, loss=1.647, backward_time=0.008, grad_norm=67.251, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-16 22:04:54,941 (trainer:762) INFO: 20epoch:train:681-720batch: iter_time=4.160e-05, forward_time=0.036, loss_ctc=1.739, loss=1.739, backward_time=0.008, grad_norm=69.120, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-16 22:04:57,653 (trainer:762) INFO: 20epoch:train:721-760batch: iter_time=4.293e-05, forward_time=0.036, loss_ctc=1.907, loss=1.907, backward_time=0.008, grad_norm=71.364, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 22:05:00,282 (trainer:762) INFO: 20epoch:train:761-800batch: iter_time=4.099e-05, forward_time=0.035, loss_ctc=1.590, loss=1.590, backward_time=0.008, grad_norm=62.435, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-16 22:05:04,252 (trainer:357) INFO: 20epoch results: [train] iter_time=1.679e-04, forward_time=0.035, loss_ctc=1.735, loss=1.735, backward_time=0.008, grad_norm=67.996, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.97 seconds, total_count=16000, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=110.754, cer_ctc=0.321, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=110.754, time=1.14 seconds, total_count=500, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.75 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 22:05:05,300 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:05:05,301 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/19epoch.pth +[stan] 2024-01-16 22:05:05,301 (trainer:291) INFO: 21/30epoch started. Estimated time to finish: 9 minutes and 48.51 seconds +[stan] 2024-01-16 22:05:08,203 (trainer:762) INFO: 21epoch:train:1-40batch: iter_time=0.003, forward_time=0.035, loss_ctc=1.873, loss=1.873, backward_time=0.008, grad_norm=73.282, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.290 +[stan] 2024-01-16 22:05:10,957 (trainer:762) INFO: 21epoch:train:41-80batch: iter_time=4.207e-05, forward_time=0.036, loss_ctc=1.927, loss=1.927, backward_time=0.008, grad_norm=68.139, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 22:05:13,625 (trainer:762) INFO: 21epoch:train:81-120batch: iter_time=4.251e-05, forward_time=0.035, loss_ctc=1.820, loss=1.820, backward_time=0.008, grad_norm=69.722, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-16 22:05:16,242 (trainer:762) INFO: 21epoch:train:121-160batch: iter_time=4.267e-05, forward_time=0.035, loss_ctc=1.760, loss=1.760, backward_time=0.008, grad_norm=63.277, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-16 22:05:18,993 (trainer:762) INFO: 21epoch:train:161-200batch: iter_time=4.156e-05, forward_time=0.036, loss_ctc=1.681, loss=1.681, backward_time=0.008, grad_norm=63.746, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 22:05:21,813 (trainer:762) INFO: 21epoch:train:201-240batch: iter_time=4.333e-05, forward_time=0.037, loss_ctc=1.961, loss=1.961, backward_time=0.008, grad_norm=70.966, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-16 22:05:24,228 (trainer:762) INFO: 21epoch:train:241-280batch: iter_time=4.227e-05, forward_time=0.032, loss_ctc=1.321, loss=1.321, backward_time=0.008, grad_norm=58.979, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-16 22:05:27,027 (trainer:762) INFO: 21epoch:train:281-320batch: iter_time=4.429e-05, forward_time=0.037, loss_ctc=1.916, loss=1.916, backward_time=0.008, grad_norm=72.120, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-16 22:05:29,781 (trainer:762) INFO: 21epoch:train:321-360batch: iter_time=4.231e-05, forward_time=0.036, loss_ctc=1.795, loss=1.795, backward_time=0.008, grad_norm=67.351, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 22:05:32,533 (trainer:762) INFO: 21epoch:train:361-400batch: iter_time=4.613e-05, forward_time=0.036, loss_ctc=1.565, loss=1.565, backward_time=0.008, grad_norm=63.891, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 22:05:35,058 (trainer:762) INFO: 21epoch:train:401-440batch: iter_time=4.189e-05, forward_time=0.033, loss_ctc=1.565, loss=1.565, backward_time=0.008, grad_norm=65.771, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-16 22:05:37,584 (trainer:762) INFO: 21epoch:train:441-480batch: iter_time=4.296e-05, forward_time=0.033, loss_ctc=1.511, loss=1.511, backward_time=0.008, grad_norm=64.883, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-16 22:05:40,336 (trainer:762) INFO: 21epoch:train:481-520batch: iter_time=4.300e-05, forward_time=0.036, loss_ctc=1.679, loss=1.679, backward_time=0.008, grad_norm=66.113, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 22:05:43,092 (trainer:762) INFO: 21epoch:train:521-560batch: iter_time=4.237e-05, forward_time=0.036, loss_ctc=1.763, loss=1.763, backward_time=0.008, grad_norm=69.291, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 22:05:45,905 (trainer:762) INFO: 21epoch:train:561-600batch: iter_time=4.306e-05, forward_time=0.037, loss_ctc=1.817, loss=1.817, backward_time=0.008, grad_norm=70.203, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-16 22:05:48,527 (trainer:762) INFO: 21epoch:train:601-640batch: iter_time=4.169e-05, forward_time=0.035, loss_ctc=1.696, loss=1.696, backward_time=0.008, grad_norm=68.080, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-16 22:05:51,125 (trainer:762) INFO: 21epoch:train:641-680batch: iter_time=4.450e-05, forward_time=0.034, loss_ctc=1.683, loss=1.683, backward_time=0.008, grad_norm=68.106, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-16 22:05:53,931 (trainer:762) INFO: 21epoch:train:681-720batch: iter_time=4.412e-05, forward_time=0.037, loss_ctc=1.770, loss=1.770, backward_time=0.008, grad_norm=70.844, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-16 22:05:56,719 (trainer:762) INFO: 21epoch:train:721-760batch: iter_time=4.293e-05, forward_time=0.037, loss_ctc=1.843, loss=1.843, backward_time=0.008, grad_norm=68.344, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 22:05:59,156 (trainer:762) INFO: 21epoch:train:761-800batch: iter_time=3.932e-05, forward_time=0.032, loss_ctc=1.303, loss=1.303, backward_time=0.008, grad_norm=57.536, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-16 22:06:03,120 (trainer:357) INFO: 21epoch results: [train] iter_time=1.755e-04, forward_time=0.035, loss_ctc=1.712, loss=1.712, backward_time=0.008, grad_norm=67.032, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.93 seconds, total_count=16800, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=109.921, cer_ctc=0.318, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=109.921, time=1.14 seconds, total_count=525, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.75 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 22:06:04,089 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:06:04,090 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/20epoch.pth +[stan] 2024-01-16 22:06:04,090 (trainer:291) INFO: 22/30epoch started. Estimated time to finish: 8 minutes and 49.64 seconds +[stan] 2024-01-16 22:06:07,176 (trainer:762) INFO: 22epoch:train:1-40batch: iter_time=0.003, forward_time=0.037, loss_ctc=1.642, loss=1.642, backward_time=0.008, grad_norm=66.674, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.308 +[stan] 2024-01-16 22:06:09,647 (trainer:762) INFO: 22epoch:train:41-80batch: iter_time=4.426e-05, forward_time=0.033, loss_ctc=1.492, loss=1.492, backward_time=0.008, grad_norm=63.832, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-16 22:06:12,393 (trainer:762) INFO: 22epoch:train:81-120batch: iter_time=4.249e-05, forward_time=0.036, loss_ctc=1.695, loss=1.695, backward_time=0.008, grad_norm=64.243, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 22:06:15,084 (trainer:762) INFO: 22epoch:train:121-160batch: iter_time=4.218e-05, forward_time=0.036, loss_ctc=1.768, loss=1.768, backward_time=0.008, grad_norm=70.827, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-16 22:06:17,706 (trainer:762) INFO: 22epoch:train:161-200batch: iter_time=4.179e-05, forward_time=0.035, loss_ctc=1.429, loss=1.429, backward_time=0.008, grad_norm=60.061, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-16 22:06:20,415 (trainer:762) INFO: 22epoch:train:201-240batch: iter_time=4.523e-05, forward_time=0.036, loss_ctc=1.533, loss=1.533, backward_time=0.008, grad_norm=64.356, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 22:06:22,742 (trainer:762) INFO: 22epoch:train:241-280batch: iter_time=4.510e-05, forward_time=0.031, loss_ctc=1.336, loss=1.336, backward_time=0.008, grad_norm=62.609, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-16 22:06:25,941 (trainer:762) INFO: 22epoch:train:281-320batch: iter_time=4.347e-05, forward_time=0.042, loss_ctc=1.983, loss=1.983, backward_time=0.009, grad_norm=74.637, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.320 +[stan] 2024-01-16 22:06:28,440 (trainer:762) INFO: 22epoch:train:321-360batch: iter_time=4.443e-05, forward_time=0.033, loss_ctc=1.460, loss=1.460, backward_time=0.008, grad_norm=59.453, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-16 22:06:31,233 (trainer:762) INFO: 22epoch:train:361-400batch: iter_time=4.189e-05, forward_time=0.037, loss_ctc=1.749, loss=1.749, backward_time=0.008, grad_norm=71.211, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 22:06:33,834 (trainer:762) INFO: 22epoch:train:401-440batch: iter_time=4.533e-05, forward_time=0.034, loss_ctc=1.776, loss=1.776, backward_time=0.008, grad_norm=63.236, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-16 22:06:36,461 (trainer:762) INFO: 22epoch:train:441-480batch: iter_time=4.278e-05, forward_time=0.035, loss_ctc=1.655, loss=1.655, backward_time=0.008, grad_norm=63.934, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-16 22:06:38,942 (trainer:762) INFO: 22epoch:train:481-520batch: iter_time=4.198e-05, forward_time=0.033, loss_ctc=1.483, loss=1.483, backward_time=0.008, grad_norm=65.900, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-16 22:06:41,672 (trainer:762) INFO: 22epoch:train:521-560batch: iter_time=4.219e-05, forward_time=0.036, loss_ctc=1.866, loss=1.866, backward_time=0.008, grad_norm=65.118, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 22:06:44,490 (trainer:762) INFO: 22epoch:train:561-600batch: iter_time=4.218e-05, forward_time=0.037, loss_ctc=1.656, loss=1.656, backward_time=0.008, grad_norm=64.327, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.282 +[stan] 2024-01-16 22:06:47,029 (trainer:762) INFO: 22epoch:train:601-640batch: iter_time=4.325e-05, forward_time=0.034, loss_ctc=1.499, loss=1.499, backward_time=0.008, grad_norm=59.524, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-16 22:06:49,765 (trainer:762) INFO: 22epoch:train:641-680batch: iter_time=4.443e-05, forward_time=0.036, loss_ctc=1.661, loss=1.661, backward_time=0.008, grad_norm=65.178, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 22:06:52,518 (trainer:762) INFO: 22epoch:train:681-720batch: iter_time=4.606e-05, forward_time=0.036, loss_ctc=1.715, loss=1.715, backward_time=0.008, grad_norm=65.240, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 22:06:55,164 (trainer:762) INFO: 22epoch:train:721-760batch: iter_time=4.451e-05, forward_time=0.035, loss_ctc=1.582, loss=1.582, backward_time=0.008, grad_norm=63.213, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-16 22:06:57,794 (trainer:762) INFO: 22epoch:train:761-800batch: iter_time=3.929e-05, forward_time=0.035, loss_ctc=1.550, loss=1.550, backward_time=0.008, grad_norm=64.445, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-16 22:07:01,752 (trainer:357) INFO: 22epoch results: [train] iter_time=1.743e-04, forward_time=0.035, loss_ctc=1.626, loss=1.626, backward_time=0.008, grad_norm=64.901, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268, time=53.77 seconds, total_count=17600, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=111.455, cer_ctc=0.322, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=111.455, time=1.15 seconds, total_count=550, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 22:07:02,822 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:07:02,823 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/21epoch.pth +[stan] 2024-01-16 22:07:02,823 (trainer:291) INFO: 23/30epoch started. Estimated time to finish: 7 minutes and 50.75 seconds +[stan] 2024-01-16 22:07:05,834 (trainer:762) INFO: 23epoch:train:1-40batch: iter_time=0.002, forward_time=0.037, loss_ctc=1.649, loss=1.649, backward_time=0.008, grad_norm=67.505, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.301 +[stan] 2024-01-16 22:07:08,665 (trainer:762) INFO: 23epoch:train:41-80batch: iter_time=4.171e-05, forward_time=0.037, loss_ctc=1.862, loss=1.862, backward_time=0.008, grad_norm=68.933, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-16 22:07:11,400 (trainer:762) INFO: 23epoch:train:81-120batch: iter_time=4.294e-05, forward_time=0.036, loss_ctc=1.662, loss=1.662, backward_time=0.008, grad_norm=61.363, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 22:07:13,861 (trainer:762) INFO: 23epoch:train:121-160batch: iter_time=4.165e-05, forward_time=0.033, loss_ctc=1.454, loss=1.454, backward_time=0.008, grad_norm=61.145, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-16 22:07:16,455 (trainer:762) INFO: 23epoch:train:161-200batch: iter_time=4.535e-05, forward_time=0.034, loss_ctc=1.380, loss=1.380, backward_time=0.008, grad_norm=61.959, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-16 22:07:19,181 (trainer:762) INFO: 23epoch:train:201-240batch: iter_time=4.111e-05, forward_time=0.036, loss_ctc=1.620, loss=1.620, backward_time=0.008, grad_norm=61.467, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 22:07:21,889 (trainer:762) INFO: 23epoch:train:241-280batch: iter_time=4.060e-05, forward_time=0.036, loss_ctc=1.536, loss=1.536, backward_time=0.008, grad_norm=63.412, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 22:07:24,475 (trainer:762) INFO: 23epoch:train:281-320batch: iter_time=4.378e-05, forward_time=0.034, loss_ctc=1.524, loss=1.524, backward_time=0.008, grad_norm=62.471, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-16 22:07:27,451 (trainer:762) INFO: 23epoch:train:321-360batch: iter_time=4.500e-05, forward_time=0.039, loss_ctc=1.805, loss=1.805, backward_time=0.009, grad_norm=72.243, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.298 +[stan] 2024-01-16 22:07:29,918 (trainer:762) INFO: 23epoch:train:361-400batch: iter_time=4.133e-05, forward_time=0.033, loss_ctc=1.500, loss=1.500, backward_time=0.008, grad_norm=63.193, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-16 22:07:32,712 (trainer:762) INFO: 23epoch:train:401-440batch: iter_time=4.136e-05, forward_time=0.037, loss_ctc=1.496, loss=1.496, backward_time=0.008, grad_norm=62.082, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 22:07:35,101 (trainer:762) INFO: 23epoch:train:441-480batch: iter_time=4.160e-05, forward_time=0.032, loss_ctc=1.455, loss=1.455, backward_time=0.008, grad_norm=62.410, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-16 22:07:37,945 (trainer:762) INFO: 23epoch:train:481-520batch: iter_time=4.284e-05, forward_time=0.037, loss_ctc=1.779, loss=1.779, backward_time=0.008, grad_norm=66.826, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.284 +[stan] 2024-01-16 22:07:40,424 (trainer:762) INFO: 23epoch:train:521-560batch: iter_time=4.206e-05, forward_time=0.033, loss_ctc=1.358, loss=1.358, backward_time=0.008, grad_norm=57.043, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-16 22:07:43,175 (trainer:762) INFO: 23epoch:train:561-600batch: iter_time=4.428e-05, forward_time=0.036, loss_ctc=1.757, loss=1.757, backward_time=0.008, grad_norm=65.166, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 22:07:45,985 (trainer:762) INFO: 23epoch:train:601-640batch: iter_time=4.188e-05, forward_time=0.037, loss_ctc=1.735, loss=1.735, backward_time=0.008, grad_norm=65.419, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-16 22:07:48,779 (trainer:762) INFO: 23epoch:train:641-680batch: iter_time=4.204e-05, forward_time=0.037, loss_ctc=1.749, loss=1.749, backward_time=0.008, grad_norm=69.196, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 22:07:51,627 (trainer:762) INFO: 23epoch:train:681-720batch: iter_time=4.201e-05, forward_time=0.038, loss_ctc=1.696, loss=1.696, backward_time=0.008, grad_norm=68.575, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-16 22:07:54,019 (trainer:762) INFO: 23epoch:train:721-760batch: iter_time=4.553e-05, forward_time=0.032, loss_ctc=1.413, loss=1.413, backward_time=0.008, grad_norm=60.782, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-16 22:07:56,564 (trainer:762) INFO: 23epoch:train:761-800batch: iter_time=4.023e-05, forward_time=0.034, loss_ctc=1.497, loss=1.497, backward_time=0.008, grad_norm=61.690, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-16 22:08:00,514 (trainer:357) INFO: 23epoch results: [train] iter_time=1.604e-04, forward_time=0.035, loss_ctc=1.596, loss=1.596, backward_time=0.008, grad_norm=64.144, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.81 seconds, total_count=18400, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=112.131, cer_ctc=0.322, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=112.131, time=1.14 seconds, total_count=575, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 22:08:01,591 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:08:01,593 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/22epoch.pth +[stan] 2024-01-16 22:08:01,593 (trainer:291) INFO: 24/30epoch started. Estimated time to finish: 6 minutes and 51.88 seconds +[stan] 2024-01-16 22:08:04,846 (trainer:762) INFO: 24epoch:train:1-40batch: iter_time=0.003, forward_time=0.040, loss_ctc=1.831, loss=1.831, backward_time=0.009, grad_norm=70.637, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.325 +[stan] 2024-01-16 22:08:07,343 (trainer:762) INFO: 24epoch:train:41-80batch: iter_time=4.408e-05, forward_time=0.033, loss_ctc=1.428, loss=1.428, backward_time=0.008, grad_norm=58.417, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-16 22:08:10,045 (trainer:762) INFO: 24epoch:train:81-120batch: iter_time=4.422e-05, forward_time=0.036, loss_ctc=1.430, loss=1.430, backward_time=0.008, grad_norm=65.420, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-16 22:08:12,616 (trainer:762) INFO: 24epoch:train:121-160batch: iter_time=4.201e-05, forward_time=0.034, loss_ctc=1.338, loss=1.338, backward_time=0.008, grad_norm=61.860, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-16 22:08:15,378 (trainer:762) INFO: 24epoch:train:161-200batch: iter_time=4.170e-05, forward_time=0.036, loss_ctc=1.519, loss=1.519, backward_time=0.008, grad_norm=61.066, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 22:08:18,152 (trainer:762) INFO: 24epoch:train:201-240batch: iter_time=4.307e-05, forward_time=0.037, loss_ctc=1.615, loss=1.615, backward_time=0.008, grad_norm=62.942, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-16 22:08:20,739 (trainer:762) INFO: 24epoch:train:241-280batch: iter_time=4.113e-05, forward_time=0.034, loss_ctc=1.355, loss=1.355, backward_time=0.008, grad_norm=62.419, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-16 22:08:23,410 (trainer:762) INFO: 24epoch:train:281-320batch: iter_time=4.403e-05, forward_time=0.035, loss_ctc=1.530, loss=1.530, backward_time=0.008, grad_norm=67.051, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-16 22:08:26,225 (trainer:762) INFO: 24epoch:train:321-360batch: iter_time=4.317e-05, forward_time=0.037, loss_ctc=1.811, loss=1.811, backward_time=0.008, grad_norm=63.644, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-16 22:08:28,913 (trainer:762) INFO: 24epoch:train:361-400batch: iter_time=4.503e-05, forward_time=0.036, loss_ctc=1.685, loss=1.685, backward_time=0.008, grad_norm=65.749, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-16 22:08:31,446 (trainer:762) INFO: 24epoch:train:401-440batch: iter_time=4.465e-05, forward_time=0.034, loss_ctc=1.403, loss=1.403, backward_time=0.008, grad_norm=59.966, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-16 22:08:34,005 (trainer:762) INFO: 24epoch:train:441-480batch: iter_time=4.414e-05, forward_time=0.034, loss_ctc=1.400, loss=1.400, backward_time=0.008, grad_norm=58.912, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-16 22:08:37,003 (trainer:762) INFO: 24epoch:train:481-520batch: iter_time=4.260e-05, forward_time=0.039, loss_ctc=1.633, loss=1.633, backward_time=0.009, grad_norm=63.456, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.300 +[stan] 2024-01-16 22:08:39,483 (trainer:762) INFO: 24epoch:train:521-560batch: iter_time=4.236e-05, forward_time=0.033, loss_ctc=1.363, loss=1.363, backward_time=0.008, grad_norm=61.745, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-16 22:08:42,265 (trainer:762) INFO: 24epoch:train:561-600batch: iter_time=4.251e-05, forward_time=0.037, loss_ctc=1.632, loss=1.632, backward_time=0.008, grad_norm=69.958, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-16 22:08:44,735 (trainer:762) INFO: 24epoch:train:601-640batch: iter_time=4.349e-05, forward_time=0.033, loss_ctc=1.511, loss=1.511, backward_time=0.008, grad_norm=63.297, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-16 22:08:47,520 (trainer:762) INFO: 24epoch:train:641-680batch: iter_time=4.309e-05, forward_time=0.037, loss_ctc=1.556, loss=1.556, backward_time=0.008, grad_norm=63.181, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-16 22:08:50,272 (trainer:762) INFO: 24epoch:train:681-720batch: iter_time=4.330e-05, forward_time=0.036, loss_ctc=1.598, loss=1.598, backward_time=0.008, grad_norm=64.483, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 22:08:53,057 (trainer:762) INFO: 24epoch:train:721-760batch: iter_time=4.379e-05, forward_time=0.037, loss_ctc=1.667, loss=1.667, backward_time=0.008, grad_norm=63.705, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-16 22:08:55,567 (trainer:762) INFO: 24epoch:train:761-800batch: iter_time=3.996e-05, forward_time=0.033, loss_ctc=1.343, loss=1.343, backward_time=0.008, grad_norm=65.042, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-16 22:08:59,550 (trainer:357) INFO: 24epoch results: [train] iter_time=1.719e-04, forward_time=0.036, loss_ctc=1.532, loss=1.532, backward_time=0.008, grad_norm=63.648, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.05 seconds, total_count=19200, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=111.769, cer_ctc=0.326, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=111.769, time=1.14 seconds, total_count=600, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.77 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 22:09:00,545 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:09:00,547 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/23epoch.pth +[stan] 2024-01-16 22:09:00,547 (trainer:291) INFO: 25/30epoch started. Estimated time to finish: 5 minutes and 53.07 seconds +[stan] 2024-01-16 22:09:03,259 (trainer:762) INFO: 25epoch:train:1-40batch: iter_time=0.003, forward_time=0.033, loss_ctc=1.390, loss=1.390, backward_time=0.008, grad_norm=54.156, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 22:09:06,319 (trainer:762) INFO: 25epoch:train:41-80batch: iter_time=4.294e-05, forward_time=0.040, loss_ctc=1.823, loss=1.823, backward_time=0.009, grad_norm=71.431, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.306 +[stan] 2024-01-16 22:09:08,847 (trainer:762) INFO: 25epoch:train:81-120batch: iter_time=4.417e-05, forward_time=0.034, loss_ctc=1.264, loss=1.264, backward_time=0.008, grad_norm=59.348, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-16 22:09:11,426 (trainer:762) INFO: 25epoch:train:121-160batch: iter_time=4.323e-05, forward_time=0.034, loss_ctc=1.487, loss=1.487, backward_time=0.008, grad_norm=62.544, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-16 22:09:14,369 (trainer:762) INFO: 25epoch:train:161-200batch: iter_time=4.489e-05, forward_time=0.039, loss_ctc=1.799, loss=1.799, backward_time=0.009, grad_norm=70.649, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.294 +[stan] 2024-01-16 22:09:16,883 (trainer:762) INFO: 25epoch:train:201-240batch: iter_time=4.202e-05, forward_time=0.033, loss_ctc=1.511, loss=1.511, backward_time=0.008, grad_norm=60.272, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-16 22:09:19,546 (trainer:762) INFO: 25epoch:train:241-280batch: iter_time=4.224e-05, forward_time=0.035, loss_ctc=1.445, loss=1.445, backward_time=0.008, grad_norm=62.781, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-16 22:09:22,116 (trainer:762) INFO: 25epoch:train:281-320batch: iter_time=4.558e-05, forward_time=0.034, loss_ctc=1.404, loss=1.404, backward_time=0.008, grad_norm=61.718, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-16 22:09:24,917 (trainer:762) INFO: 25epoch:train:321-360batch: iter_time=4.321e-05, forward_time=0.037, loss_ctc=1.550, loss=1.550, backward_time=0.008, grad_norm=64.270, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-16 22:09:27,535 (trainer:762) INFO: 25epoch:train:361-400batch: iter_time=4.264e-05, forward_time=0.035, loss_ctc=1.277, loss=1.277, backward_time=0.008, grad_norm=56.869, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-16 22:09:30,272 (trainer:762) INFO: 25epoch:train:401-440batch: iter_time=4.179e-05, forward_time=0.036, loss_ctc=1.537, loss=1.537, backward_time=0.008, grad_norm=64.203, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-16 22:09:32,959 (trainer:762) INFO: 25epoch:train:441-480batch: iter_time=4.270e-05, forward_time=0.036, loss_ctc=1.524, loss=1.524, backward_time=0.008, grad_norm=63.632, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-16 22:09:35,772 (trainer:762) INFO: 25epoch:train:481-520batch: iter_time=4.346e-05, forward_time=0.037, loss_ctc=1.554, loss=1.554, backward_time=0.008, grad_norm=62.246, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-16 22:09:38,120 (trainer:762) INFO: 25epoch:train:521-560batch: iter_time=4.246e-05, forward_time=0.031, loss_ctc=1.178, loss=1.178, backward_time=0.008, grad_norm=61.389, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-16 22:09:40,994 (trainer:762) INFO: 25epoch:train:561-600batch: iter_time=4.203e-05, forward_time=0.038, loss_ctc=1.802, loss=1.802, backward_time=0.008, grad_norm=68.783, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.287 +[stan] 2024-01-16 22:09:43,557 (trainer:762) INFO: 25epoch:train:601-640batch: iter_time=4.372e-05, forward_time=0.034, loss_ctc=1.320, loss=1.320, backward_time=0.008, grad_norm=58.900, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-16 22:09:46,484 (trainer:762) INFO: 25epoch:train:641-680batch: iter_time=4.224e-05, forward_time=0.039, loss_ctc=1.588, loss=1.588, backward_time=0.009, grad_norm=61.264, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.293 +[stan] 2024-01-16 22:09:49,031 (trainer:762) INFO: 25epoch:train:681-720batch: iter_time=4.355e-05, forward_time=0.034, loss_ctc=1.387, loss=1.387, backward_time=0.008, grad_norm=59.132, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-16 22:09:51,428 (trainer:762) INFO: 25epoch:train:721-760batch: iter_time=4.256e-05, forward_time=0.032, loss_ctc=1.209, loss=1.209, backward_time=0.008, grad_norm=56.001, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-16 22:09:54,283 (trainer:762) INFO: 25epoch:train:761-800batch: iter_time=3.987e-05, forward_time=0.038, loss_ctc=1.526, loss=1.526, backward_time=0.008, grad_norm=63.087, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-16 22:09:58,277 (trainer:357) INFO: 25epoch results: [train] iter_time=1.779e-04, forward_time=0.035, loss_ctc=1.479, loss=1.479, backward_time=0.008, grad_norm=62.134, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.81 seconds, total_count=20000, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=112.235, cer_ctc=0.325, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=112.235, time=1.15 seconds, total_count=625, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.77 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 22:09:59,365 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:09:59,367 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/24epoch.pth +[stan] 2024-01-16 22:09:59,367 (trainer:291) INFO: 26/30epoch started. Estimated time to finish: 4 minutes and 54.22 seconds +[stan] 2024-01-16 22:10:02,399 (trainer:762) INFO: 26epoch:train:1-40batch: iter_time=0.002, forward_time=0.037, loss_ctc=1.693, loss=1.693, backward_time=0.008, grad_norm=64.832, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.303 +[stan] 2024-01-16 22:10:05,091 (trainer:762) INFO: 26epoch:train:41-80batch: iter_time=4.242e-05, forward_time=0.036, loss_ctc=1.540, loss=1.540, backward_time=0.008, grad_norm=63.316, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-16 22:10:07,967 (trainer:762) INFO: 26epoch:train:81-120batch: iter_time=4.453e-05, forward_time=0.038, loss_ctc=1.560, loss=1.560, backward_time=0.008, grad_norm=64.385, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.288 +[stan] 2024-01-16 22:10:10,454 (trainer:762) INFO: 26epoch:train:121-160batch: iter_time=4.448e-05, forward_time=0.033, loss_ctc=1.340, loss=1.340, backward_time=0.008, grad_norm=59.883, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-16 22:10:13,250 (trainer:762) INFO: 26epoch:train:161-200batch: iter_time=4.172e-05, forward_time=0.037, loss_ctc=1.554, loss=1.554, backward_time=0.008, grad_norm=63.345, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 22:10:15,638 (trainer:762) INFO: 26epoch:train:201-240batch: iter_time=4.300e-05, forward_time=0.032, loss_ctc=1.224, loss=1.224, backward_time=0.008, grad_norm=54.578, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-16 22:10:18,499 (trainer:762) INFO: 26epoch:train:241-280batch: iter_time=4.438e-05, forward_time=0.038, loss_ctc=1.456, loss=1.456, backward_time=0.008, grad_norm=63.458, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.286 +[stan] 2024-01-16 22:10:21,334 (trainer:762) INFO: 26epoch:train:281-320batch: iter_time=4.238e-05, forward_time=0.037, loss_ctc=1.575, loss=1.575, backward_time=0.008, grad_norm=65.502, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-16 22:10:23,667 (trainer:762) INFO: 26epoch:train:321-360batch: iter_time=4.329e-05, forward_time=0.031, loss_ctc=1.103, loss=1.103, backward_time=0.008, grad_norm=52.231, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-16 22:10:26,548 (trainer:762) INFO: 26epoch:train:361-400batch: iter_time=4.411e-05, forward_time=0.038, loss_ctc=1.715, loss=1.715, backward_time=0.008, grad_norm=65.578, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.288 +[stan] 2024-01-16 22:10:29,287 (trainer:762) INFO: 26epoch:train:401-440batch: iter_time=4.343e-05, forward_time=0.036, loss_ctc=1.600, loss=1.600, backward_time=0.008, grad_norm=68.236, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-16 22:10:31,892 (trainer:762) INFO: 26epoch:train:441-480batch: iter_time=4.274e-05, forward_time=0.035, loss_ctc=1.420, loss=1.420, backward_time=0.008, grad_norm=58.470, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-16 22:10:34,579 (trainer:762) INFO: 26epoch:train:481-520batch: iter_time=4.277e-05, forward_time=0.035, loss_ctc=1.473, loss=1.473, backward_time=0.008, grad_norm=63.648, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-16 22:10:37,263 (trainer:762) INFO: 26epoch:train:521-560batch: iter_time=4.164e-05, forward_time=0.035, loss_ctc=1.544, loss=1.544, backward_time=0.008, grad_norm=63.190, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-16 22:10:39,825 (trainer:762) INFO: 26epoch:train:561-600batch: iter_time=4.302e-05, forward_time=0.034, loss_ctc=1.461, loss=1.461, backward_time=0.008, grad_norm=63.098, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-16 22:10:42,603 (trainer:762) INFO: 26epoch:train:601-640batch: iter_time=4.297e-05, forward_time=0.037, loss_ctc=1.595, loss=1.595, backward_time=0.008, grad_norm=65.401, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-16 22:10:45,360 (trainer:762) INFO: 26epoch:train:641-680batch: iter_time=4.242e-05, forward_time=0.036, loss_ctc=1.624, loss=1.624, backward_time=0.008, grad_norm=61.729, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 22:10:47,853 (trainer:762) INFO: 26epoch:train:681-720batch: iter_time=4.371e-05, forward_time=0.033, loss_ctc=1.186, loss=1.186, backward_time=0.008, grad_norm=54.360, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-16 22:10:50,641 (trainer:762) INFO: 26epoch:train:721-760batch: iter_time=4.470e-05, forward_time=0.037, loss_ctc=1.546, loss=1.546, backward_time=0.008, grad_norm=66.360, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 22:10:53,199 (trainer:762) INFO: 26epoch:train:761-800batch: iter_time=4.124e-05, forward_time=0.034, loss_ctc=1.321, loss=1.321, backward_time=0.008, grad_norm=57.959, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-16 22:10:57,171 (trainer:357) INFO: 26epoch results: [train] iter_time=1.566e-04, forward_time=0.035, loss_ctc=1.476, loss=1.476, backward_time=0.008, grad_norm=61.978, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.9 seconds, total_count=20800, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=113.843, cer_ctc=0.319, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=113.843, time=1.15 seconds, total_count=650, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.75 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 22:10:58,208 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:10:58,210 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/25epoch.pth +[stan] 2024-01-16 22:10:58,210 (trainer:291) INFO: 27/30epoch started. Estimated time to finish: 3 minutes and 55.38 seconds +[stan] 2024-01-16 22:11:01,264 (trainer:762) INFO: 27epoch:train:1-40batch: iter_time=0.003, forward_time=0.037, loss_ctc=1.621, loss=1.621, backward_time=0.008, grad_norm=65.783, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.305 +[stan] 2024-01-16 22:11:03,975 (trainer:762) INFO: 27epoch:train:41-80batch: iter_time=4.821e-05, forward_time=0.036, loss_ctc=1.341, loss=1.341, backward_time=0.008, grad_norm=58.502, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 22:11:06,808 (trainer:762) INFO: 27epoch:train:81-120batch: iter_time=4.245e-05, forward_time=0.037, loss_ctc=1.473, loss=1.473, backward_time=0.008, grad_norm=66.332, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-16 22:11:09,400 (trainer:762) INFO: 27epoch:train:121-160batch: iter_time=4.343e-05, forward_time=0.034, loss_ctc=1.403, loss=1.403, backward_time=0.008, grad_norm=61.121, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-16 22:11:12,021 (trainer:762) INFO: 27epoch:train:161-200batch: iter_time=4.183e-05, forward_time=0.035, loss_ctc=1.485, loss=1.485, backward_time=0.008, grad_norm=59.678, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-16 22:11:14,648 (trainer:762) INFO: 27epoch:train:201-240batch: iter_time=4.414e-05, forward_time=0.035, loss_ctc=1.340, loss=1.340, backward_time=0.008, grad_norm=58.736, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-16 22:11:17,582 (trainer:762) INFO: 27epoch:train:241-280batch: iter_time=4.335e-05, forward_time=0.039, loss_ctc=1.659, loss=1.659, backward_time=0.008, grad_norm=69.312, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.293 +[stan] 2024-01-16 22:11:20,078 (trainer:762) INFO: 27epoch:train:281-320batch: iter_time=4.246e-05, forward_time=0.033, loss_ctc=1.220, loss=1.220, backward_time=0.008, grad_norm=58.085, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-16 22:11:22,708 (trainer:762) INFO: 27epoch:train:321-360batch: iter_time=4.373e-05, forward_time=0.036, loss_ctc=1.454, loss=1.454, backward_time=0.008, grad_norm=64.170, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-16 22:11:25,734 (trainer:762) INFO: 27epoch:train:361-400batch: iter_time=4.380e-05, forward_time=0.040, loss_ctc=1.801, loss=1.801, backward_time=0.009, grad_norm=64.276, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.302 +[stan] 2024-01-16 22:11:28,211 (trainer:762) INFO: 27epoch:train:401-440batch: iter_time=4.152e-05, forward_time=0.033, loss_ctc=1.183, loss=1.183, backward_time=0.008, grad_norm=54.360, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-16 22:11:30,661 (trainer:762) INFO: 27epoch:train:441-480batch: iter_time=4.428e-05, forward_time=0.033, loss_ctc=1.108, loss=1.108, backward_time=0.008, grad_norm=55.661, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-16 22:11:33,606 (trainer:762) INFO: 27epoch:train:481-520batch: iter_time=4.326e-05, forward_time=0.039, loss_ctc=1.742, loss=1.742, backward_time=0.009, grad_norm=65.082, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.294 +[stan] 2024-01-16 22:11:36,249 (trainer:762) INFO: 27epoch:train:521-560batch: iter_time=4.288e-05, forward_time=0.035, loss_ctc=1.443, loss=1.443, backward_time=0.008, grad_norm=62.442, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-16 22:11:38,899 (trainer:762) INFO: 27epoch:train:561-600batch: iter_time=4.322e-05, forward_time=0.035, loss_ctc=1.292, loss=1.292, backward_time=0.008, grad_norm=58.388, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.265 +[stan] 2024-01-16 22:11:41,710 (trainer:762) INFO: 27epoch:train:601-640batch: iter_time=4.364e-05, forward_time=0.037, loss_ctc=1.591, loss=1.591, backward_time=0.008, grad_norm=62.514, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-16 22:11:44,288 (trainer:762) INFO: 27epoch:train:641-680batch: iter_time=4.170e-05, forward_time=0.034, loss_ctc=1.499, loss=1.499, backward_time=0.008, grad_norm=61.733, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-16 22:11:46,890 (trainer:762) INFO: 27epoch:train:681-720batch: iter_time=4.420e-05, forward_time=0.035, loss_ctc=1.372, loss=1.372, backward_time=0.008, grad_norm=58.614, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-16 22:11:49,650 (trainer:762) INFO: 27epoch:train:721-760batch: iter_time=4.329e-05, forward_time=0.036, loss_ctc=1.329, loss=1.329, backward_time=0.008, grad_norm=59.785, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-16 22:11:52,327 (trainer:762) INFO: 27epoch:train:761-800batch: iter_time=4.304e-05, forward_time=0.035, loss_ctc=1.326, loss=1.326, backward_time=0.008, grad_norm=58.591, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-16 22:11:56,310 (trainer:357) INFO: 27epoch results: [train] iter_time=2.125e-04, forward_time=0.036, loss_ctc=1.434, loss=1.434, backward_time=0.008, grad_norm=61.158, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270, time=54.19 seconds, total_count=21600, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=114.519, cer_ctc=0.323, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=114.519, time=1.14 seconds, total_count=675, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.77 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 22:11:57,350 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:11:57,351 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/26epoch.pth +[stan] 2024-01-16 22:11:57,351 (trainer:291) INFO: 28/30epoch started. Estimated time to finish: 2 minutes and 56.56 seconds +[stan] 2024-01-16 22:12:00,244 (trainer:762) INFO: 28epoch:train:1-40batch: iter_time=0.003, forward_time=0.035, loss_ctc=1.412, loss=1.412, backward_time=0.008, grad_norm=59.430, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.289 +[stan] 2024-01-16 22:12:02,870 (trainer:762) INFO: 28epoch:train:41-80batch: iter_time=4.331e-05, forward_time=0.035, loss_ctc=1.385, loss=1.385, backward_time=0.008, grad_norm=58.890, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-16 22:12:05,646 (trainer:762) INFO: 28epoch:train:81-120batch: iter_time=4.752e-05, forward_time=0.037, loss_ctc=1.427, loss=1.427, backward_time=0.008, grad_norm=62.063, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-16 22:12:08,364 (trainer:762) INFO: 28epoch:train:121-160batch: iter_time=4.087e-05, forward_time=0.036, loss_ctc=1.283, loss=1.283, backward_time=0.008, grad_norm=57.613, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 22:12:10,862 (trainer:762) INFO: 28epoch:train:161-200batch: iter_time=4.016e-05, forward_time=0.033, loss_ctc=1.312, loss=1.312, backward_time=0.008, grad_norm=57.778, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-16 22:12:13,672 (trainer:762) INFO: 28epoch:train:201-240batch: iter_time=4.267e-05, forward_time=0.037, loss_ctc=1.597, loss=1.597, backward_time=0.008, grad_norm=62.981, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-16 22:12:16,397 (trainer:762) INFO: 28epoch:train:241-280batch: iter_time=4.415e-05, forward_time=0.036, loss_ctc=1.337, loss=1.337, backward_time=0.008, grad_norm=56.261, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-16 22:12:18,913 (trainer:762) INFO: 28epoch:train:281-320batch: iter_time=4.034e-05, forward_time=0.033, loss_ctc=1.319, loss=1.319, backward_time=0.008, grad_norm=60.650, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-16 22:12:21,704 (trainer:762) INFO: 28epoch:train:321-360batch: iter_time=4.503e-05, forward_time=0.037, loss_ctc=1.457, loss=1.457, backward_time=0.008, grad_norm=65.986, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 22:12:24,225 (trainer:762) INFO: 28epoch:train:361-400batch: iter_time=4.203e-05, forward_time=0.033, loss_ctc=1.309, loss=1.309, backward_time=0.008, grad_norm=59.175, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-16 22:12:27,054 (trainer:762) INFO: 28epoch:train:401-440batch: iter_time=4.272e-05, forward_time=0.037, loss_ctc=1.274, loss=1.274, backward_time=0.008, grad_norm=61.486, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.283 +[stan] 2024-01-16 22:12:29,733 (trainer:762) INFO: 28epoch:train:441-480batch: iter_time=4.616e-05, forward_time=0.035, loss_ctc=1.501, loss=1.501, backward_time=0.008, grad_norm=62.872, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-16 22:12:32,187 (trainer:762) INFO: 28epoch:train:481-520batch: iter_time=4.563e-05, forward_time=0.033, loss_ctc=1.285, loss=1.285, backward_time=0.008, grad_norm=58.410, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-16 22:12:35,327 (trainer:762) INFO: 28epoch:train:521-560batch: iter_time=4.273e-05, forward_time=0.041, loss_ctc=1.818, loss=1.818, backward_time=0.009, grad_norm=67.821, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.314 +[stan] 2024-01-16 22:12:37,763 (trainer:762) INFO: 28epoch:train:561-600batch: iter_time=4.236e-05, forward_time=0.032, loss_ctc=1.156, loss=1.156, backward_time=0.008, grad_norm=53.480, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-16 22:12:40,281 (trainer:762) INFO: 28epoch:train:601-640batch: iter_time=4.244e-05, forward_time=0.033, loss_ctc=1.150, loss=1.150, backward_time=0.008, grad_norm=54.881, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-16 22:12:43,301 (trainer:762) INFO: 28epoch:train:641-680batch: iter_time=4.297e-05, forward_time=0.040, loss_ctc=1.498, loss=1.498, backward_time=0.009, grad_norm=59.610, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.302 +[stan] 2024-01-16 22:12:45,786 (trainer:762) INFO: 28epoch:train:681-720batch: iter_time=4.270e-05, forward_time=0.033, loss_ctc=1.233, loss=1.233, backward_time=0.008, grad_norm=55.724, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-16 22:12:48,499 (trainer:762) INFO: 28epoch:train:721-760batch: iter_time=4.190e-05, forward_time=0.036, loss_ctc=1.330, loss=1.330, backward_time=0.008, grad_norm=60.363, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 22:12:51,063 (trainer:762) INFO: 28epoch:train:761-800batch: iter_time=4.073e-05, forward_time=0.034, loss_ctc=1.302, loss=1.302, backward_time=0.008, grad_norm=55.086, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-16 22:12:55,061 (trainer:357) INFO: 28epoch results: [train] iter_time=1.816e-04, forward_time=0.035, loss_ctc=1.369, loss=1.369, backward_time=0.008, grad_norm=59.528, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268, time=53.79 seconds, total_count=22400, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=112.433, cer_ctc=0.324, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=112.433, time=1.14 seconds, total_count=700, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.78 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 22:12:56,163 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:12:56,164 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/27epoch.pth +[stan] 2024-01-16 22:12:56,164 (trainer:291) INFO: 29/30epoch started. Estimated time to finish: 1 minute and 57.71 seconds +[stan] 2024-01-16 22:12:59,167 (trainer:762) INFO: 29epoch:train:1-40batch: iter_time=0.002, forward_time=0.037, loss_ctc=1.319, loss=1.319, backward_time=0.008, grad_norm=57.855, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.300 +[stan] 2024-01-16 22:13:01,834 (trainer:762) INFO: 29epoch:train:41-80batch: iter_time=4.151e-05, forward_time=0.035, loss_ctc=1.318, loss=1.318, backward_time=0.008, grad_norm=59.733, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-16 22:13:04,766 (trainer:762) INFO: 29epoch:train:81-120batch: iter_time=4.172e-05, forward_time=0.039, loss_ctc=1.594, loss=1.594, backward_time=0.008, grad_norm=66.028, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.293 +[stan] 2024-01-16 22:13:07,189 (trainer:762) INFO: 29epoch:train:121-160batch: iter_time=4.166e-05, forward_time=0.032, loss_ctc=1.189, loss=1.189, backward_time=0.008, grad_norm=54.577, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-16 22:13:09,817 (trainer:762) INFO: 29epoch:train:161-200batch: iter_time=4.160e-05, forward_time=0.035, loss_ctc=1.207, loss=1.207, backward_time=0.008, grad_norm=57.298, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-16 22:13:12,611 (trainer:762) INFO: 29epoch:train:201-240batch: iter_time=4.288e-05, forward_time=0.037, loss_ctc=1.322, loss=1.322, backward_time=0.008, grad_norm=61.668, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-16 22:13:15,225 (trainer:762) INFO: 29epoch:train:241-280batch: iter_time=4.382e-05, forward_time=0.035, loss_ctc=1.346, loss=1.346, backward_time=0.008, grad_norm=58.824, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-16 22:13:17,899 (trainer:762) INFO: 29epoch:train:281-320batch: iter_time=4.484e-05, forward_time=0.035, loss_ctc=1.344, loss=1.344, backward_time=0.008, grad_norm=58.239, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-16 22:13:20,470 (trainer:762) INFO: 29epoch:train:321-360batch: iter_time=4.375e-05, forward_time=0.034, loss_ctc=1.276, loss=1.276, backward_time=0.008, grad_norm=55.948, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-16 22:13:23,256 (trainer:762) INFO: 29epoch:train:361-400batch: iter_time=4.306e-05, forward_time=0.037, loss_ctc=1.518, loss=1.518, backward_time=0.008, grad_norm=63.581, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-16 22:13:25,972 (trainer:762) INFO: 29epoch:train:401-440batch: iter_time=4.430e-05, forward_time=0.036, loss_ctc=1.256, loss=1.256, backward_time=0.008, grad_norm=55.690, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 22:13:28,602 (trainer:762) INFO: 29epoch:train:441-480batch: iter_time=4.202e-05, forward_time=0.035, loss_ctc=1.255, loss=1.255, backward_time=0.008, grad_norm=58.514, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-16 22:13:31,295 (trainer:762) INFO: 29epoch:train:481-520batch: iter_time=4.265e-05, forward_time=0.036, loss_ctc=1.317, loss=1.317, backward_time=0.008, grad_norm=58.072, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-16 22:13:34,063 (trainer:762) INFO: 29epoch:train:521-560batch: iter_time=4.163e-05, forward_time=0.037, loss_ctc=1.439, loss=1.439, backward_time=0.008, grad_norm=60.894, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-16 22:13:36,669 (trainer:762) INFO: 29epoch:train:561-600batch: iter_time=4.228e-05, forward_time=0.035, loss_ctc=1.438, loss=1.438, backward_time=0.008, grad_norm=59.943, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-16 22:13:39,330 (trainer:762) INFO: 29epoch:train:601-640batch: iter_time=4.100e-05, forward_time=0.035, loss_ctc=1.363, loss=1.363, backward_time=0.008, grad_norm=62.229, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-16 22:13:42,057 (trainer:762) INFO: 29epoch:train:641-680batch: iter_time=4.253e-05, forward_time=0.036, loss_ctc=1.283, loss=1.283, backward_time=0.008, grad_norm=59.818, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 22:13:44,923 (trainer:762) INFO: 29epoch:train:681-720batch: iter_time=4.558e-05, forward_time=0.038, loss_ctc=1.448, loss=1.448, backward_time=0.008, grad_norm=62.044, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.287 +[stan] 2024-01-16 22:13:47,366 (trainer:762) INFO: 29epoch:train:721-760batch: iter_time=4.289e-05, forward_time=0.032, loss_ctc=1.219, loss=1.219, backward_time=0.008, grad_norm=57.838, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-16 22:13:50,044 (trainer:762) INFO: 29epoch:train:761-800batch: iter_time=3.951e-05, forward_time=0.035, loss_ctc=1.259, loss=1.259, backward_time=0.008, grad_norm=57.748, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-16 22:13:53,995 (trainer:357) INFO: 29epoch results: [train] iter_time=1.567e-04, forward_time=0.035, loss_ctc=1.335, loss=1.335, backward_time=0.008, grad_norm=59.327, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.95 seconds, total_count=23200, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=116.273, cer_ctc=0.319, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=116.273, time=1.14 seconds, total_count=725, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 22:13:54,991 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:13:54,993 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/28epoch.pth +[stan] 2024-01-16 22:13:54,993 (trainer:291) INFO: 30/30epoch started. Estimated time to finish: 58.85 seconds +[stan] 2024-01-16 22:13:57,934 (trainer:762) INFO: 30epoch:train:1-40batch: iter_time=0.003, forward_time=0.035, loss_ctc=1.358, loss=1.358, backward_time=0.008, grad_norm=60.121, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.294 +[stan] 2024-01-16 22:14:00,606 (trainer:762) INFO: 30epoch:train:41-80batch: iter_time=4.467e-05, forward_time=0.035, loss_ctc=1.314, loss=1.314, backward_time=0.008, grad_norm=57.995, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-16 22:14:03,360 (trainer:762) INFO: 30epoch:train:81-120batch: iter_time=4.176e-05, forward_time=0.036, loss_ctc=1.421, loss=1.421, backward_time=0.008, grad_norm=59.604, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-16 22:14:06,234 (trainer:762) INFO: 30epoch:train:121-160batch: iter_time=4.761e-05, forward_time=0.038, loss_ctc=1.325, loss=1.325, backward_time=0.008, grad_norm=58.509, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.287 +[stan] 2024-01-16 22:14:08,631 (trainer:762) INFO: 30epoch:train:161-200batch: iter_time=4.134e-05, forward_time=0.032, loss_ctc=1.170, loss=1.170, backward_time=0.008, grad_norm=58.336, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-16 22:14:11,198 (trainer:762) INFO: 30epoch:train:201-240batch: iter_time=4.214e-05, forward_time=0.034, loss_ctc=1.295, loss=1.295, backward_time=0.008, grad_norm=57.037, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-16 22:14:14,211 (trainer:762) INFO: 30epoch:train:241-280batch: iter_time=4.221e-05, forward_time=0.040, loss_ctc=1.403, loss=1.403, backward_time=0.009, grad_norm=58.381, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.301 +[stan] 2024-01-16 22:14:16,667 (trainer:762) INFO: 30epoch:train:281-320batch: iter_time=4.281e-05, forward_time=0.033, loss_ctc=1.153, loss=1.153, backward_time=0.008, grad_norm=55.987, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-16 22:14:19,383 (trainer:762) INFO: 30epoch:train:321-360batch: iter_time=4.180e-05, forward_time=0.036, loss_ctc=1.357, loss=1.357, backward_time=0.008, grad_norm=57.856, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-16 22:14:21,968 (trainer:762) INFO: 30epoch:train:361-400batch: iter_time=4.244e-05, forward_time=0.034, loss_ctc=1.153, loss=1.153, backward_time=0.008, grad_norm=57.899, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-16 22:14:24,698 (trainer:762) INFO: 30epoch:train:401-440batch: iter_time=4.213e-05, forward_time=0.036, loss_ctc=1.233, loss=1.233, backward_time=0.008, grad_norm=55.564, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 22:14:27,428 (trainer:762) INFO: 30epoch:train:441-480batch: iter_time=4.214e-05, forward_time=0.036, loss_ctc=1.376, loss=1.376, backward_time=0.008, grad_norm=58.240, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-16 22:14:30,165 (trainer:762) INFO: 30epoch:train:481-520batch: iter_time=4.344e-05, forward_time=0.036, loss_ctc=1.328, loss=1.328, backward_time=0.008, grad_norm=57.342, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-16 22:14:32,740 (trainer:762) INFO: 30epoch:train:521-560batch: iter_time=4.301e-05, forward_time=0.034, loss_ctc=1.116, loss=1.116, backward_time=0.008, grad_norm=54.175, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-16 22:14:35,343 (trainer:762) INFO: 30epoch:train:561-600batch: iter_time=4.214e-05, forward_time=0.034, loss_ctc=1.175, loss=1.175, backward_time=0.008, grad_norm=54.994, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-16 22:14:38,306 (trainer:762) INFO: 30epoch:train:601-640batch: iter_time=4.312e-05, forward_time=0.039, loss_ctc=1.520, loss=1.520, backward_time=0.009, grad_norm=61.769, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.296 +[stan] 2024-01-16 22:14:40,782 (trainer:762) INFO: 30epoch:train:641-680batch: iter_time=4.269e-05, forward_time=0.033, loss_ctc=1.146, loss=1.146, backward_time=0.008, grad_norm=56.842, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-16 22:14:43,565 (trainer:762) INFO: 30epoch:train:681-720batch: iter_time=4.242e-05, forward_time=0.037, loss_ctc=1.335, loss=1.335, backward_time=0.008, grad_norm=59.596, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-16 22:14:46,174 (trainer:762) INFO: 30epoch:train:721-760batch: iter_time=4.397e-05, forward_time=0.035, loss_ctc=1.259, loss=1.259, backward_time=0.008, grad_norm=56.979, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-16 22:14:48,810 (trainer:762) INFO: 30epoch:train:761-800batch: iter_time=4.300e-05, forward_time=0.035, loss_ctc=1.346, loss=1.346, backward_time=0.008, grad_norm=57.215, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-16 22:14:52,822 (trainer:357) INFO: 30epoch results: [train] iter_time=1.903e-04, forward_time=0.035, loss_ctc=1.289, loss=1.289, backward_time=0.008, grad_norm=57.722, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269, time=53.89 seconds, total_count=24000, gpu_max_cached_mem_GB=10.023, [valid] loss_ctc=115.705, cer_ctc=0.323, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=115.705, time=1.15 seconds, total_count=750, gpu_max_cached_mem_GB=10.023, [att_plot] time=2.79 seconds, total_count=0, gpu_max_cached_mem_GB=10.023 +[stan] 2024-01-16 22:14:53,899 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:14:53,900 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/29epoch.pth +[stan] 2024-01-16 22:14:53,900 (trainer:488) INFO: The training was finished at 30 epochs +[stan] 2024-01-16 22:14:53,915 (average_nbest_models:69) INFO: Averaging 5best models: criterion="valid.loss": test_pr/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave_5best.pth +# Accounting: time=1770 threads=1 +# Ended (code 0) at Tue Jan 16 22:14:54 CST 2024, elapsed time 1770 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_10min/valid.loss.ave.pth 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a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/RESULTS.md b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/RESULTS.md new file mode 100644 index 0000000000000000000000000000000000000000..8b2068394b0735e59cc150a195512f38cfe0f68d --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/RESULTS.md @@ -0,0 +1,45 @@ +[INFO] /home/stan/Desktop/espnet/egs2/ml_superb/asr1/../../../tools/activate_python.sh is not present + +# RESULTS +## Environments +- date: `Wed Jan 17 01:57:30 CST 2024` +- python version: `3.8.10 (default, Jun 4 2021, 15:09:15) [GCC 7.5.0]` +- espnet version: `espnet 202310` +- pytorch version: `pytorch 1.12.0+cu113` +- Git hash: `aa855dffb81937a097ee03089926a0d5256426e2` + - Commit date: `Tue Jan 16 19:36:29 2024 +0800` + +## test_pr/asr_train_asr_s3prl_houlsby_deu1_1h +### WER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|decode_asr_asr_model_valid.loss.ave/test_10min_deu1|661|8099|19.5|62.4|18.1|4.9|85.4|100.0| + +### CER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|decode_asr_asr_model_valid.loss.ave/test_10min_deu1|661|54401|77.7|8.2|14.0|5.5|27.7|100.0| + +### TER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +## test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave +### WER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|org/dev_10min_deu1|207|1298|23.2|63.6|13.3|7.6|84.4|99.5| + +### CER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|org/dev_10min_deu1|207|9248|81.3|6.7|11.9|5.0|23.6|99.5| + +### TER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/att_ws/swc_deu_001201/encoder.encoders.0.self_attn.10ep.png b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/att_ws/swc_deu_001201/encoder.encoders.0.self_attn.10ep.png new file mode 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sha256:a61d3944c98c8299ce3664634a874955dc76e23f52bb1bbe576b6ef12772189a +size 63406809 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..de27c19ba7613f6d2b6e9cc38528f42a5907c30e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml @@ -0,0 +1,251 @@ +config: conf/tuning/train_asr_s3prl_houlsby.yaml +print_config: false +log_level: INFO +drop_last_iter: false +dry_run: false +iterator_type: sequence +valid_iterator_type: null +output_dir: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h +ngpu: 1 +seed: 0 +num_workers: 4 +num_att_plot: 3 +dist_backend: nccl +dist_init_method: env:// +dist_world_size: null +dist_rank: null +local_rank: 0 +dist_master_addr: null +dist_master_port: null +dist_launcher: null +multiprocessing_distributed: false +unused_parameters: true +sharded_ddp: false +cudnn_enabled: true +cudnn_benchmark: false +cudnn_deterministic: true +collect_stats: false +write_collected_feats: false +max_epoch: 30 +patience: null +val_scheduler_criterion: +- valid +- loss +early_stopping_criterion: +- valid +- loss +- min +best_model_criterion: +- - valid + - loss + - min +keep_nbest_models: 5 +nbest_averaging_interval: 0 +grad_clip: 5.0 +grad_clip_type: 2.0 +grad_noise: false +accum_grad: 4 +no_forward_run: false +resume: true +train_dtype: float32 +use_amp: false +log_interval: null +use_matplotlib: true +use_tensorboard: true +create_graph_in_tensorboard: false +use_wandb: false +wandb_project: null +wandb_id: null +wandb_entity: null +wandb_name: null +wandb_model_log_interval: -1 +detect_anomaly: false +use_adapter: true +adapter: houlsby +save_adapter_only: true +adapter_conf: + bottleneck: 32 +pretrain_path: null +init_param: [] +ignore_init_mismatch: false +freeze_param: +- frontend.upstream +num_iters_per_epoch: 800 +batch_size: 8 +valid_batch_size: null +batch_bins: 1000000 +valid_batch_bins: null +train_shape_file: +- test_pr/asr_stats_deu1_1h/train/speech_shape +- test_pr/asr_stats_deu1_1h/train/text_shape.char +valid_shape_file: +- test_pr/asr_stats_deu1_1h/valid/speech_shape +- test_pr/asr_stats_deu1_1h/valid/text_shape.char +batch_type: sorted +valid_batch_type: null +fold_length: +- 80000 +- 150 +sort_in_batch: descending +shuffle_within_batch: false +sort_batch: descending +multiple_iterator: false +chunk_length: 500 +chunk_shift_ratio: 0.5 +num_cache_chunks: 1024 +chunk_excluded_key_prefixes: [] +chunk_default_fs: null +train_data_path_and_name_and_type: +- - dump/raw/train_1h_deu1/wav.scp + - speech + - sound +- - dump/raw/train_1h_deu1/text + - text + - text +valid_data_path_and_name_and_type: +- - dump/raw/dev_10min_deu1/wav.scp + - speech + - sound +- - dump/raw/dev_10min_deu1/text + - text + - text +allow_variable_data_keys: false +max_cache_size: 0.0 +max_cache_fd: 32 +allow_multi_rates: false +valid_max_cache_size: null +exclude_weight_decay: false +exclude_weight_decay_conf: {} +optim: adam +optim_conf: + lr: 0.0001 + weight_decay: 1.0e-06 +scheduler: null +scheduler_conf: {} +token_list: +- +- +- E +- +- N +- I +- R +- S +- T +- A +- D +- H +- U +- L +- G +- C +- O +- M +- B +- F +- Z +- W +- K +- P +- V +- Ü +- Ä +- Ö +- J +- Y +- X +- Q +- – +- É +- Ã +- Ū +- Ō +- Š +- Ć +- '4' +- '1' +- '2' +- '3' +- +init: null +input_size: null +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: null + zero_infinity: true + brctc_risk_strategy: exp + brctc_group_strategy: end + brctc_risk_factor: 0.0 +joint_net_conf: null +use_preprocessor: true +use_lang_prompt: false +use_nlp_prompt: false +token_type: char +bpemodel: null +non_linguistic_symbols: null +cleaner: null +g2p: null +speech_volume_normalize: null +rir_scp: null +rir_apply_prob: 1.0 +noise_scp: null +noise_apply_prob: 1.0 +noise_db_range: '13_15' +short_noise_thres: 0.5 +aux_ctc_tasks: [] +frontend: s3prl +frontend_conf: + frontend_conf: + upstream: hubert_base + download_dir: ./hub + multilayer_feature: true + fs: 16k +specaug: specaug +specaug_conf: + apply_time_warp: true + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 27 + num_freq_mask: 2 + apply_time_mask: true + time_mask_width_ratio_range: + - 0.0 + - 0.05 + num_time_mask: 10 +normalize: utterance_mvn +normalize_conf: {} +model: espnet +model_conf: + ctc_weight: 1.0 + extract_feats_in_collect_stats: false +preencoder: linear +preencoder_conf: + input_size: 768 + output_size: 80 +encoder: transformer +encoder_conf: + output_size: 256 + attention_heads: 8 + linear_units: 1024 + num_blocks: 2 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: conv2d2 + normalize_before: true +postencoder: null +postencoder_conf: {} +decoder: null +decoder_conf: {} +preprocessor: default +preprocessor_conf: {} +required: +- output_dir +- token_list +version: '202310' +distributed: false diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.1.log b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.1.log new file mode 100644 index 0000000000000000000000000000000000000000..0efb4155a8b60af583a663a8db67e6529f47601b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.1.log @@ -0,0 +1,591 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1 --config conf/decode_asr.yaml +# Started at Wed Jan 17 01:50:22 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1 --config conf/decode_asr.yaml +2024-01-17 01:50:24,090 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml +2024-01-17 01:50:24,108 (asr:523) INFO: Vocabulary size: 44 +2024-01-17 01:50:24,170 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 01:50:24,170 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 01:50:24,281 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 01:50:25,569 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 01:50:26,821 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 01:50:26,821 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 01:50:26,821 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 01:50:26,854 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-17 01:50:26,929 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 01:50:27,039 (asr_inference:494) INFO: speech length: 62080 +2024-01-17 01:50:28,247 (beam_search:428) INFO: decoder input length: 94 +2024-01-17 01:50:28,247 (beam_search:429) INFO: max output length: 94 +2024-01-17 01:50:28,247 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:28,432 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:28,432 (beam_search:476) INFO: -27.28 * 1.0 = -27.28 for ctc +2024-01-17 01:50:28,432 (beam_search:479) INFO: total log probability: -27.28 +2024-01-17 01:50:28,432 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:50:28,432 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:28,433 (beam_search:483) INFO: best hypo: DEIERVERLIEBTEUNGEHEARZOGIERANSCLÄEKESEINESFATESNICHTBERACGTDETAB + +2024-01-17 01:50:28,457 (asr_inference:494) INFO: speech length: 26240 +2024-01-17 01:50:28,465 (beam_search:428) INFO: decoder input length: 38 +2024-01-17 01:50:28,465 (beam_search:429) INFO: max output length: 38 +2024-01-17 01:50:28,465 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:28,501 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:28,501 (beam_search:476) INFO: -7.72 * 1.0 = -7.72 for ctc +2024-01-17 01:50:28,502 (beam_search:479) INFO: total log probability: -7.72 +2024-01-17 01:50:28,502 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:50:28,502 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:28,502 (beam_search:483) INFO: best hypo: DIEINDEANSESTERDTENALS + +2024-01-17 01:50:28,503 (asr_inference:494) INFO: speech length: 22080 +2024-01-17 01:50:28,510 (beam_search:428) INFO: decoder input length: 32 +2024-01-17 01:50:28,510 (beam_search:429) INFO: max output length: 32 +2024-01-17 01:50:28,510 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:28,536 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:28,536 (beam_search:476) INFO: -9.44 * 1.0 = -9.44 for ctc +2024-01-17 01:50:28,536 (beam_search:479) INFO: total log probability: -9.44 +2024-01-17 01:50:28,536 (beam_search:480) INFO: normalized log probability: -0.45 +2024-01-17 01:50:28,536 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:28,536 (beam_search:483) INFO: best hypo: ARKEINGROSEERFOLGK + +2024-01-17 01:50:28,537 (asr_inference:494) INFO: speech length: 29440 +2024-01-17 01:50:28,544 (beam_search:428) INFO: decoder input length: 43 +2024-01-17 01:50:28,544 (beam_search:429) INFO: max output length: 43 +2024-01-17 01:50:28,544 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:28,589 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:28,589 (beam_search:476) INFO: -8.17 * 1.0 = -8.17 for ctc +2024-01-17 01:50:28,589 (beam_search:479) INFO: total log probability: -8.17 +2024-01-17 01:50:28,589 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:50:28,589 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:28,589 (beam_search:483) INFO: best hypo: GOSENSCHEHMICHEINVERBRICKEN + +2024-01-17 01:50:28,590 (asr_inference:494) INFO: speech length: 45120 +2024-01-17 01:50:28,598 (beam_search:428) INFO: decoder input length: 68 +2024-01-17 01:50:28,598 (beam_search:429) INFO: max output length: 68 +2024-01-17 01:50:28,598 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:28,698 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:28,698 (beam_search:476) INFO: -13.78 * 1.0 = -13.78 for ctc +2024-01-17 01:50:28,699 (beam_search:479) INFO: total log probability: -13.78 +2024-01-17 01:50:28,699 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:50:28,699 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:28,699 (beam_search:483) INFO: best hypo: URDENACHMEHREREARLEUTRUNGSBÜCHEVERFFNTLICHT + +2024-01-17 01:50:28,700 (asr_inference:494) INFO: speech length: 35680 +2024-01-17 01:50:28,707 (beam_search:428) INFO: decoder input length: 53 +2024-01-17 01:50:28,707 (beam_search:429) INFO: max output length: 53 +2024-01-17 01:50:28,707 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:28,762 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:28,762 (beam_search:476) INFO: -4.92 * 1.0 = -4.92 for ctc +2024-01-17 01:50:28,762 (beam_search:479) INFO: total log probability: -4.92 +2024-01-17 01:50:28,762 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:50:28,762 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:28,762 (beam_search:483) INFO: best hypo: VORBEREITENBIERTEIGGETUNGT + +2024-01-17 01:50:28,763 (asr_inference:494) INFO: speech length: 32160 +2024-01-17 01:50:28,771 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:50:28,771 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:50:28,771 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:28,812 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:28,812 (beam_search:476) INFO: -8.66 * 1.0 = -8.66 for ctc +2024-01-17 01:50:28,812 (beam_search:479) INFO: total log probability: -8.66 +2024-01-17 01:50:28,812 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:50:28,812 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:28,813 (beam_search:483) INFO: best hypo: DOMENTESHLIESLICGINE + +2024-01-17 01:50:28,814 (asr_inference:494) INFO: speech length: 53120 +2024-01-17 01:50:28,822 (beam_search:428) INFO: decoder input length: 80 +2024-01-17 01:50:28,822 (beam_search:429) INFO: max output length: 80 +2024-01-17 01:50:28,823 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:28,924 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:28,924 (beam_search:476) INFO: -16.10 * 1.0 = -16.10 for ctc +2024-01-17 01:50:28,924 (beam_search:479) INFO: total log probability: -16.10 +2024-01-17 01:50:28,924 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:50:28,924 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:28,924 (beam_search:483) INFO: best hypo: TAUTAGVÜRDENTAUTVONKÖNICHVERERCHÜLE + +2024-01-17 01:50:28,925 (asr_inference:494) INFO: speech length: 49280 +2024-01-17 01:50:28,934 (beam_search:428) INFO: decoder input length: 74 +2024-01-17 01:50:28,934 (beam_search:429) INFO: max output length: 74 +2024-01-17 01:50:28,934 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:29,048 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:29,048 (beam_search:476) INFO: -15.43 * 1.0 = -15.43 for ctc +2024-01-17 01:50:29,048 (beam_search:479) INFO: total log probability: -15.43 +2024-01-17 01:50:29,048 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:50:29,048 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:29,048 (beam_search:483) INFO: best hypo: DARUNDERSINDMATILDEARSENSISWECHTERDESKREULZIS + +2024-01-17 01:50:29,050 (asr_inference:494) INFO: speech length: 51040 +2024-01-17 01:50:29,058 (beam_search:428) INFO: decoder input length: 77 +2024-01-17 01:50:29,058 (beam_search:429) INFO: max output length: 77 +2024-01-17 01:50:29,058 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:29,182 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:29,182 (beam_search:476) INFO: -17.17 * 1.0 = -17.17 for ctc +2024-01-17 01:50:29,182 (beam_search:479) INFO: total log probability: -17.17 +2024-01-17 01:50:29,182 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:50:29,182 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:29,182 (beam_search:483) INFO: best hypo: ENINENSTÄTENMEHRUNDMEHRIEROLEDERTRADIZNELNISH + +2024-01-17 01:50:29,184 (asr_inference:494) INFO: speech length: 42240 +2024-01-17 01:50:29,192 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:50:29,192 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:50:29,192 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:29,247 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:29,247 (beam_search:476) INFO: -8.95 * 1.0 = -8.95 for ctc +2024-01-17 01:50:29,247 (beam_search:479) INFO: total log probability: -8.95 +2024-01-17 01:50:29,247 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:50:29,247 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:29,247 (beam_search:483) INFO: best hypo: ZDENENWELTLEUFICHKEIIT + +2024-01-17 01:50:29,248 (asr_inference:494) INFO: speech length: 56160 +2024-01-17 01:50:29,257 (beam_search:428) INFO: decoder input length: 85 +2024-01-17 01:50:29,257 (beam_search:429) INFO: max output length: 85 +2024-01-17 01:50:29,257 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:29,378 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:29,379 (beam_search:476) INFO: -12.98 * 1.0 = -12.98 for ctc +2024-01-17 01:50:29,379 (beam_search:479) INFO: total log probability: -12.98 +2024-01-17 01:50:29,379 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:50:29,379 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:29,379 (beam_search:483) INFO: best hypo: RACHRETDSHOFESUNDDESADETZSFÜRDENFRIEFET + +2024-01-17 01:50:29,380 (asr_inference:494) INFO: speech length: 21120 +2024-01-17 01:50:29,387 (beam_search:428) INFO: decoder input length: 30 +2024-01-17 01:50:29,387 (beam_search:429) INFO: max output length: 30 +2024-01-17 01:50:29,387 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:29,414 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:29,414 (beam_search:476) INFO: -4.20 * 1.0 = -4.20 for ctc +2024-01-17 01:50:29,414 (beam_search:479) INFO: total log probability: -4.20 +2024-01-17 01:50:29,414 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:50:29,414 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:29,414 (beam_search:483) INFO: best hypo: ZEITANGABENVERZICHTET + +2024-01-17 01:50:29,415 (asr_inference:494) INFO: speech length: 70080 +2024-01-17 01:50:29,425 (beam_search:428) INFO: decoder input length: 107 +2024-01-17 01:50:29,425 (beam_search:429) INFO: max output length: 107 +2024-01-17 01:50:29,425 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:29,597 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:29,597 (beam_search:476) INFO: -16.35 * 1.0 = -16.35 for ctc +2024-01-17 01:50:29,597 (beam_search:479) INFO: total log probability: -16.35 +2024-01-17 01:50:29,597 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:50:29,597 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:29,598 (beam_search:483) INFO: best hypo: ALLACHTZEHNHUNDERTACHTZICHMITOTTUOBRAMMSAUFSEITZ + +2024-01-17 01:50:29,599 (asr_inference:494) INFO: speech length: 48960 +2024-01-17 01:50:29,607 (beam_search:428) INFO: decoder input length: 74 +2024-01-17 01:50:29,607 (beam_search:429) INFO: max output length: 74 +2024-01-17 01:50:29,607 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:29,696 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:29,696 (beam_search:476) INFO: -19.11 * 1.0 = -19.11 for ctc +2024-01-17 01:50:29,696 (beam_search:479) INFO: total log probability: -19.11 +2024-01-17 01:50:29,696 (beam_search:480) INFO: normalized log probability: -0.50 +2024-01-17 01:50:29,696 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:29,696 (beam_search:483) INFO: best hypo: MÜLENWESENSIBTEHNUNEDCHUNDZWANZI + +2024-01-17 01:50:29,697 (asr_inference:494) INFO: speech length: 17280 +2024-01-17 01:50:29,704 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:50:29,704 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:50:29,704 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:29,722 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:29,722 (beam_search:476) INFO: -5.49 * 1.0 = -5.49 for ctc +2024-01-17 01:50:29,722 (beam_search:479) INFO: total log probability: -5.49 +2024-01-17 01:50:29,722 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:50:29,722 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:29,722 (beam_search:483) INFO: best hypo: ASDERFICHRICH + +2024-01-17 01:50:29,723 (asr_inference:494) INFO: speech length: 93920 +2024-01-17 01:50:29,734 (beam_search:428) INFO: decoder input length: 144 +2024-01-17 01:50:29,734 (beam_search:429) INFO: max output length: 144 +2024-01-17 01:50:29,734 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:30,098 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:30,098 (beam_search:476) INFO: -32.15 * 1.0 = -32.15 for ctc +2024-01-17 01:50:30,098 (beam_search:479) INFO: total log probability: -32.15 +2024-01-17 01:50:30,098 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:50:30,098 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:30,099 (beam_search:483) INFO: best hypo: SIDEMABSCLSSIMJAHRENUNZENHNDRZWAEUNACHTZICGUNDERNAMEREINEERSTELÄNGEREREISEACSPANEN + +2024-01-17 01:50:30,100 (asr_inference:494) INFO: speech length: 35040 +2024-01-17 01:50:30,108 (beam_search:428) INFO: decoder input length: 52 +2024-01-17 01:50:30,108 (beam_search:429) INFO: max output length: 52 +2024-01-17 01:50:30,108 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:30,156 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:30,156 (beam_search:476) INFO: -9.44 * 1.0 = -9.44 for ctc +2024-01-17 01:50:30,156 (beam_search:479) INFO: total log probability: -9.44 +2024-01-17 01:50:30,156 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:50:30,156 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:30,156 (beam_search:483) INFO: best hypo: VÜRNSCHATSOVURGEZEICHNET + +2024-01-17 01:50:30,157 (asr_inference:494) INFO: speech length: 68640 +2024-01-17 01:50:30,167 (beam_search:428) INFO: decoder input length: 105 +2024-01-17 01:50:30,167 (beam_search:429) INFO: max output length: 105 +2024-01-17 01:50:30,167 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:30,384 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:30,384 (beam_search:476) INFO: -20.45 * 1.0 = -20.45 for ctc +2024-01-17 01:50:30,384 (beam_search:479) INFO: total log probability: -20.45 +2024-01-17 01:50:30,384 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:50:30,384 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:30,385 (beam_search:483) INFO: best hypo: FEITENSTEINSFLSTENDIGEGESCHICHTENUNDDIEAUGSBRGESTADBGESCHIEGEDESELTREN + +2024-01-17 01:50:30,386 (asr_inference:494) INFO: speech length: 58240 +2024-01-17 01:50:30,395 (beam_search:428) INFO: decoder input length: 88 +2024-01-17 01:50:30,395 (beam_search:429) INFO: max output length: 88 +2024-01-17 01:50:30,395 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:30,534 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:30,534 (beam_search:476) INFO: -11.96 * 1.0 = -11.96 for ctc +2024-01-17 01:50:30,535 (beam_search:479) INFO: total log probability: -11.96 +2024-01-17 01:50:30,535 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:50:30,535 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:30,535 (beam_search:483) INFO: best hypo: NACHDIESENZERSTÖHRUNGENWURDEDERASSCHWIEDERAUFBLHN + +2024-01-17 01:50:30,536 (asr_inference:494) INFO: speech length: 117280 +2024-01-17 01:50:30,549 (beam_search:428) INFO: decoder input length: 181 +2024-01-17 01:50:30,549 (beam_search:429) INFO: max output length: 181 +2024-01-17 01:50:30,549 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:31,061 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:31,061 (beam_search:476) INFO: -25.65 * 1.0 = -25.65 for ctc +2024-01-17 01:50:31,061 (beam_search:479) INFO: total log probability: -25.65 +2024-01-17 01:50:31,061 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:50:31,061 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:31,062 (beam_search:483) INFO: best hypo: MACHTENEINFLUSREICHENHANSIEARTENMBEIMKOMISARISCHEINGESETZTENBÜRGEMEISTERMARKERTIHREAUFWAHTUN + +2024-01-17 01:50:31,063 (asr_inference:494) INFO: speech length: 28480 +2024-01-17 01:50:31,070 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:50:31,070 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:50:31,070 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:31,110 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:31,110 (beam_search:476) INFO: -6.91 * 1.0 = -6.91 for ctc +2024-01-17 01:50:31,110 (beam_search:479) INFO: total log probability: -6.91 +2024-01-17 01:50:31,110 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:50:31,110 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:31,111 (beam_search:483) INFO: best hypo: ALSSENTRALDESHANDESKOTOR + +2024-01-17 01:50:31,112 (asr_inference:494) INFO: speech length: 38080 +2024-01-17 01:50:31,119 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:50:31,119 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:50:31,119 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:31,191 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:31,191 (beam_search:476) INFO: -11.65 * 1.0 = -11.65 for ctc +2024-01-17 01:50:31,191 (beam_search:479) INFO: total log probability: -11.65 +2024-01-17 01:50:31,191 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:50:31,191 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:31,191 (beam_search:483) INFO: best hypo: SONDERSTELLUNGINEHEILBDERSTADTKRFEL + +2024-01-17 01:50:31,192 (asr_inference:494) INFO: speech length: 29440 +2024-01-17 01:50:31,199 (beam_search:428) INFO: decoder input length: 43 +2024-01-17 01:50:31,199 (beam_search:429) INFO: max output length: 43 +2024-01-17 01:50:31,199 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:31,240 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:31,240 (beam_search:476) INFO: -5.94 * 1.0 = -5.94 for ctc +2024-01-17 01:50:31,240 (beam_search:479) INFO: total log probability: -5.94 +2024-01-17 01:50:31,240 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:50:31,240 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:31,240 (beam_search:483) INFO: best hypo: FINESICHINHALOBAOKTERIEN + +2024-01-17 01:50:31,241 (asr_inference:494) INFO: speech length: 67360 +2024-01-17 01:50:31,251 (beam_search:428) INFO: decoder input length: 103 +2024-01-17 01:50:31,251 (beam_search:429) INFO: max output length: 103 +2024-01-17 01:50:31,251 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:31,430 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:31,430 (beam_search:476) INFO: -13.68 * 1.0 = -13.68 for ctc +2024-01-17 01:50:31,430 (beam_search:479) INFO: total log probability: -13.68 +2024-01-17 01:50:31,430 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:50:31,430 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:31,430 (beam_search:483) INFO: best hypo: AUFDERBSEITEFINDEZICHDASEBENFALSVONMEIKELOMPONIRT + +2024-01-17 01:50:31,431 (asr_inference:494) INFO: speech length: 81760 +2024-01-17 01:50:31,441 (beam_search:428) INFO: decoder input length: 125 +2024-01-17 01:50:31,441 (beam_search:429) INFO: max output length: 125 +2024-01-17 01:50:31,441 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:31,717 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:31,717 (beam_search:476) INFO: -21.08 * 1.0 = -21.08 for ctc +2024-01-17 01:50:31,717 (beam_search:479) INFO: total log probability: -21.08 +2024-01-17 01:50:31,717 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:50:31,717 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:31,718 (beam_search:483) INFO: best hypo: INHANDEARTISCHEZEITHATEDIEZERKEGESESCHAFTKEINENAUSCHAGEBENDENENFLSMIER + +2024-01-17 01:50:31,719 (asr_inference:494) INFO: speech length: 107360 +2024-01-17 01:50:31,731 (beam_search:428) INFO: decoder input length: 165 +2024-01-17 01:50:31,731 (beam_search:429) INFO: max output length: 165 +2024-01-17 01:50:31,731 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:32,168 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:32,168 (beam_search:476) INFO: -23.07 * 1.0 = -23.07 for ctc +2024-01-17 01:50:32,168 (beam_search:479) INFO: total log probability: -23.07 +2024-01-17 01:50:32,168 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:50:32,168 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:32,169 (beam_search:483) INFO: best hypo: DARSDERCHVEWENDUNVONAUFTRIEBSKÖRPANODERHOLZEINEGERINGEREMITTLERETDICHTEALSWASSERHAT + +2024-01-17 01:50:32,170 (asr_inference:494) INFO: speech length: 19520 +2024-01-17 01:50:32,177 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:50:32,177 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:50:32,177 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:32,201 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:32,201 (beam_search:476) INFO: -5.02 * 1.0 = -5.02 for ctc +2024-01-17 01:50:32,201 (beam_search:479) INFO: total log probability: -5.02 +2024-01-17 01:50:32,201 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:50:32,201 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:32,201 (beam_search:483) INFO: best hypo: DRAMEITDESIERUNGEN + +2024-01-17 01:50:32,202 (asr_inference:494) INFO: speech length: 24480 +2024-01-17 01:50:32,209 (beam_search:428) INFO: decoder input length: 36 +2024-01-17 01:50:32,209 (beam_search:429) INFO: max output length: 36 +2024-01-17 01:50:32,209 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:32,237 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:32,237 (beam_search:476) INFO: -7.24 * 1.0 = -7.24 for ctc +2024-01-17 01:50:32,237 (beam_search:479) INFO: total log probability: -7.24 +2024-01-17 01:50:32,237 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:50:32,237 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:32,237 (beam_search:483) INFO: best hypo: UMMSEBENORFÜNWON + +2024-01-17 01:50:32,238 (asr_inference:494) INFO: speech length: 33120 +2024-01-17 01:50:32,246 (beam_search:428) INFO: decoder input length: 49 +2024-01-17 01:50:32,246 (beam_search:429) INFO: max output length: 49 +2024-01-17 01:50:32,246 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:32,296 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:32,297 (beam_search:476) INFO: -6.31 * 1.0 = -6.31 for ctc +2024-01-17 01:50:32,297 (beam_search:479) INFO: total log probability: -6.31 +2024-01-17 01:50:32,297 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:50:32,297 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:32,297 (beam_search:483) INFO: best hypo: DESALBRECHTDIEBADESTOCHTER + +2024-01-17 01:50:32,298 (asr_inference:494) INFO: speech length: 33440 +2024-01-17 01:50:32,305 (beam_search:428) INFO: decoder input length: 50 +2024-01-17 01:50:32,305 (beam_search:429) INFO: max output length: 50 +2024-01-17 01:50:32,305 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:32,348 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:32,348 (beam_search:476) INFO: -6.61 * 1.0 = -6.61 for ctc +2024-01-17 01:50:32,348 (beam_search:479) INFO: total log probability: -6.61 +2024-01-17 01:50:32,348 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:50:32,348 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:32,348 (beam_search:483) INFO: best hypo: TATBAMBARESCHESTATZR + +2024-01-17 01:50:32,350 (asr_inference:494) INFO: speech length: 23840 +2024-01-17 01:50:32,357 (beam_search:428) INFO: decoder input length: 35 +2024-01-17 01:50:32,357 (beam_search:429) INFO: max output length: 35 +2024-01-17 01:50:32,357 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:32,388 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:32,388 (beam_search:476) INFO: -6.36 * 1.0 = -6.36 for ctc +2024-01-17 01:50:32,388 (beam_search:479) INFO: total log probability: -6.36 +2024-01-17 01:50:32,388 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:50:32,388 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:32,388 (beam_search:483) INFO: best hypo: DRSLIETBESONDERSLIEBTE + +2024-01-17 01:50:32,389 (asr_inference:494) INFO: speech length: 90240 +2024-01-17 01:50:32,400 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 01:50:32,400 (beam_search:429) INFO: max output length: 138 +2024-01-17 01:50:32,400 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:32,732 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:32,732 (beam_search:476) INFO: -21.34 * 1.0 = -21.34 for ctc +2024-01-17 01:50:32,732 (beam_search:479) INFO: total log probability: -21.34 +2024-01-17 01:50:32,732 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:50:32,732 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:32,733 (beam_search:483) INFO: best hypo: AUFKUNDDESWAHSENDENPOPLIKUMSINTRESSESWRDEDERAUFTRITSORTÜRIEPRIMERISTALESUNGE + +2024-01-17 01:50:32,734 (asr_inference:494) INFO: speech length: 21120 +2024-01-17 01:50:32,741 (beam_search:428) INFO: decoder input length: 30 +2024-01-17 01:50:32,741 (beam_search:429) INFO: max output length: 30 +2024-01-17 01:50:32,741 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:32,765 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:32,765 (beam_search:476) INFO: -6.22 * 1.0 = -6.22 for ctc +2024-01-17 01:50:32,766 (beam_search:479) INFO: total log probability: -6.22 +2024-01-17 01:50:32,766 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:50:32,766 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:32,766 (beam_search:483) INFO: best hypo: NDFREIDLICHSPBIELE + +2024-01-17 01:50:32,767 (asr_inference:494) INFO: speech length: 55840 +2024-01-17 01:50:32,775 (beam_search:428) INFO: decoder input length: 85 +2024-01-17 01:50:32,775 (beam_search:429) INFO: max output length: 85 +2024-01-17 01:50:32,775 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:32,917 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:32,917 (beam_search:476) INFO: -14.61 * 1.0 = -14.61 for ctc +2024-01-17 01:50:32,917 (beam_search:479) INFO: total log probability: -14.61 +2024-01-17 01:50:32,917 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:50:32,917 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:32,917 (beam_search:483) INFO: best hypo: DSDIEREIDENSTURTZERLATIEUNBESCHADEBERSTANDENHATE + +2024-01-17 01:50:32,919 (asr_inference:494) INFO: speech length: 29440 +2024-01-17 01:50:32,926 (beam_search:428) INFO: decoder input length: 43 +2024-01-17 01:50:32,926 (beam_search:429) INFO: max output length: 43 +2024-01-17 01:50:32,926 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:32,966 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:32,966 (beam_search:476) INFO: -8.43 * 1.0 = -8.43 for ctc +2024-01-17 01:50:32,966 (beam_search:479) INFO: total log probability: -8.43 +2024-01-17 01:50:32,966 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:50:32,966 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:32,967 (beam_search:483) INFO: best hypo: JAHRENASCHENENDZWEIIMEN + +2024-01-17 01:50:32,968 (asr_inference:494) INFO: speech length: 70080 +2024-01-17 01:50:32,977 (beam_search:428) INFO: decoder input length: 107 +2024-01-17 01:50:32,977 (beam_search:429) INFO: max output length: 107 +2024-01-17 01:50:32,977 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:33,141 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:33,141 (beam_search:476) INFO: -13.89 * 1.0 = -13.89 for ctc +2024-01-17 01:50:33,141 (beam_search:479) INFO: total log probability: -13.89 +2024-01-17 01:50:33,141 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:50:33,141 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:33,142 (beam_search:483) INFO: best hypo: DRRABMALEUNDGRABGKAPÄLENODEROLTATENNACHALT + +2024-01-17 01:50:33,143 (asr_inference:494) INFO: speech length: 53440 +2024-01-17 01:50:33,151 (beam_search:428) INFO: decoder input length: 81 +2024-01-17 01:50:33,151 (beam_search:429) INFO: max output length: 81 +2024-01-17 01:50:33,151 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:33,285 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:33,285 (beam_search:476) INFO: -17.60 * 1.0 = -17.60 for ctc +2024-01-17 01:50:33,285 (beam_search:479) INFO: total log probability: -17.60 +2024-01-17 01:50:33,285 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:50:33,285 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:33,285 (beam_search:483) INFO: best hypo: JUNENEUZENHUNDREHSUNEUNZIGKÖNEKTDERRSEINEBEIDENJOPS + +2024-01-17 01:50:33,287 (asr_inference:494) INFO: speech length: 20160 +2024-01-17 01:50:33,293 (beam_search:428) INFO: decoder input length: 29 +2024-01-17 01:50:33,293 (beam_search:429) INFO: max output length: 29 +2024-01-17 01:50:33,293 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:33,316 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:33,316 (beam_search:476) INFO: -7.67 * 1.0 = -7.67 for ctc +2024-01-17 01:50:33,316 (beam_search:479) INFO: total log probability: -7.67 +2024-01-17 01:50:33,316 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:50:33,316 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:33,316 (beam_search:483) INFO: best hypo: INGEPORTINEOPET + +2024-01-17 01:50:33,317 (asr_inference:494) INFO: speech length: 58880 +2024-01-17 01:50:33,326 (beam_search:428) INFO: decoder input length: 89 +2024-01-17 01:50:33,326 (beam_search:429) INFO: max output length: 89 +2024-01-17 01:50:33,326 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:33,461 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:33,461 (beam_search:476) INFO: -13.72 * 1.0 = -13.72 for ctc +2024-01-17 01:50:33,461 (beam_search:479) INFO: total log probability: -13.72 +2024-01-17 01:50:33,461 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:50:33,461 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:33,461 (beam_search:483) INFO: best hypo: NEUNUNSECHZIGDERMELIERKONTWOLALBUMSCHATZEIN + +2024-01-17 01:50:33,462 (asr_inference:494) INFO: speech length: 21440 +2024-01-17 01:50:33,469 (beam_search:428) INFO: decoder input length: 31 +2024-01-17 01:50:33,469 (beam_search:429) INFO: max output length: 31 +2024-01-17 01:50:33,469 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:33,483 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:33,483 (beam_search:476) INFO: -4.49 * 1.0 = -4.49 for ctc +2024-01-17 01:50:33,483 (beam_search:479) INFO: total log probability: -4.49 +2024-01-17 01:50:33,483 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-17 01:50:33,483 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:33,484 (beam_search:483) INFO: best hypo: TADUCHCOM + +2024-01-17 01:50:33,484 (asr_inference:494) INFO: speech length: 72800 +2024-01-17 01:50:33,494 (beam_search:428) INFO: decoder input length: 111 +2024-01-17 01:50:33,494 (beam_search:429) INFO: max output length: 111 +2024-01-17 01:50:33,494 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:33,721 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:33,721 (beam_search:476) INFO: -15.64 * 1.0 = -15.64 for ctc +2024-01-17 01:50:33,721 (beam_search:479) INFO: total log probability: -15.64 +2024-01-17 01:50:33,721 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:50:33,721 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:33,721 (beam_search:483) INFO: best hypo: ONEHENNICHTDENROSHANDELSKAUFLEUTENGESELSCHAFTLICGGLEICHGESTETWAREN + +2024-01-17 01:50:33,722 (asr_inference:494) INFO: speech length: 25600 +2024-01-17 01:50:33,729 (beam_search:428) INFO: decoder input length: 37 +2024-01-17 01:50:33,730 (beam_search:429) INFO: max output length: 37 +2024-01-17 01:50:33,730 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:33,766 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:33,766 (beam_search:476) INFO: -6.63 * 1.0 = -6.63 for ctc +2024-01-17 01:50:33,766 (beam_search:479) INFO: total log probability: -6.63 +2024-01-17 01:50:33,766 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:50:33,766 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:33,766 (beam_search:483) INFO: best hypo: VODERNARUNGUNDVOMKLIEMA + +2024-01-17 01:50:33,767 (asr_inference:494) INFO: speech length: 18400 +2024-01-17 01:50:33,774 (beam_search:428) INFO: decoder input length: 26 +2024-01-17 01:50:33,774 (beam_search:429) INFO: max output length: 26 +2024-01-17 01:50:33,774 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:33,787 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:33,787 (beam_search:476) INFO: -2.02 * 1.0 = -2.02 for ctc +2024-01-17 01:50:33,787 (beam_search:479) INFO: total log probability: -2.02 +2024-01-17 01:50:33,787 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:50:33,787 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:33,787 (beam_search:483) INFO: best hypo: APOLOEINS + +2024-01-17 01:50:33,788 (asr_inference:494) INFO: speech length: 28480 +2024-01-17 01:50:33,795 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:50:33,795 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:50:33,795 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:33,836 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:33,836 (beam_search:476) INFO: -6.93 * 1.0 = -6.93 for ctc +2024-01-17 01:50:33,836 (beam_search:479) INFO: total log probability: -6.93 +2024-01-17 01:50:33,836 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:50:33,836 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:33,836 (beam_search:483) INFO: best hypo: BRÜYELELUNDHÖRTNACHKELEN + +2024-01-17 01:50:33,837 (asr_inference:494) INFO: speech length: 20000 +2024-01-17 01:50:33,844 (beam_search:428) INFO: decoder input length: 29 +2024-01-17 01:50:33,844 (beam_search:429) INFO: max output length: 29 +2024-01-17 01:50:33,844 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:33,865 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:33,865 (beam_search:476) INFO: -3.27 * 1.0 = -3.27 for ctc +2024-01-17 01:50:33,865 (beam_search:479) INFO: total log probability: -3.27 +2024-01-17 01:50:33,865 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:50:33,865 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:33,865 (beam_search:483) INFO: best hypo: TWARINENKLOSTE + +2024-01-17 01:50:33,867 (asr_inference:494) INFO: speech length: 132480 +2024-01-17 01:50:33,880 (beam_search:428) INFO: decoder input length: 204 +2024-01-17 01:50:33,880 (beam_search:429) INFO: max output length: 204 +2024-01-17 01:50:33,880 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:34,578 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:34,578 (beam_search:476) INFO: -42.10 * 1.0 = -42.10 for ctc +2024-01-17 01:50:34,578 (beam_search:479) INFO: total log probability: -42.10 +2024-01-17 01:50:34,578 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:50:34,578 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:34,579 (beam_search:483) INFO: best hypo: DIVWRZEMOFIZEHENKANEWALENSTANDTUNTTEUTEEINEMSCHUNGAUSKÖRSCHENKANDEWALUNDPOLITSCHEMKABERETMITKOMDIELMENTENDARSTELLTUN + +2024-01-17 01:50:34,580 (asr_inference:494) INFO: speech length: 25280 +2024-01-17 01:50:34,587 (beam_search:428) INFO: decoder input length: 37 +2024-01-17 01:50:34,588 (beam_search:429) INFO: max output length: 37 +2024-01-17 01:50:34,588 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:34,626 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:34,626 (beam_search:476) INFO: -11.99 * 1.0 = -11.99 for ctc +2024-01-17 01:50:34,626 (beam_search:479) INFO: total log probability: -11.99 +2024-01-17 01:50:34,626 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:50:34,626 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:34,627 (beam_search:483) INFO: best hypo: DIEWENSTEIUNGESLIEDESFÜRTEN + +2024-01-17 01:50:34,628 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 01:50:34,636 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:50:34,636 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:50:34,636 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:34,728 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:34,728 (beam_search:476) INFO: -12.43 * 1.0 = -12.43 for ctc +2024-01-17 01:50:34,728 (beam_search:479) INFO: total log probability: -12.43 +2024-01-17 01:50:34,728 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:50:34,728 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:34,728 (beam_search:483) INFO: best hypo: NANTITZIEGLERDIARMORDUMDERBRNAURUNEN + +2024-01-17 01:50:34,729 (asr_inference:494) INFO: speech length: 20480 +2024-01-17 01:50:34,736 (beam_search:428) INFO: decoder input length: 29 +2024-01-17 01:50:34,736 (beam_search:429) INFO: max output length: 29 +2024-01-17 01:50:34,736 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:34,758 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:34,758 (beam_search:476) INFO: -4.64 * 1.0 = -4.64 for ctc +2024-01-17 01:50:34,758 (beam_search:479) INFO: total log probability: -4.64 +2024-01-17 01:50:34,758 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:50:34,758 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:34,758 (beam_search:483) INFO: best hypo: INTEROHRISTVEL + +2024-01-17 01:50:34,759 (asr_inference:494) INFO: speech length: 157120 +2024-01-17 01:50:34,774 (beam_search:428) INFO: decoder input length: 243 +2024-01-17 01:50:34,774 (beam_search:429) INFO: max output length: 243 +2024-01-17 01:50:34,774 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:35,655 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:35,655 (beam_search:476) INFO: -24.38 * 1.0 = -24.38 for ctc +2024-01-17 01:50:35,655 (beam_search:479) INFO: total log probability: -24.38 +2024-01-17 01:50:35,655 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:50:35,655 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:35,656 (beam_search:483) INFO: best hypo: DIESTRÄNGEDRFORGENGERLEITUNGWURDENZWICHENEUNZEHNHUNDERTNEUNUNDZWANZIGUNDNEUNZHNHUNDERTDREINFÜNFZIGAICHIERLOGESCHERGRABEN + +2024-01-17 01:50:35,657 (asr_inference:494) INFO: speech length: 19840 +2024-01-17 01:50:35,664 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:50:35,664 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:50:35,664 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:35,680 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:35,680 (beam_search:476) INFO: -4.05 * 1.0 = -4.05 for ctc +2024-01-17 01:50:35,680 (beam_search:479) INFO: total log probability: -4.05 +2024-01-17 01:50:35,680 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:50:35,680 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:35,680 (beam_search:483) INFO: best hypo: IMGEGENSATZ + +# Accounting: time=14 threads=1 +# Ended (code 0) at Wed Jan 17 01:50:36 CST 2024, elapsed time 14 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.2.log b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.2.log new file mode 100644 index 0000000000000000000000000000000000000000..33c5aa9efc854a14f97a8a6b5ccf0e9dcd7ba810 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.2.log @@ -0,0 +1,591 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2 --config conf/decode_asr.yaml +# Started at Wed Jan 17 01:50:36 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2 --config conf/decode_asr.yaml +2024-01-17 01:50:37,510 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml +2024-01-17 01:50:37,528 (asr:523) INFO: Vocabulary size: 44 +2024-01-17 01:50:37,590 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 01:50:37,590 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 01:50:37,701 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 01:50:39,004 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 01:50:40,241 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 01:50:40,241 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 01:50:40,241 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 01:50:40,273 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-17 01:50:40,348 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 01:50:40,460 (asr_inference:494) INFO: speech length: 30720 +2024-01-17 01:50:41,672 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:50:41,672 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:50:41,672 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:41,720 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:41,720 (beam_search:476) INFO: -12.18 * 1.0 = -12.18 for ctc +2024-01-17 01:50:41,720 (beam_search:479) INFO: total log probability: -12.18 +2024-01-17 01:50:41,720 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:50:41,720 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:41,720 (beam_search:483) INFO: best hypo: VWARBEVONÖRDIGENSNLAUNDHORD + +2024-01-17 01:50:41,744 (asr_inference:494) INFO: speech length: 24000 +2024-01-17 01:50:41,752 (beam_search:428) INFO: decoder input length: 35 +2024-01-17 01:50:41,753 (beam_search:429) INFO: max output length: 35 +2024-01-17 01:50:41,753 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:41,780 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:41,780 (beam_search:476) INFO: -4.79 * 1.0 = -4.79 for ctc +2024-01-17 01:50:41,780 (beam_search:479) INFO: total log probability: -4.79 +2024-01-17 01:50:41,780 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:50:41,780 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:41,780 (beam_search:483) INFO: best hypo: LEIFVERANSTALTUMEN + +2024-01-17 01:50:41,781 (asr_inference:494) INFO: speech length: 44800 +2024-01-17 01:50:41,791 (beam_search:428) INFO: decoder input length: 67 +2024-01-17 01:50:41,791 (beam_search:429) INFO: max output length: 67 +2024-01-17 01:50:41,791 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:41,887 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:41,887 (beam_search:476) INFO: -10.71 * 1.0 = -10.71 for ctc +2024-01-17 01:50:41,887 (beam_search:479) INFO: total log probability: -10.71 +2024-01-17 01:50:41,887 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:50:41,887 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:41,887 (beam_search:483) INFO: best hypo: SUOWERDENHEUTEINDEREGELALEDORTLEBENDENBRUN + +2024-01-17 01:50:41,889 (asr_inference:494) INFO: speech length: 19520 +2024-01-17 01:50:41,895 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:50:41,895 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:50:41,895 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:41,918 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:41,918 (beam_search:476) INFO: -9.23 * 1.0 = -9.23 for ctc +2024-01-17 01:50:41,918 (beam_search:479) INFO: total log probability: -9.23 +2024-01-17 01:50:41,918 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:50:41,918 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:41,919 (beam_search:483) INFO: best hypo: IEDAFÜREWÜSCHLMER + +2024-01-17 01:50:41,920 (asr_inference:494) INFO: speech length: 21760 +2024-01-17 01:50:41,927 (beam_search:428) INFO: decoder input length: 31 +2024-01-17 01:50:41,927 (beam_search:429) INFO: max output length: 31 +2024-01-17 01:50:41,927 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:41,952 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:41,952 (beam_search:476) INFO: -7.47 * 1.0 = -7.47 for ctc +2024-01-17 01:50:41,952 (beam_search:479) INFO: total log probability: -7.47 +2024-01-17 01:50:41,952 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:50:41,952 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:41,953 (beam_search:483) INFO: best hypo: DESHANSEATENFÜRE + +2024-01-17 01:50:41,954 (asr_inference:494) INFO: speech length: 24160 +2024-01-17 01:50:41,961 (beam_search:428) INFO: decoder input length: 35 +2024-01-17 01:50:41,961 (beam_search:429) INFO: max output length: 35 +2024-01-17 01:50:41,961 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:41,988 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:41,988 (beam_search:476) INFO: -6.96 * 1.0 = -6.96 for ctc +2024-01-17 01:50:41,988 (beam_search:479) INFO: total log probability: -6.96 +2024-01-17 01:50:41,988 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:50:41,988 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:41,988 (beam_search:483) INFO: best hypo: HÄBILSAGNESBERNAU + +2024-01-17 01:50:41,989 (asr_inference:494) INFO: speech length: 23360 +2024-01-17 01:50:41,996 (beam_search:428) INFO: decoder input length: 34 +2024-01-17 01:50:41,996 (beam_search:429) INFO: max output length: 34 +2024-01-17 01:50:41,996 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:42,025 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:42,025 (beam_search:476) INFO: -4.58 * 1.0 = -4.58 for ctc +2024-01-17 01:50:42,025 (beam_search:479) INFO: total log probability: -4.58 +2024-01-17 01:50:42,025 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:50:42,025 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:42,026 (beam_search:483) INFO: best hypo: LEBENSWEISEVERKÖARPER + +2024-01-17 01:50:42,027 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 01:50:42,035 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:50:42,035 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:50:42,035 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:42,135 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:42,135 (beam_search:476) INFO: -16.47 * 1.0 = -16.47 for ctc +2024-01-17 01:50:42,135 (beam_search:479) INFO: total log probability: -16.47 +2024-01-17 01:50:42,135 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:50:42,135 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:42,135 (beam_search:483) INFO: best hypo: IDEFALTDESVIELENHAMBURGANZUKATOLESCHFRMMEN + +2024-01-17 01:50:42,137 (asr_inference:494) INFO: speech length: 35360 +2024-01-17 01:50:42,144 (beam_search:428) INFO: decoder input length: 53 +2024-01-17 01:50:42,144 (beam_search:429) INFO: max output length: 53 +2024-01-17 01:50:42,144 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:42,204 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:42,204 (beam_search:476) INFO: -9.88 * 1.0 = -9.88 for ctc +2024-01-17 01:50:42,204 (beam_search:479) INFO: total log probability: -9.88 +2024-01-17 01:50:42,204 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:50:42,204 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:42,204 (beam_search:483) INFO: best hypo: KOLTOURENDDERTHEFTAUSTAUSCHEN + +2024-01-17 01:50:42,205 (asr_inference:494) INFO: speech length: 27840 +2024-01-17 01:50:42,212 (beam_search:428) INFO: decoder input length: 41 +2024-01-17 01:50:42,212 (beam_search:429) INFO: max output length: 41 +2024-01-17 01:50:42,212 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:42,253 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:42,253 (beam_search:476) INFO: -6.65 * 1.0 = -6.65 for ctc +2024-01-17 01:50:42,253 (beam_search:479) INFO: total log probability: -6.65 +2024-01-17 01:50:42,253 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:50:42,253 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:42,253 (beam_search:483) INFO: best hypo: MJAHRZWEITAUSENDVERTONDTE + +2024-01-17 01:50:42,254 (asr_inference:494) INFO: speech length: 80480 +2024-01-17 01:50:42,265 (beam_search:428) INFO: decoder input length: 123 +2024-01-17 01:50:42,265 (beam_search:429) INFO: max output length: 123 +2024-01-17 01:50:42,265 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:42,526 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:42,526 (beam_search:476) INFO: -13.39 * 1.0 = -13.39 for ctc +2024-01-17 01:50:42,526 (beam_search:479) INFO: total log probability: -13.39 +2024-01-17 01:50:42,526 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:50:42,526 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:42,527 (beam_search:483) INFO: best hypo: DASEDIESELEITUNGSCHNLERVOLÄENDTENKÖNEALSDERBAUMEISTERDENKÖNERDOM + +2024-01-17 01:50:42,528 (asr_inference:494) INFO: speech length: 44160 +2024-01-17 01:50:42,536 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:50:42,536 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:50:42,536 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:42,631 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:42,631 (beam_search:476) INFO: -13.99 * 1.0 = -13.99 for ctc +2024-01-17 01:50:42,631 (beam_search:479) INFO: total log probability: -13.99 +2024-01-17 01:50:42,631 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:50:42,631 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:42,631 (beam_search:483) INFO: best hypo: EIERHINRICHTUNGDERBANAURIENHABESICLICHTUM + +2024-01-17 01:50:42,633 (asr_inference:494) INFO: speech length: 16960 +2024-01-17 01:50:42,640 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:50:42,640 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:50:42,640 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:42,648 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:42,648 (beam_search:476) INFO: -4.62 * 1.0 = -4.62 for ctc +2024-01-17 01:50:42,648 (beam_search:479) INFO: total log probability: -4.62 +2024-01-17 01:50:42,648 (beam_search:480) INFO: normalized log probability: -0.58 +2024-01-17 01:50:42,648 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:42,648 (beam_search:483) INFO: best hypo: LUOREI + +2024-01-17 01:50:42,649 (asr_inference:494) INFO: speech length: 41920 +2024-01-17 01:50:42,657 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:50:42,657 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:50:42,657 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:42,736 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:42,736 (beam_search:476) INFO: -7.87 * 1.0 = -7.87 for ctc +2024-01-17 01:50:42,736 (beam_search:479) INFO: total log probability: -7.87 +2024-01-17 01:50:42,736 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:50:42,736 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:42,736 (beam_search:483) INFO: best hypo: DERZEITDERBESTIEKENERDEREIFELEITUNG + +2024-01-17 01:50:42,737 (asr_inference:494) INFO: speech length: 45920 +2024-01-17 01:50:42,745 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:50:42,745 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:50:42,745 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:42,828 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:42,828 (beam_search:476) INFO: -8.51 * 1.0 = -8.51 for ctc +2024-01-17 01:50:42,828 (beam_search:479) INFO: total log probability: -8.51 +2024-01-17 01:50:42,828 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:50:42,828 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:42,828 (beam_search:483) INFO: best hypo: VOKUSBESWISENSHAFTLICHENINTERESSES + +2024-01-17 01:50:42,830 (asr_inference:494) INFO: speech length: 16160 +2024-01-17 01:50:42,836 (beam_search:428) INFO: decoder input length: 23 +2024-01-17 01:50:42,836 (beam_search:429) INFO: max output length: 23 +2024-01-17 01:50:42,836 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:42,854 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:42,854 (beam_search:476) INFO: -3.42 * 1.0 = -3.42 for ctc +2024-01-17 01:50:42,854 (beam_search:479) INFO: total log probability: -3.42 +2024-01-17 01:50:42,854 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:50:42,854 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:42,854 (beam_search:483) INFO: best hypo: TEMEZUBEGEISTEN + +2024-01-17 01:50:42,856 (asr_inference:494) INFO: speech length: 61280 +2024-01-17 01:50:42,864 (beam_search:428) INFO: decoder input length: 93 +2024-01-17 01:50:42,864 (beam_search:429) INFO: max output length: 93 +2024-01-17 01:50:42,865 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:43,006 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:43,006 (beam_search:476) INFO: -8.22 * 1.0 = -8.22 for ctc +2024-01-17 01:50:43,006 (beam_search:479) INFO: total log probability: -8.22 +2024-01-17 01:50:43,006 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:50:43,006 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:43,006 (beam_search:483) INFO: best hypo: METERUNDKONTEDAMITAUVONINENBERGANGENWERDEN + +2024-01-17 01:50:43,007 (asr_inference:494) INFO: speech length: 28160 +2024-01-17 01:50:43,014 (beam_search:428) INFO: decoder input length: 41 +2024-01-17 01:50:43,014 (beam_search:429) INFO: max output length: 41 +2024-01-17 01:50:43,014 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:43,057 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:43,057 (beam_search:476) INFO: -11.49 * 1.0 = -11.49 for ctc +2024-01-17 01:50:43,057 (beam_search:479) INFO: total log probability: -11.49 +2024-01-17 01:50:43,057 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:50:43,057 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:43,057 (beam_search:483) INFO: best hypo: HATKABERBESSELLISTEDERNÜH + +2024-01-17 01:50:43,058 (asr_inference:494) INFO: speech length: 17920 +2024-01-17 01:50:43,065 (beam_search:428) INFO: decoder input length: 25 +2024-01-17 01:50:43,065 (beam_search:429) INFO: max output length: 25 +2024-01-17 01:50:43,065 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:43,085 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:43,085 (beam_search:476) INFO: -6.31 * 1.0 = -6.31 for ctc +2024-01-17 01:50:43,085 (beam_search:479) INFO: total log probability: -6.31 +2024-01-17 01:50:43,085 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:50:43,085 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:43,085 (beam_search:483) INFO: best hypo: DERFREINENZIGKLOP + +2024-01-17 01:50:43,087 (asr_inference:494) INFO: speech length: 35200 +2024-01-17 01:50:43,094 (beam_search:428) INFO: decoder input length: 52 +2024-01-17 01:50:43,094 (beam_search:429) INFO: max output length: 52 +2024-01-17 01:50:43,094 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:43,152 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:43,152 (beam_search:476) INFO: -10.32 * 1.0 = -10.32 for ctc +2024-01-17 01:50:43,152 (beam_search:479) INFO: total log probability: -10.32 +2024-01-17 01:50:43,152 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:50:43,152 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:43,152 (beam_search:483) INFO: best hypo: DENGRSLIWIERUFDELSTEZUSETZEN + +2024-01-17 01:50:43,153 (asr_inference:494) INFO: speech length: 56320 +2024-01-17 01:50:43,162 (beam_search:428) INFO: decoder input length: 85 +2024-01-17 01:50:43,162 (beam_search:429) INFO: max output length: 85 +2024-01-17 01:50:43,162 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:43,290 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:43,290 (beam_search:476) INFO: -9.25 * 1.0 = -9.25 for ctc +2024-01-17 01:50:43,290 (beam_search:479) INFO: total log probability: -9.25 +2024-01-17 01:50:43,290 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:50:43,290 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:43,290 (beam_search:483) INFO: best hypo: WIELANGDIESEKAPLANSSTÄLLEAUFRECHTERHETENWURD + +2024-01-17 01:50:43,292 (asr_inference:494) INFO: speech length: 64960 +2024-01-17 01:50:43,301 (beam_search:428) INFO: decoder input length: 99 +2024-01-17 01:50:43,301 (beam_search:429) INFO: max output length: 99 +2024-01-17 01:50:43,301 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:43,486 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:43,486 (beam_search:476) INFO: -19.33 * 1.0 = -19.33 for ctc +2024-01-17 01:50:43,486 (beam_search:479) INFO: total log probability: -19.33 +2024-01-17 01:50:43,486 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:50:43,486 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:43,486 (beam_search:483) INFO: best hypo: SIWAHRENWASCHEINLICHBEREITZDREISIGSIKUNDENDACHAUSPRCHTESVEUR + +2024-01-17 01:50:43,487 (asr_inference:494) INFO: speech length: 36640 +2024-01-17 01:50:43,495 (beam_search:428) INFO: decoder input length: 55 +2024-01-17 01:50:43,495 (beam_search:429) INFO: max output length: 55 +2024-01-17 01:50:43,495 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:43,562 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:43,562 (beam_search:476) INFO: -6.94 * 1.0 = -6.94 for ctc +2024-01-17 01:50:43,562 (beam_search:479) INFO: total log probability: -6.94 +2024-01-17 01:50:43,562 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:50:43,562 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:43,562 (beam_search:483) INFO: best hypo: METERGESAMTLINGEUNDBISZUZEHNMITER + +2024-01-17 01:50:43,563 (asr_inference:494) INFO: speech length: 24320 +2024-01-17 01:50:43,570 (beam_search:428) INFO: decoder input length: 35 +2024-01-17 01:50:43,570 (beam_search:429) INFO: max output length: 35 +2024-01-17 01:50:43,570 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:43,600 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:43,600 (beam_search:476) INFO: -5.45 * 1.0 = -5.45 for ctc +2024-01-17 01:50:43,600 (beam_search:479) INFO: total log probability: -5.45 +2024-01-17 01:50:43,600 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:50:43,600 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:43,600 (beam_search:483) INFO: best hypo: FEINERITZENUNSPALTE + +2024-01-17 01:50:43,601 (asr_inference:494) INFO: speech length: 48960 +2024-01-17 01:50:43,609 (beam_search:428) INFO: decoder input length: 74 +2024-01-17 01:50:43,610 (beam_search:429) INFO: max output length: 74 +2024-01-17 01:50:43,610 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:43,710 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:43,710 (beam_search:476) INFO: -11.93 * 1.0 = -11.93 for ctc +2024-01-17 01:50:43,710 (beam_search:479) INFO: total log probability: -11.93 +2024-01-17 01:50:43,710 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:50:43,710 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:43,710 (beam_search:483) INFO: best hypo: DINMANVONAUSSNDIEKELERHINABPFLIESENSIET + +2024-01-17 01:50:43,711 (asr_inference:494) INFO: speech length: 21440 +2024-01-17 01:50:43,718 (beam_search:428) INFO: decoder input length: 31 +2024-01-17 01:50:43,718 (beam_search:429) INFO: max output length: 31 +2024-01-17 01:50:43,718 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:43,744 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:43,744 (beam_search:476) INFO: -11.75 * 1.0 = -11.75 for ctc +2024-01-17 01:50:43,744 (beam_search:479) INFO: total log probability: -11.75 +2024-01-17 01:50:43,744 (beam_search:480) INFO: normalized log probability: -0.51 +2024-01-17 01:50:43,744 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:43,745 (beam_search:483) INFO: best hypo: ENEINTERIUSAGTEBRAUN + +2024-01-17 01:50:43,746 (asr_inference:494) INFO: speech length: 20960 +2024-01-17 01:50:43,753 (beam_search:428) INFO: decoder input length: 30 +2024-01-17 01:50:43,753 (beam_search:429) INFO: max output length: 30 +2024-01-17 01:50:43,753 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:43,778 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:43,778 (beam_search:476) INFO: -8.39 * 1.0 = -8.39 for ctc +2024-01-17 01:50:43,778 (beam_search:479) INFO: total log probability: -8.39 +2024-01-17 01:50:43,778 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:50:43,778 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:43,778 (beam_search:483) INFO: best hypo: DASFÜNFTDEVENGERIUM + +2024-01-17 01:50:43,779 (asr_inference:494) INFO: speech length: 41280 +2024-01-17 01:50:43,787 (beam_search:428) INFO: decoder input length: 62 +2024-01-17 01:50:43,787 (beam_search:429) INFO: max output length: 62 +2024-01-17 01:50:43,787 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:43,863 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:43,863 (beam_search:476) INFO: -8.63 * 1.0 = -8.63 for ctc +2024-01-17 01:50:43,863 (beam_search:479) INFO: total log probability: -8.63 +2024-01-17 01:50:43,863 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:50:43,863 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:43,864 (beam_search:483) INFO: best hypo: RESENSIEMANCHMALWEIDETIEREIESCHAFE + +2024-01-17 01:50:43,865 (asr_inference:494) INFO: speech length: 50560 +2024-01-17 01:50:43,873 (beam_search:428) INFO: decoder input length: 76 +2024-01-17 01:50:43,873 (beam_search:429) INFO: max output length: 76 +2024-01-17 01:50:43,873 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:43,958 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:43,958 (beam_search:476) INFO: -8.68 * 1.0 = -8.68 for ctc +2024-01-17 01:50:43,958 (beam_search:479) INFO: total log probability: -8.68 +2024-01-17 01:50:43,958 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:50:43,958 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:43,959 (beam_search:483) INFO: best hypo: SIÖRENDENARTIKELDIESEINRÜVIEU + +2024-01-17 01:50:43,960 (asr_inference:494) INFO: speech length: 24480 +2024-01-17 01:50:43,967 (beam_search:428) INFO: decoder input length: 36 +2024-01-17 01:50:43,967 (beam_search:429) INFO: max output length: 36 +2024-01-17 01:50:43,967 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:43,996 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:43,996 (beam_search:476) INFO: -5.39 * 1.0 = -5.39 for ctc +2024-01-17 01:50:43,997 (beam_search:479) INFO: total log probability: -5.39 +2024-01-17 01:50:43,997 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:50:43,997 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:43,997 (beam_search:483) INFO: best hypo: KUSEISGELENTERKOCH + +2024-01-17 01:50:43,998 (asr_inference:494) INFO: speech length: 53440 +2024-01-17 01:50:44,006 (beam_search:428) INFO: decoder input length: 81 +2024-01-17 01:50:44,006 (beam_search:429) INFO: max output length: 81 +2024-01-17 01:50:44,006 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:44,058 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:44,058 (beam_search:476) INFO: -5.66 * 1.0 = -5.66 for ctc +2024-01-17 01:50:44,058 (beam_search:479) INFO: total log probability: -5.66 +2024-01-17 01:50:44,058 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:50:44,058 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:44,058 (beam_search:483) INFO: best hypo: HANWENTSTIFTUNGE + +2024-01-17 01:50:44,059 (asr_inference:494) INFO: speech length: 56320 +2024-01-17 01:50:44,068 (beam_search:428) INFO: decoder input length: 85 +2024-01-17 01:50:44,068 (beam_search:429) INFO: max output length: 85 +2024-01-17 01:50:44,068 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:44,199 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:44,199 (beam_search:476) INFO: -15.28 * 1.0 = -15.28 for ctc +2024-01-17 01:50:44,199 (beam_search:479) INFO: total log probability: -15.28 +2024-01-17 01:50:44,199 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:50:44,199 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:44,199 (beam_search:483) INFO: best hypo: NEUNZEHNHUDERTACHTZIENALHAINSIEARTENANGESIEN + +2024-01-17 01:50:44,200 (asr_inference:494) INFO: speech length: 29280 +2024-01-17 01:50:44,208 (beam_search:428) INFO: decoder input length: 43 +2024-01-17 01:50:44,208 (beam_search:429) INFO: max output length: 43 +2024-01-17 01:50:44,208 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:44,246 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:44,247 (beam_search:476) INFO: -6.97 * 1.0 = -6.97 for ctc +2024-01-17 01:50:44,247 (beam_search:479) INFO: total log probability: -6.97 +2024-01-17 01:50:44,247 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:50:44,247 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:44,247 (beam_search:483) INFO: best hypo: MEHRERERESNACHIMTOUN + +2024-01-17 01:50:44,248 (asr_inference:494) INFO: speech length: 69920 +2024-01-17 01:50:44,258 (beam_search:428) INFO: decoder input length: 107 +2024-01-17 01:50:44,258 (beam_search:429) INFO: max output length: 107 +2024-01-17 01:50:44,258 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:44,482 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:44,483 (beam_search:476) INFO: -19.87 * 1.0 = -19.87 for ctc +2024-01-17 01:50:44,483 (beam_search:479) INFO: total log probability: -19.87 +2024-01-17 01:50:44,483 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:50:44,483 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:44,483 (beam_search:483) INFO: best hypo: ACHSTIGESGERICHTSZURLANDESWEITENBELIEBENKOLINARISCHESPEZILITETERMÖGE + +2024-01-17 01:50:44,484 (asr_inference:494) INFO: speech length: 59040 +2024-01-17 01:50:44,493 (beam_search:428) INFO: decoder input length: 90 +2024-01-17 01:50:44,493 (beam_search:429) INFO: max output length: 90 +2024-01-17 01:50:44,493 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:44,652 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:44,652 (beam_search:476) INFO: -18.07 * 1.0 = -18.07 for ctc +2024-01-17 01:50:44,652 (beam_search:479) INFO: total log probability: -18.07 +2024-01-17 01:50:44,652 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:50:44,652 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:44,652 (beam_search:483) INFO: best hypo: KOLLETSCUNDEINZWEITJOBALSPANISCHLÄHRERINHEMTNVORLSEAN + +2024-01-17 01:50:44,653 (asr_inference:494) INFO: speech length: 41760 +2024-01-17 01:50:44,661 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:50:44,661 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:50:44,661 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:44,733 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:44,733 (beam_search:476) INFO: -9.20 * 1.0 = -9.20 for ctc +2024-01-17 01:50:44,733 (beam_search:479) INFO: total log probability: -9.20 +2024-01-17 01:50:44,733 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:50:44,733 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:44,733 (beam_search:483) INFO: best hypo: BORENKEINESWEGXSALLÄEGEBÜRTIGEN + +2024-01-17 01:50:44,734 (asr_inference:494) INFO: speech length: 30560 +2024-01-17 01:50:44,741 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:50:44,741 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:50:44,741 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:44,782 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:44,782 (beam_search:476) INFO: -5.16 * 1.0 = -5.16 for ctc +2024-01-17 01:50:44,782 (beam_search:479) INFO: total log probability: -5.16 +2024-01-17 01:50:44,782 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:50:44,782 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:44,782 (beam_search:483) INFO: best hypo: ISTIERKÖRBERBAUGREFTIG + +2024-01-17 01:50:44,783 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:50:44,791 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:50:44,791 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:50:44,791 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:44,853 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:44,853 (beam_search:476) INFO: -11.50 * 1.0 = -11.50 for ctc +2024-01-17 01:50:44,853 (beam_search:479) INFO: total log probability: -11.50 +2024-01-17 01:50:44,853 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:50:44,853 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:44,853 (beam_search:483) INFO: best hypo: ANLESLICHTERNEUJASANDGSPRACHREKE + +2024-01-17 01:50:44,854 (asr_inference:494) INFO: speech length: 28480 +2024-01-17 01:50:44,861 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:50:44,861 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:50:44,861 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:44,899 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:44,899 (beam_search:476) INFO: -5.91 * 1.0 = -5.91 for ctc +2024-01-17 01:50:44,899 (beam_search:479) INFO: total log probability: -5.91 +2024-01-17 01:50:44,899 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:50:44,899 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:44,899 (beam_search:483) INFO: best hypo: MITWINDVONSCRECHINTEN + +2024-01-17 01:50:44,900 (asr_inference:494) INFO: speech length: 53760 +2024-01-17 01:50:44,909 (beam_search:428) INFO: decoder input length: 81 +2024-01-17 01:50:44,909 (beam_search:429) INFO: max output length: 81 +2024-01-17 01:50:44,909 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:45,034 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:45,034 (beam_search:476) INFO: -19.70 * 1.0 = -19.70 for ctc +2024-01-17 01:50:45,034 (beam_search:479) INFO: total log probability: -19.70 +2024-01-17 01:50:45,034 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:50:45,034 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:45,034 (beam_search:483) INFO: best hypo: DENGREÖSTENTELDERBEZIÜRGSWERTRETUNGÖRDINENAUS + +2024-01-17 01:50:45,035 (asr_inference:494) INFO: speech length: 20480 +2024-01-17 01:50:45,042 (beam_search:428) INFO: decoder input length: 29 +2024-01-17 01:50:45,042 (beam_search:429) INFO: max output length: 29 +2024-01-17 01:50:45,042 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:45,069 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:45,069 (beam_search:476) INFO: -9.19 * 1.0 = -9.19 for ctc +2024-01-17 01:50:45,069 (beam_search:479) INFO: total log probability: -9.19 +2024-01-17 01:50:45,069 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:50:45,069 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:45,069 (beam_search:483) INFO: best hypo: ACHTHNHUNEREIUNDZWENZI + +2024-01-17 01:50:45,070 (asr_inference:494) INFO: speech length: 39520 +2024-01-17 01:50:45,078 (beam_search:428) INFO: decoder input length: 59 +2024-01-17 01:50:45,078 (beam_search:429) INFO: max output length: 59 +2024-01-17 01:50:45,078 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:45,151 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:45,151 (beam_search:476) INFO: -10.89 * 1.0 = -10.89 for ctc +2024-01-17 01:50:45,151 (beam_search:479) INFO: total log probability: -10.89 +2024-01-17 01:50:45,151 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:50:45,151 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:45,151 (beam_search:483) INFO: best hypo: ESROSSENADELSANGESAMMITENREICHTUMS + +2024-01-17 01:50:45,152 (asr_inference:494) INFO: speech length: 29600 +2024-01-17 01:50:45,159 (beam_search:428) INFO: decoder input length: 44 +2024-01-17 01:50:45,159 (beam_search:429) INFO: max output length: 44 +2024-01-17 01:50:45,160 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:45,207 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:45,207 (beam_search:476) INFO: -15.71 * 1.0 = -15.71 for ctc +2024-01-17 01:50:45,207 (beam_search:479) INFO: total log probability: -15.71 +2024-01-17 01:50:45,207 (beam_search:480) INFO: normalized log probability: -0.48 +2024-01-17 01:50:45,207 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:45,207 (beam_search:483) INFO: best hypo: ESOENIHTELSEHSOLPOKATZIUN + +2024-01-17 01:50:45,208 (asr_inference:494) INFO: speech length: 36320 +2024-01-17 01:50:45,216 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:50:45,216 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:50:45,216 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:45,277 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:45,277 (beam_search:476) INFO: -11.03 * 1.0 = -11.03 for ctc +2024-01-17 01:50:45,277 (beam_search:479) INFO: total log probability: -11.03 +2024-01-17 01:50:45,277 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:50:45,277 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:45,278 (beam_search:483) INFO: best hypo: TEILHABEDEVRMEROSMANUNDJIRGENZ + +2024-01-17 01:50:45,279 (asr_inference:494) INFO: speech length: 32160 +2024-01-17 01:50:45,286 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:50:45,286 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:50:45,286 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:45,336 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:45,336 (beam_search:476) INFO: -10.31 * 1.0 = -10.31 for ctc +2024-01-17 01:50:45,336 (beam_search:479) INFO: total log probability: -10.31 +2024-01-17 01:50:45,336 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:50:45,336 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:45,336 (beam_search:483) INFO: best hypo: NERMTERFRAUNENTERAUTINSLT + +2024-01-17 01:50:45,337 (asr_inference:494) INFO: speech length: 59360 +2024-01-17 01:50:45,346 (beam_search:428) INFO: decoder input length: 90 +2024-01-17 01:50:45,346 (beam_search:429) INFO: max output length: 90 +2024-01-17 01:50:45,346 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:45,497 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:45,497 (beam_search:476) INFO: -14.96 * 1.0 = -14.96 for ctc +2024-01-17 01:50:45,497 (beam_search:479) INFO: total log probability: -14.96 +2024-01-17 01:50:45,497 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:50:45,497 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:45,497 (beam_search:483) INFO: best hypo: AURDIEOÖSTANDEUTCHERHÖRBUCHFVERLAGMITSITZINMÜNCHEN + +2024-01-17 01:50:45,498 (asr_inference:494) INFO: speech length: 55520 +2024-01-17 01:50:45,507 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:50:45,507 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:50:45,507 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:45,633 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:45,633 (beam_search:476) INFO: -15.16 * 1.0 = -15.16 for ctc +2024-01-17 01:50:45,633 (beam_search:479) INFO: total log probability: -15.16 +2024-01-17 01:50:45,633 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:50:45,633 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:45,633 (beam_search:483) INFO: best hypo: FARPIKTMENTEUNDCHEMICHEVORPROTRUCKTEHERSTELT + +2024-01-17 01:50:45,635 (asr_inference:494) INFO: speech length: 172480 +2024-01-17 01:50:45,651 (beam_search:428) INFO: decoder input length: 267 +2024-01-17 01:50:45,651 (beam_search:429) INFO: max output length: 267 +2024-01-17 01:50:45,651 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:46,551 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:46,551 (beam_search:476) INFO: -26.62 * 1.0 = -26.62 for ctc +2024-01-17 01:50:46,551 (beam_search:479) INFO: total log probability: -26.62 +2024-01-17 01:50:46,551 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:50:46,551 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:46,552 (beam_search:483) INFO: best hypo: ARPLICHENPREUSSISCHENFREIHERENSTANDINDERZALANSCHLUSSFRAGEENTSCHIEDENGEGENDENSINARAUFDIESEITEBISMARGSGESTLLT + +2024-01-17 01:50:46,553 (asr_inference:494) INFO: speech length: 67040 +2024-01-17 01:50:46,563 (beam_search:428) INFO: decoder input length: 102 +2024-01-17 01:50:46,563 (beam_search:429) INFO: max output length: 102 +2024-01-17 01:50:46,563 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:46,736 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:46,736 (beam_search:476) INFO: -16.18 * 1.0 = -16.18 for ctc +2024-01-17 01:50:46,736 (beam_search:479) INFO: total log probability: -16.18 +2024-01-17 01:50:46,736 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:50:46,736 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:46,736 (beam_search:483) INFO: best hypo: WENDIWÄLENVONSELSERFORGWELENUNDOFFENZUOTAGELIEGEN + +2024-01-17 01:50:46,738 (asr_inference:494) INFO: speech length: 47200 +2024-01-17 01:50:46,746 (beam_search:428) INFO: decoder input length: 71 +2024-01-17 01:50:46,746 (beam_search:429) INFO: max output length: 71 +2024-01-17 01:50:46,746 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:46,841 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:46,841 (beam_search:476) INFO: -14.67 * 1.0 = -14.67 for ctc +2024-01-17 01:50:46,842 (beam_search:479) INFO: total log probability: -14.67 +2024-01-17 01:50:46,842 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:50:46,842 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:46,842 (beam_search:483) INFO: best hypo: DSVONGNNACHBARBAUTRUBBAREITSBEGONWUORT + +2024-01-17 01:50:46,843 (asr_inference:494) INFO: speech length: 73600 +2024-01-17 01:50:46,853 (beam_search:428) INFO: decoder input length: 112 +2024-01-17 01:50:46,853 (beam_search:429) INFO: max output length: 112 +2024-01-17 01:50:46,853 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:47,054 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:47,054 (beam_search:476) INFO: -14.98 * 1.0 = -14.98 for ctc +2024-01-17 01:50:47,054 (beam_search:479) INFO: total log probability: -14.98 +2024-01-17 01:50:47,054 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:50:47,054 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:47,054 (beam_search:483) INFO: best hypo: WERDENPRERGENDEELEMENTEDEHANSIEATENTUMSTZUSAMENGEFAST + +2024-01-17 01:50:47,055 (asr_inference:494) INFO: speech length: 39360 +2024-01-17 01:50:47,063 (beam_search:428) INFO: decoder input length: 59 +2024-01-17 01:50:47,063 (beam_search:429) INFO: max output length: 59 +2024-01-17 01:50:47,063 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:47,137 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:47,137 (beam_search:476) INFO: -17.73 * 1.0 = -17.73 for ctc +2024-01-17 01:50:47,137 (beam_search:479) INFO: total log probability: -17.73 +2024-01-17 01:50:47,137 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-17 01:50:47,137 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:47,138 (beam_search:483) INFO: best hypo: DESSLIETZWUREALSFOLCSTLIETANGESEIEN + +# Accounting: time=11 threads=1 +# Ended (code 0) at Wed Jan 17 01:50:47 CST 2024, elapsed time 11 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.3.log b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.3.log new file mode 100644 index 0000000000000000000000000000000000000000..7fc02e191e1d310d96a63b54ee47ed65bb8f70c7 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.3.log @@ -0,0 +1,591 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3 --config conf/decode_asr.yaml +# Started at Wed Jan 17 01:50:47 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3 --config conf/decode_asr.yaml +2024-01-17 01:50:48,939 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml +2024-01-17 01:50:48,958 (asr:523) INFO: Vocabulary size: 44 +2024-01-17 01:50:49,020 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 01:50:49,020 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 01:50:49,130 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 01:50:50,433 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 01:50:51,650 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 01:50:51,650 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 01:50:51,650 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 01:50:51,683 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-17 01:50:51,758 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 01:50:51,870 (asr_inference:494) INFO: speech length: 32800 +2024-01-17 01:50:53,081 (beam_search:428) INFO: decoder input length: 49 +2024-01-17 01:50:53,081 (beam_search:429) INFO: max output length: 49 +2024-01-17 01:50:53,081 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:53,138 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:53,138 (beam_search:476) INFO: -13.45 * 1.0 = -13.45 for ctc +2024-01-17 01:50:53,138 (beam_search:479) INFO: total log probability: -13.45 +2024-01-17 01:50:53,138 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:50:53,138 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:53,139 (beam_search:483) INFO: best hypo: DERZORNDEMHAUSVERLAGSURUBGEHÖRT + +2024-01-17 01:50:53,163 (asr_inference:494) INFO: speech length: 47680 +2024-01-17 01:50:53,173 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 01:50:53,173 (beam_search:429) INFO: max output length: 72 +2024-01-17 01:50:53,173 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:53,287 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:53,287 (beam_search:476) INFO: -14.64 * 1.0 = -14.64 for ctc +2024-01-17 01:50:53,287 (beam_search:479) INFO: total log probability: -14.64 +2024-01-17 01:50:53,287 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:50:53,287 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:53,287 (beam_search:483) INFO: best hypo: VÜRDEÖNFTIGENBORTBÜCHERENTWICKETEDIPAPIERFERPRI + +2024-01-17 01:50:53,288 (asr_inference:494) INFO: speech length: 16960 +2024-01-17 01:50:53,295 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:50:53,295 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:50:53,295 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:53,310 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:53,310 (beam_search:476) INFO: -6.51 * 1.0 = -6.51 for ctc +2024-01-17 01:50:53,310 (beam_search:479) INFO: total log probability: -6.51 +2024-01-17 01:50:53,310 (beam_search:480) INFO: normalized log probability: -0.47 +2024-01-17 01:50:53,310 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:53,310 (beam_search:483) INFO: best hypo: HAMBREWUOKS + +2024-01-17 01:50:53,311 (asr_inference:494) INFO: speech length: 79040 +2024-01-17 01:50:53,321 (beam_search:428) INFO: decoder input length: 121 +2024-01-17 01:50:53,321 (beam_search:429) INFO: max output length: 121 +2024-01-17 01:50:53,321 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:53,532 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:53,532 (beam_search:476) INFO: -15.93 * 1.0 = -15.93 for ctc +2024-01-17 01:50:53,532 (beam_search:479) INFO: total log probability: -15.93 +2024-01-17 01:50:53,532 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:50:53,532 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:53,532 (beam_search:483) INFO: best hypo: RDIWASIEARDLIGENLANDZITZEPETRIBENAUFANDSEISBEMBAU + +2024-01-17 01:50:53,533 (asr_inference:494) INFO: speech length: 69120 +2024-01-17 01:50:53,543 (beam_search:428) INFO: decoder input length: 105 +2024-01-17 01:50:53,543 (beam_search:429) INFO: max output length: 105 +2024-01-17 01:50:53,543 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:53,742 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:53,742 (beam_search:476) INFO: -17.16 * 1.0 = -17.16 for ctc +2024-01-17 01:50:53,742 (beam_search:479) INFO: total log probability: -17.16 +2024-01-17 01:50:53,742 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:50:53,742 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:53,743 (beam_search:483) INFO: best hypo: JAHRZWEITAUSENZWÖLFINDENBELINERKLUPSOSECHSONDREISIGVELLIGT + +2024-01-17 01:50:53,744 (asr_inference:494) INFO: speech length: 47360 +2024-01-17 01:50:53,752 (beam_search:428) INFO: decoder input length: 71 +2024-01-17 01:50:53,752 (beam_search:429) INFO: max output length: 71 +2024-01-17 01:50:53,752 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:53,837 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:53,837 (beam_search:476) INFO: -11.54 * 1.0 = -11.54 for ctc +2024-01-17 01:50:53,838 (beam_search:479) INFO: total log probability: -11.54 +2024-01-17 01:50:53,838 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:50:53,838 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:53,838 (beam_search:483) INFO: best hypo: SECHEHNUNENDFÜNFZIGHAITSBÜNDNISD + +2024-01-17 01:50:53,839 (asr_inference:494) INFO: speech length: 44000 +2024-01-17 01:50:53,847 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:50:53,847 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:50:53,847 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:53,926 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:53,927 (beam_search:476) INFO: -13.46 * 1.0 = -13.46 for ctc +2024-01-17 01:50:53,927 (beam_search:479) INFO: total log probability: -13.46 +2024-01-17 01:50:53,927 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:50:53,927 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:53,927 (beam_search:483) INFO: best hypo: DASPROLEHMBEIESEMPERADOCHSONIST + +2024-01-17 01:50:53,928 (asr_inference:494) INFO: speech length: 75520 +2024-01-17 01:50:53,938 (beam_search:428) INFO: decoder input length: 115 +2024-01-17 01:50:53,938 (beam_search:429) INFO: max output length: 115 +2024-01-17 01:50:53,938 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:54,082 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:54,082 (beam_search:476) INFO: -18.82 * 1.0 = -18.82 for ctc +2024-01-17 01:50:54,082 (beam_search:479) INFO: total log probability: -18.82 +2024-01-17 01:50:54,082 (beam_search:480) INFO: normalized log probability: -0.47 +2024-01-17 01:50:54,082 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:54,082 (beam_search:483) INFO: best hypo: ARMENWESENTETICAMALIESIEVEIKEINGN + +2024-01-17 01:50:54,083 (asr_inference:494) INFO: speech length: 116960 +2024-01-17 01:50:54,096 (beam_search:428) INFO: decoder input length: 180 +2024-01-17 01:50:54,096 (beam_search:429) INFO: max output length: 180 +2024-01-17 01:50:54,096 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:54,571 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:54,571 (beam_search:476) INFO: -25.37 * 1.0 = -25.37 for ctc +2024-01-17 01:50:54,571 (beam_search:479) INFO: total log probability: -25.37 +2024-01-17 01:50:54,571 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:50:54,571 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:54,572 (beam_search:483) INFO: best hypo: NIHTEINMALEINEANNSATZWEISENTUSOCHUNGZUIEREMVERHALTENINERZEITDESNATZUONASOTZELISMUS + +2024-01-17 01:50:54,573 (asr_inference:494) INFO: speech length: 37760 +2024-01-17 01:50:54,581 (beam_search:428) INFO: decoder input length: 56 +2024-01-17 01:50:54,581 (beam_search:429) INFO: max output length: 56 +2024-01-17 01:50:54,581 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:54,640 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:54,640 (beam_search:476) INFO: -9.48 * 1.0 = -9.48 for ctc +2024-01-17 01:50:54,640 (beam_search:479) INFO: total log probability: -9.48 +2024-01-17 01:50:54,640 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:50:54,640 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:54,640 (beam_search:483) INFO: best hypo: LIZENSVÜERFRIEDOGOMENTATION + +2024-01-17 01:50:54,641 (asr_inference:494) INFO: speech length: 46560 +2024-01-17 01:50:54,649 (beam_search:428) INFO: decoder input length: 70 +2024-01-17 01:50:54,649 (beam_search:429) INFO: max output length: 70 +2024-01-17 01:50:54,649 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:54,759 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:54,759 (beam_search:476) INFO: -15.92 * 1.0 = -15.92 for ctc +2024-01-17 01:50:54,759 (beam_search:479) INFO: total log probability: -15.92 +2024-01-17 01:50:54,759 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:50:54,759 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:54,760 (beam_search:483) INFO: best hypo: DIMACHZIHNTNIERHUNDERDIEGARTENHEUSERVORDENTOREN + +2024-01-17 01:50:54,761 (asr_inference:494) INFO: speech length: 27040 +2024-01-17 01:50:54,768 (beam_search:428) INFO: decoder input length: 40 +2024-01-17 01:50:54,768 (beam_search:429) INFO: max output length: 40 +2024-01-17 01:50:54,768 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:54,800 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:54,800 (beam_search:476) INFO: -4.23 * 1.0 = -4.23 for ctc +2024-01-17 01:50:54,800 (beam_search:479) INFO: total log probability: -4.23 +2024-01-17 01:50:54,800 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:50:54,800 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:54,801 (beam_search:483) INFO: best hypo: GANSIMSTIELDERZEIT + +2024-01-17 01:50:54,802 (asr_inference:494) INFO: speech length: 58720 +2024-01-17 01:50:54,811 (beam_search:428) INFO: decoder input length: 89 +2024-01-17 01:50:54,811 (beam_search:429) INFO: max output length: 89 +2024-01-17 01:50:54,811 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:54,952 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:54,952 (beam_search:476) INFO: -16.45 * 1.0 = -16.45 for ctc +2024-01-17 01:50:54,952 (beam_search:479) INFO: total log probability: -16.45 +2024-01-17 01:50:54,952 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:50:54,952 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:54,952 (beam_search:483) INFO: best hypo: BERBRÜÖLUNDHÜÖRTERREICHEDELEITUNGSCLISLICHKÖN + +2024-01-17 01:50:54,953 (asr_inference:494) INFO: speech length: 35520 +2024-01-17 01:50:54,961 (beam_search:428) INFO: decoder input length: 53 +2024-01-17 01:50:54,961 (beam_search:429) INFO: max output length: 53 +2024-01-17 01:50:54,961 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:55,015 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:55,015 (beam_search:476) INFO: -5.86 * 1.0 = -5.86 for ctc +2024-01-17 01:50:55,015 (beam_search:479) INFO: total log probability: -5.86 +2024-01-17 01:50:55,015 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:50:55,015 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:55,015 (beam_search:483) INFO: best hypo: AUSZEICHNUMENFREMDERHEREN + +2024-01-17 01:50:55,016 (asr_inference:494) INFO: speech length: 28000 +2024-01-17 01:50:55,023 (beam_search:428) INFO: decoder input length: 41 +2024-01-17 01:50:55,023 (beam_search:429) INFO: max output length: 41 +2024-01-17 01:50:55,023 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:55,063 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:55,063 (beam_search:476) INFO: -8.23 * 1.0 = -8.23 for ctc +2024-01-17 01:50:55,063 (beam_search:479) INFO: total log probability: -8.23 +2024-01-17 01:50:55,063 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:50:55,063 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:55,064 (beam_search:483) INFO: best hypo: DISCHEFTELLEREIAUFZUGEBEN + +2024-01-17 01:50:55,064 (asr_inference:494) INFO: speech length: 49760 +2024-01-17 01:50:55,073 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:50:55,073 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:50:55,073 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:55,172 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:55,172 (beam_search:476) INFO: -13.86 * 1.0 = -13.86 for ctc +2024-01-17 01:50:55,172 (beam_search:479) INFO: total log probability: -13.86 +2024-01-17 01:50:55,172 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:50:55,172 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:55,173 (beam_search:483) INFO: best hypo: DAZUTZEHEDIEGEGNUNGMITVERLETZTENTIEREN + +2024-01-17 01:50:55,174 (asr_inference:494) INFO: speech length: 25920 +2024-01-17 01:50:55,181 (beam_search:428) INFO: decoder input length: 38 +2024-01-17 01:50:55,181 (beam_search:429) INFO: max output length: 38 +2024-01-17 01:50:55,181 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:55,205 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:55,206 (beam_search:476) INFO: -6.28 * 1.0 = -6.28 for ctc +2024-01-17 01:50:55,206 (beam_search:479) INFO: total log probability: -6.28 +2024-01-17 01:50:55,206 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:50:55,206 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:55,206 (beam_search:483) INFO: best hypo: JENENISCHSTIFT + +2024-01-17 01:50:55,207 (asr_inference:494) INFO: speech length: 17280 +2024-01-17 01:50:55,213 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:50:55,213 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:50:55,213 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:55,233 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:55,233 (beam_search:476) INFO: -3.77 * 1.0 = -3.77 for ctc +2024-01-17 01:50:55,233 (beam_search:479) INFO: total log probability: -3.77 +2024-01-17 01:50:55,233 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:50:55,233 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:55,233 (beam_search:483) INFO: best hypo: WESTLICHVONKÖLEN + +2024-01-17 01:50:55,234 (asr_inference:494) INFO: speech length: 23360 +2024-01-17 01:50:55,241 (beam_search:428) INFO: decoder input length: 34 +2024-01-17 01:50:55,241 (beam_search:429) INFO: max output length: 34 +2024-01-17 01:50:55,241 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:55,274 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:55,274 (beam_search:476) INFO: -8.23 * 1.0 = -8.23 for ctc +2024-01-17 01:50:55,274 (beam_search:479) INFO: total log probability: -8.23 +2024-01-17 01:50:55,274 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:50:55,274 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:55,274 (beam_search:483) INFO: best hypo: DIESTÄNDIGINBERIEBWAREN + +2024-01-17 01:50:55,275 (asr_inference:494) INFO: speech length: 25600 +2024-01-17 01:50:55,282 (beam_search:428) INFO: decoder input length: 37 +2024-01-17 01:50:55,282 (beam_search:429) INFO: max output length: 37 +2024-01-17 01:50:55,282 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:55,320 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:55,320 (beam_search:476) INFO: -10.50 * 1.0 = -10.50 for ctc +2024-01-17 01:50:55,320 (beam_search:479) INFO: total log probability: -10.50 +2024-01-17 01:50:55,320 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:50:55,320 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:55,320 (beam_search:483) INFO: best hypo: DIVOMBARBIIRCHASIERTWERDE + +2024-01-17 01:50:55,321 (asr_inference:494) INFO: speech length: 43680 +2024-01-17 01:50:55,329 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:50:55,329 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:50:55,329 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:55,425 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:55,425 (beam_search:476) INFO: -19.68 * 1.0 = -19.68 for ctc +2024-01-17 01:50:55,425 (beam_search:479) INFO: total log probability: -19.68 +2024-01-17 01:50:55,425 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:50:55,425 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:55,425 (beam_search:483) INFO: best hypo: ERSCHENOCHENWEITERERAUFSETZVONKRISTIERNMEIELT + +2024-01-17 01:50:55,426 (asr_inference:494) INFO: speech length: 79680 +2024-01-17 01:50:55,436 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 01:50:55,436 (beam_search:429) INFO: max output length: 122 +2024-01-17 01:50:55,436 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:55,651 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:55,651 (beam_search:476) INFO: -19.58 * 1.0 = -19.58 for ctc +2024-01-17 01:50:55,651 (beam_search:479) INFO: total log probability: -19.58 +2024-01-17 01:50:55,651 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:50:55,651 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:55,651 (beam_search:483) INFO: best hypo: WALLSEBSTEXTREMEREICHTUMKEINESWEHTENUNMITELBERENZUGAN + +2024-01-17 01:50:55,652 (asr_inference:494) INFO: speech length: 25440 +2024-01-17 01:50:55,659 (beam_search:428) INFO: decoder input length: 37 +2024-01-17 01:50:55,659 (beam_search:429) INFO: max output length: 37 +2024-01-17 01:50:55,659 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:55,692 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:55,692 (beam_search:476) INFO: -5.13 * 1.0 = -5.13 for ctc +2024-01-17 01:50:55,692 (beam_search:479) INFO: total log probability: -5.13 +2024-01-17 01:50:55,692 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:50:55,692 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:55,692 (beam_search:483) INFO: best hypo: GEBTEUCHNICHELBERAUF + +2024-01-17 01:50:55,693 (asr_inference:494) INFO: speech length: 67680 +2024-01-17 01:50:55,703 (beam_search:428) INFO: decoder input length: 103 +2024-01-17 01:50:55,703 (beam_search:429) INFO: max output length: 103 +2024-01-17 01:50:55,703 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:55,873 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:55,873 (beam_search:476) INFO: -13.07 * 1.0 = -13.07 for ctc +2024-01-17 01:50:55,873 (beam_search:479) INFO: total log probability: -13.07 +2024-01-17 01:50:55,873 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:50:55,873 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:55,873 (beam_search:483) INFO: best hypo: AHATDIESENPRAUCNEUNZEHNHUNDERWEIUNDFÜNFZIGGENÜB + +2024-01-17 01:50:55,874 (asr_inference:494) INFO: speech length: 54400 +2024-01-17 01:50:55,883 (beam_search:428) INFO: decoder input length: 82 +2024-01-17 01:50:55,883 (beam_search:429) INFO: max output length: 82 +2024-01-17 01:50:55,883 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:56,006 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:56,006 (beam_search:476) INFO: -13.12 * 1.0 = -13.12 for ctc +2024-01-17 01:50:56,006 (beam_search:479) INFO: total log probability: -13.12 +2024-01-17 01:50:56,006 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:50:56,006 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:56,006 (beam_search:483) INFO: best hypo: WODELEITUNGÜBEDIEALTEHÜRTERLEITUNGEFÜRTWURDE + +2024-01-17 01:50:56,007 (asr_inference:494) INFO: speech length: 73600 +2024-01-17 01:50:56,017 (beam_search:428) INFO: decoder input length: 112 +2024-01-17 01:50:56,017 (beam_search:429) INFO: max output length: 112 +2024-01-17 01:50:56,017 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:56,201 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:56,201 (beam_search:476) INFO: -16.55 * 1.0 = -16.55 for ctc +2024-01-17 01:50:56,201 (beam_search:479) INFO: total log probability: -16.55 +2024-01-17 01:50:56,201 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:50:56,201 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:56,201 (beam_search:483) INFO: best hypo: INEBLIEBTEKÖALSCHROCKTROPEASDEMÖNERUMNANDDIEÖNA + +2024-01-17 01:50:56,202 (asr_inference:494) INFO: speech length: 49120 +2024-01-17 01:50:56,210 (beam_search:428) INFO: decoder input length: 74 +2024-01-17 01:50:56,210 (beam_search:429) INFO: max output length: 74 +2024-01-17 01:50:56,210 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:56,284 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:56,284 (beam_search:476) INFO: -4.86 * 1.0 = -4.86 for ctc +2024-01-17 01:50:56,284 (beam_search:479) INFO: total log probability: -4.86 +2024-01-17 01:50:56,284 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:50:56,284 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:56,285 (beam_search:483) INFO: best hypo: GEWARDENSEIUNDALBRECHTSICH + +2024-01-17 01:50:56,286 (asr_inference:494) INFO: speech length: 42560 +2024-01-17 01:50:56,294 (beam_search:428) INFO: decoder input length: 64 +2024-01-17 01:50:56,294 (beam_search:429) INFO: max output length: 64 +2024-01-17 01:50:56,294 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:56,379 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:56,380 (beam_search:476) INFO: -12.33 * 1.0 = -12.33 for ctc +2024-01-17 01:50:56,380 (beam_search:479) INFO: total log probability: -12.33 +2024-01-17 01:50:56,380 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:50:56,380 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:56,380 (beam_search:483) INFO: best hypo: DRTAGESBEDAFEINESWACHSENDENANWITEMINAR + +2024-01-17 01:50:56,381 (asr_inference:494) INFO: speech length: 30560 +2024-01-17 01:50:56,388 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:50:56,388 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:50:56,388 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:56,434 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:56,434 (beam_search:476) INFO: -8.50 * 1.0 = -8.50 for ctc +2024-01-17 01:50:56,434 (beam_search:479) INFO: total log probability: -8.50 +2024-01-17 01:50:56,434 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:50:56,434 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:56,434 (beam_search:483) INFO: best hypo: SIBSINULERZIEHNOBERALTER + +2024-01-17 01:50:56,435 (asr_inference:494) INFO: speech length: 35840 +2024-01-17 01:50:56,443 (beam_search:428) INFO: decoder input length: 53 +2024-01-17 01:50:56,443 (beam_search:429) INFO: max output length: 53 +2024-01-17 01:50:56,443 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:56,497 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:56,497 (beam_search:476) INFO: -5.30 * 1.0 = -5.30 for ctc +2024-01-17 01:50:56,497 (beam_search:479) INFO: total log probability: -5.30 +2024-01-17 01:50:56,497 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:50:56,497 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:56,497 (beam_search:483) INFO: best hypo: WEITERHINLISIGNACRHWEISEN + +2024-01-17 01:50:56,498 (asr_inference:494) INFO: speech length: 46560 +2024-01-17 01:50:56,506 (beam_search:428) INFO: decoder input length: 70 +2024-01-17 01:50:56,506 (beam_search:429) INFO: max output length: 70 +2024-01-17 01:50:56,506 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:56,587 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:56,588 (beam_search:476) INFO: -11.78 * 1.0 = -11.78 for ctc +2024-01-17 01:50:56,588 (beam_search:479) INFO: total log probability: -11.78 +2024-01-17 01:50:56,588 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:50:56,588 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:56,588 (beam_search:483) INFO: best hypo: SUMGRÜNDUNGSTDRTUMKONTEAMBEREITZ + +2024-01-17 01:50:56,589 (asr_inference:494) INFO: speech length: 44160 +2024-01-17 01:50:56,597 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:50:56,597 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:50:56,597 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:56,671 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:56,671 (beam_search:476) INFO: -9.83 * 1.0 = -9.83 for ctc +2024-01-17 01:50:56,672 (beam_search:479) INFO: total log probability: -9.83 +2024-01-17 01:50:56,672 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:50:56,672 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:56,672 (beam_search:483) INFO: best hypo: KEINLECSCHLAGENMÖGLICHNACHFTEILE + +2024-01-17 01:50:56,673 (asr_inference:494) INFO: speech length: 57280 +2024-01-17 01:50:56,682 (beam_search:428) INFO: decoder input length: 87 +2024-01-17 01:50:56,682 (beam_search:429) INFO: max output length: 87 +2024-01-17 01:50:56,682 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:56,820 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:56,820 (beam_search:476) INFO: -14.11 * 1.0 = -14.11 for ctc +2024-01-17 01:50:56,820 (beam_search:479) INFO: total log probability: -14.11 +2024-01-17 01:50:56,820 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:50:56,820 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:56,821 (beam_search:483) INFO: best hypo: IRTIATOLISCHEKÖRSCHESANGPETERANDERSTELLEDEALT + +2024-01-17 01:50:56,822 (asr_inference:494) INFO: speech length: 64800 +2024-01-17 01:50:56,831 (beam_search:428) INFO: decoder input length: 99 +2024-01-17 01:50:56,831 (beam_search:429) INFO: max output length: 99 +2024-01-17 01:50:56,831 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:57,022 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:57,022 (beam_search:476) INFO: -22.81 * 1.0 = -22.81 for ctc +2024-01-17 01:50:57,022 (beam_search:479) INFO: total log probability: -22.81 +2024-01-17 01:50:57,022 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:50:57,022 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:57,023 (beam_search:483) INFO: best hypo: ERFAKNACUNGDESROTWEIZENSTRARTABESCHNBALTDIERTOFELEISERSAT + +2024-01-17 01:50:57,024 (asr_inference:494) INFO: speech length: 26560 +2024-01-17 01:50:57,031 (beam_search:428) INFO: decoder input length: 39 +2024-01-17 01:50:57,031 (beam_search:429) INFO: max output length: 39 +2024-01-17 01:50:57,031 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:57,074 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:57,075 (beam_search:476) INFO: -10.13 * 1.0 = -10.13 for ctc +2024-01-17 01:50:57,075 (beam_search:479) INFO: total log probability: -10.13 +2024-01-17 01:50:57,075 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:50:57,075 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:57,075 (beam_search:483) INFO: best hypo: KNNEMITDIESENNACHKOMINZEUGEN + +2024-01-17 01:50:57,076 (asr_inference:494) INFO: speech length: 33600 +2024-01-17 01:50:57,083 (beam_search:428) INFO: decoder input length: 50 +2024-01-17 01:50:57,083 (beam_search:429) INFO: max output length: 50 +2024-01-17 01:50:57,084 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:57,140 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:57,140 (beam_search:476) INFO: -8.85 * 1.0 = -8.85 for ctc +2024-01-17 01:50:57,140 (beam_search:479) INFO: total log probability: -8.85 +2024-01-17 01:50:57,140 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:50:57,140 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:57,140 (beam_search:483) INFO: best hypo: ALENEUNENFOLGENDERHÖRSTIEREIER + +2024-01-17 01:50:57,142 (asr_inference:494) INFO: speech length: 24640 +2024-01-17 01:50:57,149 (beam_search:428) INFO: decoder input length: 36 +2024-01-17 01:50:57,149 (beam_search:429) INFO: max output length: 36 +2024-01-17 01:50:57,149 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:57,178 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:57,178 (beam_search:476) INFO: -8.35 * 1.0 = -8.35 for ctc +2024-01-17 01:50:57,178 (beam_search:479) INFO: total log probability: -8.35 +2024-01-17 01:50:57,178 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:50:57,178 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:57,179 (beam_search:483) INFO: best hypo: SCHIBPSMITBEHATENSOR + +2024-01-17 01:50:57,180 (asr_inference:494) INFO: speech length: 60640 +2024-01-17 01:50:57,189 (beam_search:428) INFO: decoder input length: 92 +2024-01-17 01:50:57,189 (beam_search:429) INFO: max output length: 92 +2024-01-17 01:50:57,189 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:57,348 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:57,348 (beam_search:476) INFO: -18.50 * 1.0 = -18.50 for ctc +2024-01-17 01:50:57,348 (beam_search:479) INFO: total log probability: -18.50 +2024-01-17 01:50:57,348 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:50:57,348 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:57,349 (beam_search:483) INFO: best hypo: KOLGEANRERSBOCHNEVERZIGHTETEAUFERNIPRSÖNICHEBEWERTUNG + +2024-01-17 01:50:57,350 (asr_inference:494) INFO: speech length: 21120 +2024-01-17 01:50:57,357 (beam_search:428) INFO: decoder input length: 30 +2024-01-17 01:50:57,357 (beam_search:429) INFO: max output length: 30 +2024-01-17 01:50:57,357 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:57,380 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:57,380 (beam_search:476) INFO: -3.83 * 1.0 = -3.83 for ctc +2024-01-17 01:50:57,380 (beam_search:479) INFO: total log probability: -3.83 +2024-01-17 01:50:57,380 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:50:57,380 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:57,381 (beam_search:483) INFO: best hypo: BWENIGERINTRÜSTET + +2024-01-17 01:50:57,382 (asr_inference:494) INFO: speech length: 39840 +2024-01-17 01:50:57,389 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:50:57,389 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:50:57,389 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:57,464 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:57,464 (beam_search:476) INFO: -6.75 * 1.0 = -6.75 for ctc +2024-01-17 01:50:57,464 (beam_search:479) INFO: total log probability: -6.75 +2024-01-17 01:50:57,464 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:50:57,464 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:57,464 (beam_search:483) INFO: best hypo: WEITERHINVERSORGDEDELEITUNGTERMEN + +2024-01-17 01:50:57,465 (asr_inference:494) INFO: speech length: 33760 +2024-01-17 01:50:57,473 (beam_search:428) INFO: decoder input length: 50 +2024-01-17 01:50:57,473 (beam_search:429) INFO: max output length: 50 +2024-01-17 01:50:57,473 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:57,523 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:57,523 (beam_search:476) INFO: -9.75 * 1.0 = -9.75 for ctc +2024-01-17 01:50:57,523 (beam_search:479) INFO: total log probability: -9.75 +2024-01-17 01:50:57,523 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:50:57,523 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:57,523 (beam_search:483) INFO: best hypo: WARTETENDAFÜERABEMITEINIG + +2024-01-17 01:50:57,524 (asr_inference:494) INFO: speech length: 57440 +2024-01-17 01:50:57,533 (beam_search:428) INFO: decoder input length: 87 +2024-01-17 01:50:57,533 (beam_search:429) INFO: max output length: 87 +2024-01-17 01:50:57,533 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:57,670 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:57,670 (beam_search:476) INFO: -15.86 * 1.0 = -15.86 for ctc +2024-01-17 01:50:57,670 (beam_search:479) INFO: total log probability: -15.86 +2024-01-17 01:50:57,670 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:50:57,670 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:57,671 (beam_search:483) INFO: best hypo: LEDIKLICHANTUNDFNKLEINMUNIERTEINSEINERETZENSONDE + +2024-01-17 01:50:57,672 (asr_inference:494) INFO: speech length: 21600 +2024-01-17 01:50:57,679 (beam_search:428) INFO: decoder input length: 31 +2024-01-17 01:50:57,679 (beam_search:429) INFO: max output length: 31 +2024-01-17 01:50:57,679 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:57,706 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:57,706 (beam_search:476) INFO: -12.88 * 1.0 = -12.88 for ctc +2024-01-17 01:50:57,706 (beam_search:479) INFO: total log probability: -12.88 +2024-01-17 01:50:57,706 (beam_search:480) INFO: normalized log probability: -0.54 +2024-01-17 01:50:57,706 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:57,706 (beam_search:483) INFO: best hypo: EMIAHBERNEUZENERTFÜM + +2024-01-17 01:50:57,707 (asr_inference:494) INFO: speech length: 160480 +2024-01-17 01:50:57,723 (beam_search:428) INFO: decoder input length: 248 +2024-01-17 01:50:57,723 (beam_search:429) INFO: max output length: 248 +2024-01-17 01:50:57,723 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:58,653 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:58,653 (beam_search:476) INFO: -39.41 * 1.0 = -39.41 for ctc +2024-01-17 01:50:58,653 (beam_search:479) INFO: total log probability: -39.41 +2024-01-17 01:50:58,653 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:50:58,653 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:58,654 (beam_search:483) INFO: best hypo: ERSEDEMFARTFALDESBÜRGERECHTZUNDDERINFÜHRNGDEFREITZYÜGICKEITIMZWANSIGSNERHNDERTWANDETTESICHDIESEANSCHAUNGANSERTSWEISENDARHIN + +2024-01-17 01:50:58,656 (asr_inference:494) INFO: speech length: 60800 +2024-01-17 01:50:58,664 (beam_search:428) INFO: decoder input length: 92 +2024-01-17 01:50:58,665 (beam_search:429) INFO: max output length: 92 +2024-01-17 01:50:58,665 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:58,802 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:58,802 (beam_search:476) INFO: -8.20 * 1.0 = -8.20 for ctc +2024-01-17 01:50:58,802 (beam_search:479) INFO: total log probability: -8.20 +2024-01-17 01:50:58,802 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:50:58,802 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:58,803 (beam_search:483) INFO: best hypo: DESZWISTBACRESBERREINBACHEINEBOGENBRÜCKEVO + +2024-01-17 01:50:58,804 (asr_inference:494) INFO: speech length: 34080 +2024-01-17 01:50:58,811 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:50:58,811 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:50:58,811 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:58,874 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:58,874 (beam_search:476) INFO: -13.37 * 1.0 = -13.37 for ctc +2024-01-17 01:50:58,874 (beam_search:479) INFO: total log probability: -13.37 +2024-01-17 01:50:58,874 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:50:58,874 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:58,874 (beam_search:483) INFO: best hypo: ACHZINULESEXSUNDREISIGBURDEDEHMBURG + +2024-01-17 01:50:58,876 (asr_inference:494) INFO: speech length: 17440 +2024-01-17 01:50:58,882 (beam_search:428) INFO: decoder input length: 25 +2024-01-17 01:50:58,882 (beam_search:429) INFO: max output length: 25 +2024-01-17 01:50:58,882 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:58,886 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:58,886 (beam_search:476) INFO: -1.27 * 1.0 = -1.27 for ctc +2024-01-17 01:50:58,886 (beam_search:479) INFO: total log probability: -1.27 +2024-01-17 01:50:58,886 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:50:58,887 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:58,887 (beam_search:483) INFO: best hypo: AM + +2024-01-17 01:50:58,888 (asr_inference:494) INFO: speech length: 76000 +2024-01-17 01:50:58,897 (beam_search:428) INFO: decoder input length: 116 +2024-01-17 01:50:58,897 (beam_search:429) INFO: max output length: 116 +2024-01-17 01:50:58,897 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:59,100 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:59,100 (beam_search:476) INFO: -18.05 * 1.0 = -18.05 for ctc +2024-01-17 01:50:59,100 (beam_search:479) INFO: total log probability: -18.05 +2024-01-17 01:50:59,100 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:50:59,100 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:59,100 (beam_search:483) INFO: best hypo: UFGRUNDDERKONTENENTALSBEREACHTZEHNHUNDERELFBANOTT + +2024-01-17 01:50:59,102 (asr_inference:494) INFO: speech length: 75200 +2024-01-17 01:50:59,111 (beam_search:428) INFO: decoder input length: 115 +2024-01-17 01:50:59,111 (beam_search:429) INFO: max output length: 115 +2024-01-17 01:50:59,111 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:50:59,342 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:50:59,342 (beam_search:476) INFO: -26.47 * 1.0 = -26.47 for ctc +2024-01-17 01:50:59,342 (beam_search:479) INFO: total log probability: -26.47 +2024-01-17 01:50:59,342 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:50:59,342 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:50:59,343 (beam_search:483) INFO: best hypo: WEITERESMALMUSTENDENUNDBLEITBRANDIEVWARBUNGFÜRDASBUCHSEÄBSTWANEMN + +2024-01-17 01:50:59,344 (asr_inference:494) INFO: speech length: 132800 +2024-01-17 01:50:59,358 (beam_search:428) INFO: decoder input length: 205 +2024-01-17 01:50:59,358 (beam_search:429) INFO: max output length: 205 +2024-01-17 01:50:59,358 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:00,090 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:00,090 (beam_search:476) INFO: -55.69 * 1.0 = -55.69 for ctc +2024-01-17 01:51:00,090 (beam_search:479) INFO: total log probability: -55.69 +2024-01-17 01:51:00,090 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:51:00,090 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:00,091 (beam_search:483) INFO: best hypo: DIENAHRICHVMSEGKTERBÜGELICHDEMOGRATICHENFEPRAREVOLUTZIONVENACHTZEHNHUNDERTACHTUNDVERZICHENFANKREICHWURDENHMBURGMITIOBELAUFGEOME + +2024-01-17 01:51:00,092 (asr_inference:494) INFO: speech length: 34240 +2024-01-17 01:51:00,100 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:51:00,100 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:51:00,100 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:00,152 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:00,153 (beam_search:476) INFO: -10.57 * 1.0 = -10.57 for ctc +2024-01-17 01:51:00,153 (beam_search:479) INFO: total log probability: -10.57 +2024-01-17 01:51:00,153 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:51:00,153 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:00,153 (beam_search:483) INFO: best hypo: UBLIEBTZWEIJAREONENTERBRECHN + +2024-01-17 01:51:00,154 (asr_inference:494) INFO: speech length: 29280 +2024-01-17 01:51:00,161 (beam_search:428) INFO: decoder input length: 43 +2024-01-17 01:51:00,161 (beam_search:429) INFO: max output length: 43 +2024-01-17 01:51:00,161 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:00,205 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:00,205 (beam_search:476) INFO: -10.33 * 1.0 = -10.33 for ctc +2024-01-17 01:51:00,205 (beam_search:479) INFO: total log probability: -10.33 +2024-01-17 01:51:00,205 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:51:00,205 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:00,205 (beam_search:483) INFO: best hypo: ZEALREICENGASTSPILUNTERWEGS + +# Accounting: time=13 threads=1 +# Ended (code 0) at Wed Jan 17 01:51:00 CST 2024, elapsed time 13 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.4.log b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.4.log new file mode 100644 index 0000000000000000000000000000000000000000..085fbfb1cc8be281b7e5e6ba2909bd84b59b9c0c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/asr_inference.4.log @@ -0,0 +1,580 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4 --config conf/decode_asr.yaml +# Started at Wed Jan 17 01:51:00 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4 --config conf/decode_asr.yaml +2024-01-17 01:51:02,004 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml +2024-01-17 01:51:02,022 (asr:523) INFO: Vocabulary size: 44 +2024-01-17 01:51:02,084 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 01:51:02,084 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 01:51:02,194 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 01:51:03,482 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 01:51:04,716 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 01:51:04,716 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 01:51:04,716 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 01:51:04,749 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-17 01:51:04,824 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 01:51:04,935 (asr_inference:494) INFO: speech length: 23680 +2024-01-17 01:51:06,131 (beam_search:428) INFO: decoder input length: 34 +2024-01-17 01:51:06,131 (beam_search:429) INFO: max output length: 34 +2024-01-17 01:51:06,131 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:06,160 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:06,160 (beam_search:476) INFO: -6.24 * 1.0 = -6.24 for ctc +2024-01-17 01:51:06,160 (beam_search:479) INFO: total log probability: -6.24 +2024-01-17 01:51:06,160 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:51:06,160 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:06,160 (beam_search:483) INFO: best hypo: CRANTIETETGENÜTEN + +2024-01-17 01:51:06,185 (asr_inference:494) INFO: speech length: 20160 +2024-01-17 01:51:06,193 (beam_search:428) INFO: decoder input length: 29 +2024-01-17 01:51:06,193 (beam_search:429) INFO: max output length: 29 +2024-01-17 01:51:06,193 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:06,218 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:06,218 (beam_search:476) INFO: -4.51 * 1.0 = -4.51 for ctc +2024-01-17 01:51:06,218 (beam_search:479) INFO: total log probability: -4.51 +2024-01-17 01:51:06,218 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:51:06,218 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:06,218 (beam_search:483) INFO: best hypo: INBEROKARAUSTATE + +2024-01-17 01:51:06,219 (asr_inference:494) INFO: speech length: 101920 +2024-01-17 01:51:06,232 (beam_search:428) INFO: decoder input length: 157 +2024-01-17 01:51:06,232 (beam_search:429) INFO: max output length: 157 +2024-01-17 01:51:06,232 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:06,654 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:06,654 (beam_search:476) INFO: -24.75 * 1.0 = -24.75 for ctc +2024-01-17 01:51:06,654 (beam_search:479) INFO: total log probability: -24.75 +2024-01-17 01:51:06,654 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:51:06,654 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:06,655 (beam_search:483) INFO: best hypo: DASERICHTVOMBEIVARGENSANEMOTORDESAUSINDIEZUDIESERZEITNEUENSTEHNDENABEITASIEDLUNENZU + +2024-01-17 01:51:06,656 (asr_inference:494) INFO: speech length: 29760 +2024-01-17 01:51:06,664 (beam_search:428) INFO: decoder input length: 44 +2024-01-17 01:51:06,664 (beam_search:429) INFO: max output length: 44 +2024-01-17 01:51:06,664 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:06,709 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:06,709 (beam_search:476) INFO: -9.43 * 1.0 = -9.43 for ctc +2024-01-17 01:51:06,709 (beam_search:479) INFO: total log probability: -9.43 +2024-01-17 01:51:06,709 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:51:06,709 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:06,709 (beam_search:483) INFO: best hypo: DIMIZAMDIHERRECHENSTOBE + +2024-01-17 01:51:06,710 (asr_inference:494) INFO: speech length: 16320 +2024-01-17 01:51:06,717 (beam_search:428) INFO: decoder input length: 23 +2024-01-17 01:51:06,717 (beam_search:429) INFO: max output length: 23 +2024-01-17 01:51:06,717 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:06,734 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:06,734 (beam_search:476) INFO: -5.49 * 1.0 = -5.49 for ctc +2024-01-17 01:51:06,734 (beam_search:479) INFO: total log probability: -5.49 +2024-01-17 01:51:06,734 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:51:06,734 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:06,734 (beam_search:483) INFO: best hypo: KEISERFERDINANT + +2024-01-17 01:51:06,735 (asr_inference:494) INFO: speech length: 76640 +2024-01-17 01:51:06,745 (beam_search:428) INFO: decoder input length: 117 +2024-01-17 01:51:06,745 (beam_search:429) INFO: max output length: 117 +2024-01-17 01:51:06,745 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:06,987 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:06,987 (beam_search:476) INFO: -22.92 * 1.0 = -22.92 for ctc +2024-01-17 01:51:06,987 (beam_search:479) INFO: total log probability: -22.92 +2024-01-17 01:51:06,987 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:51:06,987 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:06,987 (beam_search:483) INFO: best hypo: VOMFERNERESCHSUHERFERNZSGSAVERBUGNERINDEMFERNSEFILENDASIEWIGELIET + +2024-01-17 01:51:06,989 (asr_inference:494) INFO: speech length: 29280 +2024-01-17 01:51:06,996 (beam_search:428) INFO: decoder input length: 43 +2024-01-17 01:51:06,996 (beam_search:429) INFO: max output length: 43 +2024-01-17 01:51:06,996 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:07,042 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:07,042 (beam_search:476) INFO: -7.59 * 1.0 = -7.59 for ctc +2024-01-17 01:51:07,042 (beam_search:479) INFO: total log probability: -7.59 +2024-01-17 01:51:07,043 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:51:07,043 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:07,043 (beam_search:483) INFO: best hypo: WOREINSEINEMBESTENZEITEIER + +2024-01-17 01:51:07,044 (asr_inference:494) INFO: speech length: 37120 +2024-01-17 01:51:07,052 (beam_search:428) INFO: decoder input length: 55 +2024-01-17 01:51:07,052 (beam_search:429) INFO: max output length: 55 +2024-01-17 01:51:07,052 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:07,115 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:07,115 (beam_search:476) INFO: -9.16 * 1.0 = -9.16 for ctc +2024-01-17 01:51:07,115 (beam_search:479) INFO: total log probability: -9.16 +2024-01-17 01:51:07,115 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:51:07,115 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:07,116 (beam_search:483) INFO: best hypo: SEÖRENDENARTIKELFISCHENDCIEBS + +2024-01-17 01:51:07,117 (asr_inference:494) INFO: speech length: 20000 +2024-01-17 01:51:07,123 (beam_search:428) INFO: decoder input length: 29 +2024-01-17 01:51:07,123 (beam_search:429) INFO: max output length: 29 +2024-01-17 01:51:07,123 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:07,143 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:07,143 (beam_search:476) INFO: -3.69 * 1.0 = -3.69 for ctc +2024-01-17 01:51:07,143 (beam_search:479) INFO: total log probability: -3.69 +2024-01-17 01:51:07,143 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:51:07,143 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:07,143 (beam_search:483) INFO: best hypo: UNDTEFARMOWIE + +2024-01-17 01:51:07,144 (asr_inference:494) INFO: speech length: 47520 +2024-01-17 01:51:07,153 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 01:51:07,153 (beam_search:429) INFO: max output length: 72 +2024-01-17 01:51:07,153 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:07,245 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:07,245 (beam_search:476) INFO: -13.15 * 1.0 = -13.15 for ctc +2024-01-17 01:51:07,245 (beam_search:479) INFO: total log probability: -13.15 +2024-01-17 01:51:07,245 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:51:07,245 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:07,245 (beam_search:483) INFO: best hypo: RESEPTIUONDEHECHSENDMATIGVONKRISTA + +2024-01-17 01:51:07,247 (asr_inference:494) INFO: speech length: 77760 +2024-01-17 01:51:07,256 (beam_search:428) INFO: decoder input length: 119 +2024-01-17 01:51:07,256 (beam_search:429) INFO: max output length: 119 +2024-01-17 01:51:07,256 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:07,510 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:07,510 (beam_search:476) INFO: -17.00 * 1.0 = -17.00 for ctc +2024-01-17 01:51:07,510 (beam_search:479) INFO: total log probability: -17.00 +2024-01-17 01:51:07,510 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:51:07,510 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:07,510 (beam_search:483) INFO: best hypo: DIGESAMTERANLAGEWARBESETVERZWEIHUNDERSECHZIGNACHKRISTUSINBEDRIEB + +2024-01-17 01:51:07,512 (asr_inference:494) INFO: speech length: 41440 +2024-01-17 01:51:07,520 (beam_search:428) INFO: decoder input length: 62 +2024-01-17 01:51:07,520 (beam_search:429) INFO: max output length: 62 +2024-01-17 01:51:07,520 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:07,603 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:07,603 (beam_search:476) INFO: -14.66 * 1.0 = -14.66 for ctc +2024-01-17 01:51:07,603 (beam_search:479) INFO: total log probability: -14.66 +2024-01-17 01:51:07,603 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:51:07,603 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:07,603 (beam_search:483) INFO: best hypo: DERESDEFASTFUTDLIFERSORIDESWAGEBOCH + +2024-01-17 01:51:07,605 (asr_inference:494) INFO: speech length: 19840 +2024-01-17 01:51:07,611 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:51:07,611 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:51:07,611 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:07,633 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:07,633 (beam_search:476) INFO: -4.30 * 1.0 = -4.30 for ctc +2024-01-17 01:51:07,633 (beam_search:479) INFO: total log probability: -4.30 +2024-01-17 01:51:07,633 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:51:07,633 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:07,633 (beam_search:483) INFO: best hypo: EINEMKABELBAUNEN + +2024-01-17 01:51:07,634 (asr_inference:494) INFO: speech length: 43040 +2024-01-17 01:51:07,642 (beam_search:428) INFO: decoder input length: 65 +2024-01-17 01:51:07,642 (beam_search:429) INFO: max output length: 65 +2024-01-17 01:51:07,642 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:07,727 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:07,727 (beam_search:476) INFO: -9.64 * 1.0 = -9.64 for ctc +2024-01-17 01:51:07,727 (beam_search:479) INFO: total log probability: -9.64 +2024-01-17 01:51:07,727 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:51:07,727 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:07,727 (beam_search:483) INFO: best hypo: MORLEDUNGMITDEGEFAHRVERBUNDENGEWES + +2024-01-17 01:51:07,728 (asr_inference:494) INFO: speech length: 28320 +2024-01-17 01:51:07,735 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:51:07,735 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:51:07,735 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:07,782 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:07,782 (beam_search:476) INFO: -10.71 * 1.0 = -10.71 for ctc +2024-01-17 01:51:07,782 (beam_search:479) INFO: total log probability: -10.71 +2024-01-17 01:51:07,782 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:51:07,782 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:07,782 (beam_search:483) INFO: best hypo: DEELTISTENPFIERDERENENASEHAB + +2024-01-17 01:51:07,784 (asr_inference:494) INFO: speech length: 67360 +2024-01-17 01:51:07,793 (beam_search:428) INFO: decoder input length: 103 +2024-01-17 01:51:07,793 (beam_search:429) INFO: max output length: 103 +2024-01-17 01:51:07,793 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:08,007 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:08,007 (beam_search:476) INFO: -17.29 * 1.0 = -17.29 for ctc +2024-01-17 01:51:08,007 (beam_search:479) INFO: total log probability: -17.29 +2024-01-17 01:51:08,007 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:51:08,007 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:08,007 (beam_search:483) INFO: best hypo: SONENAUCHDERNATZUONAHLSUOZERDISTISCHENKUNZTAUFFASSUNGERECHTWERDEN + +2024-01-17 01:51:08,009 (asr_inference:494) INFO: speech length: 30400 +2024-01-17 01:51:08,016 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:51:08,016 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:51:08,016 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:08,061 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:08,061 (beam_search:476) INFO: -8.33 * 1.0 = -8.33 for ctc +2024-01-17 01:51:08,061 (beam_search:479) INFO: total log probability: -8.33 +2024-01-17 01:51:08,061 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:51:08,061 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:08,061 (beam_search:483) INFO: best hypo: DIEWLLZICHDESHANSEARTEN + +2024-01-17 01:51:08,062 (asr_inference:494) INFO: speech length: 35520 +2024-01-17 01:51:08,070 (beam_search:428) INFO: decoder input length: 53 +2024-01-17 01:51:08,070 (beam_search:429) INFO: max output length: 53 +2024-01-17 01:51:08,070 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:08,128 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:08,128 (beam_search:476) INFO: -8.75 * 1.0 = -8.75 for ctc +2024-01-17 01:51:08,128 (beam_search:479) INFO: total log probability: -8.75 +2024-01-17 01:51:08,128 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:51:08,129 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:08,129 (beam_search:483) INFO: best hypo: AUCNACHKOMMENEINTNICHTBEKANT + +2024-01-17 01:51:08,130 (asr_inference:494) INFO: speech length: 32960 +2024-01-17 01:51:08,138 (beam_search:428) INFO: decoder input length: 49 +2024-01-17 01:51:08,138 (beam_search:429) INFO: max output length: 49 +2024-01-17 01:51:08,138 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:08,196 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:08,196 (beam_search:476) INFO: -10.44 * 1.0 = -10.44 for ctc +2024-01-17 01:51:08,196 (beam_search:479) INFO: total log probability: -10.44 +2024-01-17 01:51:08,196 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:51:08,196 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:08,196 (beam_search:483) INFO: best hypo: ERENTEXSTEDENEINDRUGKTZEVERMITTE + +2024-01-17 01:51:08,198 (asr_inference:494) INFO: speech length: 31360 +2024-01-17 01:51:08,205 (beam_search:428) INFO: decoder input length: 46 +2024-01-17 01:51:08,205 (beam_search:429) INFO: max output length: 46 +2024-01-17 01:51:08,205 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:08,257 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:08,257 (beam_search:476) INFO: -9.69 * 1.0 = -9.69 for ctc +2024-01-17 01:51:08,257 (beam_search:479) INFO: total log probability: -9.69 +2024-01-17 01:51:08,257 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:51:08,257 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:08,257 (beam_search:483) INFO: best hypo: VERDINSTMDASKÖNARLIETVERLIEN + +2024-01-17 01:51:08,259 (asr_inference:494) INFO: speech length: 107520 +2024-01-17 01:51:08,271 (beam_search:428) INFO: decoder input length: 165 +2024-01-17 01:51:08,271 (beam_search:429) INFO: max output length: 165 +2024-01-17 01:51:08,271 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:08,657 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:08,657 (beam_search:476) INFO: -29.25 * 1.0 = -29.25 for ctc +2024-01-17 01:51:08,657 (beam_search:479) INFO: total log probability: -29.25 +2024-01-17 01:51:08,657 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:51:08,657 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:08,657 (beam_search:483) INFO: best hypo: BWOLLHOFMEINVONHOFMEINZWALLDAUSWERGGROSENEINFLUSASPÄERERDICHTEAUSÜBT + +2024-01-17 01:51:08,659 (asr_inference:494) INFO: speech length: 37120 +2024-01-17 01:51:08,667 (beam_search:428) INFO: decoder input length: 55 +2024-01-17 01:51:08,667 (beam_search:429) INFO: max output length: 55 +2024-01-17 01:51:08,667 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:08,725 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:08,725 (beam_search:476) INFO: -13.32 * 1.0 = -13.32 for ctc +2024-01-17 01:51:08,725 (beam_search:479) INFO: total log probability: -13.32 +2024-01-17 01:51:08,725 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-17 01:51:08,725 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:08,725 (beam_search:483) INFO: best hypo: MMSOERMNSTALSTASOBERHAUPFVEN + +2024-01-17 01:51:08,726 (asr_inference:494) INFO: speech length: 23200 +2024-01-17 01:51:08,733 (beam_search:428) INFO: decoder input length: 34 +2024-01-17 01:51:08,733 (beam_search:429) INFO: max output length: 34 +2024-01-17 01:51:08,733 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:08,761 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:08,761 (beam_search:476) INFO: -5.68 * 1.0 = -5.68 for ctc +2024-01-17 01:51:08,761 (beam_search:479) INFO: total log probability: -5.68 +2024-01-17 01:51:08,761 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:51:08,761 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:08,761 (beam_search:483) INFO: best hypo: EFREIEDOKOMETATION + +2024-01-17 01:51:08,762 (asr_inference:494) INFO: speech length: 34400 +2024-01-17 01:51:08,770 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:51:08,770 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:51:08,770 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:08,832 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:08,832 (beam_search:476) INFO: -7.57 * 1.0 = -7.57 for ctc +2024-01-17 01:51:08,832 (beam_search:479) INFO: total log probability: -7.57 +2024-01-17 01:51:08,832 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:51:08,832 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:08,832 (beam_search:483) INFO: best hypo: GESTALTUNMBESKAVERSWIEDERSPIEGELT + +2024-01-17 01:51:08,833 (asr_inference:494) INFO: speech length: 27200 +2024-01-17 01:51:08,841 (beam_search:428) INFO: decoder input length: 40 +2024-01-17 01:51:08,841 (beam_search:429) INFO: max output length: 40 +2024-01-17 01:51:08,841 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:08,879 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:08,879 (beam_search:476) INFO: -6.65 * 1.0 = -6.65 for ctc +2024-01-17 01:51:08,879 (beam_search:479) INFO: total log probability: -6.65 +2024-01-17 01:51:08,879 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:51:08,879 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:08,879 (beam_search:483) INFO: best hypo: DERESMTERAUFWANDWITAUF + +2024-01-17 01:51:08,881 (asr_inference:494) INFO: speech length: 96000 +2024-01-17 01:51:08,892 (beam_search:428) INFO: decoder input length: 147 +2024-01-17 01:51:08,892 (beam_search:429) INFO: max output length: 147 +2024-01-17 01:51:08,892 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:09,246 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:09,246 (beam_search:476) INFO: -18.72 * 1.0 = -18.72 for ctc +2024-01-17 01:51:09,246 (beam_search:479) INFO: total log probability: -18.72 +2024-01-17 01:51:09,246 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:51:09,246 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:09,247 (beam_search:483) INFO: best hypo: OBGLEICHAMBORKDIESEMANGEHÖRTEUNDEINENOBILITIERUNGDUCHENKEISERDAMITKEINEDRC + +2024-01-17 01:51:09,248 (asr_inference:494) INFO: speech length: 71520 +2024-01-17 01:51:09,258 (beam_search:428) INFO: decoder input length: 109 +2024-01-17 01:51:09,258 (beam_search:429) INFO: max output length: 109 +2024-01-17 01:51:09,258 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:09,481 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:09,481 (beam_search:476) INFO: -16.07 * 1.0 = -16.07 for ctc +2024-01-17 01:51:09,482 (beam_search:479) INFO: total log probability: -16.07 +2024-01-17 01:51:09,482 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:51:09,482 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:09,482 (beam_search:483) INFO: best hypo: DASTURCHDENSICGAUSWEITENTENWELTHANDELARBEITUNDWOHLSTANDVERSPRACH + +2024-01-17 01:51:09,483 (asr_inference:494) INFO: speech length: 80800 +2024-01-17 01:51:09,493 (beam_search:428) INFO: decoder input length: 124 +2024-01-17 01:51:09,493 (beam_search:429) INFO: max output length: 124 +2024-01-17 01:51:09,494 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:09,768 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:09,768 (beam_search:476) INFO: -23.48 * 1.0 = -23.48 for ctc +2024-01-17 01:51:09,768 (beam_search:479) INFO: total log probability: -23.48 +2024-01-17 01:51:09,768 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:51:09,768 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:09,768 (beam_search:483) INFO: best hypo: FÜÖRDIEZEITTMITEDESNEUNZEHNTEJAHNDERBEKLAGDEDEERCHIETEKTMATINHALLE + +2024-01-17 01:51:09,769 (asr_inference:494) INFO: speech length: 42080 +2024-01-17 01:51:09,777 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:51:09,777 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:51:09,777 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:09,849 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:09,849 (beam_search:476) INFO: -10.57 * 1.0 = -10.57 for ctc +2024-01-17 01:51:09,849 (beam_search:479) INFO: total log probability: -10.57 +2024-01-17 01:51:09,849 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:51:09,849 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:09,849 (beam_search:483) INFO: best hypo: EITBONDESKANZERHEMUTSCHMITTLENTE + +2024-01-17 01:51:09,851 (asr_inference:494) INFO: speech length: 56480 +2024-01-17 01:51:09,859 (beam_search:428) INFO: decoder input length: 86 +2024-01-17 01:51:09,859 (beam_search:429) INFO: max output length: 86 +2024-01-17 01:51:09,859 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:09,967 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:09,967 (beam_search:476) INFO: -9.36 * 1.0 = -9.36 for ctc +2024-01-17 01:51:09,967 (beam_search:479) INFO: total log probability: -9.36 +2024-01-17 01:51:09,967 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:51:09,967 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:09,968 (beam_search:483) INFO: best hypo: DINAMENGODEFREIIMSTATZHANDBOCHTZ + +2024-01-17 01:51:09,969 (asr_inference:494) INFO: speech length: 27200 +2024-01-17 01:51:09,976 (beam_search:428) INFO: decoder input length: 40 +2024-01-17 01:51:09,976 (beam_search:429) INFO: max output length: 40 +2024-01-17 01:51:09,976 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:10,017 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:10,017 (beam_search:476) INFO: -12.93 * 1.0 = -12.93 for ctc +2024-01-17 01:51:10,017 (beam_search:479) INFO: total log probability: -12.93 +2024-01-17 01:51:10,017 (beam_search:480) INFO: normalized log probability: -0.43 +2024-01-17 01:51:10,017 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:10,017 (beam_search:483) INFO: best hypo: WENAUCHEENERGEWISENLETAGIE + +2024-01-17 01:51:10,018 (asr_inference:494) INFO: speech length: 18400 +2024-01-17 01:51:10,025 (beam_search:428) INFO: decoder input length: 26 +2024-01-17 01:51:10,025 (beam_search:429) INFO: max output length: 26 +2024-01-17 01:51:10,025 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:10,043 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:10,043 (beam_search:476) INFO: -5.81 * 1.0 = -5.81 for ctc +2024-01-17 01:51:10,043 (beam_search:479) INFO: total log probability: -5.81 +2024-01-17 01:51:10,043 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:51:10,043 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:10,043 (beam_search:483) INFO: best hypo: KALKOLIEREBAEG + +2024-01-17 01:51:10,044 (asr_inference:494) INFO: speech length: 52000 +2024-01-17 01:51:10,052 (beam_search:428) INFO: decoder input length: 79 +2024-01-17 01:51:10,052 (beam_search:429) INFO: max output length: 79 +2024-01-17 01:51:10,052 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:10,175 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:10,175 (beam_search:476) INFO: -23.01 * 1.0 = -23.01 for ctc +2024-01-17 01:51:10,175 (beam_search:479) INFO: total log probability: -23.01 +2024-01-17 01:51:10,175 (beam_search:480) INFO: normalized log probability: -0.43 +2024-01-17 01:51:10,175 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:10,175 (beam_search:483) INFO: best hypo: ANGEFNGDEZSAHUNERVFNMZISCÜULEREINENDIELIGIEITEN + +2024-01-17 01:51:10,176 (asr_inference:494) INFO: speech length: 90080 +2024-01-17 01:51:10,187 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 01:51:10,187 (beam_search:429) INFO: max output length: 138 +2024-01-17 01:51:10,187 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:10,463 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:10,463 (beam_search:476) INFO: -19.89 * 1.0 = -19.89 for ctc +2024-01-17 01:51:10,463 (beam_search:479) INFO: total log probability: -19.89 +2024-01-17 01:51:10,463 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:51:10,463 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:10,463 (beam_search:483) INFO: best hypo: VIELMENSCHENSANENRISLIALSNAHRUNGSKONGRENTENUNDALSPOTENELGEFA + +2024-01-17 01:51:10,465 (asr_inference:494) INFO: speech length: 16480 +2024-01-17 01:51:10,471 (beam_search:428) INFO: decoder input length: 23 +2024-01-17 01:51:10,471 (beam_search:429) INFO: max output length: 23 +2024-01-17 01:51:10,471 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:10,491 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:10,491 (beam_search:476) INFO: -9.20 * 1.0 = -9.20 for ctc +2024-01-17 01:51:10,491 (beam_search:479) INFO: total log probability: -9.20 +2024-01-17 01:51:10,491 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:51:10,491 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:10,491 (beam_search:483) INFO: best hypo: DENUFTRITVEKÖRZE + +2024-01-17 01:51:10,492 (asr_inference:494) INFO: speech length: 182560 +2024-01-17 01:51:10,509 (beam_search:428) INFO: decoder input length: 283 +2024-01-17 01:51:10,509 (beam_search:429) INFO: max output length: 283 +2024-01-17 01:51:10,510 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:11,659 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:11,659 (beam_search:476) INFO: -55.56 * 1.0 = -55.56 for ctc +2024-01-17 01:51:11,659 (beam_search:479) INFO: total log probability: -55.56 +2024-01-17 01:51:11,659 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:51:11,659 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:11,660 (beam_search:483) INFO: best hypo: MIDMSTANVOMDREITZHNNIULIEZWEITAUSENSWERFDERINHERSTETUNDERDELIZENSKRERTZUCOMONSEIZREWUSCHENSCHERELEITDREITPUNKNLANPORTETUNDUNTEDE + +2024-01-17 01:51:11,661 (asr_inference:494) INFO: speech length: 24480 +2024-01-17 01:51:11,668 (beam_search:428) INFO: decoder input length: 36 +2024-01-17 01:51:11,668 (beam_search:429) INFO: max output length: 36 +2024-01-17 01:51:11,668 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:11,702 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:11,702 (beam_search:476) INFO: -3.43 * 1.0 = -3.43 for ctc +2024-01-17 01:51:11,702 (beam_search:479) INFO: total log probability: -3.43 +2024-01-17 01:51:11,702 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:51:11,702 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:11,703 (beam_search:483) INFO: best hypo: EINEKLEINEREBOGENBRÜCKE + +2024-01-17 01:51:11,704 (asr_inference:494) INFO: speech length: 22080 +2024-01-17 01:51:11,711 (beam_search:428) INFO: decoder input length: 32 +2024-01-17 01:51:11,711 (beam_search:429) INFO: max output length: 32 +2024-01-17 01:51:11,711 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:11,739 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:11,739 (beam_search:476) INFO: -9.77 * 1.0 = -9.77 for ctc +2024-01-17 01:51:11,739 (beam_search:479) INFO: total log probability: -9.77 +2024-01-17 01:51:11,739 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-17 01:51:11,739 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:11,739 (beam_search:483) INFO: best hypo: SICHNUNVERSENWEITRKOM + +2024-01-17 01:51:11,741 (asr_inference:494) INFO: speech length: 36800 +2024-01-17 01:51:11,748 (beam_search:428) INFO: decoder input length: 55 +2024-01-17 01:51:11,749 (beam_search:429) INFO: max output length: 55 +2024-01-17 01:51:11,749 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:11,808 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:11,808 (beam_search:476) INFO: -7.15 * 1.0 = -7.15 for ctc +2024-01-17 01:51:11,808 (beam_search:479) INFO: total log probability: -7.15 +2024-01-17 01:51:11,808 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:51:11,808 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:11,808 (beam_search:483) INFO: best hypo: AUSDEMGEMEÄLDEZUENTFERENEN + +2024-01-17 01:51:11,809 (asr_inference:494) INFO: speech length: 64320 +2024-01-17 01:51:11,819 (beam_search:428) INFO: decoder input length: 98 +2024-01-17 01:51:11,819 (beam_search:429) INFO: max output length: 98 +2024-01-17 01:51:11,819 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:11,994 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:11,994 (beam_search:476) INFO: -21.67 * 1.0 = -21.67 for ctc +2024-01-17 01:51:11,994 (beam_search:479) INFO: total log probability: -21.67 +2024-01-17 01:51:11,994 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:51:11,994 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:11,994 (beam_search:483) INFO: best hypo: ISDCHÖAROPÄSCHERICHTLINENEUNZICGHVIERNERTEXSNEUNZSICHEWI + +2024-01-17 01:51:11,996 (asr_inference:494) INFO: speech length: 17600 +2024-01-17 01:51:12,002 (beam_search:428) INFO: decoder input length: 25 +2024-01-17 01:51:12,002 (beam_search:429) INFO: max output length: 25 +2024-01-17 01:51:12,002 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:12,019 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:12,019 (beam_search:476) INFO: -6.92 * 1.0 = -6.92 for ctc +2024-01-17 01:51:12,019 (beam_search:479) INFO: total log probability: -6.92 +2024-01-17 01:51:12,019 (beam_search:480) INFO: normalized log probability: -0.43 +2024-01-17 01:51:12,019 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:12,019 (beam_search:483) INFO: best hypo: ALEUMSELTAUF + +2024-01-17 01:51:12,020 (asr_inference:494) INFO: speech length: 55200 +2024-01-17 01:51:12,029 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:51:12,029 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:51:12,029 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:12,121 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:12,121 (beam_search:476) INFO: -7.44 * 1.0 = -7.44 for ctc +2024-01-17 01:51:12,121 (beam_search:479) INFO: total log probability: -7.44 +2024-01-17 01:51:12,121 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:51:12,121 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:12,121 (beam_search:483) INFO: best hypo: NDSIESEIAUCHWIEEINEFIRSTEN + +2024-01-17 01:51:12,122 (asr_inference:494) INFO: speech length: 26080 +2024-01-17 01:51:12,129 (beam_search:428) INFO: decoder input length: 38 +2024-01-17 01:51:12,129 (beam_search:429) INFO: max output length: 38 +2024-01-17 01:51:12,129 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:12,166 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:12,166 (beam_search:476) INFO: -10.96 * 1.0 = -10.96 for ctc +2024-01-17 01:51:12,166 (beam_search:479) INFO: total log probability: -10.96 +2024-01-17 01:51:12,166 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-17 01:51:12,166 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:12,166 (beam_search:483) INFO: best hypo: NEUNZEHNHUNDERTNEUNZEN + +2024-01-17 01:51:12,167 (asr_inference:494) INFO: speech length: 53920 +2024-01-17 01:51:12,176 (beam_search:428) INFO: decoder input length: 82 +2024-01-17 01:51:12,176 (beam_search:429) INFO: max output length: 82 +2024-01-17 01:51:12,176 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:12,282 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:12,283 (beam_search:476) INFO: -11.50 * 1.0 = -11.50 for ctc +2024-01-17 01:51:12,283 (beam_search:479) INFO: total log probability: -11.50 +2024-01-17 01:51:12,283 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:51:12,283 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:12,283 (beam_search:483) INFO: best hypo: STATISSENHABENDERÖMISCHEINJENIUREM + +2024-01-17 01:51:12,284 (asr_inference:494) INFO: speech length: 26400 +2024-01-17 01:51:12,291 (beam_search:428) INFO: decoder input length: 39 +2024-01-17 01:51:12,291 (beam_search:429) INFO: max output length: 39 +2024-01-17 01:51:12,291 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:12,329 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:12,329 (beam_search:476) INFO: -7.09 * 1.0 = -7.09 for ctc +2024-01-17 01:51:12,329 (beam_search:479) INFO: total log probability: -7.09 +2024-01-17 01:51:12,329 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:51:12,329 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:12,329 (beam_search:483) INFO: best hypo: DELKRISLIEBEHRUNDMENSCH + +2024-01-17 01:51:12,331 (asr_inference:494) INFO: speech length: 21120 +2024-01-17 01:51:12,338 (beam_search:428) INFO: decoder input length: 30 +2024-01-17 01:51:12,338 (beam_search:429) INFO: max output length: 30 +2024-01-17 01:51:12,338 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:12,363 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:12,364 (beam_search:476) INFO: -10.21 * 1.0 = -10.21 for ctc +2024-01-17 01:51:12,364 (beam_search:479) INFO: total log probability: -10.21 +2024-01-17 01:51:12,364 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:51:12,364 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:12,364 (beam_search:483) INFO: best hypo: MIESICHENSKURELISCHE + +2024-01-17 01:51:12,365 (asr_inference:494) INFO: speech length: 37120 +2024-01-17 01:51:12,372 (beam_search:428) INFO: decoder input length: 55 +2024-01-17 01:51:12,372 (beam_search:429) INFO: max output length: 55 +2024-01-17 01:51:12,373 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:12,441 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:12,441 (beam_search:476) INFO: -13.24 * 1.0 = -13.24 for ctc +2024-01-17 01:51:12,441 (beam_search:479) INFO: total log probability: -13.24 +2024-01-17 01:51:12,441 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:51:12,441 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:12,441 (beam_search:483) INFO: best hypo: BERTOTCHUMLGEBDESDREIVARERZIONERMI + +2024-01-17 01:51:12,442 (asr_inference:494) INFO: speech length: 79680 +2024-01-17 01:51:12,452 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 01:51:12,452 (beam_search:429) INFO: max output length: 122 +2024-01-17 01:51:12,452 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:12,738 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:12,738 (beam_search:476) INFO: -26.42 * 1.0 = -26.42 for ctc +2024-01-17 01:51:12,738 (beam_search:479) INFO: total log probability: -26.42 +2024-01-17 01:51:12,738 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:51:12,738 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:12,738 (beam_search:483) INFO: best hypo: KZUGSGEBIEDTERWESICHTERACHZEHNHUNDATZWEIUNSIEBZIGEGRÜNETEJLUSTDUNETIONALPARK + +2024-01-17 01:51:12,740 (asr_inference:494) INFO: speech length: 17120 +2024-01-17 01:51:12,747 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:51:12,747 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:51:12,747 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:12,759 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:12,760 (beam_search:476) INFO: -5.72 * 1.0 = -5.72 for ctc +2024-01-17 01:51:12,760 (beam_search:479) INFO: total log probability: -5.72 +2024-01-17 01:51:12,760 (beam_search:480) INFO: normalized log probability: -0.48 +2024-01-17 01:51:12,760 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:12,760 (beam_search:483) INFO: best hypo: EFINITION + +2024-01-17 01:51:12,761 (asr_inference:494) INFO: speech length: 66240 +2024-01-17 01:51:12,770 (beam_search:428) INFO: decoder input length: 101 +2024-01-17 01:51:12,770 (beam_search:429) INFO: max output length: 101 +2024-01-17 01:51:12,770 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:12,949 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:12,949 (beam_search:476) INFO: -18.71 * 1.0 = -18.71 for ctc +2024-01-17 01:51:12,949 (beam_search:479) INFO: total log probability: -18.71 +2024-01-17 01:51:12,949 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:51:12,949 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:12,950 (beam_search:483) INFO: best hypo: UMINEUNIVESITETZVILAZWEISEMSTERKUNSGESCHCHTZUSTUDIERN + +2024-01-17 01:51:12,951 (asr_inference:494) INFO: speech length: 22880 +2024-01-17 01:51:12,958 (beam_search:428) INFO: decoder input length: 33 +2024-01-17 01:51:12,958 (beam_search:429) INFO: max output length: 33 +2024-01-17 01:51:12,958 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:12,983 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:12,983 (beam_search:476) INFO: -7.59 * 1.0 = -7.59 for ctc +2024-01-17 01:51:12,983 (beam_search:479) INFO: total log probability: -7.59 +2024-01-17 01:51:12,983 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:51:12,983 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:12,983 (beam_search:483) INFO: best hypo: DIETOTZEREGRINE + +# Accounting: time=13 threads=1 +# Ended (code 0) at Wed Jan 17 01:51:13 CST 2024, elapsed time 13 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/calculate_rtf.log b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/calculate_rtf.log new file mode 100644 index 0000000000000000000000000000000000000000..e6ab57230c491524064e3256c3d1468deb29ffa2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/calculate_rtf.log @@ -0,0 +1,9 @@ +# pyscripts/utils/calculate_rtf.py --log-dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir --log-name asr_inference --input-shift 0.0625 --start-times-marker "speech length" --end-times-marker "best hypo" --inf-num 1 +# Started at Wed Jan 17 01:51:13 CST 2024 +# +Total audio duration: 601.310 [sec] +Total decoding time: 31.373 [sec] +RTF: 0.052 +Latency: 151.560 [ms/sentence] +# Accounting: time=0 threads=1 +# Ended (code 0) at Wed Jan 17 01:51:13 CST 2024, elapsed time 0 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.1.scp b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.1.scp new file mode 100644 index 0000000000000000000000000000000000000000..3be02abae20d1a8f79ae6b79e939cf252d56adda --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.1.scp @@ -0,0 +1,52 @@ +swc_deu_001201 dump/raw/org/dev_10min_deu1/data/format.1/swc_deu_001201.flac +swc_deu_001202 dump/raw/org/dev_10min_deu1/data/format.1/swc_deu_001202.flac +swc_deu_001203 dump/raw/org/dev_10min_deu1/data/format.1/swc_deu_001203.flac +swc_deu_001204 dump/raw/org/dev_10min_deu1/data/format.1/swc_deu_001204.flac +swc_deu_001205 dump/raw/org/dev_10min_deu1/data/format.1/swc_deu_001205.flac +swc_deu_001206 dump/raw/org/dev_10min_deu1/data/format.1/swc_deu_001206.flac +swc_deu_001207 dump/raw/org/dev_10min_deu1/data/format.1/swc_deu_001207.flac +swc_deu_001208 dump/raw/org/dev_10min_deu1/data/format.2/swc_deu_001208.flac +swc_deu_001209 dump/raw/org/dev_10min_deu1/data/format.2/swc_deu_001209.flac +swc_deu_001210 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a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.4.scp b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.4.scp new file mode 100644 index 0000000000000000000000000000000000000000..0dfdfcf1c657e4dbd7f486bca9e463558fa851a5 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/keys.4.scp @@ -0,0 +1,51 @@ +swc_deu_001357 dump/raw/org/dev_10min_deu1/data/format.24/swc_deu_001357.flac +swc_deu_001358 dump/raw/org/dev_10min_deu1/data/format.24/swc_deu_001358.flac +swc_deu_001359 dump/raw/org/dev_10min_deu1/data/format.24/swc_deu_001359.flac +swc_deu_001360 dump/raw/org/dev_10min_deu1/data/format.25/swc_deu_001360.flac +swc_deu_001361 dump/raw/org/dev_10min_deu1/data/format.25/swc_deu_001361.flac +swc_deu_001362 dump/raw/org/dev_10min_deu1/data/format.25/swc_deu_001362.flac +swc_deu_001363 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dump/raw/org/dev_10min_deu1/data/format.27/swc_deu_001375.flac +swc_deu_001376 dump/raw/org/dev_10min_deu1/data/format.27/swc_deu_001376.flac +swc_deu_001377 dump/raw/org/dev_10min_deu1/data/format.27/swc_deu_001377.flac +swc_deu_001378 dump/raw/org/dev_10min_deu1/data/format.28/swc_deu_001378.flac +swc_deu_001379 dump/raw/org/dev_10min_deu1/data/format.28/swc_deu_001379.flac +swc_deu_001380 dump/raw/org/dev_10min_deu1/data/format.28/swc_deu_001380.flac +swc_deu_001381 dump/raw/org/dev_10min_deu1/data/format.28/swc_deu_001381.flac +swc_deu_001382 dump/raw/org/dev_10min_deu1/data/format.28/swc_deu_001382.flac +swc_deu_001383 dump/raw/org/dev_10min_deu1/data/format.28/swc_deu_001383.flac +swc_deu_001384 dump/raw/org/dev_10min_deu1/data/format.29/swc_deu_001384.flac +swc_deu_001385 dump/raw/org/dev_10min_deu1/data/format.29/swc_deu_001385.flac +swc_deu_001386 dump/raw/org/dev_10min_deu1/data/format.29/swc_deu_001386.flac +swc_deu_001387 dump/raw/org/dev_10min_deu1/data/format.29/swc_deu_001387.flac +swc_deu_001388 dump/raw/org/dev_10min_deu1/data/format.29/swc_deu_001388.flac +swc_deu_001389 dump/raw/org/dev_10min_deu1/data/format.29/swc_deu_001389.flac +swc_deu_001390 dump/raw/org/dev_10min_deu1/data/format.30/swc_deu_001390.flac +swc_deu_001391 dump/raw/org/dev_10min_deu1/data/format.30/swc_deu_001391.flac +swc_deu_001392 dump/raw/org/dev_10min_deu1/data/format.30/swc_deu_001392.flac +swc_deu_001393 dump/raw/org/dev_10min_deu1/data/format.30/swc_deu_001393.flac +swc_deu_001394 dump/raw/org/dev_10min_deu1/data/format.30/swc_deu_001394.flac +swc_deu_001395 dump/raw/org/dev_10min_deu1/data/format.30/swc_deu_001395.flac +swc_deu_001396 dump/raw/org/dev_10min_deu1/data/format.31/swc_deu_001396.flac +swc_deu_001397 dump/raw/org/dev_10min_deu1/data/format.31/swc_deu_001397.flac +swc_deu_001398 dump/raw/org/dev_10min_deu1/data/format.31/swc_deu_001398.flac +swc_deu_001399 dump/raw/org/dev_10min_deu1/data/format.31/swc_deu_001399.flac +swc_deu_001400 dump/raw/org/dev_10min_deu1/data/format.31/swc_deu_001400.flac +swc_deu_001401 dump/raw/org/dev_10min_deu1/data/format.31/swc_deu_001401.flac +swc_deu_001402 dump/raw/org/dev_10min_deu1/data/format.32/swc_deu_001402.flac +swc_deu_001403 dump/raw/org/dev_10min_deu1/data/format.32/swc_deu_001403.flac +swc_deu_001404 dump/raw/org/dev_10min_deu1/data/format.32/swc_deu_001404.flac +swc_deu_001405 dump/raw/org/dev_10min_deu1/data/format.32/swc_deu_001405.flac +swc_deu_001406 dump/raw/org/dev_10min_deu1/data/format.32/swc_deu_001406.flac +swc_deu_001407 dump/raw/org/dev_10min_deu1/data/format.32/swc_deu_001407.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..0e520207ad83636bc7c8be14032a469d4ed0d954 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/score @@ -0,0 +1,52 @@ +swc_deu_001201 tensor(-27.2812) +swc_deu_001202 tensor(-7.7247) +swc_deu_001203 tensor(-9.4369) +swc_deu_001204 tensor(-8.1747) +swc_deu_001205 tensor(-13.7753) +swc_deu_001206 tensor(-4.9230) +swc_deu_001207 tensor(-8.6615) +swc_deu_001208 tensor(-16.1020) +swc_deu_001209 tensor(-15.4309) +swc_deu_001210 tensor(-17.1736) +swc_deu_001211 tensor(-8.9541) +swc_deu_001212 tensor(-12.9838) +swc_deu_001213 tensor(-4.2041) +swc_deu_001214 tensor(-16.3466) +swc_deu_001215 tensor(-19.1089) +swc_deu_001216 tensor(-5.4853) +swc_deu_001217 tensor(-32.1465) +swc_deu_001218 tensor(-9.4414) +swc_deu_001219 tensor(-20.4470) +swc_deu_001220 tensor(-11.9562) +swc_deu_001221 tensor(-25.6542) +swc_deu_001222 tensor(-6.9054) +swc_deu_001223 tensor(-11.6484) +swc_deu_001224 tensor(-5.9376) +swc_deu_001225 tensor(-13.6834) +swc_deu_001226 tensor(-21.0824) +swc_deu_001227 tensor(-23.0667) +swc_deu_001228 tensor(-5.0203) +swc_deu_001229 tensor(-7.2377) +swc_deu_001230 tensor(-6.3112) +swc_deu_001231 tensor(-6.6119) +swc_deu_001232 tensor(-6.3559) +swc_deu_001233 tensor(-21.3440) +swc_deu_001234 tensor(-6.2212) +swc_deu_001235 tensor(-14.6094) +swc_deu_001236 tensor(-8.4346) +swc_deu_001237 tensor(-13.8860) +swc_deu_001238 tensor(-17.6036) +swc_deu_001239 tensor(-7.6736) +swc_deu_001240 tensor(-13.7162) +swc_deu_001241 tensor(-4.4946) +swc_deu_001242 tensor(-15.6359) +swc_deu_001243 tensor(-6.6336) +swc_deu_001244 tensor(-2.0153) +swc_deu_001245 tensor(-6.9332) +swc_deu_001246 tensor(-3.2669) +swc_deu_001247 tensor(-42.1004) +swc_deu_001248 tensor(-11.9917) +swc_deu_001249 tensor(-12.4268) +swc_deu_001250 tensor(-4.6397) +swc_deu_001251 tensor(-24.3756) +swc_deu_001252 tensor(-4.0452) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..e775c4ce022a166bf8f6bd92ae887e460ff548d1 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/text @@ -0,0 +1,52 @@ +swc_deu_001201 DEI ERVERLIEBTE UNGE HEARZOG IE RANSCLÄEKESEINESFATES NICHTBERACGTDETAB +swc_deu_001202 DIE IN DE ANSESTERDTEN ALS +swc_deu_001203 ARKEINGROSE ERFOLGK +swc_deu_001204 GOSEN SCHEHMICHEIN VERBRICKEN +swc_deu_001205 URDEN ACH MEHRERE AR LEUTRUNGSBÜCHE VER FFNTLICHT +swc_deu_001206 VORBEREITEN BIER TEIG GETUNGT +swc_deu_001207 DOMENTE SHLIESLICG IN E +swc_deu_001208 TAUTAG VÜRDENTAUT VON KÖNICH VERERCHÜLE +swc_deu_001209 DARUNDER SIND MATILDE ARSENSIS WECHTER DES KREULZIS +swc_deu_001210 EN INENSTÄTEN MEHR UND MEHR IEROLE DERTRADI Z NELN ISH +swc_deu_001211 Z DENEN WELTLEUFICHKEIIT +swc_deu_001212 RACHRE TDSHOFES UND DES ADETZS FÜR DEN FRIEFET +swc_deu_001213 ZEIT ANGABEN VERZICHTET +swc_deu_001214 ALL ACHTZEHN HUNDERT ACHTZICHMIT OTTUOBRAMMS AUFSEITZ +swc_deu_001215 MÜLEN WESEN SIBTEHN UNED CHUNDZWANZI +swc_deu_001216 AS DER FICH RICH +swc_deu_001217 SIDEM ABSCLSS IMJAHRENUNZEN HNDR ZWAEUN ACHTZICG UND ERNAMER EINE ERSTE LÄNGERE REISE ACSPANEN +swc_deu_001218 VÜRNSCHATSO VURGEZEICHNET +swc_deu_001219 FEITENSTEINS FLSTENDIGEGESCHICHTEN UND DIE AUGSBRGESTADBGESCHIEG EDES ELTREN +swc_deu_001220 NACH DIESENZERSTÖHRUNGEN WURDE DERASSCHWIEDER AUFBLHN +swc_deu_001221 MACHTEN EINFLUSREICHEN HAN SIE ARTENM BEIM KOMISARISCHEIN GESETZTEN BÜRGEMEISTER MARKERT IHRE AUFWAHTUN +swc_deu_001222 ALS SENTRALDESHANDES KOTOR +swc_deu_001223 SONDER STELLUNG INEHEILB DERSTADT KRFEL +swc_deu_001224 FINE SICH INHALOBAOKTERIEN +swc_deu_001225 AUF DER B SEITE FINDE ZICH DAS EBENFALS VON MEIKEL OMPONIRT +swc_deu_001226 IN HANDE ARTISCHE ZEIT HATE DIE ZERKEGESESCHAFT KEINEN AUSCHAGEBENDEN ENFLSMIER +swc_deu_001227 DAR SDERCH VEWENDUN VON AUFTRIEBSKÖRPAN ODER HOLZ EINE GERINGERE MITTLERETDICHTE ALS WASSER HAT +swc_deu_001228 DRAMEI TDE SIERUNGEN +swc_deu_001229 UMM SEBEN OR FÜNWON +swc_deu_001230 DES ALBRECHT DIE BADES TOCHTER +swc_deu_001231 TAT BAM BARESCHE STATZR +swc_deu_001232 DRSLIET BESONDERSLIEBTE +swc_deu_001233 AUF KUND DES WAHSENDEN POPLIKUMS INTRESSES WRDE DER AUFTRITS ORT ÜR IE PRIMER ISTALESUNGE +swc_deu_001234 ND FREIDLICH SPBIELE +swc_deu_001235 DS DIE REIDEN STURTZ ERLATIE UN BESCHADE BER STANDEN HATE +swc_deu_001236 JAHREN ASCHENEND ZWEI IMEN +swc_deu_001237 DR RABMALE UND GRAB GKAPÄLEN ODER OL TATEN NACHALT +swc_deu_001238 JUNENEUZEN HUNDR EHSUNEUNZIG KÖNEKTDER RSEINE BEIDEN JOPS +swc_deu_001239 IN GE PORTINE OPET +swc_deu_001240 NEUNUN SECHZIG DER MELIER KONTWOL ALBUMSCHATZ EIN +swc_deu_001241 TADUCHCOM +swc_deu_001242 ONE HEN NICHT DEN ROSHANDELS KAUFLEUTEN GESELSCHAFTLICG GLEICHGESTET WAREN +swc_deu_001243 VO DERNARUNG UND VOM KLIEMA +swc_deu_001244 APOLO EINS +swc_deu_001245 BRÜYELEL UND HÖRT NACHKELEN +swc_deu_001246 TWAR IN EN KLOSTE +swc_deu_001247 DIVWRZEM OFIZEHEN KANEWALEN STANDT UNT TEUTE EINEMSCHUNG AUS KÖRSCHEN KANDEWAL UND POLITSCHEM KABERET MIT KOM DIELMENTEN DARSTELLT UN +swc_deu_001248 DIE WENSTEIUNGESLIEDES FÜRTEN +swc_deu_001249 NANTIT ZIEGLER DI ARMORDUM DER BRNAURUNEN +swc_deu_001250 INTEROHR IST VE L +swc_deu_001251 DIE STRÄNGE DR FORGENGER LEITUNG WURDEN ZWICHE NEUNZEHN HUNDERT NEUNUNDZWANZIG UND NEUNZHN HUNDERT DREINFÜNFZIG AICHIERLOGESCH ERGRABEN +swc_deu_001252 IM GEGENSATZ diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..66845e6a396a872665085a99b6bfe6c784641bd1 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/token @@ -0,0 +1,52 @@ +swc_deu_001201 D E I E R V E R L I E B T E U N G E H E A R Z O G I E R A N S C L Ä E K E S E I N E S F A T E S N I C H T B E R A C G T D E T A B +swc_deu_001202 D I E I N D E A N S E S T E R D T E N A L S +swc_deu_001203 A R K E I N G R O S E E R F O L G K +swc_deu_001204 G O S E N S C H E H M I C H E I N V E R B R I C K E N +swc_deu_001205 U R D E N A C H M E H R E R E A R L E U T R U N G S B Ü C H E V E R F F N T L I C H T +swc_deu_001206 V O R B E R E I T E N B I E R T E I G G E T U N G T +swc_deu_001207 D O M E N T E S H L I E S L I C G I N E +swc_deu_001208 T A U T A G V Ü R D E N T A U T V O N K Ö N I C H V E R E R C H Ü L E +swc_deu_001209 D A R U N D E R S I N D M A T I L D E A R S E N S I S W E C H T E R D E S K R E U L Z I S +swc_deu_001210 E N I N E N S T Ä T E N M E H R U N D M E H R I E R O L E D E R T R A D I Z N E L N I S H +swc_deu_001211 Z D E N E N W E L T L E U F I C H K E I I T +swc_deu_001212 R A C H R E T D S H O F E S U N D D E S A D E T Z S F Ü R D E N F R I E F E T +swc_deu_001213 Z E I T A N G A B E N V E R Z I C H T E T +swc_deu_001214 A L L A C H T Z E H N H U N D E R T A C H T Z I C H M I T O T T U O B R A M M S A U F S E I T Z +swc_deu_001215 M Ü L E N W E S E N S I B T E H N U N E D C H U N D Z W A N Z I +swc_deu_001216 A S D E R F I C H R I C H +swc_deu_001217 S I D E M A B S C L S S I M J A H R E N U N Z E N H N D R Z W A E U N A C H T Z I C G U N D E R N A M E R E I N E E R S T E L Ä N G E R E R E I S E A C S P A N E N +swc_deu_001218 V Ü R N S C H A T S O V U R G E Z E I C H N E T +swc_deu_001219 F E I T E N S T E I N S F L S T E N D I G E G E S C H I C H T E N U N D D I E A U G S B R G E S T A D B G E S C H I E G E D E S E L T R E N +swc_deu_001220 N A C H D I E S E N Z E R S T Ö H R U N G E N W U R D E D E R A S S C H W I E D E R A U F B L H N +swc_deu_001221 M A C H T E N E I N F L U S R E I C H E N H A N S I E A R T E N M B E I M K O M I S A R I S C H E I N G E S E T Z T E N B Ü R G E M E I S T E R M A R K E R T I H R E A U F W A H T U N +swc_deu_001222 A L S S E N T R A L D E S H A N D E S K O T O R +swc_deu_001223 S O N D E R S T E L L U N G I N E H E I L B D E R S T A D T K R F E L +swc_deu_001224 F I N E S I C H I N H A L O B A O K T E R I E N +swc_deu_001225 A U F D E R B S E I T E F I N D E Z I C H D A S E B E N F A L S V O N M E I K E L O M P O N I R T +swc_deu_001226 I N H A N D E A R T I S C H E Z E I T H A T E D I E Z E R K E G E S E S C H A F T K E I N E N A U S C H A G E B E N D E N E N F L S M I E R +swc_deu_001227 D A R S D E R C H V E W E N D U N V O N A U F T R I E B S K Ö R P A N O D E R H O L Z E I N E G E R I N G E R E M I T T L E R E T D I C H T E A L S W A S S E R H A T +swc_deu_001228 D R A M E I T D E S I E R U N G E N +swc_deu_001229 U M M S E B E N O R F Ü N W O N +swc_deu_001230 D E S A L B R E C H T D I E B A D E S T O C H T E R +swc_deu_001231 T A T B A M B A R E S C H E S T A T Z R +swc_deu_001232 D R S L I E T B E S O N D E R S L I E B T E +swc_deu_001233 A U F K U N D D E S W A H S E N D E N P O P L I K U M S I N T R E S S E S W R D E D E R A U F T R I T S O R T Ü R I E P R I M E R I S T A L E S U N G E +swc_deu_001234 N D F R E I D L I C H S P B I E L E +swc_deu_001235 D S D I E R E I D E N S T U R T Z E R L A T I E U N B E S C H A D E B E R S T A N D E N H A T E +swc_deu_001236 J A H R E N A S C H E N E N D Z W E I I M E N +swc_deu_001237 D R R A B M A L E U N D G R A B G K A P Ä L E N O D E R O L T A T E N N A C H A L T +swc_deu_001238 J U N E N E U Z E N H U N D R E H S U N E U N Z I G K Ö N E K T D E R R S E I N E B E I D E N J O P S +swc_deu_001239 I N G E P O R T I N E O P E T +swc_deu_001240 N E U N U N S E C H Z I G D E R M E L I E R K O N T W O L A L B U M S C H A T Z E I N +swc_deu_001241 T A D U C H C O M +swc_deu_001242 O N E H E N N I C H T D E N R O S H A N D E L S K A U F L E U T E N G E S E L S C H A F T L I C G G L E I C H G E S T E T W A R E N +swc_deu_001243 V O D E R N A R U N G U N D V O M K L I E M A +swc_deu_001244 A P O L O E I N S +swc_deu_001245 B R Ü Y E L E L U N D H Ö R T N A C H K E L E N +swc_deu_001246 T W A R I N E N K L O S T E +swc_deu_001247 D I V W R Z E M O F I Z E H E N K A N E W A L E N S T A N D T U N T T E U T E E I N E M S C H U N G A U S K Ö R S C H E N K A N D E W A L U N D P O L I T S C H E M K A B E R E T M I T K O M D I E L M E N T E N D A R S T E L L T U N +swc_deu_001248 D I E W E N S T E I U N G E S L I E D E S F Ü R T E N +swc_deu_001249 N A N T I T Z I E G L E R D I A R M O R D U M D E R B R N A U R U N E N +swc_deu_001250 I N T E R O H R I S T V E L +swc_deu_001251 D I E S T R Ä N G E D R F O R G E N G E R L E I T U N G W U R D E N Z W I C H E N E U N Z E H N H U N D E R T N E U N U N D Z W A N Z I G U N D N E U N Z H N H U N D E R T D R E I N F Ü N F Z I G A I C H I E R L O G E S C H E R G R A B E N +swc_deu_001252 I M G E G E N S A T Z diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..c7d29e0ea2ad356e3f5be60e25faecded0582677 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.1/1best_recog/token_int @@ -0,0 +1,52 @@ +swc_deu_001201 10 2 5 3 2 6 24 2 6 13 5 2 18 8 2 3 12 4 14 2 3 11 2 9 6 20 16 14 3 5 2 3 6 9 4 7 15 13 26 2 22 2 7 2 5 4 2 7 19 9 8 2 7 3 4 5 15 11 8 18 2 6 9 15 14 8 10 2 8 9 18 +swc_deu_001202 10 5 2 3 5 4 3 10 2 3 9 4 7 2 7 8 2 6 10 8 2 4 3 9 13 7 +swc_deu_001203 9 6 22 2 5 4 14 6 16 7 2 3 2 6 19 16 13 14 22 +swc_deu_001204 14 16 7 2 4 3 7 15 11 2 11 17 5 15 11 2 5 4 3 24 2 6 18 6 5 15 22 2 4 +swc_deu_001205 12 6 10 2 4 3 9 15 11 3 17 2 11 6 2 6 2 3 9 6 3 13 2 12 8 6 12 4 14 7 18 25 15 11 2 3 24 2 6 3 19 19 4 8 13 5 15 11 8 +swc_deu_001206 24 16 6 18 2 6 2 5 8 2 4 3 18 5 2 6 3 8 2 5 14 3 14 2 8 12 4 14 8 +swc_deu_001207 10 16 17 2 4 8 2 3 7 11 13 5 2 7 13 5 15 14 3 5 4 3 2 +swc_deu_001208 8 9 12 8 9 14 3 24 25 6 10 2 4 8 9 12 8 3 24 16 4 3 22 27 4 5 15 11 3 24 2 6 2 6 15 11 25 13 2 +swc_deu_001209 10 9 6 12 4 10 2 6 3 7 5 4 10 3 17 9 8 5 13 10 2 3 9 6 7 2 4 7 5 7 3 21 2 15 11 8 2 6 3 10 2 7 3 22 6 2 12 13 20 5 7 +swc_deu_001210 2 4 3 5 4 2 4 7 8 26 8 2 4 3 17 2 11 6 3 12 4 10 3 17 2 11 6 3 5 2 6 16 13 2 3 10 2 6 8 6 9 10 5 3 20 3 4 2 13 4 3 5 7 11 +swc_deu_001211 20 3 10 2 4 2 4 3 21 2 13 8 13 2 12 19 5 15 11 22 2 5 5 8 +swc_deu_001212 6 9 15 11 6 2 3 8 10 7 11 16 19 2 7 3 12 4 10 3 10 2 7 3 9 10 2 8 20 7 3 19 25 6 3 10 2 4 3 19 6 5 2 19 2 8 +swc_deu_001213 20 2 5 8 3 9 4 14 9 18 2 4 3 24 2 6 20 5 15 11 8 2 8 +swc_deu_001214 9 13 13 3 9 15 11 8 20 2 11 4 3 11 12 4 10 2 6 8 3 9 15 11 8 20 5 15 11 17 5 8 3 16 8 8 12 16 18 6 9 17 17 7 3 9 12 19 7 2 5 8 20 +swc_deu_001215 17 25 13 2 4 3 21 2 7 2 4 3 7 5 18 8 2 11 4 3 12 4 2 10 3 15 11 12 4 10 20 21 9 4 20 5 +swc_deu_001216 9 7 3 10 2 6 3 19 5 15 11 3 6 5 15 11 +swc_deu_001217 7 5 10 2 17 3 9 18 7 15 13 7 7 3 5 17 28 9 11 6 2 4 12 4 20 2 4 3 11 4 10 6 3 20 21 9 2 12 4 3 9 15 11 8 20 5 15 14 3 12 4 10 3 2 6 4 9 17 2 6 3 2 5 4 2 3 2 6 7 8 2 3 13 26 4 14 2 6 2 3 6 2 5 7 2 3 9 15 7 23 9 4 2 4 +swc_deu_001218 24 25 6 4 7 15 11 9 8 7 16 3 24 12 6 14 2 20 2 5 15 11 4 2 8 +swc_deu_001219 19 2 5 8 2 4 7 8 2 5 4 7 3 19 13 7 8 2 4 10 5 14 2 14 2 7 15 11 5 15 11 8 2 4 3 12 4 10 3 10 5 2 3 9 12 14 7 18 6 14 2 7 8 9 10 18 14 2 7 15 11 5 2 14 3 2 10 2 7 3 2 13 8 6 2 4 +swc_deu_001220 4 9 15 11 3 10 5 2 7 2 4 20 2 6 7 8 27 11 6 12 4 14 2 4 3 21 12 6 10 2 3 10 2 6 9 7 7 15 11 21 5 2 10 2 6 3 9 12 19 18 13 11 4 +swc_deu_001221 17 9 15 11 8 2 4 3 2 5 4 19 13 12 7 6 2 5 15 11 2 4 3 11 9 4 3 7 5 2 3 9 6 8 2 4 17 3 18 2 5 17 3 22 16 17 5 7 9 6 5 7 15 11 2 5 4 3 14 2 7 2 8 20 8 2 4 3 18 25 6 14 2 17 2 5 7 8 2 6 3 17 9 6 22 2 6 8 3 5 11 6 2 3 9 12 19 21 9 11 8 12 4 +swc_deu_001222 9 13 7 3 7 2 4 8 6 9 13 10 2 7 11 9 4 10 2 7 3 22 16 8 16 6 +swc_deu_001223 7 16 4 10 2 6 3 7 8 2 13 13 12 4 14 3 5 4 2 11 2 5 13 18 3 10 2 6 7 8 9 10 8 3 22 6 19 2 13 +swc_deu_001224 19 5 4 2 3 7 5 15 11 3 5 4 11 9 13 16 18 9 16 22 8 2 6 5 2 4 +swc_deu_001225 9 12 19 3 10 2 6 3 18 3 7 2 5 8 2 3 19 5 4 10 2 3 20 5 15 11 3 10 9 7 3 2 18 2 4 19 9 13 7 3 24 16 4 3 17 2 5 22 2 13 3 16 17 23 16 4 5 6 8 +swc_deu_001226 5 4 3 11 9 4 10 2 3 9 6 8 5 7 15 11 2 3 20 2 5 8 3 11 9 8 2 3 10 5 2 3 20 2 6 22 2 14 2 7 2 7 15 11 9 19 8 3 22 2 5 4 2 4 3 9 12 7 15 11 9 14 2 18 2 4 10 2 4 3 2 4 19 13 7 17 5 2 6 +swc_deu_001227 10 9 6 3 7 10 2 6 15 11 3 24 2 21 2 4 10 12 4 3 24 16 4 3 9 12 19 8 6 5 2 18 7 22 27 6 23 9 4 3 16 10 2 6 3 11 16 13 20 3 2 5 4 2 3 14 2 6 5 4 14 2 6 2 3 17 5 8 8 13 2 6 2 8 10 5 15 11 8 2 3 9 13 7 3 21 9 7 7 2 6 3 11 9 8 +swc_deu_001228 10 6 9 17 2 5 3 8 10 2 3 7 5 2 6 12 4 14 2 4 +swc_deu_001229 12 17 17 3 7 2 18 2 4 3 16 6 3 19 25 4 21 16 4 +swc_deu_001230 10 2 7 3 9 13 18 6 2 15 11 8 3 10 5 2 3 18 9 10 2 7 3 8 16 15 11 8 2 6 +swc_deu_001231 8 9 8 3 18 9 17 3 18 9 6 2 7 15 11 2 3 7 8 9 8 20 6 +swc_deu_001232 10 6 7 13 5 2 8 3 18 2 7 16 4 10 2 6 7 13 5 2 18 8 2 +swc_deu_001233 9 12 19 3 22 12 4 10 3 10 2 7 3 21 9 11 7 2 4 10 2 4 3 23 16 23 13 5 22 12 17 7 3 5 4 8 6 2 7 7 2 7 3 21 6 10 2 3 10 2 6 3 9 12 19 8 6 5 8 7 3 16 6 8 3 25 6 3 5 2 3 23 6 5 17 2 6 3 5 7 8 9 13 2 7 12 4 14 2 +swc_deu_001234 4 10 3 19 6 2 5 10 13 5 15 11 3 7 23 18 5 2 13 2 +swc_deu_001235 10 7 3 10 5 2 3 6 2 5 10 2 4 3 7 8 12 6 8 20 3 2 6 13 9 8 5 2 3 12 4 3 18 2 7 15 11 9 10 2 3 18 2 6 3 7 8 9 4 10 2 4 3 11 9 8 2 +swc_deu_001236 28 9 11 6 2 4 3 9 7 15 11 2 4 2 4 10 3 20 21 2 5 3 5 17 2 4 +swc_deu_001237 10 6 3 6 9 18 17 9 13 2 3 12 4 10 3 14 6 9 18 3 14 22 9 23 26 13 2 4 3 16 10 2 6 3 16 13 3 8 9 8 2 4 3 4 9 15 11 9 13 8 +swc_deu_001238 28 12 4 2 4 2 12 20 2 4 3 11 12 4 10 6 3 2 11 7 12 4 2 12 4 20 5 14 3 22 27 4 2 22 8 10 2 6 3 6 7 2 5 4 2 3 18 2 5 10 2 4 3 28 16 23 7 +swc_deu_001239 5 4 3 14 2 3 23 16 6 8 5 4 2 3 16 23 2 8 +swc_deu_001240 3 4 2 12 4 12 4 3 7 2 15 11 20 5 14 3 10 2 6 3 17 2 13 5 2 6 3 22 16 4 8 21 16 13 3 9 13 18 12 17 7 15 11 9 8 20 3 2 5 4 +swc_deu_001241 8 9 10 12 15 11 15 16 17 +swc_deu_001242 16 4 2 3 11 2 4 3 4 5 15 11 8 3 10 2 4 3 6 16 7 11 9 4 10 2 13 7 3 22 9 12 19 13 2 12 8 2 4 3 14 2 7 2 13 7 15 11 9 19 8 13 5 15 14 3 14 13 2 5 15 11 14 2 7 8 2 8 3 21 9 6 2 4 +swc_deu_001243 24 16 3 10 2 6 4 9 6 12 4 14 3 12 4 10 3 24 16 17 3 22 13 5 2 17 9 +swc_deu_001244 9 23 16 13 16 3 2 5 4 7 +swc_deu_001245 18 6 25 29 2 13 2 13 3 12 4 10 3 11 27 6 8 3 4 9 15 11 22 2 13 2 4 +swc_deu_001246 8 21 9 6 3 5 4 3 2 4 3 22 13 16 7 8 2 +swc_deu_001247 10 5 24 21 6 20 2 17 3 16 19 5 20 2 11 2 4 3 22 9 4 2 21 9 13 2 4 3 7 8 9 4 10 8 3 12 4 8 3 8 2 12 8 2 3 2 5 4 2 17 7 15 11 12 4 14 3 9 12 7 3 22 27 6 7 15 11 2 4 3 22 9 4 10 2 21 9 13 3 12 4 10 3 23 16 13 5 8 7 15 11 2 17 3 22 9 18 2 6 2 8 3 17 5 8 3 22 16 17 3 10 5 2 13 17 2 4 8 2 4 3 10 9 6 7 8 2 13 13 8 3 12 4 +swc_deu_001248 10 5 2 3 21 2 4 7 8 2 5 12 4 14 2 7 13 5 2 10 2 7 3 19 25 6 8 2 4 +swc_deu_001249 4 9 4 8 5 8 3 20 5 2 14 13 2 6 3 10 5 3 9 6 17 16 6 10 12 17 3 10 2 6 3 18 6 4 9 12 6 12 4 2 4 +swc_deu_001250 5 4 8 2 6 16 11 6 3 5 7 8 3 24 2 3 13 +swc_deu_001251 10 5 2 3 7 8 6 26 4 14 2 3 10 6 3 19 16 6 14 2 4 14 2 6 3 13 2 5 8 12 4 14 3 21 12 6 10 2 4 3 20 21 5 15 11 2 3 4 2 12 4 20 2 11 4 3 11 12 4 10 2 6 8 3 4 2 12 4 12 4 10 20 21 9 4 20 5 14 3 12 4 10 3 4 2 12 4 20 11 4 3 11 12 4 10 2 6 8 3 10 6 2 5 4 19 25 4 19 20 5 14 3 9 5 15 11 5 2 6 13 16 14 2 7 15 11 3 2 6 14 6 9 18 2 4 +swc_deu_001252 5 17 3 14 2 14 2 4 7 9 8 20 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..25ffa4c412e920f812b1a5fbc5a7cabccc0d9c13 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/score @@ -0,0 +1,52 @@ +swc_deu_001253 tensor(-12.1796) +swc_deu_001254 tensor(-4.7920) +swc_deu_001255 tensor(-10.7116) +swc_deu_001256 tensor(-9.2295) +swc_deu_001257 tensor(-7.4713) +swc_deu_001258 tensor(-6.9582) +swc_deu_001259 tensor(-4.5759) +swc_deu_001260 tensor(-16.4660) +swc_deu_001261 tensor(-9.8764) +swc_deu_001262 tensor(-6.6527) +swc_deu_001263 tensor(-13.3887) +swc_deu_001264 tensor(-13.9906) +swc_deu_001265 tensor(-4.6216) +swc_deu_001266 tensor(-7.8718) +swc_deu_001267 tensor(-8.5063) +swc_deu_001268 tensor(-3.4202) +swc_deu_001269 tensor(-8.2194) +swc_deu_001270 tensor(-11.4945) +swc_deu_001271 tensor(-6.3149) +swc_deu_001272 tensor(-10.3158) +swc_deu_001273 tensor(-9.2455) +swc_deu_001274 tensor(-19.3320) +swc_deu_001275 tensor(-6.9352) +swc_deu_001276 tensor(-5.4525) +swc_deu_001277 tensor(-11.9270) +swc_deu_001278 tensor(-11.7521) +swc_deu_001279 tensor(-8.3918) +swc_deu_001280 tensor(-8.6255) +swc_deu_001281 tensor(-8.6796) +swc_deu_001282 tensor(-5.3923) +swc_deu_001283 tensor(-5.6611) +swc_deu_001284 tensor(-15.2767) +swc_deu_001285 tensor(-6.9719) +swc_deu_001286 tensor(-19.8688) +swc_deu_001287 tensor(-18.0681) +swc_deu_001288 tensor(-9.1969) +swc_deu_001289 tensor(-5.1638) +swc_deu_001290 tensor(-11.5033) +swc_deu_001291 tensor(-5.9123) +swc_deu_001292 tensor(-19.6961) +swc_deu_001293 tensor(-9.1933) +swc_deu_001294 tensor(-10.8934) +swc_deu_001295 tensor(-15.7092) +swc_deu_001296 tensor(-11.0321) +swc_deu_001297 tensor(-10.3141) +swc_deu_001298 tensor(-14.9636) +swc_deu_001299 tensor(-15.1580) +swc_deu_001300 tensor(-26.6249) +swc_deu_001301 tensor(-16.1779) +swc_deu_001302 tensor(-14.6708) +swc_deu_001303 tensor(-14.9750) +swc_deu_001304 tensor(-17.7293) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..fba443f83a30ee327fb2ad66ea6e37fbf8a6a0f6 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/text @@ -0,0 +1,52 @@ +swc_deu_001253 VWARBE VON ÖRDIGENSN LAUNDHORD +swc_deu_001254 LEIF VERANSTALTUMEN +swc_deu_001255 SUOWERDENHEUTE IN DEREGEL ALE DORTLEBENDEN BRUN +swc_deu_001256 IEDA FÜRE WÜSCHLMER +swc_deu_001257 DES HAN SE ATEN FÜRE +swc_deu_001258 HÄBILS AGNES BERNAU +swc_deu_001259 LEBENSWEISE VERKÖARPER +swc_deu_001260 IDEFALTDES VIELEN HAMBURGAN ZU KATOLESCH FRMMEN +swc_deu_001261 KOLTOUR END DER THEFT AUSTAUSCHEN +swc_deu_001262 MJAHRZWEI TAUSEND VERTONDTE +swc_deu_001263 DAS E DIESE LEITUNG SCHNLER VOLÄENDTEN KÖNE ALS DER BAUMEISTER DEN KÖNERDOM +swc_deu_001264 EIER HINRICHTUNG DER BANAURIEN HABES IC LICHT UM +swc_deu_001265 LUOREI +swc_deu_001266 DERZEIT DER BESTIE KENERDER EIFELEITUNG +swc_deu_001267 VOKUS BES WISENSHAFTLICHEN INTERESSES +swc_deu_001268 TEME ZU BEGEISTEN +swc_deu_001269 METER UND KONTE DAMIT AU VON INEN BERGANGEN WERDEN +swc_deu_001270 HAT KABER BES SELLISTE DER NÜH +swc_deu_001271 DER FREIN ENZIGKLOP +swc_deu_001272 DEN GRSLI WIER UF DELSTE ZUSETZEN +swc_deu_001273 WIE LANG DIESE KAPLANSSTÄLLE AUFRECHTER HETEN WURD +swc_deu_001274 SIWAHREN WASCHEINLICHBEREITZ DREISIG SI KUNDEN DACH AUSPRCHTES VEUR +swc_deu_001275 METER GESAMTLINGE UND BIS ZUZEHN MITER +swc_deu_001276 FEINE RITZEN UN SPALTE +swc_deu_001277 DINMAN VON AUSSN DIEKELER HINABPFLIESEN SIET +swc_deu_001278 ENE INTERIUSAGTEBRAUN +swc_deu_001279 DAS FÜNFTDEVEN GERIUM +swc_deu_001280 RESEN SIEMANCHMAL WEIDE TIERE IE SCHAFE +swc_deu_001281 SI ÖRENDEN ARTIKEL DIE SEIN RÜVIEU +swc_deu_001282 KUSE IS GELENTER KOCH +swc_deu_001283 HANWENT STIFTUNGE +swc_deu_001284 NEUNZEHN HUDERT ACHTZIEN AL HAINSIE ARTEN ANGESIEN +swc_deu_001285 MEHRERER ES NACH IM TOUN +swc_deu_001286 ACHSTIG ES GERICHTS ZUR LANDES WEITEN BELIEBEN KOLINARISCHE SPEZILITET ERMÖGE +swc_deu_001287 KOLLETSC UND EIN ZWEIT JOB ALSPANISCHLÄHRER IN HEMTN VORLSEAN +swc_deu_001288 BOREN KEINES WEGXS ALLÄEGEBÜRTIGEN +swc_deu_001289 IST IER KÖRBERBAUGREFTIG +swc_deu_001290 ANLESLICHTER NEUJAS ANDGSPRACHREKE +swc_deu_001291 MIT WIND VON SCRECHINTEN +swc_deu_001292 DEN GREÖSTEN TELDER BE ZIÜRGSWERTRETUNG ÖRDINEN AUS +swc_deu_001293 ACHTHN HUNER EIUNDZWENZI +swc_deu_001294 ES ROSSEN ADELS ANGESAMMITEN REICHTUMS +swc_deu_001295 E SOE NIHT EL SEH SOL POKATZIUN +swc_deu_001296 TEILHABEDE VRMER OSMAN UND JIRGENZ +swc_deu_001297 NERM TER FRAUNENTE RAUT INSLT +swc_deu_001298 AURDIEO ÖST AN DEUTCHER HÖR BUCHFVERLAG MIT SITZ INMÜNCHEN +swc_deu_001299 FAR PIKTMENTE UND CHEMICHE VORPROTRUCKTE HERSTELT +swc_deu_001300 ARPLICHEN PREUSSISCHEN FREI HEREN STAND IN DER ZAL ANSCHLUSSFRAGE ENTSCHIEDEN GEGEN DENSINAR AUF DIESEITE BISMARGS GESTLLT +swc_deu_001301 WEN DI WÄLEN VON SELS ER FORGWELEN UND OFFEN ZUOTAGELIEGEN +swc_deu_001302 DS VONGN NACHBAR BAUTRUB BAREITS BEGON WUORT +swc_deu_001303 WERDEN PRERGEN DE ELEMENTE DE HANSIE ATEN TUMST ZUSAMEN GEFAST +swc_deu_001304 DES SLIETZWURE ALS FOLCSTLIET AN GESEIEN diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..6800b17ed8dd9df4d30e275366c8cdd40c2a2457 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/token @@ -0,0 +1,52 @@ +swc_deu_001253 V W A R B E V O N Ö R D I G E N S N L A U N D H O R D +swc_deu_001254 L E I F V E R A N S T A L T U M E N +swc_deu_001255 S U O W E R D E N H E U T E I N D E R E G E L A L E D O R T L E B E N D E N B R U N +swc_deu_001256 I E D A F Ü R E W Ü S C H L M E R +swc_deu_001257 D E S H A N S E A T E N F Ü R E +swc_deu_001258 H Ä B I L S A G N E S B E R N A U +swc_deu_001259 L E B E N S W E I S E V E R K Ö A R P E R +swc_deu_001260 I D E F A L T D E S V I E L E N H A M B U R G A N Z U K A T O L E S C H F R M M E N +swc_deu_001261 K O L T O U R E N D D E R T H E F T A U S T A U S C H E N +swc_deu_001262 M J A H R Z W E I T A U S E N D V E R T O N D T E +swc_deu_001263 D A S E D I E S E L E I T U N G S C H N L E R V O L Ä E N D T E N K Ö N E A L S D E R B A U M E I S T E R D E N K Ö N E R D O M +swc_deu_001264 E I E R H I N R I C H T U N G D E R B A N A U R I E N H A B E S I C L I C H T U M +swc_deu_001265 L U O R E I +swc_deu_001266 D E R Z E I T D E R B E S T I E K E N E R D E R E I F E L E I T U N G +swc_deu_001267 V O K U S B E S W I S E N S H A F T L I C H E N I N T E R E S S E S +swc_deu_001268 T E M E Z U B E G E I S T E N +swc_deu_001269 M E T E R U N D K O N T E D A M I T A U V O N I N E N B E R G A N G E N W E R D E N +swc_deu_001270 H A T K A B E R B E S S E L L I S T E D E R N Ü H +swc_deu_001271 D E R F R E I N E N Z I G K L O P +swc_deu_001272 D E N G R S L I W I E R U F D E L S T E Z U S E T Z E N +swc_deu_001273 W I E L A N G D I E S E K A P L A N S S T Ä L L E A U F R E C H T E R H E T E N W U R D +swc_deu_001274 S I W A H R E N W A S C H E I N L I C H B E R E I T Z D R E I S I G S I K U N D E N D A C H A U S P R C H T E S V E U R +swc_deu_001275 M E T E R G E S A M T L I N G E U N D B I S Z U Z E H N M I T E R +swc_deu_001276 F E I N E R I T Z E N U N S P A L T E +swc_deu_001277 D I N M A N V O N A U S S N D I E K E L E R H I N A B P F L I E S E N S I E T +swc_deu_001278 E N E I N T E R I U S A G T E B R A U N +swc_deu_001279 D A S F Ü N F T D E V E N G E R I U M +swc_deu_001280 R E S E N S I E M A N C H M A L W E I D E T I E R E I E S C H A F E +swc_deu_001281 S I Ö R E N D E N A R T I K E L D I E S E I N R Ü V I E U +swc_deu_001282 K U S E I S G E L E N T E R K O C H +swc_deu_001283 H A N W E N T S T I F T U N G E +swc_deu_001284 N E U N Z E H N H U D E R T A C H T Z I E N A L H A I N S I E A R T E N A N G E S I E N +swc_deu_001285 M E H R E R E R E S N A C H I M T O U N +swc_deu_001286 A C H S T I G E S G E R I C H T S Z U R L A N D E S W E I T E N B E L I E B E N K O L I N A R I S C H E S P E Z I L I T E T E R M Ö G E +swc_deu_001287 K O L L E T S C U N D E I N Z W E I T J O B A L S P A N I S C H L Ä H R E R I N H E M T N V O R L S E A N +swc_deu_001288 B O R E N K E I N E S W E G X S A L L Ä E G E B Ü R T I G E N +swc_deu_001289 I S T I E R K Ö R B E R B A U G R E F T I G +swc_deu_001290 A N L E S L I C H T E R N E U J A S A N D G S P R A C H R E K E +swc_deu_001291 M I T W I N D V O N S C R E C H I N T E N +swc_deu_001292 D E N G R E Ö S T E N T E L D E R B E Z I Ü R G S W E R T R E T U N G Ö R D I N E N A U S +swc_deu_001293 A C H T H N H U N E R E I U N D Z W E N Z I +swc_deu_001294 E S R O S S E N A D E L S A N G E S A M M I T E N R E I C H T U M S +swc_deu_001295 E S O E N I H T E L S E H S O L P O K A T Z I U N +swc_deu_001296 T E I L H A B E D E V R M E R O S M A N U N D J I R G E N Z +swc_deu_001297 N E R M T E R F R A U N E N T E R A U T I N S L T +swc_deu_001298 A U R D I E O Ö S T A N D E U T C H E R H Ö R B U C H F V E R L A G M I T S I T Z I N M Ü N C H E N +swc_deu_001299 F A R P I K T M E N T E U N D C H E M I C H E V O R P R O T R U C K T E H E R S T E L T +swc_deu_001300 A R P L I C H E N P R E U S S I S C H E N F R E I H E R E N S T A N D I N D E R Z A L A N S C H L U S S F R A G E E N T S C H I E D E N G E G E N D E N S I N A R A U F D I E S E I T E B I S M A R G S G E S T L L T +swc_deu_001301 W E N D I W Ä L E N V O N S E L S E R F O R G W E L E N U N D O F F E N Z U O T A G E L I E G E N +swc_deu_001302 D S V O N G N N A C H B A R B A U T R U B B A R E I T S B E G O N W U O R T +swc_deu_001303 W E R D E N P R E R G E N D E E L E M E N T E D E H A N S I E A T E N T U M S T Z U S A M E N G E F A S T +swc_deu_001304 D E S S L I E T Z W U R E A L S F O L C S T L I E T A N G E S E I E N diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..757a9fa6a958a071754d5f977fab8bd9fdfe8ba8 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.2/1best_recog/token_int @@ -0,0 +1,52 @@ +swc_deu_001253 24 21 9 6 18 2 3 24 16 4 3 27 6 10 5 14 2 4 7 4 3 13 9 12 4 10 11 16 6 10 +swc_deu_001254 13 2 5 19 3 24 2 6 9 4 7 8 9 13 8 12 17 2 4 +swc_deu_001255 7 12 16 21 2 6 10 2 4 11 2 12 8 2 3 5 4 3 10 2 6 2 14 2 13 3 9 13 2 3 10 16 6 8 13 2 18 2 4 10 2 4 3 18 6 12 4 +swc_deu_001256 5 2 10 9 3 19 25 6 2 3 21 25 7 15 11 13 17 2 6 +swc_deu_001257 10 2 7 3 11 9 4 3 7 2 3 9 8 2 4 3 19 25 6 2 +swc_deu_001258 11 26 18 5 13 7 3 9 14 4 2 7 3 18 2 6 4 9 12 +swc_deu_001259 13 2 18 2 4 7 21 2 5 7 2 3 24 2 6 22 27 9 6 23 2 6 +swc_deu_001260 5 10 2 19 9 13 8 10 2 7 3 24 5 2 13 2 4 3 11 9 17 18 12 6 14 9 4 3 20 12 3 22 9 8 16 13 2 7 15 11 3 19 6 17 17 2 4 +swc_deu_001261 22 16 13 8 16 12 6 3 2 4 10 3 10 2 6 3 8 11 2 19 8 3 9 12 7 8 9 12 7 15 11 2 4 +swc_deu_001262 17 28 9 11 6 20 21 2 5 3 8 9 12 7 2 4 10 3 24 2 6 8 16 4 10 8 2 +swc_deu_001263 10 9 7 3 2 3 10 5 2 7 2 3 13 2 5 8 12 4 14 3 7 15 11 4 13 2 6 3 24 16 13 26 2 4 10 8 2 4 3 22 27 4 2 3 9 13 7 3 10 2 6 3 18 9 12 17 2 5 7 8 2 6 3 10 2 4 3 22 27 4 2 6 10 16 17 +swc_deu_001264 2 5 2 6 3 11 5 4 6 5 15 11 8 12 4 14 3 10 2 6 3 18 9 4 9 12 6 5 2 4 3 11 9 18 2 7 3 5 15 3 13 5 15 11 8 3 12 17 +swc_deu_001265 13 12 16 6 2 5 +swc_deu_001266 10 2 6 20 2 5 8 3 10 2 6 3 18 2 7 8 5 2 3 22 2 4 2 6 10 2 6 3 2 5 19 2 13 2 5 8 12 4 14 +swc_deu_001267 24 16 22 12 7 3 18 2 7 3 21 5 7 2 4 7 11 9 19 8 13 5 15 11 2 4 3 5 4 8 2 6 2 7 7 2 7 +swc_deu_001268 8 2 17 2 3 20 12 3 18 2 14 2 5 7 8 2 4 +swc_deu_001269 17 2 8 2 6 3 12 4 10 3 22 16 4 8 2 3 10 9 17 5 8 3 9 12 3 24 16 4 3 5 4 2 4 3 18 2 6 14 9 4 14 2 4 3 21 2 6 10 2 4 +swc_deu_001270 11 9 8 3 22 9 18 2 6 3 18 2 7 3 7 2 13 13 5 7 8 2 3 10 2 6 3 4 25 11 +swc_deu_001271 10 2 6 3 19 6 2 5 4 3 2 4 20 5 14 22 13 16 23 +swc_deu_001272 10 2 4 3 14 6 7 13 5 3 21 5 2 6 3 12 19 3 10 2 13 7 8 2 3 20 12 7 2 8 20 2 4 +swc_deu_001273 21 5 2 3 13 9 4 14 3 10 5 2 7 2 3 22 9 23 13 9 4 7 7 8 26 13 13 2 3 9 12 19 6 2 15 11 8 2 6 3 11 2 8 2 4 3 21 12 6 10 +swc_deu_001274 7 5 21 9 11 6 2 4 3 21 9 7 15 11 2 5 4 13 5 15 11 18 2 6 2 5 8 20 3 10 6 2 5 7 5 14 3 7 5 3 22 12 4 10 2 4 3 10 9 15 11 3 9 12 7 23 6 15 11 8 2 7 3 24 2 12 6 +swc_deu_001275 17 2 8 2 6 3 14 2 7 9 17 8 13 5 4 14 2 3 12 4 10 3 18 5 7 3 20 12 20 2 11 4 3 17 5 8 2 6 +swc_deu_001276 19 2 5 4 2 3 6 5 8 20 2 4 3 12 4 3 7 23 9 13 8 2 +swc_deu_001277 10 5 4 17 9 4 3 24 16 4 3 9 12 7 7 4 3 10 5 2 22 2 13 2 6 3 11 5 4 9 18 23 19 13 5 2 7 2 4 3 7 5 2 8 +swc_deu_001278 2 4 2 3 5 4 8 2 6 5 12 7 9 14 8 2 18 6 9 12 4 +swc_deu_001279 10 9 7 3 19 25 4 19 8 10 2 24 2 4 3 14 2 6 5 12 17 +swc_deu_001280 6 2 7 2 4 3 7 5 2 17 9 4 15 11 17 9 13 3 21 2 5 10 2 3 8 5 2 6 2 3 5 2 3 7 15 11 9 19 2 +swc_deu_001281 7 5 3 27 6 2 4 10 2 4 3 9 6 8 5 22 2 13 3 10 5 2 3 7 2 5 4 3 6 25 24 5 2 12 +swc_deu_001282 22 12 7 2 3 5 7 3 14 2 13 2 4 8 2 6 3 22 16 15 11 +swc_deu_001283 11 9 4 21 2 4 8 3 7 8 5 19 8 12 4 14 2 +swc_deu_001284 4 2 12 4 20 2 11 4 3 11 12 10 2 6 8 3 9 15 11 8 20 5 2 4 3 9 13 3 11 9 5 4 7 5 2 3 9 6 8 2 4 3 9 4 14 2 7 5 2 4 +swc_deu_001285 17 2 11 6 2 6 2 6 3 2 7 3 4 9 15 11 3 5 17 3 8 16 12 4 +swc_deu_001286 9 15 11 7 8 5 14 3 2 7 3 14 2 6 5 15 11 8 7 3 20 12 6 3 13 9 4 10 2 7 3 21 2 5 8 2 4 3 18 2 13 5 2 18 2 4 3 22 16 13 5 4 9 6 5 7 15 11 2 3 7 23 2 20 5 13 5 8 2 8 3 2 6 17 27 14 2 +swc_deu_001287 22 16 13 13 2 8 7 15 3 12 4 10 3 2 5 4 3 20 21 2 5 8 3 28 16 18 3 9 13 7 23 9 4 5 7 15 11 13 26 11 6 2 6 3 5 4 3 11 2 17 8 4 3 24 16 6 13 7 2 9 4 +swc_deu_001288 18 16 6 2 4 3 22 2 5 4 2 7 3 21 2 14 30 7 3 9 13 13 26 2 14 2 18 25 6 8 5 14 2 4 +swc_deu_001289 5 7 8 3 5 2 6 3 22 27 6 18 2 6 18 9 12 14 6 2 19 8 5 14 +swc_deu_001290 9 4 13 2 7 13 5 15 11 8 2 6 3 4 2 12 28 9 7 3 9 4 10 14 7 23 6 9 15 11 6 2 22 2 +swc_deu_001291 17 5 8 3 21 5 4 10 3 24 16 4 3 7 15 6 2 15 11 5 4 8 2 4 +swc_deu_001292 10 2 4 3 14 6 2 27 7 8 2 4 3 8 2 13 10 2 6 3 18 2 3 20 5 25 6 14 7 21 2 6 8 6 2 8 12 4 14 3 27 6 10 5 4 2 4 3 9 12 7 +swc_deu_001293 9 15 11 8 11 4 3 11 12 4 2 6 3 2 5 12 4 10 20 21 2 4 20 5 +swc_deu_001294 2 7 3 6 16 7 7 2 4 3 9 10 2 13 7 3 9 4 14 2 7 9 17 17 5 8 2 4 3 6 2 5 15 11 8 12 17 7 +swc_deu_001295 2 3 7 16 2 3 4 5 11 8 3 2 13 3 7 2 11 3 7 16 13 3 23 16 22 9 8 20 5 12 4 +swc_deu_001296 8 2 5 13 11 9 18 2 10 2 3 24 6 17 2 6 3 16 7 17 9 4 3 12 4 10 3 28 5 6 14 2 4 20 +swc_deu_001297 4 2 6 17 3 8 2 6 3 19 6 9 12 4 2 4 8 2 3 6 9 12 8 3 5 4 7 13 8 3 +swc_deu_001298 9 12 6 10 5 2 16 3 27 7 8 3 9 4 3 10 2 12 8 15 11 2 6 3 11 27 6 3 18 12 15 11 19 24 2 6 13 9 14 3 17 5 8 3 7 5 8 20 3 5 4 17 25 4 15 11 2 4 +swc_deu_001299 19 9 6 3 23 5 22 8 17 2 4 8 2 3 12 4 10 3 15 11 2 17 5 15 11 2 3 24 16 6 23 6 16 8 6 12 15 22 8 2 3 11 2 6 7 8 2 13 8 +swc_deu_001300 9 6 23 13 5 15 11 2 4 3 23 6 2 12 7 7 5 7 15 11 2 4 3 19 6 2 5 3 11 2 6 2 4 3 7 8 9 4 10 3 5 4 3 10 2 6 3 20 9 13 3 9 4 7 15 11 13 12 7 7 19 6 9 14 2 3 2 4 8 7 15 11 5 2 10 2 4 3 14 2 14 2 4 3 10 2 4 7 5 4 9 6 3 9 12 19 3 10 5 2 7 2 5 8 2 3 18 5 7 17 9 6 14 7 3 14 2 7 8 13 13 8 +swc_deu_001301 21 2 4 3 10 5 3 21 26 13 2 4 3 24 16 4 3 7 2 13 7 3 2 6 3 19 16 6 14 21 2 13 2 4 3 12 4 10 3 16 19 19 2 4 3 20 12 16 8 9 14 2 13 5 2 14 2 4 +swc_deu_001302 10 7 3 24 16 4 14 4 3 4 9 15 11 18 9 6 3 18 9 12 8 6 12 18 3 18 9 6 2 5 8 7 3 18 2 14 16 4 3 21 12 16 6 8 +swc_deu_001303 21 2 6 10 2 4 3 23 6 2 6 14 2 4 3 10 2 3 2 13 2 17 2 4 8 2 3 10 2 3 11 9 4 7 5 2 3 9 8 2 4 3 8 12 17 7 8 3 20 12 7 9 17 2 4 3 14 2 19 9 7 8 +swc_deu_001304 10 2 7 3 7 13 5 2 8 20 21 12 6 2 3 9 13 7 3 19 16 13 15 7 8 13 5 2 8 3 9 4 3 14 2 7 2 5 2 4 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..810bc73d753943b5e639b3778e2d7b06fb9271a3 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/score @@ -0,0 +1,52 @@ +swc_deu_001305 tensor(-13.4479) +swc_deu_001306 tensor(-14.6367) +swc_deu_001307 tensor(-6.5133) +swc_deu_001308 tensor(-15.9303) +swc_deu_001309 tensor(-17.1624) +swc_deu_001310 tensor(-11.5434) +swc_deu_001311 tensor(-13.4631) +swc_deu_001312 tensor(-18.8217) +swc_deu_001313 tensor(-25.3673) +swc_deu_001314 tensor(-9.4786) +swc_deu_001315 tensor(-15.9164) +swc_deu_001316 tensor(-4.2287) +swc_deu_001317 tensor(-16.4453) +swc_deu_001318 tensor(-5.8636) +swc_deu_001319 tensor(-8.2308) +swc_deu_001320 tensor(-13.8620) +swc_deu_001321 tensor(-6.2835) +swc_deu_001322 tensor(-3.7747) +swc_deu_001323 tensor(-8.2326) +swc_deu_001324 tensor(-10.4998) +swc_deu_001325 tensor(-19.6826) +swc_deu_001326 tensor(-19.5771) +swc_deu_001327 tensor(-5.1349) +swc_deu_001328 tensor(-13.0677) +swc_deu_001329 tensor(-13.1182) +swc_deu_001330 tensor(-16.5463) +swc_deu_001331 tensor(-4.8647) +swc_deu_001332 tensor(-12.3349) +swc_deu_001333 tensor(-8.4995) +swc_deu_001334 tensor(-5.3028) +swc_deu_001335 tensor(-11.7793) +swc_deu_001336 tensor(-9.8268) +swc_deu_001337 tensor(-14.1149) +swc_deu_001338 tensor(-22.8084) +swc_deu_001339 tensor(-10.1263) +swc_deu_001340 tensor(-8.8516) +swc_deu_001341 tensor(-8.3467) +swc_deu_001342 tensor(-18.4963) +swc_deu_001343 tensor(-3.8331) +swc_deu_001344 tensor(-6.7460) +swc_deu_001345 tensor(-9.7519) +swc_deu_001346 tensor(-15.8559) +swc_deu_001347 tensor(-12.8761) +swc_deu_001348 tensor(-39.4089) +swc_deu_001349 tensor(-8.1955) +swc_deu_001350 tensor(-13.3729) +swc_deu_001351 tensor(-1.2661) +swc_deu_001352 tensor(-18.0496) +swc_deu_001353 tensor(-26.4731) +swc_deu_001354 tensor(-55.6905) +swc_deu_001355 tensor(-10.5691) +swc_deu_001356 tensor(-10.3348) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..446714eb1c8accc1c9b7a2885a97b4507b56aa14 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/text @@ -0,0 +1,52 @@ +swc_deu_001305 DER ZO RNDEM HAUSVERLAGS URUBGEHÖRT +swc_deu_001306 VÜR DE ÖNFTIGEN BORT BÜCHER ENT WICKETE DI PAPIERFERPRI +swc_deu_001307 HAMBRE WUOKS +swc_deu_001308 R DI WASIE ARDLIGEN LANDZITZE PETRIBEN AUFAND SEIS BEMBAU +swc_deu_001309 JAHRZWEI TAUSEN ZWÖLF INDEN BELINER KLUP SOSECHSONDREISIG VELLIGT +swc_deu_001310 SECHEHN UNENDFÜNFZIGH AITS BÜNDNIS D +swc_deu_001311 DAS PROLEHM BE IESEM PERADOCHSON IST +swc_deu_001312 ARMEN WESEN TETIC AMALIE S IEVEIKEINGN +swc_deu_001313 NIHT EINMALEINE ANNSATZWEISE NTU SOCHUNG ZU IEREM VERHALTEN IN ERZEIT DES NATZUONASOTZELISMUS +swc_deu_001314 LIZENS VÜERFRIE DO GOMENTATION +swc_deu_001315 DIM ACHZIHNT NIER HUNDER DIEGARTEN HEUSER VORDENTOREN +swc_deu_001316 GANS IMSTIELDER ZEIT +swc_deu_001317 BER BRÜÖL UND HÜÖRT ERREICH E DE LEITUNGSCLISLICHKÖN +swc_deu_001318 AUS ZEICHNUMEN FREM DER HEREN +swc_deu_001319 DISCHEFTELLEREI AUF ZUGEBEN +swc_deu_001320 DA ZUTZEHE DI EGEGNUNGMIT VERLETZTENTIEREN +swc_deu_001321 JENENISCH STIFT +swc_deu_001322 WESTLICH VON KÖLEN +swc_deu_001323 DIE STÄNDIG IN BERIEBWAREN +swc_deu_001324 DI VOM BAR BIIRCHASIERTWERDE +swc_deu_001325 ERSCHE NOCHEN WEITERERAUFSETZVON KRISTIERNMEIELT +swc_deu_001326 WALL SEBST EXTREMEREICHTUM KEINES WEHTEN UNMITELBEREN ZUGAN +swc_deu_001327 GEBT EUCHNICH ELBER AUF +swc_deu_001328 A HAT DIESEN PRAUC NEUNZEHN HUNDER WEIUND FÜNFZIG GENÜB +swc_deu_001329 WO DELEITUNG ÜBE DIE ALTE HÜRTERLEIT UNGEFÜRTWURDE +swc_deu_001330 INE BLIEBTE KÖALSCHROCKTROPEAS DEM ÖNER UM NANDDIE ÖNA +swc_deu_001331 GEWARDEN SEI UND ALBRECHTSICH +swc_deu_001332 DRTAGESBEDAF EINES WACHSENDEN ANWITEMIN AR +swc_deu_001333 SIB SIN ULERZIEHN OBER ALTER +swc_deu_001334 WEITER HIN LISIG NACRHWEISEN +swc_deu_001335 SUM GRÜNDUNGSTDRTUM KONTEAM BEREITZ +swc_deu_001336 KEINLECSCHLAGEN MÖGLICH NACHFTEILE +swc_deu_001337 IRTI ATOLISCHE KÖRSCHE SANGPETER AN DER STELLE DE ALT +swc_deu_001338 ER FAKNACUNG DES ROT WEIZENS TRART ABESCHN BALT DI ERTOFEL EIS ERSAT +swc_deu_001339 KNNE MIT DIESEN NACHKOMIN ZEUGEN +swc_deu_001340 ALE NEUNEN FOLGEN DER HÖRSTIEREIER +swc_deu_001341 SCHIBPSMIT BEHATENSOR +swc_deu_001342 KOLGE AN RERS BOCHNE VERZIGHTETE AUFERNI PRSÖNICHE BEWERTUNG +swc_deu_001343 BWENIGER IN TRÜSTET +swc_deu_001344 WEITER HIN VERSORG DE DE LEITUNG TERMEN +swc_deu_001345 WARTETEN DAFÜER ABE MIT EINIG +swc_deu_001346 LEDIKLICH ANTUNDFN KLEIN MUNIERTE IN SEINERETZENSONDE +swc_deu_001347 EM IAHBERNEUZEN ERTFÜM +swc_deu_001348 ERS E DEM FARTFAL DES BÜRGERECHTZ UND DER INFÜHRNG DE FREITZYÜGICKEIT IM ZWANSIGS NERHNDERT WANDET TE SICH DIESE ANSCHAUNG ANSERT SWEISEN DARHIN +swc_deu_001349 DES ZWIST BACRES BERREINBACH EINE BOGEN BRÜCKE VO +swc_deu_001350 ACHZINULE SEXSUNDREISIG BURDE DE HMBURG +swc_deu_001351 AM +swc_deu_001352 UF GRUND DER KONTENEN TALSBERE ACHTZEHN HUNDER ELF BAN OTT +swc_deu_001353 WEITERESMALMUSTENDEN UND BLEITBRAN DIEVWARBUNGFÜR DAS BUCHSEÄBST WANEMN +swc_deu_001354 DIENAHRICH VM SEGKTER BÜGELICHDEMOGRATICHEN FE PRAREVOLUTZION VEN ACHTZEHN HUNDERT ACHT UND VERZICHEN FANKREICHWURDEN HMBURG MIT IOBEL AUFGEOME +swc_deu_001355 UBLIEBTZWEIJARE ONE NTERBRECHN +swc_deu_001356 ZEALREICENGASTSPILUNTERWEGS diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..a22efa9b5e9f0450e4cb6b57e1db02ffed6d7a77 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/token @@ -0,0 +1,52 @@ +swc_deu_001305 D E R Z O R N D E M H A U S V E R L A G S U R U B G E H Ö R T +swc_deu_001306 V Ü R D E Ö N F T I G E N B O R T B Ü C H E R E N T W I C K E T E D I P A P I E R F E R P R I +swc_deu_001307 H A M B R E W U O K S +swc_deu_001308 R D I W A S I E A R D L I G E N L A N D Z I T Z E P E T R I B E N A U F A N D S E I S B E M B A U +swc_deu_001309 J A H R Z W E I T A U S E N Z W Ö L F I N D E N B E L I N E R K L U P S O S E C H S O N D R E I S I G V E L L I G T +swc_deu_001310 S E C H E H N U N E N D F Ü N F Z I G H A I T S B Ü N D N I S D +swc_deu_001311 D A S P R O L E H M B E I E S E M P E R A D O C H S O N I S T +swc_deu_001312 A R M E N W E S E N T E T I C A M A L I E S I E V E I K E I N G N +swc_deu_001313 N I H T E I N M A L E I N E A N N S A T Z W E I S E N T U S O C H U N G Z U I E R E M V E R H A L T E N I N E R Z E I T D E S N A T Z U O N A S O T Z E L I S M U S +swc_deu_001314 L I Z E N S V Ü E R F R I E D O G O M E N T A T I O N +swc_deu_001315 D I M A C H Z I H N T N I E R H U N D E R D I E G A R T E N H E U S E R V O R D E N T O R E N +swc_deu_001316 G A N S I M S T I E L D E R Z E I T +swc_deu_001317 B E R B R Ü Ö L U N D H Ü Ö R T E R R E I C H E D E L E I T U N G S C L I S L I C H K Ö N +swc_deu_001318 A U S Z E I C H N U M E N F R E M D E R H E R E N +swc_deu_001319 D I S C H E F T E L L E R E I A U F Z U G E B E N +swc_deu_001320 D A Z U T Z E H E D I E G E G N U N G M I T V E R L E T Z T E N T I E R E N +swc_deu_001321 J E N E N I S C H S T I F T +swc_deu_001322 W E S T L I C H V O N K Ö L E N +swc_deu_001323 D I E S T Ä N D I G I N B E R I E B W A R E N +swc_deu_001324 D I V O M B A R B I I R C H A S I E R T W E R D E +swc_deu_001325 E R S C H E N O C H E N W E I T E R E R A U F S E T Z V O N K R I S T I E R N M E I E L T +swc_deu_001326 W A L L S E B S T E X T R E M E R E I C H T U M K E I N E S W E H T E N U N M I T E L B E R E N Z U G A N +swc_deu_001327 G E B T E U C H N I C H E L B E R A U F +swc_deu_001328 A H A T D I E S E N P R A U C N E U N Z E H N H U N D E R W E I U N D F Ü N F Z I G G E N Ü B +swc_deu_001329 W O D E L E I T U N G Ü B E D I E A L T E H Ü R T E R L E I T U N G E F Ü R T W U R D E +swc_deu_001330 I N E B L I E B T E K Ö A L S C H R O C K T R O P E A S D E M Ö N E R U M N A N D D I E Ö N A +swc_deu_001331 G E W A R D E N S E I U N D A L B R E C H T S I C H +swc_deu_001332 D R T A G E S B E D A F E I N E S W A C H S E N D E N A N W I T E M I N A R +swc_deu_001333 S I B S I N U L E R Z I E H N O B E R A L T E R +swc_deu_001334 W E I T E R H I N L I S I G N A C R H W E I S E N +swc_deu_001335 S U M G R Ü N D U N G S T D R T U M K O N T E A M B E R E I T Z +swc_deu_001336 K E I N L E C S C H L A G E N M Ö G L I C H N A C H F T E I L E +swc_deu_001337 I R T I A T O L I S C H E K Ö R S C H E S A N G P E T E R A N D E R S T E L L E D E A L T +swc_deu_001338 E R F A K N A C U N G D E S R O T W E I Z E N S T R A R T A B E S C H N B A L T D I E R T O F E L E I S E R S A T +swc_deu_001339 K N N E M I T D I E S E N N A C H K O M I N Z E U G E N +swc_deu_001340 A L E N E U N E N F O L G E N D E R H Ö R S T I E R E I E R +swc_deu_001341 S C H I B P S M I T B E H A T E N S O R +swc_deu_001342 K O L G E A N R E R S B O C H N E V E R Z I G H T E T E A U F E R N I P R S Ö N I C H E B E W E R T U N G +swc_deu_001343 B W E N I G E R I N T R Ü S T E T +swc_deu_001344 W E I T E R H I N V E R S O R G D E D E L E I T U N G T E R M E N +swc_deu_001345 W A R T E T E N D A F Ü E R A B E M I T E I N I G +swc_deu_001346 L E D I K L I C H A N T U N D F N K L E I N M U N I E R T E I N S E I N E R E T Z E N S O N D E +swc_deu_001347 E M I A H B E R N E U Z E N E R T F Ü M +swc_deu_001348 E R S E D E M F A R T F A L D E S B Ü R G E R E C H T Z U N D D E R I N F Ü H R N G D E F R E I T Z Y Ü G I C K E I T I M Z W A N S I G S N E R H N D E R T W A N D E T T E S I C H D I E S E A N S C H A U N G A N S E R T S W E I S E N D A R H I N +swc_deu_001349 D E S Z W I S T B A C R E S B E R R E I N B A C H E I N E B O G E N B R Ü C K E V O +swc_deu_001350 A C H Z I N U L E S E X S U N D R E I S I G B U R D E D E H M B U R G +swc_deu_001351 A M +swc_deu_001352 U F G R U N D D E R K O N T E N E N T A L S B E R E A C H T Z E H N H U N D E R E L F B A N O T T +swc_deu_001353 W E I T E R E S M A L M U S T E N D E N U N D B L E I T B R A N D I E V W A R B U N G F Ü R D A S B U C H S E Ä B S T W A N E M N +swc_deu_001354 D I E N A H R I C H V M S E G K T E R B Ü G E L I C H D E M O G R A T I C H E N F E P R A R E V O L U T Z I O N V E N A C H T Z E H N H U N D E R T A C H T U N D V E R Z I C H E N F A N K R E I C H W U R D E N H M B U R G M I T I O B E L A U F G E O M E +swc_deu_001355 U B L I E B T Z W E I J A R E O N E N T E R B R E C H N +swc_deu_001356 Z E A L R E I C E N G A S T S P I L U N T E R W E G S diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..cfd14120786a79539b3eb35dbed7ca360c7692df --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.3/1best_recog/token_int @@ -0,0 +1,52 @@ +swc_deu_001305 10 2 6 3 20 16 3 6 4 10 2 17 3 11 9 12 7 24 2 6 13 9 14 7 3 12 6 12 18 14 2 11 27 6 8 +swc_deu_001306 24 25 6 3 10 2 3 27 4 19 8 5 14 2 4 3 18 16 6 8 3 18 25 15 11 2 6 3 2 4 8 3 21 5 15 22 2 8 2 3 10 5 3 23 9 23 5 2 6 19 2 6 23 6 5 +swc_deu_001307 11 9 17 18 6 2 3 21 12 16 22 7 +swc_deu_001308 6 3 10 5 3 21 9 7 5 2 3 9 6 10 13 5 14 2 4 3 13 9 4 10 20 5 8 20 2 3 23 2 8 6 5 18 2 4 3 9 12 19 9 4 10 3 7 2 5 7 3 18 2 17 18 9 12 +swc_deu_001309 28 9 11 6 20 21 2 5 3 8 9 12 7 2 4 3 20 21 27 13 19 3 5 4 10 2 4 3 18 2 13 5 4 2 6 3 22 13 12 23 3 7 16 7 2 15 11 7 16 4 10 6 2 5 7 5 14 3 24 2 13 13 5 14 8 +swc_deu_001310 7 2 15 11 2 11 4 3 12 4 2 4 10 19 25 4 19 20 5 14 11 3 9 5 8 7 3 18 25 4 10 4 5 7 3 10 +swc_deu_001311 10 9 7 3 23 6 16 13 2 11 17 3 18 2 3 5 2 7 2 17 3 23 2 6 9 10 16 15 11 7 16 4 3 5 7 8 +swc_deu_001312 9 6 17 2 4 3 21 2 7 2 4 3 8 2 8 5 15 3 9 17 9 13 5 2 3 7 3 5 2 24 2 5 22 2 5 4 14 4 +swc_deu_001313 4 5 11 8 3 2 5 4 17 9 13 2 5 4 2 3 9 4 4 7 9 8 20 21 2 5 7 2 3 4 8 12 3 7 16 15 11 12 4 14 3 20 12 3 5 2 6 2 17 3 24 2 6 11 9 13 8 2 4 3 5 4 3 2 6 20 2 5 8 3 10 2 7 3 4 9 8 20 12 16 4 9 7 16 8 20 2 13 5 7 17 12 7 +swc_deu_001314 13 5 20 2 4 7 3 24 25 2 6 19 6 5 2 3 10 16 3 14 16 17 2 4 8 9 8 5 16 4 +swc_deu_001315 10 5 17 3 9 15 11 20 5 11 4 8 3 4 5 2 6 3 11 12 4 10 2 6 3 10 5 2 14 9 6 8 2 4 3 11 2 12 7 2 6 3 24 16 6 10 2 4 8 16 6 2 4 +swc_deu_001316 14 9 4 7 3 5 17 7 8 5 2 13 10 2 6 3 20 2 5 8 +swc_deu_001317 18 2 6 3 18 6 25 27 13 3 12 4 10 3 11 25 27 6 8 3 2 6 6 2 5 15 11 3 2 3 10 2 3 13 2 5 8 12 4 14 7 15 13 5 7 13 5 15 11 22 27 4 +swc_deu_001318 9 12 7 3 20 2 5 15 11 4 12 17 2 4 3 19 6 2 17 3 10 2 6 3 11 2 6 2 4 +swc_deu_001319 10 5 7 15 11 2 19 8 2 13 13 2 6 2 5 3 9 12 19 3 20 12 14 2 18 2 4 +swc_deu_001320 10 9 3 20 12 8 20 2 11 2 3 10 5 3 2 14 2 14 4 12 4 14 17 5 8 3 24 2 6 13 2 8 20 8 2 4 8 5 2 6 2 4 +swc_deu_001321 28 2 4 2 4 5 7 15 11 3 7 8 5 19 8 +swc_deu_001322 21 2 7 8 13 5 15 11 3 24 16 4 3 22 27 13 2 4 +swc_deu_001323 10 5 2 3 7 8 26 4 10 5 14 3 5 4 3 18 2 6 5 2 18 21 9 6 2 4 +swc_deu_001324 10 5 3 24 16 17 3 18 9 6 3 18 5 5 6 15 11 9 7 5 2 6 8 21 2 6 10 2 +swc_deu_001325 2 6 7 15 11 2 3 4 16 15 11 2 4 3 21 2 5 8 2 6 2 6 9 12 19 7 2 8 20 24 16 4 3 22 6 5 7 8 5 2 6 4 17 2 5 2 13 8 +swc_deu_001326 21 9 13 13 3 7 2 18 7 8 3 2 30 8 6 2 17 2 6 2 5 15 11 8 12 17 3 22 2 5 4 2 7 3 21 2 11 8 2 4 3 12 4 17 5 8 2 13 18 2 6 2 4 3 20 12 14 9 4 +swc_deu_001327 14 2 18 8 3 2 12 15 11 4 5 15 11 3 2 13 18 2 6 3 9 12 19 +swc_deu_001328 9 3 11 9 8 3 10 5 2 7 2 4 3 23 6 9 12 15 3 4 2 12 4 20 2 11 4 3 11 12 4 10 2 6 3 21 2 5 12 4 10 3 19 25 4 19 20 5 14 3 14 2 4 25 18 +swc_deu_001329 21 16 3 10 2 13 2 5 8 12 4 14 3 25 18 2 3 10 5 2 3 9 13 8 2 3 11 25 6 8 2 6 13 2 5 8 3 12 4 14 2 19 25 6 8 21 12 6 10 2 +swc_deu_001330 5 4 2 3 18 13 5 2 18 8 2 3 22 27 9 13 7 15 11 6 16 15 22 8 6 16 23 2 9 7 3 10 2 17 3 27 4 2 6 3 12 17 3 4 9 4 10 10 5 2 3 27 4 9 +swc_deu_001331 14 2 21 9 6 10 2 4 3 7 2 5 3 12 4 10 3 9 13 18 6 2 15 11 8 7 5 15 11 +swc_deu_001332 10 6 8 9 14 2 7 18 2 10 9 19 3 2 5 4 2 7 3 21 9 15 11 7 2 4 10 2 4 3 9 4 21 5 8 2 17 5 4 3 9 6 +swc_deu_001333 7 5 18 3 7 5 4 3 12 13 2 6 20 5 2 11 4 3 16 18 2 6 3 9 13 8 2 6 +swc_deu_001334 21 2 5 8 2 6 3 11 5 4 3 13 5 7 5 14 3 4 9 15 6 11 21 2 5 7 2 4 3 +swc_deu_001335 7 12 17 3 14 6 25 4 10 12 4 14 7 8 10 6 8 12 17 3 22 16 4 8 2 9 17 3 18 2 6 2 5 8 20 +swc_deu_001336 22 2 5 4 13 2 15 7 15 11 13 9 14 2 4 3 17 27 14 13 5 15 11 3 4 9 15 11 19 8 2 5 13 2 +swc_deu_001337 5 6 8 5 3 9 8 16 13 5 7 15 11 2 3 22 27 6 7 15 11 2 3 7 9 4 14 23 2 8 2 6 3 9 4 3 10 2 6 3 7 8 2 13 13 2 3 10 2 3 9 13 8 +swc_deu_001338 2 6 3 19 9 22 4 9 15 12 4 14 3 10 2 7 3 6 16 8 3 21 2 5 20 2 4 7 3 8 6 9 6 8 3 9 18 2 7 15 11 4 3 18 9 13 8 3 10 5 3 2 6 8 16 19 2 13 3 2 5 7 3 2 6 7 9 8 +swc_deu_001339 22 4 4 2 3 17 5 8 3 10 5 2 7 2 4 3 4 9 15 11 22 16 17 5 4 3 20 2 12 14 2 4 +swc_deu_001340 9 13 2 3 4 2 12 4 2 4 3 19 16 13 14 2 4 3 10 2 6 3 11 27 6 7 8 5 2 6 2 5 2 6 +swc_deu_001341 7 15 11 5 18 23 7 17 5 8 3 18 2 11 9 8 2 4 7 16 6 +swc_deu_001342 22 16 13 14 2 3 9 4 3 6 2 6 7 3 18 16 15 11 4 2 3 24 2 6 20 5 14 11 8 2 8 2 3 9 12 19 2 6 4 5 3 23 6 7 27 4 5 15 11 2 3 18 2 21 2 6 8 12 4 14 +swc_deu_001343 18 21 2 4 5 14 2 6 3 5 4 3 8 6 25 7 8 2 8 +swc_deu_001344 21 2 5 8 2 6 3 11 5 4 3 24 2 6 7 16 6 14 3 10 2 3 10 2 3 13 2 5 8 12 4 14 3 8 2 6 17 2 4 +swc_deu_001345 21 9 6 8 2 8 2 4 3 10 9 19 25 2 6 3 9 18 2 3 17 5 8 3 2 5 4 5 14 +swc_deu_001346 13 2 10 5 22 13 5 15 11 3 9 4 8 12 4 10 19 4 3 22 13 2 5 4 3 17 12 4 5 2 6 8 2 3 5 4 3 7 2 5 4 2 6 2 8 20 2 4 7 16 4 10 2 +swc_deu_001347 2 17 3 5 9 11 18 2 6 4 2 12 20 2 4 3 2 6 8 19 25 17 +swc_deu_001348 2 6 7 3 2 3 10 2 17 3 19 9 6 8 19 9 13 3 10 2 7 3 18 25 6 14 2 6 2 15 11 8 20 3 12 4 10 3 10 2 6 3 5 4 19 25 11 6 4 14 3 10 2 3 19 6 2 5 8 20 29 25 14 5 15 22 2 5 8 3 5 17 3 20 21 9 4 7 5 14 7 3 4 2 6 11 4 10 2 6 8 3 21 9 4 10 2 8 3 8 2 3 7 5 15 11 3 10 5 2 7 2 3 9 4 7 15 11 9 12 4 14 3 9 4 7 2 6 8 3 7 21 2 5 7 2 4 3 10 9 6 11 5 4 +swc_deu_001349 10 2 7 3 20 21 5 7 8 3 18 9 15 6 2 7 3 18 2 6 6 2 5 4 18 9 15 11 3 2 5 4 2 3 18 16 14 2 4 3 18 6 25 15 22 2 3 24 16 +swc_deu_001350 9 15 11 20 5 4 12 13 2 3 7 2 30 7 12 4 10 6 2 5 7 5 14 3 18 12 6 10 2 3 10 2 3 11 17 18 12 6 14 +swc_deu_001351 9 17 +swc_deu_001352 12 19 3 14 6 12 4 10 3 10 2 6 3 22 16 4 8 2 4 2 4 3 8 9 13 7 18 2 6 2 3 9 15 11 8 20 2 11 4 3 11 12 4 10 2 6 3 2 13 19 3 18 9 4 3 16 8 8 +swc_deu_001353 21 2 5 8 2 6 2 7 17 9 13 17 12 7 8 2 4 10 2 4 3 12 4 10 3 18 13 2 5 8 18 6 9 4 3 10 5 2 24 21 9 6 18 12 4 14 19 25 6 3 10 9 7 3 18 12 15 11 7 2 26 18 7 8 3 21 9 4 2 17 4 +swc_deu_001354 10 5 2 4 9 11 6 5 15 11 3 24 17 3 7 2 14 22 8 2 6 3 18 25 14 2 13 5 15 11 10 2 17 16 14 6 9 8 5 15 11 2 4 3 19 2 3 23 6 9 6 2 24 16 13 12 8 20 5 16 4 3 24 2 4 3 9 15 11 8 20 2 11 4 3 11 12 4 10 2 6 8 3 9 15 11 8 3 12 4 10 3 24 2 6 20 5 15 11 2 4 3 19 9 4 22 6 2 5 15 11 21 12 6 10 2 4 3 11 17 18 12 6 14 3 17 5 8 3 5 16 18 2 13 3 9 12 19 14 2 16 17 2 +swc_deu_001355 12 18 13 5 2 18 8 20 21 2 5 28 9 6 2 3 16 4 2 3 4 8 2 6 18 6 2 15 11 4 +swc_deu_001356 20 2 9 13 6 2 5 15 2 4 14 9 7 8 7 23 5 13 12 4 8 2 6 21 2 14 7 3 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..e324ece1bbb1f535ada1cb0c3a499b25faa17a99 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/score @@ -0,0 +1,51 @@ +swc_deu_001357 tensor(-6.2378) +swc_deu_001358 tensor(-4.5101) +swc_deu_001359 tensor(-24.7493) +swc_deu_001360 tensor(-9.4284) +swc_deu_001361 tensor(-5.4927) +swc_deu_001362 tensor(-22.9188) +swc_deu_001363 tensor(-7.5927) +swc_deu_001364 tensor(-9.1570) +swc_deu_001365 tensor(-3.6889) +swc_deu_001366 tensor(-13.1492) +swc_deu_001367 tensor(-16.9964) +swc_deu_001368 tensor(-14.6608) +swc_deu_001369 tensor(-4.3023) +swc_deu_001370 tensor(-9.6449) +swc_deu_001371 tensor(-10.7050) +swc_deu_001372 tensor(-17.2914) +swc_deu_001373 tensor(-8.3277) +swc_deu_001374 tensor(-8.7493) +swc_deu_001375 tensor(-10.4424) +swc_deu_001376 tensor(-9.6863) +swc_deu_001377 tensor(-29.2476) +swc_deu_001378 tensor(-13.3243) +swc_deu_001379 tensor(-5.6826) +swc_deu_001380 tensor(-7.5686) +swc_deu_001381 tensor(-6.6532) +swc_deu_001382 tensor(-18.7212) +swc_deu_001383 tensor(-16.0726) +swc_deu_001384 tensor(-23.4841) +swc_deu_001385 tensor(-10.5735) +swc_deu_001386 tensor(-9.3610) +swc_deu_001387 tensor(-12.9269) +swc_deu_001388 tensor(-5.8080) +swc_deu_001389 tensor(-23.0094) +swc_deu_001390 tensor(-19.8903) +swc_deu_001391 tensor(-9.2005) +swc_deu_001392 tensor(-55.5649) +swc_deu_001393 tensor(-3.4347) +swc_deu_001394 tensor(-9.7740) +swc_deu_001395 tensor(-7.1497) +swc_deu_001396 tensor(-21.6702) +swc_deu_001397 tensor(-6.9184) +swc_deu_001398 tensor(-7.4350) +swc_deu_001399 tensor(-10.9575) +swc_deu_001400 tensor(-11.4970) +swc_deu_001401 tensor(-7.0880) +swc_deu_001402 tensor(-10.2133) +swc_deu_001403 tensor(-13.2352) +swc_deu_001404 tensor(-26.4249) +swc_deu_001405 tensor(-5.7208) +swc_deu_001406 tensor(-18.7085) +swc_deu_001407 tensor(-7.5909) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..95d19ec9520fde8ab26d543fd5fe7db58d52269a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/text @@ -0,0 +1,51 @@ +swc_deu_001357 CRANTIE TET GENÜTEN +swc_deu_001358 IN BEROKAR AUSTATE +swc_deu_001359 DAS ERICHT VOM BEIVARGENSANE MOTOR DES AUS IN DIE ZU DIESERZEITNEU EN STEHNDEN ABEIT ASIEDLUNENZU +swc_deu_001360 DIMI ZAMD IH ER RECHEN STOBE +swc_deu_001361 KEISERFERDINANT +swc_deu_001362 VOM FERNERESCHSUHER FERNZSGSAVERBUGNER IN DEM FERNSEFILEN DAS IEWIGELIET +swc_deu_001363 WORE IN SEINEM BESTEN ZEITE IER +swc_deu_001364 SEÖREN DEN ARTIKEL FISCHEND CIEBS +swc_deu_001365 UND TEFAR MOWIE +swc_deu_001366 RE SEPTIUONDE HECHSEN DMATIG VON KRISTA +swc_deu_001367 DIGESAMTER ANLAGE WARBESET VER ZWEI HUNDER SECHZIG NACH KRISTUS IN BEDRIEB +swc_deu_001368 DER ES DE FASTFUTD LIFERSOR IDES WAGEBOCH +swc_deu_001369 EINEM KABELBAUNEN +swc_deu_001370 MORLE DUNG MIT DE GEFAHRVERBUNDEN GEWES +swc_deu_001371 DE ELTISTEN PFIERDERENEN ASEHAB +swc_deu_001372 SONEN AUCH DERNAT ZUONAHL SUOZ ERDISTISCHEN KUNZT AUF FASSUNGERECHTWERDEN +swc_deu_001373 DIE WLLZICH DES HANSE ARTEN +swc_deu_001374 AUC NACHKOMMEN EINT NICHTBEKANT +swc_deu_001375 ERENTEXSTE DEN EINDRUGKTZE VERMITTE +swc_deu_001376 VER DINST M DASKÖNARLIET VERLIEN +swc_deu_001377 BWOLL HOFMEIN VON HOFMEINZ WALL DAUS WERG GROSENEINFLUS A SPÄERERDICHTE AUS ÜBT +swc_deu_001378 MMSO ERMNSTALSTAS OBERHAUPFVEN +swc_deu_001379 EFREIE DO KOMETATION +swc_deu_001380 GESTALTUNM BES KAVERS WIEDER SPIEGELT +swc_deu_001381 DER ESMTER AUFWAND WIT AUF +swc_deu_001382 OBGLEICHAMBORK DIESEM ANGEHÖRTE UND EINE NOBILI TIERUNG DUCHENKEISER DAMIT KEINE DRC +swc_deu_001383 DA STURCH DENSICG AUSWEITENTEN WELTHANDEL ARBEIT UND WOHLSTAND VERSPRACH +swc_deu_001384 FÜÖR DIE ZEITT MITE DES NEUNZEHNTE JA HNDER BEKLAG DEDE ERCHIE TEKTMATIN HALLE +swc_deu_001385 EITBONDESKANZER HEMUTSCHMITT LENTE +swc_deu_001386 DI NAMEN GODE FREI IM STATZ HANDBOCHTZ +swc_deu_001387 WEN AUCHE ENERGEWISENLETAGIE +swc_deu_001388 KALKOLIERE BAEG +swc_deu_001389 ANGEFNGDEZSAHUNER VFNM ZISCÜULER EINEN DIELIGIEITEN +swc_deu_001390 VIELMENSCHEN SANEN RISLI ALS NAHRUNGSKONGRENTEN UND ALSPOTEN ELGEFA +swc_deu_001391 DEN UFTRIT VE KÖRZE +swc_deu_001392 MI DM STAN VOM DREITZHN NIULIEZWEI TAUSEN SWERF DER INHERSTET UNDER DELIZENS KRERTZU COMONS EIZREWUSCHEN SCHER E LEITDREIT PUNK NL ANPORTET UND UNTE DE +swc_deu_001393 EINE KLEINEREBOGEN BRÜCKE +swc_deu_001394 SICH NUNVERSENWEITRKOM +swc_deu_001395 AUS DEM GEMEÄLDE ZU ENT FERENEN +swc_deu_001396 IS DCH ÖAROPÄSCHERICHTLINE NEUNZICGH VIERNERTEXSNEUNZSICH EWI +swc_deu_001397 ALE UMSELT AUF +swc_deu_001398 ND SIE SEI AUCH WIE EINE FIRSTEN +swc_deu_001399 NEUNZEHN HUNDERT NEUN ZEN +swc_deu_001400 STATISSEN HABEN DE RÖMISCHE IN JEN IUREM +swc_deu_001401 DELKRISLIE BEHR UND MENSCH +swc_deu_001402 MIESICHENS KURELISCHE +swc_deu_001403 BERTOTCHUMLGEB DES DREIVARER ZIONER MI +swc_deu_001404 K ZUGSGEBIEDT ERWESICH TER ACHZEHN HUNDATZWEIUN SIEBZIG E GRÜNETE JLUSTDUNET IONALPARK +swc_deu_001405 E FINITION +swc_deu_001406 UM INE UNIVESITET ZVILA ZWEISE MSTER KUNSGESCHCHT ZUSTUDIERN +swc_deu_001407 DIE TOTZ ERE GRINE diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..c9a529ee9d287743e330cf2b10fb4f6c3f6845a7 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/token @@ -0,0 +1,51 @@ +swc_deu_001357 C R A N T I E T E T G E N Ü T E N +swc_deu_001358 I N B E R O K A R A U S T A T E +swc_deu_001359 D A S E R I C H T V O M B E I V A R G E N S A N E M O T O R D E S A U S I N D I E Z U D I E S E R Z E I T N E U E N S T E H N D E N A B E I T A S I E D L U N E N Z U +swc_deu_001360 D I M I Z A M D I H E R R E C H E N S T O B E +swc_deu_001361 K E I S E R F E R D I N A N T +swc_deu_001362 V O M F E R N E R E S C H S U H E R F E R N Z S G S A V E R B U G N E R I N D E M F E R N S E F I L E N D A S I E W I G E L I E T +swc_deu_001363 W O R E I N S E I N E M B E S T E N Z E I T E I E R +swc_deu_001364 S E Ö R E N D E N A R T I K E L F I S C H E N D C I E B S +swc_deu_001365 U N D T E F A R M O W I E +swc_deu_001366 R E S E P T I U O N D E H E C H S E N D M A T I G V O N K R I S T A +swc_deu_001367 D I G E S A M T E R A N L A G E W A R B E S E T V E R Z W E I H U N D E R S E C H Z I G N A C H K R I S T U S I N B E D R I E B +swc_deu_001368 D E R E S D E F A S T F U T D L I F E R S O R I D E S W A G E B O C H +swc_deu_001369 E I N E M K A B E L B A U N E N +swc_deu_001370 M O R L E D U N G M I T D E G E F A H R V E R B U N D E N G E W E S +swc_deu_001371 D E E L T I S T E N P F I E R D E R E N E N A S E H A B +swc_deu_001372 S O N E N A U C H D E R N A T Z U O N A H L S U O Z E R D I S T I S C H E N K U N Z T A U F F A S S U N G E R E C H T W E R D E N +swc_deu_001373 D I E W L L Z I C H D E S H A N S E A R T E N +swc_deu_001374 A U C N A C H K O M M E N E I N T N I C H T B E K A N T +swc_deu_001375 E R E N T E X S T E D E N E I N D R U G K T Z E V E R M I T T E +swc_deu_001376 V E R D I N S T M D A S K Ö N A R L I E T V E R L I E N +swc_deu_001377 B W O L L H O F M E I N V O N H O F M E I N Z W A L L D A U S W E R G G R O S E N E I N F L U S A S P Ä E R E R D I C H T E A U S Ü B T +swc_deu_001378 M M S O E R M N S T A L S T A S O B E R H A U P F V E N +swc_deu_001379 E F R E I E D O K O M E T A T I O N +swc_deu_001380 G E S T A L T U N M B E S K A V E R S W I E D E R S P I E G E L T +swc_deu_001381 D E R E S M T E R A U F W A N D W I T A U F +swc_deu_001382 O B G L E I C H A M B O R K D I E S E M A N G E H Ö R T E U N D E I N E N O B I L I T I E R U N G D U C H E N K E I S E R D A M I T K E I N E D R C +swc_deu_001383 D A S T U R C H D E N S I C G A U S W E I T E N T E N W E L T H A N D E L A R B E I T U N D W O H L S T A N D V E R S P R A C H +swc_deu_001384 F Ü Ö R D I E Z E I T T M I T E D E S N E U N Z E H N T E J A H N D E R B E K L A G D E D E E R C H I E T E K T M A T I N H A L L E +swc_deu_001385 E I T B O N D E S K A N Z E R H E M U T S C H M I T T L E N T E +swc_deu_001386 D I N A M E N G O D E F R E I I M S T A T Z H A N D B O C H T Z +swc_deu_001387 W E N A U C H E E N E R G E W I S E N L E T A G I E +swc_deu_001388 K A L K O L I E R E B A E G +swc_deu_001389 A N G E F N G D E Z S A H U N E R V F N M Z I S C Ü U L E R E I N E N D I E L I G I E I T E N +swc_deu_001390 V I E L M E N S C H E N S A N E N R I S L I A L S N A H R U N G S K O N G R E N T E N U N D A L S P O T E N E L G E F A +swc_deu_001391 D E N U F T R I T V E K Ö R Z E +swc_deu_001392 M I D M S T A N V O M D R E I T Z H N N I U L I E Z W E I T A U S E N S W E R F D E R I N H E R S T E T U N D E R D E L I Z E N S K R E R T Z U C O M O N S E I Z R E W U S C H E N S C H E R E L E I T D R E I T P U N K N L A N P O R T E T U N D U N T E D E +swc_deu_001393 E I N E K L E I N E R E B O G E N B R Ü C K E +swc_deu_001394 S I C H N U N V E R S E N W E I T R K O M +swc_deu_001395 A U S D E M G E M E Ä L D E Z U E N T F E R E N E N +swc_deu_001396 I S D C H Ö A R O P Ä S C H E R I C H T L I N E N E U N Z I C G H V I E R N E R T E X S N E U N Z S I C H E W I +swc_deu_001397 A L E U M S E L T A U F +swc_deu_001398 N D S I E S E I A U C H W I E E I N E F I R S T E N +swc_deu_001399 N E U N Z E H N H U N D E R T N E U N Z E N +swc_deu_001400 S T A T I S S E N H A B E N D E R Ö M I S C H E I N J E N I U R E M +swc_deu_001401 D E L K R I S L I E B E H R U N D M E N S C H +swc_deu_001402 M I E S I C H E N S K U R E L I S C H E +swc_deu_001403 B E R T O T C H U M L G E B D E S D R E I V A R E R Z I O N E R M I +swc_deu_001404 K Z U G S G E B I E D T E R W E S I C H T E R A C H Z E H N H U N D A T Z W E I U N S I E B Z I G E G R Ü N E T E J L U S T D U N E T I O N A L P A R K +swc_deu_001405 E F I N I T I O N +swc_deu_001406 U M I N E U N I V E S I T E T Z V I L A Z W E I S E M S T E R K U N S G E S C H C H T Z U S T U D I E R N +swc_deu_001407 D I E T O T Z E R E G R I N E diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..a7732c5a636be57beab257698ff8c6ce82afabe2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/logdir/output.4/1best_recog/token_int @@ -0,0 +1,51 @@ +swc_deu_001357 15 6 9 4 8 5 2 3 8 2 8 3 14 2 4 25 8 2 4 +swc_deu_001358 5 4 3 18 2 6 16 22 9 6 3 9 12 7 8 9 8 2 +swc_deu_001359 10 9 7 3 2 6 5 15 11 8 3 24 16 17 3 18 2 5 24 9 6 14 2 4 7 9 4 2 3 17 16 8 16 6 3 10 2 7 3 9 12 7 3 5 4 3 10 5 2 3 20 12 3 10 5 2 7 2 6 20 2 5 8 4 2 12 3 2 4 3 7 8 2 11 4 10 2 4 3 9 18 2 5 8 3 9 7 5 2 10 13 12 4 2 4 20 12 +swc_deu_001360 10 5 17 5 3 20 9 17 10 3 5 11 3 2 6 3 6 2 15 11 2 4 3 7 8 16 18 2 +swc_deu_001361 22 2 5 7 2 6 19 2 6 10 5 4 9 4 8 +swc_deu_001362 24 16 17 3 19 2 6 4 2 6 2 7 15 11 7 12 11 2 6 3 19 2 6 4 20 7 14 7 9 24 2 6 18 12 14 4 2 6 3 5 4 3 10 2 17 3 19 2 6 4 7 2 19 5 13 2 4 3 10 9 7 3 5 2 21 5 14 2 13 5 2 8 +swc_deu_001363 21 16 6 2 3 5 4 3 7 2 5 4 2 17 3 18 2 7 8 2 4 3 20 2 5 8 2 3 5 2 6 +swc_deu_001364 7 2 27 6 2 4 3 10 2 4 3 9 6 8 5 22 2 13 3 19 5 7 15 11 2 4 10 3 15 5 2 18 7 +swc_deu_001365 12 4 10 3 8 2 19 9 6 3 17 16 21 5 2 +swc_deu_001366 6 2 3 7 2 23 8 5 12 16 4 10 2 3 11 2 15 11 7 2 4 3 10 17 9 8 5 14 3 24 16 4 3 22 6 5 7 8 9 +swc_deu_001367 10 5 14 2 7 9 17 8 2 6 3 9 4 13 9 14 2 3 21 9 6 18 2 7 2 8 3 24 2 6 3 20 21 2 5 3 11 12 4 10 2 6 3 7 2 15 11 20 5 14 3 4 9 15 11 3 22 6 5 7 8 12 7 3 5 4 3 18 2 10 6 5 2 18 +swc_deu_001368 10 2 6 3 2 7 3 10 2 3 19 9 7 8 19 12 8 10 3 13 5 19 2 6 7 16 6 3 5 10 2 7 3 21 9 14 2 18 16 15 11 +swc_deu_001369 2 5 4 2 17 3 22 9 18 2 13 18 9 12 4 2 4 +swc_deu_001370 17 16 6 13 2 3 10 12 4 14 3 17 5 8 3 10 2 3 14 2 19 9 11 6 24 2 6 18 12 4 10 2 4 3 14 2 21 2 7 +swc_deu_001371 10 2 3 2 13 8 5 7 8 2 4 3 23 19 5 2 6 10 2 6 2 4 2 4 3 9 7 2 11 9 18 +swc_deu_001372 7 16 4 2 4 3 9 12 15 11 3 10 2 6 4 9 8 3 20 12 16 4 9 11 13 3 7 12 16 20 3 2 6 10 5 7 8 5 7 15 11 2 4 3 22 12 4 20 8 3 9 12 19 3 19 9 7 7 12 4 14 2 6 2 15 11 8 21 2 6 10 2 4 +swc_deu_001373 10 5 2 3 21 13 13 20 5 15 11 3 10 2 7 3 11 9 4 7 2 3 9 6 8 2 4 +swc_deu_001374 9 12 15 3 4 9 15 11 22 16 17 17 2 4 3 2 5 4 8 3 4 5 15 11 8 18 2 22 9 4 8 +swc_deu_001375 2 6 2 4 8 2 30 7 8 2 3 10 2 4 3 2 5 4 10 6 12 14 22 8 20 2 3 24 2 6 17 5 8 8 2 +swc_deu_001376 24 2 6 3 10 5 4 7 8 3 17 3 10 9 7 22 27 4 9 6 13 5 2 8 3 24 2 6 13 5 2 4 +swc_deu_001377 18 21 16 13 13 3 11 16 19 17 2 5 4 3 24 16 4 3 11 16 19 17 2 5 4 20 3 21 9 13 13 3 10 9 12 7 3 21 2 6 14 3 14 6 16 7 2 4 2 5 4 19 13 12 7 3 9 3 7 23 26 2 6 2 6 10 5 15 11 8 2 3 9 12 7 3 25 18 8 +swc_deu_001378 17 17 7 16 3 2 6 17 4 7 8 9 13 7 8 9 7 3 16 18 2 6 11 9 12 23 19 24 2 4 +swc_deu_001379 2 19 6 2 5 2 3 10 16 3 22 16 17 2 8 9 8 5 16 4 +swc_deu_001380 14 2 7 8 9 13 8 12 4 17 3 18 2 7 3 22 9 24 2 6 7 3 21 5 2 10 2 6 3 7 23 5 2 14 2 13 8 +swc_deu_001381 10 2 6 3 2 7 17 8 2 6 3 9 12 19 21 9 4 10 3 21 5 8 3 9 12 19 +swc_deu_001382 16 18 14 13 2 5 15 11 9 17 18 16 6 22 3 10 5 2 7 2 17 3 9 4 14 2 11 27 6 8 2 3 12 4 10 3 2 5 4 2 3 4 16 18 5 13 5 3 8 5 2 6 12 4 14 3 10 12 15 11 2 4 22 2 5 7 2 6 3 10 9 17 5 8 3 22 2 5 4 2 3 10 6 15 +swc_deu_001383 10 9 3 7 8 12 6 15 11 3 10 2 4 7 5 15 14 3 9 12 7 21 2 5 8 2 4 8 2 4 3 21 2 13 8 11 9 4 10 2 13 3 9 6 18 2 5 8 3 12 4 10 3 21 16 11 13 7 8 9 4 10 3 24 2 6 7 23 6 9 15 11 +swc_deu_001384 19 25 27 6 3 10 5 2 3 20 2 5 8 8 3 17 5 8 2 3 10 2 7 3 4 2 12 4 20 2 11 4 8 2 3 28 9 3 11 4 10 2 6 3 18 2 22 13 9 14 3 10 2 10 2 3 2 6 15 11 5 2 3 8 2 22 8 17 9 8 5 4 3 11 9 13 13 2 +swc_deu_001385 2 5 8 18 16 4 10 2 7 22 9 4 20 2 6 3 11 2 17 12 8 7 15 11 17 5 8 8 3 13 2 4 8 2 +swc_deu_001386 10 5 3 4 9 17 2 4 3 14 16 10 2 3 19 6 2 5 3 5 17 3 7 8 9 8 20 3 11 9 4 10 18 16 15 11 8 20 +swc_deu_001387 21 2 4 3 9 12 15 11 2 3 2 4 2 6 14 2 21 5 7 2 4 13 2 8 9 14 5 2 +swc_deu_001388 22 9 13 22 16 13 5 2 6 2 3 18 9 2 14 +swc_deu_001389 9 4 14 2 19 4 14 10 2 20 7 9 11 12 4 2 6 3 24 19 4 17 3 20 5 7 15 25 12 13 2 6 3 2 5 4 2 4 3 10 5 2 13 5 14 5 2 5 8 2 4 +swc_deu_001390 24 5 2 13 17 2 4 7 15 11 2 4 3 7 9 4 2 4 3 6 5 7 13 5 3 9 13 7 3 4 9 11 6 12 4 14 7 22 16 4 14 6 2 4 8 2 4 3 12 4 10 3 9 13 7 23 16 8 2 4 3 2 13 14 2 19 9 +swc_deu_001391 10 2 4 3 12 19 8 6 5 8 3 24 2 3 22 27 6 20 2 +swc_deu_001392 17 5 3 10 17 3 7 8 9 4 3 24 16 17 3 10 6 2 5 8 20 11 4 3 4 5 12 13 5 2 20 21 2 5 3 8 9 12 7 2 4 3 7 21 2 6 19 3 10 2 6 3 5 4 11 2 6 7 8 2 8 3 12 4 10 2 6 3 10 2 13 5 20 2 4 7 3 22 6 2 6 8 20 12 3 15 16 17 16 4 7 3 2 5 20 6 2 21 12 7 15 11 2 4 3 7 15 11 2 6 3 2 3 13 2 5 8 10 6 2 5 8 3 23 12 4 22 3 4 13 3 9 4 23 16 6 8 2 8 3 12 4 10 3 12 4 8 2 3 10 2 +swc_deu_001393 2 5 4 2 3 22 13 2 5 4 2 6 2 18 16 14 2 4 3 18 6 25 15 22 2 +swc_deu_001394 7 5 15 11 3 4 12 4 24 2 6 7 2 4 21 2 5 8 6 22 16 17 +swc_deu_001395 9 12 7 3 10 2 17 3 14 2 17 2 26 13 10 2 3 20 12 3 2 4 8 3 19 2 6 2 4 2 4 +swc_deu_001396 5 7 3 10 15 11 3 27 9 6 16 23 26 7 15 11 2 6 5 15 11 8 13 5 4 2 3 4 2 12 4 20 5 15 14 11 3 24 5 2 6 4 2 6 8 2 30 7 4 2 12 4 20 7 5 15 11 3 2 21 5 +swc_deu_001397 9 13 2 3 12 17 7 2 13 8 3 9 12 19 +swc_deu_001398 4 10 3 7 5 2 3 7 2 5 3 9 12 15 11 3 21 5 2 3 2 5 4 2 3 19 5 6 7 8 2 4 +swc_deu_001399 4 2 12 4 20 2 11 4 3 11 12 4 10 2 6 8 3 4 2 12 4 3 20 2 4 +swc_deu_001400 7 8 9 8 5 7 7 2 4 3 11 9 18 2 4 3 10 2 3 6 27 17 5 7 15 11 2 3 5 4 3 28 2 4 3 5 12 6 2 17 +swc_deu_001401 10 2 13 22 6 5 7 13 5 2 3 18 2 11 6 3 12 4 10 3 17 2 4 7 15 11 +swc_deu_001402 17 5 2 7 5 15 11 2 4 7 3 22 12 6 2 13 5 7 15 11 2 +swc_deu_001403 18 2 6 8 16 8 15 11 12 17 13 14 2 18 3 10 2 7 3 10 6 2 5 24 9 6 2 6 3 20 5 16 4 2 6 3 17 5 +swc_deu_001404 22 3 20 12 14 7 14 2 18 5 2 10 8 3 2 6 21 2 7 5 15 11 3 8 2 6 3 9 15 11 20 2 11 4 3 11 12 4 10 9 8 20 21 2 5 12 4 3 7 5 2 18 20 5 14 3 2 3 14 6 25 4 2 8 2 3 28 13 12 7 8 10 12 4 2 8 3 5 16 4 9 13 23 9 6 22 +swc_deu_001405 2 3 19 5 4 5 8 5 16 4 +swc_deu_001406 12 17 3 5 4 2 3 12 4 5 24 2 7 5 8 2 8 3 20 24 5 13 9 3 20 21 2 5 7 2 3 17 7 8 2 6 3 22 12 4 7 14 2 7 15 11 15 11 8 3 20 12 7 8 12 10 5 2 6 4 +swc_deu_001407 10 5 2 3 8 16 8 20 3 2 6 2 3 14 6 5 4 2 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score new file mode 100644 index 0000000000000000000000000000000000000000..b4d2896eba01023b3fa0ae3154d8d5d3cd56c261 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score @@ -0,0 +1,207 @@ +swc_deu_001201 tensor(-27.2812) +swc_deu_001202 tensor(-7.7247) +swc_deu_001203 tensor(-9.4369) +swc_deu_001204 tensor(-8.1747) +swc_deu_001205 tensor(-13.7753) +swc_deu_001206 tensor(-4.9230) +swc_deu_001207 tensor(-8.6615) +swc_deu_001208 tensor(-16.1020) +swc_deu_001209 tensor(-15.4309) +swc_deu_001210 tensor(-17.1736) +swc_deu_001211 tensor(-8.9541) +swc_deu_001212 tensor(-12.9838) +swc_deu_001213 tensor(-4.2041) +swc_deu_001214 tensor(-16.3466) +swc_deu_001215 tensor(-19.1089) +swc_deu_001216 tensor(-5.4853) +swc_deu_001217 tensor(-32.1465) +swc_deu_001218 tensor(-9.4414) +swc_deu_001219 tensor(-20.4470) +swc_deu_001220 tensor(-11.9562) +swc_deu_001221 tensor(-25.6542) +swc_deu_001222 tensor(-6.9054) +swc_deu_001223 tensor(-11.6484) +swc_deu_001224 tensor(-5.9376) +swc_deu_001225 tensor(-13.6834) +swc_deu_001226 tensor(-21.0824) +swc_deu_001227 tensor(-23.0667) +swc_deu_001228 tensor(-5.0203) +swc_deu_001229 tensor(-7.2377) +swc_deu_001230 tensor(-6.3112) +swc_deu_001231 tensor(-6.6119) +swc_deu_001232 tensor(-6.3559) +swc_deu_001233 tensor(-21.3440) +swc_deu_001234 tensor(-6.2212) +swc_deu_001235 tensor(-14.6094) +swc_deu_001236 tensor(-8.4346) +swc_deu_001237 tensor(-13.8860) +swc_deu_001238 tensor(-17.6036) +swc_deu_001239 tensor(-7.6736) +swc_deu_001240 tensor(-13.7162) +swc_deu_001241 tensor(-4.4946) +swc_deu_001242 tensor(-15.6359) +swc_deu_001243 tensor(-6.6336) +swc_deu_001244 tensor(-2.0153) +swc_deu_001245 tensor(-6.9332) +swc_deu_001246 tensor(-3.2669) +swc_deu_001247 tensor(-42.1004) +swc_deu_001248 tensor(-11.9917) +swc_deu_001249 tensor(-12.4268) +swc_deu_001250 tensor(-4.6397) +swc_deu_001251 tensor(-24.3756) +swc_deu_001252 tensor(-4.0452) +swc_deu_001253 tensor(-12.1796) +swc_deu_001254 tensor(-4.7920) +swc_deu_001255 tensor(-10.7116) +swc_deu_001256 tensor(-9.2295) +swc_deu_001257 tensor(-7.4713) +swc_deu_001258 tensor(-6.9582) +swc_deu_001259 tensor(-4.5759) +swc_deu_001260 tensor(-16.4660) +swc_deu_001261 tensor(-9.8764) +swc_deu_001262 tensor(-6.6527) +swc_deu_001263 tensor(-13.3887) +swc_deu_001264 tensor(-13.9906) +swc_deu_001265 tensor(-4.6216) +swc_deu_001266 tensor(-7.8718) +swc_deu_001267 tensor(-8.5063) +swc_deu_001268 tensor(-3.4202) +swc_deu_001269 tensor(-8.2194) +swc_deu_001270 tensor(-11.4945) +swc_deu_001271 tensor(-6.3149) +swc_deu_001272 tensor(-10.3158) +swc_deu_001273 tensor(-9.2455) +swc_deu_001274 tensor(-19.3320) +swc_deu_001275 tensor(-6.9352) +swc_deu_001276 tensor(-5.4525) +swc_deu_001277 tensor(-11.9270) 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tensor(-15.9303) +swc_deu_001309 tensor(-17.1624) +swc_deu_001310 tensor(-11.5434) +swc_deu_001311 tensor(-13.4631) +swc_deu_001312 tensor(-18.8217) +swc_deu_001313 tensor(-25.3673) +swc_deu_001314 tensor(-9.4786) +swc_deu_001315 tensor(-15.9164) +swc_deu_001316 tensor(-4.2287) +swc_deu_001317 tensor(-16.4453) +swc_deu_001318 tensor(-5.8636) +swc_deu_001319 tensor(-8.2308) +swc_deu_001320 tensor(-13.8620) +swc_deu_001321 tensor(-6.2835) +swc_deu_001322 tensor(-3.7747) +swc_deu_001323 tensor(-8.2326) +swc_deu_001324 tensor(-10.4998) +swc_deu_001325 tensor(-19.6826) +swc_deu_001326 tensor(-19.5771) +swc_deu_001327 tensor(-5.1349) +swc_deu_001328 tensor(-13.0677) +swc_deu_001329 tensor(-13.1182) +swc_deu_001330 tensor(-16.5463) +swc_deu_001331 tensor(-4.8647) +swc_deu_001332 tensor(-12.3349) +swc_deu_001333 tensor(-8.4995) +swc_deu_001334 tensor(-5.3028) +swc_deu_001335 tensor(-11.7793) +swc_deu_001336 tensor(-9.8268) +swc_deu_001337 tensor(-14.1149) +swc_deu_001338 tensor(-22.8084) 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tensor(-4.3023) +swc_deu_001370 tensor(-9.6449) +swc_deu_001371 tensor(-10.7050) +swc_deu_001372 tensor(-17.2914) +swc_deu_001373 tensor(-8.3277) +swc_deu_001374 tensor(-8.7493) +swc_deu_001375 tensor(-10.4424) +swc_deu_001376 tensor(-9.6863) +swc_deu_001377 tensor(-29.2476) +swc_deu_001378 tensor(-13.3243) +swc_deu_001379 tensor(-5.6826) +swc_deu_001380 tensor(-7.5686) +swc_deu_001381 tensor(-6.6532) +swc_deu_001382 tensor(-18.7212) +swc_deu_001383 tensor(-16.0726) +swc_deu_001384 tensor(-23.4841) +swc_deu_001385 tensor(-10.5735) +swc_deu_001386 tensor(-9.3610) +swc_deu_001387 tensor(-12.9269) +swc_deu_001388 tensor(-5.8080) +swc_deu_001389 tensor(-23.0094) +swc_deu_001390 tensor(-19.8903) +swc_deu_001391 tensor(-9.2005) +swc_deu_001392 tensor(-55.5649) +swc_deu_001393 tensor(-3.4347) +swc_deu_001394 tensor(-9.7740) +swc_deu_001395 tensor(-7.1497) +swc_deu_001396 tensor(-21.6702) +swc_deu_001397 tensor(-6.9184) +swc_deu_001398 tensor(-7.4350) +swc_deu_001399 tensor(-10.9575) +swc_deu_001400 tensor(-11.4970) +swc_deu_001401 tensor(-7.0880) +swc_deu_001402 tensor(-10.2133) +swc_deu_001403 tensor(-13.2352) +swc_deu_001404 tensor(-26.4249) +swc_deu_001405 tensor(-5.7208) +swc_deu_001406 tensor(-18.7085) +swc_deu_001407 tensor(-7.5909) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..4ff31dd9284200eb20d20c5a2f5ba31043a09ae7 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/hyp.trn @@ -0,0 +1,207 @@ +D E I E R V E R L I E B T E U N G E H E A R Z O G I E R A N S C L Ä E K E S E I N E S F A T E S N I C H T B E R A C G T D E T A B (swc_deu_001201-swc_deu_001201) +D I E I N D E A N S E S T E R D T E N A L S (swc_deu_001202-swc_deu_001202) +A R K E I N G R O S E E R F O L G K (swc_deu_001203-swc_deu_001203) +G O S E N S C H E H M I C H E I N V E R B R I C K E N (swc_deu_001204-swc_deu_001204) +U R D E N A C H M E H R E R E A R L E U T R U N G S B Ü C H E V E R F F N T L I C H T (swc_deu_001205-swc_deu_001205) +V O R B E R E I T E N B I E R T E I G G E T U N G T (swc_deu_001206-swc_deu_001206) +D O M E N T E S H L I E S L I C G I N E (swc_deu_001207-swc_deu_001207) +T A U T A G V Ü R D E N T A U T V O N K Ö N I C H V E R E R C H Ü L E (swc_deu_001208-swc_deu_001208) +D A R U N D E R S I N D M A T I L D E A R S E N S I S W E C H T E R D E S K R E U L Z I S (swc_deu_001209-swc_deu_001209) +E N I N E N S T Ä T E N M E H R U N D M E H R I E R O L E D E R T R A D I Z N E L N I S H (swc_deu_001210-swc_deu_001210) +Z D E N E N W E L T L E U F I C H K E I I T (swc_deu_001211-swc_deu_001211) +R A C H R E T D S H O F E S U N D D E S A D E T Z S F Ü R D E N F R I E F E T (swc_deu_001212-swc_deu_001212) +Z E I T A N G A B E N V E R Z I C H T E T (swc_deu_001213-swc_deu_001213) +A L L A C H T Z E H N H U N D E R T A C H T Z I C H M I T O T T U O B R A M M S A U F S E I T Z (swc_deu_001214-swc_deu_001214) +M Ü L E N W E S E N S I B T E H N U N E D C H U N D Z W A N Z I (swc_deu_001215-swc_deu_001215) +A S D E R F I C H R I C H (swc_deu_001216-swc_deu_001216) +S I D E M A B S C L S S I M J A H R E N U N Z E N H N D R Z W A E U N A C H T Z I C G U N D E R N A M E R E I N E E R S T E L Ä N G E R E R E I S E A C S P A N E N (swc_deu_001217-swc_deu_001217) +V Ü R N S C H A T S O V U R G E Z E I C H N E T (swc_deu_001218-swc_deu_001218) +F E I T E N S T E I N S F L S T E N D I G E G E S C H I C H T E N U N D D I E A U G S B R G E S T A D B G E S C H I E G E D E S E L T R E N (swc_deu_001219-swc_deu_001219) +N A C H D I E S E N Z E R S T Ö H R U N G E N W U R D E D E R A S S C H W I E D E R A U F B L H N (swc_deu_001220-swc_deu_001220) +M A C H T E N E I N F L U S R E I C H E N H A N S I E A R T E N M B E I M K O M I S A R I S C H E I N G E S E T Z T E N B Ü R G E M E I S T E R M A R K E R T I H R E A U F W A H T U N (swc_deu_001221-swc_deu_001221) +A L S S E N T R A L D E S H A N D E S K O T O R (swc_deu_001222-swc_deu_001222) +S O N D E R S T E L L U N G I N E H E I L B D E R S T A D T K R F E L (swc_deu_001223-swc_deu_001223) +F I N E S I C H I N H A L O B A O K T E R I E N (swc_deu_001224-swc_deu_001224) +A U F D E R B S E I T E F I N D E Z I C H D A S E B E N F A L S V O N M E I K E L O M P O N I R T (swc_deu_001225-swc_deu_001225) +I N H A N D E A R T I S C H E Z E I T H A T E D I E Z E R K E G E S E S C H A F T K E I N E N A U S C H A G E B E N D E N E N F L S M I E R (swc_deu_001226-swc_deu_001226) +D A R S D E R C H V E W E N D U N V O N A U F T R I E B S K Ö R P A N O D E R H O L Z E I N E G E R I N G E R E M I T T L E R E T D I C H T E A L S W A S S E R H A T (swc_deu_001227-swc_deu_001227) +D R A M E I T D E S I E R U N G E N (swc_deu_001228-swc_deu_001228) +U M M S E B E N O R F Ü N W O N (swc_deu_001229-swc_deu_001229) +D E S A L B R E C H T D I E B A D E S T O C H T E R (swc_deu_001230-swc_deu_001230) +T A T B A M B A R E S C H E S T A T Z R (swc_deu_001231-swc_deu_001231) +D R S L I E T B E S O N D E R S L I E B T E (swc_deu_001232-swc_deu_001232) +A U F K U N D D E S W A H S E N D E N P O P L I K U M S I N T R E S S E S W R D E D E R A U F T R I T S O R T Ü R I E P R I M E R I S T A L E S U N G E (swc_deu_001233-swc_deu_001233) +N D F R E I D L I C H S P B I E L E (swc_deu_001234-swc_deu_001234) +D S D I E R E I D E N S T U R T Z E R L A T I E U N B E S C H A D E B E R S T A N D E N H A T E (swc_deu_001235-swc_deu_001235) +J A H R E N A S C H E N E N D Z W E I I M E N (swc_deu_001236-swc_deu_001236) +D R R A B M A L E U N D G R A B G K A P Ä L E N O D E R O L T A T E N N A C H A L T (swc_deu_001237-swc_deu_001237) +J U N E N E U Z E N H U N D R E H S U N E U N Z I G K Ö N E K T D E R R S E I N E B E I D E N J O P S (swc_deu_001238-swc_deu_001238) +I N G E P O R T I N E O P E T (swc_deu_001239-swc_deu_001239) +N E U N U N S E C H Z I G D E R M E L I E R K O N T W O L A L B U M S C H A T Z E I N (swc_deu_001240-swc_deu_001240) +T A D U C H C O M (swc_deu_001241-swc_deu_001241) +O N E H E N N I C H T D E N R O S H A N D E L S K A U F L E U T E N G E S E L S C H A F T L I C G G L E I C H G E S T E T W A R E N (swc_deu_001242-swc_deu_001242) +V O D E R N A R U N G U N D V O M K L I E M A (swc_deu_001243-swc_deu_001243) +A P O L O E I N S (swc_deu_001244-swc_deu_001244) +B R Ü Y E L E L U N D H Ö R T N A C H K E L E N (swc_deu_001245-swc_deu_001245) +T W A R I N E N K L O S T E (swc_deu_001246-swc_deu_001246) +D I V W R Z E M O F I Z E H E N K A N E W A L E N S T A N D T U N T T E U T E E I N E M S C H U N G A U S K Ö R S C H E N K A N D E W A L U N D P O L I T S C H E M K A B E R E T M I T K O M D I E L M E N T E N D A R S T E L L T U N (swc_deu_001247-swc_deu_001247) +D I E W E N S T E I U N G E S L I E D E S F Ü R T E N (swc_deu_001248-swc_deu_001248) +N A N T I T Z I E G L E R D I A R M O R D U M D E R B R N A U R U N E N (swc_deu_001249-swc_deu_001249) +I N T E R O H R I S T V E L (swc_deu_001250-swc_deu_001250) +D I E S T R Ä N G E D R F O R G E N G E R L E I T U N G W U R D E N Z W I C H E N E U N Z E H N H U N D E R T N E U N U N D Z W A N Z I G U N D N E U N Z H N H U N D E R T D R E I N F Ü N F Z I G A I C H I E R L O G E S C H E R G R A B E N (swc_deu_001251-swc_deu_001251) +I M G E G E N S A T Z (swc_deu_001252-swc_deu_001252) +V W A R B E V O N Ö R D I G E N S N L A U N D H O R D (swc_deu_001253-swc_deu_001253) +L E I F V E R A N S T A L T U M E N (swc_deu_001254-swc_deu_001254) +S U O W E R D E N H E U T E I N D E R E G E L A L E D O R T L E B E N D E N B R U N (swc_deu_001255-swc_deu_001255) +I E D A F Ü R E W Ü S C H L M E R (swc_deu_001256-swc_deu_001256) +D E S H A N S E A T E N F Ü R E (swc_deu_001257-swc_deu_001257) +H Ä B I L S A G N E S B E R N A U (swc_deu_001258-swc_deu_001258) +L E B E N S W E I S E V E R K Ö A R P E R (swc_deu_001259-swc_deu_001259) +I D E F A L T D E S V I E L E N H A M B U R G A N Z U K A T O L E S C H F R M M E N (swc_deu_001260-swc_deu_001260) +K O L T O U R E N D D E R T H E F T A U S T A U S C H E N (swc_deu_001261-swc_deu_001261) +M J A H R Z W E I T A U S E N D V E R T O N D T E (swc_deu_001262-swc_deu_001262) +D A S E D I E S E L E I T U N G S C H N L E R V O L Ä E N D T E N K Ö N E A L S D E R B A U M E I S T E R D E N K Ö N E R D O M (swc_deu_001263-swc_deu_001263) +E I E R H I N R I C H T U N G D E R B A N A U R I E N H A B E S I C L I C H T U M (swc_deu_001264-swc_deu_001264) +L U O R E I (swc_deu_001265-swc_deu_001265) +D E R Z E I T D E R B E S T I E K E N E R D E R E I F E L E I T U N G (swc_deu_001266-swc_deu_001266) +V O K U S B E S W I S E N S H A F T L I C H E N I N T E R E S S E S (swc_deu_001267-swc_deu_001267) +T E M E Z U B E G E I S T E N (swc_deu_001268-swc_deu_001268) +M E T E R U N D K O N T E D A M I T A U V O N I N E N B E R G A N G E N W E R D E N (swc_deu_001269-swc_deu_001269) +H A T K A B E R B E S S E L L I S T E D E R N Ü H (swc_deu_001270-swc_deu_001270) +D E R F R E I N E N Z I G K L O P (swc_deu_001271-swc_deu_001271) +D E N G R S L I W I E R U F D E L S T E Z U S E T Z E N (swc_deu_001272-swc_deu_001272) +W I E L A N G D I E S E K A P L A N S S T Ä L L E A U F R E C H T E R H E T E N W U R D (swc_deu_001273-swc_deu_001273) +S I W A H R E N W A S C H E I N L I C H B E R E I T Z D R E I S I G S I K U N D E N D A C H A U S P R C H T E S V E U R (swc_deu_001274-swc_deu_001274) +M E T E R G E S A M T L I N G E U N D B I S Z U Z E H N M I T E R (swc_deu_001275-swc_deu_001275) +F E I N E R I T Z E N U N S P A L T E (swc_deu_001276-swc_deu_001276) +D I N M A N V O N A U S S N D I E K E L E R H I N A B P F L I E S E N S I E T (swc_deu_001277-swc_deu_001277) +E N E I N T E R I U S A G T E B R A U N (swc_deu_001278-swc_deu_001278) +D A S F Ü N F T D E V E N G E R I U M (swc_deu_001279-swc_deu_001279) +R E S E N S I E M A N C H M A L W E I D E T I E R E I E S C H A F E (swc_deu_001280-swc_deu_001280) +S I Ö R E N D E N A R T I K E L D I E S E I N R Ü V I E U (swc_deu_001281-swc_deu_001281) +K U S E I S G E L E N T E R K O C H (swc_deu_001282-swc_deu_001282) +H A N W E N T S T I F T U N G E (swc_deu_001283-swc_deu_001283) +N E U N Z E H N H U D E R T A C H T Z I E N A L H A I N S I E A R T E N A N G E S I E N (swc_deu_001284-swc_deu_001284) +M E H R E R E R E S N A C H I M T O U N (swc_deu_001285-swc_deu_001285) +A C H S T I G E S G E R I C H T S Z U R L A N D E S W E I T E N B E L I E B E N K O L I N A R I S C H E S P E Z I L I T E T E R M Ö G E (swc_deu_001286-swc_deu_001286) +K O L L E T S C U N D E I N Z W E I T J O B A L S P A N I S C H L Ä H R E R I N H E M T N V O R L S E A N (swc_deu_001287-swc_deu_001287) +B O R E N K E I N E S W E G X S A L L Ä E G E B Ü R T I G E N (swc_deu_001288-swc_deu_001288) +I S T I E R K Ö R B E R B A U G R E F T I G (swc_deu_001289-swc_deu_001289) +A N L E S L I C H T E R N E U J A S A N D G S P R A C H R E K E (swc_deu_001290-swc_deu_001290) +M I T W I N D V O N S C R E C H I N T E N (swc_deu_001291-swc_deu_001291) +D E N G R E Ö S T E N T E L D E R B E Z I Ü R G S W E R T R E T U N G Ö R D I N E N A U S (swc_deu_001292-swc_deu_001292) +A C H T H N H U N E R E I U N D Z W E N Z I (swc_deu_001293-swc_deu_001293) +E S R O S S E N A D E L S A N G E S A M M I T E N R E I C H T U M S (swc_deu_001294-swc_deu_001294) +E S O E N I H T E L S E H S O L P O K A T Z I U N (swc_deu_001295-swc_deu_001295) +T E I L H A B E D E V R M E R O S M A N U N D J I R G E N Z (swc_deu_001296-swc_deu_001296) +N E R M T E R F R A U N E N T E R A U T I N S L T (swc_deu_001297-swc_deu_001297) +A U R D I E O Ö S T A N D E U T C H E R H Ö R B U C H F V E R L A G M I T S I T Z I N M Ü N C H E N (swc_deu_001298-swc_deu_001298) +F A R P I K T M E N T E U N D C H E M I C H E V O R P R O T R U C K T E H E R S T E L T (swc_deu_001299-swc_deu_001299) +A R P L I C H E N P R E U S S I S C H E N F R E I H E R E N S T A N D I N D E R Z A L A N S C H L U S S F R A G E E N T S C H I E D E N G E G E N D E N S I N A R A U F D I E S E I T E B I S M A R G S G E S T L L T (swc_deu_001300-swc_deu_001300) +W E N D I W Ä L E N V O N S E L S E R F O R G W E L E N U N D O F F E N Z U O T A G E L I E G E N (swc_deu_001301-swc_deu_001301) +D S V O N G N N A C H B A R B A U T R U B B A R E I T S B E G O N W U O R T (swc_deu_001302-swc_deu_001302) +W E R D E N P R E R G E N D E E L E M E N T E D E H A N S I E A T E N T U M S T Z U S A M E N G E F A S T (swc_deu_001303-swc_deu_001303) +D E S S L I E T Z W U R E A L S F O L C S T L I E T A N G E S E I E N (swc_deu_001304-swc_deu_001304) +D E R Z O R N D E M H A U S V E R L A G S U R U B G E H Ö R T (swc_deu_001305-swc_deu_001305) +V Ü R D E Ö N F T I G E N B O R T B Ü C H E R E N T W I C K E T E D I P A P I E R F E R P R I (swc_deu_001306-swc_deu_001306) +H A M B R E W U O K S (swc_deu_001307-swc_deu_001307) +R D I W A S I E A R D L I G E N L A N D Z I T Z E P E T R I B E N A U F A N D S E I S B E M B A U (swc_deu_001308-swc_deu_001308) +J A H R Z W E I T A U S E N Z W Ö L F I N D E N B E L I N E R K L U P S O S E C H S O N D R E I S I G V E L L I G T (swc_deu_001309-swc_deu_001309) +S E C H E H N U N E N D F Ü N F Z I G H A I T S B Ü N D N I S D (swc_deu_001310-swc_deu_001310) +D A S P R O L E H M B E I E S E M P E R A D O C H S O N I S T (swc_deu_001311-swc_deu_001311) +A R M E N W E S E N T E T I C A M A L I E S I E V E I K E I N G N (swc_deu_001312-swc_deu_001312) +N I H T E I N M A L E I N E A N N S A T Z W E I S E N T U S O C H U N G Z U I E R E M V E R H A L T E N I N E R Z E I T D E S N A T Z U O N A S O T Z E L I S M U S (swc_deu_001313-swc_deu_001313) +L I Z E N S V Ü E R F R I E D O G O M E N T A T I O N (swc_deu_001314-swc_deu_001314) +D I M A C H Z I H N T N I E R H U N D E R D I E G A R T E N H E U S E R V O R D E N T O R E N (swc_deu_001315-swc_deu_001315) +G A N S I M S T I E L D E R Z E I T (swc_deu_001316-swc_deu_001316) +B E R B R Ü Ö L U N D H Ü Ö R T E R R E I C H E D E L E I T U N G S C L I S L I C H K Ö N (swc_deu_001317-swc_deu_001317) +A U S Z E I C H N U M E N F R E M D E R H E R E N (swc_deu_001318-swc_deu_001318) +D I S C H E F T E L L E R E I A U F Z U G E B E N (swc_deu_001319-swc_deu_001319) +D A Z U T Z E H E D I E G E G N U N G M I T V E R L E T Z T E N T I E R E N (swc_deu_001320-swc_deu_001320) +J E N E N I S C H S T I F T (swc_deu_001321-swc_deu_001321) +W E S T L I C H V O N K Ö L E N (swc_deu_001322-swc_deu_001322) +D I E S T Ä N D I G I N B E R I E B W A R E N (swc_deu_001323-swc_deu_001323) +D I V O M B A R B I I R C H A S I E R T W E R D E (swc_deu_001324-swc_deu_001324) +E R S C H E N O C H E N W E I T E R E R A U F S E T Z V O N K R I S T I E R N M E I E L T (swc_deu_001325-swc_deu_001325) +W A L L S E B S T E X T R E M E R E I C H T U M K E I N E S W E H T E N U N M I T E L B E R E N Z U G A N (swc_deu_001326-swc_deu_001326) +G E B T E U C H N I C H E L B E R A U F (swc_deu_001327-swc_deu_001327) +A H A T D I E S E N P R A U C N E U N Z E H N H U N D E R W E I U N D F Ü N F Z I G G E N Ü B (swc_deu_001328-swc_deu_001328) +W O D E L E I T U N G Ü B E D I E A L T E H Ü R T E R L E I T U N G E F Ü R T W U R D E (swc_deu_001329-swc_deu_001329) +I N E B L I E B T E K Ö A L S C H R O C K T R O P E A S D E M Ö N E R U M N A N D D I E Ö N A (swc_deu_001330-swc_deu_001330) +G E W A R D E N S E I U N D A L B R E C H T S I C H (swc_deu_001331-swc_deu_001331) +D R T A G E S B E D A F E I N E S W A C H S E N D E N A N W I T E M I N A R (swc_deu_001332-swc_deu_001332) +S I B S I N U L E R Z I E H N O B E R A L T E R (swc_deu_001333-swc_deu_001333) +W E I T E R H I N L I S I G N A C R H W E I S E N (swc_deu_001334-swc_deu_001334) +S U M G R Ü N D U N G S T D R T U M K O N T E A M B E R E I T Z (swc_deu_001335-swc_deu_001335) +K E I N L E C S C H L A G E N M Ö G L I C H N A C H F T E I L E (swc_deu_001336-swc_deu_001336) +I R T I A T O L I S C H E K Ö R S C H E S A N G P E T E R A N D E R S T E L L E D E A L T (swc_deu_001337-swc_deu_001337) +E R F A K N A C U N G D E S R O T W E I Z E N S T R A R T A B E S C H N B A L T D I E R T O F E L E I S E R S A T (swc_deu_001338-swc_deu_001338) +K N N E M I T D I E S E N N A C H K O M I N Z E U G E N (swc_deu_001339-swc_deu_001339) +A L E N E U N E N F O L G E N D E R H Ö R S T I E R E I E R (swc_deu_001340-swc_deu_001340) +S C H I B P S M I T B E H A T E N S O R (swc_deu_001341-swc_deu_001341) +K O L G E A N R E R S B O C H N E V E R Z I G H T E T E A U F E R N I P R S Ö N I C H E B E W E R T U N G (swc_deu_001342-swc_deu_001342) +B W E N I G E R I N T R Ü S T E T (swc_deu_001343-swc_deu_001343) +W E I T E R H I N V E R S O R G D E D E L E I T U N G T E R M E N (swc_deu_001344-swc_deu_001344) +W A R T E T E N D A F Ü E R A B E M I T E I N I G (swc_deu_001345-swc_deu_001345) +L E D I K L I C H A N T U N D F N K L E I N M U N I E R T E I N S E I N E R E T Z E N S O N D E (swc_deu_001346-swc_deu_001346) +E M I A H B E R N E U Z E N E R T F Ü M (swc_deu_001347-swc_deu_001347) +E R S E D E M F A R T F A L D E S B Ü R G E R E C H T Z U N D D E R I N F Ü H R N G D E F R E I T Z Y Ü G I C K E I T I M Z W A N S I G S N E R H N D E R T W A N D E T T E S I C H D I E S E A N S C H A U N G A N S E R T S W E I S E N D A R H I N (swc_deu_001348-swc_deu_001348) +D E S Z W I S T B A C R E S B E R R E I N B A C H E I N E B O G E N B R Ü C K E V O (swc_deu_001349-swc_deu_001349) +A C H Z I N U L E S E X S U N D R E I S I G B U R D E D E H M B U R G (swc_deu_001350-swc_deu_001350) +A M (swc_deu_001351-swc_deu_001351) +U F G R U N D D E R K O N T E N E N T A L S B E R E A C H T Z E H N H U N D E R E L F B A N O T T (swc_deu_001352-swc_deu_001352) +W E I T E R E S M A L M U S T E N D E N U N D B L E I T B R A N D I E V W A R B U N G F Ü R D A S B U C H S E Ä B S T W A N E M N (swc_deu_001353-swc_deu_001353) +D I E N A H R I C H V M S E G K T E R B Ü G E L I C H D E M O G R A T I C H E N F E P R A R E V O L U T Z I O N V E N A C H T Z E H N H U N D E R T A C H T U N D V E R Z I C H E N F A N K R E I C H W U R D E N H M B U R G M I T I O B E L A U F G E O M E (swc_deu_001354-swc_deu_001354) +U B L I E B T Z W E I J A R E O N E N T E R B R E C H N (swc_deu_001355-swc_deu_001355) +Z E A L R E I C E N G A S T S P I L U N T E R W E G S (swc_deu_001356-swc_deu_001356) +C R A N T I E T E T G E N Ü T E N (swc_deu_001357-swc_deu_001357) +I N B E R O K A R A U S T A T E (swc_deu_001358-swc_deu_001358) +D A S E R I C H T V O M B E I V A R G E N S A N E M O T O R D E S A U S I N D I E Z U D I E S E R Z E I T N E U E N S T E H N D E N A B E I T A S I E D L U N E N Z U (swc_deu_001359-swc_deu_001359) +D I M I Z A M D I H E R R E C H E N S T O B E (swc_deu_001360-swc_deu_001360) +K E I S E R F E R D I N A N T (swc_deu_001361-swc_deu_001361) +V O M F E R N E R E S C H S U H E R F E R N Z S G S A V E R B U G N E R I N D E M F E R N S E F I L E N D A S I E W I G E L I E T (swc_deu_001362-swc_deu_001362) +W O R E I N S E I N E M B E S T E N Z E I T E I E R (swc_deu_001363-swc_deu_001363) +S E Ö R E N D E N A R T I K E L F I S C H E N D C I E B S (swc_deu_001364-swc_deu_001364) +U N D T E F A R M O W I E (swc_deu_001365-swc_deu_001365) +R E S E P T I U O N D E H E C H S E N D M A T I G V O N K R I S T A (swc_deu_001366-swc_deu_001366) +D I G E S A M T E R A N L A G E W A R B E S E T V E R Z W E I H U N D E R S E C H Z I G N A C H K R I S T U S I N B E D R I E B (swc_deu_001367-swc_deu_001367) +D E R E S D E F A S T F U T D L I F E R S O R I D E S W A G E B O C H (swc_deu_001368-swc_deu_001368) +E I N E M K A B E L B A U N E N (swc_deu_001369-swc_deu_001369) +M O R L E D U N G M I T D E G E F A H R V E R B U N D E N G E W E S (swc_deu_001370-swc_deu_001370) +D E E L T I S T E N P F I E R D E R E N E N A S E H A B (swc_deu_001371-swc_deu_001371) +S O N E N A U C H D E R N A T Z U O N A H L S U O Z E R D I S T I S C H E N K U N Z T A U F F A S S U N G E R E C H T W E R D E N (swc_deu_001372-swc_deu_001372) +D I E W L L Z I C H D E S H A N S E A R T E N (swc_deu_001373-swc_deu_001373) +A U C N A C H K O M M E N E I N T N I C H T B E K A N T (swc_deu_001374-swc_deu_001374) +E R E N T E X S T E D E N E I N D R U G K T Z E V E R M I T T E (swc_deu_001375-swc_deu_001375) +V E R D I N S T M D A S K Ö N A R L I E T V E R L I E N (swc_deu_001376-swc_deu_001376) +B W O L L H O F M E I N V O N H O F M E I N Z W A L L D A U S W E R G G R O S E N E I N F L U S A S P Ä E R E R D I C H T E A U S Ü B T (swc_deu_001377-swc_deu_001377) +M M S O E R M N S T A L S T A S O B E R H A U P F V E N (swc_deu_001378-swc_deu_001378) +E F R E I E D O K O M E T A T I O N (swc_deu_001379-swc_deu_001379) +G E S T A L T U N M B E S K A V E R S W I E D E R S P I E G E L T (swc_deu_001380-swc_deu_001380) +D E R E S M T E R A U F W A N D W I T A U F (swc_deu_001381-swc_deu_001381) +O B G L E I C H A M B O R K D I E S E M A N G E H Ö R T E U N D E I N E N O B I L I T I E R U N G D U C H E N K E I S E R D A M I T K E I N E D R C (swc_deu_001382-swc_deu_001382) +D A S T U R C H D E N S I C G A U S W E I T E N T E N W E L T H A N D E L A R B E I T U N D W O H L S T A N D V E R S P R A C H (swc_deu_001383-swc_deu_001383) +F Ü Ö R D I E Z E I T T M I T E D E S N E U N Z E H N T E J A H N D E R B E K L A G D E D E E R C H I E T E K T M A T I N H A L L E (swc_deu_001384-swc_deu_001384) +E I T B O N D E S K A N Z E R H E M U T S C H M I T T L E N T E (swc_deu_001385-swc_deu_001385) +D I N A M E N G O D E F R E I I M S T A T Z H A N D B O C H T Z (swc_deu_001386-swc_deu_001386) +W E N A U C H E E N E R G E W I S E N L E T A G I E (swc_deu_001387-swc_deu_001387) +K A L K O L I E R E B A E G (swc_deu_001388-swc_deu_001388) +A N G E F N G D E Z S A H U N E R V F N M Z I S C Ü U L E R E I N E N D I E L I G I E I T E N (swc_deu_001389-swc_deu_001389) +V I E L M E N S C H E N S A N E N R I S L I A L S N A H R U N G S K O N G R E N T E N U N D A L S P O T E N E L G E F A (swc_deu_001390-swc_deu_001390) +D E N U F T R I T V E K Ö R Z E (swc_deu_001391-swc_deu_001391) +M I D M S T A N V O M D R E I T Z H N N I U L I E Z W E I T A U S E N S W E R F D E R I N H E R S T E T U N D E R D E L I Z E N S K R E R T Z U C O M O N S E I Z R E W U S C H E N S C H E R E L E I T D R E I T P U N K N L A N P O R T E T U N D U N T E D E (swc_deu_001392-swc_deu_001392) +E I N E K L E I N E R E B O G E N B R Ü C K E (swc_deu_001393-swc_deu_001393) +S I C H N U N V E R S E N W E I T R K O M (swc_deu_001394-swc_deu_001394) +A U S D E M G E M E Ä L D E Z U E N T F E R E N E N (swc_deu_001395-swc_deu_001395) +I S D C H Ö A R O P Ä S C H E R I C H T L I N E N E U N Z I C G H V I E R N E R T E X S N E U N Z S I C H E W I (swc_deu_001396-swc_deu_001396) +A L E U M S E L T A U F (swc_deu_001397-swc_deu_001397) +N D S I E S E I A U C H W I E E I N E F I R S T E N (swc_deu_001398-swc_deu_001398) +N E U N Z E H N H U N D E 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U M E N T E S C H L I E S S L I C H I N (swc_deu_001207-swc_deu_001207) +T R A U E R T A G F Ü R D E N T O D V O N K Ö N I G F R I E D R I C H W I L H E L M (swc_deu_001208-swc_deu_001208) +D A R U N T E R S I N D M A T I L D E A S E N S I S W Ä C H T E R D E S K R E U Z E S (swc_deu_001209-swc_deu_001209) +I N N E N S T Ä D T E N M E H R U N D M E H R D I E R O L L E D E R T R A D I T I O N E L L E N F I S H (swc_deu_001210-swc_deu_001210) +Z U D E N E N W E L T L Ä U F I G K E I T (swc_deu_001211-swc_deu_001211) +R A C H E D E S H O F E S U N D D E S A D E L S F Ü R D E N F R E V E L (swc_deu_001212-swc_deu_001212) +Z E I T A N G A B E N V E R Z I C H T E T E (swc_deu_001213-swc_deu_001213) +A L S A C H T Z E H N H U N D E R T A C H T Z I G M I T O T T O B R A H M S A U F S A T Z (swc_deu_001214-swc_deu_001214) +E I N T A U S E N D S I E B E N H U N D E R T A C H T U N D Z W A N Z I G – (swc_deu_001215-swc_deu_001215) +D A S S D E R F I S C H F R I S C H (swc_deu_001216-swc_deu_001216) +S E I N E M A B S C H L U S S I M J A H R E N E U N Z E H N H U N D E R T Z W E I U N D A C H T Z I G U N T E R N A H M E R E I N E E R S T E L Ä N G E R E R E I S E N A C H S P A N I E N (swc_deu_001217-swc_deu_001217) +V O N C H A S Ô T V O R G E Z E I C H N E T (swc_deu_001218-swc_deu_001218) +F A L C K E N S T E I N S V O L L S T Ä N D I G E G E S C H I C H T E N U N D D I E A U G S B U R G E R S T A D T G E S C H I C H T E D E S Ä L T E R E N (swc_deu_001219-swc_deu_001219) +N A C H D I E S E N Z E R S T Ö R U N G E N W U R D E D I E R A S C H W I E D E R A U F B L Ü H E N D E (swc_deu_001220-swc_deu_001220) +M A C H T E N E I N F L U S S R E I C H E N H A N S E A T E N B E I M K O M M I S S A R I S C H E I N G E S E T Z T E N B Ü R G E R M E I S T E R M A R K E R T I H R E A U F W A R T U N G (swc_deu_001221-swc_deu_001221) +A L S Z E N T R A L E S H A N D E L S K O N T O R (swc_deu_001222-swc_deu_001222) +S O N D E R S T E L L U N G I N N E R H A L B D E R S T A D T K R E F E L D (swc_deu_001223-swc_deu_001223) +F I N D E T S I C H I N H A L O B A K T E R I E N (swc_deu_001224-swc_deu_001224) +A U F D E R B S E I T E F I N D E T S I C H D A S E B E N F A L L S V O N M I C H A E L K O M P O N I E R T E (swc_deu_001225-swc_deu_001225) +I N H A N S E A T I S C H E R Z E I T H A T T E D I E Z I R K E L G E S E L L S C H A F T K E I N E N A U S S C H L A G G E B E N D E N E I N F L U S S M E H R (swc_deu_001226-swc_deu_001226) +D A E S D U R C H V E R W E N D U N G V O N A U F T R I E B S K Ö R P E R N O D E R H O L Z E I N E G E R I N G E R E M I T T L E R E D I C H T E A L S W A S S E R H A T (swc_deu_001227-swc_deu_001227) +D R A M A T I S I E R U N G E N (swc_deu_001228-swc_deu_001228) +U M 7 5 5 (swc_deu_001229-swc_deu_001229) +D A S S A L B R E C H T D I E B A D E R S T O C H T E R (swc_deu_001230-swc_deu_001230) +T H A T B A R B A R I S C H E R S T A A T S R A I S O N (swc_deu_001231-swc_deu_001231) +D E R D A S L I E D B E S O N D E R S L I E B T E (swc_deu_001232-swc_deu_001232) +A U F G R U N D D E S W A C H S E N D E N P U B L I K U M S I N T E R E S S E S W U R D E D E R A U F T R I T T S O R T F Ü R D I E P R I M A V I S T A L E S U N G E N (swc_deu_001233-swc_deu_001233) +U N D F R E I L I C H T S P I E L E (swc_deu_001234-swc_deu_001234) +D A S S D I E D R E I D E N S T U R Z R E L A T I V U N B E S C H A D E T Ü B E R S T A N D E N H A T T E N (swc_deu_001235-swc_deu_001235) +J A H R E N E R S C H I E N E N Z W E I I M M E R (swc_deu_001236-swc_deu_001236) +G R A B M A L E U N D G R A B K A P E L L E N O D E R W O H L T A T E N N A C H H A L T I G (swc_deu_001237-swc_deu_001237) +J U N I N E U N Z E H N H U N D E R T S E C H S U N D N E U N Z I G K Ü N D I G T E E R S E I N E B E I D E N J O B S (swc_deu_001238-swc_deu_001238) +E I N G P R O T E I N G E K O P P E L T (swc_deu_001239-swc_deu_001239) +N E U N U N D S E C H Z I G D E R M E D I A C O N T R O L A L B U M C H A R T S E I N (swc_deu_001240-swc_deu_001240) +D A D U R C H K O M M T (swc_deu_001241-swc_deu_001241) +O H N E H I N N I C H T D E N G R O S S H A N D E L S K A U F L E U T E N G E S E L L S C H A F T L I C H G L E I C H G E S T E L L T W A R E N (swc_deu_001242-swc_deu_001242) +V O N D E R N A H R U N G U N D V O M K L I M A (swc_deu_001243-swc_deu_001243) +A P O L L O E I N S (swc_deu_001244-swc_deu_001244) +B R Ü H L U N D H Ü R T H N A C H K Ö L N (swc_deu_001245-swc_deu_001245) +E T W A I N E I N K L O S T E R (swc_deu_001246-swc_deu_001246) +Z U M O F F I Z I E L L E N K A R N E V A L E N T S T A N D U N D H E U T E E I N E M I S C H U N G A U S K Ö L S C H E M K A R N E V A L U N D P O L I T I S C H E M K A B A R E T T M I T C O M E D Y E L E M E N T E N D A R S T E L L T U N D (swc_deu_001247-swc_deu_001247) +D I E Z U R E N T S T E H U N G D E S L I E D E S F Ü H R T E N (swc_deu_001248-swc_deu_001248) +N A N N T E Z I E G L E R D I E E R M O R D U N G D E R B E R N A U E R I N (swc_deu_001249-swc_deu_001249) +W I N T E R R U H E I S T V O R A L L E M (swc_deu_001250-swc_deu_001250) +D I E S T R Ä N G E D E R V O R G Ä N G E R L E I T U N G W U R D E N Z W I S C H E N N E U N Z E H N H U N D E R T N E U N U N D Z W A N Z I G U N D N E U N Z E H N H U N D E R T D R E I U N D F Ü N F Z I G A R C H Ä O L O G I S C H E R G R A B E N (swc_deu_001251-swc_deu_001251) +I M G E G E N S A T Z (swc_deu_001252-swc_deu_001252) +F A R B E N V O N U E R D I N G E N S I N D B L A U U N D R O T (swc_deu_001253-swc_deu_001253) +L I V E V E R A N S T A L T U N G E N (swc_deu_001254-swc_deu_001254) +S O W E R D E N H E U T E I N D E R R E G E L A L L E D O R T L E B E N D E N B R A U N B Ä R E N (swc_deu_001255-swc_deu_001255) +L I E D E R F Ü R R E V U E F I L M E (swc_deu_001256-swc_deu_001256) +D E S H A N S E A T E N F Ü H R E N (swc_deu_001257-swc_deu_001257) +H E B B E L S A G N E S B E R N A U E R (swc_deu_001258-swc_deu_001258) +L E B E N S W E I S E V E R K Ö R P E R N (swc_deu_001259-swc_deu_001259) +W I E D E R F A L L D E S V I E L E N H A M B U R G E R N Z U K A T H O L I S C H F R O M M E N (swc_deu_001260-swc_deu_001260) +K U L T U R U N D W I R T S C H A F T A U S T A U S C H E N (swc_deu_001261-swc_deu_001261) +J A H R Z W E I T A U S E N D V E R T O N T E (swc_deu_001262-swc_deu_001262) +D A S S E R D I E S E L E I T U N G S C H N E L L E R V O L L E N D E N K Ö N N E A L S D E R B A U M E I S T E R D E N K Ö L N E R D O M (swc_deu_001263-swc_deu_001263) +H I N R I C H T U N G D E R B E R N A U E R I N H A B E E S S I C H S C H L I C H T U M (swc_deu_001264-swc_deu_001264) +L U D W I G (swc_deu_001265-swc_deu_001265) +D E R Z E I T D E R B E S T E K E N N E R D E R E I F E L L E I T U N G (swc_deu_001266-swc_deu_001266) +F O K U S D E S W I S S E N S C H A F T L I C H E N I N T E R E S S E S (swc_deu_001267-swc_deu_001267) +T H E M A Z U B E G E I S T E R N (swc_deu_001268-swc_deu_001268) +M E T E R U N D K O N N T E D A M I T A U C H V O N I N N E N B E G A N G E N W E R D E N (swc_deu_001269-swc_deu_001269) +H A R D C O V E R B E S T S E L L E R L I S T E D E R N E W (swc_deu_001270-swc_deu_001270) +D E R F R E I E N E N Z Y K L O P Ä D I E (swc_deu_001271-swc_deu_001271) +D E N G R I Z Z L Y W I E D E R A U F D I E L I S T E Z U S E T Z E N (swc_deu_001272-swc_deu_001272) +L A N G D I E S E K A P L A N S S T E L L E A U F R E C H T E R H A L T E N W U R D E (swc_deu_001273-swc_deu_001273) +S I E W A R E N W A H R S C H E I N L I C H B E R E I T S D R E I S S I G S E K U N D E N N A C H A U S B R U C H D E S F E U E R S (swc_deu_001274-swc_deu_001274) +M E T E R N G E S A M T L Ä N G E U N D B I S Z U Z E H N M E T E R N (swc_deu_001275-swc_deu_001275) +F E I N E R I T Z E N U N D S P A L T E N (swc_deu_001276-swc_deu_001276) +D E N M A N V O N A U S S E N D I E K E H L E H I N A B F L I E S S E N S I E H T (swc_deu_001277-swc_deu_001277) +E I N E M I N T E R V I E W S A G T E B R O W N (swc_deu_001278-swc_deu_001278) +D A S F Ü N F T E E V A N G E L I U M (swc_deu_001279-swc_deu_001279) +R E I S S E N S I E M A N C H M A L W E I D E T I E R E W I E S C H A F E (swc_deu_001280-swc_deu_001280) +S I E H Ö R E N D E N A R T I K E L D E S I G N R E V I E W (swc_deu_001281-swc_deu_001281) +C H A K U Z A I S T G E L E R N T E R K O C H (swc_deu_001282-swc_deu_001282) +H A N S W E N D T S T I F T U N G (swc_deu_001283-swc_deu_001283) +N E U N Z E H N H U N D E R T A C H T Z E H N A L S H A N S E A T E N A N G E S E H E N (swc_deu_001284-swc_deu_001284) +M E H R E R E E S N A C H I H M T H U N (swc_deu_001285-swc_deu_001285) +A U F S T I E G D E S G E R I C H T S Z U R L A N D E S W E I T B E L I E B T E N K U L I N A R I S C H E N S P E Z I A L I T Ä T E R M Ö G L I C H T E (swc_deu_001286-swc_deu_001286) +C O L L E G E U N D E I N E N Z W E I T J O B A L S S P A N I S C H L E H R E R I N H A M P T O N F A L L S A N (swc_deu_001287-swc_deu_001287) +W U R D E N K E I N E S W E G S A L L E G E B Ü R T I G E N (swc_deu_001288-swc_deu_001288) +I S T I H R K Ö R P E R B A U K R Ä F T I G (swc_deu_001289-swc_deu_001289) +A N L Ä S S L I C H D E R N E U J A H R E S A N S P R A C H E K I M (swc_deu_001290-swc_deu_001290) +M I T W I N D V O N S C H R Ä G H I N T E N (swc_deu_001291-swc_deu_001291) +D E N G R Ö S S T E N T E I L D E R B E Z I R K S V E R T R E T U N G U E R D I N G E N A U S (swc_deu_001292-swc_deu_001292) +A C H T Z E H N H U N D E R T E I N U N D Z W A N Z I G (swc_deu_001293-swc_deu_001293) +D E S G R O S S E N A D E L S A N G E S A M M E L T E N R E I C H T U M S (swc_deu_001294-swc_deu_001294) +S O L L T E N N I C H T A L S S E X U E L L E P R O V O K A T I O N (swc_deu_001295-swc_deu_001295) +T E I L H A B E R D E R F I R M A G O S S M A N N U N D J Ü R G E N S (swc_deu_001296-swc_deu_001296) +D E R K R A U T I N S E L B I L D E T S I E D I E G E M E I N D E (swc_deu_001297-swc_deu_001297) +A U D I O I S T E I N D E U T S C H E R H Ö R B U C H V E R L A G M I T S I T Z I N M Ü N C H E N (swc_deu_001298-swc_deu_001298) +F A R B P I G M E N T E U N D C H E M I S C H E V O R P R O D U K T E H E R S T E L L T (swc_deu_001299-swc_deu_001299) +E R B L I C H E N P R E U S S I S C H E N F R E I H E R R E N S T A N D I N D E R Z O L L A N S C H L U S S F R A G E E N T S C H I E D E N G E G E N D E N S E N A T A U F D I E S E I T E B I S M A R C K S G E S T E L L T (swc_deu_001300-swc_deu_001300) +W E N N D I E Q U E L L E N V O N S E L B S T H E R V O R Q U E L L E N U N D O F F E N Z U T A G E L I E G E N (swc_deu_001301-swc_deu_001301) +D A S V O M N A C H B A R B A U T R U P P B E R E I T S B E G O N N E N W U R D E (swc_deu_001302-swc_deu_001302) +W E R D E N P R Ä G E N D E E L E M E N T E D E S H A N S E A T E N T U M S Z U S A M M E N G E F A S S T (swc_deu_001303-swc_deu_001303) +D A S L I E D W U R D E A L S V O L K S L I E D A N G E S E H E N (swc_deu_001304-swc_deu_001304) +D E R Z U R R A N D O M H O U S E V E R L A G S G R U P P E G E H Ö R T (swc_deu_001305-swc_deu_001305) +F Ü R D I E K Ü N F T I G E N B O R D B Ü C H E R E N T W I C K E L T E D I E P A P I E R F A B R I K (swc_deu_001306-swc_deu_001306) +H A M B U R G W U C H S (swc_deu_001307-swc_deu_001307) +F Ü R D I E Q U A S I A D L I G E N L A N D S I T Z E B E T R I E B E N E A U F W A N D – S E I E S B E I M B A U (swc_deu_001308-swc_deu_001308) +J A H R Z W E I T A U S E N D Z W Ö L F I N D E N B E R L I N E R C L U B S O S E C H S U N D D R E I S S I G V E R L E G T (swc_deu_001309-swc_deu_001309) +S E C H Z E H N H U N D E R T F Ü N F Z I G A L S B Ü N D N I S D I E (swc_deu_001310-swc_deu_001310) +P R O B L E M B E I D I E S E M P A R A D O X O N I S T (swc_deu_001311-swc_deu_001311) +A R M E N W E S E N T Ä T I G A M A L I E S I E V E K I N G (swc_deu_001312-swc_deu_001312) +N I C H T E I N M A L E I N E A N S A T Z W E I S E U N T E R S U C H U N G Z U I H R E M V E R H A L T E N I N D E R Z E I T D E S N A T I O N A L S O Z I A L I S M U S (swc_deu_001313-swc_deu_001313) +L I Z E N Z F Ü R F R E I E D O K U M E N T A T I O N (swc_deu_001314-swc_deu_001314) +I M A C H T Z E H N T E J A H R H U N D E R T D I E G A R T E N H Ä U S E R V O R D E N T O R E N (swc_deu_001315-swc_deu_001315) +G A N Z I M S T I L D E R Z E I T (swc_deu_001316-swc_deu_001316) +Ü B E R B R Ü H L U N D H Ü R T H E R R E I C H T E D I E L E I T U N G S C H L I E S S L I C H K Ö L N (swc_deu_001317-swc_deu_001317) +A U S Z E I C H N U N G E N F R E M D E R H E R R E N (swc_deu_001318-swc_deu_001318) +D I E S C H R I F T S T E L L E R E I A U F Z U G E B E N (swc_deu_001319-swc_deu_001319) +Z Ä H L E N D I E B E G E G N U N G M I T V E R L E T Z T E N T I E R E N (swc_deu_001320-swc_deu_001320) +J E N I S C H S T I F T (swc_deu_001321-swc_deu_001321) +W E S T L I C H V O N K Ö L N (swc_deu_001322-swc_deu_001322) +D I E S T Ä N D I G I N B E T R I E B W A R E N (swc_deu_001323-swc_deu_001323) +D I E V O M B A R B I E R R A S I E R T W E R D E N (swc_deu_001324-swc_deu_001324) +E R S C H I E N N O C H E I N W E I T E R E R A U F S A T Z V O N C H R I S T I A N M E Y E R (swc_deu_001325-swc_deu_001325) +W E I L S E L B S T E X T R E M E R R E I C H T U M K E I N E S W E G S D E N U N M I T T E L B A R E N Z U G A N G (swc_deu_001326-swc_deu_001326) +G E B T E U C H N I C H T S E L B E R A U F (swc_deu_001327-swc_deu_001327) +H A T D I E S E N B R A U C H N E U N Z E H N H U N D E R T Z W E I U N D F Ü N F Z I G G E G E N Ü B E R (swc_deu_001328-swc_deu_001328) +W O D I E L E I T U N G Ü B E R D I E A L T E H Ü R T H E R L E I T U N G G E F Ü H R T W U R D E (swc_deu_001329-swc_deu_001329) +E I N E B E L I E B T E K Ö L S C H R O C K T R U P P E A U S D E M K Ö L N E R U M L A N D D I E H Ö H N E R (swc_deu_001330-swc_deu_001330) +G E W O R D E N S E I U N D A L B R E C H T S I C H (swc_deu_001331-swc_deu_001331) +D E R T A G E S B E D A R F E I N E S E R W A C H S E N E N A N V I T A M I N A (swc_deu_001332-swc_deu_001332) +S I E B Z E H N H U N D E R T Z E H N O B E R A L T E R (swc_deu_001333-swc_deu_001333) +W E I T E R H I N L I E S S S I C H N A C H W E I S E N (swc_deu_001334-swc_deu_001334) +Z U M G R Ü N D U N G S D A T U M K O N N T E M A N B E R E I T S (swc_deu_001335-swc_deu_001335) +K E I N L E C K S C H L A G E N M Ö G L I C H N A C H T E I L E (swc_deu_001336-swc_deu_001336) +W I R D D I E K A T H O L I S C H E K I R C H E S T Ü C K P E T E R A N D E R S T E L L E D E R A L T E N (swc_deu_001337-swc_deu_001337) +D E R V E R K N A P P U N G D E S B R O T W E I Z E N S T R A T A B E R S C H O N B A L D D I E K A R T O F F E L A L S E R S A T Z (swc_deu_001338-swc_deu_001338) +K Ö N N E N M I T D I E S E N N A C H K O M M E N Z E U G E N (swc_deu_001339-swc_deu_001339) +A L L E N E U E N F O L G E N D E R H Ö R S P I E L R E I H E (swc_deu_001340-swc_deu_001340) +C H I P S M I T B R A T E N S O S S E (swc_deu_001341-swc_deu_001341) +K O L L E G E A N D R E A S B U C H N E R V E R Z I C H T E T E A U F E I N E P E R S Ö N L I C H E B E W E R T U N G (swc_deu_001342-swc_deu_001342) +W E N I G E R E N T R Ü S T E T (swc_deu_001343-swc_deu_001343) +W E I T E R H I N V E R S O R G T E D I E L E I T U N G T H E R M E N (swc_deu_001344-swc_deu_001344) +W A R T E T E N D A F Ü R A B E R M I T E I N I G E N (swc_deu_001345-swc_deu_001345) +L E D I G L I C H A N T O N V O N K L E I N M O N I E R T E I N S E I N E R R E Z E N S I O N D E R (swc_deu_001346-swc_deu_001346) +I M J A H R E N E U N Z E H N H U N D E R T F Ü N F (swc_deu_001347-swc_deu_001347) +E R S T M I T D E M F O R T F A L L D E S B Ü R G E R R E C H T S U N D D E R E I N F Ü H R U N G D E R F R E I Z Ü G I G K E I T I M Z W A N Z I G S T E J A H R H U N D E R T W A N D E L T E S I C H D I E S E A N S C H A U U N G A N S A T Z W E I S E D A H I N (swc_deu_001348-swc_deu_001348) +D E S S W I S T B A C H E S B E I R H E I N B A C H E I N E B O G E N B R Ü C K E V O N (swc_deu_001349-swc_deu_001349) +A C H T Z E H N H U N D E R T S E C H S U N D D R E I S S I G W U R D E D E R H A M B U R G E R (swc_deu_001350-swc_deu_001350) +A M K A R N E V A L S S O N N T A G (swc_deu_001351-swc_deu_001351) +A U F G R U N D D E R K O N T I N E N T A L S P E R R E A C H T Z E H N H U N D E R T E L F B A N K R O T T (swc_deu_001352-swc_deu_001352) +W E I T E R E S M A L M U S S T E N D A N U N D B L Y T H E B R O W N D I E W E R B U N G F Ü R D A S B U C H S E L B S T Ü B E R N E H M E N (swc_deu_001353-swc_deu_001353) +D I E N A C H R I C H T V O M S I E G D E R B Ü R G E R L I C H D E M O K R A T I S C H E N F E B R U A R R E V O L U T I O N V O N A C H T Z E H N H U N D E R T A C H T U N D V I E R Z I G I N F R A N K R E I C H W U R D E I N H A M B U R G M I T J U B E L A U F G E N O M M E N (swc_deu_001354-swc_deu_001354) +Z W E I J A H R E O H N E U N T E R B R E C H U N G (swc_deu_001355-swc_deu_001355) +Z A H L R E I C H E N G A S T S P I E L E N U N T E R W E G S (swc_deu_001356-swc_deu_001356) +Q U A N T I T Ä T G E N Ü G T E N (swc_deu_001357-swc_deu_001357) +B A R O C K E R A U S S T A T T U N G (swc_deu_001358-swc_deu_001358) +D A S G E R I C H T V O M B E I W A G E N S E I N E S M O T O R R A D E S A U S I N D I E Z U D I E S E R Z E I T N E U E N T S T E H E N D E N A R B E I T E R S I E D L U N G E N Z U (swc_deu_001359-swc_deu_001359) +D I E M I T S A M T I H R E R R E C H E N S T U B E (swc_deu_001360-swc_deu_001360) +K A I S E R F E R D I N A N D (swc_deu_001361-swc_deu_001361) +V O M F E R N S E H R E G I S S E U R F R A N Z X A V E R B O G N E R I N D E M F E R N S E H F I L M D A S E W I G E L I E D (swc_deu_001362-swc_deu_001362) +W U R D E I N S E I N E N B E S T E N Z E I T E N D E R (swc_deu_001363-swc_deu_001363) +S I E H Ö R E N D E N A R T I K E L F I S H A N D C H I P S (swc_deu_001364-swc_deu_001364) +U N D T V M O V I E (swc_deu_001365-swc_deu_001365) +R E Z E P T I O N D E R H E X E N T H E M A T I K V O N C H R I S T A (swc_deu_001366-swc_deu_001366) +D I E G E S A M T E A N L A G E W A R B I S E T W A Z W E I H U N D E R T S E C H Z I G N A C H C H R I S T U S I N B E T R I E B (swc_deu_001367-swc_deu_001367) +D E R E R S T E F A S T F O O D L I E F E R S E R V I C E W A R G E B O R E N (swc_deu_001368-swc_deu_001368) +E I N E M K A B E L B A U M (swc_deu_001369-swc_deu_001369) +E R M O R D U N G M I T D E R G E F A H R V E R B U N D E N G E W E S E N (swc_deu_001370-swc_deu_001370) +D E R Ä L T E S T E N P F E R D E R E N N E N A U S S E R H A L B (swc_deu_001371-swc_deu_001371) +S O N D E R N A U C H D E R N A T I O N A L S O Z I A L I S T I S C H E N K U N S T A U F F A S S U N G G E R E C H T W E R D E N (swc_deu_001372-swc_deu_001372) +D I E W E L T S I C H T D E S H A N S E A T E N (swc_deu_001373-swc_deu_001373) +A U C H N A C H K O M M E N S I N D N I C H T B E K A N N T (swc_deu_001374-swc_deu_001374) +I H R E N T E X T E N D E N E I N D R U C K Z U V E R M I T T E L N (swc_deu_001375-swc_deu_001375) +V E R D I E N S T E U M D A S K Ö L N E R L I E D V E R L I E H E N (swc_deu_001376-swc_deu_001376) +O B W O H L H O F F M A N N V O N H O F F M A N N S W A L D A U S W E R K G R O S S E N E I N F L U S S A U F S P Ä T E R E D I C H T E R A U S Ü B T E (swc_deu_001377-swc_deu_001377) +U M S O E R N S T A L S S T A A T S O B E R H A U P T V O N (swc_deu_001378-swc_deu_001378) +D O K U M E N T A T I O N (swc_deu_001379-swc_deu_001379) +G E S T A L T U N G D E S C O V E R S W I D E R S P I E G E L T (swc_deu_001380-swc_deu_001380) +D E R G E S A M T E A U F W A N D W I R D A U F (swc_deu_001381-swc_deu_001381) +O B G L E I C H H A M B U R G D I E S E M A N G E H Ö R T E U N D E I N E N O B I L I T I E R U N G D U R C H D E N K A I S E R D A M I T K E I N E D U R C H (swc_deu_001382-swc_deu_001382) +D A E S D U R C H D E N S I C H A U S W E I T E N D E N W E L T H A N D E L A R B E I T U N D W O H L S T A N D V E R S P R A C H (swc_deu_001383-swc_deu_001383) +F Ü R D I E Z E I T M I T T E D E S N E U N Z E H N T E J A H R H U N D E R T S B E K L A G T E D E R A R C H I T E K T M A R T I N H A L L E R (swc_deu_001384-swc_deu_001384) +A L T B U N D E S K A N Z L E R H E L M U T S C H M I D T L E H N T E (swc_deu_001385-swc_deu_001385) +D E N N A M E N G O D E F F R O Y I M S T A A T S H A N D B U C H Z U S T R E I C H E N (swc_deu_001386-swc_deu_001386) +W E N N A U C H M I T E I N E R G E W I S S E N L E T H A R G I E (swc_deu_001387-swc_deu_001387) +K A L K U L I E R B A R (swc_deu_001388-swc_deu_001388) +A N G E F A N G E N E Z W E I H U N D E R T F Ü N F Z I G S C H Ü L E R E I N E N D E L E G I E R T E N (swc_deu_001389-swc_deu_001389) +V I E L E M E N S C H E N S A H E N D E N G R I Z Z L Y A L S N A H R U N G S K O N K U R R E N T E N U N D A L S P O T E N T I E L L E G E F A H R (swc_deu_001390-swc_deu_001390) +D E N A U F T R I T T V E R K Ü R Z E N (swc_deu_001391-swc_deu_001391) +D E M S T A N D V O M D E R I N H A L T S T E H T U N T E R D E R L I Z E N Z C R E A T I V E C O M M O N S A T T R I B U T I O N S H A R E A L I K E D R E I P U N K T N U L L U N P O R T E D U N D U N T E R D E R (swc_deu_001392-swc_deu_001392) +E I N E K L E I N E R E B O G E N B R Ü C K E (swc_deu_001393-swc_deu_001393) +S I C H N U N F Ü R S E I N W E I T E R K O M M E N (swc_deu_001394-swc_deu_001394) +A U S D E M G E M Ä L D E Z U E N T F E R N E N (swc_deu_001395-swc_deu_001395) +N A C H E U R O P Ä I S C H E R R I C H T L I N I E N E U N Z I G V I E R H U N D E R T S E C H S U N D N E U N Z I G E W G (swc_deu_001396-swc_deu_001396) +E I N E M U M F E L D A U F (swc_deu_001397-swc_deu_001397) +U N D S I E S E I A U C H W I E E I N E F Ü R S T I N (swc_deu_001398-swc_deu_001398) +N E U N Z E H N H U N D E R T N E U N Z E H N (swc_deu_001399-swc_deu_001399) +S T A T T D E S S E N H A B E N D I E R Ö M I S C H E N I N G E N I E U R E (swc_deu_001400-swc_deu_001400) +G R I Z Z L Y B Ä R U N D M E N S C H (swc_deu_001401-swc_deu_001401) +M U S I C I A N S C O A L I T I O N (swc_deu_001402-swc_deu_001402) +B E R T O L D H U M M E L G I B T E S D R E I V A R I A T I O N E N M I T (swc_deu_001403-swc_deu_001403) +R Ü C K Z U G S G E B I E T E R W I E S S I C H D E R A C H T Z E H N H U N D E R T Z W E I U N D S I E B Z I G G E G R Ü N D E T E Y E L L O W S T O N E N A T I O N A L P A R K (swc_deu_001404-swc_deu_001404) +D E F I N I T I O N (swc_deu_001405-swc_deu_001405) +U M A N D E R U N I V E R S I T Ä T S E V I L L A Z W E I S E M E S T E R K U N S T G E S C H I C H T E Z U S T U D I E R E N (swc_deu_001406-swc_deu_001406) +T R O T Z I H R E R G E R I N G E N (swc_deu_001407-swc_deu_001407) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/result.txt b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..7d6d54d2ad6c0f48b515bc70810c04cb9cc8fb48 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/result.txt @@ -0,0 +1,2525 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,--------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/hyp.trn| +|--------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001201 | 1 75 | 80.0 6.7 13.3 8.0 28.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001202 | 1 27 | 88.9 3.7 7.4 3.7 14.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001203 | 1 23 | 78.3 0.0 21.7 4.3 26.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001204 | 1 27 | 81.5 7.4 11.1 18.5 37.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001205 | 1 53 | 84.9 5.7 9.4 1.9 17.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001206 | 1 30 | 90.0 3.3 6.7 3.3 13.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001207 | 1 25 | 80.0 4.0 16.0 8.0 28.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001208 | 1 49 | 61.2 12.2 26.5 6.1 44.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001209 | 1 49 | 93.9 6.1 0.0 4.1 10.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001210 | 1 60 | 80.0 6.7 13.3 3.3 23.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001211 | 1 23 | 87.0 8.7 4.3 8.7 21.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001212 | 1 44 | 88.6 6.8 4.5 9.1 20.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001213 | 1 23 | 95.7 0.0 4.3 4.3 8.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001214 | 1 52 | 88.5 9.6 1.9 3.8 15.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001215 | 1 43 | 65.1 7.0 27.9 11.6 46.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001216 | 1 21 | 76.2 0.0 23.8 0.0 23.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001217 | 1 108 | 81.5 2.8 15.7 2.8 21.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001218 | 1 24 | 79.2 16.7 4.2 8.3 29.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001219 | 1 85 | 78.8 8.2 12.9 2.4 23.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001220 | 1 59 | 86.4 0.0 13.6 3.4 16.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001221 | 1 103 | 93.2 1.0 5.8 5.8 12.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001222 | 1 27 | 85.2 3.7 11.1 7.4 22.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001223 | 1 42 | 85.7 2.4 11.9 4.8 19.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001224 | 1 28 | 89.3 0.0 10.7 3.6 14.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001225 | 1 65 | 86.2 3.1 10.8 1.5 15.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001226 | 1 89 | 83.1 2.2 14.6 3.4 20.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001227 | 1 99 | 91.9 3.0 5.1 1.0 9.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001228 | 1 16 | 87.5 12.5 0.0 25.0 37.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001229 | 1 6 | 50.0 50.0 0.0 216.7 266.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001230 | 1 31 | 90.3 3.2 6.5 3.2 12.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001231 | 1 30 | 63.3 10.0 26.7 3.3 40.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001232 | 1 29 | 75.9 3.4 20.7 0.0 24.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001233 | 1 95 | 85.3 5.3 9.5 3.2 17.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001234 | 1 19 | 89.5 5.3 5.3 10.5 21.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001235 | 1 62 | 83.9 1.6 14.5 6.5 22.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001236 | 1 28 | 82.1 7.1 10.7 3.6 21.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001237 | 1 51 | 84.3 3.9 11.8 9.8 25.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001238 | 1 67 | 74.6 9.0 16.4 1.5 26.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001239 | 1 23 | 65.2 4.3 30.4 8.7 43.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001240 | 1 48 | 85.4 12.5 2.1 4.2 18.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001241 | 1 13 | 53.8 15.4 30.8 0.0 46.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001242 | 1 78 | 89.7 2.6 7.7 2.6 12.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001243 | 1 29 | 89.7 0.0 10.3 3.4 13.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001244 | 1 11 | 90.9 0.0 9.1 0.0 9.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001245 | 1 25 | 80.0 12.0 8.0 16.0 36.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001246 | 1 19 | 84.2 0.0 15.8 5.3 21.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001247 | 1 138 | 81.9 9.4 8.7 5.1 23.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001248 | 1 37 | 73.0 5.4 21.6 0.0 27.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001249 | 1 43 | 79.1 9.3 11.6 7.0 27.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001250 | 1 24 | 58.3 12.5 29.2 0.0 41.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001251 | 1 139 | 91.4 4.3 4.3 1.4 10.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001252 | 1 12 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001253 | 1 38 | 63.2 10.5 26.3 5.3 42.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001254 | 1 20 | 80.0 10.0 10.0 5.0 25.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001255 | 1 58 | 79.3 0.0 20.7 1.7 22.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001256 | 1 21 | 52.4 28.6 19.0 9.5 57.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001257 | 1 20 | 90.0 0.0 10.0 10.0 20.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001258 | 1 22 | 77.3 9.1 13.6 0.0 22.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001259 | 1 22 | 95.5 0.0 4.5 4.5 9.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001260 | 1 56 | 78.6 5.4 16.1 0.0 21.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001261 | 1 33 | 78.8 15.2 6.1 6.1 27.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001262 | 1 26 | 96.2 0.0 3.8 7.7 11.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001263 | 1 81 | 90.1 1.2 8.6 1.2 11.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001264 | 1 51 | 80.4 2.0 17.6 11.8 31.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001265 | 1 6 | 50.0 33.3 16.7 16.7 66.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001266 | 1 41 | 92.7 0.0 7.3 2.4 9.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001267 | 1 39 | 89.7 5.1 5.1 0.0 10.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001268 | 1 19 | 84.2 5.3 10.5 0.0 15.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001269 | 1 53 | 92.5 0.0 7.5 1.9 9.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001270 | 1 33 | 66.7 24.2 9.1 0.0 33.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001271 | 1 23 | 73.9 4.3 21.7 4.3 30.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001272 | 1 42 | 73.8 4.8 21.4 0.0 26.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001273 | 1 47 | 91.5 4.3 4.3 10.6 19.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001274 | 1 75 | 78.7 8.0 13.3 2.7 24.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001275 | 1 41 | 87.8 4.9 7.3 0.0 12.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001276 | 1 24 | 91.7 0.0 8.3 0.0 8.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001277 | 1 48 | 85.4 2.1 12.5 4.2 18.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001278 | 1 27 | 66.7 11.1 22.2 0.0 33.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001279 | 1 21 | 81.0 14.3 4.8 4.8 23.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001280 | 1 42 | 90.5 0.0 9.5 2.4 11.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001281 | 1 35 | 82.9 5.7 11.4 8.6 25.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001282 | 1 26 | 73.1 7.7 19.2 0.0 26.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001283 | 1 19 | 84.2 0.0 15.8 5.3 21.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001284 | 1 49 | 89.8 2.0 8.2 10.2 20.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001285 | 1 24 | 91.7 4.2 4.2 4.2 12.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001286 | 1 84 | 83.3 4.8 11.9 3.6 20.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001287 | 1 65 | 76.9 13.8 9.2 3.1 26.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001288 | 1 33 | 87.9 6.1 6.1 9.1 21.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001289 | 1 25 | 80.0 16.0 4.0 0.0 20.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001290 | 1 37 | 73.0 8.1 18.9 10.8 37.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001291 | 1 26 | 84.6 7.7 7.7 0.0 15.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001292 | 1 53 | 84.9 5.7 9.4 5.7 20.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001293 | 1 30 | 76.7 3.3 20.0 0.0 23.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001294 | 1 41 | 90.2 2.4 7.3 0.0 9.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001295 | 1 38 | 57.9 15.8 26.3 7.9 50.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001296 | 1 40 | 72.5 10.0 17.5 2.5 30.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001297 | 1 38 | 36.8 21.1 42.1 18.4 81.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001298 | 1 57 | 91.2 3.5 5.3 7.0 15.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001299 | 1 48 | 89.6 6.3 4.2 6.3 16.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001300 | 1 125 | 90.4 5.6 4.0 1.6 11.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001301 | 1 66 | 77.3 9.1 13.6 1.5 24.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001302 | 1 46 | 78.3 8.7 13.0 8.7 30.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001303 | 1 58 | 93.1 1.7 5.2 12.1 19.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001304 | 1 38 | 78.9 18.4 2.6 7.9 28.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001305 | 1 41 | 70.7 12.2 17.1 2.4 31.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001306 | 1 57 | 82.5 8.8 8.8 5.3 22.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001307 | 1 13 | 69.2 23.1 7.7 0.0 30.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001308 | 1 68 | 76.5 4.4 19.1 2.9 26.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001309 | 1 73 | 82.2 6.8 11.0 0.0 17.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001310 | 1 40 | 77.5 7.5 15.0 5.0 27.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001311 | 1 32 | 84.4 6.3 9.4 21.9 37.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001312 | 1 33 | 93.9 6.1 0.0 15.2 21.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001313 | 1 97 | 86.6 6.2 7.2 3.1 16.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001314 | 1 30 | 80.0 13.3 6.7 6.7 26.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001315 | 1 56 | 80.4 8.9 10.7 5.4 25.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001316 | 1 21 | 85.7 4.8 9.5 4.8 19.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001317 | 1 60 | 81.7 3.3 15.0 1.7 20.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001318 | 1 29 | 89.7 3.4 6.9 6.9 17.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001319 | 1 31 | 80.6 3.2 16.1 3.2 22.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001320 | 1 42 | 81.0 9.5 9.5 9.5 28.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001321 | 1 13 | 100.0 0.0 0.0 15.4 15.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001322 | 1 17 | 100.0 0.0 0.0 5.9 5.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001323 | 1 28 | 92.9 0.0 7.1 0.0 7.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001324 | 1 30 | 80.0 10.0 10.0 3.3 23.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001325 | 1 54 | 75.9 9.3 14.8 3.7 27.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001326 | 1 65 | 81.5 7.7 10.8 1.5 20.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001327 | 1 26 | 88.5 0.0 11.5 0.0 11.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001328 | 1 59 | 86.4 1.7 11.9 5.1 18.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001329 | 1 58 | 84.5 0.0 15.5 1.7 17.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001330 | 1 63 | 77.8 4.8 17.5 3.2 25.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001331 | 1 30 | 93.3 3.3 3.3 0.0 6.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001332 | 1 46 | 82.6 4.3 13.0 4.3 21.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001333 | 1 31 | 71.0 12.9 16.1 6.5 35.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001334 | 1 31 | 80.6 3.2 16.1 6.5 25.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001335 | 1 37 | 81.1 10.8 8.1 2.7 21.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001336 | 1 35 | 94.3 0.0 5.7 2.9 8.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001337 | 1 63 | 74.6 7.9 17.5 1.6 27.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001338 | 1 77 | 76.6 9.1 14.3 2.6 26.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001339 | 1 35 | 88.6 2.9 8.6 0.0 11.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001340 | 1 35 | 88.6 2.9 8.6 5.7 17.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001341 | 1 21 | 76.2 9.5 14.3 14.3 38.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001342 | 1 66 | 81.8 9.1 9.1 0.0 18.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001343 | 1 17 | 94.1 5.9 0.0 11.8 17.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001344 | 1 39 | 92.3 2.6 5.1 5.1 12.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001345 | 1 31 | 90.3 0.0 9.7 3.2 12.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001346 | 1 58 | 81.0 8.6 10.3 1.7 20.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001347 | 1 30 | 56.7 16.7 26.7 0.0 43.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001348 | 1 150 | 84.7 6.7 8.7 4.7 20.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001349 | 1 50 | 88.0 6.0 6.0 4.0 16.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001350 | 1 53 | 66.0 7.5 26.4 0.0 34.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001351 | 1 19 | 10.5 0.0 89.5 0.0 89.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001352 | 1 60 | 88.3 5.0 6.7 3.3 15.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001353 | 1 84 | 73.8 9.5 16.7 1.2 27.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001354 | 1 159 | 81.1 6.3 12.6 2.5 21.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001355 | 1 29 | 79.3 0.0 20.7 24.1 44.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001356 | 1 33 | 78.8 0.0 21.2 3.0 24.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001357 | 1 18 | 77.8 16.7 5.6 11.1 33.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001358 | 1 20 | 60.0 15.0 25.0 15.0 55.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001359 | 1 107 | 84.1 5.6 10.3 0.9 16.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001360 | 1 29 | 75.9 17.2 6.9 3.4 27.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001361 | 1 16 | 81.3 12.5 6.3 0.0 18.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001362 | 1 73 | 79.5 11.0 9.6 8.2 28.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001363 | 1 33 | 84.8 9.1 6.1 0.0 15.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001364 | 1 36 | 80.6 5.6 13.9 5.6 25.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001365 | 1 13 | 76.9 23.1 0.0 15.4 38.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001366 | 1 39 | 74.4 15.4 10.3 10.3 35.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001367 | 1 77 | 85.7 6.5 7.8 3.9 18.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001368 | 1 45 | 68.9 17.8 13.3 4.4 35.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001369 | 1 15 | 93.3 6.7 0.0 13.3 20.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001370 | 1 42 | 85.7 0.0 14.3 7.1 21.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001371 | 1 36 | 77.8 5.6 16.7 2.8 25.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001372 | 1 71 | 84.5 7.0 8.5 11.3 26.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001373 | 1 27 | 85.2 7.4 7.4 7.4 22.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001374 | 1 34 | 85.3 5.9 8.8 0.0 14.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001375 | 1 39 | 76.9 10.3 12.8 2.6 25.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001376 | 1 39 | 74.4 5.1 20.5 2.6 28.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001377 | 1 86 | 77.9 9.3 12.8 4.7 26.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001378 | 1 35 | 71.4 8.6 20.0 5.7 34.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001379 | 1 13 | 84.6 7.7 7.7 61.5 76.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001380 | 1 35 | 88.6 11.4 0.0 5.7 17.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001381 | 1 28 | 85.7 3.6 10.7 3.6 17.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001382 | 1 91 | 87.9 3.3 8.8 1.1 13.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001383 | 1 75 | 92.0 4.0 4.0 0.0 8.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001384 | 1 83 | 84.3 3.6 12.0 6.0 21.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001385 | 1 38 | 78.9 10.5 10.5 0.0 21.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001386 | 1 50 | 60.0 14.0 26.0 2.0 42.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001387 | 1 38 | 71.1 2.6 26.3 0.0 28.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001388 | 1 12 | 83.3 16.7 0.0 25.0 41.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001389 | 1 58 | 70.7 10.3 19.0 6.9 36.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001390 | 1 84 | 75.0 4.8 20.2 0.0 25.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001391 | 1 22 | 77.3 9.1 13.6 0.0 22.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001392 | 1 127 | 70.1 19.7 10.2 29.1 59.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001393 | 1 25 | 96.0 0.0 4.0 4.0 8.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001394 | 1 30 | 66.7 6.7 26.7 0.0 33.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001395 | 1 28 | 100.0 0.0 0.0 10.7 10.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001396 | 1 71 | 67.6 9.9 22.5 8.5 40.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001397 | 1 16 | 62.5 25.0 12.5 0.0 37.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001398 | 1 33 | 90.9 6.1 3.0 0.0 9.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001399 | 1 25 | 96.0 0.0 4.0 4.0 8.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001400 | 1 42 | 83.3 4.8 11.9 7.1 23.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001401 | 1 21 | 76.2 19.0 4.8 28.6 52.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001402 | 1 19 | 47.4 52.6 0.0 10.5 63.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001403 | 1 43 | 67.4 16.3 16.3 4.7 37.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001404 | 1 98 | 76.5 6.1 17.3 5.1 28.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001405 | 1 10 | 90.0 0.0 10.0 10.0 20.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001406 | 1 72 | 77.8 4.2 18.1 1.4 23.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001407 | 1 20 | 65.0 5.0 30.0 20.0 55.0 100.0 | +|====================================================================================================================| +| Sum/Avg | 207 9248 | 81.3 6.7 11.9 5.0 23.6 99.5 | +|====================================================================================================================| +| Mean | 1.0 44.7 | 80.5 7.4 12.1 6.6 26.0 99.5 | +| S.D. | 0.0 27.5 | 11.5 7.3 9.2 16.2 21.5 7.0 | +| Median | 1.0 38.0 | 81.7 6.0 10.3 4.0 22.6 100.0 | +`--------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,--------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/hyp.trn| +|--------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001201 | 1 75 | 60 5 10 6 21 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001202 | 1 27 | 24 1 2 1 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001203 | 1 23 | 18 0 5 1 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001204 | 1 27 | 22 2 3 5 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001205 | 1 53 | 45 3 5 1 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001206 | 1 30 | 27 1 2 1 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001207 | 1 25 | 20 1 4 2 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001208 | 1 49 | 30 6 13 3 22 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001209 | 1 49 | 46 3 0 2 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001210 | 1 60 | 48 4 8 2 14 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001211 | 1 23 | 20 2 1 2 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001212 | 1 44 | 39 3 2 4 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001213 | 1 23 | 22 0 1 1 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001214 | 1 52 | 46 5 1 2 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001215 | 1 43 | 28 3 12 5 20 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001216 | 1 21 | 16 0 5 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001217 | 1 108 | 88 3 17 3 23 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001218 | 1 24 | 19 4 1 2 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001219 | 1 85 | 67 7 11 2 20 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001220 | 1 59 | 51 0 8 2 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001221 | 1 103 | 96 1 6 6 13 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001222 | 1 27 | 23 1 3 2 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001223 | 1 42 | 36 1 5 2 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001224 | 1 28 | 25 0 3 1 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001225 | 1 65 | 56 2 7 1 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001226 | 1 89 | 74 2 13 3 18 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001227 | 1 99 | 91 3 5 1 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001228 | 1 16 | 14 2 0 4 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001229 | 1 6 | 3 3 0 13 16 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001230 | 1 31 | 28 1 2 1 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001231 | 1 30 | 19 3 8 1 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001232 | 1 29 | 22 1 6 0 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001233 | 1 95 | 81 5 9 3 17 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001234 | 1 19 | 17 1 1 2 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001235 | 1 62 | 52 1 9 4 14 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001236 | 1 28 | 23 2 3 1 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001237 | 1 51 | 43 2 6 5 13 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001238 | 1 67 | 50 6 11 1 18 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001239 | 1 23 | 15 1 7 2 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001240 | 1 48 | 41 6 1 2 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001241 | 1 13 | 7 2 4 0 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001242 | 1 78 | 70 2 6 2 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001243 | 1 29 | 26 0 3 1 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001244 | 1 11 | 10 0 1 0 1 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001245 | 1 25 | 20 3 2 4 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001246 | 1 19 | 16 0 3 1 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001247 | 1 138 | 113 13 12 7 32 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001248 | 1 37 | 27 2 8 0 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001249 | 1 43 | 34 4 5 3 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001250 | 1 24 | 14 3 7 0 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001251 | 1 139 | 127 6 6 2 14 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001252 | 1 12 | 12 0 0 0 0 0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001253 | 1 38 | 24 4 10 2 16 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001254 | 1 20 | 16 2 2 1 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001255 | 1 58 | 46 0 12 1 13 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001256 | 1 21 | 11 6 4 2 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001257 | 1 20 | 18 0 2 2 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001258 | 1 22 | 17 2 3 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001259 | 1 22 | 21 0 1 1 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001260 | 1 56 | 44 3 9 0 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001261 | 1 33 | 26 5 2 2 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001262 | 1 26 | 25 0 1 2 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001263 | 1 81 | 73 1 7 1 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001264 | 1 51 | 41 1 9 6 16 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001265 | 1 6 | 3 2 1 1 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001266 | 1 41 | 38 0 3 1 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001267 | 1 39 | 35 2 2 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001268 | 1 19 | 16 1 2 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001269 | 1 53 | 49 0 4 1 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001270 | 1 33 | 22 8 3 0 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001271 | 1 23 | 17 1 5 1 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001272 | 1 42 | 31 2 9 0 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001273 | 1 47 | 43 2 2 5 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001274 | 1 75 | 59 6 10 2 18 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001275 | 1 41 | 36 2 3 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001276 | 1 24 | 22 0 2 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001277 | 1 48 | 41 1 6 2 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001278 | 1 27 | 18 3 6 0 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001279 | 1 21 | 17 3 1 1 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001280 | 1 42 | 38 0 4 1 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001281 | 1 35 | 29 2 4 3 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001282 | 1 26 | 19 2 5 0 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001283 | 1 19 | 16 0 3 1 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001284 | 1 49 | 44 1 4 5 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001285 | 1 24 | 22 1 1 1 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001286 | 1 84 | 70 4 10 3 17 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001287 | 1 65 | 50 9 6 2 17 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001288 | 1 33 | 29 2 2 3 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001289 | 1 25 | 20 4 1 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001290 | 1 37 | 27 3 7 4 14 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001291 | 1 26 | 22 2 2 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001292 | 1 53 | 45 3 5 3 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001293 | 1 30 | 23 1 6 0 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001294 | 1 41 | 37 1 3 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001295 | 1 38 | 22 6 10 3 19 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001296 | 1 40 | 29 4 7 1 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001297 | 1 38 | 14 8 16 7 31 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001298 | 1 57 | 52 2 3 4 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001299 | 1 48 | 43 3 2 3 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001300 | 1 125 | 113 7 5 2 14 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001301 | 1 66 | 51 6 9 1 16 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001302 | 1 46 | 36 4 6 4 14 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001303 | 1 58 | 54 1 3 7 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001304 | 1 38 | 30 7 1 3 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001305 | 1 41 | 29 5 7 1 13 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001306 | 1 57 | 47 5 5 3 13 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001307 | 1 13 | 9 3 1 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001308 | 1 68 | 52 3 13 2 18 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001309 | 1 73 | 60 5 8 0 13 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001310 | 1 40 | 31 3 6 2 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001311 | 1 32 | 27 2 3 7 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001312 | 1 33 | 31 2 0 5 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001313 | 1 97 | 84 6 7 3 16 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001314 | 1 30 | 24 4 2 2 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001315 | 1 56 | 45 5 6 3 14 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001316 | 1 21 | 18 1 2 1 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001317 | 1 60 | 49 2 9 1 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001318 | 1 29 | 26 1 2 2 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001319 | 1 31 | 25 1 5 1 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001320 | 1 42 | 34 4 4 4 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001321 | 1 13 | 13 0 0 2 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001322 | 1 17 | 17 0 0 1 1 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001323 | 1 28 | 26 0 2 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001324 | 1 30 | 24 3 3 1 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001325 | 1 54 | 41 5 8 2 15 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001326 | 1 65 | 53 5 7 1 13 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001327 | 1 26 | 23 0 3 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001328 | 1 59 | 51 1 7 3 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001329 | 1 58 | 49 0 9 1 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001330 | 1 63 | 49 3 11 2 16 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001331 | 1 30 | 28 1 1 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001332 | 1 46 | 38 2 6 2 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001333 | 1 31 | 22 4 5 2 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001334 | 1 31 | 25 1 5 2 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001335 | 1 37 | 30 4 3 1 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001336 | 1 35 | 33 0 2 1 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001337 | 1 63 | 47 5 11 1 17 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001338 | 1 77 | 59 7 11 2 20 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001339 | 1 35 | 31 1 3 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001340 | 1 35 | 31 1 3 2 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001341 | 1 21 | 16 2 3 3 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001342 | 1 66 | 54 6 6 0 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001343 | 1 17 | 16 1 0 2 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001344 | 1 39 | 36 1 2 2 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001345 | 1 31 | 28 0 3 1 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001346 | 1 58 | 47 5 6 1 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001347 | 1 30 | 17 5 8 0 13 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001348 | 1 150 | 127 10 13 7 30 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001349 | 1 50 | 44 3 3 2 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001350 | 1 53 | 35 4 14 0 18 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001351 | 1 19 | 2 0 17 0 17 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001352 | 1 60 | 53 3 4 2 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001353 | 1 84 | 62 8 14 1 23 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001354 | 1 159 | 129 10 20 4 34 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001355 | 1 29 | 23 0 6 7 13 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001356 | 1 33 | 26 0 7 1 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001357 | 1 18 | 14 3 1 2 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001358 | 1 20 | 12 3 5 3 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001359 | 1 107 | 90 6 11 1 18 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001360 | 1 29 | 22 5 2 1 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001361 | 1 16 | 13 2 1 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001362 | 1 73 | 58 8 7 6 21 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001363 | 1 33 | 28 3 2 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001364 | 1 36 | 29 2 5 2 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001365 | 1 13 | 10 3 0 2 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001366 | 1 39 | 29 6 4 4 14 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001367 | 1 77 | 66 5 6 3 14 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001368 | 1 45 | 31 8 6 2 16 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001369 | 1 15 | 14 1 0 2 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001370 | 1 42 | 36 0 6 3 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001371 | 1 36 | 28 2 6 1 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001372 | 1 71 | 60 5 6 8 19 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001373 | 1 27 | 23 2 2 2 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001374 | 1 34 | 29 2 3 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001375 | 1 39 | 30 4 5 1 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001376 | 1 39 | 29 2 8 1 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001377 | 1 86 | 67 8 11 4 23 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001378 | 1 35 | 25 3 7 2 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001379 | 1 13 | 11 1 1 8 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001380 | 1 35 | 31 4 0 2 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001381 | 1 28 | 24 1 3 1 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001382 | 1 91 | 80 3 8 1 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001383 | 1 75 | 69 3 3 0 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001384 | 1 83 | 70 3 10 5 18 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001385 | 1 38 | 30 4 4 0 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001386 | 1 50 | 30 7 13 1 21 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001387 | 1 38 | 27 1 10 0 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001388 | 1 12 | 10 2 0 3 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001389 | 1 58 | 41 6 11 4 21 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001390 | 1 84 | 63 4 17 0 21 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001391 | 1 22 | 17 2 3 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001392 | 1 127 | 89 25 13 37 75 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001393 | 1 25 | 24 0 1 1 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001394 | 1 30 | 20 2 8 0 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001395 | 1 28 | 28 0 0 3 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001396 | 1 71 | 48 7 16 6 29 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001397 | 1 16 | 10 4 2 0 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001398 | 1 33 | 30 2 1 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001399 | 1 25 | 24 0 1 1 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001400 | 1 42 | 35 2 5 3 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001401 | 1 21 | 16 4 1 6 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001402 | 1 19 | 9 10 0 2 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001403 | 1 43 | 29 7 7 2 16 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001404 | 1 98 | 75 6 17 5 28 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001405 | 1 10 | 9 0 1 1 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001406 | 1 72 | 56 3 13 1 17 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001407 | 1 20 | 13 1 6 4 11 1 | +|====================================================================================================================| +| Sum | 207 9248 | 7522 622 1104 460 2186 206 | +|====================================================================================================================| +| Mean | 1.0 44.7 | 36.3 3.0 5.3 2.2 10.6 1.0 | +| S.D. | 0.0 27.5 | 23.4 2.9 4.2 3.1 7.8 0.1 | +| Median | 1.0 38.0 | 29.0 2.0 5.0 2.0 9.0 1.0 | +`--------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_cer/hyp.trn + +Speakers: + 0: swc_deu_001201 + 1: swc_deu_001202 + 2: swc_deu_001203 + 3: swc_deu_001204 + 4: swc_deu_001205 + 5: swc_deu_001206 + 6: swc_deu_001207 + 7: swc_deu_001208 + 8: swc_deu_001209 + 9: swc_deu_001210 + 10: swc_deu_001211 + 11: swc_deu_001212 + 12: swc_deu_001213 + 13: swc_deu_001214 + 14: swc_deu_001215 + 15: swc_deu_001216 + 16: swc_deu_001217 + 17: swc_deu_001218 + 18: swc_deu_001219 + 19: swc_deu_001220 + 20: swc_deu_001221 + 21: swc_deu_001222 + 22: swc_deu_001223 + 23: swc_deu_001224 + 24: swc_deu_001225 + 25: swc_deu_001226 + 26: swc_deu_001227 + 27: swc_deu_001228 + 28: swc_deu_001229 + 29: swc_deu_001230 + 30: swc_deu_001231 + 31: swc_deu_001232 + 32: swc_deu_001233 + 33: swc_deu_001234 + 34: swc_deu_001235 + 35: swc_deu_001236 + 36: swc_deu_001237 + 37: swc_deu_001238 + 38: swc_deu_001239 + 39: swc_deu_001240 + 40: swc_deu_001241 + 41: swc_deu_001242 + 42: swc_deu_001243 + 43: swc_deu_001244 + 44: swc_deu_001245 + 45: swc_deu_001246 + 46: swc_deu_001247 + 47: swc_deu_001248 + 48: swc_deu_001249 + 49: swc_deu_001250 + 50: swc_deu_001251 + 51: swc_deu_001252 + 52: swc_deu_001253 + 53: swc_deu_001254 + 54: swc_deu_001255 + 55: swc_deu_001256 + 56: swc_deu_001257 + 57: swc_deu_001258 + 58: swc_deu_001259 + 59: swc_deu_001260 + 60: swc_deu_001261 + 61: swc_deu_001262 + 62: swc_deu_001263 + 63: swc_deu_001264 + 64: swc_deu_001265 + 65: swc_deu_001266 + 66: swc_deu_001267 + 67: swc_deu_001268 + 68: swc_deu_001269 + 69: swc_deu_001270 + 70: swc_deu_001271 + 71: swc_deu_001272 + 72: swc_deu_001273 + 73: swc_deu_001274 + 74: swc_deu_001275 + 75: swc_deu_001276 + 76: swc_deu_001277 + 77: swc_deu_001278 + 78: swc_deu_001279 + 79: swc_deu_001280 + 80: swc_deu_001281 + 81: swc_deu_001282 + 82: swc_deu_001283 + 83: swc_deu_001284 + 84: swc_deu_001285 + 85: swc_deu_001286 + 86: swc_deu_001287 + 87: swc_deu_001288 + 88: swc_deu_001289 + 89: swc_deu_001290 + 90: swc_deu_001291 + 91: swc_deu_001292 + 92: swc_deu_001293 + 93: swc_deu_001294 + 94: swc_deu_001295 + 95: swc_deu_001296 + 96: swc_deu_001297 + 97: swc_deu_001298 + 98: swc_deu_001299 + 99: swc_deu_001300 + 100: swc_deu_001301 + 101: swc_deu_001302 + 102: swc_deu_001303 + 103: swc_deu_001304 + 104: swc_deu_001305 + 105: swc_deu_001306 + 106: swc_deu_001307 + 107: swc_deu_001308 + 108: swc_deu_001309 + 109: swc_deu_001310 + 110: swc_deu_001311 + 111: swc_deu_001312 + 112: swc_deu_001313 + 113: swc_deu_001314 + 114: swc_deu_001315 + 115: swc_deu_001316 + 116: swc_deu_001317 + 117: swc_deu_001318 + 118: swc_deu_001319 + 119: swc_deu_001320 + 120: swc_deu_001321 + 121: swc_deu_001322 + 122: swc_deu_001323 + 123: swc_deu_001324 + 124: swc_deu_001325 + 125: swc_deu_001326 + 126: swc_deu_001327 + 127: swc_deu_001328 + 128: swc_deu_001329 + 129: swc_deu_001330 + 130: swc_deu_001331 + 131: swc_deu_001332 + 132: swc_deu_001333 + 133: swc_deu_001334 + 134: swc_deu_001335 + 135: swc_deu_001336 + 136: swc_deu_001337 + 137: swc_deu_001338 + 138: swc_deu_001339 + 139: swc_deu_001340 + 140: swc_deu_001341 + 141: swc_deu_001342 + 142: swc_deu_001343 + 143: swc_deu_001344 + 144: swc_deu_001345 + 145: swc_deu_001346 + 146: swc_deu_001347 + 147: swc_deu_001348 + 148: swc_deu_001349 + 149: swc_deu_001350 + 150: swc_deu_001351 + 151: swc_deu_001352 + 152: swc_deu_001353 + 153: swc_deu_001354 + 154: swc_deu_001355 + 155: swc_deu_001356 + 156: swc_deu_001357 + 157: swc_deu_001358 + 158: swc_deu_001359 + 159: swc_deu_001360 + 160: swc_deu_001361 + 161: swc_deu_001362 + 162: swc_deu_001363 + 163: swc_deu_001364 + 164: swc_deu_001365 + 165: swc_deu_001366 + 166: swc_deu_001367 + 167: swc_deu_001368 + 168: swc_deu_001369 + 169: swc_deu_001370 + 170: swc_deu_001371 + 171: swc_deu_001372 + 172: swc_deu_001373 + 173: swc_deu_001374 + 174: swc_deu_001375 + 175: swc_deu_001376 + 176: swc_deu_001377 + 177: swc_deu_001378 + 178: swc_deu_001379 + 179: swc_deu_001380 + 180: swc_deu_001381 + 181: swc_deu_001382 + 182: swc_deu_001383 + 183: swc_deu_001384 + 184: swc_deu_001385 + 185: swc_deu_001386 + 186: swc_deu_001387 + 187: swc_deu_001388 + 188: swc_deu_001389 + 189: swc_deu_001390 + 190: swc_deu_001391 + 191: swc_deu_001392 + 192: swc_deu_001393 + 193: swc_deu_001394 + 194: swc_deu_001395 + 195: swc_deu_001396 + 196: swc_deu_001397 + 197: swc_deu_001398 + 198: swc_deu_001399 + 199: swc_deu_001400 + 200: swc_deu_001401 + 201: swc_deu_001402 + 202: swc_deu_001403 + 203: swc_deu_001404 + 204: swc_deu_001405 + 205: swc_deu_001406 + 206: swc_deu_001407 + +Speaker sentences 0: swc_deu_001201 #utts: 1 +id: (swc_deu_001201-swc_deu_001201) +Scores: (#C #S #D #I) 60 5 10 6 +REF: d e R * * v e r l i e b t e J u n g e h e * r z o g D i e r a T s c H l Ä G e * s e i n e s V a t e R s n i c h t b e * a c H t * e t H a b E +HYP: d e I E R v e r l i e b t e * u n g e h e A r z o g * i e r a N s c * l Ä * e K E s e i n e s ******* F a t e * s n i c h t ******* b e R a c G t D e t ******* * a b * +Eval: S I I D I D S D D I S D S D D I S I D D D + +Speaker sentences 1: swc_deu_001202 #utts: 1 +id: (swc_deu_001202-swc_deu_001202) +Scores: (#C #S #D #I) 24 1 2 1 +REF: d i e i n d e N H a n s e s t * Ä d t e n a l s +HYP: d i e i n d e * * a n s e s t E R d t e n a l s +Eval: D D I S + +Speaker sentences 2: swc_deu_001203 #utts: 1 +id: (swc_deu_001203-swc_deu_001203) +Scores: (#C #S #D #I) 18 0 5 1 +REF: W a r k e i n g r o S s e R e r f o l g * +HYP: * a r ******* k e i n ******* g r o * s e * e r f o l g K +Eval: D D D D D I + +Speaker sentences 3: swc_deu_001204 #utts: 1 +id: (swc_deu_001204-swc_deu_001204) +Scores: (#C #S #D #I) 22 2 3 5 +REF: g R o S s e n * c h e * m i S c h e * n * F A b r i * k e n +HYP: g * o * s e n S c h e H m i * c h e I n V E R b r i C k e n +Eval: D D I I D I I S S I + +Speaker sentences 4: swc_deu_001205 #utts: 1 +id: (swc_deu_001205-swc_deu_001205) +Scores: (#C #S #D #I) 45 3 5 1 +REF: W u r d e n a U c h m e h r e r e E r ******* l Ä u t E r u n g s b Ü c h e R v e r Ö f f E n t l i c h t +HYP: * u r d e n a * c h m e h r e r e A r l E u t * r u n g s b Ü c h e * v e r f f * n t l i c h t +Eval: D D S I S D D S D + +Speaker sentences 5: swc_deu_001206 #utts: 1 +id: (swc_deu_001206-swc_deu_001206) +Scores: (#C #S #D #I) 27 1 2 1 +REF: v o r b e r e i T E t e n b i e r ******* t e i g g e t u n K t +HYP: v o r b e r e i * * t e n b i e r t e i g g e t u n G t +Eval: D D I S + +Speaker sentences 6: swc_deu_001207 #utts: 1 +id: (swc_deu_001207-swc_deu_001207) +Scores: (#C #S #D #I) 20 1 4 2 +REF: d o K U m e n t e s C h l i e S s l i c H i n ******* * +HYP: d o * * m e n t e s * h l i e * s l i c G i n E +Eval: D D D D S I I + +Speaker sentences 7: swc_deu_001208 #utts: 1 +id: (swc_deu_001208-swc_deu_001208) +Scores: (#C #S #D #I) 30 6 13 3 +REF: t R a u E R t a g F Ü r d e n t * O D v o n k Ö n i * G * F r I e D r I c h W I l H e L M +HYP: t * a u * * t a g V Ü r ******* d e n ******* t A U T v o n k Ö n i C H V E r * e * r * c h ******* * Ü l * e * * +Eval: D D D S D D I S S I S I S D D D D D S D D D + +Speaker sentences 8: swc_deu_001209 #utts: 1 +id: (swc_deu_001209-swc_deu_001209) +Scores: (#C #S #D #I) 46 3 0 2 +REF: d a r u n T e r s i n d m a t i l d e a * s e n s i s w Ä c h t e r d e s k r e u * z E s +HYP: d a r u n D e r s i n d m a t i l d e a R s e n s i s w E c h t e r d e s k r e u L z I s +Eval: S I S I S + +Speaker sentences 9: swc_deu_001210 #utts: 1 +id: (swc_deu_001210-swc_deu_001210) +Scores: (#C #S #D #I) 48 4 8 2 +REF: I n ******* * n e n s t Ä D t e n m e h r u n d m e h r D i e r o L l e d e r t r a d i T I O n e L l E n F i s h +HYP: E n I n e n s t Ä * t e n m e h r u n d m e h r * i e ******* r o * l e d e r ******* t r a d i Z n e * l * n * i s h +Eval: S I I D D D D D S S S D D D + +Speaker sentences 10: swc_deu_001211 #utts: 1 +id: (swc_deu_001211-swc_deu_001211) +Scores: (#C #S #D #I) 20 2 1 2 +REF: z U d e n e n w e l t l Ä u f i * G k e * i t +HYP: z * d e n e n w e l t l E u f i C H k e I i t +Eval: D S I S I + +Speaker sentences 11: swc_deu_001212 #utts: 1 +id: (swc_deu_001212-swc_deu_001212) +Scores: (#C #S #D #I) 39 3 2 4 +REF: r a c h * e * d E s h o f e s u n d d e s a d e * L s f Ü r d e n f r * e V e L +HYP: r a c h R e T d * s ******* h o f e s u n d d e s a d e T Z s f Ü r d e n f r I e F e T +Eval: I I D D I S I S S + +Speaker sentences 12: swc_deu_001213 #utts: 1 +id: (swc_deu_001213-swc_deu_001213) +Scores: (#C #S #D #I) 22 0 1 1 +REF: z e i t ******* a n g a b e n v e r z i c h t e t E +HYP: z e i t a n g a b e n v e r z i c h t e t * +Eval: I D + +Speaker sentences 13: swc_deu_001214 #utts: 1 +id: (swc_deu_001214-swc_deu_001214) +Scores: (#C #S #D #I) 46 5 1 2 +REF: a l S a c h t z e h n h u n d e r t a c h t z i G m i t o t t * o b r a H m s a u f s * A t z +HYP: a l L a c h t z e h n h u n d e r t a c h t z i C H m i t o t t U o ******* b r a M m s a u f s E I t z +Eval: S S S I D S I S + +Speaker sentences 14: swc_deu_001215 #utts: 1 +id: (swc_deu_001215-swc_deu_001215) +Scores: (#C #S #D #I) 28 3 12 5 +REF: * ** * e I n T A U s e n D s i E b * e * n H u n D e R T A c h T u n d z w a n z i G – +HYP: M Ü L e * n * W E s e n * s i * b T e H n * u n * e * D * c h * u n d z w a n z i * ******* *** +Eval: I I I D D S S D D I I D D D S D D D D D + +Speaker sentences 15: swc_deu_001216 #utts: 1 +id: (swc_deu_001216-swc_deu_001216) +Scores: (#C #S #D #I) 16 0 5 0 +REF: D a S s d e r f i S c h F r i S c h +HYP: * a * s d e r f i * c h * r i * c h +Eval: D D D D D + +Speaker sentences 16: swc_deu_001217 #utts: 1 +id: (swc_deu_001217-swc_deu_001217) +Scores: (#C #S #D #I) 88 3 17 3 +REF: s E i N e m a b s c H l U s s i m j a h r e n E u n z e H n h U n d E r T z w * e I u n D a c h t z i * g u n * T e r n a H m e r e i n e e r s t e l Ä n g e r e r e i s e N a c H s p a n I e n +HYP: s * i D e m a b s c * l * s s i m ******* j a h r e ******* n * u n z e * n h * n d * r * z w A e * u n a c h t z i C g u n D e r n a * m ******* e r e i n e e r s t e l Ä n g e r e r e i s e * a c * ******* s p a n * e n +Eval: D S D D D D D D D D D I D S I I S D D D D D D + +Speaker sentences 17: swc_deu_001218 #utts: 1 +id: (swc_deu_001218-swc_deu_001218) +Scores: (#C #S #D #I) 19 4 1 2 +REF: v ** O n c h a * s Ô T v O r g e z e i c h n e t +HYP: v Ü R n S c h a T s ** O v U r g e z e i c h n e t +Eval: I S S I D S S + +Speaker sentences 18: swc_deu_001219 #utts: 1 +id: (swc_deu_001219-swc_deu_001219) +Scores: (#C #S #D #I) 67 7 11 2 +REF: f A L C K e n s t e i n s V O L l s t Ä n d i g e g e s c h i c h t e n u n d d i e a u g s b U r g e R s t a d T g e s c h i C H T e * * d e s Ä l t E r e n +HYP: f * E I T e n s t e i n s * * F l s t E n d i g e ******* g e s c h i c h t e n u n d d i e a u g s b * r g e * ******* s t a d B g e s c h i * * * e G E d e s E l t * r e n +Eval: D S S S D D S S D D D D S D D D I I S D + +Speaker sentences 19: swc_deu_001220 #utts: 1 +id: (swc_deu_001220-swc_deu_001220) +Scores: (#C #S #D #I) 51 0 8 2 +REF: n a c h d i e s e n z e r s t Ö * r u n g e n w u r d e d I e r a * s c h w i e d e r a u f b l Ü h E n D E +HYP: n a c h d i e s e n ******* z e r s t Ö H r u n g e n w u r d e d * e ******* r a S s c h ******* w i e d e r a u f b l ** h * n * * +Eval: D I D D I D D D D D + +Speaker sentences 20: swc_deu_001221 #utts: 1 +id: (swc_deu_001221-swc_deu_001221) +Scores: (#C #S #D #I) 96 1 6 6 +REF: m a c h t e n e i n f l u S s r e i c h e n h a n ******* s * e ******* a * t e n * b e i m k o M m i S s a r i s c h e i n ******* g e s e t z t e n b Ü r g e R m e i s t e r m a r k e r t i h r e a u f w a R t u n G +HYP: m a c h t e n e i n f l u * s r e i c h e n h a n s I e a R t e n M b e i m k o * m i * s a r i s c h ******* e i n g e s e t z t e n b Ü r g e * m e i s t e r m a r k e r t i h r e a u f w a H t u n * +Eval: D I I I I I D D D I D S D + +Speaker sentences 21: swc_deu_001222 #utts: 1 +id: (swc_deu_001222-swc_deu_001222) +Scores: (#C #S #D #I) 23 1 3 2 +REF: a l s Z e n t r a l * e s h a n d e L s ******* k o N t o r +HYP: a l s S e n t r a l D e s ******* h a n d e * s k o * t o r +Eval: S I D D I D + +Speaker sentences 22: swc_deu_001223 #utts: 1 +id: (swc_deu_001223-swc_deu_001223) +Scores: (#C #S #D #I) 36 1 5 2 +REF: s o n d e r ******* s t e l l u n g i N n e R h * A l b d e r s t a d t k r E f e l D +HYP: s o n d e r s t e l l u n g i * n e * h E I l b d e r ******* s t a d t k r * f e l * +Eval: I D D I S D D D + +Speaker sentences 23: swc_deu_001224 #utts: 1 +id: (swc_deu_001224-swc_deu_001224) +Scores: (#C #S #D #I) 25 0 3 1 +REF: f i n D e T s i c h i n h a l o b a * k t e r i e n +HYP: f i n * e * s i c h i n ******* h a l o b a O k t e r i e n +Eval: D D D I + +Speaker sentences 24: swc_deu_001225 #utts: 1 +id: (swc_deu_001225-swc_deu_001225) +Scores: (#C #S #D #I) 56 2 7 1 +REF: a u f d e r b s e i t e f i n d e T S i c h d a s e b e n f a L l s v o n m * i C H A e l K o m p o n i E r t E +HYP: a u f d e r b s e i t e f i n d e * Z i c h d a s e b e n f a * l s v o n m E i * * K e l * o m p o n i * r t * +Eval: D S D I D D S D D D + +Speaker sentences 25: swc_deu_001226 #utts: 1 +id: (swc_deu_001226-swc_deu_001226) +Scores: (#C #S #D #I) 74 2 13 3 +REF: i n h a n S e ******* a * t i s c h e R z e i t h a T t e d i e z I r k e L g e s e L L s c h a f t k e i n e n a u S s c h L a G g e b e n d e n e I n f l U S s m * e H r +HYP: i n h a n D e a R t i s c h e * z e i t h a * t e d i e z E r k e * g e s e * * s c h a f t k e i n e n a u * s c h * a * g e b e n d e n e * n f l * * s ******* m I e * r +Eval: S I I D D S D D D D D D D D D D I D + +Speaker sentences 26: swc_deu_001227 #utts: 1 +id: (swc_deu_001227-swc_deu_001227) +Scores: (#C #S #D #I) 91 3 5 1 +REF: d a * E s d U r c h v e R w e n d u n G v o n a u f t r i e b s k Ö r p E R n o d e r h o l z e i n e g e r i n g e r e m i t t l e r e d i c h t e a l s w a s s e r h a t +HYP: d a R * s ******* d E r c h v e * w e n d u n * v o n a u f t r i e b s k Ö r p * A n o d e r h o l z e i n e g e r i n g e r e m i t t l e r e T d i c h t e a l s w a s s e r h a t +Eval: I D D S D D D S S + +Speaker sentences 27: swc_deu_001228 #utts: 1 +id: (swc_deu_001228-swc_deu_001228) +Scores: (#C #S #D #I) 14 2 0 4 +REF: d r a m * * A t * * I s i e r u n g e n +HYP: d r a m E I t D E s i e r u n g e n +Eval: I I S I I S + +Speaker sentences 28: swc_deu_001229 #utts: 1 +id: (swc_deu_001229-swc_deu_001229) +Scores: (#C #S #D #I) 3 3 0 13 +REF: u * m ******* * * * * * ******* * * * ** * 7 5 5 +HYP: u M m S E B E N O R F Ü N W O N +Eval: I I I I I I I I I I I I I S S S + +Speaker sentences 29: swc_deu_001230 #utts: 1 +id: (swc_deu_001230-swc_deu_001230) +Scores: (#C #S #D #I) 28 1 2 1 +REF: d A S s a l b r e c h t d i e b a d e R s ******* t o c h t e r +HYP: d * E s a l b r e c h t d i e b a d e * s t o c h t e r +Eval: D S D I + +Speaker sentences 30: swc_deu_001231 #utts: 1 +id: (swc_deu_001231-swc_deu_001231) +Scores: (#C #S #D #I) 19 3 8 1 +REF: t H a t b a * R b a r I s c h e R s t A a t S r A I S O N +HYP: t * a t b a M b a r E s c h e * s t * a t Z r * * * * * +Eval: D I S S D D S D D D D D + +Speaker sentences 31: swc_deu_001232 #utts: 1 +id: (swc_deu_001232-swc_deu_001232) +Scores: (#C #S #D #I) 22 1 6 0 +REF: d E r D A s l i e D b e s o n d e r s l i e b t e +HYP: d * r ******* * * s ******* l i e T b e s o n d e r s ******* l i e b t e +Eval: D D D D D S D + +Speaker sentences 32: swc_deu_001233 #utts: 1 +id: (swc_deu_001233-swc_deu_001233) +Scores: (#C #S #D #I) 81 5 9 3 +REF: a u f G R u n d d e s w a C h s e n d e n p U B l i k u m s ******* i n t E r e s s e s w U r d e d e r a u f t r i T t s ******* o r t F Ü r D i e p r i m * A V i s t a l e s u n g e N +HYP: a u f K u n d d e s w a * h s e n d e n p O P l i k u m s i n t * r e s s e s w * r d e d e r a u f t r i * t s o r t * Ü r * i e p r i m E R * i s t a ******* l e s u n g e * +Eval: S S D S S I D D D I D D I S D D D + +Speaker sentences 33: swc_deu_001234 #utts: 1 +id: (swc_deu_001234-swc_deu_001234) +Scores: (#C #S #D #I) 17 1 1 2 +REF: U n d f r e i * l i c h T s p * i e l e +HYP: * n d f r e i D l i c h s p B i e l e +Eval: D I S I + +Speaker sentences 34: swc_deu_001235 #utts: 1 +id: (swc_deu_001235-swc_deu_001235) +Scores: (#C #S #D #I) 52 1 9 4 +REF: d A S s d i e D r e i d e n s t u r * z R e * l a t i V u n ******* b e s c h a d e T Ü b e r ******* s t a n d e n h a T t e N +HYP: d * * s d i e * r e i ******* d e n s t u r T z * e R l a t i E u n b e s c h a d e * ** b e r s t a n d e n h a * t e * +Eval: D D D D I D I S I D D I D D + +Speaker sentences 35: swc_deu_001236 #utts: 1 +id: (swc_deu_001236-swc_deu_001236) +Scores: (#C #S #D #I) 23 2 3 1 +REF: j a h r e n E R s c h I e n e n * z w e i i M m e R +HYP: j a h r e n * A s c h * e n e n D z w e i i * m e N +Eval: D S D I D S + +Speaker sentences 36: swc_deu_001237 #utts: 1 +id: (swc_deu_001237-swc_deu_001237) +Scores: (#C #S #D #I) 43 2 6 5 +REF: * * G r a b m a l e u n d g r a b ******* * k a p E L l e n o d e r W o H l ******* t a t e n n a c H h a l t I G +HYP: D R r a b m a l e u n d g r a b G k a p * Ä l e n o d e r * o * l t a t e n n a c * h a l t * * +Eval: I I S I I D S D D I D D D + +Speaker sentences 37: swc_deu_001238 #utts: 1 +id: (swc_deu_001238-swc_deu_001238) +Scores: (#C #S #D #I) 50 6 11 1 +REF: j u n I n e u N z e H n h u n d E r T S e C h s u N D n e u n z i g k Ü n D I G t E e r * s e i n e b e i d e n j o B s +HYP: j u n * E n e u * z e * n h u n d * r * * e * h s u * * n e u n z i g k Ö n * E K t * D e r R s e i n e b e i d e n j o P s +Eval: D S D D D D D D D D S D S S D S I S + +Speaker sentences 38: swc_deu_001239 #utts: 1 +id: (swc_deu_001239-swc_deu_001239) +Scores: (#C #S #D #I) 15 1 7 2 +REF: E i n g * p R o * t E i n G e K o P p e L t +HYP: * i n g E p * o R t * i n ******* * e o * p e * t +Eval: D I D I D D D S D D + +Speaker sentences 39: swc_deu_001240 #utts: 1 +id: (swc_deu_001240-swc_deu_001240) +Scores: (#C #S #D #I) 41 6 1 2 +REF: n e u n u n D s e c h z i g d e r m e D i * A C o n t R o l a l b u m * c h a R t S e i n +HYP: n e u n u n s e c h z i g d e r m e L i E R K o n t W o l a l b u m S c h a * t Z e i n +Eval: S S I S S S I D S + +Speaker sentences 40: swc_deu_001241 #utts: 1 +id: (swc_deu_001241-swc_deu_001241) +Scores: (#C #S #D #I) 7 2 4 0 +REF: D a d u R c h K o M m T +HYP: T a d u * c h ******* C o * m * +Eval: S D D S D D + +Speaker sentences 41: swc_deu_001242 #utts: 1 +id: (swc_deu_001242-swc_deu_001242) +Scores: (#C #S #D #I) 70 2 6 2 +REF: o H n e ******* h I n n i c h t d e n G r o S s h a n d e l s ******* k a u f l e u t e n g e s e L l s c h a f t l i c H g l e i c h g e s t e L L t w a r e n +HYP: o * n e h E n n i c h t d e n * r o * s h a n d e l s k a u f l e u t e n g e s e * l s c h a f t l i c G g l e i c h g e s t e * * t w a r e n +Eval: D I S D D I D S D D + +Speaker sentences 42: swc_deu_001243 #utts: 1 +id: (swc_deu_001243-swc_deu_001243) +Scores: (#C #S #D #I) 26 0 3 1 +REF: v o N d e r n a H r u n g u n d v o m k l i * m a +HYP: v o * d e r ******* n a * r u n g u n d v o m k l i E m a +Eval: D D D I + +Speaker sentences 43: swc_deu_001244 #utts: 1 +id: (swc_deu_001244-swc_deu_001244) +Scores: (#C #S #D #I) 10 0 1 0 +REF: a p o L l o e i n s +HYP: a p o * l o e i n s +Eval: D + +Speaker sentences 44: swc_deu_001245 #utts: 1 +id: (swc_deu_001245-swc_deu_001245) +Scores: (#C #S #D #I) 20 3 2 4 +REF: b r Ü * * * H l u n d h Ü r t H n a c h k Ö l * n +HYP: b r Ü Y E L E l u n d h Ö r t * n a c h ******* k E l E n +Eval: I I I S S D D S I + +Speaker sentences 45: swc_deu_001246 #utts: 1 +id: (swc_deu_001246-swc_deu_001246) +Scores: (#C #S #D #I) 16 0 3 1 +REF: E t w a * i n e I n k l o s t e R +HYP: * t w a R i n e * n k l o s t e * +Eval: D I D D + +Speaker sentences 46: swc_deu_001247 #utts: 1 +id: (swc_deu_001247-swc_deu_001247) +Scores: (#C #S #D #I) 113 13 12 7 +REF: * * * * * z U m o F f i z I e L L e n k a R n e V a l e n T s t a n d * u n D H e u t e e i n e m I s c h u n g a u s k Ö L s c h e M k a R n * e V a l u n d p o l i t I s c h e m k a b A r e T t m i t C o m E d Y e l E m e n t e n d a r s t e l l t u n D +HYP: D I V W R z E m o * f i z * e * H e n k a * n e W a l ******* e n s t a n d T u n T T e u t e e i n e ******* m * s c h u n g a u s k Ö R s c h e N k a * n D e W a l u n d p o l i t * s c h e m k a b E r e * t m i t K o m d I e l * m e n t e n d a r s t e l l t u n * +Eval: I I I I I S D D D S D S D S I S S D D S S D I S D S D S S S D D + +Speaker sentences 47: swc_deu_001248 #utts: 1 +id: (swc_deu_001248-swc_deu_001248) +Scores: (#C #S #D #I) 27 2 8 0 +REF: d i e Z U R e n T s t e H u n g D e s l i e d e s f Ü H r t e n +HYP: d i e * * * W e n * s t e I u n g ******* * e s ******* l i e d e s f Ü * r t e n +Eval: D D D S D S D D D D + +Speaker sentences 48: swc_deu_001249 #utts: 1 +id: (swc_deu_001249-swc_deu_001249) +Scores: (#C #S #D #I) 34 4 5 3 +REF: n a N n t * E z i e g l e r d i E E r m o r d u N G d e r b E r n a u E r * * I n +HYP: n a * n t I T z i e g l e r d i * A r m o r d u * M d e r b * r n a u * r U N E n +Eval: D I S D S D S D D I I S + +Speaker sentences 49: swc_deu_001250 #utts: 1 +id: (swc_deu_001250-swc_deu_001250) +Scores: (#C #S #D #I) 14 3 7 0 +REF: W i n t e R r U h E i s t v O R A L l E M +HYP: * i n t e * r O h R i s t v * E * * l * * +Eval: D D S S D S D D D D + +Speaker sentences 50: swc_deu_001251 #utts: 1 +id: (swc_deu_001251-swc_deu_001251) +Scores: (#C #S #D #I) 127 6 6 2 +REF: d i e s t r Ä n g e d E r V o r g Ä n g e r ******* l e i t u n g w u r d e n z w i S c h e N n e u n z e h n h u n d e r t n e u n u n d z w a n z i g u n d n e u n z E h n h u n d e r t d r e i U n D f Ü n f z i g a R c h * Ä O l o g I s c h e r g r a b e n +HYP: d i e s t r Ä n g e d * r F o r g E n g e r l e i t u n g w u r d e n z w i * c h e * n e u n z e h n h u n d e r t n e u n u n d z w a n z i g u n d n e u n z * h n h u n d e r t d r e i * n * f Ü n f z i g a I c h I E R l o g E s c h e r g r a b e n +Eval: D S S I D D D D D S I S S S + +Speaker sentences 51: swc_deu_001252 #utts: 1 +id: (swc_deu_001252-swc_deu_001252) +Scores: (#C #S #D #I) 12 0 0 0 +REF: i m g e g e n s a t z +HYP: i m g e g e n s a t z +Eval: + +Speaker sentences 52: swc_deu_001253 #utts: 1 +id: (swc_deu_001253-swc_deu_001253) +Scores: (#C #S #D #I) 24 4 10 2 +REF: * F a r b e N v o n U E r d i N g e n s I n D B l a U u n d R o * T +HYP: V W a r b e * v o n * Ö r d i * g e n ******* s * n * * l a * ******* u n d ******* H o R D +Eval: I S D D S D D D D D D D D S I S + +Speaker sentences 53: swc_deu_001254 #utts: 1 +id: (swc_deu_001254-swc_deu_001254) +Scores: (#C #S #D #I) 16 2 2 1 +REF: l * i V E v e r a n s t a l t u N G e n +HYP: l E i * F v e r a n s t a l t u * M e n +Eval: I D S D S + +Speaker sentences 54: swc_deu_001255 #utts: 1 +id: (swc_deu_001255-swc_deu_001255) +Scores: (#C #S #D #I) 46 0 12 1 +REF: s * o w e r d e n h e u t e i n d e R r e g e l a L l e d o r t l e b e n d e n b r A u N B Ä R E n +HYP: s U o ******* w e r d e n ******* h e u t e i n d e * ******* r e g e l a * l e d o r t ******* l e b e n d e n b r * u * * ** * * n +Eval: I D D D D D D D D D D D D + +Speaker sentences 55: swc_deu_001256 #utts: 1 +id: (swc_deu_001256-swc_deu_001256) +Scores: (#C #S #D #I) 11 6 4 2 +REF: L i e d E R f Ü R r e ******* V U E F I l m e * +HYP: * i e d * A f Ü * ******* r e W Ü S C H l m e R +Eval: D D S D D I S S S S S I + +Speaker sentences 56: swc_deu_001257 #utts: 1 +id: (swc_deu_001257-swc_deu_001257) +Scores: (#C #S #D #I) 18 0 2 2 +REF: d e s h a n ******* s e ******* a t e n f Ü H r e N +HYP: d e s h a n s e a t e n f Ü * r e * +Eval: I I D D + +Speaker sentences 57: swc_deu_001258 #utts: 1 +id: (swc_deu_001258-swc_deu_001258) +Scores: (#C #S #D #I) 17 2 3 0 +REF: h E B b E l s a g n e s b e r n a u E R +HYP: h * Ä b I l s a g n e s b e r n a u * * +Eval: D S S D D + +Speaker sentences 58: swc_deu_001259 #utts: 1 +id: (swc_deu_001259-swc_deu_001259) +Scores: (#C #S #D #I) 21 0 1 1 +REF: l e b e n s w e i s e v e r k Ö * r p e r N +HYP: l e b e n s w e i s e v e r k Ö A r p e r * +Eval: I D + +Speaker sentences 59: swc_deu_001260 #utts: 1 +id: (swc_deu_001260-swc_deu_001260) +Scores: (#C #S #D #I) 44 3 9 0 +REF: W i E d e R f a L l d e s v i e l e n h a m b u r g E R n z u k a t H o l I s c h f r O m m e n +HYP: * i * ******* d e * ******* f a * l T d e s v i e l e n h a m b u r g * A n z u k a t * o l E s c h f r * m m e n +Eval: D D D D D D S D S D S D + +Speaker sentences 60: swc_deu_001261 #utts: 1 +id: (swc_deu_001261-swc_deu_001261) +Scores: (#C #S #D #I) 26 5 2 2 +REF: k U l t * u r U n d W I r ******* t S C h A f t a u s t a u s c h e n +HYP: k O l t O u r E n d D E r t * * h E f t a u s t a u s c h e n +Eval: S I S S S I D D S + +Speaker sentences 61: swc_deu_001262 #utts: 1 +id: (swc_deu_001262-swc_deu_001262) +Scores: (#C #S #D #I) 25 0 1 2 +REF: * j a h r z w e i t a u s e n d v e r t o n * t e +HYP: M j a h r ******* z w e i t a u s e n d v e r t o n D t e +Eval: I D I + +Speaker sentences 62: swc_deu_001263 #utts: 1 +id: (swc_deu_001263-swc_deu_001263) +Scores: (#C #S #D #I) 73 1 7 1 +REF: d a S s e R d i e s e l e i t u n g s c h n E L l e r v o l L e n d * e n k Ö N n e a l s d e r b a u m e i s t e r d e n k Ö L n e r d o m +HYP: d a * s e * d i e s e l e i t u n g s c h n * * l e r v o l Ä e n d T e n k Ö * n e a l s d e r b a u m e i s t e r d e n k Ö * n e r ******* d o m +Eval: D D D D S I D D D + +Speaker sentences 63: swc_deu_001264 #utts: 1 +id: (swc_deu_001264-swc_deu_001264) +Scores: (#C #S #D #I) 41 1 9 6 +REF: * * * * ******* h i n r i c h t u n g d e r b E R n a u E r i * n h a b E e s S i c H S C H l i c h t u m +HYP: E I E R h i n r i c h t u n g d e r b * A n a u * r i E n h a b * ******* e s * i c * * * * l i c h t u m +Eval: I I I I I D S D I D D D D D D D + +Speaker sentences 64: swc_deu_001265 #utts: 1 +id: (swc_deu_001265-swc_deu_001265) +Scores: (#C #S #D #I) 3 2 1 1 +REF: l u * D W i G +HYP: l u O R E i * +Eval: I S S D + +Speaker sentences 65: swc_deu_001266 #utts: 1 +id: (swc_deu_001266-swc_deu_001266) +Scores: (#C #S #D #I) 38 0 3 1 +REF: d e r z e i t d e r b e s t * e k e N n e r d e r e i f e L l e i t u n g +HYP: d e r z e i t d e r b e s t I e k e * n e r ******* d e r e i f e * l e i t u n g +Eval: I D D D + +Speaker sentences 66: swc_deu_001267 #utts: 1 +id: (swc_deu_001267-swc_deu_001267) +Scores: (#C #S #D #I) 35 2 2 0 +REF: F o k u s D e s w i S s e n s C h a f t l i c h e n i n t e r e s s e s +HYP: V o k u s B e s w i * s e n s * h a f t l i c h e n i n t e r e s s e s +Eval: S S D D + +Speaker sentences 67: swc_deu_001268 #utts: 1 +id: (swc_deu_001268-swc_deu_001268) +Scores: (#C #S #D #I) 16 1 2 0 +REF: t H e m A z u b e g e i s t e R n +HYP: t * e m E z u b e g e i s t e * n +Eval: D S D + +Speaker sentences 68: swc_deu_001269 #utts: 1 +id: (swc_deu_001269-swc_deu_001269) +Scores: (#C #S #D #I) 49 0 4 1 +REF: m e t e r u n d k o N n t e d a m i t a u C H v o n i N n e n b e * g a n g e n w e r d e n +HYP: m e t e r u n d k o * n t e d a m i t a u * * v o n i * n e n b e R g a n g e n w e r d e n +Eval: D D D D I + +Speaker sentences 69: swc_deu_001270 #utts: 1 +id: (swc_deu_001270-swc_deu_001270) +Scores: (#C #S #D #I) 22 8 3 0 +REF: h a R D C O V e r b e s T s e L l E R l i s t e d e r n E W +HYP: h a T K A B e r b e s s e * l * * l i s t e d e r n Ü H +Eval: S S S S S S D D D S S + +Speaker sentences 70: swc_deu_001271 #utts: 1 +id: (swc_deu_001271-swc_deu_001271) +Scores: (#C #S #D #I) 17 1 5 1 +REF: d e r f r e i E n e n z * Y k l o p Ä D I E +HYP: d e r f r e i * n e n z I G k l o p ** * * * +Eval: D I S D D D D + +Speaker sentences 71: swc_deu_001272 #utts: 1 +id: (swc_deu_001272-swc_deu_001272) +Scores: (#C #S #D #I) 31 2 9 0 +REF: d e n g r I Z Z l Y w i E D e r A u f d I e l I s t e z u s e t z e n +HYP: d e n g r * * S l I w i * * e r * u f d * e ******* l * s t e z u ******* s e t z e n +Eval: D D S S D D D D D D D + +Speaker sentences 72: swc_deu_001273 #utts: 1 +id: (swc_deu_001273-swc_deu_001273) +Scores: (#C #S #D #I) 43 2 2 5 +REF: * * * ******* l a n g d i e s e k a p l a n s s t E l l e a u f r e c h t e r ******* h A L t e n w u r d E +HYP: W I E l a n g d i e s e k a p l a n s s t Ä l l e a u f r e c h t e r h * E t e n w u r d * +Eval: I I I I S I D S D + +Speaker sentences 73: swc_deu_001274 #utts: 1 +id: (swc_deu_001274-swc_deu_001274) +Scores: (#C #S #D #I) 59 6 10 2 +REF: s i E w a * r e n w a H R s c h e i n l i c h b e r e i t S d r e i S s i g s * E k u n d e n N a c h a u s B r U c h D e s F e u E r S +HYP: s i * ******* w a H r e n w a * * s c h e i n l i c h ******* b e r e i t Z d r e i * s i g s I k u n d e n D a c h a u s P r * c h ******* T e s V e u * r * +Eval: D D I D D D S D I S S S D D S S D D + +Speaker sentences 74: swc_deu_001275 #utts: 1 +id: (swc_deu_001275-swc_deu_001275) +Scores: (#C #S #D #I) 36 2 3 0 +REF: m e t e r N g e s a m t l Ä n g e u n d b i s z u z e h n m E t e r N +HYP: m e t e r * g e s a m t l I n g e u n d b i s z u ******* z e h n m I t e r * +Eval: D S D S D + +Speaker sentences 75: swc_deu_001276 #utts: 1 +id: (swc_deu_001276-swc_deu_001276) +Scores: (#C #S #D #I) 22 0 2 0 +REF: f e i n e r i t z e n u n D s p a l t e N +HYP: f e i n e r i t z e n u n * s p a l t e * +Eval: D D + +Speaker sentences 76: swc_deu_001277 #utts: 1 +id: (swc_deu_001277-swc_deu_001277) +Scores: (#C #S #D #I) 41 1 6 2 +REF: d E n m a n v o n a u s s E n d i e k e H l e * h i n a b * f l i e S s e n s i e H t +HYP: d I n ******* m a n v o n a u s s * n d i e ******* k e * l e R h i n a b P f l i e * s e n s i e * t +Eval: S D D D D I I D D + +Speaker sentences 77: swc_deu_001278 #utts: 1 +id: (swc_deu_001278-swc_deu_001278) +Scores: (#C #S #D #I) 18 3 6 0 +REF: e I n e M i n t e r V i E W s a g t e b r O W n +HYP: e * n e * i n t e r * i * * U s a g t e ******* b r A U n +Eval: D D D D D S D S S + +Speaker sentences 78: swc_deu_001279 #utts: 1 +id: (swc_deu_001279-swc_deu_001279) +Scores: (#C #S #D #I) 17 3 1 1 +REF: d a s f Ü n f t E e v A n ******* g e L i u m +HYP: d a s f Ü n f t * D e v E n g e R i u m +Eval: D S S I S + +Speaker sentences 79: swc_deu_001280 #utts: 1 +id: (swc_deu_001280-swc_deu_001280) +Scores: (#C #S #D #I) 38 0 4 1 +REF: r e I S s e n s i e m a n c h m a l w e i d e ******* t i e r e W i e s c h a f e +HYP: r e * * s e n s i e ******* m a n c h m a l w e i d e t i e r e * i e s c h a f e +Eval: D D D I D + +Speaker sentences 80: swc_deu_001281 #utts: 1 +id: (swc_deu_001281-swc_deu_001281) +Scores: (#C #S #D #I) 29 2 4 3 +REF: s i E H Ö r e n d e n a r t i k e l d * e ******* s * i G n r E v i e W +HYP: s i * * Ö r e n ******* d e n a r t i k e l d I e s E i * n r Ü v i e U +Eval: D D D I I I D S S + +Speaker sentences 81: swc_deu_001282 #utts: 1 +id: (swc_deu_001282-swc_deu_001282) +Scores: (#C #S #D #I) 19 2 5 0 +REF: C H A k u Z A i s T g e l e R n t e r k o c h +HYP: * * * k u S E i s * g e l e * n t e r k o c h +Eval: D D D S S D D + +Speaker sentences 82: swc_deu_001283 #utts: 1 +id: (swc_deu_001283-swc_deu_001283) +Scores: (#C #S #D #I) 16 0 3 1 +REF: h a n S w e n D t s t i f t u n g * +HYP: h a n * ******* w e n * t s t i f t u n g E +Eval: D D D I + +Speaker sentences 83: swc_deu_001284 #utts: 1 +id: (swc_deu_001284-swc_deu_001284) +Scores: (#C #S #D #I) 44 1 4 5 +REF: n e u n z e h n h u N d e r t a c h t z * e H n a l S h a * n s * e ******* a * t e n a n g e s E H e n +HYP: n e u n z e h n h u * d e r t a c h t z I e * n a l * h a I n s I e a R t e n a n g e s * I e n +Eval: D I D D I I I I D S + +Speaker sentences 84: swc_deu_001285 #utts: 1 +id: (swc_deu_001285-swc_deu_001285) +Scores: (#C #S #D #I) 22 1 1 1 +REF: m e h r e r e * e s n a c h i H m t H u n +HYP: m e h r e r e R e s n a c h i * m t O u n +Eval: I D S + +Speaker sentences 85: swc_deu_001286 #utts: 1 +id: (swc_deu_001286-swc_deu_001286) +Scores: (#C #S #D #I) 70 4 10 3 +REF: a U F s t i E g D e s g e r i c h t s z u r l a n d e s ******* w e i t * * b e l i e b T e n k U l i n a r i s c h e N s p e z i A l i t Ä t e r m Ö g L I C H T e +HYP: a C H s t i * g * e s g e r i c h t s z u r l a n d e s w e i t E N b e l i e b * e n k O l i n a r i s c h e * s p e z i * l i t E t e r m Ö g * * * * * e +Eval: S S D D I I I D S D D S D D D D D + +Speaker sentences 86: swc_deu_001287 #utts: 1 +id: (swc_deu_001287-swc_deu_001287) +Scores: (#C #S #D #I) 50 9 6 2 +REF: C o l l e * G E u n d e i N E n z w e i t ******* j o b a l S s p a n i s c h l E h r e r i n h A m P t O n F A L l s a n +HYP: K o l l e T S C u n d e i * * n z w e i t j o b a l * ******* s p a n i s c h l Ä h r e r i n h E m * t * n V O R l s E a n +Eval: S I S S D D I D D S S D D S S S S + +Speaker sentences 87: swc_deu_001288 #utts: 1 +id: (swc_deu_001288-swc_deu_001288) +Scores: (#C #S #D #I) 29 2 2 3 +REF: W U r D e n k e i n e s ******* w e g * s a l l ** e g e b Ü r t i g e n +HYP: B O r * e n k e i n e s w e g X s a l l Ä e ******* g e b Ü r t i g e n +Eval: S S D I I I D + +Speaker sentences 88: swc_deu_001289 #utts: 1 +id: (swc_deu_001289-swc_deu_001289) +Scores: (#C #S #D #I) 20 4 1 0 +REF: i s t i H r k Ö r P e r b a u K r Ä f t i g +HYP: i s t i E r k Ö r B e r b a u ******* G r E f t i g +Eval: S S D S S + +Speaker sentences 89: swc_deu_001290 #utts: 1 +id: (swc_deu_001290-swc_deu_001290) +Scores: (#C #S #D #I) 27 3 7 4 +REF: a n l Ä S s l i c h D e r n e u j a H R E s ******* a n * * s p r a c h * e k I M +HYP: a n l ** E s l i c h ******* T e r n e u j a * * * s a n D G s p r a c h R e ******* k * E +Eval: D S D S D D D I I I I D D S + +Speaker sentences 90: swc_deu_001291 #utts: 1 +id: (swc_deu_001291-swc_deu_001291) +Scores: (#C #S #D #I) 22 2 2 0 +REF: m i t w i n d v o n s c H r Ä G h i n t e n +HYP: m i t w i n d v o n s c * r ** E C h i n t e n +Eval: D D S S + +Speaker sentences 91: swc_deu_001292 #utts: 1 +id: (swc_deu_001292-swc_deu_001292) +Scores: (#C #S #D #I) 45 3 5 3 +REF: d e n g r * Ö S s t e n t e I l d e r b e ******* z i ** r K s V e r t r e t u n g U E r d i n G e n a u s +HYP: d e n g r E Ö * s t e n t e * l ******* d e r b e z i Ü r G s W e r t r e t u n g * Ö r d i n * e n a u s +Eval: I D D D I I S S D S D + +Speaker sentences 92: swc_deu_001293 #utts: 1 +id: (swc_deu_001293-swc_deu_001293) +Scores: (#C #S #D #I) 23 1 6 0 +REF: a c h t Z E h n h u n D e r T e i N u n d z w A n z i G +HYP: a c h t * * h n h u n * e r * e i * u n d z w E n z i * +Eval: D D D D D S D + +Speaker sentences 93: swc_deu_001294 #utts: 1 +id: (swc_deu_001294-swc_deu_001294) +Scores: (#C #S #D #I) 37 1 3 0 +REF: D e s G r o s s e n a d e l s a n g e s a m m E L t e n r e i c h t u m s +HYP: * e s * r o s s e n a d e l s a n g e s a m m * I t e n r e i c h t u m s +Eval: D D D S + +Speaker sentences 94: swc_deu_001295 #utts: 1 +id: (swc_deu_001295-swc_deu_001295) +Scores: (#C #S #D #I) 22 6 10 3 +REF: * ******* s o L L T e N n i C h t A l S s e X U E L l E p R O V o k a t * i O n +HYP: E s o * * * e * n i * h t E l * s e H S O l * p * * * o k a t Z i U n +Eval: I I D D D D D S D S S S S D D D D I S + +Speaker sentences 95: swc_deu_001296 #utts: 1 +id: (swc_deu_001296-swc_deu_001296) +Scores: (#C #S #D #I) 29 4 7 1 +REF: t e i l h a b e R d e R F I r m * A G o S s m a N n u n d j Ü r g e n S +HYP: t e i l h a b e * ******* d e * * V r m E R * o * s m a * n u n d j I r g e n Z +Eval: D D D D S I S D D D S S + +Speaker sentences 96: swc_deu_001297 #utts: 1 +id: (swc_deu_001297-swc_deu_001297) +Scores: (#C #S #D #I) 14 8 16 7 +REF: * * * * ******* D e r K r a u T I n S e L B I L D E t S I e * D I E G E M E i n * D E +HYP: N E R M T e r F r a u * * n * e * ******* * * * * N t ******* * * e R A U T * * * * i n S L T +Eval: I I I I I S S D D D D D D D D D S D D D I S S S D D D D I S S + +Speaker sentences 97: swc_deu_001298 #utts: 1 +id: (swc_deu_001298-swc_deu_001298) +Scores: (#C #S #D #I) 52 2 3 4 +REF: a u * d i * o I s t E I n d e u t S c h e r h Ö r ******* b u c h * v e r l a g m i t s i t z i n m Ü n c h e n +HYP: a u R d i E o Ö s t * A n d e u t * c h e r h Ö r b u c h F v e r l a g m i t s i t z i n ******* m Ü n c h e n +Eval: I I S D S D I I D + +Speaker sentences 98: swc_deu_001299 #utts: 1 +id: (swc_deu_001299-swc_deu_001299) +Scores: (#C #S #D #I) 43 3 2 3 +REF: f a r B p i * G m e n t e u n d c h e m i S c h e v o r p r o * D u * k t e h e r s t e L l t +HYP: f a r p i K T m e n t e u n d c h e m i * c h e v o r p r o T R u C k t e h e r s t e * l t +Eval: S I S D I S I D + +Speaker sentences 99: swc_deu_001300 #utts: 1 +id: (swc_deu_001300-swc_deu_001300) +Scores: (#C #S #D #I) 113 7 5 2 +REF: E r B l i c h e n p r e u s s i s c h e n f r e i ******* h e R r e n ******* s t a n d i n d e r z O l L a n s c h l u s s f r a g e e n t s c h i e d e n g e g e n d e n s E n a T a u f d i e s e i t e b i s m a r C K s g e s t E l l t +HYP: A r P l i c h e n p r e u s s i s c h e n f r e i h e * r e n s t a n d i n d e r z A l a n s c h l u s s f r a g e e n t s c h i e d e n g e g e n d e n ******* s I n a R a u f d i e ******* s e i t e b i s m a r * G s g e s t * l l t +Eval: S S I D I S S D S S D D S D + +Speaker sentences 100: swc_deu_001301 #utts: 1 +id: (swc_deu_001301-swc_deu_001301) +Scores: (#C #S #D #I) 51 6 9 1 +REF: w e N n d i E Q U E L l e n v o n s e l B s T H e r ******* V o r Q U e L l e n u n d o f f e n z u t a g e l i e g e n +HYP: w e * n d i * * * W Ä l e n v o n s e l * s * * e r F o r G W e * l e n u n d o f f e n z u O t a g e ******* l i e g e n +Eval: D D D D S S D D D I S S S D S D + +Speaker sentences 101: swc_deu_001302 #utts: 1 +id: (swc_deu_001302-swc_deu_001302) +Scores: (#C #S #D #I) 36 4 6 4 +REF: d A s v o * * M n a c h b a r ******* b a u t r u P P b E r e i t s b e g o N N E n w u * r D E +HYP: d * s v o N G N n a c h b a r b a u t r u * B b A r e i t s b e g o * * * n w u O r * T +Eval: D I I S I D S S D D D I D S + +Speaker sentences 102: swc_deu_001303 #utts: 1 +id: (swc_deu_001303-swc_deu_001303) +Scores: (#C #S #D #I) 54 1 3 7 +REF: w e r d e n p r * Ä g e n ******* d e e l e m e n t e d e S h a n s * e ******* a t e n ******* t u m s * z u s a M m e n ******* g e f a S s t +HYP: w e r d e n p r E R g e n d e e l e m e n t e d e * h a n s I e a t e n t u m s T z u s a * m e n g e f a * s t +Eval: I S I D I I I I D I D + +Speaker sentences 103: swc_deu_001304 #utts: 1 +id: (swc_deu_001304-swc_deu_001304) +Scores: (#C #S #D #I) 30 7 1 3 +REF: d A s * l i e D w u r D e a l s V o l K s * l i e D a n ******* g e s e H e n +HYP: d E s S l i e T Z w u r * e a l s F o l C s T l i e T a n g e s e I e n +Eval: S I S S D S S I S I S + +Speaker sentences 104: swc_deu_001305 #utts: 1 +id: (swc_deu_001305-swc_deu_001305) +Scores: (#C #S #D #I) 29 5 7 1 +REF: d e r z U R r A n d O m h O u s E v e r l a g s ******* G r u P P E g e h Ö r t +HYP: d e r z * O r * n d E m h A u s * ******* v e r l a g s U r u * * * B g e h Ö r t +Eval: D S D S S D D I S D D D S + +Speaker sentences 105: swc_deu_001306 #utts: 1 +id: (swc_deu_001306-swc_deu_001306) +Scores: (#C #S #D #I) 47 5 5 3 +REF: F Ü r d I e K Ü n f t i g e n b o r * D b Ü c h e r e n t ******* w i c k e L t e d i E p a p i e r f * A B r i K +HYP: V Ü r d * e * Ö n f t i g e n b o r T b Ü c h e r e n t w i c k e * t e d i * p a p i e r f E R P r i * +Eval: S D D S I S I D D I S S D + +Speaker sentences 106: swc_deu_001307 #utts: 1 +id: (swc_deu_001307-swc_deu_001307) +Scores: (#C #S #D #I) 9 3 1 0 +REF: h a m b U r G w u C H s +HYP: h a m b * r E w u O K s +Eval: D S S S + +Speaker sentences 107: swc_deu_001308 #utts: 1 +id: (swc_deu_001308-swc_deu_001308) +Scores: (#C #S #D #I) 52 3 13 2 +REF: F Ü r d i E Q U a s i * a * d l i g e n l a n d S i t z e B e t r i E b e n E a u f W a n d – s e i E s b e I m b a u +HYP: * ** r d i * * W a s i E a R d l i g e n l a n d Z i t z e P e t r i * b e n * a u f * a n d ******* *** s e i ******* * s b e * m ******* b a u +Eval: D D D D S I I S S D D D D D D D D D + +Speaker sentences 108: swc_deu_001309 #utts: 1 +id: (swc_deu_001309-swc_deu_001309) +Scores: (#C #S #D #I) 60 5 8 0 +REF: j a h r z w e i t a u s e n D z w Ö l f i n d e n b e R l i n e r C l u B s o s e c h s U n D d r e i S s i g v e R l E g t +HYP: j a h r ******* z w e i t a u s e n * z w Ö l f i n ******* d e n b e * l i n e r K l u P s ******* o ******* s e c h s O n * d r e i * s i g v e L l I g t +Eval: D D D D S S D D S D D S S + +Speaker sentences 109: swc_deu_001310 #utts: 1 +id: (swc_deu_001310-swc_deu_001310) +Scores: (#C #S #D #I) 31 3 6 2 +REF: s e c h Z e h n H u n D e R T f Ü n f z i g * a * L s b Ü n d n i s d I E +HYP: s e c h * e h n * u n * e * N D f Ü n f z i g H a I T s b Ü n d n i s d * * +Eval: D D D D S S I I S D D + +Speaker sentences 110: swc_deu_001311 #utts: 1 +id: (swc_deu_001311-swc_deu_001311) +Scores: (#C #S #D #I) 27 2 3 7 +REF: * * * ******* p r o B l e * m b e I D i e s e m p A r a d o * * X o n i s t +HYP: D A S p r o * l e H m b e * * i e s e m p E r a d o C H S o n i s t +Eval: I I I I D I D D S I I S + +Speaker sentences 111: swc_deu_001312 #utts: 1 +id: (swc_deu_001312-swc_deu_001312) +Scores: (#C #S #D #I) 31 2 0 5 +REF: a r m e n ******* w e s e n t Ä t i G a m a l i e s ******* i e v e * k * i n g * +HYP: a r m e n w e s e n t E t i C a m a l i e s i e v e I k E i n g N +Eval: I S S I I I I + +Speaker sentences 112: swc_deu_001313 #utts: 1 +id: (swc_deu_001313-swc_deu_001313) +Scores: (#C #S #D #I) 84 6 7 3 +REF: n i C h t e i n m a l e i n e a * n s a t z w e i s e U n t E R s U c h u n g z u i H r e m v e r h a l t e n i n D e r z e i t d e s n a t * I o n a L s o * z I A l i s m u s +HYP: n i * h t e i n m a l ******* e i n e a N n s a t z w e i s e * n t U s O c h u n g z u i E r e m v e r h a l t e n i n * e r ******* z e i t d e s n a t Z U o n a * s o T z * E l i s m u s +Eval: D D I D S S S S D D I S D I D S + +Speaker sentences 113: swc_deu_001314 #utts: 1 +id: (swc_deu_001314-swc_deu_001314) +Scores: (#C #S #D #I) 24 4 2 2 +REF: l i z e n Z F Ü * r f r E i e d o ******* K U m e n t a t i o n +HYP: l i z e n S V Ü E r ******* f r * i e d o G O m e n t a t i o n +Eval: S S I D D I S S + +Speaker sentences 114: swc_deu_001315 #utts: 1 +id: (swc_deu_001315-swc_deu_001315) +Scores: (#C #S #D #I) 45 5 6 3 +REF: * i m a c h T z E h n t E J A H r ******* h u n d e r T d i e g a r t e n ******* h Ä u s e r v o r d e n t o r e n +HYP: D i m a c h * z I h n t * N I E r h u n d e r * d i e ******* g a r t e n h E u s e r v o r ******* d e n ******* t o r e n +Eval: I D S D S S S I D D I S D D + +Speaker sentences 115: swc_deu_001316 #utts: 1 +id: (swc_deu_001316-swc_deu_001316) +Scores: (#C #S #D #I) 18 1 2 1 +REF: g a n Z i m s t i * l d e r z e i t +HYP: g a n S i m ******* s t i E l ******* d e r z e i t +Eval: S D I D + +Speaker sentences 116: swc_deu_001317 #utts: 1 +id: (swc_deu_001317-swc_deu_001317) +Scores: (#C #S #D #I) 49 2 9 1 +REF: Ü b e r b r Ü H l u n d h Ü ** r t H e r r e i c h T e d I e l e i t u n g s c H l i E S s l i c h k Ö L n +HYP: ** b e r b r Ü Ö l u n d h Ü Ö r t * e r r e i c h e d * e l e i t u n g ******* s c * l i * * s l i c h ******* k Ö * n +Eval: D S I D S D D D D D D D + +Speaker sentences 117: swc_deu_001318 #utts: 1 +id: (swc_deu_001318-swc_deu_001318) +Scores: (#C #S #D #I) 26 1 2 2 +REF: a u s ******* z e i c h n u N G e n f r e m ******* d e r h e R r e n +HYP: a u s z e i c h n u * M e n f r e m d e r h e * r e n +Eval: I D S I D + +Speaker sentences 118: swc_deu_001319 #utts: 1 +id: (swc_deu_001319-swc_deu_001319) +Scores: (#C #S #D #I) 25 1 5 1 +REF: d i E s c h R I f T S t e l l e r e i a u f ******* z u g e b e n +HYP: d i * ******* s c h * E f * * t e l l e r e i a u f z u g e b e n +Eval: D D D S D D I + +Speaker sentences 119: swc_deu_001320 #utts: 1 +id: (swc_deu_001320-swc_deu_001320) +Scores: (#C #S #D #I) 34 4 4 4 +REF: * * ******* z Ä H L e * N d i E B e g e g n u n g m i t v e r l e t z t e n t i e r e n +HYP: D A z U T Z e H E d i * * e g e g n u n g ******* m i t v e r l e t z t e n ******* t i e r e n +Eval: I I I S S S I S D D D D + +Speaker sentences 120: swc_deu_001321 #utts: 1 +id: (swc_deu_001321-swc_deu_001321) +Scores: (#C #S #D #I) 13 0 0 2 +REF: j * * e n i s c h s t i f t +HYP: j E N e n i s c h s t i f t +Eval: I I + +Speaker sentences 121: swc_deu_001322 #utts: 1 +id: (swc_deu_001322-swc_deu_001322) +Scores: (#C #S #D #I) 17 0 0 1 +REF: w e s t l i c h v o n k Ö l * n +HYP: w e s t l i c h v o n k Ö l E n +Eval: I + +Speaker sentences 122: swc_deu_001323 #utts: 1 +id: (swc_deu_001323-swc_deu_001323) +Scores: (#C #S #D #I) 26 0 2 0 +REF: d i e s t Ä n d i g i n b e T r i e b w a r e n +HYP: d i e s t Ä n d i g i n b e * r i e b ******* w a r e n +Eval: D D + +Speaker sentences 123: swc_deu_001324 #utts: 1 +id: (swc_deu_001324-swc_deu_001324) +Scores: (#C #S #D #I) 24 3 3 1 +REF: d i E v o m b a r ******* b i E r R a s i e r t w e r d e N +HYP: d i * v o m b a r b i I r C H a s i e r t ******* w e r d e * +Eval: D I S S S D D + +Speaker sentences 124: swc_deu_001325 #utts: 1 +id: (swc_deu_001325-swc_deu_001325) +Scores: (#C #S #D #I) 41 5 8 2 +REF: e r s c h I e N n o c h e I n w e i t e r e r a u f s A t z v o n C H r i s t i * A n m e Y e * R +HYP: e r s c h * e * n o c h ******* e * n w e i t e r e r ******* a u f s E t z ******* v o n * K r i s t i E R n ******* m e I e L T +Eval: D D D D D S D D S I S D S I S + +Speaker sentences 125: swc_deu_001326 #utts: 1 +id: (swc_deu_001326-swc_deu_001326) +Scores: (#C #S #D #I) 53 5 7 1 +REF: w E I l s e L b s t e x t r e m e R r e i c h t u m k e i n e s ******* w e G S D e n u n m i T t e l b A r e n z u g a n G +HYP: w A L l s e * b s t e x t r e m e * ******* r e i c h t u m k e i n e s w e * * H T e n u n m i * t e l b E r e n z u g a n * +Eval: S S D D D I D D S S D S D + +Speaker sentences 126: swc_deu_001327 #utts: 1 +id: (swc_deu_001327-swc_deu_001327) +Scores: (#C #S #D #I) 23 0 3 0 +REF: g e b t e u c h n i c h T S e l b e r a u f +HYP: g e b t e u c h ******* n i c h * * e l b e r a u f +Eval: D D D + +Speaker sentences 127: swc_deu_001328 #utts: 1 +id: (swc_deu_001328-swc_deu_001328) +Scores: (#C #S #D #I) 51 1 7 3 +REF: * ******* h a t d i e s e n B r a u c H n e u n z e h n h u n d e r T Z w e i u n d ******* f Ü n f z i g G E g e n Ü b E R +HYP: A h a t d i e s e n P r a u c * n e u n z e h n h u n d e r * * w e i u n d f Ü n f z i g * * g e n Ü b * * +Eval: I I S D D D I D D D D + +Speaker sentences 128: swc_deu_001329 #utts: 1 +id: (swc_deu_001329-swc_deu_001329) +Scores: (#C #S #D #I) 49 0 9 1 +REF: w o d I e l e i t u n g Ü b e R d i e a l t e h Ü r t H e r l e i t ******* u n G g e f Ü H r t w u r d e +HYP: w o d * e ******* l e i t u n g Ü b e * d i e a l t e h Ü r t * e r ******* l e i t u n * ******* g e f Ü * r t ******* w u r d e +Eval: D D D D D I D D D D + +Speaker sentences 129: swc_deu_001330 #utts: 1 +id: (swc_deu_001330-swc_deu_001330) +Scores: (#C #S #D #I) 49 3 11 2 +REF: E i n e b E l i e b t e k Ö * l s c h r o c k t r U P p e a U s d e m K Ö L n e r u m ******* L a n d d i e H Ö H n E R +HYP: * i n e b * l i e b t e k Ö A l s c h r o c k t r * O p e ******* a * s d e m * Ö * n e r u m N a n d ******* d i e * Ö * n * A +Eval: D D I D S D D D D I S D D D D S + +Speaker sentences 130: swc_deu_001331 #utts: 1 +id: (swc_deu_001331-swc_deu_001331) +Scores: (#C #S #D #I) 28 1 1 0 +REF: g e w O r d e n s e i u n d a l b r e c h t s i c h +HYP: g e w A r d e n s e i u n d a l b r e c h t ******* s i c h +Eval: S D + +Speaker sentences 131: swc_deu_001332 #utts: 1 +id: (swc_deu_001332-swc_deu_001332) +Scores: (#C #S #D #I) 38 2 6 2 +REF: d E r t a g e s b e d a R f e i n e s E R w a c h s e n * e n a n V i t A m i n a * +HYP: d * r ******* t a g e s b e d a * f e i n e s * * w a c h s e n D e n a n ******* W i t E m i n a R +Eval: D D D D D I D S S I + +Speaker sentences 132: swc_deu_001333 #utts: 1 +id: (swc_deu_001333-swc_deu_001333) +Scores: (#C #S #D #I) 22 4 5 2 +REF: s i E b Z E H n H u N D e r T z * e h n o b e r ******* a l t e r +HYP: s i * b S I n * u * L e r * ******* z I e h n o b e r a l t e r +Eval: D S S S D D S D D I I + +Speaker sentences 133: swc_deu_001334 #utts: 1 +id: (swc_deu_001334-swc_deu_001334) +Scores: (#C #S #D #I) 25 1 5 2 +REF: w e i t e r ******* h i n l i E S S s i C H n a c * h w e i s e n +HYP: w e i t e r h i n l i * * * ******* s i * G n a c R h w e i s e n +Eval: I D D D D D S I + +Speaker sentences 134: swc_deu_001335 #utts: 1 +id: (swc_deu_001335-swc_deu_001335) +Scores: (#C #S #D #I) 30 4 3 1 +REF: Z u m g r Ü n d u n g s * d A t u m k o N n t e M a N b e r e i t S +HYP: S u m g r Ü n d u n g s T d R t u m k o * n t e ******* * a M b e r e i t Z +Eval: S I S D D D S S + +Speaker sentences 135: swc_deu_001336 #utts: 1 +id: (swc_deu_001336-swc_deu_001336) +Scores: (#C #S #D #I) 33 0 2 1 +REF: k e i n l e c K s c h l a g e n m Ö g l i c h n a c h * t e i l e +HYP: k e i n ******* l e c * s c h l a g e n m Ö g l i c h n a c h F t e i l e +Eval: D D I + +Speaker sentences 136: swc_deu_001337 #utts: 1 +id: (swc_deu_001337-swc_deu_001337) +Scores: (#C #S #D #I) 47 5 11 1 +REF: W i r D D i E K a t H o l i s c h e k I r * c h e s T Ü C K p e t e r a n d e r s t e l l e d e R a l t E N +HYP: * i r * ******* T i * * a t * o l i s c h e k Ö r S c h e s * ** A N G p e t e r a n d e r s t e l l e d e * a l t * * +Eval: D D D S D D D S I D D S S S D D D + +Speaker sentences 137: swc_deu_001338 #utts: 1 +id: (swc_deu_001338-swc_deu_001338) +Scores: (#C #S #D #I) 59 7 11 2 +REF: D e r V E R k n a P P u n g d e s B r o t ******* w e i z e n s t r a * t a b e R s c h O n b a l D d i E K A r t o F f e l A L s e r s a t Z +HYP: * e r * F A k n a * C u n g d e s * r o t w e i z e n s t r a R t a b e * ******* s c h * n b a l T d i * * E r t o * f e l E I s e r s a t * +Eval: D D S S D S D I I D D D S D D S D S S D + +Speaker sentences 138: swc_deu_001339 #utts: 1 +id: (swc_deu_001339-swc_deu_001339) +Scores: (#C #S #D #I) 31 1 3 0 +REF: k Ö n n e N m i t d i e s e n n a c h k o M m E n z e u g e n +HYP: k ** n n e * m i t d i e s e n n a c h k o * m I n z e u g e n +Eval: D D D S + +Speaker sentences 139: swc_deu_001340 #utts: 1 +id: (swc_deu_001340-swc_deu_001340) +Scores: (#C #S #D #I) 31 1 3 2 +REF: a L l e n e u * e n f o l g e n d e r h Ö r s P i e L r e i H e * +HYP: a * l e n e u N e n f o l g e n d e r h Ö r s T i e * r e i * e R +Eval: D I S D D I + +Speaker sentences 140: swc_deu_001341 #utts: 1 +id: (swc_deu_001341-swc_deu_001341) +Scores: (#C #S #D #I) 16 2 3 3 +REF: * c h i * p s m i t b * R a t e n s o S S E +HYP: S c h i B p s ******* m i t b E H a t e n s o * * R +Eval: I I D I S D D S + +Speaker sentences 141: swc_deu_001342 #utts: 1 +id: (swc_deu_001342-swc_deu_001342) +Scores: (#C #S #D #I) 54 6 6 0 +REF: k o L l E g e a n D r e A s b U c h n e R v e r z i C h t e t e a u f e I n E p E r s Ö n L i c h e b e w e r t u n g +HYP: k o * l * g e a n r e R s b O c h n e * v e r z i G h t e t e a u f ******* e R n I p * r s Ö n * i c h e b e w e r t u n g +Eval: D D S S S D S D S S D D + +Speaker sentences 142: swc_deu_001343 #utts: 1 +id: (swc_deu_001343-swc_deu_001343) +Scores: (#C #S #D #I) 16 1 0 2 +REF: * w e n i g e r E n ******* t r Ü s t e t +HYP: B w e n i g e r I n t r Ü s t e t +Eval: I S I + +Speaker sentences 143: swc_deu_001344 #utts: 1 +id: (swc_deu_001344-swc_deu_001344) +Scores: (#C #S #D #I) 36 1 2 2 +REF: w e i t e r ******* h i n v e r s o r g ******* T e d I e l e i t u n g t H e r m e n +HYP: w e i t e r h i n v e r s o r g D e d * e l e i t u n g t * e r m e n +Eval: I I S D D + +Speaker sentences 144: swc_deu_001345 #utts: 1 +id: (swc_deu_001345-swc_deu_001345) +Scores: (#C #S #D #I) 28 0 3 1 +REF: w a r t e t e n d a f Ü * r a b e R m i t e i n i g E N +HYP: w a r t e t e n d a f Ü E r a b e * m i t e i n i g * * +Eval: I D D D + +Speaker sentences 145: swc_deu_001346 #utts: 1 +id: (swc_deu_001346-swc_deu_001346) +Scores: (#C #S #D #I) 47 5 6 1 +REF: l e d i G l i c h a n t O n V O n k l e i n m O n i e r t e i n s e i n e R r e * z e n s I o n d e R +HYP: l e d i K l i c h a n t U n ******* D F n k l e i n m U n i e r t e i n s e i n e * ******* r e T z e n s * o n ******* d e * +Eval: S S D S S S D D I D D D + +Speaker sentences 146: swc_deu_001347 #utts: 1 +id: (swc_deu_001347-swc_deu_001347) +Scores: (#C #S #D #I) 17 5 8 0 +REF: I m J a h R e n e u N z e H n H U N D e r t f Ü N F +HYP: E m I a h B e R n e u * z e * n * * * * e r t ******* f Ü * M +Eval: S S S S D D D D D D D D S + +Speaker sentences 147: swc_deu_001348 #utts: 1 +id: (swc_deu_001348-swc_deu_001348) +Scores: (#C #S #D #I) 127 10 13 7 +REF: e r s T M I T d e m f O r t f a L l d e s b Ü r g e R r e c h t S u n d d e r E i n f Ü h r U n g d e R f r e i * z * Ü g i G k e i t i m z w a n Z i g s T E J A H r h U n d e r t w a n d e * L t e s i c h d i e s e a n s c h a U u n g a n s * A t ******* Z w e i s e * d a * h i n +HYP: e r s * * * E d e m f A r t f a * l d e s b Ü r g e * r e c h t Z u n d d e r * i n f Ü h r * n g d e * f r e i T z Y Ü g i C k e i t i m z w a n S i g s * * * N E r h * n d e r t w a n d e T t e s i c h d i e s e a n s c h a * u n g a n s E R t S w e i s e N d a R h i n +Eval: D D D S S D D S D D D I I S S D D D S S D I S D I S I S I I + +Speaker sentences 148: swc_deu_001349 #utts: 1 +id: (swc_deu_001349-swc_deu_001349) +Scores: (#C #S #D #I) 44 3 3 2 +REF: d e s S w i s t ******* b a c H e s b e I r H e i n b a c h e i n e b o g e n ******* b r Ü c k e v o N +HYP: d e s Z w i s t b a c R e s b e * ******* r R e i n b a c h e i n e b o g e n b r Ü c k e v o * +Eval: S I S D D S I D + +Speaker sentences 149: swc_deu_001350 #utts: 1 +id: (swc_deu_001350-swc_deu_001350) +Scores: (#C #S #D #I) 35 4 14 0 +REF: a c h T z E H n H u N D e R T s e C H s u n D d r e i S s i g W u r d e d e R h A m b u r g E R +HYP: a c h * z * I n ******* * u * L e * * s e * X s u n * d r e i * s i g B u r d e d e * h * m b u r g * * +Eval: D D S D D D S D D D S D D S D D D D + +Speaker sentences 150: swc_deu_001351 #utts: 1 +id: (swc_deu_001351-swc_deu_001351) +Scores: (#C #S #D #I) 2 0 17 0 +REF: a m K A R N E V A L S S O N N T A G +HYP: a m ******* * * * * * * * * * * * * * * * * +Eval: D D D D D D D D D D D D D D D D D + +Speaker sentences 151: swc_deu_001352 #utts: 1 +id: (swc_deu_001352-swc_deu_001352) +Scores: (#C #S #D #I) 53 3 4 2 +REF: A u f ******* g r u n d d e r k o n t I n e n ******* t a l s P e R r e a c h t z e h n h u n d e r T e l f b a n K R o t t +HYP: * u f g r u n d d e r k o n t E n e n t a l s B e * r e a c h t z e h n h u n d e r * e l f b a n * o t t +Eval: D I S I S D D D S + +Speaker sentences 152: swc_deu_001353 #utts: 1 +id: (swc_deu_001353-swc_deu_001353) +Scores: (#C #S #D #I) 62 8 14 1 +REF: w e i t e r e s m a l m u S s t e n d A n u n d b l Y T H e * b r O W n d i e w E r b u n g f Ü r d a s b u c h s e L b s t Ü B E R n e H m E n +HYP: w e i t e r e s ******* m a l ******* m u * s t e n ******* d E n u n d b l * * * e I T b r * A n d i e V w A r b u n g ******* f Ü r d a s b u c h ******* s e Ä b s t ** * W A n e * m * n +Eval: D D D D S D D D I S D S S S D D S D D S S D D + +Speaker sentences 153: swc_deu_001354 #utts: 1 +id: (swc_deu_001354-swc_deu_001354) +Scores: (#C #S #D #I) 129 10 20 4 +REF: d i e n a C h r i c h T v O m s I e g D e r b Ü R g e R l i c h d e m o K r a t i S c h e n f e ******* B r U a R r e v o l u t * i o n v O n a c h t z e h n h u n d e r t a c h t ******* u n d ******* v I e r z i G I n f R a n k r e i c h w u r d e I n h A m b u r g m i t J U b e l a u f g e N o M m e N +HYP: d i e ******* n a * h r i c h * v * m s * e g K T e r b Ü * g e * l i c h ******* d e m o G r a t i * c h e n f e P r * a * r e v o l u t Z i o n v E n a c h t z e h n h u n d e r t a c h t u n d v * e r z i C H E n f * a n k r e i c h ******* w u r d e ******* * n h * m b u r g m i t I O b e l a u f g e * o * m e * +Eval: D D D D D S S D D D S D I S D D I S I I D S S S D D D D D S S D D D + +Speaker sentences 154: swc_deu_001355 #utts: 1 +id: (swc_deu_001355-swc_deu_001355) +Scores: (#C #S #D #I) 23 0 6 7 +REF: * * * * * * * z w e i j a H r e o H n e U n t e r b r e c h U n G +HYP: U B L I E B T z w e i ******* j a * r e o * n e * n t e r b r e c h * n * +Eval: I I I I I I I D D D D D D + +Speaker sentences 155: swc_deu_001356 #utts: 1 +id: (swc_deu_001356-swc_deu_001356) +Scores: (#C #S #D #I) 26 0 7 1 +REF: z * a H l r e i c H e n g a s t s p i E l E N u n t e r w e g s +HYP: z E a * l r e i c * e n ******* g a s t s p i * l * * ******* u n t e r w e g s +Eval: I D D D D D D D + +Speaker sentences 156: swc_deu_001357 #utts: 1 +id: (swc_deu_001357-swc_deu_001357) +Scores: (#C #S #D #I) 14 3 1 2 +REF: Q U a n t i * ******* t Ä t g e n Ü G t e n +HYP: C R a n t i E t E t g e n Ü * t e n +Eval: S S I I S D + +Speaker sentences 157: swc_deu_001358 #utts: 1 +id: (swc_deu_001358-swc_deu_001358) +Scores: (#C #S #D #I) 12 3 5 3 +REF: * * ******* b A r o C k E r a u S s t a T t U N G +HYP: I N b E r o * k A r a u * s t a * t * * E +Eval: I I I S D S D D D D S + +Speaker sentences 158: swc_deu_001359 #utts: 1 +id: (swc_deu_001359-swc_deu_001359) +Scores: (#C #S #D #I) 90 6 11 1 +REF: d a s G e r i c h t v o m b e i W a * g e n s E I n e S m o t o R r A d e s a u s i n d i e z u d i e s e r z e i t n e u e n T s t e h E n d e n a R b e i t E R s i e d l u n G e n z u +HYP: d a s * e r i c h t v o m b e i V a R g e n ******* s * A n e * m o t o * r d e s a u s i n d i e z u d i e s e r ******* z e i t ******* n e u e n s t e h * n d e n a * b e i t A s i e d l u n * e n ******* z u +Eval: D S I D D S D D S D D S D D S S D D + +Speaker sentences 159: swc_deu_001360 #utts: 1 +id: (swc_deu_001360-swc_deu_001360) +Scores: (#C #S #D #I) 22 5 2 1 +REF: d i E m i T S a m T i h R e r r e c h e n ******* s t U b e +HYP: d i * ******* m i Z a m D i h e r r e c h e n s t O b e +Eval: D D S S S S I S + +Speaker sentences 160: swc_deu_001361 #utts: 1 +id: (swc_deu_001361-swc_deu_001361) +Scores: (#C #S #D #I) 13 2 1 0 +REF: k A i s e r f e r d i n a n D +HYP: k E i s e r ******* f e r d i n a n T +Eval: S D S + +Speaker sentences 161: swc_deu_001362 #utts: 1 +id: (swc_deu_001362-swc_deu_001362) +Scores: (#C #S #D #I) 58 8 7 6 +REF: v o m f e r n S e H r e G I S s E u * * r f * r A n z * X a v e r b O g n e r i n d e m f e r n s e H f i l * M d a s * e w i g e l i e D +HYP: v o m f e r n * e * r e S C H s * u H E r f E r * n z S G S a v e r ******* b U g n e r i n d e m f e r n s e * f i l E N d a s I e w i g e ******* l i e T +Eval: D D S S S D I I I D I S S D S D I S I D S + +Speaker sentences 162: swc_deu_001363 #utts: 1 +id: (swc_deu_001363-swc_deu_001363) +Scores: (#C #S #D #I) 28 3 2 0 +REF: w U r D e i n s e i n e N b e s t e n z e i t e N D e r +HYP: w O r * e i n s e i n e M b e s t e n z e i t e * I e r +Eval: S D S D S + +Speaker sentences 163: swc_deu_001364 #utts: 1 +id: (swc_deu_001364-swc_deu_001364) +Scores: (#C #S #D #I) 29 2 5 2 +REF: s I e H Ö r e n d e n a r t i k e l f i s * h A n d c H i * P s +HYP: s * e ******* * Ö r e n d e n a r t i k e l f i s C h ******* E n d c * i E B s +Eval: D D D I D S D I S + +Speaker sentences 164: swc_deu_001365 #utts: 1 +id: (swc_deu_001365-swc_deu_001365) +Scores: (#C #S #D #I) 10 3 0 2 +REF: u n d t * * V m o V i e +HYP: u n d t E F A R m o W i e +Eval: I I S S S + +Speaker sentences 165: swc_deu_001366 #utts: 1 +id: (swc_deu_001366-swc_deu_001366) +Scores: (#C #S #D #I) 29 6 4 4 +REF: r e ******* Z e p t i * o n d e R h e * * X e n T H E m a t i K v o n C H r i s t a +HYP: r e S e p t i U o n ******* d e * h e C H S e n * D m a t i G v o n * K r i s t a +Eval: I S I D D I I S D S S S D S + +Speaker sentences 166: swc_deu_001367 #utts: 1 +id: (swc_deu_001367-swc_deu_001367) +Scores: (#C #S #D #I) 66 5 6 3 +REF: d i E g e s a m t e * a n l a g e w a r b I s e t ******* * W A z w e i h u n d e r T s e c h z i g n a c h C H r i s t u s i n b e T r i e b +HYP: d i * ******* g e s a m t e R a n l a g e w a r ******* b E s ******* e t V E R z w e i h u n d e r * s e c h z i g n a c h * K r i s t u s i n b e D r i e b +Eval: D D I D S D I I S S D D S S + +Speaker sentences 167: swc_deu_001368 #utts: 1 +id: (swc_deu_001368-swc_deu_001368) +Scores: (#C #S #D #I) 31 8 6 2 +REF: d e r e R s ******* T e f a s t f O O d l i E f e r s E r V i C e * w a R g e b o R E N +HYP: d e r e * s D e f a s t ******* f U T d l i * f e r s O r i D e S w a * ******* g e b o * C H +Eval: D I S D S S D S S S I D D D S S + +Speaker sentences 168: swc_deu_001369 #utts: 1 +id: (swc_deu_001369-swc_deu_001369) +Scores: (#C #S #D #I) 14 1 0 2 +REF: e i n e m k a b e l b a u * * M +HYP: e i n e m k a b e l b a u N E N +Eval: I I S + +Speaker sentences 169: swc_deu_001370 #utts: 1 +id: (swc_deu_001370-swc_deu_001370) +Scores: (#C #S #D #I) 36 0 6 3 +REF: E R m o r * * ******* d u n g m i t d e R g e f a h r v e r b u n d e n g e w e s E N +HYP: * * m o r L E d u n g m i t d e * g e f a h r ******* v e r b u n d e n g e w e s * * +Eval: D D I I I D D D D + +Speaker sentences 170: swc_deu_001371 #utts: 1 +id: (swc_deu_001371-swc_deu_001371) +Scores: (#C #S #D #I) 28 2 6 1 +REF: d e R Ä l t E s t e n p f * e r d e r e N n e n a U S s e R h a L b +HYP: d e * E l t I s t e n p f I e r d e r e * n e n a * * s e * h a * b +Eval: D S S I D D D D D + +Speaker sentences 171: swc_deu_001372 #utts: 1 +id: (swc_deu_001372-swc_deu_001372) +Scores: (#C #S #D #I) 60 5 6 8 +REF: s o n D e R n a u c h d e r n a t ******* * I o n a * l ******* s * o z ******* I A L i s t i s c h e n k u n S t ******* a u f ******* f a s s u n G g e r e c h t w e r d e n +HYP: s o n * e * n a u c h d e r ******* n a t Z U o n a H l s U o z E R D i s t i s c h e n k u n Z t a u f f a s s u n * ******* g e r e c h t ******* w e r d e n +Eval: D D D I I S I I I I S S S S I I D D D + +Speaker sentences 172: swc_deu_001373 #utts: 1 +id: (swc_deu_001373-swc_deu_001373) +Scores: (#C #S #D #I) 23 2 2 2 +REF: d i e w E l T S i c h T d e s h a n s e ******* a * t e n +HYP: d i e w * l L Z i c h * d e s h a n s e a R t e n +Eval: D S S D I I + +Speaker sentences 173: swc_deu_001374 #utts: 1 +id: (swc_deu_001374-swc_deu_001374) +Scores: (#C #S #D #I) 29 2 3 0 +REF: a u c H n a c h k o m m e n S i n D n i c h t b e k a N n t +HYP: a u c * n a c h k o m m e n E i n T n i c h t ******* b e k a * n t +Eval: D S S D D + +Speaker sentences 174: swc_deu_001375 #utts: 1 +id: (swc_deu_001375-swc_deu_001375) +Scores: (#C #S #D #I) 30 4 5 1 +REF: I H r e n t e x * t e N d e n e i n d r u C k z U v e r m i t t e L N +HYP: * E r e n ******* t e x S t e * d e n e i n d r u G k T z E v e r m i t t e * * +Eval: D S D I D S S S D D + +Speaker sentences 175: swc_deu_001376 #utts: 1 +id: (swc_deu_001376-swc_deu_001376) +Scores: (#C #S #D #I) 29 2 8 1 +REF: v e r ******* d i E n s t E U m d a s k Ö L n E r l i e D v e r l i E H e n +HYP: v e r d i * n s t * * m d a s ******* k Ö * n A r ******* l i e T v e r l i * * e n +Eval: I D D D D D S D S D D + +Speaker sentences 176: swc_deu_001377 #utts: 1 +id: (swc_deu_001377-swc_deu_001377) +Scores: (#C #S #D #I) 67 8 11 4 +REF: O b w o H l h o F f m A N n v o n h o F f m * A n N S w a * l ******* d a u s w e r K g r o S s e n e i n f l u S s a U F s p Ä T e r e d i c h t e R a u s ******* Ü b t E +HYP: * b w o L l h o * f m E I n v o n h o * f m E I n Z w a L l d a u s w e r G g r o * s e n ******* e i n f l u * s a * * s p Ä * e r e R d i c h t e * a u s Ü b t * +Eval: D S D S S D I S S S I I S D D D D D D S D I D + +Speaker sentences 177: swc_deu_001378 #utts: 1 +id: (swc_deu_001378-swc_deu_001378) +Scores: (#C #S #D #I) 25 3 7 2 +REF: U m s o e r * n s t a l S s t A a T s ******* o b e r h a u p T v O n +HYP: * m M s o e r M n s t ******* a l * ******* s t * a * s o b e r h a u p * F v E n +Eval: D S I D D D D D I D S S + +Speaker sentences 178: swc_deu_001379 #utts: 1 +id: (swc_deu_001379-swc_deu_001379) +Scores: (#C #S #D #I) 11 1 1 8 +REF: * * * * * * ******* d o ******* k U m e N t a t i o n +HYP: E F R E I E d o k O m e * t a t i o n +Eval: I I I I I I I I S D + +Speaker sentences 179: swc_deu_001380 #utts: 1 +id: (swc_deu_001380-swc_deu_001380) +Scores: (#C #S #D #I) 31 4 0 2 +REF: g e s t a l t u n G D e s C O v e r s w i * d e r ******* s p i e g e l t +HYP: g e s t a l t u n M B e s K A v e r s w i E d e r s p i e g e l t +Eval: S S S S I I + +Speaker sentences 180: swc_deu_001381 #utts: 1 +id: (swc_deu_001381-swc_deu_001381) +Scores: (#C #S #D #I) 24 1 3 1 +REF: d e r G e s A m t e * a u f w a n d w i R D a u f +HYP: d e r * e s * m t e R a u f w a n d w i * T a u f +Eval: D D I D S + +Speaker sentences 181: swc_deu_001382 #utts: 1 +id: (swc_deu_001382-swc_deu_001382) +Scores: (#C #S #D #I) 80 3 8 1 +REF: o b g l e i c H h a m b U r G d i e s e m a n g e h Ö r t e u n d e i n e n o b i l i ******* t i e r u n g d u R c h D e n k A i s e r d a m i t k e i n e d U r c H +HYP: o b g l e i c * ******* h a m b O r K d i e s e m a n g e h Ö r t e u n d e i n e n o b i l i t i e r u n g d u * c h ******* * e n ******* k E i s e r d a m i t k e i n e d * r c * +Eval: D D S S I D D D D S D D + +Speaker sentences 182: swc_deu_001383 #utts: 1 +id: (swc_deu_001383-swc_deu_001383) +Scores: (#C #S #D #I) 69 3 3 0 +REF: d a E s D u r c h d e n s i c H a u s w e i t e n D e n w e l t h a n d e l a r b e i t u n d w o h l s t a n d v e r s p r a c h +HYP: d a * s ******* T u r c h d e n ******* s i c G a u s w e i t e n T e n w e l t h a n d e l a r b e i t u n d w o h l s t a n d v e r s p r a c h +Eval: D D S D S S + +Speaker sentences 183: swc_deu_001384 #utts: 1 +id: (swc_deu_001384-swc_deu_001384) +Scores: (#C #S #D #I) 70 3 10 5 +REF: f Ü ** r d i e z e i * t m i T t e d e s n e u n z e h n t e j a H R h U n d e r T S b e k l a g T E d e * R A r c h i * ******* t e k t m a R t i n h a l l e R +HYP: f Ü Ö r d i e z e i T t m i * t e d e s n e u n z e h n t e j a * h * n d e r * * b e k l a g * * d e D E E r c h i E t e k t ******* m a * t i n h a l l e * +Eval: I I D D S D D D D D I S S I I D D D + +Speaker sentences 184: swc_deu_001385 #utts: 1 +id: (swc_deu_001385-swc_deu_001385) +Scores: (#C #S #D #I) 30 4 4 0 +REF: A L t b U n d e s k a n z L e r h e L m u t s c h m i D t l e H n t e +HYP: E I t b O n d e s k a n z * e r h e * m u t ******* s c h m i T t l e * n t e +Eval: S S S D D D S D + +Speaker sentences 185: swc_deu_001386 #utts: 1 +id: (swc_deu_001386-swc_deu_001386) +Scores: (#C #S #D #I) 30 7 13 1 +REF: d E N n a m e n g o d e F f r O Y i m s t A a t * S h a n d b U c h Z U S t R E I C H E N +HYP: d * I n a m e n g o d e f r E I i m s t * a t Z h a n d b O c h ******* * * ******* * t * * * * * * Z +Eval: D S S S S D I S S D D D D D D D D D D D S + +Speaker sentences 186: swc_deu_001387 #utts: 1 +id: (swc_deu_001387-swc_deu_001387) +Scores: (#C #S #D #I) 27 1 10 0 +REF: w e N n a u c h M I T e I n e r g e w i S s e n l e t H a R g i e +HYP: w e * n a u c h ******* * * E e * n e r ******* g e w i * s e n ******* l e t * a * g i e +Eval: D D D D S D D D D D D + +Speaker sentences 187: swc_deu_001388 #utts: 1 +id: (swc_deu_001388-swc_deu_001388) +Scores: (#C #S #D #I) 10 2 0 3 +REF: k a l k U l i e r * ******* b a * R +HYP: k a l k O l i e r E b a E G +Eval: S I I I S + +Speaker sentences 188: swc_deu_001389 #utts: 1 +id: (swc_deu_001389-swc_deu_001389) +Scores: (#C #S #D #I) 41 6 11 4 +REF: a n g e f A n g E N e z W E I h u n D e r T * f Ü n * F z i G s c H Ü * l e r e i n e n d * e l E g i e R t e n +HYP: a n g e f * n g * D e ******* z * * S A h u n * e r * V f ** n M z i * ******* s c * Ü U l e r e i n e n d I e l I g i e I t e n +Eval: D D S D D D S S D D I D I S D D D I I S S + +Speaker sentences 189: swc_deu_001390 #utts: 1 +id: (swc_deu_001390-swc_deu_001390) +Scores: (#C #S #D #I) 63 4 17 0 +REF: v i e l E m e n s c h e n s a H E n D e n G r i Z Z l Y a l s n a h r u n g s k o n K U R r e n t e n u n d a l s p o t e n T I e L l E g e f a H R +HYP: v i e l * ******* m e n s c h e n s a * * n ******* * e n * r i * S l I a l s n a h r u n g s k o n * * G r e n t e n u n d a l s ******* p o t e n * e * l * ******* g e f a * * +Eval: D D D D D D D D S S D D S D D S D D D D D + +Speaker sentences 190: swc_deu_001391 #utts: 1 +id: (swc_deu_001391-swc_deu_001391) +Scores: (#C #S #D #I) 17 2 3 0 +REF: d e n A u f t r i T t v e R k Ü r z e N +HYP: d e n * u f t r i * t v e k Ö r z e * +Eval: D D S S D + +Speaker sentences 191: swc_deu_001392 #utts: 1 +id: (swc_deu_001392-swc_deu_001392) +Scores: (#C #S #D #I) 89 25 13 37 +REF: * * ******* d E m s t a n D v o m d E r i * * * * ******* n H A l * * * * * * ******* t * * * * * s T e * * ******* * H T U n * * * * t e R * * d e r * * l i z e n Z C r e A t I V E c o M m o n s A T T r I B u * T I O n s * h A r ******* e A l I K e * d r e i * p u n k T n U L l U n p o r t e D u n d u n t e R d e R +HYP: M I d * m s t a n * v o m d * r E i T Z H N n I U l I E Z W E I t A U S E N s W e R F D E R I n H E R S t e T U N d e r D E l i z e n S K r e R t * Z U c o * m o n s E I Z r E W u S C H E n s C h E r e * l * * e I T d r e i T p u n k * n * * l A n p o r t e T u n d u n t e * d e * +Eval: I I I D D D S I I I I I S S I I I I I I I I I I I I S I I I I S S S I I I I S I I I I S S S D S S D S S S S S I S S S I S I D D D I S I D D D S S D D + +Speaker sentences 192: swc_deu_001393 #utts: 1 +id: (swc_deu_001393-swc_deu_001393) +Scores: (#C #S #D #I) 24 0 1 1 +REF: e i n e k l e i n e r e b o g e n ******* b r Ü c k e +HYP: e i n e k l e i n e r e ******* b o g e n b r Ü c k e +Eval: D I + +Speaker sentences 193: swc_deu_001394 #utts: 1 +id: (swc_deu_001394-swc_deu_001394) +Scores: (#C #S #D #I) 20 2 8 0 +REF: s i c h n u n F Ü r s e I n w e i t E r k o M m E N +HYP: s i c h n u n ******* V E r ******* s e * n ******* w e i t * r k o * m * * +Eval: D S S D D D D D D D + +Speaker sentences 194: swc_deu_001395 #utts: 1 +id: (swc_deu_001395-swc_deu_001395) +Scores: (#C #S #D #I) 28 0 0 3 +REF: a u s d e m g e m * Ä l d e z u e n t ******* f e r * n e n +HYP: a u s d e m g e m E Ä l d e z u e n t f e r E n e n +Eval: I I I + +Speaker sentences 195: swc_deu_001396 #utts: 1 +id: (swc_deu_001396-swc_deu_001396) +Scores: (#C #S #D #I) 48 7 16 6 +REF: * * N A c h E U r o p Ä I s c h e R r i c h t l i n I e n e u n z i * g * v i e r H U n D e r t S e C H s U N D n e u n z * i * G e w G +HYP: I S D c h Ö A r o p Ä * s c h e * ******* r i c h t l i n * e n e u n z i C g H v i e r ******* * * n * e r t ******* * e * X s * * * n e u n z S i C H e ******* w ******* I +Eval: I I S S S S D D D D I I D D D D D D D S D D D I I S D D S + +Speaker sentences 196: swc_deu_001397 #utts: 1 +id: (swc_deu_001397-swc_deu_001397) +Scores: (#C #S #D #I) 10 4 2 0 +REF: E I N e M u m F e l D a u f +HYP: * A L e * u m S e l T a u f +Eval: D S S D S S + +Speaker sentences 197: swc_deu_001398 #utts: 1 +id: (swc_deu_001398-swc_deu_001398) +Scores: (#C #S #D #I) 30 2 1 0 +REF: U n d s i e s e i a u c h w i e e i n e f Ü r s t I n +HYP: * n d s i e s e i a u c h w i e e i n e f I r s t E n +Eval: D S S + +Speaker sentences 198: swc_deu_001399 #utts: 1 +id: (swc_deu_001399-swc_deu_001399) +Scores: (#C #S #D #I) 24 0 1 1 +REF: n e u n z e h n h u n d e r t n e u n ******* z e H n +HYP: n e u n z e h n h u n d e r t n e u n z e * n +Eval: I D + +Speaker sentences 199: swc_deu_001400 #utts: 1 +id: (swc_deu_001400-swc_deu_001400) +Scores: (#C #S #D #I) 35 2 5 3 +REF: s t a T t D E s s e n h a b e n d I e r Ö m i s c h e N i n ******* G e n ******* i E u r e * +HYP: s t a * t * I s s e n h a b e n d * e r Ö m i s c h e * i n J e n i * u r e M +Eval: D D S D D I S I D I + +Speaker sentences 200: swc_deu_001401 #utts: 1 +id: (swc_deu_001401-swc_deu_001401) +Scores: (#C #S #D #I) 16 4 1 6 +REF: * * * G r i Z Z l * * Y b * Ä r u n d m e n s c h +HYP: D E L K r i * S l I E b E H r u n d m e n s c h +Eval: I I I S D S I I S I S + +Speaker sentences 201: swc_deu_001402 #utts: 1 +id: (swc_deu_001402-swc_deu_001402) +Scores: (#C #S #D #I) 9 10 0 2 +REF: m * U s i c I A n s * C O A l i T I O N +HYP: m I E s i c H E n s K U R E l i S C H E +Eval: I S S S I S S S S S S S + +Speaker sentences 202: swc_deu_001403 #utts: 1 +id: (swc_deu_001403-swc_deu_001403) +Scores: (#C #S #D #I) 29 7 7 2 +REF: b e r t o L D h u M m E l g I b T * e s d r e i v a r * I A T i o n e N m i T +HYP: b e r t o * T C h u * m * l ******* g E b * D e s d r e i ******* v a r E R Z i o n e R m i * +Eval: D S S D D D S D I D I S S S S D + +Speaker sentences 203: swc_deu_001404 #utts: 1 +id: (swc_deu_001404-swc_deu_001404) +Scores: (#C #S #D #I) 75 6 17 5 +REF: R Ü C k ******* z u g s g e b i e * t e r w I e S s i c h D e r a c h T z e h n h u n d E R t z w e i u n D s i e b z i g G e ******* g r Ü n D e t e Y E L l O W s t * O n e N A t ******* i o n a l p a r k +HYP: * ** * k z u g s g e b i e D t e r w * e * ******* s i c h T e r a c h * z e h n h u n d * A t ******* z w e i u n s i e b z i g * e g r Ü n * e t e * * J l * U s t D U n e ******* * * t i o n a l p a r k +Eval: D D D I I D D D S D D S D S D I D D D S D S I S D D D I + +Speaker sentences 204: swc_deu_001405 #utts: 1 +id: (swc_deu_001405-swc_deu_001405) +Scores: (#C #S #D #I) 9 0 1 1 +REF: D e ******* f i n i t i o n +HYP: * e f i n i t i o n +Eval: D I + +Speaker sentences 205: swc_deu_001406 #utts: 1 +id: (swc_deu_001406-swc_deu_001406) +Scores: (#C #S #D #I) 56 3 13 1 +REF: u m A n D e R u n i v e R s i t Ä t S E v i L l a z w e i s e ******* m E s t e r k u n s T g e s c h I c h t E z u s t u d i e r E n +HYP: u m I n ******* * e * u n i v e * s i t E t * Z v i * l a z w e i ******* s e m * s t e r k u n s * g e s c h * c h t * z u ******* s t u d i e r * n +Eval: S D D D D S D S D D I D D D D D D + +Speaker sentences 206: swc_deu_001407 #utts: 1 +id: (swc_deu_001407-swc_deu_001407) +Scores: (#C #S #D #I) 13 1 6 4 +REF: * * * ******* t R o t z I H r e R g E r i n G e N +HYP: D I E t * o t z * E r e * g * r i n * e * +Eval: I I I I D D S D D D D + + diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..c1ee6f8f240bdd7d181571879745a6d24a8311c9 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/hyp.trn @@ -0,0 +1,207 @@ +DEI ERVERLIEBTE UNGE HEARZOG IE RANSCLÄEKESEINESFATES NICHTBERACGTDETAB (swc_deu_001201-swc_deu_001201) +DIE IN DE ANSESTERDTEN ALS (swc_deu_001202-swc_deu_001202) +ARKEINGROSE ERFOLGK (swc_deu_001203-swc_deu_001203) +GOSEN SCHEHMICHEIN VERBRICKEN (swc_deu_001204-swc_deu_001204) +URDEN ACH MEHRERE AR LEUTRUNGSBÜCHE VER FFNTLICHT (swc_deu_001205-swc_deu_001205) +VORBEREITEN BIER TEIG GETUNGT (swc_deu_001206-swc_deu_001206) +DOMENTE SHLIESLICG IN E (swc_deu_001207-swc_deu_001207) +TAUTAG VÜRDENTAUT VON KÖNICH VERERCHÜLE (swc_deu_001208-swc_deu_001208) +DARUNDER SIND MATILDE ARSENSIS WECHTER DES KREULZIS (swc_deu_001209-swc_deu_001209) +EN INENSTÄTEN MEHR UND MEHR IEROLE DERTRADI Z NELN ISH (swc_deu_001210-swc_deu_001210) +Z DENEN WELTLEUFICHKEIIT (swc_deu_001211-swc_deu_001211) +RACHRE TDSHOFES UND DES ADETZS FÜR DEN FRIEFET (swc_deu_001212-swc_deu_001212) +ZEIT ANGABEN VERZICHTET (swc_deu_001213-swc_deu_001213) +ALL ACHTZEHN HUNDERT ACHTZICHMIT OTTUOBRAMMS AUFSEITZ (swc_deu_001214-swc_deu_001214) +MÜLEN WESEN SIBTEHN UNED CHUNDZWANZI (swc_deu_001215-swc_deu_001215) +AS DER FICH RICH (swc_deu_001216-swc_deu_001216) +SIDEM ABSCLSS IMJAHRENUNZEN HNDR ZWAEUN ACHTZICG UND ERNAMER EINE ERSTE LÄNGERE REISE ACSPANEN (swc_deu_001217-swc_deu_001217) +VÜRNSCHATSO VURGEZEICHNET (swc_deu_001218-swc_deu_001218) +FEITENSTEINS FLSTENDIGEGESCHICHTEN UND DIE AUGSBRGESTADBGESCHIEG EDES ELTREN (swc_deu_001219-swc_deu_001219) +NACH DIESENZERSTÖHRUNGEN WURDE DERASSCHWIEDER AUFBLHN (swc_deu_001220-swc_deu_001220) +MACHTEN EINFLUSREICHEN HAN SIE ARTENM BEIM KOMISARISCHEIN GESETZTEN BÜRGEMEISTER MARKERT IHRE AUFWAHTUN (swc_deu_001221-swc_deu_001221) +ALS SENTRALDESHANDES KOTOR (swc_deu_001222-swc_deu_001222) +SONDER STELLUNG INEHEILB DERSTADT KRFEL (swc_deu_001223-swc_deu_001223) +FINE SICH INHALOBAOKTERIEN (swc_deu_001224-swc_deu_001224) +AUF DER B SEITE FINDE ZICH DAS EBENFALS VON MEIKEL OMPONIRT (swc_deu_001225-swc_deu_001225) +IN HANDE ARTISCHE ZEIT HATE DIE ZERKEGESESCHAFT KEINEN AUSCHAGEBENDEN ENFLSMIER (swc_deu_001226-swc_deu_001226) +DAR SDERCH VEWENDUN VON AUFTRIEBSKÖRPAN ODER HOLZ EINE GERINGERE MITTLERETDICHTE ALS WASSER HAT (swc_deu_001227-swc_deu_001227) +DRAMEI TDE SIERUNGEN (swc_deu_001228-swc_deu_001228) +UMM SEBEN OR FÜNWON (swc_deu_001229-swc_deu_001229) +DES ALBRECHT DIE BADES TOCHTER (swc_deu_001230-swc_deu_001230) +TAT BAM BARESCHE STATZR (swc_deu_001231-swc_deu_001231) +DRSLIET BESONDERSLIEBTE (swc_deu_001232-swc_deu_001232) +AUF KUND DES WAHSENDEN POPLIKUMS INTRESSES WRDE DER AUFTRITS ORT ÜR IE PRIMER ISTALESUNGE (swc_deu_001233-swc_deu_001233) +ND FREIDLICH SPBIELE (swc_deu_001234-swc_deu_001234) +DS DIE REIDEN STURTZ ERLATIE UN BESCHADE BER STANDEN HATE (swc_deu_001235-swc_deu_001235) +JAHREN ASCHENEND ZWEI IMEN (swc_deu_001236-swc_deu_001236) +DR RABMALE UND GRAB GKAPÄLEN ODER OL TATEN NACHALT (swc_deu_001237-swc_deu_001237) +JUNENEUZEN HUNDR EHSUNEUNZIG KÖNEKTDER RSEINE BEIDEN JOPS (swc_deu_001238-swc_deu_001238) +IN GE PORTINE OPET (swc_deu_001239-swc_deu_001239) +NEUNUN SECHZIG DER MELIER KONTWOL ALBUMSCHATZ EIN (swc_deu_001240-swc_deu_001240) +TADUCHCOM (swc_deu_001241-swc_deu_001241) +ONE HEN NICHT DEN ROSHANDELS KAUFLEUTEN GESELSCHAFTLICG GLEICHGESTET WAREN (swc_deu_001242-swc_deu_001242) +VO DERNARUNG UND VOM KLIEMA (swc_deu_001243-swc_deu_001243) +APOLO EINS (swc_deu_001244-swc_deu_001244) +BRÜYELEL UND HÖRT NACHKELEN (swc_deu_001245-swc_deu_001245) +TWAR IN EN KLOSTE (swc_deu_001246-swc_deu_001246) +DIVWRZEM OFIZEHEN KANEWALEN STANDT UNT TEUTE EINEMSCHUNG AUS KÖRSCHEN KANDEWAL UND POLITSCHEM KABERET MIT KOM DIELMENTEN DARSTELLT UN (swc_deu_001247-swc_deu_001247) +DIE WENSTEIUNGESLIEDES FÜRTEN (swc_deu_001248-swc_deu_001248) +NANTIT ZIEGLER DI ARMORDUM DER BRNAURUNEN (swc_deu_001249-swc_deu_001249) +INTEROHR IST VE L (swc_deu_001250-swc_deu_001250) +DIE STRÄNGE DR FORGENGER LEITUNG WURDEN ZWICHE NEUNZEHN HUNDERT NEUNUNDZWANZIG UND NEUNZHN HUNDERT DREINFÜNFZIG AICHIERLOGESCH ERGRABEN (swc_deu_001251-swc_deu_001251) +IM GEGENSATZ (swc_deu_001252-swc_deu_001252) +VWARBE VON ÖRDIGENSN LAUNDHORD (swc_deu_001253-swc_deu_001253) +LEIF VERANSTALTUMEN (swc_deu_001254-swc_deu_001254) +SUOWERDENHEUTE IN DEREGEL ALE DORTLEBENDEN BRUN (swc_deu_001255-swc_deu_001255) +IEDA FÜRE WÜSCHLMER (swc_deu_001256-swc_deu_001256) +DES HAN SE ATEN FÜRE (swc_deu_001257-swc_deu_001257) +HÄBILS AGNES BERNAU (swc_deu_001258-swc_deu_001258) +LEBENSWEISE VERKÖARPER (swc_deu_001259-swc_deu_001259) +IDEFALTDES VIELEN HAMBURGAN ZU KATOLESCH FRMMEN (swc_deu_001260-swc_deu_001260) +KOLTOUR END DER THEFT AUSTAUSCHEN (swc_deu_001261-swc_deu_001261) +MJAHRZWEI TAUSEND VERTONDTE (swc_deu_001262-swc_deu_001262) +DAS E DIESE LEITUNG SCHNLER VOLÄENDTEN KÖNE ALS DER BAUMEISTER DEN KÖNERDOM (swc_deu_001263-swc_deu_001263) +EIER HINRICHTUNG DER BANAURIEN HABES IC LICHT UM (swc_deu_001264-swc_deu_001264) +LUOREI (swc_deu_001265-swc_deu_001265) +DERZEIT DER BESTIE KENERDER EIFELEITUNG (swc_deu_001266-swc_deu_001266) +VOKUS BES WISENSHAFTLICHEN INTERESSES (swc_deu_001267-swc_deu_001267) +TEME ZU BEGEISTEN (swc_deu_001268-swc_deu_001268) +METER UND KONTE DAMIT AU VON INEN BERGANGEN WERDEN (swc_deu_001269-swc_deu_001269) +HAT KABER BES SELLISTE DER NÜH (swc_deu_001270-swc_deu_001270) +DER FREIN ENZIGKLOP (swc_deu_001271-swc_deu_001271) +DEN GRSLI WIER UF DELSTE ZUSETZEN (swc_deu_001272-swc_deu_001272) +WIE LANG DIESE KAPLANSSTÄLLE AUFRECHTER HETEN WURD (swc_deu_001273-swc_deu_001273) +SIWAHREN WASCHEINLICHBEREITZ DREISIG SI KUNDEN DACH AUSPRCHTES VEUR (swc_deu_001274-swc_deu_001274) +METER GESAMTLINGE UND BIS ZUZEHN MITER (swc_deu_001275-swc_deu_001275) +FEINE RITZEN UN SPALTE (swc_deu_001276-swc_deu_001276) +DINMAN VON AUSSN DIEKELER HINABPFLIESEN SIET (swc_deu_001277-swc_deu_001277) +ENE INTERIUSAGTEBRAUN (swc_deu_001278-swc_deu_001278) +DAS FÜNFTDEVEN GERIUM (swc_deu_001279-swc_deu_001279) +RESEN SIEMANCHMAL WEIDE TIERE IE SCHAFE (swc_deu_001280-swc_deu_001280) +SI ÖRENDEN ARTIKEL DIE SEIN RÜVIEU (swc_deu_001281-swc_deu_001281) +KUSE IS GELENTER KOCH (swc_deu_001282-swc_deu_001282) +HANWENT STIFTUNGE (swc_deu_001283-swc_deu_001283) +NEUNZEHN HUDERT ACHTZIEN AL HAINSIE ARTEN ANGESIEN (swc_deu_001284-swc_deu_001284) +MEHRERER ES NACH IM TOUN (swc_deu_001285-swc_deu_001285) +ACHSTIG ES GERICHTS ZUR LANDES WEITEN BELIEBEN KOLINARISCHE SPEZILITET ERMÖGE (swc_deu_001286-swc_deu_001286) +KOLLETSC UND EIN ZWEIT JOB ALSPANISCHLÄHRER IN HEMTN VORLSEAN (swc_deu_001287-swc_deu_001287) +BOREN KEINES WEGXS ALLÄEGEBÜRTIGEN (swc_deu_001288-swc_deu_001288) +IST IER KÖRBERBAUGREFTIG (swc_deu_001289-swc_deu_001289) +ANLESLICHTER NEUJAS ANDGSPRACHREKE (swc_deu_001290-swc_deu_001290) +MIT WIND VON SCRECHINTEN (swc_deu_001291-swc_deu_001291) +DEN GREÖSTEN TELDER BE ZIÜRGSWERTRETUNG ÖRDINEN AUS (swc_deu_001292-swc_deu_001292) +ACHTHN HUNER EIUNDZWENZI (swc_deu_001293-swc_deu_001293) +ES ROSSEN ADELS ANGESAMMITEN REICHTUMS (swc_deu_001294-swc_deu_001294) +E SOE NIHT EL SEH SOL POKATZIUN (swc_deu_001295-swc_deu_001295) +TEILHABEDE VRMER OSMAN UND JIRGENZ (swc_deu_001296-swc_deu_001296) +NERM TER FRAUNENTE RAUT INSLT (swc_deu_001297-swc_deu_001297) +AURDIEO ÖST AN DEUTCHER HÖR BUCHFVERLAG MIT SITZ INMÜNCHEN (swc_deu_001298-swc_deu_001298) +FAR PIKTMENTE UND CHEMICHE VORPROTRUCKTE HERSTELT (swc_deu_001299-swc_deu_001299) +ARPLICHEN PREUSSISCHEN FREI HEREN STAND IN DER ZAL ANSCHLUSSFRAGE ENTSCHIEDEN GEGEN DENSINAR AUF DIESEITE BISMARGS GESTLLT (swc_deu_001300-swc_deu_001300) +WEN DI WÄLEN VON SELS ER FORGWELEN UND OFFEN ZUOTAGELIEGEN (swc_deu_001301-swc_deu_001301) +DS VONGN NACHBAR BAUTRUB BAREITS BEGON WUORT (swc_deu_001302-swc_deu_001302) +WERDEN PRERGEN DE ELEMENTE DE HANSIE ATEN TUMST ZUSAMEN GEFAST (swc_deu_001303-swc_deu_001303) +DES SLIETZWURE ALS FOLCSTLIET AN GESEIEN (swc_deu_001304-swc_deu_001304) +DER ZO RNDEM HAUSVERLAGS URUBGEHÖRT (swc_deu_001305-swc_deu_001305) +VÜR DE ÖNFTIGEN BORT BÜCHER ENT WICKETE DI PAPIERFERPRI (swc_deu_001306-swc_deu_001306) +HAMBRE WUOKS (swc_deu_001307-swc_deu_001307) +R DI WASIE ARDLIGEN LANDZITZE PETRIBEN AUFAND SEIS BEMBAU (swc_deu_001308-swc_deu_001308) +JAHRZWEI TAUSEN ZWÖLF INDEN BELINER KLUP SOSECHSONDREISIG VELLIGT (swc_deu_001309-swc_deu_001309) +SECHEHN UNENDFÜNFZIGH AITS BÜNDNIS D (swc_deu_001310-swc_deu_001310) +DAS PROLEHM BE IESEM PERADOCHSON IST (swc_deu_001311-swc_deu_001311) +ARMEN WESEN TETIC AMALIE S IEVEIKEINGN (swc_deu_001312-swc_deu_001312) +NIHT EINMALEINE ANNSATZWEISE NTU SOCHUNG ZU IEREM VERHALTEN IN ERZEIT DES NATZUONASOTZELISMUS (swc_deu_001313-swc_deu_001313) +LIZENS VÜERFRIE DO GOMENTATION (swc_deu_001314-swc_deu_001314) +DIM ACHZIHNT NIER HUNDER DIEGARTEN HEUSER VORDENTOREN (swc_deu_001315-swc_deu_001315) +GANS IMSTIELDER ZEIT (swc_deu_001316-swc_deu_001316) +BER BRÜÖL UND HÜÖRT ERREICH E DE LEITUNGSCLISLICHKÖN (swc_deu_001317-swc_deu_001317) +AUS ZEICHNUMEN FREM DER HEREN (swc_deu_001318-swc_deu_001318) +DISCHEFTELLEREI AUF ZUGEBEN (swc_deu_001319-swc_deu_001319) +DA ZUTZEHE DI EGEGNUNGMIT VERLETZTENTIEREN (swc_deu_001320-swc_deu_001320) +JENENISCH STIFT (swc_deu_001321-swc_deu_001321) +WESTLICH VON KÖLEN (swc_deu_001322-swc_deu_001322) +DIE STÄNDIG IN BERIEBWAREN (swc_deu_001323-swc_deu_001323) +DI VOM BAR BIIRCHASIERTWERDE (swc_deu_001324-swc_deu_001324) +ERSCHE NOCHEN WEITERERAUFSETZVON KRISTIERNMEIELT (swc_deu_001325-swc_deu_001325) +WALL SEBST EXTREMEREICHTUM KEINES WEHTEN UNMITELBEREN ZUGAN (swc_deu_001326-swc_deu_001326) +GEBT EUCHNICH ELBER AUF (swc_deu_001327-swc_deu_001327) +A HAT DIESEN PRAUC NEUNZEHN HUNDER WEIUND FÜNFZIG GENÜB (swc_deu_001328-swc_deu_001328) +WO DELEITUNG ÜBE DIE ALTE HÜRTERLEIT UNGEFÜRTWURDE (swc_deu_001329-swc_deu_001329) +INE BLIEBTE KÖALSCHROCKTROPEAS DEM ÖNER UM NANDDIE ÖNA (swc_deu_001330-swc_deu_001330) +GEWARDEN SEI UND ALBRECHTSICH (swc_deu_001331-swc_deu_001331) +DRTAGESBEDAF EINES WACHSENDEN ANWITEMIN AR (swc_deu_001332-swc_deu_001332) +SIB SIN ULERZIEHN OBER ALTER (swc_deu_001333-swc_deu_001333) +WEITER HIN LISIG NACRHWEISEN (swc_deu_001334-swc_deu_001334) +SUM GRÜNDUNGSTDRTUM KONTEAM BEREITZ (swc_deu_001335-swc_deu_001335) +KEINLECSCHLAGEN MÖGLICH NACHFTEILE (swc_deu_001336-swc_deu_001336) +IRTI ATOLISCHE KÖRSCHE SANGPETER AN DER STELLE DE ALT (swc_deu_001337-swc_deu_001337) +ER FAKNACUNG DES ROT WEIZENS TRART ABESCHN BALT DI ERTOFEL EIS ERSAT (swc_deu_001338-swc_deu_001338) +KNNE MIT DIESEN NACHKOMIN ZEUGEN (swc_deu_001339-swc_deu_001339) +ALE NEUNEN FOLGEN DER HÖRSTIEREIER (swc_deu_001340-swc_deu_001340) +SCHIBPSMIT BEHATENSOR (swc_deu_001341-swc_deu_001341) +KOLGE AN RERS BOCHNE VERZIGHTETE AUFERNI PRSÖNICHE BEWERTUNG (swc_deu_001342-swc_deu_001342) +BWENIGER IN TRÜSTET (swc_deu_001343-swc_deu_001343) +WEITER HIN VERSORG DE DE LEITUNG TERMEN (swc_deu_001344-swc_deu_001344) +WARTETEN DAFÜER ABE MIT EINIG (swc_deu_001345-swc_deu_001345) +LEDIKLICH ANTUNDFN KLEIN MUNIERTE IN SEINERETZENSONDE (swc_deu_001346-swc_deu_001346) +EM IAHBERNEUZEN ERTFÜM (swc_deu_001347-swc_deu_001347) +ERS E DEM FARTFAL DES BÜRGERECHTZ UND DER INFÜHRNG DE FREITZYÜGICKEIT IM ZWANSIGS NERHNDERT WANDET TE SICH DIESE ANSCHAUNG ANSERT SWEISEN DARHIN (swc_deu_001348-swc_deu_001348) +DES ZWIST BACRES BERREINBACH EINE BOGEN BRÜCKE VO (swc_deu_001349-swc_deu_001349) +ACHZINULE SEXSUNDREISIG BURDE DE HMBURG (swc_deu_001350-swc_deu_001350) +AM (swc_deu_001351-swc_deu_001351) +UF GRUND DER KONTENEN TALSBERE ACHTZEHN HUNDER ELF BAN OTT (swc_deu_001352-swc_deu_001352) +WEITERESMALMUSTENDEN UND BLEITBRAN DIEVWARBUNGFÜR DAS BUCHSEÄBST WANEMN (swc_deu_001353-swc_deu_001353) +DIENAHRICH VM SEGKTER BÜGELICHDEMOGRATICHEN FE PRAREVOLUTZION VEN ACHTZEHN HUNDERT ACHT UND VERZICHEN FANKREICHWURDEN HMBURG MIT IOBEL AUFGEOME (swc_deu_001354-swc_deu_001354) +UBLIEBTZWEIJARE ONE NTERBRECHN (swc_deu_001355-swc_deu_001355) +ZEALREICENGASTSPILUNTERWEGS (swc_deu_001356-swc_deu_001356) +CRANTIE TET GENÜTEN (swc_deu_001357-swc_deu_001357) +IN BEROKAR AUSTATE (swc_deu_001358-swc_deu_001358) +DAS ERICHT VOM BEIVARGENSANE MOTOR DES AUS IN DIE ZU DIESERZEITNEU EN STEHNDEN ABEIT ASIEDLUNENZU (swc_deu_001359-swc_deu_001359) +DIMI ZAMD IH ER RECHEN STOBE (swc_deu_001360-swc_deu_001360) +KEISERFERDINANT (swc_deu_001361-swc_deu_001361) +VOM FERNERESCHSUHER FERNZSGSAVERBUGNER IN DEM FERNSEFILEN DAS IEWIGELIET (swc_deu_001362-swc_deu_001362) +WORE IN SEINEM BESTEN ZEITE IER (swc_deu_001363-swc_deu_001363) +SEÖREN DEN ARTIKEL FISCHEND CIEBS (swc_deu_001364-swc_deu_001364) +UND TEFAR MOWIE (swc_deu_001365-swc_deu_001365) +RE SEPTIUONDE HECHSEN DMATIG VON KRISTA (swc_deu_001366-swc_deu_001366) +DIGESAMTER ANLAGE WARBESET VER ZWEI HUNDER SECHZIG NACH KRISTUS IN BEDRIEB (swc_deu_001367-swc_deu_001367) +DER ES DE FASTFUTD LIFERSOR IDES WAGEBOCH (swc_deu_001368-swc_deu_001368) +EINEM KABELBAUNEN (swc_deu_001369-swc_deu_001369) +MORLE DUNG MIT DE GEFAHRVERBUNDEN GEWES (swc_deu_001370-swc_deu_001370) +DE ELTISTEN PFIERDERENEN ASEHAB (swc_deu_001371-swc_deu_001371) +SONEN AUCH DERNAT ZUONAHL SUOZ ERDISTISCHEN KUNZT AUF FASSUNGERECHTWERDEN (swc_deu_001372-swc_deu_001372) +DIE WLLZICH DES HANSE ARTEN (swc_deu_001373-swc_deu_001373) +AUC NACHKOMMEN EINT NICHTBEKANT (swc_deu_001374-swc_deu_001374) +ERENTEXSTE DEN EINDRUGKTZE VERMITTE (swc_deu_001375-swc_deu_001375) +VER DINST M DASKÖNARLIET VERLIEN (swc_deu_001376-swc_deu_001376) +BWOLL HOFMEIN VON HOFMEINZ WALL DAUS WERG GROSENEINFLUS A SPÄERERDICHTE AUS ÜBT (swc_deu_001377-swc_deu_001377) +MMSO ERMNSTALSTAS OBERHAUPFVEN (swc_deu_001378-swc_deu_001378) +EFREIE DO KOMETATION (swc_deu_001379-swc_deu_001379) +GESTALTUNM BES KAVERS WIEDER SPIEGELT (swc_deu_001380-swc_deu_001380) +DER ESMTER AUFWAND WIT AUF (swc_deu_001381-swc_deu_001381) +OBGLEICHAMBORK DIESEM ANGEHÖRTE UND EINE NOBILI TIERUNG DUCHENKEISER DAMIT KEINE DRC (swc_deu_001382-swc_deu_001382) +DA STURCH DENSICG AUSWEITENTEN WELTHANDEL ARBEIT UND WOHLSTAND VERSPRACH (swc_deu_001383-swc_deu_001383) +FÜÖR DIE ZEITT MITE DES NEUNZEHNTE JA HNDER BEKLAG DEDE ERCHIE TEKTMATIN HALLE (swc_deu_001384-swc_deu_001384) +EITBONDESKANZER HEMUTSCHMITT LENTE (swc_deu_001385-swc_deu_001385) +DI NAMEN GODE FREI IM STATZ HANDBOCHTZ (swc_deu_001386-swc_deu_001386) +WEN AUCHE ENERGEWISENLETAGIE (swc_deu_001387-swc_deu_001387) +KALKOLIERE BAEG (swc_deu_001388-swc_deu_001388) +ANGEFNGDEZSAHUNER VFNM ZISCÜULER EINEN DIELIGIEITEN (swc_deu_001389-swc_deu_001389) +VIELMENSCHEN SANEN RISLI ALS NAHRUNGSKONGRENTEN UND ALSPOTEN ELGEFA (swc_deu_001390-swc_deu_001390) +DEN UFTRIT VE KÖRZE (swc_deu_001391-swc_deu_001391) +MI DM STAN VOM DREITZHN NIULIEZWEI TAUSEN SWERF DER INHERSTET UNDER DELIZENS KRERTZU COMONS EIZREWUSCHEN SCHER E LEITDREIT PUNK NL ANPORTET UND UNTE DE (swc_deu_001392-swc_deu_001392) +EINE KLEINEREBOGEN BRÜCKE (swc_deu_001393-swc_deu_001393) +SICH NUNVERSENWEITRKOM (swc_deu_001394-swc_deu_001394) +AUS DEM GEMEÄLDE ZU ENT FERENEN (swc_deu_001395-swc_deu_001395) +IS DCH ÖAROPÄSCHERICHTLINE NEUNZICGH VIERNERTEXSNEUNZSICH EWI (swc_deu_001396-swc_deu_001396) +ALE UMSELT AUF (swc_deu_001397-swc_deu_001397) +ND SIE SEI AUCH WIE EINE FIRSTEN (swc_deu_001398-swc_deu_001398) +NEUNZEHN HUNDERT NEUN ZEN (swc_deu_001399-swc_deu_001399) +STATISSEN HABEN DE RÖMISCHE IN JEN IUREM (swc_deu_001400-swc_deu_001400) +DELKRISLIE BEHR UND MENSCH (swc_deu_001401-swc_deu_001401) +MIESICHENS KURELISCHE (swc_deu_001402-swc_deu_001402) +BERTOTCHUMLGEB DES DREIVARER ZIONER MI (swc_deu_001403-swc_deu_001403) +K ZUGSGEBIEDT ERWESICH TER ACHZEHN HUNDATZWEIUN SIEBZIG E GRÜNETE JLUSTDUNET IONALPARK (swc_deu_001404-swc_deu_001404) +E FINITION (swc_deu_001405-swc_deu_001405) +UM INE UNIVESITET ZVILA ZWEISE MSTER KUNSGESCHCHT ZUSTUDIERN (swc_deu_001406-swc_deu_001406) +DIE TOTZ ERE GRINE (swc_deu_001407-swc_deu_001407) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..0aad4d12cd21f4608e3b2073807b9e63516b202a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/ref.trn @@ -0,0 +1,207 @@ +DER VERLIEBTE JUNGE HERZOG DIE RATSCHLÄGE SEINES VATERS NICHT BEACHTET HABE (swc_deu_001201-swc_deu_001201) +DIE IN DEN HANSESTÄDTEN ALS (swc_deu_001202-swc_deu_001202) +WAR KEIN GROSSER ERFOLG (swc_deu_001203-swc_deu_001203) +GROSSEN CHEMISCHEN FABRIKEN (swc_deu_001204-swc_deu_001204) +WURDEN AUCH MEHRERE ERLÄUTERUNGSBÜCHER VERÖFFENTLICHT (swc_deu_001205-swc_deu_001205) +VORBEREITETEN BIERTEIG GETUNKT (swc_deu_001206-swc_deu_001206) +DOKUMENTE SCHLIESSLICH IN (swc_deu_001207-swc_deu_001207) +TRAUERTAG FÜR DEN TOD VON KÖNIG FRIEDRICH WILHELM (swc_deu_001208-swc_deu_001208) +DARUNTER SIND MATILDE ASENSIS WÄCHTER DES KREUZES (swc_deu_001209-swc_deu_001209) +INNENSTÄDTEN MEHR UND MEHR DIE ROLLE DER TRADITIONELLEN FISH (swc_deu_001210-swc_deu_001210) +ZU DENEN WELTLÄUFIGKEIT (swc_deu_001211-swc_deu_001211) +RACHE DES HOFES UND DES ADELS FÜR DEN FREVEL (swc_deu_001212-swc_deu_001212) +ZEITANGABEN VERZICHTETE (swc_deu_001213-swc_deu_001213) +ALS ACHTZEHN HUNDERT ACHTZIG MIT OTTO BRAHMS AUFSATZ (swc_deu_001214-swc_deu_001214) +EIN TAUSEND SIEBEN HUNDERT ACHTUNDZWANZIG – (swc_deu_001215-swc_deu_001215) +DASS DER FISCH FRISCH (swc_deu_001216-swc_deu_001216) +SEINEM ABSCHLUSS IM JAHRE NEUNZEHN HUNDERT ZWEIUNDACHTZIG UNTERNAHM ER EINE ERSTE LÄNGERE REISE NACH SPANIEN (swc_deu_001217-swc_deu_001217) +VON CHASÔT VORGEZEICHNET (swc_deu_001218-swc_deu_001218) +FALCKENSTEINS VOLLSTÄNDIGE GESCHICHTEN UND DIE AUGSBURGER STADTGESCHICHTE DES ÄLTEREN (swc_deu_001219-swc_deu_001219) +NACH DIESEN ZERSTÖRUNGEN WURDE DIE RASCH WIEDER AUFBLÜHENDE (swc_deu_001220-swc_deu_001220) +MACHTEN EINFLUSSREICHEN HANSEATEN BEIM KOMMISSARISCH EINGESETZTEN BÜRGERMEISTER MARKERT IHRE AUFWARTUNG (swc_deu_001221-swc_deu_001221) +ALS ZENTRALES HANDELSKONTOR (swc_deu_001222-swc_deu_001222) +SONDERSTELLUNG INNERHALB DER STADT KREFELD (swc_deu_001223-swc_deu_001223) +FINDET SICH IN HALOBAKTERIEN (swc_deu_001224-swc_deu_001224) +AUF DER B SEITE FINDET SICH DAS EBENFALLS VON MICHAEL KOMPONIERTE (swc_deu_001225-swc_deu_001225) +IN HANSEATISCHER ZEIT HATTE DIE ZIRKELGESELLSCHAFT KEINEN AUSSCHLAGGEBENDEN EINFLUSS MEHR (swc_deu_001226-swc_deu_001226) +DA ES DURCH VERWENDUNG VON AUFTRIEBSKÖRPERN ODER HOLZ EINE GERINGERE MITTLERE DICHTE ALS WASSER HAT (swc_deu_001227-swc_deu_001227) +DRAMATISIERUNGEN (swc_deu_001228-swc_deu_001228) +UM 755 (swc_deu_001229-swc_deu_001229) +DASS ALBRECHT DIE BADERSTOCHTER (swc_deu_001230-swc_deu_001230) +THAT BARBARISCHER STAATSRAISON (swc_deu_001231-swc_deu_001231) +DER DAS LIED BESONDERS LIEBTE (swc_deu_001232-swc_deu_001232) +AUFGRUND DES WACHSENDEN PUBLIKUMSINTERESSES WURDE DER AUFTRITTSORT FÜR DIE PRIMA VISTA LESUNGEN (swc_deu_001233-swc_deu_001233) +UND FREILICHTSPIELE (swc_deu_001234-swc_deu_001234) +DASS DIE DREI DEN STURZ RELATIV UNBESCHADET ÜBERSTANDEN HATTEN (swc_deu_001235-swc_deu_001235) +JAHREN ERSCHIENEN ZWEI IMMER (swc_deu_001236-swc_deu_001236) +GRABMALE UND GRABKAPELLEN ODER WOHLTATEN NACHHALTIG (swc_deu_001237-swc_deu_001237) +JUNI NEUNZEHN HUNDERT SECHSUNDNEUNZIG KÜNDIGTE ER SEINE BEIDEN JOBS (swc_deu_001238-swc_deu_001238) +EIN G PROTEIN GEKOPPELT (swc_deu_001239-swc_deu_001239) +NEUNUNDSECHZIG DER MEDIA CONTROL ALBUMCHARTS EIN (swc_deu_001240-swc_deu_001240) +DADURCH KOMMT (swc_deu_001241-swc_deu_001241) +OHNEHIN NICHT DEN GROSSHANDELSKAUFLEUTEN GESELLSCHAFTLICH GLEICHGESTELLT WAREN (swc_deu_001242-swc_deu_001242) +VON DER NAHRUNG UND VOM KLIMA (swc_deu_001243-swc_deu_001243) +APOLLO EINS (swc_deu_001244-swc_deu_001244) +BRÜHL UND HÜRTH NACH KÖLN (swc_deu_001245-swc_deu_001245) +ETWA IN EIN KLOSTER (swc_deu_001246-swc_deu_001246) +ZUM OFFIZIELLEN KARNEVAL ENTSTAND UND HEUTE EINE MISCHUNG AUS KÖLSCHEM KARNEVAL UND POLITISCHEM KABARETT MIT COMEDYELEMENTEN DARSTELLT UND (swc_deu_001247-swc_deu_001247) +DIE ZUR ENTSTEHUNG DES LIEDES FÜHRTEN (swc_deu_001248-swc_deu_001248) +NANNTE ZIEGLER DIE ERMORDUNG DER BERNAUERIN (swc_deu_001249-swc_deu_001249) +WINTERRUHE IST VOR ALLEM (swc_deu_001250-swc_deu_001250) +DIE STRÄNGE DER VORGÄNGERLEITUNG WURDEN ZWISCHEN NEUNZEHN HUNDERT NEUNUNDZWANZIG UND NEUNZEHN HUNDERT DREIUNDFÜNFZIG ARCHÄOLOGISCH ERGRABEN (swc_deu_001251-swc_deu_001251) +IM GEGENSATZ (swc_deu_001252-swc_deu_001252) +FARBEN VON UERDINGEN SIND BLAU UND ROT (swc_deu_001253-swc_deu_001253) +LIVE VERANSTALTUNGEN (swc_deu_001254-swc_deu_001254) +SO WERDEN HEUTE IN DER REGEL ALLE DORT LEBENDEN BRAUNBÄREN (swc_deu_001255-swc_deu_001255) +LIEDER FÜR REVUEFILME (swc_deu_001256-swc_deu_001256) +DES HANSEATEN FÜHREN (swc_deu_001257-swc_deu_001257) +HEBBELS AGNES BERNAUER (swc_deu_001258-swc_deu_001258) +LEBENSWEISE VERKÖRPERN (swc_deu_001259-swc_deu_001259) +WIE DER FALL DES VIELEN HAMBURGERN ZU KATHOLISCH FROMMEN (swc_deu_001260-swc_deu_001260) +KULTUR UND WIRTSCHAFT AUSTAUSCHEN (swc_deu_001261-swc_deu_001261) +JAHR ZWEI TAUSEND VERTONTE (swc_deu_001262-swc_deu_001262) +DASS ER DIESE LEITUNG SCHNELLER VOLLENDEN KÖNNE ALS DER BAUMEISTER DEN KÖLNER DOM (swc_deu_001263-swc_deu_001263) +HINRICHTUNG DER BERNAUERIN HABE ES SICH SCHLICHT UM (swc_deu_001264-swc_deu_001264) +LUDWIG (swc_deu_001265-swc_deu_001265) +DERZEIT DER BESTE KENNER DER EIFELLEITUNG (swc_deu_001266-swc_deu_001266) +FOKUS DES WISSENSCHAFTLICHEN INTERESSES (swc_deu_001267-swc_deu_001267) +THEMA ZU BEGEISTERN (swc_deu_001268-swc_deu_001268) +METER UND KONNTE DAMIT AUCH VON INNEN BEGANGEN WERDEN (swc_deu_001269-swc_deu_001269) +HARDCOVER BESTSELLERLISTE DER NEW (swc_deu_001270-swc_deu_001270) +DER FREIEN ENZYKLOPÄDIE (swc_deu_001271-swc_deu_001271) +DEN GRIZZLY WIEDER AUF DIE LISTE ZU SETZEN (swc_deu_001272-swc_deu_001272) +LANG DIESE KAPLANSSTELLE AUFRECHTERHALTEN WURDE (swc_deu_001273-swc_deu_001273) +SIE WAREN WAHRSCHEINLICH BEREITS DREISSIG SEKUNDEN NACH AUSBRUCH DES FEUERS (swc_deu_001274-swc_deu_001274) +METERN GESAMTLÄNGE UND BIS ZU ZEHN METERN (swc_deu_001275-swc_deu_001275) +FEINE RITZEN UND SPALTEN (swc_deu_001276-swc_deu_001276) +DEN MAN VON AUSSEN DIE KEHLE HINABFLIESSEN SIEHT (swc_deu_001277-swc_deu_001277) +EINEM INTERVIEW SAGTE BROWN (swc_deu_001278-swc_deu_001278) +DAS FÜNFTE EVANGELIUM (swc_deu_001279-swc_deu_001279) +REISSEN SIE MANCHMAL WEIDETIERE WIE SCHAFE (swc_deu_001280-swc_deu_001280) +SIE HÖREN DEN ARTIKEL DESIGN REVIEW (swc_deu_001281-swc_deu_001281) +CHAKUZA IST GELERNTER KOCH (swc_deu_001282-swc_deu_001282) +HANS WENDT STIFTUNG (swc_deu_001283-swc_deu_001283) +NEUNZEHN HUNDERT ACHTZEHN ALS HANSEATEN ANGESEHEN (swc_deu_001284-swc_deu_001284) +MEHRERE ES NACH IHM THUN (swc_deu_001285-swc_deu_001285) +AUFSTIEG DES GERICHTS ZUR LANDESWEIT BELIEBTEN KULINARISCHEN SPEZIALITÄT ERMÖGLICHTE (swc_deu_001286-swc_deu_001286) +COLLEGE UND EINEN ZWEITJOB ALS SPANISCHLEHRER IN HAMPTON FALLS AN (swc_deu_001287-swc_deu_001287) +WURDEN KEINESWEGS ALLE GEBÜRTIGEN (swc_deu_001288-swc_deu_001288) +IST IHR KÖRPERBAU KRÄFTIG (swc_deu_001289-swc_deu_001289) +ANLÄSSLICH DER NEUJAHRESANSPRACHE KIM (swc_deu_001290-swc_deu_001290) +MIT WIND VON SCHRÄG HINTEN (swc_deu_001291-swc_deu_001291) +DEN GRÖSSTEN TEIL DER BEZIRKSVERTRETUNG UERDINGEN AUS (swc_deu_001292-swc_deu_001292) +ACHTZEHN HUNDERT EINUNDZWANZIG (swc_deu_001293-swc_deu_001293) +DES GROSSEN ADELS ANGESAMMELTEN REICHTUMS (swc_deu_001294-swc_deu_001294) +SOLLTEN NICHT ALS SEXUELLE PROVOKATION (swc_deu_001295-swc_deu_001295) +TEILHABER DER FIRMA GOSSMANN UND JÜRGENS (swc_deu_001296-swc_deu_001296) +DER KRAUTINSEL BILDET SIE DIE GEMEINDE (swc_deu_001297-swc_deu_001297) +AUDIO IST EIN DEUTSCHER HÖRBUCHVERLAG MIT SITZ IN MÜNCHEN (swc_deu_001298-swc_deu_001298) +FARBPIGMENTE UND CHEMISCHE VORPRODUKTE HERSTELLT (swc_deu_001299-swc_deu_001299) +ERBLICHEN PREUSSISCHEN FREIHERRENSTAND IN DER ZOLLANSCHLUSSFRAGE ENTSCHIEDEN GEGEN DEN SENAT AUF DIE SEITE BISMARCKS GESTELLT (swc_deu_001300-swc_deu_001300) +WENN DIE QUELLEN VON SELBST HERVORQUELLEN UND OFFEN ZU TAGE LIEGEN (swc_deu_001301-swc_deu_001301) +DAS VOM NACHBARBAUTRUPP BEREITS BEGONNEN WURDE (swc_deu_001302-swc_deu_001302) +WERDEN PRÄGENDE ELEMENTE DES HANSEATENTUMS ZUSAMMENGEFASST (swc_deu_001303-swc_deu_001303) +DAS LIED WURDE ALS VOLKSLIED ANGESEHEN (swc_deu_001304-swc_deu_001304) +DER ZUR RANDOM HOUSE VERLAGSGRUPPE GEHÖRT (swc_deu_001305-swc_deu_001305) +FÜR DIE KÜNFTIGEN BORDBÜCHER ENTWICKELTE DIE PAPIERFABRIK (swc_deu_001306-swc_deu_001306) +HAMBURG WUCHS (swc_deu_001307-swc_deu_001307) +FÜR DIE QUASI ADLIGEN LANDSITZE BETRIEBENE AUFWAND – SEI ES BEIM BAU (swc_deu_001308-swc_deu_001308) +JAHR ZWEI TAUSEND ZWÖLF IN DEN BERLINER CLUB S O SECHSUNDDREISSIG VERLEGT (swc_deu_001309-swc_deu_001309) +SECHZEHN HUNDERT FÜNFZIG ALS BÜNDNIS DIE (swc_deu_001310-swc_deu_001310) +PROBLEM BEI DIESEM PARADOXON IST (swc_deu_001311-swc_deu_001311) +ARMENWESEN TÄTIG AMALIE SIEVEKING (swc_deu_001312-swc_deu_001312) +NICHT EINMAL EINE ANSATZWEISE UNTERSUCHUNG ZU IHREM VERHALTEN IN DER ZEIT DES NATIONALSOZIALISMUS (swc_deu_001313-swc_deu_001313) +LIZENZ FÜR FREIE DOKUMENTATION (swc_deu_001314-swc_deu_001314) +IM ACHTZEHNTE JAHRHUNDERT DIE GARTENHÄUSER VOR DEN TOREN (swc_deu_001315-swc_deu_001315) +GANZ IM STIL DER ZEIT (swc_deu_001316-swc_deu_001316) +ÜBER BRÜHL UND HÜRTH ERREICHTE DIE LEITUNG SCHLIESSLICH KÖLN (swc_deu_001317-swc_deu_001317) +AUSZEICHNUNGEN FREMDER HERREN (swc_deu_001318-swc_deu_001318) +DIE SCHRIFTSTELLEREI AUFZUGEBEN (swc_deu_001319-swc_deu_001319) +ZÄHLEN DIE BEGEGNUNG MIT VERLETZTEN TIEREN (swc_deu_001320-swc_deu_001320) +JENISCH STIFT (swc_deu_001321-swc_deu_001321) +WESTLICH VON KÖLN (swc_deu_001322-swc_deu_001322) +DIE STÄNDIG IN BETRIEB WAREN (swc_deu_001323-swc_deu_001323) +DIE VOM BARBIER RASIERT WERDEN (swc_deu_001324-swc_deu_001324) +ERSCHIEN NOCH EIN WEITERER AUFSATZ VON CHRISTIAN MEYER (swc_deu_001325-swc_deu_001325) +WEIL SELBST EXTREMER REICHTUM KEINESWEGS DEN UNMITTELBAREN ZUGANG (swc_deu_001326-swc_deu_001326) +GEBT EUCH NICHT SELBER AUF (swc_deu_001327-swc_deu_001327) +HAT DIESEN BRAUCH NEUNZEHN HUNDERT ZWEIUNDFÜNFZIG GEGENÜBER (swc_deu_001328-swc_deu_001328) +WO DIE LEITUNG ÜBER DIE ALTE HÜRTHER LEITUNG GEFÜHRT WURDE (swc_deu_001329-swc_deu_001329) +EINE BELIEBTE KÖLSCHROCKTRUPPE AUS DEM KÖLNER UMLAND DIE HÖHNER (swc_deu_001330-swc_deu_001330) +GEWORDEN SEI UND ALBRECHT SICH (swc_deu_001331-swc_deu_001331) +DER TAGESBEDARF EINES ERWACHSENEN AN VITAMIN A (swc_deu_001332-swc_deu_001332) +SIEBZEHN HUNDERT ZEHN OBERALTER (swc_deu_001333-swc_deu_001333) +WEITERHIN LIESS SICH NACHWEISEN (swc_deu_001334-swc_deu_001334) +ZUM GRÜNDUNGSDATUM KONNTE MAN BEREITS (swc_deu_001335-swc_deu_001335) +KEIN LECKSCHLAGEN MÖGLICH NACHTEILE (swc_deu_001336-swc_deu_001336) +WIRD DIE KATHOLISCHE KIRCHE STÜCK PETER AN DER STELLE DER ALTEN (swc_deu_001337-swc_deu_001337) +DER VERKNAPPUNG DES BROTWEIZENS TRAT ABER SCHON BALD DIE KARTOFFEL ALS ERSATZ (swc_deu_001338-swc_deu_001338) +KÖNNEN MIT DIESEN NACHKOMMEN ZEUGEN (swc_deu_001339-swc_deu_001339) +ALLE NEUEN FOLGEN DER HÖRSPIELREIHE (swc_deu_001340-swc_deu_001340) +CHIPS MIT BRATENSOSSE (swc_deu_001341-swc_deu_001341) +KOLLEGE ANDREAS BUCHNER VERZICHTETE AUF EINE PERSÖNLICHE BEWERTUNG (swc_deu_001342-swc_deu_001342) +WENIGER ENTRÜSTET (swc_deu_001343-swc_deu_001343) +WEITERHIN VERSORGTE DIE LEITUNG THERMEN (swc_deu_001344-swc_deu_001344) +WARTETEN DAFÜR ABER MIT EINIGEN (swc_deu_001345-swc_deu_001345) +LEDIGLICH ANTON VON KLEIN MONIERTE IN SEINER REZENSION DER (swc_deu_001346-swc_deu_001346) +IM JAHRE NEUNZEHN HUNDERT FÜNF (swc_deu_001347-swc_deu_001347) +ERST MIT DEM FORTFALL DES BÜRGERRECHTS UND DER EINFÜHRUNG DER FREIZÜGIGKEIT IM ZWANZIGSTE JAHRHUNDERT WANDELTE SICH DIESE ANSCHAUUNG ANSATZWEISE DAHIN (swc_deu_001348-swc_deu_001348) +DES SWISTBACHES BEI RHEINBACH EINE BOGENBRÜCKE VON (swc_deu_001349-swc_deu_001349) +ACHTZEHN HUNDERT SECHSUNDDREISSIG WURDE DER HAMBURGER (swc_deu_001350-swc_deu_001350) +AM KARNEVALSSONNTAG (swc_deu_001351-swc_deu_001351) +AUFGRUND DER KONTINENTALSPERRE ACHTZEHN HUNDERT ELF BANKROTT (swc_deu_001352-swc_deu_001352) +WEITERES MAL MUSSTEN DAN UND BLYTHE BROWN DIE WERBUNG FÜR DAS BUCH SELBST ÜBERNEHMEN (swc_deu_001353-swc_deu_001353) +DIE NACHRICHT VOM SIEG DER BÜRGERLICH DEMOKRATISCHEN FEBRUARREVOLUTION VON ACHTZEHN HUNDERT ACHTUNDVIERZIG IN FRANKREICH WURDE IN HAMBURG MIT JUBEL AUFGENOMMEN (swc_deu_001354-swc_deu_001354) +ZWEI JAHRE OHNE UNTERBRECHUNG (swc_deu_001355-swc_deu_001355) +ZAHLREICHEN GASTSPIELEN UNTERWEGS (swc_deu_001356-swc_deu_001356) +QUANTITÄT GENÜGTEN (swc_deu_001357-swc_deu_001357) +BAROCKER AUSSTATTUNG (swc_deu_001358-swc_deu_001358) +DAS GERICHT VOM BEIWAGEN SEINES MOTORRADES AUS IN DIE ZU DIESER ZEIT NEU ENTSTEHENDEN ARBEITERSIEDLUNGEN ZU (swc_deu_001359-swc_deu_001359) +DIE MITSAMT IHRER RECHENSTUBE (swc_deu_001360-swc_deu_001360) +KAISER FERDINAND (swc_deu_001361-swc_deu_001361) +VOM FERNSEHREGISSEUR FRANZ XAVER BOGNER IN DEM FERNSEHFILM DAS EWIGE LIED (swc_deu_001362-swc_deu_001362) +WURDE IN SEINEN BESTEN ZEITEN DER (swc_deu_001363-swc_deu_001363) +SIE HÖREN DEN ARTIKEL FISH AND CHIPS (swc_deu_001364-swc_deu_001364) +UND T V MOVIE (swc_deu_001365-swc_deu_001365) +REZEPTION DER HEXENTHEMATIK VON CHRISTA (swc_deu_001366-swc_deu_001366) +DIE GESAMTE ANLAGE WAR BIS ETWA ZWEI HUNDERT SECHZIG NACH CHRISTUS IN BETRIEB (swc_deu_001367-swc_deu_001367) +DER ERSTE FAST FOOD LIEFERSERVICE WAR GEBOREN (swc_deu_001368-swc_deu_001368) +EINEM KABELBAUM (swc_deu_001369-swc_deu_001369) +ERMORDUNG MIT DER GEFAHR VERBUNDEN GEWESEN (swc_deu_001370-swc_deu_001370) +DER ÄLTESTEN PFERDERENNEN AUSSERHALB (swc_deu_001371-swc_deu_001371) +SONDERN AUCH DER NATIONALSOZIALISTISCHEN KUNSTAUFFASSUNG GERECHT WERDEN (swc_deu_001372-swc_deu_001372) +DIE WELTSICHT DES HANSEATEN (swc_deu_001373-swc_deu_001373) +AUCH NACHKOMMEN SIND NICHT BEKANNT (swc_deu_001374-swc_deu_001374) +IHREN TEXTEN DEN EINDRUCK ZU VERMITTELN (swc_deu_001375-swc_deu_001375) +VERDIENSTE UM DAS KÖLNER LIED VERLIEHEN (swc_deu_001376-swc_deu_001376) +OBWOHL HOFFMANN VON HOFFMANNSWALDAUS WERK GROSSEN EINFLUSS AUF SPÄTERE DICHTER AUSÜBTE (swc_deu_001377-swc_deu_001377) +UM SO ERNST ALS STAATSOBERHAUPT VON (swc_deu_001378-swc_deu_001378) +DOKUMENTATION (swc_deu_001379-swc_deu_001379) +GESTALTUNG DES COVERS WIDERSPIEGELT (swc_deu_001380-swc_deu_001380) +DER GESAMTE AUFWAND WIRD AUF (swc_deu_001381-swc_deu_001381) +OBGLEICH HAMBURG DIESEM ANGEHÖRTE UND EINE NOBILITIERUNG DURCH DEN KAISER DAMIT KEINE DURCH (swc_deu_001382-swc_deu_001382) +DA ES DURCH DEN SICH AUSWEITENDEN WELTHANDEL ARBEIT UND WOHLSTAND VERSPRACH (swc_deu_001383-swc_deu_001383) +FÜR DIE ZEIT MITTE DES NEUNZEHNTE JAHRHUNDERTS BEKLAGTE DER ARCHITEKT MARTIN HALLER (swc_deu_001384-swc_deu_001384) +ALTBUNDESKANZLER HELMUT SCHMIDT LEHNTE (swc_deu_001385-swc_deu_001385) +DEN NAMEN GODEFFROY IM STAATSHANDBUCH ZU STREICHEN (swc_deu_001386-swc_deu_001386) +WENN AUCH MIT EINER GEWISSEN LETHARGIE (swc_deu_001387-swc_deu_001387) +KALKULIERBAR (swc_deu_001388-swc_deu_001388) +ANGEFANGENE ZWEI HUNDERT FÜNFZIG SCHÜLER EINEN DELEGIERTEN (swc_deu_001389-swc_deu_001389) +VIELE MENSCHEN SAHEN DEN GRIZZLY ALS NAHRUNGSKONKURRENTEN UND ALS POTENTIELLE GEFAHR (swc_deu_001390-swc_deu_001390) +DEN AUFTRITT VERKÜRZEN (swc_deu_001391-swc_deu_001391) +DEM STAND VOM DER INHALT STEHT UNTER DER LIZENZ CREATIVE COMMONS ATTRIBUTION SHARE ALIKE DREI PUNKT NULL UNPORTED UND UNTER DER (swc_deu_001392-swc_deu_001392) +EINE KLEINERE BOGENBRÜCKE (swc_deu_001393-swc_deu_001393) +SICH NUN FÜR SEIN WEITERKOMMEN (swc_deu_001394-swc_deu_001394) +AUS DEM GEMÄLDE ZU ENTFERNEN (swc_deu_001395-swc_deu_001395) +NACH EUROPÄISCHER RICHTLINIE NEUNZIG VIER HUNDERT SECHSUNDNEUNZIG E W G (swc_deu_001396-swc_deu_001396) +EINEM UMFELD AUF (swc_deu_001397-swc_deu_001397) +UND SIE SEI AUCH WIE EINE FÜRSTIN (swc_deu_001398-swc_deu_001398) +NEUNZEHN HUNDERT NEUNZEHN (swc_deu_001399-swc_deu_001399) +STATTDESSEN HABEN DIE RÖMISCHEN INGENIEURE (swc_deu_001400-swc_deu_001400) +GRIZZLYBÄR UND MENSCH (swc_deu_001401-swc_deu_001401) +MUSICIANS COALITION (swc_deu_001402-swc_deu_001402) +BERTOLD HUMMEL GIBT ES DREI VARIATIONEN MIT (swc_deu_001403-swc_deu_001403) +RÜCKZUGSGEBIET ERWIES SICH DER ACHTZEHN HUNDERT ZWEIUNDSIEBZIG GEGRÜNDETE YELLOWSTONE NATIONALPARK (swc_deu_001404-swc_deu_001404) +DEFINITION (swc_deu_001405-swc_deu_001405) +UM AN DER UNIVERSITÄT SEVILLA ZWEI SEMESTER KUNSTGESCHICHTE ZU STUDIEREN (swc_deu_001406-swc_deu_001406) +TROTZ IHRER GERINGEN (swc_deu_001407-swc_deu_001407) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/result.txt b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..fe29f287c80af3f3bfbb8ff61f7f2936b7c4eb1f --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/result.txt @@ -0,0 +1,2525 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,--------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/hyp.trn| +|--------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001201 | 1 11 | 0.0 63.6 36.4 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001202 | 1 5 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001203 | 1 4 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001204 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001205 | 1 5 | 20.0 80.0 0.0 40.0 120.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001206 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001207 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001208 | 1 8 | 12.5 50.0 37.5 0.0 87.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001209 | 1 7 | 42.9 57.1 0.0 0.0 57.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001210 | 1 9 | 33.3 66.7 0.0 11.1 77.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001211 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001212 | 1 9 | 44.4 44.4 11.1 0.0 55.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001213 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001214 | 1 8 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001215 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001216 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001217 | 1 15 | 26.7 60.0 13.3 0.0 73.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001218 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001219 | 1 9 | 22.2 55.6 22.2 0.0 77.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001220 | 1 8 | 25.0 37.5 37.5 0.0 75.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001221 | 1 10 | 40.0 60.0 0.0 20.0 80.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001222 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001223 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001224 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001225 | 1 11 | 54.5 45.5 0.0 0.0 45.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001226 | 1 10 | 40.0 50.0 10.0 10.0 70.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001227 | 1 15 | 53.3 33.3 13.3 0.0 46.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001228 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001229 | 1 2 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001230 | 1 4 | 50.0 50.0 0.0 25.0 75.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001231 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001232 | 1 5 | 0.0 40.0 60.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001233 | 1 12 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001234 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001235 | 1 9 | 11.1 88.9 0.0 11.1 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001236 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001237 | 1 6 | 33.3 66.7 0.0 50.0 116.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001238 | 1 9 | 11.1 66.7 22.2 0.0 88.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001239 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001240 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001241 | 1 2 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001242 | 1 7 | 42.9 57.1 0.0 28.6 85.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001243 | 1 6 | 33.3 50.0 16.7 0.0 66.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001244 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001245 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001246 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001247 | 1 18 | 22.2 72.2 5.6 5.6 83.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001248 | 1 6 | 16.7 33.3 50.0 0.0 83.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001249 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001250 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001251 | 1 15 | 60.0 40.0 0.0 6.7 46.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001252 | 1 2 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001253 | 1 7 | 14.3 42.9 42.9 0.0 85.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001254 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001255 | 1 10 | 10.0 50.0 40.0 0.0 90.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001256 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001257 | 1 3 | 33.3 66.7 0.0 66.7 133.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001258 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001259 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001260 | 1 9 | 22.2 44.4 33.3 0.0 77.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001261 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001262 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001263 | 1 13 | 46.2 46.2 7.7 0.0 53.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001264 | 1 8 | 37.5 50.0 12.5 12.5 75.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001265 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001266 | 1 6 | 33.3 50.0 16.7 0.0 66.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001267 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001268 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001269 | 1 9 | 55.6 44.4 0.0 0.0 44.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001270 | 1 4 | 25.0 75.0 0.0 50.0 125.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001271 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001272 | 1 8 | 12.5 62.5 25.0 0.0 87.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001273 | 1 5 | 40.0 60.0 0.0 40.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001274 | 1 10 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001275 | 1 7 | 28.6 57.1 14.3 0.0 71.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001276 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001277 | 1 8 | 12.5 62.5 25.0 0.0 87.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001278 | 1 4 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001279 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001280 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001281 | 1 6 | 16.7 66.7 16.7 16.7 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001282 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001283 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001284 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001285 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001286 | 1 9 | 22.2 77.8 0.0 11.1 88.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001287 | 1 10 | 20.0 70.0 10.0 0.0 80.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001288 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001289 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001290 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001291 | 1 5 | 60.0 20.0 20.0 0.0 40.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001292 | 1 7 | 28.6 71.4 0.0 0.0 71.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001293 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001294 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001295 | 1 5 | 0.0 100.0 0.0 40.0 140.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001296 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001297 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001298 | 1 9 | 22.2 66.7 11.1 11.1 88.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001299 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001300 | 1 15 | 40.0 46.7 13.3 20.0 80.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001301 | 1 11 | 27.3 54.5 18.2 9.1 81.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001302 | 1 6 | 0.0 100.0 0.0 16.7 116.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001303 | 1 6 | 33.3 66.7 0.0 66.7 133.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001304 | 1 6 | 16.7 66.7 16.7 16.7 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001305 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001306 | 1 7 | 0.0 100.0 0.0 28.6 128.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001307 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001308 | 1 12 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001309 | 1 12 | 8.3 58.3 33.3 0.0 91.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001310 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001311 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001312 | 1 4 | 25.0 75.0 0.0 50.0 125.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001313 | 1 13 | 30.8 61.5 7.7 0.0 69.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001314 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001315 | 1 8 | 0.0 87.5 12.5 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001316 | 1 5 | 20.0 40.0 40.0 0.0 80.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001317 | 1 9 | 11.1 77.8 11.1 0.0 88.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001318 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001319 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001320 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001321 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001322 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001323 | 1 5 | 60.0 20.0 20.0 0.0 40.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001324 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001325 | 1 8 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001326 | 1 8 | 0.0 87.5 12.5 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001327 | 1 5 | 40.0 40.0 20.0 0.0 60.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001328 | 1 7 | 42.9 57.1 0.0 28.6 85.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001329 | 1 10 | 30.0 40.0 30.0 0.0 70.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001330 | 1 9 | 11.1 77.8 11.1 0.0 88.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001331 | 1 5 | 40.0 40.0 20.0 0.0 60.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001332 | 1 7 | 14.3 57.1 28.6 0.0 85.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001333 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001334 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001335 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001336 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001337 | 1 11 | 27.3 54.5 18.2 0.0 72.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001338 | 1 12 | 8.3 91.7 0.0 0.0 91.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001339 | 1 5 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001340 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001341 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001342 | 1 8 | 12.5 87.5 0.0 0.0 87.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001343 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001344 | 1 5 | 20.0 80.0 0.0 40.0 120.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001345 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001346 | 1 9 | 22.2 44.4 33.3 0.0 77.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001347 | 1 5 | 0.0 60.0 40.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001348 | 1 20 | 35.0 65.0 0.0 10.0 75.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001349 | 1 7 | 28.6 71.4 0.0 14.3 85.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001350 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001351 | 1 2 | 50.0 0.0 50.0 0.0 50.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001352 | 1 7 | 42.9 57.1 0.0 42.9 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001353 | 1 14 | 14.3 35.7 50.0 0.0 85.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001354 | 1 20 | 15.0 70.0 15.0 0.0 85.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001355 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001356 | 1 3 | 0.0 33.3 66.7 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001357 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001358 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001359 | 1 16 | 37.5 56.3 6.3 0.0 62.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001360 | 1 4 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001361 | 1 2 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001362 | 1 11 | 36.4 36.4 27.3 0.0 63.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001363 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001364 | 1 7 | 28.6 42.9 28.6 0.0 71.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001365 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001366 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001367 | 1 13 | 38.5 46.2 15.4 0.0 61.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001368 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001369 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001370 | 1 6 | 16.7 66.7 16.7 16.7 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001371 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001372 | 1 7 | 14.3 85.7 0.0 28.6 114.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001373 | 1 4 | 50.0 50.0 0.0 25.0 75.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001374 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001375 | 1 6 | 16.7 50.0 33.3 0.0 83.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001376 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001377 | 1 11 | 9.1 90.9 0.0 9.1 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001378 | 1 6 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001379 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001380 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001381 | 1 5 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001382 | 1 13 | 46.2 38.5 15.4 0.0 53.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001383 | 1 11 | 54.5 27.3 18.2 0.0 45.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001384 | 1 12 | 25.0 75.0 0.0 8.3 83.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001385 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001386 | 1 7 | 28.6 57.1 14.3 14.3 85.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001387 | 1 6 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001388 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001389 | 1 7 | 14.3 57.1 28.6 0.0 85.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001390 | 1 11 | 18.2 54.5 27.3 0.0 81.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001391 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001392 | 1 21 | 14.3 85.7 0.0 14.3 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001393 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001394 | 1 5 | 20.0 20.0 60.0 0.0 80.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001395 | 1 5 | 60.0 40.0 0.0 20.0 60.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001396 | 1 10 | 0.0 60.0 40.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001397 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001398 | 1 7 | 71.4 28.6 0.0 0.0 28.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001399 | 1 3 | 66.7 33.3 0.0 33.3 66.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001400 | 1 5 | 20.0 80.0 0.0 40.0 120.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001401 | 1 3 | 66.7 33.3 0.0 33.3 66.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001402 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001403 | 1 7 | 0.0 71.4 28.6 0.0 100.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001404 | 1 10 | 0.0 100.0 0.0 10.0 110.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001405 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001406 | 1 10 | 10.0 70.0 20.0 0.0 90.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001407 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|====================================================================================================================| +| Sum/Avg | 207 1298 | 23.2 63.6 13.3 7.6 84.4 99.5 | +|====================================================================================================================| +| Mean | 1.0 6.3 | 21.7 66.2 12.0 11.9 90.2 99.5 | +| S.D. | 0.0 3.8 | 19.7 21.9 16.0 26.7 35.5 7.0 | +| Median | 1.0 5.0 | 20.0 66.7 0.0 0.0 85.7 100.0 | +`--------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,--------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/hyp.trn| +|--------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001201 | 1 11 | 0 7 4 0 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001202 | 1 5 | 3 2 0 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001203 | 1 4 | 0 2 2 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001204 | 1 3 | 0 3 0 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001205 | 1 5 | 1 4 0 2 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001206 | 1 3 | 0 3 0 1 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001207 | 1 3 | 1 2 0 1 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001208 | 1 8 | 1 4 3 0 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001209 | 1 7 | 3 4 0 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001210 | 1 9 | 3 6 0 1 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001211 | 1 3 | 1 2 0 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001212 | 1 9 | 4 4 1 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001213 | 1 2 | 0 2 0 1 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001214 | 1 8 | 2 4 2 0 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001215 | 1 6 | 0 5 1 0 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001216 | 1 4 | 1 3 0 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001217 | 1 15 | 4 9 2 0 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001218 | 1 3 | 0 2 1 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001219 | 1 9 | 2 5 2 0 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001220 | 1 8 | 2 3 3 0 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001221 | 1 10 | 4 6 0 2 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001222 | 1 3 | 1 2 0 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001223 | 1 5 | 0 5 0 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001224 | 1 4 | 1 2 1 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001225 | 1 11 | 6 5 0 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001226 | 1 10 | 4 5 1 1 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001227 | 1 15 | 8 5 2 0 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001228 | 1 1 | 0 1 0 2 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001229 | 1 2 | 0 2 0 2 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001230 | 1 4 | 2 2 0 1 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001231 | 1 3 | 0 3 0 1 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001232 | 1 5 | 0 2 3 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001233 | 1 12 | 2 10 0 2 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001234 | 1 2 | 0 2 0 1 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001235 | 1 9 | 1 8 0 1 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001236 | 1 4 | 2 2 0 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001237 | 1 6 | 2 4 0 3 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001238 | 1 9 | 1 6 2 0 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001239 | 1 4 | 0 4 0 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001240 | 1 6 | 2 4 0 1 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001241 | 1 2 | 0 1 1 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001242 | 1 7 | 3 4 0 2 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001243 | 1 6 | 2 3 1 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001244 | 1 2 | 1 1 0 0 1 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001245 | 1 5 | 1 3 1 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001246 | 1 4 | 1 3 0 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001247 | 1 18 | 4 13 1 1 15 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001248 | 1 6 | 1 2 3 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001249 | 1 6 | 2 4 0 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001250 | 1 4 | 1 3 0 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001251 | 1 15 | 9 6 0 1 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001252 | 1 2 | 2 0 0 0 0 0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001253 | 1 7 | 1 3 3 0 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001254 | 1 2 | 0 2 0 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001255 | 1 10 | 1 5 4 0 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001256 | 1 3 | 0 3 0 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001257 | 1 3 | 1 2 0 2 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001258 | 1 3 | 1 2 0 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001259 | 1 2 | 1 1 0 0 1 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001260 | 1 9 | 2 4 3 0 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001261 | 1 4 | 1 3 0 1 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001262 | 1 4 | 1 2 1 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001263 | 1 13 | 6 6 1 0 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001264 | 1 8 | 3 4 1 1 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001265 | 1 1 | 0 1 0 0 1 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001266 | 1 6 | 2 3 1 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001267 | 1 4 | 1 3 0 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001268 | 1 3 | 1 2 0 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001269 | 1 9 | 5 4 0 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001270 | 1 4 | 1 3 0 2 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001271 | 1 3 | 1 2 0 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001272 | 1 8 | 1 5 2 0 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001273 | 1 5 | 2 3 0 2 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001274 | 1 10 | 0 8 2 0 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001275 | 1 7 | 2 4 1 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001276 | 1 4 | 2 2 0 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001277 | 1 8 | 1 5 2 0 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001278 | 1 4 | 0 2 2 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001279 | 1 3 | 1 2 0 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001280 | 1 6 | 1 5 0 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001281 | 1 6 | 1 4 1 1 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001282 | 1 4 | 1 3 0 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001283 | 1 3 | 0 2 1 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001284 | 1 6 | 1 5 0 1 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001285 | 1 5 | 2 3 0 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001286 | 1 9 | 2 7 0 1 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001287 | 1 10 | 2 7 1 0 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001288 | 1 4 | 0 4 0 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001289 | 1 4 | 1 2 1 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001290 | 1 4 | 0 3 1 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001291 | 1 5 | 3 1 1 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001292 | 1 7 | 2 5 0 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001293 | 1 3 | 0 3 0 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001294 | 1 5 | 2 3 0 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001295 | 1 5 | 0 5 0 2 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001296 | 1 6 | 1 4 1 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001297 | 1 6 | 0 5 1 0 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001298 | 1 9 | 2 6 1 1 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001299 | 1 5 | 1 4 0 1 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001300 | 1 15 | 6 7 2 3 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001301 | 1 11 | 3 6 2 1 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001302 | 1 6 | 0 6 0 1 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001303 | 1 6 | 2 4 0 4 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001304 | 1 6 | 1 4 1 1 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001305 | 1 6 | 1 4 1 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001306 | 1 7 | 0 7 0 2 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001307 | 1 2 | 0 2 0 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001308 | 1 12 | 0 9 3 0 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001309 | 1 12 | 1 7 4 0 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001310 | 1 6 | 1 4 1 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001311 | 1 5 | 1 4 0 1 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001312 | 1 4 | 1 3 0 2 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001313 | 1 13 | 4 8 1 0 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001314 | 1 4 | 0 4 0 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001315 | 1 8 | 0 7 1 0 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001316 | 1 5 | 1 2 2 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001317 | 1 9 | 1 7 1 0 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001318 | 1 3 | 0 3 0 2 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001319 | 1 3 | 0 3 0 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001320 | 1 6 | 0 5 1 0 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001321 | 1 2 | 1 1 0 0 1 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001322 | 1 3 | 2 1 0 0 1 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001323 | 1 5 | 3 1 1 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001324 | 1 5 | 1 3 1 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001325 | 1 8 | 0 4 4 0 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001326 | 1 8 | 0 7 1 0 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001327 | 1 5 | 2 2 1 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001328 | 1 7 | 3 4 0 2 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001329 | 1 10 | 3 4 3 0 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001330 | 1 9 | 1 7 1 0 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001331 | 1 5 | 2 2 1 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001332 | 1 7 | 1 4 2 0 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001333 | 1 4 | 0 4 0 1 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001334 | 1 4 | 0 4 0 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001335 | 1 5 | 0 4 1 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001336 | 1 4 | 1 2 1 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001337 | 1 11 | 3 6 2 0 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001338 | 1 12 | 1 11 0 0 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001339 | 1 5 | 3 2 0 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001340 | 1 5 | 2 3 0 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001341 | 1 3 | 0 2 1 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001342 | 1 8 | 1 7 0 0 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001343 | 1 2 | 0 2 0 1 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001344 | 1 5 | 1 4 0 2 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001345 | 1 5 | 2 3 0 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001346 | 1 9 | 2 4 3 0 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001347 | 1 5 | 0 3 2 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001348 | 1 20 | 7 13 0 2 15 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001349 | 1 7 | 2 5 0 1 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001350 | 1 6 | 0 5 1 0 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001351 | 1 2 | 1 0 1 0 1 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001352 | 1 7 | 3 4 0 3 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001353 | 1 14 | 2 5 7 0 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001354 | 1 20 | 3 14 3 0 17 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001355 | 1 4 | 0 3 1 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001356 | 1 3 | 0 1 2 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001357 | 1 2 | 0 2 0 1 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001358 | 1 2 | 0 2 0 1 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001359 | 1 16 | 6 9 1 0 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001360 | 1 4 | 0 4 0 2 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001361 | 1 2 | 0 1 1 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001362 | 1 11 | 4 4 3 0 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001363 | 1 6 | 2 4 0 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001364 | 1 7 | 2 3 2 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001365 | 1 4 | 1 2 1 0 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001366 | 1 5 | 1 4 0 1 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001367 | 1 13 | 5 6 2 0 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001368 | 1 7 | 1 6 0 0 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001369 | 1 2 | 1 1 0 0 1 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001370 | 1 6 | 1 4 1 1 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001371 | 1 4 | 0 4 0 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001372 | 1 7 | 1 6 0 2 8 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001373 | 1 4 | 2 2 0 1 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001374 | 1 5 | 1 3 1 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001375 | 1 6 | 1 3 2 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001376 | 1 6 | 0 5 1 0 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001377 | 1 11 | 1 10 0 1 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001378 | 1 6 | 0 3 3 0 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001379 | 1 1 | 0 1 0 2 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001380 | 1 4 | 0 4 0 1 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001381 | 1 5 | 3 2 0 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001382 | 1 13 | 6 5 2 0 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001383 | 1 11 | 6 3 2 0 5 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001384 | 1 12 | 3 9 0 1 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001385 | 1 4 | 0 3 1 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001386 | 1 7 | 2 4 1 1 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001387 | 1 6 | 0 3 3 0 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001388 | 1 1 | 0 1 0 1 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001389 | 1 7 | 1 4 2 0 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001390 | 1 11 | 2 6 3 0 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001391 | 1 3 | 1 2 0 1 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001392 | 1 21 | 3 18 0 3 21 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001393 | 1 3 | 1 2 0 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001394 | 1 5 | 1 1 3 0 4 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001395 | 1 5 | 3 2 0 1 3 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001396 | 1 10 | 0 6 4 0 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001397 | 1 3 | 1 2 0 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001398 | 1 7 | 5 2 0 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001399 | 1 3 | 2 1 0 1 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001400 | 1 5 | 1 4 0 2 6 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001401 | 1 3 | 2 1 0 1 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001402 | 1 2 | 0 2 0 0 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001403 | 1 7 | 0 5 2 0 7 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001404 | 1 10 | 0 10 0 1 11 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001405 | 1 1 | 0 1 0 1 2 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001406 | 1 10 | 1 7 2 0 9 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| swc_deu_001407 | 1 3 | 0 3 0 1 4 1 | +|====================================================================================================================| +| Sum | 207 1298 | 301 825 172 99 1096 206 | +|====================================================================================================================| +| Mean | 1.0 6.3 | 1.5 4.0 0.8 0.5 5.3 1.0 | +| S.D. | 0.0 3.8 | 1.6 2.6 1.1 0.8 3.1 0.1 | +| Median | 1.0 5.0 | 1.0 4.0 0.0 0.0 5.0 1.0 | +`--------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/score_wer/hyp.trn + +Speakers: + 0: swc_deu_001201 + 1: swc_deu_001202 + 2: swc_deu_001203 + 3: swc_deu_001204 + 4: swc_deu_001205 + 5: swc_deu_001206 + 6: swc_deu_001207 + 7: swc_deu_001208 + 8: swc_deu_001209 + 9: swc_deu_001210 + 10: swc_deu_001211 + 11: swc_deu_001212 + 12: swc_deu_001213 + 13: swc_deu_001214 + 14: swc_deu_001215 + 15: swc_deu_001216 + 16: swc_deu_001217 + 17: swc_deu_001218 + 18: swc_deu_001219 + 19: swc_deu_001220 + 20: swc_deu_001221 + 21: swc_deu_001222 + 22: swc_deu_001223 + 23: swc_deu_001224 + 24: swc_deu_001225 + 25: swc_deu_001226 + 26: swc_deu_001227 + 27: swc_deu_001228 + 28: swc_deu_001229 + 29: swc_deu_001230 + 30: swc_deu_001231 + 31: swc_deu_001232 + 32: swc_deu_001233 + 33: swc_deu_001234 + 34: swc_deu_001235 + 35: swc_deu_001236 + 36: swc_deu_001237 + 37: swc_deu_001238 + 38: swc_deu_001239 + 39: swc_deu_001240 + 40: swc_deu_001241 + 41: swc_deu_001242 + 42: swc_deu_001243 + 43: swc_deu_001244 + 44: swc_deu_001245 + 45: swc_deu_001246 + 46: swc_deu_001247 + 47: swc_deu_001248 + 48: swc_deu_001249 + 49: swc_deu_001250 + 50: swc_deu_001251 + 51: swc_deu_001252 + 52: swc_deu_001253 + 53: swc_deu_001254 + 54: swc_deu_001255 + 55: swc_deu_001256 + 56: swc_deu_001257 + 57: swc_deu_001258 + 58: swc_deu_001259 + 59: swc_deu_001260 + 60: swc_deu_001261 + 61: swc_deu_001262 + 62: swc_deu_001263 + 63: swc_deu_001264 + 64: swc_deu_001265 + 65: swc_deu_001266 + 66: swc_deu_001267 + 67: swc_deu_001268 + 68: swc_deu_001269 + 69: swc_deu_001270 + 70: swc_deu_001271 + 71: swc_deu_001272 + 72: swc_deu_001273 + 73: swc_deu_001274 + 74: swc_deu_001275 + 75: swc_deu_001276 + 76: swc_deu_001277 + 77: swc_deu_001278 + 78: swc_deu_001279 + 79: swc_deu_001280 + 80: swc_deu_001281 + 81: swc_deu_001282 + 82: swc_deu_001283 + 83: swc_deu_001284 + 84: swc_deu_001285 + 85: swc_deu_001286 + 86: swc_deu_001287 + 87: swc_deu_001288 + 88: swc_deu_001289 + 89: swc_deu_001290 + 90: swc_deu_001291 + 91: swc_deu_001292 + 92: swc_deu_001293 + 93: swc_deu_001294 + 94: swc_deu_001295 + 95: swc_deu_001296 + 96: swc_deu_001297 + 97: swc_deu_001298 + 98: swc_deu_001299 + 99: swc_deu_001300 + 100: swc_deu_001301 + 101: swc_deu_001302 + 102: swc_deu_001303 + 103: swc_deu_001304 + 104: swc_deu_001305 + 105: swc_deu_001306 + 106: swc_deu_001307 + 107: swc_deu_001308 + 108: swc_deu_001309 + 109: swc_deu_001310 + 110: swc_deu_001311 + 111: swc_deu_001312 + 112: swc_deu_001313 + 113: swc_deu_001314 + 114: swc_deu_001315 + 115: swc_deu_001316 + 116: swc_deu_001317 + 117: swc_deu_001318 + 118: swc_deu_001319 + 119: swc_deu_001320 + 120: swc_deu_001321 + 121: swc_deu_001322 + 122: swc_deu_001323 + 123: swc_deu_001324 + 124: swc_deu_001325 + 125: swc_deu_001326 + 126: swc_deu_001327 + 127: swc_deu_001328 + 128: swc_deu_001329 + 129: swc_deu_001330 + 130: swc_deu_001331 + 131: swc_deu_001332 + 132: swc_deu_001333 + 133: swc_deu_001334 + 134: swc_deu_001335 + 135: swc_deu_001336 + 136: swc_deu_001337 + 137: swc_deu_001338 + 138: swc_deu_001339 + 139: swc_deu_001340 + 140: swc_deu_001341 + 141: swc_deu_001342 + 142: swc_deu_001343 + 143: swc_deu_001344 + 144: swc_deu_001345 + 145: swc_deu_001346 + 146: swc_deu_001347 + 147: swc_deu_001348 + 148: swc_deu_001349 + 149: swc_deu_001350 + 150: swc_deu_001351 + 151: swc_deu_001352 + 152: swc_deu_001353 + 153: swc_deu_001354 + 154: swc_deu_001355 + 155: swc_deu_001356 + 156: swc_deu_001357 + 157: swc_deu_001358 + 158: swc_deu_001359 + 159: swc_deu_001360 + 160: swc_deu_001361 + 161: swc_deu_001362 + 162: swc_deu_001363 + 163: swc_deu_001364 + 164: swc_deu_001365 + 165: swc_deu_001366 + 166: swc_deu_001367 + 167: swc_deu_001368 + 168: swc_deu_001369 + 169: swc_deu_001370 + 170: swc_deu_001371 + 171: swc_deu_001372 + 172: swc_deu_001373 + 173: swc_deu_001374 + 174: swc_deu_001375 + 175: swc_deu_001376 + 176: swc_deu_001377 + 177: swc_deu_001378 + 178: swc_deu_001379 + 179: swc_deu_001380 + 180: swc_deu_001381 + 181: swc_deu_001382 + 182: swc_deu_001383 + 183: swc_deu_001384 + 184: swc_deu_001385 + 185: swc_deu_001386 + 186: swc_deu_001387 + 187: swc_deu_001388 + 188: swc_deu_001389 + 189: swc_deu_001390 + 190: swc_deu_001391 + 191: swc_deu_001392 + 192: swc_deu_001393 + 193: swc_deu_001394 + 194: swc_deu_001395 + 195: swc_deu_001396 + 196: swc_deu_001397 + 197: swc_deu_001398 + 198: swc_deu_001399 + 199: swc_deu_001400 + 200: swc_deu_001401 + 201: swc_deu_001402 + 202: swc_deu_001403 + 203: swc_deu_001404 + 204: swc_deu_001405 + 205: swc_deu_001406 + 206: swc_deu_001407 + +Speaker sentences 0: swc_deu_001201 #utts: 1 +id: (swc_deu_001201-swc_deu_001201) +Scores: (#C #S #D #I) 0 7 4 0 +REF: DER VERLIEBTE JUNGE HERZOG DIE RATSCHLÄGE SEINES VATERS NICHT BEACHTET HABE +HYP: *** ********* ***** ****** DEI ERVERLIEBTE UNGE HEARZOG IE RANSCLÄEKESEINESFATES NICHTBERACGTDETAB +Eval: D D D D S S S S S S S + +Speaker sentences 1: swc_deu_001202 #utts: 1 +id: (swc_deu_001202-swc_deu_001202) +Scores: (#C #S #D #I) 3 2 0 0 +REF: die in DEN HANSESTÄDTEN als +HYP: die in DE ANSESTERDTEN als +Eval: S S + +Speaker sentences 2: swc_deu_001203 #utts: 1 +id: (swc_deu_001203-swc_deu_001203) +Scores: (#C #S #D #I) 0 2 2 0 +REF: WAR KEIN GROSSER ERFOLG +HYP: *** **** ARKEINGROSE ERFOLGK +Eval: D D S S + +Speaker sentences 3: swc_deu_001204 #utts: 1 +id: (swc_deu_001204-swc_deu_001204) +Scores: (#C #S #D #I) 0 3 0 0 +REF: GROSSEN CHEMISCHEN FABRIKEN +HYP: GOSEN SCHEHMICHEIN VERBRICKEN +Eval: S S S + +Speaker sentences 4: swc_deu_001205 #utts: 1 +id: (swc_deu_001205-swc_deu_001205) +Scores: (#C #S #D #I) 1 4 0 2 +REF: WURDEN AUCH mehrere ** *************** ERLÄUTERUNGSBÜCHER VERÖFFENTLICHT +HYP: URDEN ACH mehrere AR LEUTRUNGSBÜCHE VER FFNTLICHT +Eval: S S I I S S + +Speaker sentences 5: swc_deu_001206 #utts: 1 +id: (swc_deu_001206-swc_deu_001206) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *********** VORBEREITETEN BIERTEIG GETUNKT +HYP: VORBEREITEN BIER TEIG GETUNGT +Eval: I S S S + +Speaker sentences 6: swc_deu_001207 #utts: 1 +id: (swc_deu_001207-swc_deu_001207) +Scores: (#C #S #D #I) 1 2 0 1 +REF: DOKUMENTE SCHLIESSLICH in * +HYP: DOMENTE SHLIESLICG in E +Eval: S S I + +Speaker sentences 7: swc_deu_001208 #utts: 1 +id: (swc_deu_001208-swc_deu_001208) +Scores: (#C #S #D #I) 1 4 3 0 +REF: TRAUERTAG FÜR DEN TOD von KÖNIG FRIEDRICH WILHELM +HYP: ********* **** TAUTAG VÜRDENTAUT von ****** KÖNICH VERERCHÜLE +Eval: D D S S D S S + +Speaker sentences 8: swc_deu_001209 #utts: 1 +id: (swc_deu_001209-swc_deu_001209) +Scores: (#C #S #D #I) 3 4 0 0 +REF: DARUNTER sind matilde ASENSIS WÄCHTER des KREUZES +HYP: DARUNDER sind matilde ARSENSIS WECHTER des KREULZIS +Eval: S S S S + +Speaker sentences 9: swc_deu_001210 #utts: 1 +id: (swc_deu_001210-swc_deu_001210) +Scores: (#C #S #D #I) 3 6 0 1 +REF: ** INNENSTÄDTEN mehr und mehr DIE ROLLE DER TRADITIONELLEN FISH +HYP: EN INENSTÄTEN mehr und mehr IEROLE DERTRADI Z NELN ISH +Eval: I S S S S S S + +Speaker sentences 10: swc_deu_001211 #utts: 1 +id: (swc_deu_001211-swc_deu_001211) +Scores: (#C #S #D #I) 1 2 0 0 +REF: ZU denen WELTLÄUFIGKEIT +HYP: Z denen WELTLEUFICHKEIIT +Eval: S S + +Speaker sentences 11: swc_deu_001212 #utts: 1 +id: (swc_deu_001212-swc_deu_001212) +Scores: (#C #S #D #I) 4 4 1 0 +REF: RACHE DES HOFES und des ADELS fÜr den FREVEL +HYP: ***** RACHRE TDSHOFES und des ADETZS fÜr den FRIEFET +Eval: D S S S S + +Speaker sentences 12: swc_deu_001213 #utts: 1 +id: (swc_deu_001213-swc_deu_001213) +Scores: (#C #S #D #I) 0 2 0 1 +REF: **** ZEITANGABEN VERZICHTETE +HYP: ZEIT ANGABEN VERZICHTET +Eval: I S S + +Speaker sentences 13: swc_deu_001214 #utts: 1 +id: (swc_deu_001214-swc_deu_001214) +Scores: (#C #S #D #I) 2 4 2 0 +REF: ALS achtzehn hundert ACHTZIG MIT OTTO BRAHMS AUFSATZ +HYP: ALL achtzehn hundert ******* *** ACHTZICHMIT OTTUOBRAMMS AUFSEITZ +Eval: S D D S S S + +Speaker sentences 14: swc_deu_001215 #utts: 1 +id: (swc_deu_001215-swc_deu_001215) +Scores: (#C #S #D #I) 0 5 1 0 +REF: EIN TAUSEND SIEBEN HUNDERT ACHTUNDZWANZIG – +HYP: *** MÜLEN WESEN SIBTEHN UNED CHUNDZWANZI +Eval: D S S S S S + +Speaker sentences 15: swc_deu_001216 #utts: 1 +id: (swc_deu_001216-swc_deu_001216) +Scores: (#C #S #D #I) 1 3 0 0 +REF: DASS der FISCH FRISCH +HYP: AS der FICH RICH +Eval: S S S + +Speaker sentences 16: swc_deu_001217 #utts: 1 +id: (swc_deu_001217-swc_deu_001217) +Scores: (#C #S #D #I) 4 9 2 0 +REF: SEINEM ABSCHLUSS IM JAHRE NEUNZEHN HUNDERT ZWEIUNDACHTZIG UNTERNAHM ER eine erste lÄngere reise NACH SPANIEN +HYP: ****** SIDEM ABSCLSS IMJAHRENUNZEN HNDR ZWAEUN ACHTZICG UND ERNAMER eine erste lÄngere reise **** ACSPANEN +Eval: D S S S S S S S S D S + +Speaker sentences 17: swc_deu_001218 #utts: 1 +id: (swc_deu_001218-swc_deu_001218) +Scores: (#C #S #D #I) 0 2 1 0 +REF: VON CHASÔT VORGEZEICHNET +HYP: *** VÜRNSCHATSO VURGEZEICHNET +Eval: D S S + +Speaker sentences 18: swc_deu_001219 #utts: 1 +id: (swc_deu_001219-swc_deu_001219) +Scores: (#C #S #D #I) 2 5 2 0 +REF: FALCKENSTEINS VOLLSTÄNDIGE GESCHICHTEN und die AUGSBURGER STADTGESCHICHTE DES ÄLTEREN +HYP: ************* FEITENSTEINS FLSTENDIGEGESCHICHTEN und die ********** AUGSBRGESTADBGESCHIEG EDES ELTREN +Eval: D S S D S S S + +Speaker sentences 19: swc_deu_001220 #utts: 1 +id: (swc_deu_001220-swc_deu_001220) +Scores: (#C #S #D #I) 2 3 3 0 +REF: nach DIESEN ZERSTÖRUNGEN wurde DIE RASCH WIEDER AUFBLÜHENDE +HYP: nach ****** DIESENZERSTÖHRUNGEN wurde *** ***** DERASSCHWIEDER AUFBLHN +Eval: D S D D S S + +Speaker sentences 20: swc_deu_001221 #utts: 1 +id: (swc_deu_001221-swc_deu_001221) +Scores: (#C #S #D #I) 4 6 0 2 +REF: machten ************** *** EINFLUSSREICHEN HANSEATEN beim KOMMISSARISCH EINGESETZTEN BÜRGERMEISTER markert ihre AUFWARTUNG +HYP: machten EINFLUSREICHEN HAN SIE ARTENM beim KOMISARISCHEIN GESETZTEN BÜRGEMEISTER markert ihre AUFWAHTUN +Eval: I I S S S S S S + +Speaker sentences 21: swc_deu_001222 #utts: 1 +id: (swc_deu_001222-swc_deu_001222) +Scores: (#C #S #D #I) 1 2 0 0 +REF: als ZENTRALES HANDELSKONTOR +HYP: als SENTRALDESHANDES KOTOR +Eval: S S + +Speaker sentences 22: swc_deu_001223 #utts: 1 +id: (swc_deu_001223-swc_deu_001223) +Scores: (#C #S #D #I) 0 5 0 0 +REF: SONDERSTELLUNG INNERHALB DER STADT KREFELD +HYP: SONDER STELLUNG INEHEILB DERSTADT KRFEL +Eval: S S S S S + +Speaker sentences 23: swc_deu_001224 #utts: 1 +id: (swc_deu_001224-swc_deu_001224) +Scores: (#C #S #D #I) 1 2 1 0 +REF: FINDET sich IN HALOBAKTERIEN +HYP: FINE sich ** INHALOBAOKTERIEN +Eval: S D S + +Speaker sentences 24: swc_deu_001225 #utts: 1 +id: (swc_deu_001225-swc_deu_001225) +Scores: (#C #S #D #I) 6 5 0 0 +REF: auf der b seite FINDET SICH das EBENFALLS von MICHAEL KOMPONIERTE +HYP: auf der b seite FINDE ZICH das EBENFALS von MEIKEL OMPONIRT +Eval: S S S S S + +Speaker sentences 25: swc_deu_001226 #utts: 1 +id: (swc_deu_001226-swc_deu_001226) +Scores: (#C #S #D #I) 4 5 1 1 +REF: in ***** HANSEATISCHER zeit HATTE die ZIRKELGESELLSCHAFT keinen AUSSCHLAGGEBENDEN EINFLUSS MEHR +HYP: in HANDE ARTISCHE zeit HATE die ZERKEGESESCHAFT keinen ***************** AUSCHAGEBENDEN ENFLSMIER +Eval: I S S S D S S + +Speaker sentences 26: swc_deu_001227 #utts: 1 +id: (swc_deu_001227-swc_deu_001227) +Scores: (#C #S #D #I) 8 5 2 0 +REF: DA ES DURCH VERWENDUNG von AUFTRIEBSKÖRPERN oder holz eine geringere MITTLERE DICHTE als wasser hat +HYP: ** DAR SDERCH VEWENDUN von AUFTRIEBSKÖRPAN oder holz eine geringere ******** MITTLERETDICHTE als wasser hat +Eval: D S S S S D S + +Speaker sentences 27: swc_deu_001228 #utts: 1 +id: (swc_deu_001228-swc_deu_001228) +Scores: (#C #S #D #I) 0 1 0 2 +REF: ****** *** DRAMATISIERUNGEN +HYP: DRAMEI TDE SIERUNGEN +Eval: I I S + +Speaker sentences 28: swc_deu_001229 #utts: 1 +id: (swc_deu_001229-swc_deu_001229) +Scores: (#C #S #D #I) 0 2 0 2 +REF: *** ***** UM 755 +HYP: UMM SEBEN OR FÜNWON +Eval: I I S S + +Speaker sentences 29: swc_deu_001230 #utts: 1 +id: (swc_deu_001230-swc_deu_001230) +Scores: (#C #S #D #I) 2 2 0 1 +REF: DASS albrecht die ***** BADERSTOCHTER +HYP: DES albrecht die BADES TOCHTER +Eval: S I S + +Speaker sentences 30: swc_deu_001231 #utts: 1 +id: (swc_deu_001231-swc_deu_001231) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** THAT BARBARISCHER STAATSRAISON +HYP: TAT BAM BARESCHE STATZR +Eval: I S S S + +Speaker sentences 31: swc_deu_001232 #utts: 1 +id: (swc_deu_001232-swc_deu_001232) +Scores: (#C #S #D #I) 0 2 3 0 +REF: DER DAS LIED BESONDERS LIEBTE +HYP: *** *** **** DRSLIET BESONDERSLIEBTE +Eval: D D D S S + +Speaker sentences 32: swc_deu_001233 #utts: 1 +id: (swc_deu_001233-swc_deu_001233) +Scores: (#C #S #D #I) 2 10 0 2 +REF: *** AUFGRUND des ********* WACHSENDEN PUBLIKUMSINTERESSES WURDE der AUFTRITTSORT FÜR DIE PRIMA VISTA LESUNGEN +HYP: AUF KUND des WAHSENDEN POPLIKUMS INTRESSES WRDE der AUFTRITS ORT ÜR IE PRIMER ISTALESUNGE +Eval: I S I S S S S S S S S S + +Speaker sentences 33: swc_deu_001234 #utts: 1 +id: (swc_deu_001234-swc_deu_001234) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ** UND FREILICHTSPIELE +HYP: ND FREIDLICH SPBIELE +Eval: I S S + +Speaker sentences 34: swc_deu_001235 #utts: 1 +id: (swc_deu_001235-swc_deu_001235) +Scores: (#C #S #D #I) 1 8 0 1 +REF: DASS die ****** DREI DEN STURZ RELATIV UNBESCHADET ÜBERSTANDEN HATTEN +HYP: DS die REIDEN STURTZ ERLATIE UN BESCHADE BER STANDEN HATE +Eval: S I S S S S S S S + +Speaker sentences 35: swc_deu_001236 #utts: 1 +id: (swc_deu_001236-swc_deu_001236) +Scores: (#C #S #D #I) 2 2 0 0 +REF: jahren ERSCHIENEN zwei IMMER +HYP: jahren ASCHENEND zwei IMEN +Eval: S S + +Speaker sentences 36: swc_deu_001237 #utts: 1 +id: (swc_deu_001237-swc_deu_001237) +Scores: (#C #S #D #I) 2 4 0 3 +REF: ** GRABMALE und **** GRABKAPELLEN oder ** WOHLTATEN NACHHALTIG +HYP: DR RABMALE und GRAB GKAPÄLEN oder OL TATEN NACHALT +Eval: I S I S I S S + +Speaker sentences 37: swc_deu_001238 #utts: 1 +id: (swc_deu_001238-swc_deu_001238) +Scores: (#C #S #D #I) 1 6 2 0 +REF: JUNI NEUNZEHN HUNDERT SECHSUNDNEUNZIG KÜNDIGTE ER SEINE beiden JOBS +HYP: **** ******** JUNENEUZEN HUNDR EHSUNEUNZIG KÖNEKTDER RSEINE beiden JOPS +Eval: D D S S S S S S + +Speaker sentences 38: swc_deu_001239 #utts: 1 +id: (swc_deu_001239-swc_deu_001239) +Scores: (#C #S #D #I) 0 4 0 0 +REF: EIN G PROTEIN GEKOPPELT +HYP: IN GE PORTINE OPET +Eval: S S S S + +Speaker sentences 39: swc_deu_001240 #utts: 1 +id: (swc_deu_001240-swc_deu_001240) +Scores: (#C #S #D #I) 2 4 0 1 +REF: ****** NEUNUNDSECHZIG der MEDIA CONTROL ALBUMCHARTS ein +HYP: NEUNUN SECHZIG der MELIER KONTWOL ALBUMSCHATZ ein +Eval: I S S S S + +Speaker sentences 40: swc_deu_001241 #utts: 1 +id: (swc_deu_001241-swc_deu_001241) +Scores: (#C #S #D #I) 0 1 1 0 +REF: DADURCH KOMMT +HYP: ******* TADUCHCOM +Eval: D S + +Speaker sentences 41: swc_deu_001242 #utts: 1 +id: (swc_deu_001242-swc_deu_001242) +Scores: (#C #S #D #I) 3 4 0 2 +REF: *** OHNEHIN nicht den ********** GROSSHANDELSKAUFLEUTEN GESELLSCHAFTLICH GLEICHGESTELLT waren +HYP: ONE HEN nicht den ROSHANDELS KAUFLEUTEN GESELSCHAFTLICG GLEICHGESTET waren +Eval: I S I S S S + +Speaker sentences 42: swc_deu_001243 #utts: 1 +id: (swc_deu_001243-swc_deu_001243) +Scores: (#C #S #D #I) 2 3 1 0 +REF: VON DER NAHRUNG und vom KLIMA +HYP: *** VO DERNARUNG und vom KLIEMA +Eval: D S S S + +Speaker sentences 43: swc_deu_001244 #utts: 1 +id: (swc_deu_001244-swc_deu_001244) +Scores: (#C #S #D #I) 1 1 0 0 +REF: APOLLO eins +HYP: APOLO eins +Eval: S + +Speaker sentences 44: swc_deu_001245 #utts: 1 +id: (swc_deu_001245-swc_deu_001245) +Scores: (#C #S #D #I) 1 3 1 0 +REF: BRÜHL und HÜRTH NACH KÖLN +HYP: BRÜYELEL und ****** HÖRT NACHKELEN +Eval: S D S S + +Speaker sentences 45: swc_deu_001246 #utts: 1 +id: (swc_deu_001246-swc_deu_001246) +Scores: (#C #S #D #I) 1 3 0 0 +REF: ETWA in EIN KLOSTER +HYP: TWAR in EN KLOSTE +Eval: S S S + +Speaker sentences 46: swc_deu_001247 #utts: 1 +id: (swc_deu_001247-swc_deu_001247) +Scores: (#C #S #D #I) 4 13 1 1 +REF: ZUM OFFIZIELLEN KARNEVAL ENTSTAND UND HEUTE EINE MISCHUNG aus KÖLSCHEM KARNEVAL und POLITISCHEM KABARETT mit *** COMEDYELEMENTEN darstellt UND +HYP: *** DIVWRZEM OFIZEHEN KANEWALEN STANDT UNT TEUTE EINEMSCHUNG aus KÖRSCHEN KANDEWAL und POLITSCHEM KABERET mit KOM DIELMENTEN darstellt UN +Eval: D S S S S S S S S S S S I S S + +Speaker sentences 47: swc_deu_001248 #utts: 1 +id: (swc_deu_001248-swc_deu_001248) +Scores: (#C #S #D #I) 1 2 3 0 +REF: die ZUR ENTSTEHUNG DES LIEDES FÜHRTEN +HYP: die *** ********** *** WENSTEIUNGESLIEDES FÜRTEN +Eval: D D D S S + +Speaker sentences 48: swc_deu_001249 #utts: 1 +id: (swc_deu_001249-swc_deu_001249) +Scores: (#C #S #D #I) 2 4 0 0 +REF: NANNTE ziegler DIE ERMORDUNG der BERNAUERIN +HYP: NANTIT ziegler DI ARMORDUM der BRNAURUNEN +Eval: S S S S + +Speaker sentences 49: swc_deu_001250 #utts: 1 +id: (swc_deu_001250-swc_deu_001250) +Scores: (#C #S #D #I) 1 3 0 0 +REF: WINTERRUHE ist VOR ALLEM +HYP: INTEROHR ist VE L +Eval: S S S + +Speaker sentences 50: swc_deu_001251 #utts: 1 +id: (swc_deu_001251-swc_deu_001251) +Scores: (#C #S #D #I) 9 6 0 1 +REF: die strÄnge ** DER VORGÄNGERLEITUNG wurden ZWISCHEN neunzehn hundert neunundzwanzig und NEUNZEHN hundert DREIUNDFÜNFZIG ARCHÄOLOGISCH ergraben +HYP: die strÄnge DR FORGENGER LEITUNG wurden ZWICHE neunzehn hundert neunundzwanzig und NEUNZHN hundert DREINFÜNFZIG AICHIERLOGESCH ergraben +Eval: I S S S S S S + +Speaker sentences 51: swc_deu_001252 #utts: 1 +id: (swc_deu_001252-swc_deu_001252) +Scores: (#C #S #D #I) 2 0 0 0 +REF: im gegensatz +HYP: im gegensatz +Eval: + +Speaker sentences 52: swc_deu_001253 #utts: 1 +id: (swc_deu_001253-swc_deu_001253) +Scores: (#C #S #D #I) 1 3 3 0 +REF: FARBEN von UERDINGEN SIND BLAU UND ROT +HYP: VWARBE von ********* **** **** ÖRDIGENSN LAUNDHORD +Eval: S D D D S S + +Speaker sentences 53: swc_deu_001254 #utts: 1 +id: (swc_deu_001254-swc_deu_001254) +Scores: (#C #S #D #I) 0 2 0 0 +REF: LIVE VERANSTALTUNGEN +HYP: LEIF VERANSTALTUMEN +Eval: S S + +Speaker sentences 54: swc_deu_001255 #utts: 1 +id: (swc_deu_001255-swc_deu_001255) +Scores: (#C #S #D #I) 1 5 4 0 +REF: SO WERDEN HEUTE in DER REGEL ALLE DORT LEBENDEN BRAUNBÄREN +HYP: ** ****** SUOWERDENHEUTE in *** ***** DEREGEL ALE DORTLEBENDEN BRUN +Eval: D D S D D S S S S + +Speaker sentences 55: swc_deu_001256 #utts: 1 +id: (swc_deu_001256-swc_deu_001256) +Scores: (#C #S #D #I) 0 3 0 0 +REF: LIEDER FÜR REVUEFILME +HYP: IEDA FÜRE WÜSCHLMER +Eval: S S S + +Speaker sentences 56: swc_deu_001257 #utts: 1 +id: (swc_deu_001257-swc_deu_001257) +Scores: (#C #S #D #I) 1 2 0 2 +REF: des *** ** HANSEATEN FÜHREN +HYP: des HAN SE ATEN FÜRE +Eval: I I S S + +Speaker sentences 57: swc_deu_001258 #utts: 1 +id: (swc_deu_001258-swc_deu_001258) +Scores: (#C #S #D #I) 1 2 0 0 +REF: HEBBELS agnes BERNAUER +HYP: HÄBILS agnes BERNAU +Eval: S S + +Speaker sentences 58: swc_deu_001259 #utts: 1 +id: (swc_deu_001259-swc_deu_001259) +Scores: (#C #S #D #I) 1 1 0 0 +REF: lebensweise VERKÖRPERN +HYP: lebensweise VERKÖARPER +Eval: S + +Speaker sentences 59: swc_deu_001260 #utts: 1 +id: (swc_deu_001260-swc_deu_001260) +Scores: (#C #S #D #I) 2 4 3 0 +REF: WIE DER FALL DES vielen HAMBURGERN zu KATHOLISCH FROMMEN +HYP: *** *** **** IDEFALTDES vielen HAMBURGAN zu KATOLESCH FRMMEN +Eval: D D D S S S S + +Speaker sentences 60: swc_deu_001261 #utts: 1 +id: (swc_deu_001261-swc_deu_001261) +Scores: (#C #S #D #I) 1 3 0 1 +REF: ******* KULTUR UND WIRTSCHAFT austauschen +HYP: KOLTOUR END DER THEFT austauschen +Eval: I S S S + +Speaker sentences 61: swc_deu_001262 #utts: 1 +id: (swc_deu_001262-swc_deu_001262) +Scores: (#C #S #D #I) 1 2 1 0 +REF: JAHR ZWEI tausend VERTONTE +HYP: **** MJAHRZWEI tausend VERTONDTE +Eval: D S S + +Speaker sentences 62: swc_deu_001263 #utts: 1 +id: (swc_deu_001263-swc_deu_001263) +Scores: (#C #S #D #I) 6 6 1 0 +REF: DASS ER diese leitung SCHNELLER VOLLENDEN KÖNNE als der baumeister den KÖLNER DOM +HYP: DAS E diese leitung SCHNLER VOLÄENDTEN KÖNE als der baumeister den ******* KÖNERDOM +Eval: S S S S S D S + +Speaker sentences 63: swc_deu_001264 #utts: 1 +id: (swc_deu_001264-swc_deu_001264) +Scores: (#C #S #D #I) 3 4 1 1 +REF: **** hinrichtung der BERNAUERIN HABE ES SICH SCHLICHT um +HYP: EIER hinrichtung der ********** BANAURIEN HABES IC LICHT um +Eval: I D S S S S + +Speaker sentences 64: swc_deu_001265 #utts: 1 +id: (swc_deu_001265-swc_deu_001265) +Scores: (#C #S #D #I) 0 1 0 0 +REF: LUDWIG +HYP: LUOREI +Eval: S + +Speaker sentences 65: swc_deu_001266 #utts: 1 +id: (swc_deu_001266-swc_deu_001266) +Scores: (#C #S #D #I) 2 3 1 0 +REF: derzeit der BESTE KENNER DER EIFELLEITUNG +HYP: derzeit der ***** BESTIE KENERDER EIFELEITUNG +Eval: D S S S + +Speaker sentences 66: swc_deu_001267 #utts: 1 +id: (swc_deu_001267-swc_deu_001267) +Scores: (#C #S #D #I) 1 3 0 0 +REF: FOKUS DES WISSENSCHAFTLICHEN interesses +HYP: VOKUS BES WISENSHAFTLICHEN interesses +Eval: S S S + +Speaker sentences 67: swc_deu_001268 #utts: 1 +id: (swc_deu_001268-swc_deu_001268) +Scores: (#C #S #D #I) 1 2 0 0 +REF: THEMA zu BEGEISTERN +HYP: TEME zu BEGEISTEN +Eval: S S + +Speaker sentences 68: swc_deu_001269 #utts: 1 +id: (swc_deu_001269-swc_deu_001269) +Scores: (#C #S #D #I) 5 4 0 0 +REF: meter und KONNTE damit AUCH von INNEN BEGANGEN werden +HYP: meter und KONTE damit AU von INEN BERGANGEN werden +Eval: S S S S + +Speaker sentences 69: swc_deu_001270 #utts: 1 +id: (swc_deu_001270-swc_deu_001270) +Scores: (#C #S #D #I) 1 3 0 2 +REF: *** ***** HARDCOVER BESTSELLERLISTE der NEW +HYP: HAT KABER BES SELLISTE der NÜH +Eval: I I S S S + +Speaker sentences 70: swc_deu_001271 #utts: 1 +id: (swc_deu_001271-swc_deu_001271) +Scores: (#C #S #D #I) 1 2 0 0 +REF: der FREIEN ENZYKLOPÄDIE +HYP: der FREIN ENZIGKLOP +Eval: S S + +Speaker sentences 71: swc_deu_001272 #utts: 1 +id: (swc_deu_001272-swc_deu_001272) +Scores: (#C #S #D #I) 1 5 2 0 +REF: den GRIZZLY WIEDER AUF DIE LISTE ZU SETZEN +HYP: den ******* ****** GRSLI WIER UF DELSTE ZUSETZEN +Eval: D D S S S S S + +Speaker sentences 72: swc_deu_001273 #utts: 1 +id: (swc_deu_001273-swc_deu_001273) +Scores: (#C #S #D #I) 2 3 0 2 +REF: *** lang diese ************** KAPLANSSTELLE AUFRECHTERHALTEN WURDE +HYP: WIE lang diese KAPLANSSTÄLLE AUFRECHTER HETEN WURD +Eval: I I S S S + +Speaker sentences 73: swc_deu_001274 #utts: 1 +id: (swc_deu_001274-swc_deu_001274) +Scores: (#C #S #D #I) 0 8 2 0 +REF: SIE WAREN WAHRSCHEINLICH BEREITS DREISSIG SEKUNDEN NACH AUSBRUCH DES FEUERS +HYP: *** ***** SIWAHREN WASCHEINLICHBEREITZ DREISIG SI KUNDEN DACH AUSPRCHTES VEUR +Eval: D D S S S S S S S S + +Speaker sentences 74: swc_deu_001275 #utts: 1 +id: (swc_deu_001275-swc_deu_001275) +Scores: (#C #S #D #I) 2 4 1 0 +REF: METERN GESAMTLÄNGE und bis ZU ZEHN METERN +HYP: METER GESAMTLINGE und bis ** ZUZEHN MITER +Eval: S S D S S + +Speaker sentences 75: swc_deu_001276 #utts: 1 +id: (swc_deu_001276-swc_deu_001276) +Scores: (#C #S #D #I) 2 2 0 0 +REF: feine ritzen UND SPALTEN +HYP: feine ritzen UN SPALTE +Eval: S S + +Speaker sentences 76: swc_deu_001277 #utts: 1 +id: (swc_deu_001277-swc_deu_001277) +Scores: (#C #S #D #I) 1 5 2 0 +REF: DEN MAN von AUSSEN DIE KEHLE HINABFLIESSEN SIEHT +HYP: *** DINMAN von ****** AUSSN DIEKELER HINABPFLIESEN SIET +Eval: D S D S S S S + +Speaker sentences 77: swc_deu_001278 #utts: 1 +id: (swc_deu_001278-swc_deu_001278) +Scores: (#C #S #D #I) 0 2 2 0 +REF: EINEM INTERVIEW SAGTE BROWN +HYP: ***** ********* ENE INTERIUSAGTEBRAUN +Eval: D D S S + +Speaker sentences 78: swc_deu_001279 #utts: 1 +id: (swc_deu_001279-swc_deu_001279) +Scores: (#C #S #D #I) 1 2 0 0 +REF: das FÜNFTE EVANGELIUM +HYP: das FÜNFTDEVEN GERIUM +Eval: S S + +Speaker sentences 79: swc_deu_001280 #utts: 1 +id: (swc_deu_001280-swc_deu_001280) +Scores: (#C #S #D #I) 1 5 0 0 +REF: REISSEN SIE MANCHMAL WEIDETIERE WIE schafe +HYP: RESEN SIEMANCHMAL WEIDE TIERE IE schafe +Eval: S S S S S + +Speaker sentences 80: swc_deu_001281 #utts: 1 +id: (swc_deu_001281-swc_deu_001281) +Scores: (#C #S #D #I) 1 4 1 1 +REF: SIE HÖREN DEN artikel *** DESIGN REVIEW +HYP: *** SI ÖRENDEN artikel DIE SEIN RÜVIEU +Eval: D S S I S S + +Speaker sentences 81: swc_deu_001282 #utts: 1 +id: (swc_deu_001282-swc_deu_001282) +Scores: (#C #S #D #I) 1 3 0 0 +REF: CHAKUZA IST GELERNTER koch +HYP: KUSE IS GELENTER koch +Eval: S S S + +Speaker sentences 82: swc_deu_001283 #utts: 1 +id: (swc_deu_001283-swc_deu_001283) +Scores: (#C #S #D #I) 0 2 1 0 +REF: HANS WENDT STIFTUNG +HYP: **** HANWENT STIFTUNGE +Eval: D S S + +Speaker sentences 83: swc_deu_001284 #utts: 1 +id: (swc_deu_001284-swc_deu_001284) +Scores: (#C #S #D #I) 1 5 0 1 +REF: neunzehn ****** HUNDERT ACHTZEHN ALS HANSEATEN ANGESEHEN +HYP: neunzehn HUDERT ACHTZIEN AL HAINSIE ARTEN ANGESIEN +Eval: I S S S S S + +Speaker sentences 84: swc_deu_001285 #utts: 1 +id: (swc_deu_001285-swc_deu_001285) +Scores: (#C #S #D #I) 2 3 0 0 +REF: MEHRERE es nach IHM THUN +HYP: MEHRERER es nach IM TOUN +Eval: S S S + +Speaker sentences 85: swc_deu_001286 #utts: 1 +id: (swc_deu_001286-swc_deu_001286) +Scores: (#C #S #D #I) 2 7 0 1 +REF: AUFSTIEG DES gerichts zur ****** LANDESWEIT BELIEBTEN KULINARISCHEN SPEZIALITÄT ERMÖGLICHTE +HYP: ACHSTIG ES gerichts zur LANDES WEITEN BELIEBEN KOLINARISCHE SPEZILITET ERMÖGE +Eval: S S I S S S S S + +Speaker sentences 86: swc_deu_001287 #utts: 1 +id: (swc_deu_001287-swc_deu_001287) +Scores: (#C #S #D #I) 2 7 1 0 +REF: COLLEGE und EINEN ZWEITJOB ALS SPANISCHLEHRER in HAMPTON FALLS AN +HYP: KOLLETSC und EIN ZWEIT JOB ALSPANISCHLÄHRER in ******* HEMTN VORLSEAN +Eval: S S S S S D S S + +Speaker sentences 87: swc_deu_001288 #utts: 1 +id: (swc_deu_001288-swc_deu_001288) +Scores: (#C #S #D #I) 0 4 0 0 +REF: WURDEN KEINESWEGS ALLE GEBÜRTIGEN +HYP: BOREN KEINES WEGXS ALLÄEGEBÜRTIGEN +Eval: S S S S + +Speaker sentences 88: swc_deu_001289 #utts: 1 +id: (swc_deu_001289-swc_deu_001289) +Scores: (#C #S #D #I) 1 2 1 0 +REF: ist IHR KÖRPERBAU KRÄFTIG +HYP: ist *** IER KÖRBERBAUGREFTIG +Eval: D S S + +Speaker sentences 89: swc_deu_001290 #utts: 1 +id: (swc_deu_001290-swc_deu_001290) +Scores: (#C #S #D #I) 0 3 1 0 +REF: ANLÄSSLICH DER NEUJAHRESANSPRACHE KIM +HYP: *********** ANLESLICHTER NEUJAS ANDGSPRACHREKE +Eval: D S S S + +Speaker sentences 90: swc_deu_001291 #utts: 1 +id: (swc_deu_001291-swc_deu_001291) +Scores: (#C #S #D #I) 3 1 1 0 +REF: mit wind von SCHRÄG HINTEN +HYP: mit wind von ******* SCRECHINTEN +Eval: D S + +Speaker sentences 91: swc_deu_001292 #utts: 1 +id: (swc_deu_001292-swc_deu_001292) +Scores: (#C #S #D #I) 2 5 0 0 +REF: den GRÖSSTEN TEIL DER BEZIRKSVERTRETUNG UERDINGEN aus +HYP: den GREÖSTEN TELDER BE ZIÜRGSWERTRETUNG ÖRDINEN aus +Eval: S S S S S + +Speaker sentences 92: swc_deu_001293 #utts: 1 +id: (swc_deu_001293-swc_deu_001293) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ACHTZEHN HUNDERT EINUNDZWANZIG +HYP: ACHTHN HUNER EIUNDZWENZI +Eval: S S S + +Speaker sentences 93: swc_deu_001294 #utts: 1 +id: (swc_deu_001294-swc_deu_001294) +Scores: (#C #S #D #I) 2 3 0 0 +REF: DES GROSSEN adels ANGESAMMELTEN reichtums +HYP: ES ROSSEN adels ANGESAMMITEN reichtums +Eval: S S S + +Speaker sentences 94: swc_deu_001295 #utts: 1 +id: (swc_deu_001295-swc_deu_001295) +Scores: (#C #S #D #I) 0 5 0 2 +REF: * *** SOLLTEN NICHT ALS SEXUELLE PROVOKATION +HYP: E SOE NIHT EL SEH SOL POKATZIUN +Eval: I I S S S S S + +Speaker sentences 95: swc_deu_001296 #utts: 1 +id: (swc_deu_001296-swc_deu_001296) +Scores: (#C #S #D #I) 1 4 1 0 +REF: TEILHABER DER FIRMA GOSSMANN und JÜRGENS +HYP: ********* TEILHABEDE VRMER OSMAN und JIRGENZ +Eval: D S S S S + +Speaker sentences 96: swc_deu_001297 #utts: 1 +id: (swc_deu_001297-swc_deu_001297) +Scores: (#C #S #D #I) 0 5 1 0 +REF: DER KRAUTINSEL BILDET SIE DIE GEMEINDE +HYP: *** NERM TER FRAUNENTE RAUT INSLT +Eval: D S S S S S + +Speaker sentences 97: swc_deu_001298 #utts: 1 +id: (swc_deu_001298-swc_deu_001298) +Scores: (#C #S #D #I) 2 6 1 1 +REF: ******* AUDIO IST EIN DEUTSCHER HÖRBUCHVERLAG mit sitz IN MÜNCHEN +HYP: AURDIEO ÖST AN DEUTCHER HÖR BUCHFVERLAG mit sitz ** INMÜNCHEN +Eval: I S S S S S D S + +Speaker sentences 98: swc_deu_001299 #utts: 1 +id: (swc_deu_001299-swc_deu_001299) +Scores: (#C #S #D #I) 1 4 0 1 +REF: *** FARBPIGMENTE und CHEMISCHE VORPRODUKTE HERSTELLT +HYP: FAR PIKTMENTE und CHEMICHE VORPROTRUCKTE HERSTELT +Eval: I S S S S + +Speaker sentences 99: swc_deu_001300 #utts: 1 +id: (swc_deu_001300-swc_deu_001300) +Scores: (#C #S #D #I) 6 7 2 3 +REF: ERBLICHEN preussischen **** ***** FREIHERRENSTAND in der *** ZOLLANSCHLUSSFRAGE entschieden gegen DEN SENAT auf DIE SEITE BISMARCKS GESTELLT +HYP: ARPLICHEN preussischen FREI HEREN STAND in der ZAL ANSCHLUSSFRAGE entschieden gegen *** DENSINAR auf *** DIESEITE BISMARGS GESTLLT +Eval: S I I S I S D S D S S S + +Speaker sentences 100: swc_deu_001301 #utts: 1 +id: (swc_deu_001301-swc_deu_001301) +Scores: (#C #S #D #I) 3 6 2 1 +REF: WENN DIE QUELLEN von **** SELBST HERVORQUELLEN und offen ZU TAGE LIEGEN +HYP: WEN DI WÄLEN von SELS ER FORGWELEN und offen ** **** ZUOTAGELIEGEN +Eval: S S S I S S D D S + +Speaker sentences 101: swc_deu_001302 #utts: 1 +id: (swc_deu_001302-swc_deu_001302) +Scores: (#C #S #D #I) 0 6 0 1 +REF: ** DAS VOM NACHBARBAUTRUPP BEREITS BEGONNEN WURDE +HYP: DS VONGN NACHBAR BAUTRUB BAREITS BEGON WUORT +Eval: I S S S S S S + +Speaker sentences 102: swc_deu_001303 #utts: 1 +id: (swc_deu_001303-swc_deu_001303) +Scores: (#C #S #D #I) 2 4 0 4 +REF: werden ******* PRÄGENDE elemente ** ****** **** DES HANSEATENTUMS ZUSAMMENGEFASST +HYP: werden PRERGEN DE elemente DE HANSIE ATEN TUMST ZUSAMEN GEFAST +Eval: I S I I I S S S + +Speaker sentences 103: swc_deu_001304 #utts: 1 +id: (swc_deu_001304-swc_deu_001304) +Scores: (#C #S #D #I) 1 4 1 1 +REF: DAS LIED WURDE als ********** VOLKSLIED ANGESEHEN +HYP: *** DES SLIETZWURE als FOLCSTLIET AN GESEIEN +Eval: D S S I S S + +Speaker sentences 104: swc_deu_001305 #utts: 1 +id: (swc_deu_001305-swc_deu_001305) +Scores: (#C #S #D #I) 1 4 1 0 +REF: der ZUR RANDOM HOUSE VERLAGSGRUPPE GEHÖRT +HYP: der *** ZO RNDEM HAUSVERLAGS URUBGEHÖRT +Eval: D S S S S + +Speaker sentences 105: swc_deu_001306 #utts: 1 +id: (swc_deu_001306-swc_deu_001306) +Scores: (#C #S #D #I) 0 7 0 2 +REF: **** ** FÜR DIE KÜNFTIGEN BORDBÜCHER ENTWICKELTE DIE PAPIERFABRIK +HYP: VÜR DE ÖNFTIGEN BORT BÜCHER ENT WICKETE DI PAPIERFERPRI +Eval: I I S S S S S S S + +Speaker sentences 106: swc_deu_001307 #utts: 1 +id: (swc_deu_001307-swc_deu_001307) +Scores: (#C #S #D #I) 0 2 0 0 +REF: HAMBURG WUCHS +HYP: HAMBRE WUOKS +Eval: S S + +Speaker sentences 107: swc_deu_001308 #utts: 1 +id: (swc_deu_001308-swc_deu_001308) +Scores: (#C #S #D #I) 0 9 3 0 +REF: FÜR DIE QUASI ADLIGEN LANDSITZE BETRIEBENE AUFWAND – SEI ES BEIM BAU +HYP: **** *** ***** R DI WASIE ARDLIGEN LANDZITZE PETRIBEN AUFAND SEIS BEMBAU +Eval: D D D S S S S S S S S S + +Speaker sentences 108: swc_deu_001309 #utts: 1 +id: (swc_deu_001309-swc_deu_001309) +Scores: (#C #S #D #I) 1 7 4 0 +REF: JAHR ZWEI TAUSEND zwÖlf IN DEN BERLINER CLUB S O SECHSUNDDREISSIG VERLEGT +HYP: **** JAHRZWEI TAUSEN zwÖlf ** *** ******** INDEN BELINER KLUP SOSECHSONDREISIG VELLIGT +Eval: D S S D D D S S S S S + +Speaker sentences 109: swc_deu_001310 #utts: 1 +id: (swc_deu_001310-swc_deu_001310) +Scores: (#C #S #D #I) 1 4 1 0 +REF: SECHZEHN HUNDERT FÜNFZIG ALS bÜndnis DIE +HYP: ******** SECHEHN UNENDFÜNFZIGH AITS bÜndnis D +Eval: D S S S S + +Speaker sentences 110: swc_deu_001311 #utts: 1 +id: (swc_deu_001311-swc_deu_001311) +Scores: (#C #S #D #I) 1 4 0 1 +REF: *** PROBLEM BEI DIESEM PARADOXON ist +HYP: DAS PROLEHM BE IESEM PERADOCHSON ist +Eval: I S S S S + +Speaker sentences 111: swc_deu_001312 #utts: 1 +id: (swc_deu_001312-swc_deu_001312) +Scores: (#C #S #D #I) 1 3 0 2 +REF: ***** ARMENWESEN TÄTIG amalie * SIEVEKING +HYP: ARMEN WESEN TETIC amalie S IEVEIKEINGN +Eval: I S S I S + +Speaker sentences 112: swc_deu_001313 #utts: 1 +id: (swc_deu_001313-swc_deu_001313) +Scores: (#C #S #D #I) 4 8 1 0 +REF: NICHT EINMAL EINE ANSATZWEISE UNTERSUCHUNG zu IHREM verhalten in DER ZEIT des NATIONALSOZIALISMUS +HYP: NIHT EINMALEINE ANNSATZWEISE NTU SOCHUNG zu IEREM verhalten in *** ERZEIT des NATZUONASOTZELISMUS +Eval: S S S S S S D S S + +Speaker sentences 113: swc_deu_001314 #utts: 1 +id: (swc_deu_001314-swc_deu_001314) +Scores: (#C #S #D #I) 0 4 0 0 +REF: LIZENZ FÜR FREIE DOKUMENTATION +HYP: LIZENS VÜERFRIE DO GOMENTATION +Eval: S S S S + +Speaker sentences 114: swc_deu_001315 #utts: 1 +id: (swc_deu_001315-swc_deu_001315) +Scores: (#C #S #D #I) 0 7 1 0 +REF: IM ACHTZEHNTE JAHRHUNDERT DIE GARTENHÄUSER VOR DEN TOREN +HYP: ** DIM ACHZIHNT NIER HUNDER DIEGARTEN HEUSER VORDENTOREN +Eval: D S S S S S S S + +Speaker sentences 115: swc_deu_001316 #utts: 1 +id: (swc_deu_001316-swc_deu_001316) +Scores: (#C #S #D #I) 1 2 2 0 +REF: GANZ IM STIL DER zeit +HYP: **** ** GANS IMSTIELDER zeit +Eval: D D S S + +Speaker sentences 116: swc_deu_001317 #utts: 1 +id: (swc_deu_001317-swc_deu_001317) +Scores: (#C #S #D #I) 1 7 1 0 +REF: ÜBER BRÜHL und HÜRTH ERREICHTE DIE LEITUNG SCHLIESSLICH KÖLN +HYP: BER BRÜÖL und ****** HÜÖRT ERREICH E DE LEITUNGSCLISLICHKÖN +Eval: S S D S S S S S + +Speaker sentences 117: swc_deu_001318 #utts: 1 +id: (swc_deu_001318-swc_deu_001318) +Scores: (#C #S #D #I) 0 3 0 2 +REF: *** ********** AUSZEICHNUNGEN FREMDER HERREN +HYP: AUS ZEICHNUMEN FREM DER HEREN +Eval: I I S S S + +Speaker sentences 118: swc_deu_001319 #utts: 1 +id: (swc_deu_001319-swc_deu_001319) +Scores: (#C #S #D #I) 0 3 0 0 +REF: DIE SCHRIFTSTELLEREI AUFZUGEBEN +HYP: DISCHEFTELLEREI AUF ZUGEBEN +Eval: S S S + +Speaker sentences 119: swc_deu_001320 #utts: 1 +id: (swc_deu_001320-swc_deu_001320) +Scores: (#C #S #D #I) 0 5 1 0 +REF: ZÄHLEN DIE BEGEGNUNG MIT VERLETZTEN TIEREN +HYP: ******* DA ZUTZEHE DI EGEGNUNGMIT VERLETZTENTIEREN +Eval: D S S S S S + +Speaker sentences 120: swc_deu_001321 #utts: 1 +id: (swc_deu_001321-swc_deu_001321) +Scores: (#C #S #D #I) 1 1 0 0 +REF: JENISCH stift +HYP: JENENISCH stift +Eval: S + +Speaker sentences 121: swc_deu_001322 #utts: 1 +id: (swc_deu_001322-swc_deu_001322) +Scores: (#C #S #D #I) 2 1 0 0 +REF: westlich von KÖLN +HYP: westlich von KÖLEN +Eval: S + +Speaker sentences 122: swc_deu_001323 #utts: 1 +id: (swc_deu_001323-swc_deu_001323) +Scores: (#C #S #D #I) 3 1 1 0 +REF: die stÄndig in BETRIEB WAREN +HYP: die stÄndig in ******* BERIEBWAREN +Eval: D S + +Speaker sentences 123: swc_deu_001324 #utts: 1 +id: (swc_deu_001324-swc_deu_001324) +Scores: (#C #S #D #I) 1 3 1 0 +REF: DIE vom BARBIER RASIERT WERDEN +HYP: DI vom ******* BAR BIIRCHASIERTWERDE +Eval: S D S S + +Speaker sentences 124: swc_deu_001325 #utts: 1 +id: (swc_deu_001325-swc_deu_001325) +Scores: (#C #S #D #I) 0 4 4 0 +REF: ERSCHIEN NOCH EIN WEITERER AUFSATZ VON CHRISTIAN MEYER +HYP: ******** **** *** ******** ERSCHE NOCHEN WEITERERAUFSETZVON KRISTIERNMEIELT +Eval: D D D D S S S S + +Speaker sentences 125: swc_deu_001326 #utts: 1 +id: (swc_deu_001326-swc_deu_001326) +Scores: (#C #S #D #I) 0 7 1 0 +REF: WEIL SELBST EXTREMER REICHTUM KEINESWEGS DEN UNMITTELBAREN ZUGANG +HYP: **** WALL SEBST EXTREMEREICHTUM KEINES WEHTEN UNMITELBEREN ZUGAN +Eval: D S S S S S S S + +Speaker sentences 126: swc_deu_001327 #utts: 1 +id: (swc_deu_001327-swc_deu_001327) +Scores: (#C #S #D #I) 2 2 1 0 +REF: gebt EUCH NICHT SELBER auf +HYP: gebt **** EUCHNICH ELBER auf +Eval: D S S + +Speaker sentences 127: swc_deu_001328 #utts: 1 +id: (swc_deu_001328-swc_deu_001328) +Scores: (#C #S #D #I) 3 4 0 2 +REF: * hat diesen BRAUCH neunzehn ****** HUNDERT ZWEIUNDFÜNFZIG GEGENÜBER +HYP: A hat diesen PRAUC neunzehn HUNDER WEIUND FÜNFZIG GENÜB +Eval: I S I S S S + +Speaker sentences 128: swc_deu_001329 #utts: 1 +id: (swc_deu_001329-swc_deu_001329) +Scores: (#C #S #D #I) 3 4 3 0 +REF: wo DIE LEITUNG ÜBER die alte HÜRTHER LEITUNG GEFÜHRT WURDE +HYP: wo *** DELEITUNG ÜBE die alte ******** ******* HÜRTERLEIT UNGEFÜRTWURDE +Eval: D S S D D S S + +Speaker sentences 129: swc_deu_001330 #utts: 1 +id: (swc_deu_001330-swc_deu_001330) +Scores: (#C #S #D #I) 1 7 1 0 +REF: EINE BELIEBTE KÖLSCHROCKTRUPPE AUS dem KÖLNER UMLAND DIE HÖHNER +HYP: **** INE BLIEBTE KÖALSCHROCKTROPEAS dem ÖNER UM NANDDIE ÖNA +Eval: D S S S S S S S + +Speaker sentences 130: swc_deu_001331 #utts: 1 +id: (swc_deu_001331-swc_deu_001331) +Scores: (#C #S #D #I) 2 2 1 0 +REF: GEWORDEN sei und ALBRECHT SICH +HYP: GEWARDEN sei und ******** ALBRECHTSICH +Eval: S D S + +Speaker sentences 131: swc_deu_001332 #utts: 1 +id: (swc_deu_001332-swc_deu_001332) +Scores: (#C #S #D #I) 1 4 2 0 +REF: DER TAGESBEDARF eines ERWACHSENEN AN VITAMIN A +HYP: *** DRTAGESBEDAF eines *********** WACHSENDEN ANWITEMIN AR +Eval: D S D S S S + +Speaker sentences 132: swc_deu_001333 #utts: 1 +id: (swc_deu_001333-swc_deu_001333) +Scores: (#C #S #D #I) 0 4 0 1 +REF: *** SIEBZEHN HUNDERT ZEHN OBERALTER +HYP: SIB SIN ULERZIEHN OBER ALTER +Eval: I S S S S + +Speaker sentences 133: swc_deu_001334 #utts: 1 +id: (swc_deu_001334-swc_deu_001334) +Scores: (#C #S #D #I) 0 4 0 0 +REF: WEITERHIN LIESS SICH NACHWEISEN +HYP: WEITER HIN LISIG NACRHWEISEN +Eval: S S S S + +Speaker sentences 134: swc_deu_001335 #utts: 1 +id: (swc_deu_001335-swc_deu_001335) +Scores: (#C #S #D #I) 0 4 1 0 +REF: ZUM GRÜNDUNGSDATUM KONNTE MAN BEREITS +HYP: *** SUM GRÜNDUNGSTDRTUM KONTEAM BEREITZ +Eval: D S S S S + +Speaker sentences 135: swc_deu_001336 #utts: 1 +id: (swc_deu_001336-swc_deu_001336) +Scores: (#C #S #D #I) 1 2 1 0 +REF: KEIN LECKSCHLAGEN mÖglich NACHTEILE +HYP: **** KEINLECSCHLAGEN mÖglich NACHFTEILE +Eval: D S S + +Speaker sentences 136: swc_deu_001337 #utts: 1 +id: (swc_deu_001337-swc_deu_001337) +Scores: (#C #S #D #I) 3 6 2 0 +REF: WIRD DIE KATHOLISCHE KIRCHE STÜCK PETER an der stelle DER ALTEN +HYP: **** *** IRTI ATOLISCHE KÖRSCHE SANGPETER an der stelle DE ALT +Eval: D D S S S S S S + +Speaker sentences 137: swc_deu_001338 #utts: 1 +id: (swc_deu_001338-swc_deu_001338) +Scores: (#C #S #D #I) 1 11 0 0 +REF: DER VERKNAPPUNG des BROTWEIZENS TRAT ABER SCHON BALD DIE KARTOFFEL ALS ERSATZ +HYP: ER FAKNACUNG des ROT WEIZENS TRART ABESCHN BALT DI ERTOFEL EIS ERSAT +Eval: S S S S S S S S S S S + +Speaker sentences 138: swc_deu_001339 #utts: 1 +id: (swc_deu_001339-swc_deu_001339) +Scores: (#C #S #D #I) 3 2 0 0 +REF: KÖNNEN mit diesen NACHKOMMEN zeugen +HYP: KNNE mit diesen NACHKOMIN zeugen +Eval: S S + +Speaker sentences 139: swc_deu_001340 #utts: 1 +id: (swc_deu_001340-swc_deu_001340) +Scores: (#C #S #D #I) 2 3 0 0 +REF: ALLE NEUEN folgen der HÖRSPIELREIHE +HYP: ALE NEUNEN folgen der HÖRSTIEREIER +Eval: S S S + +Speaker sentences 140: swc_deu_001341 #utts: 1 +id: (swc_deu_001341-swc_deu_001341) +Scores: (#C #S #D #I) 0 2 1 0 +REF: CHIPS MIT BRATENSOSSE +HYP: ***** SCHIBPSMIT BEHATENSOR +Eval: D S S + +Speaker sentences 141: swc_deu_001342 #utts: 1 +id: (swc_deu_001342-swc_deu_001342) +Scores: (#C #S #D #I) 1 7 0 0 +REF: KOLLEGE ANDREAS BUCHNER VERZICHTETE AUF EINE PERSÖNLICHE bewertung +HYP: KOLGE AN RERS BOCHNE VERZIGHTETE AUFERNI PRSÖNICHE bewertung +Eval: S S S S S S S + +Speaker sentences 142: swc_deu_001343 #utts: 1 +id: (swc_deu_001343-swc_deu_001343) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ******** WENIGER ENTRÜSTET +HYP: BWENIGER IN TRÜSTET +Eval: I S S + +Speaker sentences 143: swc_deu_001344 #utts: 1 +id: (swc_deu_001344-swc_deu_001344) +Scores: (#C #S #D #I) 1 4 0 2 +REF: ****** *** WEITERHIN VERSORGTE DIE leitung THERMEN +HYP: WEITER HIN VERSORG DE DE leitung TERMEN +Eval: I I S S S S + +Speaker sentences 144: swc_deu_001345 #utts: 1 +id: (swc_deu_001345-swc_deu_001345) +Scores: (#C #S #D #I) 2 3 0 0 +REF: warteten DAFÜR ABER mit EINIGEN +HYP: warteten DAFÜER ABE mit EINIG +Eval: S S S + +Speaker sentences 145: swc_deu_001346 #utts: 1 +id: (swc_deu_001346-swc_deu_001346) +Scores: (#C #S #D #I) 2 4 3 0 +REF: LEDIGLICH ANTON VON klein MONIERTE in SEINER REZENSION DER +HYP: ********* LEDIKLICH ANTUNDFN klein MUNIERTE in ****** ********* SEINERETZENSONDE +Eval: D S S S D D S + +Speaker sentences 146: swc_deu_001347 #utts: 1 +id: (swc_deu_001347-swc_deu_001347) +Scores: (#C #S #D #I) 0 3 2 0 +REF: IM JAHRE NEUNZEHN HUNDERT FÜNF +HYP: ** ***** EM IAHBERNEUZEN ERTFÜM +Eval: D D S S S + +Speaker sentences 147: swc_deu_001348 #utts: 1 +id: (swc_deu_001348-swc_deu_001348) +Scores: (#C #S #D #I) 7 13 0 2 +REF: ERST MIT dem FORTFALL des BÜRGERRECHTS und der EINFÜHRUNG DER FREIZÜGIGKEIT im ******** ZWANZIGSTE JAHRHUNDERT WANDELTE sich diese ********* ANSCHAUUNG ANSATZWEISE DAHIN +HYP: ERS E dem FARTFAL des BÜRGERECHTZ und der INFÜHRNG DE FREITZYÜGICKEIT im ZWANSIGS NERHNDERT WANDET TE sich diese ANSCHAUNG ANSERT SWEISEN DARHIN +Eval: S S S S S S S I S S S I S S S + +Speaker sentences 148: swc_deu_001349 #utts: 1 +id: (swc_deu_001349-swc_deu_001349) +Scores: (#C #S #D #I) 2 5 0 1 +REF: des SWISTBACHES BEI RHEINBACH eine ***** BOGENBRÜCKE VON +HYP: des ZWIST BACRES BERREINBACH eine BOGEN BRÜCKE VO +Eval: S S S I S S + +Speaker sentences 149: swc_deu_001350 #utts: 1 +id: (swc_deu_001350-swc_deu_001350) +Scores: (#C #S #D #I) 0 5 1 0 +REF: ACHTZEHN HUNDERT SECHSUNDDREISSIG WURDE DER HAMBURGER +HYP: ******** ACHZINULE SEXSUNDREISIG BURDE DE HMBURG +Eval: D S S S S S + +Speaker sentences 150: swc_deu_001351 #utts: 1 +id: (swc_deu_001351-swc_deu_001351) +Scores: (#C #S #D #I) 1 0 1 0 +REF: am KARNEVALSSONNTAG +HYP: am **************** +Eval: D + +Speaker sentences 151: swc_deu_001352 #utts: 1 +id: (swc_deu_001352-swc_deu_001352) +Scores: (#C #S #D #I) 3 4 0 3 +REF: ** AUFGRUND der ******** KONTINENTALSPERRE achtzehn HUNDERT elf *** BANKROTT +HYP: UF GRUND der KONTENEN TALSBERE achtzehn HUNDER elf BAN OTT +Eval: I S I S S I S + +Speaker sentences 152: swc_deu_001353 #utts: 1 +id: (swc_deu_001353-swc_deu_001353) +Scores: (#C #S #D #I) 2 5 7 0 +REF: WEITERES MAL MUSSTEN DAN und BLYTHE BROWN DIE WERBUNG FÜR das BUCH SELBST ÜBERNEHMEN +HYP: ******** *** ******* WEITERESMALMUSTENDEN und ****** ***** *** BLEITBRAN DIEVWARBUNGFÜR das **** BUCHSEÄBST WANEMN +Eval: D D D S D D D S S D S S + +Speaker sentences 153: swc_deu_001354 #utts: 1 +id: (swc_deu_001354-swc_deu_001354) +Scores: (#C #S #D #I) 3 14 3 0 +REF: DIE NACHRICHT VOM SIEG DER BÜRGERLICH DEMOKRATISCHEN FEBRUARREVOLUTION VON achtzehn hundert ACHTUNDVIERZIG IN FRANKREICH WURDE IN HAMBURG mit JUBEL AUFGENOMMEN +HYP: *** ********* DIENAHRICH VM SEGKTER BÜGELICHDEMOGRATICHEN FE PRAREVOLUTZION VEN achtzehn hundert ************** ACHT UND VERZICHEN FANKREICHWURDEN HMBURG mit IOBEL AUFGEOME +Eval: D D S S S S S S S D S S S S S S S + +Speaker sentences 154: swc_deu_001355 #utts: 1 +id: (swc_deu_001355-swc_deu_001355) +Scores: (#C #S #D #I) 0 3 1 0 +REF: ZWEI JAHRE OHNE UNTERBRECHUNG +HYP: **** UBLIEBTZWEIJARE ONE NTERBRECHN +Eval: D S S S + +Speaker sentences 155: swc_deu_001356 #utts: 1 +id: (swc_deu_001356-swc_deu_001356) +Scores: (#C #S #D #I) 0 1 2 0 +REF: ZAHLREICHEN GASTSPIELEN UNTERWEGS +HYP: *********** *********** ZEALREICENGASTSPILUNTERWEGS +Eval: D D S + +Speaker sentences 156: swc_deu_001357 #utts: 1 +id: (swc_deu_001357-swc_deu_001357) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ******* QUANTITÄT GENÜGTEN +HYP: CRANTIE TET GENÜTEN +Eval: I S S + +Speaker sentences 157: swc_deu_001358 #utts: 1 +id: (swc_deu_001358-swc_deu_001358) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ** BAROCKER AUSSTATTUNG +HYP: IN BEROKAR AUSTATE +Eval: I S S + +Speaker sentences 158: swc_deu_001359 #utts: 1 +id: (swc_deu_001359-swc_deu_001359) +Scores: (#C #S #D #I) 6 9 1 0 +REF: das GERICHT vom BEIWAGEN SEINES MOTORRADES aus in die zu DIESER ZEIT NEU ENTSTEHENDEN ARBEITERSIEDLUNGEN ZU +HYP: das ERICHT vom BEIVARGENSANE MOTOR DES aus in die zu ****** DIESERZEITNEU EN STEHNDEN ABEIT ASIEDLUNENZU +Eval: S S S S D S S S S S + +Speaker sentences 159: swc_deu_001360 #utts: 1 +id: (swc_deu_001360-swc_deu_001360) +Scores: (#C #S #D #I) 0 4 0 2 +REF: **** **** DIE MITSAMT IHRER RECHENSTUBE +HYP: DIMI ZAMD IH ER RECHEN STOBE +Eval: I I S S S S + +Speaker sentences 160: swc_deu_001361 #utts: 1 +id: (swc_deu_001361-swc_deu_001361) +Scores: (#C #S #D #I) 0 1 1 0 +REF: KAISER FERDINAND +HYP: ****** KEISERFERDINANT +Eval: D S + +Speaker sentences 161: swc_deu_001362 #utts: 1 +id: (swc_deu_001362-swc_deu_001362) +Scores: (#C #S #D #I) 4 4 3 0 +REF: vom FERNSEHREGISSEUR FRANZ XAVER BOGNER in dem FERNSEHFILM das EWIGE LIED +HYP: vom **************** ***** FERNERESCHSUHER FERNZSGSAVERBUGNER in dem FERNSEFILEN das ***** IEWIGELIET +Eval: D D S S S D S + +Speaker sentences 162: swc_deu_001363 #utts: 1 +id: (swc_deu_001363-swc_deu_001363) +Scores: (#C #S #D #I) 2 4 0 0 +REF: WURDE in SEINEN besten ZEITEN DER +HYP: WORE in SEINEM besten ZEITE IER +Eval: S S S S + +Speaker sentences 163: swc_deu_001364 #utts: 1 +id: (swc_deu_001364-swc_deu_001364) +Scores: (#C #S #D #I) 2 3 2 0 +REF: SIE HÖREN den artikel FISH AND CHIPS +HYP: *** SEÖREN den artikel **** FISCHEND CIEBS +Eval: D S D S S + +Speaker sentences 164: swc_deu_001365 #utts: 1 +id: (swc_deu_001365-swc_deu_001365) +Scores: (#C #S #D #I) 1 2 1 0 +REF: und T V MOVIE +HYP: und * TEFAR MOWIE +Eval: D S S + +Speaker sentences 165: swc_deu_001366 #utts: 1 +id: (swc_deu_001366-swc_deu_001366) +Scores: (#C #S #D #I) 1 4 0 1 +REF: ** REZEPTION DER HEXENTHEMATIK von CHRISTA +HYP: RE SEPTIUONDE HECHSEN DMATIG von KRISTA +Eval: I S S S S + +Speaker sentences 166: swc_deu_001367 #utts: 1 +id: (swc_deu_001367-swc_deu_001367) +Scores: (#C #S #D #I) 5 6 2 0 +REF: DIE GESAMTE anlage WAR BIS ETWA zwei HUNDERT sechzig nach CHRISTUS in BETRIEB +HYP: *** DIGESAMTER anlage *** WARBESET VER zwei HUNDER sechzig nach KRISTUS in BEDRIEB +Eval: D S D S S S S S + +Speaker sentences 167: swc_deu_001368 #utts: 1 +id: (swc_deu_001368-swc_deu_001368) +Scores: (#C #S #D #I) 1 6 0 0 +REF: der ERSTE FAST FOOD LIEFERSERVICE WAR GEBOREN +HYP: der ES DE FASTFUTD LIFERSOR IDES WAGEBOCH +Eval: S S S S S S + +Speaker sentences 168: swc_deu_001369 #utts: 1 +id: (swc_deu_001369-swc_deu_001369) +Scores: (#C #S #D #I) 1 1 0 0 +REF: einem KABELBAUM +HYP: einem KABELBAUNEN +Eval: S + +Speaker sentences 169: swc_deu_001370 #utts: 1 +id: (swc_deu_001370-swc_deu_001370) +Scores: (#C #S #D #I) 1 4 1 1 +REF: ***** ERMORDUNG mit DER GEFAHR VERBUNDEN GEWESEN +HYP: MORLE DUNG mit *** DE GEFAHRVERBUNDEN GEWES +Eval: I S D S S S + +Speaker sentences 170: swc_deu_001371 #utts: 1 +id: (swc_deu_001371-swc_deu_001371) +Scores: (#C #S #D #I) 0 4 0 0 +REF: DER ÄLTESTEN PFERDERENNEN AUSSERHALB +HYP: DE ELTISTEN PFIERDERENEN ASEHAB +Eval: S S S S + +Speaker sentences 171: swc_deu_001372 #utts: 1 +id: (swc_deu_001372-swc_deu_001372) +Scores: (#C #S #D #I) 1 6 0 2 +REF: SONDERN auch ****** ******* DER NATIONALSOZIALISTISCHEN KUNSTAUFFASSUNG GERECHT WERDEN +HYP: SONEN auch DERNAT ZUONAHL SUOZ ERDISTISCHEN KUNZT AUF FASSUNGERECHTWERDEN +Eval: S I I S S S S S + +Speaker sentences 172: swc_deu_001373 #utts: 1 +id: (swc_deu_001373-swc_deu_001373) +Scores: (#C #S #D #I) 2 2 0 1 +REF: die WELTSICHT des ***** HANSEATEN +HYP: die WLLZICH des HANSE ARTEN +Eval: S I S + +Speaker sentences 173: swc_deu_001374 #utts: 1 +id: (swc_deu_001374-swc_deu_001374) +Scores: (#C #S #D #I) 1 3 1 0 +REF: AUCH nachkommen SIND NICHT BEKANNT +HYP: AUC nachkommen **** EINT NICHTBEKANT +Eval: S D S S + +Speaker sentences 174: swc_deu_001375 #utts: 1 +id: (swc_deu_001375-swc_deu_001375) +Scores: (#C #S #D #I) 1 3 2 0 +REF: IHREN TEXTEN den EINDRUCK ZU VERMITTELN +HYP: ***** ERENTEXSTE den ******** EINDRUGKTZE VERMITTE +Eval: D S D S S + +Speaker sentences 175: swc_deu_001376 #utts: 1 +id: (swc_deu_001376-swc_deu_001376) +Scores: (#C #S #D #I) 0 5 1 0 +REF: VERDIENSTE UM DAS KÖLNER LIED VERLIEHEN +HYP: ********** VER DINST M DASKÖNARLIET VERLIEN +Eval: D S S S S S + +Speaker sentences 176: swc_deu_001377 #utts: 1 +id: (swc_deu_001377-swc_deu_001377) +Scores: (#C #S #D #I) 1 10 0 1 +REF: OBWOHL HOFFMANN von ******** HOFFMANNSWALDAUS WERK GROSSEN EINFLUSS AUF SPÄTERE DICHTER AUSÜBTE +HYP: BWOLL HOFMEIN von HOFMEINZ WALL DAUS WERG GROSENEINFLUS A SPÄERERDICHTE AUS ÜBT +Eval: S S I S S S S S S S S + +Speaker sentences 177: swc_deu_001378 #utts: 1 +id: (swc_deu_001378-swc_deu_001378) +Scores: (#C #S #D #I) 0 3 3 0 +REF: UM SO ERNST ALS STAATSOBERHAUPT VON +HYP: ** ** ***** MMSO ERMNSTALSTAS OBERHAUPFVEN +Eval: D D D S S S + +Speaker sentences 178: swc_deu_001379 #utts: 1 +id: (swc_deu_001379-swc_deu_001379) +Scores: (#C #S #D #I) 0 1 0 2 +REF: ****** ** DOKUMENTATION +HYP: EFREIE DO KOMETATION +Eval: I I S + +Speaker sentences 179: swc_deu_001380 #utts: 1 +id: (swc_deu_001380-swc_deu_001380) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ********** GESTALTUNG DES COVERS WIDERSPIEGELT +HYP: GESTALTUNM BES KAVERS WIEDER SPIEGELT +Eval: I S S S S + +Speaker sentences 180: swc_deu_001381 #utts: 1 +id: (swc_deu_001381-swc_deu_001381) +Scores: (#C #S #D #I) 3 2 0 0 +REF: der GESAMTE aufwand WIRD auf +HYP: der ESMTER aufwand WIT auf +Eval: S S + +Speaker sentences 181: swc_deu_001382 #utts: 1 +id: (swc_deu_001382-swc_deu_001382) +Scores: (#C #S #D #I) 6 5 2 0 +REF: OBGLEICH HAMBURG diesem angehÖrte und eine NOBILITIERUNG DURCH DEN KAISER damit keine DURCH +HYP: ******** OBGLEICHAMBORK diesem angehÖrte und eine ************* NOBILI TIERUNG DUCHENKEISER damit keine DRC +Eval: D S D S S S S + +Speaker sentences 182: swc_deu_001383 #utts: 1 +id: (swc_deu_001383-swc_deu_001383) +Scores: (#C #S #D #I) 6 3 2 0 +REF: da ES DURCH DEN SICH AUSWEITENDEN welthandel arbeit und wohlstand versprach +HYP: da ** ***** STURCH DENSICG AUSWEITENTEN welthandel arbeit und wohlstand versprach +Eval: D D S S S + +Speaker sentences 183: swc_deu_001384 #utts: 1 +id: (swc_deu_001384-swc_deu_001384) +Scores: (#C #S #D #I) 3 9 0 1 +REF: FÜR die ZEIT MITTE des neunzehnte ** JAHRHUNDERTS BEKLAGTE DER ARCHITEKT MARTIN HALLER +HYP: FÜÖR die ZEITT MITE des neunzehnte JA HNDER BEKLAG DEDE ERCHIE TEKTMATIN HALLE +Eval: S S S I S S S S S S + +Speaker sentences 184: swc_deu_001385 #utts: 1 +id: (swc_deu_001385-swc_deu_001385) +Scores: (#C #S #D #I) 0 3 1 0 +REF: ALTBUNDESKANZLER HELMUT SCHMIDT LEHNTE +HYP: **************** EITBONDESKANZER HEMUTSCHMITT LENTE +Eval: D S S S + +Speaker sentences 185: swc_deu_001386 #utts: 1 +id: (swc_deu_001386-swc_deu_001386) +Scores: (#C #S #D #I) 2 4 1 1 +REF: DEN namen **** GODEFFROY im STAATSHANDBUCH ZU STREICHEN +HYP: DI namen GODE FREI im ************** STATZ HANDBOCHTZ +Eval: S I S D S S + +Speaker sentences 186: swc_deu_001387 #utts: 1 +id: (swc_deu_001387-swc_deu_001387) +Scores: (#C #S #D #I) 0 3 3 0 +REF: WENN AUCH MIT EINER GEWISSEN LETHARGIE +HYP: **** **** *** WEN AUCHE ENERGEWISENLETAGIE +Eval: D D D S S S + +Speaker sentences 187: swc_deu_001388 #utts: 1 +id: (swc_deu_001388-swc_deu_001388) +Scores: (#C #S #D #I) 0 1 0 1 +REF: ********** KALKULIERBAR +HYP: KALKOLIERE BAEG +Eval: I S + +Speaker sentences 188: swc_deu_001389 #utts: 1 +id: (swc_deu_001389-swc_deu_001389) +Scores: (#C #S #D #I) 1 4 2 0 +REF: ANGEFANGENE ZWEI HUNDERT FÜNFZIG SCHÜLER einen DELEGIERTEN +HYP: *********** **** ANGEFNGDEZSAHUNER VFNM ZISCÜULER einen DIELIGIEITEN +Eval: D D S S S S + +Speaker sentences 189: swc_deu_001390 #utts: 1 +id: (swc_deu_001390-swc_deu_001390) +Scores: (#C #S #D #I) 2 6 3 0 +REF: VIELE MENSCHEN SAHEN DEN GRIZZLY als NAHRUNGSKONKURRENTEN und ALS POTENTIELLE GEFAHR +HYP: ***** ******** VIELMENSCHEN SANEN RISLI als NAHRUNGSKONGRENTEN und *** ALSPOTEN ELGEFA +Eval: D D S S S S D S S + +Speaker sentences 190: swc_deu_001391 #utts: 1 +id: (swc_deu_001391-swc_deu_001391) +Scores: (#C #S #D #I) 1 2 0 1 +REF: den ****** AUFTRITT VERKÜRZEN +HYP: den UFTRIT VE KÖRZE +Eval: I S S + +Speaker sentences 191: swc_deu_001392 #utts: 1 +id: (swc_deu_001392-swc_deu_001392) +Scores: (#C #S #D #I) 3 18 0 3 +REF: ** DEM STAND vom DER INHALT STEHT UNTER der ********* ***** LIZENZ CREATIVE COMMONS ATTRIBUTION SHARE ALIKE DREI PUNKT NULL UNPORTED und UNTER DER +HYP: MI DM STAN vom DREITZHN NIULIEZWEI TAUSEN SWERF der INHERSTET UNDER DELIZENS KRERTZU COMONS EIZREWUSCHEN SCHER E LEITDREIT PUNK NL ANPORTET und UNTE DE +Eval: I S S S S S S I I S S S S S S S S S S S S + +Speaker sentences 192: swc_deu_001393 #utts: 1 +id: (swc_deu_001393-swc_deu_001393) +Scores: (#C #S #D #I) 1 2 0 0 +REF: eine KLEINERE BOGENBRÜCKE +HYP: eine KLEINEREBOGEN BRÜCKE +Eval: S S + +Speaker sentences 193: swc_deu_001394 #utts: 1 +id: (swc_deu_001394-swc_deu_001394) +Scores: (#C #S #D #I) 1 1 3 0 +REF: sich NUN FÜR SEIN WEITERKOMMEN +HYP: sich *** **** **** NUNVERSENWEITRKOM +Eval: D D D S + +Speaker sentences 194: swc_deu_001395 #utts: 1 +id: (swc_deu_001395-swc_deu_001395) +Scores: (#C #S #D #I) 3 2 0 1 +REF: aus dem GEMÄLDE zu *** ENTFERNEN +HYP: aus dem GEMEÄLDE zu ENT FERENEN +Eval: S I S + +Speaker sentences 195: swc_deu_001396 #utts: 1 +id: (swc_deu_001396-swc_deu_001396) +Scores: (#C #S #D #I) 0 6 4 0 +REF: NACH EUROPÄISCHER RICHTLINIE NEUNZIG VIER HUNDERT SECHSUNDNEUNZIG E W G +HYP: **** ************* ********** ******* IS DCH ÖAROPÄSCHERICHTLINE NEUNZICGH VIERNERTEXSNEUNZSICH EWI +Eval: D D D D S S S S S S + +Speaker sentences 196: swc_deu_001397 #utts: 1 +id: (swc_deu_001397-swc_deu_001397) +Scores: (#C #S #D #I) 1 2 0 0 +REF: EINEM UMFELD auf +HYP: ALE UMSELT auf +Eval: S S + +Speaker sentences 197: swc_deu_001398 #utts: 1 +id: (swc_deu_001398-swc_deu_001398) +Scores: (#C #S #D #I) 5 2 0 0 +REF: UND sie sei auch wie eine FÜRSTIN +HYP: ND sie sei auch wie eine FIRSTEN +Eval: S S + +Speaker sentences 198: swc_deu_001399 #utts: 1 +id: (swc_deu_001399-swc_deu_001399) +Scores: (#C #S #D #I) 2 1 0 1 +REF: neunzehn hundert **** NEUNZEHN +HYP: neunzehn hundert NEUN ZEN +Eval: I S + +Speaker sentences 199: swc_deu_001400 #utts: 1 +id: (swc_deu_001400-swc_deu_001400) +Scores: (#C #S #D #I) 1 4 0 2 +REF: STATTDESSEN haben ** ********* DIE RÖMISCHEN INGENIEURE +HYP: STATISSEN haben DE RÖMISCHE IN JEN IUREM +Eval: S I I S S S + +Speaker sentences 200: swc_deu_001401 #utts: 1 +id: (swc_deu_001401-swc_deu_001401) +Scores: (#C #S #D #I) 2 1 0 1 +REF: ********** GRIZZLYBÄR und mensch +HYP: DELKRISLIE BEHR und mensch +Eval: I S + +Speaker sentences 201: swc_deu_001402 #utts: 1 +id: (swc_deu_001402-swc_deu_001402) +Scores: (#C #S #D #I) 0 2 0 0 +REF: MUSICIANS COALITION +HYP: MIESICHENS KURELISCHE +Eval: S S + +Speaker sentences 202: swc_deu_001403 #utts: 1 +id: (swc_deu_001403-swc_deu_001403) +Scores: (#C #S #D #I) 0 5 2 0 +REF: BERTOLD HUMMEL GIBT ES DREI VARIATIONEN MIT +HYP: ******* ****** BERTOTCHUMLGEB DES DREIVARER ZIONER MI +Eval: D D S S S S S + +Speaker sentences 203: swc_deu_001404 #utts: 1 +id: (swc_deu_001404-swc_deu_001404) +Scores: (#C #S #D #I) 0 10 0 1 +REF: * RÜCKZUGSGEBIET ERWIES SICH DER ACHTZEHN HUNDERT ZWEIUNDSIEBZIG GEGRÜNDETE YELLOWSTONE NATIONALPARK +HYP: K ZUGSGEBIEDT ERWESICH TER ACHZEHN HUNDATZWEIUN SIEBZIG E GRÜNETE JLUSTDUNET IONALPARK +Eval: I S S S S S S S S S S + +Speaker sentences 204: swc_deu_001405 #utts: 1 +id: (swc_deu_001405-swc_deu_001405) +Scores: (#C #S #D #I) 0 1 0 1 +REF: * DEFINITION +HYP: E FINITION +Eval: I S + +Speaker sentences 205: swc_deu_001406 #utts: 1 +id: (swc_deu_001406-swc_deu_001406) +Scores: (#C #S #D #I) 1 7 2 0 +REF: um AN DER UNIVERSITÄT SEVILLA ZWEI SEMESTER KUNSTGESCHICHTE ZU STUDIEREN +HYP: um ** *** INE UNIVESITET ZVILA ZWEISE MSTER KUNSGESCHCHT ZUSTUDIERN +Eval: D D S S S S S S S + +Speaker sentences 206: swc_deu_001407 #utts: 1 +id: (swc_deu_001407-swc_deu_001407) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** TROTZ IHRER GERINGEN +HYP: DIE TOTZ ERE GRINE +Eval: I S S S + + diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/text b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/text new file mode 100644 index 0000000000000000000000000000000000000000..dedf9168ad8ea393e5977b779d8fb405ec75d5a5 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/text @@ -0,0 +1,207 @@ +swc_deu_001201 DEI ERVERLIEBTE UNGE HEARZOG IE RANSCLÄEKESEINESFATES NICHTBERACGTDETAB +swc_deu_001202 DIE IN DE ANSESTERDTEN ALS +swc_deu_001203 ARKEINGROSE ERFOLGK +swc_deu_001204 GOSEN SCHEHMICHEIN VERBRICKEN +swc_deu_001205 URDEN ACH MEHRERE AR LEUTRUNGSBÜCHE VER FFNTLICHT +swc_deu_001206 VORBEREITEN BIER TEIG GETUNGT +swc_deu_001207 DOMENTE SHLIESLICG IN E +swc_deu_001208 TAUTAG VÜRDENTAUT VON KÖNICH VERERCHÜLE +swc_deu_001209 DARUNDER SIND MATILDE ARSENSIS WECHTER DES KREULZIS +swc_deu_001210 EN INENSTÄTEN MEHR UND MEHR IEROLE DERTRADI Z NELN ISH +swc_deu_001211 Z DENEN WELTLEUFICHKEIIT +swc_deu_001212 RACHRE TDSHOFES UND DES ADETZS FÜR DEN FRIEFET +swc_deu_001213 ZEIT ANGABEN VERZICHTET +swc_deu_001214 ALL ACHTZEHN HUNDERT ACHTZICHMIT OTTUOBRAMMS AUFSEITZ +swc_deu_001215 MÜLEN WESEN SIBTEHN UNED CHUNDZWANZI +swc_deu_001216 AS DER FICH RICH +swc_deu_001217 SIDEM ABSCLSS IMJAHRENUNZEN HNDR ZWAEUN ACHTZICG UND ERNAMER EINE ERSTE LÄNGERE REISE ACSPANEN +swc_deu_001218 VÜRNSCHATSO VURGEZEICHNET +swc_deu_001219 FEITENSTEINS FLSTENDIGEGESCHICHTEN UND DIE AUGSBRGESTADBGESCHIEG EDES ELTREN +swc_deu_001220 NACH DIESENZERSTÖHRUNGEN WURDE DERASSCHWIEDER AUFBLHN +swc_deu_001221 MACHTEN EINFLUSREICHEN HAN SIE ARTENM BEIM KOMISARISCHEIN GESETZTEN BÜRGEMEISTER MARKERT IHRE AUFWAHTUN +swc_deu_001222 ALS SENTRALDESHANDES KOTOR +swc_deu_001223 SONDER STELLUNG INEHEILB DERSTADT KRFEL +swc_deu_001224 FINE SICH INHALOBAOKTERIEN +swc_deu_001225 AUF DER B SEITE FINDE ZICH DAS EBENFALS VON MEIKEL OMPONIRT +swc_deu_001226 IN HANDE ARTISCHE ZEIT HATE DIE ZERKEGESESCHAFT KEINEN AUSCHAGEBENDEN ENFLSMIER +swc_deu_001227 DAR SDERCH VEWENDUN VON AUFTRIEBSKÖRPAN ODER HOLZ EINE GERINGERE MITTLERETDICHTE ALS WASSER HAT +swc_deu_001228 DRAMEI TDE SIERUNGEN +swc_deu_001229 UMM SEBEN OR FÜNWON +swc_deu_001230 DES ALBRECHT DIE BADES TOCHTER +swc_deu_001231 TAT BAM BARESCHE STATZR +swc_deu_001232 DRSLIET BESONDERSLIEBTE +swc_deu_001233 AUF KUND DES WAHSENDEN POPLIKUMS INTRESSES WRDE DER AUFTRITS ORT ÜR IE PRIMER ISTALESUNGE +swc_deu_001234 ND FREIDLICH SPBIELE +swc_deu_001235 DS DIE REIDEN STURTZ ERLATIE UN BESCHADE BER STANDEN HATE +swc_deu_001236 JAHREN ASCHENEND ZWEI IMEN +swc_deu_001237 DR RABMALE UND GRAB GKAPÄLEN ODER OL TATEN NACHALT +swc_deu_001238 JUNENEUZEN HUNDR EHSUNEUNZIG KÖNEKTDER RSEINE BEIDEN JOPS +swc_deu_001239 IN GE PORTINE OPET +swc_deu_001240 NEUNUN SECHZIG DER MELIER KONTWOL ALBUMSCHATZ EIN +swc_deu_001241 TADUCHCOM +swc_deu_001242 ONE HEN NICHT DEN ROSHANDELS KAUFLEUTEN GESELSCHAFTLICG GLEICHGESTET WAREN +swc_deu_001243 VO DERNARUNG UND VOM KLIEMA +swc_deu_001244 APOLO EINS +swc_deu_001245 BRÜYELEL UND HÖRT NACHKELEN +swc_deu_001246 TWAR IN EN KLOSTE +swc_deu_001247 DIVWRZEM OFIZEHEN KANEWALEN STANDT UNT TEUTE EINEMSCHUNG AUS KÖRSCHEN KANDEWAL UND POLITSCHEM KABERET MIT KOM DIELMENTEN DARSTELLT UN +swc_deu_001248 DIE WENSTEIUNGESLIEDES FÜRTEN +swc_deu_001249 NANTIT ZIEGLER DI ARMORDUM DER BRNAURUNEN +swc_deu_001250 INTEROHR IST VE L +swc_deu_001251 DIE STRÄNGE DR FORGENGER LEITUNG WURDEN ZWICHE NEUNZEHN HUNDERT NEUNUNDZWANZIG UND NEUNZHN HUNDERT DREINFÜNFZIG AICHIERLOGESCH ERGRABEN +swc_deu_001252 IM GEGENSATZ +swc_deu_001253 VWARBE VON ÖRDIGENSN LAUNDHORD +swc_deu_001254 LEIF VERANSTALTUMEN +swc_deu_001255 SUOWERDENHEUTE IN DEREGEL ALE DORTLEBENDEN BRUN +swc_deu_001256 IEDA FÜRE WÜSCHLMER +swc_deu_001257 DES HAN SE ATEN FÜRE +swc_deu_001258 HÄBILS AGNES BERNAU +swc_deu_001259 LEBENSWEISE VERKÖARPER +swc_deu_001260 IDEFALTDES VIELEN HAMBURGAN ZU KATOLESCH FRMMEN +swc_deu_001261 KOLTOUR END DER THEFT AUSTAUSCHEN +swc_deu_001262 MJAHRZWEI TAUSEND VERTONDTE +swc_deu_001263 DAS E DIESE LEITUNG SCHNLER VOLÄENDTEN KÖNE ALS DER BAUMEISTER DEN KÖNERDOM +swc_deu_001264 EIER HINRICHTUNG DER BANAURIEN HABES IC LICHT UM +swc_deu_001265 LUOREI +swc_deu_001266 DERZEIT DER BESTIE KENERDER EIFELEITUNG +swc_deu_001267 VOKUS BES WISENSHAFTLICHEN INTERESSES +swc_deu_001268 TEME ZU BEGEISTEN +swc_deu_001269 METER UND KONTE DAMIT AU VON INEN BERGANGEN WERDEN +swc_deu_001270 HAT KABER BES SELLISTE DER NÜH +swc_deu_001271 DER FREIN ENZIGKLOP +swc_deu_001272 DEN GRSLI WIER UF DELSTE ZUSETZEN +swc_deu_001273 WIE LANG DIESE KAPLANSSTÄLLE AUFRECHTER HETEN WURD +swc_deu_001274 SIWAHREN WASCHEINLICHBEREITZ DREISIG SI KUNDEN DACH AUSPRCHTES VEUR +swc_deu_001275 METER GESAMTLINGE UND BIS ZUZEHN MITER +swc_deu_001276 FEINE RITZEN UN SPALTE +swc_deu_001277 DINMAN VON AUSSN DIEKELER HINABPFLIESEN SIET +swc_deu_001278 ENE INTERIUSAGTEBRAUN +swc_deu_001279 DAS FÜNFTDEVEN GERIUM +swc_deu_001280 RESEN SIEMANCHMAL WEIDE TIERE IE SCHAFE +swc_deu_001281 SI ÖRENDEN ARTIKEL DIE SEIN RÜVIEU +swc_deu_001282 KUSE IS GELENTER KOCH +swc_deu_001283 HANWENT STIFTUNGE +swc_deu_001284 NEUNZEHN HUDERT ACHTZIEN AL HAINSIE ARTEN ANGESIEN +swc_deu_001285 MEHRERER ES NACH IM TOUN +swc_deu_001286 ACHSTIG ES GERICHTS ZUR LANDES WEITEN BELIEBEN KOLINARISCHE SPEZILITET ERMÖGE +swc_deu_001287 KOLLETSC UND EIN ZWEIT JOB ALSPANISCHLÄHRER IN HEMTN VORLSEAN +swc_deu_001288 BOREN KEINES WEGXS ALLÄEGEBÜRTIGEN +swc_deu_001289 IST IER KÖRBERBAUGREFTIG +swc_deu_001290 ANLESLICHTER NEUJAS ANDGSPRACHREKE +swc_deu_001291 MIT WIND VON SCRECHINTEN +swc_deu_001292 DEN GREÖSTEN TELDER BE ZIÜRGSWERTRETUNG ÖRDINEN AUS +swc_deu_001293 ACHTHN HUNER EIUNDZWENZI +swc_deu_001294 ES ROSSEN ADELS ANGESAMMITEN REICHTUMS +swc_deu_001295 E SOE NIHT EL SEH SOL POKATZIUN +swc_deu_001296 TEILHABEDE VRMER OSMAN UND JIRGENZ +swc_deu_001297 NERM TER FRAUNENTE RAUT INSLT +swc_deu_001298 AURDIEO ÖST AN DEUTCHER HÖR BUCHFVERLAG MIT SITZ INMÜNCHEN +swc_deu_001299 FAR PIKTMENTE UND CHEMICHE VORPROTRUCKTE HERSTELT +swc_deu_001300 ARPLICHEN PREUSSISCHEN FREI HEREN STAND IN DER ZAL ANSCHLUSSFRAGE ENTSCHIEDEN GEGEN DENSINAR AUF DIESEITE BISMARGS GESTLLT +swc_deu_001301 WEN DI WÄLEN VON SELS ER FORGWELEN UND OFFEN ZUOTAGELIEGEN +swc_deu_001302 DS VONGN NACHBAR BAUTRUB BAREITS BEGON WUORT +swc_deu_001303 WERDEN PRERGEN DE ELEMENTE DE HANSIE ATEN TUMST ZUSAMEN GEFAST +swc_deu_001304 DES SLIETZWURE ALS FOLCSTLIET AN GESEIEN +swc_deu_001305 DER ZO RNDEM HAUSVERLAGS URUBGEHÖRT +swc_deu_001306 VÜR DE ÖNFTIGEN BORT BÜCHER ENT WICKETE DI PAPIERFERPRI +swc_deu_001307 HAMBRE WUOKS +swc_deu_001308 R DI WASIE ARDLIGEN LANDZITZE PETRIBEN AUFAND SEIS BEMBAU +swc_deu_001309 JAHRZWEI TAUSEN ZWÖLF INDEN BELINER KLUP SOSECHSONDREISIG VELLIGT +swc_deu_001310 SECHEHN UNENDFÜNFZIGH AITS BÜNDNIS D +swc_deu_001311 DAS PROLEHM BE IESEM PERADOCHSON IST +swc_deu_001312 ARMEN WESEN TETIC AMALIE S IEVEIKEINGN +swc_deu_001313 NIHT EINMALEINE ANNSATZWEISE NTU SOCHUNG ZU IEREM VERHALTEN IN ERZEIT DES NATZUONASOTZELISMUS +swc_deu_001314 LIZENS VÜERFRIE DO GOMENTATION +swc_deu_001315 DIM ACHZIHNT NIER HUNDER DIEGARTEN HEUSER VORDENTOREN +swc_deu_001316 GANS IMSTIELDER ZEIT +swc_deu_001317 BER BRÜÖL UND HÜÖRT ERREICH E DE LEITUNGSCLISLICHKÖN +swc_deu_001318 AUS ZEICHNUMEN FREM DER HEREN +swc_deu_001319 DISCHEFTELLEREI AUF ZUGEBEN +swc_deu_001320 DA ZUTZEHE DI EGEGNUNGMIT VERLETZTENTIEREN +swc_deu_001321 JENENISCH STIFT +swc_deu_001322 WESTLICH VON KÖLEN +swc_deu_001323 DIE STÄNDIG IN BERIEBWAREN +swc_deu_001324 DI VOM BAR BIIRCHASIERTWERDE +swc_deu_001325 ERSCHE NOCHEN WEITERERAUFSETZVON KRISTIERNMEIELT +swc_deu_001326 WALL SEBST EXTREMEREICHTUM KEINES WEHTEN UNMITELBEREN ZUGAN +swc_deu_001327 GEBT EUCHNICH ELBER AUF +swc_deu_001328 A HAT DIESEN PRAUC NEUNZEHN HUNDER WEIUND FÜNFZIG GENÜB +swc_deu_001329 WO DELEITUNG ÜBE DIE ALTE HÜRTERLEIT UNGEFÜRTWURDE +swc_deu_001330 INE BLIEBTE KÖALSCHROCKTROPEAS DEM ÖNER UM NANDDIE ÖNA +swc_deu_001331 GEWARDEN SEI UND ALBRECHTSICH +swc_deu_001332 DRTAGESBEDAF EINES WACHSENDEN ANWITEMIN AR +swc_deu_001333 SIB SIN ULERZIEHN OBER ALTER +swc_deu_001334 WEITER HIN LISIG NACRHWEISEN +swc_deu_001335 SUM GRÜNDUNGSTDRTUM KONTEAM BEREITZ +swc_deu_001336 KEINLECSCHLAGEN MÖGLICH NACHFTEILE +swc_deu_001337 IRTI ATOLISCHE KÖRSCHE SANGPETER AN DER STELLE DE ALT +swc_deu_001338 ER FAKNACUNG DES ROT WEIZENS TRART ABESCHN BALT DI ERTOFEL EIS ERSAT +swc_deu_001339 KNNE MIT DIESEN NACHKOMIN ZEUGEN +swc_deu_001340 ALE NEUNEN FOLGEN DER HÖRSTIEREIER +swc_deu_001341 SCHIBPSMIT BEHATENSOR +swc_deu_001342 KOLGE AN RERS BOCHNE VERZIGHTETE 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J A H R Z W E I T A U S E N Z W Ö L F I N D E N B E L I N E R K L U P S O S E C H S O N D R E I S I G V E L L I G T +swc_deu_001310 S E C H E H N U N E N D F Ü N F Z I G H A I T S B Ü N D N I S D +swc_deu_001311 D A S P R O L E H M B E I E S E M P E R A D O C H S O N I S T +swc_deu_001312 A R M E N W E S E N T E T I C A M A L I E S I E V E I K E I N G N +swc_deu_001313 N I H T E I N M A L E I N E A N N S A T Z W E I S E N T U S O C H U N G Z U I E R E M V E R H A L T E N I N E R Z E I T D E S N A T Z U O N A S O T Z E L I S M U S +swc_deu_001314 L I Z E N S V Ü E R F R I E D O G O M E N T A T I O N +swc_deu_001315 D I M A C H Z I H N T N I E R H U N D E R D I E G A R T E N H E U S E R V O R D E N T O R E N +swc_deu_001316 G A N S I M S T I E L D E R Z E I T +swc_deu_001317 B E R B R Ü Ö L U N D H Ü Ö R T E R R E I C H E D E L E I T U N G S C L I S L I C H K Ö N +swc_deu_001318 A U S Z E I C H N U M E N F R E M D E R H E R E N +swc_deu_001319 D I S C H E F T E L L E R E I A U F Z U G E B E N +swc_deu_001320 D A Z U T Z E H E D I E G E G N U N G M I T V E R L E T Z T E N T I E R E N +swc_deu_001321 J E N E N I S C H S T I F T +swc_deu_001322 W E S T L I C H V O N K Ö L E N +swc_deu_001323 D I E S T Ä N D I G I N B E R I E B W A R E N +swc_deu_001324 D I V O M B A R B I I R C H A S I E R T W E R D E +swc_deu_001325 E R S C H E N O C H E N W E I T E R E R A U F S E T Z V O N K R I S T I E R N M E I E L T +swc_deu_001326 W A L L S E B S T E X T R E M E R E I C H T U M K E I N E S W E H T E N U N M I T E L B E R E N Z U G A N +swc_deu_001327 G E B T E U C H N I C H E L B E R A U F +swc_deu_001328 A H A T D I E S E N P R A U C N E U N Z E H N H U N D E R W E I U N D F Ü N F Z I G G E N Ü B +swc_deu_001329 W O D E L E I T U N G Ü B E D I E A L T E H Ü R T E R L E I T U N G E F Ü R T W U R D E +swc_deu_001330 I N E B L I E B T E K Ö A L S C H R O C K T R O P E A S D E M Ö N E R U M N A N D D I E Ö N A +swc_deu_001331 G E W A R D E N S E I U N D A L B R E C H T S I C H +swc_deu_001332 D R T A G E S B E D A F E I N E S W A C H S E N D E N A N W I T E M I N A R +swc_deu_001333 S I B S I N U L E R Z I E H N O B E R A L T E R +swc_deu_001334 W E I T E R H I N L I S I G N A C R H W E I S E N +swc_deu_001335 S U M G R Ü N D U N G S T D R T U M K O N T E A M B E R E I T Z +swc_deu_001336 K E I N L E C S C H L A G E N M Ö G L I C H N A C H F T E I L E +swc_deu_001337 I R T I A T O L I S C H E K Ö R S C H E S A N G P E T E R A N D E R S T E L L E D E A L T +swc_deu_001338 E R F A K N A C U N G D E S R O T W E I Z E N S T R A R T A B E S C H N B A L T D I E R T O F E L E I S E R S A T +swc_deu_001339 K N N E M I T D I E S E N N A C H K O M I N Z E U G E N +swc_deu_001340 A L E N E U N E N F O L G E N D E R H Ö R S T I E R E I E R +swc_deu_001341 S C H I B P S M I T B E H A T E N S O R +swc_deu_001342 K O L G E A N R E R S B O C H N E V E R Z I G H T E T E A U F E R N I P R S Ö N I C H E B E W E R T U N G +swc_deu_001343 B W E N I G E R I N T R Ü S T E T +swc_deu_001344 W E I T E R H I N V E R S O R G D E D E L E I T U N G T E R M E N +swc_deu_001345 W A R T E T E N D A F Ü E R A B E M I T E I N I G +swc_deu_001346 L E D I K L I C H A N T U N D F N K L E I N M U N I E R T E I N S E I N E R E T Z E N S O N D E +swc_deu_001347 E M I A H B E R N E U Z E N E R T F Ü M +swc_deu_001348 E R S E D E M F A R T F A L D E S B Ü R G E R E C H T Z U N D D E R I N F Ü H R N G D E F R E I T Z Y Ü G I C K E I T I M Z W A N S I G S N E R H N D E R T W A N D E T T E S I C H D I E S E A N S C H A U N G A N S E R T S W E I S E N D A R H I N +swc_deu_001349 D E S Z W I S T B A C R E S B E R R E I N B A C H E I N E B O G E N B R Ü C K E V O +swc_deu_001350 A C H Z I N U L E S E X S U N D R E I S I G B U R D E D E H M B U R G +swc_deu_001351 A M +swc_deu_001352 U F G R U N D D E R K O N T E N E N T A L S B E R E A C H T Z E H N H U N D E R E L F B A N O T T +swc_deu_001353 W E I T E R E S M A L M U S T E N D E N U N D B L E I T B R A N D I E V W A R B U N G F Ü R D A S B U C H S E Ä B S T W A N E M N +swc_deu_001354 D I E N A H R I C H V M S E G K T E R B Ü G E L I C H D E M O G R A T I C H E N F E P R A R E V O L U T Z I O N V E N A C H T Z E H N H U N D E R T A C H T U N D V E R Z I C H E N F A N K R E I C H W U R D E N H M B U R G M I T I O B E L A U F G E O M E +swc_deu_001355 U B L I E B T Z W E I J A R E O N E N T E R B R E C H N +swc_deu_001356 Z E A L R E I C E N G A S T S P I L U N T E R W E G S +swc_deu_001357 C R A N T I E T E T G E N Ü T E N +swc_deu_001358 I N B E R O K A R A U S T A T E +swc_deu_001359 D A S E R I C H T V O M B E I V A R G E N S A N E M O T O R D E S A U S I N D I E Z U D I E S E R Z E I T N E U E N S T E H N D E N A B E I T A S I E D L U N E N Z U +swc_deu_001360 D I M I Z A M D I H E R R E C H E N S T O B E +swc_deu_001361 K E I S E R F E R D I N A N T +swc_deu_001362 V O M F E R N E R E S C H S U H E R F E R N Z S G S A V E R B U G N E R I N D E M F E R N S E F I L E N D A S I E W I G E L I E T +swc_deu_001363 W O R E I N S E I N E M B E S T E N Z E I T E I E R +swc_deu_001364 S E Ö R E N D E N A R T I K E L F I S C H E N D C I E B S +swc_deu_001365 U N D T E F A R M O W I E +swc_deu_001366 R E S E P T I U O N D E H E C H S E N D M A T I G V O N K R I S T A +swc_deu_001367 D I G E S A M T E R A N L A G E W A R B E S E T V E R Z W E I H U N D E R S E C H Z I G N A C H K R I S T U S I N B E D R I E B +swc_deu_001368 D E R E S D E F A S T F U T D L I F E R S O R I D E S W A G E B O C H +swc_deu_001369 E I N E M K A B E L B A U N E N +swc_deu_001370 M O R L E D U N G M I T D E G E F A H R V E R B U N D E N G E W E S +swc_deu_001371 D E E L T I S T E N P F I E R D E R E N E N A S E H A B +swc_deu_001372 S O N E N A U C H D E R N A T Z U O N A H L S U O Z E R D I S T I S C H E N K U N Z T A U F F A S S U N G E R E C H T W E R D E N +swc_deu_001373 D I E W L L Z I C H D E S H A N S E A R T E N +swc_deu_001374 A U C N A C H K O M M E N E I N T N I C H T B E K A N T +swc_deu_001375 E R E N T E X S T E D E N E I N D R U G K T Z E V E R M I T T E +swc_deu_001376 V E R D I N S T M D A S K Ö N A R L I E T V E R L I E N +swc_deu_001377 B W O L L H O F M E I N V O N H O F M E I N Z W A L L D A U S W E R G G R O S E N E I N F L U S A S P Ä E R E R D I C H T E A U S Ü B T +swc_deu_001378 M M S O E R M N S T A L S T A S O B E R H A U P F V E N +swc_deu_001379 E F R E I E D O K O M E T A T I O N +swc_deu_001380 G E S T A L T U N M B E S K A V E R S W I E D E R S P I E G E L T +swc_deu_001381 D E R E S M T E R A U F W A N D W I T A U F +swc_deu_001382 O B G L E I C H A M B O R K D I E S E M A N G E H Ö R T E U N D E I N E N O B I L I T I E R U N G D U C H E N K E I S E R D A M I T K E I N E D R C +swc_deu_001383 D A S T U R C H D E N S I C G A U S W E I T E N T E N W E L T H A N D E L A R B E I T U N D W O H L S T A N D V E R S P R A C H +swc_deu_001384 F Ü Ö R D I E Z E I T T M I T E D E S N E U N Z E H N T E J A H N D E R B E K L A G D E D E E R C H I E T E K T M A T I N H A L L E +swc_deu_001385 E I T B O N D E S K A N Z E R H E M U T S C H M I T T L E N T E +swc_deu_001386 D I N A M E N G O D E F R E I I M S T A T Z H A N D B O C H T Z +swc_deu_001387 W E N A U C H E E N E R G E W I S E N L E T A G I E +swc_deu_001388 K A L K O L I E R E B A E G +swc_deu_001389 A N G E F N G D E Z S A H U N E R V F N M Z I S C Ü U L E R E I N E N D I E L I G I E I T E N +swc_deu_001390 V I E L M E N S C H E N S A N E N R I S L I A L S N A H R U N G S K O N G R E N T E N U N D A L S P O T E N E L G E F A +swc_deu_001391 D E N U F T R I T V E K Ö R Z E +swc_deu_001392 M I D M S T A N V O M D R E I T Z H N N I U L I E Z W E I T A U S E N S W E R F D E R I N H E R S T E T U N D E R D E L I Z E N S K R E R T Z U C O M O N S E I Z R E W U S C H E N S C H E R E L E I T D R E I T P U N K N L A N P O R T E T U N D U N T E D E +swc_deu_001393 E I N E K L E I N E R E B O G E N B R Ü C K E +swc_deu_001394 S I C H N U N V E R S E N W E I T R K O M +swc_deu_001395 A U S D E M G E M E Ä L D E Z U E N T F E R E N E N +swc_deu_001396 I S D C H Ö A R O P Ä S C H E R I C H T L I N E N E U N Z I C G H V I E R N E R T E X S N E U N Z S I C H E W I +swc_deu_001397 A L E U M S E L T A U F +swc_deu_001398 N D S I E S E I A U C H W I E E I N E F I R S T E N +swc_deu_001399 N E U N Z E H N H U N D E R T N E U N Z E N +swc_deu_001400 S T A T I S S E N H A B E N D E R Ö M I S C H E I N J E N I U R E M +swc_deu_001401 D E L K R I S L I E B E H R U N D M E N S C H +swc_deu_001402 M I E S I C H E N S K U R E L I S C H E +swc_deu_001403 B E R T O T C H U M L G E B D E S D R E I V A R E R Z I O N E R M I +swc_deu_001404 K Z U G S G E B I E D T E R W E S I C H T E R A C H Z E H N H U N D A T Z W E I U N S I E B Z I G E G R Ü N E T E J L U S T D U N E T I O N A L P A R K +swc_deu_001405 E F I N I T I O N +swc_deu_001406 U M I N E U N I V E S I T E T Z V I L A Z W E I S E M S T E R K U N S G E S C H C H T Z U S T U D I E R N +swc_deu_001407 D I E T O T Z E R E G R I N E diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/token_int new file mode 100644 index 0000000000000000000000000000000000000000..429c29bae0a002806eab63759459ad557ac82308 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_deu1/token_int @@ -0,0 +1,207 @@ +swc_deu_001201 10 2 5 3 2 6 24 2 6 13 5 2 18 8 2 3 12 4 14 2 3 11 2 9 6 20 16 14 3 5 2 3 6 9 4 7 15 13 26 2 22 2 7 2 5 4 2 7 19 9 8 2 7 3 4 5 15 11 8 18 2 6 9 15 14 8 10 2 8 9 18 +swc_deu_001202 10 5 2 3 5 4 3 10 2 3 9 4 7 2 7 8 2 6 10 8 2 4 3 9 13 7 +swc_deu_001203 9 6 22 2 5 4 14 6 16 7 2 3 2 6 19 16 13 14 22 +swc_deu_001204 14 16 7 2 4 3 7 15 11 2 11 17 5 15 11 2 5 4 3 24 2 6 18 6 5 15 22 2 4 +swc_deu_001205 12 6 10 2 4 3 9 15 11 3 17 2 11 6 2 6 2 3 9 6 3 13 2 12 8 6 12 4 14 7 18 25 15 11 2 3 24 2 6 3 19 19 4 8 13 5 15 11 8 +swc_deu_001206 24 16 6 18 2 6 2 5 8 2 4 3 18 5 2 6 3 8 2 5 14 3 14 2 8 12 4 14 8 +swc_deu_001207 10 16 17 2 4 8 2 3 7 11 13 5 2 7 13 5 15 14 3 5 4 3 2 +swc_deu_001208 8 9 12 8 9 14 3 24 25 6 10 2 4 8 9 12 8 3 24 16 4 3 22 27 4 5 15 11 3 24 2 6 2 6 15 11 25 13 2 +swc_deu_001209 10 9 6 12 4 10 2 6 3 7 5 4 10 3 17 9 8 5 13 10 2 3 9 6 7 2 4 7 5 7 3 21 2 15 11 8 2 6 3 10 2 7 3 22 6 2 12 13 20 5 7 +swc_deu_001210 2 4 3 5 4 2 4 7 8 26 8 2 4 3 17 2 11 6 3 12 4 10 3 17 2 11 6 3 5 2 6 16 13 2 3 10 2 6 8 6 9 10 5 3 20 3 4 2 13 4 3 5 7 11 +swc_deu_001211 20 3 10 2 4 2 4 3 21 2 13 8 13 2 12 19 5 15 11 22 2 5 5 8 +swc_deu_001212 6 9 15 11 6 2 3 8 10 7 11 16 19 2 7 3 12 4 10 3 10 2 7 3 9 10 2 8 20 7 3 19 25 6 3 10 2 4 3 19 6 5 2 19 2 8 +swc_deu_001213 20 2 5 8 3 9 4 14 9 18 2 4 3 24 2 6 20 5 15 11 8 2 8 +swc_deu_001214 9 13 13 3 9 15 11 8 20 2 11 4 3 11 12 4 10 2 6 8 3 9 15 11 8 20 5 15 11 17 5 8 3 16 8 8 12 16 18 6 9 17 17 7 3 9 12 19 7 2 5 8 20 +swc_deu_001215 17 25 13 2 4 3 21 2 7 2 4 3 7 5 18 8 2 11 4 3 12 4 2 10 3 15 11 12 4 10 20 21 9 4 20 5 +swc_deu_001216 9 7 3 10 2 6 3 19 5 15 11 3 6 5 15 11 +swc_deu_001217 7 5 10 2 17 3 9 18 7 15 13 7 7 3 5 17 28 9 11 6 2 4 12 4 20 2 4 3 11 4 10 6 3 20 21 9 2 12 4 3 9 15 11 8 20 5 15 14 3 12 4 10 3 2 6 4 9 17 2 6 3 2 5 4 2 3 2 6 7 8 2 3 13 26 4 14 2 6 2 3 6 2 5 7 2 3 9 15 7 23 9 4 2 4 +swc_deu_001218 24 25 6 4 7 15 11 9 8 7 16 3 24 12 6 14 2 20 2 5 15 11 4 2 8 +swc_deu_001219 19 2 5 8 2 4 7 8 2 5 4 7 3 19 13 7 8 2 4 10 5 14 2 14 2 7 15 11 5 15 11 8 2 4 3 12 4 10 3 10 5 2 3 9 12 14 7 18 6 14 2 7 8 9 10 18 14 2 7 15 11 5 2 14 3 2 10 2 7 3 2 13 8 6 2 4 +swc_deu_001220 4 9 15 11 3 10 5 2 7 2 4 20 2 6 7 8 27 11 6 12 4 14 2 4 3 21 12 6 10 2 3 10 2 6 9 7 7 15 11 21 5 2 10 2 6 3 9 12 19 18 13 11 4 +swc_deu_001221 17 9 15 11 8 2 4 3 2 5 4 19 13 12 7 6 2 5 15 11 2 4 3 11 9 4 3 7 5 2 3 9 6 8 2 4 17 3 18 2 5 17 3 22 16 17 5 7 9 6 5 7 15 11 2 5 4 3 14 2 7 2 8 20 8 2 4 3 18 25 6 14 2 17 2 5 7 8 2 6 3 17 9 6 22 2 6 8 3 5 11 6 2 3 9 12 19 21 9 11 8 12 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3 2 5 4 2 3 4 16 18 5 13 5 3 8 5 2 6 12 4 14 3 10 12 15 11 2 4 22 2 5 7 2 6 3 10 9 17 5 8 3 22 2 5 4 2 3 10 6 15 +swc_deu_001383 10 9 3 7 8 12 6 15 11 3 10 2 4 7 5 15 14 3 9 12 7 21 2 5 8 2 4 8 2 4 3 21 2 13 8 11 9 4 10 2 13 3 9 6 18 2 5 8 3 12 4 10 3 21 16 11 13 7 8 9 4 10 3 24 2 6 7 23 6 9 15 11 +swc_deu_001384 19 25 27 6 3 10 5 2 3 20 2 5 8 8 3 17 5 8 2 3 10 2 7 3 4 2 12 4 20 2 11 4 8 2 3 28 9 3 11 4 10 2 6 3 18 2 22 13 9 14 3 10 2 10 2 3 2 6 15 11 5 2 3 8 2 22 8 17 9 8 5 4 3 11 9 13 13 2 +swc_deu_001385 2 5 8 18 16 4 10 2 7 22 9 4 20 2 6 3 11 2 17 12 8 7 15 11 17 5 8 8 3 13 2 4 8 2 +swc_deu_001386 10 5 3 4 9 17 2 4 3 14 16 10 2 3 19 6 2 5 3 5 17 3 7 8 9 8 20 3 11 9 4 10 18 16 15 11 8 20 +swc_deu_001387 21 2 4 3 9 12 15 11 2 3 2 4 2 6 14 2 21 5 7 2 4 13 2 8 9 14 5 2 +swc_deu_001388 22 9 13 22 16 13 5 2 6 2 3 18 9 2 14 +swc_deu_001389 9 4 14 2 19 4 14 10 2 20 7 9 11 12 4 2 6 3 24 19 4 17 3 20 5 7 15 25 12 13 2 6 3 2 5 4 2 4 3 10 5 2 13 5 14 5 2 5 8 2 4 +swc_deu_001390 24 5 2 13 17 2 4 7 15 11 2 4 3 7 9 4 2 4 3 6 5 7 13 5 3 9 13 7 3 4 9 11 6 12 4 14 7 22 16 4 14 6 2 4 8 2 4 3 12 4 10 3 9 13 7 23 16 8 2 4 3 2 13 14 2 19 9 +swc_deu_001391 10 2 4 3 12 19 8 6 5 8 3 24 2 3 22 27 6 20 2 +swc_deu_001392 17 5 3 10 17 3 7 8 9 4 3 24 16 17 3 10 6 2 5 8 20 11 4 3 4 5 12 13 5 2 20 21 2 5 3 8 9 12 7 2 4 3 7 21 2 6 19 3 10 2 6 3 5 4 11 2 6 7 8 2 8 3 12 4 10 2 6 3 10 2 13 5 20 2 4 7 3 22 6 2 6 8 20 12 3 15 16 17 16 4 7 3 2 5 20 6 2 21 12 7 15 11 2 4 3 7 15 11 2 6 3 2 3 13 2 5 8 10 6 2 5 8 3 23 12 4 22 3 4 13 3 9 4 23 16 6 8 2 8 3 12 4 10 3 12 4 8 2 3 10 2 +swc_deu_001393 2 5 4 2 3 22 13 2 5 4 2 6 2 18 16 14 2 4 3 18 6 25 15 22 2 +swc_deu_001394 7 5 15 11 3 4 12 4 24 2 6 7 2 4 21 2 5 8 6 22 16 17 +swc_deu_001395 9 12 7 3 10 2 17 3 14 2 17 2 26 13 10 2 3 20 12 3 2 4 8 3 19 2 6 2 4 2 4 +swc_deu_001396 5 7 3 10 15 11 3 27 9 6 16 23 26 7 15 11 2 6 5 15 11 8 13 5 4 2 3 4 2 12 4 20 5 15 14 11 3 24 5 2 6 4 2 6 8 2 30 7 4 2 12 4 20 7 5 15 11 3 2 21 5 +swc_deu_001397 9 13 2 3 12 17 7 2 13 8 3 9 12 19 +swc_deu_001398 4 10 3 7 5 2 3 7 2 5 3 9 12 15 11 3 21 5 2 3 2 5 4 2 3 19 5 6 7 8 2 4 +swc_deu_001399 4 2 12 4 20 2 11 4 3 11 12 4 10 2 6 8 3 4 2 12 4 3 20 2 4 +swc_deu_001400 7 8 9 8 5 7 7 2 4 3 11 9 18 2 4 3 10 2 3 6 27 17 5 7 15 11 2 3 5 4 3 28 2 4 3 5 12 6 2 17 +swc_deu_001401 10 2 13 22 6 5 7 13 5 2 3 18 2 11 6 3 12 4 10 3 17 2 4 7 15 11 +swc_deu_001402 17 5 2 7 5 15 11 2 4 7 3 22 12 6 2 13 5 7 15 11 2 +swc_deu_001403 18 2 6 8 16 8 15 11 12 17 13 14 2 18 3 10 2 7 3 10 6 2 5 24 9 6 2 6 3 20 5 16 4 2 6 3 17 5 +swc_deu_001404 22 3 20 12 14 7 14 2 18 5 2 10 8 3 2 6 21 2 7 5 15 11 3 8 2 6 3 9 15 11 20 2 11 4 3 11 12 4 10 9 8 20 21 2 5 12 4 3 7 5 2 18 20 5 14 3 2 3 14 6 25 4 2 8 2 3 28 13 12 7 8 10 12 4 2 8 3 5 16 4 9 13 23 9 6 22 +swc_deu_001405 2 3 19 5 4 5 8 5 16 4 +swc_deu_001406 12 17 3 5 4 2 3 12 4 5 24 2 7 5 8 2 8 3 20 24 5 13 9 3 20 21 2 5 7 2 3 17 7 8 2 6 3 22 12 4 7 14 2 7 15 11 15 11 8 3 20 12 7 8 12 10 5 2 6 4 +swc_deu_001407 10 5 2 3 8 16 8 20 3 2 6 2 3 14 6 5 4 2 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/run.sh b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/run.sh new file mode 100755 index 0000000000000000000000000000000000000000..dc41d1ce95f2016341e7383d0cd743391f53f70e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/run.sh @@ -0,0 +1 @@ +./asr.sh --ngpu 1 --stage 11 --stop_stage 13 --nj 32 --inference_nj 4 --gpu_inference true --lang deu1 --inference_asr_model valid.loss.ave.pth --local_data_opts '--duration 1h --lid false --multilingual false --single_lang deu1' --use_lm false --token_type char --feats_type raw --feats_normalize utterance_mvn --asr_config conf/tuning/train_asr_s3prl_houlsby.yaml --inference_config conf/decode_asr.yaml --train_set train_1h_deu1 --valid_set dev_10min_deu1 --test_sets 'dev_10min_deu1 test_10min_deu1' --asr_tag train_asr_s3prl_houlsby_deu1_1h --expdir test_pr --asr_stats_dir test_pr/asr_stats_deu1_1h --local_score_opts 'false false monolingual' --stage 12 "$@"; exit $? diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.1.log b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.1.log new file mode 100644 index 0000000000000000000000000000000000000000..03b7d0568fdc41a9e7399e82cb6e1ce2fd260e2b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.1.log @@ -0,0 +1,1845 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1 --config conf/decode_asr.yaml +# Started at Wed Jan 17 01:51:13 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1 --config conf/decode_asr.yaml +2024-01-17 01:51:14,971 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml +2024-01-17 01:51:14,989 (asr:523) INFO: Vocabulary size: 44 +2024-01-17 01:51:15,051 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 01:51:15,051 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 01:51:15,161 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 01:51:16,453 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 01:51:17,708 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 01:51:17,708 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 01:51:17,708 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 01:51:17,741 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-17 01:51:17,815 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 01:51:17,928 (asr_inference:494) INFO: speech length: 140637 +2024-01-17 01:51:19,134 (beam_search:428) INFO: decoder input length: 217 +2024-01-17 01:51:19,134 (beam_search:429) INFO: max output length: 217 +2024-01-17 01:51:19,134 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:19,805 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:19,805 (beam_search:476) INFO: -37.24 * 1.0 = -37.24 for ctc +2024-01-17 01:51:19,805 (beam_search:479) INFO: total log probability: -37.24 +2024-01-17 01:51:19,805 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:51:19,805 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:19,806 (beam_search:483) INFO: best hypo: DEBERDIGNGMACHTEINEREUSSTWICHTIGENSECHAINENDEDERPÄTITIONANLINGOWANÜRFÜRDESENJANERSOSSBEGENADIGUNM + +2024-01-17 01:51:19,830 (asr_inference:494) INFO: speech length: 83197 +2024-01-17 01:51:19,841 (beam_search:428) INFO: decoder input length: 127 +2024-01-17 01:51:19,841 (beam_search:429) INFO: max output length: 127 +2024-01-17 01:51:19,841 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:20,103 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:20,103 (beam_search:476) INFO: -18.82 * 1.0 = -18.82 for ctc +2024-01-17 01:51:20,103 (beam_search:479) INFO: total log probability: -18.82 +2024-01-17 01:51:20,103 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:51:20,103 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:20,103 (beam_search:483) INFO: best hypo: DACHABESIETDIEWOLULGJEDEMHERINERINRUNKGEBLIEBENENWARTEGESPOCHENN + +2024-01-17 01:51:20,105 (asr_inference:494) INFO: speech length: 181277 +2024-01-17 01:51:20,121 (beam_search:428) INFO: decoder input length: 281 +2024-01-17 01:51:20,121 (beam_search:429) INFO: max output length: 281 +2024-01-17 01:51:20,121 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:21,244 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:21,245 (beam_search:476) INFO: -40.61 * 1.0 = -40.61 for ctc +2024-01-17 01:51:21,245 (beam_search:479) INFO: total log probability: -40.61 +2024-01-17 01:51:21,245 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:51:21,245 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:21,245 (beam_search:483) INFO: best hypo: ERSUMACHTURWARERAUFMALEBRCHTERDENKAFIDIESONDESCHIENINZSZIMERUNDIESPÄHRLINGEDIEDSSAUSDENHECSESECKENGEFALNEFOTEAKONAUFBIKTEN + +2024-01-17 01:51:21,247 (asr_inference:494) INFO: speech length: 67837 +2024-01-17 01:51:21,257 (beam_search:428) INFO: decoder input length: 103 +2024-01-17 01:51:21,257 (beam_search:429) INFO: max output length: 103 +2024-01-17 01:51:21,257 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:21,436 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:21,436 (beam_search:476) INFO: -22.42 * 1.0 = -22.42 for ctc +2024-01-17 01:51:21,436 (beam_search:479) INFO: total log probability: -22.42 +2024-01-17 01:51:21,436 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:51:21,436 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:21,437 (beam_search:483) INFO: best hypo: SSICHERLICHANIHRNGEBORTZTACKHÄTEERBEIIEBLEIBENKÖNENT + +2024-01-17 01:51:21,438 (asr_inference:494) INFO: speech length: 109868 +2024-01-17 01:51:21,450 (beam_search:428) INFO: decoder input length: 169 +2024-01-17 01:51:21,450 (beam_search:429) INFO: max output length: 169 +2024-01-17 01:51:21,450 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:21,941 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:21,941 (beam_search:476) INFO: -33.51 * 1.0 = -33.51 for ctc +2024-01-17 01:51:21,941 (beam_search:479) INFO: total log probability: -33.51 +2024-01-17 01:51:21,941 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:51:21,941 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:21,942 (beam_search:483) INFO: best hypo: DERSALBÖMMUSANDAUORTWUORMENTCHENSCHIRICGKEITENHABENDIESOUCHEINERSEITSERKLÄERENANGEBOTEMACHENWRALEI + +2024-01-17 01:51:21,943 (asr_inference:494) INFO: speech length: 104637 +2024-01-17 01:51:21,955 (beam_search:428) INFO: decoder input length: 161 +2024-01-17 01:51:21,955 (beam_search:429) INFO: max output length: 161 +2024-01-17 01:51:21,955 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:22,358 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:22,358 (beam_search:476) INFO: -32.66 * 1.0 = -32.66 for ctc +2024-01-17 01:51:22,358 (beam_search:479) INFO: total log probability: -32.66 +2024-01-17 01:51:22,358 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:51:22,358 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:22,359 (beam_search:483) INFO: best hypo: UESMANRAFDIVELTKOMTUMSEBSTIEDERINSONDZUOHARBENDEDIVEREHUNKDERANENVORTETZTNN + +2024-01-17 01:51:22,360 (asr_inference:494) INFO: speech length: 159093 +2024-01-17 01:51:22,376 (beam_search:428) INFO: decoder input length: 246 +2024-01-17 01:51:22,376 (beam_search:429) INFO: max output length: 246 +2024-01-17 01:51:22,376 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:23,273 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:23,273 (beam_search:476) INFO: -42.61 * 1.0 = -42.61 for ctc +2024-01-17 01:51:23,273 (beam_search:479) INFO: total log probability: -42.61 +2024-01-17 01:51:23,273 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:51:23,273 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:23,274 (beam_search:483) INFO: best hypo: ABÜRNPUNZENELICHERSCHULBILDUNGRUNTENELICHEMÖGLICHTEITRAUHDERWEITERBILUNGUNDDASEGEHNVONGEDENKTAGENEMICHUNAUFLSLICHTRAME + +2024-01-17 01:51:23,275 (asr_inference:494) INFO: speech length: 156957 +2024-01-17 01:51:23,291 (beam_search:428) INFO: decoder input length: 243 +2024-01-17 01:51:23,291 (beam_search:429) INFO: max output length: 243 +2024-01-17 01:51:23,291 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:24,125 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:24,125 (beam_search:476) INFO: -42.36 * 1.0 = -42.36 for ctc +2024-01-17 01:51:24,125 (beam_search:479) INFO: total log probability: -42.36 +2024-01-17 01:51:24,125 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:51:24,125 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:24,126 (beam_search:483) INFO: best hypo: EINANSASIERNSAKZISISTJERGETWIEDERGANSKUTZWISCHENUNSABERIERDUNICHTALESGESTIESTGETIEERINRUNGANDASBÜSENICHTWEH + +2024-01-17 01:51:24,128 (asr_inference:494) INFO: speech length: 46077 +2024-01-17 01:51:24,136 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:51:24,136 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:51:24,136 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:24,204 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:24,204 (beam_search:476) INFO: -10.80 * 1.0 = -10.80 for ctc +2024-01-17 01:51:24,204 (beam_search:479) INFO: total log probability: -10.80 +2024-01-17 01:51:24,204 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:51:24,204 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:24,204 (beam_search:483) INFO: best hypo: DNEINWEIBERBRAUCHRICHEICHTT + +2024-01-17 01:51:24,205 (asr_inference:494) INFO: speech length: 93277 +2024-01-17 01:51:24,216 (beam_search:428) INFO: decoder input length: 143 +2024-01-17 01:51:24,216 (beam_search:429) INFO: max output length: 143 +2024-01-17 01:51:24,216 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:24,477 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:24,477 (beam_search:476) INFO: -18.38 * 1.0 = -18.38 for ctc +2024-01-17 01:51:24,477 (beam_search:479) INFO: total log probability: -18.38 +2024-01-17 01:51:24,477 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:51:24,477 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:24,477 (beam_search:483) INFO: best hypo: TENDENGOTHATNICHVERGEBLICHENEHMEGERUFENSAKTEDERCHIVER + +2024-01-17 01:51:24,479 (asr_inference:494) INFO: speech length: 197437 +2024-01-17 01:51:24,496 (beam_search:428) INFO: decoder input length: 306 +2024-01-17 01:51:24,496 (beam_search:429) INFO: max output length: 306 +2024-01-17 01:51:24,496 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:25,802 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:25,802 (beam_search:476) INFO: -45.20 * 1.0 = -45.20 for ctc +2024-01-17 01:51:25,802 (beam_search:479) INFO: total log probability: -45.20 +2024-01-17 01:51:25,802 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:51:25,802 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:25,803 (beam_search:483) INFO: best hypo: NUREINESWEISSICHDIESERFURCHTBARENFRAGENTGEGENZUSETZENUNDSCHLEUDERERDARSWARTINDIEWARKSCHALDIEGLUTLEINESLIEBESWILENDZISTSTERKARALSTRENUNGT + +2024-01-17 01:51:25,804 (asr_inference:494) INFO: speech length: 85277 +2024-01-17 01:51:25,815 (beam_search:428) INFO: decoder input length: 131 +2024-01-17 01:51:25,815 (beam_search:429) INFO: max output length: 131 +2024-01-17 01:51:25,815 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:26,080 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:26,080 (beam_search:476) INFO: -23.90 * 1.0 = -23.90 for ctc +2024-01-17 01:51:26,080 (beam_search:479) INFO: total log probability: -23.90 +2024-01-17 01:51:26,080 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:51:26,080 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:26,080 (beam_search:483) INFO: best hypo: DTOMSAMEIGEWANEINGROSSENSIEGNCHENERLANGNGHARDNNECKEGENSCHLCHTN + +2024-01-17 01:51:26,081 (asr_inference:494) INFO: speech length: 97597 +2024-01-17 01:51:26,093 (beam_search:428) INFO: decoder input length: 150 +2024-01-17 01:51:26,093 (beam_search:429) INFO: max output length: 150 +2024-01-17 01:51:26,093 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:26,414 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:26,414 (beam_search:476) INFO: -25.00 * 1.0 = -25.00 for ctc +2024-01-17 01:51:26,414 (beam_search:479) INFO: total log probability: -25.00 +2024-01-17 01:51:26,414 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:51:26,414 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:26,414 (beam_search:483) INFO: best hypo: SOSEINAHMEDEMSICHTIÜRBEITAKUNNCHTAFFNANKANBRESCHERUNDWELKOMMEN + +2024-01-17 01:51:26,415 (asr_inference:494) INFO: speech length: 71677 +2024-01-17 01:51:26,425 (beam_search:428) INFO: decoder input length: 109 +2024-01-17 01:51:26,425 (beam_search:429) INFO: max output length: 109 +2024-01-17 01:51:26,425 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:26,586 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:26,586 (beam_search:476) INFO: -14.03 * 1.0 = -14.03 for ctc +2024-01-17 01:51:26,586 (beam_search:479) INFO: total log probability: -14.03 +2024-01-17 01:51:26,586 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:51:26,586 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:26,586 (beam_search:483) INFO: best hypo: ENARBERICHVERTSEIEINENIHRERUNWISSENHEITN + +2024-01-17 01:51:26,587 (asr_inference:494) INFO: speech length: 117117 +2024-01-17 01:51:26,600 (beam_search:428) INFO: decoder input length: 180 +2024-01-17 01:51:26,600 (beam_search:429) INFO: max output length: 180 +2024-01-17 01:51:26,600 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:27,095 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:27,095 (beam_search:476) INFO: -28.96 * 1.0 = -28.96 for ctc +2024-01-17 01:51:27,095 (beam_search:479) INFO: total log probability: -28.96 +2024-01-17 01:51:27,095 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:51:27,095 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:27,096 (beam_search:483) INFO: best hypo: UFONDERTRITENUNTEREDUNGANSAGTEMISTERHEVESCHEMWARMERDIEPERSONINHOHREMASERVERDECHTIGHTNN + +2024-01-17 01:51:27,097 (asr_inference:494) INFO: speech length: 130973 +2024-01-17 01:51:27,111 (beam_search:428) INFO: decoder input length: 202 +2024-01-17 01:51:27,111 (beam_search:429) INFO: max output length: 202 +2024-01-17 01:51:27,111 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:27,774 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:27,774 (beam_search:476) INFO: -38.50 * 1.0 = -38.50 for ctc +2024-01-17 01:51:27,774 (beam_search:479) INFO: total log probability: -38.50 +2024-01-17 01:51:27,774 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:51:27,774 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:27,774 (beam_search:483) INFO: best hypo: ICHDENKEDEMTNEARUNDSANEVERMIEWERDNESRECHTVONDERFINDENDASTUDIESELBTANGIEBSTUNSEVRDENFREUNTLICHGEGENDIESEIT + +2024-01-17 01:51:27,776 (asr_inference:494) INFO: speech length: 137117 +2024-01-17 01:51:27,790 (beam_search:428) INFO: decoder input length: 212 +2024-01-17 01:51:27,790 (beam_search:429) INFO: max output length: 212 +2024-01-17 01:51:27,790 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:28,432 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:28,433 (beam_search:476) INFO: -31.06 * 1.0 = -31.06 for ctc +2024-01-17 01:51:28,433 (beam_search:479) INFO: total log probability: -31.06 +2024-01-17 01:51:28,433 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:51:28,433 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:28,433 (beam_search:483) INFO: best hypo: ETZSSCHLUGDIEHLLEFLAMERAUFUNDNUNERKANTEERUNDSDIEVWENCHIMERZUSAMENGEDRENGTDNDEMWINKELSTANDEN + +2024-01-17 01:51:28,434 (asr_inference:494) INFO: speech length: 80317 +2024-01-17 01:51:28,445 (beam_search:428) INFO: decoder input length: 123 +2024-01-17 01:51:28,445 (beam_search:429) INFO: max output length: 123 +2024-01-17 01:51:28,445 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:28,662 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:28,662 (beam_search:476) INFO: -15.65 * 1.0 = -15.65 for ctc +2024-01-17 01:51:28,662 (beam_search:479) INFO: total log probability: -15.65 +2024-01-17 01:51:28,662 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:51:28,662 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:28,662 (beam_search:483) INFO: best hypo: DERSEINERSELEANSPONENDDASERMUNTERNTERWAUTVORWERLTZS + +2024-01-17 01:51:28,663 (asr_inference:494) INFO: speech length: 70327 +2024-01-17 01:51:28,673 (beam_search:428) INFO: decoder input length: 107 +2024-01-17 01:51:28,673 (beam_search:429) INFO: max output length: 107 +2024-01-17 01:51:28,673 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:28,848 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:28,848 (beam_search:476) INFO: -18.52 * 1.0 = -18.52 for ctc +2024-01-17 01:51:28,848 (beam_search:479) INFO: total log probability: -18.52 +2024-01-17 01:51:28,848 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:51:28,848 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:28,848 (beam_search:483) INFO: best hypo: FOMICAFDENBESUCHTESTONESCHEMINISAPRESEDENTENONT + +2024-01-17 01:51:28,850 (asr_inference:494) INFO: speech length: 90397 +2024-01-17 01:51:28,861 (beam_search:428) INFO: decoder input length: 139 +2024-01-17 01:51:28,861 (beam_search:429) INFO: max output length: 139 +2024-01-17 01:51:28,861 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:29,157 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:29,157 (beam_search:476) INFO: -33.27 * 1.0 = -33.27 for ctc +2024-01-17 01:51:29,157 (beam_search:479) INFO: total log probability: -33.27 +2024-01-17 01:51:29,157 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:51:29,157 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:29,157 (beam_search:483) INFO: best hypo: DUWASFÜHRERFOLGUNGENWASVÜRNARCHSTELUNENHABRIGNICHZUADULENGEHABTNN + +2024-01-17 01:51:29,159 (asr_inference:494) INFO: speech length: 159837 +2024-01-17 01:51:29,174 (beam_search:428) INFO: decoder input length: 247 +2024-01-17 01:51:29,174 (beam_search:429) INFO: max output length: 247 +2024-01-17 01:51:29,174 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:29,936 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:29,936 (beam_search:476) INFO: -40.21 * 1.0 = -40.21 for ctc +2024-01-17 01:51:29,936 (beam_search:479) INFO: total log probability: -40.21 +2024-01-17 01:51:29,936 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:51:29,936 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:29,936 (beam_search:483) INFO: best hypo: ZSIGOEINERWARENESTIVONAUZUOLTFURENEINKAUMARWACHNESIUNGESDINKAMZUMIEHERANGEHÜBPFTUNBETELTENEIN + +2024-01-17 01:51:29,938 (asr_inference:494) INFO: speech length: 139037 +2024-01-17 01:51:29,952 (beam_search:428) INFO: decoder input length: 215 +2024-01-17 01:51:29,952 (beam_search:429) INFO: max output length: 215 +2024-01-17 01:51:29,952 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:30,604 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:30,604 (beam_search:476) INFO: -43.34 * 1.0 = -43.34 for ctc +2024-01-17 01:51:30,604 (beam_search:479) INFO: total log probability: -43.34 +2024-01-17 01:51:30,604 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:51:30,604 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:30,604 (beam_search:483) INFO: best hypo: AICHWERICINEMBOTHINFAHNDEDSBOTERANLENENDSIERZOEGDERNALESGANZELLEINUSIEANICTUMZUKÖMANTN + +2024-01-17 01:51:30,606 (asr_inference:494) INFO: speech length: 129277 +2024-01-17 01:51:30,619 (beam_search:428) INFO: decoder input length: 199 +2024-01-17 01:51:30,619 (beam_search:429) INFO: max output length: 199 +2024-01-17 01:51:30,619 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:31,187 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:31,187 (beam_search:476) INFO: -26.94 * 1.0 = -26.94 for ctc +2024-01-17 01:51:31,187 (beam_search:479) INFO: total log probability: -26.94 +2024-01-17 01:51:31,187 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:51:31,187 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:31,187 (beam_search:483) INFO: best hypo: ALSNEREINMALNOCHDENRAUCHVONANEMHAUSERAUSDERFERNERAUFSTEIGENZUSENUMDANBERRIEGKZUSTERBEN + +2024-01-17 01:51:31,189 (asr_inference:494) INFO: speech length: 129309 +2024-01-17 01:51:31,202 (beam_search:428) INFO: decoder input length: 200 +2024-01-17 01:51:31,202 (beam_search:429) INFO: max output length: 200 +2024-01-17 01:51:31,202 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:31,761 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:31,761 (beam_search:476) INFO: -23.07 * 1.0 = -23.07 for ctc +2024-01-17 01:51:31,761 (beam_search:479) INFO: total log probability: -23.07 +2024-01-17 01:51:31,761 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:51:31,761 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:31,761 (beam_search:483) INFO: best hypo: DIETENZERENARBERLAKAUFDENKNIENVORBRAMASBILTNISINNAHMENLOSERSEHNSOCHTUNDWEINTEJAMERVOLL + +2024-01-17 01:51:31,763 (asr_inference:494) INFO: speech length: 79357 +2024-01-17 01:51:31,773 (beam_search:428) INFO: decoder input length: 121 +2024-01-17 01:51:31,773 (beam_search:429) INFO: max output length: 121 +2024-01-17 01:51:31,773 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:32,018 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:32,018 (beam_search:476) INFO: -28.66 * 1.0 = -28.66 for ctc +2024-01-17 01:51:32,018 (beam_search:479) INFO: total log probability: -28.66 +2024-01-17 01:51:32,018 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:51:32,018 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:32,018 (beam_search:483) INFO: best hypo: DECHTFERTICHMICHTENDIWERGLICHKEITENCHNICHTAUFDIGICBEGUFENKARN + +2024-01-17 01:51:32,019 (asr_inference:494) INFO: speech length: 97917 +2024-01-17 01:51:32,031 (beam_search:428) INFO: decoder input length: 150 +2024-01-17 01:51:32,031 (beam_search:429) INFO: max output length: 150 +2024-01-17 01:51:32,031 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:32,386 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:32,386 (beam_search:476) INFO: -33.63 * 1.0 = -33.63 for ctc +2024-01-17 01:51:32,386 (beam_search:479) INFO: total log probability: -33.63 +2024-01-17 01:51:32,386 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:51:32,386 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:32,387 (beam_search:483) INFO: best hypo: DTICHERGERTEMICHTANWENICHAURFACHTEREISWARSOFUNDERSCHÖNENGEWESENDASFLIENNN + +2024-01-17 01:51:32,388 (asr_inference:494) INFO: speech length: 140957 +2024-01-17 01:51:32,402 (beam_search:428) INFO: decoder input length: 218 +2024-01-17 01:51:32,402 (beam_search:429) INFO: max output length: 218 +2024-01-17 01:51:32,403 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:33,092 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:33,092 (beam_search:476) INFO: -39.12 * 1.0 = -39.12 for ctc +2024-01-17 01:51:33,092 (beam_search:479) INFO: total log probability: -39.12 +2024-01-17 01:51:33,092 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:51:33,092 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:33,093 (beam_search:483) INFO: best hypo: NCHDEMESCHONDENGANZSENFORMITAGMIIMVERBRACHTKAMSDENHOBNACHTISCHINSKRNSCHEHAUSUMKASPALEWOLZUSERGENNN + +2024-01-17 01:51:33,094 (asr_inference:494) INFO: speech length: 60778 +2024-01-17 01:51:33,104 (beam_search:428) INFO: decoder input length: 92 +2024-01-17 01:51:33,104 (beam_search:429) INFO: max output length: 92 +2024-01-17 01:51:33,104 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:33,244 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:33,244 (beam_search:476) INFO: -13.34 * 1.0 = -13.34 for ctc +2024-01-17 01:51:33,245 (beam_search:479) INFO: total log probability: -13.34 +2024-01-17 01:51:33,245 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:51:33,245 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:33,245 (beam_search:483) INFO: best hypo: ERWAEINALTERHIRHTVOLMEDIEZINESCERGININALITET + +2024-01-17 01:51:33,246 (asr_inference:494) INFO: speech length: 70397 +2024-01-17 01:51:33,256 (beam_search:428) INFO: decoder input length: 107 +2024-01-17 01:51:33,256 (beam_search:429) INFO: max output length: 107 +2024-01-17 01:51:33,256 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:33,448 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:33,448 (beam_search:476) INFO: -23.91 * 1.0 = -23.91 for ctc +2024-01-17 01:51:33,448 (beam_search:479) INFO: total log probability: -23.91 +2024-01-17 01:51:33,448 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:51:33,448 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:33,449 (beam_search:483) INFO: best hypo: NTDASVOLUCHDERMIETARSEINEVONDELRICHGSKEITENHARBEMSERN + +2024-01-17 01:51:33,450 (asr_inference:494) INFO: speech length: 162877 +2024-01-17 01:51:33,465 (beam_search:428) INFO: decoder input length: 252 +2024-01-17 01:51:33,465 (beam_search:429) INFO: max output length: 252 +2024-01-17 01:51:33,465 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:34,334 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:34,334 (beam_search:476) INFO: -26.97 * 1.0 = -26.97 for ctc +2024-01-17 01:51:34,334 (beam_search:479) INFO: total log probability: -26.97 +2024-01-17 01:51:34,334 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:51:34,334 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:34,335 (beam_search:483) INFO: best hypo: ESIESANALLEENGSTLICGUNDBETRÜBTAUSUNDAUCHERARENESASCHWEHRMÖTICHDAWIEDIEANDERENUNDSTÜTZSTERDASHAUPTINDIEHAN + +2024-01-17 01:51:34,336 (asr_inference:494) INFO: speech length: 188157 +2024-01-17 01:51:34,354 (beam_search:428) INFO: decoder input length: 291 +2024-01-17 01:51:34,354 (beam_search:429) INFO: max output length: 291 +2024-01-17 01:51:34,354 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:35,578 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:35,578 (beam_search:476) INFO: -47.20 * 1.0 = -47.20 for ctc +2024-01-17 01:51:35,578 (beam_search:479) INFO: total log probability: -47.20 +2024-01-17 01:51:35,578 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:51:35,578 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:35,579 (beam_search:483) INFO: best hypo: UNTERDENDAMENMEISTJIONGEFRSCHEGESICHTAUNTERDENHERENNEBENJUENTLICHIENZSOUCHEMITFALTIASTIERNUNDBEREITZSMEHRADERMINDERMONDUMGLENSTEMSCHÄDEL + +2024-01-17 01:51:35,580 (asr_inference:494) INFO: speech length: 64125 +2024-01-17 01:51:35,590 (beam_search:428) INFO: decoder input length: 98 +2024-01-17 01:51:35,590 (beam_search:429) INFO: max output length: 98 +2024-01-17 01:51:35,590 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:35,742 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:35,742 (beam_search:476) INFO: -15.94 * 1.0 = -15.94 for ctc +2024-01-17 01:51:35,743 (beam_search:479) INFO: total log probability: -15.94 +2024-01-17 01:51:35,743 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:51:35,743 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:35,743 (beam_search:483) INFO: best hypo: SEITAGENSCHONHATESBESONDERSTDREUENDGEKLUNGERT + +2024-01-17 01:51:35,744 (asr_inference:494) INFO: speech length: 27357 +2024-01-17 01:51:35,751 (beam_search:428) INFO: decoder input length: 40 +2024-01-17 01:51:35,751 (beam_search:429) INFO: max output length: 40 +2024-01-17 01:51:35,751 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:35,771 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:35,771 (beam_search:476) INFO: -3.61 * 1.0 = -3.61 for ctc +2024-01-17 01:51:35,771 (beam_search:479) INFO: total log probability: -3.61 +2024-01-17 01:51:35,771 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:51:35,771 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:35,771 (beam_search:483) INFO: best hypo: SONDERBAR + +2024-01-17 01:51:35,772 (asr_inference:494) INFO: speech length: 133757 +2024-01-17 01:51:35,786 (beam_search:428) INFO: decoder input length: 206 +2024-01-17 01:51:35,786 (beam_search:429) INFO: max output length: 206 +2024-01-17 01:51:35,786 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:36,378 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:36,378 (beam_search:476) INFO: -37.65 * 1.0 = -37.65 for ctc +2024-01-17 01:51:36,379 (beam_search:479) INFO: total log probability: -37.65 +2024-01-17 01:51:36,379 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:51:36,379 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:36,379 (beam_search:483) INFO: best hypo: ERBVONERBEMHEIMSTANDMITENHRGATENVOLWEMUOTUNGDANKGBARKEIDANDERGROFTCFDEREINMECHTINGNGN + +2024-01-17 01:51:36,380 (asr_inference:494) INFO: speech length: 99677 +2024-01-17 01:51:36,392 (beam_search:428) INFO: decoder input length: 153 +2024-01-17 01:51:36,392 (beam_search:429) INFO: max output length: 153 +2024-01-17 01:51:36,392 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:36,758 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:36,758 (beam_search:476) INFO: -23.52 * 1.0 = -23.52 for ctc +2024-01-17 01:51:36,758 (beam_search:479) INFO: total log probability: -23.52 +2024-01-17 01:51:36,758 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:51:36,758 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:36,759 (beam_search:483) INFO: best hypo: TIERWARJIEDERMENSCHEINUNDERUNDFASTALLESWASMENSCHENTATENETASSWONDARBARESN + +2024-01-17 01:51:36,760 (asr_inference:494) INFO: speech length: 48637 +2024-01-17 01:51:36,769 (beam_search:428) INFO: decoder input length: 73 +2024-01-17 01:51:36,769 (beam_search:429) INFO: max output length: 73 +2024-01-17 01:51:36,769 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:36,858 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:36,858 (beam_search:476) INFO: -15.40 * 1.0 = -15.40 for ctc +2024-01-17 01:51:36,858 (beam_search:479) INFO: total log probability: -15.40 +2024-01-17 01:51:36,858 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-17 01:51:36,858 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:36,858 (beam_search:483) INFO: best hypo: WELCHEJERWEIESIEENDLENKSTFÜRTN + +2024-01-17 01:51:36,860 (asr_inference:494) INFO: speech length: 101437 +2024-01-17 01:51:36,871 (beam_search:428) INFO: decoder input length: 156 +2024-01-17 01:51:36,871 (beam_search:429) INFO: max output length: 156 +2024-01-17 01:51:36,871 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:37,265 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:37,265 (beam_search:476) INFO: -26.01 * 1.0 = -26.01 for ctc +2024-01-17 01:51:37,265 (beam_search:479) INFO: total log probability: -26.01 +2024-01-17 01:51:37,265 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:51:37,265 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:37,265 (beam_search:483) INFO: best hypo: IEWRTENASNICHTIENTEREMSCHANKTISCUNDKEINERERERDIENSTLEUTEBEFANDZEICHINDERSTUBE + +2024-01-17 01:51:37,266 (asr_inference:494) INFO: speech length: 155517 +2024-01-17 01:51:37,282 (beam_search:428) INFO: decoder input length: 240 +2024-01-17 01:51:37,282 (beam_search:429) INFO: max output length: 240 +2024-01-17 01:51:37,282 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:38,138 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:38,138 (beam_search:476) INFO: -33.94 * 1.0 = -33.94 for ctc +2024-01-17 01:51:38,138 (beam_search:479) INFO: total log probability: -33.94 +2024-01-17 01:51:38,138 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:51:38,138 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:38,139 (beam_search:483) INFO: best hypo: NALSDIEHERSCHAFTAUSDERKILCHERTRATSTANDENDIELEUTEUMHEHRUMSIEVORBEIGEHENZUSEHENUNDAMKIRLCHOFTORERWARTEEEINMANNN + +2024-01-17 01:51:38,140 (asr_inference:494) INFO: speech length: 42512 +2024-01-17 01:51:38,149 (beam_search:428) INFO: decoder input length: 64 +2024-01-17 01:51:38,149 (beam_search:429) INFO: max output length: 64 +2024-01-17 01:51:38,149 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:38,234 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:38,234 (beam_search:476) INFO: -19.12 * 1.0 = -19.12 for ctc +2024-01-17 01:51:38,234 (beam_search:479) INFO: total log probability: -19.12 +2024-01-17 01:51:38,234 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:51:38,234 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:38,234 (beam_search:483) INFO: best hypo: ASMSENERTOHNOMDEMTARISMUSENGENTUTDE + +2024-01-17 01:51:38,235 (asr_inference:494) INFO: speech length: 37670 +2024-01-17 01:51:38,243 (beam_search:428) INFO: decoder input length: 56 +2024-01-17 01:51:38,243 (beam_search:429) INFO: max output length: 56 +2024-01-17 01:51:38,243 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:38,317 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:38,317 (beam_search:476) INFO: -17.38 * 1.0 = -17.38 for ctc +2024-01-17 01:51:38,317 (beam_search:479) INFO: total log probability: -17.38 +2024-01-17 01:51:38,317 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:51:38,317 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:38,317 (beam_search:483) INFO: best hypo: LCHGELAUERASESGUDENITBERMEINEHERDOKT + +2024-01-17 01:51:38,319 (asr_inference:494) INFO: speech length: 106653 +2024-01-17 01:51:38,330 (beam_search:428) INFO: decoder input length: 164 +2024-01-17 01:51:38,330 (beam_search:429) INFO: max output length: 164 +2024-01-17 01:51:38,330 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:38,705 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:38,705 (beam_search:476) INFO: -23.07 * 1.0 = -23.07 for ctc +2024-01-17 01:51:38,705 (beam_search:479) INFO: total log probability: -23.07 +2024-01-17 01:51:38,705 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:51:38,705 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:38,705 (beam_search:483) INFO: best hypo: ENTORIMANFANKGEWANERKEINEAUFMERAMKEITVÜRANDRERDINGEALSFÜRDERSESSEN + +2024-01-17 01:51:38,706 (asr_inference:494) INFO: speech length: 127517 +2024-01-17 01:51:38,720 (beam_search:428) INFO: decoder input length: 197 +2024-01-17 01:51:38,720 (beam_search:429) INFO: max output length: 197 +2024-01-17 01:51:38,720 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:39,272 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:39,272 (beam_search:476) INFO: -25.37 * 1.0 = -25.37 for ctc +2024-01-17 01:51:39,272 (beam_search:479) INFO: total log probability: -25.37 +2024-01-17 01:51:39,272 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:51:39,272 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:39,273 (beam_search:483) INFO: best hypo: DIESFLÄSCHIENZOGERJETZTEILICHERVORWERENDJENESCMITWASSARFILTENUNDBOTESDERUNGVERZYÜSANN + +2024-01-17 01:51:39,274 (asr_inference:494) INFO: speech length: 128911 +2024-01-17 01:51:39,288 (beam_search:428) INFO: decoder input length: 199 +2024-01-17 01:51:39,288 (beam_search:429) INFO: max output length: 199 +2024-01-17 01:51:39,288 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:39,953 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:39,953 (beam_search:476) INFO: -43.89 * 1.0 = -43.89 for ctc +2024-01-17 01:51:39,953 (beam_search:479) INFO: total log probability: -43.89 +2024-01-17 01:51:39,953 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:51:39,953 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:39,954 (beam_search:483) INFO: best hypo: ESERWASOCRICHTIONWICHTICHTERSHINERDOCHERTZANSCPOSFOLLELGESAKTATWEWERDENAUCHANENZEITPUNKDERIDUKTIUONKOMMENDESDGÜ + +2024-01-17 01:51:39,955 (asr_inference:494) INFO: speech length: 51677 +2024-01-17 01:51:39,964 (beam_search:428) INFO: decoder input length: 78 +2024-01-17 01:51:39,964 (beam_search:429) INFO: max output length: 78 +2024-01-17 01:51:39,964 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:40,066 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:40,066 (beam_search:476) INFO: -14.04 * 1.0 = -14.04 for ctc +2024-01-17 01:51:40,066 (beam_search:479) INFO: total log probability: -14.04 +2024-01-17 01:51:40,066 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:51:40,066 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:40,066 (beam_search:483) INFO: best hypo: DNICHDOCHMUTERWERKESEJERZTNCHNIGN + +2024-01-17 01:51:40,067 (asr_inference:494) INFO: speech length: 81569 +2024-01-17 01:51:40,077 (beam_search:428) INFO: decoder input length: 125 +2024-01-17 01:51:40,077 (beam_search:429) INFO: max output length: 125 +2024-01-17 01:51:40,077 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:40,317 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:40,317 (beam_search:476) INFO: -18.18 * 1.0 = -18.18 for ctc +2024-01-17 01:51:40,317 (beam_search:479) INFO: total log probability: -18.18 +2024-01-17 01:51:40,317 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:51:40,317 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:40,318 (beam_search:483) INFO: best hypo: BIAHABENINENDEZENJAHNRECHTENKEBITIUNGZUBASILIENAUFGEBAUTPR + +2024-01-17 01:51:40,319 (asr_inference:494) INFO: speech length: 53597 +2024-01-17 01:51:40,327 (beam_search:428) INFO: decoder input length: 81 +2024-01-17 01:51:40,328 (beam_search:429) INFO: max output length: 81 +2024-01-17 01:51:40,328 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:40,441 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:40,441 (beam_search:476) INFO: -21.85 * 1.0 = -21.85 for ctc +2024-01-17 01:51:40,441 (beam_search:479) INFO: total log probability: -21.85 +2024-01-17 01:51:40,441 (beam_search:480) INFO: normalized log probability: -0.47 +2024-01-17 01:51:40,441 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:40,442 (beam_search:483) INFO: best hypo: DTTSSIEEVÜRDESCHNICHTVÜERANDRABPFONTT + +2024-01-17 01:51:40,443 (asr_inference:494) INFO: speech length: 17793 +2024-01-17 01:51:40,449 (beam_search:428) INFO: decoder input length: 25 +2024-01-17 01:51:40,450 (beam_search:429) INFO: max output length: 25 +2024-01-17 01:51:40,450 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:40,468 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:40,468 (beam_search:476) INFO: -6.64 * 1.0 = -6.64 for ctc +2024-01-17 01:51:40,468 (beam_search:479) INFO: total log probability: -6.64 +2024-01-17 01:51:40,468 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:51:40,468 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:40,469 (beam_search:483) INFO: best hypo: LECHLIFENSEMETZU + +2024-01-17 01:51:40,470 (asr_inference:494) INFO: speech length: 88797 +2024-01-17 01:51:40,480 (beam_search:428) INFO: decoder input length: 136 +2024-01-17 01:51:40,480 (beam_search:429) INFO: max output length: 136 +2024-01-17 01:51:40,480 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:40,725 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:40,725 (beam_search:476) INFO: -21.84 * 1.0 = -21.84 for ctc +2024-01-17 01:51:40,725 (beam_search:479) INFO: total log probability: -21.84 +2024-01-17 01:51:40,725 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:51:40,725 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:40,726 (beam_search:483) INFO: best hypo: GOATWASSIIEARZHÄLTERHÖRENSINURESISSEINGANSEROMANN + +2024-01-17 01:51:40,727 (asr_inference:494) INFO: speech length: 67517 +2024-01-17 01:51:40,736 (beam_search:428) INFO: decoder input length: 103 +2024-01-17 01:51:40,736 (beam_search:429) INFO: max output length: 103 +2024-01-17 01:51:40,736 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:40,899 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:40,899 (beam_search:476) INFO: -19.54 * 1.0 = -19.54 for ctc +2024-01-17 01:51:40,899 (beam_search:479) INFO: total log probability: -19.54 +2024-01-17 01:51:40,899 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:51:40,899 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:40,899 (beam_search:483) INFO: best hypo: SEINEMTERKANIMOFLUSSWASSRGEBENDESSEIBPWEINDER + +2024-01-17 01:51:40,900 (asr_inference:494) INFO: speech length: 194317 +2024-01-17 01:51:40,918 (beam_search:428) INFO: decoder input length: 301 +2024-01-17 01:51:40,918 (beam_search:429) INFO: max output length: 301 +2024-01-17 01:51:40,918 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:42,233 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:42,233 (beam_search:476) INFO: -51.55 * 1.0 = -51.55 for ctc +2024-01-17 01:51:42,233 (beam_search:479) INFO: total log probability: -51.55 +2024-01-17 01:51:42,233 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:51:42,233 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:42,234 (beam_search:483) INFO: best hypo: UNESWRSCHASMINESTAWIRTEÖMENBUSAMMITERNETZAGENTURAMVIERTENJUNIZUMERSENMALPRESENTIERNWIESICHDIENETZBETREIBERUNDDIEKRAFTDEARKEDINEUNETLHNEVORSTERUND + +2024-01-17 01:51:42,235 (asr_inference:494) INFO: speech length: 140637 +2024-01-17 01:51:42,249 (beam_search:428) INFO: decoder input length: 217 +2024-01-17 01:51:42,249 (beam_search:429) INFO: max output length: 217 +2024-01-17 01:51:42,250 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:42,959 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:42,959 (beam_search:476) INFO: -38.92 * 1.0 = -38.92 for ctc +2024-01-17 01:51:42,959 (beam_search:479) INFO: total log probability: -38.92 +2024-01-17 01:51:42,959 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:51:42,959 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:42,960 (beam_search:483) INFO: best hypo: EVWARHATESECHTZITANTVORTODESSFVECHERVONDEMGETERBEFREITUNDZUCHTEZUNDFIENBEDERSMALEGATENBOTDKAEINENAUSWIHN + +2024-01-17 01:51:42,961 (asr_inference:494) INFO: speech length: 100477 +2024-01-17 01:51:42,972 (beam_search:428) INFO: decoder input length: 154 +2024-01-17 01:51:42,972 (beam_search:429) INFO: max output length: 154 +2024-01-17 01:51:42,972 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:43,328 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:43,329 (beam_search:476) INFO: -35.32 * 1.0 = -35.32 for ctc +2024-01-17 01:51:43,329 (beam_search:479) INFO: total log probability: -35.32 +2024-01-17 01:51:43,329 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-17 01:51:43,329 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:43,329 (beam_search:483) INFO: best hypo: DABECHMEINWERFÜRUTELIENLASSENNODERNOCHEINANLAUFNEMENUNDTESROLÄNDNSOLTEN + +2024-01-17 01:51:43,330 (asr_inference:494) INFO: speech length: 179357 +2024-01-17 01:51:43,347 (beam_search:428) INFO: decoder input length: 278 +2024-01-17 01:51:43,347 (beam_search:429) INFO: max output length: 278 +2024-01-17 01:51:43,347 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:44,398 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:44,398 (beam_search:476) INFO: -45.50 * 1.0 = -45.50 for ctc +2024-01-17 01:51:44,398 (beam_search:479) INFO: total log probability: -45.50 +2024-01-17 01:51:44,398 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:51:44,398 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:44,398 (beam_search:483) INFO: best hypo: ERWADASGETZIERNDERSTUNDDETEITEIEAUFTRAKTEMADMUNMSCHLLDIEACHASTANDUNIGEAUFENSEIDENTÜKEZUSAMFELDETEFÜRSCHONZUSORGENGN + +2024-01-17 01:51:44,400 (asr_inference:494) INFO: speech length: 30717 +2024-01-17 01:51:44,407 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:51:44,407 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:51:44,407 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:44,437 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:44,437 (beam_search:476) INFO: -10.69 * 1.0 = -10.69 for ctc +2024-01-17 01:51:44,437 (beam_search:479) INFO: total log probability: -10.69 +2024-01-17 01:51:44,437 (beam_search:480) INFO: normalized log probability: -0.59 +2024-01-17 01:51:44,437 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:44,437 (beam_search:483) INFO: best hypo: TIWRDENHCHSEM + +2024-01-17 01:51:44,438 (asr_inference:494) INFO: speech length: 65053 +2024-01-17 01:51:44,447 (beam_search:428) INFO: decoder input length: 99 +2024-01-17 01:51:44,447 (beam_search:429) INFO: max output length: 99 +2024-01-17 01:51:44,447 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:44,600 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:44,600 (beam_search:476) INFO: -17.44 * 1.0 = -17.44 for ctc +2024-01-17 01:51:44,600 (beam_search:479) INFO: total log probability: -17.44 +2024-01-17 01:51:44,600 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:51:44,600 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:44,600 (beam_search:483) INFO: best hypo: ABAETIBSODAFVORGABENDASMACHMERNERDOLIKNIGHS + +2024-01-17 01:51:44,601 (asr_inference:494) INFO: speech length: 138415 +2024-01-17 01:51:44,615 (beam_search:428) INFO: decoder input length: 214 +2024-01-17 01:51:44,616 (beam_search:429) INFO: max output length: 214 +2024-01-17 01:51:44,616 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:45,284 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:45,284 (beam_search:476) INFO: -33.30 * 1.0 = -33.30 for ctc +2024-01-17 01:51:45,284 (beam_search:479) INFO: total log probability: -33.30 +2024-01-17 01:51:45,284 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:51:45,284 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:45,285 (beam_search:483) INFO: best hypo: ALSUNSREEDEBEKANTWORDERWADIÜRSIOGNOMIEDERWELTERSPBOGERUNGEFÄRDIEINERSKALBESDASUMERSENMALONHNHÖR + +2024-01-17 01:51:45,286 (asr_inference:494) INFO: speech length: 69094 +2024-01-17 01:51:45,296 (beam_search:428) INFO: decoder input length: 105 +2024-01-17 01:51:45,296 (beam_search:429) INFO: max output length: 105 +2024-01-17 01:51:45,296 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:45,489 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:45,489 (beam_search:476) INFO: -15.65 * 1.0 = -15.65 for ctc +2024-01-17 01:51:45,489 (beam_search:479) INFO: total log probability: -15.65 +2024-01-17 01:51:45,489 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:51:45,489 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:45,489 (beam_search:483) INFO: best hypo: IZERMAHNSIGEFÄLICHTAUFUNDESKLANGDIEEINJAMANDERHILVER + +2024-01-17 01:51:45,491 (asr_inference:494) INFO: speech length: 105109 +2024-01-17 01:51:45,502 (beam_search:428) INFO: decoder input length: 162 +2024-01-17 01:51:45,503 (beam_search:429) INFO: max output length: 162 +2024-01-17 01:51:45,503 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:45,887 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:45,887 (beam_search:476) INFO: -22.40 * 1.0 = -22.40 for ctc +2024-01-17 01:51:45,887 (beam_search:479) INFO: total log probability: -22.40 +2024-01-17 01:51:45,887 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:51:45,887 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:45,888 (beam_search:483) INFO: best hypo: RDOKTORSAGTEEINEFRAUDIESHNOGRINERMDIESOAFTZUINENKOMISTEINLIGANICHKRAN + +2024-01-17 01:51:45,889 (asr_inference:494) INFO: speech length: 217117 +2024-01-17 01:51:45,909 (beam_search:428) INFO: decoder input length: 337 +2024-01-17 01:51:45,909 (beam_search:429) INFO: max output length: 337 +2024-01-17 01:51:45,909 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:47,575 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:47,575 (beam_search:476) INFO: -39.93 * 1.0 = -39.93 for ctc +2024-01-17 01:51:47,575 (beam_search:479) INFO: total log probability: -39.93 +2024-01-17 01:51:47,575 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:51:47,575 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:47,576 (beam_search:483) INFO: best hypo: DIEALTEERINRUNGANDENFRÜHRENTAUMTAUCHTEREBENFALSWIEDERAUFUNDUNWIELKÖRLICFSSTBEIDEBEHAUPTUNGDASDESELEDENKÖRPERVERLASSENUNDZUIMZURÜCKERENKÖNESHINESIERORDENDLIG + +2024-01-17 01:51:47,578 (asr_inference:494) INFO: speech length: 119837 +2024-01-17 01:51:47,591 (beam_search:428) INFO: decoder input length: 185 +2024-01-17 01:51:47,591 (beam_search:429) INFO: max output length: 185 +2024-01-17 01:51:47,591 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:48,095 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:48,095 (beam_search:476) INFO: -43.51 * 1.0 = -43.51 for ctc +2024-01-17 01:51:48,095 (beam_search:479) INFO: total log probability: -43.51 +2024-01-17 01:51:48,095 (beam_search:480) INFO: normalized log probability: -0.43 +2024-01-17 01:51:48,095 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:48,095 (beam_search:483) INFO: best hypo: ALSIEOFTDENALKONDZURIKERTEVANDZIINDIEZEITUNKLIESENDDIEWERENRESFORTZEINESANGELNKTWARHT + +2024-01-17 01:51:48,097 (asr_inference:494) INFO: speech length: 178877 +2024-01-17 01:51:48,113 (beam_search:428) INFO: decoder input length: 277 +2024-01-17 01:51:48,113 (beam_search:429) INFO: max output length: 277 +2024-01-17 01:51:48,113 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:49,124 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:49,124 (beam_search:476) INFO: -35.79 * 1.0 = -35.79 for ctc +2024-01-17 01:51:49,124 (beam_search:479) INFO: total log probability: -35.79 +2024-01-17 01:51:49,124 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:51:49,124 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:49,125 (beam_search:483) INFO: best hypo: TEEHRWAHEINKINDDERSTRASEVONKLEINAUFABERINIMLEBTEVONJEHERINEGWISSESSEENSOCHTNACHEINEREHRBARENBIRGERICHENEXISTENST + +2024-01-17 01:51:49,126 (asr_inference:494) INFO: speech length: 89947 +2024-01-17 01:51:49,137 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 01:51:49,137 (beam_search:429) INFO: max output length: 138 +2024-01-17 01:51:49,137 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:49,484 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:49,484 (beam_search:476) INFO: -38.40 * 1.0 = -38.40 for ctc +2024-01-17 01:51:49,484 (beam_search:479) INFO: total log probability: -38.40 +2024-01-17 01:51:49,484 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:51:49,484 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:49,484 (beam_search:483) INFO: best hypo: AITUNESTIGJRUNGKTÜHNUNZIENIGEINERGROPEFERANFODLICHONAMWÜEFÜNEDENGEMEINWUHLFERNWODLICHUN + +2024-01-17 01:51:49,486 (asr_inference:494) INFO: speech length: 47197 +2024-01-17 01:51:49,494 (beam_search:428) INFO: decoder input length: 71 +2024-01-17 01:51:49,494 (beam_search:429) INFO: max output length: 71 +2024-01-17 01:51:49,494 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:49,561 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:49,561 (beam_search:476) INFO: -8.57 * 1.0 = -8.57 for ctc +2024-01-17 01:51:49,561 (beam_search:479) INFO: total log probability: -8.57 +2024-01-17 01:51:49,562 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:51:49,562 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:49,562 (beam_search:483) INFO: best hypo: WASMEINLIEBESKINDWASKAN + +2024-01-17 01:51:49,563 (asr_inference:494) INFO: speech length: 192157 +2024-01-17 01:51:49,580 (beam_search:428) INFO: decoder input length: 298 +2024-01-17 01:51:49,580 (beam_search:429) INFO: max output length: 298 +2024-01-17 01:51:49,580 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:50,819 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:50,819 (beam_search:476) INFO: -49.28 * 1.0 = -49.28 for ctc +2024-01-17 01:51:50,819 (beam_search:479) INFO: total log probability: -49.28 +2024-01-17 01:51:50,819 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:51:50,819 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:50,820 (beam_search:483) INFO: best hypo: UNDDANWOLTICHDENANBLICGDERANICHTMISENDIEMERGEBLEBEMWARENVORALMABARBWAESIDARUMZUTOUNWEINESYSELICISERBETEINIGAMASSENGETRÜSTETZUSEENTO + +2024-01-17 01:51:50,822 (asr_inference:494) INFO: speech length: 63071 +2024-01-17 01:51:50,832 (beam_search:428) INFO: decoder input length: 96 +2024-01-17 01:51:50,832 (beam_search:429) INFO: max output length: 96 +2024-01-17 01:51:50,832 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:50,997 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:50,997 (beam_search:476) INFO: -27.80 * 1.0 = -27.80 for ctc +2024-01-17 01:51:50,997 (beam_search:479) INFO: total log probability: -27.80 +2024-01-17 01:51:50,997 (beam_search:480) INFO: normalized log probability: -0.46 +2024-01-17 01:51:50,997 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:50,998 (beam_search:483) INFO: best hypo: EDASAUCHWIEUNDSGENSETDCHEBSHNUNTDASTUZENKÖNNENEÖRM + +2024-01-17 01:51:50,999 (asr_inference:494) INFO: speech length: 88733 +2024-01-17 01:51:51,009 (beam_search:428) INFO: decoder input length: 136 +2024-01-17 01:51:51,010 (beam_search:429) INFO: max output length: 136 +2024-01-17 01:51:51,010 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:51,325 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:51,325 (beam_search:476) INFO: -30.17 * 1.0 = -30.17 for ctc +2024-01-17 01:51:51,325 (beam_search:479) INFO: total log probability: -30.17 +2024-01-17 01:51:51,325 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:51:51,325 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:51,325 (beam_search:483) INFO: best hypo: SEINEGESCHFTICHELAUFBAHNHARBESTIEVENSNALSKÖCHENBOINEINEMOTÄLFIERENGRADESBGON + +2024-01-17 01:51:51,327 (asr_inference:494) INFO: speech length: 133917 +2024-01-17 01:51:51,341 (beam_search:428) INFO: decoder input length: 207 +2024-01-17 01:51:51,341 (beam_search:429) INFO: max output length: 207 +2024-01-17 01:51:51,341 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:52,037 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:52,037 (beam_search:476) INFO: -46.95 * 1.0 = -46.95 for ctc +2024-01-17 01:51:52,037 (beam_search:479) INFO: total log probability: -46.95 +2024-01-17 01:51:52,037 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:51:52,037 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:52,037 (beam_search:483) INFO: best hypo: NFÜLEICHTENSEGUTIESEANSICHENDESSCHOFSNHASEZUMELDENZACTEDERTATSCENDERIMARMEHREINMANDESGESCRIEBENENWURTESWIEDERTADN + +2024-01-17 01:51:52,039 (asr_inference:494) INFO: speech length: 174845 +2024-01-17 01:51:52,055 (beam_search:428) INFO: decoder input length: 271 +2024-01-17 01:51:52,055 (beam_search:429) INFO: max output length: 271 +2024-01-17 01:51:52,055 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:53,103 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:53,103 (beam_search:476) INFO: -48.10 * 1.0 = -48.10 for ctc +2024-01-17 01:51:53,103 (beam_search:479) INFO: total log probability: -48.10 +2024-01-17 01:51:53,103 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:51:53,103 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:53,104 (beam_search:483) INFO: best hypo: EMANDANMORDENEHRHOPERSICHSCHWÄTSCHCKTEDENLARKEIENDEBONUNGVORJABACHSUNTLIESUMEINENTEREDUNGBITENDEMANKAMMITEROTSCHAFTZURÜGDETN + +2024-01-17 01:51:53,106 (asr_inference:494) INFO: speech length: 104157 +2024-01-17 01:51:53,118 (beam_search:428) INFO: decoder input length: 160 +2024-01-17 01:51:53,118 (beam_search:429) INFO: max output length: 160 +2024-01-17 01:51:53,118 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:53,498 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:53,498 (beam_search:476) INFO: -29.21 * 1.0 = -29.21 for ctc +2024-01-17 01:51:53,498 (beam_search:479) INFO: total log probability: -29.21 +2024-01-17 01:51:53,498 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:51:53,498 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:53,499 (beam_search:483) INFO: best hypo: NTNUEINWENICHTRAURICHWURDESENIMDASSELBERKAMENSINNIEZUFRIEDENSCHIENNN + +2024-01-17 01:51:53,500 (asr_inference:494) INFO: speech length: 185117 +2024-01-17 01:51:53,518 (beam_search:428) INFO: decoder input length: 287 +2024-01-17 01:51:53,518 (beam_search:429) INFO: max output length: 287 +2024-01-17 01:51:53,518 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:54,663 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:54,663 (beam_search:476) INFO: -49.59 * 1.0 = -49.59 for ctc +2024-01-17 01:51:54,663 (beam_search:479) INFO: total log probability: -49.59 +2024-01-17 01:51:54,663 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:51:54,663 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:54,664 (beam_search:483) INFO: best hypo: EINSOMAHRWAHMANOVEMBARTARKLAGMITZONENGLITZSANNÜBERDEHUPTSTABTUNDUNDERDNLINDENDRENKTERINETAUSENDKAPFIGEMENSCHENMÄNGERAUOVONIEDERU + +2024-01-17 01:51:54,666 (asr_inference:494) INFO: speech length: 70237 +2024-01-17 01:51:54,675 (beam_search:428) INFO: decoder input length: 107 +2024-01-17 01:51:54,675 (beam_search:429) INFO: max output length: 107 +2024-01-17 01:51:54,675 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:54,833 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:54,833 (beam_search:476) INFO: -13.97 * 1.0 = -13.97 for ctc +2024-01-17 01:51:54,833 (beam_search:479) INFO: total log probability: -13.97 +2024-01-17 01:51:54,833 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:51:54,833 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:54,833 (beam_search:483) INFO: best hypo: KOMITMIHMEINSONDENICHPRAUCHEDEINELIEBE + +2024-01-17 01:51:54,835 (asr_inference:494) INFO: speech length: 102717 +2024-01-17 01:51:54,846 (beam_search:428) INFO: decoder input length: 158 +2024-01-17 01:51:54,846 (beam_search:429) INFO: max output length: 158 +2024-01-17 01:51:54,846 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:55,233 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:55,233 (beam_search:476) INFO: -29.19 * 1.0 = -29.19 for ctc +2024-01-17 01:51:55,234 (beam_search:479) INFO: total log probability: -29.19 +2024-01-17 01:51:55,234 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:51:55,234 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:55,234 (beam_search:483) INFO: best hypo: DNTNWORSAINKESICHTRDEINWNICHNACHTDENKLICHARSOOWIEVONEINERERINERHUNGERHÄLTNN + +2024-01-17 01:51:55,235 (asr_inference:494) INFO: speech length: 77906 +2024-01-17 01:51:55,246 (beam_search:428) INFO: decoder input length: 119 +2024-01-17 01:51:55,246 (beam_search:429) INFO: max output length: 119 +2024-01-17 01:51:55,246 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:55,491 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:55,491 (beam_search:476) INFO: -28.45 * 1.0 = -28.45 for ctc +2024-01-17 01:51:55,491 (beam_search:479) INFO: total log probability: -28.45 +2024-01-17 01:51:55,491 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:51:55,491 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:55,491 (beam_search:483) INFO: best hypo: NWOTOUIERDENWATIONENSDROCKSTEIGENUNDTATZUESTASSESTEMIEENGEFÜRTWON + +2024-01-17 01:51:55,493 (asr_inference:494) INFO: speech length: 111677 +2024-01-17 01:51:55,505 (beam_search:428) INFO: decoder input length: 172 +2024-01-17 01:51:55,505 (beam_search:429) INFO: max output length: 172 +2024-01-17 01:51:55,505 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:55,973 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:55,973 (beam_search:476) INFO: -34.27 * 1.0 = -34.27 for ctc +2024-01-17 01:51:55,973 (beam_search:479) INFO: total log probability: -34.27 +2024-01-17 01:51:55,973 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:51:55,973 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:55,973 (beam_search:483) INFO: best hypo: NETGEWATEERMITENSETZSNDIESCHEUSLICHETEUFLSCHERAFENFRATZSEDIEBEDESMETCHENSCHLTERSCIELTEN + +2024-01-17 01:51:55,975 (asr_inference:494) INFO: speech length: 119037 +2024-01-17 01:51:55,987 (beam_search:428) INFO: decoder input length: 183 +2024-01-17 01:51:55,987 (beam_search:429) INFO: max output length: 183 +2024-01-17 01:51:55,987 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:56,472 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:56,472 (beam_search:476) INFO: -39.25 * 1.0 = -39.25 for ctc +2024-01-17 01:51:56,472 (beam_search:479) INFO: total log probability: -39.25 +2024-01-17 01:51:56,472 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-17 01:51:56,472 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:56,472 (beam_search:483) INFO: best hypo: TTERARDEWERTNIKTEDASGÖRDEINEGEWISSENWRTSCHAUFBERNHATWRTSCHOFISTEETWASFARGUNSENTNN + +2024-01-17 01:51:56,474 (asr_inference:494) INFO: speech length: 70077 +2024-01-17 01:51:56,484 (beam_search:428) INFO: decoder input length: 107 +2024-01-17 01:51:56,484 (beam_search:429) INFO: max output length: 107 +2024-01-17 01:51:56,484 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:56,673 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:56,673 (beam_search:476) INFO: -21.65 * 1.0 = -21.65 for ctc +2024-01-17 01:51:56,673 (beam_search:479) INFO: total log probability: -21.65 +2024-01-17 01:51:56,673 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:51:56,673 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:56,673 (beam_search:483) INFO: best hypo: WOLTERINWAHEIDIELÖRSENDTÖRTENNUNDTKNDERSCHELISENNN + +2024-01-17 01:51:56,674 (asr_inference:494) INFO: speech length: 140317 +2024-01-17 01:51:56,689 (beam_search:428) INFO: decoder input length: 217 +2024-01-17 01:51:56,689 (beam_search:429) INFO: max output length: 217 +2024-01-17 01:51:56,689 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:57,396 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:57,396 (beam_search:476) INFO: -35.82 * 1.0 = -35.82 for ctc +2024-01-17 01:51:57,396 (beam_search:479) INFO: total log probability: -35.82 +2024-01-17 01:51:57,396 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:51:57,396 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:57,397 (beam_search:483) INFO: best hypo: ATSEÄTDISERESPEKTVOLWUOBEIERNUREINIGESELDENVERSCHLUKTEWASSIMBEIDENBELEBTENLANGENWÖRTEANDESFTANFORKAN + +2024-01-17 01:51:57,399 (asr_inference:494) INFO: speech length: 98077 +2024-01-17 01:51:57,410 (beam_search:428) INFO: decoder input length: 151 +2024-01-17 01:51:57,410 (beam_search:429) INFO: max output length: 151 +2024-01-17 01:51:57,410 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:57,750 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:57,750 (beam_search:476) INFO: -26.73 * 1.0 = -26.73 for ctc +2024-01-17 01:51:57,750 (beam_search:479) INFO: total log probability: -26.73 +2024-01-17 01:51:57,750 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:51:57,750 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:57,750 (beam_search:483) INFO: best hypo: DLORTFONDLELERORUVERTNICHZSENDBEHRENDESSENBINICHGEWISVERSETZTEERT + +2024-01-17 01:51:57,751 (asr_inference:494) INFO: speech length: 92157 +2024-01-17 01:51:57,762 (beam_search:428) INFO: decoder input length: 141 +2024-01-17 01:51:57,762 (beam_search:429) INFO: max output length: 141 +2024-01-17 01:51:57,762 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:58,052 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:58,052 (beam_search:476) INFO: -17.20 * 1.0 = -17.20 for ctc +2024-01-17 01:51:58,052 (beam_search:479) INFO: total log probability: -17.20 +2024-01-17 01:51:58,052 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:51:58,052 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:58,052 (beam_search:483) INFO: best hypo: KAMGLEICHFALSINSSCHLAFTZIMMARAUFEINENNAGELINDERNEHRTESBETESN + +2024-01-17 01:51:58,053 (asr_inference:494) INFO: speech length: 122943 +2024-01-17 01:51:58,066 (beam_search:428) INFO: decoder input length: 190 +2024-01-17 01:51:58,066 (beam_search:429) INFO: max output length: 190 +2024-01-17 01:51:58,066 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:58,694 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:58,694 (beam_search:476) INFO: -51.51 * 1.0 = -51.51 for ctc +2024-01-17 01:51:58,694 (beam_search:479) INFO: total log probability: -51.51 +2024-01-17 01:51:58,694 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:51:58,694 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:58,694 (beam_search:483) INFO: best hypo: DASESDISCHANGSDIENESARKRISTEGDIESCHANGSVÜRINZTERNATZIONALEREGENBISIEINIMPRONSIEBPLIENDERSEDJALEMAKPOTAFOLIENTIERN + +2024-01-17 01:51:58,696 (asr_inference:494) INFO: speech length: 96637 +2024-01-17 01:51:58,707 (beam_search:428) INFO: decoder input length: 148 +2024-01-17 01:51:58,707 (beam_search:429) INFO: max output length: 148 +2024-01-17 01:51:58,707 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:59,049 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:59,049 (beam_search:476) INFO: -20.51 * 1.0 = -20.51 for ctc +2024-01-17 01:51:59,049 (beam_search:479) INFO: total log probability: -20.51 +2024-01-17 01:51:59,049 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:51:59,049 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:59,050 (beam_search:483) INFO: best hypo: ANFANGSFIELDEREGENSCHRAGUNDPEITSTEERSIEEINEDANDIENDERESEITEDESWAGENS + +2024-01-17 01:51:59,051 (asr_inference:494) INFO: speech length: 104317 +2024-01-17 01:51:59,063 (beam_search:428) INFO: decoder input length: 160 +2024-01-17 01:51:59,063 (beam_search:429) INFO: max output length: 160 +2024-01-17 01:51:59,063 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:59,436 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:59,437 (beam_search:476) INFO: -24.67 * 1.0 = -24.67 for ctc +2024-01-17 01:51:59,437 (beam_search:479) INFO: total log probability: -24.67 +2024-01-17 01:51:59,437 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:51:59,437 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:59,437 (beam_search:483) INFO: best hypo: MFASTLEICHENIGENBERMESSONGERESWERTESAUFTZOGEBEMSICHENTSCLOSSENHATENN + +2024-01-17 01:51:59,438 (asr_inference:494) INFO: speech length: 74689 +2024-01-17 01:51:59,449 (beam_search:428) INFO: decoder input length: 114 +2024-01-17 01:51:59,449 (beam_search:429) INFO: max output length: 114 +2024-01-17 01:51:59,449 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:59,688 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:59,688 (beam_search:476) INFO: -22.66 * 1.0 = -22.66 for ctc +2024-01-17 01:51:59,688 (beam_search:479) INFO: total log probability: -22.66 +2024-01-17 01:51:59,688 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:51:59,688 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:59,689 (beam_search:483) INFO: best hypo: SEISTBDIEFRARGERMENCHLICHENARBETNDIEFRAGEWASKANTECHNISCGELÖSTWERDENDA + +2024-01-17 01:51:59,690 (asr_inference:494) INFO: speech length: 86237 +2024-01-17 01:51:59,700 (beam_search:428) INFO: decoder input length: 132 +2024-01-17 01:51:59,700 (beam_search:429) INFO: max output length: 132 +2024-01-17 01:51:59,700 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:51:59,990 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:51:59,991 (beam_search:476) INFO: -28.89 * 1.0 = -28.89 for ctc +2024-01-17 01:51:59,991 (beam_search:479) INFO: total log probability: -28.89 +2024-01-17 01:51:59,991 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:51:59,991 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:51:59,991 (beam_search:483) INFO: best hypo: NUTISEARFAHRIEWAUFIEREDEMESIGBNOTZSTENWASSARSTELLENDISEROTANGEWIESENNN + +2024-01-17 01:51:59,992 (asr_inference:494) INFO: speech length: 119229 +2024-01-17 01:52:00,005 (beam_search:428) INFO: decoder input length: 184 +2024-01-17 01:52:00,005 (beam_search:429) INFO: max output length: 184 +2024-01-17 01:52:00,005 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:00,524 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:00,525 (beam_search:476) INFO: -20.20 * 1.0 = -20.20 for ctc +2024-01-17 01:52:00,525 (beam_search:479) INFO: total log probability: -20.20 +2024-01-17 01:52:00,525 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:52:00,525 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:00,525 (beam_search:483) INFO: best hypo: DIEBEIDENMISTENHIEOBENAUFDEMGEBPFELGESTANDTENHABENUNDERSPRACHDIEALTENWAURTEVORSECHIN + +2024-01-17 01:52:00,527 (asr_inference:494) INFO: speech length: 119197 +2024-01-17 01:52:00,539 (beam_search:428) INFO: decoder input length: 184 +2024-01-17 01:52:00,539 (beam_search:429) INFO: max output length: 184 +2024-01-17 01:52:00,539 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:00,946 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:00,946 (beam_search:476) INFO: -25.27 * 1.0 = -25.27 for ctc +2024-01-17 01:52:00,946 (beam_search:479) INFO: total log probability: -25.27 +2024-01-17 01:52:00,946 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:52:00,946 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:00,947 (beam_search:483) INFO: best hypo: ENTLICHBIKTESEDRIKGAUFWEISSNJUUIGALESVONDENARMENLEUTENFRAKTEER + +2024-01-17 01:52:00,948 (asr_inference:494) INFO: speech length: 103174 +2024-01-17 01:52:00,960 (beam_search:428) INFO: decoder input length: 159 +2024-01-17 01:52:00,960 (beam_search:429) INFO: max output length: 159 +2024-01-17 01:52:00,960 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:01,408 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:01,408 (beam_search:476) INFO: -32.02 * 1.0 = -32.02 for ctc +2024-01-17 01:52:01,408 (beam_search:479) INFO: total log probability: -32.02 +2024-01-17 01:52:01,408 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:52:01,408 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:01,408 (beam_search:483) INFO: best hypo: SEUDEINEWINDRBARETZUSAMARBEIZWSCHENBUNDUNDLÄNDERNINDIESENRAGENGIBITSESENTRESANDENPROJEGTENU + +2024-01-17 01:52:01,410 (asr_inference:494) INFO: speech length: 141597 +2024-01-17 01:52:01,425 (beam_search:428) INFO: decoder input length: 219 +2024-01-17 01:52:01,425 (beam_search:429) INFO: max output length: 219 +2024-01-17 01:52:01,425 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:02,158 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:02,158 (beam_search:476) INFO: -45.38 * 1.0 = -45.38 for ctc +2024-01-17 01:52:02,158 (beam_search:479) INFO: total log probability: -45.38 +2024-01-17 01:52:02,158 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:52:02,158 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:02,159 (beam_search:483) INFO: best hypo: DKASBARFERHARTEANGENMRTZELTENSEIMPLATZSSEINGLIEDERJARSEINENAUGENBANGIEVERSTEINRTELETUMZWEITENMALIENIKTEN + +2024-01-17 01:52:02,160 (asr_inference:494) INFO: speech length: 125597 +2024-01-17 01:52:02,173 (beam_search:428) INFO: decoder input length: 194 +2024-01-17 01:52:02,173 (beam_search:429) INFO: max output length: 194 +2024-01-17 01:52:02,173 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:02,721 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:02,721 (beam_search:476) INFO: -25.67 * 1.0 = -25.67 for ctc +2024-01-17 01:52:02,721 (beam_search:479) INFO: total log probability: -25.67 +2024-01-17 01:52:02,721 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:52:02,721 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:02,722 (beam_search:483) INFO: best hypo: EINIGEZEITDANACHFRAKTEERMICHOPICHGLAUBERDASDEREISGANGDENSCLITENDESANDERENZERSTÖRTABE + +2024-01-17 01:52:02,723 (asr_inference:494) INFO: speech length: 105216 +2024-01-17 01:52:02,735 (beam_search:428) INFO: decoder input length: 162 +2024-01-17 01:52:02,735 (beam_search:429) INFO: max output length: 162 +2024-01-17 01:52:02,735 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:02,989 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:02,990 (beam_search:476) INFO: -24.03 * 1.0 = -24.03 for ctc +2024-01-17 01:52:02,990 (beam_search:479) INFO: total log probability: -24.03 +2024-01-17 01:52:02,990 (beam_search:480) INFO: normalized log probability: -0.46 +2024-01-17 01:52:02,990 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:02,990 (beam_search:483) INFO: best hypo: AENENBLUSSENICHTGENEINERSCHORCGSTAREDELVALIN + +2024-01-17 01:52:02,991 (asr_inference:494) INFO: speech length: 50304 +2024-01-17 01:52:03,000 (beam_search:428) INFO: decoder input length: 76 +2024-01-17 01:52:03,000 (beam_search:429) INFO: max output length: 76 +2024-01-17 01:52:03,000 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:03,046 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:03,046 (beam_search:476) INFO: -10.32 * 1.0 = -10.32 for ctc +2024-01-17 01:52:03,046 (beam_search:479) INFO: total log probability: -10.32 +2024-01-17 01:52:03,046 (beam_search:480) INFO: normalized log probability: -0.57 +2024-01-17 01:52:03,046 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:03,046 (beam_search:483) INFO: best hypo: JILSCKOMIRSCHEN + +2024-01-17 01:52:03,047 (asr_inference:494) INFO: speech length: 77184 +2024-01-17 01:52:03,057 (beam_search:428) INFO: decoder input length: 118 +2024-01-17 01:52:03,057 (beam_search:429) INFO: max output length: 118 +2024-01-17 01:52:03,057 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:03,250 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:03,250 (beam_search:476) INFO: -20.17 * 1.0 = -20.17 for ctc +2024-01-17 01:52:03,250 (beam_search:479) INFO: total log probability: -20.17 +2024-01-17 01:52:03,250 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:52:03,250 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:03,251 (beam_search:483) INFO: best hypo: SDEMBEIEARBEITEDEEISAUSHRELSKRAFTAFEINENFAHME + +2024-01-17 01:52:03,252 (asr_inference:494) INFO: speech length: 102528 +2024-01-17 01:52:03,264 (beam_search:428) INFO: decoder input length: 158 +2024-01-17 01:52:03,264 (beam_search:429) INFO: max output length: 158 +2024-01-17 01:52:03,264 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:03,625 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:03,625 (beam_search:476) INFO: -30.50 * 1.0 = -30.50 for ctc +2024-01-17 01:52:03,625 (beam_search:479) INFO: total log probability: -30.50 +2024-01-17 01:52:03,625 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:52:03,625 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:03,625 (beam_search:483) INFO: best hypo: EINTERITTOHEIKOSSESOCHOPERWITDTNICHTITANMITARMESIKEINERENOHOPERREICT + +2024-01-17 01:52:03,626 (asr_inference:494) INFO: speech length: 92928 +2024-01-17 01:52:03,637 (beam_search:428) INFO: decoder input length: 143 +2024-01-17 01:52:03,637 (beam_search:429) INFO: max output length: 143 +2024-01-17 01:52:03,638 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:03,866 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:03,866 (beam_search:476) INFO: -19.78 * 1.0 = -19.78 for ctc +2024-01-17 01:52:03,866 (beam_search:479) INFO: total log probability: -19.78 +2024-01-17 01:52:03,866 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:52:03,866 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:03,867 (beam_search:483) INFO: best hypo: IERSOUNKAENDRHKÖNZSLICHERBEFROCHTUNGKZURWERT + +2024-01-17 01:52:03,868 (asr_inference:494) INFO: speech length: 94848 +2024-01-17 01:52:03,879 (beam_search:428) INFO: decoder input length: 146 +2024-01-17 01:52:03,879 (beam_search:429) INFO: max output length: 146 +2024-01-17 01:52:03,879 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:04,163 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:04,163 (beam_search:476) INFO: -22.23 * 1.0 = -22.23 for ctc +2024-01-17 01:52:04,163 (beam_search:479) INFO: total log probability: -22.23 +2024-01-17 01:52:04,163 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:52:04,163 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:04,164 (beam_search:483) INFO: best hypo: DIENACHTARDIEFNEFLERFLIENVONMITERJULIEBISMITEAOFTOBER + +2024-01-17 01:52:04,165 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:52:04,173 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:52:04,173 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:52:04,173 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:04,198 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:04,198 (beam_search:476) INFO: -13.60 * 1.0 = -13.60 for ctc +2024-01-17 01:52:04,198 (beam_search:479) INFO: total log probability: -13.60 +2024-01-17 01:52:04,198 (beam_search:480) INFO: normalized log probability: -0.97 +2024-01-17 01:52:04,198 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:04,198 (beam_search:483) INFO: best hypo: DEREARHAAT + +2024-01-17 01:52:04,199 (asr_inference:494) INFO: speech length: 41472 +2024-01-17 01:52:04,207 (beam_search:428) INFO: decoder input length: 62 +2024-01-17 01:52:04,207 (beam_search:429) INFO: max output length: 62 +2024-01-17 01:52:04,207 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:04,232 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:04,232 (beam_search:476) INFO: -6.77 * 1.0 = -6.77 for ctc +2024-01-17 01:52:04,232 (beam_search:479) INFO: total log probability: -6.77 +2024-01-17 01:52:04,232 (beam_search:480) INFO: normalized log probability: -0.62 +2024-01-17 01:52:04,232 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:04,232 (beam_search:483) INFO: best hypo: ENDHERN + +2024-01-17 01:52:04,233 (asr_inference:494) INFO: speech length: 111744 +2024-01-17 01:52:04,245 (beam_search:428) INFO: decoder input length: 172 +2024-01-17 01:52:04,245 (beam_search:429) INFO: max output length: 172 +2024-01-17 01:52:04,245 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:04,657 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:04,657 (beam_search:476) INFO: -18.56 * 1.0 = -18.56 for ctc +2024-01-17 01:52:04,657 (beam_search:479) INFO: total log probability: -18.56 +2024-01-17 01:52:04,657 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:52:04,657 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:04,657 (beam_search:483) INFO: best hypo: NUTZERKNENIHRELERSEZEICENONEINABSPEICHERVERWALTENUNDBETANENNOTZANTEILEN + +2024-01-17 01:52:04,659 (asr_inference:494) INFO: speech length: 57984 +2024-01-17 01:52:04,668 (beam_search:428) INFO: decoder input length: 88 +2024-01-17 01:52:04,668 (beam_search:429) INFO: max output length: 88 +2024-01-17 01:52:04,668 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:04,741 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:04,741 (beam_search:476) INFO: -10.40 * 1.0 = -10.40 for ctc +2024-01-17 01:52:04,741 (beam_search:479) INFO: total log probability: -10.40 +2024-01-17 01:52:04,741 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-17 01:52:04,741 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:04,741 (beam_search:483) INFO: best hypo: DIEDUMBOSCOKATIERALE + +2024-01-17 01:52:04,742 (asr_inference:494) INFO: speech length: 122880 +2024-01-17 01:52:04,755 (beam_search:428) INFO: decoder input length: 189 +2024-01-17 01:52:04,755 (beam_search:429) INFO: max output length: 189 +2024-01-17 01:52:04,755 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:05,179 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:05,180 (beam_search:476) INFO: -44.31 * 1.0 = -44.31 for ctc +2024-01-17 01:52:05,180 (beam_search:479) INFO: total log probability: -44.31 +2024-01-17 01:52:05,180 (beam_search:480) INFO: normalized log probability: -0.58 +2024-01-17 01:52:05,180 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:05,180 (beam_search:483) INFO: best hypo: SAULBASSZELZUDENNROTISTENDIESEIDAREUMEMFÜMECEÜEUELESERENZELLEN + +2024-01-17 01:52:05,181 (asr_inference:494) INFO: speech length: 80640 +2024-01-17 01:52:05,192 (beam_search:428) INFO: decoder input length: 123 +2024-01-17 01:52:05,192 (beam_search:429) INFO: max output length: 123 +2024-01-17 01:52:05,192 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:05,418 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:05,418 (beam_search:476) INFO: -20.63 * 1.0 = -20.63 for ctc +2024-01-17 01:52:05,418 (beam_search:479) INFO: total log probability: -20.63 +2024-01-17 01:52:05,418 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:52:05,418 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:05,418 (beam_search:483) INFO: best hypo: INKÜNÜWURSERBENEMENWELENBEUGKEINESIELBWONDEREICHEN + +2024-01-17 01:52:05,420 (asr_inference:494) INFO: speech length: 126720 +2024-01-17 01:52:05,433 (beam_search:428) INFO: decoder input length: 195 +2024-01-17 01:52:05,433 (beam_search:429) INFO: max output length: 195 +2024-01-17 01:52:05,433 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:05,982 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:05,982 (beam_search:476) INFO: -30.56 * 1.0 = -30.56 for ctc +2024-01-17 01:52:05,982 (beam_search:479) INFO: total log probability: -30.56 +2024-01-17 01:52:05,982 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:52:05,982 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:05,983 (beam_search:483) INFO: best hypo: EITEREWICHTIGEINDESTRIEZWEIGESEDIENICRMICHANIGGALWANOPLASTIGMITEILBAUUNTIEHUTZVERARBEITOUNGA + +2024-01-17 01:52:05,984 (asr_inference:494) INFO: speech length: 101760 +2024-01-17 01:52:05,996 (beam_search:428) INFO: decoder input length: 156 +2024-01-17 01:52:05,996 (beam_search:429) INFO: max output length: 156 +2024-01-17 01:52:05,996 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:06,345 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:06,345 (beam_search:476) INFO: -20.31 * 1.0 = -20.31 for ctc +2024-01-17 01:52:06,345 (beam_search:479) INFO: total log probability: -20.31 +2024-01-17 01:52:06,345 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:52:06,345 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:06,345 (beam_search:483) INFO: best hypo: ÜBERDENAUTORSNICHTBEKANDVERMUTLICSTMTEHRAUSEMDEUTSHENSPRACHGBIEDT + +2024-01-17 01:52:06,347 (asr_inference:494) INFO: speech length: 74112 +2024-01-17 01:52:06,357 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 01:52:06,357 (beam_search:429) INFO: max output length: 113 +2024-01-17 01:52:06,357 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:06,462 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:06,462 (beam_search:476) INFO: -15.74 * 1.0 = -15.74 for ctc +2024-01-17 01:52:06,463 (beam_search:479) INFO: total log probability: -15.74 +2024-01-17 01:52:06,463 (beam_search:480) INFO: normalized log probability: -0.54 +2024-01-17 01:52:06,463 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:06,463 (beam_search:483) INFO: best hypo: NSTUERISMIDENENTOPLIPATE + +2024-01-17 01:52:06,464 (asr_inference:494) INFO: speech length: 66432 +2024-01-17 01:52:06,474 (beam_search:428) INFO: decoder input length: 101 +2024-01-17 01:52:06,474 (beam_search:429) INFO: max output length: 101 +2024-01-17 01:52:06,474 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:06,606 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:06,606 (beam_search:476) INFO: -17.88 * 1.0 = -17.88 for ctc +2024-01-17 01:52:06,606 (beam_search:479) INFO: total log probability: -17.88 +2024-01-17 01:52:06,606 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:52:06,606 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:06,606 (beam_search:483) INFO: best hypo: DIEHARMEIEREBLEMEROSSISCHIGHTOUCHT + +2024-01-17 01:52:06,608 (asr_inference:494) INFO: speech length: 87552 +2024-01-17 01:52:06,619 (beam_search:428) INFO: decoder input length: 134 +2024-01-17 01:52:06,619 (beam_search:429) INFO: max output length: 134 +2024-01-17 01:52:06,619 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:06,850 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:06,851 (beam_search:476) INFO: -35.68 * 1.0 = -35.68 for ctc +2024-01-17 01:52:06,851 (beam_search:479) INFO: total log probability: -35.68 +2024-01-17 01:52:06,851 (beam_search:480) INFO: normalized log probability: -0.63 +2024-01-17 01:52:06,851 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:06,851 (beam_search:483) INFO: best hypo: WICHLIENEMANACHUAREMEAUKTUREEIERTETDNTHMACHBERNN + +2024-01-17 01:52:06,852 (asr_inference:494) INFO: speech length: 79104 +2024-01-17 01:52:06,862 (beam_search:428) INFO: decoder input length: 121 +2024-01-17 01:52:06,863 (beam_search:429) INFO: max output length: 121 +2024-01-17 01:52:06,863 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:07,036 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:07,036 (beam_search:476) INFO: -22.87 * 1.0 = -22.87 for ctc +2024-01-17 01:52:07,036 (beam_search:479) INFO: total log probability: -22.87 +2024-01-17 01:52:07,036 (beam_search:480) INFO: normalized log probability: -0.50 +2024-01-17 01:52:07,036 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:07,036 (beam_search:483) INFO: best hypo: ORDEALIDEGESSYKLDEANLANAUNISCHEWABEN + +2024-01-17 01:52:07,037 (asr_inference:494) INFO: speech length: 110016 +2024-01-17 01:52:07,049 (beam_search:428) INFO: decoder input length: 169 +2024-01-17 01:52:07,049 (beam_search:429) INFO: max output length: 169 +2024-01-17 01:52:07,049 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:07,284 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:07,284 (beam_search:476) INFO: -15.31 * 1.0 = -15.31 for ctc +2024-01-17 01:52:07,284 (beam_search:479) INFO: total log probability: -15.31 +2024-01-17 01:52:07,284 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:52:07,284 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:07,284 (beam_search:483) INFO: best hypo: EIEHEIDENNAMEINWINCHENWORERAUCGSTAT + +2024-01-17 01:52:07,286 (asr_inference:494) INFO: speech length: 144000 +2024-01-17 01:52:07,301 (beam_search:428) INFO: decoder input length: 222 +2024-01-17 01:52:07,301 (beam_search:429) INFO: max output length: 222 +2024-01-17 01:52:07,301 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:07,867 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:07,867 (beam_search:476) INFO: -28.34 * 1.0 = -28.34 for ctc +2024-01-17 01:52:07,867 (beam_search:479) INFO: total log probability: -28.34 +2024-01-17 01:52:07,867 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:52:07,867 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:07,867 (beam_search:483) INFO: best hypo: INERUNGUNDEUSSRERNATICGGENENALSGETRENTITAILEEINESNATIGAUCHGEMEINSAMVOROMMEN + +2024-01-17 01:52:07,869 (asr_inference:494) INFO: speech length: 73728 +2024-01-17 01:52:07,879 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 01:52:07,879 (beam_search:429) INFO: max output length: 113 +2024-01-17 01:52:07,879 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:08,040 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:08,040 (beam_search:476) INFO: -14.44 * 1.0 = -14.44 for ctc +2024-01-17 01:52:08,040 (beam_search:479) INFO: total log probability: -14.44 +2024-01-17 01:52:08,040 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:52:08,040 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:08,040 (beam_search:483) INFO: best hypo: DABEIEBELEGTERRDIEPLÄTZSEVIERUNDTREIG + +2024-01-17 01:52:08,041 (asr_inference:494) INFO: speech length: 100224 +2024-01-17 01:52:08,052 (beam_search:428) INFO: decoder input length: 154 +2024-01-17 01:52:08,053 (beam_search:429) INFO: max output length: 154 +2024-01-17 01:52:08,053 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:08,300 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:08,300 (beam_search:476) INFO: -23.95 * 1.0 = -23.95 for ctc +2024-01-17 01:52:08,300 (beam_search:479) INFO: total log probability: -23.95 +2024-01-17 01:52:08,300 (beam_search:480) INFO: normalized log probability: -0.45 +2024-01-17 01:52:08,300 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:08,300 (beam_search:483) INFO: best hypo: KINDRABIEISSIETOCHTERZWEIARROFESENERLARTENZA + +2024-01-17 01:52:08,302 (asr_inference:494) INFO: speech length: 65664 +2024-01-17 01:52:08,311 (beam_search:428) INFO: decoder input length: 100 +2024-01-17 01:52:08,311 (beam_search:429) INFO: max output length: 100 +2024-01-17 01:52:08,311 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:08,443 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:08,443 (beam_search:476) INFO: -18.33 * 1.0 = -18.33 for ctc +2024-01-17 01:52:08,443 (beam_search:479) INFO: total log probability: -18.33 +2024-01-17 01:52:08,443 (beam_search:480) INFO: normalized log probability: -0.43 +2024-01-17 01:52:08,443 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:08,443 (beam_search:483) INFO: best hypo: ISKLAUBERASFÜRTNISTINIRISTIERISTUNG + +2024-01-17 01:52:08,444 (asr_inference:494) INFO: speech length: 62976 +2024-01-17 01:52:08,454 (beam_search:428) INFO: decoder input length: 96 +2024-01-17 01:52:08,454 (beam_search:429) INFO: max output length: 96 +2024-01-17 01:52:08,454 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:08,579 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:08,579 (beam_search:476) INFO: -16.17 * 1.0 = -16.17 for ctc +2024-01-17 01:52:08,579 (beam_search:479) INFO: total log probability: -16.17 +2024-01-17 01:52:08,579 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:52:08,579 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:08,580 (beam_search:483) INFO: best hypo: DASSESEINERXSTRENSCHLECHTERICHSLIENIER + +2024-01-17 01:52:08,581 (asr_inference:494) INFO: speech length: 70656 +2024-01-17 01:52:08,591 (beam_search:428) INFO: decoder input length: 108 +2024-01-17 01:52:08,591 (beam_search:429) INFO: max output length: 108 +2024-01-17 01:52:08,591 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:08,724 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:08,724 (beam_search:476) INFO: -10.07 * 1.0 = -10.07 for ctc +2024-01-17 01:52:08,724 (beam_search:479) INFO: total log probability: -10.07 +2024-01-17 01:52:08,725 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:52:08,725 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:08,725 (beam_search:483) INFO: best hypo: HERLORCHENBLESTSEINHAGERESGESICHT + +2024-01-17 01:52:08,726 (asr_inference:494) INFO: speech length: 54912 +2024-01-17 01:52:08,735 (beam_search:428) INFO: decoder input length: 83 +2024-01-17 01:52:08,735 (beam_search:429) INFO: max output length: 83 +2024-01-17 01:52:08,735 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:08,804 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:08,804 (beam_search:476) INFO: -9.97 * 1.0 = -9.97 for ctc +2024-01-17 01:52:08,804 (beam_search:479) INFO: total log probability: -9.97 +2024-01-17 01:52:08,804 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:52:08,804 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:08,804 (beam_search:483) INFO: best hypo: MOKARENFINETOUNFER + +2024-01-17 01:52:08,805 (asr_inference:494) INFO: speech length: 65088 +2024-01-17 01:52:08,815 (beam_search:428) INFO: decoder input length: 99 +2024-01-17 01:52:08,815 (beam_search:429) INFO: max output length: 99 +2024-01-17 01:52:08,815 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:08,938 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:08,938 (beam_search:476) INFO: -9.73 * 1.0 = -9.73 for ctc +2024-01-17 01:52:08,938 (beam_search:479) INFO: total log probability: -9.73 +2024-01-17 01:52:08,938 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:52:08,938 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:08,939 (beam_search:483) INFO: best hypo: TINGEBOKABERHATDERDREIGESCHICSTET + +2024-01-17 01:52:08,940 (asr_inference:494) INFO: speech length: 93888 +2024-01-17 01:52:08,951 (beam_search:428) INFO: decoder input length: 144 +2024-01-17 01:52:08,951 (beam_search:429) INFO: max output length: 144 +2024-01-17 01:52:08,951 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:09,230 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:09,231 (beam_search:476) INFO: -26.53 * 1.0 = -26.53 for ctc +2024-01-17 01:52:09,231 (beam_search:479) INFO: total log probability: -26.53 +2024-01-17 01:52:09,231 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:52:09,231 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:09,231 (beam_search:483) INFO: best hypo: LCESKOMTWICLISTSAANMDASOLCHEDADTENUDIESEREBENERFASTWERDEN + +2024-01-17 01:52:09,232 (asr_inference:494) INFO: speech length: 76032 +2024-01-17 01:52:09,242 (beam_search:428) INFO: decoder input length: 116 +2024-01-17 01:52:09,242 (beam_search:429) INFO: max output length: 116 +2024-01-17 01:52:09,242 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:09,415 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:09,415 (beam_search:476) INFO: -11.56 * 1.0 = -11.56 for ctc +2024-01-17 01:52:09,415 (beam_search:479) INFO: total log probability: -11.56 +2024-01-17 01:52:09,415 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:52:09,415 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:09,416 (beam_search:483) INFO: best hypo: STRAMINHENGEGENEGITEINERMUNICHESPOSIEREN + +2024-01-17 01:52:09,417 (asr_inference:494) INFO: speech length: 57600 +2024-01-17 01:52:09,426 (beam_search:428) INFO: decoder input length: 87 +2024-01-17 01:52:09,426 (beam_search:429) INFO: max output length: 87 +2024-01-17 01:52:09,426 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:09,536 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:09,536 (beam_search:476) INFO: -13.53 * 1.0 = -13.53 for ctc +2024-01-17 01:52:09,536 (beam_search:479) INFO: total log probability: -13.53 +2024-01-17 01:52:09,536 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:52:09,536 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:09,537 (beam_search:483) INFO: best hypo: BENICHUMKAUFEIERHPOTEGBERECHTIGT + +2024-01-17 01:52:09,538 (asr_inference:494) INFO: speech length: 106368 +2024-01-17 01:52:09,550 (beam_search:428) INFO: decoder input length: 164 +2024-01-17 01:52:09,550 (beam_search:429) INFO: max output length: 164 +2024-01-17 01:52:09,550 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:09,773 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:09,774 (beam_search:476) INFO: -27.67 * 1.0 = -27.67 for ctc +2024-01-17 01:52:09,774 (beam_search:479) INFO: total log probability: -27.67 +2024-01-17 01:52:09,774 (beam_search:480) INFO: normalized log probability: -0.63 +2024-01-17 01:52:09,774 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:09,774 (beam_search:483) INFO: best hypo: TUNEEUNLENSSENERSHENFVENDENDENBMNMIE + +2024-01-17 01:52:09,775 (asr_inference:494) INFO: speech length: 62208 +2024-01-17 01:52:09,785 (beam_search:428) INFO: decoder input length: 95 +2024-01-17 01:52:09,785 (beam_search:429) INFO: max output length: 95 +2024-01-17 01:52:09,785 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:09,874 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:09,874 (beam_search:476) INFO: -21.57 * 1.0 = -21.57 for ctc +2024-01-17 01:52:09,874 (beam_search:479) INFO: total log probability: -21.57 +2024-01-17 01:52:09,874 (beam_search:480) INFO: normalized log probability: -0.74 +2024-01-17 01:52:09,874 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:09,874 (beam_search:483) INFO: best hypo: HKÖUENDERENSNDEREEISLNDUN + +2024-01-17 01:52:09,875 (asr_inference:494) INFO: speech length: 142848 +2024-01-17 01:52:09,890 (beam_search:428) INFO: decoder input length: 221 +2024-01-17 01:52:09,890 (beam_search:429) INFO: max output length: 221 +2024-01-17 01:52:09,890 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:10,551 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:10,551 (beam_search:476) INFO: -38.81 * 1.0 = -38.81 for ctc +2024-01-17 01:52:10,551 (beam_search:479) INFO: total log probability: -38.81 +2024-01-17 01:52:10,551 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:52:10,551 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:10,551 (beam_search:483) INFO: best hypo: ONDIEPOFISINLLINTESTÜTZUNDAMASSEARTDIEENABTEILUNGWAENDISEBAGENDERKONKROWENSUNDOCHUNDOLEGEN + +2024-01-17 01:52:10,553 (asr_inference:494) INFO: speech length: 90240 +2024-01-17 01:52:10,564 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 01:52:10,564 (beam_search:429) INFO: max output length: 138 +2024-01-17 01:52:10,564 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:10,815 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:10,815 (beam_search:476) INFO: -25.03 * 1.0 = -25.03 for ctc +2024-01-17 01:52:10,815 (beam_search:479) INFO: total log probability: -25.03 +2024-01-17 01:52:10,815 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-17 01:52:10,815 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:10,815 (beam_search:483) INFO: best hypo: SIEDIENTEUNESTASUNDTEOKMFTVÜRBELGISCHEBISATZUSTRUPEN + +2024-01-17 01:52:10,817 (asr_inference:494) INFO: speech length: 89862 +2024-01-17 01:52:10,827 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 01:52:10,827 (beam_search:429) INFO: max output length: 138 +2024-01-17 01:52:10,827 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:11,016 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:11,016 (beam_search:476) INFO: -15.52 * 1.0 = -15.52 for ctc +2024-01-17 01:52:11,016 (beam_search:479) INFO: total log probability: -15.52 +2024-01-17 01:52:11,016 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:52:11,016 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:11,016 (beam_search:483) INFO: best hypo: DAMISSNWISCSPLENENMEINTEDERZHAHNAHRZT + +2024-01-17 01:52:11,018 (asr_inference:494) INFO: speech length: 158825 +2024-01-17 01:52:11,033 (beam_search:428) INFO: decoder input length: 246 +2024-01-17 01:52:11,033 (beam_search:429) INFO: max output length: 246 +2024-01-17 01:52:11,033 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:11,673 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:11,673 (beam_search:476) INFO: -34.14 * 1.0 = -34.14 for ctc +2024-01-17 01:52:11,673 (beam_search:479) INFO: total log probability: -34.14 +2024-01-17 01:52:11,673 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:52:11,673 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:11,674 (beam_search:483) INFO: best hypo: AUSDEMSPITEREIMNACHFORGETIEMNIMARKEDROEUOLSSOWIEEIMLIGEAKONKURRENDTENLNDENNEI + +2024-01-17 01:52:11,675 (asr_inference:494) INFO: speech length: 130752 +2024-01-17 01:52:11,689 (beam_search:428) INFO: decoder input length: 202 +2024-01-17 01:52:11,689 (beam_search:429) INFO: max output length: 202 +2024-01-17 01:52:11,689 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:12,166 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:12,166 (beam_search:476) INFO: -29.35 * 1.0 = -29.35 for ctc +2024-01-17 01:52:12,166 (beam_search:479) INFO: total log probability: -29.35 +2024-01-17 01:52:12,166 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:52:12,166 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:12,167 (beam_search:483) INFO: best hypo: IEAUCASENSTENDRANACHWATENGERFÜRTIEKUMSWALDASKONNDORKETGTERIOMNICHT + +2024-01-17 01:52:12,168 (asr_inference:494) INFO: speech length: 44352 +2024-01-17 01:52:12,176 (beam_search:428) INFO: decoder input length: 67 +2024-01-17 01:52:12,176 (beam_search:429) INFO: max output length: 67 +2024-01-17 01:52:12,176 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:12,229 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:12,229 (beam_search:476) INFO: -11.71 * 1.0 = -11.71 for ctc +2024-01-17 01:52:12,229 (beam_search:479) INFO: total log probability: -11.71 +2024-01-17 01:52:12,229 (beam_search:480) INFO: normalized log probability: -0.51 +2024-01-17 01:52:12,229 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:12,230 (beam_search:483) INFO: best hypo: SMIFUCHENTIKAGEOAUF + +2024-01-17 01:52:12,231 (asr_inference:494) INFO: speech length: 114048 +2024-01-17 01:52:12,243 (beam_search:428) INFO: decoder input length: 176 +2024-01-17 01:52:12,243 (beam_search:429) INFO: max output length: 176 +2024-01-17 01:52:12,243 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:12,356 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:12,357 (beam_search:476) INFO: -8.25 * 1.0 = -8.25 for ctc +2024-01-17 01:52:12,357 (beam_search:479) INFO: total log probability: -8.25 +2024-01-17 01:52:12,357 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-17 01:52:12,357 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:12,357 (beam_search:483) INFO: best hypo: WIERIEIEALEINEN + +2024-01-17 01:52:12,358 (asr_inference:494) INFO: speech length: 117504 +2024-01-17 01:52:12,370 (beam_search:428) INFO: decoder input length: 181 +2024-01-17 01:52:12,370 (beam_search:429) INFO: max output length: 181 +2024-01-17 01:52:12,370 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:12,704 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:12,704 (beam_search:476) INFO: -29.98 * 1.0 = -29.98 for ctc +2024-01-17 01:52:12,704 (beam_search:479) INFO: total log probability: -29.98 +2024-01-17 01:52:12,704 (beam_search:480) INFO: normalized log probability: -0.48 +2024-01-17 01:52:12,704 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:12,705 (beam_search:483) INFO: best hypo: DUMISGWERTWASBWEISWURTROCDSSDESIUNBISENSVOLSCHUNGEL + +2024-01-17 01:52:12,706 (asr_inference:494) INFO: speech length: 137088 +2024-01-17 01:52:12,720 (beam_search:428) INFO: decoder input length: 212 +2024-01-17 01:52:12,720 (beam_search:429) INFO: max output length: 212 +2024-01-17 01:52:12,720 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:13,162 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:13,162 (beam_search:476) INFO: -24.41 * 1.0 = -24.41 for ctc +2024-01-17 01:52:13,162 (beam_search:479) INFO: total log probability: -24.41 +2024-01-17 01:52:13,162 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:52:13,162 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:13,162 (beam_search:483) INFO: best hypo: HABTEMADERSCHAISTDIEREWANSCHVEREKÜBICHISTREICHERENTERFREINDEN + +2024-01-17 01:52:13,164 (asr_inference:494) INFO: speech length: 92544 +2024-01-17 01:52:13,175 (beam_search:428) INFO: decoder input length: 142 +2024-01-17 01:52:13,175 (beam_search:429) INFO: max output length: 142 +2024-01-17 01:52:13,175 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:13,390 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:13,390 (beam_search:476) INFO: -14.51 * 1.0 = -14.51 for ctc +2024-01-17 01:52:13,390 (beam_search:479) INFO: total log probability: -14.51 +2024-01-17 01:52:13,390 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:52:13,390 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:13,391 (beam_search:483) INFO: best hypo: GLEICHZEITIGWUODENSPOTWETENTALWEISEVERBULEN + +2024-01-17 01:52:13,392 (asr_inference:494) INFO: speech length: 61056 +2024-01-17 01:52:13,401 (beam_search:428) INFO: decoder input length: 93 +2024-01-17 01:52:13,401 (beam_search:429) INFO: max output length: 93 +2024-01-17 01:52:13,401 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:13,428 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:13,429 (beam_search:476) INFO: -7.06 * 1.0 = -7.06 for ctc +2024-01-17 01:52:13,429 (beam_search:479) INFO: total log probability: -7.06 +2024-01-17 01:52:13,429 (beam_search:480) INFO: normalized log probability: -0.78 +2024-01-17 01:52:13,429 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:13,429 (beam_search:483) INFO: best hypo: SEEGEN + +2024-01-17 01:52:13,430 (asr_inference:494) INFO: speech length: 38016 +2024-01-17 01:52:13,437 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:52:13,437 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:52:13,437 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:13,456 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:13,456 (beam_search:476) INFO: -6.94 * 1.0 = -6.94 for ctc +2024-01-17 01:52:13,456 (beam_search:479) INFO: total log probability: -6.94 +2024-01-17 01:52:13,456 (beam_search:480) INFO: normalized log probability: -0.77 +2024-01-17 01:52:13,456 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:13,456 (beam_search:483) INFO: best hypo: TIABE + +2024-01-17 01:52:13,457 (asr_inference:494) INFO: speech length: 112128 +2024-01-17 01:52:13,469 (beam_search:428) INFO: decoder input length: 173 +2024-01-17 01:52:13,469 (beam_search:429) INFO: max output length: 173 +2024-01-17 01:52:13,469 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:13,862 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:13,863 (beam_search:476) INFO: -25.38 * 1.0 = -25.38 for ctc +2024-01-17 01:52:13,863 (beam_search:479) INFO: total log probability: -25.38 +2024-01-17 01:52:13,863 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:52:13,863 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:13,863 (beam_search:483) INFO: best hypo: ZUDEMFARSAHERENKLOSTALANGJAREDIEMTERDESNOWITZSENMEISTASUNPRIORA + +2024-01-17 01:52:13,864 (asr_inference:494) INFO: speech length: 67968 +2024-01-17 01:52:13,874 (beam_search:428) INFO: decoder input length: 104 +2024-01-17 01:52:13,874 (beam_search:429) INFO: max output length: 104 +2024-01-17 01:52:13,874 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:14,024 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:14,024 (beam_search:476) INFO: -11.43 * 1.0 = -11.43 for ctc +2024-01-17 01:52:14,024 (beam_search:479) INFO: total log probability: -11.43 +2024-01-17 01:52:14,024 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:52:14,024 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:14,025 (beam_search:483) INFO: best hypo: HEIDENHEIDENENSTMTEINERERTZTEVERMILIEAR + +2024-01-17 01:52:14,026 (asr_inference:494) INFO: speech length: 31872 +2024-01-17 01:52:14,033 (beam_search:428) INFO: decoder input length: 47 +2024-01-17 01:52:14,033 (beam_search:429) INFO: max output length: 47 +2024-01-17 01:52:14,034 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:14,061 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:14,061 (beam_search:476) INFO: -10.70 * 1.0 = -10.70 for ctc +2024-01-17 01:52:14,061 (beam_search:479) INFO: total log probability: -10.70 +2024-01-17 01:52:14,061 (beam_search:480) INFO: normalized log probability: -0.67 +2024-01-17 01:52:14,061 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:14,061 (beam_search:483) INFO: best hypo: ARESPZIMPTPENN + +2024-01-17 01:52:14,062 (asr_inference:494) INFO: speech length: 25344 +2024-01-17 01:52:14,069 (beam_search:428) INFO: decoder input length: 37 +2024-01-17 01:52:14,069 (beam_search:429) INFO: max output length: 37 +2024-01-17 01:52:14,069 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:14,086 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:14,087 (beam_search:476) INFO: -5.53 * 1.0 = -5.53 for ctc +2024-01-17 01:52:14,087 (beam_search:479) INFO: total log probability: -5.53 +2024-01-17 01:52:14,087 (beam_search:480) INFO: normalized log probability: -0.46 +2024-01-17 01:52:14,087 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:14,087 (beam_search:483) INFO: best hypo: ZWAIEUNGR + +2024-01-17 01:52:14,088 (asr_inference:494) INFO: speech length: 143424 +2024-01-17 01:52:14,102 (beam_search:428) INFO: decoder input length: 222 +2024-01-17 01:52:14,102 (beam_search:429) INFO: max output length: 222 +2024-01-17 01:52:14,102 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:14,471 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:14,471 (beam_search:476) INFO: -28.77 * 1.0 = -28.77 for ctc +2024-01-17 01:52:14,471 (beam_search:479) INFO: total log probability: -28.77 +2024-01-17 01:52:14,471 (beam_search:480) INFO: normalized log probability: -0.53 +2024-01-17 01:52:14,471 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:14,471 (beam_search:483) INFO: best hypo: TTTTTEBEMFALSENAUGNGANGIIEDEENTIKALTREINDEREFA + +2024-01-17 01:52:14,472 (asr_inference:494) INFO: speech length: 82368 +2024-01-17 01:52:14,483 (beam_search:428) INFO: decoder input length: 126 +2024-01-17 01:52:14,483 (beam_search:429) INFO: max output length: 126 +2024-01-17 01:52:14,483 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:14,737 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:14,737 (beam_search:476) INFO: -30.07 * 1.0 = -30.07 for ctc +2024-01-17 01:52:14,737 (beam_search:479) INFO: total log probability: -30.07 +2024-01-17 01:52:14,737 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-17 01:52:14,737 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:14,737 (beam_search:483) INFO: best hypo: DIESERSTDETARFEABSELWENDTENEINHERMSCHERSCHOLMITDRTKANDTESENOFEN + +2024-01-17 01:52:14,738 (asr_inference:494) INFO: speech length: 34944 +2024-01-17 01:52:14,746 (beam_search:428) INFO: decoder input length: 52 +2024-01-17 01:52:14,746 (beam_search:429) INFO: max output length: 52 +2024-01-17 01:52:14,746 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:14,784 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:14,784 (beam_search:476) INFO: -5.83 * 1.0 = -5.83 for ctc +2024-01-17 01:52:14,784 (beam_search:479) INFO: total log probability: -5.83 +2024-01-17 01:52:14,784 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:52:14,784 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:14,784 (beam_search:483) INFO: best hypo: ALSOESCHIORENICS + +2024-01-17 01:52:14,785 (asr_inference:494) INFO: speech length: 36864 +2024-01-17 01:52:14,793 (beam_search:428) INFO: decoder input length: 55 +2024-01-17 01:52:14,793 (beam_search:429) INFO: max output length: 55 +2024-01-17 01:52:14,793 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:14,844 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:14,844 (beam_search:476) INFO: -9.76 * 1.0 = -9.76 for ctc +2024-01-17 01:52:14,844 (beam_search:479) INFO: total log probability: -9.76 +2024-01-17 01:52:14,844 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:52:14,844 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:14,844 (beam_search:483) INFO: best hypo: WIEKONMAHNSISCHSCHÜTZE + +2024-01-17 01:52:14,846 (asr_inference:494) INFO: speech length: 92160 +2024-01-17 01:52:14,856 (beam_search:428) INFO: decoder input length: 141 +2024-01-17 01:52:14,856 (beam_search:429) INFO: max output length: 141 +2024-01-17 01:52:14,856 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:15,171 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:15,171 (beam_search:476) INFO: -27.62 * 1.0 = -27.62 for ctc +2024-01-17 01:52:15,171 (beam_search:479) INFO: total log probability: -27.62 +2024-01-17 01:52:15,171 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:52:15,171 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:15,171 (beam_search:483) INFO: best hypo: AUFÜMFMONATENLAGEINEIMFINTLICHAEPLOTERANZSTDIEBISTAHEINERHLTLICHENVOR + +2024-01-17 01:52:15,172 (asr_inference:494) INFO: speech length: 96384 +2024-01-17 01:52:15,183 (beam_search:428) INFO: decoder input length: 148 +2024-01-17 01:52:15,183 (beam_search:429) INFO: max output length: 148 +2024-01-17 01:52:15,183 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:15,524 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:15,524 (beam_search:476) INFO: -46.87 * 1.0 = -46.87 for ctc +2024-01-17 01:52:15,524 (beam_search:479) INFO: total log probability: -46.87 +2024-01-17 01:52:15,524 (beam_search:480) INFO: normalized log probability: -0.56 +2024-01-17 01:52:15,524 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:15,524 (beam_search:483) INFO: best hypo: ZIELISTERSDIEVEENTSTIMUGENESOFTJESESTEBZMITSNNRSGHETZICHKATZGONZUÜBARPOLFMSH + +2024-01-17 01:52:15,526 (asr_inference:494) INFO: speech length: 104832 +2024-01-17 01:52:15,538 (beam_search:428) INFO: decoder input length: 161 +2024-01-17 01:52:15,538 (beam_search:429) INFO: max output length: 161 +2024-01-17 01:52:15,538 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:15,911 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:15,912 (beam_search:476) INFO: -40.68 * 1.0 = -40.68 for ctc +2024-01-17 01:52:15,912 (beam_search:479) INFO: total log probability: -40.68 +2024-01-17 01:52:15,912 (beam_search:480) INFO: normalized log probability: -0.50 +2024-01-17 01:52:15,912 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:15,912 (beam_search:483) INFO: best hypo: BNNINTEEINENWAHRMNENGEMPRENENIONDBEUUSCNISSISTDIKÖNDELEBESSERUNSHEILTEN + +2024-01-17 01:52:15,913 (asr_inference:494) INFO: speech length: 120576 +2024-01-17 01:52:15,926 (beam_search:428) INFO: decoder input length: 186 +2024-01-17 01:52:15,926 (beam_search:429) INFO: max output length: 186 +2024-01-17 01:52:15,926 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:16,405 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:16,405 (beam_search:476) INFO: -45.40 * 1.0 = -45.40 for ctc +2024-01-17 01:52:16,405 (beam_search:479) INFO: total log probability: -45.40 +2024-01-17 01:52:16,405 (beam_search:480) INFO: normalized log probability: -0.49 +2024-01-17 01:52:16,405 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:16,406 (beam_search:483) INFO: best hypo: DIEANDIEWIERENSOCHÜÖRISTANNURNKENGALAUFHENUNDEANDALLEGUNMBIEMTDELLNNERUSLANGELIGT + +2024-01-17 01:52:16,407 (asr_inference:494) INFO: speech length: 72576 +2024-01-17 01:52:16,417 (beam_search:428) INFO: decoder input length: 111 +2024-01-17 01:52:16,417 (beam_search:429) INFO: max output length: 111 +2024-01-17 01:52:16,417 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:16,573 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:16,573 (beam_search:476) INFO: -20.95 * 1.0 = -20.95 for ctc +2024-01-17 01:52:16,573 (beam_search:479) INFO: total log probability: -20.95 +2024-01-17 01:52:16,573 (beam_search:480) INFO: normalized log probability: -0.46 +2024-01-17 01:52:16,573 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:16,573 (beam_search:483) INFO: best hypo: IERETUARGKEISTENDIESARZEITGUGEFLNGENSC + +2024-01-17 01:52:16,574 (asr_inference:494) INFO: speech length: 97536 +2024-01-17 01:52:16,585 (beam_search:428) INFO: decoder input length: 150 +2024-01-17 01:52:16,585 (beam_search:429) INFO: max output length: 150 +2024-01-17 01:52:16,585 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:16,949 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:16,949 (beam_search:476) INFO: -39.93 * 1.0 = -39.93 for ctc +2024-01-17 01:52:16,949 (beam_search:479) INFO: total log probability: -39.93 +2024-01-17 01:52:16,949 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:52:16,949 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:16,950 (beam_search:483) INFO: best hypo: ETDIESTREIKERBEGENDIMSGIENBERHUNERSINFÜHRTICDIEGOEBLEHECHTUNGMASYÜTOSSTE + +2024-01-17 01:52:16,951 (asr_inference:494) INFO: speech length: 72192 +2024-01-17 01:52:16,961 (beam_search:428) INFO: decoder input length: 110 +2024-01-17 01:52:16,961 (beam_search:429) INFO: max output length: 110 +2024-01-17 01:52:16,961 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:17,121 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:17,121 (beam_search:476) INFO: -13.71 * 1.0 = -13.71 for ctc +2024-01-17 01:52:17,121 (beam_search:479) INFO: total log probability: -13.71 +2024-01-17 01:52:17,121 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:52:17,121 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:17,121 (beam_search:483) INFO: best hypo: ERSTVONDORTKONTEERSEIMWEGFREIVORSETZEN + +2024-01-17 01:52:17,122 (asr_inference:494) INFO: speech length: 119808 +2024-01-17 01:52:17,135 (beam_search:428) INFO: decoder input length: 185 +2024-01-17 01:52:17,135 (beam_search:429) INFO: max output length: 185 +2024-01-17 01:52:17,135 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:17,524 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:17,524 (beam_search:476) INFO: -24.41 * 1.0 = -24.41 for ctc +2024-01-17 01:52:17,524 (beam_search:479) INFO: total log probability: -24.41 +2024-01-17 01:52:17,525 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:52:17,525 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:17,525 (beam_search:483) INFO: best hypo: SIEERHEBPZICHHEUTEIMARNOCKUTERKENBERAUSSDIEMNSCHWEMLANDTHERUSS + +2024-01-17 01:52:17,526 (asr_inference:494) INFO: speech length: 61056 +2024-01-17 01:52:17,535 (beam_search:428) INFO: decoder input length: 93 +2024-01-17 01:52:17,535 (beam_search:429) INFO: max output length: 93 +2024-01-17 01:52:17,535 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:17,643 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:17,643 (beam_search:476) INFO: -13.04 * 1.0 = -13.04 for ctc +2024-01-17 01:52:17,643 (beam_search:479) INFO: total log probability: -13.04 +2024-01-17 01:52:17,643 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:52:17,643 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:17,643 (beam_search:483) INFO: best hypo: TIANARESCHENINSLENGEHEZUSBAHHE + +2024-01-17 01:52:17,644 (asr_inference:494) INFO: speech length: 84864 +2024-01-17 01:52:17,655 (beam_search:428) INFO: decoder input length: 130 +2024-01-17 01:52:17,655 (beam_search:429) INFO: max output length: 130 +2024-01-17 01:52:17,655 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:17,923 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:17,923 (beam_search:476) INFO: -34.74 * 1.0 = -34.74 for ctc +2024-01-17 01:52:17,923 (beam_search:479) INFO: total log probability: -34.74 +2024-01-17 01:52:17,923 (beam_search:480) INFO: normalized log probability: -0.48 +2024-01-17 01:52:17,923 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:17,923 (beam_search:483) INFO: best hypo: WESSNSCHAFLERHAHABENDESIMENTATZUNPESERETNOBEIFOARAUNBEROBATET + +2024-01-17 01:52:17,924 (asr_inference:494) INFO: speech length: 102528 +2024-01-17 01:52:17,936 (beam_search:428) INFO: decoder input length: 158 +2024-01-17 01:52:17,936 (beam_search:429) INFO: max output length: 158 +2024-01-17 01:52:17,936 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:18,257 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:18,257 (beam_search:476) INFO: -27.65 * 1.0 = -27.65 for ctc +2024-01-17 01:52:18,258 (beam_search:479) INFO: total log probability: -27.65 +2024-01-17 01:52:18,258 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:52:18,258 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:18,258 (beam_search:483) INFO: best hypo: SEINIGISCHEHTZSBEZTIUNGENREISCHTENBISNORDDOMEHRICKAUNDASIEREN + +2024-01-17 01:52:18,259 (asr_inference:494) INFO: speech length: 139776 +2024-01-17 01:52:18,273 (beam_search:428) INFO: decoder input length: 216 +2024-01-17 01:52:18,273 (beam_search:429) INFO: max output length: 216 +2024-01-17 01:52:18,273 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:18,927 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:18,927 (beam_search:476) INFO: -36.14 * 1.0 = -36.14 for ctc +2024-01-17 01:52:18,927 (beam_search:479) INFO: total log probability: -36.14 +2024-01-17 01:52:18,927 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:52:18,927 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:18,928 (beam_search:483) INFO: best hypo: SALREICIEPVORDEREDLATIERUNGENBEIDEUTSCHENÖROPARUNDWELTLESTESCAFTERNSOIEOLÖBISCHENSPILENVORLGKTEN + +2024-01-17 01:52:18,929 (asr_inference:494) INFO: speech length: 76032 +2024-01-17 01:52:18,939 (beam_search:428) INFO: decoder input length: 116 +2024-01-17 01:52:18,939 (beam_search:429) INFO: max output length: 116 +2024-01-17 01:52:18,939 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:19,131 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:19,132 (beam_search:476) INFO: -25.03 * 1.0 = -25.03 for ctc +2024-01-17 01:52:19,132 (beam_search:479) INFO: total log probability: -25.03 +2024-01-17 01:52:19,132 (beam_search:480) INFO: normalized log probability: -0.45 +2024-01-17 01:52:19,132 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:19,132 (beam_search:483) INFO: best hypo: INENERTALESCERTUMBLETERMTDSOETSIKITAUFERBAGBANG + +2024-01-17 01:52:19,133 (asr_inference:494) INFO: speech length: 63360 +2024-01-17 01:52:19,142 (beam_search:428) INFO: decoder input length: 96 +2024-01-17 01:52:19,142 (beam_search:429) INFO: max output length: 96 +2024-01-17 01:52:19,143 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:19,303 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:19,303 (beam_search:476) INFO: -20.71 * 1.0 = -20.71 for ctc +2024-01-17 01:52:19,303 (beam_search:479) INFO: total log probability: -20.71 +2024-01-17 01:52:19,303 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:52:19,303 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:19,303 (beam_search:483) INFO: best hypo: WITEIMWAHMLITRENKINBAUFLESSEIEKERLTEBESEAUSHAUELT + +2024-01-17 01:52:19,304 (asr_inference:494) INFO: speech length: 68352 +2024-01-17 01:52:19,314 (beam_search:428) INFO: decoder input length: 104 +2024-01-17 01:52:19,314 (beam_search:429) INFO: max output length: 104 +2024-01-17 01:52:19,314 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:19,373 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:19,373 (beam_search:476) INFO: -12.73 * 1.0 = -12.73 for ctc +2024-01-17 01:52:19,373 (beam_search:479) INFO: total log probability: -12.73 +2024-01-17 01:52:19,373 (beam_search:480) INFO: normalized log probability: -0.75 +2024-01-17 01:52:19,373 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:19,373 (beam_search:483) INFO: best hypo: WOLLEDEMÖEEWES + +2024-01-17 01:52:19,374 (asr_inference:494) INFO: speech length: 82944 +2024-01-17 01:52:19,384 (beam_search:428) INFO: decoder input length: 127 +2024-01-17 01:52:19,384 (beam_search:429) INFO: max output length: 127 +2024-01-17 01:52:19,384 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:19,608 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:19,608 (beam_search:476) INFO: -28.66 * 1.0 = -28.66 for ctc +2024-01-17 01:52:19,608 (beam_search:479) INFO: total log probability: -28.66 +2024-01-17 01:52:19,608 (beam_search:480) INFO: normalized log probability: -0.48 +2024-01-17 01:52:19,608 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:19,609 (beam_search:483) INFO: best hypo: OSTANDISIENEEINEBOCHENACHEMESTUVORNMUNDIMPGLULIEN + +2024-01-17 01:52:19,610 (asr_inference:494) INFO: speech length: 98304 +2024-01-17 01:52:19,621 (beam_search:428) INFO: decoder input length: 151 +2024-01-17 01:52:19,621 (beam_search:429) INFO: max output length: 151 +2024-01-17 01:52:19,621 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:19,875 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:19,875 (beam_search:476) INFO: -15.84 * 1.0 = -15.84 for ctc +2024-01-17 01:52:19,876 (beam_search:479) INFO: total log probability: -15.84 +2024-01-17 01:52:19,876 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:52:19,876 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:19,876 (beam_search:483) INFO: best hypo: EMMITELEITERHATENDEXZEMDERHARSCHAFTDASDOFINER + +2024-01-17 01:52:19,877 (asr_inference:494) INFO: speech length: 104448 +2024-01-17 01:52:19,889 (beam_search:428) INFO: decoder input length: 161 +2024-01-17 01:52:19,889 (beam_search:429) INFO: max output length: 161 +2024-01-17 01:52:19,889 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:20,159 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:20,159 (beam_search:476) INFO: -25.37 * 1.0 = -25.37 for ctc +2024-01-17 01:52:20,159 (beam_search:479) INFO: total log probability: -25.37 +2024-01-17 01:52:20,159 (beam_search:480) INFO: normalized log probability: -0.45 +2024-01-17 01:52:20,159 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:20,159 (beam_search:483) INFO: best hypo: DIENAMSCIEPLETRAGNOCHREITERDEFALTSOLGEFADMASEHATIE + +2024-01-17 01:52:20,161 (asr_inference:494) INFO: speech length: 86016 +2024-01-17 01:52:20,171 (beam_search:428) INFO: decoder input length: 132 +2024-01-17 01:52:20,171 (beam_search:429) INFO: max output length: 132 +2024-01-17 01:52:20,171 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:20,346 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:20,347 (beam_search:476) INFO: -20.18 * 1.0 = -20.18 for ctc +2024-01-17 01:52:20,347 (beam_search:479) INFO: total log probability: -20.18 +2024-01-17 01:52:20,347 (beam_search:480) INFO: normalized log probability: -0.47 +2024-01-17 01:52:20,347 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:20,347 (beam_search:483) INFO: best hypo: PLUKANNSWITDEBUSLCHRANGVONERODERFOREN + +2024-01-17 01:52:20,348 (asr_inference:494) INFO: speech length: 31872 +2024-01-17 01:52:20,355 (beam_search:428) INFO: decoder input length: 47 +2024-01-17 01:52:20,355 (beam_search:429) INFO: max output length: 47 +2024-01-17 01:52:20,355 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:20,379 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:20,380 (beam_search:476) INFO: -6.96 * 1.0 = -6.96 for ctc +2024-01-17 01:52:20,380 (beam_search:479) INFO: total log probability: -6.96 +2024-01-17 01:52:20,380 (beam_search:480) INFO: normalized log probability: -0.50 +2024-01-17 01:52:20,380 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:20,380 (beam_search:483) INFO: best hypo: IERDOCREGOL + +2024-01-17 01:52:20,381 (asr_inference:494) INFO: speech length: 133632 +2024-01-17 01:52:20,395 (beam_search:428) INFO: decoder input length: 206 +2024-01-17 01:52:20,395 (beam_search:429) INFO: max output length: 206 +2024-01-17 01:52:20,395 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:20,970 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:20,970 (beam_search:476) INFO: -37.04 * 1.0 = -37.04 for ctc +2024-01-17 01:52:20,970 (beam_search:479) INFO: total log probability: -37.04 +2024-01-17 01:52:20,970 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:52:20,970 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:20,971 (beam_search:483) INFO: best hypo: ALLERDINGSERGAHBENWEITERHREPRÜFUNGENDASSSMITTELFRISTIGKEINPEDARFRISCEUCHEAUTOBANGERWEN + +2024-01-17 01:52:20,972 (asr_inference:494) INFO: speech length: 125568 +2024-01-17 01:52:20,985 (beam_search:428) INFO: decoder input length: 194 +2024-01-17 01:52:20,985 (beam_search:429) INFO: max output length: 194 +2024-01-17 01:52:20,985 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:21,417 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:21,417 (beam_search:476) INFO: -27.60 * 1.0 = -27.60 for ctc +2024-01-17 01:52:21,417 (beam_search:479) INFO: total log probability: -27.60 +2024-01-17 01:52:21,417 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:52:21,417 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:21,417 (beam_search:483) INFO: best hypo: UNGEKERTKANENFREIPRIEFEINEARAUSSCHREIBUNGALTSVOBELFREIGEMEINDZEINEN + +2024-01-17 01:52:21,418 (asr_inference:494) INFO: speech length: 115584 +2024-01-17 01:52:21,431 (beam_search:428) INFO: decoder input length: 178 +2024-01-17 01:52:21,431 (beam_search:429) INFO: max output length: 178 +2024-01-17 01:52:21,431 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:21,769 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:21,769 (beam_search:476) INFO: -25.72 * 1.0 = -25.72 for ctc +2024-01-17 01:52:21,769 (beam_search:479) INFO: total log probability: -25.72 +2024-01-17 01:52:21,769 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:52:21,769 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:21,769 (beam_search:483) INFO: best hypo: MIEZAGKROTESGEABSCHNITESEINENEINFLUSSELEUCHSCHOSTAKOWICH + +2024-01-17 01:52:21,771 (asr_inference:494) INFO: speech length: 100992 +2024-01-17 01:52:21,782 (beam_search:428) INFO: decoder input length: 155 +2024-01-17 01:52:21,782 (beam_search:429) INFO: max output length: 155 +2024-01-17 01:52:21,782 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:22,097 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:22,097 (beam_search:476) INFO: -17.84 * 1.0 = -17.84 for ctc +2024-01-17 01:52:22,097 (beam_search:479) INFO: total log probability: -17.84 +2024-01-17 01:52:22,097 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:52:22,097 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:22,098 (beam_search:483) INFO: best hypo: RVEREINEDERPIEONIEREAUFDMGEBIETDERUTZIUNGDERSONENENERGEE + +2024-01-17 01:52:22,099 (asr_inference:494) INFO: speech length: 73344 +2024-01-17 01:52:22,109 (beam_search:428) INFO: decoder input length: 112 +2024-01-17 01:52:22,109 (beam_search:429) INFO: max output length: 112 +2024-01-17 01:52:22,109 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:22,301 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:22,301 (beam_search:476) INFO: -24.12 * 1.0 = -24.12 for ctc +2024-01-17 01:52:22,301 (beam_search:479) INFO: total log probability: -24.12 +2024-01-17 01:52:22,301 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-17 01:52:22,301 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:22,301 (beam_search:483) INFO: best hypo: ACHVENMEDIKUNDENAFDENERENGENMSICHÖFLICHKEITBEWAN + +# Accounting: time=69 threads=1 +# Ended (code 0) at Wed Jan 17 01:52:22 CST 2024, elapsed time 69 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.2.log b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.2.log new file mode 100644 index 0000000000000000000000000000000000000000..abb146dad9e043ab517bebcc72fa3513ee8a9324 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.2.log @@ -0,0 +1,1834 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2 --config conf/decode_asr.yaml +# Started at Wed Jan 17 01:52:22 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2 --config conf/decode_asr.yaml +2024-01-17 01:52:24,106 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml +2024-01-17 01:52:24,124 (asr:523) INFO: Vocabulary size: 44 +2024-01-17 01:52:24,186 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 01:52:24,186 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 01:52:24,297 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 01:52:25,590 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 01:52:26,833 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 01:52:26,833 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 01:52:26,833 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 01:52:26,865 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-17 01:52:26,940 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 01:52:27,052 (asr_inference:494) INFO: speech length: 36096 +2024-01-17 01:52:28,259 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:52:28,259 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:52:28,259 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:28,310 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:28,310 (beam_search:476) INFO: -8.98 * 1.0 = -8.98 for ctc +2024-01-17 01:52:28,310 (beam_search:479) INFO: total log probability: -8.98 +2024-01-17 01:52:28,310 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:52:28,310 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:28,310 (beam_search:483) INFO: best hypo: DIEBEMASCHINERSTFERTICH + +2024-01-17 01:52:28,335 (asr_inference:494) INFO: speech length: 134016 +2024-01-17 01:52:28,351 (beam_search:428) INFO: decoder input length: 207 +2024-01-17 01:52:28,351 (beam_search:429) INFO: max output length: 207 +2024-01-17 01:52:28,351 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:28,779 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:28,779 (beam_search:476) INFO: -20.51 * 1.0 = -20.51 for ctc +2024-01-17 01:52:28,779 (beam_search:479) INFO: total log probability: -20.51 +2024-01-17 01:52:28,779 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:52:28,779 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:28,779 (beam_search:483) INFO: best hypo: INDEARCHAISCHENPERIODEWURDENRSTIVORMINDESOCKEBASSINTUYKILD + +2024-01-17 01:52:28,781 (asr_inference:494) INFO: speech length: 79104 +2024-01-17 01:52:28,791 (beam_search:428) INFO: decoder input length: 121 +2024-01-17 01:52:28,791 (beam_search:429) INFO: max output length: 121 +2024-01-17 01:52:28,791 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:28,923 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:28,923 (beam_search:476) INFO: -16.40 * 1.0 = -16.40 for ctc +2024-01-17 01:52:28,923 (beam_search:479) INFO: total log probability: -16.40 +2024-01-17 01:52:28,923 (beam_search:480) INFO: normalized log probability: -0.48 +2024-01-17 01:52:28,923 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:28,923 (beam_search:483) INFO: best hypo: DICOMÖÜDIERSEESEALSTERSTFÜN + +2024-01-17 01:52:28,924 (asr_inference:494) INFO: speech length: 57984 +2024-01-17 01:52:28,933 (beam_search:428) INFO: decoder input length: 88 +2024-01-17 01:52:28,933 (beam_search:429) INFO: max output length: 88 +2024-01-17 01:52:28,933 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:29,003 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:29,003 (beam_search:476) INFO: -15.04 * 1.0 = -15.04 for ctc +2024-01-17 01:52:29,003 (beam_search:479) INFO: total log probability: -15.04 +2024-01-17 01:52:29,003 (beam_search:480) INFO: normalized log probability: -0.63 +2024-01-17 01:52:29,003 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:29,003 (beam_search:483) INFO: best hypo: ARTUÄGETVERGNEAMUMS + +2024-01-17 01:52:29,004 (asr_inference:494) INFO: speech length: 127104 +2024-01-17 01:52:29,017 (beam_search:428) INFO: decoder input length: 196 +2024-01-17 01:52:29,017 (beam_search:429) INFO: max output length: 196 +2024-01-17 01:52:29,017 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:29,563 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:29,564 (beam_search:476) INFO: -49.75 * 1.0 = -49.75 for ctc +2024-01-17 01:52:29,564 (beam_search:479) INFO: total log probability: -49.75 +2024-01-17 01:52:29,564 (beam_search:480) INFO: normalized log probability: -0.49 +2024-01-17 01:52:29,564 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:29,564 (beam_search:483) INFO: best hypo: TEARMITENTETEINEEFÜRKKREISCHEINTLNATZEUNEALIEKEÄRDENSABENVORELENINMSCHKÜTELUNENENZSACKUR + +2024-01-17 01:52:29,566 (asr_inference:494) INFO: speech length: 122112 +2024-01-17 01:52:29,578 (beam_search:428) INFO: decoder input length: 188 +2024-01-17 01:52:29,578 (beam_search:429) INFO: max output length: 188 +2024-01-17 01:52:29,579 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:30,016 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:30,016 (beam_search:476) INFO: -22.97 * 1.0 = -22.97 for ctc +2024-01-17 01:52:30,016 (beam_search:479) INFO: total log probability: -22.97 +2024-01-17 01:52:30,016 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:52:30,016 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:30,017 (beam_search:483) INFO: best hypo: DERSONEINESBERETNANZBEGANSEINIEFOSBEIKAERIWEIDENSPORTFREUNDENWANEEIKEL + +2024-01-17 01:52:30,018 (asr_inference:494) INFO: speech length: 115584 +2024-01-17 01:52:30,031 (beam_search:428) INFO: decoder input length: 178 +2024-01-17 01:52:30,031 (beam_search:429) INFO: max output length: 178 +2024-01-17 01:52:30,031 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:30,440 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:30,440 (beam_search:476) INFO: -24.73 * 1.0 = -24.73 for ctc +2024-01-17 01:52:30,440 (beam_search:479) INFO: total log probability: -24.73 +2024-01-17 01:52:30,440 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:52:30,440 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:30,440 (beam_search:483) INFO: best hypo: INDIESENJAHRGABESIEDENOMEEINENSINGESUNDSECHSONDEISIGNOMEREIENSALLEBEN + +2024-01-17 01:52:30,442 (asr_inference:494) INFO: speech length: 117888 +2024-01-17 01:52:30,454 (beam_search:428) INFO: decoder input length: 182 +2024-01-17 01:52:30,454 (beam_search:429) INFO: max output length: 182 +2024-01-17 01:52:30,454 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:30,829 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:30,829 (beam_search:476) INFO: -16.86 * 1.0 = -16.86 for ctc +2024-01-17 01:52:30,829 (beam_search:479) INFO: total log probability: -16.86 +2024-01-17 01:52:30,829 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:52:30,829 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:30,829 (beam_search:483) INFO: best hypo: NORDWESTLICHVONHAKHAUSENBEFINDESICHDIORTSCHAFTHAKENBRUCHN + +2024-01-17 01:52:30,830 (asr_inference:494) INFO: speech length: 150528 +2024-01-17 01:52:30,845 (beam_search:428) INFO: decoder input length: 233 +2024-01-17 01:52:30,845 (beam_search:429) INFO: max output length: 233 +2024-01-17 01:52:30,846 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:31,486 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:31,486 (beam_search:476) INFO: -28.46 * 1.0 = -28.46 for ctc +2024-01-17 01:52:31,486 (beam_search:479) INFO: total log probability: -28.46 +2024-01-17 01:52:31,486 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:52:31,486 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:31,487 (beam_search:483) INFO: best hypo: IMORTKNAENBURGGIENVIELESOZIALEEINERICHTEUNGENVONEREMANLAMPRECHTUNDERMAINHÖTEAUS + +2024-01-17 01:52:31,488 (asr_inference:494) INFO: speech length: 100800 +2024-01-17 01:52:31,499 (beam_search:428) INFO: decoder input length: 155 +2024-01-17 01:52:31,499 (beam_search:429) INFO: max output length: 155 +2024-01-17 01:52:31,499 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:31,870 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:31,870 (beam_search:476) INFO: -17.34 * 1.0 = -17.34 for ctc +2024-01-17 01:52:31,870 (beam_search:479) INFO: total log probability: -17.34 +2024-01-17 01:52:31,870 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:52:31,870 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:31,870 (beam_search:483) INFO: best hypo: ICHWERDEFOLÖKLICHDENRATÜBERDIEMPALMENTVORGETRAGENENBEDENKTENINVORMIEREN + +2024-01-17 01:52:31,872 (asr_inference:494) INFO: speech length: 90432 +2024-01-17 01:52:31,883 (beam_search:428) INFO: decoder input length: 139 +2024-01-17 01:52:31,883 (beam_search:429) INFO: max output length: 139 +2024-01-17 01:52:31,883 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:32,175 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:32,176 (beam_search:476) INFO: -26.87 * 1.0 = -26.87 for ctc +2024-01-17 01:52:32,176 (beam_search:479) INFO: total log probability: -26.87 +2024-01-17 01:52:32,176 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:52:32,176 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:32,176 (beam_search:483) INFO: best hypo: ESERETRAURECGEWESENEINSOWICHTDIESTEMANICHTEMKONSETFABSCHENZUKÖN + +2024-01-17 01:52:32,177 (asr_inference:494) INFO: speech length: 123264 +2024-01-17 01:52:32,190 (beam_search:428) INFO: decoder input length: 190 +2024-01-17 01:52:32,190 (beam_search:429) INFO: max output length: 190 +2024-01-17 01:52:32,190 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:32,591 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:32,591 (beam_search:476) INFO: -30.46 * 1.0 = -30.46 for ctc +2024-01-17 01:52:32,591 (beam_search:479) INFO: total log probability: -30.46 +2024-01-17 01:52:32,592 (beam_search:480) INFO: normalized log probability: -0.43 +2024-01-17 01:52:32,592 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:32,592 (beam_search:483) INFO: best hypo: NOCHTISSIMTOTIMKLEICHENJAHGAMMSGUTZBRISTIGANANDEREVISEZEA + +2024-01-17 01:52:32,593 (asr_inference:494) INFO: speech length: 151488 +2024-01-17 01:52:32,608 (beam_search:428) INFO: decoder input length: 234 +2024-01-17 01:52:32,608 (beam_search:429) INFO: max output length: 234 +2024-01-17 01:52:32,608 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:33,158 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:33,158 (beam_search:476) INFO: -32.62 * 1.0 = -32.62 for ctc +2024-01-17 01:52:33,158 (beam_search:479) INFO: total log probability: -32.62 +2024-01-17 01:52:33,158 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-17 01:52:33,158 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:33,159 (beam_search:483) INFO: best hypo: KOTDANACHGABESEINEINWERBERVORDMINTDEMTKANDKAMNENDVONSCAKESHOUENBACH + +2024-01-17 01:52:33,160 (asr_inference:494) INFO: speech length: 73728 +2024-01-17 01:52:33,170 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 01:52:33,170 (beam_search:429) INFO: max output length: 113 +2024-01-17 01:52:33,170 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:33,234 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:33,234 (beam_search:476) INFO: -10.86 * 1.0 = -10.86 for ctc +2024-01-17 01:52:33,234 (beam_search:479) INFO: total log probability: -10.86 +2024-01-17 01:52:33,234 (beam_search:480) INFO: normalized log probability: -0.64 +2024-01-17 01:52:33,234 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:33,234 (beam_search:483) INFO: best hypo: DASITBSEAET + +2024-01-17 01:52:33,236 (asr_inference:494) INFO: speech length: 37632 +2024-01-17 01:52:33,243 (beam_search:428) INFO: decoder input length: 56 +2024-01-17 01:52:33,243 (beam_search:429) INFO: max output length: 56 +2024-01-17 01:52:33,243 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:33,296 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:33,296 (beam_search:476) INFO: -9.57 * 1.0 = -9.57 for ctc +2024-01-17 01:52:33,296 (beam_search:479) INFO: total log probability: -9.57 +2024-01-17 01:52:33,296 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:52:33,296 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:33,297 (beam_search:483) INFO: best hypo: WISIESMINLECHZEITAUSHR + +2024-01-17 01:52:33,298 (asr_inference:494) INFO: speech length: 115200 +2024-01-17 01:52:33,310 (beam_search:428) INFO: decoder input length: 177 +2024-01-17 01:52:33,310 (beam_search:429) INFO: max output length: 177 +2024-01-17 01:52:33,310 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:33,692 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:33,692 (beam_search:476) INFO: -26.45 * 1.0 = -26.45 for ctc +2024-01-17 01:52:33,692 (beam_search:479) INFO: total log probability: -26.45 +2024-01-17 01:52:33,692 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:52:33,692 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:33,692 (beam_search:483) INFO: best hypo: NACHEDEMDOCHFBEFINDERSIGHARUCHDERKMKANIUNNASIONALLBACHKERBOT + +2024-01-17 01:52:33,694 (asr_inference:494) INFO: speech length: 87552 +2024-01-17 01:52:33,704 (beam_search:428) INFO: decoder input length: 134 +2024-01-17 01:52:33,704 (beam_search:429) INFO: max output length: 134 +2024-01-17 01:52:33,704 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:33,906 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:33,906 (beam_search:476) INFO: -17.48 * 1.0 = -17.48 for ctc +2024-01-17 01:52:33,906 (beam_search:479) INFO: total log probability: -17.48 +2024-01-17 01:52:33,906 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:52:33,906 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:33,906 (beam_search:483) INFO: best hypo: IESORENDEAKÖNDENDESDJELIEBEDENTORDBESIGTAT + +2024-01-17 01:52:33,908 (asr_inference:494) INFO: speech length: 139968 +2024-01-17 01:52:33,922 (beam_search:428) INFO: decoder input length: 216 +2024-01-17 01:52:33,922 (beam_search:429) INFO: max output length: 216 +2024-01-17 01:52:33,922 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:34,408 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:34,408 (beam_search:476) INFO: -22.42 * 1.0 = -22.42 for ctc +2024-01-17 01:52:34,408 (beam_search:479) INFO: total log probability: -22.42 +2024-01-17 01:52:34,408 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:52:34,408 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:34,408 (beam_search:483) INFO: best hypo: BETECKTSSTDIEREPRESENTHERTIEFGESTALTEDEWILERMITDEINERNMANDSARTDACH + +2024-01-17 01:52:34,410 (asr_inference:494) INFO: speech length: 111168 +2024-01-17 01:52:34,422 (beam_search:428) INFO: decoder input length: 171 +2024-01-17 01:52:34,422 (beam_search:429) INFO: max output length: 171 +2024-01-17 01:52:34,422 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:34,744 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:34,744 (beam_search:476) INFO: -16.25 * 1.0 = -16.25 for ctc +2024-01-17 01:52:34,744 (beam_search:479) INFO: total log probability: -16.25 +2024-01-17 01:52:34,744 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:52:34,744 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:34,744 (beam_search:483) INFO: best hypo: DIESESIETLUNGESMITERORTSCHACFTDELLACHZUSAMENGEWAKZEN + +2024-01-17 01:52:34,746 (asr_inference:494) INFO: speech length: 82560 +2024-01-17 01:52:34,756 (beam_search:428) INFO: decoder input length: 126 +2024-01-17 01:52:34,756 (beam_search:429) INFO: max output length: 126 +2024-01-17 01:52:34,756 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:34,871 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:34,871 (beam_search:476) INFO: -11.05 * 1.0 = -11.05 for ctc +2024-01-17 01:52:34,871 (beam_search:479) INFO: total log probability: -11.05 +2024-01-17 01:52:34,871 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:52:34,871 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:34,871 (beam_search:483) INFO: best hypo: WARISCHNEINMALIENDEMKLO + +2024-01-17 01:52:34,872 (asr_inference:494) INFO: speech length: 74496 +2024-01-17 01:52:34,882 (beam_search:428) INFO: decoder input length: 114 +2024-01-17 01:52:34,882 (beam_search:429) INFO: max output length: 114 +2024-01-17 01:52:34,882 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:34,982 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:34,982 (beam_search:476) INFO: -8.53 * 1.0 = -8.53 for ctc +2024-01-17 01:52:34,982 (beam_search:479) INFO: total log probability: -8.53 +2024-01-17 01:52:34,982 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:52:34,982 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:34,982 (beam_search:483) INFO: best hypo: BORAURISTISTAUCHVOLOR + +2024-01-17 01:52:34,983 (asr_inference:494) INFO: speech length: 135936 +2024-01-17 01:52:34,997 (beam_search:428) INFO: decoder input length: 210 +2024-01-17 01:52:34,997 (beam_search:429) INFO: max output length: 210 +2024-01-17 01:52:34,997 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:35,559 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:35,560 (beam_search:476) INFO: -43.02 * 1.0 = -43.02 for ctc +2024-01-17 01:52:35,560 (beam_search:479) INFO: total log probability: -43.02 +2024-01-17 01:52:35,560 (beam_search:480) INFO: normalized log probability: -0.45 +2024-01-17 01:52:35,560 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:35,560 (beam_search:483) INFO: best hypo: DIEHEXVONDRSTRASEWUDENSNVONALFETDIOLEKBESEINEFESTENSEISCHOESCHABLNEENGÜLSIERTE + +2024-01-17 01:52:35,561 (asr_inference:494) INFO: speech length: 86976 +2024-01-17 01:52:35,572 (beam_search:428) INFO: decoder input length: 133 +2024-01-17 01:52:35,572 (beam_search:429) INFO: max output length: 133 +2024-01-17 01:52:35,572 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:35,842 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:35,842 (beam_search:476) INFO: -26.90 * 1.0 = -26.90 for ctc +2024-01-17 01:52:35,842 (beam_search:479) INFO: total log probability: -26.90 +2024-01-17 01:52:35,842 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:52:35,842 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:35,843 (beam_search:483) INFO: best hypo: AINHARSPÄITEVEXSLTERELTZUNELFTNATZSUNMBEVODEELFVUNGEREICHE + +2024-01-17 01:52:35,844 (asr_inference:494) INFO: speech length: 163008 +2024-01-17 01:52:35,860 (beam_search:428) INFO: decoder input length: 252 +2024-01-17 01:52:35,860 (beam_search:429) INFO: max output length: 252 +2024-01-17 01:52:35,860 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:36,361 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:36,361 (beam_search:476) INFO: -26.15 * 1.0 = -26.15 for ctc +2024-01-17 01:52:36,361 (beam_search:479) INFO: total log probability: -26.15 +2024-01-17 01:52:36,361 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:52:36,362 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:36,362 (beam_search:483) INFO: best hypo: INDERLANDVITCHERFKANDERERTRARGKTDEUTLIWEDORZIERTWERDENT + +2024-01-17 01:52:36,363 (asr_inference:494) INFO: speech length: 124416 +2024-01-17 01:52:36,376 (beam_search:428) INFO: decoder input length: 192 +2024-01-17 01:52:36,376 (beam_search:429) INFO: max output length: 192 +2024-01-17 01:52:36,376 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:36,724 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:36,724 (beam_search:476) INFO: -18.74 * 1.0 = -18.74 for ctc +2024-01-17 01:52:36,724 (beam_search:479) INFO: total log probability: -18.74 +2024-01-17 01:52:36,724 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:52:36,724 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:36,724 (beam_search:483) INFO: best hypo: MANSURSPIERTEINSEINERHEIMATSTADTKEEWORFIERALLALL + +2024-01-17 01:52:36,726 (asr_inference:494) INFO: speech length: 135360 +2024-01-17 01:52:36,740 (beam_search:428) INFO: decoder input length: 209 +2024-01-17 01:52:36,740 (beam_search:429) INFO: max output length: 209 +2024-01-17 01:52:36,740 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:37,021 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:37,021 (beam_search:476) INFO: -16.42 * 1.0 = -16.42 for ctc +2024-01-17 01:52:37,021 (beam_search:479) INFO: total log probability: -16.42 +2024-01-17 01:52:37,021 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:52:37,021 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:37,022 (beam_search:483) INFO: best hypo: ERTRADERREIMAUHALUNDELNLAOUTABUEOBEI + +2024-01-17 01:52:37,023 (asr_inference:494) INFO: speech length: 146304 +2024-01-17 01:52:37,038 (beam_search:428) INFO: decoder input length: 226 +2024-01-17 01:52:37,038 (beam_search:429) INFO: max output length: 226 +2024-01-17 01:52:37,038 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:37,591 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:37,592 (beam_search:476) INFO: -32.96 * 1.0 = -32.96 for ctc +2024-01-17 01:52:37,592 (beam_search:479) INFO: total log probability: -32.96 +2024-01-17 01:52:37,592 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:52:37,592 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:37,592 (beam_search:483) INFO: best hypo: MITFÜRTWAREHRDIEROWPTHJADELEILSDERECHTLICHEROLLEDIESSOBETSEICENENGEMEIND + +2024-01-17 01:52:37,594 (asr_inference:494) INFO: speech length: 254400 +2024-01-17 01:52:37,617 (beam_search:428) INFO: decoder input length: 395 +2024-01-17 01:52:37,617 (beam_search:429) INFO: max output length: 395 +2024-01-17 01:52:37,617 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:39,364 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:39,365 (beam_search:476) INFO: -58.99 * 1.0 = -58.99 for ctc +2024-01-17 01:52:39,365 (beam_search:479) INFO: total log probability: -58.99 +2024-01-17 01:52:39,365 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:52:39,365 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:39,365 (beam_search:483) INFO: best hypo: LETZTEWOCHOGABDASMETIEBEKANDGASESVONEPELÜBERVIERNDASHWALTEFORFELEVONÜBERITZEUNINTOMIRTWORDENWAIEDESUNTERNEHNALSNICHTSCHERIENPEEITETE + +2024-01-17 01:52:39,367 (asr_inference:494) INFO: speech length: 483840 +2024-01-17 01:52:39,412 (beam_search:428) INFO: decoder input length: 753 +2024-01-17 01:52:39,412 (beam_search:429) INFO: max output length: 753 +2024-01-17 01:52:39,412 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:44,450 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:44,450 (beam_search:476) INFO: -98.90 * 1.0 = -98.90 for ctc +2024-01-17 01:52:44,450 (beam_search:479) INFO: total log probability: -98.90 +2024-01-17 01:52:44,450 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-17 01:52:44,450 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:44,451 (beam_search:483) INFO: best hypo: EJÜURSEJIMNESTIGUNDTERSTÜTZSTEDENBERIEFDESOLÜMPISCHENKOMITISDERVEREINIGTENSTATENUNDDACIPTIERTESASAPBPTULUTENOTWENDIKEITDASIHTDIEOLÜMPISCHEVERMILIEFÜREINGSICHERESUNMFELLTFÜRALEUNSERERSPORTLEREINSETT + +2024-01-17 01:52:44,453 (asr_inference:494) INFO: speech length: 178560 +2024-01-17 01:52:44,470 (beam_search:428) INFO: decoder input length: 276 +2024-01-17 01:52:44,470 (beam_search:429) INFO: max output length: 276 +2024-01-17 01:52:44,470 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:45,607 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:45,607 (beam_search:476) INFO: -61.58 * 1.0 = -61.58 for ctc +2024-01-17 01:52:45,607 (beam_search:479) INFO: total log probability: -61.58 +2024-01-17 01:52:45,608 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:52:45,608 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:45,608 (beam_search:483) INFO: best hypo: DALICKENEAPRETSKOMPETIBELMITCHTRNERTZWEIPUNDELFARACHTENRZWEIPUNGDELFPEUNDCHTEONETZWEIPUNGDELFGESEINVERASGESDIASSISTATIONVERFÜGTBERDUALRADIO + +2024-01-17 01:52:45,610 (asr_inference:494) INFO: speech length: 98880 +2024-01-17 01:52:45,621 (beam_search:428) INFO: decoder input length: 152 +2024-01-17 01:52:45,621 (beam_search:429) INFO: max output length: 152 +2024-01-17 01:52:45,621 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:45,932 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:45,932 (beam_search:476) INFO: -21.17 * 1.0 = -21.17 for ctc +2024-01-17 01:52:45,932 (beam_search:479) INFO: total log probability: -21.17 +2024-01-17 01:52:45,932 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:52:45,932 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:45,933 (beam_search:483) INFO: best hypo: ERBEZEICHNESDIEIERÜCHTEALSPOLISCHESGISCHEÄTZSUNDTALLBENHEITZ + +2024-01-17 01:52:45,934 (asr_inference:494) INFO: speech length: 166080 +2024-01-17 01:52:45,950 (beam_search:428) INFO: decoder input length: 257 +2024-01-17 01:52:45,950 (beam_search:429) INFO: max output length: 257 +2024-01-17 01:52:45,950 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:47,004 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:47,004 (beam_search:476) INFO: -56.45 * 1.0 = -56.45 for ctc +2024-01-17 01:52:47,004 (beam_search:479) INFO: total log probability: -56.45 +2024-01-17 01:52:47,004 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:52:47,004 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:47,004 (beam_search:483) INFO: best hypo: LETEWOCHEGABTASEMIEITHIEBEKANDASISVONEBELÜBERVIRNDREISIWEITREFORFVELEVONBERHITZUNINVORMIRURDENWADIEDASUNTERNEHMEMALSNICHCHWRWEGENBEZEIGNETE + +2024-01-17 01:52:47,006 (asr_inference:494) INFO: speech length: 130560 +2024-01-17 01:52:47,020 (beam_search:428) INFO: decoder input length: 201 +2024-01-17 01:52:47,020 (beam_search:429) INFO: max output length: 201 +2024-01-17 01:52:47,020 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:47,683 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:47,683 (beam_search:476) INFO: -51.65 * 1.0 = -51.65 for ctc +2024-01-17 01:52:47,683 (beam_search:479) INFO: total log probability: -51.65 +2024-01-17 01:52:47,683 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:52:47,683 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:47,684 (beam_search:483) INFO: best hypo: NACHDEDERDMLEUNEHHNDERTRALNSECHZIGEBAURTWORDENWARKAMDIAHRESSEILIHNBERFLUTUNGDESDEMENTEMNFLSVERTALENZUMSTILSTANT + +2024-01-17 01:52:47,686 (asr_inference:494) INFO: speech length: 172800 +2024-01-17 01:52:47,702 (beam_search:428) INFO: decoder input length: 267 +2024-01-17 01:52:47,702 (beam_search:429) INFO: max output length: 267 +2024-01-17 01:52:47,702 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:48,900 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:48,900 (beam_search:476) INFO: -59.13 * 1.0 = -59.13 for ctc +2024-01-17 01:52:48,900 (beam_search:479) INFO: total log probability: -59.13 +2024-01-17 01:52:48,900 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:52:48,900 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:48,901 (beam_search:483) INFO: best hypo: ERWAUCHAMSTECHENVONGELSCHEINVEVIELEDENDEBETEILICGTAKTELEBEISHPIESENEARBETSCHLISENIEPRIMEHMINISTERPORTRESAFDERFORDERSERTDERKANADSCHENFÜNFUNDUNDEROLLRNUTENEIN + +2024-01-17 01:52:48,903 (asr_inference:494) INFO: speech length: 168960 +2024-01-17 01:52:48,919 (beam_search:428) INFO: decoder input length: 261 +2024-01-17 01:52:48,919 (beam_search:429) INFO: max output length: 261 +2024-01-17 01:52:48,919 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:49,696 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:49,696 (beam_search:476) INFO: -37.29 * 1.0 = -37.29 for ctc +2024-01-17 01:52:49,696 (beam_search:479) INFO: total log probability: -37.29 +2024-01-17 01:52:49,696 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:52:49,696 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:49,697 (beam_search:483) INFO: best hypo: DIEAUPTSTATVERMODAWIERNISTKICHINADIEEINEIMPISPACHESTRUMENISCHABARVIELEMENTENCSPRECHENROSSELC + +2024-01-17 01:52:49,698 (asr_inference:494) INFO: speech length: 519360 +2024-01-17 01:52:49,746 (beam_search:428) INFO: decoder input length: 809 +2024-01-17 01:52:49,746 (beam_search:429) INFO: max output length: 809 +2024-01-17 01:52:49,746 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:54,842 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:54,842 (beam_search:476) INFO: -92.57 * 1.0 = -92.57 for ctc +2024-01-17 01:52:54,842 (beam_search:479) INFO: total log probability: -92.57 +2024-01-17 01:52:54,842 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-17 01:52:54,842 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:54,843 (beam_search:483) INFO: best hypo: SZWICHENDENEINZENENDÜNESTIENHERSTDENAUCHUNMBESTENDIEGEZEITENGETALLTEPROINDZEDIEBEKANTESTEDIESEPERIODENOADIEPOCHEDERDRAILKÖNINGREICHEDIESECHTICHIERELAZWISCHENDERHAHNUNGDERIENDIENESTISTATVADT + +2024-01-17 01:52:54,845 (asr_inference:494) INFO: speech length: 232320 +2024-01-17 01:52:54,865 (beam_search:428) INFO: decoder input length: 360 +2024-01-17 01:52:54,865 (beam_search:429) INFO: max output length: 360 +2024-01-17 01:52:54,865 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:52:56,336 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:52:56,336 (beam_search:476) INFO: -51.69 * 1.0 = -51.69 for ctc +2024-01-17 01:52:56,336 (beam_search:479) INFO: total log probability: -51.69 +2024-01-17 01:52:56,336 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:52:56,336 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:52:56,337 (beam_search:483) INFO: best hypo: AMANDERENENEDRSPEKTRUMSHWANELTMANSICHENEINICHTWIDERZURKENDENDIWIDEUMDASALESANERSMACHNMOSSASSTIENESGEMACTERUNDSICALESTZUALGEMACHT + +2024-01-17 01:52:56,338 (asr_inference:494) INFO: speech length: 515520 +2024-01-17 01:52:56,386 (beam_search:428) INFO: decoder input length: 803 +2024-01-17 01:52:56,386 (beam_search:429) INFO: max output length: 803 +2024-01-17 01:52:56,386 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:03,639 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:03,639 (beam_search:476) INFO: -147.75 * 1.0 = -147.75 for ctc +2024-01-17 01:53:03,639 (beam_search:479) INFO: total log probability: -147.75 +2024-01-17 01:53:03,639 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:53:03,639 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:03,640 (beam_search:483) INFO: best hypo: DIEMEISTENDINTERPITEATIONENDESTICHNOLOGISCHENDERTEIMINIESNUSTALENZWEIALGEMEINEVORSCHTEUNGENEINERSEITSTSTDINTWICKLUMDERTICHNOLOGIESLLPSTEINEMWEGOLGTDERWEITGENTIENSEISUTDTORELEODERPOLIISCHEINPLSNAMENDIGTUNDANDERERSEITDASTICNERÜGIEIERERSEITSAUSWIRKUNGENAUFGESALSCHAFTNHRTDIEEHRINHÄRENDASSZUTTIALBEDIENSID + +2024-01-17 01:53:03,643 (asr_inference:494) INFO: speech length: 301440 +2024-01-17 01:53:03,670 (beam_search:428) INFO: decoder input length: 468 +2024-01-17 01:53:03,670 (beam_search:429) INFO: max output length: 468 +2024-01-17 01:53:03,670 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:06,190 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:06,191 (beam_search:476) INFO: -73.88 * 1.0 = -73.88 for ctc +2024-01-17 01:53:06,191 (beam_search:479) INFO: total log probability: -73.88 +2024-01-17 01:53:06,191 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:53:06,191 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:06,192 (beam_search:483) INFO: best hypo: WISCHEDENEINZENDNARTIENHERCHTENACHUNMBESTENDIGEZEITENGETAILTEROWINZENDIBEKANDESTEDEPERIODENWADIEPOCHRODERDEKÖNIGREICHEDIESECHTICARLANGTISCHENDERHANUNDTERINDENRTIESTTFVAND + +2024-01-17 01:53:06,193 (asr_inference:494) INFO: speech length: 234240 +2024-01-17 01:53:06,214 (beam_search:428) INFO: decoder input length: 363 +2024-01-17 01:53:06,214 (beam_search:429) INFO: max output length: 363 +2024-01-17 01:53:06,214 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:08,057 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:08,057 (beam_search:476) INFO: -67.05 * 1.0 = -67.05 for ctc +2024-01-17 01:53:08,057 (beam_search:479) INFO: total log probability: -67.05 +2024-01-17 01:53:08,058 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:53:08,058 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:08,059 (beam_search:483) INFO: best hypo: DIEMLIEKTZUFORGIEBIEZIZICHTESTOOMENTAUFDEMGRENSTREILTINDENDIEPALISINENSEREINZURÜEGSETZENDERGRENZENINDENZUSTANDVORDEMSERXSTALGLEGRIEGVORNNENEHNUNDERTSEBEONUSESTIGORDER + +2024-01-17 01:53:08,061 (asr_inference:494) INFO: speech length: 185280 +2024-01-17 01:53:08,078 (beam_search:428) INFO: decoder input length: 287 +2024-01-17 01:53:08,078 (beam_search:429) INFO: max output length: 287 +2024-01-17 01:53:08,078 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:09,071 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:09,071 (beam_search:476) INFO: -54.50 * 1.0 = -54.50 for ctc +2024-01-17 01:53:09,071 (beam_search:479) INFO: total log probability: -54.50 +2024-01-17 01:53:09,071 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:53:09,071 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:09,072 (beam_search:483) INFO: best hypo: MITIMPELUSTGRECHEHERSPACHKNNSEURDERWESTENVONSEIMEVIELOSOPISCHENUNDWISENSCHAFLICHENWORTZENKRICHENENABISCHNITEN + +2024-01-17 01:53:09,074 (asr_inference:494) INFO: speech length: 233280 +2024-01-17 01:53:09,095 (beam_search:428) INFO: decoder input length: 362 +2024-01-17 01:53:09,095 (beam_search:429) INFO: max output length: 362 +2024-01-17 01:53:09,095 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:11,058 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:11,058 (beam_search:476) INFO: -93.31 * 1.0 = -93.31 for ctc +2024-01-17 01:53:11,058 (beam_search:479) INFO: total log probability: -93.31 +2024-01-17 01:53:11,058 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:53:11,058 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:11,059 (beam_search:483) INFO: best hypo: WIRSTMITERAUSAGEDIESIUERSAUSIEÜBEREINDASTENNTRESSNUNDSREATLEDENDVEREINUNDIRESPORTSBPSSRGEDIENDISTENWRNEHALBUNSRRGENSATIONDEHNVOLEVERNDRUNGVERANDTREIBENANSTATEINERTIERIZETVIZIERUNGVORZUNE + +2024-01-17 01:53:11,061 (asr_inference:494) INFO: speech length: 201600 +2024-01-17 01:53:11,079 (beam_search:428) INFO: decoder input length: 312 +2024-01-17 01:53:11,079 (beam_search:429) INFO: max output length: 312 +2024-01-17 01:53:11,079 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:12,258 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:12,258 (beam_search:476) INFO: -50.59 * 1.0 = -50.59 for ctc +2024-01-17 01:53:12,258 (beam_search:479) INFO: total log probability: -50.59 +2024-01-17 01:53:12,258 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:53:12,258 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:12,259 (beam_search:483) INFO: best hypo: IREUTSFATENNASSANGTPIKARSBOGBIETENACHZEITFÜREINAUFENTALINERSTATKREUTZVATPASRSIERESINDFVORDERIESUNSLICHTBEFREITSIEBEDNGEN + +2024-01-17 01:53:12,260 (asr_inference:494) INFO: speech length: 297600 +2024-01-17 01:53:12,287 (beam_search:428) INFO: decoder input length: 462 +2024-01-17 01:53:12,287 (beam_search:429) INFO: max output length: 462 +2024-01-17 01:53:12,287 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:14,168 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:14,169 (beam_search:476) INFO: -52.57 * 1.0 = -52.57 for ctc +2024-01-17 01:53:14,169 (beam_search:479) INFO: total log probability: -52.57 +2024-01-17 01:53:14,169 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:53:14,169 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:14,169 (beam_search:483) INFO: best hypo: EREISENDEVERDENRINGENDGEWANTAUFJETWEDEARTVONUNWENTEZUACHTENDIEIEGIEIETBIETRIFTDADISSIGHAUFALEREISEPLENEAUSIERKENKANT + +2024-01-17 01:53:14,171 (asr_inference:494) INFO: speech length: 298560 +2024-01-17 01:53:14,198 (beam_search:428) INFO: decoder input length: 464 +2024-01-17 01:53:14,198 (beam_search:429) INFO: max output length: 464 +2024-01-17 01:53:14,198 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:16,056 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:16,056 (beam_search:476) INFO: -70.29 * 1.0 = -70.29 for ctc +2024-01-17 01:53:16,056 (beam_search:479) INFO: total log probability: -70.29 +2024-01-17 01:53:16,056 (beam_search:480) INFO: normalized log probability: -0.51 +2024-01-17 01:53:16,056 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:16,057 (beam_search:483) INFO: best hypo: SEBESARTDASDERKOLZUNGSPUNGKTDELINMINDIEENBIELTWERTIKALUNTRETONDTALDRITEENDEEFIKTISTDEPLASFÜRTESAUPTMUODIISTSIEBEISSIN + +2024-01-17 01:53:16,059 (asr_inference:494) INFO: speech length: 246720 +2024-01-17 01:53:16,081 (beam_search:428) INFO: decoder input length: 383 +2024-01-17 01:53:16,081 (beam_search:429) INFO: max output length: 383 +2024-01-17 01:53:16,081 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:18,300 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:18,300 (beam_search:476) INFO: -87.52 * 1.0 = -87.52 for ctc +2024-01-17 01:53:18,300 (beam_search:479) INFO: total log probability: -87.52 +2024-01-17 01:53:18,300 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:53:18,300 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:18,301 (beam_search:483) INFO: best hypo: SEITNUNZENURTACHTENACHTICHMSTEWALUNDRANSBERENSEINDAMITWELEUNBEOBACHTEBETZEUGENKÖNDASZWEGINDERWALKEINEUMSCHLEGEVERHNENSINDUNDASKEINUMSCHLEGEEINGEOFENWERDENAUSERJENEDERTORDUNGSMISEHLTNENATTRSIRDTENELER + +2024-01-17 01:53:18,303 (asr_inference:494) INFO: speech length: 238080 +2024-01-17 01:53:18,324 (beam_search:428) INFO: decoder input length: 369 +2024-01-17 01:53:18,324 (beam_search:429) INFO: max output length: 369 +2024-01-17 01:53:18,324 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:20,079 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:20,079 (beam_search:476) INFO: -54.85 * 1.0 = -54.85 for ctc +2024-01-17 01:53:20,079 (beam_search:479) INFO: total log probability: -54.85 +2024-01-17 01:53:20,079 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:53:20,079 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:20,080 (beam_search:483) INFO: best hypo: OTERARISTKANNERDESBIEZAUBENDERZWEISCHALIGEHAUPTSTATUNDSELTENSICHICEINEREIEVUNKUNZSTGELERIENUNDMUSEENAUSDIEKANENDERSVERGANGENHELTUNDGEGENWARTBRESENTIERE + +2024-01-17 01:53:20,081 (asr_inference:494) INFO: speech length: 71040 +2024-01-17 01:53:20,091 (beam_search:428) INFO: decoder input length: 108 +2024-01-17 01:53:20,091 (beam_search:429) INFO: max output length: 108 +2024-01-17 01:53:20,091 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:20,291 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:20,291 (beam_search:476) INFO: -20.07 * 1.0 = -20.07 for ctc +2024-01-17 01:53:20,291 (beam_search:479) INFO: total log probability: -20.07 +2024-01-17 01:53:20,291 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:53:20,291 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:20,291 (beam_search:483) INFO: best hypo: DIESEPAREKÖRENSICHVEREINADEBPTIONSPLANDVRERBEBEENSCHEIDEN + +2024-01-17 01:53:20,293 (asr_inference:494) INFO: speech length: 120000 +2024-01-17 01:53:20,305 (beam_search:428) INFO: decoder input length: 185 +2024-01-17 01:53:20,305 (beam_search:429) INFO: max output length: 185 +2024-01-17 01:53:20,305 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:20,837 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:20,838 (beam_search:476) INFO: -38.21 * 1.0 = -38.21 for ctc +2024-01-17 01:53:20,838 (beam_search:479) INFO: total log probability: -38.21 +2024-01-17 01:53:20,838 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:53:20,838 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:20,838 (beam_search:483) INFO: best hypo: INFOLGEDESSENSENZWEIFISHTABENAUSGESTORMENDZWEWEITRUSENVOMAUSTERBEBETROTDAUNEDERGELARSZYÜFER + +2024-01-17 01:53:20,840 (asr_inference:494) INFO: speech length: 193920 +2024-01-17 01:53:20,858 (beam_search:428) INFO: decoder input length: 300 +2024-01-17 01:53:20,858 (beam_search:429) INFO: max output length: 300 +2024-01-17 01:53:20,858 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:21,802 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:21,802 (beam_search:476) INFO: -42.08 * 1.0 = -42.08 for ctc +2024-01-17 01:53:21,802 (beam_search:479) INFO: total log probability: -42.08 +2024-01-17 01:53:21,802 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:53:21,802 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:21,803 (beam_search:483) INFO: best hypo: TRANZENSENNIHRNATILICHENMOGEBNGAMBESTENAUSWIEDERSTENSIALSODERVERSUCHUNGAUCHNUREINEXMKLAUDWERN + +2024-01-17 01:53:21,804 (asr_inference:494) INFO: speech length: 218880 +2024-01-17 01:53:21,824 (beam_search:428) INFO: decoder input length: 339 +2024-01-17 01:53:21,824 (beam_search:429) INFO: max output length: 339 +2024-01-17 01:53:21,824 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:22,950 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:22,950 (beam_search:476) INFO: -39.63 * 1.0 = -39.63 for ctc +2024-01-17 01:53:22,950 (beam_search:479) INFO: total log probability: -39.63 +2024-01-17 01:53:22,950 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:53:22,950 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:22,951 (beam_search:483) INFO: best hypo: AUFDERNAHSEITEKÖNTEISMERMARIERGEBENDADIGROSTEDUNESTESWEREINFERAFODIELAVERANDIOBERFLECHAUFTUSTEGEN + +2024-01-17 01:53:22,953 (asr_inference:494) INFO: speech length: 345600 +2024-01-17 01:53:22,983 (beam_search:428) INFO: decoder input length: 537 +2024-01-17 01:53:22,983 (beam_search:429) INFO: max output length: 537 +2024-01-17 01:53:22,983 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:25,778 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:25,778 (beam_search:476) INFO: -66.70 * 1.0 = -66.70 for ctc +2024-01-17 01:53:25,778 (beam_search:479) INFO: total log probability: -66.70 +2024-01-17 01:53:25,778 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:53:25,779 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:25,779 (beam_search:483) INFO: best hypo: ERFÜCKTECHINZUDASSIJEDOCHNICHTEARZUOAUFGIEVORDERTWERDENSOLLTERNFERTFLICHTUNGENEINDZUGEENDIEÜBERIERENINTWITLUNGSTANDIEREVERANTORDUNGUNDIRERFÄIGKATENHIENAUSNGEENT + +2024-01-17 01:53:25,781 (asr_inference:494) INFO: speech length: 344640 +2024-01-17 01:53:25,812 (beam_search:428) INFO: decoder input length: 536 +2024-01-17 01:53:25,812 (beam_search:429) INFO: max output length: 536 +2024-01-17 01:53:25,812 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:28,567 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:28,567 (beam_search:476) INFO: -75.96 * 1.0 = -75.96 for ctc +2024-01-17 01:53:28,567 (beam_search:479) INFO: total log probability: -75.96 +2024-01-17 01:53:28,567 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-17 01:53:28,568 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:28,568 (beam_search:483) INFO: best hypo: TCIERTUELIHIELFISTELUNGENSINTINDISOFTERENGIEBAUDUNSOLENAHBEITSCHRITDENDIEDESCHÜLERALEINMÖGLICHERWEISENIHTBEVELTIGENKÖRHINTERFRAGENNEIELEGENUNDDERGLEREN + +2024-01-17 01:53:28,570 (asr_inference:494) INFO: speech length: 138240 +2024-01-17 01:53:28,585 (beam_search:428) INFO: decoder input length: 213 +2024-01-17 01:53:28,585 (beam_search:429) INFO: max output length: 213 +2024-01-17 01:53:28,585 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:29,262 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:29,262 (beam_search:476) INFO: -43.28 * 1.0 = -43.28 for ctc +2024-01-17 01:53:29,262 (beam_search:479) INFO: total log probability: -43.28 +2024-01-17 01:53:29,262 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:53:29,262 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:29,262 (beam_search:483) INFO: best hypo: AMFÜNFZEHNNAGSTNEUNZHNHUDERTVIRZIVIELIALIERTENNSÜTFRANKREICHEINDINWASIONWORDEAPERESCHENROGUNGENRD + +2024-01-17 01:53:29,264 (asr_inference:494) INFO: speech length: 178560 +2024-01-17 01:53:29,280 (beam_search:428) INFO: decoder input length: 276 +2024-01-17 01:53:29,280 (beam_search:429) INFO: max output length: 276 +2024-01-17 01:53:29,280 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:30,064 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:30,064 (beam_search:476) INFO: -37.57 * 1.0 = -37.57 for ctc +2024-01-17 01:53:30,064 (beam_search:479) INFO: total log probability: -37.57 +2024-01-17 01:53:30,064 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:53:30,064 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:30,065 (beam_search:483) INFO: best hypo: ERGRIFACHALLSANWASSENSWASSERKARMSEBSTENGROSERDENOSAURIEWIDERTIERECGSWERIMNICHTGEWAKEN + +2024-01-17 01:53:30,066 (asr_inference:494) INFO: speech length: 144000 +2024-01-17 01:53:30,081 (beam_search:428) INFO: decoder input length: 222 +2024-01-17 01:53:30,081 (beam_search:429) INFO: max output length: 222 +2024-01-17 01:53:30,081 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:30,858 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:30,858 (beam_search:476) INFO: -44.99 * 1.0 = -44.99 for ctc +2024-01-17 01:53:30,858 (beam_search:479) INFO: total log probability: -44.99 +2024-01-17 01:53:30,858 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:53:30,858 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:30,859 (beam_search:483) INFO: best hypo: SEITERKRNDUNGVORASUNTIORFÜNFZENDESENUODREISCIESESPAREGWEIGELUNGVIELVONSEIMINDIGEHNENKARAKTERNDSEINEIDENITETZUBEWAREN + +2024-01-17 01:53:30,860 (asr_inference:494) INFO: speech length: 560640 +2024-01-17 01:53:30,918 (beam_search:428) INFO: decoder input length: 873 +2024-01-17 01:53:30,918 (beam_search:429) INFO: max output length: 873 +2024-01-17 01:53:30,918 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:37,049 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:37,049 (beam_search:476) INFO: -113.36 * 1.0 = -113.36 for ctc +2024-01-17 01:53:37,049 (beam_search:479) INFO: total log probability: -113.36 +2024-01-17 01:53:37,049 (beam_search:480) INFO: normalized log probability: -0.46 +2024-01-17 01:53:37,049 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:37,050 (beam_search:483) INFO: best hypo: STROZSDENISDERANTELLANNIEXSDIEWINDESTIGHTDEBINDERGESAMTENRUPEDERLEUTEEMITDUGDERKOLOSEROFENBADERNNOCHGERINENSEXSTAUSENDERINDGESAENDREIHUNDENDREIGAUSENLELTEDIEINSÜTAFRICKERTUEINEMBIESTINTENDEITPUNGTANGESTETSITT + +2024-01-17 01:53:37,053 (asr_inference:494) INFO: speech length: 125760 +2024-01-17 01:53:37,066 (beam_search:428) INFO: decoder input length: 194 +2024-01-17 01:53:37,066 (beam_search:429) INFO: max output length: 194 +2024-01-17 01:53:37,066 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:37,665 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:37,666 (beam_search:476) INFO: -38.11 * 1.0 = -38.11 for ctc +2024-01-17 01:53:37,666 (beam_search:479) INFO: total log probability: -38.11 +2024-01-17 01:53:37,666 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:53:37,666 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:37,666 (beam_search:483) INFO: best hypo: ENSCHELZWEITAUSENSECHSELEUTERTASKONTINUMKONZETASEINEMITODEUMOBGENSATIONDSHLFENLEISTUMGSFEGEZUWERDEN + +2024-01-17 01:53:37,668 (asr_inference:494) INFO: speech length: 371520 +2024-01-17 01:53:37,701 (beam_search:428) INFO: decoder input length: 578 +2024-01-17 01:53:37,701 (beam_search:429) INFO: max output length: 578 +2024-01-17 01:53:37,701 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:39,759 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:39,759 (beam_search:476) INFO: -55.82 * 1.0 = -55.82 for ctc +2024-01-17 01:53:39,759 (beam_search:479) INFO: total log probability: -55.82 +2024-01-17 01:53:39,759 (beam_search:480) INFO: normalized log probability: -0.47 +2024-01-17 01:53:39,759 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:39,759 (beam_search:483) INFO: best hypo: INDIESEPIERIODENDEROEROPESCHENGICHIGHTESTANDDIERLICHUNDMECHTIHEGEVORDENERATOLISCHIYKIECHEAUFDENPRÜSTANDTT + +2024-01-17 01:53:39,761 (asr_inference:494) INFO: speech length: 220800 +2024-01-17 01:53:39,781 (beam_search:428) INFO: decoder input length: 342 +2024-01-17 01:53:39,781 (beam_search:429) INFO: max output length: 342 +2024-01-17 01:53:39,782 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:41,671 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:41,671 (beam_search:476) INFO: -80.00 * 1.0 = -80.00 for ctc +2024-01-17 01:53:41,671 (beam_search:479) INFO: total log probability: -80.00 +2024-01-17 01:53:41,671 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:53:41,671 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:41,672 (beam_search:483) INFO: best hypo: DIEERSDRCHTENDIBZICEMFHLUNGIFTASEINENEUDEPLOMATSCHINIZATIEVEVOERENDEDIESENJAHRESEGRIFENWERDENSOLLTEUMDIERAGISHENGRENZENGEGEBERFENTLIHENTERWETIONDZUSICHARNUNDPLOMATSCHBIZIEOMIZEINACHBANIEDERRTUSTE + +2024-01-17 01:53:41,674 (asr_inference:494) INFO: speech length: 174720 +2024-01-17 01:53:41,691 (beam_search:428) INFO: decoder input length: 270 +2024-01-17 01:53:41,691 (beam_search:429) INFO: max output length: 270 +2024-01-17 01:53:41,691 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:42,435 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:42,435 (beam_search:476) INFO: -37.48 * 1.0 = -37.48 for ctc +2024-01-17 01:53:42,435 (beam_search:479) INFO: total log probability: -37.48 +2024-01-17 01:53:42,435 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:53:42,435 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:42,436 (beam_search:483) INFO: best hypo: DISPETETEINIUTEGELEGEGHEITDASNOTLICHTUSENDAEHIMEMMEHRAUDERWENIERRUNDUMDIEURDUNKELST + +2024-01-17 01:53:42,437 (asr_inference:494) INFO: speech length: 312960 +2024-01-17 01:53:42,465 (beam_search:428) INFO: decoder input length: 486 +2024-01-17 01:53:42,465 (beam_search:429) INFO: max output length: 486 +2024-01-17 01:53:42,465 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:44,690 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:44,690 (beam_search:476) INFO: -70.93 * 1.0 = -70.93 for ctc +2024-01-17 01:53:44,690 (beam_search:479) INFO: total log probability: -70.93 +2024-01-17 01:53:44,690 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:53:44,690 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:44,691 (beam_search:483) INFO: best hypo: PROFSSORENPAMELEVERGUSSONVONDERNWÜUSTIAFDANDIMERKTANSOALISTENSCHEINEINEGEELIEGRANZEZUASCRETENWENDIEVOTTOUNSWEITEVONVERDECTIGEVERFNTICHEN + +2024-01-17 01:53:44,693 (asr_inference:494) INFO: speech length: 140160 +2024-01-17 01:53:44,707 (beam_search:428) INFO: decoder input length: 216 +2024-01-17 01:53:44,707 (beam_search:429) INFO: max output length: 216 +2024-01-17 01:53:44,707 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:45,434 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:45,434 (beam_search:476) INFO: -51.72 * 1.0 = -51.72 for ctc +2024-01-17 01:53:45,434 (beam_search:479) INFO: total log probability: -51.72 +2024-01-17 01:53:45,434 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:53:45,434 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:45,435 (beam_search:483) INFO: best hypo: ESKANSICHLONEINEELKATZUKAUFNDIEZUTRITENWDERTUUSGEWELENPAGSENHETAFRIKARDERZUALENZUÜTFRIKANSCHNERTONALPARXSGEWERT + +2024-01-17 01:53:45,436 (asr_inference:494) INFO: speech length: 147840 +2024-01-17 01:53:45,451 (beam_search:428) INFO: decoder input length: 228 +2024-01-17 01:53:45,451 (beam_search:429) INFO: max output length: 228 +2024-01-17 01:53:45,451 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:46,268 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:46,268 (beam_search:476) INFO: -46.63 * 1.0 = -46.63 for ctc +2024-01-17 01:53:46,268 (beam_search:479) INFO: total log probability: -46.63 +2024-01-17 01:53:46,268 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:53:46,268 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:46,269 (beam_search:483) INFO: best hypo: DIEPRÜKESOLEMSETEMBERZWEITAUENSIBSHNFOLSTENDITNBETRIEAUFNEHMNISWRRWAREDSIPASIANISCHENZOLPUNKTEDANFERTIGSTELLZEINWERT + +2024-01-17 01:53:46,271 (asr_inference:494) INFO: speech length: 188160 +2024-01-17 01:53:46,289 (beam_search:428) INFO: decoder input length: 291 +2024-01-17 01:53:46,289 (beam_search:429) INFO: max output length: 291 +2024-01-17 01:53:46,289 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:47,625 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:47,625 (beam_search:476) INFO: -69.86 * 1.0 = -69.86 for ctc +2024-01-17 01:53:47,625 (beam_search:479) INFO: total log probability: -69.86 +2024-01-17 01:53:47,625 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:53:47,625 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:47,626 (beam_search:483) INFO: best hypo: WERENDEIERXPRMINTELLEIMSTFNELAGEUSEINSCHEINTDIEBOLERMOTLIETÄTZUSENGUNGGBTESBSSERKEINEMITIKAMÄNTEDIALSEINDRDIZUBEHANDLUNBESTENDEINVEKTIONGEEGNETNACHEWIESEORDEN + +2024-01-17 01:53:47,628 (asr_inference:494) INFO: speech length: 194080 +2024-01-17 01:53:47,646 (beam_search:428) INFO: decoder input length: 301 +2024-01-17 01:53:47,646 (beam_search:429) INFO: max output length: 301 +2024-01-17 01:53:47,646 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:49,118 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:49,118 (beam_search:476) INFO: -70.61 * 1.0 = -70.61 for ctc +2024-01-17 01:53:49,118 (beam_search:479) INFO: total log probability: -70.61 +2024-01-17 01:53:49,118 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:53:49,118 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:49,119 (beam_search:483) INFO: best hypo: EINEUSERSTLEPAFTERDEBECHEWECHSELVANSTADTWAIERWUKDEMPLANEINALGEMEINENSTATENKONGRESTZUBERUFENUNDKONDESIVOLLOUFIKTNONUNICHTÜBERDESORZULEGEDEROGRMMUNDENORTESZUSAMTRETSEINIGEN + +2024-01-17 01:53:49,121 (asr_inference:494) INFO: speech length: 270240 +2024-01-17 01:53:49,146 (beam_search:428) INFO: decoder input length: 420 +2024-01-17 01:53:49,146 (beam_search:429) INFO: max output length: 420 +2024-01-17 01:53:49,146 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:51,178 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:51,178 (beam_search:476) INFO: -52.66 * 1.0 = -52.66 for ctc +2024-01-17 01:53:51,178 (beam_search:479) INFO: total log probability: -52.66 +2024-01-17 01:53:51,178 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:53:51,178 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:51,179 (beam_search:483) INFO: best hypo: ERWUSTENICHTWASIMDASLEBENKOSSBARESGERAUPTATESCPANKRAFTUNDMUTDDASSESINFEIKUNDSCOEUGEMACHTATEUNDFÄICHZUDENHOHNDINGENZUODENENUNGETRÜBPTEITFROLDENGEHÖRT + +2024-01-17 01:53:51,180 (asr_inference:494) INFO: speech length: 292320 +2024-01-17 01:53:51,207 (beam_search:428) INFO: decoder input length: 454 +2024-01-17 01:53:51,207 (beam_search:429) INFO: max output length: 454 +2024-01-17 01:53:51,207 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:54,207 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:54,207 (beam_search:476) INFO: -86.53 * 1.0 = -86.53 for ctc +2024-01-17 01:53:54,207 (beam_search:479) INFO: total log probability: -86.53 +2024-01-17 01:53:54,207 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:53:54,207 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:54,208 (beam_search:483) INFO: best hypo: DIESERUNGEMANNHISKAKALITZIENUNDBEFANSIGGRADEUEWANDERSCHAFTALSINIMGEANTENKÖNIGREICHDBEKANDNACHUNGWENDERPRNZESENVERLESENWURDEEISAKTDESCHNEIDERWIENESWEITRNIHTSISTEINWEIPAUCHNHNICTKÜKÖLSTUNDDUTKÖNIGSEIDAMZUOWERDENDASGEISSETMIALEDINGST + +2024-01-17 01:53:54,210 (asr_inference:494) INFO: speech length: 279040 +2024-01-17 01:53:54,236 (beam_search:428) INFO: decoder input length: 433 +2024-01-17 01:53:54,236 (beam_search:429) INFO: max output length: 433 +2024-01-17 01:53:54,236 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:56,761 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:56,761 (beam_search:476) INFO: -83.05 * 1.0 = -83.05 for ctc +2024-01-17 01:53:56,761 (beam_search:479) INFO: total log probability: -83.05 +2024-01-17 01:53:56,761 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:53:56,761 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:56,762 (beam_search:483) INFO: best hypo: NOCFÜNFMINUTENNDIEWOLKENDEBEWUSTLOSIGKEITBEGANNZUSCHWINDENIERTWUSTEHSERWOLDASIHNMEIMEIGNENBETELAGUNDDASDIEROTEOGLOTNICHTANDERSWALSDASVEUEIMKAMINDERINDASTUBEESWANACHTEINEKEREBRANTEAFDEMTISCHE + +2024-01-17 01:53:56,764 (asr_inference:494) INFO: speech length: 199840 +2024-01-17 01:53:56,782 (beam_search:428) INFO: decoder input length: 310 +2024-01-17 01:53:56,782 (beam_search:429) INFO: max output length: 310 +2024-01-17 01:53:56,782 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:53:58,203 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:53:58,203 (beam_search:476) INFO: -69.78 * 1.0 = -69.78 for ctc +2024-01-17 01:53:58,203 (beam_search:479) INFO: total log probability: -69.78 +2024-01-17 01:53:58,203 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:53:58,203 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:53:58,204 (beam_search:483) INFO: best hypo: EICHEIEVETRENGUNENWEBECHTEUNTERHALTENCOMTANEMPRPEITITZALERDIHOCHFLODESSEXSUENBEDFFTIGKEITSOFNDESENDEGENANDENSILLISCHNREAKTIONSODEIEDERSTANSPILDONGENDEMER + +2024-01-17 01:53:58,206 (asr_inference:494) INFO: speech length: 320000 +2024-01-17 01:53:58,235 (beam_search:428) INFO: decoder input length: 497 +2024-01-17 01:53:58,235 (beam_search:429) INFO: max output length: 497 +2024-01-17 01:53:58,235 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:00,829 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:00,829 (beam_search:476) INFO: -59.91 * 1.0 = -59.91 for ctc +2024-01-17 01:54:00,829 (beam_search:479) INFO: total log probability: -59.91 +2024-01-17 01:54:00,829 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:54:00,829 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:00,830 (beam_search:483) INFO: best hypo: TABERAFENGEHRENBEIHAGKENBEANDIEKISTENWANNTNUNSOHRTEICHAUFACEZUSEINENEINKLRERSCHÖNAGEDANKENGANGDENICHIRGENDIEMITDEMBAUCHAUSGEHEKTABENMOSSDENAFFENNDENKTENMI + +2024-01-17 01:54:00,831 (asr_inference:494) INFO: speech length: 304160 +2024-01-17 01:54:00,859 (beam_search:428) INFO: decoder input length: 473 +2024-01-17 01:54:00,859 (beam_search:429) INFO: max output length: 473 +2024-01-17 01:54:00,859 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:03,965 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:03,965 (beam_search:476) INFO: -111.91 * 1.0 = -111.91 for ctc +2024-01-17 01:54:03,966 (beam_search:479) INFO: total log probability: -111.91 +2024-01-17 01:54:03,966 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:54:03,966 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:03,967 (beam_search:483) INFO: best hypo: ISSSPATREERNESMENSCHENDENZIKÄNENFRAGTEILEISEWELCHEUNBEMEAKTAMICHERANGETRETENWAUMICHINDTGEGNETEDASSNENFANTESIKOPFSEIUNTSCHUBTZEICHUNGILICUNTUDIEANDONDLÄTTERUNATÜLICGSPACRICHIEUNBAHEILTDENEWEINSEITOEUESPETRIEMISTEROTSCHSTES + +2024-01-17 01:54:03,969 (asr_inference:494) INFO: speech length: 233120 +2024-01-17 01:54:03,989 (beam_search:428) INFO: decoder input length: 362 +2024-01-17 01:54:03,989 (beam_search:429) INFO: max output length: 362 +2024-01-17 01:54:03,989 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:05,833 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:05,833 (beam_search:476) INFO: -77.37 * 1.0 = -77.37 for ctc +2024-01-17 01:54:05,833 (beam_search:479) INFO: total log probability: -77.37 +2024-01-17 01:54:05,833 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:54:05,833 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:05,834 (beam_search:483) INFO: best hypo: ICHWEISSICHSERKRANKGBINSAIGTESINEREINRWEILEVORNPAMINUTENVERSCHTEICHMICHEETTEUMZUREENUNDFÜLDEDASICKENGLIEDMERENÜORNKAMESWEREGUTDWENICHENGIMÜTELEICHTDANKÖNTERBEVORICSTERDEN + +2024-01-17 01:54:05,836 (asr_inference:494) INFO: speech length: 226080 +2024-01-17 01:54:05,856 (beam_search:428) INFO: decoder input length: 351 +2024-01-17 01:54:05,856 (beam_search:429) INFO: max output length: 351 +2024-01-17 01:54:05,856 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:07,562 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:07,562 (beam_search:476) INFO: -57.43 * 1.0 = -57.43 for ctc +2024-01-17 01:54:07,562 (beam_search:479) INFO: total log probability: -57.43 +2024-01-17 01:54:07,562 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:54:07,562 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:07,563 (beam_search:483) INFO: best hypo: SOABERISTWERUNSERWESENSKUNDOTSELLVERDACHEROMHATZICHEDOCDERCHLANGENKNEULDESALKENSATANGESHLUNGENUNGÜBERDEMFÜNKIENDENLIEBIISDIFENSERNISTESHASSESELAGERTWASWONDERDAN + +2024-01-17 01:54:07,565 (asr_inference:494) INFO: speech length: 166240 +2024-01-17 01:54:07,581 (beam_search:428) INFO: decoder input length: 257 +2024-01-17 01:54:07,581 (beam_search:429) INFO: max output length: 257 +2024-01-17 01:54:07,581 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:08,553 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:08,553 (beam_search:476) INFO: -50.91 * 1.0 = -50.91 for ctc +2024-01-17 01:54:08,553 (beam_search:479) INFO: total log probability: -50.91 +2024-01-17 01:54:08,553 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:54:08,553 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:08,554 (beam_search:483) INFO: best hypo: BESIVIRELIEAGEBLIEBENABERSIWAGETZWUNGKZUGEHNEADIÜNKLITKEITBEIDENMALSZEITENEINESACHEWAAUWÄCHINGETZSHÄRDHORLSTRENGEGEHALTENWURDE + +2024-01-17 01:54:08,555 (asr_inference:494) INFO: speech length: 261600 +2024-01-17 01:54:08,578 (beam_search:428) INFO: decoder input length: 406 +2024-01-17 01:54:08,579 (beam_search:429) INFO: max output length: 406 +2024-01-17 01:54:08,579 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:11,144 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:11,144 (beam_search:476) INFO: -91.63 * 1.0 = -91.63 for ctc +2024-01-17 01:54:11,144 (beam_search:479) INFO: total log probability: -91.63 +2024-01-17 01:54:11,144 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:54:11,144 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:11,145 (beam_search:483) INFO: best hypo: NBLICKLICHFÜLTEWIEHREAMSICHTENÜBERMICHIRERMPINDUNGEDFÜHRMICHNICHTUMEINATOUMVERINDETWAHEMNUÜBEHAUPTKANEENDERUNGFEECHWARMDICHSEISIREMERSTEINATENAUDGEWELCHESNIEMALSDUCHTRENENGENETZTNIEMASINERTLICHKEITAUFGELEUCHTETATEAMEN + +2024-01-17 01:54:11,147 (asr_inference:494) INFO: speech length: 320000 +2024-01-17 01:54:11,176 (beam_search:428) INFO: decoder input length: 497 +2024-01-17 01:54:11,176 (beam_search:429) INFO: max output length: 497 +2024-01-17 01:54:11,176 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:14,909 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:14,909 (beam_search:476) INFO: -121.57 * 1.0 = -121.57 for ctc +2024-01-17 01:54:14,909 (beam_search:479) INFO: total log probability: -121.57 +2024-01-17 01:54:14,909 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-17 01:54:14,909 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:14,910 (beam_search:483) INFO: best hypo: NSODERSSEMISTZERGUTWINEHÖUGLIGNERFÄERIDSICFREUNWÜEWERENSCHNÄLDRNACHANDENSOLBEAUFPRECHENSHNALREITENDMITÜRNOCFORDERNCHDASLAGEREICHENERSTIGEAUDIPFÄERDEDINAUSGEROTATENUNDFLOGEMGALOPBTDAVONDIESALHÜTETENIRUNSDERFÄERDEIDERDEREKZEFOLENWEREGERADEAUSNDERSPADEN + +2024-01-17 01:54:14,913 (asr_inference:494) INFO: speech length: 313600 +2024-01-17 01:54:14,941 (beam_search:428) INFO: decoder input length: 487 +2024-01-17 01:54:14,941 (beam_search:429) INFO: max output length: 487 +2024-01-17 01:54:14,941 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:18,552 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:18,552 (beam_search:476) INFO: -91.85 * 1.0 = -91.85 for ctc +2024-01-17 01:54:18,552 (beam_search:479) INFO: total log probability: -91.85 +2024-01-17 01:54:18,552 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:54:18,552 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:18,554 (beam_search:483) INFO: best hypo: WALDIEBERMITPÄECHPBESTRICHENWAHRBIEBEINERVONDENGELENENPANTOFLENFESTHÄNGENUNDINDERANGSDACHSNICHTERANINITZONEMEMUNDIEISDENLETZTENSCHITVONDERTREPETADDERHATSTZWILFAUSGESCHLAGENDARARWAGENUNDFERDEVERSCHUNDENUNDASCHENPOTESTANDINSEINSCHENKLEIDERAUFERUNGLNSTRASE + +2024-01-17 01:54:18,556 (asr_inference:494) INFO: speech length: 183840 +2024-01-17 01:54:18,573 (beam_search:428) INFO: decoder input length: 285 +2024-01-17 01:54:18,573 (beam_search:429) INFO: max output length: 285 +2024-01-17 01:54:18,573 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:19,557 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:19,558 (beam_search:476) INFO: -53.05 * 1.0 = -53.05 for ctc +2024-01-17 01:54:19,558 (beam_search:479) INFO: total log probability: -53.05 +2024-01-17 01:54:19,558 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-17 01:54:19,558 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:19,558 (beam_search:483) INFO: best hypo: IEIENOMDESASVERMAGEEINVINSTERANSTLAGEREBESENTZYÜDENIESISCHRICKALSAKTEHRGIETDASMENIGEGITANUNBUTVERGISNSVERMÄDEN + +2024-01-17 01:54:19,560 (asr_inference:494) INFO: speech length: 284960 +2024-01-17 01:54:19,586 (beam_search:428) INFO: decoder input length: 443 +2024-01-17 01:54:19,586 (beam_search:429) INFO: max output length: 443 +2024-01-17 01:54:19,586 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:21,851 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:21,851 (beam_search:476) INFO: -42.83 * 1.0 = -42.83 for ctc +2024-01-17 01:54:21,851 (beam_search:479) INFO: total log probability: -42.83 +2024-01-17 01:54:21,851 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:54:21,851 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:21,852 (beam_search:483) INFO: best hypo: NURDERDOCKTORUNDIEWERTERENSOLLENVORSEINEAUGENKOMMENERKLÄATEDIETRINERINGROSSEMAMTSEIFERDAMITWADIEFRAROBERSTGANSEINFERSTANDENUNDPIRXSTERFREITKERTESIMITIEREN + +2024-01-17 01:54:21,854 (asr_inference:494) INFO: speech length: 298240 +2024-01-17 01:54:21,881 (beam_search:428) INFO: decoder input length: 463 +2024-01-17 01:54:21,881 (beam_search:429) INFO: max output length: 463 +2024-01-17 01:54:21,881 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:24,624 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:24,624 (beam_search:476) INFO: -70.41 * 1.0 = -70.41 for ctc +2024-01-17 01:54:24,624 (beam_search:479) INFO: total log probability: -70.41 +2024-01-17 01:54:24,624 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:54:24,624 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:24,626 (beam_search:483) INFO: best hypo: KAARUNTRÜSTICHÜBERDIELAGEDASKÖNZTLRSERBEGANZUWEINENUNSCHLCHTZTDELANGEINDEORGEHALTENENHNDEDERKONSLEAWATETEBISKASICBERUICHTHATTEUNDENSCHLOSICHDANDARERKEINANDERNAUSIGFANDTDERNOCHZUMEITERSCREIBEN + +2024-01-17 01:54:24,627 (asr_inference:494) INFO: speech length: 320000 +2024-01-17 01:54:24,656 (beam_search:428) INFO: decoder input length: 497 +2024-01-17 01:54:24,656 (beam_search:429) INFO: max output length: 497 +2024-01-17 01:54:24,656 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:28,353 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:28,353 (beam_search:476) INFO: -108.08 * 1.0 = -108.08 for ctc +2024-01-17 01:54:28,353 (beam_search:479) INFO: total log probability: -108.08 +2024-01-17 01:54:28,353 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:54:28,353 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:28,355 (beam_search:483) INFO: best hypo: ONDIMFERDEHERDENDERPATSCHENUNDSAGNUNZSDASSIFENEAPATSCHEMFÄRDUNDSEBENSUVIELEWARENUNPRENIGEBENWIRDENÜFÜRINKEIOWABFERTDASINDUNRIKLIGARFODUMAPATSCHENFÄRDEZOHLENALSORCHTIGHERARSCHLDEREMTODEEBESERGEFALLENUNDERIEBLUTVERGIESENWECHESUNBEVORSTANDWEISEFÄERDEHENDTLER + +2024-01-17 01:54:28,357 (asr_inference:494) INFO: speech length: 228640 +2024-01-17 01:54:28,377 (beam_search:428) INFO: decoder input length: 355 +2024-01-17 01:54:28,377 (beam_search:429) INFO: max output length: 355 +2024-01-17 01:54:28,378 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:30,337 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:30,337 (beam_search:476) INFO: -87.90 * 1.0 = -87.90 for ctc +2024-01-17 01:54:30,337 (beam_search:479) INFO: total log probability: -87.90 +2024-01-17 01:54:30,337 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:54:30,337 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:30,338 (beam_search:483) INFO: best hypo: DASMATONEHÜTCHENVONSCWATZAMSAMETKATIÜRSAIERELANGENLOKENGEDRÜCTDIEREWANGNUMFLOSSENNDÜBERSCHLTENHERABWEITENSOTRATEIDRSEINFECHRELENTLICHGEBOUDEUNDSTEBPTETZWISCENEREINDERHEIBGEBLÄNETNOFKNERAUFENDAB + +2024-01-17 01:54:30,340 (asr_inference:494) INFO: speech length: 251040 +2024-01-17 01:54:30,363 (beam_search:428) INFO: decoder input length: 390 +2024-01-17 01:54:30,363 (beam_search:429) INFO: max output length: 390 +2024-01-17 01:54:30,363 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:32,418 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:32,418 (beam_search:476) INFO: -80.05 * 1.0 = -80.05 for ctc +2024-01-17 01:54:32,418 (beam_search:479) INFO: total log probability: -80.05 +2024-01-17 01:54:32,418 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:54:32,418 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:32,419 (beam_search:483) INFO: best hypo: TUMUSTERSTENZAGENALENSÜNTHAFTENSTREBENUNENTIEVEREUIUNDDEMUDDIEFÜHRBIERHLLINGERFLENGEGENDIEDUGEFREELTESTTIEJÜMLNGEWLCHEFENSCHESOSOLNEGEFLONSOUCHTENNAUENERWERKTATUNFANDENIN + +2024-01-17 01:54:32,421 (asr_inference:494) INFO: speech length: 171520 +2024-01-17 01:54:32,437 (beam_search:428) INFO: decoder input length: 265 +2024-01-17 01:54:32,437 (beam_search:429) INFO: max output length: 265 +2024-01-17 01:54:32,437 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:33,425 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:33,425 (beam_search:476) INFO: -39.68 * 1.0 = -39.68 for ctc +2024-01-17 01:54:33,425 (beam_search:479) INFO: total log probability: -39.68 +2024-01-17 01:54:33,425 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:54:33,425 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:33,426 (beam_search:483) INFO: best hypo: ERLIESSEINEGRETENIHTVORTSCHLÄBENAMALLERWINIGXSTEABERINDENGROSSENDVOGELBAUARUSIEALEENEINEMTONEBFEIFENMOUSTENWIERSTESAKTE + +2024-01-17 01:54:33,427 (asr_inference:494) INFO: speech length: 268800 +2024-01-17 01:54:33,452 (beam_search:428) INFO: decoder input length: 417 +2024-01-17 01:54:33,452 (beam_search:429) INFO: max output length: 417 +2024-01-17 01:54:33,452 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:36,046 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:36,046 (beam_search:476) INFO: -93.32 * 1.0 = -93.32 for ctc +2024-01-17 01:54:36,046 (beam_search:479) INFO: total log probability: -93.32 +2024-01-17 01:54:36,046 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:54:36,046 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:36,047 (beam_search:483) INFO: best hypo: VRNHESKOMALTENUNHALIGEBEGEISTRNGVIELEBIELTASTELÜGENHAFTENABELWELDTRENEALSERERMOCHDIEBULERISCHEÜLBEKAETEWEIBIENGESTALTENSOBERHAFASSTELENINDEMVONLEBENTEMODELEDIKANDATIONGVONDENALTENMAHMOBILDERRBERFORMNDBIELUNGINDNAM + +2024-01-17 01:54:36,049 (asr_inference:494) INFO: speech length: 320000 +2024-01-17 01:54:36,077 (beam_search:428) INFO: decoder input length: 497 +2024-01-17 01:54:36,078 (beam_search:429) INFO: max output length: 497 +2024-01-17 01:54:36,078 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:39,661 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:39,661 (beam_search:476) INFO: -98.12 * 1.0 = -98.12 for ctc +2024-01-17 01:54:39,661 (beam_search:479) INFO: total log probability: -98.12 +2024-01-17 01:54:39,661 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:54:39,661 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:39,663 (beam_search:483) INFO: best hypo: BWEGUNGUNTATDENSTENZUGIERSTEMTEDIEFOEUNANGEGEBNEINKGREDENZIERNMICRÜBENHANFEEICHENUNSAUERAMFANALLEINDEMFEIFNKOPFERNWESENABERINFÜNFTNHAUPTSTOFHAICHNICGGENANDJERTTROCHNDSCHMEKTIGDASEHONSTÜCHENFILSCHUDERBEISEINISEIGPLIESTENRAUHAUCHGEGDENHEMEUNGEGEDI + +2024-01-17 01:54:39,664 (asr_inference:494) INFO: speech length: 302560 +2024-01-17 01:54:39,692 (beam_search:428) INFO: decoder input length: 470 +2024-01-17 01:54:39,692 (beam_search:429) INFO: max output length: 470 +2024-01-17 01:54:39,692 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:42,985 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:42,985 (beam_search:476) INFO: -85.37 * 1.0 = -85.37 for ctc +2024-01-17 01:54:42,985 (beam_search:479) INFO: total log probability: -85.37 +2024-01-17 01:54:42,986 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:54:42,986 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:42,987 (beam_search:483) INFO: best hypo: UNDDASFOURSTANDAUFUNDFLACKEATUNKOCHDASESSENFERTICHUNDDERBRATENBRUTZELTERFORTUNDERKOCHGABDEMKÜCHENIUNGENEINEORFEIGEUNDDEMARKTROPFTEDASUNFÄERTIGHDARWARTDIEHOCHTZEITONDEMKÖNICHSONMIEDONGRÖUSIENGEFEIERTUNSIELIETNEFERGNÜTEBISANIERENDE + +2024-01-17 01:54:42,989 (asr_inference:494) INFO: speech length: 203520 +2024-01-17 01:54:43,007 (beam_search:428) INFO: decoder input length: 315 +2024-01-17 01:54:43,007 (beam_search:429) INFO: max output length: 315 +2024-01-17 01:54:43,007 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:44,264 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:44,264 (beam_search:476) INFO: -52.14 * 1.0 = -52.14 for ctc +2024-01-17 01:54:44,264 (beam_search:479) INFO: total log probability: -52.14 +2024-01-17 01:54:44,265 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:54:44,265 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:44,265 (beam_search:483) INFO: best hypo: UNMDDESERMINICHNACTRAGENBOLEWENICHIDERSHFÄNSTIGWAGINSEINOHMEINEMRARTDERHERFARAEHADIEINALLMPRECHTGEHAUTUNDICHMAAMUNMRECHTABER + +2024-01-17 01:54:44,267 (asr_inference:494) INFO: speech length: 162720 +2024-01-17 01:54:44,283 (beam_search:428) INFO: decoder input length: 252 +2024-01-17 01:54:44,283 (beam_search:429) INFO: max output length: 252 +2024-01-17 01:54:44,283 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:45,166 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:45,166 (beam_search:476) INFO: -55.71 * 1.0 = -55.71 for ctc +2024-01-17 01:54:45,166 (beam_search:479) INFO: total log probability: -55.71 +2024-01-17 01:54:45,166 (beam_search:480) INFO: normalized log probability: -0.43 +2024-01-17 01:54:45,166 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:45,167 (beam_search:483) INFO: best hypo: GÄCHENEMASENOWINIGEKRAMBETRUGEREITEESICHKILIEFRMIGAUSHUMUSTEDERERDESMINDGEGENFLIGEDESPRENKISCHOSAUFANGENNDZUWIBRINEN + +2024-01-17 01:54:45,168 (asr_inference:494) INFO: speech length: 320000 +2024-01-17 01:54:45,197 (beam_search:428) INFO: decoder input length: 497 +2024-01-17 01:54:45,197 (beam_search:429) INFO: max output length: 497 +2024-01-17 01:54:45,197 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:49,082 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:49,082 (beam_search:476) INFO: -109.70 * 1.0 = -109.70 for ctc +2024-01-17 01:54:49,082 (beam_search:479) INFO: total log probability: -109.70 +2024-01-17 01:54:49,082 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:54:49,082 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:49,084 (beam_search:483) INFO: best hypo: DERFCKSREICHTEEMDEUNFRITICHERIEDENSPFEIVERHNDERMANTATWACKARSEINESEXSZYGENSAGKTEDERROSIGEISACHTETNICHTAUFDIVERSCHIEDENEHAUDERMENSCHENDENDIKÖNSICHMITFABEBESCHMIERENMINTZUDTEUSCHENSODEERSIDASHETZSANDIEHTZENDERKLIGERVOBERÜBTENSTAMEDERKAEIOASEINTAPTVERUNERSCHROKENNTREUDASMEINEIGEHÄNG + +2024-01-17 01:54:49,086 (asr_inference:494) INFO: speech length: 251200 +2024-01-17 01:54:49,109 (beam_search:428) INFO: decoder input length: 390 +2024-01-17 01:54:49,109 (beam_search:429) INFO: max output length: 390 +2024-01-17 01:54:49,109 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:51,172 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:51,172 (beam_search:476) INFO: -50.67 * 1.0 = -50.67 for ctc +2024-01-17 01:54:51,172 (beam_search:479) INFO: total log probability: -50.67 +2024-01-17 01:54:51,172 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:54:51,172 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:51,173 (beam_search:483) INFO: best hypo: ALLSASWIEMETIERBEGEGNITSCIEBSICHDEUICHUNDÜBEREINANDERBALTUNTERSCHEIBENWERINKONTAKTDERISTERERHANDNDEMEINIGEIERNAHMONDERMEINIGEWEIDELESCHENEINANDERAUSBEIDEVERSCHLINGENSICH + +2024-01-17 01:54:51,175 (asr_inference:494) INFO: speech length: 281920 +2024-01-17 01:54:51,201 (beam_search:428) INFO: decoder input length: 438 +2024-01-17 01:54:51,201 (beam_search:429) INFO: max output length: 438 +2024-01-17 01:54:51,201 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:53,921 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:53,921 (beam_search:476) INFO: -80.91 * 1.0 = -80.91 for ctc +2024-01-17 01:54:53,921 (beam_search:479) INFO: total log probability: -80.91 +2024-01-17 01:54:53,921 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:54:53,921 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:53,923 (beam_search:483) INFO: best hypo: ERMÖSTEDENENFACHENRONITENKÖRALDESMALESMITALLEERKLEHRENENUNZRECHTWESSUNGENIMITKRAUSENWIGUNVESCHNARKENUNVERBREMENICHTRETEIDIEPERSONDESERAUSGEBESNDBITETICHIÜNSTIGELISERTUOLISTIEDUWEITELISESTFOLDENDISDEGÜTTISTMERHEN + +2024-01-17 01:54:53,924 (asr_inference:494) INFO: speech length: 294560 +2024-01-17 01:54:53,951 (beam_search:428) INFO: decoder input length: 458 +2024-01-17 01:54:53,951 (beam_search:429) INFO: max output length: 458 +2024-01-17 01:54:53,951 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:56,929 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:56,929 (beam_search:476) INFO: -88.21 * 1.0 = -88.21 for ctc +2024-01-17 01:54:56,929 (beam_search:479) INFO: total log probability: -88.21 +2024-01-17 01:54:56,929 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:54:56,929 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:56,931 (beam_search:483) INFO: best hypo: DIEOFDAMENBEKAMENKRMPFERUNDTIEKÖNIGENUNIEPROMZESSENENDIERERALLALIBSENHÜNZCHENWERNDERMALTERUFDENHOSGENOMHADENBEMERKENZUIRENSCRÄCTENDASDILIELERARMARANDFABENENUNDORANSCAEDENSEIDENKLEIDERALEICTBESETMITDENHESLICSTENÖFLÄGENWANE + +2024-01-17 01:54:56,932 (asr_inference:494) INFO: speech length: 211200 +2024-01-17 01:54:56,952 (beam_search:428) INFO: decoder input length: 327 +2024-01-17 01:54:56,952 (beam_search:429) INFO: max output length: 327 +2024-01-17 01:54:56,952 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:54:58,554 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:54:58,555 (beam_search:476) INFO: -62.82 * 1.0 = -62.82 for ctc +2024-01-17 01:54:58,555 (beam_search:479) INFO: total log probability: -62.82 +2024-01-17 01:54:58,555 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:54:58,555 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:54:58,556 (beam_search:483) INFO: best hypo: VONLIEDANDIEIESINGENUNKLVIERPIESENDIESISPIELNVONGEILTBÜÖRSENDIESIHIEGKELNVONVANZÖÜSCHENBÜCHENDIESÜBERSETZENKONTEBISMENGEMÖÜTWERENDICHLAUSTEZONACHAMNGAUFGESTACHETWURDE + +2024-01-17 01:54:58,557 (asr_inference:494) INFO: speech length: 224320 +2024-01-17 01:54:58,578 (beam_search:428) INFO: decoder input length: 348 +2024-01-17 01:54:58,578 (beam_search:429) INFO: max output length: 348 +2024-01-17 01:54:58,578 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:00,329 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:00,329 (beam_search:476) INFO: -67.77 * 1.0 = -67.77 for ctc +2024-01-17 01:55:00,329 (beam_search:479) INFO: total log probability: -67.77 +2024-01-17 01:55:00,329 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:55:00,329 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:00,330 (beam_search:483) INFO: best hypo: AMENNATENWANBLOSIRENZIGERSCMOCWANIHREKASTANIENBRANFLÄCHTENWILCHENWILDEUNNATÜRLICHERANMUNDAUIRSCHLTENERABVIELENCHNAMEINBOGENGFEINKATOUNGSNDZEICHETEMMTOSRSORKFALTIOMGRSSER + +2024-01-17 01:55:00,331 (asr_inference:494) INFO: speech length: 264800 +2024-01-17 01:55:00,355 (beam_search:428) INFO: decoder input length: 411 +2024-01-17 01:55:00,355 (beam_search:429) INFO: max output length: 411 +2024-01-17 01:55:00,355 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:03,047 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:03,047 (beam_search:476) INFO: -87.22 * 1.0 = -87.22 for ctc +2024-01-17 01:55:03,047 (beam_search:479) INFO: total log probability: -87.22 +2024-01-17 01:55:03,047 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:55:03,047 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:03,048 (beam_search:483) INFO: best hypo: AURWIEDEAUSDETCHLANDNOCHASIELENEINEMANDERENSTARTKONTEMENEFAHNWASERGEGNSTANDUNDESESLTADDIESUNTEREDUNGGEWISENSEIANVRMUTETEDSSESICUMERKLIERUNGDERMATZIERÜBEIERABSICHTENUNDUNDIEVERMITLUNGDERMECHTETZWICHNDNMARSTATENUNROSPOTANIENHANDLE + +2024-01-17 01:55:03,050 (asr_inference:494) INFO: speech length: 205120 +2024-01-17 01:55:03,069 (beam_search:428) INFO: decoder input length: 318 +2024-01-17 01:55:03,069 (beam_search:429) INFO: max output length: 318 +2024-01-17 01:55:03,069 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:04,530 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:04,530 (beam_search:476) INFO: -51.61 * 1.0 = -51.61 for ctc +2024-01-17 01:55:04,530 (beam_search:479) INFO: total log probability: -51.61 +2024-01-17 01:55:04,530 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:55:04,530 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:04,531 (beam_search:483) INFO: best hypo: LASUNSWENIGSENSEINERZEITLANGVERSUOCHENINDIEVERNWIERUFDESESBEISIMITEINANDERAUSREICHENDADASZUSAMMENHÄNGENDEVIEDUESAGSTEIGENTLICHEUERLLEMENTISVERSETZTEIDRT + +2024-01-17 01:55:04,533 (asr_inference:494) INFO: speech length: 270400 +2024-01-17 01:55:04,558 (beam_search:428) INFO: decoder input length: 420 +2024-01-17 01:55:04,558 (beam_search:429) INFO: max output length: 420 +2024-01-17 01:55:04,558 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:06,831 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:06,831 (beam_search:476) INFO: -80.81 * 1.0 = -80.81 for ctc +2024-01-17 01:55:06,831 (beam_search:479) INFO: total log probability: -80.81 +2024-01-17 01:55:06,831 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:55:06,831 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:06,832 (beam_search:483) INFO: best hypo: VEASCHINENVORKOMMNSEFÜRDENZUDERVERMOTUNGDASFRAUWISEDIKLEINENVWISENVERBRENERIESOLBESEINSUSTACHGEHEITZTABENDASDERTPLADENZSPRANGAUSSTEMOLEINFÜRCHTELICHEREROCHWAGENUMEWORDENSEIN + +2024-01-17 01:55:06,834 (asr_inference:494) INFO: speech length: 161920 +2024-01-17 01:55:06,849 (beam_search:428) INFO: decoder input length: 250 +2024-01-17 01:55:06,849 (beam_search:429) INFO: max output length: 250 +2024-01-17 01:55:06,849 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:07,758 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:07,758 (beam_search:476) INFO: -27.88 * 1.0 = -27.88 for ctc +2024-01-17 01:55:07,758 (beam_search:479) INFO: total log probability: -27.88 +2024-01-17 01:55:07,758 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:55:07,758 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:07,759 (beam_search:483) INFO: best hypo: UNDGINGDEMSCHREIENNACHSOSAHERENTLICHEINHOHNBAUMUNDOBENDERRAUFSASEINKLEINESKINDUNTERDEMBAUMABARLAKEINEFRAUDIESCHLIEF + +2024-01-17 01:55:07,760 (asr_inference:494) INFO: speech length: 286880 +2024-01-17 01:55:07,786 (beam_search:428) INFO: decoder input length: 446 +2024-01-17 01:55:07,786 (beam_search:429) INFO: max output length: 446 +2024-01-17 01:55:07,786 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:10,411 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:10,411 (beam_search:476) INFO: -81.80 * 1.0 = -81.80 for ctc +2024-01-17 01:55:10,412 (beam_search:479) INFO: total log probability: -81.80 +2024-01-17 01:55:10,412 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:55:10,412 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:10,413 (beam_search:483) INFO: best hypo: SIEHATENSEUEBNDIEFISCHERGARDNERWALCHDENACHTIYBERTAUSGEUORHVENWADENCHEREINGEZOGENNDIESELEUITERGERHEOTENAGENSCHEIMLISCHVERSCHIEDENENNCTZIONENRNABORLDEROLULOPÄISCHERKARKTEBERALLENAUSGETRÜKTWAT + +2024-01-17 01:55:10,415 (asr_inference:494) INFO: speech length: 173600 +2024-01-17 01:55:10,431 (beam_search:428) INFO: decoder input length: 269 +2024-01-17 01:55:10,431 (beam_search:429) INFO: max output length: 269 +2024-01-17 01:55:10,431 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:11,252 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:11,252 (beam_search:476) INFO: -33.08 * 1.0 = -33.08 for ctc +2024-01-17 01:55:11,252 (beam_search:479) INFO: total log probability: -33.08 +2024-01-17 01:55:11,252 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:55:11,252 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:11,253 (beam_search:483) INFO: best hypo: NENEINICHSCHÄMEMEICHLASMICHENDEINEMBUSENMEINGESICHTVERBERGENGERSINKTENSGRASNIDEUNDZIESINACH + +2024-01-17 01:55:11,254 (asr_inference:494) INFO: speech length: 226240 +2024-01-17 01:55:11,275 (beam_search:428) INFO: decoder input length: 351 +2024-01-17 01:55:11,275 (beam_search:429) INFO: max output length: 351 +2024-01-17 01:55:11,275 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:13,065 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:13,065 (beam_search:476) INFO: -58.51 * 1.0 = -58.51 for ctc +2024-01-17 01:55:13,065 (beam_search:479) INFO: total log probability: -58.51 +2024-01-17 01:55:13,065 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:55:13,065 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:13,066 (beam_search:483) INFO: best hypo: DIEKINDERARBARSASENVORDEMWALTUNDALSIEDIEREIKNECHTERVONWEITEMLAUFENSAHNSPRACHRLENSHENZUMPFUNDEFOGELVERLÄSTUMICHNICHZOERLASICHTICHAUCHNICHTSOSPRACHFONDEFOGELNUNUNDNIMERMR + +2024-01-17 01:55:13,068 (asr_inference:494) INFO: speech length: 180480 +2024-01-17 01:55:13,085 (beam_search:428) INFO: decoder input length: 279 +2024-01-17 01:55:13,085 (beam_search:429) INFO: max output length: 279 +2024-01-17 01:55:13,085 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:14,098 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:14,098 (beam_search:476) INFO: -36.39 * 1.0 = -36.39 for ctc +2024-01-17 01:55:14,098 (beam_search:479) INFO: total log probability: -36.39 +2024-01-17 01:55:14,098 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:55:14,098 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:14,098 (beam_search:483) INFO: best hypo: WIEDERSCHLZEINSEINEHLDIEGUNGSRIEDEHERVORHUBDERLERABRACHTEAMKLAENSOMMARMORDENGMITSEINENSCHUHLKINDERNEINGESANGSTÄNTIE + +2024-01-17 01:55:14,100 (asr_inference:494) INFO: speech length: 23360 +2024-01-17 01:55:14,107 (beam_search:428) INFO: decoder input length: 34 +2024-01-17 01:55:14,107 (beam_search:429) INFO: max output length: 34 +2024-01-17 01:55:14,107 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:14,137 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:14,137 (beam_search:476) INFO: -9.36 * 1.0 = -9.36 for ctc +2024-01-17 01:55:14,137 (beam_search:479) INFO: total log probability: -9.36 +2024-01-17 01:55:14,137 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:55:14,137 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:14,137 (beam_search:483) INFO: best hypo: STDTWIEIESEINSOHLEN + +2024-01-17 01:55:14,138 (asr_inference:494) INFO: speech length: 66880 +2024-01-17 01:55:14,148 (beam_search:428) INFO: decoder input length: 102 +2024-01-17 01:55:14,148 (beam_search:429) INFO: max output length: 102 +2024-01-17 01:55:14,148 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:14,315 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:14,315 (beam_search:476) INFO: -8.59 * 1.0 = -8.59 for ctc +2024-01-17 01:55:14,315 (beam_search:479) INFO: total log probability: -8.59 +2024-01-17 01:55:14,315 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:55:14,315 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:14,315 (beam_search:483) INFO: best hypo: DERENCHWINGENENDURCEINEZUSERTSCHALTUNGSTUFENLOS + +2024-01-17 01:55:14,316 (asr_inference:494) INFO: speech length: 32800 +2024-01-17 01:55:14,324 (beam_search:428) INFO: decoder input length: 49 +2024-01-17 01:55:14,324 (beam_search:429) INFO: max output length: 49 +2024-01-17 01:55:14,324 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:14,379 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:14,379 (beam_search:476) INFO: -12.26 * 1.0 = -12.26 for ctc +2024-01-17 01:55:14,379 (beam_search:479) INFO: total log probability: -12.26 +2024-01-17 01:55:14,379 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:55:14,379 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:14,380 (beam_search:483) INFO: best hypo: DIEAUFALEBEIDERSETWRTALUNGU + +2024-01-17 01:55:14,381 (asr_inference:494) INFO: speech length: 18560 +2024-01-17 01:55:14,387 (beam_search:428) INFO: decoder input length: 26 +2024-01-17 01:55:14,387 (beam_search:429) INFO: max output length: 26 +2024-01-17 01:55:14,387 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:14,410 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:14,410 (beam_search:476) INFO: -7.62 * 1.0 = -7.62 for ctc +2024-01-17 01:55:14,410 (beam_search:479) INFO: total log probability: -7.62 +2024-01-17 01:55:14,410 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:55:14,410 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:14,410 (beam_search:483) INFO: best hypo: UMDEÜBERLEBENDEND + +2024-01-17 01:55:14,411 (asr_inference:494) INFO: speech length: 68800 +2024-01-17 01:55:14,421 (beam_search:428) INFO: decoder input length: 105 +2024-01-17 01:55:14,421 (beam_search:429) INFO: max output length: 105 +2024-01-17 01:55:14,421 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:14,622 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:14,623 (beam_search:476) INFO: -12.98 * 1.0 = -12.98 for ctc +2024-01-17 01:55:14,623 (beam_search:479) INFO: total log probability: -12.98 +2024-01-17 01:55:14,623 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:55:14,623 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:14,623 (beam_search:483) INFO: best hypo: SPÄTERWURDENTAILWEISESOGAACHTPARERLELELOHSTREIFENEINGESETZT + +2024-01-17 01:55:14,624 (asr_inference:494) INFO: speech length: 34720 +2024-01-17 01:55:14,632 (beam_search:428) INFO: decoder input length: 52 +2024-01-17 01:55:14,632 (beam_search:429) INFO: max output length: 52 +2024-01-17 01:55:14,632 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:14,681 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:14,681 (beam_search:476) INFO: -5.56 * 1.0 = -5.56 for ctc +2024-01-17 01:55:14,681 (beam_search:479) INFO: total log probability: -5.56 +2024-01-17 01:55:14,681 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:55:14,681 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:14,682 (beam_search:483) INFO: best hypo: MORDEBEKANDUNDVERLANGTE + +2024-01-17 01:55:14,683 (asr_inference:494) INFO: speech length: 35040 +2024-01-17 01:55:14,690 (beam_search:428) INFO: decoder input length: 52 +2024-01-17 01:55:14,690 (beam_search:429) INFO: max output length: 52 +2024-01-17 01:55:14,690 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:14,747 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:14,747 (beam_search:476) INFO: -8.80 * 1.0 = -8.80 for ctc +2024-01-17 01:55:14,747 (beam_search:479) INFO: total log probability: -8.80 +2024-01-17 01:55:14,747 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:55:14,747 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:14,748 (beam_search:483) INFO: best hypo: BUNDEWEGESETZDIESTMVONWIHLEN + +2024-01-17 01:55:14,749 (asr_inference:494) INFO: speech length: 42720 +2024-01-17 01:55:14,757 (beam_search:428) INFO: decoder input length: 64 +2024-01-17 01:55:14,757 (beam_search:429) INFO: max output length: 64 +2024-01-17 01:55:14,757 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:14,782 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:14,782 (beam_search:476) INFO: -3.56 * 1.0 = -3.56 for ctc +2024-01-17 01:55:14,782 (beam_search:479) INFO: total log probability: -3.56 +2024-01-17 01:55:14,782 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:55:14,782 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:14,782 (beam_search:483) INFO: best hypo: GESCHCHTE + +2024-01-17 01:55:14,783 (asr_inference:494) INFO: speech length: 17120 +2024-01-17 01:55:14,789 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:55:14,789 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:55:14,789 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:14,805 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:14,805 (beam_search:476) INFO: -5.05 * 1.0 = -5.05 for ctc +2024-01-17 01:55:14,805 (beam_search:479) INFO: total log probability: -5.05 +2024-01-17 01:55:14,805 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:55:14,805 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:14,805 (beam_search:483) INFO: best hypo: SPALTUNGFEÄG + +2024-01-17 01:55:14,806 (asr_inference:494) INFO: speech length: 37120 +2024-01-17 01:55:14,814 (beam_search:428) INFO: decoder input length: 55 +2024-01-17 01:55:14,814 (beam_search:429) INFO: max output length: 55 +2024-01-17 01:55:14,814 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:14,873 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:14,874 (beam_search:476) INFO: -10.05 * 1.0 = -10.05 for ctc +2024-01-17 01:55:14,874 (beam_search:479) INFO: total log probability: -10.05 +2024-01-17 01:55:14,874 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:55:14,874 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:14,874 (beam_search:483) INFO: best hypo: SCHATPAEBORNDIEUSERENDFERNDIS + +2024-01-17 01:55:14,875 (asr_inference:494) INFO: speech length: 35200 +2024-01-17 01:55:14,882 (beam_search:428) INFO: decoder input length: 52 +2024-01-17 01:55:14,882 (beam_search:429) INFO: max output length: 52 +2024-01-17 01:55:14,882 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:14,937 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:14,937 (beam_search:476) INFO: -8.03 * 1.0 = -8.03 for ctc +2024-01-17 01:55:14,937 (beam_search:479) INFO: total log probability: -8.03 +2024-01-17 01:55:14,937 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:55:14,937 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:14,937 (beam_search:483) INFO: best hypo: UMWEITERINHUMANITEREHILFEZU + +2024-01-17 01:55:14,939 (asr_inference:494) INFO: speech length: 56480 +2024-01-17 01:55:14,947 (beam_search:428) INFO: decoder input length: 86 +2024-01-17 01:55:14,947 (beam_search:429) INFO: max output length: 86 +2024-01-17 01:55:14,947 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:15,074 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:15,074 (beam_search:476) INFO: -7.97 * 1.0 = -7.97 for ctc +2024-01-17 01:55:15,074 (beam_search:479) INFO: total log probability: -7.97 +2024-01-17 01:55:15,074 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:55:15,074 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:15,075 (beam_search:483) INFO: best hypo: SIERKAMTENDIENEUEICHENESCHEREGIERUNGNICHTAN + +2024-01-17 01:55:15,076 (asr_inference:494) INFO: speech length: 48480 +2024-01-17 01:55:15,084 (beam_search:428) INFO: decoder input length: 73 +2024-01-17 01:55:15,084 (beam_search:429) INFO: max output length: 73 +2024-01-17 01:55:15,084 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:15,207 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:15,207 (beam_search:476) INFO: -20.42 * 1.0 = -20.42 for ctc +2024-01-17 01:55:15,207 (beam_search:479) INFO: total log probability: -20.42 +2024-01-17 01:55:15,207 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:55:15,207 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:15,207 (beam_search:483) INFO: best hypo: DIEORAUFÜGENVONANDRENZWANISTENSETEMBERZWERDENDACHTI + +2024-01-17 01:55:15,208 (asr_inference:494) INFO: speech length: 54240 +2024-01-17 01:55:15,217 (beam_search:428) INFO: decoder input length: 82 +2024-01-17 01:55:15,217 (beam_search:429) INFO: max output length: 82 +2024-01-17 01:55:15,217 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:15,362 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:15,362 (beam_search:476) INFO: -25.50 * 1.0 = -25.50 for ctc +2024-01-17 01:55:15,362 (beam_search:479) INFO: total log probability: -25.50 +2024-01-17 01:55:15,362 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:55:15,362 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:15,362 (beam_search:483) INFO: best hypo: ERWIESIEMICHTSCHLDICODERMITSCHLICHMACENANTODERNSMITGESE + +2024-01-17 01:55:15,363 (asr_inference:494) INFO: speech length: 24480 +2024-01-17 01:55:15,370 (beam_search:428) INFO: decoder input length: 36 +2024-01-17 01:55:15,370 (beam_search:429) INFO: max output length: 36 +2024-01-17 01:55:15,370 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:15,403 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:15,404 (beam_search:476) INFO: -8.12 * 1.0 = -8.12 for ctc +2024-01-17 01:55:15,404 (beam_search:479) INFO: total log probability: -8.12 +2024-01-17 01:55:15,404 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:55:15,404 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:15,404 (beam_search:483) INFO: best hypo: DIEMEDERERTTUMMAREINEN + +2024-01-17 01:55:15,405 (asr_inference:494) INFO: speech length: 19520 +2024-01-17 01:55:15,411 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:55:15,411 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:55:15,411 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:15,437 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:15,437 (beam_search:476) INFO: -5.57 * 1.0 = -5.57 for ctc +2024-01-17 01:55:15,437 (beam_search:479) INFO: total log probability: -5.57 +2024-01-17 01:55:15,437 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:55:15,437 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:15,437 (beam_search:483) INFO: best hypo: NDTEIFENTIESENBEIDER + +2024-01-17 01:55:15,438 (asr_inference:494) INFO: speech length: 51520 +2024-01-17 01:55:15,447 (beam_search:428) INFO: decoder input length: 78 +2024-01-17 01:55:15,447 (beam_search:429) INFO: max output length: 78 +2024-01-17 01:55:15,447 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:15,567 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:15,567 (beam_search:476) INFO: -12.33 * 1.0 = -12.33 for ctc +2024-01-17 01:55:15,567 (beam_search:479) INFO: total log probability: -12.33 +2024-01-17 01:55:15,567 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:55:15,567 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:15,568 (beam_search:483) INFO: best hypo: KREISWALEFORSCHLAGUNDEINELANDESLISTENDERZEITNEN + +2024-01-17 01:55:15,569 (asr_inference:494) INFO: speech length: 133760 +2024-01-17 01:55:15,583 (beam_search:428) INFO: decoder input length: 206 +2024-01-17 01:55:15,583 (beam_search:429) INFO: max output length: 206 +2024-01-17 01:55:15,583 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:16,297 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:16,297 (beam_search:476) INFO: -48.14 * 1.0 = -48.14 for ctc +2024-01-17 01:55:16,297 (beam_search:479) INFO: total log probability: -48.14 +2024-01-17 01:55:16,297 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:55:16,297 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:16,298 (beam_search:483) INFO: best hypo: ANUMSERZUNDESAGEINFORMEINESTFÜNFZEHNTEILENLIEDERZYKLUOSZWETSEDCHTWURDEPESLASKÖABERTINNEBERABETUNGVONHAUSTHAREMEN + +2024-01-17 01:55:16,300 (asr_inference:494) INFO: speech length: 31520 +2024-01-17 01:55:16,307 (beam_search:428) INFO: decoder input length: 47 +2024-01-17 01:55:16,308 (beam_search:429) INFO: max output length: 47 +2024-01-17 01:55:16,308 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:16,358 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:16,358 (beam_search:476) INFO: -10.07 * 1.0 = -10.07 for ctc +2024-01-17 01:55:16,358 (beam_search:479) INFO: total log probability: -10.07 +2024-01-17 01:55:16,358 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:55:16,358 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:16,358 (beam_search:483) INFO: best hypo: IEDIEOLGENDETAPELEDARSTELLT + +2024-01-17 01:55:16,359 (asr_inference:494) INFO: speech length: 16320 +2024-01-17 01:55:16,366 (beam_search:428) INFO: decoder input length: 23 +2024-01-17 01:55:16,366 (beam_search:429) INFO: max output length: 23 +2024-01-17 01:55:16,366 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:16,383 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:16,383 (beam_search:476) INFO: -6.28 * 1.0 = -6.28 for ctc +2024-01-17 01:55:16,383 (beam_search:479) INFO: total log probability: -6.28 +2024-01-17 01:55:16,383 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:55:16,383 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:16,383 (beam_search:483) INFO: best hypo: UMSTRUMFLOSBE + +2024-01-17 01:55:16,384 (asr_inference:494) INFO: speech length: 53760 +2024-01-17 01:55:16,392 (beam_search:428) INFO: decoder input length: 81 +2024-01-17 01:55:16,392 (beam_search:429) INFO: max output length: 81 +2024-01-17 01:55:16,392 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:16,510 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:16,510 (beam_search:476) INFO: -12.42 * 1.0 = -12.42 for ctc +2024-01-17 01:55:16,510 (beam_search:479) INFO: total log probability: -12.42 +2024-01-17 01:55:16,510 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:55:16,510 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:16,510 (beam_search:483) INFO: best hypo: DEBUNDESWALLITERBISTZUMSIMONEUNZIGSTENTAG + +2024-01-17 01:55:16,511 (asr_inference:494) INFO: speech length: 41600 +2024-01-17 01:55:16,519 (beam_search:428) INFO: decoder input length: 62 +2024-01-17 01:55:16,519 (beam_search:429) INFO: max output length: 62 +2024-01-17 01:55:16,519 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:16,597 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:16,598 (beam_search:476) INFO: -9.73 * 1.0 = -9.73 for ctc +2024-01-17 01:55:16,598 (beam_search:479) INFO: total log probability: -9.73 +2024-01-17 01:55:16,598 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:55:16,598 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:16,598 (beam_search:483) INFO: best hypo: ORIRICHEWORDENDEUSCHERNICHTMITWELEN + +2024-01-17 01:55:16,599 (asr_inference:494) INFO: speech length: 47360 +2024-01-17 01:55:16,607 (beam_search:428) INFO: decoder input length: 71 +2024-01-17 01:55:16,607 (beam_search:429) INFO: max output length: 71 +2024-01-17 01:55:16,607 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:16,690 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:16,690 (beam_search:476) INFO: -12.77 * 1.0 = -12.77 for ctc +2024-01-17 01:55:16,690 (beam_search:479) INFO: total log probability: -12.77 +2024-01-17 01:55:16,690 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:55:16,690 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:16,690 (beam_search:483) INFO: best hypo: AUSSFIRNMUSTEINKUSERKOTERBEGN + +2024-01-17 01:55:16,692 (asr_inference:494) INFO: speech length: 37280 +2024-01-17 01:55:16,699 (beam_search:428) INFO: decoder input length: 56 +2024-01-17 01:55:16,699 (beam_search:429) INFO: max output length: 56 +2024-01-17 01:55:16,699 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:16,763 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:16,764 (beam_search:476) INFO: -10.45 * 1.0 = -10.45 for ctc +2024-01-17 01:55:16,764 (beam_search:479) INFO: total log probability: -10.45 +2024-01-17 01:55:16,764 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:55:16,764 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:16,764 (beam_search:483) INFO: best hypo: VERGLEICHPANZEAHLENWERTUMGEANDE + +2024-01-17 01:55:16,765 (asr_inference:494) INFO: speech length: 20480 +2024-01-17 01:55:16,771 (beam_search:428) INFO: decoder input length: 29 +2024-01-17 01:55:16,771 (beam_search:429) INFO: max output length: 29 +2024-01-17 01:55:16,771 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:16,799 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:16,799 (beam_search:476) INFO: -6.24 * 1.0 = -6.24 for ctc +2024-01-17 01:55:16,799 (beam_search:479) INFO: total log probability: -6.24 +2024-01-17 01:55:16,799 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:55:16,799 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:16,799 (beam_search:483) INFO: best hypo: BETRCHTEDEALGEMEINHEI + +2024-01-17 01:55:16,800 (asr_inference:494) INFO: speech length: 46560 +2024-01-17 01:55:16,808 (beam_search:428) INFO: decoder input length: 70 +2024-01-17 01:55:16,808 (beam_search:429) INFO: max output length: 70 +2024-01-17 01:55:16,808 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:16,908 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:16,908 (beam_search:476) INFO: -11.75 * 1.0 = -11.75 for ctc +2024-01-17 01:55:16,908 (beam_search:479) INFO: total log probability: -11.75 +2024-01-17 01:55:16,908 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:55:16,908 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:16,908 (beam_search:483) INFO: best hypo: UNTERSCHITLICHEAUFASSUNGENGABESNURDAHRÜBER + +2024-01-17 01:55:16,909 (asr_inference:494) INFO: speech length: 116320 +2024-01-17 01:55:16,922 (beam_search:428) INFO: decoder input length: 179 +2024-01-17 01:55:16,922 (beam_search:429) INFO: max output length: 179 +2024-01-17 01:55:16,922 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:17,431 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:17,431 (beam_search:476) INFO: -28.88 * 1.0 = -28.88 for ctc +2024-01-17 01:55:17,431 (beam_search:479) INFO: total log probability: -28.88 +2024-01-17 01:55:17,431 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:55:17,431 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:17,432 (beam_search:483) INFO: best hypo: DOLLBEIMBUNESLIKISTENBERSERDORTMUNDNACHVOLGERDESUNMITELBAZUVORZORÜCKETREDENRENASIRFENRÖBER + +2024-01-17 01:55:17,433 (asr_inference:494) INFO: speech length: 20000 +2024-01-17 01:55:17,439 (beam_search:428) INFO: decoder input length: 29 +2024-01-17 01:55:17,439 (beam_search:429) INFO: max output length: 29 +2024-01-17 01:55:17,439 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:17,465 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:17,465 (beam_search:476) INFO: -11.27 * 1.0 = -11.27 for ctc +2024-01-17 01:55:17,465 (beam_search:479) INFO: total log probability: -11.27 +2024-01-17 01:55:17,465 (beam_search:480) INFO: normalized log probability: -0.47 +2024-01-17 01:55:17,465 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:17,465 (beam_search:483) INFO: best hypo: NUNZENERDCHTUNACTZIG + +2024-01-17 01:55:17,466 (asr_inference:494) INFO: speech length: 22400 +2024-01-17 01:55:17,473 (beam_search:428) INFO: decoder input length: 32 +2024-01-17 01:55:17,473 (beam_search:429) INFO: max output length: 32 +2024-01-17 01:55:17,473 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:17,498 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:17,498 (beam_search:476) INFO: -6.49 * 1.0 = -6.49 for ctc +2024-01-17 01:55:17,498 (beam_search:479) INFO: total log probability: -6.49 +2024-01-17 01:55:17,498 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:55:17,498 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:17,499 (beam_search:483) INFO: best hypo: REINENZYGLOPETDIE + +2024-01-17 01:55:17,500 (asr_inference:494) INFO: speech length: 61760 +2024-01-17 01:55:17,509 (beam_search:428) INFO: decoder input length: 94 +2024-01-17 01:55:17,509 (beam_search:429) INFO: max output length: 94 +2024-01-17 01:55:17,509 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:17,684 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:17,684 (beam_search:476) INFO: -19.22 * 1.0 = -19.22 for ctc +2024-01-17 01:55:17,684 (beam_search:479) INFO: total log probability: -19.22 +2024-01-17 01:55:17,684 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:55:17,684 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:17,684 (beam_search:483) INFO: best hypo: DERVOTOSTROMISÜBERVIELEGRÜSSENORNUNGELINEARZUMLICHTENFAL + +2024-01-17 01:55:17,685 (asr_inference:494) INFO: speech length: 37920 +2024-01-17 01:55:17,693 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:55:17,693 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:55:17,693 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:17,764 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:17,764 (beam_search:476) INFO: -12.91 * 1.0 = -12.91 for ctc +2024-01-17 01:55:17,764 (beam_search:479) INFO: total log probability: -12.91 +2024-01-17 01:55:17,765 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:55:17,765 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:17,765 (beam_search:483) INFO: best hypo: DASHTFÜKLEINEPARTEINGROSEAUSWIRKUNG + +2024-01-17 01:55:17,766 (asr_inference:494) INFO: speech length: 33440 +2024-01-17 01:55:17,773 (beam_search:428) INFO: decoder input length: 50 +2024-01-17 01:55:17,773 (beam_search:429) INFO: max output length: 50 +2024-01-17 01:55:17,773 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:17,822 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:17,823 (beam_search:476) INFO: -5.43 * 1.0 = -5.43 for ctc +2024-01-17 01:55:17,823 (beam_search:479) INFO: total log probability: -5.43 +2024-01-17 01:55:17,823 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:55:17,823 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:17,823 (beam_search:483) INFO: best hypo: ISDEITERATIEVETIEFENSUCH + +2024-01-17 01:55:17,824 (asr_inference:494) INFO: speech length: 56000 +2024-01-17 01:55:17,833 (beam_search:428) INFO: decoder input length: 85 +2024-01-17 01:55:17,833 (beam_search:429) INFO: max output length: 85 +2024-01-17 01:55:17,833 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:17,951 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:17,951 (beam_search:476) INFO: -11.79 * 1.0 = -11.79 for ctc +2024-01-17 01:55:17,951 (beam_search:479) INFO: total log probability: -11.79 +2024-01-17 01:55:17,951 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:55:17,951 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:17,951 (beam_search:483) INFO: best hypo: DIESKÖNNENUMBEISPIELKONDENSEATORENSEIN + +2024-01-17 01:55:17,952 (asr_inference:494) INFO: speech length: 64160 +2024-01-17 01:55:17,962 (beam_search:428) INFO: decoder input length: 98 +2024-01-17 01:55:17,962 (beam_search:429) INFO: max output length: 98 +2024-01-17 01:55:17,962 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:18,134 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:18,134 (beam_search:476) INFO: -12.86 * 1.0 = -12.86 for ctc +2024-01-17 01:55:18,134 (beam_search:479) INFO: total log probability: -12.86 +2024-01-17 01:55:18,134 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:55:18,134 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:18,135 (beam_search:483) INFO: best hypo: ALSDIEKURSAUFKOBERHALTENDENSOJETICHENSCHIVERABTRETEN + +2024-01-17 01:55:18,136 (asr_inference:494) INFO: speech length: 88800 +2024-01-17 01:55:18,146 (beam_search:428) INFO: decoder input length: 136 +2024-01-17 01:55:18,146 (beam_search:429) INFO: max output length: 136 +2024-01-17 01:55:18,146 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:18,483 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:18,483 (beam_search:476) INFO: -29.83 * 1.0 = -29.83 for ctc +2024-01-17 01:55:18,483 (beam_search:479) INFO: total log probability: -29.83 +2024-01-17 01:55:18,483 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:55:18,483 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:18,484 (beam_search:483) INFO: best hypo: BUNESTAGWAHLNUNZHUNERDREIUNFÜFZIGWURDERSMALSNACHEINEMVOMBUNDESTAIGSEBSTERLSENGESETZ + +2024-01-17 01:55:18,485 (asr_inference:494) INFO: speech length: 34240 +2024-01-17 01:55:18,493 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:55:18,493 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:55:18,493 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:18,555 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:18,555 (beam_search:476) INFO: -12.04 * 1.0 = -12.04 for ctc +2024-01-17 01:55:18,555 (beam_search:479) INFO: total log probability: -12.04 +2024-01-17 01:55:18,555 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:55:18,555 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:18,555 (beam_search:483) INFO: best hypo: BUNDEWAIGESETZVIELFACHEINERTWURDEN + +2024-01-17 01:55:18,557 (asr_inference:494) INFO: speech length: 37280 +2024-01-17 01:55:18,564 (beam_search:428) INFO: decoder input length: 56 +2024-01-17 01:55:18,564 (beam_search:429) INFO: max output length: 56 +2024-01-17 01:55:18,564 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:18,631 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:18,631 (beam_search:476) INFO: -12.24 * 1.0 = -12.24 for ctc +2024-01-17 01:55:18,631 (beam_search:479) INFO: total log probability: -12.24 +2024-01-17 01:55:18,631 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:55:18,631 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:18,631 (beam_search:483) INFO: best hypo: ERÜBERLAGERDENVORTUSTROMEUNDTREGT + +2024-01-17 01:55:18,632 (asr_inference:494) INFO: speech length: 45920 +2024-01-17 01:55:18,640 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:55:18,640 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:55:18,640 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:18,734 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:18,734 (beam_search:476) INFO: -10.77 * 1.0 = -10.77 for ctc +2024-01-17 01:55:18,735 (beam_search:479) INFO: total log probability: -10.77 +2024-01-17 01:55:18,735 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:55:18,735 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:18,735 (beam_search:483) INFO: best hypo: DROINTEKRATIUMNDERBEIDENDEUTSCHENSTATEN + +2024-01-17 01:55:18,736 (asr_inference:494) INFO: speech length: 25920 +2024-01-17 01:55:18,743 (beam_search:428) INFO: decoder input length: 38 +2024-01-17 01:55:18,743 (beam_search:429) INFO: max output length: 38 +2024-01-17 01:55:18,743 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:18,777 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:18,777 (beam_search:476) INFO: -5.92 * 1.0 = -5.92 for ctc +2024-01-17 01:55:18,777 (beam_search:479) INFO: total log probability: -5.92 +2024-01-17 01:55:18,777 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:55:18,777 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:18,777 (beam_search:483) INFO: best hypo: BERLINERWÜLMEUSENSTAT + +2024-01-17 01:55:18,778 (asr_inference:494) INFO: speech length: 17920 +2024-01-17 01:55:18,785 (beam_search:428) INFO: decoder input length: 25 +2024-01-17 01:55:18,785 (beam_search:429) INFO: max output length: 25 +2024-01-17 01:55:18,785 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:18,804 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:18,805 (beam_search:476) INFO: -11.69 * 1.0 = -11.69 for ctc +2024-01-17 01:55:18,805 (beam_search:479) INFO: total log probability: -11.69 +2024-01-17 01:55:18,805 (beam_search:480) INFO: normalized log probability: -0.58 +2024-01-17 01:55:18,805 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:18,805 (beam_search:483) INFO: best hypo: AFIEZELERFÜHRUNGEN + +2024-01-17 01:55:18,806 (asr_inference:494) INFO: speech length: 51840 +2024-01-17 01:55:18,814 (beam_search:428) INFO: decoder input length: 78 +2024-01-17 01:55:18,814 (beam_search:429) INFO: max output length: 78 +2024-01-17 01:55:18,814 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:18,935 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:18,935 (beam_search:476) INFO: -11.27 * 1.0 = -11.27 for ctc +2024-01-17 01:55:18,935 (beam_search:479) INFO: total log probability: -11.27 +2024-01-17 01:55:18,935 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:55:18,935 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:18,935 (beam_search:483) INFO: best hypo: BEDERVERHETENSWALWITZUSETZLICGDIEEINHALTUNGDER + +2024-01-17 01:55:18,936 (asr_inference:494) INFO: speech length: 35520 +2024-01-17 01:55:18,944 (beam_search:428) INFO: decoder input length: 53 +2024-01-17 01:55:18,944 (beam_search:429) INFO: max output length: 53 +2024-01-17 01:55:18,944 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:19,013 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:19,013 (beam_search:476) INFO: -15.36 * 1.0 = -15.36 for ctc +2024-01-17 01:55:19,013 (beam_search:479) INFO: total log probability: -15.36 +2024-01-17 01:55:19,013 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:55:19,013 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:19,013 (beam_search:483) INFO: best hypo: WIEVWENIHTISOLANENOCHAMPOULTZDERZEIT + +2024-01-17 01:55:19,014 (asr_inference:494) INFO: speech length: 58720 +2024-01-17 01:55:19,023 (beam_search:428) INFO: decoder input length: 89 +2024-01-17 01:55:19,023 (beam_search:429) INFO: max output length: 89 +2024-01-17 01:55:19,023 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:19,180 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:19,180 (beam_search:476) INFO: -16.70 * 1.0 = -16.70 for ctc +2024-01-17 01:55:19,180 (beam_search:479) INFO: total log probability: -16.70 +2024-01-17 01:55:19,180 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:55:19,180 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:19,180 (beam_search:483) INFO: best hypo: REDOCHETWRDIEDUCHFÜHRENVONWALWERBUNGAUFKOSTEDESTATES + +2024-01-17 01:55:19,181 (asr_inference:494) INFO: speech length: 18720 +2024-01-17 01:55:19,188 (beam_search:428) INFO: decoder input length: 27 +2024-01-17 01:55:19,188 (beam_search:429) INFO: max output length: 27 +2024-01-17 01:55:19,188 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:19,211 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:19,211 (beam_search:476) INFO: -7.78 * 1.0 = -7.78 for ctc +2024-01-17 01:55:19,211 (beam_search:479) INFO: total log probability: -7.78 +2024-01-17 01:55:19,211 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:55:19,211 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:19,212 (beam_search:483) INFO: best hypo: DASNIHMRUNDKGESETZ + +2024-01-17 01:55:19,213 (asr_inference:494) INFO: speech length: 42080 +2024-01-17 01:55:19,221 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:55:19,221 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:55:19,221 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:19,295 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:19,295 (beam_search:476) INFO: -8.42 * 1.0 = -8.42 for ctc +2024-01-17 01:55:19,295 (beam_search:479) INFO: total log probability: -8.42 +2024-01-17 01:55:19,295 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:55:19,295 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:19,295 (beam_search:483) INFO: best hypo: HEIMATVERTRIEBENUNDHEUSLICHEGEWAL + +2024-01-17 01:55:19,296 (asr_inference:494) INFO: speech length: 42240 +2024-01-17 01:55:19,304 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:55:19,304 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:55:19,304 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:19,384 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:19,384 (beam_search:476) INFO: -9.98 * 1.0 = -9.98 for ctc +2024-01-17 01:55:19,384 (beam_search:479) INFO: total log probability: -9.98 +2024-01-17 01:55:19,384 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:55:19,384 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:19,384 (beam_search:483) INFO: best hypo: NDSPEICHERIENINEINRWAGTESCHLNGEAB + +2024-01-17 01:55:19,385 (asr_inference:494) INFO: speech length: 145760 +2024-01-17 01:55:19,400 (beam_search:428) INFO: decoder input length: 225 +2024-01-17 01:55:19,400 (beam_search:429) INFO: max output length: 225 +2024-01-17 01:55:19,400 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:20,134 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:20,135 (beam_search:476) INFO: -32.44 * 1.0 = -32.44 for ctc +2024-01-17 01:55:20,135 (beam_search:479) INFO: total log probability: -32.44 +2024-01-17 01:55:20,135 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:55:20,135 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:20,135 (beam_search:483) INFO: best hypo: ORREIGINALTONBENDERUNDDIEDOKOMITATIONDESTUDIOSWURDENNENZEHNHUNDERTZWUOHNSIEBZIGINDERSIMENSERCHIEFÜBERSTELT + +2024-01-17 01:55:20,137 (asr_inference:494) INFO: speech length: 79520 +2024-01-17 01:55:20,147 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 01:55:20,147 (beam_search:429) INFO: max output length: 122 +2024-01-17 01:55:20,147 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:20,311 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:20,312 (beam_search:476) INFO: -13.93 * 1.0 = -13.93 for ctc +2024-01-17 01:55:20,312 (beam_search:479) INFO: total log probability: -13.93 +2024-01-17 01:55:20,312 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:55:20,312 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:20,312 (beam_search:483) INFO: best hypo: SOMISSENAUFEINEMSTATGSCHNREKETENUBODT + +2024-01-17 01:55:20,313 (asr_inference:494) INFO: speech length: 27680 +2024-01-17 01:55:20,320 (beam_search:428) INFO: decoder input length: 41 +2024-01-17 01:55:20,320 (beam_search:429) INFO: max output length: 41 +2024-01-17 01:55:20,320 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:20,354 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:20,354 (beam_search:476) INFO: -5.03 * 1.0 = -5.03 for ctc +2024-01-17 01:55:20,354 (beam_search:479) INFO: total log probability: -5.03 +2024-01-17 01:55:20,354 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:55:20,354 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:20,354 (beam_search:483) INFO: best hypo: FLÖTENSPIELEDLICHER + +2024-01-17 01:55:20,355 (asr_inference:494) INFO: speech length: 52160 +2024-01-17 01:55:20,363 (beam_search:428) INFO: decoder input length: 79 +2024-01-17 01:55:20,364 (beam_search:429) INFO: max output length: 79 +2024-01-17 01:55:20,364 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:20,481 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:20,481 (beam_search:476) INFO: -9.70 * 1.0 = -9.70 for ctc +2024-01-17 01:55:20,481 (beam_search:479) INFO: total log probability: -9.70 +2024-01-17 01:55:20,481 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:55:20,481 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:20,481 (beam_search:483) INFO: best hypo: DRASTISCHMODERARNEELIKTRONISCHEKLANGESCHALTUNG + +2024-01-17 01:55:20,482 (asr_inference:494) INFO: speech length: 177600 +2024-01-17 01:55:20,499 (beam_search:428) INFO: decoder input length: 275 +2024-01-17 01:55:20,499 (beam_search:429) INFO: max output length: 275 +2024-01-17 01:55:20,499 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:21,701 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:21,701 (beam_search:476) INFO: -44.55 * 1.0 = -44.55 for ctc +2024-01-17 01:55:21,701 (beam_search:479) INFO: total log probability: -44.55 +2024-01-17 01:55:21,701 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:55:21,701 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:21,702 (beam_search:483) INFO: best hypo: ANCHLISENWODEDIESOHAMITETEMANDARTZTZALIEDERPARTEINAHDIMSEMVERFAHNENSPRECHENDERANZALIHRERZWEITSTMMPROPRZUNALAUFDIELANESLISTEDERPARTEIUNTERVERTEIERT + +2024-01-17 01:55:21,704 (asr_inference:494) INFO: speech length: 44000 +2024-01-17 01:55:21,712 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:55:21,712 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:55:21,712 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:21,793 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:21,793 (beam_search:476) INFO: -15.90 * 1.0 = -15.90 for ctc +2024-01-17 01:55:21,793 (beam_search:479) INFO: total log probability: -15.90 +2024-01-17 01:55:21,793 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-17 01:55:21,793 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:21,793 (beam_search:483) INFO: best hypo: OCFNDERNARTHOBOMBADIEOUNDTEÖKÜNFTE + +2024-01-17 01:55:21,795 (asr_inference:494) INFO: speech length: 21920 +2024-01-17 01:55:21,801 (beam_search:428) INFO: decoder input length: 32 +2024-01-17 01:55:21,801 (beam_search:429) INFO: max output length: 32 +2024-01-17 01:55:21,801 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:21,828 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:21,829 (beam_search:476) INFO: -6.68 * 1.0 = -6.68 for ctc +2024-01-17 01:55:21,829 (beam_search:479) INFO: total log probability: -6.68 +2024-01-17 01:55:21,829 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:55:21,829 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:21,829 (beam_search:483) INFO: best hypo: DERFREINENTZUKLOPE + +2024-01-17 01:55:21,830 (asr_inference:494) INFO: speech length: 16960 +2024-01-17 01:55:21,837 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:55:21,837 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:55:21,837 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:21,856 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:21,856 (beam_search:476) INFO: -9.14 * 1.0 = -9.14 for ctc +2024-01-17 01:55:21,856 (beam_search:479) INFO: total log probability: -9.14 +2024-01-17 01:55:21,856 (beam_search:480) INFO: normalized log probability: -0.46 +2024-01-17 01:55:21,856 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:21,857 (beam_search:483) INFO: best hypo: MTLERBEILELHINDEN + +2024-01-17 01:55:21,858 (asr_inference:494) INFO: speech length: 68000 +2024-01-17 01:55:21,867 (beam_search:428) INFO: decoder input length: 104 +2024-01-17 01:55:21,867 (beam_search:429) INFO: max output length: 104 +2024-01-17 01:55:21,867 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:22,085 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:22,085 (beam_search:476) INFO: -19.28 * 1.0 = -19.28 for ctc +2024-01-17 01:55:22,085 (beam_search:479) INFO: total log probability: -19.28 +2024-01-17 01:55:22,085 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:55:22,085 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:22,085 (beam_search:483) INFO: best hypo: WERWIGEGEINEVERBRECHTENSRECHSGRFTICHZUINERREITTRAFEVONMINDESENSEINE + +2024-01-17 01:55:22,087 (asr_inference:494) INFO: speech length: 54880 +2024-01-17 01:55:22,095 (beam_search:428) INFO: decoder input length: 83 +2024-01-17 01:55:22,095 (beam_search:429) INFO: max output length: 83 +2024-01-17 01:55:22,095 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:22,232 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:22,232 (beam_search:476) INFO: -16.85 * 1.0 = -16.85 for ctc +2024-01-17 01:55:22,232 (beam_search:479) INFO: total log probability: -16.85 +2024-01-17 01:55:22,232 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:55:22,232 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:22,232 (beam_search:483) INFO: best hypo: DRGSCHWINDIGKEITZWERTUNGERAGENDREIBEFEINHUDERACHT + +2024-01-17 01:55:22,233 (asr_inference:494) INFO: speech length: 31520 +2024-01-17 01:55:22,240 (beam_search:428) INFO: decoder input length: 47 +2024-01-17 01:55:22,241 (beam_search:429) INFO: max output length: 47 +2024-01-17 01:55:22,241 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:22,292 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:22,292 (beam_search:476) INFO: -11.97 * 1.0 = -11.97 for ctc +2024-01-17 01:55:22,292 (beam_search:479) INFO: total log probability: -11.97 +2024-01-17 01:55:22,292 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:55:22,292 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:22,293 (beam_search:483) INFO: best hypo: IEBORIOSAMERSTENIGBORIESAMSER + +2024-01-17 01:55:22,294 (asr_inference:494) INFO: speech length: 49920 +2024-01-17 01:55:22,302 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:55:22,302 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:55:22,302 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:22,415 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:22,415 (beam_search:476) INFO: -18.87 * 1.0 = -18.87 for ctc +2024-01-17 01:55:22,415 (beam_search:479) INFO: total log probability: -18.87 +2024-01-17 01:55:22,415 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:55:22,415 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:22,415 (beam_search:483) INFO: best hypo: NACHDMSEARNARGÜFVERFARENAUFDIELENDERERTEILT + +2024-01-17 01:55:22,416 (asr_inference:494) INFO: speech length: 50080 +2024-01-17 01:55:22,425 (beam_search:428) INFO: decoder input length: 76 +2024-01-17 01:55:22,425 (beam_search:429) INFO: max output length: 76 +2024-01-17 01:55:22,425 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:22,522 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:22,522 (beam_search:476) INFO: -11.99 * 1.0 = -11.99 for ctc +2024-01-17 01:55:22,522 (beam_search:479) INFO: total log probability: -11.99 +2024-01-17 01:55:22,522 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:55:22,522 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:22,522 (beam_search:483) INFO: best hypo: REFORMENGOBERSCHAFSUNDABRSTUNGSCHITE + +2024-01-17 01:55:22,523 (asr_inference:494) INFO: speech length: 39840 +2024-01-17 01:55:22,531 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:55:22,531 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:55:22,531 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:22,587 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:22,587 (beam_search:476) INFO: -10.39 * 1.0 = -10.39 for ctc +2024-01-17 01:55:22,587 (beam_search:479) INFO: total log probability: -10.39 +2024-01-17 01:55:22,587 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:55:22,587 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:22,587 (beam_search:483) INFO: best hypo: SIEREAANPORTETUNDNDERDE + +2024-01-17 01:55:22,588 (asr_inference:494) INFO: speech length: 49600 +2024-01-17 01:55:22,596 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:55:22,596 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:55:22,596 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:22,695 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:22,695 (beam_search:476) INFO: -9.91 * 1.0 = -9.91 for ctc +2024-01-17 01:55:22,695 (beam_search:479) INFO: total log probability: -9.91 +2024-01-17 01:55:22,695 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:55:22,695 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:22,695 (beam_search:483) INFO: best hypo: ANDEMESTICHEGREFTEAUFGEGENRVOLEZEN + +# Accounting: time=181 threads=1 +# Ended (code 0) at Wed Jan 17 01:55:23 CST 2024, elapsed time 181 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.3.log b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.3.log new file mode 100644 index 0000000000000000000000000000000000000000..3cccfd04060d19efd67d1b5a05c27fb15ecae31a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.3.log @@ -0,0 +1,1834 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.3 --config conf/decode_asr.yaml +# Started at Wed Jan 17 01:55:23 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.3 --config conf/decode_asr.yaml +2024-01-17 01:55:24,511 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml +2024-01-17 01:55:24,529 (asr:523) INFO: Vocabulary size: 44 +2024-01-17 01:55:24,591 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 01:55:24,591 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 01:55:24,702 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 01:55:25,994 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 01:55:27,228 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 01:55:27,228 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 01:55:27,228 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 01:55:27,261 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-17 01:55:27,336 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 01:55:27,447 (asr_inference:494) INFO: speech length: 22880 +2024-01-17 01:55:28,654 (beam_search:428) INFO: decoder input length: 33 +2024-01-17 01:55:28,654 (beam_search:429) INFO: max output length: 33 +2024-01-17 01:55:28,654 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:28,686 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:28,686 (beam_search:476) INFO: -5.41 * 1.0 = -5.41 for ctc +2024-01-17 01:55:28,686 (beam_search:479) INFO: total log probability: -5.41 +2024-01-17 01:55:28,686 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:55:28,686 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:28,686 (beam_search:483) INFO: best hypo: ITUNTERANDEREMVERWENDE + +2024-01-17 01:55:28,710 (asr_inference:494) INFO: speech length: 16800 +2024-01-17 01:55:28,718 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:55:28,718 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:55:28,718 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:28,734 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:28,734 (beam_search:476) INFO: -5.44 * 1.0 = -5.44 for ctc +2024-01-17 01:55:28,734 (beam_search:479) INFO: total log probability: -5.44 +2024-01-17 01:55:28,734 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:55:28,734 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:28,734 (beam_search:483) INFO: best hypo: AUSWIKEPEDIER + +2024-01-17 01:55:28,735 (asr_inference:494) INFO: speech length: 19680 +2024-01-17 01:55:28,742 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:55:28,742 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:55:28,742 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:28,761 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:28,761 (beam_search:476) INFO: -3.34 * 1.0 = -3.34 for ctc +2024-01-17 01:55:28,761 (beam_search:479) INFO: total log probability: -3.34 +2024-01-17 01:55:28,761 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:55:28,761 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:28,761 (beam_search:483) INFO: best hypo: UNDKOBARKRISE + +2024-01-17 01:55:28,762 (asr_inference:494) INFO: speech length: 83360 +2024-01-17 01:55:28,774 (beam_search:428) INFO: decoder input length: 128 +2024-01-17 01:55:28,774 (beam_search:429) INFO: max output length: 128 +2024-01-17 01:55:28,774 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:29,098 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:29,098 (beam_search:476) INFO: -35.11 * 1.0 = -35.11 for ctc +2024-01-17 01:55:29,098 (beam_search:479) INFO: total log probability: -35.11 +2024-01-17 01:55:29,098 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:55:29,098 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:29,098 (beam_search:483) INFO: best hypo: ENLTZTDARWALAUFGRUNDEIGENERWEILVORSCHLÄGEUNETEBRUCHENMINISSENSFÜFABGERUNENDVERTRETENSIND + +2024-01-17 01:55:29,100 (asr_inference:494) INFO: speech length: 60000 +2024-01-17 01:55:29,109 (beam_search:428) INFO: decoder input length: 91 +2024-01-17 01:55:29,109 (beam_search:429) INFO: max output length: 91 +2024-01-17 01:55:29,109 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:29,261 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:29,261 (beam_search:476) INFO: -8.75 * 1.0 = -8.75 for ctc +2024-01-17 01:55:29,261 (beam_search:479) INFO: total log probability: -8.75 +2024-01-17 01:55:29,261 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:55:29,261 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:29,262 (beam_search:483) INFO: best hypo: VERPREITUNGIEDIOLOGESCHAPROPARGANDERDERSUPERMICHTEUND + +2024-01-17 01:55:29,263 (asr_inference:494) INFO: speech length: 35360 +2024-01-17 01:55:29,270 (beam_search:428) INFO: decoder input length: 53 +2024-01-17 01:55:29,270 (beam_search:429) INFO: max output length: 53 +2024-01-17 01:55:29,270 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:29,328 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:29,328 (beam_search:476) INFO: -9.43 * 1.0 = -9.43 for ctc +2024-01-17 01:55:29,328 (beam_search:479) INFO: total log probability: -9.43 +2024-01-17 01:55:29,328 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:55:29,328 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:29,328 (beam_search:483) INFO: best hypo: KOMMIHSAUFDERELITETBERTHAGEN + +2024-01-17 01:55:29,329 (asr_inference:494) INFO: speech length: 43680 +2024-01-17 01:55:29,337 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:55:29,337 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:55:29,337 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:29,426 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:29,426 (beam_search:476) INFO: -11.98 * 1.0 = -11.98 for ctc +2024-01-17 01:55:29,426 (beam_search:479) INFO: total log probability: -11.98 +2024-01-17 01:55:29,426 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:55:29,426 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:29,426 (beam_search:483) INFO: best hypo: ALSDERKALTIKLIEGSIGVORTWERENZUSPITZTE + +2024-01-17 01:55:29,428 (asr_inference:494) INFO: speech length: 68000 +2024-01-17 01:55:29,437 (beam_search:428) INFO: decoder input length: 104 +2024-01-17 01:55:29,437 (beam_search:429) INFO: max output length: 104 +2024-01-17 01:55:29,437 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:29,619 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:29,619 (beam_search:476) INFO: -17.06 * 1.0 = -17.06 for ctc +2024-01-17 01:55:29,619 (beam_search:479) INFO: total log probability: -17.06 +2024-01-17 01:55:29,619 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:55:29,619 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:29,620 (beam_search:483) INFO: best hypo: SIHEITSPERSONALODEREACHUNDENNUSEHRSCHWIERIGBETETENERN + +2024-01-17 01:55:29,621 (asr_inference:494) INFO: speech length: 25600 +2024-01-17 01:55:29,628 (beam_search:428) INFO: decoder input length: 37 +2024-01-17 01:55:29,628 (beam_search:429) INFO: max output length: 37 +2024-01-17 01:55:29,628 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:29,664 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:29,664 (beam_search:476) INFO: -6.23 * 1.0 = -6.23 for ctc +2024-01-17 01:55:29,664 (beam_search:479) INFO: total log probability: -6.23 +2024-01-17 01:55:29,664 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:55:29,664 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:29,664 (beam_search:483) INFO: best hypo: DAURHAFDESBLEIBERECHTUND + +2024-01-17 01:55:29,665 (asr_inference:494) INFO: speech length: 27040 +2024-01-17 01:55:29,673 (beam_search:428) INFO: decoder input length: 40 +2024-01-17 01:55:29,673 (beam_search:429) INFO: max output length: 40 +2024-01-17 01:55:29,673 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:29,718 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:29,718 (beam_search:476) INFO: -15.94 * 1.0 = -15.94 for ctc +2024-01-17 01:55:29,718 (beam_search:479) INFO: total log probability: -15.94 +2024-01-17 01:55:29,718 (beam_search:480) INFO: normalized log probability: -0.46 +2024-01-17 01:55:29,718 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:29,718 (beam_search:483) INFO: best hypo: EBENSOWIEDEASMOTIFTERLESUNBÜT + +2024-01-17 01:55:29,719 (asr_inference:494) INFO: speech length: 32800 +2024-01-17 01:55:29,727 (beam_search:428) INFO: decoder input length: 49 +2024-01-17 01:55:29,727 (beam_search:429) INFO: max output length: 49 +2024-01-17 01:55:29,727 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:29,774 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:29,774 (beam_search:476) INFO: -10.30 * 1.0 = -10.30 for ctc +2024-01-17 01:55:29,774 (beam_search:479) INFO: total log probability: -10.30 +2024-01-17 01:55:29,774 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:55:29,774 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:29,774 (beam_search:483) INFO: best hypo: WENFÜNIEMARNNACHPRÜCFBEIST + +2024-01-17 01:55:29,775 (asr_inference:494) INFO: speech length: 40640 +2024-01-17 01:55:29,783 (beam_search:428) INFO: decoder input length: 61 +2024-01-17 01:55:29,783 (beam_search:429) INFO: max output length: 61 +2024-01-17 01:55:29,783 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:29,863 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:29,863 (beam_search:476) INFO: -12.10 * 1.0 = -12.10 for ctc +2024-01-17 01:55:29,863 (beam_search:479) INFO: total log probability: -12.10 +2024-01-17 01:55:29,863 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:55:29,863 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:29,863 (beam_search:483) INFO: best hypo: ISTIEKLEWADEARFORSCHENVONEINRICHTUNGEN + +2024-01-17 01:55:29,864 (asr_inference:494) INFO: speech length: 68960 +2024-01-17 01:55:29,874 (beam_search:428) INFO: decoder input length: 105 +2024-01-17 01:55:29,874 (beam_search:429) INFO: max output length: 105 +2024-01-17 01:55:29,874 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:30,069 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:30,069 (beam_search:476) INFO: -20.22 * 1.0 = -20.22 for ctc +2024-01-17 01:55:30,069 (beam_search:479) INFO: total log probability: -20.22 +2024-01-17 01:55:30,069 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:55:30,069 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:30,069 (beam_search:483) INFO: best hypo: GESEHENDARVONBÜRDENSEBSTDANOCHDIENTSPECHENDENPALKAOTFIELEN + +2024-01-17 01:55:30,071 (asr_inference:494) INFO: speech length: 42720 +2024-01-17 01:55:30,079 (beam_search:428) INFO: decoder input length: 64 +2024-01-17 01:55:30,079 (beam_search:429) INFO: max output length: 64 +2024-01-17 01:55:30,079 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:30,159 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:30,159 (beam_search:476) INFO: -10.84 * 1.0 = -10.84 for ctc +2024-01-17 01:55:30,159 (beam_search:479) INFO: total log probability: -10.84 +2024-01-17 01:55:30,159 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:55:30,159 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:30,159 (beam_search:483) INFO: best hypo: SPÄCHENBENUTICHDEARTEMLUFTLIEFVERT + +2024-01-17 01:55:30,160 (asr_inference:494) INFO: speech length: 36320 +2024-01-17 01:55:30,168 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:55:30,168 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:55:30,168 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:30,237 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:30,237 (beam_search:476) INFO: -12.22 * 1.0 = -12.22 for ctc +2024-01-17 01:55:30,237 (beam_search:479) INFO: total log probability: -12.22 +2024-01-17 01:55:30,237 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:55:30,237 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:30,237 (beam_search:483) INFO: best hypo: EMOGLICHENSHUTZSIMFORNENGENKRANKREITE + +2024-01-17 01:55:30,238 (asr_inference:494) INFO: speech length: 23680 +2024-01-17 01:55:30,245 (beam_search:428) INFO: decoder input length: 34 +2024-01-17 01:55:30,245 (beam_search:429) INFO: max output length: 34 +2024-01-17 01:55:30,245 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:30,280 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:30,280 (beam_search:476) INFO: -5.91 * 1.0 = -5.91 for ctc +2024-01-17 01:55:30,280 (beam_search:479) INFO: total log probability: -5.91 +2024-01-17 01:55:30,280 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:55:30,280 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:30,280 (beam_search:483) INFO: best hypo: SCHNEINENLICHENVERSUCHGAR + +2024-01-17 01:55:30,281 (asr_inference:494) INFO: speech length: 119520 +2024-01-17 01:55:30,294 (beam_search:428) INFO: decoder input length: 184 +2024-01-17 01:55:30,294 (beam_search:429) INFO: max output length: 184 +2024-01-17 01:55:30,294 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:30,849 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:30,849 (beam_search:476) INFO: -36.18 * 1.0 = -36.18 for ctc +2024-01-17 01:55:30,849 (beam_search:479) INFO: total log probability: -36.18 +2024-01-17 01:55:30,849 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:55:30,849 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:30,850 (beam_search:483) INFO: best hypo: RONDKENSTRAHENANEINEMPEENÜBERGANGODEPIEENÜBERGANGDUSTDENNHRENVOTUFEKTINEINELEKRICHENSTROMUMWANDET + +2024-01-17 01:55:30,851 (asr_inference:494) INFO: speech length: 31520 +2024-01-17 01:55:30,859 (beam_search:428) INFO: decoder input length: 47 +2024-01-17 01:55:30,859 (beam_search:429) INFO: max output length: 47 +2024-01-17 01:55:30,859 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:30,914 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:30,915 (beam_search:476) INFO: -12.34 * 1.0 = -12.34 for ctc +2024-01-17 01:55:30,915 (beam_search:479) INFO: total log probability: -12.34 +2024-01-17 01:55:30,915 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:55:30,915 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:30,915 (beam_search:483) INFO: best hypo: BRMASTEENDESEBESTERNCHTFREIDETEN + +2024-01-17 01:55:30,916 (asr_inference:494) INFO: speech length: 17280 +2024-01-17 01:55:30,923 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:55:30,923 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:55:30,923 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:30,940 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:30,940 (beam_search:476) INFO: -7.98 * 1.0 = -7.98 for ctc +2024-01-17 01:55:30,940 (beam_search:479) INFO: total log probability: -7.98 +2024-01-17 01:55:30,940 (beam_search:480) INFO: normalized log probability: -0.47 +2024-01-17 01:55:30,940 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:30,940 (beam_search:483) INFO: best hypo: LAHENDERBEGIFT + +2024-01-17 01:55:30,941 (asr_inference:494) INFO: speech length: 55520 +2024-01-17 01:55:30,949 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:55:30,950 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:55:30,950 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:31,084 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:31,084 (beam_search:476) INFO: -16.65 * 1.0 = -16.65 for ctc +2024-01-17 01:55:31,084 (beam_search:479) INFO: total log probability: -16.65 +2024-01-17 01:55:31,084 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:55:31,084 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:31,084 (beam_search:483) INFO: best hypo: ANKFALTNISUNTERDINSTIMNNOCHEINELOGESCHEABPFOLRG + +2024-01-17 01:55:31,085 (asr_inference:494) INFO: speech length: 41120 +2024-01-17 01:55:31,093 (beam_search:428) INFO: decoder input length: 62 +2024-01-17 01:55:31,093 (beam_search:429) INFO: max output length: 62 +2024-01-17 01:55:31,093 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:31,181 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:31,181 (beam_search:476) INFO: -16.89 * 1.0 = -16.89 for ctc +2024-01-17 01:55:31,181 (beam_search:479) INFO: total log probability: -16.89 +2024-01-17 01:55:31,181 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:55:31,181 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:31,182 (beam_search:483) INFO: best hypo: KABERTLENDIESESANGEBODIEOMITENHIENHEITAB + +2024-01-17 01:55:31,183 (asr_inference:494) INFO: speech length: 78560 +2024-01-17 01:55:31,193 (beam_search:428) INFO: decoder input length: 120 +2024-01-17 01:55:31,193 (beam_search:429) INFO: max output length: 120 +2024-01-17 01:55:31,193 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:31,399 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:31,399 (beam_search:476) INFO: -20.29 * 1.0 = -20.29 for ctc +2024-01-17 01:55:31,399 (beam_search:479) INFO: total log probability: -20.29 +2024-01-17 01:55:31,399 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:55:31,399 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:31,400 (beam_search:483) INFO: best hypo: STANDVOMZWALFTENMERZZWEITAUSENZWÖLFDERINHALTSTENT + +2024-01-17 01:55:31,401 (asr_inference:494) INFO: speech length: 31680 +2024-01-17 01:55:31,408 (beam_search:428) INFO: decoder input length: 47 +2024-01-17 01:55:31,408 (beam_search:429) INFO: max output length: 47 +2024-01-17 01:55:31,408 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:31,457 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:31,457 (beam_search:476) INFO: -9.74 * 1.0 = -9.74 for ctc +2024-01-17 01:55:31,457 (beam_search:479) INFO: total log probability: -9.74 +2024-01-17 01:55:31,457 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:55:31,457 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:31,457 (beam_search:483) INFO: best hypo: RGENISATIONUNTERBRACHTEREFND + +2024-01-17 01:55:31,459 (asr_inference:494) INFO: speech length: 49920 +2024-01-17 01:55:31,467 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:55:31,467 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:55:31,467 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:31,561 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:31,561 (beam_search:476) INFO: -9.42 * 1.0 = -9.42 for ctc +2024-01-17 01:55:31,561 (beam_search:479) INFO: total log probability: -9.42 +2024-01-17 01:55:31,561 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:55:31,561 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:31,561 (beam_search:483) INFO: best hypo: VERBÜNDITZENDORDERGAHFÜSIEARBEITEN + +2024-01-17 01:55:31,563 (asr_inference:494) INFO: speech length: 24000 +2024-01-17 01:55:31,570 (beam_search:428) INFO: decoder input length: 35 +2024-01-17 01:55:31,570 (beam_search:429) INFO: max output length: 35 +2024-01-17 01:55:31,570 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:31,601 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:31,601 (beam_search:476) INFO: -11.91 * 1.0 = -11.91 for ctc +2024-01-17 01:55:31,601 (beam_search:479) INFO: total log probability: -11.91 +2024-01-17 01:55:31,601 (beam_search:480) INFO: normalized log probability: -0.48 +2024-01-17 01:55:31,601 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:31,601 (beam_search:483) INFO: best hypo: ESGELETEOLHRIHKETALDE + +2024-01-17 01:55:31,602 (asr_inference:494) INFO: speech length: 37760 +2024-01-17 01:55:31,610 (beam_search:428) INFO: decoder input length: 56 +2024-01-17 01:55:31,610 (beam_search:429) INFO: max output length: 56 +2024-01-17 01:55:31,610 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:31,679 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:31,679 (beam_search:476) INFO: -8.78 * 1.0 = -8.78 for ctc +2024-01-17 01:55:31,679 (beam_search:479) INFO: total log probability: -8.78 +2024-01-17 01:55:31,679 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:55:31,679 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:31,680 (beam_search:483) INFO: best hypo: DIEERICHTUNGDERBERLINEMAURMÜNDETEN + +2024-01-17 01:55:31,681 (asr_inference:494) INFO: speech length: 27040 +2024-01-17 01:55:31,688 (beam_search:428) INFO: decoder input length: 40 +2024-01-17 01:55:31,688 (beam_search:429) INFO: max output length: 40 +2024-01-17 01:55:31,688 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:31,728 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:31,728 (beam_search:476) INFO: -5.24 * 1.0 = -5.24 for ctc +2024-01-17 01:55:31,728 (beam_search:479) INFO: total log probability: -5.24 +2024-01-17 01:55:31,728 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:55:31,728 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:31,728 (beam_search:483) INFO: best hypo: ERICHTUNGVONKLÄERANLARGEN + +2024-01-17 01:55:31,729 (asr_inference:494) INFO: speech length: 56160 +2024-01-17 01:55:31,738 (beam_search:428) INFO: decoder input length: 85 +2024-01-17 01:55:31,738 (beam_search:429) INFO: max output length: 85 +2024-01-17 01:55:31,738 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:31,872 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:31,872 (beam_search:476) INFO: -19.86 * 1.0 = -19.86 for ctc +2024-01-17 01:55:31,872 (beam_search:479) INFO: total log probability: -19.86 +2024-01-17 01:55:31,872 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:55:31,872 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:31,873 (beam_search:483) INFO: best hypo: AFGANESTANZUNDDEMIEHÖARGHATZICHSEITIEMEINMARCTE + +2024-01-17 01:55:31,874 (asr_inference:494) INFO: speech length: 65440 +2024-01-17 01:55:31,883 (beam_search:428) INFO: decoder input length: 100 +2024-01-17 01:55:31,883 (beam_search:429) INFO: max output length: 100 +2024-01-17 01:55:31,883 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:32,057 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:32,057 (beam_search:476) INFO: -12.56 * 1.0 = -12.56 for ctc +2024-01-17 01:55:32,057 (beam_search:479) INFO: total log probability: -12.56 +2024-01-17 01:55:32,057 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:55:32,057 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:32,058 (beam_search:483) INFO: best hypo: DERVONARIOUNDSTROMVONDENLUNGENÜBERDIEBRONCHIENBIS + +2024-01-17 01:55:32,059 (asr_inference:494) INFO: speech length: 70720 +2024-01-17 01:55:32,069 (beam_search:428) INFO: decoder input length: 108 +2024-01-17 01:55:32,069 (beam_search:429) INFO: max output length: 108 +2024-01-17 01:55:32,069 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:32,250 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:32,250 (beam_search:476) INFO: -15.42 * 1.0 = -15.42 for ctc +2024-01-17 01:55:32,250 (beam_search:479) INFO: total log probability: -15.42 +2024-01-17 01:55:32,250 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:55:32,250 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:32,251 (beam_search:483) INFO: best hypo: AUSEDEMNNAHMENSENDERHRSPIELEBMITTVERFRMDEDRSPRARE + +2024-01-17 01:55:32,252 (asr_inference:494) INFO: speech length: 25920 +2024-01-17 01:55:32,259 (beam_search:428) INFO: decoder input length: 38 +2024-01-17 01:55:32,259 (beam_search:429) INFO: max output length: 38 +2024-01-17 01:55:32,259 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:32,294 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:32,294 (beam_search:476) INFO: -11.59 * 1.0 = -11.59 for ctc +2024-01-17 01:55:32,294 (beam_search:479) INFO: total log probability: -11.59 +2024-01-17 01:55:32,294 (beam_search:480) INFO: normalized log probability: -0.45 +2024-01-17 01:55:32,294 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:32,294 (beam_search:483) INFO: best hypo: UNDTIGUNDMANDARSKLAUSE + +2024-01-17 01:55:32,295 (asr_inference:494) INFO: speech length: 59040 +2024-01-17 01:55:32,304 (beam_search:428) INFO: decoder input length: 90 +2024-01-17 01:55:32,304 (beam_search:429) INFO: max output length: 90 +2024-01-17 01:55:32,304 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:32,457 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:32,457 (beam_search:476) INFO: -14.46 * 1.0 = -14.46 for ctc +2024-01-17 01:55:32,457 (beam_search:479) INFO: total log probability: -14.46 +2024-01-17 01:55:32,457 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:55:32,457 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:32,457 (beam_search:483) INFO: best hypo: KEINEABKERVONENGRUNDTLAGEDESSOZELISMOSEINSCHLIESE + +2024-01-17 01:55:32,459 (asr_inference:494) INFO: speech length: 69600 +2024-01-17 01:55:32,468 (beam_search:428) INFO: decoder input length: 106 +2024-01-17 01:55:32,468 (beam_search:429) INFO: max output length: 106 +2024-01-17 01:55:32,468 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:32,635 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:32,635 (beam_search:476) INFO: -10.64 * 1.0 = -10.64 for ctc +2024-01-17 01:55:32,635 (beam_search:479) INFO: total log probability: -10.64 +2024-01-17 01:55:32,635 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:55:32,635 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:32,636 (beam_search:483) INFO: best hypo: ITKOMPONENTENSOWOHLANNALSAUCHTIEFINDERWACFE + +2024-01-17 01:55:32,637 (asr_inference:494) INFO: speech length: 20640 +2024-01-17 01:55:32,643 (beam_search:428) INFO: decoder input length: 30 +2024-01-17 01:55:32,643 (beam_search:429) INFO: max output length: 30 +2024-01-17 01:55:32,643 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:32,666 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:32,666 (beam_search:476) INFO: -6.06 * 1.0 = -6.06 for ctc +2024-01-17 01:55:32,666 (beam_search:479) INFO: total log probability: -6.06 +2024-01-17 01:55:32,666 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:55:32,666 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:32,666 (beam_search:483) INFO: best hypo: BEDEUTUNGSVOLLWA + +2024-01-17 01:55:32,667 (asr_inference:494) INFO: speech length: 31200 +2024-01-17 01:55:32,675 (beam_search:428) INFO: decoder input length: 46 +2024-01-17 01:55:32,675 (beam_search:429) INFO: max output length: 46 +2024-01-17 01:55:32,675 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:32,727 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:32,727 (beam_search:476) INFO: -13.06 * 1.0 = -13.06 for ctc +2024-01-17 01:55:32,727 (beam_search:479) INFO: total log probability: -13.06 +2024-01-17 01:55:32,727 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:55:32,727 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:32,727 (beam_search:483) INFO: best hypo: FREIWÜELIGEHENFERTEOGANIESATZION + +2024-01-17 01:55:32,728 (asr_inference:494) INFO: speech length: 43840 +2024-01-17 01:55:32,736 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:55:32,736 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:55:32,736 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:32,825 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:32,825 (beam_search:476) INFO: -9.75 * 1.0 = -9.75 for ctc +2024-01-17 01:55:32,825 (beam_search:479) INFO: total log probability: -9.75 +2024-01-17 01:55:32,825 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:55:32,825 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:32,826 (beam_search:483) INFO: best hypo: MELEKTRONVOMWARLENZSBANDINSLEITUNGSBAN + +2024-01-17 01:55:32,827 (asr_inference:494) INFO: speech length: 35840 +2024-01-17 01:55:32,834 (beam_search:428) INFO: decoder input length: 53 +2024-01-17 01:55:32,834 (beam_search:429) INFO: max output length: 53 +2024-01-17 01:55:32,834 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:32,901 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:32,901 (beam_search:476) INFO: -15.31 * 1.0 = -15.31 for ctc +2024-01-17 01:55:32,901 (beam_search:479) INFO: total log probability: -15.31 +2024-01-17 01:55:32,901 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:55:32,901 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:32,901 (beam_search:483) INFO: best hypo: ALLEDINGSENVERGLEICHBERIFEKEMÖUGLICH + +2024-01-17 01:55:32,902 (asr_inference:494) INFO: speech length: 120160 +2024-01-17 01:55:32,915 (beam_search:428) INFO: decoder input length: 185 +2024-01-17 01:55:32,915 (beam_search:429) INFO: max output length: 185 +2024-01-17 01:55:32,915 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:33,417 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:33,417 (beam_search:476) INFO: -20.20 * 1.0 = -20.20 for ctc +2024-01-17 01:55:33,417 (beam_search:479) INFO: total log probability: -20.20 +2024-01-17 01:55:33,417 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:55:33,417 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:33,418 (beam_search:483) INFO: best hypo: DIESEKONTENABERALSEINGABEINEINENDFRICGWENZSUMSETZERDIENENODERSTEUATENZUNGONMOTOUREN + +2024-01-17 01:55:33,419 (asr_inference:494) INFO: speech length: 58240 +2024-01-17 01:55:33,428 (beam_search:428) INFO: decoder input length: 88 +2024-01-17 01:55:33,428 (beam_search:429) INFO: max output length: 88 +2024-01-17 01:55:33,428 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:33,566 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:33,567 (beam_search:476) INFO: -14.22 * 1.0 = -14.22 for ctc +2024-01-17 01:55:33,567 (beam_search:479) INFO: total log probability: -14.22 +2024-01-17 01:55:33,567 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:55:33,567 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:33,567 (beam_search:483) INFO: best hypo: TOMASHRMANSPRODUZIERTEZWEITAUSENDZWEIMITKEREBE + +2024-01-17 01:55:33,568 (asr_inference:494) INFO: speech length: 21920 +2024-01-17 01:55:33,575 (beam_search:428) INFO: decoder input length: 32 +2024-01-17 01:55:33,575 (beam_search:429) INFO: max output length: 32 +2024-01-17 01:55:33,575 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:33,599 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:33,599 (beam_search:476) INFO: -7.91 * 1.0 = -7.91 for ctc +2024-01-17 01:55:33,599 (beam_search:479) INFO: total log probability: -7.91 +2024-01-17 01:55:33,599 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:55:33,599 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:33,599 (beam_search:483) INFO: best hypo: PENÜBERGANGTREFEN + +2024-01-17 01:55:33,600 (asr_inference:494) INFO: speech length: 22560 +2024-01-17 01:55:33,607 (beam_search:428) INFO: decoder input length: 33 +2024-01-17 01:55:33,607 (beam_search:429) INFO: max output length: 33 +2024-01-17 01:55:33,607 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:33,634 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:33,634 (beam_search:476) INFO: -7.93 * 1.0 = -7.93 for ctc +2024-01-17 01:55:33,634 (beam_search:479) INFO: total log probability: -7.93 +2024-01-17 01:55:33,634 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:55:33,634 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:33,634 (beam_search:483) INFO: best hypo: DIEFALGTENHOUSSCAU + +2024-01-17 01:55:33,635 (asr_inference:494) INFO: speech length: 60160 +2024-01-17 01:55:33,644 (beam_search:428) INFO: decoder input length: 91 +2024-01-17 01:55:33,644 (beam_search:429) INFO: max output length: 91 +2024-01-17 01:55:33,644 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:33,801 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:33,801 (beam_search:476) INFO: -13.60 * 1.0 = -13.60 for ctc +2024-01-17 01:55:33,801 (beam_search:479) INFO: total log probability: -13.60 +2024-01-17 01:55:33,801 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:55:33,801 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:33,802 (beam_search:483) INFO: best hypo: ANTIESOJETSCHEDEMONSTRATIONENWURDENPLUTIGNIEDERSCHLAGE + +2024-01-17 01:55:33,803 (asr_inference:494) INFO: speech length: 44960 +2024-01-17 01:55:33,811 (beam_search:428) INFO: decoder input length: 68 +2024-01-17 01:55:33,811 (beam_search:429) INFO: max output length: 68 +2024-01-17 01:55:33,811 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:33,896 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:33,896 (beam_search:476) INFO: -8.04 * 1.0 = -8.04 for ctc +2024-01-17 01:55:33,896 (beam_search:479) INFO: total log probability: -8.04 +2024-01-17 01:55:33,896 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:55:33,896 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:33,896 (beam_search:483) INFO: best hypo: EINVIERKANALMISPOLDDENTEVERKLEINER + +2024-01-17 01:55:33,898 (asr_inference:494) INFO: speech length: 86720 +2024-01-17 01:55:33,908 (beam_search:428) INFO: decoder input length: 133 +2024-01-17 01:55:33,908 (beam_search:429) INFO: max output length: 133 +2024-01-17 01:55:33,908 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:34,210 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:34,211 (beam_search:476) INFO: -19.43 * 1.0 = -19.43 for ctc +2024-01-17 01:55:34,211 (beam_search:479) INFO: total log probability: -19.43 +2024-01-17 01:55:34,211 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:55:34,211 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:34,211 (beam_search:483) INFO: best hypo: DIESEHETENDIEVORWANDZEITENFÜREINENANGRIFAUFDIEUESAREXSTREMHERAPBGESETZT + +2024-01-17 01:55:34,212 (asr_inference:494) INFO: speech length: 39200 +2024-01-17 01:55:34,220 (beam_search:428) INFO: decoder input length: 59 +2024-01-17 01:55:34,220 (beam_search:429) INFO: max output length: 59 +2024-01-17 01:55:34,220 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:34,299 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:34,299 (beam_search:476) INFO: -8.69 * 1.0 = -8.69 for ctc +2024-01-17 01:55:34,299 (beam_search:479) INFO: total log probability: -8.69 +2024-01-17 01:55:34,299 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:55:34,299 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:34,299 (beam_search:483) INFO: best hypo: WEICHESAMNECHSTENZUMSTARTKNODENLIEGT + +2024-01-17 01:55:34,300 (asr_inference:494) INFO: speech length: 68960 +2024-01-17 01:55:34,310 (beam_search:428) INFO: decoder input length: 105 +2024-01-17 01:55:34,310 (beam_search:429) INFO: max output length: 105 +2024-01-17 01:55:34,310 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:34,513 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:34,513 (beam_search:476) INFO: -16.79 * 1.0 = -16.79 for ctc +2024-01-17 01:55:34,513 (beam_search:479) INFO: total log probability: -16.79 +2024-01-17 01:55:34,513 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:55:34,513 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:34,513 (beam_search:483) INFO: best hypo: LAHTIOGINGNDOLLZERÜCKNIEBUNDESLIEGERUNDWEXSELDEZUEINDRCHT + +2024-01-17 01:55:34,514 (asr_inference:494) INFO: speech length: 19200 +2024-01-17 01:55:34,521 (beam_search:428) INFO: decoder input length: 27 +2024-01-17 01:55:34,521 (beam_search:429) INFO: max output length: 27 +2024-01-17 01:55:34,521 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:34,541 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:34,541 (beam_search:476) INFO: -5.61 * 1.0 = -5.61 for ctc +2024-01-17 01:55:34,541 (beam_search:479) INFO: total log probability: -5.61 +2024-01-17 01:55:34,541 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:55:34,541 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:34,541 (beam_search:483) INFO: best hypo: ÜBERISEKANKEIT + +2024-01-17 01:55:34,542 (asr_inference:494) INFO: speech length: 30880 +2024-01-17 01:55:34,549 (beam_search:428) INFO: decoder input length: 46 +2024-01-17 01:55:34,549 (beam_search:429) INFO: max output length: 46 +2024-01-17 01:55:34,549 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:34,603 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:34,604 (beam_search:476) INFO: -10.89 * 1.0 = -10.89 for ctc +2024-01-17 01:55:34,604 (beam_search:479) INFO: total log probability: -10.89 +2024-01-17 01:55:34,604 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:55:34,604 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:34,604 (beam_search:483) INFO: best hypo: JAHRZWEITAUSENDFÜNVEKRITIESERTE + +2024-01-17 01:55:34,605 (asr_inference:494) INFO: speech length: 41440 +2024-01-17 01:55:34,613 (beam_search:428) INFO: decoder input length: 62 +2024-01-17 01:55:34,613 (beam_search:429) INFO: max output length: 62 +2024-01-17 01:55:34,613 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:34,698 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:34,698 (beam_search:476) INFO: -14.18 * 1.0 = -14.18 for ctc +2024-01-17 01:55:34,698 (beam_search:479) INFO: total log probability: -14.18 +2024-01-17 01:55:34,698 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:55:34,698 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:34,699 (beam_search:483) INFO: best hypo: DIESEAUFHFASUNGZURNEUTRARITEÄTUNTERSHEIDE + +2024-01-17 01:55:34,700 (asr_inference:494) INFO: speech length: 65920 +2024-01-17 01:55:34,709 (beam_search:428) INFO: decoder input length: 100 +2024-01-17 01:55:34,710 (beam_search:429) INFO: max output length: 100 +2024-01-17 01:55:34,710 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:34,888 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:34,888 (beam_search:476) INFO: -17.30 * 1.0 = -17.30 for ctc +2024-01-17 01:55:34,888 (beam_search:479) INFO: total log probability: -17.30 +2024-01-17 01:55:34,888 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:55:34,888 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:34,889 (beam_search:483) INFO: best hypo: WEDELWURDERALSKNSTLAISCHERLEITERDESSIMENSTUDIESBESTELLT + +2024-01-17 01:55:34,890 (asr_inference:494) INFO: speech length: 37920 +2024-01-17 01:55:34,898 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:55:34,898 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:55:34,898 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:34,964 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:34,964 (beam_search:476) INFO: -8.36 * 1.0 = -8.36 for ctc +2024-01-17 01:55:34,964 (beam_search:479) INFO: total log probability: -8.36 +2024-01-17 01:55:34,964 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:55:34,964 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:34,964 (beam_search:483) INFO: best hypo: WENMANDIEWLTALSKANZESBERACHTET + +2024-01-17 01:55:34,965 (asr_inference:494) INFO: speech length: 93760 +2024-01-17 01:55:34,976 (beam_search:428) INFO: decoder input length: 144 +2024-01-17 01:55:34,976 (beam_search:429) INFO: max output length: 144 +2024-01-17 01:55:34,976 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:35,315 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:35,315 (beam_search:476) INFO: -18.93 * 1.0 = -18.93 for ctc +2024-01-17 01:55:35,315 (beam_search:479) INFO: total log probability: -18.93 +2024-01-17 01:55:35,315 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:55:35,315 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:35,315 (beam_search:483) INFO: best hypo: SIEMTKRITISCHEKOMPONENTENDESDETUONATIONDZYSTEMSABSICHTLICHSCHWACHEINDWURFEN + +2024-01-17 01:55:35,316 (asr_inference:494) INFO: speech length: 19360 +2024-01-17 01:55:35,323 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:55:35,323 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:55:35,323 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:35,347 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:35,347 (beam_search:476) INFO: -5.64 * 1.0 = -5.64 for ctc +2024-01-17 01:55:35,347 (beam_search:479) INFO: total log probability: -5.64 +2024-01-17 01:55:35,347 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:55:35,347 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:35,347 (beam_search:483) INFO: best hypo: NICHWEHBERISTEDOCH + +2024-01-17 01:55:35,348 (asr_inference:494) INFO: speech length: 35680 +2024-01-17 01:55:35,356 (beam_search:428) INFO: decoder input length: 53 +2024-01-17 01:55:35,356 (beam_search:429) INFO: max output length: 53 +2024-01-17 01:55:35,356 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:35,421 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:35,421 (beam_search:476) INFO: -7.66 * 1.0 = -7.66 for ctc +2024-01-17 01:55:35,421 (beam_search:479) INFO: total log probability: -7.66 +2024-01-17 01:55:35,421 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:55:35,421 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:35,421 (beam_search:483) INFO: best hypo: ERBODEINEVEREINIGUNGDEUTSCHANSAN + +2024-01-17 01:55:35,422 (asr_inference:494) INFO: speech length: 54240 +2024-01-17 01:55:35,431 (beam_search:428) INFO: decoder input length: 82 +2024-01-17 01:55:35,431 (beam_search:429) INFO: max output length: 82 +2024-01-17 01:55:35,431 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:35,498 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:35,498 (beam_search:476) INFO: -11.20 * 1.0 = -11.20 for ctc +2024-01-17 01:55:35,498 (beam_search:479) INFO: total log probability: -11.20 +2024-01-17 01:55:35,498 (beam_search:480) INFO: normalized log probability: -0.45 +2024-01-17 01:55:35,498 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:35,499 (beam_search:483) INFO: best hypo: BERIENNZWEITUENFÜNF + +2024-01-17 01:55:35,500 (asr_inference:494) INFO: speech length: 68000 +2024-01-17 01:55:35,509 (beam_search:428) INFO: decoder input length: 104 +2024-01-17 01:55:35,509 (beam_search:429) INFO: max output length: 104 +2024-01-17 01:55:35,509 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:35,659 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:35,659 (beam_search:476) INFO: -8.42 * 1.0 = -8.42 for ctc +2024-01-17 01:55:35,659 (beam_search:479) INFO: total log probability: -8.42 +2024-01-17 01:55:35,659 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:55:35,659 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:35,659 (beam_search:483) INFO: best hypo: KERNABGESTIMTUNDUMHÖLENDIESENENSPRECHENT + +2024-01-17 01:55:35,660 (asr_inference:494) INFO: speech length: 34080 +2024-01-17 01:55:35,668 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:55:35,668 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:55:35,668 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:35,715 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:35,715 (beam_search:476) INFO: -6.17 * 1.0 = -6.17 for ctc +2024-01-17 01:55:35,715 (beam_search:479) INFO: total log probability: -6.17 +2024-01-17 01:55:35,715 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:55:35,715 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:35,715 (beam_search:483) INFO: best hypo: AZTEUGUNGVOMNDENAMIGAUS + +2024-01-17 01:55:35,716 (asr_inference:494) INFO: speech length: 22560 +2024-01-17 01:55:35,723 (beam_search:428) INFO: decoder input length: 33 +2024-01-17 01:55:35,723 (beam_search:429) INFO: max output length: 33 +2024-01-17 01:55:35,723 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:35,745 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:35,745 (beam_search:476) INFO: -3.96 * 1.0 = -3.96 for ctc +2024-01-17 01:55:35,745 (beam_search:479) INFO: total log probability: -3.96 +2024-01-17 01:55:35,745 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:55:35,745 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:35,745 (beam_search:483) INFO: best hypo: SEMTUNDINGWER + +2024-01-17 01:55:35,746 (asr_inference:494) INFO: speech length: 26880 +2024-01-17 01:55:35,753 (beam_search:428) INFO: decoder input length: 39 +2024-01-17 01:55:35,753 (beam_search:429) INFO: max output length: 39 +2024-01-17 01:55:35,753 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:35,790 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:35,790 (beam_search:476) INFO: -11.74 * 1.0 = -11.74 for ctc +2024-01-17 01:55:35,790 (beam_search:479) INFO: total log probability: -11.74 +2024-01-17 01:55:35,790 (beam_search:480) INFO: normalized log probability: -0.43 +2024-01-17 01:55:35,790 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:35,790 (beam_search:483) INFO: best hypo: UNGVONSCHEHERUNTERNERUGEG + +2024-01-17 01:55:35,791 (asr_inference:494) INFO: speech length: 26080 +2024-01-17 01:55:35,798 (beam_search:428) INFO: decoder input length: 38 +2024-01-17 01:55:35,798 (beam_search:429) INFO: max output length: 38 +2024-01-17 01:55:35,798 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:35,828 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:35,828 (beam_search:476) INFO: -7.10 * 1.0 = -7.10 for ctc +2024-01-17 01:55:35,828 (beam_search:479) INFO: total log probability: -7.10 +2024-01-17 01:55:35,828 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:55:35,828 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:35,828 (beam_search:483) INFO: best hypo: NISCHENUNDGEWRTHEN + +2024-01-17 01:55:35,829 (asr_inference:494) INFO: speech length: 24320 +2024-01-17 01:55:35,836 (beam_search:428) INFO: decoder input length: 35 +2024-01-17 01:55:35,836 (beam_search:429) INFO: max output length: 35 +2024-01-17 01:55:35,836 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:35,860 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:35,860 (beam_search:476) INFO: -5.24 * 1.0 = -5.24 for ctc +2024-01-17 01:55:35,860 (beam_search:479) INFO: total log probability: -5.24 +2024-01-17 01:55:35,860 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:55:35,860 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:35,860 (beam_search:483) INFO: best hypo: ROBERTERFKNEDIE + +2024-01-17 01:55:35,861 (asr_inference:494) INFO: speech length: 19200 +2024-01-17 01:55:35,868 (beam_search:428) INFO: decoder input length: 27 +2024-01-17 01:55:35,868 (beam_search:429) INFO: max output length: 27 +2024-01-17 01:55:35,868 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:35,889 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:35,890 (beam_search:476) INFO: -4.78 * 1.0 = -4.78 for ctc +2024-01-17 01:55:35,890 (beam_search:479) INFO: total log probability: -4.78 +2024-01-17 01:55:35,890 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:55:35,890 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:35,890 (beam_search:483) INFO: best hypo: KAMSCHLISELICHZUNM + +2024-01-17 01:55:35,891 (asr_inference:494) INFO: speech length: 33280 +2024-01-17 01:55:35,898 (beam_search:428) INFO: decoder input length: 49 +2024-01-17 01:55:35,898 (beam_search:429) INFO: max output length: 49 +2024-01-17 01:55:35,898 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:35,914 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:35,914 (beam_search:476) INFO: -4.33 * 1.0 = -4.33 for ctc +2024-01-17 01:55:35,914 (beam_search:479) INFO: total log probability: -4.33 +2024-01-17 01:55:35,914 (beam_search:480) INFO: normalized log probability: -0.48 +2024-01-17 01:55:35,914 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:35,914 (beam_search:483) INFO: best hypo: OLSTENI + +2024-01-17 01:55:35,915 (asr_inference:494) INFO: speech length: 38560 +2024-01-17 01:55:35,923 (beam_search:428) INFO: decoder input length: 58 +2024-01-17 01:55:35,923 (beam_search:429) INFO: max output length: 58 +2024-01-17 01:55:35,923 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:35,978 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:35,978 (beam_search:476) INFO: -5.81 * 1.0 = -5.81 for ctc +2024-01-17 01:55:35,978 (beam_search:479) INFO: total log probability: -5.81 +2024-01-17 01:55:35,978 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:55:35,978 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:35,978 (beam_search:483) INFO: best hypo: STANDTENSICHVONDENURSAR + +2024-01-17 01:55:35,980 (asr_inference:494) INFO: speech length: 39680 +2024-01-17 01:55:35,987 (beam_search:428) INFO: decoder input length: 59 +2024-01-17 01:55:35,987 (beam_search:429) INFO: max output length: 59 +2024-01-17 01:55:35,987 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:36,054 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:36,054 (beam_search:476) INFO: -12.96 * 1.0 = -12.96 for ctc +2024-01-17 01:55:36,054 (beam_search:479) INFO: total log probability: -12.96 +2024-01-17 01:55:36,054 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:55:36,054 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:36,054 (beam_search:483) INFO: best hypo: ACFRIKASSTIHDESERHAHRERGEORTET + +2024-01-17 01:55:36,056 (asr_inference:494) INFO: speech length: 19200 +2024-01-17 01:55:36,062 (beam_search:428) INFO: decoder input length: 27 +2024-01-17 01:55:36,062 (beam_search:429) INFO: max output length: 27 +2024-01-17 01:55:36,062 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:36,084 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:36,084 (beam_search:476) INFO: -4.27 * 1.0 = -4.27 for ctc +2024-01-17 01:55:36,084 (beam_search:479) INFO: total log probability: -4.27 +2024-01-17 01:55:36,084 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:55:36,084 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:36,085 (beam_search:483) INFO: best hypo: DIEARMEMEUNTERTEL + +2024-01-17 01:55:36,086 (asr_inference:494) INFO: speech length: 19520 +2024-01-17 01:55:36,092 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:55:36,092 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:55:36,092 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:36,112 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:36,113 (beam_search:476) INFO: -3.90 * 1.0 = -3.90 for ctc +2024-01-17 01:55:36,113 (beam_search:479) INFO: total log probability: -3.90 +2024-01-17 01:55:36,113 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:55:36,113 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:36,113 (beam_search:483) INFO: best hypo: STALIENSETZTEM + +2024-01-17 01:55:36,114 (asr_inference:494) INFO: speech length: 18560 +2024-01-17 01:55:36,120 (beam_search:428) INFO: decoder input length: 26 +2024-01-17 01:55:36,120 (beam_search:429) INFO: max output length: 26 +2024-01-17 01:55:36,120 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:36,141 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:36,141 (beam_search:476) INFO: -6.76 * 1.0 = -6.76 for ctc +2024-01-17 01:55:36,141 (beam_search:479) INFO: total log probability: -6.76 +2024-01-17 01:55:36,141 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:55:36,141 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:36,141 (beam_search:483) INFO: best hypo: FEIHTENSAUSLEICH + +2024-01-17 01:55:36,142 (asr_inference:494) INFO: speech length: 42400 +2024-01-17 01:55:36,150 (beam_search:428) INFO: decoder input length: 64 +2024-01-17 01:55:36,150 (beam_search:429) INFO: max output length: 64 +2024-01-17 01:55:36,150 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:36,209 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:36,209 (beam_search:476) INFO: -6.60 * 1.0 = -6.60 for ctc +2024-01-17 01:55:36,209 (beam_search:479) INFO: total log probability: -6.60 +2024-01-17 01:55:36,209 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:55:36,209 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:36,209 (beam_search:483) INFO: best hypo: KLMERAUFBROSKREIBTGLEICH + +2024-01-17 01:55:36,210 (asr_inference:494) INFO: speech length: 43520 +2024-01-17 01:55:36,218 (beam_search:428) INFO: decoder input length: 65 +2024-01-17 01:55:36,218 (beam_search:429) INFO: max output length: 65 +2024-01-17 01:55:36,218 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:36,297 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:36,297 (beam_search:476) INFO: -8.61 * 1.0 = -8.61 for ctc +2024-01-17 01:55:36,297 (beam_search:479) INFO: total log probability: -8.61 +2024-01-17 01:55:36,297 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:55:36,297 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:36,297 (beam_search:483) INFO: best hypo: AMZWEITENJUNIEZWETAUSNDVIERWURDN + +2024-01-17 01:55:36,299 (asr_inference:494) INFO: speech length: 23040 +2024-01-17 01:55:36,305 (beam_search:428) INFO: decoder input length: 33 +2024-01-17 01:55:36,305 (beam_search:429) INFO: max output length: 33 +2024-01-17 01:55:36,305 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:36,332 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:36,332 (beam_search:476) INFO: -7.32 * 1.0 = -7.32 for ctc +2024-01-17 01:55:36,332 (beam_search:479) INFO: total log probability: -7.32 +2024-01-17 01:55:36,332 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:55:36,332 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:36,332 (beam_search:483) INFO: best hypo: INEBNESTAGNACHRLGT + +2024-01-17 01:55:36,333 (asr_inference:494) INFO: speech length: 65600 +2024-01-17 01:55:36,343 (beam_search:428) INFO: decoder input length: 100 +2024-01-17 01:55:36,343 (beam_search:429) INFO: max output length: 100 +2024-01-17 01:55:36,343 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:36,519 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:36,519 (beam_search:476) INFO: -11.25 * 1.0 = -11.25 for ctc +2024-01-17 01:55:36,519 (beam_search:479) INFO: total log probability: -11.25 +2024-01-17 01:55:36,519 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:55:36,519 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:36,519 (beam_search:483) INFO: best hypo: DIENATOOSSTERWEITERUNGUNDDIEEIENSEITIGEAUFKÖÜNDIGNDE + +2024-01-17 01:55:36,521 (asr_inference:494) INFO: speech length: 20960 +2024-01-17 01:55:36,527 (beam_search:428) INFO: decoder input length: 30 +2024-01-17 01:55:36,527 (beam_search:429) INFO: max output length: 30 +2024-01-17 01:55:36,527 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:36,544 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:36,544 (beam_search:476) INFO: -3.01 * 1.0 = -3.01 for ctc +2024-01-17 01:55:36,544 (beam_search:479) INFO: total log probability: -3.01 +2024-01-17 01:55:36,544 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:55:36,544 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:36,545 (beam_search:483) INFO: best hypo: THIERBEIIS + +2024-01-17 01:55:36,546 (asr_inference:494) INFO: speech length: 47680 +2024-01-17 01:55:36,554 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 01:55:36,554 (beam_search:429) INFO: max output length: 72 +2024-01-17 01:55:36,554 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:36,659 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:36,659 (beam_search:476) INFO: -15.20 * 1.0 = -15.20 for ctc +2024-01-17 01:55:36,659 (beam_search:479) INFO: total log probability: -15.20 +2024-01-17 01:55:36,659 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:55:36,659 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:36,659 (beam_search:483) INFO: best hypo: DIESERSTELLEKAMENSEMTICHMITIEDERDERKAPELETE + +2024-01-17 01:55:36,660 (asr_inference:494) INFO: speech length: 184320 +2024-01-17 01:55:36,677 (beam_search:428) INFO: decoder input length: 285 +2024-01-17 01:55:36,677 (beam_search:429) INFO: max output length: 285 +2024-01-17 01:55:36,677 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:37,847 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:37,847 (beam_search:476) INFO: -40.73 * 1.0 = -40.73 for ctc +2024-01-17 01:55:37,847 (beam_search:479) INFO: total log probability: -40.73 +2024-01-17 01:55:37,847 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:55:37,847 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:37,848 (beam_search:483) INFO: best hypo: POTZTAMABKOMENENTHIELDTZWAHRALGEMEINERVEREINBAUNENÜBERDIKÖNFTIGEGEMEINSAMEVERWALTUNGDERSIEGERMICHTEUNDVOMLIERTORUNDSETZEIEDEMLITRISIERUNG + +2024-01-17 01:55:37,850 (asr_inference:494) INFO: speech length: 59360 +2024-01-17 01:55:37,858 (beam_search:428) INFO: decoder input length: 90 +2024-01-17 01:55:37,858 (beam_search:429) INFO: max output length: 90 +2024-01-17 01:55:37,858 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:38,004 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:38,004 (beam_search:476) INFO: -18.63 * 1.0 = -18.63 for ctc +2024-01-17 01:55:38,004 (beam_search:479) INFO: total log probability: -18.63 +2024-01-17 01:55:38,004 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:55:38,004 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:38,004 (beam_search:483) INFO: best hypo: DANACHUNDERSCHIPEEINENVERDRAGBEIMWIEHFZIDENAHMO + +2024-01-17 01:55:38,005 (asr_inference:494) INFO: speech length: 22560 +2024-01-17 01:55:38,012 (beam_search:428) INFO: decoder input length: 33 +2024-01-17 01:55:38,012 (beam_search:429) INFO: max output length: 33 +2024-01-17 01:55:38,012 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:38,042 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:38,043 (beam_search:476) INFO: -10.69 * 1.0 = -10.69 for ctc +2024-01-17 01:55:38,043 (beam_search:479) INFO: total log probability: -10.69 +2024-01-17 01:55:38,043 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-17 01:55:38,043 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:38,043 (beam_search:483) INFO: best hypo: EINWEITEREWERIANDEMAG + +2024-01-17 01:55:38,044 (asr_inference:494) INFO: speech length: 77600 +2024-01-17 01:55:38,054 (beam_search:428) INFO: decoder input length: 119 +2024-01-17 01:55:38,054 (beam_search:429) INFO: max output length: 119 +2024-01-17 01:55:38,054 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:38,290 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:38,290 (beam_search:476) INFO: -13.55 * 1.0 = -13.55 for ctc +2024-01-17 01:55:38,290 (beam_search:479) INFO: total log probability: -13.55 +2024-01-17 01:55:38,290 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:55:38,290 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:38,291 (beam_search:483) INFO: best hypo: SIEWURDENMODOLAHRNDURCHLOCHSTREIVNGESTEURTUNDDIKLINGEKONDN + +2024-01-17 01:55:38,292 (asr_inference:494) INFO: speech length: 62080 +2024-01-17 01:55:38,302 (beam_search:428) INFO: decoder input length: 94 +2024-01-17 01:55:38,302 (beam_search:429) INFO: max output length: 94 +2024-01-17 01:55:38,302 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:38,474 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:38,474 (beam_search:476) INFO: -19.68 * 1.0 = -19.68 for ctc +2024-01-17 01:55:38,474 (beam_search:479) INFO: total log probability: -19.68 +2024-01-17 01:55:38,474 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:55:38,474 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:38,475 (beam_search:483) INFO: best hypo: DIEGRUNMADARTGKLAUSELBEVORZUCTUNDERDINKLEINERNPARTEINJIENE + +2024-01-17 01:55:38,476 (asr_inference:494) INFO: speech length: 41920 +2024-01-17 01:55:38,484 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:55:38,484 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:55:38,484 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:38,570 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:38,570 (beam_search:476) INFO: -16.44 * 1.0 = -16.44 for ctc +2024-01-17 01:55:38,570 (beam_search:479) INFO: total log probability: -16.44 +2024-01-17 01:55:38,570 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:55:38,570 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:38,570 (beam_search:483) INFO: best hypo: BERTONZDEMKEINEWÖGKLICHEHUGESNOTHERUSCHT + +2024-01-17 01:55:38,571 (asr_inference:494) INFO: speech length: 17600 +2024-01-17 01:55:38,578 (beam_search:428) INFO: decoder input length: 25 +2024-01-17 01:55:38,578 (beam_search:429) INFO: max output length: 25 +2024-01-17 01:55:38,578 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:38,596 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:38,596 (beam_search:476) INFO: -9.03 * 1.0 = -9.03 for ctc +2024-01-17 01:55:38,596 (beam_search:479) INFO: total log probability: -9.03 +2024-01-17 01:55:38,596 (beam_search:480) INFO: normalized log probability: -0.53 +2024-01-17 01:55:38,596 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:38,596 (beam_search:483) INFO: best hypo: NTUGMNTERZIOND + +2024-01-17 01:55:38,597 (asr_inference:494) INFO: speech length: 41600 +2024-01-17 01:55:38,605 (beam_search:428) INFO: decoder input length: 62 +2024-01-17 01:55:38,605 (beam_search:429) INFO: max output length: 62 +2024-01-17 01:55:38,605 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:38,684 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:38,684 (beam_search:476) INFO: -13.24 * 1.0 = -13.24 for ctc +2024-01-17 01:55:38,684 (beam_search:479) INFO: total log probability: -13.24 +2024-01-17 01:55:38,684 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:55:38,684 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:38,684 (beam_search:483) INFO: best hypo: ZUVORBEDIGUNGKONKRETERAPRÜSTUNSCHITE + +2024-01-17 01:55:38,686 (asr_inference:494) INFO: speech length: 32320 +2024-01-17 01:55:38,693 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:55:38,693 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:55:38,693 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:38,733 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:38,733 (beam_search:476) INFO: -6.65 * 1.0 = -6.65 for ctc +2024-01-17 01:55:38,733 (beam_search:479) INFO: total log probability: -6.65 +2024-01-17 01:55:38,733 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:55:38,733 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:38,733 (beam_search:483) INFO: best hypo: BUNDESTARGESWALRECHT + +2024-01-17 01:55:38,735 (asr_inference:494) INFO: speech length: 35040 +2024-01-17 01:55:38,742 (beam_search:428) INFO: decoder input length: 52 +2024-01-17 01:55:38,742 (beam_search:429) INFO: max output length: 52 +2024-01-17 01:55:38,742 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:38,807 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:38,807 (beam_search:476) INFO: -11.81 * 1.0 = -11.81 for ctc +2024-01-17 01:55:38,807 (beam_search:479) INFO: total log probability: -11.81 +2024-01-17 01:55:38,807 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:55:38,807 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:38,807 (beam_search:483) INFO: best hypo: ESMUSIMGREISWALEITDERVORGELETWERN + +2024-01-17 01:55:38,808 (asr_inference:494) INFO: speech length: 143360 +2024-01-17 01:55:38,823 (beam_search:428) INFO: decoder input length: 221 +2024-01-17 01:55:38,823 (beam_search:429) INFO: max output length: 221 +2024-01-17 01:55:38,823 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:39,627 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:39,627 (beam_search:476) INFO: -46.34 * 1.0 = -46.34 for ctc +2024-01-17 01:55:39,627 (beam_search:479) INFO: total log probability: -46.34 +2024-01-17 01:55:39,627 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:55:39,627 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:39,628 (beam_search:483) INFO: best hypo: HATMANEINEIMPIERESCHEBASIESFÜBPSYCHOSOZIALEPROGEAMEZUOSENKUNDERSEBSTMOUTERATEUNZURSTEARKUGDESICHEHITZGEFÜSINDEBEFEÖKERUN + +2024-01-17 01:55:39,629 (asr_inference:494) INFO: speech length: 99680 +2024-01-17 01:55:39,640 (beam_search:428) INFO: decoder input length: 153 +2024-01-17 01:55:39,640 (beam_search:429) INFO: max output length: 153 +2024-01-17 01:55:39,640 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:39,993 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:39,993 (beam_search:476) INFO: -21.81 * 1.0 = -21.81 for ctc +2024-01-17 01:55:39,993 (beam_search:479) INFO: total log probability: -21.81 +2024-01-17 01:55:39,993 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:55:39,993 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:39,993 (beam_search:483) INFO: best hypo: BEIDENERSTENFREINPLLEMENZWAHLNURDEILIESKUIMEINEUNZENHNDERTNEUNZIGINSEINE + +2024-01-17 01:55:39,995 (asr_inference:494) INFO: speech length: 44320 +2024-01-17 01:55:40,003 (beam_search:428) INFO: decoder input length: 67 +2024-01-17 01:55:40,003 (beam_search:429) INFO: max output length: 67 +2024-01-17 01:55:40,003 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:40,099 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:40,099 (beam_search:476) INFO: -12.44 * 1.0 = -12.44 for ctc +2024-01-17 01:55:40,099 (beam_search:479) INFO: total log probability: -12.44 +2024-01-17 01:55:40,099 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:55:40,099 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:40,099 (beam_search:483) INFO: best hypo: DAMMITLASSENSICBESTRALUNGSTERTENSERGENOMESEN + +2024-01-17 01:55:40,100 (asr_inference:494) INFO: speech length: 40640 +2024-01-17 01:55:40,108 (beam_search:428) INFO: decoder input length: 61 +2024-01-17 01:55:40,108 (beam_search:429) INFO: max output length: 61 +2024-01-17 01:55:40,108 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:40,193 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:40,193 (beam_search:476) INFO: -14.43 * 1.0 = -14.43 for ctc +2024-01-17 01:55:40,193 (beam_search:479) INFO: total log probability: -14.43 +2024-01-17 01:55:40,193 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:55:40,193 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:40,193 (beam_search:483) INFO: best hypo: WINIGEARSPÄTERKAMESZUEINEWEITERENKRONDNG + +2024-01-17 01:55:40,194 (asr_inference:494) INFO: speech length: 26880 +2024-01-17 01:55:40,201 (beam_search:428) INFO: decoder input length: 39 +2024-01-17 01:55:40,201 (beam_search:429) INFO: max output length: 39 +2024-01-17 01:55:40,201 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:40,230 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:40,230 (beam_search:476) INFO: -5.19 * 1.0 = -5.19 for ctc +2024-01-17 01:55:40,230 (beam_search:479) INFO: total log probability: -5.19 +2024-01-17 01:55:40,230 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:55:40,230 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:40,230 (beam_search:483) INFO: best hypo: RADIOKABERETPEILS + +2024-01-17 01:55:40,231 (asr_inference:494) INFO: speech length: 38880 +2024-01-17 01:55:40,239 (beam_search:428) INFO: decoder input length: 58 +2024-01-17 01:55:40,239 (beam_search:429) INFO: max output length: 58 +2024-01-17 01:55:40,239 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:40,315 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:40,315 (beam_search:476) INFO: -11.77 * 1.0 = -11.77 for ctc +2024-01-17 01:55:40,315 (beam_search:479) INFO: total log probability: -11.77 +2024-01-17 01:55:40,315 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:55:40,315 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:40,316 (beam_search:483) INFO: best hypo: ESTÜKTEBOMBERAUFDIESTARTWAHNENRELEN + +2024-01-17 01:55:40,317 (asr_inference:494) INFO: speech length: 75040 +2024-01-17 01:55:40,327 (beam_search:428) INFO: decoder input length: 115 +2024-01-17 01:55:40,327 (beam_search:429) INFO: max output length: 115 +2024-01-17 01:55:40,327 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:40,570 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:40,570 (beam_search:476) INFO: -18.44 * 1.0 = -18.44 for ctc +2024-01-17 01:55:40,570 (beam_search:479) INFO: total log probability: -18.44 +2024-01-17 01:55:40,570 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:55:40,570 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:40,570 (beam_search:483) INFO: best hypo: MITDIESEREGELUNGSOLEINERFAKTISCHZWEIVERCHEEINFLUSNAHMEDESERWELERAUF + +2024-01-17 01:55:40,571 (asr_inference:494) INFO: speech length: 19200 +2024-01-17 01:55:40,578 (beam_search:428) INFO: decoder input length: 27 +2024-01-17 01:55:40,578 (beam_search:429) INFO: max output length: 27 +2024-01-17 01:55:40,578 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:40,600 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:40,600 (beam_search:476) INFO: -4.78 * 1.0 = -4.78 for ctc +2024-01-17 01:55:40,600 (beam_search:479) INFO: total log probability: -4.78 +2024-01-17 01:55:40,600 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:55:40,600 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:40,600 (beam_search:483) INFO: best hypo: BEROKGKÖRICHENBAU + +2024-01-17 01:55:40,601 (asr_inference:494) INFO: speech length: 76320 +2024-01-17 01:55:40,611 (beam_search:428) INFO: decoder input length: 117 +2024-01-17 01:55:40,611 (beam_search:429) INFO: max output length: 117 +2024-01-17 01:55:40,611 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:40,886 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:40,886 (beam_search:476) INFO: -19.61 * 1.0 = -19.61 for ctc +2024-01-17 01:55:40,886 (beam_search:479) INFO: total log probability: -19.61 +2024-01-17 01:55:40,886 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:55:40,886 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:40,887 (beam_search:483) INFO: best hypo: DERHERVORAGENTWICENDENLANDEKLPENWIEDERUMHERVORAGENERLANGSAMFLUGEIGENSCHAFTEN + +2024-01-17 01:55:40,888 (asr_inference:494) INFO: speech length: 88320 +2024-01-17 01:55:40,899 (beam_search:428) INFO: decoder input length: 135 +2024-01-17 01:55:40,899 (beam_search:429) INFO: max output length: 135 +2024-01-17 01:55:40,899 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:41,223 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:41,223 (beam_search:476) INFO: -27.29 * 1.0 = -27.29 for ctc +2024-01-17 01:55:41,223 (beam_search:479) INFO: total log probability: -27.29 +2024-01-17 01:55:41,223 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:55:41,223 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:41,223 (beam_search:483) INFO: best hypo: MITERECHVERBINDUNGSFLUKTZOGEODERUMSCHULMASCHINFÜRDIEBEEEINHUNDRDENEUNVERWENDET + +2024-01-17 01:55:41,224 (asr_inference:494) INFO: speech length: 41440 +2024-01-17 01:55:41,232 (beam_search:428) INFO: decoder input length: 62 +2024-01-17 01:55:41,233 (beam_search:429) INFO: max output length: 62 +2024-01-17 01:55:41,233 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:41,315 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:41,316 (beam_search:476) INFO: -20.32 * 1.0 = -20.32 for ctc +2024-01-17 01:55:41,316 (beam_search:479) INFO: total log probability: -20.32 +2024-01-17 01:55:41,316 (beam_search:480) INFO: normalized log probability: -0.46 +2024-01-17 01:55:41,316 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:41,316 (beam_search:483) INFO: best hypo: LEISTETEMIEIZINISCHRNBSÜCHOLOGESCHEHELF + +2024-01-17 01:55:41,317 (asr_inference:494) INFO: speech length: 36160 +2024-01-17 01:55:41,324 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:55:41,324 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:55:41,324 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:41,382 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:41,382 (beam_search:476) INFO: -6.85 * 1.0 = -6.85 for ctc +2024-01-17 01:55:41,382 (beam_search:479) INFO: total log probability: -6.85 +2024-01-17 01:55:41,382 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:55:41,382 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:41,382 (beam_search:483) INFO: best hypo: KANMANDECHIMFUNGENVORBEUGEN + +2024-01-17 01:55:41,383 (asr_inference:494) INFO: speech length: 66880 +2024-01-17 01:55:41,393 (beam_search:428) INFO: decoder input length: 102 +2024-01-17 01:55:41,393 (beam_search:429) INFO: max output length: 102 +2024-01-17 01:55:41,393 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:41,595 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:41,595 (beam_search:476) INFO: -19.87 * 1.0 = -19.87 for ctc +2024-01-17 01:55:41,595 (beam_search:479) INFO: total log probability: -19.87 +2024-01-17 01:55:41,595 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:55:41,595 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:41,595 (beam_search:483) INFO: best hypo: MERDNAUSBOCHDIESERKANKEITENEHERFOLGTEINFEKTIONVERLANGSAMENKAN + +2024-01-17 01:55:41,596 (asr_inference:494) INFO: speech length: 55040 +2024-01-17 01:55:41,605 (beam_search:428) INFO: decoder input length: 83 +2024-01-17 01:55:41,605 (beam_search:429) INFO: max output length: 83 +2024-01-17 01:55:41,605 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:41,729 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:41,729 (beam_search:476) INFO: -10.62 * 1.0 = -10.62 for ctc +2024-01-17 01:55:41,729 (beam_search:479) INFO: total log probability: -10.62 +2024-01-17 01:55:41,729 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:55:41,729 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:41,729 (beam_search:483) INFO: best hypo: DIEINENEUTRDIETEÄLTUNTERALENUMSTENDENVORSAR + +2024-01-17 01:55:41,730 (asr_inference:494) INFO: speech length: 19520 +2024-01-17 01:55:41,737 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:55:41,737 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:55:41,737 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:41,756 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:41,756 (beam_search:476) INFO: -6.66 * 1.0 = -6.66 for ctc +2024-01-17 01:55:41,756 (beam_search:479) INFO: total log probability: -6.66 +2024-01-17 01:55:41,756 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:55:41,756 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:41,757 (beam_search:483) INFO: best hypo: UNDZIEGENHÖRT + +2024-01-17 01:55:41,757 (asr_inference:494) INFO: speech length: 119360 +2024-01-17 01:55:41,770 (beam_search:428) INFO: decoder input length: 184 +2024-01-17 01:55:41,770 (beam_search:429) INFO: max output length: 184 +2024-01-17 01:55:41,770 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:42,273 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:42,273 (beam_search:476) INFO: -24.66 * 1.0 = -24.66 for ctc +2024-01-17 01:55:42,273 (beam_search:479) INFO: total log probability: -24.66 +2024-01-17 01:55:42,273 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:55:42,273 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:42,273 (beam_search:483) INFO: best hypo: DASNEUNZEHNHUNDERTACHTENDREISSIGGEGRÜNDETEKOMITVIRUNAMERIKANISCHEUMTRIEBEWURDEDAFENUN + +2024-01-17 01:55:42,275 (asr_inference:494) INFO: speech length: 87040 +2024-01-17 01:55:42,286 (beam_search:428) INFO: decoder input length: 133 +2024-01-17 01:55:42,286 (beam_search:429) INFO: max output length: 133 +2024-01-17 01:55:42,286 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:42,577 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:42,577 (beam_search:476) INFO: -16.38 * 1.0 = -16.38 for ctc +2024-01-17 01:55:42,577 (beam_search:479) INFO: total log probability: -16.38 +2024-01-17 01:55:42,577 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:55:42,577 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:42,577 (beam_search:483) INFO: best hypo: ZENTRALEDERPROCKRESSIEVENUNDHORTDESINIENIÖRGESTÜTZSTENKUNSTDENKENS + +2024-01-17 01:55:42,579 (asr_inference:494) INFO: speech length: 42720 +2024-01-17 01:55:42,587 (beam_search:428) INFO: decoder input length: 64 +2024-01-17 01:55:42,587 (beam_search:429) INFO: max output length: 64 +2024-01-17 01:55:42,587 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:42,662 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:42,663 (beam_search:476) INFO: -8.79 * 1.0 = -8.79 for ctc +2024-01-17 01:55:42,663 (beam_search:479) INFO: total log probability: -8.79 +2024-01-17 01:55:42,663 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:55:42,663 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:42,663 (beam_search:483) INFO: best hypo: INDERDEROESPRESEDENTANKÖNDIGTE + +2024-01-17 01:55:42,664 (asr_inference:494) INFO: speech length: 30080 +2024-01-17 01:55:42,671 (beam_search:428) INFO: decoder input length: 44 +2024-01-17 01:55:42,671 (beam_search:429) INFO: max output length: 44 +2024-01-17 01:55:42,671 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:42,712 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:42,712 (beam_search:476) INFO: -6.75 * 1.0 = -6.75 for ctc +2024-01-17 01:55:42,712 (beam_search:479) INFO: total log probability: -6.75 +2024-01-17 01:55:42,712 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:55:42,712 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:42,712 (beam_search:483) INFO: best hypo: SNEÄCHSTUNDVORSPBEISEN + +2024-01-17 01:55:42,713 (asr_inference:494) INFO: speech length: 70240 +2024-01-17 01:55:42,723 (beam_search:428) INFO: decoder input length: 107 +2024-01-17 01:55:42,723 (beam_search:429) INFO: max output length: 107 +2024-01-17 01:55:42,723 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:42,936 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:42,936 (beam_search:476) INFO: -16.35 * 1.0 = -16.35 for ctc +2024-01-17 01:55:42,936 (beam_search:479) INFO: total log probability: -16.35 +2024-01-17 01:55:42,936 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:55:42,936 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:42,936 (beam_search:483) INFO: best hypo: DESPUNDESWAGESETZESBISTZUMDREISIGSTENJUNIZWEITAUSENDEFAUFGGEM + +2024-01-17 01:55:42,937 (asr_inference:494) INFO: speech length: 17120 +2024-01-17 01:55:42,944 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:55:42,944 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:55:42,944 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:42,959 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:42,959 (beam_search:476) INFO: -4.39 * 1.0 = -4.39 for ctc +2024-01-17 01:55:42,959 (beam_search:479) INFO: total log probability: -4.39 +2024-01-17 01:55:42,959 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:55:42,959 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:42,959 (beam_search:483) INFO: best hypo: ORIEPOSSEÖR + +2024-01-17 01:55:42,960 (asr_inference:494) INFO: speech length: 76800 +2024-01-17 01:55:42,970 (beam_search:428) INFO: decoder input length: 117 +2024-01-17 01:55:42,970 (beam_search:429) INFO: max output length: 117 +2024-01-17 01:55:42,970 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:43,190 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:43,190 (beam_search:476) INFO: -12.17 * 1.0 = -12.17 for ctc +2024-01-17 01:55:43,190 (beam_search:479) INFO: total log probability: -12.17 +2024-01-17 01:55:43,190 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:55:43,190 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:43,191 (beam_search:483) INFO: best hypo: FLIFTLINGENVONDERETNISCHENMINDERHEITDERSOMALISCHENBANTUM + +2024-01-17 01:55:43,192 (asr_inference:494) INFO: speech length: 40640 +2024-01-17 01:55:43,200 (beam_search:428) INFO: decoder input length: 61 +2024-01-17 01:55:43,200 (beam_search:429) INFO: max output length: 61 +2024-01-17 01:55:43,200 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:43,276 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:43,276 (beam_search:476) INFO: -9.18 * 1.0 = -9.18 for ctc +2024-01-17 01:55:43,276 (beam_search:479) INFO: total log probability: -9.18 +2024-01-17 01:55:43,276 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:55:43,276 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:43,277 (beam_search:483) INFO: best hypo: DIEBIEPOLAREWELTORDTNUNGZEMINTIERT + +2024-01-17 01:55:43,278 (asr_inference:494) INFO: speech length: 116640 +2024-01-17 01:55:43,290 (beam_search:428) INFO: decoder input length: 180 +2024-01-17 01:55:43,290 (beam_search:429) INFO: max output length: 180 +2024-01-17 01:55:43,290 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:43,767 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:43,767 (beam_search:476) INFO: -26.51 * 1.0 = -26.51 for ctc +2024-01-17 01:55:43,767 (beam_search:479) INFO: total log probability: -26.51 +2024-01-17 01:55:43,767 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:55:43,767 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:43,768 (beam_search:483) INFO: best hypo: ERANFANGEININTEILRIEATEODEREXSTERNANGEBRACHTEVORICHTUNGANEINENUCLIERENWAFENSYSTEMN + +2024-01-17 01:55:43,769 (asr_inference:494) INFO: speech length: 39680 +2024-01-17 01:55:43,777 (beam_search:428) INFO: decoder input length: 59 +2024-01-17 01:55:43,777 (beam_search:429) INFO: max output length: 59 +2024-01-17 01:55:43,777 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:43,858 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:43,858 (beam_search:476) INFO: -17.12 * 1.0 = -17.12 for ctc +2024-01-17 01:55:43,858 (beam_search:479) INFO: total log probability: -17.12 +2024-01-17 01:55:43,858 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:55:43,858 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:43,858 (beam_search:483) INFO: best hypo: STARTETEDIHILFSORGENISETZIONLANKFRSTIGE + +2024-01-17 01:55:43,859 (asr_inference:494) INFO: speech length: 116320 +2024-01-17 01:55:43,871 (beam_search:428) INFO: decoder input length: 179 +2024-01-17 01:55:43,872 (beam_search:429) INFO: max output length: 179 +2024-01-17 01:55:43,872 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:44,408 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:44,408 (beam_search:476) INFO: -25.30 * 1.0 = -25.30 for ctc +2024-01-17 01:55:44,408 (beam_search:479) INFO: total log probability: -25.30 +2024-01-17 01:55:44,408 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:55:44,408 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:44,408 (beam_search:483) INFO: best hypo: WENDIESEEXSTERNENERFECKTEINERICHTIGENREINVOLGEAUFTRETENUNDSICHINEHALBSPEZIEISCHAPARAMETERBEWIEGEN + +2024-01-17 01:55:44,410 (asr_inference:494) INFO: speech length: 138080 +2024-01-17 01:55:44,424 (beam_search:428) INFO: decoder input length: 213 +2024-01-17 01:55:44,424 (beam_search:429) INFO: max output length: 213 +2024-01-17 01:55:44,424 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:45,126 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:45,126 (beam_search:476) INFO: -26.78 * 1.0 = -26.78 for ctc +2024-01-17 01:55:45,126 (beam_search:479) INFO: total log probability: -26.78 +2024-01-17 01:55:45,126 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:55:45,126 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:45,126 (beam_search:483) INFO: best hypo: ZUGDEWIRTUNIONAUCHBEIDEASERSTOFPBOMBENUNDNEUINFLUKTZEUGENMITINTERKONTENENTALEREICHWEITEMITDENURSARGLEICH + +2024-01-17 01:55:45,128 (asr_inference:494) INFO: speech length: 38560 +2024-01-17 01:55:45,135 (beam_search:428) INFO: decoder input length: 58 +2024-01-17 01:55:45,135 (beam_search:429) INFO: max output length: 58 +2024-01-17 01:55:45,135 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:45,195 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:45,195 (beam_search:476) INFO: -10.82 * 1.0 = -10.82 for ctc +2024-01-17 01:55:45,195 (beam_search:479) INFO: total log probability: -10.82 +2024-01-17 01:55:45,195 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:55:45,195 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:45,195 (beam_search:483) INFO: best hypo: PENDIESTATHATIEWABPENTIEAM + +2024-01-17 01:55:45,196 (asr_inference:494) INFO: speech length: 55680 +2024-01-17 01:55:45,205 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:55:45,205 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:55:45,205 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:45,327 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:45,327 (beam_search:476) INFO: -8.10 * 1.0 = -8.10 for ctc +2024-01-17 01:55:45,327 (beam_search:479) INFO: total log probability: -8.10 +2024-01-17 01:55:45,327 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:55:45,327 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:45,327 (beam_search:483) INFO: best hypo: DIESERANSATZGILDALLGEMEINALSAUSGEWORGNDER + +2024-01-17 01:55:45,328 (asr_inference:494) INFO: speech length: 19840 +2024-01-17 01:55:45,335 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:55:45,335 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:55:45,335 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:45,361 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:45,361 (beam_search:476) INFO: -8.46 * 1.0 = -8.46 for ctc +2024-01-17 01:55:45,361 (beam_search:479) INFO: total log probability: -8.46 +2024-01-17 01:55:45,361 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:55:45,361 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:45,361 (beam_search:483) INFO: best hypo: NACHDEZUSAMMPROCHTE + +2024-01-17 01:55:45,362 (asr_inference:494) INFO: speech length: 28000 +2024-01-17 01:55:45,369 (beam_search:428) INFO: decoder input length: 41 +2024-01-17 01:55:45,369 (beam_search:429) INFO: max output length: 41 +2024-01-17 01:55:45,369 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:45,418 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:45,418 (beam_search:476) INFO: -10.63 * 1.0 = -10.63 for ctc +2024-01-17 01:55:45,418 (beam_search:479) INFO: total log probability: -10.63 +2024-01-17 01:55:45,418 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:55:45,418 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:45,418 (beam_search:483) INFO: best hypo: DEOBERLAUSITSWISCHENHEUERSWERDE + +2024-01-17 01:55:45,419 (asr_inference:494) INFO: speech length: 32160 +2024-01-17 01:55:45,426 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:55:45,427 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:55:45,427 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:45,480 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:45,480 (beam_search:476) INFO: -9.33 * 1.0 = -9.33 for ctc +2024-01-17 01:55:45,480 (beam_search:479) INFO: total log probability: -9.33 +2024-01-17 01:55:45,480 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:55:45,480 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:45,480 (beam_search:483) INFO: best hypo: DABEENZWEIEFAHSENNTERTEILELT + +2024-01-17 01:55:45,481 (asr_inference:494) INFO: speech length: 68320 +2024-01-17 01:55:45,491 (beam_search:428) INFO: decoder input length: 104 +2024-01-17 01:55:45,491 (beam_search:429) INFO: max output length: 104 +2024-01-17 01:55:45,491 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:45,688 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:45,688 (beam_search:476) INFO: -15.24 * 1.0 = -15.24 for ctc +2024-01-17 01:55:45,688 (beam_search:479) INFO: total log probability: -15.24 +2024-01-17 01:55:45,688 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:55:45,688 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:45,688 (beam_search:483) INFO: best hypo: SCHIEDENANDEREUROPERMISTERSCHAFTEILUNDWURDEMTERDIEEBIEELLF + +2024-01-17 01:55:45,690 (asr_inference:494) INFO: speech length: 43840 +2024-01-17 01:55:45,698 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:55:45,698 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:55:45,698 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:45,792 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:45,792 (beam_search:476) INFO: -13.09 * 1.0 = -13.09 for ctc +2024-01-17 01:55:45,792 (beam_search:479) INFO: total log probability: -13.09 +2024-01-17 01:55:45,792 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:55:45,792 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:45,792 (beam_search:483) INFO: best hypo: MESTERERFNETKABERSHLISGERNWEITEGEMÖGLICHKEIT + +2024-01-17 01:55:45,793 (asr_inference:494) INFO: speech length: 47200 +2024-01-17 01:55:45,801 (beam_search:428) INFO: decoder input length: 71 +2024-01-17 01:55:45,801 (beam_search:429) INFO: max output length: 71 +2024-01-17 01:55:45,801 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:45,893 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:45,893 (beam_search:476) INFO: -8.21 * 1.0 = -8.21 for ctc +2024-01-17 01:55:45,893 (beam_search:479) INFO: total log probability: -8.21 +2024-01-17 01:55:45,893 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:55:45,893 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:45,893 (beam_search:483) INFO: best hypo: EINEMAUSWERTSERFOLGINWOLSBURGELANG + +2024-01-17 01:55:45,894 (asr_inference:494) INFO: speech length: 73760 +2024-01-17 01:55:45,904 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 01:55:45,904 (beam_search:429) INFO: max output length: 113 +2024-01-17 01:55:45,904 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:46,095 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:46,095 (beam_search:476) INFO: -13.28 * 1.0 = -13.28 for ctc +2024-01-17 01:55:46,095 (beam_search:479) INFO: total log probability: -13.28 +2024-01-17 01:55:46,095 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:55:46,095 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:46,096 (beam_search:483) INFO: best hypo: MITSCHWEBUNGSSOUMMANKONTENKLISSANDIEERZEUGKTWERDEN + +2024-01-17 01:55:46,097 (asr_inference:494) INFO: speech length: 19520 +2024-01-17 01:55:46,103 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:55:46,103 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:55:46,103 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:46,127 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:46,127 (beam_search:476) INFO: -7.76 * 1.0 = -7.76 for ctc +2024-01-17 01:55:46,127 (beam_search:479) INFO: total log probability: -7.76 +2024-01-17 01:55:46,127 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:55:46,127 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:46,127 (beam_search:483) INFO: best hypo: DERBALEDIGLIHZEIKTE + +2024-01-17 01:55:46,128 (asr_inference:494) INFO: speech length: 55680 +2024-01-17 01:55:46,136 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:55:46,136 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:55:46,136 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:46,250 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:46,250 (beam_search:476) INFO: -9.99 * 1.0 = -9.99 for ctc +2024-01-17 01:55:46,250 (beam_search:479) INFO: total log probability: -9.99 +2024-01-17 01:55:46,250 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:55:46,250 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:46,250 (beam_search:483) INFO: best hypo: KOSPRITANIENEINEESTEWICHTIGEVEINBAUNG + +2024-01-17 01:55:46,251 (asr_inference:494) INFO: speech length: 22880 +2024-01-17 01:55:46,258 (beam_search:428) INFO: decoder input length: 33 +2024-01-17 01:55:46,258 (beam_search:429) INFO: max output length: 33 +2024-01-17 01:55:46,258 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:46,290 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:46,290 (beam_search:476) INFO: -16.59 * 1.0 = -16.59 for ctc +2024-01-17 01:55:46,290 (beam_search:479) INFO: total log probability: -16.59 +2024-01-17 01:55:46,290 (beam_search:480) INFO: normalized log probability: -0.61 +2024-01-17 01:55:46,290 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:46,290 (beam_search:483) INFO: best hypo: SIDACHTESHRITNESSEING + +2024-01-17 01:55:46,291 (asr_inference:494) INFO: speech length: 82560 +2024-01-17 01:55:46,302 (beam_search:428) INFO: decoder input length: 126 +2024-01-17 01:55:46,302 (beam_search:429) INFO: max output length: 126 +2024-01-17 01:55:46,302 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:46,586 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:46,586 (beam_search:476) INFO: -29.42 * 1.0 = -29.42 for ctc +2024-01-17 01:55:46,586 (beam_search:479) INFO: total log probability: -29.42 +2024-01-17 01:55:46,586 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:55:46,586 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:46,587 (beam_search:483) INFO: best hypo: WURDEMITDEBUNDEWAGESETZVORNEUNZEUNDERSICHSUNFÜFZIGEINEDAUARHTERELUGENGEFÜRT + +2024-01-17 01:55:46,588 (asr_inference:494) INFO: speech length: 31200 +2024-01-17 01:55:46,595 (beam_search:428) INFO: decoder input length: 46 +2024-01-17 01:55:46,595 (beam_search:429) INFO: max output length: 46 +2024-01-17 01:55:46,595 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:46,646 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:46,646 (beam_search:476) INFO: -6.29 * 1.0 = -6.29 for ctc +2024-01-17 01:55:46,647 (beam_search:479) INFO: total log probability: -6.29 +2024-01-17 01:55:46,647 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:55:46,647 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:46,647 (beam_search:483) INFO: best hypo: DIEANZALDERÜBEHNGMENDARDEKAN + +2024-01-17 01:55:46,648 (asr_inference:494) INFO: speech length: 61920 +2024-01-17 01:55:46,657 (beam_search:428) INFO: decoder input length: 94 +2024-01-17 01:55:46,657 (beam_search:429) INFO: max output length: 94 +2024-01-17 01:55:46,657 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:46,820 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:46,820 (beam_search:476) INFO: -12.51 * 1.0 = -12.51 for ctc +2024-01-17 01:55:46,820 (beam_search:479) INFO: total log probability: -12.51 +2024-01-17 01:55:46,820 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:55:46,820 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:46,821 (beam_search:483) INFO: best hypo: ISCHLSDIESEEINMLITERISCHESEINGREIFENINDENKOREARGRICK + +2024-01-17 01:55:46,822 (asr_inference:494) INFO: speech length: 19840 +2024-01-17 01:55:46,828 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:55:46,828 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:55:46,828 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:46,849 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:46,849 (beam_search:476) INFO: -2.56 * 1.0 = -2.56 for ctc +2024-01-17 01:55:46,849 (beam_search:479) INFO: total log probability: -2.56 +2024-01-17 01:55:46,849 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:55:46,849 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:46,850 (beam_search:483) INFO: best hypo: NATOVERBINTLICH + +2024-01-17 01:55:46,851 (asr_inference:494) INFO: speech length: 23680 +2024-01-17 01:55:46,858 (beam_search:428) INFO: decoder input length: 34 +2024-01-17 01:55:46,858 (beam_search:429) INFO: max output length: 34 +2024-01-17 01:55:46,858 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:46,883 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:46,883 (beam_search:476) INFO: -5.38 * 1.0 = -5.38 for ctc +2024-01-17 01:55:46,883 (beam_search:479) INFO: total log probability: -5.38 +2024-01-17 01:55:46,883 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:55:46,883 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:46,883 (beam_search:483) INFO: best hypo: KALTEGRIEGBENDERT + +2024-01-17 01:55:46,884 (asr_inference:494) INFO: speech length: 82880 +2024-01-17 01:55:46,895 (beam_search:428) INFO: decoder input length: 127 +2024-01-17 01:55:46,895 (beam_search:429) INFO: max output length: 127 +2024-01-17 01:55:46,895 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:47,159 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:47,159 (beam_search:476) INFO: -26.69 * 1.0 = -26.69 for ctc +2024-01-17 01:55:47,159 (beam_search:479) INFO: total log probability: -26.69 +2024-01-17 01:55:47,159 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:55:47,159 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:47,159 (beam_search:483) INFO: best hypo: AUNUNENHUNERDEEIUMTNEUNZIGUNDOSTERALIENSOWIEDERÖSTERECHSCHEABLIGER + +2024-01-17 01:55:47,161 (asr_inference:494) INFO: speech length: 155360 +2024-01-17 01:55:47,176 (beam_search:428) INFO: decoder input length: 240 +2024-01-17 01:55:47,176 (beam_search:429) INFO: max output length: 240 +2024-01-17 01:55:47,176 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:48,096 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:48,096 (beam_search:476) INFO: -33.48 * 1.0 = -33.48 for ctc +2024-01-17 01:55:48,096 (beam_search:479) INFO: total log probability: -33.48 +2024-01-17 01:55:48,096 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:55:48,096 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:48,097 (beam_search:483) INFO: best hypo: DADIESEITANFANGNEUNZEHNHUNDERTNEUNUNFÜNFZIGDORTHERSCHENDEREVOLOTIONDSRIGIONUNDDERVIEDELKASTROEINENSOZIELISTISCHENKURSEINGSCHAGENHATE + +2024-01-17 01:55:48,099 (asr_inference:494) INFO: speech length: 193440 +2024-01-17 01:55:48,117 (beam_search:428) INFO: decoder input length: 300 +2024-01-17 01:55:48,117 (beam_search:429) INFO: max output length: 300 +2024-01-17 01:55:48,117 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:49,515 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:49,515 (beam_search:476) INFO: -46.35 * 1.0 = -46.35 for ctc +2024-01-17 01:55:49,515 (beam_search:479) INFO: total log probability: -46.35 +2024-01-17 01:55:49,515 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:55:49,515 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:49,516 (beam_search:483) INFO: best hypo: NACHWEITERENFELUSTRECHENKÄMPFENUNENENZWERTEERFOLGEBEIDEGRIGSPATEINURDERUNDDREAJAHRENACBEGINDEAUSANDNDESEZUNGEINBESREUTEGÜLTIGESWAFNENSTILSTAMNSABCOMENABGESHLOSSEN + +2024-01-17 01:55:49,518 (asr_inference:494) INFO: speech length: 38795 +2024-01-17 01:55:49,526 (beam_search:428) INFO: decoder input length: 58 +2024-01-17 01:55:49,526 (beam_search:429) INFO: max output length: 58 +2024-01-17 01:55:49,526 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:49,579 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:49,580 (beam_search:476) INFO: -7.35 * 1.0 = -7.35 for ctc +2024-01-17 01:55:49,580 (beam_search:479) INFO: total log probability: -7.35 +2024-01-17 01:55:49,580 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:55:49,580 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:49,580 (beam_search:483) INFO: best hypo: MANISTERBEISERORSICHTIG + +2024-01-17 01:55:49,581 (asr_inference:494) INFO: speech length: 94000 +2024-01-17 01:55:49,592 (beam_search:428) INFO: decoder input length: 144 +2024-01-17 01:55:49,592 (beam_search:429) INFO: max output length: 144 +2024-01-17 01:55:49,592 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:49,883 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:49,883 (beam_search:476) INFO: -17.75 * 1.0 = -17.75 for ctc +2024-01-17 01:55:49,883 (beam_search:479) INFO: total log probability: -17.75 +2024-01-17 01:55:49,883 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:55:49,883 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:49,884 (beam_search:483) INFO: best hypo: DIEWERFLICHTSOLINDEUTSCHLANDLEIDEROCHNICHTABGESCHAFTWERDNEN + +2024-01-17 01:55:49,885 (asr_inference:494) INFO: speech length: 52464 +2024-01-17 01:55:49,893 (beam_search:428) INFO: decoder input length: 79 +2024-01-17 01:55:49,893 (beam_search:429) INFO: max output length: 79 +2024-01-17 01:55:49,893 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:49,987 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:49,987 (beam_search:476) INFO: -7.56 * 1.0 = -7.56 for ctc +2024-01-17 01:55:49,987 (beam_search:479) INFO: total log probability: -7.56 +2024-01-17 01:55:49,987 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:55:49,987 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:49,987 (beam_search:483) INFO: best hypo: ESGETAUCHMISPRAUCHUCHABETGEBER + +2024-01-17 01:55:49,989 (asr_inference:494) INFO: speech length: 48816 +2024-01-17 01:55:49,997 (beam_search:428) INFO: decoder input length: 74 +2024-01-17 01:55:49,997 (beam_search:429) INFO: max output length: 74 +2024-01-17 01:55:49,997 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:50,072 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:50,072 (beam_search:476) INFO: -8.02 * 1.0 = -8.02 for ctc +2024-01-17 01:55:50,072 (beam_search:479) INFO: total log probability: -8.02 +2024-01-17 01:55:50,072 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:55:50,072 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:50,072 (beam_search:483) INFO: best hypo: DIEKINDERSINDANKANKEBOEN + +2024-01-17 01:55:50,073 (asr_inference:494) INFO: speech length: 66352 +2024-01-17 01:55:50,083 (beam_search:428) INFO: decoder input length: 101 +2024-01-17 01:55:50,083 (beam_search:429) INFO: max output length: 101 +2024-01-17 01:55:50,083 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:50,244 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:50,244 (beam_search:476) INFO: -9.57 * 1.0 = -9.57 for ctc +2024-01-17 01:55:50,244 (beam_search:479) INFO: total log probability: -9.57 +2024-01-17 01:55:50,244 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:55:50,244 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:50,245 (beam_search:483) INFO: best hypo: DIETRAKWEITEDERATASTROFESOLVERDEUTLICHTWERDEN + +2024-01-17 01:55:50,246 (asr_inference:494) INFO: speech length: 30098 +2024-01-17 01:55:50,254 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:55:50,254 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:55:50,254 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:50,282 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:50,282 (beam_search:476) INFO: -16.61 * 1.0 = -16.61 for ctc +2024-01-17 01:55:50,282 (beam_search:479) INFO: total log probability: -16.61 +2024-01-17 01:55:50,282 (beam_search:480) INFO: normalized log probability: -0.98 +2024-01-17 01:55:50,282 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:50,282 (beam_search:483) INFO: best hypo: DSCANGEAUAULBE + +2024-01-17 01:55:50,283 (asr_inference:494) INFO: speech length: 52000 +2024-01-17 01:55:50,291 (beam_search:428) INFO: decoder input length: 79 +2024-01-17 01:55:50,291 (beam_search:429) INFO: max output length: 79 +2024-01-17 01:55:50,291 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:50,408 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:50,408 (beam_search:476) INFO: -11.88 * 1.0 = -11.88 for ctc +2024-01-17 01:55:50,409 (beam_search:479) INFO: total log probability: -11.88 +2024-01-17 01:55:50,409 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:55:50,409 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:50,409 (beam_search:483) INFO: best hypo: BEIMMOGANSTREITSTREITENOBESDEVEFASUNGSOGANE + +2024-01-17 01:55:50,410 (asr_inference:494) INFO: speech length: 41365 +2024-01-17 01:55:50,418 (beam_search:428) INFO: decoder input length: 62 +2024-01-17 01:55:50,418 (beam_search:429) INFO: max output length: 62 +2024-01-17 01:55:50,418 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:50,478 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:50,479 (beam_search:476) INFO: -7.67 * 1.0 = -7.67 for ctc +2024-01-17 01:55:50,479 (beam_search:479) INFO: total log probability: -7.67 +2024-01-17 01:55:50,479 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:55:50,479 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:50,479 (beam_search:483) INFO: best hypo: DAWAGEICHIERZUBEZWEIFELEN + +2024-01-17 01:55:50,480 (asr_inference:494) INFO: speech length: 49275 +2024-01-17 01:55:50,488 (beam_search:428) INFO: decoder input length: 74 +2024-01-17 01:55:50,488 (beam_search:429) INFO: max output length: 74 +2024-01-17 01:55:50,488 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:50,566 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:50,566 (beam_search:476) INFO: -9.44 * 1.0 = -9.44 for ctc +2024-01-17 01:55:50,566 (beam_search:479) INFO: total log probability: -9.44 +2024-01-17 01:55:50,566 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:55:50,566 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:50,567 (beam_search:483) INFO: best hypo: MANOLTEDENAUFGAKHENFALTRAUN + +2024-01-17 01:55:50,568 (asr_inference:494) INFO: speech length: 60075 +2024-01-17 01:55:50,577 (beam_search:428) INFO: decoder input length: 91 +2024-01-17 01:55:50,577 (beam_search:429) INFO: max output length: 91 +2024-01-17 01:55:50,577 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:50,711 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:50,711 (beam_search:476) INFO: -12.27 * 1.0 = -12.27 for ctc +2024-01-17 01:55:50,711 (beam_search:479) INFO: total log probability: -12.27 +2024-01-17 01:55:50,712 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:55:50,712 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:50,712 (beam_search:483) INFO: best hypo: DIEFENTLICHENSCHÖLDENWERDENNICHTGETILKTWERDEN + +2024-01-17 01:55:50,713 (asr_inference:494) INFO: speech length: 75094 +2024-01-17 01:55:50,722 (beam_search:428) INFO: decoder input length: 115 +2024-01-17 01:55:50,722 (beam_search:429) INFO: max output length: 115 +2024-01-17 01:55:50,722 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:50,844 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:50,844 (beam_search:476) INFO: -10.77 * 1.0 = -10.77 for ctc +2024-01-17 01:55:50,844 (beam_search:479) INFO: total log probability: -10.77 +2024-01-17 01:55:50,844 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:55:50,844 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:50,844 (beam_search:483) INFO: best hypo: BAGELTISTAUSGEZCAHLTWORDENT + +2024-01-17 01:55:50,845 (asr_inference:494) INFO: speech length: 80336 +2024-01-17 01:55:50,856 (beam_search:428) INFO: decoder input length: 123 +2024-01-17 01:55:50,856 (beam_search:429) INFO: max output length: 123 +2024-01-17 01:55:50,856 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:51,059 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:51,059 (beam_search:476) INFO: -15.43 * 1.0 = -15.43 for ctc +2024-01-17 01:55:51,059 (beam_search:479) INFO: total log probability: -15.43 +2024-01-17 01:55:51,059 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:55:51,060 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:51,060 (beam_search:483) INFO: best hypo: ERSOLLENDREIHUNDERDTAUSSENDNEUEARBESPLÄZEINSTEN + +2024-01-17 01:55:51,061 (asr_inference:494) INFO: speech length: 82000 +2024-01-17 01:55:51,071 (beam_search:428) INFO: decoder input length: 126 +2024-01-17 01:55:51,071 (beam_search:429) INFO: max output length: 126 +2024-01-17 01:55:51,071 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:51,267 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:51,267 (beam_search:476) INFO: -15.04 * 1.0 = -15.04 for ctc +2024-01-17 01:55:51,267 (beam_search:479) INFO: total log probability: -15.04 +2024-01-17 01:55:51,267 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:55:51,267 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:51,267 (beam_search:483) INFO: best hypo: DIEÖRBERVELETZUNGKANNALSBEISPIELGENDWERDENT + +2024-01-17 01:55:51,268 (asr_inference:494) INFO: speech length: 46069 +2024-01-17 01:55:51,277 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:55:51,277 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:55:51,277 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:51,355 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:51,355 (beam_search:476) INFO: -9.23 * 1.0 = -9.23 for ctc +2024-01-17 01:55:51,355 (beam_search:479) INFO: total log probability: -9.23 +2024-01-17 01:55:51,355 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:55:51,355 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:51,355 (beam_search:483) INFO: best hypo: DIESEKRENEISTÜBERSCITENBORDEN + +2024-01-17 01:55:51,356 (asr_inference:494) INFO: speech length: 68000 +2024-01-17 01:55:51,365 (beam_search:428) INFO: decoder input length: 104 +2024-01-17 01:55:51,366 (beam_search:429) INFO: max output length: 104 +2024-01-17 01:55:51,366 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:51,514 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:51,514 (beam_search:476) INFO: -24.82 * 1.0 = -24.82 for ctc +2024-01-17 01:55:51,514 (beam_search:479) INFO: total log probability: -24.82 +2024-01-17 01:55:51,514 (beam_search:480) INFO: normalized log probability: -0.53 +2024-01-17 01:55:51,514 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:51,515 (beam_search:483) INFO: best hypo: TESSTAEFAUGSBÜÖRDENKEIENZUGEAESKELHARBEN + +2024-01-17 01:55:51,516 (asr_inference:494) INFO: speech length: 53125 +2024-01-17 01:55:51,524 (beam_search:428) INFO: decoder input length: 81 +2024-01-17 01:55:51,525 (beam_search:429) INFO: max output length: 81 +2024-01-17 01:55:51,525 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:51,612 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:51,612 (beam_search:476) INFO: -5.19 * 1.0 = -5.19 for ctc +2024-01-17 01:55:51,612 (beam_search:479) INFO: total log probability: -5.19 +2024-01-17 01:55:51,612 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:55:51,612 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:51,612 (beam_search:483) INFO: best hypo: DIEINTERESSENFINDENKEINGEHÖR + +2024-01-17 01:55:51,613 (asr_inference:494) INFO: speech length: 223575 +2024-01-17 01:55:51,634 (beam_search:428) INFO: decoder input length: 347 +2024-01-17 01:55:51,634 (beam_search:429) INFO: max output length: 347 +2024-01-17 01:55:51,634 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:52,155 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:52,155 (beam_search:476) INFO: -30.55 * 1.0 = -30.55 for ctc +2024-01-17 01:55:52,155 (beam_search:479) INFO: total log probability: -30.55 +2024-01-17 01:55:52,155 (beam_search:480) INFO: normalized log probability: -0.64 +2024-01-17 01:55:52,155 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:52,155 (beam_search:483) INFO: best hypo: FELTAETABULATOARÜCSHIETASERÜTASERÜGIERSTASE + +2024-01-17 01:55:52,156 (asr_inference:494) INFO: speech length: 82000 +2024-01-17 01:55:52,166 (beam_search:428) INFO: decoder input length: 126 +2024-01-17 01:55:52,166 (beam_search:429) INFO: max output length: 126 +2024-01-17 01:55:52,166 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:52,376 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:52,376 (beam_search:476) INFO: -27.80 * 1.0 = -27.80 for ctc +2024-01-17 01:55:52,376 (beam_search:479) INFO: total log probability: -27.80 +2024-01-17 01:55:52,376 (beam_search:480) INFO: normalized log probability: -0.51 +2024-01-17 01:55:52,376 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:52,376 (beam_search:483) INFO: best hypo: DERBEOHENEMORAIUMBERESTIGDIEENHEÄEGELTENDMACHEN + +2024-01-17 01:55:52,378 (asr_inference:494) INFO: speech length: 116566 +2024-01-17 01:55:52,390 (beam_search:428) INFO: decoder input length: 180 +2024-01-17 01:55:52,390 (beam_search:429) INFO: max output length: 180 +2024-01-17 01:55:52,390 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:52,768 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:52,768 (beam_search:476) INFO: -18.79 * 1.0 = -18.79 for ctc +2024-01-17 01:55:52,768 (beam_search:479) INFO: total log probability: -18.79 +2024-01-17 01:55:52,768 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:55:52,768 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:52,768 (beam_search:483) INFO: best hypo: EEINDERITAHADDEMGESCHÄIDIKTENFREIWILIGLEISTUNGENZUKCOMENLASSEN + +2024-01-17 01:55:52,770 (asr_inference:494) INFO: speech length: 60000 +2024-01-17 01:55:52,779 (beam_search:428) INFO: decoder input length: 91 +2024-01-17 01:55:52,779 (beam_search:429) INFO: max output length: 91 +2024-01-17 01:55:52,779 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:52,866 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:52,866 (beam_search:476) INFO: -10.48 * 1.0 = -10.48 for ctc +2024-01-17 01:55:52,866 (beam_search:479) INFO: total log probability: -10.48 +2024-01-17 01:55:52,866 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:55:52,866 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:52,866 (beam_search:483) INFO: best hypo: SONDERACRECHNEBENDEBILT + +2024-01-17 01:55:52,868 (asr_inference:494) INFO: speech length: 70000 +2024-01-17 01:55:52,877 (beam_search:428) INFO: decoder input length: 107 +2024-01-17 01:55:52,877 (beam_search:429) INFO: max output length: 107 +2024-01-17 01:55:52,877 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:53,052 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:53,052 (beam_search:476) INFO: -16.33 * 1.0 = -16.33 for ctc +2024-01-17 01:55:53,052 (beam_search:479) INFO: total log probability: -16.33 +2024-01-17 01:55:53,052 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:55:53,052 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:53,052 (beam_search:483) INFO: best hypo: IERTEINENICHTERNSLICHGEMEITEWILESEKLÄHUNGABGEBEN + +2024-01-17 01:55:53,054 (asr_inference:494) INFO: speech length: 84822 +2024-01-17 01:55:53,064 (beam_search:428) INFO: decoder input length: 130 +2024-01-17 01:55:53,064 (beam_search:429) INFO: max output length: 130 +2024-01-17 01:55:53,064 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:53,225 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:53,225 (beam_search:476) INFO: -10.50 * 1.0 = -10.50 for ctc +2024-01-17 01:55:53,225 (beam_search:479) INFO: total log probability: -10.50 +2024-01-17 01:55:53,225 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:55:53,225 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:53,226 (beam_search:483) INFO: best hypo: DASMOSTEJAHAUFJEDENFALSOKOMMEN + +2024-01-17 01:55:53,227 (asr_inference:494) INFO: speech length: 80000 +2024-01-17 01:55:53,237 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 01:55:53,237 (beam_search:429) INFO: max output length: 122 +2024-01-17 01:55:53,237 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:53,426 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:53,426 (beam_search:476) INFO: -16.69 * 1.0 = -16.69 for ctc +2024-01-17 01:55:53,426 (beam_search:479) INFO: total log probability: -16.69 +2024-01-17 01:55:53,426 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:55:53,426 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:53,427 (beam_search:483) INFO: best hypo: MEHREREREKLEINSKNNSICHEINEEIPIEDRESSETEIUNG + +2024-01-17 01:55:53,428 (asr_inference:494) INFO: speech length: 131838 +2024-01-17 01:55:53,441 (beam_search:428) INFO: decoder input length: 203 +2024-01-17 01:55:53,441 (beam_search:429) INFO: max output length: 203 +2024-01-17 01:55:53,441 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:53,767 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:53,767 (beam_search:476) INFO: -24.75 * 1.0 = -24.75 for ctc +2024-01-17 01:55:53,768 (beam_search:479) INFO: total log probability: -24.75 +2024-01-17 01:55:53,768 (beam_search:480) INFO: normalized log probability: -0.47 +2024-01-17 01:55:53,768 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:53,768 (beam_search:483) INFO: best hypo: WADIÜNSTEERESHISASOSIGTZUSAMMENEHMENANSTAZU + +2024-01-17 01:55:53,769 (asr_inference:494) INFO: speech length: 71680 +2024-01-17 01:55:53,779 (beam_search:428) INFO: decoder input length: 109 +2024-01-17 01:55:53,779 (beam_search:429) INFO: max output length: 109 +2024-01-17 01:55:53,779 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:53,911 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:53,911 (beam_search:476) INFO: -14.65 * 1.0 = -14.65 for ctc +2024-01-17 01:55:53,911 (beam_search:479) INFO: total log probability: -14.65 +2024-01-17 01:55:53,911 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:55:53,911 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:53,911 (beam_search:483) INFO: best hypo: DERSCHLENEHATSANELEISTUNGANGEBOTEN + +2024-01-17 01:55:53,912 (asr_inference:494) INFO: speech length: 33267 +2024-01-17 01:55:53,920 (beam_search:428) INFO: decoder input length: 49 +2024-01-17 01:55:53,920 (beam_search:429) INFO: max output length: 49 +2024-01-17 01:55:53,920 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:53,938 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:53,938 (beam_search:476) INFO: -5.01 * 1.0 = -5.01 for ctc +2024-01-17 01:55:53,938 (beam_search:479) INFO: total log probability: -5.01 +2024-01-17 01:55:53,938 (beam_search:480) INFO: normalized log probability: -0.50 +2024-01-17 01:55:53,938 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:53,938 (beam_search:483) INFO: best hypo: SODSSIS + +2024-01-17 01:55:53,939 (asr_inference:494) INFO: speech length: 79531 +2024-01-17 01:55:53,949 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 01:55:53,949 (beam_search:429) INFO: max output length: 122 +2024-01-17 01:55:53,949 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:54,093 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:54,093 (beam_search:476) INFO: -11.03 * 1.0 = -11.03 for ctc +2024-01-17 01:55:54,093 (beam_search:479) INFO: total log probability: -11.03 +2024-01-17 01:55:54,093 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:55:54,093 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:54,093 (beam_search:483) INFO: best hypo: DIEBATRIENWARNSEARSTAGVERALTET + +2024-01-17 01:55:54,094 (asr_inference:494) INFO: speech length: 86699 +2024-01-17 01:55:54,105 (beam_search:428) INFO: decoder input length: 133 +2024-01-17 01:55:54,105 (beam_search:429) INFO: max output length: 133 +2024-01-17 01:55:54,105 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:54,267 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:54,267 (beam_search:476) INFO: -12.97 * 1.0 = -12.97 for ctc +2024-01-17 01:55:54,267 (beam_search:479) INFO: total log probability: -12.97 +2024-01-17 01:55:54,267 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:55:54,267 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:54,267 (beam_search:483) INFO: best hypo: DESESZIELWURDENORTALWALSEEREICHT + +2024-01-17 01:55:54,268 (asr_inference:494) INFO: speech length: 46731 +2024-01-17 01:55:54,277 (beam_search:428) INFO: decoder input length: 71 +2024-01-17 01:55:54,277 (beam_search:429) INFO: max output length: 71 +2024-01-17 01:55:54,277 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:54,356 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:54,356 (beam_search:476) INFO: -9.70 * 1.0 = -9.70 for ctc +2024-01-17 01:55:54,356 (beam_search:479) INFO: total log probability: -9.70 +2024-01-17 01:55:54,356 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:55:54,356 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:54,356 (beam_search:483) INFO: best hypo: DIESEWEHRUNGWIRTSERLANGELEBEN + +2024-01-17 01:55:54,357 (asr_inference:494) INFO: speech length: 73728 +2024-01-17 01:55:54,367 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 01:55:54,367 (beam_search:429) INFO: max output length: 113 +2024-01-17 01:55:54,367 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:54,486 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:54,487 (beam_search:476) INFO: -11.78 * 1.0 = -11.78 for ctc +2024-01-17 01:55:54,487 (beam_search:479) INFO: total log probability: -11.78 +2024-01-17 01:55:54,487 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:55:54,487 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:54,487 (beam_search:483) INFO: best hypo: DORZEITANOFENBASCHONVIELET + +2024-01-17 01:55:54,488 (asr_inference:494) INFO: speech length: 140686 +2024-01-17 01:55:54,502 (beam_search:428) INFO: decoder input length: 217 +2024-01-17 01:55:54,503 (beam_search:429) INFO: max output length: 217 +2024-01-17 01:55:54,503 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:54,904 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:54,905 (beam_search:476) INFO: -23.01 * 1.0 = -23.01 for ctc +2024-01-17 01:55:54,905 (beam_search:479) INFO: total log probability: -23.01 +2024-01-17 01:55:54,905 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:55:54,905 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:54,905 (beam_search:483) INFO: best hypo: ALSIGIENENNIGTEMMÄGIIERNURGANSFLICHTIGKZUUNDERFAATA + +2024-01-17 01:55:54,906 (asr_inference:494) INFO: speech length: 54085 +2024-01-17 01:55:54,915 (beam_search:428) INFO: decoder input length: 82 +2024-01-17 01:55:54,915 (beam_search:429) INFO: max output length: 82 +2024-01-17 01:55:54,915 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:54,983 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:54,983 (beam_search:476) INFO: -11.30 * 1.0 = -11.30 for ctc +2024-01-17 01:55:54,983 (beam_search:479) INFO: total log probability: -11.30 +2024-01-17 01:55:54,983 (beam_search:480) INFO: normalized log probability: -0.45 +2024-01-17 01:55:54,983 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:54,983 (beam_search:483) INFO: best hypo: ERZYMIEMERÜBERISTIAN + +2024-01-17 01:55:54,984 (asr_inference:494) INFO: speech length: 89600 +2024-01-17 01:55:54,995 (beam_search:428) INFO: decoder input length: 137 +2024-01-17 01:55:54,995 (beam_search:429) INFO: max output length: 137 +2024-01-17 01:55:54,995 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:55,194 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:55,194 (beam_search:476) INFO: -10.80 * 1.0 = -10.80 for ctc +2024-01-17 01:55:55,194 (beam_search:479) INFO: total log probability: -10.80 +2024-01-17 01:55:55,194 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:55:55,194 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:55,194 (beam_search:483) INFO: best hypo: DEMSTEHENATIÜRLICHAUCHFAMMÖGENGEGENÜBA + +2024-01-17 01:55:55,195 (asr_inference:494) INFO: speech length: 69093 +2024-01-17 01:55:55,205 (beam_search:428) INFO: decoder input length: 105 +2024-01-17 01:55:55,205 (beam_search:429) INFO: max output length: 105 +2024-01-17 01:55:55,205 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:55:55,354 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:55:55,354 (beam_search:476) INFO: -12.90 * 1.0 = -12.90 for ctc +2024-01-17 01:55:55,354 (beam_search:479) INFO: total log probability: -12.90 +2024-01-17 01:55:55,354 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:55:55,354 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:55:55,354 (beam_search:483) INFO: best hypo: DIEREALELAGEWIRTNICHTVOSTENDICHABGEBILET + +# Accounting: time=32 threads=1 +# Ended (code 0) at Wed Jan 17 01:55:55 CST 2024, elapsed time 32 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.4.log b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.4.log new file mode 100644 index 0000000000000000000000000000000000000000..3ac80ad7d6866be24606fcfbefdec3b1c94d5624 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/asr_inference.4.log @@ -0,0 +1,1834 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.4 --config conf/decode_asr.yaml +# Started at Wed Jan 17 01:55:55 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_deu1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.4 --config conf/decode_asr.yaml +2024-01-17 01:55:57,170 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml +2024-01-17 01:55:57,188 (asr:523) INFO: Vocabulary size: 44 +2024-01-17 01:55:57,251 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 01:55:57,251 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 01:55:57,362 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 01:55:58,650 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 01:55:59,872 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 01:55:59,872 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 01:55:59,872 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 01:55:59,905 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-17 01:55:59,979 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 01:56:00,095 (asr_inference:494) INFO: speech length: 62416 +2024-01-17 01:56:01,303 (beam_search:428) INFO: decoder input length: 95 +2024-01-17 01:56:01,303 (beam_search:429) INFO: max output length: 95 +2024-01-17 01:56:01,303 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:01,412 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:01,412 (beam_search:476) INFO: -10.18 * 1.0 = -10.18 for ctc +2024-01-17 01:56:01,412 (beam_search:479) INFO: total log probability: -10.18 +2024-01-17 01:56:01,412 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:56:01,412 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:01,412 (beam_search:483) INFO: best hypo: ESKANAUCHNOCHVIESCHLIMERWERDEN + +2024-01-17 01:56:01,437 (asr_inference:494) INFO: speech length: 43477 +2024-01-17 01:56:01,446 (beam_search:428) INFO: decoder input length: 65 +2024-01-17 01:56:01,446 (beam_search:429) INFO: max output length: 65 +2024-01-17 01:56:01,446 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:01,522 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:01,522 (beam_search:476) INFO: -13.94 * 1.0 = -13.94 for ctc +2024-01-17 01:56:01,522 (beam_search:479) INFO: total log probability: -13.94 +2024-01-17 01:56:01,522 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:56:01,522 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:01,523 (beam_search:483) INFO: best hypo: DIEPOLETIGINTRESIRTZICHNICHTMER + +2024-01-17 01:56:01,524 (asr_inference:494) INFO: speech length: 112000 +2024-01-17 01:56:01,538 (beam_search:428) INFO: decoder input length: 172 +2024-01-17 01:56:01,538 (beam_search:429) INFO: max output length: 172 +2024-01-17 01:56:01,538 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:01,915 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:01,915 (beam_search:476) INFO: -19.91 * 1.0 = -19.91 for ctc +2024-01-17 01:56:01,915 (beam_search:479) INFO: total log probability: -19.91 +2024-01-17 01:56:01,915 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:56:01,915 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:01,916 (beam_search:483) INFO: best hypo: INHALSFREIHEITBEDEUTERTDASEINHALDDERVERTRACKLICHENVEREINBARUNGEN + +2024-01-17 01:56:01,917 (asr_inference:494) INFO: speech length: 108000 +2024-01-17 01:56:01,929 (beam_search:428) INFO: decoder input length: 166 +2024-01-17 01:56:01,929 (beam_search:429) INFO: max output length: 166 +2024-01-17 01:56:01,929 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:02,193 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:02,193 (beam_search:476) INFO: -27.36 * 1.0 = -27.36 for ctc +2024-01-17 01:56:02,193 (beam_search:479) INFO: total log probability: -27.36 +2024-01-17 01:56:02,193 (beam_search:480) INFO: normalized log probability: -0.53 +2024-01-17 01:56:02,193 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:02,193 (beam_search:483) INFO: best hypo: DERSCHULNERVELETDISEINISACKVALSPLIENSCHLTHAFT + +2024-01-17 01:56:02,194 (asr_inference:494) INFO: speech length: 101888 +2024-01-17 01:56:02,206 (beam_search:428) INFO: decoder input length: 157 +2024-01-17 01:56:02,206 (beam_search:429) INFO: max output length: 157 +2024-01-17 01:56:02,206 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:02,448 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:02,449 (beam_search:476) INFO: -14.66 * 1.0 = -14.66 for ctc +2024-01-17 01:56:02,449 (beam_search:479) INFO: total log probability: -14.66 +2024-01-17 01:56:02,449 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:56:02,449 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:02,449 (beam_search:483) INFO: best hypo: DISESGETREIDEDENDINSBESONDEREALSFIVORTAT + +2024-01-17 01:56:02,450 (asr_inference:494) INFO: speech length: 86000 +2024-01-17 01:56:02,461 (beam_search:428) INFO: decoder input length: 132 +2024-01-17 01:56:02,461 (beam_search:429) INFO: max output length: 132 +2024-01-17 01:56:02,461 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:02,711 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:02,712 (beam_search:476) INFO: -20.03 * 1.0 = -20.03 for ctc +2024-01-17 01:56:02,712 (beam_search:479) INFO: total log probability: -20.03 +2024-01-17 01:56:02,712 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:56:02,712 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:02,712 (beam_search:483) INFO: best hypo: TÜPSCHWISEWERENSTATISCHEEIPIERTRESENVONSRVERNEINGESETZT + +2024-01-17 01:56:02,713 (asr_inference:494) INFO: speech length: 55568 +2024-01-17 01:56:02,722 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:56:02,722 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:56:02,722 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:02,813 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:02,813 (beam_search:476) INFO: -9.40 * 1.0 = -9.40 for ctc +2024-01-17 01:56:02,813 (beam_search:479) INFO: total log probability: -9.40 +2024-01-17 01:56:02,813 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:56:02,813 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:02,813 (beam_search:483) INFO: best hypo: JETZTWRESSOLANGSAHMGEGLAUPT + +2024-01-17 01:56:02,814 (asr_inference:494) INFO: speech length: 94891 +2024-01-17 01:56:02,826 (beam_search:428) INFO: decoder input length: 146 +2024-01-17 01:56:02,826 (beam_search:429) INFO: max output length: 146 +2024-01-17 01:56:02,826 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:03,056 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:03,056 (beam_search:476) INFO: -11.77 * 1.0 = -11.77 for ctc +2024-01-17 01:56:03,056 (beam_search:479) INFO: total log probability: -11.77 +2024-01-17 01:56:03,056 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:56:03,056 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:03,056 (beam_search:483) INFO: best hypo: UNASCHIETLICHEERGEBNISSEHARBENSICHEREIGNERD + +2024-01-17 01:56:03,058 (asr_inference:494) INFO: speech length: 94550 +2024-01-17 01:56:03,069 (beam_search:428) INFO: decoder input length: 145 +2024-01-17 01:56:03,069 (beam_search:429) INFO: max output length: 145 +2024-01-17 01:56:03,069 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:03,329 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:03,329 (beam_search:476) INFO: -15.17 * 1.0 = -15.17 for ctc +2024-01-17 01:56:03,329 (beam_search:479) INFO: total log probability: -15.17 +2024-01-17 01:56:03,329 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:56:03,329 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:03,329 (beam_search:483) INFO: best hypo: TERORFARDEICHTIGEWURDENNECHTVOREINGERECHTGESTELLTN + +2024-01-17 01:56:03,331 (asr_inference:494) INFO: speech length: 94346 +2024-01-17 01:56:03,342 (beam_search:428) INFO: decoder input length: 145 +2024-01-17 01:56:03,342 (beam_search:429) INFO: max output length: 145 +2024-01-17 01:56:03,342 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:03,596 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:03,596 (beam_search:476) INFO: -22.61 * 1.0 = -22.61 for ctc +2024-01-17 01:56:03,596 (beam_search:479) INFO: total log probability: -22.61 +2024-01-17 01:56:03,596 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:56:03,596 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:03,596 (beam_search:483) INFO: best hypo: AUFMACHENDIESTIFÜNICTAUSTZEHNUNDWEISKORDWASNOCLLS + +2024-01-17 01:56:03,597 (asr_inference:494) INFO: speech length: 120000 +2024-01-17 01:56:03,610 (beam_search:428) INFO: decoder input length: 185 +2024-01-17 01:56:03,610 (beam_search:429) INFO: max output length: 185 +2024-01-17 01:56:03,610 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:03,997 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:03,997 (beam_search:476) INFO: -19.98 * 1.0 = -19.98 for ctc +2024-01-17 01:56:03,997 (beam_search:479) INFO: total log probability: -19.98 +2024-01-17 01:56:03,997 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:56:03,997 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:03,998 (beam_search:483) INFO: best hypo: INKESAMTDREIUNDZWANSICPERSONENAUVERSCHIEDENPALEMENTENNIMENTEIL + +2024-01-17 01:56:03,999 (asr_inference:494) INFO: speech length: 73728 +2024-01-17 01:56:04,009 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 01:56:04,009 (beam_search:429) INFO: max output length: 113 +2024-01-17 01:56:04,009 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:04,205 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:04,205 (beam_search:476) INFO: -19.51 * 1.0 = -19.51 for ctc +2024-01-17 01:56:04,205 (beam_search:479) INFO: total log probability: -19.51 +2024-01-17 01:56:04,205 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:56:04,205 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:04,205 (beam_search:483) INFO: best hypo: VORDUNGSECFTEWERTENDEGLEUBIGEAUSCHLIESLIFTOGERURTNET + +2024-01-17 01:56:04,207 (asr_inference:494) INFO: speech length: 74240 +2024-01-17 01:56:04,216 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 01:56:04,216 (beam_search:429) INFO: max output length: 113 +2024-01-17 01:56:04,216 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:04,334 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:04,334 (beam_search:476) INFO: -11.38 * 1.0 = -11.38 for ctc +2024-01-17 01:56:04,334 (beam_search:479) INFO: total log probability: -11.38 +2024-01-17 01:56:04,334 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:56:04,334 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:04,334 (beam_search:483) INFO: best hypo: DASPOBLEMHÜWOURDEBEHOBENT + +2024-01-17 01:56:04,335 (asr_inference:494) INFO: speech length: 74949 +2024-01-17 01:56:04,345 (beam_search:428) INFO: decoder input length: 115 +2024-01-17 01:56:04,345 (beam_search:429) INFO: max output length: 115 +2024-01-17 01:56:04,345 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:04,536 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:04,537 (beam_search:476) INFO: -15.37 * 1.0 = -15.37 for ctc +2024-01-17 01:56:04,537 (beam_search:479) INFO: total log probability: -15.37 +2024-01-17 01:56:04,537 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:56:04,537 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:04,537 (beam_search:483) INFO: best hypo: FÜRDIERKÄNUNGVONUNTERROCHNRDIESKRETERPRACHE + +2024-01-17 01:56:04,538 (asr_inference:494) INFO: speech length: 56197 +2024-01-17 01:56:04,547 (beam_search:428) INFO: decoder input length: 85 +2024-01-17 01:56:04,547 (beam_search:429) INFO: max output length: 85 +2024-01-17 01:56:04,547 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:04,660 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:04,660 (beam_search:476) INFO: -10.49 * 1.0 = -10.49 for ctc +2024-01-17 01:56:04,660 (beam_search:479) INFO: total log probability: -10.49 +2024-01-17 01:56:04,660 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:56:04,660 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:04,660 (beam_search:483) INFO: best hypo: DICHINESENKÖNTENSERIELWICHTIGAWERDEN + +2024-01-17 01:56:04,661 (asr_inference:494) INFO: speech length: 43691 +2024-01-17 01:56:04,669 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:56:04,669 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:56:04,669 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:04,763 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:04,763 (beam_search:476) INFO: -19.96 * 1.0 = -19.96 for ctc +2024-01-17 01:56:04,763 (beam_search:479) INFO: total log probability: -19.96 +2024-01-17 01:56:04,763 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-17 01:56:04,763 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:04,764 (beam_search:483) INFO: best hypo: DIERTOSCLIERDEDIGLICHEREINSIGEMALFVEWNDET + +2024-01-17 01:56:04,765 (asr_inference:494) INFO: speech length: 104960 +2024-01-17 01:56:04,777 (beam_search:428) INFO: decoder input length: 161 +2024-01-17 01:56:04,777 (beam_search:429) INFO: max output length: 161 +2024-01-17 01:56:04,777 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:05,077 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:05,077 (beam_search:476) INFO: -12.31 * 1.0 = -12.31 for ctc +2024-01-17 01:56:05,077 (beam_search:479) INFO: total log probability: -12.31 +2024-01-17 01:56:05,077 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:56:05,077 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:05,077 (beam_search:483) INFO: best hypo: DASLANDENTWECKELTESECHZUEINERMILITERISCHENGROSSMACHT + +2024-01-17 01:56:05,079 (asr_inference:494) INFO: speech length: 60016 +2024-01-17 01:56:05,088 (beam_search:428) INFO: decoder input length: 91 +2024-01-17 01:56:05,088 (beam_search:429) INFO: max output length: 91 +2024-01-17 01:56:05,088 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:05,199 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:05,199 (beam_search:476) INFO: -9.34 * 1.0 = -9.34 for ctc +2024-01-17 01:56:05,199 (beam_search:479) INFO: total log probability: -9.34 +2024-01-17 01:56:05,199 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:56:05,199 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:05,199 (beam_search:483) INFO: best hypo: ESSEINDUNDBLEIENVERBRECHERBANDEN + +2024-01-17 01:56:05,200 (asr_inference:494) INFO: speech length: 42715 +2024-01-17 01:56:05,208 (beam_search:428) INFO: decoder input length: 64 +2024-01-17 01:56:05,209 (beam_search:429) INFO: max output length: 64 +2024-01-17 01:56:05,209 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:05,273 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:05,273 (beam_search:476) INFO: -5.76 * 1.0 = -5.76 for ctc +2024-01-17 01:56:05,274 (beam_search:479) INFO: total log probability: -5.76 +2024-01-17 01:56:05,274 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:56:05,274 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:05,274 (beam_search:483) INFO: best hypo: DIEZEITENWERDENSICHENDERN + +2024-01-17 01:56:05,275 (asr_inference:494) INFO: speech length: 108000 +2024-01-17 01:56:05,287 (beam_search:428) INFO: decoder input length: 166 +2024-01-17 01:56:05,287 (beam_search:429) INFO: max output length: 166 +2024-01-17 01:56:05,287 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:05,584 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:05,585 (beam_search:476) INFO: -14.08 * 1.0 = -14.08 for ctc +2024-01-17 01:56:05,585 (beam_search:479) INFO: total log probability: -14.08 +2024-01-17 01:56:05,585 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:56:05,585 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:05,585 (beam_search:483) INFO: best hypo: DEENSTIFFTINDIEACHBORUNGEINSCHIEBENWISTZUMANSCHLAG + +2024-01-17 01:56:05,586 (asr_inference:494) INFO: speech length: 108000 +2024-01-17 01:56:05,598 (beam_search:428) INFO: decoder input length: 166 +2024-01-17 01:56:05,598 (beam_search:429) INFO: max output length: 166 +2024-01-17 01:56:05,598 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:05,957 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:05,958 (beam_search:476) INFO: -24.36 * 1.0 = -24.36 for ctc +2024-01-17 01:56:05,958 (beam_search:479) INFO: total log probability: -24.36 +2024-01-17 01:56:05,958 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:56:05,958 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:05,958 (beam_search:483) INFO: best hypo: DIEAUCHTBEIMBOAAUSERVIERSAMMWVIERTBEISPILSWEISEBEILVEIERFOUGSEN + +2024-01-17 01:56:05,959 (asr_inference:494) INFO: speech length: 48485 +2024-01-17 01:56:05,968 (beam_search:428) INFO: decoder input length: 73 +2024-01-17 01:56:05,968 (beam_search:429) INFO: max output length: 73 +2024-01-17 01:56:05,968 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:06,036 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:06,036 (beam_search:476) INFO: -8.02 * 1.0 = -8.02 for ctc +2024-01-17 01:56:06,036 (beam_search:479) INFO: total log probability: -8.02 +2024-01-17 01:56:06,036 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:56:06,036 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:06,036 (beam_search:483) INFO: best hypo: DASWAHNOCHGAKHEINILISE + +2024-01-17 01:56:06,037 (asr_inference:494) INFO: speech length: 47808 +2024-01-17 01:56:06,045 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 01:56:06,045 (beam_search:429) INFO: max output length: 72 +2024-01-17 01:56:06,046 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:06,134 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:06,135 (beam_search:476) INFO: -8.65 * 1.0 = -8.65 for ctc +2024-01-17 01:56:06,135 (beam_search:479) INFO: total log probability: -8.65 +2024-01-17 01:56:06,135 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:56:06,135 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:06,135 (beam_search:483) INFO: best hypo: DIEHABENOFENBARZIMLICROSEANGST + +2024-01-17 01:56:06,136 (asr_inference:494) INFO: speech length: 48059 +2024-01-17 01:56:06,144 (beam_search:428) INFO: decoder input length: 73 +2024-01-17 01:56:06,144 (beam_search:429) INFO: max output length: 73 +2024-01-17 01:56:06,144 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:06,225 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:06,225 (beam_search:476) INFO: -7.84 * 1.0 = -7.84 for ctc +2024-01-17 01:56:06,225 (beam_search:479) INFO: total log probability: -7.84 +2024-01-17 01:56:06,225 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:56:06,225 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:06,225 (beam_search:483) INFO: best hypo: VIELEVELIERENIERENABEITSPLATZ + +2024-01-17 01:56:06,226 (asr_inference:494) INFO: speech length: 80214 +2024-01-17 01:56:06,236 (beam_search:428) INFO: decoder input length: 123 +2024-01-17 01:56:06,236 (beam_search:429) INFO: max output length: 123 +2024-01-17 01:56:06,236 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:06,378 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:06,379 (beam_search:476) INFO: -12.90 * 1.0 = -12.90 for ctc +2024-01-17 01:56:06,379 (beam_search:479) INFO: total log probability: -12.90 +2024-01-17 01:56:06,379 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:56:06,379 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:06,379 (beam_search:483) INFO: best hypo: DARFÜHRGEBTESEINENPUNKTABZOGE + +2024-01-17 01:56:06,380 (asr_inference:494) INFO: speech length: 82000 +2024-01-17 01:56:06,390 (beam_search:428) INFO: decoder input length: 126 +2024-01-17 01:56:06,390 (beam_search:429) INFO: max output length: 126 +2024-01-17 01:56:06,390 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:06,643 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:06,643 (beam_search:476) INFO: -22.78 * 1.0 = -22.78 for ctc +2024-01-17 01:56:06,643 (beam_search:479) INFO: total log probability: -22.78 +2024-01-17 01:56:06,643 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:56:06,643 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:06,643 (beam_search:483) INFO: best hypo: DIEBEIDESINDÜBEREINEUNDSICHEVERBINUNGMTDEINANDERINKONTAKTN + +2024-01-17 01:56:06,645 (asr_inference:494) INFO: speech length: 54053 +2024-01-17 01:56:06,654 (beam_search:428) INFO: decoder input length: 82 +2024-01-17 01:56:06,654 (beam_search:429) INFO: max output length: 82 +2024-01-17 01:56:06,654 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:06,743 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:06,743 (beam_search:476) INFO: -7.23 * 1.0 = -7.23 for ctc +2024-01-17 01:56:06,743 (beam_search:479) INFO: total log probability: -7.23 +2024-01-17 01:56:06,743 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:56:06,743 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:06,743 (beam_search:483) INFO: best hypo: BEIDESTÄCKENTIFINROTENZALN + +2024-01-17 01:56:06,744 (asr_inference:494) INFO: speech length: 79216 +2024-01-17 01:56:06,754 (beam_search:428) INFO: decoder input length: 121 +2024-01-17 01:56:06,754 (beam_search:429) INFO: max output length: 121 +2024-01-17 01:56:06,754 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:06,906 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:06,906 (beam_search:476) INFO: -19.03 * 1.0 = -19.03 for ctc +2024-01-17 01:56:06,906 (beam_search:479) INFO: total log probability: -19.03 +2024-01-17 01:56:06,906 (beam_search:480) INFO: normalized log probability: -0.48 +2024-01-17 01:56:06,906 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:06,907 (beam_search:483) INFO: best hypo: FÜNFTEHNUORFÜNFZEHNDORCFUONGOLLFF + +2024-01-17 01:56:06,908 (asr_inference:494) INFO: speech length: 131853 +2024-01-17 01:56:06,921 (beam_search:428) INFO: decoder input length: 204 +2024-01-17 01:56:06,921 (beam_search:429) INFO: max output length: 204 +2024-01-17 01:56:06,921 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:07,312 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:07,312 (beam_search:476) INFO: -18.35 * 1.0 = -18.35 for ctc +2024-01-17 01:56:07,312 (beam_search:479) INFO: total log probability: -18.35 +2024-01-17 01:56:07,312 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:56:07,312 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:07,312 (beam_search:483) INFO: best hypo: WIEMENSCHENAUSEINEANDERENWELTSCHENENZIIHRHEUITEUNDOCH + +2024-01-17 01:56:07,314 (asr_inference:494) INFO: speech length: 53697 +2024-01-17 01:56:07,322 (beam_search:428) INFO: decoder input length: 81 +2024-01-17 01:56:07,322 (beam_search:429) INFO: max output length: 81 +2024-01-17 01:56:07,322 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:07,419 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:07,419 (beam_search:476) INFO: -14.76 * 1.0 = -14.76 for ctc +2024-01-17 01:56:07,419 (beam_search:479) INFO: total log probability: -14.76 +2024-01-17 01:56:07,419 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:56:07,419 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:07,419 (beam_search:483) INFO: best hypo: BNDHMITEMHINTERNDESKAMLSAUFHRT + +2024-01-17 01:56:07,420 (asr_inference:494) INFO: speech length: 131822 +2024-01-17 01:56:07,434 (beam_search:428) INFO: decoder input length: 203 +2024-01-17 01:56:07,434 (beam_search:429) INFO: max output length: 203 +2024-01-17 01:56:07,434 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:07,813 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:07,813 (beam_search:476) INFO: -19.79 * 1.0 = -19.79 for ctc +2024-01-17 01:56:07,813 (beam_search:479) INFO: total log probability: -19.79 +2024-01-17 01:56:07,813 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:56:07,813 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:07,813 (beam_search:483) INFO: best hypo: ACHDEROBERFÖRTEAZUGTDEMIEDENCHIFENGRAUENBRAUENEINEN + +2024-01-17 01:56:07,814 (asr_inference:494) INFO: speech length: 71525 +2024-01-17 01:56:07,824 (beam_search:428) INFO: decoder input length: 109 +2024-01-17 01:56:07,824 (beam_search:429) INFO: max output length: 109 +2024-01-17 01:56:07,824 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:07,994 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:07,994 (beam_search:476) INFO: -10.11 * 1.0 = -10.11 for ctc +2024-01-17 01:56:07,994 (beam_search:479) INFO: total log probability: -10.11 +2024-01-17 01:56:07,994 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:56:07,994 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:07,994 (beam_search:483) INFO: best hypo: ICHWUNDEREMIGIMMARWIDERÜBERDISEERKLÄRUNGEN + +2024-01-17 01:56:07,996 (asr_inference:494) INFO: speech length: 62806 +2024-01-17 01:56:08,005 (beam_search:428) INFO: decoder input length: 96 +2024-01-17 01:56:08,005 (beam_search:429) INFO: max output length: 96 +2024-01-17 01:56:08,005 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:08,160 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:08,160 (beam_search:476) INFO: -19.98 * 1.0 = -19.98 for ctc +2024-01-17 01:56:08,160 (beam_search:479) INFO: total log probability: -19.98 +2024-01-17 01:56:08,160 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:56:08,160 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:08,161 (beam_search:483) INFO: best hypo: BAREINEMSMETRICHENKRIUPTUSTEMIRTNDERSVORGEGANG + +2024-01-17 01:56:08,162 (asr_inference:494) INFO: speech length: 74070 +2024-01-17 01:56:08,172 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 01:56:08,172 (beam_search:429) INFO: max output length: 113 +2024-01-17 01:56:08,172 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:08,269 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:08,269 (beam_search:476) INFO: -9.88 * 1.0 = -9.88 for ctc +2024-01-17 01:56:08,269 (beam_search:479) INFO: total log probability: -9.88 +2024-01-17 01:56:08,269 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:56:08,269 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:08,269 (beam_search:483) INFO: best hypo: DASISDORTVERZEICHNED + +2024-01-17 01:56:08,271 (asr_inference:494) INFO: speech length: 69120 +2024-01-17 01:56:08,280 (beam_search:428) INFO: decoder input length: 105 +2024-01-17 01:56:08,280 (beam_search:429) INFO: max output length: 105 +2024-01-17 01:56:08,280 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:08,398 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:08,398 (beam_search:476) INFO: -12.40 * 1.0 = -12.40 for ctc +2024-01-17 01:56:08,398 (beam_search:479) INFO: total log probability: -12.40 +2024-01-17 01:56:08,398 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:56:08,398 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:08,398 (beam_search:483) INFO: best hypo: GELLTSANSERGUTESTAUSCHMITE + +2024-01-17 01:56:08,399 (asr_inference:494) INFO: speech length: 81067 +2024-01-17 01:56:08,410 (beam_search:428) INFO: decoder input length: 124 +2024-01-17 01:56:08,410 (beam_search:429) INFO: max output length: 124 +2024-01-17 01:56:08,410 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:08,581 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:08,581 (beam_search:476) INFO: -14.13 * 1.0 = -14.13 for ctc +2024-01-17 01:56:08,581 (beam_search:479) INFO: total log probability: -14.13 +2024-01-17 01:56:08,581 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:56:08,581 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:08,581 (beam_search:483) INFO: best hypo: DASWEHRERWISSENSCHAFTLICHNODWENDIGT + +2024-01-17 01:56:08,583 (asr_inference:494) INFO: speech length: 81920 +2024-01-17 01:56:08,593 (beam_search:428) INFO: decoder input length: 125 +2024-01-17 01:56:08,593 (beam_search:429) INFO: max output length: 125 +2024-01-17 01:56:08,593 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:08,769 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:08,769 (beam_search:476) INFO: -14.24 * 1.0 = -14.24 for ctc +2024-01-17 01:56:08,769 (beam_search:479) INFO: total log probability: -14.24 +2024-01-17 01:56:08,769 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:56:08,769 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:08,770 (beam_search:483) INFO: best hypo: NUNRBESTIMTISSTRAFTATENKOMENENBETRACHT + +2024-01-17 01:56:08,771 (asr_inference:494) INFO: speech length: 95403 +2024-01-17 01:56:08,782 (beam_search:428) INFO: decoder input length: 147 +2024-01-17 01:56:08,782 (beam_search:429) INFO: max output length: 147 +2024-01-17 01:56:08,782 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:08,998 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:08,998 (beam_search:476) INFO: -12.73 * 1.0 = -12.73 for ctc +2024-01-17 01:56:08,998 (beam_search:479) INFO: total log probability: -12.73 +2024-01-17 01:56:08,998 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:56:08,998 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:08,998 (beam_search:483) INFO: best hypo: DARMITKANMANBARSCHENLICCHLÄCHTEINKAUFEN + +2024-01-17 01:56:08,999 (asr_inference:494) INFO: speech length: 55077 +2024-01-17 01:56:09,008 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:56:09,008 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:56:09,008 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:09,073 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:09,073 (beam_search:476) INFO: -5.80 * 1.0 = -5.80 for ctc +2024-01-17 01:56:09,073 (beam_search:479) INFO: total log probability: -5.80 +2024-01-17 01:56:09,073 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:56:09,073 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:09,073 (beam_search:483) INFO: best hypo: DAFÜHEWORDEGESOKT + +2024-01-17 01:56:09,075 (asr_inference:494) INFO: speech length: 58053 +2024-01-17 01:56:09,083 (beam_search:428) INFO: decoder input length: 88 +2024-01-17 01:56:09,083 (beam_search:429) INFO: max output length: 88 +2024-01-17 01:56:09,084 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:09,172 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:09,172 (beam_search:476) INFO: -11.51 * 1.0 = -11.51 for ctc +2024-01-17 01:56:09,172 (beam_search:479) INFO: total log probability: -11.51 +2024-01-17 01:56:09,172 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:56:09,172 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:09,172 (beam_search:483) INFO: best hypo: MANKANDERSEUTVERAUFENE + +2024-01-17 01:56:09,174 (asr_inference:494) INFO: speech length: 42000 +2024-01-17 01:56:09,182 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:56:09,182 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:56:09,182 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:09,243 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:09,243 (beam_search:476) INFO: -16.50 * 1.0 = -16.50 for ctc +2024-01-17 01:56:09,243 (beam_search:479) INFO: total log probability: -16.50 +2024-01-17 01:56:09,243 (beam_search:480) INFO: normalized log probability: -0.57 +2024-01-17 01:56:09,243 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:09,243 (beam_search:483) INFO: best hypo: SONENOCKNESTEUENTDETIOUNG + +2024-01-17 01:56:09,244 (asr_inference:494) INFO: speech length: 62667 +2024-01-17 01:56:09,254 (beam_search:428) INFO: decoder input length: 95 +2024-01-17 01:56:09,254 (beam_search:429) INFO: max output length: 95 +2024-01-17 01:56:09,254 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:09,367 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:09,367 (beam_search:476) INFO: -6.42 * 1.0 = -6.42 for ctc +2024-01-17 01:56:09,367 (beam_search:479) INFO: total log probability: -6.42 +2024-01-17 01:56:09,367 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:56:09,367 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:09,367 (beam_search:483) INFO: best hypo: DARÜBERREDEDIEPASTORINUNDREDET + +2024-01-17 01:56:09,369 (asr_inference:494) INFO: speech length: 76000 +2024-01-17 01:56:09,378 (beam_search:428) INFO: decoder input length: 116 +2024-01-17 01:56:09,378 (beam_search:429) INFO: max output length: 116 +2024-01-17 01:56:09,378 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:09,497 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:09,497 (beam_search:476) INFO: -15.23 * 1.0 = -15.23 for ctc +2024-01-17 01:56:09,497 (beam_search:479) INFO: total log probability: -15.23 +2024-01-17 01:56:09,497 (beam_search:480) INFO: normalized log probability: -0.48 +2024-01-17 01:56:09,497 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:09,497 (beam_search:483) INFO: best hypo: DENSCHALENDENDEENDOANSTELEN + +2024-01-17 01:56:09,499 (asr_inference:494) INFO: speech length: 96256 +2024-01-17 01:56:09,510 (beam_search:428) INFO: decoder input length: 148 +2024-01-17 01:56:09,510 (beam_search:429) INFO: max output length: 148 +2024-01-17 01:56:09,510 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:09,721 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:09,721 (beam_search:476) INFO: -12.64 * 1.0 = -12.64 for ctc +2024-01-17 01:56:09,721 (beam_search:479) INFO: total log probability: -12.64 +2024-01-17 01:56:09,721 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:56:09,721 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:09,722 (beam_search:483) INFO: best hypo: AUFDENERSTENBLIKSCHEINDASUNGEWÜÖNLICH + +2024-01-17 01:56:09,723 (asr_inference:494) INFO: speech length: 69627 +2024-01-17 01:56:09,733 (beam_search:428) INFO: decoder input length: 106 +2024-01-17 01:56:09,733 (beam_search:429) INFO: max output length: 106 +2024-01-17 01:56:09,733 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:09,904 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:09,904 (beam_search:476) INFO: -13.24 * 1.0 = -13.24 for ctc +2024-01-17 01:56:09,904 (beam_search:479) INFO: total log probability: -13.24 +2024-01-17 01:56:09,904 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:56:09,904 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:09,905 (beam_search:483) INFO: best hypo: DERDOLAWIRTICHTMEHRALSWERHUNGAKZIEPTIERTWERDEN + +2024-01-17 01:56:09,906 (asr_inference:494) INFO: speech length: 119037 +2024-01-17 01:56:09,918 (beam_search:428) INFO: decoder input length: 183 +2024-01-17 01:56:09,918 (beam_search:429) INFO: max output length: 183 +2024-01-17 01:56:09,918 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:10,280 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:10,280 (beam_search:476) INFO: -23.18 * 1.0 = -23.18 for ctc +2024-01-17 01:56:10,280 (beam_search:479) INFO: total log probability: -23.18 +2024-01-17 01:56:10,280 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:56:10,280 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:10,280 (beam_search:483) INFO: best hypo: ILENKOPTFFEHSTGEGENDENHALSTERINGERENDAMKÖSTISIEDENFARTER + +2024-01-17 01:56:10,282 (asr_inference:494) INFO: speech length: 62635 +2024-01-17 01:56:10,291 (beam_search:428) INFO: decoder input length: 95 +2024-01-17 01:56:10,291 (beam_search:429) INFO: max output length: 95 +2024-01-17 01:56:10,291 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:10,372 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:10,372 (beam_search:476) INFO: -8.61 * 1.0 = -8.61 for ctc +2024-01-17 01:56:10,372 (beam_search:479) INFO: total log probability: -8.61 +2024-01-17 01:56:10,372 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:56:10,372 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:10,372 (beam_search:483) INFO: best hypo: DASWURDENICTWAGEOMEN + +2024-01-17 01:56:10,374 (asr_inference:494) INFO: speech length: 48640 +2024-01-17 01:56:10,382 (beam_search:428) INFO: decoder input length: 73 +2024-01-17 01:56:10,382 (beam_search:429) INFO: max output length: 73 +2024-01-17 01:56:10,382 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:10,453 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:10,454 (beam_search:476) INFO: -13.76 * 1.0 = -13.76 for ctc +2024-01-17 01:56:10,454 (beam_search:479) INFO: total log probability: -13.76 +2024-01-17 01:56:10,454 (beam_search:480) INFO: normalized log probability: -0.46 +2024-01-17 01:56:10,454 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:10,454 (beam_search:483) INFO: best hypo: WAHATASDAMMEASVORGELESEN + +2024-01-17 01:56:10,455 (asr_inference:494) INFO: speech length: 82000 +2024-01-17 01:56:10,465 (beam_search:428) INFO: decoder input length: 126 +2024-01-17 01:56:10,465 (beam_search:429) INFO: max output length: 126 +2024-01-17 01:56:10,465 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:10,705 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:10,705 (beam_search:476) INFO: -20.39 * 1.0 = -20.39 for ctc +2024-01-17 01:56:10,705 (beam_search:479) INFO: total log probability: -20.39 +2024-01-17 01:56:10,705 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:56:10,705 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:10,705 (beam_search:483) INFO: best hypo: EIBESONDERWELTVONSACHUNGISDIKENZEMITRIGEASDERWANMERT + +2024-01-17 01:56:10,707 (asr_inference:494) INFO: speech length: 66560 +2024-01-17 01:56:10,717 (beam_search:428) INFO: decoder input length: 101 +2024-01-17 01:56:10,717 (beam_search:429) INFO: max output length: 101 +2024-01-17 01:56:10,717 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:10,810 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:10,810 (beam_search:476) INFO: -12.17 * 1.0 = -12.17 for ctc +2024-01-17 01:56:10,810 (beam_search:479) INFO: total log probability: -12.17 +2024-01-17 01:56:10,810 (beam_search:480) INFO: normalized log probability: -0.43 +2024-01-17 01:56:10,810 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:10,810 (beam_search:483) INFO: best hypo: NASMOSTZRÜCKEZALTWERDEN + +2024-01-17 01:56:10,811 (asr_inference:494) INFO: speech length: 64000 +2024-01-17 01:56:10,821 (beam_search:428) INFO: decoder input length: 97 +2024-01-17 01:56:10,821 (beam_search:429) INFO: max output length: 97 +2024-01-17 01:56:10,821 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:10,957 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:10,957 (beam_search:476) INFO: -10.75 * 1.0 = -10.75 for ctc +2024-01-17 01:56:10,957 (beam_search:479) INFO: total log probability: -10.75 +2024-01-17 01:56:10,957 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:56:10,957 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:10,957 (beam_search:483) INFO: best hypo: ZWICHENGLEUBIGERUNDSCHLDENEHERGELEITET + +2024-01-17 01:56:10,959 (asr_inference:494) INFO: speech length: 58710 +2024-01-17 01:56:10,967 (beam_search:428) INFO: decoder input length: 89 +2024-01-17 01:56:10,968 (beam_search:429) INFO: max output length: 89 +2024-01-17 01:56:10,968 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:11,084 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:11,085 (beam_search:476) INFO: -13.72 * 1.0 = -13.72 for ctc +2024-01-17 01:56:11,085 (beam_search:479) INFO: total log probability: -13.72 +2024-01-17 01:56:11,085 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:56:11,085 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:11,085 (beam_search:483) INFO: best hypo: EINABSUMUTESRECHTWIEDRECHTZIERIELLET + +2024-01-17 01:56:11,086 (asr_inference:494) INFO: speech length: 91648 +2024-01-17 01:56:11,097 (beam_search:428) INFO: decoder input length: 141 +2024-01-17 01:56:11,097 (beam_search:429) INFO: max output length: 141 +2024-01-17 01:56:11,097 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:11,287 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:11,288 (beam_search:476) INFO: -10.47 * 1.0 = -10.47 for ctc +2024-01-17 01:56:11,288 (beam_search:479) INFO: total log probability: -10.47 +2024-01-17 01:56:11,288 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:56:11,288 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:11,288 (beam_search:483) INFO: best hypo: MANBRAUCHTNEICHTANDENZUFALZUGLAUBEN + +2024-01-17 01:56:11,289 (asr_inference:494) INFO: speech length: 108923 +2024-01-17 01:56:11,301 (beam_search:428) INFO: decoder input length: 168 +2024-01-17 01:56:11,301 (beam_search:429) INFO: max output length: 168 +2024-01-17 01:56:11,301 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:11,603 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:11,603 (beam_search:476) INFO: -18.97 * 1.0 = -18.97 for ctc +2024-01-17 01:56:11,603 (beam_search:479) INFO: total log probability: -18.97 +2024-01-17 01:56:11,603 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:56:11,603 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:11,603 (beam_search:483) INFO: best hypo: ZERTLICHENWESENNURINTFALTENWUOMANILIEBEBODTVORHAREN + +2024-01-17 01:56:11,604 (asr_inference:494) INFO: speech length: 82000 +2024-01-17 01:56:11,614 (beam_search:428) INFO: decoder input length: 126 +2024-01-17 01:56:11,615 (beam_search:429) INFO: max output length: 126 +2024-01-17 01:56:11,615 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:11,829 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:11,830 (beam_search:476) INFO: -17.32 * 1.0 = -17.32 for ctc +2024-01-17 01:56:11,830 (beam_search:479) INFO: total log probability: -17.32 +2024-01-17 01:56:11,830 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:56:11,830 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:11,830 (beam_search:483) INFO: best hypo: ERZÜKLICHDEERWEISLATNDEHAFTUNGFÜRHELFPERSCONEN + +2024-01-17 01:56:11,831 (asr_inference:494) INFO: speech length: 76000 +2024-01-17 01:56:11,841 (beam_search:428) INFO: decoder input length: 116 +2024-01-17 01:56:11,841 (beam_search:429) INFO: max output length: 116 +2024-01-17 01:56:11,841 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:12,002 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:12,002 (beam_search:476) INFO: -13.25 * 1.0 = -13.25 for ctc +2024-01-17 01:56:12,002 (beam_search:479) INFO: total log probability: -13.25 +2024-01-17 01:56:12,002 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:56:12,002 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:12,002 (beam_search:483) INFO: best hypo: EIDERNOMAHLNOZUNGEDESDIVOLEBANDREITE + +2024-01-17 01:56:12,003 (asr_inference:494) INFO: speech length: 80965 +2024-01-17 01:56:12,013 (beam_search:428) INFO: decoder input length: 124 +2024-01-17 01:56:12,013 (beam_search:429) INFO: max output length: 124 +2024-01-17 01:56:12,013 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:12,218 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:12,218 (beam_search:476) INFO: -15.57 * 1.0 = -15.57 for ctc +2024-01-17 01:56:12,218 (beam_search:479) INFO: total log probability: -15.57 +2024-01-17 01:56:12,218 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:56:12,218 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:12,218 (beam_search:483) INFO: best hypo: ABERWIEISTDIESESPROBLEMIMGLOBAHLNMATSTABZOLESEN + +2024-01-17 01:56:12,219 (asr_inference:494) INFO: speech length: 54000 +2024-01-17 01:56:12,228 (beam_search:428) INFO: decoder input length: 82 +2024-01-17 01:56:12,228 (beam_search:429) INFO: max output length: 82 +2024-01-17 01:56:12,228 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:12,336 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:12,336 (beam_search:476) INFO: -15.33 * 1.0 = -15.33 for ctc +2024-01-17 01:56:12,336 (beam_search:479) INFO: total log probability: -15.33 +2024-01-17 01:56:12,336 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:56:12,336 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:12,336 (beam_search:483) INFO: best hypo: DESEIGENWERPLOKEHETPODENZIERMERLESE + +2024-01-17 01:56:12,338 (asr_inference:494) INFO: speech length: 84000 +2024-01-17 01:56:12,348 (beam_search:428) INFO: decoder input length: 129 +2024-01-17 01:56:12,348 (beam_search:429) INFO: max output length: 129 +2024-01-17 01:56:12,348 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:12,512 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:12,513 (beam_search:476) INFO: -22.60 * 1.0 = -22.60 for ctc +2024-01-17 01:56:12,513 (beam_search:479) INFO: total log probability: -22.60 +2024-01-17 01:56:12,513 (beam_search:480) INFO: normalized log probability: -0.57 +2024-01-17 01:56:12,513 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:12,513 (beam_search:483) INFO: best hypo: DASFRMMDEVERBLOKSIENOCHIELEBTEROS + +2024-01-17 01:56:12,514 (asr_inference:494) INFO: speech length: 48688 +2024-01-17 01:56:12,522 (beam_search:428) INFO: decoder input length: 74 +2024-01-17 01:56:12,522 (beam_search:429) INFO: max output length: 74 +2024-01-17 01:56:12,522 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:12,609 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:12,609 (beam_search:476) INFO: -6.84 * 1.0 = -6.84 for ctc +2024-01-17 01:56:12,609 (beam_search:479) INFO: total log probability: -6.84 +2024-01-17 01:56:12,609 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:56:12,609 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:12,610 (beam_search:483) INFO: best hypo: EINEUEBESTIMUNGISTELASENBORDEN + +2024-01-17 01:56:12,611 (asr_inference:494) INFO: speech length: 81408 +2024-01-17 01:56:12,621 (beam_search:428) INFO: decoder input length: 125 +2024-01-17 01:56:12,621 (beam_search:429) INFO: max output length: 125 +2024-01-17 01:56:12,621 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:12,753 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:12,753 (beam_search:476) INFO: -10.35 * 1.0 = -10.35 for ctc +2024-01-17 01:56:12,753 (beam_search:479) INFO: total log probability: -10.35 +2024-01-17 01:56:12,753 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:56:12,753 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:12,754 (beam_search:483) INFO: best hypo: DARRAUFSTHENGEWIESENWORDENT + +2024-01-17 01:56:12,755 (asr_inference:494) INFO: speech length: 57611 +2024-01-17 01:56:12,764 (beam_search:428) INFO: decoder input length: 88 +2024-01-17 01:56:12,764 (beam_search:429) INFO: max output length: 88 +2024-01-17 01:56:12,764 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:12,869 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:12,869 (beam_search:476) INFO: -7.31 * 1.0 = -7.31 for ctc +2024-01-17 01:56:12,869 (beam_search:479) INFO: total log probability: -7.31 +2024-01-17 01:56:12,869 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:56:12,869 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:12,870 (beam_search:483) INFO: best hypo: DIEBEFÖRKRUNGISTGANZMASIEVERARMT + +2024-01-17 01:56:12,871 (asr_inference:494) INFO: speech length: 55387 +2024-01-17 01:56:12,879 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:56:12,879 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:56:12,879 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:12,983 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:12,983 (beam_search:476) INFO: -13.54 * 1.0 = -13.54 for ctc +2024-01-17 01:56:12,983 (beam_search:479) INFO: total log probability: -13.54 +2024-01-17 01:56:12,984 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:56:12,984 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:12,984 (beam_search:483) INFO: best hypo: DIEWERDENDSGANZPESTLMNICHTMACHEN + +2024-01-17 01:56:12,985 (asr_inference:494) INFO: speech length: 90000 +2024-01-17 01:56:12,996 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 01:56:12,996 (beam_search:429) INFO: max output length: 138 +2024-01-17 01:56:12,996 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:13,242 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:13,242 (beam_search:476) INFO: -19.04 * 1.0 = -19.04 for ctc +2024-01-17 01:56:13,242 (beam_search:479) INFO: total log probability: -19.04 +2024-01-17 01:56:13,242 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:56:13,242 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:13,242 (beam_search:483) INFO: best hypo: DIEDARTENMÄNGEDIGESENDETWIERDTISTERHEBLICHGERINER + +2024-01-17 01:56:13,244 (asr_inference:494) INFO: speech length: 46528 +2024-01-17 01:56:13,252 (beam_search:428) INFO: decoder input length: 70 +2024-01-17 01:56:13,253 (beam_search:429) INFO: max output length: 70 +2024-01-17 01:56:13,253 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:13,324 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:13,324 (beam_search:476) INFO: -11.32 * 1.0 = -11.32 for ctc +2024-01-17 01:56:13,324 (beam_search:479) INFO: total log probability: -11.32 +2024-01-17 01:56:13,324 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:56:13,324 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:13,324 (beam_search:483) INFO: best hypo: DASEGEBNISISTVEFELSHWORDEN + +2024-01-17 01:56:13,326 (asr_inference:494) INFO: speech length: 105131 +2024-01-17 01:56:13,338 (beam_search:428) INFO: decoder input length: 162 +2024-01-17 01:56:13,338 (beam_search:429) INFO: max output length: 162 +2024-01-17 01:56:13,338 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:13,652 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:13,652 (beam_search:476) INFO: -17.77 * 1.0 = -17.77 for ctc +2024-01-17 01:56:13,652 (beam_search:479) INFO: total log probability: -17.77 +2024-01-17 01:56:13,652 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:56:13,652 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:13,653 (beam_search:483) INFO: best hypo: EINEBESCRENKUNGTRITESTBEIBESONDERSINTENSIEVERNUOZUNGAUF + +2024-01-17 01:56:13,654 (asr_inference:494) INFO: speech length: 116000 +2024-01-17 01:56:13,667 (beam_search:428) INFO: decoder input length: 179 +2024-01-17 01:56:13,667 (beam_search:429) INFO: max output length: 179 +2024-01-17 01:56:13,667 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:14,080 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:14,080 (beam_search:476) INFO: -22.72 * 1.0 = -22.72 for ctc +2024-01-17 01:56:14,080 (beam_search:479) INFO: total log probability: -22.72 +2024-01-17 01:56:14,080 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:56:14,080 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:14,081 (beam_search:483) INFO: best hypo: DERENDBENOTZEHATEINEHÖHRREGESCHWINDICKEITFÜRDENDAUNLOTDZUFERFÜGUNG + +2024-01-17 01:56:14,082 (asr_inference:494) INFO: speech length: 59125 +2024-01-17 01:56:14,091 (beam_search:428) INFO: decoder input length: 90 +2024-01-17 01:56:14,091 (beam_search:429) INFO: max output length: 90 +2024-01-17 01:56:14,091 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:14,217 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:14,217 (beam_search:476) INFO: -14.03 * 1.0 = -14.03 for ctc +2024-01-17 01:56:14,217 (beam_search:479) INFO: total log probability: -14.03 +2024-01-17 01:56:14,217 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:56:14,217 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:14,217 (beam_search:483) INFO: best hypo: DERSEMANTISCHETEILEWURDESKEBTISPETRACHTET + +2024-01-17 01:56:14,218 (asr_inference:494) INFO: speech length: 63541 +2024-01-17 01:56:14,228 (beam_search:428) INFO: decoder input length: 97 +2024-01-17 01:56:14,228 (beam_search:429) INFO: max output length: 97 +2024-01-17 01:56:14,228 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:14,336 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:14,336 (beam_search:476) INFO: -11.86 * 1.0 = -11.86 for ctc +2024-01-17 01:56:14,336 (beam_search:479) INFO: total log probability: -11.86 +2024-01-17 01:56:14,336 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:56:14,336 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:14,336 (beam_search:483) INFO: best hypo: DOTWIRTSEHRIELMHRGELTVERDIEND + +2024-01-17 01:56:14,337 (asr_inference:494) INFO: speech length: 122573 +2024-01-17 01:56:14,350 (beam_search:428) INFO: decoder input length: 189 +2024-01-17 01:56:14,350 (beam_search:429) INFO: max output length: 189 +2024-01-17 01:56:14,350 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:14,680 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:14,680 (beam_search:476) INFO: -20.13 * 1.0 = -20.13 for ctc +2024-01-17 01:56:14,680 (beam_search:479) INFO: total log probability: -20.13 +2024-01-17 01:56:14,680 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:56:14,680 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:14,681 (beam_search:483) INFO: best hypo: VÜERSTENISVÜRDASVERANWORDLIKEITZGIFÜLEINERMUTER + +2024-01-17 01:56:14,682 (asr_inference:494) INFO: speech length: 84480 +2024-01-17 01:56:14,693 (beam_search:428) INFO: decoder input length: 129 +2024-01-17 01:56:14,693 (beam_search:429) INFO: max output length: 129 +2024-01-17 01:56:14,693 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:14,823 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:14,823 (beam_search:476) INFO: -13.67 * 1.0 = -13.67 for ctc +2024-01-17 01:56:14,823 (beam_search:479) INFO: total log probability: -13.67 +2024-01-17 01:56:14,823 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:56:14,823 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:14,823 (beam_search:483) INFO: best hypo: DASWERTÜRDIMEIENGEMACHTE + +2024-01-17 01:56:14,824 (asr_inference:494) INFO: speech length: 89600 +2024-01-17 01:56:14,835 (beam_search:428) INFO: decoder input length: 137 +2024-01-17 01:56:14,835 (beam_search:429) INFO: max output length: 137 +2024-01-17 01:56:14,835 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:15,047 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:15,047 (beam_search:476) INFO: -18.84 * 1.0 = -18.84 for ctc +2024-01-17 01:56:15,048 (beam_search:479) INFO: total log probability: -18.84 +2024-01-17 01:56:15,048 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:56:15,048 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:15,048 (beam_search:483) INFO: best hypo: SIKANEINEGANSKLAREKAUFMPFIÄLUNGAUSSPRÄCHEN + +2024-01-17 01:56:15,049 (asr_inference:494) INFO: speech length: 82432 +2024-01-17 01:56:15,059 (beam_search:428) INFO: decoder input length: 126 +2024-01-17 01:56:15,060 (beam_search:429) INFO: max output length: 126 +2024-01-17 01:56:15,060 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:15,213 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:15,213 (beam_search:476) INFO: -10.78 * 1.0 = -10.78 for ctc +2024-01-17 01:56:15,213 (beam_search:479) INFO: total log probability: -10.78 +2024-01-17 01:56:15,213 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:56:15,213 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:15,213 (beam_search:483) INFO: best hypo: ZALREICHEPOTESTEWERDENATIKULIERD + +2024-01-17 01:56:15,214 (asr_inference:494) INFO: speech length: 79531 +2024-01-17 01:56:15,224 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 01:56:15,224 (beam_search:429) INFO: max output length: 122 +2024-01-17 01:56:15,224 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:15,360 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:15,360 (beam_search:476) INFO: -13.06 * 1.0 = -13.06 for ctc +2024-01-17 01:56:15,360 (beam_search:479) INFO: total log probability: -13.06 +2024-01-17 01:56:15,360 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:56:15,360 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:15,360 (beam_search:483) INFO: best hypo: DEDEUCHFÜHRUNGWARNICHTSECHAT + +2024-01-17 01:56:15,362 (asr_inference:494) INFO: speech length: 57280 +2024-01-17 01:56:15,371 (beam_search:428) INFO: decoder input length: 87 +2024-01-17 01:56:15,371 (beam_search:429) INFO: max output length: 87 +2024-01-17 01:56:15,371 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:15,485 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:15,485 (beam_search:476) INFO: -13.51 * 1.0 = -13.51 for ctc +2024-01-17 01:56:15,485 (beam_search:479) INFO: total log probability: -13.51 +2024-01-17 01:56:15,485 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:56:15,486 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:15,486 (beam_search:483) INFO: best hypo: DIEWEHRUNENHATÜBEAUPTKEINEDICKUNGN + +2024-01-17 01:56:15,487 (asr_inference:494) INFO: speech length: 109356 +2024-01-17 01:56:15,499 (beam_search:428) INFO: decoder input length: 168 +2024-01-17 01:56:15,499 (beam_search:429) INFO: max output length: 168 +2024-01-17 01:56:15,499 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:15,853 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:15,853 (beam_search:476) INFO: -20.43 * 1.0 = -20.43 for ctc +2024-01-17 01:56:15,853 (beam_search:479) INFO: total log probability: -20.43 +2024-01-17 01:56:15,853 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:56:15,853 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:15,854 (beam_search:483) INFO: best hypo: OBÜBIGENSSECKERSTOARAFDEREINENDURCHAUSTIELBEWUSTENLEBEMSKLOGEN + +2024-01-17 01:56:15,855 (asr_inference:494) INFO: speech length: 92000 +2024-01-17 01:56:15,866 (beam_search:428) INFO: decoder input length: 141 +2024-01-17 01:56:15,866 (beam_search:429) INFO: max output length: 141 +2024-01-17 01:56:15,866 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:16,100 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:16,100 (beam_search:476) INFO: -17.17 * 1.0 = -17.17 for ctc +2024-01-17 01:56:16,100 (beam_search:479) INFO: total log probability: -17.17 +2024-01-17 01:56:16,100 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:56:16,100 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:16,100 (beam_search:483) INFO: best hypo: MANRNSPRECHTENDIESEMFEILVONKONTRERHIERUNGSTWANG + +2024-01-17 01:56:16,102 (asr_inference:494) INFO: speech length: 74240 +2024-01-17 01:56:16,111 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 01:56:16,111 (beam_search:429) INFO: max output length: 113 +2024-01-17 01:56:16,111 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:16,233 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:16,233 (beam_search:476) INFO: -15.51 * 1.0 = -15.51 for ctc +2024-01-17 01:56:16,233 (beam_search:479) INFO: total log probability: -15.51 +2024-01-17 01:56:16,233 (beam_search:480) INFO: normalized log probability: -0.46 +2024-01-17 01:56:16,233 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:16,233 (beam_search:483) INFO: best hypo: GLOUBEGAUNSCHLDNARSENZICEINIG + +2024-01-17 01:56:16,234 (asr_inference:494) INFO: speech length: 50123 +2024-01-17 01:56:16,242 (beam_search:428) INFO: decoder input length: 76 +2024-01-17 01:56:16,242 (beam_search:429) INFO: max output length: 76 +2024-01-17 01:56:16,242 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:16,331 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:16,331 (beam_search:476) INFO: -8.21 * 1.0 = -8.21 for ctc +2024-01-17 01:56:16,331 (beam_search:479) INFO: total log probability: -8.21 +2024-01-17 01:56:16,331 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:56:16,331 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:16,332 (beam_search:483) INFO: best hypo: DASWIRTNICHTMEHRLANGESOBLEIBEN + +2024-01-17 01:56:16,333 (asr_inference:494) INFO: speech length: 72000 +2024-01-17 01:56:16,343 (beam_search:428) INFO: decoder input length: 110 +2024-01-17 01:56:16,343 (beam_search:429) INFO: max output length: 110 +2024-01-17 01:56:16,343 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:16,517 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:16,517 (beam_search:476) INFO: -18.55 * 1.0 = -18.55 for ctc +2024-01-17 01:56:16,517 (beam_search:479) INFO: total log probability: -18.55 +2024-01-17 01:56:16,517 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:56:16,517 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:16,517 (beam_search:483) INFO: best hypo: ESABUNTERSCHIETLICHTRIEREVORMENDERFRAHETRAVER + +2024-01-17 01:56:16,519 (asr_inference:494) INFO: speech length: 80000 +2024-01-17 01:56:16,529 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 01:56:16,529 (beam_search:429) INFO: max output length: 122 +2024-01-17 01:56:16,529 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:16,641 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:16,641 (beam_search:476) INFO: -11.76 * 1.0 = -11.76 for ctc +2024-01-17 01:56:16,641 (beam_search:479) INFO: total log probability: -11.76 +2024-01-17 01:56:16,641 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-17 01:56:16,641 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:16,642 (beam_search:483) INFO: best hypo: HNDESEIMEINEFREIESOFTW + +2024-01-17 01:56:16,643 (asr_inference:494) INFO: speech length: 90059 +2024-01-17 01:56:16,654 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 01:56:16,654 (beam_search:429) INFO: max output length: 138 +2024-01-17 01:56:16,654 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:16,935 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:16,935 (beam_search:476) INFO: -19.07 * 1.0 = -19.07 for ctc +2024-01-17 01:56:16,935 (beam_search:479) INFO: total log probability: -19.07 +2024-01-17 01:56:16,935 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:56:16,935 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:16,936 (beam_search:483) INFO: best hypo: OGANSTREITVERFAHNKÖNNAUCHAUSHLISLICGAUFELNDESEBENSTATFINTEN + +2024-01-17 01:56:16,937 (asr_inference:494) INFO: speech length: 77083 +2024-01-17 01:56:16,947 (beam_search:428) INFO: decoder input length: 118 +2024-01-17 01:56:16,947 (beam_search:429) INFO: max output length: 118 +2024-01-17 01:56:16,947 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:17,107 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:17,107 (beam_search:476) INFO: -13.42 * 1.0 = -13.42 for ctc +2024-01-17 01:56:17,107 (beam_search:479) INFO: total log probability: -13.42 +2024-01-17 01:56:17,107 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:56:17,107 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:17,107 (beam_search:483) INFO: best hypo: WEGENNOZSLOSAUFGEWNNETEROLEABSEITKANN + +2024-01-17 01:56:17,108 (asr_inference:494) INFO: speech length: 59285 +2024-01-17 01:56:17,117 (beam_search:428) INFO: decoder input length: 90 +2024-01-17 01:56:17,117 (beam_search:429) INFO: max output length: 90 +2024-01-17 01:56:17,117 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:17,236 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:17,236 (beam_search:476) INFO: -12.74 * 1.0 = -12.74 for ctc +2024-01-17 01:56:17,236 (beam_search:479) INFO: total log probability: -12.74 +2024-01-17 01:56:17,236 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:56:17,236 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:17,236 (beam_search:483) INFO: best hypo: DASWIRTNICHTIMARPERFEKTVUNKTZINIERN + +2024-01-17 01:56:17,237 (asr_inference:494) INFO: speech length: 55973 +2024-01-17 01:56:17,246 (beam_search:428) INFO: decoder input length: 85 +2024-01-17 01:56:17,246 (beam_search:429) INFO: max output length: 85 +2024-01-17 01:56:17,246 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:17,356 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:17,356 (beam_search:476) INFO: -11.27 * 1.0 = -11.27 for ctc +2024-01-17 01:56:17,356 (beam_search:479) INFO: total log probability: -11.27 +2024-01-17 01:56:17,356 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:56:17,356 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:17,356 (beam_search:483) INFO: best hypo: MANMUSICHANGERSCHIERENDESWAKSTUMSWEGEN + +2024-01-17 01:56:17,358 (asr_inference:494) INFO: speech length: 75606 +2024-01-17 01:56:17,368 (beam_search:428) INFO: decoder input length: 116 +2024-01-17 01:56:17,368 (beam_search:429) INFO: max output length: 116 +2024-01-17 01:56:17,368 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:17,526 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:17,527 (beam_search:476) INFO: -9.11 * 1.0 = -9.11 for ctc +2024-01-17 01:56:17,527 (beam_search:479) INFO: total log probability: -9.11 +2024-01-17 01:56:17,527 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:56:17,527 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:17,527 (beam_search:483) INFO: best hypo: WELLICHEWEGESOLLENEINGESCHLAGENWERDEN + +2024-01-17 01:56:17,528 (asr_inference:494) INFO: speech length: 79019 +2024-01-17 01:56:17,538 (beam_search:428) INFO: decoder input length: 121 +2024-01-17 01:56:17,538 (beam_search:429) INFO: max output length: 121 +2024-01-17 01:56:17,538 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:17,651 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:17,651 (beam_search:476) INFO: -12.08 * 1.0 = -12.08 for ctc +2024-01-17 01:56:17,651 (beam_search:479) INFO: total log probability: -12.08 +2024-01-17 01:56:17,651 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-17 01:56:17,651 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:17,651 (beam_search:483) INFO: best hypo: DASSWIRDTENDIEPEISEGENT + +2024-01-17 01:56:17,652 (asr_inference:494) INFO: speech length: 66485 +2024-01-17 01:56:17,662 (beam_search:428) INFO: decoder input length: 101 +2024-01-17 01:56:17,662 (beam_search:429) INFO: max output length: 101 +2024-01-17 01:56:17,662 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:17,768 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:17,768 (beam_search:476) INFO: -8.06 * 1.0 = -8.06 for ctc +2024-01-17 01:56:17,768 (beam_search:479) INFO: total log probability: -8.06 +2024-01-17 01:56:17,768 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:56:17,768 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:17,769 (beam_search:483) INFO: best hypo: DIEÜBENAMEERFOLUKTEWIRTLICH + +2024-01-17 01:56:17,770 (asr_inference:494) INFO: speech length: 80896 +2024-01-17 01:56:17,780 (beam_search:428) INFO: decoder input length: 124 +2024-01-17 01:56:17,780 (beam_search:429) INFO: max output length: 124 +2024-01-17 01:56:17,780 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:17,942 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:17,942 (beam_search:476) INFO: -11.60 * 1.0 = -11.60 for ctc +2024-01-17 01:56:17,942 (beam_search:479) INFO: total log probability: -11.60 +2024-01-17 01:56:17,942 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:56:17,942 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:17,942 (beam_search:483) INFO: best hypo: DEENTWEKLUNGSTWEITVORANGESCHRETEN + +2024-01-17 01:56:17,944 (asr_inference:494) INFO: speech length: 93696 +2024-01-17 01:56:17,955 (beam_search:428) INFO: decoder input length: 144 +2024-01-17 01:56:17,955 (beam_search:429) INFO: max output length: 144 +2024-01-17 01:56:17,955 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:18,185 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:18,185 (beam_search:476) INFO: -17.11 * 1.0 = -17.11 for ctc +2024-01-17 01:56:18,185 (beam_search:479) INFO: total log probability: -17.11 +2024-01-17 01:56:18,185 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:56:18,185 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:18,185 (beam_search:483) INFO: best hypo: DIESMTOMETRETENDANSCHONACHWANGENSTONDTENAUF + +2024-01-17 01:56:18,187 (asr_inference:494) INFO: speech length: 65493 +2024-01-17 01:56:18,196 (beam_search:428) INFO: decoder input length: 100 +2024-01-17 01:56:18,196 (beam_search:429) INFO: max output length: 100 +2024-01-17 01:56:18,196 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:18,314 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:18,314 (beam_search:476) INFO: -9.91 * 1.0 = -9.91 for ctc +2024-01-17 01:56:18,314 (beam_search:479) INFO: total log probability: -9.91 +2024-01-17 01:56:18,314 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:56:18,314 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:18,314 (beam_search:483) INFO: best hypo: SIBTEINIGROEWÄLLEVONPROZESEN + +2024-01-17 01:56:18,315 (asr_inference:494) INFO: speech length: 83286 +2024-01-17 01:56:18,326 (beam_search:428) INFO: decoder input length: 128 +2024-01-17 01:56:18,326 (beam_search:429) INFO: max output length: 128 +2024-01-17 01:56:18,326 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:18,465 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:18,465 (beam_search:476) INFO: -11.08 * 1.0 = -11.08 for ctc +2024-01-17 01:56:18,465 (beam_search:479) INFO: total log probability: -11.08 +2024-01-17 01:56:18,465 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:56:18,465 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:18,465 (beam_search:483) INFO: best hypo: SSTBEREITZMEINZWEITERAUTOMAD + +2024-01-17 01:56:18,466 (asr_inference:494) INFO: speech length: 151359 +2024-01-17 01:56:18,481 (beam_search:428) INFO: decoder input length: 234 +2024-01-17 01:56:18,481 (beam_search:429) INFO: max output length: 234 +2024-01-17 01:56:18,481 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:19,204 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:19,205 (beam_search:476) INFO: -38.61 * 1.0 = -38.61 for ctc +2024-01-17 01:56:19,205 (beam_search:479) INFO: total log probability: -38.61 +2024-01-17 01:56:19,205 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:56:19,205 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:19,205 (beam_search:483) INFO: best hypo: MPLMENTIERUNGVONHÖRINSTANDEATSZUSCUTZSPESONLICJERDARTENEBENFEISGENNERELUNSREUTEZUSAMENAREITELEICHTER + +2024-01-17 01:56:19,207 (asr_inference:494) INFO: speech length: 78400 +2024-01-17 01:56:19,217 (beam_search:428) INFO: decoder input length: 120 +2024-01-17 01:56:19,217 (beam_search:429) INFO: max output length: 120 +2024-01-17 01:56:19,217 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:19,482 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:19,482 (beam_search:476) INFO: -27.06 * 1.0 = -27.06 for ctc +2024-01-17 01:56:19,482 (beam_search:479) INFO: total log probability: -27.06 +2024-01-17 01:56:19,482 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:56:19,482 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:19,483 (beam_search:483) INFO: best hypo: ERAMTEABENDERSLIMSTEVERINDERDARMELEBENGEREDTEINSELBEVERLÄTSTWURDNIGKLAB + +2024-01-17 01:56:19,484 (asr_inference:494) INFO: speech length: 41920 +2024-01-17 01:56:19,492 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:56:19,492 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:56:19,492 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:19,565 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:19,565 (beam_search:476) INFO: -15.80 * 1.0 = -15.80 for ctc +2024-01-17 01:56:19,566 (beam_search:479) INFO: total log probability: -15.80 +2024-01-17 01:56:19,566 (beam_search:480) INFO: normalized log probability: -0.43 +2024-01-17 01:56:19,566 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:19,566 (beam_search:483) INFO: best hypo: ICMÖBRIÜEDASDEROMISSEINITIES + +2024-01-17 01:56:19,567 (asr_inference:494) INFO: speech length: 55680 +2024-01-17 01:56:19,576 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:56:19,576 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:56:19,576 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:19,711 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:19,711 (beam_search:476) INFO: -26.29 * 1.0 = -26.29 for ctc +2024-01-17 01:56:19,711 (beam_search:479) INFO: total log probability: -26.29 +2024-01-17 01:56:19,711 (beam_search:480) INFO: normalized log probability: -0.48 +2024-01-17 01:56:19,711 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:19,711 (beam_search:483) INFO: best hypo: MITLETUNDDECOFVEDASWERNECXHSLIEARÜBEDESWANZE + +2024-01-17 01:56:19,712 (asr_inference:494) INFO: speech length: 106551 +2024-01-17 01:56:19,724 (beam_search:428) INFO: decoder input length: 164 +2024-01-17 01:56:19,724 (beam_search:429) INFO: max output length: 164 +2024-01-17 01:56:19,724 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:20,205 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:20,205 (beam_search:476) INFO: -44.60 * 1.0 = -44.60 for ctc +2024-01-17 01:56:20,205 (beam_search:479) INFO: total log probability: -44.60 +2024-01-17 01:56:20,205 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:56:20,205 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:20,206 (beam_search:483) INFO: best hypo: DESDAFNICHIVERSEHENWERDNDASIMERHINWERBEFÜMTZIGTPUOZENDDERBEFÖLKÖNGEADERBESCHENUNUNNIELENLICHENGRAMLIEB + +2024-01-17 01:56:20,207 (asr_inference:494) INFO: speech length: 108799 +2024-01-17 01:56:20,219 (beam_search:428) INFO: decoder input length: 167 +2024-01-17 01:56:20,219 (beam_search:429) INFO: max output length: 167 +2024-01-17 01:56:20,219 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:20,690 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:20,690 (beam_search:476) INFO: -36.35 * 1.0 = -36.35 for ctc +2024-01-17 01:56:20,690 (beam_search:479) INFO: total log probability: -36.35 +2024-01-17 01:56:20,690 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:56:20,690 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:20,690 (beam_search:483) INFO: best hypo: SODASDERBÜRGERSCHELLONEAUSKUNFBEKOMMTOPSEINEBESCHÄRDEÜBEHABTANGENOMENWIRDOPSIEBERECHTICHTIST + +2024-01-17 01:56:20,692 (asr_inference:494) INFO: speech length: 120639 +2024-01-17 01:56:20,705 (beam_search:428) INFO: decoder input length: 186 +2024-01-17 01:56:20,705 (beam_search:429) INFO: max output length: 186 +2024-01-17 01:56:20,705 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:21,177 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:21,177 (beam_search:476) INFO: -32.55 * 1.0 = -32.55 for ctc +2024-01-17 01:56:21,177 (beam_search:479) INFO: total log probability: -32.55 +2024-01-17 01:56:21,177 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:56:21,177 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:21,178 (beam_search:483) INFO: best hypo: NRIESETTUNZERERBITIONGENISTNICHTVONEUTENABERSIABOEKONTINUNIERLICHESFEINTIONENG + +2024-01-17 01:56:21,179 (asr_inference:494) INFO: speech length: 51193 +2024-01-17 01:56:21,188 (beam_search:428) INFO: decoder input length: 77 +2024-01-17 01:56:21,188 (beam_search:429) INFO: max output length: 77 +2024-01-17 01:56:21,188 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:21,305 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:21,305 (beam_search:476) INFO: -18.93 * 1.0 = -18.93 for ctc +2024-01-17 01:56:21,305 (beam_search:479) INFO: total log probability: -18.93 +2024-01-17 01:56:21,305 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:56:21,305 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:21,305 (beam_search:483) INFO: best hypo: LDIETGANSTOLSGESAGJAEDBESCHEFTIUNGSTEIGKTDJERAN + +2024-01-17 01:56:21,306 (asr_inference:494) INFO: speech length: 159338 +2024-01-17 01:56:21,322 (beam_search:428) INFO: decoder input length: 246 +2024-01-17 01:56:21,322 (beam_search:429) INFO: max output length: 246 +2024-01-17 01:56:21,322 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:22,195 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:22,195 (beam_search:476) INFO: -58.89 * 1.0 = -58.89 for ctc +2024-01-17 01:56:22,195 (beam_search:479) INFO: total log probability: -58.89 +2024-01-17 01:56:22,195 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:56:22,195 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:22,195 (beam_search:483) INFO: best hypo: WIDASSFÜRUNISTERAUHONDERERVERTETWIEREXSPORTIEANZOUVEELLZOBILICGALWVERIMPOTIERENZSUWEDIGWIERFARSCHENKENWOLSTAT + +2024-01-17 01:56:22,197 (asr_inference:494) INFO: speech length: 59834 +2024-01-17 01:56:22,206 (beam_search:428) INFO: decoder input length: 91 +2024-01-17 01:56:22,206 (beam_search:429) INFO: max output length: 91 +2024-01-17 01:56:22,206 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:22,361 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:22,361 (beam_search:476) INFO: -20.45 * 1.0 = -20.45 for ctc +2024-01-17 01:56:22,361 (beam_search:479) INFO: total log probability: -20.45 +2024-01-17 01:56:22,361 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:56:22,361 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:22,362 (beam_search:483) INFO: best hypo: SIEHEUDERARBENIEANWESENZINDISENPROSITIEVESSIGNALE + +2024-01-17 01:56:22,363 (asr_inference:494) INFO: speech length: 161280 +2024-01-17 01:56:22,379 (beam_search:428) INFO: decoder input length: 249 +2024-01-17 01:56:22,379 (beam_search:429) INFO: max output length: 249 +2024-01-17 01:56:22,379 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:23,243 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:23,243 (beam_search:476) INFO: -36.33 * 1.0 = -36.33 for ctc +2024-01-17 01:56:23,243 (beam_search:479) INFO: total log probability: -36.33 +2024-01-17 01:56:23,243 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:56:23,243 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:23,244 (beam_search:483) INFO: best hypo: NEUNZSIGHPROZENTALLAAROPÄSCHENFILMEDIEAUSSEHALTIRESHEIMATLANDESGEZEICHTWERDENSINDVORMEDIERPROGRAMGEFERDERTWURDEN + +2024-01-17 01:56:23,245 (asr_inference:494) INFO: speech length: 92800 +2024-01-17 01:56:23,257 (beam_search:428) INFO: decoder input length: 142 +2024-01-17 01:56:23,257 (beam_search:429) INFO: max output length: 142 +2024-01-17 01:56:23,257 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:23,568 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:23,568 (beam_search:476) INFO: -23.35 * 1.0 = -23.35 for ctc +2024-01-17 01:56:23,568 (beam_search:479) INFO: total log probability: -23.35 +2024-01-17 01:56:23,568 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:56:23,568 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:23,568 (beam_search:483) INFO: best hypo: BIESOKAICHEBERGEBPLISSTERALEAUSCHUSABPTEMUNGINDIESEFOREMNICHZUSTEM + +2024-01-17 01:56:23,570 (asr_inference:494) INFO: speech length: 95994 +2024-01-17 01:56:23,581 (beam_search:428) INFO: decoder input length: 147 +2024-01-17 01:56:23,581 (beam_search:429) INFO: max output length: 147 +2024-01-17 01:56:23,581 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:23,967 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:23,967 (beam_search:476) INFO: -26.72 * 1.0 = -26.72 for ctc +2024-01-17 01:56:23,967 (beam_search:479) INFO: total log probability: -26.72 +2024-01-17 01:56:23,967 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:56:23,967 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:23,968 (beam_search:483) INFO: best hypo: BERBORTENVERINDERNDASICHEHINDERIENGEISIGENEIGENTUMDIEAUSGUMSFLICHTEVERSTECKENKONTE + +2024-01-17 01:56:23,969 (asr_inference:494) INFO: speech length: 105253 +2024-01-17 01:56:23,981 (beam_search:428) INFO: decoder input length: 162 +2024-01-17 01:56:23,981 (beam_search:429) INFO: max output length: 162 +2024-01-17 01:56:23,981 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:24,417 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:24,417 (beam_search:476) INFO: -32.22 * 1.0 = -32.22 for ctc +2024-01-17 01:56:24,417 (beam_search:479) INFO: total log probability: -32.22 +2024-01-17 01:56:24,417 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:56:24,417 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:24,418 (beam_search:483) INFO: best hypo: ISGIBDETZIMZUSAMANGERVERSTERGTENZUSAMMAUBEITEINENERSTENGANGVONEINIGENMITIELSTARTENNAC + +2024-01-17 01:56:24,419 (asr_inference:494) INFO: speech length: 223985 +2024-01-17 01:56:24,440 (beam_search:428) INFO: decoder input length: 347 +2024-01-17 01:56:24,440 (beam_search:429) INFO: max output length: 347 +2024-01-17 01:56:24,440 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:26,253 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:26,253 (beam_search:476) INFO: -65.05 * 1.0 = -65.05 for ctc +2024-01-17 01:56:26,253 (beam_search:479) INFO: total log probability: -65.05 +2024-01-17 01:56:26,253 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:56:26,253 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:26,254 (beam_search:483) INFO: best hypo: WASTIRENZYÜBERCHREITENDEZUSAHMENABEIANBELANTUNDWASTIEERVERBREITUNGINTRIGLÄNDERBETRIFTUNDTIEMECHLICHEINBEISCSPIENENENDESENERFOLKSBEISPILVÜMICHISTUNDZWARELSLMDOKGMILIERNEREDAS + +2024-01-17 01:56:26,256 (asr_inference:494) INFO: speech length: 128959 +2024-01-17 01:56:26,270 (beam_search:428) INFO: decoder input length: 199 +2024-01-17 01:56:26,270 (beam_search:429) INFO: max output length: 199 +2024-01-17 01:56:26,270 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:26,917 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:26,917 (beam_search:476) INFO: -39.79 * 1.0 = -39.79 for ctc +2024-01-17 01:56:26,917 (beam_search:479) INFO: total log probability: -39.79 +2024-01-17 01:56:26,917 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:56:26,917 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:26,918 (beam_search:483) INFO: best hypo: DASNICHTENURINPRTUGAHLDRGRICHENLANDSNERNAURENSOVERMEINTLICHGEICHENMITWIESTATENVIEDEUTSHLANODERRUSPETANIERN + +2024-01-17 01:56:26,919 (asr_inference:494) INFO: speech length: 25279 +2024-01-17 01:56:26,926 (beam_search:428) INFO: decoder input length: 37 +2024-01-17 01:56:26,926 (beam_search:429) INFO: max output length: 37 +2024-01-17 01:56:26,926 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:26,960 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:26,960 (beam_search:476) INFO: -6.50 * 1.0 = -6.50 for ctc +2024-01-17 01:56:26,960 (beam_search:479) INFO: total log probability: -6.50 +2024-01-17 01:56:26,960 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:56:26,960 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:26,960 (beam_search:483) INFO: best hypo: TVERAUSWENISVERBEIDA + +2024-01-17 01:56:26,961 (asr_inference:494) INFO: speech length: 112000 +2024-01-17 01:56:26,973 (beam_search:428) INFO: decoder input length: 172 +2024-01-17 01:56:26,973 (beam_search:429) INFO: max output length: 172 +2024-01-17 01:56:26,973 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:27,439 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:27,440 (beam_search:476) INFO: -36.22 * 1.0 = -36.22 for ctc +2024-01-17 01:56:27,440 (beam_search:479) INFO: total log probability: -36.22 +2024-01-17 01:56:27,440 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:56:27,440 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:27,440 (beam_search:483) INFO: best hypo: EALLELFLIEGALMIKDIEDERDIESESHAUSESVARSCHENIEDORDTLICHOLFIGEARALSDEIEUDURSCHNITZBÜRGERT + +2024-01-17 01:56:27,441 (asr_inference:494) INFO: speech length: 72948 +2024-01-17 01:56:27,451 (beam_search:428) INFO: decoder input length: 111 +2024-01-17 01:56:27,451 (beam_search:429) INFO: max output length: 111 +2024-01-17 01:56:27,451 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:27,678 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:27,678 (beam_search:476) INFO: -22.95 * 1.0 = -22.95 for ctc +2024-01-17 01:56:27,678 (beam_search:479) INFO: total log probability: -22.95 +2024-01-17 01:56:27,678 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:56:27,678 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:27,679 (beam_search:483) INFO: best hypo: ENSICHERDASERERBEDOEUTTUNGINAHRSFUKUMFTUOGERNOCHTZUNEHMWIERT + +2024-01-17 01:56:27,680 (asr_inference:494) INFO: speech length: 184000 +2024-01-17 01:56:27,697 (beam_search:428) INFO: decoder input length: 285 +2024-01-17 01:56:27,697 (beam_search:429) INFO: max output length: 285 +2024-01-17 01:56:27,697 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:28,948 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:28,948 (beam_search:476) INFO: -58.62 * 1.0 = -58.62 for ctc +2024-01-17 01:56:28,948 (beam_search:479) INFO: total log probability: -58.62 +2024-01-17 01:56:28,948 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:56:28,948 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:28,949 (beam_search:483) INFO: best hypo: TESKETDIERUMDIRICHTLIHNJEDESADESEFÄSTLEGUNGGUNDLEGNDASIGHEREITSNERMENFÜRDENSCHUTZFÜERDENGEFARENEINEREXSPOSITSIONGEGIÜBARONISIERENDARSTRALUNG + +2024-01-17 01:56:28,951 (asr_inference:494) INFO: speech length: 37759 +2024-01-17 01:56:28,959 (beam_search:428) INFO: decoder input length: 56 +2024-01-17 01:56:28,959 (beam_search:429) INFO: max output length: 56 +2024-01-17 01:56:28,959 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:29,013 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:29,013 (beam_search:476) INFO: -11.56 * 1.0 = -11.56 for ctc +2024-01-17 01:56:29,013 (beam_search:479) INFO: total log probability: -11.56 +2024-01-17 01:56:29,013 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-17 01:56:29,013 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:29,013 (beam_search:483) INFO: best hypo: DASSGILTESWIDERHERCHUSTEL + +2024-01-17 01:56:29,014 (asr_inference:494) INFO: speech length: 63018 +2024-01-17 01:56:29,024 (beam_search:428) INFO: decoder input length: 96 +2024-01-17 01:56:29,024 (beam_search:429) INFO: max output length: 96 +2024-01-17 01:56:29,024 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:29,187 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:29,187 (beam_search:476) INFO: -14.54 * 1.0 = -14.54 for ctc +2024-01-17 01:56:29,187 (beam_search:479) INFO: total log probability: -14.54 +2024-01-17 01:56:29,187 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:56:29,187 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:29,187 (beam_search:483) INFO: best hypo: DESENEINENEINZIGENSITZKIBTESLÄNGSDASIESTASTPURG + +2024-01-17 01:56:29,189 (asr_inference:494) INFO: speech length: 282859 +2024-01-17 01:56:29,215 (beam_search:428) INFO: decoder input length: 439 +2024-01-17 01:56:29,215 (beam_search:429) INFO: max output length: 439 +2024-01-17 01:56:29,215 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:32,155 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:32,156 (beam_search:476) INFO: -105.51 * 1.0 = -105.51 for ctc +2024-01-17 01:56:32,156 (beam_search:479) INFO: total log probability: -105.51 +2024-01-17 01:56:32,156 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:56:32,156 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:32,157 (beam_search:483) INFO: best hypo: EDASASPASIERTINMALTARDIESONLISTENDIKOBPZONDFELEAUFGEDEKTERDISVERGENBECHNERMODEWARWEDERWERENSYSTDEMAISHIKOCHOUNDFELEEBEUNTERSUCHNOCHIETERMORSELBEREGEZIELTEUNTERSUTEAATVASSINANDOGASWENALESIEORUNTERDEMANDEISCHWEINSZUGEDEKWERNSORS + +2024-01-17 01:56:32,159 (asr_inference:494) INFO: speech length: 96311 +2024-01-17 01:56:32,170 (beam_search:428) INFO: decoder input length: 148 +2024-01-17 01:56:32,170 (beam_search:429) INFO: max output length: 148 +2024-01-17 01:56:32,170 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:32,553 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:32,554 (beam_search:476) INFO: -29.53 * 1.0 = -29.53 for ctc +2024-01-17 01:56:32,554 (beam_search:479) INFO: total log probability: -29.53 +2024-01-17 01:56:32,554 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:56:32,554 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:32,554 (beam_search:483) INFO: best hypo: LINTLANDEKÜSTEDIEWANSTAEINEDIEAUFDIEROSENKATASFOFENIZUNAHMIEHNDRVRGANENEITINWEISEN + +2024-01-17 01:56:32,555 (asr_inference:494) INFO: speech length: 214332 +2024-01-17 01:56:32,575 (beam_search:428) INFO: decoder input length: 332 +2024-01-17 01:56:32,575 (beam_search:429) INFO: max output length: 332 +2024-01-17 01:56:32,575 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:34,201 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:34,201 (beam_search:476) INFO: -75.31 * 1.0 = -75.31 for ctc +2024-01-17 01:56:34,201 (beam_search:479) INFO: total log probability: -75.31 +2024-01-17 01:56:34,201 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:56:34,201 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:34,202 (beam_search:483) INFO: best hypo: DENNDICHABEBRIENZIEPFÜRDEBEICHTGESTEMMNTOWOLEINENSWEHRNFHLERINTELLTESIRTNEMITARZURAUFGEFARDERTDASAUROBEHRSCEPLLRMENDAUFDEMWEGKTZHEEIDEMEINZIGENSITZUUNDESTELTZEN + +2024-01-17 01:56:34,204 (asr_inference:494) INFO: speech length: 149440 +2024-01-17 01:56:34,219 (beam_search:428) INFO: decoder input length: 231 +2024-01-17 01:56:34,219 (beam_search:429) INFO: max output length: 231 +2024-01-17 01:56:34,219 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:35,016 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:35,016 (beam_search:476) INFO: -35.32 * 1.0 = -35.32 for ctc +2024-01-17 01:56:35,016 (beam_search:479) INFO: total log probability: -35.32 +2024-01-17 01:56:35,016 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:56:35,016 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:35,017 (beam_search:483) INFO: best hypo: INDIESEMRIFENWODENGEMEISAMMEPOLITISCHEVERABREDUNGERNIMKREISDERSIEBENUNTZWANZIGETDROFFERNUNDAUCHHUBLIKGEMACHT + +2024-01-17 01:56:35,019 (asr_inference:494) INFO: speech length: 367025 +2024-01-17 01:56:35,051 (beam_search:428) INFO: decoder input length: 571 +2024-01-17 01:56:35,051 (beam_search:429) INFO: max output length: 571 +2024-01-17 01:56:35,052 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:39,881 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:39,881 (beam_search:476) INFO: -150.35 * 1.0 = -150.35 for ctc +2024-01-17 01:56:39,881 (beam_search:479) INFO: total log probability: -150.35 +2024-01-17 01:56:39,881 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:56:39,881 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:39,882 (beam_search:483) INFO: best hypo: IBINEREROGENDESWERSHEUTEMITDEMVORCHARGESEMUMBELTAUSCHOSGESCHAFTAMINCHITWITEAUKMESIGERFEKTEORPÄICHERZESAGENDEHETENÜERHOCHRISIKRORPODUKTEINESENDRALEZULASNHAMENMÜSSENDASABRICHICHGESCAFTARMITDEMERSEIDAEMTISCHLIKTPLAUEICHDASIRTROTTEMEINENGROSENSCRITFLEICHKEINMEINSTDEINEEINGROSESCHIZUERPAEDENSEETHAEN + +2024-01-17 01:56:39,885 (asr_inference:494) INFO: speech length: 54080 +2024-01-17 01:56:39,894 (beam_search:428) INFO: decoder input length: 82 +2024-01-17 01:56:39,894 (beam_search:429) INFO: max output length: 82 +2024-01-17 01:56:39,894 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:40,008 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:40,008 (beam_search:476) INFO: -17.16 * 1.0 = -17.16 for ctc +2024-01-17 01:56:40,008 (beam_search:479) INFO: total log probability: -17.16 +2024-01-17 01:56:40,008 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:56:40,008 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:40,008 (beam_search:483) INFO: best hypo: PÄHENDANGESFÖRGFÜRZWEIENHEIBMINODENERG + +2024-01-17 01:56:40,010 (asr_inference:494) INFO: speech length: 123185 +2024-01-17 01:56:40,022 (beam_search:428) INFO: decoder input length: 190 +2024-01-17 01:56:40,022 (beam_search:429) INFO: max output length: 190 +2024-01-17 01:56:40,022 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:40,640 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:40,640 (beam_search:476) INFO: -47.14 * 1.0 = -47.14 for ctc +2024-01-17 01:56:40,640 (beam_search:479) INFO: total log probability: -47.14 +2024-01-17 01:56:40,641 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:56:40,641 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:40,641 (beam_search:483) INFO: best hypo: ZUMAKTUÄLENICTLABISKANKEINERVONUNSANEHMEDASWIWIRTLICHEARSZETDIESENWOCHENEDIWISENDSONSDIEZALUNSUMFICGKEITDROT + +2024-01-17 01:56:40,643 (asr_inference:494) INFO: speech length: 51521 +2024-01-17 01:56:40,651 (beam_search:428) INFO: decoder input length: 78 +2024-01-17 01:56:40,651 (beam_search:429) INFO: max output length: 78 +2024-01-17 01:56:40,651 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:40,774 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:40,774 (beam_search:476) INFO: -15.89 * 1.0 = -15.89 for ctc +2024-01-17 01:56:40,774 (beam_search:479) INFO: total log probability: -15.89 +2024-01-17 01:56:40,774 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:56:40,774 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:40,775 (beam_search:483) INFO: best hypo: DSNDEINFCHBEDINGNGENDINICGEKTZEPTABESENDMANKA + +2024-01-17 01:56:40,776 (asr_inference:494) INFO: speech length: 204139 +2024-01-17 01:56:40,794 (beam_search:428) INFO: decoder input length: 316 +2024-01-17 01:56:40,794 (beam_search:429) INFO: max output length: 316 +2024-01-17 01:56:40,794 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:42,248 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:42,248 (beam_search:476) INFO: -51.43 * 1.0 = -51.43 for ctc +2024-01-17 01:56:42,248 (beam_search:479) INFO: total log probability: -51.43 +2024-01-17 01:56:42,248 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:56:42,248 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:42,249 (beam_search:483) INFO: best hypo: INDESWISCHENSEISINDIRETUNGSORGANISERZIONERNIEGRÖSTENSCHÄPERWEISIEDIEMIGRANTENZWANZICHKILOMETERVERDERIEBICHENKÜSTEAUBGREITFENUNDALENARITALIENRASPORTIEREN + +2024-01-17 01:56:42,251 (asr_inference:494) INFO: speech length: 36157 +2024-01-17 01:56:42,259 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:56:42,259 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:56:42,259 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:42,320 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:42,320 (beam_search:476) INFO: -16.34 * 1.0 = -16.34 for ctc +2024-01-17 01:56:42,320 (beam_search:479) INFO: total log probability: -16.34 +2024-01-17 01:56:42,320 (beam_search:480) INFO: normalized log probability: -0.47 +2024-01-17 01:56:42,320 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:42,321 (beam_search:483) INFO: best hypo: DESEIKTDRFALLIOLIARTIEMSCHENKO + +2024-01-17 01:56:42,322 (asr_inference:494) INFO: speech length: 38399 +2024-01-17 01:56:42,330 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:56:42,330 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:56:42,330 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:42,396 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:42,396 (beam_search:476) INFO: -8.19 * 1.0 = -8.19 for ctc +2024-01-17 01:56:42,396 (beam_search:479) INFO: total log probability: -8.19 +2024-01-17 01:56:42,396 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:56:42,396 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:42,396 (beam_search:483) INFO: best hypo: EWASSERPREDIGENUNDWEINTRINKEN + +2024-01-17 01:56:42,397 (asr_inference:494) INFO: speech length: 77120 +2024-01-17 01:56:42,407 (beam_search:428) INFO: decoder input length: 118 +2024-01-17 01:56:42,407 (beam_search:429) INFO: max output length: 118 +2024-01-17 01:56:42,407 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:42,648 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:42,648 (beam_search:476) INFO: -19.90 * 1.0 = -19.90 for ctc +2024-01-17 01:56:42,648 (beam_search:479) INFO: total log probability: -19.90 +2024-01-17 01:56:42,648 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:56:42,648 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:42,649 (beam_search:483) INFO: best hypo: ÜRDIESEENSCHEIDUNGPRAUENWIARVIELEPATNARNICHTZULETZDIESTÄTTE + +2024-01-17 01:56:42,650 (asr_inference:494) INFO: speech length: 167360 +2024-01-17 01:56:42,666 (beam_search:428) INFO: decoder input length: 259 +2024-01-17 01:56:42,666 (beam_search:429) INFO: max output length: 259 +2024-01-17 01:56:42,666 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:43,659 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:43,659 (beam_search:476) INFO: -47.09 * 1.0 = -47.09 for ctc +2024-01-17 01:56:43,659 (beam_search:479) INFO: total log probability: -47.09 +2024-01-17 01:56:43,659 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:56:43,659 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:43,660 (beam_search:483) INFO: best hypo: DIEFOLGEISTEINHÖRENFLUGSVOMPORPOLISTNUNEXSTRLMISTEIENEINIGMIGITSTATENIERENBUMFEMPAROLUENSETZENDIARCOGRETERVERENDERUNGENGEGEN + +2024-01-17 01:56:43,662 (asr_inference:494) INFO: speech length: 261119 +2024-01-17 01:56:43,685 (beam_search:428) INFO: decoder input length: 405 +2024-01-17 01:56:43,685 (beam_search:429) INFO: max output length: 405 +2024-01-17 01:56:43,685 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:45,920 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:45,920 (beam_search:476) INFO: -58.13 * 1.0 = -58.13 for ctc +2024-01-17 01:56:45,920 (beam_search:479) INFO: total log probability: -58.13 +2024-01-17 01:56:45,920 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:56:45,920 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:45,921 (beam_search:483) INFO: best hypo: WALDIEINVESTITZIONERNVRANTÖRSISCHACHUNDDEUTSCHERBANKENGERETETWERDENMUSTENDURHTERGLICHENGLANDTWEITAUSENDEHNNICHTPBEITEGENUNDEHUTEMUSESEINENRIESIGENGSCHODENBERKVORDSICHTETHERTDRÜCKE + +2024-01-17 01:56:45,923 (asr_inference:494) INFO: speech length: 166079 +2024-01-17 01:56:45,939 (beam_search:428) INFO: decoder input length: 257 +2024-01-17 01:56:45,939 (beam_search:429) INFO: max output length: 257 +2024-01-17 01:56:45,939 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:47,015 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:47,015 (beam_search:476) INFO: -65.99 * 1.0 = -65.99 for ctc +2024-01-17 01:56:47,015 (beam_search:479) INFO: total log probability: -65.99 +2024-01-17 01:56:47,015 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-17 01:56:47,015 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:47,016 (beam_search:483) INFO: best hypo: IMITGITSTDADENDÜRFENNICHIEMÖGLICHKEITABENDERENAUROPÄSCHENSTARZEAMBALDERANZURHINDERNENIEREREGIONDGANZSGERZIEUNSTEMATISCORUTONFELNACHZUGGENERSINE + +2024-01-17 01:56:47,018 (asr_inference:494) INFO: speech length: 48640 +2024-01-17 01:56:47,026 (beam_search:428) INFO: decoder input length: 73 +2024-01-17 01:56:47,026 (beam_search:429) INFO: max output length: 73 +2024-01-17 01:56:47,026 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:47,131 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:47,131 (beam_search:476) INFO: -12.67 * 1.0 = -12.67 for ctc +2024-01-17 01:56:47,131 (beam_search:479) INFO: total log probability: -12.67 +2024-01-17 01:56:47,131 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:56:47,131 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:47,131 (beam_search:483) INFO: best hypo: EIMILIONMENSCHENSINAPÄNGHVONUDSERHILFER + +2024-01-17 01:56:47,132 (asr_inference:494) INFO: speech length: 89920 +2024-01-17 01:56:47,143 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 01:56:47,143 (beam_search:429) INFO: max output length: 138 +2024-01-17 01:56:47,143 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:47,497 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:47,497 (beam_search:476) INFO: -33.21 * 1.0 = -33.21 for ctc +2024-01-17 01:56:47,497 (beam_search:479) INFO: total log probability: -33.21 +2024-01-17 01:56:47,497 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:56:47,497 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:47,498 (beam_search:483) INFO: best hypo: EINFÜZHENHRGERJNGEWETINHERKADIONEINEPOLIZSISTENEINESONDEEINSATKOMANDOSNCOMAGSCHLAGDEN + +2024-01-17 01:56:47,499 (asr_inference:494) INFO: speech length: 98559 +2024-01-17 01:56:47,510 (beam_search:428) INFO: decoder input length: 151 +2024-01-17 01:56:47,510 (beam_search:429) INFO: max output length: 151 +2024-01-17 01:56:47,510 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:47,873 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:47,873 (beam_search:476) INFO: -30.31 * 1.0 = -30.31 for ctc +2024-01-17 01:56:47,873 (beam_search:479) INFO: total log probability: -30.31 +2024-01-17 01:56:47,873 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:56:47,873 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:47,874 (beam_search:483) INFO: best hypo: DIEEIDIEHEILIGEKUATMANWOSIHERETRAGENDASAUPTAUTWISSUDERALLEUNSHLENDENWECK + +2024-01-17 01:56:47,875 (asr_inference:494) INFO: speech length: 63360 +2024-01-17 01:56:47,885 (beam_search:428) INFO: decoder input length: 96 +2024-01-17 01:56:47,885 (beam_search:429) INFO: max output length: 96 +2024-01-17 01:56:47,885 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:48,049 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:48,049 (beam_search:476) INFO: -19.81 * 1.0 = -19.81 for ctc +2024-01-17 01:56:48,049 (beam_search:479) INFO: total log probability: -19.81 +2024-01-17 01:56:48,049 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:56:48,049 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:48,050 (beam_search:483) INFO: best hypo: REIDERARTKTEGETDEREFENHARBENINZWISCHENSTADGEUNDEM + +2024-01-17 01:56:48,051 (asr_inference:494) INFO: speech length: 26238 +2024-01-17 01:56:48,058 (beam_search:428) INFO: decoder input length: 38 +2024-01-17 01:56:48,058 (beam_search:429) INFO: max output length: 38 +2024-01-17 01:56:48,058 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:48,095 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:48,095 (beam_search:476) INFO: -9.14 * 1.0 = -9.14 for ctc +2024-01-17 01:56:48,095 (beam_search:479) INFO: total log probability: -9.14 +2024-01-17 01:56:48,095 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:56:48,095 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:48,095 (beam_search:483) INFO: best hypo: RDICHIEERNEINMONERDEBT + +2024-01-17 01:56:48,096 (asr_inference:494) INFO: speech length: 325708 +2024-01-17 01:56:48,126 (beam_search:428) INFO: decoder input length: 506 +2024-01-17 01:56:48,126 (beam_search:429) INFO: max output length: 506 +2024-01-17 01:56:48,126 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:51,634 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:51,634 (beam_search:476) INFO: -83.94 * 1.0 = -83.94 for ctc +2024-01-17 01:56:51,634 (beam_search:479) INFO: total log probability: -83.94 +2024-01-17 01:56:51,634 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:56:51,634 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:51,636 (beam_search:483) INFO: best hypo: DSWEGENEINWICHTIGEFRAGADIKOMITIONENEINLANDDIEKRANZSKONTROLLEVIEDEREINFÜHONNDDOCHIMSCHÄNGEUNIONBLEIBENITZUGANGKZURINOMAIONDSUSTEMEZETERAODERISDRSEINENTWIRDERODADIEFRAGEISWECHTICFÜRDIEDENISCHETEPATEUNDIESPETEUMEINEKLAREANDWORDDA + +2024-01-17 01:56:51,638 (asr_inference:494) INFO: speech length: 148799 +2024-01-17 01:56:51,653 (beam_search:428) INFO: decoder input length: 230 +2024-01-17 01:56:51,653 (beam_search:429) INFO: max output length: 230 +2024-01-17 01:56:51,653 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:52,508 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:52,508 (beam_search:476) INFO: -44.97 * 1.0 = -44.97 for ctc +2024-01-17 01:56:52,508 (beam_search:479) INFO: total log probability: -44.97 +2024-01-17 01:56:52,508 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:56:52,508 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:52,508 (beam_search:483) INFO: best hypo: DERSCHONAUSGIEFÜHRTWURDELAGESNICHTBARARNDASISEGROBEFFÄHLEGEGEBENHETISSONENESGABENERREIEVONDGLEIENNGEREIMTEITENBIETIENSWEI + +2024-01-17 01:56:52,510 (asr_inference:494) INFO: speech length: 92788 +2024-01-17 01:56:52,521 (beam_search:428) INFO: decoder input length: 142 +2024-01-17 01:56:52,521 (beam_search:429) INFO: max output length: 142 +2024-01-17 01:56:52,521 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:52,856 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:52,856 (beam_search:476) INFO: -28.65 * 1.0 = -28.65 for ctc +2024-01-17 01:56:52,856 (beam_search:479) INFO: total log probability: -28.65 +2024-01-17 01:56:52,857 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:56:52,857 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:52,857 (beam_search:483) INFO: best hypo: IVERGEMEINTCHEAFTUNGDERAUSENUOSSIEGERLTSPOLITIGBAISGOSISZIELDIESERUNJON + +2024-01-17 01:56:52,858 (asr_inference:494) INFO: speech length: 91839 +2024-01-17 01:56:52,869 (beam_search:428) INFO: decoder input length: 141 +2024-01-17 01:56:52,869 (beam_search:429) INFO: max output length: 141 +2024-01-17 01:56:52,869 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:53,220 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:53,220 (beam_search:476) INFO: -19.95 * 1.0 = -19.95 for ctc +2024-01-17 01:56:53,220 (beam_search:479) INFO: total log probability: -19.95 +2024-01-17 01:56:53,220 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:56:53,220 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:53,220 (beam_search:483) INFO: best hypo: DENSICHEHEITISEINESCWIERIGEUNDDETEILWEICHERARBEITNICHTNUERIMTÄCHNISCHENBEREICH + +2024-01-17 01:56:53,222 (asr_inference:494) INFO: speech length: 155840 +2024-01-17 01:56:53,237 (beam_search:428) INFO: decoder input length: 241 +2024-01-17 01:56:53,237 (beam_search:429) INFO: max output length: 241 +2024-01-17 01:56:53,237 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:54,062 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:54,062 (beam_search:476) INFO: -39.88 * 1.0 = -39.88 for ctc +2024-01-17 01:56:54,062 (beam_search:479) INFO: total log probability: -39.88 +2024-01-17 01:56:54,062 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:56:54,062 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:54,063 (beam_search:483) INFO: best hypo: TISEÄLTENGENDIENTERESTENVONBÜRGERNUNPOLITIKENSOWIAUSENANDERBEREMBÜRERNINGANZEROPERSTETESTEMERKINDTGANNSOBEN + +2024-01-17 01:56:54,065 (asr_inference:494) INFO: speech length: 17279 +2024-01-17 01:56:54,071 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:56:54,071 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:56:54,072 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:54,086 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:54,086 (beam_search:476) INFO: -4.86 * 1.0 = -4.86 for ctc +2024-01-17 01:56:54,087 (beam_search:479) INFO: total log probability: -4.86 +2024-01-17 01:56:54,087 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:56:54,087 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:54,087 (beam_search:483) INFO: best hypo: HERPASIDENT + +2024-01-17 01:56:54,088 (asr_inference:494) INFO: speech length: 144949 +2024-01-17 01:56:54,102 (beam_search:428) INFO: decoder input length: 224 +2024-01-17 01:56:54,103 (beam_search:429) INFO: max output length: 224 +2024-01-17 01:56:54,103 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:54,928 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:54,928 (beam_search:476) INFO: -39.09 * 1.0 = -39.09 for ctc +2024-01-17 01:56:54,928 (beam_search:479) INFO: total log probability: -39.09 +2024-01-17 01:56:54,928 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:56:54,928 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:54,929 (beam_search:483) INFO: best hypo: EFÜRTENGESPREICHEMITRESEDENTKARSEIZARDREICHNREGIRUNGSERTRETERNFRAUNUNDMENSCHNRECHTORGANISERTZIONENUNDDIEWANDDUCHAUSEMUTIGENT + +2024-01-17 01:56:54,931 (asr_inference:494) INFO: speech length: 101108 +2024-01-17 01:56:54,942 (beam_search:428) INFO: decoder input length: 155 +2024-01-17 01:56:54,942 (beam_search:429) INFO: max output length: 155 +2024-01-17 01:56:54,942 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:55,328 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:55,328 (beam_search:476) INFO: -33.50 * 1.0 = -33.50 for ctc +2024-01-17 01:56:55,328 (beam_search:479) INFO: total log probability: -33.50 +2024-01-17 01:56:55,328 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:56:55,328 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:55,328 (beam_search:483) INFO: best hypo: NGSACHEINEURSACHEFRDENWACHSNENATZIHNALISTMUSDEALINSEIDERFELICHPERSBEKTIFLOSSIST + +2024-01-17 01:56:55,330 (asr_inference:494) INFO: speech length: 48314 +2024-01-17 01:56:55,338 (beam_search:428) INFO: decoder input length: 73 +2024-01-17 01:56:55,338 (beam_search:429) INFO: max output length: 73 +2024-01-17 01:56:55,338 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:55,442 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:55,442 (beam_search:476) INFO: -19.23 * 1.0 = -19.23 for ctc +2024-01-17 01:56:55,442 (beam_search:479) INFO: total log probability: -19.23 +2024-01-17 01:56:55,442 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:56:55,442 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:55,443 (beam_search:483) INFO: best hypo: HUDEINEIMANAOHSORWEITONDIENZIEENFERNES + +2024-01-17 01:56:55,444 (asr_inference:494) INFO: speech length: 365439 +2024-01-17 01:56:55,476 (beam_search:428) INFO: decoder input length: 568 +2024-01-17 01:56:55,476 (beam_search:429) INFO: max output length: 568 +2024-01-17 01:56:55,476 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:56:59,943 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:56:59,943 (beam_search:476) INFO: -112.66 * 1.0 = -112.66 for ctc +2024-01-17 01:56:59,943 (beam_search:479) INFO: total log probability: -112.66 +2024-01-17 01:56:59,943 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:56:59,943 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:56:59,945 (beam_search:483) INFO: best hypo: HWERDEALSWIDANZMINISTERAUCHENMEINEMLANDZIEDENTAGKDAMITKONFVONDTIERTDASNATÜLICHAUCHTESBIRUSTZENGEGEBENSENMUSDASSTASHAUSHALGTDEVONDESTELUERSALERENENONSTEUERZEOLLANINENZIEZIHNTUNDDASIETAHMITAUCHIERNTUERTUNGRAGENINDEENTCHÄIDUNGENDIEVIERHIENTIESENRAMENDREFMIETAMNONTHERN + +2024-01-17 01:56:59,947 (asr_inference:494) INFO: speech length: 80623 +2024-01-17 01:56:59,957 (beam_search:428) INFO: decoder input length: 123 +2024-01-17 01:56:59,957 (beam_search:429) INFO: max output length: 123 +2024-01-17 01:56:59,957 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:00,183 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:00,183 (beam_search:476) INFO: -19.01 * 1.0 = -19.01 for ctc +2024-01-17 01:57:00,183 (beam_search:479) INFO: total log probability: -19.01 +2024-01-17 01:57:00,183 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:57:00,183 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:00,183 (beam_search:483) INFO: best hypo: AUFDEMOUOROPESCHENAUTERBEBILMAREKTINSIGESAMDERMATISCHIST + +2024-01-17 01:57:00,185 (asr_inference:494) INFO: speech length: 173416 +2024-01-17 01:57:00,201 (beam_search:428) INFO: decoder input length: 268 +2024-01-17 01:57:00,201 (beam_search:429) INFO: max output length: 268 +2024-01-17 01:57:00,201 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:01,304 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:01,304 (beam_search:476) INFO: -60.52 * 1.0 = -60.52 for ctc +2024-01-17 01:57:01,304 (beam_search:479) INFO: total log probability: -60.52 +2024-01-17 01:57:01,304 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:57:01,304 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:01,304 (beam_search:483) INFO: best hypo: OPÄHSCHUNIONHADTMIDISEINSTRUMENZSDIESCONSEEINEAKTIEVEROLLENIERNACHBARIGIONZUSPIELENUMDERMOGRATISCHEEFORMENNDERNNACHALIGENWIKTUNGERNZUTREIBE + +2024-01-17 01:57:01,306 (asr_inference:494) INFO: speech length: 95360 +2024-01-17 01:57:01,317 (beam_search:428) INFO: decoder input length: 146 +2024-01-17 01:57:01,317 (beam_search:429) INFO: max output length: 146 +2024-01-17 01:57:01,318 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:01,627 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:01,627 (beam_search:476) INFO: -21.57 * 1.0 = -21.57 for ctc +2024-01-17 01:57:01,627 (beam_search:479) INFO: total log probability: -21.57 +2024-01-17 01:57:01,627 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:57:01,627 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:01,628 (beam_search:483) INFO: best hypo: HTUTELLITERERERSCHIEMEVONAUSENGODERVONINENISTRECHTUNDOSCHIEDGLIGH + +2024-01-17 01:57:01,629 (asr_inference:494) INFO: speech length: 157103 +2024-01-17 01:57:01,644 (beam_search:428) INFO: decoder input length: 243 +2024-01-17 01:57:01,644 (beam_search:429) INFO: max output length: 243 +2024-01-17 01:57:01,644 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:02,576 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:02,576 (beam_search:476) INFO: -40.90 * 1.0 = -40.90 for ctc +2024-01-17 01:57:02,576 (beam_search:479) INFO: total log probability: -40.90 +2024-01-17 01:57:02,576 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:57:02,576 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:02,577 (beam_search:483) INFO: best hypo: EREMIMERGESAGKTEINÜBEREILTESTADTZIONIERUNGSENSHEIDUNGESUNSENICHWEIZUMJERZIGENTZEITFUNGESKEINEBEDROUNGBEISPIELSWEISAUSEMIERANGEBT + +2024-01-17 01:57:02,579 (asr_inference:494) INFO: speech length: 340122 +2024-01-17 01:57:02,609 (beam_search:428) INFO: decoder input length: 529 +2024-01-17 01:57:02,609 (beam_search:429) INFO: max output length: 529 +2024-01-17 01:57:02,609 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:05,159 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:05,159 (beam_search:476) INFO: -101.62 * 1.0 = -101.62 for ctc +2024-01-17 01:57:05,159 (beam_search:479) INFO: total log probability: -101.62 +2024-01-17 01:57:05,159 (beam_search:480) INFO: normalized log probability: -0.60 +2024-01-17 01:57:05,159 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:05,160 (beam_search:483) INFO: best hypo: DIESERFAKLEIISTEINETZYNISCHEMISACTDUNGENDEROBPUOVORONMENCHNECTZWRECTDOMLELALLRELSFFAAAAATDODSCSAAAAISSONGANDANANDENEEINESOEUCHODENCÜLAUPBLICHERANWORF + +2024-01-17 01:57:05,162 (asr_inference:494) INFO: speech length: 82231 +2024-01-17 01:57:05,172 (beam_search:428) INFO: decoder input length: 126 +2024-01-17 01:57:05,172 (beam_search:429) INFO: max output length: 126 +2024-01-17 01:57:05,172 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:05,444 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:05,444 (beam_search:476) INFO: -31.29 * 1.0 = -31.29 for ctc +2024-01-17 01:57:05,444 (beam_search:479) INFO: total log probability: -31.29 +2024-01-17 01:57:05,444 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:57:05,444 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:05,444 (beam_search:483) INFO: best hypo: DIEESPEERHATDISEUMFSENDERHETZUNTALERICHTLINDEÜRBEÜRBOATDETWINGER + +2024-01-17 01:57:05,446 (asr_inference:494) INFO: speech length: 270710 +2024-01-17 01:57:05,471 (beam_search:428) INFO: decoder input length: 420 +2024-01-17 01:57:05,471 (beam_search:429) INFO: max output length: 420 +2024-01-17 01:57:05,471 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:07,997 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:07,997 (beam_search:476) INFO: -90.38 * 1.0 = -90.38 for ctc +2024-01-17 01:57:07,997 (beam_search:479) INFO: total log probability: -90.38 +2024-01-17 01:57:07,997 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:57:07,997 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:07,998 (beam_search:483) INFO: best hypo: GIGISTWIRGLICKLADIINANFUNDEWIRSHASTGEDEVELANKVONUNDEALNEINMALMEHRJETZSTDERVERANTWOFTDUNGFÜREINEOBPTIMALEUNDFEALEMRASIEKALIFIZTIERUNGUNDRERARBEITNEHMENDARBEITDNEMERRINENDANSBESONDERJETSTRESCHNUNGSOTAGEN + +2024-01-17 01:57:08,000 (asr_inference:494) INFO: speech length: 171493 +2024-01-17 01:57:08,017 (beam_search:428) INFO: decoder input length: 265 +2024-01-17 01:57:08,017 (beam_search:429) INFO: max output length: 265 +2024-01-17 01:57:08,017 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:09,111 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:09,112 (beam_search:476) INFO: -57.99 * 1.0 = -57.99 for ctc +2024-01-17 01:57:09,112 (beam_search:479) INFO: total log probability: -57.99 +2024-01-17 01:57:09,112 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:57:09,112 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:09,112 (beam_search:483) INFO: best hypo: ANDRERAUCHOHLENDIESERKUDERGEBISERZIELNALSANDEREDISSISCWÄERTUNDIMITELABPZUOFNETWACRIKIONWIKALABRENZITZIELENODERAUCGRIECHELERDODRAUCHOMÄNIEN + +2024-01-17 01:57:09,114 (asr_inference:494) INFO: speech length: 176315 +2024-01-17 01:57:09,131 (beam_search:428) INFO: decoder input length: 273 +2024-01-17 01:57:09,131 (beam_search:429) INFO: max output length: 273 +2024-01-17 01:57:09,131 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:10,248 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:10,249 (beam_search:476) INFO: -42.89 * 1.0 = -42.89 for ctc +2024-01-17 01:57:10,249 (beam_search:479) INFO: total log probability: -42.89 +2024-01-17 01:57:10,249 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:57:10,249 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:10,249 (beam_search:483) INFO: best hypo: DERBRICHCOSESVORDERZURECHTDASESRETINGSTATLICHERSCHLTTIEDELEISÖFFENTLICHERAUFGABEBEGRIFENUNDDAHIERVONFFENLICHEAKTÜRNVORGENOMWERDENMOS + +2024-01-17 01:57:10,251 (asr_inference:494) INFO: speech length: 103359 +2024-01-17 01:57:10,263 (beam_search:428) INFO: decoder input length: 159 +2024-01-17 01:57:10,263 (beam_search:429) INFO: max output length: 159 +2024-01-17 01:57:10,263 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:10,703 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:10,703 (beam_search:476) INFO: -35.60 * 1.0 = -35.60 for ctc +2024-01-17 01:57:10,703 (beam_search:479) INFO: total log probability: -35.60 +2024-01-17 01:57:10,704 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:57:10,704 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:10,704 (beam_search:483) INFO: best hypo: DABWIESABALDNMITEINEMSOTCARLPOGAMZUTUNHARBENMSSWILDARFÜREINENSPECHENDERECHLIGEUNDLAGESCAFEN + +2024-01-17 01:57:10,706 (asr_inference:494) INFO: speech length: 23995 +2024-01-17 01:57:10,713 (beam_search:428) INFO: decoder input length: 35 +2024-01-17 01:57:10,713 (beam_search:429) INFO: max output length: 35 +2024-01-17 01:57:10,713 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:10,745 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:10,745 (beam_search:476) INFO: -9.53 * 1.0 = -9.53 for ctc +2024-01-17 01:57:10,745 (beam_search:479) INFO: total log probability: -9.53 +2024-01-17 01:57:10,745 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:57:10,745 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:10,745 (beam_search:483) INFO: best hypo: STIERNOCHNALISERENWORE + +2024-01-17 01:57:10,746 (asr_inference:494) INFO: speech length: 100774 +2024-01-17 01:57:10,758 (beam_search:428) INFO: decoder input length: 155 +2024-01-17 01:57:10,758 (beam_search:429) INFO: max output length: 155 +2024-01-17 01:57:10,758 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:11,152 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:11,152 (beam_search:476) INFO: -34.35 * 1.0 = -34.35 for ctc +2024-01-17 01:57:11,152 (beam_search:479) INFO: total log probability: -34.35 +2024-01-17 01:57:11,152 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:57:11,152 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:11,153 (beam_search:483) INFO: best hypo: MAKENENERTLIEVERLANGENGEBNLIEMERGARTFIRNDIKUNGSHVERAUSDIEAHMENEUTEBRAUCKENDASSBE + +2024-01-17 01:57:11,154 (asr_inference:494) INFO: speech length: 152959 +2024-01-17 01:57:11,169 (beam_search:428) INFO: decoder input length: 236 +2024-01-17 01:57:11,169 (beam_search:429) INFO: max output length: 236 +2024-01-17 01:57:11,169 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:12,043 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:12,043 (beam_search:476) INFO: -46.77 * 1.0 = -46.77 for ctc +2024-01-17 01:57:12,043 (beam_search:479) INFO: total log probability: -46.77 +2024-01-17 01:57:12,043 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:57:12,043 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:12,044 (beam_search:483) INFO: best hypo: GERADÜOGLEINELEPOJECKTEISDASÜBERMÄESIGHBIEROGATESCHRAUFANDRECHTICHDASTASIERSRBEINZEITAUMVONDDREIJAHRENGESENTWERDENSOLUNDUM + +2024-01-17 01:57:12,046 (asr_inference:494) INFO: speech length: 166708 +2024-01-17 01:57:12,062 (beam_search:428) INFO: decoder input length: 258 +2024-01-17 01:57:12,062 (beam_search:429) INFO: max output length: 258 +2024-01-17 01:57:12,062 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:12,853 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:12,853 (beam_search:476) INFO: -51.37 * 1.0 = -51.37 for ctc +2024-01-17 01:57:12,853 (beam_search:479) INFO: total log probability: -51.37 +2024-01-17 01:57:12,853 (beam_search:480) INFO: normalized log probability: -0.47 +2024-01-17 01:57:12,853 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:12,854 (beam_search:483) INFO: best hypo: IKANDEVORSICHERNDIAROPESCHEKOMISIONISTETKOMITDETZSUMAAREROBSEALOPECHENERSBIEKTDIEVERDISKOSSOSE + +2024-01-17 01:57:12,855 (asr_inference:494) INFO: speech length: 40959 +2024-01-17 01:57:12,863 (beam_search:428) INFO: decoder input length: 61 +2024-01-17 01:57:12,863 (beam_search:429) INFO: max output length: 61 +2024-01-17 01:57:12,863 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:12,917 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:12,917 (beam_search:476) INFO: -10.93 * 1.0 = -10.93 for ctc +2024-01-17 01:57:12,917 (beam_search:479) INFO: total log probability: -10.93 +2024-01-17 01:57:12,917 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-17 01:57:12,917 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:12,917 (beam_search:483) INFO: best hypo: SESESLAPEIHEAUFTAUCHSO + +2024-01-17 01:57:12,918 (asr_inference:494) INFO: speech length: 85432 +2024-01-17 01:57:12,929 (beam_search:428) INFO: decoder input length: 131 +2024-01-17 01:57:12,929 (beam_search:429) INFO: max output length: 131 +2024-01-17 01:57:12,929 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:13,228 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:13,228 (beam_search:476) INFO: -24.87 * 1.0 = -24.87 for ctc +2024-01-17 01:57:13,228 (beam_search:479) INFO: total log probability: -24.87 +2024-01-17 01:57:13,228 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:57:13,228 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:13,228 (beam_search:483) INFO: best hypo: IDIESENHAUSELKAMANDIEEENGÜRGEREINUNDBORGERNICHTÜBERZEUCTENNBEGEISTERN + +2024-01-17 01:57:13,230 (asr_inference:494) INFO: speech length: 176953 +2024-01-17 01:57:13,246 (beam_search:428) INFO: decoder input length: 274 +2024-01-17 01:57:13,246 (beam_search:429) INFO: max output length: 274 +2024-01-17 01:57:13,246 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:14,394 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:14,394 (beam_search:476) INFO: -46.48 * 1.0 = -46.48 for ctc +2024-01-17 01:57:14,394 (beam_search:479) INFO: total log probability: -46.48 +2024-01-17 01:57:14,394 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:57:14,394 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:14,395 (beam_search:483) INFO: best hypo: ZIALDEMOKRATENEHMNITGROSAFOEUDEZSORKENTNISDASDINGEDIEIERFORGETRAGENHABENJEBZSICHAUHIMZUSAMMENAGMITVERENDERUNGENEDENFEINCHENSTADENUMSETZE + +2024-01-17 01:57:14,397 (asr_inference:494) INFO: speech length: 171509 +2024-01-17 01:57:14,413 (beam_search:428) INFO: decoder input length: 265 +2024-01-17 01:57:14,413 (beam_search:429) INFO: max output length: 265 +2024-01-17 01:57:14,413 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:15,490 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:15,490 (beam_search:476) INFO: -48.37 * 1.0 = -48.37 for ctc +2024-01-17 01:57:15,490 (beam_search:479) INFO: total log probability: -48.37 +2024-01-17 01:57:15,490 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:57:15,490 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:15,491 (beam_search:483) INFO: best hypo: DEAHRBESCHUSTIEELDASOROPÄSCHESEMESTERHIERHERTZUNEHMENUNDDICOROBPTIOUNSSITPLARIONEREIMRAMDERLNDERBRECHTEZOVERÖFNIGENISTNIGAUSHEIGENT + +2024-01-17 01:57:15,492 (asr_inference:494) INFO: speech length: 298215 +2024-01-17 01:57:15,519 (beam_search:428) INFO: decoder input length: 463 +2024-01-17 01:57:15,519 (beam_search:429) INFO: max output length: 463 +2024-01-17 01:57:15,519 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:18,414 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:18,414 (beam_search:476) INFO: -83.36 * 1.0 = -83.36 for ctc +2024-01-17 01:57:18,414 (beam_search:479) INFO: total log probability: -83.36 +2024-01-17 01:57:18,414 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:57:18,414 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:18,415 (beam_search:483) INFO: best hypo: NDMEINMEINEBITEODERMDASWASICHMERVORSTENISDASMARGENGWIECKLICGINDERTARTEINIEGROSEEINEBREITMEHRHEITFÜRDIESEKOUSIONSPOLITIGHÜEONDFGEPOLITIGSTDIMTPÜRDIEMENSCHENVORORTDAMITIUNSATESWEENTICHEAUCHBESCRÄNKENKÖNDEDAS + +2024-01-17 01:57:18,417 (asr_inference:494) INFO: speech length: 325439 +2024-01-17 01:57:18,446 (beam_search:428) INFO: decoder input length: 506 +2024-01-17 01:57:18,446 (beam_search:429) INFO: max output length: 506 +2024-01-17 01:57:18,446 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:21,017 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:21,017 (beam_search:476) INFO: -75.15 * 1.0 = -75.15 for ctc +2024-01-17 01:57:21,017 (beam_search:479) INFO: total log probability: -75.15 +2024-01-17 01:57:21,017 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-17 01:57:21,017 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:21,018 (beam_search:483) INFO: best hypo: WNNWIEHEUTEDIEEVERRDNGVERABSCHIEDENOFERECHDASSEWIENACHEIMLANGKARUSELLSOUEIMGUDNABSLUSKOMMONDTITMMACHTERMICHEBEIEROMISIONBEDANGENIEONSTOKTIEVESACHABEITHAT + +2024-01-17 01:57:21,020 (asr_inference:494) INFO: speech length: 73280 +2024-01-17 01:57:21,030 (beam_search:428) INFO: decoder input length: 112 +2024-01-17 01:57:21,030 (beam_search:429) INFO: max output length: 112 +2024-01-17 01:57:21,030 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:57:21,237 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:57:21,237 (beam_search:476) INFO: -20.88 * 1.0 = -20.88 for ctc +2024-01-17 01:57:21,237 (beam_search:479) INFO: total log probability: -20.88 +2024-01-17 01:57:21,237 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:57:21,237 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:57:21,237 (beam_search:483) INFO: best hypo: UNZERERESCHEASCHENUNZSIEKONTROELENHABENKENENPIELEGERPRAFT + +# Accounting: time=86 threads=1 +# Ended (code 0) at Wed Jan 17 01:57:21 CST 2024, elapsed time 86 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/calculate_rtf.log b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/calculate_rtf.log new file mode 100644 index 0000000000000000000000000000000000000000..5095bd40ee074056c3da778d4ecff5902b9eda8a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/calculate_rtf.log @@ -0,0 +1,9 @@ +# pyscripts/utils/calculate_rtf.py --log-dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir --log-name asr_inference --input-shift 0.0625 --start-times-marker "speech length" --end-times-marker "best hypo" --inf-num 1 +# Started at Wed Jan 17 01:57:21 CST 2024 +# +Total audio duration: 4186.603 [sec] +Total decoding time: 348.101 [sec] +RTF: 0.083 +Latency: 526.628 [ms/sentence] +# Accounting: time=0 threads=1 +# Ended (code 0) at Wed Jan 17 01:57:21 CST 2024, elapsed time 0 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.1.scp b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.1.scp new file mode 100644 index 0000000000000000000000000000000000000000..607708d66ff795d9377b71f61af658c17e32d014 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/keys.1.scp @@ -0,0 +1,166 @@ +M-AILABS_deu_000165 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000165.flac +M-AILABS_deu_000166 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000166.flac +M-AILABS_deu_000167 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000167.flac +M-AILABS_deu_000168 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000168.flac +M-AILABS_deu_000169 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000169.flac +M-AILABS_deu_000170 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000170.flac +M-AILABS_deu_000171 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000171.flac +M-AILABS_deu_000172 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000172.flac +M-AILABS_deu_000173 dump/raw/test_10min_deu1/data/format.1/M-AILABS_deu_000173.flac 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dump/raw/test_10min_deu1/data/format.32/voxpopuli_deu_000377.flac +voxpopuli_deu_000378 dump/raw/test_10min_deu1/data/format.32/voxpopuli_deu_000378.flac +voxpopuli_deu_000379 dump/raw/test_10min_deu1/data/format.32/voxpopuli_deu_000379.flac +voxpopuli_deu_000380 dump/raw/test_10min_deu1/data/format.32/voxpopuli_deu_000380.flac +voxpopuli_deu_000381 dump/raw/test_10min_deu1/data/format.32/voxpopuli_deu_000381.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..c24411dc66257b42afe7db85c0c72130550eb3e9 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/score @@ -0,0 +1,166 @@ +M-AILABS_deu_000165 tensor(-37.2405) +M-AILABS_deu_000166 tensor(-18.8177) +M-AILABS_deu_000167 tensor(-40.6093) +M-AILABS_deu_000168 tensor(-22.4223) +M-AILABS_deu_000169 tensor(-33.5114) +M-AILABS_deu_000170 tensor(-32.6600) +M-AILABS_deu_000171 tensor(-42.6083) +M-AILABS_deu_000172 tensor(-42.3650) +M-AILABS_deu_000173 tensor(-10.8001) +M-AILABS_deu_000174 tensor(-18.3776) +M-AILABS_deu_000175 tensor(-45.1983) +M-AILABS_deu_000176 tensor(-23.8962) +M-AILABS_deu_000177 tensor(-24.9965) +M-AILABS_deu_000178 tensor(-14.0330) +M-AILABS_deu_000179 tensor(-28.9611) +M-AILABS_deu_000180 tensor(-38.5004) +M-AILABS_deu_000181 tensor(-31.0553) +M-AILABS_deu_000182 tensor(-15.6452) +M-AILABS_deu_000183 tensor(-18.5184) +M-AILABS_deu_000184 tensor(-33.2693) +M-AILABS_deu_000185 tensor(-40.2063) +M-AILABS_deu_000186 tensor(-43.3438) +M-AILABS_deu_000187 tensor(-26.9436) +M-AILABS_deu_000188 tensor(-23.0664) +M-AILABS_deu_000189 tensor(-28.6562) +M-AILABS_deu_000190 tensor(-33.6289) +M-AILABS_deu_000191 tensor(-39.1169) 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+cv_deu_000717 tensor(-28.3445) +cv_deu_000718 tensor(-14.4432) +cv_deu_000719 tensor(-23.9536) +cv_deu_000720 tensor(-18.3321) +cv_deu_000721 tensor(-16.1712) +cv_deu_000722 tensor(-10.0683) +cv_deu_000723 tensor(-9.9744) +cv_deu_000724 tensor(-9.7347) +cv_deu_000725 tensor(-26.5291) +cv_deu_000726 tensor(-11.5593) +cv_deu_000727 tensor(-13.5293) +cv_deu_000728 tensor(-27.6714) +cv_deu_000729 tensor(-21.5708) +cv_deu_000730 tensor(-38.8130) +cv_deu_000731 tensor(-25.0266) +cv_deu_000732 tensor(-15.5230) +cv_deu_000733 tensor(-34.1449) +cv_deu_000734 tensor(-29.3528) +cv_deu_000735 tensor(-11.7061) +cv_deu_000736 tensor(-8.2453) +cv_deu_000737 tensor(-29.9759) +cv_deu_000738 tensor(-24.4141) +cv_deu_000739 tensor(-14.5112) +cv_deu_000740 tensor(-7.0615) +cv_deu_000741 tensor(-6.9365) +cv_deu_000742 tensor(-25.3793) +cv_deu_000743 tensor(-11.4346) +cv_deu_000744 tensor(-10.6985) +cv_deu_000745 tensor(-5.5268) +cv_deu_000746 tensor(-28.7693) +cv_deu_000747 tensor(-30.0669) +cv_deu_000748 tensor(-5.8346) +cv_deu_000749 tensor(-9.7586) +cv_deu_000750 tensor(-27.6151) +cv_deu_000751 tensor(-46.8685) +cv_deu_000752 tensor(-40.6758) +cv_deu_000753 tensor(-45.3957) +cv_deu_000754 tensor(-20.9482) +cv_deu_000755 tensor(-39.9349) +cv_deu_000756 tensor(-13.7058) +cv_deu_000757 tensor(-24.4073) +cv_deu_000758 tensor(-13.0404) +cv_deu_000759 tensor(-34.7410) +cv_deu_000760 tensor(-27.6461) +cv_deu_000761 tensor(-36.1436) +cv_deu_000762 tensor(-25.0254) +cv_deu_000763 tensor(-20.7126) +cv_deu_000764 tensor(-12.7279) +cv_deu_000765 tensor(-28.6608) +cv_deu_000766 tensor(-15.8378) +cv_deu_000767 tensor(-25.3664) +cv_deu_000768 tensor(-20.1835) +cv_deu_000769 tensor(-6.9635) +cv_deu_000770 tensor(-37.0404) +cv_deu_000771 tensor(-27.5972) +cv_deu_000772 tensor(-25.7213) +cv_deu_000773 tensor(-17.8405) +cv_deu_000774 tensor(-24.1242) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..f8d94956f201071e5a94e65356ce53712a577f6e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/text @@ -0,0 +1,166 @@ +M-AILABS_deu_000165 DEBER DIGNG MACHTE INER EUSSTWICHTIGENSECHA IN ENDE DER PÄTIT ION ANLINGOWANÜR FÜRDES EN JANER SOSSBEGENADIGUNM +M-AILABS_deu_000166 DACHABESIET DIE WOLULGJEDEM HER INER INRUNKGEBLIEBENEN WARTE GESPOCHEN N +M-AILABS_deu_000167 ERS UM ACHT UR WAR ER AUF MALE BRCHTER DEN KAFI DIE SONDESCHIEN INZS ZIMER UND IE SPÄHRLINGE DIE DSSAUS DEN HECSE SECKEN GEFALNE FOTEAKON AUF BIKTEN +M-AILABS_deu_000168 SSICHERLICH AN IHRNGEBORTZTACKHÄTE ER BEI IE BLEIBEN KÖNENT +M-AILABS_deu_000169 DERSALBÖM MUS AN DAUORT WUOR MENTCHENSCHIRICGKEITENHABEN DIES OUCH EINER SEITS ERKLÄEREN 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BERKAM EN SIN NIE ZU FRIEDEN SCHIEN NN +M-AILABS_deu_000234 EIN SOMAHR WAHMANOVEM BARTARK LAG MITZONEN GLITZSANNÜBERDE HUPTSTABT UND UNDER DNLINDENDRENKTER INE TAUSEND KAPFIGE MENSCHENMÄNGER AUO VON IEDER U +M-AILABS_deu_000235 KOMITM IH MEIN SON DEN ICHPRAUCHE DEINE LIEBE +M-AILABS_deu_000236 DNTNWOR SAIN KESICHT RDE IN WNICHNACHTDENKLICHARSOO WIE VON EINER ER INERHUNG ER HÄLT NN +M-AILABS_deu_000237 N WOT OUI ERDE NWATIONENS DROCK STEIGENUNDTATZU ESTASSESTEMIE ENGEFÜRTWON +M-AILABS_deu_000238 NET GEWATE ERMIT EN SET ZSN DIE SCHEUSLICHE TEUFLSCHER AFENFRATZSE DIEBE DES METCHEN SCHLTERSCIELTEN +M-AILABS_deu_000239 TTERAR DE WERT NIK TE DASGÖRD EINE GEWISSEN WRTSCHAUF BERNHAT WRTSCHOF ISTE ETWASFARGUNSENTNN +M-AILABS_deu_000240 WOLT ER IN WAHEI DIEL ÖRSEND TÖRTEN NUNDT KND ERSCHELISENNN +M-AILABS_deu_000241 AT SEÄTDI SERESPEKT VOL WUOBEI ER NUR EINI GESELDEN VERSCHLUKTE WASS IM BEI DEN BELEBTEN LANGEN WÖRTEAN DES FTAN FORKAN +M-AILABS_deu_000242 DLORT FONDLELE RORU VERT NICHZS END 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SESE NTRESANDEN PROJEGTENU +M-AILABS_deu_000252 DKAS BAR FERHARTE AN GENMRTZELTENSEIMPLATZS SEIN GLIE DER JAR SEINEN AUGEN BANG IEVERSTEINRT EL ET UM ZWEITEN MALIEN IKTE N +M-AILABS_deu_000253 EINIGE ZEIT DANACHFRAKTE ER MICH O PICH GLAUBER DAS DER EIS GANG DEN SCLITEN DES ANDEREN ZER STÖRTABE +cv_deu_000698 AENEN BLUSSE NICHTGENEINER SCHORCGSTARE DEL VALIN +cv_deu_000699 JILSCKOMIRSCHEN +cv_deu_000700 SDEM BEIE ARBEITE DE EIS AUSHRELS KRAFTAF EINEN FAHME +cv_deu_000701 EINTERIT TO HEIKOSSES OCHOPERWITDT NICHT IT AN MITARMESI KEINEREN O HOPERREICT +cv_deu_000702 IER SOUN KAENDRH KÖNZSLICHER BEFROCHTUNGK ZURWERT +cv_deu_000703 DIE NACHT AR DIEF NEFLER FLIEN VONMITER JULIEBIS MITE AOF TOBER +cv_deu_000704 DEREAR H AAT +cv_deu_000705 EN DHERN +cv_deu_000706 NUTZER KNEN IHRELERSE ZEICEN ONEIN ABSPEICHER VERWALTEN UND BET ANEN NOTZAN TEILEN +cv_deu_000707 DIE DUM BOSCO KATIERALE +cv_deu_000708 SAUL BASSZEL ZU DEN N ROTISTEN DIESEIDARE UMEMF Ü MEC EÜEUELE SERENZELLEN +cv_deu_000709 IN KÜNÜWUR SER BENEMEN WELEN BEUG K EINE SIEL BWONDER EICHE N +cv_deu_000710 EITERE WICHTIGE INDESTRIEZWEIGESE DIE NICRMICHANIG GALWANOPLASTIG MITEILBAU UNTIE HUTZVER ARBEITOUNG A +cv_deu_000711 ÜBER DEN AUTOR S NICHT BEKAND VER MUTLIC STMTEHR AUS EM DEUTSHEN SPRACHGBIEDT +cv_deu_000712 NSTUER ISMI DENENTOPLIPATE +cv_deu_000713 DIE HARMEI EREBLEMER OSSISCHIGHT OUCHT +cv_deu_000714 WICHLIENEMANACH UARE ME AUKTUR E EIERTETDNTHMACHBER NN +cv_deu_000715 ORDE AL I DE GESSYK LDE ANLAN AU NISCHEWABEN +cv_deu_000716 EIEHEI DEN NAME IN WINCHEN WORER AUCGSTA T +cv_deu_000717 INERUNG UND EUSSRERNATICG GENEN ALSGETRENTITAILE EINES NATIG AUCH GEMEINSAM VOR OMMEN +cv_deu_000718 DA BEIE BELEGTER R DIEPLÄTZSE VIER UNDTREIG +cv_deu_000719 KIN DRABIE IS SIE TOCHTER ZWEIAR ROFESENERLARTENZA +cv_deu_000720 IS KLAUBER AS FÜRT NIST INIRISTIERISTUNG +cv_deu_000721 DASSES EINERXSTRENSCHLECHTERICHS LIENIER +cv_deu_000722 HERLORCH EN BLESTSEIN HAGERESGESICHT +cv_deu_000723 MOKAREN FINE TO UN FER +cv_deu_000724 T INGE BO KABERHAT DER DREIGESCHICSTET +cv_deu_000725 LCES KOMTWICLISTSA ANM DASOLCHEDADTEN U DIESER EBENER FASTWERDEN +cv_deu_000726 STRAMIN HEN GEGENE GIT EIN ERMUNICHES POSIEREN +cv_deu_000727 BENICH UM KAUF EIER HPOTEG BERECH TIGT +cv_deu_000728 TUN E EUNLENS S ENERSHENFVENDEN DEN BMNMIE +cv_deu_000729 HKÖUEN DERENSN DEREEISLNDUN +cv_deu_000730 ON DIE POFISINLLIN TESTÜTZUN DAMASSEARTDIE ENABTEILUNG WAEN DISE BAGEN DER KON KROWENS UN DOCH UND OLEGEN +cv_deu_000731 SIE DIEN TE UNEST ASUNDTEOKMFT VÜR BELGISCHE BISATZUSTRUPEN +cv_deu_000732 DAMISSN WISCSPLENEN MEINTE DERZHAHN AHRZT +cv_deu_000733 AUSDEM SPITE R EIMNACHFORGETIEM NIMARKEDROEUOLS SOWIE EIMLIGEA KONKURRENDTEN LNDEN NEI +cv_deu_000734 IE AUC AS ENSTEND RAN ACH WATENG ER FÜRTIE KUMS WAL DAS KONN DORKETGTERIOMNICHT +cv_deu_000735 SMIFUCHEN TI KAGEOAUF +cv_deu_000736 WIER IEIE ALEINEN +cv_deu_000737 DUM ISG WERTWAS BWEIS WURTROC DSS DESIUN BISENS VOLSCHUNGEL +cv_deu_000738 HABTEMA DER SCHA IST DIE RE WANSCH VEREKÜBICHISTREICHER ENTERFREINDEN +cv_deu_000739 GLEICH ZEITIG WUODEN SPOTWETEN TALWEISE VERBULEN +cv_deu_000740 SEEGEN +cv_deu_000741 TIA BE +cv_deu_000742 ZU DEM FARSAH ER EN KLOSTALANG JARE DIE MTER DES NO WITZSEN MEISTASUN PRIORA +cv_deu_000743 HEIDEN HEIDEN EN STMT EINER ERTZTE VERMILIEAR +cv_deu_000744 ARESPZIMPTPENN +cv_deu_000745 ZWAIE UNGR +cv_deu_000746 TTTTTEBEMFALS EN AUGNG ANGIIEDE ENTIKALTREINDER EFA +cv_deu_000747 DIESERSTDET ARFE AB SELWENDTEN EIN HERMSCHERSCHOL MITDRTKANDTESEN OFEN +cv_deu_000748 ALSO ESCHIORE NICS +cv_deu_000749 WIEKON MAHN SISCH SCHÜTZE +cv_deu_000750 AUFÜM FMONATEN LAG EINE IM FINTLICHA EPLOTER ANZSTDIEBISTAHEINER HLTLICHEN VOR +cv_deu_000751 ZIELISTERS DIEVE ENTSTIMUGENESOFTJESESTEBZMIT SNNRSGHETZICHKATZGON ZUÜBARPOL FMSH +cv_deu_000752 BN NINTE EINEN WAHRMNEN GEMPRENEN IONDBEUUSC NISSISTDIKÖNDELE BESSER UN SHEILTEN +cv_deu_000753 DIE AN DIE WIERENSOCHÜÖR IST ANNURNKENGAL AUFHEN UNDEAND ALLE GUNMBIEMTDELLN NERUSLANGELIGT +cv_deu_000754 IERE TU ARGKE ISTEN DIESARZEIT GUGEFLNGENSC +cv_deu_000755 ET DIE STREIKER BEGEND IM SGIEN BERHUNERS IN FÜHRTIC DIE GO EB LE HECHT UNGM ASYÜT OSSTE +cv_deu_000756 ERST VON DORT KONTE ER SEIM WEGFREI VOR SETZEN +cv_deu_000757 SIE ERHEBPZICH HEUTE IMARNOCKUTER KENBER AUSS DIEMN SCH WEMLANDTHERUSS +cv_deu_000758 TI ANARESCHEN INSLEN GEHE ZU SBAHHE +cv_deu_000759 WESSN SCHAFLERHAHABEN DESIM ENTATZ UN PESERETNO BEI FOARAUN BEROBATET +cv_deu_000760 SEINI GISCHEHT ZSBEZTIUNGEN REISCHTEN BIS NORD DOMEHRICKA UND ASIEREN +cv_deu_000761 SALREIC IEPVORDERED LATIERUNG EN BEI DEUTSCHEN ÖROPAR UND WELTLESTE SCAFTERN SO IE OLÖBISCHEN SPILEN VORLGKTEN +cv_deu_000762 IN ENERTALES CERTUM BLETERMTD SOETSIKIT AUF ERBAGBANG +cv_deu_000763 WIT EIM WAHMLITRENK IN BAUFLESSE IE KERLTE BESEAUSHAUELT +cv_deu_000764 WOLLEDEM ÖEEWES +cv_deu_000765 OSTAN DIS IENE EINE BOCHENACH EM ESTU VORNMUN DIM PGLULIEN +cv_deu_000766 EM MITEL EITERHATEN DEXZEM DER HARSCHAFT DAS DOF INER +cv_deu_000767 DIE NAM SCIEPLE TRAGNOCHREITERDE FALTSOLGEFADMASEHATIE 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I S S I S T D I K Ö N D E L E B E S S E R U N S H E I L T E N +cv_deu_000753 D I E A N D I E W I E R E N S O C H Ü Ö R I S T A N N U R N K E N G A L A U F H E N U N D E A N D A L L E G U N M B I E M T D E L L N N E R U S L A N G E L I G T +cv_deu_000754 I E R E T U A R G K E I S T E N D I E S A R Z E I T G U G E F L N G E N S C +cv_deu_000755 E T D I E S T R E I K E R B E G E N D I M S G I E N B E R H U N E R S I N F Ü H R T I C D I E G O E B L E H E C H T U N G M A S Y Ü T O S S T E +cv_deu_000756 E R S T V O N D O R T K O N T E E R S E I M W E G F R E I V O R S E T Z E N +cv_deu_000757 S I E E R H E B P Z I C H H E U T E I M A R N O C K U T E R K E N B E R A U S S D I E M N S C H W E M L A N D T H E R U S S +cv_deu_000758 T I A N A R E S C H E N I N S L E N G E H E Z U S B A H H E +cv_deu_000759 W E S S N S C H A F L E R H A H A B E N D E S I M E N T A T Z U N P E S E R E T N O B E I F O A R A U N B E R O B A T E T +cv_deu_000760 S E I N I G I S C H E H T Z S B E Z T I U N G E N R E I S C H T E N B I S N O R D D O M E H R I C K A U N D A S I E R E N +cv_deu_000761 S A L R E I C I E P V O R D E R E D L A T I E R U N G E N B E I D E U T S C H E N Ö R O P A R U N D W E L T L E S T E S C A F T E R N S O I E O L Ö B I S C H E N S P I L E N V O R L G K T E N +cv_deu_000762 I N E N E R T A L E S C E R T U M B L E T E R M T D S O E T S I K I T A U F E R B A G B A N G +cv_deu_000763 W I T E I M W A H M L I T R E N K I N B A U F L E S S E I E K E R L T E B E S E A U S H A U E L T +cv_deu_000764 W O L L E D E M Ö E E W E S +cv_deu_000765 O S T A N D I S I E N E E I N E B O C H E N A C H E M E S T U V O R N M U N D I M P G L U L I E N +cv_deu_000766 E M M I T E L E I T E R H A T E N D E X Z E M D E R H A R S C H A F T D A S D O F I N E R +cv_deu_000767 D I E N A M S C I E P L E T R A G N O C H R E I T E R D E F A L T S O L G E F A D M A S E H A T I E +cv_deu_000768 P L U K A N N S W I T D E B U S L C H R A N G V O N E R O D E R F O R E N +cv_deu_000769 I E R D O C R E G O L +cv_deu_000770 A L L E R D I N G S E R G A H B E N W E I T E R H R E P R Ü F U N G E N D A S S S M I T T E L F R I S T I G K E I N P E D A R F R I S C E U C H E A U T O B A N G E R W E N +cv_deu_000771 U N G E K E R T K A N E N F R E I P R I E F E I N E A R A U S S C H R E I B U N G A L T S V O B E L F R E I G E M E I N D Z E I N E N +cv_deu_000772 M I E Z A G K R O T E S G E A B S C H N I T E S E I N E N E I N F L U S S E L E U C H S C H O S T A K O W I C H +cv_deu_000773 R V E R E I N E D E R P I E O N I E R E A U F D M G E B I E T D E R U T Z I U N G D E R S O N E N E N E R G E E +cv_deu_000774 A C H V E N M E D I K U N D E N A F D E N E R E N G E N M S I C H Ö F L I C H K E I T B E W A N diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..b543536dad6a0a22f78c737f7445ac2bcfd2f5b7 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.1/1best_recog/token_int @@ -0,0 +1,166 @@ +M-AILABS_deu_000165 10 2 18 2 6 3 10 5 14 4 14 3 17 9 15 11 8 2 3 5 4 2 6 3 2 12 7 7 8 21 5 15 11 8 5 14 2 4 7 2 15 11 9 3 5 4 3 2 4 10 2 3 10 2 6 3 23 26 8 5 8 3 5 16 4 3 9 4 13 5 4 14 16 21 9 4 25 6 3 19 25 6 10 2 7 3 2 4 3 28 9 4 2 6 3 7 16 7 7 18 2 14 2 4 9 10 5 14 12 4 17 3 +M-AILABS_deu_000166 10 9 15 11 9 18 2 7 5 2 8 3 10 5 2 3 21 16 13 12 13 14 28 2 10 2 17 3 11 2 6 3 5 4 2 6 3 5 4 6 12 4 22 14 2 18 13 5 2 18 2 4 2 4 3 21 9 6 8 2 3 14 2 7 23 16 15 11 2 4 3 4 +M-AILABS_deu_000167 2 6 7 3 12 17 3 9 15 11 8 3 12 6 3 21 9 6 3 2 6 3 9 12 19 3 17 9 13 2 3 18 6 15 11 8 2 6 3 10 2 4 3 22 9 19 5 3 10 5 2 3 7 16 4 10 2 7 15 11 5 2 4 3 5 4 20 7 3 20 5 17 2 6 3 12 4 10 3 5 2 3 7 23 26 11 6 13 5 4 14 2 3 10 5 2 3 10 7 7 9 12 7 3 10 2 4 3 11 2 15 7 2 3 7 2 15 22 2 4 3 14 2 19 9 13 4 2 3 19 16 8 2 9 22 16 4 3 9 12 19 3 18 5 22 8 2 4 3 +M-AILABS_deu_000168 7 7 5 15 11 2 6 13 5 15 11 3 9 4 3 5 11 6 4 14 2 18 16 6 8 20 8 9 15 22 11 26 8 2 3 2 6 3 18 2 5 3 5 2 3 18 13 2 5 18 2 4 3 22 27 4 2 4 8 +M-AILABS_deu_000169 10 2 6 7 9 13 18 27 17 3 17 12 7 3 9 4 3 10 9 12 16 6 8 3 21 12 16 6 3 17 2 4 8 15 11 2 4 7 15 11 5 6 5 15 14 22 2 5 8 2 4 11 9 18 2 4 3 10 5 2 7 3 16 12 15 11 3 2 5 4 2 6 3 7 2 5 8 7 3 2 6 22 13 26 2 6 2 4 3 9 4 14 2 18 16 8 2 17 9 15 11 2 4 3 21 6 9 13 2 3 5 +M-AILABS_deu_000170 12 2 7 3 17 9 4 6 9 19 3 10 5 24 2 13 8 3 22 16 17 8 3 12 17 3 7 2 18 7 8 5 2 10 2 6 3 5 4 3 7 16 4 10 20 12 16 11 9 6 18 2 4 3 10 2 3 10 5 3 24 2 6 3 2 11 12 4 22 3 10 2 6 3 9 4 2 4 3 24 16 6 8 2 8 20 8 4 4 +M-AILABS_deu_000171 9 3 18 25 6 4 3 23 12 4 20 2 4 3 2 13 5 15 11 2 6 3 7 15 11 12 13 18 5 13 10 12 4 14 3 6 12 4 8 2 4 3 2 13 5 15 11 2 3 17 27 14 13 5 15 11 8 2 5 8 6 9 12 11 3 10 2 6 3 21 2 5 8 2 6 18 5 13 12 4 14 3 12 4 10 3 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9 4 22 14 18 9 6 22 2 5 3 10 9 4 3 10 2 6 3 14 6 16 19 8 3 15 19 3 10 2 6 3 2 5 4 3 17 2 15 11 8 5 4 14 4 14 4 +M-AILABS_deu_000199 8 5 2 6 3 21 9 6 28 5 2 3 10 2 6 17 2 4 7 15 11 2 5 4 3 12 4 10 2 6 3 12 4 10 3 19 9 7 8 9 13 13 2 7 3 21 9 7 17 2 4 7 15 11 2 4 3 8 9 8 2 4 3 2 8 3 9 7 7 21 16 4 10 9 6 18 9 6 2 7 3 4 +M-AILABS_deu_000200 21 2 13 15 11 2 3 28 2 6 3 21 2 5 2 3 7 5 2 3 2 4 10 3 13 2 4 22 7 8 19 25 6 8 3 4 +M-AILABS_deu_000201 5 2 21 6 8 2 4 3 9 7 3 4 5 15 11 8 5 2 4 8 2 3 6 2 17 3 7 15 11 9 4 22 8 5 7 15 3 12 4 10 3 22 2 5 4 2 6 3 2 6 2 6 3 10 5 2 4 7 8 13 2 12 8 2 3 18 2 19 9 4 10 20 2 5 15 11 5 4 3 10 2 6 3 7 8 12 18 2 +M-AILABS_deu_000202 4 9 13 7 3 10 5 2 3 11 2 6 3 7 15 11 9 19 8 3 9 12 7 3 10 2 6 3 22 5 13 15 11 2 6 3 8 6 9 8 3 7 8 9 4 10 2 4 3 10 5 2 3 13 2 12 8 2 3 12 17 3 11 2 11 6 3 12 17 3 7 5 2 3 24 16 6 18 2 5 14 2 11 2 4 3 20 12 7 2 11 2 4 3 12 4 10 3 9 17 3 22 5 6 13 15 11 16 19 3 8 16 6 2 6 21 9 6 8 2 3 2 3 2 5 4 17 9 4 4 4 +M-AILABS_deu_000203 9 7 17 7 2 4 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10 4 5 15 11 3 10 16 15 11 3 17 12 8 2 6 3 21 2 6 22 2 3 7 2 3 28 2 6 20 8 3 4 15 11 4 5 14 3 4 +M-AILABS_deu_000209 18 5 9 3 11 9 18 2 4 3 5 4 2 4 10 2 20 2 4 28 9 11 4 6 2 15 11 8 3 2 4 22 2 3 18 5 8 5 12 4 14 3 20 12 18 9 7 5 13 5 2 4 3 9 12 19 14 2 18 9 12 8 23 6 +M-AILABS_deu_000210 10 8 8 7 3 7 5 2 2 24 25 6 10 2 3 7 15 11 3 4 5 15 11 8 3 24 25 2 6 3 9 4 10 6 3 9 18 23 19 16 4 3 8 8 +M-AILABS_deu_000211 13 2 15 11 13 5 19 2 4 7 2 17 2 8 20 12 +M-AILABS_deu_000212 14 16 9 8 3 21 9 7 7 5 3 5 2 3 9 6 3 20 11 26 13 8 2 6 3 11 27 6 2 4 3 7 5 4 12 6 3 2 7 3 5 7 3 7 2 5 4 14 9 4 7 2 6 16 17 9 4 3 4 +M-AILABS_deu_000213 7 2 5 4 2 3 17 8 2 6 22 9 4 5 17 16 3 19 13 12 7 7 21 9 7 7 6 14 2 18 2 4 3 10 2 7 3 7 2 5 18 23 21 2 5 4 10 3 2 6 3 +M-AILABS_deu_000214 12 4 2 7 21 6 7 15 11 9 7 3 17 5 4 2 7 8 9 3 21 5 6 8 3 2 27 17 2 4 3 18 12 7 9 17 17 5 8 2 6 4 2 8 20 3 9 14 2 4 8 12 6 3 9 17 3 24 5 2 6 8 2 4 3 28 12 4 5 3 20 12 17 3 2 6 7 2 4 17 9 13 3 23 6 2 7 2 4 8 5 2 6 4 3 21 5 2 7 5 15 11 3 10 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diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..3e181ba5af3ff500ea674934a8022a49515b641c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/score @@ -0,0 +1,165 @@ +cv_deu_000775 tensor(-8.9764) +cv_deu_000776 tensor(-20.5073) +cv_deu_000777 tensor(-16.4045) +cv_deu_000778 tensor(-15.0419) +cv_deu_000779 tensor(-49.7511) +cv_deu_000780 tensor(-22.9737) +cv_deu_000781 tensor(-24.7326) +cv_deu_000782 tensor(-16.8567) +cv_deu_000783 tensor(-28.4648) +cv_deu_000784 tensor(-17.3375) +cv_deu_000785 tensor(-26.8736) +cv_deu_000786 tensor(-30.4628) +cv_deu_000787 tensor(-32.6192) +cv_deu_000788 tensor(-10.8581) +cv_deu_000789 tensor(-9.5691) +cv_deu_000790 tensor(-26.4505) +cv_deu_000791 tensor(-17.4771) +cv_deu_000792 tensor(-22.4178) +cv_deu_000793 tensor(-16.2528) +cv_deu_000794 tensor(-11.0482) +cv_deu_000795 tensor(-8.5283) +cv_deu_000796 tensor(-43.0245) +cv_deu_000797 tensor(-26.8977) +cv_deu_000798 tensor(-26.1502) +cv_deu_000799 tensor(-18.7353) +cv_deu_000800 tensor(-16.4169) +cv_deu_000801 tensor(-32.9591) +fleurs_deu_000378 tensor(-58.9903) +fleurs_deu_000379 tensor(-98.9036) +fleurs_deu_000380 tensor(-61.5778) +fleurs_deu_000381 tensor(-21.1676) +fleurs_deu_000382 tensor(-56.4506) +fleurs_deu_000383 tensor(-51.6535) +fleurs_deu_000384 tensor(-59.1295) +fleurs_deu_000385 tensor(-37.2905) +fleurs_deu_000386 tensor(-92.5722) +fleurs_deu_000387 tensor(-51.6912) +fleurs_deu_000388 tensor(-147.7540) +fleurs_deu_000389 tensor(-73.8813) +fleurs_deu_000390 tensor(-67.0492) +fleurs_deu_000391 tensor(-54.4951) +fleurs_deu_000392 tensor(-93.3065) +fleurs_deu_000393 tensor(-50.5866) 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tensor(-11.2693) +swc_deu_001436 tensor(-6.4889) +swc_deu_001437 tensor(-19.2162) +swc_deu_001438 tensor(-12.9061) +swc_deu_001439 tensor(-5.4289) +swc_deu_001440 tensor(-11.7939) +swc_deu_001441 tensor(-12.8562) +swc_deu_001442 tensor(-29.8297) +swc_deu_001443 tensor(-12.0394) +swc_deu_001444 tensor(-12.2408) +swc_deu_001445 tensor(-10.7659) +swc_deu_001446 tensor(-5.9170) +swc_deu_001447 tensor(-11.6939) +swc_deu_001448 tensor(-11.2744) +swc_deu_001449 tensor(-15.3559) +swc_deu_001450 tensor(-16.7001) +swc_deu_001451 tensor(-7.7783) +swc_deu_001452 tensor(-8.4195) +swc_deu_001453 tensor(-9.9767) +swc_deu_001454 tensor(-32.4449) +swc_deu_001455 tensor(-13.9264) +swc_deu_001456 tensor(-5.0291) +swc_deu_001457 tensor(-9.7010) +swc_deu_001458 tensor(-44.5458) +swc_deu_001459 tensor(-15.8999) +swc_deu_001460 tensor(-6.6774) +swc_deu_001461 tensor(-9.1391) +swc_deu_001462 tensor(-19.2785) +swc_deu_001463 tensor(-16.8488) +swc_deu_001464 tensor(-11.9665) +swc_deu_001465 tensor(-18.8686) +swc_deu_001466 tensor(-11.9896) +swc_deu_001467 tensor(-10.3911) +swc_deu_001468 tensor(-9.9116) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..cd3fbe23de4f63cfcc175ecfda31b72eb019d511 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/text @@ -0,0 +1,165 @@ +cv_deu_000775 DIE BEMASCHINERST FERTICH +cv_deu_000776 IN DE ARCHAISCHEN PERIODE WURDEN RSTI VORMIN DES OCKEBASSINTUYKILD +cv_deu_000777 DI COMÖÜDIER SEESE ALSTER STFÜN +cv_deu_000778 ARTUÄ GET VERGNEAMUMS +cv_deu_000779 TEARMIT ENTET EINE EFÜRK KREISCHE INTLNATZEUNEALIE KEÄRDENS ABEN VORELEN INMSCHKÜTE LUNENENZS ACKUR +cv_deu_000780 DERSON EINES BERETNANZ 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a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..191f7211896b33ff6ca979de70233c5c3f3b66cd --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/token @@ -0,0 +1,165 @@ +cv_deu_000775 D I E B E M A S C H I N E R S T F E R T I C H +cv_deu_000776 I N D E A R C H A I S C H E N P E R I O D E W U R D E N R S T I V O R M I N D E S O C K E B A S S I N T U Y K I L D +cv_deu_000777 D I C O M Ö Ü D I E R S E E S E A L S T E R S T F Ü N +cv_deu_000778 A R T U Ä G E T V E R G N E A M U M S +cv_deu_000779 T E A R M I T E N T E T E I N E E F Ü R K K R E I S C H E I N T L N A T Z E U N E A L I E K E Ä R D E N S A B E N V O R E L E N I N M S C H K Ü T E L U N E N E N Z S A C K U R +cv_deu_000780 D E R S O N E I N E S B E R E T N A N Z B E G A N S E I N I E F O S B E I K A E R I W E I D E N S P O R T F R E U N D E N W A N E E I K E L +cv_deu_000781 I N D I E S E N J A H R G A B E S I E D E N O M E E I N E N S I N G E S U N D S E C H S O N D E I S I G N O M E R E I E N S A L L E B E N +cv_deu_000782 N O R D W E S T L I C H V O N H A K H A U S E N B E F I N D E S I C H D I O R T S C H A F T H A K E N B R U C H N +cv_deu_000783 I M O R T K N A E N B U R G G I E N V I E L E S O Z I A L E E I N E R I C H T E U N G E N V O N E R E M A N L A M P R E C H T U N D E R M A I N H Ö T E A U S +cv_deu_000784 I C H W E R D E F O L Ö K L I C H D E N R A T Ü B E R D I E M P A L M E N T V O R G E T R A G E N E N B E D E N K T E N I N V O R M I E R E N +cv_deu_000785 E S E R E T R A U R E C G E W E S E N E I N S O W I C H T D I E S T E M A N I C H T E M K O N S E T F A B S C H E N Z U K Ö N +cv_deu_000786 N O C H T I S S I M T O T I M K L E I C H E 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E L E V O N B E R H I T Z U N I N V O R M I R U R D E N W A D I E D A S U N T E R N E H M E M A L S N I C H C H W R W E G E N B E Z E I G N E T E +fleurs_deu_000383 N A C H D E D E R D M L E U N E H H N D E R T R A L N S E C H Z I G E B A U R T W O R D E N W A R K A M D I A H R E S S E I L I H N B E R F L U T U N G D E S D E M E N T E M N F L S V E R T A L E N Z U M S T I L S T A N T +fleurs_deu_000384 E R W A U C H A M S T E C H E N V O N G E L S C H E I N V E V I E L E D E N D E B E T E I L I C G T A K T E L E B E I S H P I E S E N E A R B E T S C H L I S E N I E P R I M E H M I N I S T E R P O R T R E S A F D E R F O R D E R S E R T D E R K A N A D S C H E N F Ü N F U N D U N D E R O L L R N U T E N E I N +fleurs_deu_000385 D I E A U P T S T A T V E R M O D A W I E R N I S T K I C H I N A D I E E I N E I M P I S P A C H E S T R U M E N I S C H A B A R V I E L E M E N T E N C S P R E C H E N R O S S E L C +fleurs_deu_000386 S Z W I C H E N D E N E I N Z E N E N D Ü N E S T I E N H E R S T D E N A U C H U N M B E S T E N D I E G E Z E I T E N G E T A L L T E P R O I N D Z E D I E B E K A N T E S T E D I E S E P E R I O D E N O A D I E P O C H E D E R D R A I L K Ö N I N G R E I C H E D I E S E C H T I C H I E R E L A Z W I S C H E N D E R H A H N U N G D E R I E N D I E N E S T I S T A T V A D T +fleurs_deu_000387 A M A N D E R E N E N E D R S P E K T R U M S H W A N E L T M A N S I C H E N E I N I C H T W I D E R Z U R K E N D E N D I W I D E U M D A S A L E S A N E R S M A C H N M O S S A S S T I E N E S G E M A C T E R U N D S I C A L E S T Z U A L G E M A C H T +fleurs_deu_000388 D I E M E I S T E N D I N T E R P I T E A T I O N E N D E S T I C H N O L O G I S C H E N D E R T E I M I N I E S N U S T A L E N Z W E I A L G E M E I N E V O R S C H T E U N G E N E I N E R S E I T S T S T D I N T W I C K L U M D E R T I C H N O L O G I E S L L P S T E I N E M W E G O L G T D E R W E I T G E N T I E N S E I S U T D T O R E L E O D E R P O L I I S C H E I N P L S N A M E N D I G T U N D A N D E R E R S E I T D A S T I C N E R Ü G I E I E R E R S E I T S A U S W I R K U N G E N A U F G E S A L S C H A F T N H R T D I E E H R I N H Ä R E N D A S S Z U T T I A L B E D I E N S I D +fleurs_deu_000389 W I S C H E D E N E I N Z E N D N A R T I E N H E R C H T E N A C H U N M B E S T E N D I G E Z E I T E N G E T A I L T E R O W I N Z E N D I B E K A N D E S T E D E P E R I O D E N W A D I E P O C H R O D E R D E K Ö N I G R E I C H E D I E S E C H T I C A R L A N G T I S C H E N D E R H A N U N D T E R I N D E N R T I E S T T F V A N D +fleurs_deu_000390 D I E M L I E K T Z U F O R G I E B I E Z I Z I C H T E S T O O M E N T A U F D E M G R E N S T R E I L T I N D E N D I E P A L I S I N E N S E R E I N Z U R Ü E G S E T Z E N D E R G R E N Z E N I N D E N Z U S T A N D V O R D E M S E R X S T A L G L E G R I E G V O R N N E N E H N U N D E R T S E B E O N U S E S T I G O R D E R +fleurs_deu_000391 M I T I M P E L U S T G R E C H E H E R S P A C H K N N S E U R D E R W E S T E N V O N S E I M E V I E L O S O P I S C H E N U N D W I S E N S C H A F L I C H E N W O R T Z E N K R I C H E N E N A B I S C H N I T E N +fleurs_deu_000392 W I R S T M I T E R A U S A G E D I E S I U E R S A U S I E Ü B E R E I N D A S T E N N T R E S S N U N D S R E A T L E D E N D V E R E I N U N D I R E S P O R T S B P S S R G E D I E N D I S T E N W R N E H A L B U N S R R G E N S A T I O N D E H N V O L E V E R N D R U N G V E R A N D T R E I B E N A N S T A T E I N E R T I E R I Z E T V I Z I E R U N G V O R Z U N E +fleurs_deu_000393 I R E U T S F A T E N N A S S A N G T P I K A R S B O G B I E T E N A C H Z E I T F Ü R E I N A U F E N T A L I N E R S T A T K R E U T Z V A T P A S R S I E R E S I N D F V O R D E R I E S U N S L I C H T B E F R E I T S I E B E D N G E N +fleurs_deu_000394 E R E I S E N D E V E R D E N R I N G E N D G E W A N T A U F J E T W E D E A R T V O N U N W E N T E Z U A C H T E N D I E I E G I E I E T B I E T R I F T D A D I S S I G H A U F A L E R E I S E P L E N E A U S I E R K E N K A N T +fleurs_deu_000395 S E B E S A R T D A S D E R K O L Z U N G S P U N G K T D E L I N M I N D I E E N B I E L T W E R T I K A L U N T R E T O N D T A L D R I T E E N D E E F I K T I S T D E P L A S F Ü R T E S A U P T M U O D I I S T S I E B E I S S I N +fleurs_deu_000396 S E I T N U N Z E N U R T A C H T E N A C H T I C H M S T E W A L U N D R A N S B E R E N S E I N D A M I T W E L E U N B E O B A C H T E B E T Z E U G E N K Ö N D A S Z W E G I N D E R W A L K E I N E U M S C H L E G E V E R H N E N S I N D U N D A S K E I N U M S C H L E G E E I N G E O F E N W E R D E N A U S E R J E N E D E R T O R D U N G S M I S E H L T N E N A T T R S I R D T E N E L E R +fleurs_deu_000397 O T E R A R I S T K A N N E R D E S B I E Z A U B E N D E R Z W E I S C H A L I G E H A U P T S T A T U N D S E L T E N S I C H I C E I N E R E I E V U N K U N Z S T G E L E R I E N U N D M U S E E N A U S D I E K A N E N D E R S V E R G A N G E N H E L T U N D G E G E N W A R T B R E S E N T I E R E +fleurs_deu_000398 D I E S E P A R E K Ö R E N S I C H V E R E I N A D E B P T I O N S P L A N D V R E R B E B E E N S C H E I D E N +fleurs_deu_000399 I N F O L G E D E S S E N S E N Z W E I F I S H T A B E N A U S G E S T O R M E N D Z W E W E I T R U S E N V O M A U S T E R B E B E T R O T D A U N E D E R G E L A R S Z Y Ü F E R +fleurs_deu_000400 T R A N Z E N S E N N I H R N A T I L I C H E N M O G E B N G A M B E S T E N A U S W I E D E R S T E N S I A L S O D E R V E R S U C H U N G A U C H N U R E I N E X M K L A U D W E R N +fleurs_deu_000401 A U F D E R N A H S E I T E K Ö N T E I S M E R M A R I E R G E B E N D A D I G R O S T E D U N E S T E S W E R E I N F E R A F O D I E L A V E R A N D I O B E R F L E C H A U F T U S T E G E N +fleurs_deu_000402 E R F Ü C K T E C H I N Z U D A S S I J E D O C H N I C H T E A R Z U O A U F G I E V O R D E R T W E R D E N S O L L T E R N F E R T F L I C H T U N G E N E I N D Z U G E E N D I E Ü B E R I E R E N I N T W I T L U N G S T A N D I E R E V E R A N T O R D U N G U N D I R E R F Ä I G K A T E N H I E N A U S N G E E N T +fleurs_deu_000403 T C I E R T U E L I H I E L F I S T E L U N G E N S I N T I N D I S O F T E R E N G I E B A U D U N S O L E N A H B E I T S C H R I T D E N D I E D E S C H Ü L E R A L E I N M Ö G L I C H E R W E I S E N I H T B E V E L T I G E N K Ö R H I N T E R F R A G E N N E I E L E G E N U N D D E R G L E R E N +fleurs_deu_000404 A M F Ü N F Z E H N N A G S T N E U N Z H N H U D E R T V I R Z I V I E L I A L I E R T E N N S Ü T F R A N K R E I C H E I N D I N W A S I O N W O R D E A P E R E S C H E N R O G U N G E N R D +fleurs_deu_000405 E R G R I F A C H A L L S A N W A S S E N S W A S S E R K A R M S E B S T E N G R O S E R D E N O S A U R I E W I D E R T I E R E C G S W E R I M N I C H T G E W A K E N +fleurs_deu_000406 S E I T E R K R N D U N G V O R A S U N T I O R F Ü N F Z E N D E S E N U O D R E I S C I E S E S P A R E G W E I G E L U N G V I E L V O N S E I M I N D I G E H N E N K A R A K T E R N D S E I N E I D E N I T E T Z U B E W A R E N +fleurs_deu_000407 S T R O Z S D E N I S D E R A N T E L L A N N I E X S D I E W I N D E S T I G H T D E B I N D E R G E S A M T E N R U P E D E R L E U T E E M I T D U G D E R K O L O S E R O F E N B A D E R N N O C H G E R I N E N S E X S T A U S E N D E R I N D G E S A E N D R E I H U N D E N D R E I G A U S E N L E L T E D I E I N S Ü T A F R I C K E R T U E I N E M B I E S T I N T E N D E I T P U N G T A N G E S T E T S I T T +fleurs_deu_000408 E N S C H E L Z W E I T A U S E N S E C H S E L E U T E R T A S K O N T I N U M K O N Z E T A S E I N E M I T O D E U M O B G E N S A T I O N D S H L F E N L E I S T U M G S F E G E Z U W E R D E N +fleurs_deu_000409 I N D I E S E P I E R I O D E N D E R O E R O P E S C H E N G I C H I G H T E S T A N D D I E R L I C H U N D M E C H T I H E G E V O R D E N E R A T O L I S C H I Y K I E C H E A U F D E N P R Ü S T A N D T T +fleurs_deu_000410 D I E E R S D R C H T E N D I B Z I C E M F H L U N G I F T A S E I N E N E U D E P L O M A T S C H I N I Z A T I E V E V O E R E N D E D I E S E N J A H R E S E G R I F E N W E R D E N S O L L T E U M D I E R A G I S H E N G R E N Z E N G E G E B E R F E N T L I H E N T E R W E T I O N D Z U S I C H A R N U N D P L O M A T S C H B I Z I E O M I Z E I N A C H B A N I E D E R R T U S T E +fleurs_deu_000411 D I S P E T E T E I N I U T E G E L E G E G H E I T D A S N O T L I C H T U S E N D A E H I M E M M E H R A U D E R W E N I E R R U N D U M D I E U R D U N K E L S T +fleurs_deu_000412 P R O F S S O R E N P A M E L E V E R G U S S O N V O N D E R N W Ü U S T I A F D A N D I M E R K T A N S O A L I S T E N S C H E I N E I N E G E E L I E G R A N Z E Z U A S C R E T E N W E N D I E V O T T O U N S W E I T E V O N V E R D E C T I G E V E R F N T I C H E N +fleurs_deu_000413 E S K A N S I C H L O N E I N E E L K A T Z U K A U F N D I E Z U T R I T E N W D E R T U U S G E W E L E N P A G S E N H E T A F R I K A R D E R Z U A L E N Z U Ü T F R I K A N S C H N E R T O N A L P A R X S G E W E R T +fleurs_deu_000414 D I E P R Ü K E S O L E M S E T E M B E R Z W E I T A U E N S I B S H N F O L S T E N D I T N B E T R I E A U F N E H M N I S W R R W A R E D S I P A S I A N I S C H E N Z O L P U N K T E D A N F E R T I G S T E L L Z E I N W E R T +fleurs_deu_000415 W E R E N D E I E R X P R M I N T E L L E I M S T F N E L A G E U S E I N S C H E I N T D I E B O L E R M O T L I E T Ä T Z U S E N G U N G G B T E S B S S E R K E I N E M I T I K A M Ä N T E D I A L S E I N D R D I Z U B E H A N D L U N B E S T E N D E I N V E K T I O N G E E G N E T N A C H E W I E S E O R D E N +mls_deu_000281 E I N E U S E R S T L E P A F T E R D E B E C H E W E C H S E L V A N S T A D T W A I E R W U K D E M P L A N E I N A L G E M E I N E N S T A T E N K O N G R E S T Z U B E R U F E N U N D K O N D E S I V O L L O U F I K T N O N U N I C H T Ü B E R D E S O R Z U L E G E D E R O G R M M U N D E N O R T E S Z U S A M T R E T S E I N I G E N +mls_deu_000282 E R W U S T E N I C H T W A S I M D A S L E B E N K O S S B A R E S G E R A U P T A T E S C P A N K R A F T U N D M U T D D A S S E S I N F E I K U N D S C O E U G E M A C H T A T E U N D F Ä I C H Z U D E N H O H N D I N G E N Z U O D E N E N U N G E T R Ü B P T E I T F R O L D E N G E H Ö R T +mls_deu_000283 D I E S E R U N G E M A N N H I S K A K A L I T Z I E N U N D B E F A N S I G G R A D E U E W A N D E R S C H A F T A L S I N I M G E A N T E N K Ö N I G R E I C H D B E K A N D N A C H U N G W E N D E R P R N Z E S E N V E R L E S E N W U R D E E I S A K T D E S C H N E I D E R W I E N E S W E I T R N I H T S I S T E I N W E I P A U C H N H N I C T K Ü K Ö L S T U N D D U T K Ö N I G S E I D A M Z U O W E R D E N D A S G E I S S E T M I A L E D I N G S T +mls_deu_000284 N O C F Ü N F M I N U T E N N D I E W O L K E N D E B E W U S T L O S I G K E I T B E G A N N Z U S C H W I N D E N I E R T W U S T E H S E R W O L D A S I H N M E I M E I G N E N B E T E L A G U N D D A S D I E R O T E O G L O T N I C H T A N D E R S W A L S D A S V E U E I M K A M I N D E R I N D A S T U B E E S W A N A C H T E I N E K E R E B R A N T E A F D E M T I S C H E +mls_deu_000285 E I C H E I E V E T R E N G U N E N W E B E C H T E U N T E R H A L T E N C O M T A N E M P R P E I T I T Z A L E R D I H O C H F L O D E S S E X S U E N B E D F F T I G K E I T S O F N D E S E N D E G E N A N D E N S I L L I S C H N R E A K T I O N S O D E I E D E R S T A N S P I L D O N G E N D E M E R +mls_deu_000286 T A B E R A F E N G E H R E N B E I H A G K E N B E A N D I E K I S T E N W A N N T N U N S O H R T E I C H A U F A C E Z U S E I N E N E I N K L R E R S C H Ö N A G E D A N K E N G A N G D E N I C H I R G E N D I E M I T D E M B A U C H A U S G E H E K T A B E N M O S S D E N A F F E N N D E N K T E N M I +mls_deu_000287 I S S S P A T R E E R N E S M E N S C H E N D E N Z I K Ä N E N F R A G T E I L E I S E W E L C H E U N B E M E A K T A M I C H E R A N G E T R E T E N W A U M I C H I N D T G E G N E T E D A S S N E N F A N T E S I K O P F S E I U N T S C H U B T Z E I C H U N G I L I C U N T U D I E A N D O N D L Ä T T E R U N A T Ü L I C G S P A C R I C H I E U N B A H E I L T D E N E W E I N S E I T O E U E S P E T R I E M I S T E R O T S C H S T E S +mls_deu_000288 I C H W E I S S I C H S E R K R A N K G B I N S A I G T E S I N E R E I N R W E I L E V O R N P A M I N U T E N V E R S C H T E I C H M I C H E E T T E U M Z U R E E N U N D F Ü L D E D A S I C K E N G L I E D M E R E N Ü O R N K A M E S W E R E G U T D W E N I C H E N G I M Ü T E L E I C H T D A N K Ö N T E R B E V O R I C S T E R D E N +mls_deu_000289 S O A B E R I S T W E R U N S E R W E S E N S K U N D O T S E L L V E R D A C H E R O M H A T Z I C H E D O C D E R C H L A N G E N K N E U L D E S A L K E N S A T A N G E S H L U N G E N U N G Ü B E R D E M F Ü N K I E N D E N L I E B I I S D I F E N S E R N I S T E S H A S S E S E L A G E R T W A S W O N D E R D A N +mls_deu_000290 B E S I V I R E L I E A G E B L I E B E N A B E R S I W A G E T Z W U N G K Z U G E H N E A D I Ü N K L I T K E I T B E I D E N M A L S Z E I T E N E I N E S A C H E W A A U W Ä C H I N G E T Z S H Ä R D H O R L S T R E N G E G E H A L T E N W U R D E +mls_deu_000291 N B L I C K L I C H F Ü L T E W I E H R E A M S I C H T E N Ü B E R M I C H I R E R M P I N D U N G E D F Ü H R M I C H N I C H T U M E I N A T O U M V E R I N D E T W A H E M N U Ü B E H A U P T K A N E E N D E R U N G F E E C H W A R M D I C H S E I S I R E M E R S T E I N A T E N A U D G E W E L C H E S N I E M A L S D U C H T R E N E N G E N E T Z T N I E M A S I N E R T L I C H K E I T A U F G E L E U C H T E T A T E A M E N +mls_deu_000292 N S O D E R S S E M I S T Z E R G U T W I N E H Ö U G L I G N E R F Ä E R I D S I C F R E U N W Ü E W E R E N S C H N Ä L D R N A C H A N D E N S O L B E A U F P R E C H E N S H N A L R E I T E N D M I T Ü R N O C F O R D E R N C H D A S L A G E R E I C H E N E R S T I G E A U D I P F Ä E R D E D I N A U S G E R O T A T E N U N D F L O G E M G A L O P B T D A V O N D I E S A L H Ü T E T E N I R U N S D E R F Ä E R D E I D E R D E R E K Z E F O L E N W E R E G E R A D E A U S N D E R S P A D E N +mls_deu_000293 W A L D I E B E R M I T P Ä E C H P B E S T R I C H E N W A H R B I E B E I N E R V O N D E N G E L E N E N P A N T O F L E N F E S T H Ä N G E N U N D I N D E R A N G S D A C H S N I C H T E R A N I N I T Z O N E M E M U N D I E I S D E N L E T Z T E N S C H I T V O N D E R T R E P E T A D D E R H A T S T Z W I L F A U S G E S C H L A G E N D A R A R W A G E N U N D F E R D E V E R S C H U N D E N U N D A S C H E N P O T E S T A N D I N S E I N S C H E N K L E I D E R A U F E R U N G L N S T R A S E +mls_deu_000294 I E I E N O M D E S A S V E R M A G E E I N V I N S T E R A N S T L A G E R E B E S E N T Z Y Ü D E N I E S I S C H R I C K A L S A K T E H R G I E T D A S M E N I G E G I T A N U N B U T V E R G I S N S V E R M Ä D E N +mls_deu_000295 N U R D E R D O C K T O R U N D I E W E R T E R E N S O L L E N V O R S E I N E A U G E N K O M M E N E R K L Ä A T E D I E T R I N E R I N G R O S S E M A M T S E I F E R D A M I T W A D I E F R A R O B E R S T G A N S E I N F E R S T A N D E N U N D P I R X S T E R F R E I T K E R T E S I M I T I E R E N +mls_deu_000296 K A A R U N T R Ü S T I C H Ü B E R D I E L A G E D A S K Ö N Z T L R S E R B E G A N Z U W E I N E N U N S C H L C H T Z T D E L A N G E I N D E O R G E H A L T E N E N H N D E D E R K O N S L E A W A T E T E B I S K A S I C B E R U I C H T H A T T E U N D E N S C H L O S I C H D A N D A R E R K E I N A N D E R N A U S I G F A N D T D E R N O C H Z U M E I T E R S C R E I B E N +mls_deu_000297 O N D I M F E R D E H E R D E N D E R P A T S C H E N U N D S A G N U N Z S D A S S I F E N E A P A T S C H E M F Ä R D U N D S E B E N S U V I E L E W A R E N U N P R E N I G E B E N W I R D E N Ü F Ü R I N K E I O W A B F E R T D A S I N D U N R I K L I G A R F O D U M A P A T S C H E N F Ä R D E Z O H L E N A L S O R C H T I G H E R A R S C H L D E R E M T O D E E B E S E R G E F A L L E N U N D E R I E B L U T V E R G I E S E N W E C H E S U N B E V O R S T A N D W E I S E F Ä E R D E H E N D T L E R +mls_deu_000298 D A S M A T O N E H Ü T C H E N V O N S C W A T Z A M S A M E T K A T I Ü R S A I E R E L A N G E N L O K E N G E D R Ü C T D I E R E W A N G N U M F L O S S E N N D Ü B E R S C H L T E N H E R A B W E I T E N S O T R A T E I D R S E I N F E C H R E L E N T L I C H G E B O U D E U N D S T E B P T E T Z W I S C E N E R E I N D E R H E I B G E B L Ä N E T N O F K N E R A U F E N D A B +mls_deu_000299 T U M U S T E R S T E N Z A G E N A L E N S Ü N T H A F T E N S T R E B E N U N E N T I E V E R E U I U N D D E M U D D I E F Ü H R B I E R H L L I N G E R F L E N G E G E N D I E D U G E F R E E L T E S T T I E J Ü M L N G E W L C H E F E N S C H E S O S O L N E G E F L O N S O U C H T E N N A U E N E R W E R K T A T U N F A N D E N I N +mls_deu_000300 E R L I E S S E I N E G R E T E N I H T V O R T S C H L Ä B E N A M A L L E R W I N I G X S T E A B E R I N D E N G R O S S E N D V O G E L B A U A R U S I E A L E E N E I N E M T O N E B F E I F E N M O U S T E N W I E R S T E S A K T E +mls_deu_000301 V R N H E S K O M A L T E N U N H A L I G E B E G E I S T R N G V I E L E B I E L T A S T E L Ü G E N H A F T E N A B E L W E L D T R E N E A L S E R E R M O C H D I E B U L E R I S C H E Ü L B E K A E T E W E I B I E N G E S T A L T E N S O B E R H A F A S S T E L E N I N D E M V O N L E B E N T E M O D E L E D I K A N D A T I O N G V O N D E N A L T E N M A H M O B I L D E R R B E R F O R M N D B I E L U N G I N D N A M +mls_deu_000302 B W E G U N G U N T A T D E N S T E N Z U G I E R S T E M T E D I E F O E U N A N G E G E B N E I N K G R E D E N Z I E R N M I C R Ü B E N H A N F E E I C H E N U N S A U E R A M F A N A L L E I N D E M F E I F N K O P F E R N W E S E N A B E R I N F Ü N F T N H A U P T S T O F H A I C H N I C G G E N A N D J E R T T R O C H N D S C H M E K T I G D A S E H O N S T Ü C H E N F I L S C H U D E R B E I S E I N I S E I G P L I E S T E N R A U H A U C H G E G D E N H E M E U N G E G E D I +mls_deu_000303 U N D D A S F O U R S T A N D A U F U N D F L A C K E A T U N K O C H D A S E S S E N F E R T I C H U N D D E R B R A T E N B R U T Z E L T E R F O R T U N D E R K O C H G A B D E M K Ü C H E N I U N G E N E I N E O R F E I G E U N D D E M A R K T R O P F T E D A S U N F Ä E R T I G H D A R W A R T D I E H O C H T Z E I T O N D E M K Ö N I C H S O N M I E D O N G R Ö U S I E N G E F E I E R T U N S I E L I E T N E F E R G N Ü T E B I S A N I E R E N D E +mls_deu_000304 U N M D D E S E R M I N I C H N A C T R A G E N B O L E W E N I C H I D E R S H F Ä N S T I G W A G I N S E I N O H M E I N E M R A R T D E R H E R F A R A E H A D I E I N A L L M P R E C H T G E H A U T U N D I C H M A A M U N M R E C H T A B E R +mls_deu_000305 G Ä C H E N E M A S E N O W I N I G E K R A M B E T R U G E R E I T E E S I C H K I L I E F R M I G A U S H U M U S T E D E R E R D E S M I N D G E G E N F L I G E D E S P R E N K I S C H O S A U F A N G E N N D Z U W I B R I N E N +mls_deu_000306 D E R F C K S R E I C H T E E M D E U N F R I T I C H E R I E D E N S P F E I V E R H N D E R M A N T A T W A C K A R S E I N E S E X S Z Y G E N S A G K T E D E R R O S I G E I S A C H T E T N I C H T A U F D I V E R S C H I E D E N E H A U D E R M E N S C H E N D E N D I K Ö N S I C H M I T F A B E B E S C H M I E R E N M I N T Z U D T E U S C H E N S O D E E R S I D A S H E T Z S A N D I E H T Z E N D E R K L I G E R V O B E R Ü B T E N S T A M E D E R K A E I O A S E I N T A P T V E R U N E R S C H R O K E N N T R E U D A S M E I N E I G E H Ä N G +mls_deu_000307 A L L S A S W I E M E T I E R B E G E G N I T S C I E B S I C H D E U I C H U N D Ü B E R E I N A N D E R B A L T U N T E R S C H E I B E N W E R I N K O N T A K T D E R I S T E R E R H A N D N D E M E I N I G E I E R N A H M O N D E R M E I N I G E W E I D E L E S C H E N E I N A N D E R A U S B E I D E V E R S C H L I N G E N S I C H +mls_deu_000308 E R M Ö S T E D E N E N F A C H E N R O N I T E N K Ö R A L D E S M A L E S M I T A L L E E R K L E H R E N E N U N Z R E C H T W E S S U N G E N I M I T K R A U S E N W I G U N V E S C H N A R K E N U N V E R B R E M E N I C H T R E T E I D I E P E R S O N D E S E R A U S G E B E S N D B I T E T I C H I Ü N S T I G E L I S E R T U O L I S T I E D U W E I T E L I S E S T F O L D E N D I S D E G Ü T T I S T M E R H E N +mls_deu_000309 D I E O F D A M E N B E K A M E N K R M P F E R U N D T I E K Ö N I G E N U N I E P R O M Z E S S E N E N D I E R E R A L L A L I B S E N H Ü N Z C H E N W E R N D E R M A L T E R U F D E N H O S G E N O M H A D E N B E M E R K E N Z U I R E N S C R Ä C T E N D A S D I L I E L E R A R M A R A N D F A B E N E N U N D O R A N S C A E D E N S E I D E N K L E I D E R A L E I C T B E S E T M I T D E N H E S L I C S T E N Ö F L Ä G E N W A N E +mls_deu_000310 V O N L I E D A N D I E I E S I N G E N U N K L V I E R P I E S E N D I E S I S P I E L N V O N G E I L T B Ü Ö R S E N D I E S I H I E G K E L N V O N V A N Z Ö Ü S C H E N B Ü C H E N D I E S Ü B E R S E T Z E N K O N T E B I S M E N G E M Ö Ü T W E R E N D I C H L A U S T E Z O N A C H A M N G A U F G E S T A C H E T W U R D E +mls_deu_000311 A M E N N A T E N W A N B L O S I R E N Z I G E R S C M O C W A N I H R E K A S T A N I E N B R A N F L Ä C H T E N W I L C H E N W I L D E U N N A T Ü R L I C H E R A N M U N D A U I R S C H L T E N E R A B V I E L E N C H N A M E I N B O G E N G F E I N K A T O U N G S N D Z E I C H E T E M M T O S R S O R K F A L T I O M G R S S E R +mls_deu_000312 A U R W I E D E A U S D E T C H L A N D N O C H A S I E L E N E I N E M A N D E R E N S T A R T K O N T E M E N E F A H N W A S E R G E G N S T A N D U N D E S E S L T A D D I E S U N T E R E D U N G G E W I S E N S E I A N V R M U T E T E D S S E S I C U M E R K L I E R U N G D E R M A T Z I E R Ü B E I E R A B S I C H T E N U N D U N D I E V E R M I T L U N G D E R M E C H T E T Z W I C H N D N M A R S T A T E N U N R O S P O T A N I E N H A N D L E +mls_deu_000313 L A S U N S W E N I G S E N S E I N E R Z E I T L A N G V E R S U O C H E N I N D I E V E R N W I E R U F D E S E S B E I S I M I T E I N A N D E R A U S R E I C H E N D A D A S Z U S A M M E N H Ä N G E N D E V I E D U E S A G S T E I G E N T L I C H E U E R L L E M E N T I S V E R S E T Z T E I D R T +mls_deu_000314 V E A S C H I N E N V O R K O M M N S E F Ü R D E N Z U D E R V E R M O T U N G D A S F R A U W I S E D I K L E I N E N V W I S E N V E R B R E N E R I E S O L B E S E I N S U S T A C H G E H E I T Z T A B E N D A S D E R T P L A D E N Z S P R A N G A U S S T E M O L E I N F Ü R C H T E L I C H E R E R O C H W A G E N U M E W O R D E N S E I N +mls_deu_000315 U N D G I N G D E M S C H R E I E N N A C H S O S A H E R E N T L I C H E I N H O H N B A U M U N D O B E N D E R R A U F S A S E I N K L E I N E S K I N D U N T E R D E M B A U M A B A R L A K E I N E F R A U D I E S C H L I E F +mls_deu_000316 S I E H A T E N S E U E B N D I E F I S C H E R G A R D N E R W A L C H D E N A C H T I Y B E R T A U S G E U O R H V E N W A D E N C H E R E I N G E Z O G E N N D I E S E L E U I T E R G E R H E O T E N A G E N S C H E I M L I S C H V E R S C H I E D E N E N N C T Z I O N E N R N A B O R L D E R O L U L O P Ä I S C H E R K A R K T E B E R A L L E N A U S G E T R Ü K T W A T +mls_deu_000317 N E N E I N I C H S C H Ä M E M E I C H L A S M I C H E N D E I N E M B U S E N M E I N G E S I C H T V E R B E R G E N G E R S I N K T E N S G R A S N I D E U N D Z I E S I N A C H +mls_deu_000318 D I E K I N D E R A R B A R S A S E N V O R D E M W A L T U N D A L S I E D I E R E I K N E C H T E R V O N W E I T E M L A U F E N S A H N S P R A C H R L E N S H E N Z U M P F U N D E F O G E L V E R L Ä S T U M I C H N I C H Z O E R L A S I C H T I C H A U C H N I C H T S O S P R A C H F O N D E F O G E L N U N U N D N I M E R M R +mls_deu_000319 W I E D E R S C H L Z E I N S E I N E H L D I E G U N G S R I E D E H E R V O R H U B D E R L E R A B R A C H T E A M K L A E N S O M M A R M O R D E N G M I T S E I N E N S C H U H L K I N D E R N E I N G E S A N G S T Ä N T I E +swc_deu_001408 S T D T W I E I E S E I N S O H L E N +swc_deu_001409 D E R E N C H W I N G E N E N D U R C E I N E Z U S E R T S C H A L T U N G S T U F E N L O S +swc_deu_001410 D I E A U F A L E B E I D E R S E T W R T A L U N G U +swc_deu_001411 U M D E Ü B E R L E B E N D E N D +swc_deu_001412 S P Ä T E R W U R D E N T A I L W E I S E S O G A A C H T P A R E R L E L E L O H S T R E I F E N E I N G E S E T Z T +swc_deu_001413 M O R D E B E K A N D U N D V E R L A N G T E +swc_deu_001414 B U N D E W E G E S E T Z D I E S T M V O N W I H L E N +swc_deu_001415 G E S C H C H T E +swc_deu_001416 S P A L T U N G F E Ä G +swc_deu_001417 S C H A T P A E B O R N D I E U S E R E N D F E R N D I S +swc_deu_001418 U M W E I T E R I N H U M A N I T E R E H I L F E Z U +swc_deu_001419 S I E R K A M T E N D I E N E U E I C H E N E S C H E R E G I E R U N G N I C H T A N +swc_deu_001420 D I E O R A U F Ü G E N V O N A N D R E N Z W A N I S T E N S E T E M B E R Z W E R D E N D A C H T I +swc_deu_001421 E R W I E S I E M I C H T S C H L D I C O D E R M I T S C H L I C H M A C E N A N T O D E R N S M I T G E S E +swc_deu_001422 D I E M E D E R E R T T U M M A R E I N E N +swc_deu_001423 N D T E I F E N T I E S E N B E I D E R +swc_deu_001424 K R E I S W A L E F O R S C H L A G U N D E I N E L A N D E S L I S T E N D E R Z E I T N E N +swc_deu_001425 A N U M S E R Z U N D E S A G E I N F O R M E I N E S T F Ü N F Z E H N T E I L E N L I E D E R Z Y K L U O S Z W E T S E D C H T W U R D E P E S L A S K Ö A B E R T I N N E B E R A B E T U N G V O N H A U S T H A R E M E N +swc_deu_001426 I E D I E O L G E N D E T A P E L E D A R S T E L L T +swc_deu_001427 U M S T R U M F L O S B E +swc_deu_001428 D E B U N D E S W A L L I T E R B I S T Z U M S I M O N E U N Z I G S T E N T A G +swc_deu_001429 O R I R I C H E W O R D E N D E U S C H E R N I C H T M I T W E L E N +swc_deu_001430 A U S S F I R N M U S T E I N K U S E R K O T E R B E G N +swc_deu_001431 V E R G L E I C H P A N Z E A H L E N W E R T U M G E A N D E +swc_deu_001432 B E T R C H T E D E A L G E M E I N H E I +swc_deu_001433 U N T E R S C H I T L I C H E A U F A S S U N G E N G A B E S N U R D A H R Ü B E R +swc_deu_001434 D O L L B E I M B U N E S L I K I S T E N B E R S E R D O R T M U N D N A C H V O L G E R D E S U N M I T E L B A Z U V O R Z O R Ü C K E T R E D E N R E N A S I R F E N R Ö B E R +swc_deu_001435 N U N Z E N E R D C H T U N A C T Z I G +swc_deu_001436 R E I N E N Z Y G L O P E T D I E +swc_deu_001437 D E R V O T O S T R O M I S Ü B E R V I E L E G R Ü S S E N O R N U N G E L I N E A R Z U M L I C H T E N F A L +swc_deu_001438 D A S H T F Ü K L E I N E P A R T E I N G R O S E A U S W I R K U N G +swc_deu_001439 I S D E I T E R A T I E V E T I E F E N S U C H +swc_deu_001440 D I E S K Ö N N E N U M B E I S P I E L K O N D E N S E A T O R E N S E I N +swc_deu_001441 A L S D I E K U R S A U F K O B E R H A L T E N D E N S O J E T I C H E N S C H I V E R A B T R E T E N +swc_deu_001442 B U N E S T A G W A H L N U N Z H U N E R D R E I U N F Ü F Z I G W U R D E R S M A L S N A C H E I N E M V O M B U N D E S T A I G S E B S T E R L S E N G E S E T Z +swc_deu_001443 B U N D E W A I G E S E T Z V I E L F A C H E I N E R T W U R D E N +swc_deu_001444 E R Ü B E R L A G E R D E N V O R T U S T R O M E U N D T R E G T +swc_deu_001445 D R O I N T E K R A T I U M N D E R B E I D E N D E U T S C H E N S T A T E N +swc_deu_001446 B E R L I N E R W Ü L M E U S E N S T A T +swc_deu_001447 A F I E Z E L E R F Ü H R U N G E N +swc_deu_001448 B E D E R V E R H E T E N S W A L W I T Z U S E T Z L I C G D I E E I N H A L T U N G D E R +swc_deu_001449 W I E V W E N I H T I S O L A N E N O C H A M P O U L T Z D E R Z E I T +swc_deu_001450 R E D O C H E T W R D I E D U C H F Ü H R E N V O N W A L W E R B U N G A U F K O S T E D E S T A T E S +swc_deu_001451 D A S N I H M R U N D K G E S E T Z +swc_deu_001452 H E I M A T V E R T R I E B E N U N D H E U S L I C H E G E W A L +swc_deu_001453 N D S P E I C H E R I E N I N E I N R W A G T E S C H L N G E A B +swc_deu_001454 O R R E I G I N A L T O N B E N D E R U N D D I E D O K O M I T A T I O N D E S T U D I O S W U R D E N N E N Z E H N H U N D E R T Z W U O H N S I E B Z I G I N D E R S I M E N S E R C H I E F Ü B E R S T E L T +swc_deu_001455 S O M I S S E N A U F E I N E M S T A T G S C H N R E K E T E N U B O D T +swc_deu_001456 F L Ö T E N S P I E L E D L I C H E R +swc_deu_001457 D R A S T I S C H M O D E R A R N E E L I K T R O N I S C H E K L A N G E S C H A L T U N G +swc_deu_001458 A N C H L I S E N W O D E D I E S O H A M I T E T E M A N D A R T Z T Z A L I E D E R P A R T E I N A H D I M S E M V E R F A H N E N S P R E C H E N D E R A N Z A L I H R E R Z W E I T S T M M P R O P R Z U N A L A U F D I E L A N E S L I S T E D E R P A R T E I U N T E R V E R T E I E R T +swc_deu_001459 O C F N D E R N A R T H O B O M B A D I E O U N D T E Ö K Ü N F T E +swc_deu_001460 D E R F R E I N E N T Z U K L O P E +swc_deu_001461 M T L E R B E I L E L H I N D E N +swc_deu_001462 W E R W I G E G E I N E V E R B R E C H T E N S R E C H S G R F T I C H Z U I N E R R E I T T R A F E V O N M I N D E S E N S E I N E +swc_deu_001463 D R G S C H W I N D I G K E I T Z W E R T U N G E R A G E N D R E I B E F E I N H U D E R A C H T +swc_deu_001464 I E B O R I O S A M E R S T E N I G B O R I E S A M S E R +swc_deu_001465 N A C H D M S E A R N A R G Ü F V E R F A R E N A U F D I E L E N D E R E R T E I L T +swc_deu_001466 R E F O R M E N G O B E R S C H A F S U N D A B R S T U N G S C H I T E +swc_deu_001467 S I E R E A A N P O R T E T U N D N D E R D E +swc_deu_001468 A N D E M E S T I C H E G R E F T E A U F G E G E N R V O L E Z E N diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..9ffabd54073e8c80d5c93d0d293468945a6ea91e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.2/1best_recog/token_int @@ -0,0 +1,165 @@ +cv_deu_000775 10 5 2 3 18 2 17 9 7 15 11 5 4 2 6 7 8 3 19 2 6 8 5 15 11 3 +cv_deu_000776 5 4 3 10 2 3 9 6 15 11 9 5 7 15 11 2 4 3 23 2 6 5 16 10 2 3 21 12 6 10 2 4 3 6 7 8 5 3 24 16 6 17 5 4 3 10 2 7 3 16 15 22 2 18 9 7 7 5 4 8 12 29 22 5 13 10 3 +cv_deu_000777 10 5 3 15 16 17 27 25 10 5 2 6 3 7 2 2 7 2 3 9 13 7 8 2 6 3 7 8 19 25 4 3 +cv_deu_000778 9 6 8 12 26 3 14 2 8 3 24 2 6 14 4 2 9 17 12 17 7 3 +cv_deu_000779 3 8 2 9 6 17 5 8 3 2 4 8 2 8 3 2 5 4 2 3 2 19 25 6 22 3 22 6 2 5 7 15 11 2 3 5 4 8 13 4 9 8 20 2 12 4 2 9 13 5 2 3 22 2 26 6 10 2 4 7 3 9 18 2 4 3 24 16 6 2 13 2 4 3 5 4 17 7 15 11 22 25 8 2 3 13 12 4 2 4 2 4 20 7 3 9 15 22 12 6 +cv_deu_000780 10 2 6 7 16 4 3 2 5 4 2 7 3 18 2 6 2 8 4 9 4 20 3 18 2 14 9 4 3 7 2 5 4 5 2 3 19 16 7 18 2 5 22 9 2 6 5 3 21 2 5 3 10 2 4 3 7 23 16 6 8 19 6 2 12 4 10 2 4 3 21 9 4 2 3 2 5 22 2 13 +cv_deu_000781 5 4 3 10 5 2 7 2 4 28 9 11 6 14 9 18 2 7 5 2 10 2 4 3 16 17 2 3 2 5 4 2 4 7 5 4 14 2 7 3 12 4 10 3 7 2 15 11 7 16 4 3 10 2 5 7 5 14 3 4 16 17 2 6 3 2 5 2 4 7 3 9 13 13 2 18 2 4 +cv_deu_000782 4 16 6 10 3 21 2 7 8 13 5 15 11 3 24 16 4 3 11 9 22 11 9 12 7 2 4 3 18 2 19 5 4 10 2 3 7 5 15 11 3 10 5 16 3 6 8 7 15 11 9 19 8 11 9 22 2 4 3 18 6 12 15 11 3 4 +cv_deu_000783 5 17 3 16 6 8 3 22 4 9 2 4 3 18 12 6 14 3 14 5 2 4 3 24 5 2 13 2 3 7 16 20 5 9 13 2 3 2 5 4 2 6 5 15 11 8 2 12 4 14 2 4 3 24 16 4 3 2 6 2 17 9 4 3 13 9 17 23 6 2 15 11 8 3 12 4 10 2 6 3 17 9 5 4 3 11 27 8 2 3 9 12 7 3 +cv_deu_000784 5 15 11 3 21 2 6 10 2 3 19 16 13 27 22 13 5 15 11 3 10 2 4 3 6 9 8 3 25 18 2 6 3 10 5 2 17 3 23 9 13 17 2 4 8 3 24 16 6 14 2 8 6 9 14 2 4 2 4 3 18 2 10 2 4 22 8 2 4 3 5 4 3 24 16 6 17 5 2 6 2 4 3 +cv_deu_000785 2 7 3 2 6 2 8 6 9 12 6 2 15 14 2 21 2 7 2 4 3 2 5 4 3 7 16 3 21 5 15 11 8 10 5 2 7 8 2 17 9 4 5 15 11 8 2 17 3 22 16 4 7 2 8 3 19 9 18 7 15 11 2 4 3 20 12 3 22 27 4 3 +cv_deu_000786 4 16 15 11 8 5 7 3 7 5 17 3 8 16 8 3 5 17 3 22 13 2 5 15 11 2 4 3 28 9 11 3 14 9 17 17 3 7 3 14 12 8 20 3 18 6 5 7 8 5 14 3 9 4 3 9 4 10 2 6 2 3 24 5 7 2 20 2 9 +cv_deu_000787 22 16 8 3 10 9 4 9 15 11 3 14 9 18 2 7 3 2 5 4 2 5 4 3 21 2 6 18 2 6 3 24 16 6 10 3 17 5 4 8 10 2 17 8 22 9 4 10 22 9 17 4 2 4 10 3 24 16 4 3 7 15 9 22 2 7 11 3 16 12 2 4 3 18 9 15 11 +cv_deu_000788 10 9 7 3 5 8 3 18 7 2 3 3 9 2 8 +cv_deu_000789 21 5 7 5 2 3 7 17 5 4 3 13 2 15 11 20 2 5 8 3 9 12 7 3 11 6 +cv_deu_000790 4 9 15 11 2 3 10 2 17 3 10 16 15 11 19 3 18 2 19 5 4 3 10 2 6 3 7 5 14 11 3 9 6 12 15 11 3 10 2 6 3 22 17 22 9 4 5 12 4 3 4 9 7 5 16 4 9 13 13 18 9 15 11 22 2 6 18 16 8 3 +cv_deu_000791 5 2 7 16 6 2 4 3 10 2 9 22 27 4 10 2 4 3 10 2 7 3 10 28 2 13 5 2 18 2 10 2 4 8 16 6 10 3 18 2 7 5 14 8 9 8 3 +cv_deu_000792 18 2 8 2 15 22 8 3 7 7 8 10 5 2 3 6 2 23 6 2 7 2 4 8 11 2 6 8 5 2 19 3 14 2 7 8 9 13 8 2 3 10 2 3 21 5 13 2 6 3 17 5 8 10 3 2 5 4 2 6 4 3 17 9 4 10 7 9 6 8 3 10 9 15 11 +cv_deu_000793 10 5 2 3 7 2 3 7 5 2 8 13 12 4 14 3 2 7 3 17 5 8 2 6 3 16 6 8 7 15 11 9 15 19 8 3 10 2 13 13 9 15 11 3 20 12 7 9 17 2 4 3 14 2 21 9 22 20 2 4 3 +cv_deu_000794 21 9 6 5 7 15 11 4 3 2 5 4 17 9 13 5 2 4 10 2 17 3 22 13 16 3 +cv_deu_000795 18 16 6 9 12 6 3 5 7 8 3 5 7 8 3 9 12 15 11 3 24 16 13 16 6 +cv_deu_000796 10 5 2 3 11 2 30 3 24 16 4 10 6 3 7 8 6 9 7 2 3 21 12 10 2 4 7 4 3 24 16 4 3 9 13 19 2 8 3 10 5 16 13 2 22 3 18 2 7 2 5 4 2 3 19 2 7 8 2 4 3 7 2 5 3 7 15 11 16 3 2 3 7 15 11 9 18 13 4 2 3 2 4 3 14 25 13 7 5 2 6 8 2 +cv_deu_000797 9 5 4 3 11 9 6 7 23 26 5 8 2 3 24 2 30 7 13 8 2 6 3 2 13 8 20 12 4 3 2 13 19 8 3 4 9 8 20 7 3 12 4 17 3 18 2 3 24 16 10 2 3 2 13 19 3 24 12 4 14 2 6 2 5 15 11 2 3 +cv_deu_000798 5 4 3 10 2 6 3 13 9 4 10 24 5 8 15 11 2 6 19 3 22 9 4 10 2 6 3 2 6 8 6 9 6 14 22 8 3 10 2 12 8 13 5 3 21 2 10 16 6 20 5 2 6 8 3 21 2 6 10 2 4 3 8 +cv_deu_000799 17 9 4 3 7 12 6 7 23 5 2 6 8 2 3 5 4 3 7 2 5 4 2 6 3 11 2 5 17 9 8 3 7 8 9 10 8 3 22 2 2 3 21 16 6 19 5 2 6 3 9 13 13 3 9 13 13 3 +cv_deu_000800 2 6 8 6 9 3 10 2 6 3 6 2 5 17 9 12 11 9 13 12 4 10 2 13 3 4 13 9 16 12 8 9 18 12 2 16 18 2 5 3 +cv_deu_000801 17 5 8 3 19 25 6 8 3 21 9 6 3 2 11 6 3 10 5 2 3 6 16 21 23 8 11 28 9 10 2 13 3 2 5 13 7 3 10 2 6 2 15 11 8 13 5 15 11 2 6 16 13 13 2 3 10 5 2 7 3 7 16 18 2 8 7 2 5 15 2 4 2 4 3 14 2 17 2 5 4 10 3 +fleurs_deu_000378 13 2 8 20 8 2 21 16 15 11 16 3 14 9 18 3 10 9 7 3 17 2 8 5 2 3 18 2 22 9 4 10 3 14 9 7 2 7 3 24 16 4 3 2 23 2 13 3 25 18 2 6 3 24 5 2 6 4 10 3 9 7 3 11 21 9 13 8 2 3 19 16 6 19 2 13 2 3 24 16 4 3 25 18 2 6 3 5 8 20 2 12 4 3 5 4 8 16 17 5 6 8 21 16 6 10 2 4 21 9 3 5 2 10 2 7 3 12 4 8 2 6 4 2 11 4 3 9 13 7 3 4 5 15 11 8 3 7 15 11 2 6 3 5 2 4 3 23 2 2 5 8 2 8 2 3 +fleurs_deu_000379 2 28 25 12 6 7 2 3 28 5 17 4 2 7 8 5 14 3 12 4 10 3 8 2 6 3 7 8 25 8 20 7 8 3 2 3 10 2 4 3 18 2 6 5 2 19 3 10 2 7 3 16 13 25 17 23 5 7 15 11 2 4 3 22 16 17 5 8 5 7 3 10 2 6 3 24 2 6 3 2 5 4 5 14 8 2 4 3 7 8 9 8 2 4 3 12 4 10 3 10 9 15 5 23 8 5 2 6 8 3 2 7 3 9 7 3 9 23 18 23 8 12 13 12 8 2 3 4 16 8 3 21 2 4 10 5 22 2 5 8 3 10 9 7 5 11 8 3 10 5 2 3 16 13 25 17 23 5 7 15 11 2 3 24 2 6 3 17 5 13 5 2 3 19 25 6 3 2 5 4 14 7 5 15 11 2 6 2 7 3 12 4 17 19 2 13 13 8 3 19 25 6 3 9 13 2 3 12 4 7 2 6 2 6 3 7 23 16 6 8 13 2 6 3 2 5 4 7 2 8 8 +fleurs_deu_000380 10 9 13 5 15 3 22 2 4 2 3 9 23 6 2 8 7 22 16 17 23 2 8 5 18 2 13 17 5 8 3 15 11 8 6 4 2 6 8 20 21 2 5 3 23 12 4 10 3 2 13 19 3 9 6 9 15 11 8 2 4 6 3 20 21 2 5 3 23 12 4 14 10 3 2 13 19 23 2 3 12 4 10 3 15 11 8 2 16 4 2 8 20 21 2 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6 2 19 8 2 3 9 12 19 3 14 2 14 2 4 3 6 24 16 13 2 20 2 4 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.3/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.3/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..becffa04882394b3c50cd609f82dc934207021da --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.3/1best_recog/score @@ -0,0 +1,165 @@ +swc_deu_001469 tensor(-5.4136) +swc_deu_001470 tensor(-5.4442) +swc_deu_001471 tensor(-3.3429) +swc_deu_001472 tensor(-35.1121) +swc_deu_001473 tensor(-8.7509) +swc_deu_001474 tensor(-9.4286) +swc_deu_001475 tensor(-11.9802) +swc_deu_001476 tensor(-17.0604) +swc_deu_001477 tensor(-6.2282) +swc_deu_001478 tensor(-15.9367) +swc_deu_001479 tensor(-10.3018) +swc_deu_001480 tensor(-12.0965) 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tensor(-7.5636) +voxforge_deu_000894 tensor(-8.0158) +voxforge_deu_000895 tensor(-9.5745) +voxforge_deu_000897 tensor(-16.6137) +voxforge_deu_000898 tensor(-11.8780) +voxforge_deu_000899 tensor(-7.6656) +voxforge_deu_000900 tensor(-9.4385) +voxforge_deu_000901 tensor(-12.2706) +voxforge_deu_000902 tensor(-10.7745) +voxforge_deu_000903 tensor(-15.4295) +voxforge_deu_000904 tensor(-15.0436) +voxforge_deu_000905 tensor(-9.2342) +voxforge_deu_000906 tensor(-24.8237) +voxforge_deu_000907 tensor(-5.1890) +voxforge_deu_000908 tensor(-30.5477) +voxforge_deu_000909 tensor(-27.7951) +voxforge_deu_000910 tensor(-18.7923) +voxforge_deu_000911 tensor(-10.4784) +voxforge_deu_000912 tensor(-16.3252) +voxforge_deu_000913 tensor(-10.5035) +voxforge_deu_000914 tensor(-16.6937) +voxforge_deu_000915 tensor(-24.7510) +voxforge_deu_000917 tensor(-14.6531) +voxforge_deu_000918 tensor(-5.0130) +voxforge_deu_000919 tensor(-11.0298) +voxforge_deu_000920 tensor(-12.9725) +voxforge_deu_000921 tensor(-9.7015) +voxforge_deu_000922 tensor(-11.7815) +voxforge_deu_000923 tensor(-23.0137) +voxforge_deu_000924 tensor(-11.2998) +voxforge_deu_000925 tensor(-10.8047) +voxforge_deu_000926 tensor(-12.9043) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.3/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.3/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..bda0bc6edeebbc27e502d91505f9efba2862422d --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.3/1best_recog/text @@ -0,0 +1,165 @@ +swc_deu_001469 IT UNTER ANDEREM VERWENDE +swc_deu_001470 AUSWIKEPEDIER +swc_deu_001471 UND KOBARKRISE +swc_deu_001472 ENLTZTDARWAL AUFGRUND EIGENER WEIL VORSCHLÄGE UNETEBRUCHEN MINISSENS FÜF ABGERUNE ND VERTRETEN SIND +swc_deu_001473 VERPREITUNG IEDIOLOGESCHAPROPARGANDER 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N Z S B A N D I N S L E I T U N G S B A N +swc_deu_001505 A L L E D I N G S E N V E R G L E I C H B E R I F E K E M Ö U G L I C H +swc_deu_001506 D I E S E K O N T E N A B E R A L S E I N G A B E I N E I N E N D F R I C G W E N Z S U M S E T Z E R D I E N E N O D E R S T E U A T E N Z U N G O N M O T O U R E N +swc_deu_001507 T O M A S H R M A N S P R O D U Z I E R T E Z W E I T A U S E N D Z W E I M I T K E R E B E +swc_deu_001508 P E N Ü B E R G A N G T R E F E N +swc_deu_001509 D I E F A L G T E N H O U S S C A U +swc_deu_001510 A N T I E S O J E T S C H E D E M O N S T R A T I O N E N W U R D E N P L U T I G N I E D E R S C H L A G E +swc_deu_001511 E I N V I E R K A N A L M I S P O L D D E N T E V E R K L E I N E R +swc_deu_001512 D I E S E H E T E N D I E V O R W A N D Z E I T E N F Ü R E I N E N A N G R I F A U F D I E U E S A R E X S T R E M H E R A P B G E S E T Z T +swc_deu_001513 W E I C H E S A M N E C H S T E N Z U M S T A R T K N O D E N L I E G T +swc_deu_001514 L A H T I O G I N G N D O L L Z E R Ü C K N I E B U N D E S L I E G E R U N D W E X S E L D E Z U E I N D R C H T +swc_deu_001515 Ü B E R I S E K A N K E I T +swc_deu_001516 J A H R Z W E I T A U S E N D F Ü N V E K R I T I E S E R T E +swc_deu_001517 D I E S E A U F H F A S U N G Z U R N E U T R A R I T E Ä T U N T E R S H E I D E +swc_deu_001518 W E D E L W U R D E R A L S K N S T L A I S C H E R L E I T E R D E S S I M E N S T U D I E S B E S T E L L T +swc_deu_001519 W E N M A N D I E W L T A L S K A N Z E S B E R A C H T E T +swc_deu_001520 S I E M T K R I T I S C H E K O M P O N E N T E N D E S D E T U O N A T I O N D Z Y S T E M S A B S I C H T L I C H S C H W A C H E I N D W U R F E N +swc_deu_001521 N I C H W E H B E R I S T E D O C H +swc_deu_001522 E R B O D E I N E V E R E I N I G U N G D E U T S C H A N S A N +swc_deu_001523 B E R I E N N Z W E I T U E N F Ü N F +swc_deu_001524 K E R N A B G E S T I M T U N D U M H Ö L E N D I E S E N E N S P R E C H E N T +swc_deu_001525 A Z T E U G U N G V O M N D E N A M I G A U S +swc_deu_001526 S E M T U N D I N G W E R +swc_deu_001527 U N G V O N S C H E H E R U N T E R N E R U G E G +swc_deu_001528 N I S C H E N U N D G E W R T H E N +swc_deu_001529 R O B E R T E R F K N E D I E +swc_deu_001530 K A M S C H L I S E L I C H Z U N M +swc_deu_001531 O L S T E N I +swc_deu_001532 S T A N D T E N S I C H V O N D E N U R S A R +swc_deu_001533 A C F R I K A S S T I H D E S E R H A H R E R G E O R T E T +swc_deu_001534 D I E A R M E M E U N T E R T E L +swc_deu_001535 S T A L I E N S E T Z T E M +swc_deu_001536 F E I H T E N S A U S L E I C H +swc_deu_001537 K L M E R A U F B R O S K R E I B T G L E I C H +swc_deu_001538 A M Z W E I T E N J U N I E Z W E T A U S N D V I E R W U R D N +swc_deu_001539 I N E B N E S T A G N A C H R L G T +swc_deu_001540 D I E N A T O O S S T E R W E I T E R U N G U N D D I E E I E N S E I T I G E A U F K Ö Ü N D I G N D E +swc_deu_001541 T H I E R B E I I S +swc_deu_001542 D I E S E R S T E L L E K A M E N S E M T I C H M I T I E D E R D E R K A P E L E T E +swc_deu_001543 P O T Z T A M A B K O M E N E N T H I E L D T Z W A H R A L G E M E I N E R V E R E I N B A U N E N Ü B E R D I K Ö N F T I G E G E M E I N S A M E V E R W A L T U N G D E R S I E G E R M I C H T E U N D V O M L I E R T O R U N D S E T Z E I E D E M L I T R I S I E R U N G +swc_deu_001544 D A N A C H U N D E R S C H I P E E I N E N V E R D R A G B E I M W I E H F Z I D E N A H M O +swc_deu_001545 E I N W E I T E R E W E R I A N D E M A G +swc_deu_001546 S I E W U R D E N M O D O L A H R N D U R C H L O C H S T R E I V N G E S T E U R T U N D D I K L I N G E K O N D N +swc_deu_001547 D I E G R U N M A D A R T G K L A U S E L B E V O R Z U C T U N D E R D I N K L E I N E R N P A R T E I N J I E N E +swc_deu_001548 B E R T O N Z D E M K E I N E W Ö G K L I C H E H U G E S N O T H E R U S C H T +swc_deu_001549 N T U G M N T E R Z I O N D +swc_deu_001550 Z U V O R B E D I G U N G K O N K R E T E R A P R Ü S T U N S C H I T E +swc_deu_001551 B U N D E S T A R G E S W A L R E C H T +swc_deu_001552 E S M U S I M G R E I S W A L E I T D E R V O R G E L E T W E R N +swc_deu_001553 H A T M A N E I N E I M P I E R E S C H E B A S I E S F Ü B P S Y C H O S O Z I A L E P R O G E A M E Z U O S E N K U N D E R S E B S T M O U T E R A T E U N Z U R S T E A R K U G D E S I C H E H I T Z G E F Ü S I N D E B E F E Ö K E R U N +swc_deu_001554 B E I D E N E R S T E N F R E I N P L L E M E N Z W A H L N U R D E I L I E S K U I M E I N E U N Z E N H N D E R T N E U N Z I G I N S E I N E +swc_deu_001555 D A M M I T L A S S E N S I C B E S T R A L U N G S T E R T E N S E R G E N O M E S E N +swc_deu_001556 W I N I G E A R S P Ä T E R K A M E S Z U E I N E W E I T E R E N K R O N D N G +swc_deu_001557 R A D I O K A B E R E T P E I L S +swc_deu_001558 E S T Ü K T E B O M B E R A U F D I E S T A R T W A H N E N R E L E N +swc_deu_001559 M I T D I E S E R E G E L U N G S O L E I N E R F A K T I S C H Z W E I V E R C H E E I N F L U S N A H M E D E S E R W E L E R A U F +swc_deu_001560 B E R O K G K Ö R I C H E N B A U +swc_deu_001561 D E R H E R V O R A G E N T W I C E N D E N L A N D E K L P E N W I E D E R U M H E R V O R A G E N E R L A N G S A M F L U G E I G E N S C H A F T E N +swc_deu_001562 M I T E R E C H V E R B I N D U N G S F L U K T Z O G E O D E R U M S C H U L M A S C H I N F Ü R D I E B E E E I N H U N D R D E N E U N V E R W E N D E T +swc_deu_001563 L E I S T E T E M I E I Z I N I S C H R N B S Ü C H O L O G E S C H E H E L F +swc_deu_001564 K A N M A N D E C H I M F U N G E N V O R B E U G E N +swc_deu_001565 M E R D N A U S B O C H D I E S E R K A N K E I T E N E H E R F O L G T E I N F E K T I O N V E R L A N G S A M E N K A N +swc_deu_001566 D I E I N E N E U T R D I E T E Ä L T U N T E R A L E N U M S T E N D E N V O R S A R +swc_deu_001567 U N D Z I E G E N H Ö R T +swc_deu_001568 D A S N E U N Z E H N H U N D E R T A C H T E N D R E I S S I G G E G R Ü N D E T E K O M I T V I R U N A M E R I K A N I S C H E U M T R I E B E W U R D E D A F E N U N +swc_deu_001569 Z E N T R A L E D E R P R O C K R E S S I E V E N U N D H O R T D E S I N I E N I Ö R G E S T Ü T Z S T E N K U N S T D E N K E N S +swc_deu_001570 I N D E R D E R O E S P R E S E D E N T A N K Ö N D I G T E +swc_deu_001571 S N E Ä C H S T U N D V O R S P B E I S E N +swc_deu_001572 D E S P U N D E S W A G E S E T Z E S B I S T Z U M D R E I S I G S T E N J U N I Z W E I T A U S E N D E F A U F G G E M +swc_deu_001573 O R I E P O S S E Ö R +swc_deu_001574 F L I F T L I N G E N V O N D E R E T N I S C H E N M I N D E R H E I T D E R S O M A L I S C H E N B A N T U M +swc_deu_001575 D I E B I E P O L A R E W E L T O R D T N U N G Z E M I N T I E R T +swc_deu_001576 E R A N F A N G E I N I N T E I L R I E A T E O D E R E X S T E R N A N G E B R A C H T E V O R I C H T U N G A N E I N E N U C L I E R E N W A F E N S Y S T E M N +swc_deu_001577 S T A R T E T E D I H I L F S O R G E N I S E T Z I O N L A N K F R S T I G E +swc_deu_001578 W E N D I E S E E X S T E R N E N E R F E C K T E I N E R I C H T I G E N R E I N V O L G E A U F T R E T E N U N D S I C H I N E H A L B S P E Z I E I S C H A P A R A M E T E R B E W I E G E N +swc_deu_001579 Z U G D E W I R T U N I O N A U C H B E I D E A S E R S T O F P B O M B E N U N D N E U I N F L U K T Z E U G E N M I T I N T E R K O N T E N E N T A L E R E I C H W E I T E M I T D E N U R S A R G L E I C H +swc_deu_001580 P E N D I E S T A T H A T I E W A B P E N T I E A M +swc_deu_001581 D I E S E R A N S A T Z G I L D A L L G E M E I N A L S A U S G E W O R G N D E R +swc_deu_001582 N A C H D E Z U S A M M P R O C H T E +swc_deu_001583 D E O B E R L A U S I T S W I S C H E N H E U E R S W E R D E +swc_deu_001584 D A B E E N Z W E I E F A H S E N N T E R T E I L E L T +swc_deu_001585 S C H I E D E N A N D E R E U R O P E R M I S T E R S C H A F T E I L U N D W U R D E M T E R D I E E B I E E L L F +swc_deu_001586 M E S T E R E R F N E T K A B E R S H L I S G E R N W E I T E G E M Ö G L I C H K E I T +swc_deu_001587 E I N E M A U S W E R T S E R F O L G I N W O L S B U R G E L A N G +swc_deu_001588 M I T S C H W E B U N G S S O U M M A N K O N T E N K L I S S A N D I E E R Z E U G K T W E R D E N +swc_deu_001589 D E R B A L E D I G L I H Z E I K T E +swc_deu_001590 K O S P R I T A N I E N E I N E E S T E W I C H T I G E V E I N B A U N G +swc_deu_001591 S I D A C H T E S H R I T N E S S E I N G +swc_deu_001592 W U R D E M I T D E B U N D E W A G E S E T Z V O R N E U N Z E U N D E R S I C H S U N F Ü F Z I G E I N E D A U A R H T E R E L U G E N G E F Ü R T +swc_deu_001593 D I E A N Z A L D E R Ü B E H N G M E N D A R D E K A N +swc_deu_001594 I S C H L S D I E S E E I N M L I T E R I S C H E S E I N G R E I F E N I N D E N K O R E A R G R I C K +swc_deu_001595 N A T O V E R B I N T L I C H +swc_deu_001596 K A L T E G R I E G B E N D E R T +swc_deu_001597 A U N U N E N H U N E R D E E I U M T N E U N Z I G U N D O S T E R A L I E N S O W I E D E R Ö S T E R E C H S C H E A B L I G E R +swc_deu_001598 D A D I E S E I T A N F A N G N E U N Z E H N H U N D E R T N E U N U N F Ü N F Z I G D O R T H E R S C H E N D E R E V O L O T I O N D S R I G I O N U N D D E R V I E D E L K A S T R O E I N E N S O Z I E L I S T I S C H E N K U R S E I N G S C H A G E N H A T E +swc_deu_001599 N A C H W E I T E R E N F E L U S T R E C H E N K Ä M P F E N U N E N E N Z W E R T E E R F O L G E B E I D E G R I G S P A T E I N U R D E R U N D D R E A J A H R E N A C B E G I N D E A U S A N D N D E S E Z U N G E I N B E S R E U T E G Ü L T I G E S W A F N E N S T I L S T A M N S A B C O M E N A B G E S H L O S S E N +voxforge_deu_000891 M A N I S T E R B E I S E R O R S I C H T I G +voxforge_deu_000892 D I E W E R F L I C H T S O L I N D E U T S C H L A N D L E I D E R O C H N I C H T A B G E S C H A F T W E R D N E N +voxforge_deu_000893 E S G E T A U C H M I S P R A U C H U C H A B E T G E B E R +voxforge_deu_000894 D I E K I N D E R S I N D A N K A N K E B O E N +voxforge_deu_000895 D I E T R A K W E I T E D E R A T A S T R O F E S O L V E R D E U T L I C H T W E R D E N +voxforge_deu_000897 D S C A N G E A U A U L B E +voxforge_deu_000898 B E I M M O G A N S T R E I T S T R E I T E N O B E S D E V E F A S U N G S O G A N E +voxforge_deu_000899 D A W A G E I C H I E R Z U B E Z W E I F E L E N +voxforge_deu_000900 M A N O L T E D E N A U F G A K H E N F A L T R A U N +voxforge_deu_000901 D I E F E N T L I C H E N S C H Ö L D E N W E R D E N N I C H T G E T I L K T W E R D E N +voxforge_deu_000902 B A G E L T I S T A U S G E Z C A H L T W O R D E N T +voxforge_deu_000903 E R S O L L E N D R E I H U N D E R D T A U S S E N D N E U E A R B E S P L Ä Z E I N S T E N +voxforge_deu_000904 D I E Ö R B E R V E L E T Z U N G K A N N A L S B E I S P I E L G E N D W E R D E N T +voxforge_deu_000905 D I E S E K R E N E I S T Ü B E R S C I T E N B O R D E N +voxforge_deu_000906 T E S S T A E F A U G S B Ü Ö R D E N K E I E N Z U G E A E S K E L H A R B E N +voxforge_deu_000907 D I E I N T E R E S S E N F I N D E N K E I N G E H Ö R +voxforge_deu_000908 F E L T A E T A B U L A T O A R Ü C S H I E T A S E R Ü T A S E R Ü G I E R S T A S E +voxforge_deu_000909 D E R B E O H E N E M O R A I U M B E R E S T I G D I E E N H E Ä E G E L T E N D M A C H E N +voxforge_deu_000910 E E I N D E R I T A H A D D E M G E S C H Ä I D I K T E N F R E I W I L I G L E I S T U N G E N Z U K C O M E N L A S S E N +voxforge_deu_000911 S O N D E R A C R E C H N E B E N D E B I L T +voxforge_deu_000912 I E R T E I N E N I C H T E R N S L I C H G E M E I T E W I L E S E K L Ä H U N G A B G E B E N +voxforge_deu_000913 D A S M O S T E J A H A U F J E D E N F A L S O K O M M E N +voxforge_deu_000914 M E H R E R E R E K L E I N S K N N S I C H E I N E E I P I E D R E S S E T E I U N G +voxforge_deu_000915 W A D I Ü N S T E E R E S H I S A S O S I G T Z U S A M M E N E H M E N A N S T A Z U +voxforge_deu_000917 D E R S C H L E N E H A T S A N E L E I S T U N G A N G E B O T E N +voxforge_deu_000918 S O D S S I S +voxforge_deu_000919 D I E B A T R I E N W A R N S E A R S T A G V E R A L T E T +voxforge_deu_000920 D E S E S Z I E L W U R D E N O R T A L W A L S E E R E I C H T +voxforge_deu_000921 D I E S E W E H R U N G W I R T S E R L A N G E L E B E N +voxforge_deu_000922 D O R Z E I T A N O F E N B A S C H O N V I E L E T +voxforge_deu_000923 A L S I G I E N E N N I G T E M M Ä G I I E R N U R G A N S F L I C H T I G K Z U U N D E R F A A T A +voxforge_deu_000924 E R Z Y M I E M E R Ü B E R I S T I A N +voxforge_deu_000925 D E M S T E H E N A T I Ü R L I C H A U C H F A M M Ö G E N G E G E N Ü B A +voxforge_deu_000926 D I E R E A L E L A G E W I R T N I C H T V O S T E N D I C H A B G E B I L E T diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.3/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.3/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..3fc959b315ca1718c88d12d524d1647ae88dcc43 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.3/1best_recog/token_int @@ -0,0 +1,165 @@ +swc_deu_001469 5 8 3 12 4 8 2 6 3 9 4 10 2 6 2 17 3 24 2 6 21 2 4 10 2 +swc_deu_001470 9 12 7 21 5 22 2 23 2 10 5 2 6 +swc_deu_001471 12 4 10 3 22 16 18 9 6 22 6 5 7 2 +swc_deu_001472 2 4 13 8 20 8 10 9 6 21 9 13 3 9 12 19 14 6 12 4 10 3 2 5 14 2 4 2 6 3 21 2 5 13 3 24 16 6 7 15 11 13 26 14 2 3 12 4 2 8 2 18 6 12 15 11 2 4 3 17 5 4 5 7 7 2 4 7 3 19 25 19 3 9 18 14 2 6 12 4 2 3 4 10 3 24 2 6 8 6 2 8 2 4 3 7 5 4 10 +swc_deu_001473 24 2 6 23 6 2 5 8 12 4 14 3 5 2 10 5 16 13 16 14 2 7 15 11 9 23 6 16 23 9 6 14 9 4 10 2 6 3 10 2 6 3 7 12 23 2 6 17 5 15 11 8 2 3 12 4 10 +swc_deu_001474 22 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C H E N W E S E N N U R I N T F A L T E N W U O M A N I L I E B E B O D T V O R H A R E N +voxforge_deu_000982 E R Z Ü K L I C H D E E R W E I S L A T N D E H A F T U N G F Ü R H E L F P E R S C O N E N +voxforge_deu_000983 E I D E R N O M A H L N O Z U N G E D E S D I V O L E B A N D R E I T E +voxforge_deu_000984 A B E R W I E I S T D I E S E S P R O B L E M I M G L O B A H L N M A T S T A B Z O L E S E N +voxforge_deu_000985 D E S E I G E N W E R P L O K E H E T P O D E N Z I E R M E R L E S E +voxforge_deu_000986 D A S F R M M D E V E R B L O K S I E N O C H I E L E B T E R O S +voxforge_deu_000987 E I N E U E B E S T I M U N G I S T E L A S E N B O R D E N +voxforge_deu_000988 D A R R A U F S T H E N G E W I E S E N W O R D E N T +voxforge_deu_000989 D I E B E F Ö R K R U N G I S T G A N Z M A S I E V E R A R M T +voxforge_deu_000990 D I E W E R D E N D S G A N Z P E S T L M N I C H T M A C H E N +voxforge_deu_000991 D I E D A R T E N M Ä N G E D I G E S E N D E T W I E R D T I S T E R H E B L I C H G E R I N E R +voxforge_deu_000992 D A S E G E B N I S I S T V E F E L S H W O R D E N +voxforge_deu_000993 E I N E B E S C R E N K U N G T R I T E S T B E I B E S O N D E R S I N T E N S I E V E R N U O Z U N G A U F +voxforge_deu_000994 D E R E N D B E N O T Z E H A T E I N E H Ö H R R E G E S C H W I N D I C K E I T F Ü R D E N D A U N L O T D Z U F E R F Ü G U N G +voxforge_deu_000995 D E R S E M A N T I S C H E T E I L E W U R D E S K E B T I S P E T R A C H T E T +voxforge_deu_000996 D O T W I R T S E H R I E L M H R G E L T V E R D I E N D +voxforge_deu_000997 V Ü E R S T E N I S V Ü R D A S V E R A N W O R D L I K E I T Z G I F Ü L E I N E R M U T E R +voxforge_deu_000998 D A S W E R T Ü R D I M E I E N G E M A C H T E +voxforge_deu_000999 S I K A N E I N E G A N S K L A R E K A U F M P F I Ä L U N G A U S S P R Ä C H E N +voxforge_deu_001000 Z A L R E I C H E P O T E S T E W E R D E N A T I K U L I E R D +voxforge_deu_001001 D E D E U C H F Ü H R U N G W A R N I C H T S E C H A T +voxforge_deu_001002 D I E W E H R U N E N H A T Ü B E A U P T K E I N E D I C K U N G N +voxforge_deu_001003 O B Ü B I G E N S S E C K E R S T O A R A F D E R E I N E N D U R C H A U S T I E L B E W U S T E N L E B E M S K L O G E N +voxforge_deu_001004 M A N R N S P R E C H T E N D I E S E M F E I L V O N K O N T R E R H I E R U N G S T W A N G +voxforge_deu_001006 G L O U B E G A U N S C H L D N A R S E N Z I C E I N I G +voxforge_deu_001007 D A S W I R T N I C H T M E H R L A N G E S O B L E I B E N +voxforge_deu_001008 E S A B U N T E R S C H I E T L I C H T R I E R E V O R M E N D E R F R A H E T R A V E R +voxforge_deu_001009 H N D E S E I M E I N E F R E I E S O F T W +voxforge_deu_001010 O G A N S T R E I T V E R F A H N K Ö N N A U C H A U S H L I S L I C G A U F E L N D E S E B E N S T A T F I N T E N +voxforge_deu_001011 W E G E N N O Z S L O S A U F G E W N N E T E R O L E A B S E I T K A N N +voxforge_deu_001012 D A S W I R T N I C H T I M A R P E R F E K T V U N K T Z I N I E R N +voxforge_deu_001013 M A N M U S I C H A N G E R S C H I E R E N D E S W A K S T U M S W E G E N +voxforge_deu_001014 W E L L I C H E W E G E S O L L E N E I N G E S C H L A G E N W E R D E N +voxforge_deu_001015 D A S S W I R D T E N D I E P E I S E G E N T +voxforge_deu_001016 D I E Ü B E N A M E E R F O L U K T E W I R T L I C H +voxforge_deu_001017 D E E N T W E K L U N G S T W E I T V O R A N G E S C H R E T E N +voxforge_deu_001018 D I E S M T O M E T R E T E N D A N S C H O N A C H W A N G E N S T O N D T E N A U F +voxforge_deu_001019 S I B T E I N I G R O E W Ä L L E V O N P R O Z E S E N +voxforge_deu_001020 S S T B E R E I T Z M E I N Z W E I T E R A U T O M A D +voxpopuli_deu_000309 M P L M E N T I E R U N G V O N H Ö R I N S T A N D E A T S Z U S C U T Z S P E S O N L I C J E R D A R T E N E B E N F E I S G E N N E R E L U N S R E U T E Z U S A M E N A R E I T E L E I C H T E R +voxpopuli_deu_000310 E R A M T E A B E N D E R S L I M S T E V E R I N D E R D A R M E L E B E N G E R E D T E I N S E L B E V E R L Ä T S T W U R D N I G K L A B +voxpopuli_deu_000311 I C M Ö B R I Ü E D A S D E R O M I S S E I N I T I E S +voxpopuli_deu_000312 M I T L E T U N D D E C O F V E D A S W E R N E C X H S L I E A R Ü B E D E S W A N Z E +voxpopuli_deu_000313 D E S D A F N I C H I V E R S E H E N W E R D N D A S I M E R H I N W E R B E F Ü M T Z I G T P U O Z E N D D E R B E F Ö L K Ö N G E A D E R B E S C H E N U N U N N I E L E N L I C H E N G R A M L I E B +voxpopuli_deu_000314 S O D A S D E R B Ü R G E R S C H E L L O N E A U S K U N F B E K O M M T O P S E I N E B E S C H Ä R D E Ü B E H A B T A N G E N O M E N W I R D O P S I E B E R E C H T I C H T I S T +voxpopuli_deu_000315 N R I E S E T T U N Z E R E R B I T I O N G E N I S T N I C H T V O N E U T E N A B E R S I A B O E K O N T I N U N I E R L I C H E S F E I N T I O N E N G +voxpopuli_deu_000316 L D I E T G A N S T O L S G E S A G J A E D B E S C H E F T I U N G S T E I G K T D J E R A N +voxpopuli_deu_000317 W I D A S S F Ü R U N I S T E R A U H O N D E R E R V E R T E T W I E R E X S P O R T I E A N Z O U V E E L L Z O B I L I C G A L W V E R I M P O T I E R E N Z S U W E D I G W I E R F A R S C H E N K E N W O L S T A T +voxpopuli_deu_000318 S I E H E U D E R A R B E N I E A N W E S E N Z I N D I S E N P R O S I T I E V E S S I G N A L E +voxpopuli_deu_000319 N E U N Z S I G H P R O Z E N T A L L A A R O P Ä S C H E N F I L M E D I E A U S S E H A L T I R E S H E I M A T L A N D E S G E Z E I C H T W E R D E N S I N D V O R M E D I E R P R O G R A M G E F E R D E R T W U R D E N +voxpopuli_deu_000320 B I E S O K A I C H E B E R G E B P L I S S T E R A L E A U S C H U S A B P T E M U N G I N D I E S E F O R E M N I C H Z U S T E M +voxpopuli_deu_000321 B E R B O R T E N V E R I N D E R N D A S I C H E H I N D E R I E N G E I S I G E N E I G E N T U M D I E A U S G U M S F L I C H T E V E R S T E C K E N K O N T E +voxpopuli_deu_000322 I S G I B D E T Z I M Z U S A M A N G E R V E R S T E R G T E N Z U S A M M A U B E I T E I N E N E R S T E N G A N G V O N E I N I G E N M I T I E L S T A R T E N N A C +voxpopuli_deu_000323 W A S T I R E N Z Y Ü B E R C H R E I T E N D E Z U S A H M E N A B E I A N B E L A N T U N D W A S T I E E R V E R B R E I T U N G I N T R I G L Ä N D E R B E T R I F T U N D T I E M E C H L I C H E I N B E I S C S P I E N E N E N D E S E N E R F O L K S B E I S P I L V Ü M I C H I S T U N D Z W A R E L S L M D O K G M I L I E R N E R E D A S +voxpopuli_deu_000324 D A S N I C H T E N U R I N P R T U G A H L D R G R I C H E N L A N D S N E R N A U R E N S O V E R M E I N T L I C H G E I C H E N M I T W I E S T A T E N V I E D E U T S H L A N O D E R R U S P E T A N I E R N +voxpopuli_deu_000325 T V E R A U S W E N I S V E R B E I D A +voxpopuli_deu_000326 E A L L E L F L I E G A L M I K D I E D E R D I E S E S H A U S E S V A R S C H E N I E D O R D T L I C H O L F I G E A R A L S D E I E U D U R S C H N I T Z B Ü R G E R T +voxpopuli_deu_000327 E N S I C H E R D A S E R E R B E D O E U T T U N G I N A H R S F U K U M F T U O G E R N O C H T Z U N E H M W I E R T +voxpopuli_deu_000328 T E S K E T D I E R U M D I R I C H T L I H N J E D E S A D E S E F Ä S T L E G U N G G U N D L E G N D A S I G H E R E I T S N E R M E N F Ü R D E N S C H U T Z F Ü E R D E N G E F A R E N E I N E R E X S P O S I T S I O N G E G I Ü B A R O N I S I E R E N D A R S T R A L U N G +voxpopuli_deu_000329 D A S S G I L T E S W I D E R H E R C H U S T E L +voxpopuli_deu_000330 D E S E N E I N E N E I N Z I G E N S I T Z K I B T E S L Ä N G S D A S I E S T A S T P U R G +voxpopuli_deu_000331 E D A S A S P A S I E R T I N M A L T A R D I E S O N L I S T E N D I K O B P Z O N D F E L E A U F G E D E K T E R D I S V E R G E N B E C H N E R M O D E W A R W E D E R W E R E N S Y S T D E M A I S H I K O C H O U N D F E L E E B E U N T E R S U C H N O C H I E T E R M O R S E L B E R E G E Z I E L T E U N T E R S U T E A A T V A S S I N A N D O G A S W E N A L E S I E O R U N T E R D E M A N D E I S C H W E I N S Z U G E D E K W E R N S O R S +voxpopuli_deu_000332 L I N T L A N D E K Ü S T E D I E W A N S T A E I N E D I E A U F D I E R O S E N K A T A S F O F E N I Z U N A H M I E H N D R V R G A N E N E I T I N W E I S E N +voxpopuli_deu_000333 D E N N D I C H A B E B R I E N Z I E P F Ü R D E B E I C H T G E S T E M M N T O W O L E I N E N S W E H R N F H L E R I N T E L L T E S I R T N E M I T A R Z U R A U F G E F A R D E R T D A S A U R O B E H R S C E P L L R M E N D A U F D E M W E G K T Z H E E I D E M E I N Z I G E N S I T Z U U N D E S T E L T Z E N +voxpopuli_deu_000334 I N D I E S E M R I F E N W O D E N G E M E I S A M M E P O L I T I S C H E V E R A B R E D U N G E R N I M K R E I S D E R S I E B E N U N T Z W A N Z I G E T D R O F F E R N U N D A U C H H U B L I K G E M A C H T +voxpopuli_deu_000335 I B I N E R E R O G E N D E S W E R S H E U T E M I T D E M V O R C H A R G E S E M U M B E L T A U S C H O S G E S C H A F T A M I N C H I T W I T E A U K M E S I G E R F E K T E O R P Ä I C H E R Z E S A G E N D E H E T E N Ü E R H O C H R I S I K R O R P O D U K T E I N E S E N D R A L E Z U L A S N H A M E N M Ü S S E N D A S A B R I C H I C H G E S C A F T A R M I T D E M E R S E I D A E M T I S C H L I K T P L A U E I C H D A S I R T R O T T E M E I N E N G R O S E N S C R I T F L E I C H K E I N M E I N S T D E I N E E I N G R O S E S C H I Z U E R P A E D E N S E E T H A E N +voxpopuli_deu_000336 P Ä H E N D A N G E S F Ö R G F Ü R Z W E I E N H E I B M I N O D E N E R G +voxpopuli_deu_000337 Z U M A K T U Ä L E N I C T L A B I S K A N K E I N E R V O N U N S A N E H M E D A S W I W I R T L I C H E A R S Z E T D I E S E N W O C H E N E D I W I S E N D S O N S D I E Z A L U N S U M F I C G K E I T D R O T +voxpopuli_deu_000338 D S N D E I N F C H B E D I N G N G E N D I N I C G E K T Z E P T A B E S E N D M A N K A +voxpopuli_deu_000339 I N D E S W I S C H E N S E I S I N D I R E T U N G S O R G A N I S E R Z I O N E R N I E G R Ö S T E N S C H Ä P E R W E I S I E D I E M I G R A N T E N Z W A N Z I C H K I L O M E T E R V E R D E R I E B I C H E N K Ü S T E A U B G R E I T F E N U N D A L E N A R I T A L I E N R A S P O R T I E R E N +voxpopuli_deu_000340 D E S E I K T D R F A L L I O L I A R T I E M S C H E N K O +voxpopuli_deu_000341 E W A S S E R P R E D I G E N U N D W E I N T R I N K E N +voxpopuli_deu_000342 Ü R D I E S E E N S C H E I D U N G P R A U E N W I A R V I E L E P A T N A R N I C H T Z U L E T Z D I E S T Ä T T E +voxpopuli_deu_000343 D I E F O L G E I S T E I N H Ö R E N F L U G S V O M P O R P O L I S T N U N E X S T R L M I S T E I E N E I N I G M I G I T S T A T E N I E R E N B U M F E M P A R O L U E N S E T Z E N D I A R C O G R E T E R V E R E N D E R U N G E N G E G E N +voxpopuli_deu_000344 W A L D I E I N V E S T I T Z I O N E R N V R A N T Ö R S I S C H A C H U N D D E U T S C H E R B A N K E N G E R E T E T W E R D E N M U S T E N D U R H T E R G L I C H E N G L A N D T W E I T A U S E N D E H N N I C H T P B E I T E G E N U N D E H U T E M U S E S E I N E N R I E S I G E N G S C H O D E N B E R K V O R D S I C H T E T H E R T D R Ü C K E +voxpopuli_deu_000345 I M I T G I T S T D A D E N D Ü R F E N N I C H I E M Ö G L I C H K E I T A B E N D E R E N A U R O P Ä S C H E N S T A R Z E A M B A L D E R A N Z U R H I N D E R N E N I E R E R E G I O N D G A N Z S G E R Z I E U N S T E M A T I S C O R U T O N F E L N A C H Z U G G E N E R S I N E +voxpopuli_deu_000346 E I M I L I O N M E N S C H E N S I N A P Ä N G H V O N U D S E R H I L F E R +voxpopuli_deu_000347 E I N F Ü Z H E N H R G E R J N G E W E T I N H E R K A D I O N E I N E P O L I Z S I S T E N E I N E S O N D E E I N S A T K O M A N D O S N C O M A G S C H L A G D E N +voxpopuli_deu_000348 D I E E I D I E H E I L I G E K U A T M A N W O S I H E R E T R A G E N D A S A U P T A U T W I S S U D E R A L L E U N S H L E N D E N W E C K +voxpopuli_deu_000349 R E I D E R A R T K T E G E T D E R E F E N H A R B E N I N Z W I S C H E N S T A D G E U N D E M +voxpopuli_deu_000350 R D I C H I E E R N E I N M O N E R D E B T +voxpopuli_deu_000351 D S W E G E N E I N W I C H T I G E F R A G A D I K O M I T I O N E N E I N L A N D D I E K R A N Z S K O N T R O L L E V I E D E R E I N F Ü H O N N D D O C H I M S C H Ä N G E U N I O N B L E I B E N I T Z U G A N G K Z U R I N O M A I O N D S U S T E M E Z E T E R A O D E R I S D R S E I N E N T W I R D E R O D A D I E F R A G E I S W E C H T I C F Ü R D I E D E N I S C H E T E P A T E U N D I E S P E T E U M E I N E K L A R E A N D W O R D D A +voxpopuli_deu_000352 D E R S C H O N A U S G I E F Ü H R T W U R D E L A G E S N I C H T B A R A R N D A S I S E G R O B E F F Ä H L E G E G E B E N H E T I S S O N E N E S G A B E N E R R E I E V O N D G L E I E N N G E R E I M T E I T E N B I E T I E N S W E I +voxpopuli_deu_000353 I V E R G E M E I N T C H E A F T U N G D E R A U S E N U O S S I E G E R L T S P O L I T I G B A I S G O S I S Z I E L D I E S E R U N J O N +voxpopuli_deu_000354 D E N S I C H E H E I T I S E I N E S C W I E R I G E U N D D E T E I L W E I C H E R A R B E I T N I C H T N U E R I M T Ä C H N I S C H E N B E R E I C H +voxpopuli_deu_000355 T I S E Ä L T E N G E N D I E N T E R E S T E N V O N B Ü R G E R N U N P O L I T I K E N S O W I A U S E N A N D E R B E R E M B Ü R E R N I N G A N Z E R O P E R S T E T E S T E M E R K I N D T G A N N S O B E N +voxpopuli_deu_000356 H E R P A S I D E N T +voxpopuli_deu_000357 E F Ü R T E N G E S P R E I C H E M I T R E S E D E N T K A R S E I Z A R D R E I C H N R E G I R U N G S E R T R E T E R N F R A U N U N D M E N S C H N R E C H T O R G A N I S E R T Z I O N E N U N D D I E W A N D D U C H A U S E M U T I G E N T +voxpopuli_deu_000358 N G S A C H E I N E U R S A C H E F R D E N W A C H S N E N A T Z I H N A L I S T M U S D E A L I N S E I D E R F E L I C H P E R S B E K T I F L O S S I S T +voxpopuli_deu_000359 H U D E I N E I M A N A O H S O R W E I T O N D I E N Z I E E N F E R N E S +voxpopuli_deu_000360 H W E R D E A L S W I D A N Z M I N I S T E R A U C H E N M E I N E M L A N D Z I E D E N T A G K D A M I T K O N F V O N D T I E R T D A S N A T Ü L I C H A U C H T E S B I R U S T Z E N G E G E B E N S E N M U S D A S S T A S H A U S H A L G T D E V O N D E S T E L U E R S A L E R E N E N O N S T E U E R Z E O L L A N I N E N Z I E Z I H N T U N D D A S I E T A H M I T A U C H I E R N T U E R T U N G R A G E N I N D E E N T C H Ä I D U N G E N D I E V I E R H I E N T I E S E N R A M E N D R E F M I E T A M N O N T H E R N +voxpopuli_deu_000361 A U F D E M O U O R O P E S C H E N A U T E R B E B I L M A R E K T I N S I G E S A M D E R M A T I S C H I S T +voxpopuli_deu_000362 O P Ä H S C H U N I O N H A D T M I D I S E I N S T R U M E N Z S D I E S C O N S E E I N E A K T I E V E R O L L E N I E R N A C H B A R I G I O N Z U S P I E L E N U M D E R M O G R A T I S C H E E F O R M E N N D E R N N A C H A L I G E N W I K T U N G E R N Z U T R E I B E +voxpopuli_deu_000363 H T U T E L L I T E R E R E R S C H I E M E V O N A U S E N G O D E R V O N I N E N I S T R E C H T U N D O S C H I E D G L I G H +voxpopuli_deu_000364 E R E M I M E R G E S A G K T E I N Ü B E R E I L T E S T A D T Z I O N I E R U N G S E N S H E I D U N G E S U N S E N I C H W E I Z U M J E R Z I G E N T Z E I T F U N G E S K E I N E B E D R O U N G B E I S P I E L S W E I S A U S E M I E R A N G E B T +voxpopuli_deu_000365 D I E S E R F A K L E I I S T E I N E T Z Y N I S C H E M I S A C T D U N G E N D E R O B P U O V O R O N M E N C H N E C T Z W R E C T D O M L E L A L L R E L S F F A A A A A T D O D S C S A A A A I S S O N G A N D A N A N D E N E E I N E S O E U C H O D E N C Ü L A U P B L I C H E R A N W O R F +voxpopuli_deu_000366 D I E E S P E E R H A T D I S E U M F S E N D E R H E T Z U N T A L E R I C H T L I N D E Ü R B E Ü R B O A T D E T W I N G E R +voxpopuli_deu_000367 G I G I S T W I R G L I C K L A D I I N A N F U N D E W I R S H A S T G E D E V E L A N K V O N U N D E A L N E I N M A L M E H R J E T Z S T D E R V E R A N T W O F T D U N G F Ü R E I N E O B P T I M A L E U N D F E A L E M R A S I E K A L I F I Z T I E R U N G U N D R E R A R B E I T N E H M E N D A R B E I T D N E M E R R I N E N D A N S B E S O N D E R J E T S T R E S C H N U N G S O T A G E N +voxpopuli_deu_000368 A N D R E R A U C H O H L E N D I E S E R K U D E R G E B I S E R Z I E L N A L S A N D E R E D I S S I S C W Ä E R T U N D I M I T E L A B P Z U O F N E T W A C R I K I O N W I K A L A B R E N Z I T Z I E L E N O D E R A U C G R I E C H E L E R D O D R A U C H O M Ä N I E N +voxpopuli_deu_000369 D E R B R I C H C O S E S V O R D E R Z U R E C H T D A S E S R E T I N G S T A T L I C H E R S C H L T T I E D E L E I S Ö F F E N T L I C H E R A U F G A B E B E G R I F E N U N D D A H I E R V O N F F E N L I C H E A K T Ü R N V O R G E N O M W E R D E N M O S +voxpopuli_deu_000370 D A B W I E S A B A L D N M I T E I N E M S O T C A R L P O G A M Z U T U N H A R B E N M S S W I L D A R F Ü R E I N E N S P E C H E N D E R E C H L I G E U N D L A G E S C A F E N +voxpopuli_deu_000371 S T I E R N O C H N A L I S E R E N W O R E +voxpopuli_deu_000372 M A K E N E N E R T L I E V E R L A N G E N G E B N L I E M E R G A R T F I R N D I K U N G S H V E R A U S D I E A H M E N E U T E B R A U C K E N D A S S B E +voxpopuli_deu_000373 G E R A D Ü O G L E I N E L E P O J E C K T E I S D A S Ü B E R M Ä E S I G H B I E R O G A T E S C H R A U F A N D R E C H T I C H D A S T A S I E R S R B E I N Z E I T A U M V O N D D R E I J A H R E N G E S E N T W E R D E N S O L U N D U M +voxpopuli_deu_000374 I K A N D E V O R S I C H E R N D I A R O P E S C H E K O M I S I O N I S T E T K O M I T D E T Z S U M A A R E R O B S E A L O P E C H E N E R S B I E K T D I E V E R D I S K O S S O S E +voxpopuli_deu_000375 S E S E S L A P E I H E A U F T A U C H S O +voxpopuli_deu_000376 I D I E S E N H A U S E L K A M A N D I E E E N G Ü R G E R E I N U N D B O R G E R N I C H T Ü B E R Z E U C T E N N B E G E I S T E R N +voxpopuli_deu_000377 Z I A L D E M O K R A T E N E H M N I T G R O S A F O E U D E Z S O R K E N T N I S D A S D I N G E D I E I E R F O R G E T R A G E N H A B E N J E B Z S I C H A U H I M Z U S A M M E N A G M I T V E R E N D E R U N G E N E D E N F E I N C H E N S T A D E N U M S E T Z E +voxpopuli_deu_000378 D E A H R B E S C H U S T I E E L D A S O R O P Ä S C H E S E M E S T E R H I E R H E R T Z U N E H M E N U N D D I C O R O B P T I O U N S S I T P L A R I O N E R E I M R A M D E R L N D E R B R E C H T E Z O V E R Ö F N I G E N I S T N I G A U S H E I G E N T +voxpopuli_deu_000379 N D M E I N M E I N E B I T E O D E R M D A S W A S I C H M E R V O R S T E N I S D A S M A R G E N G W I E C K L I C G I N D E R T A R T E I N I E G R O S E E I N E B R E I T M E H R H E I T F Ü R D I E S E K O U S I O N S P O L I T I G H Ü E O N D F G E P O L I T I G S T D I M T P Ü R D I E M E N S C H E N V O R O R T D A M I T I U N S A T E S W E E N T I C H E A U C H B E S C R Ä N K E N K Ö N D E D A S +voxpopuli_deu_000380 W N N W I E H E U T E D I E E V E R R D N G V E R A B S C H I E D E N O F E R E C H D A S S E W I E N A C H E I M L A N G K A R U S E L L S O U E I M G U D N A B S L U S K O M M O N D T I T M M A C H T E R M I C H E B E I E R O M I S I O N B E D A N G E N I E O N S T O K T I E V E S A C H A B E I T H A T +voxpopuli_deu_000381 U N Z E R E R E S C H E A S C H E N U N Z S I E K O N T R O E L E N H A B E N K E N E N P I E L E G E R P R A F T diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.4/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.4/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..8328221a2044f76f8eec6d140cee3c11214cf69b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/logdir/output.4/1best_recog/token_int @@ -0,0 +1,165 @@ +voxforge_deu_000927 2 7 22 9 4 3 9 12 15 11 3 4 16 15 11 3 24 5 2 3 7 15 11 13 5 17 2 6 21 2 6 10 2 4 +voxforge_deu_000928 10 5 2 3 23 16 13 2 8 5 14 3 5 4 8 6 2 7 5 6 8 20 5 15 11 3 4 5 15 11 8 17 2 6 3 +voxforge_deu_000929 5 4 11 9 13 7 19 6 2 5 11 2 5 8 3 18 2 10 2 12 8 2 6 8 3 10 9 7 3 2 3 5 4 11 9 13 10 3 10 2 6 3 24 2 6 8 6 9 15 22 13 5 15 11 2 4 3 24 2 6 3 2 5 4 18 9 6 12 4 14 2 4 3 +voxforge_deu_000930 10 2 6 7 15 11 12 13 4 2 6 3 24 2 13 2 8 3 10 5 7 2 5 4 5 7 9 15 22 3 24 9 13 7 23 13 5 3 2 4 7 15 11 13 8 11 9 19 8 3 +voxforge_deu_000931 10 5 7 2 7 3 14 2 8 6 2 5 10 2 3 10 2 4 10 3 5 4 7 3 18 2 7 16 4 10 2 6 2 3 9 13 7 3 19 5 3 24 16 6 8 9 3 8 +voxforge_deu_000932 8 25 23 7 15 11 21 5 7 2 3 21 2 6 2 4 3 7 8 9 8 5 7 15 11 2 3 2 5 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tensor(-46.4796) +voxpopuli_deu_000378 tensor(-48.3746) +voxpopuli_deu_000379 tensor(-83.3618) +voxpopuli_deu_000380 tensor(-75.1533) +voxpopuli_deu_000381 tensor(-20.8834) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..279ac5140414e9f91ce10bc094cc8efb49e27008 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/hyp.trn @@ -0,0 +1,661 @@ +D E B E R D I G N G M A C H T E I N E R E U S S T W I C H T I G E N S E C H A I N E N D E D E R P Ä T I T I O N A N L I N G O W A N Ü R F Ü R D E S E N J A N E R S O S S B E G E N A D I G U N M (M-AILABS_deu_000165-M-AILABS_deu_000165) +D A C H A B E S I E T D I E W O L U L G J E D E M H E R I N E R I N R U N K G E B L I E B E N E N W A R T E G E S P O C H E N N (M-AILABS_deu_000166-M-AILABS_deu_000166) +E R S U M A C H T U R W A R E R A U F M A L E B R C H T E R D E N K A F I D I E S O N D E S C H I E N I N Z S Z I M E R U N D I E S P Ä H R L I N G E D I E D S S A U S D E N H E C S E S E C K E N G E F A L N E F O T E A K O N A U F B I K T E N (M-AILABS_deu_000167-M-AILABS_deu_000167) +S S I C H E R L I C H A N I H R N G E B O R T Z T A C K H Ä T E E R B E I I E B L E I B E N K Ö N E N T (M-AILABS_deu_000168-M-AILABS_deu_000168) +D E R S A L B Ö M M U S A N D A U O R T W U O R M E N T C H E N S C H I R I C G K E I T E N H A B E N D I E S O U C H E I N E R S E I T S E R K L Ä E R E N A N G E B O T E M A C H E N W R A L E I (M-AILABS_deu_000169-M-AILABS_deu_000169) +U E S M A N R A F D I V E L T K O M T U M S E B S T I E D E R I N S O N D Z U O H A R B E N D E D I V E R E H U N K D E R A N E N V O R T E T Z T N N (M-AILABS_deu_000170-M-AILABS_deu_000170) +A B Ü R N P U N Z E N E L I C H E R S C H U L B I L D U N G R U N T E N E L I C H E M Ö G L I C H T E I T R A U H D E R W E I T E R B I L U N G U N D D A S E G E H N V O N G E D E N K T A G E N E M I C H U N A U F L S L I C H T R A M E (M-AILABS_deu_000171-M-AILABS_deu_000171) +E I N A N S A S I E R N S A K Z I S I S T J E R G E T W I E D E R G A N S K U T Z W I S C H E N U N S A B E R I E R D U N I C H T A L E S G E S T I E S T G E T I E E R I N R U N G A N D A S B Ü S E N I C H T W E H (M-AILABS_deu_000172-M-AILABS_deu_000172) +D N E I N W E I B E R B R A U C H R I C H E I C H T T (M-AILABS_deu_000173-M-AILABS_deu_000173) +T E N D E N G O T H A T N I C H V E R G E B L I C H E N E H M E G E R U F E N S A K T E D E R C H I V E R (M-AILABS_deu_000174-M-AILABS_deu_000174) +N U R E I N E S W E I S S I C H D I E S E R F U R C H T B A R E N F R A G E N T G E G E N Z U S E T Z E N U N D S C H L E U D E R E R D A R S W A R T I N D I E W A R K S C H A L D I E G L U T L E I N E S L I E B E S W I L E N D Z I S T S T E R K A R A L S T R E N U N G T (M-AILABS_deu_000175-M-AILABS_deu_000175) +D T O M S A M E I G E W A N E I N G R O S S E N S I E G N C H E N E R L A N G N G H A R D N N E C K E G E N S C H L C H T N (M-AILABS_deu_000176-M-AILABS_deu_000176) +S O S E I N A H M E D E M S I C H T I Ü R B E I T A K U N N C H T A F F N A N K A N B R E S C H E R U N D W E L K O M M E N (M-AILABS_deu_000177-M-AILABS_deu_000177) +E N A R B E R I C H V E R T S E I E I N E N I H R E R U N W I S S E N H E I T N (M-AILABS_deu_000178-M-AILABS_deu_000178) +U F O N D E R T R I T E N U N T E R E D U N G A N S A G T E M I S T E R H E V E S C H E M W A R M E R D I E P E R S O N I N H O H R E M A S E R V E R D E C H T I G H T N N (M-AILABS_deu_000179-M-AILABS_deu_000179) +I C H D E N K E D E M T N E A R U N D S A N E V E R M I E W E R D N E S R E C H T V O N D E R F I N D E N D A S T U D I E S E L B T A N G I E B S T U N S E V R D E N F R E U N T L I C H G E G E N D I E S E I T (M-AILABS_deu_000180-M-AILABS_deu_000180) +E T Z S S C H L U G D I E H L L E F L A M E R A U F U N D N U N E R K A N T E E R U N D S D I E V W E N C H I M E R Z U S A M E N G E D R E N G T D N D E M W I N K E L S T A N D E N (M-AILABS_deu_000181-M-AILABS_deu_000181) +D E R S E I N E R S E L E A N S P O N E N D D A S E R M U N T E R N T E R W A U T V O R W E R L T Z S (M-AILABS_deu_000182-M-AILABS_deu_000182) +F O M I C A F D E N B E S U C H T E S T O N E S C H E M I N I S A P R E S E D E N T E N O N T (M-AILABS_deu_000183-M-AILABS_deu_000183) +D U W A S F Ü H R E R F O L G U N G E N W A S V Ü R N A R C H S T E L U N E N H A B R I G N I C H Z U A D U L E N G E H A B T N N (M-AILABS_deu_000184-M-AILABS_deu_000184) +Z S I G O E I N E R W A R E N E S T I V O N A U Z U O L T F U R E N E I N K A U M A R W A C H N E S I U N G E S D I N K A M Z U M I E H E R A N G E H Ü B P F T U N B E T E L T E N E I N (M-AILABS_deu_000185-M-AILABS_deu_000185) +A I C H W E R I C I N E M B O T H I N F A H N D E D S B O T E R A N L E N E N D S I E R Z O E G D E R N A L E S G A N Z E L L E I N U S I E A N I C T U M Z U K Ö M A N T N (M-AILABS_deu_000186-M-AILABS_deu_000186) +A L S N E R E I N M A L N O C H D E N R A U C H V O N A N E M H A U S E R A U S D E R F E R N E R A U F S T E I G E N Z U S E N U M D A N B E R R I E G K Z U S T E R B E N (M-AILABS_deu_000187-M-AILABS_deu_000187) +D I E T E N Z E R E N A R B E R L A K A U F D E N K N I E N V O R B R A M A S B I L T N I S I N N A H M E N L O S E R S E H N S O C H T U N D W E I N T E J A M E R V O L L (M-AILABS_deu_000188-M-AILABS_deu_000188) +D E C H T F E R T I C H M I C H T E N D I W E R G L I C H K E I T E N C H N I C H T A U F D I G I C B E G U F E N K A R N (M-AILABS_deu_000189-M-AILABS_deu_000189) +D T I C H E R G E R T E M I C H T A N W E N I C H A U R F A C H T E R E I S W A R S O F U N D E R S C H Ö N E N G E W E S E N D A S F L I E N N N (M-AILABS_deu_000190-M-AILABS_deu_000190) +N C H D E M E S C H O N D E N G A N Z S E N F O R M I T A G M I I M V E R B R A C H T K A M S D E N H O B N A C H T I S C H I N S K R N S C H E H A U S U M K A S P A L E W O L Z U S E R G E N N N (M-AILABS_deu_000191-M-AILABS_deu_000191) +E R W A E I N A L T E R H I R H T V O L M E D I E Z I N E S C E R G I N I N A L I T E T (M-AILABS_deu_000192-M-AILABS_deu_000192) +N T D A S V O L U C H D E R M I E T A R S E I N E V O N D E L R I C H G S K E I T E N H A R B E M S E R N (M-AILABS_deu_000193-M-AILABS_deu_000193) +E S I E S A N A L L E E N G S T L I C G U N D B E T R Ü B T A U S U N D A U C H E R A R E N E S A S C H W E H R M Ö T I C H D A W I E D I E A N D E R E N U N D S T Ü T Z S T E R D A S H A U P T I N D I E H A N (M-AILABS_deu_000194-M-AILABS_deu_000194) +U N T E R D E N D A M E N M E I S T J I O N G E F R S C H E G E S I C H T A U N T E R D E N H E R E N N E B E N J U E N T L I C H I E N Z S O U C H E M I T F A L T I A S T I E R N U N D B E R E I T Z S M E H R A D E R M I N D E R M O N D U M G L E N S T E M S C H Ä D E L (M-AILABS_deu_000195-M-AILABS_deu_000195) +S E I T A G E N S C H O N H A T E S B E S O N D E R S T D R E U E N D G E K L U N G E R T (M-AILABS_deu_000196-M-AILABS_deu_000196) +S O N D E R B A R (M-AILABS_deu_000197-M-AILABS_deu_000197) +E R B V O N E R B E M H E I M S T A N D M I T E N H R G A T E N V O L W E M U O T U N G D A N K G B A R K E I D A N D E R G R O F T C F D E R E I N M E C H T I N G N G N (M-AILABS_deu_000198-M-AILABS_deu_000198) +T I E R W A R J I E D E R M E N S C H E I N U N D E R U N D F A S T A L L E S W A S M E N S C H E N T A T E N E T A S S W O N D A R B A R E S N (M-AILABS_deu_000199-M-AILABS_deu_000199) +W E L C H E J E R W E I E S I E E N D L E N K S T F Ü R T N (M-AILABS_deu_000200-M-AILABS_deu_000200) +I E W R T E N A S N I C H T I E N T E R E M S C H A N K T I S C U N D K E I N E R E R E R D I E N S T L E U T E B E F A N D Z E I C H I N D E R S T U B E (M-AILABS_deu_000201-M-AILABS_deu_000201) +N A L S D I E H E R S C H A F T A U S D E R K I L C H E R T R A T S T A N D E N D I E L E U T E U M H E H R U M S I E V O R B E I G E H E N Z U S E H E N U N D A M K I R L C H O F T O R E R W A R T E E E I N M A N N N (M-AILABS_deu_000202-M-AILABS_deu_000202) +A S M S E N E R T O H N O M D E M T A R I S M U S E N G E N T U T D E (M-AILABS_deu_000203-M-AILABS_deu_000203) +L C H G E L A U E R A S E S G U D E N I T B E R M E I N E H E R D O K T (M-AILABS_deu_000204-M-AILABS_deu_000204) +E N T O R I M A N F A N K G E W A N E R K E I N E A U F M E R A M K E I T V Ü R A N D R E R D I N G E A L S F Ü R D E R S E S S E N (M-AILABS_deu_000205-M-AILABS_deu_000205) +D I E S F L Ä S C H I E N Z O G E R J E T Z T E I L I C H E R V O R W E R E N D J E N E S C M I T W A S S A R F I L T E N U N D B O T E S D E R U N G V E R Z Y Ü S A N N (M-AILABS_deu_000206-M-AILABS_deu_000206) +E S E R W A S O C R I C H T I O N W I C H T I C H T E R S H I N E R D O C H E R T Z A N S C P O S F O L L E L G E S A K T A T W E W E R D E N A U C H A N E N Z E I T P U N K D E R I D U K T I U O N K O M M E N D E S D G Ü (M-AILABS_deu_000207-M-AILABS_deu_000207) +D N I C H D O C H M U T E R W E R K E S E J E R Z T N C H N I G N (M-AILABS_deu_000208-M-AILABS_deu_000208) +B I A H A B E N I N E N D E Z E N J A H N R E C H T E N K E B I T I U N G Z U B A S I L I E N A U F G E B A U T P R (M-AILABS_deu_000209-M-AILABS_deu_000209) +D T T S S I E E V Ü R D E S C H N I C H T V Ü E R A N D R A B P F O N T T (M-AILABS_deu_000210-M-AILABS_deu_000210) +L E C H L I F E N S E M E T Z U (M-AILABS_deu_000211-M-AILABS_deu_000211) +G O A T W A S S I I E A R Z H Ä L T E R H Ö R E N S I N U R E S I S S E I N G A N S E R O M A N N (M-AILABS_deu_000212-M-AILABS_deu_000212) +S E I N E M T E R K A N I M O F L U S S W A S S R G E B E N D E S S E I B P W E I N D E R (M-AILABS_deu_000213-M-AILABS_deu_000213) +U N E S W R S C H A S M I N E S T A W I R T E Ö M E N B U S A M M I T E R N E T Z A G E N T U R A M V I E R T E N J U N I Z U M E R S E N M A L P R E S E N T I E R N W I E S I C H D I E N E T Z B E T R E I B E R U N D D I E K R A F T D E A R K E D I N E U N E T L H N E V O R S T E R U N D (M-AILABS_deu_000214-M-AILABS_deu_000214) +E V W A R H A T E S E C H T Z I T A N T V O R T O D E S S F V E C H E R V O N D E M G E T E R B E F R E I T U N D Z U C H T E Z U N D F I E N B E D E R S M A L E G A T E N B O T D K A E I N E N A U S W I H N (M-AILABS_deu_000215-M-AILABS_deu_000215) +D A B E C H M E I N W E R F Ü R U T E L I E N L A S S E N N O D E R N O C H E I N A N L A U F N E M E N U N D T E S R O L Ä N D N S O L T E N (M-AILABS_deu_000216-M-AILABS_deu_000216) +E R W A D A S G E T Z I E R N D E R S T U N D D E T E I T E I E A U F T R A K T E M A D M U N M S C H L L D I E A C H A S T A N D U N I G E A U F E N S E I D E N T Ü K E Z U S A M F E L D E T E F Ü R S C H O N Z U S O R G E N G N (M-AILABS_deu_000217-M-AILABS_deu_000217) +T I W R D E N H C H S E M (M-AILABS_deu_000218-M-AILABS_deu_000218) +A B A E T I B S O D A F V O R G A B E N D A S M A C H M E R N E R D O L I K N I G H S (M-AILABS_deu_000219-M-AILABS_deu_000219) +A L S U N S R E E D E B E K A N T W O R D E R W A D I Ü R S I O G N O M I E D E R W E L T E R S P B O G E R U N G E F Ä R D I E I N E R S K A L B E S D A S U M E R S E N M A L O N H N H Ö R (M-AILABS_deu_000220-M-AILABS_deu_000220) +I Z E R M A H N S I G E F Ä L I C H T A U F U N D E S K L A N G D I E E I N J A M A N D E R H I L V E R (M-AILABS_deu_000221-M-AILABS_deu_000221) +R D O K T O R S A G T E E I N E F R A U D I E S H N O G R I N E R M D I E S O A F T Z U I N E N K O M I S T E I N L I G A N I C H K R A N (M-AILABS_deu_000222-M-AILABS_deu_000222) +D I E A L T E E R I N R U N G A N D E N F R Ü H R E N T A U M T A U C H T E R E B E N F A L S W I E D E R A U F U N D U N W I E L K Ö R L I C F S S T B E I D E B E H A U P T U N G D A S D E S E L E D E N K Ö R P E R V E R L A S S E N U N D Z U I M Z U R Ü C K E R E N K Ö N E S H I N E S I E R O R D E N D L I G (M-AILABS_deu_000223-M-AILABS_deu_000223) +A L S I E O F T D E N A L K O N D Z U R I K E R T E V A N D Z I I N D I E Z E I T U N K L I E S E N D D I E W E R E N R E S F O R T Z E I N E S A N G E L N K T W A R H T (M-AILABS_deu_000224-M-AILABS_deu_000224) +T E E H R W A H E I N K I N D D E R S T R A S E V O N K L E I N A U F A B E R I N I M L E B T E V O N J E H E R I N E G W I S S E S S E E N S O C H T N A C H E I N E R E H R B A R E N B I R G E R I C H E N E X I S T E N S T (M-AILABS_deu_000225-M-AILABS_deu_000225) +A I T U N E S T I G J R U N G K T Ü H N U N Z I E N I G E I N E R G R O P E F E R A N F O D L I C H O N A M W Ü E F Ü N E D E N G E M E I N W U H L F E R N W O D L I C H U N (M-AILABS_deu_000226-M-AILABS_deu_000226) +W A S M E I N L I E B E S K I N D W A S K A N (M-AILABS_deu_000227-M-AILABS_deu_000227) +U N D D A N W O L T I C H D E N A N B L I C G D E R A N I C H T M I S E N D I E M E R G E B L E B E M W A R E N V O R A L M A B A R B W A E S I D A R U M Z U T O U N W E I N E S Y S E L I C I S E R B E T E I N I G A M A S S E N G E T R Ü S T E T Z U S E E N T O (M-AILABS_deu_000228-M-AILABS_deu_000228) +E D A S A U C H W I E U N D S G E N S E T D C H E B S H N U N T D A S T U Z E N K Ö N N E N E Ö R M (M-AILABS_deu_000229-M-AILABS_deu_000229) +S E I N E G E S C H F T I C H E L A U F B A H N H A R B E S T I E V E N S N A L S K Ö C H E N B O I N E I N E M O T Ä L F I E R E N G R A D E S B G O N (M-AILABS_deu_000230-M-AILABS_deu_000230) +N F Ü L E I C H T E N S E G U T I E S E A N S I C H E N D E S S C H O F S N H A S E Z U M E L D E N Z A C T E D E R T A T S C E N D E R I M A R M E H R E I N M A N D E S G E S C R I E B E N E N W U R T E S W I E D E R T A D N (M-AILABS_deu_000231-M-AILABS_deu_000231) +E M A N D A N M O R D E N E H R H O P E R S I C H S C H W Ä T S C H C K T E D E N L A R K E I E N D E B O N U N G V O R J A B A C H S U N T L I E S U M E I N E N T E R E D U N G B I T E N D E M A N K A M M I T E R O T S C H A F T Z U R Ü G D E T N (M-AILABS_deu_000232-M-AILABS_deu_000232) +N T N U E I N W E N I C H T R A U R I C H W U R D E S E N I M D A S S E L B E R K A M E N S I N N I E Z U F R I E D E N S C H I E N N N (M-AILABS_deu_000233-M-AILABS_deu_000233) +E I N S O M A H R W A H M A N O V E M B A R T A R K L A G M I T Z O N E N G L I T Z S A N N Ü B E R D E H U P T S T A B T U N D U N D E R D N L I N D E N D R E N K T E R I N E T A U S E N D K A P F I G E M E N S C H E N M Ä N G E R A U O V O N I E D E R U (M-AILABS_deu_000234-M-AILABS_deu_000234) +K O M I T M I H M E I N S O N D E N I C H P R A U C H E D E I N E L I E B E (M-AILABS_deu_000235-M-AILABS_deu_000235) +D N T N W O R S A I N K E S I C H T R D E I N W N I C H N A C H T D E N K L I C H A R S O O W I E V O N E I N E R E R I N E R H U N G E R H Ä L T N N (M-AILABS_deu_000236-M-AILABS_deu_000236) +N W O T O U I E R D E N W A T I O N E N S D R O C K S T E I G E N U N D T A T Z U E S T A S S E S T E M I E E N G E F Ü R T W O N (M-AILABS_deu_000237-M-AILABS_deu_000237) +N E T G E W A T E E R M I T E N S E T Z S N D I E S C H E U S L I C H E T E U F L S C H E R A F E N F R A T Z S E D I E B E D E S M E T C H E N S C H L T E R S C I E L T E N (M-AILABS_deu_000238-M-AILABS_deu_000238) +T T E R A R D E W E R T N I K T E D A S G Ö R D E I N E G E W I S S E N W R T S C H A U F B E R N H A T W R T S C H O F I S T E E T W A S F A R G U N S E N T N N (M-AILABS_deu_000239-M-AILABS_deu_000239) +W O L T E R I N W A H E I D I E L Ö R S E N D T Ö R T E N N U N D T K N D E R S C H E L I S E N N N (M-AILABS_deu_000240-M-AILABS_deu_000240) +A T S E Ä T D I S E R E S P E K T V O L W U O B E I E R N U R E I N I G E S E L D E N V E R S C H L U K T E W A S S I M B E I D E N B E L E B T E N L A N G E N W Ö R T E A N D E S F T A N F O R K A N (M-AILABS_deu_000241-M-AILABS_deu_000241) +D L O R T F O N D L E L E R O R U V E R T N I C H Z S E N D B E H R E N D E S S E N B I N I C H G E W I S V E R S E T Z T E E R T (M-AILABS_deu_000242-M-AILABS_deu_000242) +K A M G L E I C H F A L S I N S S C H L A F T Z I M M A R A U F E I N E N N A G E L I N D E R N E H R T E S B E T E S N (M-AILABS_deu_000243-M-AILABS_deu_000243) +D A S E S D I S C H A N G S D I E N E S A R K R I S T E G D I E S C H A N G S V Ü R I N Z T E R N A T Z I O N A L E R E G E N B I S I E I N I M P R O N S I E B P L I E N D E R S E D J A L E M A K P O T A F O L I E N T I E R N (M-AILABS_deu_000244-M-AILABS_deu_000244) +A N F A N G S F I E L D E R E G E N S C H R A G U N D P E I T S T E E R S I E E I N E D A N D I E N D E R E S E I T E D E S W A G E N S (M-AILABS_deu_000245-M-AILABS_deu_000245) +M F A S T L E I C H E N I G E N B E R M E S S O N G E R E S W E R T E S A U F T Z O G E B E M S I C H E N T S C L O S S E N H A T E N N (M-AILABS_deu_000246-M-AILABS_deu_000246) +S E I S T B D I E F R A R G E R M E N C H L I C H E N A R B E T N D I E F R A G E W A S K A N T E C H N I S C G E L Ö S T W E R D E N D A (M-AILABS_deu_000247-M-AILABS_deu_000247) +N U T I S E A R F A H R I E W A U F I E R E D E M E S I G B N O T Z S T E N W A S S A R S T E L L E N D I S E R O T A N G E W I E S E N N N (M-AILABS_deu_000248-M-AILABS_deu_000248) +D I E B E I D E N M I S T E N H I E O B E N A U F D E M G E B P F E L G E S T A N D T E N H A B E N U N D E R S P R A C H D I E A L T E N W A U R T E V O R S E C H I N (M-AILABS_deu_000249-M-AILABS_deu_000249) +E N T L I C H B I K T E S E D R I K G A U F W E I S S N J U U I G A L E S V O N D E N A R M E N L E U T E N F R A K T E E R (M-AILABS_deu_000250-M-AILABS_deu_000250) +S E U D E I N E W I N D R B A R E T Z U S A M A R B E I Z W S C H E N B U N D U N D L Ä N D E R N I N D I E S E N R A G E N G I B I T S E S E N T R E S A N D E N P R O J E G T E N U (M-AILABS_deu_000251-M-AILABS_deu_000251) +D K A S B A R F E R H A R T E A N G E N M R T Z E L T E N S E I M P L A T Z S S E I N G L I E D E R J A R S E I N E N A U G E N B A N G I E V E R S T E I N R T E L E T U M Z W E I T E N M A L I E N I K T E N (M-AILABS_deu_000252-M-AILABS_deu_000252) +E I N I G E Z E I T D A N A C H F R A K T E E R M I C H O P I C H G L A U B E R D A S D E R E I S G A N G D E N S C L I T E N D E S A N D E R E N Z E R S T Ö R T A B E (M-AILABS_deu_000253-M-AILABS_deu_000253) +A E N E N B L U S S E N I C H T G E N E I N E R S C H O R C G S T A R E D E L V A L I N (cv_deu_000698-cv_deu_000698) +J I L S C K O M I R S C H E N (cv_deu_000699-cv_deu_000699) +S D E M B E I E A R B E I T E D E E I S A U S H R E L S K R A F T A F E I N E N F A H M E (cv_deu_000700-cv_deu_000700) +E I N T E R I T T O H E I K O S S E S O C H O P E R W I T D T N I C H T I T A N M I T A R M E S I K E I N E R E N O H O P E R R E I C T (cv_deu_000701-cv_deu_000701) +I E R S O U N K A E N D R H K Ö N Z S L I C H E R B E F R O C H T U N G K Z U R W E R T (cv_deu_000702-cv_deu_000702) +D I E N A C H T A R D I E F N E F L E R F L I E N V O N M I T E R J U L I E B I S M I T E A O F T O B E R (cv_deu_000703-cv_deu_000703) +D E R E A R H A A T (cv_deu_000704-cv_deu_000704) +E N D H E R N (cv_deu_000705-cv_deu_000705) +N U T Z E R K N E N I H R E L E R S E Z E I C E N O N E I N A B S P E I C H E R V E R W A L T E N U N D B E T A N E N N O T Z A N T E I L E N (cv_deu_000706-cv_deu_000706) +D I E D U M B O S C O K A T I E R A L E (cv_deu_000707-cv_deu_000707) +S A U L B A S S Z E L Z U D E N N R O T I S T E N D I E S E I D A R E U M E M F Ü M E C E Ü E U E L E S E R E N Z E L L E N (cv_deu_000708-cv_deu_000708) +I N K Ü N Ü W U R S E R B E N E M E N W E L E N B E U G K E I N E S I E L B W O N D E R E I C H E N (cv_deu_000709-cv_deu_000709) +E I T E R E W I C H T I G E I N D E S T R I E Z W E I G E S E D I E N I C R M I C H A N I G G A L W A N O P L A S T I G M I T E I L B A U U N T I E H U T Z V E R A R B E I T O U N G A (cv_deu_000710-cv_deu_000710) +Ü B E R D E N A U T O R S N I C H T B E K A N D V E R M U T L I C S T M T E H R A U S E M D E U T S H E N S P R A C H G B I E D T (cv_deu_000711-cv_deu_000711) +N S T U E R I S M I D E N E N T O P L I P A T E (cv_deu_000712-cv_deu_000712) +D I E H A R M E I E R E B L E M E R O S S I S C H I G H T O U C H T (cv_deu_000713-cv_deu_000713) +W I C H L I E N E M A N A C H U A R E M E A U K T U R E E I E R T E T D N T H M A C H B E R N N (cv_deu_000714-cv_deu_000714) +O R D E A L I D E G E S S Y K L D E A N L A N A U N I S C H E W A B E N (cv_deu_000715-cv_deu_000715) +E I E H E I D E N N A M E I N W I N C H E N W O R E R A U C G S T A T (cv_deu_000716-cv_deu_000716) +I N E R U N G U N D E U S S R E R N A T I C G G E N E N A L S G E T R E N T I T A I L E E I N E S N A T I G A U C H G E M E I N S A M V O R O M M E N (cv_deu_000717-cv_deu_000717) +D A B E I E B E L E G T E R R D I E P L Ä T Z S E V I E R U N D T R E I G (cv_deu_000718-cv_deu_000718) +K I N D R A B I E I S S I E T O C H T E R Z W E I A R R O F E S E N E R L A R T E N Z A (cv_deu_000719-cv_deu_000719) +I S K L A U B E R A S F Ü R T N I S T I N I R I S T I E R I S T U N G (cv_deu_000720-cv_deu_000720) +D A S S E S E I N E R X S T R E N S C H L E C H T E R I C H S L I E N I E R (cv_deu_000721-cv_deu_000721) +H E R L O R C H E N B L E S T S E I N H A G E R E S G E S I C H T (cv_deu_000722-cv_deu_000722) +M O K A R E N F I N E T O U N F E R (cv_deu_000723-cv_deu_000723) +T I N G E B O K A B E R H A T D E R D R E I G E S C H I C S T E T (cv_deu_000724-cv_deu_000724) +L C E S K O M T W I C L I S T S A A N M D A S O L C H E D A D T E N U D I E S E R E B E N E R F A S T W E R D E N (cv_deu_000725-cv_deu_000725) +S T R A M I N H E N G E G E N E G I T E I N E R M U N I C H E S P O S I E R E N (cv_deu_000726-cv_deu_000726) +B E N I C H U M K A U F E I E R H P O T E G B E R E C H T I G T (cv_deu_000727-cv_deu_000727) +T U N E E U N L E N S S E N E R S H E N F V E N D E N D E N B M N M I E (cv_deu_000728-cv_deu_000728) +H K Ö U E N D E R E N S N D E R E E I S L N D U N (cv_deu_000729-cv_deu_000729) +O N D I E P O F I S I N L L I N T E S T Ü T Z U N D A M A S S E A R T D I E E N A B T E I L U N G W A E N D I S E B A G E N D E R K O N K R O W E N S U N D O C H U N D O L E G E N (cv_deu_000730-cv_deu_000730) +S I E D I E N T E U N E S T A S U N D T E O K M F T V Ü R B E L G I S C H E B I S A T Z U S T R U P E N (cv_deu_000731-cv_deu_000731) +D A M I S S N W I S C S P L E N E N M E I N T E D E R Z H A H N A H R Z T (cv_deu_000732-cv_deu_000732) +A U S D E M S P I T E R E I M N A C H F O R G E T I E M N I M A R K E D R O E U O L S S O W I E E I M L I G E A K O N K U R R E N D T E N L N D E N N E I (cv_deu_000733-cv_deu_000733) +I E A U C A S E N S T E N D R A N A C H W A T E N G E R F Ü R T I E K U M S W A L D A S K O N N D O R K E T G T E R I O M N I C H T (cv_deu_000734-cv_deu_000734) +S M I F U C H E N T I K A G E O A U F (cv_deu_000735-cv_deu_000735) +W I E R I E I E A L E I N E N (cv_deu_000736-cv_deu_000736) +D U M I S G W E R T W A S B W E I S W U R T R O C D S S D E S I U N B I S E N S V O L S C H U N G E L (cv_deu_000737-cv_deu_000737) +H A B T E M A D E R S C H A I S T D I E R E W A N S C H V E R E K Ü B I C H I S T R E I C H E R E N T E R F R E I N D E N (cv_deu_000738-cv_deu_000738) +G L E I C H Z E I T I G W U O D E N S P O T W E T E N T A L W E I S E V E R B U L E N (cv_deu_000739-cv_deu_000739) +S E E G E N (cv_deu_000740-cv_deu_000740) +T I A B E (cv_deu_000741-cv_deu_000741) +Z U D E M F A R S A H E R E N K L O S T A L A N G J A R E D I E M T E R D E S N O W I T Z S E N M E I S T A S U N P R I O R A (cv_deu_000742-cv_deu_000742) +H E I D E N H E I D E N E N S T M T E I N E R E R T Z T E V E R M I L I E A R (cv_deu_000743-cv_deu_000743) +A R E S P Z I M P T P E N N (cv_deu_000744-cv_deu_000744) +Z W A I E U N G R (cv_deu_000745-cv_deu_000745) +T T T T T E B E M F A L S E N A U G N G A N G I I E D E E N T I K A L T R E I N D E R E F A (cv_deu_000746-cv_deu_000746) +D I E S E R S T D E T A R F E A B S E L W E N D T E N E I N H E R M S C H E R S C H O L M I T D R T K A N D T E S E N O F E N (cv_deu_000747-cv_deu_000747) +A L S O E S C H I O R E N I C S (cv_deu_000748-cv_deu_000748) +W I E K O N M A H N S I S C H S C H Ü T Z E (cv_deu_000749-cv_deu_000749) +A U F Ü M F M O N A T E N L A G E I N E I M F I N T L I C H A E P L O T E R A N Z S T D I E B I S T A H E I N E R H L T L I C H E N V O R (cv_deu_000750-cv_deu_000750) +Z I E L I S T E R S D I E V E E N T S T I M U G E N E S O F T J E S E S T E B Z M I T S N N R S G H E T Z I C H K A T Z G O N Z U Ü B A R P O L F M S H (cv_deu_000751-cv_deu_000751) +B N N I N T E E I N E N W A H R M N E N G E M P R E N E N I O N D B E U U S C N I S S I S T D I K Ö N D E L E B E S S E R U N S H E I L T E N (cv_deu_000752-cv_deu_000752) +D I E A N D I E W I E R E N S O C H Ü Ö R I S T A N N U R N K E N G A L A U F H E N U N D E A N D A L L E G U N M B I E M T D E L L N N E R U S L A N G E L I G T (cv_deu_000753-cv_deu_000753) +I E R E T U A R G K E I S T E N D I E S A R Z E I T G U G E F L N G E N S C (cv_deu_000754-cv_deu_000754) +E T D I E S T R E I K E R B E G E N D I M S G I E N B E R H U N E R S I N F Ü H R T I C D I E G O E B L E H E C H T U N G M A S Y Ü T O S S T E (cv_deu_000755-cv_deu_000755) +E R S T V O N D O R T K O N T E E R S E I M W E G F R E I V O R S E T Z E N (cv_deu_000756-cv_deu_000756) +S I E E R H E B P Z I C H H E U T E I M A R N O C K U T E R K E N B E R A U S S D I E M N S C H W E M L A N D T H E R U S S (cv_deu_000757-cv_deu_000757) +T I A N A R E S C H E N I N S L E N G E H E Z U S B A H H E (cv_deu_000758-cv_deu_000758) +W E S S N S C H A F L E R H A H A B E N D E S I M E N T A T Z U N P E S E R E T N O B E I F O A R A U N B E R O B A T E T (cv_deu_000759-cv_deu_000759) +S E I N I G I S C H E H T Z S B E Z T I U N G E N R E I S C H T E N B I S N O R D D O M E H R I C K A U N D A S I E R E N (cv_deu_000760-cv_deu_000760) +S A L R E I C I E P V O R D E R E D L A T I E R U N G E N B E I D E U T S C H E N Ö R O P A R U N D W E L T L E S T E S C A F T E R N S O I E O L Ö B I S C H E N S P I L E N V O R L G K T E N (cv_deu_000761-cv_deu_000761) +I N E N E R T A L E S C E R T U M B L E T E R M T D S O E T S I K I T A U F E R B A G B A N G (cv_deu_000762-cv_deu_000762) +W I T E I M W A H M L I T R E N K I N B A U F L E S S E I E K E R L T E B E S E A U S H A U E L T (cv_deu_000763-cv_deu_000763) +W O L L E D E M Ö E E W E S (cv_deu_000764-cv_deu_000764) +O S T A N D I S I E N E E I N E B O C H E N A C H E M E S T U V O R N M U N D I M P G L U L I E N (cv_deu_000765-cv_deu_000765) +E M M I T E L E I T E R H A T E N D E X Z E M D E R H A R S C H A F T D A S D O F I N E R (cv_deu_000766-cv_deu_000766) +D I E N A M S C I E P L E T R A G N O C H R E I T E R D E F A L T S O L G E F A D M A S E H A T I E (cv_deu_000767-cv_deu_000767) +P L U K A N N S W I T D E B U S L C H R A N G V O N E R O D E R F O R E N (cv_deu_000768-cv_deu_000768) +I E R D O C R E G O L (cv_deu_000769-cv_deu_000769) +A L L E R D I N G S E R G A H B E N W E I T E R H R E P R Ü F U N G E N D A S S S M I T T E L F R I S T I G K E I N P E D A R F R I S C E U C H E A U T O B A N G E R W E N (cv_deu_000770-cv_deu_000770) +U N G E K E R T K A N E N F R E I P R I E F E I N E A R A U S S C H R E I B U N G A L T S V O B E L F R E I G E M E I N D Z E I N E N (cv_deu_000771-cv_deu_000771) +M I E Z A G K R O T E S G E A B S C H N I T E S E I N E N E I N F L U S S E L E U C H S C H O S T A K O W I C H (cv_deu_000772-cv_deu_000772) +R V E R E I N E D E R P I E O N I E R E A U F D M G E B I E T D E R U T Z I U N G D E R S O N E N E N E R G E E (cv_deu_000773-cv_deu_000773) +A C H V E N M E D I K U N D E N A F D E N E R E N G E N M S I C H Ö F L I C H K E I T B E W A N (cv_deu_000774-cv_deu_000774) +D I E B E M A S C H I N E R S T F E R T I C H (cv_deu_000775-cv_deu_000775) +I N D E A R C H A I S C H E N P E R I O D E W U R D E N R S T I V O R M I N D E S O C K E B A S S I N T U Y K I L D (cv_deu_000776-cv_deu_000776) +D I C O M Ö Ü D I E R S E E S E A L S T E R S T F Ü N (cv_deu_000777-cv_deu_000777) +A R T U Ä G E T V E R G N E A M U M S (cv_deu_000778-cv_deu_000778) +T E A R M I T E N T E T E I N E E F Ü R K K R E I S C H E I N T L N A T Z E U N E A L I E K E Ä R D E N S A B E N V O R E L E N I N M S C H K Ü T E L U N E N E N Z S A C K U R (cv_deu_000779-cv_deu_000779) +D E R S O N E I N E S B E R E T N A N Z B E G A N S E I N I E F O S B E I K A E R I W E I D E N S P O R T F R E U N D E N W A N E E I K E L (cv_deu_000780-cv_deu_000780) +I N D I E S E N J A H R G A B E S I E D E N O M E E I N E N S I N G E S U N D S E C H S O N D E I S I G N O M E R E I E N S A L L E B E N (cv_deu_000781-cv_deu_000781) +N O R D W E S T L I C H V O N H A K H A U S E N B E F I N D E S I C H D I O R T S C H A F T H A K E N B R U C H N (cv_deu_000782-cv_deu_000782) +I M O R T K N A E N B U R G G I E N V I E L E S O Z I A L E E I N E R I C H T E U N G E N V O N E R E M A N L A M P R E C H T U N D E R M A I N H Ö T E A U S (cv_deu_000783-cv_deu_000783) +I C H W E R D E F O L Ö K L I C H D E N R A T Ü B E R D I E M P A L M E N T V O R G E T R A G E N E N B E D E N K T E N I N V O R M I E R E N (cv_deu_000784-cv_deu_000784) +E S E R E T R A U R E C G E W E S E N E I N S O W I C H T D I E S T E M A N I C H T E M K O N S E T F A B S C H E N Z U K Ö N (cv_deu_000785-cv_deu_000785) +N O C H T I S S I M T O T I M K L E I C H E N J A H G A M M S G U T Z B R I S T I G A N A N D E R E V I S E Z E A (cv_deu_000786-cv_deu_000786) +K O T D A N A C H G A B E S E I N E I N W E R B E R V O R D M I N T D E M T K A N D K A M N E N D V O N S C A K E S H O U E N B A C H (cv_deu_000787-cv_deu_000787) +D A S I T B S E A E T (cv_deu_000788-cv_deu_000788) +W I S I E S M I N L E C H Z E I T A U S H R (cv_deu_000789-cv_deu_000789) +N A C H E D E M D O C H F B E F I N D E R S I G H A R U C H D E R K M K A N I U N N A S I O N A L L B A C H K E R B O T (cv_deu_000790-cv_deu_000790) +I E S O R E N D E A K Ö N D E N D E S D J E L I E B E D E N T O R D B E S I G T A T (cv_deu_000791-cv_deu_000791) +B E T E C K T S S T D I E R E P R E S E N T H E R T I E F G E S T A L T E D E W I L E R M I T D E I N E R N M A N D S A R T D A C H (cv_deu_000792-cv_deu_000792) +D I E S E S I E T L U N G E S M I T E R O R T S C H A C F T D E L L A C H Z U S A M E N G E W A K Z E N (cv_deu_000793-cv_deu_000793) +W A R I S C H N E I N M A L I E N D E M K L O (cv_deu_000794-cv_deu_000794) +B O R A U R I S T I S T A U C H V O L O R (cv_deu_000795-cv_deu_000795) +D I E H E X V O N D R S T R A S E W U D E N S N V O N A L F E T D I O L E K B E S E I N E F E S T E N S E I S C H O E S C H A B L N E E N G Ü L S I E R T E (cv_deu_000796-cv_deu_000796) +A I N H A R S P Ä I T E V E X S L T E R E L T Z U N E L F T N A T Z S U N M B E V O D E E L F V U N G E R E I C H E (cv_deu_000797-cv_deu_000797) +I N D E R L A N D V I T C H E R F K A N D E R E R T R A R G K T D E U T L I W E D O R Z I E R T W E R D E N T (cv_deu_000798-cv_deu_000798) +M A N S U R S P I E R T E I N S E I N E R H E I M A T S T A D T K E E W O R F I E R A L L A L L (cv_deu_000799-cv_deu_000799) +E R T R A D E R R E I M A U H A L U N D E L N L A O U T A B U E O B E I (cv_deu_000800-cv_deu_000800) +M I T F Ü R T W A R E H R D I E R O W P T H J A D E L E I L S D E R E C H T L I C H E R O L L E D I E S S O B E T S E I C E N E N G E M E I N D (cv_deu_000801-cv_deu_000801) +L E T Z T E W O C H O G A B D A S M E T I E B E K A N D G A S E S V O N E P E L Ü B E R V I E R N D A S H W A L T E F O R F E L E V O N Ü B E R I T Z E U N I N T O M I R T W O R D E N W A I E D E S U N T E R N E H N A L S N I C H T S C H E R I E N P E E I T E T E (fleurs_deu_000378-fleurs_deu_000378) +E J Ü U R S E J I M N E S T I G U N D T E R S T Ü T Z S T E D E N B E R I E F D E S O L Ü M P I S C H E N K O M I T I S D E R V E R E I N I G T E N S T A T E N U N D D A C I P T I E R T E S A S A P B P T U L U T E N O T W E N D I K E I T D A S I H T D I E O L Ü M P I S C H E V E R M I L I E F Ü R E I N G S I C H E R E S U N M F E L L T F Ü R A L E U N S E R E R S P O R T L E R E I N S E T T (fleurs_deu_000379-fleurs_deu_000379) +D A L I C K E N E A P R E T S K O M P E T I B E L M I T C H T R N E R T Z W E I P U N D E L F A R A C H T E N R Z W E I P U N G D E L F P E U N D C H T E O N E T Z W E I P U N G D E L F G E S E I N V E R A S G E S D I A S S I S T A T I O N V E R F Ü G T B E R D U A L R A D I O (fleurs_deu_000380-fleurs_deu_000380) +E R B E Z E I C H N E S D I E I E R Ü C H T E A L S P O L I S C H E S G I S C H E Ä T Z S U N D T A L L B E N H E I T Z (fleurs_deu_000381-fleurs_deu_000381) +L E T E W O C H E G A B T A S E M I E I T H I E B E K A N D A S I S V O N E B E L Ü B E R V I R N D R E I S I W E I T R E F O R F V E L E V O N B E R H I T Z U N I N V O R M I R U R D E N W A D I E D A S U N T E R N E H M E M A L S N I C H C H W R W E G E N B E Z E I G N E T E (fleurs_deu_000382-fleurs_deu_000382) +N A C H D E D E R D M L E U N E H H N D E R T R A L N S E C H Z I G E B A U R T W O R D E N W A R K A M D I A H R E S S E I L I H N B E R F L U T U N G D E S D E M E N T E M N F L S V E R T A L E N Z U M S T I L S T A N T (fleurs_deu_000383-fleurs_deu_000383) +E R W A U C H A M S T E C H E N V O N G E L S C H E I N V E V I E L E D E N D E B E T E I L I C G T A K T E L E B E I S H P I E S E N E A R B E T S C H L I S E N I E P R I M E H M I N I S T E R P O R T R E S A F D E R F O R D E R S E R T D E R K A N A D S C H E N F Ü N F U N D U N D E R O L L R N U T E N E I N (fleurs_deu_000384-fleurs_deu_000384) +D I E A U P T S T A T V E R M O D A W I E R N I S T K I C H I N A D I E E I N E I M P I S P A C H E S T R U M E N I S C H A B A R V I E L E M E N T E N C S P R E C H E N R O S S E L C (fleurs_deu_000385-fleurs_deu_000385) +S Z W I C H E N D E N E I N Z E N E N D Ü N E S T I E N H E R S T D E N A U C H U N M B E S T E N D I E G E Z E I T E N G E T A L L T E P R O I N D Z E D I E B E K A N T E S T E D I E S E P E R I O D E N O A D I E P O C H E D E R D R A I L K Ö N I N G R E I C H E D I E S E C H T I C H I E R E L A Z W I S C H E N D E R H A H N U N G D E R I E N D I E N E S T I S T A T V A D T (fleurs_deu_000386-fleurs_deu_000386) +A M A N D E R E N E N E D R S P E K T R U M S H W A N E L T M A N S I C H E N E I N I C H T W I D E R Z U R K E N D E N D I W I D E U M D A S A L E S A N E R S M A C H N M O S S A S S T I E N E S G E M A C T E R U N D S I C A L E S T Z U A L G E M A C H T (fleurs_deu_000387-fleurs_deu_000387) +D I E M E I S T E N D I N T E R P I T E A T I O N E N D E S T I C H N O L O G I S C H E N D E R T E I M I N I E S N U S T A L E N Z W E I A L G E M E I N E V O R S C H T E U N G E N E I N E R S E I T S T S T D I N T W I C K L U M D E R T I C H N O L O G I E S L L P S T E I N E M W E G O L G T D E R W E I T G E N T I E N S E I S U T D T O R E L E O D E R P O L I I S C H E I N P L S N A M E N D I G T U N D A N D E R E R S E I T D A S T I C N E R Ü G I E I E R E R S E I T S A U S W I R K U N G E N A U F G E S A L S C H A F T N H R T D I E E H R I N H Ä R E N D A S S Z U T T I A L B E D I E N S I D (fleurs_deu_000388-fleurs_deu_000388) +W I S C H E D E N E I N Z E N D N A R T I E N H E R C H T E N A C H U N M B E S T E N D I G E Z E I T E N G E T A I L T E R O W I N Z E N D I B E K A N D E S T E D E P E R I O D E N W A D I E P O C H R O D E R D E K Ö N I G R E I C H E D I E S E C H T I C A R L A N G T I S C H E N D E R H A N U N D T E R I N D E N R T I E S T T F V A N D (fleurs_deu_000389-fleurs_deu_000389) +D I E M L I E K T Z U F O R G I E B I E Z I Z I C H T E S T O O M E N T A U F D E M G R E N S T R E I L T I N D E N D I E P A L I S I N E N S E R E I N Z U R Ü E G S E T Z E N D E R G R E N Z E N I N D E N Z U S T A N D V O R D E M S E R X S T A L G L E G R I E G V O R N N E N E H N U N D E R T S E B E O N U S E S T I G O R D E R (fleurs_deu_000390-fleurs_deu_000390) +M I T I M P E L U S T G R E C H E H E R S P A C H K N N S E U R D E R W E S T E N V O N S E I M E V I E L O S O P I S C H E N U N D W I S E N S C H A F L I C H E N W O R T Z E N K R I C H E N E N A B I S C H N I T E N (fleurs_deu_000391-fleurs_deu_000391) +W I R S T M I T E R A U S A G E D I E S I U E R S A U S I E Ü B E R E I N D A S T E N N T R E S S N U N D S R E A T L E D E N D V E R E I N U N D I R E S P O R T S B P S S R G E D I E N D I S T E N W R N E H A L B U N S R R G E N S A T I O N D E H N V O L E V E R N D R U N G V E R A N D T R E I B E N A N S T A T E I N E R T I E R I Z E T V I Z I E R U N G V O R Z U N E (fleurs_deu_000392-fleurs_deu_000392) +I R E U T S F A T E N N A S S A N G T P I K A R S B O G B I E T E N A C H Z E I T F Ü R E I N A U F E N T A L I N E R S T A T K R E U T Z V A T P A S R S I E R E S I N D F V O R D E R I E S U N S L I C H T B E F R E I T S I E B E D N G E N (fleurs_deu_000393-fleurs_deu_000393) +E R E I S E N D E V E R D E N R I N G E N D G E W A N T A U F J E T W E D E A R T V O N U N W E N T E Z U A C H T E N D I E I E G I E I E T B I E T R I F T D A D I S S I G H A U F A L E R E I S E P L E N E A U S I E R K E N K A N T (fleurs_deu_000394-fleurs_deu_000394) +S E B E S A R T D A S D E R K O L Z U N G S P U N G K T D E L I N M I N D I E E N B I E L T W E R T I K A L U N T R E T O N D T A L D R I T E E N D E E F I K T I S T D E P L A S F Ü R T E S A U P T M U O D I I S T S I E B E I S S I N (fleurs_deu_000395-fleurs_deu_000395) +S E I T N U N Z E N U R T A C H T E N A C H T I C H M S T E W A L U N D R A N S B E R E N S E I N D A M I T W E L E U N B E O B A C H T E B E T Z E U G E N K Ö N D A S Z W E G I N D E R W A L K E I N E U M S C H L E G E V E R H N E N S I N D U N D A S K E I N U M S C H L E G E E I N G E O F E N W E R D E N A U S E R J E N E D E R T O R D U N G S M I S E H L T N E N A T T R S I R D T E N E L E R (fleurs_deu_000396-fleurs_deu_000396) +O T E R A R I S T K A N N E R D E S B I E Z A U B E N D E R Z W E I S C H A L I G E H A U P T S T A T U N D S E L T E N S I C H I C E I N E R E I E V U N K U N Z S T G E L E R I E N U N D M U S E E N A U S D I E K A N E N D E R S V E R G A N G E N H E L T U N D G E G E N W A R T B R E S E N T I E R E (fleurs_deu_000397-fleurs_deu_000397) +D I E S E P A R E K Ö R E N S I C H V E R E I N A D E B P T I O N S P L A N D V R E R B E B E E N S C H E I D E N (fleurs_deu_000398-fleurs_deu_000398) +I N F O L G E D E S S E N S E N Z W E I F I S H T A B E N A U S G E S T O R M E N D Z W E W E I T R U S E N V O M A U S T E R B E B E T R O T D A U N E D E R G E L A R S Z Y Ü F E R (fleurs_deu_000399-fleurs_deu_000399) +T R A N Z E N S E N N I H R N A T I L I C H E N M O G E B N G A M B E S T E N A U S W I E D E R S T E N S I A L S O D E R V E R S U C H U N G A U C H N U R E I N E X M K L A U D W E R N (fleurs_deu_000400-fleurs_deu_000400) +A U F D E R N A H S E I T E K Ö N T E I S M E R M A R I E R G E B E N D A D I G R O S T E D U N E S T E S W E R E I N F E R A F O D I E L A V E R A N D I O B E R F L E C H A U F T U S T E G E N (fleurs_deu_000401-fleurs_deu_000401) +E R F Ü C K T E C H I N Z U D A S S I J E D O C H N I C H T E A R Z U O A U F G I E V O R D E R T W E R D E N S O L L T E R N F E R T F L I C H T U N G E N E I N D Z U G E E N D I E Ü B E R I E R E N I N T W I T L U N G S T A N D I E R E V E R A N T O R D U N G U N D I R E R F Ä I G K A T E N H I E N A U S N G E E N T (fleurs_deu_000402-fleurs_deu_000402) +T C I E R T U E L I H I E L F I S T E L U N G E N S I N T I N D I S O F T E R E N G I E B A U D U N S O L E N A H B E I T S C H R I T D E N D I E D E S C H Ü L E R A L E I N M Ö G L I C H E R W E I S E N I H T B E V E L T I G E N K Ö R H I N T E R F R A G E N N E I E L E G E N U N D D E R G L E R E N (fleurs_deu_000403-fleurs_deu_000403) +A M F Ü N F Z E H N N A G S T N E U N Z H N H U D E R T V I R Z I V I E L I A L I E R T E N N S Ü T F R A N K R E I C H E I N D I N W A S I O N W O R D E A P E R E S C H E N R O G U N G E N R D (fleurs_deu_000404-fleurs_deu_000404) +E R G R I F A C H A L L S A N W A S S E N S W A S S E R K A R M S E B S T E N G R O S E R D E N O S A U R I E W I D E R T I E R E C G S W E R I M N I C H T G E W A K E N (fleurs_deu_000405-fleurs_deu_000405) +S E I T E R K R N D U N G V O R A S U N T I O R F Ü N F Z E N D E S E N U O D R E I S C I E S E S P A R E G W E I G E L U N G V I E L V O N S E I M I N D I G E H N E N K A R A K T E R N D S E I N E I D E N I T E T Z U B E W A R E N (fleurs_deu_000406-fleurs_deu_000406) +S T R O Z S D E N I S D E R A N T E L L A N N I E X S D I E W I N D E S T I G H T D E B I N D E R G E S A M T E N R U P E D E R L E U T E E M I T D U G D E R K O L O S E R O F E N B A D E R N N O C H G E R I N E N S E X S T A U S E N D E R I N D G E S A E N D R E I H U N D E N D R E I G A U S E N L E L T E D I E I N S Ü T A F R I C K E R T U E I N E M B I E S T I N T E N D E I T P U N G T A N G E S T E T S I T T (fleurs_deu_000407-fleurs_deu_000407) +E N S C H E L Z W E I T A U S E N S E C H S E L E U T E R T A S K O N T I N U M K O N Z E T A S E I N E M I T O D E U M O B G E N S A T I O N D S H L F E N L E I S T U M G S F E G E Z U W E R D E N (fleurs_deu_000408-fleurs_deu_000408) +I N D I E S E P I E R I O D E N D E R O E R O P E S C H E N G I C H I G H T E S T A N D D I E R L I C H U N D M E C H T I H E G E V O R D E N E R A T O L I S C H I Y K I E C H E A U F D E N P R Ü S T A N D T T (fleurs_deu_000409-fleurs_deu_000409) +D I E E R S D R C H T E N D I B Z I C E M F H L U N G I F T A S E I N E N E U D E P L O M A T S C H I N I Z A T I E V E V O E R E N D E D I E S E N J A H R E S E G R I F E N W E R D E N S O L L T E U M D I E R A G I S H E N G R E N Z E N G E G E B E R F E N T L I H E N T E R W E T I O N D Z U S I C H A R N U N D P L O M A T S C H B I Z I E O M I Z E I N A C H B A N I E D E R R T U S T E (fleurs_deu_000410-fleurs_deu_000410) +D I S P E T E T E I N I U T E G E L E G E G H E I T D A S N O T L I C H T U S E N D A E H I M E M M E H R A U D E R W E N I E R R U N D U M D I E U R D U N K E L S T (fleurs_deu_000411-fleurs_deu_000411) +P R O F S S O R E N P A M E L E V E R G U S S O N V O N D E R N W Ü U S T I A F D A N D I M E R K T A N S O A L I S T E N S C H E I N E I N E G E E L I E G R A N Z E Z U A S C R E T E N W E N D I E V O T T O U N S W E I T E V O N V E R D E C T I G E V E R F N T I C H E N (fleurs_deu_000412-fleurs_deu_000412) +E S K A N S I C H L O N E I N E E L K A T Z U K A U F N D I E Z U T R I T E N W D E R T U U S G E W E L E N P A G S E N H E T A F R I K A R D E R Z U A L E N Z U Ü T F R I K A N S C H N E R T O N A L P A R X S G E W E R T (fleurs_deu_000413-fleurs_deu_000413) +D I E P R Ü K E S O L E M S E T E M B E R Z W E I T A U E N S I B S H N F O L S T E N D I T N B E T R I E A U F N E H M N I S W R R W A R E D S I P A S I A N I S C H E N Z O L P U N K T E D A N F E R T I G S T E L L Z E I N W E R T (fleurs_deu_000414-fleurs_deu_000414) +W E R E N D E I E R X P R M I N T E L L E I M S T F N E L A G E U S E I N S C H E I N T D I E B O L E R M O T L I E T Ä T Z U S E N G U N G G B T E S B S S E R K E I N E M I T I K A M Ä N T E D I A L S E I N D R D I Z U B E H A N D L U N B E S T E N D E I N V E K T I O N G E E G N E T N A C H E W I E S E O R D E N (fleurs_deu_000415-fleurs_deu_000415) +E I N E U S E R S T L E P A F T E R D E B E C H E W E C H S E L V A N S T A D T W A I E R W U K D E M P L A N E I N A L G E M E I N E N S T A T E N K O N G R E S T Z U B E R U F E N U N D K O N D E S I V O L L O U F I K T N O N U N I C H T Ü B E R D E S O R Z U L E G E D E R O G R M M U N D E N O R T E S Z U S A M T R E T S E I N I G E N (mls_deu_000281-mls_deu_000281) +E R W U S T E N I C H T W A S I M D A S L E B E N K O S S B A R E S G E R A U P T A T E S C P A N K R A F T U N D M U T D D A S S E S I N F E I K U N D S C O E U G E M A C H T A T E U N D F Ä I C H Z U D E N H O H N D I N G E N Z U O D E N E N U N G E T R Ü B P T E I T F R O L D E N G E H Ö R T (mls_deu_000282-mls_deu_000282) +D I E S E R U N G E M A N N H I S K A K A L I T Z I E N U N D B E F A N S I G G R A D E U E W A N D E R S C H A F T A L S I N I M G E A N T E N K Ö N I G R E I C H D B E K A N D N A C H U N G W E N D E R P R N Z E S E N V E R L E S E N W U R D E E I S A K T D E S C H N E I D E R W I E N E S W E I T R N I H T S I S T E I N W E I P A U C H N H N I C T K Ü K Ö L S T U N D D U T K Ö N I G S E I D A M Z U O W E R D E N D A S G E I S S E T M I A L E D I N G S T (mls_deu_000283-mls_deu_000283) +N O C F Ü N F M I N U T E N N D I E W O L K E N D E B E W U S T L O S I G K E I T B E G A N N Z U S C H W I N D E N I E R T W U S T E H S E R W O L D A S I H N M E I M E I G N E N B E T E L A G U N D D A S D I E R O T E O G L O T N I C H T A N D E R S W A L S D A S V E U E I M K A M I N D E R I N D A S T U B E E S W A N A C H T E I N E K E R E B R A N T E A F D E M T I S C H E (mls_deu_000284-mls_deu_000284) +E I C H E I E V E T R E N G U N E N W E B E C H T E U N T E R H A L T E N C O M T A N E M P R P E I T I T Z A L E R D I H O C H F L O D E S S E X S U E N B E D F F T I G K E I T S O F N D E S E N D E G E N A N D E N S I L L I S C H N R E A K T I O N S O D E I E D E R S T A N S P I L D O N G E N D E M E R (mls_deu_000285-mls_deu_000285) +T A B E R A F E N G E H R E N B E I H A G K E N B E A N D I E K I S T E N W A N N T N U N S O H R T E I C H A U F A C E Z U S E I N E N E I N K L R E R S C H Ö N A G E D A N K E N G A N G D E N I C H I R G E N D I E M I T D E M B A U C H A U S G E H E K T A B E N M O S S D E N A F F E N N D E N K T E N M I (mls_deu_000286-mls_deu_000286) +I S S S P A T R E E R N E S M E N S C H E N D E N Z I K Ä N E N F R A G T E I L E I S E W E L C H E U N B E M E A K T A M I C H E R A N G E T R E T E N W A U M I C H I N D T G E G N E T E D A S S N E N F A N T E S I K O P F S E I U N T S C H U B T Z E I C H U N G I L I C U N T U D I E A N D O N D L Ä T T E R U N A T Ü L I C G S P A C R I C H I E U N B A H E I L T D E N E W E I N S E I T O E U E S P E T R I E M I S T E R O T S C H S T E S (mls_deu_000287-mls_deu_000287) +I C H W E I S S I C H S E R K R A N K G B I N S A I G T E S I N E R E I N R W E I L E V O R N P A M I N U T E N V E R S C H T E I C H M I C H E E T T E U M Z U R E E N U N D F Ü L D E D A S I C K E N G L I E D M E R E N Ü O R N K A M E S W E R E G U T D W E N I C H E N G I M Ü T E L E I C H T D A N K Ö N T E R B E V O R I C S T E R D E N (mls_deu_000288-mls_deu_000288) +S O A B E R I S T W E R U N S E R W E S E N S K U N D O T S E L L V E R D A C H E R O M H A T Z I C H E D O C D E R C H L A N G E N K N E U L D E S A L K E N S A T A N G E S H L U N G E N U N G Ü B E R D E M F Ü N K I E N D E N L I E B I I S D I F E N S E R N I S T E S H A S S E S E L A G E R T W A S W O N D E R D A N (mls_deu_000289-mls_deu_000289) +B E S I V I R E L I E A G E B L I E B E N A B E R S I W A G E T Z W U N G K Z U G E H N E A D I Ü N K L I T K E I T B E I D E N M A L S Z E I T E N E I N E S A C H E W A A U W Ä C H I N G E T Z S H Ä R D H O R L S T R E N G E G E H A L T E N W U R D E (mls_deu_000290-mls_deu_000290) +N B L I C K L I C H F Ü L T E W I E H R E A M S I C H T E N Ü B E R M I C H I R E R M P I N D U N G E D F Ü H R M I C H N I C H T U M E I N A T O U M V E R I N D E T W A H E M N U Ü B E H A U P T K A N E E N D E R U N G F E E C H W A R M D I C H S E I S I R E M E R S T E I N A T E N A U D G E W E L C H E S N I E M A L S D U C H T R E N E N G E N E T Z T N I E M A S I N E R T L I C H K E I T A U F G E L E U C H T E T A T E A M E N (mls_deu_000291-mls_deu_000291) +N S O D E R S S E M I S T Z E R G U T W I N E H Ö U G L I G N E R F Ä E R I D S I C F R E U N W Ü E W E R E N S C H N Ä L D R N A C H A N D E N S O L B E A U F P R E C H E N S H N A L R E I T E N D M I T Ü R N O C F O R D E R N C H D A S L A G E R E I C H E N E R S T I G E A U D I P F Ä E R D E D I N A U S G E R O T A T E N U N D F L O G E M G A L O P B T D A V O N D I E S A L H Ü T E T E N I R U N S D E R F Ä E R D E I D E R D E R E K Z E F O L E N W E R E G E R A D E A U S N D E R S P A D E N (mls_deu_000292-mls_deu_000292) +W A L D I E B E R M I T P Ä E C H P B E S T R I C H E N W A H R B I E B E I N E R V O N D E N G E L E N E N P A N T O F L E N F E S T H Ä N G E N U N D I N D E R A N G S D A C H S N I C H T E R A N I N I T Z O N E M E M U N D I E I S D E N L E T Z T E N S C H I T V O N D E R T R E P E T A D D E R H A T S T Z W I L F A U S G E S C H L A G E N D A R A R W A G E N U N D F E R D E V E R S C H U N D E N U N D A S C H E N P O T E S T A N D I N S E I N S C H E N K L E I D E R A U F E R U N G L N S T R A S E (mls_deu_000293-mls_deu_000293) +I E I E N O M D E S A S V E R M A G E E I N V I N S T E R A N S T L A G E R E B E S E N T Z Y Ü D E N I E S I S C H R I C K A L S A K T E H R G I E T D A S M E N I G E G I T A N U N B U T V E R G I S N S V E R M Ä D E N (mls_deu_000294-mls_deu_000294) +N U R D E R D O C K T O R U N D I E W E R T E R E N S O L L E N V O R S E I N E A U G E N K O M M E N E R K L Ä A T E D I E T R I N E R I N G R O S S E M A M T S E I F E R D A M I T W A D I E F R A R O B E R S T G A N S E I N F E R S T A N D E N U N D P I R X S T E R F R E I T K E R T E S I M I T I E R E N (mls_deu_000295-mls_deu_000295) +K A A R U N T R Ü S T I C H Ü B E R D I E L A G E D A S K Ö N Z T L R S E R B E G A N Z U W E I N E N U N S C H L C H T Z T D E L A N G E I N D E O R G E H A L T E N E N H N D E D E R K O N S L E A W A T E T E B I S K A S I C B E R U I C H T H A T T E U N D E N S C H L O S I C H D A N D A R E R K E I N A N D E R N A U S I G F A N D T D E R N O C H Z U M E I T E R S C R E I B E N (mls_deu_000296-mls_deu_000296) +O N D I M F E R D E H E R D E N D E R P A T S C H E N U N D S A G N U N Z S D A S S I F E N E A P A T S C H E M F Ä R D U N D S E B E N S U V I E L E W A R E N U N P R E N I G E B E N W I R D E N Ü F Ü R I N K E I O W A B F E R T D A S I N D U N R I K L I G A R F O D U M A P A T S C H E N F Ä R D E Z O H L E N A L S O R C H T I G H E R A R S C H L D E R E M T O D E E B E S E R G E F A L L E N U N D E R I E B L U T V E R G I E S E N W E C H E S U N B E V O R S T A N D W E I S E F Ä E R D E H E N D T L E R (mls_deu_000297-mls_deu_000297) +D A S M A T O N E H Ü T C H E N V O N S C W A T Z A M S A M E T K A T I Ü R S A I E R E L A N G E N L O K E N G E D R Ü C T D I E R E W A N G N U M F L O S S E N N D Ü B E R S C H L T E N H E R A B W E I T E N S O T R A T E I D R S E I N F E C H R E L E N T L I C H G E B O U D E U N D S T E B P T E T Z W I S C E N E R E I N D E R H E I B G E B L Ä N E T N O F K N E R A U F E N D A B (mls_deu_000298-mls_deu_000298) +T U M U S T E R S T E N Z A G E N A L E N S Ü N T H A F T E N S T R E B E N U N E N T I E V E R E U I U N D D E M U D D I E F Ü H R B I E R H L L I N G E R F L E N G E G E N D I E D U G E F R E E L T E S T T I E J Ü M L N G E W L C H E F E N S C H E S O S O L N E G E F L O N S O U C H T E N N A U E N E R W E R K T A T U N F A N D E N I N (mls_deu_000299-mls_deu_000299) +E R L I E S S E I N E G R E T E N I H T V O R T S C H L Ä B E N A M A L L E R W I N I G X S T E A B E R I N D E N G R O S S E N D V O G E L B A U A R U S I E A L E E N E I N E M T O N E B F E I F E N M O U S T E N W I E R S T E S A K T E (mls_deu_000300-mls_deu_000300) +V R N H E S K O M A L T E N U N H A L I G E B E G E I S T R N G V I E L E B I E L T A S T E L Ü G E N H A F T E N A B E L W E L D T R E N E A L S E R E R M O C H D I E B U L E R I S C H E Ü L B E K A E T E W E I B I E N G E S T A L T E N S O B E R H A F A S S T E L E N I N D E M V O N L E B E N T E M O D E L E D I K A N D A T I O N G V O N D E N A L T E N M A H M O B I L D E R R B E R F O R M N D B I E L U N G I N D N A M (mls_deu_000301-mls_deu_000301) +B W E G U N G U N T A T D E N S T E N Z U G I E R S T E M T E D I E F O E U N A N G E G E B N E I N K G R E D E N Z I E R N M I C R Ü B E N H A N F E E I C H E N U N S A U E R A M F A N A L L E I N D E M F E I F N K O P F E R N W E S E N A B E R I N F Ü N F T N H A U P T S T O F H A I C H N I C G G E N A N D J E R T T R O C H N D S C H M E K T I G D A S E H O N S T Ü C H E N F I L S C H U D E R B E I S E I N I S E I G P L I E S T E N R A U H A U C H G E G D E N H E M E U N G E G E D I (mls_deu_000302-mls_deu_000302) +U N D D A S F O U R S T A N D A U F U N D F L A C K E A T U N K O C H D A S E S S E N F E R T I C H U N D D E R B R A T E N B R U T Z E L T E R F O R T U N D E R K O C H G A B D E M K Ü C H E N I U N G E N E I N E O R F E I G E U N D D E M A R K T R O P F T E D A S U N F Ä E R T I G H D A R W A R T D I E H O C H T Z E I T O N D E M K Ö N I C H S O N M I E D O N G R Ö U S I E N G E F E I E R T U N S I E L I E T N E F E R G N Ü T E B I S A N I E R E N D E (mls_deu_000303-mls_deu_000303) +U N M D D E S E R M I N I C H N A C T R A G E N B O L E W E N I C H I D E R S H F Ä N S T I G W A G I N S E I N O H M E I N E M R A R T D E R H E R F A R A E H A D I E I N A L L M P R E C H T G E H A U T U N D I C H M A A M U N M R E C H T A B E R (mls_deu_000304-mls_deu_000304) +G Ä C H E N E M A S E N O W I N I G E K R A M B E T R U G E R E I T E E S I C H K I L I E F R M I G A U S H U M U S T E D E R E R D E S M I N D G E G E N F L I G E D E S P R E N K I S C H O S A U F A N G E N N D Z U W I B R I N E N (mls_deu_000305-mls_deu_000305) +D E R F C K S R E I C H T E E M D E U N F R I T I C H E R I E D E N S P F E I V E R H N D E R M A N T A T W A C K A R S E I N E S E X S Z Y G E N S A G K T E D E R R O S I G E I S A C H T E T N I C H T A U F D I V E R S C H I E D E N E H A U D E R M E N S C H E N D E N D I K Ö N S I C H M I T F A B E B E S C H M I E R E N M I N T Z U D T E U S C H E N S O D E E R S I D A S H E T Z S A N D I E H T Z E N D E R K L I G E R V O B E R Ü B T E N S T A M E D E R K A E I O A S E I N T A P T V E R U N E R S C H R O K E N N T R E U D A S M E I N E I G E H Ä N G (mls_deu_000306-mls_deu_000306) +A L L S A S W I E M E T I E R B E G E G N I T S C I E B S I C H D E U I C H U N D Ü B E R E I N A N D E R B A L T U N T E R S C H E I B E N W E R I N K O N T A K T D E R I S T E R E R H A N D N D E M E I N I G E I E R N A H M O N D E R M E I N I G E W E I D E L E S C H E N E I N A N D E R A U S B E I D E V E R S C H L I N G E N S I C H (mls_deu_000307-mls_deu_000307) +E R M Ö S T E D E N E N F A C H E N R O N I T E N K Ö R A L D E S M A L E S M I T A L L E E R K L E H R E N E N U N Z R E C H T W E S S U N G E N I M I T K R A U S E N W I G U N V E S C H N A R K E N U N V E R B R E M E N I C H T R E T E I D I E P E R S O N D E S E R A U S G E B E S N D B I T E T I C H I Ü N S T I G E L I S E R T U O L I S T I E D U W E I T E L I S E S T F O L D E N D I S D E G Ü T T I S T M E R H E N (mls_deu_000308-mls_deu_000308) +D I E O F D A M E N B E K A M E N K R M P F E R U N D T I E K Ö N I G E N U N I E P R O M Z E S S E N E N D I E R E R A L L A L I B S E N H Ü N Z C H E N W E R N D E R M A L T E R U F D E N H O S G E N O M H A D E N B E M E R K E N Z U I R E N S C R Ä C T E N D A S D I L I E L E R A R M A R A N D F A B E N E N U N D O R A N S C A E D E N S E I D E N K L E I D E R A L E I C T B E S E T M I T D E N H E S L I C S T E N Ö F L Ä G E N W A N E (mls_deu_000309-mls_deu_000309) +V O N L I E D A N D I E I E S I N G E N U N K L V I E R P I E S E N D I E S I S P I E L N V O N G E I L T B Ü Ö R S E N D I E S I H I E G K E L N V O N V A N Z Ö Ü S C H E N B Ü C H E N D I E S Ü B E R S E T Z E N K O N T E B I S M E N G E M Ö Ü T W E R E N D I C H L A U S T E Z O N A C H A M N G A U F G E S T A C H E T W U R D E (mls_deu_000310-mls_deu_000310) +A M E N N A T E N W A N B L O S I R E N Z I G E R S C M O C W A N I H R E K A S T A N I E N B R A N F L Ä C H T E N W I L C H E N W I L D E U N N A T Ü R L I C H E R A N M U N D A U I R S C H L T E N E R A B V I E L E N C H N A M E I N B O G E N G F E I N K A T O U N G S N D Z E I C H E T E M M T O S R S O R K F A L T I O M G R S S E R (mls_deu_000311-mls_deu_000311) +A U R W I E D E A U S D E T C H L A N D N O C H A S I E L E N E I N E M A N D E R E N S T A R T K O N T E M E N E F A H N W A S E R G E G N S T A N D U N D E S E S L T A D D I E S U N T E R E D U N G G E W I S E N S E I A N V R M U T E T E D S S E S I C U M E R K L I E R U N G D E R M A T Z I E R Ü B E I E R A B S I C H T E N U N D U N D I E V E R M I T L U N G D E R M E C H T E T Z W I C H N D N M A R S T A T E N U N R O S P O T A N I E N H A N D L E (mls_deu_000312-mls_deu_000312) +L A S U N S W E N I G S E N S E I N E R Z E I T L A N G V E R S U O C H E N I N D I E V E R N W I E R U F D E S E S B E I S I M I T E I N A N D E R A U S R E I C H E N D A D A S Z U S A M M E N H Ä N G E N D E V I E D U E S A G S T E I G E N T L I C H E U E R L L E M E N T I S V E R S E T Z T E I D R T (mls_deu_000313-mls_deu_000313) +V E A S C H I N E N V O R K O M M N S E F Ü R D E N Z U D E R V E R M O T U N G D A S F R A U W I S E D I K L E I N E N V W I S E N V E R B R E N E R I E S O L B E S E I N S U S T A C H G E H E I T Z T A B E N D A S D E R T P L A D E N Z S P R A N G A U S S T E M O L E I N F Ü R C H T E L I C H E R E R O C H W A G E N U M E W O R D E N S E I N (mls_deu_000314-mls_deu_000314) +U N D G I N G D E M S C H R E I E N N A C H S O S A H E R E N T L I C H E I N H O H N B A U M U N D O B E N D E R R A U F S A S E I N K L E I N E S K I N D U N T E R D E M B A U M A B A R L A K E I N E F R A U D I E S C H L I E F (mls_deu_000315-mls_deu_000315) +S I E H A T E N S E U E B N D I E F I S C H E R G A R D N E R W A L C H D E N A C H T I Y B E R T A U S G E U O R H V E N W A D E N C H E R E I N G E Z O G E N N D I E S E L E U I T E R G E R H E O T E N A G E N S C H E I M L I S C H V E R S C H I E D E N E N N C T Z I O N E N R N A B O R L D E R O L U L O P Ä I S C H E R K A R K T E B E R A L L E N A U S G E T R Ü K T W A T (mls_deu_000316-mls_deu_000316) +N E N E I N I C H S C H Ä M E M E I C H L A S M I C H E N D E I N E M B U S E N M E I N G E S I C H T V E R B E R G E N G E R S I N K T E N S G R A S N I D E U N D Z I E S I N A C H (mls_deu_000317-mls_deu_000317) +D I E K I N D E R A R B A R S A S E N V O R D E M W A L T U N D A L S I E D I E R E I K N E C H T E R V O N W E I T E M L A U F E N S A H N S P R A C H R L E N S H E N Z U M P F U N D E F O G E L V E R L Ä S T U M I C H N I C H Z O E R L A S I C H T I C H A U C H N I C H T S O S P R A C H F O N D E F O G E L N U N U N D N I M E R M R (mls_deu_000318-mls_deu_000318) +W I E D E R S C H L Z E I N S E I N E H L D I E G U N G S R I E D E H E R V O R H U B D E R L E R A B R A C H T E A M K L A E N S O M M A R M O R D E N G M I T S E I N E N S C H U H L K I N D E R N E I N G E S A N G S T Ä N T I E (mls_deu_000319-mls_deu_000319) +S T D T W I E I E S E I N S O H L E N (swc_deu_001408-swc_deu_001408) +D E R E N C H W I N G E N E N D U R C E I N E Z U S E R T S C H A L T U N G S T U F E N L O S (swc_deu_001409-swc_deu_001409) +D I E A U F A L E B E I D E R S E T W R T A L U N G U (swc_deu_001410-swc_deu_001410) +U M D E Ü B E R L E B E N D E N D (swc_deu_001411-swc_deu_001411) +S P Ä T E R W U R D E N T A I L W E I S E S O G A A C H T P A R E R L E L E L O H S T R E I F E N E I N G E S E T Z T (swc_deu_001412-swc_deu_001412) +M O R D E B E K A N D U N D V E R L A N G T E (swc_deu_001413-swc_deu_001413) +B U N D E W E G E S E T Z D I E S T M V O N W I H L E N (swc_deu_001414-swc_deu_001414) +G E S C H C H T E (swc_deu_001415-swc_deu_001415) +S P A L T U N G F E Ä G (swc_deu_001416-swc_deu_001416) +S C H A T P A E B O R N D I E U S E R E N D F E R N D I S (swc_deu_001417-swc_deu_001417) +U M W E I T E R I N H U M A N I T E R E H I L F E Z U (swc_deu_001418-swc_deu_001418) +S I E R K A M T E N D I E N E U E I C H E N E S C H E R E G I E R U N G N I C H T A N (swc_deu_001419-swc_deu_001419) +D I E O R A U F Ü G E N V O N A N D R E N Z W A N I S T E N S E T E M B E R Z W E R D E N D A C H T I (swc_deu_001420-swc_deu_001420) +E R W I E S I E M I C H T S C H L D I C O D E R M I T S C H L I C H M A C E N A N T O D E R N S M I T G E S E (swc_deu_001421-swc_deu_001421) +D I E M E D E R E R T T U M M A R E I N E N (swc_deu_001422-swc_deu_001422) +N D T E I F E N T I E S E N B E I D E R (swc_deu_001423-swc_deu_001423) +K R E I S W A L E F O R S C H L A G U N D E I N E L A N D E S L I S T E N D E R Z E I T N E N (swc_deu_001424-swc_deu_001424) +A N U M S E R Z U N D E S A G E I N F O R M E I N E S T F Ü N F Z E H N T E I L E N L I E D E R Z Y K L U O S Z W E T S E D C H T W U R D E P E S L A S K Ö A B E R T I N N E B E R A B E T U N G V O N H A U S T H A R E M E N (swc_deu_001425-swc_deu_001425) +I E D I E O L G E N D E T A P E L E D A R S T E L L T (swc_deu_001426-swc_deu_001426) +U M S T R U M F L O S B E (swc_deu_001427-swc_deu_001427) +D E B U N D E S W A L L I T E R B I S T Z U M S I M O N E U N Z I G S T E N T A G (swc_deu_001428-swc_deu_001428) +O R I R I C H E W O R D E N D E U S C H E R N I C H T M I T W E L E N (swc_deu_001429-swc_deu_001429) +A U S S F I R N M U S T E I N K U S E R K O T E R B E G N (swc_deu_001430-swc_deu_001430) +V E R G L E I C H P A N Z E A H L E N W E R T U M G E A N D E (swc_deu_001431-swc_deu_001431) +B E T R C H T E D E A L G E M E I N H E I (swc_deu_001432-swc_deu_001432) +U N T E R S C H I T L I C H E A U F A S S U N G E N G A B E S N U R D A H R Ü B E R (swc_deu_001433-swc_deu_001433) +D O L L B E I M B U N E S L I K I S T E N B E R S E R D O R T M U N D N A C H V O L G E R D E S U N M I T E L B A Z U V O R Z O R Ü C K E T R E D E N R E N A S I R F E N R Ö B E R (swc_deu_001434-swc_deu_001434) +N U N Z E N E R D C H T U N A C T Z I G (swc_deu_001435-swc_deu_001435) +R E I N E N Z Y G L O P E T D I E (swc_deu_001436-swc_deu_001436) +D E R V O T O S T R O M I S Ü B E R V I E L E G R Ü S S E N O R N U N G E L I N E A R Z U M L I C H T E N F A L (swc_deu_001437-swc_deu_001437) +D A S H T F Ü K L E I N E P A R T E I N G R O S E A U S W I R K U N G (swc_deu_001438-swc_deu_001438) +I S D E I T E R A T I E V E T I E F E N S U C H (swc_deu_001439-swc_deu_001439) +D I E S K Ö N N E N U M B E I S P I E L K O N D E N S E A T O R E N S E I N (swc_deu_001440-swc_deu_001440) +A L S D I E K U R S A U F K O B E R H A L T E N D E N S O J E T I C H E N S C H I V E R A B T R E T E N (swc_deu_001441-swc_deu_001441) +B U N E S T A G W A H L N U N Z H U N E R D R E I U N F Ü F Z I G W U R D E R S M A L S N A C H E I N E M V O M B U N D E S T A I G S E B S T E R L S E N G E S E T Z (swc_deu_001442-swc_deu_001442) +B U N D E W A I G E S E T Z V I E L F A C H E I N E R T W U R D E N (swc_deu_001443-swc_deu_001443) +E R Ü B E R L A G E R D E N V O R T U S T R O M E U N D T R E G T (swc_deu_001444-swc_deu_001444) +D R O I N T E K R A T I U M N D E R B E I D E N D E U T S C H E N S T A T E N (swc_deu_001445-swc_deu_001445) +B E R L I N E R W Ü L M E U S E N S T A T (swc_deu_001446-swc_deu_001446) +A F I E Z E L E R F Ü H R U N G E N (swc_deu_001447-swc_deu_001447) +B E D E R V E R H E T E N S W A L W I T Z U S E T Z L I C G D I E E I N H A L T U N G D E R (swc_deu_001448-swc_deu_001448) +W I E V W E N I H T I S O L A N E N O C H A M P O U L T Z D E R Z E I T (swc_deu_001449-swc_deu_001449) +R E D O C H E T W R D I E D U C H F Ü H R E N V O N W A L W E R B U N G A U F K O S T E D E S T A T E S (swc_deu_001450-swc_deu_001450) +D A S N I H M R U N D K G E S E T Z (swc_deu_001451-swc_deu_001451) +H E I M A T V E R T R I E B E N U N D H E U S L I C H E G E W A L (swc_deu_001452-swc_deu_001452) +N D S P E I C H E R I E N I N E I N R W A G T E S C H L N G E A B (swc_deu_001453-swc_deu_001453) +O R R E I G I N A L T O N B E N D E R U N D D I E D O K O M I T A T I O N D E S T U D I O S W U R D E N N E N Z E H N H U N D E R T Z W U O H N S I E B Z I G I N D E R S I M E N S E R C H I E F Ü B E R S T E L T (swc_deu_001454-swc_deu_001454) +S O M I S S E N A U F E I N E M S T A T G S C H N R E K E T E N U B O D T (swc_deu_001455-swc_deu_001455) +F L Ö T E N S P I E L E D L I C H E R (swc_deu_001456-swc_deu_001456) +D R A S T I S C H M O D E R A R N E E L I K T R O N I S C H E K L A N G E S C H A L T U N G (swc_deu_001457-swc_deu_001457) +A N C H L I S E N W O D E D I E S O H A M I T E T E M A N D A R T Z T Z A L I E D E R P A R T E I N A H D I M S E M V E R F A H N E N S P R E C H E N D E R A N Z A L I H R E R Z W E I T S T M M P R O P R Z U N A L A U F D I E L A N E S L I S T E D E R P A R T E I U N T E R V E R T E I E R T (swc_deu_001458-swc_deu_001458) +O C F N D E R N A R T H O B O M B A D I E O U N D T E Ö K Ü N F T E (swc_deu_001459-swc_deu_001459) +D E R F R E I N E N T Z U K L O P E (swc_deu_001460-swc_deu_001460) +M T L E R B E I L E L H I N D E N (swc_deu_001461-swc_deu_001461) +W E R W I G E G E I N E V E R B R E C H T E N S R E C H S G R F T I C H Z U I N E R R E I T T R A F E V O N M I N D E S E N S E I N E (swc_deu_001462-swc_deu_001462) +D R G S C H W I N D I G K E I T Z W E R T U N G E R A G E N D R E I B E F E I N H U D E R A C H T (swc_deu_001463-swc_deu_001463) +I E B O R I O S A M E R S T E N I G B O R I E S A M S E R (swc_deu_001464-swc_deu_001464) +N A C H D M S E A R N A R G Ü F V E R F A R E N A U F D I E L E N D E R E R T E I L T (swc_deu_001465-swc_deu_001465) +R E F O R M E N G O B E R S C H A F S U N D A B R S T U N G S C H I T E (swc_deu_001466-swc_deu_001466) +S I E R E A A N P O R T E T U N D N D E R D E (swc_deu_001467-swc_deu_001467) +A N D E M E S T I C H E G R E F T E A U F G E G E N R V O L E Z E N (swc_deu_001468-swc_deu_001468) +I T U N T E R A N D E R E M V E R W E N D E (swc_deu_001469-swc_deu_001469) +A U S W I K E P E D I E R (swc_deu_001470-swc_deu_001470) +U N D K O B A R K R I S E (swc_deu_001471-swc_deu_001471) +E N L T Z T D A R W A L A U F G R U N D E I G E N E R W E I L V O R S C H L Ä G E U N E T E B R U C H E N M I N I S S E N S F Ü F A B G E R U N E N D V E R T R E T E N S I N D (swc_deu_001472-swc_deu_001472) +V E R P R E I T U N G I E D I O L O G E S C H A P R O P A R G A N D E R D E R S U P E R M I C H T E U N D (swc_deu_001473-swc_deu_001473) +K O M M I H S A U F D E R E L I T E T B E R T H A G E N (swc_deu_001474-swc_deu_001474) +A L S D E R K A L T I K L I E G S I G V O R T W E R E N Z U S P I T Z T E (swc_deu_001475-swc_deu_001475) +S I H E I T S P E R S O N A L O D E R E A C H U N D E N N U S E H R S C H W I E R I G B E T E T E N E R N (swc_deu_001476-swc_deu_001476) +D A U R H A F D E S B L E I B E R E C H T U N D (swc_deu_001477-swc_deu_001477) +E B E N S O W I E D E A S M O T I F T E R L E S U N B Ü T (swc_deu_001478-swc_deu_001478) +W E N F Ü N I E M A R N N A C H P R Ü C F B E I S T (swc_deu_001479-swc_deu_001479) +I S T I E K L E W A D E A R F O R S C H E N V O N E I N R I C H T U N G E N (swc_deu_001480-swc_deu_001480) +G E S E H E N D A R V O N B Ü R D E N S E B S T D A N O C H D I E N T S P E C H E N D E N P A L K A O T F I E L E N (swc_deu_001481-swc_deu_001481) +S P Ä C H E N B E N U T I C H D E A R T E M L U F T L I E F V E R T (swc_deu_001482-swc_deu_001482) +E M O G L I C H E N S H U T Z S I M F O R N E N G E N K R A N K R E I T E (swc_deu_001483-swc_deu_001483) +S C H N E I N E N L I C H E N V E R S U C H G A R (swc_deu_001484-swc_deu_001484) +R O N D K E N S T R A H E N A N E I N E M P E E N Ü B E R G A N G O D E P I E E N Ü B E R G A N G D U S T D E N N H R E N V O T U F E K T I N E I N E L E K R I C H E N S T R O M U M W A N D E T (swc_deu_001485-swc_deu_001485) +B R M A S T E E N D E S E B E S T E R N C H T F R E I D E T E N (swc_deu_001486-swc_deu_001486) +L A H E N D E R B E G I F T (swc_deu_001487-swc_deu_001487) +A N K F A L T N I S U N T E R D I N S T I M N N O C H E I N E L O G E S C H E A B P F O L R G (swc_deu_001488-swc_deu_001488) +K A B E R T L E N D I E S E S A N G E B O D I E O M I T E N H I E N H E I T A B (swc_deu_001489-swc_deu_001489) +S T A N D V O M Z W A L F T E N M E R Z Z W E I T A U S E N Z W Ö L F D E R I N H A L T S T E N T (swc_deu_001490-swc_deu_001490) +R G E N I S A T I O N U N T E R B R A C H T E R E F N D (swc_deu_001491-swc_deu_001491) +V E R B Ü N D I T Z E N D O R D E R G A H F Ü S I E A R B E I T E N (swc_deu_001492-swc_deu_001492) +E S G E L E T E O L H R I H K E T A L D E (swc_deu_001493-swc_deu_001493) +D I E E R I C H T U N G D E R B E R L I N E M A U R M Ü N D E T E N (swc_deu_001494-swc_deu_001494) +E R I C H T U N G V O N K L Ä E R A N L A R G E N (swc_deu_001495-swc_deu_001495) +A F G A N E S T A N Z U N D D E M I E H Ö A R G H A T Z I C H S E I T I E M E I N M A R C T E (swc_deu_001496-swc_deu_001496) +D E R V O N A R I O U N D S T R O M V O N D E N L U N G E N Ü B E R D I E B R O N C H I E N B I S (swc_deu_001497-swc_deu_001497) +A U S E D E M N N A H M E N S E N D E R H R S P I E L E B M I T T V E R F R M D E D R S P R A R E (swc_deu_001498-swc_deu_001498) +U N D T I G U N D M A N D A R S K L A U S E (swc_deu_001499-swc_deu_001499) +K E I N E A B K E R V O N E N G R U N D T L A G E D E S S O Z E L I S M O S E I N S C H L I E S E (swc_deu_001500-swc_deu_001500) +I T K O M P O N E N T E N S O W O H L A N N A L S A U C H T I E F I N D E R W A C F E (swc_deu_001501-swc_deu_001501) +B E D E U T U N G S V O L L W A (swc_deu_001502-swc_deu_001502) +F R E I W Ü E L I G E H E N F E R T E O G A N I E S A T Z I O N (swc_deu_001503-swc_deu_001503) +M E L E K T R O N V O M W A R L E N Z S B A N D I N S L E I T U N G S B A N (swc_deu_001504-swc_deu_001504) +A L L E D I N G S E N V E R G L E I C H B E R I F E K E M Ö U G L I C H (swc_deu_001505-swc_deu_001505) +D I E S E K O N T E N A B E R A L S E I N G A B E I N E I N E N D F R I C G W E N Z S U M S E T Z E R D I E N E N O D E R S T E U A T E N Z U N G O N M O T O U R E N (swc_deu_001506-swc_deu_001506) +T O M A S H R M A N S P R O D U Z I E R T E Z W E I T A U S E N D Z W E I M I T K E R E B E (swc_deu_001507-swc_deu_001507) +P E N Ü B E R G A N G T R E F E N (swc_deu_001508-swc_deu_001508) +D I E F A L G T E N H O U S S C A U (swc_deu_001509-swc_deu_001509) +A N T I E S O J E T S C H E D E M O N S T R A T I O N E N W U R D E N P L U T I G N I E D E R S C H L A G E (swc_deu_001510-swc_deu_001510) +E I N V I E R K A N A L M I S P O L D D E N T E V E R K L E I N E R (swc_deu_001511-swc_deu_001511) +D I E S E H E T E N D I E V O R W A N D Z E I T E N F Ü R E I N E N A N G R I F A U F D I E U E S A R E X S T R E M H E R A P B G E S E T Z T (swc_deu_001512-swc_deu_001512) +W E I C H E S A M N E C H S T E N Z U M S T A R T K N O D E N L I E G T (swc_deu_001513-swc_deu_001513) +L A H T I O G I N G N D O L L Z E R Ü C K N I E B U N D E S L I E G E R U N D W E X S E L D E Z U E I N D R C H T (swc_deu_001514-swc_deu_001514) +Ü B E R I S E K A N K E I T (swc_deu_001515-swc_deu_001515) +J A H R Z W E I T A U S E N D F Ü N V E K R I T I E S E R T E (swc_deu_001516-swc_deu_001516) +D I E S E A U F H F A S U N G Z U R N E U T R A R I T E Ä T U N T E R S H E I D E (swc_deu_001517-swc_deu_001517) +W E D E L W U R D E R A L S K N S T L A I S C H E R L E I T E R D E S S I M E N S T U D I E S B E S T E L L T (swc_deu_001518-swc_deu_001518) +W E N M A N D I E W L T A L S K A N Z E S B E R A C H T E T (swc_deu_001519-swc_deu_001519) +S I E M T K R I T I S C H E K O M P O N E N T E N D E S D E T U O N A T I O N D Z Y S T E M S A B S I C H T L I C H S C H W A C H E I N D W U R F E N (swc_deu_001520-swc_deu_001520) +N I C H W E H B E R I S T E D O C H (swc_deu_001521-swc_deu_001521) +E R B O D E I N E V E R E I N I G U N G D E U T S C H A N S A N (swc_deu_001522-swc_deu_001522) +B E R I E N N Z W E I T U E N F Ü N F (swc_deu_001523-swc_deu_001523) +K E R N A B G E S T I M T U N D U M H Ö L E N D I E S E N E N S P R E C H E N T (swc_deu_001524-swc_deu_001524) +A Z T E U G U N G V O M N D E N A M I G A U S (swc_deu_001525-swc_deu_001525) +S E M T U N D I N G W E R (swc_deu_001526-swc_deu_001526) +U N G V O N S C H E H E R U N T E R N E R U G E G (swc_deu_001527-swc_deu_001527) +N I S C H E N U N D G E W R T H E N (swc_deu_001528-swc_deu_001528) +R O B E R T E R F K N E D I E (swc_deu_001529-swc_deu_001529) +K A M S C H L I S E L I C H Z U N M (swc_deu_001530-swc_deu_001530) +O L S T E N I (swc_deu_001531-swc_deu_001531) +S T A N D T E N S I C H V O N D E N U R S A R (swc_deu_001532-swc_deu_001532) +A C F R I K A S S T I H D E S E R H A H R E R G E O R T E T (swc_deu_001533-swc_deu_001533) +D I E A R M E M E U N T E R T E L (swc_deu_001534-swc_deu_001534) +S T A L I E N S E T Z T E M (swc_deu_001535-swc_deu_001535) +F E I H T E N S A U S L E I C H (swc_deu_001536-swc_deu_001536) +K L M E R A U F B R O S K R E I B T G L E I C H (swc_deu_001537-swc_deu_001537) +A M Z W E I T E N J U N I E Z W E T A U S N D V I E R W U R D N (swc_deu_001538-swc_deu_001538) +I N E B N E S T A G N A C H R L G T (swc_deu_001539-swc_deu_001539) +D I E N A T O O S S T E R W E I T E R U N G U N D D I E E I E N S E I T I G E A U F K Ö Ü N D I G N D E (swc_deu_001540-swc_deu_001540) +T H I E R B E I I S (swc_deu_001541-swc_deu_001541) +D I E S E R S T E L L E K A M E N S E M T I C H M I T I E D E R D E R K A P E L E T E (swc_deu_001542-swc_deu_001542) +P O T Z T A M A B K O M E N E N T H I E L D T Z W A H R A L G E M E I N E R V E R E I N B A U N E N Ü B E R D I K Ö N F T I G E G E M E I N S A M E V E R W A L T U N G D E R S I E G E R M I C H T E U N D V O M L I E R T O R U N D S E T Z E I E D E M L I T R I S I E R U N G (swc_deu_001543-swc_deu_001543) +D A N A C H U N D E R S C H I P E E I N E N V E R D R A G B E I M W I E H F Z I D E N A H M O (swc_deu_001544-swc_deu_001544) +E I N W E I T E R E W E R I A N D E M A G (swc_deu_001545-swc_deu_001545) +S I E W U R D E N M O D O L A H R N D U R C H L O C H S T R E I V N G E S T E U R T U N D D I K L I N G E K O N D N (swc_deu_001546-swc_deu_001546) +D I E G R U N M A D A R T G K L A U S E L B E V O R Z U C T U N D E R D I N K L E I N E R N P A R T E I N J I E N E (swc_deu_001547-swc_deu_001547) +B E R T O N Z D E M K E I N E W Ö G K L I C H E H U G E S N O T H E R U S C H T (swc_deu_001548-swc_deu_001548) +N T U G M N T E R Z I O N D (swc_deu_001549-swc_deu_001549) +Z U V O R B E D I G U N G K O N K R E T E R A P R Ü S T U N S C H I T E (swc_deu_001550-swc_deu_001550) +B U N D E S T A R G E S W A L R E C H T (swc_deu_001551-swc_deu_001551) +E S M U S I M G R E I S W A L E I T D E R V O R G E L E T W E R N (swc_deu_001552-swc_deu_001552) +H A T M A N E I N E I M P I E R E S C H E B A S I E S F Ü B P S Y C H O S O Z I A L E P R O G E A M E Z U O S E N K U N D E R S E B S T M O U T E R A T E U N Z U R S T E A R K U G D E S I C H E H I T Z G E F Ü S I N D E B E F E Ö K E R U N (swc_deu_001553-swc_deu_001553) +B E I D E N E R S T E N F R E I N P L L E M E N Z W A H L N U R D E I L I E S K U I M E I N E U N Z E N H N D E R T N E U N Z I G I N S E I N E (swc_deu_001554-swc_deu_001554) +D A M M I T L A S S E N S I C B E S T R A L U N G S T E R T E N S E R G E N O M E S E N (swc_deu_001555-swc_deu_001555) +W I N I G E A R S P Ä T E R K A M E S Z U E I N E W E I T E R E N K R O N D N G (swc_deu_001556-swc_deu_001556) +R A D I O K A B E R E T P E I L S (swc_deu_001557-swc_deu_001557) +E S T Ü K T E B O M B E R A U F D I E S T A R T W A H N E N R E L E N (swc_deu_001558-swc_deu_001558) +M I T D I E S E R E G E L U N G S O L E I N E R F A K T I S C H Z W E I V E R C H E E I N F L U S N A H M E D E S E R W E L E R A U F (swc_deu_001559-swc_deu_001559) +B E R O K G K Ö R I C H E N B A U (swc_deu_001560-swc_deu_001560) +D E R H E R V O R A G E N T W I C E N D E N L A N D E K L P E N W I E D E R U M H E R V O R A G E N E R L A N G S A M F L U G E I G E N S C H A F T E N (swc_deu_001561-swc_deu_001561) +M I T E R E C H V E R B I N D U N G S F L U K T Z O G E O D E R U M S C H U L M A S C H I N F Ü R D I E B E E E I N H U N D R D E N E U N V E R W E N D E T (swc_deu_001562-swc_deu_001562) +L E I S T E T E M I E I Z I N I S C H R N B S Ü C H O L O G E S C H E H E L F (swc_deu_001563-swc_deu_001563) +K A N M A N D E C H I M F U N G E N V O R B E U G E N (swc_deu_001564-swc_deu_001564) +M E R D N A U S B O C H D I E S E R K A N K E I T E N E H E R F O L G T E I N F E K T I O N V E R L A N G S A M E N K A N (swc_deu_001565-swc_deu_001565) +D I E I N E N E U T R D I E T E Ä L T U N T E R A L E N U M S T E N D E N V O R S A R (swc_deu_001566-swc_deu_001566) +U N D Z I E G E N H Ö R T (swc_deu_001567-swc_deu_001567) +D A S N E U N Z E H N H U N D E R T A C H T E N D R E I S S I G G E G R Ü N D E T E K O M I T V I R U N A M E R I K A N I S C H E U M T R I E B E W U R D E D A F E N U N (swc_deu_001568-swc_deu_001568) +Z E N T R A L E D E R P R O C K R E S S I E V E N U N D H O R T D E S I N I E N I Ö R G E S T Ü T Z S T E N K U N S T D E N K E N S (swc_deu_001569-swc_deu_001569) +I N D E R D E R O E S P R E S E D E N T A N K Ö N D I G T E (swc_deu_001570-swc_deu_001570) +S N E Ä C H S T U N D V O R S P B E I S E N (swc_deu_001571-swc_deu_001571) +D E S P U N D E S W A G E S E T Z E S B I S T Z U M D R E I S I G S T E N J U N I Z W E I T A U S E N D E F A U F G G E M (swc_deu_001572-swc_deu_001572) +O R I E P O S S E Ö R (swc_deu_001573-swc_deu_001573) +F L I F T L I N G E N V O N D E R E T N I S C H E N M I N D E R H E I T D E R S O M A L I S C H E N B A N T U M (swc_deu_001574-swc_deu_001574) +D I E B I E P O L A R E W E L T O R D T N U N G Z E M I N T I E R T (swc_deu_001575-swc_deu_001575) +E R A N F A N G E I N I N T E I L R I E A T E O D E R E X S T E R N A N G E B R A C H T E V O R I C H T U N G A N E I N E N U C L I E R E N W A F E N S Y S T E M N (swc_deu_001576-swc_deu_001576) +S T A R T E T E D I H I L F S O R G E N I S E T Z I O N L A N K F R S T I G E (swc_deu_001577-swc_deu_001577) +W E N D I E S E E X S T E R N E N E R F E C K T E I N E R I C H T I G E N R E I N V O L G E A U F T R E T E N U N D S I C H I N E H A L B S P E Z I E I S C H A P A R A M E T E R B E W I E G E N (swc_deu_001578-swc_deu_001578) +Z U G D E W I R T U N I O N A U C H B E I D E A S E R S T O F P B O M B E N U N D N E U I N F L U K T Z E U G E N M I T I N T E R K O N T E N E N T A L E R E I C H W E I T E M I T D E N U R S A R G L E I C H (swc_deu_001579-swc_deu_001579) +P E N D I E S T A T H A T I E W A B P E N T I E A M (swc_deu_001580-swc_deu_001580) +D I E S E R A N S A T Z G I L D A L L G E M E I N A L S A U S G E W O R G N D E R (swc_deu_001581-swc_deu_001581) +N A C H D E Z U S A M M P R O C H T E (swc_deu_001582-swc_deu_001582) +D E O B E R L A U S I T S W I S C H E N H E U E R S W E R D E (swc_deu_001583-swc_deu_001583) +D A B E E N Z W E I E F A H S E N N T E R T E I L E L T (swc_deu_001584-swc_deu_001584) +S C H I E D E N A N D E R E U R O P E R M I S T E R S C H A F T E I L U N D W U R D E M T E R D I E E B I E E L L F (swc_deu_001585-swc_deu_001585) +M E S T E R E R F N E T K A B E R S H L I S G E R N W E I T E G E M Ö G L I C H K E I T (swc_deu_001586-swc_deu_001586) +E I N E M A U S W E R T S E R F O L G I N W O L S B U R G E L A N G (swc_deu_001587-swc_deu_001587) +M I T S C H W E B U N G S S O U M M A N K O N T E N K L I S S A N D I E E R Z E U G K T W E R D E N (swc_deu_001588-swc_deu_001588) +D E R B A L E D I G L I H Z E I K T E (swc_deu_001589-swc_deu_001589) +K O S P R I T A N I E N E I N E E S T E W I C H T I G E V E I N B A U N G (swc_deu_001590-swc_deu_001590) +S I D A C H T E S H R I T N E S S E I N G (swc_deu_001591-swc_deu_001591) +W U R D E M I T D E B U N D E W A G E S E T Z V O R N E U N Z E U N D E R S I C H S U N F Ü F Z I G E I N E D A U A R H T E R E L U G E N G E F Ü R T (swc_deu_001592-swc_deu_001592) +D I E A N Z A L D E R Ü B E H N G M E N D A R D E K A N (swc_deu_001593-swc_deu_001593) +I S C H L S D I E S E E I N M L I T E R I S C H E S E I N G R E I F E N I N D E N K O R E A R G R I C K (swc_deu_001594-swc_deu_001594) +N A T O V E R B I N T L I C H (swc_deu_001595-swc_deu_001595) +K A L T E G R I E G B E N D E R T (swc_deu_001596-swc_deu_001596) +A U N U N E N H U N E R D E E I U M T N E U N Z I G U N D O S T E R A L I E N S O W I E D E R Ö S T E R E C H S C H E A B L I G E R (swc_deu_001597-swc_deu_001597) +D A D I E S E I T A N F A N G N E U N Z E H N H U N D E R T N E U N U N F Ü N F Z I G D O R T H E R S C H E N D E R E V O L O T I O N D S R I G I O N U N D D E R V I E D E L K A S T R O E I N E N S O Z I E L I S T I S C H E N K U R S E I N G S C H A G E N H A T E (swc_deu_001598-swc_deu_001598) +N A C H W E I T E R E N F E L U S T R E C H E N K Ä M P F E N U N E N E N Z W E R T E E R F O L G E B E I D E G R I G S P A T E I N U R D E R U N D D R E A J A H R E N A C B E G I N D E A U S A N D N D E S E Z 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A T S Z U S C U T Z S P E S O N L I C J E R D A R T E N E B E N F E I S G E N N E R E L U N S R E U T E Z U S A M E N A R E I T E L E I C H T E R (voxpopuli_deu_000309-voxpopuli_deu_000309) +E R A M T E A B E N D E R S L I M S T E V E R I N D E R D A R M E L E B E N G E R E D T E I N S E L B E V E R L Ä T S T W U R D N I G K L A B (voxpopuli_deu_000310-voxpopuli_deu_000310) +I C M Ö B R I Ü E D A S D E R O M I S S E I N I T I E S (voxpopuli_deu_000311-voxpopuli_deu_000311) +M I T L E T U N D D E C O F V E D A S W E R N E C X H S L I E A R Ü B E D E S W A N Z E (voxpopuli_deu_000312-voxpopuli_deu_000312) +D E S D A F N I C H I V E R S E H E N W E R D N D A S I M E R H I N W E R B E F Ü M T Z I G T P U O Z E N D D E R B E F Ö L K Ö N G E A D E R B E S C H E N U N U N N I E L E N L I C H E N G R A M L I E B (voxpopuli_deu_000313-voxpopuli_deu_000313) +S O D A S D E R B Ü R G E R S C H E L L O N E A U S K U N F B E K O M M T O P S E I N E B E S C H Ä R D E Ü B E H A B T A N G E N O M E N W I R D O P S I E B E R E C H T I C H T I S T (voxpopuli_deu_000314-voxpopuli_deu_000314) +N R I E S E T T U N Z E R E R B I T I O N G E N I S T N I C H T V O N E U T E N A B E R S I A B O E K O N T I N U N I E R L I C H E S F E I N T I O N E N G (voxpopuli_deu_000315-voxpopuli_deu_000315) +L D I E T G A N S T O L S G E S A G J A E D B E S C H E F T I U N G S T E I G K T D J E R A N (voxpopuli_deu_000316-voxpopuli_deu_000316) +W I D A S S F Ü R U N I S T E R A U H O N D E R E R V E R T E T W I E R E X S P O R T I E A N Z O U V E E L L Z O B I L I C G A L W V E R I M P O T I E R E N Z S U W E D I G W I E R F A R S C H E N K E N W O L S T A T (voxpopuli_deu_000317-voxpopuli_deu_000317) +S I E H E U D E R A R B E N I E A N W E S E N Z I N D I S E N P R O S I T I E V E S S I G N A L E (voxpopuli_deu_000318-voxpopuli_deu_000318) +N E U N Z S I G H P R O Z E N T A L L A A R O P Ä S C H E N F I L M E D I E A U S S E H A L T I R E S H E I M A T L A N D E S G E Z E I C H T W E R D E N S I N D V O R M E D I E R P R O G R A M G E F E R D E R T W U R D E N (voxpopuli_deu_000319-voxpopuli_deu_000319) +B I E S O K A I C H E B E R G E B P L I S S T E R A L E A U S C H U S A B P T E M U N G I N D I E S E F O R E M N I C H Z U S T E M (voxpopuli_deu_000320-voxpopuli_deu_000320) +B E R B O R T E N V E R I N D E R N D A S I C H E H I N D E R I E N G E I S I G E N E I G E N T U M D I E A U S G U M S F L I C H T E V E R S T E C K E N K O N T E (voxpopuli_deu_000321-voxpopuli_deu_000321) +I S G I B D E T Z I M Z U S A M A N G E R V E R S T E R G T E N Z U S A M M A U B E I T E I N E N E R S T E N G A N G V O N E I N I G E N M I T I E L S T A R T E N N A C (voxpopuli_deu_000322-voxpopuli_deu_000322) +W A S T I R E N Z Y Ü B E R C H R E I T E N D E Z U S A H M E N A B E I A N B E L A N T U N D W A S T I E E R V E R B R E I T U N G I N T R I G L Ä N D E R B E T R I F T U N D T I E M E C H L I C H E I N B E I S C S P I E N E N E N D E S E N E R F O L K S B E I S P I L V Ü M I C H I S T U N D Z W A R E L S L M D O K G M I L I E R N E R E D A S (voxpopuli_deu_000323-voxpopuli_deu_000323) +D A S N I C H T E N U R I N P R T U G A H L D R G R I C H E N L A N D S N E R N A U R E N S O V E R M E I N T L I C H G E I C H E N M I T W I E S T A T E N V I E D E U T S H L A N O D E R R U S P E T A N I E R N (voxpopuli_deu_000324-voxpopuli_deu_000324) +T V E R A U S W E N I S V E R B E I D A (voxpopuli_deu_000325-voxpopuli_deu_000325) +E A L L E L F L I E G A L M I K D I E D E R D I E S E S H A U S E S V A R S C H E N I E D O R D T L I C H O L F I G E A R A L S D E I E U D U R S C H N I T Z B Ü R G E R T (voxpopuli_deu_000326-voxpopuli_deu_000326) +E N S I C H E R D A S E R E R B E D O E U T T U N G I N A H R S F U K U M F T U O G E R N O C H T Z U N E H M W I E R T (voxpopuli_deu_000327-voxpopuli_deu_000327) +T E S K E T D I E R U M D I R I C H T L I H N J E D E S A D E S E F Ä S T L E G U N G G U N D L E G N D A S I G H E R E I T S N E R M E N F Ü R D E N S C H U T Z F Ü E R D E N G E F A R E N E I N E R E X S P O S I T S I O N G E G I Ü B A R O N I S I E R E N D A R S T R A L U N G (voxpopuli_deu_000328-voxpopuli_deu_000328) +D A S S G I L T E S W I D E R H E R C H U S T E L (voxpopuli_deu_000329-voxpopuli_deu_000329) +D E S E N E I N E N E I N Z I G E N S I T Z K I B T E S L Ä N G S D A S I E S T A S T P U R G (voxpopuli_deu_000330-voxpopuli_deu_000330) +E D A S A S P A S I E R T I N M A L T A R D I E S O N L I S T E N D I K O B P Z O N D F E L E A U F G E D E K T E R D I S V E R G E N B E C H N E R M O D E W A R W E D E R W E R E N S Y S T D E M A I S H I K O C H O U N D F E L E E B E U N T E R S U C H N O C H I E T E R M O R S E L B E R E G E Z I E L T E U N T E R S U T E A A T V A S S I N A N D O G A S W E N A L E S I E O R U N T E R D E M A N D E I S C H W E I N S Z U G E D E K W E R N S O R S (voxpopuli_deu_000331-voxpopuli_deu_000331) +L I N T L A N D E K Ü S T E D I E W A N S T A E I N E D I E A U F D I E R O S E N K A T A S F O F E N I Z U N A H M I E H N D R V R G A N E N E I T I N W E I S E N (voxpopuli_deu_000332-voxpopuli_deu_000332) +D E N N D I C H A B E B R I E N Z I E P F Ü R D E B E I C H T G E S T E M M N T O W O L E I N E N S W E H R N F H L E R I N T E L L T E S I R T N E M I T A R Z U R A U F G E F A R D E R T D A S A U R O B E H R S C E P L L R M E N D A U F D E M W E G K T Z H E E I D E M E I N Z I G E N S I T Z U U N D E S T E L T Z E N (voxpopuli_deu_000333-voxpopuli_deu_000333) +I N D I E S E M R I F E N W O D E N G E M E I S A M M E P O L I T I S C H E V E R A B R E D U N G E R N I M K R E I S D E R S I E B E N U N T Z W A N Z I G E T D R O F F E R N U N D A U C H H U B L I K G E M A C H T (voxpopuli_deu_000334-voxpopuli_deu_000334) +I B I N E R E R O G E N D E S W E R S H E U T E M I T D E M V O R C H A R G E S E M U M B E L T A U S C H O S G E S C H A F T A M I N C H I T W I T E A U K M E S I G E R F E K T E O R P Ä I C H E R Z E S A G E N D E H E T E N Ü E R H O C H R I S I K R O R P O D U K T E I N E S E N D R A L E Z U L A S N H A M E N M Ü S S E N D A S A B R I C H I C H G E S C A F T A R M I T D E M E R S E I D A E M T I S C H L I K T P L A U E I C H D A S I R T R O T T E M E I N E N G R O S E N S C R I T F L E I C H K E I N M E I N S T D E I N E E I N G R O S E S C H I Z U E R P A E D E N S E E T H A E N (voxpopuli_deu_000335-voxpopuli_deu_000335) +P Ä H E N D A N G E S F Ö R G F Ü R Z W E I E N H E I B M I N O D E N E R G (voxpopuli_deu_000336-voxpopuli_deu_000336) +Z U M A K T U Ä L E N I C T L A B I S K A N K E I N E R V O N U N S A N E H M E D A S W I W I R T L I C H E A R S Z E T D I E S E N W O C H E N E D I W I S E N D S O N S D I E Z A L U N S U M F I C G K E I T D R O T (voxpopuli_deu_000337-voxpopuli_deu_000337) +D S N D E I N F C H B E D I N G N G E N D I N I C G E K T Z E P T A B E S E N D M A N K A (voxpopuli_deu_000338-voxpopuli_deu_000338) +I N D E S W I S C H E N S E I S I N D I R E T U N G S O R G A N I S E R Z I O N E R N I E G R Ö S T E N S C H Ä P E R W E I S I E D I E M I G R A N T E N Z W A N Z I C H K I L O M E T E R V E R D E R I E B I C H E N K Ü S T E A U B G R E I T F E N U N D A L E N A R I T A L I E N R A S P O R T I E R E N (voxpopuli_deu_000339-voxpopuli_deu_000339) +D E S E I K T D R F A L L I O L I A R T I E M S C H E N K O (voxpopuli_deu_000340-voxpopuli_deu_000340) +E W A S S E R P R E D I G E N U N D W E I N T R I N K E N (voxpopuli_deu_000341-voxpopuli_deu_000341) +Ü R D I E S E E N S C H E I D U N G P R A U E N W I A R V I E L E P A T N A R N I C H T Z U L E T Z D I E S T Ä T T E (voxpopuli_deu_000342-voxpopuli_deu_000342) +D I E F O L G E I S T E I N H Ö R E N F L U G S V O M P O R P O L I S T N U N E X S T R L M I S T E I E N E I N I G M I G I T S T A T E N I E R E N B U M F E M P A R O L U E N S E T Z E N D I A R C O G R E T E R V E R E N D E R U N G E N G E G E N (voxpopuli_deu_000343-voxpopuli_deu_000343) +W A L D I E I N V E S T I T Z I O N E R N V R A N T Ö R S I S C H A C H U N D D E U T S C H E R B A N K E N G E R E T E T W E R D E N M U S T E N D U R H T E R G L I C H E N G L A N D T W E I T A U S E N D E H N N I C H T P B E I T E G E N U N D E H U T E M U S E S E I N E N R I E S I G E N G S C H O D E N B E R K V O R D S I C H T E T H E R T D R Ü C K E (voxpopuli_deu_000344-voxpopuli_deu_000344) +I M I T G I T S T D A D E N D Ü R F E N N I C H I E M Ö G L I C H K E I T A B E N D E R E N A U R O P Ä S C H E N S T A R Z E A M B A L D E R A N Z U R H I N D E R N E N I E R E R E G I O N D G A N Z S G E R Z I E U N S T E M A T I S C O R U T O N F E L N A C H Z U G G E N E R S I N E (voxpopuli_deu_000345-voxpopuli_deu_000345) +E I M I L I O N M E N S C H E N S I N A P Ä N G H V O N U D S E R H I L F E R (voxpopuli_deu_000346-voxpopuli_deu_000346) +E I N F Ü Z H E N H R G E R J N G E W E T I N H E R K A D I O N E I N E P O L I Z S I S T E N E I N E S O N D E E I N S A T K O M A N D O S N C O M A G S C H L A G D E N (voxpopuli_deu_000347-voxpopuli_deu_000347) +D I E E I D I E H E I L I G E K U A T M A N W O S I H E R E T R A G E N D A S A U P T A U T W I S S U D E R A L L E U N S H L E N D E N W E C K (voxpopuli_deu_000348-voxpopuli_deu_000348) +R E I D E R A R T K T E G E T D E R E F E N H A R B E N I N Z W I S C H E N S T A D G E U N D E M (voxpopuli_deu_000349-voxpopuli_deu_000349) +R D I C H I E E R N E I N M O N E R D E B T (voxpopuli_deu_000350-voxpopuli_deu_000350) +D S W E G E N E I N W I C H T I G E F R A G A D I K O M I T I O N E N E I N L A N D D I E K R A N Z S K O N T R O L L E V I E D E R E I N F Ü H O N N D D O C H I M S C H Ä N G E U N I O N B L E I B E N I T Z U G A N G K Z U R I N O M A I O N D S U S T E M E Z E T E R A O D E R I S D R S E I N E N T W I R D E R O D A D I E F R A G E I S W E C H T I C F Ü R D I E D E N I S C H E T E P A T E U N D I E S P E T E U M E I N E K L A R E A N D W O R D D A (voxpopuli_deu_000351-voxpopuli_deu_000351) +D E R S C H O N A U S G I E F Ü H R T W U R D E L A G E S N I C H T B A R A R N D A S I S E G R O B E F F Ä H L E G E G E B E N H E T I S S O N E N E S G A B E N E R R E I E V O N D G L E I E N N G E R E I M T E I T E N B I E T I E N S W E I (voxpopuli_deu_000352-voxpopuli_deu_000352) +I V E R G E M E I N T C H E A F T U N G D E R A U S E N U O S S I E G E R L T S P O L I T I G B A I S G O S I S Z I E L D I E S E R U N J O N (voxpopuli_deu_000353-voxpopuli_deu_000353) +D E N S I C H E H E I T I S E I N E S C W I E R I G E U N D D E T E I L W E I C H E R A R B E I T N I C H T N U E R I M T Ä C H N I S C H E N B E R E I C H (voxpopuli_deu_000354-voxpopuli_deu_000354) +T I S E Ä L T E N G E N D I E N T E R E S T E N V O N B Ü R G E R N U N P O L I T I K E N S O W I A U S E N A N D E R B E R E M B Ü R E R N I N G A N Z E R O P E R S T E T E S T E M E R K I N D T G A N N S O B E N (voxpopuli_deu_000355-voxpopuli_deu_000355) +H E R P A S I D E N T (voxpopuli_deu_000356-voxpopuli_deu_000356) +E F Ü R T E N G E S P R E I C H E M I T R E S E D E N T K A R S E I Z A R D R E I C H N R E G I R U N G S E R T R E T E R N F R A U N U N D M E N S C H N R E C H T O R G A N I S E R T Z I O N E N U N D D I E W A N D D U C H A U S E M U T I G E N T (voxpopuli_deu_000357-voxpopuli_deu_000357) +N G S A C H E I N E U R S A C H E F R D E N W A C H S N E N A T Z I H N A L I S T M U S D E A L I N S E I D E R F E L I C H P E R S B E K T I F L O S S I S T (voxpopuli_deu_000358-voxpopuli_deu_000358) +H U D E I N E I M A N A O H S O R W E I T O N D I E N Z I E E N F E R N E S (voxpopuli_deu_000359-voxpopuli_deu_000359) +H W E R D E A L S W I D A N Z M I N I S T E R A U C H E N M E I N E M L A N D Z I E D E N T A G K D A M I T K O N F V O N D T I E R T D A S N A T Ü L I C H A U C H T E S B I R U S T Z E N G E G E B E N S E N M U S D A S S T A S H A U S H A L G T D E V O N D E S T E L U E R S A L E R E N E N O N S T E U E R Z E O L L A N I N E N Z I E Z I H N T U N D D A S I E T A H M I T A U C H I E R N T U E R T U N G R A G E N I N D E E N T C H Ä I D U N G E N D I E V I E R H I E N T I E S E N R A M E N D R E F M I E T A M N O N T H E R N (voxpopuli_deu_000360-voxpopuli_deu_000360) +A U F D E M O U O R O P E S C H E N A U T E R B E B I L M A R E K T I N S I G E S A M D E R M A T I S C H I S T (voxpopuli_deu_000361-voxpopuli_deu_000361) +O P Ä H S C H U N I O N H A D T M I D I S E I N S T R U M E N Z S D I E S C O N S E E I N E A K T I E V E R O L L E N I E R N A C H B A R I G I O N Z U S P I E L E N U M D E R M O G R A T I S C H E E F O R M E N N D E R N N A C H A L I G E N W I K T U N G E R N Z U T R E I B E (voxpopuli_deu_000362-voxpopuli_deu_000362) +H T U T E L L I T E R E R E R S C H I E M E V O N A U S E N G O D E R V O N I N E N I S T R E C H T U N D O S C H I E D G L I G H (voxpopuli_deu_000363-voxpopuli_deu_000363) +E R E M I M E R G E S A G K T E I N Ü B E R E I L T E S T A D T Z I O N I E R U N G S E N S H E I D U N G E S U N S E N I C H W E I Z U M J E R Z I G E N T Z E I T F U N G E S K E I N E B E D R O U N G B E I S P I E L S W E I S A U S E M I E R A N G E B T (voxpopuli_deu_000364-voxpopuli_deu_000364) +D I E S E R F A K L E I I S T E I N E T Z Y N I S C H E M I S A C T D U N G E N D E R O B P U O V O R O N M E N C H N E C T Z W R E C T D O M L E L A L L R E L S F F A A A A A T D O D S C S A A A A I S S O N G A N D A N A N D E N E E I N E S O E U C H O D E N C Ü L A U P B L I C H E R A N W O R F (voxpopuli_deu_000365-voxpopuli_deu_000365) +D I E E S P E E R H A T D I S E U M F S E N D E R H E T Z U N T A L E R I C H T L I N D E Ü R B E Ü R B O A T D E T W I N G E R (voxpopuli_deu_000366-voxpopuli_deu_000366) +G I G I S T W I R G L I C K L A D I I N A N F U N D E W I R S H A S T G E D E V E L A N K V O N U N D E A L N E I N M A L M E H R J E T Z S T D E R V E R A N T W O F T D U N G F Ü R E I N E O B P T I M A L E U N D F E A L E M R A S I E K A L I F I Z T I E R U N G U N D R E R A R B E I T N E H M E N D A R B E I T D N E M E R R I N E N D A N S B E S O N D E R J E T S T R E S C H N U N G S O T A G E N (voxpopuli_deu_000367-voxpopuli_deu_000367) +A N D R E R A U C H O H L E N D I E S E R K U D E R G E B I S E R Z I E L N A L S A N D E R E D I S S I S C W Ä E R T U N D I M I T E L A B P Z U O F N E T W A C R I K I O N W I K A L A B R E N Z I T Z I E L E N O D E R A U C G R I E C H E L E R D O D R A U C H O M Ä N I E N (voxpopuli_deu_000368-voxpopuli_deu_000368) +D E R B R I C H C O S E S V O R D E R Z U R E C H T D A S E S R E T I N G S T A T L I C H E R S C H L T T I E D E L E I S Ö F F E N T L I C H E R A U F G A B E B E G R I F E N U N D D A H I E R V O N F F E N L I C H E A K T Ü R N V O R G E N O M W E R D E N M O S (voxpopuli_deu_000369-voxpopuli_deu_000369) +D A B W I E S A B A L D N M I T E I N E M S O T C A R L P O G A M Z U T U N H A R B E N M S S W I L D A R F Ü R E I N E N S P E C H E N D E R E C H L I G E U N D L A G E S C A F E N (voxpopuli_deu_000370-voxpopuli_deu_000370) +S T I E R N O C H N A L I S E R E N W O R E (voxpopuli_deu_000371-voxpopuli_deu_000371) +M A K E N E N E R T L I E V E R L A N G E N G E B N L I E M E R G A R T F I R N D I K U N G S H V E R A U S D I E A H M E N E U T E B R A U C K E N D A S S B E (voxpopuli_deu_000372-voxpopuli_deu_000372) +G E R A D Ü O G L E I N E L E P O J E C K T E I S D A S Ü B E R M Ä E S I G H B I E R O G A T E S C H R A U F A N D R E C H T I C H D A S T A S I E R S R B E I N Z E I T A U M V O N D D R E I J A H R E N G E S E N T W E R D E N S O L U N D U M (voxpopuli_deu_000373-voxpopuli_deu_000373) +I K A N D E V O R S I C H E R N D I A R O P E S C H E K O M I S I O N I S T E T K O M I T D E T Z S U M A A R E R O B S E A L O P E C H E N E R S B I E K T D I E V E R D I S K O S S O S E (voxpopuli_deu_000374-voxpopuli_deu_000374) +S E S E S L A P E I H E A U F T A U C H S O (voxpopuli_deu_000375-voxpopuli_deu_000375) +I D I E S E N H A U S E L K A M A N D I E E E N G Ü R G E R E I N U N D B O R G E R N I C H T Ü B E R Z E U C T E N N B E G E I S T E R N (voxpopuli_deu_000376-voxpopuli_deu_000376) +Z I A L D E M O K R A T E N E H M N I T G R O S A F O E U D E Z S O R K E N T N I S D A S D I N G E D I E I E R F O R G E T R A G E N H A B E N J E B Z S I C H A U H I M Z U S A M M E N A G M I T V E R E N D E R U N G E N E D E N F E I N C H E N S T A D E N U M S E T Z E (voxpopuli_deu_000377-voxpopuli_deu_000377) +D E A H R B E S C H U S T I E E L D A S O R O P Ä S C H E S E M E S T E R H I E R H E R T Z U N E H M E N U N D D I C O R O B P T I O U N S S I T P L A R I O N E R E I M R A M D E R L N D E R B R E C H T E Z O V E R Ö F N I G E N I S T N I G A U S H E I G E N T (voxpopuli_deu_000378-voxpopuli_deu_000378) +N D M E I N M E I N E B I T E O D E R M D A S W A S I C H M E R V O R S T E N I S D A S M A R G E N G W I E C K L I C G I N D E R T A R T E I N I E G R O S E E I N E B R E I T M E H R H E I T F Ü R D I E S E K O U S I O N S P O L I T I G H Ü E O N D F G E P O L I T I G S T D I M T P Ü R D I E M E N S C H E N V O R O R T D A M I T I U N S A T E S W E E N T I C H E A U C H B E S C R Ä N K E N K Ö N D E D A S (voxpopuli_deu_000379-voxpopuli_deu_000379) +W N N W I E H E U T E D I E E V E R R D N G V E R A B S C H I E D E N O F E R E C H D A S S E W I E N A C H E I M L A N G K A R U S E L L S O U E I M G U D N A B S L U S K O M M O N D T I T M M A C H T E R M I C H E B E I E R O M I S I O N B E D A N G E N I E O N S T O K T I E V E S A C H A B E I T H A T (voxpopuli_deu_000380-voxpopuli_deu_000380) +U N Z E R E R E S C H E A S C H E N U N Z S I E K O N T R O E L E N H A B E N K E N E N P I E L E G E R P R A F T (voxpopuli_deu_000381-voxpopuli_deu_000381) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..7ba21c36a0fa3f69bb39cbcf3cf0a08f112cf5e4 --- /dev/null +++ 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(M-AILABS_deu_000168-M-AILABS_deu_000168) +U N D D E S H A L B M U S S M A N D O R T W O M E N S C H E N S C H W I E R I G K E I T E N H A B E N D I E S A U C H E I N E R S E I T S E R K L Ä R E N A N G E B O T E M A C H E N (M-AILABS_deu_000169-M-AILABS_deu_000169) +D A S S M A N N U R A U F D I E W E L T K O M M T U M S E L B S T W I E D E R E I N E N S O H N Z U H A B E N D E R D I E V E R E H R U N G D E R A H N E N F O R T S E T Z T (M-AILABS_deu_000170-M-AILABS_deu_000170) +D E S H A L B G E H Ö R E N K O N T I N U I E R L I C H E S C H U L B I L D U N G A U C H K O N T I N U I E R L I C H E M Ö G L I C H K E I T E N D E R W E I T E R B I L D U N G U N D D A S B E G E H E N V O N G E D E N K T A G E N F Ü R M I C H U N A U F L Ö S L I C H Z U S A M M E N (M-AILABS_deu_000171-M-AILABS_deu_000171) +M E I N A N S A S C H E N S A G T S I E E S I S T J A J E T Z T W I E D E R G A N Z G U T Z W I S C H E N U N S A B E R E H E D U N I C H T A L L E S G E S T E H S T G E H T D I E E R I N N E R U N G A N D A S B Ö S E N I C H T W E G (M-AILABS_deu_000172-M-AILABS_deu_000172) +N E I N W E I B E R B R A U C H E I C H N I C H T (M-AILABS_deu_000173-M-AILABS_deu_000173) +G O T T H A T N I C H T V E R G E B L I C H N A C H M I R G E R U F E N S A G T E D E R S C H I F F E R (M-AILABS_deu_000174-M-AILABS_deu_000174) +N U R E I N E S W E I S S I C H D I E S E R F U R C H T B A R E N F R A G E E N T G E G E N Z U S E T Z E N U N D S C H L E U D E R E D A S W O R T I N D I E W A A G S C H A L E D I E G L U T M E I N E S L I E B E S W I L L E N S I S T S T Ä R K E R A L S T R E N N U N G (M-AILABS_deu_000175-M-AILABS_deu_000175) +T O M S A R M E E G E W A N N E I N E N G R O S S E N S I E G N A C H E I N E R L A N G E N H A R T N Ä C K I G E N S C H L A C H T (M-AILABS_deu_000176-M-AILABS_deu_000176) +E S I S T E I N N A M E D E M S I C H D I E T Ü R B E I T A G U N D N A C H T Ö F F N E N K A N N B U R S C H E U N D W I L L K O M M E N (M-AILABS_deu_000177-M-AILABS_deu_000177) +A B E R I C H V E R Z E I H E I H N E N I H R E U N W I S S E N H E I T (M-AILABS_deu_000178-M-AILABS_deu_000178) +V O N D E R D R I T T E N U N T E R R E D U N G A N S A G T E M I S T E R H A V I S H A M W A R M I R D I E P E R S O N I N H O H E M M A S S E V E R D Ä C H T I G (M-AILABS_deu_000179-M-AILABS_deu_000179) +I C H D E N K E D E R A M T M A N N U N D S E I N E F A M I L I E W E R D E N E S R E C H T V O N D I R F I N D E N D A S S D U D I C H S E L B S T A N G I B S T U N D S I E W E R D E N F R E U N D L I C H G E G E N D I C H S E I N (M-AILABS_deu_000180-M-AILABS_deu_000180) +J E T Z T S C H L U G D I E H E L L E F L A M M E A U F U N D N U N E R K A N N T E E R U N S D I E W I R N O C H I M M E R Z U S A M M E N G E D R Ä N G T I N D E M W I N K E L S T A N D E N (M-AILABS_deu_000181-M-AILABS_deu_000181) +D E R S E I N E R S E E L E A N S P O R N E N D D A S E R M U N T E R N D E W O R T V O R W Ä R T S (M-AILABS_deu_000182-M-AILABS_deu_000182) +I C H F R E U E M I C H A U F D E N B E S U C H D E S T U N E S I S C H E N M I N I S T E R P R Ä S I D E N T E N (M-AILABS_deu_000183-M-AILABS_deu_000183) +W A S F Ü R V E R F O L G U N G E N W A S F Ü R N A C H S T E L L U N G E N H A B E I C H N I C H T Z U E R D U L D E N G E H A B T (M-AILABS_deu_000184-M-AILABS_deu_000184) +Z I G E U N E R W A R E N E S D I E V O N O R T Z U O R T F U H R E N E I N K A U M E R W A C H S E N E S J U N G E S D I N G K A M Z U M I R H E R A N G E H Ü P F T U N D B E T T E L T E N E I N (M-AILABS_deu_000185-M-AILABS_deu_000185) +H U C K I C H W E R D E D I C H I N E N E M B O O T H I N F A H R E N W E R D E D A S B O O T D A A N L E G E N U N D E S W I E D E R Z U R Ü C K R U D E R N A L L E S G A N Z A L L E I N B R A U C H S T D I C H G A R N I C H T D R U M Z U K Ü M M E R N (M-AILABS_deu_000186-M-AILABS_deu_000186) +A L S N U R E I N M A L N O C H D E N R A U C H V O N S E I N E M H A U S E A U S D E R F E R N E A U F S T E I G E N Z U S E H E N U M D A N N B E R U H I G T Z U S T E R B E N (M-AILABS_deu_000187-M-AILABS_deu_000187) +D I E T Ä N Z E R I N A B E R L A G A U F D E N K N I E E N V O R B R A H M A S B I L D N I S I N N A M E N L O S E R S E H N S U C H T U N D W E I N T E J A M M E R V O L L (M-AILABS_deu_000188-M-AILABS_deu_000188) +R E C H T F E R T I G T M I C H D E N N D I E W I R K L I C H K E I T N O C H N I C H T A U F D I E I C H M I C H B E R U F E N K A N N (M-AILABS_deu_000189-M-AILABS_deu_000189) +I C H Ä R G E R T E M I C H D A N N W E N N I C H A U F W A C H T E E S W A R S O W U N D E R S C H Ö N G E W E S E N D A S F L I E G E N (M-AILABS_deu_000190-M-AILABS_deu_000190) +N A C H D E M E R S C H O N D E N G A N Z E N V O R M I T T A G M I T I H M V E R B R A C H T K A M S T A N H O P E N A C H T I S C H I N S Q U A N D T S C H E H A U S U M C A S P A R L E B E W O H L Z U S A G E N (M-AILABS_deu_000191-M-AILABS_deu_000191) +E R W A R E I N A L T E R H I R T V O L L M E D I Z I N I S C H E R G E N I N A L I T Ä T (M-AILABS_deu_000192-M-AILABS_deu_000192) +D A S S W O H L A U C H D E R M I E T E R S E I N E W U N D E R L I C H K E I T E N H A B E N M Ü S S E (M-AILABS_deu_000193-M-AILABS_deu_000193) +S I E S A H E N A L L E Ä N G S T L I C H U N D B E T R Ü B T A U S U N D A U C H H E R R A R N E S A S S S C H W E R M Ü T I G D A W I E D I E A N D E R E N U N D S T Ü T Z T E D A S H A U P T I N D I E H A N D (M-AILABS_deu_000194-M-AILABS_deu_000194) +U N T E R D E N D A M E N M E I S T J U N G E F R I S C H E G E S I C H T E R U N T E R D E N H E R R E N N E B E N J U G E N D L I C H E N S O L C H E M I T F A L T I G E R S T I R N U N D B E R E I T S M E H R O D E R M I N D E R M O N D U M G L Ä N Z T E M S C H Ä D E L (M-AILABS_deu_000195-M-AILABS_deu_000195) +S E I T T A G E N S C H O N H A T T E E S B E S O N D E R S D R Ä U E N D G E K L U N G E N (M-AILABS_deu_000196-M-AILABS_deu_000196) +S O N D E R B A R (M-AILABS_deu_000197-M-AILABS_deu_000197) +E R B V O N E R B E N H E I M S T A N D M I T S E I N E R G A T T I N V O L L W E H M U T U N D D A N K B A R K E I T A N D E R G R U F T A U F D E R E R E I N E N M Ä C H T I G E N (M-AILABS_deu_000198-M-AILABS_deu_000198) +I H R W A R J E D E R M E N S C H E I N W U N D E R U N D F A S T A L L E S W A S M E N S C H E N T A T E N E T W A S W U N D E R B A R E S (M-AILABS_deu_000199-M-AILABS_deu_000199) +W E L C H E I H R W E G S I E E N T L Ä N G S T F Ü H R T E (M-AILABS_deu_000200-M-AILABS_deu_000200) +D I E W I R T I N S A S S N I C H T H I N T E R I H R E M S C H A N K T I S C H U N D K E I N E R I H R E R D I E N S T L E U T E B E F A N D S I C H I N D E R S T U B E (M-AILABS_deu_000201-M-AILABS_deu_000201) +A L S D I E H E R R S C H A F T A U S D E R K I R C H E T R A T S T A N D E N D I E L E U T E U M H E R U M S I E V O R B E I G E H E N Z U S E H E N U N D A M K I R C H H O F T H O R E W A R T E T E E I N M A N N (M-AILABS_deu_000202-M-AILABS_deu_000202) +W A S M Ü S S E N W I R T U N U M D E M T E R R O R I S M U S E N T G E G E N Z U T R E T E N (M-AILABS_deu_000203-M-AILABS_deu_000203) +I C H G L A U B E D A S S S I E E S G U T M I T M I R M E I N E N H E R R D O K T O R (M-AILABS_deu_000204-M-AILABS_deu_000204) +D O C H I M A N F A N G G E W A N N E R K E I N E A U F M E R K S A M K E I T F Ü R A N D E R E D I N G E A L S F Ü R D A S E S S E N (M-AILABS_deu_000205-M-AILABS_deu_000205) +D I E S F L Ä S C H C H E N Z O G E R J E T Z T E I L I G H E R V O R W Ä H R E N D J E N E S I C H M I T W A S S E R F Ü L L T E N U N D B O T E S D E R J U N G F E R Z Ü S A N (M-AILABS_deu_000206-M-AILABS_deu_000206) +D E S H A L B W A R E S A U C H R I C H T I G U N D W I C H T I G D A S S C H I N A D O C H J E T Z T A N S P R U C H S V O L L G E S A G T H A T W I R W E R D E N A U C H A N D E N Z E I T P U N K T D E R R E D U K T I O N K O M M E N (M-AILABS_deu_000207-M-AILABS_deu_000207) +N I C H T D O C H M U T T E R W E C K E S I E J E T Z T N O C H N I C H T (M-AILABS_deu_000208-M-AILABS_deu_000208) +J A W I R H A B E N I N D E N L E T Z T E N J A H R E N R E C H T E N G E B E Z I E H U N G E N Z U B R A S I L I E N A U F G E B A U T (M-AILABS_deu_000209-M-AILABS_deu_000209) +S I E W Ü R D E S I C H N I C H T F Ü R A N D E R E O P F E R N (M-AILABS_deu_000210-M-AILABS_deu_000210) +R I E F E N S I E M I R Z U (M-AILABS_deu_000211-M-AILABS_deu_000211) +G O T T W A S S I E I H R E R Z Ä H L T E H Ö R E N S I E N U R E S I S T E I N G A N Z E R R O M A N (M-AILABS_deu_000212-M-AILABS_deu_000212) +S E I N E M U T T E R K A N N I H M N U R F L U S S W A S S E R G E B E N D E S H A L B W E I N T E R (M-AILABS_deu_000213-M-AILABS_deu_000213) +D E R B U N D E W I R T S C H A F T S M I N I S T E R W I R D Z U S A M M E N M I T D E R N E T Z A G E N T U R A M V I E R T E J U N I Z U M E R S T E N M A L P R Ä S E N T I E R E N W I E S I C H D I E N E T Z B E T R E I B E R U N D D I E K R A F T W E R K E D I E N E U E N N E T Z P L Ä N E V O R S T E L L E N (M-AILABS_deu_000214-M-AILABS_deu_000214) +E V A H A T T E S I C H Z I T T E R N D V O R T O D E S S C H W Ä C H E V O N D E M G I T T E R B E F R E I T U N D S U C H T E Z U E N T F L I E H E N A B E R D E R S C H M A L E G A R T E N B O T K E I N E N A U S W E G (M-AILABS_deu_000215-M-AILABS_deu_000215) +O B I C H M E I N W E R K F Ü R H E U T E L I E G E N L A S S E N O D E R N O C H E I N E N A N L A U F N E H M E N U N D E S V O L L E N D E N S O L L T E (M-AILABS_deu_000216-M-AILABS_deu_000216) +E R W A R D A S G Ö T Z C H E N D E R S T U N D E D I E T A I T A I B E A U F T R A G T E M A D A M E A N G E L E D I E A U C H D A S T A N D U N D D I E G E K A U F T E N S E I D E N S T Ü C K E Z U S A M M E N F A L T E T E F Ü R T S C H U N Z U S O R G E N (M-AILABS_deu_000217-M-AILABS_deu_000217) +I C H W E R D E N A C H S E H E N (M-AILABS_deu_000218-M-AILABS_deu_000218) +A B E R T I P P S O D E R V O R G A B E N D A S M A C H E N W I R N A T Ü R L I C H N I C H T (M-AILABS_deu_000219-M-AILABS_deu_000219) +A L S U N S E R E I D E E B E K A N N T W U R D E W A R D I E P H Y S I O G N O M I E D E R W A L T E R S B U R G E R U N G E F Ä H R D I E E I N E S K A L B E S D A S Z U M E R S T E N M A L E D O N N E R N H Ö R T (M-AILABS_deu_000220-M-AILABS_deu_000220) +B I T T E M A C H E N S I E G E F Ä L L I G S T A U F U N D E S K L A N G W I E E I N J A M M E R N D E R H I L F E R U F (M-AILABS_deu_000221-M-AILABS_deu_000221) +H E R R D O K T O R S A G T E E I N E F R A U D I E S C H N U R R G R I N E D I E S O O F T Z U I H N E N K O M M T I S T E I G E N T L I C H G A R N I C H T K R A N K (M-AILABS_deu_000222-M-AILABS_deu_000222) +D I E A L T E E R I N N E R U N G A N D E N F R Ü H E R E N T R A U M T A U C H T E E B E N F A L L S W I E D E R A U F U N D U N W I L L K Ü R L I C H F A S T B E I D E R B E H A U P T U N G D A S S D I E S E E L E D E N K Ö R P E R V E R L A S S E N U N D Z U I H M Z U R Ü C K K E H R E N K Ö N N E S C H I E N E S I H R O R D E N T L I C H (M-AILABS_deu_000223-M-AILABS_deu_000223) +A L S S I E A U F D E N B A L K O N Z U R Ü C K K E H R T E F A N D S I E I H N D I E Z E I T U N G L E S E N D D I E W Ä H R E N D I H R E S F O R T S E I N S A N G E L A N G T W A R (M-AILABS_deu_000224-M-AILABS_deu_000224) +E R W A R E I N K I N D D E R S T R A S S E V O N K L E I N A U F A B E R I N I H M L E B T E V O N J E H E R E I N E G E W I S S E S E H N S U C H T N A C H E I N E R E H R B A R E N B Ü R G E R L I C H E N E X I S T E N Z (M-AILABS_deu_000225-M-AILABS_deu_000225) +U N D W I R A L S B U N D E S R E G I E R U N G F Ü H L E N U N S H I E R N I C H T E I N E R G R U P P E V E R A N T W O R T L I C H S O N D E R N W I R F Ü H L E N U N S D E M G E M E I N W O H L V E R A N T W O R T L I C H (M-AILABS_deu_000226-M-AILABS_deu_000226) +W A S M E I N L I E B E S K I N D W A S K A N N (M-AILABS_deu_000227-M-AILABS_deu_000227) +U N D D A N N W O L L T E I C H D E N A N B L I C K D E R E R N I C H T M I S S E N D I E M I R G E B L I E B E N W A R E N V O R A L L E M A B E R W A R E S M I R D A R U M Z U T U N M E I N E S Ü S S E E L I S A B E T H E I N I G E R M A S S E N G E T R Ö S T E T Z U S E H E N (M-AILABS_deu_000228-M-AILABS_deu_000228) +A B E R I C H G L A U B E D A S S W I R U N S A U C H G E G E N S E I T I G E I N B I S S C H E N U N T E R S T Ü T Z E N K Ö N N E N (M-AILABS_deu_000229-M-AILABS_deu_000229) +S E I N E G E S C H Ä F T L I C H E L A U F B A H N H A B E S T E F E N S O N A L S K Ü C H E N B O Y I N E I N E M H O T E L V I E R T E N G R A D E S B E G O N N E N (M-AILABS_deu_000230-M-AILABS_deu_000230) +V I E L L E I C H T T Ä T E N S I E G U T D I E S E A N S I C H T E N D E S B I S C H O F S N A C H H A U S E Z U M E L D E N S A G T E D E R T A J E N D E R I M M E R M E H R E I N M A N N D E S G E S C H R I E B E N E N W O R T E S W I E D E R T A T (M-AILABS_deu_000231-M-AILABS_deu_000231) +A M A N D E R N M O R G E N E R H O B E R S I C H S P Ä T S C H I C K T E D E N L A K A I E N I N D I E W O H N U N G F E U E R B A C H S U N D L I E S S U M E I N E U N T E R R E D U N G B I T T E N D E R M A N N K A M M I T D E R B O T S C H A F T Z U R Ü C K (M-AILABS_deu_000232-M-AILABS_deu_000232) +N U R E I N W E N I G T R A U R I G W U R D E E S W E N N I M M E R D A S S E L B E K A M W E N N S I E N I E Z U F R I E D E N S C H I E N E N (M-AILABS_deu_000233-M-AILABS_deu_000233) +E I N S O M M E R W A R M E R N O V E M B E R T A G L A G M I T S O N N E N G L I T Z E R N Ü B E R D E R H A U P T S T A D T U N D U N T E R D E N L I N D E N D R Ä N G T E E I N E T A U S E N D K Ö P F I G E M E N S C H E N M E N G E A U F U N D N I E D E R (M-AILABS_deu_000234-M-AILABS_deu_000234) +K O M M M I T M I R M E I N S O H N D E N N I C H B R A U C H E D E I N E L I E B E (M-AILABS_deu_000235-M-AILABS_deu_000235) +N U R S E I N G E S I C H T W U R D E E I N W E N I G N A C H D E N K L I C H E R S O W I E V O N E I N E R E R I N N E R U N G E R H E L L T (M-AILABS_deu_000236-M-AILABS_deu_000236) +D A N N W I R D A U C H W I E D E R D E R I N N O V A T I O N S D R U C K S T E I G E N U N D D A Z U I S T D A S S Y S T E M J A E I N G E F Ü H R T W O R D E N (M-AILABS_deu_000237-M-AILABS_deu_000237) +J E T Z T G E W A H R T E E R M I T E N T S E T Z E N D I E S C H E U S S L I C H E T E U F L I S C H E A F F E N F R A T Z E D I E Ü B E R D E S M Ä D C H E N S S C H U L T E R S C H I E L T E (M-AILABS_deu_000238-M-AILABS_deu_000238) +J A D E R W I R T N I C K T E D A S G E H Ö R T E I N E M G E W I S S E N W U T S C H O W B E R N H A R D W U T S C H O W I S T E T W A S V E R R U F E N (M-AILABS_deu_000239-M-AILABS_deu_000239) +W O L L T I H R I N W A H R H E I T D I E L Ö W E N T Ö T E N U N D K Ö N N T I H R S C H I E S S E N (M-AILABS_deu_000240-M-AILABS_deu_000240) +B A T C E D D I E S E H R R E S P E K T V O L L W O B E I E R N U R E I N I G E S I L B E N V E R S C H L U C K T E W A S I H M B E I D E N B E L I E B T E N L A N G E N W Ö R T E R N D E S Ö F T E R N V O R K A M (M-AILABS_deu_000241-M-AILABS_deu_000241) +L O R D F A U N T L E R O Y W I R D N I C H T S E N T B E H R E N D E S S E N B I N I C H G E W I S S V E R S E T Z T E E R (M-AILABS_deu_000242-M-AILABS_deu_000242) +K A M G L E I C H F A L L S I N S S C H L A F Z I M M E R A U F E I N E N N A G E L I N D E R N Ä H E D E S B E T T E S (M-AILABS_deu_000243-M-AILABS_deu_000243) +U N D D A S I S T D I E C H A N C E D I E I N D I E S E R K R I S E S T E C K T D I E C H A N C E F Ü R I N T E R N A T I O N A L E R E G E L N D I E S I C H A N D E N P R I N Z I P I E N D E R S O Z I A L E N M A R K T W I R T S C H A F T O R I E N T I E R E N (M-AILABS_deu_000244-M-AILABS_deu_000244) +A N F A N G S F I E L D E R R E G E N S C H R Ä G U N D P E I T S C H T E E R S T D I E E I N E U N D D A N N D I E A N D E R E S E I T E D E S W A G E N S (M-AILABS_deu_000245-M-AILABS_deu_000245) +F A S T L E I C H T S I N N I G E N B E M E S S U N G I H R E S W E R T E S A U F Z U G E B E N S I C H E N T S C H L O S S E N H A T T E (M-AILABS_deu_000246-M-AILABS_deu_000246) +D A S H E I S S T D I E F R A G E D E R M E N S C H L I C H E N A R B E I T U N D D I E F R A G E W A S K A N N T E C H N I S C H G E L Ö S T W E R D E N (M-AILABS_deu_000247-M-AILABS_deu_000247) +D I E S A F A R I W A R A U F D I E R E G E L M Ä S S I G B E N U T Z T E N W A S S E R S T E L L E N D I E S E R R O U T E A N G E W I E S E N (M-AILABS_deu_000248-M-AILABS_deu_000248) +D I E B E I D E N M Ü S S T E N H I E R O B E N A U F D E M G I P F E L G E S T A N D E N H A B E N U N D E R S P R A C H D I E A L T E N W O R T E V O R S I C H H I N (M-AILABS_deu_000249-M-AILABS_deu_000249) +E N D L I C H B L I C K T E C E D R I K A U F W E I S S N E W I C K A L L E S V O N D E N A R M E N L E U T E N F R A G T E E R (M-AILABS_deu_000250-M-AILABS_deu_000250) +D A S S E S H E U T E E I N E W U N D E R B A R E Z U S A M M E N A R B E I T Z W I S C H E N B U N D U N D L Ä N D E R N I N D I E S E N F R A G E N G I B T M I T S E H R S E H R I N T E R E S S A N T E N P R O J E K T E N (M-AILABS_deu_000251-M-AILABS_deu_000251) +C A S P A R V E R H A R R T E A N G E W U R Z E L T A N S E I N E M P L A T Z S E I N E G L I E D E R J A S E I N E A U G E N W A R E N W I E V E R S T E I N E R T A L S E R Z U M Z W E I T E N M A L H I N B L I C K T E (M-AILABS_deu_000252-M-AILABS_deu_000252) +E I N I G E Z E I T D A N A C H F R A G T E E R M I C H O B I C H G L A U B E D A S S D E R E I S G A N G D E N S C H L I T T E N D E S A N D E R E N Z E R S T Ö R T H A B E (M-AILABS_deu_000253-M-AILABS_deu_000253) +A B E R N U N B L O S S N I C H T I N E I N E S C H O C K S T A R R E V E R F A L L E N (cv_deu_000698-cv_deu_000698) +J A I C H K O M M E J A S C H O N (cv_deu_000699-cv_deu_000699) +N E B E N B E I A R B E I T E T E E R A L S A U S H I L F S K R A F T A U F E I N E R F A R M (cv_deu_000700-cv_deu_000700) +E I N T E R R I T O R I A L G R Ö S S E R E S E U R O P A W I R D N I C H T M I T E I N E M E T A T M Ä S S I G K L E I N E R E N E U R O P A E R R E I C H T (cv_deu_000701-cv_deu_000701) +I H R S O H N K A M D U R C H K Ü N S T L I C H E B E F R U C H T U N G Z U R W E L T (cv_deu_000702-cv_deu_000702) +D I E N A C H T A K T I V E N F A L T E R F L I E G E N V O N M I T T E J U L I B I S M I T T E O K T O B E R (cv_deu_000703-cv_deu_000703) +A C H T (cv_deu_000704-cv_deu_000704) +F Ü N F (cv_deu_000705-cv_deu_000705) +N U T Z E R K Ö N N E N I H R E L E S E Z E I C H E N O N L I N E A B S P E I C H E R N V E R W A L T E N U N D M I T A N D E R E N N U T Z E R N T E I L E N (cv_deu_000706-cv_deu_000706) +D I E D O N B O S C O K A T H (cv_deu_000707-cv_deu_000707) +S A U L B A S S Z Ä H L T Z U D E N I N N O V A T I V S T E N D E S I G N E R N U N D F I L M E M A C H E R N S E I N E R Z E I T (cv_deu_000708-cv_deu_000708) +I N G R Ü N Ü B E R S I L B E R N E M W E L L E N B A L K E N E I N E S I L B E R N E E I C H E (cv_deu_000709-cv_deu_000709) +W E I T E R E W I C H T I G E I N D U S T R I E Z W E I G E S I N D D I E M I K R O M E C H A N I K G A L V A N O P L A S T I K M E T A L L B A U U N D D I E H O L Z V E R A R B E I T U N G (cv_deu_000710-cv_deu_000710) +Ü B E R D E N A U T O R I S T N I C H T S B E K A N N T V E R M U T L I C H S T A M M T E E R A U S D E M D E U T S C H E N S P R A C H G E B I E T (cv_deu_000711-cv_deu_000711) +M A N S T E U E R T E S M I T E I N E M D O P P E L P A D D E L (cv_deu_000712-cv_deu_000712) +W I R H A B E N E I N P R O B L E M A U F O S I S C H I C H T A C H T (cv_deu_000713-cv_deu_000713) +W I R S P I E L E N I M M E R N O C H A B E R D A S L E B E N A U F T O U R I S T D E R Z E I T N I C H T M A C H B A R (cv_deu_000714-cv_deu_000714) +H E U T E Z E I G T S I C H D E R G R Ö S S T E T E I L D E R A N L A G E A L S E N G L I S C H E R G A R T E N (cv_deu_000715-cv_deu_000715) +S E I N E R E S I D E N Z N A H M E R I N M Ü N C H E N W O E R A U C H S T A R B (cv_deu_000716-cv_deu_000716) +I N N E R E R U N D Ä U S S E R E R N A R T H E X K Ö N N E N A L S G E T R E N N T E T E I L E E I N E S N A R T H E X A U C H G E M E I N S A M V O R K O M M E N (cv_deu_000717-cv_deu_000717) +D A B E I B E L E G T E E R D I E P L Ä T Z E V I E R U N D D R E I (cv_deu_000718-cv_deu_000718) +K I M D A R B Y I S T D I E T O C H T E R Z W E I E R P R O F E S S I O N E L L E R T Ä N Z E R (cv_deu_000719-cv_deu_000719) +I C H G L A U B E D A S F Ü H R T N I C H T I N D I E R I C H T I G E R I C H T U N G (cv_deu_000720-cv_deu_000720) +D A S I S T E I N E E X T R E M S C H L E C H T E R I C H T L I N I E (cv_deu_000721-cv_deu_000721) +H E R R L U R C H E N T B L Ö S S T E S E I N H A G E R E S G E S I C H T (cv_deu_000722-cv_deu_000722) +N U R C A R M E N F I N D E T D A S U N F A I R (cv_deu_000723-cv_deu_000723) +I N G E B O R G K R A B B E H A T T E D R E I G E S C H W I S T E R (cv_deu_000724-cv_deu_000724) +E S K O M M T W I R K L I C H D A R A U F A N D A S S S O L C H E D A T E N A U F D I E S E R E B E N E E R F A S S T W E R D E N (cv_deu_000725-cv_deu_000725) +S T R U M M I N G H I N G E G E N E R G I B T E I N H A R M O N I S C H E S P U L S I E R E N (cv_deu_000726-cv_deu_000726) +B I N I C H Z U M K A U F E I N E R H Y P O T H E K B E R E C H T I G T (cv_deu_000727-cv_deu_000727) +T E H E R A N I S T D I E H A U P T S T A D T V O M I R A N (cv_deu_000728-cv_deu_000728) +K O H L E N H Y D R A T E S I N D B E S S E R A L S I H R R U F (cv_deu_000729-cv_deu_000729) +O H N E D I E P R O F E S S I O N E L L E U N T E R S T Ü T Z U N G D E R M A S E R A T I R E N N A B T E I L U N G W A R E N D I E S E W A G E N D E R K O N K U R R E N Z N U N D O C H U N T E R L E G E N (cv_deu_000730-cv_deu_000730) +S I E D I E N T E Z U N Ä C H S T A L S U N T E R K U N F T F Ü R B E L G I S C H E B E S A T Z U N G S T R U P P E N (cv_deu_000731-cv_deu_000731) +D A M Ü S S E N W I R S P R E N G E N M E I N T E D E R Z A H N A R Z T (cv_deu_000732-cv_deu_000732) +A U S S E R D E M S P I E L T E E R B E I M N A C H F O L G E T E A M N E W M A R K E T R O Y A L S S O W I E B E I M L I G A K O N K U R R E N T E N L O N D O N K N I G H T S (cv_deu_000733-cv_deu_000733) +W I E A U C H D A S I N S T A N T R U N O F F V O T I N G E R F Ü L L T D I E C O O M B S W A H L D A S C O N D O R C E T K R I T E R I U M N I C H T (cv_deu_000734-cv_deu_000734) +S M I T H W U C H S I N C H I C A G O A U F (cv_deu_000735-cv_deu_000735) +W I R S I N D H I E R A L L E I N (cv_deu_000736-cv_deu_000736) +D U M M I S T W E R E T W A S W E I S S A B E R T R O T Z D E S B E S S E R E N W I S S E N S F A L S C H H A N D E L T (cv_deu_000737-cv_deu_000737) +H A U P T T H E M A D E R S H O W I S T D I E R E V A N C H E F Ü R Ü B L E S T R E I C H E U N T E R F R E U N D E N (cv_deu_000738-cv_deu_000738) +G L E I C H Z E I T I G W U R D E N S P O R T W E T T E N T E I L W E I S E V E R B O T E N (cv_deu_000739-cv_deu_000739) +S I E B E N (cv_deu_000740-cv_deu_000740) +J A (cv_deu_000741-cv_deu_000741) +Z U D E M V E R S A H E R I M K L O S T E R L A N G E J A H R E D I E Ä M T E R D E S N O V I Z E N M E I S T E R S U N D P R I O R (cv_deu_000742-cv_deu_000742) +H E I D E N H A I N E N T S T A M M T E E I N E R Ä R Z T E F A M I L I E (cv_deu_000743-cv_deu_000743) +A C H T (cv_deu_000744-cv_deu_000744) +Z W E I (cv_deu_000745-cv_deu_000745) +E B E N F A L L S I N A U G G E N A N G E S I E D E L T S I N D D I E K E L T E R E I D E R F A (cv_deu_000746-cv_deu_000746) +D I E S E S T E H T A U C H F Ü R A B S O L V E N T E N E I N H E I M I S C H E R S C H U L E N M I T D E U T S C H K E N N T N I S S E N O F F E N (cv_deu_000747-cv_deu_000747) +A L S O I C H H Ö R E N I C H T S (cv_deu_000748-cv_deu_000748) +W I E K A N N M A N S I C H S C H Ü T Z E N (cv_deu_000749-cv_deu_000749) +N A C H F Ü N F M O N A T E N L A G E I N E E M P F I N D L I C H E R E P L A T T E A L S D I E B I S D A H I N E R H Ä L T L I C H E N V O R (cv_deu_000750-cv_deu_000750) +Z I E L I S T E S D I E Ü B E R E I N S T I M M U N G E I N E S S O F T W A R E S Y S T E M S M I T S E I N E R S P E Z I F I K A T I O N Z U Ü B E R P R Ü F E N (cv_deu_000751-cv_deu_000751) +M I T E I N E M W A R M E N G E T R Ä N K I M B A U C H L Ä S S T S I C H D I E K Ä L T E B E S S E R A U S H A L T E N (cv_deu_000752-cv_deu_000752) +D I E A N T I V I R E N S O F T W A R E I S T A M O K G E L A U F E N U N D H A T A L L E C O M P U T E R I M H A U S L A H M G E L E G T (cv_deu_000753-cv_deu_000753) +I H R E K L O A K E I S T I N D I E S E R Z E I T K U G E L F Ö R M I G (cv_deu_000754-cv_deu_000754) +D I E S T R E C K E B E G I N N T I M S Ü D E N V E R O N A S U N D F Ü H R T D U R C H D I E P O E B E N E R I C H T U N G S Ü D O S T E N (cv_deu_000755-cv_deu_000755) +E R S T V O N D O R T K O N N T E E R S E I N E N W E G F R E I F O R T S E T Z E N (cv_deu_000756-cv_deu_000756) +S I E E R H E B T S I C H H E U T E I M M E R N O C H G U T E R K E N N B A R A U S D E M S C H W E M M L A N D H E R A U S (cv_deu_000757-cv_deu_000757) +D I E K A N A R I S C H E N I N S E L N G E H Ö R E N Z U S P A N I E N (cv_deu_000758-cv_deu_000758) +W I S S E N S C H A F T L E R H A B E N D I E S E M U T A T I O N B I S H E R N U R B E I F R A U E N B E O B A C H T E T (cv_deu_000759-cv_deu_000759) +S E I N E G E S C H Ä F T S B E Z I E H U N G E N R E I C H T E N B I S N O R D A M E R I K A U N D A S I E N (cv_deu_000760-cv_deu_000760) +Z A H L R E I C H E V O R D E R E P L A T Z I E R U N G E N B E I D E U T S C H E N E U R O P A U N D W E L T M E I S T E R S C H A F T E N S O W I E O L Y M P I S C H E N S P I E L E N F O L G T E N (cv_deu_000761-cv_deu_000761) +I N E I N E R T A G E S Z E I T U N G B L Ä T T E R N D S I T Z T S I E G F R I E D A U F E I N E R P A R K B A N K (cv_deu_000762-cv_deu_000762) +M I T E I N E M W A R M E N G E T R Ä N K I M B A U C H L Ä S S T S I C H D I E K Ä L T E B E S S E R A U S H A L T E N (cv_deu_000763-cv_deu_000763) +F O L G E D E M Q U E R V E R W E I S (cv_deu_000764-cv_deu_000764) +O S T E R N I S T I M M E R E I N E W O C H E N A C H D E M E R S T E N V O L L M O N D I M F R Ü H L I N G (cv_deu_000765-cv_deu_000765) +I M M I T T E L A L T E R H A T T E N W E C H S E L N D E H E R R S C H A F T E N D A S D O R F I N N E (cv_deu_000766-cv_deu_000766) +D E N N A M E N G H I B L I T R A G E N A U C H W E I T E R E F A H R Z E U G E V O N M A S E R A T I (cv_deu_000767-cv_deu_000767) +D U K A N N S T M I T D E M B U S N A C H F R A N K F U R T A N D E R O D E R F A H R E N (cv_deu_000768-cv_deu_000768) +M I R D O C H E G A L (cv_deu_000769-cv_deu_000769) +A L L E R D I N G S E R G A B E N W E I T E R E P R Ü F U N G E N D A S S E S M I T T E L F R I S T I G K E I N E N B E D A R F F Ü R E I N E S O L C H E A U T O B A H N G Ä B E (cv_deu_000770-cv_deu_000770) +U M G E K E H R T K A N N E I N F R E I B R I E F E I N E A U S S C H R E I B U N G A L S V O G E L F R E I G E M E I N T S E I N (cv_deu_000771-cv_deu_000771) +B I Z A R R G R O T E S K E A B S C H N I T T E Z E I G E N E I N F L Ü S S E D U R C H S C H O S T A K O W I T S C H (cv_deu_000772-cv_deu_000772) +E R W A R E I N E R D E R P I O N I E R E A U F D E M G E B I E T D E R N U T Z U N G D E R S O N N E N E N E R G I E (cv_deu_000773-cv_deu_000773) +A U C H W E N N M I R D I E K U N D E N A U F D I E N E R V E N G E H E N M U S S I C H H Ö F L I C H K E I T B E W A H R E N (cv_deu_000774-cv_deu_000774) +D I E S P Ü L M A S C H I N E I S T F E R T I G (cv_deu_000775-cv_deu_000775) +I N D E R A R C H A I S C H E N P E R I O D E W U R D E N E R S T E F O R M E N D E S A C K E R B A U S E N T W I C K E L T (cv_deu_000776-cv_deu_000776) +D I E K O M Ö D I E S E I B E S S E R A L S D E R E R S T E F I L M (cv_deu_000777-cv_deu_000777) +A K T U E L L G I L T F O L G E N D E R M O D U S (cv_deu_000778-cv_deu_000778) +D A M I T E N D E T E I N E E R F O L G R E I C H E I N T E R N A T I O N A L E B I L D U N G S A R B E I T V O R A L L E M I M M U S I S C H K U L T U R E L L E N B E R E I C H (cv_deu_000779-cv_deu_000779) +D E R S O H N E I N E S B E R G M A N N S B E G A N N S E I N E F U S S B A L L K A R R I E R E B E I D E N S P O R T F R E U N D E N W A N N E E I C K E L (cv_deu_000780-cv_deu_000780) +I N D I E S E M J A H R G A B E S S I E B E N N U M M E R E I N S S I N G L E S U N D S E C H S U N D D R E I S S I G N U M M E R E I N S A L B E N (cv_deu_000781-cv_deu_000781) +N O R D W E S T L I C H V O N H A C K H A U S E N B E F I N D E T S I C H D I E O R T S C H A F T H A C K E N B R O I C H (cv_deu_000782-cv_deu_000782) +I M O R T G N A R R E N B U R G G I N G E N V I E L E S O Z I A L E E I N R I C H T U N G E N V O N H E R M A N N L A M P R E C H T U N D D E R M A R I E N H Ü T T E A U S (cv_deu_000783-cv_deu_000783) +I C H W E R D E F O L G L I C H D E N R A T Ü B E R D I E I M P A R L A M E N T V O R G E T R A G E N E N B E D E N K E N I N F O R M I E R E N (cv_deu_000784-cv_deu_000784) +E S W Ä R E T R A U R I G G E W E S E N E I N S O W I C H T I G E S T H E M A N I C H T I M K O N S E N S V E R A B S C H I E D E N Z U K Ö N N E N (cv_deu_000785-cv_deu_000785) +N A C H D E S S E N T O D I M G L E I C H E N J A H R K A M E S K U R Z F R I S T I G A N A N D E R E B E S I T Z E R (cv_deu_000786-cv_deu_000786) +K U R Z D A N A C H G A B E S E I N E N W E R B E S P O T M I T D E M C A N C A N V O N J A C Q U E S O F F E N B A C H (cv_deu_000787-cv_deu_000787) +D A S I S T B E S S E R (cv_deu_000788-cv_deu_000788) +W I E S I E H T E S M I T G L E I T Z E I T A U S (cv_deu_000789-cv_deu_000789) +N A H E D E M D O R F B E F I N D E T S I C H A U C H D E R G R A N D C A N Y O N N A T I O N A L P A R K A I R P O R T (cv_deu_000790-cv_deu_000790) +S I E S O L L E N V E R K Ü N D E N D A S S D I E L I E B E D E N T O D B E S I E G T H A T (cv_deu_000791-cv_deu_000791) +B E D E C K T I S T D I E R E P R Ä S E N T A T I V G E S T A L T E T E V I L L A M I T E I N E M M A N S A R D D A C H (cv_deu_000792-cv_deu_000792) +D I E S E S I E D L U N G I S T M I T D E R O R T S C H A F T D E L L A C H Z U S A M M E N G E W A C H S E N (cv_deu_000793-cv_deu_000793) +W A R T I H R S C H O N E I N M A L I N D E M C L U B (cv_deu_000794-cv_deu_000794) +W O R A U C H I S T I S T A U C H F E U E R (cv_deu_000795-cv_deu_000795) +D I R E K T V O N D E R S T R A S S E W U R D E N S I E V O N A L F R E D B I O L E K F Ü R S E I N E F E R N S E H S H O W S H O W B Ü H N E E N G A G I E R T (cv_deu_000796-cv_deu_000796) +E I N J A H R S P Ä T E R W E C H S E L T E E R Z U H E A L T H N E T U N D E R W U R D E E R F O L G R E I C H E R (cv_deu_000797-cv_deu_000797) +I N D E R L A N D W I R T S C H A F T K A N N D E R E R T R A G D E U T L I C H R E D U Z I E R T W E R D E N (cv_deu_000798-cv_deu_000798) +M A N S O U R S P I E L T E I N S E I N E R H E I M A T S T A D T K A I R O F Ü R A L A H L Y (cv_deu_000799-cv_deu_000799) +E R T R A T D E R F R E I M A U R E R L O G E L A U T A R O B E I (cv_deu_000800-cv_deu_000800) +M I T „ F Ü R S T “ W A R E H E R D I E S O Z I A L E A L S D I E R E C H T L I C H E R O L L E D E S S O B E Z E I C H N E T E N G E M E I N T (cv_deu_000801-cv_deu_000801) +L E T Z T E W O C H E G A B D A S M E T I B E K A N N T D A S S E S V O N A P P L E Ü B E R 3 4 W E I T E R E V O R F Ä L L E V O N Ü B E R H I T Z U N G I N F O R M I E R T W O R D E N W A R D I E D A S U N T E R N E H M E N A L S N I C H T S C H W E R W I E G E N D B E Z E I C H N E T E (fleurs_deu_000378-fleurs_deu_000378) +U S A G Y M N A S T I C S U N T E R S T Ü T Z T D E N B R I E F D E S O L Y M P I S C H E N K O M I T E E S D E R V E R E I N I G T E N S T A A T E N U N D A K Z E P T I E R T E S A L S A B S O L U T E N O T W E N D I G K E I T D A S S S I C H D I E O L Y M P I S C H E F A M I L I E F Ü R E I N S I C H E R E S U M F E L D F Ü R A L L E U N S E R E S P O R T L E R E I N S E T Z T (fleurs_deu_000379-fleurs_deu_000379) +D A D U R C H K A N N E R A B W Ä R T S K O M P A T I B E L M I T 8 0 2 1 1 A 8 0 2 1 1 B U N D 8 0 2 1 1 G S E I N V O R A U S G E S E T Z T D I E B A S I S S T A T I O N V E R F Ü G T Ü B E R D U A L R A D I O (fleurs_deu_000380-fleurs_deu_000380) +E R B E Z E I C H N E T E D I E G E R Ü C H T E A L S P O L I T I S C H E S G E S C H W Ä T Z U N D A L B E R N H E I T (fleurs_deu_000381-fleurs_deu_000381) +L E T Z T E W O C H E G A B D A S M E T I B E K A N N T D A S S E S V O N A P P L E Ü B E R 3 4 W E I T E R E V O R F Ä L L E V O N Ü B E R H I T Z U N G I N F O R M I E R T W O R D E N W A R D I E D A S U N T E R N E H M E N A L S N I C H T S C H W E R W I E G E N D B E Z E I C H N E T E (fleurs_deu_000382-fleurs_deu_000382) +N A C H D E M D E R D A M M 1 9 6 3 E R B A U T W O R D E N W A R K A M E N D I E J A H R E S Z E I T L I C H E N Ü B E R F L U T U N G E N D I E S E D I M E N T E I M F L U S S V E R T E I L E N Z U M S T I L L S T A N D (fleurs_deu_000383-fleurs_deu_000383) +E R W A R A U C H A M S T E C H E N V O N G E L D S C H E I N E N F Ü R V I E L E L Ä N D E R B E T E I L I G T A K T U E L L E B E I S P I E L E S E I N E R A R B E I T S C H L I E S S E N D I E P R E M I E R M I N I S T E R P O R T R A I T S A U F D E R V O R D E R S E I T E D E R K A N A D I S C H E N 5 U N D 1 0 0 D O L L A R N O T E N E I N (fleurs_deu_000384-fleurs_deu_000384) +D I E H A U P T S T A D T V O N M O L D A W I E N I S T K I S C H I N A U D I E E I N H E I M I S C H E S P R A C H E I S T R U M Ä N I S C H A B E R V I E L E M E N S C H E N S P R E C H E N A U C H R U S S I S C H (fleurs_deu_000385-fleurs_deu_000385) +Z W I S C H E N D E N E I N Z E L N E N D Y N A S T I E N H E R R S C H T E N A U C H U N B E S T Ä N D I G E Z E I T E N G E T E I L T E R P R O V I N Z E N D I E B E K A N N T E S T E D I E S E R P E R I O D E N W A R D I E E P O C H E D E R D R E I K Ö N I G R E I C H E D I E 6 0 J A H R E L A N G Z W I S C H E N D E R H A N U N D D E R J I N D Y N A S T I E S T A T T F A N D (fleurs_deu_000386-fleurs_deu_000386) +A M A N D E R E N E N D E D E S S P E K T R U M S V E R W A N D E L T M A N S I C H I N E I N N I C H T W I E D E R Z U E R K E N N E N D E S I N D I V I D U U M D A S A L L E S A N D E R S M A C H E N M U S S A L S D A S T E A M E S G E M A C H T H A T U N D S I C H A L L E S Z U E I G E N M A C H T (fleurs_deu_000387-fleurs_deu_000387) +D I E M E I S T E N I N T E R P R E T A T I O N E N D E S T E C H N O L O G I S C H E N D E T E R M I N I S M U S T E I L E N Z W E I A L L G E M E I N E V O R S T E L L U N G E N E I N E R S E I T S D A S S D I E E N T W I C K L U N G D E R T E C H N O L O G I E S E L B S T E I N E M W E G F O L G T D E R W E I T G E H E N D J E N S E I T S K U L T U R E L L E R O D E R P O L I T I S C H E R E I N F L U S S N A H M E L I E G T U N D A N D E R E R S E I T S D A S S T E C H N O L O G I E I H R E R S E I T S A U S W I R K U N G E N A U F G E S E L L S C H A F T E N H A T D I E E H E R I N H Ä R E N T A L S S O Z I A L B E D I N G T S I N D (fleurs_deu_000388-fleurs_deu_000388) +Z W I S C H E N D E N E I N Z E L N E N D Y N A S T I E N H E R R S C H T E N A U C H U N B E S T Ä N D I G E Z E I T E N G E T E I L T E R P R O V I N Z E N D I E B E K A N N T E S T E D I E S E R P E R I O D E N W A R D I E E P O C H E D E R D R E I K Ö N I G R E I C H E D I E 6 0 J A H R E L A N G Z W I S C H E N D E R H A N U N D D E R J I N D Y N A S T I E S T A T T F A N D (fleurs_deu_000389-fleurs_deu_000389) +D E M L E A K Z U F O L G E B E Z I E H T S I C H D A S D O K U M E N T A U F D E N G R E N Z S T R E I T I N D E M D I E P A L Ä S T I N E N S E R E I N Z U R Ü C K S E T Z E N D E R G R E N Z E N I N D E N Z U S T A N D V O R D E M S E C H S T A G E K R I E G V O N 1 9 6 7 F O R D E R N (fleurs_deu_000390-fleurs_deu_000390) +M I T D E M V E R L U S T G R I E C H I S C H E R S P R A C H K E N N T N I S S E W A R D E R W E S T E N V O N S E I N E N P H I L O S O P H I S C H E N U N D W I S S E N S C H A F T L I C H E N W U R Z E L N I N G R I E C H E N L A N D A B G E S C H N I T T E N (fleurs_deu_000391-fleurs_deu_000391) +W I R S T I M M E N M I T D E R A U S S A G E D E S U S O C Ü B E R E I N D A S S D E N I N T E R E S S E N U N S E R E R A T H L E T E N U N D V E R E I N E U N D I H R E S S P O R T S B E S S E R G E D I E N T I S T W E N N W I R I N N E R H A L B U N S E R E R O R G A N I S A T I O N S I N N V O L L E V E R Ä N D E R U N G E N V O R A N T R E I B E N A N S T A T T E I N E D E Z E R T I F I Z I E R U N G V O R Z U N E H M E N (fleurs_deu_000392-fleurs_deu_000392) +D I E K R E U Z F A H R T E N N A C H S A N K T P E T E R S B U R G B I E T E N A U C H Z E I T F Ü R E I N E N A U F E N T H A L T I N D E R S T A D T K R E U Z F A H R T P A S S A G I E R E S I N D V O N D E R V I S U M P F L I C H T B E F R E I T S I E H E B E D I N G U N G E N (fleurs_deu_000393-fleurs_deu_000393) +R E I S E N D E W E R D E N D R I N G E N D G E W A R N T A U F J E D W E D E A R T V O N U N W E T T E R Z U A C H T E N D I E I H R G E B I E T B E T R I F F T D A D I E S S I C H A U F A L L E R E I S E P L Ä N E A U S W I R K E N K A N N (fleurs_deu_000394-fleurs_deu_000394) +S I E B E S A G T D A S S D E R K R E U Z U N G S P U N K T D E R L I N I E N D I E E I N B I L D V E R T I K A L U N D H O R I Z O N T A L D R I T T E L N D E R E F F E K T I V S T E P L A T Z F Ü R D A S H A U P T M O T I V I S T S I E H E B E I S P I E L (fleurs_deu_000395-fleurs_deu_000395) +S E I T 1 9 8 8 M Ü S S E N W A H L U R N E N T R A N S P A R E N T S E I N D A M I T W Ä H L E R U N D B E O B A C H T E R B E Z E U G E N K Ö N N E N D A S S Z U B E G I N N D E R W A H L K E I N E U M S C H L Ä G E V O R H A N D E N S I N D U N D D A S S K E I N E U M S C H L Ä G E E I N G E W O R F E N W E R D E N A U S S E R J E N E D E R O R D N U N G S G E M Ä S S G E Z Ä H L T E N U N D A U T O R I S I E R T E N W Ä H L E R (fleurs_deu_000396-fleurs_deu_000396) +O T T A W A I S T K A N A D A S B E Z A U B E R N D E Z W E I S P R A C H I G E H A U P T S T A D T U N D Z E I C H N E T S I C H D U R C H E I N E R E I H E V O N K U N S T G A L E R I E N U N D M U S E E N A U S D I E K A N A D A S V E R G A N G E N H E I T U N D G E G E N W A R T P R Ä S E N T I E R E N (fleurs_deu_000397-fleurs_deu_000397) +D I E S E P A A R E K Ö N N E N S I C H F Ü R E I N E N A D O P T I O N S P L A N F Ü R I H R B A B Y E N T S C H E I D E N (fleurs_deu_000398-fleurs_deu_000398) +I N F O L G E D E S S E N S I N D Z W E I F I S C H A R T E N A U S G E S T O R B E N U N D Z W E I W E I T E R E S I N D V O M A U S S T E R B E N B E D R O H T D A R U N T E R D E R G I L A C Y P H A (fleurs_deu_000399-fleurs_deu_000399) +P F L A N Z E N S E H E N I N I H R E R N A T Ü R L I C H E N U M G E B U N G A M B E S T E N A U S W I D E R S T E H E N S I E A L S O D E R V E R S U C H U N G A U C H N U R E I N E X E M P L A R Z U E N T F E R N E N (fleurs_deu_000400-fleurs_deu_000400) +A U F D E R N A H S E I T E K Ö N N T E E S M E H R M A R I A G E B E N D A D I E K R U S T E D Ü N N E R I S T E S W A R E I N F A C H E R F Ü R D I E L A V A A N D I E O B E R F L Ä C H E A U F Z U S T E I G E N (fleurs_deu_000401-fleurs_deu_000401) +E R F Ü G T E H I N Z U D A S S S I E J E D O C H N I C H T D A Z U A U F G E F O R D E R T W E R D E N S O L L T E N V E R P F L I C H T U N G E N E I N Z U G E H E N D I E Ü B E R I H R E N E N T W I C K L U N G S S T A N D I H R E V E R A N T W O R T U N G U N D I H R E F Ä H I G K E I T E N H I N A U S G E H E N (fleurs_deu_000402-fleurs_deu_000402) +V I R T U E L L E H I L F E S T E L L U N G E N S I N D I N D I E S O F T W A R E E I N G E B A U T U N D S O L L E N A R B E I T S S C H R I T T E D I E D E R S C H Ü L E R A L L E I N M Ö G L I C H E R W E I S E N I C H T B E W Ä L T I G E N K A N N H I N T E R F R A G E N N A H E L E G E N U N D E R K L Ä R E N (fleurs_deu_000403-fleurs_deu_000403) +A M 1 5 A U G U S T 1 9 4 0 F I E L E N D I E A L L I I E R T E N I N S Ü D F R A N K R E I C H E I N D I E I N V A S I O N W U R D E O P E R A T I O N D R A G O O N G E N A N N T (fleurs_deu_000404-fleurs_deu_000404) +E R G R I F F A U C H A L L E S A N W A S I N S W A S S E R K A M S E L B S T E I N G R O S S E R D I N O S A U R I E R W I E D E R T R E X W A R I H M N I C H T G E W A C H S E N (fleurs_deu_000405-fleurs_deu_000405) +S E I T D E R G R Ü N D U N G V O N A S U N C I Ó N 1 5 3 7 I S T E S P A R A G U A Y G E L U N G E N V I E L V O N S E I N E M I N D I G E N E N C H A R A K T E R U N D S E I N E R I D E N T I T Ä T Z U B E W A H R E N (fleurs_deu_000406-fleurs_deu_000406) +T R O T Z D E M I S T D E R A N T E I L A N X D R T B I N D E R G E S A M T E N G R U P P E D E R L E U T E M I T T U B E R K U L O S E O F F E N B A R D E N N O C H G E R I N G 6 0 0 0 D E R I N S G E S A M T 3 3 0 0 0 0 L E U T E D I E I N S Ü D A F R I K A Z U E I N E M B E S T I M M T E N Z E I T P U N K T A N G E S T E C K T S I N D (fleurs_deu_000407-fleurs_deu_000407) +A N G E L 2 0 0 6 E R L Ä U T E R T D A S K O N T I N U U M K O N Z E P T A L S E I N E M E T H O D E U M O R G A N I S A T I O N E N Z U H E L F E N L E I S T U N G S F Ä H I G E R Z U W E R D E N (fleurs_deu_000408-fleurs_deu_000408) +I N D I E S E R P E R I O D E D E R E U R O P Ä I S C H E N G E S C H I C H T E S T A N D D I E R E I C H U N D M Ä C H T I G G E W O R D E N E K A T H O L I S C H E K I R C H E A U F D E M P R Ü F S T A N D (fleurs_deu_000409-fleurs_deu_000409) +D I E E R S T E D E R 7 8 E M P F E H L U N G E N I S T D A S S E I N E N E U E D I P L O M A T I S C H E I N I T I A T I V E V O R E N D E D I E S E S J A H R E S E R G R I F F E N W E R D E N S O L L T E U M D I E I R A K I S C H E N G R E N Z E N G E G E N Ü B E R F E I N D L I C H E N I N T E R V E N T I O N E N Z U S I C H E R N U N D D I P L O M A T I S C H E B E Z I E H U N G E N M I T S E I N E N N A C H B A R N W I E D E R H E R Z U S T E L L E N (fleurs_deu_000410-fleurs_deu_000410) +D I E S B I E T E T E I N E G U T E G E L E G E N H E I T D A S N O R D L I C H T Z U S E H E N D A D E R H I M M E L M E H R O D E R W E N I G E R R U N D U M D I E U H R D U N K E L I S T (fleurs_deu_000411-fleurs_deu_000411) +P R O F E S S O R I N P A M E L A F E R G U S O N V O N D E R U N I V E R S I T Y O F D U N D E E M E R K T A N J O U R N A L I S T E N S C H E I N E N E I N E G E F Ä H R L I C H E G R E N Z E Z U Ü B E R S C H R E I T E N W E N N S I E F O T O S U S W V O N V E R D Ä C H T I G E N V E R Ö F F E N T L I C H E N (fleurs_deu_000412-fleurs_deu_000412) +E S K A N N S I C H A U C H L O H N E N E I N E W I L D C A R D Z U K A U F E N D I E Z U T R I T T E N T W E D E R Z U A U S G E W Ä H L T E N P A R K S I N S Ü D A F R I K A O D E R Z U A L L E N S Ü D A F R I K A N I S C H E N N A T I O N A L P A R K S G E W Ä H R T (fleurs_deu_000413-fleurs_deu_000413) +D I E B R Ü C K E S O L L I M S E P T E M B E R 2 0 1 7 V O L L S T Ä N D I G D E N B E T R I E B A U F N E H M E N E S W I R D E R W A R T E T D A S S D I E B R A S I L I A N I S C H E N Z O L L K O N T R O L L P U N K T E D A N N F E R T I G G E S T E L L T S E I N W E R D E N (fleurs_deu_000414-fleurs_deu_000414) +W Ä H R E N D E I N E X P E R I M E N T E L L E R I M P F S T O F F I N D E R L A G E Z U S E I N S C H E I N T D I E E B O L A M O R T A L I T Ä T Z U S E N K E N G I B T E S B I S H E R K E I N E M E D I K A M E N T E D I E A L S E I N D E U T I G Z U R B E H A N D L U N G B E S T E H E N D E R I N F E K T I O N E N G E E I G N E T N A C H G E W I E S E N W U R D E N (fleurs_deu_000415-fleurs_deu_000415) +E I N Ä U S S E R S T L E B H A F T E R D E P E S C H E N W E C H S E L F A N D S T A T T M A N E R W O G D E N P L A N E I N E N A L L G E M E I N E N S T A A T E N K O N G R E S S Z U B E R U F E N U N D K O N N T E S I C H V O R L Ä U F I G N U R N O C H N I C H T Ü B E R D A S V O R Z U L E G E N D E P R O G R A M M U N D D E N O R T D E S Z U S A M M E N T R I T T S E I N I G E N (mls_deu_000281-mls_deu_000281) +E R W U S S T E N I C H T W A S I H M D A S L E B E N K O S T B A R E S G E R A U B T H A T T E S P A N N K R A F T U N D M U T D A S S E S I H N F E I G U N D S C H E U G E M A C H T H A T T E U N F Ä H I G Z U D E N H O H E N D I N G E N Z U D E N E N U N G E T R Ü B T E M I T F R E U D E G E H Ö R T (mls_deu_000282-mls_deu_000282) +D I E S E R J U N G E M A N N H I E S S K A C K E R L I T Z C H E N U N D B E F A N D S I C H G E R A D E A U F D E R W A N D E R S C H A F T A L S I N D E M G E N A N N T E N K Ö N I G R E I C H D I E B E K A N N T M A C H U N G W E G E N D E R P R I N Z E S S I N V E R L E S E N W U R D E E I S A G T E D E R S C H N E I D E R W E N N E S W E I T E R N I C H T S I S T E I N W E I B H A B I C H N O C H N I C H T G E K Ü S S T U N D D E S K Ö N I G S E I D A M Z U W E R D E N D A S G E L Ü S T E T M I C H A L L E R D I N G S (mls_deu_000283-mls_deu_000283) +N O C H F Ü N F M I N U T E N U N D D I E W O L K E N D E R B E W U S S T L O S I G K E I T B E G A N N E N Z U S C H W I N D E N J E T Z T W U S S T E I C H S E H R W O H L D A S S I C H I N M E I N E M E I G E N E N B E T T E L A G U N D D A S S D I E R O T E G L U T N I C H T S A N D E R E S W A R A L S D A S F E U E R I M K A M I N D E R K I N D E R S T U B E E S W A R N A C H T E I N E K E R Z E B R A N N T E A U F D E M T I S C H E (mls_deu_000284-mls_deu_000284) +W E L C H E D I E S E V E R D R Ä N G U N G E N W I E W Ä C H T E R U N T E R H A L T E N K O M M T D A N N I M P U B E R T Ä T S A L T E R D I E H O C H F L U T D E R S E X U E L L E N B E D Ü R F T I G K E I T S O F I N D E T S I E A N D E N G E N A N N T E N S E E L I S C H E N R E A K T I O N S O D E R W I D E R S T A N D S B I L D U N G E N D Ä M M E (mls_deu_000285-mls_deu_000285) +A B E R A F F E N G E H Ö R E N B E I H A G E N B E C K A N D I E K I S T E N W A N D N U N S O H Ö R T E I C H A U F A F F E Z U S E I N E I N K L A R E R S C H Ö N E R G E D A N K E N G A N G D E N I C H I R G E N D W I E M I T D E M B A U C H A U S G E H E C K T H A B E N M U S S D E N N A F F E N D E N K E N M I T (mls_deu_000286-mls_deu_000286) +I S T E S D A S P O R T R Ä T E I N E S M E N S C H E N D E N S I E K E N N E N F R A G T E E L I Z A W E L C H E U N B E M E R K T A N M I C H H E R A N G E T R E T E N W A R I C H E N T G E G N E T E D A S S E S N U R E I N P H A N T A S I E K O P F S E I U N D S C H O B D I E Z E I C H N U N G E I L I G U N T E R D I E A N D E R N B L Ä T T E R N A T Ü R L I C H S P R A C H I C H D I E U N W A H R H E I T D E N N E S W A R E I N S E H R G E T R E U E S P O R T R Ä T M R R O C H E S T E R S (mls_deu_000287-mls_deu_000287) +I C H W E I S S D A S S I C H S E H R K R A N K B I N S A G T E S I E N A C H E I N E R W E I L E V O R E I N P A A R M I N U T E N V E R S U C H T E I C H M I C H I M B E T T E U M Z U D R E H E N U N D F Ü H L T E D A S S I C H K E I N G L I E D M E H R R Ü H R E N K A N N E S W Ä R E G U T W E N N I C H M E I N G E M Ü T E R L E I C H T E R N K Ö N N T E B E V O R I C H S T E R B E (mls_deu_000288-mls_deu_000288) +S O A B E R I S T Z W A R U N S E R W E S E N S G R U N D G O T T S E L B E R D A H E R U M H A T S I C H J E D O C H D E R S C H L A N G E N K N Ä U E L D E S A L T E N S A T A N G E S C H L U N G E N U N D Ü B E R D E M F Ü N K C H E N D E R L I E B E I S T D I E F I N S T E R N I S D E S H A S S E S G E L A G E R T W A S W U N D E R D A N N (mls_deu_000289-mls_deu_000289) +B E S S I E W Ä R E L I E B E R G E B L I E B E N A B E R S I E W A R G E Z W U N G E N Z U G E H E N W E I L D I E P Ü N K T L I C H K E I T B E I D E N M A H L Z E I T E N E I N E S A C H E W A R A U F W E L C H E I N G A T E S H E A D H A L L S T R E N G E G E H A L T E N W U R D E (mls_deu_000290-mls_deu_000290) +A U G E N B L I C K L I C H F Ü H L T E W I E I H R E A N S I C H T E N Ü B E R M I C H I H R E E M P F I N D U N G E N F Ü R M I C H N I C H T U M E I N A T O M V E R Ä N D E R T W A R E N Ü B E R H A U P T K E I N E R Ä N D E R U N G F Ä H I G W A R E N I C H S A H E S I H R E M V E R S T E I N E R T E N A U G E W E L C H E S N I E M A L S D U R C H T R Ä N E N G E N E T Z T N I E M A L S I N Z Ä R T L I C H K E I T A U F G E L E U C H T E T H A T T E A N (mls_deu_000291-mls_deu_000291) +B R U D E R S A M I S T S E H R G U T W E N N D E R H Ä U P T L I N G I H N E R F Ä H R T W I R D E R S I C H F R E U E N U N D W I R W E R D E N S C H N E L L D A N A C H H A N D E L N S O W O L L E N W I R A U F B R E C H E N U N D S C H N E L L R E I T E N D A M I T W I R N O C H V O R N A C H T D A S L A G E R E R R E I C H E N W I R S T I E G E N A U F D I E P F E R D E D I E N U N A U S G E R U H T H A T T E N U N D F L O G E N I M G A L O P P D A V O N D I E S M A L H Ü T E T E N W I R U N S D E R F Ä H R T E W I E D E R D I R E K T Z U F O L G E N W I R R I T T E N G E R A D E A U S U N D E R S P A R T E N U N S (mls_deu_000292-mls_deu_000292) +W E I L D I E A B E R M I T P E C H B E S T R I C H E N W A R B L I E B E I N E R V O N D E N G O L D E N E N P A N T O F F E L N F E S T H Ä N G E N U N D I N D E R A N G S T D A C H T E S N I C H T D A R A N I H N M I T Z U N E H M E N U N D W I E E S D E N L E T Z T E N S C H R I T T V O N D E R T R E P P E T A T D A H A T T E E S Z W Ö L F A U S G E S C H L A G E N D A W A R W A G E N U N D P F E R D E V E R S C H W U N D E N U N D A S C H E N P U T T E L S T A N D I N S E I N E N A S C H E N K L E I D E R N A U F D E R D U N K E L N S T R A S S E (mls_deu_000293-mls_deu_000293) +I L L N A H M D A S G L A S V O M A U G E E I N F I N S T E R E R E R N S T L A G E R T E Ü B E R S E I N E N Z Ü G E N E S I S T S C H R E C K L I C H S A G T E E R I C H H A B D A S M E I N I G E G E T A N U M B L U T V E R G I E S S E N Z U V E R M E I D E N (mls_deu_000294-mls_deu_000294) +N U R D E R D O K T O R U N D D I E W Ä R T E R I N S O L L E N V O R S E I N E A U G E N K O M M E N E R K L Ä R T E D I E T R I N E I N G R O S S E M A M T S E I F E R D A M I T W A R D I E F R A U O B E R S T G A N Z E I N V E R S T A N D E N U N D H Ö C H S T E R F R E U T K E H R T E S I E M I T I H R E N (mls_deu_000295-mls_deu_000295) +K W A R U N T R Ö S T L I C H Ü B E R D I E L A G E D E S K Ü N S T L E R S E R B E G A N N Z U W E I N E N U N D S C H L U C H Z T E L A N G E I N D I E V O R G E H A L T E N E N H Ä N D E D E R K Ü N S T L E R W A R T E T E B I S K S I C H B E R U H I G T H A T T E U N D E N T S C H L O S S S I C H D A N N D A E R K E I N E N A N D E R E N A U S W E G F A N D D E N N O C H Z U M W E I T E R S C H R E I B E N (mls_deu_000296-mls_deu_000296) +V O N D E N P F E R D E H E R D E N D E R A P A C H E N U N D S A G T E N U N S D A S S S I E F Ü R E I N A P A C H E N P F E R D U N S E B E N S O V I E L E W A R E N U N D B R A N D Y G E B E N W Ü R D E N W I E F Ü R E I N K I O W A P F E R D D A S I N D U N S E R E K R I E G E R F O R T U M A P A C H E N P F E R D E Z U H O L E N A L S O R I C H T I G W E R W A R S C H U L D A N D E M T O D E D E R B I S H E R G E F A L L E N E N U N D A N D E M B L U T V E R G I E S S E N W E L C H E S N U N B E V O R S T A N D W E I S S E P F E R D E H Ä N D L E R (mls_deu_000297-mls_deu_000297) +D A S A M A Z O N E N H Ü T C H E N V O N S C H W A R Z E M S A M M E T G R A Z I Ö S A U F I H R E L A N G E N L O C K E N G E D R Ü C K T D I E I H R E W A N G E N U M F L O S S E N U N D Ü B E R I H R E S C H U L T E R N H E R A B W A L L T E N S O T R A T S I E I N D A S E I N F A C H E L Ä N D L I C H E G E B Ä U D E U N D S C H W E B T E Z W I S C H E N D E N R E I H E N D E R H A L B G E B L E N D E T E N D O R F K I N D E R A U F U N D A B (mls_deu_000298-mls_deu_000298) +D U M U S S T E R S T E N T S A G E N A L L E M S Ü N D H A F T E N S T R E B E N U N D I N T I E F E R R E U E U N D D E M U T D I E F Ü R B I T T E D E R H E I L I G E N E R F L E H E N G E G E N D I E D U G E F R E V E L T H A S T D I E J Ü N G L I N G E W E L C H E F R A N C E S K O S O L A N G E G E F L O H E N S U C H T E N I H N A U F I N S E I N E R W E R K S T A T T U N D F A N D E N I H N (mls_deu_000299-mls_deu_000299) +E R L I E S S S E I N E G R E T E L N I C H T F O R T S C H L E P P E N A M A L L E R W E N I G S T E N A B E R I N D E N G R O S S E N V O G E L B A U E R W O S I E A L L E I N E I N E M T O N E P F E I F E N M U S S T E N W I E E R S T E T S S A G T E (mls_deu_000300-mls_deu_000300) +F R A N C E S K O M A L T E I N U N H E I L I G E R B E G E I S T E R U N G V I E L E B I L D E R A U S D E R L Ü G E N H A F T E N F A B E L W E L T K E I N E R A L S E R V E R M O C H T E D I E B U H L E R I S C H E Ü P P I G K E I T D E R W E I B L I C H E N G E S T A L T E N S O W A H R H A F T D A R Z U S T E L L E N I N D E M E R V O N L E B E N D E N M O D E L L E N D I E K A R N A T I O N V O N D E N A L T E N M A R M O R B I L D E R N A B E R F O R M U N D B I L D U N G E N T N A H M (mls_deu_000301-mls_deu_000301) +B E W E G U N G U N D T A T D E N E R S T E N Z U G J A E S S T I M M T E D I E V O R H I N A N G E G E B E N E N I N G R E D I E N Z I E N N Ä M L I C H R Ü B E N H A N F E I C H E L N U N D S A U E R A M P F E R W A R E N A L L E I N D E M P F E I F E N K O P F E A N W E S E N D A B E R E I N E N F Ü N F T E N H A U P T S T O F F H A T T E I C H N I C H T G E N A N N T J E T Z T R O C H U N D S C H M E C K T E I C H D A S S A U C H E I N S T Ü C K C H E N F I L Z S C H U H D A B E I S E I N M Ü S S E I C H B L I E S D E N R A U C H A U C H G E G E N D E N H I M M E L U N D G E G E N D I E (mls_deu_000302-mls_deu_000302) +U N D D A S F E U E R S T A N D A U F U N D F L A C K E R T E U N D K O C H T E D A S E S S E N F E R T I G U N D D E R B R A T E N B R U T Z E L T E F O R T U N D D E R K O C H G A B D E M K Ü C H E N J U N G E N E I N E O H R F E I G E U N D D I E M A G D R U P F T E D A S H U H N F E R T I G D A W A R D D I E H O C H Z E I T V O N D E M K Ö N I G S S O H N M I T D O R N R Ö S C H E N G E F E I E R T U N D S I E L E B T E N V E R G N Ü G T B I S A N I H R E N D E (mls_deu_000303-mls_deu_000303) +U N D D A S S E R M I R N I C H T N A C H T R A G E N W O L L E W E N N I C H W I D E R S P E N S T I G W A R G E G E N S E I N E N W O H L M E I N E N D E N R A T D E R H E R R P F A R R E R H A T J A I N A L L E M R E C H T G E H A B T U N D I C H W A R I M U N R E C H T A B E R (mls_deu_000304-mls_deu_000304) +O B G L E I C H S E I N E M A S S E N U R W E N I G E G R A M M B E T R U G E R B R E I T E T E S I C H K E G E L F Ö R M I G A U S U N D M U S S T E D A H E R D A S I H M E N T G E G E N F L I E G E N D E S P R E N G G E S C H O S S A U F F A N G E N U N D Z U R R U H E B R I N G E N (mls_deu_000305-mls_deu_000305) +D E R F U C H S R E I C H T E S A M D I E U N F R I E D L I C H E F R I E D E N S P F E I F E H I N D E R M A N N T A T W A C K E R S E I N E S E C H S Z Ü G E U N D S A G T E D E R G R O S S E G E I S T A C H T E T N I C H T A U F D I E V E R S C H I E D E N E H A U T D E R M E N S C H E N D E N N D I E K Ö N N E N S I C H M I T F A R B E B E S C H M I E R E N U M I H N Z U T Ä U S C H E N S O N D E R N E R S I E H T D A S H E R Z A N D I E H E R Z E N D E R K R I E G E R V O M B E R Ü H M T E N S T A M M E D E R K I O W A S S I N D T A P F E R U N E R S C H R O C K E N U N D T R E U D A S M E I N I G E H Ä N G T (mls_deu_000306-mls_deu_000306) +A L L E S W A S W I R M I T I H R B E G E G N E T S C H I E B T S I C H D U R C H U N D Ü B E R E I N A N D E R B A L D U N T E R S C H R E I B E N W I R E I N E N K O N T R A K T D A I S T I H R E H A N D U N D D I E M E I N I G E I H R N A M E U N D D E R M E I N I G E B E I D E L Ö S C H E N E I N A N D E R A U S B E I D E V E R S C H L I N G E N S I C H (mls_deu_000307-mls_deu_000307) +E R M Ü S S T E D E N E I N F A C H E N C H R O N I K E N C H O R A L D E S M A L E R S M I T A L L E R L E I E R K L Ä R U N G E N U N D Z U R E C H T W E I S U N G E N W I E M I T K R A U S E N F I G U R E N V E R S C H N Ö R K E L N U N D V E R B R Ä M E N I C H T R E T E I N D I E P E R S O N D E S H E R A U S G E B E R S U N D B I T T E D I C H G Ü N S T I G E R L E S E R D U W O L L E S T E H E D U W E I T E R L I E S E S T F O L G E N D E S D I R G Ü T I G S T M E R K E N (mls_deu_000308-mls_deu_000308) +D I E H O F D A M E N B E K A M E N K R Ä M P F E U N D D I E K Ö N I G I N U N D D I E P R I N Z E S S I N N E N D I E I H R E A L L E R L I E B S T E N H Ü N D C H E N W Ä H R E N D D E R M A H L Z E I T A U F D E N S C H O S S G E N O M M E N H A T T E N B E M E R K T E N Z U I H R E M S C H R E C K E N D A S S D I E L I L A A M A R A N T F A R B E N E N U N D O R A N G E G E L B E N S E I D E N K L E I D E R A L L E D I C H T B E S Ä T M I T D E N H Ä S S L I C H S T E N Ö L F L E C K E N W A R E N (mls_deu_000309-mls_deu_000309) +V O N L I E D E R N D I E S I E S I N G E N U N D K L A V I E R P I E C E N D I E S I E S P I E L E N V O N G E L D B Ö R S E N D I E S I E H Ä K E L N V O N F R A N Z Ö S I S C H E N B Ü C H E R N D I E S I E Ü B E R S E T Z E N K O N N T E B I S M E I N G E M Ü T W Ä H R E N D I C H L A U S C H T E Z U R N A C H A H M U N G A U F G E S T A C H E L T W U R D E (mls_deu_000310-mls_deu_000310) +A R M E U N D N A C K E N W A R E N B L O S S I H R E I N Z I G E R S C H M U C K W A R E N I H R E K A S T A N I E N B R A U N E N F L E C H T E N W E L C H E I N W I L D E R U N D N A T Ü R L I C H E R A N M U T A U F I H R E S C H U L T E R N H E R A B F I E L E N I C H N A H M E I N E N B O G E N F E I N E N K A R T O N S U N D Z E I C H N E T E M I T G R O S S E R S O R G F A L T D I E U M R I S S E (mls_deu_000311-mls_deu_000311) +A B E R W E D E R A U S D E U T S C H L A N D N O C H A U S I R G E N D E I N E M A N D E R E N S T A A T K O N N T E M A N E R F A H R E N W A S D E R G E G E N S T A N D U N D D A S R E S U L T A T D I E S E R U N T E R R E D U N G E N G E W E S E N S E I M A N V E R M U T E T E D A S S E S S I C H U M E R K L Ä R U N G E N D E R M A R T I E R Ü B E R I H R E A B S I C H T E N U N D U M D I E V E R M I T T L U N G D E R M Ä C H T E Z W I S C H E N D E N M A R S S T A A T E N U N D G R O S S B R I T A N N I E N H A N D L E (mls_deu_000312-mls_deu_000312) +L A S S U N S W E N I G S T E N S E I N E Z E I T L A N G V E R S U C H E N I N W I E F E R N W I R A U F D I E S E W E I S E M I T E I N A N D E R A U S R E I C H E N D A D A S Z U S A M M E N H Ä N G E N D E W I E D U S A G S T E I G E N T L I C H E U E R E L E M E N T I S T V E R S E T Z T E (mls_deu_000313-mls_deu_000313) +V E R S C H I E D E N E V O R K O M M N I S S E F Ü H R T E N Z U D E R V E R M U T U N G D A S S F R A U W I E S E D I E K L E I N E N W E S E N V E R B R E N N E S I E S O L L B I S W E I L E N S O S T A R K G E H E I Z T H A B E N D A S S D I E H E R D P L A T T E N Z E R S P R A N G E N A U S S E R D E M S O L L E I N F Ü R C H T E R L I C H E R G E R U C H W A H R G E N O M M E N W O R D E N S E I N (mls_deu_000314-mls_deu_000314) +U N D G I N G D E M S C H R E I E N N A C H S O S A H E R E N D L I C H E I N E N H O H E N B A U M U N D O B E N D A R A U F S A S S E I N K L E I N E S K I N D U N T E R D E M B A U M A B E R L A G E I N E F R A U D I E S C H L I E F (mls_deu_000315-mls_deu_000315) +S I E H A T T E N S O E B E N D I E F I S C H E R G A R N E W E L C H E D I E N A C H T Ü B E R A U S G E W O R F E N W A R E N H E R E I N G E Z O G E N D I E S E E L E U T E G E H Ö R T E N A U G E N S C H E I N L I C H V E R S C H I E D E N E N N A T I O N E N A N O B W O H L D E R E U R O P Ä I S C H E C H A R A K T E R B E I A L L E N A U S G E D R Ü C K T W A R (mls_deu_000316-mls_deu_000316) +N E I N N E I N I C H S C H Ä M E M I C H L A S S M I C H A N D E I N E M B U S E N M E I N G E S I C H T V E R B E R G E N E R S I N K T I N S G R A S N I E D E R U N D Z I E H T S I E N A C H (mls_deu_000317-mls_deu_000317) +D I E K I N D E R A B E R S A S S E N V O R D E M W A L D U N D A L S S I E D I E D R E I K N E C H T E V O N W E I T E M L A U F E N S A H E N S P R A C H L E H N C H E N Z U M F U N D E V O G E L V E R L Ä S S T D U M I C H N I C H T S O V E R L A S S I C H D I C H A U C H N I C H T S O S P R A C H F U N D E V O G E L N U N U N D N I M M E R M E H R (mls_deu_000318-mls_deu_000318) +W I E D E R S C H U L Z E I N S E I N E R H U L D I G U N G S R E D E H E R V O R H O B D E R L E H R E R B R A C H T E A M K L A R E N S O M M E R M O R G E N M I T S E I N E N S C H U L K I N D E R N E I N G E S A N G S S T Ä N D C H E N (mls_deu_000319-mls_deu_000319) +W I E S I E S E I N S O L L T E N (swc_deu_001408-swc_deu_001408) +D E R E N S C H W I N G U N G E N D U R C H E I N E Z U S A T Z S C H A L T U N G S T U F E N L O S (swc_deu_001409-swc_deu_001409) +D I E A U F A L L E B E I D E R S I T Z V E R T E I L U N G Z U (swc_deu_001410-swc_deu_001410) +U M D E N Ü B E R L E B E N D E N D E R (swc_deu_001411-swc_deu_001411) +S P Ä T E R W U R D E N T E I L W E I S E S O G A R A C H T P A R A L L E L E L O C H S T R E I F E N E I N G E S E T Z T (swc_deu_001412-swc_deu_001412) +M O R D E B E K A N N T U N D V E R L A N G T E (swc_deu_001413-swc_deu_001413) +B W A H L G D I E S T I M M E N V O N W Ä H L E R N (swc_deu_001414-swc_deu_001414) +G E S C H I C H T E (swc_deu_001415-swc_deu_001415) +S P A L T U N G F Ä H I G (swc_deu_001416-swc_deu_001416) +S T A D T P A D E R B O R N D I E Ä U S S E R E N F E I E R N D E S (swc_deu_001417-swc_deu_001417) +W E I T E R H I N H U M A N I T Ä R E H I L F E Z U (swc_deu_001418-swc_deu_001418) +S I E E R K A N N T E N D I E N E U E C H I N E S I S C H E R E G I E R U N G N I C H T A N (swc_deu_001419-swc_deu_001419) +D I E U R A U F F Ü H R U N G F A N D A M D R E I U N D Z W A N Z I G S T E S E P T E M B E R Z W E I T A U S E N D A C H T I N (swc_deu_001420-swc_deu_001420) +E R W I L L S I C H N I C H T S C H U L D I G O D E R M I T S C H U L D I G M A C H E N A M T O D E E I N E S M I T G E S E L L E N (swc_deu_001421-swc_deu_001421) +D I E M I T D E R E R S T S T I M M E E I N E N (swc_deu_001422-swc_deu_001422) +U N D H A L F E N D I E S E N B E I D E R (swc_deu_001423-swc_deu_001423) +K R E I S W A H L V O R S C H L A G U N D E I N E L A N D E S L I S T E U N T E R Z E I C H N E N (swc_deu_001424-swc_deu_001424) +E I N E U M S E T Z U N G D E R S A G E I N F O R M E I N E S F Ü N F Z E H N T E I L I G E N L I E D E R Z Y K L U S Z W E I T A U S E N D A C H T W U R D E P R E U S S L E R S K R A B A T I N E I N E R B E A R B E I T U N G V O N H O R S T H A W E M A N N (swc_deu_001425-swc_deu_001425) +W I E D I E F O L G E N D E T A B E L L E D A R S T E L L T (swc_deu_001426-swc_deu_001426) +Z U M S T R O M F L U S S B E I (swc_deu_001427-swc_deu_001427) +D E M B U N D E S W A H L L E I T E R B I S Z U M S I E B E N U N D N E U N Z I G S T E T A G (swc_deu_001428-swc_deu_001428) +V O L L J Ä H R I G G E W O R D E N E D E U T S C H E N I C H T M I T W Ä H L E N (swc_deu_001429-swc_deu_001429) +A U S F Ü H R U N G M U S S E I N G U T E R Q U A R T E R B A C K I N (swc_deu_001430-swc_deu_001430) +V E R G L E I C H B A R E N Z A H L E N W E R T U M G E W A N D E L T (swc_deu_001431-swc_deu_001431) +B E T R A C H T E T E A L L G E M E I N H E I T (swc_deu_001432-swc_deu_001432) +U N T E R S C H I E D L I C H E A U F F A S S U N G E N G A B E S N U R D A R Ü B E R (swc_deu_001433-swc_deu_001433) +D O L L B E I M B U N D E S L I G I S T E N B O R U S S I A D O R T M U N D N A C H F O L G E R D E S U N M I T T E L B A R Z U V O R Z U R Ü C K G E T R E T E N E N T R A I N E R S J Ü R G E N R Ö B E R (swc_deu_001434-swc_deu_001434) +N E U N Z E H N H U N D E R T A C H T U N D A C H T Z I G (swc_deu_001435-swc_deu_001435) +F R E I E N E N Z Y K L O P Ä D I E (swc_deu_001436-swc_deu_001436) +D E R P H O T O S T R O M I S T Ü B E R V I E L E G R Ö S S E N O R D N U N G E N L I N E A R Z U M L I C H T E I N F A L L (swc_deu_001437-swc_deu_001437) +D A S H A T T E F Ü R K L E I N E P A R T E I E N G R O S S E A U S W I R K U N G E N (swc_deu_001438-swc_deu_001438) +I S T D I E I T E R A T I V E T I E F E N S U C H E (swc_deu_001439-swc_deu_001439) +D I E S K Ö N N E N Z U M B E I S P I E L K O N D E N S A T O R E N S E I N (swc_deu_001440-swc_deu_001440) +A L S D I E K U R S A U F K U B A H A L T E N D E N S O W J E T I S C H E N S C H I F F E A B D R E H T E N (swc_deu_001441-swc_deu_001441) +B U N D E S T A G S W A H L N E U N Z E H N H U N D E R T D R E I U N D F Ü N F Z I G W U R D E E R S T M A L S N A C H E I N E M V O M B U N D E S T A G S E L B S T E R L A S S E N E N G E S E T Z (swc_deu_001442-swc_deu_001442) +B U N D E S W A H L G E S E T Z V I E L F A C H G E Ä N D E R T W O R D E N (swc_deu_001443-swc_deu_001443) +E R Ü B E R L A G E R T D E N P H O T O S T R O M U N D T R Ä G T (swc_deu_001444-swc_deu_001444) +T R O T Z I N T E G R A T I O N D E R B E I D E N D E U T S C H E N S T A A T E N (swc_deu_001445-swc_deu_001445) +B E R L I N E R W Ü H L M Ä U S E N S T A T T (swc_deu_001446-swc_deu_001446) +O F F I Z I E L L E F Ü H R U N G E N (swc_deu_001447-swc_deu_001447) +B E I D E R V E R H Ä L T N I S W A H L W I R D Z U S Ä T Z L I C H D I E E I N H A L T U N G D E R (swc_deu_001448-swc_deu_001448) +W I E W E N I G D I E I N S U L A N E R N O C H A M P U L S D E R Z E I T (swc_deu_001449-swc_deu_001449) +J E D O C H E T W A D I E D U R C H F Ü H R U N G V O N W A H L W E R B U N G A U F K O S T E N D E S S T A A T E S (swc_deu_001450-swc_deu_001450) +D A S N I C H T I M G R U N D G E S E T Z (swc_deu_001451-swc_deu_001451) +H E I M A T V E R T R I E B E N U N D H Ä U S L I C H E G E W A L T (swc_deu_001452-swc_deu_001452) +U N D S P E I C H E R E I H N I N E I N E R W A R T E S C H L A N G E A B (swc_deu_001453-swc_deu_001453) +O R I G I N A L T O N B Ä N D E R U N D D I E D O K U M E N T A T I O N D E S S T U D I O S W U R D E N N E U N Z E H N H U N D E R T Z W E I U N D S I E B Z I G I N D A S S I E M E N S A R C H I V Ü B E R S T E L L T (swc_deu_001454-swc_deu_001454) +S O M Ü S S E N A U F E I N E M S T R A T E G I S C H E N R A K E T E N U B O O T (swc_deu_001455-swc_deu_001455) +F L Ö T E N S P I E L Ä H N L I C H E (swc_deu_001456-swc_deu_001456) +D R A S T I S C H M O D E R N E E L E K T R O N I S C H E K L A N G G E S T A L T U N G (swc_deu_001457-swc_deu_001457) +A N S C H L I E S S E N D W U R D E N D I E S O E R M I T T E L T E M A N D A T S Z A H L J E D E R P A R T E I N A C H D E M S E L B E N V E R F A H R E N E N T S P R E C H E N D D E R A N Z A H L I H R E R Z W E I T S T I M M E N P R O P O R T I O N A L A U F D I E L A N D E S L I S T E N D E R P A R T E I U N T E R V E R T E I L T (swc_deu_001458-swc_deu_001458) +O P F E R N D E R N A T O B O M B A R D I E R U N G U N T E R K Ü N F T E (swc_deu_001459-swc_deu_001459) +D E R F R E I E N E N Z Y K L O P Ä D I E (swc_deu_001460-swc_deu_001460) +M I T T L E R W E I L E F I N D E N (swc_deu_001461-swc_deu_001461) +W E R W E G E N E I N E S V E R B R E C H E N S R E C H T S K R Ä F T I G Z U E I N E R F R E I H E I T S S T R A F E V O N M I N D E S T E N S E I N E M (swc_deu_001462-swc_deu_001462) +D E R G E S C H W I N D I G K E I T S W E R T U N G E R R A N G E N D R E I B F E I N H U N D E R T A C H T (swc_deu_001463-swc_deu_001463) +L I B O R I U S A M E R S T E N L I B O R I S A M S T A G (swc_deu_001464-swc_deu_001464) +N A C H D E M S A I N T E L A G U Ë V E R F A H R E N A U F D I E L Ä N D E R V E R T E I L T (swc_deu_001465-swc_deu_001465) +R E F O R M E N G O R B A T S C H O W S U N D A B R Ü S T U N G S S C H R I T T E (swc_deu_001466-swc_deu_001466) +N U L L U N P O R T E D U N D U N T E R D E R (swc_deu_001467-swc_deu_001467) +A N D E M W E S T L I C H E K R Ä F T E A U F G E G E N R E V O L U T I O N Ä R E R (swc_deu_001468-swc_deu_001468) +W I R D U N T E R A N D E R E M V E R W E N D E T (swc_deu_001469-swc_deu_001469) +A U S W I K I P E D I A (swc_deu_001470-swc_deu_001470) +U N D K U B A K R I S E (swc_deu_001471-swc_deu_001471) +L E T Z T E R W A H L A U F G R U N D E I G E N E R W A H L V O R S C H L Ä G E U N U N T E R B R O C H E N M I T M I N D E S T E N S F Ü N F A B G E O R D N E T E N V E R T R E T E N S I N D (swc_deu_001472-swc_deu_001472) +V E R B R E I T U N G I D E O L O G I S C H E R P R O P A G A N D A D E R S U P E R M Ä C H T E U N D (swc_deu_001473-swc_deu_001473) +W E B C O M I C S A U F D I E R E A L I T Ä T Ü B E R T R A G E N (swc_deu_001474-swc_deu_001474) +A L S D E R K A L T E K R I E G S I C H F O R T W Ä H R E N D Z U S P I T Z E (swc_deu_001475-swc_deu_001475) +S I C H E R H E I T S P E R S O N A L O D E R W A C H H U N D E N N U R S E H R S C H W I E R I G B E T R E T E N W E R D E N (swc_deu_001476-swc_deu_001476) +D A U E R H A F T E S B L E I B E R E C H T U N D (swc_deu_001477-swc_deu_001477) +E B E N S O W I E D A S M O T I V D E R E R L Ö S U N G D U R C H (swc_deu_001478-swc_deu_001478) +W E N N F Ü R N I E M A N D E N N A C H P R Ü F B A R I S T (swc_deu_001479-swc_deu_001479) +P R I V A T E E R F O R S C H U N G V O N E I N R I C H T U N G E N (swc_deu_001480-swc_deu_001480) +A B G E S E H E N D A V O N W Ü R D E N S E L B S T D A N N N O C H D I E E N T S P R E C H E N D E N P A L C O D E S F E H L E N (swc_deu_001481-swc_deu_001481) +S P R E C H E N B E N Ö T I G T E A T E M L U F T L I E F E R T (swc_deu_001482-swc_deu_001482) +M Ö G L I C H E N S C H U T Z I M P F U N G E N G E G E N K R A N K H E I T E N (swc_deu_001483-swc_deu_001483) +S C H O N E I N E N Ä H N L I C H E N V E R S U C H G A B (swc_deu_001484-swc_deu_001484) +A N E I N E M P N Ü B E R G A N G O D E R P I N Ü B E R G A N G D U R C H D E N I N N E R E N P H O T O E F F E K T I N E I N E N E L E K T R I S C H E N S T R O M U M W A N D E L T (swc_deu_001485-swc_deu_001485) +B E I M M E I S T E R I N D E R S I L V E S T E R N A C H T F R E I B I T T E N (swc_deu_001486-swc_deu_001486) +J A H R E N D E R B E G R I F F V A D D I N G (swc_deu_001487-swc_deu_001487) +R A N G V E R H Ä L T N I S U N T E R D E N S T I M M E N N O C H E I N E L O G I S C H E A B F O L G E (swc_deu_001488-swc_deu_001488) +K R A B A T L E H N T D I E S E S A N G E B O T J E D O C H M I T E N T S C H I E D E N H E I T A B (swc_deu_001489-swc_deu_001489) +S T A N D V O M D E R I N H A L T S T E H T U N T E R (swc_deu_001490-swc_deu_001490) +O R G A N I S A T I O N U N T E R B R A C H D A R A U F H I N D I E (swc_deu_001491-swc_deu_001491) +V E R B Ü N D E T S I N D O D E R G A R F Ü R S I E A R B E I T E N (swc_deu_001492-swc_deu_001492) +F E S T G E L E G T E V O L L J Ä H R I G K E I T S A L T E R (swc_deu_001493-swc_deu_001493) +D I E E R R I C H T U N G D E R B E R L I N E R M A U E R M Ü N D E T E N (swc_deu_001494-swc_deu_001494) +E R R I C H T U N G V O N K L Ä R A N L A G E N (swc_deu_001495-swc_deu_001495) +A F G H A N I S T A N S U N D I M I R A K H A T S I C H S E I T D E M E I N M A R S C H (swc_deu_001496-swc_deu_001496) +D E R P H O N A T I O N S S T R O M V O N D E N L U N G E N Ü B E R D I E B R O N C H I E N B I S (swc_deu_001497-swc_deu_001497) +A U S S E R D E M N A H M E N S E N D E R H Ö R S P I E L E M I T V E R F R E M D E T E R S P R A C H E (swc_deu_001498-swc_deu_001498) +U N D D I E G R U N D M A N D A T S K L A U S E L (swc_deu_001499-swc_deu_001499) +K E I N E A B K E H R V O N D E N G R U N D L A G E N D E S S O Z I A L I S M U S E I N S C H L I E S S E (swc_deu_001500-swc_deu_001500) +M I T K O M P O N E N T E N S O W O H L A N A L S A U C H T I E F I N D E R W A F F E (swc_deu_001501-swc_deu_001501) +B E D E U T U N G S V O L L W A R (swc_deu_001502-swc_deu_001502) +F R E I W I L L I G E H E L F E R D E R (swc_deu_001503-swc_deu_001503) +U M E L E K T R O N E N V O M V A L E N Z B A N D I N S L E I T U N G S B A N D (swc_deu_001504-swc_deu_001504) +A L L E R D I N G S S I N D V E R G L E I C H B A R E E F F E K T E M Ö G L I C H (swc_deu_001505-swc_deu_001505) +D I E S E K O N N T E N A B E R A L S E I N G A B E I N E I N E N F R E Q U E N Z U M S E T Z E R D I E N E N O D E R S T E U E R T E N S Y N C H R O N M O T O R E N (swc_deu_001506-swc_deu_001506) +T H O M A S H E R M A N N S P R O D U Z I E R T E Z W E I T A U S E N D Z W E I M I T G R E B E (swc_deu_001507-swc_deu_001507) +P N Ü B E R G A N G T R E F F E N (swc_deu_001508-swc_deu_001508) +D I E F A L K E N H O R S T S H O W (swc_deu_001509-swc_deu_001509) +A N T I S O W J E T I S C H E D E M O N S T R A T I O N E N W U R D E N B L U T I G N I E D E R G E S C H L A G E N (swc_deu_001510-swc_deu_001510) +E I N V I E R K A N A L M I S C H P U L T D I E N T E F Ü R K L E I N E R E (swc_deu_001511-swc_deu_001511) +D I E S E H Ä T T E N D I E V O R W A R N Z E I T E N F Ü R E I N E N A N G R I F F A U F D I E U S A E X T R E M H E R A B G E S E T Z T (swc_deu_001512-swc_deu_001512) +W E L C H E S A M N Ä C H S T E N Z U M S T A R T K N O T E N L I E G T (swc_deu_001513-swc_deu_001513) +L A Z I O G I N G D O L L Z U R Ü C K I N D I E B U N D E S L I G A U N D W E C H S E L T E Z U E I N T R A C H T (swc_deu_001514-swc_deu_001514) +Ü B E R D I E S E K R A N K H E I T (swc_deu_001515-swc_deu_001515) +J A H R Z W E I T A U S E N D F Ü N F K R I T I S I E R T E (swc_deu_001516-swc_deu_001516) +D I E S E A U F F A S S U N G Z U R N E U T R A L I T Ä T U N T E R S C H E I D E T (swc_deu_001517-swc_deu_001517) +R I E D L W U R D E A L S K Ü N S T L E R I S C H E R L E I T E R D E S S I E M E N S S T U D I O S B E S T E L L T (swc_deu_001518-swc_deu_001518) +W E N N M A N D I E W E L T A L S G A N Z E S B E T R A C H T E T (swc_deu_001519-swc_deu_001519) +S I N D K R I T I S C H E K O M P O N E N T E N D E S D E T O N A T I O N S S Y S T E M S A B S I C H T L I C H S C H W A C H E N T W O R F E N (swc_deu_001520-swc_deu_001520) +N I C H T W Ä H L B A R I S T J E D O C H (swc_deu_001521-swc_deu_001521) +E R B O T E I N E V E R E I N I G U N G D E U T S C H L A N D S A N (swc_deu_001522-swc_deu_001522) +B E R L I N Z W E I T A U S E N D F Ü N F (swc_deu_001523-swc_deu_001523) +K E R N A B G E S T I M M T U N D U M H Ü L L E N D I E S E N E N T S P R E C H E N D (swc_deu_001524-swc_deu_001524) +E R Z E U G U N G V O N D Y N A M I K A U S (swc_deu_001525-swc_deu_001525) +Z I M T U N D I N G W E R (swc_deu_001526-swc_deu_001526) +V O N S C H W E R E R U N T E R E R N Ä H R U N G (swc_deu_001527-swc_deu_001527) +N Ü S S E N U N D G E W Ü R Z E N (swc_deu_001528-swc_deu_001528) +R O B E R T F K E N N E D Y (swc_deu_001529-swc_deu_001529) +K A M S C H L I E S S L I C H Z U M (swc_deu_001530-swc_deu_001530) +V O L L S T Ä N D I G K E I T (swc_deu_001531-swc_deu_001531) +S T A N D E N S I C H V O N D E N U S A (swc_deu_001532-swc_deu_001532) +A F R I K A S Ü D L I C H D E R S A H A R A G E O R T E T (swc_deu_001533-swc_deu_001533) +D I E A R M E E M E U T E R T E (swc_deu_001534-swc_deu_001534) +S T A L I N S E T Z T E I M (swc_deu_001535-swc_deu_001535) +V E R H Ä L T N I S A U S G L E I C H (swc_deu_001536-swc_deu_001536) +P R O S C R I B E D G L E I C H (swc_deu_001537-swc_deu_001537) +A M Z W E I T E J U N I Z W E I T A U S E N D V I E R W U R D E N (swc_deu_001538-swc_deu_001538) +I N D E N B U N D E S T A G N A C H R Ü C K T (swc_deu_001539-swc_deu_001539) +D I E N A T O O S T E R W E I T E R U N G U N D D I E E I N S E I T I G E A U F K Ü N D I G U N G D E S (swc_deu_001540-swc_deu_001540) +H I E R B E I I S T (swc_deu_001541-swc_deu_001541) +D I E S E R S T E L L E K A M E N S Ä M T L I C H E M I T G L I E D E R D E R K A P E L L E D E R (swc_deu_001542-swc_deu_001542) +P O T S D A M E R A B K O M M E N E N T H I E L T Z W A R A L L G E M E I N E V E R E I N B A R U N G E N Ü B E R D I E K Ü N F T I G E G E M E I N S A M E V E R W A L T U N G D E R S I E G E R M Ä C H T E U N D F O R M U L I E R T E G R U N D S Ä T Z E W I E D E M I L I T A R I S I E R U N G (swc_deu_001543-swc_deu_001543) +D A N A C H U N T E R S C H R I E B E R E I N E N V E R T R A G B E I M B F C D Y N A M O (swc_deu_001544-swc_deu_001544) +E I N E W E I T E R E V A R I A N T E M A G (swc_deu_001545-swc_deu_001545) +S I E W U R D E N M O D U L A R D U R C H L O C H S T R E I F E N G E S T E U E R T U N D D I E K L Ä N G E K O N N T E N (swc_deu_001546-swc_deu_001546) +D I E G R U N D M A N D A T S K L A U S E L B E V O R Z U G T U N T E R D E N K L E I N E N P A R T E I E N J E N E (swc_deu_001547-swc_deu_001547) +A B E R T R O T Z D E M K E I N E W I R K L I C H E H U N G E R S N O T H E R R S C H T (swc_deu_001548-swc_deu_001548) +U N D D O K U M E N T A T I O N D E R O B J E K T E (swc_deu_001549-swc_deu_001549) +Z U R V O R B E D I N G U N G K O N K R E T E R A B R Ü S T U N G S S C H R I T T E (swc_deu_001550-swc_deu_001550) +B U N D E S T A G S W A H L R E C H T (swc_deu_001551-swc_deu_001551) +E S M U S S D E M K R E I S W A H L L E I T E R V O R G E L E G T W E R D E N (swc_deu_001552-swc_deu_001552) +H A T M A N E I N E E M P I R I S C H E B A S I S F Ü R P S Y C H O S O Z I A L E P R O G R A M M E Z U R S E N K U N G D E R S E L B S T M O R D R A T E U N D Z U R S T Ä R K U N G D E S S I C H E R H E I T S G E F Ü H L S I N D E R B E V Ö L K E R U N G (swc_deu_001553-swc_deu_001553) +B E I D E N E R S T E N F R E I E N P A R L A M E N T S W A H L E N W U R D E I L I E S C U I M M A I N E U N Z E H N H U N D E R T N E U N Z I G I N S E I N E M (swc_deu_001554-swc_deu_001554) +D A M I T L A S S E N S I C H B E S T R A H L U N G S S T Ä R K E N S E H R G E N A U M E S S E N (swc_deu_001555-swc_deu_001555) +W E N I G E J A H R E S P Ä T E R K A M E S Z U E I N E R W E I T E R E N G R Ü N D U N G (swc_deu_001556-swc_deu_001556) +R A D I O K A B A R E T T P R E I S (swc_deu_001557-swc_deu_001557) +B E S T Ü C K T E B O M B E R A U F D I E S T A R T B A H N E N R O L L E N (swc_deu_001558-swc_deu_001558) +M I T D I E S E R R E G E L U N G S O L L E I N E F A K T I S C H Z W E I F A C H E E I N F L U S S N A H M E D I E S E R W Ä H L E R A U F (swc_deu_001559-swc_deu_001559) +B A R O C K E R K I R C H E N B A U (swc_deu_001560-swc_deu_001560) +D E R H E R V O R R A G E N D W I R K E N D E N L A N D E K L A P P E N W I E D E R U M H E R V O R R A G E N D E L A N G S A M F L U G E I G E N S C H A F T E N (swc_deu_001561-swc_deu_001561) +M I L I T Ä R I S C H E V E R B I N D U N G S F L U G Z E U G E O D E R U M S C H U L M A S C H I N E N F Ü R D I E B F E I N H U N D E R T N E U N V E R W E N D E T (swc_deu_001562-swc_deu_001562) +L E I S T E T E M E D I Z I N I S C H E U N D P S Y C H O L O G I S C H E H I L F E (swc_deu_001563-swc_deu_001563) +K A N N M A N D U R C H I M P F U N G E N V O R B E U G E N (swc_deu_001564-swc_deu_001564) +M A N D E N A U S B R U C H D I E S E R K R A N K H E I T N A C H E R F O L G T E R I N F E K T I O N V E R L A N G S A M E N K A N N (swc_deu_001565-swc_deu_001565) +D I E E I N E N E U T R A L I T Ä T U N T E R A L L E N U M S T Ä N D E N V O R S A H (swc_deu_001566-swc_deu_001566) +U N D Z I E G E N H I R T E N (swc_deu_001567-swc_deu_001567) +D A S N E U N Z E H N H U N D E R T A C H T U N D D R E I S S I G G E G R Ü N D E T E K O M I T E E F Ü R U N A M E R I K A N I S C H E U M T R I E B E W U R D E D A F Ü R N U N (swc_deu_001568-swc_deu_001568) +Z E N T R A L E D E R P R O G R E S S I V E N U N D H O R T D E S I N G E N I E U R G E S T Ü T Z T E N K U N S T D E N K E N S (swc_deu_001569-swc_deu_001569) +I N D E R D E R U S P R Ä S I D E N T A N K Ü N D I G T E (swc_deu_001570-swc_deu_001570) +S N A C K S U N D V O R S P E I S E N (swc_deu_001571-swc_deu_001571) +D E S B U N D E S W A H L G E S E T Z E S B I S Z U M D R E I S S I G S T E J U N I Z W E I T A U S E N D E L F A U F G E G E B E N (swc_deu_001572-swc_deu_001572) +H E N R I P O U S S E U R (swc_deu_001573-swc_deu_001573) +F L Ü C H T L I N G E N V O N D E R E T H N I S C H E N M I N D E R H E I T D E R S O M A L I S C H E N B A N T U (swc_deu_001574-swc_deu_001574) +D I E B I P O L A R E W E L T O R D N U N G Z E M E N T I E R T (swc_deu_001575-swc_deu_001575) +E I N E I N T E G R I E R T E O D E R E X T E R N A N G E B R A C H T E V O R R I C H T U N G A N E I N E M N U K L E A R E N W A F F E N S Y S T E M (swc_deu_001576-swc_deu_001576) +S T A R T E T E D I E H I L F S O R G A N I S A T I O N L A N G F R I S T I G E (swc_deu_001577-swc_deu_001577) +W E N N D I E S E E X T E R N E N E F F E K T E I N D E R R I C H T I G E N R E I H E N F O L G E A U F T R E T E N U N D S I C H I N N E R H A L B S P E Z I F I S C H E R P A R A M E T E R B E W E G E N (swc_deu_001578-swc_deu_001578) +Z O G D I E S O W J E T U N I O N A U C H B E I D E N W A S S E R S T O F F B O M B E N U N D N E U E N F L U G Z E U G E N M I T I N T E R K O N T I N E N T A L E R R E I C H W E I T E M I T D E N U S A G L E I C H (swc_deu_001579-swc_deu_001579) +D I E S T A D T H A T I H R W A P P E N T I E R (swc_deu_001580-swc_deu_001580) +D I E S E R A N S A T Z G I L T A L L G E M E I N A L S A U S G E W O G E N E R (swc_deu_001581-swc_deu_001581) +N A C H D E M Z U S A M M E N B R U C H D E R (swc_deu_001582-swc_deu_001582) +D E R O B E R L A U S I T Z Z W I S C H E N H O Y E R S W E R D A (swc_deu_001583-swc_deu_001583) +D A B E I I N Z W E I P H A S E N U N T E R T E I L T (swc_deu_001584-swc_deu_001584) +S C H W E D E N A N D E R E U R O P A M E I S T E R S C H A F T T E I L U N D W U R D E M I T D E R D F B E L F (swc_deu_001585-swc_deu_001585) +M E I S T E R E R Ö F F N E T K R A B A T S C H L I E S S L I C H E I N E W E I T E R E M Ö G L I C H K E I T (swc_deu_001586-swc_deu_001586) +E I N E M A U S W Ä R T S E R F O L G I N W O L F S B U R G G E L A N G (swc_deu_001587-swc_deu_001587) +M I T S C H W E B U N G S S U M M E R N K O N N T E N G L I S S A N D I E R Z E U G T W E R D E N (swc_deu_001588-swc_deu_001588) +D E R A B E R L E D I G L I C H Z E I G T E (swc_deu_001589-swc_deu_001589) +G R O S S B R I T A N N I E N E I N E E R S T E W I C H T I G E V E R E I N B A R U N G (swc_deu_001590-swc_deu_001590) +S I E H T A U C H D A S W I T N E S S I N G (swc_deu_001591-swc_deu_001591) +W U R D E M I T D E M B U N D E S W A H L G E S E T Z V O N N E U N Z E H N H U N D E R T S E C H S U N D F Ü N F Z I G E I N E D A U E R H A F T E R E G E L U N G E I N G E F Ü H R T (swc_deu_001592-swc_deu_001592) +D I E A N Z A H L D E R Ü B E R H A N G M A N D A T E K A N N (swc_deu_001593-swc_deu_001593) +B E S C H L O S S D I E S E R E I N M I L I T Ä R I S C H E S E I N G R E I F E N I N D E N K O R E A K R I E G (swc_deu_001594-swc_deu_001594) +N A T O V E R B I N D L I C H E (swc_deu_001595-swc_deu_001595) +K A L T E K R I E G B E E N D E T (swc_deu_001596-swc_deu_001596) +V N E U N Z E H N H U N D E R T D R E I U N D N E U N Z I G U N D A U S T R A L I E N S O W I E D E R Ö S T E R R E I C H I S C H E A B L E G E R (swc_deu_001597-swc_deu_001597) +D A D I E S E I T A N F A N G N E U N Z E H N H U N D E R T N E U N U N D F Ü N F Z I G D O R T H E R R S C H E N D E R E V O L U T I O N S R E G I E R U N G U N T E R F I D E L C A S T R O E I N E N S O Z I A L I S T I S C H E N K U R S E I N G E S C H L A G E N H A T T E (swc_deu_001598-swc_deu_001598) +N A C H W E I T E R E N V E R L U S T R E I C H E N K Ä M P F E N O H N E N E N N E N S W E R T E E R F O L G E B E I D E R K R I E G S P A R T E I E N W U R D E R U N D D R E I J A H R E N A C H B E G I N N D E R A U S E I N A N D E R S E T Z U N G E I N B I S H E U T E G Ü L T I G E S W A F F E N S T I L L S T A N D S A B K O M M E N A B G E S C H L O S S E N (swc_deu_001599-swc_deu_001599) +M A N I S T D A B E I S E H R V O R S I C H T I G (voxforge_deu_000891-voxforge_deu_000891) +D I E W E H R P F L I C H T S O L L I N D E U T S C H L A N D L E I D E R N O C H N I C H T A B G E S C H A F F T W E R D E N (voxforge_deu_000892-voxforge_deu_000892) +E S G I B T A U C H M I S S B R A U C H D U R C H A R B E I T G E B E R (voxforge_deu_000893-voxforge_deu_000893) +D I E K I N D E R S I N D D A N N K R A N K G E W O R D E N (voxforge_deu_000894-voxforge_deu_000894) +D I E T R A G W E I T E D E R K A T A S T R O P H E S O L L V E R D E U T L I C H T W E R D E N (voxforge_deu_000895-voxforge_deu_000895) +Ä H (voxforge_deu_000897-voxforge_deu_000897) +B E I M O R G A N S T R E I T S T R E I T E N O B E R S T E V E R F A S S U N G S O R G A N E (voxforge_deu_000898-voxforge_deu_000898) +D A S W A G E I C H J A Z U B E Z W E I F E L N (voxforge_deu_000899-voxforge_deu_000899) +M A N S O L L T E D E N E N A U F G A R K E I N E N F A L L T R A U E N (voxforge_deu_000900-voxforge_deu_000900) +D I E Ö F F E N T L I C H E N S C H U L D E N W E R D E N N I C H T G E T I L G T W E R D E N (voxforge_deu_000901-voxforge_deu_000901) +D A S G E L D I S T A U S G E Z A H L T W O R D E N (voxforge_deu_000902-voxforge_deu_000902) +E S S O L L E N D R E I H U N D E R T T A U S E N D N E U E A R B E I T S P L Ä T Z E E N T S T E H E N (voxforge_deu_000903-voxforge_deu_000903) +D I E K Ö R P E R V E R L E T Z 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R H I N M E H R A L S 5 0 D E R B E V Ö L K E R U N G D E R E U R O P Ä I S C H E N U N I O N I M L Ä N D L I C H E N R A U M L E B T (voxpopuli_deu_000313-voxpopuli_deu_000313) +W I R W O L L E N A L S O D A S S D E R B Ü R G E R S C H N E L L E R E I N E A U S K U N F T B E K O M M T O B S E I N E B E S C H W E R D E Ü B E R H A U P T A N G E N O M M E N W I R D O B S I E B E R E C H T I G T I S T (voxpopuli_deu_000314-voxpopuli_deu_000314) +E I N „ R E S E T U N S E R E R B E Z I E H U N G E N I S T N I C H T V O N N Ö T E N A B E R S E H R W O H L K O N T I N U I E R L I C H E S F E I N T U N I N G (voxpopuli_deu_000315-voxpopuli_deu_000315) +U N D D A W I R D G A N Z S T O L Z G E S A G T D I E B E S C H Ä F T I G U N G S T E I G T J A A N (voxpopuli_deu_000316-voxpopuli_deu_000316) +I C H W I L L S A G E N W I E E S I S T F Ü R U N S I S T D E R E U R O U N T E R B E W E R T E T W I R E X P O R T I E R E N Z U V I E L Z U B I L L I G U N D W I R I M P O R T I E R E N Z U W E N I 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E N Z U S A M M E N A R B E I T E I N E N E R S T E N G A N G V O N E I N I G E N M I T G L I E D S T A A T E N (voxpopuli_deu_000322-voxpopuli_deu_000322) +W A S D I E G R E N Z Ü B E R S C H R E I T E N D E Z U S A M M E N A R B E I T A N B E L A N G T U N D D I E V E R B R E I T U N G I N D R I T T L Ä N D E R B E T R I F F T H I E R M Ö C H T E I C H E I N B E I S P I E L N E N N E N D A S E I N E R F O L G S B E I S P I E L F Ü R M I C H I S T U N D Z W A R S L U M D O G M I L L I O N Ä R (voxpopuli_deu_000323-voxpopuli_deu_000323) +U N D D A S N I C H T N U R I N P O R T U G A L O D E R G R I E C H E N L A N D S O N D E R N A U C H I N S O V E R M E I N T L I C H R E I C H E N M I T G L I E D S T A A T E N W I E D E U T S C H L A N D O D E R G R O S S B R I T A N N I E N (voxpopuli_deu_000324-voxpopuli_deu_000324) +D I E Z E I T F Ü R A U S R E D E N I S T V O R B E I (voxpopuli_deu_000325-voxpopuli_deu_000325) +S I E A L L E F L I E G E N A L S M I T G L I E D E R D I E S E S H A U S E S W A H R S C H E I N L I C H D E U T L I C H H Ä U F I G E R A L S D E R E U D U R C H S C H N I T T S B Ü R G E R (voxpopuli_deu_000326-voxpopuli_deu_000326) +U N D I C H B I N S I C H E R D A S S I H R E B E D E U T U N G I N N A H E R Z U K U N F T S O G A R N O C H Z U N E H M E N W I R D (voxpopuli_deu_000327-voxpopuli_deu_000327) +E S G E H T H I E R U M D I E R I C H T L I N I E D E S R A T E S Z U R F E S T L E G U N G G R U N D L E G E N D E R S I C H E R H E I T S N O R M E N F Ü R D E N S C H U T Z V O R D E N G E F A H R E N E I N E R E X P O S I T I O N G E G E N Ü B E R I O N I S I E R E N D E R S T R A H L U N G (voxpopuli_deu_000328-voxpopuli_deu_000328) +D A S G I L T E S W I E D E R H E R Z U S T E L L E N (voxpopuli_deu_000329-voxpopuli_deu_000329) +D I E S E N E I N E N E I N Z I G E N S I T Z G I B T E S L Ä N G S T D A S I S T S T R A S S B U R G (voxpopuli_deu_000330-voxpopuli_deu_000330) +W I R S E H E N J A G E R A D E D A S S D A S P A S S I E R T I N M A L T A D I E J O U R N A L I S T I N D I E K O R R U P T I O N S F Ä L L E A U F G E D E C K T H A T I S T V O R W E N I G E N W O C H E N E R M O R D E T W O R D E N W E D E R W E R D E N S Y S T E M A T I S C H D I E K O R R U P T I O N S F Ä L L E U N T E R S U C H T N O C H W I R D D E R M O R D S E L B E R G E Z I E L T U N T E R S U C H T M A N H A T F A S T D E N E I N D R U C K A L S O B H I E R A L L E S U N T E R D E M M A N T E L D E S S C H W E I G E N S Z U G E D E C K T W E R D E N S O L L (voxpopuli_deu_000331-voxpopuli_deu_000331) +D O R T S T E H E N Ü B E R A L L E N T L A N G D E R K Ü S T E D I E W A R N S T E I N E D I E A U F D I E G R O S S E N K A T A S T R O P H E N M I T T S U N A M I S I N D E R V E R G A N G E N H E I T H I N W E I S E N (voxpopuli_deu_000332-voxpopuli_deu_000332) +H E R R P R Ä S I D E N T I C H H A B E I M P R I N Z I P F Ü R D E N B E R I C H T G E S T I M M T O B W O H L E R E I N E N S C H W E R E N F E H L E R E N T H Ä L T E S W I R D N Ä M L I C H D A Z U A U F G E F O R D E R T D A S E U R O P Ä I S C H E P A R L A M E N T A U F D E M W E G Z U E I N E M E I N Z I G E N S I T Z Z U U N T E R S T Ü T Z E N (voxpopuli_deu_000333-voxpopuli_deu_000333) +I N D I E S E N T R E F F E N W U R D E N G E M E I N S A M E P O L I T I S C H E V E R A B R E D U N G E N I M K R E I S D E R 2 7 G E T R O F F E N U N D A U C H P U B L I K G E M A C H T (voxpopuli_deu_000334-voxpopuli_deu_000334) +I C H B I N D E R Ü B E R Z E U G U N G D A S S W I R E S H E U T E M I T D E M V O R S C H L A G A U S D E M U M W E L T A U S S C H U S S G E S C H A F F T H A B E N E I N E N S C H R I T T W E I T E R Z U K O M M E N E S I S T N I C H T P E R F E K T E U R O P Ä I S C H E Ä R Z T E S A G E N W I R H Ä T T E N F Ü R H O C H R I S I K O P R O D U K T E E I N E Z E N T R A L E Z U L A S S U N G H A B E N M Ü S S E N D A S H A B E I C H N I C H T G E S C H A F F T A B E R M I T D E M W A S H E U T E A U F D E M T I S C H L I E G T S C H A F F E N W I R W O H L T R O T Z D E M E I N E N G R O S S E N S C H R I T T V I E L L E I C H T K E I N E N M E I L E N S T E I N A B E R E I N E N G R O S S E N S C H R I T T H I N Z U M E H R P A T I E N T E N S I C H E R H E I T (voxpopuli_deu_000335-voxpopuli_deu_000335) +F R A U P R Ä S I D E N T I N F R A U K O M M I S S A R I N L I E B E K O L L E G E N (voxpopuli_deu_000336-voxpopuli_deu_000336) +Z U M A K T U E L L E N I C H G L A U B E E S K A N N K E I N E R V O N U N S A N N E H M E N D A S S W I R W I R K L I C H E R S T S E I T D I E S E M W O C H E N E N D E W I S S E N D A S S U N S D I E Z A H L U N G S U N F Ä H I G K E I T D R O H T (voxpopuli_deu_000337-voxpopuli_deu_000337) +D A S S I N D E I N F A C H B E D I N G U N G E N D I E N I C H T A K Z E P T A B E L S I N D (voxpopuli_deu_000338-voxpopuli_deu_000338) +I N D E R Z W I S C H E N Z E I T S I N D D I E R E T T U N G S O R G A N I S A T I O N E N D I E G R Ö S S T E N S C H L E P P E R W E I L S I E D I E M I G R A N T E N 2 0 K I L O M E T E R V O R D E R L I B Y S C H E N K Ü S T E A U F G R E I F E N U N D A L L E N A C H I T A L I E N T R A N S P O R T I E R E N (voxpopuli_deu_000339-voxpopuli_deu_000339) +D A S Z E I G T D E R F A L L J U L I A T I M O S C H E N K O (voxpopuli_deu_000340-voxpopuli_deu_000340) +W I R D Ü R F E N N I C H T W A S S E R P R E D I G E N U N D W E I N T R I N K E N (voxpopuli_deu_000341-voxpopuli_deu_000341) +F Ü R D I E S E E N T S C H E I D U N G B R A U C H E N W I R V I E L E P A R T N E R N I C H T Z U L E T Z T D I E S T Ä D T E (voxpopuli_deu_000342-voxpopuli_deu_000342) +D I E F O L G E I S T E I N H Ö H E N F L U G V O N P O P U L I S T E N U N D E X T R E M I S T E N I N E I N I G E N M I T G L I E D S T A A T E N I H R E N D U M P F E N P A R O L E N S E T Z E N W I R K O N K R E T E V E R Ä N D E R U N G E N T G E G E N (voxpopuli_deu_000343-voxpopuli_deu_000343) +W E I L D I E I N V E S T I T I O N E N F R A N Z Ö S I S C H E R U N D D E U T S C H E R B A N K E N G E R E T T E T W E R D E N M U S S T E N D U R F T E G R I E C H E N L A N D 2 0 1 0 N I C H T P L E I T E G E H E N U N D H E U T E M U S S E S E I N E N R I E S I G E N S C H U L D E N B E R G V O R S I C H H E R S C H I E B E N (voxpopuli_deu_000344-voxpopuli_deu_000344) +D I E M I T G L I E D S T A A T E N D Ü R F E N N I C H T D I E M Ö G L I C H K E I T H A B E N D E N E U R O P Ä I S C H E N S T A A T S A N W A L T D A R A N Z U H I N D E R N I N I H R E N R E G I O N E N G A N Z G E Z I E L T U N D S Y S T E M A T I S C H K O R R U P T I O N S F Ä L L E N N A C H Z U G E H E N (voxpopuli_deu_000345-voxpopuli_deu_000345) +D R E I M I L L I O N E N M E N S C H E N S I N D A B H Ä N G I G V O N U N S E R E R H I L F E (voxpopuli_deu_000346-voxpopuli_deu_000346) +E I N V I E R Z E H N J Ä H R I G E R J U N G E W I R D I N H A K K A R I V O N E I N E M P O L I Z I S T E N E I N E S S O N D E R E I N S A T Z K O M M A N D O S I N S K O M A G E S C H L A G E N (voxpopuli_deu_000347-voxpopuli_deu_000347) +W I E E I N E H E I L I G E K U H H A T M A N V O R S I C H H E R G E T R A G E N D A S O P T O U T M Ü S S E U N T E R A L L E N U M S T Ä N D E N W E G (voxpopuli_deu_000348-voxpopuli_deu_000348) +D R E I D E R A R T I G E T R E F F E N H A B E N I N Z W I S C H E N S T A T T G E F U N D E N (voxpopuli_deu_000349-voxpopuli_deu_000349) +I C H H O F F E E S D A U E R T N I C H T W I E D E R N E U N M O N A T E (voxpopuli_deu_000350-voxpopuli_deu_000350) +D E S W E G E N E I N E W I C H T I G E F R A G E A N D I E K O M M I S S I O N K A N N E I N L A N D D I E G R E N Z K O N T R O L L E W I E D E R E I N F Ü H R E N U N D G L E I C H Z E I T I G I N D E R S C H E N G E N U N I O N B L E I B E N M I T Z U G A N G Z U M I N F O R M A T I O N S S Y S T E M E T C O D E R I S T D A S E I N E N T W E D E R O D E R D I E F R A G E I S T W I C H T I G F Ü R D I E D Ä N I S C H E D E B A T T E U N D I C H B I T T E U M E I N E K L A R E A N T W O R T (voxpopuli_deu_000351-voxpopuli_deu_000351) +W I E H E U T E S C H O N A U S G E F Ü H R T W U R D E L A G E S N I C H T D A R A N D A S S E S H I E R G R O B E F E H L E R G E G E B E N H Ä T T E S O N D E R N E S G A B E I N E R E I H E V O N K L E I N E N U N G E R E I M T H E I T E N B Z W (voxpopuli_deu_000352-voxpopuli_deu_000352) +E I N E V E R G E M E I N S C H A F T U N G D E R A U S S E N U N D S I C H E R H E I T S P O L I T I K A L S G R O S S E S Z I E L D I E S E R U N I O N (voxpopuli_deu_000353-voxpopuli_deu_000353) +D E N N S I C H E R H E I T I S T E I N E S C H W I E R I G E U N D D E T A I L R E I C H E A R B E I T N I C H T N U R I M T E C H N I S C H E N B E R E I C H (voxpopuli_deu_000354-voxpopuli_deu_000354) +K I N D E R U N D P O L I T I K S E L T E N L I E G E N D I E I N T E R E S S E N V O N B Ü R G E R N U N D P O L I T I K E R N S O W E I T A U S E I N A N D E R B E I D E N B Ü R G E R N I N G A N Z E U R O P A S T E H T D A S T H E M A K I N D G A N Z O B E N (voxpopuli_deu_000355-voxpopuli_deu_000355) +H E R R P R Ä S I D E N T (voxpopuli_deu_000356-voxpopuli_deu_000356) +W I R F Ü H R T E N G E S P R Ä C H E M I T P R Ä S I D E N T K A R Z A I Z A H L R E I C H E N R E G I E R U N G S V E R T R E T E R N F R A U E N U N D M E N S C H E N R E C H T S O R G A N I S A T I O N E N U N D D I E W A R E N D U R C H A U S E R M U T I G E N D (voxpopuli_deu_000357-voxpopuli_deu_000357) +D A S I S T Ü B R I G E N S A U C H E I N E U R S A C H E F Ü R D E N W A C H S E N D E N N A T I O N A L I S M U S D E R A L L E R D I N G S L E I D E R V Ö L L I G P E R S P E K T I V L O S I S T (voxpopuli_deu_000358-voxpopuli_deu_000358) +H E U T E S I N D W I R I M M E R N O C H S O W E I T V O N D I E S E M Z I E L E N T F E R N T (voxpopuli_deu_000359-voxpopuli_deu_000359) +I C H W E R D E A L S F I N A N Z M I N I S T E R A U C H I N M E I N E M L A N D J E D E N T A G D A M I T K O N F R O N T I E R T D A S S N A T Ü R L I C H A U C H D A S B E W U S S T S E I N G E G E B E N S E I N M U S S D A S S S T A A T S H A U S H A L T E V O N D E N S T E U E R Z A H L E R I N N E N U N D S T E U E R Z A H L E R N F I N A N Z I E R T S I N D U N D D A S S W I R D A M I T A U C H D I E V E R A N T W O R T U N G T R A G E N B E I D E N E N T S C H E I D U N G E N D I E W I R H I E R I N D I E S E M R A H M E N T R E F F E N M E I N E D A M E N U N D H E R R E N (voxpopuli_deu_000360-voxpopuli_deu_000360) +A U F D E M E U R O P Ä I S C H E N A U T O M O B I L M A R K T I N S G E S A M T D R A M A T I S C H I S T (voxpopuli_deu_000361-voxpopuli_deu_000361) +D I E E U R O P Ä I S C H E U N I O N H A T M I T D I E S E M I N S T R U M E N T D I E C H A N C E E I N E A K T I V E R O L L E I N I H R E R N A C H B A R R E G I O N Z U S P I E L E N U M D E M O K R A T I S C H E R E F O R M E N U N D E I N E N A C H H A L T I G E E N T W I C K L U N G V O R A N Z U T R E I B E N (voxpopuli_deu_000362-voxpopuli_deu_000362) +D I E S I C H T A U F T O T A L I T Ä R E R E G I M E V O N A U S S E N O D E R V O N I N N E N I S T R E C H T U N T E R S C H I E D L I C H (voxpopuli_deu_000363-voxpopuli_deu_000363) +W I R H A B E N I M M E R G E S A G T D A S S E I N E Ü B E R E I L T E S T A T I O N I E R U N G S E N T S C H E I D U N G U N S I N N I G I S T W E I L E S Z U M J E T Z I G E N Z E I T P U N K T K E I N E B E D R O H U N G B E I S P I E L S W E I S E A U S D E M I R A N G I B T (voxpopuli_deu_000364-voxpopuli_deu_000364) +D I E S E R V E R G L E I C H I S T E I N E Z Y N I S C H E M I S S A C H T U N G D E R O P F E R V O N M E N S C H E N R E C H T S V E R L E T Z U N G E N I N A L L E R W E L T E R I S T Z U M A N D E R E N E I N S O L C H U N G L A U B L I C H E R A N W U R F (voxpopuli_deu_000365-voxpopuli_deu_000365) +D I E S P E H A T D I E S E U M F A S S E N D E H O R I Z O N T A L E R I C H T L I N I E B E F Ü R W O R T E T (voxpopuli_deu_000366-voxpopuli_deu_000366) +D E N N E I N E S I S T W I R K L I C H K L A R D I E F I N A N Z U N D W I R T S C H A F T S K R I S E V E R L A N G T V O N U N S A L L E N E I N M A L M E H R D E R V E R A N T W O R T U N G F Ü R E I N E O P T I M A L E U N D V O R A L L E M R A S C H E Q U A L I F I Z I E R U N G U N S E R E R A R B E I T N E H M E R U N D A R B E I T N E H M E R I N N E N G A N Z B E S O N D E R S J E T Z T R E C H N U N G Z U T R A G E N (voxpopuli_deu_000367-voxpopuli_deu_000367) +E S T L A N D O D E R A U C H P O L E N D I E S E H R G U T E E R G E B N I S S E E R Z I E L E N A L S A N D E R E D I E S I C H S C H W E R T U N D I E M I T T E L A B Z U R U F E N E T W A R E G I O N E N W I E K A L A B R I E N S I Z I L I E N O D E R A U C H G R I E C H E N L A N D O D E R R U M Ä N I E N (voxpopuli_deu_000368-voxpopuli_deu_000368) +D E R B E R I C H T G A U Z È S F O R D E R T Z U R E C H T D A S S D A S R A T I N G S T A A T L I C H E R S C H U L D T I T E L A L S Ö F F E N T L I C H E A U F G A B E B E G R I F F E N U N D D A H E R V O N Ö F F E N T L I C H E N A K T E U R E N V O R G E N O M M E N W E R D E N M U S S (voxpopuli_deu_000369-voxpopuli_deu_000369) +D A W I R E S A B E R N U N M I T E I N E M S O Z I A L P R O G R A M M Z U T U N H A B E N M Ü S S E N W I R D A F Ü R E I N E E N T S P R E C H E N D E R E C H T L I C H E G R U N D L A G E S C H A F F E N (voxpopuli_deu_000370-voxpopuli_deu_000370) +A B E R D A S M Ü S S E N W I R N O C H A N A L Y S I E R E N (voxpopuli_deu_000371-voxpopuli_deu_000371) +M A N K A N N N A T Ü R L I C H V E R L A N G E N G E B E N W I R M E H R G E L D F Ü R E N T W I C K L U N G S H I L F E A U S D I E A R M E N L E U T E B R A U C H E N D A S (voxpopuli_deu_000372-voxpopuli_deu_000372) +G E R A D E F Ü R K L E I N E R E P R O J E K T E I S T D A S E I N Ü B E R M Ä S S I G E R B Ü R O K R A T I S C H E R A U F W A N D R I C H T I G D A S S D A S J E T Z T A U F E I N E N Z E I T R A U M V O N D R E I J A H R E N G E S E N K T W E R D E N S O L L (voxpopuli_deu_000373-voxpopuli_deu_000373) +I C H K A N N N U R V E R S I C H E R N D I E E U R O P Ä I S C H E K O M M I S S I O N I S T C O M M I T T E D Z U R E U R O P Ä I S C H E N P E R S P E K T I V E D E S K O S O V O (voxpopuli_deu_000374-voxpopuli_deu_000374) +A B E R H I E R I M H A U S E I S T E S S E H R O F T A U C H S O (voxpopuli_deu_000375-voxpopuli_deu_000375) +M I T D I E S E M H A U S H A L T K A N N M A N D I E E U B Ü R G E R I N N E N U N D B Ü R G E R N I C H T Ü B E R Z E U G E N U N D B E G E I S T E R N (voxpopuli_deu_000376-voxpopuli_deu_000376) +W I R A L S S O Z I A L D E M O K R A T E N N E H M E N M I T G R O S S E R F R E U D E Z U R K E N N T N I S D A S S D I N G E D I E W I R V O R G E T R A G E N H A B E N J E T Z T I M Z U S A M M E N H A N G M I T D E N V E R Ä N D E R U N G E N I N D E N V E R E I N I G T E N S T A A T E N U M G E S E T Z T W E R D E N (voxpopuli_deu_000377-voxpopuli_deu_000377) +D E R B E S C H L U S S D A S E U R O P Ä I S C H E S E M E S T E R H E R Z U N E H M E N U N D D I E K O R R U P T I O N S S I T U A T I O N I M R A H M E N D E R L Ä N D E R B E R I C H T E Z U V E R Ö F F E N T L I C H E N I S T N I C H T A U S R E I C H E N D (voxpopuli_deu_000378-voxpopuli_deu_000378) +U N D M E I N E B I T T E O D E R D A S W A S I C H M I R V O R S T E L L E I S T D A S S M O R G E N W I R K L I C H I N D E R T A T E I N E G R O S S E E I N E B R E I T E M E H R H E I T F Ü R D I E S E K O H Ä S I O N S P O L I T I K F Ü R U N S E R E P O L I T I K S T I M M T F Ü R D I E M E N S C H E N V O R O R T D A M I T W I R U N S A U F D A S W E S E N T L I C H E B E S C H R Ä N K E N K Ö N N E N (voxpopuli_deu_000379-voxpopuli_deu_000379) +W E N N W I R H E U T E D I E S E V E R O R D N U N G V E R A B S C H I E D E N H O F F E I C H D A S S W I R N A C H E I N E M L A N G E N W E G Z U E I N E M G U T E N A B S C H L U S S K O M M E N U N D I C H M Ö C H T E M I C H B E I D E R K O M M I S S I O N B E D A N K E N D I E U N S D U R C H K O N S T R U K T I V E S A C H A R B E I T (voxpopuli_deu_000380-voxpopuli_deu_000380) +U N S E R E K O N T R O L L E N H A B E N K E I N E N B E L E G E R B R A C H T I C H K A N N (voxpopuli_deu_000381-voxpopuli_deu_000381) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/result.txt b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..14d400e289d621ba90ae3e2a2ab2ca92609bfa9a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/result.txt @@ -0,0 +1,7481 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,-----------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/hyp.trn| +|-----------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------------+---------------------+----------------------------------------------------------------| +| m | 89 8584 | 79.0 7.5 13.5 5.4 26.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000698 | 1 51 | 70.6 17.6 11.8 7.8 37.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000699 | 1 21 | 42.9 28.6 28.6 0.0 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000700 | 1 54 | 72.2 14.8 13.0 11.1 38.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000701 | 1 90 | 60.0 25.6 14.4 1.1 41.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000702 | 1 50 | 78.0 14.0 8.0 6.0 28.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000703 | 1 64 | 79.7 7.8 12.5 10.9 31.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000704 | 1 4 | 75.0 25.0 0.0 200.0 225.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000705 | 1 4 | 25.0 50.0 25.0 125.0 200.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000706 | 1 90 | 83.3 5.6 11.1 2.2 18.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000707 | 1 18 | 83.3 16.7 0.0 27.8 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000708 | 1 75 | 60.0 26.7 13.3 10.7 50.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000709 | 1 55 | 70.9 21.8 7.3 18.2 47.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000710 | 1 105 | 80.0 13.3 6.7 3.8 23.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000711 | 1 86 | 84.9 2.3 12.8 2.3 17.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000712 | 1 37 | 54.1 10.8 35.1 5.4 51.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000713 | 1 41 | 63.4 24.4 12.2 4.9 41.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000714 | 1 72 | 47.2 20.8 31.9 6.9 59.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000715 | 1 66 | 48.5 18.2 33.3 0.0 51.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000716 | 1 50 | 68.0 14.0 18.0 2.0 34.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000717 | 1 94 | 75.5 13.8 10.6 1.1 25.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000718 | 1 41 | 90.2 2.4 7.3 12.2 22.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000719 | 1 55 | 70.9 16.4 12.7 3.6 32.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000720 | 1 51 | 66.7 9.8 23.5 2.0 35.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000721 | 1 40 | 77.5 12.5 10.0 10.0 32.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000722 | 1 42 | 78.6 7.1 14.3 0.0 21.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000723 | 1 28 | 53.6 21.4 25.0 3.6 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000724 | 1 38 | 76.3 7.9 15.8 15.8 39.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000725 | 1 77 | 71.4 5.2 23.4 6.5 35.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000726 | 1 52 | 75.0 11.5 13.5 1.9 26.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000727 | 1 42 | 83.3 4.8 11.9 2.4 19.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000728 | 1 35 | 37.1 51.4 11.4 31.4 94.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000729 | 1 37 | 35.1 32.4 32.4 5.4 70.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000730 | 1 116 | 75.9 8.6 15.5 6.0 30.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000731 | 1 66 | 78.8 7.6 13.6 3.0 24.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000732 | 1 42 | 81.0 9.5 9.5 7.1 26.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000733 | 1 99 | 74.7 6.1 19.2 6.1 31.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000734 | 1 84 | 65.5 20.2 14.3 8.3 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000735 | 1 26 | 57.7 15.4 26.9 7.7 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000736 | 1 20 | 65.0 5.0 30.0 15.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000737 | 1 71 | 63.4 18.3 18.3 1.4 38.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000738 | 1 69 | 75.4 15.9 8.7 8.7 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000739 | 1 50 | 86.0 8.0 6.0 2.0 16.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000740 | 1 6 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000741 | 1 2 | 50.0 50.0 0.0 200.0 250.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000742 | 1 78 | 80.8 9.0 10.3 7.7 26.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000743 | 1 40 | 80.0 12.5 7.5 20.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000744 | 1 4 | 50.0 50.0 0.0 250.0 300.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000745 | 1 4 | 75.0 25.0 0.0 150.0 175.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000746 | 1 56 | 66.1 12.5 21.4 12.5 46.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000747 | 1 83 | 66.3 13.3 20.5 4.8 38.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000748 | 1 20 | 70.0 15.0 15.0 5.0 35.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000749 | 1 26 | 84.6 3.8 11.5 7.7 23.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000750 | 1 83 | 74.7 13.3 12.0 6.0 31.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000751 | 1 92 | 63.0 17.4 19.6 7.6 44.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000752 | 1 71 | 67.6 21.1 11.3 23.9 56.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000753 | 1 80 | 65.0 28.7 6.3 20.0 55.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000754 | 1 42 | 64.3 31.0 4.8 7.1 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000755 | 1 82 | 72.0 19.5 8.5 15.9 43.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000756 | 1 50 | 86.0 6.0 8.0 0.0 14.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000757 | 1 73 | 79.5 8.2 12.3 8.2 28.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000758 | 1 41 | 70.7 12.2 17.1 2.4 31.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000759 | 1 69 | 72.5 15.9 11.6 11.6 39.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000760 | 1 61 | 88.5 8.2 3.3 16.4 27.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000761 | 1 111 | 83.8 9.0 7.2 6.3 22.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000762 | 1 66 | 57.6 19.7 22.7 3.0 45.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000763 | 1 71 | 60.6 14.1 25.4 4.2 43.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000764 | 1 21 | 57.1 14.3 28.6 0.0 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000765 | 1 64 | 67.2 18.8 14.1 4.7 37.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000766 | 1 59 | 69.5 15.3 15.3 5.1 35.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000767 | 1 59 | 59.3 25.4 15.3 6.8 47.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000768 | 1 55 | 60.0 10.9 29.1 3.6 43.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000769 | 1 13 | 61.5 15.4 23.1 7.7 46.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000770 | 1 102 | 82.4 6.9 10.8 8.8 26.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000771 | 1 74 | 87.8 6.8 5.4 6.8 18.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000772 | 1 64 | 79.7 14.1 6.3 3.1 23.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000773 | 1 70 | 87.1 4.3 8.6 7.1 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000774 | 1 75 | 70.7 2.7 26.7 1.3 30.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000775 | 1 27 | 74.1 14.8 11.1 3.7 29.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000776 | 1 71 | 78.9 14.1 7.0 0.0 21.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000777 | 1 41 | 61.0 9.8 29.3 4.9 43.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000778 | 1 28 | 46.4 25.0 28.6 3.6 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000779 | 1 99 | 60.6 25.3 14.1 14.1 53.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000780 | 1 88 | 78.4 9.1 12.5 3.4 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000781 | 1 83 | 77.1 9.6 13.3 7.2 30.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000782 | 1 68 | 88.2 2.9 8.8 5.9 17.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000783 | 1 99 | 86.9 2.0 11.1 5.1 18.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000784 | 1 83 | 92.8 2.4 4.8 3.6 10.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000785 | 1 87 | 73.6 6.9 19.5 1.1 27.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000786 | 1 70 | 72.9 21.4 5.7 4.3 31.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000787 | 1 71 | 76.1 16.9 7.0 15.5 39.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000788 | 1 14 | 71.4 21.4 7.1 7.1 35.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000789 | 1 30 | 66.7 10.0 23.3 10.0 43.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000790 | 1 71 | 70.4 19.7 9.9 7.0 36.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000791 | 1 55 | 70.9 10.9 18.2 1.8 30.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000792 | 1 68 | 82.4 16.2 1.5 11.8 29.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000793 | 1 62 | 85.5 6.5 8.1 4.8 19.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000794 | 1 33 | 66.7 6.1 27.3 3.0 36.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000795 | 1 27 | 70.4 22.2 7.4 0.0 29.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000796 | 1 93 | 72.0 15.1 12.9 12.9 40.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000797 | 1 69 | 65.2 18.8 15.9 14.5 49.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000798 | 1 63 | 82.5 6.3 11.1 11.1 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000799 | 1 55 | 81.8 12.7 5.5 9.1 27.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000800 | 1 38 | 73.7 13.2 13.2 15.8 42.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000801 | 1 85 | 78.8 9.4 11.8 8.2 29.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000378 | 1 170 | 75.3 10.0 14.7 5.9 30.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000379 | 1 221 | 87.3 7.2 5.4 10.0 22.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000380 | 1 121 | 67.8 18.2 14.0 43.8 76.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000381 | 1 68 | 86.8 5.9 7.4 7.4 20.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000382 | 1 170 | 77.1 8.2 14.7 9.4 32.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000383 | 1 128 | 71.9 10.2 18.0 16.4 44.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000384 | 1 200 | 77.0 6.0 17.0 5.5 28.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000385 | 1 124 | 72.6 7.3 20.2 1.6 29.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000386 | 1 221 | 81.4 8.1 10.4 8.1 26.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000387 | 1 180 | 72.2 6.7 21.1 1.7 29.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000388 | 1 366 | 79.0 7.4 13.7 4.4 25.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000389 | 1 221 | 76.9 6.3 16.7 3.6 26.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000390 | 1 171 | 80.7 12.9 6.4 18.1 37.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000391 | 1 148 | 73.6 6.8 19.6 1.4 27.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000392 | 1 248 | 71.4 6.9 21.8 6.5 35.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000393 | 1 162 | 73.5 7.4 19.1 2.5 29.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000394 | 1 143 | 86.0 6.3 7.7 5.6 19.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000395 | 1 151 | 74.2 10.6 15.2 6.0 31.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000396 | 1 252 | 73.4 8.7 17.9 8.3 34.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000397 | 1 176 | 82.4 10.8 6.8 7.4 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000398 | 1 72 | 73.6 12.5 13.9 1.4 27.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000399 | 1 116 | 74.1 10.3 15.5 5.2 31.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000400 | 1 129 | 74.4 5.4 20.2 2.3 27.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000401 | 1 129 | 76.7 8.5 14.7 2.3 25.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000402 | 1 181 | 85.1 8.3 6.6 7.2 22.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000403 | 1 179 | 82.7 9.5 7.8 7.3 24.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000404 | 1 105 | 65.7 24.8 9.5 19.0 53.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000405 | 1 110 | 77.3 7.3 15.5 2.7 25.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000406 | 1 129 | 76.0 13.2 10.9 14.0 38.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000407 | 1 200 | 79.0 14.5 6.5 27.0 48.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000408 | 1 113 | 73.5 13.3 13.3 13.3 39.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000409 | 1 120 | 81.7 10.0 8.3 5.8 24.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000410 | 1 263 | 73.0 8.7 18.3 3.8 30.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000411 | 1 115 | 74.8 6.1 19.1 0.0 25.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000412 | 1 180 | 72.2 10.6 17.2 4.4 32.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000413 | 1 158 | 67.1 12.7 20.3 0.6 33.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000414 | 1 161 | 67.7 9.3 23.0 8.1 40.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000415 | 1 214 | 73.8 7.0 19.2 3.7 29.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000281 | 1 225 | 77.3 9.3 13.3 0.9 23.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000282 | 1 182 | 85.7 5.5 8.8 2.7 17.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000283 | 1 314 | 76.1 5.4 18.5 1.0 24.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000284 | 1 267 | 80.5 1.9 17.6 0.4 19.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000285 | 1 204 | 71.6 11.8 16.7 2.9 31.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000286 | 1 192 | 88.5 2.1 9.4 5.7 17.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000287 | 1 296 | 71.6 10.1 18.2 4.1 32.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000288 | 1 236 | 74.2 5.9 19.9 2.1 28.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000289 | 1 206 | 79.6 7.8 12.6 1.5 21.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000290 | 1 169 | 74.0 7.7 18.3 1.8 27.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000291 | 1 269 | 81.4 5.6 13.0 4.8 23.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000292 | 1 378 | 67.2 9.0 23.8 2.4 35.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000293 | 1 333 | 83.2 4.5 12.3 1.8 18.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000294 | 1 157 | 62.4 12.1 25.5 3.8 41.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000295 | 1 186 | 90.9 6.5 2.7 2.2 11.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000296 | 1 246 | 82.1 4.5 13.4 3.3 21.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000297 | 1 333 | 75.1 9.3 15.6 4.5 29.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000298 | 1 264 | 72.3 8.0 19.7 2.7 30.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000299 | 1 239 | 73.6 6.7 19.7 2.5 28.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000300 | 1 151 | 83.4 5.3 11.3 2.6 19.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000301 | 1 288 | 73.6 6.6 19.8 3.5 29.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000302 | 1 355 | 71.8 6.2 22.0 0.8 29.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000303 | 1 284 | 86.6 6.3 7.0 3.9 17.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000304 | 1 170 | 72.9 8.2 18.8 3.5 30.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000305 | 1 166 | 65.7 9.6 24.7 2.4 36.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000306 | 1 372 | 79.3 4.6 16.1 2.2 22.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000307 | 1 214 | 84.1 6.5 9.3 2.8 18.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000308 | 1 283 | 77.4 7.1 15.5 1.1 23.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000309 | 1 294 | 75.9 8.5 15.6 1.7 25.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000310 | 1 213 | 82.2 3.8 14.1 3.8 21.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000311 | 1 238 | 73.9 4.2 21.8 2.5 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000312 | 1 310 | 79.7 4.8 15.5 1.3 21.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000313 | 1 171 | 90.6 4.1 5.3 7.0 16.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000314 | 1 235 | 77.4 6.8 15.7 1.7 24.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000315 | 1 145 | 89.7 2.8 7.6 1.4 11.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000316 | 1 212 | 84.9 9.0 6.1 10.4 25.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000317 | 1 118 | 84.7 1.7 13.6 3.4 18.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000318 | 1 213 | 85.0 4.7 10.3 2.3 17.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000319 | 1 137 | 86.9 4.4 8.8 3.6 16.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001408 | 1 20 | 80.0 5.0 15.0 25.0 45.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001409 | 1 55 | 89.1 5.5 5.5 3.6 14.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001410 | 1 38 | 76.3 10.5 13.2 0.0 23.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001411 | 1 23 | 87.0 0.0 13.0 0.0 13.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001412 | 1 68 | 91.2 4.4 4.4 1.5 10.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001413 | 1 27 | 92.6 3.7 3.7 0.0 7.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001414 | 1 30 | 63.3 16.7 20.0 26.7 63.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001415 | 1 10 | 90.0 0.0 10.0 0.0 10.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001416 | 1 14 | 85.7 0.0 14.3 7.1 21.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001417 | 1 39 | 69.2 5.1 25.6 5.1 35.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001418 | 1 29 | 86.2 6.9 6.9 10.3 24.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001419 | 1 53 | 83.0 5.7 11.3 0.0 17.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001420 | 1 73 | 67.1 11.0 21.9 1.4 34.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001421 | 1 77 | 68.8 7.8 23.4 2.6 33.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001422 | 1 28 | 71.4 10.7 17.9 3.6 32.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001423 | 1 25 | 76.0 16.0 8.0 0.0 24.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001424 | 1 53 | 88.7 5.7 5.7 3.8 15.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001425 | 1 149 | 78.5 7.4 14.1 4.0 25.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001426 | 1 34 | 85.3 2.9 11.8 2.9 17.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001427 | 1 18 | 66.7 11.1 22.2 5.6 38.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001428 | 1 52 | 80.8 3.8 15.4 5.8 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001429 | 1 45 | 73.3 8.9 17.8 4.4 31.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001430 | 1 40 | 62.5 20.0 17.5 5.0 42.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001431 | 1 37 | 83.8 5.4 10.8 2.7 18.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001432 | 1 25 | 84.0 4.0 12.0 8.0 24.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001433 | 1 48 | 91.7 2.1 6.3 2.1 10.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001434 | 1 114 | 79.8 9.6 10.5 2.6 22.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001435 | 1 31 | 64.5 6.5 29.0 0.0 35.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001436 | 1 19 | 78.9 10.5 10.5 10.5 31.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001437 | 1 70 | 88.6 4.3 7.1 1.4 12.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001438 | 1 49 | 79.6 0.0 20.4 0.0 20.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001439 | 1 29 | 89.7 0.0 10.3 3.4 13.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001440 | 1 43 | 95.3 0.0 4.7 7.0 11.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001441 | 1 62 | 85.5 9.7 4.8 1.6 16.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001442 | 1 111 | 81.1 1.8 17.1 0.9 19.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001443 | 1 41 | 80.5 9.8 9.8 2.4 22.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001444 | 1 38 | 81.6 10.5 7.9 2.6 21.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001445 | 1 46 | 87.0 6.5 6.5 2.2 15.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001446 | 1 25 | 88.0 4.0 8.0 0.0 12.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001447 | 1 20 | 75.0 10.0 15.0 5.0 30.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001448 | 1 57 | 80.7 7.0 12.3 3.5 22.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001449 | 1 45 | 75.6 8.9 15.6 6.7 31.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001450 | 1 67 | 85.1 4.5 10.4 1.5 16.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001451 | 1 24 | 83.3 0.0 16.7 4.2 20.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001452 | 1 38 | 92.1 2.6 5.3 0.0 7.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001453 | 1 43 | 86.0 4.7 9.3 0.0 14.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001454 | 1 124 | 85.5 8.1 6.5 3.2 17.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001455 | 1 48 | 81.3 6.3 12.5 0.0 18.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001456 | 1 19 | 84.2 15.8 0.0 10.5 26.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001457 | 1 47 | 93.6 4.3 2.1 6.4 12.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001458 | 1 190 | 81.6 5.3 13.2 3.2 21.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001459 | 1 41 | 75.6 7.3 17.1 7.3 31.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001460 | 1 23 | 78.3 4.3 17.4 8.7 30.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001461 | 1 19 | 78.9 10.5 10.5 5.3 26.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001462 | 1 87 | 79.3 5.7 14.9 3.4 24.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001463 | 1 62 | 87.1 1.6 11.3 1.6 14.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001464 | 1 33 | 72.7 21.2 6.1 3.0 30.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001465 | 1 55 | 78.2 7.3 14.5 5.5 27.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001466 | 1 44 | 79.5 9.1 11.4 2.3 22.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001467 | 1 27 | 63.0 25.9 11.1 7.4 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001468 | 1 47 | 72.3 10.6 17.0 2.1 29.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001469 | 1 28 | 85.7 3.6 10.7 0.0 14.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001470 | 1 13 | 76.9 15.4 7.7 7.7 30.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001471 | 1 13 | 92.3 7.7 0.0 7.7 15.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001472 | 1 107 | 79.4 8.4 12.1 4.7 25.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001473 | 1 56 | 85.7 10.7 3.6 5.4 19.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001474 | 1 37 | 70.3 10.8 18.9 5.4 35.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001475 | 1 45 | 80.0 11.1 8.9 4.4 24.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001476 | 1 70 | 81.4 1.4 17.1 0.0 18.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001477 | 1 27 | 92.6 3.7 3.7 3.7 11.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001478 | 1 39 | 64.1 15.4 20.5 5.1 41.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001479 | 1 34 | 70.6 5.9 23.5 2.9 32.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001480 | 1 37 | 81.1 16.2 2.7 16.2 35.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001481 | 1 75 | 77.3 5.3 17.3 2.7 25.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001482 | 1 35 | 85.7 11.4 2.9 14.3 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001483 | 1 43 | 76.7 7.0 16.3 9.3 32.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001484 | 1 33 | 78.8 6.1 15.2 0.0 21.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001485 | 1 107 | 81.3 6.5 12.1 18.7 37.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001486 | 1 45 | 62.2 15.6 22.2 2.2 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001487 | 1 26 | 50.0 7.7 42.3 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001488 | 1 59 | 79.7 8.5 11.9 3.4 23.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001489 | 1 57 | 70.2 8.8 21.1 1.8 31.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001490 | 1 32 | 81.3 0.0 18.8 96.9 115.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001491 | 1 37 | 64.9 10.8 24.3 5.4 40.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001492 | 1 40 | 82.5 10.0 7.5 5.0 22.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001493 | 1 32 | 62.5 9.4 28.1 0.0 37.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001494 | 1 42 | 92.9 0.0 7.1 0.0 7.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001495 | 1 26 | 96.2 0.0 3.8 11.5 15.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001496 | 1 52 | 76.9 15.4 7.7 9.6 32.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001497 | 1 57 | 93.0 5.3 1.8 5.3 12.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001498 | 1 58 | 86.2 5.2 8.6 5.2 19.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001499 | 1 27 | 74.1 7.4 18.5 7.4 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001500 | 1 60 | 88.3 3.3 8.3 3.3 15.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001501 | 1 52 | 92.3 1.9 5.8 3.8 11.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001502 | 1 18 | 94.4 0.0 5.6 5.6 11.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001503 | 1 22 | 77.3 22.7 0.0 54.5 77.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001504 | 1 45 | 88.9 2.2 8.9 4.4 15.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001505 | 1 45 | 77.8 8.9 13.3 2.2 24.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001506 | 1 94 | 88.3 8.5 3.2 7.4 19.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001507 | 1 55 | 90.9 1.8 7.3 3.6 12.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001508 | 1 20 | 85.0 5.0 10.0 0.0 15.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001509 | 1 20 | 65.0 25.0 10.0 10.0 45.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001510 | 1 62 | 90.3 4.8 4.8 1.6 11.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001511 | 1 44 | 79.5 9.1 11.4 0.0 20.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001512 | 1 82 | 92.7 2.4 4.9 4.9 12.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001513 | 1 41 | 92.7 7.3 0.0 2.4 9.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001514 | 1 67 | 82.1 9.0 9.0 9.0 26.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001515 | 1 20 | 80.0 0.0 20.0 0.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001516 | 1 34 | 94.1 2.9 2.9 8.8 14.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001517 | 1 46 | 89.1 2.2 8.7 6.5 17.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001518 | 1 66 | 86.4 4.5 9.1 3.0 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001519 | 1 39 | 87.2 2.6 10.3 0.0 12.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001520 | 1 79 | 92.4 7.6 0.0 7.6 15.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001521 | 1 24 | 75.0 12.5 12.5 0.0 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001522 | 1 39 | 92.3 2.6 5.1 2.6 10.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001523 | 1 24 | 83.3 0.0 16.7 12.5 29.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001524 | 1 48 | 89.6 4.2 6.3 2.1 12.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001525 | 1 25 | 84.0 12.0 4.0 8.0 24.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001526 | 1 15 | 86.7 13.3 0.0 0.0 13.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001527 | 1 27 | 66.7 11.1 22.2 14.8 48.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001528 | 1 19 | 78.9 15.8 5.3 10.5 31.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001529 | 1 16 | 75.0 6.3 18.8 18.8 43.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001530 | 1 20 | 85.0 5.0 10.0 5.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001531 | 1 15 | 40.0 6.7 53.3 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001532 | 1 26 | 92.3 3.8 3.8 7.7 15.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001533 | 1 33 | 72.7 15.2 12.1 12.1 39.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001534 | 1 18 | 94.4 0.0 5.6 11.1 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001535 | 1 16 | 93.8 0.0 6.3 6.3 12.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001536 | 1 19 | 68.4 15.8 15.8 10.5 42.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001537 | 1 17 | 76.5 17.6 5.9 58.8 82.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001538 | 1 39 | 89.7 2.6 7.7 2.6 12.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001539 | 1 26 | 69.2 7.7 23.1 0.0 30.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001540 | 1 59 | 93.2 0.0 6.8 8.5 15.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001541 | 1 11 | 90.9 0.0 9.1 18.2 27.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001542 | 1 56 | 82.1 3.6 14.3 0.0 17.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001543 | 1 164 | 87.2 4.9 7.9 3.0 15.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001544 | 1 54 | 79.6 13.0 7.4 7.4 27.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001545 | 1 25 | 80.0 12.0 8.0 4.0 24.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001546 | 1 70 | 84.3 7.1 8.6 2.9 18.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001547 | 1 65 | 84.6 7.7 7.7 7.7 23.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001548 | 1 49 | 79.6 8.2 12.2 2.0 22.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001549 | 1 29 | 34.5 10.3 55.2 6.9 72.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001550 | 1 45 | 84.4 2.2 13.3 2.2 17.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001551 | 1 19 | 94.7 0.0 5.3 21.1 26.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001552 | 1 44 | 77.3 4.5 18.2 4.5 27.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001553 | 1 147 | 82.3 6.8 10.9 5.4 23.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001554 | 1 94 | 80.9 5.3 13.8 0.0 19.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001555 | 1 55 | 78.2 5.5 16.4 1.8 23.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001556 | 1 53 | 79.2 5.7 15.1 0.0 20.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001557 | 1 19 | 84.2 5.3 10.5 5.3 21.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001558 | 1 43 | 88.4 4.7 7.0 4.7 16.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001559 | 1 80 | 86.3 3.8 10.0 3.8 17.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001560 | 1 19 | 73.7 15.8 10.5 10.5 36.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001561 | 1 87 | 90.8 3.4 5.7 6.9 16.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001562 | 1 94 | 81.9 7.4 10.6 3.2 21.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001563 | 1 46 | 76.1 10.9 13.0 4.3 28.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001564 | 1 34 | 82.4 5.9 11.8 0.0 17.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001565 | 1 76 | 82.9 6.6 10.5 2.6 19.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001566 | 1 49 | 85.7 6.1 8.2 8.2 22.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001567 | 1 16 | 81.3 6.3 12.5 6.3 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001568 | 1 100 | 91.0 5.0 4.0 1.0 10.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001569 | 1 71 | 94.4 4.2 1.4 8.5 14.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001570 | 1 35 | 85.7 11.4 2.9 5.7 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001571 | 1 21 | 90.5 9.5 0.0 19.0 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001572 | 1 75 | 86.7 5.3 8.0 2.7 16.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001573 | 1 14 | 64.3 14.3 21.4 7.1 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001574 | 1 64 | 92.2 3.1 4.7 3.1 10.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001575 | 1 35 | 94.3 2.9 2.9 11.4 17.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001576 | 1 84 | 88.1 6.0 6.0 15.5 27.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001577 | 1 43 | 88.4 7.0 4.7 4.7 16.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001578 | 1 116 | 87.1 3.4 9.5 4.3 17.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001579 | 1 126 | 86.5 7.1 6.3 2.4 15.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001580 | 1 28 | 75.0 10.7 14.3 17.9 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001581 | 1 45 | 95.6 2.2 2.2 4.4 8.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001582 | 1 26 | 69.2 15.4 15.4 3.8 34.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001583 | 1 36 | 80.6 11.1 8.3 5.6 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001584 | 1 31 | 83.9 9.7 6.5 12.9 29.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001585 | 1 68 | 85.3 5.9 8.8 7.4 22.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001586 | 1 61 | 68.9 8.2 23.0 0.0 31.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001587 | 1 40 | 92.5 2.5 5.0 5.0 12.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001588 | 1 54 | 90.7 3.7 5.6 9.3 18.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001589 | 1 25 | 72.0 8.0 20.0 0.0 28.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001590 | 1 48 | 79.2 6.3 14.6 2.1 22.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001591 | 1 25 | 68.0 20.0 12.0 12.0 44.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001592 | 1 103 | 75.7 3.9 20.4 0.0 24.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001593 | 1 35 | 80.0 5.7 14.3 5.7 25.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001594 | 1 63 | 84.1 7.9 7.9 1.6 17.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001595 | 1 17 | 88.2 5.9 5.9 5.9 17.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001596 | 1 19 | 84.2 5.3 10.5 5.3 21.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001597 | 1 82 | 79.3 8.5 12.2 2.4 23.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001598 | 1 154 | 88.3 5.2 6.5 3.9 15.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001599 | 1 204 | 81.4 6.4 12.3 2.0 20.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000891 | 1 29 | 79.3 6.9 13.8 3.4 24.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000892 | 1 72 | 93.1 1.4 5.6 1.4 8.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000893 | 1 41 | 80.5 4.9 14.6 0.0 19.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000894 | 1 35 | 77.1 2.9 20.0 0.0 22.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000895 | 1 54 | 90.7 3.7 5.6 0.0 9.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000897 | 1 2 | 0.0 100.0 0.0 600.0 700.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000898 | 1 51 | 86.3 3.9 9.8 3.9 17.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000899 | 1 29 | 82.8 10.3 6.9 3.4 20.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000900 | 1 43 | 67.4 2.3 30.2 0.0 32.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000901 | 1 53 | 86.8 3.8 9.4 0.0 13.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000902 | 1 30 | 86.7 6.7 6.7 10.0 23.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000903 | 1 57 | 82.5 5.3 12.3 5.3 22.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000904 | 1 53 | 83.0 3.8 13.2 5.7 22.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000905 | 1 37 | 83.8 5.4 10.8 0.0 16.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000906 | 1 63 | 57.1 12.7 30.2 1.6 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000907 | 1 32 | 96.9 0.0 3.1 0.0 3.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000908 | 1 62 | 64.5 9.7 25.8 0.0 35.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000909 | 1 61 | 62.3 18.0 19.7 6.6 44.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000910 | 1 70 | 90.0 4.3 5.7 5.7 15.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000911 | 1 34 | 76.5 2.9 20.6 0.0 23.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000912 | 1 64 | 79.7 4.7 15.6 0.0 20.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000913 | 1 38 | 89.5 2.6 7.9 2.6 13.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000914 | 1 50 | 80.0 4.0 16.0 16.0 36.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000915 | 1 63 | 73.0 4.8 22.2 1.6 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000917 | 1 42 | 81.0 4.8 14.3 2.4 21.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000918 | 1 10 | 60.0 10.0 30.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000919 | 1 39 | 84.6 5.1 10.3 0.0 15.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000920 | 1 40 | 77.5 10.0 12.5 2.5 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000921 | 1 35 | 88.6 5.7 5.7 0.0 11.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000922 | 1 33 | 78.8 3.0 18.2 12.1 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000923 | 1 67 | 70.1 11.9 17.9 4.5 34.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000924 | 1 30 | 63.3 6.7 30.0 3.3 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000925 | 1 44 | 86.4 9.1 4.5 4.5 18.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000926 | 1 48 | 83.3 6.3 10.4 2.1 18.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000927 | 1 39 | 87.2 0.0 12.8 0.0 12.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000928 | 1 35 | 80.0 5.7 14.3 11.4 31.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000929 | 1 73 | 91.8 2.7 5.5 4.1 12.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000930 | 1 59 | 64.4 16.9 18.6 1.7 37.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000931 | 1 49 | 77.6 18.4 4.1 2.0 24.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000932 | 1 66 | 83.3 4.5 12.1 4.5 21.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000933 | 1 33 | 87.9 3.0 9.1 3.0 15.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000934 | 1 47 | 83.0 10.6 6.4 8.5 25.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000935 | 1 55 | 89.1 9.1 1.8 7.3 18.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000936 | 1 67 | 74.6 7.5 17.9 1.5 26.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000937 | 1 63 | 81.0 9.5 9.5 19.0 38.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000938 | 1 64 | 71.9 14.1 14.1 1.6 29.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000939 | 1 25 | 96.0 0.0 4.0 24.0 28.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000940 | 1 54 | 87.0 3.7 9.3 5.6 18.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000941 | 1 47 | 83.0 4.3 12.8 0.0 17.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000942 | 1 58 | 63.8 10.3 25.9 5.2 41.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000943 | 1 59 | 94.9 5.1 0.0 1.7 6.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000944 | 1 36 | 94.4 0.0 5.6 5.6 11.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000945 | 1 29 | 96.6 3.4 0.0 0.0 3.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000946 | 1 57 | 91.2 3.5 5.3 3.5 12.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000947 | 1 62 | 82.3 12.9 4.8 21.0 38.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000948 | 1 28 | 75.0 10.7 14.3 3.6 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000950 | 1 40 | 87.5 0.0 12.5 2.5 15.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000951 | 1 34 | 91.2 2.9 5.9 0.0 8.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000952 | 1 30 | 93.3 6.7 0.0 13.3 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000953 | 1 69 | 92.8 0.0 7.2 5.8 13.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000954 | 1 34 | 88.2 2.9 8.8 2.9 14.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000955 | 1 34 | 94.1 5.9 0.0 11.8 17.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000956 | 1 68 | 85.3 1.5 13.2 2.9 17.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000957 | 1 41 | 82.9 2.4 14.6 0.0 17.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000958 | 1 61 | 86.9 4.9 8.2 6.6 19.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000959 | 1 52 | 88.5 3.8 7.7 3.8 15.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000960 | 1 60 | 76.7 8.3 15.0 1.7 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000961 | 1 24 | 91.7 4.2 4.2 0.0 8.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000962 | 1 36 | 77.8 5.6 16.7 2.8 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000963 | 1 35 | 94.3 5.7 0.0 14.3 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000964 | 1 43 | 86.0 4.7 9.3 4.7 18.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000965 | 1 48 | 83.3 4.2 12.5 4.2 20.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000966 | 1 19 | 78.9 15.8 5.3 10.5 31.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000967 | 1 31 | 74.2 9.7 16.1 6.5 32.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000968 | 1 39 | 53.8 10.3 35.9 2.6 48.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000969 | 1 36 | 97.2 0.0 2.8 0.0 2.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000970 | 1 40 | 65.0 7.5 27.5 0.0 35.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000971 | 1 45 | 93.3 0.0 6.7 2.2 8.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000972 | 1 56 | 87.5 3.6 8.9 3.6 16.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000973 | 1 69 | 76.8 10.1 13.0 4.3 27.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000974 | 1 28 | 78.6 3.6 17.9 0.0 21.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000975 | 1 29 | 82.8 3.4 13.8 6.9 24.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000976 | 1 74 | 73.0 8.1 18.9 2.7 29.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000977 | 1 29 | 75.9 10.3 13.8 0.0 24.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000978 | 1 44 | 88.6 2.3 9.1 4.5 15.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000979 | 1 47 | 72.3 14.9 12.8 0.0 27.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000980 | 1 42 | 92.9 0.0 7.1 2.4 9.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000981 | 1 62 | 85.5 3.2 11.3 3.2 17.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000982 | 1 58 | 82.8 3.4 13.8 6.9 24.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000983 | 1 53 | 71.7 3.8 24.5 3.8 32.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000984 | 1 57 | 84.2 5.3 10.5 1.8 17.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000985 | 1 46 | 67.4 17.4 15.2 2.2 34.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000986 | 1 41 | 75.6 12.2 12.2 2.4 26.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000987 | 1 40 | 82.5 2.5 15.0 0.0 17.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000988 | 1 29 | 89.7 3.4 6.9 13.8 24.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000989 | 1 39 | 84.6 7.7 7.7 0.0 15.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000990 | 1 41 | 82.9 4.9 12.2 0.0 17.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000991 | 1 55 | 92.7 1.8 5.5 9.1 16.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000992 | 1 34 | 79.4 2.9 17.6 0.0 20.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000993 | 1 65 | 92.3 3.1 4.6 3.1 10.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000994 | 1 78 | 87.2 9.0 3.8 3.8 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000995 | 1 47 | 85.1 6.4 8.5 0.0 14.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000996 | 1 38 | 78.9 7.9 13.2 0.0 21.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000997 | 1 58 | 79.3 10.3 10.3 3.4 24.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000998 | 1 31 | 80.6 6.5 12.9 6.5 25.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000999 | 1 51 | 86.3 9.8 3.9 0.0 13.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001000 | 1 38 | 89.5 2.6 7.9 0.0 10.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001001 | 1 33 | 81.8 9.1 9.1 9.1 27.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001002 | 1 39 | 84.6 10.3 5.1 7.7 23.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001003 | 1 69 | 89.9 5.8 4.3 5.8 15.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001004 | 1 50 | 84.0 14.0 2.0 6.0 22.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001006 | 1 39 | 64.1 15.4 20.5 0.0 35.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001007 | 1 36 | 94.4 2.8 2.8 0.0 5.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001008 | 1 57 | 73.7 12.3 14.0 1.8 28.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001009 | 1 38 | 60.5 5.3 34.2 0.0 39.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001010 | 1 80 | 78.8 3.8 17.5 3.8 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001011 | 1 44 | 79.5 6.8 13.6 4.5 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001012 | 1 42 | 83.3 9.5 7.1 2.4 19.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001013 | 1 44 | 75.0 9.1 15.9 6.8 31.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001014 | 1 39 | 100.0 0.0 0.0 5.1 5.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001015 | 1 28 | 75.0 17.9 7.1 3.6 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001016 | 1 31 | 87.1 6.5 6.5 6.5 19.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001017 | 1 41 | 85.4 4.9 9.8 2.4 17.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001018 | 1 55 | 81.8 5.5 12.7 1.8 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001019 | 1 39 | 82.1 5.1 12.8 0.0 17.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001020 | 1 35 | 82.9 5.7 11.4 0.0 17.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000309 | 1 137 | 73.0 5.1 21.9 2.2 29.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000310 | 1 102 | 60.8 11.8 27.5 5.9 45.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000311 | 1 55 | 49.1 10.9 40.0 1.8 52.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000312 | 1 56 | 62.5 12.5 25.0 19.6 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000313 | 1 119 | 66.4 18.5 15.1 10.1 43.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000314 | 1 131 | 73.3 4.6 22.1 1.5 28.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000315 | 1 92 | 77.2 14.1 8.7 5.4 28.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000316 | 1 60 | 63.3 16.7 20.0 3.3 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000317 | 1 149 | 68.5 12.1 19.5 7.4 38.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000318 | 1 64 | 78.1 3.1 18.8 9.4 31.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000319 | 1 125 | 86.4 8.0 5.6 7.2 20.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000320 | 1 82 | 72.0 9.8 18.3 9.8 37.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000321 | 1 102 | 77.5 12.7 9.8 2.9 25.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000322 | 1 110 | 74.5 7.3 18.2 4.5 30.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000323 | 1 197 | 79.7 7.1 13.2 12.2 32.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000324 | 1 140 | 77.1 6.4 16.4 2.1 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000325 | 1 32 | 50.0 12.5 37.5 9.4 59.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000326 | 1 109 | 72.5 11.0 16.5 5.5 33.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000327 | 1 80 | 70.0 8.8 21.3 11.3 41.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000328 | 1 170 | 81.2 8.2 10.6 5.3 24.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000329 | 1 31 | 74.2 6.5 19.4 3.2 29.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000330 | 1 60 | 83.3 6.7 10.0 0.0 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000331 | 1 339 | 63.1 9.7 27.1 5.0 41.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000332 | 1 129 | 66.7 4.7 28.7 1.6 34.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000333 | 1 210 | 71.0 11.0 18.1 6.2 35.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000334 | 1 110 | 89.1 6.4 4.5 18.2 29.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000335 | 1 454 | 62.6 8.8 28.6 2.9 40.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000336 | 1 48 | 35.4 33.3 31.3 20.8 85.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000337 | 1 148 | 72.3 6.8 20.9 3.4 31.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000338 | 1 54 | 72.2 11.1 16.7 11.1 38.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000339 | 1 182 | 81.3 6.6 12.1 6.0 24.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000340 | 1 36 | 72.2 13.9 13.9 5.6 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000341 | 1 49 | 67.3 0.0 32.7 0.0 32.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000342 | 1 74 | 86.5 4.1 9.5 2.7 16.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000343 | 1 148 | 80.4 10.1 9.5 5.4 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000344 | 1 191 | 82.7 11.0 6.3 13.1 30.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000345 | 1 179 | 71.5 10.6 17.9 7.3 35.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000346 | 1 55 | 72.7 5.5 21.8 3.6 30.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000347 | 1 112 | 74.1 9.8 16.1 2.7 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000348 | 1 93 | 74.2 12.9 12.9 5.4 31.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000349 | 1 53 | 84.9 11.3 3.8 13.2 28.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000350 | 1 44 | 38.6 11.4 50.0 9.1 70.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000351 | 1 295 | 76.6 10.2 13.2 3.7 27.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000352 | 1 149 | 77.9 7.4 14.8 8.7 30.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000353 | 1 87 | 77.0 12.6 10.3 5.7 28.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000354 | 1 92 | 89.1 3.3 7.6 4.3 15.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000355 | 1 156 | 69.2 5.1 25.6 3.2 34.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000356 | 1 14 | 71.4 7.1 21.4 0.0 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000357 | 1 149 | 81.9 8.1 10.1 2.7 20.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000358 | 1 114 | 66.7 7.0 26.3 2.6 36.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000359 | 1 58 | 63.8 10.3 25.9 5.2 41.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000360 | 1 345 | 73.0 10.4 16.5 3.5 30.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000361 | 1 60 | 85.0 8.3 6.7 8.3 23.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000362 | 1 184 | 75.5 6.0 18.5 3.8 28.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000363 | 1 83 | 71.1 8.4 20.5 8.4 37.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000364 | 1 162 | 76.5 7.4 16.0 6.8 30.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000365 | 1 151 | 66.9 23.2 9.9 19.9 53.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000366 | 1 63 | 82.5 9.5 7.9 28.6 46.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000367 | 1 250 | 76.8 8.8 14.4 6.8 30.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000368 | 1 183 | 71.0 7.1 21.9 6.6 35.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000369 | 1 169 | 78.7 7.7 13.6 2.4 23.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000370 | 1 122 | 74.6 7.4 18.0 3.3 28.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000371 | 1 36 | 44.4 8.3 47.2 13.9 69.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000372 | 1 103 | 68.0 15.5 16.5 5.8 37.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000373 | 1 154 | 74.0 11.0 14.9 5.8 31.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000374 | 1 104 | 71.2 13.5 15.4 18.3 47.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000375 | 1 42 | 38.1 19.0 42.9 0.0 61.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000376 | 1 90 | 78.9 6.7 14.4 3.3 24.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000377 | 1 188 | 70.2 6.9 22.9 5.9 35.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000378 | 1 149 | 77.2 9.4 13.4 14.8 37.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000379 | 1 246 | 80.9 8.1 11.0 7.7 26.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000380 | 1 204 | 72.1 8.8 19.1 6.4 34.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000381 | 1 54 | 68.5 9.3 22.2 37.0 68.5 100.0 | +|=================================================================================================================| +| Sum/Avg | 661 54401 | 77.7 8.2 14.0 5.5 27.7 100.0 | +|=================================================================================================================| +| Mean | 1.2 94.9 | 77.7 9.1 13.3 8.5 30.8 100.0 | +| S.D. | 3.7 362.2 | 11.4 7.8 8.1 31.0 36.5 0.0 | +| Median | 1.0 55.0 | 79.3 7.4 12.3 4.2 25.9 100.0 | +`-----------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,-----------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/hyp.trn| +|-----------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------------+---------------------+----------------------------------------------------------------| +| m | 89 8584 | 6779 646 1159 466 2271 89 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000698 | 1 51 | 36 9 6 4 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000699 | 1 21 | 9 6 6 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000700 | 1 54 | 39 8 7 6 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000701 | 1 90 | 54 23 13 1 37 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000702 | 1 50 | 39 7 4 3 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000703 | 1 64 | 51 5 8 7 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000704 | 1 4 | 3 1 0 8 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000705 | 1 4 | 1 2 1 5 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000706 | 1 90 | 75 5 10 2 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000707 | 1 18 | 15 3 0 5 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000708 | 1 75 | 45 20 10 8 38 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000709 | 1 55 | 39 12 4 10 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000710 | 1 105 | 84 14 7 4 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000711 | 1 86 | 73 2 11 2 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000712 | 1 37 | 20 4 13 2 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000713 | 1 41 | 26 10 5 2 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000714 | 1 72 | 34 15 23 5 43 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000715 | 1 66 | 32 12 22 0 34 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000716 | 1 50 | 34 7 9 1 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000717 | 1 94 | 71 13 10 1 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000718 | 1 41 | 37 1 3 5 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000719 | 1 55 | 39 9 7 2 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000720 | 1 51 | 34 5 12 1 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000721 | 1 40 | 31 5 4 4 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000722 | 1 42 | 33 3 6 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000723 | 1 28 | 15 6 7 1 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000724 | 1 38 | 29 3 6 6 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000725 | 1 77 | 55 4 18 5 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000726 | 1 52 | 39 6 7 1 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000727 | 1 42 | 35 2 5 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000728 | 1 35 | 13 18 4 11 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000729 | 1 37 | 13 12 12 2 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000730 | 1 116 | 88 10 18 7 35 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000731 | 1 66 | 52 5 9 2 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000732 | 1 42 | 34 4 4 3 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000733 | 1 99 | 74 6 19 6 31 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000734 | 1 84 | 55 17 12 7 36 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000735 | 1 26 | 15 4 7 2 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000736 | 1 20 | 13 1 6 3 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000737 | 1 71 | 45 13 13 1 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000738 | 1 69 | 52 11 6 6 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000739 | 1 50 | 43 4 3 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000740 | 1 6 | 4 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000741 | 1 2 | 1 1 0 4 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000742 | 1 78 | 63 7 8 6 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000743 | 1 40 | 32 5 3 8 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000744 | 1 4 | 2 2 0 10 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000745 | 1 4 | 3 1 0 6 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000746 | 1 56 | 37 7 12 7 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000747 | 1 83 | 55 11 17 4 32 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000748 | 1 20 | 14 3 3 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000749 | 1 26 | 22 1 3 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000750 | 1 83 | 62 11 10 5 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000751 | 1 92 | 58 16 18 7 41 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000752 | 1 71 | 48 15 8 17 40 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000753 | 1 80 | 52 23 5 16 44 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000754 | 1 42 | 27 13 2 3 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000755 | 1 82 | 59 16 7 13 36 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000756 | 1 50 | 43 3 4 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000757 | 1 73 | 58 6 9 6 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000758 | 1 41 | 29 5 7 1 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000759 | 1 69 | 50 11 8 8 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000760 | 1 61 | 54 5 2 10 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000761 | 1 111 | 93 10 8 7 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000762 | 1 66 | 38 13 15 2 30 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000763 | 1 71 | 43 10 18 3 31 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000764 | 1 21 | 12 3 6 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000765 | 1 64 | 43 12 9 3 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000766 | 1 59 | 41 9 9 3 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000767 | 1 59 | 35 15 9 4 28 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000768 | 1 55 | 33 6 16 2 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000769 | 1 13 | 8 2 3 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000770 | 1 102 | 84 7 11 9 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000771 | 1 74 | 65 5 4 5 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000772 | 1 64 | 51 9 4 2 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000773 | 1 70 | 61 3 6 5 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000774 | 1 75 | 53 2 20 1 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000775 | 1 27 | 20 4 3 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000776 | 1 71 | 56 10 5 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000777 | 1 41 | 25 4 12 2 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000778 | 1 28 | 13 7 8 1 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000779 | 1 99 | 60 25 14 14 53 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000780 | 1 88 | 69 8 11 3 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000781 | 1 83 | 64 8 11 6 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000782 | 1 68 | 60 2 6 4 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000783 | 1 99 | 86 2 11 5 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000784 | 1 83 | 77 2 4 3 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000785 | 1 87 | 64 6 17 1 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000786 | 1 70 | 51 15 4 3 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000787 | 1 71 | 54 12 5 11 28 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000788 | 1 14 | 10 3 1 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000789 | 1 30 | 20 3 7 3 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000790 | 1 71 | 50 14 7 5 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000791 | 1 55 | 39 6 10 1 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000792 | 1 68 | 56 11 1 8 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000793 | 1 62 | 53 4 5 3 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000794 | 1 33 | 22 2 9 1 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000795 | 1 27 | 19 6 2 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000796 | 1 93 | 67 14 12 12 38 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000797 | 1 69 | 45 13 11 10 34 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000798 | 1 63 | 52 4 7 7 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000799 | 1 55 | 45 7 3 5 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000800 | 1 38 | 28 5 5 6 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000801 | 1 85 | 67 8 10 7 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000378 | 1 170 | 128 17 25 10 52 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000379 | 1 221 | 193 16 12 22 50 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000380 | 1 121 | 82 22 17 53 92 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000381 | 1 68 | 59 4 5 5 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000382 | 1 170 | 131 14 25 16 55 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000383 | 1 128 | 92 13 23 21 57 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000384 | 1 200 | 154 12 34 11 57 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000385 | 1 124 | 90 9 25 2 36 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000386 | 1 221 | 180 18 23 18 59 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000387 | 1 180 | 130 12 38 3 53 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000388 | 1 366 | 289 27 50 16 93 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000389 | 1 221 | 170 14 37 8 59 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000390 | 1 171 | 138 22 11 31 64 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000391 | 1 148 | 109 10 29 2 41 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000392 | 1 248 | 177 17 54 16 87 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000393 | 1 162 | 119 12 31 4 47 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000394 | 1 143 | 123 9 11 8 28 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000395 | 1 151 | 112 16 23 9 48 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000396 | 1 252 | 185 22 45 21 88 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000397 | 1 176 | 145 19 12 13 44 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000398 | 1 72 | 53 9 10 1 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000399 | 1 116 | 86 12 18 6 36 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000400 | 1 129 | 96 7 26 3 36 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000401 | 1 129 | 99 11 19 3 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000402 | 1 181 | 154 15 12 13 40 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000403 | 1 179 | 148 17 14 13 44 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000404 | 1 105 | 69 26 10 20 56 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000405 | 1 110 | 85 8 17 3 28 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000406 | 1 129 | 98 17 14 18 49 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000407 | 1 200 | 158 29 13 54 96 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000408 | 1 113 | 83 15 15 15 45 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000409 | 1 120 | 98 12 10 7 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000410 | 1 263 | 192 23 48 10 81 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000411 | 1 115 | 86 7 22 0 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000412 | 1 180 | 130 19 31 8 58 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000413 | 1 158 | 106 20 32 1 53 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000414 | 1 161 | 109 15 37 13 65 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000415 | 1 214 | 158 15 41 8 64 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000281 | 1 225 | 174 21 30 2 53 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000282 | 1 182 | 156 10 16 5 31 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000283 | 1 314 | 239 17 58 3 78 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000284 | 1 267 | 215 5 47 1 53 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000285 | 1 204 | 146 24 34 6 64 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000286 | 1 192 | 170 4 18 11 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000287 | 1 296 | 212 30 54 12 96 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000288 | 1 236 | 175 14 47 5 66 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000289 | 1 206 | 164 16 26 3 45 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000290 | 1 169 | 125 13 31 3 47 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000291 | 1 269 | 219 15 35 13 63 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000292 | 1 378 | 254 34 90 9 133 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000293 | 1 333 | 277 15 41 6 62 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000294 | 1 157 | 98 19 40 6 65 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000295 | 1 186 | 169 12 5 4 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000296 | 1 246 | 202 11 33 8 52 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000297 | 1 333 | 250 31 52 15 98 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000298 | 1 264 | 191 21 52 7 80 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000299 | 1 239 | 176 16 47 6 69 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000300 | 1 151 | 126 8 17 4 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000301 | 1 288 | 212 19 57 10 86 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000302 | 1 355 | 255 22 78 3 103 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000303 | 1 284 | 246 18 20 11 49 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000304 | 1 170 | 124 14 32 6 52 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000305 | 1 166 | 109 16 41 4 61 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000306 | 1 372 | 295 17 60 8 85 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000307 | 1 214 | 180 14 20 6 40 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000308 | 1 283 | 219 20 44 3 67 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000309 | 1 294 | 223 25 46 5 76 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000310 | 1 213 | 175 8 30 8 46 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000311 | 1 238 | 176 10 52 6 68 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000312 | 1 310 | 247 15 48 4 67 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000313 | 1 171 | 155 7 9 12 28 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000314 | 1 235 | 182 16 37 4 57 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000315 | 1 145 | 130 4 11 2 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000316 | 1 212 | 180 19 13 22 54 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000317 | 1 118 | 100 2 16 4 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000318 | 1 213 | 181 10 22 5 37 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000319 | 1 137 | 119 6 12 5 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001408 | 1 20 | 16 1 3 5 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001409 | 1 55 | 49 3 3 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001410 | 1 38 | 29 4 5 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001411 | 1 23 | 20 0 3 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001412 | 1 68 | 62 3 3 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001413 | 1 27 | 25 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001414 | 1 30 | 19 5 6 8 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001415 | 1 10 | 9 0 1 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001416 | 1 14 | 12 0 2 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001417 | 1 39 | 27 2 10 2 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001418 | 1 29 | 25 2 2 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001419 | 1 53 | 44 3 6 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001420 | 1 73 | 49 8 16 1 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001421 | 1 77 | 53 6 18 2 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001422 | 1 28 | 20 3 5 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001423 | 1 25 | 19 4 2 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001424 | 1 53 | 47 3 3 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001425 | 1 149 | 117 11 21 6 38 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001426 | 1 34 | 29 1 4 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001427 | 1 18 | 12 2 4 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001428 | 1 52 | 42 2 8 3 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001429 | 1 45 | 33 4 8 2 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001430 | 1 40 | 25 8 7 2 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001431 | 1 37 | 31 2 4 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001432 | 1 25 | 21 1 3 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001433 | 1 48 | 44 1 3 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001434 | 1 114 | 91 11 12 3 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001435 | 1 31 | 20 2 9 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001436 | 1 19 | 15 2 2 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001437 | 1 70 | 62 3 5 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001438 | 1 49 | 39 0 10 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001439 | 1 29 | 26 0 3 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001440 | 1 43 | 41 0 2 3 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001441 | 1 62 | 53 6 3 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001442 | 1 111 | 90 2 19 1 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001443 | 1 41 | 33 4 4 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001444 | 1 38 | 31 4 3 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001445 | 1 46 | 40 3 3 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001446 | 1 25 | 22 1 2 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001447 | 1 20 | 15 2 3 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001448 | 1 57 | 46 4 7 2 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001449 | 1 45 | 34 4 7 3 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001450 | 1 67 | 57 3 7 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001451 | 1 24 | 20 0 4 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001452 | 1 38 | 35 1 2 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001453 | 1 43 | 37 2 4 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001454 | 1 124 | 106 10 8 4 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001455 | 1 48 | 39 3 6 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001456 | 1 19 | 16 3 0 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001457 | 1 47 | 44 2 1 3 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001458 | 1 190 | 155 10 25 6 41 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001459 | 1 41 | 31 3 7 3 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001460 | 1 23 | 18 1 4 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001461 | 1 19 | 15 2 2 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001462 | 1 87 | 69 5 13 3 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001463 | 1 62 | 54 1 7 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001464 | 1 33 | 24 7 2 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001465 | 1 55 | 43 4 8 3 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001466 | 1 44 | 35 4 5 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001467 | 1 27 | 17 7 3 2 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001468 | 1 47 | 34 5 8 1 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001469 | 1 28 | 24 1 3 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001470 | 1 13 | 10 2 1 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001471 | 1 13 | 12 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001472 | 1 107 | 85 9 13 5 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001473 | 1 56 | 48 6 2 3 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001474 | 1 37 | 26 4 7 2 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001475 | 1 45 | 36 5 4 2 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001476 | 1 70 | 57 1 12 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001477 | 1 27 | 25 1 1 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001478 | 1 39 | 25 6 8 2 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001479 | 1 34 | 24 2 8 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001480 | 1 37 | 30 6 1 6 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001481 | 1 75 | 58 4 13 2 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001482 | 1 35 | 30 4 1 5 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001483 | 1 43 | 33 3 7 4 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001484 | 1 33 | 26 2 5 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001485 | 1 107 | 87 7 13 20 40 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001486 | 1 45 | 28 7 10 1 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001487 | 1 26 | 13 2 11 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001488 | 1 59 | 47 5 7 2 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001489 | 1 57 | 40 5 12 1 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001490 | 1 32 | 26 0 6 31 37 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001491 | 1 37 | 24 4 9 2 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001492 | 1 40 | 33 4 3 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001493 | 1 32 | 20 3 9 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001494 | 1 42 | 39 0 3 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001495 | 1 26 | 25 0 1 3 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001496 | 1 52 | 40 8 4 5 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001497 | 1 57 | 53 3 1 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001498 | 1 58 | 50 3 5 3 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001499 | 1 27 | 20 2 5 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001500 | 1 60 | 53 2 5 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001501 | 1 52 | 48 1 3 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001502 | 1 18 | 17 0 1 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001503 | 1 22 | 17 5 0 12 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001504 | 1 45 | 40 1 4 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001505 | 1 45 | 35 4 6 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001506 | 1 94 | 83 8 3 7 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001507 | 1 55 | 50 1 4 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001508 | 1 20 | 17 1 2 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001509 | 1 20 | 13 5 2 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001510 | 1 62 | 56 3 3 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001511 | 1 44 | 35 4 5 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001512 | 1 82 | 76 2 4 4 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001513 | 1 41 | 38 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001514 | 1 67 | 55 6 6 6 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001515 | 1 20 | 16 0 4 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001516 | 1 34 | 32 1 1 3 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001517 | 1 46 | 41 1 4 3 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001518 | 1 66 | 57 3 6 2 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001519 | 1 39 | 34 1 4 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001520 | 1 79 | 73 6 0 6 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001521 | 1 24 | 18 3 3 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001522 | 1 39 | 36 1 2 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001523 | 1 24 | 20 0 4 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001524 | 1 48 | 43 2 3 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001525 | 1 25 | 21 3 1 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001526 | 1 15 | 13 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001527 | 1 27 | 18 3 6 4 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001528 | 1 19 | 15 3 1 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001529 | 1 16 | 12 1 3 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001530 | 1 20 | 17 1 2 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001531 | 1 15 | 6 1 8 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001532 | 1 26 | 24 1 1 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001533 | 1 33 | 24 5 4 4 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001534 | 1 18 | 17 0 1 2 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001535 | 1 16 | 15 0 1 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001536 | 1 19 | 13 3 3 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001537 | 1 17 | 13 3 1 10 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001538 | 1 39 | 35 1 3 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001539 | 1 26 | 18 2 6 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001540 | 1 59 | 55 0 4 5 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001541 | 1 11 | 10 0 1 2 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001542 | 1 56 | 46 2 8 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001543 | 1 164 | 143 8 13 5 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001544 | 1 54 | 43 7 4 4 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001545 | 1 25 | 20 3 2 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001546 | 1 70 | 59 5 6 2 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001547 | 1 65 | 55 5 5 5 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001548 | 1 49 | 39 4 6 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001549 | 1 29 | 10 3 16 2 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001550 | 1 45 | 38 1 6 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001551 | 1 19 | 18 0 1 4 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001552 | 1 44 | 34 2 8 2 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001553 | 1 147 | 121 10 16 8 34 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001554 | 1 94 | 76 5 13 0 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001555 | 1 55 | 43 3 9 1 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001556 | 1 53 | 42 3 8 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001557 | 1 19 | 16 1 2 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001558 | 1 43 | 38 2 3 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001559 | 1 80 | 69 3 8 3 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001560 | 1 19 | 14 3 2 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001561 | 1 87 | 79 3 5 6 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001562 | 1 94 | 77 7 10 3 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001563 | 1 46 | 35 5 6 2 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001564 | 1 34 | 28 2 4 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001565 | 1 76 | 63 5 8 2 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001566 | 1 49 | 42 3 4 4 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001567 | 1 16 | 13 1 2 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001568 | 1 100 | 91 5 4 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001569 | 1 71 | 67 3 1 6 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001570 | 1 35 | 30 4 1 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001571 | 1 21 | 19 2 0 4 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001572 | 1 75 | 65 4 6 2 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001573 | 1 14 | 9 2 3 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001574 | 1 64 | 59 2 3 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001575 | 1 35 | 33 1 1 4 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001576 | 1 84 | 74 5 5 13 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001577 | 1 43 | 38 3 2 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001578 | 1 116 | 101 4 11 5 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001579 | 1 126 | 109 9 8 3 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001580 | 1 28 | 21 3 4 5 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001581 | 1 45 | 43 1 1 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001582 | 1 26 | 18 4 4 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001583 | 1 36 | 29 4 3 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001584 | 1 31 | 26 3 2 4 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001585 | 1 68 | 58 4 6 5 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001586 | 1 61 | 42 5 14 0 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001587 | 1 40 | 37 1 2 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001588 | 1 54 | 49 2 3 5 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001589 | 1 25 | 18 2 5 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001590 | 1 48 | 38 3 7 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001591 | 1 25 | 17 5 3 3 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001592 | 1 103 | 78 4 21 0 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001593 | 1 35 | 28 2 5 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001594 | 1 63 | 53 5 5 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001595 | 1 17 | 15 1 1 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001596 | 1 19 | 16 1 2 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001597 | 1 82 | 65 7 10 2 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001598 | 1 154 | 136 8 10 6 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001599 | 1 204 | 166 13 25 4 42 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000891 | 1 29 | 23 2 4 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000892 | 1 72 | 67 1 4 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000893 | 1 41 | 33 2 6 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000894 | 1 35 | 27 1 7 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000895 | 1 54 | 49 2 3 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000897 | 1 2 | 0 2 0 12 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000898 | 1 51 | 44 2 5 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000899 | 1 29 | 24 3 2 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000900 | 1 43 | 29 1 13 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000901 | 1 53 | 46 2 5 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000902 | 1 30 | 26 2 2 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000903 | 1 57 | 47 3 7 3 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000904 | 1 53 | 44 2 7 3 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000905 | 1 37 | 31 2 4 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000906 | 1 63 | 36 8 19 1 28 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000907 | 1 32 | 31 0 1 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000908 | 1 62 | 40 6 16 0 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000909 | 1 61 | 38 11 12 4 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000910 | 1 70 | 63 3 4 4 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000911 | 1 34 | 26 1 7 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000912 | 1 64 | 51 3 10 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000913 | 1 38 | 34 1 3 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000914 | 1 50 | 40 2 8 8 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000915 | 1 63 | 46 3 14 1 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000917 | 1 42 | 34 2 6 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000918 | 1 10 | 6 1 3 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000919 | 1 39 | 33 2 4 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000920 | 1 40 | 31 4 5 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000921 | 1 35 | 31 2 2 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000922 | 1 33 | 26 1 6 4 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000923 | 1 67 | 47 8 12 3 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000924 | 1 30 | 19 2 9 1 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000925 | 1 44 | 38 4 2 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000926 | 1 48 | 40 3 5 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000927 | 1 39 | 34 0 5 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000928 | 1 35 | 28 2 5 4 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000929 | 1 73 | 67 2 4 3 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000930 | 1 59 | 38 10 11 1 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000931 | 1 49 | 38 9 2 1 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000932 | 1 66 | 55 3 8 3 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000933 | 1 33 | 29 1 3 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000934 | 1 47 | 39 5 3 4 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000935 | 1 55 | 49 5 1 4 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000936 | 1 67 | 50 5 12 1 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000937 | 1 63 | 51 6 6 12 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000938 | 1 64 | 46 9 9 1 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000939 | 1 25 | 24 0 1 6 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000940 | 1 54 | 47 2 5 3 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000941 | 1 47 | 39 2 6 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000942 | 1 58 | 37 6 15 3 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000943 | 1 59 | 56 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000944 | 1 36 | 34 0 2 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000945 | 1 29 | 28 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000946 | 1 57 | 52 2 3 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000947 | 1 62 | 51 8 3 13 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000948 | 1 28 | 21 3 4 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000950 | 1 40 | 35 0 5 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000951 | 1 34 | 31 1 2 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000952 | 1 30 | 28 2 0 4 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000953 | 1 69 | 64 0 5 4 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000954 | 1 34 | 30 1 3 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000955 | 1 34 | 32 2 0 4 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000956 | 1 68 | 58 1 9 2 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000957 | 1 41 | 34 1 6 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000958 | 1 61 | 53 3 5 4 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000959 | 1 52 | 46 2 4 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000960 | 1 60 | 46 5 9 1 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000961 | 1 24 | 22 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000962 | 1 36 | 28 2 6 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000963 | 1 35 | 33 2 0 5 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000964 | 1 43 | 37 2 4 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000965 | 1 48 | 40 2 6 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000966 | 1 19 | 15 3 1 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000967 | 1 31 | 23 3 5 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000968 | 1 39 | 21 4 14 1 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000969 | 1 36 | 35 0 1 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000970 | 1 40 | 26 3 11 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000971 | 1 45 | 42 0 3 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000972 | 1 56 | 49 2 5 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000973 | 1 69 | 53 7 9 3 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000974 | 1 28 | 22 1 5 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000975 | 1 29 | 24 1 4 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000976 | 1 74 | 54 6 14 2 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000977 | 1 29 | 22 3 4 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000978 | 1 44 | 39 1 4 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000979 | 1 47 | 34 7 6 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000980 | 1 42 | 39 0 3 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000981 | 1 62 | 53 2 7 2 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000982 | 1 58 | 48 2 8 4 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000983 | 1 53 | 38 2 13 2 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000984 | 1 57 | 48 3 6 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000985 | 1 46 | 31 8 7 1 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000986 | 1 41 | 31 5 5 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000987 | 1 40 | 33 1 6 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000988 | 1 29 | 26 1 2 4 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000989 | 1 39 | 33 3 3 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000990 | 1 41 | 34 2 5 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000991 | 1 55 | 51 1 3 5 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000992 | 1 34 | 27 1 6 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000993 | 1 65 | 60 2 3 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000994 | 1 78 | 68 7 3 3 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000995 | 1 47 | 40 3 4 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000996 | 1 38 | 30 3 5 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000997 | 1 58 | 46 6 6 2 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000998 | 1 31 | 25 2 4 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000999 | 1 51 | 44 5 2 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001000 | 1 38 | 34 1 3 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001001 | 1 33 | 27 3 3 3 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001002 | 1 39 | 33 4 2 3 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001003 | 1 69 | 62 4 3 4 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001004 | 1 50 | 42 7 1 3 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001006 | 1 39 | 25 6 8 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001007 | 1 36 | 34 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001008 | 1 57 | 42 7 8 1 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001009 | 1 38 | 23 2 13 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001010 | 1 80 | 63 3 14 3 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001011 | 1 44 | 35 3 6 2 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001012 | 1 42 | 35 4 3 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001013 | 1 44 | 33 4 7 3 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001014 | 1 39 | 39 0 0 2 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001015 | 1 28 | 21 5 2 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001016 | 1 31 | 27 2 2 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001017 | 1 41 | 35 2 4 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001018 | 1 55 | 45 3 7 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001019 | 1 39 | 32 2 5 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001020 | 1 35 | 29 2 4 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000309 | 1 137 | 100 7 30 3 40 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000310 | 1 102 | 62 12 28 6 46 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000311 | 1 55 | 27 6 22 1 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000312 | 1 56 | 35 7 14 11 32 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000313 | 1 119 | 79 22 18 12 52 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000314 | 1 131 | 96 6 29 2 37 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000315 | 1 92 | 71 13 8 5 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000316 | 1 60 | 38 10 12 2 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000317 | 1 149 | 102 18 29 11 58 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000318 | 1 64 | 50 2 12 6 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000319 | 1 125 | 108 10 7 9 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000320 | 1 82 | 59 8 15 8 31 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000321 | 1 102 | 79 13 10 3 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000322 | 1 110 | 82 8 20 5 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000323 | 1 197 | 157 14 26 24 64 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000324 | 1 140 | 108 9 23 3 35 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000325 | 1 32 | 16 4 12 3 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000326 | 1 109 | 79 12 18 6 36 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000327 | 1 80 | 56 7 17 9 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000328 | 1 170 | 138 14 18 9 41 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000329 | 1 31 | 23 2 6 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000330 | 1 60 | 50 4 6 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000331 | 1 339 | 214 33 92 17 142 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000332 | 1 129 | 86 6 37 2 45 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000333 | 1 210 | 149 23 38 13 74 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000334 | 1 110 | 98 7 5 20 32 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000335 | 1 454 | 284 40 130 13 183 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000336 | 1 48 | 17 16 15 10 41 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000337 | 1 148 | 107 10 31 5 46 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000338 | 1 54 | 39 6 9 6 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000339 | 1 182 | 148 12 22 11 45 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000340 | 1 36 | 26 5 5 2 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000341 | 1 49 | 33 0 16 0 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000342 | 1 74 | 64 3 7 2 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000343 | 1 148 | 119 15 14 8 37 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000344 | 1 191 | 158 21 12 25 58 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000345 | 1 179 | 128 19 32 13 64 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000346 | 1 55 | 40 3 12 2 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000347 | 1 112 | 83 11 18 3 32 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000348 | 1 93 | 69 12 12 5 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000349 | 1 53 | 45 6 2 7 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000350 | 1 44 | 17 5 22 4 31 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000351 | 1 295 | 226 30 39 11 80 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000352 | 1 149 | 116 11 22 13 46 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000353 | 1 87 | 67 11 9 5 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000354 | 1 92 | 82 3 7 4 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000355 | 1 156 | 108 8 40 5 53 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000356 | 1 14 | 10 1 3 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000357 | 1 149 | 122 12 15 4 31 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000358 | 1 114 | 76 8 30 3 41 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000359 | 1 58 | 37 6 15 3 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000360 | 1 345 | 252 36 57 12 105 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000361 | 1 60 | 51 5 4 5 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000362 | 1 184 | 139 11 34 7 52 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000363 | 1 83 | 59 7 17 7 31 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000364 | 1 162 | 124 12 26 11 49 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000365 | 1 151 | 101 35 15 30 80 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000366 | 1 63 | 52 6 5 18 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000367 | 1 250 | 192 22 36 17 75 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000368 | 1 183 | 130 13 40 12 65 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000369 | 1 169 | 133 13 23 4 40 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000370 | 1 122 | 91 9 22 4 35 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000371 | 1 36 | 16 3 17 5 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000372 | 1 103 | 70 16 17 6 39 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000373 | 1 154 | 114 17 23 9 49 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000374 | 1 104 | 74 14 16 19 49 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000375 | 1 42 | 16 8 18 0 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000376 | 1 90 | 71 6 13 3 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000377 | 1 188 | 132 13 43 11 67 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000378 | 1 149 | 115 14 20 22 56 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000379 | 1 246 | 199 20 27 19 66 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000380 | 1 204 | 147 18 39 13 70 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000381 | 1 54 | 37 5 12 20 37 1 | +|=================================================================================================================| +| Sum | 661 54401 | 42294 4474 7633 2978 15085 661 | +|=================================================================================================================| +| Mean | 1.2 94.9 | 73.8 7.8 13.3 5.2 26.3 1.2 | +| S.D. | 3.7 362.2 | 285.7 27.5 49.8 20.1 96.5 3.7 | +| Median | 1.0 55.0 | 43.0 4.0 7.0 3.0 14.0 1.0 | +`-----------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_cer/hyp.trn + +Speakers: + 0: m + 1: cv_deu_000698 + 2: cv_deu_000699 + 3: cv_deu_000700 + 4: cv_deu_000701 + 5: cv_deu_000702 + 6: cv_deu_000703 + 7: cv_deu_000704 + 8: cv_deu_000705 + 9: cv_deu_000706 + 10: cv_deu_000707 + 11: cv_deu_000708 + 12: cv_deu_000709 + 13: cv_deu_000710 + 14: cv_deu_000711 + 15: cv_deu_000712 + 16: cv_deu_000713 + 17: cv_deu_000714 + 18: cv_deu_000715 + 19: cv_deu_000716 + 20: cv_deu_000717 + 21: cv_deu_000718 + 22: cv_deu_000719 + 23: cv_deu_000720 + 24: cv_deu_000721 + 25: cv_deu_000722 + 26: cv_deu_000723 + 27: cv_deu_000724 + 28: cv_deu_000725 + 29: cv_deu_000726 + 30: cv_deu_000727 + 31: cv_deu_000728 + 32: cv_deu_000729 + 33: cv_deu_000730 + 34: cv_deu_000731 + 35: cv_deu_000732 + 36: cv_deu_000733 + 37: cv_deu_000734 + 38: cv_deu_000735 + 39: cv_deu_000736 + 40: cv_deu_000737 + 41: cv_deu_000738 + 42: cv_deu_000739 + 43: cv_deu_000740 + 44: cv_deu_000741 + 45: cv_deu_000742 + 46: cv_deu_000743 + 47: cv_deu_000744 + 48: cv_deu_000745 + 49: cv_deu_000746 + 50: cv_deu_000747 + 51: cv_deu_000748 + 52: cv_deu_000749 + 53: cv_deu_000750 + 54: cv_deu_000751 + 55: cv_deu_000752 + 56: cv_deu_000753 + 57: cv_deu_000754 + 58: cv_deu_000755 + 59: cv_deu_000756 + 60: cv_deu_000757 + 61: cv_deu_000758 + 62: cv_deu_000759 + 63: cv_deu_000760 + 64: cv_deu_000761 + 65: cv_deu_000762 + 66: cv_deu_000763 + 67: cv_deu_000764 + 68: cv_deu_000765 + 69: cv_deu_000766 + 70: cv_deu_000767 + 71: cv_deu_000768 + 72: cv_deu_000769 + 73: cv_deu_000770 + 74: cv_deu_000771 + 75: cv_deu_000772 + 76: cv_deu_000773 + 77: cv_deu_000774 + 78: cv_deu_000775 + 79: cv_deu_000776 + 80: cv_deu_000777 + 81: cv_deu_000778 + 82: cv_deu_000779 + 83: cv_deu_000780 + 84: cv_deu_000781 + 85: cv_deu_000782 + 86: cv_deu_000783 + 87: cv_deu_000784 + 88: cv_deu_000785 + 89: cv_deu_000786 + 90: cv_deu_000787 + 91: cv_deu_000788 + 92: cv_deu_000789 + 93: cv_deu_000790 + 94: cv_deu_000791 + 95: cv_deu_000792 + 96: cv_deu_000793 + 97: cv_deu_000794 + 98: cv_deu_000795 + 99: cv_deu_000796 + 100: cv_deu_000797 + 101: cv_deu_000798 + 102: cv_deu_000799 + 103: cv_deu_000800 + 104: cv_deu_000801 + 105: fleurs_deu_000378 + 106: fleurs_deu_000379 + 107: fleurs_deu_000380 + 108: fleurs_deu_000381 + 109: fleurs_deu_000382 + 110: fleurs_deu_000383 + 111: fleurs_deu_000384 + 112: fleurs_deu_000385 + 113: fleurs_deu_000386 + 114: fleurs_deu_000387 + 115: fleurs_deu_000388 + 116: fleurs_deu_000389 + 117: fleurs_deu_000390 + 118: fleurs_deu_000391 + 119: fleurs_deu_000392 + 120: fleurs_deu_000393 + 121: fleurs_deu_000394 + 122: fleurs_deu_000395 + 123: fleurs_deu_000396 + 124: fleurs_deu_000397 + 125: fleurs_deu_000398 + 126: fleurs_deu_000399 + 127: fleurs_deu_000400 + 128: fleurs_deu_000401 + 129: fleurs_deu_000402 + 130: fleurs_deu_000403 + 131: fleurs_deu_000404 + 132: fleurs_deu_000405 + 133: fleurs_deu_000406 + 134: fleurs_deu_000407 + 135: fleurs_deu_000408 + 136: fleurs_deu_000409 + 137: fleurs_deu_000410 + 138: fleurs_deu_000411 + 139: fleurs_deu_000412 + 140: fleurs_deu_000413 + 141: fleurs_deu_000414 + 142: fleurs_deu_000415 + 143: mls_deu_000281 + 144: mls_deu_000282 + 145: mls_deu_000283 + 146: mls_deu_000284 + 147: mls_deu_000285 + 148: mls_deu_000286 + 149: mls_deu_000287 + 150: mls_deu_000288 + 151: mls_deu_000289 + 152: mls_deu_000290 + 153: mls_deu_000291 + 154: mls_deu_000292 + 155: mls_deu_000293 + 156: mls_deu_000294 + 157: mls_deu_000295 + 158: mls_deu_000296 + 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voxforge_deu_000900 + 383: voxforge_deu_000901 + 384: voxforge_deu_000902 + 385: voxforge_deu_000903 + 386: voxforge_deu_000904 + 387: voxforge_deu_000905 + 388: voxforge_deu_000906 + 389: voxforge_deu_000907 + 390: voxforge_deu_000908 + 391: voxforge_deu_000909 + 392: voxforge_deu_000910 + 393: voxforge_deu_000911 + 394: voxforge_deu_000912 + 395: voxforge_deu_000913 + 396: voxforge_deu_000914 + 397: voxforge_deu_000915 + 398: voxforge_deu_000917 + 399: voxforge_deu_000918 + 400: voxforge_deu_000919 + 401: voxforge_deu_000920 + 402: voxforge_deu_000921 + 403: voxforge_deu_000922 + 404: voxforge_deu_000923 + 405: voxforge_deu_000924 + 406: voxforge_deu_000925 + 407: voxforge_deu_000926 + 408: voxforge_deu_000927 + 409: voxforge_deu_000928 + 410: voxforge_deu_000929 + 411: voxforge_deu_000930 + 412: voxforge_deu_000931 + 413: voxforge_deu_000932 + 414: voxforge_deu_000933 + 415: voxforge_deu_000934 + 416: voxforge_deu_000935 + 417: voxforge_deu_000936 + 418: voxforge_deu_000937 + 419: voxforge_deu_000938 + 420: voxforge_deu_000939 + 421: voxforge_deu_000940 + 422: voxforge_deu_000941 + 423: voxforge_deu_000942 + 424: voxforge_deu_000943 + 425: voxforge_deu_000944 + 426: voxforge_deu_000945 + 427: voxforge_deu_000946 + 428: voxforge_deu_000947 + 429: voxforge_deu_000948 + 430: voxforge_deu_000950 + 431: voxforge_deu_000951 + 432: voxforge_deu_000952 + 433: voxforge_deu_000953 + 434: voxforge_deu_000954 + 435: voxforge_deu_000955 + 436: voxforge_deu_000956 + 437: voxforge_deu_000957 + 438: voxforge_deu_000958 + 439: voxforge_deu_000959 + 440: voxforge_deu_000960 + 441: voxforge_deu_000961 + 442: voxforge_deu_000962 + 443: voxforge_deu_000963 + 444: voxforge_deu_000964 + 445: voxforge_deu_000965 + 446: voxforge_deu_000966 + 447: voxforge_deu_000967 + 448: voxforge_deu_000968 + 449: voxforge_deu_000969 + 450: voxforge_deu_000970 + 451: voxforge_deu_000971 + 452: voxforge_deu_000972 + 453: voxforge_deu_000973 + 454: voxforge_deu_000974 + 455: voxforge_deu_000975 + 456: voxforge_deu_000976 + 457: voxforge_deu_000977 + 458: voxforge_deu_000978 + 459: voxforge_deu_000979 + 460: voxforge_deu_000980 + 461: voxforge_deu_000981 + 462: voxforge_deu_000982 + 463: voxforge_deu_000983 + 464: voxforge_deu_000984 + 465: voxforge_deu_000985 + 466: voxforge_deu_000986 + 467: voxforge_deu_000987 + 468: voxforge_deu_000988 + 469: voxforge_deu_000989 + 470: voxforge_deu_000990 + 471: voxforge_deu_000991 + 472: voxforge_deu_000992 + 473: voxforge_deu_000993 + 474: voxforge_deu_000994 + 475: voxforge_deu_000995 + 476: voxforge_deu_000996 + 477: voxforge_deu_000997 + 478: voxforge_deu_000998 + 479: voxforge_deu_000999 + 480: voxforge_deu_001000 + 481: voxforge_deu_001001 + 482: voxforge_deu_001002 + 483: voxforge_deu_001003 + 484: voxforge_deu_001004 + 485: voxforge_deu_001006 + 486: voxforge_deu_001007 + 487: voxforge_deu_001008 + 488: voxforge_deu_001009 + 489: voxforge_deu_001010 + 490: voxforge_deu_001011 + 491: voxforge_deu_001012 + 492: voxforge_deu_001013 + 493: voxforge_deu_001014 + 494: voxforge_deu_001015 + 495: voxforge_deu_001016 + 496: voxforge_deu_001017 + 497: voxforge_deu_001018 + 498: voxforge_deu_001019 + 499: voxforge_deu_001020 + 500: voxpopuli_deu_000309 + 501: voxpopuli_deu_000310 + 502: voxpopuli_deu_000311 + 503: voxpopuli_deu_000312 + 504: voxpopuli_deu_000313 + 505: voxpopuli_deu_000314 + 506: voxpopuli_deu_000315 + 507: voxpopuli_deu_000316 + 508: voxpopuli_deu_000317 + 509: voxpopuli_deu_000318 + 510: voxpopuli_deu_000319 + 511: voxpopuli_deu_000320 + 512: voxpopuli_deu_000321 + 513: voxpopuli_deu_000322 + 514: voxpopuli_deu_000323 + 515: voxpopuli_deu_000324 + 516: voxpopuli_deu_000325 + 517: voxpopuli_deu_000326 + 518: voxpopuli_deu_000327 + 519: voxpopuli_deu_000328 + 520: voxpopuli_deu_000329 + 521: voxpopuli_deu_000330 + 522: voxpopuli_deu_000331 + 523: voxpopuli_deu_000332 + 524: voxpopuli_deu_000333 + 525: voxpopuli_deu_000334 + 526: voxpopuli_deu_000335 + 527: voxpopuli_deu_000336 + 528: voxpopuli_deu_000337 + 529: voxpopuli_deu_000338 + 530: voxpopuli_deu_000339 + 531: voxpopuli_deu_000340 + 532: voxpopuli_deu_000341 + 533: voxpopuli_deu_000342 + 534: voxpopuli_deu_000343 + 535: voxpopuli_deu_000344 + 536: voxpopuli_deu_000345 + 537: voxpopuli_deu_000346 + 538: voxpopuli_deu_000347 + 539: voxpopuli_deu_000348 + 540: voxpopuli_deu_000349 + 541: voxpopuli_deu_000350 + 542: voxpopuli_deu_000351 + 543: voxpopuli_deu_000352 + 544: voxpopuli_deu_000353 + 545: voxpopuli_deu_000354 + 546: voxpopuli_deu_000355 + 547: voxpopuli_deu_000356 + 548: voxpopuli_deu_000357 + 549: voxpopuli_deu_000358 + 550: voxpopuli_deu_000359 + 551: voxpopuli_deu_000360 + 552: voxpopuli_deu_000361 + 553: voxpopuli_deu_000362 + 554: voxpopuli_deu_000363 + 555: voxpopuli_deu_000364 + 556: voxpopuli_deu_000365 + 557: voxpopuli_deu_000366 + 558: voxpopuli_deu_000367 + 559: voxpopuli_deu_000368 + 560: voxpopuli_deu_000369 + 561: voxpopuli_deu_000370 + 562: voxpopuli_deu_000371 + 563: voxpopuli_deu_000372 + 564: voxpopuli_deu_000373 + 565: voxpopuli_deu_000374 + 566: voxpopuli_deu_000375 + 567: voxpopuli_deu_000376 + 568: voxpopuli_deu_000377 + 569: voxpopuli_deu_000378 + 570: voxpopuli_deu_000379 + 571: voxpopuli_deu_000380 + 572: voxpopuli_deu_000381 + +Speaker sentences 0: m #utts: 89 +id: (m-ailabs_deu_000165-m-ailabs_deu_000165) +Scores: (#C #S #D #I) 92 15 18 4 +REF: d I e b E e r ******* d i g U n g m a c h t e E i n e r Ä u S s E R s t w i c h t i g e n s A c h E E i n e n d e d e r p E t i t ******* i o n a n D E n g o U V E R n E U r f Ü r d e s I n D I a n e r ******* J o E s b e g * n a d i g u n G +HYP: d * e ******* b * e r d i g * n g m a c h t e * i n e r E u * s * * s t ******* w i c h t i g e n ******* s E c h A * i n e n d e d e r p Ä t i t i o n a n ******* L I n ******* g o * * W A n * Ü r f Ü r ******* d e s E n J a n e r S o * s S b e g E n a d i g u n M +Eval: D D D I D D S D D D D D S S D S I D S S D D D S S D S D S S S I S D S I S + +id: (m-ailabs_deu_000166-m-ailabs_deu_000166) +Scores: (#C #S #D #I) 63 5 10 4 +REF: d a h a b e s i e * d i e w o * H l j e d e m h I e r i n D e r E R i N n E r u n G g e b l i e b e n e n w O r t e g e s p R o c h e * ******* n +HYP: d a C h a b e ******* s i e T d i e w o L U l G j e d e m h * e r i n ******* * e r * * i * n * r u n * K g e b l i e b e n e n w A r t e g e s p * o c h e N n +Eval: S D I I S S D D D D D D D D S S D I I + +id: (m-ailabs_deu_000167-m-ailabs_deu_000167) +Scores: (#C #S #D #I) 134 10 15 4 +REF: e r s T u m a c h t u H r w a r e r a u f m a l e b r A c h t e * d e n k a F f E E d i e s o n N e s c h i e n i n * s z i M m e r u n d D i e s p ** E r l i n g e d i e d A s a u s d e n h Ä c K s e L s Ä c k e n g e f a L l E n e f U T t e R k o R n a u f ******* P i C k t e n +HYP: e r s * u m a c h t u * r w a r e r a u f m a l e b r * c h t e R d e n k a * f * I d i e s o n D e ******* s c h i e n i n Z s z i * m e r u n d * i e s p Ä H r l i n g e d i e d * s S a u s d e n h E c * s e s E c k e n g e f a * l * n e f * O t e A k o * n a u f B i * k t e n +Eval: D D D I D D S S D I D D I S D S S D S S D D D S S D I S D + +id: (m-ailabs_deu_000168-m-ailabs_deu_000168) +Scores: (#C #S #D #I) 51 6 5 2 +REF: * s i c h e r l i c h a n i h r E M g e b U r t S t a G h Ä T t e e r b e i i H R b l e i b e n k Ö N n e n * +HYP: S s i c h e r l i c h a n i h r * * N g e b O r t Z t a C K h Ä * t e e r b e i i * E b l e i b e n k Ö * n e n T +Eval: I D D S S S S S D D S D I + +id: (m-ailabs_deu_000169-m-ailabs_deu_000169) +Scores: (#C #S #D #I) 91 2 12 18 +REF: U N D d e * s H a l b ** * m u S s M a n d * * o r t w * o * m e n S c h e n s c h W i E r i * g k e i t e n h a b e n d i e s A u c h e i n e r ******* s e i t s e r k l Ä * r e n a n g e b o t e m a c h e n ******* * * * * * ******* * +HYP: * * * ******* d e R s * a l b Ö M m u * s * a n d A U o r t w U o R m e n T c h e n ******* s c h * i * r i C g k e i t e n ******* h a b e n d i e s O u c h e i n e r s e i t s e r k l Ä E r e n a n g e b o t e ******* m a c h e n W R A L E I +Eval: D D D D I D I I D D I I I I S D D D I D S I I D I I I I I I I I + +id: (m-ailabs_deu_000170-m-ailabs_deu_000170) +Scores: (#C #S #D #I) 78 7 21 4 +REF: D A S s m a N n U r a U f d i E W e l t k o M m t u m s e L b s t W i e d e r E i N E n s o H n z u h a * b e n d e R d i E v e r ******* e h R u n G d e r a H n e n F o r t S e t z t * * +HYP: * U E s m a * ******* n * r ******* a * f d i * ******* V e l t k o * m t u m s e * b s t ******* * i e d e r * i * * n s o * n D z u O h a R b e n d e * d i * v e r e h * u n K d e r a * n e n V o r t * e t z t N N +Eval: D S S D D D D D D D S D D D D D D D D S S I D D I D S D S D I I + +id: (m-ailabs_deu_000171-m-ailabs_deu_000171) +Scores: (#C #S #D #I) 115 16 29 5 +REF: D E S H a L B G E H Ö r E n K O n T I n U I e R l i c h e * s c h u l b i l d u n g A u C H K O n t I n U I e R l i c h e m Ö g l i c h K e i t * * E N d e r w e i t e r b i l D u n g u n d d a s B e g e h E n v o n g e ******* d e n k t a g e n F Ü R m i c h u n ******* a u f l Ö s l i c h Z U S a M m e N +HYP: * * * * a * * * * B Ü r * n P U n Z E n * e * l i c h e R s c h u l b i l d u n g R u * * ******* * * n t E n * e * l i c h e m Ö g l i c h T e i t R A U H d e r w e i t e r b i l * u n g u n d d a s * e g e h * n v o n g e d e n k t a g e n * ** * E m i c h u n a u f l ** s l i c h ******* * T R a * m e * +Eval: D D D D D D D D S S D S S S S D S D I S D D D D D S D S D S I I S S D D D I D D D S I D D D S S D D + +id: (m-ailabs_deu_000172-m-ailabs_deu_000172) +Scores: (#C #S #D #I) 111 11 21 5 +REF: M e i n a n s a s C H e * n s a G T S i E E s i s t j * A J e T Z t w i e d e r g a n Z G u t z w i s c h e n u n s a b e r * e H E d u n i c h t a L l e s g e s t * e H s t g e H t D i e e r ******* i N n E r u n g a n d a s b Ö s e n i c h t w e G +HYP: * e i n a n s a s I e R n s a * * K Z i * * s i s t ******* j E R G e * * t w i e d e r g a n * S K u t z w i s c h e n ******* u n s a b e r I e * R d u ******* n i c h t a * l e s ******* g e s t I e * s t g e * t ******* * i e e r i * n * r u n g a n ******* d a s b Ü s e n i c h t w e H +Eval: D S S I D D S S D D D I S S D D D S S D I D S D D D I D D D D I D D D S S + +id: (m-ailabs_deu_000173-m-ailabs_deu_000173) +Scores: (#C #S #D #I) 24 2 3 2 +REF: * n e i n w e i b e r b r a u c h E i c h N i c h * t +HYP: D n e i n w e i b e r ******* b r a u c h * R i c h ******* E i c h T t +Eval: I D D S D S I + +id: (m-ailabs_deu_000174-m-ailabs_deu_000174) +Scores: (#C #S #D #I) 48 5 8 7 +REF: * * * * * * ******* g o T t h a t n i c h T v e r g e b l i c h n A C h m I R g e r u f e n s a G t e d e r S c h i F F e r +HYP: T E N D E N g o * t h a t ******* n i c h * v e r g e b l i c h E n * E h ******* m * E g e r u f e n s a K t e d e r * c h i * V e r +Eval: I I I I I I I D D D S D S D D S S D D S + +id: (m-ailabs_deu_000175-m-ailabs_deu_000175) +Scores: (#C #S #D #I) 144 8 6 6 +REF: n u r e i n e s w e i s s i c h d i e s e r f u r c h t b a r e n f r a g E e n t ******* g e g e n ******* z u s e t z e n u n d s c h l e u d e r e d a * s w O r t i n d i e w a A G s c h a l E d i e g l u t M e i n e s l i e b e s ******* w i L l e n * S i s t s t Ä r k E r a l s t r e N n u n g * +HYP: n u r e i n e s w e i s s ******* i c h d i e s e r f u r c h t b a r e n f r a g * ******* e n t g e g e n z u s e t z e n u n d s c h l e u d e r e R d a R s w A r t i n d i e w a R K s c h a l * d i e g l u t L e i n e s l i e b e s w i * l e n D Z i s t s t E r k A r a l s t r e * n u n g T +Eval: D D D I I S I S S S D S I D I S S S D I + +id: (m-ailabs_deu_000176-m-ailabs_deu_000176) +Scores: (#C #S #D #I) 60 4 12 6 +REF: * t o m s a R m E e g e ******* w a N n e i N E n g r o s s e n s i e g n A c h e I n e r l a n g E n * h a r * * T n Ä c k I g e n s c h l A c h t * +HYP: D t o m s a * m * e I g e w a * n ******* e i * * n ******* g r o s s e n s i e g n * c h ******* e * n e r l a n g * n G h a r D N n E c k E g e n s c h l * c h t N +Eval: I D D S I D D D D D D D D D I I I S S S D I + +id: (m-ailabs_deu_000177-m-ailabs_deu_000177) +Scores: (#C #S #D #I) 64 7 14 3 +REF: E s I s T e i N n a * m e d e m s i c h D i E T Ü r b e i t a G u n D n A c h t Ö f f n E n k a N n b U r * s c h e * u n d w I l L k o m m e n +HYP: * s ******* O s * e i * ******* n a H m e d e m s i c h ******* T i * * Ü r b e i t a * K u n * n * c h t ******* A f f n A n k a * n b * r E s c h e R u n d w E l k o m m e n +Eval: D D S D D D I D S D D D S D D D S S D D I I S S + +id: (m-ailabs_deu_000178-m-ailabs_deu_000178) +Scores: (#C #S #D #I) 37 1 3 9 +REF: * * ******* a * b e r i c h v e r * ******* Z e i H e i H n e n i h r e * u n w i s s e n ******* h e i t * +HYP: E N a R b e r i c h ******* v e r T S e i * e i * n e n i h r e R u n w i s s e n h e i t N +Eval: I I I I D I I S D D I I I + +id: (m-ailabs_deu_000179-m-ailabs_deu_000179) +Scores: (#C #S #D #I) 83 7 7 8 +REF: * V o n d e r D r i T t e n u n t e R r e d u n g a n s a g t e m i s t e r h A v I s * h A m w a r m I r d i e p e r s o n i n h o h * e M m a S s e * v e r d Ä c h t i g * * * * +HYP: U F o n d e r T r i * t e n u n t e * r e d u n g a n s a g t e m i s t e r ******* h E v E s C h E m w a r ******* m E r d i e p e r s o n i n h o h R e * ******* m a * s e R v e r d E c h t i g H T N N +Eval: I S S D D D S S I S D S I D D D I S I I I I + +id: (m-ailabs_deu_000180-m-ailabs_deu_000180) +Scores: (#C #S #D #I) 110 12 18 3 +REF: i c h d e n k e d e R A m t * M a N N u n d s E I n e * F A m I L i e w e r d E n e s r e c h t v o n d I r f i n d e n d a S s D u d i C H s e l b S t a n g i * b s t u n D s I e W E r d e n f r e u n D l i c h g e g e n d i C H s e i N +HYP: i c h d e n k e d e * * m t N E a * R u n d s * A n e V E R m * * i e w e r d * n ******* e s ******* r e c h t v o n d E r f i n d e n d a * s ******* T u d i * E s e l b * t a n g i E b s t u n * s * e * V r d e n f r e u n T l i c h g e g e n d i * * E s e i T +Eval: D D I S D S D S I S S D D D D D S D D S D S D I D D D S S D D S S + +id: (m-ailabs_deu_000181-m-ailabs_deu_000181) +Scores: (#C #S #D #I) 99 5 11 4 +REF: J e t z T s c h l u g d i e h E l l e f l a M m e * a u f u n d n u n e r ******* k a N n t e e r u n * s d i e w I R n O c h i M m e r z u s a M m e n ******* g e d r Ä n g t I n d e m w i n k e l s t a n d e n +HYP: * e t z S s c h l u g d i e h * l l e f l a * m e R a u f u n d n u n e r k a * n t e e r u n D s d i e V w * E n * c h i * m e r ******* z u s a * m e n g e d r E n g t ******* D n d e m w i n k e l ******* s t a n d e n +Eval: D S D D I I D I S D S D D D D I S D S D + +id: (m-ailabs_deu_000182-m-ailabs_deu_000182) +Scores: (#C #S #D #I) 50 5 2 3 +REF: d e r s e i n e r s E e l e a n s p o R n e n d d a s e r ******* m u n t e r n D e w O R t v o r w Ä r * t * s +HYP: d e r s e i n e r s * e l e a n s p o * n e n d d a s e r m u n t e r n T e R w A U t v o r w E r L t Z s +Eval: D D I S S S S S I I + +id: (m-ailabs_deu_000183-m-ailabs_deu_000183) +Scores: (#C #S #D #I) 43 6 16 4 +REF: I C H f R E U E m i c H a U f d e n b e s u c h D e s t U n e S I s c h e N m i n i s T E R p r Ä s I d e n t e * ******* * n * +HYP: * * * ******* f * * * * O m i c * a * f d e n b e s u c h ******* T e s t O n e * * s c h e * m i n i s * * A p r E s E d e n t e N O n T +Eval: D D D D D D D D S D D D S S D D D D D S S S I I I I + +id: (m-ailabs_deu_000184-m-ailabs_deu_000184) +Scores: (#C #S #D #I) 63 4 10 7 +REF: * * w a s f Ü * r V e r ******* f o l g u n g e n w a s F Ü r n a * c h s t e L l u n G e n h a b E i C H n i c h T z u E R d u l D e n g e h a b t * * +HYP: D U w a s ******* f Ü H r * e r f o l g u n g e n w a s V Ü r n a R c h s t e * l u n * e n h a b * R i * * G n i c h * z u * A d u l * e n g e h a b t N N +Eval: I I D I D I S I D D D S D D S D D S D I I + +id: (m-ailabs_deu_000185-m-ailabs_deu_000185) +Scores: (#C #S #D #I) 98 8 12 3 +REF: z * i g * e U n e r w a r e n e s D i E v o n O R T z u o R t f u H r e n e i n k a u m E r w a c h S E n e s J u n g e s d i n G k a m z u m i R h e r a n g e h Ü * p f t u n D b e T t e l t e n e i n +HYP: z S i g O e I n e r w a r e n e s ******* T i * v o n * A U z u ******* o L t f u * r e n e i n k a u m A r w a c h * * n e s I u n g e s d i n * ******* k a m z u ******* m i E h e r a n g e h Ü B p f t u n * b e * t e l t e n e i n +Eval: I I S D S D D S S D S D S D D S D D D S I D D + +id: (m-ailabs_deu_000186-m-ailabs_deu_000186) +Scores: (#C #S #D #I) 91 16 46 1 +REF: H U C K i c h w e r D E D i c H i N E n e m b O o t h i n ******* f a h R E n W E R d e d A s b O o t D A a n l E G e n U n d E s W i E D e r z U R Ü C K R U d e r n a L l e s g a n z A l l e i n B R A u C H s T D i C H G a R n i c H t D R u m z u k Ü m M E R n +HYP: * * * A i c h w e r * * * i c * i * ******* * n e m b * o t ******* h i n f a h * * n * * * d e d * s b * o t ******* E R a n l * * e n ******* E n d * s ******* * i * e r z * * ** O E G d e r n a * l e s g a n z ******* E l l e i n * * * u * * s * * i * E * a * ******* n i c t * * u m z u ******* k Ö m A N T n +Eval: D D D S D D D D D D D D D I D D D D D D D D S S D D D S D D D D S D D D S S S S D D S D D D D D D D D S D D D S D D D S S S S + +id: (m-ailabs_deu_000187-m-ailabs_deu_000187) +Scores: (#C #S #D #I) 95 6 7 2 +REF: a l s n U r e i n m a l n o c h d e n r a u c h v o n S E I n e m h a u s e a u s d e r f e r n e * a u f s t e i g e n z u s E H e n u m d a N n b e r U H i * g T z u s t e r b e n +HYP: a l s n E r e i n m a l n o c h d e n r a u c h v o n * * A n e m h a u s e R a u s d e r f e r n e R a u f s t e i g e n z u ******* s * * e n u m d a * n ******* b e r R i E g K z u s t e r b e n +Eval: S D D S S I D D D D D S S I S + +id: (m-ailabs_deu_000188-m-ailabs_deu_000188) +Scores: (#C #S #D #I) 92 5 5 3 +REF: d i e t Ä n z e r I n a * b e r l a G a u f d e n k n i E e n v o r b r a H m a s b i l D n i s i n n a * m e n l o s e r s e h n s U c h ******* t u n d w e i n t e j a M m e r v o l l +HYP: d i e t E n z e r E n a R b e r l a * K a u f d e n k n i * e n v o r b r a * m a s b i l T n i s i n n a H m e n l o s e r ******* s e h n s O c h t u n d w e i n t e j a * m e r v o l l +Eval: S S I D S D D S I D S I D + +id: (m-ailabs_deu_000189-m-ailabs_deu_000189) +Scores: (#C #S #D #I) 58 10 12 2 +REF: R e c h t ******* f e r t i G T m i c h D e N n d i E w I r K l i c h k e i t n O c h n i c h t a u f d I E i C H M i c H b e * R u f e n k a N n +HYP: D e c h t f e r t i C H m i c h ******* T e * n ******* d i * w E r G l i c h k e i t E n * c h ******* n i c h t a u f d * * ******* i * G * i c * b e G u f e n k a R n +Eval: S I S S D S D D D S S S D D D D D D S D D I S S + +id: (m-ailabs_deu_000190-m-ailabs_deu_000190) +Scores: (#C #S #D #I) 70 4 8 10 +REF: * * i c h * Ä r g e r t e m i c h D a N n w e N n i c h a u * ******* f W a c h t e * e * s w a r s o W u n d e r s c h Ö * * n g e w e s e n d a s f l i e * G E n +HYP: D T i c h E ** r g e r t e m i c h ******* T a * n ******* w e * n ******* i c h a u R f * a c h t e R e I s w a r s o ******* F u n d e r s c h Ö N E n g e w e s e n d a s f l i e N N n +Eval: I I I D D S D D D D I I D I I D S I I I S S + +id: (m-ailabs_deu_000191-m-ailabs_deu_000191) +Scores: (#C #S #D #I) 98 11 18 7 +REF: n A c h ******* d e m e R s c h o n d e n g a n z * e n V o r m i T t a g m i T i H m v e r b r a c h t k a m s ******* T A n ******* h o P E n a c h t i s c h i n S Q U A n D T s c h e h a u s u m C a s p a R l e B E w o H l z u * S A g e * * n +HYP: n * c h d e m ******* e * ******* s c h o n d e n g a n z S e n F o r m i * t a g m i * i * m v e r b r a c h t k a m ******* s D E n h o * B n a c h ******* t i s c h i n * S K R n * * s c h e ******* h a u s u m K a s p a * ******* l e * w o * l z u S E R g e N N n +Eval: D I D D D I S D D D D I S S I D S D D S S S D D D S D D D S D I S S I I + +id: (m-ailabs_deu_000192-m-ailabs_deu_000192) +Scores: (#C #S #D #I) 44 3 5 3 +REF: e r w a R e i n a l t e r h i r * t v o L l m e d i * ******* z i n I s c H e r g E n i n a l i t Ä t +HYP: e r ******* w a * e i n a l t e r h i r H t v o * l ******* m e d i E z i n E s c * e r g I n i n a l i t E t +Eval: D D I D D I I S D S S + +id: (m-ailabs_deu_000193-m-ailabs_deu_000193) +Scores: (#C #S #D #I) 48 5 7 9 +REF: * * d a S s W o H l A u c h d e r m i e ******* t E r s e i n e W U n d e R l * i c h * * k e i t e n h a * b e N m Ü S s e * * +HYP: N T d a * s V o * l * u c h d e r m i e t A r s e i n e V O n d e * l R i c h G S k e i t e n h a R b e * ******* m ** s e R N +Eval: I I D S D D I S S S D I I I I D D D S I I + +id: (m-ailabs_deu_000194-m-ailabs_deu_000194) +Scores: (#C #S #D #I) 112 5 12 7 +REF: * ******* s i e s a H E n a l l e Ä n g s t l i c H u n d b e t r Ü b t a u s u n d a u c H h e R r a r * n e s a S S s c h w e * r ******* m Ü t i * G d a w i e d i e a n d e r e n u n d s t Ü t z * t e d a s h a u p t i n d i e h a n D +HYP: E s i e ******* s a * * n a l l e E n g s t l i c G u n d b e t r Ü b t ******* a u s u n d a u c * ******* h e * r a r E n e s a * * ******* s c h w e H r m Ö t i C H d a w i e ******* d i e a n d e r e n u n d s t Ü t z S t e R d a s h a u p t i n d i e h a n * +Eval: I I D D D S S D D D D I D D D I I S I S D I S D + +id: (m-ailabs_deu_000195-m-ailabs_deu_000195) +Scores: (#C #S #D #I) 143 6 10 10 +REF: u n t e r d e n d a m e n m e i s t j * U n g e f r I s c h e g e ******* s i c h t E R u n t e r d e n h e R r e n n e b e n j u G e n D l i c h * e n * s o L c h e m i t f a l t i G E R * s t i * r n u n d b e r e i t * s m e h r * O d e r m i n d e r m o n d ******* u m ******* g l Ä n Z t e m s c h Ä d e l +HYP: u n t e r d e n d a m e n m e i s t j I O n g e f r * s c h e g e s i c h t * * A u n t e r d e n h e * r e n n e b e n j u * e n T l i c h I e n Z s o U c h e m i t f a l t i * * * A s t i E r n u n d b e r e i t Z s ******* m e h r A * d e r m i n d e r m o n d u m g l E n S t e m s c h Ä d e l +Eval: I S D I D D S D D S I I S D D D I I I D I D I I S S + +id: (m-ailabs_deu_000196-m-ailabs_deu_000196) +Scores: (#C #S #D #I) 47 3 3 1 +REF: s e i T t a g e n s c h o n h a T t e E s b e s o n d e r s d r Ä u e n d g e k l u n g e * N +HYP: s e i * t a g e n s c h o n h a * t e * s b e s o n d e r s T d r E u e n d g e k l u n g e R T +Eval: D D D S S I S + +id: (m-ailabs_deu_000197-m-ailabs_deu_000197) +Scores: (#C #S #D #I) 9 0 0 1 +REF: s o n d e r ******* b a r +HYP: s o n d e r b a r +Eval: I + +id: (m-ailabs_deu_000198-m-ailabs_deu_000198) +Scores: (#C #S #D #I) 88 9 12 6 +REF: e r b v o n e r b e N h e i m s t a n d m i t S e I n ******* E r g a T t I n v o L l w e H m u * t u n D d a n k * b a r k e i T * a n d e r g r U f t A U f d E R e r e i N E n m Ä c h t i * g * E n +HYP: e r b v o n e r b e M h e i m s t a n d m i t * e * n H r g a * t E n v o * l w e m u O t ******* u n G d a n k G b a r k e i * D a n d e r g r O f t * C f d * * ******* e r e i * * n m E c h t i N g N G n +Eval: S D D I S D S D S I D S I D I S D S D D D D D S I I S + +id: (m-ailabs_deu_000199-m-ailabs_deu_000199) +Scores: (#C #S #D #I) 72 5 6 5 +REF: * i H r w a r j * e ******* d e r m e n s c h e i n W u n d e r u n d f a s t a l l e s w a s m e n s c h e n t a t e n e t W a s w U n d E r b a r e s ******* * +HYP: T i E r w a r ******* j I e d e r ******* m e n s c h ******* e i n * u n d e r u n d f a s t ******* a l l e s w a s ******* m e n s c h e n t a t e n e t a s S w O n d A r b a r e s N +Eval: I S D I I D D D D D S S S S I I + +id: (m-ailabs_deu_000200-m-ailabs_deu_000200) +Scores: (#C #S #D #I) 26 7 2 3 +REF: w e l c h e I H r w e * G s i e e n * T l Ä n G s t f Ü H r t ******* E +HYP: w e l c h e J E r w e I E s i e e n D l E n K s t ******* f Ü * r t N +Eval: S S I S I S S S D D I S + +id: (m-ailabs_deu_000201-m-ailabs_deu_000201) +Scores: (#C #S #D #I) 83 4 13 1 +REF: D i e w I r t I n S a S s n i c h t H i * n t e R I H r e m s c h a n k t i s c H u n d k e i n e r I H r e r d i e n s t l e u t e b e f a n d S i c h i n d e r s t u b e +HYP: * i e ******* w * r t E n * a * s n i c h t ******* * i E n t e * * * r e m s c h a n k t i s c * u n d k e i n e r * E r e r d i e n s t l e u t e b e f a n d Z E i c h ******* i n d e r s t u b e +Eval: D D D S D D D D I D D D D D S S S D + +id: (m-ailabs_deu_000202-m-ailabs_deu_000202) +Scores: (#C #S #D #I) 120 4 4 7 +REF: * a l s d i e h e r R s c h a f t a u s d e r k i R c h e * t r a t s t a n d e n d i e l e u t e u m ******* h e * r u m s i e v o r b e i g e h e n z u s e h e n u n d a m k i r * c H h o f ******* t H o r e w a r t e T e e i n m a * n n +HYP: N a l s d i e h e r s c h a f t a u s d e r k i L c h e R t r a t s t a n d e n d i e l e u t e u m h e H r u m s i e v o r b e i g e h e n z u ******* s e h e n u n d a m k i r L c * h o f t * o r e R w a r t e e e i n ******* m a N n n +Eval: I S S I I I D I D I D S S D I + +id: (m-ailabs_deu_000203-m-ailabs_deu_000203) +Scores: (#C #S #D #I) 33 7 14 1 +REF: W a s m Ü S s e n W I r t * U n U m d e m t E R R O r i s m u s e n T G E g e n Z u t R E T e N +HYP: * a s ******* m ** * s e n * E r t O H n ******* O m d e m t * * * A r i s m u s e n * * g e n T u t * * D e * +Eval: D D D D D S I S D S D D D S D D S S D D S D + +id: (m-ailabs_deu_000204-m-ailabs_deu_000204) +Scores: (#C #S #D #I) 33 6 14 2 +REF: I c h g * l a u B e * D a S s S I e E s g u T M i t M I r m e i n e N h e R r d o k t O R +HYP: L c h g E l a u * e R * a * s * * e * s ******* g u D E N i t ******* B E r ******* m e i n e * h e * r ******* d o k t * * +Eval: S I D I D D D D D D S S S D S S D D D D D D + +id: (m-ailabs_deu_000205-m-ailabs_deu_000205) +Scores: (#C #S #D #I) 64 10 6 3 +REF: * D O C H i m a n f a n G g e w a N n e r k e i n e a u f m e r K S a m ******* k e i t F Ü r a n d E r e d i n g e a l s f Ü r d * A s e s s e n +HYP: E N T O R i m a n f a n K g e w a * n ******* e r k e i n e a u f m e r * a m k e i t V Ü r a n d * r e R d i n g e a l s ******* f Ü r ******* d E R s e s s e n +Eval: I S S S S S S D D D S I S D S D D I S + +id: (m-ailabs_deu_000206-m-ailabs_deu_000206) +Scores: (#C #S #D #I) 91 6 11 4 +REF: d i e s f l Ä s c h C H e n z o g e r j e t z t e i l i G h e r ******* v o r w Ä H r e n d j e n e s I c H m i t w a s s E r f Ü L l t e n u n d b o t e s d e r J u n g ******* F e r z * Ü s a * n +HYP: d i e s f l Ä s c h * I e n z o g ******* e r ******* j e t z t e i l i * C h e r v o r w ** E r e n d j e n e s * c * m i t w a s s A r ******* f ** I l t e n u n d b o t ******* e s d e r * u n g V e r z Y Ü s a N n +Eval: D S D D D S I D S D D S D D S D D I S I I + +id: (m-ailabs_deu_000207-m-ailabs_deu_000207) +Scores: (#C #S #D #I) 98 15 27 14 +REF: D e s H A L B w a R E s A U c H r i c h t i G U n D w i c h t i G D A S s C h i n * A d o c h J e * t z T a n s P R U c * H s V o * l * l g e s a G t H a t w I R w e r d e n a u c h a n D e n z e i t ******* p u n k T d e r R E d u k t i * o n k o m m e n * * * ******* * * ** +HYP: * e s * * * E R w a * ******* * s * O c * ******* r i c h t i * O n * w i c h t i C H T E R s * h i n E R d o c h ******* * e R t z * a n s * * * c P O s F o L l E l g e s a K t ******* * a t w * * E w e r d e n a u c h a n ******* * e n z e i t p u n k * d e r * I d u k t i U o n k o m m e n D E S D G Ü +Eval: D D D D S S D D D D S D D D S D S S S S S D I S D D I D D D D I S S I I S D D D D S D D I D D S I I I I I I I I + +id: (m-ailabs_deu_000208-m-ailabs_deu_000208) +Scores: (#C #S #D #I) 34 5 5 1 +REF: * n i c h T d o c h m u T t e r w e C k e s I e j e T z t n O c h n i C H T +HYP: D n i c h * d o c h m u * t e r w e R k e s * e j e R z t n * c h ******* n i G N +Eval: I D D S D S D D S S S + +id: (m-ailabs_deu_000209-m-ailabs_deu_000209) +Scores: (#C #S #D #I) 56 6 18 2 +REF: J A W i R h a b e n i n D e n L e T z T e n j a h R E n r e c h t e n G e b E Z i E H u n g E N z u b R a s i l i e n a u f g e b a u t * * +HYP: * * ******* B i A h a b e n i n ******* * e n ******* D e * z * e n ******* j a h * * n ******* r e c h t e n K e b * * i T I u n g * * z u ******* b * a s i l i e n a u f g e b a u t P R +Eval: D D D S S D D D S D D D D D D S D D S S D D D D I I + +id: (m-ailabs_deu_000210-m-ailabs_deu_000210) +Scores: (#C #S #D #I) 29 5 4 10 +REF: * * * * ******* s i e W Ü r d e s I c h n i c h t F Ü * r a n d E r E * O p f E R n ******* * * +HYP: D T T S s i e E V Ü r d e s * c h n i c h t V Ü E r a n d * r * A B p f * O n T T +Eval: I I I I I S S D S I D D I S D S I I I + +id: (m-ailabs_deu_000211-m-ailabs_deu_000211) +Scores: (#C #S #D #I) 9 3 5 4 +REF: R I e * * * * f e n s I e m I R z u +HYP: * L e C H L I f e n ******* s * e ******* m * E T z u +Eval: D S I I I I D D D D S S + +id: (m-ailabs_deu_000212-m-ailabs_deu_000212) +Scores: (#C #S #D #I) 49 4 10 6 +REF: g o T t w a s s i E i H R E r ******* z Ä h ** l t e * h Ö r e n s i E n u r e s i s T * e i n g a n Z e R r o m a * ******* n +HYP: g o A t w a s ******* s i * i * E A r z ** h Ä l t e R h Ö r e n s i * ******* n u r e s i s * S e i n ******* g a n S e * ******* r o m a N n +Eval: S D D D S S I D I I D D D I D S D D I I + +id: (m-ailabs_deu_000213-m-ailabs_deu_000213) +Scores: (#C #S #D #I) 43 6 11 1 +REF: s e i n e m U T t e r k a N n i H m N U R f l u s s w a s s E r g e b e n d e s ******* H A L b w e i n T e r +HYP: s e i n e m * * t e r ******* k a * n ******* i * m ******* * * O f l u s s w a s s * r ******* g e b e n d e s S E I b P w e i n D e r +Eval: D D D D D D D D D S D D I S S S S S + +id: (m-ailabs_deu_000214-m-ailabs_deu_000214) +Scores: (#C #S #D #I) 142 12 28 12 +REF: D E R B u n D e * w I r T s c h a F T s ******* m i n I s t E R w i r D Z U S A M m e n * * * * * m i t D e r n e t z ******* a g e n t u r a m v i e r t e * j u n i z u m e r s T e n m a l p r Ä s e n t i e r E n w i e s i c h d i e n e t z ******* b e t r e i b e r u n d d i e k r a f t W e * r k e d i E n e u E N n e t Z P l Ä n e v o r s t e L L E n * +HYP: * * * ******* * u n * e S w * r * s c h a * * s m i n E s t * A w i r T * * * E Ö m e n B U S A M m i t ******* * e r ******* n e t z a g e n t u r a m v i e r t e N j u n i z u m e r s * e n ******* m a l p r E s e n t i e r * n w i e ******* s i c h d i e ******* n e t z b e t r e i b e r u n d d i e k r a f t D e A r k e d i * ******* n e u * * ******* n e t * l H n e v o r s t e R U n D +Eval: D D D D D D I D D D D I S D S S D D D S S I I I I I D D D I I D D S D D D I S I D D D D D D S S S S S I + +id: (m-ailabs_deu_000215-m-ailabs_deu_000215) +Scores: (#C #S #D #I) 101 13 17 8 +REF: e v * a h a T t e s I c h z i T t E R n D v o r t o d e s ******* s C H W Ä c h e * v o n d e m g I T t e r b e ******* f r e i t u n d S u c h t e z u E n * T f L i E H e n A b e R d e r s C H m a l e g a R t e n b o t * k * e i n e n a u s w * E G +HYP: e v W a R h a * t e s E c h T z i * t * A n T v o r t o d e s s * F V E c h e R v o n d e m g * E t e r b e f r e i t ******* u n d ******* Z u c h t e z u ******* * n D f * i * * e n * b e * d e r s * * m a l e g a * t e n b o t D k A e i n e n a u s w I H N +Eval: I S D S S D D S S I D S S S I D S I D D S D D I S D D D D D D D D I I I S S + +id: (m-ailabs_deu_000216-m-ailabs_deu_000216) +Scores: (#C #S #D #I) 72 5 17 5 +REF: * O b I c h m e i n w e r K f Ü r H E u t e l i E G e n l a s s e n * o d e r n o c h e i N E n a n ******* l a u f n e H m e n u n d e s V o L l E n d E n s o L l t e ******* * +HYP: D A b ******* E c h m e i n w e r * f Ü r ******* * * u t e l i * * e n l a s s e n N o d e r ******* n o c h ******* e i * * n a n l a u f ******* n e * m e n ******* u n d T e s R o * l Ä n d * n s o * l t e N +Eval: I S D S D D D D D D I D D D D I D D D S S D S D D I I + +id: (m-ailabs_deu_000217-m-ailabs_deu_000217) +Scores: (#C #S #D #I) 116 17 19 7 +REF: e r w a R d a s g Ö t z C H e * n d e r s t u n d E d I e t A i t A i B e ******* a u f t r a G t e m a d A m E A n * * G E l E d i e a U c h D a ******* s t a n d u n D D i E g e K a u f T e n s e i d e n S t Ü C k e z u s a M m E N f A l T e t e f Ü r T s c h U n z u s o r g e * * n +HYP: e r w a * d a s g E t z I e R n d e r s t u n d * d * e t E i t E i * e a u f t r a K t e m a d * m * U n M S C H l L d i e a * c h * a s t a n d u n * * i * ******* g e a u f * e n s e i d e n t Ü k e ******* z u s a * m * f E l D e t e f Ü r ******* * s c h O n z u ******* s o r g e N G n +Eval: D S S S I D D S S D I S D D S I I S S S D D I D D D D S D S S D D D S S S D D S D I I + +id: (m-ailabs_deu_000218-m-ailabs_deu_000218) +Scores: (#C #S #D #I) 10 2 7 3 +REF: * i C H w E r ******* d e n ******* A c h s E H e N +HYP: T i * * ******* w * r d e ******* n H c h s * * e M +Eval: I D D D D I D I S D D S + +id: (m-ailabs_deu_000219-m-ailabs_deu_000219) +Scores: (#C #S #D #I) 36 12 7 2 +REF: a b * ******* e R t i P P s o d E R v o r g a b e n d a s m a c h E N W I r n A T Ü R l i C H n i C h T +HYP: a b A e * t i * B s o d A F v o r g a b e n d a s m a c h * M * E r ******* n E R D O l i * * K n i G h S +Eval: I I D D S S S D S D S D S S S S D D S S S + +id: (m-ailabs_deu_000220-m-ailabs_deu_000220) +Scores: (#C #S #D #I) 100 7 20 5 +REF: a l s u n s E r e * I d E e b e k a N n t w U r d e * w a R d i E P H Y s i o g n o m i e d e r w A l t e r s * b U R g e r u n g e ******* f Ä H r d i E e i n e * s k a l b e s d a s Z u m e r s T e n m a l E D o N n E R n h Ö r T +HYP: a l s u n s * r e E d * e b e k a * n t ******* w O r d e R w a * ******* d i * * Ü R s i o g n o m i e d e r w E l t e r s P b * O g e r u n g e f Ä * r d i * e i n e R s k a l b e s d a s * u m e r s * e n ******* m a l * * o * n * H n ******* h Ö r * +Eval: D I S D D D S I D D D D S S S I D S I D D I D D D D D D D S D D + +id: (m-ailabs_deu_000221-m-ailabs_deu_000221) +Scores: (#C #S #D #I) 54 6 12 2 +REF: B i T T e * m a C h E n s i E g e ******* f Ä L l i G S t a u f u n d e s k l a n g W i e e i n j a M m E R n d e r h i l F e r U F +HYP: * i * Z e R m a * h * n ******* s i * ******* g e f Ä * l i C H t a u f u n d e s k l a n g D i e e i n j a * m * A n d e r h i l V e r * * +Eval: D D S I D D D D D I D S S S D D S S D D + +id: (m-ailabs_deu_000222-m-ailabs_deu_000222) +Scores: (#C #S #D #I) 77 2 22 3 +REF: H E R r d o k t o r s a g t e e i n e f r a u d i e s C h ******* n U R R g r i n e * * d i e s o O f t z u i H n e n k o M m T i s t e i G E n T l i C H g a R n i c h T k r a n K +HYP: * * * r d o k t o r s a g t e e i n e ******* f r a u d i e s * h n * * O g r i n e R M d i e ******* s o A f t z u i * n e n k o * m * i s t e i * * n * l i * * ******* g a * ******* n i c h * ******* k r a n * +Eval: D D D D D I D D S I I D S D D D D D D D D D D D D D D + +id: (m-ailabs_deu_000223-m-ailabs_deu_000223) +Scores: (#C #S #D #I) 176 7 20 4 +REF: d i e a l t e e r ******* i N n E r u n g a n d e n f r Ü h E r e n t R a u m t a u c h t e * e b e n f a L l s w i e d e r a u f u n d u n w i * l L k Ü r l i c H f A s t b e i d e R b e ******* h a u p t u n g d a S s d I e s E e l e d e n k Ö r p e r v e r l a s s e n u n d z u i H m z u r Ü c K k e H r e n k Ö N n e s C h i E n e s i H r o r d e n T l i C H +HYP: d i e a l t e e r i * n r u n g ******* a n d e n f r Ü h * r e n t * a u m t a u c h t e R e b e n f a * l s ******* w i e d e r a u f u n d u n w i E l k Ö r l i c * f S s t b e i d e * b e h a u p t u n g d a * s d * e s * e l e d e n k Ö r p e r v e r l a s s e n u n d z u i * m z u r Ü c * k e * r e n k Ö * n e s * h i * n ******* e s ******* i E r o r d e n D l i * G +Eval: I D S D D D I D D I S S D S D I D D D D D D D D D D D S S D S + +id: (m-ailabs_deu_000224-m-ailabs_deu_000224) +Scores: (#C #S #D #I) 84 9 16 6 +REF: a l S s i e A U f d e n B a l k o n * z u r Ü C K k e H r t e F a n d S i E i H n d i e z e i t u n G l * e s e n d d i e w Ä H r e n D I H r e s f o r t ******* S e i n * s a n g e l A n G t w a r * * +HYP: a l * s i e * O f T d e n * a l k o n D z u r ** * I k e * r t e V a n d ******* Z i * i * n d i e z e i t u n * K l I e s e n d d i e w ** E r e n * * * r e s f o r t Z e i n E s a n g e l * n K t ******* w a r H T +Eval: D D S S D I D D S D S D S D D D S I D S D D D I S I D S D I I + +id: (m-ailabs_deu_000225-m-ailabs_deu_000225) +Scores: (#C #S #D #I) 121 5 8 7 +REF: * e R * * * w a R e i n k i n d d e r s t r a S s e v o n k l e i n a u f a b e r i n i H m l e b t e v o n j e h e r E i n e g E w i s s e * s e H n ******* s U c h t n a c h e i n e r e h r b a r e n b Ü r g e r L i c h e n e x i s t e n * Z +HYP: T e * E H R w a H e i n k i n d d e r s t r a * s e v o n k l e i n a u f a b e r i n i * m ******* l e b t e v o n j e h e r * i n e g * w i s s e S s e E n s O c h t n a c h ******* e i n e r e h r b a r e n b I r g e r * i c h e n e x i s t e n S T +Eval: I D I I I S D D D D D I S I S D S D I S + +id: (m-ailabs_deu_000226-m-ailabs_deu_000226) +Scores: (#C #S #D #I) 70 23 37 2 +REF: U N D W I R a L S B u n D e s R E g I E r u n g F Ü h L E n u n S H i e R n i C H T e i n e r g r U P p e V e r a n T W o R T l i c h S o n D E R N w I R F Ü H L e N U n S d e M g e m e i n w O h l V e r A n T w o R T l i c h * * +HYP: * * * ******* * * * ******* a I T * u n * e s T I g * J r u n g K T Ü h * * n ******* u n * ******* Z i e * ******* n i * * G e i n e r g r * O p e ******* F e r a n * F o * D l i c h * o n * * A M w * * ******* * Ü * * e * F Ü n E d e N g e m e i n w U h l ******* F e r n * w o * D l i c h U N +Eval: D D D D D D D D S S D D S S D S S S D D D D D S D D D D S D S D S D S D S D D D S S D D D D D D D S S S S S D S S D D S I I + +id: (m-ailabs_deu_000227-m-ailabs_deu_000227) +Scores: (#C #S #D #I) 25 0 4 1 +REF: w a s m e i n l i e ******* b e s k i n d w a s k a N n +HYP: w a s m e i n ******* l i e b e s ******* k i n d w a s ******* k a * n +Eval: D I D D D + +id: (m-ailabs_deu_000228-m-ailabs_deu_000228) +Scores: (#C #S #D #I) 136 13 19 7 +REF: u n d d a N n w o L l t E i c h d e n a n b l i c K d e R E r n i c h t m i S s e n d i e m I r g e b l I e b e N w a r e n v o r a L l E m a b E r w a R e s M i R d a r u m z u t * u n M e i n e s Ü S s e E l * * i s * * A b e t H e i n i g E R m a s s e n g e t r Ö s t e t z u s e H e n * * +HYP: u n d d a * n w o * l t * i c h d e n a n b l i c G d e * r A n i c h t ******* m i * s e n d i e ******* m E r g e b l * e b e M w a r e n v o r a * l * m a b A r B w a * e s * i * d a r u m z u ******* t O u n W e i n e s ** Y s e * l I C i s E R b e t * e i n i g * A m a s s e n g e t r Ü s t e t z u ******* s e e n T O +Eval: D D D S D S S D D D S D S D D S S D D D D I S D S D I I I I S D D S S D S I I + +id: (m-ailabs_deu_000229-m-ailabs_deu_000229) +Scores: (#C #S #D #I) 40 12 26 7 +REF: A B e R I C H G L a u B E D A S S w i R u n * s A U C H G E g e n s e I t I G * E I N b I S s C h E n u n t E R s t Ü T z e n k Ö n n e n ******* * ** * * +HYP: * * e * ******* D A S * * a u * * ******* * * C H w i E u n D s ******* * * * * * * g e n s e * t * * D C H E b * * s * h * n u n t D A s t ** U z e n k Ö n n e n E Ö R M +Eval: D D D D S S S D D D D D D D S S S I D D D D D D D D D D I S S S D D D D S S D S I I I I I + +id: (m-ailabs_deu_000230-m-ailabs_deu_000230) +Scores: (#C #S #D #I) 76 5 15 3 +REF: s e i n e g e s c h Ä f t L i c h e l a u f b a h n h a * b e s t * e F e n s O n a l s k Ü c h e n ******* b o Y i n e i n e M H o t E l V i e r T e n g r a d e s b E g o N N E n +HYP: s e i n e g e s c h ** f t * i c h e ******* l a u f b a h n ******* h a R b e ******* s t I e V e n s * n a l s ******* k Ö c h e n b o * i n e i n e * M o t Ä l F i e r * e n ******* g r a d e s b * g o * * * n +Eval: D D D D I D I S D D S I D D S S S D D D D D D + +id: (m-ailabs_deu_000231-m-ailabs_deu_000231) +Scores: (#C #S #D #I) 116 9 26 4 +REF: V I E L l e i c h T T Ä t e n s I e g u t D i e s e a n s i c h T e n d e s B I s c h o f s n A C H h a U s e z u m e l d e n S a G t e d e r t a * * J e n d e r i M m E r m e h r e i n m a N n d e s g e s c H r i e b e n e n w O r t e s w i e d e r t a * ******* T +HYP: * N F Ü l e i c h * ******* * ** t e n s * e ******* g u t ******* * i e s e a n s i c h * e n ******* d e s * * s c h o f s ******* n * * * h a * s e z u ******* m e l d e n Z a C t e ******* d e r ******* t a T S C e n d e r i * m A r m e h r e i n m a * n ******* d e s g e s c * r i e b e n e n w U r t e s w i e ******* d e r t a D N +Eval: D S S S D D D D D D D D D D D D D D D D D D S S D D I I S D S D D D S D I I S + +id: (m-ailabs_deu_000232-m-ailabs_deu_000232) +Scores: (#C #S #D #I) 119 15 23 11 +REF: A m a n ******* d E R n m o r G e n e * r h o B e r s i c h s * * P Ä t s c h I c k t e d e n l a * k A i e N I n d I e W o H n u n g * F E U E R b a c h s u n D l i e S s u m e i n e U n t e R r e ******* d u n g b i T t e n d e R m a N n k a m m i t D e r B o t s c h a f t z u r Ü * ******* * * C K +HYP: E m ******* a n d * A n m o r D e n e H r h o * P e r ******* s i c h s C H W Ä t s c h * c k t e d e n l a R k E i e * ******* * n d * e B o * n u n g V O R J A b a c h s ******* u n * T l i e * s ******* u m e i n e * n t e * r e d u n g b i * t e n d e * ******* m a * n k a m m i t ******* * e r * o t s c h a f t z u r Ü G D E T N +Eval: S D I D S S I D S D I I S D I S D D D D S D I S S S S S D D S D D D D I D D D D D D D I I I I S S + +id: (m-ailabs_deu_000233-m-ailabs_deu_000233) +Scores: (#C #S #D #I) 70 5 11 9 +REF: * * ******* n u R e i n w e n i * G t r a u r i * G w u r d E e s W e N n i M m E R d a s ******* s e l ******* b e k a m W e N n s i E n i e z u ******* f r i e d e n s c h i e n ******* E n +HYP: N T n u * e i n ******* w e n i C H t r a u r i C H w u r d * ******* e s * e * n i * m * * d a s s e l b e R k a m * e * n s i N n i e z u f r i e d e n s c h i e n N n +Eval: I I I D D I S I S D D D D D D D I I S D D S I I S + +id: (m-ailabs_deu_000234-m-ailabs_deu_000234) +Scores: (#C #S #D #I) 118 18 13 10 +REF: e i n s o m M E r ******* w a R m E R n o v e m ******* b E r t a * G l a g m i t S o N n e n ******* g l i t z E R n Ü b e r d e R h A u p t s t a D t u n d u n T e r d E n l i n d e n d r Ä n G t e * E i n e t a u s e n d ******* k Ö p f i g e m e n s c h e n m E n g e * a u F * U n D N i e d e r ******* * +HYP: e i n s o m A H r w a H m * * A n o v e m b A r t a R K l a g m i t ******* Z o * n e n g l i t z S A n N Ü b e r ******* d e * h * u p t s t a B t u n d u n D e r d * n ******* l i n d e n ******* d r E n K t e R * i n e t a u s e n d k A p f i g e m e n s c h e n m Ä n g e R a u O V O n * * i e d e r U +Eval: S S I S D D S I S I S D S D I S S S D D D S S D D D S S I D I S S I S I S D D I I + +id: (m-ailabs_deu_000235-m-ailabs_deu_000235) +Scores: (#C #S #D #I) 42 2 7 1 +REF: k o M M m i t m ******* i R m e i n s o H n d e N n i c h B r a u c h e d e i n e l i e b e +HYP: k o * * ******* m i t ******* m i H m e i n s o * n d e * n i c h ******* P r a u c h e d e i n e l i e b e +Eval: D D D D I S D D D S + +id: (m-ailabs_deu_000236-m-ailabs_deu_000236) +Scores: (#C #S #D #I) 69 7 7 12 +REF: * * * n * U r s E i n G e s i c h t W U r d e E i n w E n i G n a c h * d e n k l i c h E r s * o w i e v o n e i n e r e r ******* i N n e r * u n g e r ******* h E L l t ******* * * +HYP: D N T n W O r s A i n K e s i c h t * * r d e * i n w * n i C H n a c h T d e n k l i c h A r ******* s O o w i e v o n e i n e r e r i * n e r H u n g e r h * Ä l t N N +Eval: I I I I S S S D D D D S S I S D I I D I I D S I I I + +id: (m-ailabs_deu_000237-m-ailabs_deu_000237) +Scores: (#C #S #D #I) 58 11 26 4 +REF: D A N n w I R D A u C H W i E D e r d e R I N n O V a t i o * * n s ******* d r U c k s t e i g e n u n d D a * z u I s t D a s s Y s t e m J A e I n g e f Ü H r t w o R D E n +HYP: * * * n w * O T O u * * ******* * i * e r ******* d e * * * n * W a t i o N E n s d r O c k s t e i g e n ******* u n d ******* T a T z u E s t ******* * a s ******* s E s t e m ******* I E e * n g e f Ü * r t ******* w o * * * n +Eval: D D D D S S S D D D D D S D D D D D S I I I S D D S I S D D D S D S S D D D D D D + +id: (m-ailabs_deu_000238-m-ailabs_deu_000238) +Scores: (#C #S #D #I) 91 5 15 4 +REF: J e T Z t g e w a H R t e e r m i t e n T s e t ******* z E n d i e s c h e u S s l i c h e t e u f l I s c h e * a F f e n f r a t z * e d i e Ü b e R d e s m Ä D c h e n S s c h U l t e r s c H i e l t e * +HYP: N e * * t g e w a * * t e e r ******* m i t e n s e t z S n d i e s c h e u * s l i c h e t e u f l * s c h e R a * f e n f r a t z S e d i e ******* ** b e * d e s m E T c h e n * s c h * l t e r ******* s c * i e l t e N +Eval: S D D D D D S I S D D I D I D D D S S D D D D I + +id: (m-ailabs_deu_000239-m-ailabs_deu_000239) +Scores: (#C #S #D #I) 69 13 8 11 +REF: * * * J a * d e R w I r t n i C k ******* t e d a s g E H Ö r T e i n e M g e w i s s e n w U t s c h * O W b e r n h a R D w U t s c h o W i s t * e t w a s V E r R u * F e * * * n +HYP: T T E R a R d e * w E r t n i * k t e d a s ******* g * * Ö r D e i n e * g e w i s s e n w R t s c h A U F b e r n h a * T w R t s c h o F i s t E e t w a s ******* F A r G u N S e N T N n +Eval: I I I S I D S D I D D D S D S I S S D S S S I D S S S I S I I I + +id: (m-ailabs_deu_000240-m-ailabs_deu_000240) +Scores: (#C #S #D #I) 45 5 11 9 +REF: w o L l t I H r i n w a H R h e i T d i e l ******* Ö * W e n * t Ö * t e n * u n d * k Ö N n T I H r s c h I e * S s e * * n +HYP: w o * l t * E r i n w a * * h e i * d i e ******* l Ö R S e n D t Ö R t e n N u n d T k ** * n D * E r ******* s c h * e L I s e N N n +Eval: D D S D D D D I I S I I I I D D S D S D D I S I I + +id: (m-ailabs_deu_000241-m-ailabs_deu_000241) +Scores: (#C #S #D #I) 106 8 12 5 +REF: B a t C e ** D d i E s e H R r e s p e k t ******* v o L l w * o b e i e r n u r e i n i ******* g e s I l B e n v e r s c h l u C k t e w a * s i H m b e i d e n b e l I e b t e n l a n g e n w Ö r t e R n d e s Ö f t E R n V o r k a M +HYP: * a t S e Ä T d i * s e * * ******* r e s p e k t v o * l w U o b e i e r n u r e i n i g e ******* s E l D e n v e r s c h l u * k t e w a S s i * m b e i d e n b e l * e b t e n l a n g e n w Ö r t e A n d e s ** f t * A n F o r k a N +Eval: D S I S D D D D I D I I D S S D I D D S D D S S S + +id: (m-ailabs_deu_000242-m-ailabs_deu_000242) +Scores: (#C #S #D #I) 60 9 3 8 +REF: * l o r D f A U n * * T l e ******* r o * Y W I r D n i c h T s e n * T b e h r e n d e s s e n b i n i c h g e w i S s v e r s e t z t e e r ******* * +HYP: D l o r T f * O n D L E l e r o R U V E r T n i c h Z s e n D b e h r e n d e s s e n b i n ******* i c h g e w i * s v e r s e t z t e e r T +Eval: I S D S I I S I I S S S S S I S D D I I + +id: (m-ailabs_deu_000243-m-ailabs_deu_000243) +Scores: (#C #S #D #I) 63 4 4 2 +REF: k a m g l e i c h f a L l s i n s s c h l a f * z i m m E r a u f e i n e n n a g e l i n d e r n Ä h E D e s b e T t e s * +HYP: k a m g l e i c h f a * l s i n s s c h l a f T z i m m A r a u f e i n e n n a g e l ******* i n ******* d e r n E h R T e s b e * t e s N +Eval: D I S D D S S S D I + +id: (m-ailabs_deu_000244-m-ailabs_deu_000244) +Scores: (#C #S #D #I) 97 22 35 7 +REF: U N D d a s I s T d i E c h a n C E d i e I n D I e s E r k r i S E s t e C K T d i e c h a n C E F Ü r i n * t e r n a t * i o n a l e r e g e L n D i E s i C H * A n D E N p r I n Z i * * p * i e n d e r s * O Z I a l e N m a R k T W I R t S C H a f T o R i e n t i e r E n +HYP: * * * ******* d a s ******* E s * d i * S c h a n G S d i e * n ******* * * e s A r ******* k r i * * ******* s t e * * G d i e S c h a n G S V Ü r i n Z t e r n a t Z i o n a l e ******* r e g e * n B i * ******* s i * * E I n ******* * I M p r O n S i E B p L i e n d e r ******* s E D J a l e * m a * k * * P O t * * * a f * o L i e n t i e r * n +Eval: D D D D D S D D S S S D D D D S D D D D D D S S S S S I I D D S D D D D I S D D S S S S I I I D I S S S D D D D S S D D D D S D + +id: (m-ailabs_deu_000245-m-ailabs_deu_000245) +Scores: (#C #S #D #I) 78 1 15 0 +REF: a n f a n g s f i e l d e R r e g e n s c h r Ä g u n d p e i t s C H t e e r s T D i e e i n e U N D d a N n d i e A n d e r e s e i t e d e s w a g e n s +HYP: a n f a n g s ******* f i e l ******* d e * ******* r e g e n s c h r A g u n d p e i t s * * t e e r s * * i e e i n e ******* * * * d a * n d i e * n d e r e ******* s e i t e d e s w a g e n s +Eval: D D D D S D D D D D D D D D D D + +id: (m-ailabs_deu_000246-m-ailabs_deu_000246) +Scores: (#C #S #D #I) 66 6 5 9 +REF: * f a s t l e i c h T S I N n i g e n b e * ******* m e s s U n g I H r e s w e r t e s a u f * ******* z U g e b e N s i c h e n t ******* s c H l o s s e n h a T t e ******* * * +HYP: M f a s t l e i c h * * E n i g e n b e R m e s s O n g * E r e s w e r t e s a u f T z O g e b e M s i c h e n t s c * l o s s e n h a * t e N N +Eval: I D D S S I I S D S I I S S I D D I I I + +id: (m-ailabs_deu_000247-m-ailabs_deu_000247) +Scores: (#C #S #D #I) 71 0 20 5 +REF: D A s H e i S s t * d i e f r a * g E D e r m e n S c h l i c h e n a r b e I t U n D d i e f r a g e w a s k a N n t e c h n i s c H g e l Ö s t w e r d e n ******* * * +HYP: * * s ******* * e i * s t B d i e f r a R g * ******* * e r ******* m e n * c h l i c h e n a r b e * t * n * ******* d i e f r a g e ******* w a s k a * n ******* t e c h n i s c * ******* g e l Ö s t ******* w e r d e n D A +Eval: D D D D D I I D D D D D D D D D D D D D D D I I I + +id: (m-ailabs_deu_000248-m-ailabs_deu_000248) +Scores: (#C #S #D #I) 60 8 14 10 +REF: * * D i * e S A f a * r i * w A R a u f D i e r e G e L m Ä S s i g b E n U t z * t e n w a s s E r s t e l l e n d i E s e R r o U t E a n ******* g e w i e s e * ******* * n +HYP: N U T i S e A R f a H r i E w * * ******* a u f * i e ******* r e D e * m ** E s i g b * n O t z S t e n w a s s A r s t e l l e n d i * s e * ******* r o * t * ******* a n g e w i e s e N N n +Eval: I I S I S S S I I D D D D D S D D S D S I S D D D D D D I I I I + +id: (m-ailabs_deu_000249-m-ailabs_deu_000249) +Scores: (#C #S #D #I) 93 4 5 3 +REF: d i e b e i d e n m Ü S s t e n h i e R o b e n a u f d e m g * I p f e l g e s t a n d * e n h a b e n u n d e r s p r a c h d i e a l t e n w * O r t e v o r s I c H h i n +HYP: d i e b e i d e n m ** I s t e n h i e * o b e n a u f d e m g E B p f e l g e s t a n d T e n h a b e n u n d e r s p r a c h d i e a l t e n w A U r t e v o r ******* s E c * ******* h i n +Eval: D S D I S I I S D S D D + +id: (m-ailabs_deu_000250-m-ailabs_deu_000250) +Scores: (#C #S #D #I) 64 6 6 3 +REF: e n D l i c h b L i C k t e * C e d r i k * a u f w e i s s * N E W i C K a L l e s v o n d e n a r m e n l e u t e n f r a G t e e r +HYP: e n T l i c h b * i * k t e S * e d r i k G a u f w e i s s N J U U i * G a * l e s ******* v o n d e n a r m e n l e u t e n f r a K t e e r +Eval: S D D I D I I S S S D S D D S + +id: (m-ailabs_deu_000251-m-ailabs_deu_000251) +Scores: (#C #S #D #I) 99 5 26 1 +REF: D A S S E s H e u T e E i n e w U n d E r b a r e z u s a M m E N a r b e i T z w I s c h e n b u n d u n d l Ä n d e r n i n d i e s e n F r a g e n g i b T M i t s e H R s e H R I n t E r e S s a n T e n p r o j e K t e n * +HYP: * * * * ******* * s ******* * e u D e * i n e w I n d * r b a r e T z u s a * m * * a r b e i * z w * s c h e n b u n d u n d l Ä n d e r n i n d i e s e n * r a g e n g i b * * i t s e * * ******* s e * * * n t * r e * s a n D e n p r o j e G t e n U +Eval: D D D D D D D D S D S D S D D D D D D D D D D D D D D D D S S I + +id: (m-ailabs_deu_000252-m-ailabs_deu_000252) +Scores: (#C #S #D #I) 100 10 18 13 +REF: * C a s ******* P a r V e r h a R r t e a n ******* g e W U r * z e l t A n s e i N E m p l a t z * s e i n E g l i e ******* d e r j a * s e i n e * a u g e n W a R E n * W i e v e r s t e i n E r t A l S e R Z u m z w e i t e n ******* m a l H i * n B L i C k t e ******* * +HYP: D K a s B a r F e r h a * r t e a n g e N M r T z e l t ******* E n ******* s e i * * m ******* p l a t z S s e i n * g l i e d e r j a R s e i n e N a u g e n B a * * n G * i e ******* v e r s t e i n * r t E l * e T * u m z w e i t e n m a l ******* * i E n * i * k t e N +Eval: I S I S S D I S S I D S D D D D I D I I I S D D I D D D S D S D I D D I D S D I I + +id: (m-ailabs_deu_000253-m-ailabs_deu_000253) +Scores: (#C #S #D #I) 96 1 7 4 +REF: e i n i g e z e i t d a n a c h f r a G t e e r m i c h o B * i c h g l a u b e * d a S s d e r e i s ******* g a n g d e n s c H l i T t e n d e s a n d e r e n z e r ******* s t Ö r t H a b e +HYP: e i n i g e z e i t d a n a c h ******* f r a K t e e r m i c h o * P i c h g l a u b e R d a * s d e r e i s g a n g d e n s c * l i * t e n d e s a n d e r e n z e r s t Ö r t ******* * a b e +Eval: D S D I I D I D D I D D + +Speaker sentences 1: cv_deu_000698 #utts: 1 +id: (cv_deu_000698-cv_deu_000698) +Scores: (#C #S #D #I) 36 9 6 4 +REF: a B e R n U n b l O s s * n i c h t I n e i n e * s c h o * c K s t a R r e V e * R F a L l E n +HYP: a * e * ******* n E n b l U s s E n i c h t G E n ******* e i n e R s c h o R c G s t a * r e D e L V a * l I n +Eval: D D D S S I S S D I I S D S I S S D S + +Speaker sentences 2: cv_deu_000699 #utts: 1 +id: (cv_deu_000699-cv_deu_000699) +Scores: (#C #S #D #I) 9 6 6 0 +REF: j A i C H k o M m E J A s c h O n +HYP: j * ******* i L S C k o * m * ******* * I R s c h E n +Eval: D D S S S D D D D S S S + +Speaker sentences 3: cv_deu_000700 #utts: 1 +id: (cv_deu_000700-cv_deu_000700) +Scores: (#C #S #D #I) 39 8 7 6 +REF: * N e B E N b e i * a r b e i T E t e * e R A L s a u s h * I l F s ******* k r a f t a U f e i n e R f a R m * +HYP: S D e * M b e i E a r b e i * * t e D e * E I s a u s h R E l * s k r a f t ******* a * f e i n e N f a H m E +Eval: I S D S S I D D I D S S I S D I D D S S I + +Speaker sentences 4: cv_deu_000701 #utts: 1 +id: (cv_deu_000701-cv_deu_000701) +Scores: (#C #S #D #I) 54 23 13 1 +REF: e i n t e R r i t O R I A L G R Ö s s E R e s E U R o p A w i R d * n i c h t M i t E I n E m E t a T m Ä S s i G k L e i n e r e n E U R o p A e r r e i c H t +HYP: e i n ******* t e * r i t T O H E I K O s s * * e s O C H o p E R w i T d T n i c h t * i t * A n m ******* I t a R m ** E s i * k * e i n e r e n O H o p * ******* e r r e i c * t +Eval: D D S S S S S S S S S D D S S S S S S I D D S S D S S D S D D S S S D D D + +Speaker sentences 5: cv_deu_000702 #utts: 1 +id: (cv_deu_000702-cv_deu_000702) +Scores: (#C #S #D #I) 39 7 4 3 +REF: i H r s o H n k a M d U r C h k Ü n * s T l i c h e * b e f r U c h t u n g * z u r w e L t +HYP: i E r s o U n k a E N d * r * h k Ö n Z s * l i c h e R b e f r O c h t u n g K z u r ******* w e R t +Eval: S S S S D D S I D I S I D S + +Speaker sentences 6: cv_deu_000703 #utts: 1 +id: (cv_deu_000703-cv_deu_000703) +Scores: (#C #S #D #I) 51 5 8 7 +REF: d i e n a c h t ******* a * K T i V e * ******* n f A l T e r f l i E G e n v o n m i T t e * j u l i b i s m i T t e * o * K t o b e r +HYP: d i e n a c h t a R D i * e F n E f * l * e r f l i * * e n v o n ******* m i * t e R j u l i E b i s m i * t e A o F t o b e r +Eval: I I S S D I I S D D D D D D I S D I I S + +Speaker sentences 7: cv_deu_000704 #utts: 1 +id: (cv_deu_000704-cv_deu_000704) +Scores: (#C #S #D #I) 3 1 0 8 +REF: * * * * a * C h ******* * * t +HYP: D E R E a R h A A t +Eval: I I I I I S I I I + +Speaker sentences 8: cv_deu_000705 #utts: 1 +id: (cv_deu_000705-cv_deu_000705) +Scores: (#C #S #D #I) 1 2 1 5 +REF: F Ü n ******* * * * * F +HYP: * E n D H E R N +Eval: D S I I I I I S + +Speaker sentences 9: cv_deu_000706 #utts: 1 +id: (cv_deu_000706-cv_deu_000706) +Scores: (#C #S #D #I) 75 5 10 2 +REF: n u t z e r k Ö N n e n i h r e l e * s e ******* z e i c H e n o n L i n E a b s p e i c h e r N v e r w a l t e n u n d M I t a n D E R e n n U t z E R n t e i l e n +HYP: n u t z e r k ** * n e n i h r e ******* l e R s e z e i c * e n o n E i n * a b s p e i c h e r * v e r w a l t e n u n d B E t a n * * * e n n O t z * A n t e i l e n +Eval: D D D I I D S D D S S D D D S D S + +Speaker sentences 10: cv_deu_000707 #utts: 1 +id: (cv_deu_000707-cv_deu_000707) +Scores: (#C #S #D #I) 15 3 0 5 +REF: d i e d O N b o s c o k a t * * * * * H +HYP: d i e d U M b o s c o k a t I E R A L E +Eval: S S I I I I I S + +Speaker sentences 11: cv_deu_000708 #utts: 1 +id: (cv_deu_000708-cv_deu_000708) +Scores: (#C #S #D #I) 45 20 10 8 +REF: s a u l b a s s z Ä H l T z u d e n I n ******* N o V A t i V s t e n d * e s * i G N E r N u N D f ******* I L m e M A c H e ** R N S e I N e r * z e * * I T +HYP: s a u l b a s s ******* z ** E l * z u d e n * n R o * * t i * s t e n d I e s E i * D A r E u M E M f Ü m e * * c e Ü E U E L e S e r E N z e L L E N +Eval: D D S D D I S D D D I I D S S S S S S I S S D D S I S S S S S S I S I I S S + +Speaker sentences 12: cv_deu_000709 #utts: 1 +id: (cv_deu_000709-cv_deu_000709) +Scores: (#C #S #D #I) 39 12 4 10 +REF: i n G R Ü n Ü B E r s * I L b e R n e m * * w e L l e n ******* b A L K E N e i n e s i * l ******* b E R n * e * e i c h e ******* * +HYP: i n * K Ü n ******* Ü W U r s E R b e * n e m E N w e * l e n b E U G K e i n e s i E l b W O n D e R e i c h e N +Eval: D S D S S I S S D I I D I S S S S S I I S S I I I I + +Speaker sentences 13: cv_deu_000710 #utts: 1 +id: (cv_deu_000710-cv_deu_000710) +Scores: (#C #S #D #I) 84 14 7 4 +REF: W e i t e r e w i c h t i g e i n d U s t r i e z w e i g e s I N D d i e M i K r O m E c h a n i K g a l V a n o p l a s t i K m E t A L l b a u u n D D i e h O L z v e r ******* a r b e i t * u n g ******* * +HYP: * e i t e r e w i c h t i g e i n d E s t r i e z w e i g e ******* s * * E d i e N i C r * m I c h a n i G g a l W a n o p l a s t i G m I t E I l b a u u n * ******* T i e h U T z v e r a r b e i t O u n g A +Eval: D S D D D S S S D S S S S S S S D D S S S I I I I + +Speaker sentences 14: cv_deu_000711 #utts: 1 +id: (cv_deu_000711-cv_deu_000711) +Scores: (#C #S #D #I) 73 2 11 2 +REF: Ü b e r d e n a u t o r I s T n i c h t S b e k a N n T v e r ******* m u t l i c H s t A M m t e E r a u s D e m d e u t s C h e n s p r a c h g E b i e * t +HYP: Ü b e r d e n a u t o r * s * n i c h t * b e k a * n D v e r m u t l i c * s t * * m t e ******* H r a u s * e m d e u t s * h e n s p r a c h g * b i e D t +Eval: D D D D S I D D D D S D D D I + +Speaker sentences 15: cv_deu_000712 #utts: 1 +id: (cv_deu_000712-cv_deu_000712) +Scores: (#C #S #D #I) 20 4 13 2 +REF: M A n s t E u e r T E s m i T * e I n e M D o P p E l * p a D D e L +HYP: * * n ******* s t * u e r * I s ******* m i * D e * n e * N T o * p * l I p a * T e * +Eval: D D D D D S D D I D D S S D D I D S D + +Speaker sentences 16: cv_deu_000713 #utts: 1 +id: (cv_deu_000713-cv_deu_000713) +Scores: (#C #S #D #I) 26 10 5 2 +REF: W i R h a B E N e i N P r O b l e m A U F o * s i s c h i C h t * A c h t +HYP: D i E h a * * R M e i * E r E b l e m ******* * E R o S s i s c h i G h t O U c h t +Eval: S S D D S S D S S D D S S I S I S + +Speaker sentences 17: cv_deu_000714 #utts: 1 +id: (cv_deu_000714-cv_deu_000714) +Scores: (#C #S #D #I) 34 15 23 5 +REF: w i R S P i E L e n I M M e R n O c h * a B E r D A S L E B e N a u F t O u r I S T * D e r Z e I t n I C H t m a c h b A r ******* * * +HYP: w i * C H L i * * e n ******* * * * e M A n A c h U a * * r ******* * * E * * M e * a u * K t * u r * * E E I e r T e * t D n * * * t H m a c h b E r N N +Eval: D S S S D D D D D D S S S I D D D D D S D D S D D S D D D S I S S D S D D D S S I I I + +Speaker sentences 18: cv_deu_000715 #utts: 1 +id: (cv_deu_000715-cv_deu_000715) +Scores: (#C #S #D #I) 32 12 22 0 +REF: H E U T e Z E I G T S i C H d e R g R Ö s s T E T E I l d e R a n l a G E a L S E n G L i s c h e R G a R T e n +HYP: * O R D e * * * A L * i * * d e * g * E s s Y K * * * l ******* d e * a n l a * N a * U * n * * i s c h e * ******* W a * B e n +Eval: D S S S D D D S S D D D D D S S S D D D D D D S D S D D D D D S D S + +Speaker sentences 19: cv_deu_000716 #utts: 1 +id: (cv_deu_000716-cv_deu_000716) +Scores: (#C #S #D #I) 34 7 9 1 +REF: S e i N e R e S i ******* d e n Z n a H m e R i n M Ü n c h e n w o e r a u c H s t a R B +HYP: * e i * e ******* H e * i d e n * n a * m ******* e * i n W I n c h e n w o R e r a u c * G s t a T +Eval: D D D S D I D D D D S S S D S S S + +Speaker sentences 20: cv_deu_000717 #utts: 1 +id: (cv_deu_000717-cv_deu_000717) +Scores: (#C #S #D #I) 71 13 10 1 +REF: i N n e r * E R u n d Ä u s s E r e r n a R t H E X K Ö N n e n a l s g e t r e N n t E t E i l e e i n e s n a R t H E X a u c h g e m e i n s a m v o r K o m m e n +HYP: i * n e r U N G u n d E u s s * r e r ******* n a * t I C G * G E n e n a l s ******* g e t r e * n t * I t A i l e e i n e s n a * t * I G a u c h g e m e i n s a m v o r o m m e n +Eval: D I S S S D D D S S S D S S D D D S S D D S S S + +Speaker sentences 21: cv_deu_000718 #utts: 1 +id: (cv_deu_000718-cv_deu_000718) +Scores: (#C #S #D #I) 37 1 3 5 +REF: d a ******* b e i * b e l e g t e * E r d i e p l Ä t z * e v i e r u n d D r e i * +HYP: d a b e i E b e l e g t e R * r d i e ******* p l Ä t z S e v i e r u n d ******* T r e i G +Eval: I I I D D I D S I + +Speaker sentences 22: cv_deu_000719 #utts: 1 +id: (cv_deu_000719-cv_deu_000719) +Scores: (#C #S #D #I) 39 9 7 2 +REF: k i M d A r * b * Y i s T D i e t o c h t e r z w e i E r P r o f e S s I O n e L l E r t Ä n z E R +HYP: k i N d * r A b I E i s * S i e t o c h t e r z w e i A r * r o f e * s * E n e R l A r ******* t E n z * A +Eval: S D I I S D S S D D D S S S D S D S + +Speaker sentences 23: cv_deu_000720 #utts: 1 +id: (cv_deu_000720-cv_deu_000720) +Scores: (#C #S #D #I) 34 5 12 1 +REF: i C H G l a u b e * D a s f Ü H r t n i C H t i n D i E r i C H t i G e r i C H t u n g +HYP: i * S K l a u b e R * a s f Ü * r t n i * S t i n ******* * i * ******* r i * S t i * e ******* r i * S t u n g +Eval: D S S I D D D S D D D D D S D D D S + +Speaker sentences 24: cv_deu_000721 #utts: 1 +id: (cv_deu_000721-cv_deu_000721) +Scores: (#C #S #D #I) 31 5 4 4 +REF: d a s I s T e i n e E x * t r e M s c h l e c h t e r i c h * T l i * n i e * +HYP: d a s S E s * e i n e ******* R x S t r e * N s c h l e c h t e ******* r i c h S l i E n i e R +Eval: S S D D S I D S D I S I I + +Speaker sentences 25: cv_deu_000722 #utts: 1 +id: (cv_deu_000722-cv_deu_000722) +Scores: (#C #S #D #I) 33 3 6 0 +REF: h e R r l U r c h e n T b l Ö S s t E s e i n h a g e r e s g e s i c h t +HYP: h e * r ******* l O r c h e n b l ** E s t * ******* s e i n h a g e r e s ******* g e s i c h t +Eval: D D S S D S D D D + +Speaker sentences 26: cv_deu_000723 #utts: 1 +id: (cv_deu_000723-cv_deu_000723) +Scores: (#C #S #D #I) 15 6 7 1 +REF: N U R C a r M e n f i n D e T D A S u n ******* f A I r +HYP: * * M O K a r * e n f i n * e * * T O u n f * E r +Eval: D D S S S D D D D S S I D S + +Speaker sentences 27: cv_deu_000724 #utts: 1 +id: (cv_deu_000724-cv_deu_000724) +Scores: (#C #S #D #I) 29 3 6 6 +REF: * ******* i n g e ******* b o R G k R a B b e h a t ******* T e * d r e i g e s c h W i * s t e R +HYP: T i n g e b o * * k * a * b e R h a t D e R d r e i ******* g e s c h * i C s t e T +Eval: I I I D D D D S I S I D D I S + +Speaker sentences 28: cv_deu_000725 #utts: 1 +id: (cv_deu_000725-cv_deu_000725) +Scores: (#C #S #D #I) 55 4 18 5 +REF: * * e s k o M m t w i R K l i C H D A R a U F a n * d a S S s o l c h e d a * t e n A u F d i e s e r e b e n E e r ******* f a S s t w e r d e n +HYP: L C e s k o * m t ******* w i * C l i * * ******* S T S a * * a n M d a * * ******* s o l c h e ******* d a D t e n * u * d i e s e r e b e n * ******* e r f a * s t ******* w e r d e n +Eval: I I D D D S D D D S S S D D I D D D D I D D D D I D D + +Speaker sentences 29: cv_deu_000726 #utts: 1 +id: (cv_deu_000726-cv_deu_000726) +Scores: (#C #S #D #I) 39 6 7 1 +REF: s t r U M m i n G h I n ******* g e g e n e R g i B t e i n H A r m O n i S c h e s p U L s i e r e n +HYP: s t r * A m i n * h E n g e g e n ******* e g i * t e i n * E r m U n i * c h e s p * O s i e r e n +Eval: D S D S I D S D D S S D D S + +Speaker sentences 30: cv_deu_000727 #utts: 1 +id: (cv_deu_000727-cv_deu_000727) +Scores: (#C #S #D #I) 35 2 5 1 +REF: b I n i c h Z u m k a u f e i N e r h Y p o t H e K b e r e c h ******* t i g t +HYP: b E n ******* i c h * u m k a u f e i * e r h * p o t * e G b e r e c h t i g t +Eval: S D D D D D S I + +Speaker sentences 31: cv_deu_000728 #utts: 1 +id: (cv_deu_000728-cv_deu_000728) +Scores: (#C #S #D #I) 13 18 4 11 +REF: t * * ******* e H e * * R A n * I s T D I e * h A U P T S T A d * T V O M I R A n * * * +HYP: t U N e e U N L E n S * s * E N e R S h * E N F V E N d E N D E N * B M n M I E +Eval: I I I S I I S S I D D S S I S D S S S S S S I S S S S D S S I I I + +Speaker sentences 32: cv_deu_000729 #utts: 1 +id: (cv_deu_000729-cv_deu_000729) +Scores: (#C #S #D #I) 13 12 12 2 +REF: * k O H L e n H Y d * r A T e s I n D B E S S e r A L S i H R R u F +HYP: H k * Ö U e n * d E r * * e N s * n * * * * D e r ******* * * E E i S L N D u N +Eval: I D S S D S I D D S D D D D D S D D D S S S S S S S + +Speaker sentences 33: cv_deu_000730 #utts: 1 +id: (cv_deu_000730-cv_deu_000730) +Scores: (#C #S #D #I) 88 10 18 7 +REF: o H n E d i e p R o f E S s i O n E l l E U n ******* t e R s t Ü t z u n G d E R m a * s e R a * t * i R e ******* N n a b t e i l u n g w a R e n d i E s e W a g e n d e r k o n ******* k U r * R e n Z N u n d o c h u n T E R l e g e n +HYP: o * n * d i e p * o f * I s i * n * l l * ******* I n t e * s t Ü t z u n * d * * A m a S s e * a R t D i * e E n a b t e i l u n g w a * e n d i * s e B a g e n d e r k o n k * r O W e n S * u n d o c h u n D O l e g e n +Eval: D D D D S D D D D S I D D D D S I D I I D I S D D S I D I S S D S S S + +Speaker sentences 34: cv_deu_000731 #utts: 1 +id: (cv_deu_000731-cv_deu_000731) +Scores: (#C #S #D #I) 52 5 9 2 +REF: s i e d i e n ******* t e Z u n Ä C H s t a L s u n * t e R k U N f t F Ü r b e l g i s c h e b E s a t z u N G s t r u P p e n +HYP: s i e d i e n t e * u n ** * E s t a * s ******* u n D t e O k * M f t V Ü r b e l g i s c h e b I s a t z u * * s t r u * p e n +Eval: I D D D S D D I S D S S S D D D + +Speaker sentences 35: cv_deu_000732 #utts: 1 +id: (cv_deu_000732-cv_deu_000732) +Scores: (#C #S #D #I) 34 4 4 3 +REF: d a m Ü s s E n w i R s p R e n G e n m e i n t e d e r z * a h n ******* a * r z t +HYP: d a ******* m I s s * n w i S C s p L e n * e n m e i n t e d e r ******* z H a h n a H r z t +Eval: D S D S S S D D I I I + +Speaker sentences 36: cv_deu_000733 #utts: 1 +id: (cv_deu_000733-cv_deu_000733) +Scores: (#C #S #D #I) 74 6 19 6 +REF: a u S s E R d e m s p i E L t e E r B e i m n a c h f o L g e t * e A m n E W m a r k e T r o * Y A l s s o w i e B e i m l i g * a ******* k o n k u r r e n * t e n l O n d O n K n * i G H T S +HYP: a u * s * * d e m s p i * * t e * r * e i m ******* n a c h f o R g e t I e * m n * I m a r k e * D r o E U O l s s o w i e * e i m ******* l i g E a k o n k u r r e n D t e n l * n d E n * n E i * * * * +Eval: D D D D D D D D S I D D S D S I S S D D I I I D S D I D D D D + +Speaker sentences 37: cv_deu_000734 #utts: 1 +id: (cv_deu_000734-cv_deu_000734) +Scores: (#C #S #D #I) 55 17 12 7 +REF: W i e a u c H D a s I n s t A n * T r U n ******* * O F F V O t I n g e r ******* f Ü L L t D i e C O O m B s ******* w a H l d a s C o * n ******* d o r C e t K R I t e r i U m n i c h t +HYP: * i e a u c * * a s E n s t E n D r A n A C H W A t E n g e r f Ü * R t ******* * i e * K U m * s w a * l d a s K o N n d o r K e t * * G t e r i O m ******* n i c h t +Eval: D D D S S I S S I I S S S S S S I D S D D D S S D I D S I I S D D S S D + +Speaker sentences 38: cv_deu_000735 #utts: 1 +id: (cv_deu_000735-cv_deu_000735) +Scores: (#C #S #D #I) 15 4 7 2 +REF: s m i T H W u c h S I n C H i ******* C a g * o a u f +HYP: s m i * * ******* F u c h * ******* E n * T i K a g E o ******* a u f +Eval: D D D S D D S D S I S I D + +Speaker sentences 39: cv_deu_000736 #utts: 1 +id: (cv_deu_000736-cv_deu_000736) +Scores: (#C #S #D #I) 13 1 6 3 +REF: w i * r S i N D H i e R a L l e i * * n +HYP: w i E r * i * * ******* E i e * a * l e i N E n +Eval: I D D D D S D D I I + +Speaker sentences 40: cv_deu_000737 #utts: 1 +id: (cv_deu_000737-cv_deu_000737) +Scores: (#C #S #D #I) 45 13 13 1 +REF: d u M m i s T w e r E t w a s * w e i S s A B E r t r o T Z d E s B e S s E R E n W i S s e n s F A l s c H h A n D e l T +HYP: d u * m i s G w e r ******* * t w a s B w e i * s * W U r ******* t r o * C d S s D e * s * I U n B i * s e n s V O l s c * ******* h U n G e l * +Eval: D S D D I D D S S D D S S S D D S S S D S S D D S S D + +Speaker sentences 41: cv_deu_000738 #utts: 1 +id: (cv_deu_000738-cv_deu_000738) +Scores: (#C #S #D #I) 52 11 6 6 +REF: h a U P T t H e m a d e r s * h O W i s t d i e r e ******* V a n * c h E F Ü r * Ü b * L E s t r e i c h e * U n t e r f r e U n d e n +HYP: h a * * B t * e m a d e r s C h * A i s t d i e r e W a n S c h * V E r E K Ü b I C H I s t r e i c h e R E n t e r ******* f r e I n d e n +Eval: D D S D I D S I S I D S S I S I S S S I S D S + +Speaker sentences 42: cv_deu_000739 #utts: 1 +id: (cv_deu_000739-cv_deu_000739) +Scores: (#C #S #D #I) 43 4 3 1 +REF: g l e i c h ******* z e i t i g w u R d e n s p o R t w e T t e n t E I l w e i s e v e r b O T e n +HYP: g l e i c h z e i t i g w u O d e n s p o * t w e * t e n t * A l w e i s e v e r b U L e n +Eval: I S D D D S S S + +Speaker sentences 43: cv_deu_000740 #utts: 1 +id: (cv_deu_000740-cv_deu_000740) +Scores: (#C #S #D #I) 4 2 0 0 +REF: s I e B e n +HYP: s E e G e n +Eval: S S + +Speaker sentences 44: cv_deu_000741 #utts: 1 +id: (cv_deu_000741-cv_deu_000741) +Scores: (#C #S #D #I) 1 1 0 4 +REF: * J a ******* * * +HYP: T I a B E +Eval: I S I I I + +Speaker sentences 45: cv_deu_000742 #utts: 1 +id: (cv_deu_000742-cv_deu_000742) +Scores: (#C #S #D #I) 63 7 8 6 +REF: z u ******* d e m V E r s a h e r I M k l o s t E R l a n g E j a H r e d i e Ä m t e r d e s n o ******* V i * z * e n ******* m e i s t E R s u n D p r i o r * +HYP: z u d e m F A r s a h e r E N k l o s t * * A l a n g * j a * r e d i e ** m t e r d e s n o W i T z S e n m e i s t * A s ******* u n * p r i o r A +Eval: I S S S S D D S D D D I S I I I D S D D I + +Speaker sentences 46: cv_deu_000743 #utts: 1 +id: (cv_deu_000743-cv_deu_000743) +Scores: (#C #S #D #I) 32 5 3 8 +REF: h e i d e n ******* h A i * * n e n T s t A M m t E e i n e r Ä r * z t e ******* * F A m i l i e * * +HYP: h e i d e n h E i D E n e n s t * * m t * e i n e r E r T z t e V E R m i l i e A R +Eval: I S I I S D D D S I I I S S I I + +Speaker sentences 47: cv_deu_000744 #utts: 1 +id: (cv_deu_000744-cv_deu_000744) +Scores: (#C #S #D #I) 2 2 0 10 +REF: a * * * * * * C H t * * * * +HYP: a R E S P Z I M P t P E N N +Eval: I I I I I I S S I I I I + +Speaker sentences 48: cv_deu_000745 #utts: 1 +id: (cv_deu_000745-cv_deu_000745) +Scores: (#C #S #D #I) 3 1 0 6 +REF: z w * * e ******* * * * I +HYP: z w A I e U N G R +Eval: I I I I I I S + +Speaker sentences 49: cv_deu_000746 #utts: 1 +id: (cv_deu_000746-cv_deu_000746) +Scores: (#C #S #D #I) 37 7 12 7 +REF: * * * * * e b e N f a L l s I n a u G g E n * a n g E S i e d e L T S I n D D i E k E l t E r e i d e r * f a +HYP: T T T T T e b e M f a * l s E n a u * g * n G a n g * I i e d e * * * E n * ******* T i * ******* k A l t * r e i N d e r E f a +Eval: I I I I I S D S D D I D S D D D S D D S D D S D S I + +Speaker sentences 50: cv_deu_000747 #utts: 1 +id: (cv_deu_000747-cv_deu_000747) +Scores: (#C #S #D #I) 55 11 17 4 +REF: d i e s e s t * e H t a U C H f Ü R a b ******* s O l V e n * t e n e i n ******* h e I m I s c h e r s c h U l E N m i t d E U t S C H k E n N t N I S s e n o F f e n +HYP: d i e s e R s t D e * t a * * * R f ** E a b s E l W e n D t e n e i n h e R m * s c h e r ******* s c h O l * * m i t ******* d * R t * * * k A n D t * * E s e n o * f e n +Eval: S I D D D D S D S I S S I I S D D S D D D D S D D D S S D D S D + +Speaker sentences 51: cv_deu_000748 #utts: 1 +id: (cv_deu_000748-cv_deu_000748) +Scores: (#C #S #D #I) 14 3 3 1 +REF: a l s o * I c h H Ö r e n i c H T s +HYP: a l s o E S c h ******* I O r e n i c * * s +Eval: I S D S S D D + +Speaker sentences 52: cv_deu_000749 #utts: 1 +id: (cv_deu_000749-cv_deu_000749) +Scores: (#C #S #D #I) 22 1 3 2 +REF: w i e k A N n m a * n s i * c h s c h Ü t z e N +HYP: w i e ******* k * O n m a H n s i S c h s c h Ü t z e * +Eval: D D S I I D + +Speaker sentences 53: cv_deu_000750 #utts: 1 +id: (cv_deu_000750-cv_deu_000750) +Scores: (#C #S #D #I) 62 11 10 5 +REF: N a C H f Ü N F * m o n a t e n l a g e i n e E m P f i n D l i c h E R e p l A T t e * a * L s d i e b i s D a h * i n e r ******* h Ä l t l i c h e n v o r +HYP: * a * * U f Ü * M F m o n a t e n l a g e i n e I m f i n T l i c h A e ******* p l * O t e R a N Z s T d i e ******* b i s ******* T a h E i n ******* e r h ** l t l i c h e n v o r +Eval: D D D S D S I S S S S S D D S I I S S D D S I D I D + +Speaker sentences 54: cv_deu_000751 #utts: 1 +id: (cv_deu_000751-cv_deu_000751) +Scores: (#C #S #D #I) 58 16 18 7 +REF: z i e l i s t e * s d i e Ü B e R e I n * s t i M m u N g e I n e S s o f t W A R e s Y s t e M S m i t s E I n E r s * P e * z i F I k a t * I o n z u Ü b E r p * R Ü f * E N +HYP: z i e l ******* i s t ******* e R s d i e ******* ** V e e * n T s t i * m u * g ******* e * n e * ******* s o f t * * J e s E s t e * B Z m i t s * * n N r ******* s G H e T z i C H k a t Z G o n z u ******* Ü b A r p O L f M S H +Eval: D D I D D S S D I D D D D D D D D S S D S S D D S D I S I S S I S D S I S S I S S + +Speaker sentences 55: cv_deu_000752 #utts: 1 +id: (cv_deu_000752-cv_deu_000752) +Scores: (#C #S #D #I) 48 15 8 17 +REF: * * ******* M i * t * e i n e M w a * r m * e n g e * T r Ä n * K i * M b * A u * c H * L Ä s * s t S i C H d I e K Ä l T e b e s s e r A u * ******* s h * A l t e n +HYP: B N N i N t E e i n e N w a H r m N e n g e M P r E n E N i O N D b E U u S c * N I S s I s t ******* D i K Ö N d * e ******* * ** l * e b e s s e r * u N s h E I l t e n +Eval: I I I S I I S I I I S S I S I S S I S I D I S S I D S S S S D D D D D D I I I S + +Speaker sentences 56: cv_deu_000753 #utts: 1 +id: (cv_deu_000753-cv_deu_000753) +Scores: (#C #S #D #I) 52 23 5 16 +REF: d i e a n ******* T i * ******* V i * r e n s o F T W A r E i s t a * * * M O k * g E l ******* a u f * e n u n d H a * T a l l e * C O m * * P U t * e R I M * H A u s l a H M g e l E g t +HYP: d i e a n D i E W i E r e n s o C H Ü Ö r * i s t a N N U R N k E N g A l a u f H e n u n d ******* E a N D a l l e G U N m B I E M t D e * L L N N E R u s ******* l a * N g e l I g t +Eval: I S I I S I S S S S D I I I S S I S S I I D S I S I S S I I S S I D S S S I S S D D S S + +Speaker sentences 57: cv_deu_000754 #utts: 1 +id: (cv_deu_000754-cv_deu_000754) +Scores: (#C #S #D #I) 27 13 2 3 +REF: i H r e K L O a * * k e i s t I n d i e s E r z e i t K u g e * l F Ö R M I G +HYP: i E r e T U a R G k e i s t ******* E n d i e s A r ******* z e i t G u g e F l N G E N S C +Eval: S S S S I I D S S D S I S S S S S S + +Speaker sentences 58: cv_deu_000755 #utts: 1 +id: (cv_deu_000755-cv_deu_000755) +Scores: (#C #S #D #I) 59 16 7 13 +REF: * * ******* d i e s t r e C k e * b e g I N n T i m s Ü D e n V e r * O n * A s U n D f Ü h r t D U R c H d i e P o ******* e b E N e R I c h t ******* u n g * * s * Ü * D o * s t e N +HYP: E T d i e s t r e I k e R b e g * E n D i m s G I e n B e r H U n E R s I n * f Ü h r t ******* * * I c * d i e G o e b L e H E c h t u n g M A s Y Ü T o S s t e * +Eval: I I I S I D S S S S S I S I S S D D D D S D S I S S S S I I I I I S I D + +Speaker sentences 59: cv_deu_000756 #utts: 1 +id: (cv_deu_000756-cv_deu_000756) +Scores: (#C #S #D #I) 43 3 4 0 +REF: e r s t v o n d o r t k o N n t e e r s e i N E N w e g f r e i F o r T s e t z e n +HYP: e r s t v o n d o r t k o * n t e e r s e i * * M w e g ******* f r e i V o r s e t z e n +Eval: D D D S D S S + +Speaker sentences 60: cv_deu_000757 #utts: 1 +id: (cv_deu_000757-cv_deu_000757) +Scores: (#C #S #D #I) 58 6 9 6 +REF: s i e e r h e b T S i c h h e u t e i M m E r n o c H G u t e r ******* k e N n b A r a u * s d * e m * s c h ******* w e M m l a n d h e r A u * s +HYP: s i e e r h e b * P Z i c h h e u t e i * m A r ******* n o c * ******* K u t ******* e r k e * n b E r a u S s d I e m N s c h w e * m l a n d T h e r * u S s +Eval: D S S D S D D D S D I D S I I I I D S D I + +Speaker sentences 61: cv_deu_000758 #utts: 1 +id: (cv_deu_000758-cv_deu_000758) +Scores: (#C #S #D #I) 29 5 7 1 +REF: D i E K a n a r I s c h e n i n s E l * n g e h Ö R e N z u s P a N I e N +HYP: T i * * a n a r E s c h e n i n s * l E n g e h ** * e * z u s B a H H e * +Eval: S D D S D I D D D S S S D + +Speaker sentences 62: cv_deu_000759 #utts: 1 +id: (cv_deu_000759-cv_deu_000759) +Scores: (#C #S #D #I) 50 11 8 8 +REF: w I s s E n ******* s c h a f T l e r * h a b e n d I e s * E M U t a t * I O n B I s H e r * n U R b e i f * * r a u E n b e * o b a C H t e t +HYP: w E s s * n s c h a f * l e r H A h a b e n d * e s I M E N t a t Z U n P E s * e r E T n * O b e i f O A r a u * n b e R o b a * * t e t +Eval: S D I D I S D I S S S I S S S S D I S D S I I D I D D + +Speaker sentences 63: cv_deu_000760 #utts: 1 +id: (cv_deu_000760-cv_deu_000760) +Scores: (#C #S #D #I) 54 5 2 10 +REF: s e i n E g E s c h Ä F t ******* * s b e z * i E H u n g e n r e i * c h t e n b i s n o r * ******* d A m e * r i * k a u n d a s i * * e n +HYP: s e i n I g I s c h E H t Z s b e z T i * * u n g e n r e i S c h t e n b i s n o r D d O m e H r i C k a u n d a s i E R e n +Eval: S S S S I I I D D I I I S I I I I + +Speaker sentences 64: cv_deu_000761 #utts: 1 +id: (cv_deu_000761-cv_deu_000761) +Scores: (#C #S #D #I) 93 10 8 7 +REF: Z a H l r e i c ******* H e v o r d e r e * P l a t Z i e r u n g ******* e n b e i d e u t s c h e n E U r o p a * u n d w e l t M e I s t e R s c H a f t e * n s o W i e o l Y M P i s c h e n s p i E l e n F o * l g * t e n +HYP: S a * l r e i c I e P v o r d e r e D * l a t * i e r u n g e n b e i d e u t s c h e n * Ö r o p a R u n d w e l t L e * s t e s c * a f t e R n s o i e o l * Ö B i s c h e n s p i * l e n V o R l g K t e n +Eval: S D I S S I D D I D S I S D S D I S D S S D S I I + +Speaker sentences 65: cv_deu_000762 #utts: 1 +id: (cv_deu_000762-cv_deu_000762) +Scores: (#C #S #D #I) 38 13 15 2 +REF: i n e I n e r t a G e s ******* Z e I t u N G b l Ä T t e r * N d s I T Z t s i E G F R i E D a u f E I N e r P a R K b a n K +HYP: i n e * n e r ******* t a L e s C e R t u * M b l ** E t e r M T d s * O E t ******* s i * * * K i * T a u f * * * e r ******* B a * G b a n G +Eval: D D S I S S D S D S I S D S S D D D D S D S D D D D S D S S + +Speaker sentences 66: cv_deu_000763 #utts: 1 +id: (cv_deu_000763-cv_deu_000763) +Scores: (#C #S #D #I) 43 10 18 3 +REF: M i t e i N E m w a R m E N G E t r Ä n k i M b a u C H l Ä S s T s I C H D i e k * Ä l t e b e S s e R a u s h a * * l t E N +HYP: W i t e i * * m w a H m * * ******* L I t r E n k i N b a u * * F l ** E s * ******* s * * E * i e k E R l t e b e * s e * ******* a u s h a U E l t * * +Eval: S D D S D D D S S S S D D S D S D D D D S D I S D D D I I D D + +Speaker sentences 67: cv_deu_000764 #utts: 1 +id: (cv_deu_000764-cv_deu_000764) +Scores: (#C #S #D #I) 12 3 6 0 +REF: F o l G e d e m Q U e R V e R w e I s +HYP: W o l L e ******* d e m * Ö e * * e * w e * s +Eval: S S D D S D D D D + +Speaker sentences 68: cv_deu_000765 #utts: 1 +id: (cv_deu_000765-cv_deu_000765) +Scores: (#C #S #D #I) 43 12 9 3 +REF: o s t E R n * i s T i M M e R e i n e W o c h e n a c h D e m e R s t E N v o L L m O n D * i m F R Ü H l i * n G +HYP: o s t * A n D i s * i E N e * e i n e B o c h e ******* n a c h * e m e * s t * U v o R N m U n * D i m P G L U l i E n * +Eval: D S I D S S D S D D D D S S S S D I S S S S I D + +Speaker sentences 69: cv_deu_000766 #utts: 1 +id: (cv_deu_000766-cv_deu_000766) +Scores: (#C #S #D #I) 41 9 9 3 +REF: I m m i T t e l ******* A L t e r h a T t e n W e C H S e L N d e * h E R r s c h a f t E N d a s d o R f i N n e * +HYP: E m m i * t e l E I t e r ******* h a * t e n D e * X Z e M d e R h * A r s c h a f t * * d a s d o * f i * n e R +Eval: S D I S S D D S D S S S S I D S D D D D I + +Speaker sentences 70: cv_deu_000767 #utts: 1 +id: (cv_deu_000767-cv_deu_000767) +Scores: (#C #S #D #I) 35 15 9 4 +REF: d * e N n a m E N G H i * B l I t r a g E n A U c h W e i t e r * e f a H R Z E U g e V O N m a s e R a t i * +HYP: d I e * n a m * * S C i E P l E t r a g * n ******* * O c h ******* R e i t e r D e f a L T S O L g e ******* * F A D m a s e H a t i E +Eval: I D D D S S I S S D D D S D S I S S S S S D D S S S S I + +Speaker sentences 71: cv_deu_000768 #utts: 1 +id: (cv_deu_000768-cv_deu_000768) +Scores: (#C #S #D #I) 33 6 16 2 +REF: * D u k a n n s T M i t d e M b u s N A c h F r a n K F U R T * A n D e r o d e r f A H r e n +HYP: P L u ******* k a n n s * ******* W i t d e * b u s ******* * L c h ******* * r a n * * * * G V O n * e r ******* o d e r ******* f * O r e n +Eval: I S D D D S D D D S D D D D D D S I S D D D D S + +Speaker sentences 72: cv_deu_000769 #utts: 1 +id: (cv_deu_000769-cv_deu_000769) +Scores: (#C #S #D #I) 8 2 3 1 +REF: M i * r d o c H e g A l +HYP: * i E r ******* d o c * R e g O l +Eval: D I D D S S + +Speaker sentences 73: cv_deu_000770 #utts: 1 +id: (cv_deu_000770-cv_deu_000770) +Scores: (#C #S #D #I) 84 7 11 9 +REF: a l l e r ******* d i n g s e r ******* g a * b e n w e i t e * * r e p r Ü f u n g e n d a s s E s m i t t e l f r i s t i g k e i N E n B e d a r F f Ü r E i N E S O L c h e a u t o b a H n g * * Ä B e ******* * +HYP: a l l e r d i n g s e r g a H b e n w e i t e R H r e p r Ü f u n g e n d a s s * s m i t t e l f r i s t i g k e i * * n P e d a r * ******* f ** r ******* * i * S C E U c h e a u t o b a * n ******* g E R W e N +Eval: I I I I I D D D S D D D D D D S S S S D D I I S S I I + +Speaker sentences 74: cv_deu_000771 #utts: 1 +id: (cv_deu_000771-cv_deu_000771) +Scores: (#C #S #D #I) 65 5 4 5 +REF: u M g e k e H r t k a N n e I n f r e i B r i e f e i n e * * a u s s c h r e i b u n g a l * s v o G e l f r e i g e m e i n T S e i * * n +HYP: u N g e k e * r t k a * n e * n f r e i P r i e f e i n e A R a u s s c h r e i b u n g a l T s v o B e l f r e i g e m e i n * D Z e i N E n +Eval: S D D D S I I I S D S S I I + +Speaker sentences 75: cv_deu_000772 #utts: 1 +id: (cv_deu_000772-cv_deu_000772) +Scores: (#C #S #D #I) 51 9 4 2 +REF: B i * z a R R G r o t e s K e a b s c h n i T t e Z e i G e n e i n f l Ü s s e * D u R c h s c h o s t a k o w i T S c h +HYP: M i E z a G K r o t e s G e a b s c h n i * t e S e i N e n e i n f l U s s e L E u * c h s c h o s t a k o w i * * c h +Eval: S I S S S S D S S S I S D D D + +Speaker sentences 76: cv_deu_000773 #utts: 1 +id: (cv_deu_000773-cv_deu_000773) +Scores: (#C #S #D #I) 61 3 6 5 +REF: E r W A r e i n e R d e r p i * o ******* n i e r e a u f d E m g e b i e t d e r N u t z * u n g d e r s o N n e n e ******* n e r ******* g I e +HYP: * r ******* V E r e i n e * d e r p i E o n i e r e a u f d * m g e b i e t d e r * u t z I u n g d e r s o * n e n e n e r g E e +Eval: D D S S D I I D D I D I I S + +Speaker sentences 77: cv_deu_000774 #utts: 1 +id: (cv_deu_000774-cv_deu_000774) +Scores: (#C #S #D #I) 53 2 20 1 +REF: a U c h W e N n m I R d i E k u n d e n a U f d I e n e ******* r V e n g E H e n m U S s i c H h Ö f l i c h k e i t b e w a H R E n +HYP: a * c h ******* V e * n m * * E d i * k u n d e n a * f d * e ******* n e r * e n g * * e n m * * s ******* i c * ******* h Ö f l i c h k e i t b e w a * * * n +Eval: D D S D D D S D D D D I D D D D D D D D D D D + +Speaker sentences 78: cv_deu_000775 #utts: 1 +id: (cv_deu_000775-cv_deu_000775) +Scores: (#C #S #D #I) 20 4 3 1 +REF: d i e S P Ü L m a s c h i n e I s t f e r t i * G +HYP: d i e * * B E m a s c h i n e ******* R s t f e r t i C H +Eval: D D S S D S I S + +Speaker sentences 79: cv_deu_000776 #utts: 1 +id: (cv_deu_000776-cv_deu_000776) +Scores: (#C #S #D #I) 56 10 5 0 +REF: i n d e R a r c h a i s c h e n p e r i o d e w u r d e n E r s t E F o r m E n d e s A c k e R b a U s E n t W I C k E l T +HYP: i n d e * a r c h a i s c h e n p e r i o d e w u r d e n * r s t I V o r m I n d e s O c k e * b a * s S I n t * U Y k I l D +Eval: D D S S S S D D S S D S S S S + +Speaker sentences 80: cv_deu_000777 #utts: 1 +id: (cv_deu_000777-cv_deu_000777) +Scores: (#C #S #D #I) 25 4 12 2 +REF: d i E K o m Ö ** d i e * s e I B e S s e R a l s D e r E R s t E f I L M +HYP: d i * C o m Ö Ü d i e R s e * ******* * e * s e * a l s ******* T e r * * s t * ******* f * Ü N +Eval: D S I I D D D D D D S D D D D D S S + +Speaker sentences 81: cv_deu_000778 #utts: 1 +id: (cv_deu_000778-cv_deu_000778) +Scores: (#C #S #D #I) 13 7 8 1 +REF: a K t u E L L g I L t F O L g E n D e R m O D u * s +HYP: a R t u * * Ä g * E t V E R g * n * e * A m * * u M s +Eval: S D D S D S S S S D D D S D D I + +Speaker sentences 82: cv_deu_000779 #utts: 1 +id: (cv_deu_000779-cv_deu_000779) +Scores: (#C #S #D #I) 60 25 14 14 +REF: * D a * m i t e n D e t e i n e e R f ** * O L G r e i * c h e i n t E R n a t * I O n * a l * e * B I L d U n G s ******* a R b e I T v o r A L l e M i M m U S I s c h k U L t U R e ******* l * L e n B E R E I c * * H +HYP: T E a R m i t e n T e t e i n e e * f Ü R K K r e i S c h e i n t * L n a t Z E U n E a l I e K E Ä R d E n * s a * b e * N v o r ******* * E l e N i * N m * * * s c h k * Ü t * * e l U N e n E N Z S A c K U R +Eval: I S I S D I I S S S I D S I S S I I I S S S S D I D D S D D S S D S D D D D S D D I I S S S S S S S I I S + +Speaker sentences 83: cv_deu_000780 #utts: 1 +id: (cv_deu_000780-cv_deu_000780) +Scores: (#C #S #D #I) 69 8 11 3 +REF: d e r s o H n e i n e s b e r G M A n * n S b e g a N n s e i n * e f U S s b A L L k a R R I e r E B e i d e n s p o r t f r e u n d e n w a N n e ******* e i C k e l +HYP: d e r ******* s o * n e i n e s b e r * E T n A n Z b e g a * n s e i n I e f * O s b * E I k a * * * e r I W e i d e n s p o r t f r e u n d e n w a * n e e i * k e l +Eval: D D D S S I S D I D S D S S D D D S S D I D + +Speaker sentences 84: cv_deu_000781 #utts: 1 +id: (cv_deu_000781-cv_deu_000781) +Scores: (#C #S #D #I) 64 8 11 6 +REF: i n d i e s e M j a h r g a b e S s i e B e n N U M m e R e i n * S s i n g L e s u n d s e c h s U n D d R e i S s i g n U M m e r ******* e i * n s ******* a * l * b e n +HYP: i n d i e s e * N j a h r ******* g a b ******* e * ******* s i e D e n * * O m e e i n E N s i n g * e s u n d s e c h s O n d * e i * s i g n * O m e r e i E n s a L l E b e n +Eval: D S D D D D S D D S S I S D S S D D D S I I I I I + +Speaker sentences 85: cv_deu_000782 #utts: 1 +id: (cv_deu_000782-cv_deu_000782) +Scores: (#C #S #D #I) 60 2 6 4 +REF: n o r d ******* w e s t l i c h v o n h a C k h a u s e n b e f i n d e T s i c h d i E O r t s c h a f t h a C k e n ******* b r O I c h ******* * +HYP: n o r d w e s t l i c h v o n h a * k h a u s e n b e f i n d e * s i c h d i O * r t s c h a f t ******* h a * k e n b r * U c h N +Eval: I D D S D D D I D S I I + +Speaker sentences 86: cv_deu_000783 #utts: 1 +id: (cv_deu_000783-cv_deu_000783) +Scores: (#C #S #D #I) 86 2 11 5 +REF: i m o r t G n a R R e n ******* b u r g g i N G e n v i e l e s o z i a l e e i n * r i c h t * u n g e n v o n H e r * m a N n l a m p r e c h t u n D d e r m a R i E n ******* h Ü T t e a u s +HYP: i m o r t K n a * * e n b u r g g i * * e n v i e l e s o z i a l e e i n E r i c h t E u n g e n v o n * e r E m a * n l a m p r e c h t u n * ******* d e r m a * i * n h ** Ö t e a u s +Eval: S D D I D D I I D I D D D D D I D S + +Speaker sentences 87: cv_deu_000784 #utts: 1 +id: (cv_deu_000784-cv_deu_000784) +Scores: (#C #S #D #I) 77 2 4 3 +REF: i c h w e r d e f o l ** G l i c h d e n r a t Ü b e r d i e I m p a R l A m e n t v o r g e t r a g e n e n b e d e n k * e n i n ******* F o r m i e r e n +HYP: i c h w e r d e f o l Ö K l i c h d e n r a t Ü b e r d i e ******* * m p a * l * m e n t v o r g e t r a g e n e n b e d e n k T e n i n V o r m i e r e n +Eval: I S D D D D I I S + +Speaker sentences 88: cv_deu_000785 #utts: 1 +id: (cv_deu_000785-cv_deu_000785) +Scores: (#C #S #D #I) 64 6 17 1 +REF: e s W Ä r e t r a u r I G g e w e s e n e i n s o w i c h t * i G e s t H e m a n i c h t I m k o n s e N S V E R a b s c h I E D e n z u k Ö N N E n +HYP: e s * E r e ******* t r a u r * E C g e w e s e n e i n s o w i c h t D i * e s ******* t * e m a ******* n i c h t ******* E m k o n s e * T * * F a b s c h * * * e n z u k Ö * * * n +Eval: D S D D S S I D D D D D S D S D D S D D D D D D + +Speaker sentences 89: cv_deu_000786 #utts: 1 +id: (cv_deu_000786-cv_deu_000786) +Scores: (#C #S #D #I) 51 15 4 3 +REF: n A c h D E s ******* s E N t o D i m G l e i c h e n j a h R K a * m E s K u R z ******* F r i s t i g a n a n d e r e B E s I T z e R +HYP: n O c h ******* T I s s I M t o T i m K l e i c h e n j a h * G a M m * s G u T z B r i s t i g a n a n d e r e V I s * E z e A +Eval: S D S S I S S S S D S I D S S I S S S D S S + +Speaker sentences 90: cv_deu_000787 #utts: 1 +id: (cv_deu_000787-cv_deu_000787) +Scores: (#C #S #D #I) 54 12 5 11 +REF: k U R Z d a n a c h g a b e s e i n e * n w e r b e * S P o * T m i * t d e m C a n * C a * * * n * v o n J A c Q U e s * o F F e n ******* b a c h +HYP: k * O T d a n a c h g a b ******* e s e i n e I n w e r b e R V o R D m i N t ******* d e m T K a n D K a M N E n D v o n * S c A K e s H o * U e n b a c h +Eval: D S S D I I S S I S I D S S I S I I I I D S S S I D S I + +Speaker sentences 91: cv_deu_000788 #utts: 1 +id: (cv_deu_000788-cv_deu_000788) +Scores: (#C #S #D #I) 10 3 1 1 +REF: d a s i S t b * e S S e R +HYP: d a s i * t b S e A e T +Eval: D I S S S + +Speaker sentences 92: cv_deu_000789 #utts: 1 +id: (cv_deu_000789-cv_deu_000789) +Scores: (#C #S #D #I) 20 3 7 3 +REF: w i E s i e H T E s m i T G l e I T z e i t a u s ******* * * +HYP: w i * ******* s i e * * * s ******* m i N * l e C H z e i t a u s H R +Eval: D D D D D D S D S S I I I + +Speaker sentences 93: cv_deu_000790 #utts: 1 +id: (cv_deu_000790-cv_deu_000790) +Scores: (#C #S #D #I) 50 14 7 5 +REF: n a * h e d e m d o * R f b e f i n ******* d e T s i C h a * u c h d e r G R A N D C a n Y O n n a T i o n a l P a * R k A I r P o R t +HYP: n a C h e d e m d o C H f b e f i n d e R s i G h a R u c h d e r * * * * K M K a n I U n n a S i o n a l L B a C H k ******* * E r B o * t +Eval: I I S I S S I D D D D S S S S S S S S I S D D S S D + +Speaker sentences 94: cv_deu_000791 #utts: 1 +id: (cv_deu_000791-cv_deu_000791) +Scores: (#C #S #D #I) 39 6 10 1 +REF: S i e s o L L e n V e R k Ü n d e n d A S s d I e l i e b e d e n t o * d b e s i E g t H a t +HYP: * i e ******* s o * R e n D e A k Ö n d e n d * E s d J e ******* l i e b e ******* d e n ******* t o R d b e s i * g t ******* * a t +Eval: D D D S S S S D S S D D D I D D D + +Speaker sentences 95: cv_deu_000792 #utts: 1 +id: (cv_deu_000792-cv_deu_000792) +Scores: (#C #S #D #I) 56 11 1 8 +REF: b e D e c k t I s t d i e r e p r Ä s e n t * * A t i * V g e s t a l t e ******* T e V i l L A m i t * e i n e * M m a n * s a r * D d a c h +HYP: b e T e c k t S s t ******* d i e r e p r E s e n t H E R t i E F g e s t a l t e D e W i l E R m i t D e i n e R N m a n D s a r T d a c h +Eval: S S D S I I S I S I S S S S I I S I I S + +Speaker sentences 96: cv_deu_000793 #utts: 1 +id: (cv_deu_000793-cv_deu_000793) +Scores: (#C #S #D #I) 53 4 5 3 +REF: d i e ******* s e s i e D l u n g I s T m i t D e r o r t s c h a * f t d e l l a c h z u s a M m e n ******* g e w a C H S e n +HYP: d i e s e s i e T l u n g E s * m i t ******* * e r o r t s c h a C f t d e l l a c h z u s a * m e n g e w a * K Z e n +Eval: I S S D D D I D I D S S + +Speaker sentences 97: cv_deu_000794 #utts: 1 +id: (cv_deu_000794-cv_deu_000794) +Scores: (#C #S #D #I) 22 2 9 1 +REF: w a r T i H R s c h O n e i n m a l i * n d e m C l U B +HYP: w a r * ******* i * * ******* s c h * n e i n m a l ******* i E n ******* d e m K l * O +Eval: D D D D D D D I D S D S + +Speaker sentences 98: cv_deu_000795 #utts: 1 +id: (cv_deu_000795-cv_deu_000795) +Scores: (#C #S #D #I) 19 6 2 0 +REF: W o r a u C H i s t i s t a u c h F E U E r +HYP: B o ******* r a u * R i s t i s t a u c h V O L O r +Eval: S D D S S S S S + +Speaker sentences 99: cv_deu_000796 #utts: 1 +id: (cv_deu_000796-cv_deu_000796) +Scores: (#C #S #D #I) 67 14 12 12 +REF: d i R e ******* * K T v o n d E r s t r a S s e w u R d e n s I E v o n a l f R e D B i o l e k F Ü R s e i n e f e * * R n ******* s e * H s * h o ******* W s * h O W b Ü H n e e n ******* g ** A G i e r t * +HYP: d i * e H E X v o n ******* d * r s t r a * s e w u * d e n ******* s * N v o n a l f * e T D i o l e k * ** B E s e i n e f e S T E n s e I s C h o E s C h * A b ** L n e e n g Ü L S i e r t E +Eval: D I I S S D D D D D D S D S S D D S S I I S I I S I I S I D S D S I I S S I + +Speaker sentences 100: cv_deu_000797 #utts: 1 +id: (cv_deu_000797-cv_deu_000797) +Scores: (#C #S #D #I) 45 13 11 10 +REF: E i n J a H r s p Ä * t e R W e C H s E l t e * e R z u * H e A l * t H n E t * * u n D * e R W U R d e e R f ******* * O L g * r e i c h e R +HYP: A i n H a * r ******* s p Ä I t e * V e * X s * l t e R e L T z u N * e * l F t * n A t Z S u n M B e * * V O d e e L f V U N g E r e i c h e * +Eval: S S D D I D S D S D I S S I D D I D S I I S I D D S S S I I S S I D + +Speaker sentences 101: cv_deu_000798 #utts: 1 +id: (cv_deu_000798-cv_deu_000798) +Scores: (#C #S #D #I) 52 4 7 7 +REF: i n d e r l a n d W i R t S c h * A f T k a N n d e r e r t r a * g * * d e u t l i C H R e d * U z i e r t w e r d e n ******* * +HYP: i n d e r l a n d V i * t * c h E R f * k a * n ******* d e r e r t r a R g K T d e u t l i * * W e d O R z i e r t w e r d e n T +Eval: S D D I S D D D I I I D D S I S I I + +Speaker sentences 102: cv_deu_000799 #utts: 1 +id: (cv_deu_000799-cv_deu_000799) +Scores: (#C #S #D #I) 45 7 3 5 +REF: m a n ******* s O u r s p i e L t e i n s e i n e r h e i m a t ******* s t a d t k * A I R o f * Ü r a * l a H l Y +HYP: m a n s * u r ******* s p i e R t e i n s e i n e r h e i m a t s t a d t k E E W o R f I E r a L l a * l L +Eval: I D D S I I S S S S I S I D S + +Speaker sentences 103: cv_deu_000800 #utts: 1 +id: (cv_deu_000800-cv_deu_000800) +Scores: (#C #S #D #I) 28 5 5 6 +REF: e r t r a T d e r F r e i m a u R E R l * O G e * * l a * u t a * * R o b e i +HYP: e r ******* t r a * d e r * r e i m a u * H A l U N D e L N l a O u t a B U E o ******* b e i +Eval: D D D D S S I S S I I I I I S D + +Speaker sentences 104: cv_deu_000801 #utts: 1 +id: (cv_deu_000801-cv_deu_000801) +Scores: (#C #S #D #I) 67 8 10 7 +REF: m i t „ f Ü r S t “ w a r e h E r d i e S o * * * Z I a L e * * A l s d I e r e c h t l i c h e r o l l e d * e s s o b e * Z e i c H N e T e n g e m e i n T +HYP: m i t *** f Ü r * t *** w a r e h * r d i e R o W P T H J a D e L E I l s d * e ******* r e c h t l i c h e ******* r o l l e d I e s s o ******* b e T S e i c * * e N e n g e m e i n D +Eval: D D D D S I I I S S S I I S D D D I D I S D D S S + +Speaker sentences 105: fleurs_deu_000378 #utts: 1 +id: (fleurs_deu_000378-fleurs_deu_000378) +Scores: (#C #S #D #I) 128 17 25 10 +REF: l e t z t e w o c h E g a b d a s m e t i * b e k a N n T D a S s e s v o n A p P l E Ü b * * ******* * * e r * * 3 4 * w E I t E R e V o r f Ä L l e v o n Ü b e r H i t z * u n G i n F o R m i E r t w o r d e n w a R D i e d A s u n t e r n e h M E n a l s n i c h t s c h W e r W i E G e n D B e Z e i C H N e t e +HYP: l e t z t e ******* w o c h O g a b d a s m e t i E b e k a * n D G a * s ******* e s v o n E p E l * Ü b E R V I e r N D A S H w A L t * * e F o r f ** E l e v o n Ü b e r i t z E u n * i n T o * m i * r t ******* w o r d e n ******* w a * * i e ******* d E s u n t e r n e h * * n a l s n i c h t s c h * e r i * * e n * P e * e i * * T e t e +Eval: D S I D S S D D S S D I I I I I I I S S I S S D D S D S S I D S D D D D D D D S D D D S D D D S D D D S + +Speaker sentences 106: fleurs_deu_000379 #utts: 1 +id: (fleurs_deu_000379-fleurs_deu_000379) +Scores: (#C #S #D #I) 193 16 12 22 +REF: * * ** u * s A G Y m n A s t i C S u n * ******* t e r ******* s t Ü t z * t ******* * d e n b * r i e f d e s o l Y m p i s c h e n k o m i t E E s d e r v e r ******* e i n i g t e n s t A a t e n u n d * a K Z E p t i e r t e s a L s a * b * S O l u t e n o t ******* w e n d i G k e i t d a S S s i C h * d i e o l Y m p i s c h e * * F A m i l i e f Ü r e i n s i c h e r e s u * m f e * l D f Ü r a L l e u n s e r e * s p o r t l e r e i n s e t Z t +HYP: E J Ü u R s E J I m n E s t i * G u n D t e r s t Ü t z S t E d e n b E r i e f d e s o l Ü m p i s c h e n k o m i t * I s d e r v e r e i n i g t e n s t * a t e n u n d D a * C I p t i e r t e s a * s a P b P T U l u t e n o t w e n d i * k e i t d a * * ******* s i * h T d i e o l Ü m p i s c h e V E R m i l i e f Ü r e i n G s i c h e r e s u N m f e L l T f Ü r a * l e u n s e r e R s p o r t l e r e i n s e t * t +Eval: I I I I S S S S D S I I I I I I I S D S I D I D S S D I I S S I D D D D D I S I I S S S I I S D I D + +Speaker sentences 107: fleurs_deu_000380 #utts: 1 +id: (fleurs_deu_000380-fleurs_deu_000380) +Scores: (#C #S #D #I) 82 22 17 53 +REF: d a D U R c H k A N n e R a B W Ä r * t s k o m p A t i b e l m i * ******* * * * * * * * t * * * * ******* * * * * * 8 0 2 1 1 a * * * * * * ******* * * * * * * * * * 8 0 2 1 1 B u n d * * * * * * * * * * 8 0 2 1 1 g * * * * * * s e i n v O r a U s g e s E T Z T d i E B a s I s * s t a t i o n v e r f Ü g t Ü b e r d u a l r a d i o +HYP: d a * L I c * k * E n ******* e * a * * P r E t s k o m p E t i b e l ******* m i T C H T R N E R t Z W E I P U N D E L F A R a C H T E N R Z W E I P U N G D E L F P E u n d C H T E O N E T Z W E I P U N g D E L F G E s e i n v E r a * s g e s * * * * d i * * a s * s I s t a t i o n v e r f Ü g t ** b e r d u a l r a d i o +Eval: D S S D D S D D D D S I S D I I I I I I I I I I I I I I I I I I I S S S S S I I I I I I I I I I I I I I I I S S S S S S I I I I I I I I I I S S S S S I I I I I I S D D D D D D D D I D + +Speaker sentences 108: fleurs_deu_000381 #utts: 1 +id: (fleurs_deu_000381-fleurs_deu_000381) +Scores: (#C #S #D #I) 59 4 5 5 +REF: e r b e z e i c h n e T E d i e G e r Ü c h t e a l s p o l I T i s c h e s g E s c h W Ä t z * u n d * a * l ******* b e R n h e i t * +HYP: e r b e z e i c h n e * S d i e I e r Ü c h t e a l s p o l * * i s c h e s ******* g I s c h E Ä t z S u n d T a L l b e * n h e i t Z +Eval: D S S D D D S S I I I I D I + +Speaker sentences 109: fleurs_deu_000382 #utts: 1 +id: (fleurs_deu_000382-fleurs_deu_000382) +Scores: (#C #S #D #I) 131 14 25 16 +REF: l e t Z T e w o c h e g a b * D a s * m * e * t * i * b e k a N n T d a S s E s v o n A P P l E Ü b e r * * * * * * * * 3 4 w e i t E r e V o r f Ä L l e v o n Ü b e r h i t z u n G i n ******* F o r m i E r T W O r d e n w a R d i e d a s u n t e r n e h m e N a l s n i c h T S c h ******* w E r w I e g e n D b e z e i C H n e t e +HYP: l e t * e w o c h e ******* g a b T * a s E m I e I t H i E b e k a * n * ******* d a * s ******* I s v o n E B E l * Ü b e r V I R N D R E I S I w e i t * r e F o r f V E l e ******* v o n ** b e r h i t z u n * i n V o r m i * r * * U r d e n w a * d i e ******* d a s u n t e r n e h m e M a l s n i c h * ******* * c h w * r w * e g e n * b e z e i * G n e t e +Eval: D S D I D I I I I I D D D D D S S S S D I I I I I I I I S S D S S S D D D I S D D D S D D S D D D I D D D D S + +Speaker sentences 110: fleurs_deu_000383 #utts: 1 +id: (fleurs_deu_000383-fleurs_deu_000383) +Scores: (#C #S #D #I) 92 13 23 21 +REF: n a c h ******* d e M d e r * * ******* * * * * * * * * d * * * * a M M * * * 1 9 6 3 e R b a u * t w o r d e n w a r k a m E N d i E J a h r e s ******* Z e i T l i C h E n Ü b e r f l u t u n g E N d I e s E d I m e n t e I m f l U S s v e r t E I l e n z u m s t i L l s t a n D +HYP: n a c h d e * d e r D M L E U N E H H N d E R T R a L N S E C H Z I G e b a u R t w o r d e n ******* w a r ******* k a m * * d i * ******* * a h r e s S e i * l i * h * n ** b e r f l u t u n g * * d * e ******* s d E m e n t e * m N f l * * s ******* v e r t * A l e n ******* z u m s t i * l s t a n T +Eval: I D I I I I I I I I I I I I I I I S S I I I S S S S S I D D D D D D D I S D D D D D D D D S S D S D D D D S D D S + +Speaker sentences 111: fleurs_deu_000384 #utts: 1 +id: (fleurs_deu_000384-fleurs_deu_000384) +Scores: (#C #S #D #I) 154 12 34 11 +REF: e r w A R a u c h a m s t e c h e n v o n g e l D s c h e i n * e N F Ü R v i e l e L Ä n d e R b e t e i l i * g t a k t U e L l e b e i s * p i E L e s e I n e R a r ******* b e I t s c h l i E S s e n D i e p r E m I e R m i n i s t e r ******* p o r t r A I T s a U f d e r V o r d e r s e I t E d e r k a n a d I s c h e n * ** * 5 u n d 1 0 0 d * * ******* o l l A r n O t e n e i n +HYP: e r ******* w * * ******* a u c h a m s t e c h e n v o n g e l * s c h e i n V e * ******* * ** * v i e l e ******* D E n d e * b e t e i l i C g t a k t * e l e ******* b e i s H p i * * e ******* s e * n e * a r b e * t ******* s c h l i * * s e n ******* * i e ******* p r I m * e H m i n i s t e r p o r t r * * E s a * f d e r F o r d e r s e R t * d e r k a n a d * s c h e n F Ü N F u n d * U N d E R o l l * r n U t e n e i n +Eval: D D D D D I D D D D D D S S D I D S D I D D D D D I D D D D D D D S D S I D D S D S S D D I I I S D S S I I I D S + +Speaker sentences 112: fleurs_deu_000385 #utts: 1 +id: (fleurs_deu_000385-fleurs_deu_000385) +Scores: (#C #S #D #I) 90 9 25 2 +REF: d i e H a u p t s t a D t v O N m o L d a w i e * n i s t k i S c h i n a U d i e e i n H e i m * i S C H E s p R a c h e I s t r u m Ä n i s c h a b E r v i e l e m e n S C H e n s p r e c h e n A U C H r U s s I S c H +HYP: d i e * a u p t s t a * t v * E R m o * d a w i e R n i s t k i * c h i n a * d i e e i n * e i m P i * * * * ******* s p * a c h e * s t ******* r u m E n i s c h a b A r ******* v i e l e ******* m e n * * T e n C s p r e c h e n ******* * * * * r O s s E L c * +Eval: D D D S S D I D D D I D D D D D D D D S S D D D D S S D D D D D S S S D + +Speaker sentences 113: fleurs_deu_000386 #utts: 1 +id: (fleurs_deu_000386-fleurs_deu_000386) +Scores: (#C #S #D #I) 180 18 23 18 +REF: * z w i S c h e n d e n e i n z e L n e n d Y n A s t i e n h e R r s C H t * e n a u c h u n * ******* b e s t Ä n d i * g e z e i t e n g e t E I l t e R p r o V i n * z e N d i e b e k a N n t e s t e d i e s e R p e r i o d e n W a R d i E e ******* p o c h e d e r d r E i k Ö n i * g r e i c h e d i e * * 6 0 J A h ******* * * r e l a N G z w i s c h e n d e r h a * n u n D d e r J i * n ******* d * Y n A s t i E s t a t T F a N d * +HYP: S z w i * c h e n d e n e i n z e * n e n d Ü n E s t i e n h e * r s * * t D e n a u c h u n M b e s t E n d i E g e z e i t e n g e t A L l t e * p r o * i n D z e * ******* d i e b e k a * n t e s t e ******* d i e s e * p e r i o d e n O a * ******* d i * e p o c h e ******* d e r ******* d r A i L k Ö n i N g r e i c h e d i e S E C H T I C h I E r e ******* l a * * z w i s c h e n d e r h a H n u n G d e r * i E n d I E n E s t i * s t a t V a * d T +Eval: I D D S S D D D I I I S I S S D D I D D D D D S D D D I D D S S I I I S S S S S I I I D D D I S D I I I S S D S S D I + +Speaker sentences 114: fleurs_deu_000387 #utts: 1 +id: (fleurs_deu_000387-fleurs_deu_000387) +Scores: (#C #S #D #I) 130 12 38 3 +REF: a m a n d e r e n e n D e d E S s p e k t r u m s V E R w a n D e l t m a n s i c h I n e i N n i c h t w i E d e r ******* z u E r k E N N e n d e S I n d i V i d * U u m d a s a L l e s a n D e r s m a c h E n m U s s a L s D A s t * e A M e s g e m a c H t H A T u n d s i c H a L l e s z u E I g e N m a c h t +HYP: a m a n d e r e n ******* e n * e ******* d * R s p e k t r u m s * * H w a n * e l t ******* m a n ******* s i c h ******* E n e i * ******* n i c h t w i * d e r z u * r k * * * e n d e * * n d i W i d E u m d a s a * l e s a n * e r s m a c h * n ******* m O s s a * s ******* * * s ******* t I e * * N e s g e m a c * t ******* * E R u n d s i c * a * l e s T z u ******* A L g e * ******* m a c h t +Eval: D D D D S D D S D D D D S D D D I D D D D D D S I S D D D D S D D D D D I D D S D D D S S D D S D S S D D + +Speaker sentences 115: fleurs_deu_000388 #utts: 1 +id: (fleurs_deu_000388-fleurs_deu_000388) +Scores: (#C #S #D #I) 289 27 50 16 +REF: d i e m e i s t e n * i n t e r ******* p R E t * a t i o n e n d e s t E c h n o l o g i s c h e n d e * t e R m i n i * s * M u s t E I l e n z w e i a L l g e m e i n e v o r s * * t e L L u n g e n e i n e r ******* s e i t s D A S s d I E E n t w i c k l u N G d e r t E c h n o l o g i e s E l B s t e i n e m w e g F o l g t d e r w e i t g E H e n D J e n s e i T s K u * L t U r e L l e R o d e r p o l i T i s c h E R e i n F l U S s n a H m e * L i E g t u n d a n d e r e r s e i t S d a S s t E c H n O L O g i e i H r e r s e i t s a u s w i r k u n g ******* e n a u f g e s E L l s c h a f t E n h A t d i e e h E r i n h Ä r e n T a L s +HYP: d i e m e i s t e n D i n t e r p * I t E a t i o n e n d e s ******* t I c h n o l o g i s c h e n ******* d e R t e I m i n i E s N u s ******* t * A l e n z w e i ******* a * l g e m e i n e v o r s C H t e * * u n g e n ******* e i n e r s e i t s * * T s T d * * I n t w i c k l u * M d e r ******* t I c h n o l o g i e ******* s L l P s t e i n e m w e g ******* * o l g t ******* d e r ******* w e i t g * * e n T I e n s e i * s * u T D t O r e * l e * ******* o d e r p o l i * i s c h * * ******* e i n P l * * s n a * m e N D i * g t ******* u n d a n d e r e r s e i t * d a * s ******* t I c n E R Ü g i e i E r e r s e i t s a u s w i r k u n g e n a u f ******* g e s * A l s c h a f t * n ******* h R t d i e e h * r i n h Ä r e n D a * s +Eval: I I D S I D S D I S I I S D D S D D I I D D D I D D S S D D S D S D S D S S D D D D D D S S D D I S S D D D D D D D S D D D I S D D D D D S S S S S S I D D S D D S D S D + +>> REF: s O z * * * i a l b e d i * n G T s i N d +>> HYP: s * z U T T i a l b e d i E n * * s i * d +>> Eval: D I I I I D D D + +Speaker sentences 116: fleurs_deu_000389 #utts: 1 +id: (fleurs_deu_000389-fleurs_deu_000389) +Scores: (#C #S #D #I) 170 14 37 8 +REF: Z w i s c h e N d e n e i n z E L N e n d Y n a S t i e n h e R r S c h t e n a U c h u n * b e s t Ä n d i g e z e i t e n g e t E i l t e R P r o V i n z e n d i E b e k a N n T e s t e d I E S e R p e r i o d e n w a R d i E e ******* p o c h * E d e r d R e I k Ö n i g r e i c h e d i e 6 0 J A h * R E * * l a n g * Z W i s c h e n d e r h a n u n d D e r J i n d Y n A S t i e s T A t t f * a n d +HYP: * w i s c h e * d e n e i n z * * * e n d * n a R t i e n h e * r * c h t e n a * c h u n M b e s t E n d i g e ******* z e i t e n g e t A i l t e * * r o W i n z e n d i * b e k a * n D e s t e d * * * e * ******* p e r i o d e n w a * ******* d i * e p o c h R O d e r ******* d * e * ******* k Ö n i g r e i c h e d i e * * S E C h T I C A R l a n g T * * i s c h e n d e r h a n u n d ******* T e r ******* * i n d E n * R t i e ******* s * * t t f V a n d +Eval: D D D D D D S D D D I S D S D D S D D S D D D D D D D D I I S D D D D D D S S S I S S I I I D D D S D D S D S D D D I + +Speaker sentences 117: fleurs_deu_000390 #utts: 1 +id: (fleurs_deu_000390-fleurs_deu_000390) +Scores: (#C #S #D #I) 138 22 11 31 +REF: d * e m l * e A k z u f o L g * e b * e z i E H T S i c h D A s D o K U m e n t a u f d e N g r e n Z s t r e i * t i n d e M d i e p a l Ä s T i n e n s e r e i n z u r Ü C K s e t z e n d e r g r e n z e n i n d e n z u s t a n d v o r d e m s e C H s t a * g * e K r i e g v o * * ******* * * * * * * ******* * n * * * * * 1 9 6 7 * * * * * * * * F o r d e r N +HYP: d I e m ******* l I e * k T z u f o R g I e ******* b I e z i * * * ******* Z i c h ******* T E s T o O m e n t a u f d e M g r e n * s t r e i L t i n d e N d i e p a l I s i n e n s e r e i n z u r Ü E G s e t z e n d e r g r e n z e n i n d e n z u s t a n d v o r ******* d e m s e R X s t a L g L e G r i e g v o R N N E N E H N U n D E R T S E B E O N U S E S T I G o r d e r * +Eval: I D I D S S I D I D D D D S D S S S S S S D I S S S S S D S S I I S I I I I I I I I I I I I I I I I S S S S I I I I I I I I S D + +Speaker sentences 118: fleurs_deu_000391 #utts: 1 +id: (fleurs_deu_000391-fleurs_deu_000391) +Scores: (#C #S #D #I) 109 10 29 2 +REF: m i t D E m V e R l u s t g r I e c h I S C h e r s p R a c h k E N n T n I S s e W A r d e r w e s t e n v o n s e i N e N P H i * l o s o p H i s c h e n u n d w i S s e n s c h a f T l i c h e n w U r * z e L N I n G r i E c h e n L A n D a b G E s c h n i T t e n +HYP: m i t * I m P e * l u s t ******* g r * e c h * * E h e r ******* s p * a c h k * * n * n * * s e * U r d e r w e s t e n v o n ******* s e i M e * * V i E l o s o p * i s c h e n u n d w i * s e n s c h a f * l i c h e n w O r T z e * * * n ******* K r i * c h e n * E n * a b * I s c h n i * t e n +Eval: D S S D D D D D S D D D D D D D D S D S D D S I D D D S I D D D D S D D S D D S D + +Speaker sentences 119: fleurs_deu_000392 #utts: 1 +id: (fleurs_deu_000392-fleurs_deu_000392) +Scores: (#C #S #D #I) 177 17 54 16 +REF: w i r s t I M M E N m i t D e r a u S s a g * * * e * * D e * s * u s O C Ü b e r e i n d a S s D e n I n t E r e s s E n u n * s E r e R a t H l e T e N U n d v e r e i n E u n D * i H r e S s p o r t s b E s s E r g e d i e n T i s t W e N n w I r I N n e R h a l b u n s E R E r O r g A n I s a t i o n S I n N v o L l e v e r Ä n d E r u n g E N v O r a n * t r e i b e n a n s t a T t e i n e * * * D e * ******* * z e R t I F i z i e r u n g v o r z u n E H M e N +HYP: w i r s t * * * * * ******* m i t ******* * e r a u * s a g E D I e S I U e R s A u s I E Ü b e r e i n ******* d a * s ******* T e n * n t * r e s s * n ******* u n D s * r e * ******* a t * l e D e * ******* * n d v e r e i n * ******* u n * D i * r e * ******* s p o r t s ******* b P s s * r g e d i e n * D i s t * e * n w * r * * n e * h a l b u n s * * * r * r g E n s a t i o n D E H n v o * l e ******* v e r n d * r u n g * * v E r a n D t r e i b e n a n s t a * t e i n e R T I * e R I z e * t V i z i e r u n g v o r z u n * * * e * +Eval: D D D D D D D D D I I I I I S I I S S D D D S D D D D I D D D D S D D D D D D I D D D D S D D S D D D D D D D D D D S S S S S S D D S D D D S I D I I I D I I I D S S D D D D + +Speaker sentences 120: fleurs_deu_000393 #utts: 1 +id: (fleurs_deu_000393-fleurs_deu_000393) +Scores: (#C #S #D #I) 119 12 31 4 +REF: D i E K r e u * Z f a H R t e n n a C H s a n K t p E T E r s b U R g b i e t e n a U c h z e i t f Ü r e i N E n a u f e n t H a l T i n D e r s t a D t k r e u * z F a H R t p a s * s A G i e r e s i n d v o N d e r V i * s u M P F l i c h t b e f r e i t s i E H e b e d I N G U n g e n +HYP: * i * * r e u T S f a * * t e n n a * * S s a n G t ******* p I K A r s b * O g b i e t e n a * c h z e i t ******* f Ü r e i * * n a u f e n t * a l * i n ******* * e r ******* s t a * t k r e u T z V a * * t p a s R s * * i e r e s i n d F v o R d e r * i E s u * N S l i c h t b e f r e i t s i * * e b e d * * * * n g e n +Eval: D D D I S D D D D S S D S S S D S D D D D D D D D D D I S D D I D D S S D I D S S D D D D D D + +Speaker sentences 121: fleurs_deu_000394 #utts: 1 +id: (fleurs_deu_000394-fleurs_deu_000394) +Scores: (#C #S #D #I) 123 9 11 8 +REF: * r e i s e n ******* d e W e r d e n D r i n g e n d g e w a R n t a u f j e * D w e d e a r t v o n u n ******* w e T t e R z u a c h t e n d i e i H R g * e B i e t b * e t r i F f t d a d i E s s i C h a u f a L l e r e i s e ******* p l Ä n e a u s W i * r k e n k a n N +HYP: E r e i s e n d e V e r d e n * r i n g e n d g e w a * n t a u f j e T w e d e a r t v o n u n w e N t e * z u ******* a c h t e n d i e i * * E g I e i e t b I e t r i * f t d a ******* d i * s s i G h a u f a * l e ******* r e i s e p l E n e a u s i E r k e n k a n T +Eval: I I S D D I S I S D D D D S I S I D D D S D D I S S I S + +Speaker sentences 122: fleurs_deu_000395 #utts: 1 +id: (fleurs_deu_000395-fleurs_deu_000395) +Scores: (#C #S #D #I) 112 16 23 9 +REF: s I e b e ******* s a G t d a S s d e r k R E U z u n g s p u n * k t d e R l i n * i E n d i e e I n b i * l D V e r t i k a l u n D H O r * I Z o n * t a l d r i T t e L n d e R e F f E k t ******* i V s t * e p l a T Z f Ü r D A s H a u p t m * o T i V i s t s i E H e b e i s P i E L +HYP: s * e b e s a R t d a * s d e r k * O L z u n g s p u n G k t d e * ******* l i n M i * n d i e e * n b i E l T W e r t i k a l u n T * * r E T o n D t a l d r i * t e E n d e * e * f I k t i * s t D e ******* p l a * S f Ü r ******* T E s ******* * a u p t m U o D i * i s t ******* s i * * e b e i s S i * N +Eval: D I S D D S S I D D I D D I S S S D D I S S I D S D D S I D I D D S D S S D D I S D D D D S D S + +Speaker sentences 123: fleurs_deu_000396 #utts: 1 +id: (fleurs_deu_000396-fleurs_deu_000396) +Scores: (#C #S #D #I) 185 22 45 21 +REF: s e i t * * * * * 1 9 8 8 M Ü S S e n W a * * * * * h ******* * * * * ******* * * l u R n E N T r a n s P A r e n T s e i n d a m i t w Ä H l e R u n D b e o b a c h t e R b e * z e u g e n k Ö N N E n d a S s z U B e g i N n d e r w a H l k e i n e u m s c h l Ä g e v O r h A n D e n s i n d u n D d a S s k e i n E u m s c h l Ä g e e i n g e W o R f e n w e r d e n a u S s e r j e n e d e r * o r d N u n g s G E m Ä S s G e Z Ä h l t E n U n D a U t O r I s i E r * t e n W Ä H l e r +HYP: s e i t N U N Z E N U R T A C H T e n * a C H T I C h M S T E W A l u * n * D * r a n s B E r e n * ******* s e i n d a m i t w ** E l e * u n * ******* b e o b a c h t e * b e T z e u g e n ******* k Ö * * * n d a * s z * W e g i * n ******* d e r w a * l ******* k e i n e u m s c h l E g e v E r h * n * e n ******* s i n d u n * ******* d a * s ******* k e i n * u m s c h l E g e e i n g e * o * f e n w e r d e n a u * s e r j e n e d e r T o r d * u n g s * m ** I s * e * ** h l t * n ******* E n * a * t T r s i * r D t e n * ** E l e r +Eval: I I I I I S S S S S S S S D I I I I I I I I I I I I I D D S D S S D D D S D D D D I D D D D D D S D D D D S S D D D D D D D D S D D D I D D S D S D D D D D S D D S S D I D D S + +Speaker sentences 124: fleurs_deu_000397 #utts: 1 +id: (fleurs_deu_000397-fleurs_deu_000397) +Scores: (#C #S #D #I) 145 19 12 13 +REF: o t * T a W A i s t k a * n * A d A s b * e z a u b e R n ******* d e * z w e i s P R a C H i ******* g e h a u p t s t a D t u n d Z e I C H N e T s i c h D U R c H e i n e r e i H e v O n k u n * s t g A l e r i e n u n d m u s e e n a u s d i e k a n * A d * A s v e r g a n g e n ******* h e I t u n d g e g e n ******* w a r t P r Ä s e n ******* t i e r e N +HYP: o t E R a * R i s t k a N n E R d E s b I e z a u b e * n d e R z w e i s C H a * L i g e h a u p t s t a * t u n d S e * * L T e N s i c h * * I c * e i n e ******* r e i * e v U n k u n Z s t g E l e r i e n u n d m u s e e n a u s d i e k a n E N d E R s v e r g a n g e n h e L t u n d g e g e n w a r t B r E s e n t i e r e * +Eval: I S D S I I S S I D I I S S D S I D S D D S S S D D S D D D S I S I S I S I S I S S I D + +Speaker sentences 125: fleurs_deu_000398 #utts: 1 +id: (fleurs_deu_000398-fleurs_deu_000398) +Scores: (#C #S #D #I) 53 9 10 1 +REF: d i e s e p A a r e k Ö N N e n s i c h F Ü r e i N E n a d * O p t i o n s p l a n F Ü r I H r b A b Y e n T s c h e i d e n +HYP: d i e s e p * a r e ******* k Ö * R e n s i c h V E r ******* e i * * n a d E B p t i o n s p l a n ******* D V r * E r ******* b E b E e n * s c h e i d e n +Eval: D D D S S S D D D I S D S S D S D S S D + +Speaker sentences 126: fleurs_deu_000399 #utts: 1 +id: (fleurs_deu_000399-fleurs_deu_000399) +Scores: (#C #S #D #I) 86 12 18 6 +REF: i n ******* f o l ******* g e d e s s e n s I n D z w e i f i s C h * ******* a R T e n a u s g e s t o r B e N U n d z w e I w e i t E r E s I n D v o m a u S s t e r b e N b e D r o H t d a R u n T e R d e r g I l a * C y ** P H A +HYP: i n f o l g e d e s s e n ******* s E n * z w e i f i s * h T a * B e n a u s g e s t o r M e * * n d ******* z w e * ******* w e i t * r * U s E n * v o m a u * s t e r b e * b e T r o * t d a * u n * e * d e r g E l a R S Z y Ü F E R +Eval: I I D S D D I I D S S D D D D D D D S S D D D S D D D D S I S S I S S S + +Speaker sentences 127: fleurs_deu_000400 #utts: 1 +id: (fleurs_deu_000400-fleurs_deu_000400) +Scores: (#C #S #D #I) 96 7 26 3 +REF: P F L a n z e n s E H e n I n i h R E r n a t Ü R l i c h e n U m * g e b U n g a m b e s t e n a u s w i * d e r ******* s t E H e n s i E a l s o d e r v e r s u c h u n g a u c h n u r e i n e x E m P l a R Z u E N T F e r N E n +HYP: * T R a n z e n ******* s * * e n * n ******* i h * * r ******* n a t ** I l i c h e n * m O g e b * n g ******* a m b e s t e n a u s w i E d e r s t * * e n ******* s i * a l s o d e r ******* v e r s u c h u n g a u c h n u r e i n ******* e x * m K l a * ******* * u * D W e r * * n +Eval: D S S D D D D D D D D D S D I D D I I D D D D D D D S D D D D S S S D D + +Speaker sentences 128: fleurs_deu_000401 #utts: 1 +id: (fleurs_deu_000401-fleurs_deu_000401) +Scores: (#C #S #D #I) 99 11 19 3 +REF: a u f d e r n a h s e i t e k Ö N n t e E s m e H r m a r i * A g e b e n d a d i E K r U s t e d Ü N n e R I s t e s w A r e i n ******* f A C H e r f Ü R d i e l a v * A a n d i E o b e r f l Ä c h E a u f Z u s t e I g e n +HYP: a u f d e r n a h s e i t e k Ö * n t e I s m e * r ******* m a r i E R g e b e n d a ******* d i * G r O s t e d ** U n e * ******* * s t e s w E r e i n f * * * e r A f ** * O d i e l a v E R a n ******* d i * ******* o b e r f l E c h * a u f T u s t e * g e n +Eval: D S D D I S D D S S D S D D D S I D D D S D D S I S D D D S D S D + +Speaker sentences 129: fleurs_deu_000402 #utts: 1 +id: (fleurs_deu_000402-fleurs_deu_000402) +Scores: (#C #S #D #I) 154 15 12 13 +REF: e r f Ü * G t e h i n ******* z u d a S s s i E j e d o c h n i c h t D a * ******* z u a u f g * e ******* F o r d e r t w e r d e n s o l l t e * n V e r P f l i c h t u n g e n e i n * z u g e H e n d i e Ü b e r i H r e n E n t w i C K l u n g S s t a n d i H r e v e r a n t W o r T u n g u n d i H r e f Ä H i g k E I t e n h i * n a u s ******* * g e H e n ******* * +HYP: e r f Ü C K t e C h i n z u d a * s ******* s i * ******* j e d o c h n i c h t ******* E a R z u O a u f g I e V o r d e r t w e r d e n s o l l t e R n F e r T f l i c h t u n g e n e i n D z u g e * e n d i e Ü b e r i E r e n I n t w i * T l u n g * s t a n d i E r e v e r a n t * o r D u n g u n d i r e R f Ä * i g k * A t e n h i E n a u s N g e * e n T +Eval: I S S I D D D D D S I I S I I S I S S I D S S D S D S D S S S D D S I I I D I I + +Speaker sentences 130: fleurs_deu_000403 #utts: 1 +id: (fleurs_deu_000403-fleurs_deu_000403) +Scores: (#C #S #D #I) 148 17 14 13 +REF: * V i * r t u ******* e L l E h i * l ******* f E s t e L l u n g e n s i n D i n d i E s o f t W A r E e I n g * e ******* b a u T u n D s o L l e n a R b e i t S s c h r i t T e * d ******* i e d e R s c h Ü l e r a L l e i n m Ö g l i c h ******* e r ******* w e i s e n i C h t b e W Ä l t i g e n k A N N h i n t e r ******* f r a g e n n A H e l e g e n u n d * e r K l Ä r e n +HYP: T C i E r t u e * l I h i E l f I s t e * l u n g e n s i n T i n d i * ******* s o f t E r * e * n g I e b a u D u n * s o * l e n a H b e i t * s c h r i t D e N d i e ******* d e * s c h Ü l e r a * l e i n m Ö g l i c h e r w e i s e n i * h t b e V E l t i g e n k * Ö R h i n t e r f r a g e n n E I e l e g e n u n d D e r G l E r e n +Eval: I S I I D S I I S D S D D S S D D I I S D D S D S I I D D D I I D S S D S S I S S I S S + +Speaker sentences 131: fleurs_deu_000404 #utts: 1 +id: (fleurs_deu_000404-fleurs_deu_000404) +Scores: (#C #S #D #I) 69 26 10 20 +REF: a m * ** * * * * 1 5 * a U g U s t * * * 1 9 4 0 * F I e * * ******* * * L E N D i e * * a L l I i e r t e n I n s Ü * D f r a n k r e i c h e i n d i E I N V a s i o n w U r d e O p e r * A T I O n D r A g O O n g e N A n N T +HYP: a m F Ü N F Z E H N N a g * s t N E U N Z H N H U D e R T V I R Z I V i e L I a * l * i e r t e n * n s Ü T f r a n k r e i c h e i n d i N * * W a s i o n w O r d e A p e r E S C H E n * r O g * U n g e * * n R D +Eval: I I I I I I S S I S D I I I S S S S I S S I I I I I S S S S I I D D D I S S D D S S S I S S S S D S D S D D S S + +Speaker sentences 132: fleurs_deu_000405 #utts: 1 +id: (fleurs_deu_000405-fleurs_deu_000405) +Scores: (#C #S #D #I) 85 8 17 3 +REF: e r g r i F f a U c h a l l E s a n w a s I n s w a s s e r k a * m s e L b s t e I n g r o S s e r d I n o s a u r i e R w i E d e r t * r e * X w A r i H m n i c h t g e w a C H S e n +HYP: e r g r i * f ******* a * c h a l l * s a n w a s S E n s w a s s e r ******* k a R m s e * b s t e * n g r o * s e r ******* d E n o s a u r i e * w i * ******* d e r t I E r e C G S w E r ******* i * m ******* n i c h t g e w a * * K e n +Eval: D D D D S S D I D D D D S D D D I S I S S S D D D D D S + +Speaker sentences 133: fleurs_deu_000406 #utts: 1 +id: (fleurs_deu_000406-fleurs_deu_000406) +Scores: (#C #S #D #I) 98 17 14 18 +REF: s e i t D e r G r Ü n d u n g v o N a s u n C i * * ******* * ** * * * Ó n ******* * * * * * 1 5 3 7 i s T e s * * p a r A g U A Y g e l u n g E N v i e l v o n s e i N E m i n d i g e * n e n C H a r a k t e r U n d s e i n e R i d e ******* n T i t Ä t z u b e w a H r e n +HYP: s e i t ******* * e r K r ** n d u n g v o R a s u n T i O R F Ü N F Z E n D E S E N U O D R E i s C I e s E S p a r E g W E I g e l u n g * * v i e l v o n s e i * * m i n d i g e H n e n * K a r a k t e r * n d ******* s e i n e * i d e n * i t E t ******* z u b e w a * r e n +Eval: D D S D S S I I I I I I I I S I I I I I I S S S S S S S I I S S S S D D D D I D S D D D I D S D D + +Speaker sentences 134: fleurs_deu_000407 #utts: 1 +id: (fleurs_deu_000407-fleurs_deu_000407) +Scores: (#C #S #D #I) 158 29 13 54 +REF: * t r o T z * ******* d e M i s T d e r a n t e I l a * n * * x * * * * ******* * * * d * * * * * R t * * ******* b i n d e r g e s a m t e n G r u P p e d e r l e u t * e m i t T u * ******* B e r ******* k U l o s e * o F f e n b a R d e * n ******* n o c h g e r i * * n * * * * G 6 0 0 0 d e r i n S g e s a M T * * * * * * * * * * * * * * * 3 3 0 0 0 0 l e U t e d i e i n s Ü D a f r i * k * A Z u e i n e m b * e s t i M M t e n * Z e i t p u n K t a n g e s t e C K t s i N D +HYP: S t r o * z S d e N i s * d e r a n t e * l L a N n I E x S D I E W I N d E S T I G H t D E b i n d e r g e s a m t e n * r u * p e d e r l e u t E e m i t D u G D e r k O l o s e R o * f e n b a * d e R n n o c h g e r i N E n S E X S T A U S E N d e r i n D g e s a E N D R E I H U N D E N D R E I G A U S E N l e L t e d i e i n ******* s Ü T a f r i C k E R T u e i n e m b I e s t i * N t e n D * e i t p u n G t ******* a n g e s t e * * t s i T T +Eval: I D I I S D D S I I I I I I I I I I I I I I I I S I I I D D I S I I S I S I D D I I I I I I I I S S S S S S S S S I I I I I I I I I I I I I I I S S S S S S S D S I I S S I D S I D S D D D S S + +Speaker sentences 135: fleurs_deu_000408 #utts: 1 +id: (fleurs_deu_000408-fleurs_deu_000408) +Scores: (#C #S #D #I) 83 15 15 15 +REF: A n * * G e l * * * * * * 2 0 0 6 * e * * * ******* R l Ä u t e r t D a s k o n t i n U u m ******* k o n z e P t a L s e i n e m E t H o d e u m o R g A n I s a t i o N E n Z U h E l f e n l e i s t u N g s ******* f Ä H I g e R z u w e r d e n +HYP: E n S C H e l Z W E I T A U S E N S e C H S E l E u t e r t * a s ******* k o n t i n * u m k o n z e * t ******* a * s e i n e m I t * o d e u m o B g E n * s a t i o * * n ******* D S h * l f e n l e i s t u M g s f ** * E g e * z u w e r d e n +Eval: S I I S I I I I I I S S S S I I I I I S S D D D I D D D S D S S D D D D S S D S I D D S D + +Speaker sentences 136: fleurs_deu_000409 #utts: 1 +id: (fleurs_deu_000409-fleurs_deu_000409) +Scores: (#C #S #D #I) 98 12 10 7 +REF: i n d i e s e R p * e r i o d e * d e r e U r o p Ä I s c h e n g E S c h i C h t e s t a n d d i e r E i c h u n d m Ä c h t i * G g e W o r d e n e * K a t H o l i s c h E k i R c h e a u f d e M p r Ü F s t a n d * ******* * +HYP: i n d i e s e * p I e r i o d e N d e r O e * r o p ** E s c h e n ******* g * I c h i G h t e s t a n d d i e ******* r L i c h u n d m E c h t i H E g e V o r d e n e R * a t * o l i s c h I Y k i E c h e a u f ******* d e N p r Ü * s t a n d T T +Eval: D I I S D D S D D S S D S S I S S I D D S S S D S D I I I + +Speaker sentences 137: fleurs_deu_000410 #utts: 1 +id: (fleurs_deu_000410-fleurs_deu_000410) +Scores: (#C #S #D #I) 192 23 48 10 +REF: d i e e r s ******* * * ******* * * t e * d E R 7 8 e m P f E h l u n g E N i S t D a S s e i n e n e u E d I p l o m a t I s c h E i n i T I a t i * v e v o * r e n d e d i e s e S j a h r e s e R g r i F f e n w e r d e n s o l l t e u m d i e I r a K i s C h e n g r e n z e n g e g e N Ü b e r f e I n D l i C h e N I n t e r V e N t i o n E N z u s i c h E r n u n d D I p l o m a t I s c h E b E z i E H U N G e N m i T S e i N E N n a c h b a R n W i e d e r H E r Z u ******* s t E L L e N +HYP: d i e e r s D R C H t e N d I B Z I C e m f * h l u n g * * i F t ******* * a * s e i n e n e u * d E p l o m a t * s c h * i n i Z a t i E v e ******* v o E r ******* e n d e d i e s e N j a h r e s ******* e g r i * f e n w e r d e n ******* s o l l t e u m d i e * r a G i s * h e n g r e n z e n g e g e * b e r ******* f e * n T l i * h e * ******* * n t e r W e * t i o n * D z u ******* s i c h A r n u n d * * p l o m a t * s c h * b I z i * * * * * e O m i * ******* Z e i * * * ******* n a c h b a * n * i e d e r * r T u s t * * * e * +Eval: I I I I I I I S S S S S S D D D S D D D D S D D S S I D I D S D S D D D S D D S D D S D D D D S D D S D S D D D D S D D D D D S D D S D D D D D D D S S I D D D D + +Speaker sentences 138: fleurs_deu_000411 #utts: 1 +id: (fleurs_deu_000411-fleurs_deu_000411) +Scores: (#C #S #D #I) 86 7 22 0 +REF: d i E s B I e t e t e i n E G u t e g e l e g e N h e i t d a s n o R D l i c h t Z u s E H e n d a D e R h i M m e L m e h r O d e r w e n i G e r r u n d u m d i e u H r d u n k e l I s t +HYP: d i * s ******* * P e t e t e i n I * u t e ******* g e l e g e G h e i t d a s n o * T l i c h t ******* * u ******* s * * e n d a ******* * e * h i * m e M m e h r A U d e r ******* w e n i * e r r u n d u m ******* d i e ******* u * r ******* d u n k e l * s t +Eval: D D D S S D D S D S D D D D D D D D D S S S D D D D D D D + +Speaker sentences 139: fleurs_deu_000412 #utts: 1 +id: (fleurs_deu_000412-fleurs_deu_000412) +Scores: (#C #S #D #I) 130 19 31 8 +REF: p r o f E s s o r I n p a m e l A F e r g u * s o n v o n d e r U n I V E R s I t Y O f d U n d E E m e r k t a n J o U R N a l i s t e n s c h e i N E n e i n e g e F Ä H R l i C H e g r E n z e z u Ü B E R s c H r e I t e n w e N n S i e F o * t o S u * ******* s w * * * * v o n v e r d Ä c H t i g e N v e r Ö F f E n t L i c h e n +HYP: p r o f * s s o r E n p a m e l E V e r g u S s o n v o n d e r * n W Ü U s * t I A f d A n d * * I m e r k t a n S o * * * a l i s t e n ******* s c h e i * * n e i n e g e * ** * E l i * * e ******* g r A n z e z u ******* ** * * A s c * r e * t e n w e * n D i e V o T t o * u N s w E I T E v o n ******* v e r d E c * t i g e * v e r ** f * n t * i c h e n +Eval: D S S S I D S S S S D S S S D D S S D D D D D D D D D S D D D S D D D D S D D D S S I D I I I I I I D S D D D S D D + +Speaker sentences 140: fleurs_deu_000413 #utts: 1 +id: (fleurs_deu_000413-fleurs_deu_000413) +Scores: (#C #S #D #I) 106 20 32 1 +REF: e s k a N n s i C H A U c h l o H N E n e i n e W I l D C a R D z u k a u f E n d i e z u t r i T t e n T w E d e r Z u A u s g e w Ä H l T e n p a R K s I n * S Ü D a f r i k a O d e r z u a L l e n S Ü D A f r i k a n I s c h E n N A t I o n a l p a r K s g e w Ä H r t +HYP: e s ******* k a * n ******* s i * * * * c h l o * * * n e i n e * E l * ******* K a * T z u k a u f * n ******* d i e z u t r i * t e n w * d e r ******* T u * u s g e w ** E l * e n p a * G s ******* E n H E T a f r i k a ******* R d e r z u a * l e n Z U Ü T f r i k a n * s c h * n ******* E R t * o n a l p a r X s ******* g e w ** E r t +Eval: D D D D D D D D D D D S D D S D S D D D S D D S D D S D D S D S I S S S D S D S S S S D D D S S D S D D S + +Speaker sentences 141: fleurs_deu_000414 #utts: 1 +id: (fleurs_deu_000414-fleurs_deu_000414) +Scores: (#C #S #D #I) 109 15 37 13 +REF: d i e B r Ü C k e s o L l I m s e P t e m b e r ******* * * * * * * * * * * * * 2 0 1 7 V o L l s t Ä n d i G D E n b e t r i e B a u f n e h m E n E s w I r D E r w a r T e T d A S s D i E B R a s I L i a n i s c h e n z O L L K O N T R o L l p u n k t e d a N n f e r t i g G E s t e l l T S e i n w e r D E N +HYP: d i e P r Ü * k e ******* s o * l ******* E m ******* s e t e m b e r Z W E I T A U E N S I B S H N F o * l s t E n d i T * * n b e t r i e * a u f n e h m * n I s ******* w * r * * r w a r * e * d * * s * i * * P a s * i a n i s c h e n z * * * * * * * * o * l p u n k t e d a * n f e r t i g * * s t e l l * Z e i n w e r * * T +Eval: S D D D D S D S I I I I I I I I I I I I I S S S S S D S S D D D D S D D D D D D D D D D D S D S D D D D D D D D D D D D D S D D S + +Speaker sentences 142: fleurs_deu_000415 #utts: 1 +id: (fleurs_deu_000415-fleurs_deu_000415) +Scores: (#C #S #D #I) 158 15 41 8 +REF: w Ä H r e n D * e i N e * x p E r I m E n t e l l e R i m P F s t O F f I n D e R l a g e Z u s e i n s c h e i n t d i E e b o l * A m o R t A l i * t Ä t z u s e n * K E n * g I b t e s b I s H e r k e i n e m E D i k a m E n t e d i E a l s e i n d E U T i G z u R b e h a n d l u n G b e s t E H e n d e R i n ******* F e k t i o N E n g e ******* e I g n e t n a c h G e w i e s e N W U r d e n +HYP: w ** E r e n * D e i * e R x p * r * m I n t e l l e * i m * * s t * * f * n ******* * e * l a g e * u ******* s e i n ******* s c h e i n t ******* d i * e b o l E R m o * t * l i E t Ä t ******* z u s e n G U n G g * b t ******* e s b * s S e r ******* k e i n e ******* m I T i k a m Ä n t e d i * ******* a l s e i n d R D i * z u * b e h a n d l u n * b e s t * * e n d e * i n V e k t i o * * n ******* g e e * g n e t n a c h e w i e s e * * O r d e n +Eval: D S D I D I D D S D D D D D D D D D D D D D D I S D D I D I S S I D D D S D D S S S D D S S S D D D D D D I S D D D I D S D D S + +Speaker sentences 143: mls_deu_000281 #utts: 1 +id: (mls_deu_000281-mls_deu_000281) +Scores: (#C #S #D #I) 174 21 30 2 +REF: e i n Ä u S s e r s t l e B H a f t e r d e ******* P e S c h e N w e c h s e l F a n D s t a T t M a N e r w O G d e N p l a n e i N E n a L l g e m e i n e n s t A a t e n ******* k o n g r e S s z u b e r u f e n u n d k o N n T e s i C H v o R l Ä u f i G N U R n o C H n i c h t Ü b e r d A s V o r z u l e g e N d e P r o g r A m m u n D d e n o r t D e s z u s a M m E N t r I T t s e i n i g e n +HYP: e i n E u * s e r s t ******* l e * P a f t e r d e B e * c h e w e c h s e l V a n * ******* s t a D t W a I e r w U K d e M p l a n e i * * n a * l g e m e i n e n s t * a t e n k o n g r e * s T z u ******* b e r u f e n u n d k o * n D e s i * * v o L l O u f i * ******* * K T n o * N U n i c h t Ü b e r d E s * o r z u l e g e * d e * r o g r * m m u n * d e n o r t ******* * e s z u s a * m * * t r * E t s e i n i g e n +Eval: S D D D S I S D S S D D S S S S S S D D D D I D S D D S D D S S D D D S S D S S S D D D D D D D D D D D S + +Speaker sentences 144: mls_deu_000282 #utts: 1 +id: (mls_deu_000282-mls_deu_000282) +Scores: (#C #S #D #I) 156 10 16 5 +REF: e r w u S s t e n i c h t w a s i H m d a s l e b e n k o s T b a r e s g e r a u B t H a T t e s * p a n N k r a f t u n d m u t * d a s s e s i H n f e i G u n d s c H e u g e m a c h t H a T t e u n * f Ä H i * G z u d e n h o h E n d i n g e n z u d e n e n u n g e t r Ü b * t e M i t f r E U d e g e h Ö r t +HYP: e r w u * s t e ******* n i c h t w a s i * m d a s l e b e n k o s S b a r e s g e r a u P t ******* * a * t e s C p a n k r a f t ******* u n d m u t D d a s s e s i * n f e i K u n d ******* s c O e u ******* g e m a c h t ******* * a * t e u n D f Ä * i C H z u d e n h o h * n d i n g e n z u O d e n e n u n g e t r Ü b P t e * i t f r O L d e N g e h Ö r t +Eval: D D D S S D D D I S D I D S D S D D D D I D I S D S I D S S S + +Speaker sentences 145: mls_deu_000283 #utts: 1 +id: (mls_deu_000283-mls_deu_000283) +Scores: (#C #S #D #I) 239 17 58 3 +REF: d i e s e r J u n g e m a n n h i E S s k a C k E R l i t z C H e n u n d b e f a n D s i C H g E r a d e A u F D e R w a n d e r s c h a f t a l s i n D E m g e N a N n t e n k Ö n i g r e i c h d I E b e k a n * n T M a c h u n g w E G e n d e r p r I n z e S s I n v e r l e s e n w u r d e e i s a G t E d e R s c h n e i d e r w * e N n e s w e i t E r n i C h t s i s t e i n w e i B H a B I c h n O C h n i c H t G E k Ü S s t u n d d E S k Ö n i g s e i d a m z u w e r d e n d a s g e L Ü s T e t m i C H a L l e R d i n g s * +HYP: d i e s e r * u n g e ******* m a n n ******* h i * * s k a * k * A l i t z * I e n u n d ******* b e f a n * s i * G g * r a d e * u * * e * ******* w a n d e r s c h a f t a l s i n ******* * I m g e * a * n t e n k Ö n i g r e i c h d * * b e k a n D n * * a c h u n g ******* w * * e n ******* d e r p r * n z e * s E n v e r l e s e n w u r d e e i s a K t * d e * ******* s c h n e i d e r w I e * n ******* e s w e i t * r ******* n i * h t s ******* i s t e i n w e i * P a * ******* U c h ******* n * * h ******* n i c * t ******* K Ü k Ö L s t ******* u n d d * U T k Ö n i g s e i d a m z u O w e r d e n d a s ******* g e * I s S e t ******* m i * * a * l e * d i n g s T +Eval: D D D D D D D S D S D D D S D D D D D D D D S D D D D I D D D D D D D D S S D D D I D D D D D D D S D D S D D D D D D S S S S D D S S S D D S S D D D D D I + +Speaker sentences 146: mls_deu_000284 #utts: 1 +id: (mls_deu_000284-mls_deu_000284) +Scores: (#C #S #D #I) 215 5 47 1 +REF: n o c H f Ü n f m i n u t e n U n D d i e w o l k e n d e R b e w u S s t l o s i g k e i t b e g a N n E n z u s c h w i n d e n J e T Z t w u S s t e I C h s e H r w o H l d a S s i C h I n m e i N E m e i g E n e n b e T t e l a g u n d d a S s d i e r o t e * g l U t n i c h t S a n d e r E s w a R A l s d a s F e u e R i m k a m i n d e r K i n d E R s t u b e e s w a R n a c h t e i n e k e r Z e b r a N n t e a U f d e m t i s c h e +HYP: n o c * f Ü n f ******* m i n u t e n * n * d i e w o l k e n ******* d e * b e w u * s t l o s i g k e i t b e g a * n * n z u ******* s c h w i n d e n ******* I e * R t w u * s t e * * h ******* s e * r w o * l ******* d a * s ******* i * h ******* * n m e i * * m e i g * n e n b e * t e ******* l a g u n d d a * s d i e r o t e O g l O t ******* n i c h t * a n d e r * s ******* w a * * l s d a s V e u e * i m k a m i n ******* d e r * i n d * A s t u b e e s w a * ******* n a c h t e i n e k e r * e ******* b r a * n t e a * f d e m ******* t i s c h e +Eval: D D D D D D D D D D D S D S D D D D D D D D D D D D D D D D D D I S D D D D D D S D D D D S D D D D D D D + +Speaker sentences 147: mls_deu_000285 #utts: 1 +id: (mls_deu_000285-mls_deu_000285) +Scores: (#C #S #D #I) 146 24 34 6 +REF: W e L c h e D i E S e v e R D r Ä n g u n G e n w I e W Ä c h t e R u n t e r h a l t e n K o M m T D a N n I m p U B e R t Ä t * S a l T e r d i E h o c h ******* f l U T d e R s e x * u E L L e n b e d Ü R f t i g k e i t s o f I n d e T s I e A n d e N g e n a N n T e n s E E l i s c h E n r e a k t i o n s o d e R W i * d e r ******* s t a n D s B i l d U n g e n d Ä M m e * +HYP: * e I c h e * i * * e v e * T r E n g u n * e n w * e B E c h t e * u n t e r h a l t e n ******* C o * m * T a * n ******* E m ******* p R P e I t I t Z a l e r d i * h o c h f l * * O d e S s e x S u * * e n b e d ** F f t i g k e i t s o ******* f * n d e * s * e * n ******* d e * g e n a * n D e n s I L l i s c h * n r e a k t i o n s ******* o d e * * i E d e r s t a n * s P i l d O n g e n d ** E m e R +Eval: D S D D D D S S D D S S D D S D D S D D S D S S S S I S S D I D D S S I D D S D S D D D D D D D D S S S D D D D I I D S S D S I + +Speaker sentences 148: mls_deu_000286 #utts: 1 +id: (mls_deu_000286-mls_deu_000286) +Scores: (#C #S #D #I) 170 4 18 11 +REF: * ******* a b e r a F f e n g e h Ö r e n b e i h a g * e n ******* b e C K a n d i e k i s t e n ******* w a * n D n u n s o h Ö r t e i c h a u f a F F e z u s e i * * n e i n k l A r e r s c h Ö n E R g e d a n k e n g a n g d e n i c h i r g e n ******* d W i e m i t d e m b a u c h a u s g e h e C k t H a b e n m U s s d e N n a f f e * n d e n k * e n m i T +HYP: T a b e r a * f e n g e h ** r e n b e i h a g K e n b e * * a n d i e k i s t e n w a N n T n u n s o h ** r t e i c h a u f a * C e z u ******* s e i N E n e i n k l * r e r s c h Ö n * A g e d a n k e n g a n g d e n ******* i c h i r g e n d * i e m i t ******* d e m b a u c h a u s g e h e * k t ******* * a b e n ******* m O s s d e * n a f f e N n d e n k T e n m i * +Eval: I I D D I I D D I I S D D S D I I D D S D I D D D D D D S D I I D + +Speaker sentences 149: mls_deu_000287 #utts: 1 +id: (mls_deu_000287-mls_deu_000287) +Scores: (#C #S #D #I) 212 30 54 12 +REF: i s T E s D A s p O R t r Ä T e I n e s m e n s c h e n d e n S i E k E N n e n f r a g t e E l * i Z A w e l c h e u n ******* b e m e R k t a N m i c H h e r a n ******* g e t r e t e n w a R * * i c h E n * t g e g n e t e d a S s E s n U R e I n P H a n t A s i E k o p f s e i u n D s c h O b D I E z e i c h N u n g E i l i G u n t E R d i e a n d E R n B l Ä t t e r * n a t Ü R l i c H s p R a c H i c h D i e u n W a H R h e i * t d e N n e S w A R e i n s e H R G E t R e u e s p O R t r Ä T m * * R r o * * c h E s t e R s +HYP: i s * * s * * s p * A t r ** * E e R n e s m e n s c h e n d e n Z i * ******* k * Ä n e n f r a g t e I l E i S E w e l c h e u n b e m e A k t ******* a * ******* m i c * ******* h e r a n g e t r e t e n w a * U M i c h ******* I n D t g e g n e t e d a * s * s n * * e * n * F a n t E s i * k o p f ******* s e i u n * T s c h U b ******* * * T z e i c h u n g * i l i C u n t * U d i e a n d * O n ******* D l Ä t t e r U n a t Ü * l i c * G s p * a c * R i c h * i e u n B a * * h e i L t d e * n ******* e * w * * ******* e i n s e * * * I t O e u e s ******* p * E t r ** I E m I S T E r o T S c h * s t e * s +Eval: D D D D D S D D S S S D D D S S I S S I S D D D D D I D I I D S I D D D D D D S S D D D S S D D D S S D S D S D S D S I D D S D D S D S D D I D D D D D D D D D S S D D S D S S I I S S I I D D + +Speaker sentences 150: mls_deu_000288 #utts: 1 +id: (mls_deu_000288-mls_deu_000288) +Scores: (#C #S #D #I) 175 14 47 5 +REF: i c h w e i S s D A S s i c h s e H r k r a n k b i n s a * g t e s i E n A C H e i n E r w e i l e v o r E I n p A a R m i n u t e n v e r s U c h t e i c h m i c h I M B e t t e u m ******* z u D r e H e n u n d f Ü H l T e d a S s i c H k e I n g l i e d m e H r R Ü H r E n k a N N e s w Ä r e g u t * w e N n i c h M e I n g E m Ü t e R l e i c h t E R n k Ö N n t e * b e v o r i c H s t e r B e * +HYP: i c h w e i * s * * * s ******* i c h ******* s e * r k r a n k G b i n s a I g t e ******* s i * ******* n * * E R e i n * r ******* w e i l e v o r ******* * * n p * a * ******* m i n u t e n v e r s * c h t e i c h ******* m i c h ******* * E * e t t e u m z u * r e * e n u n d f Ü * l D e d a * s ******* i c * k e * n ******* g l i e d m e * r E N Ü O r * n k a * M e s w E r e ******* g u t D w e * n ******* i c h * e * n ******* g I m Ü t e * l e i c h t D A n k Ö * n t e R b e v o r ******* i c * ******* s t e r D e N +Eval: D D D D D D D S I D D D D D S S D D D D D D D D D D D D S D I D D D S D D D D D D S S S D D S S D I D D D D D S D S S D I D D D S I + +Speaker sentences 151: mls_deu_000289 #utts: 1 +id: (mls_deu_000289-mls_deu_000289) +Scores: (#C #S #D #I) 164 16 26 3 +REF: s o a b e r i s t Z w A r u n ******* s e r w e s e n s G R u n d G o T t s e * l B e r d a h e r U m h a t S i c h J e d o c H d e r S c h l a n g e n ******* k n Ä u E l d e s a l T e n s a t a n g e s C h l u n g e n u n D Ü b e r d e m f Ü n k C H e n d e R l i e b E i s T d i E f I n s T e r n i s D e s h a s s e s G e l a g e r t w a s w U n d e r d a N n +HYP: s o ******* a b e r i s t * w E r u n s e r ******* w e s e n s K u n d * o * t ******* s e L l V e r d a C h e r O m h a t ******* Z i c h ******* * e d o c * d e r ******* * c h l a n g e n k n E u * l d e s a l K e n ******* s a t a n ******* g e s * h l u n g e n u n G Ü b e r d e m f Ü n k * I e n ******* d e N l i e b I i s * d i * f E n s * e r n i s ******* T e s ******* h a s s e s * e l a g e r t w a s w O n d e r ******* d a * n +Eval: D D S I D S S D D D I S S S D S D D D D D I S D S D D D S D S D S S D D S D D S D D S D D + +Speaker sentences 152: mls_deu_000290 #utts: 1 +id: (mls_deu_000290-mls_deu_000290) +Scores: (#C #S #D #I) 125 13 31 3 +REF: b e S s i E W Ä r e l i E B e R g e b l i e b e n a b e r s i E w a R g e * z w u n g E N z u g e h E n W e I L d i E P Ü n k T l i C H k e i t b e i d e n m a H l * z e i t e n e i n e s a c h e w a R a u F w E L c h E i n g A t E s ******* h E A d h A L l s t r e n g e g e h a l t e n w u r d e +HYP: b e * s i * ******* V I r e ******* l i * * e * A g e b l i e b e n a b e r ******* s i * ******* w a * ******* g e T z w u n g * * K z u ******* g e h * n * e * A d i * * Ü n k * l i * T k e i t b e i ******* d e n ******* m a * l S z e i t e n e i n e s a c h e ******* w a * a u * w * Ä c h * i n g E t Z s h Ä R d h O R l s t r e n g e ******* g e h a l t e n w u r d e +Eval: D D D S S D D D D S D D D D D I D D S D D D D S D D D D S D D D I D D D D S D S S I S S S S D + +Speaker sentences 153: mls_deu_000291 #utts: 1 +id: (mls_deu_000291-mls_deu_000291) +Scores: (#C #S #D #I) 219 15 35 13 +REF: A U G E n b l i c k l i c h f Ü H l t e w i e I h r e a N s i c h t e n Ü b e r m i c h i H r e * E m ******* p F i n d u n g e N f Ü * r m i c h n i c h t u m e i n a t o * m v e r ******* Ä n d e R t w a R e * n * Ü b e R h a u p t k E I n e R Ä n d e r u n g f * Ä H I G w a r E N i c h s A H e * s i H r e m V e r s t e i n E R t e n a u * g e w e l c h e s n i e m a l s d u R c h t r Ä n e n g e n e t z t n i e m a L s i n Z Ä r t l i c h k e i t a u f ******* g e l e u c h t e t H a T t e a * * n +HYP: * * * * n b l i c k l i c h f Ü * l t e ******* w i e ******* * h r e a M s i c h t e n ******* Ü b e r ******* m i c h i * r e R * m p * i n d u n g e D f Ü H r ******* m i c h n i c h t ******* u m ******* e i n a t o U m ******* v e r I n d e * t ******* w a H e M n U Ü b e * h a u p t k * A n e * E n d e r u n g f E E C H w a r M D i c h s * * ******* e I s i * r e m * e r s t e i n * A t e n a u D g e w e l c h e s n i e m a l s d u * c h ******* t r E n e n g e n e t z t n i e m a * s i n * E r t l i c h k e i t a u f g e l e u c h t e t ******* * a * t e a M E n +Eval: D D D D D D D D S D D D I D I D S I D D D I D I S D D S I I D D S D S I S S S S S S D D D I D D D S I D D S D D S I D D D I I + +Speaker sentences 154: mls_deu_000292 #utts: 1 +id: (mls_deu_000292-mls_deu_000292) +Scores: (#C #S #D #I) 254 34 90 9 +REF: * B R U d e r * s A m i s t S e H r g u t w E N n D e R h Ä u P T l i N g I H n e r ******* f Ä H r T W i R D E R s i c H f r E U e N u n D w I R w e r D e n s c h n E L l d A n a c H h a n d e L n s O W o L l E N W I R a u f B r e c h e N U n D s C h n E L l r e i t e n d A m i t W I r n o c H V o r * * n A c h T d a s l a g e R E R r e i c h e n W I r s t i E g e N a u F d i E P f ** e r d e d i E N U n a u s g e r U H t H a T t e n u n d f l o g e N I m g a l o p P d a ******* v o n d i e s M a l h Ü t e t e n W i r u n s d e r +HYP: N S O d e r S s E m ******* i s t Z e * r g u t w * I n * e * h Ö u * G l i * g ******* * * n ******* e r f Ä E r * * i * * * D s i c * f r * * e * ******* u n * ******* w * Ü E w e r * e n s c h n * Ä l ******* d R n a c * ******* h a n d e * n s * ******* * o * l * * * B E a u f P r e c h e * ******* * n * ******* s * h n * A l ******* r e i t e n ******* d m i t * Ü r n o c * F o r D E R n * c h * d a s l a g e * * * r e i c h e n * E r ******* s t i * g e * a u * d i P * f Ä e r d e d i * * * n a u s g e r * O t ******* * a * t e n u n d f l o g e * ******* * m g a l o p B T d a v o n d i e s a l h Ü t e t e n * i r ******* u n s d e r +Eval: I S S S I S D S D D S D D S D S D D D D D I S D D D D D S D D D D D D D D S S D D S D S D D D D D D D D D D S S S D D D D D D D S D D S D S D S I I S D D D D D D S D D D D S D I D D D D S D D D D D D S S I S D D + +>> REF: f Ä H r T e W i E d e r d I r e k T z U f o l G e n w I R r I T T e N g e r a d e ******* a u s U n d e r ******* s p a R T e N U n S +>> HYP: f Ä E r D e * i * d e r d E r e k * ******* z E f o l * e n w * * E r * * * e * g e r a d e a u s ******* * n d ******* e r s p a * D e * ******* * n * +>> Eval: S S D D S D D S D D D S D D D D I D D D I D S D D D D + +Speaker sentences 155: mls_deu_000293 #utts: 1 +id: (mls_deu_000293-mls_deu_000293) +Scores: (#C #S #D #I) 277 15 41 6 +REF: w E I l d i e A b e r m i t p ** e c h b e s t r i c h e n w a * r b L i e b e i n e r v o n d e n g O l D e n e n p a n t o f F e L n f e s t h Ä n g e n u n d i n d e r a n g s T d a c h T E s n i c h t D A r a n i H n M i t z U n e H m e N u n d W i e E s d e n l e t z t e n s c h R i T t v o n d e r t r e P p e t a T d A h a T t E E s z w Ö l f a u s ******* g e s c h l a g e n d a * W a r w a g e n u n d P f e r d e v e r s c h W u n d e n u n d a s c h e n ******* p U T t e L s t a n d i n s e i N E n A s c h e n ******* k l e i d e r N a u f D e r D u n +HYP: w * A l ******* d i e * b e r m i t p Ä e c h P b e s t r i c h e n w a H r b * i e b e i n e r v o n ******* d e n g E l * e n e n p a n t o f L e * n f e s t h Ä n g e n u n d i n d e r a n g s * d a c h * * s n i c h t ******* * E r a n i * n * i t z O n e * m e M u n d * i e I s d e n l e t z t e n ******* s c h * i * t v o n ******* d e r ******* t r e * p e ******* t a D d E R h a * t * ******* * s T z w I l f a u s g e s c h l a g e n d a R * a r w a g e n u n d * f e r d e v e r s c h * u n d e n u n d a s c h e n p * O t e * ******* s t a n d i n s e i * * n * s c h e n k l e i d e r * a u f * e r * u n +Eval: D S D D I S I D D S D S D D D D D D S D D S D S D S D D D D D D D S S S D D D D S S I I D D D I D S D D D D D I D D D + +>> REF: K E l n s t r a S s e +>> HYP: * G l n s t r a * s e +>> Eval: D S D + +Speaker sentences 156: mls_deu_000294 #utts: 1 +id: (mls_deu_000294-mls_deu_000294) +Scores: (#C #S #D #I) 98 19 40 6 +REF: i L L n A H m d A s G L a s v * O m a U g e e i n F i n s t E R e r E R n s t l a g e r T e Ü b e R s E I N e n z * Ü G e n * e s i S T s c h r E c k * l I C H s a G t e E r * i C H H A B d a s m e I n i g e g E t a n u M b L u t ******* v e r g i E S s E n Z U v e r m E I d e n +HYP: i E I E n * O m d E s * * a s ******* v E R m ******* a * g e e i n V i n s t * * e r * A n s t ******* l a g e r * e ** b e * ******* s * * * e n T z Y Ü D e n I e s ******* i * * ******* s c h r I c k A l * * * s a K t e ******* H r G i * * ******* * * E T d a s m e * n i g e ******* g I t a n ******* u N b * u t v e r g i * * s * n ******* * S v e r m * Ä d e n +Eval: S S S D S S D D D I S D D S D D D S D D D D D D D D S I S I D D D D S I D D D S D S I D D D D D S S D D S D S D I D D D D D S D S + +Speaker sentences 157: mls_deu_000295 #utts: 1 +id: (mls_deu_000295-mls_deu_000295) +Scores: (#C #S #D #I) 169 12 5 4 +REF: n u r d e r d o * k t o r u n D d i e w Ä r t e r I n s o l l e n v o r s e i n e a u g e n k o m m e n e r ******* k l Ä R t e d i e t r i n e * i n g r o s s e m a m t ******* s e i f e r d a m i t w a R d i e f r a U o b e r s t g a n Z e i n V e r s t a n d e n u n d H Ö C H s t e r f r e U t k e H r t e s i E m i t i H r e n +HYP: n u r d e r d o C k t o r u n * d i e w E r t e r E n s o l l e n v o r s e i n e a u g e n k o m m e n e r k l Ä A t e d i e ******* t r i n e R i n g r o s s e m a m t s e i f e r d a m i t w a * d i e f r a R o b e r s t g a n S e i n F e r s t a n d e n u n d P I R X s t e r f r e I t k e * r t e s i * m i t i E r e n +Eval: I D S S I S D I I D S S S S S S S S D D S + +Speaker sentences 158: mls_deu_000296 #utts: 1 +id: (mls_deu_000296-mls_deu_000296) +Scores: (#C #S #D #I) 202 11 33 8 +REF: k W a r u n ******* t r Ö s t L i c h Ü b e r d i e l a g e d E s k Ü n S t l E r s e r b e g a N n z u w e i n e n u n D s c h l U c h * z t ******* * e l a n g e i n d I e V o r g e ******* h a l t e n e n h Ä n d e d e r k Ü n s T l e R w a R t e t e b i s k s i c H b e r u H i * G t h a t t e u n d e n T s c h l o S S s i c h d a N n d a * e r k e i N E n a n d e r E n a u s W E g f a n d * d e N n o c h z u m W e i t e r s c H r e i b e n +HYP: k ******* A a r u n t r Ü s t * i c h ******* Ü b e r ******* d i e l a g e d A s k Ö n Z t l * r s e r ******* b e g a * n z u w e i n e n u n * ******* s c h l * c h T z t D e l a n g e i n d * e * o r g e h a l t e n e n ******* h ** n d e d e r k O n s * l e * A w a * t e t e b i s ******* k A s i c * b e r u * i C H t ******* h a t t e u n d e n * s c h l o * * ******* s i c h d a * n d a R e r k e i * * n a n d e r * n a u s * I g ******* f a n d T d e R n o c h z u m * e i t e r s c * r e i b e n +Eval: D S I S D D D S S S D D D D D D I I I D D I D D S D D S D D S D D I S D D D D D D I D D D D S D I S D D + +Speaker sentences 159: mls_deu_000297 #utts: 1 +id: (mls_deu_000297-mls_deu_000297) +Scores: (#C #S #D #I) 250 31 52 15 +REF: V o n d E N P f e r d e h e r d e n d e r A p a * * c h e n u n d s a g T E n u n * s d a S s s i E f Ü R e I n a ******* p a * * c h e N P f E r d u n * s e b e n s O v i e l e w a r e n u n D B r A n D Y g e b e n w Ü r d e n W I E f Ü r E i n k * i o w a P f e r D d a s i n d u n S E r E k R i E g E r f o R T u m a p a * * c h e n P f E r d e z U h O l e n a l s o r I c h t i g * W e r W a r s c h U l d A N D e m t o d e D e R b I s H e r g e f a l l E N e n u n d A N D e M b l u t v e r g i e S s e n w e L c h e s N u n b e ******* v o r s +HYP: * o n ******* d I M * f e r d e h e r d e n d e r * p a T S c h e n u n d s a g * n ******* u n Z s ******* d a * s ******* s i * f ** * e * n E a p a T S c h e M f Ä r d ******* u n D s e b e n s U v i e l e w a r e n ******* u n * P r E n * * I g e b e n w I r d e n * * * Ü f Ü r ******* * i n k E i o w a B f e r T d a ******* s i n d ******* u n * r * I k L i * g A r f o * D u m a p a T S c h e n f Ä r d e z O h * l e n ******* a l s o ******* r * c h t i g H * e r ******* * a r ******* s c h * l d ******* E R * e m t o d e * e * b E s e r g e f a l l * * e n u n d ******* * E R I e * b l u t v e r g i e * s e n w e * c h e s * u n b e v o r s +Eval: D D S S D D I I D S D I D D D D D D D S I I I S S S D I S D D S S D D S S D D D S D D I S S D D D S D S S D S D S I I S S S D D D D I D D D D D D S S D D D S S D D D D S S S D D D D I + +>> REF: t a n d w e i S s e P f ** e r d e ******* h Ä n d * l e r +>> HYP: t a n d w e i * s e * f Ä e r d e h E n d T l e r +>> Eval: D D I I S I + +Speaker sentences 160: mls_deu_000298 #utts: 1 +id: (mls_deu_000298-mls_deu_000298) +Scores: (#C #S #D #I) 191 21 52 7 +REF: d a s A m a Z o n e N h Ü t ******* c h e n v o n s c H w a R z E m s a M m e t G R a Z i ** Ö s a U F i H r e l a n g e n l o C k e n g e d r Ü c K t d i e I H r e w a n g E n u m ******* f l o s s e n U n d Ü b e R I H r E s c h U l t e R n h e r a b w A L L t e n s o t r a t S I e i N d A s e i n f A c h * e l Ä n D l i c h E g e b Ä u d e u n d s C H W e b * t e z w i s c H e n D e N r e i H E n d e r h A L b ******* g e b l E n D e t E n D o R f ******* k I n D e r a u f U n d a b +HYP: d a s * m a T o n e h Ü t c h e n v o n ******* s c * w a T z A m s a * m e t * K a T i Ü R s ******* a * * i E r e l a n g e n ******* l o * k e n ******* g e d r Ü c * t d i e ******* * * r e ******* w a n g * n ******* u m f l o s s e n * n d Ü b e * ******* * * r * ******* s c h * l t e * n ******* h e r a b w * E I t e n s o t r a t ******* * * e i * d R s e i n f E c h R e ******* l E n T l i c h * g e b O u d e u n d ******* s * * T e b P t e T z w i s c * e n ******* * e * r e i * * n d e r ******* h E I b g e b l Ä n * e t * n * o * f k * n * e r a u f ******* E n d a b +Eval: D S S I D D S S D D S S I S D D D S D D D D D D D D D D I D D D D D D D D D D D S S D D D D S S I D S S D S D D D S I S D D D D D D D S S I S D D D D I D D D S + +Speaker sentences 161: mls_deu_000299 #utts: 1 +id: (mls_deu_000299-mls_deu_000299) +Scores: (#C #S #D #I) 176 16 47 6 +REF: D u m u S s t e r s t e n T S a g e n a L l e M s Ü n D h a f t e n s t r e b e n u n D I n t i e F e R r e u E u n d d e m u T d i e f Ü * r ******* b i T T E D e r h E I l i G E n e r ******* f l E H e n g e g e n d i e d u g e f r e V e l t H A s t D i e j Ü N G l I n g e w E l c h e f R A n * c * e s K o s o l A n G e g e f l o H E n s * u c h t e n I H n a u F I N S e I n e r w e r k S t a T t u n D f a n d e n i H n +HYP: T u ******* m u * s t e r s t e n Z a g e n a * l e N s Ü n T h a f t e n ******* s t r e b e n u n * ******* E n t i e V e * ******* r e u * I u n d d e m u D d i e f Ü H r b i * * * ******* * e r h * L l i * * n G e r f l * * e n g e g e n d i e ******* d u g e f r e * e l t ******* * E s t T i e j Ü * M l * n g e w * l c h e f * E n S c H e s o ******* s o ******* l * n * e g e f l o * * n s O u c h t e n * * n a u * ******* * * ******* * e * n e r w e r k * t a * t ******* u n * f a n d e n i * n +Eval: S D D S S D S S D D D S S D D D S S I I D D D D D D S D D S I D D D D D D S S D S D D D S I I S D D D D D D I D D D D D D D D D D D D D D + +Speaker sentences 162: mls_deu_000300 #utts: 1 +id: (mls_deu_000300-mls_deu_000300) +Scores: (#C #S #D #I) 126 8 17 4 +REF: e r l i e S s s e i n e g r e t e L n i C h t F o r t s c h l E P P e n a m a l l e r w E n i g * s t e N a b e r i n d e n g r o s s e n * v o g e l b a u E r W O s i e a L l e I n e i n e m t o n e * P f e i f e n m * u S s t e n w i E e r s t e T S s a G t e +HYP: e r ******* l i e * s s e i n e g r e t e * n i * h t V o r t s c h l * Ä B e n a m a l l e r w I n i g X s t e * a b e r i n ******* d e n g r o s s e n D v o g e l b a u A r * * U s i e a * l e E n e i n e m t o n e B * f e i f e n m O u * s t e n w i * ******* e r ******* s t e * * s a K t e +Eval: D D D D S D S S S I D D I S D D S D S I D I D D D D D D S + +Speaker sentences 163: mls_deu_000301 #utts: 1 +id: (mls_deu_000301-mls_deu_000301) +Scores: (#C #S #D #I) 212 19 57 10 +REF: F r A n ******* C e s k o m a l t e I n u n h E I l i g e R b e g e i s t E r U n g v i e l e b i L D e * R a U s D e R l Ü g e n h a f t e n F a b e l w e l T * K e I n e R a l s e r V e r m o c h T E d i e b u H l e r i s c h e Ü P P I G k * e I t D e R w e i b L i C H e n g e s t a l t e n s o W A H r ******* h a f T D a R Z U s t e L l e n i n ******* d e m E R v o n l e b e n D e N m o d e L l e N d i E k a R n * a t i o n * v o n d e n a l t e n m a R m o R b i l d e r N A b e r f o r m U n d b i * l D u n g E n * T n a H m +HYP: V r * n H e s k o ******* m a l t e ******* * n u n h * A l i g e * b e g e i s t * r * n g v i e l e ******* b i * * e L T a * s ******* T e * l Ü g e n h a f t e n * a b e l w e l D T R e * n e * ******* a l s e r ******* * e r m o c h * * d i e b u * l e r i s c h e ******* Ü * L B E k A e * t ******* * e * w e i b * i * * e n ******* g e s t a l t e n s o ******* * B E r h a f * * a * * S s t e * l e n i n d e m ******* * * v o n l e b e n T e * m o d e * l e * d i * ******* k a * n D a t i o n G v o n ******* d e n a l t e n m a H m o b i l d e r * R b e r ******* f o r m ******* * n d b i E l * u n g ******* I n D n a * m +Eval: S D I S D D D D S D D D D D D I S D D S D D S I S D D D D D D D D D D S S S I D D D D D D D D D D S S I D D D D S D I D D D S D D D D D D I I D S S D S D D D I D D S I S D + +Speaker sentences 164: mls_deu_000302 #utts: 1 +id: (mls_deu_000302-mls_deu_000302) +Scores: (#C #S #D #I) 255 22 78 3 +REF: b E w e g u n g u n D t a t d e n E R s t e n z u g J A e S s t I M m t e d i e V o R H I n a n g e g e b E n e N i n * g r e d I e n z i e N n Ä m L i c H r Ü b e n h a n f * e i c h e L n u n D s a u e r a m P f E R W a R E n a l l e i n d e m P f e i f E n k o p f e A n w e s e n D a b e r E i N E n f Ü n f t E n h a u p t s t o F f h a T T E i c h n i c H T g e n a N n T j e * t Z t r o c h U n d s c h m e C k t E i C H d a s S A U C h E I n s t Ü C K c h e n f i l Z s c h u H D A b e i s e i n M Ü S s e i C H B l i e s D e n r a u C h a u c h g e g E N d +HYP: b * w e g u n g u n * t a t d e n * * s t e n z u g * * I e R s t * E m t e d i e F o * E U n a n g e g e b * n e * i n K g r e d * e n z i e * R n ** m * i c * r Ü b e n h a n f E e i c h e * n ******* u n * s a u e r a m * f * * ******* * a * * n a l l e i n d e m * f e i f * n k o p f e ******* R n w e s e n * a b e r ******* * i * * n f Ü n f t * n h a u p t s t o * f ******* h a * * * ******* i c h ******* n i c * G g e n a * n * D j e R t * t ******* r o c h * n d s c h m e * k t * ******* i * G d a s E * * * h ******* * O n ******* s t Ü * * c h e n f i l * s c h u D E R b e i ******* s e i n * ** I s e i * * G P l i e s ******* T e n r a u * h ******* a u c h ******* g e g * * d +Eval: D D D D D D S S D S S D S S D D I D D S D D D I D D D D D D D D D D D D D S D D D D D D D D D D D D D D S D D S I D D D D D D D S S D D D D D S D D D D S S S S D D D S D D S S D S D D D D D + +>> REF: e n h I M m e L u n D g e g e N d i E +>> HYP: e n h * E m e * u n * ******* g e g e * ******* d i * +>> Eval: D S D D D D D D + +Speaker sentences 165: mls_deu_000303 #utts: 1 +id: (mls_deu_000303-mls_deu_000303) +Scores: (#C #S #D #I) 246 18 20 11 +REF: u n d d a s f E u E r s t a n d a u f u n d f l a c k e * R t E u n D k o c h T E d a s e s s e n f e r t i * G u n d d e r b r a t e n b r u t z e l t e * f o r t u n D d e r k o c h g a b d e m k Ü c h e n ******* J u n g e n e i n e o H r ******* f e i g e u n d d I e m a G D r U p f t e d a s H u H n f ** e r t i g * d a w a r D d i e h o c h * z e i t V o n d e m k Ö n i G S s o H n m i T d o R n * r Ö * s C H e n g e f e i e r t u n D s i e l * e B t E n V e r g n Ü G t b i s a n i H r e n d e +HYP: u n d d a s f O u * r s t a n d a u f u n d f l a c k e A t * u n * k o c h * * d a s e s s e n f e r t i C H u n d d e r b r a t e n b r u t z e l t e R f o r t u n * d e r k o c h g a b d e m k Ü c h e n I u n g e n e i n e o * r f e i g e u n d d * e m a R K T r O p f t e d a s * u * n ******* f Ä e r t i g H d a R w a r T d i e h o c h T z e i t * o n d e m k Ö n i C H s o * n m i E d o * n G r Ö U s * I e n g e f e i e r t u n * s i e ******* l I e * t * n E F e r g n Ü * t E b i s a n i E r e n d e +Eval: S D I S D D D D I S I D I S D I D S S S S D D D I I S S I D S S D S D I I D S D D I D D S S D S S + +Speaker sentences 166: mls_deu_000304 #utts: 1 +id: (mls_deu_000304-mls_deu_000304) +Scores: (#C #S #D #I) 124 14 32 6 +REF: u n * d d A S s e r m i R n i c h T n a c H t r a g e n W o L l e w e N n i c h W i d e r s * P E n s t i g w a R G E g E n s e i N E n W o h L m e i N E n D e N r a * t d e r h e R r P f a R r E R * h a T J A i n a l l E m r e c h t g e h a B t u n d i c h W a R I m u n * ******* r e c h t a b e r +HYP: u n M d d * E s ******* e r ******* m i * ******* n i c h * n a c * t r a g e n B o * l e w e * n ******* i c h * i d e r s H F Ä n s t i g w a * ******* * * g I n s e i * * n * o h * m e i * * n * e M r a R t d e r h e * r ******* * f a * r * A E h a * ******* D I E i n a l l * m P r e c h t g e h a U t u n d i c h ******* M a * A m u n M r e c h t a b e r +Eval: I D S D D D D D D S D D D D I S S D D D D S D D D D D D D S I D D D D D S I D D S S S D S S D S D S I I + +Speaker sentences 167: mls_deu_000305 #utts: 1 +id: (mls_deu_000305-mls_deu_000305) +Scores: (#C #S #D #I) 109 16 41 4 +REF: O B g L E I c h S e I n e m a S s e n U R w E n i g e G r a M m b e t r u g e R B r e i t e T e s i c h k * E G e L f Ö r m i g a u s u N D m u S s t e d A H e r * * d A s I H m E n * T g e g e n f l i E g e N d e s p r e n G G E s c h o S s a u F f a n g e n U n d z u R R U H E b r i n G e n +HYP: * * g * * Ä c h ******* * e * n e ******* m a * s e n * * O w I n i g e ******* K r a * m b e t r u g ******* e * ******* * r e i t e e ******* s i c h k I L I e f ** r m i g a u s H u * * ******* m u * s t e ******* d * * e r E R d E s * * m I n D g e g e n f l i * g e * d e ******* s p r e n * K I s c h o * s a u * f a n g e n * n d ******* z u * * * * W I b r i n * e n +Eval: D D D D S D D D D D D D S S D S D D D D D S D I S S S D S D D D D D D D I I S D D S I S D D D D S S D D D D D D D D S S D + +Speaker sentences 168: mls_deu_000306 #utts: 1 +id: (mls_deu_000306-mls_deu_000306) +Scores: (#C #S #D #I) 295 17 60 8 +REF: d e r f U c H s r e i c h t e S A m d I e u n f r i E D L i c h e F r i e d e n s p f e i ******* F e * h I n d e r m a N n t a t w a c k E r s e i n e s e C H s z Ü g e U n D s a g * t e d e r G r o S s E g e i s T a c h t e t n i c h t a u f d i E v e r s c h i e d e n e h a u T d e r m e n s c h e n d e N n d i E k Ö N N E n s i c h m i t f a R b e b e s c h m i e r e n U m i H n z u * t Ä u s c h e n s o N d e R N e r s i E H T d a s h e R z a n d i e h E R z e n d e r k R i E g e r v o M b e r Ü H M t e n s t a M m e d e r k * * i o W a s S i n +HYP: d e r f * c K s ******* r e i c h t e * E m d * e u n f r i * * T i c h e * r i e d e n s p f e i V e R h * n d e r m a * n ******* t a t w a c k A r ******* s e i n e ******* s e * X s z Y g e ******* * n * s a g K t e d e r * r o * s * I g e i s * a c h t e t ******* n i c h t a u f ******* d i * ******* v e r s c h i e d e n e h a u * ******* d e r m e n s c h e n d e * n d i * ******* k Ö * * * n s i c h ******* m i t f a * b e ******* b e s c h m i e r e n * m ******* i * n T z u D t E u s c h e n s o * d e * * e r ******* s i * * * d a s h e T z S a n d i e h * T z e n d e r k L i * g e r v o * b e r Ü * B t e n ******* s t a * m e ******* d e r k A E i o * a s ******* E i n +Eval: D S D D S D D D S D I S I D D D S D D D S S D D D I D D D S D D D D D D D D D D D D D D D D D D D S I S D D D D D D D S S D S S D D D S D D D I I D D S + +>> REF: D t a p * F e r u n e r s c h r o C k e n U n D t r e u d a s m e i n * i g e h Ä n g T +>> HYP: * t a p T V e r u n e r s c h r o * k e n * n * t r e u d a s m e i n E i g e ******* h Ä n g * +>> Eval: D I S D D D I D D + +Speaker sentences 169: mls_deu_000307 #utts: 1 +id: (mls_deu_000307-mls_deu_000307) +Scores: (#C #S #D #I) 180 14 20 6 +REF: a l l E s W a s w i R m I t i H r b e g e g n E t s c H i e b T s i c h d * u R c h u n d Ü b e r ******* e i n a n d e r b a l D u n t e r s c h R e i b e n w I r E i N E n k o n t R a k t d * A i s t I H r e h a n d U n D d I e m e i n i g e i H r n a * m E U n D d e r m e i n i g e B e i ******* d e l Ö s c h e n e i n a n d e r a u s b e i ******* d e v e r s c h l i n g e n s i c h +HYP: a l l * s * a s ******* w i E m E t i E r b e g e g n I t s c * i e b * s i c h d E u I c h ******* u n d Ü b e r e i n a n d e r b a l T u n t e r s c h * e i b e n w E r * i * * n k o n t * a k t d E R i s t * E r e R h a n d * n * d * e m e i n i g e i E r n a H m * O n * ******* d e r m e i n i g e W e i d e ******* l E s c h e n e i n a n d e r a u s b e i d e v e r s c h l i n g e n ******* s i c h +Eval: D D D S S S S D D I S D I S D S D D D D I S D S S D D D S I D S D D S I D S I D + +Speaker sentences 170: mls_deu_000308 #utts: 1 +id: (mls_deu_000308-mls_deu_000308) +Scores: (#C #S #D #I) 219 20 44 3 +REF: e r m Ü S s t e d e n e I n f a c h e n C H r o n i K e n C H O r a l d e s m a l e R s m i t a L l E R l e I e r ******* k l * Ä r U n G e n u n D z U r e c h t w e I s u n g e n W i E m i t k r a u s e n F i g u R E n v e R s c h n Ö r k e L n u n D v e r b r Ä m e n i c h t r e t e i N d i e p e r s o n d e s H e r a u s g e b e R s U n d b i T t e D i c h G Ü n s t i g e R l E s e r D u W o L l E s t E H e d u w e i t e R l i E s e s t f o l G e n d E s d I R g Ü * t i G s t m e r K e n +HYP: e r m ** Ö s t e d e n e * n f a c h e n * * r o n i T e n * K Ö r a l ******* d e s m a l e * s m i t a * l * * l e * e r k l E H r E n * e n ******* u n * z * r e c h t w e S s u n g e n * i * ******* m i t k r a u s e n W i g u * * n v e * s c h n A r k e * n ******* u n * v e r b r E m e n i c h t r e t e i * d i e p e r s o n ******* d e s ******* * e r a u s g e b e * s ******* * n d b i * t e ******* T i c h I Ü n s t i g e * ******* l I s e r T u * o * l I s t * I e d u w e i t e * ******* l i * s e s t f o l D e n d I s d * * E g Ü T t i * s t m e r H e n +Eval: D S D D D S D S S D D D D D D I I S S D D D D S D D D S D D D S D D D S D D D D D D D D D S S D D S S D D S D S D D D S S D D S I D S + +Speaker sentences 171: mls_deu_000309 #utts: 1 +id: (mls_deu_000309-mls_deu_000309) +Scores: (#C #S #D #I) 223 25 46 5 +REF: d i e H o f d a m e n b e k a m e n k r Ä m p f e * u n d D i e k Ö n i g I n u n D D i e p r I N z e s s I N n e n d i e I H r e * a l l E R l i E b s T e n h Ü n D c h e n w Ä H r E n D d e r m a H l Z e I T A u f d e n S C h o S s g e n o M m E N h a T T e n b e m e r k T e n z u i H r e M s c H r E c K e n d a S s d i E l i * l * A a * m a r a n T f a R b e n e n u n d o r a n G E G e L B e n s e i d e n k l e i d e r a L l e D i c H t b e s Ä t m i t d e n h Ä S s l i c H s t e n Ö L f l E C K e n w a R e N +HYP: d i e * o f d a m e n b e k a m e n k r ** m p f e R u n d ******* T i e ******* k Ö n i g E n u n * * i e ******* p r O M z e s s * E n e n d i e ******* * * r e R a l l * A l i * b s * e n h Ü n Z c h e n w ** E r * n * d e r ******* m a * l T e * R * u f d e n * * h o * s ******* g e n o * m * * h a * D e n b e m e r k * e n z u i * r e N s c * r Ä c T e n d a * s d i * ******* l i E l E R a R m a r a n D f a * b e n e n u n d ******* o r a n S C A e * D e n s e i d e n k l e i d e r a * l e * i c * t b e s E t m i t d e n h ** E s l i c * s t e n Ö * f l * Ä G e n w a N e * +Eval: D D I D S D S D D D S S D S D D D I D S D D S D S D D D D S D S D D D D D D D D D S D D S D S S D D D I I S I S D D S S S D S D D D S D S D D D S S S D + +Speaker sentences 172: mls_deu_000310 #utts: 1 +id: (mls_deu_000310-mls_deu_000310) +Scores: (#C #S #D #I) 175 8 30 8 +REF: v o n l i e d E R n d i e S i e s i n g e n u n D k l A v i e r ******* p i e C e n d i e s i E s p i e l E n v o n g e * l * D b ** Ö r s e n d i e s i E h * * Ä k e l n v o n F R a n z Ö S I s c h e n b Ü c h e R n d i e s I E Ü b e r s e t z e n k o N n t e b i s m e I n g e m ** Ü t w Ä H r e n d i c h l a u s C H t e z U R n a c h ******* a H m U n g a u f g e s t a c h e L t w u r d e +HYP: v o n l i e d * A n d i e * i e s i n g e n u n * k l * v i e r p i e S e n d i e ******* s i * ******* s p i e l * n v o n g e I l T b Ü Ö r s e n d i e ******* s i * h I E G k e l n v o n * V a n z Ö * Ü s c h e n b Ü c h e * n d i e ******* s * * Ü b e r s e t z e n k o * n t e b i s ******* m e * n ******* g e m Ö Ü t w ** E r e n d ******* i c h ******* l a u s * * t e z * * O n a c h a * m * n g a u f g e s t a c h e * t w u r d e +Eval: D S D D D I S D D D D I I S I D D I I S D S D S D D D D D D D D I D S D D D D D D S I D D D + +Speaker sentences 173: mls_deu_000311 #utts: 1 +id: (mls_deu_000311-mls_deu_000311) +Scores: (#C #S #D #I) 176 10 52 6 +REF: a R m e U n D n a C K e n w a R E n b l o S s i H r e I n z i g e r s c H m U c K w a R E n i h r e k a s t a n i e n b r a U N E n f l E c h t e n w E l c h E I n w i l d e R u n D n a t Ü r l i c h e r a n m u * T a u F i H r E s c h U l t e R n H e r a b F i e l e n I c h n a H m e i N E n b o g e n f e i N E n k a R t o * n * s U n d z e i c h N e t e * m I t G R o S s E r s o r G f a l t D i E U m * r I s s e * +HYP: a * m e ******* * n * n a * T e n w a * * n ******* b l o * s i * r e * n z i g e r s c * m O c * w a * * n i h r e ******* k a s t a n i e n b r a * * * n ******* f l Ä c h t e n w I l c h * E n w i l d e * u n * n a t Ü r l i c h e r a n m u N D a u * i * r * ******* s c h * l t e * n ******* * e r a b V i e l e n * c h ******* n a * m e i * * n b o g e n G f e i * * n k a * t o U n G s * n d ******* z e i c h * e t e M m * t * * o * s * r ******* s o r K f a l t ******* * i * O m G r * s s e R +Eval: D D D D D S D D D D D D D S D D D D D D D D S S D S D D I S D D D D D D D D S D D D D D S D D D I I D D D I D D D D D D S D D D S I D I + +Speaker sentences 174: mls_deu_000312 #utts: 1 +id: (mls_deu_000312-mls_deu_000312) +Scores: (#C #S #D #I) 247 15 48 4 +REF: a B E r w * e d e R a u s d e U t S c h l a n d n o c h a U s i R G e n D e i n e m a n d e r e n s t a A t k o N n t e m A n e R f a h R E n w a s D e r g e g E n s t a n d u n D d A s R e s U l t a T d i e s E R u n t e R r e d u n g E N g e w E s e n s e i M a n v E r m u t e t e d A s s e S s i c H u m e r k l * Ä r u n g E N d e r m a R t * i e r Ü b e R i H r E a b s i c h t e n u n d u M d i e v e r m i T t l u n g d e r m Ä c h t e * z w i S c h E n d E n m a r S s t A a t e n u n D G r o S s B R I t a N n i e n h a n d l e +HYP: a * U r w I e d e * a u s d e * t * c h l a n d n o c h ******* a * s i E L e n e i n e m a n d e r e n s t a R t k o * n t e ******* m E n e * f a h * * n w a s * e r g e g * n s t a n d u n * ******* d E s * e s * l t a D d i e s * * u n t e * r e d u n g * * g e w I s e n s e i * a n v * r m u t e t e d * s s ******* e * ******* s i c * u m e r k l I E r u n g * * d e r m a * t Z i e r Ü b e * i E r * a b s i c h t e n u n d u N d i e ******* v e r m i * t l u n g d e r m E c h t e T z w i * c h * n ******* d * n m a r * s t * a t e n u n * * r o * s * P O t a * n i e n ******* h a n d l e +Eval: D S I D D D D D S S S S D D S D D D D D D D S D D S D D D D D S D D D D D D D I S D D D I D S D S D D S I D D D D D D D D D D S S D D + +Speaker sentences 175: mls_deu_000313 #utts: 1 +id: (mls_deu_000313-mls_deu_000313) +Scores: (#C #S #D #I) 155 7 9 12 +REF: l a S s u n s w e n i g s T e n s e i n e * z e i t l a n g v e r s u * c h e n i n ******* W i e ******* F e r n w i * r A u f d I e s e * W e i s E m i t ******* e i n a n d e r a u s r e i c h e n d a d a s z u s a m m e n h Ä n g e n d e W i e d u s a g s t e i g e n t l i c h e u e r E l e m e n t i s T v e r s e t z t e ******* * * * * +HYP: l a * s ******* u n s w e n i g s * e n s e i n e R z e i t l a n g v e r s u O c h e n i n D i e V e r n w i E r * u f d * e s e S B e i s * I m i t e i n a n d e r a u s r e i c h e n d a ******* d a s z u s a m m e n h Ä n g e n d e V i e ******* d u E s a g s t e i g e n t l i c h e u e r L l e m e n t i s * v e r s e t z t e I D R T +Eval: D D D I I I S I S I D D I S D S I D S D S S D I I I I I + +Speaker sentences 176: mls_deu_000314 #utts: 1 +id: (mls_deu_000314-mls_deu_000314) +Scores: (#C #S #D #I) 182 16 37 4 +REF: v e R s c h i E D e n E v o r k o m m n I S s e f Ü H r T e n z u d e r v e r ******* m U t u n g d a S s f r a u w i E s e d i E k l e i n e n * w E s e n v e r b r e N n e * S i e s o L l b I s W e i L E n s O s t a R K g e h e i * z t H a b e n d a S s d I E H e r D p l a T T e n z E R s p r a n g E N a u s s E R D e m S o L l e i n f Ü r c h t e R l i c h e r G e r U c h w a H R g e n O M m e N w o r d e n s e i n +HYP: v e A s c h i * N e n * v o r k o m m n * * s e f Ü * r D e n z u d e r v e r m O t u n g ******* d a * s f r a u w i * s e d i * k l e i n e n V w I s e n v e r b r e * n e R * i e ******* s o * l b E s e i * * n s U s t a C H g e h e i T z t ******* * a b e n d a * s d * * * e r T p l a * D e n z * * s p r a n g * * a u s s * T e m * o * l ******* e i n f Ü r c h t e * l i c h e r * e r O c h ******* w a * * g e n * U m e * w o r d e n s e i n +Eval: S D S D D D D S I S D D D D I S D I D D D S S D D S S S I D D D D D D S D S D D D D D S S D D D D D S D D D D S D + +Speaker sentences 177: mls_deu_000315 #utts: 1 +id: (mls_deu_000315-mls_deu_000315) +Scores: (#C #S #D #I) 130 4 11 2 +REF: u n d g i n g d e m s c h r e i e n n a c h s o s a h e r e n D l i c h e i N E n h o h E n b a u m u n d o b e n d * * A r a u f s a S s e i n k l e i n e s k i n d u n t e r d e m b a u m a b E r l a G e i n e f r a u d i e s c h l i e f +HYP: u n d g i n g d e m s c h r e i e n n a c h s o ******* s a h e r e n T l i c h e i * * n ******* h o h * n b a u m u n d o b e n ******* d E R r a u f ******* s a * s e i n k l e i n e s k i n d u n t e r d e m b a u m a b A r ******* l a K e i n e f r a u ******* d i e ******* s c h l i e f +Eval: D S D D D D D I I S D D S D S D D + +Speaker sentences 178: mls_deu_000316 #utts: 1 +id: (mls_deu_000316-mls_deu_000316) +Scores: (#C #S #D #I) 180 19 13 22 +REF: s i e h a T t e n s O e B e * n d i e f i s c h e r ******* g a r * n e * w E l c h E d I e n a c h t * Ü b e r * a u s g e ******* W o r * ******* F e n w a R e n * h e r e i n g e ******* z o g e n * d i e s E e l e u * t e * g e * ******* h Ö R t e n a U g e n s c h e i N l i * c h v e r s c h i e d e n e n n A t * i o n e n A n O b W o H l d e r * E u R o p Ä i s c h e * C H a r A k t e R b e I a l l e n a u s ******* g e * D r Ü C k t w a R +HYP: s i e h a * t e n s * e U e B n d i e f i s c h e r g a r D n e R w A l c h * d * e n a c h t I Y b e r T a u s g e U o r H V e n w a D e n C h e r e i n g e z o g e n N d i e s * e l e u I t e R g e R h E O t e n a * g e n s c h e i M l i S c h v e r s c h i e d e n e n n C t Z i o n e n ******* R n A b * o R l d e r O L u L o p Ä i s c h e R * K a r * k t e * b e R a l l e n a u s g e T r Ü * k t ******* w a T +Eval: D D S I I I I S D D I S I I S I I S S I I I D I I I I S S D S I S I D S S D S I S S I D S D D S I I S D D S + +Speaker sentences 179: mls_deu_000317 #utts: 1 +id: (mls_deu_000317-mls_deu_000317) +Scores: (#C #S #D #I) 100 2 16 4 +REF: n e I N n e i n i c h s c h Ä m e m * i c h l a S s m i c h A n d e i n e m b u s e n m e i n g e ******* s i c h t v e r ******* b e r g e n * e r s i n k t I n s g r a s n i E d e R u n d z i e H T s i E n a c h +HYP: n e * * ******* n e i n ******* i c h s c h Ä m e m E i c h l a * s ******* m i c h E n d e i n e m b u s e n m e i n ******* g e s i c h t v e r b e r g e n G e r s i n k t ******* E n s g r a s ******* n i * d e * u n d ******* z i e * * s i * ******* n a c h +Eval: D D D D I D D S D I I I D S D D D D D D D D + +Speaker sentences 180: mls_deu_000318 #utts: 1 +id: (mls_deu_000318-mls_deu_000318) +Scores: (#C #S #D #I) 181 10 22 5 +REF: d i e k i n d e r a * b E r s a S s e n v o r d e m w a l D u n d a l S s i e d i e D r e i k n e c h t e * v o n w e i t e m l a u f e n s a h E n s p r a c h l e H n C h e n z u m f u n d e ******* V o g e l v e r l Ä S s t D u m i c h n i c h T S o V e r l a S s i c h D i c h a u c h n i c h t s o s p r a c h f U n d e ******* V o g e l n u n u n d n i M m e r ******* m E H r +HYP: d i e k i n d e r a R b A r s a * s e n v o r d e m w a l T u n d a l * ******* s i e ******* d i e * r e i ******* k n e c h t e R v o n w e i t e m l a u f e n s a h * n ******* s p r a c h R l e * n S h e n z u m P f u n d e F o g e l v e r l Ä * s t ******* * u ******* m i c h n i c h * Z o * e r l a * s i c h ******* T i c h a u c h ******* n i c h t s o ******* s p r a c h f O n d e F o g e l n u n u n d n i * m e r m * * r +Eval: I S D S D D D D D I D D S D S S I S D D D D D S D D D S D D S I S D I D D + +Speaker sentences 181: mls_deu_000319 #utts: 1 +id: (mls_deu_000319-mls_deu_000319) +Scores: (#C #S #D #I) 119 6 12 5 +REF: w i e d e r s c h U l z e i n s e i n e R h U l d i * g u n g s r * e d e h e r ******* v o r h O b d e r l E H R e r b r a c h t e a m k l a R e n s o m m E r m o r G e n * m i t s e i n e n s c h u * l k i n d e r n e i n g e s a n g S s t Ä n D C H e N +HYP: w i e ******* d e r s c h * l z e i n s e i n e * h * l d i E g u n g s r I e d e h e r v o r h U b d e r l * * * e r A b r a c h t e a m k l a * e n s o m m A r m o r D e n G m i t s e i n e n s c h u H l k i n d e r n e i n ******* g e s a n g * s t Ä n * T I e * +Eval: D D D D I I I S D D D S D S S I I D D D S S D + +Speaker sentences 182: swc_deu_001408 #utts: 1 +id: (swc_deu_001408-swc_deu_001408) +Scores: (#C #S #D #I) 16 1 3 5 +REF: * * * * ******* w i e S i e s e i n s o L l T e n +HYP: S T D T w i e ******* * i e s e i n s o H l * e n +Eval: I I I I I D D S D + +Speaker sentences 183: swc_deu_001409 #utts: 1 +id: (swc_deu_001409-swc_deu_001409) +Scores: (#C #S #D #I) 49 3 3 2 +REF: d e r e n S c h w i n g U n G e n d u r c H e i n e z u s * A t Z s c h a l t u n g s t u f e n ******* l o s +HYP: d e r e n * c h w i n g E n * e n d u r c * e i n e z u s E R t s c h a l t u n g s t u f e n l o s +Eval: D S D D I S S I + +Speaker sentences 184: swc_deu_001410 #utts: 1 +id: (swc_deu_001410-swc_deu_001410) +Scores: (#C #S #D #I) 29 4 5 0 +REF: d i e a u f a L l e b e i d e r s I t Z V E r t E I l u n g Z u +HYP: d i e a u f a * l e b e i d e r ******* s E t * W r t * A l u n g * u +Eval: D D S D S S D S D + +Speaker sentences 185: swc_deu_001411 #utts: 1 +id: (swc_deu_001411-swc_deu_001411) +Scores: (#C #S #D #I) 20 0 3 0 +REF: u m d e N Ü b e r l e b e n d e n d E R +HYP: u m d e * Ü b e r l e b e n d e n d * * +Eval: D D D + +Speaker sentences 186: swc_deu_001412 #utts: 1 +id: (swc_deu_001412-swc_deu_001412) +Scores: (#C #S #D #I) 62 3 3 1 +REF: s p Ä t e r w u r d e n t E i l w e i s e s o g a R a c h t p a r A L l e l e l o C h ******* s t r e i f e n e i n g e s e t z t +HYP: s p Ä t e r ******* w u r d e n t A i l w e i s e s o g a * a c h t p a r E R l e l e l o * h s t r e i f e n e i n g e s e t z t +Eval: D S D S S D I + +Speaker sentences 187: swc_deu_001413 #utts: 1 +id: (swc_deu_001413-swc_deu_001413) +Scores: (#C #S #D #I) 25 1 1 0 +REF: m o r d e b e k a N n T u n d v e r l a n g t e +HYP: m o r d e b e k a * n D u n d v e r l a n g t e +Eval: D S + +Speaker sentences 188: swc_deu_001414 #utts: 1 +id: (swc_deu_001414-swc_deu_001414) +Scores: (#C #S #D #I) 19 5 6 8 +REF: b * * * * ******* w * * * A H L G d i e s t I M m E N v o n w Ä h l e R n +HYP: b U N D E w E G E S E T Z d i e ******* s t * * m * * v o n w I h l e * n +Eval: I I I I I I I I S S S S D D D D D S D + +Speaker sentences 189: swc_deu_001415 #utts: 1 +id: (swc_deu_001415-swc_deu_001415) +Scores: (#C #S #D #I) 9 0 1 0 +REF: g e s c h I c h t e +HYP: g e s c h * c h t e +Eval: D + +Speaker sentences 190: swc_deu_001416 #utts: 1 +id: (swc_deu_001416-swc_deu_001416) +Scores: (#C #S #D #I) 12 0 2 1 +REF: s p a l t u n g f * Ä H I g +HYP: s p a l t u n g f E Ä * * g +Eval: I D D + +Speaker sentences 191: swc_deu_001417 #utts: 1 +id: (swc_deu_001417-swc_deu_001417) +Scores: (#C #S #D #I) 27 2 10 2 +REF: s * T a D t p a D e R b o r n d i e Ä u S s e r e n * f E I e r n d E s +HYP: s C H a * t ******* p a * e * b o r n d i e ******* ** u * s e r e n D f * * e r n ******* d I s +Eval: I S D D D D D D D I D D D S + +Speaker sentences 192: swc_deu_001418 #utts: 1 +id: (swc_deu_001418-swc_deu_001418) +Scores: (#C #S #D #I) 25 2 2 3 +REF: * * w e i t e r H i n h u m a n i ******* t Ä r e h i l f e z u +HYP: U M w e i t e r i n ******* h u m a n i t E r e h i l f e ******* z u +Eval: I I S D I S D + +Speaker sentences 193: swc_deu_001419 #utts: 1 +id: (swc_deu_001419-swc_deu_001419) +Scores: (#C #S #D #I) 44 3 6 0 +REF: s i E e r k a N N t e n d i e n e u e c h I n e S I s c h e r e g i e r u n g n i c h t a n +HYP: s i * ******* e r k a * M t e n d i e n e u e I c h E n e * * s c h e ******* r e g i e r u n g n i c h t a n +Eval: D D D S S S D D D + +Speaker sentences 194: swc_deu_001420 #utts: 1 +id: (swc_deu_001420-swc_deu_001420) +Scores: (#C #S #D #I) 49 8 16 1 +REF: d i e U r a u F f Ü H R U n G F A n D a M d r e I U n D z w a n Z i G s t e * s e P t e m b e r z w e I T A U S e n d a c h t i N +HYP: d i e O r a u * f Ü * G E n * V O n * a N d r e * * n * z w a n * i * s t e N s e * t e m b e r z w e * ******* * * R D e n d a c h t ******* i * +Eval: S D D S S D S S D S D D D D D I D D D D D S S D D + +Speaker sentences 195: swc_deu_001421 #utts: 1 +id: (swc_deu_001421-swc_deu_001421) +Scores: (#C #S #D #I) 53 6 18 2 +REF: e r w i L L s i C H N i c h t s c h U l d i G o d e r m i t s c h U l D i * G m a c H e n a M t o d e * E I n E s m i t g e s E L L e N +HYP: e r ******* w i * E s i * E M i c h t ******* s c h * l d i C o d e r ******* m i t s c h * l * i C H m a c * e n a * N t o d e R * * n * s ******* m i t g e s * * * e * +Eval: D D S D S S D D S D D D I S D D S I D D D D D D D D + +Speaker sentences 196: swc_deu_001422 #utts: 1 +id: (swc_deu_001422-swc_deu_001422) +Scores: (#C #S #D #I) 20 3 5 1 +REF: d i e m I T d e r e r S t S t I m m * E e i n e n +HYP: d i e ******* m * * E d e r e r * t * t U m m A R e i n e n +Eval: D D D S D D S I S + +Speaker sentences 197: swc_deu_001423 #utts: 1 +id: (swc_deu_001423-swc_deu_001423) +Scores: (#C #S #D #I) 19 4 2 0 +REF: U n d H A L f e n D i e s e n b e i d e r +HYP: * n d ******* T E I f e n T i e s e n b e i d e r +Eval: D D S S S S + +Speaker sentences 198: swc_deu_001424 #utts: 1 +id: (swc_deu_001424-swc_deu_001424) +Scores: (#C #S #D #I) 47 3 3 2 +REF: k r e i s w a H l * ******* V o r s c h l a g u n d e i n e l a n d e s l i s t e U n T e r z e i C H n e n +HYP: k r e i s w a * l E F o r s c h l a g u n d e i n e l a n d e s l i s t e * n D e r z e i * T n e n +Eval: D I I S D S D S + +Speaker sentences 199: swc_deu_001425 #utts: 1 +id: (swc_deu_001425-swc_deu_001425) +Scores: (#C #S #D #I) 117 11 21 6 +REF: E I n E u m s e * T z u n G d e R s a g e i n f o r m e i n e s * f Ü n f z e h n t e i l I G e n l i e d e r ******* z y k l u * s z w e I t A U s e N d A c h t w u r d e p R e U S s l E R s k R a b * A t i n * e I N e r B E a R b e I t u n g v o n h O R s t h a W e m A N n +HYP: * A n * u m s e R z u n * d e * s a g e i n f o r m e i n e s T f Ü n f z e h n t e i l * * e n l i e d e r z y k l u O s z w e * t * s e * d * c h t w u r d e p * e * * s l * A s k Ö a b E R t i n N e * B e r * * a * b e * t u n g v o n h A U s t ******* h a R e m * E n +Eval: D S D I S D D I D D I I D D S D D D D D D S S I S I D S D D D D S S D S D S + +Speaker sentences 200: swc_deu_001426 #utts: 1 +id: (swc_deu_001426-swc_deu_001426) +Scores: (#C #S #D #I) 29 1 4 1 +REF: W i e d i e F o l g e n ******* d e t a B e L l e d a r s t e l l t +HYP: * i e d i e * o l g e n d e t a P e * l e ******* d a r s t e l l t +Eval: D D I S D D + +Speaker sentences 201: swc_deu_001427 #utts: 1 +id: (swc_deu_001427-swc_deu_001427) +Scores: (#C #S #D #I) 12 2 4 1 +REF: Z u m s t r O m ******* f l U S s b e I +HYP: * u m s t r U m f l * O s ******* b e * +Eval: D S I D S D D + +Speaker sentences 202: swc_deu_001428 #utts: 1 +id: (swc_deu_001428-swc_deu_001428) +Scores: (#C #S #D #I) 42 2 8 3 +REF: d e M b u n d e s ******* w a H l l E i t e r b i s * z u m s i E B E N U N D n e u n z i g s t e * t a g +HYP: d e * b u n d e s w a * l l * i t e r b i s T z u m s i * * * * * M O n e u n z i g s t e N t a g +Eval: D I D D I D D D D D S S I + +Speaker sentences 203: swc_deu_001429 #utts: 1 +id: (swc_deu_001429-swc_deu_001429) +Scores: (#C #S #D #I) 33 4 8 2 +REF: V o L L J Ä H r i * G G e w o r d e n E d e u T s c h e * n i c h t m i t w Ä H l e n +HYP: * o * * * R I r i C H * e w o r d e n * d e u * s c h e R n i c h t m i t w ** E l e n +Eval: D D D D S S I S D D D I D S + +Speaker sentences 204: swc_deu_001430 #utts: 1 +id: (swc_deu_001430-swc_deu_001430) +Scores: (#C #S #D #I) 25 8 7 2 +REF: a u * s f Ü H r U n G m u s S e i n G u T e r Q U A R t e r ******* b A C K I n +HYP: a u S s f ** I r * n * m u s T e i n K u S e r * * K O t e r b * E G * n +Eval: I D S D D S S S D D S S I D S S D + +Speaker sentences 205: swc_deu_001431 #utts: 1 +id: (swc_deu_001431-swc_deu_001431) +Scores: (#C #S #D #I) 31 2 4 1 +REF: v e r g l e i c h B a R E n z * a h l e n w e r t u m g e W a n d e L T +HYP: v e r g l e i c h P a * * n z E a h l e n w e r t u m g e a n d e * * +Eval: S D D I S D D + +Speaker sentences 206: swc_deu_001432 #utts: 1 +id: (swc_deu_001432-swc_deu_001432) +Scores: (#C #S #D #I) 21 1 3 2 +REF: b e t r A c h t e ******* T e a L l g e ******* m e i n h e i T +HYP: b e t r * c h t e D e a * l g e m e i n h e i * +Eval: D I S D I D + +Speaker sentences 207: swc_deu_001433 #utts: 1 +id: (swc_deu_001433-swc_deu_001433) +Scores: (#C #S #D #I) 44 1 3 1 +REF: u n t e r s c h i E D l i c h e a u F f a s s u n g e n g a b e s n u r d a * r Ü b e r +HYP: u n t e r s c h i * T l i c h e a u * f a s s u n g e n g a b ******* e s n u r d a H r Ü b e r +Eval: D S D D I + +Speaker sentences 208: swc_deu_001434 #utts: 1 +id: (swc_deu_001434-swc_deu_001434) +Scores: (#C #S #D #I) 91 11 12 3 +REF: d o l l b e i m b u n D e s l i G i s t e n b O r U S s I A d o r t m u n d n a c h ******* F o l g e r d e s u n m i T t e l ******* b a R z u ******* v o r z U r Ü c k G e t r E T e N e n T r A I n E R s J Ü r G e n r Ö b e r +HYP: d o l l b e i m b u n * e s l i K i s t e n b E r * * s E R d o r t m u n d n a c h V o l g e r d e s u n m i * t e l b a * z u v o r z O r Ü c k * e t r * * e D e n * r * E n * A s * I r F e n r Ö b e r +Eval: D S S D D S S I S D I D I S D D D S D D S D S D S S + +Speaker sentences 209: swc_deu_001435 #utts: 1 +id: (swc_deu_001435-swc_deu_001435) +Scores: (#C #S #D #I) 20 2 9 0 +REF: n E u n z e H N H U n D e r T A c h t u n D a c H t z i g +HYP: n * u n z e * * ******* * * n * e r D * c h t u n a c * t z i g +Eval: D D D D D D D S D S D + +Speaker sentences 210: swc_deu_001436 #utts: 1 +id: (swc_deu_001436-swc_deu_001436) +Scores: (#C #S #D #I) 15 2 2 2 +REF: F r e i E n e n ******* z y K l o p * Ä d i e +HYP: * r e i * n e n z y G l o p E T d i e +Eval: D D I S I S + +Speaker sentences 211: swc_deu_001437 #utts: 1 +id: (swc_deu_001437-swc_deu_001437) +Scores: (#C #S #D #I) 62 3 5 1 +REF: d e r P H o t o s t r o m i s T Ü b e r v i e l e g r Ö s s e n o r D n u n g e N l i n e a r z u m l i c h t ******* e I n f a L l +HYP: d e r * V o t o s t r o m i s * Ü b e r v i e l e g r Ü s s e n o r n u n g e * l i n e a r z u m l i c h t e * n f a * l +Eval: D S D S S D I D D + +Speaker sentences 212: swc_deu_001438 #utts: 1 +id: (swc_deu_001438-swc_deu_001438) +Scores: (#C #S #D #I) 39 0 10 0 +REF: d a s h A T t E f Ü R k l e i n e p a r t e i E n g r o S s e a u s w i r k u n g E N +HYP: d a s ******* h * * t * f Ü * ******* k l e i n e p a r t e i * n g r o * s e a u s w i r k u n g * * +Eval: D D D D D D D D D D + +Speaker sentences 213: swc_deu_001439 #utts: 1 +id: (swc_deu_001439-swc_deu_001439) +Scores: (#C #S #D #I) 26 0 3 1 +REF: i s T d I e i t e r a t i * v e t i e f e n s u c h E +HYP: i s * d * e i t e r a t i E v e t i e f e n s u c h * +Eval: D D I D + +Speaker sentences 214: swc_deu_001440 #utts: 1 +id: (swc_deu_001440-swc_deu_001440) +Scores: (#C #S #D #I) 41 0 2 3 +REF: d i e s k Ö n n e n Z u m b e i ******* s p i e l k o n d e n ******* s * a t o r e n s e i n +HYP: d i e s ******* k Ö n n e n * u m b e i s p i e l k o n d e n s E a t o r e n s e i n +Eval: D D I I I + +Speaker sentences 215: swc_deu_001441 #utts: 1 +id: (swc_deu_001441-swc_deu_001441) +Scores: (#C #S #D #I) 53 6 3 1 +REF: a l s d i e k u r s a u f k U b A h a l t e n d e n s o W j e t i S c h e n s c h i F F e * a b D r e H t e n +HYP: a l s d i e k u r s a u f k O b E R h a l t e n d e n s o j e t i * c h e n s c h i * V e R a b T r e * t e n +Eval: S S S S D D S I S D + +Speaker sentences 216: swc_deu_001442 #utts: 1 +id: (swc_deu_001442-swc_deu_001442) +Scores: (#C #S #D #I) 90 2 19 1 +REF: b u n D e s t a g S w a h l n E u n z E H N h u n D e r T d r e i u n D f Ü N f z i g w u r d e E r s T m a l s n a c h e i n e m v o m b u n d e s t a * g s e L b s t e r l A S s E N e n g e s e t z +HYP: b u n * e s t a g w a h l ******* n * u n z * * * ******* h u n * e r * ******* d r e i u n f Ü * f z i g w u r d e * r s * m a l s n a c h e i n e m v o m b u n d e s t a I g ******* s e * b s t e r l * * s * * e n g e s e t z +Eval: D S D D D D D D D D D S D D D I D D D D D D + +Speaker sentences 217: swc_deu_001443 #utts: 1 +id: (swc_deu_001443-swc_deu_001443) +Scores: (#C #S #D #I) 33 4 4 1 +REF: b u n d e S w a H L g e s e t z v i e l f a c h G e ******* Ä n D e r t w O r d e n +HYP: b u n d e w a * I g e s e t z v i e l f a c h ******* * e I n * e r t w U r d e n +Eval: S D S D D I S D S + +Speaker sentences 218: swc_deu_001444 #utts: 1 +id: (swc_deu_001444-swc_deu_001444) +Scores: (#C #S #D #I) 31 4 3 1 +REF: e r Ü b e r l a g e r T d e n P H o * t O s t r o m u n d t r Ä g t +HYP: e r Ü b e r l a g e r * d e n * V o R t U s t r o m E u n d ******* t r E g t +Eval: D D S I S S D S + +Speaker sentences 219: swc_deu_001445 #utts: 1 +id: (swc_deu_001445-swc_deu_001445) +Scores: (#C #S #D #I) 40 3 3 1 +REF: T r o T Z i n t e G r a t i * O n d e r b e i d e n d e u t s c h e n s t A a t e n +HYP: D r o * * i n t e K r a t i U M n d e r b e i d e n d e u t s c h e n s t * a t e n +Eval: S D D S I S D + +Speaker sentences 220: swc_deu_001446 #utts: 1 +id: (swc_deu_001446-swc_deu_001446) +Scores: (#C #S #D #I) 22 1 2 0 +REF: b e r l i n e r w Ü H l m Ä u s e n s t a T t +HYP: b e r l i n e r w Ü * l m E u s e n s t a * t +Eval: D S D + +Speaker sentences 221: swc_deu_001447 #utts: 1 +id: (swc_deu_001447-swc_deu_001447) +Scores: (#C #S #D #I) 15 2 3 1 +REF: O F f i * z I e L l e f Ü h r u n g e n +HYP: * A f i E z * e * l e R f Ü h r u n g e n +Eval: D S I D D S + +Speaker sentences 222: swc_deu_001448 #utts: 1 +id: (swc_deu_001448-swc_deu_001448) +Scores: (#C #S #D #I) 46 4 7 2 +REF: b e I d e r v e r h Ä L t * n I s ******* w a H l w i R D z u s Ä t z l i c H d i e e i n h a l t u n g d e r +HYP: b e * d e r ******* v e r h ** E t E n * s w a * l ******* w i * T z u s E t z l i c G d i e e i n h a l t u n g d e r +Eval: D D D S I D I D D D S S S + +Speaker sentences 223: swc_deu_001449 #utts: 1 +id: (swc_deu_001449-swc_deu_001449) +Scores: (#C #S #D #I) 34 4 7 3 +REF: w i e w e n i * G D i E I N s U l a n e R n o c h a m p * u l * S d e r z e i t +HYP: w i e V w e n i H T * i * * * s O l a n e * n o c h a m ******* p O u l T Z d e r ******* z e i t +Eval: S I S D D D D S D D I I S D + +Speaker sentences 224: swc_deu_001450 #utts: 1 +id: (swc_deu_001450-swc_deu_001450) +Scores: (#C #S #D #I) 57 3 7 1 +REF: J e ******* d o c h e t w A d i e d u R c h f Ü h r U n G v o n w a H l w e r b u n g a u f k o s t e N d e S s t A a t e s +HYP: R e d o c h e t w R d i e ******* d u * c h f Ü h r E n * v o n w a * l w e r b u n g a u f k o s t e * d e * s t * a t e s +Eval: S I S D D S D D D D D + +Speaker sentences 225: swc_deu_001451 #utts: 1 +id: (swc_deu_001451-swc_deu_001451) +Scores: (#C #S #D #I) 20 0 4 1 +REF: d a s n i C h T I m G r u n d * g e s e t z +HYP: d a s n i * h * * m * r u n d K g e s e t z +Eval: D D D D I + +Speaker sentences 226: swc_deu_001452 #utts: 1 +id: (swc_deu_001452-swc_deu_001452) +Scores: (#C #S #D #I) 35 1 2 0 +REF: h e i m a t v e r t r i e b e n u n d h Ä u s l i c h e g e w a l T +HYP: h e i m a t v e r t r i e b e n u n d h E u s l i c h e ******* g e w a l * +Eval: S D D + +Speaker sentences 227: swc_deu_001453 #utts: 1 +id: (swc_deu_001453-swc_deu_001453) +Scores: (#C #S #D #I) 37 2 4 0 +REF: U n d s p e i c h e r E i H n i n e i n E r w a R t e s c h l A n g e a b +HYP: * n d s p e i c h e r * i E n i n e i n * r w a G t e s c h l * n g e a b +Eval: D D S D S D + +Speaker sentences 228: swc_deu_001454 #utts: 1 +id: (swc_deu_001454-swc_deu_001454) +Scores: (#C #S #D #I) 106 10 8 4 +REF: o * r * i g i n a l t o n ******* b Ä n d e r u n d d i e d o k U m E N t a t i o n d e S s t u d i o s w u r d e n n e U n z e h n h u n d e r t z w E I U n D s i e b z i g i n d A S s i E m e n s A r c h i * V Ü b e r s t e L l t +HYP: o R r E i g i n a l t o n b E n d e r u n d d i e d o k O m * I t a t i o n ******* d e * ******* s t u d i o s w u r d e n n e * n z e h n h u n d e r t z w U O H n * s i e b z i g i n d E R s i * m e n s E r c h i E F Ü b e r s t e * l t +Eval: I I I S S D S D D D D S S S D S S D S I S D + +Speaker sentences 229: swc_deu_001455 #utts: 1 +id: (swc_deu_001455-swc_deu_001455) +Scores: (#C #S #D #I) 39 3 6 0 +REF: s o m Ü s s e n a u f e i n e m s t R a t E g I s c h E n r A k e t e n u b o O t +HYP: s o ******* m I s s e n a u f e i n e m s t * a t * g * s c h * n r E k e t e n u ******* b o D t +Eval: D S D D D D S D S + +Speaker sentences 230: swc_deu_001456 #utts: 1 +id: (swc_deu_001456-swc_deu_001456) +Scores: (#C #S #D #I) 16 3 0 2 +REF: f l Ö t e n ******* s p i e l Ä H N l i c h e * +HYP: f l Ö t e n s p i e l E D l i c h e R +Eval: I S S S I + +Speaker sentences 231: swc_deu_001457 #utts: 1 +id: (swc_deu_001457-swc_deu_001457) +Scores: (#C #S #D #I) 44 2 1 3 +REF: d r a s t i s c h m o d e * * r n e e l E k t r o n i s c h e k l a n G g e s * T a l t u n g +HYP: d r a s t i s c h m o d e R A r n e e l I k t r o n i s c h e k l a n * g e s C H a l t u n g +Eval: I I S D I S + +Speaker sentences 232: swc_deu_001458 #utts: 1 +id: (swc_deu_001458-swc_deu_001458) +Scores: (#C #S #D #I) 155 10 25 6 +REF: a n S c h l i E S s e n D w U R d e N d i e s o * E R m i T t e L t e m a n d a * t * S z a H l J e d e r p a r t e i n a C h d E m ******* s E L B e N v e r f a h R E n e n T s p r e c h e n D d e r a n z a H l i h r e r z w e i t s t I m m E N p r o p O r T I O n a l a u f d i e l a n D e s l i s t e N d e r p a r t e i u n t e r ******* v e r t e i * L t +HYP: a n * c h l i * * s e n * w * O d e * d i e s o H * A m i * t e * t e m a n d a R t Z T z a * l I e d e r p a r t e i n a * h d I m s * * * e M v e r f a h * * n e n s p r e c h e n * d e r a n z a * l i h r e r z w e i t s t * m m * * p r o p * r * Z U n a l a u f d i e l a n * e s l i s t e * d e r p a r t e i u n t e r v e r t e i E R t +Eval: D D D D D S D I D S D D I I S D S D S I D D D S D D S D D D D D D D S S D D I I S + +Speaker sentences 233: swc_deu_001459 #utts: 1 +id: (swc_deu_001459-swc_deu_001459) +Scores: (#C #S #D #I) 31 3 7 3 +REF: o P f E R n d e r n a * t * o b o m b a R d i e R U N G u n * t e R k Ü n f t e +HYP: o C f * * n ******* d e r n a R t H o b o m b a * d i e * * * O u n D t e Ö k Ü n f t e +Eval: S D D D I I D D D D S I S + +Speaker sentences 234: swc_deu_001460 #utts: 1 +id: (swc_deu_001460-swc_deu_001460) +Scores: (#C #S #D #I) 18 1 4 2 +REF: d e r f r e i E n e n * z * Y k l o p Ä D I e +HYP: d e r f r e i * n e n T z U k l o p ** * * e +Eval: D I I S D D D + +Speaker sentences 235: swc_deu_001461 #utts: 1 +id: (swc_deu_001461-swc_deu_001461) +Scores: (#C #S #D #I) 15 2 2 1 +REF: m I T t l e r W e i l e * F i n d e n +HYP: m * * t l e r B e i l e L H i n d e n +Eval: D D S I S + +Speaker sentences 236: swc_deu_001462 #utts: 1 +id: (swc_deu_001462-swc_deu_001462) +Scores: (#C #S #D #I) 69 5 13 3 +REF: w e r w E g e N e i n e S v e r b r e c h * e n S * r e c h T s K r Ä f t i * G z u E i n e r F r E I H e i t S S t r a f e v o n m i n d e s T e n s e i n e M +HYP: w e r w I g e G e i n e * v e r b r e c h T e n * S r e c h * s G r ** f t i C H z u * i n e r * r * * * e i t * t r a f e v o n ******* m i n d e s * e n s e i n e * +Eval: S S D I D I D S D I S D D D D D D S D D D + +Speaker sentences 237: swc_deu_001463 #utts: 1 +id: (swc_deu_001463-swc_deu_001463) +Scores: (#C #S #D #I) 54 1 7 1 +REF: d E r g E s c h w i n d i g k e i t S w e r t u n g e R r a N g e n d r e i b * f e i n h u N d e r T a c h t +HYP: d * r g * s c h w i n d i g k e i t Z w e r t u n g e * r a * g e n d r e i b E f e i n ******* h u * d e r * a c h t +Eval: D D S D D I D D D + +Speaker sentences 238: swc_deu_001464 #utts: 1 +id: (swc_deu_001464-swc_deu_001464) +Scores: (#C #S #D #I) 24 7 2 1 +REF: L i * b o r i U s a m e r s t e n L I b o r i s a m s T A G +HYP: * i E b o r i O s a m e r s t e n I G b o r i E s a m s * E R +Eval: D I S S S S S D S S + +Speaker sentences 239: swc_deu_001465 #utts: 1 +id: (swc_deu_001465-swc_deu_001465) +Scores: (#C #S #D #I) 43 4 8 3 +REF: n a c h d E m s * a I n T E L a * ******* g U Ë v e r f a H r e n a u f d i e l Ä n d e r V e r t e i l t +HYP: n a c h d * m s E a R n * * * a R g * Ü F v e r f a * r e n a u f d i e ******* l E n d e r * e r t e i l t +Eval: D I S D D D I I D S S D D S D + +Speaker sentences 240: swc_deu_001466 #utts: 1 +id: (swc_deu_001466-swc_deu_001466) +Scores: (#C #S #D #I) 35 4 5 1 +REF: r e ******* f o r m e n g o R b A T s c h O W s u n d a b r Ü s t u n g S s c h R i T t e +HYP: r e f o r m e n g o * b E R s c h A F s u n d a b r ** s t u n g * s c h * i * t e +Eval: I D S S S S D D D D + +Speaker sentences 241: swc_deu_001467 #utts: 1 +id: (swc_deu_001467-swc_deu_001467) +Scores: (#C #S #D #I) 17 7 3 2 +REF: * * N U L L U n p o r t e D u n d U n T e r d e R +HYP: S I E R E A A n p o r t e T u n d * n D e r ******* d e * +Eval: I I S S S S S S D S D D + +Speaker sentences 242: swc_deu_001468 #utts: 1 +id: (swc_deu_001468-swc_deu_001468) +Scores: (#C #S #D #I) 34 5 8 1 +REF: a n d e m W e s t L i c h e K r Ä f t e a u f g e g e n ******* r E v o l U T I O N Ä R e R +HYP: a n d e m * e s t * i c h e G r E f t e a u f g e g e n r * v o l * * * * * E Z e N +Eval: D D S S I D D D D D D S S S + +Speaker sentences 243: swc_deu_001469 #utts: 1 +id: (swc_deu_001469-swc_deu_001469) +Scores: (#C #S #D #I) 24 1 3 0 +REF: W i R D u n t e r a n d e r e m v e r w e n d e T +HYP: * i * T u n t e r a n d e r e m v e r w e n d e * +Eval: D D S D + +Speaker sentences 244: swc_deu_001470 #utts: 1 +id: (swc_deu_001470-swc_deu_001470) +Scores: (#C #S #D #I) 10 2 1 1 +REF: a u s w i k I p e d i * A +HYP: a u s ******* w i k E p e d i E R +Eval: D S I S + +Speaker sentences 245: swc_deu_001471 #utts: 1 +id: (swc_deu_001471-swc_deu_001471) +Scores: (#C #S #D #I) 12 1 0 1 +REF: u n d k U b a * k r i s e +HYP: u n d k O b a R k r i s e +Eval: S I + +Speaker sentences 246: swc_deu_001472 #utts: 1 +id: (swc_deu_001472-swc_deu_001472) +Scores: (#C #S #D #I) 85 9 13 5 +REF: L e * * t z t * E r w a H l a u f g r u n d e i g e n e r w A H l ******* v o r s c h l Ä g e u n U N t e R b r O c h e n M I T m i n D E s T e n s f Ü N f a b g e O r D n e T E n * v e r t r e t e n s i n d +HYP: * e N L t z t D A r ******* w a * l a u f g r u n d e i g e n e r w E I l v o r s c h l Ä g e u n * E t e * b r U c h e n ******* * * * m i n * I s S e n s f Ü * f a b g e * r U n e * n D v e r t r e t e n s i n d +Eval: D I I I S D D S S I D S D S D D D D D S S D D S D S I + +Speaker sentences 247: swc_deu_001473 #utts: 1 +id: (swc_deu_001473-swc_deu_001473) +Scores: (#C #S #D #I) 48 6 2 3 +REF: v e r B r e i t u n g i * d E o l o g I s c h E R p r o p a * g a n d * A d e r s u p e r m Ä c h t e u n d +HYP: v e r P r e i t u n g i E d I o l o g E s c h * * A p r o p a R g a n d E R d e r s u p e r m I c h t e u n d +Eval: S I S S D D S I I S S + +Speaker sentences 248: swc_deu_001474 #utts: 1 +id: (swc_deu_001474-swc_deu_001474) +Scores: (#C #S #D #I) 26 4 7 2 +REF: W E B C o * m i C s a u f d I e r e A l i t Ä t Ü b e r t ******* R a g e n +HYP: * * * K o M m i H s a u f d * e ******* r e * l i t E t ** b e r t H a g e n +Eval: D D D S I S D D D S D I S + +Speaker sentences 249: swc_deu_001475 #utts: 1 +id: (swc_deu_001475-swc_deu_001475) +Scores: (#C #S #D #I) 36 5 4 2 +REF: a l s d e r k a l t E k R i e g s i C H F o r t ******* w Ä H r e n D z u s p i t z * e +HYP: a l s d e r k a l t * I k L i e g s i * G V o r t w ** E r e n * z u s p i t z T e +Eval: D S S D S S I D S D I + +Speaker sentences 250: swc_deu_001476 #utts: 1 +id: (swc_deu_001476-swc_deu_001476) +Scores: (#C #S #D #I) 57 1 12 0 +REF: s i C H E R h e i t s p e r s o n a l o d e r W a c H h u n d e n n u R s e h r s c h w i e r i g b e t R e t e n W e r D E n +HYP: s i * * * * h e i t s p e r s o n a l o d e r ******* E a c * h u n d e n n u * ******* s e h r s c h w i e r i g b e t * e t e n * e r * * n +Eval: D D D D D S D D D D D D D + +Speaker sentences 251: swc_deu_001477 #utts: 1 +id: (swc_deu_001477-swc_deu_001477) +Scores: (#C #S #D #I) 25 1 1 1 +REF: d a u E r h a f ******* T e s b l e i b e r e c h t u n d +HYP: d a u * r h a f D e s b l e i b e r e c h t u n d +Eval: D I S + +Speaker sentences 252: swc_deu_001478 #utts: 1 +id: (swc_deu_001478-swc_deu_001478) +Scores: (#C #S #D #I) 25 6 8 2 +REF: e b e n s o w i e d * a s m o t i V D e r E R l * Ö s u n G D U R C H +HYP: e b e n s o ******* w i e ******* d E a s m o t i * F T e r * * l E s u n * * * B Ü T +Eval: D D I D S S D D I S D D D S S S + +Speaker sentences 253: swc_deu_001479 #utts: 1 +id: (swc_deu_001479-swc_deu_001479) +Scores: (#C #S #D #I) 24 2 8 1 +REF: w e N n f Ü R n i e m a N D E n n a c h p r Ü * f b A R i s t +HYP: w e * n ******* f Ü * ******* n i e m a * * R n n a c h p r Ü C f b * * E i s t +Eval: D D D D D D S I D D S + +Speaker sentences 254: swc_deu_001480 #utts: 1 +id: (swc_deu_001480-swc_deu_001480) +Scores: (#C #S #D #I) 30 6 1 6 +REF: * * P R i * * * * V a T e E r f o r s c h U n G v o n e i n r i c h t u n g e n +HYP: I S T i E K L E W a D e A r f o r s c h E n * v o n e i n r i c h t u n g e n +Eval: I I S S I I I I S S S S D + +Speaker sentences 255: swc_deu_001481 #utts: 1 +id: (swc_deu_001481-swc_deu_001481) +Scores: (#C #S #D #I) 58 4 13 2 +REF: A B g e s e h e n d a * v o n W Ü r d e n s e L b s t d a N N n o c h d i E e n t s p R e c h e n d e n p a l C o D E S f * e H l e n +HYP: * * g e s e h e n d a R v o n B Ü r d e n s e * b s t ******* d a * * ******* n o c h d i * ******* e n t s p * e c h e n d e n p a l K A o * * T f I e * l e n +Eval: D D I S D D D D D D D D S S D D S I D + +Speaker sentences 256: swc_deu_001482 #utts: 1 +id: (swc_deu_001482-swc_deu_001482) +Scores: (#C #S #D #I) 30 4 1 5 +REF: s p R E c h e n b e n Ö t i * * G T e a * t e m ******* l u f t l i e f * e r t +HYP: s p * Ä c h e n b e n U t i C H D e a R t e m l u f t l i e f V e r t +Eval: D S S I I S S I I I + +Speaker sentences 257: swc_deu_001483 #utts: 1 +id: (swc_deu_001483-swc_deu_001483) +Scores: (#C #S #D #I) 33 3 7 4 +REF: * m Ö g l i c h e n s C h u t z * ******* i m P f * U n G e n G E g e n k r a n k H e i t e N +HYP: E m O g l i c h e n ******* s * h u t z S i m * f O R n * e n * * g e n k r a n k R e i t e * +Eval: I S D D I I D I S D D D S D + +Speaker sentences 258: swc_deu_001484 #utts: 1 +id: (swc_deu_001484-swc_deu_001484) +Scores: (#C #S #D #I) 26 2 5 0 +REF: s c h O n e i N E n Ä H n l i c h e n v e r s u c h g a B +HYP: s c h * n e i * * n ** E n l i c h e n v e r s u c h ******* g a R +Eval: D D D D S D S + +Speaker sentences 259: swc_deu_001485 #utts: 1 +id: (swc_deu_001485-swc_deu_001485) +Scores: (#C #S #D #I) 87 7 13 20 +REF: * * * * ******* * * * * * * * * * * ******* a n e i n e m p * n Ü b e r g a n g o d e R p i * ******* * n Ü b e r g a n g d u R C H d e n I N n E r e n P H o t O E F f e k t i n e i N E n e l e k T r i S c h e n s t r o m u m w a n d e L t +HYP: R O N D K E N S T R A H E N a n e i n e m p E E n Ü b e r g a n g o d e * p i E E n Ü b e r g a n g d u * * S T d e n * * n H r e n * V o t * U f e k t i n e i * * n e l e k * r i * c h e n ******* s t r o m u m w a n d e * t +Eval: I I I I I I I I I I I I I I I I I S D I I I D D S S D D S D S D S S D D D D D D + +Speaker sentences 260: swc_deu_001486 #utts: 1 +id: (swc_deu_001486-swc_deu_001486) +Scores: (#C #S #D #I) 28 7 10 1 +REF: b E I M m E I s t e R I n d e R s I L V e s t e r ******* n A c h t f r e i B I T t e n +HYP: b * * * R m * A s t e * E n ******* d e * s * E B e s t e r n * c h t f r e i * D E t e n +Eval: D D D S D S D S D D D S S I D D S S + +Speaker sentences 261: swc_deu_001487 #utts: 1 +id: (swc_deu_001487-swc_deu_001487) +Scores: (#C #S #D #I) 13 2 11 0 +REF: J a h R e n d e r b e g R i F f V A D D I N G +HYP: L a h * e n d e r ******* b e g * i * f ******* * * * * * * T +Eval: S D D D D D D D D D D D S + +Speaker sentences 262: swc_deu_001488 #utts: 1 +id: (swc_deu_001488-swc_deu_001488) +Scores: (#C #S #D #I) 47 5 7 2 +REF: R a n G V E R H Ä l t n i s u n t e r d E n s t i M m E n n o c h e i n e l o g I s c h e a b * f o l * g E +HYP: * a n * * * K F A l t n i s u n t e r d I n s t i * m * n n o c h e i n e l o g E s c h e a b P f o l R g * +Eval: D D D D S S S S D D S I I D + +Speaker sentences 263: swc_deu_001489 #utts: 1 +id: (swc_deu_001489-swc_deu_001489) +Scores: (#C #S #D #I) 40 5 12 1 +REF: k R a b * A t l e H n T d i e s e s a n g e b o T J e D o C H m i t e n T S C h i E D e n h e i t a b +HYP: k * a b E R t ******* l e * n * ******* d i e s e s a n g e b o * D I e o * * m i t e n * * h i * * e n h e i t a b +Eval: D I S D D D D D S S S D D D D S D D + +Speaker sentences 264: swc_deu_001490 #utts: 1 +id: (swc_deu_001490-swc_deu_001490) +Scores: (#C #S #D #I) 26 0 6 31 +REF: s t a n d v o * ******* * * * * * * * * ******* m * * * ******* * * * * ******* * * * * * * * * ** * * d e r i n h a l t s t e H T U n t E R +HYP: s t a n d v o M Z W A L F T E N m E R Z Z W E I T A U S E N Z W Ö L F d e r i n h a l t ******* s t e * * * n t * * +Eval: I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I D D D D D D + +Speaker sentences 265: swc_deu_001491 #utts: 1 +id: (swc_deu_001491-swc_deu_001491) +Scores: (#C #S #D #I) 24 4 9 2 +REF: O r g A n i s a t ******* i o n u n t e r ******* b r a c h D A r A U f H I n d I E +HYP: * r g E n i s a t i o n ******* u n t e r b r a c h ******* T E r * E f * * n ******* d * * +Eval: D S I D I D S S D S D D D D D + +Speaker sentences 266: swc_deu_001492 #utts: 1 +id: (swc_deu_001492-swc_deu_001492) +Scores: (#C #S #D #I) 33 4 3 2 +REF: v e r ******* b Ü n d E t S I n d o * d e r g a R f Ü R s i e a r b e i t e n +HYP: v e r b Ü n d I t ******* Z E n d o R d e r g a * H f Ü * s i e a r b e i t e n +Eval: I S D S S I D S D + +Speaker sentences 267: swc_deu_001493 #utts: 1 +id: (swc_deu_001493-swc_deu_001493) +Scores: (#C #S #D #I) 20 3 9 0 +REF: F e s T g e l e G t e V o L l J Ä h r i G k e I t S a l T e R +HYP: * e s * g e l e * t e * o * l * ** h r i H k e * t a l D e * +Eval: D D D D D D D S D S S D + +Speaker sentences 268: swc_deu_001494 #utts: 1 +id: (swc_deu_001494-swc_deu_001494) +Scores: (#C #S #D #I) 39 0 3 0 +REF: d i e e R r i c h t u n g d e r b e r l i n e R m a u E r m Ü n d e t e n +HYP: d i e e * r i c h t u n g d e r b e r l i n e * m a u * r m Ü n d e t e n +Eval: D D D + +Speaker sentences 269: swc_deu_001495 #utts: 1 +id: (swc_deu_001495-swc_deu_001495) +Scores: (#C #S #D #I) 25 0 1 3 +REF: e R r i c h t u n g v o n k l Ä * r ******* a n l a * g e n +HYP: e * r i c h t u n g v o n k l Ä E r a n l a R g e n +Eval: D I I I + +Speaker sentences 270: swc_deu_001496 #utts: 1 +id: (swc_deu_001496-swc_deu_001496) +Scores: (#C #S #D #I) 40 8 4 5 +REF: a f g H a n I s t a n S u n d * I m i * * R a * K h a t S i c h s e i t D e m e i n m a r S c * H +HYP: a f g * a n E s t a n Z u n d D E m ******* i E H Ö a R G h a t Z i c h s e i t ******* I e m e i n m a r * c T E +Eval: D S S I S D I I S I S S D S D I S + +Speaker sentences 271: swc_deu_001497 #utts: 1 +id: (swc_deu_001497-swc_deu_001497) +Scores: (#C #S #D #I) 53 3 1 3 +REF: d e r P H o n a * T i o * n * S s t r o m v o n d e n l u n g e n Ü b e r d i e b r o n c h i e n b i s +HYP: d e r * V o n a R i o U n D s t r o m v o n d e n l u n g e n Ü b e r d i e b r o n c h i e n b i s +Eval: D S I S I I S + +Speaker sentences 272: swc_deu_001498 #utts: 1 +id: (swc_deu_001498-swc_deu_001498) +Scores: (#C #S #D #I) 50 3 5 3 +REF: a u S s e R d e m * n a h m e n s e n d e r h Ö r s p i e l e * m i * t v e r f r E m d e T E r s p r a C H e +HYP: a u * s e d e m N n a h m e n s e n d e r h ** r s p i e l e B m i T t v e r f r * m d e * D r s p r a * R e +Eval: D S I D I I D D S D S + +Speaker sentences 273: swc_deu_001499 #utts: 1 +id: (swc_deu_001499-swc_deu_001499) +Scores: (#C #S #D #I) 20 2 5 2 +REF: u n d D i E g R u n d m a n d a * T s ******* k l a u s e L +HYP: u n d ******* T i * ******* g * u n d m a n d a R s k l a u s e * +Eval: D S D D D I S I D + +Speaker sentences 274: swc_deu_001500 #utts: 1 +id: (swc_deu_001500-swc_deu_001500) +Scores: (#C #S #D #I) 53 2 5 2 +REF: k e i n e a b ******* k e H r v o n D e n g r u n d * l a g e N d e s s o z I A l i s m U s e i n s c h l i e S s e +HYP: k e i n e a b k e * r v o n * e n g r u n d T l a g e * d e s s o z * E l i s m O s e i n s c h l i e * s e +Eval: I D D I D D S S D + +Speaker sentences 275: swc_deu_001501 #utts: 1 +id: (swc_deu_001501-swc_deu_001501) +Scores: (#C #S #D #I) 48 1 3 2 +REF: M i t k o m p o n e n t e n s o ******* w o h l a * n a l s a u c h t i e f i n d e r w a F f e +HYP: * i t k o m p o n e n t e n s o w o h l a N n a l s a u c h ******* t i e f i n d e r ******* w a C f e +Eval: D I I D D S + +Speaker sentences 276: swc_deu_001502 #utts: 1 +id: (swc_deu_001502-swc_deu_001502) +Scores: (#C #S #D #I) 17 0 1 1 +REF: b e d e u t u n g s ******* v o l l w a R +HYP: b e d e u t u n g s v o l l w a * +Eval: I D + +Speaker sentences 277: swc_deu_001503 #utts: 1 +id: (swc_deu_001503-swc_deu_001503) +Scores: (#C #S #D #I) 17 5 0 12 +REF: f r e i w I L l i g e h e L f e r * * * * * * D e * * * * * * R +HYP: f r e i w Ü E l i g e h e N f e r T E O G A N I e S A T Z I O N +Eval: S S S I I I I I I S I I I I I I S + +Speaker sentences 278: swc_deu_001504 #utts: 1 +id: (swc_deu_001504-swc_deu_001504) +Scores: (#C #S #D #I) 40 1 4 2 +REF: U m e l e k t r o N E n v o m V a * l e n z * b a n d i n s l e i t u n g s b a n D +HYP: * m e l e k t r o * * n v o m W a R l e n z S b a n d i n s l e i t u n g s b a n * +Eval: D D D S I I D + +Speaker sentences 279: swc_deu_001505 #utts: 1 +id: (swc_deu_001505-swc_deu_001505) +Scores: (#C #S #D #I) 35 4 6 1 +REF: a l l e R d i n g S s I n D v e r g l e i c h b A r E E F f e k T e m Ö * g l i c h +HYP: a l l e * d i n g * ******* s E n * v e r g l e i c h b E r * * I f e k e m Ö U g l i c h +Eval: D D D S D S D D S S I + +Speaker sentences 280: swc_deu_001506 #utts: 1 +id: (swc_deu_001506-swc_deu_001506) +Scores: (#C #S #D #I) 83 8 3 7 +REF: d i e s e k o N n t e n a b e r a l s e i n g a b e i n e i n e n * f r * * E Q U e n z * ******* u m s e t z e r d i e n e n o d e r s t e u E R t e n S Y n C H R o n ******* m o t o * r e n +HYP: d i e s e k o * n t e n a b e r a l s e i n g a b e i n e i n e n D f r I C G W e n z S u m s e t z e r d i e n e n o d e r s t e u * A t e n Z U n * G o n m o t o U r e n +Eval: D I I I S S S I I D S S S D S S I I + +Speaker sentences 281: swc_deu_001507 #utts: 1 +id: (swc_deu_001507-swc_deu_001507) +Scores: (#C #S #D #I) 50 1 4 2 +REF: t H o m a s h E r m a N n s p r o d u z i e r t e z w e i t a u s e n d z ******* w e i m i t * G r e b e +HYP: t * o m a s h * r m a * n s p r o d u z i e r t e z w e i t a u s e n d z w e i ******* m i t K E r e b e +Eval: D D D I D I S + +Speaker sentences 282: swc_deu_001508 #utts: 1 +id: (swc_deu_001508-swc_deu_001508) +Scores: (#C #S #D #I) 17 1 2 0 +REF: p n Ü b e r g a n g t r e F f e n +HYP: p E n ******* Ü b e r g a n g t r e * f e n +Eval: S D D + +Speaker sentences 283: swc_deu_001509 #utts: 1 +id: (swc_deu_001509-swc_deu_001509) +Scores: (#C #S #D #I) 13 5 2 2 +REF: d i e f a l * K e n ******* h o R s T s H O W +HYP: d i e f a l G T e n h o U s * ******* s C A U +Eval: I S I S D D S S S + +Speaker sentences 284: swc_deu_001510 #utts: 1 +id: (swc_deu_001510-swc_deu_001510) +Scores: (#C #S #D #I) 56 3 3 1 +REF: a n t i * s o W j e t I s c h e d e m o n s t r a t i o n e n w u r d e n B l u t i g n i e d e r G E s c h l a g e N +HYP: a n t i E s o j e t s c h e d e m o n s t r a t i o n e n w u r d e n P l u t i g n i e d e r * * s c h l a g e * +Eval: I S S S D D D + +Speaker sentences 285: swc_deu_001511 #utts: 1 +id: (swc_deu_001511-swc_deu_001511) +Scores: (#C #S #D #I) 35 4 5 0 +REF: e i n v i e r k a n a l m i s C H p U l T d I e n t e F Ü r k l e i n e r E +HYP: e i n v i e r k a n a l m i s * * p O l D d * e n t e V E r ******* k l e i n e r * +Eval: D D S S D S S D D + +Speaker sentences 286: swc_deu_001512 #utts: 1 +id: (swc_deu_001512-swc_deu_001512) +Scores: (#C #S #D #I) 76 2 4 4 +REF: d i e s e h Ä T t e n d i e v o r w a R n * z e i t e n f Ü r e i n e n a n g r i F f a u f d i e u s a * e x * t r e m h e r a * b g e s e t z t +HYP: d i e s e h ** E t e n d i e v o r w a * n D z e i t e n f Ü r ******* e i n e n a n g r i * f a u f d i e u E s a R e x S t r e m h e r a P b g e s e t z t +Eval: D S D I D D S I I I + +Speaker sentences 287: swc_deu_001513 #utts: 1 +id: (swc_deu_001513-swc_deu_001513) +Scores: (#C #S #D #I) 38 3 0 1 +REF: w e L c h e s a m n Ä c h s t e n z u m s t a r t ******* k n o T e n l i e g t +HYP: w e I c h e s a m n E c h s t e n z u m s t a r t k n o D e n l i e g t +Eval: S S I S + +Speaker sentences 288: swc_deu_001514 #utts: 1 +id: (swc_deu_001514-swc_deu_001514) +Scores: (#C #S #D #I) 55 6 6 6 +REF: l a * * Z i o g i n g * d o l l z U r Ü c k I n D i e b u n d e s ******* l i * g * A u n d w e C H s e l T e z u e i n T r A c h t +HYP: l a H T i o ******* g i n g N d o l l ******* z E r Ü c k * n * i e b u n d e s l i E g E R u n d w e * X s e l D e z u e i n D r * c h t +Eval: I I S D I D S D D I I I S D S S S D + +Speaker sentences 289: swc_deu_001515 #utts: 1 +id: (swc_deu_001515-swc_deu_001515) +Scores: (#C #S #D #I) 16 0 4 0 +REF: Ü b e r D i E s e k R a n k H e i t +HYP: Ü b e r * i * s e k * a n k * e i t +Eval: D D D D + +Speaker sentences 290: swc_deu_001516 #utts: 1 +id: (swc_deu_001516-swc_deu_001516) +Scores: (#C #S #D #I) 32 1 1 3 +REF: j a h r z w e i t a u s e n d f Ü n ******* * F k r i t i * s I e r t e +HYP: j a h r z w e i t a u s e n d f Ü n V E k r i t i E s * e r t e +Eval: I I S I D + +Speaker sentences 291: swc_deu_001517 #utts: 1 +id: (swc_deu_001517-swc_deu_001517) +Scores: (#C #S #D #I) 41 1 4 3 +REF: d i e s e a u f * f a S s u n g z u r n e u t r a L i t * Ä t u n t e r ******* s C h e i d e T +HYP: d i e s e a u f H f a * s u n g z u r ******* n e u t r a R i t E Ä t u n t e r s * h e i d e * +Eval: I D D S I I D D + +Speaker sentences 292: swc_deu_001518 #utts: 1 +id: (swc_deu_001518-swc_deu_001518) +Scores: (#C #S #D #I) 57 3 6 2 +REF: R I e d * l w u r d e * a l s k Ü n s t l E R i s c h e r l e i t e r d e s s i E m e n S s t u d i O s b e s t e l l t +HYP: * W e d E l w u r d e R a l s k ** n s t l * A i s c h e r ******* l e i t e r d e s s i * m e n * s t u d i E s b e s t e l l t +Eval: D S I I D D S D D D S + +Speaker sentences 293: swc_deu_001519 #utts: 1 +id: (swc_deu_001519-swc_deu_001519) +Scores: (#C #S #D #I) 34 1 4 0 +REF: w e N n m a n d i e w E l t a l s G a n z e s b e T r a c h t e t +HYP: w e * n ******* m a n d i e w * l t a l s K a n z e s b e * r a c h t e t +Eval: D D D S D + +Speaker sentences 294: swc_deu_001520 #utts: 1 +id: (swc_deu_001520-swc_deu_001520) +Scores: (#C #S #D #I) 73 6 0 6 +REF: s i * N D k r i t i s c h e k o m p o n e n t e n d e s d e t * o n a t i o n * S S y s t e m s a b s i c h ******* t l i c h s c h w a c h e * n * T w O r f e n +HYP: s i E M T k r i t i s c h e k o m p o n e n t e n d e s d e t U o n a t i o n D Z y s t e m s a b s i c h t l i c h s c h w a c h e I n D w U r f e n +Eval: I S S I I S S I I I S S + +Speaker sentences 295: swc_deu_001521 #utts: 1 +id: (swc_deu_001521-swc_deu_001521) +Scores: (#C #S #D #I) 18 3 3 0 +REF: n i c h T w Ä h L b A r i s T J e d o c h +HYP: n i c h * w E h * b E r i s * T e d o c h +Eval: D S D S D S + +Speaker sentences 296: swc_deu_001522 #utts: 1 +id: (swc_deu_001522-swc_deu_001522) +Scores: (#C #S #D #I) 36 1 2 1 +REF: e r b o T e i n e v e r ******* e i n i g u n g d e u t s c h L a n D s a n +HYP: e r b o D e i n e v e r e i n i g u n g d e u t s c h * a n * s a n +Eval: S I D D + +Speaker sentences 297: swc_deu_001523 #utts: 1 +id: (swc_deu_001523-swc_deu_001523) +Scores: (#C #S #D #I) 20 0 4 3 +REF: b e r L i * * ******* n z w e i t A u S e n D f Ü n f +HYP: b e r * i E N n z w e i t * u * e n * f Ü n f +Eval: D I I I D D D + +Speaker sentences 298: swc_deu_001524 #utts: 1 +id: (swc_deu_001524-swc_deu_001524) +Scores: (#C #S #D #I) 43 2 3 1 +REF: k e r n a b g e s t i M m t u n d u m ******* h Ü L l e n d i e s e n e n T s p r e c h e n D +HYP: k e r n a b g e s t i * m t u n d u m h ** Ö l e n d i e s e n e n * s p r e c h e n T +Eval: D I D S D S + +Speaker sentences 299: swc_deu_001525 #utts: 1 +id: (swc_deu_001525-swc_deu_001525) +Scores: (#C #S #D #I) 21 3 1 2 +REF: E R z * e u g u n g v o * n d Y n a m i K a u s +HYP: * A z T e u g u n g v o M n d E n a m i G a u s +Eval: D S I I S S + +Speaker sentences 300: swc_deu_001526 #utts: 1 +id: (swc_deu_001526-swc_deu_001526) +Scores: (#C #S #D #I) 13 2 0 0 +REF: Z I m t u n d i n g w e r +HYP: S E m t u n d i n g w e r +Eval: S S + +Speaker sentences 301: swc_deu_001527 #utts: 1 +id: (swc_deu_001527-swc_deu_001527) +Scores: (#C #S #D #I) 18 3 6 4 +REF: * * * v o n s c h W e R e r u n t E R e r n Ä H r u * N g +HYP: U N G v o n ******* s c h * e H e r ******* u n t * * e r n ** E r u G E g +Eval: I I I D D S D D D D S I S + +Speaker sentences 302: swc_deu_001528 #utts: 1 +id: (swc_deu_001528-swc_deu_001528) +Scores: (#C #S #D #I) 15 3 1 2 +REF: n Ü s * S e n u n d g e w Ü r * Z e n +HYP: n I s C H e n u n d g e w ** r T H e n +Eval: S I S D I S + +Speaker sentences 303: swc_deu_001529 #utts: 1 +id: (swc_deu_001529-swc_deu_001529) +Scores: (#C #S #D #I) 12 1 3 3 +REF: r o b e r t * * f k E N n e d * Y +HYP: r o b e r t E R f ******* k * * n e d I E +Eval: I I D D D I S + +Speaker sentences 304: swc_deu_001530 #utts: 1 +id: (swc_deu_001530-swc_deu_001530) +Scores: (#C #S #D #I) 17 1 2 1 +REF: k a m s c h l i E s S l i c h z u * m +HYP: k a m ******* s c h l i * s E l i c h z u N m +Eval: D D S I + +Speaker sentences 305: swc_deu_001531 #utts: 1 +id: (swc_deu_001531-swc_deu_001531) +Scores: (#C #S #D #I) 6 1 8 0 +REF: V o L l s t Ä n D I G K E i T +HYP: * o * l s t E n * * * * * i * +Eval: D D S D D D D D D + +Speaker sentences 306: swc_deu_001532 #utts: 1 +id: (swc_deu_001532-swc_deu_001532) +Scores: (#C #S #D #I) 24 1 1 2 +REF: s t a n d * e n s i c h v o n d e n u s a * +HYP: s t a n d T e n s i c h v o n d e n u R s ******* a R +Eval: I S D I + +Speaker sentences 307: swc_deu_001533 #utts: 1 +id: (swc_deu_001533-swc_deu_001533) +Scores: (#C #S #D #I) 24 5 4 4 +REF: a * f r i k a s Ü D L i C h d e R s * * A h a * r A g e o r t e t +HYP: a C f r i k a ******* s ** S T i * h d e * s E R h a H r E R g e o r t e t +Eval: I D D S S D D I I S I S S + +Speaker sentences 308: swc_deu_001534 #utts: 1 +id: (swc_deu_001534-swc_deu_001534) +Scores: (#C #S #D #I) 17 0 1 2 +REF: d i e a r m E e m e u * t e r t e * +HYP: d i e a r m * e m e u N t e r t e L +Eval: D I I + +Speaker sentences 309: swc_deu_001535 #utts: 1 +id: (swc_deu_001535-swc_deu_001535) +Scores: (#C #S #D #I) 15 0 1 1 +REF: s t a l i * n s e t z t e I m +HYP: s t a l i E n s e t z t e * m +Eval: I D + +Speaker sentences 310: swc_deu_001536 #utts: 1 +id: (swc_deu_001536-swc_deu_001536) +Scores: (#C #S #D #I) 13 3 3 2 +REF: V e R h Ä L t * n I s ******* a u s G l e i c h +HYP: F e I h ** * t E n * s a u s l e i c h +Eval: S S D D I D I S + +Speaker sentences 311: swc_deu_001537 #utts: 1 +id: (swc_deu_001537-swc_deu_001537) +Scores: (#C #S #D #I) 13 3 1 10 +REF: * * * * * ******* * * * P r o s C r * i b E D g l e i c h +HYP: K L M E R A U F B r o s K r E i b * T g l e i c h +Eval: I I I I I I I I I S S I D S + +Speaker sentences 312: swc_deu_001538 #utts: 1 +id: (swc_deu_001538-swc_deu_001538) +Scores: (#C #S #D #I) 35 1 3 1 +REF: a m z w e i t e * j u n i z w e I t a u s E n d v i e r w u r d E n +HYP: a m z w e i t e N j u n i E z w e * t a u s * n d v i e r w u r d * n +Eval: I S D D D + +Speaker sentences 313: swc_deu_001539 #utts: 1 +id: (swc_deu_001539-swc_deu_001539) +Scores: (#C #S #D #I) 18 2 6 0 +REF: i n D e N b U n D e s t a g n a c h r Ü C K t +HYP: i n ******* * e * b * n * e s t a g n a c h r ** L G t +Eval: D D D D D D S S + +Speaker sentences 314: swc_deu_001540 #utts: 1 +id: (swc_deu_001540-swc_deu_001540) +Scores: (#C #S #D #I) 55 0 4 5 +REF: d i e n a t o o * s t ******* e r w e i t e r u n g u n d d i e e i * n ******* s e i t i g e a u f k ** Ü n d i g U n G d e S +HYP: d i e ******* n a t o o S s t e r w e i t e r u n g u n d d i e e i E n s e i t i g e a u f k Ö Ü n d i g * n * d e * +Eval: D I I I I I D D D + +Speaker sentences 315: swc_deu_001541 #utts: 1 +id: (swc_deu_001541-swc_deu_001541) +Scores: (#C #S #D #I) 10 0 1 2 +REF: * ******* h i e r b e i i s T +HYP: T h i e r b e i i s * +Eval: I I D + +Speaker sentences 316: swc_deu_001542 #utts: 1 +id: (swc_deu_001542-swc_deu_001542) +Scores: (#C #S #D #I) 46 2 8 0 +REF: d i e s e r s t e l l e k a m e n s Ä m t L i c h E m i t G L i e d e r d e r k a p e L l e D e R +HYP: d i e s e r s t e l l e k a m e n s E m t * i c h * m i t * * i e d e r ******* d e r k a p e * l e ******* T e * +Eval: S D D D D D D D S D + +Speaker sentences 317: swc_deu_001543 #utts: 1 +id: (swc_deu_001543-swc_deu_001543) +Scores: (#C #S #D #I) 143 8 13 5 +REF: p o t S D a m E R a b k o M m e n e n t ******* h i e l * t z w a * r a L l g e m e i n e v e r ******* e i n b a R u n G e n Ü b e r d i E k Ü n f t i g e g e m e i n s a m e v e r w a l t u n g d e r s i e g e r m Ä c h t e u n d F o R m U l i e r ******* t E G r u n d s Ä t z e W i e d e m I l i t A r i s i e r u n g +HYP: p o t Z T a m * * a b k o * m e n e n t h i e l D t z w a H r a * l g e m e i n e R v e r e i n b a * u n * e n Ü b e r d i * k Ö n f t i g e g e m e i n s a m e v e r w a l t u n g d e r s i e g e r m I c h t e u n d V o * m * l i e r t O * r u n d s E t z e * i e d e m * l i t * r i s i e r u n g +Eval: S S D D D I I I D S I D D D S S S D D I S D S D D D + +Speaker sentences 318: swc_deu_001544 #utts: 1 +id: (swc_deu_001544-swc_deu_001544) +Scores: (#C #S #D #I) 43 7 4 4 +REF: d a n a c h u n T e r s c h R i E B e R e i n e n v e r T r a g b e i m * * B f * C d Y n a * m o +HYP: d a n a c h u n D e r s c h * i * * P e * e i n e n v e r D r a g b e i m W I E H f Z I d E n a H m o +Eval: S D D D S D S I I S S I S S I + +Speaker sentences 319: swc_deu_001545 #utts: 1 +id: (swc_deu_001545-swc_deu_001545) +Scores: (#C #S #D #I) 20 3 2 1 +REF: e i n E w e i t e r e V A r i ******* a n T e m a g +HYP: e i n * w e i t e r e W E r i a n D e ******* m a g +Eval: D S S I S D + +Speaker sentences 320: swc_deu_001546 #utts: 1 +id: (swc_deu_001546-swc_deu_001546) +Scores: (#C #S #D #I) 59 5 6 2 +REF: s i e w u r d e n m o d U l a * r * d u r c h l o c h s t r e i F E n g e s t e u E r t u n d d i E k l Ä n g e k o N n T E n +HYP: s i e ******* w u r d e n m o d O l a H r N d u r c h l o c h s t r e i V n g e s t e u * r t u n d d i * ******* k l I n g e k o * n * D n +Eval: D S I I S S D D D S D D S + +Speaker sentences 321: swc_deu_001547 #utts: 1 +id: (swc_deu_001547-swc_deu_001547) +Scores: (#C #S #D #I) 55 5 5 5 +REF: d i e g r u n D m a N d a * t * S k l a u s e l b e ******* v o r z u G t u n T e r d E n k l e i n e * n p a r t e i E n j * e n e +HYP: d i e ******* g r u n m a * d a R t G k l a u s e l b e v o r z u C t u n D e r ******* d I n ******* k l e i n e R n p a r t e i * n j I e n e +Eval: D S D I I S I S S D S D I D I + +Speaker sentences 322: swc_deu_001548 #utts: 1 +id: (swc_deu_001548-swc_deu_001548) +Scores: (#C #S #D #I) 39 4 6 1 +REF: A b e r t R o T z d e m k e i n e w I R k l i c h e h u N g e R s ******* n o t h e r R s c h t +HYP: * b e r ******* t * o N z d e m k e i n e w Ö G k l i c h e h u * g e * s n o t ******* h e r U s c h t +Eval: D D D S S S D D I D S + +Speaker sentences 323: swc_deu_001549 #utts: 1 +id: (swc_deu_001549-swc_deu_001549) +Scores: (#C #S #D #I) 10 3 16 2 +REF: U n D D O K u * m E n t * A T i o n d E R O B J E K T E +HYP: * n * ******* * * T u G m * n t E R Z i o n d * * ******* * * * * * * * +Eval: D D D D D S I D I S S D D D D D D D D D D + +Speaker sentences 324: swc_deu_001550 #utts: 1 +id: (swc_deu_001550-swc_deu_001550) +Scores: (#C #S #D #I) 38 1 6 1 +REF: z u R v o r ******* b e d i N g u n g k o n k r e t e r a B r Ü s t u n G S s c h R i T t e +HYP: z u * v o r b e d i * g u n g k o n k r e t e r a P r Ü s t u n * * s c h * i * t e +Eval: D I D S D D D D + +Speaker sentences 325: swc_deu_001551 #utts: 1 +id: (swc_deu_001551-swc_deu_001551) +Scores: (#C #S #D #I) 18 0 1 4 +REF: b u n d e s ******* t a * g * s ******* w a H l r e c h t +HYP: b u n d e s t a R g E s w a * l r e c h t +Eval: I I I I D + +Speaker sentences 326: swc_deu_001552 #utts: 1 +id: (swc_deu_001552-swc_deu_001552) +Scores: (#C #S #D #I) 34 2 8 2 +REF: e s m u S s D E m K r e i s ******* w a H L l e i t * e r v o r g e l e G t w e r D E n +HYP: e s m u * s * I m G r e i s w a * * l e i t D e r v o r g e l e * t ******* w e r * * n +Eval: D D S S I D D I D D D D + +Speaker sentences 327: swc_deu_001553 #utts: 1 +id: (swc_deu_001553-swc_deu_001553) +Scores: (#C #S #D #I) 121 10 16 8 +REF: h a t m a n e i n e E m ******* p i * r I s c h e b a s i * s f Ü R p s y c h o s o z i a l e p r o g R a M m e z u R s e n k u n G d e r s e L b s t ******* m o * R D r a t e u n D z u r s t * Ä r k u N g d e S s i c h e R h E i t * S g e f Ü H L s i n d e R b e * V Ö L k e r u n G +HYP: h a t m a n e i n e I m p i E r E s c h e b a s i E s f Ü * B p s y c h o s o z i a l e p r o g E a * m e z u O s e n k u n * d e r ******* s e * b s t m o U T E r a t e u n * z u r s t E A r k u * g d e * s i c h e * h * i t Z g e f Ü * * s i n d e * ******* b e F E Ö * k e r u n * +Eval: S I I S I D S S D S D D D I I S S D I S D D D D I S D D D D I S D D + +Speaker sentences 328: swc_deu_001554 #utts: 1 +id: (swc_deu_001554-swc_deu_001554) +Scores: (#C #S #D #I) 76 5 13 0 +REF: b e i d e n e r s t e n f r e i E n p A R l A m e n T S w a h l E n W u r d e i l i e s C u i M m A i n e u n z e H n h U n d e r t n e u n z i g i n s e i n e M +HYP: b e i ******* d e n e r s t e n ******* f r e i * n p * L l E m e n * Z w a h l * n * u r d e i l i e s K u i * ******* m E i ******* n e u n z e * n h * n d e r t n e u n z i g i n s e i n e * +Eval: D D D D S S D S D D S D D S D D D D + +Speaker sentences 329: swc_deu_001555 #utts: 1 +id: (swc_deu_001555-swc_deu_001555) +Scores: (#C #S #D #I) 43 3 9 1 +REF: d a * m i t l a s s e n s i c H b e s t r a H l u n g S s t Ä r K e n s e H r g e n A U m e S s e n +HYP: d a M m i t l a s s e n ******* s i c * b e s t r a * l u n g * s t E r T e n s e * r ******* g e n * * O m e * s e n +Eval: I D D D D S S D D D D S D + +Speaker sentences 330: swc_deu_001556 #utts: 1 +id: (swc_deu_001556-swc_deu_001556) +Scores: (#C #S #D #I) 42 3 8 0 +REF: w E n i g e J a H r E s p Ä t e r k a m e s z u e i n e R w e i t e r e n G r Ü n d U n g +HYP: w I n i g e ******* * a * r * s p Ä t e r k a m ******* e s z u e i n e * w e i t e r e n ******* K r O n d * n g +Eval: S D D D D D D D S S D + +Speaker sentences 331: swc_deu_001557 #utts: 1 +id: (swc_deu_001557-swc_deu_001557) +Scores: (#C #S #D #I) 16 1 2 1 +REF: r a d i o k a b A r e T t p R e i * s +HYP: r a d i o k a b E r e * t p * e i L s +Eval: S D D I + +Speaker sentences 332: swc_deu_001558 #utts: 1 +id: (swc_deu_001558-swc_deu_001558) +Scores: (#C #S #D #I) 38 2 3 2 +REF: B e s t Ü C k ******* t e b o m b e r a u f d i e s t a r t ******* B a h n e n r O L l e n +HYP: * e s t Ü * k t e b o m b e r a u f d i e s t a r t W a h n e n r * E l e n +Eval: D D I I S D S + +Speaker sentences 333: swc_deu_001559 #utts: 1 +id: (swc_deu_001559-swc_deu_001559) +Scores: (#C #S #D #I) 69 3 8 3 +REF: m i t d i e s e R r e g e l u n g s o L l e i n e * f a k t i s c h z w e i ******* * F A c h e e i n f l u S s n a h m e d I e s e r w Ä H l e r a u f +HYP: m i t ******* d i e s e * ******* r e g e l u n g s o * l e i n e R f a k t i s c h z w e i V E R c h e e i n f l u * s n a h m e d * e s e r ******* w ** E l e r a u f +Eval: D D D D I I I S S D D D D S + +Speaker sentences 334: swc_deu_001560 #utts: 1 +id: (swc_deu_001560-swc_deu_001560) +Scores: (#C #S #D #I) 14 3 2 2 +REF: b A r o C k E R k I r * c h e n ******* b a u +HYP: b E r o * k * G k Ö r I c h e n b a u +Eval: S D D S S I I + +Speaker sentences 335: swc_deu_001561 #utts: 1 +id: (swc_deu_001561-swc_deu_001561) +Scores: (#C #S #D #I) 79 3 5 6 +REF: d e r h e r ******* v o R r a g e n D w i R K e n ******* d e n l a n d e k l A P p e n w i e d e r u m h e r ******* v o R r a g e n D e * l a n g s a m ******* f l u g ******* e i g e n s c h a f t e n +HYP: d e r h e r v o * r a g e n T w i * C e n d e n l a n d e k l * * p e n w i e d e r u m h e r v o * r a g e n e R l a n g s a m f l u g e i g e n s c h a f t e n +Eval: I D S D S I D D I D S I I I + +Speaker sentences 336: swc_deu_001562 #utts: 1 +id: (swc_deu_001562-swc_deu_001562) +Scores: (#C #S #D #I) 77 7 10 3 +REF: m I L i t Ä r I S c h E v e r ******* b i n d u n g s f l u * G z E U g e o d e r u m s c h u l m a s c h i N E n f Ü r d i e b * F e i n h u n d E r T n e u n v e r w e n d e t +HYP: m * * i t E r * E c h * v e r b i n d u n g s f l u K T z * O g e o d e r u m s c h u l m a s c h i * * n f Ü r d i e b E E e i n ******* h u n d * r D E n e u n ******* v e r w e n d e t +Eval: D D S D S D I I S D S D D I S D D S S D + +Speaker sentences 337: swc_deu_001563 #utts: 1 +id: (swc_deu_001563-swc_deu_001563) +Scores: (#C #S #D #I) 35 5 6 2 +REF: l e i s t e t e m * e D i ******* z i n i s c h E U n D P s Y c h o l o g I s c h e h I l f E +HYP: l e i s t e t e ******* m I e * i z i n i s c h R * n * B s Ü c h o l o g E s c h e ******* h E l f * +Eval: D I D I S D D S S S D S D + +Speaker sentences 338: swc_deu_001564 #utts: 1 +id: (swc_deu_001564-swc_deu_001564) +Scores: (#C #S #D #I) 28 2 4 0 +REF: k a N n m a n d U R c h i m P f u n g e n v o r b e u g e n +HYP: k a * n ******* m a n ******* d * E c h i m f u n g e n v o r b e u g e n +Eval: D D D D S S + +Speaker sentences 339: swc_deu_001565 #utts: 1 +id: (swc_deu_001565-swc_deu_001565) +Scores: (#C #S #D #I) 63 5 8 2 +REF: m A N d E n a u s b R U c h d i e s e r k R a n k H e i t n A C h e r ******* f o l g t e R i n f e k t i o n v e r l a n g ******* s a m e n k a N n +HYP: m * E R d * n a u s b * O c h d i e s e r k * a n k * e i t E n * E h e r f o l g t e * i n f e k t i o n v e r l a n g s a m e n k a * n +Eval: D S S D D S D D S D S I D I D + +Speaker sentences 340: swc_deu_001566 #utts: 1 +id: (swc_deu_001566-swc_deu_001566) +Scores: (#C #S #D #I) 42 3 4 4 +REF: d i E e i n e n e u t r A L i * ******* t * Ä * t u n t e r a L l e n u m s t Ä n d e n v o r s a H +HYP: d i * ******* e i n e n e u t r * D i E t E Ä L t u n t e r a * l e n u m s t E n d e n v o r s a R +Eval: D D D S I I I I D S S + +Speaker sentences 341: swc_deu_001567 #utts: 1 +id: (swc_deu_001567-swc_deu_001567) +Scores: (#C #S #D #I) 13 1 2 1 +REF: u n d z i e g e n ******* h I r t E N +HYP: u n d z i e g e n h Ö r t * * +Eval: I S D D + +Speaker sentences 342: swc_deu_001568 #utts: 1 +id: (swc_deu_001568-swc_deu_001568) +Scores: (#C #S #D #I) 91 5 4 1 +REF: d a s n e u n z e h n h u n d e r t a c h t U n D d r e i s s i g g e g r Ü n d e t e k o m i t E E F Ü r u n ******* a m e r i k a n i s c h e u m t r i e b e w u r d e d a f Ü R N u n +HYP: d a s n e u n z e h n h u n d e r t a c h t E n * d r e i s s i g g e g r Ü n d e t e k o m i t * * V I r u n a m e r i k a n i s c h e u m t r i e b e w u r d e d a f E N * u n +Eval: S D D D S S I S S D + +Speaker sentences 343: swc_deu_001569 #utts: 1 +id: (swc_deu_001569-swc_deu_001569) +Scores: (#C #S #D #I) 67 3 1 6 +REF: z e n t r a l e d e r p r o * G r e s s i * v e n u n d h o r t d e s i n ******* G e n ******* i E U r g e s t Ü t z * t e n k u n s t ******* d e n k e n s +HYP: z e n t r a l e d e r p r o C K r e s s i E v e n u n d h o r t d e s i n I e n i * Ö r g e s t Ü t z S t e n k u n s t d e n k e n s +Eval: I S I I S I D S I I + +Speaker sentences 344: swc_deu_001570 #utts: 1 +id: (swc_deu_001570-swc_deu_001570) +Scores: (#C #S #D #I) 30 4 1 2 +REF: i n d e r d e r U * s p r Ä s I d e n t a n ******* k Ü n d i g t e +HYP: i n d e r ******* d e r O E s p r E s E d e n t a n k Ö n d i g t e +Eval: D S I S S I S + +Speaker sentences 345: swc_deu_001571 #utts: 1 +id: (swc_deu_001571-swc_deu_001571) +Scores: (#C #S #D #I) 19 2 0 4 +REF: s ******* n * A c K s * u n d v o r s p * e i s e n +HYP: s n E Ä c H s T u n d v o r s p B e i s e n +Eval: I I S S I I + +Speaker sentences 346: swc_deu_001572 #utts: 1 +id: (swc_deu_001572-swc_deu_001572) +Scores: (#C #S #D #I) 65 4 6 2 +REF: d e s B u n d e s ******* w a H L g e s e t z e s b i s z u m d r e i S s i g s t e * j u n i z w e i t a u s e n d e L f a u f g E g E B e N +HYP: d e s P u n d e s w a * * g e s e t z e s b i s T z u m d r e i * s i g s t e N j u n i z w e i t a u s e n d e * f a u f g g * * e M +Eval: S I D D S D I D S D D S + +Speaker sentences 347: swc_deu_001573 #utts: 1 +id: (swc_deu_001573-swc_deu_001573) +Scores: (#C #S #D #I) 9 2 3 1 +REF: H E N r i * p o U s s e U r +HYP: * * O r i E p o * s s e Ö r +Eval: D D S I D S + +Speaker sentences 348: swc_deu_001574 #utts: 1 +id: (swc_deu_001574-swc_deu_001574) +Scores: (#C #S #D #I) 59 2 3 2 +REF: f l Ü C H t l i n g e n v o n d e r e t H n i s c h e n m i n d e r h e i t d e r s o m a l i s c h e n b a n ******* t u * +HYP: f l ** I F t l i n g e n v o n d e r e t * n i s c h e n m i n d e r h e i t d e r ******* s o m a l i s c h e n b a n t u M +Eval: D S S D D I I + +Speaker sentences 349: swc_deu_001575 #utts: 1 +id: (swc_deu_001575-swc_deu_001575) +Scores: (#C #S #D #I) 33 1 1 4 +REF: d i e b i * ******* p o l a r e w e l t ******* o r d * n u n g z e m E n t i e r t +HYP: d i e b i E p o l a r e ******* w e l t o r d T n u n g z e m I n t i e r t +Eval: I I D I I S + +Speaker sentences 350: swc_deu_001576 #utts: 1 +id: (swc_deu_001576-swc_deu_001576) +Scores: (#C #S #D #I) 74 5 5 13 +REF: e * ******* * * * I n * ******* e * * i n t e * G r i e R t e o d e r e x * t e r n a n g e b r a c h t e v o R r i c h t u n g a n e i n e M n u K l * e A r e n w a F f e n s y s t e m * +HYP: e R A N F A n G e I N i n t e I L r i e A t e o d e r e x S t e r n a n g e b r a c h t e ******* v o * r i c h t u n g a n e i n e * n u C l I e r e n ******* w a * f e n s y s t e m N +Eval: I I I I I S I I I I I S S I D D D S I S D D I + +Speaker sentences 351: swc_deu_001577 #utts: 1 +id: (swc_deu_001577-swc_deu_001577) +Scores: (#C #S #D #I) 38 3 2 2 +REF: s t a r t e t e d i E h i l f s o r g A n i s A t * i o n l a n * G f r I s t i g e +HYP: s t a r t e t e d i * h i l f s o r g E n i s E t Z i o n l a n K f r * s t i g e +Eval: D S S I I S D + +Speaker sentences 352: swc_deu_001578 #utts: 1 +id: (swc_deu_001578-swc_deu_001578) +Scores: (#C #S #D #I) 101 4 11 5 +REF: w e N n d i e s e e x * t e r n e n e F f e * k t e i n D e R r i c h t i g e n r e i H E n ******* F o l g e a u f t r e t e n u n d s i c h i N n e R h a l b s p e z i * F i s c h E R p a r a m e t e r b e w * e g e n +HYP: w e * n d i e s e e x S t e r n e n e R f e C k t e i n * e * ******* r i c h t i g e n r e i * * n V o l g e a u f t r e t e n u n d s i c h i * n e * h a l b ******* s p e z i E i s c h * * A p a r a m e t e r b e w I e g e n +Eval: D I S I D D D D D I S D D D I S D D S I + +Speaker sentences 353: swc_deu_001579 #utts: 1 +id: (swc_deu_001579-swc_deu_001579) +Scores: (#C #S #D #I) 109 9 8 3 +REF: z O g d I e S O w J E t u n i o n a u c h b e i d e N W a S s e r s t o f ******* F b o m b e n u n d n e u E n f l u * G z e u g e n m i t i n t e r ******* k o n t I n e n t a l e R r e i c h w e i t e m i t d e n u s a g l e i c h +HYP: z U g d * e * * w I R t u n i o n a u c h b e i d e * * a * s e r s t o f P b o m b e n u n d n e u I n f l u K T z e u g e n m i t i n t e r k o n t E n e n t a l e * ******* r e i c h w e i t e m i t d e n u R s a R g l e i c h +Eval: S D D D S S D D D I S S I S I S D D S S + +Speaker sentences 354: swc_deu_001580 #utts: 1 +id: (swc_deu_001580-swc_deu_001580) +Scores: (#C #S #D #I) 21 3 4 5 +REF: * * * ******* d i e s t a D t h a t i H R w a P p e n t i e * R +HYP: P E N d i e s t a * t ******* h a t i * * E w a B p e n t i e A M +Eval: I I I I D D D D S S I S + +Speaker sentences 355: swc_deu_001581 #utts: 1 +id: (swc_deu_001581-swc_deu_001581) +Scores: (#C #S #D #I) 43 1 1 2 +REF: d i e s e r a n s a t z g i l T a l l g e m e i n a l s a u s g e w o * g E n * e r +HYP: d i e s e r a n s a t z g i l D a l l g e m e i n a l s a u s g e w o R g * n D e r +Eval: S I D I + +Speaker sentences 356: swc_deu_001582 #utts: 1 +id: (swc_deu_001582-swc_deu_001582) +Scores: (#C #S #D #I) 18 4 4 1 +REF: n a c h d e M z u ******* s a m m E N B r U c h D e R +HYP: n a c h d e * z u s a m m * P r O c h ******* T e * +Eval: D I D S S S D S D + +Speaker sentences 357: swc_deu_001583 #utts: 1 +id: (swc_deu_001583-swc_deu_001583) +Scores: (#C #S #D #I) 29 4 3 2 +REF: d e R o b e r l a u s i t Z * Z w i s c h e n h O Y e r s ******* w e r d A +HYP: d e * o b e r l a u s i t * S w i s c h e n ******* h E U e r s w e r d E +Eval: D D I S D S S I S + +Speaker sentences 358: swc_deu_001584 #utts: 1 +id: (swc_deu_001584-swc_deu_001584) +Scores: (#C #S #D #I) 26 3 2 4 +REF: d a b e I I n z w e i * P H a * s e n U n t e r t e i * * l t +HYP: d a b e * E n z w e i E F a H s e n * n t e r t e i L E l t +Eval: D S I S S I D I I + +Speaker sentences 359: swc_deu_001585 #utts: 1 +id: (swc_deu_001585-swc_deu_001585) +Scores: (#C #S #D #I) 58 4 6 5 +REF: s c h W e d e n a n d e r e u r o p * A m E i s t e r s c h a f T t e i l u n d w u r d e m I t D e r d * * F b * e * l f +HYP: s c h I e d e n a n d e r e u r o p E R m * i s t e r s c h a f * ******* t e i l u n d w u r d e m * t ******* * e r d I E E b I E e L l f +Eval: S I S D D D D D D I I S I S I + +Speaker sentences 360: swc_deu_001586 #utts: 1 +id: (swc_deu_001586-swc_deu_001586) +Scores: (#C #S #D #I) 42 5 14 0 +REF: m e I s t e r e r Ö F f n e t k R a b A T s C h l i E S s L I C H e I n E w e i t e R e m Ö g l i c h k e i t +HYP: m e * s t e r e r ** * f n e t k * a b * E R s * h l i * * s * * * * G e R n * w e i t e G e ******* m Ö g l i c h k e i t +Eval: D D D D D S S D D D D D D D S S D S D + +Speaker sentences 361: swc_deu_001587 #utts: 1 +id: (swc_deu_001587-swc_deu_001587) +Scores: (#C #S #D #I) 37 1 2 2 +REF: e i n e m a u s w Ä r t s ******* e r ******* f o l g i n w o l F s b u r G g e l a n g +HYP: e i n e m a u s w E r t s e r f o l g i n w o l * s b u r * g e l a n g +Eval: S I I D D + +Speaker sentences 362: swc_deu_001588 #utts: 1 +id: (swc_deu_001588-swc_deu_001588) +Scores: (#C #S #D #I) 49 2 3 5 +REF: m i t s c h w e b u n g s ******* s * u m m E R n k o N n t e n G l i s s a n ******* d i * e r z e u g * t w e r d e n +HYP: m i t s c h w e b u n g s s O u m m * A n k o * n t e n K l i s s a n d i E e r z e u g K t ******* w e r d e n +Eval: I I D S D S I I I D + +Speaker sentences 363: swc_deu_001589 #utts: 1 +id: (swc_deu_001589-swc_deu_001589) +Scores: (#C #S #D #I) 18 2 5 0 +REF: d e r A b E R l e d i g l i C h z e i G t e +HYP: d e r ******* * b * * A l e d i g l i * h z e i K t e +Eval: D D D D S D S + +Speaker sentences 364: swc_deu_001590 #utts: 1 +id: (swc_deu_001590-swc_deu_001590) +Scores: (#C #S #D #I) 38 3 7 1 +REF: G R o S s B r i t a N n i e n e i n e e R s t e w i c h t i g e v e R E i n ******* b a R u n g +HYP: * K o * s P r i t a * n i e n e i n e e * s t e w i c h t i g e ******* v e * i n b a * u n g +Eval: D S D S D D D D S I D + +Speaker sentences 365: swc_deu_001591 #utts: 1 +id: (swc_deu_001591-swc_deu_001591) +Scores: (#C #S #D #I) 17 5 3 3 +REF: s i E H T a U c h * * D A S W i t n e s s * i n g +HYP: s i * * D a * c h T E S H R i t n e s s E i n g +Eval: D D S D I I S S S S I + +Speaker sentences 366: swc_deu_001592 #utts: 1 +id: (swc_deu_001592-swc_deu_001592) +Scores: (#C #S #D #I) 78 4 21 0 +REF: w u r d e m i t d e M b u n d e S w a H L g e s e t z v o N n e u n z e H N H u n d e r T s E c h s u n D f Ü N f z i g e i n e d a u E r h A F t e r E G e l u N g e I n g e f Ü H r t +HYP: w u r d e ******* m i t d e * b u n d e w a * * g e s e t z ******* v o * R n e u n z e * * ******* * u n d e r * s I c h s u n * f Ü * f z i g e i n e d a u A r h * * t e ******* r * * e l u * g e * n g e f Ü * r t +Eval: D D S D D D D S D D D D D S D D S D D D D D D D D + +Speaker sentences 367: swc_deu_001593 #utts: 1 +id: (swc_deu_001593-swc_deu_001593) +Scores: (#C #S #D #I) 28 2 5 2 +REF: d i e a n z a H l d e r Ü b e R h A n g m A n ******* d a * T e k a N n +HYP: d i e a n z a * l ******* d e r Ü b e * h * n g m E n d a R D e k a * n +Eval: D D D D S I I S D + +Speaker sentences 368: swc_deu_001594 #utts: 1 +id: (swc_deu_001594-swc_deu_001594) +Scores: (#C #S #D #I) 53 5 5 1 +REF: B E s c h l O S s d i e s e R e i n m I l i t Ä r i s c h e s e i n g r e i f e n i n d e n k o r e a * K r i E G +HYP: * I s c h l * * s d i e s e * e i n m * l i t E r i s c h e s e i n g r e i f e n i n d e n k o r e a R G r i C K +Eval: D S D D D D S I S S S + +Speaker sentences 369: swc_deu_001595 #utts: 1 +id: (swc_deu_001595-swc_deu_001595) +Scores: (#C #S #D #I) 15 1 1 1 +REF: n a t o v e r ******* b i n D l i c h E +HYP: n a t o v e r b i n T l i c h * +Eval: I S D + +Speaker sentences 370: swc_deu_001596 #utts: 1 +id: (swc_deu_001596-swc_deu_001596) +Scores: (#C #S #D #I) 16 1 2 1 +REF: k a l t e K r i e g b E e n d e * t +HYP: k a l t e ******* G r i e g b * e n d e R t +Eval: D S D I + +Speaker sentences 371: swc_deu_001597 #utts: 1 +id: (swc_deu_001597-swc_deu_001597) +Scores: (#C #S #D #I) 65 7 10 2 +REF: V n E u n Z e H n h u n D e r T d R e i u * N D n e u n z i g u n d A U s t * r a l i e n s o w i e d e r Ö s t e R r e I c h I s c h e a b l E g e r +HYP: A U n * u n * e * n h u n * e r * d E e i u M T n e u n z i g u n d * O s t E r a l i e n s o w i e ******* d e r Ö s t e * r e * c h * s c h e a b l I g e r +Eval: S S D D D D D S I S S D S I D D D D S + +Speaker sentences 372: swc_deu_001598 #utts: 1 +id: (swc_deu_001598-swc_deu_001598) +Scores: (#C #S #D #I) 136 8 10 6 +REF: d a d i e s e i t a n f a n g n e u n z e h n h u n d e r t n e u n u n D f Ü n f z i g d o r t h e R r s c h e n ******* d e r e v o l U t i o n * s r E g i E R U n G u n * ******* T e r F i * ******* d e l C a s t r o e i n e n s o z i A l i s t i s c h e n k u r s e i n g E s c h L a g e n h a T t e +HYP: d a ******* d i e s e i t a n f a n g ******* n e u n z e h n h u n d e r t n e u n u n f Ü n f z i g d o r t h e * r s c h e n d e r e v o l O t i o n D s r I g i * * O n * u n D D e r V i E d e l K a s t r o e i n e n s o z i E l i s t i s c h e n k u r s e i n g * s c h * a g e n ******* h a * t e +Eval: D D S D I S I S D D S D I I S S I I S S D D D D + +Speaker sentences 373: swc_deu_001599 #utts: 1 +id: (swc_deu_001599-swc_deu_001599) +Scores: (#C #S #D #I) 166 13 25 4 +REF: n a c h w e i t e r e n V e R l u s t r e I c h e n k Ä m p f e n O H n e n e N n E N S w e r t e e r ******* f o l g e b e i d e R K r i E g s p a R t e i E n W u r d e r u n d d r e I j a h r e n a c H b e g i N n d e R a u s E I n A n d e R s e T z u n g e i n b I s H e u t e g Ü l t i g e s w a f F e n ******* s t i L l s t a * n D s ******* a b K o M m e n a b g e s C h l o s s e n +HYP: n a c h ******* w e i t e r e n F e * l u s t r e * c h e n ******* k Ä m p f e n * U n e ******* n e * n * Z w e r t e e r f o l g e b e i d e * G r i * g s p a * t e i * n * u r d e r u n d d r e * A j a h r e ******* n a c * b e g i * n ******* d e * a u s * A n D n d e s e * z u n g e i n b E s R e u t e g Ü l t i g e s w a f N e n s t i * l s t a M n * s a b C o * m e n a b g e s * h l o s s e n +Eval: D S D D D D S D D D S S I D S D D D D D S D D D D D D S S S D S S S I D I D I S D D + +Speaker sentences 374: voxforge_deu_000891 #utts: 1 +id: (voxforge_deu_000891-voxforge_deu_000891) +Scores: (#C #S #D #I) 23 2 4 1 +REF: m a n i s t D A b e i s ******* e H r V o r s i c h t i g +HYP: m a n i s t ******* E R b e i ******* s e * r * o r s i c h t i g +Eval: D S S D I D D + +Speaker sentences 375: voxforge_deu_000892 #utts: 1 +id: (voxforge_deu_000892-voxforge_deu_000892) +Scores: (#C #S #D #I) 67 1 4 1 +REF: d i e w e H r P f l i c h t s o L l i n d e u t s c h l a n d l e i d e r N o c h n i c h t a b g e s c h a F f t w e r d * e n +HYP: d i e w e * r f l i c h t s o * l i n d e u t s c h l a n d l e i d e r * o c h n i c h t a b g e s c h a * f t w e r d N e n +Eval: D S D D D I + +Speaker sentences 376: voxforge_deu_000893 #utts: 1 +id: (voxforge_deu_000893-voxforge_deu_000893) +Scores: (#C #S #D #I) 33 2 6 0 +REF: e s g I B t a u c h m i S s B r a u c h D u R c h a R b e I t g e b e r +HYP: e s g * E t a u c h m i * s P r a u c h * u * c h a * b e * t g e b e r +Eval: D S D S D D D D + +Speaker sentences 377: voxforge_deu_000894 #utts: 1 +id: (voxforge_deu_000894-voxforge_deu_000894) +Scores: (#C #S #D #I) 27 1 7 0 +REF: d i e k i n d e r s i n D d a N n k R a n k G e W o R D e n +HYP: d i e k i n d e r s i n * d a * n k * a n k ******* * e B o * * e n +Eval: D D D D D S D D + +Speaker sentences 378: voxforge_deu_000895 #utts: 1 +id: (voxforge_deu_000895-voxforge_deu_000895) +Scores: (#C #S #D #I) 49 2 3 0 +REF: d i e t r a G w e i t e d e r K a t a s t r o P H e s o L l v e r d e u t l i c h t w e r d e n +HYP: d i e t r a K w e i t e d e r * a t a s t r o * F e s o * l v e r d e u t l i c h t w e r d e n +Eval: S D D S D + +Speaker sentences 379: voxforge_deu_000897 #utts: 1 +id: (voxforge_deu_000897-voxforge_deu_000897) +Scores: (#C #S #D #I) 0 2 0 12 +REF: * * * * * * * * * * * * Ä H +HYP: D S C A N G E A U A U L B E +Eval: I I I I I I I I I I I I S S + +Speaker sentences 380: voxforge_deu_000898 #utts: 1 +id: (voxforge_deu_000898-voxforge_deu_000898) +Scores: (#C #S #D #I) 44 2 5 2 +REF: b e i m * o R g a n s t r e i t s t r e i t e n o b e R s ******* T e v e R f a S s u n g s o R g a n e +HYP: b e i m M o * g a n s t r e i t s t r e i t e n o b e * s D e ******* v e f a * s u n g s o * g a n e +Eval: I D D I S D S D D + +Speaker sentences 381: voxforge_deu_000899 #utts: 1 +id: (voxforge_deu_000899-voxforge_deu_000899) +Scores: (#C #S #D #I) 24 3 2 1 +REF: d a S w a g e i c h J A z u b e z w e i f e l * n +HYP: d a * w a g e ******* i c h I E R z u b e z w e i f e l E n +Eval: D D S S S I + +Speaker sentences 382: voxforge_deu_000900 #utts: 1 +id: (voxforge_deu_000900-voxforge_deu_000900) +Scores: (#C #S #D #I) 29 1 13 0 +REF: m a n S o L l t e d E N e n a u f g a R k E I N e n f a L l t r a u E n +HYP: m a n * o * l t e d * * e n a u f ******* g a * ******* k * * H e n ******* f a * l ******* t r a u * n +Eval: D D D D D D D D D S D D D D + +Speaker sentences 383: voxforge_deu_000901 #utts: 1 +id: (voxforge_deu_000901-voxforge_deu_000901) +Scores: (#C #S #D #I) 46 2 5 0 +REF: d i e Ö F f e n t l i c h e n s c h U l d e n w e r d e n n i c h t g e t i l G t w e r d e n +HYP: d i e ** * f e n t l i c h e n ******* s c h Ö l d e n w e r d e n n i c h t ******* g e t i l K t ******* w e r d e n +Eval: D D D S D S D + +Speaker sentences 384: voxforge_deu_000902 #utts: 1 +id: (voxforge_deu_000902-voxforge_deu_000902) +Scores: (#C #S #D #I) 26 2 2 3 +REF: D a S g e l D i s t a u s g e z * a h l t w o r d e n ******* * +HYP: B a * ******* g e l T i s t a u s g e z C a h l t w o r d e n T +Eval: S D D S I I I + +Speaker sentences 385: voxforge_deu_000903 #utts: 1 +id: (voxforge_deu_000903-voxforge_deu_000903) +Scores: (#C #S #D #I) 47 3 7 3 +REF: e S s o l l e n d r e i ******* h u n d e r * T t a u * s e n d n e u e a r b e I T s p l Ä T z e E n T s t E H e n +HYP: e * R s o l l e n d r e i h u n d e r D t a u S s e n d n e u e a r b e * * s p l Ä * z e I n * s t * * e n +Eval: D S I I S I D D D S D D D + +Speaker sentences 386: voxforge_deu_000904 #utts: 1 +id: (voxforge_deu_000904-voxforge_deu_000904) +Scores: (#C #S #D #I) 44 2 7 3 +REF: d i e K Ö r P e r ******* v e R l e t z u n g k a n n a l s b e i s p i e l g e N A N n T w e r d e n ******* * +HYP: d i e * Ö r B e r v e * l e t z u n g k a n n a l s ******* b e i s p i e l ******* g e * * * n D w e r d e n T +Eval: D S I D D D D D D S I I + +Speaker sentences 387: voxforge_deu_000905 #utts: 1 +id: (voxforge_deu_000905-voxforge_deu_000905) +Scores: (#C #S #D #I) 31 2 4 0 +REF: d i e s e G r e n Z e i s t Ü b e r s c H R i T t e n W o r d e n +HYP: d i e s e K r e n * e i s t Ü b e r s c * * i * t e n B o r d e n +Eval: S D D D D S + +Speaker sentences 388: voxforge_deu_000906 #utts: 1 +id: (voxforge_deu_000906-voxforge_deu_000906) +Scores: (#C #S #D #I) 36 8 19 1 +REF: D A S s s t R a F V e R f O L G u N g s b E H Ö r d e n k e i N e n z u g R I F F a U F D A s G e l D h a * b e n +HYP: * T E s s t a * * e * f * * A u * g s b * Ü Ö r d e n k e i * e n ******* z u g * * * E a * * ******* * E s K e l * ******* h a R b e n +Eval: D S S S D D D D D S D D S D D D D D S D D D D S S D D I + +Speaker sentences 389: voxforge_deu_000907 #utts: 1 +id: (voxforge_deu_000907-voxforge_deu_000907) +Scores: (#C #S #D #I) 31 0 1 0 +REF: d i e i n t e r e s s e n f i n d e n k e i n g e h Ö r +HYP: d i e i n t e r e s s e n f i n d e n k e i n ******* g e h Ö r +Eval: D + +Speaker sentences 390: voxforge_deu_000908 #utts: 1 +id: (voxforge_deu_000908-voxforge_deu_000908) +Scores: (#C #S #D #I) 40 6 16 0 +REF: P f e I l t a S T e t a b u l a t o R r Ü c K s C h R i T T t a s T e r Ü C K t a s T e r Ü C K L Ö s C H t a s T e +HYP: * f e * l t a * * e t a b u l a t o A r Ü c * s * h * i * E t a s * e ******* r Ü * * t a s * e r Ü G I E R s * * t a s * e +Eval: D D D D S D D D D S D D D D D S S S S D D D + +Speaker sentences 391: voxforge_deu_000909 #utts: 1 +id: (voxforge_deu_000909-voxforge_deu_000909) +Scores: (#C #S #D #I) 38 11 12 4 +REF: d e r b e T R o F F e n e m U S S E i N b e r ******* e C H t i g T E S * i N T e R e * S S e ** g e l t e n d m a c h e n +HYP: d e r ******* b e * o * H e n e ******* m * O R A i U M b e r e * S t i g * * * D i * * e * e N H e Ä E g e l t e n d m a c h e n +Eval: D D S D S D D S S S S S I D S D D D I D D D I S S I S + +Speaker sentences 392: voxforge_deu_000910 #utts: 1 +id: (voxforge_deu_000910-voxforge_deu_000910) +Scores: (#C #S #D #I) 63 3 4 4 +REF: * e i n d * r i T t E R h a T d e m g e s c h Ä * d i G t e n f r e i w i L l i g l e i s t u n g e n z u k * o M m e n l a s s e n +HYP: E e i n d E r i * t * A h a D d e m g e s c h Ä I d i K t e n f r e i w i * l i g l e i s t u n g e n z u k C o * m e n l a s s e n +Eval: I I D D S S I S D I D + +Speaker sentences 393: voxforge_deu_000911 #utts: 1 +id: (voxforge_deu_000911-voxforge_deu_000911) +Scores: (#C #S #D #I) 26 1 7 0 +REF: s o n d e r N a U c H r e c h T S n e b e n d e M b i l D +HYP: s o n d e r * a * c * ******* r e c h * * n e b e n d e * b i l T +Eval: D D D D D D D S + +Speaker sentences 394: voxforge_deu_000912 #utts: 1 +id: (voxforge_deu_000912-voxforge_deu_000912) +Scores: (#C #S #D #I) 51 3 10 0 +REF: S i e H A t e i n e n i c h t e r n s T l i c h g e m e i N t e w i L l e N s e R k l Ä R u n g a b G E g e b e n +HYP: * i e ******* * R t e i n e n i c h t e r n s * l i c h ******* g e m e i * t e w i * l e * s e k l Ä H u n g a b * * g e b e n +Eval: D D D S D D D D D S S D D + +Speaker sentences 395: voxforge_deu_000913 #utts: 1 +id: (voxforge_deu_000913-voxforge_deu_000913) +Scores: (#C #S #D #I) 34 1 3 1 +REF: d a s m U S s t e j a * a u f j e d e n f a L l s o k o m m e n +HYP: d a s m * O s t e j a H a u f j e d e n f a * l ******* s o k o m m e n +Eval: D S I D D + +Speaker sentences 396: voxforge_deu_000914 #utts: 1 +id: (voxforge_deu_000914-voxforge_deu_000914) +Scores: (#C #S #D #I) 40 2 8 8 +REF: m e h * * r e r e C l I e * n T s k Ö N n E n s i c h e i n e * i ******* p * * A d r e s s e t e i L E n * +HYP: m e h R E r e r e K l * e I n * s k ** * n * n ******* s i c h e i n e E i p I E * d r e s s e t e i * U n G +Eval: I I S D I D D D D D I I I I D D S I + +Speaker sentences 397: voxforge_deu_000915 #utts: 1 +id: (voxforge_deu_000915-voxforge_deu_000915) +Scores: (#C #S #D #I) 46 3 14 1 +REF: w a R d i E G Ü n s t I G e r e E s h i E S s a L s o s i C H * z u s a m m e N n e h m e n a n s t a T T z u +HYP: w a * ******* d i * * Ü n s t E e r e * s ******* h i * * s a * s o s i * G T z u s a m m e * n e h m e n a n s t a * * ******* z u +Eval: D D D D S S D D D D D D S I D D D D + +Speaker sentences 398: voxforge_deu_000917 #utts: 1 +id: (voxforge_deu_000917-voxforge_deu_000917) +Scores: (#C #S #D #I) 34 2 6 1 +REF: d e R * s c h U l D n e R h a t s E I n e l e i s t u n g a n g e b o t e n +HYP: d e * R s c h * l E n e * h a t ******* s * A n e ******* l e i s t u n g a n g e b o t e n +Eval: D I D S D D D S D + +Speaker sentences 399: voxforge_deu_000918 #utts: 1 +id: (voxforge_deu_000918-voxforge_deu_000918) +Scores: (#C #S #D #I) 6 1 3 0 +REF: s o d A s s E s +HYP: s o ******* d * s s ******* I s +Eval: D D D S + +Speaker sentences 400: voxforge_deu_000919 #utts: 1 +id: (voxforge_deu_000919-voxforge_deu_000919) +Scores: (#C #S #D #I) 33 2 4 0 +REF: d i e b a T t E r i e n w a r E n s e H r s t a R K v e r a l t e t +HYP: d i e b a * t * r i e n w a r * n s e A r s t a * G v e r a l t e t +Eval: D D D S D S + +Speaker sentences 401: voxforge_deu_000920 #utts: 1 +id: (voxforge_deu_000920-voxforge_deu_000920) +Scores: (#C #S #D #I) 31 4 5 1 +REF: d I e s e s z i e l w u r d e n ******* U r t E I l w E I s e e R r e i c h t +HYP: d * e s e s z i e l w u r d e ******* n O r ******* t * A l w A L s e e * r e i c h t +Eval: D D I S D D S S S D + +Speaker sentences 402: voxforge_deu_000921 #utts: 1 +id: (voxforge_deu_000921-voxforge_deu_000921) +Scores: (#C #S #D #I) 31 2 2 0 +REF: d i e s e w Ä h r u n g w i r D s e H r l a n g e l e b e n +HYP: d i e s e w E h r u n g w i r T s e * r l a n g e ******* l e b e n +Eval: S S D D + +Speaker sentences 403: voxforge_deu_000922 #utts: 1 +id: (voxforge_deu_000922-voxforge_deu_000922) +Scores: (#C #S #D #I) 26 1 6 4 +REF: d o r T z * i T t E R n o F f e n ******* b a R s c h o n v i e l e ******* * +HYP: d o r * z E i * t * A n o * f e n b a * ******* s c h o n v i e l e T +Eval: D I D D S D I D D I I + +Speaker sentences 404: voxforge_deu_000923 #utts: 1 +id: (voxforge_deu_000923-voxforge_deu_000923) +Scores: (#C #S #D #I) 47 8 12 3 +REF: a l S s i E g i * n G e n n i C K t e * m A G g i E i H r n u r g a n Z f l Ü c h t i g z u u n d D e r * V a t E R +HYP: a l * ******* s i * ******* g i E n * e n n i * G t e M m * Ä g i * i E r n u r ******* g a n * S f l I c h t i g K z u u n d * e r F A a t * A +Eval: D D D D I D D S I D S D S D D S S S D I S D S + +Speaker sentences 405: voxforge_deu_000924 #utts: 1 +id: (voxforge_deu_000924-voxforge_deu_000924) +Scores: (#C #S #D #I) 19 2 9 1 +REF: e r ******* z Ä H L m i R m e H r Ü b e R C H r i s t i a n +HYP: e r z ** * * Y m i * E m e * r Ü b e * ******* * * r i s t i a n +Eval: I D D D S D S D D D D D + +Speaker sentences 406: voxforge_deu_000925 #utts: 1 +id: (voxforge_deu_000925-voxforge_deu_000925) +Scores: (#C #S #D #I) 38 4 2 2 +REF: d e m s t e h e N n a t * Ü r l i c h a u c h V E R m Ö g e n g e g e n ******* Ü b E R +HYP: d e m s t e h e * n a t I Ü r l i c h a u c h F A M m Ö g e n g e g e n Ü b * A +Eval: D I S S S I D S + +Speaker sentences 407: voxforge_deu_000926 #utts: 1 +id: (voxforge_deu_000926-voxforge_deu_000926) +Scores: (#C #S #D #I) 40 3 5 1 +REF: d i e r e a l e l a g e w i r D n i c h t v o L L s t Ä n d i * G a b g e b i l D e t +HYP: d i e r e a l e ******* l a g e w i r * T n i c h t v o * * s t E n d i C H a b g e b i l * e t +Eval: D D S D D S I S D + +Speaker sentences 408: voxforge_deu_000927 #utts: 1 +id: (voxforge_deu_000927-voxforge_deu_000927) +Scores: (#C #S #D #I) 34 0 5 0 +REF: e s k a N n a u c h n o c h v i e L s c h l i M m e r w e r d e n +HYP: e s ******* k a * n a u c h n o c h v i e * s c h l i * m e r ******* w e r d e n +Eval: D D D D D + +Speaker sentences 409: voxforge_deu_000928 #utts: 1 +id: (voxforge_deu_000928-voxforge_deu_000928) +Scores: (#C #S #D #I) 28 2 5 4 +REF: d i e p o l I t i K i n t E r e S s i E r t * * * * n i c h t m e H r +HYP: d i e p o l E t i G i n t * r e * s i * r t Z I C H n i c h t ******* m e * r +Eval: S S D D D I I I I D D + +Speaker sentences 410: voxforge_deu_000929 #utts: 1 +id: (voxforge_deu_000929-voxforge_deu_000929) +Scores: (#C #S #D #I) 67 2 4 3 +REF: i n h a l T s f r e i h e i t b e d e u t e * t d a S s D e R i n h a l T d e r v e r t r a * G l i c h e n v e r ******* e i n b a r u n g e n +HYP: i n h a l * s f r e i h e i t b e d e u t e R t d a * s * e * i n h a l D d e r v e r t r a C K l i c h e n v e r e i n b a r u n g e n +Eval: D I D D D S I S I + +Speaker sentences 411: voxforge_deu_000930 #utts: 1 +id: (voxforge_deu_000930-voxforge_deu_000930) +Scores: (#C #S #D #I) 38 10 11 1 +REF: d e r s c h u l D n e r v e R l e t Z T E s e i n E s * O R G F a l T s p F l i C H T e n s c h U l D h a f t +HYP: d e r ******* s c h u l * n e r v e * l e t * D I s e i n * I s A C K V a l * s p * l i * * e n ******* s c h * l T h a f t +Eval: D D D D S S S D S I S S S S D D D D S D D S + +Speaker sentences 412: voxforge_deu_000931 #utts: 1 +id: (voxforge_deu_000931-voxforge_deu_000931) +Scores: (#C #S #D #I) 38 9 2 1 +REF: d i E s e s g e t r e i d e d I e n T i n s ******* b e s o n d e r e a l s V i E H F U t T E R +HYP: d i * s e s g e t r e i d e d * e n D i n s b e s o n d e r e a l s F i V O R t A T +Eval: D D S I S S S S S S S S + +Speaker sentences 413: voxforge_deu_000932 #utts: 1 +id: (voxforge_deu_000932-voxforge_deu_000932) +Scores: (#C #S #D #I) 55 3 8 3 +REF: t Y p I s c h E R w E i s e w e r D e n s t a t i s c h e * i p * * A D r e S s e n v o n s E r v e r n e i n g e s e t z t +HYP: t Ü p * s c h * * w * i s e w e r * e n s t a t i s c h e E i p I E R T r e * s e n v o n ******* s * r v e r n e i n g e s e t z t +Eval: S D D D D D I I I S S D D D + +Speaker sentences 414: voxforge_deu_000933 #utts: 1 +id: (voxforge_deu_000933-voxforge_deu_000933) +Scores: (#C #S #D #I) 29 1 3 1 +REF: j e t z t w I r D e s s o l a n g s a * m g e g l a u B t +HYP: j e t z t ******* w * r * e s s o l a n g s a H m g e g l a u P t +Eval: D D D I S + +Speaker sentences 415: voxforge_deu_000934 #utts: 1 +id: (voxforge_deu_000934-voxforge_deu_000934) +Scores: (#C #S #D #I) 39 5 3 4 +REF: u n T E R s c h i e D l i c h e e r * e I G n i s s e h a * b e n s i c h e r ******* e i g n e * T +HYP: u n * A s c h i e T l i c h e e r G e * B n i s s e h a R b e n ******* s i c h e r e i g n e R D +Eval: D S S S I D S I D I I S + +Speaker sentences 416: voxforge_deu_000935 #utts: 1 +id: (voxforge_deu_000935-voxforge_deu_000935) +Scores: (#C #S #D #I) 49 5 1 4 +REF: t e R r o r ******* V E r d * Ä c h t i g e w u r d e n n I c h t v o r e i n g e r I c h t g e s t e l l t ******* * +HYP: t e * r o r F A r d E I c h t i g e w u r d e n n E c h t v o r e i n g e r E c h t g e s t e l l t N +Eval: D I S S I S S S I I + +Speaker sentences 417: voxforge_deu_000936 #utts: 1 +id: (voxforge_deu_000936-voxforge_deu_000936) +Scores: (#C #S #D #I) 50 5 12 1 +REF: a u f m a c h e n d i e s t i E f E L n i c H t a u s * z I e h E n u n d w e i S s G o T T w a s n o c H A l l E s +HYP: a u f m a c h e n d i e ******* s t i f * Ü n i c * t a u s T z * e h * n u n d w e i * s ******* K o * R D w a s ******* n o c * * l l * s +Eval: D S D S D I D D D D S D S S D D D D + +Speaker sentences 418: voxforge_deu_000937 #utts: 1 +id: (voxforge_deu_000937-voxforge_deu_000937) +Scores: (#C #S #D #I) 51 6 6 12 +REF: i n S G e s a m t * * * * * * * * * * * * 2 3 p e r s o n e n a u S v e r s c h i e d E N e n p a R l A m e n t e n n E H m e n t e i l +HYP: i n K e s a m t D R E I U N D Z W A N S I C p e r s o n e n a u * ******* v e r s c h i e d * * e n p a * l E m e n t e n n * I m e n t e i l +Eval: S S I I I I I I I I I I I I S S D D D D D S D S + +Speaker sentences 419: voxforge_deu_000938 #utts: 1 +id: (voxforge_deu_000938-voxforge_deu_000938) +Scores: (#C #S #D #I) 46 9 9 1 +REF: F o r d E R u n g s R e c H t e w e r D e n d e M g l Ä u b i g e R a u S s c h l i e S s l i C H Z U g e * O r D n e t +HYP: V o r d * * u n g s * e c F t e w e r T e n ******* d e * g l E u b i g e * a u * s c h l i e * s l i * F T O g e R U r T n e t +Eval: S D D D S S D D S D D D D S S S I S S + +Speaker sentences 420: voxforge_deu_000939 #utts: 1 +id: (voxforge_deu_000939-voxforge_deu_000939) +Scores: (#C #S #D #I) 24 0 1 6 +REF: d a s p R o b l e m * ** w * u r d e b e ******* h o b e n ******* * +HYP: d a s p * o b l e m H Ü w O u r d e b e h o b e n T +Eval: D I I I I I I + +Speaker sentences 421: voxforge_deu_000940 #utts: 1 +id: (voxforge_deu_000940-voxforge_deu_000940) +Scores: (#C #S #D #I) 47 2 5 3 +REF: f Ü r d i E e r ******* k E N n u n g v o n u n t e r B r o c h E n E r d i * s ******* k r e t e r S p r a c h e +HYP: f Ü r d i * e r k * Ä n u n g v o n u n t e r r o c h * n * r d i E s k r e t e r * p r a c h e +Eval: D I D S S D D I I D + +Speaker sentences 422: voxforge_deu_000941 #utts: 1 +id: (voxforge_deu_000941-voxforge_deu_000941) +Scores: (#C #S #D #I) 39 2 6 0 +REF: d i E C h i n e s e n k Ö N n t e n s e H r V i e l w i c h t i g E R w e r d e n +HYP: d i C * h i n e s e n k Ö * n t e n s e * r * i e l w i c h t i g * * A w e r d e n +Eval: S D D D D D D S + +Speaker sentences 423: voxforge_deu_000942 #utts: 1 +id: (voxforge_deu_000942-voxforge_deu_000942) +Scores: (#C #S #D #I) 37 6 15 3 +REF: d i E S e r * * s c H l Ü S S e L W I r D L e ******* d i g l i c h e I N e i n Z i g e S m a l v e R w E n d e t +HYP: d i * * e r T O s c * l ** * I e * ******* * * r * D e d i g l i c h ******* e * R e i n S i g e * ******* m a l F v e w * n d e t +Eval: D D I I D D D S D D D D D S I D D S S D D S S D + +Speaker sentences 424: voxforge_deu_000943 #utts: 1 +id: (voxforge_deu_000943-voxforge_deu_000943) +Scores: (#C #S #D #I) 56 3 0 1 +REF: d a s l a n d e n t ******* w I c k e l t e s I c h z u e i n e r m i l i t Ä r i s c h e n g r o s s m a c h t +HYP: d a s l a n d e n t w E c k e l t e s E c h z u e i n e r m i l i t E r i s c h e n g r o s s m a c h t +Eval: I S S S + +Speaker sentences 425: voxforge_deu_000944 #utts: 1 +id: (voxforge_deu_000944-voxforge_deu_000944) +Scores: (#C #S #D #I) 34 0 2 2 +REF: e s s * i n d u n d b l e i B e n v e r b r e c h e r ******* b a n d e n +HYP: e s ******* s E i n d u n d b l e i * e n v e r b r e c h e r b a n d e n +Eval: D I D I + +Speaker sentences 426: voxforge_deu_000945 #utts: 1 +id: (voxforge_deu_000945-voxforge_deu_000945) +Scores: (#C #S #D #I) 28 1 0 0 +REF: d i e z e i t e n w e r d e n s i c h Ä n d e r n +HYP: d i e z e i t e n w e r d e n s i c h E n d e r n +Eval: S + +Speaker sentences 427: voxforge_deu_000946 #utts: 1 +id: (voxforge_deu_000946-voxforge_deu_000946) +Scores: (#C #S #D #I) 52 2 3 2 +REF: d * e n s t i * f t i n d i e a c h S b o H r u n g e i n s c h i e b e n B i s z u m a n s c h l a g +HYP: d E e n s t i F f t i n d i e a c h * b o * r u n g e i n s c h i e b e n W i s T z u m ******* a n s c h l a g +Eval: I I D D S S D + +Speaker sentences 428: voxforge_deu_000947 #utts: 1 +id: (voxforge_deu_000947-voxforge_deu_000947) +Scores: (#C #S #D #I) 51 8 3 13 +REF: d i e a u c h * b e i m b R o * * W s e r W i * r K s a m w * i * r D b e i s p i E l s ******* w e i s e b e i M * F i * r E f o * * * * X +HYP: d i e a u c h T b e i m b * o A A U s e r V i E r * s a m M w V i E r T b e i s p i * l s w e i s e b e i L V E i E r f o U G S E N +Eval: I D I I S S I D S I I S D I S I S I S I I I I S + +Speaker sentences 429: voxforge_deu_000948 #utts: 1 +id: (voxforge_deu_000948-voxforge_deu_000948) +Scores: (#C #S #D #I) 21 3 4 1 +REF: d a s w a R n o c h g a R k * e i n E K R i s e +HYP: d a s ******* w a H n o c h g a * ******* k H e i n I * L i s e +Eval: D S D D I S D S + +Speaker sentences 430: voxforge_deu_000950 #utts: 1 +id: (voxforge_deu_000950-voxforge_deu_000950) +Scores: (#C #S #D #I) 35 0 5 1 +REF: d i e h a b e n o F f e n ******* b a r z i E m l i c H G r o S s e a n g s t +HYP: d i e h a b e n o * f e n b a r z i * m l i c * * r o * s e a n g s t +Eval: D I D D D D + +Speaker sentences 431: voxforge_deu_000951 #utts: 1 +id: (voxforge_deu_000951-voxforge_deu_000951) +Scores: (#C #S #D #I) 31 1 2 0 +REF: v i e l e v e R l i e r e n i H r e n a R b e i t s p l a t z +HYP: v i e l e v e * l i e r e n i E r e n a * b e i t s p l a t z +Eval: D S D + +Speaker sentences 432: voxforge_deu_000952 #utts: 1 +id: (voxforge_deu_000952-voxforge_deu_000952) +Scores: (#C #S #D #I) 28 2 0 4 +REF: d a * f Ü * r g I b t e s e i n e n p u n k t a b z U g ******* * +HYP: d a R f Ü H r g E b t e s e i n e n p u n k t a b z O g E +Eval: I I S S I I + +Speaker sentences 433: voxforge_deu_000953 #utts: 1 +id: (voxforge_deu_000953-voxforge_deu_000953) +Scores: (#C #S #D #I) 64 0 5 4 +REF: d i e b e i d e N s i n d Ü b e r e i n e u n * s i c h E R e v e r b i n D u n g m I t ******* * e i n a n d e r i n k o n t a k t * +HYP: d i e b e i d e * s i n d Ü b e r e i n e u n D s i c h * * e v e r b i n * u n g m * t D e i n a n d e r i n k o n t a k t N +Eval: D I D D D D I I I + +Speaker sentences 434: voxforge_deu_000954 #utts: 1 +id: (voxforge_deu_000954-voxforge_deu_000954) +Scores: (#C #S #D #I) 30 1 3 1 +REF: b e i ******* d e s t E c k e n t i E f i n r o t e n z a H l E n +HYP: b e i d e s t Ä c k e n t i * f i n r o t e n z a * l * n +Eval: I S D D D + +Speaker sentences 435: voxforge_deu_000955 #utts: 1 +id: (voxforge_deu_000955-voxforge_deu_000955) +Scores: (#C #S #D #I) 32 2 0 4 +REF: f Ü n f Z e h n u H r f Ü n f z e h n d o r * f * o n g o * l * f +HYP: f Ü n f T e h n u O r f Ü n f z e h n d o r C f U o n g o L l F f +Eval: S S I I I I + +Speaker sentences 436: voxforge_deu_000956 #utts: 1 +id: (voxforge_deu_000956-voxforge_deu_000956) +Scores: (#C #S #D #I) 58 1 9 2 +REF: w i e m e n s c h e n a u s e i n e R a n d e r * n w e l t E R s c h I e n e n S i E i h r h e u * t e u n d D o c h +HYP: w i e ******* m e n s c h e n a u s e i n e * a n d e r E n w e l t * * s c h * e n e n ******* Z i * i h r ******* h e u I t e u n d * o c h +Eval: D D I D D D D S D D I D + +Speaker sentences 437: voxforge_deu_000957 #utts: 1 +id: (voxforge_deu_000957-voxforge_deu_000957) +Scores: (#C #S #D #I) 34 1 6 0 +REF: b Ü n d I G m i t D e m h i n t e r n d e s k a m E l s a u f h Ö r t +HYP: b ** n d * * H m i t * e m h i n t e r n d e s k a m * l s a u f h ** r t +Eval: D D D S D D D + +Speaker sentences 438: voxforge_deu_000958 #utts: 1 +id: (voxforge_deu_000958-voxforge_deu_000958) +Scores: (#C #S #D #I) 53 3 5 4 +REF: a c h d e r o b e r ******* f Ö r S t e R z u C K t * e m i T d e n S c h i E f e n g r a u e n b r a u e n e i * * n +HYP: a c h ******* d e r o b e r f Ö r * t e A z u * G t D e m i E d e n * c h i * f e n g r a u e n b r a u e n e i N E n +Eval: D I D S D S I S D D I I + +Speaker sentences 439: voxforge_deu_000959 #utts: 1 +id: (voxforge_deu_000959-voxforge_deu_000959) +Scores: (#C #S #D #I) 46 2 4 2 +REF: i c h w u n d e r e m i C H i m ******* m E r w i E d e r Ü b e r d i E s e e r ******* k l Ä r u n g e n +HYP: i c h w u n d e r e ******* m i * G i m m A r w i * d e r Ü b e r d i * s e e r k l Ä r u n g e n +Eval: D D S I S D D I + +Speaker sentences 440: voxforge_deu_000960 #utts: 1 +id: (voxforge_deu_000960-voxforge_deu_000960) +Scores: (#C #S #D #I) 46 5 9 1 +REF: b E I e i n e m s Y M m e t r i S c h e n k r * Y p t O S Y s t e m W i r D A n d e r s v o r g e g a n g E N +HYP: b A R e i n e m s * * m e t r i * c h e n k r I U p t * * U s t e m * i r T * n d e r s v o r g e g a n g * * +Eval: S S D D D I S D D S D S D D D + +Speaker sentences 441: voxforge_deu_000961 #utts: 1 +id: (voxforge_deu_000961-voxforge_deu_000961) +Scores: (#C #S #D #I) 22 1 1 0 +REF: d a s i s T d o r t v e r z e i c h n e T +HYP: d a s i s * d o r t v e r z e i c h n e D +Eval: D S + +Speaker sentences 442: voxforge_deu_000962 #utts: 1 +id: (voxforge_deu_000962-voxforge_deu_000962) +Scores: (#C #S #D #I) 28 2 6 1 +REF: g e * l D I s T E I n s e H r g u t e s t a u s c h m i T t e L +HYP: g e L l T * s * * A n s e * r g u t e s t a u s c h m i * t e * +Eval: I S D D D S D D D + +Speaker sentences 443: voxforge_deu_000963 #utts: 1 +id: (voxforge_deu_000963-voxforge_deu_000963) +Scores: (#C #S #D #I) 33 2 0 5 +REF: d a s w * Ä r e * w i s s e n s c h a f t l i c h n o * T w e n d i g ******* * +HYP: d a s w E H r e R w i s s e n s c h a f t l i c h n o D w e n d i g T +Eval: I S I I S I I + +Speaker sentences 444: voxforge_deu_000964 #utts: 1 +id: (voxforge_deu_000964-voxforge_deu_000964) +Scores: (#C #S #D #I) 37 2 4 2 +REF: n u * r b e s t i M m t * E s t r a f t a t e n k o M m e n I n b e t r a c h t +HYP: n u N r ******* b e s t i * m t I S s t r a f t a t e n k o * m e n ******* E n b e t r a c h t +Eval: I D D I S D D S + +Speaker sentences 445: voxforge_deu_000965 #utts: 1 +id: (voxforge_deu_000965-voxforge_deu_000965) +Scores: (#C #S #D #I) 40 2 6 2 +REF: d a * m i t k a N n m a n W a H r s c h e I n l i c H S c h l E c h t e i n ******* k a u f e n +HYP: d a R m i t k a * n ******* m a n B a * r s c h e * n l i c * * c h l Ä c h t e i n k a u f e n +Eval: I D D S D D D D S I + +Speaker sentences 446: voxforge_deu_000966 #utts: 1 +id: (voxforge_deu_000966-voxforge_deu_000966) +Scores: (#C #S #D #I) 15 3 1 2 +REF: d a f Ü * R w U r d e g e ******* s o R G t +HYP: d a f Ü H E w O r d e g e s o * K t +Eval: I S S I D S + +Speaker sentences 447: voxforge_deu_000967 #utts: 1 +id: (voxforge_deu_000967-voxforge_deu_000967) +Scores: (#C #S #D #I) 23 3 5 2 +REF: m a n k a N n d A S s e H R G u t v e r K a u f e n ******* * +HYP: m a n k a * n ******* d E R s e * * * u t v e r a u f e n E +Eval: D D S S D D D S I I + +Speaker sentences 448: voxforge_deu_000968 #utts: 1 +id: (voxforge_deu_000968-voxforge_deu_000968) +Scores: (#C #S #D #I) 21 4 14 1 +REF: s o n D e R n A U c H I n D e R s t e u e R H I n t * e R Z i E H u n g +HYP: s o n * e * n * O c * ******* K n ******* * e * ******* s t e u e * * * n t D e * T i * O u n g +Eval: D D D S D D S D D D D D D D I D S D S + +Speaker sentences 449: voxforge_deu_000969 #utts: 1 +id: (voxforge_deu_000969-voxforge_deu_000969) +Scores: (#C #S #D #I) 35 0 1 0 +REF: d a r Ü b e r r e d e T d i e p a s t o r i n u n d r e d e t +HYP: d a r Ü b e r r e d e * d i e p a s t o r i n u n d r e d e t +Eval: D + +Speaker sentences 450: voxforge_deu_000970 #utts: 1 +id: (voxforge_deu_000970-voxforge_deu_000970) +Scores: (#C #S #D #I) 26 3 11 0 +REF: d e n s c h a l T e R I n d e n d R I T T e n G a n G s t e L l e n +HYP: d e n ******* s c h a l * e * ******* * n d e n d * * * E e n D O a n * ******* s t e * l e n +Eval: D D D D D D D D S S S D D D + +Speaker sentences 451: voxforge_deu_000971 #utts: 1 +id: (voxforge_deu_000971-voxforge_deu_000971) +Scores: (#C #S #D #I) 42 0 3 1 +REF: a u f d e n e r s t e n b l i C k s c h e i n T d a s u n g e w ** Ö H n l i c h +HYP: a u f d e n e r s t e n b l i * k s c h e i n * d a s u n g e w Ü Ö * n l i c h +Eval: D D I D + +Speaker sentences 452: voxforge_deu_000972 #utts: 1 +id: (voxforge_deu_000972-voxforge_deu_000972) +Scores: (#C #S #D #I) 49 2 5 2 +REF: d e r d o L l a R w i r D N i c h t m e h r a l s w Ä H r * u n g a k z * e p t i e r t w e r d e n +HYP: d e r d o * l a * w i r T * i c h t ******* m e h r a l s w ** E r H u n g a k z I e p t i e r t w e r d e n +Eval: D D S D D D S I I + +Speaker sentences 453: voxforge_deu_000973 #utts: 1 +id: (voxforge_deu_000973-voxforge_deu_000973) +Scores: (#C #S #D #I) 53 7 9 3 +REF: i H R e n k o p * f f e * s t g e g e n d e n h a l s D e r J Ü n g e r e n d a N N k Ü S s t E s i e d e n V a * t e r +HYP: i * L e n k o p T f f e H s t ******* g e g e n d e n h a l s ******* T e r * I n g e r e n d a * * M k ** Ö s t * I s i e ******* d e n F a R t e r +Eval: D S I I D D S D S D D S D S D S D S I + +Speaker sentences 454: voxforge_deu_000974 #utts: 1 +id: (voxforge_deu_000974-voxforge_deu_000974) +Scores: (#C #S #D #I) 22 1 5 0 +REF: d a s w u r d e n i c H t w a H R g e N o M m e n +HYP: d a s w u r d e n i c * t ******* w a * * g e o * m e n +Eval: D D D D S D + +Speaker sentences 455: voxforge_deu_000975 #utts: 1 +id: (voxforge_deu_000975-voxforge_deu_000975) +Scores: (#C #S #D #I) 24 1 4 2 +REF: M a N h a t D a s d a * m * a L s v o r g e l e s e n +HYP: W a * h a t ******* * a s d a M m E a * s v o r g e l e s e n +Eval: S D D D I I D + +Speaker sentences 456: voxforge_deu_000976 #utts: 1 +id: (voxforge_deu_000976-voxforge_deu_000976) +Scores: (#C #S #D #I) 54 6 14 2 +REF: B e i b e s o n d e r S w e R t v o L L E n s a c h E n * i s T d i E G R e n z e N i E D r i g e R a L s d e r w a R E n ******* W e r t +HYP: * e i b e s o n d e r * w e L t v o * * * n ******* s a c h U n G i s * d i * * K e n z e M i * T r i g e * a * s d e r w a * * n M e r t +Eval: D D S D D D D S I D D D S S D S D D D D I S + +Speaker sentences 457: voxforge_deu_000977 #utts: 1 +id: (voxforge_deu_000977-voxforge_deu_000977) +Scores: (#C #S #D #I) 22 3 4 0 +REF: D a s m U S s z U r Ü c k G e z a H l t w e r d e n +HYP: N a s m * O s T z * r Ü c k * e z a * l t w e r d e n +Eval: S D S S D D D + +Speaker sentences 458: voxforge_deu_000978 #utts: 1 +id: (voxforge_deu_000978-voxforge_deu_000978) +Scores: (#C #S #D #I) 39 1 4 2 +REF: z w i S c h e n g l Ä u b i g e r u n d s c h U l d * n e R h e r ******* g e l e i t e t +HYP: z w i * c h e n g l E u b i g e r u n d ******* s c h * l d E n e * h e r g e l e i t e t +Eval: D S D D I D I + +Speaker sentences 459: voxforge_deu_000979 #utts: 1 +id: (voxforge_deu_000979-voxforge_deu_000979) +Scores: (#C #S #D #I) 34 7 6 0 +REF: e i n a b s O L u t e s r e c h t w U R d E r e c h t S W i D r i G V e R l e T Z t +HYP: e i n a b s U M u t e s r e c h t w I E d * r e c h t * Z i E r i * * e L l e * * t +Eval: S S S S D D S S D D S D D + +Speaker sentences 460: voxforge_deu_000980 #utts: 1 +id: (voxforge_deu_000980-voxforge_deu_000980) +Scores: (#C #S #D #I) 39 0 3 1 +REF: m a n b r a u c h t n * i c h t a n d e n z u f a L l z u g l a u b e n +HYP: m a n b r a u c h t ******* n E i c h t a n ******* d e n z u f a * l z u g l a u b e n +Eval: D I D D + +Speaker sentences 461: voxforge_deu_000981 #utts: 1 +id: (voxforge_deu_000981-voxforge_deu_000981) +Scores: (#C #S #D #I) 53 2 7 2 +REF: z Ä r t l i c h e n w e s e n n u r E n t f a l t e n w * o m a n i H R l i e b e b o * t v o r h a r T e n +HYP: z E r t l i c h e n w e s e n n u r I n t f a l t e n w U o ******* m a n i * * ******* l i e b e ******* b o D t v o r ******* h a r * e n +Eval: S S I D D D D D I D D + +Speaker sentences 462: voxforge_deu_000982 #utts: 1 +id: (voxforge_deu_000982-voxforge_deu_000982) +Scores: (#C #S #D #I) 48 2 8 4 +REF: B e * ******* z Ü G l i c h d e R B e * w e i s l a S t U n D d e R h a f t u n g f Ü r h I l f S p e r s * o n e n +HYP: * e R z Ü K l i c h d e * * e R w e i s l a * t * n * d e * h a f t u n g f Ü r h E l f * p e r s C o n e n +Eval: D I I S D D I D D D D S D I + +Speaker sentences 463: voxforge_deu_000983 #utts: 1 +id: (voxforge_deu_000983-voxforge_deu_000983) +Scores: (#C #S #D #I) 38 2 13 2 +REF: B e i d e r n o R m a * l E N n U T z u n g * G I B T e s d i E v o L l e b a n d B r e i t e +HYP: * e i d e r n o * m a H l * * n * O z u n g E * * * * D e s d i * v o * l e ******* b a n d * r e i t e +Eval: D D I D D D S I D D D D S D D D D + +Speaker sentences 464: voxforge_deu_000984 #utts: 1 +id: (voxforge_deu_000984-voxforge_deu_000984) +Scores: (#C #S #D #I) 48 3 6 1 +REF: a b e r w i e i s t d i e s e s p r o b l e m i m g l o b a * l E n m a S S s t a b z U l Ö s e n +HYP: a b e r w i e i s t ******* d i e s e s ******* p r o b l e m i m g l o b a H l * n ******* m a * T s t a b z * O l E s e n +Eval: D D I D D D S D S S + +Speaker sentences 465: voxforge_deu_000985 #utts: 1 +id: (voxforge_deu_000985-voxforge_deu_000985) +Scores: (#C #S #D #I) 31 8 7 1 +REF: d A s e i g e n E w e * B l o G e R h Ä L t p o T e n T i e L L m e H r l e s e R +HYP: d E s e i g e n * ******* w e R P l o K e h ** E t ******* p o D e n Z i e * R m e * r l e s e * +Eval: S D D I S S S D S D S S D S D D + +Speaker sentences 466: voxforge_deu_000986 #utts: 1 +id: (voxforge_deu_000986-voxforge_deu_000986) +Scores: (#C #S #D #I) 31 5 5 1 +REF: d a s f r E m d e W e * b l o G s i e H T n o c h B e l e b t e r A U s +HYP: d a s ******* f r M m d e ******* V e R b l o K s i e * * n o c h I e l e b t e r * O s +Eval: D S D S I S D D S D S + +Speaker sentences 467: voxforge_deu_000987 #utts: 1 +id: (voxforge_deu_000987-voxforge_deu_000987) +Scores: (#C #S #D #I) 33 1 6 0 +REF: e i N E n e u e b e s t i M m u n g i s t e R l a S s e n W o r d e n +HYP: e i * * ******* n e u e b e s t i * m u n g i s t e * l a * s e n B o r d e n +Eval: D D D D D D S + +Speaker sentences 468: voxforge_deu_000988 #utts: 1 +id: (voxforge_deu_000988-voxforge_deu_000988) +Scores: (#C #S #D #I) 26 1 2 4 +REF: d a * r a u f I s T * h I n g e w i e s e n w o r d e n ******* * +HYP: d a R r a u f * s * T h E n g e w i e s e n w o r d e n T +Eval: I D D I S I I + +Speaker sentences 469: voxforge_deu_000989 #utts: 1 +id: (voxforge_deu_000989-voxforge_deu_000989) +Scores: (#C #S #D #I) 33 3 3 0 +REF: d i e b e V Ö L k E r u n g i s t g a n z m a S s i V v e r a r m t +HYP: d i e b e F Ö R k * r u n g i s t g a n z ******* m a * s i E v e r a r m t +Eval: S S D D D S + +Speaker sentences 470: voxforge_deu_000990 #utts: 1 +id: (voxforge_deu_000990-voxforge_deu_000990) +Scores: (#C #S #D #I) 34 2 5 0 +REF: d i e w e r d e n d A s g a n z B e s t I M m T n i c h t m a c h e n +HYP: d i e ******* w e r d e n d * s g a n z ******* P e s t * L m * n i c h t m a c h e n +Eval: D D D S D S D + +Speaker sentences 471: voxforge_deu_000991 #utts: 1 +id: (voxforge_deu_000991-voxforge_deu_000991) +Scores: (#C #S #D #I) 51 1 3 5 +REF: d i e d a * t e n ******* m E n g e d i E g e s e n d e t w i * r d * i s t e r ******* h e b l i c h g e r i n G e r +HYP: d i e d a R t e n m Ä n g e d i * g e s e n d e t w i E r d T i s t e r h e b l i c h ******* g e r i n * e r +Eval: I I S D I I I D D + +Speaker sentences 472: voxforge_deu_000992 #utts: 1 +id: (voxforge_deu_000992-voxforge_deu_000992) +Scores: (#C #S #D #I) 27 1 6 0 +REF: d a s e R g e b n i s i s t v e R f Ä l s C h T w o r d e n +HYP: d a s ******* e * g e b n i s i s t v e * f E l s * h * ******* w o r d e n +Eval: D D D S D D D + +Speaker sentences 473: voxforge_deu_000993 #utts: 1 +id: (voxforge_deu_000993-voxforge_deu_000993) +Scores: (#C #S #D #I) 60 2 3 2 +REF: e i n e b e s c H r Ä n k u n g t r i T t e R s t b e i b e ******* s o n d e r s i n t e n s i * v e r n u T z u n g a u f +HYP: e i n e b e s c * r E n k u n g t r i * t e * s t b e i b e s o n d e r s i n t e n s i E v e r n u O z u n g a u f +Eval: D S D D I I S + +Speaker sentences 474: voxforge_deu_000994 #utts: 1 +id: (voxforge_deu_000994-voxforge_deu_000994) +Scores: (#C #S #D #I) 68 7 3 3 +REF: d e r e n d ******* b e n U t z e R h a t e i n e h Ö h E r e g e s c h w i n d i G k e i t f Ü r d e n d O W n ******* l o A d z u R V e r ******* f Ü g u n g +HYP: d e r e n d b e n O t z e * h a t e i n e h Ö h R r e g e s c h w i n d i C k e i t f Ü r d e n d A U n l o T d z u * ******* F e r f Ü g u n g +Eval: I S D S S S S I S D D S I + +Speaker sentences 475: voxforge_deu_000995 #utts: 1 +id: (voxforge_deu_000995-voxforge_deu_000995) +Scores: (#C #S #D #I) 40 3 4 0 +REF: d e r s e m a n t i s c h e t e i l w u r d e s k e P t i s C H B e t r a c h t e t +HYP: d e r s e m a n t i s c h e ******* t e i l E w u r d e ******* s k e B t i s * * P e t r a c h t e t +Eval: D S D S D D S + +Speaker sentences 476: voxforge_deu_000996 #utts: 1 +id: (voxforge_deu_000996-voxforge_deu_000996) +Scores: (#C #S #D #I) 30 3 5 0 +REF: d o R t w i r D s e h r V i e l m E h r g e l D v e r d i e n T +HYP: d o * t w i r T s e h r * i e l ******* m * h r g e l * T v e r d i e n D +Eval: D S D D D D S S + +Speaker sentences 477: voxforge_deu_000997 #utts: 1 +id: (voxforge_deu_000997-voxforge_deu_000997) +Scores: (#C #S #D #I) 46 6 6 2 +REF: v ** e r s t Ä N D n i s F Ü r d a s v e r ******* a n T w o r T l i C H k e i t S g E f Ü H l e i n e r m u T t e r +HYP: v Ü e r s t ** * E n i s V Ü r d a s v e r a n w o r D l i * * k e i t Z g I f Ü * l e i n e r m u * t e r +Eval: I D D S S I S S D D S S D D + +Speaker sentences 478: voxforge_deu_000998 #utts: 1 +id: (voxforge_deu_000998-voxforge_deu_000998) +Scores: (#C #S #D #I) 25 2 4 2 +REF: d a s w I r D F Ü r d i E m e D i e n g e m a c h t ******* * +HYP: d a s w E r T * Ü r d i * ******* m e * i e n g e m a c h t E +Eval: S S D D D D I I + +Speaker sentences 479: voxforge_deu_000999 #utts: 1 +id: (voxforge_deu_000999-voxforge_deu_000999) +Scores: (#C #S #D #I) 44 5 2 0 +REF: s i E k a N n e i n e g a n Z k l a r e k a u f E m p f E H l u n g a u s s p r E c h e n +HYP: s i * k a * n e i n e g a n S k l a r e k a u f m p f I Ä l u n g a u s s p r Ä c h e n +Eval: D D S S S S S + +Speaker sentences 480: voxforge_deu_001000 #utts: 1 +id: (voxforge_deu_001000-voxforge_deu_001000) +Scores: (#C #S #D #I) 34 1 3 0 +REF: z a H l r e i c h e p R o t e s t e w e r d e n a R t i k u l i e r T +HYP: z a * l r e i c h e p * o t e s t e w e r d e n a * t i k u l i e r D +Eval: D D D S + +Speaker sentences 481: voxforge_deu_001001 #utts: 1 +id: (voxforge_deu_001001-voxforge_deu_001001) +Scores: (#C #S #D #I) 27 3 3 3 +REF: d I e d * u R c h ******* f Ü h r u n g w a r n i c h t s I c h * E R +HYP: d * e d E u * c h f Ü h r u n g w a r ******* n i c h t s E c h A T +Eval: D I D I D S I S S + +Speaker sentences 482: voxforge_deu_001002 #utts: 1 +id: (voxforge_deu_001002-voxforge_deu_001002) +Scores: (#C #S #D #I) 33 4 2 3 +REF: d i e w Ä h r u n * G h a t Ü b e R H a u p t k e i n e d E c k u n g ******* * +HYP: d i e w E h r u n E N h a t Ü b e * a u p t ******* k e i n e d I c k u n g N +Eval: S I S D S D S I I + +Speaker sentences 483: voxforge_deu_001003 #utts: 1 +id: (voxforge_deu_001003-voxforge_deu_001003) +Scores: (#C #S #D #I) 62 4 3 4 +REF: o b Ü b R i g e n s s e c k e r s D o * r * f d e r e i n e n d u r c h ******* a u s Z i e l ******* b e w u S s t e n l e b e N s k l U g e n +HYP: o b Ü b * i g e n s s e c k e r s T o A r A f d e r e i n e n d u r c h a u s ******* T i e l b e w u * s t e n l e b e M s k l O g e n +Eval: D S I I I D S I D S S + +Speaker sentences 484: voxforge_deu_001004 #utts: 1 +id: (voxforge_deu_001004-voxforge_deu_001004) +Scores: (#C #S #D #I) 42 7 1 3 +REF: m a n * s p r I c h t I n d i e s e m f A L l v o n k o n t r * A h i e r u n g s * Z w a n g +HYP: m a n R N s p r E c h t ******* E n d i e s e m f E I l v o n k o n t r E R h i e r u n g s T w a n g +Eval: I S S D S S S I S I S + +Speaker sentences 485: voxforge_deu_001006 #utts: 1 +id: (voxforge_deu_001006-voxforge_deu_001006) +Scores: (#C #S #D #I) 25 6 8 0 +REF: g l Ä u b I g E R u n D s c h U l d n E r s I n D S i c H e i n i g +HYP: g l O u b E g * A u n * ******* s c h * l d n A r s E n * ******* Z i c * ******* e i n i g +Eval: S S D S D D D S S D D S D D + +Speaker sentences 486: voxforge_deu_001007 #utts: 1 +id: (voxforge_deu_001007-voxforge_deu_001007) +Scores: (#C #S #D #I) 34 1 1 0 +REF: d a s w i r D n i c h t m e h r l a n g e s o b l e i b e n +HYP: d a s w i r T n i c h t m e h r l a n g e s o ******* b l e i b e n +Eval: S D + +Speaker sentences 487: voxforge_deu_001008 #utts: 1 +id: (voxforge_deu_001008-voxforge_deu_001008) +Scores: (#C #S #D #I) 42 7 8 1 +REF: e s G a b u n t e r s c h i e D l i c h S C H W e r e F o r m e n d e r f r E I h e I T S S t r a F e * +HYP: e s * a b u n t e r s c h i e T l i c h ******* * T R I e r e V o r m e n d e r f r * A h e * * * * t r a V e R +Eval: D S D D S S S S D S D D D D S I + +Speaker sentences 488: voxforge_deu_001009 #utts: 1 +id: (voxforge_deu_001009-voxforge_deu_001009) +Scores: (#C #S #D #I) 23 2 13 0 +REF: E S h A n d e L T s I C H U m e i n e f r e i e s o f t w A R E +HYP: * * ******* h * n d e * * s * * E I m ******* e i n e f r e i e ******* s o f t w * * * +Eval: D D D D D D D D S S D D D D D + +Speaker sentences 489: voxforge_deu_001010 #utts: 1 +id: (voxforge_deu_001010-voxforge_deu_001010) +Scores: (#C #S #D #I) 63 3 14 3 +REF: o R g a n s t r e i t ******* v e r ******* f a h R E n k Ö N n E n a u c h a u S s C h l i E S s l i c H a u f D e R l A n d e s ******* e b e n E S t a T t f i n D e n +HYP: o * g a n s t r e i t v e r f a h * * n k Ö * n * n a u c h a u * s * h l i * * s l i c G a u f * e * l * n d e s e b e n S * t a * t f i n T e n +Eval: D I I D D D D D D D D S D D D I S D D S + +Speaker sentences 490: voxforge_deu_001011 #utts: 1 +id: (voxforge_deu_001011-voxforge_deu_001011) +Scores: (#C #S #D #I) 35 3 6 2 +REF: w e g e n n U T z * l o s a u f g e w E n D e t e r U R l * a U b s Z e i t k a n n +HYP: w e g e n n * O z S l o s a u f g e w * n N e t e r * O l E a * b s * e i t ******* k a n n +Eval: D S I D S D S I D D D + +Speaker sentences 491: voxforge_deu_001012 #utts: 1 +id: (voxforge_deu_001012-voxforge_deu_001012) +Scores: (#C #S #D #I) 35 4 3 1 +REF: d a s w i r D n i c h t i m M E r p e r f e k t F u n k t * i O n i e r E n +HYP: d a s w i r T n i c h t i m A r ******* p e r f e k t V u n k t Z i * n i e r * n +Eval: S S S D S I D D + +Speaker sentences 492: voxforge_deu_001013 #utts: 1 +id: (voxforge_deu_001013-voxforge_deu_001013) +Scores: (#C #S #D #I) 33 4 7 3 +REF: m a n m u S S s i c h E n g * * * A G i e r e n d e s w a C H s t u m s w e g e n +HYP: m a n ******* m u * * ******* s i c h A n g E R S C H i e r e n d e s ******* w a * K s t u m s ******* w e g e n +Eval: D D D D S I I I S S D D S D + +Speaker sentences 493: voxforge_deu_001014 #utts: 1 +id: (voxforge_deu_001014-voxforge_deu_001014) +Scores: (#C #S #D #I) 39 0 0 2 +REF: w e * l * c h e w e g e s o l l e n e i n g e s c h l a g e n w e r d e n +HYP: w e L l I c h e w e g e s o l l e n e i n g e s c h l a g e n w e r d e n +Eval: I I + +Speaker sentences 494: voxforge_deu_001015 #utts: 1 +id: (voxforge_deu_001015-voxforge_deu_001015) +Scores: (#C #S #D #I) 21 5 2 1 +REF: d a s w i r d * I n d i e p R e i s e g e H E N +HYP: d a s S w i r d T E n d i e p * e i s e ******* g e N T +Eval: S I S D D S S S + +Speaker sentences 495: voxforge_deu_001016 #utts: 1 +id: (voxforge_deu_001016-voxforge_deu_001016) +Scores: (#C #S #D #I) 27 2 2 2 +REF: d i e Ü b e R n a H m e e r ******* f o l * G t e w Ö r t l i c h +HYP: d i e Ü b e * n a * m e e r f o l U K t e w I r t l i c h +Eval: D D I I S S + +Speaker sentences 496: voxforge_deu_001017 #utts: 1 +id: (voxforge_deu_001017-voxforge_deu_001017) +Scores: (#C #S #D #I) 35 2 4 1 +REF: d I e e n t ******* w I C k l u n g I s t w e i t v o r a n g e s c h r I T t e n +HYP: d * e e n t w * E k l u n g * s t w e i t v o r a n g e s c h r * E t e n +Eval: D I D S D D S + +Speaker sentences 497: voxforge_deu_001018 #utts: 1 +id: (voxforge_deu_001018-voxforge_deu_001018) +Scores: (#C #S #D #I) 45 3 7 1 +REF: d i e s Y m P t o m e t r e t e n d a N n s c h o N n a c h w E n I g e n s t U n d * e n a u f +HYP: d i e s * m t o m e t r e t e n d a * n ******* s c h o * ******* n a c h ******* w A n * g e n s t O n d T e n a u f +Eval: D S D D D D D S D S I + +Speaker sentences 498: voxforge_deu_001019 #utts: 1 +id: (voxforge_deu_001019-voxforge_deu_001019) +Scores: (#C #S #D #I) 32 2 5 0 +REF: E s G i b t e i n E g r o S S e w E l l e v o n p r o z e S s e n +HYP: * s * i b t e i n I g r o * * e w Ä l l e v o n p r o z e * s e n +Eval: D D S D D S D + +Speaker sentences 499: voxforge_deu_001020 #utts: 1 +id: (voxforge_deu_001020-voxforge_deu_001020) +Scores: (#C #S #D #I) 29 2 4 0 +REF: E s I s t b e r e i t S m e i n z w e i t e r a u t o m a T +HYP: * s * s t b e r e i t * Z m e i n ******* z w e i t e r a u t o m a D +Eval: D D D S D S + +Speaker sentences 500: voxpopuli_deu_000309 #utts: 1 +id: (voxpopuli_deu_000309-voxpopuli_deu_000309) +Scores: (#C #S #D #I) 100 7 30 3 +REF: B R U S S L A N D S I m p l E m e n t i e r u n g v o n h Ö H E r E n s t a n d * a R D s z u M s c H u t z p e R s Ö n l i c H e r d a * t e n e b e n f A L L s g e * n e r e L l u n s E r e G u t e z u s a M m e n a r B e i t e R l e i c h t e r N +HYP: * ******* * * * * * * * * * ******* * m p l * m e n t i e r u n g v o n h Ö * * r I n s t a n d E a * T s z u * ******* s c * u t z S p e * s O n l i c J e r d a R t e n ******* e b e n f * E I s g e N n e r e * l u n s * r e * u t e z u s a * m e n a r * e i t e * l e i c h t e r * +Eval: D D D D D D D D D D D D D D D D S I D S D D D S D S S I D D S S I D D D D D D D + +Speaker sentences 501: voxpopuli_deu_000310 #utts: 1 +id: (voxpopuli_deu_000310-voxpopuli_deu_000310) +Scores: (#C #S #D #I) 62 12 28 6 +REF: P O L I Z e I B E a m t e H a b e n d A S s C H l i M m s t e v e r H i n d e r T H a B E N I H R l e b e n g e r e T t e T U N D S i n D s e l b e R v e r l E t Z t w O r d E n * * * * * * +HYP: * * * * * e * * R a m t e * a b e n ******* d * E R s * * l i * m s t e v e r i n d e r * D a * * R * * M E l e b e n g e r e D t e * ******* * * * * i n * s e l b e * v e r l Ä t S t ******* w U r d * n I G K L A B +Eval: D D D D D D D S D D D S S D D D S D S D D S D D S S S D D D D D D D D S S D S D I I I I I I + +Speaker sentences 502: voxpopuli_deu_000311 #utts: 1 +id: (voxpopuli_deu_000311-voxpopuli_deu_000311) +Scores: (#C #S #D #I) 27 6 22 1 +REF: D A S I S T N i c H T m Ö G L i C H d a S s d e r K o M m i s ******* s A R n i C H t H i e R I s T +HYP: * * * ******* * * * ******* * i c * * m Ö B R i Ü E d a * s d e r * o * m i s s * E I n i * * t * i e * ******* * s * +Eval: D D D D D D D D D D D S S S S D D D I D S S D D D D D D D + +Speaker sentences 503: voxpopuli_deu_000312 #utts: 1 +id: (voxpopuli_deu_000312-voxpopuli_deu_000312) +Scores: (#C #S #D #I) 35 7 14 11 +REF: 1 9 m i t G l I e D u n * ******* d * H o f F e d a S s w I r I M n Ä c * h s * T e N J a H r Ü b e R d * * ******* * a * * S +HYP: * * ******* m i t * l * e T u n D d E C o f V e d a * s w E r ******* * * n E c X h s L I e * ******* * a * r Ü b e * d E S W a N Z E +Eval: D D D D D S I I I S S D S D D D S I I S D D D D D I I I I I I S + +Speaker sentences 504: voxpopuli_deu_000313 #utts: 1 +id: (voxpopuli_deu_000313-voxpopuli_deu_000313) +Scores: (#C #S #D #I) 79 22 18 12 +REF: * e s d a R f n i c h T Ü B e r s e h e n w e r d E n d a S s i M m e r h i n * * * * * * ** m * * E H R A L S 5 0 d e r b e V Ö l k E R U n g D e R e U r O P Ä I s c h e n u n I O n * i M l Ä n D l i c h e n r a U m l * e b T +HYP: D e s d a * f n i c h * I V e r s e h e n w e r d * n d a * s i * m e r h i n W E R B E F Ü m T Z I G T P U O Z E N D d e r ******* b e F Ö l k * * Ö n g * e A D e * r * * B E s c h e n ******* u n * U n N i E l E n * l i c h e n G r a * m ******* l I e b * +Eval: I D D S S D D D I I I I I I I I I S S S S S S S S S S D S D D S D S S D D D S S D D S I S S D S D D I D + +Speaker sentences 505: voxpopuli_deu_000314 #utts: 1 +id: (voxpopuli_deu_000314-voxpopuli_deu_000314) +Scores: (#C #S #D #I) 96 6 29 2 +REF: W I R W O L L E N A L s o d a S s d e r b Ü r g e r s c h N e l l E R E I n e a u s ******* k u n f T b e k o m m t o B s e i n e b e s c h W E r d e Ü b e R h a U P t a n g e n o M m e n w i r d o B s i e b e r e c h t i * G t i s t +HYP: * * * ******* * * * * * * ******* * * s o ******* d a * s d e r b Ü r g e r s c h * e l l * * ******* * O n e a u s k u n f * b e k o m m t o * P s e i n e b e s c h * Ä r d e Ü b e * h a * B t a n g e n o * m e n ******* w i r d o * P s i e b e r e c h t i C H t ******* i s t +Eval: D D D D D D D D D D D D D D D D D D D D S I D D S D S D D S D D D S I S D + +Speaker sentences 506: voxpopuli_deu_000315 #utts: 1 +id: (voxpopuli_deu_000315-voxpopuli_deu_000315) +Scores: (#C #S #D #I) 71 13 8 5 +REF: E I n „ r * e s e * t u n S e r e r b E Z i E H U n g e n i s t n i c h t v o n N Ö t e n a b e r s E H R W o H L k o n t i n u * ******* i e r l i c h e s f e i n t * U n I n g +HYP: * * n *** r I e s e T t u n Z e r e r b * * i T I O n g e n i s t n i c h t v o n E U t e n a b e r ******* s * I A B o * E k o n t i n u N i e r l i c h e s f e i n t I O n E n g +Eval: D D D I I S D D S S S S S D D S S S S D S I I I S S + +Speaker sentences 507: voxpopuli_deu_000316 #utts: 1 +id: (voxpopuli_deu_000316-voxpopuli_deu_000316) +Scores: (#C #S #D #I) 38 10 12 2 +REF: U N D d A W i R D g a n Z s t o l Z g e s a g T D I e b e s c h Ä f t i G u n g s t e i g * t j * A a n +HYP: * * * L d * ******* * i E T g a n * ******* s t o l * S g e s a g * J A e D b e s c h E f t i * u n g ******* s t e i g K t D j E R a n +Eval: D D D S D D D S S D D D S D S S S S D D I S I S + +Speaker sentences 508: voxpopuli_deu_000317 #utts: 1 +id: (voxpopuli_deu_000317-voxpopuli_deu_000317) +Scores: (#C #S #D #I) 102 18 29 11 +REF: I C H w i L L S a G E N W I E E s I s T f Ü r u n S i s T D e r E u R o U n * T e r B e * W e r t e t w i * r e x * p o r t i e R E n z * u v I e * l z U b i L l i * g U N D w * I r i m ******* p o R t i e r e n z * u w e N i g w i * r V E r s c h e n k e n w o H l s t a N D +HYP: * * * ******* w i * * ******* D a * * * ******* * * * ******* * s * s * f Ü r u n * i s * T e r A u H o * n D e r e R V e r t e t w i E r e x S p o r t i e A n z O u v E e L l z O b i * l i C g * * A L w V E r i m p o * t i e r e n ******* z S u ******* w e D i g w i E r F A r s c h e n k e n w o * l s t a * T +Eval: D D D D D D D S D D D D D D D D D D D D D S S S D I S S I S I I S S I S I S D I D D S S I S I D D I D S I S S D D S + +Speaker sentences 509: voxpopuli_deu_000318 #utts: 1 +id: (voxpopuli_deu_000318-voxpopuli_deu_000318) +Scores: (#C #S #D #I) 50 2 12 6 +REF: D A S S s i e h e u T e * a * b e n D H i e R a n w e s e n D S i n d i s T e I n p * o s i t i * ******* v e s s i g n a l * +HYP: * * * * ******* s i e h e u D e R a R b e n * * i e * a n w e s e n * Z i n d i s * ******* e * n p R o s i t i E v e s s i g n a l E +Eval: D D D D D S I I D D D D S D D D I I I I + +Speaker sentences 510: voxpopuli_deu_000319 #utts: 1 +id: (voxpopuli_deu_000319-voxpopuli_deu_000319) +Scores: (#C #S #D #I) 108 10 7 9 +REF: * * * * * * * 9 0 p r o z e n t a l l E R E U r o p Ä I s c h e n f i l m e d i e a u s s e R h a l B i H r e s h e i m a t l a n d e s g e z e i * G t w e r d e n s i n d v o M m e d i * A p r o g r a M m g e f Ö r d e r t w O r d e n +HYP: N E U N Z S I G H p r o z e n t a l l * A * A r o p Ä * s c h e n f i l m e d i e a u s s e * h a l T i * r e s ******* h e i m a t l a n d e s g e z e i C H t w e r d e n s i n d v o R m e d i E R p r o g r a * m g e f E r d e r t w U r d e n +Eval: I I I I I I I S S D S D S D D S D D I S S I S D S S + +Speaker sentences 511: voxpopuli_deu_000320 #utts: 1 +id: (voxpopuli_deu_000320-voxpopuli_deu_000320) +Scores: (#C #S #D #I) 59 8 15 8 +REF: W i e s o k a N N i c h D e M e r ******* g e b * N i s * D e r ******* * * * a u S s c h u s S a b S t I M m u n g i n d i e s e R f o r * m n i c h T z u s t I M M e N +HYP: B i e s o ******* k a * * ******* i c h ******* * e * B e r g e b P L i s S T e r A L E a u * s c h u s a b P t * E m u n g i n d i e s e * f o r E m n i c h * ******* z u s t * * * e M +Eval: S D D D D D D D S I I S I S I I I I D S S D S D I D D D D D S + +Speaker sentences 512: voxpopuli_deu_000321 #utts: 1 +id: (voxpopuli_deu_000321-voxpopuli_deu_000321) +Scores: (#C #S #D #I) 79 13 10 3 +REF: W I r W o L L t e n v e r H i n d e r n d a S S s i c h * h i n T e r D i E S e M g e i s T i g e n e i g e n t u m d i e a u s K u N F T s P f l i c h t * v e r s t e c k e n k A n * N +HYP: B E r ******* B o * R t e n v e r i n d e r n d a * * ******* s i c h E h i n D e r * i * * e N g e i s * i g e n e i g e n t u m d i e a u s G u * M s f l i c h t E v e r s t e c k e n k O n T E +Eval: S S D S D S S D D D I S D D D S D S D S S S I S I S + +Speaker sentences 513: voxpopuli_deu_000322 #utts: 1 +id: (voxpopuli_deu_000322-voxpopuli_deu_000322) +Scores: (#C #S #D #I) 82 8 20 5 +REF: E s g i b T J e t z T i m z u ******* s a M m E N H a n g M I T D e r v e r s t Ä r K t e n z u s a m m E N a R b e i t e i n e n e r s t e n g a n g v o n e i n i g e n m i t G L i e D s t a A t e * ******* n * * +HYP: I s ******* g i b * D e t z * ******* i m z u s a * m * * * a n g ******* * * * ******* * e r ******* v e r s t E r G t e n z u s a m m * a U b e i t e i n e n e r s t e n g a n g ******* v o n e i n i g e n ******* m i t * * i e L s t a R t e N n A C +Eval: S D D S D D I D D D D D D D D D D D S S D S S D D D D S S I I I I + +Speaker sentences 514: voxpopuli_deu_000323 #utts: 1 +id: (voxpopuli_deu_000323-voxpopuli_deu_000323) +Scores: (#C #S #D #I) 157 14 26 24 +REF: w a s D i E G r e n z * Ü b e r S c h r e i t e n ******* d e z u s a M m e n a R b e i T a n b e l a n G t u n d * * * D i * ******* e * v e r b r e i t u n g i n D r i T T l Ä n d e r b e t r i F f t * * * H i e R m Ö c h T E i c h e i n b e i * * s p i e L n e N n e n d A s e I n e r ******* f o l G s b e i s p i E l F Ü R m i c h i s t u n d z w a r * * s l U m ******* d o * g m i L l i * * O n Ä r * * * * +HYP: w a s ******* T i * * r e n z Y Ü b e r * c h r e i t e n d e z u s a H m e n a * b e i * a n b e l a n * t u n d W A S T i E e R v e r b r e i t u n g i n ******* T r i G l Ä n d e r b e t r i * f t U N D T i e * ******* m E c h * * L i c h e i n ******* b e i S C s p i e * ******* n e * n e n ******* d E s e * n e r f o l K s b e i s p i * l ******* V Ü * ******* m i c h i s t u n d z w a r E L s l * m d o K g ******* m i * l i E R n E r E D A S +Eval: D S D D I D I S D D D I I I S I I I D S S S D I I I S D D S D D S D I I D D D D S D I S D D S D D I I D I I D D I I S S I I I I + +Speaker sentences 515: voxpopuli_deu_000324 #utts: 1 +id: (voxpopuli_deu_000324-voxpopuli_deu_000324) +Scores: (#C #S #D #I) 108 9 23 3 +REF: U N D d a s n i c h t n u r i n p O r t u g a * l O d E r g r i E c h e n l a n d s O n D e r n a u C H I n s o v e r m e i n t l i c h * R e i c h e n m i t G L i e D s t A a t e n W i e d e u t s C h l a n D o d e r G r O S s B R I t a N n i e * n +HYP: * * * ******* d a s n i c h t E n u r i n p * r t u g a H l * d * r g r i * c h e n l a n d s * n * e r n a u * * R E n ******* s o v e r m e i n t l i c h G * e i c h e n m i t * W i e s t * a t e n V i e ******* d e u t s * h l a n * o d e r * r * U s * P E t a * n i e R n +Eval: D D D D S D I D D D D D D D S S D I D D S S D S D D D D D S D S S D I + +Speaker sentences 516: voxpopuli_deu_000325 #utts: 1 +id: (voxpopuli_deu_000325-voxpopuli_deu_000325) +Scores: (#C #S #D #I) 16 4 12 3 +REF: D I E Z E I t F Ü r a u s R E D e n i s T v O r b e i ******* * * +HYP: * * * ******* * * * t ******* V E r a u s * * W e n ******* i s * v E r b e i D A +Eval: D D D D D D D D S S D D S D D S I I I + +Speaker sentences 517: voxpopuli_deu_000326 #utts: 1 +id: (voxpopuli_deu_000326-voxpopuli_deu_000326) +Scores: (#C #S #D #I) 79 12 18 6 +REF: S I e a l l e f l i e g E N a l S m i T G L i e d e r d i e s e s h a u s e s W a H r s c h e I n L i C H d * E U t l i c H h Ä U f i g e * r a l s d e R * e U * d u r C H s c h n i T t S b Ü r g e r ******* * +HYP: * * e ******* a l l e L f l i e g * * ******* a l * m i K D i e d e r d i e s e s ******* h a u s e s V a * r s c h e * n i * E d O R D t l i c * ******* h O L f i g e A r a l s d e * I e * U d u r * * s c h n i * t Z b Ü r g e r T +Eval: D D D S D D D D S S S D S D D S D S I S S D D S S I D I D I D D D S I I + +Speaker sentences 518: voxpopuli_deu_000327 #utts: 1 +id: (voxpopuli_deu_000327-voxpopuli_deu_000327) +Scores: (#C #S #D #I) 56 7 17 9 +REF: U N D I C H B I n s i c h e r d a S s I H r e * b e d * e u * ******* t u n g i N n a h E r ******* * Z u ******* k u N f t S o g A r n o c h * z u n e h m E N w i * r D +HYP: * * * ******* * * * ******* * E n s i c h e r d a * s * E r e R b e d O e u T t u n g i * ******* n a h * r S F u k u M f t ******* U o g E r n o c h T z u n e h m * * w i E r T +Eval: D D D D D D D D D S D D S I I I I D D D I I S I S D S S I D D I S + +Speaker sentences 519: voxpopuli_deu_000328 #utts: 1 +id: (voxpopuli_deu_000328-voxpopuli_deu_000328) +Scores: (#C #S #D #I) 138 14 18 9 +REF: * ******* e s G e H t H i e r u m d i E r i c h t l i * n I e d e s R a T e s Z U R f E s t l e g u n g g R u n d l e g E n ******* d E R s i C h e r H e i t s ******* n O r m e n f Ü r d e n s c h u t z * V O r d e n g e f a H r e n e i n e r e x * p o s i t * i o ******* n g e g E N Ü b E r I o n i s i e r e n d E r s t r a H l u n g +HYP: T e s ******* K e * t ******* D i e r ******* u m d i * ******* r i c h t l i H n J e d e s * a D e s * * E f Ä s t l e g u n g g * u n d l e g * n d * * A s i G h e r * e i t s n E r m e n f Ü r d e n s c h u t z F Ü E r d e n g e f a * r e n e i n e r e x S p o s i t S i o n g e g * I Ü b A r * o n i s i e r e n d A r s t r a * l u n g +Eval: I I D S D D S D D D I S D S D D S S D D I D D S S D I S I S S D I I I D S S D S D + +Speaker sentences 520: voxpopuli_deu_000329 #utts: 1 +id: (voxpopuli_deu_000329-voxpopuli_deu_000329) +Scores: (#C #S #D #I) 23 2 6 1 +REF: d a s g i l t e s w i E d e r h e r * Z u s t e L l E N +HYP: d a s S g i l t ******* e s w i * d e r ******* h e r C H u s t e * l * * +Eval: S D D D I S D D D + +Speaker sentences 521: voxpopuli_deu_000330 #utts: 1 +id: (voxpopuli_deu_000330-voxpopuli_deu_000330) +Scores: (#C #S #D #I) 50 4 6 0 +REF: d I e s e n e i n e n e i n z i g e n s i t z G i b t e s l Ä n g s T d a s i S T s t R a s S B u r g +HYP: d * e s e n e i n e n e i n z i g e n ******* s i t z K i b t e s l Ä n g s * d a s ******* i * E s t * a s T P u r g +Eval: D D S D D D S D S S + +Speaker sentences 522: voxpopuli_deu_000331 #utts: 1 +id: (voxpopuli_deu_000331-voxpopuli_deu_000331) +Scores: (#C #S #D #I) 214 33 92 17 +REF: W I R S E H E N J A G E R A D e d a S s D a s p a S s i e r t i n m a l t a * d i e J o U R n A l i s t I n d i E k o R R U p T I o n S f Ä L l e a u f g e d e C k t H A T * i s T v O R W e N I g e n W O c h E n e r m o R d e T w O r D E N w e d e r w e r D e n s y s t * e m a T i s C h D i E k o R R U P T I o * n S f Ä l * L e * * u n t e r s u c h T n o c h W i R D D e r m o r D s e l b e r ******* * g e z i e l t ******* * u n t e r s u C H t M a N H a t F a s T D E n E I n d R U C K a L s O B H I e R a L l e s ******* * * * * u n t e r d e M m a n T E L d e S s c h w e i G E n s +HYP: * * * ******* * * * * * ******* * * ******* * * * * * e d a * s ******* * a s ******* p a * s i e r t i n m a l t a R d i e S o * * n * l i s t E n d i * k o * * B p Z o n D f ** E l e a u f g e d e * k t ******* * E R D i s * v * * ******* * e * R g e n B E c h * n ******* e r m o * d e * w A r * * * w e d e r w e r * e n s y s t D e m a * i s * h ******* * i * ******* k o * * * C H o U n D f E l E e B E u n t e r s u c h * n o c h * i * * E T e r m o r * s e l b e r E g e z i e l t E u n t e r s u * * t ******* E a * * a t ******* V a s * ******* S I n * A n d * * * O G a * s ******* * * * W e N a * l e s I E O R u n t e r d e * m a n * * * ******* d e * I s c h w e i * * n s +Eval: D D D D D D D D D D D D D D D D D D D D D D D I S D D D S D D D S S S S D S D D D S S I D D D D D D S S S D D D D S D D D D I D D D D D D D D D S S S I S S I S I I D D D D S S D I I I I D D D S D D D S D D S S D S D D D S S D D D D D S S D I I I I I D D D D D D S D D + +>> REF: z u g e d e C k T w e r D E n s o * L L +>> HYP: z u g e d e * k * w e r * * n ******* s o R S +>> Eval: D D D D D I S S + +Speaker sentences 523: voxpopuli_deu_000332 #utts: 1 +id: (voxpopuli_deu_000332-voxpopuli_deu_000332) +Scores: (#C #S #D #I) 86 6 37 2 +REF: D O R T S T E H E N Ü B E R A L l E n t l a n G d e R k Ü s t e d i e w a R n s t * e i n e d i e a u f d i e G r o S s e n k a t a s T R o P H e n M i T T S u n a * m i S I n d E r v E r g a n G e n H e i t H i n w e i s e n +HYP: * * * * ******* * * * * * * ******* ** * * * * * l ******* I n t l a n * d e * k Ü s t e d i e ******* w a * n s t A e i n e d i e a u f d i e * r o * s e n k a t a s * F o * F e n * i * * Z u n a H m i * E H n d * r v * r g a n * e n * e i t ******* * i n w e i s e n +Eval: D D D D D D D D D D D D D D D D D D D S D D D D I D D D S D S D D D S I D S S D D D D D D + +Speaker sentences 524: voxpopuli_deu_000333 #utts: 1 +id: (voxpopuli_deu_000333-voxpopuli_deu_000333) +Scores: (#C #S #D #I) 149 23 38 13 +REF: H E R R P R Ä S I d e * n T i c H h a b e I M P r i * n ******* z i * p f Ü r d e N b e R i c h t g e s t I m m * t o B w o H l e R E i n e n s C H w e * r E n f E h l e r E n t H Ä l t e s W i r D n Ä m L i * C H D A z u * a u f ******* g e f O r d e r t d a s E u r o * P Ä I s c H e p A R l A m e n T a u f d e m w e g * z * U e i N e m e i n z i g e n s i t Z z u u n T e R s t * Ü t z e n +HYP: * * * * ******* * * ** * * d e N n D i c * ******* h a b e ******* * * ******* B r i E n z i E p ******* f Ü r ******* d e * b e * i c h t g e s t E m m N t o * w o * l ******* e * * i n e n s * * w e H r * n f * h l e r I n t E L l t e s * i r T n E m * i T A R * * z u R a u f g e f A r d e r t d a s A u r o B E H R s c * e p * L l R m e n D a u f d e m w e g K T z H E e i D e m e i n z i g e n ******* s i t * z u u n D e * s t E L t z e n +Eval: D D D D D D D D D D I S D D D D D D S I I I D D D D S I D D D D D D D I D D S S S D S S D I S S D D I I S S I S S S D D S S S I S I S S D D S D I S + +Speaker sentences 525: voxpopuli_deu_000334 #utts: 1 +id: (voxpopuli_deu_000334-voxpopuli_deu_000334) +Scores: (#C #S #D #I) 98 7 5 20 +REF: i n d i e ******* s e N T r E F f e n w U R d e n g e m e i N s a m E * * p o l i t i s c h e v e r ******* a b r e d u n g e * n i m k r e i s d e r * * * * 2 7 * * * * * * * * * g e t * r o f f e * n u n d a u c h P u b l i k g e m a c h t +HYP: i n d i e s e M * r * I f e n w * O d e n g e m e i s a m * M E p o l i t i s c h e ******* v e r a b r e d u n g e R n i m k r e i s d e r S I E B E N U N T Z W A N Z I g e t D r o f f e R n u n d a u c h H u b l i k g e m a c h t +Eval: I S D D S D S S D I I D I I I I I I S S I I I I I I I I I I I S + +Speaker sentences 526: voxpopuli_deu_000335 #utts: 1 +id: (voxpopuli_deu_000335-voxpopuli_deu_000335) +Scores: (#C #S #D #I) 284 40 130 13 +REF: i C H b i n D e r Ü B e r Z E U g U n G d A S s w I r E s h e u t e m i t d e m v o r S c h L a * g A U s D e m u m W e l t ******* a u S s c h U S s g e s c h a F f t H a B E N E i N E n S c h R i T t w E i t e R Z u ******* k O M m E N e S I s T N i C H T P e r f e k t e U R o * p Ä i S c h e Ä r z T e s a g e n W I R h Ä T t e n F Ü * r h o c h r i s i k * o P r * o d u k t e E i n e Z e n T r a l e z u l a S s U n G h a B e n m Ü s s e n d a s H a b E i c h N i c h T g e s c H a F f t a B E r m i t d e m W A S H e U T E a U F D e m t i s c h l i E G t S C H a F F e +HYP: i * * ******* b i n ******* * e r ** * e r * O g E n * d * E s w E r ******* * s ******* h e u t e m i t ******* d e m v o r * c h * a R g ******* * E s * e m u m B e l t a u * s c h * O s g e s c h a * f t ******* * a * * M * i * * n ******* * c h * i * t ******* w * i t e A u k * * m * * ******* e * ******* * s * ******* * i * * G * e r f e k t e * o R p Ä i * c h e ** r z * e ******* s a g e n ******* * * D E h ** E t e n * Ü E r h o c h r i s i k R o * r P o d u k t e * i n e S e n D r a l e ******* z u l a * s * n * h a M e n m Ü s s e n d a s ******* * a b * R i c h ******* * i c h * g e s c * a * f t a * * r ******* m i t ******* d e m ******* * E R S e * I D a * * * e m ******* t i s c h ******* l i * K t ******* * P L a * U e +Eval: D D D D D D D D S S S D D S S D D D D D D I D D S D S I D D S D D D D D S D D D D D D D D D S S I D D D D D D D D D D D D D S D D S I D D D D D D D S S D S D I I D I D S S D D D D S D D D S D D D D D D D D D D D S S S D S S D D D D D D S D D S S D S + +>> REF: N W i * R W O H L * * t r o t Z D e m e i n e n g r o S s e n s c H r i T t V I E L l e i c h T k e i N E n m e i L E n s t * e i n A B e R e i N E n g r o S s e N s c H R I T T h i N z u M e H r p a T I e N T e n s I C H e * R h * e I T +>> HYP: * ******* * i C H * D A S I R t r o t * T e m e i n e n g r o * s e n s c * r i * t * * * F l e i c h * k e i * * n m e i * * n s t D e i n ******* * * e * e i * * n g r o * s e * ******* s c * * * * * ******* h i * ******* z u * e * r ******* p a * * e * D e n s * E e T h A e * N +>> Eval: D D D I S D S S S I I D S D D D D D D S D D D D D I D D D D D D D D D D D D D D D D D D D D D D D S D S S I S I D S + +Speaker sentences 527: voxpopuli_deu_000336 #utts: 1 +id: (voxpopuli_deu_000336-voxpopuli_deu_000336) +Scores: (#C #S #D #I) 17 16 15 10 +REF: F R A U p R Ä S I D e n ******* T I n * * * f ** r * * A U K O M M i S S A R i N L i * E B e * K O L L e * g E N +HYP: * * * * ******* p * Ä * * H e n D A n G E S f Ö r G F Ü R * Z W E i E N H E i B M i N O D e N * * * * e R g * * +Eval: D D D D D D D D S I S S I I I I I I S S D S S S S S S S S S I S S I D D D D I D D + +Speaker sentences 528: voxpopuli_deu_000337 #utts: 1 +id: (voxpopuli_deu_000337-voxpopuli_deu_000337) +Scores: (#C #S #D #I) 107 10 31 5 +REF: z u m a k t u E L l e n i c H G l a U b E E s k a N n k e i n e r v o N * u n S * a N n e h m e N d a S s w i R w i r K l i c h e * r s T S e I t d i e s e M w o c h e n ******* e N d E w i S s e n d A S s U n s d i e z a H l u n G s u N f Ä H i * g k e i t d r o H t +HYP: z u m a k t u * Ä l e n i c T * l a * b * ******* I s ******* k a * n ******* k e i n e r v o * N u n * S a * n e h m e * d a * s w i * ******* w i r T l i c h e A r s * Z e * t ******* d i e s e N w o c h e n e d * I w i * s e n ******* d * * s ******* O n s d i e ******* z a * l u n * s u M f ** * i C g k e i t ******* d r o * t +Eval: D S S D D D D S D D D D I D I D D D D D S I D S D D S I S D S D D D D D S D D D S D D I D D + +Speaker sentences 529: voxpopuli_deu_000338 #utts: 1 +id: (voxpopuli_deu_000338-voxpopuli_deu_000338) +Scores: (#C #S #D #I) 39 6 9 6 +REF: d A S s I n d e i n f A c h b e d i n g U n g e n d i E n i c H T A k * z e p t a b * * e * L S I n ******* * D +HYP: d * * ******* s * n d e i n f * c h b e d i n g * n g e n d i * ******* n i c * G E k T z e p t a b E S e N D M A n K A +Eval: D D D D D D D D D S S I I I I S S S I I S + +Speaker sentences 530: voxpopuli_deu_000339 #utts: 1 +id: (voxpopuli_deu_000339-voxpopuli_deu_000339) +Scores: (#C #S #D #I) 148 12 22 11 +REF: i n d e R Z w i s c h e n ******* Z e i T s i n D d i E r e T t u n g s o r g a n i s * A T i o n e * n D i e g r Ö S s t e n s c h L E P p e r w e i L s i e d i e m i g r a n t e n * * * * * 2 0 k i l o m e t e r v O r d e r L i * b Y S c h e n k Ü s t ******* e a u F g r e i * f e n u n d a L l e n a C H i t a l i e n T r a N s p o r t i e r e n +HYP: i n d e * ******* S w i s c h e n S e i * ******* s i n * d i * r e * t u n g s o r g a n i s E R Z i o n e R n * i e ******* g r Ö * s t e n s c h * * Ä p e r w e i * ******* s i e d i e m i g r a n t e n Z W A N Z I C H k i l o m e t e r v E r d e r ******* * i E b * I c h e n k Ü s t e a u B g r e i T f e n u n d a * l e ******* n a * R i t a l i e n * r a * s p o r t i e r e n +Eval: D D S I S D D D D D I S S I D D D D D S D D I I I I I S S S S D D I D S I S I D D D S D D + +Speaker sentences 531: voxpopuli_deu_000340 #utts: 1 +id: (voxpopuli_deu_000340-voxpopuli_deu_000340) +Scores: (#C #S #D #I) 26 5 5 2 +REF: d A S Z e i G t d E r f a l l J U l i a * t i * m O s c h e n k o +HYP: d * E S e i K t ******* d * r f a l l ******* I O l i a R t i E m * s c h e n k o +Eval: D S S S D D D S S I I D + +Speaker sentences 532: voxpopuli_deu_000341 #utts: 1 +id: (voxpopuli_deu_000341-voxpopuli_deu_000341) +Scores: (#C #S #D #I) 33 0 16 0 +REF: W I R D Ü R F e N N I C H T w a s s e r p r e d i g e n u n d w e i n t r i n k e n +HYP: * * * ******* * ** * * e * ******* * * * * * w a s s e r p r e d i g e n u n d w e i n ******* t r i n k e n +Eval: D D D D D D D D D D D D D D D D + +Speaker sentences 533: voxpopuli_deu_000342 #utts: 1 +id: (voxpopuli_deu_000342-voxpopuli_deu_000342) +Scores: (#C #S #D #I) 64 3 7 2 +REF: F Ü r d i e s e e n T s c h e i ******* d u n g B r a u C H e n w i * r v i e l e p a R t n E r n i c h t z u l e t z T d i e s t Ä D t e +HYP: * Ü r d i e s e e n * s c h e i d u n g P r a u * * e n w i A r v i e l e p a * t n A r n i c h t ******* z u l e t z * d i e s t Ä T t e +Eval: D D I S D D I D S D D S + +Speaker sentences 534: voxpopuli_deu_000343 #utts: 1 +id: (voxpopuli_deu_000343-voxpopuli_deu_000343) +Scores: (#C #S #D #I) 119 15 14 8 +REF: d i e f o l g e i s t e i n h Ö H e n f l u g ******* * v o N p o * p U l i s t E n u n D e x * t r E m i s t e N i * n e i n i g E N m i T g L i E D s t A a t e n i H r e n D u m P f e N p a r o l * e n s e t z e n W i * r K o N K r e t e * v e r Ä n d e r u n g e n T g e g e n +HYP: d i e ******* f o l g e i s t e i n h Ö R e n f l u g S v o M p o R p O l i s t n ******* u n * e x S t r L m i s t e * ******* i E n e i n i g * * m i * g * i T s t * a t e n ******* i E r e n B u m * f e M p a r o l U e n s e t z e n ******* D i A r C o * G r e t e R v e r E n d e r u n g e n g e g e n +Eval: D S I I S I S S D D I S D D I D D D D S S D D S S D S I D S I S D S I S S + +Speaker sentences 535: voxpopuli_deu_000344 #utts: 1 +id: (voxpopuli_deu_000344-voxpopuli_deu_000344) +Scores: (#C #S #D #I) 158 21 12 25 +REF: w E I l d i e i n v e s t i t * i o n e * n F r a n Z Ö * s i s c h * E R u n d d e u t s c h e r b a n k e n g e r e T t e t w e r d e n m u S s t e n d u r F t e * g R i E c h e n * l a n d * * * * * * * * * * * 2 0 1 0 n i c h t p L e i t e g E H e n u n d h E u t e m u S s e s e i n e n r i e s i g e n * s c h U L d e n ******* b e r G v o r * s i c h ******* * * * h e r * S C H I E B e N +HYP: w * A l d i e i n v e s t i t Z i o n e R n V r a n T Ö R s i s c h A C H u n d d e u t s c h e r b a n k e n g e r e * t e t w e r d e n m u * s t e n d u r H t e R g L i * c h e n G l a n d T W E I T A U S E N D E H N n i c h t ******* p B e i t e g * * e n u n d E h * u t e m u * s ******* e s e i n e n r i e s i g e n G s c h * O d e n b e r K v o r D s i c h T E T h e r T D R Ü C K e * +Eval: D S I I S S I I S S D D S I S D I I I I I I I I I I I I S S S S D S D D S D D D I D S I S I I I I I I S S S S S S D + +Speaker sentences 536: voxpopuli_deu_000345 #utts: 1 +id: (voxpopuli_deu_000345-voxpopuli_deu_000345) +Scores: (#C #S #D #I) 128 19 32 13 +REF: D i E m i t g L i E D s ******* t A a T e n d Ü r f e n n i c h T D i e m Ö g l i c h k e i t H a b e n d * * e n E u r o p Ä I s c h e n s t a A T S a N W a l T d A r a n z u h i n d e r n I n i H r e N r e g ******* i o n E N g a n z g e * ******* z i e L T u n D S Y s t e m a t i s C H K o R r u P t I o n S f Ä L L e N n a c h z u * ******* g e * H e * * * n * +HYP: * i * ******* m i t g * i * T s t D a D e n d Ü r f e n n i c h * * i e m Ö g l i c h k e i t ******* * a b e n d E R e n A u r o p Ä * s c h e n ******* s t a R Z E a M B a l * d E r a n ******* z u R h i n d e r n ******* E n i E r e * ******* r e g i o n * D g a n z S g e R z i e * * u n * ******* * * s t e m a t i s * * C o * r u t * o n * f ** * * e * L n a c h z u G g e N e R S I n E +Eval: D D D D D S I S S D D D D I I S D D S S S S S D S D S D S S D D I D S S I I D D D D D D D D S D S D D D D D D S I I I S I I I I + +Speaker sentences 537: voxpopuli_deu_000346 #utts: 1 +id: (voxpopuli_deu_000346-voxpopuli_deu_000346) +Scores: (#C #S #D #I) 40 3 12 2 +REF: D R e i m i L l i o N E n m e n s c h e n s i n D a B H Ä n g I G v o n u * N s E R e r h i l f e * +HYP: * * e i ******* m i * l i o * * n m e n s c h e n s i n * a * P Ä n g * H v o n u D s * * e r ******* h i l f e R +Eval: D D D D D D D D S D S I S D D D I + +Speaker sentences 538: voxpopuli_deu_000347 #utts: 1 +id: (voxpopuli_deu_000347-voxpopuli_deu_000347) +Scores: (#C #S #D #I) 83 11 18 3 +REF: e i n V I E R z E h * n J Ä h r I g e r j U n g e w I R D i n h A K k a R i V o n e i n e M p o l i z * i s t e n e i n e S s o n d e R e i n s a t Z k o M m a n d o s I n S K o m a g E s c h l a g * e n +HYP: e i n ******* * * F Ü z * h E n * ** h r g e r j * n g e w * E T i n ******* h E R k a D i * o n e i n e * p o l i z S i s t e n e i n e * ******* s o n d e e i n s a t k o * m a n d o s * n * C o m a ******* g * s c h l a g D e n +Eval: D D D S S D I D D S D D S S D S S S D D I D D S S D D D S D D I + +Speaker sentences 539: voxpopuli_deu_000348 #utts: 1 +id: (voxpopuli_deu_000348-voxpopuli_deu_000348) +Scores: (#C #S #D #I) 69 12 12 5 +REF: W i e e i ******* * N e h e i l i g e k u H H a t m a n V o R s i C H h e r G e t r a g e n d a s * O p t O u t M Ü s s E u N T e r a l l e N u M s * T Ä n d e n w e * G +HYP: D i e e i D I e h e i l i g e ******* k u * * a t m a n ******* W o * ******* s i * * h e r * e t r a g e n d a s A U p t A u t W I s s * u * D e r a l l e * u N s H L E n d e n w e C K +Eval: S I I S D D D D S D D D D D I S S S S D D S D S I S S I S + +Speaker sentences 540: voxpopuli_deu_000349 #utts: 1 +id: (voxpopuli_deu_000349-voxpopuli_deu_000349) +Scores: (#C #S #D #I) 45 6 2 7 +REF: D r e i d e r ******* a r t I G e * * t * * r e F f e n h a * b e n i n z w i s c h e n s t a T T g e F u n ******* d e N +HYP: * r e i d e r a r t K T e G E t D E r e * f e n h a R b e n i n z w i s c h e n s t a D g e u n d e M +Eval: D I S S I I I I D I S S S I S + +Speaker sentences 541: voxpopuli_deu_000350 #utts: 1 +id: (voxpopuli_deu_000350-voxpopuli_deu_000350) +Scores: (#C #S #D #I) 17 5 22 4 +REF: I C H H O F F E E S D A U E r T N i c h T W i e D e r n e U n m o n * * A T e * * +HYP: * * * ******* * * * * * ******* * * ******* * * * * r D * i c h * ******* * i e e r n e I n ******* m o n E R D e B T +Eval: D D D D D D D D D D D D D D D D D S D D D D S S D I I S S I I + +Speaker sentences 542: voxpopuli_deu_000351 #utts: 1 +id: (voxpopuli_deu_000351-voxpopuli_deu_000351) +Scores: (#C #S #D #I) 226 30 39 11 +REF: d E s w e g e n e i n E w i c h t i g e f r a g E a N d i E k o M m i S S i o n K A N n e i n l a n d d i e G r E n z * k o n t r o l l e W i e d e r e i n f Ü h R E n U n d G L E I c h Z E I T I G i N D E R s c h E n g e N u n i o n b l e i b e n M i t z u g a n g z u M i n F o R m a T i o n S s Y s t e m * * e t * * C o d e r i s T d A s e i n e n t w * E d e r o d E R d i e f r a g e i s T w I c h t i G f Ü r d i e d Ä n i s c h e D e B a T t e u n D * i C H B I T t e u m e i n e k l a r e a n * T w o r * ******* * T +HYP: d * s w e g e n e i n * ******* w i c h t i g e f r a g * ******* a * d i * k o * m i * T i o n * * E n e i n l a n d d i e K r A n z S k o n t r o l l e V i e d e r e i n f Ü h * O n * n d * * D O c h * * * * * * i * ******* * * M s c h Ä n g e * u n i o n b l e i b e n * i t ******* z u g a n g K z u R i n o * m a * i o n D s U s t e m E Z e t E R A o d e r i s * d R s e i n e n t w I R d e r o d * A d i e ******* f r a g e i s * w E c h t i C f Ü r d i e ******* d E n i s c h e T e P a * t e u n * D i * * E S P E t e u m e i n e k l a r e a n D w o r D D A +Eval: D D D D D D D D D S D D S S S I S D S D D D S S D D D D D D D D D D S S D D D S S S D D S S I I I I S D S I S D S D D S S D S S S D D I D D S S S S I S I I I S + +Speaker sentences 543: voxpopuli_deu_000352 #utts: 1 +id: (voxpopuli_deu_000352-voxpopuli_deu_000352) +Scores: (#C #S #D #I) 116 11 22 13 +REF: W I E H e U T E s c h o n a u s g * e f Ü h r t w u r d e l a g e s n i c h t D a r a * n d a S s E s H I e R g r o b e * f E h l e R g e g e b e n h Ä t T E s o n D e R n e s g a b e I n e * r e i H e v o n * K l e i N e n U n g e r e i m t H e i t e n b * * * * * * Z w * * +HYP: * * * ******* D e * * R s c h o n a u s g I e f Ü h r t w u r d e l a g ******* e s ******* n i c h t B a r a R n d a * s ******* I s * * e * g r o b e F f Ä h l e * g e g e b e n h E t I S s o n * e n ******* e s g a b ******* e * n e R r e i * e v o n D G l e i * e n * n g e r e i m t * e i t e n b I E T I E N S w E I +Eval: D D D D S D D S I D D S I D D S D D D I S D S S S D S D D D I D I S D D D I I I I I I S I I + +Speaker sentences 544: voxpopuli_deu_000353 #utts: 1 +id: (voxpopuli_deu_000353-voxpopuli_deu_000353) +Scores: (#C #S #D #I) 67 11 9 5 +REF: E i N E v e r ******* g e m e i n S c h * a f t u n g d e r a u S s e n u N D s i C H e r H E I t s ******* p o l i t i K * a L s g R o s S E s z i e l d i e s e r u n ******* I o n +HYP: * i * * ******* v e r g e m e i n T c h E a f t u n g d e r a u * s e n ******* u O S s i E G e r * * L t s p o l i t i G B a I s g * o s I s z i e l d i e s e r u n J o n +Eval: D D D D I S I D D S S S S D D S I S I S D S S I S + +Speaker sentences 545: voxpopuli_deu_000354 #utts: 1 +id: (voxpopuli_deu_000354-voxpopuli_deu_000354) +Scores: (#C #S #D #I) 82 3 7 4 +REF: d e N n s i c h e R h e i t i s T e i n e s c H w i e r i g e u n d d e ******* t A i l ******* R e i c h e * a r b e i t n i c h t n u * r i m t E c h n i s c h e n b e r e i c h +HYP: d e * n ******* s i c h e * h e i t i s * e i n e ******* s c * w i e r i g e u n d d e t E i l W e i c h e R a r b e i t n i c h t n u E r i m ******* t Ä c h n i s c h e n b e r e i c h +Eval: D D D D D D I S I S I I D S + +Speaker sentences 546: voxpopuli_deu_000355 #utts: 1 +id: (voxpopuli_deu_000355-voxpopuli_deu_000355) +Scores: (#C #S #D #I) 108 8 40 5 +REF: K I N D E R U N D P O L I t i K s e ** l t e n L I E g e n d i e I n t e r e s S e n v o n b Ü r g e r n u n D p o l i t i k e R n s o w E i T a u s e I n a n d e r b e I D e N b Ü r G e r n i n g a n z e U r ******* o p A s t e H t D A s t H e m * A k i n d * g a * n Z o b e n +HYP: * * * * * * ******* * * * ******* * * * * t i * ******* s e Ä l t e n * * * g e n d i e ******* * n t e r e s T e n v o n b Ü r g e r n ******* u n * p o l i t i k e * n s o ******* w * i * a u s e * n a n d e r b e R * e M b Ü r * e r n ******* i n ******* g a n z ******* e * r o p E R s t e * t ******* * E s ******* t * e m E R k i n d T g a N n S o b e n +Eval: D D D D D D D D D D D D D D D D D I D D D D D S D D D D D D D S D S D D D D D I S S D D D S D D I S I I S + +Speaker sentences 547: voxpopuli_deu_000356 #utts: 1 +id: (voxpopuli_deu_000356-voxpopuli_deu_000356) +Scores: (#C #S #D #I) 10 1 3 0 +REF: h e R r p R Ä s i d e n t +HYP: h e * r ******* p * A s i d e n t +Eval: D D D S + +Speaker sentences 548: voxpopuli_deu_000357 #utts: 1 +id: (voxpopuli_deu_000357-voxpopuli_deu_000357) +Scores: (#C #S #D #I) 122 12 15 4 +REF: W I R f Ü H r t e n g e s p r * Ä c h e m i t P r Ä s I d e n t k a r Z A i z a H L r e i c h E n r e g i E r u n g s V e r t r e t e r n f r a u E n u n d m e n s c h E n r e c h t S o r g a n i s * A t * i o n e n u n d d i e w a R E n * d u R c h a u s e R m u t i g e n D +HYP: * * * E f Ü * r t e n g e s p r E I c h e m i t * r E s E d e n t k a r S E i z a R D r e i c h * n r e g i * r u n g s e r t r e t e r n f r a u * n ******* u n d m e n s c h * n r e c h t o r g a n i s E R t Z i o n e n u n d d i e w a * * n D d u * c h a u s ******* e * m u t i g e n T +Eval: D D D S D I S D S S S S S S D D S D D D S I S I D D I D D D S + +Speaker sentences 549: voxpopuli_deu_000358 #utts: 1 +id: (voxpopuli_deu_000358-voxpopuli_deu_000358) +Scores: (#C #S #D #I) 76 8 30 3 +REF: D A S I S T Ü B R I g E N s a U c h e i n e u r s a c h e f Ü r d e n w a c h s E n D e N n a t * i O n a l i s * m u s d e R a L l E R D i n G s L e i d e r V Ö L l i * G p e r s P e k t i V l o s i s t +HYP: * * * ******* * * * ******* ** * * N g * * s a * c h e i n e u r s a c h e ******* f ** r ******* d e n w a c h s * n * e * ******* n a t Z i H n a l i s T m u s d e * a * l * * * i n * s ******* * e i d e r * F E l i C H p e r s B e k t i F l o s S i s t +Eval: D D D D D D D D D D D S D D D D D D D D D D I S I D D D D D D D D D S S I S S S S + +Speaker sentences 550: voxpopuli_deu_000359 #utts: 1 +id: (voxpopuli_deu_000359-voxpopuli_deu_000359) +Scores: (#C #S #D #I) 37 6 15 3 +REF: h E u T e S i n D W I R i M m E R n * o C h s o w e i t V o n d i E S e M z i e L e n T f e r n ******* * T +HYP: h * u D e * i n * * * E i * m * * A n A o * h s o R w e i t * o n d i * * e * N z i e * e n * f e r n E S +Eval: D S D D D D S D D D S I D S D D D D S D D I I S + +Speaker sentences 551: voxpopuli_deu_000360 #utts: 1 +id: (voxpopuli_deu_000360-voxpopuli_deu_000360) +Scores: (#C #S #D #I) 252 36 57 12 +REF: I C h w e r ******* d e a l s F i N a n z m i n i s t e r a u c h I n m e i n e m l a n d J e d e n t a g * d a m i t k o n f R o n * t i e r t d a S s n a t Ü R l i c h a u c h D A s b E W u S s t S e I n g e g e b e n s e I n m u S s d a S s s t A a T s h a u s h a l * t * e v o n d e N s t e * u e r ******* Z a H l e r I N n e N U n * D s t e u e r ******* z A H l E R n F i n A n z i e R T S i * n D u n d d a S s W i R D a * m i t a u c h D i E V e r A n t W O r t u n g T r a g e n B E i d e N e n t S c h E i d u n g e n d i e W i * r h i e R I n D i e s e M r a H m e n T r e F f E N m E i N e +HYP: * * h ******* w e r d e ******* a l s ******* W i D a n z m i n i s t e r a u c h ******* E n m e i n e m l a n d Z I e d e n t a g K d a m i t k o n f V o n D t i e r t d a * s n a t Ü * l i c h a u c h ******* T E s b I R u * s t Z e * n ******* g e g e b e n ******* s e * n ******* m u * s d a * s s t * a * s h a u s h a l G t D e v o n d e * s t e L u e r S a * l e r * E n e * * n O N s t e u e r z E O l L A n * i n E n z i e * * ******* Z i H n T u n d d a * s * i * E T a H m i t a u c h * i * ******* * e r * n t U E r t u n g ******* * r a g e n * * i N d e * e n t c h Ä i d u n g e n d i e ******* V i E r ******* h i e * ******* * n T i e s e N r a * m e n D r e * f * * m * i * e +Eval: D D D I D D S S D S S S I S I D D D S S S S D S D D D D D D D D D I I D I I S D D S D D I S I S S S S D S D D D S I S D D D S S I D D D D D S S D D D D S D S S D S I D D D D S S D S D D D D D + +>> REF: D a m E n U n D h e R r E n +>> HYP: ******* T a m n ******* O n * T h e * r * n +>> Eval: D S S D S D S D D + +Speaker sentences 552: voxpopuli_deu_000361 #utts: 1 +id: (voxpopuli_deu_000361-voxpopuli_deu_000361) +Scores: (#C #S #D #I) 51 5 4 5 +REF: a u f d e m E u * r o p Ä I s c h e n a u t * O M O b i l m a r * k t i n s * g e s a m T d * r A m a t i s c h i s t +HYP: a u f d e m O u O r o p ** E s c h e n a u t E R B E b i l m a r E k t i n s I g e s a m * d E r * m a t i s c h ******* i s t +Eval: S I D S I S S S I I D I D D + +Speaker sentences 553: voxpopuli_deu_000362 #utts: 1 +id: (voxpopuli_deu_000362-voxpopuli_deu_000362) +Scores: (#C #S #D #I) 139 11 34 7 +REF: D I E E U R o p Ä I s c h E u n i o n h a * t m i T d i E s e M i n s t r u m e n * T d i e * c H A n C e e i n e a k t i V e * * r o l l e I n i H R e r n a c h b a R r E g i o n z u s p i e l e n u m d e * m o K r a t i s c h e R e ******* f o r m e n U n d e I n E n a c H h a l T i g E e n T w i C k L u n g V O r A n z u t r e i b e N +HYP: * * * ******* * * * o p Ä H s c h * ******* u n i o n h a D t ******* m i * ******* d i * s e * i n s t r u m e n Z S d i e S c * O n S e e i n e a k t i * e V E r o l l e ******* * n ******* i * * e r ******* n a c h b a r I g i o n ******* z u s p i e l e n u m d e R m o G r a t i s c h e * e f o r m e n * n d ******* e R n * n a c * h a l * i g * e n w i * k T u n g * E r * n z u t r e i b e * +Eval: D D D D D D D S D D I D D D D D I S I D S S D I I D D D D D D S S D I S D I D D S D D D D S D S D S D D + +Speaker sentences 554: voxpopuli_deu_000363 #utts: 1 +id: (voxpopuli_deu_000363-voxpopuli_deu_000363) +Scores: (#C #S #D #I) 59 7 17 7 +REF: D I E S I C h t A u F t O T A l i t Ä r e r e * * * G i * m e v o n a u S s e n * o d e r v o n i N n e n i s T * r e c h t u n T E R s c h i e d * l i C h +HYP: * * * ******* * * * h t ******* * u * ******* t * E L l i t E r e ******* r e R S C H i E m e v o n a u * s e n G o d e r v o n i * n e n i s * T r e c h t u n * D O s c h i e d G l i G h +Eval: D D D D D D D D D D D D S S S D I I I S I D I D D I D S S I S + +Speaker sentences 555: voxpopuli_deu_000364 #utts: 1 +id: (voxpopuli_deu_000364-voxpopuli_deu_000364) +Scores: (#C #S #D #I) 124 12 26 11 +REF: W I r H A B e N i M m e r g e s a g * t D A S S e i n E Ü b e r ******* e i l t e s t a * t * i o n i e r u n g s e n T s C h e i d u n g * * u n ******* s I N n I G i S T w e i L E S z u m j e T z i g e n z e i t ******* P u n * ******* K T k e i n e b e d r o H u n g b e i s p i e l s w e i s E a u s D e m i * r a n g I b t +HYP: * E r * * * e M i * m e r g e s a g K t ******* * * * * e i n * Ü b e r e i l t e s t a D t Z i o n i e r u n g s e n s * h e i d u n g E S u n s * E n * * ******* i C H w e i * ******* * * z u m j e R z i g e n T z e i t F u n G E S k e i n e ******* b e d r o * u n g b e i s p i e l s w e i s * ******* a u s * e m i E r a n ******* g E b t +Eval: D S D D D S D I D D D D D D I I I S D I I I D S D D D S S D D D D S S I S I I S S D D D D D I D S + +Speaker sentences 556: voxpopuli_deu_000365 #utts: 1 +id: (voxpopuli_deu_000365-voxpopuli_deu_000365) +Scores: (#C #S #D #I) 101 35 15 30 +REF: d i e s e r V E R G l e i C H i s t e i n e * z y n i s c h e m i s S a c H t ******* * u n g * * d e r o * p F E R V o n m e n S c h E n R e c H t S V E r L e * t Z U N G e N I N a l l * e * R * * * * W E L t * * * * * * E R i s T Z U M a n d * * * E R e n * e i n * s o * L c h * * U n ******* * G l a u * b l i c h e r a n w U r f +HYP: d i e s e r ******* * F A K l e i * * ******* i s t e i n e T z y n i s c h e m i s a c * t D u n g E N d e r o B p U O V O R o n m e n * c h * n e c * t * Z W r * e C t D O M L e * ******* * L a l l R e L S F F A A A A A t D O D S C S A A A A i s * S O N G a n d A N A N D e n E e i n E s o E U c h O D E n C Ü l a u P b l i c h e r a n w O r f +Eval: D D S S S D D D I S D I I I I I S S S S S D D S D D S S D I S S S S D D D S I I S I I I I S S S I I I I I I S S S S D S S S S I I I S S I I I S I I S I I S I S + +Speaker sentences 557: voxpopuli_deu_000366 #utts: 1 +id: (voxpopuli_deu_000366-voxpopuli_deu_000366) +Scores: (#C #S #D #I) 52 6 5 18 +REF: d i e * s ******* p e * * h a t d i E s e u m f A S s e n d e * h O R I z O n ******* t a l e r i c h t l i n I e ******* ** * b e F Ü r W o R t * e t ******* * * * ******* * * * +HYP: d i e E s p e E R h a t d i * s e u m f * * s e n d e R h * E T z U n t a l e r i c h t l i n D e Ü R b e * Ü r B o A t D e t W I N G E R +Eval: I I I I D D D I D S S S I S I I I D S S I I I I I I I I I + +Speaker sentences 558: voxpopuli_deu_000367 #utts: 1 +id: (voxpopuli_deu_000367-voxpopuli_deu_000367) +Scores: (#C #S #D #I) 192 22 36 17 +REF: D E N N E i N E S i s t w i r K l i c H k l a R d i E F i n a n Z u n d ******* * w i r T s C h a F T s K R I S e v e R l a n G T v o n u n * S a L l E n e i n m a l m e h r ******* * * * * * * d e r v e r a n t w o R t * u n g f Ü r e i n e o * p t i m a l e u n d V O R a L l e m r a s C H e Q U a l i f i z * i e r u n g u n S E r e r a r b e i t n e h m e R U n d a r b e i t * n e H m e * ******* r i N n e n G a n Z b e s o n d e r S j e t Z t r e * c h n u n g Z U t R a g e n +HYP: * * * * ******* G i * * * G i s t w i r G l i c * k l a * d i * * i n a n F u n d E w i r * s * h a * * s T G E D e v e * l a n * K v o n ******* u n D E a * l * n e i n m a l ******* m e h r J E T Z S T d e r v e r a n t w o F t D u n g f Ü r e i n e o B p t i m a l e u n d * * F E a * l e m r a s * I e ******* * K a l i f i z T i e r u n g u n D r e r a r b e i t n e h m e * * n d a r b e i t D n e * m e R r i * n e n D a n S b e s o n d e r * j e t S t ******* r e S c h n u n g * S O t * a g e n +Eval: D D D D D S D D D S S D D D D S I I D D D D S S S S D D S D I S D D D I I I I I I I S I I D D S S D D S D D S I S S D D I D I I D S S D S D I D S S D + +Speaker sentences 559: voxpopuli_deu_000368 #utts: 1 +id: (voxpopuli_deu_000368-voxpopuli_deu_000368) +Scores: (#C #S #D #I) 130 13 40 12 +REF: E S T L a n d O D e r a u c h P o * l e n d i e s e H r G u T E e r ******* g e b N i S s E e r ******* z i e l E n a l s a n d e r e d i E s i C H s c H w ** e r t u n d i E m i T t e l a b * z u R U f E n e t w a r * E G i o N E n w i E k a l a b r I e n S i * z i * l I e n o d e r a u c H g r i e c h e N l A N D * o d E r R u * * * m Ä n i e n +HYP: * * * * a n d ******* * R e r a u c h ******* * o H l e n d i e ******* s e * r ******* K u * * D e r g e b * i * s * ******* e r z i e l * n ******* a l s a n d e r e d i S s i * * s c * w Ä e r ******* t u n d i * ******* m i * t e l a b P z u O f * n e t w a C r I K i o * * n ******* w i * ******* k a l a b r * e n ******* Z i T z i E l * e n ******* o d e r a u c * g r i e c h e * l * E R D o d * r A u C H O m Ä n i e n +Eval: D D D D D D S D D I D D D S D D S I D D D D I D D S D D D I D D D D I S S D S I S S D D D D D D D S I I D D D D D S S I D S I I I + +Speaker sentences 560: voxpopuli_deu_000369 #utts: 1 +id: (voxpopuli_deu_000369-voxpopuli_deu_000369) +Scores: (#C #S #D #I) 133 13 23 4 +REF: d e r b E r i c h T G A U Z È s F o r d e r T z u r e c h t d a S s D A s r A t i n g s t A a t l i c h e r s c h U l * D t i * T e l A L s Ö f f e n t l i c h e * a u f g a b e b e g r i F f e n u n d d a h * e r v o n Ö f f e n T l i c h e N a k t E U r E n v o r g e n o M m E N w e r d e n m U S s +HYP: d e r ******* b * r i c h * * C O S E s V o r d e r * z u ******* r e c h t d a * s * E s r E t i n g s t * a t l i c h e r ******* s c h * l T t i E D e l E I s Ö f f e n t l i c h e R a u f g a b e ******* b e g r i * f e n u n d d a h I e r v o n ** f f e n * l i c h e * a k t * Ü r * n v o r g e n o * m * * ******* w e r d e n m * O s +Eval: D D D D S S S S S D D D D S S D D D I S I S S S I D D I D D D D S D D D D D D S + +Speaker sentences 561: voxpopuli_deu_000370 #utts: 1 +id: (voxpopuli_deu_000370-voxpopuli_deu_000370) +Scores: (#C #S #D #I) 91 9 22 4 +REF: d a w i R e s a b E R N U n m i t e i n e m s o Z I a * l ******* p R o g R a M m z u t u n h a * b e n m Ü s s E N w i R d a * f Ü r e i n E e n T s p R e c h e n d e r e c h T l i C H e G R u n d l a g e s c H a F f e n +HYP: d a B w i * ******* e s a b * * A L D n m i t e i n e m s o T C a R l p * o g * a * m z u ******* t u n h a R b e n m ** s s * * ******* w i L d a R f Ü r e i n * e n s p * e c h e n d e ******* r e c h * l i * G e * * u n d l a g e ******* s c * a * f e n +Eval: S D D D D S S S S S I I D D D D I D D D D S I D S D D D D S D D D D D + +Speaker sentences 562: voxpopuli_deu_000371 #utts: 1 +id: (voxpopuli_deu_000371-voxpopuli_deu_000371) +Scores: (#C #S #D #I) 16 3 17 5 +REF: A B E R D A s M Ü S S e N W I r n o c h A n a l Y s I e r e n ******* * * * * +HYP: * * * * ******* * * s ******* * ** T I e * ******* * * r n o c h ******* * n a l I s * e r e n W O R E +Eval: D D D D D D D D D D S S D D D D D D S D I I I I I + +Speaker sentences 563: voxpopuli_deu_000372 #utts: 1 +id: (voxpopuli_deu_000372-voxpopuli_deu_000372) +Scores: (#C #S #D #I) 70 16 17 6 +REF: m a N k A N n n A T Ü r * l i C H v e r l a n g e n g e b E n W i R m e H r g E L D f Ü r E n T W i C k L u n g s h I L F e * a u s d i e a R m e n L e u t e b r a u c H e n d a * s ******* * * +HYP: m a * k * E n E n * * E r T l i * E v e r l a n g e n g e b * n ******* L i * E m e * r g * A R T f I r ******* * n D i * k * u n g s h * V e R a u s d i e a H m e n ******* * e u t e b r a u c K e n d a S s B E +Eval: D D S S D D S I D S D D S D S D D S S S S D D S S D D D S S I S D D S I I I I + +Speaker sentences 564: voxpopuli_deu_000373 #utts: 1 +id: (voxpopuli_deu_000373-voxpopuli_deu_000373) +Scores: (#C #S #D #I) 114 17 23 9 +REF: g e r a d E F Ü R K l e i n e R e p R o j e * k t e i s T d a s E I N Ü b e r ******* m Ä S s i g E R b * Ü r o K R a t I s c h E r a u f W a n d r I c h t i * G d a S s D a s J e T Z T A U F e i N E n z e i t R a u m v o n * d r e i j a h r e n g e s e n K t w e r d e n s o l * * * * L +HYP: g e r a d * * Ü O G l e i n e L e p * o j e C k t e i s * d a s ******* * * * Ü b e r m Ä E s i g * H b I E r o * G a t E s c h * r a u f * a n d r E c h t i C H d a * s T a s I e * * R * S R B e i * * n z e i t * a u m v o n D d r e i ******* j a h r e n g e s e n * t ******* w e r d e n ******* s o l U N D U M +Eval: D D S S S D I D D D D D I S D S I S D S S D D S I S D S S D D S D S S S D D D I D D D D I I I I S + +Speaker sentences 565: voxpopuli_deu_000374 #utts: 1 +id: (voxpopuli_deu_000374-voxpopuli_deu_000374) +Scores: (#C #S #D #I) 74 14 16 19 +REF: i C H k a N N n U R v E r s i c h e r n d i E E U r o p Ä I s c h e k o M m i S s i o n i s t * * C o M m i t T e D z * u * ******* * ******* * r e U r o P Ä I s * * * * * * c h e n P e r s * P e k t * i * v e * d E s k o * s o V O +HYP: i * * ******* k a * * ******* n D E v O r s i c h e r n d i * ******* * A r o p ** E s c h e k o * m i * s i o n i s t E T K o * m i t D e T z S u M A A r e * r o * B s E A L O P E c h e n * e r s B I e k t D i E v e R d I s k o S s o S E +Eval: D D D D D D S S S D D D S D S D D I I S D S S I I I I I I D D S S I I I I I I D I S I I I S I S S + +Speaker sentences 566: voxpopuli_deu_000375 #utts: 1 +id: (voxpopuli_deu_000375-voxpopuli_deu_000375) +Scores: (#C #S #D #I) 16 8 18 0 +REF: A B e R H I e R I M H a U S e i S T e S S E H R O f t a u c h s o +HYP: * S e * ******* * S e * ******* * * S L a * P e ******* i * * H e * * * * * A U f t ******* a u c h s o +Eval: D S D D D S D D D D S S D S D D D S D D D D D S S D + +Speaker sentences 567: voxpopuli_deu_000376 #utts: 1 +id: (voxpopuli_deu_000376-voxpopuli_deu_000376) +Scores: (#C #S #D #I) 71 6 13 3 +REF: M i T d i e s e M h a u s H A l T k a N N m a n d i e * e U B Ü r g e r I N N e * n u n d b Ü r g e r n i c h t Ü b e r z e u * G e n U n D b e g e i s t e r n +HYP: * i * d i e s e N h a u s * E l * k a * * ******* m a n d i e E e N G Ü r g e r * * * e I n ******* u n d b O r g e r n i c h t Ü b e r z e u C T e n * n * b e g e i s t e r n +Eval: D D S D S D D D D I S S D D D I D S I S D D + +Speaker sentences 568: voxpopuli_deu_000377 #utts: 1 +id: (voxpopuli_deu_000377-voxpopuli_deu_000377) +Scores: (#C #S #D #I) 132 13 43 11 +REF: W I R A L S S O z i a l d e ******* m o k r a t e N n e h m E n M i t g r o S s E R F R e u d e z * U r k e N n t n i s d a S s d i n g e d i e W i * r V o r g e t r a g e n h a b e n j e T z ******* * * * * ******* * * T i m z u s a m m e n H a N g m i t D E N v e r Ä n d e r u n g e n I N d e n V E R e i n I G T e n s t A a T e n u m G E s e t z T W E R D e N +HYP: * * * ******* * * * ******* * * z i a l d e m o k r a t e * ******* n e h m * n ******* * i t g r o * s A F * O e u d e z S O r ******* k e * n t n i s d a * s d i n g e d i e * i E r F o r g e t r a g e n h a b e n j e B z S I C H A U H i m z u s a m m e n * a * g m i t ******* * * * v e r E n d e r u n g e n ******* * E d e n ******* * * F e i n * C H e n s t * a D e n u m * * s e t z * ******* * * * * e * +Eval: D D D D D D D D D D I D D D D D D S S D S I S D D D D I S S I I I I I I I I S D D D D D D S D D S D D D S D S S D S D D D D D D D D D + +Speaker sentences 569: voxpopuli_deu_000378 #utts: 1 +id: (voxpopuli_deu_000378-voxpopuli_deu_000378) +Scores: (#C #S #D #I) 115 14 20 22 +REF: d e * * r b e s c h L u s ******* * * * ******* * S d a s E U r o p Ä I s c h e s e m e s t e r * * * * h e r * ******* z u n e h m e n u n d d i E K o r R U p t i o * n s ******* s i t * U a * T i o n ******* * * * i m r a H m E N d e r l Ä n d e r b E r I c h t e z U v e r Ö F f E n T L i C H e n i s t n i C H T a u s R e i C H e n D +HYP: d e A H r ******* b e s c h * u s T I E E L d a s ******* * O r o p Ä * s c h e s e m e s t e r H I E R h e r T z u n e h m e n u n d d i * C o r O B p t i o U n s s i t P L a R i o n E R E i m r a * m * * d e r l ** n d e r b * r E c h t e z O v e r Ö * f * n * * i * G e n i s t ******* n i * * G a u s H e i * G e n T +Eval: I I D D I I I I I I S D D S D I I I I I I D S S S I I I S I S I I I I D D D D D S S D D D D D S D D D S S D S S + +Speaker sentences 570: voxpopuli_deu_000379 #utts: 1 +id: (voxpopuli_deu_000379-voxpopuli_deu_000379) +Scores: (#C #S #D #I) 199 20 27 19 +REF: U n d ******* * * * * m e i n e b i T t e o d e r * d a s w a s i c h m I r v o r s t e L L E i s T d a S s m O r g e n w i * R k l i c H i n d e r t a * t e i n * e g r o S s e e i n e b r e i t E m e h r h e i t f Ü r d i e s e k o H Ä s i o n s ******* p o l i t i K F Ü R U n S E R e p o l i t i K s t * i M m t F Ü r d i e m e n s c h e n v o r o r t d a m i t W i R u n s a U F D A s w e S e n t L i c h e ******* * * * * b e s c H r Ä n k e n k Ö n N e ******* * * N +HYP: * n d M E I N m e i n e b i * t e o d e r M d a s w a s i c h m E r v o r s t e * * N i s * d a * s m A r g e n G w i E C k l i c G i n ******* d e r ******* t a R t e i n I e g r o * s e e i n e b r e i t * m e h r h e i t f Ü r d i e s e k o * U s i o n s p o l i t i * G H Ü E O n D F G e ******* p o l i t i * G s t D i * m t P Ü r d i e ******* m e n s c h e n v o r ******* o r t d a m i t ******* * i * ******* u n s a * * T E s w e * e n t * i c h e A U C H b e s c * r Ä n k e n ******* k Ö n D e D A S +Eval: D I I I I I D I S D D S D D S S I S S D D I I D D D S I D S S S S S S S D D S I D S D D D D D D D D S S D D I I I I I D D S I I I S + +Speaker sentences 571: voxpopuli_deu_000380 #utts: 1 +id: (voxpopuli_deu_000380-voxpopuli_deu_000380) +Scores: (#C #S #D #I) 147 18 39 13 +REF: w E n n w i R h e u t e d i e S e v e r O r d N U n g v e r a b s c h i e d e n H o F f e * I c h d a s s * w i R n a c h e i N E m l a n g * E N W e * G * Z u e i N E m g u T E n a b s C H l u S s k o m m E N U n d * i C H m Ö c h t e m i c h * b e i D e r K o M m i S s i o n b e d a n K e n D i e U N S D U R C H K o n s t R U k t i * v e s a c h ******* a R b e i * ******* * * t +HYP: w * n n ******* w i E h e u t e d i e e v e r * r d * * n g v e r a b s c h i e d e n * o * f e R E c h d a s s E w i E n a c h e i * * m l a n g K A R U S e L L S O u e i * * m g u * D n ******* a b s * * l u * s ******* k o m m * * ******* O n d T i T M m A c h t e R m i c h E b e i * e r * o * m i * s i o n b e d a n G e n * i e ******* * * * ******* * * * * * ******* * o n s t * O k t i E v e s a c h a * b e i T H A t +Eval: D D S S D D D D D I S I S D D I S S S S I S I S D D D S D D D D D D D D S I S S S S I D D D D S D D D D D D D D D D D D D D S I I D I I I I + +Speaker sentences 572: voxpopuli_deu_000381 #utts: 1 +id: (voxpopuli_deu_000381-voxpopuli_deu_000381) +Scores: (#C #S #D #I) 37 5 12 20 +REF: u n * * * * * * * * * * * s * * e * ******* * * * * R e k o n t r o L l e n h a b e n k e I n e n * B e l e g e r B r a C H t I C H K A N N +HYP: u n Z E R E R E S C H E A s C H e N U N Z S I e k o n t r o E l e n h a b e n k e * n e n P I e l e g ******* e r P r a * F t ******* * * * ******* * * * * +Eval: I I I I I I I I I I I I I I I I I I I S S D I S D S D S D D D D D D D D D + + diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..59b400d0a5f9d82f44ab0e420f56af2703ed9cbc --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/hyp.trn @@ -0,0 +1,661 @@ +DEBER DIGNG MACHTE INER EUSSTWICHTIGENSECHA IN ENDE DER PÄTIT ION ANLINGOWANÜR FÜRDES EN JANER SOSSBEGENADIGUNM (M-AILABS_deu_000165-M-AILABS_deu_000165) +DACHABESIET DIE WOLULGJEDEM HER INER INRUNKGEBLIEBENEN WARTE GESPOCHEN N (M-AILABS_deu_000166-M-AILABS_deu_000166) +ERS UM ACHT UR WAR ER AUF MALE BRCHTER DEN KAFI DIE SONDESCHIEN INZS ZIMER UND IE SPÄHRLINGE DIE DSSAUS DEN HECSE SECKEN GEFALNE FOTEAKON AUF BIKTEN (M-AILABS_deu_000167-M-AILABS_deu_000167) +SSICHERLICH AN IHRNGEBORTZTACKHÄTE ER BEI IE BLEIBEN KÖNENT (M-AILABS_deu_000168-M-AILABS_deu_000168) +DERSALBÖM MUS AN DAUORT WUOR MENTCHENSCHIRICGKEITENHABEN DIES OUCH EINER SEITS ERKLÄEREN ANGEBOTEMACHEN WRALE I (M-AILABS_deu_000169-M-AILABS_deu_000169) +UES MANRAF DIVELT KOMT UM SEBSTIEDER IN SONDZUOHARBEN DE DI VER EHUNK DER ANEN VORTETZTNN (M-AILABS_deu_000170-M-AILABS_deu_000170) +A BÜRN PUNZEN ELICHER SCHULBILDUNG RUNTEN ELICHE MÖGLICHTEITRAUH DER WEITERBILUNG UND DAS EGEHN VON GE DENKTAGEN EMICH UN AUFLSLICHTRAME (M-AILABS_deu_000171-M-AILABS_deu_000171) +EIN ANSAS IERN SAKZI S ISTJER GET WIEDER GANSKUT ZWISCHENUNS ABER IER DUNICHT ALESGESTIEST GETIE ER INRUNG ANDAS BÜSE NICHT WEH (M-AILABS_deu_000172-M-AILABS_deu_000172) +DNEIN WEIBERBRAUCHRICHEICHTT (M-AILABS_deu_000173-M-AILABS_deu_000173) +TENDEN GOT HATNICH VERGEBLICHENEHME GERUFEN SAKTE DER CHIVER (M-AILABS_deu_000174-M-AILABS_deu_000174) +NUR EINES WEISSICH DIESER FURCHTBAREN FRAGENT GEGEN ZUSETZEN UND SCHLEUDERERDARS WART IN DIE WARKSCHAL DIE GLUT LEINES LIEBES WILENDZ IST STERKAR ALS TRENUNGT (M-AILABS_deu_000175-M-AILABS_deu_000175) +DTOMS AMEIGE WANEINGROSSEN SIEG NCHENER LANGNG HARDN NECKEGEN SCHLCHTN (M-AILABS_deu_000176-M-AILABS_deu_000176) +SOS EINAHME DEM SICHTI ÜR BEI TAKUN NCHTAFFNAN KAN BRESCHER UND WEL KOMMEN (M-AILABS_deu_000177-M-AILABS_deu_000177) +EN ARBER ICHVERT SEIE INEN IHRER UNWISSEN HEITN (M-AILABS_deu_000178-M-AILABS_deu_000178) +UFON DER TRITEN UNTEREDUNG AN SAGTE MISTERHEVESCHEM WARMER DIE PERSON IN HOHREMASER VERDECHTIGHTNN (M-AILABS_deu_000179-M-AILABS_deu_000179) +ICH DENKE DE MTNEAR UND SANE VERMIE WERDNESRECHT VON DER FINDEN DASTU DIE SELBT ANGIEBST UN SE VRDEN FREUNTLICH GEGEN DIESEIT (M-AILABS_deu_000180-M-AILABS_deu_000180) +ETZS SCHLUG DIE HLLE FLAMER AUF UND NUN ER KANTE ER UNDS DIEVWE NCH IMERZUSAMEN GEDRENGTDN DEM WINKELSTANDEN (M-AILABS_deu_000181-M-AILABS_deu_000181) +DER SEINER SELE ANSPONEND DAS ER MUNTERNTERWAUT VORWERLTZS (M-AILABS_deu_000182-M-AILABS_deu_000182) +FOMIC AF DEN BESUCHTES TONESCHE MINISAPRESEDENTEN ONT (M-AILABS_deu_000183-M-AILABS_deu_000183) +DUWASFÜHR ER FOLGUNGEN WAS VÜR NARCHSTELUNEN HABRIGNICH ZU ADULEN GEHABTNN (M-AILABS_deu_000184-M-AILABS_deu_000184) +ZSIGOEINER WAREN ESTI VON AU ZUOLT FUREN EIN KAUM ARWACHNES IUNGES DINKAM ZUMIE HERANGEHÜBPFT UN BETELTE NEIN (M-AILABS_deu_000185-M-AILABS_deu_000185) +A ICH WER IC INEM BOTHIN FAHN DE DS BOTER ANLENEND SI ER ZOEG DERN ALES GANZELLEIN US IE ANIC T UM ZUKÖMANTN (M-AILABS_deu_000186-M-AILABS_deu_000186) +ALS NER EINMAL NOCH DEN RAUCH VON ANEM HAUSERAUS DER FERNER AUFSTEIGEN ZUSEN UM DANBER RIEGK ZU STERBEN (M-AILABS_deu_000187-M-AILABS_deu_000187) +DIE TENZEREN ARBER LAKAUF DEN KNIEN VOR BRAMAS BILTNIS IN NAHMENLOSERSEHNSOCH T UND WEINTE JAMERVOLL (M-AILABS_deu_000188-M-AILABS_deu_000188) +DECHT FERTICH MICHTENDI WERGLICHKEITENCHNICHT AUF DIG IC BEG UFEN KARN (M-AILABS_deu_000189-M-AILABS_deu_000189) +DTICHE RGERTE MICHTANWENICH AUR FACHTER EIS WAR SOFUNDERSCHÖNEN GEWESEN DAS FLIEN NN (M-AILABS_deu_000190-M-AILABS_deu_000190) +NCH DEMESCHON DEN GANZSEN FORMITAG MI IM VERBRACHT KAMS DEN HOB NACHTISCH IN SKRNSCHEHAUS UM KASPALE WOL ZUS ERGENNN (M-AILABS_deu_000191-M-AILABS_deu_000191) +ERWA EIN ALTER HIRHT VOLMEDIE ZINESCER GININALITET (M-AILABS_deu_000192-M-AILABS_deu_000192) +NTDAS VOL UCH DER MIE TAR SEINE VONDELRICHGSKEITEN HARBEM SERN (M-AILABS_deu_000193-M-AILABS_deu_000193) +E SIESAN ALLE ENGSTLICG UND BETRÜBTAUS UND AUCHER ARENE SASCHWEHR MÖTICH DA WIEDIE ANDEREN UND STÜTZSTERDAS HAUPT IN DIE HAN (M-AILABS_deu_000194-M-AILABS_deu_000194) +UNTER DEN DAMEN MEIST JIONGE FRSCHE GE SICHTAUNTER DEN HEREN NEBEN JUENTLICHIEN ZSOUCHE MIT FALTI ASTIERN UND BEREITZSMEHRA DER MINDER MOND UM GLENSTEM SCHÄDEL (M-AILABS_deu_000195-M-AILABS_deu_000195) +SEI TAGEN SCHON HATE S BESONDERSTDREUEND GEKLUNGERT (M-AILABS_deu_000196-M-AILABS_deu_000196) +SONDER BAR (M-AILABS_deu_000197-M-AILABS_deu_000197) +ERB VON ERBEMHEIM STAND MIT EN HR GATEN VOL WE MUOTUNG DANKGBARKEI DAN DER GROFT CF DER EIN MECHTINGNGN (M-AILABS_deu_000198-M-AILABS_deu_000198) +TIER WARJIE DERMENSCHEIN UNDER UND FASTALLES WASMENSCHEN TATEN ET ASSWONDARBARES N (M-AILABS_deu_000199-M-AILABS_deu_000199) +WELCHE JER WEIE SIE END LENKSTFÜRT N (M-AILABS_deu_000200-M-AILABS_deu_000200) +IEWRTEN AS NICHTIENTE REM SCHANKTISC UND KEINER ERER DIENSTLEUTE BEFANDZEICHIN DER STUBE (M-AILABS_deu_000201-M-AILABS_deu_000201) +NALS DIE HER SCHAFT AUS DER KILCHER TRAT STANDEN DIE LEUTE UM HEHR UM SIE VORBEIGEHEN ZUSEHEN UND AM KIRLCHOF TORERWARTE E EINMANNN (M-AILABS_deu_000202-M-AILABS_deu_000202) +ASMSEN ER TOHNOM DEM TARISMUS EN GENTUTDE (M-AILABS_deu_000203-M-AILABS_deu_000203) +LCH GELAUER AS E SGUDENITBERMEINE HERDOKT (M-AILABS_deu_000204-M-AILABS_deu_000204) +EN TORIM ANFANK GEWANER KEINE AUFMER AM KEIT VÜR ANDRERDINGE ALSFÜRDERS ESSEN (M-AILABS_deu_000205-M-AILABS_deu_000205) +DIES FLÄSCHIEN ZOGERJETZT EILICHER VOR WEREND JENE SC MIT WASSARFILTEN UND BOTES DER UNG VER ZYÜS ANN (M-AILABS_deu_000206-M-AILABS_deu_000206) +ESERWAS OCRICHTI ON WICHTICHTERS HINER DOCHERTZ ANSCPOSFOLLEL GESAKTAT WEWERDEN AUCH ANEN ZEIT PUNK DER IDUKTIUON KOMMENDES DGÜ (M-AILABS_deu_000207-M-AILABS_deu_000207) +DNICH DOCH MUTER WERKE SE JERZT NCHNIG N (M-AILABS_deu_000208-M-AILABS_deu_000208) +BIA HABEN INENDEZENJAHNRECHT ENKE BITIUNG ZUBASILIEN AUFGEBAUTPR (M-AILABS_deu_000209-M-AILABS_deu_000209) +DTTS SIEEVÜRDE SCH NICHT VÜER ANDR ABPFON TT (M-AILABS_deu_000210-M-AILABS_deu_000210) +LECHLIFENSEMETZU (M-AILABS_deu_000211-M-AILABS_deu_000211) +GOAT WASSI IE AR ZHÄLTER HÖREN SINUR ES IS SEINGANSEROMAN N (M-AILABS_deu_000212-M-AILABS_deu_000212) +SEINE MTERKANIMO FLUSSWASSRGEBEN DES SEIBPWEIND ER (M-AILABS_deu_000213-M-AILABS_deu_000213) +UNESWRSCHAS MINESTA WIRT EÖMEN BUSAMMITERNETZ AGENTUR AM VIERTEN JUNI ZUM ERSENMAL PRESENTIERN WIESICH DIENETZ BETREIBER UND DIE KRAFTDEARKE DINEUNET LHNE VORSTER UND (M-AILABS_deu_000214-M-AILABS_deu_000214) +EVWARHATE SECHTZITANT VOR TODES SFVECHER VON DEM GETER BE FREITUNDZUCHTE ZUND FIEN BE DER SMALE GATEN BOTD KAEINEN AUSWIHN (M-AILABS_deu_000215-M-AILABS_deu_000215) +DABECH MEIN WER FÜRUTE LIEN LASSEN NODERNOCHEIN AN LAUFNEMENUNDTES ROLÄNDN SOLTE N (M-AILABS_deu_000216-M-AILABS_deu_000216) +ER WA DAS GETZ IERN DER STUND DE TEITEI E AUFTRAKTE MADM UNMSCHLL DIE ACH A STAND UN IGE AUFEN SEIDEN TÜ KEZUSAM FELDETE FÜRSCHON ZUSORGENGN (M-AILABS_deu_000217-M-AILABS_deu_000217) +TIWR DEN HCHSEM (M-AILABS_deu_000218-M-AILABS_deu_000218) +ABA E TIBS ODAF VORGABEN DAS MACHM ERNERDOLIKNIGHS (M-AILABS_deu_000219-M-AILABS_deu_000219) +ALS UNSRE E DE BEKANTWORDER WADI ÜRSIOGNOMIE DER WELTERSPBOGER UNGE FÄR DI EINERS KALBES DAS UM ERSENMAL ONHNHÖR (M-AILABS_deu_000220-M-AILABS_deu_000220) +IZER MAHNSIGE FÄLICHT AUF UND ES KLANG DIE EIN JAMANDER HILVER (M-AILABS_deu_000221-M-AILABS_deu_000221) +R DOKTOR SAGTE EINEFRAU DIE SH NOGRINERM DIESO AFT ZU INEN KOM IST EINLIGANICHKRAN (M-AILABS_deu_000222-M-AILABS_deu_000222) +DIE ALTE ER IN RUNGAN DEN FRÜHREN TAUM TAUCHTER EBENFALSWIEDER AUF UND UNWIEL KÖRLIC FSST BEI DE BE HAUPTUNG DAS DE SELE DEN KÖRPER VERLASSEN UND ZU IM ZURÜCKEREN KÖNE SHINESIER ORDENDLIG (M-AILABS_deu_000223-M-AILABS_deu_000223) +AL SIE OFTDEN ALKOND ZURIKERTE VANDZI IN DIE ZEITUNKLIESEND DIE WEREN RES FORT ZEINES ANGELNKTWARHT (M-AILABS_deu_000224-M-AILABS_deu_000224) +TE EHRWAH EIN KIND DER STRASE VON KLEIN AUF ABER IN IMLEBTE VON JEHER INE GWISSES SEEN SOCHT NACHEINER EHRBAREN BIRGERICHEN EXISTENST (M-AILABS_deu_000225-M-AILABS_deu_000225) +AIT UNESTIGJRUNGKTÜHNUNZIENIG EINER GROPEFERANFODLICH ONAM WÜEFÜNE DEN GEMEINWUHLFER NWODLICHUN (M-AILABS_deu_000226-M-AILABS_deu_000226) +WAS MEINLIE BESKIND WASKAN (M-AILABS_deu_000227-M-AILABS_deu_000227) +UND DAN WOLT ICH DEN ANBLICG DE RANICHTMISEN DIEMER GEBLEBEM WAREN VOR ALM ABARBWA ES I DARUM ZUTOUN WEINE SYSE LICISER BET EINIGAMASSEN GETRÜSTET ZUSE ENTO (M-AILABS_deu_000228-M-AILABS_deu_000228) +EDAS AUCH WIE UNDS GENSET DCHE BSHN UNTDASTUZEN KÖNNEN EÖRM (M-AILABS_deu_000229-M-AILABS_deu_000229) +SEINE GESCHFTICHELAUFBAHNHARBESTIEVENSN ALSKÖCHEN BO IN EINE MOTÄL FIERENGRADES BGON (M-AILABS_deu_000230-M-AILABS_deu_000230) +NFÜLEICHTEN SEGUTIESE ANSICHENDES SCHOFSN HASE ZUMELDEN ZACTEDERTATSCEN DER IMAR MEHR EIN MANDES GESCRIEBENEN WURTES WIEDER TAD N (M-AILABS_deu_000231-M-AILABS_deu_000231) +EMAN DAN MORDEN EHRHOPERSICH SCHWÄT SCHCKTE DEN LARKEIEN DE BONUNG VOR JABACHSUNTLIESUM EINE NTERE DUNG BITEN DEMAN KAM MITER OTSCHAFT ZURÜG DETN (M-AILABS_deu_000232-M-AILABS_deu_000232) +NT NU EINWENICH TRAURICH WURDES EN IM DAS SEL BERKAM EN SIN NIE ZU FRIEDEN SCHIEN NN (M-AILABS_deu_000233-M-AILABS_deu_000233) +EIN SOMAHR WAHMANOVEM BARTARK LAG MITZONEN GLITZSANNÜBERDE HUPTSTABT UND UNDER DNLINDENDRENKTER INE TAUSEND KAPFIGE MENSCHENMÄNGER AUO VON IEDER U (M-AILABS_deu_000234-M-AILABS_deu_000234) +KOMITM IH MEIN SON DEN ICHPRAUCHE DEINE LIEBE (M-AILABS_deu_000235-M-AILABS_deu_000235) +DNTNWOR SAIN KESICHT RDE IN WNICHNACHTDENKLICHARSOO WIE VON EINER ER INERHUNG ER HÄLT NN (M-AILABS_deu_000236-M-AILABS_deu_000236) +N WOT OUI ERDE NWATIONENS DROCK STEIGENUNDTATZU ESTASSESTEMIE ENGEFÜRTWON (M-AILABS_deu_000237-M-AILABS_deu_000237) +NET GEWATE ERMIT EN SET ZSN DIE SCHEUSLICHE TEUFLSCHER AFENFRATZSE DIEBE DES METCHEN SCHLTERSCIELTEN (M-AILABS_deu_000238-M-AILABS_deu_000238) +TTERAR DE WERT NIK TE DASGÖRD EINE GEWISSEN WRTSCHAUF BERNHAT WRTSCHOF ISTE ETWASFARGUNSENTNN (M-AILABS_deu_000239-M-AILABS_deu_000239) +WOLT ER IN WAHEI DIEL ÖRSEND TÖRTEN NUNDT KND ERSCHELISENNN (M-AILABS_deu_000240-M-AILABS_deu_000240) +AT SEÄTDI SERESPEKT VOL WUOBEI ER NUR EINI GESELDEN VERSCHLUKTE WASS IM BEI DEN BELEBTEN LANGEN WÖRTEAN DES FTAN FORKAN (M-AILABS_deu_000241-M-AILABS_deu_000241) +DLORT FONDLELE RORU VERT NICHZS END BEHREN DESSEN BINICH GEWIS VERSETZTE ER T (M-AILABS_deu_000242-M-AILABS_deu_000242) +KAM GLEICHFALS INS SCHLAFTZIMMAR AUF EINEN NAGELINDER NEHR TES BETESN (M-AILABS_deu_000243-M-AILABS_deu_000243) +DASES DISCHANGS DIE NESARKRISTEG DIESCHANGS VÜR INZTERNATZIONALEREGEN BISI EINIM PRONSIEBPLIEN DERSE DJALE MAKPOTAF OLIENTIERN (M-AILABS_deu_000244-M-AILABS_deu_000244) +ANFANGSFIELDEREGEN SCHRAG UND PEITSTE ERS IE EINE DAN DIE NDERESEITE DES WAGENS (M-AILABS_deu_000245-M-AILABS_deu_000245) +MFAST LEICH ENIGEN BER MESSONG ERES WERTES AUFT ZOGEBEM SICH ENT SCLOSSEN HATE NN (M-AILABS_deu_000246-M-AILABS_deu_000246) +SEIST BDIE FRARGERMENCHLICHEN ARBET NDIE FRAGEWAS KANTECHNISCGELÖSTWERDEN DA (M-AILABS_deu_000247-M-AILABS_deu_000247) +NUTISEAR FAHRIE WAUF IEREDEMESIG BNOTZSTEN WASSARSTELLEN DISEROTAN GEWIESEN NN (M-AILABS_deu_000248-M-AILABS_deu_000248) +DIE BEIDEN MISTEN HIE OBEN AUF DEM GEBPFEL GESTANDTEN HABEN UND ER SPRACH DIE ALTEN WAURTE VORSECHIN (M-AILABS_deu_000249-M-AILABS_deu_000249) +ENTLICH BIKTES EDRIKG AUF WEISSN JUUIG ALESVON DEN ARMEN LEUTEN FRAKTE ER (M-AILABS_deu_000250-M-AILABS_deu_000250) +SEUDE INE WINDRBARETZUSAMARBEI ZWSCHEN BUND UND LÄNDERN IN DIESEN RAGEN GIB IT SESE NTRESANDEN PROJEGTENU (M-AILABS_deu_000251-M-AILABS_deu_000251) +DKAS BAR FERHARTE AN GENMRTZELTENSEIMPLATZS SEIN GLIE DER JAR SEINEN AUGEN BANG IEVERSTEINRT EL ET UM ZWEITEN MALIEN IKTE N (M-AILABS_deu_000252-M-AILABS_deu_000252) +EINIGE ZEIT DANACHFRAKTE ER MICH O PICH GLAUBER DAS DER EIS GANG DEN SCLITEN DES ANDEREN ZER STÖRTABE (M-AILABS_deu_000253-M-AILABS_deu_000253) +AENEN BLUSSE NICHTGENEINER SCHORCGSTARE DEL VALIN (cv_deu_000698-cv_deu_000698) +JILSCKOMIRSCHEN (cv_deu_000699-cv_deu_000699) +SDEM BEIE ARBEITE DE EIS AUSHRELS KRAFTAF EINEN FAHME (cv_deu_000700-cv_deu_000700) +EINTERIT TO HEIKOSSES OCHOPERWITDT NICHT IT AN MITARMESI KEINEREN O HOPERREICT (cv_deu_000701-cv_deu_000701) +IER SOUN KAENDRH KÖNZSLICHER BEFROCHTUNGK ZURWERT (cv_deu_000702-cv_deu_000702) +DIE NACHT AR DIEF NEFLER FLIEN VONMITER JULIEBIS MITE AOF TOBER (cv_deu_000703-cv_deu_000703) +DEREAR H AAT (cv_deu_000704-cv_deu_000704) +EN DHERN (cv_deu_000705-cv_deu_000705) +NUTZER KNEN IHRELERSE ZEICEN ONEIN ABSPEICHER VERWALTEN UND BET ANEN NOTZAN TEILEN (cv_deu_000706-cv_deu_000706) +DIE DUM BOSCO KATIERALE (cv_deu_000707-cv_deu_000707) +SAUL BASSZEL ZU DEN N ROTISTEN DIESEIDARE UMEMF Ü MEC EÜEUELE SERENZELLEN (cv_deu_000708-cv_deu_000708) +IN KÜNÜWUR SER BENEMEN WELEN BEUG K EINE SIEL BWONDER EICHE N (cv_deu_000709-cv_deu_000709) +EITERE WICHTIGE INDESTRIEZWEIGESE DIE NICRMICHANIG GALWANOPLASTIG MITEILBAU UNTIE HUTZVER ARBEITOUNG A (cv_deu_000710-cv_deu_000710) +ÜBER DEN AUTOR S NICHT BEKAND VER MUTLIC STMTEHR AUS EM DEUTSHEN SPRACHGBIEDT (cv_deu_000711-cv_deu_000711) +NSTUER ISMI DENENTOPLIPATE (cv_deu_000712-cv_deu_000712) +DIE HARMEI EREBLEMER OSSISCHIGHT OUCHT (cv_deu_000713-cv_deu_000713) +WICHLIENEMANACH UARE ME AUKTUR E EIERTETDNTHMACHBER NN (cv_deu_000714-cv_deu_000714) +ORDE AL I DE GESSYK LDE ANLAN AU NISCHEWABEN (cv_deu_000715-cv_deu_000715) +EIEHEI DEN NAME IN WINCHEN WORER AUCGSTA T (cv_deu_000716-cv_deu_000716) +INERUNG UND EUSSRERNATICG GENEN ALSGETRENTITAILE EINES NATIG AUCH GEMEINSAM VOR OMMEN (cv_deu_000717-cv_deu_000717) +DA BEIE BELEGTER R DIEPLÄTZSE VIER UNDTREIG (cv_deu_000718-cv_deu_000718) +KIN DRABIE IS SIE TOCHTER ZWEIAR ROFESENERLARTENZA (cv_deu_000719-cv_deu_000719) +IS KLAUBER AS FÜRT NIST INIRISTIERISTUNG (cv_deu_000720-cv_deu_000720) +DASSES EINERXSTRENSCHLECHTERICHS LIENIER (cv_deu_000721-cv_deu_000721) +HERLORCH EN BLESTSEIN HAGERESGESICHT (cv_deu_000722-cv_deu_000722) +MOKAREN FINE TO UN FER (cv_deu_000723-cv_deu_000723) +T INGE BO KABERHAT DER DREIGESCHICSTET (cv_deu_000724-cv_deu_000724) +LCES KOMTWICLISTSA ANM DASOLCHEDADTEN U DIESER EBENER FASTWERDEN (cv_deu_000725-cv_deu_000725) +STRAMIN HEN GEGENE GIT EIN ERMUNICHES POSIEREN (cv_deu_000726-cv_deu_000726) +BENICH UM KAUF EIER HPOTEG BERECH TIGT (cv_deu_000727-cv_deu_000727) +TUN E EUNLENS S ENERSHENFVENDEN DEN BMNMIE (cv_deu_000728-cv_deu_000728) +HKÖUEN DERENSN DEREEISLNDUN (cv_deu_000729-cv_deu_000729) +ON DIE POFISINLLIN TESTÜTZUN DAMASSEARTDIE ENABTEILUNG WAEN DISE BAGEN DER KON KROWENS UN DOCH UND OLEGEN (cv_deu_000730-cv_deu_000730) +SIE DIEN TE UNEST ASUNDTEOKMFT VÜR BELGISCHE BISATZUSTRUPEN (cv_deu_000731-cv_deu_000731) +DAMISSN WISCSPLENEN MEINTE DERZHAHN AHRZT (cv_deu_000732-cv_deu_000732) +AUSDEM SPITE R EIMNACHFORGETIEM NIMARKEDROEUOLS SOWIE EIMLIGEA KONKURRENDTEN LNDEN NEI (cv_deu_000733-cv_deu_000733) +IE AUC AS ENSTEND RAN ACH WATENG ER FÜRTIE KUMS WAL DAS KONN DORKETGTERIOMNICHT (cv_deu_000734-cv_deu_000734) +SMIFUCHEN TI KAGEOAUF (cv_deu_000735-cv_deu_000735) +WIER IEIE ALEINEN (cv_deu_000736-cv_deu_000736) +DUM ISG WERTWAS BWEIS WURTROC DSS DESIUN BISENS VOLSCHUNGEL (cv_deu_000737-cv_deu_000737) +HABTEMA DER SCHA IST DIE RE WANSCH VEREKÜBICHISTREICHER ENTERFREINDEN (cv_deu_000738-cv_deu_000738) +GLEICH ZEITIG WUODEN SPOTWETEN TALWEISE VERBULEN (cv_deu_000739-cv_deu_000739) +SEEGEN (cv_deu_000740-cv_deu_000740) +TIA BE (cv_deu_000741-cv_deu_000741) +ZU DEM FARSAH ER EN KLOSTALANG JARE DIE MTER DES NO WITZSEN MEISTASUN PRIORA (cv_deu_000742-cv_deu_000742) +HEIDEN HEIDEN EN STMT EINER ERTZTE VERMILIEAR (cv_deu_000743-cv_deu_000743) +ARESPZIMPTPENN (cv_deu_000744-cv_deu_000744) +ZWAIE UNGR (cv_deu_000745-cv_deu_000745) +TTTTTEBEMFALS EN AUGNG ANGIIEDE ENTIKALTREINDER EFA (cv_deu_000746-cv_deu_000746) +DIESERSTDET ARFE AB SELWENDTEN EIN HERMSCHERSCHOL MITDRTKANDTESEN OFEN (cv_deu_000747-cv_deu_000747) +ALSO ESCHIORE NICS (cv_deu_000748-cv_deu_000748) +WIEKON MAHN SISCH SCHÜTZE (cv_deu_000749-cv_deu_000749) +AUFÜM FMONATEN LAG EINE IM FINTLICHA EPLOTER ANZSTDIEBISTAHEINER HLTLICHEN VOR (cv_deu_000750-cv_deu_000750) +ZIELISTERS DIEVE ENTSTIMUGENESOFTJESESTEBZMIT SNNRSGHETZICHKATZGON ZUÜBARPOL FMSH (cv_deu_000751-cv_deu_000751) +BN NINTE EINEN WAHRMNEN GEMPRENEN IONDBEUUSC NISSISTDIKÖNDELE BESSER UN SHEILTEN (cv_deu_000752-cv_deu_000752) +DIE AN DIE WIERENSOCHÜÖR IST ANNURNKENGAL AUFHEN UNDEAND ALLE GUNMBIEMTDELLN NERUSLANGELIGT (cv_deu_000753-cv_deu_000753) +IERE TU ARGKE ISTEN DIESARZEIT GUGEFLNGENSC (cv_deu_000754-cv_deu_000754) +ET DIE STREIKER BEGEND IM SGIEN BERHUNERS IN FÜHRTIC DIE GO EB LE HECHT UNGM ASYÜT OSSTE (cv_deu_000755-cv_deu_000755) +ERST VON DORT KONTE ER SEIM WEGFREI VOR SETZEN (cv_deu_000756-cv_deu_000756) +SIE ERHEBPZICH HEUTE IMARNOCKUTER KENBER AUSS DIEMN SCH WEMLANDTHERUSS (cv_deu_000757-cv_deu_000757) +TI ANARESCHEN INSLEN GEHE ZU SBAHHE (cv_deu_000758-cv_deu_000758) +WESSN SCHAFLERHAHABEN DESIM ENTATZ UN PESERETNO BEI FOARAUN BEROBATET (cv_deu_000759-cv_deu_000759) +SEINI GISCHEHT ZSBEZTIUNGEN REISCHTEN BIS NORD DOMEHRICKA UND ASIEREN (cv_deu_000760-cv_deu_000760) +SALREIC IEPVORDERED LATIERUNG EN BEI DEUTSCHEN ÖROPAR UND WELTLESTE SCAFTERN SO IE OLÖBISCHEN SPILEN VORLGKTEN (cv_deu_000761-cv_deu_000761) +IN ENERTALES CERTUM BLETERMTD SOETSIKIT AUF ERBAGBANG (cv_deu_000762-cv_deu_000762) +WIT EIM WAHMLITRENK IN BAUFLESSE IE KERLTE BESEAUSHAUELT (cv_deu_000763-cv_deu_000763) +WOLLEDEM ÖEEWES (cv_deu_000764-cv_deu_000764) +OSTAN DIS IENE EINE BOCHENACH EM ESTU VORNMUN DIM PGLULIEN (cv_deu_000765-cv_deu_000765) +EM MITEL EITERHATEN DEXZEM DER HARSCHAFT DAS DOF INER (cv_deu_000766-cv_deu_000766) +DIE NAM SCIEPLE TRAGNOCHREITERDE FALTSOLGEFADMASEHATIE (cv_deu_000767-cv_deu_000767) +PLUKANNSWIT DE BUSLCHRANG VON ERODERFOREN (cv_deu_000768-cv_deu_000768) +IERDOCREGOL (cv_deu_000769-cv_deu_000769) +ALLER DINGS ER GAHBEN WEITERHRE PRÜFUNGEN DASS S MITTELFRISTIG KEIN PEDARFRIS CEUCHE AUTOBANGER WE N (cv_deu_000770-cv_deu_000770) +UNGEKERT KAN EN FREIPRIEF EINE ARAUSSCHREIBUNG ALTS VOBELFREI GEMEINDZEINEN (cv_deu_000771-cv_deu_000771) +MIEZAG KROTESGE ABSCHNITE SEINEN EINFLUSSE LEUCH SCHOSTAKOWICH (cv_deu_000772-cv_deu_000772) +RVER EINE DER PIEO NIERE AUF DM GEBIET DER UTZIUNG DER SONENE NER GEE (cv_deu_000773-cv_deu_000773) +ACHVEN MEDI KUNDEN AF DENE REN GEN MSICHÖFLICHKEIT BEWAN (cv_deu_000774-cv_deu_000774) +DIE BEMASCHINERST FERTICH (cv_deu_000775-cv_deu_000775) +IN DE ARCHAISCHEN PERIODE WURDEN RSTI VORMIN DES OCKEBASSINTUYKILD (cv_deu_000776-cv_deu_000776) +DI COMÖÜDIER SEESE ALSTER STFÜN (cv_deu_000777-cv_deu_000777) +ARTUÄ GET VERGNEAMUMS (cv_deu_000778-cv_deu_000778) +TEARMIT ENTET EINE EFÜRK KREISCHE INTLNATZEUNEALIE KEÄRDENS ABEN VORELEN INMSCHKÜTE LUNENENZS ACKUR (cv_deu_000779-cv_deu_000779) +DERSON EINES BERETNANZ BEGAN SEINIE FOSBEIKAERI WEI DEN SPORTFREUNDEN WANE EIKEL (cv_deu_000780-cv_deu_000780) +IN DIESENJAHRGABESIEDEN OME EINENSINGES UND SECHSON DEISIG NOMER EIENS ALLEBEN (cv_deu_000781-cv_deu_000781) +NORD WESTLICH VON HAKHAUSEN BEFINDE SICH DIO RTSCHAFTHAKEN BRUCH N (cv_deu_000782-cv_deu_000782) +IM ORT KNAEN BURG GIEN VIELE SOZIALE EINERICHTEUNGEN VON EREMAN LAMPRECHT UNDER MAIN HÖTE AUS (cv_deu_000783-cv_deu_000783) +ICH WERDE FOLÖKLICH DEN RAT ÜBER DIEM PALMENT VORGETRAGENEN BEDENKTEN IN VORMIEREN (cv_deu_000784-cv_deu_000784) +ES ERETRAURECGEWESEN EIN SO WICHTDIESTEMANICHTEM KONSET FABSCHEN ZU KÖN (cv_deu_000785-cv_deu_000785) +NOCHTIS SIM TOT IM KLEICHEN JAH GAMM S GUTZ BRISTIG AN ANDERE VISEZEA (cv_deu_000786-cv_deu_000786) +KOT DANACH GABES EINEIN WERBER VORD MINTDEMTKANDKAMNEND VON SCAKESH OUEN BACH (cv_deu_000787-cv_deu_000787) +DAS IT BSE AET (cv_deu_000788-cv_deu_000788) +WISIE SMIN LECHZEIT AUS HR (cv_deu_000789-cv_deu_000789) +NACHE DEM DOCHF BEFIN DER SIGH ARUCH DER KMKANIUN NASIONALLBACHKERBOT (cv_deu_000790-cv_deu_000790) +IESOREN DEAKÖNDEN DES DJELIEBEDENTORD BESIGTAT (cv_deu_000791-cv_deu_000791) +BETECKT SSTDIE REPRESENTHERTIEF GESTALTE DE WILER MITD EINERN MANDSART DACH (cv_deu_000792-cv_deu_000792) +DIE SE SIETLUNG ES MITER ORTSCHACFT DELLACH ZUSAMEN GEWAKZEN (cv_deu_000793-cv_deu_000793) +WARISCHN EINMALIENDEM KLO (cv_deu_000794-cv_deu_000794) +BORAUR IST IST AUCH VOLOR (cv_deu_000795-cv_deu_000795) +DIE HEX VONDR STRASE WUDENSN VON ALFET DIOLEK BESEINE FESTEN SEI SCHO E SCHABLNE EN GÜLSIERTE (cv_deu_000796-cv_deu_000796) +AIN HARSPÄITE VEXSLTER ELTZUN ELFT NATZS UNM BE VODE ELF VUNGEREICHE (cv_deu_000797-cv_deu_000797) +IN DER LANDVITCHERF KANDER ERTRARGKT DEUTLI WEDORZIERT WERDEN T (cv_deu_000798-cv_deu_000798) +MAN SURSPIERTE IN SEINER HEIMAT STADT KEE WORFIER ALL ALL (cv_deu_000799-cv_deu_000799) +ERTRA DER REIMAUHALUNDEL NLAOUTABUEOBEI (cv_deu_000800-cv_deu_000800) +MIT FÜRT WAR EHR DIE ROWPTHJADEL EILS DERECHTLICHEROLLE DIES SOBETSEICENEN GEMEIND (cv_deu_000801-cv_deu_000801) +LETZTEWOCHO GAB DAS METIE BEKAND GASES VON EPEL ÜBER VIERND AS HWALTE FORFELE VON ÜBER ITZEUN INTOMIRTWORDENWA IEDES UNTERNEHN ALS NICHT SCHER IEN PEEITETE (fleurs_deu_000378-fleurs_deu_000378) +EJÜURSE JIMNESTIG UND TER STÜTZST E DEN BERIEF DES OLÜMPISCHEN KOMITIS DER VER EINIGTEN STATEN UND DACIPTIERT ES AS APBPTULUTE NOT WENDIKEIT DASIHT DIE OLÜMPISCHE VER MILIE FÜR EINGSICHERES UNMFELLT FÜR ALE UNSERER SPORTLER EINSETT (fleurs_deu_000379-fleurs_deu_000379) +DALIC KENE APRETSKOMPETIBELMIT CHTRNERTZWEI PUND ELF ARACHTENR ZWEI PUNGD ELFPE UND CHTEONETZWEIPUNGD ELFGESEIN VERASGES DI ASSISTATION VERFÜGT BER DUALRADIO (fleurs_deu_000380-fleurs_deu_000380) +ER BEZEICHNES DIE IERÜCHTE ALS POLISCHESGISCHEÄTZS UNDT ALL BENHEITZ (fleurs_deu_000381-fleurs_deu_000381) +LET E WOCHEGABT AS EMIEITHIE BEKANDASIS VON EBEL ÜBER VIRNDREISI WEITRE FORFVELEVON BERHITZUN IN VORMIR URDEN WA DIEDAS UNTERNEHMEM ALS NICHCH WRWEGEN BEZEIGNETE (fleurs_deu_000382-fleurs_deu_000382) +NACH DE DERDM LEUNEH HNDERTRALN SECHZIG E BAURT WORDENWARKAM DIAHRES SEILIHN BERFLUTUNG DES DEMENTE MNFLSVERTALENZUM STILSTANT (fleurs_deu_000383-fleurs_deu_000383) +ERWAUCH AM STECHEN VON GELSCHEINVE VIELEDENDE BETEILICGT AKTE LEBEISHPIESENE AR BETSCHLISENIEPRIMEHMINISTER PORTRES AF DER FORDERSERT DER KANADSCHEN FÜNF UND UNDER OLLRNUTEN EIN (fleurs_deu_000384-fleurs_deu_000384) +DIE AUPTSTAT VERMODAWIERN IST KICHINA DIE EINEIMPISPACHE STRUMENISCH ABARVIELEMENTENCSPRECHEN ROSSELC (fleurs_deu_000385-fleurs_deu_000385) +SZWICHEN DEN EINZENEN DÜNESTIEN HERSTDEN AUCH UNM BESTENDIEGE ZEITEN GETALLTE PROINDZEDIE BEKANTESTEDIESE PERIODEN OADI E POCHEDERDRAILKÖNINGREICHE DIE SECHTICH IERELA ZWISCHEN DER HAHN UNG DER IEN DIENESTI STAT VADT (fleurs_deu_000386-fleurs_deu_000386) +AM ANDERENENEDR SPEKTRUMS HWANELTMANSICHEN EINICHT WIDER ZURKENDE NDIWIDE UM DAS ALES ANERS MACHNMOSS ASSTIENES GEMACTER UND SIC ALESTZUALGEMACHT (fleurs_deu_000387-fleurs_deu_000387) +DIE MEISTEND INTER PITEATIONEN DESTICHNOLOGISCHENDERTEIMINIESN USTALEN ZWEIALGEMEINE VORSCHTEUNGENEINER SEITS TSTD INTWICKLUM DERTICHNOLOGIESLLPST EINEM WEGOLGTDERWEITGENT IENSEIS UTDTORELEODER POLIISCHEINPLSNAMEN DIGTUND ANDERERSEIT DASTIC NERÜGIE IERERSEITS AUSWIRKUNG EN AUFGESALSCHAFTNHRT DIE EHR INHÄREND AS SZUTTIAL BEDIEN SID (fleurs_deu_000388-fleurs_deu_000388) +WISCHE DEN EINZEN DNARTIEN HERCHTEN ACH UNMBESTENDIGEZEITEN GETAILTE ROWINZEN DI BEKANDESTE DEPERIODEN WADI E POCHRO DERDEKÖNIGREICHE DIE SECHTIC ARLANGT ISCHEN DER HAN UNDTERINDENRTIESTTFVAND (fleurs_deu_000389-fleurs_deu_000389) +DIEMLIEKTZUFORGIEBIEZIZICHTES TO OMENT AUF DEM GRENSTREILT IN DEN DIE PALIS INENSER EIN ZURÜEGSETZEN DER GRENZEN IN DEN ZUSTAND VORDEM SERXSTALGLEGRIEG VORN NENEHN UNDERT SEBEO NUSESTIG ORDER (fleurs_deu_000390-fleurs_deu_000390) +MIT IM PELUSTGRECHEHERSPACHKNNSE UR DER WESTEN VONSEIME VIELOSOPISCHEN UND WISENSCHAFLICHEN WORTZE NKRICHENEN ABISCHNITEN (fleurs_deu_000391-fleurs_deu_000391) +WIR STMITER AUSAGEDIES IUERS AUSIE ÜBEREINDASTEN NTRESSNUNDSREATLEDEND VEREINUN DIRESPORTSBPSSR GEDIENDIST EN WR NEHALB UNSR RGEN SATIONDEHN VOLEVER NDRUNG VERANDTREIBEN ANSTAT EINERTI ER IZET VIZIERUNG VORZUNE (fleurs_deu_000392-fleurs_deu_000392) +I REUTSFATEN NASSANGTPIKARSBOG BIETEN ACH ZEITFÜR EIN AUFENTAL INERSTAT KREUTZVATPASRSIERE SINDFVOR DER IESUNSLICHT BEFREIT SIE BEDNGEN (fleurs_deu_000393-fleurs_deu_000393) +EREISEN DE VERDEN RINGEND GEWANT AUF JET WEDE ART VON UN WENTE ZUACHTEN DIE IEGIE IET BIETRIFT DADIS SIGH AUF ALEREISE PLENE AUS IERKEN KANT (fleurs_deu_000394-fleurs_deu_000394) +SE BE SART DAS DER KOLZUNGSPUNGKT DELINMIN DIE EN BIELT WERTIKAL UNT RET ONDTAL DRITEEN DE EFIKT ISTDEPLAS FÜRTESAUPTMUODI ISTSIE BEISSIN (fleurs_deu_000395-fleurs_deu_000395) +SEIT NUNZENURT ACHTEN ACHTICH MSTE WALUND RANSBERENSEIN DAMIT WELE UNBEOBACHTE BETZEUGENKÖN DAS Z WEGINDER WALKEINE UMSCHLEGE VERHNENSIND UNDASKEIN UMSCHLEGE EINGEOFEN WERDEN AUSER JENE DERT ORDUNGS MIS EHLTNEN ATTR SIRDTEN ELER (fleurs_deu_000396-fleurs_deu_000396) +OTERAR IST KANNERDES BIEZAUBEN DER ZWEISCHALI GE HAUPTSTAT UND SELTEN SICH IC EINEREIE VUN KUNZSTGELERIEN UND MUSEEN AUS DIE KANENDERS VERGANGEN HELT UND GEGEN WART BRESEN TIERE (fleurs_deu_000397-fleurs_deu_000397) +DIESE PAREKÖREN SICH VEREIN ADEBPTIONSPLANDVR ERBEBE ENSCHEIDEN (fleurs_deu_000398-fleurs_deu_000398) +IN FOL GEDESSENSEN ZWEI FISHT ABEN AUSGESTORME NDZWEWEITRUSEN VOM AUSTERBE BETROT DAUNE DER GELARSZYÜFER (fleurs_deu_000399-fleurs_deu_000399) +TRANZENSEN NIHRNATILICHEN MOGEBNGAM BESTEN AUS WIEDER STENSI ALSO DERVERSUCHUNG AUCH NUR EINEXMKLAU D WERN (fleurs_deu_000400-fleurs_deu_000400) +AUF DER NAHSEITE KÖNTE IS MERMARIER GEBEN DADI GROSTE DUNEST ES WER EIN FERAFODIE LAVER ANDIOBERFLECH AUFTUSTEGEN (fleurs_deu_000401-fleurs_deu_000401) +ER FÜCKTECHIN ZU DASSIJEDOCH NICHTEAR ZUOAUFGIE VORDERT WERDEN SOLLTERN FERTFLICHTUNGEN EINDZUGEEN DIE ÜBER IEREN INTWITLUNGSTAND IERE VERANTORDUNG UND I RERFÄIGKATEN HIENAUS NGEEN T (fleurs_deu_000402-fleurs_deu_000402) +TCIERTU ELI HIEL FISTELUNGEN SINT IN DISOFT ER ENGIE BAUD UN SOLEN AHBEITSCHRITDE ND IEDE SCHÜLER ALEIN MÖGLICH ER WEISE NIHT BEVELTIGEN KÖR HINTER FRAGEN NEIELEGEN UND DERGLEREN (fleurs_deu_000403-fleurs_deu_000403) +AM FÜNFZEHN NA GST NEUNZHN HUDERT VIRZI VIELI ALIERTEN N SÜT FRANKREICH EIN DIN WASION WORDE APERESCHEN ROGUN GENRD (fleurs_deu_000404-fleurs_deu_000404) +ER GRIFACH ALLS AN WASSENS WASSERKARM SEBST EN GROSERDENOSAURIE WIDER TIERECGSWERIMNICHT GEWAKEN (fleurs_deu_000405-fleurs_deu_000405) +SEITER KRNDUNG VOR ASUNTIOR FÜNFZEN DE SENUODREISCIES ESPAREGWEI GELUNG VIEL VON SEIM INDIGEHNEN KARAKTER NDSEINE IDE NITETZU BEWAREN (fleurs_deu_000406-fleurs_deu_000406) +STROZS DEN IS DER ANTELLANN IEXSDIE WINDESTIGHTDE B IN DER GESAMTEN RUPE DER LEUTEE MIT DUG DER KOLOSER OFENBA DERN NOCH GERINENSEXSTAUSEN DER INDGESAEN DREIHUNDENDREIG AUSEN LELTE DIE INSÜTAFRICKER TU EINEM BIESTINTEND EITPUNGTANGESTET SITT (fleurs_deu_000407-fleurs_deu_000407) +ENSCHEL ZWEITAUSEN SECHS ELEUTERT ASKONTINUM KONZETAS EINE MITODE UM OBGENSATIONDS HLFEN LEISTUMGS FEGE ZU WERDEN (fleurs_deu_000408-fleurs_deu_000408) +IN DIESE PIERIODEN DEROEROPESCHENGICHIGHTE STAND DIERLICH UND MECHTIHE GEVORDENER ATOLISCHIYKIECHE AUFDEN PRÜSTANDT T (fleurs_deu_000409-fleurs_deu_000409) +DIE ERS DR CHTEN DIBZIC EM FHLUNG IFTAS EINE NEU DEPLOMATSCH INI ZATIEVEVOERENDE DIESEN JAHRESE GRIFEN WERDENSOLLTE UM DIE RAGISHEN GRENZEN GEGE BERFENTLIHENTERWETIOND ZUSICHARN UND PLOMATSCH BIZIEO MIZEINACHBAN IEDER RTU STE (fleurs_deu_000410-fleurs_deu_000410) +DISPETET EINI UTEGELEGEGHEIT DAS NOTLICHTUSEN DAE HIMEM MEHRAUDERWENIER RUND UMDIEURDUNKEL ST (fleurs_deu_000411-fleurs_deu_000411) +PROFSSOREN PAMELE VERGUSSON VON DER N WÜUSTI AF DANDIMERKT AN SOALISTENSCHEIN EINE GEELIEGRANZE ZUASCRETEN WEN DIE VOTTO UN SWEITE VONVERDECTIGE VER FNTICHEN (fleurs_deu_000412-fleurs_deu_000412) +ESKANSI CH LON EINE ELKAT ZU KAUFNDIE ZUTRIT EN WDERTU USGEWELEN PAGSEN HET AFRIKARDER ZU ALENZUÜT FRIKANSCHNERTONALPARXSGEWERT (fleurs_deu_000413-fleurs_deu_000413) +DIE PRÜKESOLEMSE TEMBER ZWEITAUEN SIB SHN FOLSTENDIT N BETRIE AUFNEHMN ISWR RWARE DS I PAS IANISCHEN ZOLPUNKTE DAN FERTIG STELL ZEIN WERT (fleurs_deu_000414-fleurs_deu_000414) +WEREN DEI ERXPRMINTELLE IMSTF NE LAGE USEINSCHEINTDI EBOLERMOTLIETÄTZU SENG UNG GBTES BSSERKEINEMITIKAMÄNTE DIALS EINDR DI ZU BEHANDLUN BESTENDE IN VEKTIONGE EGNET NACH EWIESE ORDEN (fleurs_deu_000415-fleurs_deu_000415) +EIN EUSERSTLEPAFTER DE BECHE WECHSEL VANSTADT WAI ERWUK DEM PLAN EIN ALGEMEINEN STATEN KONGRESTZUBERUFEN UND KONDE SI VOLLOUFIKT NONUNICHT ÜBER DES ORZULEGEDE ROGRMM UN DEN ORTES ZUSAMTRETS EINIGEN (mls_deu_000281-mls_deu_000281) +ER WUSTENICHT WAS IM DAS LEBEN KOSSBARES GERAUPTATE SCPAN KRAFTUND MUTD DASS ES IN FEIK UNDSCOEUGEMACHTATE UNDFÄICH ZU DEN HOHN DINGEN ZUODENEN UNGETRÜBPTE ITFROLDENGEHÖRT (mls_deu_000282-mls_deu_000282) 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MENSCHEN DEN ZIKÄNEN FRAGTE ILEISE WELCHE UN BEMEAKTAMICHERAN GETRETEN WA UMICHINDTGEGNETE DAS S N EN FANTESIKOPFSEI UNTSCHUBT ZEICH UNG ILIC UNTU DIE ANDONDLÄTTER UNATÜLICGSPACRICH IE UNBAHEILT DENE WEIN SE ITOEUESPETRIEMISTEROTSCHSTES (mls_deu_000287-mls_deu_000287) +ICH WEIS SICHSER KRANKGBIN SAIGTESINEREINRWEILE VORN PAMINUTEN VERSCHTE ICHMICHE ETTE UM ZUREEN UND FÜLDE DASIC KENGLIED MERENÜORN KAM ES WEREGUTD WENICH ENGIMÜT ELEICHTDAN KÖNTER BEVORICSTERDEN (mls_deu_000288-mls_deu_000288) +SOABER IST WER UN SERWESENS KUND OTSELLVER DACHEROM HATZICHEDOC DERCHLANGEN KNEUL DES ALKENSATANGESHLUNGEN UNG ÜBER DEM FÜNKIENDEN LIEBI IS DI FENSERNISTESHASSES ELAGERT WAS WONDERDAN (mls_deu_000289-mls_deu_000289) +BESIVIRELIEAGEBLIEBEN ABERSIWAGETZWUNGKZUGEHN EA DI ÜNKLITKEIT BEIDENMALSZEITEN EINE SACHEWA AU WÄCH IN GETZS HÄRD HORL STRENGEGEHALTEN WURDE (mls_deu_000290-mls_deu_000290) +NBLICKLICH FÜLTEWIEHRE AMSICHTENÜBERMICH IRER M PINDUNGED FÜHRMICH NICHTUMEIN ATOUMVER INDETWAHEMN UÜBEHAUPT 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DOCKTOR UN DIE WERTEREN SOLLEN VOR SEINE AUGEN KOMMEN ER KLÄATE DIETRINER IN GROSSEM AMT SEIFER DAMIT WA DIE FRAR OBERST GANS EINFERSTANDEN UND PIRXST ERFREIT KERTE SI MIT IEREN (mls_deu_000295-mls_deu_000295) +KAAR UN TRÜSTICHÜBERDIE LAGE DAS KÖNZTLRS ERBEGAN ZU WEINEN UNSCHLCHTZT DE LANGE IN DE ORGE HALTENENHNDE DER KONSLEAWATETE BISKASIC BERUICHTHATTE UND ENSCHLOSICH DAN DAR ER KEIN ANDERN AUSIGFANDT DERNOCH ZUM EITERSCREIBEN (mls_deu_000296-mls_deu_000296) +ONDIM FERDEHERDEN DER PATSCHEN UND SAG NUNZSDASSI F ENEA PATSCHEM FÄRDUNDS EBENSU VIELE WARENUN PRENIGEBEN WIRDEN ÜFÜRIN KEIOWABFERT DASINDUN RIKLIGAR FOD UM APATSCHEN FÄRDE ZO HLENALSORCHTIGH ERARSCHLDER EM TODE E BES ER GEFALLEN UNDERIE BLUTVERGIESEN WECHES UN BE VORSTAND WEISE FÄERDE HENDTLER (mls_deu_000297-mls_deu_000297) +DAS MATONE HÜT CHEN VONSCWATZAM SAMET KATIÜRSA IERE LANGENLOKENGEDRÜCT DIEREWANGNUM FLOSSEN ND ÜBERSCHLTENHERABWEITEN SO TRATE I DRS EINFECHRELENTLICH GEBOUDE UNDSTEBPTETZWISCENE REIN DERHEIB GEBLÄNETN OF KNER AUFEND AB (mls_deu_000298-mls_deu_000298) +TUMUST ERST EN ZAGEN ALEN SÜNTHAFTENSTREBEN UNEN TIEVEREUIUND DEMUD DIE FÜHR BIER HLLINGER FLEN GEGEN DIEDU GEFREELTEST TIE JÜMLNGE WLCHE FENSCHES OSOLNE GEFLON SOUCHTEN N AUENER WERKTATUN FANDEN IN (mls_deu_000299-mls_deu_000299) +ERLIES SEINE GRETE NIHT VORTSCHLÄBEN AM ALLERWINIGXSTE ABER INDEN GROSSEND VOGELBAUAR USIE ALE EN EINEM TONEB FEIFEN MOUSTEN WIERSTE SAKTE (mls_deu_000300-mls_deu_000300) +VRN HESKOMALTEN UNHALIGE BEGEISTRNG VIELEBIELT ASTE LÜGENHAFTEN ABELWELD TRENEALS ERERMOCH DIE BULERISCHEÜLBEKAETE WEIBIENGESTALTEN SOBER HAF ASSTELEN IN DEM VON LEBENTE MODELE DIKANDATIONG VONDEN ALTEN MAHMO BILDER RBERFORMND BIELUNGIND NAM (mls_deu_000301-mls_deu_000301) +BWEGUNG UN TAT DEN STEN ZUG IER STEMTE DIE FOEUN ANGEGEBNE INKGREDENZIERNMIC RÜBEN HANFE EICHENUN SAUERAMFAN ALLE IN DEM FEIFNKOPFERNWESEN ABERIN FÜNFTN HAUPTSTOFHAICHNICG GENANDJERTTROCH ND SCHMEKTIG DASE HONSTÜCHEN FILSCHUDER BEISEIN ISE IGPLIESTEN RAUHAUCHGEG DEN HEME UNGEGEDI (mls_deu_000302-mls_deu_000302) +UND DAS FOUR STAND AUF UND FLACKEA T UN KOCH DAS ESSEN FERTICH UND DER BRATEN BRUTZELTER FORT UN DER KOCH GAB DEM KÜCHEN IUNGEN EINE OR FEIGE UND DE MARKTROPFTE DAS UNFÄERTIGH DARWART DIE HOCHTZEIT ON DEM KÖNICHSON MIE DONGRÖUSIEN GEFEIERT UN SIELIETNEFERGNÜTEBIS AN IER ENDE (mls_deu_000303-mls_deu_000303) +UNMD DESERMINICH NACTRAGEN BOLE WENICH IDERSHFÄNSTIG WAGIN SEIN OHMEINEM RART DER HERFARA EHADIEIN ALLMPRECHT GEHAUT UND ICHMA AM UNM RECHT ABER (mls_deu_000304-mls_deu_000304) +GÄCHENEMASE NOWINIGEKRAM BETRUGEREITE ESICH KILIE FRMIG AUSHUMUSTEDER ERDES M IND GEGENFLIGEDESPRENKISCHOS AUFANGEN NDZU WIBRINEN (mls_deu_000305-mls_deu_000305) +DER FCKSREICHTE EM DE UNFRITICHE RIEDENSPFEI VER HN DER MANTAT WACKARSEINESEXS ZYGEN SAGKTE DER ROSIGEIS ACHTETNICHT AUFDIVERSCHIEDENE HAUDER MENSCHEN DEN DIKÖN SICHMIT FABEBESCHMIEREN MINTZU DTEUSCHEN SODE ERSI DAS HETZSAN DIE HTZEN DER KLIGER VO BERÜBTENSTAMEDER KAEIOASEIN TAPTVER UNERSCHROKEN N TREU DAS MEINEIGEHÄNG (mls_deu_000306-mls_deu_000306) +ALLS ASWIE MET IER BEGEGNIT SCIEB SICH DEUICHUND ÜBER EINANDER BALT UNTERSCHEIBEN WER IN KONTAKT DER IST ERERHAND N DE MEINIGE IER NAHM ONDER MEINIGE WEI DELESCHEN EINANDER AUS BEI DE VERSCHLINGENSICH (mls_deu_000307-mls_deu_000307) +ER MÖSTE DEN ENFACHEN RONITEN KÖRALDES MALES MIT ALLE ER KLEHRENENUN ZRECHTWESSUNGEN IMIT KRAUSEN WIGUN VESCHNARKENUN VERBREMEN ICH TRETE I DIE PERSONDESERAUSGEBESND BITETICH IÜNSTIGELISER TU OLIST IE DU WEITELISEST FOLDENDIS DEGÜTTIST MERHEN (mls_deu_000308-mls_deu_000308) +DIE OFDAMEN BEKAMEN KRMPFER UNDTIEKÖNIGEN UN IEPROMZESSENEN DIERER ALLALIBSEN HÜNZCHEN WERN DERMALTER UF DEN HOSGENOM HADEN BEMERKEN ZU IREN SCRÄCTEN DAS DILIELER ARMARANDFABENEN UNDORANSCAEDEN SEIDENKLEIDER ALE ICT BESET MIT DEN HESLICSTEN ÖFLÄGEN WANE (mls_deu_000309-mls_deu_000309) +VON LIEDAN DIE IE SINGEN UN KLVIER PIESEN DIESISPIELN VON GEILT BÜÖRSEN DIESI HIEGKELN VON VANZÖÜSCHEN BÜCHEN DIES ÜBERSETZEN KONTE BISMENGEMÖÜT WERENDICHLAUSTE ZONACH AMNG AUFGESTACHET WURDE (mls_deu_000310-mls_deu_000310) +AMEN NATEN WANBLOS IR ENZIGER SCMOC WAN IHREKASTANIENBRANFLÄCHTEN WILCH EN WILDE UN NATÜRLICHER ANMUND AU IRSCHLTENERABVIELEN CHNAM EIN BOGENGFEIN KATOUNGS NDZEICHETEM MT OSRSORKFALTI OMGRSSER (mls_deu_000311-mls_deu_000311) +AUR WIEDE AUS DETCHLAND NOCHAS IELEN EINEM ANDEREN START KONTEMEN EFAHN WAS ER GEGNSTAND UNDES ESLTAD DIES UNTEREDUNG GEWISEN SEI AN VRMUTETE DSSESIC UM ERKLIERUNG DER MATZIER ÜBE IER ABSICHTEN UND UN DIEVERMITLUNG DER MECHTET ZWICHNDN MARSTATEN UN ROSPOTANIENHANDLE (mls_deu_000312-mls_deu_000312) +LASUNS WENIGSENS EINER ZEITLANG VERSUOCHEN IN DIE VERN WIER UF DESES BEISIMIT EINANDER AUSREICHEN DADAS ZUSAMMENHÄNGENDE VIEDUESAGST EIGENTLICH EUER LLEMENT IS VERSETZTE IDRT (mls_deu_000313-mls_deu_000313) +VEASCHINEN VORKOMMNSE FÜRDEN ZU DER VER MOTUNGDAS FRAU WISE DI KLEINEN VWISEN VERBRENER IESOL BES EIN SU STACH GEHEITZTABEN DAS D ERTPLADEN ZSPRANG AUSST EM OLEIN FÜRCHTELICHER EROCHWAGENUME WORDEN SEIN (mls_deu_000314-mls_deu_000314) +UND GING DEM SCHREIEN NACH SOSAH ER ENTLICH EINHOHN BAUM UND OBENDER RAUFSAS EIN KLEINES KIND UNTER DEM BAUM ABARLAK EINE FRAUDIESCHLIEF (mls_deu_000315-mls_deu_000315) +SIE HATEN SEUEBN DIE FISCHER GARDNER WALCH DE NACHT IYBERT AUSGE UORH VEN WADEN CHEREIN GE ZOGEN NDIE SELEUITER GER HEOTEN AGENSCHEIMLISCH VERSCHIEDENEN NCTZIONENRN ABORL DER OLULOPÄISCHER KARKTE BER ALLEN AUS GET RÜKTWAT (mls_deu_000316-mls_deu_000316) +NENEINICH SCHÄME MEICH LASMICH EN DEINEM BUSEN MEINGE SICHT VER BERGENG ER SINKTENS GRASNIDE UNDZIE SINACH (mls_deu_000317-mls_deu_000317) +DIE KINDER ARBAR SASEN VOR DEM WALT UND ALSIEDIE REIKNECHTER VON WEITEM LAUFEN SAHNSPRACHRLENSHEN ZUMPFUNDE FOGEL VERLÄSTUMICH NICH ZO ERLAS ICHTICH AUCHNICHT SOSPRACH FONDE FOGEL NUN UND NIMER MR (mls_deu_000318-mls_deu_000318) +WIEDER SCHLZE IN SEINE HLDIEGUNGSRIEDE HER VORHUB DER LERABRACHTE AM KLAEN SOMMARMORDENG MIT SEINEN SCHUHLKINDERN EINGESANGSTÄNTIE 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(voxpopuli_deu_000338-voxpopuli_deu_000338) +IN DESWISCHEN SEISIN DI RETUNGSORGANISERZIONERN IEGRÖSTEN SCHÄPER WEISIE DIE MIGRANTEN ZWANZICHKILOMETER VER DERIEBICHEN KÜST E AUBGREITFEN UND ALENAR ITALIEN RASPORTIEREN (voxpopuli_deu_000339-voxpopuli_deu_000339) +DE SEIKTDR FALLIOLIAR TIEMSCHENKO (voxpopuli_deu_000340-voxpopuli_deu_000340) +E WASSER PREDIGEN UND WEINTRINKEN (voxpopuli_deu_000341-voxpopuli_deu_000341) +ÜR DIESE ENSCHEI DUNG PRAUEN WIAR VIELE PATNAR NICHTZULETZ DIE STÄTTE (voxpopuli_deu_000342-voxpopuli_deu_000342) +DIEFOLGE IST EIN HÖRENFLUG S VOM PORPOLIST NUN EXSTRLMISTEIEN EINIG MIGIT STATENIEREN BUMFEM PAROLUEN SETZENDIAR COGRETER VERENDERUNG EN GEGEN (voxpopuli_deu_000343-voxpopuli_deu_000343) +WAL DIE INVESTITZIONERN VRANTÖRSISCHACH UND DEUTSCHER BANKEN GERETET WERDEN MUSTEN DURHTER GLICHENGLANDT WEITAUSEND EHN NICHTPBEITEGEN UNDEHUTE MUSES EINEN RIESIGENG SCHODEN BERK VORD SICH TET HERT DRÜCKE (voxpopuli_deu_000344-voxpopuli_deu_000344) +IMITGITS TDADEN DÜRFEN NICH 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(voxpopuli_deu_000351-voxpopuli_deu_000351) +DER SCHON AUSGIEFÜHRT WURDE LAGESNICHT BARARN DASIS E GROBEF FÄHLE GEGEBEN HETIS SONE NES GABENER REIE VOND GLEIEN NGEREIMTEITEN BIETIENSWEI (voxpopuli_deu_000352-voxpopuli_deu_000352) +IVER GEMEINTCHEAFTUNG DER AUSENUOS SIEGERLTS POLITIG BAIS GOS IS ZIEL DIESER UN JON (voxpopuli_deu_000353-voxpopuli_deu_000353) +DENSICHEHEIT IS EINESCWIERIGE UND DE TEIL WEICHER ARBEIT NICHT NUER IMTÄCHNISCHEN BEREICH (voxpopuli_deu_000354-voxpopuli_deu_000354) +TISEÄLTEN GEN DIENTERESTEN VON BÜRGERNUN POLITIKEN SOWI AUSENANDER BER EM BÜRERNINGANZER OPERSTETESTEMER KINDT GANNS OBEN (voxpopuli_deu_000355-voxpopuli_deu_000355) +HERPASIDENT (voxpopuli_deu_000356-voxpopuli_deu_000356) +EFÜRTEN GESPREICHE MIT RESEDENT KARSEI ZARDREICHN REGIRUNGS ERTRETERN FRAUNUND MENSCHNRECHT ORGANISERTZIONEN UND DIE WAND DUCHAUSEMUTIGENT (voxpopuli_deu_000357-voxpopuli_deu_000357) +NGS ACH EINE URSACHEFRDEN WACHSNENATZIHNALISTMUS DE ALINSEIDER FELICH PERSBEKTIFLOSSIST (voxpopuli_deu_000358-voxpopuli_deu_000358) +HUDE IN E IMANAOH SORWEIT ON DIENZIE ENFERN ES (voxpopuli_deu_000359-voxpopuli_deu_000359) +HWER DEALSWIDANZMINISTER AUCHEN MEINEM LANDZIEDEN TAGK DAMIT KONFVONDTIERT DAS NATÜLICH AUCHTES BIRUSTZENGEGEBENSENMUS DAS STASHAUSHALGTDE VON DE STELUER SALERENE NON STEUER ZEOLLAN INENZIEZIHNT UND DAS IETAHMIT AUCH IERNTUERTUNGRAGEN INDE ENT CHÄIDUNGEN DIEVIERHIEN TIESEN RAMEN DREF MIETAM NONTHERN (voxpopuli_deu_000360-voxpopuli_deu_000360) +AUF DEM OUOROPESCHEN AUTERBEBILMAREKT INSIGESAM DERMATISCHIST (voxpopuli_deu_000361-voxpopuli_deu_000361) +OPÄHSCHUNION HADTMIDISE INSTRUMENZS DIE SCONSE EINE AKTIE VEROLLENIERNACHBA RIGIONZU SPIELEN UM DERMOGRATISCHE E FORMEN NDERN NACHALIG EN WIKTUNG ERNZUTREIBE (voxpopuli_deu_000362-voxpopuli_deu_000362) +HTUTELLITERERERSCHIEME VON AUSENG ODER VON INEN IS TRECHT UNDOSCHIEDGLIGH (voxpopuli_deu_000363-voxpopuli_deu_000363) +ER EM IMER GESAGKT EIN ÜBER EILTE STADTZIONIERUNGSEN SHEIDUNGES UN SENICH WEI ZUM JERZIGENTZEIT FUNG ES KEINEBEDROUNG BEISPIELSWEISAUS EM IERANGEBT (voxpopuli_deu_000364-voxpopuli_deu_000364) +DIESERFAKLEIIST EINET ZYNISCHE MIS ACT DUNGEN DER OBPUOVORON MENCHN ECTZWRECTDOMLEL ALLRELS FFAAAAATDODSCSAAAAIS SONGANDANANDENE EINE SOEUCH ODEN CÜLAUPBLICHER ANWORF (voxpopuli_deu_000365-voxpopuli_deu_000365) +DIE ES PE ERHAT DISE UMFSENDER HETZUN TALE RICHTLINDE ÜR BEÜRBOATDET WIN GER (voxpopuli_deu_000366-voxpopuli_deu_000366) +GIGIST WIRGLIC KLA DI INANF UND E WIRSHASTGEDE VELANK VONUNDE ALN EINMALMEHR JETZST DER VERANTWOFTDUNG FÜR EINE OBPTIMALE UND FEALEM RASIEKALIFIZTIERUNG UND RER ARBEITNEHME ND ARBEITDNEMER RINEN DANS BESONDER JETSTRESCHNUNG SOTAGEN (voxpopuli_deu_000367-voxpopuli_deu_000367) +ANDRER AUCHOHLEN DIESERKUDER GEBISER ZIELNALS ANDERE DIS SI SCWÄERTUN DIMITEL ABPZU OFN ETWACRIK IONWIKALABRENZITZIELENODER AUC GRIECHELER DODR AUCHOMÄNIEN (voxpopuli_deu_000368-voxpopuli_deu_000368) +DERBRICH COSES VORDER ZURECHT DAS ES RETING STATLICHERSCHLT TIEDEL EIS ÖFFENTLICHER AUFGABEBEGRIFEN UND DAHIER VON FFENLICHE AKTÜRN VORGENOMWERDEN MOS (voxpopuli_deu_000369-voxpopuli_deu_000369) +DABWIES ABALDN MIT EINEM SOTCARL POGAM ZUTUN HARBEN MSSWIL DARFÜR EIN EN SPECHENDERECHLIGE UNDLAGESCAFEN (voxpopuli_deu_000370-voxpopuli_deu_000370) +STIER NOCHNALISEREN WORE (voxpopuli_deu_000371-voxpopuli_deu_000371) +MA KENENERTLIE VERLANGEN GEBNLIEMER GARTFIRND IKUNGSH VER AUS DIE AHMENEUTE BRAUCKEN DASS BE (voxpopuli_deu_000372-voxpopuli_deu_000372) +GERAD ÜO GLEINELE POJECKTE IS DAS ÜBER MÄESIGH BIEROGATESCHR AUFAND RECHTICH DAS TAS IER SRBEIN ZEITAUM VOND DREIJAHREN GESENTWERDENSOLUNDUM (voxpopuli_deu_000373-voxpopuli_deu_000373) +IKANDE VORSICHERN DIAROPESCHE KOMISION ISTE TKOMITDET ZSUM A AR EROB SEALOPECHEN ERSBIEKTDIEVER DIS KOSSOSE (voxpopuli_deu_000374-voxpopuli_deu_000374) +SESESLAPEIHE AUFTAUCH SO (voxpopuli_deu_000375-voxpopuli_deu_000375) +I DIESEN HAUSEL KAMAN DIE EEN GÜRGEREINUND BORGER NICHT ÜBERZEUCTEN N BEGEISTERN (voxpopuli_deu_000376-voxpopuli_deu_000376) +ZIALDE MOKRATENEHMNIT GROSAF OEUDE ZSORKENTNIS DAS DINGE DIE IER FORGETRAGEN HABEN JEBZ SICH AUH IM ZUSAMMENAG MIT VERENDERUNGENE DENFEINCHEN STADEN UMSETZE (voxpopuli_deu_000377-voxpopuli_deu_000377) +DEAHRBESCHUS TIE EL DASOROPÄSCHE SEMESTER HIERHERT ZUNEHMEN UND DI COROBPTIOUNS SITPLAR ION ERE IM RAM DER LNDERBRECHTE ZO VERÖFNIGEN ISTNIG AUSHEIGENT (voxpopuli_deu_000378-voxpopuli_deu_000378) +ND MEIN MEINE BITE ODERM DAS WAS ICH MER VORSTEN IS DAS MARGENGWIECKLICG INDERTART EINIE GROSE EINE BREIT MEHRHEIT FÜR DIESE KOUSIONS POLITIGHÜE ONDFGEPOLITIGSTDIMT PÜR DIEMENSCHEN VORORT DAMITIUNS A TES WEENTICHE AUCH BESCRÄNKENKÖNDE DAS (voxpopuli_deu_000379-voxpopuli_deu_000379) +WNNWIE HEUTE DIE E VERRDNG VERABSCHIEDEN OFER ECH DASSE WIE NACH EIM LANGKARUSELL SOU EIM GUDNABSLUSKOMMONDT ITM MACHTERMICHE BEI ER OMISION BEDANGEN IEONSTOKTIEVE SACH ABEIT HAT (voxpopuli_deu_000380-voxpopuli_deu_000380) +UNZERERESCHEASCHEN UNZSIE KONTROELEN HABEN KENEN PIELEGERPRAFT (voxpopuli_deu_000381-voxpopuli_deu_000381) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..f48bbb8bf0fb07a038de1b6657debe20427aeef9 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/ref.trn @@ -0,0 +1,661 @@ +DIE BEERDIGUNG MACHTE EINER ÄUSSERST WICHTIGEN SACHE EIN ENDE DER PETITION AN DEN GOUVERNEUR FÜR DES INDIANERJOES BEGNADIGUNG (M-AILABS_deu_000165-M-AILABS_deu_000165) +DA HABE SIE DIE WOHL JEDEM HIER IN DER ERINNERUNG GEBLIEBENEN WORTE GESPROCHEN (M-AILABS_deu_000166-M-AILABS_deu_000166) +ERST UM ACHT UHR WAR ER AUF MALE BRACHTE DEN KAFFEE DIE SONNE SCHIEN INS ZIMMER UND DIE SPERLINGE DIE DAS AUS DEN HÄCKSELSÄCKEN GEFALLENE FUTTERKORN AUFPICKTEN (M-AILABS_deu_000167-M-AILABS_deu_000167) +SICHERLICH AN IHREM GEBURTSTAG HÄTTE ER BEI IHR BLEIBEN KÖNNEN (M-AILABS_deu_000168-M-AILABS_deu_000168) +UND DESHALB MUSS MAN DORT WO MENSCHEN SCHWIERIGKEITEN HABEN DIES AUCH EINERSEITS ERKLÄREN ANGEBOTE MACHEN (M-AILABS_deu_000169-M-AILABS_deu_000169) +DASS MAN NUR AUF DIE WELT KOMMT UM SELBST WIEDER EINEN SOHN ZU HABEN DER DIE VEREHRUNG DER AHNEN FORTSETZT (M-AILABS_deu_000170-M-AILABS_deu_000170) +DESHALB GEHÖREN KONTINUIERLICHE SCHULBILDUNG AUCH KONTINUIERLICHE MÖGLICHKEITEN DER WEITERBILDUNG UND DAS BEGEHEN VON GEDENKTAGEN FÜR MICH UNAUFLÖSLICH ZUSAMMEN (M-AILABS_deu_000171-M-AILABS_deu_000171) +MEIN ANSASCHEN SAGT SIE ES IST JA JETZT WIEDER GANZ GUT ZWISCHEN UNS ABER EHE DU NICHT ALLES GESTEHST GEHT DIE ERINNERUNG AN DAS BÖSE NICHT WEG (M-AILABS_deu_000172-M-AILABS_deu_000172) +NEIN WEIBER BRAUCHE ICH NICHT (M-AILABS_deu_000173-M-AILABS_deu_000173) +GOTT HAT NICHT VERGEBLICH NACH MIR GERUFEN SAGTE DER SCHIFFER (M-AILABS_deu_000174-M-AILABS_deu_000174) +NUR EINES WEISS ICH DIESER FURCHTBAREN FRAGE ENTGEGENZUSETZEN UND SCHLEUDERE DAS WORT IN DIE WAAGSCHALE DIE GLUT MEINES LIEBESWILLENS IST STÄRKER ALS TRENNUNG (M-AILABS_deu_000175-M-AILABS_deu_000175) +TOMS ARMEE GEWANN EINEN GROSSEN SIEG NACH EINER LANGEN HARTNÄCKIGEN SCHLACHT (M-AILABS_deu_000176-M-AILABS_deu_000176) +ES IST EIN NAME DEM SICH DIE TÜR BEI TAG UND NACHT ÖFFNEN KANN BURSCHE UND WILLKOMMEN (M-AILABS_deu_000177-M-AILABS_deu_000177) +ABER ICH VERZEIHE IHNEN IHRE UNWISSENHEIT (M-AILABS_deu_000178-M-AILABS_deu_000178) +VON DER DRITTEN UNTERREDUNG AN SAGTE MISTER HAVISHAM WAR MIR DIE PERSON IN HOHEM MASSE VERDÄCHTIG (M-AILABS_deu_000179-M-AILABS_deu_000179) +ICH DENKE DER AMTMANN UND SEINE FAMILIE WERDEN ES RECHT VON DIR FINDEN DASS DU DICH SELBST ANGIBST UND SIE WERDEN FREUNDLICH GEGEN DICH SEIN (M-AILABS_deu_000180-M-AILABS_deu_000180) +JETZT SCHLUG DIE HELLE FLAMME AUF UND NUN ERKANNTE ER UNS DIE WIR NOCH IMMER ZUSAMMENGEDRÄNGT IN DEM WINKEL STANDEN (M-AILABS_deu_000181-M-AILABS_deu_000181) +DER SEINER SEELE ANSPORNEND DAS ERMUNTERNDE WORT VORWÄRTS (M-AILABS_deu_000182-M-AILABS_deu_000182) +ICH FREUE MICH AUF DEN BESUCH DES TUNESISCHEN MINISTERPRÄSIDENTEN (M-AILABS_deu_000183-M-AILABS_deu_000183) +WAS FÜR VERFOLGUNGEN WAS FÜR NACHSTELLUNGEN HABE ICH NICHT ZU ERDULDEN GEHABT (M-AILABS_deu_000184-M-AILABS_deu_000184) +ZIGEUNER WAREN ES DIE VON ORT ZU ORT FUHREN EIN KAUM ERWACHSENES JUNGES DING KAM ZU MIR HERANGEHÜPFT UND BETTELTE NEIN (M-AILABS_deu_000185-M-AILABS_deu_000185) +HUCK ICH WERDE DICH IN ENEM BOOT HINFAHREN WERDE DAS BOOT DA ANLEGEN UND ES WIEDER ZURÜCKRUDERN ALLES GANZ ALLEIN BRAUCHST DICH GAR NICHT DRUM ZU KÜMMERN (M-AILABS_deu_000186-M-AILABS_deu_000186) +ALS NUR EINMAL NOCH DEN RAUCH VON SEINEM HAUSE AUS DER FERNE AUFSTEIGEN ZU SEHEN UM DANN BERUHIGT ZU STERBEN (M-AILABS_deu_000187-M-AILABS_deu_000187) +DIE TÄNZERIN ABER LAG AUF DEN KNIEEN VOR BRAHMAS BILDNIS IN NAMENLOSER SEHNSUCHT UND WEINTE JAMMERVOLL (M-AILABS_deu_000188-M-AILABS_deu_000188) +RECHTFERTIGT MICH DENN DIE WIRKLICHKEIT NOCH NICHT AUF DIE ICH MICH BERUFEN KANN (M-AILABS_deu_000189-M-AILABS_deu_000189) +ICH ÄRGERTE MICH DANN WENN ICH AUFWACHTE ES WAR SO WUNDERSCHÖN GEWESEN DAS FLIEGEN (M-AILABS_deu_000190-M-AILABS_deu_000190) +NACHDEM ER SCHON DEN GANZEN VORMITTAG MIT IHM VERBRACHT KAM STANHOPE NACH TISCH INS QUANDTSCHE HAUS UM CASPAR LEBEWOHL ZU SAGEN (M-AILABS_deu_000191-M-AILABS_deu_000191) +ER WAR EIN ALTER HIRT VOLL MEDIZINISCHER GENINALITÄT (M-AILABS_deu_000192-M-AILABS_deu_000192) +DASS WOHL AUCH DER MIETER SEINE WUNDERLICHKEITEN HABEN MÜSSE (M-AILABS_deu_000193-M-AILABS_deu_000193) +SIE SAHEN ALLE ÄNGSTLICH UND BETRÜBT AUS UND AUCH HERR ARNE SASS SCHWERMÜTIG DA WIE DIE ANDEREN UND STÜTZTE DAS HAUPT IN DIE HAND (M-AILABS_deu_000194-M-AILABS_deu_000194) +UNTER DEN DAMEN MEIST JUNGE FRISCHE GESICHTER UNTER DEN HERREN NEBEN JUGENDLICHEN SOLCHE MIT FALTIGER STIRN UND BEREITS MEHR ODER MINDER MONDUMGLÄNZTEM SCHÄDEL (M-AILABS_deu_000195-M-AILABS_deu_000195) +SEIT TAGEN SCHON HATTE ES BESONDERS DRÄUEND GEKLUNGEN (M-AILABS_deu_000196-M-AILABS_deu_000196) +SONDERBAR (M-AILABS_deu_000197-M-AILABS_deu_000197) +ERB VON ERBENHEIM STAND MIT SEINER GATTIN VOLL WEHMUT UND DANKBARKEIT AN DER GRUFT AUF DER ER EINEN MÄCHTIGEN (M-AILABS_deu_000198-M-AILABS_deu_000198) +IHR WAR JEDER MENSCH EIN WUNDER UND FAST ALLES WAS MENSCHEN TATEN ETWAS WUNDERBARES (M-AILABS_deu_000199-M-AILABS_deu_000199) +WELCHE IHR WEG SIE ENTLÄNGST FÜHRTE (M-AILABS_deu_000200-M-AILABS_deu_000200) +DIE WIRTIN SASS NICHT HINTER IHREM SCHANKTISCH UND KEINER IHRER DIENSTLEUTE BEFAND SICH IN DER STUBE (M-AILABS_deu_000201-M-AILABS_deu_000201) +ALS DIE HERRSCHAFT AUS DER KIRCHE TRAT STANDEN DIE LEUTE UMHER UM SIE VORBEIGEHEN ZU SEHEN UND AM KIRCHHOFTHORE WARTETE EIN MANN (M-AILABS_deu_000202-M-AILABS_deu_000202) +WAS MÜSSEN WIR TUN UM DEM TERRORISMUS ENTGEGENZUTRETEN (M-AILABS_deu_000203-M-AILABS_deu_000203) +ICH GLAUBE DASS SIE ES GUT MIT MIR MEINEN HERR DOKTOR (M-AILABS_deu_000204-M-AILABS_deu_000204) +DOCH IM ANFANG GEWANN ER KEINE AUFMERKSAMKEIT FÜR ANDERE DINGE ALS FÜR DAS ESSEN (M-AILABS_deu_000205-M-AILABS_deu_000205) +DIES FLÄSCHCHEN ZOG ER JETZT EILIG HERVOR WÄHREND JENE SICH MIT WASSER FÜLLTEN UND BOT ES DER JUNGFER ZÜS AN (M-AILABS_deu_000206-M-AILABS_deu_000206) +DESHALB WAR ES AUCH RICHTIG UND WICHTIG DASS CHINA DOCH JETZT ANSPRUCHSVOLL GESAGT HAT WIR WERDEN AUCH AN DEN ZEITPUNKT DER REDUKTION KOMMEN (M-AILABS_deu_000207-M-AILABS_deu_000207) +NICHT DOCH MUTTER WECKE SIE JETZT NOCH NICHT (M-AILABS_deu_000208-M-AILABS_deu_000208) +JA WIR HABEN IN DEN LETZTEN JAHREN RECHT ENGE BEZIEHUNGEN ZU BRASILIEN AUFGEBAUT (M-AILABS_deu_000209-M-AILABS_deu_000209) +SIE WÜRDE SICH NICHT FÜR ANDERE OPFERN (M-AILABS_deu_000210-M-AILABS_deu_000210) +RIEFEN SIE MIR ZU (M-AILABS_deu_000211-M-AILABS_deu_000211) +GOTT WAS SIE IHR ERZÄHLTE HÖREN SIE NUR ES IST EIN GANZER ROMAN (M-AILABS_deu_000212-M-AILABS_deu_000212) +SEINE MUTTER KANN IHM NUR FLUSSWASSER GEBEN DESHALB WEINT ER (M-AILABS_deu_000213-M-AILABS_deu_000213) +DER BUNDEWIRTSCHAFTSMINISTER WIRD ZUSAMMEN MIT DER NETZAGENTUR AM VIERTE JUNI ZUM ERSTEN MAL PRÄSENTIEREN WIE SICH DIE NETZBETREIBER UND DIE KRAFTWERKE DIE NEUEN NETZPLÄNE VORSTELLEN (M-AILABS_deu_000214-M-AILABS_deu_000214) +EVA HATTE SICH ZITTERND VOR TODESSCHWÄCHE VON DEM GITTER BEFREIT UND SUCHTE ZU ENTFLIEHEN ABER DER SCHMALE GARTEN BOT KEINEN AUSWEG (M-AILABS_deu_000215-M-AILABS_deu_000215) +OB ICH MEIN WERK FÜR HEUTE LIEGEN LASSEN ODER NOCH EINEN ANLAUF NEHMEN UND ES VOLLENDEN SOLLTE (M-AILABS_deu_000216-M-AILABS_deu_000216) +ER WAR DAS GÖTZCHEN DER STUNDE DIE TAITAI BEAUFTRAGTE MADAME ANGELE DIE AUCH DASTAND UND DIE GEKAUFTEN SEIDENSTÜCKE ZUSAMMENFALTETE FÜR TSCHUN ZU SORGEN (M-AILABS_deu_000217-M-AILABS_deu_000217) +ICH WERDE NACHSEHEN (M-AILABS_deu_000218-M-AILABS_deu_000218) +ABER TIPPS ODER VORGABEN DAS MACHEN WIR NATÜRLICH NICHT (M-AILABS_deu_000219-M-AILABS_deu_000219) +ALS UNSERE IDEE BEKANNT WURDE WAR DIE PHYSIOGNOMIE DER WALTERSBURGER UNGEFÄHR DIE EINES KALBES DAS ZUM ERSTEN MALE DONNERN HÖRT (M-AILABS_deu_000220-M-AILABS_deu_000220) +BITTE MACHEN SIE GEFÄLLIGST AUF UND ES KLANG WIE EIN JAMMERNDER HILFERUF (M-AILABS_deu_000221-M-AILABS_deu_000221) +HERR DOKTOR SAGTE EINE FRAU DIE SCHNURRGRINE DIE SO OFT ZU IHNEN KOMMT IST EIGENTLICH GAR NICHT KRANK (M-AILABS_deu_000222-M-AILABS_deu_000222) +DIE ALTE ERINNERUNG AN DEN FRÜHEREN TRAUM TAUCHTE EBENFALLS WIEDER AUF UND UNWILLKÜRLICH FAST BEI DER BEHAUPTUNG DASS DIE SEELE DEN KÖRPER VERLASSEN UND ZU IHM ZURÜCKKEHREN KÖNNE SCHIEN ES IHR ORDENTLICH (M-AILABS_deu_000223-M-AILABS_deu_000223) +ALS SIE AUF DEN BALKON ZURÜCKKEHRTE FAND SIE IHN DIE ZEITUNG LESEND DIE WÄHREND IHRES FORTSEINS ANGELANGT WAR (M-AILABS_deu_000224-M-AILABS_deu_000224) +ER WAR EIN KIND DER STRASSE VON KLEIN AUF ABER IN IHM LEBTE VON JEHER EINE GEWISSE SEHNSUCHT NACH EINER EHRBAREN BÜRGERLICHEN EXISTENZ (M-AILABS_deu_000225-M-AILABS_deu_000225) +UND WIR ALS BUNDESREGIERUNG FÜHLEN UNS HIER NICHT EINER GRUPPE VERANTWORTLICH SONDERN WIR FÜHLEN UNS DEM GEMEINWOHL VERANTWORTLICH (M-AILABS_deu_000226-M-AILABS_deu_000226) +WAS MEIN LIEBES KIND WAS KANN (M-AILABS_deu_000227-M-AILABS_deu_000227) +UND DANN WOLLTE ICH DEN ANBLICK DERER NICHT MISSEN DIE MIR GEBLIEBEN WAREN VOR ALLEM ABER WAR ES MIR DARUM ZU TUN MEINE SÜSSE ELISABETH EINIGERMASSEN GETRÖSTET ZU SEHEN (M-AILABS_deu_000228-M-AILABS_deu_000228) +ABER ICH GLAUBE DASS WIR UNS AUCH GEGENSEITIG EIN BISSCHEN UNTERSTÜTZEN KÖNNEN (M-AILABS_deu_000229-M-AILABS_deu_000229) +SEINE GESCHÄFTLICHE LAUFBAHN HABE STEFENSON ALS KÜCHENBOY IN EINEM HOTEL VIERTEN GRADES BEGONNEN (M-AILABS_deu_000230-M-AILABS_deu_000230) +VIELLEICHT TÄTEN SIE GUT DIESE ANSICHTEN DES BISCHOFS NACH HAUSE ZU MELDEN SAGTE DER TAJEN DER IMMER MEHR EIN MANN DES GESCHRIEBENEN WORTES WIE DER TAT (M-AILABS_deu_000231-M-AILABS_deu_000231) +AM ANDERN MORGEN ERHOB ER SICH SPÄT SCHICKTE DEN LAKAIEN IN DIE WOHNUNG FEUERBACHS UND LIESS UM EINE UNTERREDUNG BITTEN DER MANN KAM MIT DER BOTSCHAFT ZURÜCK (M-AILABS_deu_000232-M-AILABS_deu_000232) +NUR EIN WENIG TRAURIG WURDE ES WENN IMMER DASSELBE KAM WENN SIE NIE ZUFRIEDEN SCHIENEN (M-AILABS_deu_000233-M-AILABS_deu_000233) +EIN SOMMERWARMER NOVEMBERTAG LAG MIT SONNENGLITZERN ÜBER DER HAUPTSTADT UND UNTER DEN LINDEN DRÄNGTE EINE TAUSENDKÖPFIGE MENSCHENMENGE AUF UND NIEDER (M-AILABS_deu_000234-M-AILABS_deu_000234) +KOMM MIT MIR MEIN SOHN DENN ICH BRAUCHE DEINE LIEBE (M-AILABS_deu_000235-M-AILABS_deu_000235) +NUR SEIN GESICHT WURDE EIN WENIG NACHDENKLICHER SO WIE VON EINER ERINNERUNG ERHELLT (M-AILABS_deu_000236-M-AILABS_deu_000236) +DANN WIRD AUCH WIEDER DER INNOVATIONSDRUCK STEIGEN UND DAZU IST DAS SYSTEM JA EINGEFÜHRT WORDEN (M-AILABS_deu_000237-M-AILABS_deu_000237) +JETZT GEWAHRTE ER MIT ENTSETZEN DIE SCHEUSSLICHE TEUFLISCHE AFFENFRATZE DIE ÜBER DES MÄDCHENS SCHULTER SCHIELTE (M-AILABS_deu_000238-M-AILABS_deu_000238) +JA DER WIRT NICKTE DAS GEHÖRT EINEM GEWISSEN WUTSCHOW BERNHARD WUTSCHOW IST ETWAS VERRUFEN (M-AILABS_deu_000239-M-AILABS_deu_000239) +WOLLT IHR IN WAHRHEIT DIE LÖWEN TÖTEN UND KÖNNT IHR SCHIESSEN (M-AILABS_deu_000240-M-AILABS_deu_000240) +BAT CEDDIE SEHR RESPEKTVOLL WOBEI ER NUR EINIGE SILBEN VERSCHLUCKTE WAS IHM BEI DEN BELIEBTEN LANGEN WÖRTERN DES ÖFTERN VORKAM (M-AILABS_deu_000241-M-AILABS_deu_000241) +LORD FAUNTLEROY WIRD NICHTS ENTBEHREN DESSEN BIN ICH GEWISS VERSETZTE ER (M-AILABS_deu_000242-M-AILABS_deu_000242) +KAM GLEICHFALLS INS SCHLAFZIMMER AUF EINEN NAGEL IN DER NÄHE DES BETTES (M-AILABS_deu_000243-M-AILABS_deu_000243) +UND DAS IST DIE CHANCE DIE IN DIESER KRISE STECKT DIE CHANCE FÜR INTERNATIONALE REGELN DIE SICH AN DEN PRINZIPIEN DER SOZIALEN MARKTWIRTSCHAFT ORIENTIEREN (M-AILABS_deu_000244-M-AILABS_deu_000244) +ANFANGS FIEL DER REGEN SCHRÄG UND PEITSCHTE ERST DIE EINE UND DANN DIE ANDERE SEITE DES WAGENS (M-AILABS_deu_000245-M-AILABS_deu_000245) +FAST LEICHTSINNIGEN BEMESSUNG IHRES WERTES AUFZUGEBEN SICH ENTSCHLOSSEN HATTE (M-AILABS_deu_000246-M-AILABS_deu_000246) +DAS HEISST DIE FRAGE DER MENSCHLICHEN ARBEIT UND DIE FRAGE WAS KANN TECHNISCH GELÖST WERDEN (M-AILABS_deu_000247-M-AILABS_deu_000247) +DIE SAFARI WAR AUF DIE REGELMÄSSIG BENUTZTEN WASSERSTELLEN DIESER ROUTE ANGEWIESEN (M-AILABS_deu_000248-M-AILABS_deu_000248) +DIE BEIDEN MÜSSTEN HIER OBEN AUF DEM GIPFEL GESTANDEN HABEN UND ER SPRACH DIE ALTEN WORTE VOR SICH HIN (M-AILABS_deu_000249-M-AILABS_deu_000249) +ENDLICH BLICKTE CEDRIK AUF WEISS NEWICK ALLES VON DEN ARMEN LEUTEN FRAGTE ER (M-AILABS_deu_000250-M-AILABS_deu_000250) +DASS ES HEUTE EINE WUNDERBARE ZUSAMMENARBEIT ZWISCHEN BUND UND LÄNDERN IN DIESEN FRAGEN GIBT MIT SEHR SEHR INTERESSANTEN PROJEKTEN (M-AILABS_deu_000251-M-AILABS_deu_000251) +CASPAR VERHARRTE ANGEWURZELT AN SEINEM PLATZ SEINE GLIEDER JA SEINE AUGEN WAREN WIE VERSTEINERT ALS ER ZUM ZWEITENMAL HINBLICKTE (M-AILABS_deu_000252-M-AILABS_deu_000252) +EINIGE ZEIT DANACH FRAGTE ER MICH OB ICH GLAUBE DASS DER EISGANG DEN SCHLITTEN DES ANDEREN ZERSTÖRT HABE (M-AILABS_deu_000253-M-AILABS_deu_000253) +ABER NUN BLOSS NICHT IN EINE SCHOCKSTARRE VERFALLEN (cv_deu_000698-cv_deu_000698) +JA ICH KOMME JA SCHON (cv_deu_000699-cv_deu_000699) +NEBENBEI ARBEITETE ER ALS AUSHILFSKRAFT AUF EINER FARM (cv_deu_000700-cv_deu_000700) +EIN TERRITORIAL GRÖSSERES EUROPA WIRD NICHT MIT EINEM ETATMÄSSIG KLEINEREN EUROPA ERREICHT (cv_deu_000701-cv_deu_000701) +IHR SOHN KAM DURCH KÜNSTLICHE BEFRUCHTUNG ZUR WELT (cv_deu_000702-cv_deu_000702) +DIE NACHTAKTIVEN FALTER FLIEGEN VON MITTE JULI BIS MITTE OKTOBER (cv_deu_000703-cv_deu_000703) +ACHT (cv_deu_000704-cv_deu_000704) +FÜNF (cv_deu_000705-cv_deu_000705) +NUTZER KÖNNEN IHRE LESEZEICHEN ONLINE ABSPEICHERN VERWALTEN UND MIT ANDEREN NUTZERN TEILEN (cv_deu_000706-cv_deu_000706) +DIE DON BOSCO KATH (cv_deu_000707-cv_deu_000707) +SAUL BASS ZÄHLT ZU DEN INNOVATIVSTEN DESIGNERN UND FILMEMACHERN SEINER ZEIT (cv_deu_000708-cv_deu_000708) +IN GRÜN ÜBER SILBERNEM WELLENBALKEN EINE SILBERNE EICHE (cv_deu_000709-cv_deu_000709) +WEITERE WICHTIGE INDUSTRIEZWEIGE SIND DIE MIKROMECHANIK GALVANOPLASTIK METALLBAU UND DIE HOLZVERARBEITUNG (cv_deu_000710-cv_deu_000710) +ÜBER DEN AUTOR IST NICHTS BEKANNT VERMUTLICH STAMMTE ER AUS DEM DEUTSCHEN SPRACHGEBIET (cv_deu_000711-cv_deu_000711) +MAN STEUERT ES MIT EINEM DOPPELPADDEL (cv_deu_000712-cv_deu_000712) +WIR HABEN EIN PROBLEM AUF OSISCHICHT ACHT (cv_deu_000713-cv_deu_000713) +WIR SPIELEN IMMER NOCH ABER DAS LEBEN AUF TOUR IST DERZEIT NICHT MACHBAR (cv_deu_000714-cv_deu_000714) +HEUTE ZEIGT SICH DER GRÖSSTE TEIL DER ANLAGE ALS ENGLISCHER GARTEN (cv_deu_000715-cv_deu_000715) +SEINE RESIDENZ NAHM ER IN MÜNCHEN WO ER AUCH STARB (cv_deu_000716-cv_deu_000716) +INNERER UND ÄUSSERER NARTHEX KÖNNEN ALS GETRENNTE TEILE EINES NARTHEX AUCH GEMEINSAM VORKOMMEN (cv_deu_000717-cv_deu_000717) +DABEI BELEGTE ER DIE PLÄTZE VIER UND DREI (cv_deu_000718-cv_deu_000718) +KIM DARBY IST DIE TOCHTER ZWEIER PROFESSIONELLER TÄNZER (cv_deu_000719-cv_deu_000719) +ICH GLAUBE DAS FÜHRT NICHT IN DIE RICHTIGE RICHTUNG (cv_deu_000720-cv_deu_000720) +DAS IST EINE EXTREM SCHLECHTE RICHTLINIE (cv_deu_000721-cv_deu_000721) +HERR LURCH ENTBLÖSSTE SEIN HAGERES GESICHT (cv_deu_000722-cv_deu_000722) +NUR CARMEN FINDET DAS UNFAIR (cv_deu_000723-cv_deu_000723) +INGEBORG KRABBE HATTE DREI GESCHWISTER (cv_deu_000724-cv_deu_000724) +ES KOMMT WIRKLICH DARAUF AN DASS SOLCHE DATEN AUF DIESER EBENE ERFASST WERDEN (cv_deu_000725-cv_deu_000725) +STRUMMING HINGEGEN ERGIBT EIN HARMONISCHES PULSIEREN (cv_deu_000726-cv_deu_000726) +BIN ICH ZUM KAUF EINER HYPOTHEK BERECHTIGT (cv_deu_000727-cv_deu_000727) +TEHERAN IST DIE HAUPTSTADT VOM IRAN (cv_deu_000728-cv_deu_000728) +KOHLENHYDRATE SIND BESSER ALS IHR RUF (cv_deu_000729-cv_deu_000729) +OHNE DIE PROFESSIONELLE UNTERSTÜTZUNG DER MASERATIRENNABTEILUNG WAREN DIESE WAGEN DER KONKURRENZ NUN DOCH UNTERLEGEN (cv_deu_000730-cv_deu_000730) +SIE DIENTE ZUNÄCHST ALS UNTERKUNFT FÜR BELGISCHE BESATZUNGSTRUPPEN (cv_deu_000731-cv_deu_000731) +DA MÜSSEN WIR SPRENGEN MEINTE DER ZAHNARZT (cv_deu_000732-cv_deu_000732) +AUSSERDEM SPIELTE ER BEIM NACHFOLGETEAM NEWMARKET ROYALS SOWIE BEIM LIGAKONKURRENTEN LONDON KNIGHTS (cv_deu_000733-cv_deu_000733) +WIE AUCH DAS INSTANTRUNOFFVOTING ERFÜLLT DIE COOMBSWAHL DAS CONDORCETKRITERIUM NICHT (cv_deu_000734-cv_deu_000734) +SMITH WUCHS IN CHICAGO AUF (cv_deu_000735-cv_deu_000735) +WIR SIND HIER ALLEIN (cv_deu_000736-cv_deu_000736) +DUMM IST WER ETWAS WEISS ABER TROTZ DES BESSEREN WISSENS FALSCH HANDELT (cv_deu_000737-cv_deu_000737) +HAUPTTHEMA DER SHOW IST DIE REVANCHE FÜR ÜBLE STREICHE UNTER FREUNDEN (cv_deu_000738-cv_deu_000738) +GLEICHZEITIG WURDEN SPORTWETTEN TEILWEISE VERBOTEN (cv_deu_000739-cv_deu_000739) +SIEBEN (cv_deu_000740-cv_deu_000740) +JA (cv_deu_000741-cv_deu_000741) +ZUDEM VERSAH ER IM KLOSTER LANGE JAHRE DIE ÄMTER DES NOVIZENMEISTERS UND PRIOR (cv_deu_000742-cv_deu_000742) +HEIDENHAIN ENTSTAMMTE EINER ÄRZTEFAMILIE (cv_deu_000743-cv_deu_000743) +ACHT (cv_deu_000744-cv_deu_000744) +ZWEI (cv_deu_000745-cv_deu_000745) +EBENFALLS IN AUGGEN ANGESIEDELT SIND DIE KELTEREI DER FA (cv_deu_000746-cv_deu_000746) +DIESE STEHT AUCH FÜR ABSOLVENTEN EINHEIMISCHER SCHULEN MIT DEUTSCHKENNTNISSEN OFFEN (cv_deu_000747-cv_deu_000747) +ALSO ICH HÖRE NICHTS (cv_deu_000748-cv_deu_000748) +WIE KANN MAN SICH SCHÜTZEN (cv_deu_000749-cv_deu_000749) +NACH FÜNF MONATEN LAG EINE EMPFINDLICHERE PLATTE ALS DIE BIS DAHIN ERHÄLTLICHEN VOR (cv_deu_000750-cv_deu_000750) +ZIEL IST ES DIE ÜBEREINSTIMMUNG EINES SOFTWARESYSTEMS MIT SEINER SPEZIFIKATION ZU ÜBERPRÜFEN (cv_deu_000751-cv_deu_000751) +MIT EINEM WARMEN GETRÄNK IM BAUCH LÄSST SICH DIE KÄLTE BESSER AUSHALTEN (cv_deu_000752-cv_deu_000752) +DIE ANTIVIRENSOFTWARE IST AMOK GELAUFEN UND HAT ALLE COMPUTER IM HAUS LAHMGELEGT (cv_deu_000753-cv_deu_000753) +IHRE KLOAKE IST IN DIESER ZEIT KUGELFÖRMIG (cv_deu_000754-cv_deu_000754) +DIE STRECKE BEGINNT IM SÜDEN VERONAS UND FÜHRT DURCH DIE POEBENE RICHTUNG SÜDOSTEN (cv_deu_000755-cv_deu_000755) +ERST VON DORT KONNTE ER SEINEN WEG FREI FORTSETZEN (cv_deu_000756-cv_deu_000756) +SIE ERHEBT SICH HEUTE IMMER NOCH GUT ERKENNBAR AUS DEM SCHWEMMLAND HERAUS (cv_deu_000757-cv_deu_000757) +DIE KANARISCHEN INSELN GEHÖREN ZU SPANIEN (cv_deu_000758-cv_deu_000758) +WISSENSCHAFTLER HABEN DIESE MUTATION BISHER NUR BEI FRAUEN BEOBACHTET (cv_deu_000759-cv_deu_000759) +SEINE GESCHÄFTSBEZIEHUNGEN REICHTEN BIS NORDAMERIKA UND ASIEN (cv_deu_000760-cv_deu_000760) +ZAHLREICHE VORDERE PLATZIERUNGEN BEI DEUTSCHEN EUROPA UND WELTMEISTERSCHAFTEN SOWIE OLYMPISCHEN SPIELEN FOLGTEN (cv_deu_000761-cv_deu_000761) +IN EINER TAGESZEITUNG BLÄTTERND SITZT SIEGFRIED AUF EINER PARKBANK (cv_deu_000762-cv_deu_000762) +MIT EINEM WARMEN GETRÄNK IM BAUCH LÄSST SICH DIE KÄLTE BESSER AUSHALTEN (cv_deu_000763-cv_deu_000763) +FOLGE DEM QUERVERWEIS (cv_deu_000764-cv_deu_000764) +OSTERN IST IMMER EINE WOCHE NACH DEM ERSTEN VOLLMOND IM FRÜHLING (cv_deu_000765-cv_deu_000765) +IM MITTELALTER HATTEN WECHSELNDE HERRSCHAFTEN DAS DORF INNE (cv_deu_000766-cv_deu_000766) +DEN NAMEN GHIBLI TRAGEN AUCH WEITERE FAHRZEUGE VON MASERATI (cv_deu_000767-cv_deu_000767) +DU KANNST MIT DEM BUS NACH FRANKFURT AN DER ODER FAHREN (cv_deu_000768-cv_deu_000768) +MIR DOCH EGAL (cv_deu_000769-cv_deu_000769) +ALLERDINGS ERGABEN WEITERE PRÜFUNGEN DASS ES MITTELFRISTIG KEINEN BEDARF FÜR EINE SOLCHE AUTOBAHN GÄBE (cv_deu_000770-cv_deu_000770) +UMGEKEHRT KANN EIN FREIBRIEF EINE AUSSCHREIBUNG ALS VOGELFREI GEMEINT SEIN (cv_deu_000771-cv_deu_000771) +BIZARRGROTESKE ABSCHNITTE ZEIGEN EINFLÜSSE DURCH SCHOSTAKOWITSCH (cv_deu_000772-cv_deu_000772) +ER WAR EINER DER PIONIERE AUF DEM GEBIET DER NUTZUNG DER SONNENENERGIE (cv_deu_000773-cv_deu_000773) +AUCH WENN MIR DIE KUNDEN AUF DIE NERVEN GEHEN MUSS ICH HÖFLICHKEIT BEWAHREN (cv_deu_000774-cv_deu_000774) +DIE SPÜLMASCHINE IST FERTIG (cv_deu_000775-cv_deu_000775) +IN DER ARCHAISCHEN PERIODE WURDEN ERSTE FORMEN DES ACKERBAUS ENTWICKELT (cv_deu_000776-cv_deu_000776) +DIE KOMÖDIE SEI BESSER ALS DER ERSTE FILM (cv_deu_000777-cv_deu_000777) +AKTUELL GILT FOLGENDER MODUS (cv_deu_000778-cv_deu_000778) +DAMIT ENDET EINE ERFOLGREICHE INTERNATIONALE BILDUNGSARBEIT VOR ALLEM IM MUSISCHKULTURELLEN BEREICH (cv_deu_000779-cv_deu_000779) +DER SOHN EINES BERGMANNS BEGANN SEINE FUSSBALLKARRIERE BEI DEN SPORTFREUNDEN WANNEEICKEL (cv_deu_000780-cv_deu_000780) +IN DIESEM JAHR GAB ES SIEBEN NUMMEREINSSINGLES UND SECHSUNDDREISSIG NUMMEREINSALBEN (cv_deu_000781-cv_deu_000781) +NORDWESTLICH VON HACKHAUSEN BEFINDET SICH DIE ORTSCHAFT HACKENBROICH (cv_deu_000782-cv_deu_000782) +IM ORT GNARRENBURG GINGEN VIELE SOZIALE EINRICHTUNGEN VON HERMANN LAMPRECHT UND DER MARIENHÜTTE AUS (cv_deu_000783-cv_deu_000783) +ICH WERDE FOLGLICH DEN RAT ÜBER DIE IM PARLAMENT VORGETRAGENEN BEDENKEN INFORMIEREN (cv_deu_000784-cv_deu_000784) +ES WÄRE TRAURIG GEWESEN EIN SO WICHTIGES THEMA NICHT IM KONSENS VERABSCHIEDEN ZU KÖNNEN (cv_deu_000785-cv_deu_000785) +NACH DESSEN TOD IM GLEICHEN JAHR KAM ES KURZFRISTIG AN ANDERE BESITZER (cv_deu_000786-cv_deu_000786) +KURZ DANACH GAB ES EINEN WERBESPOT MIT DEM CANCAN VON JACQUES OFFENBACH (cv_deu_000787-cv_deu_000787) +DAS IST BESSER (cv_deu_000788-cv_deu_000788) +WIE SIEHT ES MIT GLEITZEIT AUS (cv_deu_000789-cv_deu_000789) +NAHE DEM DORF BEFINDET SICH AUCH DER GRAND CANYON NATIONAL PARK AIRPORT (cv_deu_000790-cv_deu_000790) +SIE SOLLEN VERKÜNDEN DASS DIE LIEBE DEN TOD BESIEGT HAT (cv_deu_000791-cv_deu_000791) +BEDECKT IST DIE REPRÄSENTATIV GESTALTETE VILLA MIT EINEM MANSARDDACH (cv_deu_000792-cv_deu_000792) +DIESE SIEDLUNG IST MIT DER ORTSCHAFT DELLACH ZUSAMMENGEWACHSEN (cv_deu_000793-cv_deu_000793) +WART IHR SCHON EINMAL IN DEM CLUB (cv_deu_000794-cv_deu_000794) +WO RAUCH IST IST AUCH FEUER (cv_deu_000795-cv_deu_000795) +DIREKT VON DER STRASSE WURDEN SIE VON ALFRED BIOLEK FÜR SEINE FERNSEHSHOW SHOWBÜHNE ENGAGIERT (cv_deu_000796-cv_deu_000796) +EIN JAHR SPÄTER WECHSELTE ER ZU HEALTH NET UND ER WURDE ERFOLGREICHER (cv_deu_000797-cv_deu_000797) +IN DER LANDWIRTSCHAFT KANN DER ERTRAG DEUTLICH REDUZIERT WERDEN (cv_deu_000798-cv_deu_000798) +MANSOUR SPIELTE IN SEINER HEIMATSTADT KAIRO FÜR AL AHLY (cv_deu_000799-cv_deu_000799) +ER TRAT DER FREIMAURERLOGE LAUTARO BEI (cv_deu_000800-cv_deu_000800) +MIT „FÜRST“ WAR EHER DIE SOZIALE ALS DIE RECHTLICHE ROLLE DES SO BEZEICHNETEN GEMEINT (cv_deu_000801-cv_deu_000801) +LETZTE WOCHE GAB DAS METI BEKANNT DASS ES VON APPLE ÜBER 34 WEITERE VORFÄLLE VON ÜBERHITZUNG INFORMIERT WORDEN WAR DIE DAS UNTERNEHMEN ALS NICHT SCHWERWIEGEND BEZEICHNETE (fleurs_deu_000378-fleurs_deu_000378) +USA GYMNASTICS UNTERSTÜTZT DEN BRIEF DES OLYMPISCHEN KOMITEES DER VEREINIGTEN STAATEN UND AKZEPTIERT ES ALS ABSOLUTE NOTWENDIGKEIT DASS SICH DIE OLYMPISCHE FAMILIE FÜR EIN SICHERES UMFELD FÜR ALLE UNSERE SPORTLER EINSETZT (fleurs_deu_000379-fleurs_deu_000379) +DADURCH KANN ER ABWÄRTSKOMPATIBEL MIT 80211A 80211B UND 80211G SEIN VORAUSGESETZT DIE BASISSTATION VERFÜGT ÜBER DUALRADIO (fleurs_deu_000380-fleurs_deu_000380) +ER BEZEICHNETE DIE GERÜCHTE ALS POLITISCHES GESCHWÄTZ UND ALBERNHEIT (fleurs_deu_000381-fleurs_deu_000381) +LETZTE WOCHE GAB DAS METI BEKANNT DASS ES VON APPLE ÜBER 34 WEITERE VORFÄLLE VON ÜBERHITZUNG INFORMIERT WORDEN WAR DIE DAS UNTERNEHMEN ALS NICHT SCHWERWIEGEND BEZEICHNETE (fleurs_deu_000382-fleurs_deu_000382) +NACHDEM DER DAMM 1963 ERBAUT WORDEN WAR KAMEN DIE JAHRESZEITLICHEN ÜBERFLUTUNGEN DIE SEDIMENTE IM FLUSS VERTEILEN ZUM STILLSTAND (fleurs_deu_000383-fleurs_deu_000383) +ER WAR AUCH AM STECHEN VON GELDSCHEINEN FÜR VIELE LÄNDER BETEILIGT AKTUELLE BEISPIELE SEINER ARBEIT SCHLIESSEN DIE PREMIERMINISTERPORTRAITS AUF DER VORDERSEITE DER KANADISCHEN 5 UND 100DOLLARNOTEN EIN (fleurs_deu_000384-fleurs_deu_000384) +DIE HAUPTSTADT VON MOLDAWIEN IST KISCHINAU DIE EINHEIMISCHE SPRACHE IST RUMÄNISCH ABER VIELE MENSCHEN SPRECHEN AUCH RUSSISCH (fleurs_deu_000385-fleurs_deu_000385) +ZWISCHEN DEN EINZELNEN DYNASTIEN HERRSCHTEN AUCH UNBESTÄNDIGE ZEITEN GETEILTER PROVINZEN DIE BEKANNTESTE DIESER PERIODEN WAR DIE EPOCHE DER DREI KÖNIGREICHE DIE 60 JAHRE LANG ZWISCHEN DER HAN UND DER JINDYNASTIE STATTFAND (fleurs_deu_000386-fleurs_deu_000386) +AM ANDEREN ENDE DES SPEKTRUMS VERWANDELT MAN SICH IN EIN NICHT WIEDERZUERKENNENDES INDIVIDUUM DAS ALLES ANDERS MACHEN MUSS ALS DAS TEAM ES GEMACHT HAT UND SICH ALLES ZU EIGEN MACHT (fleurs_deu_000387-fleurs_deu_000387) +DIE MEISTEN INTERPRETATIONEN DES TECHNOLOGISCHEN DETERMINISMUS TEILEN ZWEI ALLGEMEINE VORSTELLUNGEN EINERSEITS DASS DIE ENTWICKLUNG DER TECHNOLOGIE SELBST EINEM WEG FOLGT DER WEITGEHEND JENSEITS KULTURELLER ODER POLITISCHER EINFLUSSNAHME LIEGT UND ANDERERSEITS DASS TECHNOLOGIE IHRERSEITS AUSWIRKUNGEN AUF GESELLSCHAFTEN HAT DIE EHER INHÄRENT ALS SOZIAL BEDINGT SIND (fleurs_deu_000388-fleurs_deu_000388) +ZWISCHEN DEN EINZELNEN DYNASTIEN HERRSCHTEN AUCH UNBESTÄNDIGE ZEITEN GETEILTER PROVINZEN DIE BEKANNTESTE DIESER PERIODEN WAR DIE EPOCHE DER DREI KÖNIGREICHE DIE 60 JAHRE LANG ZWISCHEN DER HAN UND DER JINDYNASTIE STATTFAND (fleurs_deu_000389-fleurs_deu_000389) +DEM LEAK ZUFOLGE BEZIEHT SICH DAS DOKUMENT AUF DEN GRENZSTREIT IN DEM DIE PALÄSTINENSER EIN ZURÜCKSETZEN DER GRENZEN IN DEN ZUSTAND VOR DEM SECHSTAGEKRIEG VON 1967 FORDERN (fleurs_deu_000390-fleurs_deu_000390) +MIT DEM VERLUST GRIECHISCHER SPRACHKENNTNISSE WAR DER WESTEN VON SEINEN PHILOSOPHISCHEN UND WISSENSCHAFTLICHEN WURZELN IN GRIECHENLAND ABGESCHNITTEN (fleurs_deu_000391-fleurs_deu_000391) +WIR STIMMEN MIT DER AUSSAGE DES USOC ÜBEREIN DASS DEN INTERESSEN UNSERER ATHLETEN UND VEREINE UND IHRES SPORTS BESSER GEDIENT IST WENN WIR INNERHALB UNSERER ORGANISATION SINNVOLLE VERÄNDERUNGEN VORANTREIBEN ANSTATT EINE DEZERTIFIZIERUNG VORZUNEHMEN (fleurs_deu_000392-fleurs_deu_000392) +DIE KREUZFAHRTEN NACH SANKT PETERSBURG BIETEN AUCH ZEIT FÜR EINEN AUFENTHALT IN DER STADT KREUZFAHRTPASSAGIERE SIND VON DER VISUMPFLICHT BEFREIT SIEHE BEDINGUNGEN (fleurs_deu_000393-fleurs_deu_000393) +REISENDE WERDEN DRINGEND GEWARNT AUF JEDWEDE ART VON UNWETTER ZU ACHTEN DIE IHR GEBIET BETRIFFT DA DIES SICH AUF ALLE REISEPLÄNE AUSWIRKEN KANN (fleurs_deu_000394-fleurs_deu_000394) +SIE BESAGT DASS DER KREUZUNGSPUNKT DER LINIEN DIE EIN BILD VERTIKAL UND HORIZONTAL DRITTELN DER EFFEKTIVSTE PLATZ FÜR DAS HAUPTMOTIV IST SIEHE BEISPIEL (fleurs_deu_000395-fleurs_deu_000395) +SEIT 1988 MÜSSEN WAHLURNEN TRANSPARENT SEIN DAMIT WÄHLER UND BEOBACHTER BEZEUGEN KÖNNEN DASS ZU BEGINN DER WAHL KEINE UMSCHLÄGE VORHANDEN SIND UND DASS KEINE UMSCHLÄGE EINGEWORFEN WERDEN AUSSER JENE DER ORDNUNGSGEMÄSS GEZÄHLTEN UND AUTORISIERTEN WÄHLER (fleurs_deu_000396-fleurs_deu_000396) +OTTAWA IST KANADAS BEZAUBERNDE ZWEISPRACHIGE HAUPTSTADT UND ZEICHNET SICH DURCH EINE REIHE VON KUNSTGALERIEN UND MUSEEN AUS DIE KANADAS VERGANGENHEIT UND GEGENWART PRÄSENTIEREN (fleurs_deu_000397-fleurs_deu_000397) +DIESE PAARE KÖNNEN SICH FÜR EINEN ADOPTIONSPLAN FÜR IHR BABY ENTSCHEIDEN (fleurs_deu_000398-fleurs_deu_000398) +INFOLGEDESSEN SIND ZWEI FISCHARTEN AUSGESTORBEN UND ZWEI WEITERE SIND VOM AUSSTERBEN BEDROHT DARUNTER DER GILA CYPHA (fleurs_deu_000399-fleurs_deu_000399) +PFLANZEN SEHEN IN IHRER NATÜRLICHEN UMGEBUNG AM BESTEN AUS WIDERSTEHEN SIE ALSO DER VERSUCHUNG AUCH NUR EIN EXEMPLAR ZU ENTFERNEN (fleurs_deu_000400-fleurs_deu_000400) +AUF DER NAHSEITE KÖNNTE ES MEHR MARIA GEBEN DA DIE KRUSTE DÜNNER IST ES WAR EINFACHER FÜR DIE LAVA AN DIE OBERFLÄCHE AUFZUSTEIGEN (fleurs_deu_000401-fleurs_deu_000401) +ER FÜGTE HINZU DASS SIE JEDOCH NICHT DAZU AUFGEFORDERT WERDEN SOLLTEN VERPFLICHTUNGEN EINZUGEHEN DIE ÜBER IHREN ENTWICKLUNGSSTAND IHRE VERANTWORTUNG UND IHRE FÄHIGKEITEN HINAUSGEHEN (fleurs_deu_000402-fleurs_deu_000402) +VIRTUELLE HILFESTELLUNGEN SIND IN DIE SOFTWARE EINGEBAUT UND SOLLEN ARBEITSSCHRITTE DIE DER SCHÜLER ALLEIN MÖGLICHERWEISE NICHT BEWÄLTIGEN KANN HINTERFRAGEN NAHELEGEN UND ERKLÄREN (fleurs_deu_000403-fleurs_deu_000403) +AM 15 AUGUST 1940 FIELEN DIE ALLIIERTEN IN SÜDFRANKREICH EIN DIE INVASION WURDE OPERATION DRAGOON GENANNT (fleurs_deu_000404-fleurs_deu_000404) +ER GRIFF AUCH ALLES AN WAS INS WASSER KAM SELBST EIN GROSSER DINOSAURIER WIE DER T REX WAR IHM NICHT GEWACHSEN (fleurs_deu_000405-fleurs_deu_000405) +SEIT DER GRÜNDUNG VON ASUNCIÓN 1537 IST ES PARAGUAY GELUNGEN VIEL VON SEINEM INDIGENEN CHARAKTER UND SEINER IDENTITÄT ZU BEWAHREN (fleurs_deu_000406-fleurs_deu_000406) +TROTZDEM IST DER ANTEIL AN XDRTB IN DER GESAMTEN GRUPPE DER LEUTE MIT TUBERKULOSE OFFENBAR DENNOCH GERING 6000 DER INSGESAMT 330000 LEUTE DIE IN SÜDAFRIKA ZU EINEM BESTIMMTEN ZEITPUNKT ANGESTECKT SIND (fleurs_deu_000407-fleurs_deu_000407) +ANGEL 2006 ERLÄUTERT DAS KONTINUUMKONZEPT ALS EINE METHODE UM ORGANISATIONEN ZU HELFEN LEISTUNGSFÄHIGER ZU WERDEN (fleurs_deu_000408-fleurs_deu_000408) +IN DIESER PERIODE DER EUROPÄISCHEN GESCHICHTE STAND DIE REICH UND MÄCHTIG GEWORDENE KATHOLISCHE KIRCHE AUF DEM PRÜFSTAND (fleurs_deu_000409-fleurs_deu_000409) +DIE ERSTE DER 78 EMPFEHLUNGEN IST DASS EINE NEUE DIPLOMATISCHE INITIATIVE VOR ENDE DIESES JAHRES ERGRIFFEN WERDEN SOLLTE UM DIE IRAKISCHEN GRENZEN GEGENÜBER FEINDLICHEN INTERVENTIONEN ZU SICHERN UND DIPLOMATISCHE BEZIEHUNGEN MIT SEINEN NACHBARN WIEDERHERZUSTELLEN (fleurs_deu_000410-fleurs_deu_000410) +DIES BIETET EINE GUTE GELEGENHEIT DAS NORDLICHT ZU SEHEN DA DER HIMMEL MEHR ODER WENIGER RUND UM DIE UHR DUNKEL IST (fleurs_deu_000411-fleurs_deu_000411) +PROFESSORIN PAMELA FERGUSON VON DER UNIVERSITY OF DUNDEE MERKT AN JOURNALISTEN SCHEINEN EINE GEFÄHRLICHE GRENZE ZU ÜBERSCHREITEN WENN SIE FOTOS USW VON VERDÄCHTIGEN VERÖFFENTLICHEN (fleurs_deu_000412-fleurs_deu_000412) +ES KANN SICH AUCH LOHNEN EINE WILD CARD ZU KAUFEN DIE ZUTRITT ENTWEDER ZU AUSGEWÄHLTEN PARKS IN SÜDAFRIKA ODER ZU ALLEN SÜDAFRIKANISCHEN NATIONALPARKS GEWÄHRT (fleurs_deu_000413-fleurs_deu_000413) +DIE BRÜCKE SOLL IM SEPTEMBER 2017 VOLLSTÄNDIG DEN BETRIEB AUFNEHMEN ES WIRD ERWARTET DASS DIE BRASILIANISCHEN ZOLLKONTROLLPUNKTE DANN FERTIG GESTELLT SEIN WERDEN (fleurs_deu_000414-fleurs_deu_000414) +WÄHREND EIN EXPERIMENTELLER IMPFSTOFF IN DER LAGE ZU SEIN SCHEINT DIE EBOLAMORTALITÄT ZU SENKEN GIBT ES BISHER KEINE MEDIKAMENTE DIE ALS EINDEUTIG ZUR BEHANDLUNG BESTEHENDER INFEKTIONEN GEEIGNET NACHGEWIESEN WURDEN (fleurs_deu_000415-fleurs_deu_000415) +EIN ÄUSSERST LEBHAFTER DEPESCHENWECHSEL FAND STATT MAN ERWOG DEN PLAN EINEN ALLGEMEINEN STAATENKONGRESS ZU BERUFEN UND KONNTE SICH VORLÄUFIG NUR NOCH NICHT ÜBER DAS VORZULEGENDE PROGRAMM UND DEN ORT DES ZUSAMMENTRITTS EINIGEN (mls_deu_000281-mls_deu_000281) +ER WUSSTE NICHT WAS IHM DAS LEBEN KOSTBARES GERAUBT HATTE SPANNKRAFT UND MUT DASS ES IHN FEIG UND SCHEU GEMACHT HATTE UNFÄHIG ZU DEN HOHEN DINGEN ZU DENEN UNGETRÜBTE MITFREUDE GEHÖRT (mls_deu_000282-mls_deu_000282) +DIESER JUNGE MANN HIESS KACKERLITZCHEN UND BEFAND SICH GERADE AUF DER WANDERSCHAFT ALS IN DEM GENANNTEN KÖNIGREICH DIE BEKANNTMACHUNG WEGEN DER PRINZESSIN VERLESEN WURDE EI SAGTE DER SCHNEIDER WENN ES WEITER NICHTS IST EIN WEIB HAB ICH NOCH NICHT GEKÜSST UND DES KÖNIGS EIDAM ZU WERDEN DAS GELÜSTET MICH ALLERDINGS (mls_deu_000283-mls_deu_000283) +NOCH FÜNF MINUTEN UND DIE WOLKEN DER BEWUSSTLOSIGKEIT BEGANNEN ZU SCHWINDEN JETZT WUSSTE ICH SEHR WOHL DASS ICH IN MEINEM EIGENEN BETTE LAG UND DASS DIE ROTE GLUT NICHTS ANDERES WAR ALS DAS FEUER IM KAMIN DER KINDERSTUBE ES WAR NACHT EINE KERZE BRANNTE AUF DEM TISCHE (mls_deu_000284-mls_deu_000284) +WELCHE DIESE VERDRÄNGUNGEN WIE WÄCHTER UNTERHALTEN KOMMT DANN IM PUBERTÄTSALTER DIE HOCHFLUT DER SEXUELLEN BEDÜRFTIGKEIT SO FINDET SIE AN DEN GENANNTEN SEELISCHEN REAKTIONS ODER WIDERSTANDSBILDUNGEN DÄMME (mls_deu_000285-mls_deu_000285) +ABER AFFEN GEHÖREN BEI HAGENBECK AN DIE KISTENWAND NUN SO HÖRTE ICH AUF AFFE ZU SEIN EIN KLARER SCHÖNER GEDANKENGANG DEN ICH IRGENDWIE MIT DEM BAUCH AUSGEHECKT HABEN MUSS DENN AFFEN DENKEN MIT (mls_deu_000286-mls_deu_000286) +IST ES DAS PORTRÄT EINES MENSCHEN DEN SIE KENNEN FRAGTE ELIZA WELCHE UNBEMERKT AN MICH HERANGETRETEN WAR ICH ENTGEGNETE DASS ES NUR EIN PHANTASIEKOPF SEI UND SCHOB DIE ZEICHNUNG EILIG UNTER DIE ANDERN BLÄTTER NATÜRLICH SPRACH ICH DIE UNWAHRHEIT DENN ES WAR EIN SEHR GETREUES PORTRÄT MR ROCHESTERS (mls_deu_000287-mls_deu_000287) +ICH WEISS DASS ICH SEHR KRANK BIN SAGTE SIE NACH EINER WEILE VOR EIN PAAR MINUTEN VERSUCHTE ICH MICH IM BETTE UMZUDREHEN UND FÜHLTE DASS ICH KEIN GLIED MEHR RÜHREN KANN ES WÄRE GUT WENN ICH MEIN GEMÜT ERLEICHTERN KÖNNTE BEVOR ICH STERBE (mls_deu_000288-mls_deu_000288) +SO ABER IST ZWAR UNSER WESENSGRUND GOTT SELBER DA HERUM HAT SICH JEDOCH DER SCHLANGENKNÄUEL DES ALTEN SATAN GESCHLUNGEN UND ÜBER DEM FÜNKCHEN DER LIEBE IST DIE FINSTERNIS DES HASSES GELAGERT WAS WUNDER DANN (mls_deu_000289-mls_deu_000289) +BESSIE WÄRE LIEBER GEBLIEBEN ABER SIE WAR GEZWUNGEN ZU GEHEN WEIL DIE PÜNKTLICHKEIT BEI DEN MAHLZEITEN EINE SACHE WAR AUF WELCHE IN GATESHEAD HALL STRENGE GEHALTEN WURDE (mls_deu_000290-mls_deu_000290) +AUGENBLICKLICH FÜHLTE WIE IHRE ANSICHTEN ÜBER MICH IHRE EMPFINDUNGEN FÜR MICH NICHT UM EIN ATOM VERÄNDERT WAREN ÜBERHAUPT KEINER ÄNDERUNG FÄHIG WAREN ICH SAH ES IHREM VERSTEINERTEN AUGE WELCHES NIEMALS DURCH TRÄNEN GENETZT NIEMALS IN ZÄRTLICHKEIT AUFGELEUCHTET HATTE AN (mls_deu_000291-mls_deu_000291) +BRUDER SAM IST SEHR GUT WENN DER HÄUPTLING IHN ERFÄHRT WIRD ER SICH FREUEN UND WIR WERDEN SCHNELL DANACH HANDELN SO WOLLEN WIR AUFBRECHEN UND SCHNELL REITEN DAMIT WIR NOCH VOR NACHT DAS LAGER ERREICHEN WIR STIEGEN AUF DIE PFERDE DIE NUN AUSGERUHT HATTEN UND FLOGEN IM GALOPP DAVON DIESMAL HÜTETEN WIR UNS DER FÄHRTE WIEDER DIREKT ZU FOLGEN WIR RITTEN GERADEAUS UND ERSPARTEN UNS (mls_deu_000292-mls_deu_000292) +WEIL DIE ABER MIT PECH BESTRICHEN WAR BLIEB EINER VON DEN GOLDENEN PANTOFFELN FESTHÄNGEN UND IN DER ANGST DACHT ES NICHT DARAN IHN MITZUNEHMEN UND WIE ES DEN LETZTEN SCHRITT VON DER TREPPE TAT DA HATTE ES ZWÖLF AUSGESCHLAGEN DA WAR WAGEN UND PFERDE VERSCHWUNDEN UND ASCHENPUTTEL STAND IN SEINEN ASCHENKLEIDERN AUF DER DUNKELN STRASSE (mls_deu_000293-mls_deu_000293) +ILL NAHM DAS GLAS VOM AUGE EIN FINSTERER ERNST LAGERTE ÜBER SEINEN ZÜGEN ES IST SCHRECKLICH SAGTE ER ICH HAB DAS MEINIGE GETAN UM BLUTVERGIESSEN ZU VERMEIDEN (mls_deu_000294-mls_deu_000294) +NUR DER DOKTOR UND DIE WÄRTERIN SOLLEN VOR SEINE AUGEN KOMMEN ERKLÄRTE DIE TRINE IN GROSSEM AMTSEIFER DAMIT WAR DIE FRAU OBERST GANZ EINVERSTANDEN UND HÖCHST ERFREUT KEHRTE SIE MIT IHREN (mls_deu_000295-mls_deu_000295) +K WAR UNTRÖSTLICH ÜBER DIE LAGE DES KÜNSTLERS ER BEGANN ZU WEINEN UND SCHLUCHZTE LANGE IN DIE VORGEHALTENEN HÄNDE DER KÜNSTLER WARTETE BIS K SICH BERUHIGT HATTE UND ENTSCHLOSS SICH DANN DA ER KEINEN ANDEREN AUSWEG FAND DENNOCH ZUM WEITERSCHREIBEN (mls_deu_000296-mls_deu_000296) +VON DEN PFERDEHERDEN DER APACHEN UND SAGTEN UNS DASS SIE FÜR EIN APACHENPFERD UNS EBENSO VIELE WAREN UND BRANDY GEBEN WÜRDEN WIE FÜR EIN KIOWAPFERD DA SIND UNSERE KRIEGER FORT UM APACHENPFERDE ZU HOLEN ALSO RICHTIG WER WAR SCHULD AN DEM TODE DER BISHER GEFALLENEN UND AN DEM BLUTVERGIESSEN WELCHES NUN BEVORSTAND WEISSE PFERDEHÄNDLER (mls_deu_000297-mls_deu_000297) +DAS AMAZONENHÜTCHEN VON SCHWARZEM SAMMET GRAZIÖS AUF IHRE LANGEN LOCKEN GEDRÜCKT DIE IHRE WANGEN UMFLOSSEN UND ÜBER IHRE SCHULTERN HERABWALLTEN SO TRAT SIE IN DAS EINFACHE LÄNDLICHE GEBÄUDE UND SCHWEBTE ZWISCHEN DEN REIHEN DER HALBGEBLENDETEN DORFKINDER AUF UND AB (mls_deu_000298-mls_deu_000298) +DU MUSST ERST ENTSAGEN ALLEM SÜNDHAFTEN STREBEN UND IN TIEFER REUE UND DEMUT DIE FÜRBITTE DER HEILIGEN ERFLEHEN GEGEN DIE DU GEFREVELT HAST DIE JÜNGLINGE WELCHE FRANCESKO SO LANGE GEFLOHEN SUCHTEN IHN AUF IN SEINER WERKSTATT UND FANDEN IHN (mls_deu_000299-mls_deu_000299) +ER LIESS SEINE GRETEL NICHT FORTSCHLEPPEN AM ALLERWENIGSTEN ABER IN DEN GROSSEN VOGELBAUER WO SIE ALLE IN EINEM TONE PFEIFEN MUSSTEN WIE ER STETS SAGTE (mls_deu_000300-mls_deu_000300) +FRANCESKO MALTE IN UNHEILIGER BEGEISTERUNG VIELE BILDER AUS DER LÜGENHAFTEN FABELWELT KEINER ALS ER VERMOCHTE DIE BUHLERISCHE ÜPPIGKEIT DER WEIBLICHEN GESTALTEN SO WAHRHAFT DARZUSTELLEN INDEM ER VON LEBENDEN MODELLEN DIE KARNATION VON DEN ALTEN MARMORBILDERN ABER FORM UND BILDUNG ENTNAHM (mls_deu_000301-mls_deu_000301) +BEWEGUNG UND TAT DEN ERSTEN ZUG JA ES STIMMTE DIE VORHIN ANGEGEBENEN INGREDIENZIEN NÄMLICH RÜBEN HANF EICHELN UND SAUERAMPFER WAREN ALLE IN DEM PFEIFENKOPFE ANWESEND ABER EINEN FÜNFTEN HAUPTSTOFF HATTE ICH NICHT GENANNT JETZT ROCH UND SCHMECKTE ICH DASS AUCH EIN STÜCKCHEN FILZSCHUH DABEI SEIN MÜSSE ICH BLIES DEN RAUCH AUCH GEGEN DEN HIMMEL UND GEGEN DIE (mls_deu_000302-mls_deu_000302) +UND DAS FEUER STAND AUF UND FLACKERTE UND KOCHTE DAS ESSEN FERTIG UND DER BRATEN BRUTZELTE FORT UND DER KOCH GAB DEM KÜCHENJUNGEN EINE OHRFEIGE UND DIE MAGD RUPFTE DAS HUHN FERTIG DA WARD DIE HOCHZEIT VON DEM KÖNIGSSOHN MIT DORNRÖSCHEN GEFEIERT UND SIE LEBTEN VERGNÜGT BIS AN IHR ENDE (mls_deu_000303-mls_deu_000303) +UND DASS ER MIR NICHT NACHTRAGEN WOLLE WENN ICH WIDERSPENSTIG WAR GEGEN SEINEN WOHLMEINENDEN RAT DER HERR PFARRER HAT JA IN ALLEM RECHT GEHABT UND ICH WAR IM UNRECHT ABER (mls_deu_000304-mls_deu_000304) +OBGLEICH SEINE MASSE NUR WENIGE GRAMM BETRUG ER BREITETE SICH KEGELFÖRMIG AUS UND MUSSTE DAHER DAS IHM ENTGEGENFLIEGENDE SPRENGGESCHOSS AUFFANGEN UND ZUR RUHE BRINGEN (mls_deu_000305-mls_deu_000305) +DER FUCHS REICHTE SAM DIE UNFRIEDLICHE FRIEDENSPFEIFE HIN DER MANN TAT WACKER SEINE SECHS ZÜGE UND SAGTE DER GROSSE GEIST ACHTET NICHT AUF DIE VERSCHIEDENE HAUT DER MENSCHEN DENN DIE KÖNNEN SICH MIT FARBE BESCHMIEREN UM IHN ZU TÄUSCHEN SONDERN ER SIEHT DAS HERZ AN DIE HERZEN DER KRIEGER VOM BERÜHMTEN STAMME DER KIOWAS SIND TAPFER UNERSCHROCKEN UND TREU DAS MEINIGE HÄNGT (mls_deu_000306-mls_deu_000306) +ALLES WAS WIR MIT IHR BEGEGNET SCHIEBT SICH DURCH UND ÜBEREINANDER BALD UNTERSCHREIBEN WIR EINEN KONTRAKT DA IST IHRE HAND UND DIE MEINIGE IHR NAME UND DER MEINIGE BEIDE LÖSCHEN EINANDER AUS BEIDE VERSCHLINGEN 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(voxforge_deu_001016-voxforge_deu_001016) +DIE ENTWICKLUNG IST WEIT VORANGESCHRITTEN (voxforge_deu_001017-voxforge_deu_001017) +DIE SYMPTOME TRETEN DANN SCHON NACH WENIGEN STUNDEN AUF (voxforge_deu_001018-voxforge_deu_001018) +ES GIBT EINE GROSSE WELLE VON PROZESSEN (voxforge_deu_001019-voxforge_deu_001019) +ES IST BEREITS MEIN ZWEITER AUTOMAT (voxforge_deu_001020-voxforge_deu_001020) +B RUSSLANDS IMPLEMENTIERUNG VON HÖHEREN STANDARDS ZUM SCHUTZ PERSÖNLICHER DATEN EBENFALLS GENERELL UNSERE GUTE ZUSAMMENARBEIT ERLEICHTERN (voxpopuli_deu_000309-voxpopuli_deu_000309) +POLIZEIBEAMTE HABEN DAS SCHLIMMSTE VERHINDERT HABEN IHR LEBEN GERETTET UND SIND SELBER VERLETZT WORDEN (voxpopuli_deu_000310-voxpopuli_deu_000310) +DAS IST NICHT MÖGLICH DASS DER KOMMISSAR NICHT HIER IST (voxpopuli_deu_000311-voxpopuli_deu_000311) +19 MITGLIED UND HOFFE DASS WIR IM NÄCHSTEN JAHR ÜBER DAS (voxpopuli_deu_000312-voxpopuli_deu_000312) +ES DARF NICHT ÜBERSEHEN WERDEN DASS IMMERHIN MEHR ALS 50 DER BEVÖLKERUNG DER EUROPÄISCHEN UNION IM LÄNDLICHEN RAUM LEBT (voxpopuli_deu_000313-voxpopuli_deu_000313) +WIR WOLLEN ALSO DASS DER BÜRGER SCHNELLER EINE AUSKUNFT BEKOMMT OB SEINE BESCHWERDE ÜBERHAUPT ANGENOMMEN WIRD OB SIE BERECHTIGT IST (voxpopuli_deu_000314-voxpopuli_deu_000314) +EIN „RESET UNSERER BEZIEHUNGEN IST NICHT VONNÖTEN ABER SEHR WOHL KONTINUIERLICHES FEINTUNING (voxpopuli_deu_000315-voxpopuli_deu_000315) +UND DA WIRD GANZ STOLZ GESAGT DIE BESCHÄFTIGUNG STEIGT JA AN (voxpopuli_deu_000316-voxpopuli_deu_000316) +ICH WILL SAGEN WIE ES IST FÜR UNS IST DER EURO UNTERBEWERTET WIR EXPORTIEREN ZU VIEL ZU BILLIG UND WIR IMPORTIEREN ZU WENIG WIR VERSCHENKEN WOHLSTAND (voxpopuli_deu_000317-voxpopuli_deu_000317) +DASS SIE HEUTE ABEND HIER ANWESEND SIND IST EIN POSITIVES SIGNAL (voxpopuli_deu_000318-voxpopuli_deu_000318) +90 PROZENT ALLER EUROPÄISCHEN FILME DIE AUSSERHALB IHRES HEIMATLANDES GEZEIGT WERDEN SIND VOM MEDIA PROGRAMM GEFÖRDERT WORDEN (voxpopuli_deu_000319-voxpopuli_deu_000319) +WIESO KANN ICH DEM ERGEBNIS DER AUSSCHUSSABSTIMMUNG IN DIESER FORM NICHT ZUSTIMMEN (voxpopuli_deu_000320-voxpopuli_deu_000320) +WIR WOLLTEN VERHINDERN DASS SICH HINTER DIESEM GEISTIGEN EIGENTUM DIE AUSKUNFTSPFLICHT VERSTECKEN KANN (voxpopuli_deu_000321-voxpopuli_deu_000321) +ES GIBT JETZT IM ZUSAMMENHANG MIT DER VERSTÄRKTEN ZUSAMMENARBEIT EINEN ERSTEN GANG VON EINIGEN MITGLIEDSTAATEN (voxpopuli_deu_000322-voxpopuli_deu_000322) +WAS DIE GRENZÜBERSCHREITENDE ZUSAMMENARBEIT ANBELANGT UND DIE VERBREITUNG IN DRITTLÄNDER BETRIFFT HIER MÖCHTE ICH EIN BEISPIEL NENNEN DAS EIN ERFOLGSBEISPIEL FÜR MICH IST UND ZWAR SLUMDOG MILLIONÄR (voxpopuli_deu_000323-voxpopuli_deu_000323) +UND DAS NICHT NUR IN PORTUGAL ODER GRIECHENLAND SONDERN AUCH IN SO VERMEINTLICH REICHEN MITGLIEDSTAATEN WIE DEUTSCHLAND ODER GROSSBRITANNIEN (voxpopuli_deu_000324-voxpopuli_deu_000324) +DIE ZEIT FÜR AUSREDEN IST VORBEI (voxpopuli_deu_000325-voxpopuli_deu_000325) +SIE ALLE FLIEGEN ALS MITGLIEDER DIESES HAUSES WAHRSCHEINLICH DEUTLICH HÄUFIGER ALS DER EU DURCHSCHNITTSBÜRGER (voxpopuli_deu_000326-voxpopuli_deu_000326) +UND ICH BIN SICHER DASS IHRE BEDEUTUNG IN NAHER ZUKUNFT SOGAR NOCH ZUNEHMEN WIRD (voxpopuli_deu_000327-voxpopuli_deu_000327) +ES GEHT HIER UM DIE RICHTLINIE DES RATES ZUR FESTLEGUNG GRUNDLEGENDER SICHERHEITSNORMEN FÜR DEN SCHUTZ VOR DEN GEFAHREN EINER EXPOSITION GEGENÜBER IONISIERENDER STRAHLUNG (voxpopuli_deu_000328-voxpopuli_deu_000328) +DAS GILT ES WIEDER HERZUSTELLEN (voxpopuli_deu_000329-voxpopuli_deu_000329) +DIESEN EINEN EINZIGEN SITZ GIBT ES LÄNGST DAS IST STRASSBURG (voxpopuli_deu_000330-voxpopuli_deu_000330) +WIR SEHEN JA GERADE DASS DAS PASSIERT IN MALTA DIE JOURNALISTIN DIE KORRUPTIONSFÄLLE AUFGEDECKT HAT IST VOR WENIGEN WOCHEN ERMORDET WORDEN WEDER WERDEN SYSTEMATISCH DIE KORRUPTIONSFÄLLE UNTERSUCHT NOCH WIRD DER MORD SELBER GEZIELT UNTERSUCHT MAN HAT FAST DEN EINDRUCK ALS OB HIER ALLES UNTER DEM MANTEL DES SCHWEIGENS ZUGEDECKT WERDEN SOLL (voxpopuli_deu_000331-voxpopuli_deu_000331) +DORT STEHEN ÜBERALL ENTLANG DER KÜSTE DIE WARNSTEINE DIE AUF DIE GROSSEN KATASTROPHEN MIT TSUNAMIS IN DER VERGANGENHEIT HINWEISEN (voxpopuli_deu_000332-voxpopuli_deu_000332) +HERR PRÄSIDENT ICH HABE IM PRINZIP FÜR DEN BERICHT GESTIMMT OBWOHL ER EINEN SCHWEREN FEHLER ENTHÄLT ES WIRD NÄMLICH DAZU AUFGEFORDERT DAS EUROPÄISCHE PARLAMENT AUF DEM WEG ZU EINEM EINZIGEN SITZ ZU UNTERSTÜTZEN (voxpopuli_deu_000333-voxpopuli_deu_000333) +IN DIESEN TREFFEN WURDEN GEMEINSAME POLITISCHE VERABREDUNGEN IM KREIS DER 27 GETROFFEN UND AUCH PUBLIK GEMACHT (voxpopuli_deu_000334-voxpopuli_deu_000334) +ICH BIN DER ÜBERZEUGUNG DASS WIR ES HEUTE MIT DEM VORSCHLAG AUS DEM UMWELTAUSSCHUSS GESCHAFFT HABEN EINEN SCHRITT WEITERZUKOMMEN ES IST NICHT PERFEKT EUROPÄISCHE ÄRZTE SAGEN WIR HÄTTEN FÜR HOCHRISIKOPRODUKTE EINE ZENTRALE ZULASSUNG HABEN MÜSSEN DAS HABE ICH NICHT GESCHAFFT ABER MIT DEM WAS HEUTE AUF DEM TISCH LIEGT SCHAFFEN WIR WOHL TROTZDEM EINEN GROSSEN SCHRITT VIELLEICHT KEINEN MEILENSTEIN ABER EINEN GROSSEN SCHRITT HIN ZU MEHR PATIENTENSICHERHEIT (voxpopuli_deu_000335-voxpopuli_deu_000335) +FRAU PRÄSIDENTIN FRAU KOMMISSARIN LIEBE KOLLEGEN (voxpopuli_deu_000336-voxpopuli_deu_000336) +ZUM AKTUELLEN ICH GLAUBE ES KANN KEINER VON UNS ANNEHMEN DASS WIR WIRKLICH ERST SEIT DIESEM WOCHENENDE WISSEN DASS UNS DIE ZAHLUNGSUNFÄHIGKEIT DROHT (voxpopuli_deu_000337-voxpopuli_deu_000337) +DAS SIND EINFACH BEDINGUNGEN DIE NICHT AKZEPTABEL SIND (voxpopuli_deu_000338-voxpopuli_deu_000338) +IN DER ZWISCHENZEIT SIND DIE RETTUNGSORGANISATIONEN DIE GRÖSSTEN SCHLEPPER WEIL SIE DIE MIGRANTEN 20 KILOMETER VOR DER LIBYSCHEN KÜSTE AUFGREIFEN UND ALLE NACH ITALIEN TRANSPORTIEREN (voxpopuli_deu_000339-voxpopuli_deu_000339) +DAS ZEIGT DER FALL JULIA TIMOSCHENKO (voxpopuli_deu_000340-voxpopuli_deu_000340) +WIR DÜRFEN NICHT WASSER PREDIGEN UND WEIN TRINKEN (voxpopuli_deu_000341-voxpopuli_deu_000341) +FÜR DIESE ENTSCHEIDUNG BRAUCHEN WIR VIELE PARTNER NICHT ZULETZT DIE STÄDTE (voxpopuli_deu_000342-voxpopuli_deu_000342) 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UMSTÄNDEN WEG (voxpopuli_deu_000348-voxpopuli_deu_000348) +DREI DERARTIGE TREFFEN HABEN INZWISCHEN STATTGEFUNDEN (voxpopuli_deu_000349-voxpopuli_deu_000349) +ICH HOFFE ES DAUERT NICHT WIEDER NEUN MONATE (voxpopuli_deu_000350-voxpopuli_deu_000350) +DESWEGEN EINE WICHTIGE FRAGE AN DIE KOMMISSION KANN EIN LAND DIE GRENZKONTROLLE WIEDER EINFÜHREN UND GLEICHZEITIG IN DER SCHENGEN UNION BLEIBEN MIT ZUGANG ZUM INFORMATIONSSYSTEM ETC ODER IST DAS EIN ENTWEDER ODER DIE FRAGE IST WICHTIG FÜR DIE DÄNISCHE DEBATTE UND ICH BITTE UM EINE KLARE ANTWORT (voxpopuli_deu_000351-voxpopuli_deu_000351) +WIE HEUTE SCHON AUSGEFÜHRT WURDE LAG ES NICHT DARAN DASS ES HIER GROBE FEHLER GEGEBEN HÄTTE SONDERN ES GAB EINE REIHE VON KLEINEN UNGEREIMTHEITEN BZW (voxpopuli_deu_000352-voxpopuli_deu_000352) +EINE VERGEMEINSCHAFTUNG DER AUSSEN UND SICHERHEITSPOLITIK ALS GROSSES ZIEL DIESER UNION (voxpopuli_deu_000353-voxpopuli_deu_000353) +DENN SICHERHEIT IST EINE SCHWIERIGE UND DETAILREICHE ARBEIT NICHT NUR IM TECHNISCHEN BEREICH (voxpopuli_deu_000354-voxpopuli_deu_000354) +KINDER UND POLITIK SELTEN LIEGEN DIE INTERESSEN VON BÜRGERN UND POLITIKERN SO WEIT AUSEINANDER BEI DEN BÜRGERN IN GANZ EUROPA STEHT DAS THEMA KIND GANZ OBEN (voxpopuli_deu_000355-voxpopuli_deu_000355) +HERR PRÄSIDENT (voxpopuli_deu_000356-voxpopuli_deu_000356) +WIR FÜHRTEN GESPRÄCHE MIT PRÄSIDENT KARZAI ZAHLREICHEN REGIERUNGSVERTRETERN FRAUEN UND MENSCHENRECHTSORGANISATIONEN UND DIE WAREN DURCHAUS ERMUTIGEND (voxpopuli_deu_000357-voxpopuli_deu_000357) +DAS IST ÜBRIGENS AUCH EINE URSACHE FÜR DEN WACHSENDEN NATIONALISMUS DER ALLERDINGS LEIDER VÖLLIG PERSPEKTIVLOS IST (voxpopuli_deu_000358-voxpopuli_deu_000358) +HEUTE SIND WIR IMMER NOCH SO WEIT VON DIESEM ZIEL ENTFERNT (voxpopuli_deu_000359-voxpopuli_deu_000359) +ICH WERDE ALS FINANZMINISTER AUCH IN MEINEM LAND JEDEN TAG DAMIT KONFRONTIERT DASS NATÜRLICH AUCH DAS BEWUSSTSEIN GEGEBEN SEIN MUSS DASS STAATSHAUSHALTE VON DEN STEUERZAHLERINNEN UND STEUERZAHLERN FINANZIERT SIND UND DASS WIR DAMIT AUCH DIE VERANTWORTUNG TRAGEN BEI DEN ENTSCHEIDUNGEN DIE WIR HIER IN DIESEM RAHMEN TREFFEN MEINE DAMEN UND HERREN (voxpopuli_deu_000360-voxpopuli_deu_000360) +AUF DEM EUROPÄISCHEN AUTOMOBILMARKT INSGESAMT DRAMATISCH IST (voxpopuli_deu_000361-voxpopuli_deu_000361) +DIE EUROPÄISCHE UNION HAT MIT DIESEM INSTRUMENT DIE CHANCE EINE AKTIVE ROLLE IN IHRER NACHBARREGION ZU SPIELEN UM DEMOKRATISCHE REFORMEN UND EINE NACHHALTIGE ENTWICKLUNG VORANZUTREIBEN (voxpopuli_deu_000362-voxpopuli_deu_000362) +DIE SICHT AUF TOTALITÄRE REGIME VON AUSSEN ODER VON INNEN IST RECHT UNTERSCHIEDLICH (voxpopuli_deu_000363-voxpopuli_deu_000363) +WIR HABEN IMMER GESAGT DASS EINE ÜBEREILTE STATIONIERUNGSENTSCHEIDUNG UNSINNIG IST WEIL ES ZUM JETZIGEN ZEITPUNKT KEINE BEDROHUNG BEISPIELSWEISE AUS DEM IRAN GIBT (voxpopuli_deu_000364-voxpopuli_deu_000364) +DIESER VERGLEICH IST EINE ZYNISCHE MISSACHTUNG DER OPFER VON MENSCHENRECHTSVERLETZUNGEN IN ALLER WELT ER IST ZUM ANDEREN EIN SOLCH UNGLAUBLICHER ANWURF (voxpopuli_deu_000365-voxpopuli_deu_000365) +DIE SPE HAT DIESE UMFASSENDE HORIZONTALE RICHTLINIE BEFÜRWORTET (voxpopuli_deu_000366-voxpopuli_deu_000366) +DENN EINES IST WIRKLICH KLAR DIE FINANZ UND WIRTSCHAFTSKRISE VERLANGT VON UNS ALLEN EINMAL MEHR DER VERANTWORTUNG FÜR EINE OPTIMALE UND VOR ALLEM RASCHE QUALIFIZIERUNG UNSERER ARBEITNEHMER UND ARBEITNEHMERINNEN GANZ BESONDERS JETZT RECHNUNG ZU TRAGEN (voxpopuli_deu_000367-voxpopuli_deu_000367) +ESTLAND ODER AUCH POLEN DIE SEHR GUTE ERGEBNISSE ERZIELEN ALS ANDERE DIE SICH SCHWER TUN DIE MITTEL ABZURUFEN ETWA REGIONEN WIE KALABRIEN SIZILIEN ODER AUCH GRIECHENLAND ODER RUMÄNIEN (voxpopuli_deu_000368-voxpopuli_deu_000368) +DER BERICHT GAUZÈS FORDERT ZU RECHT DASS DAS RATING STAATLICHER SCHULDTITEL ALS ÖFFENTLICHE AUFGABE BEGRIFFEN UND DAHER VON ÖFFENTLICHEN AKTEUREN VORGENOMMEN WERDEN MUSS (voxpopuli_deu_000369-voxpopuli_deu_000369) +DA WIR ES ABER NUN MIT EINEM SOZIALPROGRAMM ZU TUN HABEN MÜSSEN WIR DAFÜR EINE ENTSPRECHENDE RECHTLICHE GRUNDLAGE SCHAFFEN (voxpopuli_deu_000370-voxpopuli_deu_000370) +ABER DAS MÜSSEN WIR NOCH ANALYSIEREN (voxpopuli_deu_000371-voxpopuli_deu_000371) +MAN KANN NATÜRLICH VERLANGEN GEBEN WIR MEHR GELD FÜR ENTWICKLUNGSHILFE AUS DIE ARMEN LEUTE BRAUCHEN DAS (voxpopuli_deu_000372-voxpopuli_deu_000372) +GERADE FÜR KLEINERE PROJEKTE IST DAS EIN ÜBERMÄSSIGER BÜROKRATISCHER AUFWAND RICHTIG DASS DAS JETZT AUF EINEN ZEITRAUM VON DREI JAHREN GESENKT WERDEN SOLL (voxpopuli_deu_000373-voxpopuli_deu_000373) +ICH KANN NUR VERSICHERN DIE EUROPÄISCHE KOMMISSION IST COMMITTED ZUR EUROPÄISCHEN PERSPEKTIVE DES KOSOVO (voxpopuli_deu_000374-voxpopuli_deu_000374) +ABER HIER IM HAUSE IST ES SEHR OFT AUCH SO (voxpopuli_deu_000375-voxpopuli_deu_000375) +MIT DIESEM HAUSHALT KANN MAN DIE EU BÜRGERINNEN UND BÜRGER NICHT ÜBERZEUGEN UND BEGEISTERN (voxpopuli_deu_000376-voxpopuli_deu_000376) +WIR ALS SOZIALDEMOKRATEN NEHMEN MIT GROSSER FREUDE ZUR KENNTNIS DASS DINGE DIE WIR VORGETRAGEN HABEN JETZT IM ZUSAMMENHANG MIT DEN VERÄNDERUNGEN IN DEN VEREINIGTEN STAATEN UMGESETZT WERDEN (voxpopuli_deu_000377-voxpopuli_deu_000377) +DER BESCHLUSS DAS EUROPÄISCHE SEMESTER HERZUNEHMEN UND DIE KORRUPTIONSSITUATION IM RAHMEN DER LÄNDERBERICHTE ZU VERÖFFENTLICHEN IST NICHT AUSREICHEND (voxpopuli_deu_000378-voxpopuli_deu_000378) +UND MEINE BITTE ODER DAS WAS ICH MIR VORSTELLE IST DASS MORGEN WIRKLICH IN DER TAT EINE GROSSE EINE BREITE MEHRHEIT FÜR DIESE KOHÄSIONSPOLITIK FÜR UNSERE POLITIK STIMMT FÜR DIE MENSCHEN VOR ORT DAMIT WIR UNS AUF DAS WESENTLICHE BESCHRÄNKEN KÖNNEN (voxpopuli_deu_000379-voxpopuli_deu_000379) +WENN WIR HEUTE DIESE VERORDNUNG VERABSCHIEDEN HOFFE ICH DASS WIR NACH EINEM LANGEN WEG ZU EINEM GUTEN ABSCHLUSS KOMMEN UND ICH MÖCHTE MICH BEI DER KOMMISSION BEDANKEN DIE UNS DURCH KONSTRUKTIVE SACHARBEIT (voxpopuli_deu_000380-voxpopuli_deu_000380) +UNSERE KONTROLLEN HABEN KEINEN BELEG ERBRACHT ICH KANN (voxpopuli_deu_000381-voxpopuli_deu_000381) diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/result.txt b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..43b0a1dd6dca823ce03f4331ac7a548fed6b577b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/result.txt @@ -0,0 +1,7445 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,-----------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/hyp.trn| +|-----------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------------+---------------------+----------------------------------------------------------------| +| m | 89 1395 | 22.2 60.9 16.8 3.5 81.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000698 | 1 8 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000699 | 1 5 | 0.0 20.0 80.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000700 | 1 8 | 0.0 100.0 0.0 12.5 112.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000701 | 1 12 | 8.3 83.3 8.3 0.0 91.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000702 | 1 8 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000703 | 1 10 | 10.0 90.0 0.0 10.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000704 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000705 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000706 | 1 12 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000707 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000708 | 1 11 | 27.3 63.6 9.1 18.2 90.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000709 | 1 8 | 37.5 62.5 0.0 50.0 112.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000710 | 1 11 | 18.2 72.7 9.1 9.1 90.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000711 | 1 13 | 30.8 69.2 0.0 0.0 69.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000712 | 1 6 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000713 | 1 7 | 0.0 71.4 28.6 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000714 | 1 13 | 0.0 53.8 46.2 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000715 | 1 11 | 0.0 81.8 18.2 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000716 | 1 10 | 10.0 70.0 20.0 0.0 90.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000717 | 1 13 | 30.8 46.2 23.1 7.7 76.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000718 | 1 8 | 12.5 75.0 12.5 0.0 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000719 | 1 8 | 12.5 75.0 12.5 0.0 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000720 | 1 9 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000721 | 1 6 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000722 | 1 6 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000723 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000724 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000725 | 1 13 | 7.7 53.8 38.5 0.0 92.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000726 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000727 | 1 7 | 14.3 71.4 14.3 14.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000728 | 1 6 | 0.0 100.0 0.0 16.7 116.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000729 | 1 6 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000730 | 1 14 | 21.4 78.6 0.0 14.3 92.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000731 | 1 8 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000732 | 1 7 | 14.3 57.1 28.6 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000733 | 1 12 | 8.3 75.0 16.7 0.0 91.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000734 | 1 10 | 10.0 90.0 0.0 40.0 130.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000735 | 1 5 | 0.0 60.0 40.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000736 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000737 | 1 12 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000738 | 1 11 | 27.3 54.5 18.2 0.0 72.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000739 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000740 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000741 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000742 | 1 13 | 23.1 69.2 7.7 15.4 92.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000743 | 1 4 | 25.0 75.0 0.0 75.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000744 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000745 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000746 | 1 9 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000747 | 1 10 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000748 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000749 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000750 | 1 13 | 23.1 53.8 23.1 0.0 76.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000751 | 1 12 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000752 | 1 12 | 8.3 66.7 25.0 8.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000753 | 1 12 | 25.0 50.0 25.0 16.7 91.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000754 | 1 7 | 0.0 85.7 14.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000755 | 1 13 | 23.1 69.2 7.7 38.5 115.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000756 | 1 9 | 44.4 55.6 0.0 0.0 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000757 | 1 12 | 16.7 58.3 25.0 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000758 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000759 | 1 9 | 11.1 88.9 0.0 0.0 88.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000760 | 1 7 | 28.6 71.4 0.0 28.6 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000761 | 1 12 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000762 | 1 9 | 22.2 55.6 22.2 0.0 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000763 | 1 12 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000764 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000765 | 1 11 | 9.1 81.8 9.1 0.0 90.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000766 | 1 8 | 12.5 87.5 0.0 12.5 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000767 | 1 9 | 0.0 55.6 44.4 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000768 | 1 11 | 0.0 45.5 54.5 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000769 | 1 3 | 0.0 33.3 66.7 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000770 | 1 14 | 21.4 71.4 7.1 14.3 92.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000771 | 1 10 | 10.0 80.0 10.0 0.0 90.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000772 | 1 6 | 0.0 100.0 0.0 16.7 116.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000773 | 1 12 | 41.7 50.0 8.3 25.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000774 | 1 13 | 7.7 61.5 30.8 0.0 92.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000775 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000776 | 1 10 | 50.0 40.0 10.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000777 | 1 8 | 0.0 62.5 37.5 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000778 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000779 | 1 11 | 9.1 90.9 0.0 9.1 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000780 | 1 11 | 27.3 63.6 9.1 9.1 81.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000781 | 1 10 | 10.0 90.0 0.0 0.0 90.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000782 | 1 8 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000783 | 1 14 | 50.0 50.0 0.0 7.1 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000784 | 1 12 | 50.0 41.7 8.3 8.3 58.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000785 | 1 14 | 28.6 35.7 35.7 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000786 | 1 12 | 25.0 75.0 0.0 8.3 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000787 | 1 12 | 16.7 66.7 16.7 8.3 91.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000788 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000789 | 1 6 | 16.7 50.0 33.3 16.7 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000790 | 1 12 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000791 | 1 10 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000792 | 1 9 | 0.0 100.0 0.0 11.1 111.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000793 | 1 8 | 12.5 87.5 0.0 12.5 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000794 | 1 7 | 0.0 42.9 57.1 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000795 | 1 6 | 50.0 33.3 16.7 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000796 | 1 14 | 7.1 85.7 7.1 21.4 114.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000797 | 1 12 | 0.0 91.7 8.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000798 | 1 9 | 33.3 55.6 11.1 11.1 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000799 | 1 9 | 22.2 77.8 0.0 11.1 88.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000800 | 1 6 | 16.7 50.0 33.3 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000801 | 1 14 | 21.4 57.1 21.4 0.0 78.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000378 | 1 26 | 26.9 57.7 15.4 7.7 80.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000379 | 1 31 | 29.0 67.7 3.2 16.1 87.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000380 | 1 16 | 18.8 81.3 0.0 18.8 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000381 | 1 9 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000382 | 1 26 | 11.5 76.9 11.5 0.0 88.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000383 | 1 18 | 0.0 88.9 11.1 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000384 | 1 27 | 25.9 51.9 22.2 3.7 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000385 | 1 17 | 17.6 41.2 41.2 0.0 82.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000386 | 1 31 | 25.8 54.8 19.4 9.7 83.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000387 | 1 30 | 13.3 46.7 40.0 0.0 86.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000388 | 1 44 | 6.8 65.9 27.3 0.0 93.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000389 | 1 31 | 12.9 61.3 25.8 0.0 87.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000390 | 1 27 | 33.3 51.9 14.8 11.1 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000391 | 1 17 | 23.5 52.9 23.5 0.0 76.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000392 | 1 33 | 3.0 72.7 24.2 0.0 97.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000393 | 1 22 | 13.6 59.1 27.3 0.0 86.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000394 | 1 23 | 21.7 73.9 4.3 13.0 91.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000395 | 1 23 | 8.7 78.3 13.0 4.3 95.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000396 | 1 35 | 11.4 68.6 20.0 5.7 94.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000397 | 1 23 | 34.8 60.9 4.3 21.7 87.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000398 | 1 11 | 18.2 45.5 36.4 0.0 81.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000399 | 1 16 | 18.8 62.5 18.8 6.3 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000400 | 1 20 | 25.0 45.0 30.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000401 | 1 23 | 21.7 52.2 26.1 0.0 78.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000402 | 1 23 | 21.7 69.6 8.7 8.7 87.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000403 | 1 22 | 13.6 86.4 0.0 27.3 113.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000404 | 1 16 | 12.5 87.5 0.0 18.8 106.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000405 | 1 21 | 9.5 47.6 42.9 0.0 90.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000406 | 1 20 | 10.0 80.0 10.0 0.0 90.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000407 | 1 31 | 29.0 64.5 6.5 16.1 87.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000408 | 1 15 | 26.7 73.3 0.0 0.0 73.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000409 | 1 17 | 17.6 58.8 23.5 0.0 82.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000410 | 1 34 | 17.6 70.6 11.8 2.9 85.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000411 | 1 21 | 9.5 42.9 47.6 0.0 90.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000412 | 1 24 | 16.7 75.0 8.3 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000413 | 1 24 | 12.5 58.3 29.2 0.0 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000414 | 1 22 | 9.1 90.9 0.0 0.0 90.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000415 | 1 29 | 3.4 79.3 17.2 0.0 96.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000281 | 1 32 | 18.8 68.8 12.5 0.0 81.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000282 | 1 31 | 29.0 48.4 22.6 0.0 71.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000283 | 1 50 | 16.0 44.0 40.0 0.0 84.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000284 | 1 47 | 14.9 53.2 31.9 0.0 85.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000285 | 1 26 | 0.0 92.3 7.7 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000286 | 1 33 | 33.3 54.5 12.1 9.1 75.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000287 | 1 48 | 10.4 60.4 29.2 0.0 89.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000288 | 1 43 | 7.0 51.2 41.9 0.0 93.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000289 | 1 34 | 14.7 55.9 29.4 0.0 85.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000290 | 1 27 | 11.1 48.1 40.7 0.0 88.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000291 | 1 39 | 12.8 66.7 20.5 0.0 87.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000292 | 1 65 | 7.7 66.2 26.2 0.0 92.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000293 | 1 55 | 23.6 58.2 18.2 1.8 78.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000294 | 1 27 | 3.7 48.1 48.1 0.0 96.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000295 | 1 31 | 48.4 51.6 0.0 3.2 54.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000296 | 1 40 | 22.5 55.0 22.5 0.0 77.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000297 | 1 54 | 9.3 66.7 24.1 1.9 92.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000298 | 1 39 | 7.7 61.5 30.8 0.0 92.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000299 | 1 39 | 10.3 64.1 25.6 0.0 89.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000300 | 1 25 | 16.0 64.0 20.0 0.0 84.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000301 | 1 40 | 10.0 62.5 27.5 0.0 90.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000302 | 1 57 | 15.8 47.4 36.8 0.0 84.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000303 | 1 50 | 46.0 42.0 12.0 6.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000304 | 1 30 | 10.0 60.0 30.0 0.0 90.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000305 | 1 24 | 0.0 58.3 41.7 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000306 | 1 62 | 14.5 53.2 32.3 0.0 85.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000307 | 1 35 | 17.1 74.3 8.6 0.0 82.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000308 | 1 42 | 19.0 57.1 23.8 0.0 81.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000309 | 1 41 | 17.1 63.4 19.5 0.0 82.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000310 | 1 32 | 21.9 59.4 18.8 0.0 78.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000311 | 1 35 | 2.9 65.7 31.4 0.0 97.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000312 | 1 46 | 19.6 65.2 15.2 0.0 80.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000313 | 1 24 | 25.0 58.3 16.7 12.5 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000314 | 1 32 | 18.8 75.0 6.3 0.0 81.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000315 | 1 28 | 53.6 25.0 21.4 0.0 46.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000316 | 1 28 | 21.4 75.0 3.6 21.4 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000317 | 1 22 | 18.2 50.0 31.8 4.5 86.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000318 | 1 37 | 27.0 48.6 24.3 2.7 75.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000319 | 1 18 | 27.8 55.6 16.7 5.6 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001408 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001409 | 1 6 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001410 | 1 7 | 42.9 57.1 0.0 0.0 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001411 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001412 | 1 8 | 25.0 62.5 12.5 12.5 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001413 | 1 4 | 75.0 25.0 0.0 0.0 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001414 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001415 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001416 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001417 | 1 6 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001418 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001419 | 1 8 | 37.5 25.0 37.5 0.0 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001420 | 1 10 | 10.0 70.0 20.0 0.0 90.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001421 | 1 12 | 0.0 58.3 41.7 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001422 | 1 5 | 20.0 40.0 40.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001423 | 1 5 | 40.0 40.0 20.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001424 | 1 5 | 60.0 40.0 0.0 20.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001425 | 1 21 | 28.6 66.7 4.8 14.3 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001426 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001427 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001428 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001429 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001430 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001431 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001432 | 1 2 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001433 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001434 | 1 13 | 38.5 61.5 0.0 23.1 84.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001435 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001436 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001437 | 1 9 | 55.6 44.4 0.0 22.2 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001438 | 1 7 | 0.0 71.4 28.6 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001439 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001440 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001441 | 1 9 | 44.4 55.6 0.0 0.0 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001442 | 1 13 | 38.5 46.2 15.4 0.0 61.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001443 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001444 | 1 6 | 33.3 33.3 33.3 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001445 | 1 6 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001446 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001447 | 1 2 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001448 | 1 8 | 37.5 50.0 12.5 0.0 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001449 | 1 9 | 11.1 55.6 33.3 0.0 88.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001450 | 1 10 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001451 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001452 | 1 5 | 60.0 20.0 20.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001453 | 1 7 | 28.6 71.4 0.0 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001454 | 1 16 | 31.3 56.3 12.5 6.3 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001455 | 1 8 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001456 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001457 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001458 | 1 23 | 34.8 65.2 0.0 13.0 78.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001459 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001460 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001461 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001462 | 1 11 | 18.2 81.8 0.0 0.0 81.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001463 | 1 9 | 33.3 55.6 11.1 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001464 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001465 | 1 9 | 22.2 66.7 11.1 0.0 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001466 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001467 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001468 | 1 6 | 50.0 50.0 0.0 16.7 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001469 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001470 | 1 2 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001471 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001472 | 1 12 | 33.3 58.3 8.3 8.3 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001473 | 1 6 | 33.3 50.0 16.7 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001474 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001475 | 1 7 | 28.6 71.4 0.0 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001476 | 1 8 | 12.5 62.5 25.0 0.0 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001477 | 1 3 | 66.7 33.3 0.0 33.3 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001478 | 1 7 | 0.0 71.4 28.6 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001479 | 1 5 | 0.0 40.0 60.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001480 | 1 4 | 50.0 50.0 0.0 25.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001481 | 1 11 | 0.0 63.6 36.4 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001482 | 1 4 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001483 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001484 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001485 | 1 17 | 29.4 58.8 11.8 17.6 88.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001486 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001487 | 1 4 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001488 | 1 8 | 37.5 62.5 0.0 0.0 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001489 | 1 8 | 25.0 50.0 25.0 12.5 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001490 | 1 6 | 33.3 66.7 0.0 50.0 116.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001491 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001492 | 1 7 | 28.6 57.1 14.3 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001493 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001494 | 1 6 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001495 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001496 | 1 9 | 22.2 55.6 22.2 0.0 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001497 | 1 9 | 88.9 11.1 0.0 22.2 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001498 | 1 7 | 28.6 71.4 0.0 14.3 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001499 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001500 | 1 8 | 37.5 62.5 0.0 12.5 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001501 | 1 10 | 30.0 50.0 20.0 10.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001502 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001503 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001504 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001505 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001506 | 1 12 | 58.3 41.7 0.0 33.3 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001507 | 1 8 | 37.5 62.5 0.0 0.0 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001508 | 1 4 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001509 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001510 | 1 5 | 40.0 60.0 0.0 40.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001511 | 1 7 | 42.9 42.9 14.3 0.0 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001512 | 1 14 | 28.6 57.1 14.3 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001513 | 1 6 | 50.0 50.0 0.0 16.7 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001514 | 1 11 | 18.2 81.8 0.0 0.0 81.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001515 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001516 | 1 5 | 60.0 40.0 0.0 20.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001517 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001518 | 1 9 | 33.3 55.6 11.1 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001519 | 1 7 | 28.6 57.1 14.3 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001520 | 1 8 | 50.0 50.0 0.0 37.5 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001521 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001522 | 1 6 | 50.0 50.0 0.0 16.7 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001523 | 1 4 | 50.0 50.0 0.0 25.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001524 | 1 6 | 50.0 50.0 0.0 16.7 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001525 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001526 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001527 | 1 3 | 0.0 33.3 66.7 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001528 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001529 | 1 3 | 33.3 33.3 33.3 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001530 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001531 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001532 | 1 7 | 42.9 28.6 28.6 0.0 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001533 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001534 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001535 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001536 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001537 | 1 2 | 50.0 50.0 0.0 50.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001538 | 1 7 | 28.6 57.1 14.3 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001539 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001540 | 1 8 | 25.0 75.0 0.0 12.5 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001541 | 1 2 | 50.0 50.0 0.0 50.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001542 | 1 8 | 37.5 37.5 25.0 0.0 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001543 | 1 18 | 27.8 72.2 0.0 11.1 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001544 | 1 10 | 30.0 50.0 20.0 0.0 70.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001545 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001546 | 1 10 | 20.0 60.0 20.0 10.0 90.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001547 | 1 8 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001548 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001549 | 1 4 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001550 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001551 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001552 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001553 | 1 20 | 25.0 70.0 5.0 5.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001554 | 1 14 | 14.3 57.1 28.6 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001555 | 1 7 | 0.0 57.1 42.9 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001556 | 1 9 | 22.2 44.4 33.3 0.0 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001557 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001558 | 1 6 | 50.0 50.0 0.0 33.3 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001559 | 1 11 | 18.2 63.6 18.2 0.0 81.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001560 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001561 | 1 7 | 28.6 71.4 0.0 85.7 157.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001562 | 1 12 | 25.0 50.0 25.0 8.3 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001563 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001564 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001565 | 1 10 | 20.0 70.0 10.0 10.0 90.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001566 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001567 | 1 2 | 50.0 50.0 0.0 50.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001568 | 1 12 | 50.0 50.0 0.0 8.3 58.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001569 | 1 8 | 62.5 37.5 0.0 37.5 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001570 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001571 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001572 | 1 10 | 40.0 60.0 0.0 10.0 70.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001573 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001574 | 1 8 | 37.5 62.5 0.0 0.0 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001575 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001576 | 1 10 | 20.0 70.0 10.0 20.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001577 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001578 | 1 15 | 33.3 60.0 6.7 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001579 | 1 19 | 31.6 57.9 10.5 5.3 73.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001580 | 1 5 | 20.0 40.0 40.0 20.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001581 | 1 6 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001582 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001583 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001584 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001585 | 1 13 | 30.8 46.2 23.1 0.0 69.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001586 | 1 7 | 0.0 57.1 42.9 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001587 | 1 5 | 60.0 40.0 0.0 40.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001588 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001589 | 1 4 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001590 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001591 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001592 | 1 12 | 8.3 58.3 33.3 0.0 91.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001593 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001594 | 1 8 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001595 | 1 2 | 50.0 50.0 0.0 50.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001596 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001597 | 1 10 | 10.0 80.0 10.0 0.0 90.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001598 | 1 18 | 27.8 55.6 16.7 22.2 94.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001599 | 1 23 | 13.0 73.9 13.0 8.7 95.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000891 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000892 | 1 10 | 50.0 50.0 0.0 10.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000893 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000894 | 1 6 | 33.3 50.0 16.7 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000895 | 1 7 | 57.1 42.9 0.0 0.0 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000897 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000898 | 1 5 | 40.0 60.0 0.0 20.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000899 | 1 6 | 16.7 50.0 33.3 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000900 | 1 8 | 12.5 37.5 50.0 0.0 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000901 | 1 7 | 28.6 28.6 42.9 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000902 | 1 5 | 40.0 40.0 20.0 20.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000903 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000904 | 1 7 | 42.9 28.6 28.6 28.6 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000905 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000906 | 1 8 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000907 | 1 5 | 60.0 20.0 20.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000908 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000909 | 1 8 | 12.5 75.0 12.5 0.0 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000910 | 1 9 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000911 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000912 | 1 8 | 25.0 62.5 12.5 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000913 | 1 8 | 50.0 37.5 12.5 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000914 | 1 8 | 12.5 75.0 12.5 12.5 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000915 | 1 10 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000917 | 1 6 | 16.7 50.0 33.3 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000918 | 1 3 | 0.0 33.3 66.7 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000919 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000920 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000921 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000922 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000923 | 1 13 | 7.7 53.8 38.5 0.0 92.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000924 | 1 5 | 0.0 60.0 40.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000925 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000926 | 1 7 | 14.3 57.1 28.6 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000927 | 1 7 | 28.6 42.9 28.6 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000928 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000929 | 1 8 | 12.5 87.5 0.0 12.5 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000930 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000931 | 1 6 | 33.3 66.7 0.0 50.0 116.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000932 | 1 8 | 25.0 62.5 12.5 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000933 | 1 6 | 33.3 50.0 16.7 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000934 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000935 | 1 7 | 57.1 42.9 0.0 28.6 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000936 | 1 11 | 18.2 54.5 27.3 0.0 81.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000937 | 1 8 | 25.0 62.5 12.5 12.5 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000938 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000939 | 1 4 | 25.0 75.0 0.0 50.0 125.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000940 | 1 7 | 28.6 71.4 0.0 42.9 114.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000941 | 1 7 | 0.0 85.7 14.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000942 | 1 8 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000943 | 1 8 | 62.5 37.5 0.0 12.5 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000944 | 1 5 | 20.0 60.0 20.0 20.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000945 | 1 5 | 80.0 20.0 0.0 0.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000946 | 1 9 | 33.3 44.4 22.2 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000947 | 1 9 | 22.2 77.8 0.0 11.1 88.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000948 | 1 6 | 16.7 50.0 33.3 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000950 | 1 6 | 50.0 50.0 0.0 16.7 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000951 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000952 | 1 5 | 40.0 60.0 0.0 20.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000953 | 1 10 | 50.0 50.0 0.0 10.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000954 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000955 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000956 | 1 12 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000957 | 1 7 | 28.6 57.1 14.3 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000958 | 1 10 | 30.0 70.0 0.0 0.0 70.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000959 | 1 8 | 25.0 75.0 0.0 12.5 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000960 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000961 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000962 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000963 | 1 4 | 50.0 50.0 0.0 50.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000964 | 1 6 | 33.3 33.3 33.3 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000965 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000966 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000967 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000968 | 1 5 | 0.0 40.0 60.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000969 | 1 6 | 83.3 16.7 0.0 0.0 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000970 | 1 7 | 14.3 28.6 57.1 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000971 | 1 7 | 57.1 42.9 0.0 0.0 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000972 | 1 9 | 33.3 55.6 11.1 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000973 | 1 13 | 7.7 53.8 38.5 0.0 92.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000974 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000975 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000976 | 1 11 | 9.1 81.8 9.1 9.1 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000977 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000978 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000979 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000980 | 1 8 | 37.5 37.5 25.0 0.0 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000981 | 1 11 | 18.2 45.5 36.4 0.0 81.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000982 | 1 8 | 25.0 75.0 0.0 12.5 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000983 | 1 9 | 11.1 66.7 22.2 0.0 88.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000984 | 1 10 | 30.0 30.0 40.0 0.0 70.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000985 | 1 7 | 0.0 85.7 14.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000986 | 1 7 | 14.3 57.1 28.6 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000987 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000988 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000989 | 1 6 | 50.0 33.3 16.7 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000990 | 1 7 | 28.6 42.9 28.6 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000991 | 1 8 | 37.5 62.5 0.0 12.5 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000992 | 1 5 | 20.0 40.0 40.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000993 | 1 9 | 33.3 66.7 0.0 11.1 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000994 | 1 11 | 45.5 54.5 0.0 18.2 72.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000995 | 1 6 | 16.7 33.3 50.0 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000996 | 1 7 | 14.3 57.1 28.6 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000997 | 1 6 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000998 | 1 6 | 33.3 50.0 16.7 16.7 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000999 | 1 7 | 28.6 71.4 0.0 14.3 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001000 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001001 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001002 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001003 | 1 8 | 37.5 62.5 0.0 12.5 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001004 | 1 7 | 28.6 42.9 28.6 14.3 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001006 | 1 6 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001007 | 1 7 | 57.1 28.6 14.3 0.0 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001008 | 1 7 | 28.6 57.1 14.3 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001009 | 1 7 | 0.0 57.1 42.9 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001010 | 1 8 | 25.0 75.0 0.0 37.5 112.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001011 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001012 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001013 | 1 7 | 0.0 42.9 57.1 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001014 | 1 5 | 80.0 20.0 0.0 0.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001015 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001016 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001017 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001018 | 1 9 | 33.3 33.3 33.3 11.1 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001019 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001020 | 1 6 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000309 | 1 16 | 6.3 62.5 31.3 0.0 93.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000310 | 1 14 | 0.0 71.4 28.6 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000311 | 1 10 | 10.0 60.0 30.0 0.0 90.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000312 | 1 11 | 9.1 81.8 9.1 0.0 90.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000313 | 1 19 | 0.0 63.2 36.8 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000314 | 1 20 | 15.0 50.0 35.0 0.0 85.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000315 | 1 12 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000316 | 1 11 | 9.1 27.3 63.6 0.0 90.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000317 | 1 26 | 3.8 80.8 15.4 3.8 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000318 | 1 11 | 9.1 81.8 9.1 0.0 90.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000319 | 1 17 | 29.4 64.7 5.9 0.0 70.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000320 | 1 12 | 8.3 75.0 16.7 0.0 91.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000321 | 1 13 | 23.1 69.2 7.7 15.4 92.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000322 | 1 15 | 13.3 60.0 26.7 0.0 86.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000323 | 1 27 | 14.8 63.0 22.2 7.4 92.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000324 | 1 19 | 15.8 63.2 21.1 0.0 84.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000325 | 1 6 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000326 | 1 14 | 7.1 71.4 21.4 7.1 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000327 | 1 14 | 7.1 78.6 14.3 7.1 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000328 | 1 23 | 26.1 60.9 13.0 8.7 82.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000329 | 1 5 | 0.0 40.0 60.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000330 | 1 10 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000331 | 1 51 | 13.7 60.8 25.5 2.0 88.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000332 | 1 19 | 21.1 47.4 31.6 0.0 78.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000333 | 1 33 | 15.2 60.6 24.2 3.0 87.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000334 | 1 16 | 43.8 56.3 0.0 12.5 68.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000335 | 1 67 | 3.0 61.2 35.8 0.0 97.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000336 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000337 | 1 23 | 4.3 60.9 34.8 0.0 95.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000338 | 1 8 | 0.0 87.5 12.5 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000339 | 1 25 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000340 | 1 6 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000341 | 1 8 | 37.5 25.0 37.5 0.0 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000342 | 1 11 | 27.3 63.6 9.1 9.1 81.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000343 | 1 20 | 10.0 85.0 5.0 0.0 90.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000344 | 1 25 | 28.0 64.0 8.0 12.0 84.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000345 | 1 22 | 4.5 77.3 18.2 0.0 95.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000346 | 1 8 | 25.0 62.5 12.5 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000347 | 1 14 | 0.0 92.9 7.1 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000348 | 1 17 | 5.9 82.4 11.8 0.0 94.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000349 | 1 6 | 16.7 83.3 0.0 66.7 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000350 | 1 8 | 0.0 62.5 37.5 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000351 | 1 47 | 23.4 61.7 14.9 4.3 80.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000352 | 1 25 | 12.0 68.0 20.0 0.0 88.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000353 | 1 11 | 27.3 72.7 0.0 18.2 90.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000354 | 1 13 | 23.1 69.2 7.7 0.0 76.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000355 | 1 26 | 7.7 50.0 42.3 0.0 92.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000356 | 1 2 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000357 | 1 16 | 18.8 68.8 12.5 6.3 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000358 | 1 16 | 6.3 50.0 43.8 0.0 93.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000359 | 1 11 | 0.0 81.8 18.2 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000360 | 1 51 | 11.8 56.9 31.4 2.0 90.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000361 | 1 7 | 28.6 57.1 14.3 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000362 | 1 25 | 16.0 56.0 28.0 4.0 88.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000363 | 1 13 | 23.1 46.2 30.8 0.0 76.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000364 | 1 22 | 4.5 86.4 9.1 0.0 95.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000365 | 1 21 | 4.8 81.0 14.3 0.0 95.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000366 | 1 8 | 12.5 87.5 0.0 62.5 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000367 | 1 35 | 14.3 74.3 11.4 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000368 | 1 28 | 3.6 60.7 35.7 0.0 96.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000369 | 1 23 | 13.0 69.6 17.4 0.0 87.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000370 | 1 19 | 10.5 63.2 26.3 0.0 89.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000371 | 1 6 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000372 | 1 16 | 18.8 62.5 18.8 0.0 81.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000373 | 1 23 | 8.7 73.9 17.4 0.0 91.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000374 | 1 14 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000375 | 1 10 | 10.0 20.0 70.0 0.0 90.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000376 | 1 14 | 21.4 64.3 14.3 0.0 78.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000377 | 1 27 | 18.5 51.9 29.6 7.4 88.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000378 | 1 18 | 22.2 72.2 5.6 22.2 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000379 | 1 41 | 22.0 58.5 19.5 2.4 80.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000380 | 1 32 | 12.5 65.6 21.9 3.1 90.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000381 | 1 8 | 12.5 50.0 37.5 12.5 100.0 100.0 | +|=================================================================================================================| +| Sum/Avg | 661 8099 | 19.5 62.4 18.1 4.9 85.4 100.0 | +|=================================================================================================================| +| Mean | 1.2 14.1 | 20.3 64.5 15.2 9.0 88.8 100.0 | +| S.D. | 3.7 58.8 | 17.1 18.2 16.3 21.9 27.4 0.0 | +| Median | 1.0 8.0 | 18.8 63.6 12.5 0.0 87.5 100.0 | +`-----------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,-----------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/hyp.trn| +|-----------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------------+---------------------+----------------------------------------------------------------| +| m | 89 1395 | 310 850 235 49 1134 89 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000698 | 1 8 | 0 6 2 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000699 | 1 5 | 0 1 4 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000700 | 1 8 | 0 8 0 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000701 | 1 12 | 1 10 1 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000702 | 1 8 | 0 6 2 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000703 | 1 10 | 1 9 0 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000704 | 1 1 | 0 1 0 2 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000705 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000706 | 1 12 | 4 8 0 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000707 | 1 4 | 2 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000708 | 1 11 | 3 7 1 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000709 | 1 8 | 3 5 0 4 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000710 | 1 11 | 2 8 1 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000711 | 1 13 | 4 9 0 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000712 | 1 6 | 0 3 3 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000713 | 1 7 | 0 5 2 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000714 | 1 13 | 0 7 6 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000715 | 1 11 | 0 9 2 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000716 | 1 10 | 1 7 2 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000717 | 1 13 | 4 6 3 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000718 | 1 8 | 1 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000719 | 1 8 | 1 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000720 | 1 9 | 0 6 3 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000721 | 1 6 | 0 3 3 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000722 | 1 6 | 0 4 2 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000723 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000724 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000725 | 1 13 | 1 7 5 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000726 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000727 | 1 7 | 1 5 1 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000728 | 1 6 | 0 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000729 | 1 6 | 0 3 3 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000730 | 1 14 | 3 11 0 2 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000731 | 1 8 | 2 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000732 | 1 7 | 1 4 2 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000733 | 1 12 | 1 9 2 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000734 | 1 10 | 1 9 0 4 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000735 | 1 5 | 0 3 2 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000736 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000737 | 1 12 | 0 9 3 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000738 | 1 11 | 3 6 2 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000739 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000740 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000741 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000742 | 1 13 | 3 9 1 2 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000743 | 1 4 | 1 3 0 3 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000744 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000745 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000746 | 1 9 | 0 6 3 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000747 | 1 10 | 0 8 2 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000748 | 1 4 | 1 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000749 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000750 | 1 13 | 3 7 3 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000751 | 1 12 | 0 6 6 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000752 | 1 12 | 1 8 3 1 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000753 | 1 12 | 3 6 3 2 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000754 | 1 7 | 0 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000755 | 1 13 | 3 9 1 5 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000756 | 1 9 | 4 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000757 | 1 12 | 2 7 3 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000758 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000759 | 1 9 | 1 8 0 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000760 | 1 7 | 2 5 0 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000761 | 1 12 | 3 9 0 3 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000762 | 1 9 | 2 5 2 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000763 | 1 12 | 0 8 4 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000764 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000765 | 1 11 | 1 9 1 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000766 | 1 8 | 1 7 0 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000767 | 1 9 | 0 5 4 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000768 | 1 11 | 0 5 6 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000769 | 1 3 | 0 1 2 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000770 | 1 14 | 3 10 1 2 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000771 | 1 10 | 1 8 1 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000772 | 1 6 | 0 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000773 | 1 12 | 5 6 1 3 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000774 | 1 13 | 1 8 4 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000775 | 1 4 | 1 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000776 | 1 10 | 5 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000777 | 1 8 | 0 5 3 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000778 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000779 | 1 11 | 1 10 0 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000780 | 1 11 | 3 7 1 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000781 | 1 10 | 1 9 0 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000782 | 1 8 | 2 6 0 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000783 | 1 14 | 7 7 0 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000784 | 1 12 | 6 5 1 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000785 | 1 14 | 4 5 5 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000786 | 1 12 | 3 9 0 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000787 | 1 12 | 2 8 2 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000788 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000789 | 1 6 | 1 3 2 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000790 | 1 12 | 2 8 2 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000791 | 1 10 | 0 5 5 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000792 | 1 9 | 0 9 0 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000793 | 1 8 | 1 7 0 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000794 | 1 7 | 0 3 4 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000795 | 1 6 | 3 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000796 | 1 14 | 1 12 1 3 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000797 | 1 12 | 0 11 1 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000798 | 1 9 | 3 5 1 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000799 | 1 9 | 2 7 0 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000800 | 1 6 | 1 3 2 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_deu_000801 | 1 14 | 3 8 3 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000378 | 1 26 | 7 15 4 2 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000379 | 1 31 | 9 21 1 5 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000380 | 1 16 | 3 13 0 3 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000381 | 1 9 | 3 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000382 | 1 26 | 3 20 3 0 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000383 | 1 18 | 0 16 2 0 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000384 | 1 27 | 7 14 6 1 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000385 | 1 17 | 3 7 7 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000386 | 1 31 | 8 17 6 3 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000387 | 1 30 | 4 14 12 0 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000388 | 1 44 | 3 29 12 0 41 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000389 | 1 31 | 4 19 8 0 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000390 | 1 27 | 9 14 4 3 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000391 | 1 17 | 4 9 4 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000392 | 1 33 | 1 24 8 0 32 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000393 | 1 22 | 3 13 6 0 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000394 | 1 23 | 5 17 1 3 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000395 | 1 23 | 2 18 3 1 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000396 | 1 35 | 4 24 7 2 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000397 | 1 23 | 8 14 1 5 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000398 | 1 11 | 2 5 4 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000399 | 1 16 | 3 10 3 1 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000400 | 1 20 | 5 9 6 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000401 | 1 23 | 5 12 6 0 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000402 | 1 23 | 5 16 2 2 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000403 | 1 22 | 3 19 0 6 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000404 | 1 16 | 2 14 0 3 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000405 | 1 21 | 2 10 9 0 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000406 | 1 20 | 2 16 2 0 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000407 | 1 31 | 9 20 2 5 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000408 | 1 15 | 4 11 0 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000409 | 1 17 | 3 10 4 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000410 | 1 34 | 6 24 4 1 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000411 | 1 21 | 2 9 10 0 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000412 | 1 24 | 4 18 2 0 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000413 | 1 24 | 3 14 7 0 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000414 | 1 22 | 2 20 0 0 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_deu_000415 | 1 29 | 1 23 5 0 28 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000281 | 1 32 | 6 22 4 0 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000282 | 1 31 | 9 15 7 0 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000283 | 1 50 | 8 22 20 0 42 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000284 | 1 47 | 7 25 15 0 40 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000285 | 1 26 | 0 24 2 0 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000286 | 1 33 | 11 18 4 3 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000287 | 1 48 | 5 29 14 0 43 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000288 | 1 43 | 3 22 18 0 40 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000289 | 1 34 | 5 19 10 0 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000290 | 1 27 | 3 13 11 0 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000291 | 1 39 | 5 26 8 0 34 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000292 | 1 65 | 5 43 17 0 60 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000293 | 1 55 | 13 32 10 1 43 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000294 | 1 27 | 1 13 13 0 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000295 | 1 31 | 15 16 0 1 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000296 | 1 40 | 9 22 9 0 31 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000297 | 1 54 | 5 36 13 1 50 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000298 | 1 39 | 3 24 12 0 36 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000299 | 1 39 | 4 25 10 0 35 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000300 | 1 25 | 4 16 5 0 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000301 | 1 40 | 4 25 11 0 36 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000302 | 1 57 | 9 27 21 0 48 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000303 | 1 50 | 23 21 6 3 30 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000304 | 1 30 | 3 18 9 0 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000305 | 1 24 | 0 14 10 0 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000306 | 1 62 | 9 33 20 0 53 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000307 | 1 35 | 6 26 3 0 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000308 | 1 42 | 8 24 10 0 34 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000309 | 1 41 | 7 26 8 0 34 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000310 | 1 32 | 7 19 6 0 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000311 | 1 35 | 1 23 11 0 34 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000312 | 1 46 | 9 30 7 0 37 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000313 | 1 24 | 6 14 4 3 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000314 | 1 32 | 6 24 2 0 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000315 | 1 28 | 15 7 6 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000316 | 1 28 | 6 21 1 6 28 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000317 | 1 22 | 4 11 7 1 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000318 | 1 37 | 10 18 9 1 28 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_deu_000319 | 1 18 | 5 10 3 1 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001408 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001409 | 1 6 | 2 4 0 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001410 | 1 7 | 3 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001411 | 1 4 | 2 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001412 | 1 8 | 2 5 1 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001413 | 1 4 | 3 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001414 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001415 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001416 | 1 2 | 1 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001417 | 1 6 | 0 3 3 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001418 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001419 | 1 8 | 3 2 3 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001420 | 1 10 | 1 7 2 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001421 | 1 12 | 0 7 5 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001422 | 1 5 | 1 2 2 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001423 | 1 5 | 2 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001424 | 1 5 | 3 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001425 | 1 21 | 6 14 1 3 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001426 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001427 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001428 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001429 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001430 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001431 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001432 | 1 2 | 0 2 0 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001433 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001434 | 1 13 | 5 8 0 3 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001435 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001436 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001437 | 1 9 | 5 4 0 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001438 | 1 7 | 0 5 2 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001439 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001440 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001441 | 1 9 | 4 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001442 | 1 13 | 5 6 2 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001443 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001444 | 1 6 | 2 2 2 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001445 | 1 6 | 3 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001446 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001447 | 1 2 | 0 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001448 | 1 8 | 3 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001449 | 1 9 | 1 5 3 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001450 | 1 10 | 2 8 0 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001451 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001452 | 1 5 | 3 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001453 | 1 7 | 2 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001454 | 1 16 | 5 9 2 1 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001455 | 1 8 | 2 4 2 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001456 | 1 1 | 0 1 0 2 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001457 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001458 | 1 23 | 8 15 0 3 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001459 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001460 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001461 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001462 | 1 11 | 2 9 0 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001463 | 1 9 | 3 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001464 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001465 | 1 9 | 2 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001466 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001467 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001468 | 1 6 | 3 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001469 | 1 4 | 2 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001470 | 1 2 | 0 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001471 | 1 2 | 1 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001472 | 1 12 | 4 7 1 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001473 | 1 6 | 2 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001474 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001475 | 1 7 | 2 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001476 | 1 8 | 1 5 2 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001477 | 1 3 | 2 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001478 | 1 7 | 0 5 2 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001479 | 1 5 | 0 2 3 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001480 | 1 4 | 2 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001481 | 1 11 | 0 7 4 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001482 | 1 4 | 0 4 0 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001483 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001484 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001485 | 1 17 | 5 10 2 3 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001486 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001487 | 1 4 | 0 2 2 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001488 | 1 8 | 3 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001489 | 1 8 | 2 4 2 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001490 | 1 6 | 2 4 0 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001491 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001492 | 1 7 | 2 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001493 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001494 | 1 6 | 3 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001495 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001496 | 1 9 | 2 5 2 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001497 | 1 9 | 8 1 0 2 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001498 | 1 7 | 2 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001499 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001500 | 1 8 | 3 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001501 | 1 10 | 3 5 2 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001502 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001503 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001504 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001505 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001506 | 1 12 | 7 5 0 4 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001507 | 1 8 | 3 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001508 | 1 4 | 0 2 2 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001509 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001510 | 1 5 | 2 3 0 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001511 | 1 7 | 3 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001512 | 1 14 | 4 8 2 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001513 | 1 6 | 3 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001514 | 1 11 | 2 9 0 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001515 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001516 | 1 5 | 3 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001517 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001518 | 1 9 | 3 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001519 | 1 7 | 2 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001520 | 1 8 | 4 4 0 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001521 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001522 | 1 6 | 3 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001523 | 1 4 | 2 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001524 | 1 6 | 3 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001525 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001526 | 1 3 | 2 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001527 | 1 3 | 0 1 2 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001528 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001529 | 1 3 | 1 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001530 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001531 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001532 | 1 7 | 3 2 2 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001533 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001534 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001535 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001536 | 1 1 | 0 1 0 2 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001537 | 1 2 | 1 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001538 | 1 7 | 2 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001539 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001540 | 1 8 | 2 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001541 | 1 2 | 1 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001542 | 1 8 | 3 3 2 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001543 | 1 18 | 5 13 0 2 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001544 | 1 10 | 3 5 2 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001545 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001546 | 1 10 | 2 6 2 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001547 | 1 8 | 0 8 0 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001548 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001549 | 1 4 | 0 2 2 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001550 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001551 | 1 1 | 0 1 0 2 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001552 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001553 | 1 20 | 5 14 1 1 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001554 | 1 14 | 2 8 4 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001555 | 1 7 | 0 4 3 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001556 | 1 9 | 2 4 3 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001557 | 1 2 | 1 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001558 | 1 6 | 3 3 0 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001559 | 1 11 | 2 7 2 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001560 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001561 | 1 7 | 2 5 0 6 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001562 | 1 12 | 3 6 3 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001563 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001564 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001565 | 1 10 | 2 7 1 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001566 | 1 7 | 1 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001567 | 1 2 | 1 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001568 | 1 12 | 6 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001569 | 1 8 | 5 3 0 3 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001570 | 1 7 | 1 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001571 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001572 | 1 10 | 4 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001573 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001574 | 1 8 | 3 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001575 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001576 | 1 10 | 2 7 1 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001577 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001578 | 1 15 | 5 9 1 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001579 | 1 19 | 6 11 2 1 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001580 | 1 5 | 1 2 2 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001581 | 1 6 | 4 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001582 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001583 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001584 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001585 | 1 13 | 4 6 3 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001586 | 1 7 | 0 4 3 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001587 | 1 5 | 3 2 0 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001588 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001589 | 1 4 | 0 2 2 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001590 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001591 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001592 | 1 12 | 1 7 4 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001593 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001594 | 1 8 | 4 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001595 | 1 2 | 1 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001596 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001597 | 1 10 | 1 8 1 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001598 | 1 18 | 5 10 3 4 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_deu_001599 | 1 23 | 3 17 3 2 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000891 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000892 | 1 10 | 5 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000893 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000894 | 1 6 | 2 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000895 | 1 7 | 4 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000897 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000898 | 1 5 | 2 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000899 | 1 6 | 1 3 2 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000900 | 1 8 | 1 3 4 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000901 | 1 7 | 2 2 3 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000902 | 1 5 | 2 2 1 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000903 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000904 | 1 7 | 3 2 2 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000905 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000906 | 1 8 | 0 6 2 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000907 | 1 5 | 3 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000908 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000909 | 1 8 | 1 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000910 | 1 9 | 3 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000911 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000912 | 1 8 | 2 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000913 | 1 8 | 4 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000914 | 1 8 | 1 6 1 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000915 | 1 10 | 0 8 2 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000917 | 1 6 | 1 3 2 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000918 | 1 3 | 0 1 2 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000919 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000920 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000921 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000922 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000923 | 1 13 | 1 7 5 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000924 | 1 5 | 0 3 2 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000925 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000926 | 1 7 | 1 4 2 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000927 | 1 7 | 2 3 2 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000928 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000929 | 1 8 | 1 7 0 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000930 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000931 | 1 6 | 2 4 0 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000932 | 1 8 | 2 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000933 | 1 6 | 2 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000934 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000935 | 1 7 | 4 3 0 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000936 | 1 11 | 2 6 3 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000937 | 1 8 | 2 5 1 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000938 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000939 | 1 4 | 1 3 0 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000940 | 1 7 | 2 5 0 3 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000941 | 1 7 | 0 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000942 | 1 8 | 0 6 2 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000943 | 1 8 | 5 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000944 | 1 5 | 1 3 1 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000945 | 1 5 | 4 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000946 | 1 9 | 3 4 2 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000947 | 1 9 | 2 7 0 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000948 | 1 6 | 1 3 2 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000950 | 1 6 | 3 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000951 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000952 | 1 5 | 2 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000953 | 1 10 | 5 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000954 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000955 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000956 | 1 12 | 3 6 3 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000957 | 1 7 | 2 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000958 | 1 10 | 3 7 0 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000959 | 1 8 | 2 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000960 | 1 7 | 1 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000961 | 1 4 | 2 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000962 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000963 | 1 4 | 2 2 0 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000964 | 1 6 | 2 2 2 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000965 | 1 6 | 0 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000966 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000967 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000968 | 1 5 | 0 2 3 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000969 | 1 6 | 5 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000970 | 1 7 | 1 2 4 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000971 | 1 7 | 4 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000972 | 1 9 | 3 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000973 | 1 13 | 1 7 5 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000974 | 1 4 | 2 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000975 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000976 | 1 11 | 1 9 1 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000977 | 1 4 | 1 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000978 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000979 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000980 | 1 8 | 3 3 2 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000981 | 1 11 | 2 5 4 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000982 | 1 8 | 2 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000983 | 1 9 | 1 6 2 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000984 | 1 10 | 3 3 4 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000985 | 1 7 | 0 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000986 | 1 7 | 1 4 2 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000987 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000988 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000989 | 1 6 | 3 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000990 | 1 7 | 2 3 2 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000991 | 1 8 | 3 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000992 | 1 5 | 1 2 2 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000993 | 1 9 | 3 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000994 | 1 11 | 5 6 0 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000995 | 1 6 | 1 2 3 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000996 | 1 7 | 1 4 2 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000997 | 1 6 | 2 4 0 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000998 | 1 6 | 2 3 1 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_000999 | 1 7 | 2 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001000 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001001 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001002 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001003 | 1 8 | 3 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001004 | 1 7 | 2 3 2 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001006 | 1 6 | 0 3 3 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001007 | 1 7 | 4 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001008 | 1 7 | 2 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001009 | 1 7 | 0 4 3 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001010 | 1 8 | 2 6 0 3 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001011 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001012 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001013 | 1 7 | 0 3 4 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001014 | 1 5 | 4 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001015 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001016 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001017 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001018 | 1 9 | 3 3 3 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001019 | 1 7 | 1 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_deu_001020 | 1 6 | 0 4 2 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000309 | 1 16 | 1 10 5 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000310 | 1 14 | 0 10 4 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000311 | 1 10 | 1 6 3 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000312 | 1 11 | 1 9 1 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000313 | 1 19 | 0 12 7 0 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000314 | 1 20 | 3 10 7 0 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000315 | 1 12 | 2 10 0 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000316 | 1 11 | 1 3 7 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000317 | 1 26 | 1 21 4 1 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000318 | 1 11 | 1 9 1 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000319 | 1 17 | 5 11 1 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000320 | 1 12 | 1 9 2 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000321 | 1 13 | 3 9 1 2 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000322 | 1 15 | 2 9 4 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000323 | 1 27 | 4 17 6 2 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000324 | 1 19 | 3 12 4 0 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000325 | 1 6 | 0 4 2 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000326 | 1 14 | 1 10 3 1 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000327 | 1 14 | 1 11 2 1 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000328 | 1 23 | 6 14 3 2 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000329 | 1 5 | 0 2 3 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000330 | 1 10 | 2 6 2 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000331 | 1 51 | 7 31 13 1 45 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000332 | 1 19 | 4 9 6 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000333 | 1 33 | 5 20 8 1 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000334 | 1 16 | 7 9 0 2 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000335 | 1 67 | 2 41 24 0 65 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000336 | 1 6 | 0 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000337 | 1 23 | 1 14 8 0 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000338 | 1 8 | 0 7 1 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000339 | 1 25 | 5 15 5 0 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000340 | 1 6 | 0 4 2 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000341 | 1 8 | 3 2 3 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000342 | 1 11 | 3 7 1 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000343 | 1 20 | 2 17 1 0 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000344 | 1 25 | 7 16 2 3 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000345 | 1 22 | 1 17 4 0 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000346 | 1 8 | 2 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000347 | 1 14 | 0 13 1 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000348 | 1 17 | 1 14 2 0 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000349 | 1 6 | 1 5 0 4 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000350 | 1 8 | 0 5 3 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000351 | 1 47 | 11 29 7 2 38 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000352 | 1 25 | 3 17 5 0 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000353 | 1 11 | 3 8 0 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000354 | 1 13 | 3 9 1 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000355 | 1 26 | 2 13 11 0 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000356 | 1 2 | 0 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000357 | 1 16 | 3 11 2 1 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000358 | 1 16 | 1 8 7 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000359 | 1 11 | 0 9 2 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000360 | 1 51 | 6 29 16 1 46 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000361 | 1 7 | 2 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000362 | 1 25 | 4 14 7 1 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000363 | 1 13 | 3 6 4 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000364 | 1 22 | 1 19 2 0 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000365 | 1 21 | 1 17 3 0 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000366 | 1 8 | 1 7 0 5 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000367 | 1 35 | 5 26 4 0 30 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000368 | 1 28 | 1 17 10 0 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000369 | 1 23 | 3 16 4 0 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000370 | 1 19 | 2 12 5 0 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000371 | 1 6 | 0 3 3 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000372 | 1 16 | 3 10 3 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000373 | 1 23 | 2 17 4 0 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000374 | 1 14 | 0 14 0 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000375 | 1 10 | 1 2 7 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000376 | 1 14 | 3 9 2 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000377 | 1 27 | 5 14 8 2 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000378 | 1 18 | 4 13 1 4 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000379 | 1 41 | 9 24 8 1 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000380 | 1 32 | 4 21 7 1 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_deu_000381 | 1 8 | 1 4 3 1 8 1 | +|=================================================================================================================| +| Sum | 661 8099 | 1580 5053 1466 395 6914 661 | +|=================================================================================================================| +| Mean | 1.2 14.1 | 2.8 8.8 2.6 0.7 12.1 1.2 | +| S.D. | 3.7 58.8 | 13.1 35.8 10.3 2.3 47.9 3.7 | +| Median | 1.0 8.0 | 2.0 5.0 1.0 0.0 7.0 1.0 | +`-----------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/score_wer/hyp.trn + +Speakers: + 0: m + 1: cv_deu_000698 + 2: cv_deu_000699 + 3: cv_deu_000700 + 4: cv_deu_000701 + 5: cv_deu_000702 + 6: cv_deu_000703 + 7: cv_deu_000704 + 8: cv_deu_000705 + 9: cv_deu_000706 + 10: cv_deu_000707 + 11: cv_deu_000708 + 12: cv_deu_000709 + 13: cv_deu_000710 + 14: cv_deu_000711 + 15: cv_deu_000712 + 16: cv_deu_000713 + 17: cv_deu_000714 + 18: cv_deu_000715 + 19: cv_deu_000716 + 20: cv_deu_000717 + 21: cv_deu_000718 + 22: 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#S #D #I) 11 14 2 2 +REF: ERST um acht UHR war er auf male BRACHTE den KAFFEE die SONNE SCHIEN INS ZIMMER und DIE SPERLINGE die DAS AUS den ***** ****** HÄCKSELSÄCKEN GEFALLENE FUTTERKORN AUFPICKTEN +HYP: ERS um acht UR war er auf male BRCHTER den KAFI die ***** SONDESCHIEN INZS ZIMER und IE SPÄHRLINGE die *** DSSAUS den HECSE SECKEN GEFALNE FOTEAKON AUF BIKTEN +Eval: S S S S D S S S S S D S I I S S S S + +id: (m-ailabs_deu_000168-m-ailabs_deu_000168) +Scores: (#C #S #D #I) 4 4 2 0 +REF: SICHERLICH an IHREM GEBURTSTAG HÄTTE er bei IHR bleiben KÖNNEN +HYP: SSICHERLICH an ***** ********** IHRNGEBORTZTACKHÄTE er bei IE bleiben KÖNENT +Eval: S D D S S S + +id: (m-ailabs_deu_000169-m-ailabs_deu_000169) +Scores: (#C #S #D #I) 0 14 1 0 +REF: UND DESHALB MUSS MAN DORT WO MENSCHEN SCHWIERIGKEITEN HABEN DIES AUCH EINERSEITS ERKLÄREN ANGEBOTE MACHEN +HYP: *** DERSALBÖM MUS AN DAUORT WUOR MENTCHENSCHIRICGKEITENHABEN DIES OUCH EINER SEITS ERKLÄEREN ANGEBOTEMACHEN WRALE I +Eval: D S S S S S S S S S S S S S S + +id: (m-ailabs_deu_000170-m-ailabs_deu_000170) +Scores: (#C #S #D #I) 2 13 5 0 +REF: DASS MAN NUR AUF DIE WELT KOMMT um SELBST WIEDER EINEN SOHN ZU HABEN DER DIE VEREHRUNG der AHNEN FORTSETZT +HYP: **** *** *** UES MANRAF DIVELT KOMT um ****** ****** SEBSTIEDER IN SONDZUOHARBEN DE DI VER EHUNK der ANEN VORTETZTNN +Eval: D D D S S S S D D S S S S S S S S S + +id: (m-ailabs_deu_000171-m-ailabs_deu_000171) +Scores: (#C #S #D #I) 5 13 0 1 +REF: * DESHALB GEHÖREN KONTINUIERLICHE schulbildung AUCH KONTINUIERLICHE MÖGLICHKEITEN der WEITERBILDUNG und das BEGEHEN von GEDENKTAGEN FÜR MICH UNAUFLÖSLICH ZUSAMMEN +HYP: A BÜRN PUNZEN ELICHER schulbildung RUNTEN ELICHE MÖGLICHTEITRAUH der WEITERBILUNG und das EGEHN von GE DENKTAGEN EMICH UN AUFLSLICHTRAME +Eval: I S S S S S S S S S S S S S + +id: (m-ailabs_deu_000172-m-ailabs_deu_000172) +Scores: (#C #S #D #I) 3 18 6 0 +REF: MEIN ANSASCHEN SAGT SIE ES IST JA JETZT wieder GANZ GUT ZWISCHEN UNS aber EHE DU NICHT ALLES GESTEHST GEHT DIE ERINNERUNG AN DAS BÖSE nicht WEG +HYP: **** EIN ANSAS IERN SAKZI S ISTJER GET wieder **** *** GANSKUT ZWISCHENUNS aber *** ** ***** IER DUNICHT ALESGESTIEST GETIE ER INRUNG ANDAS BÜSE nicht WEH +Eval: D S S S S S S S D D S S D D D S S S S S S S S S + +id: (m-ailabs_deu_000173-m-ailabs_deu_000173) +Scores: (#C #S #D #I) 0 2 3 0 +REF: NEIN WEIBER BRAUCHE ICH NICHT +HYP: **** ****** ******* DNEIN WEIBERBRAUCHRICHEICHTT +Eval: D D D S S + +id: (m-ailabs_deu_000174-m-ailabs_deu_000174) +Scores: (#C #S #D #I) 2 6 2 0 +REF: GOTT HAT NICHT VERGEBLICH NACH MIR gerufen SAGTE der SCHIFFER +HYP: **** *** TENDEN GOT HATNICH VERGEBLICHENEHME gerufen SAKTE der CHIVER +Eval: D D S S S S S S + +id: (m-ailabs_deu_000175-m-ailabs_deu_000175) +Scores: (#C #S #D #I) 11 10 2 2 +REF: nur eines WEISS ICH dieser furchtbaren ******* FRAGE ENTGEGENZUSETZEN und SCHLEUDERE DAS WORT in die WAAGSCHALE die glut ****** MEINES LIEBESWILLENS ist STÄRKER als TRENNUNG +HYP: nur eines ***** WEISSICH dieser furchtbaren FRAGENT GEGEN ZUSETZEN und ********** SCHLEUDERERDARS WART in die WARKSCHAL die glut LEINES LIEBES WILENDZ ist STERKAR als TRENUNGT +Eval: D S I S S D S S S I S S S S + +id: (m-ailabs_deu_000176-m-ailabs_deu_000176) +Scores: (#C #S #D #I) 1 8 2 0 +REF: TOMS ARMEE GEWANN EINEN GROSSEN sieg NACH EINER LANGEN HARTNÄCKIGEN SCHLACHT +HYP: **** ***** DTOMS AMEIGE WANEINGROSSEN sieg NCHENER LANGNG HARDN NECKEGEN SCHLCHTN +Eval: D D S S S S S S S S + +id: (m-ailabs_deu_000177-m-ailabs_deu_000177) +Scores: (#C #S #D #I) 3 9 5 1 +REF: ES IST EIN NAME dem SICH DIE TÜR bei TAG UND NACHT ÖFFNEN KANN BURSCHE und *** WILLKOMMEN +HYP: ** *** SOS EINAHME dem **** SICHTI ÜR bei *** *** TAKUN NCHTAFFNAN KAN BRESCHER und WEL KOMMEN +Eval: D D S S D S S D D S S S S I S + +id: (m-ailabs_deu_000178-m-ailabs_deu_000178) +Scores: (#C #S #D #I) 0 6 0 2 +REF: ** ***** ABER ICH VERZEIHE IHNEN IHRE UNWISSENHEIT +HYP: EN ARBER ICHVERT SEIE INEN IHRER UNWISSEN HEITN +Eval: I I S S S S S S + +id: (m-ailabs_deu_000179-m-ailabs_deu_000179) +Scores: (#C #S #D #I) 6 7 3 0 +REF: VON der DRITTEN UNTERREDUNG an sagte MISTER HAVISHAM WAR MIR die person in HOHEM MASSE VERDÄCHTIG +HYP: UFON der TRITEN UNTEREDUNG an sagte ****** ******** MISTERHEVESCHEM WARMER die person in ***** HOHREMASER VERDECHTIGHTNN +Eval: S S S D D S S D S S + +id: (m-ailabs_deu_000180-m-ailabs_deu_000180) +Scores: (#C #S #D #I) 6 15 4 0 +REF: ich denke DER AMTMANN und SEINE FAMILIE WERDEN ES RECHT von DIR finden DASS DU DICH SELBST ANGIBST UND SIE WERDEN FREUNDLICH gegen DICH SEIN +HYP: ich denke DE MTNEAR und ***** ******* SANE VERMIE WERDNESRECHT von DER finden **** DASTU DIE SELBT ANGIEBST UN SE VRDEN FREUNTLICH gegen **** DIESEIT +Eval: S S D D S S S S D S S S S S S S S D S + +id: (m-ailabs_deu_000181-m-ailabs_deu_000181) +Scores: (#C #S #D #I) 7 11 2 0 +REF: JETZT schlug die HELLE FLAMME auf und nun ERKANNTE er UNS DIE WIR NOCH IMMER ZUSAMMENGEDRÄNGT IN dem WINKEL STANDEN +HYP: ETZS schlug die HLLE FLAMER auf und nun ******** er KANTE ER UNDS DIEVWE NCH IMERZUSAMEN GEDRENGTDN dem ****** WINKELSTANDEN +Eval: S S S D S S S S S S S D S + +id: (m-ailabs_deu_000182-m-ailabs_deu_000182) +Scores: (#C #S #D #I) 3 5 0 0 +REF: der seiner SEELE ANSPORNEND das ERMUNTERNDE WORT VORWÄRTS +HYP: der seiner SELE ANSPONEND das ER MUNTERNTERWAUT VORWERLTZS +Eval: S S S S S + +id: (m-ailabs_deu_000183-m-ailabs_deu_000183) +Scores: (#C #S #D #I) 1 6 2 0 +REF: ICH FREUE MICH AUF den BESUCH DES TUNESISCHEN MINISTERPRÄSIDENTEN +HYP: *** ***** FOMIC AF den BESUCHTES TONESCHE MINISAPRESEDENTEN ONT +Eval: D D S S S S S S + +id: (m-ailabs_deu_000184-m-ailabs_deu_000184) +Scores: (#C #S #D #I) 2 8 2 0 +REF: WAS FÜR VERFOLGUNGEN was FÜR NACHSTELLUNGEN HABE ICH NICHT zu ERDULDEN GEHABT +HYP: DUWASFÜHR ER FOLGUNGEN was **** ************** VÜR NARCHSTELUNEN HABRIGNICH zu ADULEN GEHABTNN +Eval: S S S D D S S S S S + +id: (m-ailabs_deu_000185-m-ailabs_deu_000185) +Scores: (#C #S #D #I) 5 12 4 0 +REF: ZIGEUNER waren ES DIE von ORT ZU ORT FUHREN ein kaum ERWACHSENES JUNGES DING KAM ZU MIR HERANGEHÜPFT UND BETTELTE nein +HYP: ZSIGOEINER waren ** ESTI von *** AU ZUOLT FUREN ein kaum *********** ****** ARWACHNES IUNGES DINKAM ZUMIE HERANGEHÜBPFT UN BETELTE nein +Eval: S D S D S S S D D S S S S S S S + +id: (m-ailabs_deu_000186-m-ailabs_deu_000186) +Scores: (#C #S #D #I) 1 22 4 0 +REF: HUCK ich WERDE DICH IN ENEM BOOT HINFAHREN WERDE DAS BOOT DA ANLEGEN UND ES WIEDER ZURÜCKRUDERN ALLES GANZ ALLEIN BRAUCHST DICH GAR NICHT DRUM ZU KÜMMERN +HYP: A ich ***** **** ** **** WER IC INEM BOTHIN FAHN DE DS BOTER ANLENEND SI ER ZOEG DERN ALES GANZELLEIN US IE ANIC T UM ZUKÖMANTN +Eval: S D D D D S S S S S S S S S S S S S S S S S S S S S + +id: (m-ailabs_deu_000187-m-ailabs_deu_000187) +Scores: (#C #S #D #I) 11 7 2 0 +REF: als NUR einmal noch den rauch von SEINEM HAUSE AUS der FERNE aufsteigen ZU SEHEN um DANN BERUHIGT zu sterben +HYP: als NER einmal noch den rauch von ****** ANEM HAUSERAUS der FERNER aufsteigen ** ZUSEN um DANBER RIEGK zu sterben +Eval: S D S S S D S S S + +id: (m-ailabs_deu_000188-m-ailabs_deu_000188) +Scores: (#C #S #D #I) 6 9 1 0 +REF: die TÄNZERIN ABER LAG AUF den KNIEEN vor BRAHMAS BILDNIS in NAMENLOSER SEHNSUCHT und weinte JAMMERVOLL +HYP: die ********* TENZEREN ARBER LAKAUF den KNIEN vor BRAMAS BILTNIS in NAHMENLOSERSEHNSOCH T und weinte JAMERVOLL +Eval: D S S S S S S S S S + +id: (m-ailabs_deu_000189-m-ailabs_deu_000189) +Scores: (#C #S #D #I) 1 9 3 0 +REF: RECHTFERTIGT MICH DENN DIE WIRKLICHKEIT NOCH NICHT auf DIE ICH MICH BERUFEN KANN +HYP: ************ **** **** DECHT FERTICH MICHTENDI WERGLICHKEITENCHNICHT auf DIG IC BEG UFEN KARN +Eval: D D D S S S S S S S S S + +id: (m-ailabs_deu_000190-m-ailabs_deu_000190) +Scores: (#C #S #D #I) 3 8 3 1 +REF: ICH ÄRGERTE MICH DANN WENN ICH AUFWACHTE ES war SO WUNDERSCHÖN gewesen das ***** FLIEGEN +HYP: *** ******** DTICHE RGERTE MICHTANWENICH AUR FACHTER EIS war ** SOFUNDERSCHÖNEN gewesen das FLIEN NN +Eval: D D S S S S S S D S I S + +id: (m-ailabs_deu_000191-m-ailabs_deu_000191) +Scores: (#C #S #D #I) 3 16 2 0 +REF: NACHDEM ER SCHON den GANZEN VORMITTAG MIT IHM verbracht KAM STANHOPE NACH TISCH INS QUANDTSCHE HAUS um CASPAR LEBEWOHL ZU SAGEN +HYP: ******* NCH DEMESCHON den GANZSEN FORMITAG MI IM verbracht *** KAMS DEN HOB NACHTISCH IN SKRNSCHEHAUS um KASPALE WOL ZUS ERGENNN +Eval: D S S S S S S D S S S S S S S S S S + +id: (m-ailabs_deu_000192-m-ailabs_deu_000192) +Scores: (#C #S #D #I) 2 5 1 0 +REF: ER WAR ein alter HIRT VOLL MEDIZINISCHER GENINALITÄT +HYP: ** ERWA ein alter HIRHT VOLMEDIE ZINESCER GININALITET +Eval: D S S S S S + +id: (m-ailabs_deu_000193-m-ailabs_deu_000193) +Scores: (#C #S #D #I) 2 7 0 1 +REF: DASS WOHL AUCH der *** MIETER seine WUNDERLICHKEITEN HABEN MÜSSE +HYP: NTDAS VOL UCH der MIE TAR seine VONDELRICHGSKEITEN HARBEM SERN +Eval: S S S I S S S S + +id: (m-ailabs_deu_000194-m-ailabs_deu_000194) +Scores: (#C #S #D #I) 9 11 4 0 +REF: SIE SAHEN alle ÄNGSTLICH und BETRÜBT AUS und AUCH HERR ARNE SASS SCHWERMÜTIG da WIE DIE anderen und STÜTZTE DAS haupt in die HAND +HYP: E SIESAN alle ENGSTLICG und ******** BETRÜBTAUS und **** AUCHER ARENE SASCHWEHR MÖTICH da *** WIEDIE anderen und ******** STÜTZSTERDAS haupt in die HAN +Eval: S S S D S D S S S S D S D S S + +id: (m-ailabs_deu_000195-m-ailabs_deu_000195) +Scores: (#C #S #D #I) 10 12 1 2 +REF: unter den damen meist JUNGE FRISCHE GESICHTER UNTER den HERREN neben JUGENDLICHEN SOLCHE mit FALTIGER STIRN und BEREITS MEHR ODER minder **** ** MONDUMGLÄNZTEM schÄdel +HYP: unter den damen meist JIONGE FRSCHE GE SICHTAUNTER den HEREN neben JUENTLICHIEN ZSOUCHE mit FALTI ASTIERN und ******* BEREITZSMEHRA DER minder MOND UM GLENSTEM schÄdel +Eval: S S S S S S S S S D S S I I S + +id: (m-ailabs_deu_000196-m-ailabs_deu_000196) +Scores: (#C #S #D #I) 2 5 1 0 +REF: SEIT tagen schon HATTE ES BESONDERS DRÄUEND GEKLUNGEN +HYP: SEI tagen schon ***** HATE S BESONDERSTDREUEND GEKLUNGERT +Eval: S D S S S S + +id: (m-ailabs_deu_000197-m-ailabs_deu_000197) +Scores: (#C #S #D #I) 0 1 0 1 +REF: ****** SONDERBAR +HYP: SONDER BAR +Eval: I S + +id: (m-ailabs_deu_000198-m-ailabs_deu_000198) +Scores: (#C #S #D #I) 6 12 1 1 +REF: erb von ERBENHEIM stand mit ** SEINER GATTIN VOLL WEHMUT UND DANKBARKEIT AN der GRUFT AUF der ER EINEN MÄCHTIGEN +HYP: erb von ERBEMHEIM stand mit EN HR GATEN VOL WE MUOTUNG DANKGBARKEI DAN der GROFT CF der ** EIN MECHTINGNGN +Eval: S I S S S S S S S S S D S S + +id: (m-ailabs_deu_000199-m-ailabs_deu_000199) +Scores: (#C #S #D #I) 2 8 4 1 +REF: IHR WAR JEDER MENSCH EIN WUNDER und FAST ALLES WAS MENSCHEN taten ** ETWAS WUNDERBARES +HYP: *** *** TIER WARJIE DERMENSCHEIN UNDER und **** ***** FASTALLES WASMENSCHEN taten ET ASSWONDARBARES N +Eval: D D S S S S D D S S I S S + +id: (m-ailabs_deu_000200-m-ailabs_deu_000200) +Scores: (#C #S #D #I) 2 4 0 1 +REF: welche IHR WEG sie *** ENTLÄNGST FÜHRTE +HYP: welche JER WEIE sie END LENKSTFÜRT N +Eval: S S I S S + +id: (m-ailabs_deu_000201-m-ailabs_deu_000201) +Scores: (#C #S #D #I) 5 7 4 0 +REF: DIE WIRTIN SASS NICHT HINTER IHREM SCHANKTISCH und keiner IHRER dienstleute BEFAND SICH IN der stube +HYP: *** ****** IEWRTEN AS NICHTIENTE REM SCHANKTISC und keiner ERER dienstleute ****** **** BEFANDZEICHIN der stube +Eval: D D S S S S S S D D S + +id: (m-ailabs_deu_000202-m-ailabs_deu_000202) +Scores: (#C #S #D #I) 12 9 1 2 +REF: ALS die *** HERRSCHAFT aus der KIRCHE trat standen die leute ** UMHER um sie vorbeigehen ZU SEHEN und am KIRCHHOFTHORE WARTETE EIN MANN +HYP: NALS die HER SCHAFT aus der KILCHER trat standen die leute UM HEHR um sie vorbeigehen ** ZUSEHEN und am KIRLCHOF TORERWARTE E EINMANNN +Eval: S I S S I S D S S S S S + +id: (m-ailabs_deu_000203-m-ailabs_deu_000203) +Scores: (#C #S #D #I) 1 5 2 1 +REF: WAS MÜSSEN WIR TUN UM dem ******** TERRORISMUS ENTGEGENZUTRETEN +HYP: *** ******* ASMSEN ER TOHNOM dem TARISMUS EN GENTUTDE +Eval: D D S S S I S S + +id: (m-ailabs_deu_000204-m-ailabs_deu_000204) +Scores: (#C #S #D #I) 0 6 5 0 +REF: ICH GLAUBE DASS SIE ES GUT MIT MIR MEINEN HERR DOKTOR +HYP: *** ****** **** *** ** LCH GELAUER AS E SGUDENITBERMEINE HERDOKT +Eval: D D D D D S S S S S S + +id: (m-ailabs_deu_000205-m-ailabs_deu_000205) +Scores: (#C #S #D #I) 2 10 2 0 +REF: DOCH IM ANFANG GEWANN ER keine AUFMERKSAMKEIT FÜR ANDERE DINGE ALS FÜR DAS essen +HYP: **** EN TORIM ANFANK GEWANER keine ************** AUFMER AM KEIT VÜR ANDRERDINGE ALSFÜRDERS essen +Eval: D S S S S D S S S S S S + +id: (m-ailabs_deu_000206-m-ailabs_deu_000206) +Scores: (#C #S #D #I) 5 11 4 1 +REF: dies FLÄSCHCHEN ZOG ER JETZT EILIG HERVOR WÄHREND jene SICH mit WASSER FÜLLTEN und BOT ES der *** JUNGFER ZÜS AN +HYP: dies *********** *** FLÄSCHIEN ZOGERJETZT EILICHER VOR WEREND jene SC mit ****** WASSARFILTEN und *** BOTES der UNG VER ZYÜS ANN +Eval: D D S S S S S S D S D S I S S S + +id: (m-ailabs_deu_000207-m-ailabs_deu_000207) +Scores: (#C #S #D #I) 2 14 7 1 +REF: DESHALB WAR ES AUCH RICHTIG UND WICHTIG DASS CHINA DOCH JETZT ANSPRUCHSVOLL GESAGT HAT WIR WERDEN auch AN DEN ZEITPUNKT der ********* REDUKTION KOMMEN +HYP: ******* *** ** **** ******* *** ******* ESERWAS OCRICHTI ON WICHTICHTERS HINER DOCHERTZ ANSCPOSFOLLEL GESAKTAT WEWERDEN auch ANEN ZEIT PUNK der IDUKTIUON KOMMENDES DGÜ +Eval: D D D D D D D S S S S S S S S S S S S I S S + +id: (m-ailabs_deu_000208-m-ailabs_deu_000208) +Scores: (#C #S #D #I) 1 7 0 0 +REF: NICHT doch MUTTER WECKE SIE JETZT NOCH NICHT +HYP: DNICH doch MUTER WERKE SE JERZT NCHNIG N +Eval: S S S S S S S + +id: (m-ailabs_deu_000209-m-ailabs_deu_000209) +Scores: (#C #S #D #I) 1 6 6 0 +REF: JA WIR haben IN DEN LETZTEN JAHREN RECHT ENGE BEZIEHUNGEN ZU BRASILIEN AUFGEBAUT +HYP: ** BIA haben ** *** ******* ****** ***** INENDEZENJAHNRECHT ENKE BITIUNG ZUBASILIEN AUFGEBAUTPR +Eval: D S D D D D D S S S S S + +id: (m-ailabs_deu_000210-m-ailabs_deu_000210) +Scores: (#C #S #D #I) 1 6 0 1 +REF: SIE WÜRDE SICH nicht ***** FÜR ANDERE OPFERN +HYP: DTTS SIEEVÜRDE SCH nicht VÜER ANDR ABPFON TT +Eval: S S S I S S S + +id: (m-ailabs_deu_000211-m-ailabs_deu_000211) +Scores: (#C #S #D #I) 0 1 3 0 +REF: RIEFEN SIE MIR ZU +HYP: ****** *** *** LECHLIFENSEMETZU +Eval: D D D S + +id: (m-ailabs_deu_000212-m-ailabs_deu_000212) +Scores: (#C #S #D #I) 2 9 2 0 +REF: GOTT WAS SIE IHR ERZÄHLTE hÖren SIE NUR es IST EIN GANZER ROMAN +HYP: GOAT WASSI IE AR ZHÄLTER hÖren *** SINUR es *** IS SEINGANSEROMAN N +Eval: S S S S S D S D S S S + +id: (m-ailabs_deu_000213-m-ailabs_deu_000213) +Scores: (#C #S #D #I) 2 4 4 0 +REF: seine MUTTER KANN IHM NUR FLUSSWASSER GEBEN DESHALB WEINT er +HYP: seine ****** **** *** *** MTERKANIMO FLUSSWASSRGEBEN DES SEIBPWEIND er +Eval: D D D D S S S S + +id: (m-ailabs_deu_000214-m-ailabs_deu_000214) +Scores: (#C #S #D #I) 5 17 3 0 +REF: DER BUNDEWIRTSCHAFTSMINISTER WIRD ZUSAMMEN MIT DER NETZAGENTUR am VIERTE juni zum ERSTEN MAL PRÄSENTIEREN WIE SICH DIE NETZBETREIBER und die KRAFTWERKE DIE NEUEN NETZPLÄNE VORSTELLEN +HYP: *** UNESWRSCHAS MINESTA WIRT EÖMEN BUSAMMITERNETZ AGENTUR am VIERTEN juni zum ****** *** ERSENMAL PRESENTIERN WIESICH DIENETZ BETREIBER und die KRAFTDEARKE DINEUNET LHNE VORSTER UND +Eval: D S S S S S S S D D S S S S S S S S S S + +id: (m-ailabs_deu_000215-m-ailabs_deu_000215) +Scores: (#C #S #D #I) 4 14 3 1 +REF: EVA HATTE SICH ZITTERND vor ***** TODESSCHWÄCHE von dem GITTER BEFREIT UND SUCHTE ZU ENTFLIEHEN ABER der SCHMALE GARTEN BOT KEINEN AUSWEG +HYP: *** ***** EVWARHATE SECHTZITANT vor TODES SFVECHER von dem ****** GETER BE FREITUNDZUCHTE ZUND FIEN BE der SMALE GATEN BOTD KAEINEN AUSWIHN +Eval: D D S S I S D S S S S S S S S S S S + +id: (m-ailabs_deu_000216-m-ailabs_deu_000216) +Scores: (#C #S #D #I) 2 10 5 0 +REF: OB ICH mein WERK FÜR HEUTE LIEGEN lassen ODER NOCH EINEN ANLAUF NEHMEN UND ES VOLLENDEN SOLLTE +HYP: ** DABECH mein **** WER FÜRUTE LIEN lassen **** **** ***** NODERNOCHEIN AN LAUFNEMENUNDTES ROLÄNDN SOLTE N +Eval: D S D S S S D D D S S S S S S + +id: (m-ailabs_deu_000217-m-ailabs_deu_000217) +Scores: (#C #S #D #I) 4 19 0 3 +REF: er WAR das **** GÖTZCHEN der ***** STUNDE DIE TAITAI BEAUFTRAGTE MADAME ANGELE die *** AUCH DASTAND UND DIE GEKAUFTEN SEIDENSTÜCKE ZUSAMMENFALTETE FÜR TSCHUN ZU SORGEN +HYP: er WA das GETZ IERN der STUND DE TEITEI E AUFTRAKTE MADM UNMSCHLL die ACH A STAND UN IGE AUFEN SEIDEN TÜ KEZUSAM FELDETE FÜRSCHON ZUSORGENGN +Eval: S I S I S S S S S S I S S S S S S S S S S S + +id: (m-ailabs_deu_000218-m-ailabs_deu_000218) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ICH WERDE NACHSEHEN +HYP: TIWR DEN HCHSEM +Eval: S S S + +id: (m-ailabs_deu_000219-m-ailabs_deu_000219) +Scores: (#C #S #D #I) 2 5 2 1 +REF: *** ABER TIPPS ODER vorgaben das MACHEN WIR NATÜRLICH NICHT +HYP: ABA E TIBS ODAF vorgaben das ****** *** MACHM ERNERDOLIKNIGHS +Eval: I S S S D D S S + +id: (m-ailabs_deu_000220-m-ailabs_deu_000220) +Scores: (#C #S #D #I) 4 13 3 1 +REF: als UNSERE IDEE BEKANNT WURDE WAR DIE PHYSIOGNOMIE der ************* WALTERSBURGER UNGEFÄHR DIE EINES kalbes das ZUM ERSTEN MALE DONNERN HÖRT +HYP: als ****** UNSRE E DE BEKANTWORDER WADI ÜRSIOGNOMIE der WELTERSPBOGER UNGE FÄR DI EINERS kalbes das *** ****** UM ERSENMAL ONHNHÖR +Eval: D S S S S S S I S S S S D D S S S + +id: (m-ailabs_deu_000221-m-ailabs_deu_000221) +Scores: (#C #S #D #I) 5 6 1 0 +REF: BITTE MACHEN SIE GEFÄLLIGST auf und es klang WIE ein JAMMERNDER HILFERUF +HYP: ***** IZER MAHNSIGE FÄLICHT auf und es klang DIE ein JAMANDER HILVER +Eval: D S S S S S S + +id: (m-ailabs_deu_000222-m-ailabs_deu_000222) +Scores: (#C #S #D #I) 5 9 4 0 +REF: HERR doktor sagte EINE FRAU die SCHNURRGRINE DIE SO OFT zu IHNEN KOMMT ist EIGENTLICH GAR NICHT KRANK +HYP: R doktor sagte **** EINEFRAU die SH NOGRINERM DIESO AFT zu INEN KOM ist ********** *** ***** EINLIGANICHKRAN +Eval: S D S S S S S S S D D D S + +id: (m-ailabs_deu_000223-m-ailabs_deu_000223) +Scores: (#C #S #D #I) 11 18 3 3 +REF: die alte ** ERINNERUNG AN den FRÜHEREN TRAUM TAUCHTE EBENFALLS WIEDER auf und ****** UNWILLKÜRLICH FAST bei ** DER BEHAUPTUNG DASS DIE SEELE den kÖrper verlassen und zu IHM ZURÜCKKEHREN KÖNNE SCHIEN ES IHR ORDENTLICH +HYP: die alte ER IN RUNGAN den ********* FRÜHREN TAUM TAUCHTER EBENFALSWIEDER auf und UNWIEL KÖRLIC FSST bei DE BE HAUPTUNG DAS DE SELE den kÖrper verlassen und zu *** ************* IM ZURÜCKEREN KÖNE SHINESIER ORDENDLIG +Eval: I S S D S S S S I S S I S S S S S D D S S S S S + +id: (m-ailabs_deu_000224-m-ailabs_deu_000224) +Scores: (#C #S #D #I) 3 12 3 0 +REF: ALS sie AUF DEN BALKON ZURÜCKKEHRTE FAND SIE IHN die ZEITUNG LESEND die WÄHREND IHRES FORTSEINS ANGELANGT WAR +HYP: AL sie *** *** OFTDEN ALKOND ZURIKERTE VANDZI IN die ******* ZEITUNKLIESEND die WEREN RES FORT ZEINES ANGELNKTWARHT +Eval: S D D S S S S S D S S S S S S + +id: (m-ailabs_deu_000225-m-ailabs_deu_000225) +Scores: (#C #S #D #I) 11 11 1 0 +REF: ER WAR ein kind der STRASSE von klein auf aber in IHM LEBTE von jeher EINE GEWISSE SEHNSUCHT NACH EINER ehrbaren BÜRGERLICHEN EXISTENZ +HYP: TE EHRWAH ein kind der STRASE von klein auf aber in *** IMLEBTE von jeher INE GWISSES SEEN SOCHT NACHEINER ehrbaren BIRGERICHEN EXISTENST +Eval: S S S D S S S S S S S S + +id: (m-ailabs_deu_000226-m-ailabs_deu_000226) +Scores: (#C #S #D #I) 1 8 9 0 +REF: UND WIR ALS BUNDESREGIERUNG FÜHLEN UNS HIER NICHT einer GRUPPE VERANTWORTLICH SONDERN WIR FÜHLEN UNS DEM GEMEINWOHL VERANTWORTLICH +HYP: *** *** *** *************** ******* *** AIT UNESTIGJRUNGKTÜHNUNZIENIG einer ****** ************** ******* GROPEFERANFODLICH ONAM WÜEFÜNE DEN GEMEINWUHLFER NWODLICHUN +Eval: D D D D D D S S D D D S S S S S S + +id: (m-ailabs_deu_000227-m-ailabs_deu_000227) +Scores: (#C #S #D #I) 1 3 2 0 +REF: was MEIN LIEBES KIND WAS KANN +HYP: was **** ****** MEINLIE BESKIND WASKAN +Eval: D D S S S + +id: (m-ailabs_deu_000228-m-ailabs_deu_000228) +Scores: (#C #S #D #I) 7 19 3 0 +REF: und DANN WOLLTE ich den ANBLICK DERER NICHT MISSEN DIE MIR GEBLIEBEN waren vor ALLEM ABER WAR es MIR darum ZU TUN MEINE SÜSSE ELISABETH EINIGERMASSEN GETRÖSTET ZU SEHEN +HYP: und DAN WOLT ich den ******* ***** ANBLICG DE RANICHTMISEN DIEMER GEBLEBEM waren vor ***** ALM ABARBWA es I darum ZUTOUN WEINE SYSE LICISER BET EINIGAMASSEN GETRÜSTET ZUSE ENTO +Eval: S S D D S S S S S D S S S S S S S S S S S S + +id: (m-ailabs_deu_000229-m-ailabs_deu_000229) +Scores: (#C #S #D #I) 1 8 3 1 +REF: ABER ICH GLAUBE DASS WIR UNS AUCH GEGENSEITIG EIN BISSCHEN UNTERSTÜTZEN kÖnnen ***** +HYP: **** *** ****** EDAS AUCH WIE UNDS GENSET DCHE BSHN UNTDASTUZEN kÖnnen EÖRM +Eval: D D D S S S S S S S S I + +id: (m-ailabs_deu_000230-m-ailabs_deu_000230) +Scores: (#C #S #D #I) 2 7 4 0 +REF: seine GESCHÄFTLICHE LAUFBAHN HABE STEFENSON ALS KÜCHENBOY in EINEM HOTEL VIERTEN GRADES BEGONNEN +HYP: seine ************** ******** **** GESCHFTICHELAUFBAHNHARBESTIEVENSN ALSKÖCHEN BO in ***** EINE MOTÄL FIERENGRADES BGON +Eval: D D D S S S D S S S S + +id: (m-ailabs_deu_000231-m-ailabs_deu_000231) +Scores: (#C #S #D #I) 3 14 9 0 +REF: VIELLEICHT TÄTEN SIE GUT DIESE ANSICHTEN DES BISCHOFS NACH HAUSE ZU MELDEN SAGTE DER TAJEN der IMMER mehr ein MANN DES GESCHRIEBENEN WORTES WIE DER TAT +HYP: ********** ****** *** *** ***** ********* *** ******** NFÜLEICHTEN SEGUTIESE ANSICHENDES SCHOFSN HASE ZUMELDEN ZACTEDERTATSCEN der IMAR mehr ein **** MANDES GESCRIEBENEN WURTES WIEDER TAD N +Eval: D D D D D D D D S S S S S S S S D S S S S S S + +id: (m-ailabs_deu_000232-m-ailabs_deu_000232) +Scores: (#C #S #D #I) 3 19 5 0 +REF: AM ANDERN MORGEN ERHOB ER SICH SPÄT SCHICKTE den LAKAIEN IN DIE WOHNUNG FEUERBACHS UND LIESS UM eine UNTERREDUNG BITTEN DER MANN kam MIT DER BOTSCHAFT ZURÜCK +HYP: ** ****** EMAN DAN MORDEN EHRHOPERSICH SCHWÄT SCHCKTE den ******* ** *** LARKEIEN DE BONUNG VOR JABACHSUNTLIESUM eine NTERE DUNG BITEN DEMAN kam MITER OTSCHAFT ZURÜG DETN +Eval: D D S S S S S S D D D S S S S S S S S S S S S S + +id: (m-ailabs_deu_000233-m-ailabs_deu_000233) +Scores: (#C #S #D #I) 1 14 0 2 +REF: NUR EIN WENIG TRAURIG WURDE ES WENN IMMER DASSELBE KAM WENN SIE nie ** ******* ZUFRIEDEN SCHIENEN +HYP: NT NU EINWENICH TRAURICH WURDES EN IM DAS SEL BERKAM EN SIN nie ZU FRIEDEN SCHIEN NN +Eval: S S S S S S S S S S S S I I S S + +id: (m-ailabs_deu_000234-m-ailabs_deu_000234) +Scores: (#C #S #D #I) 3 15 2 1 +REF: ein ****** SOMMERWARMER NOVEMBERTAG lag MIT SONNENGLITZERN ÜBER DER HAUPTSTADT und UNTER DEN LINDEN DRÄNGTE EINE TAUSENDKÖPFIGE MENSCHENMENGE AUF UND NIEDER +HYP: ein SOMAHR WAHMANOVEM BARTARK lag *** ************** MITZONEN GLITZSANNÜBERDE HUPTSTABT und UNDER DNLINDENDRENKTER INE TAUSEND KAPFIGE MENSCHENMÄNGER AUO VON IEDER U +Eval: I S S D D S S S S S S S S S S S S S + +id: (m-ailabs_deu_000235-m-ailabs_deu_000235) +Scores: (#C #S #D #I) 3 5 2 0 +REF: KOMM MIT MIR mein SOHN DENN ICH BRAUCHE deine liebe +HYP: **** KOMITM IH mein **** SON DEN ICHPRAUCHE deine liebe +Eval: D S S D S S S + +id: (m-ailabs_deu_000236-m-ailabs_deu_000236) +Scores: (#C #S #D #I) 3 8 2 3 +REF: NUR SEIN GESICHT WURDE EIN WENIG NACHDENKLICHER SO wie von einer ** ******** ** ERINNERUNG ERHELLT +HYP: *** **** DNTNWOR SAIN KESICHT RDE IN WNICHNACHTDENKLICHARSOO wie von einer ER INERHUNG ER HÄLT NN +Eval: D D S S S S S S I I I S S + +id: (m-ailabs_deu_000237-m-ailabs_deu_000237) +Scores: (#C #S #D #I) 0 9 6 0 +REF: DANN WIRD AUCH WIEDER DER INNOVATIONSDRUCK STEIGEN UND DAZU IST DAS SYSTEM JA EINGEFÜHRT WORDEN +HYP: **** **** **** ****** *** **************** N WOT OUI ERDE NWATIONENS DROCK STEIGENUNDTATZU ESTASSESTEMIE ENGEFÜRTWON +Eval: D D D D D D S S S S S S S S S + +id: (m-ailabs_deu_000238-m-ailabs_deu_000238) +Scores: (#C #S #D #I) 2 11 2 1 +REF: *** JETZT GEWAHRTE ER MIT ENTSETZEN die SCHEUSSLICHE TEUFLISCHE AFFENFRATZE DIE ÜBER des MÄDCHENS SCHULTER SCHIELTE +HYP: NET GEWATE ERMIT EN SET ZSN die ************ SCHEUSLICHE TEUFLSCHER AFENFRATZSE DIEBE des ********* METCHEN SCHLTERSCIELTEN +Eval: I S S S S S D S S S S D S S + +id: (m-ailabs_deu_000239-m-ailabs_deu_000239) +Scores: (#C #S #D #I) 1 12 1 0 +REF: JA DER WIRT NICKTE DAS GEHÖRT EINEM gewissen WUTSCHOW BERNHARD WUTSCHOW IST ETWAS VERRUFEN +HYP: TTERAR DE WERT NIK TE DASGÖRD EINE gewissen ******** WRTSCHAUF BERNHAT WRTSCHOF ISTE ETWASFARGUNSENTNN +Eval: S S S S S S S D S S S S S + +id: (m-ailabs_deu_000240-m-ailabs_deu_000240) +Scores: (#C #S #D #I) 1 9 1 0 +REF: WOLLT IHR in WAHRHEIT DIE LÖWEN TÖTEN UND KÖNNT IHR SCHIESSEN +HYP: WOLT ER in ******** WAHEI DIEL ÖRSEND TÖRTEN NUNDT KND ERSCHELISENNN +Eval: S S D S S S S S S S + +id: (m-ailabs_deu_000241-m-ailabs_deu_000241) +Scores: (#C #S #D #I) 6 14 0 0 +REF: BAT CEDDIE SEHR RESPEKTVOLL WOBEI er nur EINIGE SILBEN VERSCHLUCKTE WAS IHM bei den BELIEBTEN langen WÖRTERN des ÖFTERN VORKAM +HYP: AT SEÄTDI SERESPEKT VOL WUOBEI er nur EINI GESELDEN VERSCHLUKTE WASS IM bei den BELEBTEN langen WÖRTEAN des FTAN FORKAN +Eval: S S S S S S S S S S S S S S + +id: (m-ailabs_deu_000242-m-ailabs_deu_000242) +Scores: (#C #S #D #I) 3 7 1 3 +REF: ***** ******** LORD FAUNTLEROY WIRD NICHTS ENTBEHREN dessen BIN ICH GEWISS versetzte er * +HYP: DLORT FONDLELE RORU VERT NICHZS END BEHREN dessen *** BINICH GEWIS versetzte er T +Eval: I I S S S S S D S S I + +id: (m-ailabs_deu_000243-m-ailabs_deu_000243) +Scores: (#C #S #D #I) 4 6 2 0 +REF: kam GLEICHFALLS ins SCHLAFZIMMER auf einen NAGEL IN DER NÄHE DES BETTES +HYP: kam GLEICHFALS ins SCHLAFTZIMMAR auf einen ***** ** NAGELINDER NEHR TES BETESN +Eval: S S D D S S S S + +id: (m-ailabs_deu_000244-m-ailabs_deu_000244) +Scores: (#C #S #D #I) 1 13 10 0 +REF: UND DAS IST DIE CHANCE DIE IN DIESER KRISE STECKT die CHANCE FÜR INTERNATIONALE REGELN DIE SICH AN DEN PRINZIPIEN DER SOZIALEN MARKTWIRTSCHAFT ORIENTIEREN +HYP: *** *** *** *** ****** *** ** ****** DASES DISCHANGS die ****** **** NESARKRISTEG DIESCHANGS VÜR INZTERNATZIONALEREGEN BISI EINIM PRONSIEBPLIEN DERSE DJALE MAKPOTAF OLIENTIERN +Eval: D D D D D D D D S S D D S S S S S S S S S S S + +id: (m-ailabs_deu_000245-m-ailabs_deu_000245) +Scores: (#C #S #D #I) 5 7 5 0 +REF: ANFANGS FIEL DER REGEN SCHRÄG und PEITSCHTE ERST DIE eine UND DANN die ANDERE SEITE des wagens +HYP: ******* **** *** ANFANGSFIELDEREGEN SCHRAG und PEITSTE ERS IE eine *** DAN die ****** NDERESEITE des wagens +Eval: D D D S S S S S D S D S + +id: (m-ailabs_deu_000246-m-ailabs_deu_000246) +Scores: (#C #S #D #I) 2 7 0 5 +REF: ***** ***** FAST LEICHTSINNIGEN BEMESSUNG IHRES wertes **** AUFZUGEBEN sich *** ******** ENTSCHLOSSEN HATTE +HYP: MFAST LEICH ENIGEN BER MESSONG ERES wertes AUFT ZOGEBEM sich ENT SCLOSSEN HATE NN +Eval: I I S S S S I S I I S S + +id: (m-ailabs_deu_000247-m-ailabs_deu_000247) +Scores: (#C #S #D #I) 0 8 7 0 +REF: DAS HEISST DIE FRAGE DER MENSCHLICHEN ARBEIT UND DIE FRAGE WAS KANN TECHNISCH GELÖST WERDEN +HYP: *** ****** *** ***** *** ************ ****** SEIST BDIE FRARGERMENCHLICHEN ARBET NDIE FRAGEWAS KANTECHNISCGELÖSTWERDEN DA +Eval: D D D D D D D S S S S S S S S + +id: (m-ailabs_deu_000248-m-ailabs_deu_000248) +Scores: (#C #S #D #I) 0 9 2 0 +REF: DIE SAFARI WAR AUF DIE REGELMÄSSIG BENUTZTEN WASSERSTELLEN DIESER ROUTE ANGEWIESEN +HYP: *** ****** NUTISEAR FAHRIE WAUF IEREDEMESIG BNOTZSTEN WASSARSTELLEN DISEROTAN GEWIESEN NN +Eval: D D S S S S S S S S S + +id: (m-ailabs_deu_000249-m-ailabs_deu_000249) +Scores: (#C #S #D #I) 11 6 2 0 +REF: die beiden MÜSSTEN HIER oben auf dem GIPFEL GESTANDEN haben und er sprach die alten WORTE VOR SICH HIN +HYP: die beiden MISTEN HIE oben auf dem GEBPFEL GESTANDTEN haben und er sprach die alten ***** *** WAURTE VORSECHIN +Eval: S S S S D D S S + +id: (m-ailabs_deu_000250-m-ailabs_deu_000250) +Scores: (#C #S #D #I) 5 7 1 0 +REF: ENDLICH BLICKTE CEDRIK auf WEISS NEWICK ALLES VON den armen leuten FRAGTE er +HYP: ENTLICH BIKTES EDRIKG auf ***** WEISSN JUUIG ALESVON den armen leuten FRAKTE er +Eval: S S S D S S S S + +id: (m-ailabs_deu_000251-m-ailabs_deu_000251) +Scores: (#C #S #D #I) 5 10 4 0 +REF: DASS ES HEUTE EINE WUNDERBARE ZUSAMMENARBEIT ZWISCHEN bund und lÄndern in diesen FRAGEN GIBT MIT SEHR SEHR INTERESSANTEN PROJEKTEN +HYP: **** ** ***** SEUDE INE WINDRBARETZUSAMARBEI ZWSCHEN bund und lÄndern in diesen ****** RAGEN GIB IT SESE NTRESANDEN PROJEGTENU +Eval: D D D S S S S D S S S S S S + +id: (m-ailabs_deu_000252-m-ailabs_deu_000252) +Scores: (#C #S #D #I) 2 17 0 1 +REF: CASPAR VERHARRTE ANGEWURZELT an SEINEM PLATZ SEINE GLIEDER JA SEINE augen **** WAREN WIE VERSTEINERT ALS ER ZUM ZWEITENMAL HINBLICKTE +HYP: DKAS BAR FERHARTE an GENMRTZELTENSEIMPLATZS SEIN GLIE DER JAR SEINEN augen BANG IEVERSTEINRT EL ET UM ZWEITEN MALIEN IKTE N +Eval: S S S S S S S S S I S S S S S S S S + +id: (m-ailabs_deu_000253-m-ailabs_deu_000253) +Scores: (#C #S #D #I) 8 9 1 1 +REF: einige zeit DANACH FRAGTE er mich OB ICH GLAUBE DASS der *** EISGANG den SCHLITTEN des anderen ZERSTÖRT HABE +HYP: einige zeit ****** DANACHFRAKTE er mich O PICH GLAUBER DAS der EIS GANG den SCLITEN des anderen ZER STÖRTABE +Eval: D S S S S S I S S S S + +Speaker sentences 1: cv_deu_000698 #utts: 1 +id: (cv_deu_000698-cv_deu_000698) +Scores: (#C #S #D #I) 0 6 2 0 +REF: ABER NUN BLOSS NICHT IN EINE SCHOCKSTARRE VERFALLEN +HYP: **** *** AENEN BLUSSE NICHTGENEINER SCHORCGSTARE DEL VALIN +Eval: D D S S S S S S + +Speaker sentences 2: cv_deu_000699 #utts: 1 +id: (cv_deu_000699-cv_deu_000699) +Scores: (#C #S #D #I) 0 1 4 0 +REF: JA ICH KOMME JA SCHON +HYP: ** *** ***** ** JILSCKOMIRSCHEN +Eval: D D D D S + +Speaker sentences 3: cv_deu_000700 #utts: 1 +id: (cv_deu_000700-cv_deu_000700) +Scores: (#C #S #D #I) 0 8 0 1 +REF: **** NEBENBEI ARBEITETE ER ALS AUSHILFSKRAFT AUF EINER FARM +HYP: SDEM BEIE ARBEITE DE EIS AUSHRELS KRAFTAF EINEN FAHME +Eval: I S S S S S S S S + +Speaker sentences 4: cv_deu_000701 #utts: 1 +id: (cv_deu_000701-cv_deu_000701) +Scores: (#C #S #D #I) 1 10 1 0 +REF: EIN TERRITORIAL GRÖSSERES EUROPA WIRD nicht MIT EINEM ETATMÄSSIG KLEINEREN EUROPA ERREICHT +HYP: *** EINTERIT TO HEIKOSSES OCHOPERWITDT nicht IT AN MITARMESI KEINEREN O HOPERREICT +Eval: D S S S S S S S S S S + +Speaker sentences 5: cv_deu_000702 #utts: 1 +id: (cv_deu_000702-cv_deu_000702) +Scores: (#C #S #D #I) 0 6 2 0 +REF: IHR SOHN KAM DURCH KÜNSTLICHE BEFRUCHTUNG ZUR WELT +HYP: *** **** IER SOUN KAENDRH KÖNZSLICHER BEFROCHTUNGK ZURWERT +Eval: D D S S S S S S + +Speaker sentences 6: cv_deu_000703 #utts: 1 +id: (cv_deu_000703-cv_deu_000703) +Scores: (#C #S #D #I) 1 9 0 1 +REF: die ***** NACHTAKTIVEN FALTER FLIEGEN VON MITTE JULI BIS MITTE OKTOBER +HYP: die NACHT AR DIEF NEFLER FLIEN VONMITER JULIEBIS MITE AOF TOBER +Eval: I S S S S S S S S S + +Speaker sentences 7: cv_deu_000704 #utts: 1 +id: (cv_deu_000704-cv_deu_000704) +Scores: (#C #S #D #I) 0 1 0 2 +REF: ****** * ACHT +HYP: DEREAR H AAT +Eval: I I S + +Speaker sentences 8: cv_deu_000705 #utts: 1 +id: (cv_deu_000705-cv_deu_000705) +Scores: (#C #S #D #I) 0 1 0 1 +REF: ** FÜNF +HYP: EN DHERN +Eval: I S + +Speaker sentences 9: cv_deu_000706 #utts: 1 +id: (cv_deu_000706-cv_deu_000706) +Scores: (#C #S #D #I) 4 8 0 0 +REF: nutzer KÖNNEN IHRE LESEZEICHEN ONLINE ABSPEICHERN verwalten und MIT ANDEREN NUTZERN teilen +HYP: nutzer KNEN IHRELERSE ZEICEN ONEIN ABSPEICHER verwalten und BET ANEN NOTZAN teilen +Eval: S S S S S S S S + +Speaker sentences 10: cv_deu_000707 #utts: 1 +id: (cv_deu_000707-cv_deu_000707) +Scores: (#C #S #D #I) 2 2 0 0 +REF: die DON bosco KATH +HYP: die DUM bosco KATIERALE +Eval: S S + +Speaker sentences 11: cv_deu_000708 #utts: 1 +id: (cv_deu_000708-cv_deu_000708) +Scores: (#C #S #D #I) 3 7 1 2 +REF: saul BASS ZÄHLT zu den * ******** INNOVATIVSTEN DESIGNERN UND FILMEMACHERN SEINER ZEIT +HYP: saul **** BASSZEL zu den N ROTISTEN DIESEIDARE UMEMF Ü MEC EÜEUELE SERENZELLEN +Eval: D S I I S S S S S S + +Speaker sentences 12: cv_deu_000709 #utts: 1 +id: (cv_deu_000709-cv_deu_000709) +Scores: (#C #S #D #I) 3 5 0 4 +REF: in ********* *** GRÜN ÜBER SILBERNEM WELLENBALKEN eine **** SILBERNE eiche * +HYP: in KÜNÜWUR SER BENEMEN WELEN BEUG K eine SIEL BWONDER eiche N +Eval: I I S S S S I S I + +Speaker sentences 13: cv_deu_000710 #utts: 1 +id: (cv_deu_000710-cv_deu_000710) +Scores: (#C #S #D #I) 2 8 1 1 +REF: WEITERE wichtige INDUSTRIEZWEIGE SIND die ************ MIKROMECHANIK GALVANOPLASTIK METALLBAU UND DIE HOLZVERARBEITUNG +HYP: EITERE wichtige *************** INDESTRIEZWEIGESE die NICRMICHANIG GALWANOPLASTIG MITEILBAU UNTIE HUTZVER ARBEITOUNG A +Eval: S D S I S S S S S S + +Speaker sentences 14: cv_deu_000711 #utts: 1 +id: (cv_deu_000711-cv_deu_000711) +Scores: (#C #S #D #I) 4 9 0 0 +REF: Über den autor IST NICHTS BEKANNT VERMUTLICH STAMMTE ER aus DEM DEUTSCHEN SPRACHGEBIET +HYP: Über den autor S NICHT BEKAND VER MUTLIC STMTEHR aus EM DEUTSHEN SPRACHGBIEDT +Eval: S S S S S S S S S + +Speaker sentences 15: cv_deu_000712 #utts: 1 +id: (cv_deu_000712-cv_deu_000712) +Scores: (#C #S #D #I) 0 3 3 0 +REF: MAN STEUERT ES MIT EINEM DOPPELPADDEL +HYP: *** ******* ** NSTUER ISMI DENENTOPLIPATE +Eval: D D D S S S + +Speaker sentences 16: cv_deu_000713 #utts: 1 +id: (cv_deu_000713-cv_deu_000713) +Scores: (#C #S #D #I) 0 5 2 0 +REF: WIR HABEN EIN PROBLEM AUF OSISCHICHT ACHT +HYP: *** ***** DIE HARMEI EREBLEMER OSSISCHIGHT OUCHT +Eval: D D S S S S S + +Speaker sentences 17: cv_deu_000714 #utts: 1 +id: (cv_deu_000714-cv_deu_000714) +Scores: (#C #S #D #I) 0 7 6 0 +REF: WIR SPIELEN IMMER NOCH ABER DAS LEBEN AUF TOUR IST DERZEIT NICHT MACHBAR +HYP: *** ******* ***** **** **** *** WICHLIENEMANACH UARE ME AUKTUR E EIERTETDNTHMACHBER NN +Eval: D D D D D D S S S S S S S + +Speaker sentences 18: cv_deu_000715 #utts: 1 +id: (cv_deu_000715-cv_deu_000715) +Scores: (#C #S #D #I) 0 9 2 0 +REF: HEUTE ZEIGT SICH DER GRÖSSTE TEIL DER ANLAGE ALS ENGLISCHER GARTEN +HYP: ***** ***** ORDE AL I DE GESSYK LDE ANLAN AU NISCHEWABEN +Eval: D D S S S S S S S S S + +Speaker sentences 19: cv_deu_000716 #utts: 1 +id: (cv_deu_000716-cv_deu_000716) +Scores: (#C #S #D #I) 1 7 2 0 +REF: SEINE RESIDENZ NAHM ER in MÜNCHEN WO ER AUCH STARB +HYP: ***** EIEHEI DEN NAME in ******** WINCHEN WORER AUCGSTA T +Eval: D S S S D S S S S + +Speaker sentences 20: cv_deu_000717 #utts: 1 +id: (cv_deu_000717-cv_deu_000717) +Scores: (#C #S #D #I) 4 6 3 1 +REF: INNERER und ÄUSSERER NARTHEX KÖNNEN ALS GETRENNTE TEILE eines NARTHEX auch gemeinsam *** VORKOMMEN +HYP: INERUNG und ********* ******* ******* EUSSRERNATICG GENEN ALSGETRENTITAILE eines NATIG auch gemeinsam VOR OMMEN +Eval: S D D D S S S S I S + +Speaker sentences 21: cv_deu_000718 #utts: 1 +id: (cv_deu_000718-cv_deu_000718) +Scores: (#C #S #D #I) 1 6 1 0 +REF: DABEI BELEGTE ER DIE PLÄTZE vier UND DREI +HYP: DA BEIE BELEGTER R DIEPLÄTZSE vier *** UNDTREIG +Eval: S S S S S D S + +Speaker sentences 22: cv_deu_000719 #utts: 1 +id: (cv_deu_000719-cv_deu_000719) +Scores: (#C #S #D #I) 1 6 1 0 +REF: KIM DARBY IST DIE tochter ZWEIER PROFESSIONELLER TÄNZER +HYP: KIN DRABIE IS SIE tochter ****** ZWEIAR ROFESENERLARTENZA +Eval: S S S S D S S + +Speaker sentences 23: cv_deu_000720 #utts: 1 +id: (cv_deu_000720-cv_deu_000720) +Scores: (#C #S #D #I) 0 6 3 0 +REF: ICH GLAUBE DAS FÜHRT NICHT IN DIE RICHTIGE RICHTUNG +HYP: *** ****** *** IS KLAUBER AS FÜRT NIST INIRISTIERISTUNG +Eval: D D D S S S S S S + +Speaker sentences 24: cv_deu_000721 #utts: 1 +id: (cv_deu_000721-cv_deu_000721) +Scores: (#C #S #D #I) 0 3 3 0 +REF: DAS IST EINE EXTREM SCHLECHTE RICHTLINIE +HYP: *** *** **** DASSES EINERXSTRENSCHLECHTERICHS LIENIER +Eval: D D D S S S + +Speaker sentences 25: cv_deu_000722 #utts: 1 +id: (cv_deu_000722-cv_deu_000722) +Scores: (#C #S #D #I) 0 4 2 0 +REF: HERR LURCH ENTBLÖSSTE SEIN HAGERES GESICHT +HYP: **** ***** HERLORCH EN BLESTSEIN HAGERESGESICHT +Eval: D D S S S S + +Speaker sentences 26: cv_deu_000723 #utts: 1 +id: (cv_deu_000723-cv_deu_000723) +Scores: (#C #S #D #I) 0 5 0 0 +REF: NUR CARMEN FINDET DAS UNFAIR +HYP: MOKAREN FINE TO UN FER +Eval: S S S S S + +Speaker sentences 27: cv_deu_000724 #utts: 1 +id: (cv_deu_000724-cv_deu_000724) +Scores: (#C #S #D #I) 0 5 0 1 +REF: * INGEBORG KRABBE HATTE DREI GESCHWISTER +HYP: T INGE BO KABERHAT DER DREIGESCHICSTET +Eval: I S S S S S + +Speaker sentences 28: cv_deu_000725 #utts: 1 +id: (cv_deu_000725-cv_deu_000725) +Scores: (#C #S #D #I) 1 7 5 0 +REF: ES KOMMT WIRKLICH DARAUF AN DASS SOLCHE DATEN AUF dieser EBENE ERFASST WERDEN +HYP: ** ***** ******** ****** LCES KOMTWICLISTSA ANM DASOLCHEDADTEN U dieser ***** EBENER FASTWERDEN +Eval: D D D D S S S S S D S S + +Speaker sentences 29: cv_deu_000726 #utts: 1 +id: (cv_deu_000726-cv_deu_000726) +Scores: (#C #S #D #I) 1 5 0 1 +REF: ******* STRUMMING HINGEGEN ERGIBT ein HARMONISCHES PULSIEREN +HYP: STRAMIN HEN GEGENE GIT ein ERMUNICHES POSIEREN +Eval: I S S S S S + +Speaker sentences 30: cv_deu_000727 #utts: 1 +id: (cv_deu_000727-cv_deu_000727) +Scores: (#C #S #D #I) 1 5 1 1 +REF: BIN ICH ZUM kauf **** EINER HYPOTHEK BERECHTIGT +HYP: *** BENICH UM kauf EIER HPOTEG BERECH TIGT +Eval: D S S I S S S + +Speaker sentences 31: cv_deu_000728 #utts: 1 +id: (cv_deu_000728-cv_deu_000728) +Scores: (#C #S #D #I) 0 6 0 1 +REF: *** TEHERAN IST DIE HAUPTSTADT VOM IRAN +HYP: TUN E EUNLENS S ENERSHENFVENDEN DEN BMNMIE +Eval: I S S S S S S + +Speaker sentences 32: cv_deu_000729 #utts: 1 +id: (cv_deu_000729-cv_deu_000729) +Scores: (#C #S #D #I) 0 3 3 0 +REF: KOHLENHYDRATE SIND BESSER ALS IHR RUF +HYP: ************* **** ****** HKÖUEN DERENSN DEREEISLNDUN +Eval: D D D S S S + +Speaker sentences 33: cv_deu_000730 #utts: 1 +id: (cv_deu_000730-cv_deu_000730) +Scores: (#C #S #D #I) 3 11 0 2 +REF: OHNE die PROFESSIONELLE UNTERSTÜTZUNG DER MASERATIRENNABTEILUNG WAREN DIESE WAGEN der *** KONKURRENZ NUN doch *** UNTERLEGEN +HYP: ON die POFISINLLIN TESTÜTZUN DAMASSEARTDIE ENABTEILUNG WAEN DISE BAGEN der KON KROWENS UN doch UND OLEGEN +Eval: S S S S S S S S I S S I S + +Speaker sentences 34: cv_deu_000731 #utts: 1 +id: (cv_deu_000731-cv_deu_000731) +Scores: (#C #S #D #I) 2 6 0 0 +REF: sie DIENTE ZUNÄCHST ALS UNTERKUNFT FÜR belgische BESATZUNGSTRUPPEN +HYP: sie DIEN TE UNEST ASUNDTEOKMFT VÜR belgische BISATZUSTRUPEN +Eval: S S S S S S + +Speaker sentences 35: cv_deu_000732 #utts: 1 +id: (cv_deu_000732-cv_deu_000732) +Scores: (#C #S #D #I) 1 4 2 0 +REF: DA MÜSSEN WIR SPRENGEN meinte DER ZAHNARZT +HYP: ** ******* DAMISSN WISCSPLENEN meinte DERZHAHN AHRZT +Eval: D D S S S S + +Speaker sentences 36: cv_deu_000733 #utts: 1 +id: (cv_deu_000733-cv_deu_000733) +Scores: (#C #S #D #I) 1 9 2 0 +REF: AUSSERDEM SPIELTE ER BEIM NACHFOLGETEAM NEWMARKET ROYALS sowie BEIM LIGAKONKURRENTEN LONDON KNIGHTS +HYP: ********* ******* AUSDEM SPITE R EIMNACHFORGETIEM NIMARKEDROEUOLS sowie EIMLIGEA KONKURRENDTEN LNDEN NEI +Eval: D D S S S S S S S S S + +Speaker sentences 37: cv_deu_000734 #utts: 1 +id: (cv_deu_000734-cv_deu_000734) +Scores: (#C #S #D #I) 1 9 0 4 +REF: ** *** ** ******* WIE AUCH DAS INSTANTRUNOFFVOTING ERFÜLLT DIE COOMBSWAHL das CONDORCETKRITERIUM NICHT +HYP: IE AUC AS ENSTEND RAN ACH WATENG ER FÜRTIE KUMS WAL das KONN DORKETGTERIOMNICHT +Eval: I I I I S S S S S S S S S + +Speaker sentences 38: cv_deu_000735 #utts: 1 +id: (cv_deu_000735-cv_deu_000735) +Scores: (#C #S #D #I) 0 3 2 0 +REF: SMITH WUCHS IN CHICAGO AUF +HYP: ***** ***** SMIFUCHEN TI KAGEOAUF +Eval: D D S S S + +Speaker sentences 39: cv_deu_000736 #utts: 1 +id: (cv_deu_000736-cv_deu_000736) +Scores: (#C #S #D #I) 0 3 1 0 +REF: WIR SIND HIER ALLEIN +HYP: *** WIER IEIE ALEINEN +Eval: D S S S + +Speaker sentences 40: cv_deu_000737 #utts: 1 +id: (cv_deu_000737-cv_deu_000737) +Scores: (#C #S #D #I) 0 9 3 0 +REF: DUMM IST WER ETWAS WEISS ABER TROTZ DES BESSEREN WISSENS FALSCH HANDELT +HYP: **** *** *** DUM ISG WERTWAS BWEIS WURTROC DSS DESIUN BISENS VOLSCHUNGEL +Eval: D D D S S S S S S S S S + +Speaker sentences 41: cv_deu_000738 #utts: 1 +id: (cv_deu_000738-cv_deu_000738) +Scores: (#C #S #D #I) 3 6 2 0 +REF: HAUPTTHEMA der SHOW ist die REVANCHE FÜR ÜBLE STREICHE UNTER FREUNDEN +HYP: HABTEMA der SCHA ist die ******** **** RE WANSCH VEREKÜBICHISTREICHER ENTERFREINDEN +Eval: S S D D S S S S + +Speaker sentences 42: cv_deu_000739 #utts: 1 +id: (cv_deu_000739-cv_deu_000739) +Scores: (#C #S #D #I) 0 5 0 1 +REF: ****** GLEICHZEITIG WURDEN SPORTWETTEN TEILWEISE VERBOTEN +HYP: GLEICH ZEITIG WUODEN SPOTWETEN TALWEISE VERBULEN +Eval: I S S S S S + +Speaker sentences 43: cv_deu_000740 #utts: 1 +id: (cv_deu_000740-cv_deu_000740) +Scores: (#C #S #D #I) 0 1 0 0 +REF: SIEBEN +HYP: SEEGEN +Eval: S + +Speaker sentences 44: cv_deu_000741 #utts: 1 +id: (cv_deu_000741-cv_deu_000741) +Scores: (#C #S #D #I) 0 1 0 1 +REF: *** JA +HYP: TIA BE +Eval: I S + +Speaker sentences 45: cv_deu_000742 #utts: 1 +id: (cv_deu_000742-cv_deu_000742) +Scores: (#C #S #D #I) 3 9 1 2 +REF: ** ZUDEM VERSAH er IM KLOSTER LANGE JAHRE die ÄMTER des ** NOVIZENMEISTERS UND PRIOR +HYP: ZU DEM FARSAH er ** EN KLOSTALANG JARE die MTER des NO WITZSEN MEISTASUN PRIORA +Eval: I S S D S S S S I S S S + +Speaker sentences 46: cv_deu_000743 #utts: 1 +id: (cv_deu_000743-cv_deu_000743) +Scores: (#C #S #D #I) 1 3 0 3 +REF: ****** ****** HEIDENHAIN ENTSTAMMTE einer ****** ÄRZTEFAMILIE +HYP: HEIDEN HEIDEN EN STMT einer ERTZTE VERMILIEAR +Eval: I I S S I S + +Speaker sentences 47: cv_deu_000744 #utts: 1 +id: (cv_deu_000744-cv_deu_000744) +Scores: (#C #S #D #I) 0 1 0 0 +REF: ACHT +HYP: ARESPZIMPTPENN +Eval: S + +Speaker sentences 48: cv_deu_000745 #utts: 1 +id: (cv_deu_000745-cv_deu_000745) +Scores: (#C #S #D #I) 0 1 0 1 +REF: ***** ZWEI +HYP: ZWAIE UNGR +Eval: I S + +Speaker sentences 49: cv_deu_000746 #utts: 1 +id: (cv_deu_000746-cv_deu_000746) +Scores: (#C #S #D #I) 0 6 3 0 +REF: EBENFALLS IN AUGGEN ANGESIEDELT SIND DIE KELTEREI DER FA +HYP: ********* ** ****** TTTTTEBEMFALS EN AUGNG ANGIIEDE ENTIKALTREINDER EFA +Eval: D D D S S S S S S + +Speaker sentences 50: cv_deu_000747 #utts: 1 +id: (cv_deu_000747-cv_deu_000747) +Scores: (#C #S #D #I) 0 8 2 0 +REF: DIESE STEHT AUCH FÜR ABSOLVENTEN EINHEIMISCHER SCHULEN MIT DEUTSCHKENNTNISSEN OFFEN +HYP: ***** ***** DIESERSTDET ARFE AB SELWENDTEN EIN HERMSCHERSCHOL MITDRTKANDTESEN OFEN +Eval: D D S S S S S S S S + +Speaker sentences 51: cv_deu_000748 #utts: 1 +id: (cv_deu_000748-cv_deu_000748) +Scores: (#C #S #D #I) 1 2 1 0 +REF: also ICH HÖRE NICHTS +HYP: also *** ESCHIORE NICS +Eval: D S S + +Speaker sentences 52: cv_deu_000749 #utts: 1 +id: (cv_deu_000749-cv_deu_000749) +Scores: (#C #S #D #I) 0 4 1 0 +REF: WIE KANN MAN SICH SCHÜTZEN +HYP: *** WIEKON MAHN SISCH SCHÜTZE +Eval: D S S S S + +Speaker sentences 53: cv_deu_000750 #utts: 1 +id: (cv_deu_000750-cv_deu_000750) +Scores: (#C #S #D #I) 3 7 3 0 +REF: NACH FÜNF MONATEN lag eine EMPFINDLICHERE PLATTE ALS DIE BIS DAHIN ERHÄLTLICHEN vor +HYP: **** AUFÜM FMONATEN lag eine ************** ****** IM FINTLICHA EPLOTER ANZSTDIEBISTAHEINER HLTLICHEN vor +Eval: D S S D D S S S S S + +Speaker sentences 54: cv_deu_000751 #utts: 1 +id: (cv_deu_000751-cv_deu_000751) +Scores: (#C #S #D #I) 0 6 6 0 +REF: ZIEL IST ES DIE ÜBEREINSTIMMUNG EINES SOFTWARESYSTEMS MIT SEINER SPEZIFIKATION ZU ÜBERPRÜFEN +HYP: **** *** ** *** **************** ***** ZIELISTERS DIEVE ENTSTIMUGENESOFTJESESTEBZMIT SNNRSGHETZICHKATZGON ZUÜBARPOL FMSH +Eval: D D D D D D S S S S S S + +Speaker sentences 55: cv_deu_000752 #utts: 1 +id: (cv_deu_000752-cv_deu_000752) +Scores: (#C #S #D #I) 1 8 3 1 +REF: MIT EINEM WARMEN GETRÄNK IM BAUCH LÄSST SICH DIE KÄLTE besser ** AUSHALTEN +HYP: *** ***** ****** BN NINTE EINEN WAHRMNEN GEMPRENEN IONDBEUUSC NISSISTDIKÖNDELE besser UN SHEILTEN +Eval: D D D S S S S S S S I S + +Speaker sentences 56: cv_deu_000753 #utts: 1 +id: (cv_deu_000753-cv_deu_000753) +Scores: (#C #S #D #I) 3 6 3 2 +REF: *** ** die ANTIVIRENSOFTWARE ist AMOK GELAUFEN UND HAT alle COMPUTER IM HAUS LAHMGELEGT +HYP: DIE AN die WIERENSOCHÜÖR ist **** ANNURNKENGAL AUFHEN UNDEAND alle ******** ** GUNMBIEMTDELLN NERUSLANGELIGT +Eval: I I S D S S S D D S S + +Speaker sentences 57: cv_deu_000754 #utts: 1 +id: (cv_deu_000754-cv_deu_000754) +Scores: (#C #S #D #I) 0 6 1 0 +REF: IHRE KLOAKE IST IN DIESER ZEIT KUGELFÖRMIG +HYP: **** IERE TU ARGKE ISTEN DIESARZEIT GUGEFLNGENSC +Eval: D S S S S S S + +Speaker sentences 58: cv_deu_000755 #utts: 1 +id: (cv_deu_000755-cv_deu_000755) +Scores: (#C #S #D #I) 3 9 1 5 +REF: ** die STRECKE BEGINNT im SÜDEN VERONAS UND FÜHRT DURCH die ** ** ** ***** POEBENE RICHTUNG SÜDOSTEN +HYP: ET die STREIKER BEGEND im ****** SGIEN BERHUNERS IN FÜHRTIC die GO EB LE HECHT UNGM ASYÜT OSSTE +Eval: I S S D S S S S I I I I S S S + +Speaker sentences 59: cv_deu_000756 #utts: 1 +id: (cv_deu_000756-cv_deu_000756) +Scores: (#C #S #D #I) 4 5 0 0 +REF: erst von dort KONNTE er SEINEN WEG FREI FORTSETZEN +HYP: erst von dort KONTE er SEIM WEGFREI VOR SETZEN +Eval: S S S S S + +Speaker sentences 60: cv_deu_000757 #utts: 1 +id: (cv_deu_000757-cv_deu_000757) +Scores: (#C #S #D #I) 2 7 3 0 +REF: sie ERHEBT SICH heute IMMER NOCH GUT ERKENNBAR AUS DEM SCHWEMMLAND HERAUS +HYP: sie ****** ERHEBPZICH heute ***** **** IMARNOCKUTER KENBER AUSS DIEMN SCH WEMLANDTHERUSS +Eval: D S D D S S S S S S + +Speaker sentences 61: cv_deu_000758 #utts: 1 +id: (cv_deu_000758-cv_deu_000758) +Scores: (#C #S #D #I) 1 5 0 0 +REF: DIE KANARISCHEN INSELN GEHÖREN zu SPANIEN +HYP: TI ANARESCHEN INSLEN GEHE zu SBAHHE +Eval: S S S S S + +Speaker sentences 62: cv_deu_000759 #utts: 1 +id: (cv_deu_000759-cv_deu_000759) +Scores: (#C #S #D #I) 1 8 0 0 +REF: WISSENSCHAFTLER HABEN DIESE MUTATION BISHER NUR bei FRAUEN BEOBACHTET +HYP: WESSN SCHAFLERHAHABEN DESIM ENTATZ UN PESERETNO bei FOARAUN BEROBATET +Eval: S S S S S S S S + +Speaker sentences 63: cv_deu_000760 #utts: 1 +id: (cv_deu_000760-cv_deu_000760) +Scores: (#C #S #D #I) 2 5 0 2 +REF: ***** SEINE GESCHÄFTSBEZIEHUNGEN REICHTEN bis **** NORDAMERIKA und ASIEN +HYP: SEINI GISCHEHT ZSBEZTIUNGEN REISCHTEN bis NORD DOMEHRICKA und ASIEREN +Eval: I S S S I S S + +Speaker sentences 64: cv_deu_000761 #utts: 1 +id: (cv_deu_000761-cv_deu_000761) +Scores: (#C #S #D #I) 3 9 0 3 +REF: ******* ZAHLREICHE VORDERE PLATZIERUNGEN bei deutschen EUROPA und ********* ******** WELTMEISTERSCHAFTEN SOWIE OLYMPISCHEN SPIELEN FOLGTEN +HYP: SALREIC IEPVORDERED LATIERUNG EN bei deutschen ÖROPAR und WELTLESTE SCAFTERN SO IE OLÖBISCHEN SPILEN VORLGKTEN +Eval: I S S S S I I S S S S S + +Speaker sentences 65: cv_deu_000762 #utts: 1 +id: (cv_deu_000762-cv_deu_000762) +Scores: (#C #S #D #I) 2 5 2 0 +REF: in EINER TAGESZEITUNG BLÄTTERND SITZT SIEGFRIED auf EINER PARKBANK +HYP: in ***** ENERTALES CERTUM BLETERMTD SOETSIKIT auf ***** ERBAGBANG +Eval: D S S S S D S + +Speaker sentences 66: cv_deu_000763 #utts: 1 +id: (cv_deu_000763-cv_deu_000763) +Scores: (#C #S #D #I) 0 8 4 0 +REF: MIT EINEM WARMEN GETRÄNK IM BAUCH LÄSST SICH DIE KÄLTE BESSER AUSHALTEN +HYP: *** ***** ****** ******** WIT EIM WAHMLITRENK IN BAUFLESSE IE KERLTE BESEAUSHAUELT +Eval: D D D D S S S S S S S S + +Speaker sentences 67: cv_deu_000764 #utts: 1 +id: (cv_deu_000764-cv_deu_000764) +Scores: (#C #S #D #I) 0 2 1 0 +REF: FOLGE DEM QUERVERWEIS +HYP: ***** WOLLEDEM ÖEEWES +Eval: D S S + +Speaker sentences 68: cv_deu_000765 #utts: 1 +id: (cv_deu_000765-cv_deu_000765) +Scores: (#C #S #D #I) 1 9 1 0 +REF: OSTERN IST IMMER eine WOCHE NACH DEM ERSTEN VOLLMOND IM FRÜHLING +HYP: OSTAN DIS IENE eine ***** BOCHENACH EM ESTU VORNMUN DIM PGLULIEN +Eval: S S S D S S S S S S + +Speaker sentences 69: cv_deu_000766 #utts: 1 +id: (cv_deu_000766-cv_deu_000766) +Scores: (#C #S #D #I) 1 7 0 1 +REF: ** IM MITTELALTER HATTEN WECHSELNDE HERRSCHAFTEN das DORF INNE +HYP: EM MITEL EITERHATEN DEXZEM DER HARSCHAFT das DOF INER +Eval: I S S S S S S S + +Speaker sentences 70: cv_deu_000767 #utts: 1 +id: (cv_deu_000767-cv_deu_000767) +Scores: (#C #S #D #I) 0 5 4 0 +REF: DEN NAMEN GHIBLI TRAGEN AUCH WEITERE FAHRZEUGE VON MASERATI +HYP: *** ***** ****** ****** DIE NAM SCIEPLE TRAGNOCHREITERDE FALTSOLGEFADMASEHATIE +Eval: D D D D S S S S S + +Speaker sentences 71: cv_deu_000768 #utts: 1 +id: (cv_deu_000768-cv_deu_000768) +Scores: (#C #S #D #I) 0 5 6 0 +REF: DU KANNST MIT DEM BUS NACH FRANKFURT AN DER ODER FAHREN +HYP: ** ****** *** *** *** **** PLUKANNSWIT DE BUSLCHRANG VON ERODERFOREN +Eval: D D D D D D S S S S S + +Speaker sentences 72: cv_deu_000769 #utts: 1 +id: (cv_deu_000769-cv_deu_000769) +Scores: (#C #S #D #I) 0 1 2 0 +REF: MIR DOCH EGAL +HYP: *** **** IERDOCREGOL +Eval: D D S + +Speaker sentences 73: cv_deu_000770 #utts: 1 +id: (cv_deu_000770-cv_deu_000770) +Scores: (#C #S #D #I) 3 10 1 2 +REF: ***** ***** ALLERDINGS ERGABEN WEITERE prÜfungen dass ES mittelfristig KEINEN BEDARF FÜR EINE SOLCHE AUTOBAHN GÄBE +HYP: ALLER DINGS ER GAHBEN WEITERHRE prÜfungen dass S mittelfristig ****** KEIN PEDARFRIS CEUCHE AUTOBANGER WE N +Eval: I I S S S S D S S S S S S + +Speaker sentences 74: cv_deu_000771 #utts: 1 +id: (cv_deu_000771-cv_deu_000771) +Scores: (#C #S #D #I) 1 8 1 0 +REF: UMGEKEHRT KANN EIN FREIBRIEF eine AUSSCHREIBUNG ALS VOGELFREI GEMEINT SEIN +HYP: UNGEKERT KAN EN FREIPRIEF eine ************* ARAUSSCHREIBUNG ALTS VOBELFREI GEMEINDZEINEN +Eval: S S S S D S S S S + +Speaker sentences 75: cv_deu_000772 #utts: 1 +id: (cv_deu_000772-cv_deu_000772) +Scores: (#C #S #D #I) 0 6 0 1 +REF: ****** BIZARRGROTESKE ABSCHNITTE ZEIGEN EINFLÜSSE DURCH SCHOSTAKOWITSCH +HYP: MIEZAG KROTESGE ABSCHNITE SEINEN EINFLUSSE LEUCH SCHOSTAKOWICH +Eval: I S S S S S S + +Speaker sentences 76: cv_deu_000773 #utts: 1 +id: (cv_deu_000773-cv_deu_000773) +Scores: (#C #S #D #I) 5 6 1 3 +REF: ER WAR EINER der **** PIONIERE auf DEM gebiet der NUTZUNG der ****** *** SONNENENERGIE +HYP: ** RVER EINE der PIEO NIERE auf DM gebiet der UTZIUNG der SONENE NER GEE +Eval: D S S I S S S I I S + +Speaker sentences 77: cv_deu_000774 #utts: 1 +id: (cv_deu_000774-cv_deu_000774) +Scores: (#C #S #D #I) 1 8 4 0 +REF: AUCH WENN MIR DIE kunden AUF DIE NERVEN GEHEN MUSS ICH HÖFLICHKEIT BEWAHREN +HYP: **** **** ACHVEN MEDI kunden *** *** AF DENE REN GEN MSICHÖFLICHKEIT BEWAN +Eval: D D S S D D S S S S S S + +Speaker sentences 78: cv_deu_000775 #utts: 1 +id: (cv_deu_000775-cv_deu_000775) +Scores: (#C #S #D #I) 1 2 1 0 +REF: die SPÜLMASCHINE IST FERTIG +HYP: die ************* BEMASCHINERST FERTICH +Eval: D S S + +Speaker sentences 79: cv_deu_000776 #utts: 1 +id: (cv_deu_000776-cv_deu_000776) +Scores: (#C #S #D #I) 5 4 1 0 +REF: in DER archaischen periode wurden ERSTE FORMEN des ACKERBAUS ENTWICKELT +HYP: in DE archaischen periode wurden RSTI VORMIN des ********* OCKEBASSINTUYKILD +Eval: S S S D S + +Speaker sentences 80: cv_deu_000777 #utts: 1 +id: (cv_deu_000777-cv_deu_000777) +Scores: (#C #S #D #I) 0 5 3 0 +REF: DIE KOMÖDIE SEI BESSER ALS DER ERSTE FILM +HYP: *** ******** *** DI COMÖÜDIER SEESE ALSTER STFÜN +Eval: D D D S S S S S + +Speaker sentences 81: cv_deu_000778 #utts: 1 +id: (cv_deu_000778-cv_deu_000778) +Scores: (#C #S #D #I) 0 3 1 0 +REF: AKTUELL GILT FOLGENDER MODUS +HYP: ******* ARTUÄ GET VERGNEAMUMS +Eval: D S S S + +Speaker sentences 82: cv_deu_000779 #utts: 1 +id: (cv_deu_000779-cv_deu_000779) +Scores: (#C #S #D #I) 1 10 0 1 +REF: DAMIT ENDET eine ****** ERFOLGREICHE INTERNATIONALE BILDUNGSARBEIT VOR ALLEM IM MUSISCHKULTURELLEN BEREICH +HYP: TEARMIT ENTET eine EFÜRK KREISCHE INTLNATZEUNEALIE KEÄRDENS ABEN VORELEN INMSCHKÜTE LUNENENZS ACKUR +Eval: S S I S S S S S S S S + +Speaker sentences 83: cv_deu_000780 #utts: 1 +id: (cv_deu_000780-cv_deu_000780) +Scores: (#C #S #D #I) 3 7 1 1 +REF: DER SOHN eines BERGMANNS BEGANN SEINE FUSSBALLKARRIERE BEI den sportfreunden **** WANNEEICKEL +HYP: *** DERSON eines BERETNANZ BEGAN SEINIE FOSBEIKAERI WEI den sportfreunden WANE EIKEL +Eval: D S S S S S S I S + +Speaker sentences 84: cv_deu_000781 #utts: 1 +id: (cv_deu_000781-cv_deu_000781) +Scores: (#C #S #D #I) 1 9 0 0 +REF: in DIESEM JAHR GAB ES SIEBEN NUMMEREINSSINGLES UND SECHSUNDDREISSIG NUMMEREINSALBEN +HYP: in DIESENJAHRGABESIEDEN OME EINENSINGES UND SECHSON DEISIG NOMER EIENS ALLEBEN +Eval: S S S S S S S S S + +Speaker sentences 85: cv_deu_000782 #utts: 1 +id: (cv_deu_000782-cv_deu_000782) +Scores: (#C #S #D #I) 2 6 0 2 +REF: **** NORDWESTLICH von HACKHAUSEN BEFINDET sich *** DIE ORTSCHAFT HACKENBROICH +HYP: NORD WESTLICH von HAKHAUSEN BEFINDE sich DIO RTSCHAFTHAKEN BRUCH N +Eval: I S S S I S S S + +Speaker sentences 86: cv_deu_000783 #utts: 1 +id: (cv_deu_000783-cv_deu_000783) +Scores: (#C #S #D #I) 7 7 0 1 +REF: im ort ***** GNARRENBURG GINGEN viele soziale EINRICHTUNGEN von HERMANN lamprecht UND DER MARIENHÜTTE aus +HYP: im ort KNAEN BURG GIEN viele soziale EINERICHTEUNGEN von EREMAN lamprecht UNDER MAIN HÖTE aus +Eval: I S S S S S S S + +Speaker sentences 87: cv_deu_000784 #utts: 1 +id: (cv_deu_000784-cv_deu_000784) +Scores: (#C #S #D #I) 6 5 1 1 +REF: ich werde FOLGLICH den rat Über DIE IM PARLAMENT vorgetragenen ********* BEDENKEN INFORMIEREN +HYP: ich werde FOLÖKLICH den rat Über *** DIEM PALMENT vorgetragenen BEDENKTEN IN VORMIEREN +Eval: S D S S I S S + +Speaker sentences 88: cv_deu_000785 #utts: 1 +id: (cv_deu_000785-cv_deu_000785) +Scores: (#C #S #D #I) 4 5 5 0 +REF: es WÄRE TRAURIG GEWESEN ein so WICHTIGES THEMA NICHT IM KONSENS VERABSCHIEDEN zu KÖNNEN +HYP: es ***** ******* ERETRAURECGEWESEN ein so ********* ***** ***** WICHTDIESTEMANICHTEM KONSET FABSCHEN zu KÖN +Eval: D D S D D D S S S S + +Speaker sentences 89: cv_deu_000786 #utts: 1 +id: (cv_deu_000786-cv_deu_000786) +Scores: (#C #S #D #I) 3 9 0 1 +REF: NACH DESSEN TOD im ******** GLEICHEN JAHR KAM ES KURZFRISTIG an andere BESITZER +HYP: NOCHTIS SIM TOT im KLEICHEN JAH GAMM S GUTZ BRISTIG an andere VISEZEA +Eval: S S S I S S S S S S + +Speaker sentences 90: cv_deu_000787 #utts: 1 +id: (cv_deu_000787-cv_deu_000787) +Scores: (#C #S #D #I) 2 8 2 1 +REF: KURZ danach GAB ES EINEN WERBESPOT MIT DEM CANCAN von ******* JACQUES OFFENBACH +HYP: KOT danach *** ** GABES EINEIN WERBER VORD MINTDEMTKANDKAMNEND von SCAKESH OUEN BACH +Eval: S D D S S S S S I S S + +Speaker sentences 91: cv_deu_000788 #utts: 1 +id: (cv_deu_000788-cv_deu_000788) +Scores: (#C #S #D #I) 1 2 0 1 +REF: das ** IST BESSER +HYP: das IT BSE AET +Eval: I S S + +Speaker sentences 92: cv_deu_000789 #utts: 1 +id: (cv_deu_000789-cv_deu_000789) +Scores: (#C #S #D #I) 1 3 2 1 +REF: WIE SIEHT ES MIT GLEITZEIT aus ** +HYP: *** ***** WISIE SMIN LECHZEIT aus HR +Eval: D D S S S I + +Speaker sentences 93: cv_deu_000790 #utts: 1 +id: (cv_deu_000790-cv_deu_000790) +Scores: (#C #S #D #I) 2 8 2 0 +REF: NAHE dem DORF BEFINDET SICH AUCH der GRAND CANYON NATIONAL PARK AIRPORT +HYP: NACHE dem **** ******** DOCHF BEFIN der SIGH ARUCH DER KMKANIUN NASIONALLBACHKERBOT +Eval: S D D S S S S S S S + +Speaker sentences 94: cv_deu_000791 #utts: 1 +id: (cv_deu_000791-cv_deu_000791) +Scores: (#C #S #D #I) 0 5 5 0 +REF: SIE SOLLEN VERKÜNDEN DASS DIE LIEBE DEN TOD BESIEGT HAT +HYP: *** ****** ********** **** *** IESOREN DEAKÖNDEN DES DJELIEBEDENTORD BESIGTAT +Eval: D D D D D S S S S S + +Speaker sentences 95: cv_deu_000792 #utts: 1 +id: (cv_deu_000792-cv_deu_000792) +Scores: (#C #S #D #I) 0 9 0 1 +REF: ******* BEDECKT IST DIE REPRÄSENTATIV GESTALTETE VILLA MIT EINEM MANSARDDACH +HYP: BETECKT SSTDIE REPRESENTHERTIEF GESTALTE DE WILER MITD EINERN MANDSART DACH +Eval: I S S S S S S S S S + +Speaker sentences 96: cv_deu_000793 #utts: 1 +id: (cv_deu_000793-cv_deu_000793) +Scores: (#C #S #D #I) 1 7 0 1 +REF: DIESE SIEDLUNG IST MIT DER ORTSCHAFT dellach ******* ZUSAMMENGEWACHSEN +HYP: DIE SE SIETLUNG ES MITER ORTSCHACFT dellach ZUSAMEN GEWAKZEN +Eval: S S S S S S I S + +Speaker sentences 97: cv_deu_000794 #utts: 1 +id: (cv_deu_000794-cv_deu_000794) +Scores: (#C #S #D #I) 0 3 4 0 +REF: WART IHR SCHON EINMAL IN DEM CLUB +HYP: **** *** ***** ****** WARISCHN EINMALIENDEM KLO +Eval: D D D D S S S + +Speaker sentences 98: cv_deu_000795 #utts: 1 +id: (cv_deu_000795-cv_deu_000795) +Scores: (#C #S #D #I) 3 2 1 0 +REF: WO RAUCH ist ist auch FEUER +HYP: ** BORAUR ist ist auch VOLOR +Eval: D S S + +Speaker sentences 99: cv_deu_000796 #utts: 1 +id: (cv_deu_000796-cv_deu_000796) +Scores: (#C #S #D #I) 1 12 1 3 +REF: DIREKT VON DER STRASSE WURDEN SIE von ***** ****** ******* ALFRED BIOLEK FÜR SEINE FERNSEHSHOW SHOWBÜHNE ENGAGIERT +HYP: ****** DIE HEX VONDR STRASE WUDENSN von ALFET DIOLEK BESEINE FESTEN SEI SCHO E SCHABLNE EN GÜLSIERTE +Eval: D S S S S S I I I S S S S S S S + +Speaker sentences 100: cv_deu_000797 #utts: 1 +id: (cv_deu_000797-cv_deu_000797) +Scores: (#C #S #D #I) 0 11 1 0 +REF: EIN JAHR SPÄTER WECHSELTE ER ZU HEALTH NET UND ER WURDE ERFOLGREICHER +HYP: *** AIN HARSPÄITE VEXSLTER ELTZUN ELFT NATZS UNM BE VODE ELF VUNGEREICHE +Eval: D S S S S S S S S S S S + +Speaker sentences 101: cv_deu_000798 #utts: 1 +id: (cv_deu_000798-cv_deu_000798) +Scores: (#C #S #D #I) 3 5 1 1 +REF: in der LANDWIRTSCHAFT KANN DER ERTRAG DEUTLICH REDUZIERT werden * +HYP: in der ************** LANDVITCHERF KANDER ERTRARGKT DEUTLI WEDORZIERT werden T +Eval: D S S S S S I + +Speaker sentences 102: cv_deu_000799 #utts: 1 +id: (cv_deu_000799-cv_deu_000799) +Scores: (#C #S #D #I) 2 7 0 1 +REF: MANSOUR SPIELTE in seiner ****** HEIMATSTADT KAIRO FÜR AL AHLY +HYP: MAN SURSPIERTE in seiner HEIMAT STADT KEE WORFIER ALL ALL +Eval: S S I S S S S S + +Speaker sentences 103: cv_deu_000800 #utts: 1 +id: (cv_deu_000800-cv_deu_000800) +Scores: (#C #S #D #I) 1 3 2 0 +REF: ER TRAT der FREIMAURERLOGE LAUTARO BEI +HYP: ** ERTRA der ************** REIMAUHALUNDEL NLAOUTABUEOBEI +Eval: D S D S S + +Speaker sentences 104: cv_deu_000801 #utts: 1 +id: (cv_deu_000801-cv_deu_000801) +Scores: (#C #S #D #I) 3 8 3 0 +REF: mit „FÜRST“ war EHER DIE SOZIALE ALS die RECHTLICHE ROLLE DES SO BEZEICHNETEN GEMEINT +HYP: mit FÜRT war **** *** ******* EHR die ROWPTHJADEL EILS DERECHTLICHEROLLE DIES SOBETSEICENEN GEMEIND +Eval: S D D D S S S S S S S + +Speaker sentences 105: fleurs_deu_000378 #utts: 1 +id: (fleurs_deu_000378-fleurs_deu_000378) +Scores: (#C #S #D #I) 7 15 4 2 +REF: LETZTE WOCHE gab das METI BEKANNT DASS ES von APPLE Über ****** 34 WEITERE VORFÄLLE von ÜBERHITZUNG INFORMIERT WORDEN WAR DIE DAS UNTERNEHMEN als nicht ***** SCHWERWIEGEND BEZEICHNETE +HYP: ****** LETZTEWOCHO gab das **** METIE BEKAND GASES von EPEL Über VIERND AS HWALTE FORFELE von ************ ********** ÜBER ITZEUN INTOMIRTWORDENWA IEDES UNTERNEHN als nicht SCHER IEN PEEITETE +Eval: D S D S S S S I S S S D D S S S S S I S S + +Speaker sentences 106: fleurs_deu_000379 #utts: 1 +id: (fleurs_deu_000379-fleurs_deu_000379) +Scores: (#C #S #D #I) 9 21 1 5 +REF: ******** ********* *** USA GYMNASTICS UNTERSTÜTZT den BRIEF des OLYMPISCHEN KOMITEES der *** VEREINIGTEN STAATEN und AKZEPTIERT es ALS ABSOLUTE NOTWENDIGKEIT DASS SICH die *********** OLYMPISCHE FAMILIE fÜr EIN SICHERES UMFELD fÜr ALLE UNSERE sportler EINSETZT +HYP: EJÜURSE JIMNESTIG UND TER STÜTZST E den BERIEF des OLÜMPISCHEN KOMITIS der VER EINIGTEN STATEN und DACIPTIERT es AS APBPTULUTE NOT WENDIKEIT DASIHT die OLÜMPISCHE VER MILIE fÜr *** EINGSICHERES UNMFELLT fÜr ALE UNSERER sportler EINSETT +Eval: I I I S S S S S S I S S S S S S S S I S S D S S S S S + +Speaker sentences 107: fleurs_deu_000380 #utts: 1 +id: (fleurs_deu_000380-fleurs_deu_000380) +Scores: (#C #S #D #I) 3 13 0 3 +REF: ***** **** ******************* DADURCH KANN ER ABWÄRTSKOMPATIBEL MIT 80211A 80211B und 80211G SEIN VORAUSGESETZT DIE BASISSTATION verfÜgt ÜBER dualradio +HYP: DALIC KENE APRETSKOMPETIBELMIT CHTRNERTZWEI PUND ELF ARACHTENR ZWEI PUNGD ELFPE und CHTEONETZWEIPUNGD ELFGESEIN VERASGES DI ASSISTATION verfÜgt BER dualradio +Eval: I I I S S S S S S S S S S S S S + +Speaker sentences 108: fleurs_deu_000381 #utts: 1 +id: (fleurs_deu_000381-fleurs_deu_000381) +Scores: (#C #S #D #I) 3 6 0 0 +REF: er BEZEICHNETE die GERÜCHTE als POLITISCHES GESCHWÄTZ UND ALBERNHEIT +HYP: er BEZEICHNES die IERÜCHTE als POLISCHESGISCHEÄTZS UNDT ALL BENHEITZ +Eval: S S S S S S + +Speaker sentences 109: fleurs_deu_000382 #utts: 1 +id: (fleurs_deu_000382-fleurs_deu_000382) +Scores: (#C #S #D #I) 3 20 3 0 +REF: LETZTE WOCHE GAB DAS METI BEKANNT DASS ES von APPLE Über 34 WEITERE VORFÄLLE VON ÜBERHITZUNG INFORMIERT WORDEN WAR DIE DAS UNTERNEHMEN als NICHT SCHWERWIEGEND BEZEICHNETE +HYP: ****** ***** LET E WOCHEGABT AS EMIEITHIE BEKANDASIS von EBEL Über ** VIRNDREISI WEITRE FORFVELEVON BERHITZUN IN VORMIR URDEN WA DIEDAS UNTERNEHMEM als NICHCH WRWEGEN BEZEIGNETE +Eval: D D S S S S S S S D S S S S S S S S S S S S S + +Speaker sentences 110: fleurs_deu_000383 #utts: 1 +id: (fleurs_deu_000383-fleurs_deu_000383) +Scores: (#C #S #D #I) 0 16 2 0 +REF: NACHDEM DER DAMM 1963 ERBAUT WORDEN WAR KAMEN DIE JAHRESZEITLICHEN ÜBERFLUTUNGEN DIE SEDIMENTE IM FLUSS VERTEILEN ZUM STILLSTAND +HYP: ******* *** NACH DE DERDM LEUNEH HNDERTRALN SECHZIG E BAURT WORDENWARKAM DIAHRES SEILIHN BERFLUTUNG DES DEMENTE MNFLSVERTALENZUM STILSTANT +Eval: D D S S S S S S S S S S S S S S S S + +Speaker sentences 111: fleurs_deu_000384 #utts: 1 +id: (fleurs_deu_000384-fleurs_deu_000384) +Scores: (#C #S #D #I) 7 14 6 1 +REF: ER WAR AUCH am stechen von GELDSCHEINEN FÜR VIELE LÄNDER BETEILIGT AKTUELLE BEISPIELE SEINER ARBEIT SCHLIESSEN DIE PREMIERMINISTERPORTRAITS AUF der VORDERSEITE der KANADISCHEN 5 und ***** 100DOLLARNOTEN ein +HYP: ** *** ERWAUCH am stechen von ************ **** ***** ******* GELSCHEINVE VIELEDENDE BETEILICGT AKTE LEBEISHPIESENE AR BETSCHLISENIEPRIMEHMINISTER PORTRES AF der FORDERSERT der KANADSCHEN FÜNF und UNDER OLLRNUTEN ein +Eval: D D S D D D D S S S S S S S S S S S S I S + +Speaker sentences 112: fleurs_deu_000385 #utts: 1 +id: (fleurs_deu_000385-fleurs_deu_000385) +Scores: (#C #S #D #I) 3 7 7 0 +REF: die HAUPTSTADT VON MOLDAWIEN ist KISCHINAU die EINHEIMISCHE SPRACHE IST RUMÄNISCH ABER VIELE MENSCHEN SPRECHEN AUCH RUSSISCH +HYP: die ********** AUPTSTAT VERMODAWIERN ist KICHINA die ************ ******* *** ********** **** ***** EINEIMPISPACHE STRUMENISCH ABARVIELEMENTENCSPRECHEN ROSSELC +Eval: D S S S D D D D D D S S S S + +Speaker sentences 113: fleurs_deu_000386 #utts: 1 +id: (fleurs_deu_000386-fleurs_deu_000386) +Scores: (#C #S #D #I) 8 17 6 3 +REF: ZWISCHEN den EINZELNEN DYNASTIEN HERRSCHTEN auch *** UNBESTÄNDIGE zeiten GETEILTER PROVINZEN DIE BEKANNTESTE DIESER perioden WAR DIE EPOCHE DER DREI KÖNIGREICHE die 60 JAHRE LANG zwischen der HAN UND der *** ******** JINDYNASTIE STATTFAND +HYP: SZWICHEN den EINZENEN DÜNESTIEN HERSTDEN auch UNM BESTENDIEGE zeiten ********* ********* GETALLTE PROINDZEDIE BEKANTESTEDIESE perioden *** *** ****** OADI E POCHEDERDRAILKÖNINGREICHE die ** SECHTICH IERELA zwischen der HAHN UNG der IEN DIENESTI STAT VADT +Eval: S S S S I S D D S S S D D D S S S D S S S S I I S S + +Speaker sentences 114: fleurs_deu_000387 #utts: 1 +id: (fleurs_deu_000387-fleurs_deu_000387) +Scores: (#C #S #D #I) 4 14 12 0 +REF: am ANDEREN ENDE DES spektrums VERWANDELT MAN SICH IN EIN NICHT WIEDERZUERKENNENDES INDIVIDUUM das ALLES ANDERS MACHEN MUSS ALS DAS TEAM ES GEMACHT HAT und SICH ALLES ZU EIGEN MACHT +HYP: am ******* **** ANDERENENEDR spektrums ********** *** HWANELTMANSICHEN EINICHT WIDER ZURKENDE NDIWIDE UM das ***** ****** ****** **** *** ALES ANERS MACHNMOSS ASSTIENES GEMACTER und **** ***** ** SIC ALESTZUALGEMACHT +Eval: D D S D D S S S S S S D D D D D S S S S S D D D S S + +Speaker sentences 115: fleurs_deu_000388 #utts: 1 +id: (fleurs_deu_000388-fleurs_deu_000388) +Scores: (#C #S #D #I) 3 29 12 0 +REF: die MEISTEN INTERPRETATIONEN DES TECHNOLOGISCHEN DETERMINISMUS TEILEN ZWEI ALLGEMEINE VORSTELLUNGEN EINERSEITS DASS DIE ENTWICKLUNG DER TECHNOLOGIE SELBST einem WEG FOLGT DER WEITGEHEND JENSEITS KULTURELLER ODER POLITISCHER EINFLUSSNAHME LIEGT UND ANDERERSEITS DASS TECHNOLOGIE IHRERSEITS AUSWIRKUNGEN AUF GESELLSCHAFTEN HAT die EHER INHÄRENT ALS SOZIAL BEDINGT SIND +HYP: die ******* **************** *** *************** ************* MEISTEND INTER PITEATIONEN DESTICHNOLOGISCHENDERTEIMINIESN USTALEN ZWEIALGEMEINE VORSCHTEUNGENEINER SEITS TSTD INTWICKLUM DERTICHNOLOGIESLLPST einem *** ***** *** ********** ******** *********** **** WEGOLGTDERWEITGENT IENSEIS UTDTORELEODER POLIISCHEINPLSNAMEN DIGTUND ANDERERSEIT DASTIC NERÜGIE IERERSEITS AUSWIRKUNG EN AUFGESALSCHAFTNHRT die EHR INHÄREND AS SZUTTIAL BEDIEN SID +Eval: D D D D D S S S S S S S S S S S D D D D D D D S S S S S S S S S S S S S S S S S S + +Speaker sentences 116: fleurs_deu_000389 #utts: 1 +id: (fleurs_deu_000389-fleurs_deu_000389) +Scores: (#C #S #D #I) 4 19 8 0 +REF: ZWISCHEN den EINZELNEN DYNASTIEN HERRSCHTEN AUCH UNBESTÄNDIGE ZEITEN GETEILTER PROVINZEN DIE BEKANNTESTE DIESER PERIODEN WAR DIE EPOCHE DER DREI KÖNIGREICHE die 60 JAHRE LANG ZWISCHEN der han UND DER JINDYNASTIE STATTFAND +HYP: WISCHE den ********* ********* ********** **** EINZEN DNARTIEN HERCHTEN ACH UNMBESTENDIGEZEITEN GETAILTE ROWINZEN DI BEKANDESTE DEPERIODEN WADI E POCHRO DERDEKÖNIGREICHE die ** SECHTIC ARLANGT ISCHEN der han *** *** *********** UNDTERINDENRTIESTTFVAND +Eval: S D D D D S S S S S S S S S S S S S S D S S S D D D S + +Speaker sentences 117: fleurs_deu_000390 #utts: 1 +id: (fleurs_deu_000390-fleurs_deu_000390) +Scores: (#C #S #D #I) 9 14 4 3 +REF: DEM LEAK ZUFOLGE BEZIEHT SICH DAS DOKUMENT auf DEN GRENZSTREIT in DEM die ***** PALÄSTINENSER ein ZURÜCKSETZEN der grenzen in den zustand ****** **************** VOR DEM SECHSTAGEKRIEG VON 1967 FORDERN +HYP: *** **** ******* ******* DIEMLIEKTZUFORGIEBIEZIZICHTES TO OMENT auf DEM GRENSTREILT in DEN die PALIS INENSER ein ZURÜEGSETZEN der grenzen in den zustand VORDEM SERXSTALGLEGRIEG VORN NENEHN UNDERT SEBEO NUSESTIG ORDER +Eval: D D D D S S S S S S I S S I I S S S S S S + +Speaker sentences 118: fleurs_deu_000391 #utts: 1 +id: (fleurs_deu_000391-fleurs_deu_000391) +Scores: (#C #S #D #I) 4 9 4 0 +REF: mit DEM VERLUST GRIECHISCHER SPRACHKENNTNISSE WAR der westen VON SEINEN PHILOSOPHISCHEN und WISSENSCHAFTLICHEN WURZELN IN GRIECHENLAND ABGESCHNITTEN +HYP: mit *** ******* IM PELUSTGRECHEHERSPACHKNNSE UR der westen *** VONSEIME VIELOSOPISCHEN und ****************** WISENSCHAFLICHEN WORTZE NKRICHENEN ABISCHNITEN +Eval: D D S S S D S S D S S S S + +Speaker sentences 119: fleurs_deu_000392 #utts: 1 +id: (fleurs_deu_000392-fleurs_deu_000392) +Scores: (#C #S #D #I) 1 24 8 0 +REF: wir STIMMEN MIT DER AUSSAGE DES USOC ÜBEREIN DASS DEN INTERESSEN UNSERER ATHLETEN UND VEREINE UND IHRES SPORTS BESSER GEDIENT IST WENN WIR INNERHALB UNSERER ORGANISATION SINNVOLLE VERÄNDERUNGEN VORANTREIBEN ANSTATT EINE DEZERTIFIZIERUNG VORZUNEHMEN +HYP: wir ******* *** *** ******* *** **** ******** **** STMITER AUSAGEDIES IUERS AUSIE ÜBEREINDASTEN NTRESSNUNDSREATLEDEND VEREINUN DIRESPORTSBPSSR GEDIENDIST EN WR NEHALB UNSR RGEN SATIONDEHN VOLEVER NDRUNG VERANDTREIBEN ANSTAT EINERTI ER IZET VIZIERUNG VORZUNE +Eval: D D D D D D D D S S S S S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 120: fleurs_deu_000393 #utts: 1 +id: (fleurs_deu_000393-fleurs_deu_000393) +Scores: (#C #S #D #I) 3 13 6 0 +REF: DIE KREUZFAHRTEN NACH SANKT PETERSBURG bieten AUCH ZEIT FÜR EINEN AUFENTHALT IN DER STADT KREUZFAHRTPASSAGIERE SIND VON der VISUMPFLICHT befreit SIEHE BEDINGUNGEN +HYP: *** ************ I REUTSFATEN NASSANGTPIKARSBOG bieten **** **** **** ***** ACH ZEITFÜR EIN AUFENTAL INERSTAT KREUTZVATPASRSIERE SINDFVOR der IESUNSLICHT befreit SIE BEDNGEN +Eval: D D S S S D D D D S S S S S S S S S S + +Speaker sentences 121: fleurs_deu_000394 #utts: 1 +id: (fleurs_deu_000394-fleurs_deu_000394) +Scores: (#C #S #D #I) 5 17 1 3 +REF: ******* REISENDE WERDEN DRINGEND GEWARNT auf *** JEDWEDE art von UNWETTER ZU ACHTEN die IHR GEBIET BETRIFFT DA DIES SICH auf ******** ALLE REISEPLÄNE AUSWIRKEN KANN +HYP: EREISEN DE VERDEN RINGEND GEWANT auf JET WEDE art von UN WENTE ZUACHTEN die *** IEGIE IET BIETRIFT DADIS SIGH auf ALEREISE PLENE AUS IERKEN KANT +Eval: I S S S S I S S S S D S S S S S I S S S S + +Speaker sentences 122: fleurs_deu_000395 #utts: 1 +id: (fleurs_deu_000395-fleurs_deu_000395) +Scores: (#C #S #D #I) 2 18 3 1 +REF: ** SIE BESAGT DASS der KREUZUNGSPUNKT DER LINIEN die EIN BILD VERTIKAL UND HORIZONTAL DRITTELN DER EFFEKTIVSTE PLATZ FÜR DAS HAUPTMOTIV IST SIEHE BEISPIEL +HYP: SE BE SART DAS der ************** KOLZUNGSPUNGKT DELINMIN die *** **** EN BIELT WERTIKAL UNT RET ONDTAL DRITEEN DE EFIKT ISTDEPLAS FÜRTESAUPTMUODI ISTSIE BEISSIN +Eval: I S S S D S S D D S S S S S S S S S S S S S + +Speaker sentences 123: fleurs_deu_000396 #utts: 1 +id: (fleurs_deu_000396-fleurs_deu_000396) +Scores: (#C #S #D #I) 4 24 7 2 +REF: seit ********* 1988 MÜSSEN WAHLURNEN TRANSPARENT SEIN damit WÄHLER UND BEOBACHTER BEZEUGEN KÖNNEN DASS ZU BEGINN DER WAHL KEINE UMSCHLÄGE VORHANDEN SIND UND DASS KEINE UMSCHLÄGE EINGEWORFEN werden AUSSER jene **** DER ORDNUNGSGEMÄSS GEZÄHLTEN UND AUTORISIERTEN WÄHLER +HYP: seit NUNZENURT ACHTEN ACHTICH MSTE WALUND RANSBERENSEIN damit ******* *** ********** ******** ******* **** ** WELE UNBEOBACHTE BETZEUGENKÖN DAS Z WEGINDER WALKEINE UMSCHLEGE VERHNENSIND UNDASKEIN UMSCHLEGE EINGEOFEN werden AUSER jene DERT ORDUNGS MIS EHLTNEN ATTR SIRDTEN ELER +Eval: I S S S S S D D D D D D D S S S S S S S S S S S S S I S S S S S S + +Speaker sentences 124: fleurs_deu_000397 #utts: 1 +id: (fleurs_deu_000397-fleurs_deu_000397) +Scores: (#C #S #D #I) 8 14 1 5 +REF: OTTAWA ist ********* ********* KANADAS BEZAUBERNDE ZWEISPRACHIGE HAUPTSTADT und ZEICHNET sich DURCH EINE REIHE VON KUNSTGALERIEN und museen aus die ********* KANADAS VERGANGENHEIT und ***** **** GEGENWART PRÄSENTIEREN +HYP: OTERAR ist KANNERDES BIEZAUBEN DER ZWEISCHALI GE HAUPTSTAT und SELTEN sich ***** IC EINEREIE VUN KUNZSTGELERIEN und museen aus die KANENDERS VERGANGEN HELT und GEGEN WART BRESEN TIERE +Eval: S I I S S S S S D S S S S I S S I I S S + +Speaker sentences 125: fleurs_deu_000398 #utts: 1 +id: (fleurs_deu_000398-fleurs_deu_000398) +Scores: (#C #S #D #I) 2 5 4 0 +REF: diese PAARE KÖNNEN sich FÜR EINEN ADOPTIONSPLAN FÜR IHR BABY ENTSCHEIDEN +HYP: diese ***** PAREKÖREN sich **** ***** ************* VEREIN ADEBPTIONSPLANDVR ERBEBE ENSCHEIDEN +Eval: D S D D D S S S S + +Speaker sentences 126: fleurs_deu_000399 #utts: 1 +id: (fleurs_deu_000399-fleurs_deu_000399) +Scores: (#C #S #D #I) 3 10 3 1 +REF: ** INFOLGEDESSEN SIND zwei FISCHARTEN AUSGESTORBEN UND ZWEI WEITERE SIND vom AUSSTERBEN BEDROHT DARUNTER der GILA CYPHA +HYP: IN FOL GEDESSENSEN zwei ********** ************ FISHT ABEN AUSGESTORME NDZWEWEITRUSEN vom AUSTERBE BETROT DAUNE der **** GELARSZYÜFER +Eval: I S S D D S S S S S S S D S + +Speaker sentences 127: fleurs_deu_000400 #utts: 1 +id: (fleurs_deu_000400-fleurs_deu_000400) +Scores: (#C #S #D #I) 5 9 6 0 +REF: PFLANZEN SEHEN IN IHRER NATÜRLICHEN UMGEBUNG AM besten aus WIDERSTEHEN SIE also DER VERSUCHUNG auch nur EIN EXEMPLAR ZU ENTFERNEN +HYP: ******** ***** ** ***** TRANZENSEN NIHRNATILICHEN MOGEBNGAM besten aus WIEDER STENSI also *** DERVERSUCHUNG auch nur *** EINEXMKLAU D WERN +Eval: D D D D S S S S S D S D S S S + +Speaker sentences 128: fleurs_deu_000401 #utts: 1 +id: (fleurs_deu_000401-fleurs_deu_000401) +Scores: (#C #S #D #I) 5 12 6 0 +REF: auf der nahseite KÖNNTE ES MEHR MARIA geben DA DIE KRUSTE DÜNNER IST es WAR EINFACHER FÜR DIE LAVA AN DIE OBERFLÄCHE AUFZUSTEIGEN +HYP: auf der nahseite ******* KÖNTE IS MERMARIER geben ** *** DADI GROSTE DUNEST es *** ********* **** WER EIN FERAFODIE LAVER ANDIOBERFLECH AUFTUSTEGEN +Eval: D S S S D D S S S D D D S S S S S S + +Speaker sentences 129: fleurs_deu_000402 #utts: 1 +id: (fleurs_deu_000402-fleurs_deu_000402) +Scores: (#C #S #D #I) 5 16 2 2 +REF: er FÜGTE HINZU DASS SIE JEDOCH NICHT DAZU AUFGEFORDERT werden SOLLTEN VERPFLICHTUNGEN EINZUGEHEN die Über IHREN ENTWICKLUNGSSTAND IHRE VERANTWORTUNG und * ************* IHRE FÄHIGKEITEN HINAUSGEHEN +HYP: er ****** ***** FÜCKTECHIN ZU DASSIJEDOCH NICHTEAR ZUOAUFGIE VORDERT werden SOLLTERN FERTFLICHTUNGEN EINDZUGEEN die Über IEREN INTWITLUNGSTAND IERE VERANTORDUNG und I RERFÄIGKATEN HIENAUS NGEEN T +Eval: D D S S S S S S S S S S S S S I I S S S + +Speaker sentences 130: fleurs_deu_000403 #utts: 1 +id: (fleurs_deu_000403-fleurs_deu_000403) +Scores: (#C #S #D #I) 3 19 0 6 +REF: ******* *** VIRTUELLE HILFESTELLUNGEN SIND in ****** DIE SOFTWARE EINGEBAUT UND SOLLEN ARBEITSSCHRITTE DIE DER schÜler ***** ******** ** ALLEIN MÖGLICHERWEISE NICHT BEWÄLTIGEN KANN HINTERFRAGEN NAHELEGEN und ERKLÄREN +HYP: TCIERTU ELI HIEL FISTELUNGEN SINT in DISOFT ER ENGIE BAUD UN SOLEN AHBEITSCHRITDE ND IEDE schÜler ALEIN MÖGLICH ER WEISE NIHT BEVELTIGEN KÖR HINTER FRAGEN NEIELEGEN und DERGLEREN +Eval: I I S S S I S S S S S S S S I I I S S S S S S S S + +Speaker sentences 131: fleurs_deu_000404 #utts: 1 +id: (fleurs_deu_000404-fleurs_deu_000404) +Scores: (#C #S #D #I) 2 14 0 3 +REF: am ********* ** *** 15 AUGUST 1940 FIELEN DIE ALLIIERTEN IN SÜDFRANKREICH ein DIE INVASION WURDE OPERATION DRAGOON GENANNT +HYP: am FÜNFZEHN NA GST NEUNZHN HUDERT VIRZI VIELI ALIERTEN N SÜT FRANKREICH ein DIN WASION WORDE APERESCHEN ROGUN GENRD +Eval: I I I S S S S S S S S S S S S S S + +Speaker sentences 132: fleurs_deu_000405 #utts: 1 +id: (fleurs_deu_000405-fleurs_deu_000405) +Scores: (#C #S #D #I) 2 10 9 0 +REF: er GRIFF AUCH ALLES an WAS INS WASSER KAM SELBST EIN GROSSER DINOSAURIER WIE DER T REX WAR IHM NICHT GEWACHSEN +HYP: er ***** GRIFACH ALLS an *** *** ****** *** ****** *** ******* *********** WASSENS WASSERKARM SEBST EN GROSERDENOSAURIE WIDER TIERECGSWERIMNICHT GEWAKEN +Eval: D S S D D D D D D D D S S S S S S S S + +Speaker sentences 133: fleurs_deu_000406 #utts: 1 +id: (fleurs_deu_000406-fleurs_deu_000406) +Scores: (#C #S #D #I) 2 16 2 0 +REF: SEIT DER GRÜNDUNG VON ASUNCIÓN 1537 IST ES PARAGUAY GELUNGEN viel von SEINEM INDIGENEN CHARAKTER UND SEINER IDENTITÄT ZU BEWAHREN +HYP: **** SEITER KRNDUNG VOR ASUNTIOR FÜNFZEN DE SENUODREISCIES ESPAREGWEI GELUNG viel von ****** SEIM INDIGEHNEN KARAKTER NDSEINE IDE NITETZU BEWAREN +Eval: D S S S S S S S S S D S S S S S S S + +Speaker sentences 134: fleurs_deu_000407 #utts: 1 +id: (fleurs_deu_000407-fleurs_deu_000407) +Scores: (#C #S #D #I) 9 20 2 5 +REF: ****** TROTZDEM IST der ********* ANTEIL AN XDRTB in der gesamten GRUPPE der LEUTE mit *** *** TUBERKULOSE OFFENBAR DENNOCH GERING 6000 der ********* INSGESAMT 330000 LEUTE die IN SÜDAFRIKA ZU einem BESTIMMTEN ZEITPUNKT ANGESTECKT SIND +HYP: STROZS DEN IS der ANTELLANN IEXSDIE WINDESTIGHTDE B in der gesamten RUPE der LEUTEE mit DUG DER KOLOSER OFENBA DERN NOCH GERINENSEXSTAUSEN der INDGESAEN DREIHUNDENDREIG AUSEN LELTE die ** INSÜTAFRICKER TU einem ********** BIESTINTEND EITPUNGTANGESTET SITT +Eval: I S S I S S S S S I I S S S S S I S S S D S S D S S S + +Speaker sentences 135: fleurs_deu_000408 #utts: 1 +id: (fleurs_deu_000408-fleurs_deu_000408) +Scores: (#C #S #D #I) 4 11 0 0 +REF: ANGEL 2006 ERLÄUTERT DAS KONTINUUMKONZEPT ALS eine METHODE um ORGANISATIONEN ZU HELFEN LEISTUNGSFÄHIGER zu werden +HYP: ENSCHEL ZWEITAUSEN SECHS ELEUTERT ASKONTINUM KONZETAS eine MITODE um OBGENSATIONDS HLFEN LEISTUMGS FEGE zu werden +Eval: S S S S S S S S S S S + +Speaker sentences 136: fleurs_deu_000409 #utts: 1 +id: (fleurs_deu_000409-fleurs_deu_000409) +Scores: (#C #S #D #I) 3 10 4 0 +REF: in DIESER PERIODE DER EUROPÄISCHEN GESCHICHTE stand DIE REICH und MÄCHTIG GEWORDENE KATHOLISCHE KIRCHE AUF DEM PRÜFSTAND +HYP: in ****** ******* DIESE PIERIODEN DEROEROPESCHENGICHIGHTE stand *** DIERLICH und ******** MECHTIHE GEVORDENER ATOLISCHIYKIECHE AUFDEN PRÜSTANDT T +Eval: D D S S S D S D S S S S S S + +Speaker sentences 137: fleurs_deu_000410 #utts: 1 +id: (fleurs_deu_000410-fleurs_deu_000410) +Scores: (#C #S #D #I) 6 24 4 1 +REF: die *** ERSTE DER 78 EMPFEHLUNGEN IST DASS eine NEUE DIPLOMATISCHE INITIATIVE VOR ENDE DIESES JAHRES ERGRIFFEN WERDEN SOLLTE um die IRAKISCHEN grenzen GEGENÜBER FEINDLICHEN INTERVENTIONEN ZU SICHERN und DIPLOMATISCHE BEZIEHUNGEN MIT SEINEN NACHBARN WIEDERHERZUSTELLEN +HYP: die ERS DR CHTEN DIBZIC EM FHLUNG IFTAS eine **** ************* NEU DEPLOMATSCH INI ZATIEVEVOERENDE DIESEN JAHRESE GRIFEN WERDENSOLLTE um die RAGISHEN grenzen ********** *********** GEGE BERFENTLIHENTERWETIOND ZUSICHARN und PLOMATSCH BIZIEO MIZEINACHBAN IEDER RTU STE +Eval: I S S S S S S D D S S S S S S S S S D D S S S S S S S S S + +Speaker sentences 138: fleurs_deu_000411 #utts: 1 +id: (fleurs_deu_000411-fleurs_deu_000411) +Scores: (#C #S #D #I) 2 9 10 0 +REF: DIES BIETET EINE GUTE GELEGENHEIT das NORDLICHT ZU SEHEN DA DER HIMMEL MEHR ODER WENIGER rund UM DIE UHR DUNKEL IST +HYP: **** ****** DISPETET EINI UTEGELEGEGHEIT das ********* ** ***** ** *** NOTLICHTUSEN DAE HIMEM MEHRAUDERWENIER rund ** *** *** UMDIEURDUNKEL ST +Eval: D D S S S D D D D D S S S S D D D S S + +Speaker sentences 139: fleurs_deu_000412 #utts: 1 +id: (fleurs_deu_000412-fleurs_deu_000412) +Scores: (#C #S #D #I) 4 18 2 0 +REF: PROFESSORIN PAMELA FERGUSON von der UNIVERSITY OF DUNDEE MERKT an JOURNALISTEN SCHEINEN eine GEFÄHRLICHE GRENZE ZU ÜBERSCHREITEN WENN SIE FOTOS USW VON VERDÄCHTIGEN VERÖFFENTLICHEN +HYP: PROFSSOREN PAMELE VERGUSSON von der N WÜUSTI AF DANDIMERKT an ************ SOALISTENSCHEIN eine ************ GEELIEGRANZE ZUASCRETEN WEN DIE VOTTO UN SWEITE VONVERDECTIGE VER FNTICHEN +Eval: S S S S S S S D S D S S S S S S S S S S + +Speaker sentences 140: fleurs_deu_000413 #utts: 1 +id: (fleurs_deu_000413-fleurs_deu_000413) +Scores: (#C #S #D #I) 3 14 7 0 +REF: ES KANN SICH AUCH LOHNEN eine WILD CARD zu KAUFEN DIE ZUTRITT ENTWEDER ZU AUSGEWÄHLTEN PARKS IN SÜDAFRIKA ODER zu ALLEN SÜDAFRIKANISCHEN NATIONALPARKS GEWÄHRT +HYP: ** **** ESKANSI CH LON eine **** ELKAT zu ****** *** KAUFNDIE ZUTRIT EN WDERTU USGEWELEN PAGSEN HET AFRIKARDER zu ***** ***************** ALENZUÜT FRIKANSCHNERTONALPARXSGEWERT +Eval: D D S S S D S D D S S S S S S S S D D S S + +Speaker sentences 141: fleurs_deu_000414 #utts: 1 +id: (fleurs_deu_000414-fleurs_deu_000414) +Scores: (#C #S #D #I) 2 20 0 0 +REF: die BRÜCKE SOLL IM SEPTEMBER 2017 VOLLSTÄNDIG DEN BETRIEB AUFNEHMEN ES WIRD ERWARTET DASS DIE BRASILIANISCHEN ZOLLKONTROLLPUNKTE DANN fertig GESTELLT SEIN WERDEN +HYP: die PRÜKESOLEMSE TEMBER ZWEITAUEN SIB SHN FOLSTENDIT N BETRIE AUFNEHMN ISWR RWARE DS I PAS IANISCHEN ZOLPUNKTE DAN fertig STELL ZEIN WERT +Eval: S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 142: fleurs_deu_000415 #utts: 1 +id: (fleurs_deu_000415-fleurs_deu_000415) +Scores: (#C #S #D #I) 1 23 5 0 +REF: WÄHREND EIN EXPERIMENTELLER IMPFSTOFF IN DER lage ZU SEIN SCHEINT DIE EBOLAMORTALITÄT ZU SENKEN GIBT ES BISHER KEINE MEDIKAMENTE DIE ALS EINDEUTIG ZUR BEHANDLUNG BESTEHENDER INFEKTIONEN GEEIGNET NACHGEWIESEN WURDEN +HYP: ******** WEREN DEI ERXPRMINTELLE IMSTF NE lage ** **** ******* *** USEINSCHEINTDI EBOLERMOTLIETÄTZU SENG UNG GBTES BSSERKEINEMITIKAMÄNTE DIALS EINDR DI ZU BEHANDLUN BESTENDE IN VEKTIONGE EGNET NACH EWIESE ORDEN +Eval: D S S S S S D D D D S S S S S S S S S S S S S S S S S S + +Speaker sentences 143: mls_deu_000281 #utts: 1 +id: (mls_deu_000281-mls_deu_000281) +Scores: (#C #S #D #I) 6 22 4 0 +REF: ein ÄUSSERST LEBHAFTER DEPESCHENWECHSEL FAND STATT MAN ERWOG DEN plan EINEN ALLGEMEINEN STAATENKONGRESS ZU BERUFEN und KONNTE SICH VORLÄUFIG NUR NOCH NICHT Über DAS VORZULEGENDE PROGRAMM UND den ORT DES ZUSAMMENTRITTS einigen +HYP: ein EUSERSTLEPAFTER DE BECHE WECHSEL VANSTADT WAI ERWUK DEM plan ***** EIN ALGEMEINEN STATEN KONGRESTZUBERUFEN und ****** **** KONDE SI VOLLOUFIKT NONUNICHT Über DES ORZULEGEDE ROGRMM UN den *** ORTES ZUSAMTRETS einigen +Eval: S S S S S S S S D S S S S D D S S S S S S S S D S S + +Speaker sentences 144: mls_deu_000282 #utts: 1 +id: (mls_deu_000282-mls_deu_000282) +Scores: (#C #S #D #I) 9 15 7 0 +REF: er WUSSTE NICHT was IHM das leben KOSTBARES GERAUBT HATTE SPANNKRAFT UND MUT dass es IHN FEIG UND SCHEU GEMACHT HATTE UNFÄHIG zu den HOHEN dingen ZU DENEN UNGETRÜBTE MITFREUDE GEHÖRT +HYP: er ****** WUSTENICHT was IM das leben ********* KOSSBARES GERAUPTATE SCPAN KRAFTUND MUTD dass es *** **** *** IN FEIK UNDSCOEUGEMACHTATE UNDFÄICH zu den HOHN dingen ** ***** ZUODENEN UNGETRÜBPTE ITFROLDENGEHÖRT +Eval: D S S D S S S S S D D D S S S S S D D S S S + +Speaker sentences 145: mls_deu_000283 #utts: 1 +id: (mls_deu_000283-mls_deu_000283) +Scores: (#C #S #D #I) 8 22 20 0 +REF: dieser JUNGE MANN HIESS KACKERLITZCHEN UND BEFAND SICH GERADE AUF DER WANDERSCHAFT als IN DEM GENANNTEN kÖnigreich DIE BEKANNTMACHUNG WEGEN DER PRINZESSIN verlesen wurde ei SAGTE DER SCHNEIDER WENN ES WEITER NICHTS IST ein WEIB HAB ICH NOCH NICHT GEKÜSST UND DES KÖNIGS eidam ZU WERDEN DAS GELÜSTET MICH ALLERDINGS +HYP: dieser ***** **** ***** ************** UNGEMANNHIS KAKALITZIEN UNDBEFAN SIG GRADE U EWANDERSCHAFT als ** INIM GEANTEN kÖnigreich *** ************** D BEKANDNACHUNGWENDER PRNZESEN verlesen wurde ei ***** *** ********* **** SAKT DESCHNEIDER WIENES WEITRNIHTSIST ein **** *** *** **** ***** ******** WEI PAUCHNHNICTKÜKÖLSTUND DUTKÖNIGS eidam ** ****** *** ZUOWERDEN DASGEISSETMI ALEDINGST +Eval: D D D D S S S S S S S D S S D D S S S D D D D S S S S D D D D D D S S S D D D S S S + +Speaker sentences 146: mls_deu_000284 #utts: 1 +id: (mls_deu_000284-mls_deu_000284) +Scores: (#C #S #D #I) 7 25 15 0 +REF: NOCH FÜNF MINUTEN UND die WOLKEN DER BEWUSSTLOSIGKEIT BEGANNEN ZU SCHWINDEN JETZT WUSSTE ICH SEHR WOHL DASS ICH IN MEINEM EIGENEN BETTE LAG und DASS die ROTE GLUT NICHTS ANDERES WAR ALS das FEUER im KAMIN DER KINDERSTUBE es WAR NACHT eine KERZE BRANNTE AUF DEM TISCHE +HYP: **** NOC FÜNFMINUTEN N die ****** *** **************** ******** ** ********* ***** ****** WOLKENDE BEWUSTLOSIGKEIT BEGANN ZUSCHWINDENIERT WUSTE HSER WOLDASIHN MEIM EIGNEN BETELAG und DAS die **** **** ROTEO GLOTNICHT ANDERSWA LS das VEUE im ***** KAMINDER INDASTUBE es *** WANACHT eine ***** ******* KEREBRANTE AF DEMTISCHE +Eval: D S S S D D D D D D D D S S S S S S S S S S S D D S S S S S D S S D S D D S S S + +Speaker sentences 147: mls_deu_000285 #utts: 1 +id: (mls_deu_000285-mls_deu_000285) +Scores: (#C #S #D #I) 0 24 2 0 +REF: WELCHE DIESE VERDRÄNGUNGEN WIE WÄCHTER UNTERHALTEN KOMMT DANN IM PUBERTÄTSALTER DIE HOCHFLUT DER SEXUELLEN BEDÜRFTIGKEIT SO FINDET SIE AN DEN GENANNTEN SEELISCHEN REAKTIONS ODER WIDERSTANDSBILDUNGEN DÄMME +HYP: ****** ***** EICHE IE VETRENGUNEN WE BECHTE UNTERHALTENCOM TANEMPRPEITITZ AL ER DI HOCH FLODES SEXSU EN BEDFFTIGKEIT SOFNDE SE NDE GENANDEN SILLISCHN REAKTIONSODE IEDER STANSPILDONGEN DEMER +Eval: D D S S S S S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 148: mls_deu_000286 #utts: 1 +id: (mls_deu_000286-mls_deu_000286) +Scores: (#C #S #D #I) 11 18 4 3 +REF: * aber AFFEN GEHÖREN bei ****** HAGENBECK an die ****** KISTENWAND nun so HÖRTE ich auf AFFE ZU SEIN ein KLARER SCHÖNER gedankengang DEN ICH IRGENDWIE MIT DEM bauch AUSGEHECKT HABEN MUSS DENN AFFEN DENKEN MIT +HYP: T aber AFEN GEHREN bei HAGKEN BE an die KISTEN WANNT nun so HRTE ich auf **** ACE ZUSEINEN ein KLRER SCHÖNA gedankengang *** DENICH IRGEN DIE MITDEM bauch ********** ***** AUSGEHEKTABENMOSS DEN AFFENN DENKTEN MI +Eval: I S S I S I S S D S S S S D S S S S D D S S S S S + +Speaker sentences 149: mls_deu_000287 #utts: 1 +id: (mls_deu_000287-mls_deu_000287) +Scores: (#C #S #D #I) 5 29 14 0 +REF: IST ES DAS PORTRÄT EINES menschen den SIE KENNEN fragte ELIZA welche UNBEMERKT AN MICH HERANGETRETEN WAR ICH ENTGEGNETE DASS ES NUR EIN PHANTASIEKOPF SEI UND SCHOB DIE ZEICHNUNG EILIG UNTER DIE ANDERN BLÄTTER NATÜRLICH SPRACH ICH die UNWAHRHEIT DENN ES WAR EIN SEHR GETREUES PORTRÄT MR ROCHESTERS +HYP: *** IS S S PATREERNES menschen den *** ZIKÄNEN fragte ILEISE welche ********* ** **** ************* *** *** ********** **** ** *** UN BEMEAKTAMICHERAN GETRETEN WA UMICHINDTGEGNETE DAS S N EN FANTESIKOPFSEI UNTSCHUBT ZEICH UNG ILIC UNTU die ********** **** ANDONDLÄTTER UNATÜLICGSPACRICH IE UNBAHEILT DENE WEIN SE ITOEUESPETRIEMISTEROTSCHSTES +Eval: D S S S S D S S D D D D D D D D D D S S S S S S S S S S S S S S S D D S S S S S S S S + +Speaker sentences 150: mls_deu_000288 #utts: 1 +id: (mls_deu_000288-mls_deu_000288) +Scores: (#C #S #D #I) 3 22 18 0 +REF: ICH WEISS DASS ich SEHR KRANK BIN SAGTE SIE NACH EINER WEILE VOR EIN PAAR MINUTEN VERSUCHTE ICH MICH IM BETTE UMZUDREHEN und FÜHLTE DASS ICH KEIN GLIED MEHR RÜHREN KANN es WÄRE GUT WENN ICH MEIN GEMÜT ERLEICHTERN KÖNNTE BEVOR ICH STERBE +HYP: *** ***** **** ich **** ***** *** ***** *** **** ***** WEIS SICHSER KRANKGBIN SAIGTESINEREINRWEILE VORN PAMINUTEN VERSCHTE ICHMICHE ETTE UM ZUREEN und ******* **** *** FÜLDE DASIC KENGLIED MERENÜORN KAM es ***** *** **** *** **** WEREGUTD WENICH ENGIMÜT ELEICHTDAN KÖNTER BEVORICSTERDEN +Eval: D D D D D D D D D D S S S S S S S S S S S D D D S S S S S D D D D D S S S S S S + +Speaker sentences 151: mls_deu_000289 #utts: 1 +id: (mls_deu_000289-mls_deu_000289) +Scores: (#C #S #D #I) 5 19 10 0 +REF: SO ABER ist ZWAR UNSER WESENSGRUND GOTT SELBER DA HERUM HAT SICH JEDOCH DER SCHLANGENKNÄUEL des ALTEN SATAN GESCHLUNGEN UND Über dem FÜNKCHEN DER LIEBE IST DIE FINSTERNIS DES HASSES GELAGERT was WUNDER DANN +HYP: ** SOABER ist **** ***** *********** WER UN SERWESENS KUND OTSELLVER DACHEROM HATZICHEDOC DERCHLANGEN KNEUL des ***** ***** ALKENSATANGESHLUNGEN UNG Über dem ********* *** ***** FÜNKIENDEN LIEBI IS DI FENSERNISTESHASSES ELAGERT was ****** WONDERDAN +Eval: D S D D D S S S S S S S S S D D S S D D D S S S S S S D S + +Speaker sentences 152: mls_deu_000290 #utts: 1 +id: (mls_deu_000290-mls_deu_000290) +Scores: (#C #S #D #I) 3 13 11 0 +REF: BESSIE WÄRE LIEBER GEBLIEBEN ABER SIE WAR GEZWUNGEN ZU GEHEN WEIL DIE PÜNKTLICHKEIT BEI DEN MAHLZEITEN eine SACHE WAR AUF WELCHE in GATESHEAD HALL STRENGE GEHALTEN wurde +HYP: ****** ***** ****** ********* **** *** *** ********* ** ***** BESIVIRELIEAGEBLIEBEN ABERSIWAGETZWUNGKZUGEHN EA DI ÜNKLITKEIT BEIDENMALSZEITEN eine ***** SACHEWA AU WÄCH in GETZS HÄRD HORL STRENGEGEHALTEN wurde +Eval: D D D D D D D D D D S S S S S S D S S S S S S S + +Speaker sentences 153: mls_deu_000291 #utts: 1 +id: (mls_deu_000291-mls_deu_000291) +Scores: (#C #S #D #I) 5 26 8 0 +REF: AUGENBLICKLICH FÜHLTE WIE IHRE ANSICHTEN ÜBER MICH IHRE EMPFINDUNGEN FÜR MICH NICHT UM EIN ATOM VERÄNDERT WAREN ÜBERHAUPT KEINER ÄNDERUNG FÄHIG WAREN ich SAH ES IHREM VERSTEINERTEN AUGE welches niemals DURCH TRÄNEN genetzt NIEMALS in ZÄRTLICHKEIT AUFGELEUCHTET HATTE AN +HYP: ************** ******* *** **** ********* ***** NBLICKLICH FÜLTEWIEHRE AMSICHTENÜBERMICH IRER M PINDUNGED FÜHRMICH NICHTUMEIN ATOUMVER INDETWAHEMN UÜBEHAUPT KANE ENDERUNG FEE CH WARMD ich *** SEIS IREM ERSTEINATEN AUDGE welches niemals ***** DUCHTRENEN genetzt NIEMAS in ERTLICHKEIT AUF GELEUCHTETATE AMEN +Eval: D D D D D D S S S S S S S S S S S S S S S S D S S S S D S S S S S S + +Speaker sentences 154: mls_deu_000292 #utts: 1 +id: (mls_deu_000292-mls_deu_000292) +Scores: (#C #S #D #I) 5 43 17 0 +REF: BRUDER SAM IST SEHR gut WENN DER HÄUPTLING IHN ERFÄHRT WIRD ER SICH FREUEN UND WIR WERDEN SCHNELL DANACH HANDELN SO WOLLEN WIR AUFBRECHEN UND SCHNELL REITEN DAMIT WIR NOCH VOR NACHT das LAGER ERREICHEN WIR STIEGEN AUF DIE PFERDE DIE NUN AUSGERUHT HATTEN und FLOGEN IM GALOPP DAVON DIESMAL hÜteten WIR UNS der FÄHRTE WIEDER DIREKT ZU FOLGEN WIR RITTEN GERADEAUS UND ERSPARTEN UNS +HYP: NS ODERS SEMIST ZER gut **** *** ********** *** ******** **** ** **** ****** *** *** WIN E HÖUGLIGNER FÄER I D SIC FREUNWÜEWEREN SCHNÄLDRNACHANDEN SOL BE AUFPRECHENSHNALREITEND MIT ÜR NOC FORDERNCH das ***** ********* LAGE REICHEN ERSTIGE AU DIP FÄERDE DI N AUSGEROTATEN und FLOGEM GALOPBTDA VON DIES AL hÜteten *** IRUNS der ******* ****** ****** FÄERDE IDER DEREKZE FOLEN WERE GERADE AUSNDER SPADEN +Eval: S S S S D D D D D D D D D D D S S S S S S S S S S S S S S S S D D S S S S S S S S S S S S S S D S D D D S S S S S S S S + +Speaker sentences 155: mls_deu_000293 #utts: 1 +id: (mls_deu_000293-mls_deu_000293) +Scores: (#C #S #D #I) 13 32 10 1 +REF: WEIL DIE ABER mit PECH BESTRICHEN WAR BLIEB einer VON DEN GOLDENEN PANTOFFELN festhÄngen und in der ANGST DACHT ES NICHT DARAN IHN MITZUNEHMEN und WIE ES den LETZTEN SCHRITT VON DER TREPPE TAT DA HATTE ES ZWÖLF AUSGESCHLAGEN DA WAR wagen und PFERDE VERSCHWUNDEN und ASCHENPUTTEL STAND in **** SEINEN ASCHENKLEIDERN auf DER DUNKELN STRASSE +HYP: **** WALDIE BER mit **** PÄECHPBESTRICHEN WAHR BIEB einer *** VONDEN GELENEN PANTOFLEN festhÄngen und in der ***** ANGS DACH S NICHTERAN IN ITZONEMEM und IE IS den ******* ******* *** *** ****** *** LETZTENSCHIT VONDERTREPETAD DERHATSTZWILF AUS GESCHLAGEN DAR AR wagen und FERDE VERSCHUNDEN und ASCHEN POTESTAND in SEIN SCHEN KLEIDER auf ER UNGLN STRASE +Eval: D S S D S S S D S S S D S S S S S S S S D D D D D D S S S S S S S S S S S I S S S S S + +Speaker sentences 156: mls_deu_000294 #utts: 1 +id: (mls_deu_000294-mls_deu_000294) +Scores: (#C #S #D #I) 1 13 13 0 +REF: ILL NAHM DAS GLAS VOM AUGE ein FINSTERER ERNST LAGERTE ÜBER SEINEN ZÜGEN ES IST SCHRECKLICH SAGTE ER ICH HAB DAS MEINIGE GETAN UM BLUTVERGIESSEN ZU VERMEIDEN +HYP: *** **** *** IEIENOM DES ASVERMAGE ein ********* ***** ******* ***** ****** ****** ** *** *********** ***** VINSTER ANSTLAGERE BESENTZYÜDEN IESISCHRICKAL SAKTEHR GIETDAS MENIGEGITANUN BUT VERGISNS VERMÄDEN +Eval: D D D S S S D D D D D D D D D D S S S S S S S S S S + +Speaker sentences 157: mls_deu_000295 #utts: 1 +id: (mls_deu_000295-mls_deu_000295) +Scores: (#C #S #D #I) 15 16 0 1 +REF: nur der DOKTOR UND die WÄRTERIN sollen vor seine augen kommen ERKLÄRTE DIE TRINE in grossem *** AMTSEIFER damit WAR die FRAU oberst GANZ EINVERSTANDEN und HÖCHST ERFREUT KEHRTE SIE mit IHREN +HYP: nur der DOCKTOR UN die WERTEREN sollen vor seine augen kommen ER KLÄATE DIETRINER in grossem AMT SEIFER damit WA die FRAR oberst GANS EINFERSTANDEN und PIRXST ERFREIT KERTE SI mit IEREN +Eval: S S S S S S I S S S S S S S S S S + +Speaker sentences 158: mls_deu_000296 #utts: 1 +id: (mls_deu_000296-mls_deu_000296) +Scores: (#C #S #D #I) 9 22 9 0 +REF: K WAR UNTRÖSTLICH ÜBER DIE lage DES KÜNSTLERS ER BEGANN zu weinen UND SCHLUCHZTE lange in DIE VORGEHALTENEN HÄNDE der KÜNSTLER WARTETE BIS K SICH BERUHIGT HATTE und ENTSCHLOSS SICH DANN DA er KEINEN ANDEREN AUSWEG FAND DENNOCH zum WEITERSCHREIBEN +HYP: * *** KAAR UN TRÜSTICHÜBERDIE lage *** DAS KÖNZTLRS ERBEGAN zu weinen UNSCHLCHTZT DE lange in DE ORGE HALTENENHNDE der ********* ******* *** * KONSLEAWATETE BISKASIC BERUICHTHATTE und ********** ENSCHLOSICH DAN DAR er ****** KEIN ANDERN AUSIGFANDT DERNOCH zum EITERSCREIBEN +Eval: D D S S S D S S S S S S S S D D D D S S S D S S S D S S S S S + +Speaker sentences 159: mls_deu_000297 #utts: 1 +id: (mls_deu_000297-mls_deu_000297) +Scores: (#C #S #D #I) 5 36 13 1 +REF: VON DEN PFERDEHERDEN der APACHEN und SAGTEN UNS DASS SIE FÜR EIN APACHENPFERD UNS EBENSO viele WAREN UND BRANDY GEBEN WÜRDEN WIE FÜR EIN KIOWAPFERD DA SIND UNSERE KRIEGER FORT um APACHENPFERDE ZU HOLEN ALSO RICHTIG WER WAR SCHULD AN DEM tode * DER BISHER GEFALLENEN UND AN DEM BLUTVERGIESSEN WELCHES NUN BEVORSTAND WEISSE PFERDEHÄNDLER +HYP: *** ONDIM FERDEHERDEN der PATSCHEN und ****** *** SAG NUNZSDASSI F ENEA PATSCHEM FÄRDUNDS EBENSU viele ***** *** ****** ***** ******* *** WARENUN PRENIGEBEN WIRDEN ÜFÜRIN KEIOWABFERT DASINDUN RIKLIGAR FOD um ************* ** ***** **** APATSCHEN FÄRDE ZO HLENALSORCHTIGH ERARSCHLDER EM tode E BES ER GEFALLEN UNDERIE BLUTVERGIESEN WECHES UN BE VORSTAND WEISE FÄERDE HENDTLER +Eval: D S S S D D S S S S S S S D D D D D D S S S S S S S S D D D D S S S S S S I S S S S S S S S S S S S + +Speaker sentences 160: mls_deu_000298 #utts: 1 +id: (mls_deu_000298-mls_deu_000298) +Scores: (#C #S #D #I) 3 24 12 0 +REF: das AMAZONENHÜTCHEN VON SCHWARZEM SAMMET GRAZIÖS AUF IHRE LANGEN LOCKEN GEDRÜCKT DIE IHRE WANGEN UMFLOSSEN UND ÜBER IHRE SCHULTERN HERABWALLTEN so TRAT SIE IN DAS EINFACHE LÄNDLICHE GEBÄUDE UND SCHWEBTE ZWISCHEN DEN REIHEN DER HALBGEBLENDETEN DORFKINDER AUF UND ab +HYP: das **************** *** ********* ****** ******** *** **** MATONE HÜT CHEN VONSCWATZAM SAMET KATIÜRSA IERE LANGENLOKENGEDRÜCT DIEREWANGNUM FLOSSEN ND ÜBERSCHLTENHERABWEITEN so **** *** ** *** ******** TRATE I DRS EINFECHRELENTLICH GEBOUDE UNDSTEBPTETZWISCENE REIN DERHEIB GEBLÄNETN OF KNER AUFEND ab +Eval: D D D D D D D S S S S S S S S S S S S D D D D D S S S S S S S S S S S S + +Speaker sentences 161: mls_deu_000299 #utts: 1 +id: (mls_deu_000299-mls_deu_000299) +Scores: (#C #S #D #I) 4 25 10 0 +REF: DU MUSST erst ENTSAGEN ALLEM SÜNDHAFTEN STREBEN UND IN TIEFER REUE UND DEMUT die FÜRBITTE DER HEILIGEN ERFLEHEN gegen DIE DU GEFREVELT HAST DIE JÜNGLINGE WELCHE FRANCESKO SO LANGE GEFLOHEN SUCHTEN IHN AUF IN SEINER WERKSTATT UND fanden IHN +HYP: ** TUMUST erst ******** ***** *********** EN ZAGEN ALEN SÜNTHAFTENSTREBEN UNEN TIEVEREUIUND DEMUD die FÜHR BIER HLLINGER FLEN gegen *** ** ********* **** *** ********** DIEDU GEFREELTEST TIE JÜMLNGE WLCHE FENSCHES OSOLNE GEFLON SOUCHTEN N AUENER WERKTATUN fanden IN +Eval: D S D D D S S S S S S S S S S S D D D D D D S S S S S S S S S S S S S + +Speaker sentences 162: mls_deu_000300 #utts: 1 +id: (mls_deu_000300-mls_deu_000300) +Scores: (#C #S #D #I) 4 16 5 0 +REF: ER LIESS seine GRETEL NICHT FORTSCHLEPPEN am ALLERWENIGSTEN aber IN DEN GROSSEN VOGELBAUER WO SIE ALLE IN einem TONE PFEIFEN MUSSTEN WIE ER STETS SAGTE +HYP: ** ERLIES seine GRETE NIHT VORTSCHLÄBEN am ALLERWINIGXSTE aber ** *** INDEN GROSSEND VOGELBAUAR USIE ALE EN einem **** ******* TONEB FEIFEN MOUSTEN WIERSTE SAKTE +Eval: D S S S S S D D S S S S S S D D S S S S S + +Speaker sentences 163: mls_deu_000301 #utts: 1 +id: (mls_deu_000301-mls_deu_000301) +Scores: (#C #S #D #I) 4 25 11 0 +REF: FRANCESKO MALTE IN UNHEILIGER BEGEISTERUNG VIELE BILDER AUS DER lÜgenhaften FABELWELT KEINER ALS ER VERMOCHTE die BUHLERISCHE ÜPPIGKEIT DER WEIBLICHEN GESTALTEN SO WAHRHAFT DARZUSTELLEN INDEM ER von LEBENDEN MODELLEN DIE KARNATION VON DEN alten MARMORBILDERN ABER FORM UND BILDUNG ENTNAHM +HYP: ********* ***** ** VRN HESKOMALTEN UNHALIGE BEGEISTRNG VIELEBIELT ASTE lÜgenhaften ********* ****** ABELWELD TRENEALS ERERMOCH die *********** ********** *** BULERISCHEÜLBEKAETE WEIBIENGESTALTEN SOBER HAF ASSTELEN IN DEM von ******** ******** LEBENTE MODELE DIKANDATIONG VONDEN alten ************* MAHMO BILDER RBERFORMND BIELUNGIND NAM +Eval: D D D S S S S S S D D S S S D D D S S S S S S S D D S S S S D S S S S S + +Speaker sentences 164: mls_deu_000302 #utts: 1 +id: (mls_deu_000302-mls_deu_000302) +Scores: (#C #S #D #I) 9 27 21 0 +REF: BEWEGUNG UND tat den ERSTEN zug JA ES STIMMTE die VORHIN ANGEGEBENEN INGREDIENZIEN NÄMLICH rÜben HANF EICHELN UND SAUERAMPFER WAREN alle in dem PFEIFENKOPFE ANWESEND ABER EINEN FÜNFTEN HAUPTSTOFF HATTE ICH NICHT GENANNT JETZT ROCH UND SCHMECKTE ICH DASS AUCH EIN STÜCKCHEN FILZSCHUH DABEI SEIN MÜSSE ICH BLIES DEN RAUCH AUCH GEGEN den HIMMEL UND GEGEN DIE +HYP: BWEGUNG UN tat den STEN zug ** IER STEMTE die ****** FOEUN ANGEGEBNE INKGREDENZIERNMIC rÜben **** ******* HANFE EICHENUN SAUERAMFAN alle in dem ************ ******** **** ***** ******** ********** ***** *** ***** ******* ***** **** *** ********* *** FEIFNKOPFERNWESEN ABERIN FÜNFTN HAUPTSTOFHAICHNICG GENANDJERTTROCH ND SCHMEKTIG DASE HONSTÜCHEN FILSCHUDER BEISEIN ISE IGPLIESTEN RAUHAUCHGEG den ****** *** HEME UNGEGEDI +Eval: S S S D S S D S S S D D S S S D D D D D D D D D D D D D D D S S S S S S S S S S S S S S D D S S + +Speaker sentences 165: mls_deu_000303 #utts: 1 +id: (mls_deu_000303-mls_deu_000303) +Scores: (#C #S #D #I) 23 21 6 3 +REF: und das FEUER stand auf und ******* FLACKERTE UND KOCHTE das essen FERTIG und der braten BRUTZELTE fort UND der koch gab dem ******* KÜCHENJUNGEN eine ** OHRFEIGE und DIE MAGD RUPFTE das HUHN FERTIG DA WARD die HOCHZEIT VON dem KÖNIGSSOHN MIT DORNRÖSCHEN gefeiert UND SIE LEBTEN VERGNÜGT BIS an IHR ende +HYP: und das FOUR stand auf und FLACKEA T UN KOCH das essen FERTICH und der braten BRUTZELTER fort UN der koch gab dem KÜCHEN IUNGEN eine OR FEIGE und *** DE MARKTROPFTE das **** ****** UNFÄERTIGH DARWART die HOCHTZEIT ON dem KÖNICHSON MIE DONGRÖUSIEN gefeiert *** *** ****** UN SIELIETNEFERGNÜTEBIS an IER ende +Eval: S I S S S S S S I S I S D S S D D S S S S S S S D D D S S S + +Speaker sentences 166: mls_deu_000304 #utts: 1 +id: (mls_deu_000304-mls_deu_000304) +Scores: (#C #S #D #I) 3 18 9 0 +REF: UND DASS ER MIR NICHT NACHTRAGEN WOLLE WENN ICH WIDERSPENSTIG WAR GEGEN SEINEN WOHLMEINENDEN RAT der HERR PFARRER HAT JA IN ALLEM RECHT GEHABT und ICH WAR IM UNRECHT aber +HYP: *** **** ** *** ***** UNMD DESERMINICH NACTRAGEN BOLE WENICH IDERSHFÄNSTIG WAGIN SEIN OHMEINEM RART der **** ******* *** ** HERFARA EHADIEIN ALLMPRECHT GEHAUT und ICHMA AM UNM RECHT aber +Eval: D D D D D S S S S S S S S S S D D D D S S S S S S S S + +Speaker sentences 167: mls_deu_000305 #utts: 1 +id: (mls_deu_000305-mls_deu_000305) +Scores: (#C #S #D #I) 0 14 10 0 +REF: OBGLEICH SEINE MASSE NUR WENIGE GRAMM BETRUG ER BREITETE SICH KEGELFÖRMIG AUS UND MUSSTE DAHER DAS IHM ENTGEGENFLIEGENDE SPRENGGESCHOSS AUFFANGEN UND ZUR RUHE BRINGEN +HYP: ******** ***** ***** *** ****** ***** ****** ** ******** **** GÄCHENEMASE NOWINIGEKRAM BETRUGEREITE ESICH KILIE FRMIG AUSHUMUSTEDER ERDES M IND GEGENFLIGEDESPRENKISCHOS AUFANGEN NDZU WIBRINEN +Eval: D D D D D D D D D D S S S S S S S S S S S S S S + +Speaker sentences 168: mls_deu_000306 #utts: 1 +id: (mls_deu_000306-mls_deu_000306) +Scores: (#C #S #D #I) 9 33 20 0 +REF: der FUCHS REICHTE SAM DIE UNFRIEDLICHE FRIEDENSPFEIFE HIN der MANN TAT WACKER SEINE SECHS ZÜGE UND SAGTE der GROSSE GEIST ACHTET NICHT AUF DIE VERSCHIEDENE HAUT DER menschen DENN DIE KÖNNEN SICH MIT FARBE BESCHMIEREN UM IHN ZU TÄUSCHEN SONDERN ER SIEHT das HERZ AN die HERZEN der KRIEGER VOM BERÜHMTEN STAMME DER KIOWAS SIND TAPFER UNERSCHROCKEN UND treu das MEINIGE HÄNGT +HYP: der FCKSREICHTE EM DE UNFRITICHE RIEDENSPFEI VER HN der **** *** ****** ***** MANTAT WACKARSEINESEXS ZYGEN SAGKTE der ****** ***** ****** ***** *** ROSIGEIS ACHTETNICHT AUFDIVERSCHIEDENE HAUDER menschen **** *** ******* **** *** ***** DEN DIKÖN SICHMIT FABEBESCHMIEREN MINTZU DTEUSCHEN SODE ERSI das **** HETZSAN die HTZEN der ******* *** ********** KLIGER VO BERÜBTENSTAMEDER KAEIOASEIN TAPTVER UNERSCHROKEN N treu das ******* MEINEIGEHÄNG +Eval: S S S S S S S D D D D S S S S D D D D D S S S S D D D D D D S S S S S S S S D S S D D D S S S S S S S D S + +Speaker sentences 169: mls_deu_000307 #utts: 1 +id: (mls_deu_000307-mls_deu_000307) +Scores: (#C #S #D #I) 6 26 3 0 +REF: ALLES WAS WIR MIT IHR BEGEGNET SCHIEBT sich DURCH UND ÜBEREINANDER BALD UNTERSCHREIBEN WIR EINEN KONTRAKT DA ist IHRE HAND UND DIE meinige IHR NAME UND DER meinige BEIDE LÖSCHEN einander aus BEIDE VERSCHLINGEN SICH +HYP: ***** ALLS ASWIE MET IER BEGEGNIT SCIEB sich DEUICHUND ÜBER EINANDER BALT UNTERSCHEIBEN WER IN KONTAKT DER ist **** ERERHAND N DE meinige *** IER NAHM ONDER meinige WEI DELESCHEN einander aus BEI DE VERSCHLINGENSICH +Eval: D S S S S S S S S S S S S S S S D S S S D S S S S S S S S + +Speaker sentences 170: mls_deu_000308 #utts: 1 +id: (mls_deu_000308-mls_deu_000308) +Scores: (#C #S #D #I) 8 24 10 0 +REF: er MÜSSTE den EINFACHEN CHRONIKEN CHORAL DES MALERS mit ALLERLEI ERKLÄRUNGEN UND ZURECHTWEISUNGEN WIE MIT krausen FIGUREN VERSCHNÖRKELN UND VERBRÄMEN ich trete IN die PERSON DES HERAUSGEBERS UND BITTE DICH GÜNSTIGER LESER DU WOLLEST EHE du WEITER LIESEST FOLGENDES DIR GÜTIGST MERKEN +HYP: er MÖSTE den ********* ENFACHEN RONITEN KÖRALDES MALES mit ******** ALLE ER KLEHRENENUN ZRECHTWESSUNGEN IMIT krausen ******* WIGUN VESCHNARKENUN VERBREMEN ich trete I die ****** *** ************ *** ***** PERSONDESERAUSGEBESND BITETICH IÜNSTIGELISER TU OLIST IE du ****** ******* WEITELISEST FOLDENDIS DEGÜTTIST MERHEN +Eval: S D S S S S D S S S S S D S S S S D D D D D S S S S S S D D S S S S + +Speaker sentences 171: mls_deu_000309 #utts: 1 +id: (mls_deu_000309-mls_deu_000309) +Scores: (#C #S #D #I) 7 26 8 0 +REF: die HOFDAMEN bekamen KRÄMPFE UND DIE KÖNIGIN UND DIE PRINZESSINNEN DIE IHRE ALLERLIEBSTEN HÜNDCHEN WÄHREND DER MAHLZEIT AUF den SCHOSS GENOMMEN HATTEN BEMERKTEN zu IHREM SCHRECKEN DASS DIE LILA AMARANTFARBENEN UND ORANGEGELBEN seidenkleider ALLE DICHT BESÄT mit den HÄSSLICHSTEN ÖLFLECKEN WAREN +HYP: die OFDAMEN bekamen ******** *** *** ******** *** KRMPFER UNDTIEKÖNIGEN UN IEPROMZESSENEN DIERER ALLALIBSEN HÜNZCHEN WERN DERMALTER UF den ****** HOSGENOM HADEN BEMERKEN zu ***** ********* IREN SCRÄCTEN DAS DILIELER ARMARANDFABENEN UNDORANSCAEDEN seidenkleider ALE ICT BESET mit den HESLICSTEN ÖFLÄGEN WANE +Eval: S D D D D D S S S S S S S S S S D S S S D D S S S S S S S S S S S S + +Speaker sentences 172: mls_deu_000310 #utts: 1 +id: (mls_deu_000310-mls_deu_000310) +Scores: (#C #S #D #I) 7 19 6 0 +REF: von LIEDERN die SIE singen UND KLAVIERPIECEN DIE SIE SPIELEN von GELDBÖRSEN DIE SIE HÄKELN von FRANZÖSISCHEN BÜCHERN DIE SIE Übersetzen KONNTE BIS MEIN GEMÜT WÄHREND ICH LAUSCHTE ZUR NACHAHMUNG AUFGESTACHELT wurde +HYP: von LIEDAN die IE singen *** UN KLVIER PIESEN DIESISPIELN von GEILT BÜÖRSEN DIESI HIEGKELN von ************** VANZÖÜSCHEN BÜCHEN DIES Übersetzen ****** *** **** ****** KONTE BISMENGEMÖÜT WERENDICHLAUSTE ZONACH AMNG AUFGESTACHET wurde +Eval: S S D S S S S S S S S D S S S D D D D S S S S S S + +Speaker sentences 173: mls_deu_000311 #utts: 1 +id: (mls_deu_000311-mls_deu_000311) +Scores: (#C #S #D #I) 1 23 11 0 +REF: ARME UND NACKEN WAREN BLOSS IHR EINZIGER SCHMUCK WAREN IHRE KASTANIENBRAUNEN FLECHTEN WELCHE IN WILDER UND natÜrlicher ANMUT AUF IHRE SCHULTERN HERABFIELEN ICH NAHM EINEN BOGEN FEINEN KARTONS UND ZEICHNETE MIT GROSSER SORGFALT DIE UMRISSE +HYP: **** *** ****** ***** AMEN NATEN WANBLOS IR ENZIGER SCMOC WAN IHREKASTANIENBRANFLÄCHTEN WILCH EN WILDE UN natÜrlicher ***** *** **** ********* *********** *** **** ANMUND AU IRSCHLTENERABVIELEN CHNAM EIN BOGENGFEIN KATOUNGS NDZEICHETEM MT OSRSORKFALTI OMGRSSER +Eval: D D D D S S S S S S S S S S S S D D D D D D D S S S S S S S S S S S + +Speaker sentences 174: mls_deu_000312 #utts: 1 +id: (mls_deu_000312-mls_deu_000312) +Scores: (#C #S #D #I) 9 30 7 0 +REF: ABER WEDER aus DEUTSCHLAND NOCH AUS IRGENDEINEM anderen STAAT KONNTE MAN ERFAHREN was DER GEGENSTAND UND DAS RESULTAT DIESER UNTERREDUNGEN GEWESEN sei MAN VERMUTETE DASS ES SICH um ERKLÄRUNGEN der MARTIER ÜBER IHRE absichten und UM DIE VERMITTLUNG der MÄCHTE ZWISCHEN DEN MARSSTAATEN UND GROSSBRITANNIEN HANDLE +HYP: AUR WIEDE aus DETCHLAND NOCHAS IELEN EINEM anderen ***** START KONTEMEN EFAHN was *** ER GEGNSTAND UNDES ESLTAD DIES UNTEREDUNG GEWISEN sei *** ********* AN VRMUTETE DSSESIC um ERKLIERUNG der MATZIER ÜBE IER absichten und ** UN DIEVERMITLUNG der ******* ******** MECHTET ZWICHNDN MARSTATEN UN ROSPOTANIENHANDLE +Eval: S S S S S S D S S S D S S S S S S S D D S S S S S S S D S S D D S S S S S + +Speaker sentences 175: mls_deu_000313 #utts: 1 +id: (mls_deu_000313-mls_deu_000313) +Scores: (#C #S #D #I) 6 14 4 3 +REF: LASS UNS WENIGSTENS EINE zeitlang ********** ** VERSUCHEN INWIEFERN WIR AUF DIESE WEISE MITEINANDER ausreichen DA DAS zusammenhÄngende WIE DU SAGST eigentlich euer ELEMENT IST versetzte **** +HYP: **** LASUNS WENIGSENS EINER zeitlang VERSUOCHEN IN DIE VERN WIER UF DESES BEISIMIT EINANDER ausreichen ** DADAS zusammenhÄngende *** ** VIEDUESAGST eigentlich euer LLEMENT IS versetzte IDRT +Eval: D S S S I I S S S S S S S D S D D S S S I + +Speaker sentences 176: mls_deu_000314 #utts: 1 +id: (mls_deu_000314-mls_deu_000314) +Scores: (#C #S #D #I) 6 24 2 0 +REF: VERSCHIEDENE VORKOMMNISSE FÜHRTEN zu der VERMUTUNG DASS frau WIESE DIE kleinen WESEN VERBRENNE SIE SOLL BISWEILEN SO STARK GEHEIZT HABEN DASS DIE HERDPLATTEN ZERSPRANGEN AUSSERDEM SOLL EIN FÜRCHTERLICHER GERUCH WAHRGENOMMEN worden sein +HYP: VEASCHINEN VORKOMMNSE FÜRDEN zu der VER MOTUNGDAS frau WISE DI kleinen ***** ********* VWISEN VERBRENER IESOL BES EIN SU STACH GEHEITZTABEN DAS D ERTPLADEN ZSPRANG AUSST EM OLEIN FÜRCHTELICHER EROCHWAGENUME worden sein +Eval: S S S S S S S D D S S S S S S S S S S S S S S S S S + +Speaker sentences 177: mls_deu_000315 #utts: 1 +id: (mls_deu_000315-mls_deu_000315) +Scores: (#C #S #D #I) 15 7 6 0 +REF: und ging dem schreien nach SO SAH er ENDLICH EINEN HOHEN baum und OBEN DARAUF SASS ein kleines kind unter dem baum ABER LAG eine FRAU DIE SCHLIEF +HYP: und ging dem schreien nach ** SOSAH er ******* ENTLICH EINHOHN baum und **** OBENDER RAUFSAS ein kleines kind unter dem baum **** ABARLAK eine **** *** FRAUDIESCHLIEF +Eval: D S D S S D S S D S D D S + +Speaker sentences 178: mls_deu_000316 #utts: 1 +id: (mls_deu_000316-mls_deu_000316) +Scores: (#C #S #D #I) 6 21 1 6 +REF: sie HATTEN SOEBEN die ******* FISCHERGARNE WELCHE DIE nacht ****** ***** **** *** ÜBER AUSGEWORFEN WAREN HEREIN GEZOGEN DIE SEELEUTE GEHÖRTEN AUGENSCHEINLICH verschiedenen NATIONEN AN OBWOHL der EUROPÄISCHE CHARAKTER BEI allen *** AUSGEDRÜCKT WAR +HYP: sie HATEN SEUEBN die FISCHER GARDNER WALCH DE nacht IYBERT AUSGE UORH VEN WADEN CHEREIN GE ZOGEN NDIE SELEUITER GER HEOTEN AGENSCHEIMLISCH verschiedenen ******** NCTZIONENRN ABORL der OLULOPÄISCHER KARKTE BER allen AUS GET RÜKTWAT +Eval: S S I S S S I I I I S S S S S S S S S D S S S S S I S S + +Speaker sentences 179: mls_deu_000317 #utts: 1 +id: (mls_deu_000317-mls_deu_000317) +Scores: (#C #S #D #I) 4 11 7 1 +REF: NEIN NEIN ICH schÄme MICH LASS MICH AN deinem busen ****** MEIN GESICHT VERBERGEN er SINKT INS GRAS NIEDER UND ZIEHT SIE NACH +HYP: **** **** NENEINICH schÄme **** MEICH LASMICH EN deinem busen MEINGE SICHT VER BERGENG er ***** *** **** ****** SINKTENS GRASNIDE UNDZIE SINACH +Eval: D D S D S S S I S S S D D D D S S S S + +Speaker sentences 180: mls_deu_000318 #utts: 1 +id: (mls_deu_000318-mls_deu_000318) +Scores: (#C #S #D #I) 10 18 9 1 +REF: die kinder ABER SASSEN vor dem WALD und ALS SIE DIE DREI KNECHTE von weitem laufen SAHEN SPRACH LEHNCHEN ZUM FUNDEVOGEL VERLÄSST DU MICH NICHT SO VERLASS ICH DICH AUCH NICHT SO SPRACH FUNDEVOGEL nun und ***** NIMMERMEHR +HYP: die kinder ARBAR SASEN vor dem WALT und *** *** *** ALSIEDIE REIKNECHTER von weitem laufen ***** ****** ******** *** ********** ********* SAHNSPRACHRLENSHEN ZUMPFUNDE FOGEL VERLÄSTUMICH NICH ZO ERLAS ICHTICH AUCHNICHT SOSPRACH FONDE FOGEL nun und NIMER MR +Eval: S S S D D D S S D D D D D D S S S S S S S S S S S S I S + +Speaker sentences 181: mls_deu_000319 #utts: 1 +id: (mls_deu_000319-mls_deu_000319) +Scores: (#C #S #D #I) 5 10 3 1 +REF: WIE DER SCHULZE in ***** SEINER HULDIGUNGSREDE HERVORHOB der LEHRER BRACHTE am KLAREN SOMMERMORGEN mit seinen SCHULKINDERN EIN GESANGSSTÄNDCHEN +HYP: *** WIEDER SCHLZE in SEINE HLDIEGUNGSRIEDE HER VORHUB der ****** LERABRACHTE am KLAEN SOMMARMORDENG mit seinen ************ SCHUHLKINDERN EINGESANGSTÄNTIE +Eval: D S S I S S S D S S S D S S + +Speaker sentences 182: swc_deu_001408 #utts: 1 +id: (swc_deu_001408-swc_deu_001408) +Scores: (#C #S #D #I) 1 3 0 0 +REF: WIE SIE sein SOLLTEN +HYP: STDT WIEIE sein SOHLEN +Eval: S S S + +Speaker sentences 183: swc_deu_001409 #utts: 1 +id: (swc_deu_001409-swc_deu_001409) +Scores: (#C #S #D #I) 2 4 0 2 +REF: deren SCHWINGUNGEN DURCH eine ****** ********* ZUSATZSCHALTUNG STUFENLOS +HYP: deren CHWINGENEN DURC eine ZUSERT SCHALTUNG STUFEN LOS +Eval: S S I I S S + +Speaker sentences 184: swc_deu_001410 #utts: 1 +id: (swc_deu_001410-swc_deu_001410) +Scores: (#C #S #D #I) 3 4 0 0 +REF: die auf ALLE bei DER SITZVERTEILUNG ZU +HYP: die auf ALE bei DERSET WRTALUNG U +Eval: S S S S + +Speaker sentences 185: swc_deu_001411 #utts: 1 +id: (swc_deu_001411-swc_deu_001411) +Scores: (#C #S #D #I) 2 2 0 0 +REF: um DEN Überlebenden DER +HYP: um DE Überlebenden D +Eval: S S + +Speaker sentences 186: swc_deu_001412 #utts: 1 +id: (swc_deu_001412-swc_deu_001412) +Scores: (#C #S #D #I) 2 5 1 1 +REF: SPÄTER WURDEN TEILWEISE SOGAR acht ********* PARALLELE LOCHSTREIFEN eingesetzt +HYP: ******* SPÄTERWURDEN TAILWEISE SOGA acht PARERLELE LOH STREIFEN eingesetzt +Eval: D S S S I S S + +Speaker sentences 187: swc_deu_001413 #utts: 1 +id: (swc_deu_001413-swc_deu_001413) +Scores: (#C #S #D #I) 3 1 0 0 +REF: morde BEKANNT und verlangte +HYP: morde BEKAND und verlangte +Eval: S + +Speaker sentences 188: swc_deu_001414 #utts: 1 +id: (swc_deu_001414-swc_deu_001414) +Scores: (#C #S #D #I) 1 4 0 0 +REF: BWAHLG DIE STIMMEN von WÄHLERN +HYP: BUNDE WEGESETZ DIESTM von WIHLEN +Eval: S S S S + +Speaker sentences 189: swc_deu_001415 #utts: 1 +id: (swc_deu_001415-swc_deu_001415) +Scores: (#C #S #D #I) 0 1 0 0 +REF: GESCHICHTE +HYP: GESCHCHTE +Eval: S + +Speaker sentences 190: swc_deu_001416 #utts: 1 +id: (swc_deu_001416-swc_deu_001416) +Scores: (#C #S #D #I) 1 1 0 0 +REF: spaltung FÄHIG +HYP: spaltung FEÄG +Eval: S + +Speaker sentences 191: swc_deu_001417 #utts: 1 +id: (swc_deu_001417-swc_deu_001417) +Scores: (#C #S #D #I) 0 3 3 0 +REF: STADT PADERBORN DIE ÄUSSEREN FEIERN DES +HYP: ***** ********* *** SCHATPAEBORN DIEUSEREND FERNDIS +Eval: D D D S S S + +Speaker sentences 192: swc_deu_001418 #utts: 1 +id: (swc_deu_001418-swc_deu_001418) +Scores: (#C #S #D #I) 0 4 0 0 +REF: WEITERHIN HUMANITÄRE HILFE ZU +HYP: UMWEITER INHUMANI TERE HILFEZU +Eval: S S S S + +Speaker sentences 193: swc_deu_001419 #utts: 1 +id: (swc_deu_001419-swc_deu_001419) +Scores: (#C #S #D #I) 3 2 3 0 +REF: SIE ERKANNTEN die NEUE CHINESISCHE REGIERUNG nicht an +HYP: *** SIERKAMTEN die **** *********** NEUEICHENESCHEREGIERUNG nicht an +Eval: D S D D S + +Speaker sentences 194: swc_deu_001420 #utts: 1 +id: (swc_deu_001420-swc_deu_001420) +Scores: (#C #S #D #I) 1 7 2 0 +REF: die URAUFFÜHRUNG FAND AM DREIUNDZWANZIGSTE SEPTEMBER ZWEI TAUSEND ACHT IN +HYP: die ************* **** ORAUFÜGEN VON AN DRENZWANISTEN SETEMBER ZWERDEND ACHTI +Eval: D D S S S S S S S + +Speaker sentences 195: swc_deu_001421 #utts: 1 +id: (swc_deu_001421-swc_deu_001421) +Scores: (#C #S #D #I) 0 7 5 0 +REF: ER WILL SICH NICHT SCHULDIG ODER MITSCHULDIG MACHEN AM TODE EINES MITGESELLEN +HYP: ** **** **** ***** ******** ERWIE SIE MICHTSCHLDIC ODERMITSCHLICH MACEN ANTODER NSMITGESE +Eval: D D D D D S S S S S S S + +Speaker sentences 196: swc_deu_001422 #utts: 1 +id: (swc_deu_001422-swc_deu_001422) +Scores: (#C #S #D #I) 1 2 2 0 +REF: DIE MIT DER ERSTSTIMME einen +HYP: *** *** DIEMEDER ERTTUMMAR einen +Eval: D D S S + +Speaker sentences 197: swc_deu_001423 #utts: 1 +id: (swc_deu_001423-swc_deu_001423) +Scores: (#C #S #D #I) 2 2 1 0 +REF: UND HALFEN DIESEN bei der +HYP: *** NDTEIFEN TIESEN bei der +Eval: D S S + +Speaker sentences 198: swc_deu_001424 #utts: 1 +id: (swc_deu_001424-swc_deu_001424) +Scores: (#C #S #D #I) 3 2 0 1 +REF: ********* KREISWAHLVORSCHLAG und eine landesliste UNTERZEICHNEN +HYP: KREISWALE FORSCHLAG und eine landesliste NDERZEITNEN +Eval: I S S + +Speaker sentences 199: swc_deu_001425 #utts: 1 +id: (swc_deu_001425-swc_deu_001425) +Scores: (#C #S #D #I) 6 14 1 3 +REF: ** EINE UMSETZUNG DER sage in form ****** *************** EINES FÜNFZEHNTEILIGEN LIEDERZYKLUS ZWEI TAUSEND ACHT wurde PREUSSLERS KRABAT in EINER BEARBEITUNG von HORST HAWEMANN +HYP: AN UMSER ZUN DE sage in form EINEST FÜNFZEHNTEILEN LIEDER ZYKLUOS ZWE T SED CHT wurde PESLAS KÖABERT in NEBER ABETUNG von ***** HAUSTHAREMEN +Eval: I S S S I I S S S S S S S S S S D S + +Speaker sentences 200: swc_deu_001426 #utts: 1 +id: (swc_deu_001426-swc_deu_001426) +Scores: (#C #S #D #I) 1 4 0 0 +REF: WIE die FOLGENDE TABELLE DARSTELLT +HYP: IE die OLGEN DE TAPELEDARSTELLT +Eval: S S S S + +Speaker sentences 201: swc_deu_001427 #utts: 1 +id: (swc_deu_001427-swc_deu_001427) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ZUM STROMFLUSS BEI +HYP: UM STRUM FLOSBE +Eval: S S S + +Speaker sentences 202: swc_deu_001428 #utts: 1 +id: (swc_deu_001428-swc_deu_001428) +Scores: (#C #S #D #I) 2 4 0 1 +REF: ** DEM BUNDESWAHLLEITER BIS zum SIEBENUNDNEUNZIGSTE tag +HYP: DE BUNDES WALLITER BIST zum SIMONEUNZIGSTEN tag +Eval: I S S S S + +Speaker sentences 203: swc_deu_001429 #utts: 1 +id: (swc_deu_001429-swc_deu_001429) +Scores: (#C #S #D #I) 1 4 0 0 +REF: VOLLJÄHRIG GEWORDENE DEUTSCHE nicht MITWÄHLEN +HYP: ORIRICH EWORDEN DEUSCHER nicht MITWELEN +Eval: S S S S + +Speaker sentences 204: swc_deu_001430 #utts: 1 +id: (swc_deu_001430-swc_deu_001430) +Scores: (#C #S #D #I) 1 5 0 1 +REF: AUSFÜHRUNG MUSS ein ***** GUTER QUARTERBACK IN +HYP: AUSSFIRN MUST ein KUSER KOTER BEG N +Eval: S S I S S S + +Speaker sentences 205: swc_deu_001431 #utts: 1 +id: (swc_deu_001431-swc_deu_001431) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ************ VERGLEICHBAREN ZAHLENWERT UMGEWANDELT +HYP: VERGLEICHPAN ZEAHLENWERT UMGE ANDE +Eval: I S S S + +Speaker sentences 206: swc_deu_001432 #utts: 1 +id: (swc_deu_001432-swc_deu_001432) +Scores: (#C #S #D #I) 0 2 0 2 +REF: ******** ** BETRACHTETE ALLGEMEINHEIT +HYP: BETRCHTE DE ALGE MEINHEI +Eval: I I S S + +Speaker sentences 207: swc_deu_001433 #utts: 1 +id: (swc_deu_001433-swc_deu_001433) +Scores: (#C #S #D #I) 1 4 1 0 +REF: UNTERSCHIEDLICHE AUFFASSUNGEN GAB ES nur DARÜBER +HYP: **************** UNTERSCHITLICHE AUFASSUNGEN GABES nur DAHRÜBER +Eval: D S S S S + +Speaker sentences 208: swc_deu_001434 #utts: 1 +id: (swc_deu_001434-swc_deu_001434) +Scores: (#C #S #D #I) 5 8 0 3 +REF: doll beim BUNDESLIGISTEN BORUSSIA dortmund **** NACHFOLGER des ******* ** UNMITTELBAR ZUVOR ZURÜCKGETRETENEN TRAINERS JÜRGEN rÖber +HYP: doll beim BUNESLIKISTEN BERSER dortmund NACH VOLGER des UNMITEL BA ZU VOR ZORÜCKETREDEN RENAS IRFEN rÖber +Eval: S S I S I I S S S S S + +Speaker sentences 209: swc_deu_001435 #utts: 1 +id: (swc_deu_001435-swc_deu_001435) +Scores: (#C #S #D #I) 0 3 0 0 +REF: NEUNZEHN HUNDERT ACHTUNDACHTZIG +HYP: NUNZENERD CHTUN ACTZIG +Eval: S S S + +Speaker sentences 210: swc_deu_001436 #utts: 1 +id: (swc_deu_001436-swc_deu_001436) +Scores: (#C #S #D #I) 0 2 0 1 +REF: **** FREIEN ENZYKLOPÄDIE +HYP: REIN EN ZYGLOPETDIE +Eval: I S S + +Speaker sentences 211: swc_deu_001437 #utts: 1 +id: (swc_deu_001437-swc_deu_001437) +Scores: (#C #S #D #I) 5 4 0 2 +REF: der PHOTOSTROM IST Über viele ********** GRÖSSENORDNUNGEN linear zum ***** LICHTEINFALL +HYP: der VOTOSTROM IS Über viele GRÜSSENOR NUNGE linear zum LICHT ENFAL +Eval: S S I S I S + +Speaker sentences 212: swc_deu_001438 #utts: 1 +id: (swc_deu_001438-swc_deu_001438) +Scores: (#C #S #D #I) 0 5 2 0 +REF: DAS HATTE FÜR KLEINE PARTEIEN GROSSE AUSWIRKUNGEN +HYP: *** ***** DASHT FÜKLEINE PARTEIN GROSE AUSWIRKUNG +Eval: D D S S S S S + +Speaker sentences 213: swc_deu_001439 #utts: 1 +id: (swc_deu_001439-swc_deu_001439) +Scores: (#C #S #D #I) 0 4 0 0 +REF: IST DIE ITERATIVE TIEFENSUCHE +HYP: IS DE ITERATIEVE TIEFENSUCH +Eval: S S S S + +Speaker sentences 214: swc_deu_001440 #utts: 1 +id: (swc_deu_001440-swc_deu_001440) +Scores: (#C #S #D #I) 1 5 0 1 +REF: *********** DIES KÖNNEN ZUM BEISPIEL KONDENSATOREN sein +HYP: DIESKÖNNEN UM BEI SPIEL KONDEN SEATOREN sein +Eval: I S S S S S + +Speaker sentences 215: swc_deu_001441 #utts: 1 +id: (swc_deu_001441-swc_deu_001441) +Scores: (#C #S #D #I) 4 5 0 0 +REF: als die kurs auf KUBA HALTENDEN SOWJETISCHEN SCHIFFE ABDREHTEN +HYP: als die kurs auf KOBERHALTENDEN SO JETICHEN SCHIVER ABTRETEN +Eval: S S S S S + +Speaker sentences 216: swc_deu_001442 #utts: 1 +id: (swc_deu_001442-swc_deu_001442) +Scores: (#C #S #D #I) 5 6 2 0 +REF: BUNDESTAGSWAHL NEUNZEHN HUNDERT DREIUNDFÜNFZIG wurde ERSTMALS nach einem vom BUNDESTAG SELBST ERLASSENEN gesetz +HYP: ************** BUNESTAG WAHLNUNZHUNERDREIUN FÜFZIG wurde RSMALS nach einem vom ********* BUNDESTAIGSEBST ERLSEN gesetz +Eval: D S S S S D S S + +Speaker sentences 217: swc_deu_001443 #utts: 1 +id: (swc_deu_001443-swc_deu_001443) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ***** BUNDESWAHLGESETZ VIELFACH GEÄNDERT WORDEN +HYP: BUNDE WAIGESETZ VIELFACHE INERT WURDEN +Eval: I S S S S + +Speaker sentences 218: swc_deu_001444 #utts: 1 +id: (swc_deu_001444-swc_deu_001444) +Scores: (#C #S #D #I) 2 2 2 0 +REF: er ÜBERLAGERT den PHOTOSTROM UND TRÄGT +HYP: er ÜBERLAGER den ********** *** VORTUSTROMEUNDTREGT +Eval: S D D S + +Speaker sentences 219: swc_deu_001445 #utts: 1 +id: (swc_deu_001445-swc_deu_001445) +Scores: (#C #S #D #I) 3 3 0 0 +REF: TROTZ INTEGRATION der beiden deutschen STAATEN +HYP: DRO INTEKRATIUMN der beiden deutschen STATEN +Eval: S S S + +Speaker sentences 220: swc_deu_001446 #utts: 1 +id: (swc_deu_001446-swc_deu_001446) +Scores: (#C #S #D #I) 1 2 0 0 +REF: berliner WÜHLMÄUSEN STATT +HYP: berliner WÜLMEUSEN STAT +Eval: S S + +Speaker sentences 221: swc_deu_001447 #utts: 1 +id: (swc_deu_001447-swc_deu_001447) +Scores: (#C #S #D #I) 0 1 1 0 +REF: OFFIZIELLE FÜHRUNGEN +HYP: ********** AFIEZELERFÜHRUNGEN +Eval: D S + +Speaker sentences 222: swc_deu_001448 #utts: 1 +id: (swc_deu_001448-swc_deu_001448) +Scores: (#C #S #D #I) 3 4 1 0 +REF: BEI DER VERHÄLTNISWAHL WIRD ZUSÄTZLICH die einhaltung der +HYP: *** BE DERVERHETENS WALWIT ZUSETZLICG die einhaltung der +Eval: D S S S S + +Speaker sentences 223: swc_deu_001449 #utts: 1 +id: (swc_deu_001449-swc_deu_001449) +Scores: (#C #S #D #I) 1 5 3 0 +REF: WIE WENIG DIE INSULANER noch AM PULS DER ZEIT +HYP: *** WIEVWENIHT I SOLANE noch ** **** AMPOULTZ DERZEIT +Eval: D S S S D D S S + +Speaker sentences 224: swc_deu_001450 #utts: 1 +id: (swc_deu_001450-swc_deu_001450) +Scores: (#C #S #D #I) 2 8 0 0 +REF: JEDOCH ETWA DIE DURCHFÜHRUNG von WAHLWERBUNG auf KOSTEN DES STAATES +HYP: RE DOCH ETWR DIEDUCHFÜHREN von WALWERBUNG auf KOSTE DE STATES +Eval: S S S S S S S S + +Speaker sentences 225: swc_deu_001451 #utts: 1 +id: (swc_deu_001451-swc_deu_001451) +Scores: (#C #S #D #I) 1 3 0 0 +REF: das NICHT IM GRUNDGESETZ +HYP: das NIH M RUNDKGESETZ +Eval: S S S + +Speaker sentences 226: swc_deu_001452 #utts: 1 +id: (swc_deu_001452-swc_deu_001452) +Scores: (#C #S #D #I) 3 1 1 0 +REF: heimat vertrieben und HÄUSLICHE GEWALT +HYP: heimat vertrieben und ********** HEUSLICHEGEWAL +Eval: D S + +Speaker sentences 227: swc_deu_001453 #utts: 1 +id: (swc_deu_001453-swc_deu_001453) +Scores: (#C #S #D #I) 2 5 0 0 +REF: UND SPEICHERE IHN in EINER WARTESCHLANGE ab +HYP: ND SPEICHER IEN in EINR WAGTESCHLNGE ab +Eval: S S S S S + +Speaker sentences 228: swc_deu_001454 #utts: 1 +id: (swc_deu_001454-swc_deu_001454) +Scores: (#C #S #D #I) 5 9 2 1 +REF: ********** ORIGINAL TONBÄNDER und die DOKUMENTATION DES STUDIOS wurden NEUNZEHN hundert ZWEIUNDSIEBZIG in DAS SIEMENS ARCHIV ÜBERSTELLT +HYP: ORREIGINAL TON BENDER und die ************* *** DOKOMITATIONDESTUDIOS wurden NENZEHN hundert ZWUOHNSIEBZIG in DER SIMENS ERCHIEF ÜBERSTELT +Eval: I S S D D S S S S S S S + +Speaker sentences 229: swc_deu_001455 #utts: 1 +id: (swc_deu_001455-swc_deu_001455) +Scores: (#C #S #D #I) 2 4 2 0 +REF: SO MÜSSEN auf einem STRATEGISCHEN RAKETEN U BOOT +HYP: ** SOMISSEN auf einem ************* STATGSCHN REKETEN UBODT +Eval: D S D S S S + +Speaker sentences 230: swc_deu_001456 #utts: 1 +id: (swc_deu_001456-swc_deu_001456) +Scores: (#C #S #D #I) 0 1 0 2 +REF: ******* ***** FLÖTENSPIELÄHNLICHE +HYP: FLÖTEN SPIEL EDLICHER +Eval: I I S + +Speaker sentences 231: swc_deu_001457 #utts: 1 +id: (swc_deu_001457-swc_deu_001457) +Scores: (#C #S #D #I) 1 3 0 0 +REF: drastisch MODERNE ELEKTRONISCHE KLANGGESTALTUNG +HYP: drastisch MODERARNE ELIKTRONISCHE KLANGESCHALTUNG +Eval: S S S + +Speaker sentences 232: swc_deu_001458 #utts: 1 +id: (swc_deu_001458-swc_deu_001458) +Scores: (#C #S #D #I) 8 15 0 3 +REF: ANSCHLIESSEND WURDEN die SO ERMITTELTE MANDATSZAHL JEDER partei *** *** NACH DEMSELBEN VERFAHREN ENTSPRECHEND der ANZAHL ihrer ZWEITSTIMMEN PROPORTIONAL auf die LANDESLISTEN der partei ***** UNTERVERTEILT +HYP: ANCHLISEN WODE die SOH AMITETE MANDARTZTZAL IEDER partei NAH DIM SEM VERFAHN EN SPRECHEN der ANZAL ihrer ZWEITSTMM PROPRZUNAL auf die LANESLISTE der partei UNTER VERTEIERT +Eval: S S S S S S I I S S S S S S S S I S + +Speaker sentences 233: swc_deu_001459 #utts: 1 +id: (swc_deu_001459-swc_deu_001459) +Scores: (#C #S #D #I) 0 4 1 0 +REF: OPFERN DER NATO BOMBARDIERUNG UNTERKÜNFTE +HYP: ****** OCFNDER NARTHO BOMBADIEO UNDTEÖKÜNFTE +Eval: D S S S S + +Speaker sentences 234: swc_deu_001460 #utts: 1 +id: (swc_deu_001460-swc_deu_001460) +Scores: (#C #S #D #I) 1 2 0 1 +REF: der ***** FREIEN ENZYKLOPÄDIE +HYP: der FREIN ENTZU KLOPE +Eval: I S S + +Speaker sentences 235: swc_deu_001461 #utts: 1 +id: (swc_deu_001461-swc_deu_001461) +Scores: (#C #S #D #I) 0 2 0 0 +REF: MITTLERWEILE FINDEN +HYP: MTLERBEILEL HINDEN +Eval: S S + +Speaker sentences 236: swc_deu_001462 #utts: 1 +id: (swc_deu_001462-swc_deu_001462) +Scores: (#C #S #D #I) 2 9 0 0 +REF: wer WEGEN EINES VERBRECHENS RECHTSKRÄFTIG zu EINER FREIHEITSSTRAFE VON MINDESTENS EINEM +HYP: wer WIGEG EINE VERBRECHTEN SRECHSGRFTICH zu INER REIT TRAFE VONMINDESENS EINE +Eval: S S S S S S S S S + +Speaker sentences 237: swc_deu_001463 #utts: 1 +id: (swc_deu_001463-swc_deu_001463) +Scores: (#C #S #D #I) 3 5 1 0 +REF: DER GESCHWINDIGKEITSWERTUNG ERRANGEN drei b F EIN HUNDERT acht +HYP: DR GSCHWINDIGKEITZWERTUNG ERAGEN drei b * EF EINHUDER acht +Eval: S S S D S S + +Speaker sentences 238: swc_deu_001464 #utts: 1 +id: (swc_deu_001464-swc_deu_001464) +Scores: (#C #S #D #I) 1 3 1 0 +REF: LIBORIUS am ERSTEN LIBORI SAMSTAG +HYP: IEBORIOS am ****** ERSTENIG BORIESAMSER +Eval: S D S S + +Speaker sentences 239: swc_deu_001465 #utts: 1 +id: (swc_deu_001465-swc_deu_001465) +Scores: (#C #S #D #I) 2 6 1 0 +REF: nach DEM SAINTE LAGUË VERFAHREN auf DIE LÄNDER VERTEILT +HYP: nach DM SEARN AR GÜFVERFAREN auf *** DIELENDER ERTEILT +Eval: S S S S D S S + +Speaker sentences 240: swc_deu_001466 #utts: 1 +id: (swc_deu_001466-swc_deu_001466) +Scores: (#C #S #D #I) 1 3 0 1 +REF: ** REFORMEN GORBATSCHOWS und ABRÜSTUNGSSCHRITTE +HYP: RE FORMEN GOBERSCHAFS und ABRSTUNGSCHITE +Eval: I S S S + +Speaker sentences 241: swc_deu_001467 #utts: 1 +id: (swc_deu_001467-swc_deu_001467) +Scores: (#C #S #D #I) 1 3 1 0 +REF: NULL UNPORTED und UNTER DER +HYP: SIEREA ANPORTET und ***** NDERDE +Eval: S S D S + +Speaker sentences 242: swc_deu_001468 #utts: 1 +id: (swc_deu_001468-swc_deu_001468) +Scores: (#C #S #D #I) 3 3 0 1 +REF: an dem WESTLICHE KRÄFTE auf ***** GEGENREVOLUTIONÄRER +HYP: an dem ESTICHE GREFTE auf GEGEN RVOLEZEN +Eval: S S I S + +Speaker sentences 243: swc_deu_001469 #utts: 1 +id: (swc_deu_001469-swc_deu_001469) +Scores: (#C #S #D #I) 2 2 0 0 +REF: WIRD unter anderem VERWENDET +HYP: IT unter anderem VERWENDE +Eval: S S + +Speaker sentences 244: swc_deu_001470 #utts: 1 +id: (swc_deu_001470-swc_deu_001470) +Scores: (#C #S #D #I) 0 1 1 0 +REF: AUS WIKIPEDIA +HYP: *** AUSWIKEPEDIER +Eval: D S + +Speaker sentences 245: swc_deu_001471 #utts: 1 +id: (swc_deu_001471-swc_deu_001471) +Scores: (#C #S #D #I) 1 1 0 0 +REF: und KUBAKRISE +HYP: und KOBARKRISE +Eval: S + +Speaker sentences 246: swc_deu_001472 #utts: 1 +id: (swc_deu_001472-swc_deu_001472) +Scores: (#C #S #D #I) 4 7 1 1 +REF: LETZTER WAHL aufgrund eigener **** WAHLVORSCHLÄGE UNUNTERBROCHEN MIT MINDESTENS FÜNF ABGEORDNETEN vertreten sind +HYP: ******* ENLTZTDARWAL aufgrund eigener WEIL VORSCHLÄGE UNETEBRUCHEN MINISSENS FÜF ABGERUNE ND vertreten sind +Eval: D S I S S S S S S + +Speaker sentences 247: swc_deu_001473 #utts: 1 +id: (swc_deu_001473-swc_deu_001473) +Scores: (#C #S #D #I) 2 3 1 0 +REF: VERBREITUNG IDEOLOGISCHER PROPAGANDA der SUPERMÄCHTE und +HYP: *********** VERPREITUNG IEDIOLOGESCHAPROPARGANDER der SUPERMICHTE und +Eval: D S S S + +Speaker sentences 248: swc_deu_001474 #utts: 1 +id: (swc_deu_001474-swc_deu_001474) +Scores: (#C #S #D #I) 1 4 0 0 +REF: WEBCOMICS auf DIE REALITÄT ÜBERTRAGEN +HYP: KOMMIHS auf DERELITET BERT HAGEN +Eval: S S S S + +Speaker sentences 249: swc_deu_001475 #utts: 1 +id: (swc_deu_001475-swc_deu_001475) +Scores: (#C #S #D #I) 2 5 0 0 +REF: als der KALTE KRIEG SICH FORTWÄHREND ZUSPITZE +HYP: als der KALTIKLIEG SIG VORT WEREN ZUSPITZTE +Eval: S S S S S + +Speaker sentences 250: swc_deu_001476 #utts: 1 +id: (swc_deu_001476-swc_deu_001476) +Scores: (#C #S #D #I) 1 5 2 0 +REF: SICHERHEITSPERSONAL ODER WACHHUNDEN NUR SEHR schwierig BETRETEN WERDEN +HYP: ******************* **** SIHEITSPERSONAL ODEREACHUNDEN NUSEHR schwierig BETETEN ERN +Eval: D D S S S S S + +Speaker sentences 251: swc_deu_001477 #utts: 1 +id: (swc_deu_001477-swc_deu_001477) +Scores: (#C #S #D #I) 2 1 0 1 +REF: ******* DAUERHAFTES bleiberecht und +HYP: DAURHAF DES bleiberecht und +Eval: I S + +Speaker sentences 252: swc_deu_001478 #utts: 1 +id: (swc_deu_001478-swc_deu_001478) +Scores: (#C #S #D #I) 0 5 2 0 +REF: EBENSO WIE DAS MOTIV DER ERLÖSUNG DURCH +HYP: ****** *** EBENSOWIEDEAS MOTIFTER LE SUN BÜT +Eval: D D S S S S S + +Speaker sentences 253: swc_deu_001479 #utts: 1 +id: (swc_deu_001479-swc_deu_001479) +Scores: (#C #S #D #I) 0 2 3 0 +REF: WENN FÜR NIEMANDEN NACHPRÜFBAR IST +HYP: **** **** ********* WENFÜNIEMARN NACHPRÜCFBEIST +Eval: D D D S S + +Speaker sentences 254: swc_deu_001480 #utts: 1 +id: (swc_deu_001480-swc_deu_001480) +Scores: (#C #S #D #I) 2 2 0 1 +REF: ** PRIVATE ERFORSCHUNG von einrichtungen +HYP: IS TIEKLEWADE ARFORSCHEN von einrichtungen +Eval: I S S + +Speaker sentences 255: swc_deu_001481 #utts: 1 +id: (swc_deu_001481-swc_deu_001481) +Scores: (#C #S #D #I) 0 7 4 0 +REF: ABGESEHEN DAVON WÜRDEN SELBST DANN NOCH DIE ENTSPRECHENDEN PAL CODES FEHLEN +HYP: ********* ***** ******* ****** GESEHEN DARVON BÜRDEN SEBSTDANOCH DIENTSPECHENDEN PALKAOT FIELEN +Eval: D D D D S S S S S S S + +Speaker sentences 256: swc_deu_001482 #utts: 1 +id: (swc_deu_001482-swc_deu_001482) +Scores: (#C #S #D #I) 0 4 0 2 +REF: ******** ******** SPRECHEN BENÖTIGTE ATEMLUFT LIEFERT +HYP: SPÄCHEN BENUTICH DE ARTEM LUFT LIEFVERT +Eval: I I S S S S + +Speaker sentences 257: swc_deu_001483 #utts: 1 +id: (swc_deu_001483-swc_deu_001483) +Scores: (#C #S #D #I) 0 4 0 0 +REF: MÖGLICHEN SCHUTZIMPFUNGEN GEGEN KRANKHEITEN +HYP: EMOGLICHENSHUTZS IMFORNEN GEN KRANKREITE +Eval: S S S S + +Speaker sentences 258: swc_deu_001484 #utts: 1 +id: (swc_deu_001484-swc_deu_001484) +Scores: (#C #S #D #I) 0 4 1 0 +REF: SCHON EINEN ÄHNLICHEN VERSUCH GAB +HYP: ***** SCHN EIN ENLICHEN VERSUCHGAR +Eval: D S S S S + +Speaker sentences 259: swc_deu_001485 #utts: 1 +id: (swc_deu_001485-swc_deu_001485) +Scores: (#C #S #D #I) 5 10 2 3 +REF: **** ********** an einem P N Übergang *** ODER PIN Übergang DURCH DEN INNEREN PHOTOEFFEKT in EINEN ELEKTRISCHEN STROM UMWANDELT +HYP: ROND KENSTRAHEN an einem * PEEN Übergang ODE PIE EN Übergang DUSTDEN NHREN VOTU FEKT in ***** EIN ELEKRICHENSTROM UMWANDET +Eval: I I D S I S S S S S S D S S S + +Speaker sentences 260: swc_deu_001486 #utts: 1 +id: (swc_deu_001486-swc_deu_001486) +Scores: (#C #S #D #I) 0 5 1 0 +REF: BEIM MEISTER IN DER SILVESTERNACHT FREIBITTEN +HYP: **** BRMASTE ENDE SEBESTER NCHT FREIDETEN +Eval: D S S S S S + +Speaker sentences 261: swc_deu_001487 #utts: 1 +id: (swc_deu_001487-swc_deu_001487) +Scores: (#C #S #D #I) 0 2 2 0 +REF: JAHREN DER BEGRIFF VADDING +HYP: ****** *** LAHEN DERBEGIFT +Eval: D D S S + +Speaker sentences 262: swc_deu_001488 #utts: 1 +id: (swc_deu_001488-swc_deu_001488) +Scores: (#C #S #D #I) 3 5 0 0 +REF: RANGVERHÄLTNIS unter DEN STIMMEN noch eine LOGISCHE ABFOLGE +HYP: ANKFALTNIS unter DIN STIMN noch eine LOGESCHE ABPFOLRG +Eval: S S S S S + +Speaker sentences 263: swc_deu_001489 #utts: 1 +id: (swc_deu_001489-swc_deu_001489) +Scores: (#C #S #D #I) 2 4 2 1 +REF: KRABAT LEHNT DIESES ANGEBOT JEDOCH mit ** ENTSCHIEDENHEIT ab +HYP: ****** ***** KABERTLENDIESES ANGEBODIE O mit EN HIENHEIT ab +Eval: D D S S S I S + +Speaker sentences 264: swc_deu_001490 #utts: 1 +id: (swc_deu_001490-swc_deu_001490) +Scores: (#C #S #D #I) 2 4 0 3 +REF: stand vom ******** **** **** DER INHALT STEHT UNTER +HYP: stand vom ZWALFTEN MERZ ZWEI TAUSEN ZWÖLFDER INHALTSTE NT +Eval: I I I S S S S + +Speaker sentences 265: swc_deu_001491 #utts: 1 +id: (swc_deu_001491-swc_deu_001491) +Scores: (#C #S #D #I) 0 3 1 0 +REF: ORGANISATION UNTERBRACH DARAUFHIN DIE +HYP: ************ RGENISAT IONUNTER BRACHTEREFND +Eval: D S S S + +Speaker sentences 266: swc_deu_001492 #utts: 1 +id: (swc_deu_001492-swc_deu_001492) +Scores: (#C #S #D #I) 2 4 1 0 +REF: VERBÜNDET SIND ODER GAR FÜR sie arbeiten +HYP: ********** VER BÜNDITZEND ORDER GAHFÜ sie arbeiten +Eval: D S S S S + +Speaker sentences 267: swc_deu_001493 #utts: 1 +id: (swc_deu_001493-swc_deu_001493) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ******** FESTGELEGTE VOLLJÄHRIGKEITSALTER +HYP: ESGELETE OLHRIHKET ALDE +Eval: I S S + +Speaker sentences 268: swc_deu_001494 #utts: 1 +id: (swc_deu_001494-swc_deu_001494) +Scores: (#C #S #D #I) 3 3 0 0 +REF: die ERRICHTUNG der BERLINER MAUER mÜndeten +HYP: die ERICHTUNG der BERLINE MAUR mÜndeten +Eval: S S S + +Speaker sentences 269: swc_deu_001495 #utts: 1 +id: (swc_deu_001495-swc_deu_001495) +Scores: (#C #S #D #I) 1 2 0 1 +REF: ERRICHTUNG von ****** KLÄRANLAGEN +HYP: ERICHTUNG von KLÄER ANLARGEN +Eval: S I S + +Speaker sentences 270: swc_deu_001496 #utts: 1 +id: (swc_deu_001496-swc_deu_001496) +Scores: (#C #S #D #I) 2 5 2 0 +REF: AFGHANISTANS und IM IRAK hat SICH SEIT DEM EINMARSCH +HYP: AFGANESTANZ und ** DEMIEHÖARG hat **** ZICH SEITIEM EINMARCTE +Eval: S D S D S S S + +Speaker sentences 271: swc_deu_001497 #utts: 1 +id: (swc_deu_001497-swc_deu_001497) +Scores: (#C #S #D #I) 8 1 0 2 +REF: der ***** ***** PHONATIONSSTROM von den lungen Über die bronchien bis +HYP: der VONAR IOUND STROM von den lungen Über die bronchien bis +Eval: I I S + +Speaker sentences 272: swc_deu_001498 #utts: 1 +id: (swc_deu_001498-swc_deu_001498) +Scores: (#C #S #D #I) 2 5 0 1 +REF: **** AUSSERDEM nahmen sender HÖRSPIELE MIT VERFREMDETER SPRACHE +HYP: AUSE DEMN nahmen sender HRSPIELEB MITT VERFRMDEDR SPRARE +Eval: I S S S S S + +Speaker sentences 273: swc_deu_001499 #utts: 1 +id: (swc_deu_001499-swc_deu_001499) +Scores: (#C #S #D #I) 0 3 0 0 +REF: UND DIE GRUNDMANDATSKLAUSEL +HYP: UNDTIGUNDMANDAR S KLAUSE +Eval: S S S + +Speaker sentences 274: swc_deu_001500 #utts: 1 +id: (swc_deu_001500-swc_deu_001500) +Scores: (#C #S #D #I) 3 5 0 1 +REF: keine ** ABKEHR von DEN GRUNDLAGEN des SOZIALISMUS EINSCHLIESSE +HYP: keine AB KER von EN GRUNDTLAGE des SOZELISMOS EINSCHLIESE +Eval: I S S S S S + +Speaker sentences 275: swc_deu_001501 #utts: 1 +id: (swc_deu_001501-swc_deu_001501) +Scores: (#C #S #D #I) 3 5 2 1 +REF: MIT komponenten ** SOWOHL AN als AUCH TIEF in DER WAFFE +HYP: IT komponenten SO WOHL ANN als **** AUCHTIEF in *** DERWACFE +Eval: S I S S D S D S + +Speaker sentences 276: swc_deu_001502 #utts: 1 +id: (swc_deu_001502-swc_deu_001502) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ********** BEDEUTUNGSVOLL WAR +HYP: BEDEUTUNGS VOLL WA +Eval: I S S + +Speaker sentences 277: swc_deu_001503 #utts: 1 +id: (swc_deu_001503-swc_deu_001503) +Scores: (#C #S #D #I) 0 3 0 0 +REF: FREIWILLIGE HELFER DER +HYP: FREIWÜELIGE HENFERTE OGANIESATZION +Eval: S S S + +Speaker sentences 278: swc_deu_001504 #utts: 1 +id: (swc_deu_001504-swc_deu_001504) +Scores: (#C #S #D #I) 2 4 0 0 +REF: UM ELEKTRONEN vom VALENZBAND ins LEITUNGSBAND +HYP: M ELEKTRON vom WARLENZSBAND ins LEITUNGSBAN +Eval: S S S S + +Speaker sentences 279: swc_deu_001505 #utts: 1 +id: (swc_deu_001505-swc_deu_001505) +Scores: (#C #S #D #I) 0 5 0 0 +REF: ALLERDINGS SIND VERGLEICHBARE EFFEKTE MÖGLICH +HYP: ALLEDINGSEN VERGLEICHBER IFEK E MÖUGLICH +Eval: S S S S S + +Speaker sentences 280: swc_deu_001506 #utts: 1 +id: (swc_deu_001506-swc_deu_001506) +Scores: (#C #S #D #I) 7 5 0 4 +REF: diese KONNTEN aber als eingabe in ****** ***** EINEN FREQUENZUMSETZER dienen oder ******** **** STEUERTEN SYNCHRONMOTOREN +HYP: diese KONTEN aber als eingabe in EINEND FRICG WENZS UMSETZER dienen oder STEUATEN ZUNG ON MOTOUREN +Eval: S I I S S I I S S + +Speaker sentences 281: swc_deu_001507 #utts: 1 +id: (swc_deu_001507-swc_deu_001507) +Scores: (#C #S #D #I) 3 5 0 0 +REF: THOMAS HERMANNS produzierte zwei tausend ZWEI MIT GREBE +HYP: TOMAS HRMANS produzierte zwei tausend Z WEIMIT KEREBE +Eval: S S S S S + +Speaker sentences 282: swc_deu_001508 #utts: 1 +id: (swc_deu_001508-swc_deu_001508) +Scores: (#C #S #D #I) 0 2 2 0 +REF: P N ÜBERGANG TREFFEN +HYP: * * PENÜBERGANG TREFEN +Eval: D D S S + +Speaker sentences 283: swc_deu_001509 #utts: 1 +id: (swc_deu_001509-swc_deu_001509) +Scores: (#C #S #D #I) 1 2 0 0 +REF: die FALKENHORST SHOW +HYP: die FALGTEN HOUSSCAU +Eval: S S + +Speaker sentences 284: swc_deu_001510 #utts: 1 +id: (swc_deu_001510-swc_deu_001510) +Scores: (#C #S #D #I) 2 3 0 2 +REF: ******* *** ANTISOWJETISCHE demonstrationen wurden BLUTIG NIEDERGESCHLAGEN +HYP: ANTIESO JET SCHE demonstrationen wurden PLUTIG NIEDERSCHLAGE +Eval: I I S S S + +Speaker sentences 285: swc_deu_001511 #utts: 1 +id: (swc_deu_001511-swc_deu_001511) +Scores: (#C #S #D #I) 3 3 1 0 +REF: ein vier kanal MISCHPULT DIENTE FÜR KLEINERE +HYP: ein vier kanal ********* MISPOLD DENTE VERKLEINER +Eval: D S S S + +Speaker sentences 286: swc_deu_001512 #utts: 1 +id: (swc_deu_001512-swc_deu_001512) +Scores: (#C #S #D #I) 4 8 2 0 +REF: diese HÄTTEN die VORWARNZEITEN FÜR EINEN ANGRIFF auf die U S A EXTREM HERABGESETZT +HYP: diese HETEN die ************* VORWANDZEITEN FÜREINEN ANGRIF auf die * UES AR EXSTREM HERAPBGESETZT +Eval: S D S S S D S S S S + +Speaker sentences 287: swc_deu_001513 #utts: 1 +id: (swc_deu_001513-swc_deu_001513) +Scores: (#C #S #D #I) 3 3 0 1 +REF: WELCHES am NÄCHSTEN zum ***** STARTKNOTEN liegt +HYP: WEICHES am NECHSTEN zum START KNODEN liegt +Eval: S S I S + +Speaker sentences 288: swc_deu_001514 #utts: 1 +id: (swc_deu_001514-swc_deu_001514) +Scores: (#C #S #D #I) 2 9 0 0 +REF: LAZIO GING DOLL ZURÜCK IN DIE BUNDESLIGA und WECHSELTE zu EINTRACHT +HYP: LAHT IOGINGN DOLLZERÜCK N IE BUNDES LIEGER und WEXSELDE zu EINDRCHT +Eval: S S S S S S S S S + +Speaker sentences 289: swc_deu_001515 #utts: 1 +id: (swc_deu_001515-swc_deu_001515) +Scores: (#C #S #D #I) 1 2 0 0 +REF: Über DIESE KRANKHEIT +HYP: Über ISE KANKEIT +Eval: S S + +Speaker sentences 290: swc_deu_001516 #utts: 1 +id: (swc_deu_001516-swc_deu_001516) +Scores: (#C #S #D #I) 3 2 0 1 +REF: jahr zwei tausend **** FÜNF KRITISIERTE +HYP: jahr zwei tausend FÜN VE KRITIESERTE +Eval: I S S + +Speaker sentences 291: swc_deu_001517 #utts: 1 +id: (swc_deu_001517-swc_deu_001517) +Scores: (#C #S #D #I) 1 4 0 0 +REF: diese AUFFASSUNG ZUR NEUTRALITÄT UNTERSCHEIDET +HYP: diese AUFHFASUNG ZURNEUTRARITEÄT UNTER SHEIDE +Eval: S S S S + +Speaker sentences 292: swc_deu_001518 #utts: 1 +id: (swc_deu_001518-swc_deu_001518) +Scores: (#C #S #D #I) 3 5 1 0 +REF: RIEDL WURDE als KÜNSTLERISCHER LEITER des SIEMENS STUDIOS bestellt +HYP: WEDEL WURDER als *************** KNSTLAISCHERLEITER des SIMEN STUDIES bestellt +Eval: S S D S S S + +Speaker sentences 293: swc_deu_001519 #utts: 1 +id: (swc_deu_001519-swc_deu_001519) +Scores: (#C #S #D #I) 2 4 1 0 +REF: WENN MAN die WELT als GANZES BETRACHTET +HYP: **** WENMAN die WLT als KANZES BERACHTET +Eval: D S S S S + +Speaker sentences 294: swc_deu_001520 #utts: 1 +id: (swc_deu_001520-swc_deu_001520) +Scores: (#C #S #D #I) 4 4 0 3 +REF: SIND kritische komponenten des ************ ******* DETONATIONSSYSTEMS ABSICHTLICH schwach **** ENTWORFEN +HYP: SIEMT kritische komponenten des DETUONATIOND ZYSTEMS ABSICH TLICH schwach EIND WURFEN +Eval: S I I S S I S + +Speaker sentences 295: swc_deu_001521 #utts: 1 +id: (swc_deu_001521-swc_deu_001521) +Scores: (#C #S #D #I) 0 4 0 0 +REF: NICHT WÄHLBAR IST JEDOCH +HYP: NICH WEHBER IS TEDOCH +Eval: S S S S + +Speaker sentences 296: swc_deu_001522 #utts: 1 +id: (swc_deu_001522-swc_deu_001522) +Scores: (#C #S #D #I) 3 3 0 1 +REF: er BOT eine *** VEREINIGUNG DEUTSCHLANDS an +HYP: er BOD eine VER EINIGUNG DEUTSCHANS an +Eval: S I S S + +Speaker sentences 297: swc_deu_001523 #utts: 1 +id: (swc_deu_001523-swc_deu_001523) +Scores: (#C #S #D #I) 2 2 0 1 +REF: ****** BERLIN zwei TAUSEND fÜnf +HYP: BERIEN N zwei TUEN fÜnf +Eval: I S S + +Speaker sentences 298: swc_deu_001524 #utts: 1 +id: (swc_deu_001524-swc_deu_001524) +Scores: (#C #S #D #I) 3 3 0 1 +REF: kern ABGESTIMMT und ** UMHÜLLEN diesen ENTSPRECHEND +HYP: kern ABGESTIMT und UM HÖLEN diesen ENSPRECHENT +Eval: S I S S + +Speaker sentences 299: swc_deu_001525 #utts: 1 +id: (swc_deu_001525-swc_deu_001525) +Scores: (#C #S #D #I) 1 3 0 0 +REF: ERZEUGUNG VON DYNAMIK aus +HYP: AZTEUGUNG VOMN DENAMIG aus +Eval: S S S + +Speaker sentences 300: swc_deu_001526 #utts: 1 +id: (swc_deu_001526-swc_deu_001526) +Scores: (#C #S #D #I) 2 1 0 0 +REF: ZIMT und ingwer +HYP: SEMT und ingwer +Eval: S + +Speaker sentences 301: swc_deu_001527 #utts: 1 +id: (swc_deu_001527-swc_deu_001527) +Scores: (#C #S #D #I) 0 1 2 0 +REF: VON SCHWERER UNTERERNÄHRUNG +HYP: *** ******** UNGVONSCHEHERUNTERNERUGEG +Eval: D D S + +Speaker sentences 302: swc_deu_001528 #utts: 1 +id: (swc_deu_001528-swc_deu_001528) +Scores: (#C #S #D #I) 1 2 0 0 +REF: NÜSSEN und GEWÜRZEN +HYP: NISCHEN und GEWRTHEN +Eval: S S + +Speaker sentences 303: swc_deu_001529 #utts: 1 +id: (swc_deu_001529-swc_deu_001529) +Scores: (#C #S #D #I) 1 1 1 0 +REF: robert F KENNEDY +HYP: robert * ERFKNEDIE +Eval: D S + +Speaker sentences 304: swc_deu_001530 #utts: 1 +id: (swc_deu_001530-swc_deu_001530) +Scores: (#C #S #D #I) 0 2 1 0 +REF: KAM SCHLIESSLICH ZUM +HYP: *** KAMSCHLISELICH ZUNM +Eval: D S S + +Speaker sentences 305: swc_deu_001531 #utts: 1 +id: (swc_deu_001531-swc_deu_001531) +Scores: (#C #S #D #I) 0 1 0 0 +REF: VOLLSTÄNDIGKEIT +HYP: OLSTENI +Eval: S + +Speaker sentences 306: swc_deu_001532 #utts: 1 +id: (swc_deu_001532-swc_deu_001532) +Scores: (#C #S #D #I) 3 2 2 0 +REF: STANDEN sich von den U S A +HYP: STANDTEN sich von den * * URSAR +Eval: S D D S + +Speaker sentences 307: swc_deu_001533 #utts: 1 +id: (swc_deu_001533-swc_deu_001533) +Scores: (#C #S #D #I) 0 4 1 0 +REF: AFRIKA SÜDLICH DER SAHARA GEORTET +HYP: ****** ACFRIKASSTIH DE SER HAHRERGEORTET +Eval: D S S S S + +Speaker sentences 308: swc_deu_001534 #utts: 1 +id: (swc_deu_001534-swc_deu_001534) +Scores: (#C #S #D #I) 1 2 0 0 +REF: die ARMEE MEUTERTE +HYP: die ARME MEUNTERTEL +Eval: S S + +Speaker sentences 309: swc_deu_001535 #utts: 1 +id: (swc_deu_001535-swc_deu_001535) +Scores: (#C #S #D #I) 1 2 0 0 +REF: STALIN setzte IM +HYP: STALIEN setzte M +Eval: S S + +Speaker sentences 310: swc_deu_001536 #utts: 1 +id: (swc_deu_001536-swc_deu_001536) +Scores: (#C #S #D #I) 0 1 0 2 +REF: ******** *** VERHÄLTNISAUSGLEICH +HYP: FEIHTENS AUS LEICH +Eval: I I S + +Speaker sentences 311: swc_deu_001537 #utts: 1 +id: (swc_deu_001537-swc_deu_001537) +Scores: (#C #S #D #I) 1 1 0 1 +REF: ***** PROSCRIBED gleich +HYP: KLMER AUFBROSKREIBT gleich +Eval: I S + +Speaker sentences 312: swc_deu_001538 #utts: 1 +id: (swc_deu_001538-swc_deu_001538) +Scores: (#C #S #D #I) 2 4 1 0 +REF: am ZWEITE JUNI ZWEI TAUSEND vier WURDEN +HYP: am ****** ZWEITEN JUNIEZWE TAUSND vier WURDN +Eval: D S S S S + +Speaker sentences 313: swc_deu_001539 #utts: 1 +id: (swc_deu_001539-swc_deu_001539) +Scores: (#C #S #D #I) 0 3 1 0 +REF: IN DEN BUNDESTAG NACHRÜCKT +HYP: ** INE BNESTAG NACHRLGT +Eval: D S S S + +Speaker sentences 314: swc_deu_001540 #utts: 1 +id: (swc_deu_001540-swc_deu_001540) +Scores: (#C #S #D #I) 2 6 0 1 +REF: DIE NATO OSTERWEITERUNG und die **** EINSEITIGE AUFKÜNDIGUNG DES +HYP: DIENATO OSST ERWEITERUNG und die EIEN SEITIGE AUFKÖÜNDIGN DE +Eval: S S S I S S S + +Speaker sentences 315: swc_deu_001541 #utts: 1 +id: (swc_deu_001541-swc_deu_001541) +Scores: (#C #S #D #I) 1 1 0 1 +REF: * hierbei IST +HYP: T hierbei IS +Eval: I S + +Speaker sentences 316: swc_deu_001542 #utts: 1 +id: (swc_deu_001542-swc_deu_001542) +Scores: (#C #S #D #I) 3 3 2 0 +REF: dieser stelle kamen SÄMTLICHE MITGLIEDER DER KAPELLE DER +HYP: dieser stelle kamen ********** ********** SEMTICH MITIEDERDER KAPELETE +Eval: D D S S S + +Speaker sentences 317: swc_deu_001543 #utts: 1 +id: (swc_deu_001543-swc_deu_001543) +Scores: (#C #S #D #I) 5 13 0 2 +REF: ******* POTSDAMER ABKOMMEN ENTHIELT ZWAR ALLGEMEINE VEREINBARUNGEN Über DIE KÜNFTIGE gemeinsame verwaltung der SIEGERMÄCHTE und ******* FORMULIERTE GRUNDSÄTZE WIE DEMILITARISIERUNG +HYP: POTZTAM ABKOMEN ENT HIELDT ZWAHR ALGEMEINERVER EINBAUNEN Über DI KÖNFTIGE gemeinsame verwaltung der SIEGERMICHTE und VOMLIER TO RUNDSETZE IE DEMLITRISIERUNG +Eval: I S S S S S S S S S I S S S S + +Speaker sentences 318: swc_deu_001544 #utts: 1 +id: (swc_deu_001544-swc_deu_001544) +Scores: (#C #S #D #I) 3 5 2 0 +REF: danach UNTERSCHRIEB ER einen VERTRAG beim B F C DYNAMO +HYP: danach ************ UNDERSCHIPE einen VERDRAG beim * WIEHF ZI DENAHMO +Eval: D S S D S S S + +Speaker sentences 319: swc_deu_001545 #utts: 1 +id: (swc_deu_001545-swc_deu_001545) +Scores: (#C #S #D #I) 1 3 0 0 +REF: EINE weitere VARIANTE MAG +HYP: EIN weitere WERI ANDEMAG +Eval: S S S + +Speaker sentences 320: swc_deu_001546 #utts: 1 +id: (swc_deu_001546-swc_deu_001546) +Scores: (#C #S #D #I) 2 6 2 1 +REF: SIE WURDEN MODULAR durch ********* LOCHSTREIFEN GESTEUERT und DIE KLÄNGE KONNTEN +HYP: *** SIEWURDEN MODOLAHRN durch LOCHSTREI VN GESTEURT und *** DIKLINGE KONDN +Eval: D S S I S S D S S + +Speaker sentences 321: swc_deu_001547 #utts: 1 +id: (swc_deu_001547-swc_deu_001547) +Scores: (#C #S #D #I) 0 8 0 0 +REF: DIE GRUNDMANDATSKLAUSEL BEVORZUGT UNTER DEN KLEINEN PARTEIEN JENE +HYP: DIEGRUN MADARTG KLAUSEL BE VORZUCT UNDERDINKLEINERN PARTEIN JIENE +Eval: S S S S S S S S + +Speaker sentences 322: swc_deu_001548 #utts: 1 +id: (swc_deu_001548-swc_deu_001548) +Scores: (#C #S #D #I) 1 4 1 0 +REF: ABER TROTZDEM keine WIRKLICHE HUNGERSNOT HERRSCHT +HYP: **** BERTONZDEM keine WÖGKLICHE HUGES NOTHERUSCHT +Eval: D S S S S + +Speaker sentences 323: swc_deu_001549 #utts: 1 +id: (swc_deu_001549-swc_deu_001549) +Scores: (#C #S #D #I) 0 2 2 0 +REF: UND DOKUMENTATION DER OBJEKTE +HYP: *** ************* NTUGMNTERZION D +Eval: D D S S + +Speaker sentences 324: swc_deu_001550 #utts: 1 +id: (swc_deu_001550-swc_deu_001550) +Scores: (#C #S #D #I) 1 3 0 1 +REF: ** ZUR VORBEDINGUNG konkreter ABRÜSTUNGSSCHRITTE +HYP: ZU VOR BEDIGUNG konkreter APRÜSTUNSCHITE +Eval: I S S S + +Speaker sentences 325: swc_deu_001551 #utts: 1 +id: (swc_deu_001551-swc_deu_001551) +Scores: (#C #S #D #I) 0 1 0 2 +REF: ****** ****** BUNDESTAGSWAHLRECHT +HYP: BUNDES TARGES WALRECHT +Eval: I I S + +Speaker sentences 326: swc_deu_001552 #utts: 1 +id: (swc_deu_001552-swc_deu_001552) +Scores: (#C #S #D #I) 1 5 0 0 +REF: es MUSS DEM KREISWAHLLEITER VORGELEGT WERDEN +HYP: es MUS IM GREIS WALEITDER VORGELETWERN +Eval: S S S S S + +Speaker sentences 327: swc_deu_001553 #utts: 1 +id: (swc_deu_001553-swc_deu_001553) +Scores: (#C #S #D #I) 5 14 1 1 +REF: hat man eine EMPIRISCHE BASIS FÜR PSYCHOSOZIALE PROGRAMME ZUR SENKUNG DER SELBSTMORDRATE UND zur ******** STÄRKUNG DES SICHERHEITSGEFÜHLS in DER BEVÖLKERUNG +HYP: hat man eine IM PIERESCHE BASIES FÜBPSYCHOSOZIALE PROGEAME ZUO SENKUN DERSEBST MOUTERATE UN zur STEARKUG DE SICHEHITZ GEFÜS in *** DEBEFEÖKERUN +Eval: S S S S S S S S S S I S S S D S + +Speaker sentences 328: swc_deu_001554 #utts: 1 +id: (swc_deu_001554-swc_deu_001554) +Scores: (#C #S #D #I) 2 8 4 0 +REF: BEI DEN ERSTEN FREIEN PARLAMENTSWAHLEN WURDE ILIESCU IM MAI NEUNZEHN HUNDERT neunzig in SEINEM +HYP: *** *** ****** ****** BEIDEN ERSTENFREIN PLLEMENZWAHLN URDE ILIESKU IMEINEUNZEN HNDERT neunzig in SEINE +Eval: D D D D S S S S S S S S + +Speaker sentences 329: swc_deu_001555 #utts: 1 +id: (swc_deu_001555-swc_deu_001555) +Scores: (#C #S #D #I) 0 4 3 0 +REF: DAMIT LASSEN SICH BESTRAHLUNGSSTÄRKEN SEHR GENAU MESSEN +HYP: ***** ****** **** DAMMIT LASSENSIC BESTRALUNGSTERTEN SERGENOMESEN +Eval: D D D S S S S + +Speaker sentences 330: swc_deu_001556 #utts: 1 +id: (swc_deu_001556-swc_deu_001556) +Scores: (#C #S #D #I) 2 4 3 0 +REF: WENIGE JAHRE spÄter KAM ES zu EINER WEITEREN GRÜNDUNG +HYP: ****** WINIGEAR spÄter *** KAMES zu ***** EINE WEITERENKRONDNG +Eval: D S D S D S S + +Speaker sentences 331: swc_deu_001557 #utts: 1 +id: (swc_deu_001557-swc_deu_001557) +Scores: (#C #S #D #I) 1 1 0 0 +REF: radio KABARETTPREIS +HYP: radio KABERETPEILS +Eval: S + +Speaker sentences 332: swc_deu_001558 #utts: 1 +id: (swc_deu_001558-swc_deu_001558) +Scores: (#C #S #D #I) 3 3 0 2 +REF: ****** BESTÜCKTE bomber auf die ***** STARTBAHNEN ROLLEN +HYP: ESTÜK TE bomber auf die START WAHNEN RELEN +Eval: I S I S S + +Speaker sentences 333: swc_deu_001559 #utts: 1 +id: (swc_deu_001559-swc_deu_001559) +Scores: (#C #S #D #I) 2 7 2 0 +REF: MIT DIESER REGELUNG SOLL EINE faktisch ZWEIFACHE EINFLUSSNAHME DIESER WÄHLER auf +HYP: *** ****** MITDIESEREGELUNG SOL EINER faktisch ZWEI VERCHE EINFLUSNAHME DESERWELER auf +Eval: D D S S S S S S S + +Speaker sentences 334: swc_deu_001560 #utts: 1 +id: (swc_deu_001560-swc_deu_001560) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ****** BAROCKER KIRCHENBAU +HYP: BEROKG KÖRICHEN BAU +Eval: I S S + +Speaker sentences 335: swc_deu_001561 #utts: 1 +id: (swc_deu_001561-swc_deu_001561) +Scores: (#C #S #D #I) 2 5 0 6 +REF: der *** ******** HERVORRAGEND WIRKENDEN LANDEKLAPPEN wiederum *** ******* ** ******* HERVORRAGENDE LANGSAMFLUGEIGENSCHAFTEN +HYP: der HER VORAGENT WICEN DEN LANDEKLPEN wiederum HER VORAGEN ER LANGSAM FLUG EIGENSCHAFTEN +Eval: I I S S S I I I I S S + +Speaker sentences 336: swc_deu_001562 #utts: 1 +id: (swc_deu_001562-swc_deu_001562) +Scores: (#C #S #D #I) 3 6 3 1 +REF: ******** MILITÄRISCHE VERBINDUNGSFLUGZEUGE oder UMSCHULMASCHINEN fÜr die B F EIN HUNDERT NEUN VERWENDET +HYP: MITERECH VER BINDUNGSFLUKTZOGE oder UMSCHULMASCHIN fÜr die * * *** BE E EINHUNDRDENEUNVERWENDET +Eval: I S S S D D D S S S + +Speaker sentences 337: swc_deu_001563 #utts: 1 +id: (swc_deu_001563-swc_deu_001563) +Scores: (#C #S #D #I) 0 4 1 0 +REF: LEISTETE MEDIZINISCHE UND PSYCHOLOGISCHE HILFE +HYP: ******** LEISTETEMIEI ZINISCHR N BSÜCHOLOGESCHEHELF +Eval: D S S S S + +Speaker sentences 338: swc_deu_001564 #utts: 1 +id: (swc_deu_001564-swc_deu_001564) +Scores: (#C #S #D #I) 1 3 1 0 +REF: KANN MAN DURCH IMPFUNGEN vorbeugen +HYP: **** KANMANDECH IM FUNGEN vorbeugen +Eval: D S S S + +Speaker sentences 339: swc_deu_001565 #utts: 1 +id: (swc_deu_001565-swc_deu_001565) +Scores: (#C #S #D #I) 2 7 1 1 +REF: MAN DEN AUSBRUCH dieser KRANKHEIT NACH ERFOLGTER infektion ******* VERLANGSAMEN KANN +HYP: *** MERDN AUSBOCH dieser KANKEITENEH ER FOLGTE infektion VERLANG SAMEN KAN +Eval: D S S S S S I S S + +Speaker sentences 340: swc_deu_001566 #utts: 1 +id: (swc_deu_001566-swc_deu_001566) +Scores: (#C #S #D #I) 1 6 0 0 +REF: DIE EINE NEUTRALITÄT unter ALLEN UMSTÄNDEN VORSAH +HYP: DIEINE NEUTRDIE TEÄLT unter ALEN UMSTENDEN VORSAR +Eval: S S S S S S + +Speaker sentences 341: swc_deu_001567 #utts: 1 +id: (swc_deu_001567-swc_deu_001567) +Scores: (#C #S #D #I) 1 1 0 1 +REF: und ****** ZIEGENHIRTEN +HYP: und ZIEGEN HÖRT +Eval: I S + +Speaker sentences 342: swc_deu_001568 #utts: 1 +id: (swc_deu_001568-swc_deu_001568) +Scores: (#C #S #D #I) 6 6 0 1 +REF: das neunzehn hundert ACHTUNDDREISSIG gegrÜndete ***** KOMITEE FÜR UNAMERIKANISCHE umtriebe wurde DAFÜR NUN +HYP: das neunzehn hundert ACHTENDREISSIG gegrÜndete KOMIT VIR UN AMERIKANISCHE umtriebe wurde DAFEN UN +Eval: S I S S S S S + +Speaker sentences 343: swc_deu_001569 #utts: 1 +id: (swc_deu_001569-swc_deu_001569) +Scores: (#C #S #D #I) 5 3 0 3 +REF: zentrale der PROGRESSIVEN und hort des ** *** **************** INGENIEURGESTÜTZTEN KUNSTDENKENS +HYP: zentrale der PROCKRESSIEVEN und hort des IN IEN IÖRGESTÜTZSTEN KUNST DENKENS +Eval: S I I I S S + +Speaker sentences 344: swc_deu_001570 #utts: 1 +id: (swc_deu_001570-swc_deu_001570) +Scores: (#C #S #D #I) 1 6 0 0 +REF: in DER DER U S PRÄSIDENT ANKÜNDIGTE +HYP: in DERDER O ES PRESEDENT AN KÖNDIGTE +Eval: S S S S S S + +Speaker sentences 345: swc_deu_001571 #utts: 1 +id: (swc_deu_001571-swc_deu_001571) +Scores: (#C #S #D #I) 1 2 0 1 +REF: * SNACKS und VORSPEISEN +HYP: S NEÄCHST und VORSPBEISEN +Eval: I S S + +Speaker sentences 346: swc_deu_001572 #utts: 1 +id: (swc_deu_001572-swc_deu_001572) +Scores: (#C #S #D #I) 4 6 0 1 +REF: des BUNDESWAHLGESETZES BIS ZUM DREISSIGSTE juni zwei tausend ** ELF AUFGEGEBEN +HYP: des PUNDES WAGESETZES BISTZUM DREISIGSTEN juni zwei tausend EF AUFG GEM +Eval: S S S S I S S + +Speaker sentences 347: swc_deu_001573 #utts: 1 +id: (swc_deu_001573-swc_deu_001573) +Scores: (#C #S #D #I) 0 2 0 0 +REF: HENRI POUSSEUR +HYP: ORIE POSSEÖR +Eval: S S + +Speaker sentences 348: swc_deu_001574 #utts: 1 +id: (swc_deu_001574-swc_deu_001574) +Scores: (#C #S #D #I) 3 5 0 0 +REF: FLÜCHTLINGEN von der ETHNISCHEN minderheit DER SOMALISCHEN BANTU +HYP: FLIFTLINGEN von der ETNISCHEN minderheit DERSOMALISCHEN BAN TUM +Eval: S S S S S + +Speaker sentences 349: swc_deu_001575 #utts: 1 +id: (swc_deu_001575-swc_deu_001575) +Scores: (#C #S #D #I) 1 3 0 1 +REF: die *** BIPOLARE WELTORDNUNG ZEMENTIERT +HYP: die BIE POLAREWELT ORDTNUNG ZEMINTIERT +Eval: I S S S + +Speaker sentences 350: swc_deu_001576 #utts: 1 +id: (swc_deu_001576-swc_deu_001576) +Scores: (#C #S #D #I) 2 7 1 2 +REF: ** ****** EINE INTEGRIERTE oder EXTERN ANGEBRACHTE VORRICHTUNG an EINEM NUKLEAREN WAFFENSYSTEM +HYP: ER ANFANG EIN INTEILRIEATE oder ****** EXSTERN ANGEBRACHTEVORICHTUNG an EINE NUCLIE RENWAFENSYSTEMN +Eval: I I S S D S S S S S + +Speaker sentences 351: swc_deu_001577 #utts: 1 +id: (swc_deu_001577-swc_deu_001577) +Scores: (#C #S #D #I) 1 3 0 1 +REF: startete ** DIE HILFSORGANISATION LANGFRISTIGE +HYP: startete DI HILFSORGENISETZION LANK FRSTIGE +Eval: I S S S + +Speaker sentences 352: swc_deu_001578 #utts: 1 +id: (swc_deu_001578-swc_deu_001578) +Scores: (#C #S #D #I) 5 9 1 0 +REF: WENN diese EXTERNEN EFFEKTE in DER RICHTIGEN REIHENFOLGE auftreten und sich INNERHALB SPEZIFISCHER PARAMETER BEWEGEN +HYP: WEN diese EXSTERNEN ERFECKTE in ERICHTIGEN REIN VOLGE auftreten und sich ********* INEHALBSPEZIE ISCHAPARAMETER BEWIEGEN +Eval: S S S S S S D S S S + +Speaker sentences 353: swc_deu_001579 #utts: 1 +id: (swc_deu_001579-swc_deu_001579) +Scores: (#C #S #D #I) 6 11 2 1 +REF: ZOG DIE SOWJETUNION auch bei ** DEN WASSERSTOFFBOMBEN und NEUEN FLUGZEUGEN mit INTERKONTINENTALER REICHWEITE mit den U S A GLEICH +HYP: ZUG DE WIRTUNION auch bei DE ASERSTOF PBOMBEN und NEUIN FLUKTZEUGEN mit INTER KONTENENTALEREICHWEITE mit den * * URS ARGLEICH +Eval: S S S I S S S S S S D D S S + +Speaker sentences 354: swc_deu_001580 #utts: 1 +id: (swc_deu_001580-swc_deu_001580) +Scores: (#C #S #D #I) 1 2 2 1 +REF: *** die STADT HAT IHR WAPPENTIER +HYP: PEN die ***** *** STATHAT IEWABPENTIEAM +Eval: I D D S S + +Speaker sentences 355: swc_deu_001581 #utts: 1 +id: (swc_deu_001581-swc_deu_001581) +Scores: (#C #S #D #I) 4 2 0 0 +REF: dieser ansatz GILT allgemein als AUSGEWOGENER +HYP: dieser ansatz GILD allgemein als AUSGEWORGNDER +Eval: S S + +Speaker sentences 356: swc_deu_001582 #utts: 1 +id: (swc_deu_001582-swc_deu_001582) +Scores: (#C #S #D #I) 1 3 0 1 +REF: nach ** DEM ZUSAMMENBRUCH DER +HYP: nach DE ZU SAMM PROCHTE +Eval: I S S S + +Speaker sentences 357: swc_deu_001583 #utts: 1 +id: (swc_deu_001583-swc_deu_001583) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ** DER OBERLAUSITZ ZWISCHEN HOYERSWERDA +HYP: DE OBERLAUSIT S WISCHENHEUERS WERDE +Eval: I S S S S + +Speaker sentences 358: swc_deu_001584 #utts: 1 +id: (swc_deu_001584-swc_deu_001584) +Scores: (#C #S #D #I) 1 4 0 1 +REF: DABEI IN zwei * PHASEN UNTERTEILT +HYP: DABE EN zwei E FAHSEN NTERTEILELT +Eval: S S I S S + +Speaker sentences 359: swc_deu_001585 #utts: 1 +id: (swc_deu_001585-swc_deu_001585) +Scores: (#C #S #D #I) 4 6 3 0 +REF: SCHWEDEN an der EUROPAMEISTERSCHAFT TEIL und wurde MIT DER D F B ELF +HYP: SCHIEDEN an der ******************* EUROPERMISTERSCHAFTEIL und wurde *** *** MTER DIE E BIEELLF +Eval: S D S D D S S S S + +Speaker sentences 360: swc_deu_001586 #utts: 1 +id: (swc_deu_001586-swc_deu_001586) +Scores: (#C #S #D #I) 0 4 3 0 +REF: MEISTER ERÖFFNET KRABAT SCHLIESSLICH EINE WEITERE MÖGLICHKEIT +HYP: ******* ********* ****** MESTER ERFNET KABERSHLISGERN WEITEGEMÖGLICHKEIT +Eval: D D D S S S S + +Speaker sentences 361: swc_deu_001587 #utts: 1 +id: (swc_deu_001587-swc_deu_001587) +Scores: (#C #S #D #I) 3 2 0 2 +REF: einem ******** ** AUSWÄRTSERFOLG in WOLFSBURG gelang +HYP: einem AUSWERTS ER FOLG in WOLSBUR gelang +Eval: I I S S + +Speaker sentences 362: swc_deu_001588 #utts: 1 +id: (swc_deu_001588-swc_deu_001588) +Scores: (#C #S #D #I) 1 5 0 1 +REF: mit ********** SCHWEBUNGSSUMMERN KONNTEN GLISSANDI ERZEUGT WERDEN +HYP: mit SCHWEBUNGS SOUMMAN KONTEN KLISSAN DIE ERZEUGKTWERDEN +Eval: I S S S S S + +Speaker sentences 363: swc_deu_001589 #utts: 1 +id: (swc_deu_001589-swc_deu_001589) +Scores: (#C #S #D #I) 0 2 2 0 +REF: DER ABER LEDIGLICH ZEIGTE +HYP: *** **** DERBALEDIGLIH ZEIKTE +Eval: D D S S + +Speaker sentences 364: swc_deu_001590 #utts: 1 +id: (swc_deu_001590-swc_deu_001590) +Scores: (#C #S #D #I) 1 4 0 1 +REF: GROSSBRITANNIEN eine **** ERSTE WICHTIGE VEREINBARUNG +HYP: KOSPRITANIEN eine ESTE WICHTIGEVE IN BAUNG +Eval: S I S S S + +Speaker sentences 365: swc_deu_001591 #utts: 1 +id: (swc_deu_001591-swc_deu_001591) +Scores: (#C #S #D #I) 0 4 0 1 +REF: *** SIEHT AUCH DAS WITNESSING +HYP: SID ACHTE S H RITNESSEING +Eval: I S S S S + +Speaker sentences 366: swc_deu_001592 #utts: 1 +id: (swc_deu_001592-swc_deu_001592) +Scores: (#C #S #D #I) 1 7 4 0 +REF: WURDE MIT DEM BUNDESWAHLGESETZ VON NEUNZEHN HUNDERT SECHSUNDFÜNFZIG eine DAUERHAFTE REGELUNG EINGEFÜHRT +HYP: ***** *** *** WURDEMIT DE BUNDE WAGESETZVORNEUNZEUNDER SICHSUNFÜFZIG eine ********** DAUARHTERELUG ENGEFÜRT +Eval: D D D S S S S S D S S + +Speaker sentences 367: swc_deu_001593 #utts: 1 +id: (swc_deu_001593-swc_deu_001593) +Scores: (#C #S #D #I) 1 4 0 0 +REF: die ANZAHL DER ÜBERHANGMANDATE KANN +HYP: die ANZALDER ÜBEHNGMEN DARDE KAN +Eval: S S S S + +Speaker sentences 368: swc_deu_001594 #utts: 1 +id: (swc_deu_001594-swc_deu_001594) +Scores: (#C #S #D #I) 4 4 0 0 +REF: BESCHLOSS DIESER ein MILITÄRISCHES eingreifen in den KOREAKRIEG +HYP: ISCHLS DIESE ein MLITERISCHES eingreifen in den KOREARGRICK +Eval: S S S S + +Speaker sentences 369: swc_deu_001595 #utts: 1 +id: (swc_deu_001595-swc_deu_001595) +Scores: (#C #S #D #I) 1 1 0 1 +REF: nato *** VERBINDLICHE +HYP: nato VER BINTLICH +Eval: I S + +Speaker sentences 370: swc_deu_001596 #utts: 1 +id: (swc_deu_001596-swc_deu_001596) +Scores: (#C #S #D #I) 0 2 1 0 +REF: KALTE KRIEG BEENDET +HYP: ***** KALTEGRIEG BENDERT +Eval: D S S + +Speaker sentences 371: swc_deu_001597 #utts: 1 +id: (swc_deu_001597-swc_deu_001597) +Scores: (#C #S #D #I) 1 8 1 0 +REF: V NEUNZEHN HUNDERT DREIUNDNEUNZIG und AUSTRALIEN SOWIE DER ÖSTERREICHISCHE ABLEGER +HYP: AUNUNEN HUNER DEEIUMT NEUNZIG und ********** OSTERALIEN SOWIEDER ÖSTERECHSCHE ABLIGER +Eval: S S S S D S S S S + +Speaker sentences 372: swc_deu_001598 #utts: 1 +id: (swc_deu_001598-swc_deu_001598) +Scores: (#C #S #D #I) 5 10 3 4 +REF: DA DIE seit ANFANG NEUNZEHN hundert ****** NEUNUNDFÜNFZIG dort ******** ** ****************** HERRSCHENDE REVOLUTIONSREGIERUNG UNTER FIDEL CASTRO einen SOZIALISTISCHEN kurs EINGESCHLAGEN HATTE +HYP: ** DADIE seit ****** ANFANGNEUNZEHN hundert NEUNUN FÜNFZIG dort HERSCHEN DE REVOLOTIONDSRIGION UND DER VIE DEL KASTRO einen SOZIELISTISCHEN kurs ************* EINGSCHAGENHATE +Eval: D S D S I S I I I S S S S S S D S + +Speaker sentences 373: swc_deu_001599 #utts: 1 +id: (swc_deu_001599-swc_deu_001599) +Scores: (#C #S #D #I) 3 17 3 2 +REF: NACH WEITEREN VERLUSTREICHEN KÄMPFEN OHNE NENNENSWERTE ERFOLGE BEIDER KRIEGSPARTEIEN WURDE rund DREI JAHRE NACH BEGINN DER AUSEINANDERSETZUNG ein BIS HEUTE gÜltiges ****** ********** WAFFENSTILLSTANDSABKOMMEN ABGESCHLOSSEN +HYP: **** NACHWEITEREN FELUSTRECHENKÄMPFEN UNENEN ZWERTE ER FOLGE BEIDE GRIGSPATEIN URDE rund **** ***** DREAJAHRENAC BEGINDE AUSANDNDE SEZUNG ein BES REUTE gÜltiges WAFNEN STILSTAMNS ABCOMEN ABGESHLOSSEN +Eval: D S S S S S S S S S D D S S S S S S I I S S + +Speaker sentences 374: voxforge_deu_000891 #utts: 1 +id: (voxforge_deu_000891-voxforge_deu_000891) +Scores: (#C #S #D #I) 1 3 1 0 +REF: man IST DABEI SEHR VORSICHTIG +HYP: man *** ISTERBEIS ER ORSICHTIG +Eval: D S S S + +Speaker sentences 375: voxforge_deu_000892 #utts: 1 +id: (voxforge_deu_000892-voxforge_deu_000892) +Scores: (#C #S #D #I) 5 5 0 1 +REF: die *** WEHRPFLICHT SOLL in deutschland leider NOCH nicht ABGESCHAFFT WERDEN +HYP: die WER FLICHT SOL in deutschland leider OCH nicht ABGESCHAFT WERDNEN +Eval: I S S S S S + +Speaker sentences 376: voxforge_deu_000893 #utts: 1 +id: (voxforge_deu_000893-voxforge_deu_000893) +Scores: (#C #S #D #I) 2 4 0 0 +REF: es GIBT auch MISSBRAUCH DURCH ARBEITGEBER +HYP: es GET auch MISPRAUCH UCH ABETGEBER +Eval: S S S S + +Speaker sentences 377: voxforge_deu_000894 #utts: 1 +id: (voxforge_deu_000894-voxforge_deu_000894) +Scores: (#C #S #D #I) 2 3 1 0 +REF: die kinder SIND DANN KRANK GEWORDEN +HYP: die kinder **** SIN DAN KANKEBOEN +Eval: D S S S + +Speaker sentences 378: voxforge_deu_000895 #utts: 1 +id: (voxforge_deu_000895-voxforge_deu_000895) +Scores: (#C #S #D #I) 4 3 0 0 +REF: die TRAGWEITE der KATASTROPHE SOLL verdeutlicht werden +HYP: die TRAKWEITE der ATASTROFE SOL verdeutlicht werden +Eval: S S S + +Speaker sentences 379: voxforge_deu_000897 #utts: 1 +id: (voxforge_deu_000897-voxforge_deu_000897) +Scores: (#C #S #D #I) 0 1 0 0 +REF: ÄH +HYP: DSCANGEAUAULBE +Eval: S + +Speaker sentences 380: voxforge_deu_000898 #utts: 1 +id: (voxforge_deu_000898-voxforge_deu_000898) +Scores: (#C #S #D #I) 2 3 0 1 +REF: beim ORGANSTREIT streiten **** OBERSTE VERFASSUNGSORGANE +HYP: beim MOGANSTREIT streiten OBES DEVE FASUNGSOGANE +Eval: S I S S + +Speaker sentences 381: voxforge_deu_000899 #utts: 1 +id: (voxforge_deu_000899-voxforge_deu_000899) +Scores: (#C #S #D #I) 1 3 2 0 +REF: DAS WAGE ICH JA zu BEZWEIFELN +HYP: *** **** DA WAGEICHIER zu BEZWEIFELEN +Eval: D D S S S + +Speaker sentences 382: voxforge_deu_000900 #utts: 1 +id: (voxforge_deu_000900-voxforge_deu_000900) +Scores: (#C #S #D #I) 1 3 4 0 +REF: man SOLLTE DENEN AUF GAR KEINEN FALL TRAUEN +HYP: man ****** ***** *** *** OLTE DEN AUFGAKHENFALTRAUN +Eval: D D D D S S S + +Speaker sentences 383: voxforge_deu_000901 #utts: 1 +id: (voxforge_deu_000901-voxforge_deu_000901) +Scores: (#C #S #D #I) 2 2 3 0 +REF: die ÖFFENTLICHEN SCHULDEN werden NICHT GETILGT WERDEN +HYP: die ************* FENTLICHENSCHÖLDEN werden ***** ******* NICHTGETILKTWERDEN +Eval: D S D D S + +Speaker sentences 384: voxforge_deu_000902 #utts: 1 +id: (voxforge_deu_000902-voxforge_deu_000902) +Scores: (#C #S #D #I) 2 2 1 1 +REF: DAS GELD ist AUSGEZAHLT worden * +HYP: *** BAGELT ist AUSGEZCAHLT worden T +Eval: D S S I + +Speaker sentences 385: voxforge_deu_000903 #utts: 1 +id: (voxforge_deu_000903-voxforge_deu_000903) +Scores: (#C #S #D #I) 1 5 0 1 +REF: ******** ES SOLLEN DREIHUNDERTTAUSEND neue ARBEITSPLÄTZE ENTSTEHEN +HYP: ERSOLLEN DREI HUNDERD TAUSSEND neue ARBESPLÄZE INSTEN +Eval: I S S S S S + +Speaker sentences 386: voxforge_deu_000904 #utts: 1 +id: (voxforge_deu_000904-voxforge_deu_000904) +Scores: (#C #S #D #I) 3 2 2 2 +REF: die ****** KÖRPERVERLETZUNG kann ALS BEISPIEL GENANNT werden * +HYP: die ÖRBER VELETZUNG kann *** ******** ALSBEISPIELGEND werden T +Eval: I S D D S I + +Speaker sentences 387: voxforge_deu_000905 #utts: 1 +id: (voxforge_deu_000905-voxforge_deu_000905) +Scores: (#C #S #D #I) 2 3 0 0 +REF: diese GRENZE ist ÜBERSCHRITTEN WORDEN +HYP: diese KRENE ist ÜBERSCITEN BORDEN +Eval: S S S + +Speaker sentences 388: voxforge_deu_000906 #utts: 1 +id: (voxforge_deu_000906-voxforge_deu_000906) +Scores: (#C #S #D #I) 0 6 2 0 +REF: DASS STRAFVERFOLGUNGSBEHÖRDEN KEINEN ZUGRIFF AUF DAS GELD HABEN +HYP: **** ************************* TES ST AEFAUGSBÜÖRDEN KEIENZUGE AES KELHARBEN +Eval: D D S S S S S S + +Speaker sentences 389: voxforge_deu_000907 #utts: 1 +id: (voxforge_deu_000907-voxforge_deu_000907) +Scores: (#C #S #D #I) 3 1 1 0 +REF: die interessen finden KEIN GEHÖR +HYP: die interessen finden **** KEINGEHÖR +Eval: D S + +Speaker sentences 390: voxforge_deu_000908 #utts: 1 +id: (voxforge_deu_000908-voxforge_deu_000908) +Scores: (#C #S #D #I) 0 4 1 0 +REF: PFEILTASTE TABULATOR RÜCKSCHRITTTASTE RÜCKTASTE RÜCKLÖSCHTASTE +HYP: ********** FELTAE TABULATOA RÜCSHIETASERÜTASE RÜGIERSTASE +Eval: D S S S S + +Speaker sentences 391: voxforge_deu_000909 #utts: 1 +id: (voxforge_deu_000909-voxforge_deu_000909) +Scores: (#C #S #D #I) 1 6 1 0 +REF: DER BETROFFENE MUSS EIN BERECHTIGTES INTERESSE GELTEND machen +HYP: *** DERBE OHENEMOR AIUMBER ESTIG DIEEN HEÄEGELTEND machen +Eval: D S S S S S S + +Speaker sentences 392: voxforge_deu_000910 #utts: 1 +id: (voxforge_deu_000910-voxforge_deu_000910) +Scores: (#C #S #D #I) 3 6 0 0 +REF: EIN DRITTER HAT dem GESCHÄDIGTEN FREIWILLIG leistungen ZUKOMMEN lassen +HYP: EEIN DERITA HAD dem GESCHÄIDIKTEN FREIWILIG leistungen ZUKCOMEN lassen +Eval: S S S S S S + +Speaker sentences 393: voxforge_deu_000911 #utts: 1 +id: (voxforge_deu_000911-voxforge_deu_000911) +Scores: (#C #S #D #I) 1 4 1 0 +REF: SONDERN AUCH RECHTS neben DEM BILD +HYP: ******* SONDER ACRECH neben DE BILT +Eval: D S S S S + +Speaker sentences 394: voxforge_deu_000912 #utts: 1 +id: (voxforge_deu_000912-voxforge_deu_000912) +Scores: (#C #S #D #I) 2 5 1 0 +REF: SIE HAT eine nicht ERNSTLICH GEMEINTE WILLENSERKLÄRUNG ABGEGEBEN +HYP: *** IERT eine nicht ERNSLICHGEMEITE WILESE KLÄHUNG ABGEBEN +Eval: D S S S S S + +Speaker sentences 395: voxforge_deu_000913 #utts: 1 +id: (voxforge_deu_000913-voxforge_deu_000913) +Scores: (#C #S #D #I) 4 3 1 0 +REF: das MUSSTE JA auf jeden FALL SO kommen +HYP: das MOSTE JAH auf jeden **** FALSO kommen +Eval: S S D S + +Speaker sentences 396: voxforge_deu_000914 #utts: 1 +id: (voxforge_deu_000914-voxforge_deu_000914) +Scores: (#C #S #D #I) 1 6 1 1 +REF: MEHRERE CLIENTS KÖNNEN SICH eine ** IP ADRESSE TEILEN +HYP: ******* MEHRERERE KLEINS KNNSICH eine EI PIE DRESSE TEIUNG +Eval: D S S S I S S S + +Speaker sentences 397: voxforge_deu_000915 #utts: 1 +id: (voxforge_deu_000915-voxforge_deu_000915) +Scores: (#C #S #D #I) 0 8 2 0 +REF: WAR DIE GÜNSTIGERE ES HIESS ALSO SICH ZUSAMMENNEHMEN ANSTATT ZU +HYP: *** *** WADI ÜNSTE ERE SHIS ASO SIG TZUSAMMENEHMEN ANSTAZU +Eval: D D S S S S S S S S + +Speaker sentences 398: voxforge_deu_000917 #utts: 1 +id: (voxforge_deu_000917-voxforge_deu_000917) +Scores: (#C #S #D #I) 1 3 2 0 +REF: DER SCHULDNER HAT SEINE LEISTUNG angeboten +HYP: *** ********* DE RSCHLENE HATSANELEISTUNG angeboten +Eval: D D S S S + +Speaker sentences 399: voxforge_deu_000918 #utts: 1 +id: (voxforge_deu_000918-voxforge_deu_000918) +Scores: (#C #S #D #I) 0 1 2 0 +REF: SO DASS ES +HYP: ** **** SODSSIS +Eval: D D S + +Speaker sentences 400: voxforge_deu_000919 #utts: 1 +id: (voxforge_deu_000919-voxforge_deu_000919) +Scores: (#C #S #D #I) 2 4 0 0 +REF: die BATTERIEN WAREN SEHR STARK veraltet +HYP: die BATRIEN WARN SEAR STAG veraltet +Eval: S S S S + +Speaker sentences 401: voxforge_deu_000920 #utts: 1 +id: (voxforge_deu_000920-voxforge_deu_000920) +Scores: (#C #S #D #I) 1 4 1 0 +REF: DIESES ziel WURDE NUR TEILWEISE ERREICHT +HYP: DESES ziel ***** WURDEN ORTALWALSE EREICHT +Eval: S D S S S + +Speaker sentences 402: voxforge_deu_000921 #utts: 1 +id: (voxforge_deu_000921-voxforge_deu_000921) +Scores: (#C #S #D #I) 1 4 1 0 +REF: diese WÄHRUNG WIRD SEHR LANGE LEBEN +HYP: diese ******** WEHRUNG WIRT SER LANGELEBEN +Eval: D S S S S + +Speaker sentences 403: voxforge_deu_000922 #utts: 1 +id: (voxforge_deu_000922-voxforge_deu_000922) +Scores: (#C #S #D #I) 1 4 0 1 +REF: DORT ZITTERN OFFENBAR SCHON viele * +HYP: DOR ZEITAN OFEN BASCHON viele T +Eval: S S S S I + +Speaker sentences 404: voxforge_deu_000923 #utts: 1 +id: (voxforge_deu_000923-voxforge_deu_000923) +Scores: (#C #S #D #I) 1 7 5 0 +REF: ALS SIE GINGEN NICKTE MAGGIE IHR NUR GANZ FLÜCHTIG ZU und DER VATER +HYP: *** *** ****** ****** ****** ALSIGIENEN NIGTEM MÄGI IER NURGANSFLICHTIGKZU und ER FAATA +Eval: D D D D D S S S S S S S + +Speaker sentences 405: voxforge_deu_000924 #utts: 1 +id: (voxforge_deu_000924-voxforge_deu_000924) +Scores: (#C #S #D #I) 0 3 2 0 +REF: ERZÄHL MIR MEHR ÜBER CHRISTIAN +HYP: ******* *** ER ZYMIEMER ÜBERISTIAN +Eval: D D S S S + +Speaker sentences 406: voxforge_deu_000925 #utts: 1 +id: (voxforge_deu_000925-voxforge_deu_000925) +Scores: (#C #S #D #I) 2 4 0 1 +REF: dem STEHEN NATÜRLICH auch ********* VERMÖGEN GEGENÜBER +HYP: dem STEHE NATIÜRLICH auch FAMMÖGEN GEGEN ÜBA +Eval: S S I S S + +Speaker sentences 407: voxforge_deu_000926 #utts: 1 +id: (voxforge_deu_000926-voxforge_deu_000926) +Scores: (#C #S #D #I) 1 4 2 0 +REF: die REALE LAGE WIRD NICHT VOLLSTÄNDIG ABGEBILDET +HYP: die ***** **** REALELAGE WIRTNICHT VOSTENDICH ABGEBILET +Eval: D D S S S S + +Speaker sentences 408: voxforge_deu_000927 #utts: 1 +id: (voxforge_deu_000927-voxforge_deu_000927) +Scores: (#C #S #D #I) 2 3 2 0 +REF: ES KANN auch noch VIEL SCHLIMMER WERDEN +HYP: ** ESKAN auch noch **** VIE SCHLIMERWERDEN +Eval: D S D S S + +Speaker sentences 409: voxforge_deu_000928 #utts: 1 +id: (voxforge_deu_000928-voxforge_deu_000928) +Scores: (#C #S #D #I) 1 3 1 0 +REF: die POLITIK INTERESSIERT NICHT MEHR +HYP: die ******* POLETIG INTRESIRTZICH NICHTMER +Eval: D S S S + +Speaker sentences 410: voxforge_deu_000929 #utts: 1 +id: (voxforge_deu_000929-voxforge_deu_000929) +Scores: (#C #S #D #I) 1 7 0 1 +REF: INHALTSFREIHEIT BEDEUTET DASS DER INHALT der ************** VERTRAGLICHEN VEREINBARUNGEN +HYP: INHALSFREIHEIT BEDEUTERT DAS E INHALD der VERTRACKLICHEN VER EINBARUNGEN +Eval: S S S S S I S S + +Speaker sentences 411: voxforge_deu_000930 #utts: 1 +id: (voxforge_deu_000930-voxforge_deu_000930) +Scores: (#C #S #D #I) 0 5 1 0 +REF: DER SCHULDNER VERLETZTE SEINE SORGFALTSPFLICHTEN SCHULDHAFT +HYP: *** DERSCHULNER VELET DISEINISACK VALSPLI ENSCHLTHAFT +Eval: D S S S S S + +Speaker sentences 412: voxforge_deu_000931 #utts: 1 +id: (voxforge_deu_000931-voxforge_deu_000931) +Scores: (#C #S #D #I) 2 4 0 3 +REF: DIESES getreide **** DIENT INSBESONDERE als ** ***** VIEHFUTTER +HYP: DISES getreide DEND INS BESONDERE als FI VORTA T +Eval: S I S S I I S + +Speaker sentences 413: voxforge_deu_000932 #utts: 1 +id: (voxforge_deu_000932-voxforge_deu_000932) +Scores: (#C #S #D #I) 2 5 1 0 +REF: TYPISCHERWEISE WERDEN statische IP ADRESSEN VON SERVERN eingesetzt +HYP: TÜPSCHWISE WEREN statische ** EIPIE RTRESEN VONSRVERN eingesetzt +Eval: S S D S S S + +Speaker sentences 414: voxforge_deu_000933 #utts: 1 +id: (voxforge_deu_000933-voxforge_deu_000933) +Scores: (#C #S #D #I) 2 3 1 0 +REF: JETZT WIRD es so LANGSAM GEGLAUBT +HYP: ***** JETZTWR es so LANGSAHM GEGLAUPT +Eval: D S S S + +Speaker sentences 415: voxforge_deu_000934 #utts: 1 +id: (voxforge_deu_000934-voxforge_deu_000934) +Scores: (#C #S #D #I) 0 5 0 1 +REF: ** UNTERSCHIEDLICHE EREIGNISSE HABEN SICH EREIGNET +HYP: UN ASCHIETLICHE ERGEBNISSE HARBENSICH ER EIGNERD +Eval: I S S S S S + +Speaker sentences 416: voxforge_deu_000935 #utts: 1 +id: (voxforge_deu_000935-voxforge_deu_000935) +Scores: (#C #S #D #I) 4 3 0 2 +REF: ***** TERRORVERDÄCHTIGE wurden NICHT vor ein GERICHT gestellt * +HYP: TEROR FARDEICHTIGE wurden NECHT vor ein GERECHT gestellt N +Eval: I S S S I + +Speaker sentences 417: voxforge_deu_000936 #utts: 1 +id: (voxforge_deu_000936-voxforge_deu_000936) +Scores: (#C #S #D #I) 2 6 3 0 +REF: aufmachen DIE STIEFEL NICHT AUSZIEHEN und WEISS GOTT WAS NOCH ALLES +HYP: aufmachen DIESTI FÜ NICT AUSTZEHN und ***** **** *** WEISKORDWASNOC LLS +Eval: S S S S D D D S S + +Speaker sentences 418: voxforge_deu_000937 #utts: 1 +id: (voxforge_deu_000937-voxforge_deu_000937) +Scores: (#C #S #D #I) 2 5 1 1 +REF: ** INSGESAMT 23 personen AUS VERSCHIEDENEN PARLAMENTEN NEHMEN teil +HYP: IN KESAMT DREIUNDZWANSIC personen *** AUVERSCHIEDEN PALEMENTEN NIMEN teil +Eval: I S S D S S S + +Speaker sentences 419: voxforge_deu_000938 #utts: 1 +id: (voxforge_deu_000938-voxforge_deu_000938) +Scores: (#C #S #D #I) 0 5 1 0 +REF: FORDERUNGSRECHTE WERDEN DEM GLÄUBIGER AUSSCHLIESSLICH ZUGEORDNET +HYP: **************** VORDUNGSECFTE WERTENDE GLEUBIGE AUSCHLIESLIF TOGERURTNET +Eval: D S S S S S + +Speaker sentences 420: voxforge_deu_000939 #utts: 1 +id: (voxforge_deu_000939-voxforge_deu_000939) +Scores: (#C #S #D #I) 1 3 0 2 +REF: das ****** ********* PROBLEM WURDE BEHOBEN +HYP: das POBLEM HÜWOURDE BE HOBEN T +Eval: I I S S S + +Speaker sentences 421: voxforge_deu_000940 #utts: 1 +id: (voxforge_deu_000940-voxforge_deu_000940) +Scores: (#C #S #D #I) 2 5 0 3 +REF: fÜr ** DIE ERKENNUNG von ***** ****** UNTERBROCHENER DISKRETER SPRACHE +HYP: fÜr DI ER KÄNUNG von UNTER ROCHNR DIES KRETER PRACHE +Eval: I S S I I S S S + +Speaker sentences 422: voxforge_deu_000941 #utts: 1 +id: (voxforge_deu_000941-voxforge_deu_000941) +Scores: (#C #S #D #I) 0 6 1 0 +REF: DIE CHINESEN KÖNNTEN SEHR VIEL WICHTIGER WERDEN +HYP: *** DIC HINESEN KÖNTEN SER IEL WICHTIGAWERDEN +Eval: D S S S S S S + +Speaker sentences 423: voxforge_deu_000942 #utts: 1 +id: (voxforge_deu_000942-voxforge_deu_000942) +Scores: (#C #S #D #I) 0 6 2 0 +REF: DIESER SCHLÜSSEL WIRD LEDIGLICH EIN EINZIGES MAL VERWENDET +HYP: ****** ********** DIER TOSCLIER DE DIGLICHER EINSIGEMALFVE WNDET +Eval: D D S S S S S S + +Speaker sentences 424: voxforge_deu_000943 #utts: 1 +id: (voxforge_deu_000943-voxforge_deu_000943) +Scores: (#C #S #D #I) 5 3 0 1 +REF: das land *** ENTWICKELTE SICH zu einer MILITÄRISCHEN grossmacht +HYP: das land ENT WECKELTE SECH zu einer MILITERISCHEN grossmacht +Eval: I S S S + +Speaker sentences 425: voxforge_deu_000944 #utts: 1 +id: (voxforge_deu_000944-voxforge_deu_000944) +Scores: (#C #S #D #I) 1 3 1 1 +REF: ES SIND und ****** BLEIBEN VERBRECHERBANDEN +HYP: ** ESSEIND und BLEIEN VERBRECHER BANDEN +Eval: D S I S S + +Speaker sentences 426: voxforge_deu_000945 #utts: 1 +id: (voxforge_deu_000945-voxforge_deu_000945) +Scores: (#C #S #D #I) 4 1 0 0 +REF: die zeiten werden sich ÄNDERN +HYP: die zeiten werden sich ENDERN +Eval: S + +Speaker sentences 427: voxforge_deu_000946 #utts: 1 +id: (voxforge_deu_000946-voxforge_deu_000946) +Scores: (#C #S #D #I) 3 4 2 0 +REF: DEN STIFT in die ACHSBOHRUNG einschieben BIS ZUM ANSCHLAG +HYP: DEEN STIFFT in die ACHBORUNG einschieben *** *** WISTZUMANSCHLAG +Eval: S S S D D S + +Speaker sentences 428: voxforge_deu_000947 #utts: 1 +id: (voxforge_deu_000947-voxforge_deu_000947) +Scores: (#C #S #D #I) 2 7 0 1 +REF: die AUCH beim ******** BROWSER WIRKSAM WIRD BEISPIELSWEISE BEIM FIREFOX +HYP: die AUCHT beim BOAAUSER VIERSAMMWVIERT BEISPILS WEISE BEIL VEIER FOUGSEN +Eval: S I S S S S S S + +Speaker sentences 429: voxforge_deu_000948 #utts: 1 +id: (voxforge_deu_000948-voxforge_deu_000948) +Scores: (#C #S #D #I) 1 3 2 0 +REF: DAS WAR noch GAR KEINE KRISE +HYP: *** DASWAH noch *** GAKHEINI LISE +Eval: D S D S S + +Speaker sentences 430: voxforge_deu_000950 #utts: 1 +id: (voxforge_deu_000950-voxforge_deu_000950) +Scores: (#C #S #D #I) 3 3 0 1 +REF: die haben **** OFFENBAR ZIEMLICH GROSSE angst +HYP: die haben OFEN BAR ZIMLIC ROSE angst +Eval: I S S S + +Speaker sentences 431: voxforge_deu_000951 #utts: 1 +id: (voxforge_deu_000951-voxforge_deu_000951) +Scores: (#C #S #D #I) 1 3 0 0 +REF: viele VERLIEREN IHREN ARBEITSPLATZ +HYP: viele VELIEREN IEREN ABEITSPLATZ +Eval: S S S + +Speaker sentences 432: voxforge_deu_000952 #utts: 1 +id: (voxforge_deu_000952-voxforge_deu_000952) +Scores: (#C #S #D #I) 2 3 0 1 +REF: DAFÜR GIBT es einen ********** PUNKTABZUG +HYP: DARFÜHR GEBT es einen PUNKTABZOG E +Eval: S S I S + +Speaker sentences 433: voxforge_deu_000953 #utts: 1 +id: (voxforge_deu_000953-voxforge_deu_000953) +Scores: (#C #S #D #I) 5 5 0 1 +REF: die BEIDEN sind Über eine ******** UNSICHERE VERBINDUNG MITEINANDER in KONTAKT +HYP: die BEIDE sind Über eine UNDSICHE VERBINUNG MT DEINANDER in KONTAKTN +Eval: S I S S S S + +Speaker sentences 434: voxforge_deu_000954 #utts: 1 +id: (voxforge_deu_000954-voxforge_deu_000954) +Scores: (#C #S #D #I) 2 4 0 1 +REF: *** BEIDE STECKEN TIEF in roten ZAHLEN +HYP: BEI DE STÄCKEN TIF in roten ZALN +Eval: I S S S S + +Speaker sentences 435: voxforge_deu_000955 #utts: 1 +id: (voxforge_deu_000955-voxforge_deu_000955) +Scores: (#C #S #D #I) 1 5 0 0 +REF: FÜNFZEHN UHR fÜnfzehn DORF ON GOLF +HYP: FÜNFTEHN UOR fÜnfzehn DORCF UON GOLLFF +Eval: S S S S S + +Speaker sentences 436: voxforge_deu_000956 #utts: 1 +id: (voxforge_deu_000956-voxforge_deu_000956) +Scores: (#C #S #D #I) 3 6 3 0 +REF: WIE MENSCHEN aus EINER ANDERN welt ERSCHIENEN SIE IHR HEUTE und DOCH +HYP: *** WIEMENSCHEN aus EINE ANDEREN welt ********** *** SCHENENZI IHRHEUITE und OCH +Eval: D S S S D D S S S + +Speaker sentences 437: voxforge_deu_000957 #utts: 1 +id: (voxforge_deu_000957-voxforge_deu_000957) +Scores: (#C #S #D #I) 2 4 1 0 +REF: BÜNDIG MIT DEM hintern des KAMELS AUFHÖRT +HYP: ******* BNDHMIT EM hintern des KAMLS AUFHRT +Eval: D S S S S + +Speaker sentences 438: voxforge_deu_000958 #utts: 1 +id: (voxforge_deu_000958-voxforge_deu_000958) +Scores: (#C #S #D #I) 3 7 0 0 +REF: ACH DER OBERFÖRSTER ZUCKTE MIT den SCHIEFEN grauen brauen EIN +HYP: ACHDER OBER FÖRTEA ZUGTDE MIE den CHIFEN grauen brauen EINEN +Eval: S S S S S S S + +Speaker sentences 439: voxforge_deu_000959 #utts: 1 +id: (voxforge_deu_000959-voxforge_deu_000959) +Scores: (#C #S #D #I) 2 6 0 1 +REF: ich WUNDERE MICH IMMER WIEDER Über **** DIESE ERKLÄRUNGEN +HYP: ich WUNDEREMIG IM MAR WIDER Über DISE ER KLÄRUNGEN +Eval: S S S S I S S + +Speaker sentences 440: voxforge_deu_000960 #utts: 1 +id: (voxforge_deu_000960-voxforge_deu_000960) +Scores: (#C #S #D #I) 1 6 0 0 +REF: BEI einem SYMMETRISCHEN KRYPTOSYSTEM WIRD ANDERS VORGEGANGEN +HYP: BAR einem SMETRICHEN KRIUPTUSTEM IRT NDERS VORGEGANG +Eval: S S S S S S + +Speaker sentences 441: voxforge_deu_000961 #utts: 1 +id: (voxforge_deu_000961-voxforge_deu_000961) +Scores: (#C #S #D #I) 2 2 0 0 +REF: das IST dort VERZEICHNET +HYP: das IS dort VERZEICHNED +Eval: S S + +Speaker sentences 442: voxforge_deu_000962 #utts: 1 +id: (voxforge_deu_000962-voxforge_deu_000962) +Scores: (#C #S #D #I) 1 5 0 0 +REF: GELD IST EIN SEHR gutes TAUSCHMITTEL +HYP: GELLT S AN SER gutes TAUSCHMITE +Eval: S S S S S + +Speaker sentences 443: voxforge_deu_000963 #utts: 1 +id: (voxforge_deu_000963-voxforge_deu_000963) +Scores: (#C #S #D #I) 2 2 0 2 +REF: das WÄRE wissenschaftlich *** ****** NOTWENDIG +HYP: das WEHRER wissenschaftlich NOD WENDIG T +Eval: S I I S + +Speaker sentences 444: voxforge_deu_000964 #utts: 1 +id: (voxforge_deu_000964-voxforge_deu_000964) +Scores: (#C #S #D #I) 2 2 2 0 +REF: NUR BESTIMMTE straftaten KOMMEN IN betracht +HYP: *** NUNRBESTIMTIS straftaten ****** KOMENEN betracht +Eval: D S D S + +Speaker sentences 445: voxforge_deu_000965 #utts: 1 +id: (voxforge_deu_000965-voxforge_deu_000965) +Scores: (#C #S #D #I) 0 6 0 0 +REF: DAMIT KANN MAN WAHRSCHEINLICH SCHLECHT EINKAUFEN +HYP: DARMIT KANMAN BARSCHENLIC CHLÄCHT EIN KAUFEN +Eval: S S S S S S + +Speaker sentences 446: voxforge_deu_000966 #utts: 1 +id: (voxforge_deu_000966-voxforge_deu_000966) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ******* DAFÜR WURDE GESORGT +HYP: DAFÜHE WORDE GE SOKT +Eval: I S S S + +Speaker sentences 447: voxforge_deu_000967 #utts: 1 +id: (voxforge_deu_000967-voxforge_deu_000967) +Scores: (#C #S #D #I) 1 5 0 1 +REF: man ****** KANN DAS SEHR GUT VERKAUFEN +HYP: man KANDER SE UT VER AUFEN E +Eval: I S S S S S + +Speaker sentences 448: voxforge_deu_000968 #utts: 1 +id: (voxforge_deu_000968-voxforge_deu_000968) +Scores: (#C #S #D #I) 0 2 3 0 +REF: SONDERN AUCH IN DER STEUERHINTERZIEHUNG +HYP: ******* **** ** SONEN OCKNESTEUENTDETIOUNG +Eval: D D D S S + +Speaker sentences 449: voxforge_deu_000969 #utts: 1 +id: (voxforge_deu_000969-voxforge_deu_000969) +Scores: (#C #S #D #I) 5 1 0 0 +REF: darÜber REDET die pastorin und redet +HYP: darÜber REDE die pastorin und redet +Eval: S + +Speaker sentences 450: voxforge_deu_000970 #utts: 1 +id: (voxforge_deu_000970-voxforge_deu_000970) +Scores: (#C #S #D #I) 1 2 4 0 +REF: DEN SCHALTER IN den DRITTEN GANG STELLEN +HYP: *** ******** DENSCHALEN den ******* **** DEENDOANSTELEN +Eval: D D S D D S + +Speaker sentences 451: voxforge_deu_000971 #utts: 1 +id: (voxforge_deu_000971-voxforge_deu_000971) +Scores: (#C #S #D #I) 4 3 0 0 +REF: auf den ersten BLICK SCHEINT das UNGEWÖHNLICH +HYP: auf den ersten BLIK SCHEIN das UNGEWÜÖNLICH +Eval: S S S + +Speaker sentences 452: voxforge_deu_000972 #utts: 1 +id: (voxforge_deu_000972-voxforge_deu_000972) +Scores: (#C #S #D #I) 3 5 1 0 +REF: der DOLLAR WIRD NICHT MEHR als WÄHRUNG AKZEPTIERT werden +HYP: der ****** DOLA WIRT ICHTMEHR als WERHUNG AKZIEPTIERT werden +Eval: D S S S S S + +Speaker sentences 453: voxforge_deu_000973 #utts: 1 +id: (voxforge_deu_000973-voxforge_deu_000973) +Scores: (#C #S #D #I) 1 7 5 0 +REF: IHREN KOPF FEST GEGEN den HALS DER JÜNGEREN DANN KÜSSTE SIE DEN VATER +HYP: ***** ILEN KOPTF FEHSTGEGEN den **** *** ********* **** HALSTER INGEREN DAMKÖSTISIEDEN FARTER +Eval: D S S S D D D D S S S S + +Speaker sentences 454: voxforge_deu_000974 #utts: 1 +id: (voxforge_deu_000974-voxforge_deu_000974) +Scores: (#C #S #D #I) 2 2 0 0 +REF: das wurde NICHT WAHRGENOMMEN +HYP: das wurde NICTWAGE OMEN +Eval: S S + +Speaker sentences 455: voxforge_deu_000975 #utts: 1 +id: (voxforge_deu_000975-voxforge_deu_000975) +Scores: (#C #S #D #I) 1 3 1 0 +REF: MAN HAT DAS DAMALS vorgelesen +HYP: *** WA HATAS DAMMEAS vorgelesen +Eval: D S S S + +Speaker sentences 456: voxforge_deu_000976 #utts: 1 +id: (voxforge_deu_000976-voxforge_deu_000976) +Scores: (#C #S #D #I) 1 9 1 1 +REF: BEI BESONDERS WERTVOLLEN SACHEN IST DIE GRENZE NIEDRIGER ALS der *** WARENWERT +HYP: *** EI BESONDER WELTVONSACHUNG IS DI KENZE MITRIGE AS der WAN MERT +Eval: D S S S S S S S S I S + +Speaker sentences 457: voxforge_deu_000977 #utts: 1 +id: (voxforge_deu_000977-voxforge_deu_000977) +Scores: (#C #S #D #I) 1 2 1 0 +REF: DAS MUSS ZURÜCKGEZAHLT werden +HYP: *** NAS MOSTZRÜCKEZALT werden +Eval: D S S + +Speaker sentences 458: voxforge_deu_000978 #utts: 1 +id: (voxforge_deu_000978-voxforge_deu_000978) +Scores: (#C #S #D #I) 0 5 0 0 +REF: ZWISCHEN GLÄUBIGER UND SCHULDNER HERGELEITET +HYP: ZWICHEN GLEUBIGER UNDSCHLDENE HER GELEITET +Eval: S S S S S + +Speaker sentences 459: voxforge_deu_000979 #utts: 1 +id: (voxforge_deu_000979-voxforge_deu_000979) +Scores: (#C #S #D #I) 2 4 0 0 +REF: ein ABSOLUTES recht WURDE RECHTSWIDRIG VERLETZT +HYP: ein ABSUMUTES recht WIED RECHTZIERI ELLET +Eval: S S S S + +Speaker sentences 460: voxforge_deu_000980 #utts: 1 +id: (voxforge_deu_000980-voxforge_deu_000980) +Scores: (#C #S #D #I) 3 3 2 0 +REF: man BRAUCHT NICHT AN DEN ZUFALL zu glauben +HYP: man ******* ***** BRAUCHTNEICHT ANDEN ZUFAL zu glauben +Eval: D D S S S + +Speaker sentences 461: voxforge_deu_000981 #utts: 1 +id: (voxforge_deu_000981-voxforge_deu_000981) +Scores: (#C #S #D #I) 2 5 4 0 +REF: ZÄRTLICHEN wesen nur ENTFALTEN WO MAN IHR LIEBE BOT VOR HARTEN +HYP: ZERTLICHEN wesen nur ********* ** *** *** INTFALTEN WUOMAN ILIEBEBODT VORHAREN +Eval: S D D D D S S S S + +Speaker sentences 462: voxforge_deu_000982 #utts: 1 +id: (voxforge_deu_000982-voxforge_deu_000982) +Scores: (#C #S #D #I) 2 6 0 1 +REF: ** BEZÜGLICH DER BEWEISLAST UND DER haftung fÜr HILFSPERSONEN +HYP: ER ZÜKLICH DE ERWEISLAT N DE haftung fÜr HELFPERSCONEN +Eval: I S S S S S S + +Speaker sentences 463: voxforge_deu_000983 #utts: 1 +id: (voxforge_deu_000983-voxforge_deu_000983) +Scores: (#C #S #D #I) 1 6 2 0 +REF: BEI der NORMALEN NUTZUNG GIBT ES DIE VOLLE BANDBREITE +HYP: EI der ******** ******* NOMAHL NOZUNGE DES DI VOLEBANDREITE +Eval: S D D S S S S S + +Speaker sentences 464: voxforge_deu_000984 #utts: 1 +id: (voxforge_deu_000984-voxforge_deu_000984) +Scores: (#C #S #D #I) 3 3 4 0 +REF: aber wie IST DIESES PROBLEM im GLOBALEN MASSSTAB ZU LÖSEN +HYP: aber wie *** ****** ISTDIESESPROBLEM im ******** ******** GLOBAHLNMATSTAB ZOLESEN +Eval: D D S D D S S + +Speaker sentences 465: voxforge_deu_000985 #utts: 1 +id: (voxforge_deu_000985-voxforge_deu_000985) +Scores: (#C #S #D #I) 0 6 1 0 +REF: DAS EIGENE WEBLOG ERHÄLT POTENTIELL MEHR LESER +HYP: *** DES EIGENWERPLOK E HETPODENZIER MER LESE +Eval: D S S S S S S + +Speaker sentences 466: voxforge_deu_000986 #utts: 1 +id: (voxforge_deu_000986-voxforge_deu_000986) +Scores: (#C #S #D #I) 1 4 2 0 +REF: DAS FREMDE WEBLOG SIEHT noch BELEBTER AUS +HYP: *** ****** DASFRMMDEVERBLOK SIE noch IELEBTER OS +Eval: D D S S S S + +Speaker sentences 467: voxforge_deu_000987 #utts: 1 +id: (voxforge_deu_000987-voxforge_deu_000987) +Scores: (#C #S #D #I) 1 4 1 0 +REF: EINE NEUE BESTIMMUNG ist ERLASSEN WORDEN +HYP: **** EINEUE BESTIMUNG ist ELASEN BORDEN +Eval: D S S S S + +Speaker sentences 468: voxforge_deu_000988 #utts: 1 +id: (voxforge_deu_000988-voxforge_deu_000988) +Scores: (#C #S #D #I) 1 3 0 1 +REF: DARAUF IST HINGEWIESEN worden * +HYP: DARRAUF S THENGEWIESEN worden T +Eval: S S S I + +Speaker sentences 469: voxforge_deu_000989 #utts: 1 +id: (voxforge_deu_000989-voxforge_deu_000989) +Scores: (#C #S #D #I) 3 2 1 0 +REF: die BEVÖLKERUNG ist GANZ MASSIV verarmt +HYP: die BEFÖRKRUNG ist **** GANZMASIE verarmt +Eval: S D S + +Speaker sentences 470: voxforge_deu_000990 #utts: 1 +id: (voxforge_deu_000990-voxforge_deu_000990) +Scores: (#C #S #D #I) 2 3 2 0 +REF: DIE WERDEN DAS GANZ BESTIMMT nicht machen +HYP: *** ****** DIEWERDEN DS GANZPESTLM nicht machen +Eval: D D S S S + +Speaker sentences 471: voxforge_deu_000991 #utts: 1 +id: (voxforge_deu_000991-voxforge_deu_000991) +Scores: (#C #S #D #I) 3 5 0 1 +REF: die ****** DATENMENGE DIE gesendet WIRD ist ERHEBLICH GERINGER +HYP: die DARTEN MÄNGE DI gesendet WIERDT ist ER HEBLICHGERINER +Eval: I S S S S S + +Speaker sentences 472: voxforge_deu_000992 #utts: 1 +id: (voxforge_deu_000992-voxforge_deu_000992) +Scores: (#C #S #D #I) 1 2 2 0 +REF: DAS ERGEBNIS ist VERFÄLSCHT WORDEN +HYP: *** DASEGEBNIS ist *********** VEFELSHWORDEN +Eval: D S D S + +Speaker sentences 473: voxforge_deu_000993 #utts: 1 +id: (voxforge_deu_000993-voxforge_deu_000993) +Scores: (#C #S #D #I) 3 6 0 1 +REF: eine BESCHRÄNKUNG TRITT ERST bei ** BESONDERS INTENSIVER NUTZUNG auf +HYP: eine BESCRENKUNG TRIT EST bei BE SONDERS INTENSIEVER NUOZUNG auf +Eval: S S S I S S S + +Speaker sentences 474: voxforge_deu_000994 #utts: 1 +id: (voxforge_deu_000994-voxforge_deu_000994) +Scores: (#C #S #D #I) 5 6 0 2 +REF: der *** ENDBENUTZER hat eine HÖHERE GESCHWINDIGKEIT fÜr den **** DOWNLOAD ZUR VERFÜGUNG +HYP: der END BENOTZE hat eine HÖHRRE GESCHWINDICKEIT fÜr den DAUN LOTD ZUFER FÜGUNG +Eval: I S S S I S S S + +Speaker sentences 475: voxforge_deu_000995 #utts: 1 +id: (voxforge_deu_000995-voxforge_deu_000995) +Scores: (#C #S #D #I) 1 2 3 0 +REF: der SEMANTISCHE TEIL WURDE SKEPTISCH BETRACHTET +HYP: der *********** **** ***** SEMANTISCHETEILEWURDESKEBTIS PETRACHTET +Eval: D D D S S + +Speaker sentences 476: voxforge_deu_000996 #utts: 1 +id: (voxforge_deu_000996-voxforge_deu_000996) +Scores: (#C #S #D #I) 1 4 2 0 +REF: DORT WIRD sehr VIEL MEHR GELD VERDIENT +HYP: DOT WIRT sehr **** **** IELMHR GELTVERDIEND +Eval: S S D D S S + +Speaker sentences 477: voxforge_deu_000997 #utts: 1 +id: (voxforge_deu_000997-voxforge_deu_000997) +Scores: (#C #S #D #I) 2 4 0 2 +REF: VERSTÄNDNIS FÜR das *** ** VERANTWORTLICHKEITSGEFÜHL einer MUTTER +HYP: VÜERSTENIS VÜR das VER AN WORDLIKEITZGIFÜL einer MUTER +Eval: S S I I S S + +Speaker sentences 478: voxforge_deu_000998 #utts: 1 +id: (voxforge_deu_000998-voxforge_deu_000998) +Scores: (#C #S #D #I) 2 3 1 1 +REF: das WIRD FÜR DIE MEDIEN gemacht * +HYP: das **** WERT ÜR DIMEIEN gemacht E +Eval: D S S S I + +Speaker sentences 479: voxforge_deu_000999 #utts: 1 +id: (voxforge_deu_000999-voxforge_deu_000999) +Scores: (#C #S #D #I) 2 5 0 1 +REF: SIE KANN eine GANZ klare **** KAUFEMPFEHLUNG AUSSPRECHEN +HYP: SI KAN eine GANS klare KAUF MPFIÄLUNG AUSSPRÄCHEN +Eval: S S S I S S + +Speaker sentences 480: voxforge_deu_001000 #utts: 1 +id: (voxforge_deu_001000-voxforge_deu_001000) +Scores: (#C #S #D #I) 1 3 0 0 +REF: ZAHLREICHE PROTESTE werden ARTIKULIERT +HYP: ZALREICHE POTESTE werden ATIKULIERD +Eval: S S S + +Speaker sentences 481: voxforge_deu_001001 #utts: 1 +id: (voxforge_deu_001001-voxforge_deu_001001) +Scores: (#C #S #D #I) 0 5 0 1 +REF: ** DIE DURCHFÜHRUNG WAR NICHT SICHER +HYP: DE DEUCH FÜHRUNG WARNICHT SECHA T +Eval: I S S S S S + +Speaker sentences 482: voxforge_deu_001002 #utts: 1 +id: (voxforge_deu_001002-voxforge_deu_001002) +Scores: (#C #S #D #I) 2 4 0 1 +REF: die WÄHRUNG hat **** ÜBERHAUPT KEINE DECKUNG +HYP: die WEHRUNEN hat ÜBE AUPTKEINE DICKUNG N +Eval: S I S S S + +Speaker sentences 483: voxforge_deu_001003 #utts: 1 +id: (voxforge_deu_001003-voxforge_deu_001003) +Scores: (#C #S #D #I) 3 5 0 1 +REF: ob ÜBRIGENS SECKERSDORF der einen ***** DURCHAUS ZIELBEWUSSTEN LEBENSKLUGEN +HYP: ob ÜBIGENS SECKERSTOARAF der einen DURCH AUSTIEL BEWUSTEN LEBEMSKLOGEN +Eval: S S I S S S + +Speaker sentences 484: voxforge_deu_001004 #utts: 1 +id: (voxforge_deu_001004-voxforge_deu_001004) +Scores: (#C #S #D #I) 2 3 2 1 +REF: MAN SPRICHT IN diesem FALL von **************** KONTRAHIERUNGSZWANG +HYP: *** ******* MANRNSPRECHTEN diesem FEIL von KONTRERHIERUNGST WANG +Eval: D D S S I S + +Speaker sentences 485: voxforge_deu_001006 #utts: 1 +id: (voxforge_deu_001006-voxforge_deu_001006) +Scores: (#C #S #D #I) 0 3 3 0 +REF: GLÄUBIGER UND SCHULDNER SIND SICH EINIG +HYP: ********** *** ********* GLOUBEGA UNSCHLDNAR SENZICEINIG +Eval: D D D S S S + +Speaker sentences 486: voxforge_deu_001007 #utts: 1 +id: (voxforge_deu_001007-voxforge_deu_001007) +Scores: (#C #S #D #I) 4 2 1 0 +REF: das WIRD nicht mehr lange SO BLEIBEN +HYP: das WIRT nicht mehr lange ** SOBLEIBEN +Eval: S D S + +Speaker sentences 487: voxforge_deu_001008 #utts: 1 +id: (voxforge_deu_001008-voxforge_deu_001008) +Scores: (#C #S #D #I) 2 4 1 0 +REF: es GAB UNTERSCHIEDLICH SCHWERE FORMEN der FREIHEITSSTRAFE +HYP: es *** AB UNTERSCHIETLICHTRIERE VORMEN der FRAHETRAVER +Eval: D S S S S + +Speaker sentences 488: voxforge_deu_001009 #utts: 1 +id: (voxforge_deu_001009-voxforge_deu_001009) +Scores: (#C #S #D #I) 0 4 3 0 +REF: ES HANDELT SICH UM EINE FREIE SOFTWARE +HYP: ** ******* **** HNDE SE IMEINE FREIESOFTW +Eval: D D D S S S S + +Speaker sentences 489: voxforge_deu_001010 #utts: 1 +id: (voxforge_deu_001010-voxforge_deu_001010) +Scores: (#C #S #D #I) 2 6 0 3 +REF: ********** *** ORGANSTREITVERFAHREN KÖNNEN auch AUSSCHLIESSLICH auf * DER LANDESEBENE STATTFINDEN +HYP: OGANSTREIT VER FAHN KÖNN auch AUSHLISLICG auf E LNDES EBENS TATFINTEN +Eval: I I S S S I S S S + +Speaker sentences 490: voxforge_deu_001011 #utts: 1 +id: (voxforge_deu_001011-voxforge_deu_001011) +Scores: (#C #S #D #I) 1 3 1 0 +REF: wegen NUTZLOS AUFGEWENDETER URLAUBSZEIT KANN +HYP: wegen ******* NOZSLOS AUFGEWNNETER OLEABSEITKANN +Eval: D S S S + +Speaker sentences 491: voxforge_deu_001012 #utts: 1 +id: (voxforge_deu_001012-voxforge_deu_001012) +Scores: (#C #S #D #I) 2 4 0 0 +REF: das WIRD nicht IMMER PERFEKT FUNKTIONIEREN +HYP: das WIRT nicht IM ARPERFEKT VUNKTZINIERN +Eval: S S S S + +Speaker sentences 492: voxforge_deu_001013 #utts: 1 +id: (voxforge_deu_001013-voxforge_deu_001013) +Scores: (#C #S #D #I) 0 3 4 0 +REF: MAN MUSS SICH ENGAGIEREN DES WACHSTUMS WEGEN +HYP: *** **** **** ********** MANMUSICH ANGERSCHIEREN DESWAKSTUMSWEGEN +Eval: D D D D S S S + +Speaker sentences 493: voxforge_deu_001014 #utts: 1 +id: (voxforge_deu_001014-voxforge_deu_001014) +Scores: (#C #S #D #I) 4 1 0 0 +REF: WELCHE wege sollen eingeschlagen werden +HYP: WELLICHE wege sollen eingeschlagen werden +Eval: S + +Speaker sentences 494: voxforge_deu_001015 #utts: 1 +id: (voxforge_deu_001015-voxforge_deu_001015) +Scores: (#C #S #D #I) 1 4 1 0 +REF: DAS WIRD IN die PREISE GEHEN +HYP: *** DASSWIRDT EN die PEISEGEN T +Eval: D S S S S + +Speaker sentences 495: voxforge_deu_001016 #utts: 1 +id: (voxforge_deu_001016-voxforge_deu_001016) +Scores: (#C #S #D #I) 1 3 0 1 +REF: die ******** ÜBERNAHME ERFOLGTE WÖRTLICH +HYP: die ÜBENAME ER FOLUKTE WIRTLICH +Eval: I S S S + +Speaker sentences 496: voxforge_deu_001017 #utts: 1 +id: (voxforge_deu_001017-voxforge_deu_001017) +Scores: (#C #S #D #I) 1 4 0 1 +REF: ** DIE ENTWICKLUNG IST weit VORANGESCHRITTEN +HYP: DE ENT WEKLUNG ST weit VORANGESCHRETEN +Eval: I S S S S + +Speaker sentences 497: voxforge_deu_001018 #utts: 1 +id: (voxforge_deu_001018-voxforge_deu_001018) +Scores: (#C #S #D #I) 3 3 3 1 +REF: die ** SYMPTOME treten DANN SCHON NACH WENIGEN STUNDEN auf +HYP: die SM TOME treten **** ***** **** DANSCHONACHWANGEN STONDTEN auf +Eval: I S D D D S S + +Speaker sentences 498: voxforge_deu_001019 #utts: 1 +id: (voxforge_deu_001019-voxforge_deu_001019) +Scores: (#C #S #D #I) 1 6 0 0 +REF: ES GIBT EINE GROSSE WELLE von PROZESSEN +HYP: S IBT EINI GROE WÄLLE von PROZESEN +Eval: S S S S S S + +Speaker sentences 499: voxforge_deu_001020 #utts: 1 +id: (voxforge_deu_001020-voxforge_deu_001020) +Scores: (#C #S #D #I) 0 4 2 0 +REF: ES IST BEREITS MEIN ZWEITER AUTOMAT +HYP: ** *** S ST BEREITZMEINZWEITER AUTOMAD +Eval: D D S S S S + +Speaker sentences 500: voxpopuli_deu_000309 #utts: 1 +id: (voxpopuli_deu_000309-voxpopuli_deu_000309) +Scores: (#C #S #D #I) 1 10 5 0 +REF: B RUSSLANDS IMPLEMENTIERUNG von HÖHEREN STANDARDS ZUM SCHUTZ PERSÖNLICHER DATEN EBENFALLS GENERELL UNSERE GUTE ZUSAMMENARBEIT ERLEICHTERN +HYP: * ********* MPLMENTIERUNG von ******** ********* *** HÖRIN STANDEATS ZUSCUTZSPESONLICJER DARTENEBENFEIS GENNEREL UNSRE UTE ZUSAMENAREIT ELEICHTER +Eval: D D S D D D S S S S S S S S S + +Speaker sentences 501: voxpopuli_deu_000310 #utts: 1 +id: (voxpopuli_deu_000310-voxpopuli_deu_000310) +Scores: (#C #S #D #I) 0 10 4 0 +REF: POLIZEIBEAMTE HABEN DAS SCHLIMMSTE VERHINDERT HABEN IHR LEBEN GERETTET UND SIND SELBER VERLETZT WORDEN +HYP: ************* ***** *** ********** ERAMTE ABENDERSLIMSTE VER INDER DAR MELEBEN GEREDTE IN SELBE VERLÄTSTWURDNIGKLAB +Eval: D D D D S S S S S S S S S S + +Speaker sentences 502: voxpopuli_deu_000311 #utts: 1 +id: (voxpopuli_deu_000311-voxpopuli_deu_000311) +Scores: (#C #S #D #I) 1 6 3 0 +REF: DAS IST NICHT MÖGLICH DASS der KOMMISSAR NICHT HIER IST +HYP: *** *** IC MÖBRIÜE DAS der ********* OMIS SEINIT IES +Eval: D D S S S D S S S + +Speaker sentences 503: voxpopuli_deu_000312 #utts: 1 +id: (voxpopuli_deu_000312-voxpopuli_deu_000312) +Scores: (#C #S #D #I) 1 9 1 0 +REF: 19 MITGLIED und HOFFE DASS WIR IM NÄCHSTEN JAHR ÜBER DAS +HYP: ** MITLET und DE COFVE DAS WER NECXHSLIEAR ÜBE DES WANZE +Eval: D S S S S S S S S S + +Speaker sentences 504: voxpopuli_deu_000313 #utts: 1 +id: (voxpopuli_deu_000313-voxpopuli_deu_000313) +Scores: (#C #S #D #I) 0 12 7 0 +REF: ES DARF NICHT ÜBERSEHEN WERDEN DASS IMMERHIN MEHR ALS 50 DER BEVÖLKERUNG DER EUROPÄISCHEN UNION IM LÄNDLICHEN RAUM LEBT +HYP: ** **** ***** ********** ****** **** ******** DES DAF NICH IVERSEHEN WERDN DAS IMERHINWER BEFÜMTZIGTPUOZEND DERBEFÖLKÖNG EADERBESCHENUNUN NIE LENLICHENGRAMLIEB +Eval: D D D D D D D S S S S S S S S S S S S + +Speaker sentences 505: voxpopuli_deu_000314 #utts: 1 +id: (voxpopuli_deu_000314-voxpopuli_deu_000314) +Scores: (#C #S #D #I) 3 10 7 0 +REF: WIR WOLLEN ALSO DASS der bÜrger SCHNELLER EINE AUSKUNFT bekommt OB SEINE BESCHWERDE ÜBERHAUPT ANGENOMMEN WIRD OB SIE BERECHTIGT IST +HYP: *** ****** **** SODAS der bÜrger SCHELLONE AUS KUNF bekommt ** ***** ********** ********** OPSEINE BESCHÄRDE ÜBEHABT ANGENOMENWIRD OPSIE BERECHTICHTIST +Eval: D D D S S S S D D D D S S S S S S + +Speaker sentences 506: voxpopuli_deu_000315 #utts: 1 +id: (voxpopuli_deu_000315-voxpopuli_deu_000315) +Scores: (#C #S #D #I) 2 10 0 0 +REF: EIN „RESET UNSERER BEZIEHUNGEN ist nicht VONNÖTEN ABER SEHR WOHL KONTINUIERLICHES FEINTUNING +HYP: N RIESETT UNZERER BITIONGEN ist nicht VONEUTEN ABERS IABOE KONTINUN IERLICHES FEINTIONENG +Eval: S S S S S S S S S S + +Speaker sentences 507: voxpopuli_deu_000316 #utts: 1 +id: (voxpopuli_deu_000316-voxpopuli_deu_000316) +Scores: (#C #S #D #I) 1 3 7 0 +REF: UND DA WIRD GANZ STOLZ GESAGT DIE BESCHÄFTIGUNG STEIGT JA an +HYP: *** ** **** **** ***** ****** *** LDIET GANSTOLSGESAG JAEDBESCHEFTIUNGSTEIGKTDJER an +Eval: D D D D D D D S S S + +Speaker sentences 508: voxpopuli_deu_000317 #utts: 1 +id: (voxpopuli_deu_000317-voxpopuli_deu_000317) +Scores: (#C #S #D #I) 1 21 4 1 +REF: ICH WILL SAGEN WIE ES IST fÜr ** UNS IST DER EURO UNTERBEWERTET WIR EXPORTIEREN ZU VIEL ZU BILLIG UND WIR IMPORTIEREN ZU WENIG WIR VERSCHENKEN WOHLSTAND +HYP: *** **** ***** *** WIDAS S fÜr UN IS TER AUHO ND ER ERVERTET WIER EXSPORTIE AN ZOU VEELL ZO BILICG ALWVER IM POTIERENZSUWEDIG WIER FARSCHENKEN WOLSTAT +Eval: D D D D S S I S S S S S S S S S S S S S S S S S S S + +Speaker sentences 509: voxpopuli_deu_000318 #utts: 1 +id: (voxpopuli_deu_000318-voxpopuli_deu_000318) +Scores: (#C #S #D #I) 1 9 1 0 +REF: DASS sie HEUTE ABEND HIER ANWESEND SIND IST EIN POSITIVES SIGNAL +HYP: **** sie HEUDER ARBEN IE ANWESEN ZIND ISEN PROSITIE VES SIGNALE +Eval: D S S S S S S S S S + +Speaker sentences 510: voxpopuli_deu_000319 #utts: 1 +id: (voxpopuli_deu_000319-voxpopuli_deu_000319) +Scores: (#C #S #D #I) 5 11 1 0 +REF: 90 prozent ALLER EUROPÄISCHEN filme die AUSSERHALB IHRES HEIMATLANDES GEZEIGT werden sind VOM MEDIA PROGRAMM GEFÖRDERT WORDEN +HYP: NEUNZSIGH prozent ALLA AROPÄSCHEN filme die ********** AUSSEHALT IRESHEIMATLANDES GEZEICHT werden sind VOR MEDIER PROGRAM GEFERDERT WURDEN +Eval: S S S D S S S S S S S S + +Speaker sentences 511: voxpopuli_deu_000320 #utts: 1 +id: (voxpopuli_deu_000320-voxpopuli_deu_000320) +Scores: (#C #S #D #I) 1 9 2 0 +REF: WIESO KANN ICH DEM ERGEBNIS DER AUSSCHUSSABSTIMMUNG in DIESER FORM NICHT ZUSTIMMEN +HYP: ***** BIESOKAICHEBER GEBPLIS STER ALE AUSCHUS ABPTEMUNG in ****** DIESE FOREM NICHZUSTEM +Eval: D S S S S S S D S S S + +Speaker sentences 512: voxpopuli_deu_000321 #utts: 1 +id: (voxpopuli_deu_000321-voxpopuli_deu_000321) +Scores: (#C #S #D #I) 3 9 1 2 +REF: WIR WOLLTEN VERHINDERN DASS SICH HINTER DIESEM GEISTIGEN eigentum die ****** * AUSKUNFTSPFLICHT verstecken KANN +HYP: *** BERBORTEN VER INDERN DASICHE HINDER IEN GEISIGEN eigentum die AUSGUM S FLICHTE verstecken KONTE +Eval: D S S S S S S S I I S S + +Speaker sentences 513: voxpopuli_deu_000322 #utts: 1 +id: (voxpopuli_deu_000322-voxpopuli_deu_000322) +Scores: (#C #S #D #I) 2 9 4 0 +REF: ES GIBT JETZT IM ZUSAMMENHANG MIT DER VERSTÄRKTEN ZUSAMMENARBEIT einen ersten GANG VON EINIGEN MITGLIEDSTAATEN +HYP: ** **** ***** ISGIB DETZIM ZU SAMANGERVERSTERGTEN ZUSAMM AUBEIT einen ersten **** GANGVON EINIGENMITIELSTARTEN NAC +Eval: D D D S S S S S S D S S S + +Speaker sentences 514: voxpopuli_deu_000323 #utts: 1 +id: (voxpopuli_deu_000323-voxpopuli_deu_000323) +Scores: (#C #S #D #I) 4 17 6 2 +REF: WAS DIE GRENZÜBERSCHREITENDE ZUSAMMENARBEIT ANBELANGT und ****** DIE verbreitung IN DRITTLÄNDER BETRIFFT HIER MÖCHTE ICH EIN BEISPIEL NENNEN DAS EIN ERFOLGSBEISPIEL FÜR MICH ist und ****** ZWAR SLUMDOG MILLIONÄR +HYP: WASTI RENZYÜBERCHREITEN DE ZUSAHMENABEI ANBELANT und WASTIE ER verbreitung ** ************ ******** **** ******* *** INTRIG LÄNDER BETRIFTUND TIEMECHLICH EINBEISCSPIENENENDES EN ER FOLKSBEISPILVÜMICH ist und ZWAREL SLM DOKGMILIER NEREDAS +Eval: S S S S S I S D D D D D D S S S S S S S S I S S S + +Speaker sentences 515: voxpopuli_deu_000324 #utts: 1 +id: (voxpopuli_deu_000324-voxpopuli_deu_000324) +Scores: (#C #S #D #I) 3 12 4 0 +REF: UND das NICHT NUR in PORTUGAL ODER GRIECHENLAND SONDERN AUCH IN SO VERMEINTLICH REICHEN MITGLIEDSTAATEN WIE DEUTSCHLAND oder GROSSBRITANNIEN +HYP: *** das ***** NICHTENUR in ******** **** PRTUGAHL DR GRICHENLAND SNERN AURENSO VERMEINTLICHG EICHEN MITWIE STATEN VIEDEUTSHLAN oder RUSPETANIERN +Eval: D D S D D S S S S S S S S S S S + +Speaker sentences 516: voxpopuli_deu_000325 #utts: 1 +id: (voxpopuli_deu_000325-voxpopuli_deu_000325) +Scores: (#C #S #D #I) 0 4 2 0 +REF: DIE ZEIT FÜR AUSREDEN IST VORBEI +HYP: *** **** TVER AUSWENIS VERBEI DA +Eval: D D S S S S + +Speaker sentences 517: voxpopuli_deu_000326 #utts: 1 +id: (voxpopuli_deu_000326-voxpopuli_deu_000326) +Scores: (#C #S #D #I) 1 10 3 1 +REF: SIE ALLE FLIEGEN ALS MITGLIEDER DIESES HAUSES WAHRSCHEINLICH DEUTLICH HÄUFIGER als ** DER EU DURCHSCHNITTSBÜRGER +HYP: *** **** ******* EALLELFLIEGAL MIK DIEDER DIESESHAUSES VARSCHEN IE DORDTLICHOLFIGEAR als DE IE UDURSCHNITZBÜRGER T +Eval: D D D S S S S S S S I S S S + +Speaker sentences 518: voxpopuli_deu_000327 #utts: 1 +id: (voxpopuli_deu_000327-voxpopuli_deu_000327) +Scores: (#C #S #D #I) 1 11 2 1 +REF: UND ICH BIN sicher *** DASS IHRE BEDEUTUNG IN NAHER ZUKUNFT SOGAR NOCH ZUNEHMEN WIRD +HYP: *** *** EN sicher DAS ERER BEDOEUT TUNG INAHR S FU KUMFTUOGER NOCHT ZUNEHM WIERT +Eval: D D S I S S S S S S S S S S + +Speaker sentences 519: voxpopuli_deu_000328 #utts: 1 +id: (voxpopuli_deu_000328-voxpopuli_deu_000328) +Scores: (#C #S #D #I) 6 14 3 2 +REF: ES GEHT HIER UM DIE RICHTLINIE des **** RATES ZUR FESTLEGUNG GRUNDLEGENDER SICHERHEITSNORMEN fÜr den schutz VOR den GEFAHREN einer *********** EXPOSITION GEGENÜBER IONISIERENDER STRAHLUNG +HYP: ** **** **** T ESKETDIERUM DIRICHTLIHNJE des ADES E FÄSTLEGUNG GUNDLEGN DASIGHEREITS NERMEN fÜr den schutz FÜER den GEFAREN einer EXSPOSITSIO N GEGIÜBAR ONISIERENDAR STRALUNG +Eval: D D D S S S I S S S S S S S I S S S S + +Speaker sentences 520: voxpopuli_deu_000329 #utts: 1 +id: (voxpopuli_deu_000329-voxpopuli_deu_000329) +Scores: (#C #S #D #I) 0 2 3 0 +REF: DAS GILT ES WIEDER HERZUSTELLEN +HYP: *** **** ** DASSGILTES WIDERHERCHUSTEL +Eval: D D D S S + +Speaker sentences 521: voxpopuli_deu_000330 #utts: 1 +id: (voxpopuli_deu_000330-voxpopuli_deu_000330) +Scores: (#C #S #D #I) 2 6 2 0 +REF: DIESEN einen EINZIGEN SITZ GIBT es LÄNGST DAS IST STRASSBURG +HYP: DESEN einen ******** EINZIGENSITZ KIBT es ******* LÄNGS DASIE STASTPURG +Eval: S D S S D S S S + +Speaker sentences 522: voxpopuli_deu_000331 #utts: 1 +id: (voxpopuli_deu_000331-voxpopuli_deu_000331) +Scores: (#C #S #D #I) 7 31 13 1 +REF: WIR SEHEN JA GERADE DASS DAS PASSIERT in MALTA DIE JOURNALISTIN die KORRUPTIONSFÄLLE AUFGEDECKT HAT IST VOR WENIGEN WOCHEN ERMORDET WORDEN weder WERDEN SYSTEMATISCH DIE KORRUPTIONSFÄLLE UNTERSUCHT noch WIRD DER MORD selber * gezielt UNTERSUCHT MAN HAT FAST DEN EINDRUCK ALS OB HIER ALLES unter DEM MANTEL DES SCHWEIGENS ZUGEDECKT WERDEN SOLL +HYP: *** ***** ** ****** **** E DASASPASIERT in ***** *** MALTAR die SONLISTEN DI KOBP ZONDFELE AUFGEDEKTER DIS VERGEN BECHNERMODE WAR weder WEREN SYSTDEMAISHIKO CHOUNDFELE E BEUNTERSUCH noch **** IETER MOR selber E gezielt ********** *** *** E UNTERSUTEA ATVASSIN ANDOGAS WEN ALES IEOR unter *** ****** DE MANDEISCHWEINS ZUGEDEK WERNSOR S +Eval: D D D D D S S D D S S S S S S S S S S S S S S S D S S I D D D S S S S S S S D D S S S S S + +Speaker sentences 523: voxpopuli_deu_000332 #utts: 1 +id: (voxpopuli_deu_000332-voxpopuli_deu_000332) +Scores: (#C #S #D #I) 4 9 6 0 +REF: DORT STEHEN ÜBERALL ENTLANG DER kÜste DIE WARNSTEINE die auf die GROSSEN KATASTROPHEN MIT TSUNAMIS IN DER VERGANGENHEIT HINWEISEN +HYP: **** ****** ******** LINTLAN DE kÜste *** DIEWANSTAEINE die auf die ******* ************ ROSEN KATASFOFEN I ZUNAHMIEHN DR VRGANENEITINWEISEN +Eval: D D D S S D S D D S S S S S S + +Speaker sentences 524: voxpopuli_deu_000333 #utts: 1 +id: (voxpopuli_deu_000333-voxpopuli_deu_000333) +Scores: (#C #S #D #I) 5 20 8 1 +REF: HERR PRÄSIDENT ICH HABE IM PRINZIP FÜR DEN BERICHT GESTIMMT OBWOHL ER EINEN SCHWEREN FEHLER ENTHÄLT es *** WIRD NÄMLICH DAZU AUFGEFORDERT das EUROPÄISCHE PARLAMENT auf dem WEG ZU EINEM EINZIGEN SITZ zu UNTERSTÜTZEN +HYP: **** ********** *** **** ** ******* DENND ICHABEBRIEN ZIEPFÜRDE BEICHT GESTEMMNT OWOLE INEN SWEHRN FHLER INTELLT es IRT NEMITAR ZUR AUF GEFARDERT das AUROBEHRSCE PLLRMEND auf dem *** ** WEGKTZHE EIDEM EINZIGENSIT zu UNDESTELTZEN +Eval: D D D D D D S S S S S S S S S S I S S S S S S D D S S S S + +Speaker sentences 525: voxpopuli_deu_000334 #utts: 1 +id: (voxpopuli_deu_000334-voxpopuli_deu_000334) +Scores: (#C #S #D #I) 7 9 0 2 +REF: in *** *** DIESEN TREFFEN WURDEN GEMEINSAME POLITISCHE VERABREDUNGEN im kreis der 27 GETROFFEN und auch PUBLIK gemacht +HYP: in DIE SEM RIFEN WODEN GEMEI SAM MEPOLITISCHEVER ABREDUNGERN im kreis der SIEBEN UNTZWANZIGETDROFFERN und auch HUBLIK gemacht +Eval: I I S S S S S S S S S + +Speaker sentences 526: voxpopuli_deu_000335 #utts: 1 +id: (voxpopuli_deu_000335-voxpopuli_deu_000335) +Scores: (#C #S #D #I) 2 41 24 0 +REF: ICH BIN DER ÜBERZEUGUNG DASS WIR ES HEUTE MIT DEM VORSCHLAG AUS DEM UMWELTAUSSCHUSS GESCHAFFT HABEN EINEN SCHRITT WEITERZUKOMMEN ES IST NICHT PERFEKT EUROPÄISCHE ÄRZTE SAGEN WIR HÄTTEN FÜR HOCHRISIKOPRODUKTE EINE ZENTRALE ZULASSUNG HABEN mÜssen DAS HABE ICH NICHT GESCHAFFT ABER MIT DEM WAS HEUTE AUF DEM TISCH LIEGT SCHAFFEN WIR WOHL TROTZDEM einen GROSSEN SCHRITT VIELLEICHT KEINEN MEILENSTEIN ABER EINEN GROSSEN SCHRITT HIN ZU MEHR PATIENTENSICHERHEIT +HYP: *** *** *** ************ **** *** ** ***** *** *** ********* IBINER ER OGEN DES WERSHEUTE MITDEM VORCHARGES EM UMBELT AUSCHOS GESCHAFTAM INCHITWITEA U KMESIG ERFEKT E ORPÄICHE RZESAGENDEHETEN ÜER HOCHRISIKRORPODUKTE INE SENDRALEZULASN HAMEN mÜssen *** **** *** ***** ********* **** *** *** *** ***** DASABRICHICH GESCAFT ARMITDEMER SEID A EMTISCHLIKTPLAUEICH DAS IRTROTTEM einen ******* ******* ********** GROSEN SCRIT FLEICH KEIN MEINSTDEINE EIN GROSESCHIZU ERPAEDENSE ET HAEN +Eval: D D D D D D D D D D D S S S S S S S S S S S S S S S S S S S S S S S D D D D D D D D D D S S S S S S S S D D D S S S S S S S S S S + +Speaker sentences 527: voxpopuli_deu_000336 #utts: 1 +id: (voxpopuli_deu_000336-voxpopuli_deu_000336) +Scores: (#C #S #D #I) 0 6 0 0 +REF: FRAU PRÄSIDENTIN FRAU KOMMISSARIN LIEBE KOLLEGEN +HYP: PÄHEN DANG ESFÖRGFÜR ZWEIENHEIB MINODEN ERG +Eval: S S S S S S + +Speaker sentences 528: voxpopuli_deu_000337 #utts: 1 +id: (voxpopuli_deu_000337-voxpopuli_deu_000337) +Scores: (#C #S #D #I) 1 14 8 0 +REF: zum AKTUELLEN ICH GLAUBE ES KANN KEINER VON UNS ANNEHMEN DASS WIR WIRKLICH ERST SEIT DIESEM WOCHENENDE WISSEN DASS UNS DIE ZAHLUNGSUNFÄHIGKEIT DROHT +HYP: zum ********* *** ****** ** **** ****** *** *** AKTUÄLEN ICT LABISKANKEINER VO NUN SANEHME DAS WIWIRTLICH EARS ZETDIESEN WOCHEN E DIWISENDSONS DIEZALUNSUMFICGKEITDROT +Eval: D D D D D D D D S S S S S S S S S S S S S S + +Speaker sentences 529: voxpopuli_deu_000338 #utts: 1 +id: (voxpopuli_deu_000338-voxpopuli_deu_000338) +Scores: (#C #S #D #I) 0 7 1 0 +REF: DAS SIND EINFACH BEDINGUNGEN DIE NICHT AKZEPTABEL SIND +HYP: *** DSND EINFCH BEDINGNGEN DINICG EKTZEPTABESEND MAN KA +Eval: D S S S S S S S + +Speaker sentences 530: voxpopuli_deu_000339 #utts: 1 +id: (voxpopuli_deu_000339-voxpopuli_deu_000339) +Scores: (#C #S #D #I) 5 15 5 0 +REF: in DER ZWISCHENZEIT SIND DIE RETTUNGSORGANISATIONEN DIE GRÖSSTEN SCHLEPPER WEIL SIE die migranten 20 KILOMETER VOR DER LIBYSCHEN KÜSTE AUFGREIFEN und ALLE NACH italien TRANSPORTIEREN +HYP: in *** ************ **** DESWISCHEN SEISIN DI RETUNGSORGANISERZIONERN IEGRÖSTEN SCHÄPER WEISIE die migranten ** ZWANZICHKILOMETER VER DERIEBICHEN KÜST E AUBGREITFEN und **** ALENAR italien RASPORTIEREN +Eval: D D D S S S S S S S D S S S S S S D S S + +Speaker sentences 531: voxpopuli_deu_000340 #utts: 1 +id: (voxpopuli_deu_000340-voxpopuli_deu_000340) +Scores: (#C #S #D #I) 0 4 2 0 +REF: DAS ZEIGT DER FALL JULIA TIMOSCHENKO +HYP: *** ***** DE SEIKTDR FALLIOLIAR TIEMSCHENKO +Eval: D D S S S S + +Speaker sentences 532: voxpopuli_deu_000341 #utts: 1 +id: (voxpopuli_deu_000341-voxpopuli_deu_000341) +Scores: (#C #S #D #I) 3 2 3 0 +REF: WIR DÜRFEN NICHT wasser predigen und WEIN TRINKEN +HYP: *** ******* E wasser predigen und **** WEINTRINKEN +Eval: D D S D S + +Speaker sentences 533: voxpopuli_deu_000342 #utts: 1 +id: (voxpopuli_deu_000342-voxpopuli_deu_000342) +Scores: (#C #S #D #I) 3 7 1 1 +REF: FÜR diese ******* ENTSCHEIDUNG BRAUCHEN WIR viele PARTNER NICHT ZULETZT die STÄDTE +HYP: ÜR diese ENSCHEI DUNG PRAUEN WIAR viele ******* PATNAR NICHTZULETZ die STÄTTE +Eval: S I S S S D S S S + +Speaker sentences 534: voxpopuli_deu_000343 #utts: 1 +id: (voxpopuli_deu_000343-voxpopuli_deu_000343) +Scores: (#C #S #D #I) 2 17 1 0 +REF: DIE FOLGE ist ein HÖHENFLUG VON POPULISTEN UND EXTREMISTEN IN EINIGEN MITGLIEDSTAATEN IHREN DUMPFEN PAROLEN SETZEN WIR KONKRETE VERÄNDERUNG ENTGEGEN +HYP: *** DIEFOLGE ist ein HÖRENFLUG S VOM PORPOLIST NUN EXSTRLMISTEIEN EINIG MIGIT STATENIEREN BUMFEM PAROLUEN SETZENDIAR COGRETER VERENDERUNG EN GEGEN +Eval: D S S S S S S S S S S S S S S S S S + +Speaker sentences 535: voxpopuli_deu_000344 #utts: 1 +id: (voxpopuli_deu_000344-voxpopuli_deu_000344) +Scores: (#C #S #D #I) 7 16 2 3 +REF: WEIL die INVESTITIONEN FRANZÖSISCHER und deutscher banken GERETTET werden MUSSTEN DURFTE GRIECHENLAND 2010 NICHT PLEITEGEHEN UND HEUTE MUSS ES einen ********* RIESIGEN SCHULDENBERG VOR sich *** **** HERSCHIEBEN +HYP: WAL die INVESTITZIONERN VRANTÖRSISCHACH und deutscher banken GERETET werden ******* ****** MUSTEN DURHTER GLICHENGLANDT WEITAUSEND EHN NICHTPBEITEGEN UNDEHUTE MUSES einen RIESIGENG SCHODEN BERK VORD sich TET HERT DRÜCKE +Eval: S S S S D D S S S S S S S S I S S S I I S + +Speaker sentences 536: voxpopuli_deu_000345 #utts: 1 +id: (voxpopuli_deu_000345-voxpopuli_deu_000345) +Scores: (#C #S #D #I) 1 17 4 0 +REF: DIE MITGLIEDSTAATEN dÜrfen NICHT DIE MÖGLICHKEIT HABEN DEN EUROPÄISCHEN STAATSANWALT DARAN ZU HINDERN IN IHREN REGIONEN GANZ GEZIELT UND SYSTEMATISCH KORRUPTIONSFÄLLEN NACHZUGEHEN +HYP: IMITGITS TDADEN dÜrfen ***** *** ************ ***** NICH IE MÖGLICHKEITABEN DEREN AUROPÄSCHENSTARZEAMBAL DERANZURHINDERNEN IEREREG IOND GANZSGER ZIE UNSTEMATIS CORU TONFELNACHZUG GEN ERSINE +Eval: S S D D D D S S S S S S S S S S S S S S S + +Speaker sentences 537: voxpopuli_deu_000346 #utts: 1 +id: (voxpopuli_deu_000346-voxpopuli_deu_000346) +Scores: (#C #S #D #I) 2 5 1 0 +REF: DREI MILLIONEN menschen SIND ABHÄNGIG von UNSERER HILFE +HYP: **** EIMILION menschen SIN APÄNGH von UD SERHILFER +Eval: D S S S S S + +Speaker sentences 538: voxpopuli_deu_000347 #utts: 1 +id: (voxpopuli_deu_000347-voxpopuli_deu_000347) +Scores: (#C #S #D #I) 0 13 1 0 +REF: EIN VIERZEHNJÄHRIGER JUNGE WIRD IN HAKKARI VON EINEM POLIZISTEN EINES SONDEREINSATZKOMMANDOS INS KOMA GESCHLAGEN +HYP: *** EINFÜZHENHR GER JNGE WET INHERKADI ON EINE POLIZSISTEN EINESONDE EINSAT KOMANDOS N COMAGSCHLAGDEN +Eval: D S S S S S S S S S S S S S + +Speaker sentences 539: voxpopuli_deu_000348 #utts: 1 +id: (voxpopuli_deu_000348-voxpopuli_deu_000348) +Scores: (#C #S #D #I) 1 14 2 0 +REF: WIE EINE HEILIGE KUH HAT MAN VOR SICH HERGETRAGEN das OPT OUT MÜSSE UNTER ALLEN UMSTÄNDEN WEG +HYP: *** **** DIE EI DIE HEILIGEKU AT MANWOSI HERETRAGEN das AUPT AUT WISS UDER ALLE UNSHLENDEN WECK +Eval: D D S S S S S S S S S S S S S S + +Speaker sentences 540: voxpopuli_deu_000349 #utts: 1 +id: (voxpopuli_deu_000349-voxpopuli_deu_000349) +Scores: (#C #S #D #I) 1 5 0 4 +REF: *** DREI DERARTIGE TREFFEN HABEN inzwischen **** ** ** STATTGEFUNDEN +HYP: REI DER ARTKTE GETDEREFEN HARBEN inzwischen STAD GE UN DEM +Eval: I S S S S I I I S + +Speaker sentences 541: voxpopuli_deu_000350 #utts: 1 +id: (voxpopuli_deu_000350-voxpopuli_deu_000350) +Scores: (#C #S #D #I) 0 5 3 0 +REF: ICH HOFFE ES DAUERT NICHT WIEDER NEUN MONATE +HYP: *** ***** ** RD ICHIE ER NEINMONER DEBT +Eval: D D D S S S S S + +Speaker sentences 542: voxpopuli_deu_000351 #utts: 1 +id: (voxpopuli_deu_000351-voxpopuli_deu_000351) +Scores: (#C #S #D #I) 11 29 7 2 +REF: DESWEGEN EINE WICHTIGE FRAGE AN DIE KOMMISSION KANN ein land die GRENZKONTROLLE WIEDER EINFÜHREN UND GLEICHZEITIG IN DER SCHENGEN union bleiben MIT ZUGANG ZUM INFORMATIONSSYSTEM ETC oder IST DAS ein ENTWEDER ODER DIE FRAGE IST WICHTIG fÜr DIE DÄNISCHE DEBATTE UND ICH BITTE um eine klare *** **** ANTWORT +HYP: ******** **** DSWEGEN EINWICHTIGE FRAGA DI KOMITION EN ein land die ************** KRANZSKONTROLLE VIEDER EINFÜHON ND DOCH IM SCHÄNGE union bleiben *** ITZUGANGKZUR IN OMAIONDSUSTEME ZETERA oder IS DRS ein ******** ENTWIRDER ODA DIEFRAGE IS WECHTIC fÜr *** ********* DIEDENISCHE TEPATE UN DIESPETE um eine klare AND WORD DA +Eval: D D S S S S S S D S S S S S S S D S S S S S S D S S S S S D D S S S S I I S + +Speaker sentences 543: voxpopuli_deu_000352 #utts: 1 +id: (voxpopuli_deu_000352-voxpopuli_deu_000352) +Scores: (#C #S #D #I) 3 17 5 0 +REF: WIE HEUTE schon AUSGEFÜHRT wurde LAG ES NICHT DARAN DASS ES HIER GROBE FEHLER gegeben HÄTTE SONDERN ES GAB EINE REIHE VON KLEINEN UNGEREIMTHEITEN BZW +HYP: *** DER schon AUSGIEFÜHRT wurde *** ** ***** LAGESNICHT BARARN DASIS E GROBEF FÄHLE gegeben ****** HETIS SONE NES GABENER REIE VOND GLEIEN NGEREIMTEITEN BIETIENSWEI +Eval: D S S D D D S S S S S S D S S S S S S S S S + +Speaker sentences 544: voxpopuli_deu_000353 #utts: 1 +id: (voxpopuli_deu_000353-voxpopuli_deu_000353) +Scores: (#C #S #D #I) 3 8 0 2 +REF: EINE VERGEMEINSCHAFTUNG der ******** AUSSEN UND SICHERHEITSPOLITIK ALS GROSSES ziel dieser ** UNION +HYP: IVER GEMEINTCHEAFTUNG der AUSENUOS SIEGERLTS POLITIG BAIS GOS IS ziel dieser UN JON +Eval: S S I S S S S S I S + +Speaker sentences 545: voxpopuli_deu_000354 #utts: 1 +id: (voxpopuli_deu_000354-voxpopuli_deu_000354) +Scores: (#C #S #D #I) 3 9 1 0 +REF: DENN SICHERHEIT IST EINE SCHWIERIGE UND DETAILREICHE arbeit nicht NUR IM TECHNISCHEN bereich +HYP: DENSICHEHEIT IS EINESCWIERIGE UND DE TEIL WEICHER arbeit nicht *** NUER IMTÄCHNISCHEN bereich +Eval: S S S S S S S D S S + +Speaker sentences 546: voxpopuli_deu_000355 #utts: 1 +id: (voxpopuli_deu_000355-voxpopuli_deu_000355) +Scores: (#C #S #D #I) 2 13 11 0 +REF: KINDER UND POLITIK SELTEN LIEGEN DIE INTERESSEN von BÜRGERN UND POLITIKERN SO WEIT AUSEINANDER BEI DEN BÜRGERN IN GANZ EUROPA STEHT DAS THEMA KIND GANZ oben +HYP: ****** *** ******* ****** TISEÄLTEN GEN DIENTERESTEN von ******** *** ********** ** **** *********** *** BÜRGERNUN POLITIKEN SOWI AUSENANDER BER EM BÜRERNINGANZER OPERSTETESTEMER KINDT GANNS oben +Eval: D D D D S S S D D D D D D D S S S S S S S S S S + +Speaker sentences 547: voxpopuli_deu_000356 #utts: 1 +id: (voxpopuli_deu_000356-voxpopuli_deu_000356) +Scores: (#C #S #D #I) 0 1 1 0 +REF: HERR PRÄSIDENT +HYP: **** HERPASIDENT +Eval: D S + +Speaker sentences 548: voxpopuli_deu_000357 #utts: 1 +id: (voxpopuli_deu_000357-voxpopuli_deu_000357) +Scores: (#C #S #D #I) 3 11 2 1 +REF: WIR FÜHRTEN GESPRÄCHE mit ******** PRÄSIDENT KARZAI ZAHLREICHEN REGIERUNGSVERTRETERN FRAUEN UND MENSCHENRECHTSORGANISATIONEN und die WAREN DURCHAUS ERMUTIGEND +HYP: *** EFÜRTEN GESPREICHE mit RESEDENT KARSEI ZARDREICHN REGIRUNGS ERTRETERN FRAUNUND MENSCHNRECHT ORGANISERTZIONEN und die ***** WAND DUCHAUSEMUTIGENT +Eval: D S S I S S S S S S S D S S + +Speaker sentences 549: voxpopuli_deu_000358 #utts: 1 +id: (voxpopuli_deu_000358-voxpopuli_deu_000358) +Scores: (#C #S #D #I) 1 8 7 0 +REF: DAS IST ÜBRIGENS AUCH eine URSACHE FÜR DEN WACHSENDEN NATIONALISMUS DER ALLERDINGS LEIDER VÖLLIG PERSPEKTIVLOS IST +HYP: *** *** NGS ACH eine ******* **** *** ********** ************* URSACHEFRDEN WACHSNENATZIHNALISTMUS DE ALINSEIDER FELICH PERSBEKTIFLOSSIST +Eval: D D S S D D D D D S S S S S S + +Speaker sentences 550: voxpopuli_deu_000359 #utts: 1 +id: (voxpopuli_deu_000359-voxpopuli_deu_000359) +Scores: (#C #S #D #I) 0 9 2 0 +REF: HEUTE SIND WIR IMMER NOCH SO WEIT VON DIESEM ZIEL ENTFERNT +HYP: ***** **** HUDE IN E IMANAOH SORWEIT ON DIENZIE ENFERN ES +Eval: D D S S S S S S S S S + +Speaker sentences 551: voxpopuli_deu_000360 #utts: 1 +id: (voxpopuli_deu_000360-voxpopuli_deu_000360) +Scores: (#C #S #D #I) 6 29 16 1 +REF: ICH WERDE ALS FINANZMINISTER AUCH IN meinem LAND JEDEN TAG damit KONFRONTIERT DASS NATÜRLICH AUCH das BEWUSSTSEIN GEGEBEN SEIN MUSS DASS STAATSHAUSHALTE von ** DEN STEUERZAHLERINNEN UND STEUERZAHLERN FINANZIERT SIND und DASS WIR DAMIT auch DIE VERANTWORTUNG TRAGEN BEI DEN ENTSCHEIDUNGEN DIE WIR HIER IN DIESEM RAHMEN TREFFEN MEINE DAMEN UND HERREN +HYP: *** ***** *** HWER DEALSWIDANZMINISTER AUCHEN meinem **** LANDZIEDEN TAGK damit ************ **** ********** KONFVONDTIERT das *********** NATÜLICH AUCHTES BIRUSTZENGEGEBENSENMUS DAS STASHAUSHALGTDE von DE STELUER SALERENE NON STEUER ZEOLLAN INENZIEZIHNT und **** DAS IETAHMIT auch *** ************* ****** *** *** ************** *** IERNTUERTUNGRAGEN INDE ENT CHÄIDUNGEN DIEVIERHIEN TIESEN RAMEN DREF MIETAM NONTHERN +Eval: D D D S S S D S S D D D S D S S S S S I S S S S S S D S S D D D D D D D S S S S S S S S S S + +Speaker sentences 552: voxpopuli_deu_000361 #utts: 1 +id: (voxpopuli_deu_000361-voxpopuli_deu_000361) +Scores: (#C #S #D #I) 2 4 1 0 +REF: auf dem EUROPÄISCHEN AUTOMOBILMARKT INSGESAMT DRAMATISCH IST +HYP: auf dem ************* OUOROPESCHEN AUTERBEBILMAREKT INSIGESAM DERMATISCHIST +Eval: D S S S S + +Speaker sentences 553: voxpopuli_deu_000362 #utts: 1 +id: (voxpopuli_deu_000362-voxpopuli_deu_000362) +Scores: (#C #S #D #I) 4 14 7 1 +REF: DIE EUROPÄISCHE UNION HAT MIT DIESEM INSTRUMENT die CHANCE eine AKTIVE ROLLE IN IHRER NACHBARREGION ZU spielen um ************** DEMOKRATISCHE REFORMEN UND EINE NACHHALTIGE ENTWICKLUNG VORANZUTREIBEN +HYP: *** ************ ***** *** OPÄHSCHUNION HADTMIDISE INSTRUMENZS die SCONSE eine ****** ***** ** AKTIE VEROLLENIERNACHBA RIGIONZU spielen um DERMOGRATISCHE E FORMEN NDERN NACHALIG EN WIKTUNG ERNZUTREIBE +Eval: D D D D S S S S D D D S S S I S S S S S S S + +Speaker sentences 554: voxpopuli_deu_000363 #utts: 1 +id: (voxpopuli_deu_000363-voxpopuli_deu_000363) +Scores: (#C #S #D #I) 3 6 4 0 +REF: DIE SICHT AUF TOTALITÄRE REGIME von AUSSEN oder von INNEN IST RECHT UNTERSCHIEDLICH +HYP: *** ***** *** *********** HTUTELLITERERERSCHIEME von AUSENG oder von INEN IS TRECHT UNDOSCHIEDGLIGH +Eval: D D D D S S S S S S + +Speaker sentences 555: voxpopuli_deu_000364 #utts: 1 +id: (voxpopuli_deu_000364-voxpopuli_deu_000364) +Scores: (#C #S #D #I) 1 19 2 0 +REF: WIR HABEN IMMER GESAGT DASS EINE ÜBEREILTE STATIONIERUNGSENTSCHEIDUNG UNSINNIG IST WEIL ES zum JETZIGEN ZEITPUNKT KEINE BEDROHUNG BEISPIELSWEISE AUS DEM IRAN GIBT +HYP: ER EM IMER GESAGKT EIN ÜBER EILTE STADTZIONIERUNGSEN SHEIDUNGES UN SENICH WEI zum ******** ********* JERZIGENTZEIT FUNG ES KEINEBEDROUNG BEISPIELSWEISAUS EM IERANGEBT +Eval: S S S S S S S S S S S S D D S S S S S S S + +Speaker sentences 556: voxpopuli_deu_000365 #utts: 1 +id: (voxpopuli_deu_000365-voxpopuli_deu_000365) +Scores: (#C #S #D #I) 1 17 3 0 +REF: DIESER VERGLEICH IST EINE zynische MISSACHTUNG DER OPFER VON MENSCHENRECHTSVERLETZUNGEN IN ALLER WELT ER IST ZUM ANDEREN EIN SOLCH UNGLAUBLICHER ANWURF +HYP: ****** ********* DIESERFAKLEIIST EINET zynische *********** MIS ACT DUNGEN DER OBPUOVORON MENCHN ECTZWRECTDOMLEL ALLRELS FFAAAAATDODSCSAAAAIS SONGANDANANDENE EINE SOEUCH ODEN CÜLAUPBLICHER ANWORF +Eval: D D S S D S S S S S S S S S S S S S S S + +Speaker sentences 557: voxpopuli_deu_000366 #utts: 1 +id: (voxpopuli_deu_000366-voxpopuli_deu_000366) +Scores: (#C #S #D #I) 1 7 0 5 +REF: die ** ** ***** **** ********* SPE HAT DIESE UMFASSENDE HORIZONTALE RICHTLINIE BEFÜRWORTET +HYP: die ES PE ERHAT DISE UMFSENDER HETZUN TALE RICHTLINDE ÜR BEÜRBOATDET WIN GER +Eval: I I I I I S S S S S S S + +Speaker sentences 558: voxpopuli_deu_000367 #utts: 1 +id: (voxpopuli_deu_000367-voxpopuli_deu_000367) +Scores: (#C #S #D #I) 5 26 4 0 +REF: DENN EINES IST WIRKLICH KLAR DIE FINANZ und WIRTSCHAFTSKRISE VERLANGT VON UNS ALLEN EINMAL MEHR der VERANTWORTUNG fÜr eine OPTIMALE und VOR ALLEM RASCHE QUALIFIZIERUNG UNSERER ARBEITNEHMER UND ARBEITNEHMERINNEN GANZ BESONDERS JETZT RECHNUNG ZU TRAGEN +HYP: **** ***** GIGIST WIRGLIC KLA DI INANF und E WIRSHASTGEDE VELANK VONUNDE ALN EINMALMEHR JETZST der VERANTWOFTDUNG fÜr eine OBPTIMALE und *** ***** FEALEM RASIEKALIFIZTIERUNG UND RER ARBEITNEHME ND ARBEITDNEMER RINEN DANS BESONDER JETSTRESCHNUNG SOTAGEN +Eval: D D S S S S S S S S S S S S S S D D S S S S S S S S S S S S + +Speaker sentences 559: voxpopuli_deu_000368 #utts: 1 +id: (voxpopuli_deu_000368-voxpopuli_deu_000368) +Scores: (#C #S #D #I) 1 17 10 0 +REF: ESTLAND ODER AUCH POLEN DIE SEHR GUTE ERGEBNISSE ERZIELEN ALS andere DIE SICH SCHWER TUN DIE MITTEL ABZURUFEN ETWA REGIONEN WIE KALABRIEN SIZILIEN ODER AUCH GRIECHENLAND ODER RUMÄNIEN +HYP: ******* **** **** ***** *** ANDRER AUCHOHLEN DIESERKUDER GEBISER ZIELNALS andere *** **** ****** *** *** DIS SI SCWÄERTUN DIMITEL ABPZU OFN ETWACRIK IONWIKALABRENZITZIELENODER AUC GRIECHELER DODR AUCHOMÄNIEN +Eval: D D D D D S S S S S D D D D D S S S S S S S S S S S S + +Speaker sentences 560: voxpopuli_deu_000369 #utts: 1 +id: (voxpopuli_deu_000369-voxpopuli_deu_000369) +Scores: (#C #S #D #I) 3 16 4 0 +REF: DER BERICHT GAUZÈS FORDERT ZU RECHT DASS das RATING STAATLICHER SCHULDTITEL ALS ÖFFENTLICHE AUFGABE BEGRIFFEN und DAHER von ÖFFENTLICHEN AKTEUREN VORGENOMMEN WERDEN MUSS +HYP: *** ******* ******* DERBRICH COSES VORDER ZURECHT das ES RETING STATLICHERSCHLT TIEDEL EIS ÖFFENTLICHER AUFGABEBEGRIFEN und DAHIER von ************* FFENLICHE AKTÜRN VORGENOMWERDEN MOS +Eval: D D D S S S S S S S S S S S S D S S S S + +Speaker sentences 561: voxpopuli_deu_000370 #utts: 1 +id: (voxpopuli_deu_000370-voxpopuli_deu_000370) +Scores: (#C #S #D #I) 2 12 5 0 +REF: DA WIR ES ABER NUN mit einem SOZIALPROGRAMM ZU TUN HABEN MÜSSEN WIR DAFÜR EINE ENTSPRECHENDE RECHTLICHE GRUNDLAGE SCHAFFEN +HYP: ** *** ** DABWIES ABALDN mit einem ************** ** SOTCARL POGAM ZUTUN HARBEN MSSWIL DARFÜR EIN EN SPECHENDERECHLIGE UNDLAGESCAFEN +Eval: D D D S S D D S S S S S S S S S S + +Speaker sentences 562: voxpopuli_deu_000371 #utts: 1 +id: (voxpopuli_deu_000371-voxpopuli_deu_000371) +Scores: (#C #S #D #I) 0 3 3 0 +REF: ABER DAS MÜSSEN WIR NOCH ANALYSIEREN +HYP: **** *** ******* STIER NOCHNALISEREN WORE +Eval: D D D S S S + +Speaker sentences 563: voxpopuli_deu_000372 #utts: 1 +id: (voxpopuli_deu_000372-voxpopuli_deu_000372) +Scores: (#C #S #D #I) 3 10 3 0 +REF: MAN KANN NATÜRLICH verlangen GEBEN WIR MEHR GELD FÜR ENTWICKLUNGSHILFE aus die ARMEN LEUTE BRAUCHEN DAS +HYP: *** MA KENENERTLIE verlangen ***** *** GEBNLIEMER GARTFIRND IKUNGSH VER aus die AHMENEUTE BRAUCKEN DASS BE +Eval: D S S D D S S S S S S S S + +Speaker sentences 564: voxpopuli_deu_000373 #utts: 1 +id: (voxpopuli_deu_000373-voxpopuli_deu_000373) +Scores: (#C #S #D #I) 2 17 4 0 +REF: GERADE FÜR KLEINERE PROJEKTE IST das EIN ÜBERMÄSSIGER BÜROKRATISCHER AUFWAND RICHTIG DASS das JETZT AUF EINEN ZEITRAUM VON DREI JAHREN GESENKT WERDEN SOLL +HYP: GERAD ÜO GLEINELE POJECKTE IS das *** ÜBER MÄESIGH BIEROGATESCHR AUFAND RECHTICH das ***** *** ***** TAS IER SRBEIN ZEITAUM VOND DREIJAHREN GESENTWERDENSOLUNDUM +Eval: S S S S S D S S S S S D D D S S S S S S S + +Speaker sentences 565: voxpopuli_deu_000374 #utts: 1 +id: (voxpopuli_deu_000374-voxpopuli_deu_000374) +Scores: (#C #S #D #I) 0 14 0 0 +REF: ICH KANN NUR VERSICHERN DIE EUROPÄISCHE KOMMISSION IST COMMITTED ZUR EUROPÄISCHEN PERSPEKTIVE DES KOSOVO +HYP: IKANDE VORSICHERN DIAROPESCHE KOMISION ISTE TKOMITDET ZSUM A AR EROB SEALOPECHEN ERSBIEKTDIEVER DIS KOSSOSE +Eval: S S S S S S S S S S S S S S + +Speaker sentences 566: voxpopuli_deu_000375 #utts: 1 +id: (voxpopuli_deu_000375-voxpopuli_deu_000375) +Scores: (#C #S #D #I) 1 2 7 0 +REF: ABER HIER IM HAUSE IST ES SEHR OFT AUCH so +HYP: **** **** ** ***** *** ** **** SESESLAPEIHE AUFTAUCH so +Eval: D D D D D D D S S + +Speaker sentences 567: voxpopuli_deu_000376 #utts: 1 +id: (voxpopuli_deu_000376-voxpopuli_deu_000376) +Scores: (#C #S #D #I) 3 9 2 0 +REF: MIT DIESEM HAUSHALT KANN MAN die EU BÜRGERINNEN UND BÜRGER nicht ÜBERZEUGEN UND begeistern +HYP: *** I DIESEN HAUSEL KAMAN die ** EEN GÜRGEREINUND BORGER nicht ÜBERZEUCTEN N begeistern +Eval: D S S S S D S S S S S + +Speaker sentences 568: voxpopuli_deu_000377 #utts: 1 +id: (voxpopuli_deu_000377-voxpopuli_deu_000377) +Scores: (#C #S #D #I) 5 14 8 2 +REF: WIR ALS SOZIALDEMOKRATEN NEHMEN MIT GROSSER FREUDE ZUR KENNTNIS DASS dinge die WIR VORGETRAGEN haben **** **** JETZT im ZUSAMMENHANG mit DEN VERÄNDERUNGEN IN DEN VEREINIGTEN STAATEN UMGESETZT WERDEN +HYP: *** *** **************** ****** ZIALDE MOKRATENEHMNIT GROSAF OEUDE ZSORKENTNIS DAS dinge die IER FORGETRAGEN haben JEBZ SICH AUH im ZUSAMMENAG mit *** ************** ** *** VERENDERUNGENE DENFEINCHEN STADEN UMSETZE +Eval: D D D D S S S S S S S S I I S S D D D D S S S S + +Speaker sentences 569: voxpopuli_deu_000378 #utts: 1 +id: (voxpopuli_deu_000378-voxpopuli_deu_000378) +Scores: (#C #S #D #I) 4 13 1 4 +REF: DER BESCHLUSS DAS EUROPÄISCHE semester ******** HERZUNEHMEN und ** ************ ******* DIE KORRUPTIONSSITUATION im RAHMEN der LÄNDERBERICHTE ZU VERÖFFENTLICHEN IST NICHT AUSREICHEND +HYP: DEAHRBESCHUS TIE EL DASOROPÄSCHE semester HIERHERT ZUNEHMEN und DI COROBPTIOUNS SITPLAR ION ERE im RAM der *************** LNDERBRECHTE ZO VERÖFNIGEN ISTNIG AUSHEIGENT +Eval: S S S S I S I I I S S S D S S S S S + +Speaker sentences 570: voxpopuli_deu_000379 #utts: 1 +id: (voxpopuli_deu_000379-voxpopuli_deu_000379) +Scores: (#C #S #D #I) 9 24 8 1 +REF: ** UND meine BITTE ODER das was ich MIR VORSTELLE IST DASS MORGEN WIRKLICH IN DER TAT EINE GROSSE eine BREITE mehrheit fÜr diese KOHÄSIONSPOLITIK FÜR UNSERE POLITIK STIMMT FÜR DIE MENSCHEN VOR ORT DAMIT WIR UNS AUF das WESENTLICHE BESCHRÄNKEN KÖNNEN +HYP: ND MEIN meine BITE ODERM das was ich *** ********* *** MER VORSTEN IS DAS MARGENGWIECKLICG INDERTART EINIE GROSE eine BREIT mehrheit fÜr diese ***************** **** KOUSIONS POLITIGHÜE ONDFGEPOLITIGSTDIMT PÜR DIEMENSCHEN VORORT DAMITIUNS A TES WEENTICHE AUCH BESCRÄNKENKÖNDE das *********** ************ ******* +Eval: I S S S D D D S S S S S S S S S D D S S S S S S S S S S S S D D D + +Speaker sentences 571: voxpopuli_deu_000380 #utts: 1 +id: (voxpopuli_deu_000380-voxpopuli_deu_000380) +Scores: (#C #S #D #I) 4 21 7 1 +REF: WENN WIR heute *** DIESE VERORDNUNG verabschieden HOFFE ICH DASS WIR nach EINEM LANGEN WEG ZU EINEM GUTEN ABSCHLUSS KOMMEN UND ICH MÖCHTE MICH bei DER KOMMISSION BEDANKEN DIE UNS DURCH KONSTRUKTIVE SACHARBEIT +HYP: **** WNNWIE heute DIE E VERRDNG verabschieden OFER ECH DASSE WIE nach ***** ****** *** ** ***** EIM LANGKARUSELL SOU EIM GUDNABSLUSKOMMONDT ITM MACHTERMICHE bei *** ER OMISION BEDANGEN IEONSTOKTIEVE SACH ABEIT HAT +Eval: D S I S S S S S S D D D D D S S S S S S S D S S S S S S S + +Speaker sentences 572: voxpopuli_deu_000381 #utts: 1 +id: (voxpopuli_deu_000381-voxpopuli_deu_000381) +Scores: (#C #S #D #I) 1 4 3 1 +REF: ****************** UNSERE KONTROLLEN haben KEINEN BELEG ERBRACHT ICH KANN +HYP: UNZERERESCHEASCHEN UNZSIE KONTROELEN haben ****** ***** ******** KENEN PIELEGERPRAFT +Eval: I S S D D D S S + + diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/text b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/text new file mode 100644 index 0000000000000000000000000000000000000000..e48dbde059c95b904cd198df866c4e5a24415bd5 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/text @@ -0,0 +1,661 @@ 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G D A S D E S E L E D E N K Ö R P E R V E R L A S S E N U N D Z U I M Z U R Ü C K E R E N K Ö N E S H I N E S I E R O R D E N D L I G +M-AILABS_deu_000224 A L S I E O F T D E N A L K O N D Z U R I K E R T E V A N D Z I I N D I E Z E I T U N K L I E S E N D D I E W E R E N R E S F O R T Z E I N E S A N G E L N K T W A R H T +M-AILABS_deu_000225 T E E H R W A H E I N K I N D D E R S T R A S E V O N K L E I N A U F A B E R I N I M L E B T E V O N J E H E R I N E G W I S S E S S E E N S O C H T N A C H E I N E R E H R B A R E N B I R G E R I C H E N E X I S T E N S T +M-AILABS_deu_000226 A I T U N E S T I G J R U N G K T Ü H N U N Z I E N I G E I N E R G R O P E F E R A N F O D L I C H O N A M W Ü E F Ü N E D E N G E M E I N W U H L F E R N W O D L I C H U N +M-AILABS_deu_000227 W A S M E I N L I E B E S K I N D W A S K A N +M-AILABS_deu_000228 U N D D A N W O L T I C H D E N A N B L I C G D E R A N I C H T M I S E N D I E M E R G E B L E B E M W A R E N V O R A L M A B A R B W A E S I D A R U M Z U T O U N W E I N E S Y S E L I C I S E R B E T E I N I G A M A S S E N G E T R Ü S T E T Z U S E E N T O +M-AILABS_deu_000229 E D A S A U C H W I E U N D S G E N S E T D C H E B S H N U N T D A S T U Z E N K Ö N N E N E Ö R M +M-AILABS_deu_000230 S E I N E G E S C H F T I C H E L A U F B A H N H A R B E S T I E V E N S N A L S K Ö C H E N B O I N E I N E M O T Ä L F I E R E N G R A D E S B G O N +M-AILABS_deu_000231 N F Ü L E I C H T E N S E G U T I E S E A N S I C H E N D E S S C H O F S N H A S E Z U M E L D E N Z A C T E D E R T A T S C E N D E R I M A R M E H R E I N M A N D E S G E S C R I E B E N E N W U R T E S W I E D E R T A D N +M-AILABS_deu_000232 E M A N D A N M O R D E N E H R H O P E R S I C H S C H W Ä T S C H C K T E D E N L A R K E I E N D E B O N U N G V O R J A B A C H S U N T L I E S U M E I N E N T E R E D U N G B I T E N D E M A N K A M M I T E R O T S C H A F T Z U R Ü G D E T N +M-AILABS_deu_000233 N T N U E I N W E N I C H T R A U R I C H W U R D E S E 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A K P O T A F O L I E N T I E R N +M-AILABS_deu_000245 A N F A N G S F I E L D E R E G E N S C H R A G U N D P E I T S T E E R S I E E I N E D A N D I E N D E R E S E I T E D E S W A G E N S +M-AILABS_deu_000246 M F A S T L E I C H E N I G E N B E R M E S S O N G E R E S W E R T E S A U F T Z O G E B E M S I C H E N T S C L O S S E N H A T E N N +M-AILABS_deu_000247 S E I S T B D I E F R A R G E R M E N C H L I C H E N A R B E T N D I E F R A G E W A S K A N T E C H N I S C G E L Ö S T W E R D E N D A +M-AILABS_deu_000248 N U T I S E A R F A H R I E W A U F I E R E D E M E S I G B N O T Z S T E N W A S S A R S T E L L E N D I S E R O T A N G E W I E S E N N N +M-AILABS_deu_000249 D I E B E I D E N M I S T E N H I E O B E N A U F D E M G E B P F E L G E S T A N D T E N H A B E N U N D E R S P R A C H D I E A L T E N W A U R T E V O R S E C H I N +M-AILABS_deu_000250 E N T L I C H B I K T E S E D R I K G A U F W E I S S N J U U I G A L E S V O N D E N A R M E N L E U T E N F R A K T E E 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A C F T D E L L A C H Z U S A M E N G E W A K Z E N +cv_deu_000794 W A R I S C H N E I N M A L I E N D E M K L O +cv_deu_000795 B O R A U R I S T I S T A U C H V O L O R +cv_deu_000796 D I E H E X V O N D R S T R A S E W U D E N S N V O N A L F E T D I O L E K B E S E I N E F E S T E N S E I S C H O E S C H A B L N E E N G Ü L S I E R T E +cv_deu_000797 A I N H A R S P Ä I T E V E X S L T E R E L T Z U N E L F T N A T Z S U N M B E V O D E E L F V U N G E R E I C H E +cv_deu_000798 I N D E R L A N D V I T C H E R F K A N D E R E R T R A R G K T D E U T L I W E D O R Z I E R T W E R D E N T +cv_deu_000799 M A N S U R S P I E R T E I N S E I N E R H E I M A T S T A D T K E E W O R F I E R A L L A L L +cv_deu_000800 E R T R A D E R R E I M A U H A L U N D E L N L A O U T A B U E O B E I +cv_deu_000801 M I T F Ü R T W A R E H R D I E R O W P T H J A D E L E I L S D E R E C H T L I C H E R O L L E D I E S S O B E T S E I C E N E N G E M E I N D +fleurs_deu_000378 L E T Z T E W O C H O G A B D A S M E T I E B E K A N D G A S E S V O N E P E L Ü B E R V I E R N D A S H W A L T E F O R F E L E V O N Ü B E R I T Z E U N I N T O M I R T W O R D E N W A I E D E S U N T E R N E H N A L S N I C H T S C H E R I E N P E E I T E T E +fleurs_deu_000379 E J Ü U R S E J I M N E S T I G U N D T E R S T Ü T Z S T E D E N B E R I E F D E S O L Ü M P I S C H E N K O M I T I S D E R V E R E I N I G T E N S T A T E N U N D D A C I P T I E R T E S A S A P B P T U L U T E N O T W E N D I K E I T D A S I H T D I E O L Ü M P I S C H E V E R M I L I E F Ü R E I N G S I C H E R E S U N M F E L L T F Ü R A L E U N S E R E R S P O R T L E R E I N S E T T +fleurs_deu_000380 D A L I C K E N E A P R E T S K O M P E T I B E L M I T C H T R N E R T Z W E I P U N D E L F A R A C H T E N R Z W E I P U N G D E L F P E U N D C H T E O N E T Z W E I P U N G D E L F G E S E I N V E R A S G E S D I A S S I S T A T I O N V E R F Ü G T B E R D U A L R A D I O +fleurs_deu_000381 E R B E Z E I C H N E S D I E I E R Ü C H T E A L S P O L I S C H E S G I S C H E Ä T Z S U N D T A L L B E N H E I T Z +fleurs_deu_000382 L E T E W O C H E G A B T A S E M I E I T H I E B E K A N D A S I S V O N E B E L Ü B E R V I R N D R E I S I W E I T R E F O R F V E L E V O N B E R H I T Z U N I N V O R M I R U R D E N W A D I E D A S U N T E R N E H M E M A L S N I C H C H W R W E G E N B E Z E I G N E T E +fleurs_deu_000383 N A C H D E D E R D M L E U N E H H N D E R T R A L N S E C H Z I G E B A U R T W O R D E N W A R K A M D I A H R E S S E I L I H N B E R F L U T U N G D E S D E M E N T E M N F L S V E R T A L E N Z U M S T I L S T A N T +fleurs_deu_000384 E R W A U C H A M S T E C H E N V O N G E L S C H E I N V E V I E L E D E N D E B E T E I L I C G T A K T E L E B E I S H P I E S E N E A R B E T S C H L I S E N I E P R I M E H M I N I S T E R P O R T R E S A F D E R F O R D E R S E R T D E R K A N A D S C H E N F Ü N F U N D U N D E R O L L R N U T E N E I N +fleurs_deu_000385 D I E A U P T S T A T V E R M O D A W I E R N I S T K I C H I N A D I E E I N E I M P I S P A C H E S T R U M E N I S C H A B A R V I E L E M E N T E N C S P R E C H E N R O S S E L C +fleurs_deu_000386 S Z W I C H E N D E N E I N Z E N E N D Ü N E S T I E N H E R S T D E N A U C H U N M B E S T E N D I E G E Z E I T E N G E T A L L T E P R O I N D Z E D I E B E K A N T E S T E D I E S E P E R I O D E N O A D I E P O C H E D E R D R A I L K Ö N I N G R E I C H E D I E S E C H T I C H I E R E L A Z W I S C H E N D E R H A H N U N G D E R I E N D I E N E S T I S T A T V A D T +fleurs_deu_000387 A M A N D E R E N E N E D R S P E K T R U M S H W A N E L T M A N S I C H E N E I N I C H T W I D E R Z U R K E N D E N D I W I D E U M D A S A L E S A N E R S M A C H N M O S S A S S T I E N E S G E M A C T E R U N D S I C A L E S T Z U A L G E M A C H T +fleurs_deu_000388 D I E M E I S T E N D I N T E R P I T E A T I O N E N D E S T I C H N O L O G I S C H E N D E R T E I M I N I E S N U S T A L E N Z W E I A L G E M E I N E V O R S C H T E U N G E N E I N E R S E I T S T S T D I N T W I C K L U M D E R T I C H N O L O G I E S L L P S T E I N E M W E G O L G T D E R W E I T G E N T I E N S E I S U T D T O R E L E O D E R P O L I I S C H E I N P L S N A M E N D I G T U N D A N D E R E R S E I T D A S T I C N E R Ü G I E I E R E R S E I T S A U S W I R K U N G E N A U F G E S A L S C H A F T N H R T D I E E H R I N H Ä R E N D A S S Z U T T I A L B E D I E N S I D +fleurs_deu_000389 W I S C H E D E N E I N Z E N D N A R T I E N H E R C H T E N A C H U N M B E S T E N D I G E Z E I T E N G E T A I L T E R O W I N Z E N D I B E K A N D E S T E D E P E R I O D E N W A D I E P O C H R O D E R D E K Ö N I G R E I C H E D I E S E C H T I C A R L A N G T I S C H E N D E R H A N U N D T E R I N D E N R T I E S T T F V A N D +fleurs_deu_000390 D I E M L I E K T Z U F O R G I E B I E Z I Z I C H T E S T O O M E N T A U F D E M G R E N S T R E I L T I N D E N D I E P A L I S I N E N S E R E I N Z U R Ü E G S E T Z E N D E R G R E N Z E N I N D E N Z U S T A N D V O R D E M S E R X S T A L G L E G R I E G V O R N N E N E H N U N D E R T S E B E O N U S E S T I G O R D E R +fleurs_deu_000391 M I T I M P E L U S T G R E C H E H E R S P A C H K N N S E U R D E R W E S T E N V O N S E I M E V I E L O S O P I S C H E N U N D W I S E N S C H A F L I C H E N W O R T Z E N K R I C H E N E N A B I S C H N I T E N +fleurs_deu_000392 W I R S T M I T E R A U S A G E D I E S I U E R S A U S I E Ü B E R E I N D A S T E N N T R E S S N U N D S R E A T L E D E N D V E R E I N U N D I R E S P O R T S B P S S R G E D I E N D I S T E N W R N E H A L B U N S R R G E N S A T I O N D E H N V O L E V E R N D R U N G V E R A N D T R E I B E N A N S T A T E I N E R T I E R I Z E T V I Z I E R U N G V O R Z U N E +fleurs_deu_000393 I R E U T S F A T E N N A S S A N G T P I K A R S B O G B I E T E N A C H Z E I T F Ü R E I N A U F E N T A L I N E R S T A T K R E U T Z V A T P A S R S I E R E S I N D F V O R D E R I E S U N S L I C H T B E F R E I T S I E B E D N G E N +fleurs_deu_000394 E R E I S E N D E V E R D E N R I N G E N D G E W A N T A U F J E T W E D E A R T V O N U N W E N T E Z U A C H T E N D I E I E G I E I E T B I E T R I F T D A D I S S I G H A U F A L E R E I S E P L E N E A U S I E R K E N K A N T +fleurs_deu_000395 S E B E S A R T D A S D E R K O L Z U N G S P U N G K T D E L I N M I N D I E E N B I E L T W E R T I K A L U N T R E T O N D T A L D R I T E E N D E E F I K T I S T D E P L A S F Ü R T E S A U P T M U O D I I S T S I E B E I S S I N +fleurs_deu_000396 S E I T N U N Z E N U R T A C H T E N A C H T I C H M S T E W A L U N D R A N S B E R E N S E I N D A M I T W E L E U N B E O B A C H T E B E T Z E U G E N K Ö N D A S Z W E G I N D E R W A L K E I N E U M S C H L E G E V E R H N E N S I N D U N D A S K E I N U M S C H L E G E E I N G E O F E N W E R D E N A U S E R J E N E D E R T O R D U N G S M I S E H L T N E N A T T R S I R D T E N E L E R +fleurs_deu_000397 O T E R A R I S T K A N N E R D E S B I E Z A U B E N D E R Z W E I S C H A L I G E H A U P T S T A T U N D S E L T E N S I C H I C E I N E R E I E V U N K U N Z S T G E L E R I E N U N D M U S E E N A U S D I E K A N E N D E R S V E R G A N G E N H E L T U N D G E G E N W A R T B R E S E N T I E R E +fleurs_deu_000398 D I E S E P A R E K Ö R E N S I C H V E R E I N A D E B P T I O N S P L A N D V R E R B E B E E N S C H E I D E N +fleurs_deu_000399 I N F O L G E D E S S E N S E N Z W E I F I S H T A B E N A U S G E S T O R M E N D Z W E W E I T R U S E N V O M A U S T E R B E B E T R O T D A U N E D E R G E L A R S Z Y Ü F E R +fleurs_deu_000400 T R A N Z E N S E N N I H R N A T I L I C H E N M O G E B N G A M B E S T E N A U S W I E D E R S T E N S I A L S O D E R V E R S U C H U N G A U C H N U R E I N E X M K L A U D W E R N +fleurs_deu_000401 A U F D E R N A H S E I T E K Ö N T E I S M E R M A R I E R G E B E N D A D I G R O S T E D U N E S T E S W E R E I N F E R A F O D I E L A V E R A N D I O B E R F L E C H A U F T U S T E G E N +fleurs_deu_000402 E R F Ü C K T E C H I N Z U D A S S I J E D O C H N I C H T E A R Z U O A U F G I E V O R D E R T W E R D E N S O L L T E R N F E R T F L I C H T U N G E N E I N D Z U G E E N D I E Ü B E R I E R E N I N T W I T L U N G S T A N D I E R E V E R A N T O R D U N G U N D I R E R F Ä I G K A T E N H I E N A U S N G E E N T +fleurs_deu_000403 T C I E R T U E L I H I E L F I S T E L U N G E N S I N T I N D I S O F T E R E N G I E B A U D U N S O L E N A H B E I T S C H R I T D E N D I E D E S C H Ü L E R A L E I N M Ö G L I C H E R W E I S E N I H T B E V E L T I G E N K Ö R H I N T E R F R A G E N N E I E L E G E N U N D D E R G L E R E N +fleurs_deu_000404 A M F Ü N F Z E H N N A G S T N E U N Z H N H U D E R T V I R Z I V I E L I A L I E R T E N N S Ü T F R A N K R E I C H E I N D I N W A S I O N W O R D E A P E R E S C H E N R O G U N G E N R D +fleurs_deu_000405 E R G R I F A C H A L L S A N W A S S E N S W A S S E R K A R M S E B S T E N G R O S E R D E N O S A U R I E W I D E R T I E R E C G S W E R I M N I C H T G E W A K E N +fleurs_deu_000406 S E I T E R K R N D U N G V O R A S U N T I O R F Ü N F Z E N D E S E N U O D R E I S C I E S E S P A R E G W E I G E L U N G V I E L V O N S E I M I N D I G E H N E N K A R A K T E R N D S E I N E I D E N I T E T Z U B E W A R E N +fleurs_deu_000407 S T R O Z S D E N I S D E R A N T E L L A N N I E X S D I E W I N D E S T I G H T D E B I N D E R G E S A M T E N R U P E D E R L E U T E E M I T D U G D E R K O L O S E R O F E N B A D E R N N O C H G E R I N E N S E X S T A U S E N D E R I N D G E S A E N D R E I H U N D E N D R E I G A U S E N L E L T E D I E I N S Ü T A F R I C K E R T U E I N E M B I E S T I N T E N D E I T P U N G T A N G E S T E T S I T T +fleurs_deu_000408 E N S C H E L Z W E I T A U S E N S E C H S E L E U T E R T A S K O N T I N U M K O N Z E T A S E I N E M I T O D E U M O B G E N S A T I O N D S H L F E N L E I S T U M G S F E G E Z U W E R D E N +fleurs_deu_000409 I N D I E S E P I E R I O D E N D E R O E R O P E S C H E N G I C H I G H T E S T A N D D I E R L I C H U N D M E C H T I H E G E V O R D E N E R A T O L I S C H I Y K I E C H E A U F D E N P R Ü S T A N D T T +fleurs_deu_000410 D I E E R S D R C H T E N D I B Z I C E M F H L U N G I F T A S E I N E N E U D E P L O M A T S C H I N I Z A T I E V E V O E R E N D E D I E S E N J A H R E S E G R I F E N W E R D E N S O L L T E U M D I E R A G I S H E N G R E N Z E N G E G E B E R F E N T L I H E N T E R W E T I O N D Z U S I C H A R N U N D P L O M A T S C H B I Z I E O M I Z E I N A C H B A N I E D E R R T U S T E +fleurs_deu_000411 D I S P E T E T E I N I U T E G E L E G E G H E I T D A S N O T L I C H T U S E N D A E H I M E M M E H R A U D E R W E N I E R R U N D U M D I E U R D U N K E L S T +fleurs_deu_000412 P R O F S S O R E N P A M E L E V E R G U S S O N V O N D E R N W Ü U S T I A F D A N D I M E R K T A N S O A L I S T E N S C H E I N E I N E G E E L I E G R A N Z E Z U A S C R E T E N W E N D I E V O T T O U N S W E I T E V O N V E R D E C T I G E V E R F N T I C H E N +fleurs_deu_000413 E S K A N S I C H L O N E I N E E L K A T Z U K A U F N D I E Z U T R I T E N W D E R T U U S G E W E L E N P A G S E N H E T A F R I K A R D E R Z U A L E N Z U Ü T F R I K A N S C H N E R T O N A L P A R X S G E W E R T +fleurs_deu_000414 D I E P R Ü K E S O L E M S E T E M B E R Z W E I T A U E N S I B S H N F O L S T E N D I T N B E T R I E A U F N E H M N I S W R R W A R E D S I P A S I A N I S C H E N Z O L P U N K T E D A N F E R T I G S T E L L Z E I N W E R T +fleurs_deu_000415 W E R E N D E I E R X P R M I N T E L L E I M S T F N E L A G E U S E I N S C H E I N T D I E B O L E R M O T L I E T Ä T Z U S E N G U N G G B T E S B S S E R K E I N E M I T I K A M Ä N T E D I A L S E I N D R D I Z U B E H A N D L U N B E S T E N D E I N V E K T I O N G E E G N E T N A C H E W I E S E O R D E N +mls_deu_000281 E I N E U S E R S T L E P A F T E R D E B E C H E W E C H S E L V A N S T A D T W A I E R W U K D E M P L A N E I N A L G E M E I N E N S T A T E N K O N G R E S T Z U B E R U F E N U N D K O N D E S I V O L L O U F I K T N O N U N I C H T Ü B E R D E S O R Z U L E G E D E R O G R M M U N D E N O R T E S Z U S A M T R E T S E I N I G E N +mls_deu_000282 E R W U S T E N I C H T W A S I M D A S L E B E N K O S S B A R E S G E R A U P T A T E S C P A N K R A F T U N D M U T D D A S S E S I N F E I K U N D S C O E U G E M A C H T A T E U N D F Ä I C H Z U D E N H O H N D I N G E N Z U O D E N E N U N G E T R Ü B P T E I T F R O L D E N G E H Ö R T +mls_deu_000283 D I E S E R U N G E M A N N H I S K A K A L I T Z I E N U N D B E F A N S I G G R A D E U E W A N D E R S C H A F T A L S I N I M G E A N T E N K Ö N I G R E I C H D B E K A N D N A C H U N G W E N D E R P R N Z E S E N V E R L E S E N W U R D E E I S A K T D E S C H N E I D E R W I E N E S W E I T R N I H T S I S T E I N W E I P A U C H N H N I C T K Ü K Ö L S T U N D D U T K Ö N I G S E I D A M Z U O W E R D E N D A S G E I S S E T M I A L E D I N G S T +mls_deu_000284 N O C F Ü N F M I N U T E N N D I E W O L K E N D E B E W U S T L O S I G K E I T B E G A N N Z U S C H W I N D E N I E R T W U S T E H S E R W O L D A S I H N M E I M E I G N E N B E T E L A G U N D D A S D I E R O T E O G L O T N I C H T A N D E R S W A L S D A S V E U E I M K A M I N D E R I N D A S T U B E E S W A N A C H T E I N E K E R E B R A N T E A F D E M T I S C H E +mls_deu_000285 E I C H E I E V E T R E N G U N E N W E B E C H T E U N T E R H A L T E N C O M T A N E M P R P E I T I T Z A L E R D I H O C H F L O D E S S E X S U E N B E D F F T I G K E I T S O F N D E S E N D E G E N A N D E N S I L L I S C H N R E A K T I O N S O D E I E D E R S T A N S P I L D O N G E N D E M E R +mls_deu_000286 T A B E R A F E N G E H R E N B E I H A G K E N B E A N D I E K I S T E N W A N N T N U N S O H R T E I C H A U F A C E Z U S E I N E N E I N K L R E R S C H Ö N A G E D A N K E N G A N G D E N I C H I R G E N D I E M I T D E M B A U C H A U S G E H E K T A B E N M O S S D E N A F F E N N D E N K T E N M I +mls_deu_000287 I S S S P A T R E E R N E S M E N S C H E N D E N Z I K Ä N E N F R A G T E I L E I S E W E L C H E U N B E M E A K T A M I C H E R A N G E T R E T E N W A U M I C H I N D T G E G N E T E D A S S N E N F A N T E S I K O P F S E I U N T S C H U B T Z E I C H U N G I L I C U N T U D I E A N D O N D L Ä T T E R U N A T Ü L I C G S P A C R I C H I E U N B A H E I L T D E N E W E I N S E I T O E U E S P E T R I E M I S T E R O T S C H S T E S +mls_deu_000288 I C H W E I S S I C H S E R K R A N K G B I N S A I G T E S I N E R E I N R W E I L E V O R N P A M I N U T E N V E R S C H T E I C H M I C H E E T T E U M Z U R E E N U N D F Ü L D E D A S I C K E N G L I E D M E R E N Ü O R N K A M E S W E R E G U T D W E N I C H E N G I M Ü T E L E I C H T D A N K Ö N T E R B E V O R I C S T E R D E N +mls_deu_000289 S O A B E R I S T W E R U N S E R W E S E N S K U N D O T S E L L V E R D A C H E R O M H A T Z I C H E D O C D E R C H L A N G E N K N E U L D E S A L K E N S A T A N G E S H L U N G E N U N G Ü B E R D E M F Ü N K I E N D E N L I E B I I S D I F E N S E R N I S T E S H A S S E S E L A G E R T W A S W O N D E R D A N +mls_deu_000290 B E S I V I R E L I E A G E B L I E B E N A B E R S I W A G E T Z W U N G K Z U G E H N E A D I Ü N K L I T K E I T B E I D E N M A L S Z E I T E N E I N E S A C H E W A A U W Ä C H I N G E T Z S H Ä R D H O R L S T R E N G E G E H A L T E N W U R D E +mls_deu_000291 N B L I C K L I C H F Ü L T E W I E H R E A M S I C H T E N Ü B E R M I C H I R E R M P I N D U N G E D F Ü H R M I C H N I C H T U M E I N A T O U M V E R I N D E T W A H E M N U Ü B E H A U P T K A N E E N D E R U N G F E E C H W A R M D I C H S E I S I R E M E R S T E I N A T E N A U D G E W E L C H E S N I E M A L S D U C H T R E N E N G E N E T Z T N I E M A S I N E R T L I C H K E I T A U F G E L E U C H T E T A T E A M E N +mls_deu_000292 N S O D E R S S E M I S T Z E R G U T W I N E H Ö U G L I G N E R F Ä E R I D S I C F R E U N W Ü E W E R E N S C H N Ä L D R N A C H A N D E N S O L B E A U F P R E C H E N S H N A L R E I T E N D M I T Ü R N O C F O R D E R N C H D A S L A G E R E I C H E N E R S T I G E A U D I P F Ä E R D E D I N A U S G E R O T A T E N U N D F L O G E M G A L O P B T D A V O N D I E S A L H Ü T E T E N I R U N S D E R F Ä E R D E I D E R D E R E K Z E F O L E N W E R E G E R A D E A U S N D E R S P A D E N +mls_deu_000293 W A L D I E B E R M I T P Ä E C H P B E S T R I C H E N W A H R B I E B E I N E R V O N D E N G E L E N E N P A N T O F L E N F E S T H Ä N G E N U N D I N D E R A N G S D A C H S N I C H T E R A N I N I T Z O N E M E M U N D I E I S D E N L E T Z T E N S C H I T V O N D E R T R E P E T A D D E R H A T S T Z W I L F A U S G E S C H L A G E N D A R A R W A G E N U N D F E R D E V E R S C H U N D E N U N D A S C H E N P O T E S T A N D I N S E I N S C H E N K L E I D E R A U F E R U N G L N S T R A S E +mls_deu_000294 I E I E N O M D E S A S V E R M A G E E I N V I N S T E R A N S T L A G E R E B E S E N T Z Y Ü D E N I E S I S C H R I C K A L S A K T E H R G I E T D A S M E N I G E G I T A N U N B U T V E R G I S N S V E R M Ä D E N +mls_deu_000295 N U R D E R D O C K T O R U N D I E W E R T E R E N S O L L E N V O R S E I N E A U G E N K O M M E N E R K L Ä A T E D I E T R I N E R I N G R O S S E M A M T S E I F E R D A M I T W A D I E F R A R O B E R S T G A N S E I N F E R S T A N D E N U N D P I R X S T E R F R E I T K E R T E S I M I T I E R E N +mls_deu_000296 K A A R U N T R Ü S T I C H Ü B E R D I E L A G E D A S K Ö N Z T L R S E R B E G A N Z U W E I N E N U N S C H L C H T Z T D E L A N G E I N D E O R G E H A L T E N E N H N D E D E R K O N S L E A W A T E T E B I S K A S I C B E R U I C H T H A T T E U N D E N S C H L O S I C H D A N D A R E R K E I N A N D E R N A U S I G F A N D T D E R N O C H Z U M E I T E R S C R E I B E N +mls_deu_000297 O N D I M F E R D E H E R D E N D E R P A T S C H E N U N D S A G N U N Z S D A S S I F E N E A P A T S C H E M F Ä R D U N D S E B E N S U V I E L E W A R E N U N P R E N I G E B E N W I R D E N Ü F Ü R I N K E I O W A B F E R T D A S I N D U N R I K L I G A R F O D U M A P A T S C H E N F Ä R D E Z O H L E N A L S O R C H T I G H E R A R S C H L D E R E M T O D E E B E S E R G E F A L L E N U N D E R I E B L U T V E R G I E S E N W E C H E S U N B E V O R S T A N D W E I S E F Ä E R D E H E N D T L E R +mls_deu_000298 D A S M A T O N E H Ü T C H E N V O N S C W A T Z A M S A M E T K A T I Ü R S A I E R E L A N G E N L O K E N G E D R Ü C T D I E R E W A N G N U M F L O S S E N N D Ü B E R S C H L T E N H E R A B W E I T E N S O T R A T E I D R S E I N F E C H R E L E N T L I C H G E B O U D E U N D S T E B P T E T Z W I S C E N E R E I N D E R H E I B G E B L Ä N E T N O F K N E R A U F E N D A B +mls_deu_000299 T U M U S T E R S T E N Z A G E N A L E N S Ü N T H A F T E N S T R E B E N U N E N T I E V E R E U I U N D D E M U D D I E F Ü H R B I E R H L L I N G E R F L E N G E G E N D I E D U G E F R E E L T E S T T I E J Ü M L N G E W L C H E F E N S C H E S O S O L N E G E F L O N S O U C H T E N N A U E N E R W E R K T A T U N F A N D E N I N +mls_deu_000300 E R L I E S S E I N E G R E T E N I H T V O R T S C H L Ä B E N A M A L L E R W I N I G X S T E A B E R I N D E N G R O S S E N D V O G E L B A U A R U S I E A L E E N E I N E M T O N E B F E I F E N M O U S T E N W I E R S T E S A K T E +mls_deu_000301 V R N H E S K O M A L T E N U N H A L I G E B E G E I S T R N G V I E L E B I E L T A S T E L Ü G E N H A F T E N A B E L W E L D T R E N E A L S E R E R M O C H D I E B U L E R I S C H E Ü L B E K A E T E W E I B I E N G E S T A L T E N S O B E R H A F A S S T E L E N I N D E M V O N L E B E N T E M O D E L E D I K A N D A T I O N G V O N D E N A L T E N M A H M O B I L D E R R B E R F O R M N D B I E L U N G I N D N A M +mls_deu_000302 B W E G U N G U N T A T D E N S T E N Z U G I E R S T E M T E D I E F O E U N A N G E G E B N E I N K G R E D E N Z I E R N M I C R Ü B E N H A N F E E I C H E N U N S A U E R A M F A N A L L E I N D E M F E I F N K O P F E R N W E S E N A B E R I N F Ü N F T N H A U P T S T O F H A I C H N I C G G E N A N D J E R T T R O C H N D S C H M E K T I G D A S E H O N S T Ü C H E N F I L S C H U D E R B E I S E I N I S E I G P L I E S T E N R A U H A U C H G E G D E N H E M E U N G E G E D I +mls_deu_000303 U N D D A S F O U R S T A N D A U F U N D F L A C K E A T U N K O C H D A S E S S E N F E R T I C H U N D D E R B R A T E N B R U T Z E L T E R F O R T U N D E R K O C H G A B D E M K Ü C H E N I U N G E N E I N E O R F E I G E U N D D E M A R K T R O P F T E D A S U N F Ä E R T I G H D A R W A R T D I E H O C H T Z E I T O N D E M K Ö N I C H S O N M I E D O N G R Ö U S I E N G E F E I E R T U N S I E L I E T N E F E R G N Ü T E B I S A N I E R E N D E +mls_deu_000304 U N M D D E S E R M I N I C H N A C T R A G E N B O L E W E N I C H I D E R S H F Ä N S T I G W A G I N S E I N O H M E I N E M R A R T D E R H E R F A R A E H A D I E I N A L L M P R E C H T G E H A U T U N D I C H M A A M U N M R E C H T A B E R +mls_deu_000305 G Ä C H E N E M A S E N O W I N I G E K R A M B E T R U G E R E I T E E S I C H K I L I E F R M I G A U S H U M U S T E D E R E R D E S M I N D G E G E N F L I G E D E S P R E N K I S C H O S A U F A N G E N N D Z U W I B R I N E N +mls_deu_000306 D E R F C K S R E I C H T E E M D E U N F R I T I C H E R I E D E N S P F E I V E R H N D E R M A N T A T W A C K A R S E I N E S E X S Z Y G E N S A G K T E D E R R O S I G E I S A C H T E T N I C H T A U F D I V E R S C H I E D E N E H A U D E R M E N S C H E N D E N D I K Ö N S I C H M I T F A B E B E S C H M I E R E N M I N T Z U D T E U S C H E N S O D E E R S I D A S H E T Z S A N D I E H T Z E N D E R K L I G E R V O B E R Ü B T E N S T A M E D E R K A E I O A S E I N T A P T V E R U N E R S C H R O K E N N T R E U D A S M E I N E I G E H Ä N G +mls_deu_000307 A L L S A S W I E M E T I E R B E G E G N I T S C I E B S I C H D E U I C H U N D Ü B E R E I N A N D E R B A L T U N T E R S C H E I B E N W E R I N K O N T A K T D E R I S T E R E R H A N D N D E M E I N I G E I E R N A H M O N D E R M E I N I G E W E I D E L E S C H E N E I N A N D E R A U S B E I D E V E R S C H L I N G E N S I C H +mls_deu_000308 E R M Ö S T E D E N E N F A C H E N R O N I T E N K Ö R A L D E S M A L E S M I T A L L E E R K L E H R E N E N U N Z R E C H T W E S S U N G E N I M I T K R A U S E N W I G U N V E S C H N A R K E N U N V E R B R E M E N I C H T R E T E I D I E P E R S O N D E S E R A U S G E B E S N D B I T E T I C H I Ü N S T I G E L I S E R T U O L I S T I E D U W E I T E L I S E S T F O L D E N D I S D E G Ü T T I S T M E R H E N +mls_deu_000309 D I E O F D A M E N B E K A M E N K R M P F E R U N D T I E K Ö N I G E N U N I E P R O M Z E S S E N E N D I E R E R A L L A L I B S E N H Ü N Z C H E N W E R N D E R M A L T E R U F D E N H O S G E N O M H A D E N B E M E R K E N Z U I R E N S C R Ä C T E N D A S D I L I E L E R A R M A R A N D F A B E N E N U N D O R A N S C A E D E N S E I D E N K L E I D E R A L E I C T B E S E T M I T D E N H E S L I C S T E N Ö F L Ä G E N W A N E +mls_deu_000310 V O N L I E D A N D I E I E S I N G E N U N K L V I E R P I E S E N D I E S I S P I E L N V O N G E I L T B Ü Ö R S E N D I E S I H I E G K E L N V O N V A N Z Ö Ü S C H E N B Ü C H E N D I E S Ü B E R S E T Z E N K O N T E B I S M E N G E M Ö Ü T W E R E N D I C H L A U S T E Z O N A C H A M N G A U F G E S T A C H E T W U R D E +mls_deu_000311 A M E N N A T E N W A N B L O S I R E N Z I G E R S C M O C W A N I H R E K A S T A N I E N B R A N F L Ä C H T E N W I L C H E N W I L D E U N N A T Ü R L I C H E R A N M U N D A U I R S C H L T E N E R A B V I E L E N C H N A M E I N B O G E N G F E I N K A T O U N G S N D Z E I C H E T E M M T O S R S O R K F A L T I O M G R S S E R +mls_deu_000312 A U R W I E D E A U S D E T C H L A N D N O C H A S I E L E N E I N E M A N D E R E N S T A R T K O N T E M E N E F A H N W A S E R G E G N S T A N D U N D E S E S L T A D D I E S U N T E R E D U N G G E W I S E N S E I A N V R M U T E T E D S S E S I C U M E R K L I E R U N G D E R M A T Z I E R Ü B E I E R A B S I C H T E N U N D U N D I E V E R M I T L U N G D E R M E C H T E T Z W I C H N D N M A R S T A T E N U N R O S P O T A N I E N H A N D L E +mls_deu_000313 L A S U N S W E N I G S E N S E I N E R Z E I T L A N G V E R S U O C H E N I N D I E V E R N W I E R U F D E S E S B E I S I M I T E I N A N D E R A U S R E I C H E N D A D A S Z U S A M M E N H Ä N G E N D E V I E D U E S A G S T E I G E N T L I C H E U E R L L E M E N T I S V E R S E T Z T E I D R T +mls_deu_000314 V E A S C H I N E N V O R K O M M N S E F Ü R D E N Z U D E R V E R M O T U N G D A S F R A U W I S E D I K L E I N E N V W I S E N V E R B R E N E R I E S O L B E S E I N S U S T A C H G E H E I T Z T A B E N D A S D E R T P L A D E N Z S P R A N G A U S S T E M O L E I N F Ü R C H T E L I C H E R E R O C H W A G E N U M E W O R D E N S E I N +mls_deu_000315 U N D G I N G D E M S C H R E I E N N A C H S O S A H E R E N T L I C H E I N H O H N B A U M U N D O B E N D E R R A U F S A S E I N K L E I N E S K I N D U N T E R D E M B A U M A B A R L A K E I N E F R A U D I E S C H L I E F +mls_deu_000316 S I E H A T E N S E U E B N D I E F I S C H E R G A R D N E R W A L C H D E N A C H T I Y B E R T A U S G E U O R H V E N W A D E N C H E R E I N G E Z O G E N N D I E S E L E U I T E R G E R H E O T E N A G E N S C H E I M L I S C H V E R S C H I E D E N E N N C T Z I O N E N R N A B O R L D E R O L U L O P Ä I S C H E R K A R K T E B E R A L L E N A U S G E T R Ü K T W A T +mls_deu_000317 N E N E I N I C H S C H Ä M E M E I C H L A S M I C H E N D E I N E M B U S E N M E I N G E S I C H T V E R B E R G E N G E R S I N K T E N S G R A S N I D E U N D Z I E S I N A C H +mls_deu_000318 D I E K I N D E R A R B A R S A S E N V O R D E M W A L T U N D A L S I E D I E R E I K N E C H T E R V O N W E I T E M L A U F E N S A H N S P R A C H R L E N S H E N Z U M P F U N D E F O G E L V E R L Ä S T U M I C H N I C H Z O E R L A S I C H T I C H A U C H N I C H T S O S P R A C H F O N D E F O G E L N U N U N D N I M E R M R +mls_deu_000319 W I E D E R S C H L Z E I N S E I N E H L D I E G U N G S R I E D E H E R V O R H U B D E R L E R A B R A C H T E A M K L A E N S O M M A R M O R D E N G M I T S E I N E N S C H U H L K I N D E R N E I N G E S A N G S T Ä N T I E +swc_deu_001408 S T D T W I E I E S E I N S O H L E N +swc_deu_001409 D E R E N C H W I N G E N E N D U R C E I N E Z U S E R T S C H A L T U N G S T U F E N L O S +swc_deu_001410 D I E A U F A L E B E I D E R S E T W R T A L U N G U +swc_deu_001411 U M D E Ü B E R L E B E N D E N D +swc_deu_001412 S P Ä T E R W U R D E N T A I L W E I S E S O G A A C H T P A R E R L E L E L O H S T R E I F E N E I N G E S E T Z T +swc_deu_001413 M O R D E B E K A N D U N D V E R L A N G T E +swc_deu_001414 B U N D E W E G E S E T Z D I E S T M V O N W I H L E N +swc_deu_001415 G E S C H C H T E +swc_deu_001416 S P A L T U N G F E Ä G +swc_deu_001417 S C H A T P A E B O R N D I E U S E R E N D F E R N D I S +swc_deu_001418 U M W E I T E R I N H U M A N I T E R E H I L F E Z U +swc_deu_001419 S I E R K A M T E N D I E N E U E I C H E N E S C H E R E G I E R U N G N I C H T A N +swc_deu_001420 D I E O R A U F Ü G E N V O N A N D R E N Z W A N I S T E N S E T E M B E R Z W E R D E N D A C H T I +swc_deu_001421 E R W I E S I E M I C H T S C H L D I C O D E R M I T S C H L I C H M A C E N A N T O D E R N S M I T G E S E +swc_deu_001422 D I E M E D E R E R T T U M M A R E I N E N +swc_deu_001423 N D T E I F E N T I E S E N B E I D E R +swc_deu_001424 K R E I S W A L E F O R S C H L A G U N D E I N E L A N D E S L I S T E N D E R Z E I T N E N +swc_deu_001425 A N U M S E R Z U N D E S A G E I N F O R M E I N E S T F Ü N F Z E H N T E I L E N L I E D E R Z Y K L U O S Z W E T S E D C H T W U R D E P E S L A S K Ö A B E R T I N N E B E R A B E T U N G V O N H A U S T H A R E M E N +swc_deu_001426 I E D I E O L G E N D E T A P E L E D A R S T E L L T +swc_deu_001427 U M S T R U M F L O S B E +swc_deu_001428 D E B U N D E S W A L L I T E R B I S T Z U M S I M O N E U N Z I G S T E N T A G +swc_deu_001429 O R I R I C H E W O R D E N D E U S C H E R N I C H T M I T W E L E N +swc_deu_001430 A U S S F I R N M U S T E I N K U S E R K O T E R B E G N +swc_deu_001431 V E R G L E I C H P A N Z E A H L E N W E R T U M G E A N D E +swc_deu_001432 B E T R C H T E D E A L G E M E I N H E I +swc_deu_001433 U N T E R S C H I T L I C H E A U F A S S U N G E N G A B E S N U R D A H R Ü B E R +swc_deu_001434 D O L L B E I M B U N E S L I K I S T E N B E R S E R D O R T M U N D N A C H V O L G E R D E S U N M I T E L B A Z U V O R Z O R Ü C K E T R E D E N R E N A S I R F E N R Ö B E R +swc_deu_001435 N U N Z E N E R D C H T U N A C T Z I G +swc_deu_001436 R E I N E N Z Y G L O P E T D I E +swc_deu_001437 D E R V O T O S T R O M I S Ü B E R V I E L E G R Ü S S E N O R N U N G E L I N E A R Z U M L I C H T E N F A L +swc_deu_001438 D A S H T F Ü K L E I N E P A R T E I N G R O S E A U S W I R K U N G +swc_deu_001439 I S D E I T E R A T I E V E T I E F E N S U C H +swc_deu_001440 D I E S K Ö N N E N U M B E I S P I E L K O N D E N S E A T O R E N S E I N +swc_deu_001441 A L S D I E K U R S A U F K O B E R H A L T E N D E N S O J E T I C H E N S C H I V E R A B T R E T E N +swc_deu_001442 B U N E S T A G W A H L N U N Z H U N E R D R E I U N F Ü F Z I G W U R D E R S M A L S N A C H E I N E M V O M B U N D E S T A I G S E B S T E R L S E N G E S E T Z +swc_deu_001443 B U N D E W A I G E S E T Z V I E L F A C H E I N E R T W U R D E N +swc_deu_001444 E R Ü B E R L A G E R D E N V O R T U S T R O M E U N D T R E G T +swc_deu_001445 D R O I N T E K R A T I U M N D E R B E I D E N D E U T S C H E N S T A T E N +swc_deu_001446 B E R L I N E R W Ü L M E U S E N S T A T +swc_deu_001447 A F I E Z E L E R F Ü H R U N G E N +swc_deu_001448 B E D E R V E R H E T E N S W A L W I T Z U S E T Z L I C G D I E E I N H A L T U N G D E R +swc_deu_001449 W I E V W E N I H T I S O L A N E N O C H A M P O U L T Z D E R Z E I T +swc_deu_001450 R E D O C H E T W R D I E D U C H F Ü H R E N V O N W A L W E R B U N G A U F K O S T E D E S T A T E S +swc_deu_001451 D A S N I H M R U N D K G E S E T Z +swc_deu_001452 H E I M A T V E R T R I E B E N U N D H E U S L I C H E G E W A L +swc_deu_001453 N D S P E I C H E R I E N I N E I N R W A G T E S C H L N G E A B +swc_deu_001454 O R R E I G I N A L T O N B E N D E R U N D D I E D O K O M I T A T I O N D E S T U D I O S W U R D E N N E N Z E H N H U N D E R T Z W U O H N S I E B Z I G I N D E R S I M E N S E R C H I E F Ü B E R S T E L T +swc_deu_001455 S O M I S S E N A U F E I N E M S T A T G S C H N R E K E T E N U B O D T +swc_deu_001456 F L Ö T E N S P I E L E D L I C H E R +swc_deu_001457 D R A S T I S C H M O D E R A R N E E L I K T R O N I S C H E K L A N G E S C H A L T U N G +swc_deu_001458 A N C H L I S E N W O D E D I E S O H A M I T E T E M A N D A R T Z T Z A L I E D E R P A R T E I N A H D I M S E M V E R F A H N E N S P R E C H E N D E R A N Z A L I H R E R Z W E I T S T M M P R O P R Z U N A L A U F D I E L A N E S L I S T E D E R P A R T E I U N T E R V E R T E I E R T +swc_deu_001459 O C F N D E R N A R T H O B O M B A D I E O U N D T E Ö K Ü N F T E +swc_deu_001460 D E R F R E I N E N T Z U K L O P E +swc_deu_001461 M T L E R B E I L E L H I N D E N +swc_deu_001462 W E R W I G E G E I N E V E R B R E C H T E N S R E C H S G R F T I C H Z U I N E R R E I T T R A F E V O N M I N D E S E N S E I N E +swc_deu_001463 D R G S C H W I N D I G K E I T Z W E R T U N G E R A G E N D R E I B E F E I N H U D E R A C H T +swc_deu_001464 I E B O R I O S A M E R S T E N I G B O R I E S A M S E R +swc_deu_001465 N A C H D M S E A R N A R G Ü F V E R F A R E N A U F D I E L E N D E R E R T E I L T +swc_deu_001466 R E F O R M E N G O B E R S C H A F S U N D A B R S T U N G S C H I T E +swc_deu_001467 S I E R E A A N P O R T E T U N D N D E R D E +swc_deu_001468 A N D E M E S T I C H E G R E F T E A U F G E G E N R V O L E Z E N +swc_deu_001469 I T U N T E R A N D E R E M V E R W E N D E +swc_deu_001470 A U S W I K E P E D I E R +swc_deu_001471 U N D K O B A R K R I S E +swc_deu_001472 E N L T Z T D A R W A L A U F G R U N D E I G E N E R W E I L V O R S C H L Ä G E U N E T E B R U C H E N M I N I S S E N S F Ü F A B G E R U N E N D V E R T R E T E N S I N D +swc_deu_001473 V E R P R E I T U N G I E D I O L O G E S C H A P R O P A R G A N D E R D E R S U P E R M I C H T E U N D +swc_deu_001474 K O M M I H S A U F D E R E L I T E T B E R T H A G E N +swc_deu_001475 A L S D E R K A L T I K L I E G S I G V O R T W E R E N Z U S P I T Z T E +swc_deu_001476 S I H E I T S P E R S O N A L O D E R E A C H U N D E N N U S E H R S C H W I E R I G B E T E T E N E R N +swc_deu_001477 D A U R H A F D E S B L E I B E R E C H T U N D +swc_deu_001478 E B E N S O W I E D E A S M O T I F T E R L E S U N B Ü T +swc_deu_001479 W E N F Ü N I E M A R N N A C H P R Ü C F B E I S T +swc_deu_001480 I S T I E K L E W A D E A R F O R S C H E N V O N E I N R I C H T U N G E N +swc_deu_001481 G E S E H E N D A R V O N B Ü R D E N S E B S T D A N O C H D I E N T S P E C H E N D E N P A L K A O T F I E L E N +swc_deu_001482 S P Ä C H E N B E N U T I C H D E A R T E M L U F T L I E F V E R T +swc_deu_001483 E M O G L I C H E N S H U T Z S I M F O R N E N G E N K R A N K R E I T E +swc_deu_001484 S C H N E I N E N L I C H E N V E R S U C H G A R +swc_deu_001485 R O N D K E N S T R A H E N A N E I N E M P E E N Ü B E R G A N G O D E P I E E N Ü B E R G A N G D U S T D E N N H R E N V O T U F E K T I N E I N E L E K R I C H E N S T R O M U M W A N D E T +swc_deu_001486 B R M A S T E E N D E S E B E S T E R N C H T F R E I D E T E N +swc_deu_001487 L A H E N D E R B E G I F T +swc_deu_001488 A N K F A L T N I S U N T E R D I N S T I M N N O C H E I N E L O G E S C H E A B P F O L R G +swc_deu_001489 K A B E R T L E N D I E S E S A N G E B O D I E O M I T E N H I E N H E I T A B +swc_deu_001490 S T A N D V O M Z W A L F T E N M E R Z Z W E I T A U S E N Z W Ö L F D E R I N H A L T S T E N T +swc_deu_001491 R G E N I S A T I O N U N T E R B R A C H T E R E F N D +swc_deu_001492 V E R B Ü N D I T Z E N D O R D E R G A H F Ü S I E A R B E I T E N +swc_deu_001493 E S G E L E T E O L H R I H K E T A L D E +swc_deu_001494 D I E E R I C H T U N G D E R B E R L I N E M A U R M Ü N D E T E N +swc_deu_001495 E R I C H T U N G V O N K L Ä E R A N L A R G E N +swc_deu_001496 A F G A N E S T A N Z U N D D E M I E H Ö A R G H A T Z I C H S E I T I E M E I N M A R C T E +swc_deu_001497 D E R V O N A R I O U N D S T R O M V O N D E N L U N G E N Ü B E R D I E B R O N C H I E N B I S +swc_deu_001498 A U S E D E M N N A H M E N S E N D E R H R S P I E L E B M I T T V E R F R M D E D R S P R A R E +swc_deu_001499 U N D T I G U N D M A N D A R S K L A U S E +swc_deu_001500 K E I N E A B K E R V O N E N G R U N D T L A G E D E S S O Z E L I S M O S E I N S C H L I E S E +swc_deu_001501 I T K O M P O N E N T E N S O W O H L A N N A L S A U C H T I E F I N D E R W A C F E +swc_deu_001502 B E D E U T U N G S V O L L W A +swc_deu_001503 F R E I W Ü E L I G E H E N F E R T E O G A N I E S A T Z I O N +swc_deu_001504 M E L E K T R O N V O M W A R L E N Z S B A N D I N S L E I T U N G S B A N +swc_deu_001505 A L L E D I N G S E N V E R G L E I C H B E R I F E K E M Ö U G L I C H +swc_deu_001506 D I E S E K O N T E N A B E R A L S E I N G A B E I N E I N E N D F R I C G W E N Z S U M S E T Z E R D I E N E N O D E R S T E U A T E N Z U N G O N M O T O U R E N +swc_deu_001507 T O M A S H R M A N S P R O D U Z I E R T E Z W E I T A U S E N D Z W E I M I T K E R E B E +swc_deu_001508 P E N Ü B E R G A N G T R E F E N +swc_deu_001509 D I E F A L G T E N H O U S S C A U +swc_deu_001510 A N T I E S O J E T S C H E D E M O N S T R A T I O N E N W U R D E N P L U T I G N I E D E R S C H L A G E +swc_deu_001511 E I N V I E R K A N A L M I S P O L D D E N T E V E R K L E I N E R +swc_deu_001512 D I E S E H E T E N D I E V O R W A N D Z E I T E N F Ü R E I N E N A N G R I F A U F D I E U E S A R E X S T R E M H E R A P B G E S E T Z T +swc_deu_001513 W E I C H E S A M N E C H S T E N Z U M S T A R T K N O D E N L I E G T +swc_deu_001514 L A H T I O G I N G N D O L L Z E R Ü C K N I E B U N D E S L I E G E R U N D W E X S E L D E Z U E I N D R C H T +swc_deu_001515 Ü B E R I S E K A N K E I T +swc_deu_001516 J A H R Z W E I T A U S E N D F Ü N V E K R I T I E S E R T E +swc_deu_001517 D I E S E A U F H F A S U N G Z U R N E U T R A R I T E Ä T U N T E R S H E I D E +swc_deu_001518 W E D E L W U R D E R A L S K N S T L A I S C H E R L E I T E R D E S S I M E N S T U D I E S B E S T E L L T +swc_deu_001519 W E N M A N D I E W L T A L S K A N Z E S B E R A C H T E T +swc_deu_001520 S I E M T K R I T I S C H E K O M P O N E N T E N D E S D E T U O N A T I O N D Z Y S T E M S A B S I C H T L I C H S C H W A C H E I N D W U R F E N +swc_deu_001521 N I C H W E H B E R I S T E D O C H +swc_deu_001522 E R B O D E I N E V E R E I N I G U N G D E U T S C H A N S A N +swc_deu_001523 B E R I E N N Z W E I T U E N F Ü N F +swc_deu_001524 K E R N A B G E S T I M T U N D U M H Ö L E N D I E S E N E N S P R E C H E N T +swc_deu_001525 A Z T E U G U N G V O M N D E N A M I G A U S +swc_deu_001526 S E M T U N D I N G W E R +swc_deu_001527 U N G V O N S C H E H E R U N T E R N E R U G E G +swc_deu_001528 N I S C H E N U N D G E W R T H E N +swc_deu_001529 R O B E R T E R F K N E D I E +swc_deu_001530 K A M S C H L I S E L I C H Z U N M +swc_deu_001531 O L S T E N I +swc_deu_001532 S T A N D T E N S I C H V O N D E N U R S A R +swc_deu_001533 A C F R I K A S S T I H D E S E R H A H R E R G E O R T E T +swc_deu_001534 D I E A R M E M E U N T E R T E L +swc_deu_001535 S T A L I E N S E T Z T E M +swc_deu_001536 F E I H T E N S A U S L E I C H +swc_deu_001537 K L M E R A U F B R O S K R E I B T G L E I C H +swc_deu_001538 A M Z W E I T E N J U N I E Z W E T A U S N D V I E R W U R D N +swc_deu_001539 I N E B N E S T A G N A C H R L G T +swc_deu_001540 D I E N A T O O S S T E R W E I T E R U N G U N D D I E E I E N S E I T I G E A U F K Ö Ü N D I G N D E +swc_deu_001541 T H I E R B E I I S +swc_deu_001542 D I E S E R S T E L L E K A M E N S E M T I C H M I T I E D E R D E R K A P E L E T E +swc_deu_001543 P O T Z T A M A B K O M E N E N T H I E L D T Z W A H R A L G E M E I N E R V E R E I N B A U N E N Ü B E R D I K Ö N F T I G E G E M E I N S A M E V E R W A L T U N G D E R S I E G E R M I C H T E U N D V O M L I E R T O R U N D S E T Z E I E D E M L I T R I S I E R U N G +swc_deu_001544 D A N A C H U N D E R S C H I P E E I N E N V E R D R A G B E I M W I E H F Z I D E N A H M O +swc_deu_001545 E I N W E I T E R E W E R I A N D E M A G +swc_deu_001546 S I E W U R D E N M O D O L A H R N D U R C H L O C H S T R E I V N G E S T E U R T U N D D I K L I N G E K O N D N +swc_deu_001547 D I E G R U N M A D A R T G K L A U S E L B E V O R Z U C T U N D E R D I N K L E I N E R N P A R T E I N J I E N E +swc_deu_001548 B E R T O N Z D E M K E I N E W Ö G K L I C H E H U G E S N O T H E R U S C H T +swc_deu_001549 N T U G M N T E R Z I O N D +swc_deu_001550 Z U V O R B E D I G U N G K O N K R E T E R A P R Ü S T U N S C H I T E +swc_deu_001551 B U N D E S T A R G E S W A L R E C H T +swc_deu_001552 E S M U S I M G R E I S W A L E I T D E R V O R G E L E T W E R N +swc_deu_001553 H A T M A N E I N E I M P I E R E S C H E B A S I E S F Ü B P S Y C H O S O Z I A L E P R O G E A M E Z U O S E N K U N D E R S E B S T M O U T E R A T E U N Z U R S T E A R K U G D E S I C H E H I T Z G E F Ü S I N D E B E F E Ö K E R U N +swc_deu_001554 B E I D E N E R S T E N F R E I N P L L E M E N Z W A H L N U R D E I L I E S K U I M E I N E U N Z E N H N D E R T N E U N Z I G I N S E I N E +swc_deu_001555 D A M M I T L A S S E N S I C B E S T R A L U N G S T E R T E N S E R G E N O M E S E N +swc_deu_001556 W I N I G E A R S P Ä T E R K A M E S Z U E I N E W E I T E R E N K R O N D N G +swc_deu_001557 R A D I O K A B E R E T P E I L S +swc_deu_001558 E S T Ü K T E B O M B E R A U F D I E S T A R T W A H N E N R E L E N +swc_deu_001559 M I T D I E S E R E G E L U N G S O L E I N E R F A K T I S C H Z W E I V E R C H E E I N F L U S N A H M E D E S E R W E L E R A U F +swc_deu_001560 B E R O K G K Ö R I C H E N B A U +swc_deu_001561 D E R H E R V O R A G E N T W I C E N D E N L A N D E K L P E N W I E D E R U M H E R V O R A G E N E R L A N G S A M F L U G E I G E N S C H A F T E N +swc_deu_001562 M I T E R E C H V E R B I N D U N G S F L U K T Z O G E O D E R U M S C H U L M A S C H I N F Ü R D I E B E E E I N H U N D R D E N E U N V E R W E N D E T +swc_deu_001563 L E I S T E T E M I E I Z I N I S C H R N B S Ü C H O L O G E S C H E H E L F +swc_deu_001564 K A N M A N D E C H I M F U N G E N V O R B E U G E N +swc_deu_001565 M E R D N A U S B O C H D I E S E R K A N K E I T E N E H E R F O L G T E I N F E K T I O N V E R L A N G S A M E N K A N +swc_deu_001566 D I E I N E N E U T R D I E T E Ä L T U N T E R A L E N U M S T E N D E N V O R S A R +swc_deu_001567 U N D Z I E G E N H Ö R T +swc_deu_001568 D A S N E U N Z E H N H U N D E R T A C H T E N D R E I S S I G G E G R Ü N D E T E K O M I T V I R U N A M E R I K A N I S C H E U M T R I E B E W U R D E D A F E N U N +swc_deu_001569 Z E N T R A L E D E R P R O C K R E S S I E V E N U N D H O R T D E S I N I E N I Ö R G E S T Ü T Z S T E N K U N S T D E N K E N S +swc_deu_001570 I N D E R D E R O E S P R E S E D E N T A N K Ö N D I G T E +swc_deu_001571 S N E Ä C H S T U N D V O R S P B E I S E N +swc_deu_001572 D E S P U N D E S W A G E S E T Z E S B I S T Z U M D R E I S I G S T E N J U N I Z W E I T A U S E N D E F A U F G G E M +swc_deu_001573 O R I E P O S S E Ö R +swc_deu_001574 F L I F T L I N G E N V O N D E R E T N I S C H E N M I N D E R H E I T D E R S O M A L I S C H E N B A N T U M +swc_deu_001575 D I E B I E P O L A R E W E L T O R D T N U N G Z E M I N T I E R T +swc_deu_001576 E R A N F A N G E I N I N T E I L R I E A T E O D E R E X S T E R N A N G E B R A C H T E V O R I C H T U N G A N E I N E N U C L I E R E N W A F E N S Y S T E M N +swc_deu_001577 S T A R T E T E D I H I L F S O R G E N I S E T Z I O N L A N K F R S T I G E +swc_deu_001578 W E N D I E S E E X S T E R N E N E R F E C K T E I N E R I C H T I G E N R E I N V O L G E A U F T R E T E N U N D S I C H I N E H A L B S P E Z I E I S C H A P A R A M E T E R B E W I E G E N +swc_deu_001579 Z U G D E W I R T U N I O N A U C H B E I D E A S E R S T O F P B O M B E N U N D N E U I N F L U K T Z E U G E N M I T I N T E R K O N T E N E N T A L E R E I C H W E I T E M I T D E N U R S A R G L E I C H +swc_deu_001580 P E N D I E S T A T H A T I E W A B P E N T I E A M +swc_deu_001581 D I E S E R A N S A T Z G I L D A L L G E M E I N A L S A U S G E W O R G N D E R +swc_deu_001582 N A C H D E Z U S A M M P R O C H T E +swc_deu_001583 D E O B E R L A U S I T S W I S C H E N H E U E R S W E R D E +swc_deu_001584 D A B E E N Z W E I E F A H S E N N T E R T E I L E L T +swc_deu_001585 S C H I E D E N A N D E R E U R O P E R M I S T E R S C H A F T E I L U N D W U R D E M T E R D I E E B I E E L L F +swc_deu_001586 M E S T E R E R F N E T K A B E R S H L I S G E R N W E I T E G E M Ö G L I C H K E I T +swc_deu_001587 E I N E M A U S W E R T S E R F O L G I N W O L S B U R G E L A N G +swc_deu_001588 M I T S C H W E B U N G S S O U M M A N K O N T E N K L I S S A N D I E E R Z E U G K T W E R D E N +swc_deu_001589 D E R B A L E D I G L I H Z E I K T E +swc_deu_001590 K O S P R I T A N I E N E I N E E S T E W I C H T I G E V E I N B A U N G +swc_deu_001591 S I D A C H T E S H R I T N E S S E I N G +swc_deu_001592 W U R D E M I T D E B U N D E W A G E S E T Z V O R N E U N Z E U N D E R S I C H S U N F Ü F Z I G E I N E D A U A R H T E R E L U G E N G E F Ü R T +swc_deu_001593 D I E A N Z A L D E R Ü B E H N G M E N D A R D E K A N +swc_deu_001594 I S C H L S D I E S E E I N M L I T E R I S C H E S E I N G R E I F E N I N D E N K O R E A R G R I C K +swc_deu_001595 N A T O V E R B I N T L I C H +swc_deu_001596 K A L T E G R I E G B E N D E R T +swc_deu_001597 A U N U N E N H U N E R D E E I U M T N E U N Z I G U N D O S T E R A L I E N S O W I E D E R Ö S T E R E C H S C H E A B L I G E R +swc_deu_001598 D A D I E S E I T A N F A N G N E U N Z E H N H U N D E R T N E U N U N F Ü N F Z I G D O R T H E R S C H E N D E R E V O L O T I O N D S R I G I O N U N D D E R V I E D E L K A S T R O E I N E N S O Z I E L I S T I S C H E N K U R S E I N G S C H A G E N H A T E +swc_deu_001599 N A C H W E I T E R E N F E L U S T R E C H E N K Ä M P F E N U N E N E N Z W E R T E E R F O L G E B E I D E G R I G S P A T E I N U R D E R U N D D R E A J A H R E N A C B E G I N D E A U S A N D N D E S E Z U N G E I N B E S R E U T E G Ü L T I G E S W A F N E N S T I L S T A M N S A B C O M E N A B G E S H L O S S E N +voxforge_deu_000891 M A N I S T E R B E I S E R O R S I C H T I G +voxforge_deu_000892 D I E W E R F L I C H T S O L I N D E U T S C H L A N D L E I D E R O C H N I C H T A B G E S C H A F T W E R D N E N +voxforge_deu_000893 E S G E T A U C H M I S P R A U C H U C H A B E T G E B E R +voxforge_deu_000894 D I E K I N D E R S I N D A N K A N K E B O E N +voxforge_deu_000895 D I E T R A K W E I T E D E R A T A S T R O F E S O L V E R D E U T L I C H T W E R D E N +voxforge_deu_000897 D S C A N G E A U A U L B E +voxforge_deu_000898 B E I M M O G A N S T R E I T S T R E I T E N O B E S D E V E F A S U N G S O G A N E +voxforge_deu_000899 D A W A G E I C H I E R Z U B E Z W E I F E L E N +voxforge_deu_000900 M A N O L T E D E N A U F G A K H E N F A L T R A U N 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E N Z U K C O M E N L A S S E N +voxforge_deu_000911 S O N D E R A C R E C H N E B E N D E B I L T +voxforge_deu_000912 I E R T E I N E N I C H T E R N S L I C H G E M E I T E W I L E S E K L Ä H U N G A B G E B E N +voxforge_deu_000913 D A S M O S T E J A H A U F J E D E N F A L S O K O M M E N +voxforge_deu_000914 M E H R E R E R E K L E I N S K N N S I C H E I N E E I P I E D R E S S E T E I U N G +voxforge_deu_000915 W A D I Ü N S T E E R E S H I S A S O S I G T Z U S A M M E N E H M E N A N S T A Z U +voxforge_deu_000917 D E R S C H L E N E H A T S A N E L E I S T U N G A N G E B O T E N +voxforge_deu_000918 S O D S S I S +voxforge_deu_000919 D I E B A T R I E N W A R N S E A R S T A G V E R A L T E T +voxforge_deu_000920 D E S E S Z I E L W U R D E N O R T A L W A L S E E R E I C H T +voxforge_deu_000921 D I E S E W E H R U N G W I R T S E R L A N G E L E B E N +voxforge_deu_000922 D O R Z E I T A N O F E N B A S C H O N V I E L E T +voxforge_deu_000923 A L S I G I E N E N N I G T E M M Ä G I I E R N U R G A N S F L I C H T I G K Z U U N D E R F A A T A +voxforge_deu_000924 E R Z Y M I E M E R Ü B E R I S T I A N +voxforge_deu_000925 D E M S T E H E N A T I Ü R L I C H A U C H F A M M Ö G E N G E G E N Ü B A +voxforge_deu_000926 D I E R E A L E L A G E W I R T N I C H T V O S T E N D I C H A B G E B I L E T +voxforge_deu_000927 E S K A N A U C H N O C H V I E S C H L I M E R W E R D E N +voxforge_deu_000928 D I E P O L E T I G I N T R E S I R T Z I C H N I C H T M E R +voxforge_deu_000929 I N H A L S F R E I H E I T B E D E U T E R T D A S E I N H A L D D E R V E R T R A C K L I C H E N V E R E I N B A R U N G E N +voxforge_deu_000930 D E R S C H U L N E R V E L E T D I S E I N I S A C K V A L S P L I E N S C H L T H A F T +voxforge_deu_000931 D I S E S G E T R E I D E D E N D I N S B E S O N D E R E A L S F I V O R T A T +voxforge_deu_000932 T Ü P S C H W I S E W E R E N S T A T I S C H E E I P I E R T R E S E N V O N S R V E R N E I N G E S E T Z T +voxforge_deu_000933 J E T Z T W R E S S O L A N G S A H M G E G L A U P T +voxforge_deu_000934 U N A S C H I E T L I C H E E R G E B N I S S E H A R B E N S I C H E R E I G N E R D +voxforge_deu_000935 T E R O R F A R D E I C H T I G E W U R D E N N E C H T V O R E I N G E R E C H T G E S T E L L T N +voxforge_deu_000936 A U F M A C H E N D I E S T I F Ü N I C T A U S T Z E H N U N D W E I S K O R D W A S N O C L L S +voxforge_deu_000937 I N K E S A M T D R E I U N D Z W A N S I C P E R S O N E N A U V E R S C H I E D E N P A L E M E N T E N N I M E N T E I L +voxforge_deu_000938 V O R D U N G S E C F T E W E R T E N D E G L E U B I G E A U S C H L I E S L I F T O G E R U R T N E T +voxforge_deu_000939 D A S P O B L E M H Ü W O U R D E B E H O B E N T +voxforge_deu_000940 F Ü R D I E R K Ä N U N G V O N U N T E R R O C H N R D I E S K R E T E R P R A C H E +voxforge_deu_000941 D I C H I N E S E N K Ö N T E N S E R I E L W I C H T I G A W E R D E N +voxforge_deu_000942 D I E R T O S C L I E R D E D I G L I C H E R E I N S I G E M A L F V E W N D E T +voxforge_deu_000943 D A S L A N D E N T W E C K E L T E S E C H Z U E I N E R M I L I T E R I S C H E N G R O S S M A C H T +voxforge_deu_000944 E S S E I N D U N D B L E I E N V E R B R E C H E R B A N D E N +voxforge_deu_000945 D I E Z E I T E N W E R D E N S I C H E N D E R N +voxforge_deu_000946 D E E N S T I F F T I N D I E A C H B O R U N G E I N S C H I E B E N W I S T Z U M A N S C H L A G +voxforge_deu_000947 D I E A U C H T B E I M B O A A U S E R V I E R S A M M W V I E R T B E I S P I L S W E I S E B E I L V E I E R F O U G S E N +voxforge_deu_000948 D A S W A H N O C H G A K H E I N I L I S E +voxforge_deu_000950 D I E H A B E N O F E N B A R Z I M L I C R O S E A N G S T +voxforge_deu_000951 V I E L E V E L I E R E N I E R E N A B E I T S P L A T Z +voxforge_deu_000952 D A R F Ü H R G E B T E S E I N E N P U N K T A B Z O G E +voxforge_deu_000953 D I E B E I D E S I N D Ü B E R E I N E U N D S I C H E V E R B I N U N G M T D E I N A N D E R I N K O N T A K T N +voxforge_deu_000954 B E I D E S T Ä C K E N T I F I N R O T E N Z A L N +voxforge_deu_000955 F Ü N F T E H N U O R F Ü N F Z E H N D O R C F U O N G O L L F F +voxforge_deu_000956 W I E M E N S C H E N A U S E I N E A N D E R E N W E L T S C H E N E N Z I I H R H E U I T E U N D O C H +voxforge_deu_000957 B N D H M I T E M H I N T E R N D E S K A M L S A U F H R T +voxforge_deu_000958 A C H D E R O B E R F Ö R T E A Z U G T D E M I E D E N C H I F E N G R A U E N B R A U E N E I N E N +voxforge_deu_000959 I C H W U N D E R E M I G I M M A R W I D E R Ü B E R D I S E E R K L Ä R U N G E N +voxforge_deu_000960 B A R E I N E M S M E T R I C H E N K R I U P T U S T E M I R T N D E R S V O R G E G A N G +voxforge_deu_000961 D A S I S D O R T V E R Z E I C H N E D +voxforge_deu_000962 G E L L T S A N S E R G U T E S T A U S C H M I T E +voxforge_deu_000963 D A S W E H R E R W I S S E N S C H A F T L I C H N O D W E N D I G T 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G E L E S E N +voxforge_deu_000976 E I B E S O N D E R W E L T V O N S A C H U N G I S D I K E N Z E M I T R I G E A S D E R W A N M E R T +voxforge_deu_000977 N A S M O S T Z R Ü C K E Z A L T W E R D E N +voxforge_deu_000978 Z W I C H E N G L E U B I G E R U N D S C H L D E N E H E R G E L E I T E T +voxforge_deu_000979 E I N A B S U M U T E S R E C H T W I E D R E C H T Z I E R I E L L E T +voxforge_deu_000980 M A N B R A U C H T N E I C H T A N D E N Z U F A L Z U G L A U B E N +voxforge_deu_000981 Z E R T L I C H E N W E S E N N U R I N T F A L T E N W U O M A N I L I E B E B O D T V O R H A R E N +voxforge_deu_000982 E R Z Ü K L I C H D E E R W E I S L A T N D E H A F T U N G F Ü R H E L F P E R S C O N E N +voxforge_deu_000983 E I D E R N O M A H L N O Z U N G E D E S D I V O L E B A N D R E I T E +voxforge_deu_000984 A B E R W I E I S T D I E S E S P R O B L E M I M G L O B A H L N M A T S T A B Z O L E S E N +voxforge_deu_000985 D E S E I G E N W E R P L O K E H E T P O D E N Z I E R M E R L E S E +voxforge_deu_000986 D A S F R M M D E V E R B L O K S I E N O C H I E L E B T E R O S +voxforge_deu_000987 E I N E U E B E S T I M U N G I S T E L A S E N B O R D E N +voxforge_deu_000988 D A R R A U F S T H E N G E W I E S E N W O R D E N T +voxforge_deu_000989 D I E B E F Ö R K R U N G I S T G A N Z M A S I E V E R A R M T +voxforge_deu_000990 D I E W E R D E N D S G A N Z P E S T L M N I C H T M A C H E N +voxforge_deu_000991 D I E D A R T E N M Ä N G E D I G E S E N D E T W I E R D T I S T E R H E B L I C H G E R I N E R +voxforge_deu_000992 D A S E G E B N I S I S T V E F E L S H W O R D E N +voxforge_deu_000993 E I N E B E S C R E N K U N G T R I T E S T B E I B E S O N D E R S I N T E N S I E V E R N U O Z U N G A U F +voxforge_deu_000994 D E R E N D B E N O T Z E H A T E I N E H Ö H R R E G E S C H W I N D I C K E I T F Ü R D E N D A U N L O T D Z U F E R F Ü G U N G +voxforge_deu_000995 D E R S E M A N T I S C H E T E I L E W U R D E S K E B T I S P E T R A C H T E T +voxforge_deu_000996 D O T W I R T S E H R I E L M H R G E L T V E R D I E N D +voxforge_deu_000997 V Ü E R S T E N I S V Ü R D A S V E R A N W O R D L I K E I T Z G I F Ü L E I N E R M U T E R +voxforge_deu_000998 D A S W E R T Ü R D I M E I E N G E M A C H T E +voxforge_deu_000999 S I K A N E I N E G A N S K L A R E K A U F M P F I Ä L U N G A U S S P R Ä C H E N +voxforge_deu_001000 Z A L R E I C H E P O T E S T E W E R D E N A T I K U L I E R D +voxforge_deu_001001 D E D E U C H F Ü H R U N G W A R N I C H T S E C H A T +voxforge_deu_001002 D I E W E H R U N E N H A T Ü B E A U P T K E I N E D I C K U N G N +voxforge_deu_001003 O B Ü B I G E N S S E C K E R S T O A R A F D E R E I N E N D U R C H A U S T I E L B E W U S T E N L E B E M S K L O G E N +voxforge_deu_001004 M A N R N S P R E C H T E N D I E S E M F E I L V O N K O N T R E R H I E R U N G S T W A N G +voxforge_deu_001006 G L O U B E G A U N S C H L D N A R S E N Z I C E I N I G +voxforge_deu_001007 D A S W I R T N I C H T M E H R L A N G E S O B L E I B E N +voxforge_deu_001008 E S A B U N T E R S C H I E T L I C H T R I E R E V O R M E N D E R F R A H E T R A V E R +voxforge_deu_001009 H N D E S E I M E I N E F R E I E S O F T W +voxforge_deu_001010 O G A N S T R E I T V E R F A H N K Ö N N A U C H A U S H L I S L I C G A U F E L N D E S E B E N S T A T F I N T E N +voxforge_deu_001011 W E G E N N O Z S L O S A U F G E W N N E T E R O L E A B S E I T K A N N +voxforge_deu_001012 D A S W I R T N I C H T I M A R P E R F E K T V U N K T Z I N I E R N +voxforge_deu_001013 M A N M U S I C H A N G E R S C H I E R E N D E S W A K S T U M S W E G E N +voxforge_deu_001014 W E L L I C H E W E G E S O L L E N E I N G E S C H L A G E N W E R D E N +voxforge_deu_001015 D A S S W I R D T E N D I E P E I S E G E N T +voxforge_deu_001016 D I E Ü B E N A M E E R F O L U K T E W I R T L I C H +voxforge_deu_001017 D E E N T W E K L U N G S T W E I T V O R A N G E S C H R E T E N +voxforge_deu_001018 D I E S M T O M E T R E T E N D A N S C H O N A C H W A N G E N S T O N D T E N A U F +voxforge_deu_001019 S I B T E I N I G R O E W Ä L L E V O N P R O Z E S E N +voxforge_deu_001020 S S T B E R E I T Z M E I N Z W E I T E R A U T O M A D +voxpopuli_deu_000309 M P L M E N T I E R U N G V O N H Ö R I N S T A N D E A T S Z U S C U T Z S P E S O N L I C J E R D A R T E N E B E N F E I S G E N N E R E L U N S R E U T E Z U S A M E N A R E I T E L E I C H T E R +voxpopuli_deu_000310 E R A M T E A B E N D E R S L I M S T E V E R I N D E R D A R M E L E B E N G E R E D T E I N S E L B E V E R L Ä T S T W U R D N I G K L A B +voxpopuli_deu_000311 I C M Ö B R I Ü E D A S D E R O M I S S E I N I T I E S +voxpopuli_deu_000312 M I T L E T U N D D E C O F V E D A S W E R N E C X H S L I E A R Ü B E D E S W A N Z E +voxpopuli_deu_000313 D E S D A F N I C H I V E R S E H E N W E R D N D A S I M E R H I N W E R B E F Ü M T Z I G T P U O Z E N D D E R B E F Ö L K Ö N G E A D E R B E S C H E N U N U N N I E L E N L I C H E N G R A M L I E B +voxpopuli_deu_000314 S O D A S D E R B Ü R G E R S C H E L L O N E A U S K U N F B E K O M M T O P S E I N E B E S C H Ä R D E Ü B E H A B T A N G E N O M E N W I R D O P S I E B E R E C H T I C H T I S T +voxpopuli_deu_000315 N R I E S E T T U N Z E R E R B I T I O N G E N I S T N I C H T V O N E U T E N A B E R S I A B O E K O N T I N U N I E R L I C H E S F E I N T I O N E N G +voxpopuli_deu_000316 L D I E T G A N S T O L S G E S A G J A E D B E S C H E F T I U N G S T E I G K T D J E R A N +voxpopuli_deu_000317 W I D A S S F Ü R U N I S T E R A U H O N D E R E R V E R T E T W I E R E X S P O R T I E A N Z O U V E E L L Z O B I L I C G A L W V E R I M P O T I E R E N Z S U W E D I G W I E R F A R S C H E N K E N W O L S T A T +voxpopuli_deu_000318 S I E H E U D E R A R B E N I E A N W E S E N Z I N D I S E N P R O S I T I E V E S S I G N A L E +voxpopuli_deu_000319 N E U N Z S I G H P R O Z E N T A L L A A R O P Ä S C H E N F I L M E D I E A U S S E H A L T I R E S H E I M A T L A N D E S G E Z E I C H T W E R D E N S I N D V O R M E D I E R P R O G R A M G E F E R D E R T W U R D E N +voxpopuli_deu_000320 B I E S O K A I C H E B E R G E B P L I S S T E R A L E A U S C H U S A B P T E M U N G I N D I E S E F O R E M N I C H Z U S T E M +voxpopuli_deu_000321 B E R B O R T E N V E R I N D E R N D A S I C H E H I N D E R I E N G E I S I G E N E I G E N T U M D I E A U S G U M S F L I C H T E V E R S T E C K E N K O N T E +voxpopuli_deu_000322 I S G I B D E T Z I M Z U S A M A N G E R V E R S T E R G T E N Z U S A M M A U B E I T E I N E N E R S T E N G A N G V O N E I N I G E N M I T I E L S T A R T E N N A C +voxpopuli_deu_000323 W A S T I R E N Z Y Ü B E R C H R E I T E N D E Z U S A H M E N A B E I A N B E L A N T U N D W A S T I E E R V E R B R E I T U N G I N T R I G L Ä N D E R B E T R I F T U N D T I E M E C H L I C H E I N B E I S C S P I E N E N E N D E S E N E R F O L K S B E I S P I L V Ü M I C H I S T U N D Z W A R E L S L M D O K G M I L I E R N E R E D A S +voxpopuli_deu_000324 D A S N I C H T E N U R I N P R T U G A H L D R G R I C H E N L A N D S N E R N A U R E N S O V E R M E I N T L I C H G E I C H E N M I T W I E S T A T E N V I E D E U T S H L A N O D E R R U S P E T A N I E R N +voxpopuli_deu_000325 T V E R A U S W E N I S V E R B E I D A +voxpopuli_deu_000326 E A L L E L F L I E G A L M I K D I E D E R D I E S E S H A U S E S V A R S C H E N I E D O R D T L I C H O L F I G E A R A L S D E I E U D U R S C H N I T Z B Ü R G E R T +voxpopuli_deu_000327 E N S I C H E R D A S E R E R B E D O E U T T U N G I N A H R S F U K U M F T U O G E R N O C H T Z U N E H M W I E R T +voxpopuli_deu_000328 T E S K E T D I E R U M D I R I C H T L I H N J E D E S A D E S E F Ä S T L E G U N G G U N D L E G N D A S I G H E R E I T S N E R M E N F Ü R D E N S C H U T Z F Ü E R D E N G E F A R E N E I N E R E X S P O S I T S I O N G E G I Ü B A R O N I S I E R E N D A R S T R A L U N G +voxpopuli_deu_000329 D A S S G I L T E S W I D E R H E R C H U S T E L +voxpopuli_deu_000330 D E S E N E I N E N E I N Z I G E N S I T Z K I B T E S L Ä N G S D A S I E S T A S T P U R G +voxpopuli_deu_000331 E D A S A S P A S I E R T I N M A L T A R D I E S O N L I S T E N D I K O B P Z O N D F E L E A U F G E D E K T E R D I S V E R G E N B E C H N E R M O D E W A R W E D E R W E R E N S Y S T D E M A I S H I K O C H O U N D F E L E E B E U N T E R S U C H N O C H I E T E R M O R S E L B E R E G E Z I E L T E U N T E R S U T E A A T V A S S I N A N D O G A S W E N A L E S I E O R U N T E R D E M A N D E I S C H W E I N S Z U G E D E K W E R N S O R S +voxpopuli_deu_000332 L I N T L A N D E K Ü S T E D I E W A N S T A E I N E D I E A U F D I E R O S E N K A T A S F O F E N I Z U N A H M I E H N D R V R G A N E N E I T I N W E I S E N +voxpopuli_deu_000333 D E N N D I C H A B E B R I E N Z I E P F Ü R D E B E I C H T G E S T E M M N T O W O L E I N E N S W E H R N F H L E R I N T E L L T E S I R T N E M I T A R Z U R A U F G E F A R D E R T D A S A U R O B E H R S C E P L L R M E N D A U F D E M W E G K T Z H E E I D E M E I N Z I G E N S I T Z U U N D E S T E L T Z E N +voxpopuli_deu_000334 I N D I E S E M R I F E N W O D E N G E M E I S A M M E P O L I T I S C H E V E R A B R E D U N G E R N I M K R E I S D E R S I E B E N U N T Z W A N Z I G E T D R O F F E R N U N D A U C H H U B L I K G E M A C H T +voxpopuli_deu_000335 I B I N E R E R O G E N D E S W E R S H E U T E M I T D E M V O R C H A R G E S E M U M B E L T A U S C H O S G E S C H A F T A M I N C H I T W I T E A U K M E S I G E R F E K T E O R P Ä I C H E R Z E S A G E N D E H E T E N Ü E R H O C H R I S I K R O R P O D U K T E I N E S E N D R A L E Z U L A S N H A M E N M Ü S S E N D A S A B R I C H I C H G E S C A F T A R M I T D E M E R S E I D A E M T I S C H L I K T P L A U E I C H D A S I R T R O T T E M E I N E N G R O S E N S C R I T F L E I C H K E I N M E I N S T D E I N E E I N G R O S E S C H I Z U E R P A E D E N S E E T H A E N +voxpopuli_deu_000336 P Ä H E N D A N G E S F Ö R G F Ü R Z W E I E N H E I B M I N O D E N E R G +voxpopuli_deu_000337 Z U M A K T U Ä L E N I C T L A B I S K A N K E I N E R V O N U N S A N E H M E D A S W I W I R T L I C H E A R S Z E T D I E S E N W O C H E N E D I W I S E N D S O N S D I E Z A L U N S U M F I C G K E I T D R O T +voxpopuli_deu_000338 D S N D E I N F C H B E D I N G N G E N D I N I C G E K T Z E P T A B E S E N D M A N K A +voxpopuli_deu_000339 I N D E S W I S C H E N S E I S I N D I R E T U N G S O R G A N I S E R Z I O N E R N I E G R Ö S T E N S C H Ä P E R W E I S I E D I E M I G R A N T E N Z W A N Z I C H K I L O M E T E R V E R D E R I E B I C H E N K Ü S T E A U B G R E I T F E N U N D A L E N A R I T A L I E N R A S P O R T I E R E N +voxpopuli_deu_000340 D E S E I K T D R F A L L I O L I A R T I E M S C H E N K O +voxpopuli_deu_000341 E W A S S E R P R E D I G E N U N D W E I N T R I N K E N +voxpopuli_deu_000342 Ü R D I E S E E N S C H E I D U N G P R A U E N W I A R V I E L E P A T N A R N I C H T Z U L E T Z D I E S T Ä T T E +voxpopuli_deu_000343 D I E F O L G E I S T E I N H Ö R E N F L U G S V O M P O R P O L I S T N U N E X S T R L M I S T E I E N E I N I G M I G I T S T A T E N I E R E N B U M F E M P A R O L U E N S E T Z E N D I A R C O G R E T E R V E R E N D E R U N G E N G E G E N +voxpopuli_deu_000344 W A L D I E I N V E S T I T Z I O N E R N V R A N T Ö R S I S C H A C H U N D D E U T S C H E R B A N K E N G E R E T E T W E R D E N M U S T E N D U R H T E R G L I C H E N G L A N D T W E I T A U S E N D E H N N I C H T P B E I T E G E N U N D E H U T E M U S E S E I N E N R I E S I G E N G S C H O D E N B E R K V O R D S I C H T E T H E R T D R Ü C K E +voxpopuli_deu_000345 I M I T G I T S T D A D E N D Ü R F E N N I C H I E M Ö G L I C H K E I T A B E N D E R E N A U R O P Ä S C H E N S T A R Z E A M B A L D E R A N Z U R H I N D E R N E N I E R E R E G I O N D G A N Z S G E R Z I E U N S T E M A T I S C O R U T O N F E L N A C H Z U G G E N E R S I N E +voxpopuli_deu_000346 E I M I L I O N M E N S C H E N S I N A P Ä N G H V O N U D S E R H I L F E R +voxpopuli_deu_000347 E I N F Ü Z H E N H R G E R J N G E W E T I N H E R K A D I O N E I N E P O L I Z S I S T E N E I N E S O N D E E I N S A T K O M A N D O S N C O M A G S C H L A G D E N +voxpopuli_deu_000348 D I E E I D I E H E I L I G E K U A T M A N W O S I H E R E T R A G E N D A S A U P T A U T W I S S U D E R A L L E U N S H L E N D E N W E C K +voxpopuli_deu_000349 R E I D E R A R T K T E G E T D E R E F E N H A R B E N I N Z W I S C H E N S T A D G E U N D E M +voxpopuli_deu_000350 R D I C H I E E R N E I N M O N E R D E B T +voxpopuli_deu_000351 D S W E G E N E I N W I C H T I G E F R A G A D I K O M I T I O N E N E I N L A N D D I E K R A N Z S K O N T R O L L E V I E D E R E I N F Ü H O N N D D O C H I M S C H Ä N G E U N I O N B L E I B E N I T Z U G A N G K Z U R I N O M A I O N D S U S T E M E Z E T E R A O D E R I S D R S E I N E N T W I R D E R O D A D I E F R A G E I S W E C H T I C F Ü R D I E D E N I S C H E T E P A T E U N D I E S P E T E U M E I N E K L A R E A N D W O R D D A +voxpopuli_deu_000352 D E R S C H O N A U S G I E F Ü H R T W U R D E L A G E S N I C H T B A R A R N D A S I S E G R O B E F F Ä H L E G E G E B E N H E T I S S O N E N E S G A B E N E R R E I E V O N D G L E I E N N G E R E I M T E I T E N B I E T I E N S W E I +voxpopuli_deu_000353 I V E R G E M E I N T C H E A F T U N G D E R A U S E N U O S S I E G E R L T S P O L I T I G B A I S G O S I S Z I E L D I E S E R U N J O N +voxpopuli_deu_000354 D E N S I C H E H E I T I S E I N E S C W I E R I G E U N D D E T E I L W E I C H E R A R B E I T N I C H T N U E R I M T Ä C H N I S C H E N B E R E I C H +voxpopuli_deu_000355 T I S E Ä L T E N G E N D I E N T E R E S T E N V O N B Ü R G E R N U N P O L I T I K E N S O W I A U S E N A N D E R B E R E M B Ü R E R N I N G A N Z E R O P E R S T E T E S T E M E R K I N D T G A N N S O B E N +voxpopuli_deu_000356 H E R P A S I D E N T +voxpopuli_deu_000357 E F Ü R T E N G E S P R E I C H E M I T R E S E D E N T K A R S E I Z A R D R E I C H N R E G I R U N G S E R T R E T E R N F R A U N U N D M E N S C H N R E C H T O R G A N I S E R T Z I O N E N U N D D I E W A N D D U C H A U S E M U T I G E N T +voxpopuli_deu_000358 N G S A C H E I N E U R S A C H E F R D E N W A C H S N E N A T Z I H N A L I S T M U S D E A L I N S E I D E R F E L I C H P E R S B E K T I F L O S S I S T +voxpopuli_deu_000359 H U D E I N E I M A N A O H S O R W E I T O N D I E N Z I E E N F E R N E S +voxpopuli_deu_000360 H W E R D E A L S W I D A N Z M I N I S T E R A U C H E N M E I N E M L A N D Z I E D E N T A G K D A M I T K O N F V O N D T I E R T D A S N A T Ü L I C H A U C H T E S B I R U S T Z E N G E G E B E N S E N M U S D A S S T A S H A U S H A L G T D E V O N D E S T E L U E R S A L E R E N E N O N S T E U E R Z E O L L A N I N E N Z I E Z I H N T U N D D A S I E T A H M I T A U C H I E R N T U E R T U N G R A G E N I N D E E N T C H Ä I D U N G E N D I E V I E R H I E N T I E S E N R A M E N D R E F M I E T A M N O N T H E R N +voxpopuli_deu_000361 A U F D E M O U O R O P E S C H E N A U T E R B E B I L M A R E K T I N S I G E S A M D E R M A T I S C H I S T +voxpopuli_deu_000362 O P Ä H S C H U N I O N H A D T M I D I S E I N S T R U M E N Z S D I E S C O N S E E I N E A K T I E V E R O L L E N I E R N A C H B A R I G I O N Z U S P I E L E N U M D E R M O G R A T I S C H E E F O R M E N N D E R N N A C H A L I G E N W I K T U N G E R N Z U T R E I B E +voxpopuli_deu_000363 H T U T E L L I T E R E R E R S C H I E M E V O N A U S E N G O D E R V O N I N E N I S T R E C H T U N D O S C H I E D G L I G H +voxpopuli_deu_000364 E R E M I M E R G E S A G K T E I N Ü B E R E I L T E S T A D T Z I O N I E R U N G S E N S H E I D U N G E S U N S E N I C H W E I Z U M J E R Z I G E N T Z E I T F U N G E S K E I N E B E D R O U N G B E I S P I E L S W E I S A U S E M I E R A N G E B T +voxpopuli_deu_000365 D I E S E R F A K L E I I S T E I N E T Z Y N I S C H E M I S A C T D U N G E N D E R O B P U O V O R O N M E N C H N E C T Z W R E C T D O M L E L A L L R E L S F F A A A A A T D O D S C S A A A A I S S O N G A N D A N A N D E N E E I N E S O E U C H O D E N C Ü L A U P B L I C H E R A N W O R F +voxpopuli_deu_000366 D I E E S P E E R H A T D I S E U M F S E N D E R H E T Z U N T A L E R I C H T L I N D E Ü R B E Ü R B O A T D E T W I N G E R +voxpopuli_deu_000367 G I G I S T W I R G L I C K L A D I I N A N F U N D E W I R S H A S T G E D E V E L A N K V O N U N D E A L N E I N M A L M E H R J E T Z S T D E R V E R A N T W O F T D U N G F Ü R E I N E O B P T I M A L E U N D F E A L E M R A S I E K A L I F I Z T I E R U N G U N D R E R A R B E I T N E H M E N D A R B E I T D N E M E R R I N E N D A N S B E S O N D E R J E T S T R E S C H N U N G S O T A G E N +voxpopuli_deu_000368 A N D R E R A U C H O H L E N D I E S E R K U D E R G E B I S E R Z I E L N A L S A N D E R E D I S S I S C W Ä E R T U N D I M I T E L A B P Z U O F N E T W A C R I K I O N W I K A L A B R E N Z I T Z I E L E N O D E R A U C G R I E C H E L E R D O D R A U C H O M Ä N I E N +voxpopuli_deu_000369 D E R B R I C H C O S E S V O R D E R Z U R E C H T D A S E S R E T I N G S T A T L I C H E R S C H L T T I E D E L E I S Ö F F E N T L I C H E R A U F G A B E B E G R I F E N U N D D A H I E R V O N F F E N L I C H E A K T Ü R N V O R G E N O M W E R D E N M O S +voxpopuli_deu_000370 D A B W I E S A B A L D N M I T E I N E M S O T C A R L P O G A M Z U T U N H A R B E N M S S W I L D A R F Ü R E I N E N S P E C H E N D E R E C H L I G E U N D L A G E S C A F E N +voxpopuli_deu_000371 S T I E R N O C H N A L I S E R E N W O R E +voxpopuli_deu_000372 M A K E N E N E R T L I E V E R L A N G E N G E B N L I E M E R G A R T F I R N D I K U N G S H V E R A U S D I E A H M E N E U T E B R A U C K E N D A S S B E +voxpopuli_deu_000373 G E R A D Ü O G L E I N E L E P O J E C K T E I S D A S Ü B E R M Ä E S I G H B I E R O G A T E S C H R A U F A N D R E C H T I C H D A S T A S I E R S R B E I N Z E I T A U M V O N D D R E I J A H R E N G E S E N T W E R D E N S O L U N D U M +voxpopuli_deu_000374 I K A N D E V O R S I C H E R N D I A R O P E S C H E K O M I S I O N I S T E T K O M I T D E T Z S U M A A R E R O B S E A L O P E C H E N E R S B I E K T D I E V E R D I S K O S S O S E +voxpopuli_deu_000375 S E S E S L A P E I H E A U F T A U C H S O +voxpopuli_deu_000376 I D I E S E N H A U S E L K A M A N D I E E E N G Ü R G E R E I N U N D B O R G E R N I C H T Ü B E R Z E U C T E N N B E G E I S T E R N +voxpopuli_deu_000377 Z I A L D E M O K R A T E N E H M N I T G R O S A F O E U D E Z S O R K E N T N I S D A S D I N G E D I E I E R F O R G E T R A G E N H A B E N J E B Z S I C H A U H I M Z U S A M M E N A G M I T V E R E N D E R U N G E N E D E N F E I N C H E N S T A D E N U M S E T Z E +voxpopuli_deu_000378 D E A H R B E S C H U S T I E E L D A S O R O P Ä S C H E S E M E S T E R H I E R H E R T Z U N E H M E N U N D D I C O R O B P T I O U N S S I T P L A R I O N E R E I M R A M D E R L N D E R B R E C H T E Z O V E R Ö F N I G E N I S T N I G A U S H E I G E N T +voxpopuli_deu_000379 N D M E I N M E I N E B I T E O D E R M D A S W A S I C H M E R V O R S T E N I S D A S M A R G E N G W I E C K L I C G I N D E R T A R T E I N I E G R O S E E I N E B R E I T M E H R H E I T F Ü R D I E S E K O U S I O N S P O L I T I G H Ü E O N D F G E P O L I T I G S T D I M T P Ü R D I E M E N S C H E N V O R O R T D A M I T I U N S A T E S W E E N T I C H E A U C H B E S C R Ä N K E N K Ö N D E D A S +voxpopuli_deu_000380 W N N W I E H E U T E D I E E V E R R D N G V E R A B S C H I E D E N O F E R E C H D A S S E W I E N A C H E I M L A N G K A R U S E L L S O U E I M G U D N A B S L U S K O M M O N D T I T M M A C H T E R M I C H E B E I E R O M I S I O N B E D A N G E N I E O N S T O K T I E V E S A C H A B E I T H A T +voxpopuli_deu_000381 U N Z E R E R E S C H E A S C H E N U N Z S I E K O N T R O E L E N H A B E N K E N E N P I E L E G E R P R A F T diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/token_int b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/token_int new file mode 100644 index 0000000000000000000000000000000000000000..00aac741c28e973a5254b47674c5b3af69da3543 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_deu1/token_int @@ -0,0 +1,661 @@ +M-AILABS_deu_000165 10 2 18 2 6 3 10 5 14 4 14 3 17 9 15 11 8 2 3 5 4 2 6 3 2 12 7 7 8 21 5 15 11 8 5 14 2 4 7 2 15 11 9 3 5 4 3 2 4 10 2 3 10 2 6 3 23 26 8 5 8 3 5 16 4 3 9 4 13 5 4 14 16 21 9 4 25 6 3 19 25 6 10 2 7 3 2 4 3 28 9 4 2 6 3 7 16 7 7 18 2 14 2 4 9 10 5 14 12 4 17 3 +M-AILABS_deu_000166 10 9 15 11 9 18 2 7 5 2 8 3 10 5 2 3 21 16 13 12 13 14 28 2 10 2 17 3 11 2 6 3 5 4 2 6 3 5 4 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b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/images/wer.png new file mode 100644 index 0000000000000000000000000000000000000000..2d4d7a305e96df16d0c88b31dd97f2e2b45f11cb Binary files /dev/null and b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/images/wer.png differ diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/latest.pth b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/latest.pth new file mode 120000 index 0000000000000000000000000000000000000000..d52af6357e1c6c465ad7c4b26911c5298cdbde12 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/latest.pth @@ -0,0 +1 @@ +30epoch.pth \ No newline at end of file diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/run.sh b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/run.sh new file mode 100755 index 0000000000000000000000000000000000000000..2fbc7225ba31b80d124b6e7a3c94f0322bb1eaa0 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/run.sh @@ -0,0 +1 @@ +./asr.sh --ngpu 1 --stage 11 --stop_stage 13 --nj 32 --inference_nj 4 --gpu_inference true --lang deu1 --inference_asr_model valid.loss.ave.pth --local_data_opts '--duration 1h --lid false --multilingual false --single_lang deu1' --use_lm false --token_type char --feats_type raw --feats_normalize utterance_mvn --asr_config conf/tuning/train_asr_s3prl_houlsby.yaml --inference_config conf/decode_asr.yaml --train_set train_1h_deu1 --valid_set dev_10min_deu1 --test_sets 'dev_10min_deu1 test_10min_deu1' --asr_tag train_asr_s3prl_houlsby_deu1_1h --expdir test_pr --asr_stats_dir test_pr/asr_stats_deu1_1h --local_score_opts 'false false monolingual' --stage 11 "$@"; exit $? diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/tensorboard/train/events.out.tfevents.1705246478.stan.294159.0 b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/tensorboard/train/events.out.tfevents.1705246478.stan.294159.0 new file mode 100644 index 0000000000000000000000000000000000000000..52999b8cd51b59487dedcd1496af692150fbcd65 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/tensorboard/train/events.out.tfevents.1705246478.stan.294159.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b92b7e80024024c91d8c5880e6aab598869ebbd4a9d8570ff35c4d37e17a42a8 +size 24094568 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/tensorboard/train/events.out.tfevents.1705425848.stan.888851.0 b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/tensorboard/train/events.out.tfevents.1705425848.stan.888851.0 new file mode 100644 index 0000000000000000000000000000000000000000..3cb3c8d126f31270f739ad033e04fb9b5e94ce2b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/tensorboard/train/events.out.tfevents.1705425848.stan.888851.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9c2bc6971be172735302c66606c00339391b50e8a012aa766d59a77441b6d9a0 +size 24217874 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/tensorboard/valid/events.out.tfevents.1705246478.stan.294159.1 b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/tensorboard/valid/events.out.tfevents.1705246478.stan.294159.1 new file mode 100644 index 0000000000000000000000000000000000000000..545df0b1c4bc3ec089227a66cf21daedaa531379 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/tensorboard/valid/events.out.tfevents.1705246478.stan.294159.1 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0fae6e0fa3f3c078c7cb856a61b22e4bba6c509f30e92a62b76935e544e6f58d +size 11190 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/tensorboard/valid/events.out.tfevents.1705425848.stan.888851.1 b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/tensorboard/valid/events.out.tfevents.1705425848.stan.888851.1 new file mode 100644 index 0000000000000000000000000000000000000000..ada4767baa011bc1aa5262183d4c5c3938f2b7ba --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/tensorboard/valid/events.out.tfevents.1705425848.stan.888851.1 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:db0072a6f7d65beb95293c7dbe91ccccef3affea745da9a90b905215d1504643 +size 11190 diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/train.1.log b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/train.1.log new file mode 100644 index 0000000000000000000000000000000000000000..62f8bcefb73602c8dc6b04b1416a564a068fd921 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/train.1.log @@ -0,0 +1,1406 @@ +# python3 -m espnet2.bin.asr_train --use_preprocessor true --bpemodel none --token_type char --token_list data/deu1_token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_deu1/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_deu1_1h/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_deu1/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_deu1_1h/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_1h_deu1/text,text,text --train_shape_file test_pr/asr_stats_deu1_1h/train/text_shape.char --valid_data_path_and_name_and_type dump/raw/dev_10min_deu1/text,text,text --valid_shape_file test_pr/asr_stats_deu1_1h/valid/text_shape.char --ngpu 1 --multiprocessing_distributed True +# Started at Sun Jan 14 23:34:34 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_train.py --use_preprocessor true --bpemodel none --token_type char --token_list data/deu1_token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_deu1/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_deu1_1h/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_deu1/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_deu1_1h/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_1h_deu1/text,text,text --train_shape_file test_pr/asr_stats_deu1_1h/train/text_shape.char --valid_data_path_and_name_and_type dump/raw/dev_10min_deu1/text,text,text --valid_shape_file test_pr/asr_stats_deu1_1h/valid/text_shape.char --ngpu 1 --multiprocessing_distributed True +[stan] 2024-01-14 23:34:35,913 (asr:523) INFO: Vocabulary size: 44 +[stan] 2024-01-14 23:34:35,974 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +[stan] 2024-01-14 23:34:35,975 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +[stan] 2024-01-14 23:34:36,085 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +[stan] 2024-01-14 23:34:37,375 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.mask_emb.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.label_embs_concat.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.0.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.1.0.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.2.0.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.3.0.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.4.0.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.5.0.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.6.0.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_g.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_v.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,197 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.weight.requires_grad = False +[stan] 2024-01-14 23:34:38,198 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.bias.requires_grad = False +[stan] 2024-01-14 23:34:38,602 (abs_task:1275) INFO: pytorch.version=1.12.0+cu113, cuda.available=True, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True +[stan] 2024-01-14 23:34:38,604 (abs_task:1276) INFO: Model structure: +ESPnetASRModel( + (frontend): S3prlFrontend( + (upstream): S3PRLUpstream( + (upstream): UpstreamExpert( + (model): HubertModel( + (feature_extractor): ConvFeatureExtractionModel( + (conv_layers): ModuleList( + (0): Sequential( + (0): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): Fp32GroupNorm(512, 512, eps=1e-05, affine=True) + (3): GELU(approximate=none) + ) + (1): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (2): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (3): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (4): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (5): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (6): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + ) + ) + (post_extract_proj): Linear(in_features=512, out_features=768, bias=True) + (dropout_input): Dropout(p=0.1, inplace=False) + (dropout_features): Dropout(p=0.1, inplace=False) + (encoder): TransformerEncoder( + (pos_conv): Sequential( + (0): Conv1d(768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16) + (1): SamePad() + (2): GELU(approximate=none) + ) + (layers): ModuleList( + (0): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (1): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (2): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (3): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (4): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (5): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (6): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (7): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (8): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (9): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (10): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (11): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + ) + (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (final_proj): Linear(in_features=768, out_features=256, bias=True) + ) + ) + ) + (featurizer): Featurizer() + ) + (specaug): SpecAug( + (time_warp): TimeWarp(window=5, mode=bicubic) + (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq) + (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time) + ) + (normalize): UtteranceMVN(norm_means=True, norm_vars=False) + (preencoder): LinearProjection( + (linear_out): Linear(in_features=768, out_features=80, bias=True) + (dropout): Dropout(p=0.0, inplace=False) + ) + (encoder): TransformerEncoder( + (embed): Conv2dSubsampling2( + (conv): Sequential( + (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU() + (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) + (3): ReLU() + ) + (out): Sequential( + (0): Linear(in_features=9472, out_features=256, bias=True) + (1): PositionalEncoding( + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + ) + (encoders): MultiSequential( + (0): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (1): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + ) + (criterion_att): LabelSmoothingLoss( + (criterion): KLDivLoss() + ) + (ctc): CTC( + (ctc_lo): Linear(in_features=256, out_features=44, bias=True) + (ctc_loss): CTCLoss() + ) +) + +Model summary: + Class Name: ESPnetASRModel + Total Number of model parameters: 99.97 M + Number of trainable parameters: 5.27 M (5.3%) + Size: 21.08 MB + Type: torch.float32 +[stan] 2024-01-14 23:34:38,604 (abs_task:1279) INFO: Optimizer: +Adam ( +Parameter Group 0 + amsgrad: False + betas: (0.9, 0.999) + capturable: False + eps: 1e-08 + foreach: None + lr: 0.0001 + maximize: False + weight_decay: 1e-06 +) +[stan] 2024-01-14 23:34:38,604 (abs_task:1280) INFO: Scheduler: None +[stan] 2024-01-14 23:34:38,604 (abs_task:1289) INFO: Saving the configuration in test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml +[stan] 2024-01-14 23:34:38,758 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-14 23:34:38,801 (abs_task:1665) INFO: [train] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/train_1h_deu1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/train_1h_deu1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-14 23:34:38,801 (abs_task:1666) INFO: [train] Batch sampler: SortedBatchSampler(N-batch=150, batch_size=8, shape_file=test_pr/asr_stats_deu1_1h/train/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-14 23:34:38,801 (abs_task:1667) INFO: [train] mini-batch sizes summary: N-batch=150, mean=8.0, min=8, max=8 +[stan] 2024-01-14 23:34:38,812 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-14 23:34:38,813 (abs_task:1665) INFO: [valid] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_deu1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_deu1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-14 23:34:38,813 (abs_task:1666) INFO: [valid] Batch sampler: SortedBatchSampler(N-batch=25, batch_size=8, shape_file=test_pr/asr_stats_deu1_1h/valid/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-14 23:34:38,813 (abs_task:1667) INFO: [valid] mini-batch sizes summary: N-batch=25, mean=8.3, min=8, max=9 +[stan] 2024-01-14 23:34:38,814 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-14 23:34:38,824 (abs_task:1665) INFO: [plot_att] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_deu1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_deu1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-14 23:34:38,824 (abs_task:1666) INFO: [plot_att] Batch sampler: UnsortedBatchSampler(N-batch=207, batch_size=1, key_file=test_pr/asr_stats_deu1_1h/valid/speech_shape, +[stan] 2024-01-14 23:34:38,824 (abs_task:1667) INFO: [plot_att] mini-batch sizes summary: N-batch=3, mean=1.0, min=1, max=1 +[stan] 2024-01-14 23:34:38,857 (trainer:300) INFO: 1/30epoch started +[stan] 2024-01-14 23:34:42,662 (trainer:763) INFO: 1epoch:train:1-40batch: iter_time=0.002, forward_time=0.060, loss_ctc=38.645, loss=38.645, backward_time=0.008, grad_norm=324.009, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-14 23:34:44,886 (trainer:763) INFO: 1epoch:train:41-80batch: iter_time=3.910e-05, forward_time=0.030, loss_ctc=34.035, loss=34.035, backward_time=0.007, grad_norm=99.373, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.223 +[stan] 2024-01-14 23:34:47,585 (trainer:763) INFO: 1epoch:train:81-120batch: iter_time=3.957e-05, forward_time=0.036, loss_ctc=37.956, loss=37.956, backward_time=0.007, grad_norm=75.901, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-14 23:34:49,755 (trainer:763) INFO: 1epoch:train:121-160batch: iter_time=3.824e-05, forward_time=0.029, loss_ctc=32.072, loss=32.072, backward_time=0.006, grad_norm=63.269, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-14 23:34:51,972 (trainer:763) INFO: 1epoch:train:161-200batch: iter_time=3.820e-05, forward_time=0.030, loss_ctc=30.402, loss=30.402, backward_time=0.006, grad_norm=89.215, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.222 +[stan] 2024-01-14 23:34:54,170 (trainer:763) INFO: 1epoch:train:201-240batch: iter_time=4.245e-05, forward_time=0.029, loss_ctc=27.144, loss=27.144, backward_time=0.007, grad_norm=76.472, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-14 23:34:56,872 (trainer:763) INFO: 1epoch:train:241-280batch: iter_time=4.141e-05, forward_time=0.036, loss_ctc=29.065, loss=29.065, backward_time=0.007, grad_norm=94.386, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-14 23:34:59,282 (trainer:763) INFO: 1epoch:train:281-320batch: iter_time=3.898e-05, forward_time=0.032, loss_ctc=25.546, loss=25.546, backward_time=0.006, grad_norm=95.860, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-14 23:35:01,679 (trainer:763) INFO: 1epoch:train:321-360batch: iter_time=3.974e-05, forward_time=0.032, loss_ctc=22.782, loss=22.782, backward_time=0.007, grad_norm=92.803, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-14 23:35:04,073 (trainer:763) INFO: 1epoch:train:361-400batch: iter_time=3.915e-05, forward_time=0.032, loss_ctc=22.190, loss=22.190, backward_time=0.007, grad_norm=94.184, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-14 23:35:06,073 (trainer:763) INFO: 1epoch:train:401-440batch: iter_time=3.855e-05, forward_time=0.027, loss_ctc=19.098, loss=19.098, backward_time=0.006, grad_norm=65.962, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.200 +[stan] 2024-01-14 23:35:08,442 (trainer:763) INFO: 1epoch:train:441-480batch: iter_time=3.975e-05, forward_time=0.032, loss_ctc=21.312, loss=21.312, backward_time=0.007, grad_norm=85.055, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-14 23:35:10,946 (trainer:763) INFO: 1epoch:train:481-520batch: iter_time=4.073e-05, forward_time=0.033, loss_ctc=21.789, loss=21.789, backward_time=0.007, grad_norm=86.893, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 23:35:13,054 (trainer:763) INFO: 1epoch:train:521-560batch: iter_time=3.845e-05, forward_time=0.028, loss_ctc=17.948, loss=17.948, backward_time=0.006, grad_norm=73.050, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.211 +[stan] 2024-01-14 23:35:15,501 (trainer:763) INFO: 1epoch:train:561-600batch: iter_time=4.136e-05, forward_time=0.032, loss_ctc=20.128, loss=20.128, backward_time=0.007, grad_norm=77.570, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-14 23:35:18,178 (trainer:763) INFO: 1epoch:train:601-640batch: iter_time=4.046e-05, forward_time=0.035, loss_ctc=21.109, loss=21.109, backward_time=0.007, grad_norm=91.972, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-14 23:35:20,471 (trainer:763) INFO: 1epoch:train:641-680batch: iter_time=4.164e-05, forward_time=0.031, loss_ctc=19.302, loss=19.302, backward_time=0.006, grad_norm=76.212, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-14 23:35:22,516 (trainer:763) INFO: 1epoch:train:681-720batch: iter_time=4.050e-05, forward_time=0.027, loss_ctc=16.767, loss=16.767, backward_time=0.006, grad_norm=65.276, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.204 +[stan] 2024-01-14 23:35:24,974 (trainer:763) INFO: 1epoch:train:721-760batch: iter_time=3.958e-05, forward_time=0.033, loss_ctc=19.113, loss=19.113, backward_time=0.007, grad_norm=71.136, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 23:35:27,110 (trainer:763) INFO: 1epoch:train:761-800batch: iter_time=4.046e-05, forward_time=0.029, loss_ctc=17.013, loss=17.013, backward_time=0.006, grad_norm=66.821, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.213 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-14 23:35:31,292 (trainer:354) INFO: 1epoch results: [train] iter_time=1.404e-04, forward_time=0.033, loss_ctc=24.671, loss=24.671, backward_time=0.007, grad_norm=93.271, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241, time=48.29 seconds, total_count=800, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=63.767, cer_ctc=0.317, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=63.767, time=1.13 seconds, total_count=25, gpu_max_cached_mem_GB=10.941, [att_plot] time=3 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:35:32,323 (trainer:417) INFO: The best model has been updated: valid.loss +[stan] 2024-01-14 23:35:32,323 (trainer:288) INFO: 2/30epoch started. Estimated time to finish: 25 minutes and 50.53 seconds +[stan] 2024-01-14 23:35:34,747 (trainer:763) INFO: 2epoch:train:1-40batch: iter_time=0.002, forward_time=0.029, loss_ctc=17.320, loss=17.320, backward_time=0.007, grad_norm=68.455, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-14 23:35:37,534 (trainer:763) INFO: 2epoch:train:41-80batch: iter_time=4.197e-05, forward_time=0.037, loss_ctc=20.132, loss=20.132, backward_time=0.008, grad_norm=84.328, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-14 23:35:39,700 (trainer:763) INFO: 2epoch:train:81-120batch: iter_time=4.124e-05, forward_time=0.029, loss_ctc=16.194, loss=16.194, backward_time=0.007, grad_norm=80.441, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-14 23:35:42,346 (trainer:763) INFO: 2epoch:train:121-160batch: iter_time=4.276e-05, forward_time=0.035, loss_ctc=19.544, loss=19.544, backward_time=0.007, grad_norm=95.183, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.265 +[stan] 2024-01-14 23:35:44,490 (trainer:763) INFO: 2epoch:train:161-200batch: iter_time=4.211e-05, forward_time=0.029, loss_ctc=16.099, loss=16.099, backward_time=0.007, grad_norm=68.017, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.214 +[stan] 2024-01-14 23:35:46,797 (trainer:763) INFO: 2epoch:train:201-240batch: iter_time=4.047e-05, forward_time=0.031, loss_ctc=16.491, loss=16.491, backward_time=0.007, grad_norm=69.735, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-14 23:35:48,877 (trainer:763) INFO: 2epoch:train:241-280batch: iter_time=4.012e-05, forward_time=0.028, loss_ctc=14.920, loss=14.920, backward_time=0.007, grad_norm=67.248, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.208 +[stan] 2024-01-14 23:35:51,323 (trainer:763) INFO: 2epoch:train:281-320batch: iter_time=4.010e-05, forward_time=0.032, loss_ctc=17.800, loss=17.800, backward_time=0.007, grad_norm=80.350, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 23:35:53,767 (trainer:763) INFO: 2epoch:train:321-360batch: iter_time=4.137e-05, forward_time=0.032, loss_ctc=17.276, loss=17.276, backward_time=0.007, grad_norm=87.908, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 23:35:56,252 (trainer:763) INFO: 2epoch:train:361-400batch: iter_time=4.326e-05, forward_time=0.033, loss_ctc=17.126, loss=17.126, backward_time=0.008, grad_norm=79.981, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 23:35:58,943 (trainer:763) INFO: 2epoch:train:401-440batch: iter_time=4.138e-05, forward_time=0.035, loss_ctc=18.724, loss=18.724, backward_time=0.008, grad_norm=82.730, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-14 23:36:01,232 (trainer:763) INFO: 2epoch:train:441-480batch: iter_time=4.051e-05, forward_time=0.031, loss_ctc=16.273, loss=16.273, backward_time=0.007, grad_norm=76.446, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-14 23:36:03,566 (trainer:763) INFO: 2epoch:train:481-520batch: iter_time=4.069e-05, forward_time=0.031, loss_ctc=15.759, loss=15.759, backward_time=0.007, grad_norm=76.119, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-14 23:36:05,734 (trainer:763) INFO: 2epoch:train:521-560batch: iter_time=4.067e-05, forward_time=0.029, loss_ctc=14.769, loss=14.769, backward_time=0.007, grad_norm=86.177, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-14 23:36:08,057 (trainer:763) INFO: 2epoch:train:561-600batch: iter_time=4.148e-05, forward_time=0.031, loss_ctc=16.226, loss=16.226, backward_time=0.007, grad_norm=80.276, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-14 23:36:10,275 (trainer:763) INFO: 2epoch:train:601-640batch: iter_time=4.161e-05, forward_time=0.030, loss_ctc=14.362, loss=14.362, backward_time=0.007, grad_norm=79.302, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.222 +[stan] 2024-01-14 23:36:12,908 (trainer:763) INFO: 2epoch:train:641-680batch: iter_time=3.989e-05, forward_time=0.035, loss_ctc=16.960, loss=16.960, backward_time=0.008, grad_norm=88.927, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 23:36:15,249 (trainer:763) INFO: 2epoch:train:681-720batch: iter_time=4.086e-05, forward_time=0.031, loss_ctc=15.222, loss=15.222, backward_time=0.007, grad_norm=84.647, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-14 23:36:17,342 (trainer:763) INFO: 2epoch:train:721-760batch: iter_time=3.996e-05, forward_time=0.028, loss_ctc=14.100, loss=14.100, backward_time=0.007, grad_norm=85.774, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.209 +[stan] 2024-01-14 23:36:19,604 (trainer:763) INFO: 2epoch:train:761-800batch: iter_time=3.758e-05, forward_time=0.030, loss_ctc=14.868, loss=14.868, backward_time=0.007, grad_norm=82.462, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-14 23:36:23,530 (trainer:354) INFO: 2epoch results: [train] iter_time=1.511e-04, forward_time=0.031, loss_ctc=16.508, loss=16.508, backward_time=0.007, grad_norm=80.225, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236, time=47.35 seconds, total_count=1600, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=56.181, cer_ctc=0.277, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=56.181, time=1.13 seconds, total_count=50, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.73 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:36:24,377 (trainer:417) INFO: The best model has been updated: valid.loss +[stan] 2024-01-14 23:36:24,377 (trainer:288) INFO: 3/30epoch started. Estimated time to finish: 24 minutes and 37.28 seconds +[stan] 2024-01-14 23:36:26,907 (trainer:763) INFO: 3epoch:train:1-40batch: iter_time=0.002, forward_time=0.031, loss_ctc=15.056, loss=15.056, backward_time=0.007, grad_norm=83.889, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-14 23:36:29,592 (trainer:763) INFO: 3epoch:train:41-80batch: iter_time=4.051e-05, forward_time=0.035, loss_ctc=16.076, loss=16.076, backward_time=0.008, grad_norm=86.404, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-14 23:36:32,075 (trainer:763) INFO: 3epoch:train:81-120batch: iter_time=4.466e-05, forward_time=0.033, loss_ctc=16.041, loss=16.041, backward_time=0.007, grad_norm=93.353, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 23:36:34,517 (trainer:763) INFO: 3epoch:train:121-160batch: iter_time=4.179e-05, forward_time=0.032, loss_ctc=15.256, loss=15.256, backward_time=0.007, grad_norm=85.923, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 23:36:36,635 (trainer:763) INFO: 3epoch:train:161-200batch: iter_time=4.093e-05, forward_time=0.028, loss_ctc=14.137, loss=14.137, backward_time=0.007, grad_norm=78.798, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.212 +[stan] 2024-01-14 23:36:39,143 (trainer:763) INFO: 3epoch:train:201-240batch: iter_time=4.046e-05, forward_time=0.033, loss_ctc=14.760, loss=14.760, backward_time=0.007, grad_norm=79.585, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-14 23:36:41,716 (trainer:763) INFO: 3epoch:train:241-280batch: iter_time=4.194e-05, forward_time=0.034, loss_ctc=15.407, loss=15.407, backward_time=0.007, grad_norm=86.419, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 23:36:43,933 (trainer:763) INFO: 3epoch:train:281-320batch: iter_time=3.986e-05, forward_time=0.030, loss_ctc=14.444, loss=14.444, backward_time=0.007, grad_norm=81.995, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.222 +[stan] 2024-01-14 23:36:45,988 (trainer:763) INFO: 3epoch:train:321-360batch: iter_time=3.901e-05, forward_time=0.028, loss_ctc=12.957, loss=12.957, backward_time=0.007, grad_norm=81.764, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.205 +[stan] 2024-01-14 23:36:48,102 (trainer:763) INFO: 3epoch:train:361-400batch: iter_time=4.179e-05, forward_time=0.028, loss_ctc=12.866, loss=12.866, backward_time=0.007, grad_norm=81.119, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.211 +[stan] 2024-01-14 23:36:50,415 (trainer:763) INFO: 3epoch:train:401-440batch: iter_time=4.095e-05, forward_time=0.031, loss_ctc=14.360, loss=14.360, backward_time=0.007, grad_norm=83.088, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-14 23:36:52,914 (trainer:763) INFO: 3epoch:train:441-480batch: iter_time=4.179e-05, forward_time=0.033, loss_ctc=15.240, loss=15.240, backward_time=0.007, grad_norm=84.700, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 23:36:55,494 (trainer:763) INFO: 3epoch:train:481-520batch: iter_time=4.121e-05, forward_time=0.034, loss_ctc=15.584, loss=15.584, backward_time=0.008, grad_norm=91.506, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 23:36:57,938 (trainer:763) INFO: 3epoch:train:521-560batch: iter_time=4.113e-05, forward_time=0.032, loss_ctc=13.985, loss=13.985, backward_time=0.007, grad_norm=90.544, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 23:36:59,948 (trainer:763) INFO: 3epoch:train:561-600batch: iter_time=4.135e-05, forward_time=0.027, loss_ctc=11.955, loss=11.955, backward_time=0.007, grad_norm=78.365, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.201 +[stan] 2024-01-14 23:37:02,634 (trainer:763) INFO: 3epoch:train:601-640batch: iter_time=4.308e-05, forward_time=0.035, loss_ctc=15.764, loss=15.764, backward_time=0.008, grad_norm=96.165, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-14 23:37:04,839 (trainer:763) INFO: 3epoch:train:641-680batch: iter_time=3.970e-05, forward_time=0.029, loss_ctc=13.421, loss=13.421, backward_time=0.007, grad_norm=87.422, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-14 23:37:07,340 (trainer:763) INFO: 3epoch:train:681-720batch: iter_time=4.106e-05, forward_time=0.033, loss_ctc=14.448, loss=14.448, backward_time=0.007, grad_norm=95.022, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 23:37:09,740 (trainer:763) INFO: 3epoch:train:721-760batch: iter_time=4.015e-05, forward_time=0.033, loss_ctc=13.146, loss=13.146, backward_time=0.007, grad_norm=85.517, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-14 23:37:12,120 (trainer:763) INFO: 3epoch:train:761-800batch: iter_time=3.911e-05, forward_time=0.032, loss_ctc=13.638, loss=13.638, backward_time=0.007, grad_norm=84.552, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-14 23:37:16,022 (trainer:354) INFO: 3epoch results: [train] iter_time=1.586e-04, forward_time=0.032, loss_ctc=14.427, loss=14.427, backward_time=0.007, grad_norm=85.807, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239, time=47.82 seconds, total_count=2400, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=53.052, cer_ctc=0.265, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=53.052, time=1.13 seconds, total_count=75, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.7 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:37:16,881 (trainer:417) INFO: The best model has been updated: valid.loss +[stan] 2024-01-14 23:37:16,881 (trainer:288) INFO: 4/30epoch started. Estimated time to finish: 23 minutes and 42.22 seconds +[stan] 2024-01-14 23:37:19,620 (trainer:763) INFO: 4epoch:train:1-40batch: iter_time=0.002, forward_time=0.033, loss_ctc=13.920, loss=13.920, backward_time=0.007, grad_norm=91.295, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 23:37:21,979 (trainer:763) INFO: 4epoch:train:41-80batch: iter_time=4.074e-05, forward_time=0.031, loss_ctc=13.547, loss=13.547, backward_time=0.007, grad_norm=86.984, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-14 23:37:24,443 (trainer:763) INFO: 4epoch:train:81-120batch: iter_time=4.102e-05, forward_time=0.033, loss_ctc=13.627, loss=13.627, backward_time=0.007, grad_norm=97.222, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 23:37:26,440 (trainer:763) INFO: 4epoch:train:121-160batch: iter_time=3.854e-05, forward_time=0.027, loss_ctc=11.466, loss=11.466, backward_time=0.007, grad_norm=78.739, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.200 +[stan] 2024-01-14 23:37:28,887 (trainer:763) INFO: 4epoch:train:161-200batch: iter_time=4.134e-05, forward_time=0.032, loss_ctc=13.168, loss=13.168, backward_time=0.007, grad_norm=93.223, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-14 23:37:31,345 (trainer:763) INFO: 4epoch:train:201-240batch: iter_time=4.037e-05, forward_time=0.033, loss_ctc=13.671, loss=13.671, backward_time=0.007, grad_norm=89.358, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 23:37:33,607 (trainer:763) INFO: 4epoch:train:241-280batch: iter_time=4.080e-05, forward_time=0.030, loss_ctc=12.719, loss=12.719, backward_time=0.007, grad_norm=85.792, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-14 23:37:36,160 (trainer:763) INFO: 4epoch:train:281-320batch: iter_time=4.064e-05, forward_time=0.034, loss_ctc=13.829, loss=13.829, backward_time=0.007, grad_norm=98.740, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 23:37:38,425 (trainer:763) INFO: 4epoch:train:321-360batch: iter_time=4.040e-05, forward_time=0.030, loss_ctc=11.934, loss=11.934, backward_time=0.007, grad_norm=89.221, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-14 23:37:40,686 (trainer:763) INFO: 4epoch:train:361-400batch: iter_time=3.929e-05, forward_time=0.030, loss_ctc=12.315, loss=12.315, backward_time=0.007, grad_norm=85.872, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-14 23:37:43,200 (trainer:763) INFO: 4epoch:train:401-440batch: iter_time=4.014e-05, forward_time=0.033, loss_ctc=13.977, loss=13.977, backward_time=0.007, grad_norm=93.756, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-14 23:37:45,554 (trainer:763) INFO: 4epoch:train:441-480batch: iter_time=4.133e-05, forward_time=0.031, loss_ctc=12.516, loss=12.516, backward_time=0.007, grad_norm=97.913, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-14 23:37:47,976 (trainer:763) INFO: 4epoch:train:481-520batch: iter_time=4.011e-05, forward_time=0.032, loss_ctc=13.268, loss=13.268, backward_time=0.007, grad_norm=105.665, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-14 23:37:50,295 (trainer:763) INFO: 4epoch:train:521-560batch: iter_time=4.048e-05, forward_time=0.031, loss_ctc=12.945, loss=12.945, backward_time=0.007, grad_norm=94.618, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-14 23:37:52,617 (trainer:763) INFO: 4epoch:train:561-600batch: iter_time=4.044e-05, forward_time=0.031, loss_ctc=11.711, loss=11.711, backward_time=0.007, grad_norm=90.672, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-14 23:37:55,166 (trainer:763) INFO: 4epoch:train:601-640batch: iter_time=4.249e-05, forward_time=0.034, loss_ctc=13.505, loss=13.505, backward_time=0.008, grad_norm=92.955, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 23:37:57,572 (trainer:763) INFO: 4epoch:train:641-680batch: iter_time=4.238e-05, forward_time=0.032, loss_ctc=12.172, loss=12.172, backward_time=0.007, grad_norm=96.465, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-14 23:37:59,555 (trainer:763) INFO: 4epoch:train:681-720batch: iter_time=4.020e-05, forward_time=0.027, loss_ctc=10.473, loss=10.473, backward_time=0.007, grad_norm=81.507, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.198 +[stan] 2024-01-14 23:38:02,183 (trainer:763) INFO: 4epoch:train:721-760batch: iter_time=4.127e-05, forward_time=0.035, loss_ctc=13.427, loss=13.427, backward_time=0.007, grad_norm=97.399, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 23:38:04,225 (trainer:763) INFO: 4epoch:train:761-800batch: iter_time=3.802e-05, forward_time=0.027, loss_ctc=9.948, loss=9.948, backward_time=0.007, grad_norm=87.045, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.204 +[stan] 2024-01-14 23:38:08,144 (trainer:354) INFO: 4epoch results: [train] iter_time=1.587e-04, forward_time=0.031, loss_ctc=12.707, loss=12.707, backward_time=0.007, grad_norm=91.722, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237, time=47.42 seconds, total_count=3200, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=52.159, cer_ctc=0.256, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=52.159, time=1.16 seconds, total_count=100, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.68 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:38:09,113 (trainer:417) INFO: The best model has been updated: valid.loss +[stan] 2024-01-14 23:38:09,114 (trainer:288) INFO: 5/30epoch started. Estimated time to finish: 22 minutes and 46.67 seconds +[stan] 2024-01-14 23:38:12,030 (trainer:763) INFO: 5epoch:train:1-40batch: iter_time=0.002, forward_time=0.036, loss_ctc=14.672, loss=14.672, backward_time=0.008, grad_norm=108.352, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.291 +[stan] 2024-01-14 23:38:14,472 (trainer:763) INFO: 5epoch:train:41-80batch: iter_time=4.202e-05, forward_time=0.032, loss_ctc=11.859, loss=11.859, backward_time=0.007, grad_norm=90.294, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 23:38:16,407 (trainer:763) INFO: 5epoch:train:81-120batch: iter_time=3.962e-05, forward_time=0.026, loss_ctc=10.558, loss=10.558, backward_time=0.007, grad_norm=83.525, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.193 +[stan] 2024-01-14 23:38:18,809 (trainer:763) INFO: 5epoch:train:121-160batch: iter_time=4.194e-05, forward_time=0.032, loss_ctc=11.965, loss=11.965, backward_time=0.007, grad_norm=94.006, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-14 23:38:21,511 (trainer:763) INFO: 5epoch:train:161-200batch: iter_time=4.392e-05, forward_time=0.036, loss_ctc=13.777, loss=13.777, backward_time=0.008, grad_norm=99.093, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-14 23:38:23,866 (trainer:763) INFO: 5epoch:train:201-240batch: iter_time=4.170e-05, forward_time=0.031, loss_ctc=11.770, loss=11.770, backward_time=0.007, grad_norm=91.704, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-14 23:38:26,252 (trainer:763) INFO: 5epoch:train:241-280batch: iter_time=4.207e-05, forward_time=0.032, loss_ctc=11.628, loss=11.628, backward_time=0.007, grad_norm=91.431, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-14 23:38:28,477 (trainer:763) INFO: 5epoch:train:281-320batch: iter_time=4.322e-05, forward_time=0.030, loss_ctc=10.523, loss=10.523, backward_time=0.007, grad_norm=86.248, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.222 +[stan] 2024-01-14 23:38:30,906 (trainer:763) INFO: 5epoch:train:321-360batch: iter_time=4.121e-05, forward_time=0.032, loss_ctc=11.996, loss=11.996, backward_time=0.008, grad_norm=105.581, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-14 23:38:33,229 (trainer:763) INFO: 5epoch:train:361-400batch: iter_time=4.504e-05, forward_time=0.031, loss_ctc=12.032, loss=12.032, backward_time=0.007, grad_norm=108.568, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-14 23:38:35,503 (trainer:763) INFO: 5epoch:train:401-440batch: iter_time=4.203e-05, forward_time=0.030, loss_ctc=10.891, loss=10.891, backward_time=0.007, grad_norm=97.159, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-14 23:38:37,796 (trainer:763) INFO: 5epoch:train:441-480batch: iter_time=4.093e-05, forward_time=0.031, loss_ctc=11.371, loss=11.371, backward_time=0.007, grad_norm=90.073, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-14 23:38:40,507 (trainer:763) INFO: 5epoch:train:481-520batch: iter_time=4.175e-05, forward_time=0.036, loss_ctc=12.947, loss=12.947, backward_time=0.008, grad_norm=101.562, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 23:38:42,592 (trainer:763) INFO: 5epoch:train:521-560batch: iter_time=4.227e-05, forward_time=0.028, loss_ctc=10.307, loss=10.307, backward_time=0.007, grad_norm=97.904, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.208 +[stan] 2024-01-14 23:38:45,349 (trainer:763) INFO: 5epoch:train:561-600batch: iter_time=4.268e-05, forward_time=0.036, loss_ctc=13.205, loss=13.205, backward_time=0.008, grad_norm=108.897, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 23:38:47,546 (trainer:763) INFO: 5epoch:train:601-640batch: iter_time=4.349e-05, forward_time=0.029, loss_ctc=10.709, loss=10.709, backward_time=0.007, grad_norm=93.531, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-14 23:38:49,833 (trainer:763) INFO: 5epoch:train:641-680batch: iter_time=4.183e-05, forward_time=0.031, loss_ctc=10.276, loss=10.276, backward_time=0.007, grad_norm=93.296, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-14 23:38:52,273 (trainer:763) INFO: 5epoch:train:681-720batch: iter_time=4.181e-05, forward_time=0.032, loss_ctc=11.362, loss=11.362, backward_time=0.008, grad_norm=90.598, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 23:38:54,556 (trainer:763) INFO: 5epoch:train:721-760batch: iter_time=4.508e-05, forward_time=0.031, loss_ctc=10.686, loss=10.686, backward_time=0.007, grad_norm=102.517, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-14 23:38:56,975 (trainer:763) INFO: 5epoch:train:761-800batch: iter_time=4.105e-05, forward_time=0.032, loss_ctc=11.066, loss=11.066, backward_time=0.007, grad_norm=98.051, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-14 23:39:00,869 (trainer:354) INFO: 5epoch results: [train] iter_time=1.564e-04, forward_time=0.032, loss_ctc=11.680, loss=11.680, backward_time=0.007, grad_norm=96.620, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239, time=47.93 seconds, total_count=4000, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=52.155, cer_ctc=0.253, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=52.155, time=1.14 seconds, total_count=125, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.68 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:39:01,778 (trainer:417) INFO: The best model has been updated: valid.loss +[stan] 2024-01-14 23:39:01,778 (trainer:288) INFO: 6/30epoch started. Estimated time to finish: 21 minutes and 54.61 seconds +[stan] 2024-01-14 23:39:04,368 (trainer:763) INFO: 6epoch:train:1-40batch: iter_time=0.003, forward_time=0.031, loss_ctc=11.365, loss=11.365, backward_time=0.007, grad_norm=107.918, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-14 23:39:06,368 (trainer:763) INFO: 6epoch:train:41-80batch: iter_time=4.291e-05, forward_time=0.027, loss_ctc=9.401, loss=9.401, backward_time=0.007, grad_norm=90.943, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.200 +[stan] 2024-01-14 23:39:08,770 (trainer:763) INFO: 6epoch:train:81-120batch: iter_time=4.267e-05, forward_time=0.032, loss_ctc=10.571, loss=10.571, backward_time=0.007, grad_norm=98.259, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-14 23:39:11,248 (trainer:763) INFO: 6epoch:train:121-160batch: iter_time=3.983e-05, forward_time=0.033, loss_ctc=11.626, loss=11.626, backward_time=0.007, grad_norm=101.896, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 23:39:13,723 (trainer:763) INFO: 6epoch:train:161-200batch: iter_time=4.166e-05, forward_time=0.033, loss_ctc=11.743, loss=11.743, backward_time=0.007, grad_norm=104.405, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-14 23:39:16,127 (trainer:763) INFO: 6epoch:train:201-240batch: iter_time=4.055e-05, forward_time=0.032, loss_ctc=10.776, loss=10.776, backward_time=0.007, grad_norm=106.956, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-14 23:39:18,446 (trainer:763) INFO: 6epoch:train:241-280batch: iter_time=4.016e-05, forward_time=0.031, loss_ctc=10.191, loss=10.191, backward_time=0.007, grad_norm=100.674, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-14 23:39:20,496 (trainer:763) INFO: 6epoch:train:281-320batch: iter_time=3.965e-05, forward_time=0.027, loss_ctc=9.802, loss=9.802, backward_time=0.007, grad_norm=90.391, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.205 +[stan] 2024-01-14 23:39:23,081 (trainer:763) INFO: 6epoch:train:321-360batch: iter_time=4.072e-05, forward_time=0.034, loss_ctc=11.418, loss=11.418, backward_time=0.008, grad_norm=101.212, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 23:39:25,610 (trainer:763) INFO: 6epoch:train:361-400batch: iter_time=3.992e-05, forward_time=0.033, loss_ctc=11.207, loss=11.207, backward_time=0.008, grad_norm=101.986, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-14 23:39:27,718 (trainer:763) INFO: 6epoch:train:401-440batch: iter_time=4.141e-05, forward_time=0.028, loss_ctc=9.150, loss=9.150, backward_time=0.007, grad_norm=96.971, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.211 +[stan] 2024-01-14 23:39:30,275 (trainer:763) INFO: 6epoch:train:441-480batch: iter_time=4.303e-05, forward_time=0.034, loss_ctc=11.672, loss=11.672, backward_time=0.007, grad_norm=110.963, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-14 23:39:32,826 (trainer:763) INFO: 6epoch:train:481-520batch: iter_time=4.095e-05, forward_time=0.034, loss_ctc=11.157, loss=11.157, backward_time=0.008, grad_norm=100.987, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 23:39:35,153 (trainer:763) INFO: 6epoch:train:521-560batch: iter_time=4.254e-05, forward_time=0.031, loss_ctc=9.884, loss=9.884, backward_time=0.007, grad_norm=102.747, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-14 23:39:37,428 (trainer:763) INFO: 6epoch:train:561-600batch: iter_time=4.039e-05, forward_time=0.030, loss_ctc=10.032, loss=10.032, backward_time=0.007, grad_norm=107.281, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-14 23:39:39,700 (trainer:763) INFO: 6epoch:train:601-640batch: iter_time=4.008e-05, forward_time=0.030, loss_ctc=10.045, loss=10.045, backward_time=0.007, grad_norm=99.509, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-14 23:39:41,939 (trainer:763) INFO: 6epoch:train:641-680batch: iter_time=4.048e-05, forward_time=0.030, loss_ctc=9.629, loss=9.629, backward_time=0.007, grad_norm=99.812, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.224 +[stan] 2024-01-14 23:39:44,558 (trainer:763) INFO: 6epoch:train:681-720batch: iter_time=4.110e-05, forward_time=0.035, loss_ctc=11.336, loss=11.336, backward_time=0.007, grad_norm=114.074, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-14 23:39:46,943 (trainer:763) INFO: 6epoch:train:721-760batch: iter_time=4.216e-05, forward_time=0.032, loss_ctc=9.740, loss=9.740, backward_time=0.007, grad_norm=104.375, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-14 23:39:49,136 (trainer:763) INFO: 6epoch:train:761-800batch: iter_time=3.785e-05, forward_time=0.029, loss_ctc=9.715, loss=9.715, backward_time=0.007, grad_norm=101.943, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-14 23:39:53,041 (trainer:354) INFO: 6epoch results: [train] iter_time=1.643e-04, forward_time=0.031, loss_ctc=10.523, loss=10.523, backward_time=0.007, grad_norm=102.165, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237, time=47.43 seconds, total_count=4800, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=52.158, cer_ctc=0.250, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=52.158, time=1.16 seconds, total_count=150, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.68 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:39:54,062 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:39:54,062 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/1epoch.pth +[stan] 2024-01-14 23:39:54,062 (trainer:288) INFO: 7/30epoch started. Estimated time to finish: 21 minutes and 0.82 seconds +[stan] 2024-01-14 23:39:56,568 (trainer:763) INFO: 7epoch:train:1-40batch: iter_time=0.002, forward_time=0.030, loss_ctc=9.733, loss=9.733, backward_time=0.007, grad_norm=104.517, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 23:39:59,456 (trainer:763) INFO: 7epoch:train:41-80batch: iter_time=4.172e-05, forward_time=0.038, loss_ctc=12.033, loss=12.033, backward_time=0.008, grad_norm=138.699, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.289 +[stan] 2024-01-14 23:40:01,441 (trainer:763) INFO: 7epoch:train:81-120batch: iter_time=3.875e-05, forward_time=0.027, loss_ctc=8.127, loss=8.127, backward_time=0.007, grad_norm=90.944, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.198 +[stan] 2024-01-14 23:40:03,703 (trainer:763) INFO: 7epoch:train:121-160batch: iter_time=4.005e-05, forward_time=0.030, loss_ctc=9.864, loss=9.864, backward_time=0.007, grad_norm=102.229, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-14 23:40:05,864 (trainer:763) INFO: 7epoch:train:161-200batch: iter_time=4.058e-05, forward_time=0.029, loss_ctc=8.978, loss=8.978, backward_time=0.007, grad_norm=106.698, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.216 +[stan] 2024-01-14 23:40:08,351 (trainer:763) INFO: 7epoch:train:201-240batch: iter_time=4.071e-05, forward_time=0.033, loss_ctc=9.898, loss=9.898, backward_time=0.008, grad_norm=110.580, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 23:40:11,002 (trainer:763) INFO: 7epoch:train:241-280batch: iter_time=4.019e-05, forward_time=0.035, loss_ctc=10.975, loss=10.975, backward_time=0.007, grad_norm=112.977, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.265 +[stan] 2024-01-14 23:40:13,247 (trainer:763) INFO: 7epoch:train:281-320batch: iter_time=3.955e-05, forward_time=0.030, loss_ctc=9.117, loss=9.117, backward_time=0.007, grad_norm=108.327, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.224 +[stan] 2024-01-14 23:40:15,677 (trainer:763) INFO: 7epoch:train:321-360batch: iter_time=3.960e-05, forward_time=0.032, loss_ctc=10.264, loss=10.264, backward_time=0.007, grad_norm=112.840, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-14 23:40:18,234 (trainer:763) INFO: 7epoch:train:361-400batch: iter_time=4.044e-05, forward_time=0.034, loss_ctc=10.080, loss=10.080, backward_time=0.008, grad_norm=103.739, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-14 23:40:20,358 (trainer:763) INFO: 7epoch:train:401-440batch: iter_time=4.184e-05, forward_time=0.028, loss_ctc=8.705, loss=8.705, backward_time=0.007, grad_norm=98.675, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.212 +[stan] 2024-01-14 23:40:22,556 (trainer:763) INFO: 7epoch:train:441-480batch: iter_time=4.085e-05, forward_time=0.029, loss_ctc=8.622, loss=8.622, backward_time=0.007, grad_norm=103.815, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-14 23:40:24,666 (trainer:763) INFO: 7epoch:train:481-520batch: iter_time=3.963e-05, forward_time=0.028, loss_ctc=8.718, loss=8.718, backward_time=0.007, grad_norm=104.210, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.211 +[stan] 2024-01-14 23:40:27,416 (trainer:763) INFO: 7epoch:train:521-560batch: iter_time=4.220e-05, forward_time=0.036, loss_ctc=10.640, loss=10.640, backward_time=0.008, grad_norm=118.932, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 23:40:29,717 (trainer:763) INFO: 7epoch:train:561-600batch: iter_time=4.237e-05, forward_time=0.031, loss_ctc=9.061, loss=9.061, backward_time=0.007, grad_norm=104.272, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-14 23:40:32,186 (trainer:763) INFO: 7epoch:train:601-640batch: iter_time=3.988e-05, forward_time=0.033, loss_ctc=9.791, loss=9.791, backward_time=0.007, grad_norm=110.973, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-14 23:40:34,545 (trainer:763) INFO: 7epoch:train:641-680batch: iter_time=4.088e-05, forward_time=0.031, loss_ctc=8.835, loss=8.835, backward_time=0.007, grad_norm=103.567, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-14 23:40:36,864 (trainer:763) INFO: 7epoch:train:681-720batch: iter_time=3.973e-05, forward_time=0.031, loss_ctc=9.049, loss=9.049, backward_time=0.007, grad_norm=104.177, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-14 23:40:39,214 (trainer:763) INFO: 7epoch:train:721-760batch: iter_time=4.068e-05, forward_time=0.031, loss_ctc=9.258, loss=9.258, backward_time=0.007, grad_norm=103.399, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-14 23:40:41,591 (trainer:763) INFO: 7epoch:train:761-800batch: iter_time=3.812e-05, forward_time=0.032, loss_ctc=8.981, loss=8.981, backward_time=0.007, grad_norm=106.516, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-14 23:40:45,488 (trainer:354) INFO: 7epoch results: [train] iter_time=1.616e-04, forward_time=0.031, loss_ctc=9.537, loss=9.537, backward_time=0.007, grad_norm=107.504, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238, time=47.61 seconds, total_count=5600, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=53.165, cer_ctc=0.255, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=53.165, time=1.15 seconds, total_count=175, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.67 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:40:46,415 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:40:46,415 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/2epoch.pth +[stan] 2024-01-14 23:40:46,415 (trainer:288) INFO: 8/30epoch started. Estimated time to finish: 20 minutes and 7.69 seconds +[stan] 2024-01-14 23:40:48,655 (trainer:763) INFO: 8epoch:train:1-40batch: iter_time=0.003, forward_time=0.027, loss_ctc=7.310, loss=7.310, backward_time=0.007, grad_norm=98.845, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.224 +[stan] 2024-01-14 23:40:51,406 (trainer:763) INFO: 8epoch:train:41-80batch: iter_time=4.351e-05, forward_time=0.036, loss_ctc=10.258, loss=10.258, backward_time=0.008, grad_norm=114.068, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 23:40:53,542 (trainer:763) INFO: 8epoch:train:81-120batch: iter_time=4.087e-05, forward_time=0.030, loss_ctc=7.627, loss=7.627, backward_time=0.007, grad_norm=96.562, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.213 +[stan] 2024-01-14 23:40:56,110 (trainer:763) INFO: 8epoch:train:121-160batch: iter_time=3.935e-05, forward_time=0.034, loss_ctc=10.226, loss=10.226, backward_time=0.007, grad_norm=115.253, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 23:40:58,571 (trainer:763) INFO: 8epoch:train:161-200batch: iter_time=4.161e-05, forward_time=0.033, loss_ctc=9.133, loss=9.133, backward_time=0.007, grad_norm=111.243, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 23:41:00,866 (trainer:763) INFO: 8epoch:train:201-240batch: iter_time=3.945e-05, forward_time=0.031, loss_ctc=8.864, loss=8.864, backward_time=0.007, grad_norm=105.899, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-14 23:41:03,151 (trainer:763) INFO: 8epoch:train:241-280batch: iter_time=3.989e-05, forward_time=0.030, loss_ctc=7.901, loss=7.901, backward_time=0.007, grad_norm=102.475, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-14 23:41:05,496 (trainer:763) INFO: 8epoch:train:281-320batch: iter_time=4.073e-05, forward_time=0.031, loss_ctc=9.325, loss=9.325, backward_time=0.007, grad_norm=109.108, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-14 23:41:08,208 (trainer:763) INFO: 8epoch:train:321-360batch: iter_time=4.483e-05, forward_time=0.036, loss_ctc=10.120, loss=10.120, backward_time=0.008, grad_norm=121.659, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 23:41:10,356 (trainer:763) INFO: 8epoch:train:361-400batch: iter_time=4.164e-05, forward_time=0.029, loss_ctc=7.935, loss=7.935, backward_time=0.007, grad_norm=104.953, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.215 +[stan] 2024-01-14 23:41:12,800 (trainer:763) INFO: 8epoch:train:401-440batch: iter_time=4.294e-05, forward_time=0.032, loss_ctc=9.074, loss=9.074, backward_time=0.007, grad_norm=110.655, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 23:41:15,046 (trainer:763) INFO: 8epoch:train:441-480batch: iter_time=3.942e-05, forward_time=0.030, loss_ctc=8.562, loss=8.562, backward_time=0.007, grad_norm=111.043, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-14 23:41:17,426 (trainer:763) INFO: 8epoch:train:481-520batch: iter_time=4.098e-05, forward_time=0.032, loss_ctc=8.912, loss=8.912, backward_time=0.008, grad_norm=110.119, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-14 23:41:19,562 (trainer:763) INFO: 8epoch:train:521-560batch: iter_time=4.076e-05, forward_time=0.029, loss_ctc=7.698, loss=7.698, backward_time=0.007, grad_norm=106.391, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.213 +[stan] 2024-01-14 23:41:21,791 (trainer:763) INFO: 8epoch:train:561-600batch: iter_time=4.047e-05, forward_time=0.030, loss_ctc=8.416, loss=8.416, backward_time=0.007, grad_norm=109.683, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.223 +[stan] 2024-01-14 23:41:24,554 (trainer:763) INFO: 8epoch:train:601-640batch: iter_time=4.240e-05, forward_time=0.036, loss_ctc=10.380, loss=10.380, backward_time=0.008, grad_norm=129.892, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 23:41:27,030 (trainer:763) INFO: 8epoch:train:641-680batch: iter_time=4.001e-05, forward_time=0.033, loss_ctc=8.632, loss=8.632, backward_time=0.007, grad_norm=114.296, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 23:41:29,245 (trainer:763) INFO: 8epoch:train:681-720batch: iter_time=3.984e-05, forward_time=0.030, loss_ctc=7.786, loss=7.786, backward_time=0.007, grad_norm=114.808, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.221 +[stan] 2024-01-14 23:41:31,853 (trainer:763) INFO: 8epoch:train:721-760batch: iter_time=4.320e-05, forward_time=0.035, loss_ctc=8.900, loss=8.900, backward_time=0.008, grad_norm=114.615, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-14 23:41:34,048 (trainer:763) INFO: 8epoch:train:761-800batch: iter_time=3.790e-05, forward_time=0.029, loss_ctc=8.064, loss=8.064, backward_time=0.007, grad_norm=105.419, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-14 23:41:37,948 (trainer:354) INFO: 8epoch results: [train] iter_time=1.795e-04, forward_time=0.032, loss_ctc=8.756, loss=8.756, backward_time=0.007, grad_norm=110.349, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238, time=47.71 seconds, total_count=6400, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=54.750, cer_ctc=0.253, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=54.750, time=1.14 seconds, total_count=200, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.68 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:41:38,908 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:41:38,908 (trainer:288) INFO: 9/30epoch started. Estimated time to finish: 19 minutes and 15.14 seconds +[stan] 2024-01-14 23:41:41,567 (trainer:763) INFO: 9epoch:train:1-40batch: iter_time=0.003, forward_time=0.032, loss_ctc=8.932, loss=8.932, backward_time=0.008, grad_norm=111.063, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-14 23:41:44,347 (trainer:763) INFO: 9epoch:train:41-80batch: iter_time=4.560e-05, forward_time=0.037, loss_ctc=9.726, loss=9.726, backward_time=0.008, grad_norm=122.937, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 23:41:46,653 (trainer:763) INFO: 9epoch:train:81-120batch: iter_time=4.207e-05, forward_time=0.031, loss_ctc=8.354, loss=8.354, backward_time=0.007, grad_norm=112.709, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-14 23:41:48,749 (trainer:763) INFO: 9epoch:train:121-160batch: iter_time=4.028e-05, forward_time=0.028, loss_ctc=7.275, loss=7.275, backward_time=0.007, grad_norm=100.808, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.210 +[stan] 2024-01-14 23:41:50,928 (trainer:763) INFO: 9epoch:train:161-200batch: iter_time=3.908e-05, forward_time=0.029, loss_ctc=7.850, loss=7.850, backward_time=0.007, grad_norm=105.282, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.218 +[stan] 2024-01-14 23:41:53,119 (trainer:763) INFO: 9epoch:train:201-240batch: iter_time=3.988e-05, forward_time=0.029, loss_ctc=7.683, loss=7.683, backward_time=0.007, grad_norm=105.815, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-14 23:41:55,459 (trainer:763) INFO: 9epoch:train:241-280batch: iter_time=4.350e-05, forward_time=0.031, loss_ctc=8.095, loss=8.095, backward_time=0.007, grad_norm=109.661, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-14 23:41:58,204 (trainer:763) INFO: 9epoch:train:281-320batch: iter_time=4.135e-05, forward_time=0.036, loss_ctc=9.155, loss=9.155, backward_time=0.008, grad_norm=123.277, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 23:42:00,667 (trainer:763) INFO: 9epoch:train:321-360batch: iter_time=4.201e-05, forward_time=0.033, loss_ctc=8.593, loss=8.593, backward_time=0.008, grad_norm=117.460, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 23:42:03,013 (trainer:763) INFO: 9epoch:train:361-400batch: iter_time=4.100e-05, forward_time=0.031, loss_ctc=7.731, loss=7.731, backward_time=0.007, grad_norm=109.276, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-14 23:42:05,396 (trainer:763) INFO: 9epoch:train:401-440batch: iter_time=4.051e-05, forward_time=0.032, loss_ctc=8.123, loss=8.123, backward_time=0.007, grad_norm=112.410, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-14 23:42:07,490 (trainer:763) INFO: 9epoch:train:441-480batch: iter_time=4.014e-05, forward_time=0.028, loss_ctc=7.004, loss=7.004, backward_time=0.007, grad_norm=109.361, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.209 +[stan] 2024-01-14 23:42:09,916 (trainer:763) INFO: 9epoch:train:481-520batch: iter_time=4.137e-05, forward_time=0.032, loss_ctc=8.396, loss=8.396, backward_time=0.007, grad_norm=113.025, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-14 23:42:12,209 (trainer:763) INFO: 9epoch:train:521-560batch: iter_time=3.985e-05, forward_time=0.031, loss_ctc=7.707, loss=7.707, backward_time=0.007, grad_norm=110.571, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-14 23:42:14,834 (trainer:763) INFO: 9epoch:train:561-600batch: iter_time=4.312e-05, forward_time=0.035, loss_ctc=8.893, loss=8.893, backward_time=0.008, grad_norm=118.268, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-14 23:42:17,009 (trainer:763) INFO: 9epoch:train:601-640batch: iter_time=4.035e-05, forward_time=0.029, loss_ctc=7.102, loss=7.102, backward_time=0.007, grad_norm=101.558, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-14 23:42:19,669 (trainer:763) INFO: 9epoch:train:641-680batch: iter_time=4.069e-05, forward_time=0.035, loss_ctc=8.825, loss=8.825, backward_time=0.008, grad_norm=124.563, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-14 23:42:21,933 (trainer:763) INFO: 9epoch:train:681-720batch: iter_time=4.015e-05, forward_time=0.030, loss_ctc=7.495, loss=7.495, backward_time=0.007, grad_norm=115.919, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-14 23:42:24,132 (trainer:763) INFO: 9epoch:train:721-760batch: iter_time=4.048e-05, forward_time=0.029, loss_ctc=6.988, loss=6.988, backward_time=0.007, grad_norm=108.826, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-14 23:42:26,383 (trainer:763) INFO: 9epoch:train:761-800batch: iter_time=3.785e-05, forward_time=0.030, loss_ctc=7.859, loss=7.859, backward_time=0.007, grad_norm=109.212, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-14 23:42:30,320 (trainer:354) INFO: 9epoch results: [train] iter_time=1.968e-04, forward_time=0.031, loss_ctc=8.089, loss=8.089, backward_time=0.007, grad_norm=112.100, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237, time=47.56 seconds, total_count=7200, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=56.301, cer_ctc=0.254, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=56.301, time=1.14 seconds, total_count=225, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.71 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:42:31,358 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:42:31,358 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/8epoch.pth +[stan] 2024-01-14 23:42:31,358 (trainer:288) INFO: 10/30epoch started. Estimated time to finish: 18 minutes and 22.5 seconds +[stan] 2024-01-14 23:42:33,700 (trainer:763) INFO: 10epoch:train:1-40batch: iter_time=0.002, forward_time=0.028, loss_ctc=6.483, loss=6.483, backward_time=0.007, grad_norm=102.176, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-14 23:42:35,713 (trainer:763) INFO: 10epoch:train:41-80batch: iter_time=4.035e-05, forward_time=0.027, loss_ctc=6.389, loss=6.389, backward_time=0.007, grad_norm=102.242, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.201 +[stan] 2024-01-14 23:42:38,456 (trainer:763) INFO: 10epoch:train:81-120batch: iter_time=4.252e-05, forward_time=0.036, loss_ctc=9.403, loss=9.403, backward_time=0.008, grad_norm=121.898, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 23:42:40,857 (trainer:763) INFO: 10epoch:train:121-160batch: iter_time=4.155e-05, forward_time=0.032, loss_ctc=7.997, loss=7.997, backward_time=0.007, grad_norm=114.776, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-14 23:42:43,206 (trainer:763) INFO: 10epoch:train:161-200batch: iter_time=3.980e-05, forward_time=0.031, loss_ctc=7.539, loss=7.539, backward_time=0.007, grad_norm=116.821, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-14 23:42:45,780 (trainer:763) INFO: 10epoch:train:201-240batch: iter_time=3.966e-05, forward_time=0.034, loss_ctc=9.024, loss=9.024, backward_time=0.007, grad_norm=129.390, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 23:42:48,300 (trainer:763) INFO: 10epoch:train:241-280batch: iter_time=3.974e-05, forward_time=0.033, loss_ctc=8.421, loss=8.421, backward_time=0.007, grad_norm=121.847, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-14 23:42:50,453 (trainer:763) INFO: 10epoch:train:281-320batch: iter_time=3.939e-05, forward_time=0.029, loss_ctc=6.839, loss=6.839, backward_time=0.007, grad_norm=108.685, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.215 +[stan] 2024-01-14 23:42:52,840 (trainer:763) INFO: 10epoch:train:321-360batch: iter_time=4.013e-05, forward_time=0.032, loss_ctc=7.862, loss=7.862, backward_time=0.007, grad_norm=109.460, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-14 23:42:55,226 (trainer:763) INFO: 10epoch:train:361-400batch: iter_time=3.932e-05, forward_time=0.032, loss_ctc=7.701, loss=7.701, backward_time=0.007, grad_norm=115.831, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-14 23:42:57,565 (trainer:763) INFO: 10epoch:train:401-440batch: iter_time=4.143e-05, forward_time=0.031, loss_ctc=7.514, loss=7.514, backward_time=0.007, grad_norm=122.510, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-14 23:42:59,727 (trainer:763) INFO: 10epoch:train:441-480batch: iter_time=4.111e-05, forward_time=0.029, loss_ctc=6.572, loss=6.572, backward_time=0.007, grad_norm=102.853, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.216 +[stan] 2024-01-14 23:43:01,957 (trainer:763) INFO: 10epoch:train:481-520batch: iter_time=4.180e-05, forward_time=0.030, loss_ctc=6.927, loss=6.927, backward_time=0.007, grad_norm=109.623, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.223 +[stan] 2024-01-14 23:43:04,702 (trainer:763) INFO: 10epoch:train:521-560batch: iter_time=4.112e-05, forward_time=0.036, loss_ctc=8.567, loss=8.567, backward_time=0.008, grad_norm=123.386, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 23:43:07,000 (trainer:763) INFO: 10epoch:train:561-600batch: iter_time=4.338e-05, forward_time=0.031, loss_ctc=7.238, loss=7.238, backward_time=0.007, grad_norm=114.085, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-14 23:43:09,038 (trainer:763) INFO: 10epoch:train:601-640batch: iter_time=4.108e-05, forward_time=0.027, loss_ctc=6.634, loss=6.634, backward_time=0.007, grad_norm=107.863, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.204 +[stan] 2024-01-14 23:43:11,559 (trainer:763) INFO: 10epoch:train:641-680batch: iter_time=4.004e-05, forward_time=0.033, loss_ctc=7.703, loss=7.703, backward_time=0.007, grad_norm=119.674, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-14 23:43:13,970 (trainer:763) INFO: 10epoch:train:681-720batch: iter_time=4.047e-05, forward_time=0.032, loss_ctc=7.277, loss=7.277, backward_time=0.007, grad_norm=113.834, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-14 23:43:16,408 (trainer:763) INFO: 10epoch:train:721-760batch: iter_time=3.988e-05, forward_time=0.032, loss_ctc=7.716, loss=7.716, backward_time=0.007, grad_norm=119.906, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 23:43:18,884 (trainer:763) INFO: 10epoch:train:761-800batch: iter_time=3.950e-05, forward_time=0.033, loss_ctc=7.805, loss=7.805, backward_time=0.008, grad_norm=108.265, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 23:43:22,802 (trainer:354) INFO: 10epoch results: [train] iter_time=1.500e-04, forward_time=0.031, loss_ctc=7.581, loss=7.581, backward_time=0.007, grad_norm=114.256, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238, time=47.61 seconds, total_count=8000, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=57.621, cer_ctc=0.249, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=57.621, time=1.16 seconds, total_count=250, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.68 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:43:23,875 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:43:23,875 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/9epoch.pth +[stan] 2024-01-14 23:43:23,875 (trainer:288) INFO: 11/30epoch started. Estimated time to finish: 17 minutes and 30.04 seconds +[stan] 2024-01-14 23:43:26,613 (trainer:763) INFO: 11epoch:train:1-40batch: iter_time=0.002, forward_time=0.033, loss_ctc=7.576, loss=7.576, backward_time=0.007, grad_norm=118.339, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 23:43:28,823 (trainer:763) INFO: 11epoch:train:41-80batch: iter_time=4.020e-05, forward_time=0.029, loss_ctc=6.634, loss=6.634, backward_time=0.007, grad_norm=109.799, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.221 +[stan] 2024-01-14 23:43:31,287 (trainer:763) INFO: 11epoch:train:81-120batch: iter_time=4.343e-05, forward_time=0.033, loss_ctc=7.539, loss=7.539, backward_time=0.007, grad_norm=110.832, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 23:43:33,147 (trainer:763) INFO: 11epoch:train:121-160batch: iter_time=3.792e-05, forward_time=0.025, loss_ctc=5.479, loss=5.479, backward_time=0.007, grad_norm=101.764, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.186 +[stan] 2024-01-14 23:43:35,326 (trainer:763) INFO: 11epoch:train:161-200batch: iter_time=4.004e-05, forward_time=0.029, loss_ctc=6.709, loss=6.709, backward_time=0.007, grad_norm=110.029, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.218 +[stan] 2024-01-14 23:43:38,164 (trainer:763) INFO: 11epoch:train:201-240batch: iter_time=4.335e-05, forward_time=0.037, loss_ctc=8.761, loss=8.761, backward_time=0.008, grad_norm=129.932, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.284 +[stan] 2024-01-14 23:43:40,465 (trainer:763) INFO: 11epoch:train:241-280batch: iter_time=4.026e-05, forward_time=0.031, loss_ctc=6.676, loss=6.676, backward_time=0.007, grad_norm=109.669, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-14 23:43:43,265 (trainer:763) INFO: 11epoch:train:281-320batch: iter_time=4.115e-05, forward_time=0.037, loss_ctc=8.194, loss=8.194, backward_time=0.008, grad_norm=125.869, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 23:43:45,648 (trainer:763) INFO: 11epoch:train:321-360batch: iter_time=3.939e-05, forward_time=0.032, loss_ctc=8.057, loss=8.057, backward_time=0.007, grad_norm=120.584, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-14 23:43:47,691 (trainer:763) INFO: 11epoch:train:361-400batch: iter_time=3.888e-05, forward_time=0.027, loss_ctc=5.585, loss=5.585, backward_time=0.007, grad_norm=103.315, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.204 +[stan] 2024-01-14 23:43:50,252 (trainer:763) INFO: 11epoch:train:401-440batch: iter_time=4.081e-05, forward_time=0.034, loss_ctc=6.814, loss=6.814, backward_time=0.007, grad_norm=110.308, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-14 23:43:52,580 (trainer:763) INFO: 11epoch:train:441-480batch: iter_time=4.172e-05, forward_time=0.031, loss_ctc=6.919, loss=6.919, backward_time=0.007, grad_norm=109.823, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-14 23:43:54,778 (trainer:763) INFO: 11epoch:train:481-520batch: iter_time=4.157e-05, forward_time=0.029, loss_ctc=6.966, loss=6.966, backward_time=0.007, grad_norm=116.903, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-14 23:43:57,126 (trainer:763) INFO: 11epoch:train:521-560batch: iter_time=4.016e-05, forward_time=0.031, loss_ctc=6.959, loss=6.959, backward_time=0.007, grad_norm=113.237, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-14 23:43:59,626 (trainer:763) INFO: 11epoch:train:561-600batch: iter_time=4.394e-05, forward_time=0.033, loss_ctc=7.407, loss=7.407, backward_time=0.007, grad_norm=122.469, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 23:44:01,753 (trainer:763) INFO: 11epoch:train:601-640batch: iter_time=4.064e-05, forward_time=0.028, loss_ctc=6.315, loss=6.315, backward_time=0.007, grad_norm=114.568, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.213 +[stan] 2024-01-14 23:44:04,434 (trainer:763) INFO: 11epoch:train:641-680batch: iter_time=4.084e-05, forward_time=0.035, loss_ctc=7.800, loss=7.800, backward_time=0.007, grad_norm=128.831, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-14 23:44:06,452 (trainer:763) INFO: 11epoch:train:681-720batch: iter_time=3.991e-05, forward_time=0.027, loss_ctc=5.811, loss=5.811, backward_time=0.007, grad_norm=99.997, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.202 +[stan] 2024-01-14 23:44:08,734 (trainer:763) INFO: 11epoch:train:721-760batch: iter_time=3.989e-05, forward_time=0.030, loss_ctc=6.549, loss=6.549, backward_time=0.007, grad_norm=111.410, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-14 23:44:11,189 (trainer:763) INFO: 11epoch:train:761-800batch: iter_time=4.185e-05, forward_time=0.033, loss_ctc=6.773, loss=6.773, backward_time=0.007, grad_norm=115.097, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-14 23:44:15,123 (trainer:354) INFO: 11epoch results: [train] iter_time=1.622e-04, forward_time=0.031, loss_ctc=6.976, loss=6.976, backward_time=0.007, grad_norm=114.139, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236, time=47.39 seconds, total_count=8800, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=58.680, cer_ctc=0.248, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=58.680, time=1.14 seconds, total_count=275, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.72 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:44:16,033 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:44:16,033 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/10epoch.pth +[stan] 2024-01-14 23:44:16,034 (trainer:288) INFO: 12/30epoch started. Estimated time to finish: 16 minutes and 36.94 seconds +[stan] 2024-01-14 23:44:18,512 (trainer:763) INFO: 12epoch:train:1-40batch: iter_time=0.003, forward_time=0.030, loss_ctc=6.954, loss=6.954, backward_time=0.007, grad_norm=114.539, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 23:44:21,064 (trainer:763) INFO: 12epoch:train:41-80batch: iter_time=4.062e-05, forward_time=0.034, loss_ctc=7.117, loss=7.117, backward_time=0.008, grad_norm=112.236, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 23:44:23,494 (trainer:763) INFO: 12epoch:train:81-120batch: iter_time=4.273e-05, forward_time=0.032, loss_ctc=7.132, loss=7.132, backward_time=0.007, grad_norm=118.660, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-14 23:44:25,994 (trainer:763) INFO: 12epoch:train:121-160batch: iter_time=3.998e-05, forward_time=0.033, loss_ctc=7.116, loss=7.116, backward_time=0.007, grad_norm=119.451, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 23:44:28,216 (trainer:763) INFO: 12epoch:train:161-200batch: iter_time=4.393e-05, forward_time=0.030, loss_ctc=6.444, loss=6.444, backward_time=0.007, grad_norm=112.648, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.222 +[stan] 2024-01-14 23:44:31,071 (trainer:763) INFO: 12epoch:train:201-240batch: iter_time=4.425e-05, forward_time=0.038, loss_ctc=7.959, loss=7.959, backward_time=0.008, grad_norm=125.943, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-14 23:44:33,183 (trainer:763) INFO: 12epoch:train:241-280batch: iter_time=4.093e-05, forward_time=0.028, loss_ctc=6.469, loss=6.469, backward_time=0.007, grad_norm=108.363, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.211 +[stan] 2024-01-14 23:44:35,525 (trainer:763) INFO: 12epoch:train:281-320batch: iter_time=3.985e-05, forward_time=0.031, loss_ctc=6.480, loss=6.480, backward_time=0.007, grad_norm=110.169, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-14 23:44:37,593 (trainer:763) INFO: 12epoch:train:321-360batch: iter_time=3.950e-05, forward_time=0.028, loss_ctc=5.380, loss=5.380, backward_time=0.007, grad_norm=104.862, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.207 +[stan] 2024-01-14 23:44:40,155 (trainer:763) INFO: 12epoch:train:361-400batch: iter_time=4.089e-05, forward_time=0.034, loss_ctc=6.742, loss=6.742, backward_time=0.008, grad_norm=115.741, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-14 23:44:42,635 (trainer:763) INFO: 12epoch:train:401-440batch: iter_time=4.345e-05, forward_time=0.033, loss_ctc=7.216, loss=7.216, backward_time=0.007, grad_norm=118.981, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 23:44:44,737 (trainer:763) INFO: 12epoch:train:441-480batch: iter_time=4.162e-05, forward_time=0.028, loss_ctc=6.095, loss=6.095, backward_time=0.007, grad_norm=113.515, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.210 +[stan] 2024-01-14 23:44:46,919 (trainer:763) INFO: 12epoch:train:481-520batch: iter_time=3.898e-05, forward_time=0.029, loss_ctc=6.061, loss=6.061, backward_time=0.007, grad_norm=110.841, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.218 +[stan] 2024-01-14 23:44:49,232 (trainer:763) INFO: 12epoch:train:521-560batch: iter_time=4.049e-05, forward_time=0.031, loss_ctc=6.612, loss=6.612, backward_time=0.007, grad_norm=113.239, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-14 23:44:51,908 (trainer:763) INFO: 12epoch:train:561-600batch: iter_time=4.331e-05, forward_time=0.035, loss_ctc=7.630, loss=7.630, backward_time=0.008, grad_norm=120.231, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-14 23:44:54,160 (trainer:763) INFO: 12epoch:train:601-640batch: iter_time=4.076e-05, forward_time=0.030, loss_ctc=5.726, loss=5.726, backward_time=0.007, grad_norm=108.304, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-14 23:44:56,245 (trainer:763) INFO: 12epoch:train:641-680batch: iter_time=4.034e-05, forward_time=0.028, loss_ctc=5.837, loss=5.837, backward_time=0.007, grad_norm=105.933, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.208 +[stan] 2024-01-14 23:44:58,878 (trainer:763) INFO: 12epoch:train:681-720batch: iter_time=4.152e-05, forward_time=0.035, loss_ctc=6.968, loss=6.968, backward_time=0.007, grad_norm=121.310, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 23:45:01,036 (trainer:763) INFO: 12epoch:train:721-760batch: iter_time=4.080e-05, forward_time=0.029, loss_ctc=6.107, loss=6.107, backward_time=0.007, grad_norm=107.308, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.216 +[stan] 2024-01-14 23:45:03,637 (trainer:763) INFO: 12epoch:train:761-800batch: iter_time=4.020e-05, forward_time=0.034, loss_ctc=7.296, loss=7.296, backward_time=0.008, grad_norm=117.514, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-14 23:45:07,600 (trainer:354) INFO: 12epoch results: [train] iter_time=1.684e-04, forward_time=0.031, loss_ctc=6.667, loss=6.667, backward_time=0.007, grad_norm=113.989, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238, time=47.67 seconds, total_count=9600, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=59.190, cer_ctc=0.246, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=59.190, time=1.15 seconds, total_count=300, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:45:08,597 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:45:08,597 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/11epoch.pth +[stan] 2024-01-14 23:45:08,605 (trainer:288) INFO: 13/30epoch started. Estimated time to finish: 15 minutes and 44.62 seconds +[stan] 2024-01-14 23:45:11,187 (trainer:763) INFO: 13epoch:train:1-40batch: iter_time=0.002, forward_time=0.031, loss_ctc=6.124, loss=6.124, backward_time=0.007, grad_norm=109.773, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 23:45:13,491 (trainer:763) INFO: 13epoch:train:41-80batch: iter_time=4.098e-05, forward_time=0.031, loss_ctc=6.261, loss=6.261, backward_time=0.007, grad_norm=111.540, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-14 23:45:15,953 (trainer:763) INFO: 13epoch:train:81-120batch: iter_time=4.317e-05, forward_time=0.033, loss_ctc=6.820, loss=6.820, backward_time=0.007, grad_norm=115.565, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 23:45:18,282 (trainer:763) INFO: 13epoch:train:121-160batch: iter_time=3.973e-05, forward_time=0.031, loss_ctc=6.067, loss=6.067, backward_time=0.007, grad_norm=113.796, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-14 23:45:20,594 (trainer:763) INFO: 13epoch:train:161-200batch: iter_time=4.105e-05, forward_time=0.031, loss_ctc=6.189, loss=6.189, backward_time=0.007, grad_norm=107.306, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-14 23:45:23,068 (trainer:763) INFO: 13epoch:train:201-240batch: iter_time=4.222e-05, forward_time=0.033, loss_ctc=7.249, loss=7.249, backward_time=0.007, grad_norm=120.756, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-14 23:45:25,315 (trainer:763) INFO: 13epoch:train:241-280batch: iter_time=4.001e-05, forward_time=0.030, loss_ctc=5.598, loss=5.598, backward_time=0.007, grad_norm=108.832, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-14 23:45:27,882 (trainer:763) INFO: 13epoch:train:281-320batch: iter_time=4.087e-05, forward_time=0.034, loss_ctc=6.908, loss=6.908, backward_time=0.008, grad_norm=117.374, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 23:45:30,102 (trainer:763) INFO: 13epoch:train:321-360batch: iter_time=4.066e-05, forward_time=0.030, loss_ctc=5.438, loss=5.438, backward_time=0.007, grad_norm=111.919, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.222 +[stan] 2024-01-14 23:45:32,548 (trainer:763) INFO: 13epoch:train:361-400batch: iter_time=4.180e-05, forward_time=0.032, loss_ctc=6.645, loss=6.645, backward_time=0.007, grad_norm=115.272, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-14 23:45:34,873 (trainer:763) INFO: 13epoch:train:401-440batch: iter_time=4.127e-05, forward_time=0.031, loss_ctc=6.166, loss=6.166, backward_time=0.007, grad_norm=116.757, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-14 23:45:37,282 (trainer:763) INFO: 13epoch:train:441-480batch: iter_time=4.077e-05, forward_time=0.032, loss_ctc=6.628, loss=6.628, backward_time=0.007, grad_norm=116.960, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-14 23:45:39,885 (trainer:763) INFO: 13epoch:train:481-520batch: iter_time=4.061e-05, forward_time=0.034, loss_ctc=6.780, loss=6.780, backward_time=0.008, grad_norm=122.464, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-14 23:45:42,121 (trainer:763) INFO: 13epoch:train:521-560batch: iter_time=3.966e-05, forward_time=0.030, loss_ctc=5.724, loss=5.724, backward_time=0.007, grad_norm=110.035, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.224 +[stan] 2024-01-14 23:45:44,257 (trainer:763) INFO: 13epoch:train:561-600batch: iter_time=3.941e-05, forward_time=0.029, loss_ctc=5.798, loss=5.798, backward_time=0.007, grad_norm=111.910, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.213 +[stan] 2024-01-14 23:45:46,441 (trainer:763) INFO: 13epoch:train:601-640batch: iter_time=3.902e-05, forward_time=0.029, loss_ctc=5.612, loss=5.612, backward_time=0.007, grad_norm=108.609, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.218 +[stan] 2024-01-14 23:45:48,780 (trainer:763) INFO: 13epoch:train:641-680batch: iter_time=4.023e-05, forward_time=0.031, loss_ctc=6.428, loss=6.428, backward_time=0.007, grad_norm=112.120, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-14 23:45:51,244 (trainer:763) INFO: 13epoch:train:681-720batch: iter_time=4.018e-05, forward_time=0.033, loss_ctc=6.291, loss=6.291, backward_time=0.007, grad_norm=117.152, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 23:45:53,753 (trainer:763) INFO: 13epoch:train:721-760batch: iter_time=4.118e-05, forward_time=0.033, loss_ctc=6.683, loss=6.683, backward_time=0.007, grad_norm=118.687, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-14 23:45:55,901 (trainer:763) INFO: 13epoch:train:761-800batch: iter_time=3.887e-05, forward_time=0.029, loss_ctc=5.396, loss=5.396, backward_time=0.007, grad_norm=108.909, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.215 +[stan] 2024-01-14 23:45:59,820 (trainer:354) INFO: 13epoch results: [train] iter_time=1.489e-04, forward_time=0.031, loss_ctc=6.240, loss=6.240, backward_time=0.007, grad_norm=113.787, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236, time=47.36 seconds, total_count=10400, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=61.256, cer_ctc=0.246, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=61.256, time=1.14 seconds, total_count=325, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.71 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:46:00,771 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:46:00,771 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/12epoch.pth +[stan] 2024-01-14 23:46:00,771 (trainer:288) INFO: 14/30epoch started. Estimated time to finish: 14 minutes and 51.73 seconds +[stan] 2024-01-14 23:46:03,312 (trainer:763) INFO: 14epoch:train:1-40batch: iter_time=0.003, forward_time=0.031, loss_ctc=5.972, loss=5.972, backward_time=0.007, grad_norm=110.094, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-14 23:46:05,777 (trainer:763) INFO: 14epoch:train:41-80batch: iter_time=4.386e-05, forward_time=0.033, loss_ctc=5.934, loss=5.934, backward_time=0.007, grad_norm=112.677, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 23:46:08,260 (trainer:763) INFO: 14epoch:train:81-120batch: iter_time=4.172e-05, forward_time=0.033, loss_ctc=6.631, loss=6.631, backward_time=0.007, grad_norm=115.984, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 23:46:10,547 (trainer:763) INFO: 14epoch:train:121-160batch: iter_time=4.369e-05, forward_time=0.030, loss_ctc=5.872, loss=5.872, backward_time=0.007, grad_norm=115.376, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-14 23:46:13,298 (trainer:763) INFO: 14epoch:train:161-200batch: iter_time=4.151e-05, forward_time=0.036, loss_ctc=7.283, loss=7.283, backward_time=0.008, grad_norm=123.198, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-14 23:46:15,472 (trainer:763) INFO: 14epoch:train:201-240batch: iter_time=4.497e-05, forward_time=0.029, loss_ctc=5.587, loss=5.587, backward_time=0.007, grad_norm=111.487, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-14 23:46:17,445 (trainer:763) INFO: 14epoch:train:241-280batch: iter_time=4.201e-05, forward_time=0.026, loss_ctc=5.068, loss=5.068, backward_time=0.007, grad_norm=114.209, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.197 +[stan] 2024-01-14 23:46:19,855 (trainer:763) INFO: 14epoch:train:281-320batch: iter_time=4.153e-05, forward_time=0.032, loss_ctc=5.875, loss=5.875, backward_time=0.007, grad_norm=113.842, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-14 23:46:21,831 (trainer:763) INFO: 14epoch:train:321-360batch: iter_time=4.197e-05, forward_time=0.026, loss_ctc=4.777, loss=4.777, backward_time=0.007, grad_norm=103.558, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.197 +[stan] 2024-01-14 23:46:24,783 (trainer:763) INFO: 14epoch:train:361-400batch: iter_time=4.574e-05, forward_time=0.039, loss_ctc=7.616, loss=7.616, backward_time=0.008, grad_norm=126.323, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.295 +[stan] 2024-01-14 23:46:27,399 (trainer:763) INFO: 14epoch:train:401-440batch: iter_time=4.214e-05, forward_time=0.036, loss_ctc=6.366, loss=6.366, backward_time=0.007, grad_norm=116.979, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-14 23:46:29,444 (trainer:763) INFO: 14epoch:train:441-480batch: iter_time=3.968e-05, forward_time=0.027, loss_ctc=4.856, loss=4.856, backward_time=0.007, grad_norm=101.955, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.204 +[stan] 2024-01-14 23:46:32,013 (trainer:763) INFO: 14epoch:train:481-520batch: iter_time=4.246e-05, forward_time=0.034, loss_ctc=6.079, loss=6.079, backward_time=0.007, grad_norm=118.458, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 23:46:34,304 (trainer:763) INFO: 14epoch:train:521-560batch: iter_time=4.176e-05, forward_time=0.031, loss_ctc=5.920, loss=5.920, backward_time=0.007, grad_norm=111.313, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-14 23:46:36,891 (trainer:763) INFO: 14epoch:train:561-600batch: iter_time=4.078e-05, forward_time=0.034, loss_ctc=6.475, loss=6.475, backward_time=0.008, grad_norm=115.815, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-14 23:46:39,096 (trainer:763) INFO: 14epoch:train:601-640batch: iter_time=4.027e-05, forward_time=0.029, loss_ctc=5.103, loss=5.103, backward_time=0.007, grad_norm=107.561, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-14 23:46:41,730 (trainer:763) INFO: 14epoch:train:641-680batch: iter_time=4.104e-05, forward_time=0.035, loss_ctc=6.765, loss=6.765, backward_time=0.008, grad_norm=121.366, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 23:46:43,696 (trainer:763) INFO: 14epoch:train:681-720batch: iter_time=4.011e-05, forward_time=0.026, loss_ctc=4.944, loss=4.944, backward_time=0.007, grad_norm=102.409, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.196 +[stan] 2024-01-14 23:46:46,141 (trainer:763) INFO: 14epoch:train:721-760batch: iter_time=4.105e-05, forward_time=0.032, loss_ctc=6.048, loss=6.048, backward_time=0.007, grad_norm=111.666, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 23:46:48,852 (trainer:763) INFO: 14epoch:train:761-800batch: iter_time=3.948e-05, forward_time=0.036, loss_ctc=6.913, loss=6.913, backward_time=0.008, grad_norm=120.075, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 23:46:52,752 (trainer:354) INFO: 14epoch results: [train] iter_time=1.670e-04, forward_time=0.032, loss_ctc=6.004, loss=6.004, backward_time=0.007, grad_norm=113.717, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240, time=48.15 seconds, total_count=11200, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=61.670, cer_ctc=0.245, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=61.670, time=1.14 seconds, total_count=350, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.68 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:46:53,683 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:46:53,684 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/13epoch.pth +[stan] 2024-01-14 23:46:53,684 (trainer:288) INFO: 15/30epoch started. Estimated time to finish: 13 minutes and 59.8 seconds +[stan] 2024-01-14 23:46:55,945 (trainer:763) INFO: 15epoch:train:1-40batch: iter_time=0.003, forward_time=0.027, loss_ctc=4.776, loss=4.776, backward_time=0.007, grad_norm=107.221, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-14 23:46:58,238 (trainer:763) INFO: 15epoch:train:41-80batch: iter_time=4.065e-05, forward_time=0.031, loss_ctc=5.795, loss=5.795, backward_time=0.007, grad_norm=114.629, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-14 23:47:00,409 (trainer:763) INFO: 15epoch:train:81-120batch: iter_time=4.057e-05, forward_time=0.029, loss_ctc=5.152, loss=5.152, backward_time=0.007, grad_norm=105.935, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-14 23:47:03,132 (trainer:763) INFO: 15epoch:train:121-160batch: iter_time=4.604e-05, forward_time=0.036, loss_ctc=6.542, loss=6.542, backward_time=0.008, grad_norm=113.853, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 23:47:05,435 (trainer:763) INFO: 15epoch:train:161-200batch: iter_time=3.941e-05, forward_time=0.031, loss_ctc=5.406, loss=5.406, backward_time=0.007, grad_norm=108.865, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-14 23:47:08,214 (trainer:763) INFO: 15epoch:train:201-240batch: iter_time=4.178e-05, forward_time=0.037, loss_ctc=6.311, loss=6.311, backward_time=0.008, grad_norm=113.143, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-14 23:47:10,459 (trainer:763) INFO: 15epoch:train:241-280batch: iter_time=4.124e-05, forward_time=0.030, loss_ctc=5.370, loss=5.370, backward_time=0.007, grad_norm=112.040, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.224 +[stan] 2024-01-14 23:47:12,775 (trainer:763) INFO: 15epoch:train:281-320batch: iter_time=4.306e-05, forward_time=0.031, loss_ctc=6.086, loss=6.086, backward_time=0.007, grad_norm=117.372, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-14 23:47:14,768 (trainer:763) INFO: 15epoch:train:321-360batch: iter_time=4.325e-05, forward_time=0.027, loss_ctc=4.877, loss=4.877, backward_time=0.007, grad_norm=107.094, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.199 +[stan] 2024-01-14 23:47:16,955 (trainer:763) INFO: 15epoch:train:361-400batch: iter_time=4.183e-05, forward_time=0.029, loss_ctc=5.444, loss=5.444, backward_time=0.007, grad_norm=109.211, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-14 23:47:19,386 (trainer:763) INFO: 15epoch:train:401-440batch: iter_time=4.154e-05, forward_time=0.032, loss_ctc=6.081, loss=6.081, backward_time=0.007, grad_norm=116.318, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-14 23:47:21,884 (trainer:763) INFO: 15epoch:train:441-480batch: iter_time=4.125e-05, forward_time=0.033, loss_ctc=5.873, loss=5.873, backward_time=0.007, grad_norm=115.287, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 23:47:24,120 (trainer:763) INFO: 15epoch:train:481-520batch: iter_time=3.997e-05, forward_time=0.030, loss_ctc=4.867, loss=4.867, backward_time=0.007, grad_norm=107.913, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.224 +[stan] 2024-01-14 23:47:26,625 (trainer:763) INFO: 15epoch:train:521-560batch: iter_time=4.552e-05, forward_time=0.033, loss_ctc=6.331, loss=6.331, backward_time=0.007, grad_norm=113.046, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 23:47:29,198 (trainer:763) INFO: 15epoch:train:561-600batch: iter_time=4.141e-05, forward_time=0.034, loss_ctc=6.318, loss=6.318, backward_time=0.008, grad_norm=114.572, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 23:47:31,318 (trainer:763) INFO: 15epoch:train:601-640batch: iter_time=4.169e-05, forward_time=0.028, loss_ctc=4.837, loss=4.837, backward_time=0.007, grad_norm=106.124, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.212 +[stan] 2024-01-14 23:47:33,568 (trainer:763) INFO: 15epoch:train:641-680batch: iter_time=4.141e-05, forward_time=0.030, loss_ctc=5.526, loss=5.526, backward_time=0.007, grad_norm=114.100, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-14 23:47:36,221 (trainer:763) INFO: 15epoch:train:681-720batch: iter_time=4.110e-05, forward_time=0.035, loss_ctc=6.677, loss=6.677, backward_time=0.008, grad_norm=118.241, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.265 +[stan] 2024-01-14 23:47:38,540 (trainer:763) INFO: 15epoch:train:721-760batch: iter_time=4.271e-05, forward_time=0.031, loss_ctc=5.506, loss=5.506, backward_time=0.007, grad_norm=110.342, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-14 23:47:40,826 (trainer:763) INFO: 15epoch:train:761-800batch: iter_time=3.921e-05, forward_time=0.030, loss_ctc=5.395, loss=5.395, backward_time=0.007, grad_norm=108.291, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-14 23:47:44,796 (trainer:354) INFO: 15epoch results: [train] iter_time=1.714e-04, forward_time=0.031, loss_ctc=5.659, loss=5.659, backward_time=0.007, grad_norm=111.680, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236, time=47.22 seconds, total_count=12000, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=62.299, cer_ctc=0.240, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=62.299, time=1.17 seconds, total_count=375, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.73 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:47:45,853 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:47:45,853 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/14epoch.pth +[stan] 2024-01-14 23:47:45,853 (trainer:288) INFO: 16/30epoch started. Estimated time to finish: 13 minutes and 7 seconds +[stan] 2024-01-14 23:47:48,833 (trainer:763) INFO: 16epoch:train:1-40batch: iter_time=0.002, forward_time=0.036, loss_ctc=5.944, loss=5.944, backward_time=0.008, grad_norm=119.019, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.298 +[stan] 2024-01-14 23:47:50,896 (trainer:763) INFO: 16epoch:train:41-80batch: iter_time=3.937e-05, forward_time=0.028, loss_ctc=4.876, loss=4.876, backward_time=0.007, grad_norm=104.982, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.206 +[stan] 2024-01-14 23:47:53,233 (trainer:763) INFO: 16epoch:train:81-120batch: iter_time=4.064e-05, forward_time=0.031, loss_ctc=5.483, loss=5.483, backward_time=0.007, grad_norm=113.059, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-14 23:47:55,568 (trainer:763) INFO: 16epoch:train:121-160batch: iter_time=4.045e-05, forward_time=0.031, loss_ctc=5.804, loss=5.804, backward_time=0.007, grad_norm=110.664, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-14 23:47:57,898 (trainer:763) INFO: 16epoch:train:161-200batch: iter_time=4.087e-05, forward_time=0.031, loss_ctc=5.381, loss=5.381, backward_time=0.007, grad_norm=113.733, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-14 23:48:00,375 (trainer:763) INFO: 16epoch:train:201-240batch: iter_time=4.150e-05, forward_time=0.033, loss_ctc=6.217, loss=6.217, backward_time=0.008, grad_norm=113.157, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 23:48:02,708 (trainer:763) INFO: 16epoch:train:241-280batch: iter_time=4.301e-05, forward_time=0.031, loss_ctc=5.348, loss=5.348, backward_time=0.007, grad_norm=117.854, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-14 23:48:05,152 (trainer:763) INFO: 16epoch:train:281-320batch: iter_time=4.239e-05, forward_time=0.033, loss_ctc=5.692, loss=5.692, backward_time=0.007, grad_norm=112.270, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 23:48:07,651 (trainer:763) INFO: 16epoch:train:321-360batch: iter_time=4.253e-05, forward_time=0.033, loss_ctc=5.409, loss=5.409, backward_time=0.007, grad_norm=111.741, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 23:48:10,109 (trainer:763) INFO: 16epoch:train:361-400batch: iter_time=3.982e-05, forward_time=0.033, loss_ctc=5.962, loss=5.962, backward_time=0.007, grad_norm=127.522, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 23:48:12,225 (trainer:763) INFO: 16epoch:train:401-440batch: iter_time=4.063e-05, forward_time=0.028, loss_ctc=4.906, loss=4.906, backward_time=0.007, grad_norm=104.236, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.211 +[stan] 2024-01-14 23:48:14,343 (trainer:763) INFO: 16epoch:train:441-480batch: iter_time=4.231e-05, forward_time=0.028, loss_ctc=4.634, loss=4.634, backward_time=0.007, grad_norm=106.742, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.212 +[stan] 2024-01-14 23:48:16,677 (trainer:763) INFO: 16epoch:train:481-520batch: iter_time=3.981e-05, forward_time=0.031, loss_ctc=5.227, loss=5.227, backward_time=0.007, grad_norm=111.304, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-14 23:48:19,009 (trainer:763) INFO: 16epoch:train:521-560batch: iter_time=4.061e-05, forward_time=0.031, loss_ctc=5.336, loss=5.336, backward_time=0.007, grad_norm=110.687, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-14 23:48:21,547 (trainer:763) INFO: 16epoch:train:561-600batch: iter_time=4.100e-05, forward_time=0.034, loss_ctc=6.386, loss=6.386, backward_time=0.008, grad_norm=114.466, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-14 23:48:23,986 (trainer:763) INFO: 16epoch:train:601-640batch: iter_time=4.305e-05, forward_time=0.032, loss_ctc=5.652, loss=5.652, backward_time=0.007, grad_norm=111.358, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 23:48:26,377 (trainer:763) INFO: 16epoch:train:641-680batch: iter_time=4.067e-05, forward_time=0.032, loss_ctc=5.317, loss=5.317, backward_time=0.007, grad_norm=111.416, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-14 23:48:28,991 (trainer:763) INFO: 16epoch:train:681-720batch: iter_time=4.113e-05, forward_time=0.035, loss_ctc=6.007, loss=6.007, backward_time=0.008, grad_norm=112.468, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-14 23:48:30,960 (trainer:763) INFO: 16epoch:train:721-760batch: iter_time=3.960e-05, forward_time=0.026, loss_ctc=4.628, loss=4.628, backward_time=0.007, grad_norm=102.021, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.197 +[stan] 2024-01-14 23:48:33,513 (trainer:763) INFO: 16epoch:train:761-800batch: iter_time=4.056e-05, forward_time=0.034, loss_ctc=5.900, loss=5.900, backward_time=0.007, grad_norm=113.971, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 23:48:37,469 (trainer:354) INFO: 16epoch results: [train] iter_time=1.591e-04, forward_time=0.032, loss_ctc=5.505, loss=5.505, backward_time=0.007, grad_norm=112.133, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238, time=47.73 seconds, total_count=12800, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=63.971, cer_ctc=0.241, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=63.971, time=1.15 seconds, total_count=400, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:48:38,430 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:48:38,430 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/15epoch.pth +[stan] 2024-01-14 23:48:38,430 (trainer:288) INFO: 17/30epoch started. Estimated time to finish: 12 minutes and 14.63 seconds +[stan] 2024-01-14 23:48:40,840 (trainer:763) INFO: 17epoch:train:1-40batch: iter_time=0.003, forward_time=0.029, loss_ctc=5.143, loss=5.143, backward_time=0.007, grad_norm=106.801, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-14 23:48:43,407 (trainer:763) INFO: 17epoch:train:41-80batch: iter_time=4.109e-05, forward_time=0.034, loss_ctc=5.362, loss=5.362, backward_time=0.007, grad_norm=113.660, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 23:48:45,667 (trainer:763) INFO: 17epoch:train:81-120batch: iter_time=4.439e-05, forward_time=0.030, loss_ctc=5.284, loss=5.284, backward_time=0.007, grad_norm=108.386, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-14 23:48:47,621 (trainer:763) INFO: 17epoch:train:121-160batch: iter_time=4.018e-05, forward_time=0.026, loss_ctc=4.150, loss=4.150, backward_time=0.007, grad_norm=101.992, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.195 +[stan] 2024-01-14 23:48:50,212 (trainer:763) INFO: 17epoch:train:161-200batch: iter_time=4.390e-05, forward_time=0.034, loss_ctc=5.773, loss=5.773, backward_time=0.008, grad_norm=110.260, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-14 23:48:52,719 (trainer:763) INFO: 17epoch:train:201-240batch: iter_time=4.135e-05, forward_time=0.033, loss_ctc=5.551, loss=5.551, backward_time=0.007, grad_norm=117.267, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-14 23:48:55,300 (trainer:763) INFO: 17epoch:train:241-280batch: iter_time=4.139e-05, forward_time=0.034, loss_ctc=6.044, loss=6.044, backward_time=0.007, grad_norm=120.259, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 23:48:57,660 (trainer:763) INFO: 17epoch:train:281-320batch: iter_time=4.386e-05, forward_time=0.031, loss_ctc=5.318, loss=5.318, backward_time=0.008, grad_norm=114.091, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-14 23:48:59,933 (trainer:763) INFO: 17epoch:train:321-360batch: iter_time=4.088e-05, forward_time=0.030, loss_ctc=5.132, loss=5.132, backward_time=0.007, grad_norm=108.989, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-14 23:49:02,158 (trainer:763) INFO: 17epoch:train:361-400batch: iter_time=4.401e-05, forward_time=0.030, loss_ctc=4.587, loss=4.587, backward_time=0.007, grad_norm=99.794, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.222 +[stan] 2024-01-14 23:49:04,355 (trainer:763) INFO: 17epoch:train:401-440batch: iter_time=4.162e-05, forward_time=0.029, loss_ctc=4.497, loss=4.497, backward_time=0.007, grad_norm=103.239, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-14 23:49:06,710 (trainer:763) INFO: 17epoch:train:441-480batch: iter_time=4.366e-05, forward_time=0.031, loss_ctc=5.695, loss=5.695, backward_time=0.007, grad_norm=109.865, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-14 23:49:09,188 (trainer:763) INFO: 17epoch:train:481-520batch: iter_time=4.128e-05, forward_time=0.033, loss_ctc=5.950, loss=5.950, backward_time=0.007, grad_norm=115.263, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 23:49:11,552 (trainer:763) INFO: 17epoch:train:521-560batch: iter_time=4.430e-05, forward_time=0.031, loss_ctc=5.045, loss=5.045, backward_time=0.007, grad_norm=101.993, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-14 23:49:14,024 (trainer:763) INFO: 17epoch:train:561-600batch: iter_time=4.150e-05, forward_time=0.033, loss_ctc=5.976, loss=5.976, backward_time=0.007, grad_norm=112.749, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-14 23:49:16,673 (trainer:763) INFO: 17epoch:train:601-640batch: iter_time=4.110e-05, forward_time=0.035, loss_ctc=5.959, loss=5.959, backward_time=0.008, grad_norm=113.070, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.265 +[stan] 2024-01-14 23:49:18,868 (trainer:763) INFO: 17epoch:train:641-680batch: iter_time=4.022e-05, forward_time=0.029, loss_ctc=4.675, loss=4.675, backward_time=0.007, grad_norm=109.698, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-14 23:49:20,911 (trainer:763) INFO: 17epoch:train:681-720batch: iter_time=4.201e-05, forward_time=0.027, loss_ctc=4.378, loss=4.378, backward_time=0.007, grad_norm=105.766, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.204 +[stan] 2024-01-14 23:49:22,947 (trainer:763) INFO: 17epoch:train:721-760batch: iter_time=4.132e-05, forward_time=0.027, loss_ctc=3.810, loss=3.810, backward_time=0.007, grad_norm=95.870, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.204 +[stan] 2024-01-14 23:49:25,320 (trainer:763) INFO: 17epoch:train:761-800batch: iter_time=3.881e-05, forward_time=0.032, loss_ctc=5.286, loss=5.286, backward_time=0.007, grad_norm=112.857, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-14 23:49:29,311 (trainer:354) INFO: 17epoch results: [train] iter_time=1.710e-04, forward_time=0.031, loss_ctc=5.181, loss=5.181, backward_time=0.007, grad_norm=109.093, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234, time=46.97 seconds, total_count=13600, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=65.226, cer_ctc=0.244, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=65.226, time=1.17 seconds, total_count=425, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:49:30,365 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:49:30,365 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/16epoch.pth +[stan] 2024-01-14 23:49:30,365 (trainer:288) INFO: 18/30epoch started. Estimated time to finish: 11 minutes and 21.74 seconds +[stan] 2024-01-14 23:49:33,557 (trainer:763) INFO: 18epoch:train:1-40batch: iter_time=0.003, forward_time=0.039, loss_ctc=6.816, loss=6.816, backward_time=0.008, grad_norm=116.820, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.319 +[stan] 2024-01-14 23:49:35,752 (trainer:763) INFO: 18epoch:train:41-80batch: iter_time=4.049e-05, forward_time=0.029, loss_ctc=4.548, loss=4.548, backward_time=0.007, grad_norm=106.789, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-14 23:49:38,071 (trainer:763) INFO: 18epoch:train:81-120batch: iter_time=4.043e-05, forward_time=0.031, loss_ctc=4.663, loss=4.663, backward_time=0.007, grad_norm=104.582, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-14 23:49:40,800 (trainer:763) INFO: 18epoch:train:121-160batch: iter_time=4.103e-05, forward_time=0.036, loss_ctc=6.276, loss=6.276, backward_time=0.008, grad_norm=113.961, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-14 23:49:42,899 (trainer:763) INFO: 18epoch:train:161-200batch: iter_time=4.063e-05, forward_time=0.028, loss_ctc=4.391, loss=4.391, backward_time=0.007, grad_norm=106.148, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.210 +[stan] 2024-01-14 23:49:45,449 (trainer:763) INFO: 18epoch:train:201-240batch: iter_time=4.090e-05, forward_time=0.034, loss_ctc=5.686, loss=5.686, backward_time=0.008, grad_norm=110.616, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 23:49:47,816 (trainer:763) INFO: 18epoch:train:241-280batch: iter_time=4.049e-05, forward_time=0.031, loss_ctc=5.098, loss=5.098, backward_time=0.007, grad_norm=112.001, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-14 23:49:50,246 (trainer:763) INFO: 18epoch:train:281-320batch: iter_time=4.045e-05, forward_time=0.032, loss_ctc=5.370, loss=5.370, backward_time=0.007, grad_norm=107.642, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-14 23:49:52,344 (trainer:763) INFO: 18epoch:train:321-360batch: iter_time=3.927e-05, forward_time=0.028, loss_ctc=4.726, loss=4.726, backward_time=0.007, grad_norm=105.625, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.210 +[stan] 2024-01-14 23:49:54,753 (trainer:763) INFO: 18epoch:train:361-400batch: iter_time=4.123e-05, forward_time=0.032, loss_ctc=4.760, loss=4.760, backward_time=0.007, grad_norm=107.711, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-14 23:49:57,121 (trainer:763) INFO: 18epoch:train:401-440batch: iter_time=3.968e-05, forward_time=0.031, loss_ctc=5.163, loss=5.163, backward_time=0.007, grad_norm=106.982, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-14 23:49:59,420 (trainer:763) INFO: 18epoch:train:441-480batch: iter_time=3.975e-05, forward_time=0.031, loss_ctc=5.159, loss=5.159, backward_time=0.007, grad_norm=111.559, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-14 23:50:01,673 (trainer:763) INFO: 18epoch:train:481-520batch: iter_time=4.012e-05, forward_time=0.030, loss_ctc=4.478, loss=4.478, backward_time=0.007, grad_norm=105.979, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-14 23:50:04,235 (trainer:763) INFO: 18epoch:train:521-560batch: iter_time=4.163e-05, forward_time=0.034, loss_ctc=5.629, loss=5.629, backward_time=0.007, grad_norm=113.667, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-14 23:50:06,588 (trainer:763) INFO: 18epoch:train:561-600batch: iter_time=4.042e-05, forward_time=0.031, loss_ctc=5.201, loss=5.201, backward_time=0.007, grad_norm=110.195, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-14 23:50:08,939 (trainer:763) INFO: 18epoch:train:601-640batch: iter_time=4.068e-05, forward_time=0.031, loss_ctc=5.434, loss=5.434, backward_time=0.007, grad_norm=116.817, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-14 23:50:11,324 (trainer:763) INFO: 18epoch:train:641-680batch: iter_time=4.235e-05, forward_time=0.032, loss_ctc=4.819, loss=4.819, backward_time=0.007, grad_norm=108.258, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-14 23:50:13,441 (trainer:763) INFO: 18epoch:train:681-720batch: iter_time=3.962e-05, forward_time=0.028, loss_ctc=4.417, loss=4.417, backward_time=0.007, grad_norm=102.267, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.212 +[stan] 2024-01-14 23:50:15,812 (trainer:763) INFO: 18epoch:train:721-760batch: iter_time=3.996e-05, forward_time=0.032, loss_ctc=4.978, loss=4.978, backward_time=0.007, grad_norm=107.571, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-14 23:50:18,283 (trainer:763) INFO: 18epoch:train:761-800batch: iter_time=3.852e-05, forward_time=0.033, loss_ctc=5.684, loss=5.684, backward_time=0.007, grad_norm=109.578, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-14 23:50:22,256 (trainer:354) INFO: 18epoch results: [train] iter_time=1.723e-04, forward_time=0.032, loss_ctc=5.165, loss=5.165, backward_time=0.007, grad_norm=109.238, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239, time=48 seconds, total_count=14400, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=65.794, cer_ctc=0.241, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=65.794, time=1.15 seconds, total_count=450, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:50:23,318 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:50:23,319 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/17epoch.pth +[stan] 2024-01-14 23:50:23,319 (trainer:288) INFO: 19/30epoch started. Estimated time to finish: 10 minutes and 29.64 seconds +[stan] 2024-01-14 23:50:26,047 (trainer:763) INFO: 19epoch:train:1-40batch: iter_time=0.002, forward_time=0.033, loss_ctc=5.487, loss=5.487, backward_time=0.007, grad_norm=118.420, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 23:50:28,745 (trainer:763) INFO: 19epoch:train:41-80batch: iter_time=4.085e-05, forward_time=0.036, loss_ctc=5.662, loss=5.662, backward_time=0.008, grad_norm=117.854, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-14 23:50:31,014 (trainer:763) INFO: 19epoch:train:81-120batch: iter_time=4.096e-05, forward_time=0.030, loss_ctc=4.751, loss=4.751, backward_time=0.007, grad_norm=109.100, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-14 23:50:33,030 (trainer:763) INFO: 19epoch:train:121-160batch: iter_time=4.031e-05, forward_time=0.027, loss_ctc=4.201, loss=4.201, backward_time=0.007, grad_norm=101.414, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.201 +[stan] 2024-01-14 23:50:35,413 (trainer:763) INFO: 19epoch:train:161-200batch: iter_time=4.002e-05, forward_time=0.032, loss_ctc=5.310, loss=5.310, backward_time=0.007, grad_norm=110.506, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-14 23:50:37,748 (trainer:763) INFO: 19epoch:train:201-240batch: iter_time=4.113e-05, forward_time=0.031, loss_ctc=4.720, loss=4.720, backward_time=0.007, grad_norm=108.900, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-14 23:50:40,255 (trainer:763) INFO: 19epoch:train:241-280batch: iter_time=4.047e-05, forward_time=0.033, loss_ctc=5.191, loss=5.191, backward_time=0.007, grad_norm=111.313, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-14 23:50:42,258 (trainer:763) INFO: 19epoch:train:281-320batch: iter_time=4.320e-05, forward_time=0.027, loss_ctc=4.008, loss=4.008, backward_time=0.007, grad_norm=102.537, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.200 +[stan] 2024-01-14 23:50:44,787 (trainer:763) INFO: 19epoch:train:321-360batch: iter_time=4.301e-05, forward_time=0.034, loss_ctc=5.333, loss=5.333, backward_time=0.007, grad_norm=110.295, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-14 23:50:47,357 (trainer:763) INFO: 19epoch:train:361-400batch: iter_time=3.955e-05, forward_time=0.034, loss_ctc=5.923, loss=5.923, backward_time=0.008, grad_norm=118.017, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-14 23:50:49,458 (trainer:763) INFO: 19epoch:train:401-440batch: iter_time=4.071e-05, forward_time=0.028, loss_ctc=4.485, loss=4.485, backward_time=0.007, grad_norm=100.716, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.210 +[stan] 2024-01-14 23:50:51,807 (trainer:763) INFO: 19epoch:train:441-480batch: iter_time=4.275e-05, forward_time=0.031, loss_ctc=4.674, loss=4.674, backward_time=0.007, grad_norm=104.587, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-14 23:50:54,028 (trainer:763) INFO: 19epoch:train:481-520batch: iter_time=4.067e-05, forward_time=0.030, loss_ctc=4.600, loss=4.600, backward_time=0.007, grad_norm=98.853, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.222 +[stan] 2024-01-14 23:50:56,459 (trainer:763) INFO: 19epoch:train:521-560batch: iter_time=4.106e-05, forward_time=0.032, loss_ctc=5.049, loss=5.049, backward_time=0.007, grad_norm=108.795, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-14 23:50:58,941 (trainer:763) INFO: 19epoch:train:561-600batch: iter_time=4.017e-05, forward_time=0.033, loss_ctc=5.274, loss=5.274, backward_time=0.007, grad_norm=108.155, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 23:51:01,215 (trainer:763) INFO: 19epoch:train:601-640batch: iter_time=4.008e-05, forward_time=0.030, loss_ctc=4.678, loss=4.678, backward_time=0.007, grad_norm=105.846, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-14 23:51:03,600 (trainer:763) INFO: 19epoch:train:641-680batch: iter_time=4.103e-05, forward_time=0.032, loss_ctc=4.891, loss=4.891, backward_time=0.007, grad_norm=106.197, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-14 23:51:06,101 (trainer:763) INFO: 19epoch:train:681-720batch: iter_time=4.198e-05, forward_time=0.033, loss_ctc=4.915, loss=4.915, backward_time=0.007, grad_norm=106.441, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 23:51:08,499 (trainer:763) INFO: 19epoch:train:721-760batch: iter_time=4.264e-05, forward_time=0.032, loss_ctc=5.193, loss=5.193, backward_time=0.007, grad_norm=112.095, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-14 23:51:10,553 (trainer:763) INFO: 19epoch:train:761-800batch: iter_time=3.808e-05, forward_time=0.028, loss_ctc=4.249, loss=4.249, backward_time=0.007, grad_norm=106.007, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.205 +[stan] 2024-01-14 23:51:14,504 (trainer:354) INFO: 19epoch results: [train] iter_time=1.565e-04, forward_time=0.031, loss_ctc=4.930, loss=4.930, backward_time=0.007, grad_norm=108.302, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236, time=47.31 seconds, total_count=15200, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=66.128, cer_ctc=0.240, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=66.128, time=1.13 seconds, total_count=475, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:51:15,474 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:51:15,474 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/18epoch.pth +[stan] 2024-01-14 23:51:15,474 (trainer:288) INFO: 20/30epoch started. Estimated time to finish: 9 minutes and 36.99 seconds +[stan] 2024-01-14 23:51:18,277 (trainer:763) INFO: 20epoch:train:1-40batch: iter_time=0.003, forward_time=0.034, loss_ctc=5.590, loss=5.590, backward_time=0.007, grad_norm=113.211, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 23:51:20,827 (trainer:763) INFO: 20epoch:train:41-80batch: iter_time=3.976e-05, forward_time=0.034, loss_ctc=4.986, loss=4.986, backward_time=0.007, grad_norm=109.625, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 23:51:23,156 (trainer:763) INFO: 20epoch:train:81-120batch: iter_time=4.131e-05, forward_time=0.031, loss_ctc=4.540, loss=4.540, backward_time=0.007, grad_norm=102.440, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-14 23:51:25,337 (trainer:763) INFO: 20epoch:train:121-160batch: iter_time=3.909e-05, forward_time=0.029, loss_ctc=4.074, loss=4.074, backward_time=0.007, grad_norm=99.551, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.218 +[stan] 2024-01-14 23:51:27,685 (trainer:763) INFO: 20epoch:train:161-200batch: iter_time=4.011e-05, forward_time=0.031, loss_ctc=5.039, loss=5.039, backward_time=0.007, grad_norm=105.476, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-14 23:51:29,948 (trainer:763) INFO: 20epoch:train:201-240batch: iter_time=3.969e-05, forward_time=0.030, loss_ctc=4.440, loss=4.440, backward_time=0.007, grad_norm=102.989, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-14 23:51:32,325 (trainer:763) INFO: 20epoch:train:241-280batch: iter_time=4.034e-05, forward_time=0.031, loss_ctc=4.803, loss=4.803, backward_time=0.007, grad_norm=107.438, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-14 23:51:34,326 (trainer:763) INFO: 20epoch:train:281-320batch: iter_time=4.113e-05, forward_time=0.027, loss_ctc=4.005, loss=4.005, backward_time=0.007, grad_norm=103.310, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.200 +[stan] 2024-01-14 23:51:36,743 (trainer:763) INFO: 20epoch:train:321-360batch: iter_time=4.140e-05, forward_time=0.032, loss_ctc=4.927, loss=4.927, backward_time=0.007, grad_norm=106.979, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-14 23:51:39,539 (trainer:763) INFO: 20epoch:train:361-400batch: iter_time=4.060e-05, forward_time=0.037, loss_ctc=5.617, loss=5.617, backward_time=0.008, grad_norm=113.137, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 23:51:42,251 (trainer:763) INFO: 20epoch:train:401-440batch: iter_time=4.043e-05, forward_time=0.036, loss_ctc=5.254, loss=5.254, backward_time=0.008, grad_norm=104.872, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-14 23:51:44,418 (trainer:763) INFO: 20epoch:train:441-480batch: iter_time=4.271e-05, forward_time=0.029, loss_ctc=4.064, loss=4.064, backward_time=0.007, grad_norm=102.882, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-14 23:51:46,675 (trainer:763) INFO: 20epoch:train:481-520batch: iter_time=4.063e-05, forward_time=0.030, loss_ctc=4.998, loss=4.998, backward_time=0.007, grad_norm=105.562, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-14 23:51:48,874 (trainer:763) INFO: 20epoch:train:521-560batch: iter_time=4.126e-05, forward_time=0.029, loss_ctc=4.601, loss=4.601, backward_time=0.007, grad_norm=104.610, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-14 23:51:50,906 (trainer:763) INFO: 20epoch:train:561-600batch: iter_time=3.978e-05, forward_time=0.027, loss_ctc=3.885, loss=3.885, backward_time=0.007, grad_norm=99.312, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.203 +[stan] 2024-01-14 23:51:53,259 (trainer:763) INFO: 20epoch:train:601-640batch: iter_time=4.011e-05, forward_time=0.031, loss_ctc=4.699, loss=4.699, backward_time=0.007, grad_norm=110.364, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-14 23:51:55,745 (trainer:763) INFO: 20epoch:train:641-680batch: iter_time=3.940e-05, forward_time=0.033, loss_ctc=4.708, loss=4.708, backward_time=0.008, grad_norm=105.977, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 23:51:58,593 (trainer:763) INFO: 20epoch:train:681-720batch: iter_time=4.409e-05, forward_time=0.038, loss_ctc=5.635, loss=5.635, backward_time=0.008, grad_norm=114.360, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.285 +[stan] 2024-01-14 23:52:01,062 (trainer:763) INFO: 20epoch:train:721-760batch: iter_time=4.108e-05, forward_time=0.033, loss_ctc=4.963, loss=4.963, backward_time=0.008, grad_norm=107.836, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-14 23:52:03,222 (trainer:763) INFO: 20epoch:train:761-800batch: iter_time=3.800e-05, forward_time=0.029, loss_ctc=4.316, loss=4.316, backward_time=0.007, grad_norm=104.203, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.216 +[stan] 2024-01-14 23:52:07,184 (trainer:354) INFO: 20epoch results: [train] iter_time=1.770e-04, forward_time=0.032, loss_ctc=4.757, loss=4.757, backward_time=0.007, grad_norm=106.207, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239, time=47.82 seconds, total_count=16000, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=65.352, cer_ctc=0.240, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=65.352, time=1.15 seconds, total_count=500, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:52:08,196 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:52:08,197 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/19epoch.pth +[stan] 2024-01-14 23:52:08,197 (trainer:288) INFO: 21/30epoch started. Estimated time to finish: 8 minutes and 44.67 seconds +[stan] 2024-01-14 23:52:10,612 (trainer:763) INFO: 21epoch:train:1-40batch: iter_time=0.002, forward_time=0.029, loss_ctc=4.354, loss=4.354, backward_time=0.007, grad_norm=101.055, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-14 23:52:12,917 (trainer:763) INFO: 21epoch:train:41-80batch: iter_time=4.094e-05, forward_time=0.031, loss_ctc=5.005, loss=5.005, backward_time=0.007, grad_norm=106.568, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-14 23:52:15,532 (trainer:763) INFO: 21epoch:train:81-120batch: iter_time=4.057e-05, forward_time=0.035, loss_ctc=5.339, loss=5.339, backward_time=0.008, grad_norm=109.657, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-14 23:52:17,903 (trainer:763) INFO: 21epoch:train:121-160batch: iter_time=4.340e-05, forward_time=0.031, loss_ctc=4.854, loss=4.854, backward_time=0.007, grad_norm=111.563, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-14 23:52:20,172 (trainer:763) INFO: 21epoch:train:161-200batch: iter_time=3.902e-05, forward_time=0.030, loss_ctc=4.315, loss=4.315, backward_time=0.007, grad_norm=105.452, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-14 23:52:23,063 (trainer:763) INFO: 21epoch:train:201-240batch: iter_time=4.203e-05, forward_time=0.038, loss_ctc=5.531, loss=5.531, backward_time=0.008, grad_norm=110.651, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.289 +[stan] 2024-01-14 23:52:25,171 (trainer:763) INFO: 21epoch:train:241-280batch: iter_time=4.332e-05, forward_time=0.028, loss_ctc=3.998, loss=3.998, backward_time=0.007, grad_norm=97.931, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.211 +[stan] 2024-01-14 23:52:27,555 (trainer:763) INFO: 21epoch:train:281-320batch: iter_time=4.074e-05, forward_time=0.032, loss_ctc=5.028, loss=5.028, backward_time=0.007, grad_norm=107.857, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-14 23:52:29,606 (trainer:763) INFO: 21epoch:train:321-360batch: iter_time=4.001e-05, forward_time=0.027, loss_ctc=4.034, loss=4.034, backward_time=0.007, grad_norm=101.016, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.205 +[stan] 2024-01-14 23:52:31,689 (trainer:763) INFO: 21epoch:train:361-400batch: iter_time=3.940e-05, forward_time=0.028, loss_ctc=3.682, loss=3.682, backward_time=0.007, grad_norm=98.487, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.208 +[stan] 2024-01-14 23:52:34,150 (trainer:763) INFO: 21epoch:train:401-440batch: iter_time=4.151e-05, forward_time=0.033, loss_ctc=5.031, loss=5.031, backward_time=0.008, grad_norm=106.349, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 23:52:36,567 (trainer:763) INFO: 21epoch:train:441-480batch: iter_time=4.086e-05, forward_time=0.032, loss_ctc=4.641, loss=4.641, backward_time=0.007, grad_norm=107.526, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-14 23:52:38,997 (trainer:763) INFO: 21epoch:train:481-520batch: iter_time=4.035e-05, forward_time=0.032, loss_ctc=4.689, loss=4.689, backward_time=0.007, grad_norm=110.578, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-14 23:52:41,355 (trainer:763) INFO: 21epoch:train:521-560batch: iter_time=4.253e-05, forward_time=0.031, loss_ctc=4.355, loss=4.355, backward_time=0.007, grad_norm=107.193, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-14 23:52:43,934 (trainer:763) INFO: 21epoch:train:561-600batch: iter_time=4.315e-05, forward_time=0.034, loss_ctc=5.654, loss=5.654, backward_time=0.008, grad_norm=106.933, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 23:52:46,236 (trainer:763) INFO: 21epoch:train:601-640batch: iter_time=4.191e-05, forward_time=0.031, loss_ctc=4.725, loss=4.725, backward_time=0.007, grad_norm=106.462, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-14 23:52:48,686 (trainer:763) INFO: 21epoch:train:641-680batch: iter_time=4.065e-05, forward_time=0.032, loss_ctc=4.935, loss=4.935, backward_time=0.008, grad_norm=104.471, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-14 23:52:50,941 (trainer:763) INFO: 21epoch:train:681-720batch: iter_time=4.052e-05, forward_time=0.030, loss_ctc=4.714, loss=4.714, backward_time=0.007, grad_norm=107.732, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-14 23:52:53,102 (trainer:763) INFO: 21epoch:train:721-760batch: iter_time=4.030e-05, forward_time=0.029, loss_ctc=4.001, loss=4.001, backward_time=0.007, grad_norm=98.808, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.216 +[stan] 2024-01-14 23:52:55,617 (trainer:763) INFO: 21epoch:train:761-800batch: iter_time=3.962e-05, forward_time=0.033, loss_ctc=4.719, loss=4.719, backward_time=0.007, grad_norm=110.011, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-14 23:52:59,547 (trainer:354) INFO: 21epoch results: [train] iter_time=1.597e-04, forward_time=0.031, loss_ctc=4.680, loss=4.680, backward_time=0.007, grad_norm=105.815, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237, time=47.49 seconds, total_count=16800, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=66.561, cer_ctc=0.242, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=66.561, time=1.15 seconds, total_count=525, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.71 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:53:00,494 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:53:00,494 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/20epoch.pth +[stan] 2024-01-14 23:53:00,494 (trainer:288) INFO: 22/30epoch started. Estimated time to finish: 7 minutes and 52.13 seconds +[stan] 2024-01-14 23:53:02,807 (trainer:763) INFO: 22epoch:train:1-40batch: iter_time=0.003, forward_time=0.027, loss_ctc=3.770, loss=3.770, backward_time=0.007, grad_norm=97.782, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-14 23:53:05,470 (trainer:763) INFO: 22epoch:train:41-80batch: iter_time=4.298e-05, forward_time=0.035, loss_ctc=5.064, loss=5.064, backward_time=0.008, grad_norm=108.207, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-14 23:53:07,958 (trainer:763) INFO: 22epoch:train:81-120batch: iter_time=4.141e-05, forward_time=0.033, loss_ctc=5.052, loss=5.052, backward_time=0.007, grad_norm=110.485, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 23:53:10,167 (trainer:763) INFO: 22epoch:train:121-160batch: iter_time=4.047e-05, forward_time=0.029, loss_ctc=4.211, loss=4.211, backward_time=0.007, grad_norm=99.439, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.221 +[stan] 2024-01-14 23:53:12,482 (trainer:763) INFO: 22epoch:train:161-200batch: iter_time=4.102e-05, forward_time=0.031, loss_ctc=4.463, loss=4.463, backward_time=0.007, grad_norm=105.006, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-14 23:53:14,892 (trainer:763) INFO: 22epoch:train:201-240batch: iter_time=4.136e-05, forward_time=0.032, loss_ctc=4.187, loss=4.187, backward_time=0.007, grad_norm=104.697, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-14 23:53:17,144 (trainer:763) INFO: 22epoch:train:241-280batch: iter_time=4.100e-05, forward_time=0.030, loss_ctc=4.447, loss=4.447, backward_time=0.007, grad_norm=102.188, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-14 23:53:19,485 (trainer:763) INFO: 22epoch:train:281-320batch: iter_time=4.716e-05, forward_time=0.031, loss_ctc=4.870, loss=4.870, backward_time=0.007, grad_norm=105.817, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-14 23:53:22,159 (trainer:763) INFO: 22epoch:train:321-360batch: iter_time=4.195e-05, forward_time=0.035, loss_ctc=5.363, loss=5.363, backward_time=0.008, grad_norm=111.263, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-14 23:53:24,712 (trainer:763) INFO: 22epoch:train:361-400batch: iter_time=4.100e-05, forward_time=0.034, loss_ctc=4.834, loss=4.834, backward_time=0.007, grad_norm=105.107, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-14 23:53:26,781 (trainer:763) INFO: 22epoch:train:401-440batch: iter_time=4.137e-05, forward_time=0.028, loss_ctc=3.754, loss=3.754, backward_time=0.007, grad_norm=100.159, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.207 +[stan] 2024-01-14 23:53:29,289 (trainer:763) INFO: 22epoch:train:441-480batch: iter_time=4.189e-05, forward_time=0.033, loss_ctc=4.948, loss=4.948, backward_time=0.007, grad_norm=107.856, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-14 23:53:31,706 (trainer:763) INFO: 22epoch:train:481-520batch: iter_time=4.021e-05, forward_time=0.032, loss_ctc=4.019, loss=4.019, backward_time=0.007, grad_norm=99.645, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-14 23:53:34,252 (trainer:763) INFO: 22epoch:train:521-560batch: iter_time=4.141e-05, forward_time=0.034, loss_ctc=4.849, loss=4.849, backward_time=0.008, grad_norm=105.591, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-14 23:53:36,193 (trainer:763) INFO: 22epoch:train:561-600batch: iter_time=4.065e-05, forward_time=0.026, loss_ctc=3.522, loss=3.522, backward_time=0.007, grad_norm=98.696, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.194 +[stan] 2024-01-14 23:53:38,523 (trainer:763) INFO: 22epoch:train:601-640batch: iter_time=4.010e-05, forward_time=0.031, loss_ctc=4.301, loss=4.301, backward_time=0.007, grad_norm=104.568, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-14 23:53:41,081 (trainer:763) INFO: 22epoch:train:641-680batch: iter_time=4.034e-05, forward_time=0.034, loss_ctc=4.593, loss=4.593, backward_time=0.007, grad_norm=106.079, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-14 23:53:43,063 (trainer:763) INFO: 22epoch:train:681-720batch: iter_time=4.852e-05, forward_time=0.027, loss_ctc=3.765, loss=3.765, backward_time=0.007, grad_norm=97.568, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.198 +[stan] 2024-01-14 23:53:45,564 (trainer:763) INFO: 22epoch:train:721-760batch: iter_time=4.231e-05, forward_time=0.033, loss_ctc=4.764, loss=4.764, backward_time=0.007, grad_norm=102.049, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 23:53:48,051 (trainer:763) INFO: 22epoch:train:761-800batch: iter_time=3.818e-05, forward_time=0.033, loss_ctc=5.047, loss=5.047, backward_time=0.008, grad_norm=107.415, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 23:53:52,024 (trainer:354) INFO: 22epoch results: [train] iter_time=2.087e-04, forward_time=0.031, loss_ctc=4.491, loss=4.491, backward_time=0.007, grad_norm=103.981, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238, time=47.63 seconds, total_count=17600, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=67.481, cer_ctc=0.242, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=67.481, time=1.16 seconds, total_count=550, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:53:53,079 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:53:53,079 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/21epoch.pth +[stan] 2024-01-14 23:53:53,079 (trainer:288) INFO: 23/30epoch started. Estimated time to finish: 6 minutes and 59.72 seconds +[stan] 2024-01-14 23:53:55,541 (trainer:763) INFO: 23epoch:train:1-40batch: iter_time=0.002, forward_time=0.030, loss_ctc=4.123, loss=4.123, backward_time=0.007, grad_norm=100.758, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 23:53:58,183 (trainer:763) INFO: 23epoch:train:41-80batch: iter_time=4.224e-05, forward_time=0.035, loss_ctc=4.785, loss=4.785, backward_time=0.007, grad_norm=103.069, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-14 23:54:00,095 (trainer:763) INFO: 23epoch:train:81-120batch: iter_time=3.915e-05, forward_time=0.026, loss_ctc=3.070, loss=3.070, backward_time=0.007, grad_norm=93.924, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.191 +[stan] 2024-01-14 23:54:02,376 (trainer:763) INFO: 23epoch:train:121-160batch: iter_time=4.012e-05, forward_time=0.030, loss_ctc=3.776, loss=3.776, backward_time=0.007, grad_norm=97.696, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-14 23:54:04,739 (trainer:763) INFO: 23epoch:train:161-200batch: iter_time=4.122e-05, forward_time=0.031, loss_ctc=4.307, loss=4.307, backward_time=0.007, grad_norm=104.956, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-14 23:54:07,127 (trainer:763) INFO: 23epoch:train:201-240batch: iter_time=4.307e-05, forward_time=0.032, loss_ctc=5.205, loss=5.205, backward_time=0.008, grad_norm=106.236, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-14 23:54:09,902 (trainer:763) INFO: 23epoch:train:241-280batch: iter_time=4.204e-05, forward_time=0.037, loss_ctc=5.960, loss=5.960, backward_time=0.008, grad_norm=115.788, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 23:54:12,296 (trainer:763) INFO: 23epoch:train:281-320batch: iter_time=4.175e-05, forward_time=0.032, loss_ctc=4.458, loss=4.458, backward_time=0.007, grad_norm=104.120, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-14 23:54:14,581 (trainer:763) INFO: 23epoch:train:321-360batch: iter_time=3.933e-05, forward_time=0.030, loss_ctc=4.003, loss=4.003, backward_time=0.007, grad_norm=101.123, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-14 23:54:16,890 (trainer:763) INFO: 23epoch:train:361-400batch: iter_time=4.080e-05, forward_time=0.031, loss_ctc=4.145, loss=4.145, backward_time=0.007, grad_norm=100.355, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-14 23:54:19,228 (trainer:763) INFO: 23epoch:train:401-440batch: iter_time=4.106e-05, forward_time=0.031, loss_ctc=3.964, loss=3.964, backward_time=0.007, grad_norm=99.779, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-14 23:54:21,675 (trainer:763) INFO: 23epoch:train:441-480batch: iter_time=4.014e-05, forward_time=0.032, loss_ctc=4.440, loss=4.440, backward_time=0.007, grad_norm=104.412, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-14 23:54:23,911 (trainer:763) INFO: 23epoch:train:481-520batch: iter_time=3.970e-05, forward_time=0.030, loss_ctc=4.045, loss=4.045, backward_time=0.007, grad_norm=99.200, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.223 +[stan] 2024-01-14 23:54:26,303 (trainer:763) INFO: 23epoch:train:521-560batch: iter_time=4.078e-05, forward_time=0.032, loss_ctc=4.547, loss=4.547, backward_time=0.007, grad_norm=101.304, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-14 23:54:28,441 (trainer:763) INFO: 23epoch:train:561-600batch: iter_time=4.285e-05, forward_time=0.029, loss_ctc=3.814, loss=3.814, backward_time=0.007, grad_norm=100.812, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.214 +[stan] 2024-01-14 23:54:30,609 (trainer:763) INFO: 23epoch:train:601-640batch: iter_time=4.349e-05, forward_time=0.029, loss_ctc=3.786, loss=3.786, backward_time=0.007, grad_norm=97.140, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-14 23:54:33,354 (trainer:763) INFO: 23epoch:train:641-680batch: iter_time=4.109e-05, forward_time=0.036, loss_ctc=5.203, loss=5.203, backward_time=0.008, grad_norm=104.462, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-14 23:54:35,658 (trainer:763) INFO: 23epoch:train:681-720batch: iter_time=4.153e-05, forward_time=0.031, loss_ctc=4.231, loss=4.231, backward_time=0.007, grad_norm=102.763, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-14 23:54:38,143 (trainer:763) INFO: 23epoch:train:721-760batch: iter_time=4.151e-05, forward_time=0.033, loss_ctc=4.653, loss=4.653, backward_time=0.007, grad_norm=102.306, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 23:54:40,344 (trainer:763) INFO: 23epoch:train:761-800batch: iter_time=3.892e-05, forward_time=0.029, loss_ctc=4.307, loss=4.307, backward_time=0.007, grad_norm=100.312, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-14 23:54:44,281 (trainer:354) INFO: 23epoch results: [train] iter_time=1.610e-04, forward_time=0.031, loss_ctc=4.341, loss=4.341, backward_time=0.007, grad_norm=102.026, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236, time=47.34 seconds, total_count=18400, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=67.678, cer_ctc=0.240, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=67.678, time=1.16 seconds, total_count=575, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.71 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:54:45,368 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:54:45,368 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/22epoch.pth +[stan] 2024-01-14 23:54:45,368 (trainer:288) INFO: 24/30epoch started. Estimated time to finish: 6 minutes and 7.2 seconds +[stan] 2024-01-14 23:54:48,140 (trainer:763) INFO: 24epoch:train:1-40batch: iter_time=0.002, forward_time=0.034, loss_ctc=4.534, loss=4.534, backward_time=0.007, grad_norm=100.130, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 23:54:50,465 (trainer:763) INFO: 24epoch:train:41-80batch: iter_time=4.307e-05, forward_time=0.031, loss_ctc=4.138, loss=4.138, backward_time=0.007, grad_norm=103.268, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-14 23:54:52,904 (trainer:763) INFO: 24epoch:train:81-120batch: iter_time=3.986e-05, forward_time=0.032, loss_ctc=4.102, loss=4.102, backward_time=0.008, grad_norm=96.253, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 23:54:55,585 (trainer:763) INFO: 24epoch:train:121-160batch: iter_time=4.336e-05, forward_time=0.035, loss_ctc=5.062, loss=5.062, backward_time=0.007, grad_norm=106.114, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-14 23:54:57,592 (trainer:763) INFO: 24epoch:train:161-200batch: iter_time=3.948e-05, forward_time=0.027, loss_ctc=3.606, loss=3.606, backward_time=0.007, grad_norm=93.737, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.201 +[stan] 2024-01-14 23:54:59,874 (trainer:763) INFO: 24epoch:train:201-240batch: iter_time=4.044e-05, forward_time=0.030, loss_ctc=3.927, loss=3.927, backward_time=0.007, grad_norm=96.731, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-14 23:55:02,663 (trainer:763) INFO: 24epoch:train:241-280batch: iter_time=4.114e-05, forward_time=0.037, loss_ctc=5.629, loss=5.629, backward_time=0.008, grad_norm=109.217, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-14 23:55:04,744 (trainer:763) INFO: 24epoch:train:281-320batch: iter_time=4.049e-05, forward_time=0.028, loss_ctc=3.421, loss=3.421, backward_time=0.007, grad_norm=92.284, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.208 +[stan] 2024-01-14 23:55:07,017 (trainer:763) INFO: 24epoch:train:321-360batch: iter_time=4.757e-05, forward_time=0.030, loss_ctc=3.974, loss=3.974, backward_time=0.007, grad_norm=100.704, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-14 23:55:09,380 (trainer:763) INFO: 24epoch:train:361-400batch: iter_time=4.135e-05, forward_time=0.031, loss_ctc=4.133, loss=4.133, backward_time=0.007, grad_norm=101.165, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-14 23:55:11,483 (trainer:763) INFO: 24epoch:train:401-440batch: iter_time=4.114e-05, forward_time=0.028, loss_ctc=3.741, loss=3.741, backward_time=0.007, grad_norm=96.462, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.210 +[stan] 2024-01-14 23:55:14,015 (trainer:763) INFO: 24epoch:train:441-480batch: iter_time=3.965e-05, forward_time=0.034, loss_ctc=4.458, loss=4.458, backward_time=0.007, grad_norm=100.639, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-14 23:55:16,813 (trainer:763) INFO: 24epoch:train:481-520batch: iter_time=4.032e-05, forward_time=0.037, loss_ctc=5.051, loss=5.051, backward_time=0.008, grad_norm=105.151, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-14 23:55:18,968 (trainer:763) INFO: 24epoch:train:521-560batch: iter_time=3.985e-05, forward_time=0.029, loss_ctc=3.689, loss=3.689, backward_time=0.007, grad_norm=95.501, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.215 +[stan] 2024-01-14 23:55:21,365 (trainer:763) INFO: 24epoch:train:561-600batch: iter_time=4.013e-05, forward_time=0.032, loss_ctc=4.148, loss=4.148, backward_time=0.007, grad_norm=103.408, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-14 23:55:23,559 (trainer:763) INFO: 24epoch:train:601-640batch: iter_time=4.023e-05, forward_time=0.029, loss_ctc=3.703, loss=3.703, backward_time=0.007, grad_norm=95.326, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-14 23:55:25,815 (trainer:763) INFO: 24epoch:train:641-680batch: iter_time=3.993e-05, forward_time=0.030, loss_ctc=4.082, loss=4.082, backward_time=0.007, grad_norm=98.442, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-14 23:55:28,175 (trainer:763) INFO: 24epoch:train:681-720batch: iter_time=4.076e-05, forward_time=0.031, loss_ctc=4.550, loss=4.550, backward_time=0.007, grad_norm=106.457, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-14 23:55:30,670 (trainer:763) INFO: 24epoch:train:721-760batch: iter_time=4.113e-05, forward_time=0.033, loss_ctc=4.769, loss=4.769, backward_time=0.008, grad_norm=106.987, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 23:55:33,040 (trainer:763) INFO: 24epoch:train:761-800batch: iter_time=3.808e-05, forward_time=0.032, loss_ctc=3.902, loss=3.902, backward_time=0.007, grad_norm=103.302, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-14 23:55:37,016 (trainer:354) INFO: 24epoch results: [train] iter_time=1.487e-04, forward_time=0.032, loss_ctc=4.231, loss=4.231, backward_time=0.007, grad_norm=100.564, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238, time=47.75 seconds, total_count=19200, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=68.054, cer_ctc=0.238, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=68.054, time=1.15 seconds, total_count=600, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.75 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:55:37,998 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:55:37,998 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/23epoch.pth +[stan] 2024-01-14 23:55:37,998 (trainer:288) INFO: 25/30epoch started. Estimated time to finish: 5 minutes and 14.79 seconds +[stan] 2024-01-14 23:55:40,671 (trainer:763) INFO: 25epoch:train:1-40batch: iter_time=0.003, forward_time=0.032, loss_ctc=4.308, loss=4.308, backward_time=0.007, grad_norm=102.773, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-14 23:55:42,662 (trainer:763) INFO: 25epoch:train:41-80batch: iter_time=3.954e-05, forward_time=0.027, loss_ctc=3.444, loss=3.444, backward_time=0.007, grad_norm=96.833, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.199 +[stan] 2024-01-14 23:55:44,875 (trainer:763) INFO: 25epoch:train:81-120batch: iter_time=3.984e-05, forward_time=0.030, loss_ctc=3.772, loss=3.772, backward_time=0.007, grad_norm=99.171, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.221 +[stan] 2024-01-14 23:55:47,873 (trainer:763) INFO: 25epoch:train:121-160batch: iter_time=4.247e-05, forward_time=0.039, loss_ctc=5.628, loss=5.628, backward_time=0.008, grad_norm=108.255, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.300 +[stan] 2024-01-14 23:55:50,232 (trainer:763) INFO: 25epoch:train:161-200batch: iter_time=4.308e-05, forward_time=0.031, loss_ctc=4.054, loss=4.054, backward_time=0.007, grad_norm=97.357, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-14 23:55:52,658 (trainer:763) INFO: 25epoch:train:201-240batch: iter_time=4.070e-05, forward_time=0.032, loss_ctc=4.103, loss=4.103, backward_time=0.007, grad_norm=104.460, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-14 23:55:54,683 (trainer:763) INFO: 25epoch:train:241-280batch: iter_time=4.079e-05, forward_time=0.027, loss_ctc=3.485, loss=3.485, backward_time=0.007, grad_norm=94.661, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.202 +[stan] 2024-01-14 23:55:56,967 (trainer:763) INFO: 25epoch:train:281-320batch: iter_time=4.134e-05, forward_time=0.030, loss_ctc=3.837, loss=3.837, backward_time=0.007, grad_norm=99.757, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-14 23:55:59,414 (trainer:763) INFO: 25epoch:train:321-360batch: iter_time=4.152e-05, forward_time=0.033, loss_ctc=4.241, loss=4.241, backward_time=0.007, grad_norm=102.152, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-14 23:56:02,005 (trainer:763) INFO: 25epoch:train:361-400batch: iter_time=4.193e-05, forward_time=0.034, loss_ctc=4.565, loss=4.565, backward_time=0.008, grad_norm=103.222, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-14 23:56:04,244 (trainer:763) INFO: 25epoch:train:401-440batch: iter_time=4.028e-05, forward_time=0.030, loss_ctc=3.939, loss=3.939, backward_time=0.007, grad_norm=99.148, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.224 +[stan] 2024-01-14 23:56:06,264 (trainer:763) INFO: 25epoch:train:441-480batch: iter_time=4.098e-05, forward_time=0.027, loss_ctc=3.052, loss=3.052, backward_time=0.007, grad_norm=90.779, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.202 +[stan] 2024-01-14 23:56:08,658 (trainer:763) INFO: 25epoch:train:481-520batch: iter_time=4.179e-05, forward_time=0.032, loss_ctc=4.049, loss=4.049, backward_time=0.007, grad_norm=99.180, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-14 23:56:11,306 (trainer:763) INFO: 25epoch:train:521-560batch: iter_time=4.192e-05, forward_time=0.035, loss_ctc=4.723, loss=4.723, backward_time=0.008, grad_norm=104.179, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.265 +[stan] 2024-01-14 23:56:13,723 (trainer:763) INFO: 25epoch:train:561-600batch: iter_time=4.172e-05, forward_time=0.032, loss_ctc=4.300, loss=4.300, backward_time=0.007, grad_norm=102.613, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-14 23:56:15,992 (trainer:763) INFO: 25epoch:train:601-640batch: iter_time=4.034e-05, forward_time=0.030, loss_ctc=4.095, loss=4.095, backward_time=0.007, grad_norm=95.317, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-14 23:56:18,461 (trainer:763) INFO: 25epoch:train:641-680batch: iter_time=4.164e-05, forward_time=0.033, loss_ctc=4.366, loss=4.366, backward_time=0.007, grad_norm=104.475, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-14 23:56:20,863 (trainer:763) INFO: 25epoch:train:681-720batch: iter_time=4.134e-05, forward_time=0.032, loss_ctc=4.066, loss=4.066, backward_time=0.007, grad_norm=97.700, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-14 23:56:23,286 (trainer:763) INFO: 25epoch:train:721-760batch: iter_time=4.074e-05, forward_time=0.032, loss_ctc=4.025, loss=4.025, backward_time=0.007, grad_norm=103.149, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-14 23:56:26,054 (trainer:763) INFO: 25epoch:train:761-800batch: iter_time=4.168e-05, forward_time=0.036, loss_ctc=4.767, loss=4.767, backward_time=0.008, grad_norm=105.776, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-14 23:56:30,033 (trainer:354) INFO: 25epoch results: [train] iter_time=1.933e-04, forward_time=0.032, loss_ctc=4.141, loss=4.141, backward_time=0.007, grad_norm=100.548, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240, time=48.14 seconds, total_count=20000, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=67.609, cer_ctc=0.243, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=67.609, time=1.16 seconds, total_count=625, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.73 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:56:31,106 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:56:31,106 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/24epoch.pth +[stan] 2024-01-14 23:56:31,106 (trainer:288) INFO: 26/30epoch started. Estimated time to finish: 4 minutes and 22.45 seconds +[stan] 2024-01-14 23:56:33,380 (trainer:763) INFO: 26epoch:train:1-40batch: iter_time=0.002, forward_time=0.027, loss_ctc=3.395, loss=3.395, backward_time=0.007, grad_norm=98.257, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-14 23:56:35,646 (trainer:763) INFO: 26epoch:train:41-80batch: iter_time=3.922e-05, forward_time=0.030, loss_ctc=3.716, loss=3.716, backward_time=0.007, grad_norm=97.195, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-14 23:56:38,002 (trainer:763) INFO: 26epoch:train:81-120batch: iter_time=4.262e-05, forward_time=0.031, loss_ctc=3.906, loss=3.906, backward_time=0.007, grad_norm=99.173, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-14 23:56:40,232 (trainer:763) INFO: 26epoch:train:121-160batch: iter_time=4.006e-05, forward_time=0.030, loss_ctc=3.809, loss=3.809, backward_time=0.008, grad_norm=99.338, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.223 +[stan] 2024-01-14 23:56:42,603 (trainer:763) INFO: 26epoch:train:161-200batch: iter_time=4.231e-05, forward_time=0.032, loss_ctc=4.034, loss=4.034, backward_time=0.007, grad_norm=104.097, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-14 23:56:45,043 (trainer:763) INFO: 26epoch:train:201-240batch: iter_time=4.098e-05, forward_time=0.032, loss_ctc=4.135, loss=4.135, backward_time=0.007, grad_norm=98.296, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 23:56:47,335 (trainer:763) INFO: 26epoch:train:241-280batch: iter_time=4.226e-05, forward_time=0.030, loss_ctc=3.892, loss=3.892, backward_time=0.007, grad_norm=99.829, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-14 23:56:49,881 (trainer:763) INFO: 26epoch:train:281-320batch: iter_time=4.019e-05, forward_time=0.034, loss_ctc=4.550, loss=4.550, backward_time=0.008, grad_norm=101.434, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-14 23:56:52,058 (trainer:763) INFO: 26epoch:train:321-360batch: iter_time=3.956e-05, forward_time=0.029, loss_ctc=3.558, loss=3.558, backward_time=0.007, grad_norm=96.706, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.218 +[stan] 2024-01-14 23:56:54,476 (trainer:763) INFO: 26epoch:train:361-400batch: iter_time=4.014e-05, forward_time=0.032, loss_ctc=4.380, loss=4.380, backward_time=0.007, grad_norm=98.518, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-14 23:56:56,412 (trainer:763) INFO: 26epoch:train:401-440batch: iter_time=3.921e-05, forward_time=0.026, loss_ctc=2.886, loss=2.886, backward_time=0.007, grad_norm=93.830, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.193 +[stan] 2024-01-14 23:56:58,647 (trainer:763) INFO: 26epoch:train:441-480batch: iter_time=3.983e-05, forward_time=0.030, loss_ctc=3.802, loss=3.802, backward_time=0.007, grad_norm=99.792, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.223 +[stan] 2024-01-14 23:57:01,322 (trainer:763) INFO: 26epoch:train:481-520batch: iter_time=4.114e-05, forward_time=0.035, loss_ctc=4.914, loss=4.914, backward_time=0.008, grad_norm=106.159, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-14 23:57:03,964 (trainer:763) INFO: 26epoch:train:521-560batch: iter_time=4.155e-05, forward_time=0.035, loss_ctc=4.822, loss=4.822, backward_time=0.008, grad_norm=98.892, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-14 23:57:06,383 (trainer:763) INFO: 26epoch:train:561-600batch: iter_time=4.166e-05, forward_time=0.032, loss_ctc=4.846, loss=4.846, backward_time=0.008, grad_norm=104.481, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-14 23:57:09,003 (trainer:763) INFO: 26epoch:train:601-640batch: iter_time=4.342e-05, forward_time=0.035, loss_ctc=4.417, loss=4.417, backward_time=0.007, grad_norm=102.267, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-14 23:57:11,189 (trainer:763) INFO: 26epoch:train:641-680batch: iter_time=4.126e-05, forward_time=0.029, loss_ctc=3.648, loss=3.648, backward_time=0.007, grad_norm=95.361, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.218 +[stan] 2024-01-14 23:57:13,633 (trainer:763) INFO: 26epoch:train:681-720batch: iter_time=4.013e-05, forward_time=0.032, loss_ctc=4.352, loss=4.352, backward_time=0.007, grad_norm=98.464, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 23:57:16,142 (trainer:763) INFO: 26epoch:train:721-760batch: iter_time=4.149e-05, forward_time=0.033, loss_ctc=4.514, loss=4.514, backward_time=0.007, grad_norm=105.540, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-14 23:57:18,256 (trainer:763) INFO: 26epoch:train:761-800batch: iter_time=3.752e-05, forward_time=0.028, loss_ctc=3.679, loss=3.679, backward_time=0.007, grad_norm=93.934, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.211 +[stan] 2024-01-14 23:57:22,211 (trainer:354) INFO: 26epoch results: [train] iter_time=1.400e-04, forward_time=0.031, loss_ctc=4.063, loss=4.063, backward_time=0.007, grad_norm=99.578, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236, time=47.23 seconds, total_count=20800, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=69.183, cer_ctc=0.238, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=69.183, time=1.15 seconds, total_count=650, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.73 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:57:23,209 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:57:23,210 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/25epoch.pth +[stan] 2024-01-14 23:57:23,210 (trainer:288) INFO: 27/30epoch started. Estimated time to finish: 3 minutes and 29.9 seconds +[stan] 2024-01-14 23:57:25,694 (trainer:763) INFO: 27epoch:train:1-40batch: iter_time=0.003, forward_time=0.030, loss_ctc=3.560, loss=3.560, backward_time=0.007, grad_norm=98.592, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 23:57:28,354 (trainer:763) INFO: 27epoch:train:41-80batch: iter_time=4.406e-05, forward_time=0.036, loss_ctc=4.505, loss=4.505, backward_time=0.007, grad_norm=105.535, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-14 23:57:30,566 (trainer:763) INFO: 27epoch:train:81-120batch: iter_time=4.379e-05, forward_time=0.029, loss_ctc=3.660, loss=3.660, backward_time=0.007, grad_norm=96.973, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.221 +[stan] 2024-01-14 23:57:32,936 (trainer:763) INFO: 27epoch:train:121-160batch: iter_time=4.003e-05, forward_time=0.032, loss_ctc=4.240, loss=4.240, backward_time=0.007, grad_norm=98.555, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-14 23:57:35,237 (trainer:763) INFO: 27epoch:train:161-200batch: iter_time=4.255e-05, forward_time=0.031, loss_ctc=4.221, loss=4.221, backward_time=0.007, grad_norm=100.190, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-14 23:57:37,528 (trainer:763) INFO: 27epoch:train:201-240batch: iter_time=4.310e-05, forward_time=0.031, loss_ctc=3.834, loss=3.834, backward_time=0.007, grad_norm=95.536, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-14 23:57:40,287 (trainer:763) INFO: 27epoch:train:241-280batch: iter_time=4.035e-05, forward_time=0.036, loss_ctc=5.114, loss=5.114, backward_time=0.008, grad_norm=105.209, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-14 23:57:42,316 (trainer:763) INFO: 27epoch:train:281-320batch: iter_time=4.155e-05, forward_time=0.027, loss_ctc=3.564, loss=3.564, backward_time=0.007, grad_norm=92.403, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.203 +[stan] 2024-01-14 23:57:44,646 (trainer:763) INFO: 27epoch:train:321-360batch: iter_time=4.188e-05, forward_time=0.031, loss_ctc=3.648, loss=3.648, backward_time=0.007, grad_norm=93.324, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-14 23:57:46,881 (trainer:763) INFO: 27epoch:train:361-400batch: iter_time=4.315e-05, forward_time=0.030, loss_ctc=3.644, loss=3.644, backward_time=0.007, grad_norm=91.562, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.223 +[stan] 2024-01-14 23:57:49,313 (trainer:763) INFO: 27epoch:train:401-440batch: iter_time=4.112e-05, forward_time=0.032, loss_ctc=3.986, loss=3.986, backward_time=0.007, grad_norm=98.072, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-14 23:57:51,807 (trainer:763) INFO: 27epoch:train:441-480batch: iter_time=4.307e-05, forward_time=0.033, loss_ctc=4.762, loss=4.762, backward_time=0.008, grad_norm=106.808, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 23:57:54,312 (trainer:763) INFO: 27epoch:train:481-520batch: iter_time=4.210e-05, forward_time=0.033, loss_ctc=4.260, loss=4.260, backward_time=0.007, grad_norm=100.018, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-14 23:57:56,462 (trainer:763) INFO: 27epoch:train:521-560batch: iter_time=4.015e-05, forward_time=0.029, loss_ctc=3.459, loss=3.459, backward_time=0.007, grad_norm=97.460, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.215 +[stan] 2024-01-14 23:57:58,805 (trainer:763) INFO: 27epoch:train:561-600batch: iter_time=4.056e-05, forward_time=0.031, loss_ctc=4.015, loss=4.015, backward_time=0.007, grad_norm=97.857, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-14 23:58:01,260 (trainer:763) INFO: 27epoch:train:601-640batch: iter_time=4.342e-05, forward_time=0.033, loss_ctc=4.622, loss=4.622, backward_time=0.007, grad_norm=103.320, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-14 23:58:03,985 (trainer:763) INFO: 27epoch:train:641-680batch: iter_time=4.184e-05, forward_time=0.036, loss_ctc=4.961, loss=4.961, backward_time=0.008, grad_norm=105.386, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-14 23:58:06,251 (trainer:763) INFO: 27epoch:train:681-720batch: iter_time=4.000e-05, forward_time=0.030, loss_ctc=3.552, loss=3.552, backward_time=0.007, grad_norm=95.783, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-14 23:58:08,510 (trainer:763) INFO: 27epoch:train:721-760batch: iter_time=4.333e-05, forward_time=0.030, loss_ctc=3.832, loss=3.832, backward_time=0.007, grad_norm=94.461, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-14 23:58:10,701 (trainer:763) INFO: 27epoch:train:761-800batch: iter_time=3.875e-05, forward_time=0.029, loss_ctc=3.313, loss=3.313, backward_time=0.007, grad_norm=94.765, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-14 23:58:14,677 (trainer:354) INFO: 27epoch results: [train] iter_time=1.873e-04, forward_time=0.031, loss_ctc=4.038, loss=4.038, backward_time=0.007, grad_norm=98.590, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237, time=47.57 seconds, total_count=21600, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=69.978, cer_ctc=0.234, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=69.978, time=1.14 seconds, total_count=675, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.76 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:58:15,665 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:58:15,665 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/26epoch.pth +[stan] 2024-01-14 23:58:15,665 (trainer:288) INFO: 28/30epoch started. Estimated time to finish: 2 minutes and 37.42 seconds +[stan] 2024-01-14 23:58:18,066 (trainer:763) INFO: 28epoch:train:1-40batch: iter_time=0.003, forward_time=0.029, loss_ctc=3.236, loss=3.236, backward_time=0.007, grad_norm=93.088, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-14 23:58:20,334 (trainer:763) INFO: 28epoch:train:41-80batch: iter_time=3.995e-05, forward_time=0.030, loss_ctc=3.760, loss=3.760, backward_time=0.007, grad_norm=93.623, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-14 23:58:23,016 (trainer:763) INFO: 28epoch:train:81-120batch: iter_time=4.099e-05, forward_time=0.035, loss_ctc=4.543, loss=4.543, backward_time=0.008, grad_norm=102.534, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-14 23:58:25,287 (trainer:763) INFO: 28epoch:train:121-160batch: iter_time=3.931e-05, forward_time=0.030, loss_ctc=4.055, loss=4.055, backward_time=0.007, grad_norm=98.889, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-14 23:58:27,865 (trainer:763) INFO: 28epoch:train:161-200batch: iter_time=4.017e-05, forward_time=0.034, loss_ctc=4.392, loss=4.392, backward_time=0.008, grad_norm=96.716, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-14 23:58:30,354 (trainer:763) INFO: 28epoch:train:201-240batch: iter_time=3.955e-05, forward_time=0.033, loss_ctc=4.403, loss=4.403, backward_time=0.007, grad_norm=100.868, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 23:58:32,530 (trainer:763) INFO: 28epoch:train:241-280batch: iter_time=4.144e-05, forward_time=0.029, loss_ctc=3.329, loss=3.329, backward_time=0.007, grad_norm=91.617, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-14 23:58:34,704 (trainer:763) INFO: 28epoch:train:281-320batch: iter_time=3.964e-05, forward_time=0.029, loss_ctc=3.681, loss=3.681, backward_time=0.007, grad_norm=97.097, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-14 23:58:36,799 (trainer:763) INFO: 28epoch:train:321-360batch: iter_time=4.150e-05, forward_time=0.028, loss_ctc=3.480, loss=3.480, backward_time=0.007, grad_norm=91.871, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.209 +[stan] 2024-01-14 23:58:39,467 (trainer:763) INFO: 28epoch:train:361-400batch: iter_time=4.078e-05, forward_time=0.035, loss_ctc=4.653, loss=4.653, backward_time=0.008, grad_norm=102.121, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-14 23:58:41,682 (trainer:763) INFO: 28epoch:train:401-440batch: iter_time=4.097e-05, forward_time=0.030, loss_ctc=3.970, loss=3.970, backward_time=0.007, grad_norm=95.877, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.221 +[stan] 2024-01-14 23:58:43,838 (trainer:763) INFO: 28epoch:train:441-480batch: iter_time=3.948e-05, forward_time=0.029, loss_ctc=3.450, loss=3.450, backward_time=0.007, grad_norm=90.724, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.215 +[stan] 2024-01-14 23:58:46,679 (trainer:763) INFO: 28epoch:train:481-520batch: iter_time=4.235e-05, forward_time=0.037, loss_ctc=5.017, loss=5.017, backward_time=0.008, grad_norm=102.146, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.284 +[stan] 2024-01-14 23:58:48,767 (trainer:763) INFO: 28epoch:train:521-560batch: iter_time=3.961e-05, forward_time=0.028, loss_ctc=3.385, loss=3.385, backward_time=0.007, grad_norm=93.020, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.209 +[stan] 2024-01-14 23:58:51,305 (trainer:763) INFO: 28epoch:train:561-600batch: iter_time=3.994e-05, forward_time=0.034, loss_ctc=3.964, loss=3.964, backward_time=0.008, grad_norm=94.499, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-14 23:58:53,600 (trainer:763) INFO: 28epoch:train:601-640batch: iter_time=4.213e-05, forward_time=0.031, loss_ctc=3.764, loss=3.764, backward_time=0.007, grad_norm=97.109, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-14 23:58:56,064 (trainer:763) INFO: 28epoch:train:641-680batch: iter_time=4.024e-05, forward_time=0.033, loss_ctc=4.360, loss=4.360, backward_time=0.007, grad_norm=97.801, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-14 23:58:58,232 (trainer:763) INFO: 28epoch:train:681-720batch: iter_time=3.920e-05, forward_time=0.029, loss_ctc=3.395, loss=3.395, backward_time=0.007, grad_norm=93.632, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-14 23:59:00,569 (trainer:763) INFO: 28epoch:train:721-760batch: iter_time=4.066e-05, forward_time=0.031, loss_ctc=3.683, loss=3.683, backward_time=0.007, grad_norm=94.675, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-14 23:59:02,626 (trainer:763) INFO: 28epoch:train:761-800batch: iter_time=3.829e-05, forward_time=0.028, loss_ctc=3.010, loss=3.010, backward_time=0.007, grad_norm=89.922, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.206 +[stan] 2024-01-14 23:59:06,581 (trainer:354) INFO: 28epoch results: [train] iter_time=1.851e-04, forward_time=0.031, loss_ctc=3.877, loss=3.877, backward_time=0.007, grad_norm=95.891, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235, time=47.03 seconds, total_count=22400, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=70.481, cer_ctc=0.232, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=70.481, time=1.15 seconds, total_count=700, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.73 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-14 23:59:07,667 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:59:07,667 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/27epoch.pth +[stan] 2024-01-14 23:59:07,667 (trainer:288) INFO: 29/30epoch started. Estimated time to finish: 1 minute and 44.92 seconds +[stan] 2024-01-14 23:59:10,646 (trainer:763) INFO: 29epoch:train:1-40batch: iter_time=0.002, forward_time=0.036, loss_ctc=4.898, loss=4.898, backward_time=0.008, grad_norm=106.347, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.298 +[stan] 2024-01-14 23:59:12,835 (trainer:763) INFO: 29epoch:train:41-80batch: iter_time=3.998e-05, forward_time=0.029, loss_ctc=3.709, loss=3.709, backward_time=0.007, grad_norm=97.613, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-14 23:59:15,465 (trainer:763) INFO: 29epoch:train:81-120batch: iter_time=4.075e-05, forward_time=0.035, loss_ctc=4.573, loss=4.573, backward_time=0.007, grad_norm=99.958, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 23:59:17,791 (trainer:763) INFO: 29epoch:train:121-160batch: iter_time=3.937e-05, forward_time=0.031, loss_ctc=3.629, loss=3.629, backward_time=0.007, grad_norm=95.375, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-14 23:59:20,423 (trainer:763) INFO: 29epoch:train:161-200batch: iter_time=4.073e-05, forward_time=0.035, loss_ctc=4.480, loss=4.480, backward_time=0.008, grad_norm=98.982, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-14 23:59:22,624 (trainer:763) INFO: 29epoch:train:201-240batch: iter_time=4.019e-05, forward_time=0.029, loss_ctc=3.543, loss=3.543, backward_time=0.007, grad_norm=91.093, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-14 23:59:25,058 (trainer:763) INFO: 29epoch:train:241-280batch: iter_time=4.145e-05, forward_time=0.032, loss_ctc=4.241, loss=4.241, backward_time=0.007, grad_norm=90.982, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-14 23:59:27,551 (trainer:763) INFO: 29epoch:train:281-320batch: iter_time=4.096e-05, forward_time=0.033, loss_ctc=4.056, loss=4.056, backward_time=0.007, grad_norm=100.244, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-14 23:59:29,946 (trainer:763) INFO: 29epoch:train:321-360batch: iter_time=4.066e-05, forward_time=0.032, loss_ctc=3.838, loss=3.838, backward_time=0.007, grad_norm=95.258, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-14 23:59:32,003 (trainer:763) INFO: 29epoch:train:361-400batch: iter_time=3.954e-05, forward_time=0.028, loss_ctc=2.997, loss=2.997, backward_time=0.007, grad_norm=92.496, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.206 +[stan] 2024-01-14 23:59:34,682 (trainer:763) INFO: 29epoch:train:401-440batch: iter_time=4.216e-05, forward_time=0.035, loss_ctc=4.711, loss=4.711, backward_time=0.008, grad_norm=99.970, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-14 23:59:37,163 (trainer:763) INFO: 29epoch:train:441-480batch: iter_time=4.094e-05, forward_time=0.033, loss_ctc=3.919, loss=3.919, backward_time=0.007, grad_norm=98.763, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-14 23:59:39,296 (trainer:763) INFO: 29epoch:train:481-520batch: iter_time=4.496e-05, forward_time=0.029, loss_ctc=3.532, loss=3.532, backward_time=0.007, grad_norm=94.242, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.213 +[stan] 2024-01-14 23:59:41,388 (trainer:763) INFO: 29epoch:train:521-560batch: iter_time=4.020e-05, forward_time=0.028, loss_ctc=3.295, loss=3.295, backward_time=0.007, grad_norm=89.038, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.209 +[stan] 2024-01-14 23:59:43,701 (trainer:763) INFO: 29epoch:train:561-600batch: iter_time=4.478e-05, forward_time=0.031, loss_ctc=4.025, loss=4.025, backward_time=0.007, grad_norm=96.260, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-14 23:59:46,008 (trainer:763) INFO: 29epoch:train:601-640batch: iter_time=4.133e-05, forward_time=0.031, loss_ctc=3.785, loss=3.785, backward_time=0.007, grad_norm=92.682, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-14 23:59:48,449 (trainer:763) INFO: 29epoch:train:641-680batch: iter_time=4.063e-05, forward_time=0.032, loss_ctc=3.855, loss=3.855, backward_time=0.007, grad_norm=95.779, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-14 23:59:50,836 (trainer:763) INFO: 29epoch:train:681-720batch: iter_time=4.166e-05, forward_time=0.032, loss_ctc=3.674, loss=3.674, backward_time=0.007, grad_norm=97.290, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-14 23:59:53,152 (trainer:763) INFO: 29epoch:train:721-760batch: iter_time=4.071e-05, forward_time=0.031, loss_ctc=3.742, loss=3.742, backward_time=0.007, grad_norm=96.087, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-14 23:59:55,854 (trainer:763) INFO: 29epoch:train:761-800batch: iter_time=4.022e-05, forward_time=0.036, loss_ctc=4.740, loss=4.740, backward_time=0.008, grad_norm=95.038, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-14 23:59:59,795 (trainer:354) INFO: 29epoch results: [train] iter_time=1.567e-04, forward_time=0.032, loss_ctc=3.962, loss=3.962, backward_time=0.007, grad_norm=96.175, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241, time=48.26 seconds, total_count=23200, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=71.056, cer_ctc=0.236, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=71.056, time=1.14 seconds, total_count=725, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.73 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-15 00:00:00,757 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:00:00,757 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/28epoch.pth +[stan] 2024-01-15 00:00:00,757 (trainer:288) INFO: 30/30epoch started. Estimated time to finish: 52.48 seconds +[stan] 2024-01-15 00:00:03,184 (trainer:763) INFO: 30epoch:train:1-40batch: iter_time=0.003, forward_time=0.029, loss_ctc=3.438, loss=3.438, backward_time=0.007, grad_norm=91.081, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-15 00:00:05,881 (trainer:763) INFO: 30epoch:train:41-80batch: iter_time=4.273e-05, forward_time=0.036, loss_ctc=4.934, loss=4.934, backward_time=0.007, grad_norm=101.045, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-15 00:00:07,868 (trainer:763) INFO: 30epoch:train:81-120batch: iter_time=4.384e-05, forward_time=0.027, loss_ctc=3.132, loss=3.132, backward_time=0.007, grad_norm=89.404, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.199 +[stan] 2024-01-15 00:00:10,097 (trainer:763) INFO: 30epoch:train:121-160batch: iter_time=4.418e-05, forward_time=0.030, loss_ctc=3.514, loss=3.514, backward_time=0.007, grad_norm=94.514, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.223 +[stan] 2024-01-15 00:00:12,625 (trainer:763) INFO: 30epoch:train:161-200batch: iter_time=4.234e-05, forward_time=0.033, loss_ctc=4.034, loss=4.034, backward_time=0.007, grad_norm=100.736, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-15 00:00:15,136 (trainer:763) INFO: 30epoch:train:201-240batch: iter_time=4.374e-05, forward_time=0.033, loss_ctc=4.391, loss=4.391, backward_time=0.008, grad_norm=99.775, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-15 00:00:17,195 (trainer:763) INFO: 30epoch:train:241-280batch: iter_time=4.184e-05, forward_time=0.028, loss_ctc=3.018, loss=3.018, backward_time=0.007, grad_norm=87.810, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.206 +[stan] 2024-01-15 00:00:19,641 (trainer:763) INFO: 30epoch:train:281-320batch: iter_time=4.372e-05, forward_time=0.032, loss_ctc=3.944, loss=3.944, backward_time=0.007, grad_norm=91.958, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-15 00:00:21,930 (trainer:763) INFO: 30epoch:train:321-360batch: iter_time=4.731e-05, forward_time=0.030, loss_ctc=3.447, loss=3.447, backward_time=0.007, grad_norm=92.200, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-15 00:00:24,219 (trainer:763) INFO: 30epoch:train:361-400batch: iter_time=4.107e-05, forward_time=0.032, loss_ctc=3.724, loss=3.724, backward_time=0.007, grad_norm=99.016, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-15 00:00:26,796 (trainer:763) INFO: 30epoch:train:401-440batch: iter_time=4.091e-05, forward_time=0.034, loss_ctc=4.469, loss=4.469, backward_time=0.007, grad_norm=99.740, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-15 00:00:29,243 (trainer:763) INFO: 30epoch:train:441-480batch: iter_time=4.354e-05, forward_time=0.032, loss_ctc=3.926, loss=3.926, backward_time=0.007, grad_norm=89.314, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-15 00:00:31,682 (trainer:763) INFO: 30epoch:train:481-520batch: iter_time=4.144e-05, forward_time=0.032, loss_ctc=4.337, loss=4.337, backward_time=0.008, grad_norm=96.147, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-15 00:00:33,892 (trainer:763) INFO: 30epoch:train:521-560batch: iter_time=4.005e-05, forward_time=0.030, loss_ctc=3.075, loss=3.075, backward_time=0.007, grad_norm=89.986, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.221 +[stan] 2024-01-15 00:00:36,341 (trainer:763) INFO: 30epoch:train:561-600batch: iter_time=4.278e-05, forward_time=0.032, loss_ctc=4.333, loss=4.333, backward_time=0.008, grad_norm=98.014, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-15 00:00:38,808 (trainer:763) INFO: 30epoch:train:601-640batch: iter_time=4.077e-05, forward_time=0.033, loss_ctc=3.541, loss=3.541, backward_time=0.007, grad_norm=101.445, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-15 00:00:40,996 (trainer:763) INFO: 30epoch:train:641-680batch: iter_time=4.161e-05, forward_time=0.029, loss_ctc=3.820, loss=3.820, backward_time=0.007, grad_norm=95.302, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-15 00:00:43,697 (trainer:763) INFO: 30epoch:train:681-720batch: iter_time=4.183e-05, forward_time=0.036, loss_ctc=4.625, loss=4.625, backward_time=0.008, grad_norm=99.004, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-15 00:00:46,096 (trainer:763) INFO: 30epoch:train:721-760batch: iter_time=4.051e-05, forward_time=0.032, loss_ctc=3.843, loss=3.843, backward_time=0.007, grad_norm=94.735, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-15 00:00:48,173 (trainer:763) INFO: 30epoch:train:761-800batch: iter_time=3.902e-05, forward_time=0.028, loss_ctc=3.040, loss=3.040, backward_time=0.007, grad_norm=89.392, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.208 +[stan] 2024-01-15 00:00:52,111 (trainer:354) INFO: 30epoch results: [train] iter_time=1.962e-04, forward_time=0.031, loss_ctc=3.829, loss=3.829, backward_time=0.007, grad_norm=95.031, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237, time=47.49 seconds, total_count=24000, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=70.185, cer_ctc=0.229, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=70.185, time=1.14 seconds, total_count=750, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.73 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-15 00:00:53,068 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:00:53,069 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/29epoch.pth +[stan] 2024-01-15 00:00:53,069 (trainer:489) INFO: The training was finished at 30 epochs +[stan] 2024-01-15 00:00:53,086 (average_nbest_models:69) INFO: Averaging 5best models: criterion="valid.loss": test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave_5best.pth +# Accounting: time=1579 threads=1 +# Ended (code 0) at Mon Jan 15 00:00:53 CST 2024, elapsed time 1579 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/train.log b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/train.log new file mode 100644 index 0000000000000000000000000000000000000000..4391afde6c98d687837d351a1ae31823da7095e6 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_deu1_1h/train.log @@ -0,0 +1,1406 @@ +# python3 -m espnet2.bin.asr_train --use_preprocessor true --bpemodel none --token_type char --token_list data/deu1_token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_deu1/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_deu1_1h/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_deu1/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_deu1_1h/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_1h_deu1/text,text,text --train_shape_file test_pr/asr_stats_deu1_1h/train/text_shape.char --valid_data_path_and_name_and_type dump/raw/dev_10min_deu1/text,text,text --valid_shape_file test_pr/asr_stats_deu1_1h/valid/text_shape.char --ngpu 1 --multiprocessing_distributed True +# Started at Wed Jan 17 01:24:03 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_train.py --use_preprocessor true --bpemodel none --token_type char --token_list data/deu1_token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_deu1/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_deu1_1h/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_deu1_1h --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_deu1/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_deu1_1h/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_1h_deu1/text,text,text --train_shape_file test_pr/asr_stats_deu1_1h/train/text_shape.char --valid_data_path_and_name_and_type dump/raw/dev_10min_deu1/text,text,text --valid_shape_file test_pr/asr_stats_deu1_1h/valid/text_shape.char --ngpu 1 --multiprocessing_distributed True +[stan] 2024-01-17 01:24:05,100 (asr:523) INFO: Vocabulary size: 44 +[stan] 2024-01-17 01:24:05,162 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +[stan] 2024-01-17 01:24:05,162 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +[stan] 2024-01-17 01:24:05,272 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +[stan] 2024-01-17 01:24:06,565 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +[stan] 2024-01-17 01:24:07,382 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.mask_emb.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.label_embs_concat.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.0.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.1.0.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.2.0.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.3.0.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.4.0.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.5.0.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.6.0.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_g.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_v.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,383 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,384 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.weight.requires_grad = False +[stan] 2024-01-17 01:24:07,385 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.bias.requires_grad = False +[stan] 2024-01-17 01:24:07,786 (abs_task:1275) INFO: pytorch.version=1.12.0+cu113, cuda.available=True, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True +[stan] 2024-01-17 01:24:07,788 (abs_task:1276) INFO: Model structure: +ESPnetASRModel( + (frontend): S3prlFrontend( + (upstream): S3PRLUpstream( + (upstream): UpstreamExpert( + (model): HubertModel( + (feature_extractor): ConvFeatureExtractionModel( + (conv_layers): ModuleList( + (0): Sequential( + (0): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): Fp32GroupNorm(512, 512, eps=1e-05, affine=True) + (3): GELU(approximate=none) + ) + (1): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (2): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (3): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (4): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (5): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (6): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + ) + ) + (post_extract_proj): Linear(in_features=512, out_features=768, bias=True) + (dropout_input): Dropout(p=0.1, inplace=False) + (dropout_features): Dropout(p=0.1, inplace=False) + (encoder): TransformerEncoder( + (pos_conv): Sequential( + (0): Conv1d(768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16) + (1): SamePad() + (2): GELU(approximate=none) + ) + (layers): ModuleList( + (0): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (1): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (2): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (3): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (4): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (5): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (6): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (7): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (8): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (9): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (10): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (11): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + ) + (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (final_proj): Linear(in_features=768, out_features=256, bias=True) + ) + ) + ) + (featurizer): Featurizer() + ) + (specaug): SpecAug( + (time_warp): TimeWarp(window=5, mode=bicubic) + (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq) + (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time) + ) + (normalize): UtteranceMVN(norm_means=True, norm_vars=False) + (preencoder): LinearProjection( + (linear_out): Linear(in_features=768, out_features=80, bias=True) + (dropout): Dropout(p=0.0, inplace=False) + ) + (encoder): TransformerEncoder( + (embed): Conv2dSubsampling2( + (conv): Sequential( + (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU() + (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) + (3): ReLU() + ) + (out): Sequential( + (0): Linear(in_features=9472, out_features=256, bias=True) + (1): PositionalEncoding( + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + ) + (encoders): MultiSequential( + (0): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (1): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + ) + (criterion_att): LabelSmoothingLoss( + (criterion): KLDivLoss() + ) + (ctc): CTC( + (ctc_lo): Linear(in_features=256, out_features=44, bias=True) + (ctc_loss): CTCLoss() + ) +) + +Model summary: + Class Name: ESPnetASRModel + Total Number of model parameters: 99.97 M + Number of trainable parameters: 5.27 M (5.3%) + Size: 21.08 MB + Type: torch.float32 +[stan] 2024-01-17 01:24:07,788 (abs_task:1279) INFO: Optimizer: +Adam ( +Parameter Group 0 + amsgrad: False + betas: (0.9, 0.999) + capturable: False + eps: 1e-08 + foreach: None + lr: 0.0001 + maximize: False + weight_decay: 1e-06 +) +[stan] 2024-01-17 01:24:07,788 (abs_task:1280) INFO: Scheduler: None +[stan] 2024-01-17 01:24:07,789 (abs_task:1289) INFO: Saving the configuration in test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/config.yaml +[stan] 2024-01-17 01:24:07,941 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-17 01:24:07,984 (abs_task:1665) INFO: [train] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/train_1h_deu1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/train_1h_deu1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-17 01:24:07,984 (abs_task:1666) INFO: [train] Batch sampler: SortedBatchSampler(N-batch=150, batch_size=8, shape_file=test_pr/asr_stats_deu1_1h/train/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-17 01:24:07,984 (abs_task:1667) INFO: [train] mini-batch sizes summary: N-batch=150, mean=8.0, min=8, max=8 +[stan] 2024-01-17 01:24:07,995 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-17 01:24:07,995 (abs_task:1665) INFO: [valid] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_deu1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_deu1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-17 01:24:07,995 (abs_task:1666) INFO: [valid] Batch sampler: SortedBatchSampler(N-batch=25, batch_size=8, shape_file=test_pr/asr_stats_deu1_1h/valid/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-17 01:24:07,996 (abs_task:1667) INFO: [valid] mini-batch sizes summary: N-batch=25, mean=8.3, min=8, max=9 +[stan] 2024-01-17 01:24:07,996 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-17 01:24:08,008 (abs_task:1665) INFO: [plot_att] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_deu1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_deu1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-17 01:24:08,008 (abs_task:1666) INFO: [plot_att] Batch sampler: UnsortedBatchSampler(N-batch=207, batch_size=1, key_file=test_pr/asr_stats_deu1_1h/valid/speech_shape, +[stan] 2024-01-17 01:24:08,008 (abs_task:1667) INFO: [plot_att] mini-batch sizes summary: N-batch=3, mean=1.0, min=1, max=1 +[stan] 2024-01-17 01:24:08,043 (trainer:303) INFO: 1/30epoch started +[stan] 2024-01-17 01:24:11,821 (trainer:762) INFO: 1epoch:train:1-40batch: iter_time=0.002, forward_time=0.060, loss_ctc=38.645, loss=38.645, backward_time=0.008, grad_norm=324.007, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.374 +[stan] 2024-01-17 01:24:14,041 (trainer:762) INFO: 1epoch:train:41-80batch: iter_time=3.900e-05, forward_time=0.030, loss_ctc=34.035, loss=34.035, backward_time=0.007, grad_norm=99.318, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.223 +[stan] 2024-01-17 01:24:16,736 (trainer:762) INFO: 1epoch:train:81-120batch: iter_time=4.099e-05, forward_time=0.036, loss_ctc=37.960, loss=37.960, backward_time=0.007, grad_norm=76.400, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-17 01:24:18,901 (trainer:762) INFO: 1epoch:train:121-160batch: iter_time=3.900e-05, forward_time=0.029, loss_ctc=32.080, loss=32.080, backward_time=0.006, grad_norm=63.044, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.216 +[stan] 2024-01-17 01:24:21,112 (trainer:762) INFO: 1epoch:train:161-200batch: iter_time=4.337e-05, forward_time=0.030, loss_ctc=30.447, loss=30.447, backward_time=0.006, grad_norm=91.103, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.221 +[stan] 2024-01-17 01:24:23,305 (trainer:762) INFO: 1epoch:train:201-240batch: iter_time=4.228e-05, forward_time=0.029, loss_ctc=27.176, loss=27.176, backward_time=0.007, grad_norm=75.581, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-17 01:24:26,000 (trainer:762) INFO: 1epoch:train:241-280batch: iter_time=4.512e-05, forward_time=0.036, loss_ctc=29.093, loss=29.093, backward_time=0.007, grad_norm=93.907, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-17 01:24:28,415 (trainer:762) INFO: 1epoch:train:281-320batch: iter_time=3.986e-05, forward_time=0.032, loss_ctc=25.576, loss=25.576, backward_time=0.006, grad_norm=96.674, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-17 01:24:30,812 (trainer:762) INFO: 1epoch:train:321-360batch: iter_time=4.050e-05, forward_time=0.032, loss_ctc=22.792, loss=22.792, backward_time=0.007, grad_norm=93.114, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-17 01:24:33,194 (trainer:762) INFO: 1epoch:train:361-400batch: iter_time=4.051e-05, forward_time=0.032, loss_ctc=22.200, loss=22.200, backward_time=0.007, grad_norm=93.510, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-17 01:24:35,189 (trainer:762) INFO: 1epoch:train:401-440batch: iter_time=4.045e-05, forward_time=0.027, loss_ctc=19.098, loss=19.098, backward_time=0.006, grad_norm=66.715, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.199 +[stan] 2024-01-17 01:24:37,543 (trainer:762) INFO: 1epoch:train:441-480batch: iter_time=4.000e-05, forward_time=0.031, loss_ctc=21.312, loss=21.312, backward_time=0.007, grad_norm=84.292, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-17 01:24:40,035 (trainer:762) INFO: 1epoch:train:481-520batch: iter_time=3.974e-05, forward_time=0.033, loss_ctc=21.783, loss=21.783, backward_time=0.007, grad_norm=85.746, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-17 01:24:42,131 (trainer:762) INFO: 1epoch:train:521-560batch: iter_time=3.968e-05, forward_time=0.028, loss_ctc=17.934, loss=17.934, backward_time=0.006, grad_norm=71.803, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.209 +[stan] 2024-01-17 01:24:44,567 (trainer:762) INFO: 1epoch:train:561-600batch: iter_time=4.051e-05, forward_time=0.032, loss_ctc=20.122, loss=20.122, backward_time=0.007, grad_norm=77.929, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-17 01:24:47,237 (trainer:762) INFO: 1epoch:train:601-640batch: iter_time=4.119e-05, forward_time=0.035, loss_ctc=21.107, loss=21.107, backward_time=0.007, grad_norm=91.967, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-17 01:24:49,525 (trainer:762) INFO: 1epoch:train:641-680batch: iter_time=4.016e-05, forward_time=0.030, loss_ctc=19.303, loss=19.303, backward_time=0.006, grad_norm=74.927, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-17 01:24:51,559 (trainer:762) INFO: 1epoch:train:681-720batch: iter_time=4.188e-05, forward_time=0.027, loss_ctc=16.761, loss=16.761, backward_time=0.006, grad_norm=66.043, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.203 +[stan] 2024-01-17 01:24:54,011 (trainer:762) INFO: 1epoch:train:721-760batch: iter_time=4.225e-05, forward_time=0.032, loss_ctc=19.107, loss=19.107, backward_time=0.007, grad_norm=71.295, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-17 01:24:56,141 (trainer:762) INFO: 1epoch:train:761-800batch: iter_time=3.953e-05, forward_time=0.028, loss_ctc=17.006, loss=17.006, backward_time=0.006, grad_norm=66.751, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.213 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-17 01:25:00,329 (trainer:357) INFO: 1epoch results: [train] iter_time=1.400e-04, forward_time=0.032, loss_ctc=24.677, loss=24.677, backward_time=0.007, grad_norm=93.206, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240, time=48.13 seconds, total_count=800, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=63.755, cer_ctc=0.315, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=63.755, time=1.14 seconds, total_count=25, gpu_max_cached_mem_GB=10.941, [att_plot] time=3 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:25:01,392 (trainer:416) INFO: The best model has been updated: valid.loss +[stan] 2024-01-17 01:25:01,392 (trainer:291) INFO: 2/30epoch started. Estimated time to finish: 25 minutes and 47.12 seconds +[stan] 2024-01-17 01:25:03,801 (trainer:762) INFO: 2epoch:train:1-40batch: iter_time=0.002, forward_time=0.029, loss_ctc=17.322, loss=17.322, backward_time=0.007, grad_norm=68.667, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-17 01:25:06,583 (trainer:762) INFO: 2epoch:train:41-80batch: iter_time=4.273e-05, forward_time=0.037, loss_ctc=20.135, loss=20.135, backward_time=0.008, grad_norm=84.550, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.278 +[stan] 2024-01-17 01:25:08,746 (trainer:762) INFO: 2epoch:train:81-120batch: iter_time=4.159e-05, forward_time=0.029, loss_ctc=16.196, loss=16.196, backward_time=0.007, grad_norm=80.820, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.216 +[stan] 2024-01-17 01:25:11,382 (trainer:762) INFO: 2epoch:train:121-160batch: iter_time=4.196e-05, forward_time=0.035, loss_ctc=19.561, loss=19.561, backward_time=0.007, grad_norm=97.237, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-17 01:25:13,517 (trainer:762) INFO: 2epoch:train:161-200batch: iter_time=4.287e-05, forward_time=0.029, loss_ctc=16.108, loss=16.108, backward_time=0.007, grad_norm=68.327, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.213 +[stan] 2024-01-17 01:25:15,812 (trainer:762) INFO: 2epoch:train:201-240batch: iter_time=4.099e-05, forward_time=0.031, loss_ctc=16.491, loss=16.491, backward_time=0.007, grad_norm=70.081, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-17 01:25:17,887 (trainer:762) INFO: 2epoch:train:241-280batch: iter_time=4.089e-05, forward_time=0.028, loss_ctc=14.929, loss=14.929, backward_time=0.007, grad_norm=66.880, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.207 +[stan] 2024-01-17 01:25:20,325 (trainer:762) INFO: 2epoch:train:281-320batch: iter_time=4.007e-05, forward_time=0.032, loss_ctc=17.817, loss=17.817, backward_time=0.007, grad_norm=81.203, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-17 01:25:22,761 (trainer:762) INFO: 2epoch:train:321-360batch: iter_time=4.093e-05, forward_time=0.032, loss_ctc=17.285, loss=17.285, backward_time=0.007, grad_norm=88.892, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-17 01:25:25,231 (trainer:762) INFO: 2epoch:train:361-400batch: iter_time=4.497e-05, forward_time=0.033, loss_ctc=17.147, loss=17.147, backward_time=0.008, grad_norm=80.003, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-17 01:25:27,915 (trainer:762) INFO: 2epoch:train:401-440batch: iter_time=4.515e-05, forward_time=0.035, loss_ctc=18.744, loss=18.744, backward_time=0.008, grad_norm=83.008, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-17 01:25:30,201 (trainer:762) INFO: 2epoch:train:441-480batch: iter_time=4.195e-05, forward_time=0.030, loss_ctc=16.280, loss=16.280, backward_time=0.007, grad_norm=76.179, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-17 01:25:32,530 (trainer:762) INFO: 2epoch:train:481-520batch: iter_time=4.011e-05, forward_time=0.031, loss_ctc=15.773, loss=15.773, backward_time=0.007, grad_norm=76.636, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-17 01:25:34,692 (trainer:762) INFO: 2epoch:train:521-560batch: iter_time=4.048e-05, forward_time=0.029, loss_ctc=14.782, loss=14.782, backward_time=0.007, grad_norm=85.040, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.216 +[stan] 2024-01-17 01:25:37,013 (trainer:762) INFO: 2epoch:train:561-600batch: iter_time=4.089e-05, forward_time=0.031, loss_ctc=16.249, loss=16.249, backward_time=0.007, grad_norm=80.540, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-17 01:25:39,228 (trainer:762) INFO: 2epoch:train:601-640batch: iter_time=4.115e-05, forward_time=0.030, loss_ctc=14.389, loss=14.389, backward_time=0.007, grad_norm=79.428, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.221 +[stan] 2024-01-17 01:25:41,861 (trainer:762) INFO: 2epoch:train:641-680batch: iter_time=4.193e-05, forward_time=0.035, loss_ctc=16.992, loss=16.992, backward_time=0.008, grad_norm=89.498, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-17 01:25:44,197 (trainer:762) INFO: 2epoch:train:681-720batch: iter_time=4.014e-05, forward_time=0.031, loss_ctc=15.227, loss=15.227, backward_time=0.007, grad_norm=82.272, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-17 01:25:46,296 (trainer:762) INFO: 2epoch:train:721-760batch: iter_time=3.969e-05, forward_time=0.028, loss_ctc=14.116, loss=14.116, backward_time=0.007, grad_norm=85.542, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.210 +[stan] 2024-01-17 01:25:48,558 (trainer:762) INFO: 2epoch:train:761-800batch: iter_time=3.926e-05, forward_time=0.030, loss_ctc=14.905, loss=14.905, backward_time=0.007, grad_norm=83.331, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-17 01:25:52,484 (trainer:357) INFO: 2epoch results: [train] iter_time=1.437e-04, forward_time=0.031, loss_ctc=16.522, loss=16.522, backward_time=0.007, grad_norm=80.407, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236, time=47.24 seconds, total_count=1600, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=56.210, cer_ctc=0.277, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=56.210, time=1.13 seconds, total_count=50, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.72 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:25:53,375 (trainer:416) INFO: The best model has been updated: valid.loss +[stan] 2024-01-17 01:25:53,376 (trainer:291) INFO: 3/30epoch started. Estimated time to finish: 24 minutes and 34.66 seconds +[stan] 2024-01-17 01:25:55,909 (trainer:762) INFO: 3epoch:train:1-40batch: iter_time=0.002, forward_time=0.031, loss_ctc=15.081, loss=15.081, backward_time=0.007, grad_norm=84.510, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-17 01:25:58,597 (trainer:762) INFO: 3epoch:train:41-80batch: iter_time=4.506e-05, forward_time=0.035, loss_ctc=16.102, loss=16.102, backward_time=0.008, grad_norm=86.597, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-17 01:26:01,076 (trainer:762) INFO: 3epoch:train:81-120batch: iter_time=4.011e-05, forward_time=0.033, loss_ctc=16.063, loss=16.063, backward_time=0.007, grad_norm=92.574, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-17 01:26:03,513 (trainer:762) INFO: 3epoch:train:121-160batch: iter_time=4.235e-05, forward_time=0.032, loss_ctc=15.260, loss=15.260, backward_time=0.007, grad_norm=84.650, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-17 01:26:05,628 (trainer:762) INFO: 3epoch:train:161-200batch: iter_time=4.161e-05, forward_time=0.028, loss_ctc=14.166, loss=14.166, backward_time=0.007, grad_norm=77.819, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.211 +[stan] 2024-01-17 01:26:08,133 (trainer:762) INFO: 3epoch:train:201-240batch: iter_time=4.287e-05, forward_time=0.033, loss_ctc=14.776, loss=14.776, backward_time=0.007, grad_norm=77.822, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-17 01:26:10,702 (trainer:762) INFO: 3epoch:train:241-280batch: iter_time=4.225e-05, forward_time=0.034, loss_ctc=15.449, loss=15.449, backward_time=0.007, grad_norm=86.380, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-17 01:26:12,914 (trainer:762) INFO: 3epoch:train:281-320batch: iter_time=4.242e-05, forward_time=0.030, loss_ctc=14.472, loss=14.472, backward_time=0.007, grad_norm=83.330, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.221 +[stan] 2024-01-17 01:26:14,970 (trainer:762) INFO: 3epoch:train:321-360batch: iter_time=3.932e-05, forward_time=0.028, loss_ctc=12.965, loss=12.965, backward_time=0.007, grad_norm=81.124, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.205 +[stan] 2024-01-17 01:26:17,083 (trainer:762) INFO: 3epoch:train:361-400batch: iter_time=4.139e-05, forward_time=0.028, loss_ctc=12.884, loss=12.884, backward_time=0.007, grad_norm=80.278, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.211 +[stan] 2024-01-17 01:26:19,392 (trainer:762) INFO: 3epoch:train:401-440batch: iter_time=4.042e-05, forward_time=0.031, loss_ctc=14.387, loss=14.387, backward_time=0.007, grad_norm=81.626, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-17 01:26:21,891 (trainer:762) INFO: 3epoch:train:441-480batch: iter_time=4.117e-05, forward_time=0.033, loss_ctc=15.274, loss=15.274, backward_time=0.008, grad_norm=84.147, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-17 01:26:24,466 (trainer:762) INFO: 3epoch:train:481-520batch: iter_time=4.168e-05, forward_time=0.034, loss_ctc=15.598, loss=15.598, backward_time=0.008, grad_norm=89.620, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-17 01:26:26,908 (trainer:762) INFO: 3epoch:train:521-560batch: iter_time=4.014e-05, forward_time=0.032, loss_ctc=13.995, loss=13.995, backward_time=0.007, grad_norm=89.373, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-17 01:26:28,916 (trainer:762) INFO: 3epoch:train:561-600batch: iter_time=3.985e-05, forward_time=0.027, loss_ctc=11.966, loss=11.966, backward_time=0.007, grad_norm=77.557, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.201 +[stan] 2024-01-17 01:26:31,594 (trainer:762) INFO: 3epoch:train:601-640batch: iter_time=4.377e-05, forward_time=0.035, loss_ctc=15.776, loss=15.776, backward_time=0.008, grad_norm=93.819, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-17 01:26:33,793 (trainer:762) INFO: 3epoch:train:641-680batch: iter_time=3.993e-05, forward_time=0.029, loss_ctc=13.423, loss=13.423, backward_time=0.007, grad_norm=86.256, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-17 01:26:36,285 (trainer:762) INFO: 3epoch:train:681-720batch: iter_time=4.141e-05, forward_time=0.033, loss_ctc=14.449, loss=14.449, backward_time=0.007, grad_norm=94.850, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-17 01:26:38,615 (trainer:762) INFO: 3epoch:train:721-760batch: iter_time=4.141e-05, forward_time=0.031, loss_ctc=13.176, loss=13.176, backward_time=0.007, grad_norm=86.139, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-17 01:26:40,989 (trainer:762) INFO: 3epoch:train:761-800batch: iter_time=3.916e-05, forward_time=0.032, loss_ctc=13.667, loss=13.667, backward_time=0.007, grad_norm=87.201, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-17 01:26:44,990 (trainer:357) INFO: 3epoch results: [train] iter_time=1.641e-04, forward_time=0.031, loss_ctc=14.447, loss=14.447, backward_time=0.007, grad_norm=85.284, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238, time=47.69 seconds, total_count=2400, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=53.202, cer_ctc=0.264, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=53.202, time=1.14 seconds, total_count=75, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.79 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:26:45,877 (trainer:416) INFO: The best model has been updated: valid.loss +[stan] 2024-01-17 01:26:45,877 (trainer:291) INFO: 4/30epoch started. Estimated time to finish: 23 minutes and 40.51 seconds +[stan] 2024-01-17 01:26:48,612 (trainer:762) INFO: 4epoch:train:1-40batch: iter_time=0.002, forward_time=0.033, loss_ctc=13.940, loss=13.940, backward_time=0.007, grad_norm=92.734, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-17 01:26:50,970 (trainer:762) INFO: 4epoch:train:41-80batch: iter_time=4.406e-05, forward_time=0.031, loss_ctc=13.560, loss=13.560, backward_time=0.007, grad_norm=87.272, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-17 01:26:53,430 (trainer:762) INFO: 4epoch:train:81-120batch: iter_time=4.180e-05, forward_time=0.033, loss_ctc=13.665, loss=13.665, backward_time=0.007, grad_norm=100.979, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-17 01:26:55,420 (trainer:762) INFO: 4epoch:train:121-160batch: iter_time=4.266e-05, forward_time=0.027, loss_ctc=11.478, loss=11.478, backward_time=0.007, grad_norm=79.438, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.199 +[stan] 2024-01-17 01:26:57,861 (trainer:762) INFO: 4epoch:train:161-200batch: iter_time=4.354e-05, forward_time=0.032, loss_ctc=13.198, loss=13.198, backward_time=0.007, grad_norm=93.010, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-17 01:27:00,315 (trainer:762) INFO: 4epoch:train:201-240batch: iter_time=4.309e-05, forward_time=0.033, loss_ctc=13.703, loss=13.703, backward_time=0.007, grad_norm=90.578, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-17 01:27:02,576 (trainer:762) INFO: 4epoch:train:241-280batch: iter_time=4.152e-05, forward_time=0.030, loss_ctc=12.758, loss=12.758, backward_time=0.007, grad_norm=87.156, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-17 01:27:05,124 (trainer:762) INFO: 4epoch:train:281-320batch: iter_time=4.467e-05, forward_time=0.034, loss_ctc=13.876, loss=13.876, backward_time=0.007, grad_norm=99.947, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-17 01:27:07,384 (trainer:762) INFO: 4epoch:train:321-360batch: iter_time=4.351e-05, forward_time=0.030, loss_ctc=11.971, loss=11.971, backward_time=0.007, grad_norm=88.879, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-17 01:27:09,646 (trainer:762) INFO: 4epoch:train:361-400batch: iter_time=4.556e-05, forward_time=0.030, loss_ctc=12.337, loss=12.337, backward_time=0.007, grad_norm=85.300, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-17 01:27:12,159 (trainer:762) INFO: 4epoch:train:401-440batch: iter_time=4.196e-05, forward_time=0.033, loss_ctc=14.011, loss=14.011, backward_time=0.007, grad_norm=94.161, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-17 01:27:14,510 (trainer:762) INFO: 4epoch:train:441-480batch: iter_time=4.141e-05, forward_time=0.031, loss_ctc=12.546, loss=12.546, backward_time=0.007, grad_norm=99.916, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-17 01:27:16,934 (trainer:762) INFO: 4epoch:train:481-520batch: iter_time=4.203e-05, forward_time=0.032, loss_ctc=13.279, loss=13.279, backward_time=0.007, grad_norm=104.421, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-17 01:27:19,252 (trainer:762) INFO: 4epoch:train:521-560batch: iter_time=4.103e-05, forward_time=0.031, loss_ctc=12.963, loss=12.963, backward_time=0.007, grad_norm=95.739, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-17 01:27:21,576 (trainer:762) INFO: 4epoch:train:561-600batch: iter_time=4.161e-05, forward_time=0.031, loss_ctc=11.740, loss=11.740, backward_time=0.007, grad_norm=90.800, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-17 01:27:24,123 (trainer:762) INFO: 4epoch:train:601-640batch: iter_time=4.448e-05, forward_time=0.034, loss_ctc=13.515, loss=13.515, backward_time=0.008, grad_norm=92.771, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-17 01:27:26,525 (trainer:762) INFO: 4epoch:train:641-680batch: iter_time=4.278e-05, forward_time=0.032, loss_ctc=12.218, loss=12.218, backward_time=0.007, grad_norm=96.155, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-17 01:27:28,504 (trainer:762) INFO: 4epoch:train:681-720batch: iter_time=4.160e-05, forward_time=0.027, loss_ctc=10.501, loss=10.501, backward_time=0.007, grad_norm=81.584, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.198 +[stan] 2024-01-17 01:27:31,131 (trainer:762) INFO: 4epoch:train:721-760batch: iter_time=4.304e-05, forward_time=0.035, loss_ctc=13.465, loss=13.465, backward_time=0.007, grad_norm=97.502, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-17 01:27:33,172 (trainer:762) INFO: 4epoch:train:761-800batch: iter_time=4.160e-05, forward_time=0.027, loss_ctc=9.965, loss=9.965, backward_time=0.007, grad_norm=86.467, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.204 +[stan] 2024-01-17 01:27:37,069 (trainer:357) INFO: 4epoch results: [train] iter_time=1.626e-04, forward_time=0.031, loss_ctc=12.734, loss=12.734, backward_time=0.007, grad_norm=92.240, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236, time=47.37 seconds, total_count=3200, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=52.160, cer_ctc=0.257, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=52.160, time=1.13 seconds, total_count=100, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.69 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:27:37,971 (trainer:416) INFO: The best model has been updated: valid.loss +[stan] 2024-01-17 01:27:37,971 (trainer:291) INFO: 5/30epoch started. Estimated time to finish: 22 minutes and 44.53 seconds +[stan] 2024-01-17 01:27:40,905 (trainer:762) INFO: 5epoch:train:1-40batch: iter_time=0.003, forward_time=0.036, loss_ctc=14.709, loss=14.709, backward_time=0.008, grad_norm=107.693, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.293 +[stan] 2024-01-17 01:27:43,445 (trainer:762) INFO: 5epoch:train:41-80batch: iter_time=4.448e-05, forward_time=0.035, loss_ctc=11.906, loss=11.906, backward_time=0.007, grad_norm=89.722, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-17 01:27:45,372 (trainer:762) INFO: 5epoch:train:81-120batch: iter_time=4.030e-05, forward_time=0.026, loss_ctc=10.585, loss=10.585, backward_time=0.007, grad_norm=83.142, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.193 +[stan] 2024-01-17 01:27:47,770 (trainer:762) INFO: 5epoch:train:121-160batch: iter_time=4.264e-05, forward_time=0.032, loss_ctc=12.005, loss=12.005, backward_time=0.007, grad_norm=95.285, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-17 01:27:50,475 (trainer:762) INFO: 5epoch:train:161-200batch: iter_time=4.040e-05, forward_time=0.036, loss_ctc=13.774, loss=13.774, backward_time=0.008, grad_norm=97.682, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-17 01:27:52,831 (trainer:762) INFO: 5epoch:train:201-240batch: iter_time=4.044e-05, forward_time=0.031, loss_ctc=11.777, loss=11.777, backward_time=0.007, grad_norm=90.069, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-17 01:27:55,220 (trainer:762) INFO: 5epoch:train:241-280batch: iter_time=4.241e-05, forward_time=0.032, loss_ctc=11.638, loss=11.638, backward_time=0.007, grad_norm=90.795, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-17 01:27:57,447 (trainer:762) INFO: 5epoch:train:281-320batch: iter_time=4.255e-05, forward_time=0.030, loss_ctc=10.559, loss=10.559, backward_time=0.007, grad_norm=86.018, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.223 +[stan] 2024-01-17 01:27:59,876 (trainer:762) INFO: 5epoch:train:321-360batch: iter_time=4.087e-05, forward_time=0.032, loss_ctc=12.011, loss=12.011, backward_time=0.008, grad_norm=103.978, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-17 01:28:02,202 (trainer:762) INFO: 5epoch:train:361-400batch: iter_time=4.093e-05, forward_time=0.031, loss_ctc=12.063, loss=12.063, backward_time=0.007, grad_norm=110.374, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-17 01:28:04,477 (trainer:762) INFO: 5epoch:train:401-440batch: iter_time=4.357e-05, forward_time=0.030, loss_ctc=10.919, loss=10.919, backward_time=0.007, grad_norm=95.891, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-17 01:28:06,773 (trainer:762) INFO: 5epoch:train:441-480batch: iter_time=4.239e-05, forward_time=0.031, loss_ctc=11.385, loss=11.385, backward_time=0.007, grad_norm=89.968, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-17 01:28:09,489 (trainer:762) INFO: 5epoch:train:481-520batch: iter_time=4.312e-05, forward_time=0.036, loss_ctc=12.949, loss=12.949, backward_time=0.008, grad_norm=102.047, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-17 01:28:11,571 (trainer:762) INFO: 5epoch:train:521-560batch: iter_time=4.070e-05, forward_time=0.028, loss_ctc=10.311, loss=10.311, backward_time=0.007, grad_norm=98.117, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.208 +[stan] 2024-01-17 01:28:14,330 (trainer:762) INFO: 5epoch:train:561-600batch: iter_time=4.173e-05, forward_time=0.036, loss_ctc=13.221, loss=13.221, backward_time=0.008, grad_norm=108.472, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-17 01:28:16,531 (trainer:762) INFO: 5epoch:train:601-640batch: iter_time=4.057e-05, forward_time=0.029, loss_ctc=10.703, loss=10.703, backward_time=0.007, grad_norm=92.098, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-17 01:28:18,826 (trainer:762) INFO: 5epoch:train:641-680batch: iter_time=4.192e-05, forward_time=0.031, loss_ctc=10.261, loss=10.261, backward_time=0.007, grad_norm=91.173, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-17 01:28:21,269 (trainer:762) INFO: 5epoch:train:681-720batch: iter_time=4.229e-05, forward_time=0.032, loss_ctc=11.391, loss=11.391, backward_time=0.008, grad_norm=92.030, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-17 01:28:23,553 (trainer:762) INFO: 5epoch:train:721-760batch: iter_time=4.117e-05, forward_time=0.031, loss_ctc=10.668, loss=10.668, backward_time=0.007, grad_norm=101.884, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-17 01:28:25,975 (trainer:762) INFO: 5epoch:train:761-800batch: iter_time=4.003e-05, forward_time=0.032, loss_ctc=11.089, loss=11.089, backward_time=0.007, grad_norm=98.888, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-17 01:28:29,859 (trainer:357) INFO: 5epoch results: [train] iter_time=1.866e-04, forward_time=0.032, loss_ctc=11.696, loss=11.696, backward_time=0.007, grad_norm=96.266, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240, time=48.08 seconds, total_count=4000, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=51.994, cer_ctc=0.251, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=51.994, time=1.13 seconds, total_count=125, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.68 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:28:30,802 (trainer:416) INFO: The best model has been updated: valid.loss +[stan] 2024-01-17 01:28:30,803 (trainer:291) INFO: 6/30epoch started. Estimated time to finish: 21 minutes and 53.8 seconds +[stan] 2024-01-17 01:28:33,398 (trainer:762) INFO: 6epoch:train:1-40batch: iter_time=0.003, forward_time=0.031, loss_ctc=11.371, loss=11.371, backward_time=0.007, grad_norm=108.071, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-17 01:28:35,394 (trainer:762) INFO: 6epoch:train:41-80batch: iter_time=4.055e-05, forward_time=0.027, loss_ctc=9.428, loss=9.428, backward_time=0.007, grad_norm=92.570, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.200 +[stan] 2024-01-17 01:28:37,798 (trainer:762) INFO: 6epoch:train:81-120batch: iter_time=4.219e-05, forward_time=0.032, loss_ctc=10.576, loss=10.576, backward_time=0.007, grad_norm=97.007, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-17 01:28:40,288 (trainer:762) INFO: 6epoch:train:121-160batch: iter_time=4.466e-05, forward_time=0.033, loss_ctc=11.655, loss=11.655, backward_time=0.007, grad_norm=101.987, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-17 01:28:42,768 (trainer:762) INFO: 6epoch:train:161-200batch: iter_time=4.055e-05, forward_time=0.033, loss_ctc=11.778, loss=11.778, backward_time=0.007, grad_norm=104.708, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-17 01:28:45,180 (trainer:762) INFO: 6epoch:train:201-240batch: iter_time=4.185e-05, forward_time=0.032, loss_ctc=10.789, loss=10.789, backward_time=0.007, grad_norm=105.565, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-17 01:28:47,505 (trainer:762) INFO: 6epoch:train:241-280batch: iter_time=4.162e-05, forward_time=0.031, loss_ctc=10.174, loss=10.174, backward_time=0.007, grad_norm=98.974, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-17 01:28:49,561 (trainer:762) INFO: 6epoch:train:281-320batch: iter_time=4.045e-05, forward_time=0.028, loss_ctc=9.836, loss=9.836, backward_time=0.007, grad_norm=89.788, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.206 +[stan] 2024-01-17 01:28:52,163 (trainer:762) INFO: 6epoch:train:321-360batch: iter_time=4.296e-05, forward_time=0.034, loss_ctc=11.438, loss=11.438, backward_time=0.008, grad_norm=101.730, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-17 01:28:54,710 (trainer:762) INFO: 6epoch:train:361-400batch: iter_time=4.568e-05, forward_time=0.034, loss_ctc=11.260, loss=11.260, backward_time=0.008, grad_norm=102.492, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-17 01:28:56,827 (trainer:762) INFO: 6epoch:train:401-440batch: iter_time=4.362e-05, forward_time=0.028, loss_ctc=9.147, loss=9.147, backward_time=0.007, grad_norm=95.550, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.212 +[stan] 2024-01-17 01:28:59,398 (trainer:762) INFO: 6epoch:train:441-480batch: iter_time=4.289e-05, forward_time=0.034, loss_ctc=11.660, loss=11.660, backward_time=0.007, grad_norm=108.454, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-17 01:29:01,964 (trainer:762) INFO: 6epoch:train:481-520batch: iter_time=4.365e-05, forward_time=0.034, loss_ctc=11.159, loss=11.159, backward_time=0.008, grad_norm=101.177, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-17 01:29:04,296 (trainer:762) INFO: 6epoch:train:521-560batch: iter_time=4.304e-05, forward_time=0.031, loss_ctc=9.902, loss=9.902, backward_time=0.008, grad_norm=102.290, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-17 01:29:06,574 (trainer:762) INFO: 6epoch:train:561-600batch: iter_time=4.149e-05, forward_time=0.030, loss_ctc=10.015, loss=10.015, backward_time=0.007, grad_norm=105.396, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-17 01:29:08,844 (trainer:762) INFO: 6epoch:train:601-640batch: iter_time=4.498e-05, forward_time=0.030, loss_ctc=10.059, loss=10.059, backward_time=0.007, grad_norm=99.442, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-17 01:29:11,083 (trainer:762) INFO: 6epoch:train:641-680batch: iter_time=4.099e-05, forward_time=0.030, loss_ctc=9.621, loss=9.621, backward_time=0.007, grad_norm=98.160, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.224 +[stan] 2024-01-17 01:29:13,710 (trainer:762) INFO: 6epoch:train:681-720batch: iter_time=4.095e-05, forward_time=0.035, loss_ctc=11.360, loss=11.360, backward_time=0.007, grad_norm=113.730, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-17 01:29:16,098 (trainer:762) INFO: 6epoch:train:721-760batch: iter_time=4.242e-05, forward_time=0.032, loss_ctc=9.760, loss=9.760, backward_time=0.007, grad_norm=103.685, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-17 01:29:18,295 (trainer:762) INFO: 6epoch:train:761-800batch: iter_time=3.844e-05, forward_time=0.029, loss_ctc=9.716, loss=9.716, backward_time=0.007, grad_norm=102.168, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-17 01:29:22,183 (trainer:357) INFO: 6epoch results: [train] iter_time=1.713e-04, forward_time=0.031, loss_ctc=10.535, loss=10.535, backward_time=0.007, grad_norm=101.647, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237, time=47.57 seconds, total_count=4800, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=51.996, cer_ctc=0.249, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=51.996, time=1.14 seconds, total_count=150, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.67 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:29:23,207 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:29:23,208 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/1epoch.pth +[stan] 2024-01-17 01:29:23,208 (trainer:291) INFO: 7/30epoch started. Estimated time to finish: 21 minutes and 0.66 seconds +[stan] 2024-01-17 01:29:25,702 (trainer:762) INFO: 7epoch:train:1-40batch: iter_time=0.002, forward_time=0.030, loss_ctc=9.757, loss=9.757, backward_time=0.007, grad_norm=105.747, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-17 01:29:28,592 (trainer:762) INFO: 7epoch:train:41-80batch: iter_time=4.290e-05, forward_time=0.038, loss_ctc=12.032, loss=12.032, backward_time=0.008, grad_norm=138.976, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.289 +[stan] 2024-01-17 01:29:30,579 (trainer:762) INFO: 7epoch:train:81-120batch: iter_time=4.011e-05, forward_time=0.027, loss_ctc=8.152, loss=8.152, backward_time=0.007, grad_norm=91.552, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.199 +[stan] 2024-01-17 01:29:32,845 (trainer:762) INFO: 7epoch:train:121-160batch: iter_time=4.128e-05, forward_time=0.030, loss_ctc=9.876, loss=9.876, backward_time=0.007, grad_norm=102.772, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-17 01:29:35,017 (trainer:762) INFO: 7epoch:train:161-200batch: iter_time=4.086e-05, forward_time=0.029, loss_ctc=8.993, loss=8.993, backward_time=0.007, grad_norm=108.947, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-17 01:29:37,504 (trainer:762) INFO: 7epoch:train:201-240batch: iter_time=4.093e-05, forward_time=0.033, loss_ctc=9.904, loss=9.904, backward_time=0.008, grad_norm=110.298, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-17 01:29:40,156 (trainer:762) INFO: 7epoch:train:241-280batch: iter_time=4.080e-05, forward_time=0.035, loss_ctc=10.979, loss=10.979, backward_time=0.007, grad_norm=114.019, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.265 +[stan] 2024-01-17 01:29:42,405 (trainer:762) INFO: 7epoch:train:281-320batch: iter_time=4.272e-05, forward_time=0.030, loss_ctc=9.133, loss=9.133, backward_time=0.007, grad_norm=109.281, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-17 01:29:44,834 (trainer:762) INFO: 7epoch:train:321-360batch: iter_time=4.094e-05, forward_time=0.032, loss_ctc=10.326, loss=10.326, backward_time=0.007, grad_norm=114.359, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-17 01:29:47,396 (trainer:762) INFO: 7epoch:train:361-400batch: iter_time=4.474e-05, forward_time=0.034, loss_ctc=10.074, loss=10.074, backward_time=0.008, grad_norm=103.660, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-17 01:29:49,520 (trainer:762) INFO: 7epoch:train:401-440batch: iter_time=4.343e-05, forward_time=0.028, loss_ctc=8.729, loss=8.729, backward_time=0.007, grad_norm=100.013, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.212 +[stan] 2024-01-17 01:29:51,715 (trainer:762) INFO: 7epoch:train:441-480batch: iter_time=4.208e-05, forward_time=0.029, loss_ctc=8.645, loss=8.645, backward_time=0.007, grad_norm=102.946, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-17 01:29:53,824 (trainer:762) INFO: 7epoch:train:481-520batch: iter_time=4.362e-05, forward_time=0.028, loss_ctc=8.721, loss=8.721, backward_time=0.007, grad_norm=101.246, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.211 +[stan] 2024-01-17 01:29:56,580 (trainer:762) INFO: 7epoch:train:521-560batch: iter_time=4.268e-05, forward_time=0.036, loss_ctc=10.651, loss=10.651, backward_time=0.008, grad_norm=117.930, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-17 01:29:58,879 (trainer:762) INFO: 7epoch:train:561-600batch: iter_time=4.239e-05, forward_time=0.031, loss_ctc=9.064, loss=9.064, backward_time=0.007, grad_norm=102.983, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-17 01:30:01,346 (trainer:762) INFO: 7epoch:train:601-640batch: iter_time=4.079e-05, forward_time=0.033, loss_ctc=9.794, loss=9.794, backward_time=0.007, grad_norm=109.931, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-17 01:30:03,704 (trainer:762) INFO: 7epoch:train:641-680batch: iter_time=4.018e-05, forward_time=0.031, loss_ctc=8.841, loss=8.841, backward_time=0.007, grad_norm=102.186, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-17 01:30:06,030 (trainer:762) INFO: 7epoch:train:681-720batch: iter_time=4.188e-05, forward_time=0.031, loss_ctc=9.071, loss=9.071, backward_time=0.007, grad_norm=101.961, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-17 01:30:08,384 (trainer:762) INFO: 7epoch:train:721-760batch: iter_time=4.201e-05, forward_time=0.031, loss_ctc=9.282, loss=9.282, backward_time=0.008, grad_norm=102.338, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-17 01:30:10,760 (trainer:762) INFO: 7epoch:train:761-800batch: iter_time=3.939e-05, forward_time=0.032, loss_ctc=8.994, loss=8.994, backward_time=0.007, grad_norm=106.316, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-17 01:30:14,631 (trainer:357) INFO: 7epoch results: [train] iter_time=1.595e-04, forward_time=0.031, loss_ctc=9.551, loss=9.551, backward_time=0.007, grad_norm=107.373, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238, time=47.63 seconds, total_count=5600, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=53.283, cer_ctc=0.252, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=53.283, time=1.14 seconds, total_count=175, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.65 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:30:15,583 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:30:15,585 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/2epoch.pth +[stan] 2024-01-17 01:30:15,585 (trainer:291) INFO: 8/30epoch started. Estimated time to finish: 20 minutes and 7.64 seconds +[stan] 2024-01-17 01:30:17,810 (trainer:762) INFO: 8epoch:train:1-40batch: iter_time=0.003, forward_time=0.027, loss_ctc=7.335, loss=7.335, backward_time=0.007, grad_norm=98.339, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.222 +[stan] 2024-01-17 01:30:20,561 (trainer:762) INFO: 8epoch:train:41-80batch: iter_time=4.416e-05, forward_time=0.036, loss_ctc=10.317, loss=10.317, backward_time=0.008, grad_norm=116.020, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-17 01:30:22,578 (trainer:762) INFO: 8epoch:train:81-120batch: iter_time=4.365e-05, forward_time=0.027, loss_ctc=7.631, loss=7.631, backward_time=0.007, grad_norm=96.785, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.202 +[stan] 2024-01-17 01:30:25,128 (trainer:762) INFO: 8epoch:train:121-160batch: iter_time=4.094e-05, forward_time=0.034, loss_ctc=10.221, loss=10.221, backward_time=0.007, grad_norm=115.743, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-17 01:30:27,585 (trainer:762) INFO: 8epoch:train:161-200batch: iter_time=4.310e-05, forward_time=0.032, loss_ctc=9.155, loss=9.155, backward_time=0.007, grad_norm=108.006, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-17 01:30:29,883 (trainer:762) INFO: 8epoch:train:201-240batch: iter_time=4.202e-05, forward_time=0.031, loss_ctc=8.896, loss=8.896, backward_time=0.007, grad_norm=105.529, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-17 01:30:32,173 (trainer:762) INFO: 8epoch:train:241-280batch: iter_time=4.059e-05, forward_time=0.030, loss_ctc=7.888, loss=7.888, backward_time=0.007, grad_norm=102.317, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-17 01:30:34,516 (trainer:762) INFO: 8epoch:train:281-320batch: iter_time=4.216e-05, forward_time=0.031, loss_ctc=9.402, loss=9.402, backward_time=0.007, grad_norm=109.932, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-17 01:30:37,224 (trainer:762) INFO: 8epoch:train:321-360batch: iter_time=4.236e-05, forward_time=0.036, loss_ctc=10.097, loss=10.097, backward_time=0.008, grad_norm=117.213, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-17 01:30:39,363 (trainer:762) INFO: 8epoch:train:361-400batch: iter_time=4.357e-05, forward_time=0.029, loss_ctc=7.944, loss=7.944, backward_time=0.007, grad_norm=105.845, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.214 +[stan] 2024-01-17 01:30:41,802 (trainer:762) INFO: 8epoch:train:401-440batch: iter_time=4.205e-05, forward_time=0.032, loss_ctc=9.084, loss=9.084, backward_time=0.007, grad_norm=109.658, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-17 01:30:44,045 (trainer:762) INFO: 8epoch:train:441-480batch: iter_time=4.062e-05, forward_time=0.030, loss_ctc=8.594, loss=8.594, backward_time=0.007, grad_norm=111.485, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.224 +[stan] 2024-01-17 01:30:46,419 (trainer:762) INFO: 8epoch:train:481-520batch: iter_time=4.057e-05, forward_time=0.031, loss_ctc=8.925, loss=8.925, backward_time=0.007, grad_norm=109.362, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-17 01:30:48,547 (trainer:762) INFO: 8epoch:train:521-560batch: iter_time=4.030e-05, forward_time=0.029, loss_ctc=7.698, loss=7.698, backward_time=0.007, grad_norm=105.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.213 +[stan] 2024-01-17 01:30:50,766 (trainer:762) INFO: 8epoch:train:561-600batch: iter_time=4.150e-05, forward_time=0.030, loss_ctc=8.386, loss=8.386, backward_time=0.007, grad_norm=108.602, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.222 +[stan] 2024-01-17 01:30:53,523 (trainer:762) INFO: 8epoch:train:601-640batch: iter_time=4.188e-05, forward_time=0.036, loss_ctc=10.323, loss=10.323, backward_time=0.008, grad_norm=127.046, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-17 01:30:55,993 (trainer:762) INFO: 8epoch:train:641-680batch: iter_time=4.244e-05, forward_time=0.033, loss_ctc=8.617, loss=8.617, backward_time=0.007, grad_norm=112.235, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-17 01:30:58,207 (trainer:762) INFO: 8epoch:train:681-720batch: iter_time=4.302e-05, forward_time=0.030, loss_ctc=7.792, loss=7.792, backward_time=0.007, grad_norm=114.517, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.221 +[stan] 2024-01-17 01:31:00,809 (trainer:762) INFO: 8epoch:train:721-760batch: iter_time=4.610e-05, forward_time=0.034, loss_ctc=8.896, loss=8.896, backward_time=0.008, grad_norm=113.641, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-17 01:31:03,000 (trainer:762) INFO: 8epoch:train:761-800batch: iter_time=3.860e-05, forward_time=0.029, loss_ctc=8.074, loss=8.074, backward_time=0.007, grad_norm=104.366, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-17 01:31:07,009 (trainer:357) INFO: 8epoch results: [train] iter_time=1.703e-04, forward_time=0.031, loss_ctc=8.764, loss=8.764, backward_time=0.007, grad_norm=109.594, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237, time=47.48 seconds, total_count=6400, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=54.757, cer_ctc=0.254, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=54.757, time=1.16 seconds, total_count=200, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.78 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:31:07,990 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:31:07,990 (trainer:291) INFO: 9/30epoch started. Estimated time to finish: 19 minutes and 14.85 seconds +[stan] 2024-01-17 01:31:10,636 (trainer:762) INFO: 9epoch:train:1-40batch: iter_time=0.003, forward_time=0.032, loss_ctc=8.937, loss=8.937, backward_time=0.008, grad_norm=110.672, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-17 01:31:13,411 (trainer:762) INFO: 9epoch:train:41-80batch: iter_time=4.503e-05, forward_time=0.037, loss_ctc=9.718, loss=9.718, backward_time=0.008, grad_norm=118.500, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-17 01:31:15,716 (trainer:762) INFO: 9epoch:train:81-120batch: iter_time=4.131e-05, forward_time=0.031, loss_ctc=8.334, loss=8.334, backward_time=0.007, grad_norm=111.433, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-17 01:31:17,808 (trainer:762) INFO: 9epoch:train:121-160batch: iter_time=4.116e-05, forward_time=0.028, loss_ctc=7.265, loss=7.265, backward_time=0.007, grad_norm=100.858, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.209 +[stan] 2024-01-17 01:31:19,986 (trainer:762) INFO: 9epoch:train:161-200batch: iter_time=3.969e-05, forward_time=0.029, loss_ctc=7.868, loss=7.868, backward_time=0.007, grad_norm=104.103, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.218 +[stan] 2024-01-17 01:31:22,177 (trainer:762) INFO: 9epoch:train:201-240batch: iter_time=4.162e-05, forward_time=0.029, loss_ctc=7.725, loss=7.725, backward_time=0.007, grad_norm=106.767, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-17 01:31:24,516 (trainer:762) INFO: 9epoch:train:241-280batch: iter_time=4.033e-05, forward_time=0.031, loss_ctc=8.108, loss=8.108, backward_time=0.007, grad_norm=108.868, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-17 01:31:27,264 (trainer:762) INFO: 9epoch:train:281-320batch: iter_time=4.445e-05, forward_time=0.036, loss_ctc=9.187, loss=9.187, backward_time=0.008, grad_norm=126.488, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-17 01:31:29,726 (trainer:762) INFO: 9epoch:train:321-360batch: iter_time=4.158e-05, forward_time=0.033, loss_ctc=8.557, loss=8.557, backward_time=0.008, grad_norm=117.233, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-17 01:31:32,069 (trainer:762) INFO: 9epoch:train:361-400batch: iter_time=4.095e-05, forward_time=0.031, loss_ctc=7.768, loss=7.768, backward_time=0.007, grad_norm=110.647, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-17 01:31:34,450 (trainer:762) INFO: 9epoch:train:401-440batch: iter_time=4.184e-05, forward_time=0.032, loss_ctc=8.170, loss=8.170, backward_time=0.007, grad_norm=114.145, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-17 01:31:36,541 (trainer:762) INFO: 9epoch:train:441-480batch: iter_time=4.477e-05, forward_time=0.028, loss_ctc=7.012, loss=7.012, backward_time=0.007, grad_norm=109.465, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.209 +[stan] 2024-01-17 01:31:38,964 (trainer:762) INFO: 9epoch:train:481-520batch: iter_time=4.207e-05, forward_time=0.032, loss_ctc=8.480, loss=8.480, backward_time=0.007, grad_norm=115.800, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-17 01:31:41,255 (trainer:762) INFO: 9epoch:train:521-560batch: iter_time=4.023e-05, forward_time=0.031, loss_ctc=7.698, loss=7.698, backward_time=0.007, grad_norm=109.146, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-17 01:31:43,879 (trainer:762) INFO: 9epoch:train:561-600batch: iter_time=4.284e-05, forward_time=0.035, loss_ctc=8.888, loss=8.888, backward_time=0.008, grad_norm=117.062, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.262 +[stan] 2024-01-17 01:31:46,048 (trainer:762) INFO: 9epoch:train:601-640batch: iter_time=4.026e-05, forward_time=0.029, loss_ctc=7.112, loss=7.112, backward_time=0.007, grad_norm=101.226, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-17 01:31:48,703 (trainer:762) INFO: 9epoch:train:641-680batch: iter_time=4.227e-05, forward_time=0.035, loss_ctc=8.796, loss=8.796, backward_time=0.008, grad_norm=121.234, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.265 +[stan] 2024-01-17 01:31:50,965 (trainer:762) INFO: 9epoch:train:681-720batch: iter_time=4.355e-05, forward_time=0.030, loss_ctc=7.522, loss=7.522, backward_time=0.007, grad_norm=114.643, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-17 01:31:53,160 (trainer:762) INFO: 9epoch:train:721-760batch: iter_time=4.020e-05, forward_time=0.029, loss_ctc=6.995, loss=6.995, backward_time=0.007, grad_norm=106.635, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-17 01:31:55,410 (trainer:762) INFO: 9epoch:train:761-800batch: iter_time=4.022e-05, forward_time=0.030, loss_ctc=7.859, loss=7.859, backward_time=0.007, grad_norm=108.733, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-17 01:31:59,352 (trainer:357) INFO: 9epoch results: [train] iter_time=1.924e-04, forward_time=0.031, loss_ctc=8.100, loss=8.100, backward_time=0.007, grad_norm=111.683, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237, time=47.49 seconds, total_count=7200, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=55.924, cer_ctc=0.252, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=55.924, time=1.16 seconds, total_count=225, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.7 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:32:00,434 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:32:00,435 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/8epoch.pth +[stan] 2024-01-17 01:32:00,435 (trainer:291) INFO: 10/30epoch started. Estimated time to finish: 18 minutes and 22.25 seconds +[stan] 2024-01-17 01:32:02,783 (trainer:762) INFO: 10epoch:train:1-40batch: iter_time=0.002, forward_time=0.028, loss_ctc=6.504, loss=6.504, backward_time=0.007, grad_norm=102.964, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-17 01:32:04,799 (trainer:762) INFO: 10epoch:train:41-80batch: iter_time=4.148e-05, forward_time=0.027, loss_ctc=6.385, loss=6.385, backward_time=0.007, grad_norm=101.360, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.202 +[stan] 2024-01-17 01:32:07,544 (trainer:762) INFO: 10epoch:train:81-120batch: iter_time=4.190e-05, forward_time=0.036, loss_ctc=9.393, loss=9.393, backward_time=0.008, grad_norm=119.143, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-17 01:32:09,943 (trainer:762) INFO: 10epoch:train:121-160batch: iter_time=4.130e-05, forward_time=0.032, loss_ctc=7.996, loss=7.996, backward_time=0.007, grad_norm=114.344, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-17 01:32:12,295 (trainer:762) INFO: 10epoch:train:161-200batch: iter_time=4.096e-05, forward_time=0.031, loss_ctc=7.615, loss=7.615, backward_time=0.007, grad_norm=119.433, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-17 01:32:14,866 (trainer:762) INFO: 10epoch:train:201-240batch: iter_time=4.248e-05, forward_time=0.034, loss_ctc=9.108, loss=9.108, backward_time=0.007, grad_norm=132.562, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-17 01:32:17,385 (trainer:762) INFO: 10epoch:train:241-280batch: iter_time=4.142e-05, forward_time=0.033, loss_ctc=8.450, loss=8.450, backward_time=0.007, grad_norm=121.487, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-17 01:32:19,539 (trainer:762) INFO: 10epoch:train:281-320batch: iter_time=4.352e-05, forward_time=0.029, loss_ctc=6.861, loss=6.861, backward_time=0.007, grad_norm=109.866, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.215 +[stan] 2024-01-17 01:32:21,923 (trainer:762) INFO: 10epoch:train:321-360batch: iter_time=4.147e-05, forward_time=0.032, loss_ctc=7.907, loss=7.907, backward_time=0.007, grad_norm=110.475, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-17 01:32:24,308 (trainer:762) INFO: 10epoch:train:361-400batch: iter_time=4.092e-05, forward_time=0.032, loss_ctc=7.727, loss=7.727, backward_time=0.007, grad_norm=113.672, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-17 01:32:26,652 (trainer:762) INFO: 10epoch:train:401-440batch: iter_time=4.187e-05, forward_time=0.031, loss_ctc=7.485, loss=7.485, backward_time=0.007, grad_norm=122.188, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-17 01:32:28,814 (trainer:762) INFO: 10epoch:train:441-480batch: iter_time=4.240e-05, forward_time=0.029, loss_ctc=6.563, loss=6.563, backward_time=0.007, grad_norm=99.944, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.216 +[stan] 2024-01-17 01:32:31,041 (trainer:762) INFO: 10epoch:train:481-520batch: iter_time=4.208e-05, forward_time=0.030, loss_ctc=6.992, loss=6.992, backward_time=0.007, grad_norm=110.270, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.223 +[stan] 2024-01-17 01:32:33,789 (trainer:762) INFO: 10epoch:train:521-560batch: iter_time=4.221e-05, forward_time=0.036, loss_ctc=8.582, loss=8.582, backward_time=0.008, grad_norm=122.176, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.275 +[stan] 2024-01-17 01:32:36,088 (trainer:762) INFO: 10epoch:train:561-600batch: iter_time=4.031e-05, forward_time=0.031, loss_ctc=7.227, loss=7.227, backward_time=0.007, grad_norm=111.344, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-17 01:32:38,130 (trainer:762) INFO: 10epoch:train:601-640batch: iter_time=4.114e-05, forward_time=0.027, loss_ctc=6.669, loss=6.669, backward_time=0.007, grad_norm=108.993, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.204 +[stan] 2024-01-17 01:32:40,655 (trainer:762) INFO: 10epoch:train:641-680batch: iter_time=4.128e-05, forward_time=0.034, loss_ctc=7.726, loss=7.726, backward_time=0.007, grad_norm=116.962, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.252 +[stan] 2024-01-17 01:32:43,072 (trainer:762) INFO: 10epoch:train:681-720batch: iter_time=4.142e-05, forward_time=0.032, loss_ctc=7.264, loss=7.264, backward_time=0.007, grad_norm=113.702, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-17 01:32:45,505 (trainer:762) INFO: 10epoch:train:721-760batch: iter_time=4.523e-05, forward_time=0.032, loss_ctc=7.727, loss=7.727, backward_time=0.007, grad_norm=118.401, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-17 01:32:47,973 (trainer:762) INFO: 10epoch:train:761-800batch: iter_time=3.964e-05, forward_time=0.033, loss_ctc=7.807, loss=7.807, backward_time=0.008, grad_norm=108.400, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-17 01:32:51,870 (trainer:357) INFO: 10epoch results: [train] iter_time=1.549e-04, forward_time=0.031, loss_ctc=7.599, loss=7.599, backward_time=0.007, grad_norm=113.884, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238, time=47.61 seconds, total_count=8000, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=57.421, cer_ctc=0.250, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=57.421, time=1.14 seconds, total_count=250, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.68 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:32:52,875 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:32:52,877 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/9epoch.pth +[stan] 2024-01-17 01:32:52,877 (trainer:291) INFO: 11/30epoch started. Estimated time to finish: 17 minutes and 29.67 seconds +[stan] 2024-01-17 01:32:55,612 (trainer:762) INFO: 11epoch:train:1-40batch: iter_time=0.003, forward_time=0.033, loss_ctc=7.572, loss=7.572, backward_time=0.007, grad_norm=117.323, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-17 01:32:57,810 (trainer:762) INFO: 11epoch:train:41-80batch: iter_time=4.273e-05, forward_time=0.029, loss_ctc=6.663, loss=6.663, backward_time=0.007, grad_norm=110.199, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-17 01:33:00,264 (trainer:762) INFO: 11epoch:train:81-120batch: iter_time=4.150e-05, forward_time=0.032, loss_ctc=7.548, loss=7.548, backward_time=0.007, grad_norm=111.298, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-17 01:33:02,125 (trainer:762) INFO: 11epoch:train:121-160batch: iter_time=4.435e-05, forward_time=0.025, loss_ctc=5.499, loss=5.499, backward_time=0.007, grad_norm=102.027, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.186 +[stan] 2024-01-17 01:33:04,301 (trainer:762) INFO: 11epoch:train:161-200batch: iter_time=4.306e-05, forward_time=0.029, loss_ctc=6.716, loss=6.716, backward_time=0.007, grad_norm=110.178, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.218 +[stan] 2024-01-17 01:33:07,236 (trainer:762) INFO: 11epoch:train:201-240batch: iter_time=4.477e-05, forward_time=0.040, loss_ctc=8.750, loss=8.750, backward_time=0.008, grad_norm=130.742, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.293 +[stan] 2024-01-17 01:33:09,548 (trainer:762) INFO: 11epoch:train:241-280batch: iter_time=4.112e-05, forward_time=0.031, loss_ctc=6.649, loss=6.649, backward_time=0.007, grad_norm=109.222, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-17 01:33:12,355 (trainer:762) INFO: 11epoch:train:281-320batch: iter_time=4.216e-05, forward_time=0.037, loss_ctc=8.170, loss=8.170, backward_time=0.008, grad_norm=128.920, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.281 +[stan] 2024-01-17 01:33:14,737 (trainer:762) INFO: 11epoch:train:321-360batch: iter_time=4.087e-05, forward_time=0.032, loss_ctc=8.070, loss=8.070, backward_time=0.007, grad_norm=123.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-17 01:33:16,769 (trainer:762) INFO: 11epoch:train:361-400batch: iter_time=4.074e-05, forward_time=0.027, loss_ctc=5.621, loss=5.621, backward_time=0.007, grad_norm=104.063, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.203 +[stan] 2024-01-17 01:33:19,327 (trainer:762) INFO: 11epoch:train:401-440batch: iter_time=4.164e-05, forward_time=0.034, loss_ctc=6.816, loss=6.816, backward_time=0.007, grad_norm=110.860, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-17 01:33:21,658 (trainer:762) INFO: 11epoch:train:441-480batch: iter_time=4.185e-05, forward_time=0.031, loss_ctc=6.942, loss=6.942, backward_time=0.007, grad_norm=111.391, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-17 01:33:23,859 (trainer:762) INFO: 11epoch:train:481-520batch: iter_time=4.110e-05, forward_time=0.029, loss_ctc=7.039, loss=7.039, backward_time=0.007, grad_norm=117.981, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-17 01:33:26,206 (trainer:762) INFO: 11epoch:train:521-560batch: iter_time=4.659e-05, forward_time=0.031, loss_ctc=6.972, loss=6.972, backward_time=0.007, grad_norm=112.694, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-17 01:33:28,705 (trainer:762) INFO: 11epoch:train:561-600batch: iter_time=4.371e-05, forward_time=0.033, loss_ctc=7.393, loss=7.393, backward_time=0.007, grad_norm=119.434, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-17 01:33:30,835 (trainer:762) INFO: 11epoch:train:601-640batch: iter_time=4.126e-05, forward_time=0.029, loss_ctc=6.318, loss=6.318, backward_time=0.007, grad_norm=111.730, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.213 +[stan] 2024-01-17 01:33:33,514 (trainer:762) INFO: 11epoch:train:641-680batch: iter_time=4.027e-05, forward_time=0.035, loss_ctc=7.799, loss=7.799, backward_time=0.007, grad_norm=126.606, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-17 01:33:35,525 (trainer:762) INFO: 11epoch:train:681-720batch: iter_time=4.067e-05, forward_time=0.027, loss_ctc=5.833, loss=5.833, backward_time=0.007, grad_norm=100.689, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.201 +[stan] 2024-01-17 01:33:37,806 (trainer:762) INFO: 11epoch:train:721-760batch: iter_time=4.097e-05, forward_time=0.030, loss_ctc=6.535, loss=6.535, backward_time=0.007, grad_norm=110.259, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-17 01:33:40,260 (trainer:762) INFO: 11epoch:train:761-800batch: iter_time=3.995e-05, forward_time=0.033, loss_ctc=6.771, loss=6.771, backward_time=0.007, grad_norm=115.822, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-17 01:33:44,155 (trainer:357) INFO: 11epoch results: [train] iter_time=1.740e-04, forward_time=0.031, loss_ctc=6.984, loss=6.984, backward_time=0.007, grad_norm=114.227, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237, time=47.46 seconds, total_count=8800, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=58.872, cer_ctc=0.246, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=58.872, time=1.13 seconds, total_count=275, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.68 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:33:45,081 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:33:45,083 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/10epoch.pth +[stan] 2024-01-17 01:33:45,083 (trainer:291) INFO: 12/30epoch started. Estimated time to finish: 16 minutes and 36.71 seconds +[stan] 2024-01-17 01:33:47,566 (trainer:762) INFO: 12epoch:train:1-40batch: iter_time=0.003, forward_time=0.030, loss_ctc=6.967, loss=6.967, backward_time=0.007, grad_norm=112.974, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-17 01:33:50,117 (trainer:762) INFO: 12epoch:train:41-80batch: iter_time=4.124e-05, forward_time=0.034, loss_ctc=7.104, loss=7.104, backward_time=0.008, grad_norm=113.206, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-17 01:33:52,542 (trainer:762) INFO: 12epoch:train:81-120batch: iter_time=4.341e-05, forward_time=0.032, loss_ctc=7.118, loss=7.118, backward_time=0.007, grad_norm=116.742, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-17 01:33:55,042 (trainer:762) INFO: 12epoch:train:121-160batch: iter_time=4.149e-05, forward_time=0.033, loss_ctc=7.146, loss=7.146, backward_time=0.007, grad_norm=118.440, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-17 01:33:57,266 (trainer:762) INFO: 12epoch:train:161-200batch: iter_time=4.157e-05, forward_time=0.030, loss_ctc=6.443, loss=6.443, backward_time=0.007, grad_norm=114.029, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.222 +[stan] 2024-01-17 01:34:00,125 (trainer:762) INFO: 12epoch:train:201-240batch: iter_time=4.559e-05, forward_time=0.038, loss_ctc=7.962, loss=7.962, backward_time=0.008, grad_norm=122.536, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.286 +[stan] 2024-01-17 01:34:02,237 (trainer:762) INFO: 12epoch:train:241-280batch: iter_time=4.185e-05, forward_time=0.028, loss_ctc=6.474, loss=6.474, backward_time=0.007, grad_norm=108.014, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.211 +[stan] 2024-01-17 01:34:04,580 (trainer:762) INFO: 12epoch:train:281-320batch: iter_time=4.192e-05, forward_time=0.031, loss_ctc=6.516, loss=6.516, backward_time=0.007, grad_norm=111.285, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-17 01:34:06,646 (trainer:762) INFO: 12epoch:train:321-360batch: iter_time=4.143e-05, forward_time=0.028, loss_ctc=5.411, loss=5.411, backward_time=0.007, grad_norm=103.646, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.206 +[stan] 2024-01-17 01:34:09,209 (trainer:762) INFO: 12epoch:train:361-400batch: iter_time=4.110e-05, forward_time=0.034, loss_ctc=6.752, loss=6.752, backward_time=0.008, grad_norm=114.680, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-17 01:34:11,684 (trainer:762) INFO: 12epoch:train:401-440batch: iter_time=4.096e-05, forward_time=0.033, loss_ctc=7.212, loss=7.212, backward_time=0.007, grad_norm=118.319, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-17 01:34:13,780 (trainer:762) INFO: 12epoch:train:441-480batch: iter_time=4.079e-05, forward_time=0.028, loss_ctc=6.071, loss=6.071, backward_time=0.007, grad_norm=111.073, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.209 +[stan] 2024-01-17 01:34:15,963 (trainer:762) INFO: 12epoch:train:481-520batch: iter_time=4.136e-05, forward_time=0.029, loss_ctc=6.125, loss=6.125, backward_time=0.007, grad_norm=111.710, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.218 +[stan] 2024-01-17 01:34:18,276 (trainer:762) INFO: 12epoch:train:521-560batch: iter_time=4.015e-05, forward_time=0.031, loss_ctc=6.602, loss=6.602, backward_time=0.007, grad_norm=111.437, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-17 01:34:20,950 (trainer:762) INFO: 12epoch:train:561-600batch: iter_time=4.216e-05, forward_time=0.035, loss_ctc=7.684, loss=7.684, backward_time=0.008, grad_norm=121.053, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-17 01:34:23,202 (trainer:762) INFO: 12epoch:train:601-640batch: iter_time=4.081e-05, forward_time=0.030, loss_ctc=5.720, loss=5.720, backward_time=0.007, grad_norm=107.496, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-17 01:34:25,283 (trainer:762) INFO: 12epoch:train:641-680batch: iter_time=4.076e-05, forward_time=0.028, loss_ctc=5.847, loss=5.847, backward_time=0.007, grad_norm=105.247, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.208 +[stan] 2024-01-17 01:34:27,913 (trainer:762) INFO: 12epoch:train:681-720batch: iter_time=4.125e-05, forward_time=0.035, loss_ctc=6.947, loss=6.947, backward_time=0.007, grad_norm=120.041, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-17 01:34:30,072 (trainer:762) INFO: 12epoch:train:721-760batch: iter_time=4.070e-05, forward_time=0.029, loss_ctc=6.045, loss=6.045, backward_time=0.007, grad_norm=106.045, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.216 +[stan] 2024-01-17 01:34:32,672 (trainer:762) INFO: 12epoch:train:761-800batch: iter_time=3.974e-05, forward_time=0.034, loss_ctc=7.206, loss=7.206, backward_time=0.008, grad_norm=114.750, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-17 01:34:36,614 (trainer:357) INFO: 12epoch results: [train] iter_time=1.742e-04, forward_time=0.031, loss_ctc=6.668, loss=6.668, backward_time=0.007, grad_norm=113.136, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238, time=47.67 seconds, total_count=9600, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=59.602, cer_ctc=0.247, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=59.602, time=1.15 seconds, total_count=300, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.71 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:34:37,645 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:34:37,647 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/11epoch.pth +[stan] 2024-01-17 01:34:37,647 (trainer:291) INFO: 13/30epoch started. Estimated time to finish: 15 minutes and 44.41 seconds +[stan] 2024-01-17 01:34:40,233 (trainer:762) INFO: 13epoch:train:1-40batch: iter_time=0.002, forward_time=0.031, loss_ctc=6.096, loss=6.096, backward_time=0.007, grad_norm=106.891, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-17 01:34:42,536 (trainer:762) INFO: 13epoch:train:41-80batch: iter_time=4.306e-05, forward_time=0.031, loss_ctc=6.276, loss=6.276, backward_time=0.007, grad_norm=110.063, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-17 01:34:45,007 (trainer:762) INFO: 13epoch:train:81-120batch: iter_time=4.223e-05, forward_time=0.033, loss_ctc=6.828, loss=6.828, backward_time=0.007, grad_norm=113.495, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-17 01:34:47,336 (trainer:762) INFO: 13epoch:train:121-160batch: iter_time=4.030e-05, forward_time=0.031, loss_ctc=6.081, loss=6.081, backward_time=0.007, grad_norm=112.425, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-17 01:34:49,647 (trainer:762) INFO: 13epoch:train:161-200batch: iter_time=4.004e-05, forward_time=0.031, loss_ctc=6.229, loss=6.229, backward_time=0.007, grad_norm=107.362, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-17 01:34:52,123 (trainer:762) INFO: 13epoch:train:201-240batch: iter_time=4.037e-05, forward_time=0.033, loss_ctc=7.239, loss=7.239, backward_time=0.007, grad_norm=122.038, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-17 01:34:54,369 (trainer:762) INFO: 13epoch:train:241-280batch: iter_time=3.982e-05, forward_time=0.030, loss_ctc=5.579, loss=5.579, backward_time=0.007, grad_norm=109.704, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.224 +[stan] 2024-01-17 01:34:56,933 (trainer:762) INFO: 13epoch:train:281-320batch: iter_time=4.113e-05, forward_time=0.034, loss_ctc=6.886, loss=6.886, backward_time=0.008, grad_norm=117.441, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-17 01:34:59,151 (trainer:762) INFO: 13epoch:train:321-360batch: iter_time=4.220e-05, forward_time=0.030, loss_ctc=5.410, loss=5.410, backward_time=0.007, grad_norm=110.816, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.222 +[stan] 2024-01-17 01:35:01,598 (trainer:762) INFO: 13epoch:train:361-400batch: iter_time=4.091e-05, forward_time=0.032, loss_ctc=6.618, loss=6.618, backward_time=0.007, grad_norm=114.147, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-17 01:35:03,922 (trainer:762) INFO: 13epoch:train:401-440batch: iter_time=4.119e-05, forward_time=0.031, loss_ctc=6.151, loss=6.151, backward_time=0.007, grad_norm=116.939, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-17 01:35:06,333 (trainer:762) INFO: 13epoch:train:441-480batch: iter_time=4.055e-05, forward_time=0.032, loss_ctc=6.634, loss=6.634, backward_time=0.007, grad_norm=117.384, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-17 01:35:08,936 (trainer:762) INFO: 13epoch:train:481-520batch: iter_time=4.144e-05, forward_time=0.034, loss_ctc=6.794, loss=6.794, backward_time=0.008, grad_norm=121.819, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-17 01:35:11,171 (trainer:762) INFO: 13epoch:train:521-560batch: iter_time=4.076e-05, forward_time=0.030, loss_ctc=5.759, loss=5.759, backward_time=0.007, grad_norm=112.900, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.223 +[stan] 2024-01-17 01:35:13,308 (trainer:762) INFO: 13epoch:train:561-600batch: iter_time=4.287e-05, forward_time=0.029, loss_ctc=5.769, loss=5.769, backward_time=0.007, grad_norm=111.747, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.214 +[stan] 2024-01-17 01:35:15,494 (trainer:762) INFO: 13epoch:train:601-640batch: iter_time=4.042e-05, forward_time=0.029, loss_ctc=5.638, loss=5.638, backward_time=0.007, grad_norm=108.464, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.218 +[stan] 2024-01-17 01:35:17,834 (trainer:762) INFO: 13epoch:train:641-680batch: iter_time=4.083e-05, forward_time=0.031, loss_ctc=6.421, loss=6.421, backward_time=0.007, grad_norm=111.508, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-17 01:35:20,299 (trainer:762) INFO: 13epoch:train:681-720batch: iter_time=4.051e-05, forward_time=0.033, loss_ctc=6.324, loss=6.324, backward_time=0.007, grad_norm=118.293, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-17 01:35:22,813 (trainer:762) INFO: 13epoch:train:721-760batch: iter_time=4.255e-05, forward_time=0.033, loss_ctc=6.701, loss=6.701, backward_time=0.007, grad_norm=117.973, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-17 01:35:24,957 (trainer:762) INFO: 13epoch:train:761-800batch: iter_time=3.896e-05, forward_time=0.029, loss_ctc=5.426, loss=5.426, backward_time=0.007, grad_norm=108.946, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.214 +[stan] 2024-01-17 01:35:28,870 (trainer:357) INFO: 13epoch results: [train] iter_time=1.556e-04, forward_time=0.031, loss_ctc=6.243, loss=6.243, backward_time=0.007, grad_norm=113.518, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236, time=47.38 seconds, total_count=10400, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=61.220, cer_ctc=0.245, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=61.220, time=1.14 seconds, total_count=325, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.7 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:35:29,835 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:35:29,836 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/12epoch.pth +[stan] 2024-01-17 01:35:29,836 (trainer:291) INFO: 14/30epoch started. Estimated time to finish: 14 minutes and 51.58 seconds +[stan] 2024-01-17 01:35:32,380 (trainer:762) INFO: 14epoch:train:1-40batch: iter_time=0.002, forward_time=0.031, loss_ctc=5.952, loss=5.952, backward_time=0.007, grad_norm=109.160, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-17 01:35:34,848 (trainer:762) INFO: 14epoch:train:41-80batch: iter_time=4.217e-05, forward_time=0.033, loss_ctc=5.940, loss=5.940, backward_time=0.007, grad_norm=112.304, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-17 01:35:37,327 (trainer:762) INFO: 14epoch:train:81-120batch: iter_time=4.082e-05, forward_time=0.033, loss_ctc=6.664, loss=6.664, backward_time=0.007, grad_norm=115.105, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-17 01:35:39,615 (trainer:762) INFO: 14epoch:train:121-160batch: iter_time=4.326e-05, forward_time=0.030, loss_ctc=5.855, loss=5.855, backward_time=0.007, grad_norm=114.968, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-17 01:35:42,359 (trainer:762) INFO: 14epoch:train:161-200batch: iter_time=4.110e-05, forward_time=0.036, loss_ctc=7.215, loss=7.215, backward_time=0.008, grad_norm=120.290, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-17 01:35:44,531 (trainer:762) INFO: 14epoch:train:201-240batch: iter_time=4.288e-05, forward_time=0.029, loss_ctc=5.581, loss=5.581, backward_time=0.007, grad_norm=110.744, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-17 01:35:46,501 (trainer:762) INFO: 14epoch:train:241-280batch: iter_time=4.156e-05, forward_time=0.026, loss_ctc=5.041, loss=5.041, backward_time=0.007, grad_norm=113.254, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.197 +[stan] 2024-01-17 01:35:48,911 (trainer:762) INFO: 14epoch:train:281-320batch: iter_time=4.221e-05, forward_time=0.032, loss_ctc=5.870, loss=5.870, backward_time=0.007, grad_norm=116.859, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-17 01:35:50,882 (trainer:762) INFO: 14epoch:train:321-360batch: iter_time=4.090e-05, forward_time=0.026, loss_ctc=4.780, loss=4.780, backward_time=0.007, grad_norm=101.995, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.197 +[stan] 2024-01-17 01:35:53,832 (trainer:762) INFO: 14epoch:train:361-400batch: iter_time=4.289e-05, forward_time=0.039, loss_ctc=7.613, loss=7.613, backward_time=0.008, grad_norm=125.160, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.295 +[stan] 2024-01-17 01:35:56,342 (trainer:762) INFO: 14epoch:train:401-440batch: iter_time=4.144e-05, forward_time=0.033, loss_ctc=6.397, loss=6.397, backward_time=0.007, grad_norm=116.284, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-17 01:35:58,474 (trainer:762) INFO: 14epoch:train:441-480batch: iter_time=4.220e-05, forward_time=0.030, loss_ctc=4.870, loss=4.870, backward_time=0.007, grad_norm=101.650, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.213 +[stan] 2024-01-17 01:36:01,039 (trainer:762) INFO: 14epoch:train:481-520batch: iter_time=4.365e-05, forward_time=0.034, loss_ctc=6.033, loss=6.033, backward_time=0.007, grad_norm=116.534, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-17 01:36:03,328 (trainer:762) INFO: 14epoch:train:521-560batch: iter_time=4.121e-05, forward_time=0.030, loss_ctc=5.946, loss=5.946, backward_time=0.007, grad_norm=111.887, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-17 01:36:05,913 (trainer:762) INFO: 14epoch:train:561-600batch: iter_time=4.255e-05, forward_time=0.034, loss_ctc=6.450, loss=6.450, backward_time=0.008, grad_norm=112.077, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-17 01:36:08,113 (trainer:762) INFO: 14epoch:train:601-640batch: iter_time=4.223e-05, forward_time=0.029, loss_ctc=5.129, loss=5.129, backward_time=0.007, grad_norm=107.657, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-17 01:36:10,741 (trainer:762) INFO: 14epoch:train:641-680batch: iter_time=4.435e-05, forward_time=0.035, loss_ctc=6.704, loss=6.704, backward_time=0.008, grad_norm=117.630, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-17 01:36:12,701 (trainer:762) INFO: 14epoch:train:681-720batch: iter_time=4.035e-05, forward_time=0.026, loss_ctc=4.924, loss=4.924, backward_time=0.007, grad_norm=101.423, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.196 +[stan] 2024-01-17 01:36:15,146 (trainer:762) INFO: 14epoch:train:721-760batch: iter_time=4.240e-05, forward_time=0.032, loss_ctc=6.080, loss=6.080, backward_time=0.007, grad_norm=112.434, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-17 01:36:17,851 (trainer:762) INFO: 14epoch:train:761-800batch: iter_time=3.951e-05, forward_time=0.036, loss_ctc=6.972, loss=6.972, backward_time=0.008, grad_norm=120.039, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-17 01:36:21,753 (trainer:357) INFO: 14epoch results: [train] iter_time=1.636e-04, forward_time=0.032, loss_ctc=6.001, loss=6.001, backward_time=0.007, grad_norm=112.873, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240, time=48.09 seconds, total_count=11200, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=61.065, cer_ctc=0.242, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=61.065, time=1.13 seconds, total_count=350, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.7 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:36:22,716 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:36:22,718 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/13epoch.pth +[stan] 2024-01-17 01:36:22,718 (trainer:291) INFO: 15/30epoch started. Estimated time to finish: 13 minutes and 59.63 seconds +[stan] 2024-01-17 01:36:24,989 (trainer:762) INFO: 15epoch:train:1-40batch: iter_time=0.003, forward_time=0.027, loss_ctc=4.777, loss=4.777, backward_time=0.007, grad_norm=109.126, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-17 01:36:27,273 (trainer:762) INFO: 15epoch:train:41-80batch: iter_time=4.006e-05, forward_time=0.030, loss_ctc=5.841, loss=5.841, backward_time=0.007, grad_norm=114.501, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-17 01:36:29,434 (trainer:762) INFO: 15epoch:train:81-120batch: iter_time=4.099e-05, forward_time=0.029, loss_ctc=5.209, loss=5.209, backward_time=0.007, grad_norm=106.487, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.216 +[stan] 2024-01-17 01:36:32,149 (trainer:762) INFO: 15epoch:train:121-160batch: iter_time=4.402e-05, forward_time=0.036, loss_ctc=6.552, loss=6.552, backward_time=0.008, grad_norm=114.236, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-17 01:36:34,450 (trainer:762) INFO: 15epoch:train:161-200batch: iter_time=4.564e-05, forward_time=0.031, loss_ctc=5.399, loss=5.399, backward_time=0.007, grad_norm=110.599, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-17 01:36:37,226 (trainer:762) INFO: 15epoch:train:201-240batch: iter_time=4.420e-05, forward_time=0.037, loss_ctc=6.382, loss=6.382, backward_time=0.008, grad_norm=116.233, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-17 01:36:39,473 (trainer:762) INFO: 15epoch:train:241-280batch: iter_time=4.004e-05, forward_time=0.030, loss_ctc=5.372, loss=5.372, backward_time=0.007, grad_norm=112.727, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-17 01:36:41,791 (trainer:762) INFO: 15epoch:train:281-320batch: iter_time=4.516e-05, forward_time=0.031, loss_ctc=6.085, loss=6.085, backward_time=0.007, grad_norm=116.009, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-17 01:36:43,783 (trainer:762) INFO: 15epoch:train:321-360batch: iter_time=4.113e-05, forward_time=0.027, loss_ctc=4.896, loss=4.896, backward_time=0.007, grad_norm=106.940, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.199 +[stan] 2024-01-17 01:36:45,970 (trainer:762) INFO: 15epoch:train:361-400batch: iter_time=4.266e-05, forward_time=0.029, loss_ctc=5.397, loss=5.397, backward_time=0.007, grad_norm=107.479, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-17 01:36:48,404 (trainer:762) INFO: 15epoch:train:401-440batch: iter_time=4.098e-05, forward_time=0.032, loss_ctc=6.100, loss=6.100, backward_time=0.007, grad_norm=114.955, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-17 01:36:50,906 (trainer:762) INFO: 15epoch:train:441-480batch: iter_time=4.349e-05, forward_time=0.033, loss_ctc=5.839, loss=5.839, backward_time=0.007, grad_norm=114.499, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-17 01:36:53,144 (trainer:762) INFO: 15epoch:train:481-520batch: iter_time=4.164e-05, forward_time=0.030, loss_ctc=4.889, loss=4.889, backward_time=0.007, grad_norm=107.477, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.224 +[stan] 2024-01-17 01:36:55,648 (trainer:762) INFO: 15epoch:train:521-560batch: iter_time=4.283e-05, forward_time=0.033, loss_ctc=6.303, loss=6.303, backward_time=0.007, grad_norm=108.908, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-17 01:36:58,227 (trainer:762) INFO: 15epoch:train:561-600batch: iter_time=4.081e-05, forward_time=0.034, loss_ctc=6.311, loss=6.311, backward_time=0.008, grad_norm=115.972, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-17 01:37:00,352 (trainer:762) INFO: 15epoch:train:601-640batch: iter_time=4.264e-05, forward_time=0.028, loss_ctc=4.870, loss=4.870, backward_time=0.007, grad_norm=107.041, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.212 +[stan] 2024-01-17 01:37:02,603 (trainer:762) INFO: 15epoch:train:641-680batch: iter_time=4.095e-05, forward_time=0.030, loss_ctc=5.519, loss=5.519, backward_time=0.007, grad_norm=114.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-17 01:37:05,260 (trainer:762) INFO: 15epoch:train:681-720batch: iter_time=4.293e-05, forward_time=0.035, loss_ctc=6.733, loss=6.733, backward_time=0.008, grad_norm=124.073, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-17 01:37:07,573 (trainer:762) INFO: 15epoch:train:721-760batch: iter_time=4.465e-05, forward_time=0.031, loss_ctc=5.559, loss=5.559, backward_time=0.007, grad_norm=114.082, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-17 01:37:09,848 (trainer:762) INFO: 15epoch:train:761-800batch: iter_time=3.894e-05, forward_time=0.030, loss_ctc=5.397, loss=5.397, backward_time=0.007, grad_norm=108.489, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-17 01:37:13,817 (trainer:357) INFO: 15epoch results: [train] iter_time=1.927e-04, forward_time=0.031, loss_ctc=5.671, loss=5.671, backward_time=0.007, grad_norm=112.204, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236, time=47.2 seconds, total_count=12000, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=62.249, cer_ctc=0.241, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=62.249, time=1.15 seconds, total_count=375, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.75 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:37:14,874 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:37:14,875 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/14epoch.pth +[stan] 2024-01-17 01:37:14,875 (trainer:291) INFO: 16/30epoch started. Estimated time to finish: 13 minutes and 6.83 seconds +[stan] 2024-01-17 01:37:17,847 (trainer:762) INFO: 16epoch:train:1-40batch: iter_time=0.002, forward_time=0.036, loss_ctc=5.948, loss=5.948, backward_time=0.008, grad_norm=119.187, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.297 +[stan] 2024-01-17 01:37:19,907 (trainer:762) INFO: 16epoch:train:41-80batch: iter_time=4.189e-05, forward_time=0.028, loss_ctc=4.884, loss=4.884, backward_time=0.007, grad_norm=104.300, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.206 +[stan] 2024-01-17 01:37:22,231 (trainer:762) INFO: 16epoch:train:81-120batch: iter_time=4.099e-05, forward_time=0.031, loss_ctc=5.498, loss=5.498, backward_time=0.007, grad_norm=113.045, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-17 01:37:24,553 (trainer:762) INFO: 16epoch:train:121-160batch: iter_time=4.262e-05, forward_time=0.031, loss_ctc=5.830, loss=5.830, backward_time=0.007, grad_norm=109.307, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-17 01:37:26,876 (trainer:762) INFO: 16epoch:train:161-200batch: iter_time=4.062e-05, forward_time=0.031, loss_ctc=5.356, loss=5.356, backward_time=0.007, grad_norm=110.816, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-17 01:37:29,344 (trainer:762) INFO: 16epoch:train:201-240batch: iter_time=4.152e-05, forward_time=0.033, loss_ctc=6.244, loss=6.244, backward_time=0.008, grad_norm=110.916, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-17 01:37:31,663 (trainer:762) INFO: 16epoch:train:241-280batch: iter_time=4.231e-05, forward_time=0.031, loss_ctc=5.340, loss=5.340, backward_time=0.007, grad_norm=113.491, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-17 01:37:34,100 (trainer:762) INFO: 16epoch:train:281-320batch: iter_time=4.082e-05, forward_time=0.032, loss_ctc=5.697, loss=5.697, backward_time=0.007, grad_norm=110.428, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-17 01:37:36,589 (trainer:762) INFO: 16epoch:train:321-360batch: iter_time=4.213e-05, forward_time=0.033, loss_ctc=5.384, loss=5.384, backward_time=0.007, grad_norm=107.901, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-17 01:37:39,051 (trainer:762) INFO: 16epoch:train:361-400batch: iter_time=4.067e-05, forward_time=0.033, loss_ctc=5.951, loss=5.951, backward_time=0.007, grad_norm=121.189, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-17 01:37:41,164 (trainer:762) INFO: 16epoch:train:401-440batch: iter_time=4.170e-05, forward_time=0.028, loss_ctc=4.888, loss=4.888, backward_time=0.007, grad_norm=104.253, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.211 +[stan] 2024-01-17 01:37:43,289 (trainer:762) INFO: 16epoch:train:441-480batch: iter_time=4.566e-05, forward_time=0.028, loss_ctc=4.601, loss=4.601, backward_time=0.007, grad_norm=108.396, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.212 +[stan] 2024-01-17 01:37:45,619 (trainer:762) INFO: 16epoch:train:481-520batch: iter_time=4.161e-05, forward_time=0.031, loss_ctc=5.201, loss=5.201, backward_time=0.007, grad_norm=111.700, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-17 01:37:47,948 (trainer:762) INFO: 16epoch:train:521-560batch: iter_time=4.194e-05, forward_time=0.031, loss_ctc=5.367, loss=5.367, backward_time=0.007, grad_norm=110.824, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-17 01:37:50,480 (trainer:762) INFO: 16epoch:train:561-600batch: iter_time=4.162e-05, forward_time=0.033, loss_ctc=6.381, loss=6.381, backward_time=0.008, grad_norm=114.710, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-17 01:37:52,906 (trainer:762) INFO: 16epoch:train:601-640batch: iter_time=4.147e-05, forward_time=0.032, loss_ctc=5.683, loss=5.683, backward_time=0.007, grad_norm=111.540, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-17 01:37:55,289 (trainer:762) INFO: 16epoch:train:641-680batch: iter_time=4.144e-05, forward_time=0.032, loss_ctc=5.286, loss=5.286, backward_time=0.007, grad_norm=107.379, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-17 01:37:57,893 (trainer:762) INFO: 16epoch:train:681-720batch: iter_time=4.488e-05, forward_time=0.034, loss_ctc=5.986, loss=5.986, backward_time=0.008, grad_norm=111.380, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.260 +[stan] 2024-01-17 01:37:59,855 (trainer:762) INFO: 16epoch:train:721-760batch: iter_time=4.052e-05, forward_time=0.026, loss_ctc=4.613, loss=4.613, backward_time=0.007, grad_norm=102.639, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.196 +[stan] 2024-01-17 01:38:02,403 (trainer:762) INFO: 16epoch:train:761-800batch: iter_time=4.267e-05, forward_time=0.034, loss_ctc=5.885, loss=5.885, backward_time=0.007, grad_norm=114.421, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-17 01:38:06,332 (trainer:357) INFO: 16epoch results: [train] iter_time=1.637e-04, forward_time=0.031, loss_ctc=5.501, loss=5.501, backward_time=0.007, grad_norm=110.891, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238, time=47.6 seconds, total_count=12800, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=64.337, cer_ctc=0.240, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=64.337, time=1.14 seconds, total_count=400, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.71 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:38:07,313 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:38:07,315 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/15epoch.pth +[stan] 2024-01-17 01:38:07,315 (trainer:291) INFO: 17/30epoch started. Estimated time to finish: 12 minutes and 14.36 seconds +[stan] 2024-01-17 01:38:09,758 (trainer:762) INFO: 17epoch:train:1-40batch: iter_time=0.003, forward_time=0.029, loss_ctc=5.154, loss=5.154, backward_time=0.007, grad_norm=106.984, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-17 01:38:12,319 (trainer:762) INFO: 17epoch:train:41-80batch: iter_time=4.369e-05, forward_time=0.034, loss_ctc=5.395, loss=5.395, backward_time=0.007, grad_norm=114.475, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-17 01:38:14,570 (trainer:762) INFO: 17epoch:train:81-120batch: iter_time=4.497e-05, forward_time=0.030, loss_ctc=5.294, loss=5.294, backward_time=0.007, grad_norm=109.336, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-17 01:38:16,520 (trainer:762) INFO: 17epoch:train:121-160batch: iter_time=4.282e-05, forward_time=0.026, loss_ctc=4.178, loss=4.178, backward_time=0.007, grad_norm=102.024, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.195 +[stan] 2024-01-17 01:38:19,109 (trainer:762) INFO: 17epoch:train:161-200batch: iter_time=4.539e-05, forward_time=0.034, loss_ctc=5.761, loss=5.761, backward_time=0.008, grad_norm=110.684, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.259 +[stan] 2024-01-17 01:38:21,612 (trainer:762) INFO: 17epoch:train:201-240batch: iter_time=4.220e-05, forward_time=0.033, loss_ctc=5.581, loss=5.581, backward_time=0.007, grad_norm=116.561, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-17 01:38:24,187 (trainer:762) INFO: 17epoch:train:241-280batch: iter_time=4.408e-05, forward_time=0.034, loss_ctc=6.038, loss=6.038, backward_time=0.007, grad_norm=119.177, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-17 01:38:26,537 (trainer:762) INFO: 17epoch:train:281-320batch: iter_time=4.346e-05, forward_time=0.031, loss_ctc=5.323, loss=5.323, backward_time=0.007, grad_norm=113.604, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-17 01:38:28,805 (trainer:762) INFO: 17epoch:train:321-360batch: iter_time=4.577e-05, forward_time=0.030, loss_ctc=5.196, loss=5.196, backward_time=0.007, grad_norm=109.561, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-17 01:38:31,028 (trainer:762) INFO: 17epoch:train:361-400batch: iter_time=4.422e-05, forward_time=0.030, loss_ctc=4.619, loss=4.619, backward_time=0.007, grad_norm=99.456, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.222 +[stan] 2024-01-17 01:38:33,301 (trainer:762) INFO: 17epoch:train:401-440batch: iter_time=4.274e-05, forward_time=0.031, loss_ctc=4.551, loss=4.551, backward_time=0.007, grad_norm=105.252, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-17 01:38:35,664 (trainer:762) INFO: 17epoch:train:441-480batch: iter_time=4.253e-05, forward_time=0.031, loss_ctc=5.702, loss=5.702, backward_time=0.007, grad_norm=109.126, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-17 01:38:38,144 (trainer:762) INFO: 17epoch:train:481-520batch: iter_time=4.477e-05, forward_time=0.033, loss_ctc=5.936, loss=5.936, backward_time=0.007, grad_norm=115.147, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-17 01:38:40,505 (trainer:762) INFO: 17epoch:train:521-560batch: iter_time=4.155e-05, forward_time=0.031, loss_ctc=4.978, loss=4.978, backward_time=0.007, grad_norm=100.594, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-17 01:38:42,976 (trainer:762) INFO: 17epoch:train:561-600batch: iter_time=4.235e-05, forward_time=0.033, loss_ctc=5.945, loss=5.945, backward_time=0.007, grad_norm=112.755, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-17 01:38:45,617 (trainer:762) INFO: 17epoch:train:601-640batch: iter_time=4.122e-05, forward_time=0.035, loss_ctc=5.960, loss=5.960, backward_time=0.008, grad_norm=111.994, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-17 01:38:47,802 (trainer:762) INFO: 17epoch:train:641-680batch: iter_time=4.021e-05, forward_time=0.029, loss_ctc=4.692, loss=4.692, backward_time=0.007, grad_norm=110.980, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.218 +[stan] 2024-01-17 01:38:49,838 (trainer:762) INFO: 17epoch:train:681-720batch: iter_time=4.225e-05, forward_time=0.027, loss_ctc=4.429, loss=4.429, backward_time=0.007, grad_norm=107.216, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.204 +[stan] 2024-01-17 01:38:51,875 (trainer:762) INFO: 17epoch:train:721-760batch: iter_time=4.270e-05, forward_time=0.027, loss_ctc=3.905, loss=3.905, backward_time=0.007, grad_norm=97.243, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.204 +[stan] 2024-01-17 01:38:54,241 (trainer:762) INFO: 17epoch:train:761-800batch: iter_time=4.036e-05, forward_time=0.031, loss_ctc=5.266, loss=5.266, backward_time=0.007, grad_norm=111.786, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-17 01:38:58,152 (trainer:357) INFO: 17epoch results: [train] iter_time=2.059e-04, forward_time=0.031, loss_ctc=5.195, loss=5.195, backward_time=0.007, grad_norm=109.198, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235, time=47 seconds, total_count=13600, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=64.958, cer_ctc=0.244, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=64.958, time=1.13 seconds, total_count=425, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.71 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:38:59,139 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:38:59,140 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/16epoch.pth +[stan] 2024-01-17 01:38:59,140 (trainer:291) INFO: 18/30epoch started. Estimated time to finish: 11 minutes and 21.43 seconds +[stan] 2024-01-17 01:39:02,346 (trainer:762) INFO: 18epoch:train:1-40batch: iter_time=0.003, forward_time=0.039, loss_ctc=6.839, loss=6.839, backward_time=0.008, grad_norm=118.442, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.320 +[stan] 2024-01-17 01:39:04,533 (trainer:762) INFO: 18epoch:train:41-80batch: iter_time=4.236e-05, forward_time=0.029, loss_ctc=4.570, loss=4.570, backward_time=0.007, grad_norm=105.181, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-17 01:39:06,846 (trainer:762) INFO: 18epoch:train:81-120batch: iter_time=4.211e-05, forward_time=0.031, loss_ctc=4.662, loss=4.662, backward_time=0.007, grad_norm=106.926, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-17 01:39:09,569 (trainer:762) INFO: 18epoch:train:121-160batch: iter_time=4.387e-05, forward_time=0.036, loss_ctc=6.317, loss=6.317, backward_time=0.008, grad_norm=114.378, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-17 01:39:11,665 (trainer:762) INFO: 18epoch:train:161-200batch: iter_time=4.091e-05, forward_time=0.028, loss_ctc=4.414, loss=4.414, backward_time=0.007, grad_norm=106.966, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.209 +[stan] 2024-01-17 01:39:14,220 (trainer:762) INFO: 18epoch:train:201-240batch: iter_time=4.142e-05, forward_time=0.034, loss_ctc=5.678, loss=5.678, backward_time=0.008, grad_norm=108.224, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-17 01:39:16,585 (trainer:762) INFO: 18epoch:train:241-280batch: iter_time=4.180e-05, forward_time=0.031, loss_ctc=5.101, loss=5.101, backward_time=0.007, grad_norm=109.748, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-17 01:39:19,013 (trainer:762) INFO: 18epoch:train:281-320batch: iter_time=4.141e-05, forward_time=0.032, loss_ctc=5.342, loss=5.342, backward_time=0.007, grad_norm=107.899, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-17 01:39:21,105 (trainer:762) INFO: 18epoch:train:321-360batch: iter_time=4.055e-05, forward_time=0.028, loss_ctc=4.705, loss=4.705, backward_time=0.007, grad_norm=105.112, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.209 +[stan] 2024-01-17 01:39:23,512 (trainer:762) INFO: 18epoch:train:361-400batch: iter_time=4.286e-05, forward_time=0.032, loss_ctc=4.778, loss=4.778, backward_time=0.007, grad_norm=108.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-17 01:39:25,881 (trainer:762) INFO: 18epoch:train:401-440batch: iter_time=4.121e-05, forward_time=0.031, loss_ctc=5.176, loss=5.176, backward_time=0.008, grad_norm=107.142, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-17 01:39:28,179 (trainer:762) INFO: 18epoch:train:441-480batch: iter_time=4.114e-05, forward_time=0.031, loss_ctc=5.186, loss=5.186, backward_time=0.007, grad_norm=111.471, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-17 01:39:30,433 (trainer:762) INFO: 18epoch:train:481-520batch: iter_time=4.153e-05, forward_time=0.030, loss_ctc=4.412, loss=4.412, backward_time=0.007, grad_norm=105.296, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-17 01:39:33,002 (trainer:762) INFO: 18epoch:train:521-560batch: iter_time=4.242e-05, forward_time=0.034, loss_ctc=5.587, loss=5.587, backward_time=0.007, grad_norm=114.295, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-17 01:39:35,359 (trainer:762) INFO: 18epoch:train:561-600batch: iter_time=4.326e-05, forward_time=0.031, loss_ctc=5.185, loss=5.185, backward_time=0.007, grad_norm=112.131, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-17 01:39:37,711 (trainer:762) INFO: 18epoch:train:601-640batch: iter_time=4.311e-05, forward_time=0.031, loss_ctc=5.348, loss=5.348, backward_time=0.007, grad_norm=112.018, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-17 01:39:40,098 (trainer:762) INFO: 18epoch:train:641-680batch: iter_time=4.113e-05, forward_time=0.032, loss_ctc=4.859, loss=4.859, backward_time=0.007, grad_norm=106.004, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-17 01:39:42,216 (trainer:762) INFO: 18epoch:train:681-720batch: iter_time=4.157e-05, forward_time=0.028, loss_ctc=4.439, loss=4.439, backward_time=0.007, grad_norm=103.438, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.212 +[stan] 2024-01-17 01:39:44,590 (trainer:762) INFO: 18epoch:train:721-760batch: iter_time=4.201e-05, forward_time=0.032, loss_ctc=5.044, loss=5.044, backward_time=0.007, grad_norm=108.265, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-17 01:39:47,065 (trainer:762) INFO: 18epoch:train:761-800batch: iter_time=4.246e-05, forward_time=0.033, loss_ctc=5.691, loss=5.691, backward_time=0.007, grad_norm=110.741, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-17 01:39:51,034 (trainer:357) INFO: 18epoch results: [train] iter_time=1.886e-04, forward_time=0.032, loss_ctc=5.167, loss=5.167, backward_time=0.007, grad_norm=109.091, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240, time=48 seconds, total_count=14400, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=65.542, cer_ctc=0.243, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=65.542, time=1.15 seconds, total_count=450, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:39:52,135 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:39:52,136 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/17epoch.pth +[stan] 2024-01-17 01:39:52,136 (trainer:291) INFO: 19/30epoch started. Estimated time to finish: 10 minutes and 29.4 seconds +[stan] 2024-01-17 01:39:54,873 (trainer:762) INFO: 19epoch:train:1-40batch: iter_time=0.003, forward_time=0.033, loss_ctc=5.520, loss=5.520, backward_time=0.007, grad_norm=117.399, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.273 +[stan] 2024-01-17 01:39:57,569 (trainer:762) INFO: 19epoch:train:41-80batch: iter_time=4.377e-05, forward_time=0.036, loss_ctc=5.625, loss=5.625, backward_time=0.008, grad_norm=112.238, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-17 01:39:59,838 (trainer:762) INFO: 19epoch:train:81-120batch: iter_time=4.121e-05, forward_time=0.030, loss_ctc=4.715, loss=4.715, backward_time=0.007, grad_norm=107.311, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-17 01:40:01,851 (trainer:762) INFO: 19epoch:train:121-160batch: iter_time=4.155e-05, forward_time=0.027, loss_ctc=4.227, loss=4.227, backward_time=0.007, grad_norm=102.063, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.201 +[stan] 2024-01-17 01:40:04,234 (trainer:762) INFO: 19epoch:train:161-200batch: iter_time=4.086e-05, forward_time=0.032, loss_ctc=5.313, loss=5.313, backward_time=0.007, grad_norm=109.276, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-17 01:40:06,577 (trainer:762) INFO: 19epoch:train:201-240batch: iter_time=4.147e-05, forward_time=0.031, loss_ctc=4.725, loss=4.725, backward_time=0.007, grad_norm=110.038, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-17 01:40:09,090 (trainer:762) INFO: 19epoch:train:241-280batch: iter_time=4.100e-05, forward_time=0.033, loss_ctc=5.235, loss=5.235, backward_time=0.007, grad_norm=115.286, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-17 01:40:11,092 (trainer:762) INFO: 19epoch:train:281-320batch: iter_time=4.067e-05, forward_time=0.027, loss_ctc=4.028, loss=4.028, backward_time=0.007, grad_norm=106.176, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.200 +[stan] 2024-01-17 01:40:13,624 (trainer:762) INFO: 19epoch:train:321-360batch: iter_time=4.313e-05, forward_time=0.034, loss_ctc=5.308, loss=5.308, backward_time=0.007, grad_norm=106.868, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-17 01:40:16,196 (trainer:762) INFO: 19epoch:train:361-400batch: iter_time=4.084e-05, forward_time=0.034, loss_ctc=5.934, loss=5.934, backward_time=0.008, grad_norm=120.045, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-17 01:40:18,299 (trainer:762) INFO: 19epoch:train:401-440batch: iter_time=4.103e-05, forward_time=0.028, loss_ctc=4.486, loss=4.486, backward_time=0.007, grad_norm=102.956, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.210 +[stan] 2024-01-17 01:40:20,648 (trainer:762) INFO: 19epoch:train:441-480batch: iter_time=4.155e-05, forward_time=0.031, loss_ctc=4.706, loss=4.706, backward_time=0.007, grad_norm=104.747, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-17 01:40:22,879 (trainer:762) INFO: 19epoch:train:481-520batch: iter_time=4.154e-05, forward_time=0.030, loss_ctc=4.601, loss=4.601, backward_time=0.007, grad_norm=99.009, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.223 +[stan] 2024-01-17 01:40:25,314 (trainer:762) INFO: 19epoch:train:521-560batch: iter_time=4.070e-05, forward_time=0.032, loss_ctc=5.105, loss=5.105, backward_time=0.007, grad_norm=109.311, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-17 01:40:27,796 (trainer:762) INFO: 19epoch:train:561-600batch: iter_time=4.028e-05, forward_time=0.033, loss_ctc=5.298, loss=5.298, backward_time=0.007, grad_norm=109.359, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-17 01:40:30,072 (trainer:762) INFO: 19epoch:train:601-640batch: iter_time=4.053e-05, forward_time=0.030, loss_ctc=4.691, loss=4.691, backward_time=0.007, grad_norm=106.011, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-17 01:40:32,459 (trainer:762) INFO: 19epoch:train:641-680batch: iter_time=4.195e-05, forward_time=0.032, loss_ctc=4.898, loss=4.898, backward_time=0.007, grad_norm=105.508, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-17 01:40:34,958 (trainer:762) INFO: 19epoch:train:681-720batch: iter_time=4.233e-05, forward_time=0.033, loss_ctc=4.961, loss=4.961, backward_time=0.007, grad_norm=108.177, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-17 01:40:37,355 (trainer:762) INFO: 19epoch:train:721-760batch: iter_time=4.122e-05, forward_time=0.032, loss_ctc=5.261, loss=5.261, backward_time=0.007, grad_norm=110.129, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-17 01:40:39,407 (trainer:762) INFO: 19epoch:train:761-800batch: iter_time=3.813e-05, forward_time=0.028, loss_ctc=4.254, loss=4.254, backward_time=0.007, grad_norm=103.862, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.205 +[stan] 2024-01-17 01:40:43,337 (trainer:357) INFO: 19epoch results: [train] iter_time=1.681e-04, forward_time=0.031, loss_ctc=4.945, loss=4.945, backward_time=0.007, grad_norm=108.289, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236, time=47.34 seconds, total_count=15200, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=66.255, cer_ctc=0.241, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=66.255, time=1.15 seconds, total_count=475, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.71 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:40:44,340 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:40:44,342 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/18epoch.pth +[stan] 2024-01-17 01:40:44,342 (trainer:291) INFO: 20/30epoch started. Estimated time to finish: 9 minutes and 36.8 seconds +[stan] 2024-01-17 01:40:47,147 (trainer:762) INFO: 20epoch:train:1-40batch: iter_time=0.003, forward_time=0.034, loss_ctc=5.541, loss=5.541, backward_time=0.008, grad_norm=112.152, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.280 +[stan] 2024-01-17 01:40:49,698 (trainer:762) INFO: 20epoch:train:41-80batch: iter_time=4.176e-05, forward_time=0.034, loss_ctc=4.961, loss=4.961, backward_time=0.007, grad_norm=109.582, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-17 01:40:52,031 (trainer:762) INFO: 20epoch:train:81-120batch: iter_time=4.411e-05, forward_time=0.031, loss_ctc=4.535, loss=4.535, backward_time=0.008, grad_norm=100.471, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-17 01:40:54,204 (trainer:762) INFO: 20epoch:train:121-160batch: iter_time=4.193e-05, forward_time=0.029, loss_ctc=4.072, loss=4.072, backward_time=0.007, grad_norm=99.528, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-17 01:40:56,547 (trainer:762) INFO: 20epoch:train:161-200batch: iter_time=4.666e-05, forward_time=0.031, loss_ctc=5.083, loss=5.083, backward_time=0.007, grad_norm=105.122, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-17 01:40:58,806 (trainer:762) INFO: 20epoch:train:201-240batch: iter_time=4.200e-05, forward_time=0.030, loss_ctc=4.479, loss=4.479, backward_time=0.007, grad_norm=104.079, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-17 01:41:01,178 (trainer:762) INFO: 20epoch:train:241-280batch: iter_time=4.276e-05, forward_time=0.031, loss_ctc=4.719, loss=4.719, backward_time=0.007, grad_norm=109.023, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-17 01:41:03,177 (trainer:762) INFO: 20epoch:train:281-320batch: iter_time=4.333e-05, forward_time=0.027, loss_ctc=3.978, loss=3.978, backward_time=0.007, grad_norm=102.045, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.200 +[stan] 2024-01-17 01:41:05,592 (trainer:762) INFO: 20epoch:train:321-360batch: iter_time=4.292e-05, forward_time=0.032, loss_ctc=4.939, loss=4.939, backward_time=0.007, grad_norm=106.987, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-17 01:41:08,386 (trainer:762) INFO: 20epoch:train:361-400batch: iter_time=4.395e-05, forward_time=0.037, loss_ctc=5.673, loss=5.673, backward_time=0.008, grad_norm=114.334, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-17 01:41:11,094 (trainer:762) INFO: 20epoch:train:401-440batch: iter_time=4.186e-05, forward_time=0.036, loss_ctc=5.219, loss=5.219, backward_time=0.008, grad_norm=104.554, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.271 +[stan] 2024-01-17 01:41:13,262 (trainer:762) INFO: 20epoch:train:441-480batch: iter_time=4.039e-05, forward_time=0.029, loss_ctc=4.010, loss=4.010, backward_time=0.007, grad_norm=100.522, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-17 01:41:15,522 (trainer:762) INFO: 20epoch:train:481-520batch: iter_time=4.166e-05, forward_time=0.030, loss_ctc=4.964, loss=4.964, backward_time=0.007, grad_norm=104.889, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-17 01:41:17,725 (trainer:762) INFO: 20epoch:train:521-560batch: iter_time=4.148e-05, forward_time=0.029, loss_ctc=4.610, loss=4.610, backward_time=0.007, grad_norm=102.688, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-17 01:41:19,753 (trainer:762) INFO: 20epoch:train:561-600batch: iter_time=4.040e-05, forward_time=0.027, loss_ctc=3.841, loss=3.841, backward_time=0.007, grad_norm=97.985, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.203 +[stan] 2024-01-17 01:41:22,108 (trainer:762) INFO: 20epoch:train:601-640batch: iter_time=4.088e-05, forward_time=0.031, loss_ctc=4.639, loss=4.639, backward_time=0.007, grad_norm=107.408, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-17 01:41:24,594 (trainer:762) INFO: 20epoch:train:641-680batch: iter_time=4.161e-05, forward_time=0.033, loss_ctc=4.786, loss=4.786, backward_time=0.008, grad_norm=108.691, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-17 01:41:27,440 (trainer:762) INFO: 20epoch:train:681-720batch: iter_time=4.335e-05, forward_time=0.038, loss_ctc=5.622, loss=5.622, backward_time=0.008, grad_norm=113.314, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.284 +[stan] 2024-01-17 01:41:29,904 (trainer:762) INFO: 20epoch:train:721-760batch: iter_time=4.262e-05, forward_time=0.033, loss_ctc=4.899, loss=4.899, backward_time=0.008, grad_norm=106.385, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-17 01:41:32,069 (trainer:762) INFO: 20epoch:train:761-800batch: iter_time=3.987e-05, forward_time=0.029, loss_ctc=4.331, loss=4.331, backward_time=0.007, grad_norm=106.882, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.216 +[stan] 2024-01-17 01:41:36,134 (trainer:357) INFO: 20epoch results: [train] iter_time=1.798e-04, forward_time=0.032, loss_ctc=4.745, loss=4.745, backward_time=0.007, grad_norm=105.832, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239, time=47.8 seconds, total_count=16000, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=66.256, cer_ctc=0.240, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=66.256, time=1.15 seconds, total_count=500, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.84 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:41:37,117 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:41:37,119 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/19epoch.pth +[stan] 2024-01-17 01:41:37,119 (trainer:291) INFO: 21/30epoch started. Estimated time to finish: 8 minutes and 44.54 seconds +[stan] 2024-01-17 01:41:39,544 (trainer:762) INFO: 21epoch:train:1-40batch: iter_time=0.003, forward_time=0.029, loss_ctc=4.339, loss=4.339, backward_time=0.007, grad_norm=101.572, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-17 01:41:41,851 (trainer:762) INFO: 21epoch:train:41-80batch: iter_time=4.088e-05, forward_time=0.031, loss_ctc=5.008, loss=5.008, backward_time=0.007, grad_norm=105.934, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-17 01:41:44,463 (trainer:762) INFO: 21epoch:train:81-120batch: iter_time=4.100e-05, forward_time=0.035, loss_ctc=5.344, loss=5.344, backward_time=0.008, grad_norm=109.260, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-17 01:41:46,835 (trainer:762) INFO: 21epoch:train:121-160batch: iter_time=4.192e-05, forward_time=0.031, loss_ctc=4.829, loss=4.829, backward_time=0.007, grad_norm=109.412, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-17 01:41:49,107 (trainer:762) INFO: 21epoch:train:161-200batch: iter_time=4.065e-05, forward_time=0.030, loss_ctc=4.328, loss=4.328, backward_time=0.007, grad_norm=106.225, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-17 01:41:51,990 (trainer:762) INFO: 21epoch:train:201-240batch: iter_time=4.233e-05, forward_time=0.038, loss_ctc=5.593, loss=5.593, backward_time=0.008, grad_norm=111.962, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.288 +[stan] 2024-01-17 01:41:54,088 (trainer:762) INFO: 21epoch:train:241-280batch: iter_time=3.923e-05, forward_time=0.028, loss_ctc=3.973, loss=3.973, backward_time=0.007, grad_norm=98.930, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.210 +[stan] 2024-01-17 01:41:56,475 (trainer:762) INFO: 21epoch:train:281-320batch: iter_time=4.101e-05, forward_time=0.032, loss_ctc=5.088, loss=5.088, backward_time=0.007, grad_norm=107.734, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-17 01:41:58,524 (trainer:762) INFO: 21epoch:train:321-360batch: iter_time=4.046e-05, forward_time=0.027, loss_ctc=4.075, loss=4.075, backward_time=0.007, grad_norm=102.434, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.205 +[stan] 2024-01-17 01:42:00,605 (trainer:762) INFO: 21epoch:train:361-400batch: iter_time=3.965e-05, forward_time=0.028, loss_ctc=3.688, loss=3.688, backward_time=0.007, grad_norm=96.699, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.208 +[stan] 2024-01-17 01:42:03,062 (trainer:762) INFO: 21epoch:train:401-440batch: iter_time=4.558e-05, forward_time=0.033, loss_ctc=4.990, loss=4.990, backward_time=0.008, grad_norm=101.330, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-17 01:42:05,485 (trainer:762) INFO: 21epoch:train:441-480batch: iter_time=4.266e-05, forward_time=0.032, loss_ctc=4.672, loss=4.672, backward_time=0.007, grad_norm=106.043, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-17 01:42:07,914 (trainer:762) INFO: 21epoch:train:481-520batch: iter_time=4.168e-05, forward_time=0.032, loss_ctc=4.639, loss=4.639, backward_time=0.007, grad_norm=110.311, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-17 01:42:10,267 (trainer:762) INFO: 21epoch:train:521-560batch: iter_time=4.058e-05, forward_time=0.031, loss_ctc=4.335, loss=4.335, backward_time=0.007, grad_norm=104.892, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-17 01:42:12,851 (trainer:762) INFO: 21epoch:train:561-600batch: iter_time=4.183e-05, forward_time=0.034, loss_ctc=5.627, loss=5.627, backward_time=0.008, grad_norm=104.469, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-17 01:42:15,155 (trainer:762) INFO: 21epoch:train:601-640batch: iter_time=4.091e-05, forward_time=0.031, loss_ctc=4.687, loss=4.687, backward_time=0.007, grad_norm=104.811, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-17 01:42:17,606 (trainer:762) INFO: 21epoch:train:641-680batch: iter_time=4.113e-05, forward_time=0.032, loss_ctc=4.928, loss=4.928, backward_time=0.008, grad_norm=102.406, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-17 01:42:19,858 (trainer:762) INFO: 21epoch:train:681-720batch: iter_time=4.306e-05, forward_time=0.030, loss_ctc=4.740, loss=4.740, backward_time=0.007, grad_norm=107.711, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-17 01:42:22,017 (trainer:762) INFO: 21epoch:train:721-760batch: iter_time=4.070e-05, forward_time=0.029, loss_ctc=4.044, loss=4.044, backward_time=0.007, grad_norm=102.541, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.216 +[stan] 2024-01-17 01:42:24,527 (trainer:762) INFO: 21epoch:train:761-800batch: iter_time=3.910e-05, forward_time=0.033, loss_ctc=4.734, loss=4.734, backward_time=0.007, grad_norm=107.598, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-17 01:42:28,506 (trainer:357) INFO: 21epoch results: [train] iter_time=1.749e-04, forward_time=0.031, loss_ctc=4.683, loss=4.683, backward_time=0.007, grad_norm=105.114, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237, time=47.48 seconds, total_count=16800, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=67.088, cer_ctc=0.245, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=67.088, time=1.16 seconds, total_count=525, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.75 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:42:29,597 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:42:29,599 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/20epoch.pth +[stan] 2024-01-17 01:42:29,599 (trainer:291) INFO: 22/30epoch started. Estimated time to finish: 7 minutes and 52.1 seconds +[stan] 2024-01-17 01:42:31,887 (trainer:762) INFO: 22epoch:train:1-40batch: iter_time=0.003, forward_time=0.027, loss_ctc=3.763, loss=3.763, backward_time=0.007, grad_norm=95.408, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-17 01:42:34,549 (trainer:762) INFO: 22epoch:train:41-80batch: iter_time=4.285e-05, forward_time=0.035, loss_ctc=5.095, loss=5.095, backward_time=0.008, grad_norm=109.897, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-17 01:42:37,034 (trainer:762) INFO: 22epoch:train:81-120batch: iter_time=4.229e-05, forward_time=0.033, loss_ctc=5.060, loss=5.060, backward_time=0.007, grad_norm=112.030, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-17 01:42:39,237 (trainer:762) INFO: 22epoch:train:121-160batch: iter_time=4.214e-05, forward_time=0.029, loss_ctc=4.258, loss=4.258, backward_time=0.007, grad_norm=99.793, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-17 01:42:41,549 (trainer:762) INFO: 22epoch:train:161-200batch: iter_time=4.083e-05, forward_time=0.031, loss_ctc=4.512, loss=4.512, backward_time=0.007, grad_norm=104.164, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-17 01:42:43,950 (trainer:762) INFO: 22epoch:train:201-240batch: iter_time=4.278e-05, forward_time=0.032, loss_ctc=4.133, loss=4.133, backward_time=0.007, grad_norm=101.053, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-17 01:42:46,201 (trainer:762) INFO: 22epoch:train:241-280batch: iter_time=4.158e-05, forward_time=0.030, loss_ctc=4.492, loss=4.492, backward_time=0.007, grad_norm=102.813, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-17 01:42:48,539 (trainer:762) INFO: 22epoch:train:281-320batch: iter_time=4.170e-05, forward_time=0.031, loss_ctc=4.935, loss=4.935, backward_time=0.007, grad_norm=107.330, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-17 01:42:51,204 (trainer:762) INFO: 22epoch:train:321-360batch: iter_time=4.320e-05, forward_time=0.035, loss_ctc=5.423, loss=5.423, backward_time=0.008, grad_norm=114.068, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.266 +[stan] 2024-01-17 01:42:53,760 (trainer:762) INFO: 22epoch:train:361-400batch: iter_time=4.128e-05, forward_time=0.034, loss_ctc=4.845, loss=4.845, backward_time=0.008, grad_norm=106.325, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-17 01:42:55,827 (trainer:762) INFO: 22epoch:train:401-440batch: iter_time=4.201e-05, forward_time=0.028, loss_ctc=3.767, loss=3.767, backward_time=0.007, grad_norm=99.900, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.207 +[stan] 2024-01-17 01:42:58,337 (trainer:762) INFO: 22epoch:train:441-480batch: iter_time=4.292e-05, forward_time=0.033, loss_ctc=4.959, loss=4.959, backward_time=0.007, grad_norm=111.224, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-17 01:43:00,756 (trainer:762) INFO: 22epoch:train:481-520batch: iter_time=4.223e-05, forward_time=0.032, loss_ctc=4.011, loss=4.011, backward_time=0.007, grad_norm=98.778, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-17 01:43:03,304 (trainer:762) INFO: 22epoch:train:521-560batch: iter_time=4.523e-05, forward_time=0.034, loss_ctc=4.870, loss=4.870, backward_time=0.008, grad_norm=106.020, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-17 01:43:05,249 (trainer:762) INFO: 22epoch:train:561-600batch: iter_time=4.166e-05, forward_time=0.026, loss_ctc=3.527, loss=3.527, backward_time=0.007, grad_norm=97.206, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.194 +[stan] 2024-01-17 01:43:07,580 (trainer:762) INFO: 22epoch:train:601-640batch: iter_time=4.250e-05, forward_time=0.031, loss_ctc=4.312, loss=4.312, backward_time=0.007, grad_norm=105.189, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-17 01:43:10,139 (trainer:762) INFO: 22epoch:train:641-680batch: iter_time=4.307e-05, forward_time=0.034, loss_ctc=4.566, loss=4.566, backward_time=0.007, grad_norm=106.991, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-17 01:43:12,119 (trainer:762) INFO: 22epoch:train:681-720batch: iter_time=4.332e-05, forward_time=0.027, loss_ctc=3.749, loss=3.749, backward_time=0.007, grad_norm=95.523, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.198 +[stan] 2024-01-17 01:43:14,619 (trainer:762) INFO: 22epoch:train:721-760batch: iter_time=4.531e-05, forward_time=0.033, loss_ctc=4.790, loss=4.790, backward_time=0.008, grad_norm=100.827, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.250 +[stan] 2024-01-17 01:43:17,105 (trainer:762) INFO: 22epoch:train:761-800batch: iter_time=3.945e-05, forward_time=0.033, loss_ctc=5.105, loss=5.105, backward_time=0.008, grad_norm=110.834, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-17 01:43:21,021 (trainer:357) INFO: 22epoch results: [train] iter_time=1.778e-04, forward_time=0.031, loss_ctc=4.509, loss=4.509, backward_time=0.007, grad_norm=104.269, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237, time=47.58 seconds, total_count=17600, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=67.060, cer_ctc=0.244, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=67.060, time=1.13 seconds, total_count=550, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.71 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:43:22,014 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:43:22,015 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/21epoch.pth +[stan] 2024-01-17 01:43:22,015 (trainer:291) INFO: 23/30epoch started. Estimated time to finish: 6 minutes and 59.63 seconds +[stan] 2024-01-17 01:43:24,486 (trainer:762) INFO: 23epoch:train:1-40batch: iter_time=0.003, forward_time=0.030, loss_ctc=4.152, loss=4.152, backward_time=0.007, grad_norm=105.336, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-17 01:43:27,125 (trainer:762) INFO: 23epoch:train:41-80batch: iter_time=4.086e-05, forward_time=0.035, loss_ctc=4.846, loss=4.846, backward_time=0.007, grad_norm=105.255, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-17 01:43:29,035 (trainer:762) INFO: 23epoch:train:81-120batch: iter_time=3.932e-05, forward_time=0.026, loss_ctc=3.057, loss=3.057, backward_time=0.007, grad_norm=91.904, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.191 +[stan] 2024-01-17 01:43:31,315 (trainer:762) INFO: 23epoch:train:121-160batch: iter_time=4.002e-05, forward_time=0.030, loss_ctc=3.799, loss=3.799, backward_time=0.007, grad_norm=100.156, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-17 01:43:33,675 (trainer:762) INFO: 23epoch:train:161-200batch: iter_time=4.150e-05, forward_time=0.031, loss_ctc=4.361, loss=4.361, backward_time=0.007, grad_norm=103.018, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-17 01:43:36,061 (trainer:762) INFO: 23epoch:train:201-240batch: iter_time=4.125e-05, forward_time=0.032, loss_ctc=5.217, loss=5.217, backward_time=0.008, grad_norm=106.502, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238 +[stan] 2024-01-17 01:43:38,832 (trainer:762) INFO: 23epoch:train:241-280batch: iter_time=4.457e-05, forward_time=0.037, loss_ctc=5.996, loss=5.996, backward_time=0.008, grad_norm=113.915, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-17 01:43:41,223 (trainer:762) INFO: 23epoch:train:281-320batch: iter_time=4.147e-05, forward_time=0.032, loss_ctc=4.455, loss=4.455, backward_time=0.007, grad_norm=102.638, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-17 01:43:43,515 (trainer:762) INFO: 23epoch:train:321-360batch: iter_time=4.123e-05, forward_time=0.031, loss_ctc=4.016, loss=4.016, backward_time=0.007, grad_norm=103.031, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-17 01:43:45,823 (trainer:762) INFO: 23epoch:train:361-400batch: iter_time=4.415e-05, forward_time=0.031, loss_ctc=4.097, loss=4.097, backward_time=0.007, grad_norm=102.010, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-17 01:43:48,160 (trainer:762) INFO: 23epoch:train:401-440batch: iter_time=4.169e-05, forward_time=0.031, loss_ctc=4.014, loss=4.014, backward_time=0.007, grad_norm=105.275, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-17 01:43:50,608 (trainer:762) INFO: 23epoch:train:441-480batch: iter_time=4.078e-05, forward_time=0.032, loss_ctc=4.490, loss=4.490, backward_time=0.007, grad_norm=104.517, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-17 01:43:52,844 (trainer:762) INFO: 23epoch:train:481-520batch: iter_time=4.115e-05, forward_time=0.030, loss_ctc=4.065, loss=4.065, backward_time=0.007, grad_norm=98.521, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.224 +[stan] 2024-01-17 01:43:55,234 (trainer:762) INFO: 23epoch:train:521-560batch: iter_time=4.141e-05, forward_time=0.032, loss_ctc=4.590, loss=4.590, backward_time=0.007, grad_norm=105.216, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-17 01:43:57,375 (trainer:762) INFO: 23epoch:train:561-600batch: iter_time=4.017e-05, forward_time=0.029, loss_ctc=3.878, loss=3.878, backward_time=0.007, grad_norm=100.781, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.214 +[stan] 2024-01-17 01:43:59,541 (trainer:762) INFO: 23epoch:train:601-640batch: iter_time=4.412e-05, forward_time=0.029, loss_ctc=3.797, loss=3.797, backward_time=0.007, grad_norm=95.627, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.216 +[stan] 2024-01-17 01:44:02,283 (trainer:762) INFO: 23epoch:train:641-680batch: iter_time=4.153e-05, forward_time=0.036, loss_ctc=5.227, loss=5.227, backward_time=0.008, grad_norm=105.701, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.274 +[stan] 2024-01-17 01:44:04,584 (trainer:762) INFO: 23epoch:train:681-720batch: iter_time=4.341e-05, forward_time=0.031, loss_ctc=4.204, loss=4.204, backward_time=0.007, grad_norm=99.865, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-17 01:44:07,063 (trainer:762) INFO: 23epoch:train:721-760batch: iter_time=4.161e-05, forward_time=0.033, loss_ctc=4.700, loss=4.700, backward_time=0.007, grad_norm=103.916, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-17 01:44:09,261 (trainer:762) INFO: 23epoch:train:761-800batch: iter_time=3.821e-05, forward_time=0.029, loss_ctc=4.315, loss=4.315, backward_time=0.007, grad_norm=99.043, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-17 01:44:13,233 (trainer:357) INFO: 23epoch results: [train] iter_time=1.712e-04, forward_time=0.031, loss_ctc=4.364, loss=4.364, backward_time=0.007, grad_norm=102.611, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236, time=47.32 seconds, total_count=18400, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=67.167, cer_ctc=0.242, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=67.167, time=1.16 seconds, total_count=575, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.74 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:44:14,333 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:44:14,335 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/22epoch.pth +[stan] 2024-01-17 01:44:14,335 (trainer:291) INFO: 24/30epoch started. Estimated time to finish: 6 minutes and 7.13 seconds +[stan] 2024-01-17 01:44:17,106 (trainer:762) INFO: 24epoch:train:1-40batch: iter_time=0.002, forward_time=0.034, loss_ctc=4.573, loss=4.573, backward_time=0.007, grad_norm=101.795, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-17 01:44:19,430 (trainer:762) INFO: 24epoch:train:41-80batch: iter_time=4.016e-05, forward_time=0.031, loss_ctc=4.149, loss=4.149, backward_time=0.007, grad_norm=101.149, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-17 01:44:21,869 (trainer:762) INFO: 24epoch:train:81-120batch: iter_time=4.405e-05, forward_time=0.032, loss_ctc=4.092, loss=4.092, backward_time=0.008, grad_norm=97.950, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-17 01:44:24,552 (trainer:762) INFO: 24epoch:train:121-160batch: iter_time=4.197e-05, forward_time=0.035, loss_ctc=5.080, loss=5.080, backward_time=0.007, grad_norm=107.522, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-17 01:44:26,556 (trainer:762) INFO: 24epoch:train:161-200batch: iter_time=3.979e-05, forward_time=0.027, loss_ctc=3.603, loss=3.603, backward_time=0.007, grad_norm=93.601, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.200 +[stan] 2024-01-17 01:44:28,840 (trainer:762) INFO: 24epoch:train:201-240batch: iter_time=4.166e-05, forward_time=0.030, loss_ctc=3.946, loss=3.946, backward_time=0.007, grad_norm=96.319, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-17 01:44:31,633 (trainer:762) INFO: 24epoch:train:241-280batch: iter_time=4.141e-05, forward_time=0.037, loss_ctc=5.654, loss=5.654, backward_time=0.008, grad_norm=108.623, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-17 01:44:33,715 (trainer:762) INFO: 24epoch:train:281-320batch: iter_time=4.305e-05, forward_time=0.028, loss_ctc=3.449, loss=3.449, backward_time=0.007, grad_norm=94.885, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.208 +[stan] 2024-01-17 01:44:35,975 (trainer:762) INFO: 24epoch:train:321-360batch: iter_time=4.205e-05, forward_time=0.030, loss_ctc=4.007, loss=4.007, backward_time=0.007, grad_norm=100.185, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.226 +[stan] 2024-01-17 01:44:38,340 (trainer:762) INFO: 24epoch:train:361-400batch: iter_time=4.316e-05, forward_time=0.031, loss_ctc=4.092, loss=4.092, backward_time=0.007, grad_norm=101.882, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-17 01:44:40,440 (trainer:762) INFO: 24epoch:train:401-440batch: iter_time=4.179e-05, forward_time=0.028, loss_ctc=3.713, loss=3.713, backward_time=0.007, grad_norm=94.367, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.210 +[stan] 2024-01-17 01:44:42,971 (trainer:762) INFO: 24epoch:train:441-480batch: iter_time=4.262e-05, forward_time=0.034, loss_ctc=4.558, loss=4.558, backward_time=0.007, grad_norm=101.983, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-17 01:44:45,766 (trainer:762) INFO: 24epoch:train:481-520batch: iter_time=4.376e-05, forward_time=0.037, loss_ctc=5.116, loss=5.116, backward_time=0.008, grad_norm=110.915, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.279 +[stan] 2024-01-17 01:44:47,916 (trainer:762) INFO: 24epoch:train:521-560batch: iter_time=4.134e-05, forward_time=0.029, loss_ctc=3.708, loss=3.708, backward_time=0.007, grad_norm=96.615, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.215 +[stan] 2024-01-17 01:44:50,303 (trainer:762) INFO: 24epoch:train:561-600batch: iter_time=4.285e-05, forward_time=0.032, loss_ctc=4.152, loss=4.152, backward_time=0.007, grad_norm=101.975, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-17 01:44:52,491 (trainer:762) INFO: 24epoch:train:601-640batch: iter_time=4.156e-05, forward_time=0.029, loss_ctc=3.682, loss=3.682, backward_time=0.007, grad_norm=95.483, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-17 01:44:54,744 (trainer:762) INFO: 24epoch:train:641-680batch: iter_time=4.183e-05, forward_time=0.030, loss_ctc=4.125, loss=4.125, backward_time=0.007, grad_norm=100.153, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-17 01:44:57,104 (trainer:762) INFO: 24epoch:train:681-720batch: iter_time=4.279e-05, forward_time=0.031, loss_ctc=4.618, loss=4.618, backward_time=0.007, grad_norm=106.437, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-17 01:44:59,597 (trainer:762) INFO: 24epoch:train:721-760batch: iter_time=4.331e-05, forward_time=0.033, loss_ctc=4.790, loss=4.790, backward_time=0.008, grad_norm=106.668, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-17 01:45:01,955 (trainer:762) INFO: 24epoch:train:761-800batch: iter_time=3.950e-05, forward_time=0.031, loss_ctc=3.905, loss=3.905, backward_time=0.007, grad_norm=102.808, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-17 01:45:05,889 (trainer:357) INFO: 24epoch results: [train] iter_time=1.517e-04, forward_time=0.031, loss_ctc=4.251, loss=4.251, backward_time=0.007, grad_norm=101.066, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.238, time=47.69 seconds, total_count=19200, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=68.562, cer_ctc=0.241, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=68.562, time=1.14 seconds, total_count=600, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.72 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:45:06,898 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:45:06,900 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/23epoch.pth +[stan] 2024-01-17 01:45:06,900 (trainer:291) INFO: 25/30epoch started. Estimated time to finish: 5 minutes and 14.71 seconds +[stan] 2024-01-17 01:45:09,551 (trainer:762) INFO: 25epoch:train:1-40batch: iter_time=0.003, forward_time=0.032, loss_ctc=4.251, loss=4.251, backward_time=0.007, grad_norm=101.084, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.265 +[stan] 2024-01-17 01:45:11,546 (trainer:762) INFO: 25epoch:train:41-80batch: iter_time=4.167e-05, forward_time=0.027, loss_ctc=3.470, loss=3.470, backward_time=0.007, grad_norm=94.831, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.199 +[stan] 2024-01-17 01:45:13,761 (trainer:762) INFO: 25epoch:train:81-120batch: iter_time=3.989e-05, forward_time=0.030, loss_ctc=3.810, loss=3.810, backward_time=0.007, grad_norm=99.693, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.221 +[stan] 2024-01-17 01:45:16,759 (trainer:762) INFO: 25epoch:train:121-160batch: iter_time=4.315e-05, forward_time=0.039, loss_ctc=5.637, loss=5.637, backward_time=0.008, grad_norm=107.797, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.300 +[stan] 2024-01-17 01:45:19,125 (trainer:762) INFO: 25epoch:train:161-200batch: iter_time=4.016e-05, forward_time=0.031, loss_ctc=3.988, loss=3.988, backward_time=0.007, grad_norm=96.104, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236 +[stan] 2024-01-17 01:45:21,554 (trainer:762) INFO: 25epoch:train:201-240batch: iter_time=4.396e-05, forward_time=0.032, loss_ctc=4.094, loss=4.094, backward_time=0.007, grad_norm=102.823, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-17 01:45:23,580 (trainer:762) INFO: 25epoch:train:241-280batch: iter_time=3.965e-05, forward_time=0.027, loss_ctc=3.490, loss=3.490, backward_time=0.007, grad_norm=94.551, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.203 +[stan] 2024-01-17 01:45:25,857 (trainer:762) INFO: 25epoch:train:281-320batch: iter_time=4.141e-05, forward_time=0.030, loss_ctc=3.879, loss=3.879, backward_time=0.007, grad_norm=102.434, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-17 01:45:28,291 (trainer:762) INFO: 25epoch:train:321-360batch: iter_time=4.204e-05, forward_time=0.032, loss_ctc=4.270, loss=4.270, backward_time=0.007, grad_norm=103.753, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-17 01:45:30,873 (trainer:762) INFO: 25epoch:train:361-400batch: iter_time=4.176e-05, forward_time=0.034, loss_ctc=4.599, loss=4.599, backward_time=0.008, grad_norm=104.945, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-17 01:45:33,113 (trainer:762) INFO: 25epoch:train:401-440batch: iter_time=4.078e-05, forward_time=0.030, loss_ctc=3.896, loss=3.896, backward_time=0.007, grad_norm=98.376, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.224 +[stan] 2024-01-17 01:45:35,131 (trainer:762) INFO: 25epoch:train:441-480batch: iter_time=4.284e-05, forward_time=0.027, loss_ctc=3.025, loss=3.025, backward_time=0.007, grad_norm=88.438, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.202 +[stan] 2024-01-17 01:45:37,527 (trainer:762) INFO: 25epoch:train:481-520batch: iter_time=4.117e-05, forward_time=0.032, loss_ctc=4.114, loss=4.114, backward_time=0.007, grad_norm=100.701, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-17 01:45:40,170 (trainer:762) INFO: 25epoch:train:521-560batch: iter_time=4.165e-05, forward_time=0.035, loss_ctc=4.671, loss=4.671, backward_time=0.008, grad_norm=102.241, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.264 +[stan] 2024-01-17 01:45:42,588 (trainer:762) INFO: 25epoch:train:561-600batch: iter_time=4.362e-05, forward_time=0.032, loss_ctc=4.288, loss=4.288, backward_time=0.007, grad_norm=103.338, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-17 01:45:44,856 (trainer:762) INFO: 25epoch:train:601-640batch: iter_time=4.302e-05, forward_time=0.030, loss_ctc=4.067, loss=4.067, backward_time=0.007, grad_norm=95.317, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-17 01:45:47,330 (trainer:762) INFO: 25epoch:train:641-680batch: iter_time=4.148e-05, forward_time=0.033, loss_ctc=4.374, loss=4.374, backward_time=0.007, grad_norm=103.346, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-17 01:45:49,732 (trainer:762) INFO: 25epoch:train:681-720batch: iter_time=4.214e-05, forward_time=0.032, loss_ctc=4.029, loss=4.029, backward_time=0.007, grad_norm=97.174, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-17 01:45:52,155 (trainer:762) INFO: 25epoch:train:721-760batch: iter_time=4.095e-05, forward_time=0.032, loss_ctc=4.076, loss=4.076, backward_time=0.007, grad_norm=105.088, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-17 01:45:54,927 (trainer:762) INFO: 25epoch:train:761-800batch: iter_time=3.932e-05, forward_time=0.036, loss_ctc=4.741, loss=4.741, backward_time=0.008, grad_norm=105.441, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.277 +[stan] 2024-01-17 01:45:58,891 (trainer:357) INFO: 25epoch results: [train] iter_time=1.687e-04, forward_time=0.032, loss_ctc=4.138, loss=4.138, backward_time=0.007, grad_norm=100.374, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240, time=48.1 seconds, total_count=20000, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=68.342, cer_ctc=0.241, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=68.342, time=1.16 seconds, total_count=625, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.73 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:45:59,990 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:45:59,992 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/24epoch.pth +[stan] 2024-01-17 01:45:59,992 (trainer:291) INFO: 26/30epoch started. Estimated time to finish: 4 minutes and 22.39 seconds +[stan] 2024-01-17 01:46:02,273 (trainer:762) INFO: 26epoch:train:1-40batch: iter_time=0.002, forward_time=0.027, loss_ctc=3.387, loss=3.387, backward_time=0.007, grad_norm=98.060, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-17 01:46:04,540 (trainer:762) INFO: 26epoch:train:41-80batch: iter_time=4.087e-05, forward_time=0.030, loss_ctc=3.737, loss=3.737, backward_time=0.007, grad_norm=99.228, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-17 01:46:06,884 (trainer:762) INFO: 26epoch:train:81-120batch: iter_time=4.032e-05, forward_time=0.031, loss_ctc=3.953, loss=3.953, backward_time=0.007, grad_norm=100.685, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-17 01:46:09,110 (trainer:762) INFO: 26epoch:train:121-160batch: iter_time=4.050e-05, forward_time=0.030, loss_ctc=3.787, loss=3.787, backward_time=0.008, grad_norm=94.686, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.222 +[stan] 2024-01-17 01:46:11,476 (trainer:762) INFO: 26epoch:train:161-200batch: iter_time=4.187e-05, forward_time=0.032, loss_ctc=4.036, loss=4.036, backward_time=0.007, grad_norm=104.104, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-17 01:46:13,919 (trainer:762) INFO: 26epoch:train:201-240batch: iter_time=4.208e-05, forward_time=0.032, loss_ctc=4.194, loss=4.194, backward_time=0.007, grad_norm=96.942, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-17 01:46:16,217 (trainer:762) INFO: 26epoch:train:241-280batch: iter_time=3.989e-05, forward_time=0.031, loss_ctc=3.837, loss=3.837, backward_time=0.007, grad_norm=95.928, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-17 01:46:18,758 (trainer:762) INFO: 26epoch:train:281-320batch: iter_time=4.181e-05, forward_time=0.034, loss_ctc=4.533, loss=4.533, backward_time=0.008, grad_norm=103.051, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-17 01:46:20,929 (trainer:762) INFO: 26epoch:train:321-360batch: iter_time=4.117e-05, forward_time=0.029, loss_ctc=3.556, loss=3.556, backward_time=0.007, grad_norm=96.149, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-17 01:46:23,348 (trainer:762) INFO: 26epoch:train:361-400batch: iter_time=4.239e-05, forward_time=0.032, loss_ctc=4.374, loss=4.374, backward_time=0.007, grad_norm=99.586, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-17 01:46:25,285 (trainer:762) INFO: 26epoch:train:401-440batch: iter_time=4.004e-05, forward_time=0.026, loss_ctc=2.881, loss=2.881, backward_time=0.007, grad_norm=94.141, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.194 +[stan] 2024-01-17 01:46:27,519 (trainer:762) INFO: 26epoch:train:441-480batch: iter_time=4.170e-05, forward_time=0.030, loss_ctc=3.839, loss=3.839, backward_time=0.007, grad_norm=99.208, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.223 +[stan] 2024-01-17 01:46:30,192 (trainer:762) INFO: 26epoch:train:481-520batch: iter_time=4.359e-05, forward_time=0.035, loss_ctc=4.908, loss=4.908, backward_time=0.008, grad_norm=103.797, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-17 01:46:32,827 (trainer:762) INFO: 26epoch:train:521-560batch: iter_time=4.038e-05, forward_time=0.035, loss_ctc=4.893, loss=4.893, backward_time=0.008, grad_norm=99.702, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-17 01:46:35,244 (trainer:762) INFO: 26epoch:train:561-600batch: iter_time=4.366e-05, forward_time=0.032, loss_ctc=4.761, loss=4.761, backward_time=0.008, grad_norm=105.542, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-17 01:46:37,859 (trainer:762) INFO: 26epoch:train:601-640batch: iter_time=4.238e-05, forward_time=0.035, loss_ctc=4.449, loss=4.449, backward_time=0.007, grad_norm=102.844, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.261 +[stan] 2024-01-17 01:46:40,042 (trainer:762) INFO: 26epoch:train:641-680batch: iter_time=4.165e-05, forward_time=0.029, loss_ctc=3.612, loss=3.612, backward_time=0.007, grad_norm=94.759, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.218 +[stan] 2024-01-17 01:46:42,489 (trainer:762) INFO: 26epoch:train:681-720batch: iter_time=4.163e-05, forward_time=0.033, loss_ctc=4.416, loss=4.416, backward_time=0.007, grad_norm=99.168, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-17 01:46:45,001 (trainer:762) INFO: 26epoch:train:721-760batch: iter_time=4.190e-05, forward_time=0.033, loss_ctc=4.558, loss=4.558, backward_time=0.008, grad_norm=103.242, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-17 01:46:47,119 (trainer:762) INFO: 26epoch:train:761-800batch: iter_time=3.827e-05, forward_time=0.028, loss_ctc=3.699, loss=3.699, backward_time=0.007, grad_norm=94.912, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.212 +[stan] 2024-01-17 01:46:51,083 (trainer:357) INFO: 26epoch results: [train] iter_time=1.620e-04, forward_time=0.031, loss_ctc=4.070, loss=4.070, backward_time=0.007, grad_norm=99.287, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.236, time=47.2 seconds, total_count=20800, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=70.066, cer_ctc=0.233, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=70.066, time=1.14 seconds, total_count=650, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.75 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:46:52,102 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:46:52,104 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/25epoch.pth +[stan] 2024-01-17 01:46:52,104 (trainer:291) INFO: 27/30epoch started. Estimated time to finish: 3 minutes and 29.86 seconds +[stan] 2024-01-17 01:46:54,579 (trainer:762) INFO: 27epoch:train:1-40batch: iter_time=0.003, forward_time=0.030, loss_ctc=3.614, loss=3.614, backward_time=0.007, grad_norm=95.710, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-17 01:46:57,143 (trainer:762) INFO: 27epoch:train:41-80batch: iter_time=4.452e-05, forward_time=0.034, loss_ctc=4.500, loss=4.500, backward_time=0.007, grad_norm=104.324, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.256 +[stan] 2024-01-17 01:46:59,346 (trainer:762) INFO: 27epoch:train:81-120batch: iter_time=4.192e-05, forward_time=0.029, loss_ctc=3.660, loss=3.660, backward_time=0.007, grad_norm=99.463, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-17 01:47:01,713 (trainer:762) INFO: 27epoch:train:121-160batch: iter_time=4.225e-05, forward_time=0.031, loss_ctc=4.271, loss=4.271, backward_time=0.007, grad_norm=98.243, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237 +[stan] 2024-01-17 01:47:04,014 (trainer:762) INFO: 27epoch:train:161-200batch: iter_time=4.190e-05, forward_time=0.031, loss_ctc=4.179, loss=4.179, backward_time=0.007, grad_norm=98.388, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-17 01:47:06,304 (trainer:762) INFO: 27epoch:train:201-240batch: iter_time=4.521e-05, forward_time=0.031, loss_ctc=3.871, loss=3.871, backward_time=0.007, grad_norm=98.356, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.229 +[stan] 2024-01-17 01:47:09,068 (trainer:762) INFO: 27epoch:train:241-280batch: iter_time=4.196e-05, forward_time=0.037, loss_ctc=5.103, loss=5.103, backward_time=0.008, grad_norm=105.627, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.276 +[stan] 2024-01-17 01:47:11,096 (trainer:762) INFO: 27epoch:train:281-320batch: iter_time=4.202e-05, forward_time=0.027, loss_ctc=3.622, loss=3.622, backward_time=0.007, grad_norm=93.876, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.203 +[stan] 2024-01-17 01:47:13,416 (trainer:762) INFO: 27epoch:train:321-360batch: iter_time=4.222e-05, forward_time=0.031, loss_ctc=3.637, loss=3.637, backward_time=0.007, grad_norm=92.988, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-17 01:47:15,635 (trainer:762) INFO: 27epoch:train:361-400batch: iter_time=4.215e-05, forward_time=0.030, loss_ctc=3.688, loss=3.688, backward_time=0.007, grad_norm=93.686, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.222 +[stan] 2024-01-17 01:47:18,060 (trainer:762) INFO: 27epoch:train:401-440batch: iter_time=4.492e-05, forward_time=0.032, loss_ctc=3.990, loss=3.990, backward_time=0.007, grad_norm=99.057, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.242 +[stan] 2024-01-17 01:47:20,639 (trainer:762) INFO: 27epoch:train:441-480batch: iter_time=4.206e-05, forward_time=0.035, loss_ctc=4.743, loss=4.743, backward_time=0.007, grad_norm=103.214, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-17 01:47:23,146 (trainer:762) INFO: 27epoch:train:481-520batch: iter_time=4.120e-05, forward_time=0.033, loss_ctc=4.268, loss=4.268, backward_time=0.007, grad_norm=98.484, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-17 01:47:25,296 (trainer:762) INFO: 27epoch:train:521-560batch: iter_time=4.174e-05, forward_time=0.029, loss_ctc=3.451, loss=3.451, backward_time=0.007, grad_norm=97.330, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.215 +[stan] 2024-01-17 01:47:27,638 (trainer:762) INFO: 27epoch:train:561-600batch: iter_time=4.030e-05, forward_time=0.031, loss_ctc=4.010, loss=4.010, backward_time=0.007, grad_norm=98.774, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.234 +[stan] 2024-01-17 01:47:30,094 (trainer:762) INFO: 27epoch:train:601-640batch: iter_time=4.139e-05, forward_time=0.033, loss_ctc=4.621, loss=4.621, backward_time=0.007, grad_norm=103.638, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.245 +[stan] 2024-01-17 01:47:32,820 (trainer:762) INFO: 27epoch:train:641-680batch: iter_time=4.299e-05, forward_time=0.036, loss_ctc=4.919, loss=4.919, backward_time=0.008, grad_norm=106.256, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.272 +[stan] 2024-01-17 01:47:35,086 (trainer:762) INFO: 27epoch:train:681-720batch: iter_time=4.251e-05, forward_time=0.030, loss_ctc=3.541, loss=3.541, backward_time=0.007, grad_norm=94.129, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-17 01:47:37,340 (trainer:762) INFO: 27epoch:train:721-760batch: iter_time=4.127e-05, forward_time=0.030, loss_ctc=3.895, loss=3.895, backward_time=0.007, grad_norm=94.782, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.225 +[stan] 2024-01-17 01:47:39,530 (trainer:762) INFO: 27epoch:train:761-800batch: iter_time=3.815e-05, forward_time=0.029, loss_ctc=3.298, loss=3.298, backward_time=0.007, grad_norm=91.737, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-17 01:47:43,451 (trainer:357) INFO: 27epoch results: [train] iter_time=1.710e-04, forward_time=0.031, loss_ctc=4.044, loss=4.044, backward_time=0.007, grad_norm=98.403, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237, time=47.5 seconds, total_count=21600, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=70.644, cer_ctc=0.227, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=70.644, time=1.13 seconds, total_count=675, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.71 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:47:44,474 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:47:44,476 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/26epoch.pth +[stan] 2024-01-17 01:47:44,476 (trainer:291) INFO: 28/30epoch started. Estimated time to finish: 2 minutes and 37.38 seconds +[stan] 2024-01-17 01:47:46,878 (trainer:762) INFO: 28epoch:train:1-40batch: iter_time=0.003, forward_time=0.029, loss_ctc=3.206, loss=3.206, backward_time=0.007, grad_norm=89.914, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-17 01:47:49,153 (trainer:762) INFO: 28epoch:train:41-80batch: iter_time=4.148e-05, forward_time=0.030, loss_ctc=3.780, loss=3.780, backward_time=0.007, grad_norm=94.124, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-17 01:47:51,843 (trainer:762) INFO: 28epoch:train:81-120batch: iter_time=4.437e-05, forward_time=0.035, loss_ctc=4.520, loss=4.520, backward_time=0.008, grad_norm=100.823, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-17 01:47:54,118 (trainer:762) INFO: 28epoch:train:121-160batch: iter_time=4.320e-05, forward_time=0.030, loss_ctc=4.012, loss=4.012, backward_time=0.007, grad_norm=97.749, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.227 +[stan] 2024-01-17 01:47:56,701 (trainer:762) INFO: 28epoch:train:161-200batch: iter_time=4.136e-05, forward_time=0.034, loss_ctc=4.494, loss=4.494, backward_time=0.008, grad_norm=99.506, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.258 +[stan] 2024-01-17 01:47:59,192 (trainer:762) INFO: 28epoch:train:201-240batch: iter_time=4.467e-05, forward_time=0.033, loss_ctc=4.399, loss=4.399, backward_time=0.007, grad_norm=100.513, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-17 01:48:01,371 (trainer:762) INFO: 28epoch:train:241-280batch: iter_time=4.226e-05, forward_time=0.029, loss_ctc=3.361, loss=3.361, backward_time=0.007, grad_norm=93.882, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.218 +[stan] 2024-01-17 01:48:03,551 (trainer:762) INFO: 28epoch:train:281-320batch: iter_time=4.041e-05, forward_time=0.029, loss_ctc=3.656, loss=3.656, backward_time=0.007, grad_norm=94.898, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.218 +[stan] 2024-01-17 01:48:05,649 (trainer:762) INFO: 28epoch:train:321-360batch: iter_time=4.128e-05, forward_time=0.028, loss_ctc=3.476, loss=3.476, backward_time=0.007, grad_norm=94.168, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.210 +[stan] 2024-01-17 01:48:08,323 (trainer:762) INFO: 28epoch:train:361-400batch: iter_time=4.307e-05, forward_time=0.035, loss_ctc=4.623, loss=4.623, backward_time=0.008, grad_norm=98.052, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.267 +[stan] 2024-01-17 01:48:10,541 (trainer:762) INFO: 28epoch:train:401-440batch: iter_time=4.200e-05, forward_time=0.030, loss_ctc=3.956, loss=3.956, backward_time=0.007, grad_norm=96.899, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.222 +[stan] 2024-01-17 01:48:12,700 (trainer:762) INFO: 28epoch:train:441-480batch: iter_time=4.066e-05, forward_time=0.029, loss_ctc=3.452, loss=3.452, backward_time=0.007, grad_norm=92.008, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.216 +[stan] 2024-01-17 01:48:15,542 (trainer:762) INFO: 28epoch:train:481-520batch: iter_time=4.202e-05, forward_time=0.037, loss_ctc=5.027, loss=5.027, backward_time=0.008, grad_norm=99.848, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.284 +[stan] 2024-01-17 01:48:17,626 (trainer:762) INFO: 28epoch:train:521-560batch: iter_time=4.159e-05, forward_time=0.028, loss_ctc=3.362, loss=3.362, backward_time=0.007, grad_norm=94.024, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.208 +[stan] 2024-01-17 01:48:20,164 (trainer:762) INFO: 28epoch:train:561-600batch: iter_time=4.167e-05, forward_time=0.034, loss_ctc=4.029, loss=4.029, backward_time=0.007, grad_norm=93.882, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.254 +[stan] 2024-01-17 01:48:22,461 (trainer:762) INFO: 28epoch:train:601-640batch: iter_time=4.178e-05, forward_time=0.031, loss_ctc=3.739, loss=3.739, backward_time=0.007, grad_norm=96.065, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.230 +[stan] 2024-01-17 01:48:24,921 (trainer:762) INFO: 28epoch:train:641-680batch: iter_time=4.111e-05, forward_time=0.033, loss_ctc=4.369, loss=4.369, backward_time=0.007, grad_norm=98.626, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.246 +[stan] 2024-01-17 01:48:27,094 (trainer:762) INFO: 28epoch:train:681-720batch: iter_time=4.131e-05, forward_time=0.029, loss_ctc=3.366, loss=3.366, backward_time=0.007, grad_norm=92.656, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.217 +[stan] 2024-01-17 01:48:29,440 (trainer:762) INFO: 28epoch:train:721-760batch: iter_time=4.202e-05, forward_time=0.031, loss_ctc=3.699, loss=3.699, backward_time=0.007, grad_norm=95.612, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235 +[stan] 2024-01-17 01:48:31,496 (trainer:762) INFO: 28epoch:train:761-800batch: iter_time=4.030e-05, forward_time=0.028, loss_ctc=3.000, loss=3.000, backward_time=0.007, grad_norm=90.362, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.206 +[stan] 2024-01-17 01:48:35,489 (trainer:357) INFO: 28epoch results: [train] iter_time=1.831e-04, forward_time=0.031, loss_ctc=3.876, loss=3.876, backward_time=0.007, grad_norm=95.681, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.235, time=47.09 seconds, total_count=22400, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=71.370, cer_ctc=0.233, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=71.370, time=1.15 seconds, total_count=700, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.77 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:48:36,596 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:48:36,598 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/27epoch.pth +[stan] 2024-01-17 01:48:36,598 (trainer:291) INFO: 29/30epoch started. Estimated time to finish: 1 minute and 44.9 seconds +[stan] 2024-01-17 01:48:39,577 (trainer:762) INFO: 29epoch:train:1-40batch: iter_time=0.002, forward_time=0.036, loss_ctc=4.830, loss=4.830, backward_time=0.008, grad_norm=106.222, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.298 +[stan] 2024-01-17 01:48:41,766 (trainer:762) INFO: 29epoch:train:41-80batch: iter_time=4.092e-05, forward_time=0.029, loss_ctc=3.709, loss=3.709, backward_time=0.007, grad_norm=96.868, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.219 +[stan] 2024-01-17 01:48:44,398 (trainer:762) INFO: 29epoch:train:81-120batch: iter_time=4.317e-05, forward_time=0.035, loss_ctc=4.533, loss=4.533, backward_time=0.007, grad_norm=100.042, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-17 01:48:46,725 (trainer:762) INFO: 29epoch:train:121-160batch: iter_time=4.236e-05, forward_time=0.031, loss_ctc=3.669, loss=3.669, backward_time=0.007, grad_norm=94.178, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.233 +[stan] 2024-01-17 01:48:49,359 (trainer:762) INFO: 29epoch:train:161-200batch: iter_time=4.073e-05, forward_time=0.035, loss_ctc=4.443, loss=4.443, backward_time=0.008, grad_norm=100.002, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.263 +[stan] 2024-01-17 01:48:51,558 (trainer:762) INFO: 29epoch:train:201-240batch: iter_time=4.032e-05, forward_time=0.029, loss_ctc=3.609, loss=3.609, backward_time=0.007, grad_norm=92.316, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-17 01:48:53,991 (trainer:762) INFO: 29epoch:train:241-280batch: iter_time=4.159e-05, forward_time=0.032, loss_ctc=4.256, loss=4.256, backward_time=0.007, grad_norm=90.736, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-17 01:48:56,481 (trainer:762) INFO: 29epoch:train:281-320batch: iter_time=4.136e-05, forward_time=0.033, loss_ctc=3.988, loss=3.988, backward_time=0.007, grad_norm=97.461, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.249 +[stan] 2024-01-17 01:48:58,875 (trainer:762) INFO: 29epoch:train:321-360batch: iter_time=4.400e-05, forward_time=0.032, loss_ctc=3.820, loss=3.820, backward_time=0.007, grad_norm=91.813, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-17 01:49:00,928 (trainer:762) INFO: 29epoch:train:361-400batch: iter_time=4.032e-05, forward_time=0.028, loss_ctc=2.944, loss=2.944, backward_time=0.007, grad_norm=92.034, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.205 +[stan] 2024-01-17 01:49:03,608 (trainer:762) INFO: 29epoch:train:401-440batch: iter_time=4.173e-05, forward_time=0.035, loss_ctc=4.716, loss=4.716, backward_time=0.008, grad_norm=103.798, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.268 +[stan] 2024-01-17 01:49:06,091 (trainer:762) INFO: 29epoch:train:441-480batch: iter_time=4.377e-05, forward_time=0.033, loss_ctc=3.895, loss=3.895, backward_time=0.007, grad_norm=96.608, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.248 +[stan] 2024-01-17 01:49:08,224 (trainer:762) INFO: 29epoch:train:481-520batch: iter_time=4.028e-05, forward_time=0.028, loss_ctc=3.536, loss=3.536, backward_time=0.007, grad_norm=94.725, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.213 +[stan] 2024-01-17 01:49:10,321 (trainer:762) INFO: 29epoch:train:521-560batch: iter_time=4.415e-05, forward_time=0.028, loss_ctc=3.328, loss=3.328, backward_time=0.007, grad_norm=88.016, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.210 +[stan] 2024-01-17 01:49:12,633 (trainer:762) INFO: 29epoch:train:561-600batch: iter_time=4.271e-05, forward_time=0.031, loss_ctc=4.045, loss=4.045, backward_time=0.007, grad_norm=96.712, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-17 01:49:14,943 (trainer:762) INFO: 29epoch:train:601-640batch: iter_time=4.118e-05, forward_time=0.031, loss_ctc=3.799, loss=3.799, backward_time=0.007, grad_norm=93.946, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.231 +[stan] 2024-01-17 01:49:17,385 (trainer:762) INFO: 29epoch:train:641-680batch: iter_time=4.034e-05, forward_time=0.032, loss_ctc=3.893, loss=3.893, backward_time=0.007, grad_norm=93.703, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-17 01:49:19,782 (trainer:762) INFO: 29epoch:train:681-720batch: iter_time=4.091e-05, forward_time=0.032, loss_ctc=3.689, loss=3.689, backward_time=0.007, grad_norm=99.989, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.240 +[stan] 2024-01-17 01:49:22,098 (trainer:762) INFO: 29epoch:train:721-760batch: iter_time=4.089e-05, forward_time=0.031, loss_ctc=3.759, loss=3.759, backward_time=0.007, grad_norm=94.899, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.232 +[stan] 2024-01-17 01:49:24,804 (trainer:762) INFO: 29epoch:train:761-800batch: iter_time=4.022e-05, forward_time=0.036, loss_ctc=4.757, loss=4.757, backward_time=0.008, grad_norm=95.494, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-17 01:49:28,729 (trainer:357) INFO: 29epoch results: [train] iter_time=1.559e-04, forward_time=0.032, loss_ctc=3.961, loss=3.961, backward_time=0.007, grad_norm=95.978, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241, time=48.27 seconds, total_count=23200, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=72.021, cer_ctc=0.235, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=72.021, time=1.14 seconds, total_count=725, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.72 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:49:29,719 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:49:29,720 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/28epoch.pth +[stan] 2024-01-17 01:49:29,720 (trainer:291) INFO: 30/30epoch started. Estimated time to finish: 52.47 seconds +[stan] 2024-01-17 01:49:32,134 (trainer:762) INFO: 30epoch:train:1-40batch: iter_time=0.003, forward_time=0.029, loss_ctc=3.419, loss=3.419, backward_time=0.007, grad_norm=89.402, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.241 +[stan] 2024-01-17 01:49:34,833 (trainer:762) INFO: 30epoch:train:41-80batch: iter_time=4.239e-05, forward_time=0.036, loss_ctc=4.912, loss=4.912, backward_time=0.007, grad_norm=101.497, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.270 +[stan] 2024-01-17 01:49:36,818 (trainer:762) INFO: 30epoch:train:81-120batch: iter_time=4.416e-05, forward_time=0.027, loss_ctc=3.200, loss=3.200, backward_time=0.007, grad_norm=89.853, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.198 +[stan] 2024-01-17 01:49:39,042 (trainer:762) INFO: 30epoch:train:121-160batch: iter_time=4.405e-05, forward_time=0.030, loss_ctc=3.560, loss=3.560, backward_time=0.007, grad_norm=98.386, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.222 +[stan] 2024-01-17 01:49:41,571 (trainer:762) INFO: 30epoch:train:161-200batch: iter_time=4.313e-05, forward_time=0.033, loss_ctc=4.042, loss=4.042, backward_time=0.007, grad_norm=103.206, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.253 +[stan] 2024-01-17 01:49:44,085 (trainer:762) INFO: 30epoch:train:201-240batch: iter_time=4.232e-05, forward_time=0.033, loss_ctc=4.340, loss=4.340, backward_time=0.008, grad_norm=98.655, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.251 +[stan] 2024-01-17 01:49:46,134 (trainer:762) INFO: 30epoch:train:241-280batch: iter_time=4.220e-05, forward_time=0.027, loss_ctc=2.984, loss=2.984, backward_time=0.007, grad_norm=85.687, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.205 +[stan] 2024-01-17 01:49:48,578 (trainer:762) INFO: 30epoch:train:281-320batch: iter_time=4.183e-05, forward_time=0.032, loss_ctc=3.882, loss=3.882, backward_time=0.007, grad_norm=90.116, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-17 01:49:50,861 (trainer:762) INFO: 30epoch:train:321-360batch: iter_time=4.428e-05, forward_time=0.030, loss_ctc=3.469, loss=3.469, backward_time=0.007, grad_norm=91.738, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.228 +[stan] 2024-01-17 01:49:53,046 (trainer:762) INFO: 30epoch:train:361-400batch: iter_time=4.297e-05, forward_time=0.029, loss_ctc=3.724, loss=3.724, backward_time=0.007, grad_norm=97.501, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.218 +[stan] 2024-01-17 01:49:55,621 (trainer:762) INFO: 30epoch:train:401-440batch: iter_time=4.334e-05, forward_time=0.034, loss_ctc=4.435, loss=4.435, backward_time=0.007, grad_norm=98.049, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.257 +[stan] 2024-01-17 01:49:58,065 (trainer:762) INFO: 30epoch:train:441-480batch: iter_time=4.518e-05, forward_time=0.032, loss_ctc=3.923, loss=3.923, backward_time=0.007, grad_norm=91.112, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.244 +[stan] 2024-01-17 01:50:00,498 (trainer:762) INFO: 30epoch:train:481-520batch: iter_time=4.269e-05, forward_time=0.032, loss_ctc=4.410, loss=4.410, backward_time=0.008, grad_norm=98.086, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.243 +[stan] 2024-01-17 01:50:02,703 (trainer:762) INFO: 30epoch:train:521-560batch: iter_time=4.196e-05, forward_time=0.029, loss_ctc=3.087, loss=3.087, backward_time=0.007, grad_norm=89.619, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.220 +[stan] 2024-01-17 01:50:05,256 (trainer:762) INFO: 30epoch:train:561-600batch: iter_time=4.632e-05, forward_time=0.035, loss_ctc=4.320, loss=4.320, backward_time=0.008, grad_norm=97.828, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.255 +[stan] 2024-01-17 01:50:07,725 (trainer:762) INFO: 30epoch:train:601-640batch: iter_time=4.456e-05, forward_time=0.033, loss_ctc=3.512, loss=3.512, backward_time=0.007, grad_norm=101.040, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.247 +[stan] 2024-01-17 01:50:09,905 (trainer:762) INFO: 30epoch:train:641-680batch: iter_time=4.499e-05, forward_time=0.029, loss_ctc=3.781, loss=3.781, backward_time=0.007, grad_norm=95.212, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.218 +[stan] 2024-01-17 01:50:12,593 (trainer:762) INFO: 30epoch:train:681-720batch: iter_time=4.381e-05, forward_time=0.036, loss_ctc=4.616, loss=4.616, backward_time=0.008, grad_norm=98.016, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.269 +[stan] 2024-01-17 01:50:14,981 (trainer:762) INFO: 30epoch:train:721-760batch: iter_time=4.163e-05, forward_time=0.032, loss_ctc=3.886, loss=3.886, backward_time=0.007, grad_norm=95.515, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.239 +[stan] 2024-01-17 01:50:17,048 (trainer:762) INFO: 30epoch:train:761-800batch: iter_time=3.918e-05, forward_time=0.028, loss_ctc=3.023, loss=3.023, backward_time=0.007, grad_norm=88.406, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.207 +[stan] 2024-01-17 01:50:20,976 (trainer:357) INFO: 30epoch results: [train] iter_time=1.805e-04, forward_time=0.031, loss_ctc=3.826, loss=3.826, backward_time=0.007, grad_norm=94.946, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.237, time=47.4 seconds, total_count=24000, gpu_max_cached_mem_GB=10.941, [valid] loss_ctc=72.357, cer_ctc=0.236, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=72.357, time=1.15 seconds, total_count=750, gpu_max_cached_mem_GB=10.941, [att_plot] time=2.71 seconds, total_count=0, gpu_max_cached_mem_GB=10.941 +[stan] 2024-01-17 01:50:21,997 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:50:21,999 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/29epoch.pth +[stan] 2024-01-17 01:50:21,999 (trainer:488) INFO: The training was finished at 30 epochs +[stan] 2024-01-17 01:50:22,015 (average_nbest_models:69) INFO: Averaging 5best models: criterion="valid.loss": test_pr/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave_5best.pth +# Accounting: time=1579 threads=1 +# Ended (code 0) at Wed Jan 17 01:50:22 CST 2024, elapsed time 1579 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_deu1_1h/valid.loss.ave.pth 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21123854 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/RESULTS.md b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/RESULTS.md new file mode 100644 index 0000000000000000000000000000000000000000..4bef1e55f095c3486191c57f92d64a9087a2050a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/RESULTS.md @@ -0,0 +1,45 @@ +[INFO] /home/stan/Desktop/espnet/egs2/ml_superb/asr1/../../../tools/activate_python.sh is not present + +# RESULTS +## Environments +- date: `Tue Jan 16 21:45:23 CST 2024` +- python version: `3.8.10 (default, Jun 4 2021, 15:09:15) [GCC 7.5.0]` +- espnet version: `espnet 202310` +- pytorch version: `pytorch 1.12.0+cu113` +- Git hash: `aa855dffb81937a097ee03089926a0d5256426e2` + - Commit date: `Tue Jan 16 19:36:29 2024 +0800` + +## test_pr/asr_train_asr_s3prl_houlsby_eng1_10min +### WER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|decode_asr_asr_model_valid.loss.ave/test_10min_eng1|1092|11772|27.2|66.1|6.6|7.6|80.4|99.9| + +### CER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|decode_asr_asr_model_valid.loss.ave/test_10min_eng1|1092|67334|76.2|10.0|13.8|7.2|31.0|99.9| + +### TER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +## test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave +### WER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|org/dev_10min_eng1|40|1535|31.7|61.4|6.9|4.6|72.8|100.0| + +### CER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|org/dev_10min_eng1|40|8254|79.0|8.5|12.4|5.4|26.4|100.0| + +### TER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/att_ws/mls_eng_000243/encoder.encoders.0.self_attn.10ep.png 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0000000000000000000000000000000000000000..5a701808cdab1670365217081641a7ffb06bec2d --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/checkpoint.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a90cdc825415dd3165cf39930aaed14fbf49d17d4195360b59dba12b97d81333 +size 63363673 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..40f6e856b546676d4c62b5b9978a27235c889bb0 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml @@ -0,0 +1,237 @@ +config: conf/tuning/train_asr_s3prl_houlsby.yaml +print_config: false +log_level: INFO +drop_last_iter: false +dry_run: false +iterator_type: sequence +valid_iterator_type: null +output_dir: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min +ngpu: 1 +seed: 0 +num_workers: 4 +num_att_plot: 3 +dist_backend: nccl +dist_init_method: env:// +dist_world_size: null +dist_rank: null +local_rank: 0 +dist_master_addr: null +dist_master_port: null +dist_launcher: null +multiprocessing_distributed: false +unused_parameters: true +sharded_ddp: false +cudnn_enabled: true +cudnn_benchmark: false +cudnn_deterministic: true +collect_stats: false +write_collected_feats: false +max_epoch: 30 +patience: null +val_scheduler_criterion: +- valid +- loss +early_stopping_criterion: +- valid +- loss +- min +best_model_criterion: +- - valid + - loss + - min +keep_nbest_models: 5 +nbest_averaging_interval: 0 +grad_clip: 5.0 +grad_clip_type: 2.0 +grad_noise: false +accum_grad: 4 +no_forward_run: false +resume: true +train_dtype: float32 +use_amp: false +log_interval: null +use_matplotlib: true +use_tensorboard: true +create_graph_in_tensorboard: false +use_wandb: false +wandb_project: null +wandb_id: null +wandb_entity: null +wandb_name: null +wandb_model_log_interval: -1 +detect_anomaly: false +use_adapter: true +adapter: houlsby +save_adapter_only: true +adapter_conf: + bottleneck: 32 +pretrain_path: null +init_param: [] +ignore_init_mismatch: false +freeze_param: +- frontend.upstream +num_iters_per_epoch: 800 +batch_size: 8 +valid_batch_size: null +batch_bins: 1000000 +valid_batch_bins: null +train_shape_file: +- test_pr/asr_stats_eng1_10min/train/speech_shape +- test_pr/asr_stats_eng1_10min/train/text_shape.char +valid_shape_file: +- test_pr/asr_stats_eng1_10min/valid/speech_shape +- test_pr/asr_stats_eng1_10min/valid/text_shape.char +batch_type: sorted +valid_batch_type: null +fold_length: +- 80000 +- 150 +sort_in_batch: descending +shuffle_within_batch: false +sort_batch: descending +multiple_iterator: false +chunk_length: 500 +chunk_shift_ratio: 0.5 +num_cache_chunks: 1024 +chunk_excluded_key_prefixes: [] +chunk_default_fs: null +train_data_path_and_name_and_type: +- - dump/raw/train_10min_eng1/wav.scp + - speech + - sound +- - dump/raw/train_10min_eng1/text + - text + - text +valid_data_path_and_name_and_type: +- - dump/raw/dev_10min_eng1/wav.scp + - speech + - sound +- - dump/raw/dev_10min_eng1/text + - text + - text +allow_variable_data_keys: false +max_cache_size: 0.0 +max_cache_fd: 32 +allow_multi_rates: false +valid_max_cache_size: null +exclude_weight_decay: false +exclude_weight_decay_conf: {} +optim: adam +optim_conf: + lr: 0.0001 + weight_decay: 1.0e-06 +scheduler: null +scheduler_conf: {} +token_list: +- +- +- +- E +- T +- A +- O +- N +- I +- S +- H +- R +- D +- L +- U +- M +- C +- W +- F +- Y +- G +- P +- B +- V +- K +- X +- J +- Q +- Z +- +init: null +input_size: null +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: null + zero_infinity: true + brctc_risk_strategy: exp + brctc_group_strategy: end + brctc_risk_factor: 0.0 +joint_net_conf: null +use_preprocessor: true +use_lang_prompt: false +use_nlp_prompt: false +token_type: char +bpemodel: null +non_linguistic_symbols: null +cleaner: null +g2p: null +speech_volume_normalize: null +rir_scp: null +rir_apply_prob: 1.0 +noise_scp: null +noise_apply_prob: 1.0 +noise_db_range: '13_15' +short_noise_thres: 0.5 +aux_ctc_tasks: [] +frontend: s3prl +frontend_conf: + frontend_conf: + upstream: hubert_base + download_dir: ./hub + multilayer_feature: true + fs: 16k +specaug: specaug +specaug_conf: + apply_time_warp: true + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 27 + num_freq_mask: 2 + apply_time_mask: true + time_mask_width_ratio_range: + - 0.0 + - 0.05 + num_time_mask: 10 +normalize: utterance_mvn +normalize_conf: {} +model: espnet +model_conf: + ctc_weight: 1.0 + extract_feats_in_collect_stats: false +preencoder: linear +preencoder_conf: + input_size: 768 + output_size: 80 +encoder: transformer +encoder_conf: + output_size: 256 + attention_heads: 8 + linear_units: 1024 + num_blocks: 2 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: conv2d2 + normalize_before: true +postencoder: null +postencoder_conf: {} +decoder: null +decoder_conf: {} +preprocessor: default +preprocessor_conf: {} +required: +- output_dir +- token_list +version: '202310' +distributed: false diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.1.log b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.1.log new file mode 100644 index 0000000000000000000000000000000000000000..1fda41344f5ff7dcf6035598eb2eb5a3a78d86a5 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.1.log @@ -0,0 +1,129 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1 --config conf/decode_asr.yaml +# Started at Tue Jan 16 21:36:59 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1 --config conf/decode_asr.yaml +2024-01-16 21:37:00,596 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml +2024-01-16 21:37:00,614 (asr:523) INFO: Vocabulary size: 30 +2024-01-16 21:37:00,676 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 21:37:00,676 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 21:37:00,787 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 21:37:02,074 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 21:37:03,338 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 21:37:03,338 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 21:37:03,338 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 21:37:03,371 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-16 21:37:03,446 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 21:37:03,561 (asr_inference:494) INFO: speech length: 228000 +2024-01-16 21:37:04,788 (beam_search:428) INFO: decoder input length: 354 +2024-01-16 21:37:04,788 (beam_search:429) INFO: max output length: 354 +2024-01-16 21:37:04,788 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:06,585 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:06,585 (beam_search:476) INFO: -36.17 * 1.0 = -36.17 for ctc +2024-01-16 21:37:06,585 (beam_search:479) INFO: total log probability: -36.17 +2024-01-16 21:37:06,585 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:37:06,585 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:06,586 (beam_search:483) INFO: best hypo: IAETHIRUEOFTEKENAOLDESFRONYOUSHISATHASTALYISIANOWTHEITSIMPOSEABLETHAVEFATHINOUNBASIYNOHEITASYUSESTECPECETANYRETERENFORYOUGFORALIHAVEDONIOWNTNOMOROFYOU + +2024-01-16 21:37:06,611 (asr_inference:494) INFO: speech length: 256160 +2024-01-16 21:37:06,634 (beam_search:428) INFO: decoder input length: 398 +2024-01-16 21:37:06,634 (beam_search:429) INFO: max output length: 398 +2024-01-16 21:37:06,634 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:08,507 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:08,507 (beam_search:476) INFO: -29.09 * 1.0 = -29.09 for ctc +2024-01-16 21:37:08,507 (beam_search:479) INFO: total log probability: -29.09 +2024-01-16 21:37:08,507 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:37:08,507 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:08,508 (beam_search:483) INFO: best hypo: THATHEMAYSOMNTIMESBEELAKCKOUTHERCHELEDRENLERNINGTESIDEMYNEORPLAINGPRATLINDSICINGFORHELPESCOMSTOYHEARTATSSHINFULORDANSPEAKINHOWCGODTHOARTE + +2024-01-16 21:37:08,509 (asr_inference:494) INFO: speech length: 317440 +2024-01-16 21:37:08,539 (beam_search:428) INFO: decoder input length: 493 +2024-01-16 21:37:08,539 (beam_search:429) INFO: max output length: 493 +2024-01-16 21:37:08,539 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:11,698 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:11,698 (beam_search:476) INFO: -43.10 * 1.0 = -43.10 for ctc +2024-01-16 21:37:11,698 (beam_search:479) INFO: total log probability: -43.10 +2024-01-16 21:37:11,698 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:37:11,698 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:11,699 (beam_search:483) INFO: best hypo: RENSENTHINGSOFALSOREASITHEJENRALOUTBURSTOFMOLTEITODNESSPASTIONARHUTEDTOGETHERTHELITURCRISNAYTHEREDDIEKILSWITHEHARABLEFOAREOVERTHEBILYSEOFHEADSMABESEMERASTKGALADYCAPRYALINGONCORSESFOTHEROILSTOID + +2024-01-16 21:37:11,701 (asr_inference:494) INFO: speech length: 226080 +2024-01-16 21:37:11,722 (beam_search:428) INFO: decoder input length: 351 +2024-01-16 21:37:11,722 (beam_search:429) INFO: max output length: 351 +2024-01-16 21:37:11,722 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:13,463 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:13,464 (beam_search:476) INFO: -22.53 * 1.0 = -22.53 for ctc +2024-01-16 21:37:13,464 (beam_search:479) INFO: total log probability: -22.53 +2024-01-16 21:37:13,464 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:37:13,464 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:13,465 (beam_search:483) INFO: best hypo: ITMAYHAVEBENTATTHEBONESWERERFOLDEDTOGETHERANDNONEASONYHEPELYBONSFOLDEDANDLAIDWAYFOTHEPERPSCESOFINCENTATIONSUCHBUNDLESOFBONESWEPUTTHORWEAPROSESSOFPRAIRS + +2024-01-16 21:37:13,466 (asr_inference:494) INFO: speech length: 211200 +2024-01-16 21:37:13,486 (beam_search:428) INFO: decoder input length: 327 +2024-01-16 21:37:13,486 (beam_search:429) INFO: max output length: 327 +2024-01-16 21:37:13,486 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:14,968 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:14,968 (beam_search:476) INFO: -23.95 * 1.0 = -23.95 for ctc +2024-01-16 21:37:14,968 (beam_search:479) INFO: total log probability: -23.95 +2024-01-16 21:37:14,968 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:37:14,968 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:14,969 (beam_search:483) INFO: best hypo: MARSSAILESNEVERECSPERINCEDTHOSGRATTRAENSCITIONSFOMLONESTOGREANDEURTHISWASONINGTOTHEPRODINTCONDUCTTHATREPOBLIKEWHICHALWAYEPRESERVEHEPINCIPLE + +2024-01-16 21:37:14,971 (asr_inference:494) INFO: speech length: 304480 +2024-01-16 21:37:14,998 (beam_search:428) INFO: decoder input length: 473 +2024-01-16 21:37:14,998 (beam_search:429) INFO: max output length: 473 +2024-01-16 21:37:14,998 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:18,341 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:18,341 (beam_search:476) INFO: -50.97 * 1.0 = -50.97 for ctc +2024-01-16 21:37:18,341 (beam_search:479) INFO: total log probability: -50.97 +2024-01-16 21:37:18,341 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:37:18,341 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:18,342 (beam_search:483) INFO: best hypo: ATSMLEREHINGSTIONONMTHEMORINGOFUPTOERTHRTYONCODUTEDITHEAPSFHEROFCOESPERSYANDAITANDEDBYBROADYWERETOLHATTHENORMWLADENTSEREQURIRMETFORREVIUOREPROVLHADBENWAETTHATNORMLEAEPRVLOTHEMAROFWASINGTINNDSRTNGOVENRSWODBEHANDLDINFORMILY + +2024-01-16 21:37:18,344 (asr_inference:494) INFO: speech length: 234080 +2024-01-16 21:37:18,365 (beam_search:428) INFO: decoder input length: 363 +2024-01-16 21:37:18,365 (beam_search:429) INFO: max output length: 363 +2024-01-16 21:37:18,365 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:20,104 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:20,104 (beam_search:476) INFO: -34.62 * 1.0 = -34.62 for ctc +2024-01-16 21:37:20,104 (beam_search:479) INFO: total log probability: -34.62 +2024-01-16 21:37:20,104 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:37:20,104 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:20,105 (beam_search:483) INFO: best hypo: TTHEBOODISTLATDYIGCHINERHOLDONOHASTATETOLTAKLIGFORTHEPERVERSOFODSOFTHERCONCHONCSFOROMGTUCKTOTIBYBYINGVEIRSDFIASHIEISYOUSEADTERANDLETINTHEGOLL + +2024-01-16 21:37:20,107 (asr_inference:494) INFO: speech length: 299360 +2024-01-16 21:37:20,134 (beam_search:428) INFO: decoder input length: 465 +2024-01-16 21:37:20,134 (beam_search:429) INFO: max output length: 465 +2024-01-16 21:37:20,134 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:23,129 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:23,129 (beam_search:476) INFO: -30.03 * 1.0 = -30.03 for ctc +2024-01-16 21:37:23,129 (beam_search:479) INFO: total log probability: -30.03 +2024-01-16 21:37:23,129 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:37:23,129 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:23,131 (beam_search:483) INFO: best hypo: TTHIEAGANISSOFENDANDTUMPERDBYASEMBLEFATHANTHESUPROMIRSYOFLOVEOVERFERANDUNBWNTEDUMANITYANDCHIRIYFORTHEPORANHELPLESANUNCODIONALFORGIVENESOFTHEDIRSTINERESWHICHISTHENOTOFTHENOBLAENEROUSITYANDLIBERALITY + +2024-01-16 21:37:23,132 (asr_inference:494) INFO: speech length: 201120 +2024-01-16 21:37:23,150 (beam_search:428) INFO: decoder input length: 312 +2024-01-16 21:37:23,150 (beam_search:429) INFO: max output length: 312 +2024-01-16 21:37:23,150 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:24,623 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:24,623 (beam_search:476) INFO: -28.10 * 1.0 = -28.10 for ctc +2024-01-16 21:37:24,623 (beam_search:479) INFO: total log probability: -28.10 +2024-01-16 21:37:24,623 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:37:24,623 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:24,624 (beam_search:483) INFO: best hypo: THESECENDMADEFALLOEDANDHECOUPLOTHESEMERSMENBROTHEPBORTHBARKEBEFORCUCHINGAROUPTHEWENTOWARKTSURCHTHSHITELIFEDDEHACHESANDFAOWNDTHEHOLDFULLOFCORGO + +2024-01-16 21:37:24,625 (asr_inference:494) INFO: speech length: 200480 +2024-01-16 21:37:24,643 (beam_search:428) INFO: decoder input length: 311 +2024-01-16 21:37:24,643 (beam_search:429) INFO: max output length: 311 +2024-01-16 21:37:24,644 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:26,029 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:26,029 (beam_search:476) INFO: -22.14 * 1.0 = -22.14 for ctc +2024-01-16 21:37:26,029 (beam_search:479) INFO: total log probability: -22.14 +2024-01-16 21:37:26,029 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:37:26,029 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:26,030 (beam_search:483) INFO: best hypo: FONDOFHISCOMERADEANDRESPETFULEITOHISPASTURANDMASTERSEVENSCHLMUSTERSASHELADHEPREPERSFORANHODWTHWILANDTHISTRAINGOCKUPYEHIMTHUREOUTYOUTHTIID + +# Accounting: time=27 threads=1 +# Ended (code 0) at Tue Jan 16 21:37:26 CST 2024, elapsed time 27 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.2.log b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.2.log new file mode 100644 index 0000000000000000000000000000000000000000..c0117f459a45ddd7de0c9ab1735a516597fbd120 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.2.log @@ -0,0 +1,129 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2 --config conf/decode_asr.yaml +# Started at Tue Jan 16 21:37:26 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2 --config conf/decode_asr.yaml +2024-01-16 21:37:27,847 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml +2024-01-16 21:37:27,865 (asr:523) INFO: Vocabulary size: 30 +2024-01-16 21:37:27,928 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 21:37:27,928 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 21:37:28,037 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 21:37:29,324 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 21:37:30,563 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 21:37:30,563 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 21:37:30,563 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 21:37:30,596 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-16 21:37:30,671 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 21:37:30,787 (asr_inference:494) INFO: speech length: 167040 +2024-01-16 21:37:31,997 (beam_search:428) INFO: decoder input length: 258 +2024-01-16 21:37:31,997 (beam_search:429) INFO: max output length: 258 +2024-01-16 21:37:31,997 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:32,799 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:32,799 (beam_search:476) INFO: -19.51 * 1.0 = -19.51 for ctc +2024-01-16 21:37:32,799 (beam_search:479) INFO: total log probability: -19.51 +2024-01-16 21:37:32,799 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:37:32,799 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:32,800 (beam_search:483) INFO: best hypo: ASSWHETHEPRENESQUPECEAERECHETERSIXTINTYEARSHBECAMEBLINGDWHERLARGHSOFBONICESHADOLIDINGTHM + +2024-01-16 21:37:32,824 (asr_inference:494) INFO: speech length: 249280 +2024-01-16 21:37:32,848 (beam_search:428) INFO: decoder input length: 387 +2024-01-16 21:37:32,848 (beam_search:429) INFO: max output length: 387 +2024-01-16 21:37:32,848 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:35,053 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:35,053 (beam_search:476) INFO: -35.32 * 1.0 = -35.32 for ctc +2024-01-16 21:37:35,053 (beam_search:479) INFO: total log probability: -35.32 +2024-01-16 21:37:35,053 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:37:35,053 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:35,054 (beam_search:483) INFO: best hypo: AOLMOSTALLEDYTHEBATERANGEDBUTCETHTWOMENBAKINFORTTHEYFOURSYECHEOTHEROVERTHELAVABEDSTHESHIVFSWELOIEBOYWASVERYDIFICALTFORHEOALOHAYTOGRASPBROSEANDBLEADINGFROMREPETEDFLLESONTHEROUFHLAVA + +2024-01-16 21:37:35,056 (asr_inference:494) INFO: speech length: 199840 +2024-01-16 21:37:35,074 (beam_search:428) INFO: decoder input length: 310 +2024-01-16 21:37:35,074 (beam_search:429) INFO: max output length: 310 +2024-01-16 21:37:35,074 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:36,174 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:36,174 (beam_search:476) INFO: -17.31 * 1.0 = -17.31 for ctc +2024-01-16 21:37:36,174 (beam_search:479) INFO: total log probability: -17.31 +2024-01-16 21:37:36,174 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:37:36,174 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:36,174 (beam_search:483) INFO: best hypo: BPOSYIYTREBITTERRARANDAWLALPANTINGTHEWHINMONDTTHROGTHETREESOFTHECARDINANDFOMETIMETOTIMEMSEEASIFTOA + +2024-01-16 21:37:36,176 (asr_inference:494) INFO: speech length: 175680 +2024-01-16 21:37:36,192 (beam_search:428) INFO: decoder input length: 272 +2024-01-16 21:37:36,192 (beam_search:429) INFO: max output length: 272 +2024-01-16 21:37:36,192 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:37,230 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:37,230 (beam_search:476) INFO: -23.91 * 1.0 = -23.91 for ctc +2024-01-16 21:37:37,230 (beam_search:479) INFO: total log probability: -23.91 +2024-01-16 21:37:37,230 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:37:37,230 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:37,231 (beam_search:483) INFO: best hypo: HISMENTLTORPTITYFOUNDETAPONFISIOCLINDLEANCERENDERSAMEDIATACTIONANALMAENEROESRTIONDDUSTEASFULHISCONCHUSWEKNESSHOSITDSELF + +2024-01-16 21:37:37,232 (asr_inference:494) INFO: speech length: 256480 +2024-01-16 21:37:37,255 (beam_search:428) INFO: decoder input length: 398 +2024-01-16 21:37:37,255 (beam_search:429) INFO: max output length: 398 +2024-01-16 21:37:37,255 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:39,284 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:39,284 (beam_search:476) INFO: -36.75 * 1.0 = -36.75 for ctc +2024-01-16 21:37:39,284 (beam_search:479) INFO: total log probability: -36.75 +2024-01-16 21:37:39,284 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:37:39,284 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:39,285 (beam_search:483) INFO: best hypo: TNORETHAELEHOWGLADTHECINGMTHEWASNORHOUEGRATDWOERTHEREYGJUORISINGOFTHEPEPLEORHOMMENIFESSENDTDWASTHEROILBANKQCEDTHATGOODCFINGPOMEREARADTENDETDWHITHALHERCOREN + +2024-01-16 21:37:39,287 (asr_inference:494) INFO: speech length: 238400 +2024-01-16 21:37:39,308 (beam_search:428) INFO: decoder input length: 370 +2024-01-16 21:37:39,308 (beam_search:429) INFO: max output length: 370 +2024-01-16 21:37:39,308 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:41,101 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:41,101 (beam_search:476) INFO: -34.31 * 1.0 = -34.31 for ctc +2024-01-16 21:37:41,102 (beam_search:479) INFO: total log probability: -34.31 +2024-01-16 21:37:41,102 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:37:41,102 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:41,102 (beam_search:483) INFO: best hypo: ANDTHECHONCEOFTHERBENGSUCHAONUNGIANDIMISHESBYEWERYPRAPEPROSESSMAMELUKASAEORSWHOSOFEAKCUEANCQULITYEREASNINGNOTABOWTDHISODERSBUTBOULTHEIYTODOTHET + +2024-01-16 21:37:41,104 (asr_inference:494) INFO: speech length: 295040 +2024-01-16 21:37:41,131 (beam_search:428) INFO: decoder input length: 458 +2024-01-16 21:37:41,131 (beam_search:429) INFO: max output length: 458 +2024-01-16 21:37:41,131 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:44,012 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:44,012 (beam_search:476) INFO: -44.74 * 1.0 = -44.74 for ctc +2024-01-16 21:37:44,012 (beam_search:479) INFO: total log probability: -44.74 +2024-01-16 21:37:44,012 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:37:44,012 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:44,013 (beam_search:483) INFO: best hypo: TSHENOKETBUTSNOROUPLOEOATHOFTEWHENDOANSAIDIDEAERNOTOBPBHEDORFORTHEDORSHAVETODMETOLTNOOININTHATISHAREDFORMEESAIDTHEOMEONFORIMUSTTAKBACEMYAUPLSBUTTHERISONEWICHIULLGIVEYOUANDSHEHELDUPENAL + +2024-01-16 21:37:44,015 (asr_inference:494) INFO: speech length: 185440 +2024-01-16 21:37:44,032 (beam_search:428) INFO: decoder input length: 287 +2024-01-16 21:37:44,033 (beam_search:429) INFO: max output length: 287 +2024-01-16 21:37:44,033 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:45,142 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:45,142 (beam_search:476) INFO: -28.76 * 1.0 = -28.76 for ctc +2024-01-16 21:37:45,142 (beam_search:479) INFO: total log probability: -28.76 +2024-01-16 21:37:45,142 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:37:45,142 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:45,142 (beam_search:483) INFO: best hypo: OVYMORYSPOKAKCELTHORANDHERSEFINLYSPOKENIHTTWOMYPESWASWHYFEABEYARYOCMSOSONWEREOURBRYCHILDNDOTHANARTDLSPOKSAMEOAN + +2024-01-16 21:37:45,144 (asr_inference:494) INFO: speech length: 234400 +2024-01-16 21:37:45,165 (beam_search:428) INFO: decoder input length: 364 +2024-01-16 21:37:45,165 (beam_search:429) INFO: max output length: 364 +2024-01-16 21:37:45,165 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:47,143 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:47,143 (beam_search:476) INFO: -52.93 * 1.0 = -52.93 for ctc +2024-01-16 21:37:47,143 (beam_search:479) INFO: total log probability: -52.93 +2024-01-16 21:37:47,143 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:37:47,143 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:47,144 (beam_search:483) INFO: best hypo: INEVERUANYWOENHOLETDOGOTYCERCHISMCHACSGRAMOHDOUSSHEHAISHEWOUDRAVEBADORECEPEITHHASOFRGODTHENIWELLTHETENSOFWIKEDNESTEDONHVEWOMENDOREKEPERSANIWENOSHEWINUTTWELLMININAATENT + +2024-01-16 21:37:47,146 (asr_inference:494) INFO: speech length: 163200 +2024-01-16 21:37:47,162 (beam_search:428) INFO: decoder input length: 252 +2024-01-16 21:37:47,162 (beam_search:429) INFO: max output length: 252 +2024-01-16 21:37:47,162 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:48,026 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:48,026 (beam_search:476) INFO: -25.96 * 1.0 = -25.96 for ctc +2024-01-16 21:37:48,026 (beam_search:479) INFO: total log probability: -25.96 +2024-01-16 21:37:48,026 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:37:48,026 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:48,027 (beam_search:483) INFO: best hypo: THEDUKWASSUPRICSTOSEHMOTBRINGOOTSOALYABELARDDEMANDDHEOLYOURGRAICTEDREPLIHEUTLRYGASPINGFORUTERANCES + +# Accounting: time=22 threads=1 +# Ended (code 0) at Tue Jan 16 21:37:48 CST 2024, elapsed time 22 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.3.log b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.3.log new file mode 100644 index 0000000000000000000000000000000000000000..9674100bcb2d04c5c966a7ed03b40997a043cdea --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.3.log @@ -0,0 +1,129 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3 --config conf/decode_asr.yaml +# Started at Tue Jan 16 21:37:48 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3 --config conf/decode_asr.yaml +2024-01-16 21:37:49,846 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml +2024-01-16 21:37:49,864 (asr:523) INFO: Vocabulary size: 30 +2024-01-16 21:37:49,926 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 21:37:49,926 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 21:37:50,036 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 21:37:51,322 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 21:37:52,526 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 21:37:52,526 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 21:37:52,526 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 21:37:52,559 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-16 21:37:52,634 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 21:37:52,753 (asr_inference:494) INFO: speech length: 236480 +2024-01-16 21:37:53,953 (beam_search:428) INFO: decoder input length: 367 +2024-01-16 21:37:53,954 (beam_search:429) INFO: max output length: 367 +2024-01-16 21:37:53,954 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:55,829 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:55,829 (beam_search:476) INFO: -35.79 * 1.0 = -35.79 for ctc +2024-01-16 21:37:55,829 (beam_search:479) INFO: total log probability: -35.79 +2024-01-16 21:37:55,829 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:37:55,829 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:55,830 (beam_search:483) INFO: best hypo: FOREIUATSEMINGTRONSENMEUTATIONSOFCALEMABEMADWETHERISNYMICXTROFDEVORSOUTSORAESBRINSACHMICTERSTHECOMPONELISAPERENOTBYTHEMUTILALAINGAHETHECONCTUTEAMIDLINGCOLER + +2024-01-16 21:37:55,854 (asr_inference:494) INFO: speech length: 273120 +2024-01-16 21:37:55,880 (beam_search:428) INFO: decoder input length: 424 +2024-01-16 21:37:55,880 (beam_search:429) INFO: max output length: 424 +2024-01-16 21:37:55,880 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:37:58,525 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:37:58,525 (beam_search:476) INFO: -43.51 * 1.0 = -43.51 for ctc +2024-01-16 21:37:58,525 (beam_search:479) INFO: total log probability: -43.51 +2024-01-16 21:37:58,525 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:37:58,526 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:37:58,527 (beam_search:483) INFO: best hypo: ANALMOSTHESAMEINSTANTCENSRSANDYSIDSTEPINTOASHOPDORWAYHEWADEDTHEINTIKCHINCAMEUPCINSTDAUPEANDRETENDETOSTARTHTHEGLASATHDESPLAYOFHERDWERINGTUOULESWERHECONTINEDTOWOUCHBAIRICKOUSEWATISHAINBYSAIDRINNOTED + +2024-01-16 21:37:58,529 (asr_inference:494) INFO: speech length: 292800 +2024-01-16 21:37:58,555 (beam_search:428) INFO: decoder input length: 455 +2024-01-16 21:37:58,555 (beam_search:429) INFO: max output length: 455 +2024-01-16 21:37:58,555 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:01,127 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:01,127 (beam_search:476) INFO: -42.03 * 1.0 = -42.03 for ctc +2024-01-16 21:38:01,127 (beam_search:479) INFO: total log probability: -42.03 +2024-01-16 21:38:01,127 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:38:01,127 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:01,128 (beam_search:483) INFO: best hypo: TTHENTHETHIEKGRENESTAVFLOLEDOLRTHEHLBELDINANDTHEWHASNOTHINGTOESSEENTHERBUTDEMAOUDOFSOFETDFLOINGGRAYGRENSTARTHEDRUSHEDONNOWHWTHESWEIFTNESOLLWINGKEALOOETABDINTOBERSFAE + +2024-01-16 21:38:01,130 (asr_inference:494) INFO: speech length: 301760 +2024-01-16 21:38:01,157 (beam_search:428) INFO: decoder input length: 469 +2024-01-16 21:38:01,157 (beam_search:429) INFO: max output length: 469 +2024-01-16 21:38:01,157 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:03,999 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:03,999 (beam_search:476) INFO: -45.55 * 1.0 = -45.55 for ctc +2024-01-16 21:38:03,999 (beam_search:479) INFO: total log probability: -45.55 +2024-01-16 21:38:03,999 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:38:03,999 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:04,001 (beam_search:483) INFO: best hypo: IHAVEMAESURCKROFISESTOEBUROSWHENIANEUTHATHEWRENOTMYHAPENESSWASAFTERISSAOLTHATHADSTEPEDTAGHERISOARTHATYOURTENDERNESADTERNDTOCOULDILATIONAOFTERSAORTHATYOUCEIRDFORYOURSELFONLYNOTFORME + +2024-01-16 21:38:04,002 (asr_inference:494) INFO: speech length: 249120 +2024-01-16 21:38:04,025 (beam_search:428) INFO: decoder input length: 387 +2024-01-16 21:38:04,025 (beam_search:429) INFO: max output length: 387 +2024-01-16 21:38:04,025 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:05,835 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:05,836 (beam_search:476) INFO: -23.18 * 1.0 = -23.18 for ctc +2024-01-16 21:38:05,836 (beam_search:479) INFO: total log probability: -23.18 +2024-01-16 21:38:05,836 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:38:05,836 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:05,836 (beam_search:483) INFO: best hypo: YATDTHATHANDTERNEVERRALSINWINTERISEYACROALWARRINGROUNEDANDROUNMEBEFOREITALIHTETTHESENNOTHINGUNDETHEFIREDTREESBUTILNOSOMETHNGMUSBETHAR + +2024-01-16 21:38:05,838 (asr_inference:494) INFO: speech length: 268480 +2024-01-16 21:38:05,863 (beam_search:428) INFO: decoder input length: 417 +2024-01-16 21:38:05,863 (beam_search:429) INFO: max output length: 417 +2024-01-16 21:38:05,863 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:07,986 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:07,986 (beam_search:476) INFO: -32.70 * 1.0 = -32.70 for ctc +2024-01-16 21:38:07,986 (beam_search:479) INFO: total log probability: -32.70 +2024-01-16 21:38:07,986 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:38:07,986 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:07,987 (beam_search:483) INFO: best hypo: EIYISBEYONTHOUTTHATSOMEPEBLEHAEMANISETOSAFMANYTHINGSANOFCOURSESTHEJIRMEANTHASASOMAIISEASMAUTHTOWOUORETHREDASGEAFEHEFIRSIPRECOUCITIONSDEDROUPETININOWARS + +2024-01-16 21:38:07,988 (asr_inference:494) INFO: speech length: 255840 +2024-01-16 21:38:08,011 (beam_search:428) INFO: decoder input length: 397 +2024-01-16 21:38:08,011 (beam_search:429) INFO: max output length: 397 +2024-01-16 21:38:08,011 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:10,244 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:10,245 (beam_search:476) INFO: -34.80 * 1.0 = -34.80 for ctc +2024-01-16 21:38:10,245 (beam_search:479) INFO: total log probability: -34.80 +2024-01-16 21:38:10,245 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:38:10,245 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:10,246 (beam_search:483) INFO: best hypo: SONAMANCAMEOUTTOMEHIMTHISMANWASOALOHAYABERDLESSMANDBELNINGTOALESROVBURCLANHIHWICHINFESTEDTHEDSTRCTBPUSTIBLYASISTINGTHEANUNTERSOFHETEMPLINSCIRINGVICTUMSFORTHETEMBLELLTERS + +2024-01-16 21:38:10,247 (asr_inference:494) INFO: speech length: 166400 +2024-01-16 21:38:10,264 (beam_search:428) INFO: decoder input length: 257 +2024-01-16 21:38:10,264 (beam_search:429) INFO: max output length: 257 +2024-01-16 21:38:10,264 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:11,040 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:11,040 (beam_search:476) INFO: -21.28 * 1.0 = -21.28 for ctc +2024-01-16 21:38:11,040 (beam_search:479) INFO: total log probability: -21.28 +2024-01-16 21:38:11,040 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:38:11,040 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:11,040 (beam_search:483) INFO: best hypo: WEARENOTLOVEIRSYOUANIYHAPONTHISSUNYLANADTBUTCHOLLDEONWHEAVENEVERNONTLOVFSJIORYOURPE + +2024-01-16 21:38:11,042 (asr_inference:494) INFO: speech length: 283040 +2024-01-16 21:38:11,068 (beam_search:428) INFO: decoder input length: 440 +2024-01-16 21:38:11,068 (beam_search:429) INFO: max output length: 440 +2024-01-16 21:38:11,068 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:13,471 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:13,471 (beam_search:476) INFO: -39.19 * 1.0 = -39.19 for ctc +2024-01-16 21:38:13,471 (beam_search:479) INFO: total log probability: -39.19 +2024-01-16 21:38:13,471 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:38:13,471 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:13,472 (beam_search:483) INFO: best hypo: EWASMURDEDONHESORSTEVFWISOALHOSESWASIANOLMANASTANDTRACAMLYMISEREQOUODIARYOUTOKASIFEHADDIGEINHISBEATDYOHENOVEONITIONADYUCANOUNDESTAEWDINMINSHNANINGUICITYISMRDET + +2024-01-16 21:38:13,474 (asr_inference:494) INFO: speech length: 284480 +2024-01-16 21:38:13,500 (beam_search:428) INFO: decoder input length: 442 +2024-01-16 21:38:13,500 (beam_search:429) INFO: max output length: 442 +2024-01-16 21:38:13,500 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:16,139 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:16,139 (beam_search:476) INFO: -33.26 * 1.0 = -33.26 for ctc +2024-01-16 21:38:16,139 (beam_search:479) INFO: total log probability: -33.26 +2024-01-16 21:38:16,139 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:38:16,139 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:16,140 (beam_search:483) INFO: best hypo: BYGORDHISAEDTHEWONDMEHERBISNSTTHIDSMEINANOUTOMNYHOUSESOEHATDOIYCARFORTHESICRSTTHATMAVEHADNTHARHOWWEVERIUCUNOTPLINGWISPEBEFORTHEWOUCHFLNESTHEBLAUTHOUESOFHISFMNORIAREAINEVFERYSTREEPT + +# Accounting: time=28 threads=1 +# Ended (code 0) at Tue Jan 16 21:38:16 CST 2024, elapsed time 28 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.4.log b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.4.log new file mode 100644 index 0000000000000000000000000000000000000000..bff8fbe0f2d34e8f7e241b8f604b5c51d099c40e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.4.log @@ -0,0 +1,129 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4 --config conf/decode_asr.yaml +# Started at Tue Jan 16 21:38:16 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4 --config conf/decode_asr.yaml +2024-01-16 21:38:17,953 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml +2024-01-16 21:38:17,972 (asr:523) INFO: Vocabulary size: 30 +2024-01-16 21:38:18,034 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 21:38:18,034 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 21:38:18,144 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 21:38:19,437 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 21:38:20,665 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 21:38:20,665 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 21:38:20,665 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 21:38:20,697 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-16 21:38:20,773 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 21:38:20,887 (asr_inference:494) INFO: speech length: 216320 +2024-01-16 21:38:22,107 (beam_search:428) INFO: decoder input length: 335 +2024-01-16 21:38:22,107 (beam_search:429) INFO: max output length: 335 +2024-01-16 21:38:22,107 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:23,809 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:23,809 (beam_search:476) INFO: -43.58 * 1.0 = -43.58 for ctc +2024-01-16 21:38:23,809 (beam_search:479) INFO: total log probability: -43.58 +2024-01-16 21:38:23,809 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:38:23,809 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:23,810 (beam_search:483) INFO: best hypo: UOYABLETWASPULINGITNOASTIKNORASTONWASINDREGTOFHEHANDANDTHEBIYLIGRAGSEQULDONNOLONGSREABELTHERUWAROFWARTHEFOLEDSHESTRUOEDBUONOUEANDGASETHEGONBUTONLYVELEDHERSEGNCASTO + +2024-01-16 21:38:23,834 (asr_inference:494) INFO: speech length: 303680 +2024-01-16 21:38:23,862 (beam_search:428) INFO: decoder input length: 472 +2024-01-16 21:38:23,862 (beam_search:429) INFO: max output length: 472 +2024-01-16 21:38:23,862 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:26,514 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:26,514 (beam_search:476) INFO: -29.23 * 1.0 = -29.23 for ctc +2024-01-16 21:38:26,514 (beam_search:479) INFO: total log probability: -29.23 +2024-01-16 21:38:26,514 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:38:26,514 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:26,515 (beam_search:483) INFO: best hypo: ANDAFALYOFEITWASTHATANNOTHEWOMENDTOVESARNYWRICHEFORNOTHINGBETEDENTOGOSINTOMYSISTERANDFARTHEUSCSENTWAYSHESAIDISOULRATHERGOWITHIMIHAVENOMIETOSTAYHEARALONITDMYTWOEBABES + +2024-01-16 21:38:26,517 (asr_inference:494) INFO: speech length: 185120 +2024-01-16 21:38:26,535 (beam_search:428) INFO: decoder input length: 287 +2024-01-16 21:38:26,535 (beam_search:429) INFO: max output length: 287 +2024-01-16 21:38:26,535 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:27,549 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:27,549 (beam_search:476) INFO: -22.33 * 1.0 = -22.33 for ctc +2024-01-16 21:38:27,549 (beam_search:479) INFO: total log probability: -22.33 +2024-01-16 21:38:27,549 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:38:27,549 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:27,549 (beam_search:483) INFO: best hypo: INIDITACORDINGLYWHENTDRINGHATELITEMANWOLBEATANDHEPEAEDTOFTHESTOUTISYRUSHISHKCUFINEDWEITDELITALBUNEOFB + +2024-01-16 21:38:27,551 (asr_inference:494) INFO: speech length: 259520 +2024-01-16 21:38:27,574 (beam_search:428) INFO: decoder input length: 403 +2024-01-16 21:38:27,574 (beam_search:429) INFO: max output length: 403 +2024-01-16 21:38:27,574 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:29,830 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:29,830 (beam_search:476) INFO: -29.14 * 1.0 = -29.14 for ctc +2024-01-16 21:38:29,830 (beam_search:479) INFO: total log probability: -29.14 +2024-01-16 21:38:29,830 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:38:29,830 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:29,832 (beam_search:483) INFO: best hypo: ITISNOTHEDREDIULNATCMSONHOEDESMEASTHEPLENWERTHEPUSIONSATHEINGLSHFONETEBOFTENTHOUSENSLEAINBREVEWELINGTOAMBLOKERBORTHMOSNOBLYDROVETHERFOURSANDBONOPARTEAMPEYARCRONWASTEKENATWOTEL + +2024-01-16 21:38:29,833 (asr_inference:494) INFO: speech length: 259200 +2024-01-16 21:38:29,856 (beam_search:428) INFO: decoder input length: 402 +2024-01-16 21:38:29,857 (beam_search:429) INFO: max output length: 402 +2024-01-16 21:38:29,857 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:32,113 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:32,113 (beam_search:476) INFO: -38.59 * 1.0 = -38.59 for ctc +2024-01-16 21:38:32,113 (beam_search:479) INFO: total log probability: -38.59 +2024-01-16 21:38:32,113 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:38:32,113 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:32,115 (beam_search:483) INFO: best hypo: SSOMEYUOUSAGLLEHAFETEMAKINGOREARINGENSFOTHEINGCAMPENTATDNIHTWITCONSTANTLYHADOVEADPESULRESROKONBYATRIBOFBROUNMUNKESTHEEIDINTYTHOUGHTTHATLONGPOSESTIONHADGIMENTHEAPRIARELAMTOTHEGLL + +2024-01-16 21:38:32,116 (asr_inference:494) INFO: speech length: 294240 +2024-01-16 21:38:32,143 (beam_search:428) INFO: decoder input length: 457 +2024-01-16 21:38:32,143 (beam_search:429) INFO: max output length: 457 +2024-01-16 21:38:32,143 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:34,733 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:34,733 (beam_search:476) INFO: -37.21 * 1.0 = -37.21 for ctc +2024-01-16 21:38:34,733 (beam_search:479) INFO: total log probability: -37.21 +2024-01-16 21:38:34,733 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:38:34,733 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:34,734 (beam_search:483) INFO: best hypo: TOFLASHINHATOMECPLEMIDINGFORHEMEADETWEENRSSWANESTENSPARTYANDTHEOPREIFONLYFIRMINTSERTOLYETWASMORTHANAMINITTHATSMONDREMANEDATHEFIERHOUSESAOUFTERBENGGROGDBAKEOFTEREDINERINTETACK + +2024-01-16 21:38:34,736 (asr_inference:494) INFO: speech length: 303680 +2024-01-16 21:38:34,763 (beam_search:428) INFO: decoder input length: 472 +2024-01-16 21:38:34,763 (beam_search:429) INFO: max output length: 472 +2024-01-16 21:38:34,763 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:37,969 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:37,970 (beam_search:476) INFO: -54.51 * 1.0 = -54.51 for ctc +2024-01-16 21:38:37,970 (beam_search:479) INFO: total log probability: -54.51 +2024-01-16 21:38:37,970 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:38:37,970 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:37,971 (beam_search:483) INFO: best hypo: ANDINDICTIONARYANDENWEHADECOUSTENINCXSWECOLETHRAGEAVMANYFIGERSINCINGEALIFEMIOUTIONWAVEWHATFERYLIKHMUSICKSTILSOVETHESELIHELYROELIATLYROEORTHEGLASYWAYWUDGOLANDOKOMCOMEWAYANDOTHERSONGSMISISJUGHETHALERBOTONDSONGUNPERPSFORAUS + +2024-01-16 21:38:37,973 (asr_inference:494) INFO: speech length: 272480 +2024-01-16 21:38:37,998 (beam_search:428) INFO: decoder input length: 423 +2024-01-16 21:38:37,998 (beam_search:429) INFO: max output length: 423 +2024-01-16 21:38:37,998 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:40,576 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:40,576 (beam_search:476) INFO: -48.11 * 1.0 = -48.11 for ctc +2024-01-16 21:38:40,576 (beam_search:479) INFO: total log probability: -48.11 +2024-01-16 21:38:40,576 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:38:40,576 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:40,578 (beam_search:483) INFO: best hypo: THATWHICHBASHESOFHISTRYIGURSCHLLSAERGOVERMENTLYFABROCATEDBPSOMENHISTRYISFIRGURYAMRSREPRESIENTATIONOFHEVENCESLICETHELDROAWMARSENTERINGUPONTHEMPOSEABLEFIGIROFTHEHEAROWITHEJESTICILATINGCROUDINTEBAROUNED + +2024-01-16 21:38:40,579 (asr_inference:494) INFO: speech length: 233920 +2024-01-16 21:38:40,600 (beam_search:428) INFO: decoder input length: 363 +2024-01-16 21:38:40,600 (beam_search:429) INFO: max output length: 363 +2024-01-16 21:38:40,600 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:42,251 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:42,251 (beam_search:476) INFO: -26.31 * 1.0 = -26.31 for ctc +2024-01-16 21:38:42,251 (beam_search:479) INFO: total log probability: -26.31 +2024-01-16 21:38:42,251 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:38:42,251 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:42,252 (beam_search:483) INFO: best hypo: ANHEKELIVFHRDTHISHESAIDOALLOFOUREHOWGOODLYISTHATFOYSCEANDTHEESIEREBPLAIDAOLEOURLORDNEVERSMORTMYHAINGORTTRETEREOREGODLYEARETHANTHESANGING + +2024-01-16 21:38:42,253 (asr_inference:494) INFO: speech length: 305760 +2024-01-16 21:38:42,280 (beam_search:428) INFO: decoder input length: 475 +2024-01-16 21:38:42,280 (beam_search:429) INFO: max output length: 475 +2024-01-16 21:38:42,281 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:45,126 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:45,126 (beam_search:476) INFO: -41.37 * 1.0 = -41.37 for ctc +2024-01-16 21:38:45,126 (beam_search:479) INFO: total log probability: -41.37 +2024-01-16 21:38:45,126 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:38:45,126 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:45,127 (beam_search:483) INFO: best hypo: EVIDENTELYTHELARNEDBAIRANHADNOTSTDADYEDSOUCHWORKSOTHETOTEACOAHINIYIOREPIRITCATWHICHNOTIBLYTRANSLATEDBYNUGHSHABEYFRMETHESANSESGRITSIOKASEPPTUTYHASNOBOCOMEASORTHEDOCICLYMOSLAMEASTHENIGTES + +# Accounting: time=29 threads=1 +# Ended (code 0) at Tue Jan 16 21:38:45 CST 2024, elapsed time 29 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/calculate_rtf.log b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/calculate_rtf.log new file mode 100644 index 0000000000000000000000000000000000000000..61c61938478a82103ca8e7738c948c26f35d092a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/calculate_rtf.log @@ -0,0 +1,9 @@ +# pyscripts/utils/calculate_rtf.py --log-dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir --log-name asr_inference --input-shift 0.0625 --start-times-marker "speech length" --end-times-marker "best hypo" --inf-num 1 +# Started at Tue Jan 16 21:38:45 CST 2024 +# +Total audio duration: 618.040 [sec] +Total decoding time: 87.187 [sec] +RTF: 0.141 +Latency: 2179.675 [ms/sentence] +# Accounting: time=0 threads=1 +# Ended (code 0) at Tue Jan 16 21:38:45 CST 2024, elapsed time 0 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.1.scp b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.1.scp new file mode 100644 index 0000000000000000000000000000000000000000..3461e51141d223181d58a9832ae835adb929dce5 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.1.scp @@ -0,0 +1,10 @@ +mls_eng_000243 dump/raw/org/dev_10min_eng1/data/format.1/mls_eng_000243.flac +mls_eng_000244 dump/raw/org/dev_10min_eng1/data/format.1/mls_eng_000244.flac +mls_eng_000245 dump/raw/org/dev_10min_eng1/data/format.2/mls_eng_000245.flac +mls_eng_000246 dump/raw/org/dev_10min_eng1/data/format.2/mls_eng_000246.flac +mls_eng_000247 dump/raw/org/dev_10min_eng1/data/format.3/mls_eng_000247.flac +mls_eng_000248 dump/raw/org/dev_10min_eng1/data/format.3/mls_eng_000248.flac +mls_eng_000249 dump/raw/org/dev_10min_eng1/data/format.4/mls_eng_000249.flac +mls_eng_000250 dump/raw/org/dev_10min_eng1/data/format.4/mls_eng_000250.flac +mls_eng_000251 dump/raw/org/dev_10min_eng1/data/format.5/mls_eng_000251.flac +mls_eng_000252 dump/raw/org/dev_10min_eng1/data/format.5/mls_eng_000252.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.2.scp b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.2.scp new file mode 100644 index 0000000000000000000000000000000000000000..57dfd47f98c07f1c55c2f2d31b815483d6aeadef --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.2.scp @@ -0,0 +1,10 @@ +mls_eng_000253 dump/raw/org/dev_10min_eng1/data/format.6/mls_eng_000253.flac +mls_eng_000254 dump/raw/org/dev_10min_eng1/data/format.6/mls_eng_000254.flac +mls_eng_000255 dump/raw/org/dev_10min_eng1/data/format.7/mls_eng_000255.flac +mls_eng_000256 dump/raw/org/dev_10min_eng1/data/format.7/mls_eng_000256.flac +mls_eng_000257 dump/raw/org/dev_10min_eng1/data/format.8/mls_eng_000257.flac +mls_eng_000258 dump/raw/org/dev_10min_eng1/data/format.8/mls_eng_000258.flac +mls_eng_000259 dump/raw/org/dev_10min_eng1/data/format.9/mls_eng_000259.flac +mls_eng_000260 dump/raw/org/dev_10min_eng1/data/format.10/mls_eng_000260.flac +mls_eng_000261 dump/raw/org/dev_10min_eng1/data/format.11/mls_eng_000261.flac +mls_eng_000262 dump/raw/org/dev_10min_eng1/data/format.12/mls_eng_000262.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.3.scp b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.3.scp new file mode 100644 index 0000000000000000000000000000000000000000..1a36277b8bd4955924328f1ea89a248c0fa35056 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.3.scp @@ -0,0 +1,10 @@ +mls_eng_000263 dump/raw/org/dev_10min_eng1/data/format.13/mls_eng_000263.flac +mls_eng_000264 dump/raw/org/dev_10min_eng1/data/format.14/mls_eng_000264.flac +mls_eng_000265 dump/raw/org/dev_10min_eng1/data/format.15/mls_eng_000265.flac +mls_eng_000266 dump/raw/org/dev_10min_eng1/data/format.16/mls_eng_000266.flac +mls_eng_000267 dump/raw/org/dev_10min_eng1/data/format.17/mls_eng_000267.flac +mls_eng_000268 dump/raw/org/dev_10min_eng1/data/format.18/mls_eng_000268.flac +mls_eng_000269 dump/raw/org/dev_10min_eng1/data/format.19/mls_eng_000269.flac +mls_eng_000270 dump/raw/org/dev_10min_eng1/data/format.20/mls_eng_000270.flac +mls_eng_000271 dump/raw/org/dev_10min_eng1/data/format.21/mls_eng_000271.flac +mls_eng_000272 dump/raw/org/dev_10min_eng1/data/format.22/mls_eng_000272.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.4.scp b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.4.scp new file mode 100644 index 0000000000000000000000000000000000000000..cbd32313e1d13010a429ca6206e3d80b77030b6b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.4.scp @@ -0,0 +1,10 @@ +mls_eng_000273 dump/raw/org/dev_10min_eng1/data/format.23/mls_eng_000273.flac +mls_eng_000274 dump/raw/org/dev_10min_eng1/data/format.24/mls_eng_000274.flac +mls_eng_000275 dump/raw/org/dev_10min_eng1/data/format.25/mls_eng_000275.flac +mls_eng_000276 dump/raw/org/dev_10min_eng1/data/format.26/mls_eng_000276.flac +mls_eng_000277 dump/raw/org/dev_10min_eng1/data/format.27/mls_eng_000277.flac +mls_eng_000278 dump/raw/org/dev_10min_eng1/data/format.28/mls_eng_000278.flac +mls_eng_000279 dump/raw/org/dev_10min_eng1/data/format.29/mls_eng_000279.flac +mls_eng_000280 dump/raw/org/dev_10min_eng1/data/format.30/mls_eng_000280.flac +mls_eng_000281 dump/raw/org/dev_10min_eng1/data/format.31/mls_eng_000281.flac +mls_eng_000282 dump/raw/org/dev_10min_eng1/data/format.32/mls_eng_000282.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..8879dd9b677d323a262f87a8c92448aef1eb5859 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/score @@ -0,0 +1,10 @@ +mls_eng_000243 tensor(-36.1665) +mls_eng_000244 tensor(-29.0905) +mls_eng_000245 tensor(-43.1050) +mls_eng_000246 tensor(-22.5313) +mls_eng_000247 tensor(-23.9541) +mls_eng_000248 tensor(-50.9715) +mls_eng_000249 tensor(-34.6205) +mls_eng_000250 tensor(-30.0281) +mls_eng_000251 tensor(-28.0966) +mls_eng_000252 tensor(-22.1428) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..8c082cd9eb7bded922545a2205507ccaa6188083 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/text @@ -0,0 +1,10 @@ +mls_eng_000243 I AETHIR UE OF TEKEN AOLDES FRON YOU SHI SAT HASTALY ISIA NOW THE IT S IMPOSEABLE T HAVE FATHIN OUN BASIY NO HEIT AS YUSEST ECPECET ANY RETEREN FOR YOUG FORAL I HAVE DON IOWNT NO MOR OF YOU +mls_eng_000244 THAT HE MAY SOMNTIMES BE E LAKCK OUTHER CHELEDREN LERNING TE SIDE MY NE OR PLAING PRATLIND SICING FOR HELPES COMSTO Y HEART ATS SHINFULORD AN SPEAKIN HOW CGOD THO ARTE +mls_eng_000245 REN SEN THINGS OF AL SORE AS I THE JENRAL OUTBURST OF MOLTEITODNESS PASTION AR HUTED TO GETHER THE LITURCRIS NAY THE RED DIEKILS WI THE HARABLE FOARE OVER THE BILY SE OF HEADS MA BE SEME RASTKGALADY CAPRYALING ON CORSES FO THE ROIL STOID +mls_eng_000246 IT MAY HAVE BEN TAT THE BONES WERER FOLDEDTOGETHER AND NONE AS ONY HE PELY BONS FOLDED AND LAID WAY FO THE PERPSCES OF INCENTATION SUCH BUNDLES OF BONES WE PUT THORWE A PROSESS OF PRAIRS +mls_eng_000247 MARS SAILES NEVER ECSPERINCED THOS GRAT TRAENSCITIONS FOM LONES TO GREANDEUR THIS WAS ONING TO THE PRODINT CONDUCT THAT RE POBLIKE WHICH ALWAYE PRESERVE HE PINCIPLE +mls_eng_000248 AT SMLE RE HING STION ON M THE MORING OF UPTOER THRTY ON CODUTED I THE APSFHER OF COESPERSY AND AI TANDED BY BROADY WE RETOL HAT THE NORMWL ADENTSE REQURIRMET FOR REVIU OREPROVL HAD BEN WAET THAT NORMLE AE PRVL O THE MAR OF WASINGTIN ND SRTNGOVENRS WOD BE HANDLD IN FORMILY +mls_eng_000249 T THE BOODIST LATDY IG CHINER HOL DO NO HASTATE TOL TAK LIG FOR THE PERVERS OFOD SOF THER CONCHONCS FOROMG TUCK TO TI BY BYING VEIRSD FIASHIEIS YOU SEADTER AND LETIN THE GOLL +mls_eng_000250 T THIE AGAN IS SOFEND AND TUMPERD BY A SEMBLE FATH AN THE SUPROMIRSY OF LOVE OVER FER AND UN BWNTED UMANITY AND CHIRIY FOR THE POR AN HELPLESAN UN CODIONAL FOR GIVENES OF THE DIRST INERES WHICH IS THE NOT OF THE NOBL A ENEROUSITY AND LIBERALITY +mls_eng_000251 THE SECEND MADE FALLOED AND HE COUPL O THE SEMERSMEN BRO THE PBOR TH BARKE BE FOR CUCHING A ROUP THE WEN TO WARK T SURCH TH SHI TE LIFED DE HACHES AND FAOWND THE HOLD FULL OF CORGO +mls_eng_000252 FOND OFHIS COMERADE AND RESPETFULE I TO HIS PASTUR AND MASTERS EVEN SCHL MUSTERS AS HE LAD HE PREPERS FOR ANHOD WTH WIL AND THIS TRAING OCKUPYE HIM THURE OUT YOUTH TIID diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..e15b7ced9032804f374362ec195fc18948cd679b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/token @@ -0,0 +1,10 @@ +mls_eng_000243 I A E T H I R U E O F T E K E N A O L D E S F R O N Y O U S H I S A T H A S T A L Y I S I A N O W T H E I T S I M P O S E A B L E T H A V E F A T H I N O U N B A S I Y N O H E I T A S Y U S E S T E C P E C E T A N Y R E T E R E N F O R Y O U G F O R A L I H A V E D O N I O W N T N O M O R O F Y O U +mls_eng_000244 T H A T H E M A Y S O M N T I M E S B E E L A K C K O U T H E R C H E L E D R E N L E R N I N G T E S I D E M Y N E O R P L A I N G P R A T L I N D S I C I N G F O R H E L P E S C O M S T O Y H E A R T A T S S H I N F U L O R D A N S P E A K I N H O W C G O D T H O A R T E +mls_eng_000245 R E N S E N T H I N G S O F A L S O R E A S I T H E J E N R A L O U T B U R S T O F M O L T E I T O D N E S S P A S T I O N A R H U T E D T O G E T H E R T H E L I T U R C R I S N A Y T H E R E D D I E K I L S W I T H E H A R A B L E F O A R E O V E R T H E B I L Y S E O F H E A D S M A B E S E M E R A S T K G A L A D Y C A P R Y A L I N G O N C O R S E S F O T H E R O I L S T O I D +mls_eng_000246 I T M A Y H A V E B E N T A T T H E B O N E S W E R E R F O L D E D T O G E T H E R A N D N O N E A S O N Y H E P E L Y B O N S F O L D E D A N D L A I D W A Y F O T H E P E R P S C E S O F I N C E N T A T I O N S U C H B U N D L E S O F B O N E S W E P U T T H O R W E A P R O S E S S O F P R A I R S +mls_eng_000247 M A R S S A I L E S N E V E R E C S P E R I N C E D T H O S G R A T T R A E N S C I T I O N S F O M L O N E S T O G R E A N D E U R T H I S W A S O N I N G T O T H E P R O D I N T C O N D U C T T H A T R E P O B L I K E W H I C H A L W A Y E P R E S E R V E H E P I N C I P L E +mls_eng_000248 A T S M L E R E H I N G S T I O N O N M T H E M O R I N G O F U P T O E R T H R T Y O N C O D U T E D I T H E A P S F H E R O F C O E S P E R S Y A N D A I T A N D E D B Y B R O A D Y W E R E T O L H A T T H E N O R M W L A D E N T S E R E Q U R I R M E T F O R R E V I U O R E P R O V L H A D B E N W A E T T H A T N O R M L E A E P R V L O T H E M A R O F W A S I N G T I N N D S R T N G O V E N R S W O D B E H A N D L D I N F O R M I L Y +mls_eng_000249 T T H E B O O D I S T L A T D Y I G C H I N E R H O L D O N O H A S T A T E T O L T A K L I G F O R T H E P E R V E R S O F O D S O F T H E R C O N C H O N C S F O R O M G T U C K T O T I B Y B Y I N G V E I R S D F I A S H I E I S Y O U S E A D T E R A N D L E T I N T H E G O L L +mls_eng_000250 T T H I E A G A N I S S O F E N D A N D T U M P E R D B Y A S E M B L E F A T H A N T H E S U P R O M I R S Y O F L O V E O V E R F E R A N D U N B W N T E D U M A N I T Y A N D C H I R I Y F O R T H E P O R A N H E L P L E S A N U N C O D I O N A L F O R G I V E N E S O F T H E D I R S T I N E R E S W H I C H I S T H E N O T O F T H E N O B L A E N E R O U S I T Y A N D L I B E R A L I T Y +mls_eng_000251 T H E S E C E N D M A D E F A L L O E D A N D H E C O U P L O T H E S E M E R S M E N B R O T H E P B O R T H B A R K E B E F O R C U C H I N G A R O U P T H E W E N T O W A R K T S U R C H T H S H I T E L I F E D D E H A C H E S A N D F A O W N D T H E H O L D F U L L O F C O R G O +mls_eng_000252 F O N D O F H I S C O M E R A D E A N D R E S P E T F U L E I T O H I S P A S T U R A N D M A S T E R S E V E N S C H L M U S T E R S A S H E L A D H E P R E P E R S F O R A N H O D W T H W I L A N D T H I S T R A I N G O C K U P Y E H I M T H U R E O U T Y O U T H T I I D diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..fcafbce0cf39c58da6b8c38f5638da568c73535b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/token_int @@ -0,0 +1,10 @@ +mls_eng_000243 8 2 5 3 4 10 8 11 2 14 3 2 6 18 2 4 3 24 3 7 2 5 6 13 12 3 9 2 18 11 6 7 2 19 6 14 2 9 10 8 2 9 5 4 2 10 5 9 4 5 13 19 2 8 9 8 5 2 7 6 17 2 4 10 3 2 8 4 2 9 2 8 15 21 6 9 3 5 22 13 3 2 4 2 10 5 23 3 2 18 5 4 10 8 7 2 6 14 7 2 22 5 9 8 19 2 7 6 2 10 3 8 4 2 5 9 2 19 14 9 3 9 4 2 3 16 21 3 16 3 4 2 5 7 19 2 11 3 4 3 11 3 7 2 18 6 11 2 19 6 14 20 2 18 6 11 5 13 2 8 2 10 5 23 3 2 12 6 7 2 8 6 17 7 4 2 7 6 2 15 6 11 2 6 18 2 19 6 14 +mls_eng_000244 4 10 5 4 2 10 3 2 15 5 19 2 9 6 15 7 4 8 15 3 9 2 22 3 2 3 2 13 5 24 16 24 2 6 14 4 10 3 11 2 16 10 3 13 3 12 11 3 7 2 13 3 11 7 8 7 20 2 4 3 2 9 8 12 3 2 15 19 2 7 3 2 6 11 2 21 13 5 8 7 20 2 21 11 5 4 13 8 7 12 2 9 8 16 8 7 20 2 18 6 11 2 10 3 13 21 3 9 2 16 6 15 9 4 6 2 19 2 10 3 5 11 4 2 5 4 9 2 9 10 8 7 18 14 13 6 11 12 2 5 7 2 9 21 3 5 24 8 7 2 10 6 17 2 16 20 6 12 2 4 10 6 2 5 11 4 3 +mls_eng_000245 2 11 3 7 2 9 3 7 2 4 10 8 7 20 9 2 6 18 2 5 13 2 9 6 11 3 2 5 9 2 8 2 4 10 3 2 26 3 7 11 5 13 2 6 14 4 22 14 11 9 4 2 6 18 2 15 6 13 4 3 8 4 6 12 7 3 9 9 2 21 5 9 4 8 6 7 2 5 11 2 10 14 4 3 12 2 4 6 2 20 3 4 10 3 11 2 4 10 3 2 13 8 4 14 11 16 11 8 9 2 7 5 19 2 2 4 10 3 2 11 3 12 2 12 8 3 24 8 13 9 2 17 8 2 4 10 3 2 10 5 11 5 22 13 3 2 18 6 5 11 3 2 6 23 3 11 2 4 10 3 2 22 8 13 19 2 9 3 2 6 18 2 10 3 5 12 9 2 15 5 2 22 3 2 9 3 15 3 2 11 5 9 4 24 20 5 13 5 12 19 2 16 5 21 11 19 5 13 8 7 20 2 6 7 2 16 6 11 9 3 9 2 18 6 2 4 10 3 2 11 6 8 13 2 9 4 6 8 12 +mls_eng_000246 8 4 2 15 5 19 2 10 5 23 3 2 22 3 7 2 4 5 4 2 4 10 3 2 22 6 7 3 9 2 17 3 11 3 11 2 18 6 13 12 3 12 4 6 20 3 4 10 3 11 2 5 7 12 2 7 6 7 3 2 5 9 2 6 7 19 2 10 3 2 21 3 13 19 2 22 6 7 9 2 18 6 13 12 3 12 2 5 7 12 2 13 5 8 12 2 17 5 19 2 18 6 2 4 10 3 2 21 3 11 21 9 16 3 9 2 6 18 2 8 7 16 3 7 4 5 4 8 6 7 2 9 14 16 10 2 22 14 7 12 13 3 9 2 6 18 2 22 6 7 3 9 2 17 3 2 21 14 4 2 4 10 6 11 17 3 2 5 2 21 11 6 9 3 9 9 2 6 18 2 21 11 5 8 11 9 +mls_eng_000247 2 15 5 11 9 2 9 5 8 13 3 9 2 7 3 23 3 11 2 3 16 9 21 3 11 8 7 16 3 12 2 4 10 6 9 2 20 11 5 4 2 4 11 5 3 7 9 16 8 4 8 6 7 9 2 18 6 15 2 13 6 7 3 9 2 4 6 2 20 11 3 5 7 12 3 14 11 2 4 10 8 9 2 17 5 9 2 6 7 8 7 20 2 4 6 2 4 10 3 2 21 11 6 12 8 7 4 2 16 6 7 12 14 16 4 2 4 10 5 4 2 11 3 2 21 6 22 13 8 24 3 2 17 10 8 16 10 2 5 13 17 5 19 3 2 21 11 3 9 3 11 23 3 2 10 3 2 21 8 7 16 8 21 13 3 +mls_eng_000248 5 4 2 9 15 13 3 2 11 3 2 10 8 7 20 2 9 4 8 6 7 2 6 7 2 2 15 2 4 10 3 2 15 6 11 8 7 20 2 6 18 2 14 21 4 6 3 11 2 4 10 11 4 19 2 6 7 2 16 6 12 14 4 3 12 2 8 2 4 10 3 2 5 21 9 18 10 3 11 2 6 18 2 16 6 3 9 21 3 11 9 19 2 5 7 12 2 5 8 2 4 5 7 12 3 12 2 22 19 2 22 11 6 5 12 19 2 17 3 2 11 3 4 6 13 2 10 5 4 2 4 10 3 2 7 6 11 15 17 13 2 5 12 3 7 4 9 3 2 11 3 27 14 11 8 11 15 3 4 2 18 6 11 2 11 3 23 8 14 2 6 11 3 21 11 6 23 13 2 10 5 12 2 22 3 7 2 17 5 3 4 2 4 10 5 4 2 7 6 11 15 13 3 2 5 3 2 21 11 23 13 2 6 2 4 10 3 2 15 5 11 2 6 18 2 17 5 9 8 7 20 4 8 7 2 7 12 2 9 11 4 7 20 6 23 3 7 11 9 2 17 6 12 2 22 3 2 10 5 7 12 13 12 2 8 7 2 18 6 11 15 8 13 19 +mls_eng_000249 4 2 4 10 3 2 22 6 6 12 8 9 4 2 13 5 4 12 19 2 8 20 2 16 10 8 7 3 11 2 10 6 13 2 12 6 2 7 6 2 10 5 9 4 5 4 3 2 4 6 13 2 4 5 24 2 13 8 20 2 18 6 11 2 4 10 3 2 21 3 11 23 3 11 9 2 6 18 6 12 2 9 6 18 2 4 10 3 11 2 16 6 7 16 10 6 7 16 9 2 18 6 11 6 15 20 2 4 14 16 24 2 4 6 2 4 8 2 22 19 2 22 19 8 7 20 2 23 3 8 11 9 12 2 18 8 5 9 10 8 3 8 9 2 19 6 14 2 9 3 5 12 4 3 11 2 5 7 12 2 13 3 4 8 7 2 4 10 3 2 20 6 13 13 +mls_eng_000250 4 2 4 10 8 3 2 5 20 5 7 2 8 9 2 9 6 18 3 7 12 2 5 7 12 2 4 14 15 21 3 11 12 2 2 22 19 2 5 2 9 3 15 22 13 3 2 18 5 4 10 2 5 7 2 4 10 3 2 9 14 21 11 6 15 8 11 9 19 2 6 18 2 13 6 23 3 2 6 23 3 11 2 18 3 11 2 5 7 12 2 14 7 2 22 17 7 4 3 12 2 14 15 5 7 8 4 19 2 5 7 12 2 16 10 8 11 8 19 2 18 6 11 2 4 10 3 2 21 6 11 2 5 7 2 10 3 13 21 13 3 9 5 7 2 14 7 2 16 6 12 8 6 7 5 13 2 18 6 11 2 20 8 23 3 7 3 9 2 6 18 2 4 10 3 2 12 8 11 9 4 2 8 7 3 11 3 9 2 17 10 8 16 10 2 8 9 2 4 10 3 2 7 6 4 2 6 18 2 4 10 3 2 7 6 22 13 2 5 2 3 7 3 11 6 14 9 8 4 19 2 5 7 12 2 13 8 22 3 11 5 13 8 4 19 +mls_eng_000251 2 4 10 3 2 9 3 16 3 7 12 2 15 5 12 3 2 18 5 13 13 6 3 12 2 5 7 12 2 10 3 2 16 6 14 21 13 2 6 2 4 10 3 2 9 3 15 3 11 9 15 3 7 2 22 11 6 2 4 10 3 2 21 22 6 11 2 4 10 2 22 5 11 24 3 2 22 3 2 18 6 11 2 16 14 16 10 8 7 20 2 5 2 11 6 14 21 2 4 10 3 2 17 3 7 2 4 6 2 17 5 11 24 2 4 2 9 14 11 16 10 2 4 10 2 9 10 8 2 4 3 2 13 8 18 3 12 2 12 3 2 10 5 16 10 3 9 2 5 7 12 2 18 5 6 17 7 12 2 4 10 3 2 10 6 13 12 2 18 14 13 13 2 6 18 2 16 6 11 20 6 +mls_eng_000252 18 6 7 12 2 6 18 10 8 9 2 16 6 15 3 11 5 12 3 2 5 7 12 2 11 3 9 21 3 4 18 14 13 3 2 8 2 4 6 2 10 8 9 2 21 5 9 4 14 11 2 5 7 12 2 15 5 9 4 3 11 9 2 3 23 3 7 2 9 16 10 13 2 15 14 9 4 3 11 9 2 5 9 2 10 3 2 13 5 12 2 10 3 2 21 11 3 21 3 11 9 2 18 6 11 2 5 7 10 6 12 2 17 4 10 2 17 8 13 2 5 7 12 2 4 10 8 9 2 4 11 5 8 7 20 2 6 16 24 14 21 19 3 2 10 8 15 2 4 10 14 11 3 2 6 14 4 2 19 6 14 4 10 2 4 8 8 12 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..e8eaecea38625dad45811b4b8c8c709458844378 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/score @@ -0,0 +1,10 @@ +mls_eng_000253 tensor(-19.5110) +mls_eng_000254 tensor(-35.3209) +mls_eng_000255 tensor(-17.3090) +mls_eng_000256 tensor(-23.9095) +mls_eng_000257 tensor(-36.7483) +mls_eng_000258 tensor(-34.3075) +mls_eng_000259 tensor(-44.7388) +mls_eng_000260 tensor(-28.7605) +mls_eng_000261 tensor(-52.9342) +mls_eng_000262 tensor(-25.9552) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..215a64a64950bef3f1a9ac44c7c72a33c190bab1 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/text @@ -0,0 +1,10 @@ +mls_eng_000253 ASS WHE THE PRENES QUPECEA E RECHE TER SIXT INT YEAR SH BECAME BLINGD WHER LARGH SOF BON ICES HAD O LIDINGTHM +mls_eng_000254 AOLMOST ALLE DY THE BATE RANGEDBUTCE TH TWO MEN BAK IN FORT THEY FOURS YECHEOTHER OVER THE LAV A BEDS THE SHIVFS WEL OIE BOY WAS VERY DIFICALT FOR HEOALO HAY TOGRAS PBROSE AND BLEADING FROM RE PETED FLLES ON THE ROUFH LAVA +mls_eng_000255 BPOSY IY TRE BIT TERRAR AND A WLAL PANTING THE WHIN MONDT THROG THE TREES OF THE CARDIN AND FOME TIME TO TIMEMSEE AS IF TO A +mls_eng_000256 HIS MENTL TORPTITY FOUNDET A PON FISIOCL INDLEANCE RENDERS A MEDIAT ACTION AN AL MAENERO ESRTIOND DUSTEASFUL HIS CONCHUS WEKNES SHOS ITD SELF +mls_eng_000257 TNORE THAELE HOW GLAD THE CING MTHE WAS NOR HOUE GRATD WOER THE REYGJUORISING OF THE PEPLE OR HOM MENIFESSENDTD WAS THE ROIL BANKQCED THAT GOOD CFING POMEREAR ADTENDETD WHITHAL HER COREN +mls_eng_000258 AND THE CHONCE OF THER BENG SUCH A ON UNGIAN DIMISHES BY EWERY PRAPE PROSESS MAMELUK AS AEORS WHOS OF EAKCUEAN CQULITY EREASNING NOT ABOWTD HIS ODERS BUT BOUL THE IY TO DO THET +mls_eng_000259 TSHE NOKET BUT S NO ROUP LOE OATHOF TE WHENDO AN SAID I DEAER NOT OBPB HE DOR FOR THE DORSHAVE TOD ME TOLT NO OIN IN THAT IS HARED FOR ME E SAID THE OMEON FOR I MUST TAK BACE MY AUPLS BUT THERIS ONE WICH IULL GIVE YOU AND SHE HELD UPEN AL +mls_eng_000260 OV Y MORY SPOK AK CELTHOR AND HERSE FINLY SPOKEN IHT TWO MY PESWAS WHY FEABEY AR YO CM SOSON WERE OUR BRY CHILD ND O THAN AR TDLSPOK SAME OAN +mls_eng_000261 I NEVER U ANYWOEN HO LE TDOGOTY CERCHIS MCH AC SGRAMOH DOUS SHE HAIS HE WOUD RAVE B A DORE CEPEI TH HAS OF R GOD THEN I WELL THE TENSOF WIKEDNES TE DON HVE WOMEN DORE KEPERS AN I WENO SHE WINUT TWELL MININA ATENT +mls_eng_000262 THE DUK WAS SUPRICS TO SE HM OT BRING O OT SO ALY ABEL ARD DEMAND D HE OL YOUR GRAICTED REPLI HE UTLR Y GASPING FOR UTERANCES diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..0a4ca1e756af5af2a9af830bed58727af111bf4b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/token @@ -0,0 +1,10 @@ +mls_eng_000253 A S S W H E T H E P R E N E S Q U P E C E A E R E C H E T E R S I X T I N T Y E A R S H B E C A M E B L I N G D W H E R L A R G H S O F B O N I C E S H A D O L I D I N G T H M +mls_eng_000254 A O L M O S T A L L E D Y T H E B A T E R A N G E D B U T C E T H T W O M E N B A K I N F O R T T H E Y F O U R S Y E C H E O T H E R O V E R T H E L A V A B E D S T H E S H I V F S W E L O I E B O Y W A S V E R Y D I F I C A L T F O R H E O A L O H A Y T O G R A S P B R O S E A N D B L E A D I N G F R O M R E P E T E D F L L E S O N T H E R O U F H L A V A +mls_eng_000255 B P O S Y I Y T R E B I T T E R R A R A N D A W L A L P A N T I N G T H E W H I N M O N D T T H R O G T H E T R E E S O F T H E C A R D I N A N D F O M E T I M E T O T I M E M S E E A S I F T O A +mls_eng_000256 H I S M E N T L T O R P T I T Y F O U N D E T A P O N F I S I O C L I N D L E A N C E R E N D E R S A M E D I A T A C T I O N A N A L M A E N E R O E S R T I O N D D U S T E A S F U L H I S C O N C H U S W E K N E S S H O S I T D S E L F +mls_eng_000257 T N O R E T H A E L E H O W G L A D T H E C I N G M T H E W A S N O R H O U E G R A T D W O E R T H E R E Y G J U O R I S I N G O F T H E P E P L E O R H O M M E N I F E S S E N D T D W A S T H E R O I L B A N K Q C E D T H A T G O O D C F I N G P O M E R E A R A D T E N D E T D W H I T H A L H E R C O R E N +mls_eng_000258 A N D T H E C H O N C E O F T H E R B E N G S U C H A O N U N G I A N D I M I S H E S B Y E W E R Y P R A P E P R O S E S S M A M E L U K A S A E O R S W H O S O F E A K C U E A N C Q U L I T Y E R E A S N I N G N O T A B O W T D H I S O D E R S B U T B O U L T H E I Y T O D O T H E T +mls_eng_000259 T S H E N O K E T B U T S N O R O U P L O E O A T H O F T E W H E N D O A N S A I D I D E A E R N O T O B P B H E D O R F O R T H E D O R S H A V E T O D M E T O L T N O O I N I N T H A T I S H A R E D F O R M E E S A I D T H E O M E O N F O R I M U S T T A K B A C E M Y A U P L S B U T T H E R I S O N E W I C H I U L L G I V E Y O U A N D S H E H E L D U P E N A L +mls_eng_000260 O V Y M O R Y S P O K A K C E L T H O R A N D H E R S E F I N L Y S P O K E N I H T T W O M Y P E S W A S W H Y F E A B E Y A R Y O C M S O S O N W E R E O U R B R Y C H I L D N D O T H A N A R T D L S P O K S A M E O A N +mls_eng_000261 I N E V E R U A N Y W O E N H O L E T D O G O T Y C E R C H I S M C H A C S G R A M O H D O U S S H E H A I S H E W O U D R A V E B A D O R E C E P E I T H H A S O F R G O D T H E N I W E L L T H E T E N S O F W I K E D N E S T E D O N H V E W O M E N D O R E K E P E R S A N I W E N O S H E W I N U T T W E L L M I N I N A A T E N T +mls_eng_000262 T H E D U K W A S S U P R I C S T O S E H M O T B R I N G O O T S O A L Y A B E L A R D D E M A N D D H E O L Y O U R G R A I C T E D R E P L I H E U T L R Y G A S P I N G F O R U T E R A N C E S diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..991f47d35f28d67ed886c0bb9be97ebf6dd0d9f5 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/token_int @@ -0,0 +1,10 @@ +mls_eng_000253 5 9 9 2 17 10 3 2 4 10 3 2 21 11 3 7 3 9 2 27 14 21 3 16 3 5 2 3 2 11 3 16 10 3 2 4 3 11 2 9 8 25 4 2 8 7 4 2 19 3 5 11 2 9 10 2 22 3 16 5 15 3 2 22 13 8 7 20 12 2 17 10 3 11 2 13 5 11 20 10 2 9 6 18 2 22 6 7 2 8 16 3 9 2 10 5 12 2 6 2 13 8 12 8 7 20 4 10 15 +mls_eng_000254 5 6 13 15 6 9 4 2 5 13 13 3 2 12 19 2 4 10 3 2 22 5 4 3 2 11 5 7 20 3 12 22 14 4 16 3 2 4 10 2 4 17 6 2 15 3 7 2 22 5 24 2 8 7 2 18 6 11 4 2 4 10 3 19 2 18 6 14 11 9 2 19 3 16 10 3 6 4 10 3 11 2 6 23 3 11 2 4 10 3 2 13 5 23 2 5 2 22 3 12 9 2 4 10 3 2 9 10 8 23 18 9 2 17 3 13 2 6 8 3 2 22 6 19 2 17 5 9 2 23 3 11 19 2 12 8 18 8 16 5 13 4 2 18 6 11 2 10 3 6 5 13 6 2 10 5 19 2 4 6 20 11 5 9 2 21 22 11 6 9 3 2 5 7 12 2 22 13 3 5 12 8 7 20 2 18 11 6 15 2 11 3 2 21 3 4 3 12 2 18 13 13 3 9 2 6 7 2 4 10 3 2 11 6 14 18 10 2 13 5 23 5 +mls_eng_000255 22 21 6 9 19 2 8 19 2 4 11 3 2 22 8 4 2 4 3 11 11 5 11 2 5 7 12 2 5 2 17 13 5 13 2 21 5 7 4 8 7 20 2 4 10 3 2 17 10 8 7 2 15 6 7 12 4 2 4 10 11 6 20 2 4 10 3 2 4 11 3 3 9 2 6 18 2 4 10 3 2 16 5 11 12 8 7 2 5 7 12 2 18 6 15 3 2 4 8 15 3 2 4 6 2 4 8 15 3 15 9 3 3 2 5 9 2 8 18 2 4 6 2 5 +mls_eng_000256 10 8 9 2 15 3 7 4 13 2 4 6 11 21 4 8 4 19 2 18 6 14 7 12 3 4 2 5 2 21 6 7 2 18 8 9 8 6 16 13 2 8 7 12 13 3 5 7 16 3 2 11 3 7 12 3 11 9 2 5 2 15 3 12 8 5 4 2 5 16 4 8 6 7 2 5 7 2 5 13 2 15 5 3 7 3 11 6 2 3 9 11 4 8 6 7 12 2 12 14 9 4 3 5 9 18 14 13 2 10 8 9 2 16 6 7 16 10 14 9 2 17 3 24 7 3 9 2 9 10 6 9 2 8 4 12 2 9 3 13 18 +mls_eng_000257 4 7 6 11 3 2 4 10 5 3 13 3 2 10 6 17 2 20 13 5 12 2 4 10 3 2 16 8 7 20 2 15 4 10 3 2 17 5 9 2 7 6 11 2 10 6 14 3 2 20 11 5 4 12 2 17 6 3 11 2 4 10 3 2 11 3 19 20 26 14 6 11 8 9 8 7 20 2 6 18 2 4 10 3 2 21 3 21 13 3 2 6 11 2 10 6 15 2 15 3 7 8 18 3 9 9 3 7 12 4 12 2 2 17 5 9 2 4 10 3 2 11 6 8 13 2 22 5 7 24 27 16 3 12 2 4 10 5 4 2 20 6 6 12 2 16 18 8 7 20 2 21 6 15 3 11 3 5 11 2 5 12 4 3 7 12 3 4 12 2 17 10 8 4 10 5 13 2 10 3 11 2 16 6 11 3 7 +mls_eng_000258 5 7 12 2 4 10 3 2 16 10 6 7 16 3 2 6 18 2 4 10 3 11 2 22 3 7 20 2 9 14 16 10 2 5 2 6 7 2 14 7 20 8 5 7 2 12 8 15 8 9 10 3 9 2 22 19 2 3 17 3 11 19 2 21 11 5 21 3 2 21 11 6 9 3 9 9 2 15 5 15 3 13 14 24 2 5 9 2 5 3 6 11 9 2 17 10 6 9 2 6 18 2 3 5 24 16 14 3 5 7 2 16 27 14 13 8 4 19 2 3 11 3 5 9 7 8 7 20 2 7 6 4 2 5 22 6 17 4 12 2 10 8 9 2 6 12 3 11 9 2 22 14 4 2 22 6 14 13 2 4 10 3 2 8 19 2 4 6 2 12 6 2 4 10 3 4 +mls_eng_000259 4 9 10 3 2 7 6 24 3 4 2 22 14 4 2 9 2 7 6 2 11 6 14 21 2 13 6 3 2 6 5 4 10 6 18 2 4 3 2 17 10 3 7 12 6 2 5 7 2 9 5 8 12 2 8 2 12 3 5 3 11 2 7 6 4 2 6 22 21 22 2 10 3 2 12 6 11 2 18 6 11 2 4 10 3 2 12 6 11 9 10 5 23 3 2 4 6 12 2 15 3 2 4 6 13 4 2 7 6 2 6 8 7 2 8 7 2 4 10 5 4 2 8 9 2 10 5 11 3 12 2 18 6 11 2 15 3 2 3 2 9 5 8 12 2 4 10 3 2 6 15 3 6 7 2 18 6 11 2 8 2 15 14 9 4 2 4 5 24 2 22 5 16 3 2 15 19 2 5 14 21 13 9 2 22 14 4 2 4 10 3 11 8 9 2 6 7 3 2 17 8 16 10 2 8 14 13 13 2 20 8 23 3 2 19 6 14 2 5 7 12 2 9 10 3 2 10 3 13 12 2 14 21 3 7 2 5 13 +mls_eng_000260 6 23 2 19 2 15 6 11 19 2 9 21 6 24 2 5 24 2 16 3 13 4 10 6 11 2 5 7 12 2 10 3 11 9 3 2 18 8 7 13 19 2 9 21 6 24 3 7 2 8 10 4 2 4 17 6 2 15 19 2 21 3 9 17 5 9 2 17 10 19 2 18 3 5 22 3 19 2 5 11 2 19 6 2 16 15 2 9 6 9 6 7 2 17 3 11 3 2 6 14 11 2 22 11 19 2 16 10 8 13 12 2 7 12 2 6 2 4 10 5 7 2 5 11 2 4 12 13 9 21 6 24 2 9 5 15 3 2 6 5 7 +mls_eng_000261 8 2 7 3 23 3 11 2 14 2 5 7 19 17 6 3 7 2 10 6 2 13 3 2 4 12 6 20 6 4 19 2 16 3 11 16 10 8 9 2 15 16 10 2 5 16 2 9 20 11 5 15 6 10 2 12 6 14 9 2 9 10 3 2 10 5 8 9 2 10 3 2 17 6 14 12 2 11 5 23 3 2 22 2 5 2 12 6 11 3 2 16 3 21 3 8 2 4 10 2 10 5 9 2 6 18 2 11 2 20 6 12 2 4 10 3 7 2 8 2 17 3 13 13 2 4 10 3 2 4 3 7 9 6 18 2 17 8 24 3 12 7 3 9 2 4 3 2 12 6 7 2 10 23 3 2 17 6 15 3 7 2 12 6 11 3 2 24 3 21 3 11 9 2 5 7 2 8 2 17 3 7 6 2 9 10 3 2 17 8 7 14 4 2 4 17 3 13 13 2 15 8 7 8 7 5 2 5 4 3 7 4 +mls_eng_000262 4 10 3 2 12 14 24 2 17 5 9 2 9 14 21 11 8 16 9 2 4 6 2 9 3 2 10 15 2 6 4 2 22 11 8 7 20 2 6 2 6 4 2 9 6 2 5 13 19 2 5 22 3 13 2 5 11 12 2 12 3 15 5 7 12 2 12 2 10 3 2 6 13 2 19 6 14 11 2 20 11 5 8 16 4 3 12 2 11 3 21 13 8 2 10 3 2 14 4 13 11 2 19 2 20 5 9 21 8 7 20 2 18 6 11 2 14 4 3 11 5 7 16 3 9 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..1a5c3e5d2c0acc2c1f431c621f3d624bce9484b5 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/score @@ -0,0 +1,10 @@ +mls_eng_000263 tensor(-35.7861) +mls_eng_000264 tensor(-43.5149) +mls_eng_000265 tensor(-42.0253) +mls_eng_000266 tensor(-45.5519) +mls_eng_000267 tensor(-23.1777) +mls_eng_000268 tensor(-32.7009) +mls_eng_000269 tensor(-34.7996) +mls_eng_000270 tensor(-21.2846) +mls_eng_000271 tensor(-39.1876) +mls_eng_000272 tensor(-33.2570) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..602e5b6d4acf7bc92741f8595d979b16612b7689 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/text @@ -0,0 +1,10 @@ +mls_eng_000263 FORE IUAT SEMING TRONSENMEUTATIONS OF CALE MA BE MAD WE THER IS NY MICXTROF DEVOR SOUTS O RAES BRIN SACH MICTERS THE COM PON ELIS APERE NOT BY THE MUTIL ALAING AHETHE CONCTUTE A MIDLING COLER +mls_eng_000264 AN ALMOS THE SAME INSTANT CEN S RS ANDY SID STEP INTO A SHOP DORWAY HE WADED THEI NTIK CHIN CAME UP CIN STDAUPE AND RETENDE TO STAR TH THEGLAS ATH DESPLAY OF HERD WERINGTUOULES WER HE CONTINED TO WOUCH BAIRICK OU SE WAT IS HAINBY SAID RIN NOTED +mls_eng_000265 T THEN THE THIEK GRENE STAV FLOLED OLR THE HL BELDIN AND THE WHAS NOTHING TO ES SEEN THER BUT DE MAOUD OF SOFETD FLO ING GRAY GREN STAR THED RUSHED ON NOWHW THE SWEIFTNES OLL WINGKE A LOOET ABD INTO BERS FAE +mls_eng_000266 I HAVE MAE SURCKROFISES TOE BUR OS WHEN I ANEU THATHE WRE NOT MY HAPENESS WAS AFTER IS SAOL THAT H AD STEPEDT AGHER I SOAR THAT YOUR TENDERNES AD TERND TO COULDILATION AOFTER SAOR THAT YOU CEIRD FOR YOURSELF ON LY NOT FOR ME +mls_eng_000267 YATD THA THANDTER NEVER RALS IN WINTER I SEY A CROAL WARRING ROUNED AND ROUNME BEFORE IT A LIHTET THESEN NOT HING UNDE THE FIRED TREES BUT IL NO SOME THNG MUS BE THAR +mls_eng_000268 EIY IS BEYON THOUT THAT SOME PEBLE HAE MANISETO SAF MANY THINGS AN OF COURSES THE JIRMEANT HAS A SOMAIISE AS MAUTH TOWOU ORE THRE DASGE AFE HE FIRSI PRECOUCITIONS DE DROUPET IN INOWARS +mls_eng_000269 SON A MAN CAME OUT TO ME HIM THIS MAN WAS OALOHAY A BERDLESS MAND BELNING TO A LES ROVBUR CLAN HIHWICH IN FESTED THE DSTRCT BPUSTIBLY ASISTING THE AN UNTERS OF HE TEMPL INSCIRING VICTUMS FOR THE TEMBLE LLTERS +mls_eng_000270 WE ARE NOT LOVEIRS YOU AN IYH APON THIS SUNY LANAD TBUT CHOLLDEON WHE AVE NEVER NON T LOVFS JIOR YOUR PE +mls_eng_000271 EWAS MURDED ON HESORSTEVF WISOAL HOSES WAS I AN OL MAN AST ANDTR A CAM LY MISERE QOUODIAR YOU TOKASIFE HAD DIGE IN HIS BEATD YO HE NOVEONITION AD YU CANO UNDESTAEWD IN MINS HN ANINGUICITY IS MRDET +mls_eng_000272 BY GORD HISAED THE WOND ME HER BISNST THIDS ME IN AN OUTOMNY HOUSES OE HAT DO IY CAR FOR THE SICRST THAT MA VE HAD N THAR HOW WEVER IU CUNOT PLING WIS PEBEFOR THE WOUCHFLNES THE BLAUT HOUES OF HISFMNORI ARE A IN EVFERY STREEPT diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..6bc1c362c6b4ab3ad4ea4d7b003cac74f1aa378e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/token @@ -0,0 +1,10 @@ +mls_eng_000263 F O R E I U A T S E M I N G T R O N S E N M E U T A T I O N S O F C A L E M A B E M A D W E T H E R I S N Y M I C X T R O F D E V O R S O U T S O R A E S B R I N S A C H M I C T E R S T H E C O M P O N E L I S A P E R E N O T B Y T H E M U T I L A L A I N G A H E T H E C O N C T U T E A M I D L I N G C O L E R +mls_eng_000264 A N A L M O S T H E S A M E I N S T A N T C E N S R S A N D Y S I D S T E P I N T O A S H O P D O R W A Y H E W A D E D T H E I N T I K C H I N C A M E U P C I N S T D A U P E A N D R E T E N D E T O S T A R T H T H E G L A S A T H D E S P L A Y O F H E R D W E R I N G T U O U L E S W E R H E C O N T I N E D T O W O U C H B A I R I C K O U S E W A T I S H A I N B Y S A I D R I N N O T E D +mls_eng_000265 T T H E N T H E T H I E K G R E N E S T A V F L O L E D O L R T H E H L B E L D I N A N D T H E W H A S N O T H I N G T O E S S E E N T H E R B U T D E M A O U D O F S O F E T D F L O I N G G R A Y G R E N S T A R T H E D R U S H E D O N N O W H W T H E S W E I F T N E S O L L W I N G K E A L O O E T A B D I N T O B E R S F A E +mls_eng_000266 I H A V E M A E S U R C K R O F I S E S T O E B U R O S W H E N I A N E U T H A T H E W R E N O T M Y H A P E N E S S W A S A F T E R I S S A O L T H A T H A D S T E P E D T A G H E R I S O A R T H A T Y O U R T E N D E R N E S A D T E R N D T O C O U L D I L A T I O N A O F T E R S A O R T H A T Y O U C E I R D F O R Y O U R S E L F O N L Y N O T F O R M E +mls_eng_000267 Y A T D T H A T H A N D T E R N E V E R R A L S I N W I N T E R I S E Y A C R O A L W A R R I N G R O U N E D A N D R O U N M E B E F O R E I T A L I H T E T T H E S E N N O T H I N G U N D E T H E F I R E D T R E E S B U T I L N O S O M E T H N G M U S B E T H A R +mls_eng_000268 E I Y I S B E Y O N T H O U T T H A T S O M E P E B L E H A E M A N I S E T O S A F M A N Y T H I N G S A N O F C O U R S E S T H E J I R M E A N T H A S A S O M A I I S E A S M A U T H T O W O U O R E T H R E D A S G E A F E H E F I R S I P R E C O U C I T I O N S D E D R O U P E T I N I N O W A R S +mls_eng_000269 S O N A M A N C A M E O U T T O M E H I M T H I S M A N W A S O A L O H A Y A B E R D L E S S M A N D B E L N I N G T O A L E S R O V B U R C L A N H I H W I C H I N F E S T E D T H E D S T R C T B P U S T I B L Y A S I S T I N G T H E A N U N T E R S O F H E T E M P L I N S C I R I N G V I C T U M S F O R T H E T E M B L E L L T E R S +mls_eng_000270 W E A R E N O T L O V E I R S Y O U A N I Y H A P O N T H I S S U N Y L A N A D T B U T C H O L L D E O N W H E A V E N E V E R N O N T L O V F S J I O R Y O U R P E +mls_eng_000271 E W A S M U R D E D O N H E S O R S T E V F W I S O A L H O S E S W A S I A N O L M A N A S T A N D T R A C A M L Y M I S E R E Q O U O D I A R Y O U T O K A S I F E H A D D I G E I N H I S B E A T D Y O H E N O V E O N I T I O N A D Y U C A N O U N D E S T A E W D I N M I N S H N A N I N G U I C I T Y I S M R D E T +mls_eng_000272 B Y G O R D H I S A E D T H E W O N D M E H E R B I S N S T T H I D S M E I N A N O U T O M N Y H O U S E S O E H A T D O I Y C A R F O R T H E S I C R S T T H A T M A V E H A D N T H A R H O W W E V E R I U C U N O T P L I N G W I S P E B E F O R T H E W O U C H F L N E S T H E B L A U T H O U E S O F H I S F M N O R I A R E A I N E V F E R Y S T R E E P T diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..73d211b5de1009416920ce0b29bad092b4628721 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/token_int @@ -0,0 +1,10 @@ +mls_eng_000263 2 18 6 11 3 2 8 14 5 4 2 9 3 15 8 7 20 2 4 11 6 7 9 3 7 15 3 14 4 5 4 8 6 7 9 2 6 18 2 16 5 13 3 2 15 5 2 22 3 2 15 5 12 2 17 3 2 4 10 3 11 2 8 9 2 7 19 2 15 8 16 25 4 11 6 18 2 12 3 23 6 11 2 9 6 14 4 9 2 6 2 11 5 3 9 2 22 11 8 7 2 9 5 16 10 2 15 8 16 4 3 11 9 2 4 10 3 2 16 6 15 2 21 6 7 2 3 13 8 9 2 5 21 3 11 3 2 7 6 4 2 22 19 2 4 10 3 2 15 14 4 8 13 2 5 13 5 8 7 20 2 5 10 3 4 10 3 2 16 6 7 16 4 14 4 3 2 5 2 15 8 12 13 8 7 20 2 16 6 13 3 11 +mls_eng_000264 5 7 2 5 13 15 6 9 2 4 10 3 2 9 5 15 3 2 8 7 9 4 5 7 4 2 16 3 7 2 9 2 11 9 2 5 7 12 19 2 9 8 12 2 9 4 3 21 2 8 7 4 6 2 5 2 9 10 6 21 2 12 6 11 17 5 19 2 10 3 2 17 5 12 3 12 2 4 10 3 8 2 7 4 8 24 2 16 10 8 7 2 16 5 15 3 2 14 21 2 16 8 7 2 9 4 12 5 14 21 3 2 5 7 12 2 11 3 4 3 7 12 3 2 4 6 2 9 4 5 11 2 4 10 2 4 10 3 20 13 5 9 2 5 4 10 2 12 3 9 21 13 5 19 2 6 18 2 10 3 11 12 2 17 3 11 8 7 20 4 14 6 14 13 3 9 2 17 3 11 2 10 3 2 16 6 7 4 8 7 3 12 2 4 6 2 17 6 14 16 10 2 22 5 8 11 8 16 24 2 6 14 2 9 3 2 17 5 4 2 8 9 2 10 5 8 7 22 19 2 9 5 8 12 2 11 8 7 2 7 6 4 3 12 +mls_eng_000265 4 2 4 10 3 7 2 4 10 3 2 4 10 8 3 24 2 20 11 3 7 3 2 9 4 5 23 2 18 13 6 13 3 12 2 6 13 11 2 4 10 3 2 10 13 2 22 3 13 12 8 7 2 5 7 12 2 4 10 3 2 17 10 5 9 2 7 6 4 10 8 7 20 2 4 6 2 3 9 2 9 3 3 7 2 4 10 3 11 2 22 14 4 2 12 3 2 15 5 6 14 12 2 6 18 2 9 6 18 3 4 12 2 18 13 6 2 8 7 20 2 20 11 5 19 2 20 11 3 7 2 9 4 5 11 2 4 10 3 12 2 11 14 9 10 3 12 2 6 7 2 7 6 17 10 17 2 4 10 3 2 9 17 3 8 18 4 7 3 9 2 6 13 13 2 17 8 7 20 24 3 2 5 2 13 6 6 3 4 2 5 22 12 2 8 7 4 6 2 22 3 11 9 2 18 5 3 +mls_eng_000266 2 8 2 10 5 23 3 2 15 5 3 2 9 14 11 16 24 11 6 18 8 9 3 9 2 4 6 3 2 22 14 11 2 6 9 2 17 10 3 7 2 8 2 5 7 3 14 2 4 10 5 4 10 3 2 17 11 3 2 7 6 4 2 15 19 2 10 5 21 3 7 3 9 9 2 17 5 9 2 5 18 4 3 11 2 8 9 2 9 5 6 13 2 4 10 5 4 2 10 2 5 12 2 9 4 3 21 3 12 4 2 5 20 10 3 11 2 8 2 9 6 5 11 2 4 10 5 4 2 19 6 14 11 2 4 3 7 12 3 11 7 3 9 2 5 12 2 4 3 11 7 12 2 4 6 2 16 6 14 13 12 8 13 5 4 8 6 7 2 5 6 18 4 3 11 2 9 5 6 11 2 4 10 5 4 2 19 6 14 2 16 3 8 11 12 2 18 6 11 2 19 6 14 11 9 3 13 18 2 6 7 2 13 19 2 7 6 4 2 18 6 11 2 15 3 +mls_eng_000267 19 5 4 12 2 2 4 10 5 2 4 10 5 7 12 4 3 11 2 7 3 23 3 11 2 11 5 13 9 2 8 7 2 17 8 7 4 3 11 2 8 2 9 3 19 2 5 2 16 11 6 5 13 2 17 5 11 11 8 7 20 2 11 6 14 7 3 12 2 5 7 12 2 11 6 14 7 15 3 2 22 3 18 6 11 3 2 8 4 2 5 2 13 8 10 4 3 4 2 4 10 3 9 3 7 2 7 6 4 2 10 8 7 20 2 14 7 12 3 2 4 10 3 2 18 8 11 3 12 2 4 11 3 3 9 2 22 14 4 2 8 13 2 7 6 2 9 6 15 3 2 4 10 7 20 2 15 14 9 2 22 3 2 4 10 5 11 +mls_eng_000268 3 8 19 2 8 9 2 22 3 19 6 7 2 4 10 6 14 4 2 4 10 5 4 2 9 6 15 3 2 21 3 22 13 3 2 10 5 3 2 15 5 7 8 9 3 4 6 2 9 5 18 2 15 5 7 19 2 4 10 8 7 20 9 2 5 7 2 6 18 2 16 6 14 11 9 3 9 2 4 10 3 2 26 8 11 15 3 5 7 4 2 10 5 9 2 5 2 9 6 15 5 8 8 9 3 2 5 9 2 15 5 14 4 10 2 4 6 17 6 14 2 6 11 3 2 4 10 11 3 2 12 5 9 20 3 2 5 18 3 2 10 3 2 18 8 11 9 8 2 21 11 3 16 6 14 16 8 4 8 6 7 9 2 12 3 2 12 11 6 14 21 3 4 2 8 7 2 8 7 6 17 5 11 9 +mls_eng_000269 2 9 6 7 2 5 2 15 5 7 2 16 5 15 3 2 6 14 4 2 4 6 2 15 3 2 10 8 15 2 4 10 8 9 2 15 5 7 2 17 5 9 2 6 5 13 6 10 5 19 2 5 2 22 3 11 12 13 3 9 9 2 15 5 7 12 2 22 3 13 7 8 7 20 2 4 6 2 5 2 13 3 9 2 11 6 23 22 14 11 2 16 13 5 7 2 10 8 10 17 8 16 10 2 8 7 2 18 3 9 4 3 12 2 4 10 3 2 12 9 4 11 16 4 2 22 21 14 9 4 8 22 13 19 2 5 9 8 9 4 8 7 20 2 4 10 3 2 5 7 2 14 7 4 3 11 9 2 6 18 2 10 3 2 4 3 15 21 13 2 8 7 9 16 8 11 8 7 20 2 23 8 16 4 14 15 9 2 18 6 11 2 4 10 3 2 4 3 15 22 13 3 2 13 13 4 3 11 9 +mls_eng_000270 2 17 3 2 5 11 3 2 7 6 4 2 13 6 23 3 8 11 9 2 19 6 14 2 5 7 2 8 19 10 2 5 21 6 7 2 4 10 8 9 2 9 14 7 19 2 13 5 7 5 12 2 4 22 14 4 2 16 10 6 13 13 12 3 6 7 2 17 10 3 2 5 23 3 2 7 3 23 3 11 2 7 6 7 2 4 2 13 6 23 18 9 2 26 8 6 11 2 19 6 14 11 2 21 3 +mls_eng_000271 3 17 5 9 2 15 14 11 12 3 12 2 6 7 2 10 3 9 6 11 9 4 3 23 18 2 17 8 9 6 5 13 2 10 6 9 3 9 2 17 5 9 2 8 2 5 7 2 6 13 2 15 5 7 2 5 9 4 2 5 7 12 4 11 2 5 2 16 5 15 2 13 19 2 15 8 9 3 11 3 2 27 6 14 6 12 8 5 11 2 19 6 14 2 4 6 24 5 9 8 18 3 2 10 5 12 2 12 8 20 3 2 8 7 2 10 8 9 2 22 3 5 4 12 2 19 6 2 10 3 2 7 6 23 3 6 7 8 4 8 6 7 2 5 12 2 19 14 2 16 5 7 6 2 14 7 12 3 9 4 5 3 17 12 2 8 7 2 15 8 7 9 2 10 7 2 5 7 8 7 20 14 8 16 8 4 19 2 8 9 2 15 11 12 3 4 +mls_eng_000272 22 19 2 20 6 11 12 2 10 8 9 5 3 12 2 4 10 3 2 17 6 7 12 2 15 3 2 10 3 11 2 22 8 9 7 9 4 2 4 10 8 12 9 2 15 3 2 8 7 2 5 7 2 6 14 4 6 15 7 19 2 10 6 14 9 3 9 2 6 3 2 10 5 4 2 12 6 2 8 19 2 16 5 11 2 18 6 11 2 4 10 3 2 9 8 16 11 9 4 2 4 10 5 4 2 15 5 2 23 3 2 10 5 12 2 7 2 4 10 5 11 2 10 6 17 2 17 3 23 3 11 2 8 14 2 16 14 7 6 4 2 21 13 8 7 20 2 17 8 9 2 21 3 22 3 18 6 11 2 4 10 3 2 17 6 14 16 10 18 13 7 3 9 2 4 10 3 2 22 13 5 14 4 2 10 6 14 3 9 2 6 18 2 10 8 9 18 15 7 6 11 8 2 5 11 3 2 5 2 8 7 2 3 23 18 3 11 19 2 9 4 11 3 3 21 4 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..92487cbe1bda269e076a964aa80520e11d618e2f --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/score @@ -0,0 +1,10 @@ +mls_eng_000273 tensor(-43.5789) +mls_eng_000274 tensor(-29.2308) +mls_eng_000275 tensor(-22.3335) +mls_eng_000276 tensor(-29.1365) +mls_eng_000277 tensor(-38.5878) +mls_eng_000278 tensor(-37.2069) +mls_eng_000279 tensor(-54.5134) +mls_eng_000280 tensor(-48.1146) +mls_eng_000281 tensor(-26.3066) +mls_eng_000282 tensor(-41.3668) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..3a345c4e0e7e701d60aaaee9609ddc4745ac2b59 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/text @@ -0,0 +1,10 @@ +mls_eng_000273 UOYABL ET WAS PULING IT NO ASTIK NORASTON WAS INDREGT OF HE HAND AND THE BIYLI GRAGS EQULD ON NOLONG SRE ABEL THERUWAROFWARTHE FOLED SHE STRUOED BU ONOUE AND GASE THE GON BUT ONLY VELED HERSE GN CASTO +mls_eng_000274 AND A FALY OFE IT WAS THAT AN NOTHE WOMENDT OVE SARNY WRICHE FOR NOTHING BETEDEN TO GO SINTO MY SISTER AND FARTHE US CSENT WAY SHE SAID I SOULRATHER GO WITHIM I HAVE NO MI E TOSTAY HEAR ALONITD MY TWOE BABES +mls_eng_000275 INIDIT A CORDINGLY WHENTDRING HA TE LITE MAN WOL BE AT AND HE PEAED TO F THESTOUT ISYRUSHIS HKCU FINED WEITDELITAL BUNE OF B +mls_eng_000276 IT IS NO THE DREDIUL NAT CMS ON HOE DESME AS THE PLEN WER THE PUSIONS A THE INGLSH FONE TE BOF TEN THOUSEN SLEAIN BREVE WELINGTO AM BLOKER BORTH MOS NOBLY DROVE THERFOURS AND BONOPARTE AMPEYAR CRON WAS TEKEN AT WOTEL +mls_eng_000277 S SOMEYUOUS AGLLE HAFE TE MAKING ORE ARINGENS FO THEING CAMPENT ATD NIHT WIT CONSTANTLY HAD OVE AD PESUL RESROKON BY A TRIB OF BROUN MUNKES THE EIDINTY THOUGHT THAT LONG POSESTION HAD GIMEN THE A PRIARE LAM TO THE GLL +mls_eng_000278 TO FLASH IN HAT OME CPLEMIDING FOR HE MEAD ETWEEN RSS WAN ESTENS PARTY AND THE OPRE IFONLYFIR MINT SERTOLYE TWAS MOR THAN A MINIT THAT SMOND REMANED A THE FIER HOUSES AOUFTER BENG GROGD BAKE OFTERE DINER INTETACK +mls_eng_000279 AND INDICTIONARY AN DENWE HADE COUS TENINCXS WE COLE THR A GEAVMANY FIGERS IN CINGE A LIFE MIOUTION WAVEWHAT FERY LIKH MUSICK STILS OVE THE SE LIHELY ROE LIATLY ROE OR THE GLASY WAY WUD GOL AND O KOM COME WAY AND OTHER SONGS MISIS JUGHE THAL ERBOT ONDSONG UN PERPSFORAUS +mls_eng_000280 THAT WHICH BASHES OF HISTRYIG UR SCHLLS AER GOVERMENTLY FABROCATEDBP SOMEN HISTRY IS FIRGURY A MRSREPRES IENTATION OF HEVENCE S LICE THE LDROAWM AR SENTERING UPON THEMPOSEABLE FIGIR OF THE HEARO WIT HE JESTICILATING CROUD IN TEBAROUNED +mls_eng_000281 AN HE KELIVF HRD THIS HE SAID OALL OFOURE HOW GOODLY IS THAT FOYSCE AND THE ESIE REBPLAID AOLE OUR LORD NEVER SMORT MY HAING ORT TRETERE ORE GODLYEARE THAN THE SANGING +mls_eng_000282 EVIDENTELY THE LARNED BAIRAN HAD NOT STDADYED SOUCH WORK S O THE TOTEA COAHINIY I ORE PIRIT C AT WHICH NOTIBLY TRANSLATED BY NUGH SHABEY FRME THE SANSESGRIT SIOK ASEPP TUTY HAS NO BOCOME AS ORTHEDOCICLY MOSLAME AS THENIGTES diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..20e5f6671713d75b3364135af2d5311d82cee1ac --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/token @@ -0,0 +1,10 @@ +mls_eng_000273 U O Y A B L E T W A S P U L I N G I T N O A S T I K N O R A S T O N W A S I N D R E G T O F H E H A N D A N D T H E B I Y L I G R A G S E Q U L D O N N O L O N G S R E A B E L T H E R U W A R O F W A R T H E F O L E D S H E S T R U O E D B U O N O U E A N D G A S E T H E G O N B U T O N L Y V E L E D H E R S E G N C A S T O +mls_eng_000274 A N D A F A L Y O F E I T W A S T H A T A N N O T H E W O M E N D T O V E S A R N Y W R I C H E F O R N O T H I N G B E T E D E N T O G O S I N T O M Y S I S T E R A N D F A R T H E U S C S E N T W A Y S H E S A I D I S O U L R A T H E R G O W I T H I M I H A V E N O M I E T O S T A Y H E A R A L O N I T D M Y T W O E B A B E S +mls_eng_000275 I N I D I T A C O R D I N G L Y W H E N T D R I N G H A T E L I T E M A N W O L B E A T A N D H E P E A E D T O F T H E S T O U T I S Y R U S H I S H K C U F I N E D W E I T D E L I T A L B U N E O F B +mls_eng_000276 I T I S N O T H E D R E D I U L N A T C M S O N H O E D E S M E A S T H E P L E N W E R T H E P U S I O N S A T H E I N G L S H F O N E T E B O F T E N T H O U S E N S L E A I N B R E V E W E L I N G T O A M B L O K E R B O R T H M O S N O B L Y D R O V E T H E R F O U R S A N D B O N O P A R T E A M P E Y A R C R O N W A S T E K E N A T W O T E L +mls_eng_000277 S S O M E Y U O U S A G L L E H A F E T E M A K I N G O R E A R I N G E N S F O T H E I N G C A M P E N T A T D N I H T W I T C O N S T A N T L Y H A D O V E A D P E S U L R E S R O K O N B Y A T R I B O F B R O U N M U N K E S T H E E I D I N T Y T H O U G H T T H A T L O N G P O S E S T I O N H A D G I M E N T H E A P R I A R E L A M T O T H E G L L +mls_eng_000278 T O F L A S H I N H A T O M E C P L E M I D I N G F O R H E M E A D E T W E E N R S S W A N E S T E N S P A R T Y A N D T H E O P R E I F O N L Y F I R M I N T S E R T O L Y E T W A S M O R T H A N A M I N I T T H A T S M O N D R E M A N E D A T H E F I E R H O U S E S A O U F T E R B E N G G R O G D B A K E O F T E R E D I N E R I N T E T A C K +mls_eng_000279 A N D I N D I C T I O N A R Y A N D E N W E H A D E C O U S T E N I N C X S W E C O L E T H R A G E A V M A N Y F I G E R S I N C I N G E A L I F E M I O U T I O N W A V E W H A T F E R Y L I K H M U S I C K S T I L S O V E T H E S E L I H E L Y R O E L I A T L Y R O E O R T H E G L A S Y W A Y W U D G O L A N D O K O M C O M E W A Y A N D O T H E R S O N G S M I S I S J U G H E T H A L E R B O T O N D S O N G U N P E R P S F O R A U S +mls_eng_000280 T H A T W H I C H B A S H E S O F H I S T R Y I G U R S C H L L S A E R G O V E R M E N T L Y F A B R O C A T E D B P S O M E N H I S T R Y I S F I R G U R Y A M R S R E P R E S I E N T A T I O N O F H E V E N C E S L I C E T H E L D R O A W M A R S E N T E R I N G U P O N T H E M P O S E A B L E F I G I R O F T H E H E A R O W I T H E J E S T I C I L A T I N G C R O U D I N T E B A R O U N E D +mls_eng_000281 A N H E K E L I V F H R D T H I S H E S A I D O A L L O F O U R E H O W G O O D L Y I S T H A T F O Y S C E A N D T H E E S I E R E B P L A I D A O L E O U R L O R D N E V E R S M O R T M Y H A I N G O R T T R E T E R E O R E G O D L Y E A R E T H A N T H E S A N G I N G +mls_eng_000282 E V I D E N T E L Y T H E L A R N E D B A I R A N H A D N O T S T D A D Y E D S O U C H W O R K S O T H E T O T E A C O A H I N I Y I O R E P I R I T C A T W H I C H N O T I B L Y T R A N S L A T E D B Y N U G H S H A B E Y F R M E T H E S A N S E S G R I T S I O K A S E P P T U T Y H A S N O B O C O M E A S O R T H E D O C I C L Y M O S L A M E A S T H E N I G T E S diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..8ba1a9df3734cf96bf5cdc8c0d5d71a3d0e3bdbb --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/token_int @@ -0,0 +1,10 @@ +mls_eng_000273 14 6 19 5 22 13 2 3 4 2 17 5 9 2 21 14 13 8 7 20 2 8 4 2 7 6 2 5 9 4 8 24 2 7 6 11 5 9 4 6 7 2 17 5 9 2 8 7 12 11 3 20 4 2 6 18 2 10 3 2 10 5 7 12 2 5 7 12 2 4 10 3 2 22 8 19 13 8 2 20 11 5 20 9 2 3 27 14 13 12 2 6 7 2 7 6 13 6 7 20 2 9 11 3 2 5 22 3 13 2 4 10 3 11 14 17 5 11 6 18 17 5 11 4 10 3 2 18 6 13 3 12 2 9 10 3 2 9 4 11 14 6 3 12 2 22 14 2 6 7 6 14 3 2 5 7 12 2 20 5 9 3 2 4 10 3 2 20 6 7 2 22 14 4 2 6 7 13 19 2 23 3 13 3 12 2 10 3 11 9 3 2 20 7 2 16 5 9 4 6 +mls_eng_000274 5 7 12 2 5 2 18 5 13 19 2 6 18 3 2 8 4 2 17 5 9 2 4 10 5 4 2 5 7 2 7 6 4 10 3 2 17 6 15 3 7 12 4 2 6 23 3 2 9 5 11 7 19 2 17 11 8 16 10 3 2 18 6 11 2 7 6 4 10 8 7 20 2 22 3 4 3 12 3 7 2 4 6 2 20 6 2 9 8 7 4 6 2 15 19 2 9 8 9 4 3 11 2 5 7 12 2 18 5 11 4 10 3 2 14 9 2 16 9 3 7 4 2 17 5 19 2 9 10 3 2 9 5 8 12 2 8 2 9 6 14 13 11 5 4 10 3 11 2 20 6 2 17 8 4 10 8 15 2 8 2 10 5 23 3 2 7 6 2 15 8 2 3 2 4 6 9 4 5 19 2 10 3 5 11 2 5 13 6 7 8 4 12 2 15 19 2 4 17 6 3 2 22 5 22 3 9 +mls_eng_000275 2 8 7 8 12 8 4 2 5 2 16 6 11 12 8 7 20 13 19 2 17 10 3 7 4 12 11 8 7 20 2 2 10 5 2 4 3 2 13 8 4 3 2 15 5 7 2 17 6 13 2 22 3 2 5 4 2 5 7 12 2 10 3 2 21 3 5 3 12 2 2 4 6 2 18 2 4 10 3 9 4 6 14 4 2 8 9 19 11 14 9 10 8 9 2 10 24 16 14 2 18 8 7 3 12 2 17 3 8 4 12 3 13 8 4 5 13 2 22 14 7 3 2 6 18 2 22 +mls_eng_000276 8 4 2 8 9 2 7 6 2 4 10 3 2 12 11 3 12 8 14 13 2 7 5 4 2 16 15 9 2 6 7 2 10 6 3 2 12 3 9 15 3 2 5 9 2 4 10 3 2 21 13 3 7 2 17 3 11 2 4 10 3 2 21 14 9 8 6 7 9 2 5 2 4 10 3 2 8 7 20 13 9 10 2 18 6 7 3 2 4 3 2 22 6 18 2 4 3 7 2 4 10 6 14 9 3 7 2 9 13 3 5 8 7 2 22 11 3 23 3 2 17 3 13 8 7 20 4 6 2 5 15 2 22 13 6 24 3 11 2 22 6 11 4 10 2 15 6 9 2 7 6 22 13 19 2 12 11 6 23 3 2 4 10 3 11 18 6 14 11 9 2 5 7 12 2 22 6 7 6 21 5 11 4 3 2 5 15 21 3 19 5 11 2 16 11 6 7 2 17 5 9 2 4 3 24 3 7 2 5 4 2 17 6 4 3 13 +mls_eng_000277 9 2 9 6 15 3 19 14 6 14 9 2 5 20 13 13 3 2 10 5 18 3 2 4 3 2 15 5 24 8 7 20 2 6 11 3 2 5 11 8 7 20 3 7 9 2 18 6 2 4 10 3 8 7 20 2 16 5 15 21 3 7 4 2 5 4 12 2 7 8 10 4 2 17 8 4 2 16 6 7 9 4 5 7 4 13 19 2 10 5 12 2 6 23 3 2 5 12 2 21 3 9 14 13 2 11 3 9 11 6 24 6 7 2 22 19 2 5 2 4 11 8 22 2 6 18 2 22 11 6 14 7 2 15 14 7 24 3 9 2 4 10 3 2 3 8 12 8 7 4 19 2 4 10 6 14 20 10 4 2 4 10 5 4 2 13 6 7 20 2 21 6 9 3 9 4 8 6 7 2 10 5 12 2 20 8 15 3 7 2 4 10 3 2 5 2 21 11 8 5 11 3 2 13 5 15 2 4 6 2 4 10 3 2 20 13 13 +mls_eng_000278 4 6 2 18 13 5 9 10 2 8 7 2 10 5 4 2 6 15 3 2 16 21 13 3 15 8 12 8 7 20 2 18 6 11 2 10 3 2 15 3 5 12 2 3 4 17 3 3 7 2 11 9 9 2 17 5 7 2 3 9 4 3 7 9 2 21 5 11 4 19 2 5 7 12 2 4 10 3 2 6 21 11 3 2 8 18 6 7 13 19 18 8 11 2 15 8 7 4 2 9 3 11 4 6 13 19 3 2 4 17 5 9 2 15 6 11 2 4 10 5 7 2 5 2 15 8 7 8 4 2 4 10 5 4 2 9 15 6 7 12 2 11 3 15 5 7 3 12 2 5 2 4 10 3 2 18 8 3 11 2 10 6 14 9 3 9 2 5 6 14 18 4 3 11 2 22 3 7 20 2 20 11 6 20 12 2 22 5 24 3 2 6 18 4 3 11 3 2 12 8 7 3 11 2 8 7 4 3 4 5 16 24 +mls_eng_000279 5 7 12 2 8 7 12 8 16 4 8 6 7 5 11 19 2 5 7 2 12 3 7 17 3 2 10 5 12 3 2 16 6 14 9 2 4 3 7 8 7 16 25 9 2 17 3 2 16 6 13 3 2 4 10 11 2 5 2 20 3 5 23 15 5 7 19 2 18 8 20 3 11 9 2 8 7 2 16 8 7 20 3 2 5 2 13 8 18 3 2 15 8 6 14 4 8 6 7 2 17 5 23 3 17 10 5 4 2 18 3 11 19 2 13 8 24 10 2 15 14 9 8 16 24 2 9 4 8 13 9 2 6 23 3 2 4 10 3 2 9 3 2 13 8 10 3 13 19 2 11 6 3 2 13 8 5 4 13 19 2 11 6 3 2 6 11 2 4 10 3 2 20 13 5 9 19 2 17 5 19 2 17 14 12 2 20 6 13 2 5 7 12 2 6 2 24 6 15 2 16 6 15 3 2 17 5 19 2 5 7 12 2 6 4 10 3 11 2 9 6 7 20 9 2 15 8 9 8 9 2 26 14 20 10 3 2 4 10 5 13 2 3 11 22 6 4 2 6 7 12 9 6 7 20 2 14 7 2 21 3 11 21 9 18 6 11 5 14 9 +mls_eng_000280 4 10 5 4 2 17 10 8 16 10 2 22 5 9 10 3 9 2 6 18 2 10 8 9 4 11 19 8 20 2 14 11 2 9 16 10 13 13 9 2 5 3 11 2 20 6 23 3 11 15 3 7 4 13 19 2 18 5 22 11 6 16 5 4 3 12 22 21 2 9 6 15 3 7 2 10 8 9 4 11 19 2 2 8 9 2 18 8 11 20 14 11 19 2 5 2 15 11 9 11 3 21 11 3 9 2 8 3 7 4 5 4 8 6 7 2 6 18 2 10 3 23 3 7 16 3 2 9 2 13 8 16 3 2 4 10 3 2 13 12 11 6 5 17 15 2 5 11 2 9 3 7 4 3 11 8 7 20 2 14 21 6 7 2 4 10 3 15 21 6 9 3 5 22 13 3 2 18 8 20 8 11 2 6 18 2 4 10 3 2 10 3 5 11 6 2 17 8 4 2 10 3 2 26 3 9 4 8 16 8 13 5 4 8 7 20 2 16 11 6 14 12 2 8 7 2 4 3 22 5 11 6 14 7 3 12 +mls_eng_000281 5 7 2 10 3 2 24 3 13 8 23 18 2 10 11 12 2 4 10 8 9 2 10 3 2 9 5 8 12 2 6 5 13 13 2 6 18 6 14 11 3 2 10 6 17 2 20 6 6 12 13 19 2 8 9 2 4 10 5 4 2 18 6 19 9 16 3 2 5 7 12 2 4 10 3 2 3 9 8 3 2 11 3 22 21 13 5 8 12 2 5 6 13 3 2 6 14 11 2 13 6 11 12 2 7 3 23 3 11 2 9 15 6 11 4 2 15 19 2 10 5 8 7 20 2 6 11 4 2 4 11 3 4 3 11 3 2 6 11 3 2 20 6 12 13 19 3 5 11 3 2 4 10 5 7 2 4 10 3 2 9 5 7 20 8 7 20 +mls_eng_000282 3 23 8 12 3 7 4 3 13 19 2 4 10 3 2 13 5 11 7 3 12 2 22 5 8 11 5 7 2 10 5 12 2 7 6 4 2 9 4 12 5 12 19 3 12 2 9 6 14 16 10 2 17 6 11 24 2 9 2 6 2 4 10 3 2 4 6 4 3 5 2 16 6 5 10 8 7 8 19 2 8 2 6 11 3 2 21 8 11 8 4 2 16 2 5 4 2 2 17 10 8 16 10 2 7 6 4 8 22 13 19 2 4 11 5 7 9 13 5 4 3 12 2 22 19 2 7 14 20 10 2 9 10 5 22 3 19 2 18 11 15 3 2 4 10 3 2 9 5 7 9 3 9 20 11 8 4 2 9 8 6 24 2 5 9 3 21 21 2 4 14 4 19 2 10 5 9 2 7 6 2 22 6 16 6 15 3 2 5 9 2 2 6 11 4 10 3 12 6 16 8 16 13 19 2 15 6 9 13 5 15 3 2 5 9 2 4 10 3 7 8 20 4 3 9 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score new file mode 100644 index 0000000000000000000000000000000000000000..2e02d66a6b9871e8eff9580bda6cf91089c48e6f --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score @@ -0,0 +1,40 @@ +mls_eng_000243 tensor(-36.1665) +mls_eng_000244 tensor(-29.0905) +mls_eng_000245 tensor(-43.1050) +mls_eng_000246 tensor(-22.5313) +mls_eng_000247 tensor(-23.9541) +mls_eng_000248 tensor(-50.9715) +mls_eng_000249 tensor(-34.6205) +mls_eng_000250 tensor(-30.0281) +mls_eng_000251 tensor(-28.0966) +mls_eng_000252 tensor(-22.1428) +mls_eng_000253 tensor(-19.5110) +mls_eng_000254 tensor(-35.3209) +mls_eng_000255 tensor(-17.3090) +mls_eng_000256 tensor(-23.9095) +mls_eng_000257 tensor(-36.7483) +mls_eng_000258 tensor(-34.3075) +mls_eng_000259 tensor(-44.7388) +mls_eng_000260 tensor(-28.7605) +mls_eng_000261 tensor(-52.9342) +mls_eng_000262 tensor(-25.9552) +mls_eng_000263 tensor(-35.7861) +mls_eng_000264 tensor(-43.5149) +mls_eng_000265 tensor(-42.0253) +mls_eng_000266 tensor(-45.5519) +mls_eng_000267 tensor(-23.1777) +mls_eng_000268 tensor(-32.7009) +mls_eng_000269 tensor(-34.7996) +mls_eng_000270 tensor(-21.2846) +mls_eng_000271 tensor(-39.1876) +mls_eng_000272 tensor(-33.2570) +mls_eng_000273 tensor(-43.5789) +mls_eng_000274 tensor(-29.2308) +mls_eng_000275 tensor(-22.3335) +mls_eng_000276 tensor(-29.1365) +mls_eng_000277 tensor(-38.5878) +mls_eng_000278 tensor(-37.2069) +mls_eng_000279 tensor(-54.5134) +mls_eng_000280 tensor(-48.1146) +mls_eng_000281 tensor(-26.3066) +mls_eng_000282 tensor(-41.3668) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..8eff576cce5a6e19277519cf0167d4e846ff8214 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/hyp.trn @@ -0,0 +1,40 @@ +I A E T H I R U E O F T E K E N A O L D E S F R O N Y O U S H I S A T H A S T A L Y I S I A N O W T H E I T S I M P O S E A B L E T H A V E F A T H I N O U N B A S I Y N O H E I T A S Y U S E S T E C P E C E T A N Y R E T E R E N F O R Y O U G F O R A L I H A V E D O N I O W N T N O M O R O F Y O U (mls_eng_000243-mls_eng_000243) +T H A T H E M A Y S O M N T I M E S B E E L A K C K O U T H E R C H E L E D R E N L E R N I N G T E S I D E M Y N E O R P L A I N G P R A T L I N D S I C I N G F O R H E L P E S C O M S T O Y H E A R T A T S S H I N F U L O R D A N S P E A K I N H O W C G O D T H O A R T E (mls_eng_000244-mls_eng_000244) +R E N S E N T H I N G S O F A L S O R E A S I T H E J E N R A L O U T B U R S T O F M O L T E I T O D N E S S P A S T I O N A R H U T E D T O G E T H E R T H E L I T U R C R I S N A Y T H E R E D D I E K I L S W I T H E H A R A B L E F O A R E O V E R T H E B I L Y S E O F H E A D S M A B E S E M E R A S T K G A L A D Y C A P R Y A L I N G O N C O R S E S F O T H E R O I L S T O I D (mls_eng_000245-mls_eng_000245) +I T M A Y H A V E B E N T A T T H E B O N E S W E R E R F O L D E D T O G E T H E R A N D N O N E A S O N Y H E P E L Y B O N S F O L D E D A N D L A I D W A Y F O T H E P E R P S C E S O F I N C E N T A T I O N S U C H B U N D L E S O F B O N E S W E P U T T H O R W E A P R O S E S S O F P R A I R S (mls_eng_000246-mls_eng_000246) +M A R S S A I L E S N E V E R E C S P E R I N C E D T H O S G R A T T R A E N S C I T I O N S F O M L O N E S T O G R E A N D E U R T H I S W A S O N I N G T O T H E P R O D I N T C O N D U C T T H A T R E P O B L I K E W H I C H A L W A Y E P R E S E R V E H E P I N C I P L E (mls_eng_000247-mls_eng_000247) +A T S M L E R E H I N G S T I O N O N M T H E M O R I N G O F U P T O E R T H R T Y O N C O D U T E D I T H E A P S F H E R O F C O E S P E R S Y A N D A I T A N D E D B Y B R O A D Y W E R E T O L H A T T H E N O R M W L A D E N T S E R E Q U R I R M E T F O R R E V I U O R E P R O V L H A D B E N W A E T T H A T N O R M L E A E P R V L O T H E M A R O F W A S I N G T I N N D S R T N G O V E N R S W O D B E H A N D L D I N F O R M I L Y (mls_eng_000248-mls_eng_000248) +T T H E B O O D I S T L A T D Y I G C H I N E R H O L D O N O H A S T A T E T O L T A K L I G F O R T H E P E R V E R S O F O D S O F T H E R C O N C H O N C S F O R O M G T U C K T O T I B Y B Y I N G V E I R S D F I A S H I E I S Y O U S E A D T E R A N D L E T I N T H E G O L L (mls_eng_000249-mls_eng_000249) +T T H I E A G A N I S S O F E N D A N D T U M P E R D B Y A S E M B L E F A T H A N T H E S U P R O M I R S Y O F L O V E O V E R F E R A N D U N B W N T E D U M A N I T Y A N D C H I R I Y F O R T H E P O R A N H E L P L E S A N U N C O D I O N A L F O R G I V E N E S O F T H E D I R S T I N E R E S W H I C H I S T H E N O T O F T H E N O B L A E N E R O U S I T Y A N D L I B E R A L I T Y (mls_eng_000250-mls_eng_000250) +T H E S E C E N D M A D E F A L L O E D A N D H E C O U P L O T H E S E M E R S M E N B R O T H E P B O R T H B A R K E B E F O R C U C H I N G A R O U P T H E W E N T O W A R K T S U R C H T H S H I T E L I F E D D E H A C H E S A N D F A O W N D T H E H O L D F U L L O F C O R G O (mls_eng_000251-mls_eng_000251) +F O N D O F H I S C O M E R A D E A N D R E S P E T F U L E I T O H I S P A S T U R A N D M A S T E R S E V E N S C H L M U S T E R S A S H E L A D H E P R E P E R S F O R A N H O D W T H W I L A N D T H I S T R A I N G O C K U P Y E H I M T H U R E O U T Y O U T H T I I D (mls_eng_000252-mls_eng_000252) +A S S W H E T H E P R E N E S Q U P E C E A E R E C H E T E R S I X T I N T Y E A R S H B E C A M E B L I N G D W H E R L A R G H S O F B O N I C E S H A D O L I D I N G T H M (mls_eng_000253-mls_eng_000253) +A O L M O S T A L L E D Y T H E B A T E R A N G E D B U T C E T H T W O M E N B A K I N F O R T T H E Y F O U R S Y E C H E O T H E R O V E R T H E L A V A B E D S T H E S H I V F S W E L O I E B O Y W A S V E R Y D I F I C A L T F O R H E O A L O H A Y T O G R A S P B R O S E A N D B L E A D I N G F R O M R E P E T E D F L L E S O N T H E R O U F H L A V A (mls_eng_000254-mls_eng_000254) +B P O S Y I Y T R E B I T T E R R A R A N D A W L A L P A N T I N G T H E W H I N M O N D T T H R O G T H E T R E E S O F T H E C A R D I N A N D F O M E T I M E T O T I M E M S E E A S I F T O A (mls_eng_000255-mls_eng_000255) +H I S M E N T L T O R P T I T Y F O U N D E T A P O N F I S I O C L I N D L E A N C E R E N D E R S A M E D I A T A C T I O N A N A L M A E N E R O E S R T I O N D D U S T E A S F U L H I S C O N C H U S W E K N E S S H O S I T D S E L F (mls_eng_000256-mls_eng_000256) +T N O R E T H A E L E H O W G L A D T H E C I N G M T H E W A S N O R H O U E G R A T D W O E R T H E R E Y G J U O R I S I N G O F T H E P E P L E O R H O M M E N I F E S S E N D T D W A S T H E R O I L B A N K Q C E D T H A T G O O D C F I N G P O M E R E A R A D T E N D E T D W H I T H A L H E R C O R E N (mls_eng_000257-mls_eng_000257) +A N D T H E C H O N C E O F T H E R B E N G S U C H A O N U N G I A N D I M I S H E S B Y E W E R Y P R A P E P R O S E S S M A M E L U K A S A E O R S W H O S O F E A K C U E A N C Q U L I T Y E R E A S N I N G N O T A B O W T D H I S O D E R S B U T B O U L T H E I Y T O D O T H E T (mls_eng_000258-mls_eng_000258) +T S H E N O K E T B U T S N O R O U P L O E O A T H O F T E W H E N D O A N S A I D I D E A E R N O T O B P B H E D O R F O R T H E D O R S H A V E T O D M E T O L T N O O I N I N T H A T I S H A R E D F O R M E E S A I D T H E O M E O N F O R I M U S T T A K B A C E M Y A U P L S B U T T H E R I S O N E W I C H I U L L G I V E Y O U A N D S H E H E L D U P E N A L (mls_eng_000259-mls_eng_000259) +O V Y M O R Y S P O K A K C E L T H O R A N D H E R S E F I N L Y S P O K E N I H T T W O M Y P E S W A S W H Y F E A B E Y A R Y O C M S O S O N W E R E O U R B R Y C H I L D N D O T H A N A R T D L S P O K S A M E O A N (mls_eng_000260-mls_eng_000260) +I N E V E R U A N Y W O E N H O L E T D O G O T Y C E R C H I S M C H A C S G R A M O H D O U S S H E H A I S H E W O U D R A V E B A D O R E C E P E I T H H A S O F R G O D T H E N I W E L L T H E T E N S O F W I K E D N E S T E D O N H V E W O M E N D O R E K E P E R S A N I W E N O S H E W I N U T T W E L L M I N I N A A T E N T (mls_eng_000261-mls_eng_000261) +T H E D U K W A S S U P R I C S T O S E H M O T B R I N G O O T S O A L Y A B E L A R D D E M A N D D H E O L Y O U R G R A I C T E D R E P L I H E U T L R Y G A S P I N G F O R U T E R A N C E S (mls_eng_000262-mls_eng_000262) +F O R E I U A T S E M I N G T R O N S E N M E U T A T I O N S O F C A L E M A B E M A D W E T H E R I S N Y M I C X T R O F D E V O R S O U T S O R A E S B R I N S A C H M I C T E R S T H E C O M P O N E L I S A P E R E N O T B Y T H E M U T I L A L A I N G A H E T H E C O N C T U T E A M I D L I N G C O L E R (mls_eng_000263-mls_eng_000263) +A N A L M O S T H E S A M E I N S T A N T C E N S R S A N D Y S I D S T E P I N T O A S H O P D O R W A Y H E W A D E D T H E I N T I K C H I N C A M E U P C I N S T D A U P E A N D R E T E N D E T O S T A R T H T H E G L A S A T H D E S P L A Y O F H E R D W E R I N G T U O U L E S W E R H E C O N T I N E D T O W O U C H B A I R I C K O U S E W A T I S H A I N B Y S A I D R I N N O T E D (mls_eng_000264-mls_eng_000264) +T T H E N T H E T H I E K G R E N E S T A V F L O L E D O L R T H E H L B E L D I N A N D T H E W H A S N O T H I N G T O E S S E E N T H E R B U T D E M A O U D O F S O F E T D F L O I N G G R A Y G R E N S T A R T H E D R U S H E D O N N O W H W T H E S W E I F T N E S O L L W I N G K E A L O O E T A B D I N T O B E R S F A E (mls_eng_000265-mls_eng_000265) +I H A V E M A E S U R C K R O F I S E S T O E B U R O S W H E N I A N E U T H A T H E W R E N O T M Y H A P E N E S S W A S A F T E R I S S A O L T H A T H A D S T E P E D T A G H E R I S O A R T H A T Y O U R T E N D E R N E S A D T E R N D T O C O U L D I L A T I O N A O F T E R S A O R T H A T Y O U C E I R D F O R Y O U R S E L F O N L Y N O T F O R M E (mls_eng_000266-mls_eng_000266) +Y A T D T H A T H A N D T E R N E V E R R A L S I N W I N T E R I S E Y A C R O A L W A R R I N G R O U N E D A N D R O U N M E B E F O R E I T A L I H T E T T H E S E N N O T H I N G U N D E T H E F I R E D T R E E S B U T I L N O S O M E T H N G M U S B E T H A R (mls_eng_000267-mls_eng_000267) +E I Y I S B E Y O N T H O U T T H A T S O M E P E B L E H A E M A N I S E T O S A F M A N Y T H I N G S A N O F C O U R S E S T H E J I R M E A N T H A S A S O M A I I S E A S M A U T H T O W O U O R E T H R E D A S G E A F E H E F I R S I P R E C O U C I T I O N S D E D R O U P E T I N I N O W A R S (mls_eng_000268-mls_eng_000268) +S O N A M A N C A M E O U T T O M E H I M T H I S M A N W A S O A L O H A Y A B E R D L E S S M A N D B E L N I N G T O A L E S R O V B U R C L A N H I H W I C H I N F E S T E D T H E D S T R C T B P U S T I B L Y A S I S T I N G T H E A N U N T E R S O F H E T E M P L I N S C I R I N G V I C T U M S F O R T H E T E M B L E L L T E R S (mls_eng_000269-mls_eng_000269) +W E A R E N O T L O V E I R S Y O U A N I Y H A P O N T H I S S U N Y L A N A D T B U T C H O L L D E O N W H E A V E N E V E R N O N T L O V F S J I O R Y O U R P E (mls_eng_000270-mls_eng_000270) +E W A S M U R D E D O N H E S O R S T E V F W I S O A L H O S E S W A S I A N O L M A N A S T A N D T R A C A M L Y M I S E R E Q O U O D I A R Y O U T O K A S I F E H A D D I G E I N H I S B E A T D Y O H E N O V E O N I T I O N A D Y U C A N O U N D E S T A E W D I N M I N S H N A N I N G U I C I T Y I S M R D E T (mls_eng_000271-mls_eng_000271) +B Y G O R D H I S A E D T H E W O N D M E H E R B I S N S T T H I D S M E I N A N O U T O M N Y H O U S E S O E H A T D O I Y C A R F O R T H E S I C R S T T H A T M A V E H A D N T H A R H O W W E V E R I U C U N O T P L I N G W I S P E B E F O R T H E W O U C H F L N E S T H E B L A U T H O U E S O F H I S F M N O R I A R E A I N E V F E R Y S T R E E P T (mls_eng_000272-mls_eng_000272) +U O Y A B L E T W A S P U L I N G I T N O A S T I K N O R A S T O N W A S I N D R E G T O F H E H A N D A N D T H E B I Y L I G R A G S E Q U L D O N N O L O N G S R E A B E L T H E R U W A R O F W A R T H E F O L E D S H E S T R U O E D B U O N O U E A N D G A S E T H E G O N B U T O N L Y V E L E D H E R S E G N C A S T O (mls_eng_000273-mls_eng_000273) +A N D A F A L Y O F E I T W A S T H A T A N N O T H E W O M E N D T O V E S A R N Y W R I C H E F O R N O T H I N G B E T E D E N T O G O S I N T O M Y S I S T E R A N D F A R T H E U S C S E N T W A Y S H E S A I D I S O U L R A T H E R G O W I T H I M I H A V E N O M I E T O S T A Y H E A R A L O N I T D M Y T W O E B A B E S (mls_eng_000274-mls_eng_000274) +I N I D I T A C O R D I N G L Y W H E N T D R I N G H A T E L I T E M A N W O L B E A T A N D H E P E A E D T O F T H E S T O U T I S Y R U S H I S H K C U F I N E D W E I T D E L I T A L B U N E O F B (mls_eng_000275-mls_eng_000275) +I T I S N O T H E D R E D I U L N A T C M S O N H O E D E S M E A S T H E P L E N W E R T H E P U S I O N S A T H E I N G L S H F O N E T E B O F T E N T H O U S E N S L E A I N B R E V E W E L I N G T O A M B L O K E R B O R T H M O S N O B L Y D R O V E T H E R F O U R S A N D B O N O P A R T E A M P E Y A R C R O N W A S T E K E N A T W O T E L (mls_eng_000276-mls_eng_000276) +S S O M E Y U O U S A G L L E H A F E T E M A K I N G O R E A R I N G E N S F O T H E I N G C A M P E N T A T D N I H T W I T C O N S T A N T L Y H A D O V E A D P E S U L R E S R O K O N B Y A T R I B O F B R O U N M U N K E S T H E E I D I N T Y T H O U G H T T H A T L O N G P O S E S T I O N H A D G I M E N T H E A P R I A R E L A M T O T H E G L L (mls_eng_000277-mls_eng_000277) +T O F L A S H I N H A T O M E C P L E M I D I N G F O R H E M E A D E T W E E N R S S W A N E S T E N S P A R T Y A N D T H E O P R E I F O N L Y F I R M I N T S E R T O L Y E T W A S M O R T H A N A M I N I T T H A T S M O N D R E M A N E D A T H E F I E R H O U S E S A O U F T E R B E N G G R O G D B A K E O F T E R E D I N E R I N T E T A C K (mls_eng_000278-mls_eng_000278) +A N D I N D I C T I O N A R Y A N D E N W E H A D E C O U S T E N I N C X S W E C O L E T H R A G E A V M A N 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-0,0 +1,40 @@ +I A M T I R E D O F T A K I N G O R D E R S F R O M Y O U S H E S A I D H A S T I L Y I S E E N O W T H A T I T I S I M P O S S I B L E T O H A V E F A I T H I N Y O U I S E E N O W T H A T I T I S U S E L E S S T O E X P E C T A N Y R E T U R N F R O M Y O U F O R A L L I H A V E D O N E I W A N T N O M O R E O F Y O U (mls_eng_000243-mls_eng_000243) +T H A T H E M A Y S O M E T I M E S B E L I K E O T H E R C H I L D R E N L E A R N I N G B E S I D E M Y K N E E O R P L A Y I N G P R A T T L I N G S E E K I N G F O R H E L P C O M E S T O M Y H E A R T A H S I N F U L L O R D I M S P E A K I N G H O W G O O D T H O U A R T (mls_eng_000244-mls_eng_000244) +T R A N S C E N D T H I N G S O F A L L S O R T S A S I N T H E G E N E R A L O U T B U R S T O F M U L T I T U D I N O U S P A S S I O N A R E H U D D L E D T O G E T H E R T H E L U D I C R O U S N A Y T H E R I D I C U L O U S W I T H T H E H O R R I B L E F A R O V E R T H E B I L L O W Y S E A O F H E A D S M A Y B E S E E N R A S C A L I T Y C A P R I O L I N G O N H O R S E S F R O M T H E R O Y A L S T U D (mls_eng_000245-mls_eng_000245) +I T M A Y H A V E B E E N T H A T T H E B O N E S W E R E F O L D E D T O G E T H E R A N D K N O W N A S U N I H I P I L I B O N E S F O L D E D A N D L A I D A W A Y F O R P U R P O S E S O F I N C A N T A T I O N S U C H B U N D L E S O F B O N E S W E R E P U T T H R O U G H A P R O C E S S O F P R A Y E R S (mls_eng_000246-mls_eng_000246) +M A R S E I L L E S N E V E R E X P E R I E N C E D T H O S E G R E A T T R A N S I T I O N S F R O M L O W N E S S T O G R A N D E U R T H I S W A S O W I N G T O T H E P R U D E N T C O N D U C T T H A T R E P U B L I C W H I C H A L W A Y S P R E S E R V E D H E R P R I N C I P L E S (mls_eng_000247-mls_eng_000247) +A T A S M A L L B R I E F I N G S E S S I O N O N T H E M O R N I N G O F O C T O B E R T H I R T Y O N E C O N D U C T E D I N T H E A T M O S P H E R E O F C O N S P I R A C Y A N D A T T E N D E D B Y B R O D Y W E W E R E T O L D T H A T T H E N O R M A L A G E N C Y R E Q U I R E M E N T F O R R E V I E W B O A R D A P P R O V A L H A D B E E N W A I V E D T H A T N O R M A L A P P R O V A L O F T H E M A Y O R O F W A S H I N G T O N A N D C E R T A I N G O V E R N O R S W O U L D B E H A N D L E D I N F O R M A L L Y (mls_eng_000248-mls_eng_000248) +T H E B U D D H I S T L A I T Y I N C H I N A W H O D O N O T H E S I T A T E T O T A K E L I F E F O R T H E P U R P O S E O F F O O D S A L V E T H E I R C O N S C I E N C E F R O M T I M E T O T I M E B Y B U Y I N G B I R D S F I S H E S E T C E T E R A A N D L E T T I N G T H E M G O (mls_eng_000249-mls_eng_000249) +T H I S A G A I N I S S O F T E N E D A N D T E M P E R E D B Y A S I M P L E F A I T H I N T H E S U P R E M A C Y O F L O V E O V E R F E A R A N U N B O U N D E D H U M A N I T Y A N D C H A R I T Y F O R T H E P O O R A N D H E L P L E S S A N U N C O N D I T I O N A L F O R G I V E N E S S O F T H E D I R E S T I N J U R I E S W H I C H I S T H E N O T E O F T H E N O B L E A G E N E R O S I T Y A N D L I B E R A L I T Y (mls_eng_000250-mls_eng_000250) +T H E S E C O N D M A T E F O L L O W E D A N D A C O U P L E O F T H E S T E A M E R S M E N R O W E D T H E M A B O A R D T H E B A R Q U E B E F O R E T O U C H I N G A R O P E T H E Y W E N T T O W O R K T O S E A R C H T H E S H I P T H E Y L I F T E D T H E H A T C H E S A N D F O U N D T H E H O L D F U L L O F C A R G O (mls_eng_000251-mls_eng_000251) +F O N D O F H I S C O M R A D E S A N D R E S P E C T F U L T O H I S P A S T O R S A N D M A S T E R S E V E N S C H O O L M A S T E R S A S A L A D H E P R E P A R E S F O R M A N H O O D W I T H A W I L L A N D T H I S T R A I N I N G O C C U P I E S H I M T H R O U G H O U T Y O U T H T I D E (mls_eng_000252-mls_eng_000252) +W H E N T H E P R I N C E S S C O E C A R E A C H E D H E R S I X T E E N T H Y E A R S H E B E C A M E B L I N D H E R L A R G E S O F T B R O W N E Y E S H A D N O L I G H T I N T H E M (mls_eng_000253-mls_eng_000253) +A L M O S T A L L D A Y T H E B A T T L E R A G E D B E T W E E N T H E T W O M E N B A C K A N D F O R T H T H E Y F O R C E D E A C H O T H E R O V E R T H E L A V A B E D S T H E C H I E F S W E L L O I L E D B O D Y W A S V E R Y D I F F I C U L T F O R T H E O L O H E T O G R A S P B R U I S E D A N D B L E E D I N G F R O M R E P E A T E D F A L L S O N T H E R O U G H L A V A (mls_eng_000254-mls_eng_000254) +P O S Y I S H R I E K E D W I T H T E R R O R A N D I A W O K E P A N T I N G T H E W I N D M O A N E D T H R O U G H T H E T R E E S O F T H E G A R D E N A N D F R O M T I M E T O T I M E C E A S E D A S I F (mls_eng_000255-mls_eng_000255) +H I S M E N T A L T O R P I D I T Y F O U N D E D U P O N P H Y S I C A L I N D O L E N C E R E N D E R S I M M E D I A T E A C T I O N A N D A L L M A N N E R O F E X E R T I O N D I S T A S T E F U L H I S C O N S C I O U S W E A K N E S S S H O W S I T S E L F (mls_eng_000256-mls_eng_000256) +N O R T E L L H O W G L A D T H E Q U E E N M O T H E R W A S N O R H O W G R E A T W E R E T H E R E J O I C I N G O F T H E P E O P L E N O R H O W M A G N I F I C E N T W A S T H E R O Y A L B A N Q U E T T H A T G O O D Q U E E N P O M A R E A A T T E N D E D W I T H A L L H E R C O U R T (mls_eng_000257-mls_eng_000257) +A N D T H E C H A N C E O F T H E R E B E I N G S U C H A O N E A G A I N D I M I N I S H E S B Y V E R Y R A P I D P R O C E S S M A R M A D U K E A S A H O R S E W A S O F E Q U A L Q U A L I T Y R E A S O N I N G N O T A B O U T H I S O R D E R S B U T A B O U T T H E W A Y T O D O T H E M (mls_eng_000258-mls_eng_000258) +S H E K N O C K E D B U T S N O W D R O P L O O K E D O U T O F T H E W I N D O W A N D S A I D I D A R E N O T O P E N T H E D O O R F O R T H E D W A R F S H A V E T O L D M E T O L E T N O O N E I N T H A T I S H A R D F O R M E S A I D T H E W O M A N F O R I M U S T T A K E B A C K M Y A P P L E S B U T T H E R E I S O N E W H I C H I W I L L G I V E Y O U A N D S H E H E L D U P A N A P P L E (mls_eng_000259-mls_eng_000259) +A L B E R T M U R R A Y S P O K E E X C E L S I O R A N D H O R A C E F I N L E Y S P O K E N I C E T O O M Y P I E C E W A S W H Y P H O E B E A R E Y O U C O M E S O S O O N W H E R E A R E Y O U R B E R R I E S C H I L D E M M A V A N A R S D A L E S P O K E T H E S A M E O N E (mls_eng_000260-mls_eng_000260) +I N E V E R K N E W A N Y O N E W H O L I K E D T O G O T O C H U R C H A S M U C H A S G R A N D M O T H E R D O E S S H E S A Y S S H E W O U L D R A T H E R B E A D O O R K E E P E R I N T H E H O U S E O F O U R G O D T H A N T O D W E L L I N T H E T E N T S O F W I C K E D N E S S T H E Y D O N T H A V E W O M E N D O O R K E E P E R S A N D I K N O W S H E W O U L D N O T D W E L L A M I N U T E I N A T E N T (mls_eng_000261-mls_eng_000261) +T H E D U K E W A S S U R P R I S E D T O S E E H I M W H A T B R I N G S Y O U O U T S O E A R L Y A B E L A R D D E M A N D E D H E O H Y O U R G R A C E R E P L I E D T H E B U T L E R G A S P I N G F O R U T T E R A N C E (mls_eng_000262-mls_eng_000262) +F O U R Y E T S E E M I N G T R A N S M U T A T I O N S O F C O L O U R M A Y B E M A D E W H E R E T H E R E I S A N Y M I X T U R E O F D I V E R S E S O R T S O F R A Y S F O R I N S U C H M I X T U R E S T H E C O M P O N E N T C O L O U R S A P P E A R N O T B U T B Y T H E I R M U T U A L A L L A Y I N G E A C H O T H E R C O N S T I T U T E A M I D D L I N G C O L O U R (mls_eng_000263-mls_eng_000263) +I N A L M O S T T H E S A M E I N S T A N T K E N S A W S A N D Y S I D E S T E P I N T O A S H O P D O O R W A Y H E W A I T E D T H E R E U N T I L K E N C A M E U P K E N S T O P P E D A N D P R E T E N D E D T O S T A R E T H R O U G H T H E G L A S S A T T H E D I S P L A Y O F H A R D W A R E A N D T O O L S W H E R E H E C O N T I N U E D T O W A T C H B A R R A C K Y O U S E E W H A T I S E E S A N D Y S A I D K E N N O D D E D (mls_eng_000264-mls_eng_000264) +T H E N T H E T H I C K G R E E N S T U F F F L O W E D O V E R T H E W H O L E B U I L D I N G A N D T H E R E W A S N O T H I N G T O B E S E E N T H E R E B U T A M O U N D O F S O F T F L O W I N G G R A Y G R E E N S T U F F T H A T R U S H E D O N N O W W I T H T H E S W I F T N E S S O F T H E W I N D I L O O K E D U P I N T O B A R R Y S F A C E (mls_eng_000265-mls_eng_000265) +I H A V E M A D E S A C R I F I C E S T O O B U T I T W A S W H E N I K N E W T H A T T H E Y W E R E N O T M Y H A P P I N E S S I T W A S A F T E R I S A W T H A T I H A D S T O O P E D A F T E R I S A W T H A T Y O U R T E N D E R N E S S H A D T U R N E D T O C A L C U L A T I O N A F T E R I S A W T H A T Y O U C A R E D F O R Y O U R S E L F O N L Y N O T F O R M E (mls_eng_000266-mls_eng_000266) +Y E T T H E T H U N D E R N E V E R R O A R S I N W I N T E R I S E E A C R O W W H I R L I N G R O U N D A N D R O U N D B E F O R E I T A L I G H T T H E R E I S N O T H I N G U N D E R T H E F I R T R E E S B U T I K N O W S O M E T H I N G M U S T B E T H E R E (mls_eng_000267-mls_eng_000267) +I T I S B E Y O N D D O U B T T H A T S O M E P E O P L E H A D M A N A G E D T O S A V E M A N Y T H I N G S A N D O F C O U R S E T H E G E R M A N S H A D S U R M I S E D A S M U C H T W O O R T H R E E D A Y S A F T E R T H E F I R S T P E R Q U I S I T I O N S T H E Y D R O P P E D I N U N A W A R E S (mls_eng_000268-mls_eng_000268) +S O O N A M A N C A M E O U T T O M E E T H I M T H I S M A N W A S O L O H E A B E A R D L E S S M A N B E L O N G I N G T O A L A W L E S S R O B B E R C L A N W H I C H I N F E S T E D T H E D I S T R I C T P O S S I B L Y A S S I S T I N G T H E M A N H U N T E R S O F T H E T E M P L E I N S E C U R I N G V I C T I M S F O R T H E T E M P L E A L T A R S (mls_eng_000269-mls_eng_000269) +W E A R E N O T L O V E R S Y O U A N D I U P O N T H I S S U N N Y L A N E B U T C H I L D R E N W H O H A V E N E V E R K N O W N L O V E S J O Y O R P A I N (mls_eng_000270-mls_eng_000270) +W A S M U R D E R E D O N T H E D O O R S T E P O F H I S O W N H O U S E W A S T H I S A N O L D M A N A S K E D A N D R E A C A L M L Y M I S E R I C O R D I A Y O U T A L K A S I F H E H A D D I E D I N H I S B E D Y O U A R E N O V E N E T I A N A N D Y O U C A N N O T U N D E R S T A N D W H A T I T M E A N S W H E N A N I N Q U I S I T O R I S M U R D E R E D (mls_eng_000271-mls_eng_000271) +B Y G O D H E S A I D T H E Y W R O N G M E M Y B U S I N E S S L E A D S M E I N A N D O U T O F M A N Y H O U S E S B U T W H A T D O I C A R E F O R T H E S E C R E T S T H A T M A Y B E H I D D E N T H E R E H O W E V E R I C A N N O T B L A M E T H E S E P E O P L E F O R T H E I R W A T C H F U L N E S S T H E B L O O D H O U N D S O F T H E S I G N O R I A A R E I N E V E R Y S T R E E T (mls_eng_000272-mls_eng_000272) +H O R R I B L E D E A T H W A S P U L L I N G A T H E R N O T A S T I C K N O R A S T O N E W A S I N R E A C H O F H E R H A N D S A N D T H E P I T I L E S S C R A G S E C H O E D O N E L O N G S H R I E K A B O V E A L L T H E R O A R O F T H E W A T E R F A L L S H E S T R O V E T O T U R N O V E R A N D G R A S P T H E G R O U N D B U T O N L Y F E L T H E R S E L F G O I N G F A S T E R (mls_eng_000273-mls_eng_000273) +A N D T H E F O L L Y O F I T W A S T H A T A N O T H E R W O M A N O F C E R N Y W I S H E D F O R N O T H I N G B E T T E R T H A N T O G O S I N C E M Y S I S T E R A N D F A T H E R A R E S E N T A W A Y S H E S A I D I S H O U L D R A T H E R G O W I T H T H E M I H A V E N O M I N D T O S T A Y H E R E A L O N E W I T H M Y T W O B A B I E S (mls_eng_000274-mls_eng_000274) +B I L L Y D I D A C C O R D I N G L Y W O N D E R I N G W H A T T H E L I T T L E M A N W O U L D B E A T A N D H E P I C K E D T W O O F T H E S T O U T E S T R U S H E S H E C O U L D F I N D W I T H A L I T T L E B U N C H O F (mls_eng_000275-mls_eng_000275) +I T I S N O W T H E D R E A D F U L N I G H T C O M E S O N H O W D I S M A L I S T H E P L A I N F O R T H E P R U S S I A N S A N D T H E E N G L I S H F O U N D A B O V E T E N T H O U S A N D S L A I N B R A V E W E L L I N G T O N A N D B L U C H E R B O T H M O S T N O B L Y D R O V E T H E I R F O E S A N D B U O N A P A R T E S I M P E R I A L C R O W N W A S T A K E N A T W A T E R L O O (mls_eng_000276-mls_eng_000276) +S O M E Y E A R S A G O A F T E R M A K I N G O U R A R R A N G E M E N T S F O R T H E E N C A M P M E N T A T N I G H T W E C O N S T A N T L Y H A D O U R P E A C E F U L R E S T B R O K E N B Y A T R I B E O F B R O W N M O N K E Y S T H E Y E V I D E N T L Y T H O U G H T T H A T L O N G P O S S E S S I O N H A D G I V E N T H E M A P R I O R C L A I M T O T H E G R O V E (mls_eng_000277-mls_eng_000277) +T O F L A S H I N A T H O M E C L A M O U R I N G F O R H E R M A I D B E T W E E N M R S V A N E S T E N S P A R T Y A N D T H E O P E R A I F O N L Y F O R A M I N U T E C E R T A I N L Y I T W A S M O R E T H A N A M I N U T E T H A T S I M O N E R E M A I N E D A T T H E P H A Y R E H O U S E A F T E R B E I N G B R O U G H T B A C K A F T E R D I N N E R I N T A X I (mls_eng_000278-mls_eng_000278) +A N D I N D I C T I O N A R Y A N D T H E N W E H A D C A L I S T H E N I C S W E G O T H R O U G H A G R E A T M A N Y F I G U R E S A N D S I N G A L I F E O N T H E O C E A N W A V E W H A T F A I R Y L I K E M U S I C S T E A L S O V E R T H E S E A L I G H T L Y R O W L I G H T L Y R O W O E R T H E G L A S S Y W A V E S W E G O A N D O H C O M E C O M E A W A Y A N D O T H E R S O N G S M R S J U D G E T A Y L O R W R O T E O N E S O N G O N P U R P O S E F O R U S (mls_eng_000279-mls_eng_000279) +T H A T W H I C H P A S S E S A S H I S T O R Y I N O U R S C H O O L S O R G O V E R N M E N T A L L Y F A B R I C A T E D B O O K S O N H I S T O R Y I S A F O R G E R Y A M I S R E P R E S E N T A T I O N O F E V E N T S L I K E T H E O L D D R A M A C E N T E R I N G U P O N T H E I M P O S S I B L E F I G U R E O F T H E H E R O W I T H A G E S T I C U L A T I N G C R O W D I N T H E B A C K G R O U N D (mls_eng_000280-mls_eng_000280) +W H E N T H E C A L I P H H E A R D T H I S H E S A I D O J A A F A R H O W G O O D L Y I S T H A T V O I C E A N D T H E W A Z I R R E P L I E D O O U R L O R D N E V E R S M O T E M Y H E A R I N G A U G H T S W E E T E R O R G O O D L I E R T H A N T H I S S I N G I N G (mls_eng_000281-mls_eng_000281) +E V I D E N T L Y T H E L E A R N E D B A R O N H A D N O T S T U D I E D S U C H W O R K S O F T H E T O T K A H N I O R P A R R O T C H A T W H I C H N O T A B L Y T R A N S L A T E D B Y N A K H S H A B I F R O M T H E S A N S K R I T S U K A S A P T A T I H A S N O W B E C O M E A S O R T H O D O X I C A L L Y M U S L I M A S T H E N I G H T S (mls_eng_000282-mls_eng_000282) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/result.txt b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..057661f57bef6ba03e9e8a1633923d4193bbf275 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/result.txt @@ -0,0 +1,521 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,-----------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/hyp.trn| +|-----------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000243 | 1 207 | 75.8 11.1 13.0 4.3 28.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000244 | 1 169 | 85.2 6.5 8.3 7.1 21.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000245 | 1 250 | 79.6 9.6 10.8 5.6 26.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000246 | 1 190 | 86.8 6.8 6.3 4.2 17.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000247 | 1 167 | 88.6 4.8 6.6 4.8 16.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000248 | 1 316 | 75.9 7.3 16.8 3.2 27.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000249 | 1 178 | 75.3 11.8 12.9 10.7 35.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000250 | 1 259 | 86.5 4.6 8.9 3.1 16.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000251 | 1 203 | 80.3 5.9 13.8 2.5 22.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000252 | 1 178 | 84.8 4.5 10.7 5.1 20.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000253 | 1 114 | 77.2 9.6 13.2 8.8 31.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000254 | 1 235 | 83.8 5.5 10.6 5.1 21.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000255 | 1 130 | 79.2 8.5 12.3 7.7 28.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000256 | 1 152 | 80.9 7.2 11.8 4.6 23.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000257 | 1 179 | 82.1 10.1 7.8 11.7 29.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000258 | 1 181 | 82.9 8.3 8.8 6.1 23.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000259 | 1 258 | 82.9 4.7 12.4 4.7 21.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000260 | 1 172 | 64.5 13.4 22.1 4.1 39.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000261 | 1 263 | 70.7 6.1 23.2 3.8 33.1 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000262 | 1 137 | 83.2 2.2 14.6 5.8 22.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000263 | 1 228 | 71.9 8.3 19.7 3.5 31.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000264 | 1 270 | 77.4 10.0 12.6 3.0 25.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000265 | 1 219 | 78.5 9.6 11.9 6.4 27.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000266 | 1 234 | 82.1 9.0 9.0 4.7 22.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000267 | 1 164 | 86.0 7.3 6.7 7.9 22.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000268 | 1 187 | 78.6 11.8 9.6 8.0 29.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000269 | 1 219 | 86.3 5.5 8.2 3.2 16.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000270 | 1 100 | 85.0 7.0 8.0 12.0 27.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000271 | 1 227 | 71.8 9.7 18.5 4.8 33.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000272 | 1 246 | 73.2 14.2 12.6 4.5 31.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000273 | 1 245 | 66.9 11.0 22.0 3.7 36.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000274 | 1 220 | 81.4 7.7 10.9 5.0 23.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000275 | 1 141 | 72.3 10.6 17.0 5.0 32.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000276 | 1 241 | 76.8 10.8 12.4 2.1 25.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000277 | 1 229 | 79.0 10.0 10.9 5.7 26.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000278 | 1 230 | 79.1 8.3 12.6 4.8 25.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000279 | 1 295 | 76.6 8.8 14.6 6.1 29.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000280 | 1 245 | 80.4 9.4 10.2 6.1 25.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000281 | 1 168 | 78.0 13.1 8.9 8.3 30.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000282 | 1 208 | 85.1 10.1 4.8 12.0 26.9 100.0 | +|=======================================================================================================================| +| Sum/Avg | 40 8254 | 79.0 8.5 12.4 5.4 26.4 100.0 | +|=======================================================================================================================| +| Mean | 1.0 206.4 | 79.3 8.5 12.2 5.7 26.4 100.0 | +| S.D. | 0.0 48.8 | 5.6 2.7 4.4 2.5 5.5 0.0 | +| Median | 1.0 213.5 | 79.4 8.6 11.9 5.0 26.3 100.0 | +`-----------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,-----------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/hyp.trn| +|-----------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000243 | 1 207 | 157 23 27 9 59 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000244 | 1 169 | 144 11 14 12 37 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000245 | 1 250 | 199 24 27 14 65 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000246 | 1 190 | 165 13 12 8 33 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000247 | 1 167 | 148 8 11 8 27 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000248 | 1 316 | 240 23 53 10 86 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000249 | 1 178 | 134 21 23 19 63 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000250 | 1 259 | 224 12 23 8 43 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000251 | 1 203 | 163 12 28 5 45 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000252 | 1 178 | 151 8 19 9 36 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000253 | 1 114 | 88 11 15 10 36 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000254 | 1 235 | 197 13 25 12 50 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000255 | 1 130 | 103 11 16 10 37 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000256 | 1 152 | 123 11 18 7 36 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000257 | 1 179 | 147 18 14 21 53 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000258 | 1 181 | 150 15 16 11 42 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000259 | 1 258 | 214 12 32 12 56 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000260 | 1 172 | 111 23 38 7 68 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000261 | 1 263 | 186 16 61 10 87 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000262 | 1 137 | 114 3 20 8 31 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000263 | 1 228 | 164 19 45 8 72 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000264 | 1 270 | 209 27 34 8 69 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000265 | 1 219 | 172 21 26 14 61 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000266 | 1 234 | 192 21 21 11 53 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000267 | 1 164 | 141 12 11 13 36 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000268 | 1 187 | 147 22 18 15 55 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000269 | 1 219 | 189 12 18 7 37 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000270 | 1 100 | 85 7 8 12 27 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000271 | 1 227 | 163 22 42 11 75 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000272 | 1 246 | 180 35 31 11 77 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000273 | 1 245 | 164 27 54 9 90 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000274 | 1 220 | 179 17 24 11 52 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000275 | 1 141 | 102 15 24 7 46 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000276 | 1 241 | 185 26 30 5 61 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000277 | 1 229 | 181 23 25 13 61 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000278 | 1 230 | 182 19 29 11 59 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000279 | 1 295 | 226 26 43 18 87 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000280 | 1 245 | 197 23 25 15 63 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000281 | 1 168 | 131 22 15 14 51 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000282 | 1 208 | 177 21 10 25 56 1 | +|=======================================================================================================================| +| Sum | 40 8254 | 6524 705 1025 448 2178 40 | +|=======================================================================================================================| +| Mean | 1.0 206.4 | 163.1 17.6 25.6 11.2 54.5 1.0 | +| S.D. | 0.0 48.8 | 37.7 6.8 12.6 4.2 17.3 0.0 | +| Median | 1.0 213.5 | 164.0 18.5 24.0 11.0 54.0 1.0 | +`-----------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/hyp.trn + +Speakers: + 0: mls_eng_000243 + 1: mls_eng_000244 + 2: mls_eng_000245 + 3: mls_eng_000246 + 4: mls_eng_000247 + 5: mls_eng_000248 + 6: mls_eng_000249 + 7: mls_eng_000250 + 8: mls_eng_000251 + 9: mls_eng_000252 + 10: mls_eng_000253 + 11: mls_eng_000254 + 12: mls_eng_000255 + 13: mls_eng_000256 + 14: mls_eng_000257 + 15: mls_eng_000258 + 16: mls_eng_000259 + 17: mls_eng_000260 + 18: mls_eng_000261 + 19: mls_eng_000262 + 20: mls_eng_000263 + 21: mls_eng_000264 + 22: mls_eng_000265 + 23: mls_eng_000266 + 24: mls_eng_000267 + 25: mls_eng_000268 + 26: mls_eng_000269 + 27: mls_eng_000270 + 28: mls_eng_000271 + 29: mls_eng_000272 + 30: mls_eng_000273 + 31: mls_eng_000274 + 32: mls_eng_000275 + 33: mls_eng_000276 + 34: mls_eng_000277 + 35: mls_eng_000278 + 36: mls_eng_000279 + 37: mls_eng_000280 + 38: mls_eng_000281 + 39: mls_eng_000282 + +Speaker sentences 0: mls_eng_000243 #utts: 1 +id: (mls_eng_000243-mls_eng_000243) +Scores: (#C #S #D #I) 157 23 27 9 +REF: i a M t * i r ******* * e D o f t A k I n G * o R d e R s f r o M y o u s h E s a I D h a s t I l y i s E E n o w t h A T i t I s i m p o s S I b l e t O h a v e f a I t h i n Y o u * I s E E n o W T h A T i t I s * u s E L e S s t O e X p e c * t a n y r e t U r * n f R o M y o u * f o r a L l i h a v e d o n E i w A n t n o m o r E o f y o u +HYP: i a * E t H i r U e * o f t E k E n * A o L d e * s f r o N y o u s h I s a * T h a s t A l y i ******* s I A n o w t h * E i t * s i m p o s E A b l e t * h a v e f a * t h ******* i n * o u N B A s I Y n o * * h * * E i t A s Y u s * * e * s ******* t * e C p e c E t a n y r e t E r E n f * o R y o u G f o r ******* a * l i h a v e d o n * i O w * n t n o m o r * o f y o u +Eval: D S I I I D S S D I S D S S D S S D S S D S D S S D D D D I S S S S D D D D S S I D D D D D S I S I D S I D D D S D D + +Speaker sentences 1: mls_eng_000244 #utts: 1 +id: (mls_eng_000244-mls_eng_000244) +Scores: (#C #S #D #I) 144 11 14 12 +REF: t h a t h e m a y s o m E t i m e s b * ******* e l I k * E o * t h e r c h I l * d r e n l e A r n i n g B e ******* s i d e m y K n E e o r p l a Y i n g p r a T t l i n G s E E K i n g f o r h e l p * * c o m E s t o M y h e a r t a * H s * i n f u L l o r d I M s p e a k i n G h o w * g O o d t h o U a r t * +HYP: t h a t h e m a y s o m N t i m e s b E e l A k C K o U t h e r c h E l E d r e n l e * r n i n g T e s i d e m y * n * e o r p l a * i n g p r a * t l i n D s * I C i n g f o r h e l p E S c o m * s ******* t o * y h e a r t a T S s H i n f u * ******* l o r d A N s p e a k i n * h o w C g * o d t h o * a r t E +Eval: S I I S I S I S I D S I D D D D S D S S I I D D D I S I D D S S D I D D I + +Speaker sentences 2: mls_eng_000245 #utts: 1 +id: (mls_eng_000245-mls_eng_000245) +Scores: (#C #S #D #I) 199 24 27 14 +REF: T r A n ******* s C e n D t h i n g s o f a L l s o r T S a s i N t h e G e n E r a l o u t b u r s t o f m U l t * i t U d I n O U s p a s S i o n a r E h u D D L e d t o ******* g e t h e r t h e l * * u D I c r O U s n a y t h e r * * I d i * C U l O U s w i T H t h e h O R r I b l e f * a r * o v e r t h e b i L l O W y s e A o f h e a d s m a Y b e s e * e N r a s * * C a l I T y c a p r I O l i n g o n H o r s e s f R o M t h e r o Y A l s t * U d +HYP: * r E n s * e n * t h i n g s o f a * l s o r * E a s i * t h e J e n * r a l o u t b u r s t o f m O l t E i t O d * n E S s p a s T i o n a r * h u * * T e d t o g e t h e r t h e l I T u * R c r * I s n a y t h e r E D d i E K I l * * s w i * * t h e h * A r A b l e f O a r E o v e r t h e b i * l * * y s e * o f h e a d s m a * b e s e M e * r a s T K G a l A D y c a p r Y A l i n g o n C o r s e s f * o * t h e r o * I l s t O I d +Eval: D S I D D D D S D S D S I S D S S S D D D S I I I D S D S I I S I S S D D D D D S S I I D D D D D I D I I S S S S S S D D D S I S + +Speaker sentences 3: mls_eng_000246 #utts: 1 +id: (mls_eng_000246-mls_eng_000246) +Scores: (#C #S #D #I) 165 13 12 8 +REF: i t m a y h a v e b E e n t H a t t h e b o n e s w e r e * f o l d e d t o g e t h e r a n d K n o W n * a s U n * I h * I p I l I b o n E s f o l d e d a n d l a i d A w a y f o ******* * * R p U r p O s * e s o f i n c A n t a t i o n s u c h b u n d l e s o f b o n e s w E R e p u t t h R o U G H a p r o C e s s o f p r a Y E r s +HYP: i t m a y h a v e b * e n t * a t t h e b o n e s w e r e R f o l d e d ******* t o g e t h e r a n d * n o * n E a s O n Y h E p E l Y b o n * s f o l d e d a n d l a i d * w a y f o T H E p E r p * s C e s o f i n c E n t a t i o n s u c h b u n d l e s o f b o n e s w * * e p u t t h * o R W E a p r o S e s s o f p r a * I r s +Eval: D D I D D D I S I S I S S S D D I I I S S D I S D D D S S S S D S + +Speaker sentences 4: mls_eng_000247 #utts: 1 +id: (mls_eng_000247-mls_eng_000247) +Scores: (#C #S #D #I) 148 8 11 8 +REF: m a r * ******* s E i L l e s n e v e r e * X p e r i E n c e d t h o s E g r E a t t r a * n s * i t i o n s f R o m l o W n e S s t o g r * a n d e u r t h i s w a s o W i n g t o t h e p r U d E n t c o n d u c t t h a t r e ******* p U b l i * C w h i c h a l w a y S p r e s e r v e D h e R p R i n c i p l e S +HYP: m a r S s A i * l e s n e v e r e C S p e r i * n c e d t h o s * g r * a t t r a E n s C i t i o n s f * o m l o * n e * s t o g r E a n d e u r t h i s w a s o N i n g t o t h e p r O d I n t c o n d u c t t h a t r e p O b l i K E w h i c h a l w a y E p r e s e r v e * h e * p * i n c i p l e * +Eval: I I S D I S D D D I I D D D I S S S I S I S S D D D D + +Speaker sentences 5: mls_eng_000248 #utts: 1 +id: (mls_eng_000248-mls_eng_000248) +Scores: (#C #S #D #I) 240 23 53 10 +REF: a t A s m A l L B r I e ******* F i n g S E s S i o n o n ******* * t h e m o r N i n g o f O C t o B e r t h I r t y o n E c o N d u C t e d i N t h e a T M O s P h e r E o f c o N s p I r A C y a n d a * T t E n d e d b y b r o * d y w e W E r e t o l D T h a t t h e n o r m A l a G e n * C Y r e q u * i r E m e N t f o r r e v i E W B o A r D A P p r o v A l h a d b E e n w a I V e D t h a t n o r m A l * a * P p r O v A l o F t h e m a Y O r o f w a s H i n g t O n A n d C E r t A I n g o v e R n O r s w o U L d b e h a n d l E d i n ******* f o r m A L l y +HYP: a t ******* * s m * l E * r * e H i n g * * s T i o n o n M t h e m o r * i n g o f U P t o * e r t h * r t y o n * c o * d u * t e d i * t h e a * * P s F h e r * o f c o E s p E r * S y a n d a I t A n d e d b y b r o A d y w e * * r e ******* t o l * * h a t t h e n o r m W l a D e n T S E r e q u R i r * m e * t f o r r e v i * U * o * r * ******* * E p r o v * l h a d b * e n w a * * e T t h a t n o r m * l E a E p r * v * l o * t h e m a * * r o f w a s * i n g t I n * n d * S r t * * n ******* g o v e * n * r s w o * * d b e h a n d l * d i n f o r m * I l y +Eval: D D D S D D I S D D S I I D S S D D D D D D D D S S D S S D S I S S I D D D D D S S I S S I D D D S D D D D D S D D D D S D I I S D D D D D D S D D S D D D D D D D D I D S + +Speaker sentences 6: mls_eng_000249 #utts: 1 +id: (mls_eng_000249-mls_eng_000249) +Scores: (#C #S #D #I) 134 21 23 19 +REF: * ******* t h e b U D d H i s t l a I t * y i N c h i n * A W h o * d o n o T h E s I t a t e t o * t a k E l i F E f o r t h e p U r * P O s E o F f O o d s A L V E t h e I r c o n S c I E n c E f * r o m * t I M E t o t i M E b y b U y i n g * B i r D s * f i * s h * e * s * E T C e * * t e r A a n d l e T t i n G t h e M g o * * +HYP: T t h e b O O d * i s t l a * t D y i G c h i n E R * h o L d o n o * h A s * t a t e t o L t a k * l i * G f o r t h e p E r V E R s * o * ******* f * o d s * * O F t h e * r c o n * c H O n c S f O r o m G t U C K t o t i * * b y b * y i n g V E i r * s D f i A s h I e I s Y O U S e A D t e r * a n d l e * t i n * t h e * g o L L +Eval: I I S S D D I S I S D I D S D I D D S S I S S D D D D D D S S D D S S S I I S S S D D D I S D I I I I I S S S I I D D D D I I + +Speaker sentences 7: mls_eng_000250 #utts: 1 +id: (mls_eng_000250-mls_eng_000250) +Scores: (#C #S #D #I) 224 12 23 8 +REF: * ******* t h i S a g a I n i s s o f T e n E d a n d t E m p e r E d b y a s I m P l e f a I t h I n t h e s u p r E m * A C y o f l o v e o v e r f e A r a n * u n ******* b O U n D e d H u m a n i t y a n d c h A r i T y f o r t h e p O o r a n D h e l p l e S s a n u n ******* c o N d I T i o n a l f o r ******* g i v e n e S s o f t h e d i r E s t i n J U r I e s w h i c h i s t h e n o t E o f t h e n o b l E a G e n e r o * s i t y a n d l i b e r a l i t y +HYP: T t h i E a g a * n i s s o f * e n * d a n d t U m p e r * d b y a s E m B l e f a * t h A n t h e s u p r O m I R S y o f l o v e o v e r f e * r a n D u n b * W n T e d * u m a n i t y a n d c h I r i * y f o r t h e p * o r a n * h e l p l e * s ******* a n u n c o * d * * i o n a l f o r g i v e n e * s o f t h e d i r * s t i n * E r * e s w h i c h i s t h e n o t * o f t h e n o b l * a * e n e r o U s i t y a n d l i b e r a l i t y +Eval: I I S D D D S D S S D S S I S S D I I D S S D S D D D D D I D D D I D D D S D D D D I + +Speaker sentences 8: mls_eng_000251 #utts: 1 +id: (mls_eng_000251-mls_eng_000251) +Scores: (#C #S #D #I) 163 12 28 5 +REF: t h e s e c O n d m a T e f O l l o W e d a n d * A c o u p l E o F t h e s T e A m e r s m e n * r o W E D t h e M A b o A r D t h E b a r Q U e b e ******* f o r E T O u c h i n g a r o * p E t h e Y w e n T t o w O r k t O s E A r c h t h E s h i P t H e Y l i f T e d T H e h a T c h e s a n d f * o U n d t h e h o l d f u l l o f c A r g o +HYP: t h e s e c E n d m a D e f A l l o * e d a n d H E c o u p l * o * t h e s * e * m e r s ******* m e n B r o * * * t h e * P b o * r * t h * b a r * K e b e f o r * * C u c h i n g a r o U p * t h e * w e n * t o w A r k t * s * U r c h t h * s h i * t * e * l i f * e d * D e h a * c h e s a n d f A o W n d t h e h o l d f u l l o f c O r g o +Eval: S S S D I S D D D D D I D D D D S D D D D S I D D S I D D D S D D S D D D D D D S D I S S + +Speaker sentences 9: mls_eng_000252 #utts: 1 +id: (mls_eng_000252-mls_eng_000252) +Scores: (#C #S #D #I) 151 8 19 9 +REF: f o n d o f h i s c o m * r a d e S a n d r e s p e C t f u l * ******* * t o h i s p a s t O r S a n d m a s t e r s e v e n s c h O O l ******* m A s t e r s a s * A l a d h e p r e p A r E s f o r M a n h O o d w I t h A w i L l a n d t h i s t r a I N i n g o c C u p I e S h i m t h R O u * G H o u t y o u t h ******* t * i d E +HYP: f o n d o f ******* h i s c o m E r a d e * a n d r e s p e * t f u l E I t o h i s p a s t U r * a n d m a s t e r s e v e n s c h * * l m U s t e r s a s H E l a d h e p r e p E r * s f o r * a n h * o d w * t h ******* * w i * l a n d t h i s t r a * * i n g o c K u p Y e * h i m t h * * u R E o u t y o u t h t I i d * +Eval: D I D D I I I S D D D I S I S S D D D D D D D D D S S D D D I S S I I D + +Speaker sentences 10: mls_eng_000253 #utts: 1 +id: (mls_eng_000253-mls_eng_000253) +Scores: (#C #S #D #I) 88 11 15 10 +REF: * * * ******* w h e N t h e p r I n C e S s * C O e c * a ******* * r e A c h e D H e r s i x t E E n t H y e a r s h E b e c a m e b l i n * d * h e r l a r g E s o f T b R o W n E Y e s h a d N o l i G H T i n t h E m +HYP: A S S w h e * t h e p r E n * e * s Q U P e c E a E r e * c h e * T e r s i x t I n t * y e a r s h * b e c a m e b l i n G d W h e r l a r g H s o f * b * o * n I C e s h a d * o l i * * * D i n G t h * m +Eval: I I I I D S D D I S S I I I D D S S S D D I I S D D D S S D D D D S S D + +Speaker sentences 11: mls_eng_000254 #utts: 1 +id: (mls_eng_000254-mls_eng_000254) +Scores: (#C #S #D #I) 197 13 25 12 +REF: a * l m o s t a l l * d A y t h e b a T t L e r a * g e d b E t W E e N t h E t w o m e n b a C k A n D f o r t H t h e y f o * r C E D * e A c h o t h e r o v e r t h e l a v ******* a b e d s t h e C h i E f s w e l L o i L e D b o D y w a s v e r y d i F f i c U l t f o r T h e o * l o ******* h * E t o g r a s P * b r U I s e D a n d b l e E d i n g f r o m r e ******* p e A t e d f A l l * s o n t h e r o u G h l a v a +HYP: a O l m o s t a l l E d * y t h e b a * t * e r a N g e d ******* b U t * C e * t h * t w o m e n b a * k I n * f o r t * t h e y f o U r * * S Y e * c h E o t h e r o v e r t h e l a v a b e d s t h e S h i V f s w e l o i * e * b o * y w a s v e r y d i * f i c A l t f o r * h e ******* o A l o h A Y t o ******* g r a s * P b r * O s e * a n d b l e A d i n g f r o m r e p e * t e d f * l l E s o n t h e r o u F h l a v a +Eval: I I D D D I D S D S D D D S D D I D D S I D S I S S S D D D D S D D I I I S D D I D S D S I D D I S + +Speaker sentences 12: mls_eng_000255 #utts: 1 +id: (mls_eng_000255-mls_eng_000255) +Scores: (#C #S #D #I) 103 11 16 10 +REF: * p o s y i * S H r I E K e D W i t H t e r r O r a n d I A w O K E p a n t i n g t h e w * i n D m o A n E d * t h r o U g H t h e t r e e s o f t h e G a r d E n a n d f R o m * t i m e t o t i m E C e A s e D a s i f ******* * * ******* * +HYP: B p o s y i Y * T r * * * e * B i t * t e r r A r a n d A * w L A L p a n t i n g t h e w H i n * m o * n * d T t h r o * g * t h e t r e e s o f t h e C a r d I n a n d f * o m E t i m e t o t i m * ******* * e M s e E a s i f T O A +Eval: I I D S D D D D S D S S D S S S I D D D I D D S S D I D D D S S I I I I I + +Speaker sentences 13: mls_eng_000256 #utts: 1 +id: (mls_eng_000256-mls_eng_000256) +Scores: (#C #S #D #I) 123 11 18 7 +REF: h i s m e n t A l t o r p I D i t y f o u n d e D * U p o n P H Y s i * c A l i n d O l e * n c e r e n d e r s I M m e d i a t E a c t i o n a n D a L l m a N n e r o F e X E r t i o n * d I s t * a s T E f u l h i s c o n S c I O u s w e A k n e S s s h o W s i t * ******* s e l f +HYP: h i s m e n t * l t o r p * T i t y f o u n d e T A p o n * F I s i O c * l i n d * l e A n c e r e n d e r s A m e d i a t * a c t i o n a n * a * l m a E n e r ******* o * e * S r t i o n D d U s t E a s * * f u l h i s c o n * c * H u s w e * k n e * s s h o * s i t D s e l f +Eval: D D S S I S D S S I D D I S S D D D S D D D S I S I D D D D S D D D I I + +Speaker sentences 14: mls_eng_000257 #utts: 1 +id: (mls_eng_000257-mls_eng_000257) +Scores: (#C #S #D #I) 147 18 14 21 +REF: * n o r * t * * e l L h o w g l a d t h e Q U E E n * m O t h e R w a s n o r h o * W g r E a t * w * e r E t h e r e * * j * o * i C i n g o f t h e p e O p l e N o r h o W m A G n i f * I C e n * t * w a s t h e r o Y A l b a n * q U e T t h a t g o o d Q U E E n * p o m A r e a * a T t e n d e * d w * i t h a L l h e r c o U r * T +HYP: T n o r E t H A e l E h o w g l a d t h e * * C I n G m * t h e * w a s n o r h o U E g r * a t D w O e r * t h e r e Y G j U o R i S i n g o f t h e p e * p l e * o r h o M m * E n i f E S S e n D t D w a s t h e r o * I l b a n K q C e D t h a t g o o d * C F I n G p o m E r e a R a D t e n d e T d w H i t h ******* a * l h e r c o * r E N +Eval: I I I I S D D S S I D D I S D I I D I I I I S D D S D S I S S I I D S I S S D S S S I S I S I I D D D I S + +Speaker sentences 15: mls_eng_000258 #utts: 1 +id: (mls_eng_000258-mls_eng_000258) +Scores: (#C #S #D #I) 150 15 16 11 +REF: a n d t h e c h A n c e o f t h e r E b e I n g s u c h a o n E * A g A i * n d i m I N i s h e s b y * V e r y * r a p I D p r o C e s s m a R m A D u k E a s a H o r s E w * A s o f e * * Q u * a L * q u A l i t y * r e a s O n i n g n o t a b o U t * h i s o R d e r s b u t A b o u T t h e W A y t o d o t h e M +HYP: a n d t h e c h O n c e o f t h e r * b e * n g s u c h a o n * U N g * i A n d i m * * i s h e s b y E W e r y P r a p * E p r o S e s s m a * m E L u k * a s a ******* E o r s * w H O s o f e A K C u E a N C q u * l i t y E r e a s * n i n g n o t a b o W t D h i s o * d e r s b u t * b o u L t h e * I y t o d o t h e T +Eval: S D D D I S D I D D I S I D S S D S S D D S D I S I I S I S I D I D S I D D S D S S + +Speaker sentences 16: mls_eng_000259 #utts: 1 +id: (mls_eng_000259-mls_eng_000259) +Scores: (#C #S #D #I) 214 12 32 12 +REF: * s h e K n o C k e D b u t s ******* n o W D r o * p l O o K e D o U t o f t H e w * I n d o W a n D s a i d i d * a R e * n o t o * p E N T h e d O o r f o r t h e d W A r F s h a v e t o L d m e t o l E t n o o * n E i n t h a t i s h a r * d f o r m * ******* e s a i d t h e W o m * A n f o r i m u s t t a k E b a c K m y a P p l E s b u t t h e r E i s o n e w H i c h i W I l l g i v e y o u a n d s h e h e l d u p A n a P P l E +HYP: T s h e * n o * k e T b u t s n o * r o U p l * o * e * o A t H o f t * e w H E n d o * a n * s a i d i d E a * e R n o t o B p * B * h e d * o r f o r t h e d * O r * s ******* h a v e t o * d m e t o ******* l * t n o o I n * i n t h a t i s h a r E d f o r m E e s a i d t h e * o m E O n f o r i m u s t t a k * b a c E m y a U p l * s b u t t h e r * ******* i s o n e w * i c h i ******* * U l l g i v e y o u a n d s h e h e l d u p ******* E n a * * l * +Eval: I D D S I D S I D D D S S D I S D D I D I I D S D D D S D D D D D I D I I I D I S D S S D D D D D D S D S D D D + +Speaker sentences 17: mls_eng_000260 #utts: 1 +id: (mls_eng_000260-mls_eng_000260) +Scores: (#C #S #D #I) 111 23 38 7 +REF: A L B E R T m U R r A y s p o k E * E X c e l S I o r a n d h O r A C e f i n l E y s p o k e n ******* i C E t O o m y p I E C e w a s w h y P H O e * b e * a r E y o U c O m E s o s O o n w H E R e A r e Y o u r b E R r I E S c h i l d E M M A * V a n a r ******* S d A l E s p o k E T H E s a m e o * n E +HYP: * * O V Y m * O r * y s p o k * A K c e l T H o r a n d h E r * S e f i n l * y s p o k e ******* n i H T t W o m y p * * * e S w a s w h y * * F e A b e Y a r * y o * c * m * s o ******* s * o n w * * * e ******* * r e * o u r b * * r * * Y c h i l d N D O T H a n a r T d * l * ******* s p o k * ******* * * * s a m e o A n * +Eval: D D S S S S D S D D I S S S S S D S D D I S S S D D D S D D S I I D D D D D D D D D D D D D D D D S S S S S I S I S D D D D D D D D I D + +Speaker sentences 18: mls_eng_000261 #utts: 1 +id: (mls_eng_000261-mls_eng_000261) +Scores: (#C #S #D #I) 186 16 61 10 +REF: i n e v e r K N E W a n y * o N e * W h o l I K e D t * o g o t O c H U r c h A s m U c h a S * g r a N D m o T h E R d o E s s h e S a Y s S h e w o u L d r a T H e R b E a d O o r K e ******* * e p e R i N t h E h O U s E o f O U r g o d t h A n T O D w e l l I N t h e t e n T s o f w i C k e d n e S s t H e Y d o n T h A v e w o m e n d O o r K e ******* * e p e r s a n D i * K n o W s h e w O U L D n O t D w e l l A m i n U T E i n a * t e n t +HYP: i n e v e r * * * U a n y W o * e N * h o l * * e * t D o ******* g o ******* t Y c * E r c h ******* I s m * c h a C S g r a * * m o * h * * d o U s s h e H a I s * h e w o u * d r a * V e * b * a d * o r * e C e p e * ******* i * t h * h * A s * o f * * r g o d t h E n * I * w e l l ******* * * t h e t e n * s ******* o f w i * k e d n e * s t * e * d o n * h * v e w o m e n d * o r * e K e p e r s a n * i W E n o * s h e w * * * * I n U t T w e l l ******* * m i n * * * ******* i n ******* a A t e n t +Eval: D D D S I D I D D D D I D D S D S D S D S I D D D D D S S S D D D S D D D D I I D D D D D S D D D S D S D D D D D D D D D D D D D D I I D I S D D D D D S S S D D D D D D D I + +Speaker sentences 19: mls_eng_000262 #utts: 1 +id: (mls_eng_000262-mls_eng_000262) +Scores: (#C #S #D #I) 114 3 20 8 +REF: t h e d u k E w a s s u R p r i * s E D t o s E e h I m W H A t b r i n g S Y o U o U t s o E a R l y a b e l ******* a r d d e m a n d E d h e o H y o u r g r a * c * e * r e p l i E D T h e B u t l E r ******* * g a s p i n g f o r u T t e r a n c e * +HYP: t h e d u k * w a s s u * p r i C s * * t o s * e h * m * * O t b r i n g * * o * o * t s o * a * l y a b e l a r d d e m a n d d h e o L y o u r g r a I c T e D r e p l i * * * h e * u t l * r Y g a s p i n g f o r u * t e r a n c e S +Eval: D D I D D D D D D S D D D D D D I S S I I I D D D D D I I D I + +Speaker sentences 20: mls_eng_000263 #utts: 1 +id: (mls_eng_000263-mls_eng_000263) +Scores: (#C #S #D #I) 164 19 45 8 +REF: f o U r * * Y E t s E e m i n g t r A n s * * m * u t a t i o n s o f c O l O U R m a Y b e m a d E w H E R e t h e r E i s A n y m i * x t U r E o f d I v E r S E s o R t s o F r a Y s F O r i n s U c h m i X t U r E s t h e c o m ******* p o N E n T C O l O U R s a P p e A r * n o T B U t b y t h e I R m u t U A l a L l a Y i n g E a C h O t h e R c o n S T I t u t e a m i D d l i n g c o l O U r +HYP: f o * r E I U A t s * e m i n g t r O n s E N m E u t a t i o n s o f c A l * * E m a * b e m a d * w * * * e t h e r * i s * n y m i C x t * r * ******* o f d E v O r * * s o U t s o * r a E s * B r ******* i n s A c h m i C t E r * s t h e c o m p o * * n * * E l * * I s a * p e * r E n o * ******* * * t b y t h e * * m u t * I l a * l a * i n g * a * h ******* E t h e * c o n * * C t u t e a m i * d l i n g c o l * E r +Eval: D I I S S D S I I I S D D S D D D D D D D I D D D S S D D S D S D S D S S S D I D D D D S D D S D D I D D D D D D D S D D D D D S D D D S D D S + +Speaker sentences 21: mls_eng_000264 #utts: 1 +id: (mls_eng_000264-mls_eng_000264) +Scores: (#C #S #D #I) 209 27 34 8 +REF: I n a l m o s T t h e s a m e i n s t a n t K e n s A W * s ******* a n d y s i d E s t e p i n t o a s h o p d O o r w a y h e w a I T e d t h e R E U n t i L * K E n c a m e u p K E n s t * O P p e D a n d P r e t e n d e D t o s t a r E t H R O U G h t h e g l a S s a T t h E d I s p l a y o f h A r d ******* w A R e A n D t * o O l * s w H e r E h e c o n t i n U e d t o w A T c h b a R r A c k Y o u s E e w H a t i s E E S a * n D y s a i d K E n n o D D e d +HYP: A n a l m o s * t h e s a m e i n s t a n t C e n s * * R s a n d y s i d s t e p i n t o a s h o p d * o r w a y h e w a * D e d t h e * I * n t i K C H I n c a m e u p C I n s t D A U p e * a n d * r e t e n d e * t o s t a r * t * * * * * h t h e ******* g l a * s a * ******* t h * d E s p l a y o f h E r d w * * e R I n * G t U o U l E s w * e r * h e c o n t i n * e d t o w O U c h b a I r I c k * o u s * e w * a t i ******* s * * H a I n B y s a i d R I n n o * T e d +Eval: S D S D D I I S D D S D S D S I S S S S I S S D D D D D D D D D D D D D D S S I D D S S D S I S I D D D S S S S D D D D D D S I S S S D S + +Speaker sentences 22: mls_eng_000265 #utts: 1 +id: (mls_eng_000265-mls_eng_000265) +Scores: (#C #S #D #I) 172 21 26 14 +REF: * ******* t h e n t h e t h i C k g r E e n * s t U F F f l o W e d o V E r t h e W h O l E b U I l d i n G a n d t h E R e w * a s n o t h i n g t o B e * s e e n t h e r E b u t * A m * o u N d o f s o f * t * f l o W i n g g r a y ******* g r E e n s t U F F t h A T r u s h e d o n n o w w I T H t h e s w * i f t n e S s o F T H E w i n * * D I l o o K e D * U P i n t o b A R r Y s f a C e +HYP: T t h e n t h e t h i E k g r * e n E s t * A V f l o L e d o * L r t h e * h * l * b * E l d i n * a n d t h * * e w H a s n o t h i n g t o * e S s e e n t h e r * b u t D E m A o u * d o f s o f E t D f l o i n g g r a y g r * e n s t * A R t h E D r u s h e d o n n o w H w * * * t h e s w E i f t n e * s o * ******* * L L w i n G K E A l o o * e T A B D i n t o b * E r * s f a * e +Eval: I I S D I D S S S D S D D D D S D D D I D I D I S I D I I S I D D S S S S S D D D I D D D D S S I I S S D S I S S D S D D + +Speaker sentences 23: mls_eng_000266 #utts: 1 +id: (mls_eng_000266-mls_eng_000266) +Scores: (#C #S #D #I) 192 21 21 11 +REF: i h a v e m a D e s * A c * r I f i C e s t o O b u T I T W A s w h e n i K n e W t h a T t h e Y w E r e n o t m y h a P p I n e s s I T w a s a f t e r i * s a * W t h a t I H a d s t O O p e d * a F T e r i s * a W t h a t y o u r t e n d e r n e S s H a d t U r n E d t o c * A l C U l a t i o n a * f t e r I s a * W t h a t y o u c * A r E d f o r y o u r s e l f o n ******* l y n o t f o r m e +HYP: i h a v e m a * e s U R c K r O f i S e s t o E b u * ******* * R * O s w h e n i A n e U t h a * ******* t h e * w * r e n o t m y h a * p E n e s s ******* * * w a s a f t e r i S s a O L t h a t H * a d s t * E p e d T a G H e r i s O a R t h a t y o u r t e n d e r n e * s * a d t E r n * d t o c O U l D I l a t i o n a O f t e r ******* * s a O R t h a t y o u c E I r * d f o r y o u r s e l f o n l y n o t f o r m e +Eval: D I S I S S S D D D S D S S S D D D D D S D D D I I S S D D S I S S I S D D S D I S S S I D D I S I S D I + +Speaker sentences 24: mls_eng_000267 #utts: 1 +id: (mls_eng_000267-mls_eng_000267) +Scores: (#C #S #D #I) 141 12 11 13 +REF: y E t * t h E t h U n d * e r n e v e r r O a R s i n w i n t e r i s e E a c r o * W w H I r L i n g r o u n * d a n d r o u n * D b e f o r e i t a ******* l i G h * * t t h e R e I S n o t ******* h i n g u n d e R t h e f i r * * t r e e s b u t i * K n o W s o m e ******* t h I n g m u s T b e t h E r E +HYP: y A t D t h A t h A n d T e r n e v e r r * a L s i n w i n t e r i s e Y a c r o A L w * A r R i n g r o u n E d a n d r o u n M E b e f o r e i t a l i * h T E t t h e S e ******* * N n o t h i n g u n d e * t h e f i r E D t r e e s b u t i L * n o * s o m e t h * n g m u s * b e t h A r * +Eval: S I S S I D S S I S D S S I I S I D I I S D D S I D I I I D D I D D S D + +Speaker sentences 25: mls_eng_000268 #utts: 1 +id: (mls_eng_000268-mls_eng_000268) +Scores: (#C #S #D #I) 147 22 18 15 +REF: * i T i s b e y o n D * D o u B t t h a t s o m e p e O P l e h a D m a n A G e D t o s a V E m a n y t h i n g s a n D o f c o u r s e * t h e G E r m * a n S h a * ******* D s U R m * * i s e D a s m * u C h t * w o * o r * t h r E e d a Y s * * a f T e R T h e f i r s T p * e R Q u I S i t i o n s T H e Y d r o P p e D i n U n A w a r E s +HYP: E i Y i s b e y o n * T H o u * t t h a t s o m e p e * B l e h a E m a n I S e * ******* t o s a * F m a n y t h i n g s a n * o f c o u r s e S t h e J I r m E a n T h a S A s * O m A I i s e * a s m A u T h t O w o U o r E t h r * e d a * s G E a f * e * * h e f i r s I p R e C O u * C i t i o n s * D e * d r o U p e T i n I n O w a r * s +Eval: I S D I S D D S S S S D D D S D I S S I S I I S D S I I D I S I I I D D I I D D D S I S S D S D S D S S S S D + +Speaker sentences 26: mls_eng_000269 #utts: 1 +id: (mls_eng_000269-mls_eng_000269) +Scores: (#C #S #D #I) 189 12 18 7 +REF: s O o n a m a n c a m e o u t t o m E e T h i m t h i s m a n w a s o * l o h * E a b e A r d l e s s m a n * b e l O n G i n g t o a L A W l e S s r o B b E r c l a n * W h * i c h i n ******* f e s t e d t h e d I s t r I c t * p O s S i b l y a S s i s t i n g t h e M a n H u n t e r s o f T h e t e m p l E i n s E c U r i n g v i c t I m s f o r t h e t e m P l e A l t A r s +HYP: s * o n a m a n c a m e o u t t o m * e * h i m t h i s m a n w a s o A l o h A Y a b e * r d l e s s m a n D b e l * n * i n g t o a * * * l e * s r o V b U r c l a n H I h W i c h i n f e s t e d t h e d * s t r * c t B p U s T i b l y a * s i s t i n g t h e * a n u n t e r s o f * h e t e m p l * i n ******* s * c I r i n g v i c t U m s f o r t h e t e m B l e L l t E r s +Eval: D D D I I S D I D D D D D D S S I S I I D D I S S D D S D D D D S S S S S + +Speaker sentences 27: mls_eng_000270 #utts: 1 +id: (mls_eng_000270-mls_eng_000270) +Scores: (#C #S #D #I) 85 7 8 12 +REF: w e a r e n o t l o v e * r s y o u a n D i * * U p o n t h i s s u N n y l a n * E * b u t c h * I l d R e * n w h O H a v e n e v e r K n o W n ******* * l o v E s j * o Y * o * r p A I N +HYP: w e a r e n o t l o v e I r s y o u a n * i Y H A p o n t h i s s u * n y l a n A D T b u t c h O L l d * e O n w h E * a v e n e v e r * n o * n T l o v F s j I o R Y o U r p * * E +Eval: I D I I S D I S I I S D I S D D D I I S I S I I D D S + +Speaker sentences 28: mls_eng_000271 #utts: 1 +id: (mls_eng_000271-mls_eng_000271) +Scores: (#C #S #D #I) 163 22 42 11 +REF: * w a s m u r d E R e d o n T h e D O o r s t e P O f H i s o W N h o U s e * w a s T H i S a n o l D m a n a s K E D a n d * r E a c a L m ******* l y m i s e r * I C o * R d i a * y o u t A L k a s i f H e h a d d i * e D i n h i s b e * * d y o U A R e n o v e * n E t i A n a N d y O u c a N n o T u n d e R s t a N D w H A T i T m E A n s W h E n a n i n Q u i S i t O R i s m U r d E R e D +HYP: E w a s m u r d * * e d o n * h e ******* * S o r s t e * ******* V f W i s ******* o A L h o * s e S w a s * * i * a n o l * m a n a s * * T a n d T r a c a * m l y m i s e r E Q o U O d i a R y o u t * O k ******* a s ******* i f ******* * e h a d d i G e * i n h i s b e A T d y o * * H e n o ******* v e O n I t i O n a * d y * u c a * n o * u n d e * s t a * * E w * * D i N m * I n s * h * n a n ******* i n G u i C i t * Y i s m * r d * * e T +Eval: I D D D D D S D D S S D S S D I D D D D D D S I S D I I S S I S I D S D D D D I D I I D D S D I S S D D D D D D D S D D S S D S D D D S S D S D D D S + +Speaker sentences 29: mls_eng_000272 #utts: 1 +id: (mls_eng_000272-mls_eng_000272) +Scores: (#C #S #D #I) 180 35 31 11 +REF: b y g o * d h E s a I d t h e Y w R o n G m e * M Y b U s I n E s S L E A d s m e i n a n D o u t o F m A n y h o u s e s B U T W h a t d o i * c a r E f o r t h e s E c r E t S t h a t m a Y B e h I D d E n t h E r E h o * ******* w e v e r i * c A N n o t B l A M E T H E s E p e O P L e f o r t h e I R w A T c h f U l n e S s t h e b l * O O D h o u N D s o f T h E s I G n o r i A a r e ******* * i n e v * e r y s t r e e * t +HYP: b y g o R d h * I s a E d t h e * w * o n D m e H E R b I s * n * s T T H I d s m e i n a n * o u t ******* o * ******* m * n y h o u s e s * O E * h a t d o i Y c a r * f o r t h e s I c r S t * t h a t m a * V e h * A d n t h A r * h o W w e v e r i U c * U n o t P l I N G * W I s * p e * * B e ******* f o r t h e * * w O U c h f * l n e * s t h e b l A U T h o u * E s o f * h * I s F M n o r i * a r e A i n e v F e r y s t r e e P t +Eval: I D S S D D S I S S S D D S S S S D D D D D D S S D I D S S D D S D S S S D I I I D S S S S S D S S D D D S D D D S S D D I S S S D S D D S S S D I I I I + +Speaker sentences 30: mls_eng_000273 #utts: 1 +id: (mls_eng_000273-mls_eng_000273) +Scores: (#C #S #D #I) 164 27 54 9 +REF: H o R R I b l E D e A t H w a s p u L l i n g A t H E R n o T a s t i C k n o r a s t o n E w a s i n r e A C H o f h e R h a n d S a n d t h e P i T I l E S S C r a g s e C H O E d o n E * * l o n g s H r I e K a b O V e A L l t h e r * O a r o f T H E w a * t * e R f A l * L s h e s t r * o V e T O T u ******* R n o V e R a n d g R a s P t h e g R o U n D b u t o n l y F e l * T h e r s e L F g O I n G F a s t E R +HYP: U o * Y A b l * * e * t * w a s p u * l i n g I t ******* * * * n o * a ******* s t i * k n o r ******* a ******* s t o n * w a s i n D r e * G T o f h e * h a n d * a n d t h e B i * Y l * * I G r a g s e * Q U L d o n * N O l o n g s * r * e * a b * * e ******* * * l t h e ******* r U W a r ******* o f ******* * * * ******* w a R t H e f O l E D s h e s t r U o * e ******* * D B u O n ******* o U e * a n d g * a s E t h e g * o * n * b u t o n l y V e l E D h e r s e * * g * * n * C a s t * O +Eval: S D S S D D D D D S D D D D D D D D D D S D S S D D S D S D D S S D S S S D I I D D D D D D D D D I S D D D D D D I I S S I S I D D D S S I S D S D D S D D D S I S D D D D D S D S + +Speaker sentences 31: mls_eng_000274 #utts: 1 +id: (mls_eng_000274-mls_eng_000274) +Scores: (#C #S #D #I) 179 17 24 11 +REF: a n d T H E f O L l y o f * i t w a s t h a t a * ******* n o t h e R w o m A n * * o * F C E r n y w * i S h e D f o r n o t h i n g b e T t e R T H A n t o g o s i n C E m y s i s t e r a n d f a * t h e R A R E * s e n t A w a y s h e s a i d i s H o u l D r a t h e r g o w i T H t h E m i h a v e n o m i N D t o s t a y h e * r E a l o n E W i t H m y t w o * b a b I e s +HYP: a n d * * A f * A l y o f E i t w a s t h a t a N n o t h e * w o m E n D T o V E S A r n y w R i C h e * f o r n o t h i n g b e * t e * ******* * D E n t o g o s i n T O m y s i s t e r a n d f a R t h e * * U S C s e n t * w a y s h e s a i d i s * o u l * ******* r a t h e r g o w i * * ******* t h I m i h a v e n o m i E t o ******* s t a y h e A r * a l o n * ******* * i t D m y t w o E b a b * e s +Eval: D D S D S I I I D S I I I S S S I S D D D D D S S S S I D D S S I D D D D D D D S S S D I D D D D S I D + +Speaker sentences 32: mls_eng_000275 #utts: 1 +id: (mls_eng_000275-mls_eng_000275) +Scores: (#C #S #D #I) 102 15 24 7 +REF: B i L L Y d i D a C c o r d i n g l y w * O n * d E r i n g W h a T t H e l i T t L e m a n w o U l D b e a t a n d h e p I C K e d t W o O f t h e s t o u t ******* E s T r u s h E s h E c O u L D f i n * d w * i t H A l i t T l E b u n C H o f ******* * +HYP: * i * * N I d i T a c o r d i n g l y w H E n T d * r i n g * h a * t * e l i * t * e m a n w o * l * b e a t a n d h e p * E A e d t * o * f t h e ******* s t o u t I s * Y r u s h I s h * K c * u * * f i n E d w E i t * ******* D E l i t A l * b u n * E o f B +Eval: D D D S S S S I S I D D D D D D D D D S S D D D I S D S S D S D D D I I D D S S S D D S I I + +Speaker sentences 33: mls_eng_000276 #utts: 1 +id: (mls_eng_000276-mls_eng_000276) +Scores: (#C #S #D #I) 185 26 30 5 +REF: i t i s n o W t h e d r e A d F u l n I G H t c O m E s o n h o W d I s m A L I s t h e p l A I n F O r t h e p R u S s i A n s a N D t h e E n g l I s h f o U n D * * A b o V E t e n t h o u s A n D s l * a i n b r A v e w e L l i n g t o N a N D b l U C H e r b o * t h m o s T n o b l y d r o v e t h e I r f o * E s a n d b U o n A p a r t e S I m p e R I a L c r o W n w a s t A k e n a t w A t e R l O O +HYP: i t i s n o * t h e d r e * d I u l n * * A t c * m * s o n h o E d E s m * E A s t h e p l * E n W E r t h e p * u * s i O n s a * * t h e I n g l * s h f o * n E T E b o * F t e n t h o u s E n * s l E a i n b r E v e w e * l i n g t o * a * M b l * O K e r b o R t h m o s * n o b l y d r o v e t h e * r ******* f o U R s a n d b * o n O p a r t e * A m p e * Y a R c r o * n w a s t E k e n a t w O t e * l * * +Eval: D D S D D S D D S S D S S D S S S D D S D D S D D S I I S D S S D I S D D D S D S S I D D D I S D S D S D S S D S S D D D + +Speaker sentences 34: mls_eng_000277 #utts: 1 +id: (mls_eng_000277-mls_eng_000277) +Scores: (#C #S #D #I) 181 23 25 13 +REF: * ******* s o m e y E A R s a g * * O * a f * ******* t e R m a k i n g o U r * a R r A n g E M e n T s f o R t h e E n * ******* c a m p M e n t a t * n i G h t w * E c o n s t a n t l y h a d o U R P E A C e F u l r e s T B r o k E n b y a t r i b E o f b r o W n m O n k e Y s t h e Y e V i d E n t L y t h o u g h t t h a t l o n g p o S s e s S i o n h a d g i V e n t h e M a p r i O r * C l a I m t o t h e g R O V E +HYP: S s o m e ******* y U O U s a g L L E H a f E t e * m a k i n g o * r E a * r I n g * * e n * s f o * t h e ******* I n G c a m p * e n t a t D n i * h t w I T c o n s t a n t l y h a d o V E A D P e S u l r e s * ******* * r o k O n b y a t r i b * o f b r o U n m U n k e * s t h e * e * i d I n t * y t h o u g h t t h a t l o n g p o * s e s T i o n h a d g i M e n t h e * a p r i A r E * l a * m t o t h e g * * L L +Eval: I I D S S S I I S I I I D D I D S D D D D D S I I D I D I S S S S S S S S D D D S D S S D D D S D D S S D S I D D D D S S + +Speaker sentences 35: mls_eng_000278 #utts: 1 +id: (mls_eng_000278-mls_eng_000278) +Scores: (#C #S #D #I) 182 19 29 11 +REF: t o f l a s h i n * a t H o m e c * l A m O U R i n g f o r h e R m * a I d B e t w e e n M r * s V a n e s t e n s p a r t y a n d t h e o p E r A i f o n l y f O r A m i n U t E C e r t A I N l y * I t w a s m o r E t h a n a m i n U t E t h a t s I m o n E r e m a I n e d a T t h e P H A Y r E h o u s e * a * * f t e r b e I n g B r o U g H T b a C k * A f t e r * d i N n e r i n * t a X I +HYP: t o f l a s h i n H a t * o m e c P l E m * I D i n g f o r h e * m E a * d * e t w e e n * r S s W a n e s t e n s p a r t y a n d t h e o p * r E i f ******* o n l y ******* f I r ******* * m i n * t * S e r t * * O l y E * t ******* w a s m o r * t h a n a m i n I t * t h a t s * m o n D r e m a * n e d a * t h e * F I E r * h o u s e S a O U f t e r b e * n g G r o * g * D b a * k E O f t e r E d i * n e r i n T E t a C K +Eval: I D I S D S S D I D D D I S D S D D S D D D D S D D S I D D D S D D S D D D S S S D I I I D S D D S D I S I D I S S S + +Speaker sentences 36: mls_eng_000279 #utts: 1 +id: (mls_eng_000279-mls_eng_000279) +Scores: (#C #S #D #I) 226 26 43 18 +REF: a n d i n d i c t i o n a r y a n D T H e n w e h a d * c A L I s ******* t H e n i * c * s w e G o * * t h r O U G H a g R e a T m a n y f i g U r E s A n D S i n g * a l i f e * * o N t H E o C E A n w a v e w h a t f A I r y ******* l i k E m u s i c * s t E A l s o v e R t h e s e A l i G h T l y r o W l i G H t l y r o W o E r t h e g l a S s y w a V E S w * E g o * a n d o H C o m E c o m e A w a y a n d o t h e r s o n g s m * * R s j u D g * e t * a Y l O R W r * o t E o n E s o n g O n p U r p O s E f o r u s +HYP: a n d i n ******* d i c t i o n a r y a n * * D e n ******* w e h a d E c * O U s t * e n i N c X s w e C o L E t h r * * * * a g * e a * V m a n y f i g E r * s I n * C i n g E a l i f e M I o * U t * * I o * * * n w a v e ******* w h a t f * E r y l i k H m u s i c K s t * I l s o v e * t h e s e * l i * h E l y r o E l i * A t l y r o E o * r t h e g l a * s y w a * * Y w U D g o L a n d o * K o m * c o m e * w a y a n d o t h e r s o n g s m I S I s j u * g H e t H a * l * * E r B o t * o n * D s o n g U n p E r p * s * ******* f o r A u s +Eval: D D D S D I D S S I D I I S I I D D D D D D S S D S D S I I I D S D D S D D D D D S I S I D S D D D S S D S S D D D D S I S I D S D D I I S D I I D D D S I D D S S S D D D S + +Speaker sentences 37: mls_eng_000280 #utts: 1 +id: (mls_eng_000280-mls_eng_000280) +Scores: (#C #S #D #I) 197 23 25 15 +REF: t h a t w h i c h P a s S e s A S h i s t O r y i N O u r s c h O O l s * O r g o v e r N m e n t A L l y f a b r I c a t e d b O O K s o * * n h i s t O r y i s A f O r g E r y a m I s r e p r e s ******* * e n t a t i o n o f * e v e n * * T s l i K e t h e O l D d r * a * m ******* a * C e n t e r i n g u p o n t h e I m p o s S I b l e f i g U r E o f t h e h e * r o w i t H * A G e s t i c U l a t i n g c r o W d i n t H e b a C K G r o u n * d +HYP: t h a t w h i c h B a s H e s O F h i s t * r y ******* i G * u r s c h * L l s A E r g o v e r * m e n t * * l y f a b r O c a t e d ******* b * P s ******* o M E n h i s t * r y i s ******* * f I r g U r y a m R s r e p r e s I e n t a t i o n o f H e v e n C E s l i C e t h e * l * ******* d r O a W m a R S e n t e r i n g u p o n t h e ******* * m p o s E A b l e f i g I r * o f t h e h e A r o w i t * H E J e s t i c I l a t i n g c r o U d i n t * e ******* b a * * * r o u n E d +Eval: S S S S D D S D D S I S D D D S D D S S D I I D D D S S S I I I I I S S D D D I I I I S D D S S S D I D I S S S S D D D D D I + +Speaker sentences 38: mls_eng_000281 #utts: 1 +id: (mls_eng_000281-mls_eng_000281) +Scores: (#C #S #D #I) 131 22 15 14 +REF: W H E n T h e C A l i P H h E A r d t h i s h e s a i d o * J A A f * A r * h o w g o o d l y i s t h a t V o * I c e a n d t h e W A Z i R r e * p l * i E d * o * * o u r l o r d n e v e r s m o * t E m y h E a R i n g A U G H t S W E e t e r * o r * g O o d l I e * r * t h a n t h I S s I n g i n g +HYP: * * A n * h e K E l i V F h * * r d t h i s h e s a i d o A L L O f O U r E h o w g o o d l y i s t h a t F o Y S c e a n d t h e * E S i E r e B p l A i * d A o L E o u r l o r d n e v e r s m o R t * m y h * a * i n g * * O R t * T R e t e r E o r E g * o d l Y e A r E t h a n t h * E s A n g i n g +Eval: D D S D S S S S D D I S S S S I S I S I S D S S S I I D I I I I D D D D D S S D S S I I D S I I D S S + +Speaker sentences 39: mls_eng_000282 #utts: 1 +id: (mls_eng_000282-mls_eng_000282) +Scores: (#C #S #D #I) 177 21 10 25 +REF: e v i d e n t * l y t h e l E a r n e d b a * r O n h a d n o t s t * U d I e d s * u c h w o r k ******* s o F t h e t o t * * ******* * K a h * n * * ******* i o r * p A R r O t c H a t w h i c h n o t A b l y t r a n s l a t e d b y n A K h ******* s h a b * I f r O m * t h e s a n * * s K r i t s * U k A * s * A p ******* t A t I h a s n o W b E c o m e a s o r t h O d o X i c A L l y m U s l I m * a s t h e n i g H t * s +HYP: e v i d e n t E l y t h e l * a r n e d b a I r A n h a d n o t s t D A d Y e d s O u c h w o r k s o * t h e t o t E A C O a h I n I Y i o r E p * I r I t c a t w h i c h n o t I b l y t r a n s l a t e d b y n U G h s h a b E Y f r * m E t h e s a n S E s G r i t s I O k * A s E P p t U t Y h a s n o * b O c o m e a s o r t h E d o C i c * * l y m O s l A m E a s t h e ******* n i g * t E s +Eval: I D I S I S S I I D I I I I S I I I I I D S S S S S S I I S D I I I S I S D I I S I S S D S S S D D S S I D D I + + diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..84f9e975e05ffd6f9a96cb428373280195d4daf5 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/hyp.trn @@ -0,0 +1,40 @@ +I AETHIR UE OF TEKEN AOLDES FRON YOU SHI SAT HASTALY ISIA NOW THE IT S IMPOSEABLE T HAVE FATHIN OUN BASIY NO HEIT AS YUSEST ECPECET ANY RETEREN FOR YOUG FORAL I HAVE DON IOWNT NO MOR OF YOU (mls_eng_000243-mls_eng_000243) +THAT HE MAY SOMNTIMES BE E LAKCK OUTHER CHELEDREN LERNING TE SIDE MY NE OR PLAING PRATLIND SICING FOR HELPES COMSTO Y HEART ATS SHINFULORD AN SPEAKIN HOW CGOD THO ARTE (mls_eng_000244-mls_eng_000244) +REN SEN THINGS OF AL SORE AS I THE JENRAL OUTBURST OF MOLTEITODNESS PASTION AR HUTED TO GETHER THE LITURCRIS NAY THE RED DIEKILS WI THE HARABLE FOARE OVER THE BILY SE OF HEADS MA BE SEME RASTKGALADY CAPRYALING ON CORSES FO THE ROIL STOID (mls_eng_000245-mls_eng_000245) +IT MAY HAVE BEN TAT THE BONES WERER FOLDEDTOGETHER AND NONE AS ONY HE PELY BONS FOLDED AND LAID WAY FO THE PERPSCES OF INCENTATION SUCH BUNDLES OF BONES WE PUT THORWE A PROSESS OF PRAIRS (mls_eng_000246-mls_eng_000246) +MARS SAILES NEVER ECSPERINCED THOS GRAT TRAENSCITIONS FOM LONES TO GREANDEUR THIS WAS ONING TO THE PRODINT CONDUCT THAT RE POBLIKE WHICH ALWAYE PRESERVE HE PINCIPLE (mls_eng_000247-mls_eng_000247) +AT SMLE RE HING STION ON M THE MORING OF UPTOER THRTY ON CODUTED I THE APSFHER OF COESPERSY AND AI TANDED BY BROADY WE RETOL HAT THE NORMWL ADENTSE REQURIRMET FOR REVIU OREPROVL HAD BEN WAET THAT NORMLE AE PRVL O THE MAR OF WASINGTIN ND SRTNGOVENRS WOD BE HANDLD IN FORMILY (mls_eng_000248-mls_eng_000248) +T THE BOODIST LATDY IG CHINER HOL DO NO HASTATE TOL TAK LIG FOR THE PERVERS OFOD SOF THER CONCHONCS FOROMG TUCK TO TI BY BYING VEIRSD FIASHIEIS YOU SEADTER AND LETIN THE GOLL (mls_eng_000249-mls_eng_000249) +T THIE AGAN IS SOFEND AND TUMPERD BY A SEMBLE FATH AN THE SUPROMIRSY OF LOVE OVER FER AND UN BWNTED UMANITY AND CHIRIY FOR THE POR AN HELPLESAN UN CODIONAL FOR GIVENES OF THE DIRST INERES WHICH IS THE NOT OF THE NOBL A ENEROUSITY AND LIBERALITY (mls_eng_000250-mls_eng_000250) +THE SECEND MADE FALLOED AND HE COUPL O THE SEMERSMEN BRO THE PBOR TH BARKE BE FOR CUCHING A ROUP THE WEN TO WARK T SURCH TH SHI TE LIFED DE HACHES AND FAOWND THE HOLD FULL OF CORGO (mls_eng_000251-mls_eng_000251) +FOND OFHIS COMERADE AND RESPETFULE I TO HIS PASTUR AND MASTERS EVEN SCHL MUSTERS AS HE LAD HE PREPERS FOR ANHOD WTH WIL AND THIS TRAING OCKUPYE HIM THURE OUT YOUTH TIID (mls_eng_000252-mls_eng_000252) +ASS WHE THE PRENES QUPECEA E RECHE TER SIXT INT YEAR SH BECAME BLINGD WHER LARGH SOF BON ICES HAD O LIDINGTHM (mls_eng_000253-mls_eng_000253) +AOLMOST ALLE DY THE BATE RANGEDBUTCE TH TWO MEN BAK IN FORT THEY FOURS YECHEOTHER OVER THE LAV A BEDS THE SHIVFS WEL OIE BOY WAS VERY DIFICALT FOR HEOALO HAY TOGRAS PBROSE AND BLEADING FROM RE PETED FLLES ON THE ROUFH LAVA (mls_eng_000254-mls_eng_000254) +BPOSY IY TRE BIT TERRAR AND A WLAL PANTING THE WHIN MONDT THROG THE TREES OF THE CARDIN AND FOME TIME TO TIMEMSEE AS IF TO A (mls_eng_000255-mls_eng_000255) +HIS MENTL TORPTITY FOUNDET A PON FISIOCL INDLEANCE RENDERS A MEDIAT ACTION AN AL MAENERO ESRTIOND DUSTEASFUL HIS CONCHUS WEKNES SHOS ITD SELF (mls_eng_000256-mls_eng_000256) +TNORE THAELE HOW GLAD THE CING MTHE WAS NOR HOUE GRATD WOER THE REYGJUORISING OF THE PEPLE OR HOM MENIFESSENDTD WAS THE ROIL BANKQCED THAT GOOD CFING POMEREAR ADTENDETD WHITHAL HER COREN (mls_eng_000257-mls_eng_000257) +AND THE CHONCE OF THER BENG SUCH A ON UNGIAN DIMISHES BY EWERY PRAPE PROSESS MAMELUK AS AEORS WHOS OF EAKCUEAN CQULITY EREASNING NOT ABOWTD HIS ODERS BUT BOUL THE IY TO DO THET (mls_eng_000258-mls_eng_000258) +TSHE NOKET BUT S NO ROUP LOE OATHOF TE WHENDO AN SAID I DEAER NOT OBPB HE DOR FOR THE DORSHAVE TOD ME TOLT NO OIN IN THAT IS HARED FOR ME E SAID THE OMEON FOR I MUST TAK BACE MY AUPLS BUT THERIS ONE WICH IULL GIVE YOU AND SHE HELD UPEN AL (mls_eng_000259-mls_eng_000259) +OV Y MORY SPOK AK CELTHOR AND HERSE FINLY SPOKEN IHT TWO MY PESWAS WHY FEABEY AR YO CM SOSON WERE OUR BRY CHILD ND O THAN AR TDLSPOK SAME OAN (mls_eng_000260-mls_eng_000260) +I NEVER U ANYWOEN HO LE TDOGOTY CERCHIS MCH AC SGRAMOH DOUS SHE HAIS HE WOUD RAVE B A DORE CEPEI TH HAS OF R GOD THEN I WELL THE TENSOF WIKEDNES TE DON HVE WOMEN DORE KEPERS AN I WENO SHE WINUT TWELL MININA ATENT (mls_eng_000261-mls_eng_000261) +THE DUK WAS SUPRICS TO SE HM OT BRING O OT SO ALY ABEL ARD DEMAND D HE OL YOUR GRAICTED REPLI HE UTLR Y GASPING FOR UTERANCES (mls_eng_000262-mls_eng_000262) +FORE IUAT SEMING TRONSENMEUTATIONS OF CALE MA BE MAD WE THER IS NY MICXTROF DEVOR SOUTS O RAES BRIN SACH MICTERS THE COM PON ELIS APERE NOT BY THE MUTIL ALAING AHETHE CONCTUTE A MIDLING COLER (mls_eng_000263-mls_eng_000263) +AN ALMOS THE SAME INSTANT CEN S RS ANDY SID STEP INTO A SHOP DORWAY HE WADED THEI NTIK CHIN CAME UP CIN STDAUPE AND RETENDE TO STAR TH THEGLAS ATH DESPLAY OF HERD WERINGTUOULES WER HE CONTINED TO WOUCH BAIRICK OU SE WAT IS HAINBY SAID RIN NOTED (mls_eng_000264-mls_eng_000264) +T THEN THE THIEK GRENE STAV FLOLED OLR THE HL BELDIN AND THE WHAS NOTHING TO ES SEEN THER BUT DE MAOUD OF SOFETD FLO ING GRAY GREN STAR THED RUSHED ON NOWHW THE SWEIFTNES OLL WINGKE A LOOET ABD INTO BERS FAE (mls_eng_000265-mls_eng_000265) +I HAVE MAE SURCKROFISES TOE BUR OS WHEN I ANEU THATHE WRE NOT MY HAPENESS WAS AFTER IS SAOL THAT H AD STEPEDT AGHER I SOAR THAT YOUR TENDERNES AD TERND TO COULDILATION AOFTER SAOR THAT YOU CEIRD FOR YOURSELF ON LY NOT FOR ME (mls_eng_000266-mls_eng_000266) +YATD THA THANDTER NEVER RALS IN WINTER I SEY A CROAL WARRING ROUNED AND ROUNME BEFORE IT A LIHTET THESEN NOT HING UNDE THE FIRED TREES BUT IL NO SOME THNG MUS BE THAR (mls_eng_000267-mls_eng_000267) +EIY IS BEYON THOUT THAT SOME PEBLE HAE MANISETO SAF MANY THINGS AN OF COURSES THE JIRMEANT HAS A SOMAIISE AS MAUTH TOWOU ORE THRE DASGE AFE HE FIRSI PRECOUCITIONS DE DROUPET IN INOWARS (mls_eng_000268-mls_eng_000268) +SON A MAN CAME OUT TO ME HIM THIS MAN WAS OALOHAY A BERDLESS MAND BELNING TO A LES ROVBUR CLAN HIHWICH IN FESTED THE DSTRCT BPUSTIBLY ASISTING THE AN UNTERS OF HE TEMPL INSCIRING VICTUMS FOR THE TEMBLE LLTERS (mls_eng_000269-mls_eng_000269) +WE ARE NOT LOVEIRS YOU AN IYH APON THIS SUNY LANAD TBUT CHOLLDEON WHE AVE NEVER NON T LOVFS JIOR YOUR PE (mls_eng_000270-mls_eng_000270) +EWAS MURDED ON HESORSTEVF WISOAL HOSES WAS I AN OL MAN AST ANDTR A CAM LY MISERE QOUODIAR YOU TOKASIFE HAD DIGE IN HIS BEATD YO HE NOVEONITION AD YU CANO UNDESTAEWD IN MINS HN ANINGUICITY IS MRDET (mls_eng_000271-mls_eng_000271) +BY GORD HISAED THE WOND ME HER BISNST THIDS ME IN AN OUTOMNY HOUSES OE HAT DO IY CAR FOR THE SICRST THAT MA VE HAD N THAR HOW WEVER IU CUNOT PLING WIS PEBEFOR THE WOUCHFLNES THE BLAUT HOUES OF HISFMNORI ARE A IN EVFERY STREEPT (mls_eng_000272-mls_eng_000272) +UOYABL ET WAS PULING IT NO ASTIK NORASTON WAS INDREGT OF HE HAND AND THE BIYLI GRAGS EQULD ON NOLONG SRE ABEL THERUWAROFWARTHE FOLED SHE STRUOED BU ONOUE AND GASE THE GON BUT ONLY VELED HERSE GN CASTO (mls_eng_000273-mls_eng_000273) +AND A FALY OFE IT WAS THAT AN NOTHE WOMENDT OVE SARNY WRICHE FOR NOTHING BETEDEN TO GO SINTO MY SISTER AND FARTHE US CSENT WAY SHE SAID I SOULRATHER GO WITHIM I HAVE NO MI E TOSTAY HEAR ALONITD MY TWOE BABES (mls_eng_000274-mls_eng_000274) +INIDIT A CORDINGLY WHENTDRING HA TE LITE MAN WOL BE AT AND HE PEAED TO F THESTOUT ISYRUSHIS HKCU FINED WEITDELITAL BUNE OF B (mls_eng_000275-mls_eng_000275) +IT IS NO THE DREDIUL NAT CMS ON HOE DESME AS THE PLEN WER THE PUSIONS A THE INGLSH FONE TE BOF TEN THOUSEN SLEAIN BREVE WELINGTO AM BLOKER BORTH MOS NOBLY DROVE THERFOURS AND BONOPARTE AMPEYAR CRON WAS TEKEN AT WOTEL (mls_eng_000276-mls_eng_000276) +S SOMEYUOUS AGLLE HAFE TE MAKING ORE ARINGENS FO THEING CAMPENT ATD NIHT WIT CONSTANTLY HAD OVE AD PESUL RESROKON BY A TRIB OF BROUN MUNKES THE EIDINTY THOUGHT THAT LONG POSESTION HAD GIMEN THE A PRIARE LAM TO THE GLL (mls_eng_000277-mls_eng_000277) +TO FLASH IN HAT OME CPLEMIDING FOR HE MEAD ETWEEN RSS WAN ESTENS PARTY AND THE OPRE IFONLYFIR MINT SERTOLYE TWAS MOR THAN A MINIT THAT SMOND REMANED A THE FIER HOUSES AOUFTER BENG GROGD BAKE OFTERE DINER INTETACK (mls_eng_000278-mls_eng_000278) +AND INDICTIONARY AN DENWE HADE COUS TENINCXS WE COLE THR A GEAVMANY FIGERS IN CINGE A LIFE MIOUTION WAVEWHAT FERY LIKH MUSICK STILS OVE THE SE LIHELY ROE LIATLY ROE OR THE GLASY WAY WUD GOL AND O KOM COME WAY AND OTHER SONGS MISIS JUGHE THAL ERBOT ONDSONG UN PERPSFORAUS (mls_eng_000279-mls_eng_000279) +THAT WHICH BASHES OF HISTRYIG UR SCHLLS AER GOVERMENTLY FABROCATEDBP SOMEN HISTRY IS FIRGURY A MRSREPRES IENTATION OF HEVENCE S LICE THE LDROAWM AR SENTERING UPON THEMPOSEABLE FIGIR OF THE HEARO WIT HE JESTICILATING CROUD IN TEBAROUNED (mls_eng_000280-mls_eng_000280) +AN HE KELIVF HRD THIS HE SAID OALL OFOURE HOW GOODLY IS THAT FOYSCE AND THE ESIE REBPLAID AOLE OUR LORD NEVER SMORT MY HAING ORT TRETERE ORE GODLYEARE THAN THE SANGING (mls_eng_000281-mls_eng_000281) +EVIDENTELY THE LARNED BAIRAN HAD NOT STDADYED SOUCH WORK S O THE TOTEA COAHINIY I ORE PIRIT C AT WHICH NOTIBLY TRANSLATED BY NUGH SHABEY FRME THE SANSESGRIT SIOK ASEPP TUTY HAS NO BOCOME AS ORTHEDOCICLY MOSLAME AS THENIGTES (mls_eng_000282-mls_eng_000282) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..e6e203ca8e5cd5f06e81647944ebbf3a39aabc4e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/ref.trn @@ -0,0 +1,40 @@ +I AM TIRED OF TAKING ORDERS FROM YOU SHE SAID HASTILY I SEE NOW THAT IT IS IMPOSSIBLE TO HAVE FAITH IN YOU I SEE NOW THAT IT IS USELESS TO EXPECT ANY RETURN FROM YOU FOR ALL I HAVE DONE I WANT NO MORE OF YOU (mls_eng_000243-mls_eng_000243) +THAT HE MAY SOMETIMES BE LIKE OTHER CHILDREN LEARNING BESIDE MY KNEE OR PLAYING PRATTLING SEEKING FOR HELP COMES TO MY HEART AH SINFUL LORD IM SPEAKING HOW GOOD THOU ART (mls_eng_000244-mls_eng_000244) +TRANSCEND THINGS OF ALL SORTS AS IN THE GENERAL OUTBURST OF MULTITUDINOUS PASSION ARE HUDDLED TOGETHER THE LUDICROUS NAY THE RIDICULOUS WITH THE HORRIBLE FAR OVER THE BILLOWY SEA OF HEADS MAY BE SEEN RASCALITY CAPRIOLING ON HORSES FROM THE ROYAL STUD (mls_eng_000245-mls_eng_000245) +IT MAY HAVE BEEN THAT THE BONES WERE FOLDED TOGETHER AND KNOWN AS UNIHIPILI BONES FOLDED AND LAID AWAY FOR PURPOSES OF INCANTATION SUCH BUNDLES OF BONES WERE PUT THROUGH A PROCESS OF PRAYERS (mls_eng_000246-mls_eng_000246) +MARSEILLES NEVER EXPERIENCED THOSE GREAT TRANSITIONS FROM LOWNESS TO GRANDEUR THIS WAS OWING TO THE PRUDENT CONDUCT THAT REPUBLIC WHICH ALWAYS PRESERVED HER PRINCIPLES (mls_eng_000247-mls_eng_000247) +AT A SMALL BRIEFING SESSION ON THE MORNING OF OCTOBER THIRTY ONE CONDUCTED IN THE ATMOSPHERE OF CONSPIRACY AND ATTENDED BY BRODY WE WERE TOLD THAT THE NORMAL AGENCY REQUIREMENT FOR REVIEW BOARD APPROVAL HAD BEEN WAIVED THAT NORMAL APPROVAL OF THE MAYOR OF WASHINGTON AND CERTAIN GOVERNORS WOULD BE HANDLED INFORMALLY (mls_eng_000248-mls_eng_000248) +THE BUDDHIST LAITY IN CHINA WHO DO NOT HESITATE TO TAKE LIFE FOR THE PURPOSE OF FOOD SALVE THEIR CONSCIENCE FROM TIME TO TIME BY BUYING BIRDS FISHES ET CETERA AND LETTING THEM GO (mls_eng_000249-mls_eng_000249) +THIS AGAIN IS SOFTENED AND TEMPERED BY A SIMPLE FAITH IN THE SUPREMACY OF LOVE OVER FEAR AN UNBOUNDED HUMANITY AND CHARITY FOR THE POOR AND HELPLESS AN UNCONDITIONAL FORGIVENESS OF THE DIREST INJURIES WHICH IS THE NOTE OF THE NOBLE A GENEROSITY AND LIBERALITY (mls_eng_000250-mls_eng_000250) +THE SECOND MATE FOLLOWED AND A COUPLE OF THE STEAMERS MEN ROWED THEM ABOARD THE BARQUE BEFORE TOUCHING A ROPE THEY WENT TO WORK TO SEARCH THE SHIP THEY LIFTED THE HATCHES AND FOUND THE HOLD FULL OF CARGO (mls_eng_000251-mls_eng_000251) +FOND OF HIS COMRADES AND RESPECTFUL TO HIS PASTORS AND MASTERS EVEN SCHOOLMASTERS AS A LAD HE PREPARES FOR MANHOOD WITH A WILL AND THIS TRAINING OCCUPIES HIM THROUGHOUT YOUTHTIDE (mls_eng_000252-mls_eng_000252) +WHEN THE PRINCESS COECA REACHED HER SIXTEENTH YEAR SHE BECAME BLIND HER LARGE SOFT BROWN EYES HAD NO LIGHT IN THEM (mls_eng_000253-mls_eng_000253) +ALMOST ALL DAY THE BATTLE RAGED BETWEEN THE TWO MEN BACK AND FORTH THEY FORCED EACH OTHER OVER THE LAVA BEDS THE CHIEFS WELLOILED BODY WAS VERY DIFFICULT FOR THE OLOHE TO GRASP BRUISED AND BLEEDING FROM REPEATED FALLS ON THE ROUGH LAVA (mls_eng_000254-mls_eng_000254) +POSY I SHRIEKED WITH TERROR AND I AWOKE PANTING THE WIND MOANED THROUGH THE TREES OF THE GARDEN AND FROM TIME TO TIME CEASED AS IF (mls_eng_000255-mls_eng_000255) +HIS MENTAL TORPIDITY FOUNDED UPON PHYSICAL INDOLENCE RENDERS IMMEDIATE ACTION AND ALL MANNER OF EXERTION DISTASTEFUL HIS CONSCIOUS WEAKNESS SHOWS ITSELF (mls_eng_000256-mls_eng_000256) +NOR TELL HOW GLAD THE QUEEN MOTHER WAS NOR HOW GREAT WERE THE REJOICING OF THE PEOPLE NOR HOW MAGNIFICENT WAS THE ROYAL BANQUET THAT GOOD QUEEN POMAREA ATTENDED WITH ALL HER COURT (mls_eng_000257-mls_eng_000257) +AND THE CHANCE OF THERE BEING SUCH A ONE AGAIN DIMINISHES BY VERY RAPID PROCESS MARMADUKE AS A HORSE WAS OF EQUAL QUALITY REASONING NOT ABOUT HIS ORDERS BUT ABOUT THE WAY TO DO THEM (mls_eng_000258-mls_eng_000258) +SHE KNOCKED BUT SNOWDROP LOOKED OUT OF THE WINDOW AND SAID I DARE NOT OPEN THE DOOR FOR THE DWARFS HAVE TOLD ME TO LET NO ONE IN THAT IS HARD FOR ME SAID THE WOMAN FOR I MUST TAKE BACK MY APPLES BUT THERE IS ONE WHICH I WILL GIVE YOU AND SHE HELD UP AN APPLE (mls_eng_000259-mls_eng_000259) +ALBERT MURRAY SPOKE EXCELSIOR AND HORACE FINLEY SPOKE NICE TOO MY PIECE WAS WHY PHOEBE ARE YOU COME SO SOON WHERE ARE YOUR BERRIES CHILD EMMA VAN ARSDALE SPOKE THE SAME ONE (mls_eng_000260-mls_eng_000260) +I NEVER KNEW ANYONE WHO LIKED TO GO TO CHURCH AS MUCH AS GRANDMOTHER DOES SHE SAYS SHE WOULD RATHER BE A DOORKEEPER IN THE HOUSE OF OUR GOD THAN TO DWELL IN THE TENTS OF WICKEDNESS THEY DONT HAVE WOMEN DOORKEEPERS AND I KNOW SHE WOULD NOT DWELL A MINUTE IN A TENT (mls_eng_000261-mls_eng_000261) +THE DUKE WAS SURPRISED TO SEE HIM WHAT BRINGS YOU OUT SO EARLY ABELARD DEMANDED HE OH YOUR GRACE REPLIED THE BUTLER GASPING FOR UTTERANCE (mls_eng_000262-mls_eng_000262) +FOUR YET SEEMING TRANSMUTATIONS OF COLOUR MAY BE MADE WHERE THERE IS ANY MIXTURE OF DIVERSE SORTS OF RAYS FOR IN SUCH MIXTURES THE COMPONENT COLOURS APPEAR NOT BUT BY THEIR MUTUAL ALLAYING EACH OTHER CONSTITUTE A MIDDLING COLOUR (mls_eng_000263-mls_eng_000263) +IN ALMOST THE SAME INSTANT KEN SAW SANDY SIDESTEP INTO A SHOP DOORWAY HE WAITED THERE UNTIL KEN CAME UP KEN STOPPED AND PRETENDED TO STARE THROUGH THE GLASS AT THE DISPLAY OF HARDWARE AND TOOLS WHERE HE CONTINUED TO WATCH BARRACK YOU SEE WHAT I SEE SANDY SAID KEN NODDED (mls_eng_000264-mls_eng_000264) +THEN THE THICK GREEN STUFF FLOWED OVER THE WHOLE BUILDING AND THERE WAS NOTHING TO BE SEEN THERE BUT A MOUND OF SOFT FLOWING GRAYGREEN STUFF THAT RUSHED ON NOW WITH THE SWIFTNESS OF THE WIND I LOOKED UP INTO BARRYS FACE (mls_eng_000265-mls_eng_000265) +I HAVE MADE SACRIFICES TOO BUT IT WAS WHEN I KNEW THAT THEY WERE NOT MY HAPPINESS IT WAS AFTER I SAW THAT I HAD STOOPED AFTER I SAW THAT YOUR TENDERNESS HAD TURNED TO CALCULATION AFTER I SAW THAT YOU CARED FOR YOURSELF ONLY NOT FOR ME (mls_eng_000266-mls_eng_000266) +YET THE THUNDER NEVER ROARS IN WINTER I SEE A CROW WHIRLING ROUND AND ROUND BEFORE IT ALIGHT THERE IS NOTHING UNDER THE FIR TREES BUT I KNOW SOMETHING MUST BE THERE (mls_eng_000267-mls_eng_000267) +IT IS BEYOND DOUBT THAT SOME PEOPLE HAD MANAGED TO SAVE MANY THINGS AND OF COURSE THE GERMANS HAD SURMISED AS MUCH TWO OR THREE DAYS AFTER THE FIRST PERQUISITIONS THEY DROPPED IN UNAWARES (mls_eng_000268-mls_eng_000268) +SOON A MAN CAME OUT TO MEET HIM THIS MAN WAS OLOHE A BEARDLESS MAN BELONGING TO A LAWLESS ROBBER CLAN WHICH INFESTED THE DISTRICT POSSIBLY ASSISTING THE MANHUNTERS OF THE TEMPLE IN SECURING VICTIMS FOR THE TEMPLE ALTARS (mls_eng_000269-mls_eng_000269) +WE ARE NOT LOVERS YOU AND I UPON THIS SUNNY LANE BUT CHILDREN WHO HAVE NEVER KNOWN LOVES JOY OR PAIN (mls_eng_000270-mls_eng_000270) +WAS MURDERED ON THE DOORSTEP OF HIS OWN HOUSE WAS THIS AN OLD MAN ASKED ANDREA CALMLY MISERICORDIA YOU TALK AS IF HE HAD DIED IN HIS BED YOU ARE NO VENETIAN AND YOU CANNOT UNDERSTAND WHAT IT MEANS WHEN AN INQUISITOR IS MURDERED (mls_eng_000271-mls_eng_000271) +BY GOD HE SAID THEY WRONG ME MY BUSINESS LEADS ME IN AND OUT OF MANY HOUSES BUT WHAT DO I CARE FOR THE SECRETS THAT MAY BE HIDDEN THERE HOWEVER I CANNOT BLAME THESE PEOPLE FOR THEIR WATCHFULNESS THE BLOODHOUNDS OF THE SIGNORIA ARE IN EVERY STREET (mls_eng_000272-mls_eng_000272) +HORRIBLE DEATH WAS PULLING AT HER NOT A STICK NOR A STONE WAS IN REACH OF HER HANDS AND THE PITILESS CRAGS ECHOED ONE LONG SHRIEK ABOVE ALL THE ROAR OF THE WATERFALL SHE STROVE TO TURN OVER AND GRASP THE GROUND BUT ONLY FELT HERSELF GOING FASTER (mls_eng_000273-mls_eng_000273) +AND THE FOLLY OF IT WAS THAT ANOTHER WOMAN OF CERNY WISHED FOR NOTHING BETTER THAN TO GO SINCE MY SISTER AND FATHER ARE SENT AWAY SHE SAID I SHOULD RATHER GO WITH THEM I HAVE NO MIND TO STAY HERE ALONE WITH MY TWO BABIES (mls_eng_000274-mls_eng_000274) +BILLY DID ACCORDINGLY WONDERING WHAT THE LITTLE MAN WOULD BE AT AND HE PICKED TWO OF THE STOUTEST RUSHES HE COULD FIND WITH A LITTLE BUNCH OF (mls_eng_000275-mls_eng_000275) +IT IS NOW THE DREADFUL NIGHT COMES ON HOW DISMAL IS THE PLAIN FOR THE PRUSSIANS AND THE ENGLISH FOUND ABOVE TEN THOUSAND SLAIN BRAVE WELLINGTON AND BLUCHER BOTH MOST NOBLY DROVE THEIR FOES AND BUONAPARTES IMPERIAL CROWN WAS TAKEN AT WATERLOO (mls_eng_000276-mls_eng_000276) +SOME YEARS AGO AFTER MAKING OUR ARRANGEMENTS FOR THE ENCAMPMENT AT NIGHT WE CONSTANTLY HAD OUR PEACEFUL REST BROKEN BY A TRIBE OF BROWN MONKEYS THEY EVIDENTLY THOUGHT THAT LONG POSSESSION HAD GIVEN THEM A PRIOR CLAIM TO THE GROVE (mls_eng_000277-mls_eng_000277) +TO FLASH IN AT HOME CLAMOURING FOR HER MAID BETWEEN MRS VAN ESTENS PARTY AND THE OPERA IF ONLY FOR A MINUTE CERTAINLY IT WAS MORE THAN A MINUTE THAT SIMONE REMAINED AT THE PHAYRE HOUSE AFTER BEING BROUGHT BACK AFTER DINNER IN TAXI (mls_eng_000278-mls_eng_000278) +AND IN DICTIONARY AND THEN WE HAD CALISTHENICS WE GO THROUGH A GREAT MANY FIGURES AND SING A LIFE ON THE OCEAN WAVE WHAT FAIRYLIKE MUSIC STEALS OVER THE SEA LIGHTLY ROW LIGHTLY ROW OER THE GLASSY WAVES WE GO AND OH COME COME AWAY AND OTHER SONGS MRS JUDGE TAYLOR WROTE ONE SONG ON PURPOSE FOR US (mls_eng_000279-mls_eng_000279) +THAT WHICH PASSES AS HISTORY IN OUR SCHOOLS OR GOVERNMENTALLY FABRICATED BOOKS ON HISTORY IS A FORGERY A MISREPRESENTATION OF EVENTS LIKE THE OLD DRAMA CENTERING UPON THE IMPOSSIBLE FIGURE OF THE HERO WITH A GESTICULATING CROWD IN THE BACKGROUND (mls_eng_000280-mls_eng_000280) +WHEN THE CALIPH HEARD THIS HE SAID O JAAFAR HOW GOODLY IS THAT VOICE AND THE WAZIR REPLIED O OUR LORD NEVER SMOTE MY HEARING AUGHT SWEETER OR GOODLIER THAN THIS SINGING (mls_eng_000281-mls_eng_000281) +EVIDENTLY THE LEARNED BARON HAD NOT STUDIED SUCH WORKS OF THE TOTKAHNI OR PARROT CHAT WHICH NOTABLY TRANSLATED BY NAKHSHABI FROM THE SANSKRIT SUKA SAPTATI HAS NOW BECOME AS ORTHODOXICALLY MUSLIM AS THE NIGHTS (mls_eng_000282-mls_eng_000282) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/result.txt b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..9cc5c885155a2656409c39396a6188b812093662 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/result.txt @@ -0,0 +1,521 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,-----------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/hyp.trn| +|-----------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000243 | 1 47 | 27.7 55.3 17.0 2.1 74.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000244 | 1 31 | 29.0 64.5 6.5 6.5 77.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000245 | 1 42 | 40.5 59.5 0.0 7.1 66.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000246 | 1 34 | 52.9 44.1 2.9 8.8 55.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000247 | 1 24 | 37.5 62.5 0.0 8.3 70.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000248 | 1 52 | 30.8 63.5 5.8 7.7 76.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000249 | 1 34 | 20.6 76.5 2.9 2.9 82.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000250 | 1 45 | 46.7 53.3 0.0 6.7 60.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000251 | 1 39 | 25.6 74.4 0.0 0.0 74.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000252 | 1 30 | 46.7 46.7 6.7 13.3 66.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000253 | 1 21 | 19.0 71.4 9.5 14.3 95.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000254 | 1 43 | 37.2 55.8 7.0 7.0 69.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000255 | 1 26 | 46.2 50.0 3.8 7.7 61.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000256 | 1 21 | 19.0 76.2 4.8 14.3 95.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000257 | 1 33 | 39.4 57.6 3.0 0.0 60.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000258 | 1 35 | 40.0 57.1 2.9 0.0 60.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000259 | 1 58 | 44.8 46.6 8.6 3.4 58.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000260 | 1 32 | 15.6 75.0 9.4 6.3 90.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000261 | 1 54 | 18.5 64.8 16.7 1.9 83.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000262 | 1 25 | 32.0 68.0 0.0 12.0 80.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000263 | 1 39 | 17.9 71.8 10.3 2.6 84.6 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000264 | 1 51 | 29.4 62.7 7.8 3.9 74.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000265 | 1 42 | 31.0 64.3 4.8 7.1 76.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000266 | 1 48 | 41.7 50.0 8.3 2.1 60.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000267 | 1 32 | 37.5 62.5 0.0 6.3 68.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000268 | 1 34 | 26.5 70.6 2.9 2.9 76.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000269 | 1 39 | 46.2 51.3 2.6 5.1 59.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000270 | 1 21 | 28.6 71.4 0.0 4.8 76.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000271 | 1 44 | 18.2 68.2 13.6 0.0 81.8 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000272 | 1 48 | 27.1 64.6 8.3 6.3 79.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000273 | 1 48 | 20.8 58.3 20.8 0.0 79.2 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000274 | 1 46 | 41.3 50.0 8.7 2.2 60.9 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000275 | 1 27 | 22.2 63.0 14.8 3.7 81.5 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000276 | 1 42 | 31.0 66.7 2.4 2.4 71.4 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000277 | 1 40 | 32.5 67.5 0.0 2.5 70.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000278 | 1 44 | 27.3 61.4 11.4 0.0 72.7 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000279 | 1 58 | 20.7 67.2 12.1 0.0 79.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000280 | 1 40 | 25.0 62.5 12.5 5.0 80.0 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000281 | 1 32 | 43.8 56.3 0.0 0.0 56.3 100.0 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000282 | 1 34 | 32.4 64.7 2.9 17.6 85.3 100.0 | +|=======================================================================================================================| +| Sum/Avg | 40 1535 | 31.7 61.4 6.9 4.6 72.8 100.0 | +|=======================================================================================================================| +| Mean | 1.0 38.4 | 31.8 61.9 6.3 5.1 73.4 100.0 | +| S.D. | 0.0 10.0 | 10.0 8.6 5.5 4.5 10.4 0.0 | +| Median | 1.0 39.0 | 30.9 62.9 5.3 4.3 74.5 100.0 | +`-----------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,-----------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/hyp.trn| +|-----------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000243 | 1 47 | 13 26 8 1 35 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000244 | 1 31 | 9 20 2 2 24 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000245 | 1 42 | 17 25 0 3 28 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000246 | 1 34 | 18 15 1 3 19 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000247 | 1 24 | 9 15 0 2 17 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000248 | 1 52 | 16 33 3 4 40 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000249 | 1 34 | 7 26 1 1 28 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000250 | 1 45 | 21 24 0 3 27 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000251 | 1 39 | 10 29 0 0 29 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000252 | 1 30 | 14 14 2 4 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000253 | 1 21 | 4 15 2 3 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000254 | 1 43 | 16 24 3 3 30 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000255 | 1 26 | 12 13 1 2 16 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000256 | 1 21 | 4 16 1 3 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000257 | 1 33 | 13 19 1 0 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000258 | 1 35 | 14 20 1 0 21 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000259 | 1 58 | 26 27 5 2 34 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000260 | 1 32 | 5 24 3 2 29 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000261 | 1 54 | 10 35 9 1 45 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000262 | 1 25 | 8 17 0 3 20 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000263 | 1 39 | 7 28 4 1 33 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000264 | 1 51 | 15 32 4 2 38 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000265 | 1 42 | 13 27 2 3 32 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000266 | 1 48 | 20 24 4 1 29 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000267 | 1 32 | 12 20 0 2 22 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000268 | 1 34 | 9 24 1 1 26 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000269 | 1 39 | 18 20 1 2 23 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000270 | 1 21 | 6 15 0 1 16 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000271 | 1 44 | 8 30 6 0 36 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000272 | 1 48 | 13 31 4 3 38 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000273 | 1 48 | 10 28 10 0 38 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000274 | 1 46 | 19 23 4 1 28 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000275 | 1 27 | 6 17 4 1 22 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000276 | 1 42 | 13 28 1 1 30 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000277 | 1 40 | 13 27 0 1 28 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000278 | 1 44 | 12 27 5 0 32 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000279 | 1 58 | 12 39 7 0 46 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000280 | 1 40 | 10 25 5 2 32 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000281 | 1 32 | 14 18 0 0 18 1 | +|---------------------+------------------------+------------------------------------------------------------------------| +| mls_eng_000282 | 1 34 | 11 22 1 6 29 1 | +|=======================================================================================================================| +| Sum | 40 1535 | 487 942 106 70 1118 40 | +|=======================================================================================================================| +| Mean | 1.0 38.4 | 12.2 23.6 2.7 1.8 28.0 1.0 | +| S.D. | 0.0 10.0 | 4.9 6.3 2.6 1.4 7.8 0.0 | +| Median | 1.0 39.0 | 12.0 24.0 2.0 2.0 28.0 1.0 | +`-----------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/hyp.trn + +Speakers: + 0: mls_eng_000243 + 1: mls_eng_000244 + 2: mls_eng_000245 + 3: mls_eng_000246 + 4: mls_eng_000247 + 5: mls_eng_000248 + 6: mls_eng_000249 + 7: mls_eng_000250 + 8: mls_eng_000251 + 9: mls_eng_000252 + 10: mls_eng_000253 + 11: mls_eng_000254 + 12: mls_eng_000255 + 13: mls_eng_000256 + 14: mls_eng_000257 + 15: mls_eng_000258 + 16: mls_eng_000259 + 17: mls_eng_000260 + 18: mls_eng_000261 + 19: mls_eng_000262 + 20: mls_eng_000263 + 21: mls_eng_000264 + 22: mls_eng_000265 + 23: mls_eng_000266 + 24: mls_eng_000267 + 25: mls_eng_000268 + 26: mls_eng_000269 + 27: mls_eng_000270 + 28: mls_eng_000271 + 29: mls_eng_000272 + 30: mls_eng_000273 + 31: mls_eng_000274 + 32: mls_eng_000275 + 33: mls_eng_000276 + 34: mls_eng_000277 + 35: mls_eng_000278 + 36: mls_eng_000279 + 37: mls_eng_000280 + 38: mls_eng_000281 + 39: mls_eng_000282 + +Speaker sentences 0: mls_eng_000243 #utts: 1 +id: (mls_eng_000243-mls_eng_000243) +Scores: (#C #S #D #I) 13 26 8 1 +REF: i AM TIRED of TAKING ORDERS FROM you SHE SAID HASTILY I SEE now THAT it IS IMPOSSIBLE TO have FAITH IN YOU I SEE NOW THAT IT IS USELESS TO EXPECT any RETURN FROM YOU for **** ALL i have DONE I WANT no MORE of you +HYP: i AETHIR UE of TEKEN AOLDES FRON you *** SHI SAT HASTALY ISIA now THE it S IMPOSEABLE T have ***** ** *** * FATHIN OUN BASIY NO HEIT AS YUSEST ECPECET any ****** **** RETEREN for YOUG FORAL i have **** DON IOWNT no MOR of you +Eval: S S S S S D S S S S S S S S D D D D S S S S S S S S D D S I S D S S S + +Speaker sentences 1: mls_eng_000244 #utts: 1 +id: (mls_eng_000244-mls_eng_000244) +Scores: (#C #S #D #I) 9 20 2 2 +REF: that he may SOMETIMES be * ***** LIKE OTHER CHILDREN LEARNING BESIDE my KNEE or PLAYING PRATTLING SEEKING for HELP COMES TO MY heart AH SINFUL LORD IM SPEAKING how GOOD THOU ART +HYP: that he may SOMNTIMES be E LAKCK OUTHER CHELEDREN LERNING TE SIDE my NE or PLAING PRATLIND SICING for **** HELPES COMSTO Y heart ** ATS SHINFULORD AN SPEAKIN how CGOD THO ARTE +Eval: S I I S S S S S S S S S D S S S D S S S S S S S + +Speaker sentences 2: mls_eng_000245 #utts: 1 +id: (mls_eng_000245-mls_eng_000245) +Scores: (#C #S #D #I) 17 25 0 3 +REF: *** TRANSCEND things of ALL SORTS as IN the GENERAL outburst of ************* MULTITUDINOUS PASSION ARE HUDDLED TOGETHER the LUDICROUS nay the *** RIDICULOUS WITH the HORRIBLE FAR over the BILLOWY SEA of heads MAY be SEEN RASCALITY CAPRIOLING on HORSES FROM the ROYAL STUD +HYP: REN SEN things of AL SORE as I the JENRAL outburst of MOLTEITODNESS PASTION AR HUTED TO GETHER the LITURCRIS nay the RED DIEKILS WI the HARABLE FOARE over the BILY SE of heads MA be SEME RASTKGALADY CAPRYALING on CORSES FO the ROIL STOID +Eval: I S S S S S I S S S S S S I S S S S S S S S S S S S S S + +Speaker sentences 3: mls_eng_000246 #utts: 1 +id: (mls_eng_000246-mls_eng_000246) +Scores: (#C #S #D #I) 18 15 1 3 +REF: it may have BEEN THAT the bones WERE FOLDED TOGETHER and KNOWN as *** ** UNIHIPILI BONES folded and laid *** AWAY FOR PURPOSES of INCANTATION such bundles of bones WERE put THROUGH a PROCESS of PRAYERS +HYP: it may have BEN TAT the bones **** WERER FOLDEDTOGETHER and NONE as ONY HE PELY BONS folded and laid WAY FO THE PERPSCES of INCENTATION such bundles of bones WE put THORWE a PROSESS of PRAIRS +Eval: S S D S S S I I S S I S S S S S S S S + +Speaker sentences 4: mls_eng_000247 #utts: 1 +id: (mls_eng_000247-mls_eng_000247) +Scores: (#C #S #D #I) 9 15 0 2 +REF: **** MARSEILLES never EXPERIENCED THOSE GREAT TRANSITIONS FROM LOWNESS to GRANDEUR this was OWING to the PRUDENT conduct that ** REPUBLIC which ALWAYS PRESERVED HER PRINCIPLES +HYP: MARS SAILES never ECSPERINCED THOS GRAT TRAENSCITIONS FOM LONES to GREANDEUR this was ONING to the PRODINT conduct that RE POBLIKE which ALWAYE PRESERVE HE PINCIPLE +Eval: I S S S S S S S S S S I S S S S S + +Speaker sentences 5: mls_eng_000248 #utts: 1 +id: (mls_eng_000248-mls_eng_000248) +Scores: (#C #S #D #I) 16 33 3 4 +REF: at A SMALL BRIEFING SESSION on * the MORNING of OCTOBER THIRTY ONE CONDUCTED IN the ATMOSPHERE of CONSPIRACY and ** ATTENDED by BRODY we WERE TOLD THAT the NORMAL AGENCY REQUIREMENT for REVIEW BOARD APPROVAL had BEEN WAIVED that ****** NORMAL APPROVAL OF the MAYOR of WASHINGTON AND CERTAIN GOVERNORS WOULD be ****** HANDLED INFORMALLY +HYP: at SMLE RE HING STION on M the MORING of UPTOER THRTY ON CODUTED I the APSFHER of COESPERSY and AI TANDED by BROADY we **** RETOL HAT the NORMWL ADENTSE REQURIRMET for ****** REVIU OREPROVL had BEN WAET that NORMLE AE PRVL O the MAR of ********** WASINGTIN ND SRTNGOVENRS WOD be HANDLD IN FORMILY +Eval: S S S S I S S S S S S S S I S S D S S S S S D S S S S I S S S S D S S S S I S S + +Speaker sentences 6: mls_eng_000249 #utts: 1 +id: (mls_eng_000249-mls_eng_000249) +Scores: (#C #S #D #I) 7 26 1 1 +REF: * the BUDDHIST LAITY IN CHINA WHO do NOT HESITATE TO TAKE LIFE for the PURPOSE OF FOOD SALVE THEIR CONSCIENCE FROM TIME to TIME by BUYING BIRDS FISHES ET CETERA and LETTING THEM GO +HYP: T the BOODIST LATDY IG CHINER HOL do NO HASTATE TOL TAK LIG for the ******* PERVERS OFOD SOF THER CONCHONCS FOROMG TUCK to TI by BYING VEIRSD FIASHIEIS YOU SEADTER and LETIN THE GOLL +Eval: I S S S S S S S S S S D S S S S S S S S S S S S S S S S + +Speaker sentences 7: mls_eng_000250 #utts: 1 +id: (mls_eng_000250-mls_eng_000250) +Scores: (#C #S #D #I) 21 24 0 3 +REF: * THIS AGAIN is SOFTENED and TEMPERED by a SIMPLE FAITH IN the SUPREMACY of love over *** FEAR AN UNBOUNDED HUMANITY and CHARITY for the *** POOR AND HELPLESS AN UNCONDITIONAL FORGIVENESS of the DIREST INJURIES which is the NOTE of the NOBLE a GENEROSITY and liberality +HYP: T THIE AGAN is SOFEND and TUMPERD by a SEMBLE FATH AN the SUPROMIRSY of love over FER AND UN BWNTED UMANITY and CHIRIY for the POR AN HELPLESAN UN CODIONAL FOR GIVENES of the DIRST INERES which is the NOT of the NOBL a ENEROUSITY and liberality +Eval: I S S S S S S S S I S S S S S I S S S S S S S S S S S + +Speaker sentences 8: mls_eng_000251 #utts: 1 +id: (mls_eng_000251-mls_eng_000251) +Scores: (#C #S #D #I) 10 29 0 0 +REF: the SECOND MATE FOLLOWED and A COUPLE OF the STEAMERS MEN ROWED THEM ABOARD THE BARQUE BEFORE TOUCHING a ROPE THEY WENT to WORK TO SEARCH THE SHIP THEY LIFTED THE HATCHES and FOUND the hold full of CARGO +HYP: the SECEND MADE FALLOED and HE COUPL O the SEMERSMEN BRO THE PBOR TH BARKE BE FOR CUCHING a ROUP THE WEN to WARK T SURCH TH SHI TE LIFED DE HACHES and FAOWND the hold full of CORGO +Eval: S S S S S S S S S S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 9: mls_eng_000252 #utts: 1 +id: (mls_eng_000252-mls_eng_000252) +Scores: (#C #S #D #I) 14 14 2 4 +REF: fond OF HIS COMRADES and ********** RESPECTFUL to his PASTORS and masters even **** SCHOOLMASTERS as A lad he PREPARES for MANHOOD WITH A WILL and this TRAINING OCCUPIES him ***** *** THROUGHOUT YOUTHTIDE +HYP: fond ** OFHIS COMERADE and RESPETFULE I to his PASTUR and masters even SCHL MUSTERS as HE lad he PREPERS for ******* ANHOD WTH WIL and this TRAING OCKUPYE him THURE OUT YOUTH TIID +Eval: D S S I S S I S S S D S S S S S I I S S + +Speaker sentences 10: mls_eng_000253 #utts: 1 +id: (mls_eng_000253-mls_eng_000253) +Scores: (#C #S #D #I) 4 15 2 3 +REF: *** WHEN the ****** ******* PRINCESS COECA REACHED HER SIXTEENTH year SHE became BLIND HER LARGE SOFT BROWN EYES had NO LIGHT IN THEM +HYP: ASS WHE the PRENES QUPECEA E RECHE TER SIXT INT year SH became BLINGD WHER LARGH SOF BON ICES had ** ***** O LIDINGTHM +Eval: I S I I S S S S S S S S S S S S D D S S + +Speaker sentences 11: mls_eng_000254 #utts: 1 +id: (mls_eng_000254-mls_eng_000254) +Scores: (#C #S #D #I) 16 24 3 3 +REF: ALMOST ALL DAY the BATTLE RAGED BETWEEN THE two men BACK AND FORTH they FORCED EACH OTHER over the *** LAVA beds the ****** CHIEFS WELLOILED BODY was very DIFFICULT for THE OLOHE TO GRASP BRUISED and BLEEDING from ** REPEATED FALLS on the ROUGH lava +HYP: AOLMOST ALLE DY the ****** BATE RANGEDBUTCE TH two men BAK IN FORT they ****** FOURS YECHEOTHER over the LAV A beds the SHIVFS WEL OIE BOY was very DIFICALT for *** HEOALO HAY TOGRAS PBROSE and BLEADING from RE PETED FLLES on the ROUFH lava +Eval: S S S D S S S S S S D S S I S I S S S S D S S S S S I S S S + +Speaker sentences 12: mls_eng_000255 #utts: 1 +id: (mls_eng_000255-mls_eng_000255) +Scores: (#C #S #D #I) 12 13 1 2 +REF: POSY I SHRIEKED WITH TERROR and I AWOKE panting the WIND MOANED THROUGH the trees of the GARDEN and FROM time to TIME CEASED as if ** * +HYP: BPOSY IY TRE BIT TERRAR and A WLAL panting the WHIN MONDT THROG the trees of the CARDIN and FOME time to **** TIMEMSEE as if TO A +Eval: S S S S S S S S S S S S D S I I + +Speaker sentences 13: mls_eng_000256 #utts: 1 +id: (mls_eng_000256-mls_eng_000256) +Scores: (#C #S #D #I) 4 16 1 3 +REF: his ***** MENTAL TORPIDITY FOUNDED UPON PHYSICAL INDOLENCE renders * IMMEDIATE action AND ALL MANNER OF EXERTION DISTASTEFUL his ******* CONSCIOUS WEAKNESS SHOWS ITSELF +HYP: his MENTL TORPTITY FOUNDET A PON FISIOCL INDLEANCE renders A MEDIAT action *** AN AL MAENERO ESRTIOND DUSTEASFUL his CONCHUS WEKNES SHOS ITD SELF +Eval: I S S S S S S I S D S S S S S I S S S S + +Speaker sentences 14: mls_eng_000257 #utts: 1 +id: (mls_eng_000257-mls_eng_000257) +Scores: (#C #S #D #I) 13 19 1 0 +REF: NOR TELL how glad the QUEEN MOTHER was nor HOW GREAT WERE the REJOICING of the PEOPLE NOR HOW MAGNIFICENT was the ROYAL BANQUET that good QUEEN POMAREA ATTENDED WITH ALL her COURT +HYP: TNORE THAELE how glad the CING MTHE was nor HOUE GRATD WOER the REYGJUORISING of the PEPLE OR HOM MENIFESSENDTD was the ROIL BANKQCED that good ***** CFING POMEREAR ADTENDETD WHITHAL her COREN +Eval: S S S S S S S S S S S S S S D S S S S S + +Speaker sentences 15: mls_eng_000258 #utts: 1 +id: (mls_eng_000258-mls_eng_000258) +Scores: (#C #S #D #I) 14 20 1 0 +REF: and the CHANCE of THERE BEING such a ONE AGAIN DIMINISHES by VERY RAPID PROCESS MARMADUKE as A HORSE WAS of EQUAL QUALITY REASONING not ABOUT his ORDERS but ABOUT the WAY to do THEM +HYP: and the CHONCE of THER BENG such a ON UNGIAN DIMISHES by EWERY PRAPE PROSESS MAMELUK as * AEORS WHOS of EAKCUEAN CQULITY EREASNING not ABOWTD his ODERS but BOUL the IY to do THET +Eval: S S S S S S S S S S D S S S S S S S S S S + +Speaker sentences 16: mls_eng_000259 #utts: 1 +id: (mls_eng_000259-mls_eng_000259) +Scores: (#C #S #D #I) 26 27 5 2 +REF: SHE KNOCKED but * SNOWDROP LOOKED OUT OF THE WINDOW AND said i DARE not OPEN THE DOOR for the DWARFS HAVE TOLD me TO LET no ONE in that is HARD for me * said the WOMAN for i must TAKE BACK my APPLES but THERE IS one WHICH I WILL give you and she held UP AN APPLE +HYP: TSHE NOKET but S NO ROUP LOE OATHOF TE WHENDO AN said i DEAER not OBPB HE DOR for the ****** DORSHAVE TOD me ** TOLT no OIN in that is HARED for me E said the OMEON for i must TAK BACE my AUPLS but ***** THERIS one ***** WICH IULL give you and she held ** UPEN AL +Eval: S S I S S S S S S S S S S S D S S D S S S I S S S S D S D S S D S S + +Speaker sentences 17: mls_eng_000260 #utts: 1 +id: (mls_eng_000260-mls_eng_000260) +Scores: (#C #S #D #I) 5 24 3 2 +REF: ** * ALBERT MURRAY SPOKE EXCELSIOR and HORACE FINLEY SPOKE NICE TOO my PIECE WAS why PHOEBE ARE YOU COME SO SOON WHERE ARE YOUR BERRIES child EMMA VAN ARSDALE SPOKE THE same ONE +HYP: OV Y MORY SPOK AK CELTHOR and HERSE FINLY SPOKEN IHT TWO my ***** PESWAS why ****** *** FEABEY AR YO CM SOSON WERE OUR BRY child ND O THAN AR TDLSPOK same OAN +Eval: I I S S S S S S S S S D S D D S S S S S S S S S S S S S S + +Speaker sentences 18: mls_eng_000261 #utts: 1 +id: (mls_eng_000261-mls_eng_000261) +Scores: (#C #S #D #I) 10 35 9 1 +REF: i never KNEW ANYONE WHO LIKED TO GO TO CHURCH AS MUCH AS GRANDMOTHER DOES she SAYS SHE WOULD RATHER BE a DOORKEEPER IN THE HOUSE of OUR god THAN TO DWELL IN the TENTS OF WICKEDNESS THEY DONT HAVE women **** DOORKEEPERS AND i KNOW she WOULD NOT DWELL A MINUTE IN A TENT +HYP: i never **** ****** *** U ANYWOEN HO LE TDOGOTY CERCHIS MCH AC SGRAMOH DOUS she HAIS HE WOUD RAVE B a DORE CEPEI TH HAS of R god **** THEN I WELL the ***** TENSOF WIKEDNES TE DON HVE women DORE KEPERS AN i WENO she ***** *** ***** * WINUT TWELL MININA ATENT +Eval: D D D S S S S S S S S S S S S S S S S S S S S D S S S D S S S S S I S S S D D D D S S S S + +Speaker sentences 19: mls_eng_000262 #utts: 1 +id: (mls_eng_000262-mls_eng_000262) +Scores: (#C #S #D #I) 8 17 0 3 +REF: the DUKE was SURPRISED to SEE HIM WHAT BRINGS YOU OUT so *** **** EARLY ABELARD DEMANDED he OH your ******** GRACE REPLIED THE BUTLER gasping for UTTERANCE +HYP: the DUK was SUPRICS to SE HM OT BRING O OT so ALY ABEL ARD DEMAND D he OL your GRAICTED REPLI HE UTLR Y gasping for UTERANCES +Eval: S S S S S S S S I I S S S S I S S S S S + +Speaker sentences 20: mls_eng_000263 #utts: 1 +id: (mls_eng_000263-mls_eng_000263) +Scores: (#C #S #D #I) 7 28 4 1 +REF: FOUR YET SEEMING TRANSMUTATIONS of COLOUR MAY be MADE WHERE THERE is ANY MIXTURE OF DIVERSE SORTS OF RAYS FOR IN SUCH MIXTURES the *** COMPONENT COLOURS APPEAR not BUT by THEIR MUTUAL ALLAYING EACH OTHER CONSTITUTE a MIDDLING COLOUR +HYP: FORE IUAT SEMING TRONSENMEUTATIONS of CALE MA be MAD WE THER is *** ******* NY MICXTROF DEVOR SOUTS O RAES BRIN SACH MICTERS the COM PON ELIS APERE not *** by ***** THE MUTIL ALAING AHETHE CONCTUTE a MIDLING COLER +Eval: S S S S S S S S S D D S S S S S S S S S I S S S D D S S S S S S S + +Speaker sentences 21: mls_eng_000264 #utts: 1 +id: (mls_eng_000264-mls_eng_000264) +Scores: (#C #S #D #I) 15 32 4 2 +REF: IN ALMOST the same instant *** * KEN SAW SANDY SIDESTEP into a shop DOORWAY he WAITED THERE UNTIL KEN came up KEN STOPPED and PRETENDED to STARE THROUGH THE GLASS AT THE DISPLAY of HARDWARE AND TOOLS WHERE he CONTINUED to WATCH BARRACK YOU SEE WHAT I SEE SANDY said KEN NODDED +HYP: AN ALMOS the same instant CEN S RS ANDY SID STEP into a shop DORWAY he WADED THEI NTIK CHIN came up CIN STDAUPE and RETENDE to ***** ******* STAR TH THEGLAS ATH DESPLAY of ******** HERD WERINGTUOULES WER he CONTINED to ***** WOUCH BAIRICK OU SE WAT IS HAINBY said RIN NOTED +Eval: S S I I S S S S S S S S S S S S D D S S S S S D S S S S D S S S S S S S S S + +Speaker sentences 22: mls_eng_000265 #utts: 1 +id: (mls_eng_000265-mls_eng_000265) +Scores: (#C #S #D #I) 13 27 2 3 +REF: * then the THICK GREEN STUFF FLOWED OVER the WHOLE BUILDING and THERE WAS nothing to BE seen THERE but A MOUND of ****** *** SOFT FLOWING GRAYGREEN STUFF THAT rushed on NOW WITH the SWIFTNESS OF THE WIND I LOOKED UP into BARRYS FACE +HYP: T then the THIEK GRENE STAV FLOLED OLR the HL BELDIN and THE WHAS nothing to ES seen THER but DE MAOUD of SOFETD FLO ING GRAY GREN STAR THED rushed on *** NOWHW the ********* SWEIFTNES OLL WINGKE A LOOET ABD into BERS FAE +Eval: I S S S S S S S S S S S S S I I S S S S S D S D S S S S S S S S + +Speaker sentences 23: mls_eng_000266 #utts: 1 +id: (mls_eng_000266-mls_eng_000266) +Scores: (#C #S #D #I) 20 24 4 1 +REF: i have MADE SACRIFICES TOO BUT IT WAS when i KNEW THAT THEY WERE not my HAPPINESS IT was after I SAW that I HAD STOOPED AFTER i SAW that your TENDERNESS HAD TURNED to CALCULATION AFTER I SAW that you CARED for yourself ** ONLY not for me +HYP: i have **** MAE SURCKROFISES TOE BUR OS when i **** ANEU THATHE WRE not my ********* HAPENESS was after IS SAOL that H AD STEPEDT AGHER i SOAR that your TENDERNES AD TERND to *********** COULDILATION AOFTER SAOR that you CEIRD for yourself ON LY not for me +Eval: D S S S S S D S S S D S S S S S S S S S S S D S S S S I S + +Speaker sentences 24: mls_eng_000267 #utts: 1 +id: (mls_eng_000267-mls_eng_000267) +Scores: (#C #S #D #I) 12 20 0 2 +REF: YET THE THUNDER never ROARS in winter i SEE a CROW WHIRLING ROUND and ROUND before it * ALIGHT THERE IS NOTHING UNDER the FIR trees but ** I KNOW SOMETHING MUST be THERE +HYP: YATD THA THANDTER never RALS in winter i SEY a CROAL WARRING ROUNED and ROUNME before it A LIHTET THESEN NOT HING UNDE the FIRED trees but IL NO SOME THNG MUS be THAR +Eval: S S S S S S S S S I S S S S S S I S S S S S + +Speaker sentences 25: mls_eng_000268 #utts: 1 +id: (mls_eng_000268-mls_eng_000268) +Scores: (#C #S #D #I) 9 24 1 1 +REF: IT is BEYOND DOUBT that some PEOPLE HAD MANAGED TO SAVE many things AND of COURSE the ******** GERMANS HAD SURMISED as MUCH TWO OR THREE DAYS AFTER THE FIRST PERQUISITIONS THEY DROPPED in UNAWARES +HYP: EIY is BEYON THOUT that some ****** PEBLE HAE MANISETO SAF many things AN of COURSES the JIRMEANT HAS A SOMAIISE as MAUTH TOWOU ORE THRE DASGE AFE HE FIRSI PRECOUCITIONS DE DROUPET in INOWARS +Eval: S S S D S S S S S S I S S S S S S S S S S S S S S S + +Speaker sentences 26: mls_eng_000269 #utts: 1 +id: (mls_eng_000269-mls_eng_000269) +Scores: (#C #S #D #I) 18 20 1 2 +REF: SOON a man came out to MEET him this man was OLOHE a BEARDLESS MAN BELONGING to a LAWLESS ROBBER clan ******* WHICH INFESTED the DISTRICT POSSIBLY ASSISTING the ** MANHUNTERS of THE TEMPLE IN SECURING VICTIMS for the TEMPLE ALTARS +HYP: SON a man came out to ME him this man was OALOHAY a BERDLESS MAND BELNING to a LES ROVBUR clan HIHWICH IN FESTED the DSTRCT BPUSTIBLY ASISTING the AN UNTERS of *** HE TEMPL INSCIRING VICTUMS for the TEMBLE LLTERS +Eval: S S S S S S S S I S S S S S I S D S S S S S S + +Speaker sentences 27: mls_eng_000270 #utts: 1 +id: (mls_eng_000270-mls_eng_000270) +Scores: (#C #S #D #I) 6 15 0 1 +REF: we are not LOVERS you AND I UPON this SUNNY LANE BUT CHILDREN WHO HAVE never *** KNOWN LOVES JOY OR PAIN +HYP: we are not LOVEIRS you AN IYH APON this SUNY LANAD TBUT CHOLLDEON WHE AVE never NON T LOVFS JIOR YOUR PE +Eval: S S S S S S S S S S I S S S S S + +Speaker sentences 28: mls_eng_000271 #utts: 1 +id: (mls_eng_000271-mls_eng_000271) +Scores: (#C #S #D #I) 8 30 6 0 +REF: WAS MURDERED on THE DOORSTEP OF HIS OWN HOUSE was THIS an OLD man ASKED ANDREA CALMLY MISERICORDIA YOU TALK AS IF HE had DIED in his BED YOU ARE NO VENETIAN AND YOU CANNOT UNDERSTAND WHAT IT MEANS WHEN AN INQUISITOR is MURDERED +HYP: EWAS MURDED on *** ******** ** HESORSTEVF WISOAL HOSES was I an OL man AST ANDTR A CAM LY MISERE QOUODIAR YOU TOKASIFE had DIGE in his *** *** *** BEATD YO HE NOVEONITION AD YU CANO UNDESTAEWD IN MINS HN ANINGUICITY is MRDET +Eval: S S D D D S S S S S S S S S S S S S S S D D D S S S S S S S S S S S S S + +Speaker sentences 29: mls_eng_000272 #utts: 1 +id: (mls_eng_000272-mls_eng_000272) +Scores: (#C #S #D #I) 13 31 4 3 +REF: by GOD HE SAID THEY WRONG me MY BUSINESS LEADS me in AND OUT OF MANY houses BUT WHAT do I CARE for the SECRETS that ** MAY BE HIDDEN THERE HOWEVER I CANNOT BLAME THESE PEOPLE FOR THEIR WATCHFULNESS the ***** BLOODHOUNDS of THE SIGNORIA are * in EVERY STREET +HYP: by *** GORD HISAED THE WOND me HER BISNST THIDS me in *** *** AN OUTOMNY houses OE HAT do IY CAR for the SICRST that MA VE HAD N THAR HOW WEVER IU CUNOT PLING WIS PEBEFOR THE WOUCHFLNES the BLAUT HOUES of *** HISFMNORI are A in EVFERY STREEPT +Eval: D S S S S S S S D D S S S S S S S I S S S S S S S S S S S S S I S D S I S S + +Speaker sentences 30: mls_eng_000273 #utts: 1 +id: (mls_eng_000273-mls_eng_000273) +Scores: (#C #S #D #I) 10 28 10 0 +REF: HORRIBLE DEATH was PULLING AT HER NOT A STICK NOR A STONE was IN REACH of HER HANDS and the PITILESS CRAGS ECHOED ONE LONG SHRIEK ABOVE ALL THE ROAR OF THE WATERFALL she STROVE TO TURN OVER and GRASP the GROUND but only FELT HERSELF GOING FASTER +HYP: UOYABL ET was ******* ** *** *** PULING IT NO ASTIK NORASTON was ** INDREGT of HE HAND and the ******** ***** ****** *** BIYLI GRAGS EQULD ON NOLONG SRE ABEL THERUWAROFWARTHE FOLED she ****** STRUOED BU ONOUE and GASE the GON but only VELED HERSE GN CASTO +Eval: S S D D D D S S S S S D S S S D D D D S S S S S S S S S D S S S S S S S S S + +Speaker sentences 31: mls_eng_000274 #utts: 1 +id: (mls_eng_000274-mls_eng_000274) +Scores: (#C #S #D #I) 19 23 4 1 +REF: and THE FOLLY OF it was that ** ANOTHER WOMAN OF CERNY WISHED for nothing BETTER THAN to go SINCE my sister and FATHER ARE SENT AWAY she said i SHOULD RATHER go WITH THEM i have no MIND TO STAY HERE ALONE WITH my TWO BABIES +HYP: and A FALY OFE it was that AN NOTHE WOMENDT OVE SARNY WRICHE for nothing ****** BETEDEN to go SINTO my sister and FARTHE US CSENT WAY she said i ****** SOULRATHER go **** WITHIM i have no **** MI E TOSTAY HEAR ALONITD my TWOE BABES +Eval: S S S I S S S S S D S S S S S S D S D S D S S S S S S S + +Speaker sentences 32: mls_eng_000275 #utts: 1 +id: (mls_eng_000275-mls_eng_000275) +Scores: (#C #S #D #I) 6 17 4 1 +REF: BILLY DID ACCORDINGLY WONDERING WHAT THE LITTLE man WOULD be at and he PICKED TWO OF THE STOUTEST RUSHES HE COULD FIND WITH A LITTLE BUNCH of * +HYP: INIDIT A CORDINGLY WHENTDRING HA TE LITE man WOL be at and he ****** *** ** *** PEAED TO F THESTOUT ISYRUSHIS HKCU FINED WEITDELITAL BUNE of B +Eval: S S S S S S S S D D D D S S S S S S S S S I + +Speaker sentences 33: mls_eng_000276 #utts: 1 +id: (mls_eng_000276-mls_eng_000276) +Scores: (#C #S #D #I) 13 28 1 1 +REF: it is NOW the DREADFUL NIGHT COMES on HOW DISMAL IS the PLAIN FOR the PRUSSIANS AND the ****** ENGLISH FOUND ABOVE ten THOUSAND SLAIN BRAVE WELLINGTON AND BLUCHER BOTH MOST nobly drove THEIR FOES and BUONAPARTES IMPERIAL CROWN was TAKEN at WATERLOO +HYP: it is NO the DREDIUL NAT CMS on HOE DESME AS the PLEN WER the PUSIONS A the INGLSH FONE TE BOF ten THOUSEN SLEAIN BREVE WELINGTO AM BLOKER BORTH MOS nobly drove ***** THERFOURS and BONOPARTE AMPEYAR CRON was TEKEN at WOTEL +Eval: S S S S S S S S S S S I S S S S S S S S S S S D S S S S S S + +Speaker sentences 34: mls_eng_000277 #utts: 1 +id: (mls_eng_000277-mls_eng_000277) +Scores: (#C #S #D #I) 13 27 0 1 +REF: * SOME YEARS AGO AFTER making OUR ARRANGEMENTS FOR THE ENCAMPMENT AT NIGHT WE constantly had OUR PEACEFUL REST BROKEN by a TRIBE of BROWN MONKEYS THEY EVIDENTLY thought that long POSSESSION had GIVEN THEM a PRIOR CLAIM to the GROVE +HYP: S SOMEYUOUS AGLLE HAFE TE making ORE ARINGENS FO THEING CAMPENT ATD NIHT WIT constantly had OVE AD PESUL RESROKON by a TRIB of BROUN MUNKES THE EIDINTY thought that long POSESTION had GIMEN THE a PRIARE LAM to the GLL +Eval: I S S S S S S S S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 35: mls_eng_000278 #utts: 1 +id: (mls_eng_000278-mls_eng_000278) +Scores: (#C #S #D #I) 12 27 5 0 +REF: to flash in AT HOME CLAMOURING for HER MAID BETWEEN MRS VAN estens party and the OPERA IF ONLY FOR A MINUTE CERTAINLY IT WAS MORE than a MINUTE that SIMONE REMAINED AT the PHAYRE HOUSE AFTER BEING BROUGHT BACK AFTER DINNER IN TAXI +HYP: to flash in HAT OME CPLEMIDING for HE MEAD ETWEEN RSS WAN estens party and the ***** ** **** *** OPRE IFONLYFIR MINT SERTOLYE TWAS MOR than a MINIT that SMOND REMANED A the ****** FIER HOUSES AOUFTER BENG GROGD BAKE OFTERE DINER INTETACK +Eval: S S S S S S S S D D D D S S S S S S S S S S D S S S S S S S S S + +Speaker sentences 36: mls_eng_000279 #utts: 1 +id: (mls_eng_000279-mls_eng_000279) +Scores: (#C #S #D #I) 12 39 7 0 +REF: and IN DICTIONARY AND THEN WE HAD CALISTHENICS we GO THROUGH a GREAT MANY FIGURES AND SING a life ON THE OCEAN WAVE WHAT FAIRYLIKE MUSIC STEALS OVER the SEA LIGHTLY ROW LIGHTLY ROW OER the GLASSY WAVES WE GO and OH COME come AWAY and other songs MRS JUDGE TAYLOR WROTE ONE SONG ON PURPOSE FOR US +HYP: and ** INDICTIONARY AN DENWE HADE COUS TENINCXS we COLE THR a ***** GEAVMANY FIGERS IN CINGE a life ** *** MIOUTION WAVEWHAT FERY LIKH MUSICK STILS OVE the SE LIHELY ROE LIATLY ROE OR the GLASY WAY WUD GOL and O KOM come WAY and other songs *** ***** ****** MISIS JUGHE THAL ERBOT ONDSONG UN PERPSFORAUS +Eval: D S S S S S S S S D S S S S D D S S S S S S S S S S S S S S S S S S S S D D D S S S S S S S + +Speaker sentences 37: mls_eng_000280 #utts: 1 +id: (mls_eng_000280-mls_eng_000280) +Scores: (#C #S #D #I) 10 25 5 2 +REF: that which PASSES AS HISTORY IN OUR SCHOOLS OR GOVERNMENTALLY FABRICATED BOOKS ON HISTORY is ******* a FORGERY A MISREPRESENTATION of ******* EVENTS LIKE the OLD DRAMA CENTERING upon THE IMPOSSIBLE FIGURE of the HERO WITH A GESTICULATING CROWD in THE BACKGROUND +HYP: that which ****** ** BASHES OF HISTRYIG UR SCHLLS AER GOVERMENTLY FABROCATEDBP SOMEN HISTRY is FIRGURY a ******* MRSREPRES IENTATION of HEVENCE S LICE the LDROAWM AR SENTERING upon *** THEMPOSEABLE FIGIR of the HEARO WIT HE JESTICILATING CROUD in *** TEBAROUNED +Eval: D D S S S S S S S S S S I D S S I S S S S S D S S S S S S S D S + +Speaker sentences 38: mls_eng_000281 #utts: 1 +id: (mls_eng_000281-mls_eng_000281) +Scores: (#C #S #D #I) 14 18 0 0 +REF: WHEN THE CALIPH HEARD this he said O JAAFAR how goodly is that VOICE and the WAZIR REPLIED O our lord never SMOTE my HEARING AUGHT SWEETER OR GOODLIER than THIS SINGING +HYP: AN HE KELIVF HRD this he said OALL OFOURE how goodly is that FOYSCE and the ESIE REBPLAID AOLE our lord never SMORT my HAING ORT TRETERE ORE GODLYEARE than THE SANGING +Eval: S S S S S S S S S S S S S S S S S S + +Speaker sentences 39: mls_eng_000282 #utts: 1 +id: (mls_eng_000282-mls_eng_000282) +Scores: (#C #S #D #I) 11 22 1 6 +REF: EVIDENTLY the LEARNED BARON had not ******** STUDIED SUCH WORKS OF the ***** ******** * TOTKAHNI OR PARROT CHAT which NOTABLY translated by **** NAKHSHABI FROM the ********** SANSKRIT SUKA SAPTATI has NOW BECOME as ORTHODOXICALLY MUSLIM as THE NIGHTS +HYP: EVIDENTELY the LARNED BAIRAN had not STDADYED SOUCH WORK S O the TOTEA COAHINIY I ORE PIRIT C AT which NOTIBLY translated by NUGH SHABEY FRME the SANSESGRIT SIOK ASEPP TUTY has NO BOCOME as ORTHEDOCICLY MOSLAME as *** THENIGTES +Eval: S S S I S S S S I I I S S S S S I S S I S S S S S S S D S + + diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/text b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/text new file mode 100644 index 0000000000000000000000000000000000000000..a762ca72eed552b2387908a2dd31200397413bf9 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/text @@ -0,0 +1,40 @@ +mls_eng_000243 I AETHIR UE OF TEKEN AOLDES FRON YOU SHI SAT HASTALY ISIA NOW THE IT S IMPOSEABLE T HAVE FATHIN OUN BASIY NO HEIT AS YUSEST ECPECET ANY RETEREN FOR YOUG FORAL I HAVE DON IOWNT NO MOR OF YOU +mls_eng_000244 THAT HE MAY SOMNTIMES BE E LAKCK OUTHER CHELEDREN LERNING TE SIDE MY NE OR PLAING PRATLIND SICING FOR HELPES COMSTO Y HEART ATS SHINFULORD AN SPEAKIN HOW CGOD THO ARTE +mls_eng_000245 REN SEN THINGS OF AL SORE AS I THE JENRAL OUTBURST OF MOLTEITODNESS PASTION AR HUTED TO GETHER THE LITURCRIS NAY THE RED DIEKILS WI THE HARABLE FOARE OVER THE BILY SE OF HEADS MA BE SEME RASTKGALADY CAPRYALING ON CORSES FO THE ROIL STOID +mls_eng_000246 IT MAY HAVE BEN TAT THE BONES WERER FOLDEDTOGETHER AND NONE AS ONY HE PELY BONS FOLDED AND LAID WAY FO THE PERPSCES OF INCENTATION SUCH BUNDLES OF BONES WE PUT THORWE A PROSESS OF PRAIRS +mls_eng_000247 MARS SAILES NEVER ECSPERINCED THOS GRAT TRAENSCITIONS FOM LONES TO GREANDEUR THIS WAS ONING TO THE PRODINT CONDUCT THAT RE POBLIKE WHICH ALWAYE PRESERVE HE PINCIPLE +mls_eng_000248 AT SMLE RE HING STION ON M THE MORING OF UPTOER THRTY ON CODUTED I THE APSFHER OF COESPERSY AND AI TANDED BY BROADY WE RETOL HAT THE NORMWL ADENTSE REQURIRMET FOR REVIU OREPROVL HAD BEN WAET THAT NORMLE AE PRVL O THE MAR OF WASINGTIN ND SRTNGOVENRS WOD BE HANDLD IN FORMILY +mls_eng_000249 T THE BOODIST LATDY IG CHINER HOL DO NO HASTATE TOL TAK LIG FOR THE PERVERS OFOD SOF THER CONCHONCS FOROMG TUCK TO TI BY BYING VEIRSD FIASHIEIS YOU SEADTER AND LETIN THE GOLL +mls_eng_000250 T THIE AGAN IS SOFEND AND TUMPERD BY A SEMBLE FATH AN THE SUPROMIRSY OF LOVE OVER FER AND UN BWNTED UMANITY AND CHIRIY FOR THE POR AN HELPLESAN UN CODIONAL FOR GIVENES OF THE DIRST INERES WHICH IS THE NOT OF THE NOBL A ENEROUSITY AND LIBERALITY +mls_eng_000251 THE SECEND MADE FALLOED AND HE COUPL O THE SEMERSMEN BRO THE PBOR TH BARKE BE FOR CUCHING A ROUP THE WEN TO WARK T SURCH TH SHI TE LIFED DE HACHES AND FAOWND THE HOLD FULL OF CORGO +mls_eng_000252 FOND OFHIS COMERADE AND RESPETFULE I TO HIS PASTUR AND MASTERS EVEN SCHL MUSTERS AS HE LAD HE PREPERS FOR ANHOD WTH WIL AND THIS TRAING OCKUPYE HIM THURE OUT YOUTH TIID +mls_eng_000253 ASS WHE THE PRENES QUPECEA E RECHE TER SIXT INT YEAR SH BECAME BLINGD WHER LARGH SOF BON ICES HAD O LIDINGTHM +mls_eng_000254 AOLMOST ALLE DY THE BATE RANGEDBUTCE TH TWO MEN BAK IN FORT THEY FOURS YECHEOTHER OVER THE LAV A BEDS THE SHIVFS WEL OIE BOY WAS VERY DIFICALT FOR HEOALO HAY TOGRAS PBROSE AND BLEADING FROM RE PETED FLLES ON THE ROUFH LAVA +mls_eng_000255 BPOSY IY TRE BIT TERRAR AND A WLAL PANTING THE WHIN MONDT THROG THE TREES OF THE CARDIN AND FOME TIME TO TIMEMSEE AS IF TO A +mls_eng_000256 HIS MENTL TORPTITY FOUNDET A PON FISIOCL INDLEANCE RENDERS A MEDIAT ACTION AN AL MAENERO ESRTIOND DUSTEASFUL HIS CONCHUS WEKNES SHOS ITD SELF +mls_eng_000257 TNORE THAELE HOW GLAD THE CING MTHE WAS NOR HOUE GRATD WOER THE REYGJUORISING OF THE PEPLE OR HOM MENIFESSENDTD WAS THE ROIL BANKQCED THAT GOOD CFING POMEREAR ADTENDETD WHITHAL HER COREN +mls_eng_000258 AND THE CHONCE OF THER BENG SUCH A ON UNGIAN DIMISHES BY EWERY PRAPE PROSESS MAMELUK AS AEORS WHOS OF EAKCUEAN CQULITY EREASNING NOT ABOWTD HIS ODERS BUT BOUL THE IY TO DO THET +mls_eng_000259 TSHE NOKET BUT S NO ROUP LOE OATHOF TE WHENDO AN SAID I DEAER NOT OBPB HE DOR FOR THE DORSHAVE TOD ME TOLT NO OIN IN THAT IS HARED FOR ME E SAID THE OMEON FOR I MUST TAK BACE MY AUPLS BUT THERIS ONE WICH IULL GIVE YOU AND SHE HELD UPEN AL +mls_eng_000260 OV Y MORY SPOK AK CELTHOR AND HERSE FINLY SPOKEN IHT TWO MY PESWAS WHY FEABEY AR YO CM SOSON WERE OUR BRY CHILD ND O THAN AR TDLSPOK SAME OAN +mls_eng_000261 I NEVER U ANYWOEN HO LE TDOGOTY CERCHIS MCH AC SGRAMOH DOUS SHE HAIS HE WOUD RAVE B A DORE CEPEI TH HAS OF R GOD THEN I WELL THE TENSOF WIKEDNES TE DON HVE WOMEN DORE KEPERS AN I WENO SHE WINUT TWELL MININA ATENT +mls_eng_000262 THE DUK WAS SUPRICS TO SE HM OT BRING O OT SO ALY ABEL ARD DEMAND D HE OL YOUR GRAICTED REPLI HE UTLR Y GASPING FOR UTERANCES +mls_eng_000263 FORE IUAT SEMING TRONSENMEUTATIONS OF CALE MA BE MAD WE THER IS NY MICXTROF DEVOR SOUTS O RAES BRIN SACH MICTERS THE COM PON ELIS APERE NOT BY THE MUTIL ALAING AHETHE CONCTUTE A MIDLING COLER +mls_eng_000264 AN ALMOS THE SAME INSTANT CEN S RS ANDY SID STEP INTO A SHOP DORWAY HE WADED THEI NTIK CHIN CAME UP CIN STDAUPE AND RETENDE TO STAR TH THEGLAS ATH DESPLAY OF HERD WERINGTUOULES WER HE CONTINED TO WOUCH BAIRICK OU SE WAT IS HAINBY SAID RIN NOTED +mls_eng_000265 T THEN THE THIEK GRENE STAV FLOLED OLR THE HL BELDIN AND THE WHAS NOTHING TO ES SEEN THER BUT DE MAOUD OF SOFETD FLO ING GRAY GREN STAR THED RUSHED ON NOWHW THE SWEIFTNES OLL WINGKE A LOOET ABD INTO BERS FAE +mls_eng_000266 I HAVE MAE SURCKROFISES TOE BUR OS WHEN I ANEU THATHE WRE NOT MY HAPENESS WAS AFTER IS SAOL THAT H AD STEPEDT AGHER I SOAR THAT YOUR TENDERNES AD TERND TO COULDILATION AOFTER SAOR THAT YOU CEIRD FOR YOURSELF ON LY NOT FOR ME +mls_eng_000267 YATD THA THANDTER NEVER RALS IN WINTER I SEY A CROAL WARRING ROUNED AND ROUNME BEFORE IT A LIHTET THESEN NOT HING UNDE THE FIRED TREES BUT IL NO SOME THNG MUS BE THAR +mls_eng_000268 EIY IS BEYON THOUT THAT SOME PEBLE HAE MANISETO SAF MANY THINGS AN OF COURSES THE JIRMEANT HAS A SOMAIISE AS MAUTH TOWOU ORE THRE DASGE AFE HE FIRSI PRECOUCITIONS DE DROUPET IN INOWARS +mls_eng_000269 SON A MAN CAME OUT TO ME HIM THIS MAN WAS OALOHAY A BERDLESS MAND BELNING TO A LES ROVBUR CLAN HIHWICH IN FESTED THE DSTRCT BPUSTIBLY ASISTING THE AN UNTERS OF HE TEMPL INSCIRING VICTUMS FOR THE TEMBLE LLTERS +mls_eng_000270 WE ARE NOT LOVEIRS YOU AN IYH APON THIS SUNY LANAD TBUT CHOLLDEON WHE AVE NEVER NON T LOVFS JIOR YOUR PE +mls_eng_000271 EWAS MURDED ON HESORSTEVF WISOAL HOSES WAS I AN OL MAN AST ANDTR A CAM LY MISERE QOUODIAR YOU TOKASIFE HAD DIGE IN HIS BEATD YO HE NOVEONITION AD YU CANO UNDESTAEWD IN MINS HN ANINGUICITY IS MRDET +mls_eng_000272 BY GORD HISAED THE WOND ME HER BISNST THIDS ME IN AN OUTOMNY HOUSES OE HAT DO IY CAR FOR THE SICRST THAT MA VE HAD N THAR HOW WEVER IU 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CONSTANTLY HAD OVE AD PESUL RESROKON BY A TRIB OF BROUN MUNKES THE EIDINTY THOUGHT THAT LONG POSESTION HAD GIMEN THE A PRIARE LAM TO THE GLL +mls_eng_000278 TO FLASH IN HAT OME CPLEMIDING FOR HE MEAD ETWEEN RSS WAN ESTENS PARTY AND THE OPRE IFONLYFIR MINT SERTOLYE TWAS MOR THAN A MINIT THAT SMOND REMANED A THE FIER HOUSES AOUFTER BENG GROGD BAKE OFTERE DINER INTETACK +mls_eng_000279 AND INDICTIONARY AN DENWE HADE COUS TENINCXS WE COLE THR A GEAVMANY FIGERS IN CINGE A LIFE MIOUTION WAVEWHAT FERY LIKH MUSICK STILS OVE THE SE LIHELY ROE LIATLY ROE OR THE GLASY WAY WUD GOL AND O KOM COME WAY AND OTHER SONGS MISIS JUGHE THAL ERBOT ONDSONG UN PERPSFORAUS +mls_eng_000280 THAT WHICH BASHES OF HISTRYIG UR SCHLLS AER GOVERMENTLY FABROCATEDBP SOMEN HISTRY IS FIRGURY A MRSREPRES IENTATION OF HEVENCE S LICE THE LDROAWM AR SENTERING UPON THEMPOSEABLE FIGIR OF THE HEARO WIT HE JESTICILATING CROUD IN TEBAROUNED +mls_eng_000281 AN HE KELIVF HRD THIS HE SAID OALL OFOURE HOW GOODLY IS THAT FOYSCE AND THE ESIE REBPLAID AOLE OUR LORD NEVER SMORT MY HAING ORT TRETERE ORE GODLYEARE THAN THE SANGING +mls_eng_000282 EVIDENTELY THE LARNED BAIRAN HAD NOT STDADYED SOUCH WORK S O THE TOTEA COAHINIY I ORE PIRIT C AT WHICH NOTIBLY TRANSLATED BY NUGH SHABEY FRME THE SANSESGRIT SIOK ASEPP TUTY HAS NO BOCOME AS ORTHEDOCICLY MOSLAME AS THENIGTES diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/token b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/token new file mode 100644 index 0000000000000000000000000000000000000000..721cee8180d57a84815ddcc25d62b2fabc9b816f --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/token @@ -0,0 +1,40 @@ +mls_eng_000243 I A E T H I R U E O F T E K E N A O L D E S F R O N Y O U S H I S A T H A S T A L Y I S I A N O W T H E I T S I M P O S E A B L E T H A V E F A T H I N O U N B A S I Y N O H E I T A S Y U S E S T E C P E C E T A N Y R E T E R E N F O R Y O U G F O R A L I H A V E D O N I O W N T N O M O R O F Y O U +mls_eng_000244 T H A T H E M A Y S O M N T I M E S B E E L A K C K O U T H E R C H E L E D R E N L E R N I N G T E S I D E M Y N E O R P L A I N G P R A T L I N D S I C I N G F O R H E L P E S C O M S T O Y H E A R T A T S S H I N F U L O R D A N S P E A K I N H O W C G O D T H O A R T E +mls_eng_000245 R E N S E N T H I N G S O F A L S O R E A S I T H E J E N R A L O U T B U R S T O F M O L T E I T O D N E S S P A S T I O N A R H U T E D T O G E T H E R T H E L I T U R C R I S N A Y T H E R E D D I E K I L S W I T H E H A R A B L E F O A R E O V E R T H E B I L Y S E O F H E A D S M A B E S E M E R A S T K G A L A D Y C A P R Y A L I N G O N C O R S E S F O T H E R O I L S T O I D +mls_eng_000246 I T M A Y H A V E B E N T A T T H E B O N E S W E R E R F O L D E D T O G E T H E R A N D N O N E A S O N Y H E P E L Y B O N S F O L D E D A N D L A I D W A Y F O T H E P E R P S C E S O F I N C E N T A T I O N S U C H B U N D L E S O F B O N E S W E P U T T H O R W E A P R O S E S S O F P R A I R S +mls_eng_000247 M A R S S A I L E S N E V E R E C S P E R I N C E D T H O S G R A T T R A E N S C I T I O N S F O M L O N E S T O G R E A N D E U R T H I S W A S O N I N G T O T H E P R O D I N T C O N D U C T T H A T R E P O B L I K E W H I C H A L W A Y E P R E S E R V E H E P I N C I P L E +mls_eng_000248 A T S M L E R E H I N G S T I O N O N M T H E M O R I N G O F U P T O E R T H R T Y O N C O D U T E D I T H E A P S F H E R O F C O E S P E R S Y A N D A I T A N D E D B Y B R O A D Y W E R E T O L H A T T H E N O R M W L A D E N T S E R E Q U R I R M E T F O R R E V I U O R E P R O V L H A D B E N W A E T T H A T N O R M L E A E P R V L O T H E M A R O F W A S I N G T I N N D S R T N G O V E N R S W O D B E H A N D L D I N F O R M I L Y +mls_eng_000249 T T H E B O O D I S T L A T D Y I G C H I N E R H O L D O N O H A S T A T E T O L T A K L I G F O R T H E P E R V E R S O F O D S O F T H E R C O N C H O N C S F O R O M G T U C K T O T I B Y B Y I N G V E I R S D F I A S H I E I S Y O U S E A D T E R A N D L E T I N T H E G O L L +mls_eng_000250 T T H I E A G A N I S S O F E N D A N D T U M P E R D B Y A S E M B L E F A T H A N T H E S U P R O M I R S Y O F L O V E O V E R F E R A N D U N B W N T E D U M A N I T Y A N D C H I R I Y F O R T H E P O R A N H E L P L E S A N U N C O D I O N A L F O R G I V E N E S O F T H E D I R S T I N E R E S W H I C H I S T H E N O T O F T H E N O B L A E N E R O U S I T Y A N D L I B E R A L I T Y +mls_eng_000251 T H E S E C E N D M A D E F A L L O E D A N D H E C O U P L O T H E S E M E R S M E N B R O T H E P B O R T H B A R K E B E F O R C U C H I N G A R O U P T H E W E N T O W A R K T S U R C H T H S H I T E L I F E D D E H A C H E S A N D F A O W N D T H E H O L D F U L L O F C O R G O +mls_eng_000252 F O N D O F H I S C O M E R A D E A N D R E S P E T F U L E I T O H I S P A S T U R A N D M A S T E R S E V E N S C H L M U S T E R S A S H E L A D H E P R E P E R S F O R A N H O D W T H W I L A N D T H I S T R A I N G O C K U P Y E H I M T H U R E O U T Y O U T H T I I D +mls_eng_000253 A S S W H E T H E P R E N E S Q U P E C E A E R E C H E T E R S I X T I N T Y E A R S H B E C A M E B L I N G D W H E R L A R G H S O F B O N I C E S H A D O L I D I N G T H M +mls_eng_000254 A O L M O S T A L L E D Y T H E B A T E R A N G E D B U T C E T H T W O M E N B A K I N F O R T T H E Y F O U R S Y E C H E O T H E R O V E R T H E L A V A B E D S T H E S H I V F S W E L O I E B O Y W A S V E R Y D I F I C A L T F O R H E O A L O H A Y T O G R A S P B R O S E A N D B L E A D I N G F R O M R E P E T E D F L L E S O N T H E R O U F H L A V A +mls_eng_000255 B P O S Y I Y T R E B I T T E R R A R A N D A W L A L P A N T I N G T H E W H I N M O N D T T H R O G T H E T R E E S O F T H E C A R D I N A N D F O M E T I M E T O T I M E M S E E A S I F T O A +mls_eng_000256 H I S M E N T L T O R P T I T Y F O U N D E T A P O N F I S I O C L I N D L E A N C E R E N D E R S A M E D I A T A C T I O N A N A L M A E N E R O E S R T I O N D D U S T E A S F U L H I S C O N C H U S W E K N E S S H O S I T D S E L F +mls_eng_000257 T N O R E T H A E L E H O W G L A D T H E C I N G M T H E W A S N O R H O U E G R A T D W O E R T H E R E Y G J U O R I S I N G O F T H E P E P L E O R H O M M E N I F E S S E N D T D W A S T H E R O I L B A N K Q C E D T H A T G O O D C F I N G P O M E R E A R A D T E N D E T D W H I T H A L H E R C O R E N +mls_eng_000258 A N D T H E C H O N C E O F T H E R B E N G S U C H A O N U N G I A N D I M I S H E S B Y E W E R Y P R A P E P R O S E S S M A M E L U K A S A E O R S W H O S O F E A K C U E A N C Q U L I T Y E R E A S N I N G N O T A B O W T D H I S O D E R S B U T B O U L T H E I Y T O D O T H E T +mls_eng_000259 T S H E N O K E T B U T S N O R O U P L O E O A T H O F T E W H E N D O A N S A I D I D E A E R N O T O B P B H E D O R F O R T H E D O R S H A V E T O D M E T O L T N O O I N I N T H A T I S H A R E D F O R M E E S A I D T H E O M E O N F O R I M U S T T A K B A C E M Y A U P L S B U T T H E R I S O N E W I C H I U L L G I V E Y O U A N D S H E H E L D U P E N A L +mls_eng_000260 O V Y M O R Y S P O K A K C E L T H O R A N D H E R S E F I N L Y S P O K E N I H T T W O M Y P E S W A S W H Y F E A B E Y A R Y O C M S O S O N W E R E O U R B R Y C H I L D N D O T H A N A R T D L S P O K S A M E O A N +mls_eng_000261 I N E V E R U A N Y W O E N H O L E T D O G O T Y C E R C H I S M C H A C S G R A M O H D O U S S H E H A I S H E W O U D R A V E B A D O R E C E P E I T H H A S O F R G O D T H E N I W E L L T H E T E N S O F W I K E D N E S T E D O N H V E W O M E N D O R E K E P E R S A N I W E N O S H E W I N U T T W E L L M I N I N A A T E N T +mls_eng_000262 T H E D U K W A S S U P R I C S T O S E H M O T B R I N G O O T S O A L Y A B E L A R D D E M A N D D H E O L Y O U R G R A I C T E D R E P L I H E U T L R Y G A S P I N G F O R U T E R A N C E S +mls_eng_000263 F O R E I U A T S E M I N G T R O N S E N M E U T A T I O N S O F C A L E M A B E M A D W E T H E R I S N Y M I C X T R O F D E V O R S O U T S O R A E S B R I N S A C H M I C T E R S T H E C O M P O N E L I S A P E R E N O T B Y T H E M U T I L A L A I N G A H E T H E C O N C T U T E A M I D L I N G C O L E R +mls_eng_000264 A N A L M O S T H E S A M E I N S T A N T C E N S R S A N D Y S I D S T E P I N T O A S H O P D O R W A Y H E W A D E D T H E I N T I K C H I N C A M E U P C I N S T D A U P E A N D R E T E N D E T O S T A R T H T H E G L A S A T H D E S P L A Y O F H E R D W E R I N G T U O U L E S W E R H E C O N T I N E D T O W O U C H B A I R I C K O U S E W A T I S H A I N B Y S A I D R I N N O T E D +mls_eng_000265 T T H E N T H E T H I E K G R E N E S T A V F L O L E D O L R T H E H L B E L D I N A N D T H E W H A S N O T H I N G T O E S S E E N T H E R B U T D E M A O U D O F S O F E T D F L O I N G G R A Y G R E N S T A R T H E D R U S H E D O N N O W H W T H E S W E I F T N E S O L L W I N G K E A L O O E T A B D I N T O B E R S F A E +mls_eng_000266 I H A V E M A E S U R C K R O F I S E S T O E B U R O S W H E N I A N E U T H A T H E W R E N O T M Y H A P E N E S S W A S A F T E R I S S A O L T H A T H A D S T E P E D T A G H E R I S O A R T H A T Y O U R T E N D E R N E S A D T E R N D T O C O U L D I L A T I O N A O F T E R S A O R T H A T Y O U C E I R D F O R Y O U R S E L F O N L Y N O T F O R M E +mls_eng_000267 Y A T D T H A T H A N D T E R N E V E R R A L S I N W I N T E R I S E Y A C R O A L W A R R I N G R O U N E D A N D R O U N M E B E F O R E I T A L I H T E T T H E S E N N O T H I N G U N D E T H E F I R E D T R E E S B U T I L N O S O M E T H N G M U S B E T H A R +mls_eng_000268 E I Y I S B E Y O N T H O U T T H A T S O M E P E B L E H A E M A N I S E T O S A F M A N Y T H I N G S A N O F C O U R S E S T H E J I R M E A N T H A S A S O M A I I S E A S M A U T H T O W O U O R E T H R E D A S G E A F E H E F I R S I P R E C O U C I T I O N S D E D R O U P E T I N I N O W A R S +mls_eng_000269 S O N A M A N C A M E O U T T O M E H I M T H I S M A N W A S O A L O H A Y A B E R D L E S S M A N D B E L N I N G T O A L E S R O V B U R C L A N H I H W I C H I N F E S T E D T H E D S T R C T B P U S T I B L Y A S I S T I N G T H E A N U N T E R S O F H E T E M P L I N S C I R I N G V I C T U M S F O R T H E T E M B L E L L T E R S +mls_eng_000270 W E A R E N O T L O V E I R S Y O U A N I Y H A P O N T H I S S U N Y L A N A D T B U T C H O L L D E O N W H E A V E N E V E R N O N T L O V F S J I O R Y O U R P E +mls_eng_000271 E W A S M U R D E D O N H E S O R S T E V F W I S O A L H O S E S W A S I A N O L M A N A S T A N D T R A C A M L Y M I S E R E Q O U O D I A R Y O U T O K A S I F E H A D D I G E I N H I S B E A T D Y O H E N O V E O N I T I O N A D Y U C A N O U N D E S T A E W D I N M I N S H N A N I N G U I C I T Y I S M R D E T +mls_eng_000272 B Y G O R D H I S A E D T H E W O N D M E H E R B I S N S T T H I D S M E I N A N O U T O M N Y H O U S E S O E H A T D O I Y C A R F O R T H E S I C R S T T H A T M A V E H A D N T H A R H O W W E V E R I U C U N O T P L I N G W I S P E B E F O R T H E W O U C H F L N E S T H E B L A U T H O U E S O F H I S F M N O R I A R E A I N E V F E R Y S T R E E P T +mls_eng_000273 U O Y A B L E T W A S P U L I N G I T N O A S T I K N O R A S T O N W A S I N D R E G T O F H E H A N D A N D T H E B I Y L I G R A G S E Q U L D O N N O L O N G S R E A B E L T H E R U W A R O F W A R T H E F O L E D S H E S T R U O E D B U O N O U E A N D G A S E T H E G O N B U T O N L Y V E L E D H E R S E G N C A S T O +mls_eng_000274 A N D A F A L Y O F E I T W A S T H A T A N N O T H E W O M E N D T O V E S A R N Y W R I C H E F O R N O T H I N G B E T E D E N T O G O S I N T O M Y S I S T E R A N D F A R T H E U S C S E N T W A Y S H E S A I D I S O U L R A T H E R G O W I T H I M I H A V E N O M I E T O S T A Y H E A R A L O N I T D M Y T W O E B A B E S +mls_eng_000275 I N I D I T A C O R D I N G L Y W H E N T D R I N G H A T E L I T E M A N W O L B E A T A N D H E P E A E D T O F T H E S T O U T I S Y R U S H I S H K C U F I N E D W E I T D E L I T A L B U N E O F B +mls_eng_000276 I T I S N O T H E D R E D I U L N A T C M S O N H O E D E S M E A S T H E P L E N W E R T H E P U S I O N S A T H E I N G L S H F O N E T E B O F T E N T H O U S E N S L E A I N B R E V E W E L I N G T O A M B L O K E R B O R T H M O S N O B L Y D R O V E T H E R F O U R S A N D B O N O P A R T E A M P E Y A R C R O N W A S T E K E N A T W O T E L +mls_eng_000277 S S O M E Y U O U S A G L L E H A F E T E M A K I N G O R E A R I N G E N S F O T H E I N G C A M P E N T A T D N I H T W I T C O N S T A N T L Y H A D O V E A D P E S U L R E S R O K O N B Y A T R I B O F B R O U N M U N K E S T H E E I D I N T Y T H O U G H T T H A T L O N G P O S E S T I O N H A D G I M E N T H E A P R I A R E L A M T O T H E G L L +mls_eng_000278 T O F L A S H I N H A T O M E C P L E M I D I N G F O R H E M E A D E T W E E N R S S W A N E S T E N S P A R T Y A N D T H E O P R E I F O N L Y F I R M I N T S E R T O L Y E T W A S M O R T H A N A M I N I T T H A T S M O N D R E M A N E D A T H E F I E R H O U S E S A O U F T E R B E N G G R O G D B A K E O F T E R E D I N E R I N T E T A C K +mls_eng_000279 A N D I N D I C T I O N A R Y A N D E N W E H A D E C O U S T E N I N C X S W E C O L E T H R A G E A V M A N Y F I G E R S I N C I N G E A L I F E M I O U T I O N W A V E W H A T F E R Y L I K H M U S I C K S T I L S O V E T H E S E L I H E L Y R O E L I A T L Y R O E O R T H E G L A S Y W A Y W U D G O L A N D O K O M C O M E W A Y A N D O T H E R S O N G S M I S I S J U G H E T H A L E R B O T O N D S O N G U N P E R P S F O R A U S +mls_eng_000280 T H A T W H I C H B A S H E S O F H I S T R Y I G U R S C H L L S A E R G O V E R M E N T L Y F A B R O C A T E D B P S O M E N H I S T R Y I S F I R G U R Y A M R S R E P R E S I E N T A T I O N O F H E V E N C E S L I C E T H E L D R O A W M A R S E N T E R I N G U P O N T H E M P O S E A B L E F I G I R O F T H E H E A R O W I T H E J E S T I C I L A T I N G C R O U D I N T E B A R O U N E D +mls_eng_000281 A N H E K E L I V F H R D T H I S H E S A I D O A L L O F O U R E H O W G O O D L Y I S T H A T F O Y S C E A N D T H E E S I E R E B P L A I D A O L E O U R L O R D N E V E R S M O R T M Y H A I N G O R T T R E T E R E O R E G O D L Y E A R E T H A N T H E S A N G I N G +mls_eng_000282 E V I D E N T E L Y T H E L A R N E D B A I R A N H A D N O T S T D A D Y E D S O U C H W O R K S O T H E T O T E A C O A H I N I Y I O R E P I R I T C A T W H I C H N O T I B L Y T R A N S L A T E D B Y N U G H S H A B E Y F R M E T H E S A N S E S G R I T S I O K A S E P P T U T Y H A S N O B O C O M E A S O R T H E D O C I C L Y M O S L A M E A S T H E N I G T E S diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/token_int b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/token_int new file mode 100644 index 0000000000000000000000000000000000000000..12132805aca22e7556ba17b97132eb480003da11 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/token_int @@ -0,0 +1,40 @@ +mls_eng_000243 8 2 5 3 4 10 8 11 2 14 3 2 6 18 2 4 3 24 3 7 2 5 6 13 12 3 9 2 18 11 6 7 2 19 6 14 2 9 10 8 2 9 5 4 2 10 5 9 4 5 13 19 2 8 9 8 5 2 7 6 17 2 4 10 3 2 8 4 2 9 2 8 15 21 6 9 3 5 22 13 3 2 4 2 10 5 23 3 2 18 5 4 10 8 7 2 6 14 7 2 22 5 9 8 19 2 7 6 2 10 3 8 4 2 5 9 2 19 14 9 3 9 4 2 3 16 21 3 16 3 4 2 5 7 19 2 11 3 4 3 11 3 7 2 18 6 11 2 19 6 14 20 2 18 6 11 5 13 2 8 2 10 5 23 3 2 12 6 7 2 8 6 17 7 4 2 7 6 2 15 6 11 2 6 18 2 19 6 14 +mls_eng_000244 4 10 5 4 2 10 3 2 15 5 19 2 9 6 15 7 4 8 15 3 9 2 22 3 2 3 2 13 5 24 16 24 2 6 14 4 10 3 11 2 16 10 3 13 3 12 11 3 7 2 13 3 11 7 8 7 20 2 4 3 2 9 8 12 3 2 15 19 2 7 3 2 6 11 2 21 13 5 8 7 20 2 21 11 5 4 13 8 7 12 2 9 8 16 8 7 20 2 18 6 11 2 10 3 13 21 3 9 2 16 6 15 9 4 6 2 19 2 10 3 5 11 4 2 5 4 9 2 9 10 8 7 18 14 13 6 11 12 2 5 7 2 9 21 3 5 24 8 7 2 10 6 17 2 16 20 6 12 2 4 10 6 2 5 11 4 3 +mls_eng_000245 2 11 3 7 2 9 3 7 2 4 10 8 7 20 9 2 6 18 2 5 13 2 9 6 11 3 2 5 9 2 8 2 4 10 3 2 26 3 7 11 5 13 2 6 14 4 22 14 11 9 4 2 6 18 2 15 6 13 4 3 8 4 6 12 7 3 9 9 2 21 5 9 4 8 6 7 2 5 11 2 10 14 4 3 12 2 4 6 2 20 3 4 10 3 11 2 4 10 3 2 13 8 4 14 11 16 11 8 9 2 7 5 19 2 2 4 10 3 2 11 3 12 2 12 8 3 24 8 13 9 2 17 8 2 4 10 3 2 10 5 11 5 22 13 3 2 18 6 5 11 3 2 6 23 3 11 2 4 10 3 2 22 8 13 19 2 9 3 2 6 18 2 10 3 5 12 9 2 15 5 2 22 3 2 9 3 15 3 2 11 5 9 4 24 20 5 13 5 12 19 2 16 5 21 11 19 5 13 8 7 20 2 6 7 2 16 6 11 9 3 9 2 18 6 2 4 10 3 2 11 6 8 13 2 9 4 6 8 12 +mls_eng_000246 8 4 2 15 5 19 2 10 5 23 3 2 22 3 7 2 4 5 4 2 4 10 3 2 22 6 7 3 9 2 17 3 11 3 11 2 18 6 13 12 3 12 4 6 20 3 4 10 3 11 2 5 7 12 2 7 6 7 3 2 5 9 2 6 7 19 2 10 3 2 21 3 13 19 2 22 6 7 9 2 18 6 13 12 3 12 2 5 7 12 2 13 5 8 12 2 17 5 19 2 18 6 2 4 10 3 2 21 3 11 21 9 16 3 9 2 6 18 2 8 7 16 3 7 4 5 4 8 6 7 2 9 14 16 10 2 22 14 7 12 13 3 9 2 6 18 2 22 6 7 3 9 2 17 3 2 21 14 4 2 4 10 6 11 17 3 2 5 2 21 11 6 9 3 9 9 2 6 18 2 21 11 5 8 11 9 +mls_eng_000247 2 15 5 11 9 2 9 5 8 13 3 9 2 7 3 23 3 11 2 3 16 9 21 3 11 8 7 16 3 12 2 4 10 6 9 2 20 11 5 4 2 4 11 5 3 7 9 16 8 4 8 6 7 9 2 18 6 15 2 13 6 7 3 9 2 4 6 2 20 11 3 5 7 12 3 14 11 2 4 10 8 9 2 17 5 9 2 6 7 8 7 20 2 4 6 2 4 10 3 2 21 11 6 12 8 7 4 2 16 6 7 12 14 16 4 2 4 10 5 4 2 11 3 2 21 6 22 13 8 24 3 2 17 10 8 16 10 2 5 13 17 5 19 3 2 21 11 3 9 3 11 23 3 2 10 3 2 21 8 7 16 8 21 13 3 +mls_eng_000248 5 4 2 9 15 13 3 2 11 3 2 10 8 7 20 2 9 4 8 6 7 2 6 7 2 2 15 2 4 10 3 2 15 6 11 8 7 20 2 6 18 2 14 21 4 6 3 11 2 4 10 11 4 19 2 6 7 2 16 6 12 14 4 3 12 2 8 2 4 10 3 2 5 21 9 18 10 3 11 2 6 18 2 16 6 3 9 21 3 11 9 19 2 5 7 12 2 5 8 2 4 5 7 12 3 12 2 22 19 2 22 11 6 5 12 19 2 17 3 2 11 3 4 6 13 2 10 5 4 2 4 10 3 2 7 6 11 15 17 13 2 5 12 3 7 4 9 3 2 11 3 27 14 11 8 11 15 3 4 2 18 6 11 2 11 3 23 8 14 2 6 11 3 21 11 6 23 13 2 10 5 12 2 22 3 7 2 17 5 3 4 2 4 10 5 4 2 7 6 11 15 13 3 2 5 3 2 21 11 23 13 2 6 2 4 10 3 2 15 5 11 2 6 18 2 17 5 9 8 7 20 4 8 7 2 7 12 2 9 11 4 7 20 6 23 3 7 11 9 2 17 6 12 2 22 3 2 10 5 7 12 13 12 2 8 7 2 18 6 11 15 8 13 19 +mls_eng_000249 4 2 4 10 3 2 22 6 6 12 8 9 4 2 13 5 4 12 19 2 8 20 2 16 10 8 7 3 11 2 10 6 13 2 12 6 2 7 6 2 10 5 9 4 5 4 3 2 4 6 13 2 4 5 24 2 13 8 20 2 18 6 11 2 4 10 3 2 21 3 11 23 3 11 9 2 6 18 6 12 2 9 6 18 2 4 10 3 11 2 16 6 7 16 10 6 7 16 9 2 18 6 11 6 15 20 2 4 14 16 24 2 4 6 2 4 8 2 22 19 2 22 19 8 7 20 2 23 3 8 11 9 12 2 18 8 5 9 10 8 3 8 9 2 19 6 14 2 9 3 5 12 4 3 11 2 5 7 12 2 13 3 4 8 7 2 4 10 3 2 20 6 13 13 +mls_eng_000250 4 2 4 10 8 3 2 5 20 5 7 2 8 9 2 9 6 18 3 7 12 2 5 7 12 2 4 14 15 21 3 11 12 2 2 22 19 2 5 2 9 3 15 22 13 3 2 18 5 4 10 2 5 7 2 4 10 3 2 9 14 21 11 6 15 8 11 9 19 2 6 18 2 13 6 23 3 2 6 23 3 11 2 18 3 11 2 5 7 12 2 14 7 2 22 17 7 4 3 12 2 14 15 5 7 8 4 19 2 5 7 12 2 16 10 8 11 8 19 2 18 6 11 2 4 10 3 2 21 6 11 2 5 7 2 10 3 13 21 13 3 9 5 7 2 14 7 2 16 6 12 8 6 7 5 13 2 18 6 11 2 20 8 23 3 7 3 9 2 6 18 2 4 10 3 2 12 8 11 9 4 2 8 7 3 11 3 9 2 17 10 8 16 10 2 8 9 2 4 10 3 2 7 6 4 2 6 18 2 4 10 3 2 7 6 22 13 2 5 2 3 7 3 11 6 14 9 8 4 19 2 5 7 12 2 13 8 22 3 11 5 13 8 4 19 +mls_eng_000251 2 4 10 3 2 9 3 16 3 7 12 2 15 5 12 3 2 18 5 13 13 6 3 12 2 5 7 12 2 10 3 2 16 6 14 21 13 2 6 2 4 10 3 2 9 3 15 3 11 9 15 3 7 2 22 11 6 2 4 10 3 2 21 22 6 11 2 4 10 2 22 5 11 24 3 2 22 3 2 18 6 11 2 16 14 16 10 8 7 20 2 5 2 11 6 14 21 2 4 10 3 2 17 3 7 2 4 6 2 17 5 11 24 2 4 2 9 14 11 16 10 2 4 10 2 9 10 8 2 4 3 2 13 8 18 3 12 2 12 3 2 10 5 16 10 3 9 2 5 7 12 2 18 5 6 17 7 12 2 4 10 3 2 10 6 13 12 2 18 14 13 13 2 6 18 2 16 6 11 20 6 +mls_eng_000252 18 6 7 12 2 6 18 10 8 9 2 16 6 15 3 11 5 12 3 2 5 7 12 2 11 3 9 21 3 4 18 14 13 3 2 8 2 4 6 2 10 8 9 2 21 5 9 4 14 11 2 5 7 12 2 15 5 9 4 3 11 9 2 3 23 3 7 2 9 16 10 13 2 15 14 9 4 3 11 9 2 5 9 2 10 3 2 13 5 12 2 10 3 2 21 11 3 21 3 11 9 2 18 6 11 2 5 7 10 6 12 2 17 4 10 2 17 8 13 2 5 7 12 2 4 10 8 9 2 4 11 5 8 7 20 2 6 16 24 14 21 19 3 2 10 8 15 2 4 10 14 11 3 2 6 14 4 2 19 6 14 4 10 2 4 8 8 12 +mls_eng_000253 5 9 9 2 17 10 3 2 4 10 3 2 21 11 3 7 3 9 2 27 14 21 3 16 3 5 2 3 2 11 3 16 10 3 2 4 3 11 2 9 8 25 4 2 8 7 4 2 19 3 5 11 2 9 10 2 22 3 16 5 15 3 2 22 13 8 7 20 12 2 17 10 3 11 2 13 5 11 20 10 2 9 6 18 2 22 6 7 2 8 16 3 9 2 10 5 12 2 6 2 13 8 12 8 7 20 4 10 15 +mls_eng_000254 5 6 13 15 6 9 4 2 5 13 13 3 2 12 19 2 4 10 3 2 22 5 4 3 2 11 5 7 20 3 12 22 14 4 16 3 2 4 10 2 4 17 6 2 15 3 7 2 22 5 24 2 8 7 2 18 6 11 4 2 4 10 3 19 2 18 6 14 11 9 2 19 3 16 10 3 6 4 10 3 11 2 6 23 3 11 2 4 10 3 2 13 5 23 2 5 2 22 3 12 9 2 4 10 3 2 9 10 8 23 18 9 2 17 3 13 2 6 8 3 2 22 6 19 2 17 5 9 2 23 3 11 19 2 12 8 18 8 16 5 13 4 2 18 6 11 2 10 3 6 5 13 6 2 10 5 19 2 4 6 20 11 5 9 2 21 22 11 6 9 3 2 5 7 12 2 22 13 3 5 12 8 7 20 2 18 11 6 15 2 11 3 2 21 3 4 3 12 2 18 13 13 3 9 2 6 7 2 4 10 3 2 11 6 14 18 10 2 13 5 23 5 +mls_eng_000255 22 21 6 9 19 2 8 19 2 4 11 3 2 22 8 4 2 4 3 11 11 5 11 2 5 7 12 2 5 2 17 13 5 13 2 21 5 7 4 8 7 20 2 4 10 3 2 17 10 8 7 2 15 6 7 12 4 2 4 10 11 6 20 2 4 10 3 2 4 11 3 3 9 2 6 18 2 4 10 3 2 16 5 11 12 8 7 2 5 7 12 2 18 6 15 3 2 4 8 15 3 2 4 6 2 4 8 15 3 15 9 3 3 2 5 9 2 8 18 2 4 6 2 5 +mls_eng_000256 10 8 9 2 15 3 7 4 13 2 4 6 11 21 4 8 4 19 2 18 6 14 7 12 3 4 2 5 2 21 6 7 2 18 8 9 8 6 16 13 2 8 7 12 13 3 5 7 16 3 2 11 3 7 12 3 11 9 2 5 2 15 3 12 8 5 4 2 5 16 4 8 6 7 2 5 7 2 5 13 2 15 5 3 7 3 11 6 2 3 9 11 4 8 6 7 12 2 12 14 9 4 3 5 9 18 14 13 2 10 8 9 2 16 6 7 16 10 14 9 2 17 3 24 7 3 9 2 9 10 6 9 2 8 4 12 2 9 3 13 18 +mls_eng_000257 4 7 6 11 3 2 4 10 5 3 13 3 2 10 6 17 2 20 13 5 12 2 4 10 3 2 16 8 7 20 2 15 4 10 3 2 17 5 9 2 7 6 11 2 10 6 14 3 2 20 11 5 4 12 2 17 6 3 11 2 4 10 3 2 11 3 19 20 26 14 6 11 8 9 8 7 20 2 6 18 2 4 10 3 2 21 3 21 13 3 2 6 11 2 10 6 15 2 15 3 7 8 18 3 9 9 3 7 12 4 12 2 2 17 5 9 2 4 10 3 2 11 6 8 13 2 22 5 7 24 27 16 3 12 2 4 10 5 4 2 20 6 6 12 2 16 18 8 7 20 2 21 6 15 3 11 3 5 11 2 5 12 4 3 7 12 3 4 12 2 17 10 8 4 10 5 13 2 10 3 11 2 16 6 11 3 7 +mls_eng_000258 5 7 12 2 4 10 3 2 16 10 6 7 16 3 2 6 18 2 4 10 3 11 2 22 3 7 20 2 9 14 16 10 2 5 2 6 7 2 14 7 20 8 5 7 2 12 8 15 8 9 10 3 9 2 22 19 2 3 17 3 11 19 2 21 11 5 21 3 2 21 11 6 9 3 9 9 2 15 5 15 3 13 14 24 2 5 9 2 5 3 6 11 9 2 17 10 6 9 2 6 18 2 3 5 24 16 14 3 5 7 2 16 27 14 13 8 4 19 2 3 11 3 5 9 7 8 7 20 2 7 6 4 2 5 22 6 17 4 12 2 10 8 9 2 6 12 3 11 9 2 22 14 4 2 22 6 14 13 2 4 10 3 2 8 19 2 4 6 2 12 6 2 4 10 3 4 +mls_eng_000259 4 9 10 3 2 7 6 24 3 4 2 22 14 4 2 9 2 7 6 2 11 6 14 21 2 13 6 3 2 6 5 4 10 6 18 2 4 3 2 17 10 3 7 12 6 2 5 7 2 9 5 8 12 2 8 2 12 3 5 3 11 2 7 6 4 2 6 22 21 22 2 10 3 2 12 6 11 2 18 6 11 2 4 10 3 2 12 6 11 9 10 5 23 3 2 4 6 12 2 15 3 2 4 6 13 4 2 7 6 2 6 8 7 2 8 7 2 4 10 5 4 2 8 9 2 10 5 11 3 12 2 18 6 11 2 15 3 2 3 2 9 5 8 12 2 4 10 3 2 6 15 3 6 7 2 18 6 11 2 8 2 15 14 9 4 2 4 5 24 2 22 5 16 3 2 15 19 2 5 14 21 13 9 2 22 14 4 2 4 10 3 11 8 9 2 6 7 3 2 17 8 16 10 2 8 14 13 13 2 20 8 23 3 2 19 6 14 2 5 7 12 2 9 10 3 2 10 3 13 12 2 14 21 3 7 2 5 13 +mls_eng_000260 6 23 2 19 2 15 6 11 19 2 9 21 6 24 2 5 24 2 16 3 13 4 10 6 11 2 5 7 12 2 10 3 11 9 3 2 18 8 7 13 19 2 9 21 6 24 3 7 2 8 10 4 2 4 17 6 2 15 19 2 21 3 9 17 5 9 2 17 10 19 2 18 3 5 22 3 19 2 5 11 2 19 6 2 16 15 2 9 6 9 6 7 2 17 3 11 3 2 6 14 11 2 22 11 19 2 16 10 8 13 12 2 7 12 2 6 2 4 10 5 7 2 5 11 2 4 12 13 9 21 6 24 2 9 5 15 3 2 6 5 7 +mls_eng_000261 8 2 7 3 23 3 11 2 14 2 5 7 19 17 6 3 7 2 10 6 2 13 3 2 4 12 6 20 6 4 19 2 16 3 11 16 10 8 9 2 15 16 10 2 5 16 2 9 20 11 5 15 6 10 2 12 6 14 9 2 9 10 3 2 10 5 8 9 2 10 3 2 17 6 14 12 2 11 5 23 3 2 22 2 5 2 12 6 11 3 2 16 3 21 3 8 2 4 10 2 10 5 9 2 6 18 2 11 2 20 6 12 2 4 10 3 7 2 8 2 17 3 13 13 2 4 10 3 2 4 3 7 9 6 18 2 17 8 24 3 12 7 3 9 2 4 3 2 12 6 7 2 10 23 3 2 17 6 15 3 7 2 12 6 11 3 2 24 3 21 3 11 9 2 5 7 2 8 2 17 3 7 6 2 9 10 3 2 17 8 7 14 4 2 4 17 3 13 13 2 15 8 7 8 7 5 2 5 4 3 7 4 +mls_eng_000262 4 10 3 2 12 14 24 2 17 5 9 2 9 14 21 11 8 16 9 2 4 6 2 9 3 2 10 15 2 6 4 2 22 11 8 7 20 2 6 2 6 4 2 9 6 2 5 13 19 2 5 22 3 13 2 5 11 12 2 12 3 15 5 7 12 2 12 2 10 3 2 6 13 2 19 6 14 11 2 20 11 5 8 16 4 3 12 2 11 3 21 13 8 2 10 3 2 14 4 13 11 2 19 2 20 5 9 21 8 7 20 2 18 6 11 2 14 4 3 11 5 7 16 3 9 +mls_eng_000263 2 18 6 11 3 2 8 14 5 4 2 9 3 15 8 7 20 2 4 11 6 7 9 3 7 15 3 14 4 5 4 8 6 7 9 2 6 18 2 16 5 13 3 2 15 5 2 22 3 2 15 5 12 2 17 3 2 4 10 3 11 2 8 9 2 7 19 2 15 8 16 25 4 11 6 18 2 12 3 23 6 11 2 9 6 14 4 9 2 6 2 11 5 3 9 2 22 11 8 7 2 9 5 16 10 2 15 8 16 4 3 11 9 2 4 10 3 2 16 6 15 2 21 6 7 2 3 13 8 9 2 5 21 3 11 3 2 7 6 4 2 22 19 2 4 10 3 2 15 14 4 8 13 2 5 13 5 8 7 20 2 5 10 3 4 10 3 2 16 6 7 16 4 14 4 3 2 5 2 15 8 12 13 8 7 20 2 16 6 13 3 11 +mls_eng_000264 5 7 2 5 13 15 6 9 2 4 10 3 2 9 5 15 3 2 8 7 9 4 5 7 4 2 16 3 7 2 9 2 11 9 2 5 7 12 19 2 9 8 12 2 9 4 3 21 2 8 7 4 6 2 5 2 9 10 6 21 2 12 6 11 17 5 19 2 10 3 2 17 5 12 3 12 2 4 10 3 8 2 7 4 8 24 2 16 10 8 7 2 16 5 15 3 2 14 21 2 16 8 7 2 9 4 12 5 14 21 3 2 5 7 12 2 11 3 4 3 7 12 3 2 4 6 2 9 4 5 11 2 4 10 2 4 10 3 20 13 5 9 2 5 4 10 2 12 3 9 21 13 5 19 2 6 18 2 10 3 11 12 2 17 3 11 8 7 20 4 14 6 14 13 3 9 2 17 3 11 2 10 3 2 16 6 7 4 8 7 3 12 2 4 6 2 17 6 14 16 10 2 22 5 8 11 8 16 24 2 6 14 2 9 3 2 17 5 4 2 8 9 2 10 5 8 7 22 19 2 9 5 8 12 2 11 8 7 2 7 6 4 3 12 +mls_eng_000265 4 2 4 10 3 7 2 4 10 3 2 4 10 8 3 24 2 20 11 3 7 3 2 9 4 5 23 2 18 13 6 13 3 12 2 6 13 11 2 4 10 3 2 10 13 2 22 3 13 12 8 7 2 5 7 12 2 4 10 3 2 17 10 5 9 2 7 6 4 10 8 7 20 2 4 6 2 3 9 2 9 3 3 7 2 4 10 3 11 2 22 14 4 2 12 3 2 15 5 6 14 12 2 6 18 2 9 6 18 3 4 12 2 18 13 6 2 8 7 20 2 20 11 5 19 2 20 11 3 7 2 9 4 5 11 2 4 10 3 12 2 11 14 9 10 3 12 2 6 7 2 7 6 17 10 17 2 4 10 3 2 9 17 3 8 18 4 7 3 9 2 6 13 13 2 17 8 7 20 24 3 2 5 2 13 6 6 3 4 2 5 22 12 2 8 7 4 6 2 22 3 11 9 2 18 5 3 +mls_eng_000266 2 8 2 10 5 23 3 2 15 5 3 2 9 14 11 16 24 11 6 18 8 9 3 9 2 4 6 3 2 22 14 11 2 6 9 2 17 10 3 7 2 8 2 5 7 3 14 2 4 10 5 4 10 3 2 17 11 3 2 7 6 4 2 15 19 2 10 5 21 3 7 3 9 9 2 17 5 9 2 5 18 4 3 11 2 8 9 2 9 5 6 13 2 4 10 5 4 2 10 2 5 12 2 9 4 3 21 3 12 4 2 5 20 10 3 11 2 8 2 9 6 5 11 2 4 10 5 4 2 19 6 14 11 2 4 3 7 12 3 11 7 3 9 2 5 12 2 4 3 11 7 12 2 4 6 2 16 6 14 13 12 8 13 5 4 8 6 7 2 5 6 18 4 3 11 2 9 5 6 11 2 4 10 5 4 2 19 6 14 2 16 3 8 11 12 2 18 6 11 2 19 6 14 11 9 3 13 18 2 6 7 2 13 19 2 7 6 4 2 18 6 11 2 15 3 +mls_eng_000267 19 5 4 12 2 2 4 10 5 2 4 10 5 7 12 4 3 11 2 7 3 23 3 11 2 11 5 13 9 2 8 7 2 17 8 7 4 3 11 2 8 2 9 3 19 2 5 2 16 11 6 5 13 2 17 5 11 11 8 7 20 2 11 6 14 7 3 12 2 5 7 12 2 11 6 14 7 15 3 2 22 3 18 6 11 3 2 8 4 2 5 2 13 8 10 4 3 4 2 4 10 3 9 3 7 2 7 6 4 2 10 8 7 20 2 14 7 12 3 2 4 10 3 2 18 8 11 3 12 2 4 11 3 3 9 2 22 14 4 2 8 13 2 7 6 2 9 6 15 3 2 4 10 7 20 2 15 14 9 2 22 3 2 4 10 5 11 +mls_eng_000268 3 8 19 2 8 9 2 22 3 19 6 7 2 4 10 6 14 4 2 4 10 5 4 2 9 6 15 3 2 21 3 22 13 3 2 10 5 3 2 15 5 7 8 9 3 4 6 2 9 5 18 2 15 5 7 19 2 4 10 8 7 20 9 2 5 7 2 6 18 2 16 6 14 11 9 3 9 2 4 10 3 2 26 8 11 15 3 5 7 4 2 10 5 9 2 5 2 9 6 15 5 8 8 9 3 2 5 9 2 15 5 14 4 10 2 4 6 17 6 14 2 6 11 3 2 4 10 11 3 2 12 5 9 20 3 2 5 18 3 2 10 3 2 18 8 11 9 8 2 21 11 3 16 6 14 16 8 4 8 6 7 9 2 12 3 2 12 11 6 14 21 3 4 2 8 7 2 8 7 6 17 5 11 9 +mls_eng_000269 2 9 6 7 2 5 2 15 5 7 2 16 5 15 3 2 6 14 4 2 4 6 2 15 3 2 10 8 15 2 4 10 8 9 2 15 5 7 2 17 5 9 2 6 5 13 6 10 5 19 2 5 2 22 3 11 12 13 3 9 9 2 15 5 7 12 2 22 3 13 7 8 7 20 2 4 6 2 5 2 13 3 9 2 11 6 23 22 14 11 2 16 13 5 7 2 10 8 10 17 8 16 10 2 8 7 2 18 3 9 4 3 12 2 4 10 3 2 12 9 4 11 16 4 2 22 21 14 9 4 8 22 13 19 2 5 9 8 9 4 8 7 20 2 4 10 3 2 5 7 2 14 7 4 3 11 9 2 6 18 2 10 3 2 4 3 15 21 13 2 8 7 9 16 8 11 8 7 20 2 23 8 16 4 14 15 9 2 18 6 11 2 4 10 3 2 4 3 15 22 13 3 2 13 13 4 3 11 9 +mls_eng_000270 2 17 3 2 5 11 3 2 7 6 4 2 13 6 23 3 8 11 9 2 19 6 14 2 5 7 2 8 19 10 2 5 21 6 7 2 4 10 8 9 2 9 14 7 19 2 13 5 7 5 12 2 4 22 14 4 2 16 10 6 13 13 12 3 6 7 2 17 10 3 2 5 23 3 2 7 3 23 3 11 2 7 6 7 2 4 2 13 6 23 18 9 2 26 8 6 11 2 19 6 14 11 2 21 3 +mls_eng_000271 3 17 5 9 2 15 14 11 12 3 12 2 6 7 2 10 3 9 6 11 9 4 3 23 18 2 17 8 9 6 5 13 2 10 6 9 3 9 2 17 5 9 2 8 2 5 7 2 6 13 2 15 5 7 2 5 9 4 2 5 7 12 4 11 2 5 2 16 5 15 2 13 19 2 15 8 9 3 11 3 2 27 6 14 6 12 8 5 11 2 19 6 14 2 4 6 24 5 9 8 18 3 2 10 5 12 2 12 8 20 3 2 8 7 2 10 8 9 2 22 3 5 4 12 2 19 6 2 10 3 2 7 6 23 3 6 7 8 4 8 6 7 2 5 12 2 19 14 2 16 5 7 6 2 14 7 12 3 9 4 5 3 17 12 2 8 7 2 15 8 7 9 2 10 7 2 5 7 8 7 20 14 8 16 8 4 19 2 8 9 2 15 11 12 3 4 +mls_eng_000272 22 19 2 20 6 11 12 2 10 8 9 5 3 12 2 4 10 3 2 17 6 7 12 2 15 3 2 10 3 11 2 22 8 9 7 9 4 2 4 10 8 12 9 2 15 3 2 8 7 2 5 7 2 6 14 4 6 15 7 19 2 10 6 14 9 3 9 2 6 3 2 10 5 4 2 12 6 2 8 19 2 16 5 11 2 18 6 11 2 4 10 3 2 9 8 16 11 9 4 2 4 10 5 4 2 15 5 2 23 3 2 10 5 12 2 7 2 4 10 5 11 2 10 6 17 2 17 3 23 3 11 2 8 14 2 16 14 7 6 4 2 21 13 8 7 20 2 17 8 9 2 21 3 22 3 18 6 11 2 4 10 3 2 17 6 14 16 10 18 13 7 3 9 2 4 10 3 2 22 13 5 14 4 2 10 6 14 3 9 2 6 18 2 10 8 9 18 15 7 6 11 8 2 5 11 3 2 5 2 8 7 2 3 23 18 3 11 19 2 9 4 11 3 3 21 4 +mls_eng_000273 14 6 19 5 22 13 2 3 4 2 17 5 9 2 21 14 13 8 7 20 2 8 4 2 7 6 2 5 9 4 8 24 2 7 6 11 5 9 4 6 7 2 17 5 9 2 8 7 12 11 3 20 4 2 6 18 2 10 3 2 10 5 7 12 2 5 7 12 2 4 10 3 2 22 8 19 13 8 2 20 11 5 20 9 2 3 27 14 13 12 2 6 7 2 7 6 13 6 7 20 2 9 11 3 2 5 22 3 13 2 4 10 3 11 14 17 5 11 6 18 17 5 11 4 10 3 2 18 6 13 3 12 2 9 10 3 2 9 4 11 14 6 3 12 2 22 14 2 6 7 6 14 3 2 5 7 12 2 20 5 9 3 2 4 10 3 2 20 6 7 2 22 14 4 2 6 7 13 19 2 23 3 13 3 12 2 10 3 11 9 3 2 20 7 2 16 5 9 4 6 +mls_eng_000274 5 7 12 2 5 2 18 5 13 19 2 6 18 3 2 8 4 2 17 5 9 2 4 10 5 4 2 5 7 2 7 6 4 10 3 2 17 6 15 3 7 12 4 2 6 23 3 2 9 5 11 7 19 2 17 11 8 16 10 3 2 18 6 11 2 7 6 4 10 8 7 20 2 22 3 4 3 12 3 7 2 4 6 2 20 6 2 9 8 7 4 6 2 15 19 2 9 8 9 4 3 11 2 5 7 12 2 18 5 11 4 10 3 2 14 9 2 16 9 3 7 4 2 17 5 19 2 9 10 3 2 9 5 8 12 2 8 2 9 6 14 13 11 5 4 10 3 11 2 20 6 2 17 8 4 10 8 15 2 8 2 10 5 23 3 2 7 6 2 15 8 2 3 2 4 6 9 4 5 19 2 10 3 5 11 2 5 13 6 7 8 4 12 2 15 19 2 4 17 6 3 2 22 5 22 3 9 +mls_eng_000275 2 8 7 8 12 8 4 2 5 2 16 6 11 12 8 7 20 13 19 2 17 10 3 7 4 12 11 8 7 20 2 2 10 5 2 4 3 2 13 8 4 3 2 15 5 7 2 17 6 13 2 22 3 2 5 4 2 5 7 12 2 10 3 2 21 3 5 3 12 2 2 4 6 2 18 2 4 10 3 9 4 6 14 4 2 8 9 19 11 14 9 10 8 9 2 10 24 16 14 2 18 8 7 3 12 2 17 3 8 4 12 3 13 8 4 5 13 2 22 14 7 3 2 6 18 2 22 +mls_eng_000276 8 4 2 8 9 2 7 6 2 4 10 3 2 12 11 3 12 8 14 13 2 7 5 4 2 16 15 9 2 6 7 2 10 6 3 2 12 3 9 15 3 2 5 9 2 4 10 3 2 21 13 3 7 2 17 3 11 2 4 10 3 2 21 14 9 8 6 7 9 2 5 2 4 10 3 2 8 7 20 13 9 10 2 18 6 7 3 2 4 3 2 22 6 18 2 4 3 7 2 4 10 6 14 9 3 7 2 9 13 3 5 8 7 2 22 11 3 23 3 2 17 3 13 8 7 20 4 6 2 5 15 2 22 13 6 24 3 11 2 22 6 11 4 10 2 15 6 9 2 7 6 22 13 19 2 12 11 6 23 3 2 4 10 3 11 18 6 14 11 9 2 5 7 12 2 22 6 7 6 21 5 11 4 3 2 5 15 21 3 19 5 11 2 16 11 6 7 2 17 5 9 2 4 3 24 3 7 2 5 4 2 17 6 4 3 13 +mls_eng_000277 9 2 9 6 15 3 19 14 6 14 9 2 5 20 13 13 3 2 10 5 18 3 2 4 3 2 15 5 24 8 7 20 2 6 11 3 2 5 11 8 7 20 3 7 9 2 18 6 2 4 10 3 8 7 20 2 16 5 15 21 3 7 4 2 5 4 12 2 7 8 10 4 2 17 8 4 2 16 6 7 9 4 5 7 4 13 19 2 10 5 12 2 6 23 3 2 5 12 2 21 3 9 14 13 2 11 3 9 11 6 24 6 7 2 22 19 2 5 2 4 11 8 22 2 6 18 2 22 11 6 14 7 2 15 14 7 24 3 9 2 4 10 3 2 3 8 12 8 7 4 19 2 4 10 6 14 20 10 4 2 4 10 5 4 2 13 6 7 20 2 21 6 9 3 9 4 8 6 7 2 10 5 12 2 20 8 15 3 7 2 4 10 3 2 5 2 21 11 8 5 11 3 2 13 5 15 2 4 6 2 4 10 3 2 20 13 13 +mls_eng_000278 4 6 2 18 13 5 9 10 2 8 7 2 10 5 4 2 6 15 3 2 16 21 13 3 15 8 12 8 7 20 2 18 6 11 2 10 3 2 15 3 5 12 2 3 4 17 3 3 7 2 11 9 9 2 17 5 7 2 3 9 4 3 7 9 2 21 5 11 4 19 2 5 7 12 2 4 10 3 2 6 21 11 3 2 8 18 6 7 13 19 18 8 11 2 15 8 7 4 2 9 3 11 4 6 13 19 3 2 4 17 5 9 2 15 6 11 2 4 10 5 7 2 5 2 15 8 7 8 4 2 4 10 5 4 2 9 15 6 7 12 2 11 3 15 5 7 3 12 2 5 2 4 10 3 2 18 8 3 11 2 10 6 14 9 3 9 2 5 6 14 18 4 3 11 2 22 3 7 20 2 20 11 6 20 12 2 22 5 24 3 2 6 18 4 3 11 3 2 12 8 7 3 11 2 8 7 4 3 4 5 16 24 +mls_eng_000279 5 7 12 2 8 7 12 8 16 4 8 6 7 5 11 19 2 5 7 2 12 3 7 17 3 2 10 5 12 3 2 16 6 14 9 2 4 3 7 8 7 16 25 9 2 17 3 2 16 6 13 3 2 4 10 11 2 5 2 20 3 5 23 15 5 7 19 2 18 8 20 3 11 9 2 8 7 2 16 8 7 20 3 2 5 2 13 8 18 3 2 15 8 6 14 4 8 6 7 2 17 5 23 3 17 10 5 4 2 18 3 11 19 2 13 8 24 10 2 15 14 9 8 16 24 2 9 4 8 13 9 2 6 23 3 2 4 10 3 2 9 3 2 13 8 10 3 13 19 2 11 6 3 2 13 8 5 4 13 19 2 11 6 3 2 6 11 2 4 10 3 2 20 13 5 9 19 2 17 5 19 2 17 14 12 2 20 6 13 2 5 7 12 2 6 2 24 6 15 2 16 6 15 3 2 17 5 19 2 5 7 12 2 6 4 10 3 11 2 9 6 7 20 9 2 15 8 9 8 9 2 26 14 20 10 3 2 4 10 5 13 2 3 11 22 6 4 2 6 7 12 9 6 7 20 2 14 7 2 21 3 11 21 9 18 6 11 5 14 9 +mls_eng_000280 4 10 5 4 2 17 10 8 16 10 2 22 5 9 10 3 9 2 6 18 2 10 8 9 4 11 19 8 20 2 14 11 2 9 16 10 13 13 9 2 5 3 11 2 20 6 23 3 11 15 3 7 4 13 19 2 18 5 22 11 6 16 5 4 3 12 22 21 2 9 6 15 3 7 2 10 8 9 4 11 19 2 2 8 9 2 18 8 11 20 14 11 19 2 5 2 15 11 9 11 3 21 11 3 9 2 8 3 7 4 5 4 8 6 7 2 6 18 2 10 3 23 3 7 16 3 2 9 2 13 8 16 3 2 4 10 3 2 13 12 11 6 5 17 15 2 5 11 2 9 3 7 4 3 11 8 7 20 2 14 21 6 7 2 4 10 3 15 21 6 9 3 5 22 13 3 2 18 8 20 8 11 2 6 18 2 4 10 3 2 10 3 5 11 6 2 17 8 4 2 10 3 2 26 3 9 4 8 16 8 13 5 4 8 7 20 2 16 11 6 14 12 2 8 7 2 4 3 22 5 11 6 14 7 3 12 +mls_eng_000281 5 7 2 10 3 2 24 3 13 8 23 18 2 10 11 12 2 4 10 8 9 2 10 3 2 9 5 8 12 2 6 5 13 13 2 6 18 6 14 11 3 2 10 6 17 2 20 6 6 12 13 19 2 8 9 2 4 10 5 4 2 18 6 19 9 16 3 2 5 7 12 2 4 10 3 2 3 9 8 3 2 11 3 22 21 13 5 8 12 2 5 6 13 3 2 6 14 11 2 13 6 11 12 2 7 3 23 3 11 2 9 15 6 11 4 2 15 19 2 10 5 8 7 20 2 6 11 4 2 4 11 3 4 3 11 3 2 6 11 3 2 20 6 12 13 19 3 5 11 3 2 4 10 5 7 2 4 10 3 2 9 5 7 20 8 7 20 +mls_eng_000282 3 23 8 12 3 7 4 3 13 19 2 4 10 3 2 13 5 11 7 3 12 2 22 5 8 11 5 7 2 10 5 12 2 7 6 4 2 9 4 12 5 12 19 3 12 2 9 6 14 16 10 2 17 6 11 24 2 9 2 6 2 4 10 3 2 4 6 4 3 5 2 16 6 5 10 8 7 8 19 2 8 2 6 11 3 2 21 8 11 8 4 2 16 2 5 4 2 2 17 10 8 16 10 2 7 6 4 8 22 13 19 2 4 11 5 7 9 13 5 4 3 12 2 22 19 2 7 14 20 10 2 9 10 5 22 3 19 2 18 11 15 3 2 4 10 3 2 9 5 7 9 3 9 20 11 8 4 2 9 8 6 24 2 5 9 3 21 21 2 4 14 4 19 2 10 5 9 2 7 6 2 22 6 16 6 15 3 2 5 9 2 2 6 11 4 10 3 12 6 16 8 16 13 19 2 15 6 9 13 5 15 3 2 5 9 2 4 10 3 7 8 20 4 3 9 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/run.sh b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/run.sh new file mode 100755 index 0000000000000000000000000000000000000000..c1b1869c3fb0cd7433c1563b8a40f9bf8ef7d37b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/run.sh @@ -0,0 +1 @@ +./asr.sh --ngpu 1 --stage 11 --stop_stage 13 --nj 32 --inference_nj 4 --gpu_inference true --lang eng1 --inference_asr_model valid.loss.ave.pth --local_data_opts '--duration 10min --lid false --multilingual false --single_lang eng1' --use_lm false --token_type char --feats_type raw --feats_normalize utterance_mvn --asr_config conf/tuning/train_asr_s3prl_houlsby.yaml --inference_config conf/decode_asr.yaml --train_set train_10min_eng1 --valid_set dev_10min_eng1 --test_sets 'dev_10min_eng1 test_10min_eng1' --asr_tag train_asr_s3prl_houlsby_eng1_10min --expdir test_pr --asr_stats_dir test_pr/asr_stats_eng1_10min --local_score_opts 'false false monolingual' --stage 12 "$@"; exit $? diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.1.log b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.1.log new file mode 100644 index 0000000000000000000000000000000000000000..b1898602cba872ddec83fe150c81ecdacafb0a70 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.1.log @@ -0,0 +1,3022 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.1 --config conf/decode_asr.yaml +# Started at Tue Jan 16 21:38:45 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.1 --config conf/decode_asr.yaml +2024-01-16 21:38:47,109 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml +2024-01-16 21:38:47,127 (asr:523) INFO: Vocabulary size: 30 +2024-01-16 21:38:47,189 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 21:38:47,189 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 21:38:47,299 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 21:38:48,590 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 21:38:49,815 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 21:38:49,815 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 21:38:49,815 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 21:38:49,848 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-16 21:38:49,923 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 21:38:50,036 (asr_inference:494) INFO: speech length: 103766 +2024-01-16 21:38:51,236 (beam_search:428) INFO: decoder input length: 160 +2024-01-16 21:38:51,236 (beam_search:429) INFO: max output length: 160 +2024-01-16 21:38:51,236 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:51,621 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:51,621 (beam_search:476) INFO: -14.00 * 1.0 = -14.00 for ctc +2024-01-16 21:38:51,621 (beam_search:479) INFO: total log probability: -14.00 +2024-01-16 21:38:51,621 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:38:51,621 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:51,622 (beam_search:483) INFO: best hypo: HEREMAEDWELCAMPIONANTILNINTINSICTYFIVEAYARINWHCHESUFHEDATERABLACXITENT + +2024-01-16 21:38:51,646 (asr_inference:494) INFO: speech length: 66902 +2024-01-16 21:38:51,656 (beam_search:428) INFO: decoder input length: 102 +2024-01-16 21:38:51,656 (beam_search:429) INFO: max output length: 102 +2024-01-16 21:38:51,656 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:51,822 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:51,822 (beam_search:476) INFO: -6.93 * 1.0 = -6.93 for ctc +2024-01-16 21:38:51,822 (beam_search:479) INFO: total log probability: -6.93 +2024-01-16 21:38:51,822 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:38:51,822 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:51,822 (beam_search:483) INFO: best hypo: AYLIBRLCONERVETIVEHEASDEFEATEDINATYNATYTWO + +2024-01-16 21:38:51,824 (asr_inference:494) INFO: speech length: 60075 +2024-01-16 21:38:51,833 (beam_search:428) INFO: decoder input length: 91 +2024-01-16 21:38:51,833 (beam_search:429) INFO: max output length: 91 +2024-01-16 21:38:51,833 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:51,949 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:51,949 (beam_search:476) INFO: -9.58 * 1.0 = -9.58 for ctc +2024-01-16 21:38:51,949 (beam_search:479) INFO: total log probability: -9.58 +2024-01-16 21:38:51,949 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:38:51,949 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:51,949 (beam_search:483) INFO: best hypo: WONROVEDLARCODRETWOROUDEATWHANCE + +2024-01-16 21:38:51,950 (asr_inference:494) INFO: speech length: 57344 +2024-01-16 21:38:51,959 (beam_search:428) INFO: decoder input length: 87 +2024-01-16 21:38:51,959 (beam_search:429) INFO: max output length: 87 +2024-01-16 21:38:51,959 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:52,090 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:52,090 (beam_search:476) INFO: -8.72 * 1.0 = -8.72 for ctc +2024-01-16 21:38:52,090 (beam_search:479) INFO: total log probability: -8.72 +2024-01-16 21:38:52,090 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:38:52,090 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:52,090 (beam_search:483) INFO: best hypo: SOMEOTHEOUNTISHAESERVAYISFORMALTBLEYEARS + +2024-01-16 21:38:52,091 (asr_inference:494) INFO: speech length: 62806 +2024-01-16 21:38:52,101 (beam_search:428) INFO: decoder input length: 96 +2024-01-16 21:38:52,101 (beam_search:429) INFO: max output length: 96 +2024-01-16 21:38:52,101 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:52,244 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:52,244 (beam_search:476) INFO: -5.00 * 1.0 = -5.00 for ctc +2024-01-16 21:38:52,244 (beam_search:479) INFO: total log probability: -5.00 +2024-01-16 21:38:52,244 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 21:38:52,244 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:52,244 (beam_search:483) INFO: best hypo: BOTHOFTHEVIRSINSFEATHETHESONGHAPYHOLIDAY + +2024-01-16 21:38:52,245 (asr_inference:494) INFO: speech length: 116054 +2024-01-16 21:38:52,258 (beam_search:428) INFO: decoder input length: 179 +2024-01-16 21:38:52,258 (beam_search:429) INFO: max output length: 179 +2024-01-16 21:38:52,258 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:52,698 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:52,698 (beam_search:476) INFO: -15.56 * 1.0 = -15.56 for ctc +2024-01-16 21:38:52,698 (beam_search:479) INFO: total log probability: -15.56 +2024-01-16 21:38:52,698 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:38:52,698 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:52,698 (beam_search:483) INFO: best hypo: SHAKSPIERMANYREFRNCESURMADTOSHEENDSINTRACTIONDORCARICTESFOMVERIUTPLAYES + +2024-01-16 21:38:52,700 (asr_inference:494) INFO: speech length: 84651 +2024-01-16 21:38:52,710 (beam_search:428) INFO: decoder input length: 130 +2024-01-16 21:38:52,710 (beam_search:429) INFO: max output length: 130 +2024-01-16 21:38:52,710 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:52,981 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:52,981 (beam_search:476) INFO: -12.77 * 1.0 = -12.77 for ctc +2024-01-16 21:38:52,981 (beam_search:479) INFO: total log probability: -12.77 +2024-01-16 21:38:52,981 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:38:52,981 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:52,981 (beam_search:483) INFO: best hypo: IFNDYTHEPROGRAMCULDBRAKEOUTGUSTLITLFOMEITTWOFOMELIARAPROUCH + +2024-01-16 21:38:52,982 (asr_inference:494) INFO: speech length: 94208 +2024-01-16 21:38:52,993 (beam_search:428) INFO: decoder input length: 145 +2024-01-16 21:38:52,993 (beam_search:429) INFO: max output length: 145 +2024-01-16 21:38:52,993 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:53,317 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:53,317 (beam_search:476) INFO: -11.49 * 1.0 = -11.49 for ctc +2024-01-16 21:38:53,318 (beam_search:479) INFO: total log probability: -11.49 +2024-01-16 21:38:53,318 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:38:53,318 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:53,318 (beam_search:483) INFO: best hypo: THEHALBUMWASRELESEINOSTRALIARARNNINTINTOAGISTTWOTHOUSENTANELEVON + +2024-01-16 21:38:53,319 (asr_inference:494) INFO: speech length: 64171 +2024-01-16 21:38:53,329 (beam_search:428) INFO: decoder input length: 98 +2024-01-16 21:38:53,329 (beam_search:429) INFO: max output length: 98 +2024-01-16 21:38:53,329 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:53,468 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:53,468 (beam_search:476) INFO: -6.77 * 1.0 = -6.77 for ctc +2024-01-16 21:38:53,468 (beam_search:479) INFO: total log probability: -6.77 +2024-01-16 21:38:53,468 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:38:53,468 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:53,468 (beam_search:483) INFO: best hypo: HENOWPLACEFORASTRALINTLOBEPEIRTGLOURY + +2024-01-16 21:38:53,470 (asr_inference:494) INFO: speech length: 61440 +2024-01-16 21:38:53,479 (beam_search:428) INFO: decoder input length: 93 +2024-01-16 21:38:53,479 (beam_search:429) INFO: max output length: 93 +2024-01-16 21:38:53,479 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:53,626 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:53,626 (beam_search:476) INFO: -7.10 * 1.0 = -7.10 for ctc +2024-01-16 21:38:53,626 (beam_search:479) INFO: total log probability: -7.10 +2024-01-16 21:38:53,626 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:38:53,626 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:53,627 (beam_search:483) INFO: best hypo: ITITNOTNONEHOWMUCHEIFANYOFHERTLAMEMSARETRO + +2024-01-16 21:38:53,628 (asr_inference:494) INFO: speech length: 113323 +2024-01-16 21:38:53,640 (beam_search:428) INFO: decoder input length: 175 +2024-01-16 21:38:53,640 (beam_search:429) INFO: max output length: 175 +2024-01-16 21:38:53,640 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:54,100 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:54,100 (beam_search:476) INFO: -13.14 * 1.0 = -13.14 for ctc +2024-01-16 21:38:54,100 (beam_search:479) INFO: total log probability: -13.14 +2024-01-16 21:38:54,100 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:38:54,100 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:54,101 (beam_search:483) INFO: best hypo: ASMLLBISNESSONERBRAURDOPRATEDHISWEAANHAPFARMEFORSICTENEARSFROTHEAGOFWENTYTWO + +2024-01-16 21:38:54,102 (asr_inference:494) INFO: speech length: 60075 +2024-01-16 21:38:54,111 (beam_search:428) INFO: decoder input length: 91 +2024-01-16 21:38:54,111 (beam_search:429) INFO: max output length: 91 +2024-01-16 21:38:54,111 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:54,229 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:54,229 (beam_search:476) INFO: -4.41 * 1.0 = -4.41 for ctc +2024-01-16 21:38:54,229 (beam_search:479) INFO: total log probability: -4.41 +2024-01-16 21:38:54,229 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 21:38:54,229 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:54,229 (beam_search:483) INFO: best hypo: INTHENINTHSENCTRYHEWASANIRISHPOAT + +2024-01-16 21:38:54,230 (asr_inference:494) INFO: speech length: 39595 +2024-01-16 21:38:54,238 (beam_search:428) INFO: decoder input length: 59 +2024-01-16 21:38:54,238 (beam_search:429) INFO: max output length: 59 +2024-01-16 21:38:54,238 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:54,294 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:54,294 (beam_search:476) INFO: -5.90 * 1.0 = -5.90 for ctc +2024-01-16 21:38:54,294 (beam_search:479) INFO: total log probability: -5.90 +2024-01-16 21:38:54,294 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:38:54,294 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:54,294 (beam_search:483) INFO: best hypo: THEYARMAUECETBYSTRONGN + +2024-01-16 21:38:54,295 (asr_inference:494) INFO: speech length: 43691 +2024-01-16 21:38:54,303 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:38:54,303 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:38:54,303 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:54,371 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:54,371 (beam_search:476) INFO: -3.79 * 1.0 = -3.79 for ctc +2024-01-16 21:38:54,371 (beam_search:479) INFO: total log probability: -3.79 +2024-01-16 21:38:54,371 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:38:54,371 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:54,371 (beam_search:483) INFO: best hypo: THELALLEISTHEREFORVOULED + +2024-01-16 21:38:54,372 (asr_inference:494) INFO: speech length: 55979 +2024-01-16 21:38:54,381 (beam_search:428) INFO: decoder input length: 85 +2024-01-16 21:38:54,381 (beam_search:429) INFO: max output length: 85 +2024-01-16 21:38:54,381 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:54,491 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:54,491 (beam_search:476) INFO: -6.07 * 1.0 = -6.07 for ctc +2024-01-16 21:38:54,491 (beam_search:479) INFO: total log probability: -6.07 +2024-01-16 21:38:54,491 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:38:54,491 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:54,491 (beam_search:483) INFO: best hypo: INTHEARLYSTAGESCAMECLOSETOUASLEP + +2024-01-16 21:38:54,492 (asr_inference:494) INFO: speech length: 55979 +2024-01-16 21:38:54,501 (beam_search:428) INFO: decoder input length: 85 +2024-01-16 21:38:54,501 (beam_search:429) INFO: max output length: 85 +2024-01-16 21:38:54,501 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:54,623 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:54,623 (beam_search:476) INFO: -7.57 * 1.0 = -7.57 for ctc +2024-01-16 21:38:54,623 (beam_search:479) INFO: total log probability: -7.57 +2024-01-16 21:38:54,623 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:38:54,623 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:54,624 (beam_search:483) INFO: best hypo: RUNINGEVERYTHARTYMINITTHOUTSERVISTIMEMS + +2024-01-16 21:38:54,625 (asr_inference:494) INFO: speech length: 83286 +2024-01-16 21:38:54,635 (beam_search:428) INFO: decoder input length: 128 +2024-01-16 21:38:54,635 (beam_search:429) INFO: max output length: 128 +2024-01-16 21:38:54,635 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:54,874 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:54,874 (beam_search:476) INFO: -5.63 * 1.0 = -5.63 for ctc +2024-01-16 21:38:54,875 (beam_search:479) INFO: total log probability: -5.63 +2024-01-16 21:38:54,875 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 21:38:54,875 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:54,875 (beam_search:483) INFO: best hypo: ASRESILTWHENTHECOLIGEREOPENDITWHASASAALLMALECOLIGE + +2024-01-16 21:38:54,876 (asr_inference:494) INFO: speech length: 90112 +2024-01-16 21:38:54,887 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 21:38:54,887 (beam_search:429) INFO: max output length: 138 +2024-01-16 21:38:54,887 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:55,208 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:55,208 (beam_search:476) INFO: -10.32 * 1.0 = -10.32 for ctc +2024-01-16 21:38:55,208 (beam_search:479) INFO: total log probability: -10.32 +2024-01-16 21:38:55,208 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:38:55,208 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:55,208 (beam_search:483) INFO: best hypo: THETIMEBETWENTHESPONTISVERRABLEANDCANACERANYWHEFROAINITTOMUCHLONGER + +2024-01-16 21:38:55,210 (asr_inference:494) INFO: speech length: 111958 +2024-01-16 21:38:55,222 (beam_search:428) INFO: decoder input length: 172 +2024-01-16 21:38:55,222 (beam_search:429) INFO: max output length: 172 +2024-01-16 21:38:55,222 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:55,681 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:55,681 (beam_search:476) INFO: -14.97 * 1.0 = -14.97 for ctc +2024-01-16 21:38:55,681 (beam_search:479) INFO: total log probability: -14.97 +2024-01-16 21:38:55,681 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:38:55,681 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:55,681 (beam_search:483) INFO: best hypo: WEARKONTHEEEEAEASSTDARTEDINMARHCHTWOTHAUSEDANDSEVONATCOSTOFFIVEMILIONDDOLLERS + +2024-01-16 21:38:55,683 (asr_inference:494) INFO: speech length: 117419 +2024-01-16 21:38:55,696 (beam_search:428) INFO: decoder input length: 181 +2024-01-16 21:38:55,696 (beam_search:429) INFO: max output length: 181 +2024-01-16 21:38:55,696 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:56,236 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:56,236 (beam_search:476) INFO: -15.58 * 1.0 = -15.58 for ctc +2024-01-16 21:38:56,236 (beam_search:479) INFO: total log probability: -15.58 +2024-01-16 21:38:56,236 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:38:56,236 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:56,236 (beam_search:483) INFO: best hypo: HOWEVERTEWASSOMEDECAGREMENTOETHEENDINGTHEMEMWICHOMORYANDYOSHIMORYDECSKUSTEATLEANGTHOVEREMOUL + +2024-01-16 21:38:56,238 (asr_inference:494) INFO: speech length: 38230 +2024-01-16 21:38:56,246 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:38:56,246 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:38:56,246 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:56,299 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:56,299 (beam_search:476) INFO: -3.06 * 1.0 = -3.06 for ctc +2024-01-16 21:38:56,299 (beam_search:479) INFO: total log probability: -3.06 +2024-01-16 21:38:56,299 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:38:56,299 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:56,299 (beam_search:483) INFO: best hypo: THECAPLEHADNOCHILDRON + +2024-01-16 21:38:56,301 (asr_inference:494) INFO: speech length: 98304 +2024-01-16 21:38:56,312 (beam_search:428) INFO: decoder input length: 151 +2024-01-16 21:38:56,312 (beam_search:429) INFO: max output length: 151 +2024-01-16 21:38:56,312 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:56,648 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:56,648 (beam_search:476) INFO: -10.79 * 1.0 = -10.79 for ctc +2024-01-16 21:38:56,648 (beam_search:479) INFO: total log probability: -10.79 +2024-01-16 21:38:56,648 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:38:56,648 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:56,648 (beam_search:483) INFO: best hypo: THEFITIALSINGLFTHATDEBYUALTHMPARISSCOLINGHADAELABRTMUSICVIDEAO + +2024-01-16 21:38:56,650 (asr_inference:494) INFO: speech length: 102400 +2024-01-16 21:38:56,661 (beam_search:428) INFO: decoder input length: 157 +2024-01-16 21:38:56,661 (beam_search:429) INFO: max output length: 157 +2024-01-16 21:38:56,661 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:57,071 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:57,071 (beam_search:476) INFO: -11.77 * 1.0 = -11.77 for ctc +2024-01-16 21:38:57,071 (beam_search:479) INFO: total log probability: -11.77 +2024-01-16 21:38:57,071 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:38:57,071 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:57,072 (beam_search:483) INFO: best hypo: THESERISENDEDONSICXTHAORGISTTWOTHAUSENDANDFORELASTINGFRATOUTLOFSEVENTYONDDAYS + +2024-01-16 21:38:57,073 (asr_inference:494) INFO: speech length: 62806 +2024-01-16 21:38:57,083 (beam_search:428) INFO: decoder input length: 96 +2024-01-16 21:38:57,083 (beam_search:429) INFO: max output length: 96 +2024-01-16 21:38:57,083 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:57,243 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:57,243 (beam_search:476) INFO: -9.12 * 1.0 = -9.12 for ctc +2024-01-16 21:38:57,243 (beam_search:479) INFO: total log probability: -9.12 +2024-01-16 21:38:57,243 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:38:57,243 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:57,243 (beam_search:483) INFO: best hypo: HEHASALSODCONTRIBETETOTHENUNYOURCREEUOFBOKS + +2024-01-16 21:38:57,245 (asr_inference:494) INFO: speech length: 60075 +2024-01-16 21:38:57,253 (beam_search:428) INFO: decoder input length: 91 +2024-01-16 21:38:57,253 (beam_search:429) INFO: max output length: 91 +2024-01-16 21:38:57,253 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:57,386 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:57,386 (beam_search:476) INFO: -4.66 * 1.0 = -4.66 for ctc +2024-01-16 21:38:57,386 (beam_search:479) INFO: total log probability: -4.66 +2024-01-16 21:38:57,386 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 21:38:57,386 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:57,386 (beam_search:483) INFO: best hypo: BYPLACINGSMALARTOBDGECTTHROOUTTHEILME + +2024-01-16 21:38:57,388 (asr_inference:494) INFO: speech length: 39595 +2024-01-16 21:38:57,395 (beam_search:428) INFO: decoder input length: 59 +2024-01-16 21:38:57,395 (beam_search:429) INFO: max output length: 59 +2024-01-16 21:38:57,395 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:57,444 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:57,444 (beam_search:476) INFO: -5.59 * 1.0 = -5.59 for ctc +2024-01-16 21:38:57,444 (beam_search:479) INFO: total log probability: -5.59 +2024-01-16 21:38:57,444 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:38:57,444 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:57,444 (beam_search:483) INFO: best hypo: ITIFOUNEDINBRESIL + +2024-01-16 21:38:57,445 (asr_inference:494) INFO: speech length: 61440 +2024-01-16 21:38:57,454 (beam_search:428) INFO: decoder input length: 93 +2024-01-16 21:38:57,454 (beam_search:429) INFO: max output length: 93 +2024-01-16 21:38:57,454 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:57,601 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:57,601 (beam_search:476) INFO: -7.05 * 1.0 = -7.05 for ctc +2024-01-16 21:38:57,601 (beam_search:479) INFO: total log probability: -7.05 +2024-01-16 21:38:57,601 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:38:57,601 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:57,601 (beam_search:483) INFO: best hypo: ITWATHESIDOFTHECAMLYIIDENTIFIEDMORLEWIFH + +2024-01-16 21:38:57,602 (asr_inference:494) INFO: speech length: 66902 +2024-01-16 21:38:57,612 (beam_search:428) INFO: decoder input length: 102 +2024-01-16 21:38:57,612 (beam_search:429) INFO: max output length: 102 +2024-01-16 21:38:57,612 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:57,768 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:57,768 (beam_search:476) INFO: -8.04 * 1.0 = -8.04 for ctc +2024-01-16 21:38:57,768 (beam_search:479) INFO: total log probability: -8.04 +2024-01-16 21:38:57,768 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:38:57,768 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:57,768 (beam_search:483) INFO: best hypo: ECANDEDITSIGHTHEMUSTLLSOSODMITAWORKPLAN + +2024-01-16 21:38:57,769 (asr_inference:494) INFO: speech length: 54614 +2024-01-16 21:38:57,778 (beam_search:428) INFO: decoder input length: 83 +2024-01-16 21:38:57,778 (beam_search:429) INFO: max output length: 83 +2024-01-16 21:38:57,778 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:57,863 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:57,864 (beam_search:476) INFO: -3.81 * 1.0 = -3.81 for ctc +2024-01-16 21:38:57,864 (beam_search:479) INFO: total log probability: -3.81 +2024-01-16 21:38:57,864 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:38:57,864 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:57,864 (beam_search:483) INFO: best hypo: DUNDEYWONTHEMACHTHRETWOE + +2024-01-16 21:38:57,865 (asr_inference:494) INFO: speech length: 99670 +2024-01-16 21:38:57,876 (beam_search:428) INFO: decoder input length: 153 +2024-01-16 21:38:57,876 (beam_search:429) INFO: max output length: 153 +2024-01-16 21:38:57,876 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:58,268 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:58,268 (beam_search:476) INFO: -10.79 * 1.0 = -10.79 for ctc +2024-01-16 21:38:58,268 (beam_search:479) INFO: total log probability: -10.79 +2024-01-16 21:38:58,268 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:38:58,268 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:58,269 (beam_search:483) INFO: best hypo: HOWEVERTHEVILIGREMANEDICILATDTNTILTHERIVULOFTEFIRSTNOUSPAPERSECONDREPOUBLICK + +2024-01-16 21:38:58,270 (asr_inference:494) INFO: speech length: 107862 +2024-01-16 21:38:58,282 (beam_search:428) INFO: decoder input length: 166 +2024-01-16 21:38:58,282 (beam_search:429) INFO: max output length: 166 +2024-01-16 21:38:58,282 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:58,712 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:58,713 (beam_search:476) INFO: -9.79 * 1.0 = -9.79 for ctc +2024-01-16 21:38:58,713 (beam_search:479) INFO: total log probability: -9.79 +2024-01-16 21:38:58,713 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 21:38:58,713 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:58,713 (beam_search:483) INFO: best hypo: THEFIRSTERVIITHENUCHARCWASHELDNINTNFIFTYONALTHOTHEBILDIGWASNOTFULYFINISHED + +2024-01-16 21:38:58,714 (asr_inference:494) INFO: speech length: 113323 +2024-01-16 21:38:58,727 (beam_search:428) INFO: decoder input length: 175 +2024-01-16 21:38:58,727 (beam_search:429) INFO: max output length: 175 +2024-01-16 21:38:58,727 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:59,198 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:59,199 (beam_search:476) INFO: -11.23 * 1.0 = -11.23 for ctc +2024-01-16 21:38:59,199 (beam_search:479) INFO: total log probability: -11.23 +2024-01-16 21:38:59,199 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:38:59,199 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:59,199 (beam_search:483) INFO: best hypo: THEAVRIGHHOUSEHLDSIESWASTWOPONTTWOSEVONNDTHEAVRIGHFAMLYSIESWASTHREPOENTESIAROSIARO + +2024-01-16 21:38:59,200 (asr_inference:494) INFO: speech length: 79190 +2024-01-16 21:38:59,211 (beam_search:428) INFO: decoder input length: 121 +2024-01-16 21:38:59,211 (beam_search:429) INFO: max output length: 121 +2024-01-16 21:38:59,211 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:59,430 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:59,430 (beam_search:476) INFO: -9.16 * 1.0 = -9.16 for ctc +2024-01-16 21:38:59,430 (beam_search:479) INFO: total log probability: -9.16 +2024-01-16 21:38:59,430 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:38:59,430 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:59,431 (beam_search:483) INFO: best hypo: ITWASFIRSTERARDCASTONTHREDGANIOURYTWOHOUSENDANDTEN + +2024-01-16 21:38:59,432 (asr_inference:494) INFO: speech length: 47787 +2024-01-16 21:38:59,440 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 21:38:59,440 (beam_search:429) INFO: max output length: 72 +2024-01-16 21:38:59,440 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:59,531 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:59,531 (beam_search:476) INFO: -3.84 * 1.0 = -3.84 for ctc +2024-01-16 21:38:59,531 (beam_search:479) INFO: total log probability: -3.84 +2024-01-16 21:38:59,531 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 21:38:59,531 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:59,531 (beam_search:483) INFO: best hypo: THEWINGSWENOWADINASINGLEPRESING + +2024-01-16 21:38:59,532 (asr_inference:494) INFO: speech length: 64171 +2024-01-16 21:38:59,542 (beam_search:428) INFO: decoder input length: 98 +2024-01-16 21:38:59,542 (beam_search:429) INFO: max output length: 98 +2024-01-16 21:38:59,542 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:59,677 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:59,677 (beam_search:476) INFO: -8.43 * 1.0 = -8.43 for ctc +2024-01-16 21:38:59,677 (beam_search:479) INFO: total log probability: -8.43 +2024-01-16 21:38:59,677 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:38:59,677 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:59,678 (beam_search:483) INFO: best hypo: TEDOCTEOFOLOIFYINENDENYEARIGMANAGEMENT + +2024-01-16 21:38:59,679 (asr_inference:494) INFO: speech length: 61440 +2024-01-16 21:38:59,688 (beam_search:428) INFO: decoder input length: 93 +2024-01-16 21:38:59,688 (beam_search:429) INFO: max output length: 93 +2024-01-16 21:38:59,688 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:59,824 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:59,824 (beam_search:476) INFO: -9.13 * 1.0 = -9.13 for ctc +2024-01-16 21:38:59,824 (beam_search:479) INFO: total log probability: -9.13 +2024-01-16 21:38:59,824 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:38:59,824 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:59,824 (beam_search:483) INFO: best hypo: THISEOWAYTHEMAENARKGUMENOFSAIFTDYRISSKS + +2024-01-16 21:38:59,826 (asr_inference:494) INFO: speech length: 66902 +2024-01-16 21:38:59,835 (beam_search:428) INFO: decoder input length: 102 +2024-01-16 21:38:59,835 (beam_search:429) INFO: max output length: 102 +2024-01-16 21:38:59,835 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:38:59,994 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:38:59,994 (beam_search:476) INFO: -6.68 * 1.0 = -6.68 for ctc +2024-01-16 21:38:59,994 (beam_search:479) INFO: total log probability: -6.68 +2024-01-16 21:38:59,994 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:38:59,994 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:38:59,995 (beam_search:483) INFO: best hypo: HEWASALLSOADALIFHMEMBROFSCOUNDTHORPYUNITED + +2024-01-16 21:38:59,996 (asr_inference:494) INFO: speech length: 62806 +2024-01-16 21:39:00,005 (beam_search:428) INFO: decoder input length: 96 +2024-01-16 21:39:00,005 (beam_search:429) INFO: max output length: 96 +2024-01-16 21:39:00,005 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:00,153 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:00,153 (beam_search:476) INFO: -5.51 * 1.0 = -5.51 for ctc +2024-01-16 21:39:00,153 (beam_search:479) INFO: total log probability: -5.51 +2024-01-16 21:39:00,153 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 21:39:00,153 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:00,154 (beam_search:483) INFO: best hypo: SHEFHEIRSTHELGATDEFORSEBUTTHIENEVERHAPENS + +2024-01-16 21:39:00,155 (asr_inference:494) INFO: speech length: 64171 +2024-01-16 21:39:00,164 (beam_search:428) INFO: decoder input length: 98 +2024-01-16 21:39:00,164 (beam_search:429) INFO: max output length: 98 +2024-01-16 21:39:00,164 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:00,294 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:00,294 (beam_search:476) INFO: -9.25 * 1.0 = -9.25 for ctc +2024-01-16 21:39:00,294 (beam_search:479) INFO: total log probability: -9.25 +2024-01-16 21:39:00,294 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:39:00,294 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:00,294 (beam_search:483) INFO: best hypo: FOTDROPSNABLETHADTHFOTSRATACROUSE + +2024-01-16 21:39:00,295 (asr_inference:494) INFO: speech length: 87382 +2024-01-16 21:39:00,306 (beam_search:428) INFO: decoder input length: 134 +2024-01-16 21:39:00,306 (beam_search:429) INFO: max output length: 134 +2024-01-16 21:39:00,306 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:00,581 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:00,581 (beam_search:476) INFO: -10.28 * 1.0 = -10.28 for ctc +2024-01-16 21:39:00,581 (beam_search:479) INFO: total log probability: -10.28 +2024-01-16 21:39:00,581 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:00,581 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:00,582 (beam_search:483) INFO: best hypo: WHETHTHEARFLOYISFREYORFOURSTCNFECTHEENDGYOFIENCYOFTHWHNDO + +2024-01-16 21:39:00,583 (asr_inference:494) INFO: speech length: 57344 +2024-01-16 21:39:00,592 (beam_search:428) INFO: decoder input length: 87 +2024-01-16 21:39:00,592 (beam_search:429) INFO: max output length: 87 +2024-01-16 21:39:00,592 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:00,714 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:00,714 (beam_search:476) INFO: -9.12 * 1.0 = -9.12 for ctc +2024-01-16 21:39:00,714 (beam_search:479) INFO: total log probability: -9.12 +2024-01-16 21:39:00,714 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:39:00,714 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:00,714 (beam_search:483) INFO: best hypo: AFTRGETIGHEITMESERENTTHEMADTHNOUDORS + +2024-01-16 21:39:00,715 (asr_inference:494) INFO: speech length: 72363 +2024-01-16 21:39:00,725 (beam_search:428) INFO: decoder input length: 111 +2024-01-16 21:39:00,725 (beam_search:429) INFO: max output length: 111 +2024-01-16 21:39:00,725 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:00,892 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:00,892 (beam_search:476) INFO: -10.11 * 1.0 = -10.11 for ctc +2024-01-16 21:39:00,892 (beam_search:479) INFO: total log probability: -10.11 +2024-01-16 21:39:00,892 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:39:00,892 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:00,892 (beam_search:483) INFO: best hypo: FRAGMNTEONACHFACEREMARETWTHLTERSAYBESE + +2024-01-16 21:39:00,893 (asr_inference:494) INFO: speech length: 87382 +2024-01-16 21:39:00,904 (beam_search:428) INFO: decoder input length: 134 +2024-01-16 21:39:00,904 (beam_search:429) INFO: max output length: 134 +2024-01-16 21:39:00,904 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:01,207 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:01,207 (beam_search:476) INFO: -11.39 * 1.0 = -11.39 for ctc +2024-01-16 21:39:01,207 (beam_search:479) INFO: total log probability: -11.39 +2024-01-16 21:39:01,207 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:01,208 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:01,208 (beam_search:483) INFO: best hypo: FROMTHFIRSTDMINITEBOTHTEMESSHODTHEDESIRETOCOMPEETWITHEGREIOFAPROCERS + +2024-01-16 21:39:01,209 (asr_inference:494) INFO: speech length: 81920 +2024-01-16 21:39:01,219 (beam_search:428) INFO: decoder input length: 125 +2024-01-16 21:39:01,219 (beam_search:429) INFO: max output length: 125 +2024-01-16 21:39:01,219 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:01,479 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:01,479 (beam_search:476) INFO: -10.47 * 1.0 = -10.47 for ctc +2024-01-16 21:39:01,479 (beam_search:479) INFO: total log probability: -10.47 +2024-01-16 21:39:01,479 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:01,479 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:01,479 (beam_search:483) INFO: best hypo: FISICLHERIBYECERSIDSESMAYHELPTHPATIONTETOMAINTAINMUSLSTRINGTH + +2024-01-16 21:39:01,481 (asr_inference:494) INFO: speech length: 75094 +2024-01-16 21:39:01,490 (beam_search:428) INFO: decoder input length: 115 +2024-01-16 21:39:01,491 (beam_search:429) INFO: max output length: 115 +2024-01-16 21:39:01,491 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:01,692 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:01,692 (beam_search:476) INFO: -5.60 * 1.0 = -5.60 for ctc +2024-01-16 21:39:01,692 (beam_search:479) INFO: total log probability: -5.60 +2024-01-16 21:39:01,692 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 21:39:01,692 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:01,692 (beam_search:483) INFO: best hypo: HOWEVERTHETOWNEHELIVSINNOUNDWONTTOHEARABOUTHER + +2024-01-16 21:39:01,694 (asr_inference:494) INFO: speech length: 95574 +2024-01-16 21:39:01,705 (beam_search:428) INFO: decoder input length: 147 +2024-01-16 21:39:01,705 (beam_search:429) INFO: max output length: 147 +2024-01-16 21:39:01,705 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:02,027 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:02,028 (beam_search:476) INFO: -12.23 * 1.0 = -12.23 for ctc +2024-01-16 21:39:02,028 (beam_search:479) INFO: total log probability: -12.23 +2024-01-16 21:39:02,028 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:39:02,028 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:02,028 (beam_search:483) INFO: best hypo: ADISRIESAEPOENTDENTOANACTINGCHIVEJUSTISSORJOUDGEOFTHESOPREMECORT + +2024-01-16 21:39:02,029 (asr_inference:494) INFO: speech length: 84651 +2024-01-16 21:39:02,040 (beam_search:428) INFO: decoder input length: 130 +2024-01-16 21:39:02,040 (beam_search:429) INFO: max output length: 130 +2024-01-16 21:39:02,040 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:02,310 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:02,310 (beam_search:476) INFO: -10.76 * 1.0 = -10.76 for ctc +2024-01-16 21:39:02,310 (beam_search:479) INFO: total log probability: -10.76 +2024-01-16 21:39:02,311 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:02,311 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:02,311 (beam_search:483) INFO: best hypo: THESORYBESOUTCUVERINGISTENREMOVEDTANDTEBENDSARPARTHALYCOCKET + +2024-01-16 21:39:02,312 (asr_inference:494) INFO: speech length: 95574 +2024-01-16 21:39:02,323 (beam_search:428) INFO: decoder input length: 147 +2024-01-16 21:39:02,323 (beam_search:429) INFO: max output length: 147 +2024-01-16 21:39:02,323 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:02,600 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:02,600 (beam_search:476) INFO: -10.65 * 1.0 = -10.65 for ctc +2024-01-16 21:39:02,600 (beam_search:479) INFO: total log probability: -10.65 +2024-01-16 21:39:02,600 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:39:02,600 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:02,600 (beam_search:483) INFO: best hypo: THISNASTIALMOVENTWHCHEBEGONWTHSOUHHOPCAMETOASADEAND + +2024-01-16 21:39:02,602 (asr_inference:494) INFO: speech length: 75094 +2024-01-16 21:39:02,611 (beam_search:428) INFO: decoder input length: 115 +2024-01-16 21:39:02,611 (beam_search:429) INFO: max output length: 115 +2024-01-16 21:39:02,611 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:02,811 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:02,811 (beam_search:476) INFO: -10.21 * 1.0 = -10.21 for ctc +2024-01-16 21:39:02,811 (beam_search:479) INFO: total log probability: -10.21 +2024-01-16 21:39:02,811 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:39:02,811 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:02,811 (beam_search:483) INFO: best hypo: HISASEOSIATEOUSUALYCALDHIMTEORETHEGOODLOKINGGIY + +2024-01-16 21:39:02,813 (asr_inference:494) INFO: speech length: 76459 +2024-01-16 21:39:02,823 (beam_search:428) INFO: decoder input length: 117 +2024-01-16 21:39:02,823 (beam_search:429) INFO: max output length: 117 +2024-01-16 21:39:02,823 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:03,028 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:03,028 (beam_search:476) INFO: -6.03 * 1.0 = -6.03 for ctc +2024-01-16 21:39:03,028 (beam_search:479) INFO: total log probability: -6.03 +2024-01-16 21:39:03,028 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 21:39:03,028 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:03,029 (beam_search:483) INFO: best hypo: ITSMAENOFHICESWERINLUNDENWETHESECENDOFISSBELFAST + +2024-01-16 21:39:03,030 (asr_inference:494) INFO: speech length: 62806 +2024-01-16 21:39:03,039 (beam_search:428) INFO: decoder input length: 96 +2024-01-16 21:39:03,039 (beam_search:429) INFO: max output length: 96 +2024-01-16 21:39:03,039 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:03,173 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:03,174 (beam_search:476) INFO: -3.64 * 1.0 = -3.64 for ctc +2024-01-16 21:39:03,174 (beam_search:479) INFO: total log probability: -3.64 +2024-01-16 21:39:03,174 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 21:39:03,174 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:03,174 (beam_search:483) INFO: best hypo: ACTULYIHADNERBEENTOAVILIDGEBEFORTHAT + +2024-01-16 21:39:03,175 (asr_inference:494) INFO: speech length: 81920 +2024-01-16 21:39:03,185 (beam_search:428) INFO: decoder input length: 125 +2024-01-16 21:39:03,185 (beam_search:429) INFO: max output length: 125 +2024-01-16 21:39:03,185 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:03,428 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:03,428 (beam_search:476) INFO: -9.10 * 1.0 = -9.10 for ctc +2024-01-16 21:39:03,428 (beam_search:479) INFO: total log probability: -9.10 +2024-01-16 21:39:03,428 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:39:03,428 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:03,428 (beam_search:483) INFO: best hypo: HEASCHAGEITHPLADINGTOSETOFBOMSINURAPANDTHEUNIGTETATE + +2024-01-16 21:39:03,429 (asr_inference:494) INFO: speech length: 102400 +2024-01-16 21:39:03,441 (beam_search:428) INFO: decoder input length: 157 +2024-01-16 21:39:03,441 (beam_search:429) INFO: max output length: 157 +2024-01-16 21:39:03,441 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:03,783 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:03,783 (beam_search:476) INFO: -11.23 * 1.0 = -11.23 for ctc +2024-01-16 21:39:03,783 (beam_search:479) INFO: total log probability: -11.23 +2024-01-16 21:39:03,783 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:03,783 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:03,783 (beam_search:483) INFO: best hypo: MAKINGMRARSISTHETHIRDSTUDIURALBAEBYBELDENASTRALIANARTISTGOTIEAY + +2024-01-16 21:39:03,785 (asr_inference:494) INFO: speech length: 109227 +2024-01-16 21:39:03,797 (beam_search:428) INFO: decoder input length: 168 +2024-01-16 21:39:03,797 (beam_search:429) INFO: max output length: 168 +2024-01-16 21:39:03,797 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:04,221 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:04,222 (beam_search:476) INFO: -14.98 * 1.0 = -14.98 for ctc +2024-01-16 21:39:04,222 (beam_search:479) INFO: total log probability: -14.98 +2024-01-16 21:39:04,222 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:39:04,222 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:04,222 (beam_search:483) INFO: best hypo: HETHENMOVEDTOWOASINGTODDESIANDWASAPARTNEITWORDBROWNEANDTILLNINTENWENTYNIN + +2024-01-16 21:39:04,224 (asr_inference:494) INFO: speech length: 76459 +2024-01-16 21:39:04,233 (beam_search:428) INFO: decoder input length: 117 +2024-01-16 21:39:04,233 (beam_search:429) INFO: max output length: 117 +2024-01-16 21:39:04,233 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:04,442 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:04,442 (beam_search:476) INFO: -8.36 * 1.0 = -8.36 for ctc +2024-01-16 21:39:04,442 (beam_search:479) INFO: total log probability: -8.36 +2024-01-16 21:39:04,442 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:39:04,442 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:04,442 (beam_search:483) INFO: best hypo: JOSOFHIYSCOLEADTHESCOLESTHEYCOMPETGANEDINALSPORTS + +2024-01-16 21:39:04,444 (asr_inference:494) INFO: speech length: 49152 +2024-01-16 21:39:04,452 (beam_search:428) INFO: decoder input length: 74 +2024-01-16 21:39:04,452 (beam_search:429) INFO: max output length: 74 +2024-01-16 21:39:04,452 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:04,531 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:04,531 (beam_search:476) INFO: -6.03 * 1.0 = -6.03 for ctc +2024-01-16 21:39:04,531 (beam_search:479) INFO: total log probability: -6.03 +2024-01-16 21:39:04,531 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:39:04,531 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:04,531 (beam_search:483) INFO: best hypo: WELFPLUSONMACHBANDERCOAURD + +2024-01-16 21:39:04,532 (asr_inference:494) INFO: speech length: 43691 +2024-01-16 21:39:04,540 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:39:04,540 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:39:04,540 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:04,602 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:04,602 (beam_search:476) INFO: -1.37 * 1.0 = -1.37 for ctc +2024-01-16 21:39:04,602 (beam_search:479) INFO: total log probability: -1.37 +2024-01-16 21:39:04,602 (beam_search:480) INFO: normalized log probability: -0.05 +2024-01-16 21:39:04,602 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:04,603 (beam_search:483) INFO: best hypo: ITHINKIMIGHTBENOTHING + +2024-01-16 21:39:04,604 (asr_inference:494) INFO: speech length: 118784 +2024-01-16 21:39:04,616 (beam_search:428) INFO: decoder input length: 183 +2024-01-16 21:39:04,616 (beam_search:429) INFO: max output length: 183 +2024-01-16 21:39:04,616 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:05,095 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:05,096 (beam_search:476) INFO: -9.08 * 1.0 = -9.08 for ctc +2024-01-16 21:39:05,096 (beam_search:479) INFO: total log probability: -9.08 +2024-01-16 21:39:05,096 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 21:39:05,096 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:05,096 (beam_search:483) INFO: best hypo: THEHOMASBILTANDLIVEDINBYANDRJACANDCANIDYDEPETYCOLECTOTHEINTERNLREVINUSERVIS + +2024-01-16 21:39:05,097 (asr_inference:494) INFO: speech length: 90112 +2024-01-16 21:39:05,108 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 21:39:05,108 (beam_search:429) INFO: max output length: 138 +2024-01-16 21:39:05,108 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:05,405 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:05,405 (beam_search:476) INFO: -12.53 * 1.0 = -12.53 for ctc +2024-01-16 21:39:05,405 (beam_search:479) INFO: total log probability: -12.53 +2024-01-16 21:39:05,405 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:39:05,405 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:05,405 (beam_search:483) INFO: best hypo: INNINTANSICEYEFOREHEWENTBAKTOOMSEKANDENTETHEACTOSCHOULOFOAMSK + +2024-01-16 21:39:05,407 (asr_inference:494) INFO: speech length: 69632 +2024-01-16 21:39:05,416 (beam_search:428) INFO: decoder input length: 106 +2024-01-16 21:39:05,416 (beam_search:429) INFO: max output length: 106 +2024-01-16 21:39:05,416 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:05,601 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:05,601 (beam_search:476) INFO: -8.81 * 1.0 = -8.81 for ctc +2024-01-16 21:39:05,601 (beam_search:479) INFO: total log probability: -8.81 +2024-01-16 21:39:05,601 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:05,601 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:05,602 (beam_search:483) INFO: best hypo: THEBANKISJUINTLYONEDBYHIMANDHISBROUVERANDRELITIVS + +2024-01-16 21:39:05,603 (asr_inference:494) INFO: speech length: 46422 +2024-01-16 21:39:05,611 (beam_search:428) INFO: decoder input length: 70 +2024-01-16 21:39:05,611 (beam_search:429) INFO: max output length: 70 +2024-01-16 21:39:05,611 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:05,696 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:05,696 (beam_search:476) INFO: -5.50 * 1.0 = -5.50 for ctc +2024-01-16 21:39:05,696 (beam_search:479) INFO: total log probability: -5.50 +2024-01-16 21:39:05,696 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:05,696 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:05,696 (beam_search:483) INFO: best hypo: HESOPEICENTLYWANTOCOLINBREISTAL + +2024-01-16 21:39:05,698 (asr_inference:494) INFO: speech length: 69632 +2024-01-16 21:39:05,707 (beam_search:428) INFO: decoder input length: 106 +2024-01-16 21:39:05,707 (beam_search:429) INFO: max output length: 106 +2024-01-16 21:39:05,707 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:05,875 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:05,875 (beam_search:476) INFO: -9.87 * 1.0 = -9.87 for ctc +2024-01-16 21:39:05,875 (beam_search:479) INFO: total log probability: -9.87 +2024-01-16 21:39:05,875 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:39:05,875 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:05,875 (beam_search:483) INFO: best hypo: WONTHAUSENDATHUNDEDFORTYSICKCSFOARTHIDITION + +2024-01-16 21:39:05,876 (asr_inference:494) INFO: speech length: 103766 +2024-01-16 21:39:05,888 (beam_search:428) INFO: decoder input length: 160 +2024-01-16 21:39:05,888 (beam_search:429) INFO: max output length: 160 +2024-01-16 21:39:05,888 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:06,298 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:06,298 (beam_search:476) INFO: -11.35 * 1.0 = -11.35 for ctc +2024-01-16 21:39:06,298 (beam_search:479) INFO: total log probability: -11.35 +2024-01-16 21:39:06,298 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:39:06,298 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:06,298 (beam_search:483) INFO: best hypo: APATOFLITLINGLENDBEYONDWAILESITHASBEEAENCHRLYINGLISHSPEAKINGFORNINHUNTREDOEARS + +2024-01-16 21:39:06,300 (asr_inference:494) INFO: speech length: 87382 +2024-01-16 21:39:06,310 (beam_search:428) INFO: decoder input length: 134 +2024-01-16 21:39:06,310 (beam_search:429) INFO: max output length: 134 +2024-01-16 21:39:06,310 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:06,571 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:06,571 (beam_search:476) INFO: -10.19 * 1.0 = -10.19 for ctc +2024-01-16 21:39:06,571 (beam_search:479) INFO: total log probability: -10.19 +2024-01-16 21:39:06,571 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:06,571 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:06,572 (beam_search:483) INFO: best hypo: HEPLADWTHTENPLARSFORHARVFWASAGANETHTRDITIONINDDEASSPE + +2024-01-16 21:39:06,574 (asr_inference:494) INFO: speech length: 109227 +2024-01-16 21:39:06,586 (beam_search:428) INFO: decoder input length: 168 +2024-01-16 21:39:06,586 (beam_search:429) INFO: max output length: 168 +2024-01-16 21:39:06,586 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:07,024 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:07,024 (beam_search:476) INFO: -16.54 * 1.0 = -16.54 for ctc +2024-01-16 21:39:07,024 (beam_search:479) INFO: total log probability: -16.54 +2024-01-16 21:39:07,024 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:39:07,024 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:07,025 (beam_search:483) INFO: best hypo: THEREIDINGGJOUDGWASWEBSTOFAIRHOWASALEADYASIEDTOTHECORTBEFORETHISCACEWASSHEDULT + +2024-01-16 21:39:07,026 (asr_inference:494) INFO: speech length: 77824 +2024-01-16 21:39:07,036 (beam_search:428) INFO: decoder input length: 119 +2024-01-16 21:39:07,036 (beam_search:429) INFO: max output length: 119 +2024-01-16 21:39:07,036 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:07,261 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:07,261 (beam_search:476) INFO: -10.90 * 1.0 = -10.90 for ctc +2024-01-16 21:39:07,261 (beam_search:479) INFO: total log probability: -10.90 +2024-01-16 21:39:07,261 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:39:07,261 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:07,261 (beam_search:483) INFO: best hypo: BGGRATHEFIVEWASTHETHIRDOTHEMAINSERISTOFECHEALIVELOUNCH + +2024-01-16 21:39:07,263 (asr_inference:494) INFO: speech length: 109227 +2024-01-16 21:39:07,274 (beam_search:428) INFO: decoder input length: 168 +2024-01-16 21:39:07,274 (beam_search:429) INFO: max output length: 168 +2024-01-16 21:39:07,274 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:07,679 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:07,679 (beam_search:476) INFO: -11.03 * 1.0 = -11.03 for ctc +2024-01-16 21:39:07,679 (beam_search:479) INFO: total log probability: -11.03 +2024-01-16 21:39:07,679 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:39:07,679 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:07,680 (beam_search:483) INFO: best hypo: ITSMOTOISHOEVEYOUARANDWHEREVERYOUAREONTHEDIRNYOFFAIFYOAEWELCOMHER + +2024-01-16 21:39:07,681 (asr_inference:494) INFO: speech length: 50518 +2024-01-16 21:39:07,690 (beam_search:428) INFO: decoder input length: 76 +2024-01-16 21:39:07,690 (beam_search:429) INFO: max output length: 76 +2024-01-16 21:39:07,690 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:07,766 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:07,766 (beam_search:476) INFO: -4.10 * 1.0 = -4.10 for ctc +2024-01-16 21:39:07,766 (beam_search:479) INFO: total log probability: -4.10 +2024-01-16 21:39:07,766 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:39:07,766 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:07,767 (beam_search:483) INFO: best hypo: ROBETAMILEASCOTHWILTSON + +2024-01-16 21:39:07,768 (asr_inference:494) INFO: speech length: 73728 +2024-01-16 21:39:07,778 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 21:39:07,778 (beam_search:429) INFO: max output length: 113 +2024-01-16 21:39:07,778 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:07,964 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:07,965 (beam_search:476) INFO: -7.36 * 1.0 = -7.36 for ctc +2024-01-16 21:39:07,965 (beam_search:479) INFO: total log probability: -7.36 +2024-01-16 21:39:07,965 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:39:07,965 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:07,965 (beam_search:483) INFO: best hypo: AFTREWNYUARBRAKSIRAODEGREWASHEFLLINGVENCHER + +2024-01-16 21:39:07,966 (asr_inference:494) INFO: speech length: 92843 +2024-01-16 21:39:07,977 (beam_search:428) INFO: decoder input length: 143 +2024-01-16 21:39:07,977 (beam_search:429) INFO: max output length: 143 +2024-01-16 21:39:07,977 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:08,228 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:08,228 (beam_search:476) INFO: -9.19 * 1.0 = -9.19 for ctc +2024-01-16 21:39:08,228 (beam_search:479) INFO: total log probability: -9.19 +2024-01-16 21:39:08,228 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:08,228 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:08,228 (beam_search:483) INFO: best hypo: AYAMTEYMANUFACTEDAMORTLCITOFTHEADSAIDARDRACSTER + +2024-01-16 21:39:08,229 (asr_inference:494) INFO: speech length: 94208 +2024-01-16 21:39:08,240 (beam_search:428) INFO: decoder input length: 145 +2024-01-16 21:39:08,240 (beam_search:429) INFO: max output length: 145 +2024-01-16 21:39:08,240 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:08,568 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:08,568 (beam_search:476) INFO: -8.27 * 1.0 = -8.27 for ctc +2024-01-16 21:39:08,568 (beam_search:479) INFO: total log probability: -8.27 +2024-01-16 21:39:08,568 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 21:39:08,568 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:08,569 (beam_search:483) INFO: best hypo: THEESESSAYAMEDTOBILEDALEFTWINGOLTERNITIFTONOULABERANDTHEESSANDPE + +2024-01-16 21:39:08,570 (asr_inference:494) INFO: speech length: 47787 +2024-01-16 21:39:08,578 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 21:39:08,578 (beam_search:429) INFO: max output length: 72 +2024-01-16 21:39:08,578 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:08,650 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:08,650 (beam_search:476) INFO: -6.05 * 1.0 = -6.05 for ctc +2024-01-16 21:39:08,650 (beam_search:479) INFO: total log probability: -6.05 +2024-01-16 21:39:08,650 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:39:08,650 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:08,650 (beam_search:483) INFO: best hypo: HELIVSLIKHEASYONGPRSON + +2024-01-16 21:39:08,651 (asr_inference:494) INFO: speech length: 49152 +2024-01-16 21:39:08,659 (beam_search:428) INFO: decoder input length: 74 +2024-01-16 21:39:08,659 (beam_search:429) INFO: max output length: 74 +2024-01-16 21:39:08,659 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:08,747 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:08,747 (beam_search:476) INFO: -4.41 * 1.0 = -4.41 for ctc +2024-01-16 21:39:08,747 (beam_search:479) INFO: total log probability: -4.41 +2024-01-16 21:39:08,747 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:39:08,747 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:08,747 (beam_search:483) INFO: best hypo: MASTEOFSINDINENDENEARIGMANIGENT + +2024-01-16 21:39:08,748 (asr_inference:494) INFO: speech length: 76459 +2024-01-16 21:39:08,758 (beam_search:428) INFO: decoder input length: 117 +2024-01-16 21:39:08,758 (beam_search:429) INFO: max output length: 117 +2024-01-16 21:39:08,758 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:08,990 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:08,990 (beam_search:476) INFO: -11.10 * 1.0 = -11.10 for ctc +2024-01-16 21:39:08,990 (beam_search:479) INFO: total log probability: -11.10 +2024-01-16 21:39:08,990 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:08,990 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:08,991 (beam_search:483) INFO: best hypo: SHEFAILEDTOAKTHETOPTHREATHECENIONDJUNIEARTRACTRILESTHATDUN + +2024-01-16 21:39:08,992 (asr_inference:494) INFO: speech length: 42326 +2024-01-16 21:39:09,000 (beam_search:428) INFO: decoder input length: 64 +2024-01-16 21:39:09,000 (beam_search:429) INFO: max output length: 64 +2024-01-16 21:39:09,000 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:09,056 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:09,056 (beam_search:476) INFO: -6.16 * 1.0 = -6.16 for ctc +2024-01-16 21:39:09,057 (beam_search:479) INFO: total log probability: -6.16 +2024-01-16 21:39:09,057 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:39:09,057 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:09,057 (beam_search:483) INFO: best hypo: ATOAREFOLOUDINSUPORT + +2024-01-16 21:39:09,058 (asr_inference:494) INFO: speech length: 90112 +2024-01-16 21:39:09,068 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 21:39:09,068 (beam_search:429) INFO: max output length: 138 +2024-01-16 21:39:09,068 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:09,386 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:09,386 (beam_search:476) INFO: -15.39 * 1.0 = -15.39 for ctc +2024-01-16 21:39:09,386 (beam_search:479) INFO: total log probability: -15.39 +2024-01-16 21:39:09,386 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:39:09,386 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:09,387 (beam_search:483) INFO: best hypo: THEESTABLISENATENSEVENTYONANDEWEOTHELDESTCLOPSINTHESOUTHOFINGLEND + +2024-01-16 21:39:09,388 (asr_inference:494) INFO: speech length: 57344 +2024-01-16 21:39:09,397 (beam_search:428) INFO: decoder input length: 87 +2024-01-16 21:39:09,397 (beam_search:429) INFO: max output length: 87 +2024-01-16 21:39:09,397 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:09,526 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:09,526 (beam_search:476) INFO: -7.25 * 1.0 = -7.25 for ctc +2024-01-16 21:39:09,526 (beam_search:479) INFO: total log probability: -7.25 +2024-01-16 21:39:09,526 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:09,526 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:09,527 (beam_search:483) INFO: best hypo: HEWSAMEMBROFTHEGEASSCOTLENDADFISERYBORD + +2024-01-16 21:39:09,528 (asr_inference:494) INFO: speech length: 45056 +2024-01-16 21:39:09,536 (beam_search:428) INFO: decoder input length: 68 +2024-01-16 21:39:09,536 (beam_search:429) INFO: max output length: 68 +2024-01-16 21:39:09,536 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:09,609 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:09,609 (beam_search:476) INFO: -5.18 * 1.0 = -5.18 for ctc +2024-01-16 21:39:09,609 (beam_search:479) INFO: total log probability: -5.18 +2024-01-16 21:39:09,609 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:39:09,609 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:09,609 (beam_search:483) INFO: best hypo: TWOTHOUSENDNDFIVEGENTLMEN + +2024-01-16 21:39:09,610 (asr_inference:494) INFO: speech length: 96939 +2024-01-16 21:39:09,621 (beam_search:428) INFO: decoder input length: 149 +2024-01-16 21:39:09,621 (beam_search:429) INFO: max output length: 149 +2024-01-16 21:39:09,621 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:09,992 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:09,992 (beam_search:476) INFO: -10.42 * 1.0 = -10.42 for ctc +2024-01-16 21:39:09,992 (beam_search:479) INFO: total log probability: -10.42 +2024-01-16 21:39:09,992 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:39:09,992 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:09,993 (beam_search:483) INFO: best hypo: AOUREFILEADASTONGRECEPTIONINURAPADCHVEDESTOBEUTIONTUTTHATWASNOTTHECACEHER + +2024-01-16 21:39:09,994 (asr_inference:494) INFO: speech length: 55979 +2024-01-16 21:39:10,003 (beam_search:428) INFO: decoder input length: 85 +2024-01-16 21:39:10,003 (beam_search:429) INFO: max output length: 85 +2024-01-16 21:39:10,003 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:10,119 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:10,119 (beam_search:476) INFO: -8.60 * 1.0 = -8.60 for ctc +2024-01-16 21:39:10,119 (beam_search:479) INFO: total log probability: -8.60 +2024-01-16 21:39:10,119 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:39:10,119 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:10,120 (beam_search:483) INFO: best hypo: BLTHOISSTDETHESPOSTERIERANGALSTROCTHES + +2024-01-16 21:39:10,121 (asr_inference:494) INFO: speech length: 117419 +2024-01-16 21:39:10,133 (beam_search:428) INFO: decoder input length: 181 +2024-01-16 21:39:10,133 (beam_search:429) INFO: max output length: 181 +2024-01-16 21:39:10,133 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:10,609 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:10,610 (beam_search:476) INFO: -13.49 * 1.0 = -13.49 for ctc +2024-01-16 21:39:10,610 (beam_search:479) INFO: total log probability: -13.49 +2024-01-16 21:39:10,610 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:10,610 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:10,610 (beam_search:483) INFO: best hypo: HEWASALLSOATHRETIMEFRENCHENASINLHAMBPIANNINTINNINTYNINTENITYFORETWOHOUSEDANWON + +2024-01-16 21:39:10,611 (asr_inference:494) INFO: speech length: 88747 +2024-01-16 21:39:10,622 (beam_search:428) INFO: decoder input length: 136 +2024-01-16 21:39:10,622 (beam_search:429) INFO: max output length: 136 +2024-01-16 21:39:10,622 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:10,895 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:10,895 (beam_search:476) INFO: -9.46 * 1.0 = -9.46 for ctc +2024-01-16 21:39:10,895 (beam_search:479) INFO: total log probability: -9.46 +2024-01-16 21:39:10,895 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:39:10,895 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:10,896 (beam_search:483) INFO: best hypo: THEILIGESTRUCTHERSHOWNINISMAPISTAGRATEXSTENTUNCHAGETDAY + +2024-01-16 21:39:10,897 (asr_inference:494) INFO: speech length: 106496 +2024-01-16 21:39:10,909 (beam_search:428) INFO: decoder input length: 164 +2024-01-16 21:39:10,909 (beam_search:429) INFO: max output length: 164 +2024-01-16 21:39:10,909 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:11,284 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:11,284 (beam_search:476) INFO: -17.16 * 1.0 = -17.16 for ctc +2024-01-16 21:39:11,284 (beam_search:479) INFO: total log probability: -17.16 +2024-01-16 21:39:11,284 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:39:11,284 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:11,285 (beam_search:483) INFO: best hypo: RUHARISRECAGNISEDITNUKLERDESAUSTERECPORTESANDOTHESAFTYOFITSTENOLAGY + +2024-01-16 21:39:11,286 (asr_inference:494) INFO: speech length: 121515 +2024-01-16 21:39:11,299 (beam_search:428) INFO: decoder input length: 187 +2024-01-16 21:39:11,299 (beam_search:429) INFO: max output length: 187 +2024-01-16 21:39:11,299 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:11,829 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:11,830 (beam_search:476) INFO: -15.82 * 1.0 = -15.82 for ctc +2024-01-16 21:39:11,830 (beam_search:479) INFO: total log probability: -15.82 +2024-01-16 21:39:11,830 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:11,830 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:11,830 (beam_search:483) INFO: best hypo: ASOFTWOTHOUSENDODFORTENAEMTYVEEISAVALABLEWITHINTHEUNIGTEDCINGDMONVERGEINMEDIERANDSCGIY + +2024-01-16 21:39:11,832 (asr_inference:494) INFO: speech length: 47787 +2024-01-16 21:39:11,840 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 21:39:11,840 (beam_search:429) INFO: max output length: 72 +2024-01-16 21:39:11,840 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:11,911 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:11,911 (beam_search:476) INFO: -4.47 * 1.0 = -4.47 for ctc +2024-01-16 21:39:11,911 (beam_search:479) INFO: total log probability: -4.47 +2024-01-16 21:39:11,911 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:11,911 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:11,912 (beam_search:483) INFO: best hypo: NOYOURKPEANGINRANDMHOUSE + +2024-01-16 21:39:11,913 (asr_inference:494) INFO: speech length: 62806 +2024-01-16 21:39:11,922 (beam_search:428) INFO: decoder input length: 96 +2024-01-16 21:39:11,922 (beam_search:429) INFO: max output length: 96 +2024-01-16 21:39:11,922 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:12,077 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:12,077 (beam_search:476) INFO: -8.31 * 1.0 = -8.31 for ctc +2024-01-16 21:39:12,077 (beam_search:479) INFO: total log probability: -8.31 +2024-01-16 21:39:12,077 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:12,077 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:12,078 (beam_search:483) INFO: best hypo: THEDUTCHYWASECUREDITEOUTCOMEOFTHEOFICTWALR + +2024-01-16 21:39:12,079 (asr_inference:494) INFO: speech length: 79190 +2024-01-16 21:39:12,088 (beam_search:428) INFO: decoder input length: 121 +2024-01-16 21:39:12,088 (beam_search:429) INFO: max output length: 121 +2024-01-16 21:39:12,088 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:12,322 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:12,322 (beam_search:476) INFO: -6.02 * 1.0 = -6.02 for ctc +2024-01-16 21:39:12,322 (beam_search:479) INFO: total log probability: -6.02 +2024-01-16 21:39:12,322 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 21:39:12,322 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:12,323 (beam_search:483) INFO: best hypo: WITGODPACESDARTETHEMACHHIHBOTHTEMEMSOLTENATINGSOPREMISY + +2024-01-16 21:39:12,324 (asr_inference:494) INFO: speech length: 69632 +2024-01-16 21:39:12,333 (beam_search:428) INFO: decoder input length: 106 +2024-01-16 21:39:12,333 (beam_search:429) INFO: max output length: 106 +2024-01-16 21:39:12,333 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:12,529 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:12,529 (beam_search:476) INFO: -13.92 * 1.0 = -13.92 for ctc +2024-01-16 21:39:12,529 (beam_search:479) INFO: total log probability: -13.92 +2024-01-16 21:39:12,529 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:39:12,529 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:12,529 (beam_search:483) INFO: best hypo: THISVRTIONISNONTEDORBEINERYFAVTFULETOTHERIDIONLNOVHL + +2024-01-16 21:39:12,530 (asr_inference:494) INFO: speech length: 88747 +2024-01-16 21:39:12,541 (beam_search:428) INFO: decoder input length: 136 +2024-01-16 21:39:12,541 (beam_search:429) INFO: max output length: 136 +2024-01-16 21:39:12,541 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:12,834 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:12,834 (beam_search:476) INFO: -11.09 * 1.0 = -11.09 for ctc +2024-01-16 21:39:12,834 (beam_search:479) INFO: total log probability: -11.09 +2024-01-16 21:39:12,834 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:12,834 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:12,834 (beam_search:483) INFO: best hypo: THISPRSAUMPTIONISNOTFULEFILDWONHASTONOTLEATETWOCONGETDIAMITES + +2024-01-16 21:39:12,836 (asr_inference:494) INFO: speech length: 152918 +2024-01-16 21:39:12,851 (beam_search:428) INFO: decoder input length: 236 +2024-01-16 21:39:12,851 (beam_search:429) INFO: max output length: 236 +2024-01-16 21:39:12,851 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:13,567 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:13,567 (beam_search:476) INFO: -16.12 * 1.0 = -16.12 for ctc +2024-01-16 21:39:13,568 (beam_search:479) INFO: total log probability: -16.12 +2024-01-16 21:39:13,568 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:13,568 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:13,568 (beam_search:483) INFO: best hypo: NOTALETITLESINLDEDGOLDANACSTHEREVENGOFDEATADERRADMOBILOUTRUNERSANDSAKGRSONICTHEHEAGHOGK + +2024-01-16 21:39:13,570 (asr_inference:494) INFO: speech length: 109227 +2024-01-16 21:39:13,581 (beam_search:428) INFO: decoder input length: 168 +2024-01-16 21:39:13,581 (beam_search:429) INFO: max output length: 168 +2024-01-16 21:39:13,581 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:14,015 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:14,015 (beam_search:476) INFO: -11.34 * 1.0 = -11.34 for ctc +2024-01-16 21:39:14,015 (beam_search:479) INFO: total log probability: -11.34 +2024-01-16 21:39:14,015 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:39:14,015 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:14,015 (beam_search:483) INFO: best hypo: THENINTNNINTYNINDJUGMENTNOTDTATTHEINTFLONCOTHEFARTHEROFTHECUSDHISBEETHER + +2024-01-16 21:39:14,017 (asr_inference:494) INFO: speech length: 99670 +2024-01-16 21:39:14,028 (beam_search:428) INFO: decoder input length: 153 +2024-01-16 21:39:14,028 (beam_search:429) INFO: max output length: 153 +2024-01-16 21:39:14,028 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:14,348 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:14,348 (beam_search:476) INFO: -13.89 * 1.0 = -13.89 for ctc +2024-01-16 21:39:14,348 (beam_search:479) INFO: total log probability: -13.89 +2024-01-16 21:39:14,348 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:39:14,348 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:14,349 (beam_search:483) INFO: best hypo: MOKDAFSWEARSRVENGEHANDJOINSFOURSESITMULKCMTOOVERTROMMOKBETH + +2024-01-16 21:39:14,350 (asr_inference:494) INFO: speech length: 86016 +2024-01-16 21:39:14,360 (beam_search:428) INFO: decoder input length: 132 +2024-01-16 21:39:14,360 (beam_search:429) INFO: max output length: 132 +2024-01-16 21:39:14,360 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:14,653 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:14,653 (beam_search:476) INFO: -15.43 * 1.0 = -15.43 for ctc +2024-01-16 21:39:14,653 (beam_search:479) INFO: total log probability: -15.43 +2024-01-16 21:39:14,653 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:39:14,653 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:14,653 (beam_search:483) INFO: best hypo: THEEADYAVLEVILIGECORTWASALLASANCHOUSTOCAPTHEENEROUNTHEVILIGEGAPLES + +2024-01-16 21:39:14,655 (asr_inference:494) INFO: speech length: 75094 +2024-01-16 21:39:14,664 (beam_search:428) INFO: decoder input length: 115 +2024-01-16 21:39:14,664 (beam_search:429) INFO: max output length: 115 +2024-01-16 21:39:14,664 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:14,877 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:14,877 (beam_search:476) INFO: -9.07 * 1.0 = -9.07 for ctc +2024-01-16 21:39:14,877 (beam_search:479) INFO: total log probability: -9.07 +2024-01-16 21:39:14,877 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:14,877 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:14,877 (beam_search:483) INFO: best hypo: THEASANINRANKSISTEMEACHRANCAVIGMOREPOUERTHTHELOERANK + +2024-01-16 21:39:14,878 (asr_inference:494) INFO: speech length: 103766 +2024-01-16 21:39:14,890 (beam_search:428) INFO: decoder input length: 160 +2024-01-16 21:39:14,890 (beam_search:429) INFO: max output length: 160 +2024-01-16 21:39:14,890 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:15,246 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:15,246 (beam_search:476) INFO: -14.93 * 1.0 = -14.93 for ctc +2024-01-16 21:39:15,246 (beam_search:479) INFO: total log probability: -14.93 +2024-01-16 21:39:15,246 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:39:15,246 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:15,246 (beam_search:483) INFO: best hypo: THEASTABLISCHEDDEPLIMATICRLATIONDSONDEPTOMERNINTNTHNINTNSEVENTYTWO + +2024-01-16 21:39:15,248 (asr_inference:494) INFO: speech length: 96939 +2024-01-16 21:39:15,259 (beam_search:428) INFO: decoder input length: 149 +2024-01-16 21:39:15,259 (beam_search:429) INFO: max output length: 149 +2024-01-16 21:39:15,259 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:15,611 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:15,611 (beam_search:476) INFO: -11.84 * 1.0 = -11.84 for ctc +2024-01-16 21:39:15,611 (beam_search:479) INFO: total log probability: -11.84 +2024-01-16 21:39:15,611 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:39:15,611 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:15,611 (beam_search:483) INFO: best hypo: THIASFIRTHCSTENDEDTOINCLDMOREYOUCAYDATEINDECSEMBERTWOTHOUSEDNDFORTEN + +2024-01-16 21:39:15,613 (asr_inference:494) INFO: speech length: 80555 +2024-01-16 21:39:15,623 (beam_search:428) INFO: decoder input length: 123 +2024-01-16 21:39:15,623 (beam_search:429) INFO: max output length: 123 +2024-01-16 21:39:15,623 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:15,865 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:15,865 (beam_search:476) INFO: -12.29 * 1.0 = -12.29 for ctc +2024-01-16 21:39:15,865 (beam_search:479) INFO: total log probability: -12.29 +2024-01-16 21:39:15,865 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:39:15,865 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:15,865 (beam_search:483) INFO: best hypo: THEUCHGOVENTISCIRNTLYESAMINGTHELEAKLECONCICQENCESFHEROLING + +2024-01-16 21:39:15,867 (asr_inference:494) INFO: speech length: 106496 +2024-01-16 21:39:15,878 (beam_search:428) INFO: decoder input length: 164 +2024-01-16 21:39:15,879 (beam_search:429) INFO: max output length: 164 +2024-01-16 21:39:15,879 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:16,305 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:16,305 (beam_search:476) INFO: -14.54 * 1.0 = -14.54 for ctc +2024-01-16 21:39:16,305 (beam_search:479) INFO: total log probability: -14.54 +2024-01-16 21:39:16,305 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:16,305 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:16,306 (beam_search:483) INFO: best hypo: FROMNINTINTHERTYTHREETONINTINFOARTYNINTHEMERICEDLEEWONDWELVEAUTOTHEFIRSTSICSTEN + +2024-01-16 21:39:16,307 (asr_inference:494) INFO: speech length: 54614 +2024-01-16 21:39:16,315 (beam_search:428) INFO: decoder input length: 83 +2024-01-16 21:39:16,315 (beam_search:429) INFO: max output length: 83 +2024-01-16 21:39:16,315 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:16,417 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:16,417 (beam_search:476) INFO: -7.50 * 1.0 = -7.50 for ctc +2024-01-16 21:39:16,417 (beam_search:479) INFO: total log probability: -7.50 +2024-01-16 21:39:16,417 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:39:16,417 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:16,417 (beam_search:483) INFO: best hypo: THEAIRHEFELESICKWTTIVFOSHIMSELF + +2024-01-16 21:39:16,418 (asr_inference:494) INFO: speech length: 70998 +2024-01-16 21:39:16,428 (beam_search:428) INFO: decoder input length: 108 +2024-01-16 21:39:16,428 (beam_search:429) INFO: max output length: 108 +2024-01-16 21:39:16,428 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:16,616 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:16,616 (beam_search:476) INFO: -7.80 * 1.0 = -7.80 for ctc +2024-01-16 21:39:16,616 (beam_search:479) INFO: total log probability: -7.80 +2024-01-16 21:39:16,616 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:39:16,616 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:16,616 (beam_search:483) INFO: best hypo: SICXTTEMESHAEEDEVEIDEDINTOTWOGRUPSOFTHRETEMEMSACH + +2024-01-16 21:39:16,617 (asr_inference:494) INFO: speech length: 66902 +2024-01-16 21:39:16,627 (beam_search:428) INFO: decoder input length: 102 +2024-01-16 21:39:16,627 (beam_search:429) INFO: max output length: 102 +2024-01-16 21:39:16,627 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:16,811 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:16,811 (beam_search:476) INFO: -9.88 * 1.0 = -9.88 for ctc +2024-01-16 21:39:16,811 (beam_search:479) INFO: total log probability: -9.88 +2024-01-16 21:39:16,811 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:16,811 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:16,811 (beam_search:483) INFO: best hypo: THEFIRSTSESONREMIADONDWELTHDUNTWOTHOUSENDANFIFDEEN + +2024-01-16 21:39:16,812 (asr_inference:494) INFO: speech length: 84651 +2024-01-16 21:39:16,822 (beam_search:428) INFO: decoder input length: 130 +2024-01-16 21:39:16,823 (beam_search:429) INFO: max output length: 130 +2024-01-16 21:39:16,823 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:17,090 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:17,091 (beam_search:476) INFO: -11.19 * 1.0 = -11.19 for ctc +2024-01-16 21:39:17,091 (beam_search:479) INFO: total log probability: -11.19 +2024-01-16 21:39:17,091 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:17,091 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:17,091 (beam_search:483) INFO: best hypo: ITSACEDTHEWEIHBOLRDANDSISTAMTWENTYFORECOMBINGFEACESFROMBOTH + +2024-01-16 21:39:17,092 (asr_inference:494) INFO: speech length: 58710 +2024-01-16 21:39:17,101 (beam_search:428) INFO: decoder input length: 89 +2024-01-16 21:39:17,101 (beam_search:429) INFO: max output length: 89 +2024-01-16 21:39:17,101 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:17,205 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:17,205 (beam_search:476) INFO: -10.57 * 1.0 = -10.57 for ctc +2024-01-16 21:39:17,205 (beam_search:479) INFO: total log probability: -10.57 +2024-01-16 21:39:17,205 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:39:17,205 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:17,206 (beam_search:483) INFO: best hypo: VELYUETWOOHNUMBRSWONTWOANDTHRE + +2024-01-16 21:39:17,207 (asr_inference:494) INFO: speech length: 70998 +2024-01-16 21:39:17,216 (beam_search:428) INFO: decoder input length: 108 +2024-01-16 21:39:17,216 (beam_search:429) INFO: max output length: 108 +2024-01-16 21:39:17,217 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:17,420 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:17,421 (beam_search:476) INFO: -8.84 * 1.0 = -8.84 for ctc +2024-01-16 21:39:17,421 (beam_search:479) INFO: total log probability: -8.84 +2024-01-16 21:39:17,421 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:39:17,421 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:17,421 (beam_search:483) INFO: best hypo: THELORPATOFMENSDRESESWEMUCHHOURTINLEANGTHNTHOSFRWIEIN + +2024-01-16 21:39:17,422 (asr_inference:494) INFO: speech length: 64171 +2024-01-16 21:39:17,431 (beam_search:428) INFO: decoder input length: 98 +2024-01-16 21:39:17,431 (beam_search:429) INFO: max output length: 98 +2024-01-16 21:39:17,431 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:17,570 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:17,570 (beam_search:476) INFO: -6.42 * 1.0 = -6.42 for ctc +2024-01-16 21:39:17,570 (beam_search:479) INFO: total log probability: -6.42 +2024-01-16 21:39:17,570 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:39:17,570 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:17,571 (beam_search:483) INFO: best hypo: THEIGOALTHSINTERNWERCEADEDBYTHEMULERS + +2024-01-16 21:39:17,572 (asr_inference:494) INFO: speech length: 68267 +2024-01-16 21:39:17,581 (beam_search:428) INFO: decoder input length: 104 +2024-01-16 21:39:17,581 (beam_search:429) INFO: max output length: 104 +2024-01-16 21:39:17,581 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:17,719 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:17,719 (beam_search:476) INFO: -7.00 * 1.0 = -7.00 for ctc +2024-01-16 21:39:17,719 (beam_search:479) INFO: total log probability: -7.00 +2024-01-16 21:39:17,719 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:39:17,719 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:17,720 (beam_search:483) INFO: best hypo: JOSOFHIYSCOLAVERYWEKOFTHSCOLHEAR + +2024-01-16 21:39:17,721 (asr_inference:494) INFO: speech length: 57344 +2024-01-16 21:39:17,729 (beam_search:428) INFO: decoder input length: 87 +2024-01-16 21:39:17,729 (beam_search:429) INFO: max output length: 87 +2024-01-16 21:39:17,729 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:17,838 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:17,838 (beam_search:476) INFO: -6.13 * 1.0 = -6.13 for ctc +2024-01-16 21:39:17,838 (beam_search:479) INFO: total log probability: -6.13 +2024-01-16 21:39:17,838 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:17,838 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:17,838 (beam_search:483) INFO: best hypo: ASRSILOFALTHEARGUMENTGETIGTOHER + +2024-01-16 21:39:17,839 (asr_inference:494) INFO: speech length: 65536 +2024-01-16 21:39:17,849 (beam_search:428) INFO: decoder input length: 100 +2024-01-16 21:39:17,849 (beam_search:429) INFO: max output length: 100 +2024-01-16 21:39:17,849 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:17,996 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:17,996 (beam_search:476) INFO: -7.19 * 1.0 = -7.19 for ctc +2024-01-16 21:39:17,996 (beam_search:479) INFO: total log probability: -7.19 +2024-01-16 21:39:17,996 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:17,996 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:17,996 (beam_search:483) INFO: best hypo: ITHADQUARTERSARINSHEFEILDOUNIGTEDCINGDOM + +2024-01-16 21:39:17,998 (asr_inference:494) INFO: speech length: 92843 +2024-01-16 21:39:18,009 (beam_search:428) INFO: decoder input length: 143 +2024-01-16 21:39:18,009 (beam_search:429) INFO: max output length: 143 +2024-01-16 21:39:18,009 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:18,349 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:18,349 (beam_search:476) INFO: -12.85 * 1.0 = -12.85 for ctc +2024-01-16 21:39:18,349 (beam_search:479) INFO: total log probability: -12.85 +2024-01-16 21:39:18,349 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:18,349 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:18,350 (beam_search:483) INFO: best hypo: LAYLLSOFIHALYSIDETHECONTRACTONSTAGEWHTHEDIRECTEANDPRODOUSESOFTHEGOLDANIYS + +2024-01-16 21:39:18,351 (asr_inference:494) INFO: speech length: 81920 +2024-01-16 21:39:18,361 (beam_search:428) INFO: decoder input length: 125 +2024-01-16 21:39:18,361 (beam_search:429) INFO: max output length: 125 +2024-01-16 21:39:18,361 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:18,604 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:18,604 (beam_search:476) INFO: -13.19 * 1.0 = -13.19 for ctc +2024-01-16 21:39:18,604 (beam_search:479) INFO: total log probability: -13.19 +2024-01-16 21:39:18,604 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:39:18,604 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:18,604 (beam_search:483) INFO: best hypo: FISICLEFERIAPYCONHELPATIENTETOARNHOTOWAEKWTHEFOTDROUPE + +2024-01-16 21:39:18,605 (asr_inference:494) INFO: speech length: 86016 +2024-01-16 21:39:18,616 (beam_search:428) INFO: decoder input length: 132 +2024-01-16 21:39:18,616 (beam_search:429) INFO: max output length: 132 +2024-01-16 21:39:18,616 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:18,866 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:18,866 (beam_search:476) INFO: -10.47 * 1.0 = -10.47 for ctc +2024-01-16 21:39:18,866 (beam_search:479) INFO: total log probability: -10.47 +2024-01-16 21:39:18,866 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:39:18,866 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:18,866 (beam_search:483) INFO: best hypo: ITENTONTOSELTHREYHUNDREDTHOUSEDYAUNITSACHEVEFIVEFNO + +2024-01-16 21:39:18,868 (asr_inference:494) INFO: speech length: 35499 +2024-01-16 21:39:18,875 (beam_search:428) INFO: decoder input length: 53 +2024-01-16 21:39:18,875 (beam_search:429) INFO: max output length: 53 +2024-01-16 21:39:18,875 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:18,926 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:18,926 (beam_search:476) INFO: -1.28 * 1.0 = -1.28 for ctc +2024-01-16 21:39:18,926 (beam_search:479) INFO: total log probability: -1.28 +2024-01-16 21:39:18,926 (beam_search:480) INFO: normalized log probability: -0.04 +2024-01-16 21:39:18,926 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:18,926 (beam_search:483) INFO: best hypo: THENAMEMSTDUCKAFERTHAT + +2024-01-16 21:39:18,928 (asr_inference:494) INFO: speech length: 81920 +2024-01-16 21:39:18,937 (beam_search:428) INFO: decoder input length: 125 +2024-01-16 21:39:18,938 (beam_search:429) INFO: max output length: 125 +2024-01-16 21:39:18,938 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:19,149 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:19,149 (beam_search:476) INFO: -9.22 * 1.0 = -9.22 for ctc +2024-01-16 21:39:19,149 (beam_search:479) INFO: total log probability: -9.22 +2024-01-16 21:39:19,149 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:39:19,149 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:19,150 (beam_search:483) INFO: best hypo: THEILBOMLATERBRACTHEDIMENDRECORDONDCOKCUMUSICK + +2024-01-16 21:39:19,151 (asr_inference:494) INFO: speech length: 83286 +2024-01-16 21:39:19,161 (beam_search:428) INFO: decoder input length: 128 +2024-01-16 21:39:19,161 (beam_search:429) INFO: max output length: 128 +2024-01-16 21:39:19,161 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:19,376 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:19,376 (beam_search:476) INFO: -9.57 * 1.0 = -9.57 for ctc +2024-01-16 21:39:19,376 (beam_search:479) INFO: total log probability: -9.57 +2024-01-16 21:39:19,376 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:39:19,376 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:19,377 (beam_search:483) INFO: best hypo: ITEDEATORIALWESOUBMITANDITORTHEAPOLITOPRISE + +2024-01-16 21:39:19,378 (asr_inference:494) INFO: speech length: 55979 +2024-01-16 21:39:19,387 (beam_search:428) INFO: decoder input length: 85 +2024-01-16 21:39:19,387 (beam_search:429) INFO: max output length: 85 +2024-01-16 21:39:19,387 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:19,504 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:19,504 (beam_search:476) INFO: -8.58 * 1.0 = -8.58 for ctc +2024-01-16 21:39:19,504 (beam_search:479) INFO: total log probability: -8.58 +2024-01-16 21:39:19,504 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:39:19,504 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:19,505 (beam_search:483) INFO: best hypo: DJOSOFPLAYESAURFETCEDIEACHWEEOTHSHO + +2024-01-16 21:39:19,506 (asr_inference:494) INFO: speech length: 102400 +2024-01-16 21:39:19,517 (beam_search:428) INFO: decoder input length: 157 +2024-01-16 21:39:19,517 (beam_search:429) INFO: max output length: 157 +2024-01-16 21:39:19,517 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:19,888 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:19,888 (beam_search:476) INFO: -11.75 * 1.0 = -11.75 for ctc +2024-01-16 21:39:19,888 (beam_search:479) INFO: total log probability: -11.75 +2024-01-16 21:39:19,888 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:19,888 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:19,889 (beam_search:483) INFO: best hypo: THEWATFRATIMEMBILDINGUTTHEFORSESBEIGINGTOOANDRIFTHISEAVLREALYAEXSISTS + +2024-01-16 21:39:19,890 (asr_inference:494) INFO: speech length: 79190 +2024-01-16 21:39:19,900 (beam_search:428) INFO: decoder input length: 121 +2024-01-16 21:39:19,900 (beam_search:429) INFO: max output length: 121 +2024-01-16 21:39:19,900 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:20,147 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:20,147 (beam_search:476) INFO: -11.18 * 1.0 = -11.18 for ctc +2024-01-16 21:39:20,147 (beam_search:479) INFO: total log probability: -11.18 +2024-01-16 21:39:20,147 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:20,147 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:20,148 (beam_search:483) INFO: best hypo: BREAEMENCIONOFTHCONVICTIONAPERDONPAGETHREOFTHENOUYOUOKTIMES + +2024-01-16 21:39:20,149 (asr_inference:494) INFO: speech length: 69632 +2024-01-16 21:39:20,159 (beam_search:428) INFO: decoder input length: 106 +2024-01-16 21:39:20,159 (beam_search:429) INFO: max output length: 106 +2024-01-16 21:39:20,159 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:20,328 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:20,328 (beam_search:476) INFO: -7.87 * 1.0 = -7.87 for ctc +2024-01-16 21:39:20,328 (beam_search:479) INFO: total log probability: -7.87 +2024-01-16 21:39:20,328 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:20,328 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:20,328 (beam_search:483) INFO: best hypo: ORDEDBYPESIONONPICHFRMBAKRIHTTOFRUNTLEFTET + +2024-01-16 21:39:20,329 (asr_inference:494) INFO: speech length: 88747 +2024-01-16 21:39:20,340 (beam_search:428) INFO: decoder input length: 136 +2024-01-16 21:39:20,340 (beam_search:429) INFO: max output length: 136 +2024-01-16 21:39:20,340 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:20,589 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:20,589 (beam_search:476) INFO: -6.79 * 1.0 = -6.79 for ctc +2024-01-16 21:39:20,589 (beam_search:479) INFO: total log probability: -6.79 +2024-01-16 21:39:20,589 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:39:20,589 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:20,590 (beam_search:483) INFO: best hypo: HEASMEMBEROFTHECORTOTHERILCOLIGOFARTLUNDENYOUCAY + +2024-01-16 21:39:20,591 (asr_inference:494) INFO: speech length: 94208 +2024-01-16 21:39:20,602 (beam_search:428) INFO: decoder input length: 145 +2024-01-16 21:39:20,602 (beam_search:429) INFO: max output length: 145 +2024-01-16 21:39:20,602 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:20,940 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:20,940 (beam_search:476) INFO: -14.85 * 1.0 = -14.85 for ctc +2024-01-16 21:39:20,940 (beam_search:479) INFO: total log probability: -14.85 +2024-01-16 21:39:20,940 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:39:20,940 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:20,941 (beam_search:483) INFO: best hypo: DERINTHECOURSEOFHECAMPAINFIRGEANDVISITEDLLTHERTYNINWASIGTANSTATCONTYIS + +2024-01-16 21:39:20,942 (asr_inference:494) INFO: speech length: 43691 +2024-01-16 21:39:20,950 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:39:20,950 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:39:20,950 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:21,012 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:21,012 (beam_search:476) INFO: -1.21 * 1.0 = -1.21 for ctc +2024-01-16 21:39:21,012 (beam_search:479) INFO: total log probability: -1.21 +2024-01-16 21:39:21,012 (beam_search:480) INFO: normalized log probability: -0.04 +2024-01-16 21:39:21,012 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:21,012 (beam_search:483) INFO: best hypo: ASTRIPOFPAPEROFLEANTH + +2024-01-16 21:39:21,014 (asr_inference:494) INFO: speech length: 87382 +2024-01-16 21:39:21,024 (beam_search:428) INFO: decoder input length: 134 +2024-01-16 21:39:21,024 (beam_search:429) INFO: max output length: 134 +2024-01-16 21:39:21,024 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:21,289 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:21,290 (beam_search:476) INFO: -12.72 * 1.0 = -12.72 for ctc +2024-01-16 21:39:21,290 (beam_search:479) INFO: total log probability: -12.72 +2024-01-16 21:39:21,290 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:39:21,290 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:21,290 (beam_search:483) INFO: best hypo: SATOHADFREKUNTLYWERKTOGETHWTHYOUKYYAMEARONPREVIUSPOGECTS + +2024-01-16 21:39:21,291 (asr_inference:494) INFO: speech length: 109227 +2024-01-16 21:39:21,303 (beam_search:428) INFO: decoder input length: 168 +2024-01-16 21:39:21,303 (beam_search:429) INFO: max output length: 168 +2024-01-16 21:39:21,303 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:21,695 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:21,695 (beam_search:476) INFO: -12.64 * 1.0 = -12.64 for ctc +2024-01-16 21:39:21,695 (beam_search:479) INFO: total log probability: -12.64 +2024-01-16 21:39:21,695 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:21,695 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:21,696 (beam_search:483) INFO: best hypo: SHEWSBOREONDSCRENDUINGTHEEPSODBRURDCASTONFORTHNOVEMBERNINTENNINTYFOR + +2024-01-16 21:39:21,697 (asr_inference:494) INFO: speech length: 89597 +2024-01-16 21:39:21,708 (beam_search:428) INFO: decoder input length: 137 +2024-01-16 21:39:21,708 (beam_search:429) INFO: max output length: 137 +2024-01-16 21:39:21,708 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:21,978 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:21,978 (beam_search:476) INFO: -9.59 * 1.0 = -9.59 for ctc +2024-01-16 21:39:21,978 (beam_search:479) INFO: total log probability: -9.59 +2024-01-16 21:39:21,978 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:21,978 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:21,979 (beam_search:483) INFO: best hypo: AHTUREDROUWNEDSHEHADCOMINSOGENTELYTHATHEHADEVEHERDHER + +2024-01-16 21:39:21,980 (asr_inference:494) INFO: speech length: 104477 +2024-01-16 21:39:21,992 (beam_search:428) INFO: decoder input length: 161 +2024-01-16 21:39:21,992 (beam_search:429) INFO: max output length: 161 +2024-01-16 21:39:21,992 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:22,303 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:22,303 (beam_search:476) INFO: -12.43 * 1.0 = -12.43 for ctc +2024-01-16 21:39:22,303 (beam_search:479) INFO: total log probability: -12.43 +2024-01-16 21:39:22,303 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:39:22,303 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:22,304 (beam_search:483) INFO: best hypo: ATOBESHOUORHWEMUSTCEOURDORSSHOTWEMUSLATNOWOUNINHA + +2024-01-16 21:39:22,305 (asr_inference:494) INFO: speech length: 200157 +2024-01-16 21:39:22,323 (beam_search:428) INFO: decoder input length: 310 +2024-01-16 21:39:22,323 (beam_search:429) INFO: max output length: 310 +2024-01-16 21:39:22,323 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:23,406 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:23,406 (beam_search:476) INFO: -24.90 * 1.0 = -24.90 for ctc +2024-01-16 21:39:23,406 (beam_search:479) INFO: total log probability: -24.90 +2024-01-16 21:39:23,406 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:39:23,406 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:23,407 (beam_search:483) INFO: best hypo: ACIDESBENEBEGANMOKINGLYYOUMAHVEWEDEWHIICALDOTROUSWHNICOODJOSTASWILLHEDESTREYOUTHATIDOTADOANCERHIM + +2024-01-16 21:39:23,408 (asr_inference:494) INFO: speech length: 87677 +2024-01-16 21:39:23,419 (beam_search:428) INFO: decoder input length: 134 +2024-01-16 21:39:23,419 (beam_search:429) INFO: max output length: 134 +2024-01-16 21:39:23,419 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:23,711 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:23,711 (beam_search:476) INFO: -11.68 * 1.0 = -11.68 for ctc +2024-01-16 21:39:23,711 (beam_search:479) INFO: total log probability: -11.68 +2024-01-16 21:39:23,711 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:23,711 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:23,712 (beam_search:483) INFO: best hypo: THEPESENTTIRHIMSEFAPONHIMANDBOUNEDHISFORLAKEDHTLYSOATECONOTMOE + +2024-01-16 21:39:23,714 (asr_inference:494) INFO: speech length: 123197 +2024-01-16 21:39:23,726 (beam_search:428) INFO: decoder input length: 190 +2024-01-16 21:39:23,726 (beam_search:429) INFO: max output length: 190 +2024-01-16 21:39:23,726 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:24,274 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:24,275 (beam_search:476) INFO: -12.99 * 1.0 = -12.99 for ctc +2024-01-16 21:39:24,275 (beam_search:479) INFO: total log probability: -12.99 +2024-01-16 21:39:24,275 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:39:24,275 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:24,275 (beam_search:483) INFO: best hypo: NORMUSTTHOUSOLIMITHTHEOLYONOFISRILASTOTHINHHATHBUTWNENWAYINWHCHCNGORIFYHIMSELFBYTHE + +2024-01-16 21:39:24,277 (asr_inference:494) INFO: speech length: 177917 +2024-01-16 21:39:24,293 (beam_search:428) INFO: decoder input length: 275 +2024-01-16 21:39:24,293 (beam_search:429) INFO: max output length: 275 +2024-01-16 21:39:24,293 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:25,436 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:25,437 (beam_search:476) INFO: -28.74 * 1.0 = -28.74 for ctc +2024-01-16 21:39:25,437 (beam_search:479) INFO: total log probability: -28.74 +2024-01-16 21:39:25,437 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:39:25,437 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:25,437 (beam_search:483) INFO: best hypo: THELDCOMPRSONDBETWENTHIMPALSOFEXSEAKITIVEANDTHELITBRLEARTUSMANHWWODLEURNDTHATHEREONLYONEERTOPOSIEDECISIOSOALABLEINALLTHEWERLOTHINKING + +2024-01-16 21:39:25,439 (asr_inference:494) INFO: speech length: 95197 +2024-01-16 21:39:25,450 (beam_search:428) INFO: decoder input length: 146 +2024-01-16 21:39:25,450 (beam_search:429) INFO: max output length: 146 +2024-01-16 21:39:25,450 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:25,791 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:25,791 (beam_search:476) INFO: -14.75 * 1.0 = -14.75 for ctc +2024-01-16 21:39:25,791 (beam_search:479) INFO: total log probability: -14.75 +2024-01-16 21:39:25,791 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:39:25,791 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:25,792 (beam_search:483) INFO: best hypo: AVFTRTHICSPERIANCETHEENDVADERSWERCAIRFLTOCEPEASAKFEDISTENCEFROMTHEWALL + +2024-01-16 21:39:25,793 (asr_inference:494) INFO: speech length: 207117 +2024-01-16 21:39:25,812 (beam_search:428) INFO: decoder input length: 321 +2024-01-16 21:39:25,812 (beam_search:429) INFO: max output length: 321 +2024-01-16 21:39:25,812 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:27,066 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:27,066 (beam_search:476) INFO: -29.12 * 1.0 = -29.12 for ctc +2024-01-16 21:39:27,066 (beam_search:479) INFO: total log probability: -29.12 +2024-01-16 21:39:27,066 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:39:27,066 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:27,066 (beam_search:483) INFO: best hypo: MAONOUBEAERSMINGFIRTHERITHINYOATNOITIHAVEHERAMOSTMSTERIAUSTELPRGROMEMESEASEWHATISITEISSHEDADNOITISNOTUBOUTHERY + +2024-01-16 21:39:27,068 (asr_inference:494) INFO: speech length: 90557 +2024-01-16 21:39:27,079 (beam_search:428) INFO: decoder input length: 139 +2024-01-16 21:39:27,079 (beam_search:429) INFO: max output length: 139 +2024-01-16 21:39:27,079 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:27,318 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:27,318 (beam_search:476) INFO: -16.26 * 1.0 = -16.26 for ctc +2024-01-16 21:39:27,318 (beam_search:479) INFO: total log probability: -16.26 +2024-01-16 21:39:27,318 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:39:27,318 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:27,318 (beam_search:483) INFO: best hypo: DNOWHLEMISTORTHUNTONSADDIETHEBASKTOMEILTAKITW + +2024-01-16 21:39:27,319 (asr_inference:494) INFO: speech length: 216957 +2024-01-16 21:39:27,339 (beam_search:428) INFO: decoder input length: 336 +2024-01-16 21:39:27,339 (beam_search:429) INFO: max output length: 336 +2024-01-16 21:39:27,339 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:28,865 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:28,865 (beam_search:476) INFO: -27.65 * 1.0 = -27.65 for ctc +2024-01-16 21:39:28,865 (beam_search:479) INFO: total log probability: -27.65 +2024-01-16 21:39:28,865 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:39:28,865 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:28,866 (beam_search:483) INFO: best hypo: ANARABIOANNIHTECSCLAMETROGTWHITHATWASAMAGIAKNIGHTWOASINITHERSDIFRNTSORTSANIGHTSMATSAIDTHESALRANDTHENIGTBUTNUBIGHTMENESATTHESAMENIGHTYOMEAN + +2024-01-16 21:39:28,868 (asr_inference:494) INFO: speech length: 149757 +2024-01-16 21:39:28,883 (beam_search:428) INFO: decoder input length: 231 +2024-01-16 21:39:28,883 (beam_search:429) INFO: max output length: 231 +2024-01-16 21:39:28,883 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:29,638 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:29,638 (beam_search:476) INFO: -19.73 * 1.0 = -19.73 for ctc +2024-01-16 21:39:29,638 (beam_search:479) INFO: total log probability: -19.73 +2024-01-16 21:39:29,638 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:39:29,638 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:29,639 (beam_search:483) INFO: best hypo: IVETUREDOBFEOUPWARDOHUDRDOMYBESTEHANDSFORNOOTHERFLTTHEMFALWINGOUANDSUCHASYOUANDYTHINKILLTAKEYOUON + +2024-01-16 21:39:29,640 (asr_inference:494) INFO: speech length: 95677 +2024-01-16 21:39:29,651 (beam_search:428) INFO: decoder input length: 147 +2024-01-16 21:39:29,651 (beam_search:429) INFO: max output length: 147 +2024-01-16 21:39:29,651 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:29,972 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:29,972 (beam_search:476) INFO: -15.03 * 1.0 = -15.03 for ctc +2024-01-16 21:39:29,972 (beam_search:479) INFO: total log probability: -15.03 +2024-01-16 21:39:29,972 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:39:29,972 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:29,973 (beam_search:483) INFO: best hypo: GUHEWOHESEHIMHERHARTLAPEDUBBNAPROHENTIONATEVEYRINGOFHEDORBLT + +2024-01-16 21:39:29,974 (asr_inference:494) INFO: speech length: 180957 +2024-01-16 21:39:29,990 (beam_search:428) INFO: decoder input length: 280 +2024-01-16 21:39:29,990 (beam_search:429) INFO: max output length: 280 +2024-01-16 21:39:29,990 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:31,000 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:31,000 (beam_search:476) INFO: -26.18 * 1.0 = -26.18 for ctc +2024-01-16 21:39:31,000 (beam_search:479) INFO: total log probability: -26.18 +2024-01-16 21:39:31,000 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:39:31,000 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:31,000 (beam_search:483) INFO: best hypo: AATHEASBOKSSDICSONIWLCEALTHERESTWEOUSENTOMSTRBELTHEAROFACINTHTHEWLAVOUYOFORTHIMSELESASWELASFRPOSSAYD + +2024-01-16 21:39:31,002 (asr_inference:494) INFO: speech length: 192637 +2024-01-16 21:39:31,020 (beam_search:428) INFO: decoder input length: 298 +2024-01-16 21:39:31,020 (beam_search:429) INFO: max output length: 298 +2024-01-16 21:39:31,020 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:32,320 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:32,320 (beam_search:476) INFO: -18.35 * 1.0 = -18.35 for ctc +2024-01-16 21:39:32,320 (beam_search:479) INFO: total log probability: -18.35 +2024-01-16 21:39:32,320 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:39:32,320 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:32,321 (beam_search:483) INFO: best hypo: BUTINGLWASNOTITLSHOURTHATHEYCOULDNOTGEDINTHEGATSOPEDINORDANDTHREHAVYBOARRSWERHELDINPLACEBYMENESOFSTOUTSTAPLESRIVIDTETOTHESHETESOFSTDEL + +2024-01-16 21:39:32,323 (asr_inference:494) INFO: speech length: 165117 +2024-01-16 21:39:32,339 (beam_search:428) INFO: decoder input length: 255 +2024-01-16 21:39:32,339 (beam_search:429) INFO: max output length: 255 +2024-01-16 21:39:32,339 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:33,219 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:33,219 (beam_search:476) INFO: -18.11 * 1.0 = -18.11 for ctc +2024-01-16 21:39:33,219 (beam_search:479) INFO: total log probability: -18.11 +2024-01-16 21:39:33,219 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:33,219 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:33,220 (beam_search:483) INFO: best hypo: AIWOANTTHEILSIDODONCOLDLYIHENTADOSONHORESIWNUNTMENTOBRIGETHEWITMHEPUSHEISWAYFOREDWICHWAYTOTHESTABLS + +2024-01-16 21:39:33,222 (asr_inference:494) INFO: speech length: 153437 +2024-01-16 21:39:33,237 (beam_search:428) INFO: decoder input length: 237 +2024-01-16 21:39:33,237 (beam_search:429) INFO: max output length: 237 +2024-01-16 21:39:33,237 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:34,038 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:34,038 (beam_search:476) INFO: -22.69 * 1.0 = -22.69 for ctc +2024-01-16 21:39:34,038 (beam_search:479) INFO: total log probability: -22.69 +2024-01-16 21:39:34,038 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:39:34,038 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:34,039 (beam_search:483) INFO: best hypo: IRISLEITWACOCANDDEVFRTHEFIRSTIMEYOUANTEANSHOUSEANDBESIGDESTHATWASNOTIMETOAROUSESPIONITHMINDSOFANYWON + +2024-01-16 21:39:34,040 (asr_inference:494) INFO: speech length: 156637 +2024-01-16 21:39:34,055 (beam_search:428) INFO: decoder input length: 242 +2024-01-16 21:39:34,055 (beam_search:429) INFO: max output length: 242 +2024-01-16 21:39:34,055 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:34,796 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:34,796 (beam_search:476) INFO: -16.68 * 1.0 = -16.68 for ctc +2024-01-16 21:39:34,796 (beam_search:479) INFO: total log probability: -16.68 +2024-01-16 21:39:34,796 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:34,796 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:34,797 (beam_search:483) INFO: best hypo: DOUNOTREMEMERTHATESASTHYDEMONTHATDTHESPIRITHICHCKEAPESTHEISNOBLCOREAGESCEHAIUNMAUCHOABL + +2024-01-16 21:39:34,798 (asr_inference:494) INFO: speech length: 141277 +2024-01-16 21:39:34,813 (beam_search:428) INFO: decoder input length: 218 +2024-01-16 21:39:34,813 (beam_search:429) INFO: max output length: 218 +2024-01-16 21:39:34,813 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:35,311 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:35,311 (beam_search:476) INFO: -18.48 * 1.0 = -18.48 for ctc +2024-01-16 21:39:35,311 (beam_search:479) INFO: total log probability: -18.48 +2024-01-16 21:39:35,311 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:39:35,311 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:35,311 (beam_search:483) INFO: best hypo: ATMISTRBELEOACANHENOOFCONHEELIVINGALASYLIFNADDROUSYCOLIDGEHA + +2024-01-16 21:39:35,312 (asr_inference:494) INFO: speech length: 56637 +2024-01-16 21:39:35,321 (beam_search:428) INFO: decoder input length: 86 +2024-01-16 21:39:35,321 (beam_search:429) INFO: max output length: 86 +2024-01-16 21:39:35,321 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:35,435 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:35,435 (beam_search:476) INFO: -5.46 * 1.0 = -5.46 for ctc +2024-01-16 21:39:35,435 (beam_search:479) INFO: total log probability: -5.46 +2024-01-16 21:39:35,435 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:39:35,435 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:35,435 (beam_search:483) INFO: best hypo: ANDTECITONFOLLOWEDEMURLYATTHERHEALS + +2024-01-16 21:39:35,436 (asr_inference:494) INFO: speech length: 124477 +2024-01-16 21:39:35,450 (beam_search:428) INFO: decoder input length: 192 +2024-01-16 21:39:35,450 (beam_search:429) INFO: max output length: 192 +2024-01-16 21:39:35,450 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:35,976 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:35,976 (beam_search:476) INFO: -18.24 * 1.0 = -18.24 for ctc +2024-01-16 21:39:35,976 (beam_search:479) INFO: total log probability: -18.24 +2024-01-16 21:39:35,976 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:39:35,976 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:35,976 (beam_search:483) INFO: best hypo: THEFIRSTTUHWODCOSANECSPLOIONINWHICHAMOGSUCHHUNDREDSOFINFERATEDMENANDRECKLSSBORYS + +2024-01-16 21:39:35,978 (asr_inference:494) INFO: speech length: 82077 +2024-01-16 21:39:35,988 (beam_search:428) INFO: decoder input length: 126 +2024-01-16 21:39:35,988 (beam_search:429) INFO: max output length: 126 +2024-01-16 21:39:35,988 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:36,224 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:36,224 (beam_search:476) INFO: -12.17 * 1.0 = -12.17 for ctc +2024-01-16 21:39:36,225 (beam_search:479) INFO: total log probability: -12.17 +2024-01-16 21:39:36,225 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:39:36,225 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:36,225 (beam_search:483) INFO: best hypo: WONTEGATPLESERSOFMARGRETLIEATTHISTIMEWASINEADESBOY + +2024-01-16 21:39:36,226 (asr_inference:494) INFO: speech length: 132157 +2024-01-16 21:39:36,240 (beam_search:428) INFO: decoder input length: 204 +2024-01-16 21:39:36,240 (beam_search:429) INFO: max output length: 204 +2024-01-16 21:39:36,240 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:36,869 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:36,869 (beam_search:476) INFO: -20.22 * 1.0 = -20.22 for ctc +2024-01-16 21:39:36,869 (beam_search:479) INFO: total log probability: -20.22 +2024-01-16 21:39:36,869 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:39:36,869 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:36,870 (beam_search:483) INFO: best hypo: THTHINASGONUNDLONNOFRSONEORBIGACXITDENTWESHALHAVETOCOMBERMYISWITHEINERIVERNCERYONTHWERKCOINL + +2024-01-16 21:39:36,871 (asr_inference:494) INFO: speech length: 98557 +2024-01-16 21:39:36,882 (beam_search:428) INFO: decoder input length: 151 +2024-01-16 21:39:36,882 (beam_search:429) INFO: max output length: 151 +2024-01-16 21:39:36,882 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:37,142 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:37,142 (beam_search:476) INFO: -12.13 * 1.0 = -12.13 for ctc +2024-01-16 21:39:37,142 (beam_search:479) INFO: total log probability: -12.13 +2024-01-16 21:39:37,142 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:39:37,142 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:37,143 (beam_search:483) INFO: best hypo: AAYOURLATSAIDSHEWELSHEHEDHERBREATHOTHEANCSRHAL + +2024-01-16 21:39:37,144 (asr_inference:494) INFO: speech length: 142364 +2024-01-16 21:39:37,158 (beam_search:428) INFO: decoder input length: 220 +2024-01-16 21:39:37,158 (beam_search:429) INFO: max output length: 220 +2024-01-16 21:39:37,158 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:37,852 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:37,852 (beam_search:476) INFO: -17.50 * 1.0 = -17.50 for ctc +2024-01-16 21:39:37,852 (beam_search:479) INFO: total log probability: -17.50 +2024-01-16 21:39:37,852 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:37,852 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:37,853 (beam_search:483) INFO: best hypo: ROHTTOLETHEGIRLSTAHEMUSCOHTHERFOTHERTOLIVNGIPKGESSISLESLTLEOLDCABONANHNTHEHERDTHSREDFULDECRE + +2024-01-16 21:39:37,854 (asr_inference:494) INFO: speech length: 143837 +2024-01-16 21:39:37,869 (beam_search:428) INFO: decoder input length: 222 +2024-01-16 21:39:37,869 (beam_search:429) INFO: max output length: 222 +2024-01-16 21:39:37,869 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:38,604 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:38,605 (beam_search:476) INFO: -16.95 * 1.0 = -16.95 for ctc +2024-01-16 21:39:38,605 (beam_search:479) INFO: total log probability: -16.95 +2024-01-16 21:39:38,605 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:39:38,605 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:38,605 (beam_search:483) INFO: best hypo: MARGITSATDOWNTHEROGKGPARTLYTOWORMEHRSELFFOTHEDANPNESSOTHEEAVNINGHUNGOUTHERRESANDOVEFITEADMADHERCHILY + +2024-01-16 21:39:38,607 (asr_inference:494) INFO: speech length: 143197 +2024-01-16 21:39:38,621 (beam_search:428) INFO: decoder input length: 221 +2024-01-16 21:39:38,621 (beam_search:429) INFO: max output length: 221 +2024-01-16 21:39:38,621 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:39,325 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:39,325 (beam_search:476) INFO: -16.40 * 1.0 = -16.40 for ctc +2024-01-16 21:39:39,325 (beam_search:479) INFO: total log probability: -16.40 +2024-01-16 21:39:39,325 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:39,325 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:39,326 (beam_search:483) INFO: best hypo: ONOWYOARMSTAKENBOUTTHATELIDTHECINGTHEARNOTMYPRESNERSBUTMYSLAVESHOMIYPRCEUSTFROMTHECINGOFEVE + +2024-01-16 21:39:39,327 (asr_inference:494) INFO: speech length: 40157 +2024-01-16 21:39:39,335 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:39:39,335 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:39:39,335 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:39,401 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:39,401 (beam_search:476) INFO: -4.03 * 1.0 = -4.03 for ctc +2024-01-16 21:39:39,401 (beam_search:479) INFO: total log probability: -4.03 +2024-01-16 21:39:39,401 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:39:39,401 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:39,401 (beam_search:483) INFO: best hypo: HERFATHETOUTHEOMERSATION + +2024-01-16 21:39:39,402 (asr_inference:494) INFO: speech length: 164797 +2024-01-16 21:39:39,418 (beam_search:428) INFO: decoder input length: 255 +2024-01-16 21:39:39,418 (beam_search:429) INFO: max output length: 255 +2024-01-16 21:39:39,418 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:40,398 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:40,398 (beam_search:476) INFO: -21.06 * 1.0 = -21.06 for ctc +2024-01-16 21:39:40,398 (beam_search:479) INFO: total log probability: -21.06 +2024-01-16 21:39:40,398 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:40,398 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:40,399 (beam_search:483) INFO: best hypo: INACOURERWASASOURDOFDREINGTABLEINWHICHLYACOMEANDBRUSHCENIDYSEEDMUCHINTERSTDINTHETABLEANDWASAEXSAMINGATHETHEGUERURETRN + +2024-01-16 21:39:40,400 (asr_inference:494) INFO: speech length: 193597 +2024-01-16 21:39:40,418 (beam_search:428) INFO: decoder input length: 300 +2024-01-16 21:39:40,418 (beam_search:429) INFO: max output length: 300 +2024-01-16 21:39:40,418 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:41,587 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:41,588 (beam_search:476) INFO: -22.77 * 1.0 = -22.77 for ctc +2024-01-16 21:39:41,588 (beam_search:479) INFO: total log probability: -22.77 +2024-01-16 21:39:41,588 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:39:41,588 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:41,588 (beam_search:483) INFO: best hypo: IAVESOMETIMTAUHTTATMYSEFSHEAGREEDBUTOFCORIOTNOESTILIHVETBEPRTYCARFULSOMEWENISLLISOVEREBYMYDESCORLOINGOVERHEAR + +2024-01-16 21:39:41,590 (asr_inference:494) INFO: speech length: 79197 +2024-01-16 21:39:41,600 (beam_search:428) INFO: decoder input length: 121 +2024-01-16 21:39:41,600 (beam_search:429) INFO: max output length: 121 +2024-01-16 21:39:41,600 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:41,792 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:41,792 (beam_search:476) INFO: -11.40 * 1.0 = -11.40 for ctc +2024-01-16 21:39:41,792 (beam_search:479) INFO: total log probability: -11.40 +2024-01-16 21:39:41,792 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:39:41,792 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:41,792 (beam_search:483) INFO: best hypo: ISHALSTAYEREPLDTHONGANFORIMENTOSTCOFRE + +2024-01-16 21:39:41,793 (asr_inference:494) INFO: speech length: 46877 +2024-01-16 21:39:41,802 (beam_search:428) INFO: decoder input length: 71 +2024-01-16 21:39:41,802 (beam_search:429) INFO: max output length: 71 +2024-01-16 21:39:41,802 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:41,876 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:41,876 (beam_search:476) INFO: -5.04 * 1.0 = -5.04 for ctc +2024-01-16 21:39:41,876 (beam_search:479) INFO: total log probability: -5.04 +2024-01-16 21:39:41,876 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:41,876 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:41,876 (beam_search:483) INFO: best hypo: WHATDYOUDEOASTTHESORSERER + +2024-01-16 21:39:41,877 (asr_inference:494) INFO: speech length: 192045 +2024-01-16 21:39:41,895 (beam_search:428) INFO: decoder input length: 298 +2024-01-16 21:39:41,895 (beam_search:429) INFO: max output length: 298 +2024-01-16 21:39:41,895 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:43,025 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:43,025 (beam_search:476) INFO: -22.05 * 1.0 = -22.05 for ctc +2024-01-16 21:39:43,025 (beam_search:479) INFO: total log probability: -22.05 +2024-01-16 21:39:43,025 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:39:43,025 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:43,026 (beam_search:483) INFO: best hypo: WHIYTHERARANIMESYOURSHORTHINESNOTANYMOREREPLIDESROUHTIMQUETHEPINKESNDHMLSOQUEOTHELSSOIWONTHAVEMYPEBLEQUARLING + +2024-01-16 21:39:43,027 (asr_inference:494) INFO: speech length: 171373 +2024-01-16 21:39:43,043 (beam_search:428) INFO: decoder input length: 265 +2024-01-16 21:39:43,043 (beam_search:429) INFO: max output length: 265 +2024-01-16 21:39:43,044 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:44,116 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:44,116 (beam_search:476) INFO: -26.16 * 1.0 = -26.16 for ctc +2024-01-16 21:39:44,116 (beam_search:479) INFO: total log probability: -26.16 +2024-01-16 21:39:44,116 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:39:44,116 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:44,117 (beam_search:483) INFO: best hypo: TIPRAERSECLICINGCLPINGARBNGSNIPDOTFCUGSTACOFNOUSPERANDPASEINAINLARGSRABOCSSURKILERSRBENGFOLDANDADEDRADYTOMALFOTHEFINLAPEL + +2024-01-16 21:39:44,118 (asr_inference:494) INFO: speech length: 141917 +2024-01-16 21:39:44,132 (beam_search:428) INFO: decoder input length: 219 +2024-01-16 21:39:44,132 (beam_search:429) INFO: max output length: 219 +2024-01-16 21:39:44,132 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:44,828 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:44,828 (beam_search:476) INFO: -15.13 * 1.0 = -15.13 for ctc +2024-01-16 21:39:44,828 (beam_search:479) INFO: total log probability: -15.13 +2024-01-16 21:39:44,828 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:39:44,828 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:44,829 (beam_search:483) INFO: best hypo: ITWASFOREDAYSAFEDTHESUPRIYSOFLHERSHORSEWHNTHESTRANGRSLEAFTHESTDATTTHECAIROFROGEDOLDFORSTRHIRMON + +2024-01-16 21:39:44,830 (asr_inference:494) INFO: speech length: 178877 +2024-01-16 21:39:44,847 (beam_search:428) INFO: decoder input length: 277 +2024-01-16 21:39:44,847 (beam_search:429) INFO: max output length: 277 +2024-01-16 21:39:44,847 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:45,878 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:45,878 (beam_search:476) INFO: -34.93 * 1.0 = -34.93 for ctc +2024-01-16 21:39:45,878 (beam_search:479) INFO: total log probability: -34.93 +2024-01-16 21:39:45,878 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:39:45,878 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:45,879 (beam_search:483) INFO: best hypo: MPORETEMPLTONHESAIDIOUSTNOHAMMANYUARSGOWENWEBORYSEMNTOSCOUWITHMNDNTALHATSOROFHINUNOEBUTANDTILIERANCROUSHMORE + +2024-01-16 21:39:45,880 (asr_inference:494) INFO: speech length: 75677 +2024-01-16 21:39:45,890 (beam_search:428) INFO: decoder input length: 116 +2024-01-16 21:39:45,890 (beam_search:429) INFO: max output length: 116 +2024-01-16 21:39:45,890 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:46,107 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:46,108 (beam_search:476) INFO: -11.37 * 1.0 = -11.37 for ctc +2024-01-16 21:39:46,108 (beam_search:479) INFO: total log probability: -11.37 +2024-01-16 21:39:46,108 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:39:46,108 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:46,108 (beam_search:483) INFO: best hypo: IFONDTEINTHEFOARRSTNDBRUGTHEEARAPRESNEREPLYDTHECAPTON + +2024-01-16 21:39:46,109 (asr_inference:494) INFO: speech length: 164797 +2024-01-16 21:39:46,125 (beam_search:428) INFO: decoder input length: 255 +2024-01-16 21:39:46,125 (beam_search:429) INFO: max output length: 255 +2024-01-16 21:39:46,125 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:47,073 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:47,073 (beam_search:476) INFO: -27.30 * 1.0 = -27.30 for ctc +2024-01-16 21:39:47,073 (beam_search:479) INFO: total log probability: -27.30 +2024-01-16 21:39:47,073 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:39:47,073 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:47,074 (beam_search:483) INFO: best hypo: OMABECOMPITENTIDTHEFROMPERSONLCSPEIRINCEORTHEECSPERINCEOFOTHERSTOANCSERTWHTMOEORLESSCORACTKNESORATLEASTENITEMTOH + +2024-01-16 21:39:47,076 (asr_inference:494) INFO: speech length: 70077 +2024-01-16 21:39:47,085 (beam_search:428) INFO: decoder input length: 107 +2024-01-16 21:39:47,085 (beam_search:429) INFO: max output length: 107 +2024-01-16 21:39:47,086 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:47,264 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:47,264 (beam_search:476) INFO: -14.69 * 1.0 = -14.69 for ctc +2024-01-16 21:39:47,264 (beam_search:479) INFO: total log probability: -14.69 +2024-01-16 21:39:47,264 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:39:47,264 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:47,264 (beam_search:483) INFO: best hypo: LWNNINTETOLATSTREDSAIDHOAKGONBUYDIGOFHISOGAR + +2024-01-16 21:39:47,265 (asr_inference:494) INFO: speech length: 171837 +2024-01-16 21:39:47,281 (beam_search:428) INFO: decoder input length: 266 +2024-01-16 21:39:47,281 (beam_search:429) INFO: max output length: 266 +2024-01-16 21:39:47,281 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:48,321 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:48,321 (beam_search:476) INFO: -25.92 * 1.0 = -25.92 for ctc +2024-01-16 21:39:48,321 (beam_search:479) INFO: total log probability: -25.92 +2024-01-16 21:39:48,321 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:39:48,321 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:48,322 (beam_search:483) INFO: best hypo: RATWASSURPRISTOFINEHECOLDSESOPLAILYTHRTHEHIYWALOWOAHTERUBOFHERRBUTTHESNDWASABLTOSHOOTITSBEMESTRATDONTHORTHEARAENSPEIRNT + +2024-01-16 21:39:48,323 (asr_inference:494) INFO: speech length: 37757 +2024-01-16 21:39:48,331 (beam_search:428) INFO: decoder input length: 56 +2024-01-16 21:39:48,331 (beam_search:429) INFO: max output length: 56 +2024-01-16 21:39:48,331 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:48,381 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:48,382 (beam_search:476) INFO: -4.57 * 1.0 = -4.57 for ctc +2024-01-16 21:39:48,382 (beam_search:479) INFO: total log probability: -4.57 +2024-01-16 21:39:48,382 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:39:48,382 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:48,382 (beam_search:483) INFO: best hypo: THESPATEIDSPRONGOPE + +2024-01-16 21:39:48,383 (asr_inference:494) INFO: speech length: 57277 +2024-01-16 21:39:48,392 (beam_search:428) INFO: decoder input length: 87 +2024-01-16 21:39:48,392 (beam_search:429) INFO: max output length: 87 +2024-01-16 21:39:48,392 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:48,511 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:48,511 (beam_search:476) INFO: -7.97 * 1.0 = -7.97 for ctc +2024-01-16 21:39:48,511 (beam_search:479) INFO: total log probability: -7.97 +2024-01-16 21:39:48,511 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:39:48,511 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:48,511 (beam_search:483) INFO: best hypo: GCOMEDEANILWITHEGAVESOCHASUPOSITION + +2024-01-16 21:39:48,512 (asr_inference:494) INFO: speech length: 143037 +2024-01-16 21:39:48,527 (beam_search:428) INFO: decoder input length: 221 +2024-01-16 21:39:48,527 (beam_search:429) INFO: max output length: 221 +2024-01-16 21:39:48,527 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:49,249 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:49,249 (beam_search:476) INFO: -20.80 * 1.0 = -20.80 for ctc +2024-01-16 21:39:49,249 (beam_search:479) INFO: total log probability: -20.80 +2024-01-16 21:39:49,249 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:39:49,249 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:49,250 (beam_search:483) INFO: best hypo: YOSEANDTILTHESCHLPILESRINGENTEDWEWASTTLATOFTIMINSTDADYTHATNOWMABEBETERIMPLOYEDANMPRCTSCINGATHLETIC + +2024-01-16 21:39:49,252 (asr_inference:494) INFO: speech length: 76125 +2024-01-16 21:39:49,261 (beam_search:428) INFO: decoder input length: 116 +2024-01-16 21:39:49,261 (beam_search:429) INFO: max output length: 116 +2024-01-16 21:39:49,261 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:49,468 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:49,469 (beam_search:476) INFO: -9.28 * 1.0 = -9.28 for ctc +2024-01-16 21:39:49,469 (beam_search:479) INFO: total log probability: -9.28 +2024-01-16 21:39:49,469 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:49,469 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:49,469 (beam_search:483) INFO: best hypo: YOVEDNITHANWDECLAREDDARTHYTHESTENCEARJUSTOENDERFOL + +2024-01-16 21:39:49,470 (asr_inference:494) INFO: speech length: 212317 +2024-01-16 21:39:49,490 (beam_search:428) INFO: decoder input length: 329 +2024-01-16 21:39:49,490 (beam_search:429) INFO: max output length: 329 +2024-01-16 21:39:49,490 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:50,776 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:50,776 (beam_search:476) INFO: -28.15 * 1.0 = -28.15 for ctc +2024-01-16 21:39:50,776 (beam_search:479) INFO: total log probability: -28.15 +2024-01-16 21:39:50,776 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:39:50,776 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:50,777 (beam_search:483) INFO: best hypo: EMFORTWENINGTANFIVEFTHEREETWOETHEINOWASBERLYTWOENYMYWSAWAYWHNHODIONFIRDHSROCKITSTHEMDACOLOSTOLCLOWEOAPERINAMTYNESE + +2024-01-16 21:39:50,778 (asr_inference:494) INFO: speech length: 151997 +2024-01-16 21:39:50,793 (beam_search:428) INFO: decoder input length: 235 +2024-01-16 21:39:50,793 (beam_search:429) INFO: max output length: 235 +2024-01-16 21:39:50,793 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:51,632 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:51,632 (beam_search:476) INFO: -20.31 * 1.0 = -20.31 for ctc +2024-01-16 21:39:51,632 (beam_search:479) INFO: total log probability: -20.31 +2024-01-16 21:39:51,632 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:51,632 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:51,633 (beam_search:483) INFO: best hypo: THEPADNOATENCIONTOTHEFACTHATGIPKGESCISILDIDNOTONTOMARYANYOFTHEMEMFORHEHADETERMENDTHATHNITWASEGREEDWHOSHODHAVHIM + +2024-01-16 21:39:51,634 (asr_inference:494) INFO: speech length: 116157 +2024-01-16 21:39:51,646 (beam_search:428) INFO: decoder input length: 179 +2024-01-16 21:39:51,646 (beam_search:429) INFO: max output length: 179 +2024-01-16 21:39:51,646 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:52,096 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:52,096 (beam_search:476) INFO: -14.90 * 1.0 = -14.90 for ctc +2024-01-16 21:39:52,096 (beam_search:479) INFO: total log probability: -14.90 +2024-01-16 21:39:52,096 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:39:52,096 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:52,097 (beam_search:483) INFO: best hypo: WATDYOUTHIOFTHATHECRIDOPENGACOPBYOTHERECKEDANDLANGTFLATOTHELIBRYTABLEL + +2024-01-16 21:39:52,098 (asr_inference:494) INFO: speech length: 48045 +2024-01-16 21:39:52,106 (beam_search:428) INFO: decoder input length: 73 +2024-01-16 21:39:52,106 (beam_search:429) INFO: max output length: 73 +2024-01-16 21:39:52,106 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:52,177 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:52,177 (beam_search:476) INFO: -6.51 * 1.0 = -6.51 for ctc +2024-01-16 21:39:52,177 (beam_search:479) INFO: total log probability: -6.51 +2024-01-16 21:39:52,177 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:39:52,177 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:52,177 (beam_search:483) INFO: best hypo: ITLECOPIERUTASORTTIME + +2024-01-16 21:39:52,178 (asr_inference:494) INFO: speech length: 113117 +2024-01-16 21:39:52,191 (beam_search:428) INFO: decoder input length: 174 +2024-01-16 21:39:52,191 (beam_search:429) INFO: max output length: 174 +2024-01-16 21:39:52,191 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:52,615 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:52,615 (beam_search:476) INFO: -11.61 * 1.0 = -11.61 for ctc +2024-01-16 21:39:52,615 (beam_search:479) INFO: total log probability: -11.61 +2024-01-16 21:39:52,615 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:39:52,615 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:52,616 (beam_search:483) INFO: best hypo: ANDLASTTHEROUDOVEIGETDABLEPEPLEHOHADNOHARTSANDOUODNITHERSMILENORFROWN + +2024-01-16 21:39:52,617 (asr_inference:494) INFO: speech length: 52477 +2024-01-16 21:39:52,626 (beam_search:428) INFO: decoder input length: 79 +2024-01-16 21:39:52,626 (beam_search:429) INFO: max output length: 79 +2024-01-16 21:39:52,626 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:52,707 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:52,708 (beam_search:476) INFO: -5.05 * 1.0 = -5.05 for ctc +2024-01-16 21:39:52,708 (beam_search:479) INFO: total log probability: -5.05 +2024-01-16 21:39:52,708 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:52,708 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:52,708 (beam_search:483) INFO: best hypo: THEINYOLLCACHITESAITHICH + +2024-01-16 21:39:52,709 (asr_inference:494) INFO: speech length: 124637 +2024-01-16 21:39:52,722 (beam_search:428) INFO: decoder input length: 192 +2024-01-16 21:39:52,722 (beam_search:429) INFO: max output length: 192 +2024-01-16 21:39:52,722 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:53,279 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:53,279 (beam_search:476) INFO: -15.62 * 1.0 = -15.62 for ctc +2024-01-16 21:39:53,280 (beam_search:479) INFO: total log probability: -15.62 +2024-01-16 21:39:53,280 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:53,280 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:53,280 (beam_search:483) INFO: best hypo: WHTISITIQUEREDNOTFIELINGSRENBUTTHATTWASAEVALDATEMDTOSECERLITLFREADRTYSINGFOTHEANDEOVER + +2024-01-16 21:39:53,282 (asr_inference:494) INFO: speech length: 151517 +2024-01-16 21:39:53,296 (beam_search:428) INFO: decoder input length: 234 +2024-01-16 21:39:53,297 (beam_search:429) INFO: max output length: 234 +2024-01-16 21:39:53,297 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:54,099 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:54,099 (beam_search:476) INFO: -21.89 * 1.0 = -21.89 for ctc +2024-01-16 21:39:54,099 (beam_search:479) INFO: total log probability: -21.89 +2024-01-16 21:39:54,099 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:39:54,099 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:54,099 (beam_search:483) INFO: best hypo: SOEGAVETHELIRCTHETHRDUNDEDOLOSFORBOKSANDACASKOFGODOLDALFRPETERTHECLRKRANTHEAILHIMSELFANDGAVEHECAFMIW + +2024-01-16 21:39:54,101 (asr_inference:494) INFO: speech length: 204477 +2024-01-16 21:39:54,119 (beam_search:428) INFO: decoder input length: 317 +2024-01-16 21:39:54,119 (beam_search:429) INFO: max output length: 317 +2024-01-16 21:39:54,119 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:55,482 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:55,482 (beam_search:476) INFO: -20.73 * 1.0 = -20.73 for ctc +2024-01-16 21:39:55,482 (beam_search:479) INFO: total log probability: -20.73 +2024-01-16 21:39:55,482 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:39:55,482 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:55,483 (beam_search:483) INFO: best hypo: ATLIEKTHATANALSINNEDERLANTDWITHMERLYGRINTHTFATEDAWAYCHANGINGINTOALINKSWHICINTRETOSPERDFOLOWEDBYANUNONRECEWITSOURTNOUSANDPONTEDERS + +2024-01-16 21:39:55,485 (asr_inference:494) INFO: speech length: 147037 +2024-01-16 21:39:55,500 (beam_search:428) INFO: decoder input length: 227 +2024-01-16 21:39:55,500 (beam_search:429) INFO: max output length: 227 +2024-01-16 21:39:55,500 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:56,250 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:56,250 (beam_search:476) INFO: -19.22 * 1.0 = -19.22 for ctc +2024-01-16 21:39:56,250 (beam_search:479) INFO: total log probability: -19.22 +2024-01-16 21:39:56,250 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:39:56,250 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:56,251 (beam_search:483) INFO: best hypo: ASHECOLDNOTDEOEMARGRILANCDUNCONHOUSLYATHEUNGKLEDORNEROFHEOMESHEUOHARTHNDERTAKASURVINCSPLACECOOSHE + +2024-01-16 21:39:56,253 (asr_inference:494) INFO: speech length: 100797 +2024-01-16 21:39:56,264 (beam_search:428) INFO: decoder input length: 155 +2024-01-16 21:39:56,264 (beam_search:429) INFO: max output length: 155 +2024-01-16 21:39:56,264 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:56,578 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:56,579 (beam_search:476) INFO: -13.59 * 1.0 = -13.59 for ctc +2024-01-16 21:39:56,579 (beam_search:479) INFO: total log probability: -13.59 +2024-01-16 21:39:56,579 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:39:56,579 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:56,579 (beam_search:483) INFO: best hypo: ADNOHESHEREPLIDEDWITINISENKERYOUSITYDIDIGIVFTHETOYOUWHAL + +2024-01-16 21:39:56,580 (asr_inference:494) INFO: speech length: 116797 +2024-01-16 21:39:56,593 (beam_search:428) INFO: decoder input length: 180 +2024-01-16 21:39:56,593 (beam_search:429) INFO: max output length: 180 +2024-01-16 21:39:56,593 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:57,041 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:57,041 (beam_search:476) INFO: -12.69 * 1.0 = -12.69 for ctc +2024-01-16 21:39:57,042 (beam_search:479) INFO: total log probability: -12.69 +2024-01-16 21:39:57,042 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:57,042 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:57,042 (beam_search:483) INFO: best hypo: MARBORMILESANTHEAGACENTDELINGWRHELDUNDERLONGLEESTHEMUSTIFPOSABLTBERELET + +2024-01-16 21:39:57,043 (asr_inference:494) INFO: speech length: 42557 +2024-01-16 21:39:57,051 (beam_search:428) INFO: decoder input length: 64 +2024-01-16 21:39:57,051 (beam_search:429) INFO: max output length: 64 +2024-01-16 21:39:57,051 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:57,120 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:57,121 (beam_search:476) INFO: -7.10 * 1.0 = -7.10 for ctc +2024-01-16 21:39:57,121 (beam_search:479) INFO: total log probability: -7.10 +2024-01-16 21:39:57,121 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:39:57,121 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:57,121 (beam_search:483) INFO: best hypo: DACOPWAEOSTUNISTHELATEM + +2024-01-16 21:39:57,122 (asr_inference:494) INFO: speech length: 155037 +2024-01-16 21:39:57,137 (beam_search:428) INFO: decoder input length: 240 +2024-01-16 21:39:57,137 (beam_search:429) INFO: max output length: 240 +2024-01-16 21:39:57,137 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:58,010 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:58,010 (beam_search:476) INFO: -20.81 * 1.0 = -20.81 for ctc +2024-01-16 21:39:58,010 (beam_search:479) INFO: total log probability: -20.81 +2024-01-16 21:39:58,010 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:39:58,010 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:58,011 (beam_search:483) INFO: best hypo: ITDBONDEDHEARANDTHAIRABOUTHECIOKONHOUSANATFIRSTDORTHYCOULDNOTTELHATITWOSWHILTHESPEACINGOFTHECHIOCIONSNERLYDEFENDHER + +2024-01-16 21:39:58,012 (asr_inference:494) INFO: speech length: 162637 +2024-01-16 21:39:58,028 (beam_search:428) INFO: decoder input length: 252 +2024-01-16 21:39:58,028 (beam_search:429) INFO: max output length: 252 +2024-01-16 21:39:58,028 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:58,995 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:58,995 (beam_search:476) INFO: -20.78 * 1.0 = -20.78 for ctc +2024-01-16 21:39:58,995 (beam_search:479) INFO: total log probability: -20.78 +2024-01-16 21:39:58,995 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:58,995 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:58,996 (beam_search:483) INFO: best hypo: THESOLDERGAVEYALHATEROUSEDASCHUAROFHISCOMRADANDBRGTTHETUMBLINGINTOTHSTRETWHENTHEYSAWHOTHEOLRSPRESCISEPRISNERWASSCAPING + +2024-01-16 21:39:58,998 (asr_inference:494) INFO: speech length: 159677 +2024-01-16 21:39:59,013 (beam_search:428) INFO: decoder input length: 247 +2024-01-16 21:39:59,013 (beam_search:429) INFO: max output length: 247 +2024-01-16 21:39:59,013 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:39:59,905 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:39:59,905 (beam_search:476) INFO: -20.09 * 1.0 = -20.09 for ctc +2024-01-16 21:39:59,905 (beam_search:479) INFO: total log probability: -20.09 +2024-01-16 21:39:59,905 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:39:59,905 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:39:59,906 (beam_search:483) INFO: best hypo: GJIMHADREFEUSETOLETHEFELDOFGRASWHERHEWASINGAGEDANDBISILYEATINGSOTHEWISURDGUGTOUTOTHBOUGYANDJUONEDSAEBANDOARITHY + +2024-01-16 21:39:59,907 (asr_inference:494) INFO: speech length: 103197 +2024-01-16 21:39:59,919 (beam_search:428) INFO: decoder input length: 159 +2024-01-16 21:39:59,919 (beam_search:429) INFO: max output length: 159 +2024-01-16 21:39:59,919 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:00,271 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:00,271 (beam_search:476) INFO: -16.26 * 1.0 = -16.26 for ctc +2024-01-16 21:40:00,271 (beam_search:479) INFO: total log probability: -16.26 +2024-01-16 21:40:00,271 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:40:00,271 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:00,272 (beam_search:483) INFO: best hypo: GSRDNLYIMASINTERTEDNTHECACESOARBUTICANAKHEDSRTALSOFTIREPLID + +2024-01-16 21:40:00,273 (asr_inference:494) INFO: speech length: 55197 +2024-01-16 21:40:00,282 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 21:40:00,282 (beam_search:429) INFO: max output length: 84 +2024-01-16 21:40:00,282 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:00,375 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:00,375 (beam_search:476) INFO: -7.43 * 1.0 = -7.43 for ctc +2024-01-16 21:40:00,375 (beam_search:479) INFO: total log probability: -7.43 +2024-01-16 21:40:00,375 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:40:00,375 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:00,376 (beam_search:483) INFO: best hypo: ORANYMICEOREVENGGRASHOPERS + +2024-01-16 21:40:00,377 (asr_inference:494) INFO: speech length: 159837 +2024-01-16 21:40:00,392 (beam_search:428) INFO: decoder input length: 247 +2024-01-16 21:40:00,392 (beam_search:429) INFO: max output length: 247 +2024-01-16 21:40:00,392 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:01,301 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:01,301 (beam_search:476) INFO: -24.74 * 1.0 = -24.74 for ctc +2024-01-16 21:40:01,301 (beam_search:479) INFO: total log probability: -24.74 +2024-01-16 21:40:01,301 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:40:01,301 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:01,302 (beam_search:483) INFO: best hypo: ANDTHETHEPASIODONTHETELYOUWHAETODORWHATANNOTTODEHWETHEMUNYTHEYGIVEYOUANJUSTPAMENTFOYOUPAINSINTHERECSTCANGELIC + +2024-01-16 21:40:01,303 (asr_inference:494) INFO: speech length: 46077 +2024-01-16 21:40:01,312 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:40:01,312 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:40:01,312 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:01,386 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:01,386 (beam_search:476) INFO: -7.16 * 1.0 = -7.16 for ctc +2024-01-16 21:40:01,386 (beam_search:479) INFO: total log probability: -7.16 +2024-01-16 21:40:01,386 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:40:01,386 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:01,387 (beam_search:483) INFO: best hypo: WHTDISTATMEANASTTHERINCES + +2024-01-16 21:40:01,388 (asr_inference:494) INFO: speech length: 180957 +2024-01-16 21:40:01,404 (beam_search:428) INFO: decoder input length: 280 +2024-01-16 21:40:01,404 (beam_search:429) INFO: max output length: 280 +2024-01-16 21:40:01,404 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:02,356 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:02,356 (beam_search:476) INFO: -20.47 * 1.0 = -20.47 for ctc +2024-01-16 21:40:02,356 (beam_search:479) INFO: total log probability: -20.47 +2024-01-16 21:40:02,356 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:40:02,356 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:02,357 (beam_search:483) INFO: best hypo: DEHADBEEDRONEDHEWASFLOAODINGNASIOFLITANDNOWTHEDSHININGLTEFISIESSWHEAMINCQUISITIELYEOUPTOHIENDSTAR + +2024-01-16 21:40:02,358 (asr_inference:494) INFO: speech length: 197437 +2024-01-16 21:40:02,376 (beam_search:428) INFO: decoder input length: 306 +2024-01-16 21:40:02,376 (beam_search:429) INFO: max output length: 306 +2024-01-16 21:40:02,376 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:03,805 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:03,805 (beam_search:476) INFO: -33.35 * 1.0 = -33.35 for ctc +2024-01-16 21:40:03,805 (beam_search:479) INFO: total log probability: -33.35 +2024-01-16 21:40:03,805 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:40:03,805 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:03,806 (beam_search:483) INFO: best hypo: BUDOLDGNHDARIKTWOLEFTDREMEMETHETAILIREDTOYOUITHTHONERMOABLTHETHEFIRSTTHEBRONSTENDETHEWRLDOFOAPLWERSOULDGRSSINTFRMSOMEBLASTIDPLANTNOUTERSPACSFIEANOHOM + +2024-01-16 21:40:03,808 (asr_inference:494) INFO: speech length: 129437 +2024-01-16 21:40:03,821 (beam_search:428) INFO: decoder input length: 200 +2024-01-16 21:40:03,821 (beam_search:429) INFO: max output length: 200 +2024-01-16 21:40:03,821 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:04,355 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:04,355 (beam_search:476) INFO: -18.82 * 1.0 = -18.82 for ctc +2024-01-16 21:40:04,355 (beam_search:479) INFO: total log probability: -18.82 +2024-01-16 21:40:04,355 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:40:04,355 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:04,356 (beam_search:483) INFO: best hypo: APPEWIEOSPEATTHEMENANDGTHEMTOGOWAYSHECANTBREETHPORTHINGWITTHISROWDOBOUTTERHL + +2024-01-16 21:40:04,357 (asr_inference:494) INFO: speech length: 162877 +2024-01-16 21:40:04,373 (beam_search:428) INFO: decoder input length: 252 +2024-01-16 21:40:04,373 (beam_search:429) INFO: max output length: 252 +2024-01-16 21:40:04,373 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:05,197 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:05,197 (beam_search:476) INFO: -17.28 * 1.0 = -17.28 for ctc +2024-01-16 21:40:05,197 (beam_search:479) INFO: total log probability: -17.28 +2024-01-16 21:40:05,197 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:40:05,197 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:05,197 (beam_search:483) INFO: best hypo: AWHENIYTOKTHISCACEHESAIDIBULEVEDDONEINDMYHARTDICSONWASINSENTISTOBELEITBUTMYFATHASPBENROUTLYSHAC + +2024-01-16 21:40:05,199 (asr_inference:494) INFO: speech length: 58237 +2024-01-16 21:40:05,208 (beam_search:428) INFO: decoder input length: 88 +2024-01-16 21:40:05,208 (beam_search:429) INFO: max output length: 88 +2024-01-16 21:40:05,208 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:05,316 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:05,316 (beam_search:476) INFO: -11.76 * 1.0 = -11.76 for ctc +2024-01-16 21:40:05,316 (beam_search:479) INFO: total log probability: -11.76 +2024-01-16 21:40:05,316 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:40:05,316 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:05,316 (beam_search:483) INFO: best hypo: ADHAPTRSICKOVEFTHEPITOORSEATSE + +2024-01-16 21:40:05,318 (asr_inference:494) INFO: speech length: 45757 +2024-01-16 21:40:05,326 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:40:05,326 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:40:05,326 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:05,396 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:05,396 (beam_search:476) INFO: -4.84 * 1.0 = -4.84 for ctc +2024-01-16 21:40:05,396 (beam_search:479) INFO: total log probability: -4.84 +2024-01-16 21:40:05,396 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:40:05,397 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:05,397 (beam_search:483) INFO: best hypo: REMEMERTHECANNOTTOUCHUS + +2024-01-16 21:40:05,398 (asr_inference:494) INFO: speech length: 163357 +2024-01-16 21:40:05,413 (beam_search:428) INFO: decoder input length: 253 +2024-01-16 21:40:05,413 (beam_search:429) INFO: max output length: 253 +2024-01-16 21:40:05,413 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:06,320 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:06,320 (beam_search:476) INFO: -21.74 * 1.0 = -21.74 for ctc +2024-01-16 21:40:06,320 (beam_search:479) INFO: total log probability: -21.74 +2024-01-16 21:40:06,320 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:40:06,320 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:06,321 (beam_search:483) INFO: best hypo: IVMETIMEASOURGIVEMETIMEIOFTEIRSANYTHNGIHATITSAHURYIVEANYDEAEYOURMADGESTYANDONCETTHESICTTHSNOBENOSPRONCES + +2024-01-16 21:40:06,322 (asr_inference:494) INFO: speech length: 49757 +2024-01-16 21:40:06,331 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 21:40:06,331 (beam_search:429) INFO: max output length: 75 +2024-01-16 21:40:06,331 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:06,420 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:06,420 (beam_search:476) INFO: -7.67 * 1.0 = -7.67 for ctc +2024-01-16 21:40:06,420 (beam_search:479) INFO: total log probability: -7.67 +2024-01-16 21:40:06,420 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:40:06,420 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:06,421 (beam_search:483) INFO: best hypo: TONOFTROAHTDECLEARETHESALERMAN + +2024-01-16 21:40:06,422 (asr_inference:494) INFO: speech length: 138397 +2024-01-16 21:40:06,436 (beam_search:428) INFO: decoder input length: 214 +2024-01-16 21:40:06,436 (beam_search:429) INFO: max output length: 214 +2024-01-16 21:40:06,436 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:06,950 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:06,951 (beam_search:476) INFO: -13.29 * 1.0 = -13.29 for ctc +2024-01-16 21:40:06,951 (beam_search:479) INFO: total log probability: -13.29 +2024-01-16 21:40:06,951 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:40:06,951 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:06,951 (beam_search:483) INFO: best hypo: AASFORTHATSAIDMARGRITRTHEHOTILYIHOLDITHISOHONYSOITCQUEEMULDEPENSAY + +2024-01-16 21:40:06,953 (asr_inference:494) INFO: speech length: 177709 +2024-01-16 21:40:06,969 (beam_search:428) INFO: decoder input length: 275 +2024-01-16 21:40:06,969 (beam_search:429) INFO: max output length: 275 +2024-01-16 21:40:06,969 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:08,064 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:08,064 (beam_search:476) INFO: -19.68 * 1.0 = -19.68 for ctc +2024-01-16 21:40:08,065 (beam_search:479) INFO: total log probability: -19.68 +2024-01-16 21:40:08,065 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:40:08,065 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:08,065 (beam_search:483) INFO: best hypo: WEHETHESWORDSTHECINGWOSHEDWASFULFTHEPINCESNEVERTAPETONCQPHIRIFTHEYCODBETROANDSMEREDHIMSELFOVERWITHFATANDSPRANGINTTHEOVEINT + +2024-01-16 21:40:08,067 (asr_inference:494) INFO: speech length: 178989 +2024-01-16 21:40:08,083 (beam_search:428) INFO: decoder input length: 277 +2024-01-16 21:40:08,083 (beam_search:429) INFO: max output length: 277 +2024-01-16 21:40:08,083 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:09,175 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:09,175 (beam_search:476) INFO: -31.25 * 1.0 = -31.25 for ctc +2024-01-16 21:40:09,175 (beam_search:479) INFO: total log probability: -31.25 +2024-01-16 21:40:09,175 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:40:09,175 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:09,176 (beam_search:483) INFO: best hypo: YOSHOLBALYGETPARCEFROMYOUREROMEVIONDRESEVEREILLHAVESOMTOUOLSGIVENOUTHENADTEDDEPOMASHEHASTONDERSTANTHTINGSACETOLOFENCESH + +2024-01-16 21:40:09,178 (asr_inference:494) INFO: speech length: 71357 +2024-01-16 21:40:09,188 (beam_search:428) INFO: decoder input length: 109 +2024-01-16 21:40:09,188 (beam_search:429) INFO: max output length: 109 +2024-01-16 21:40:09,188 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:09,388 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:09,388 (beam_search:476) INFO: -8.91 * 1.0 = -8.91 for ctc +2024-01-16 21:40:09,388 (beam_search:479) INFO: total log probability: -8.91 +2024-01-16 21:40:09,388 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:40:09,388 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:09,388 (beam_search:483) INFO: best hypo: BYTHETIMTHEFROUSTADSADINTHESHOBEFOAREWAYFOMHELSTDON + +2024-01-16 21:40:09,390 (asr_inference:494) INFO: speech length: 46557 +2024-01-16 21:40:09,398 (beam_search:428) INFO: decoder input length: 70 +2024-01-16 21:40:09,398 (beam_search:429) INFO: max output length: 70 +2024-01-16 21:40:09,398 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:09,479 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:09,479 (beam_search:476) INFO: -6.02 * 1.0 = -6.02 for ctc +2024-01-16 21:40:09,479 (beam_search:479) INFO: total log probability: -6.02 +2024-01-16 21:40:09,479 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:40:09,479 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:09,480 (beam_search:483) INFO: best hypo: OENTHIGWOENTOESAYBEGANDCENITY + +2024-01-16 21:40:09,481 (asr_inference:494) INFO: speech length: 68317 +2024-01-16 21:40:09,490 (beam_search:428) INFO: decoder input length: 104 +2024-01-16 21:40:09,490 (beam_search:429) INFO: max output length: 104 +2024-01-16 21:40:09,490 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:09,674 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:09,674 (beam_search:476) INFO: -11.05 * 1.0 = -11.05 for ctc +2024-01-16 21:40:09,674 (beam_search:479) INFO: total log probability: -11.05 +2024-01-16 21:40:09,674 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:40:09,674 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:09,675 (beam_search:483) INFO: best hypo: THIMPORTNTRACHICWASONFIDETONOOAMBTHEREALPRPRITER + +2024-01-16 21:40:09,676 (asr_inference:494) INFO: speech length: 79104 +2024-01-16 21:40:09,686 (beam_search:428) INFO: decoder input length: 121 +2024-01-16 21:40:09,686 (beam_search:429) INFO: max output length: 121 +2024-01-16 21:40:09,686 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:09,891 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:09,891 (beam_search:476) INFO: -18.34 * 1.0 = -18.34 for ctc +2024-01-16 21:40:09,891 (beam_search:479) INFO: total log probability: -18.34 +2024-01-16 21:40:09,891 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 21:40:09,891 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:09,891 (beam_search:483) INFO: best hypo: UNNWHWEAIDEDABBLASEDONTDBASKODOVEMYTHISTIMGCGO + +2024-01-16 21:40:09,893 (asr_inference:494) INFO: speech length: 68352 +2024-01-16 21:40:09,902 (beam_search:428) INFO: decoder input length: 104 +2024-01-16 21:40:09,902 (beam_search:429) INFO: max output length: 104 +2024-01-16 21:40:09,902 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:10,068 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:10,068 (beam_search:476) INFO: -9.15 * 1.0 = -9.15 for ctc +2024-01-16 21:40:10,068 (beam_search:479) INFO: total log probability: -9.15 +2024-01-16 21:40:10,068 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:40:10,068 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:10,069 (beam_search:483) INFO: best hypo: IDTDATASEPRTSUPSECTIONWHICHDLSWITHISASPECTD + +2024-01-16 21:40:10,070 (asr_inference:494) INFO: speech length: 104832 +2024-01-16 21:40:10,082 (beam_search:428) INFO: decoder input length: 161 +2024-01-16 21:40:10,082 (beam_search:429) INFO: max output length: 161 +2024-01-16 21:40:10,082 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:10,386 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:10,386 (beam_search:476) INFO: -12.08 * 1.0 = -12.08 for ctc +2024-01-16 21:40:10,386 (beam_search:479) INFO: total log probability: -12.08 +2024-01-16 21:40:10,386 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:40:10,386 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:10,386 (beam_search:483) INFO: best hypo: OPRATIONOFTHEFRONTLANCONTDNUREDONTHEWODENDTTESSEILS + +2024-01-16 21:40:10,387 (asr_inference:494) INFO: speech length: 116352 +2024-01-16 21:40:10,400 (beam_search:428) INFO: decoder input length: 179 +2024-01-16 21:40:10,400 (beam_search:429) INFO: max output length: 179 +2024-01-16 21:40:10,400 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:10,776 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:10,776 (beam_search:476) INFO: -16.58 * 1.0 = -16.58 for ctc +2024-01-16 21:40:10,776 (beam_search:479) INFO: total log probability: -16.58 +2024-01-16 21:40:10,776 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:40:10,776 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:10,776 (beam_search:483) INFO: best hypo: MNISONFHLORIDISTWENCEPERENTOVERNCTRIMLYEWHIDRANGOFAVOMINGS + +2024-01-16 21:40:10,778 (asr_inference:494) INFO: speech length: 130944 +2024-01-16 21:40:10,791 (beam_search:428) INFO: decoder input length: 202 +2024-01-16 21:40:10,791 (beam_search:429) INFO: max output length: 202 +2024-01-16 21:40:10,791 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:11,312 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:11,312 (beam_search:476) INFO: -16.66 * 1.0 = -16.66 for ctc +2024-01-16 21:40:11,312 (beam_search:479) INFO: total log probability: -16.66 +2024-01-16 21:40:11,312 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:40:11,312 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:11,313 (beam_search:483) INFO: best hypo: FORJINTBECKINGKSHITSSTORETHFRESHPBOCKTBUTDEADESNDELEVETHEMUNTOERLEROLDCAS + +2024-01-16 21:40:11,314 (asr_inference:494) INFO: speech length: 119424 +2024-01-16 21:40:11,327 (beam_search:428) INFO: decoder input length: 184 +2024-01-16 21:40:11,327 (beam_search:429) INFO: max output length: 184 +2024-01-16 21:40:11,327 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:11,804 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:11,804 (beam_search:476) INFO: -16.57 * 1.0 = -16.57 for ctc +2024-01-16 21:40:11,804 (beam_search:479) INFO: total log probability: -16.57 +2024-01-16 21:40:11,804 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:40:11,804 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:11,805 (beam_search:483) INFO: best hypo: THEOTHEFORTINGCOUMPOSESARDTOWOYURECAMPSREFIRTTOCOLECTIVELYASTHEYUNERSTDECOLNGE + +2024-01-16 21:40:11,806 (asr_inference:494) INFO: speech length: 70272 +2024-01-16 21:40:11,816 (beam_search:428) INFO: decoder input length: 107 +2024-01-16 21:40:11,816 (beam_search:429) INFO: max output length: 107 +2024-01-16 21:40:11,816 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:11,994 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:11,995 (beam_search:476) INFO: -13.36 * 1.0 = -13.36 for ctc +2024-01-16 21:40:11,995 (beam_search:479) INFO: total log probability: -13.36 +2024-01-16 21:40:11,995 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:40:11,995 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:11,995 (beam_search:483) INFO: best hypo: ITSTWOBHADTHEDWEHAECUICKLEGORNGTOFRGETMYTAETD + +2024-01-16 21:40:11,996 (asr_inference:494) INFO: speech length: 113280 +2024-01-16 21:40:12,008 (beam_search:428) INFO: decoder input length: 174 +2024-01-16 21:40:12,008 (beam_search:429) INFO: max output length: 174 +2024-01-16 21:40:12,008 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:12,426 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:12,426 (beam_search:476) INFO: -25.35 * 1.0 = -25.35 for ctc +2024-01-16 21:40:12,426 (beam_search:479) INFO: total log probability: -25.35 +2024-01-16 21:40:12,426 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:40:12,426 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:12,426 (beam_search:483) INFO: best hypo: WONENOTOREINTEGELORYSHOHHOURDHEGINTLAIDIREDISTATYATFOALADGRORTCONTON + +2024-01-16 21:40:12,427 (asr_inference:494) INFO: speech length: 59904 +2024-01-16 21:40:12,436 (beam_search:428) INFO: decoder input length: 91 +2024-01-16 21:40:12,436 (beam_search:429) INFO: max output length: 91 +2024-01-16 21:40:12,436 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:12,509 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:12,509 (beam_search:476) INFO: -8.80 * 1.0 = -8.80 for ctc +2024-01-16 21:40:12,509 (beam_search:479) INFO: total log probability: -8.80 +2024-01-16 21:40:12,509 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-16 21:40:12,509 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:12,509 (beam_search:483) INFO: best hypo: EANMPERIALDIIAT + +2024-01-16 21:40:12,510 (asr_inference:494) INFO: speech length: 93888 +2024-01-16 21:40:12,521 (beam_search:428) INFO: decoder input length: 144 +2024-01-16 21:40:12,521 (beam_search:429) INFO: max output length: 144 +2024-01-16 21:40:12,521 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:12,761 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:12,761 (beam_search:476) INFO: -15.82 * 1.0 = -15.82 for ctc +2024-01-16 21:40:12,761 (beam_search:479) INFO: total log probability: -15.82 +2024-01-16 21:40:12,761 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:40:12,761 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:12,761 (beam_search:483) INFO: best hypo: THEESELDINCOMPNYHTDASHUDTAKESCKORITYOOTPRATION + +2024-01-16 21:40:12,763 (asr_inference:494) INFO: speech length: 112896 +2024-01-16 21:40:12,775 (beam_search:428) INFO: decoder input length: 174 +2024-01-16 21:40:12,775 (beam_search:429) INFO: max output length: 174 +2024-01-16 21:40:12,775 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:13,157 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:13,157 (beam_search:476) INFO: -15.14 * 1.0 = -15.14 for ctc +2024-01-16 21:40:13,157 (beam_search:479) INFO: total log probability: -15.14 +2024-01-16 21:40:13,157 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:40:13,157 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:13,158 (beam_search:483) INFO: best hypo: TECOINGMIYNINGCANBEDONWITGDOFHISCOARTSAOREITESPESIOLISTHORDLY + +2024-01-16 21:40:13,159 (asr_inference:494) INFO: speech length: 69696 +2024-01-16 21:40:13,169 (beam_search:428) INFO: decoder input length: 106 +2024-01-16 21:40:13,169 (beam_search:429) INFO: max output length: 106 +2024-01-16 21:40:13,169 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:13,296 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:13,296 (beam_search:476) INFO: -9.03 * 1.0 = -9.03 for ctc +2024-01-16 21:40:13,296 (beam_search:479) INFO: total log probability: -9.03 +2024-01-16 21:40:13,296 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:40:13,296 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:13,296 (beam_search:483) INFO: best hypo: GTHELSOLETHENOUSIONALRANKINGN + +2024-01-16 21:40:13,297 (asr_inference:494) INFO: speech length: 77184 +2024-01-16 21:40:13,307 (beam_search:428) INFO: decoder input length: 118 +2024-01-16 21:40:13,307 (beam_search:429) INFO: max output length: 118 +2024-01-16 21:40:13,307 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:13,449 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:13,449 (beam_search:476) INFO: -9.12 * 1.0 = -9.12 for ctc +2024-01-16 21:40:13,449 (beam_search:479) INFO: total log probability: -9.12 +2024-01-16 21:40:13,449 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:40:13,449 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:13,449 (beam_search:483) INFO: best hypo: TTARWRSGRANESBISHOPEOFNHIMORECKE + +2024-01-16 21:40:13,450 (asr_inference:494) INFO: speech length: 122880 +2024-01-16 21:40:13,463 (beam_search:428) INFO: decoder input length: 189 +2024-01-16 21:40:13,463 (beam_search:429) INFO: max output length: 189 +2024-01-16 21:40:13,463 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:13,766 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:13,767 (beam_search:476) INFO: -15.93 * 1.0 = -15.93 for ctc +2024-01-16 21:40:13,767 (beam_search:479) INFO: total log probability: -15.93 +2024-01-16 21:40:13,767 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:40:13,767 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:13,767 (beam_search:483) INFO: best hypo: AAIOUNDERTHARTTHITOLHIMANDHEOKMYPLASESPD + +2024-01-16 21:40:13,768 (asr_inference:494) INFO: speech length: 76416 +2024-01-16 21:40:13,778 (beam_search:428) INFO: decoder input length: 117 +2024-01-16 21:40:13,778 (beam_search:429) INFO: max output length: 117 +2024-01-16 21:40:13,778 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:13,925 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:13,925 (beam_search:476) INFO: -8.98 * 1.0 = -8.98 for ctc +2024-01-16 21:40:13,925 (beam_search:479) INFO: total log probability: -8.98 +2024-01-16 21:40:13,925 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:40:13,925 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:13,925 (beam_search:483) INFO: best hypo: ITHORGTIDGIVETHECITITCSATDREAETE + +2024-01-16 21:40:13,926 (asr_inference:494) INFO: speech length: 60480 +2024-01-16 21:40:13,935 (beam_search:428) INFO: decoder input length: 92 +2024-01-16 21:40:13,935 (beam_search:429) INFO: max output length: 92 +2024-01-16 21:40:13,935 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:14,040 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:14,040 (beam_search:476) INFO: -7.29 * 1.0 = -7.29 for ctc +2024-01-16 21:40:14,040 (beam_search:479) INFO: total log probability: -7.29 +2024-01-16 21:40:14,040 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:40:14,041 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:14,041 (beam_search:483) INFO: best hypo: ASTIEWVETLDENIHTOMNTHERICTOES + +2024-01-16 21:40:14,042 (asr_inference:494) INFO: speech length: 99456 +2024-01-16 21:40:14,053 (beam_search:428) INFO: decoder input length: 153 +2024-01-16 21:40:14,053 (beam_search:429) INFO: max output length: 153 +2024-01-16 21:40:14,053 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:14,356 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:14,356 (beam_search:476) INFO: -8.54 * 1.0 = -8.54 for ctc +2024-01-16 21:40:14,356 (beam_search:479) INFO: total log probability: -8.54 +2024-01-16 21:40:14,356 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:40:14,356 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:14,357 (beam_search:483) INFO: best hypo: HOEDYOURNOSTHTOCAEDTHITMAYFOMTHEFABLINGHORMOTOFONTION + +2024-01-16 21:40:14,358 (asr_inference:494) INFO: speech length: 135168 +2024-01-16 21:40:14,372 (beam_search:428) INFO: decoder input length: 209 +2024-01-16 21:40:14,372 (beam_search:429) INFO: max output length: 209 +2024-01-16 21:40:14,372 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:14,654 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:14,654 (beam_search:476) INFO: -18.08 * 1.0 = -18.08 for ctc +2024-01-16 21:40:14,654 (beam_search:479) INFO: total log probability: -18.08 +2024-01-16 21:40:14,654 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-16 21:40:14,654 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:14,654 (beam_search:483) INFO: best hypo: DTHATSONEDTSLAKETHEREPOLOMEAIIEDEI + +2024-01-16 21:40:14,656 (asr_inference:494) INFO: speech length: 115200 +2024-01-16 21:40:14,669 (beam_search:428) INFO: decoder input length: 177 +2024-01-16 21:40:14,669 (beam_search:429) INFO: max output length: 177 +2024-01-16 21:40:14,669 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:15,102 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:15,102 (beam_search:476) INFO: -20.02 * 1.0 = -20.02 for ctc +2024-01-16 21:40:15,102 (beam_search:479) INFO: total log probability: -20.02 +2024-01-16 21:40:15,102 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:40:15,102 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:15,102 (beam_search:483) INFO: best hypo: IHISRINCULIGORWASNOTPLARLYDEFIDEBOWNDGREAINTHESPITDOFTECHARIBEUNPANINSTOLAE + +2024-01-16 21:40:15,104 (asr_inference:494) INFO: speech length: 125568 +2024-01-16 21:40:15,117 (beam_search:428) INFO: decoder input length: 194 +2024-01-16 21:40:15,117 (beam_search:429) INFO: max output length: 194 +2024-01-16 21:40:15,117 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:15,503 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:15,503 (beam_search:476) INFO: -11.40 * 1.0 = -11.40 for ctc +2024-01-16 21:40:15,503 (beam_search:479) INFO: total log probability: -11.40 +2024-01-16 21:40:15,503 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:40:15,503 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:15,504 (beam_search:483) INFO: best hypo: MARSIALSHAVEROFSLASHFILNMEDGAVETHEFILMEANDATOUTOFCAN + +2024-01-16 21:40:15,505 (asr_inference:494) INFO: speech length: 41088 +2024-01-16 21:40:15,513 (beam_search:428) INFO: decoder input length: 62 +2024-01-16 21:40:15,513 (beam_search:429) INFO: max output length: 62 +2024-01-16 21:40:15,513 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:15,554 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:15,555 (beam_search:476) INFO: -10.59 * 1.0 = -10.59 for ctc +2024-01-16 21:40:15,555 (beam_search:479) INFO: total log probability: -10.59 +2024-01-16 21:40:15,555 (beam_search:480) INFO: normalized log probability: -0.53 +2024-01-16 21:40:15,555 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:15,555 (beam_search:483) INFO: best hypo: HOWPERDYITITATE + +2024-01-16 21:40:15,556 (asr_inference:494) INFO: speech length: 117504 +2024-01-16 21:40:15,568 (beam_search:428) INFO: decoder input length: 181 +2024-01-16 21:40:15,568 (beam_search:429) INFO: max output length: 181 +2024-01-16 21:40:15,568 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:15,882 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:15,882 (beam_search:476) INFO: -12.15 * 1.0 = -12.15 for ctc +2024-01-16 21:40:15,882 (beam_search:479) INFO: total log probability: -12.15 +2024-01-16 21:40:15,882 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:40:15,882 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:15,883 (beam_search:483) INFO: best hypo: HISTTDILEBEGANETOESAEMBLETEMYIKCLETDEMOSKINOSES + +2024-01-16 21:40:15,884 (asr_inference:494) INFO: speech length: 96768 +2024-01-16 21:40:15,895 (beam_search:428) INFO: decoder input length: 149 +2024-01-16 21:40:15,895 (beam_search:429) INFO: max output length: 149 +2024-01-16 21:40:15,895 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:16,154 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:16,154 (beam_search:476) INFO: -10.38 * 1.0 = -10.38 for ctc +2024-01-16 21:40:16,154 (beam_search:479) INFO: total log probability: -10.38 +2024-01-16 21:40:16,154 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:40:16,154 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:16,154 (beam_search:483) INFO: best hypo: HISALLCAPABLFINGLYTINBLTWOEAMENTEDESRUPTOFPOER + +2024-01-16 21:40:16,155 (asr_inference:494) INFO: speech length: 133248 +2024-01-16 21:40:16,169 (beam_search:428) INFO: decoder input length: 206 +2024-01-16 21:40:16,169 (beam_search:429) INFO: max output length: 206 +2024-01-16 21:40:16,169 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:16,698 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:16,698 (beam_search:476) INFO: -28.32 * 1.0 = -28.32 for ctc +2024-01-16 21:40:16,698 (beam_search:479) INFO: total log probability: -28.32 +2024-01-16 21:40:16,698 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 21:40:16,698 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:16,699 (beam_search:483) INFO: best hypo: THEFLAMETOWIAKCSCININGLINIRMLYININGSASFPOLHELISTEATONDUILRLNDASELANDDTTRY + +2024-01-16 21:40:16,700 (asr_inference:494) INFO: speech length: 44928 +2024-01-16 21:40:16,709 (beam_search:428) INFO: decoder input length: 68 +2024-01-16 21:40:16,709 (beam_search:429) INFO: max output length: 68 +2024-01-16 21:40:16,709 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:16,779 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:16,779 (beam_search:476) INFO: -7.41 * 1.0 = -7.41 for ctc +2024-01-16 21:40:16,779 (beam_search:479) INFO: total log probability: -7.41 +2024-01-16 21:40:16,779 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:40:16,779 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:16,779 (beam_search:483) INFO: best hypo: SHEDETEROUSHELYTOROWOATAH + +2024-01-16 21:40:16,781 (asr_inference:494) INFO: speech length: 95616 +2024-01-16 21:40:16,792 (beam_search:428) INFO: decoder input length: 147 +2024-01-16 21:40:16,792 (beam_search:429) INFO: max output length: 147 +2024-01-16 21:40:16,792 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:17,083 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:17,083 (beam_search:476) INFO: -12.02 * 1.0 = -12.02 for ctc +2024-01-16 21:40:17,083 (beam_search:479) INFO: total log probability: -12.02 +2024-01-16 21:40:17,083 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:40:17,083 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:17,084 (beam_search:483) INFO: best hypo: AEMTTHEORGONYSEORSOFTHEROTESEANDAGREDCREATTWOWORKINGRUMS + +2024-01-16 21:40:17,085 (asr_inference:494) INFO: speech length: 101952 +2024-01-16 21:40:17,096 (beam_search:428) INFO: decoder input length: 157 +2024-01-16 21:40:17,096 (beam_search:429) INFO: max output length: 157 +2024-01-16 21:40:17,096 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:17,388 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:17,389 (beam_search:476) INFO: -14.98 * 1.0 = -14.98 for ctc +2024-01-16 21:40:17,389 (beam_search:479) INFO: total log probability: -14.98 +2024-01-16 21:40:17,389 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:40:17,389 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:17,389 (beam_search:483) INFO: best hypo: ATHEBONSTROCTTHOFFHALDPORDWHILEOABOFHIGRENONSTORD + +2024-01-16 21:40:17,390 (asr_inference:494) INFO: speech length: 114048 +2024-01-16 21:40:17,402 (beam_search:428) INFO: decoder input length: 176 +2024-01-16 21:40:17,403 (beam_search:429) INFO: max output length: 176 +2024-01-16 21:40:17,403 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:17,796 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:17,796 (beam_search:476) INFO: -14.56 * 1.0 = -14.56 for ctc +2024-01-16 21:40:17,796 (beam_search:479) INFO: total log probability: -14.56 +2024-01-16 21:40:17,796 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:40:17,796 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:17,796 (beam_search:483) INFO: best hypo: ONLYCAMEDONTOMESGARITANDGLDFILDSOUISEILBACKEARWERUNDCONTISTED + +2024-01-16 21:40:17,797 (asr_inference:494) INFO: speech length: 148224 +2024-01-16 21:40:17,812 (beam_search:428) INFO: decoder input length: 229 +2024-01-16 21:40:17,812 (beam_search:429) INFO: max output length: 229 +2024-01-16 21:40:17,812 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:18,282 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:18,282 (beam_search:476) INFO: -16.34 * 1.0 = -16.34 for ctc +2024-01-16 21:40:18,282 (beam_search:479) INFO: total log probability: -16.34 +2024-01-16 21:40:18,282 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:40:18,282 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:18,283 (beam_search:483) INFO: best hypo: BPITHISADCHIRDYSCOLEWHOSFESANCOUCKILADINONAINMEANSTEST + +2024-01-16 21:40:18,284 (asr_inference:494) INFO: speech length: 67200 +2024-01-16 21:40:18,293 (beam_search:428) INFO: decoder input length: 102 +2024-01-16 21:40:18,293 (beam_search:429) INFO: max output length: 102 +2024-01-16 21:40:18,293 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:18,441 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:18,441 (beam_search:476) INFO: -9.96 * 1.0 = -9.96 for ctc +2024-01-16 21:40:18,441 (beam_search:479) INFO: total log probability: -9.96 +2024-01-16 21:40:18,441 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:40:18,441 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:18,441 (beam_search:483) INFO: best hypo: SOMEWENTWAYWHLOUWASERANDOTHEPEPECAMEM + +2024-01-16 21:40:18,443 (asr_inference:494) INFO: speech length: 26496 +2024-01-16 21:40:18,450 (beam_search:428) INFO: decoder input length: 39 +2024-01-16 21:40:18,450 (beam_search:429) INFO: max output length: 39 +2024-01-16 21:40:18,450 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:18,483 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:18,484 (beam_search:476) INFO: -29.90 * 1.0 = -29.90 for ctc +2024-01-16 21:40:18,484 (beam_search:479) INFO: total log probability: -29.90 +2024-01-16 21:40:18,484 (beam_search:480) INFO: normalized log probability: -1.20 +2024-01-16 21:40:18,484 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:18,484 (beam_search:483) INFO: best hypo: DADTTHANTHAHTDTCTCDNCN + +2024-01-16 21:40:18,485 (asr_inference:494) INFO: speech length: 130752 +2024-01-16 21:40:18,498 (beam_search:428) INFO: decoder input length: 202 +2024-01-16 21:40:18,498 (beam_search:429) INFO: max output length: 202 +2024-01-16 21:40:18,498 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:18,978 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:18,978 (beam_search:476) INFO: -9.49 * 1.0 = -9.49 for ctc +2024-01-16 21:40:18,978 (beam_search:479) INFO: total log probability: -9.49 +2024-01-16 21:40:18,978 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:40:18,978 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:18,978 (beam_search:483) INFO: best hypo: THATCURACONOTYWASLOKCADMANLYTHATHISTORICLEANDDEOGREFOCLEREGIONOFCURE + +2024-01-16 21:40:18,980 (asr_inference:494) INFO: speech length: 95040 +2024-01-16 21:40:18,991 (beam_search:428) INFO: decoder input length: 146 +2024-01-16 21:40:18,991 (beam_search:429) INFO: max output length: 146 +2024-01-16 21:40:18,991 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:19,182 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:19,183 (beam_search:476) INFO: -12.41 * 1.0 = -12.41 for ctc +2024-01-16 21:40:19,183 (beam_search:479) INFO: total log probability: -12.41 +2024-01-16 21:40:19,183 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:40:19,183 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:19,183 (beam_search:483) INFO: best hypo: TELVATIOATHESIHTISAMOFSILEVELEIG + +2024-01-16 21:40:19,184 (asr_inference:494) INFO: speech length: 122496 +2024-01-16 21:40:19,196 (beam_search:428) INFO: decoder input length: 189 +2024-01-16 21:40:19,196 (beam_search:429) INFO: max output length: 189 +2024-01-16 21:40:19,196 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:19,525 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:19,526 (beam_search:476) INFO: -14.01 * 1.0 = -14.01 for ctc +2024-01-16 21:40:19,526 (beam_search:479) INFO: total log probability: -14.01 +2024-01-16 21:40:19,526 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:40:19,526 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:19,526 (beam_search:483) INFO: best hypo: ATOBASTRIDETOANCHECTTCONPTEMETETEDINTOHISTON + +2024-01-16 21:40:19,527 (asr_inference:494) INFO: speech length: 72960 +2024-01-16 21:40:19,537 (beam_search:428) INFO: decoder input length: 111 +2024-01-16 21:40:19,537 (beam_search:429) INFO: max output length: 111 +2024-01-16 21:40:19,537 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:19,649 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:19,649 (beam_search:476) INFO: -6.12 * 1.0 = -6.12 for ctc +2024-01-16 21:40:19,649 (beam_search:479) INFO: total log probability: -6.12 +2024-01-16 21:40:19,649 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:40:19,649 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:19,649 (beam_search:483) INFO: best hypo: IHEAVETOWORKETHISSITODLY + +2024-01-16 21:40:19,650 (asr_inference:494) INFO: speech length: 124032 +2024-01-16 21:40:19,663 (beam_search:428) INFO: decoder input length: 191 +2024-01-16 21:40:19,663 (beam_search:429) INFO: max output length: 191 +2024-01-16 21:40:19,663 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:20,066 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:20,066 (beam_search:476) INFO: -19.59 * 1.0 = -19.59 for ctc +2024-01-16 21:40:20,066 (beam_search:479) INFO: total log probability: -19.59 +2024-01-16 21:40:20,066 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 21:40:20,066 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:20,066 (beam_search:483) INFO: best hypo: UTEDRATHERONWOSFENMWASKILAGEAGLEADWLAFGLATINGONTERENONES + +2024-01-16 21:40:20,067 (asr_inference:494) INFO: speech length: 139392 +2024-01-16 21:40:20,082 (beam_search:428) INFO: decoder input length: 215 +2024-01-16 21:40:20,082 (beam_search:429) INFO: max output length: 215 +2024-01-16 21:40:20,082 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:20,543 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:20,543 (beam_search:476) INFO: -12.23 * 1.0 = -12.23 for ctc +2024-01-16 21:40:20,543 (beam_search:479) INFO: total log probability: -12.23 +2024-01-16 21:40:20,544 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:40:20,544 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:20,544 (beam_search:483) INFO: best hypo: WHEOTHEBILINGDOSTHESELEDRBEITETHEBOYTROMBLEDTDATWHAHESO + +2024-01-16 21:40:20,545 (asr_inference:494) INFO: speech length: 100800 +2024-01-16 21:40:20,556 (beam_search:428) INFO: decoder input length: 155 +2024-01-16 21:40:20,556 (beam_search:429) INFO: max output length: 155 +2024-01-16 21:40:20,556 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:20,807 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:20,807 (beam_search:476) INFO: -11.95 * 1.0 = -11.95 for ctc +2024-01-16 21:40:20,807 (beam_search:479) INFO: total log probability: -11.95 +2024-01-16 21:40:20,807 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:40:20,807 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:20,808 (beam_search:483) INFO: best hypo: DEIOCRATANEBRHINDDBAKEIERWONITTHEOPENSEO + +2024-01-16 21:40:20,809 (asr_inference:494) INFO: speech length: 109440 +2024-01-16 21:40:20,821 (beam_search:428) INFO: decoder input length: 168 +2024-01-16 21:40:20,821 (beam_search:429) INFO: max output length: 168 +2024-01-16 21:40:20,821 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:21,208 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:21,208 (beam_search:476) INFO: -31.50 * 1.0 = -31.50 for ctc +2024-01-16 21:40:21,208 (beam_search:479) INFO: total log probability: -31.50 +2024-01-16 21:40:21,208 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-16 21:40:21,208 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:21,208 (beam_search:483) INFO: best hypo: LWORYOUETWORDINOGEATHABLYTHEOODININHECULDYUSEOANATLYSINDTOLROUMNP + +2024-01-16 21:40:21,209 (asr_inference:494) INFO: speech length: 92736 +2024-01-16 21:40:21,220 (beam_search:428) INFO: decoder input length: 142 +2024-01-16 21:40:21,220 (beam_search:429) INFO: max output length: 142 +2024-01-16 21:40:21,220 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:21,463 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:21,463 (beam_search:476) INFO: -11.19 * 1.0 = -11.19 for ctc +2024-01-16 21:40:21,463 (beam_search:479) INFO: total log probability: -11.19 +2024-01-16 21:40:21,463 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:40:21,463 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:21,463 (beam_search:483) INFO: best hypo: TRANTHWWASBORNINBELYESESITEDINDBRITOCSPONDREASS + +2024-01-16 21:40:21,464 (asr_inference:494) INFO: speech length: 52992 +2024-01-16 21:40:21,473 (beam_search:428) INFO: decoder input length: 80 +2024-01-16 21:40:21,473 (beam_search:429) INFO: max output length: 80 +2024-01-16 21:40:21,473 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:21,560 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:21,560 (beam_search:476) INFO: -7.48 * 1.0 = -7.48 for ctc +2024-01-16 21:40:21,560 (beam_search:479) INFO: total log probability: -7.48 +2024-01-16 21:40:21,560 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:40:21,560 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:21,560 (beam_search:483) INFO: best hypo: DERIRDYFASEOFLICFMOMESFAST + +2024-01-16 21:40:21,561 (asr_inference:494) INFO: speech length: 46464 +2024-01-16 21:40:21,569 (beam_search:428) INFO: decoder input length: 70 +2024-01-16 21:40:21,569 (beam_search:429) INFO: max output length: 70 +2024-01-16 21:40:21,569 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:21,610 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:21,610 (beam_search:476) INFO: -15.10 * 1.0 = -15.10 for ctc +2024-01-16 21:40:21,610 (beam_search:479) INFO: total log probability: -15.10 +2024-01-16 21:40:21,610 (beam_search:480) INFO: normalized log probability: -0.89 +2024-01-16 21:40:21,610 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:21,610 (beam_search:483) INFO: best hypo: AAATNOTETHDEE + +2024-01-16 21:40:21,612 (asr_inference:494) INFO: speech length: 52992 +2024-01-16 21:40:21,620 (beam_search:428) INFO: decoder input length: 80 +2024-01-16 21:40:21,620 (beam_search:429) INFO: max output length: 80 +2024-01-16 21:40:21,620 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:21,685 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:21,685 (beam_search:476) INFO: -11.55 * 1.0 = -11.55 for ctc +2024-01-16 21:40:21,685 (beam_search:479) INFO: total log probability: -11.55 +2024-01-16 21:40:21,685 (beam_search:480) INFO: normalized log probability: -0.48 +2024-01-16 21:40:21,685 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:21,685 (beam_search:483) INFO: best hypo: SORVEWINTOLTDLUETOEE + +2024-01-16 21:40:21,686 (asr_inference:494) INFO: speech length: 102144 +2024-01-16 21:40:21,697 (beam_search:428) INFO: decoder input length: 157 +2024-01-16 21:40:21,697 (beam_search:429) INFO: max output length: 157 +2024-01-16 21:40:21,697 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:22,035 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:22,035 (beam_search:476) INFO: -14.79 * 1.0 = -14.79 for ctc +2024-01-16 21:40:22,035 (beam_search:479) INFO: total log probability: -14.79 +2024-01-16 21:40:22,035 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:40:22,035 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:22,036 (beam_search:483) INFO: best hypo: ATOETERELURLENSTEYEORDFOMBRAKGBESTATIONINSOENIFRENDEDECTIONS + +2024-01-16 21:40:22,037 (asr_inference:494) INFO: speech length: 88128 +2024-01-16 21:40:22,048 (beam_search:428) INFO: decoder input length: 135 +2024-01-16 21:40:22,048 (beam_search:429) INFO: max output length: 135 +2024-01-16 21:40:22,048 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:22,337 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:22,337 (beam_search:476) INFO: -14.68 * 1.0 = -14.68 for ctc +2024-01-16 21:40:22,337 (beam_search:479) INFO: total log probability: -14.68 +2024-01-16 21:40:22,337 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:40:22,337 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:22,337 (beam_search:483) INFO: best hypo: AACHAECREPUPBLIKANTEREDTWOSHOUTERSINTOTHEPARROROLENPOGCOMPOTISIO + +2024-01-16 21:40:22,338 (asr_inference:494) INFO: speech length: 126336 +2024-01-16 21:40:22,352 (beam_search:428) INFO: decoder input length: 195 +2024-01-16 21:40:22,352 (beam_search:429) INFO: max output length: 195 +2024-01-16 21:40:22,352 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:22,799 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:22,799 (beam_search:476) INFO: -19.44 * 1.0 = -19.44 for ctc +2024-01-16 21:40:22,799 (beam_search:479) INFO: total log probability: -19.44 +2024-01-16 21:40:22,799 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:40:22,799 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:22,799 (beam_search:483) INFO: best hypo: TIDHERWILIMSROETHESKANGPLAYHANTSHAREDSTORYREDITHTTHOPREIPIT + +2024-01-16 21:40:22,801 (asr_inference:494) INFO: speech length: 132864 +2024-01-16 21:40:22,814 (beam_search:428) INFO: decoder input length: 205 +2024-01-16 21:40:22,814 (beam_search:429) INFO: max output length: 205 +2024-01-16 21:40:22,815 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:23,311 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:23,311 (beam_search:476) INFO: -22.95 * 1.0 = -22.95 for ctc +2024-01-16 21:40:23,311 (beam_search:479) INFO: total log probability: -22.95 +2024-01-16 21:40:23,311 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 21:40:23,311 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:23,312 (beam_search:483) INFO: best hypo: TISFASTOFELLEDWORDESTOAFBETERDNCHEIRRITYFLINDERATNYSAIDEOFOIDYETHERAURT + +2024-01-16 21:40:23,313 (asr_inference:494) INFO: speech length: 104064 +2024-01-16 21:40:23,325 (beam_search:428) INFO: decoder input length: 160 +2024-01-16 21:40:23,325 (beam_search:429) INFO: max output length: 160 +2024-01-16 21:40:23,325 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:23,715 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:23,715 (beam_search:476) INFO: -25.16 * 1.0 = -25.16 for ctc +2024-01-16 21:40:23,715 (beam_search:479) INFO: total log probability: -25.16 +2024-01-16 21:40:23,715 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:40:23,715 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:23,715 (beam_search:483) INFO: best hypo: OTHESEENXTRAGOARSTSWENESEURNLTTHEDONGONLEALNMTOFOACKGALFTERAYHATHEIRHARDTS + +2024-01-16 21:40:23,717 (asr_inference:494) INFO: speech length: 67968 +2024-01-16 21:40:23,726 (beam_search:428) INFO: decoder input length: 104 +2024-01-16 21:40:23,726 (beam_search:429) INFO: max output length: 104 +2024-01-16 21:40:23,726 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:23,844 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:23,844 (beam_search:476) INFO: -12.64 * 1.0 = -12.64 for ctc +2024-01-16 21:40:23,844 (beam_search:479) INFO: total log probability: -12.64 +2024-01-16 21:40:23,844 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 21:40:23,844 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:23,845 (beam_search:483) INFO: best hypo: AIPIHANDERONDBAKCTTOESTRLIOWM + +2024-01-16 21:40:23,846 (asr_inference:494) INFO: speech length: 88704 +2024-01-16 21:40:23,856 (beam_search:428) INFO: decoder input length: 136 +2024-01-16 21:40:23,856 (beam_search:429) INFO: max output length: 136 +2024-01-16 21:40:23,856 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:24,082 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:24,082 (beam_search:476) INFO: -8.42 * 1.0 = -8.42 for ctc +2024-01-16 21:40:24,082 (beam_search:479) INFO: total log probability: -8.42 +2024-01-16 21:40:24,082 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:40:24,082 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:24,083 (beam_search:483) INFO: best hypo: ALPREMITMETTOINTERDUSESYOUTOHERMODESTIDCQREN + +2024-01-16 21:40:24,084 (asr_inference:494) INFO: speech length: 77568 +2024-01-16 21:40:24,094 (beam_search:428) INFO: decoder input length: 119 +2024-01-16 21:40:24,094 (beam_search:429) INFO: max output length: 119 +2024-01-16 21:40:24,094 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:24,314 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:24,314 (beam_search:476) INFO: -13.19 * 1.0 = -13.19 for ctc +2024-01-16 21:40:24,314 (beam_search:479) INFO: total log probability: -13.19 +2024-01-16 21:40:24,314 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:40:24,314 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:24,314 (beam_search:483) INFO: best hypo: ENORGEIONHERLENWASBOSTOYTHENONDADETDOFMORFHENSPSTOTD + +2024-01-16 21:40:24,316 (asr_inference:494) INFO: speech length: 86400 +2024-01-16 21:40:24,326 (beam_search:428) INFO: decoder input length: 132 +2024-01-16 21:40:24,326 (beam_search:429) INFO: max output length: 132 +2024-01-16 21:40:24,326 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:24,469 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:24,469 (beam_search:476) INFO: -9.86 * 1.0 = -9.86 for ctc +2024-01-16 21:40:24,469 (beam_search:479) INFO: total log probability: -9.86 +2024-01-16 21:40:24,469 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:40:24,469 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:24,469 (beam_search:483) INFO: best hypo: ETSHEISOFMAKCOCONDESESNTH + +2024-01-16 21:40:24,470 (asr_inference:494) INFO: speech length: 77568 +2024-01-16 21:40:24,480 (beam_search:428) INFO: decoder input length: 119 +2024-01-16 21:40:24,480 (beam_search:429) INFO: max output length: 119 +2024-01-16 21:40:24,480 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:24,617 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:24,617 (beam_search:476) INFO: -7.40 * 1.0 = -7.40 for ctc +2024-01-16 21:40:24,617 (beam_search:479) INFO: total log probability: -7.40 +2024-01-16 21:40:24,617 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:40:24,617 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:24,617 (beam_search:483) INFO: best hypo: GDIMESORETHEILESTNOGTONDIST + +2024-01-16 21:40:24,619 (asr_inference:494) INFO: speech length: 87168 +2024-01-16 21:40:24,629 (beam_search:428) INFO: decoder input length: 134 +2024-01-16 21:40:24,629 (beam_search:429) INFO: max output length: 134 +2024-01-16 21:40:24,629 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:24,871 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:24,871 (beam_search:476) INFO: -18.79 * 1.0 = -18.79 for ctc +2024-01-16 21:40:24,872 (beam_search:479) INFO: total log probability: -18.79 +2024-01-16 21:40:24,872 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 21:40:24,872 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:24,872 (beam_search:483) INFO: best hypo: AAOTHOSANDONTELANCTLOLTHESHREYIGDLOLDPORABEDITONO + +2024-01-16 21:40:24,873 (asr_inference:494) INFO: speech length: 79488 +2024-01-16 21:40:24,884 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 21:40:24,884 (beam_search:429) INFO: max output length: 122 +2024-01-16 21:40:24,884 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:25,016 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:25,016 (beam_search:476) INFO: -8.89 * 1.0 = -8.89 for ctc +2024-01-16 21:40:25,016 (beam_search:479) INFO: total log probability: -8.89 +2024-01-16 21:40:25,016 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:40:25,016 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:25,016 (beam_search:483) INFO: best hypo: AICOLEDANTOPESORINATITEER + +2024-01-16 21:40:25,018 (asr_inference:494) INFO: speech length: 84010 +2024-01-16 21:40:25,028 (beam_search:428) INFO: decoder input length: 129 +2024-01-16 21:40:25,028 (beam_search:429) INFO: max output length: 129 +2024-01-16 21:40:25,028 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:25,287 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:25,287 (beam_search:476) INFO: -9.96 * 1.0 = -9.96 for ctc +2024-01-16 21:40:25,287 (beam_search:479) INFO: total log probability: -9.96 +2024-01-16 21:40:25,287 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:40:25,287 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:25,288 (beam_search:483) INFO: best hypo: FORSINPLITYGURINCHESDISNORMILYARDOUNDEDTOHENERESHHOLNOMBER + +2024-01-16 21:40:25,289 (asr_inference:494) INFO: speech length: 79872 +2024-01-16 21:40:25,299 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 21:40:25,299 (beam_search:429) INFO: max output length: 122 +2024-01-16 21:40:25,299 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:25,458 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:25,458 (beam_search:476) INFO: -6.22 * 1.0 = -6.22 for ctc +2024-01-16 21:40:25,458 (beam_search:479) INFO: total log probability: -6.22 +2024-01-16 21:40:25,458 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:40:25,458 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:25,458 (beam_search:483) INFO: best hypo: IOFWEACTILYDEOONISSOLDITWILBEF + +2024-01-16 21:40:25,459 (asr_inference:494) INFO: speech length: 65664 +2024-01-16 21:40:25,469 (beam_search:428) INFO: decoder input length: 100 +2024-01-16 21:40:25,469 (beam_search:429) INFO: max output length: 100 +2024-01-16 21:40:25,469 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:25,585 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:25,586 (beam_search:476) INFO: -9.87 * 1.0 = -9.87 for ctc +2024-01-16 21:40:25,586 (beam_search:479) INFO: total log probability: -9.87 +2024-01-16 21:40:25,586 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 21:40:25,586 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:25,586 (beam_search:483) INFO: best hypo: THEIFOOFHHICTRYSAPLSHAPEDT + +2024-01-16 21:40:25,587 (asr_inference:494) INFO: speech length: 63360 +2024-01-16 21:40:25,596 (beam_search:428) INFO: decoder input length: 96 +2024-01-16 21:40:25,596 (beam_search:429) INFO: max output length: 96 +2024-01-16 21:40:25,596 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:25,698 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:25,698 (beam_search:476) INFO: -9.62 * 1.0 = -9.62 for ctc +2024-01-16 21:40:25,698 (beam_search:479) INFO: total log probability: -9.62 +2024-01-16 21:40:25,698 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:40:25,698 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:25,698 (beam_search:483) INFO: best hypo: THEREIEACTSHANGEISNOWOBLY + +2024-01-16 21:40:25,699 (asr_inference:494) INFO: speech length: 112512 +2024-01-16 21:40:25,711 (beam_search:428) INFO: decoder input length: 173 +2024-01-16 21:40:25,711 (beam_search:429) INFO: max output length: 173 +2024-01-16 21:40:25,711 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:25,965 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:25,965 (beam_search:476) INFO: -11.37 * 1.0 = -11.37 for ctc +2024-01-16 21:40:25,965 (beam_search:479) INFO: total log probability: -11.37 +2024-01-16 21:40:25,965 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:40:25,965 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:25,965 (beam_search:483) INFO: best hypo: AWHATOUEATTODAYWHEASKANDTORKSTOMOROE + +2024-01-16 21:40:25,967 (asr_inference:494) INFO: speech length: 82944 +2024-01-16 21:40:25,977 (beam_search:428) INFO: decoder input length: 127 +2024-01-16 21:40:25,977 (beam_search:429) INFO: max output length: 127 +2024-01-16 21:40:25,977 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:26,204 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:26,204 (beam_search:476) INFO: -8.69 * 1.0 = -8.69 for ctc +2024-01-16 21:40:26,204 (beam_search:479) INFO: total log probability: -8.69 +2024-01-16 21:40:26,204 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:40:26,204 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:26,205 (beam_search:483) INFO: best hypo: ATHEWOTEDANFLOSOUTOFTESCONMNTSASHELOOAPLERIVER + +2024-01-16 21:40:26,206 (asr_inference:494) INFO: speech length: 104064 +2024-01-16 21:40:26,217 (beam_search:428) INFO: decoder input length: 160 +2024-01-16 21:40:26,217 (beam_search:429) INFO: max output length: 160 +2024-01-16 21:40:26,217 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:26,431 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:26,432 (beam_search:476) INFO: -20.56 * 1.0 = -20.56 for ctc +2024-01-16 21:40:26,432 (beam_search:479) INFO: total log probability: -20.56 +2024-01-16 21:40:26,432 (beam_search:480) INFO: normalized log probability: -0.47 +2024-01-16 21:40:26,432 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:26,432 (beam_search:483) INFO: best hypo: AMHEWHIYIDDIONYOESAYSOMTHINKCDHEADCD + +2024-01-16 21:40:26,433 (asr_inference:494) INFO: speech length: 56448 +2024-01-16 21:40:26,442 (beam_search:428) INFO: decoder input length: 86 +2024-01-16 21:40:26,442 (beam_search:429) INFO: max output length: 86 +2024-01-16 21:40:26,442 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:26,526 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:26,526 (beam_search:476) INFO: -14.64 * 1.0 = -14.64 for ctc +2024-01-16 21:40:26,526 (beam_search:479) INFO: total log probability: -14.64 +2024-01-16 21:40:26,526 (beam_search:480) INFO: normalized log probability: -0.47 +2024-01-16 21:40:26,526 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:26,526 (beam_search:483) INFO: best hypo: TAVEYOSENOMARNMEETDEEEEE + +2024-01-16 21:40:26,527 (asr_inference:494) INFO: speech length: 110976 +2024-01-16 21:40:26,540 (beam_search:428) INFO: decoder input length: 171 +2024-01-16 21:40:26,540 (beam_search:429) INFO: max output length: 171 +2024-01-16 21:40:26,540 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:26,922 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:26,922 (beam_search:476) INFO: -16.92 * 1.0 = -16.92 for ctc +2024-01-16 21:40:26,922 (beam_search:479) INFO: total log probability: -16.92 +2024-01-16 21:40:26,922 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:40:26,922 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:26,922 (beam_search:483) INFO: best hypo: ICOTDGOONEFREDAISABOUTHEDIDIOSWONSTHEDUASTINHISPARTOFTHEWERETD + +2024-01-16 21:40:26,924 (asr_inference:494) INFO: speech length: 99648 +2024-01-16 21:40:26,935 (beam_search:428) INFO: decoder input length: 153 +2024-01-16 21:40:26,935 (beam_search:429) INFO: max output length: 153 +2024-01-16 21:40:26,935 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:27,233 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:27,233 (beam_search:476) INFO: -21.38 * 1.0 = -21.38 for ctc +2024-01-16 21:40:27,233 (beam_search:479) INFO: total log probability: -21.38 +2024-01-16 21:40:27,233 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 21:40:27,233 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:27,233 (beam_search:483) INFO: best hypo: THOSOEOLADDEVFTHEARINCOREIRERAENINGDINSITOPCEYOTTHEYOARE + +2024-01-16 21:40:27,235 (asr_inference:494) INFO: speech length: 88704 +2024-01-16 21:40:27,245 (beam_search:428) INFO: decoder input length: 136 +2024-01-16 21:40:27,245 (beam_search:429) INFO: max output length: 136 +2024-01-16 21:40:27,245 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:27,464 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:27,464 (beam_search:476) INFO: -29.10 * 1.0 = -29.10 for ctc +2024-01-16 21:40:27,464 (beam_search:479) INFO: total log probability: -29.10 +2024-01-16 21:40:27,464 (beam_search:480) INFO: normalized log probability: -0.55 +2024-01-16 21:40:27,464 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:27,464 (beam_search:483) INFO: best hypo: AACOASEVESOPBDCECTISESERGLWNDSHEECLDELDARDL + +2024-01-16 21:40:27,465 (asr_inference:494) INFO: speech length: 91584 +2024-01-16 21:40:27,476 (beam_search:428) INFO: decoder input length: 141 +2024-01-16 21:40:27,476 (beam_search:429) INFO: max output length: 141 +2024-01-16 21:40:27,476 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:27,743 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:27,743 (beam_search:476) INFO: -12.53 * 1.0 = -12.53 for ctc +2024-01-16 21:40:27,743 (beam_search:479) INFO: total log probability: -12.53 +2024-01-16 21:40:27,743 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:40:27,743 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:27,743 (beam_search:483) INFO: best hypo: THESWEEDSWERANABLTOYOUSHRVEACALSWHIHERTOCEINTHMODE + +2024-01-16 21:40:27,744 (asr_inference:494) INFO: speech length: 132480 +2024-01-16 21:40:27,758 (beam_search:428) INFO: decoder input length: 204 +2024-01-16 21:40:27,758 (beam_search:429) INFO: max output length: 204 +2024-01-16 21:40:27,758 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:28,218 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:28,218 (beam_search:476) INFO: -15.79 * 1.0 = -15.79 for ctc +2024-01-16 21:40:28,218 (beam_search:479) INFO: total log probability: -15.79 +2024-01-16 21:40:28,218 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:40:28,218 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:28,218 (beam_search:483) INFO: best hypo: ITHEACKTIDNOTPORHEBITBAYINGAEREPEOSENTIPTOEBEARANTHECORTLIS + +2024-01-16 21:40:28,220 (asr_inference:494) INFO: speech length: 44544 +2024-01-16 21:40:28,228 (beam_search:428) INFO: decoder input length: 67 +2024-01-16 21:40:28,228 (beam_search:429) INFO: max output length: 67 +2024-01-16 21:40:28,228 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:28,292 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:28,292 (beam_search:476) INFO: -9.83 * 1.0 = -9.83 for ctc +2024-01-16 21:40:28,292 (beam_search:479) INFO: total log probability: -9.83 +2024-01-16 21:40:28,292 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 21:40:28,292 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:28,292 (beam_search:483) INFO: best hypo: CHONWRPLESTELEPINROULTIDN + +2024-01-16 21:40:28,293 (asr_inference:494) INFO: speech length: 105984 +2024-01-16 21:40:28,305 (beam_search:428) INFO: decoder input length: 163 +2024-01-16 21:40:28,305 (beam_search:429) INFO: max output length: 163 +2024-01-16 21:40:28,305 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:28,610 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:28,610 (beam_search:476) INFO: -14.27 * 1.0 = -14.27 for ctc +2024-01-16 21:40:28,610 (beam_search:479) INFO: total log probability: -14.27 +2024-01-16 21:40:28,610 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:40:28,611 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:28,611 (beam_search:483) INFO: best hypo: HEWACONVICTDEDAMBANISOSIPRSFORSOVIONHORSRPNISMENT + +2024-01-16 21:40:28,612 (asr_inference:494) INFO: speech length: 122496 +2024-01-16 21:40:28,624 (beam_search:428) INFO: decoder input length: 189 +2024-01-16 21:40:28,624 (beam_search:429) INFO: max output length: 189 +2024-01-16 21:40:28,624 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:29,067 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:29,067 (beam_search:476) INFO: -15.61 * 1.0 = -15.61 for ctc +2024-01-16 21:40:29,067 (beam_search:479) INFO: total log probability: -15.61 +2024-01-16 21:40:29,067 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:40:29,067 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:29,067 (beam_search:483) INFO: best hypo: THECOPLOFTWOCHOLDONADATERSOFEAURRSELENDEAANTHESINFMUTHALBRVERY + +2024-01-16 21:40:29,069 (asr_inference:494) INFO: speech length: 117504 +2024-01-16 21:40:29,081 (beam_search:428) INFO: decoder input length: 181 +2024-01-16 21:40:29,081 (beam_search:429) INFO: max output length: 181 +2024-01-16 21:40:29,081 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:29,456 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:29,456 (beam_search:476) INFO: -11.63 * 1.0 = -11.63 for ctc +2024-01-16 21:40:29,456 (beam_search:479) INFO: total log probability: -11.63 +2024-01-16 21:40:29,456 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:40:29,456 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:29,456 (beam_search:483) INFO: best hypo: NOFTHTHRERERENDEMSRECHHECUARAMOFHEMODGURITYOFHESINTICTLDT + +2024-01-16 21:40:29,458 (asr_inference:494) INFO: speech length: 160128 +2024-01-16 21:40:29,473 (beam_search:428) INFO: decoder input length: 248 +2024-01-16 21:40:29,473 (beam_search:429) INFO: max output length: 248 +2024-01-16 21:40:29,473 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:30,178 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:30,178 (beam_search:476) INFO: -23.45 * 1.0 = -23.45 for ctc +2024-01-16 21:40:30,178 (beam_search:479) INFO: total log probability: -23.45 +2024-01-16 21:40:30,178 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:40:30,178 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:30,178 (beam_search:483) INFO: best hypo: IETITERPENSAEXCEADEDINDERASTSOMERROSIPCAIRAITIINHOSOLEUNERSYOTHORAPEIRDOSTONGBOATH + +# Accounting: time=105 threads=1 +# Ended (code 0) at Tue Jan 16 21:40:30 CST 2024, elapsed time 105 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.2.log b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.2.log new file mode 100644 index 0000000000000000000000000000000000000000..24ef1c140d0ebeccccfa1d524151177c4835e6ce --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.2.log @@ -0,0 +1,3022 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.2 --config conf/decode_asr.yaml +# Started at Tue Jan 16 21:40:30 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.2 --config conf/decode_asr.yaml +2024-01-16 21:40:31,991 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml +2024-01-16 21:40:32,009 (asr:523) INFO: Vocabulary size: 30 +2024-01-16 21:40:32,071 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 21:40:32,071 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 21:40:32,183 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 21:40:33,490 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 21:40:34,718 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 21:40:34,718 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 21:40:34,718 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 21:40:34,751 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-16 21:40:34,826 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 21:40:34,941 (asr_inference:494) INFO: speech length: 95232 +2024-01-16 21:40:36,140 (beam_search:428) INFO: decoder input length: 146 +2024-01-16 21:40:36,141 (beam_search:429) INFO: max output length: 146 +2024-01-16 21:40:36,141 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:36,405 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:36,406 (beam_search:476) INFO: -10.95 * 1.0 = -10.95 for ctc +2024-01-16 21:40:36,406 (beam_search:479) INFO: total log probability: -10.95 +2024-01-16 21:40:36,406 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:40:36,406 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:36,406 (beam_search:483) INFO: best hypo: WHEARAAMEBECWENMYFLUCKANDMTHBUTERSIURETHEBOLYTOS + +2024-01-16 21:40:36,430 (asr_inference:494) INFO: speech length: 95040 +2024-01-16 21:40:36,441 (beam_search:428) INFO: decoder input length: 146 +2024-01-16 21:40:36,441 (beam_search:429) INFO: max output length: 146 +2024-01-16 21:40:36,441 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:36,742 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:36,742 (beam_search:476) INFO: -18.09 * 1.0 = -18.09 for ctc +2024-01-16 21:40:36,742 (beam_search:479) INFO: total log probability: -18.09 +2024-01-16 21:40:36,742 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:40:36,742 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:36,743 (beam_search:483) INFO: best hypo: THIFALIYERHASTLETTOICXSTDAENOULBLENCDHADHINSERDAYSEOFCOLLSTO + +2024-01-16 21:40:36,744 (asr_inference:494) INFO: speech length: 56448 +2024-01-16 21:40:36,753 (beam_search:428) INFO: decoder input length: 86 +2024-01-16 21:40:36,753 (beam_search:429) INFO: max output length: 86 +2024-01-16 21:40:36,753 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:36,825 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:36,825 (beam_search:476) INFO: -20.59 * 1.0 = -20.59 for ctc +2024-01-16 21:40:36,825 (beam_search:479) INFO: total log probability: -20.59 +2024-01-16 21:40:36,825 (beam_search:480) INFO: normalized log probability: -0.79 +2024-01-16 21:40:36,825 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:36,826 (beam_search:483) INFO: best hypo: ADDUTYASESHHHHDEN + +2024-01-16 21:40:36,827 (asr_inference:494) INFO: speech length: 61440 +2024-01-16 21:40:36,836 (beam_search:428) INFO: decoder input length: 93 +2024-01-16 21:40:36,836 (beam_search:429) INFO: max output length: 93 +2024-01-16 21:40:36,836 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:36,946 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:36,946 (beam_search:476) INFO: -6.44 * 1.0 = -6.44 for ctc +2024-01-16 21:40:36,946 (beam_search:479) INFO: total log probability: -6.44 +2024-01-16 21:40:36,946 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:40:36,946 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:36,946 (beam_search:483) INFO: best hypo: WHIYOITHATPLAINDCAEPEGOINOVER + +2024-01-16 21:40:36,947 (asr_inference:494) INFO: speech length: 178176 +2024-01-16 21:40:36,964 (beam_search:428) INFO: decoder input length: 276 +2024-01-16 21:40:36,964 (beam_search:429) INFO: max output length: 276 +2024-01-16 21:40:36,964 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:37,522 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:37,522 (beam_search:476) INFO: -27.69 * 1.0 = -27.69 for ctc +2024-01-16 21:40:37,522 (beam_search:479) INFO: total log probability: -27.69 +2024-01-16 21:40:37,522 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-16 21:40:37,522 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:37,522 (beam_search:483) INFO: best hypo: EEEHAYITAODDONDISTEETHEFORWAOFORSIOLBOSEWIOFORSOLTESE + +2024-01-16 21:40:37,524 (asr_inference:494) INFO: speech length: 73152 +2024-01-16 21:40:37,534 (beam_search:428) INFO: decoder input length: 112 +2024-01-16 21:40:37,534 (beam_search:429) INFO: max output length: 112 +2024-01-16 21:40:37,534 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:37,693 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:37,693 (beam_search:476) INFO: -6.62 * 1.0 = -6.62 for ctc +2024-01-16 21:40:37,693 (beam_search:479) INFO: total log probability: -6.62 +2024-01-16 21:40:37,693 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:40:37,693 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:37,693 (beam_search:483) INFO: best hypo: TEPLOCATIONWASPUTAPROVEITINFARIBRAELY + +2024-01-16 21:40:37,694 (asr_inference:494) INFO: speech length: 109056 +2024-01-16 21:40:37,706 (beam_search:428) INFO: decoder input length: 168 +2024-01-16 21:40:37,706 (beam_search:429) INFO: max output length: 168 +2024-01-16 21:40:37,706 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:38,025 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:38,025 (beam_search:476) INFO: -11.24 * 1.0 = -11.24 for ctc +2024-01-16 21:40:38,025 (beam_search:479) INFO: total log probability: -11.24 +2024-01-16 21:40:38,025 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:40:38,025 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:38,025 (beam_search:483) INFO: best hypo: HENRYTORLDTONSTILESEWHERHEHADTESOUNDEDRONINGINLTIN + +2024-01-16 21:40:38,026 (asr_inference:494) INFO: speech length: 127872 +2024-01-16 21:40:38,040 (beam_search:428) INFO: decoder input length: 197 +2024-01-16 21:40:38,040 (beam_search:429) INFO: max output length: 197 +2024-01-16 21:40:38,040 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:38,619 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:38,619 (beam_search:476) INFO: -20.73 * 1.0 = -20.73 for ctc +2024-01-16 21:40:38,619 (beam_search:479) INFO: total log probability: -20.73 +2024-01-16 21:40:38,619 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:40:38,619 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:38,620 (beam_search:483) INFO: best hypo: ITWASTHISCONTINUEDDTOSCETHRLINGCONFLICANDFVLVEDANDLOSESISRETIRNTORETOERESTRIULEREBRADIUO + +2024-01-16 21:40:38,621 (asr_inference:494) INFO: speech length: 144576 +2024-01-16 21:40:38,636 (beam_search:428) INFO: decoder input length: 223 +2024-01-16 21:40:38,636 (beam_search:429) INFO: max output length: 223 +2024-01-16 21:40:38,636 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:38,975 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:38,975 (beam_search:476) INFO: -21.34 * 1.0 = -21.34 for ctc +2024-01-16 21:40:38,975 (beam_search:479) INFO: total log probability: -21.34 +2024-01-16 21:40:38,976 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-16 21:40:38,976 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:38,976 (beam_search:483) INFO: best hypo: DEDHHERFANMELYHWOASFOMEPREOHONSAEEDDUDY + +2024-01-16 21:40:38,977 (asr_inference:494) INFO: speech length: 74112 +2024-01-16 21:40:38,987 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 21:40:38,987 (beam_search:429) INFO: max output length: 113 +2024-01-16 21:40:38,987 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:39,107 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:39,107 (beam_search:476) INFO: -14.96 * 1.0 = -14.96 for ctc +2024-01-16 21:40:39,107 (beam_search:479) INFO: total log probability: -14.96 +2024-01-16 21:40:39,107 (beam_search:480) INFO: normalized log probability: -0.45 +2024-01-16 21:40:39,107 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:39,107 (beam_search:483) INFO: best hypo: EWOWNTIDIEAKEFORDINMNBHPT + +2024-01-16 21:40:39,109 (asr_inference:494) INFO: speech length: 48960 +2024-01-16 21:40:39,117 (beam_search:428) INFO: decoder input length: 74 +2024-01-16 21:40:39,117 (beam_search:429) INFO: max output length: 74 +2024-01-16 21:40:39,117 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:39,183 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:39,183 (beam_search:476) INFO: -4.06 * 1.0 = -4.06 for ctc +2024-01-16 21:40:39,183 (beam_search:479) INFO: total log probability: -4.06 +2024-01-16 21:40:39,183 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:40:39,183 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:39,183 (beam_search:483) INFO: best hypo: THATWASMYDRETOSINSE + +2024-01-16 21:40:39,184 (asr_inference:494) INFO: speech length: 86400 +2024-01-16 21:40:39,195 (beam_search:428) INFO: decoder input length: 132 +2024-01-16 21:40:39,195 (beam_search:429) INFO: max output length: 132 +2024-01-16 21:40:39,195 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:39,384 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:39,384 (beam_search:476) INFO: -13.15 * 1.0 = -13.15 for ctc +2024-01-16 21:40:39,384 (beam_search:479) INFO: total log probability: -13.15 +2024-01-16 21:40:39,384 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:40:39,384 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:39,385 (beam_search:483) INFO: best hypo: HESGOSLAIRENTAMUSTEREOFDSHEAROSTCOLO + +2024-01-16 21:40:39,386 (asr_inference:494) INFO: speech length: 104064 +2024-01-16 21:40:39,397 (beam_search:428) INFO: decoder input length: 160 +2024-01-16 21:40:39,398 (beam_search:429) INFO: max output length: 160 +2024-01-16 21:40:39,398 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:39,720 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:39,721 (beam_search:476) INFO: -11.94 * 1.0 = -11.94 for ctc +2024-01-16 21:40:39,721 (beam_search:479) INFO: total log probability: -11.94 +2024-01-16 21:40:39,721 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:40:39,721 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:39,721 (beam_search:483) INFO: best hypo: THENLINTORNSTOTHECHORISHEOFSHINGSHATTBLTERANDESPEUEITE + +2024-01-16 21:40:39,722 (asr_inference:494) INFO: speech length: 44160 +2024-01-16 21:40:39,730 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:40:39,730 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:40:39,730 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:39,800 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:39,800 (beam_search:476) INFO: -10.30 * 1.0 = -10.30 for ctc +2024-01-16 21:40:39,801 (beam_search:479) INFO: total log probability: -10.30 +2024-01-16 21:40:39,801 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 21:40:39,801 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:39,801 (beam_search:483) INFO: best hypo: WUDNOTTHOSERINHEREACADRT + +2024-01-16 21:40:39,802 (asr_inference:494) INFO: speech length: 69696 +2024-01-16 21:40:39,812 (beam_search:428) INFO: decoder input length: 106 +2024-01-16 21:40:39,812 (beam_search:429) INFO: max output length: 106 +2024-01-16 21:40:39,812 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:39,932 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:39,932 (beam_search:476) INFO: -4.96 * 1.0 = -4.96 for ctc +2024-01-16 21:40:39,932 (beam_search:479) INFO: total log probability: -4.96 +2024-01-16 21:40:39,932 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:40:39,932 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:39,932 (beam_search:483) INFO: best hypo: TOELCIOLSEINCTENDFESTTHEAIYH + +2024-01-16 21:40:39,933 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 21:40:39,942 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:40:39,942 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:40:39,942 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:40,015 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:40,016 (beam_search:476) INFO: -6.33 * 1.0 = -6.33 for ctc +2024-01-16 21:40:40,016 (beam_search:479) INFO: total log probability: -6.33 +2024-01-16 21:40:40,016 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:40:40,016 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:40,016 (beam_search:483) INFO: best hypo: MYNESTCONHELPEOWITTHAITS + +2024-01-16 21:40:40,017 (asr_inference:494) INFO: speech length: 46464 +2024-01-16 21:40:40,025 (beam_search:428) INFO: decoder input length: 70 +2024-01-16 21:40:40,025 (beam_search:429) INFO: max output length: 70 +2024-01-16 21:40:40,025 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:40,090 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:40,090 (beam_search:476) INFO: -12.27 * 1.0 = -12.27 for ctc +2024-01-16 21:40:40,090 (beam_search:479) INFO: total log probability: -12.27 +2024-01-16 21:40:40,090 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-16 21:40:40,090 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:40,090 (beam_search:483) INFO: best hypo: THATSACOUDHISTOTLYON + +2024-01-16 21:40:40,091 (asr_inference:494) INFO: speech length: 59136 +2024-01-16 21:40:40,100 (beam_search:428) INFO: decoder input length: 90 +2024-01-16 21:40:40,100 (beam_search:429) INFO: max output length: 90 +2024-01-16 21:40:40,100 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:40,218 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:40,219 (beam_search:476) INFO: -8.65 * 1.0 = -8.65 for ctc +2024-01-16 21:40:40,219 (beam_search:479) INFO: total log probability: -8.65 +2024-01-16 21:40:40,219 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:40:40,219 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:40,219 (beam_search:483) INFO: best hypo: HOEFORTHEBESTANDPOBEAREFORTHEMLST + +2024-01-16 21:40:40,220 (asr_inference:494) INFO: speech length: 89472 +2024-01-16 21:40:40,231 (beam_search:428) INFO: decoder input length: 137 +2024-01-16 21:40:40,231 (beam_search:429) INFO: max output length: 137 +2024-01-16 21:40:40,231 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:40,432 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:40,432 (beam_search:476) INFO: -15.94 * 1.0 = -15.94 for ctc +2024-01-16 21:40:40,432 (beam_search:479) INFO: total log probability: -15.94 +2024-01-16 21:40:40,432 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 21:40:40,432 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:40,433 (beam_search:483) INFO: best hypo: CINISHELYTHEWHPDYOSOSHTINSTROCKYOBODICT + +2024-01-16 21:40:40,434 (asr_inference:494) INFO: speech length: 86518 +2024-01-16 21:40:40,444 (beam_search:428) INFO: decoder input length: 133 +2024-01-16 21:40:40,444 (beam_search:429) INFO: max output length: 133 +2024-01-16 21:40:40,444 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:40,624 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:40,624 (beam_search:476) INFO: -8.60 * 1.0 = -8.60 for ctc +2024-01-16 21:40:40,624 (beam_search:479) INFO: total log probability: -8.60 +2024-01-16 21:40:40,624 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:40:40,624 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:40,624 (beam_search:483) INFO: best hypo: ALLWEONEDBYTHEEVERITMORSONIKCETH + +2024-01-16 21:40:40,626 (asr_inference:494) INFO: speech length: 76416 +2024-01-16 21:40:40,636 (beam_search:428) INFO: decoder input length: 117 +2024-01-16 21:40:40,636 (beam_search:429) INFO: max output length: 117 +2024-01-16 21:40:40,636 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:40,822 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:40,822 (beam_search:476) INFO: -21.57 * 1.0 = -21.57 for ctc +2024-01-16 21:40:40,822 (beam_search:479) INFO: total log probability: -21.57 +2024-01-16 21:40:40,822 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-16 21:40:40,822 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:40,823 (beam_search:483) INFO: best hypo: AATHETHEOENGTHEWILASERINTOMORONMNEINLNDEETHER + +2024-01-16 21:40:40,824 (asr_inference:494) INFO: speech length: 72000 +2024-01-16 21:40:40,834 (beam_search:428) INFO: decoder input length: 110 +2024-01-16 21:40:40,834 (beam_search:429) INFO: max output length: 110 +2024-01-16 21:40:40,834 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:40,951 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:40,951 (beam_search:476) INFO: -10.31 * 1.0 = -10.31 for ctc +2024-01-16 21:40:40,951 (beam_search:479) INFO: total log probability: -10.31 +2024-01-16 21:40:40,951 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:40:40,951 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:40,951 (beam_search:483) INFO: best hypo: EDOBPBISTDIRICMMINDLIBPBPTH + +2024-01-16 21:40:40,952 (asr_inference:494) INFO: speech length: 149184 +2024-01-16 21:40:40,967 (beam_search:428) INFO: decoder input length: 231 +2024-01-16 21:40:40,967 (beam_search:429) INFO: max output length: 231 +2024-01-16 21:40:40,967 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:41,366 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:41,366 (beam_search:476) INFO: -18.96 * 1.0 = -18.96 for ctc +2024-01-16 21:40:41,366 (beam_search:479) INFO: total log probability: -18.96 +2024-01-16 21:40:41,366 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 21:40:41,366 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:41,366 (beam_search:483) INFO: best hypo: ATOSEIOLEPATRYHETOHERPLAEASACTINGORECTERHEHD + +2024-01-16 21:40:41,368 (asr_inference:494) INFO: speech length: 81792 +2024-01-16 21:40:41,378 (beam_search:428) INFO: decoder input length: 125 +2024-01-16 21:40:41,378 (beam_search:429) INFO: max output length: 125 +2024-01-16 21:40:41,378 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:41,594 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:41,594 (beam_search:476) INFO: -9.23 * 1.0 = -9.23 for ctc +2024-01-16 21:40:41,594 (beam_search:479) INFO: total log probability: -9.23 +2024-01-16 21:40:41,594 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:40:41,594 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:41,595 (beam_search:483) INFO: best hypo: TEBEVERLYWLEBEFLYANTESTHEIESSENTERPUOPTTONSHIM + +2024-01-16 21:40:41,596 (asr_inference:494) INFO: speech length: 59328 +2024-01-16 21:40:41,605 (beam_search:428) INFO: decoder input length: 90 +2024-01-16 21:40:41,605 (beam_search:429) INFO: max output length: 90 +2024-01-16 21:40:41,605 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:41,717 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:41,717 (beam_search:476) INFO: -6.78 * 1.0 = -6.78 for ctc +2024-01-16 21:40:41,717 (beam_search:479) INFO: total log probability: -6.78 +2024-01-16 21:40:41,717 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:40:41,717 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:41,717 (beam_search:483) INFO: best hypo: THETRACERERVISTINGWASALSOCOMPETED + +2024-01-16 21:40:41,719 (asr_inference:494) INFO: speech length: 125568 +2024-01-16 21:40:41,732 (beam_search:428) INFO: decoder input length: 194 +2024-01-16 21:40:41,732 (beam_search:429) INFO: max output length: 194 +2024-01-16 21:40:41,732 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:42,203 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:42,203 (beam_search:476) INFO: -20.39 * 1.0 = -20.39 for ctc +2024-01-16 21:40:42,203 (beam_search:479) INFO: total log probability: -20.39 +2024-01-16 21:40:42,203 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:40:42,203 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:42,204 (beam_search:483) INFO: best hypo: HATDMARSHWASAWHAROFTHEIMPRTNSOFILCTRMRYCOSKOMPYEINBYLOUGICKLRESRCHE + +2024-01-16 21:40:42,205 (asr_inference:494) INFO: speech length: 61056 +2024-01-16 21:40:42,214 (beam_search:428) INFO: decoder input length: 93 +2024-01-16 21:40:42,214 (beam_search:429) INFO: max output length: 93 +2024-01-16 21:40:42,214 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:42,310 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:42,311 (beam_search:476) INFO: -6.95 * 1.0 = -6.95 for ctc +2024-01-16 21:40:42,311 (beam_search:479) INFO: total log probability: -6.95 +2024-01-16 21:40:42,311 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:40:42,311 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:42,311 (beam_search:483) INFO: best hypo: SINHEWASBORNYUDTHEHOBOUE + +2024-01-16 21:40:42,312 (asr_inference:494) INFO: speech length: 166656 +2024-01-16 21:40:42,328 (beam_search:428) INFO: decoder input length: 258 +2024-01-16 21:40:42,328 (beam_search:429) INFO: max output length: 258 +2024-01-16 21:40:42,328 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:43,044 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:43,044 (beam_search:476) INFO: -19.80 * 1.0 = -19.80 for ctc +2024-01-16 21:40:43,044 (beam_search:479) INFO: total log probability: -19.80 +2024-01-16 21:40:43,044 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:40:43,044 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:43,044 (beam_search:483) INFO: best hypo: THISWEIENCEHEASEANOFITILYTHERHEOTOASEMACRETOADWINTHMYCOLISTIONFOASESOPERATDINMIL + +2024-01-16 21:40:43,046 (asr_inference:494) INFO: speech length: 114048 +2024-01-16 21:40:43,058 (beam_search:428) INFO: decoder input length: 176 +2024-01-16 21:40:43,059 (beam_search:429) INFO: max output length: 176 +2024-01-16 21:40:43,059 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:43,471 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:43,471 (beam_search:476) INFO: -14.42 * 1.0 = -14.42 for ctc +2024-01-16 21:40:43,471 (beam_search:479) INFO: total log probability: -14.42 +2024-01-16 21:40:43,471 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:40:43,471 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:43,471 (beam_search:483) INFO: best hypo: ITISRESPONEAULEFORWATERSOPLIANDMANEMENTOFWOTERRESOURSESANDMOHOUSTRA + +2024-01-16 21:40:43,473 (asr_inference:494) INFO: speech length: 86784 +2024-01-16 21:40:43,483 (beam_search:428) INFO: decoder input length: 133 +2024-01-16 21:40:43,483 (beam_search:429) INFO: max output length: 133 +2024-01-16 21:40:43,483 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:43,672 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:43,672 (beam_search:476) INFO: -8.41 * 1.0 = -8.41 for ctc +2024-01-16 21:40:43,672 (beam_search:479) INFO: total log probability: -8.41 +2024-01-16 21:40:43,672 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:40:43,672 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:43,673 (beam_search:483) INFO: best hypo: DISESTHOFEORESFAYEOFTHEGHORVEHESADED + +2024-01-16 21:40:43,674 (asr_inference:494) INFO: speech length: 268160 +2024-01-16 21:40:43,699 (beam_search:428) INFO: decoder input length: 416 +2024-01-16 21:40:43,699 (beam_search:429) INFO: max output length: 416 +2024-01-16 21:40:43,699 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:45,825 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:45,825 (beam_search:476) INFO: -31.38 * 1.0 = -31.38 for ctc +2024-01-16 21:40:45,825 (beam_search:479) INFO: total log probability: -31.38 +2024-01-16 21:40:45,825 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:40:45,825 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:45,826 (beam_search:483) INFO: best hypo: THEGISIAPPLATOHORGISSANCRAOLPOLISITHENGIONVAOLYOFTHEDEADCONTTANGSEVRLPERMINDSOFWHICHTHEGRATPERMENTISTHELARTEISSESEVERLESMLTONSSOVRLETEMPLESANDTHEGRATSPANKS + +2024-01-16 21:40:45,828 (asr_inference:494) INFO: speech length: 249600 +2024-01-16 21:40:45,850 (beam_search:428) INFO: decoder input length: 387 +2024-01-16 21:40:45,850 (beam_search:429) INFO: max output length: 387 +2024-01-16 21:40:45,850 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:47,782 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:47,782 (beam_search:476) INFO: -30.09 * 1.0 = -30.09 for ctc +2024-01-16 21:40:47,782 (beam_search:479) INFO: total log probability: -30.09 +2024-01-16 21:40:47,782 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:40:47,782 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:47,783 (beam_search:483) INFO: best hypo: TWOREHEINDOFTEMILEAGESWESTORNYUROBEGANTODELTTERONSTILONEOFTHEBIGISTOELINSOFTHETIMEASRESIULTOFTHEREUCSAISPEPBLBEGANTOOUSESBUTENSTOFASTONRLVDINGIIIA + +2024-01-16 21:40:47,785 (asr_inference:494) INFO: speech length: 148160 +2024-01-16 21:40:47,800 (beam_search:428) INFO: decoder input length: 229 +2024-01-16 21:40:47,800 (beam_search:429) INFO: max output length: 229 +2024-01-16 21:40:47,800 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:48,442 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:48,442 (beam_search:476) INFO: -16.99 * 1.0 = -16.99 for ctc +2024-01-16 21:40:48,443 (beam_search:479) INFO: total log probability: -16.99 +2024-01-16 21:40:48,443 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:40:48,443 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:48,443 (beam_search:483) INFO: best hypo: IFSYOUONLYGOLSHOREOUSINGSHIPORCSCKDRIONDSYOULNOTEASEPRTVESAASATWOFHOUSINNOIG + +2024-01-16 21:40:48,444 (asr_inference:494) INFO: speech length: 99840 +2024-01-16 21:40:48,456 (beam_search:428) INFO: decoder input length: 153 +2024-01-16 21:40:48,456 (beam_search:429) INFO: max output length: 153 +2024-01-16 21:40:48,456 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:48,854 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:48,854 (beam_search:476) INFO: -23.72 * 1.0 = -23.72 for ctc +2024-01-16 21:40:48,854 (beam_search:479) INFO: total log probability: -23.72 +2024-01-16 21:40:48,854 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:40:48,854 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:48,854 (beam_search:483) INFO: best hypo: BDOBALHUISMAREWIHTOADLCIOREIONIDNOTBYWABINIMPRESIONNMILERTOHOETHETARYWASRELATED + +2024-01-16 21:40:48,856 (asr_inference:494) INFO: speech length: 125760 +2024-01-16 21:40:48,869 (beam_search:428) INFO: decoder input length: 194 +2024-01-16 21:40:48,869 (beam_search:429) INFO: max output length: 194 +2024-01-16 21:40:48,869 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:49,457 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:49,457 (beam_search:476) INFO: -20.05 * 1.0 = -20.05 for ctc +2024-01-16 21:40:49,457 (beam_search:479) INFO: total log probability: -20.05 +2024-01-16 21:40:49,457 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:40:49,457 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:49,458 (beam_search:483) INFO: best hypo: TERDECTPLNDDEFIENCEBOLEHADLINGSCILSANECSLNTIEWORDMAYTHESTANDOUTANWASCLERHATHISWATHETEMTOBE + +2024-01-16 21:40:49,459 (asr_inference:494) INFO: speech length: 86400 +2024-01-16 21:40:49,470 (beam_search:428) INFO: decoder input length: 132 +2024-01-16 21:40:49,470 (beam_search:429) INFO: max output length: 132 +2024-01-16 21:40:49,470 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:49,732 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:49,732 (beam_search:476) INFO: -18.01 * 1.0 = -18.01 for ctc +2024-01-16 21:40:49,732 (beam_search:479) INFO: total log probability: -18.01 +2024-01-16 21:40:49,732 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:40:49,732 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:49,732 (beam_search:483) INFO: best hypo: THEDEASSCAIREDBYPIAKGEWICHTHNMYGRETTOCUMENTOROMOSKETOS + +2024-01-16 21:40:49,734 (asr_inference:494) INFO: speech length: 79680 +2024-01-16 21:40:49,744 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 21:40:49,744 (beam_search:429) INFO: max output length: 122 +2024-01-16 21:40:49,744 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:49,958 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:49,958 (beam_search:476) INFO: -8.97 * 1.0 = -8.97 for ctc +2024-01-16 21:40:49,958 (beam_search:479) INFO: total log probability: -8.97 +2024-01-16 21:40:49,958 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:40:49,958 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:49,959 (beam_search:483) INFO: best hypo: FORTHSPRINGBOGCKEHIDINDEDOAFFLIVENATHLOINGSTRAKE + +2024-01-16 21:40:49,960 (asr_inference:494) INFO: speech length: 63360 +2024-01-16 21:40:49,969 (beam_search:428) INFO: decoder input length: 96 +2024-01-16 21:40:49,969 (beam_search:429) INFO: max output length: 96 +2024-01-16 21:40:49,969 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:50,122 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:50,122 (beam_search:476) INFO: -9.55 * 1.0 = -9.55 for ctc +2024-01-16 21:40:50,122 (beam_search:479) INFO: total log probability: -9.55 +2024-01-16 21:40:50,122 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:40:50,122 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:50,122 (beam_search:483) INFO: best hypo: THEUSTHEHINSLWASGOFRIENDMANYBEBLLADYKINGOU + +2024-01-16 21:40:50,124 (asr_inference:494) INFO: speech length: 184640 +2024-01-16 21:40:50,141 (beam_search:428) INFO: decoder input length: 286 +2024-01-16 21:40:50,141 (beam_search:429) INFO: max output length: 286 +2024-01-16 21:40:50,141 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:51,272 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:51,272 (beam_search:476) INFO: -23.00 * 1.0 = -23.00 for ctc +2024-01-16 21:40:51,272 (beam_search:479) INFO: total log probability: -23.00 +2024-01-16 21:40:51,272 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:40:51,272 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:51,273 (beam_search:483) INFO: best hypo: THEYUSEOFEAORECORINGHASLADTOINPORNEDDECSCORVERESINTHEINTERPROTATIONOFMYGCRLRECTPRESTIONSFASIALMOVEENSWHICHLASEAFYOMILSSICKENS + +2024-01-16 21:40:51,275 (asr_inference:494) INFO: speech length: 120960 +2024-01-16 21:40:51,288 (beam_search:428) INFO: decoder input length: 186 +2024-01-16 21:40:51,288 (beam_search:429) INFO: max output length: 186 +2024-01-16 21:40:51,288 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:51,834 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:51,834 (beam_search:476) INFO: -26.81 * 1.0 = -26.81 for ctc +2024-01-16 21:40:51,834 (beam_search:479) INFO: total log probability: -26.81 +2024-01-16 21:40:51,834 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:40:51,834 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:51,835 (beam_search:483) INFO: best hypo: LSATTHENORTHISTTHEGRATSANCHURYOFRLATDYOFATHYMUSHRINGAELECEAFWORLDGHTFIMISMERIONAPBRISTIONS + +2024-01-16 21:40:51,836 (asr_inference:494) INFO: speech length: 147840 +2024-01-16 21:40:51,851 (beam_search:428) INFO: decoder input length: 228 +2024-01-16 21:40:51,851 (beam_search:429) INFO: max output length: 228 +2024-01-16 21:40:51,851 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:52,470 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:52,470 (beam_search:476) INFO: -16.68 * 1.0 = -16.68 for ctc +2024-01-16 21:40:52,470 (beam_search:479) INFO: total log probability: -16.68 +2024-01-16 21:40:52,470 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:40:52,470 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:52,470 (beam_search:483) INFO: best hypo: EIFONEBYCLOSOTHEACTIONYGORHETOWWOWGITINEALYWTOTECAPINGSIHTCLOSTOTHMOUSICKE + +2024-01-16 21:40:52,471 (asr_inference:494) INFO: speech length: 117120 +2024-01-16 21:40:52,484 (beam_search:428) INFO: decoder input length: 180 +2024-01-16 21:40:52,484 (beam_search:429) INFO: max output length: 180 +2024-01-16 21:40:52,484 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:52,909 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:52,909 (beam_search:476) INFO: -17.70 * 1.0 = -17.70 for ctc +2024-01-16 21:40:52,909 (beam_search:479) INFO: total log probability: -17.70 +2024-01-16 21:40:52,909 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:40:52,909 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:52,909 (beam_search:483) INFO: best hypo: MTYAGUSKURERISBLYHFARETHEBIGISTNDACONTINTONIDSONWHNACOMSTOWHOULDLIFT + +2024-01-16 21:40:52,911 (asr_inference:494) INFO: speech length: 98880 +2024-01-16 21:40:52,922 (beam_search:428) INFO: decoder input length: 152 +2024-01-16 21:40:52,922 (beam_search:429) INFO: max output length: 152 +2024-01-16 21:40:52,922 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:53,302 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:53,302 (beam_search:476) INFO: -13.34 * 1.0 = -13.34 for ctc +2024-01-16 21:40:53,302 (beam_search:479) INFO: total log probability: -13.34 +2024-01-16 21:40:53,302 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:40:53,302 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:53,303 (beam_search:483) INFO: best hypo: WEMENITISRECMENETHATANYWHEMENTROVLIRSAYTHEARMARDREGARLESOFACHULMARTLSTATTIST + +2024-01-16 21:40:53,304 (asr_inference:494) INFO: speech length: 148800 +2024-01-16 21:40:53,319 (beam_search:428) INFO: decoder input length: 230 +2024-01-16 21:40:53,319 (beam_search:429) INFO: max output length: 230 +2024-01-16 21:40:53,319 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:54,065 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:54,065 (beam_search:476) INFO: -21.36 * 1.0 = -21.36 for ctc +2024-01-16 21:40:54,065 (beam_search:479) INFO: total log probability: -21.36 +2024-01-16 21:40:54,065 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:40:54,065 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:54,065 (beam_search:483) INFO: best hypo: COUOAOMOFIFTYTHREBEGANHFGOVERMENTGOVERSHIPERILETHESYEARANDSINEABILLASTMUNCHLEGLIDSINGSANSECXSMARAGE + +2024-01-16 21:40:54,067 (asr_inference:494) INFO: speech length: 166080 +2024-01-16 21:40:54,083 (beam_search:428) INFO: decoder input length: 257 +2024-01-16 21:40:54,083 (beam_search:429) INFO: max output length: 257 +2024-01-16 21:40:54,083 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:55,017 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:55,017 (beam_search:476) INFO: -24.91 * 1.0 = -24.91 for ctc +2024-01-16 21:40:55,017 (beam_search:479) INFO: total log probability: -24.91 +2024-01-16 21:40:55,017 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:40:55,017 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:55,018 (beam_search:483) INFO: best hypo: ASLIHPLTIONNTHRHAYDYWASNOTTHEINDOFPOLOMTISTODYTHERUELYLOCKATEDINSIYESORACANPSESESIURTORESIONTHOSBULANMOTNTIMS + +2024-01-16 21:40:55,019 (asr_inference:494) INFO: speech length: 119040 +2024-01-16 21:40:55,032 (beam_search:428) INFO: decoder input length: 183 +2024-01-16 21:40:55,032 (beam_search:429) INFO: max output length: 183 +2024-01-16 21:40:55,032 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:55,511 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:55,511 (beam_search:476) INFO: -15.26 * 1.0 = -15.26 for ctc +2024-01-16 21:40:55,511 (beam_search:479) INFO: total log probability: -15.26 +2024-01-16 21:40:55,511 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:40:55,511 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:55,512 (beam_search:483) INFO: best hypo: THEYOUELYHAVESPETILFORNKNINERTAMEOPERSTOCAEGISANAGOMODNCAETHIMATTHEPRMIS + +2024-01-16 21:40:55,514 (asr_inference:494) INFO: speech length: 193920 +2024-01-16 21:40:55,531 (beam_search:428) INFO: decoder input length: 300 +2024-01-16 21:40:55,531 (beam_search:429) INFO: max output length: 300 +2024-01-16 21:40:55,531 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:56,561 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:56,561 (beam_search:476) INFO: -16.75 * 1.0 = -16.75 for ctc +2024-01-16 21:40:56,561 (beam_search:479) INFO: total log probability: -16.75 +2024-01-16 21:40:56,561 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:40:56,561 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:56,562 (beam_search:483) INFO: best hypo: OHETERHEANDISEANDSNOWCOEDIONSARNORMAEANDMANYCUNTRYESIHANDTRAPFITOSONMOSTELYUNINTERUPTEDALLEYERROUNED + +2024-01-16 21:40:56,563 (asr_inference:494) INFO: speech length: 169600 +2024-01-16 21:40:56,580 (beam_search:428) INFO: decoder input length: 262 +2024-01-16 21:40:56,580 (beam_search:429) INFO: max output length: 262 +2024-01-16 21:40:56,580 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:57,357 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:57,357 (beam_search:476) INFO: -22.24 * 1.0 = -22.24 for ctc +2024-01-16 21:40:57,357 (beam_search:479) INFO: total log probability: -22.24 +2024-01-16 21:40:57,357 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:40:57,357 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:57,358 (beam_search:483) INFO: best hypo: BECARFLNOTTOAELOULFABOICTOBECMETOHIDWHICHCANCOSSTRANKADGEORINASTRENCASCESSSQOARTCHE + +2024-01-16 21:40:57,360 (asr_inference:494) INFO: speech length: 104640 +2024-01-16 21:40:57,371 (beam_search:428) INFO: decoder input length: 161 +2024-01-16 21:40:57,371 (beam_search:429) INFO: max output length: 161 +2024-01-16 21:40:57,371 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:57,764 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:57,765 (beam_search:476) INFO: -16.12 * 1.0 = -16.12 for ctc +2024-01-16 21:40:57,765 (beam_search:479) INFO: total log probability: -16.12 +2024-01-16 21:40:57,765 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:40:57,765 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:57,765 (beam_search:483) INFO: best hypo: FEIRLTHLLDERNAYHAVCTPEINCEOVERCHULDOBEUSORTRMOHBHEFORBINGABENDINRNGWAY + +2024-01-16 21:40:57,766 (asr_inference:494) INFO: speech length: 95040 +2024-01-16 21:40:57,777 (beam_search:428) INFO: decoder input length: 146 +2024-01-16 21:40:57,777 (beam_search:429) INFO: max output length: 146 +2024-01-16 21:40:57,777 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:58,133 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:58,133 (beam_search:476) INFO: -19.40 * 1.0 = -19.40 for ctc +2024-01-16 21:40:58,133 (beam_search:479) INFO: total log probability: -19.40 +2024-01-16 21:40:58,133 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:40:58,133 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:58,133 (beam_search:483) INFO: best hypo: BEBLMANOTNTICHIPATTHATPETIONCANDNDERSTANINGRALONESARYFORTRVLURSRETRNINGHOME + +2024-01-16 21:40:58,135 (asr_inference:494) INFO: speech length: 87360 +2024-01-16 21:40:58,145 (beam_search:428) INFO: decoder input length: 134 +2024-01-16 21:40:58,145 (beam_search:429) INFO: max output length: 134 +2024-01-16 21:40:58,145 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:58,435 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:58,436 (beam_search:476) INFO: -13.28 * 1.0 = -13.28 for ctc +2024-01-16 21:40:58,436 (beam_search:479) INFO: total log probability: -13.28 +2024-01-16 21:40:58,436 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:40:58,436 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:58,436 (beam_search:483) INFO: best hypo: ONOTERTHEOPRIKOFOUSTILITYESBRININANTSHEATEDADNAVBLBOKAYEOFTIRMINY + +2024-01-16 21:40:58,437 (asr_inference:494) INFO: speech length: 116160 +2024-01-16 21:40:58,450 (beam_search:428) INFO: decoder input length: 179 +2024-01-16 21:40:58,450 (beam_search:429) INFO: max output length: 179 +2024-01-16 21:40:58,450 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:58,778 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:58,778 (beam_search:476) INFO: -14.32 * 1.0 = -14.32 for ctc +2024-01-16 21:40:58,778 (beam_search:479) INFO: total log probability: -14.32 +2024-01-16 21:40:58,778 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:40:58,778 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:58,779 (beam_search:483) INFO: best hypo: HTHEVENRSOFPISSADNIATTIENOFTHINDERDWEREPLEOFHSARS + +2024-01-16 21:40:58,780 (asr_inference:494) INFO: speech length: 121920 +2024-01-16 21:40:58,793 (beam_search:428) INFO: decoder input length: 188 +2024-01-16 21:40:58,793 (beam_search:429) INFO: max output length: 188 +2024-01-16 21:40:58,793 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:59,323 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:59,323 (beam_search:476) INFO: -14.93 * 1.0 = -14.93 for ctc +2024-01-16 21:40:59,323 (beam_search:479) INFO: total log probability: -14.93 +2024-01-16 21:40:59,323 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:40:59,323 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:59,324 (beam_search:483) INFO: best hypo: YUSINHIPSTTRESPBURTDGOODASBYFARTHEMOSTOFIENTWAYHMOELARGEMUTOPEBEANDGOOCROUSOATIONS + +2024-01-16 21:40:59,325 (asr_inference:494) INFO: speech length: 128640 +2024-01-16 21:40:59,339 (beam_search:428) INFO: decoder input length: 198 +2024-01-16 21:40:59,339 (beam_search:429) INFO: max output length: 198 +2024-01-16 21:40:59,339 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:40:59,957 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:40:59,957 (beam_search:476) INFO: -23.25 * 1.0 = -23.25 for ctc +2024-01-16 21:40:59,957 (beam_search:479) INFO: total log probability: -23.25 +2024-01-16 21:40:59,957 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:40:59,957 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:40:59,958 (beam_search:483) INFO: best hypo: LEBRLRETISOHMOTHERECONSTRCTIONVERNHASPOAKEASOTHAORDINGOFRECONSRCTINGCONCHACTTORSTHEDWAUTINGANINSIHERS + +2024-01-16 21:40:59,959 (asr_inference:494) INFO: speech length: 193920 +2024-01-16 21:40:59,977 (beam_search:428) INFO: decoder input length: 300 +2024-01-16 21:40:59,977 (beam_search:429) INFO: max output length: 300 +2024-01-16 21:40:59,977 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:01,030 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:01,030 (beam_search:476) INFO: -26.56 * 1.0 = -26.56 for ctc +2024-01-16 21:41:01,030 (beam_search:479) INFO: total log probability: -26.56 +2024-01-16 21:41:01,030 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:41:01,030 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:01,031 (beam_search:483) INFO: best hypo: UASBODOEBODAMRSECKLTACEYTOGETERONDGOMEATHENORMEAWLACALPRICEISFIVEHUNDREDCONDLESFROUSFORTHEMSHORETRIII + +2024-01-16 21:41:01,032 (asr_inference:494) INFO: speech length: 190080 +2024-01-16 21:41:01,050 (beam_search:428) INFO: decoder input length: 294 +2024-01-16 21:41:01,050 (beam_search:429) INFO: max output length: 294 +2024-01-16 21:41:01,050 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:02,259 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:02,259 (beam_search:476) INFO: -24.32 * 1.0 = -24.32 for ctc +2024-01-16 21:41:02,259 (beam_search:479) INFO: total log probability: -24.32 +2024-01-16 21:41:02,259 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:41:02,259 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:02,260 (beam_search:483) INFO: best hypo: THETHRECINGDMESWASONEOFTHEBLTBLUDIESTARSANANGIONTCHINESHISTRETHOUSENCSOFPEBLEDIDEDFIDINGTOSIITHHIHISSEITHEGRANPALESATSIANN + +2024-01-16 21:41:02,262 (asr_inference:494) INFO: speech length: 109440 +2024-01-16 21:41:02,274 (beam_search:428) INFO: decoder input length: 168 +2024-01-16 21:41:02,274 (beam_search:429) INFO: max output length: 168 +2024-01-16 21:41:02,274 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:02,605 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:02,605 (beam_search:476) INFO: -10.42 * 1.0 = -10.42 for ctc +2024-01-16 21:41:02,605 (beam_search:479) INFO: total log probability: -10.42 +2024-01-16 21:41:02,605 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:41:02,605 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:02,605 (beam_search:483) INFO: best hypo: THEISCOUPLESMAYCHOSTOMAKANDANDOUSIONPLANDFORTHEREBAVY + +2024-01-16 21:41:02,607 (asr_inference:494) INFO: speech length: 151680 +2024-01-16 21:41:02,622 (beam_search:428) INFO: decoder input length: 234 +2024-01-16 21:41:02,622 (beam_search:429) INFO: max output length: 234 +2024-01-16 21:41:02,622 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:03,474 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:03,474 (beam_search:476) INFO: -22.74 * 1.0 = -22.74 for ctc +2024-01-16 21:41:03,474 (beam_search:479) INFO: total log probability: -22.74 +2024-01-16 21:41:03,474 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:41:03,474 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:03,475 (beam_search:483) INFO: best hypo: NOTHINCANBEFENUTHEHATHECPERBEUTOFLSCKIABOVEANDNTHEMANYSURWUNINGMUNDSBERYLITLOTHSWLLTANBEFEENRHERFROMINSITHECAVE + +2024-01-16 21:41:03,476 (asr_inference:494) INFO: speech length: 88320 +2024-01-16 21:41:03,487 (beam_search:428) INFO: decoder input length: 135 +2024-01-16 21:41:03,487 (beam_search:429) INFO: max output length: 135 +2024-01-16 21:41:03,487 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:03,736 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:03,736 (beam_search:476) INFO: -13.22 * 1.0 = -13.22 for ctc +2024-01-16 21:41:03,736 (beam_search:479) INFO: total log probability: -13.22 +2024-01-16 21:41:03,736 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:41:03,736 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:03,736 (beam_search:483) INFO: best hypo: HEWASOEICENLYRELOKCATETOADTENBROKSHOSTPTLANDCAMBRIGE + +2024-01-16 21:41:03,738 (asr_inference:494) INFO: speech length: 171840 +2024-01-16 21:41:03,754 (beam_search:428) INFO: decoder input length: 266 +2024-01-16 21:41:03,754 (beam_search:429) INFO: max output length: 266 +2024-01-16 21:41:03,754 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:04,551 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:04,551 (beam_search:476) INFO: -27.29 * 1.0 = -27.29 for ctc +2024-01-16 21:41:04,551 (beam_search:479) INFO: total log probability: -27.29 +2024-01-16 21:41:04,551 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:41:04,551 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:04,552 (beam_search:483) INFO: best hypo: DTHANTIACANSTAIDYPULEATIONISEROUDINHERNTHITETHIISMOSTINPENECOENTRETHEWHERALDNTHPOPLIULEATION + +2024-01-16 21:41:04,553 (asr_inference:494) INFO: speech length: 209280 +2024-01-16 21:41:04,572 (beam_search:428) INFO: decoder input length: 324 +2024-01-16 21:41:04,572 (beam_search:429) INFO: max output length: 324 +2024-01-16 21:41:04,572 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:06,120 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:06,120 (beam_search:476) INFO: -41.50 * 1.0 = -41.50 for ctc +2024-01-16 21:41:06,120 (beam_search:479) INFO: total log probability: -41.50 +2024-01-16 21:41:06,120 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:41:06,120 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:06,121 (beam_search:483) INFO: best hypo: BRDGLRALOUNCSTHENCTHEPMATHRARMAEOLYNCOUTILONBUTNPLENEDESTRPTIONSARNUCDBYANODTIMTEDSTOMINOWAOVERITYOFLINWICHGESINCLTINGSBANISHANGLSHFRENCHERBICANDAPNS + +2024-01-16 21:41:06,123 (asr_inference:494) INFO: speech length: 107520 +2024-01-16 21:41:06,135 (beam_search:428) INFO: decoder input length: 165 +2024-01-16 21:41:06,135 (beam_search:429) INFO: max output length: 165 +2024-01-16 21:41:06,135 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:06,555 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:06,555 (beam_search:476) INFO: -20.41 * 1.0 = -20.41 for ctc +2024-01-16 21:41:06,555 (beam_search:479) INFO: total log probability: -20.41 +2024-01-16 21:41:06,555 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:41:06,555 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:06,555 (beam_search:483) INFO: best hypo: THISOPRSGDPRTENDETOSHTHEOAREREABORILESASTHESCAIWLBEDARCMORELESTORONTHECO + +2024-01-16 21:41:06,557 (asr_inference:494) INFO: speech length: 91520 +2024-01-16 21:41:06,568 (beam_search:428) INFO: decoder input length: 140 +2024-01-16 21:41:06,568 (beam_search:429) INFO: max output length: 140 +2024-01-16 21:41:06,568 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:06,832 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:06,832 (beam_search:476) INFO: -11.66 * 1.0 = -11.66 for ctc +2024-01-16 21:41:06,832 (beam_search:479) INFO: total log probability: -11.66 +2024-01-16 21:41:06,832 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:41:06,832 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:06,833 (beam_search:483) INFO: best hypo: FIRESCOUCREOSOVENCILYDUSTHFIERBYLVIONTHRTYFIVEPEAM + +2024-01-16 21:41:06,834 (asr_inference:494) INFO: speech length: 72960 +2024-01-16 21:41:06,844 (beam_search:428) INFO: decoder input length: 111 +2024-01-16 21:41:06,844 (beam_search:429) INFO: max output length: 111 +2024-01-16 21:41:06,844 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:07,064 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:07,064 (beam_search:476) INFO: -21.14 * 1.0 = -21.14 for ctc +2024-01-16 21:41:07,064 (beam_search:479) INFO: total log probability: -21.14 +2024-01-16 21:41:07,064 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:41:07,064 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:07,064 (beam_search:483) INFO: best hypo: HISCLOCOMICALSPECHEEONMYANINDECATENOUSINGREDCABIGHEDDOUSE + +2024-01-16 21:41:07,066 (asr_inference:494) INFO: speech length: 122880 +2024-01-16 21:41:07,079 (beam_search:428) INFO: decoder input length: 189 +2024-01-16 21:41:07,079 (beam_search:429) INFO: max output length: 189 +2024-01-16 21:41:07,079 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:07,621 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:07,621 (beam_search:476) INFO: -18.40 * 1.0 = -18.40 for ctc +2024-01-16 21:41:07,621 (beam_search:479) INFO: total log probability: -18.40 +2024-01-16 21:41:07,621 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:41:07,622 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:07,622 (beam_search:483) INFO: best hypo: ANPRTICKILEITISLETHATONCENDETECWETHEAPERSONISLINGBYANTERPRINGMYGRLCSTPERTIONCCURECTLY + +2024-01-16 21:41:07,624 (asr_inference:494) INFO: speech length: 209920 +2024-01-16 21:41:07,643 (beam_search:428) INFO: decoder input length: 325 +2024-01-16 21:41:07,643 (beam_search:429) INFO: max output length: 325 +2024-01-16 21:41:07,643 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:09,058 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:09,058 (beam_search:476) INFO: -28.02 * 1.0 = -28.02 for ctc +2024-01-16 21:41:09,058 (beam_search:479) INFO: total log probability: -28.02 +2024-01-16 21:41:09,058 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:41:09,058 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:09,059 (beam_search:483) INFO: best hypo: THESESCIALFORIDYOFTHCHURHOUIEBENINMROMFOROVERATHOUSEINYOEARSANDDISCONCONTRTIONFPOUERIMUNYWEADTOMAYTOCESTIONWETHERICTENENTWASBENGMAT + +2024-01-16 21:41:09,060 (asr_inference:494) INFO: speech length: 195840 +2024-01-16 21:41:09,078 (beam_search:428) INFO: decoder input length: 303 +2024-01-16 21:41:09,078 (beam_search:429) INFO: max output length: 303 +2024-01-16 21:41:09,078 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:10,407 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:10,407 (beam_search:476) INFO: -24.82 * 1.0 = -24.82 for ctc +2024-01-16 21:41:10,407 (beam_search:479) INFO: total log probability: -24.82 +2024-01-16 21:41:10,407 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:41:10,407 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:10,408 (beam_search:483) INFO: best hypo: THESUNDDOARBONSARTHEARGEISTTHETEORALMANGROEBELINTHEWEROEDSTRCHINGATYCOLOMETERSFIFTYMILESINTOTHEBANGWLDEASHEANANINDINHINTERLANTFOMTHAOWCOST + +2024-01-16 21:41:10,410 (asr_inference:494) INFO: speech length: 184320 +2024-01-16 21:41:10,427 (beam_search:428) INFO: decoder input length: 285 +2024-01-16 21:41:10,427 (beam_search:429) INFO: max output length: 285 +2024-01-16 21:41:10,427 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:11,685 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:11,685 (beam_search:476) INFO: -43.13 * 1.0 = -43.13 for ctc +2024-01-16 21:41:11,685 (beam_search:479) INFO: total log probability: -43.13 +2024-01-16 21:41:11,685 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:41:11,685 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:11,686 (beam_search:483) INFO: best hypo: REAGULRNONCTHNTHEMATHOLARMAEDONLYNCOTLINBUTNETENDISTRUTONERNOUCDBYANOTIMATEISSISMINTWARTVEURITYOFLNGWIGDESINKEUTINGPANIHINGLSHFRINCHERIBICKANDOAPNES + +2024-01-16 21:41:11,688 (asr_inference:494) INFO: speech length: 116160 +2024-01-16 21:41:11,700 (beam_search:428) INFO: decoder input length: 179 +2024-01-16 21:41:11,700 (beam_search:429) INFO: max output length: 179 +2024-01-16 21:41:11,700 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:12,180 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:12,180 (beam_search:476) INFO: -27.24 * 1.0 = -27.24 for ctc +2024-01-16 21:41:12,180 (beam_search:479) INFO: total log probability: -27.24 +2024-01-16 21:41:12,180 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 21:41:12,180 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:12,181 (beam_search:483) INFO: best hypo: ERWNPRTITBATINSTSIDYANOUSISTRENSPRTINCSISTAENCALOSTERWNOMPLANEOBOUTRECDPRTIONSISTOM + +2024-01-16 21:41:12,182 (asr_inference:494) INFO: speech length: 155520 +2024-01-16 21:41:12,197 (beam_search:428) INFO: decoder input length: 240 +2024-01-16 21:41:12,197 (beam_search:429) INFO: max output length: 240 +2024-01-16 21:41:12,197 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:13,139 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:13,139 (beam_search:476) INFO: -29.03 * 1.0 = -29.03 for ctc +2024-01-16 21:41:13,139 (beam_search:479) INFO: total log probability: -29.03 +2024-01-16 21:41:13,139 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:41:13,139 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:13,140 (beam_search:483) INFO: best hypo: LATNHADASFRTHANGESOTHEONSRTESMFVARMINLBILEDRIGHEEAINGWTHEEAUMASINGFRYTHIRLANCOMPETRERIGDTINGOTHEKOSIRVETHISPARYINFBIRAMANALDILL + +2024-01-16 21:41:13,142 (asr_inference:494) INFO: speech length: 137280 +2024-01-16 21:41:13,156 (beam_search:428) INFO: decoder input length: 212 +2024-01-16 21:41:13,156 (beam_search:429) INFO: max output length: 212 +2024-01-16 21:41:13,156 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:13,795 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:13,795 (beam_search:476) INFO: -24.48 * 1.0 = -24.48 for ctc +2024-01-16 21:41:13,795 (beam_search:479) INFO: total log probability: -24.48 +2024-01-16 21:41:13,795 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:41:13,795 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:13,796 (beam_search:483) INFO: best hypo: ANWNHASLNTORTHATHATLIHTATUESAROVERMUENPASTHADENSAIDETHEPOSEILITYOFSNOEICEORFRESINGTEMBTERS + +2024-01-16 21:41:13,797 (asr_inference:494) INFO: speech length: 200000 +2024-01-16 21:41:13,815 (beam_search:428) INFO: decoder input length: 310 +2024-01-16 21:41:13,815 (beam_search:429) INFO: max output length: 310 +2024-01-16 21:41:13,815 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:14,970 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:14,970 (beam_search:476) INFO: -21.62 * 1.0 = -21.62 for ctc +2024-01-16 21:41:14,970 (beam_search:479) INFO: total log probability: -21.62 +2024-01-16 21:41:14,970 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:41:14,970 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:14,971 (beam_search:483) INFO: best hypo: HSLEAEINTERUTIONISHEPRASTISOFHEBOUESAYWAKINDIRINGYORNOMOSTSLEPEREADANDFLINGASLESHOURTIMELATERENTOSICTDEINTSTIT + +2024-01-16 21:41:14,973 (asr_inference:494) INFO: speech length: 133760 +2024-01-16 21:41:14,987 (beam_search:428) INFO: decoder input length: 206 +2024-01-16 21:41:14,987 (beam_search:429) INFO: max output length: 206 +2024-01-16 21:41:14,987 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:15,561 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:15,561 (beam_search:476) INFO: -25.10 * 1.0 = -25.10 for ctc +2024-01-16 21:41:15,561 (beam_search:479) INFO: total log probability: -25.10 +2024-01-16 21:41:15,561 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:41:15,561 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:15,562 (beam_search:483) INFO: best hypo: BLISWMURLETHETWODRIYPPOURERSTOGETHEANDTENWITGQUENGWEATHANDSSCUEETHEINTOABOLWRI + +2024-01-16 21:41:15,563 (asr_inference:494) INFO: speech length: 64320 +2024-01-16 21:41:15,573 (beam_search:428) INFO: decoder input length: 98 +2024-01-16 21:41:15,573 (beam_search:429) INFO: max output length: 98 +2024-01-16 21:41:15,573 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:15,735 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:15,735 (beam_search:476) INFO: -11.16 * 1.0 = -11.16 for ctc +2024-01-16 21:41:15,735 (beam_search:479) INFO: total log probability: -11.16 +2024-01-16 21:41:15,735 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:41:15,735 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:15,736 (beam_search:483) INFO: best hypo: FOTHESPRINGBOACKEIDANDEDAFIVEDMACHTHESINGSTRE + +2024-01-16 21:41:15,737 (asr_inference:494) INFO: speech length: 114240 +2024-01-16 21:41:15,749 (beam_search:428) INFO: decoder input length: 176 +2024-01-16 21:41:15,749 (beam_search:429) INFO: max output length: 176 +2024-01-16 21:41:15,749 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:16,239 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:16,240 (beam_search:476) INFO: -25.12 * 1.0 = -25.12 for ctc +2024-01-16 21:41:16,240 (beam_search:479) INFO: total log probability: -25.12 +2024-01-16 21:41:16,240 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:41:16,240 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:16,240 (beam_search:483) INFO: best hypo: YUOSTLKTHONEXPURTDAPOLNTEARTHCOSINGTIDHDSOTESAMLBYWYEXSERTOFFORSOTHEEDGITARIYAUSALACSY + +2024-01-16 21:41:16,242 (asr_inference:494) INFO: speech length: 131520 +2024-01-16 21:41:16,255 (beam_search:428) INFO: decoder input length: 203 +2024-01-16 21:41:16,255 (beam_search:429) INFO: max output length: 203 +2024-01-16 21:41:16,255 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:16,784 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:16,784 (beam_search:476) INFO: -17.48 * 1.0 = -17.48 for ctc +2024-01-16 21:41:16,784 (beam_search:479) INFO: total log probability: -17.48 +2024-01-16 21:41:16,784 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:41:16,784 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:16,784 (beam_search:483) INFO: best hypo: THORWTHENIHTTHETWENHERDENFITYANDTOHERECOPYSWERMADNONNONASBUMELAPBORODSIDES + +2024-01-16 21:41:16,786 (asr_inference:494) INFO: speech length: 281280 +2024-01-16 21:41:16,812 (beam_search:428) INFO: decoder input length: 437 +2024-01-16 21:41:16,812 (beam_search:429) INFO: max output length: 437 +2024-01-16 21:41:16,812 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:19,248 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:19,248 (beam_search:476) INFO: -38.89 * 1.0 = -38.89 for ctc +2024-01-16 21:41:19,248 (beam_search:479) INFO: total log probability: -38.89 +2024-01-16 21:41:19,248 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:41:19,248 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:19,249 (beam_search:483) INFO: best hypo: FIRSEAMLNGISEINYATRECAMNDATIONDSTHATANNODTIBLMATIKNISHITDIOFSHDBETEKHOMBOFORTHEENDOTHICYEARTOSECURARACSPORERSEGNSTHOSTILINTERVENTIONSANDTORESTABLISTDIPLMATICRELATIONSWIITSNAVERS + +2024-01-16 21:41:19,251 (asr_inference:494) INFO: speech length: 120000 +2024-01-16 21:41:19,264 (beam_search:428) INFO: decoder input length: 185 +2024-01-16 21:41:19,264 (beam_search:429) INFO: max output length: 185 +2024-01-16 21:41:19,264 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:19,756 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:19,756 (beam_search:476) INFO: -25.02 * 1.0 = -25.02 for ctc +2024-01-16 21:41:19,756 (beam_search:479) INFO: total log probability: -25.02 +2024-01-16 21:41:19,756 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:41:19,756 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:19,757 (beam_search:483) INFO: best hypo: SANETERSBRCRESISINLTIMENTOWNWHOHTASINGESARCSENTIFFRMRESERRECURIENCSCHOCTHETRNENS + +2024-01-16 21:41:19,758 (asr_inference:494) INFO: speech length: 118080 +2024-01-16 21:41:19,771 (beam_search:428) INFO: decoder input length: 182 +2024-01-16 21:41:19,771 (beam_search:429) INFO: max output length: 182 +2024-01-16 21:41:19,771 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:20,246 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:20,246 (beam_search:476) INFO: -20.74 * 1.0 = -20.74 for ctc +2024-01-16 21:41:20,246 (beam_search:479) INFO: total log probability: -20.74 +2024-01-16 21:41:20,246 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:41:20,246 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:20,247 (beam_search:483) INFO: best hypo: AOCORDINGTOEPANSNOGELRAGEANSYWRDYLACTIOFCAESIOMNDIADINHASENDENIFIETATHEPLANT + +2024-01-16 21:41:20,248 (asr_inference:494) INFO: speech length: 184320 +2024-01-16 21:41:20,265 (beam_search:428) INFO: decoder input length: 285 +2024-01-16 21:41:20,265 (beam_search:429) INFO: max output length: 285 +2024-01-16 21:41:20,265 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:21,158 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:21,158 (beam_search:476) INFO: -21.37 * 1.0 = -21.37 for ctc +2024-01-16 21:41:21,158 (beam_search:479) INFO: total log probability: -21.37 +2024-01-16 21:41:21,158 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:41:21,158 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:21,158 (beam_search:483) INFO: best hypo: SAEGOKATIONANDRECOMONATIONSHOLFTLVERYATIONBAKONDFORTHHBEUTWENDTHETWOPALLSEWITEACHEGENERYTION + +2024-01-16 21:41:21,160 (asr_inference:494) INFO: speech length: 99840 +2024-01-16 21:41:21,171 (beam_search:428) INFO: decoder input length: 153 +2024-01-16 21:41:21,171 (beam_search:429) INFO: max output length: 153 +2024-01-16 21:41:21,171 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:21,554 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:21,554 (beam_search:476) INFO: -20.42 * 1.0 = -20.42 for ctc +2024-01-16 21:41:21,554 (beam_search:479) INFO: total log probability: -20.42 +2024-01-16 21:41:21,554 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:41:21,554 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:21,555 (beam_search:483) INFO: best hypo: ELAMNTLYCOULSTHUMANDPOTASHIMRCONSETDMUTLESOFPORESRALSOMTLELASIVERANDGOLD + +2024-01-16 21:41:21,556 (asr_inference:494) INFO: speech length: 101760 +2024-01-16 21:41:21,568 (beam_search:428) INFO: decoder input length: 156 +2024-01-16 21:41:21,568 (beam_search:429) INFO: max output length: 156 +2024-01-16 21:41:21,568 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:21,920 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:21,920 (beam_search:476) INFO: -13.34 * 1.0 = -13.34 for ctc +2024-01-16 21:41:21,920 (beam_search:479) INFO: total log probability: -13.34 +2024-01-16 21:41:21,920 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:41:21,920 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:21,921 (beam_search:483) INFO: best hypo: THEORLTIOBTWENBRANPOTHOALAGEYANEHAVEYOURSSPRTSINCSISTSANDTHERESRCGE + +2024-01-16 21:41:21,922 (asr_inference:494) INFO: speech length: 207040 +2024-01-16 21:41:21,941 (beam_search:428) INFO: decoder input length: 321 +2024-01-16 21:41:21,941 (beam_search:429) INFO: max output length: 321 +2024-01-16 21:41:21,941 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:23,239 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:23,239 (beam_search:476) INFO: -22.73 * 1.0 = -22.73 for ctc +2024-01-16 21:41:23,239 (beam_search:479) INFO: total log probability: -22.73 +2024-01-16 21:41:23,239 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:41:23,239 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:23,240 (beam_search:483) INFO: best hypo: ANCIONCHAINENTHADOUNEAKCWAYOFSHOINGDIFRENTTIMEMPEREATDSEACHESTDAKEOFCHINEOREECHFAMILYTHATWASIMPOURERWASHAEDESTINTOFDINISTY + +2024-01-16 21:41:23,241 (asr_inference:494) INFO: speech length: 159360 +2024-01-16 21:41:23,257 (beam_search:428) INFO: decoder input length: 246 +2024-01-16 21:41:23,257 (beam_search:429) INFO: max output length: 246 +2024-01-16 21:41:23,257 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:24,151 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:24,151 (beam_search:476) INFO: -30.96 * 1.0 = -30.96 for ctc +2024-01-16 21:41:24,151 (beam_search:479) INFO: total log probability: -30.96 +2024-01-16 21:41:24,151 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:41:24,151 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:24,152 (beam_search:483) INFO: best hypo: AHEMBLPOPBELEDIMERNHHSTESHLYTDRINTHESUMERISPAAMALYBUDWHTLVOILTOMETDNANYOVLALABECONTINCTICHESCHEESTOUOFISHITDSETER + +2024-01-16 21:41:24,154 (asr_inference:494) INFO: speech length: 122880 +2024-01-16 21:41:24,166 (beam_search:428) INFO: decoder input length: 189 +2024-01-16 21:41:24,166 (beam_search:429) INFO: max output length: 189 +2024-01-16 21:41:24,166 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:24,682 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:24,682 (beam_search:476) INFO: -19.60 * 1.0 = -19.60 for ctc +2024-01-16 21:41:24,682 (beam_search:479) INFO: total log probability: -19.60 +2024-01-16 21:41:24,682 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:41:24,682 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:24,683 (beam_search:483) INFO: best hypo: THEANONCTHNTWASMADEAVERTRNMATYFONOMERSATIONWHTTORKSHSPRDODENTRESEPTTHEYEAPAERORDOUN + +2024-01-16 21:41:24,684 (asr_inference:494) INFO: speech length: 220800 +2024-01-16 21:41:24,704 (beam_search:428) INFO: decoder input length: 342 +2024-01-16 21:41:24,704 (beam_search:429) INFO: max output length: 342 +2024-01-16 21:41:24,704 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:26,524 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:26,524 (beam_search:476) INFO: -53.27 * 1.0 = -53.27 for ctc +2024-01-16 21:41:26,524 (beam_search:479) INFO: total log probability: -53.27 +2024-01-16 21:41:26,524 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:41:26,524 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:26,526 (beam_search:483) INFO: best hypo: PERYSATETATHHEODRETENTOTECXEICTOSTSEUTORESULTSOFTONGHCSCOKISCDEDERMONWHTHEHERSAPPASFORDFRMYSELFNTHSRACESBUELETERSTTHOWOWLREMAEINTHERAIDANDGBPENOTIGENRYTWEWNSOUTEIRLINOPRMIARY + +2024-01-16 21:41:26,527 (asr_inference:494) INFO: speech length: 294720 +2024-01-16 21:41:26,554 (beam_search:428) INFO: decoder input length: 458 +2024-01-16 21:41:26,554 (beam_search:429) INFO: max output length: 458 +2024-01-16 21:41:26,554 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:29,193 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:29,193 (beam_search:476) INFO: -42.21 * 1.0 = -42.21 for ctc +2024-01-16 21:41:29,193 (beam_search:479) INFO: total log probability: -42.21 +2024-01-16 21:41:29,193 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:41:29,193 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:29,194 (beam_search:483) INFO: best hypo: EHEWAELSOINGAGEINGGRAINGBAKNOLTDSFORMINYOUNTRESRSONINGSEAPLESOFWHISERKINGCLEEDTHEHEAMPRIENMENININISREALPRTEREDSONTHEFIRSTFROFHONDTHFRUNTOFTHENOOCONADYINOFLOEDLERINWOHNDERDLDEL + +2024-01-16 21:41:29,196 (asr_inference:494) INFO: speech length: 203520 +2024-01-16 21:41:29,214 (beam_search:428) INFO: decoder input length: 315 +2024-01-16 21:41:29,214 (beam_search:429) INFO: max output length: 315 +2024-01-16 21:41:29,214 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:30,706 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:30,706 (beam_search:476) INFO: -35.65 * 1.0 = -35.65 for ctc +2024-01-16 21:41:30,706 (beam_search:479) INFO: total log probability: -35.65 +2024-01-16 21:41:30,706 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:41:30,706 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:30,707 (beam_search:483) INFO: best hypo: HMORTRDINHRCHESONTANHOETHENEASTERRICILNSATTEDYNGHTTURINTHEESTRWEENDBUTHECOGREWTIONSOTIMBRAKININTOSELEBRATIONATTHESROCOFMINTHTOSOLBRAKCRICESRESURECTION + +2024-01-16 21:41:30,709 (asr_inference:494) INFO: speech length: 149760 +2024-01-16 21:41:30,724 (beam_search:428) INFO: decoder input length: 231 +2024-01-16 21:41:30,724 (beam_search:429) INFO: max output length: 231 +2024-01-16 21:41:30,724 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:31,519 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:31,519 (beam_search:476) INFO: -20.63 * 1.0 = -20.63 for ctc +2024-01-16 21:41:31,519 (beam_search:479) INFO: total log probability: -20.63 +2024-01-16 21:41:31,519 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:41:31,519 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:31,519 (beam_search:483) INFO: best hypo: FILNIAGRATBODINGDUSTENATIONTHELANDOFATHOUSENLAKHESTOUSEOFILENCDSTWOOANDTHELAKANNTHECOSTAOEARKYPELOGOES + +2024-01-16 21:41:31,521 (asr_inference:494) INFO: speech length: 193920 +2024-01-16 21:41:31,538 (beam_search:428) INFO: decoder input length: 300 +2024-01-16 21:41:31,538 (beam_search:429) INFO: max output length: 300 +2024-01-16 21:41:31,538 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:32,871 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:32,871 (beam_search:476) INFO: -40.92 * 1.0 = -40.92 for ctc +2024-01-16 21:41:32,871 (beam_search:479) INFO: total log probability: -40.92 +2024-01-16 21:41:32,871 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:41:32,871 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:32,872 (beam_search:483) INFO: best hypo: ERNTTENTERANDARGHINCSINFRSLADYCESTDENOFRNDISACERSIONRANOESRPESINACHLCANDISOUSTRDAYAVENGNLHPLATHATASTADYFOFTDYOLOMEITERSTHERTYWNMILSAWYFROMWNOLSIDIS + +2024-01-16 21:41:32,874 (asr_inference:494) INFO: speech length: 172800 +2024-01-16 21:41:32,890 (beam_search:428) INFO: decoder input length: 267 +2024-01-16 21:41:32,890 (beam_search:429) INFO: max output length: 267 +2024-01-16 21:41:32,890 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:33,894 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:33,894 (beam_search:476) INFO: -24.93 * 1.0 = -24.93 for ctc +2024-01-16 21:41:33,895 (beam_search:479) INFO: total log probability: -24.93 +2024-01-16 21:41:33,895 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:41:33,895 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:33,895 (beam_search:483) INFO: best hypo: SVERWETHERIHEENARNCTEREFORNYDANDERSWHTHEFONAMONANHWHTHEPOTINCLTOCOSDAMIAGESIRISCSOSIOLDISTRUPTIONORLOSOFHMENLIFE + +2024-01-16 21:41:33,897 (asr_inference:494) INFO: speech length: 222720 +2024-01-16 21:41:33,917 (beam_search:428) INFO: decoder input length: 345 +2024-01-16 21:41:33,917 (beam_search:429) INFO: max output length: 345 +2024-01-16 21:41:33,917 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:35,470 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:35,470 (beam_search:476) INFO: -29.70 * 1.0 = -29.70 for ctc +2024-01-16 21:41:35,470 (beam_search:479) INFO: total log probability: -29.70 +2024-01-16 21:41:35,470 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:41:35,470 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:35,471 (beam_search:483) INFO: best hypo: FORESAMBLTHEMOSTCOMENSTHLIMINGEFHOTOCKRFEYFORMUTITHEHORALDISTHRYFIREILNMEAERWHCHWASTHEDOMINENTFIELMEMSIGESATHECLOESOFTHEANILOGFILEARA + +2024-01-16 21:41:35,472 (asr_inference:494) INFO: speech length: 144000 +2024-01-16 21:41:35,487 (beam_search:428) INFO: decoder input length: 222 +2024-01-16 21:41:35,487 (beam_search:429) INFO: max output length: 222 +2024-01-16 21:41:35,487 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:36,320 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:36,320 (beam_search:476) INFO: -23.27 * 1.0 = -23.27 for ctc +2024-01-16 21:41:36,320 (beam_search:479) INFO: total log probability: -23.27 +2024-01-16 21:41:36,320 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:41:36,320 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:36,321 (beam_search:483) INFO: best hypo: ITISRELATEDTOBUTYUSELYNOTIBLTINGOLPINGTILESKETORINGARMOUTENERINGTHELATERWOESDOUNEINSDECTERINGANDRECRIRINGMUSHTHIFRSKEESANDBOTS + +2024-01-16 21:41:36,322 (asr_inference:494) INFO: speech length: 203520 +2024-01-16 21:41:36,340 (beam_search:428) INFO: decoder input length: 315 +2024-01-16 21:41:36,340 (beam_search:429) INFO: max output length: 315 +2024-01-16 21:41:36,340 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:37,492 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:37,492 (beam_search:476) INFO: -22.89 * 1.0 = -22.89 for ctc +2024-01-16 21:41:37,492 (beam_search:479) INFO: total log probability: -22.89 +2024-01-16 21:41:37,492 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:41:37,492 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:37,492 (beam_search:483) INFO: best hypo: IARNINGDAMKCLOASESCANHEPTHEDRIYIHMANYHOTELSHVENIARNANDIRNINGBOROVALABLEFRLONEVENIFONHSNOTPRESENTINTHEROM + +2024-01-16 21:41:37,494 (asr_inference:494) INFO: speech length: 273760 +2024-01-16 21:41:37,519 (beam_search:428) INFO: decoder input length: 425 +2024-01-16 21:41:37,519 (beam_search:429) INFO: max output length: 425 +2024-01-16 21:41:37,519 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:39,673 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:39,673 (beam_search:476) INFO: -37.60 * 1.0 = -37.60 for ctc +2024-01-16 21:41:39,673 (beam_search:479) INFO: total log probability: -37.60 +2024-01-16 21:41:39,673 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:41:39,673 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:39,674 (beam_search:483) INFO: best hypo: EEVEDNYUNCERDHORSLYSHEESUHERCHAREUNLEITHEGLAOSEHEOTHFIRERANDSCREDEHRHANDSOUTOTHEBLASESTEREWASNOOTHELIHTINTEONMMYTHETIMNTHEWINWIDNOTOHORDEDISMRLYSTIL + +2024-01-16 21:41:39,676 (asr_inference:494) INFO: speech length: 288160 +2024-01-16 21:41:39,702 (beam_search:428) INFO: decoder input length: 448 +2024-01-16 21:41:39,702 (beam_search:429) INFO: max output length: 448 +2024-01-16 21:41:39,702 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:42,009 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:42,009 (beam_search:476) INFO: -40.88 * 1.0 = -40.88 for ctc +2024-01-16 21:41:42,009 (beam_search:479) INFO: total log probability: -40.88 +2024-01-16 21:41:42,009 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:41:42,009 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:42,010 (beam_search:483) INFO: best hypo: EMYDEARMARLDEEAERWHIDOOUDNOTDESISTFOMTHESILYPERSSOUOTOFEANADNMANDGINARYTHEADSERWHATISTHETHEOLYOUOFMUNYWEARSPBANURDSNOTSHURTSLEVEDMERSSINARYPPEGSOFAMAIYCENDS + +2024-01-16 21:41:42,012 (asr_inference:494) INFO: speech length: 196160 +2024-01-16 21:41:42,030 (beam_search:428) INFO: decoder input length: 304 +2024-01-16 21:41:42,030 (beam_search:429) INFO: max output length: 304 +2024-01-16 21:41:42,030 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:43,376 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:43,376 (beam_search:476) INFO: -34.41 * 1.0 = -34.41 for ctc +2024-01-16 21:41:43,376 (beam_search:479) INFO: total log probability: -34.41 +2024-01-16 21:41:43,376 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:41:43,376 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:43,377 (beam_search:483) INFO: best hypo: TERITCLTUBPTEISTATOTESINGLIASETHERMELINWCHPESESEPONTIEINFLEATIONTHOARSENDTINGENTTHERSTIALERESUHERSCAVLLIEATHTWOCOALDNESOFTHISPONTBENPLETION + +2024-01-16 21:41:43,379 (asr_inference:494) INFO: speech length: 287040 +2024-01-16 21:41:43,405 (beam_search:428) INFO: decoder input length: 446 +2024-01-16 21:41:43,405 (beam_search:429) INFO: max output length: 446 +2024-01-16 21:41:43,405 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:46,032 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:46,032 (beam_search:476) INFO: -31.30 * 1.0 = -31.30 for ctc +2024-01-16 21:41:46,032 (beam_search:479) INFO: total log probability: -31.30 +2024-01-16 21:41:46,032 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:41:46,032 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:46,034 (beam_search:483) INFO: best hypo: MUCHLIKANFOUNEUSNDOFORMEITYANTOHATMONSTERHOMTHETHEABONNIHTTHEFARTHEROFTHATFATLPRODGINYMADEDCILHERSELFFORVERYHARTSTESPIHTTHATHEHADRAHERRIDLWHICHNOWIHTCOUOTEVERLOUSHATSUFERDDEDLYDE + +2024-01-16 21:41:46,036 (asr_inference:494) INFO: speech length: 241120 +2024-01-16 21:41:46,057 (beam_search:428) INFO: decoder input length: 374 +2024-01-16 21:41:46,057 (beam_search:429) INFO: max output length: 374 +2024-01-16 21:41:46,057 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:48,138 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:48,138 (beam_search:476) INFO: -36.71 * 1.0 = -36.71 for ctc +2024-01-16 21:41:48,138 (beam_search:479) INFO: total log probability: -36.71 +2024-01-16 21:41:48,138 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:41:48,138 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:48,139 (beam_search:483) INFO: best hypo: HIMASHEMASERWIHPRSIONGRESIESMUNTIGTOHRETHOULSENANSTIESANDALSOTHERYSMALVLLIMSTHADOCKIEPYDEBTHEFLNTDMUENDECOESIERATIONTHISLASEMASENTWHCHNOUESEITATENUMERSCORATINDISMOSTDELIKEUPUTHEOPRATION + +2024-01-16 21:41:48,141 (asr_inference:494) INFO: speech length: 163840 +2024-01-16 21:41:48,157 (beam_search:428) INFO: decoder input length: 253 +2024-01-16 21:41:48,157 (beam_search:429) INFO: max output length: 253 +2024-01-16 21:41:48,157 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:49,071 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:49,071 (beam_search:476) INFO: -22.28 * 1.0 = -22.28 for ctc +2024-01-16 21:41:49,071 (beam_search:479) INFO: total log probability: -22.28 +2024-01-16 21:41:49,071 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:41:49,071 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:49,072 (beam_search:483) INFO: best hypo: WHISHULITHAEBENDEMEDNECROMANCSYTOINDEVEROONBINGTHESFASTOIVFOLILVEBYGIARFULELMINATIONANDCHANGETOTHEPERVECTFOD + +2024-01-16 21:41:49,074 (asr_inference:494) INFO: speech length: 313440 +2024-01-16 21:41:49,102 (beam_search:428) INFO: decoder input length: 487 +2024-01-16 21:41:49,102 (beam_search:429) INFO: max output length: 487 +2024-01-16 21:41:49,102 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:51,768 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:51,768 (beam_search:476) INFO: -35.40 * 1.0 = -35.40 for ctc +2024-01-16 21:41:51,768 (beam_search:479) INFO: total log probability: -35.40 +2024-01-16 21:41:51,768 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:41:51,768 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:51,769 (beam_search:483) INFO: best hypo: DNAYTHOVERASESBEYMYBEADTYEATIAMRIGECELOVESAIDBUTHOUTAREGUDLIHVETHRICEFOMNDARTHOULETOYELTHESOVERANGIFTSOFARTHTHEVIETORSORORDTHELORALETBROLORVINTHINGKSOFLILEWERRT + +2024-01-16 21:41:51,771 (asr_inference:494) INFO: speech length: 176960 +2024-01-16 21:41:51,788 (beam_search:428) INFO: decoder input length: 274 +2024-01-16 21:41:51,788 (beam_search:429) INFO: max output length: 274 +2024-01-16 21:41:51,788 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:52,922 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:52,922 (beam_search:476) INFO: -29.12 * 1.0 = -29.12 for ctc +2024-01-16 21:41:52,922 (beam_search:479) INFO: total log probability: -29.12 +2024-01-16 21:41:52,922 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:41:52,922 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:52,923 (beam_search:483) INFO: best hypo: BOCKESTOHVEBENAKENOLACTROLTHOHAMPEDBYILEHELTHFADGRATPINTONISFAVERISHATESCRIGEDONLYTHUCEPLANSHWITHHATDCOMUNDERISONPERSINLOPSOVATION + +2024-01-16 21:41:52,925 (asr_inference:494) INFO: speech length: 203520 +2024-01-16 21:41:52,943 (beam_search:428) INFO: decoder input length: 315 +2024-01-16 21:41:52,943 (beam_search:429) INFO: max output length: 315 +2024-01-16 21:41:52,943 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:54,331 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:54,331 (beam_search:476) INFO: -24.99 * 1.0 = -24.99 for ctc +2024-01-16 21:41:54,331 (beam_search:479) INFO: total log probability: -24.99 +2024-01-16 21:41:54,331 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:41:54,331 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:54,332 (beam_search:483) INFO: best hypo: HADRATHERSRNGOPANDHADNOTCHAINSEDINTONEIMSTHEISHYFELDINTHESTAMSCOVERINTHEMUPAGANANDTHEYAPEREDASPERFECTDINSACTSINTHEMAYOFTHEFOLNINHEAR + +2024-01-16 21:41:54,334 (asr_inference:494) INFO: speech length: 230880 +2024-01-16 21:41:54,354 (beam_search:428) INFO: decoder input length: 358 +2024-01-16 21:41:54,354 (beam_search:429) INFO: max output length: 358 +2024-01-16 21:41:54,354 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:56,181 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:56,181 (beam_search:476) INFO: -40.23 * 1.0 = -40.23 for ctc +2024-01-16 21:41:56,181 (beam_search:479) INFO: total log probability: -40.23 +2024-01-16 21:41:56,182 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:41:56,182 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:56,183 (beam_search:483) INFO: best hypo: NOTHINGSAYWOOBDECEANDHOATSOFBUTYMOUDPRESVENDTHEMSAESTOTHEUNDERSTANDINGOFTHEFORTIELETBORSONHOUEARTOKOFITTHEISBEATSWICHOHAEBRUGTOMETRANSLATDARCONSEREWITTHISSOUPESTION + +2024-01-16 21:41:56,184 (asr_inference:494) INFO: speech length: 160800 +2024-01-16 21:41:56,200 (beam_search:428) INFO: decoder input length: 249 +2024-01-16 21:41:56,200 (beam_search:429) INFO: max output length: 249 +2024-01-16 21:41:56,200 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:56,964 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:56,964 (beam_search:476) INFO: -20.49 * 1.0 = -20.49 for ctc +2024-01-16 21:41:56,964 (beam_search:479) INFO: total log probability: -20.49 +2024-01-16 21:41:56,964 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:41:56,964 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:56,965 (beam_search:483) INFO: best hypo: NOUSMEINCUPTITYANDHENERVEHIMSELFAGAINSITHISFAISWALSORDOSOVEFLUSHDHEWASTIMIDEVEINTORROUDNES + +2024-01-16 21:41:56,966 (asr_inference:494) INFO: speech length: 247360 +2024-01-16 21:41:56,989 (beam_search:428) INFO: decoder input length: 384 +2024-01-16 21:41:56,989 (beam_search:429) INFO: max output length: 384 +2024-01-16 21:41:56,989 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:41:59,027 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:41:59,027 (beam_search:476) INFO: -43.52 * 1.0 = -43.52 for ctc +2024-01-16 21:41:59,027 (beam_search:479) INFO: total log probability: -43.52 +2024-01-16 21:41:59,027 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:41:59,027 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:41:59,028 (beam_search:483) INFO: best hypo: TBECOMEMOLLIUGHELIKCKEASTHESHIEXSFLUSHTHEASREARWOAIMGEFINOEINTEMORDINGTHERDHAFTISPRINGSOLTIDITLONGAOURSOFALLREADINGANDPERSTEDHARTBYNEVERSEASINGRIMEMSESTICUNONDERSTANDIT + +2024-01-16 21:41:59,030 (asr_inference:494) INFO: speech length: 176640 +2024-01-16 21:41:59,047 (beam_search:428) INFO: decoder input length: 273 +2024-01-16 21:41:59,047 (beam_search:429) INFO: max output length: 273 +2024-01-16 21:41:59,047 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:00,025 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:00,025 (beam_search:476) INFO: -21.84 * 1.0 = -21.84 for ctc +2024-01-16 21:42:00,026 (beam_search:479) INFO: total log probability: -21.84 +2024-01-16 21:42:00,026 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:42:00,026 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:00,026 (beam_search:483) INFO: best hypo: WONFTHEHOWINGRGDERSAIDTHEALLPEHEAOVOISAPOSENSHOLFISHTHESERBITRANDDEDLYANDCOEBEYOUSEINPUITINGENINMYSTODEA + +2024-01-16 21:42:00,028 (asr_inference:494) INFO: speech length: 209280 +2024-01-16 21:42:00,047 (beam_search:428) INFO: decoder input length: 324 +2024-01-16 21:42:00,047 (beam_search:429) INFO: max output length: 324 +2024-01-16 21:42:00,047 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:01,383 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:01,383 (beam_search:476) INFO: -26.68 * 1.0 = -26.68 for ctc +2024-01-16 21:42:01,383 (beam_search:479) INFO: total log probability: -26.68 +2024-01-16 21:42:01,383 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:42:01,383 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:01,384 (beam_search:483) INFO: best hypo: THEBEUTEASROUPESOFHAVIONASLONTOADURIHTEARANDCOLETARHELOKEINBOWNLISMAGHSTYRABROURDTOUCHINGTHGRENLEVESALLATREMBLITGOUGLLIHT + +2024-01-16 21:42:01,386 (asr_inference:494) INFO: speech length: 305760 +2024-01-16 21:42:01,413 (beam_search:428) INFO: decoder input length: 475 +2024-01-16 21:42:01,413 (beam_search:429) INFO: max output length: 475 +2024-01-16 21:42:01,413 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:04,175 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:04,175 (beam_search:476) INFO: -52.13 * 1.0 = -52.13 for ctc +2024-01-16 21:42:04,175 (beam_search:479) INFO: total log probability: -52.13 +2024-01-16 21:42:04,175 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:42:04,175 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:04,176 (beam_search:483) INFO: best hypo: IADDUNORMORTHNTHATIUNDTLTHISNATAREHISAPELUDLYSETEDETHEEWORTHMTHELIFITELFTOEMWHSTRBLLBURSMEDANORYISTSAORLYHEPRTESTEITOULNOTGODASTMETOWATTHRAMUNCEANDTILIATAEXAMINBONOFTHEISES + +2024-01-16 21:42:04,178 (asr_inference:494) INFO: speech length: 227520 +2024-01-16 21:42:04,198 (beam_search:428) INFO: decoder input length: 353 +2024-01-16 21:42:04,198 (beam_search:429) INFO: max output length: 353 +2024-01-16 21:42:04,198 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:05,693 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:05,693 (beam_search:476) INFO: -27.55 * 1.0 = -27.55 for ctc +2024-01-16 21:42:05,693 (beam_search:479) INFO: total log probability: -27.55 +2024-01-16 21:42:05,693 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:42:05,693 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:05,694 (beam_search:483) INFO: best hypo: ROSCONGRESFOUNDATIONRESIONANDTITETHATORGNIHETHESANTPETERSBURIGINTERNASIONLEECENOMICFORMROUSNEFTRUSIONDSTADONEDOILENANERGYCOMBPANYW + +2024-01-16 21:42:05,695 (asr_inference:494) INFO: speech length: 265920 +2024-01-16 21:42:05,719 (beam_search:428) INFO: decoder input length: 413 +2024-01-16 21:42:05,719 (beam_search:429) INFO: max output length: 413 +2024-01-16 21:42:05,719 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:08,273 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:08,273 (beam_search:476) INFO: -41.56 * 1.0 = -41.56 for ctc +2024-01-16 21:42:08,273 (beam_search:479) INFO: total log probability: -41.56 +2024-01-16 21:42:08,273 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:42:08,273 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:08,274 (beam_search:483) INFO: best hypo: HOWGLATEDINSPACALTHEDELICEATFROSTWEKYOUEATRACTDEDNOEDOUTAMAVERDATHEDINYTRACSOMSBUTFEWUEOVASHAEREALYHADANOPRTNITYTOSTANDYTHEDETEALOTHISFRUSTESINESMYNUTLYORVCONSIDEDHATTHEEREORHINTHREYURFORDESINEATMOST + +2024-01-16 21:42:08,276 (asr_inference:494) INFO: speech length: 256160 +2024-01-16 21:42:08,299 (beam_search:428) INFO: decoder input length: 398 +2024-01-16 21:42:08,299 (beam_search:429) INFO: max output length: 398 +2024-01-16 21:42:08,299 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:10,690 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:10,690 (beam_search:476) INFO: -43.97 * 1.0 = -43.97 for ctc +2024-01-16 21:42:10,690 (beam_search:479) INFO: total log probability: -43.97 +2024-01-16 21:42:10,690 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:42:10,690 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:10,691 (beam_search:483) INFO: best hypo: THEAHANTHOFESINTRINGTOINFIKGIUONTHEMENKILVHEOFENDERORWENHIMMORVHANTHEINTENDENTODWANDTHISBECOMEACCUSFULEANUHERDSOHATHEPRIMITIVELIGESLATERSWHERECEFULINORECQUIURINGTHERIETALITIONTOBEDLMITEDTOANIYFORANOI + +2024-01-16 21:42:10,693 (asr_inference:494) INFO: speech length: 286400 +2024-01-16 21:42:10,719 (beam_search:428) INFO: decoder input length: 445 +2024-01-16 21:42:10,719 (beam_search:429) INFO: max output length: 445 +2024-01-16 21:42:10,719 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:13,259 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:13,259 (beam_search:476) INFO: -28.24 * 1.0 = -28.24 for ctc +2024-01-16 21:42:13,259 (beam_search:479) INFO: total log probability: -28.24 +2024-01-16 21:42:13,259 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:42:13,259 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:13,260 (beam_search:483) INFO: best hypo: ATSIRESSWORDTHEJUOSERETERENTHECOMPONYTHATGOEBGOSHICESBEGONWITMRTAMOMEISHENDEREDBYTHEFOBUTWHNCEAGAINTHEWORKGOSONBYLIENSFROMDRIASASREISSENTWITHROILEDGRANTANDGIFTSFRYOSESPISS + +2024-01-16 21:42:13,262 (asr_inference:494) INFO: speech length: 195680 +2024-01-16 21:42:13,280 (beam_search:428) INFO: decoder input length: 303 +2024-01-16 21:42:13,280 (beam_search:429) INFO: max output length: 303 +2024-01-16 21:42:13,280 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:14,391 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:14,391 (beam_search:476) INFO: -35.02 * 1.0 = -35.02 for ctc +2024-01-16 21:42:14,391 (beam_search:479) INFO: total log probability: -35.02 +2024-01-16 21:42:14,391 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 21:42:14,392 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:14,392 (beam_search:483) INFO: best hypo: ANTPRODKEYHAIEINANDYEAROUTESVINHUDRTFRONCESHOELVEDNITHOVENOSOBADLYBREULACSPMLAINMURTYISOCKUPYHTEOREBOHOUES + +2024-01-16 21:42:14,394 (asr_inference:494) INFO: speech length: 309280 +2024-01-16 21:42:14,422 (beam_search:428) INFO: decoder input length: 481 +2024-01-16 21:42:14,422 (beam_search:429) INFO: max output length: 481 +2024-01-16 21:42:14,422 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:17,309 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:17,309 (beam_search:476) INFO: -38.72 * 1.0 = -38.72 for ctc +2024-01-16 21:42:17,309 (beam_search:479) INFO: total log probability: -38.72 +2024-01-16 21:42:17,309 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:42:17,309 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:17,310 (beam_search:483) INFO: best hypo: TEDTHISEISALLYOURANCTSERTISTWOFEAIREFORONEOFHISOLINTCSANDYWOREYOUOWETHATTHISPLACENOMORSEYOUANGSITANTERDEFLERANSTHEESTISESTHEISMORECROUNDTOMEDAMANDRAVENDGONONISEFARACSTHEATSMYAMEINDED + +2024-01-16 21:42:17,312 (asr_inference:494) INFO: speech length: 316160 +2024-01-16 21:42:17,341 (beam_search:428) INFO: decoder input length: 491 +2024-01-16 21:42:17,341 (beam_search:429) INFO: max output length: 491 +2024-01-16 21:42:17,341 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:20,351 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:20,351 (beam_search:476) INFO: -42.48 * 1.0 = -42.48 for ctc +2024-01-16 21:42:20,351 (beam_search:479) INFO: total log probability: -42.48 +2024-01-16 21:42:20,351 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:42:20,351 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:20,352 (beam_search:483) INFO: best hypo: WHENIRETURNEDATOTHEHOUSESWHREHADBEENHAPYCHILEDONDLYAEPILOFASHIESWRAYTHADSTODAIWEPTLONGKANDTOFOROGETMYWEPINGISAIDOUTUNDEVEASCOMSEONDTHESWORTERSINASTHRSIUGFYAERNIGTIDPLAYEMYFLTTOTHESMEMON + +2024-01-16 21:42:20,354 (asr_inference:494) INFO: speech length: 275520 +2024-01-16 21:42:20,380 (beam_search:428) INFO: decoder input length: 428 +2024-01-16 21:42:20,380 (beam_search:429) INFO: max output length: 428 +2024-01-16 21:42:20,380 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:22,909 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:22,909 (beam_search:476) INFO: -39.34 * 1.0 = -39.34 for ctc +2024-01-16 21:42:22,909 (beam_search:479) INFO: total log probability: -39.34 +2024-01-16 21:42:22,909 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:42:22,909 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:22,910 (beam_search:483) INFO: best hypo: TEDOOUNOTSEEWHAUTLESERITDGIVESMEWEHAVEGRONOUPOGETERINTHISHOUSESINHEWORSABOYHISSEIMPLYANORBEAREASOUCANDTHEIHTOFTHSMYGLDLEVINHISFHACEBORDEARHEHASNOAMUSMENECEPTHICBLANGATDTHSHOPSKSCPING + +2024-01-16 21:42:22,912 (asr_inference:494) INFO: speech length: 197600 +2024-01-16 21:42:22,930 (beam_search:428) INFO: decoder input length: 306 +2024-01-16 21:42:22,930 (beam_search:429) INFO: max output length: 306 +2024-01-16 21:42:22,930 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:24,203 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:24,203 (beam_search:476) INFO: -36.63 * 1.0 = -36.63 for ctc +2024-01-16 21:42:24,203 (beam_search:479) INFO: total log probability: -36.63 +2024-01-16 21:42:24,203 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:42:24,203 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:24,203 (beam_search:483) INFO: best hypo: ITISDEVIESSBOYREVEALDINGNYELPICALORGREYUWONGATIONILOVELOVEDLOVEILNOTBETHEWONDOFCUPITBUTHEADIFTHSTATIONOFEGEVERSLERDUCTOEISTINCES + +2024-01-16 21:42:24,205 (asr_inference:494) INFO: speech length: 284000 +2024-01-16 21:42:24,231 (beam_search:428) INFO: decoder input length: 441 +2024-01-16 21:42:24,231 (beam_search:429) INFO: max output length: 441 +2024-01-16 21:42:24,231 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:26,732 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:26,732 (beam_search:476) INFO: -41.20 * 1.0 = -41.20 for ctc +2024-01-16 21:42:26,732 (beam_search:479) INFO: total log probability: -41.20 +2024-01-16 21:42:26,732 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:42:26,732 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:26,734 (beam_search:483) INFO: best hypo: SHORPLYASHESHOKHANSITHEROGODBESYUMAYDYATCHAITHEBISHOPSAIDWENSHECESEHIMANDHISLIPSMORDOFTERWORDFORESOMESICKENTSASIFHEWERINPREAREIUDMUTHEOFOLLOREHEREOULOFTHOMANDTIENSILANSETEL + +2024-01-16 21:42:26,735 (asr_inference:494) INFO: speech length: 180640 +2024-01-16 21:42:26,752 (beam_search:428) INFO: decoder input length: 280 +2024-01-16 21:42:26,752 (beam_search:429) INFO: max output length: 280 +2024-01-16 21:42:26,752 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:27,803 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:27,803 (beam_search:476) INFO: -22.49 * 1.0 = -22.49 for ctc +2024-01-16 21:42:27,803 (beam_search:479) INFO: total log probability: -22.49 +2024-01-16 21:42:27,803 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:42:27,803 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:27,804 (beam_search:483) INFO: best hypo: FOLLAEDHIMSTEAELTHELYHEANDHEWWASANASTPINGPOSRERFILINGHISBOUCKEATCAMEUPTBEHEIEDHIMANDPLUNCHEDANDLONGNIFNTOOISNACK + +2024-01-16 21:42:27,805 (asr_inference:494) INFO: speech length: 266560 +2024-01-16 21:42:27,829 (beam_search:428) INFO: decoder input length: 414 +2024-01-16 21:42:27,829 (beam_search:429) INFO: max output length: 414 +2024-01-16 21:42:27,829 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:30,164 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:30,164 (beam_search:476) INFO: -32.81 * 1.0 = -32.81 for ctc +2024-01-16 21:42:30,164 (beam_search:479) INFO: total log probability: -32.81 +2024-01-16 21:42:30,164 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:42:30,164 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:30,165 (beam_search:483) INFO: best hypo: TSAISTHCKERSIASDOUSTNOTJUPETERDISTRIBUETTOHEGOGDTHEPREPORSTIONANDDIVIDENTSPARINGLYANDSEVERALLYASAGMENDITOHISCOMANDRSWHENHISGEASTSTRANGTOONEANOTHERIVFOCKOURSIUSQULSKLEDEMIUSASYOUNERRAT + +2024-01-16 21:42:30,167 (asr_inference:494) INFO: speech length: 207520 +2024-01-16 21:42:30,186 (beam_search:428) INFO: decoder input length: 322 +2024-01-16 21:42:30,187 (beam_search:429) INFO: max output length: 322 +2024-01-16 21:42:30,187 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:31,509 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:31,509 (beam_search:476) INFO: -29.92 * 1.0 = -29.92 for ctc +2024-01-16 21:42:31,509 (beam_search:479) INFO: total log probability: -29.92 +2024-01-16 21:42:31,509 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:42:31,509 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:31,510 (beam_search:483) INFO: best hypo: EANWHRNONHULERRSTRANATOCAMETAGAININFBHOHTSOTHISNOOUSENWEPINGBERCHERULSPIRTSTILENEVERDOUTHEFATISCEPINGPUCTERGOODFORPRESENIL + +2024-01-16 21:42:31,512 (asr_inference:494) INFO: speech length: 220320 +2024-01-16 21:42:31,532 (beam_search:428) INFO: decoder input length: 342 +2024-01-16 21:42:31,532 (beam_search:429) INFO: max output length: 342 +2024-01-16 21:42:31,532 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:33,202 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:33,202 (beam_search:476) INFO: -25.10 * 1.0 = -25.10 for ctc +2024-01-16 21:42:33,202 (beam_search:479) INFO: total log probability: -25.10 +2024-01-16 21:42:33,202 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:42:33,202 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:33,203 (beam_search:483) INFO: best hypo: ANDOBECOMETHERECKERDOFWHATPEPLAEDONITHERMORAMIUBLEMOENTSTHERECKERDOFTHCONCQUESTSATPESEHOWMENDHAVELIVEDANDLEAVERDDOUGATBILTUNANLIEREDGARDIEDATREAFORERST + +2024-01-16 21:42:33,205 (asr_inference:494) INFO: speech length: 254400 +2024-01-16 21:42:33,228 (beam_search:428) INFO: decoder input length: 395 +2024-01-16 21:42:33,228 (beam_search:429) INFO: max output length: 395 +2024-01-16 21:42:33,228 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:35,354 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:35,354 (beam_search:476) INFO: -38.62 * 1.0 = -38.62 for ctc +2024-01-16 21:42:35,354 (beam_search:479) INFO: total log probability: -38.62 +2024-01-16 21:42:35,354 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:42:35,354 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:35,355 (beam_search:483) INFO: best hypo: THEOFLINGOTESOLASPETOCKINSRAINASWILATNYNSESINABLEDANSINGOFMIGISINTHEEVEININGSOCONSONDEFETANDINGORTISINGTHEOINSADDIOFULRECOIRSSISSTHELESTATALLATRMBLEBOEFORTHATPROWCETUNDER + +2024-01-16 21:42:35,357 (asr_inference:494) INFO: speech length: 243040 +2024-01-16 21:42:35,378 (beam_search:428) INFO: decoder input length: 377 +2024-01-16 21:42:35,378 (beam_search:429) INFO: max output length: 377 +2024-01-16 21:42:35,378 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:37,324 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:37,325 (beam_search:476) INFO: -39.39 * 1.0 = -39.39 for ctc +2024-01-16 21:42:37,325 (beam_search:479) INFO: total log probability: -39.39 +2024-01-16 21:42:37,325 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:42:37,325 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:37,326 (beam_search:483) INFO: best hypo: WASSTOREMNGEJENRLETAMPEAREWASCILLGENERLCOSTIENGWASBLAMEDANINDEDESNOBCOMTOPARISTHDEVICXSELNATIONSAGINSEALLWHIHTHEMOUNTONANHETROTIOUSMOEARMUSTDEVONMAKEHALASHECAN + +2024-01-16 21:42:37,327 (asr_inference:494) INFO: speech length: 211840 +2024-01-16 21:42:37,347 (beam_search:428) INFO: decoder input length: 328 +2024-01-16 21:42:37,347 (beam_search:429) INFO: max output length: 328 +2024-01-16 21:42:37,347 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:38,776 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:38,776 (beam_search:476) INFO: -19.55 * 1.0 = -19.55 for ctc +2024-01-16 21:42:38,776 (beam_search:479) INFO: total log probability: -19.55 +2024-01-16 21:42:38,776 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:42:38,776 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:38,777 (beam_search:483) INFO: best hypo: THEMOMENWASFEAVFULAMITYOFOHADNEVERSWHUNGTHEBUTLLACKEOVERHIMENBUTTHEHOBENERVEDHISANEFORADESPRETBLOANDTECUOMSERULEPROSTRAITBEFORHIM + +2024-01-16 21:42:38,779 (asr_inference:494) INFO: speech length: 208160 +2024-01-16 21:42:38,798 (beam_search:428) INFO: decoder input length: 323 +2024-01-16 21:42:38,799 (beam_search:429) INFO: max output length: 323 +2024-01-16 21:42:38,799 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:39,998 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:39,998 (beam_search:476) INFO: -27.60 * 1.0 = -27.60 for ctc +2024-01-16 21:42:39,998 (beam_search:479) INFO: total log probability: -27.60 +2024-01-16 21:42:39,998 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:42:39,998 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:39,999 (beam_search:483) INFO: best hypo: THINTHEWINSTOUTTHEGLERSTANDDRKHNDNIGCAMEONLAEINGKMYOLDCOTIONDCOUILTWASCOLLDASINMYSWETSUNTOUSTINHISSCEE + +2024-01-16 21:42:40,001 (asr_inference:494) INFO: speech length: 232640 +2024-01-16 21:42:40,022 (beam_search:428) INFO: decoder input length: 361 +2024-01-16 21:42:40,022 (beam_search:429) INFO: max output length: 361 +2024-01-16 21:42:40,022 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:41,864 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:41,864 (beam_search:476) INFO: -28.21 * 1.0 = -28.21 for ctc +2024-01-16 21:42:41,864 (beam_search:479) INFO: total log probability: -28.21 +2024-01-16 21:42:41,864 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:42:41,864 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:41,865 (beam_search:483) INFO: best hypo: YOUMAYDASOUPLETOWEREOFOURIRTATIONTOCEPPYOURFENATTIISMYOUHEWELAFYOUNEDNOTMIDTHECOUSTTHEPAREDUNOTWONTESTANDINYOURWAYBUTYOINSISTDONTHESOIMITINGORCOMPALTION + +2024-01-16 21:42:41,866 (asr_inference:494) INFO: speech length: 258080 +2024-01-16 21:42:41,889 (beam_search:428) INFO: decoder input length: 401 +2024-01-16 21:42:41,889 (beam_search:429) INFO: max output length: 401 +2024-01-16 21:42:41,889 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:44,218 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:44,218 (beam_search:476) INFO: -36.96 * 1.0 = -36.96 for ctc +2024-01-16 21:42:44,218 (beam_search:479) INFO: total log probability: -36.96 +2024-01-16 21:42:44,218 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:42:44,218 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:44,219 (beam_search:483) INFO: best hypo: WEWASBREDBYAEREVERNTERYACSNIHTETBEINGBYOTHEMENESCXSFORTOTOUHIDICKLYWASBONEANMACHATINSEVENTYNINANDHWASTHEONLYSOFLIVEROFELETEROFFIFTENITWASNTHICOUNDTTHATHEASOURDSAIFANDCOLRANDMARCKINGS + +2024-01-16 21:42:44,221 (asr_inference:494) INFO: speech length: 252160 +2024-01-16 21:42:44,244 (beam_search:428) INFO: decoder input length: 391 +2024-01-16 21:42:44,244 (beam_search:429) INFO: max output length: 391 +2024-01-16 21:42:44,244 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:46,041 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:46,041 (beam_search:476) INFO: -28.00 * 1.0 = -28.00 for ctc +2024-01-16 21:42:46,041 (beam_search:479) INFO: total log probability: -28.00 +2024-01-16 21:42:46,041 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:42:46,041 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:46,042 (beam_search:483) INFO: best hypo: EANDWHATHASTEITMAKSOFOLINTOTESECIONTTHERBYTHIHTIMEDIAFHANDERSNESERSESGIUMOSTADMRABLSEAKCKRETONTHECONTRYITSTARSMENOTAWITWICHMOSTCODSORESIT + +2024-01-16 21:42:46,044 (asr_inference:494) INFO: speech length: 262560 +2024-01-16 21:42:46,067 (beam_search:428) INFO: decoder input length: 408 +2024-01-16 21:42:46,067 (beam_search:429) INFO: max output length: 408 +2024-01-16 21:42:46,067 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:48,223 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:48,223 (beam_search:476) INFO: -31.65 * 1.0 = -31.65 for ctc +2024-01-16 21:42:48,223 (beam_search:479) INFO: total log probability: -31.65 +2024-01-16 21:42:48,223 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:42:48,223 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:48,224 (beam_search:483) INFO: best hypo: THERDLYTHALSAIDWHERTHEITISINCEARENETHERETOREACEHNORTOPOREFORTHILYANACOSISSAIDWHERETHOINALLOTHERESPECTETHEYOARRCOLIHATVERETOHSMINARADVANCEDANDVESIOUSPERSOENTDEGRADED + +2024-01-16 21:42:48,226 (asr_inference:494) INFO: speech length: 246400 +2024-01-16 21:42:48,249 (beam_search:428) INFO: decoder input length: 382 +2024-01-16 21:42:48,249 (beam_search:429) INFO: max output length: 382 +2024-01-16 21:42:48,249 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:50,177 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:50,177 (beam_search:476) INFO: -28.14 * 1.0 = -28.14 for ctc +2024-01-16 21:42:50,177 (beam_search:479) INFO: total log probability: -28.14 +2024-01-16 21:42:50,177 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:42:50,177 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:50,178 (beam_search:483) INFO: best hypo: THECINDLYFRANGISSIMPTHETINKEVRYDAYHEPASNOTASBETWENUSANDTRIYTONCKERIRGSRUSTLHEWLIMPROVEIASUORHIMHISTIMEISSOUOURTANDFREACHAIRANLIBURTYWLLSOONRESTAORHIM + +2024-01-16 21:42:50,180 (asr_inference:494) INFO: speech length: 290720 +2024-01-16 21:42:50,207 (beam_search:428) INFO: decoder input length: 452 +2024-01-16 21:42:50,207 (beam_search:429) INFO: max output length: 452 +2024-01-16 21:42:50,207 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:52,886 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:52,886 (beam_search:476) INFO: -30.69 * 1.0 = -30.69 for ctc +2024-01-16 21:42:52,886 (beam_search:479) INFO: total log probability: -30.69 +2024-01-16 21:42:52,887 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:42:52,887 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:52,888 (beam_search:483) INFO: best hypo: THISCRESTIONSITISNOLEVIDENTMAYFREKCENTLYBEUNCTEREDWESEQULLPROPRITYINOPSITWASESANDIFTHERBEANYACAINSUNGWHICHTHEYCANDBEUNCTEREDONLYINONENWAYTHEUNCSERWILDEPENDAPONTHENATEROFTHEOECATION + +2024-01-16 21:42:52,890 (asr_inference:494) INFO: speech length: 229280 +2024-01-16 21:42:52,910 (beam_search:428) INFO: decoder input length: 356 +2024-01-16 21:42:52,910 (beam_search:429) INFO: max output length: 356 +2024-01-16 21:42:52,910 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:54,629 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:54,629 (beam_search:476) INFO: -29.09 * 1.0 = -29.09 for ctc +2024-01-16 21:42:54,630 (beam_search:479) INFO: total log probability: -29.09 +2024-01-16 21:42:54,630 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:42:54,630 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:54,630 (beam_search:483) INFO: best hypo: INHISNOHTBORTHEINSTRLSYSECKNEDIONATYONOWATESCOUTSESTHBALEDWASTCEKINDDOWNRMANOLDOMENSFRCITATIONATTHLSONMORLEADMINSBYTHEAGENDTHERANDCIENTBYHMTOSERTEAS + +2024-01-16 21:42:54,632 (asr_inference:494) INFO: speech length: 59520 +2024-01-16 21:42:54,641 (beam_search:428) INFO: decoder input length: 90 +2024-01-16 21:42:54,641 (beam_search:429) INFO: max output length: 90 +2024-01-16 21:42:54,641 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:54,717 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:54,718 (beam_search:476) INFO: -8.36 * 1.0 = -8.36 for ctc +2024-01-16 21:42:54,718 (beam_search:479) INFO: total log probability: -8.36 +2024-01-16 21:42:54,718 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 21:42:54,718 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:54,718 (beam_search:483) INFO: best hypo: CRESTIONTTHEOLIGEONSH + +2024-01-16 21:42:54,719 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:42:54,727 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:42:54,727 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:42:54,727 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:54,766 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:54,766 (beam_search:476) INFO: -4.80 * 1.0 = -4.80 for ctc +2024-01-16 21:42:54,766 (beam_search:479) INFO: total log probability: -4.80 +2024-01-16 21:42:54,766 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:42:54,766 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:54,766 (beam_search:483) INFO: best hypo: OPTADEEAGOFITHES + +2024-01-16 21:42:54,767 (asr_inference:494) INFO: speech length: 49920 +2024-01-16 21:42:54,775 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 21:42:54,775 (beam_search:429) INFO: max output length: 75 +2024-01-16 21:42:54,775 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:54,847 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:54,847 (beam_search:476) INFO: -5.45 * 1.0 = -5.45 for ctc +2024-01-16 21:42:54,847 (beam_search:479) INFO: total log probability: -5.45 +2024-01-16 21:42:54,847 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:42:54,847 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:54,847 (beam_search:483) INFO: best hypo: ALAMENTOSPESIALFONTIONS + +2024-01-16 21:42:54,848 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:42:54,856 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:42:54,856 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:42:54,856 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:54,914 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:54,914 (beam_search:476) INFO: -7.90 * 1.0 = -7.90 for ctc +2024-01-16 21:42:54,914 (beam_search:479) INFO: total log probability: -7.90 +2024-01-16 21:42:54,914 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 21:42:54,914 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:54,914 (beam_search:483) INFO: best hypo: TORDEWASINANNUNEVOREITY + +2024-01-16 21:42:54,915 (asr_inference:494) INFO: speech length: 44160 +2024-01-16 21:42:54,923 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:42:54,923 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:42:54,923 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:54,987 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:54,987 (beam_search:476) INFO: -5.00 * 1.0 = -5.00 for ctc +2024-01-16 21:42:54,987 (beam_search:479) INFO: total log probability: -5.00 +2024-01-16 21:42:54,987 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:42:54,987 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:54,988 (beam_search:483) INFO: best hypo: SIESFICTIONNOVLESPRVHAN + +2024-01-16 21:42:54,989 (asr_inference:494) INFO: speech length: 28800 +2024-01-16 21:42:54,996 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 21:42:54,996 (beam_search:429) INFO: max output length: 42 +2024-01-16 21:42:54,996 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:55,021 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:55,021 (beam_search:476) INFO: -2.94 * 1.0 = -2.94 for ctc +2024-01-16 21:42:55,021 (beam_search:479) INFO: total log probability: -2.94 +2024-01-16 21:42:55,021 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:42:55,021 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:55,021 (beam_search:483) INFO: best hypo: COSTDHIBPOP + +2024-01-16 21:42:55,022 (asr_inference:494) INFO: speech length: 69120 +2024-01-16 21:42:55,032 (beam_search:428) INFO: decoder input length: 105 +2024-01-16 21:42:55,032 (beam_search:429) INFO: max output length: 105 +2024-01-16 21:42:55,032 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:55,141 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:55,141 (beam_search:476) INFO: -5.95 * 1.0 = -5.95 for ctc +2024-01-16 21:42:55,141 (beam_search:479) INFO: total log probability: -5.95 +2024-01-16 21:42:55,141 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:42:55,141 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:55,141 (beam_search:483) INFO: best hypo: INDVERSLEATBLAYETRONSFORME + +2024-01-16 21:42:55,142 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:42:55,149 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:42:55,150 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:42:55,150 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:55,189 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:55,189 (beam_search:476) INFO: -4.06 * 1.0 = -4.06 for ctc +2024-01-16 21:42:55,189 (beam_search:479) INFO: total log probability: -4.06 +2024-01-16 21:42:55,189 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:42:55,189 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:55,189 (beam_search:483) INFO: best hypo: FRINGHPROTISTANCES + +2024-01-16 21:42:55,190 (asr_inference:494) INFO: speech length: 63360 +2024-01-16 21:42:55,199 (beam_search:428) INFO: decoder input length: 96 +2024-01-16 21:42:55,199 (beam_search:429) INFO: max output length: 96 +2024-01-16 21:42:55,199 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:55,285 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:55,285 (beam_search:476) INFO: -14.19 * 1.0 = -14.19 for ctc +2024-01-16 21:42:55,285 (beam_search:479) INFO: total log probability: -14.19 +2024-01-16 21:42:55,285 (beam_search:480) INFO: normalized log probability: -0.51 +2024-01-16 21:42:55,285 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:55,285 (beam_search:483) INFO: best hypo: OFEGOUNAYFORSSHHDKE + +2024-01-16 21:42:55,287 (asr_inference:494) INFO: speech length: 74880 +2024-01-16 21:42:55,296 (beam_search:428) INFO: decoder input length: 114 +2024-01-16 21:42:55,296 (beam_search:429) INFO: max output length: 114 +2024-01-16 21:42:55,296 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:55,419 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:55,419 (beam_search:476) INFO: -5.80 * 1.0 = -5.80 for ctc +2024-01-16 21:42:55,419 (beam_search:479) INFO: total log probability: -5.80 +2024-01-16 21:42:55,419 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:42:55,419 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:55,420 (beam_search:483) INFO: best hypo: HEAROSINMOSOLIGYANDLEAGEND + +2024-01-16 21:42:55,421 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:42:55,428 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:42:55,428 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:42:55,428 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:55,468 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:55,468 (beam_search:476) INFO: -7.46 * 1.0 = -7.46 for ctc +2024-01-16 21:42:55,468 (beam_search:479) INFO: total log probability: -7.46 +2024-01-16 21:42:55,468 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 21:42:55,468 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:55,468 (beam_search:483) INFO: best hypo: BUISNSCLASSETNDNE + +2024-01-16 21:42:55,469 (asr_inference:494) INFO: speech length: 49920 +2024-01-16 21:42:55,477 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 21:42:55,477 (beam_search:429) INFO: max output length: 75 +2024-01-16 21:42:55,477 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:55,535 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:55,535 (beam_search:476) INFO: -7.46 * 1.0 = -7.46 for ctc +2024-01-16 21:42:55,535 (beam_search:479) INFO: total log probability: -7.46 +2024-01-16 21:42:55,535 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 21:42:55,535 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:55,535 (beam_search:483) INFO: best hypo: CLAIDPLAYCHORTEE + +2024-01-16 21:42:55,536 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:42:55,544 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:42:55,544 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:42:55,544 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:55,595 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:55,595 (beam_search:476) INFO: -3.89 * 1.0 = -3.89 for ctc +2024-01-16 21:42:55,595 (beam_search:479) INFO: total log probability: -3.89 +2024-01-16 21:42:55,596 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:42:55,596 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:55,596 (beam_search:483) INFO: best hypo: POSIYTRINSWEREROPORTED + +2024-01-16 21:42:55,597 (asr_inference:494) INFO: speech length: 28800 +2024-01-16 21:42:55,604 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 21:42:55,604 (beam_search:429) INFO: max output length: 42 +2024-01-16 21:42:55,604 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:55,634 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:55,634 (beam_search:476) INFO: -2.42 * 1.0 = -2.42 for ctc +2024-01-16 21:42:55,634 (beam_search:479) INFO: total log probability: -2.42 +2024-01-16 21:42:55,634 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:42:55,634 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:55,634 (beam_search:483) INFO: best hypo: ALDVICKTHEATER + +2024-01-16 21:42:55,636 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:42:55,643 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:42:55,643 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:42:55,643 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:55,687 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:55,687 (beam_search:476) INFO: -4.25 * 1.0 = -4.25 for ctc +2024-01-16 21:42:55,687 (beam_search:479) INFO: total log probability: -4.25 +2024-01-16 21:42:55,687 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:42:55,687 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:55,687 (beam_search:483) INFO: best hypo: ORTHEDOCKSMONOCKSE + +2024-01-16 21:42:55,688 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 21:42:55,696 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:42:55,696 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:42:55,696 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:55,753 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:55,753 (beam_search:476) INFO: -5.35 * 1.0 = -5.35 for ctc +2024-01-16 21:42:55,753 (beam_search:479) INFO: total log probability: -5.35 +2024-01-16 21:42:55,753 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:42:55,753 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:55,753 (beam_search:483) INFO: best hypo: NATIONSWMEMBRSTATES + +2024-01-16 21:42:55,754 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:42:55,762 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:42:55,762 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:42:55,762 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:55,796 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:55,796 (beam_search:476) INFO: -2.66 * 1.0 = -2.66 for ctc +2024-01-16 21:42:55,796 (beam_search:479) INFO: total log probability: -2.66 +2024-01-16 21:42:55,796 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:42:55,796 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:55,796 (beam_search:483) INFO: best hypo: FHETHOWILDCOP + +2024-01-16 21:42:55,797 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:42:55,804 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:42:55,804 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:42:55,804 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:55,851 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:55,851 (beam_search:476) INFO: -6.65 * 1.0 = -6.65 for ctc +2024-01-16 21:42:55,851 (beam_search:479) INFO: total log probability: -6.65 +2024-01-16 21:42:55,851 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:42:55,851 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:55,852 (beam_search:483) INFO: best hypo: CROSERICSKYUEFEITS + +2024-01-16 21:42:55,853 (asr_inference:494) INFO: speech length: 53760 +2024-01-16 21:42:55,861 (beam_search:428) INFO: decoder input length: 81 +2024-01-16 21:42:55,861 (beam_search:429) INFO: max output length: 81 +2024-01-16 21:42:55,861 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:55,939 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:55,939 (beam_search:476) INFO: -8.28 * 1.0 = -8.28 for ctc +2024-01-16 21:42:55,939 (beam_search:479) INFO: total log probability: -8.28 +2024-01-16 21:42:55,939 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:42:55,939 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:55,939 (beam_search:483) INFO: best hypo: ACTHOLFOLMEMARCKESCOPITY + +2024-01-16 21:42:55,941 (asr_inference:494) INFO: speech length: 51840 +2024-01-16 21:42:55,949 (beam_search:428) INFO: decoder input length: 78 +2024-01-16 21:42:55,949 (beam_search:429) INFO: max output length: 78 +2024-01-16 21:42:55,949 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:56,029 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:56,029 (beam_search:476) INFO: -9.58 * 1.0 = -9.58 for ctc +2024-01-16 21:42:56,029 (beam_search:479) INFO: total log probability: -9.58 +2024-01-16 21:42:56,029 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:42:56,029 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:56,029 (beam_search:483) INFO: best hypo: MOUOSICLGRUPSREASTABLASHED + +2024-01-16 21:42:56,030 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 21:42:56,038 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:42:56,038 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:42:56,038 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:56,092 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:56,092 (beam_search:476) INFO: -5.63 * 1.0 = -5.63 for ctc +2024-01-16 21:42:56,092 (beam_search:479) INFO: total log probability: -5.63 +2024-01-16 21:42:56,092 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:42:56,092 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:56,092 (beam_search:483) INFO: best hypo: PROMISENEREPACESE + +2024-01-16 21:42:56,094 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:42:56,101 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:42:56,101 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:42:56,101 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:56,134 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:56,134 (beam_search:476) INFO: -5.39 * 1.0 = -5.39 for ctc +2024-01-16 21:42:56,134 (beam_search:479) INFO: total log probability: -5.39 +2024-01-16 21:42:56,134 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:42:56,134 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:56,134 (beam_search:483) INFO: best hypo: FOLNDSIKNEKS + +2024-01-16 21:42:56,135 (asr_inference:494) INFO: speech length: 42240 +2024-01-16 21:42:56,143 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 21:42:56,143 (beam_search:429) INFO: max output length: 63 +2024-01-16 21:42:56,143 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:56,195 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:56,195 (beam_search:476) INFO: -5.33 * 1.0 = -5.33 for ctc +2024-01-16 21:42:56,195 (beam_search:479) INFO: total log probability: -5.33 +2024-01-16 21:42:56,195 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:42:56,195 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:56,195 (beam_search:483) INFO: best hypo: TOELAVIONSERYSBAST + +2024-01-16 21:42:56,196 (asr_inference:494) INFO: speech length: 72960 +2024-01-16 21:42:56,206 (beam_search:428) INFO: decoder input length: 111 +2024-01-16 21:42:56,206 (beam_search:429) INFO: max output length: 111 +2024-01-16 21:42:56,206 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:56,310 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:56,310 (beam_search:476) INFO: -10.17 * 1.0 = -10.17 for ctc +2024-01-16 21:42:56,310 (beam_search:479) INFO: total log probability: -10.17 +2024-01-16 21:42:56,310 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 21:42:56,310 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:56,311 (beam_search:483) INFO: best hypo: HNOEPOLITIOCAOEPORTYHEEE + +2024-01-16 21:42:56,312 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:42:56,319 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:42:56,319 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:42:56,320 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:56,369 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:56,369 (beam_search:476) INFO: -4.63 * 1.0 = -4.63 for ctc +2024-01-16 21:42:56,369 (beam_search:479) INFO: total log probability: -4.63 +2024-01-16 21:42:56,369 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:42:56,369 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:56,369 (beam_search:483) INFO: best hypo: ANCHONTDEAGOPACHEVED + +2024-01-16 21:42:56,370 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:42:56,377 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:42:56,378 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:42:56,378 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:56,410 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:56,410 (beam_search:476) INFO: -3.17 * 1.0 = -3.17 for ctc +2024-01-16 21:42:56,410 (beam_search:479) INFO: total log probability: -3.17 +2024-01-16 21:42:56,410 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:42:56,410 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:56,410 (beam_search:483) INFO: best hypo: FLATMUSIGNTROL + +2024-01-16 21:42:56,412 (asr_inference:494) INFO: speech length: 48000 +2024-01-16 21:42:56,420 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 21:42:56,420 (beam_search:429) INFO: max output length: 72 +2024-01-16 21:42:56,420 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:56,504 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:56,504 (beam_search:476) INFO: -7.39 * 1.0 = -7.39 for ctc +2024-01-16 21:42:56,505 (beam_search:479) INFO: total log probability: -7.39 +2024-01-16 21:42:56,505 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:42:56,505 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:56,505 (beam_search:483) INFO: best hypo: AMRICONTICNOLIDTOINOLIDYRATES + +2024-01-16 21:42:56,506 (asr_inference:494) INFO: speech length: 42240 +2024-01-16 21:42:56,514 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 21:42:56,514 (beam_search:429) INFO: max output length: 63 +2024-01-16 21:42:56,514 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:56,555 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:56,555 (beam_search:476) INFO: -4.59 * 1.0 = -4.59 for ctc +2024-01-16 21:42:56,555 (beam_search:479) INFO: total log probability: -4.59 +2024-01-16 21:42:56,555 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:42:56,555 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:56,555 (beam_search:483) INFO: best hypo: DOATESOFVARINS + +2024-01-16 21:42:56,556 (asr_inference:494) INFO: speech length: 49920 +2024-01-16 21:42:56,564 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 21:42:56,564 (beam_search:429) INFO: max output length: 75 +2024-01-16 21:42:56,564 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:56,630 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:56,630 (beam_search:476) INFO: -5.91 * 1.0 = -5.91 for ctc +2024-01-16 21:42:56,630 (beam_search:479) INFO: total log probability: -5.91 +2024-01-16 21:42:56,630 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:42:56,630 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:56,630 (beam_search:483) INFO: best hypo: POPILEITWREISTACTIONS + +2024-01-16 21:42:56,632 (asr_inference:494) INFO: speech length: 28800 +2024-01-16 21:42:56,639 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 21:42:56,639 (beam_search:429) INFO: max output length: 42 +2024-01-16 21:42:56,639 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:56,668 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:56,669 (beam_search:476) INFO: -3.33 * 1.0 = -3.33 for ctc +2024-01-16 21:42:56,669 (beam_search:479) INFO: total log probability: -3.33 +2024-01-16 21:42:56,669 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:42:56,669 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:56,669 (beam_search:483) INFO: best hypo: DUCHEWISTINDEAR + +2024-01-16 21:42:56,670 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:42:56,677 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:42:56,677 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:42:56,677 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:56,724 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:56,724 (beam_search:476) INFO: -7.08 * 1.0 = -7.08 for ctc +2024-01-16 21:42:56,724 (beam_search:479) INFO: total log probability: -7.08 +2024-01-16 21:42:56,724 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 21:42:56,724 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:56,724 (beam_search:483) INFO: best hypo: GOLDMATLRESPIENSE + +2024-01-16 21:42:56,726 (asr_inference:494) INFO: speech length: 51840 +2024-01-16 21:42:56,734 (beam_search:428) INFO: decoder input length: 78 +2024-01-16 21:42:56,734 (beam_search:429) INFO: max output length: 78 +2024-01-16 21:42:56,734 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:56,820 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:56,820 (beam_search:476) INFO: -7.53 * 1.0 = -7.53 for ctc +2024-01-16 21:42:56,820 (beam_search:479) INFO: total log probability: -7.53 +2024-01-16 21:42:56,820 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:42:56,820 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:56,821 (beam_search:483) INFO: best hypo: REASHIONSOSIALDEMOCRETICKEH + +2024-01-16 21:42:56,822 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:42:56,830 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:42:56,830 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:42:56,830 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:56,884 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:56,884 (beam_search:476) INFO: -9.60 * 1.0 = -9.60 for ctc +2024-01-16 21:42:56,884 (beam_search:479) INFO: total log probability: -9.60 +2024-01-16 21:42:56,884 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 21:42:56,884 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:56,884 (beam_search:483) INFO: best hypo: AMIRIYCONFLMEPRODUSES + +2024-01-16 21:42:56,886 (asr_inference:494) INFO: speech length: 42240 +2024-01-16 21:42:56,894 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 21:42:56,894 (beam_search:429) INFO: max output length: 63 +2024-01-16 21:42:56,894 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:56,948 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:56,948 (beam_search:476) INFO: -5.17 * 1.0 = -5.17 for ctc +2024-01-16 21:42:56,948 (beam_search:479) INFO: total log probability: -5.17 +2024-01-16 21:42:56,948 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:42:56,948 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:56,948 (beam_search:483) INFO: best hypo: FREESOFTERYFUNDATION + +2024-01-16 21:42:56,949 (asr_inference:494) INFO: speech length: 30720 +2024-01-16 21:42:56,956 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 21:42:56,956 (beam_search:429) INFO: max output length: 45 +2024-01-16 21:42:56,956 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:56,990 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:56,990 (beam_search:476) INFO: -3.67 * 1.0 = -3.67 for ctc +2024-01-16 21:42:56,990 (beam_search:479) INFO: total log probability: -3.67 +2024-01-16 21:42:56,990 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:42:56,990 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:56,990 (beam_search:483) INFO: best hypo: RILERMATIOCTHEAT + +2024-01-16 21:42:56,991 (asr_inference:494) INFO: speech length: 38400 +2024-01-16 21:42:56,999 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:42:56,999 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:42:56,999 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:57,040 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:57,040 (beam_search:476) INFO: -6.37 * 1.0 = -6.37 for ctc +2024-01-16 21:42:57,040 (beam_search:479) INFO: total log probability: -6.37 +2024-01-16 21:42:57,040 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:42:57,040 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:57,040 (beam_search:483) INFO: best hypo: ITABLEMOLOSKSH + +2024-01-16 21:42:57,041 (asr_inference:494) INFO: speech length: 49920 +2024-01-16 21:42:57,049 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 21:42:57,049 (beam_search:429) INFO: max output length: 75 +2024-01-16 21:42:57,049 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:57,117 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:57,117 (beam_search:476) INFO: -6.76 * 1.0 = -6.76 for ctc +2024-01-16 21:42:57,117 (beam_search:479) INFO: total log probability: -6.76 +2024-01-16 21:42:57,117 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:42:57,117 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:57,117 (beam_search:483) INFO: best hypo: FEATCHESINTLWDBEACHERS + +2024-01-16 21:42:57,118 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:42:57,126 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:42:57,126 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:42:57,126 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:57,179 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:57,179 (beam_search:476) INFO: -5.98 * 1.0 = -5.98 for ctc +2024-01-16 21:42:57,179 (beam_search:479) INFO: total log probability: -5.98 +2024-01-16 21:42:57,179 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:42:57,179 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:57,180 (beam_search:483) INFO: best hypo: OCFORDICTIONRYCHANGET + +2024-01-16 21:42:57,181 (asr_inference:494) INFO: speech length: 38400 +2024-01-16 21:42:57,188 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:42:57,188 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:42:57,188 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:57,241 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:57,241 (beam_search:476) INFO: -7.29 * 1.0 = -7.29 for ctc +2024-01-16 21:42:57,241 (beam_search:479) INFO: total log probability: -7.29 +2024-01-16 21:42:57,241 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:42:57,241 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:57,241 (beam_search:483) INFO: best hypo: SALCOWPIRISIONDRYHUND + +2024-01-16 21:42:57,242 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:42:57,250 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:42:57,250 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:42:57,250 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:57,292 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:57,292 (beam_search:476) INFO: -3.42 * 1.0 = -3.42 for ctc +2024-01-16 21:42:57,292 (beam_search:479) INFO: total log probability: -3.42 +2024-01-16 21:42:57,292 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:42:57,292 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:57,293 (beam_search:483) INFO: best hypo: PROWNMONISTERCIVEN + +2024-01-16 21:42:57,294 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:42:57,301 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:42:57,301 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:42:57,301 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:57,336 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:57,336 (beam_search:476) INFO: -2.89 * 1.0 = -2.89 for ctc +2024-01-16 21:42:57,336 (beam_search:479) INFO: total log probability: -2.89 +2024-01-16 21:42:57,336 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:42:57,336 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:57,336 (beam_search:483) INFO: best hypo: LANGESOFYUROCKE + +2024-01-16 21:42:57,337 (asr_inference:494) INFO: speech length: 42240 +2024-01-16 21:42:57,345 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 21:42:57,345 (beam_search:429) INFO: max output length: 63 +2024-01-16 21:42:57,345 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:57,392 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:57,392 (beam_search:476) INFO: -2.89 * 1.0 = -2.89 for ctc +2024-01-16 21:42:57,392 (beam_search:479) INFO: total log probability: -2.89 +2024-01-16 21:42:57,392 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:42:57,392 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:57,392 (beam_search:483) INFO: best hypo: SOTHEASTINGLONDT + +2024-01-16 21:42:57,393 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:42:57,400 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:42:57,400 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:42:57,400 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:57,440 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:57,440 (beam_search:476) INFO: -7.34 * 1.0 = -7.34 for ctc +2024-01-16 21:42:57,440 (beam_search:479) INFO: total log probability: -7.34 +2024-01-16 21:42:57,440 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 21:42:57,440 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:57,440 (beam_search:483) INFO: best hypo: NOURLINEDSENOMARTH + +2024-01-16 21:42:57,441 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 21:42:57,449 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:42:57,449 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:42:57,449 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:57,514 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:57,514 (beam_search:476) INFO: -9.29 * 1.0 = -9.29 for ctc +2024-01-16 21:42:57,514 (beam_search:479) INFO: total log probability: -9.29 +2024-01-16 21:42:57,514 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 21:42:57,514 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:57,515 (beam_search:483) INFO: best hypo: ECOULKREDITOPOTONNTSY + +2024-01-16 21:42:57,516 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:42:57,523 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:42:57,523 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:42:57,523 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:57,558 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:57,558 (beam_search:476) INFO: -3.25 * 1.0 = -3.25 for ctc +2024-01-16 21:42:57,558 (beam_search:479) INFO: total log probability: -3.25 +2024-01-16 21:42:57,558 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:42:57,558 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:57,558 (beam_search:483) INFO: best hypo: SOUTHEASTINGLAND + +2024-01-16 21:42:57,559 (asr_inference:494) INFO: speech length: 24960 +2024-01-16 21:42:57,566 (beam_search:428) INFO: decoder input length: 36 +2024-01-16 21:42:57,566 (beam_search:429) INFO: max output length: 36 +2024-01-16 21:42:57,566 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:57,583 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:57,583 (beam_search:476) INFO: -5.77 * 1.0 = -5.77 for ctc +2024-01-16 21:42:57,583 (beam_search:479) INFO: total log probability: -5.77 +2024-01-16 21:42:57,583 (beam_search:480) INFO: normalized log probability: -0.48 +2024-01-16 21:42:57,583 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:57,583 (beam_search:483) INFO: best hypo: MAYWEHTE + +2024-01-16 21:42:57,584 (asr_inference:494) INFO: speech length: 51840 +2024-01-16 21:42:57,593 (beam_search:428) INFO: decoder input length: 78 +2024-01-16 21:42:57,593 (beam_search:429) INFO: max output length: 78 +2024-01-16 21:42:57,593 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:57,672 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:57,672 (beam_search:476) INFO: -10.29 * 1.0 = -10.29 for ctc +2024-01-16 21:42:57,672 (beam_search:479) INFO: total log probability: -10.29 +2024-01-16 21:42:57,672 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 21:42:57,672 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:57,673 (beam_search:483) INFO: best hypo: RECOLRDHATATEOMEESCRIPES + +2024-01-16 21:42:57,674 (asr_inference:494) INFO: speech length: 67200 +2024-01-16 21:42:57,683 (beam_search:428) INFO: decoder input length: 102 +2024-01-16 21:42:57,683 (beam_search:429) INFO: max output length: 102 +2024-01-16 21:42:57,683 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:57,796 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:57,796 (beam_search:476) INFO: -9.11 * 1.0 = -9.11 for ctc +2024-01-16 21:42:57,796 (beam_search:479) INFO: total log probability: -9.11 +2024-01-16 21:42:57,796 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:42:57,796 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:57,796 (beam_search:483) INFO: best hypo: MUSICALGREPESFOMCALOFORNIEAH + +2024-01-16 21:42:57,797 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:42:57,804 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:42:57,804 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:42:57,804 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:57,837 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:57,837 (beam_search:476) INFO: -2.74 * 1.0 = -2.74 for ctc +2024-01-16 21:42:57,837 (beam_search:479) INFO: total log probability: -2.74 +2024-01-16 21:42:57,837 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:42:57,837 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:57,838 (beam_search:483) INFO: best hypo: MAINBETLETINCS + +2024-01-16 21:42:57,839 (asr_inference:494) INFO: speech length: 57600 +2024-01-16 21:42:57,847 (beam_search:428) INFO: decoder input length: 87 +2024-01-16 21:42:57,847 (beam_search:429) INFO: max output length: 87 +2024-01-16 21:42:57,848 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:57,941 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:57,941 (beam_search:476) INFO: -6.39 * 1.0 = -6.39 for ctc +2024-01-16 21:42:57,941 (beam_search:479) INFO: total log probability: -6.39 +2024-01-16 21:42:57,941 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:42:57,941 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:57,942 (beam_search:483) INFO: best hypo: PORDLISEHMUSIOCTALEINSTRMENTS + +2024-01-16 21:42:57,943 (asr_inference:494) INFO: speech length: 57600 +2024-01-16 21:42:57,951 (beam_search:428) INFO: decoder input length: 87 +2024-01-16 21:42:57,951 (beam_search:429) INFO: max output length: 87 +2024-01-16 21:42:57,951 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,037 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,037 (beam_search:476) INFO: -4.84 * 1.0 = -4.84 for ctc +2024-01-16 21:42:58,038 (beam_search:479) INFO: total log probability: -4.84 +2024-01-16 21:42:58,038 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:42:58,038 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,038 (beam_search:483) INFO: best hypo: LANWIGESOFSADYEARROAVIARE + +2024-01-16 21:42:58,039 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:42:58,046 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:42:58,046 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:42:58,046 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,089 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,089 (beam_search:476) INFO: -4.06 * 1.0 = -4.06 for ctc +2024-01-16 21:42:58,089 (beam_search:479) INFO: total log probability: -4.06 +2024-01-16 21:42:58,089 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:42:58,089 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,089 (beam_search:483) INFO: best hypo: COLDORTINTIONSEH + +2024-01-16 21:42:58,090 (asr_inference:494) INFO: speech length: 28800 +2024-01-16 21:42:58,097 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 21:42:58,098 (beam_search:429) INFO: max output length: 42 +2024-01-16 21:42:58,098 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,118 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,118 (beam_search:476) INFO: -7.50 * 1.0 = -7.50 for ctc +2024-01-16 21:42:58,118 (beam_search:479) INFO: total log probability: -7.50 +2024-01-16 21:42:58,118 (beam_search:480) INFO: normalized log probability: -0.58 +2024-01-16 21:42:58,118 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,118 (beam_search:483) INFO: best hypo: DOBEHIMHS + +2024-01-16 21:42:58,119 (asr_inference:494) INFO: speech length: 48000 +2024-01-16 21:42:58,127 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 21:42:58,128 (beam_search:429) INFO: max output length: 72 +2024-01-16 21:42:58,128 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,179 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,179 (beam_search:476) INFO: -3.70 * 1.0 = -3.70 for ctc +2024-01-16 21:42:58,179 (beam_search:479) INFO: total log probability: -3.70 +2024-01-16 21:42:58,179 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:42:58,179 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,179 (beam_search:483) INFO: best hypo: ANDYPOPKLIMINTH + +2024-01-16 21:42:58,180 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:42:58,188 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:42:58,188 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:42:58,188 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,225 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,225 (beam_search:476) INFO: -3.40 * 1.0 = -3.40 for ctc +2024-01-16 21:42:58,225 (beam_search:479) INFO: total log probability: -3.40 +2024-01-16 21:42:58,225 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:42:58,225 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,225 (beam_search:483) INFO: best hypo: GITHEYCONPOIVEIT + +2024-01-16 21:42:58,226 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:42:58,233 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:42:58,233 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:42:58,233 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,265 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,265 (beam_search:476) INFO: -3.53 * 1.0 = -3.53 for ctc +2024-01-16 21:42:58,265 (beam_search:479) INFO: total log probability: -3.53 +2024-01-16 21:42:58,265 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:42:58,265 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,265 (beam_search:483) INFO: best hypo: CINFEIANAND + +2024-01-16 21:42:58,266 (asr_inference:494) INFO: speech length: 42240 +2024-01-16 21:42:58,274 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 21:42:58,274 (beam_search:429) INFO: max output length: 63 +2024-01-16 21:42:58,274 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,340 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,340 (beam_search:476) INFO: -6.17 * 1.0 = -6.17 for ctc +2024-01-16 21:42:58,340 (beam_search:479) INFO: total log probability: -6.17 +2024-01-16 21:42:58,340 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:42:58,340 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,340 (beam_search:483) INFO: best hypo: ILECTONICKMEUSOCKLINSTRONCS + +2024-01-16 21:42:58,342 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:42:58,349 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:42:58,349 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:42:58,349 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,387 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,387 (beam_search:476) INFO: -4.37 * 1.0 = -4.37 for ctc +2024-01-16 21:42:58,387 (beam_search:479) INFO: total log probability: -4.37 +2024-01-16 21:42:58,387 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:42:58,387 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,387 (beam_search:483) INFO: best hypo: AGEMOLDTOORTERN + +2024-01-16 21:42:58,388 (asr_inference:494) INFO: speech length: 44160 +2024-01-16 21:42:58,396 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:42:58,396 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:42:58,396 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,458 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,458 (beam_search:476) INFO: -4.36 * 1.0 = -4.36 for ctc +2024-01-16 21:42:58,458 (beam_search:479) INFO: total log probability: -4.36 +2024-01-16 21:42:58,458 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:42:58,458 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,458 (beam_search:483) INFO: best hypo: LORANCTLVEMORNASTIONLE + +2024-01-16 21:42:58,459 (asr_inference:494) INFO: speech length: 48000 +2024-01-16 21:42:58,467 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 21:42:58,467 (beam_search:429) INFO: max output length: 72 +2024-01-16 21:42:58,467 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,519 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,519 (beam_search:476) INFO: -3.40 * 1.0 = -3.40 for ctc +2024-01-16 21:42:58,519 (beam_search:479) INFO: total log probability: -3.40 +2024-01-16 21:42:58,519 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:42:58,519 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,519 (beam_search:483) INFO: best hypo: LEGBACSPLPLAYARS + +2024-01-16 21:42:58,520 (asr_inference:494) INFO: speech length: 49920 +2024-01-16 21:42:58,528 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 21:42:58,528 (beam_search:429) INFO: max output length: 75 +2024-01-16 21:42:58,528 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,618 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,618 (beam_search:476) INFO: -6.01 * 1.0 = -6.01 for ctc +2024-01-16 21:42:58,618 (beam_search:479) INFO: total log probability: -6.01 +2024-01-16 21:42:58,618 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:42:58,618 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,618 (beam_search:483) INFO: best hypo: BODISOMEANTHEANCIONTMEDETRANION + +2024-01-16 21:42:58,620 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:42:58,627 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:42:58,627 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:42:58,627 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,682 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,682 (beam_search:476) INFO: -6.00 * 1.0 = -6.00 for ctc +2024-01-16 21:42:58,682 (beam_search:479) INFO: total log probability: -6.00 +2024-01-16 21:42:58,682 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:42:58,682 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,682 (beam_search:483) INFO: best hypo: OUNIGTIDSTATSREKOCONSED + +2024-01-16 21:42:58,683 (asr_inference:494) INFO: speech length: 57600 +2024-01-16 21:42:58,692 (beam_search:428) INFO: decoder input length: 87 +2024-01-16 21:42:58,692 (beam_search:429) INFO: max output length: 87 +2024-01-16 21:42:58,692 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,761 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,761 (beam_search:476) INFO: -5.74 * 1.0 = -5.74 for ctc +2024-01-16 21:42:58,761 (beam_search:479) INFO: total log probability: -5.74 +2024-01-16 21:42:58,761 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:42:58,761 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,761 (beam_search:483) INFO: best hypo: PROPOSIONLFELATYESE + +2024-01-16 21:42:58,763 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:42:58,770 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:42:58,770 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:42:58,770 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,816 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,816 (beam_search:476) INFO: -7.17 * 1.0 = -7.17 for ctc +2024-01-16 21:42:58,816 (beam_search:479) INFO: total log probability: -7.17 +2024-01-16 21:42:58,816 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:42:58,816 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,816 (beam_search:483) INFO: best hypo: SPETIALACONOMNGESORONDS + +2024-01-16 21:42:58,817 (asr_inference:494) INFO: speech length: 38400 +2024-01-16 21:42:58,824 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:42:58,824 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:42:58,824 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,855 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,855 (beam_search:476) INFO: -2.65 * 1.0 = -2.65 for ctc +2024-01-16 21:42:58,855 (beam_search:479) INFO: total log probability: -2.65 +2024-01-16 21:42:58,855 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:42:58,855 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,855 (beam_search:483) INFO: best hypo: MANSTRMWIST + +2024-01-16 21:42:58,856 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:42:58,864 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:42:58,864 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:42:58,864 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,893 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,893 (beam_search:476) INFO: -5.69 * 1.0 = -5.69 for ctc +2024-01-16 21:42:58,893 (beam_search:479) INFO: total log probability: -5.69 +2024-01-16 21:42:58,893 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 21:42:58,893 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,893 (beam_search:483) INFO: best hypo: EVENGRUCHHLS + +2024-01-16 21:42:58,895 (asr_inference:494) INFO: speech length: 30720 +2024-01-16 21:42:58,902 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 21:42:58,902 (beam_search:429) INFO: max output length: 45 +2024-01-16 21:42:58,902 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,935 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,935 (beam_search:476) INFO: -4.02 * 1.0 = -4.02 for ctc +2024-01-16 21:42:58,935 (beam_search:479) INFO: total log probability: -4.02 +2024-01-16 21:42:58,935 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:42:58,935 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,935 (beam_search:483) INFO: best hypo: BYTHEDIONSTOKK + +2024-01-16 21:42:58,936 (asr_inference:494) INFO: speech length: 28800 +2024-01-16 21:42:58,943 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 21:42:58,943 (beam_search:429) INFO: max output length: 42 +2024-01-16 21:42:58,943 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:58,974 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:58,975 (beam_search:476) INFO: -2.40 * 1.0 = -2.40 for ctc +2024-01-16 21:42:58,975 (beam_search:479) INFO: total log probability: -2.40 +2024-01-16 21:42:58,975 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:42:58,975 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:58,975 (beam_search:483) INFO: best hypo: NDARTIOCKEAHASNO + +2024-01-16 21:42:58,976 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:42:58,983 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:42:58,983 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:42:58,983 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:59,015 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:59,015 (beam_search:476) INFO: -2.76 * 1.0 = -2.76 for ctc +2024-01-16 21:42:59,015 (beam_search:479) INFO: total log probability: -2.76 +2024-01-16 21:42:59,015 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:42:59,015 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:59,015 (beam_search:483) INFO: best hypo: WASTINMUSICLES + +2024-01-16 21:42:59,017 (asr_inference:494) INFO: speech length: 49920 +2024-01-16 21:42:59,025 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 21:42:59,025 (beam_search:429) INFO: max output length: 75 +2024-01-16 21:42:59,025 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:59,097 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:59,097 (beam_search:476) INFO: -4.10 * 1.0 = -4.10 for ctc +2024-01-16 21:42:59,097 (beam_search:479) INFO: total log probability: -4.10 +2024-01-16 21:42:59,097 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:42:59,097 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:59,097 (beam_search:483) INFO: best hypo: CONEVITOFDUDAYSAMREGORT + +2024-01-16 21:42:59,098 (asr_inference:494) INFO: speech length: 51840 +2024-01-16 21:42:59,107 (beam_search:428) INFO: decoder input length: 78 +2024-01-16 21:42:59,107 (beam_search:429) INFO: max output length: 78 +2024-01-16 21:42:59,107 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:59,166 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:59,167 (beam_search:476) INFO: -4.59 * 1.0 = -4.59 for ctc +2024-01-16 21:42:59,167 (beam_search:479) INFO: total log probability: -4.59 +2024-01-16 21:42:59,167 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:42:59,167 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:59,167 (beam_search:483) INFO: best hypo: OPPICKMEMBRSTATDS + +2024-01-16 21:42:59,168 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:42:59,175 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:42:59,175 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:42:59,175 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:59,210 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:59,210 (beam_search:476) INFO: -2.73 * 1.0 = -2.73 for ctc +2024-01-16 21:42:59,210 (beam_search:479) INFO: total log probability: -2.73 +2024-01-16 21:42:59,210 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:42:59,210 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:59,210 (beam_search:483) INFO: best hypo: PRIMINISSAIDJON + +2024-01-16 21:42:59,211 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:42:59,219 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:42:59,219 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:42:59,219 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:59,262 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:59,262 (beam_search:476) INFO: -2.99 * 1.0 = -2.99 for ctc +2024-01-16 21:42:59,262 (beam_search:479) INFO: total log probability: -2.99 +2024-01-16 21:42:59,262 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:42:59,262 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:59,262 (beam_search:483) INFO: best hypo: ROACKSFORMINGMOUNT + +2024-01-16 21:42:59,263 (asr_inference:494) INFO: speech length: 30720 +2024-01-16 21:42:59,270 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 21:42:59,271 (beam_search:429) INFO: max output length: 45 +2024-01-16 21:42:59,271 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:59,303 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:59,303 (beam_search:476) INFO: -4.73 * 1.0 = -4.73 for ctc +2024-01-16 21:42:59,303 (beam_search:479) INFO: total log probability: -4.73 +2024-01-16 21:42:59,303 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:42:59,303 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:59,303 (beam_search:483) INFO: best hypo: MADGERLEAKTLMNS + +2024-01-16 21:42:59,304 (asr_inference:494) INFO: speech length: 42240 +2024-01-16 21:42:59,312 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 21:42:59,312 (beam_search:429) INFO: max output length: 63 +2024-01-16 21:42:59,312 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:59,365 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:59,365 (beam_search:476) INFO: -2.79 * 1.0 = -2.79 for ctc +2024-01-16 21:42:59,365 (beam_search:479) INFO: total log probability: -2.79 +2024-01-16 21:42:59,365 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:42:59,365 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:59,365 (beam_search:483) INFO: best hypo: POLANATIONMANAGHENTT + +2024-01-16 21:42:59,366 (asr_inference:494) INFO: speech length: 55680 +2024-01-16 21:42:59,375 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 21:42:59,375 (beam_search:429) INFO: max output length: 84 +2024-01-16 21:42:59,375 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:59,428 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:59,428 (beam_search:476) INFO: -5.50 * 1.0 = -5.50 for ctc +2024-01-16 21:42:59,428 (beam_search:479) INFO: total log probability: -5.50 +2024-01-16 21:42:59,428 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:42:59,428 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:59,428 (beam_search:483) INFO: best hypo: FRANCHEFISISST + +2024-01-16 21:42:59,429 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:42:59,436 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:42:59,436 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:42:59,436 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:59,491 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:59,491 (beam_search:476) INFO: -5.01 * 1.0 = -5.01 for ctc +2024-01-16 21:42:59,491 (beam_search:479) INFO: total log probability: -5.01 +2024-01-16 21:42:59,491 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:42:59,491 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:59,491 (beam_search:483) INFO: best hypo: HIARECOMPREITIONDRATIO + +2024-01-16 21:42:59,492 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:42:59,500 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:42:59,500 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:42:59,500 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:59,555 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:59,555 (beam_search:476) INFO: -7.12 * 1.0 = -7.12 for ctc +2024-01-16 21:42:59,555 (beam_search:479) INFO: total log probability: -7.12 +2024-01-16 21:42:59,555 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:42:59,555 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:59,556 (beam_search:483) INFO: best hypo: RECOARDNGINDESTYSOUCHATION + +2024-01-16 21:42:59,557 (asr_inference:494) INFO: speech length: 42240 +2024-01-16 21:42:59,565 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 21:42:59,565 (beam_search:429) INFO: max output length: 63 +2024-01-16 21:42:59,565 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:59,621 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:59,621 (beam_search:476) INFO: -5.60 * 1.0 = -5.60 for ctc +2024-01-16 21:42:59,621 (beam_search:479) INFO: total log probability: -5.60 +2024-01-16 21:42:59,621 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:42:59,621 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:59,621 (beam_search:483) INFO: best hypo: TEAPADEOUNLINMAGOSIAN + +2024-01-16 21:42:59,623 (asr_inference:494) INFO: speech length: 53760 +2024-01-16 21:42:59,631 (beam_search:428) INFO: decoder input length: 81 +2024-01-16 21:42:59,631 (beam_search:429) INFO: max output length: 81 +2024-01-16 21:42:59,631 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:59,711 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:59,711 (beam_search:476) INFO: -10.63 * 1.0 = -10.63 for ctc +2024-01-16 21:42:59,711 (beam_search:479) INFO: total log probability: -10.63 +2024-01-16 21:42:59,711 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 21:42:59,711 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:59,712 (beam_search:483) INFO: best hypo: HIPOPERECULDPOROTOUCSENSS + +2024-01-16 21:42:59,713 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:42:59,720 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:42:59,720 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:42:59,720 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:59,761 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:59,761 (beam_search:476) INFO: -7.06 * 1.0 = -7.06 for ctc +2024-01-16 21:42:59,761 (beam_search:479) INFO: total log probability: -7.06 +2024-01-16 21:42:59,761 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:42:59,761 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:59,761 (beam_search:483) INFO: best hypo: FINIGTSTATMUSHENENS + +2024-01-16 21:42:59,762 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:42:59,769 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:42:59,769 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:42:59,769 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:59,816 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:59,816 (beam_search:476) INFO: -5.36 * 1.0 = -5.36 for ctc +2024-01-16 21:42:59,816 (beam_search:479) INFO: total log probability: -5.36 +2024-01-16 21:42:59,817 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:42:59,817 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:59,817 (beam_search:483) INFO: best hypo: WHIDLYSUSEDLOCKALD + +2024-01-16 21:42:59,818 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:42:59,826 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:42:59,826 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:42:59,826 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:59,874 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:59,874 (beam_search:476) INFO: -3.83 * 1.0 = -3.83 for ctc +2024-01-16 21:42:59,874 (beam_search:479) INFO: total log probability: -3.83 +2024-01-16 21:42:59,874 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:42:59,874 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:59,874 (beam_search:483) INFO: best hypo: NORHEMAYCODCONTINENT + +2024-01-16 21:42:59,876 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:42:59,883 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:42:59,883 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:42:59,883 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:59,926 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:59,926 (beam_search:476) INFO: -6.75 * 1.0 = -6.75 for ctc +2024-01-16 21:42:59,926 (beam_search:479) INFO: total log probability: -6.75 +2024-01-16 21:42:59,926 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:42:59,926 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:59,926 (beam_search:483) INFO: best hypo: AFRCONMERICONREPES + +2024-01-16 21:42:59,927 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:42:59,934 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:42:59,934 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:42:59,934 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:42:59,978 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:42:59,978 (beam_search:476) INFO: -3.27 * 1.0 = -3.27 for ctc +2024-01-16 21:42:59,978 (beam_search:479) INFO: total log probability: -3.27 +2024-01-16 21:42:59,978 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:42:59,978 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:42:59,978 (beam_search:483) INFO: best hypo: THRETENDMELIGTRACTIONS + +2024-01-16 21:42:59,979 (asr_inference:494) INFO: speech length: 67200 +2024-01-16 21:42:59,988 (beam_search:428) INFO: decoder input length: 102 +2024-01-16 21:42:59,988 (beam_search:429) INFO: max output length: 102 +2024-01-16 21:42:59,988 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:00,084 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:00,084 (beam_search:476) INFO: -11.75 * 1.0 = -11.75 for ctc +2024-01-16 21:43:00,084 (beam_search:479) INFO: total log probability: -11.75 +2024-01-16 21:43:00,084 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-16 21:43:00,084 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:00,084 (beam_search:483) INFO: best hypo: UHTTHEWORDFININTNEEEEEE + +2024-01-16 21:43:00,085 (asr_inference:494) INFO: speech length: 42240 +2024-01-16 21:43:00,093 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 21:43:00,093 (beam_search:429) INFO: max output length: 63 +2024-01-16 21:43:00,093 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:00,161 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:00,161 (beam_search:476) INFO: -6.59 * 1.0 = -6.59 for ctc +2024-01-16 21:43:00,161 (beam_search:479) INFO: total log probability: -6.59 +2024-01-16 21:43:00,162 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:00,162 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:00,162 (beam_search:483) INFO: best hypo: THETOMIKMLEILENOPTOCALFISICE + +2024-01-16 21:43:00,163 (asr_inference:494) INFO: speech length: 19200 +2024-01-16 21:43:00,169 (beam_search:428) INFO: decoder input length: 27 +2024-01-16 21:43:00,169 (beam_search:429) INFO: max output length: 27 +2024-01-16 21:43:00,169 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:00,179 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:00,179 (beam_search:476) INFO: -2.15 * 1.0 = -2.15 for ctc +2024-01-16 21:43:00,179 (beam_search:479) INFO: total log probability: -2.15 +2024-01-16 21:43:00,179 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 21:43:00,179 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:00,179 (beam_search:483) INFO: best hypo: ETONE + +2024-01-16 21:43:00,180 (asr_inference:494) INFO: speech length: 19200 +2024-01-16 21:43:00,186 (beam_search:428) INFO: decoder input length: 27 +2024-01-16 21:43:00,186 (beam_search:429) INFO: max output length: 27 +2024-01-16 21:43:00,186 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:00,197 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:00,197 (beam_search:476) INFO: -1.90 * 1.0 = -1.90 for ctc +2024-01-16 21:43:00,197 (beam_search:479) INFO: total log probability: -1.90 +2024-01-16 21:43:00,198 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:00,198 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:00,198 (beam_search:483) INFO: best hypo: MYRSOL + +2024-01-16 21:43:00,199 (asr_inference:494) INFO: speech length: 59520 +2024-01-16 21:43:00,207 (beam_search:428) INFO: decoder input length: 90 +2024-01-16 21:43:00,207 (beam_search:429) INFO: max output length: 90 +2024-01-16 21:43:00,207 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:00,286 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:00,286 (beam_search:476) INFO: -6.36 * 1.0 = -6.36 for ctc +2024-01-16 21:43:00,286 (beam_search:479) INFO: total log probability: -6.36 +2024-01-16 21:43:00,286 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:00,286 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:00,287 (beam_search:483) INFO: best hypo: CONSTRCTNOUOHRALGAGE + +2024-01-16 21:43:00,288 (asr_inference:494) INFO: speech length: 30720 +2024-01-16 21:43:00,295 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 21:43:00,295 (beam_search:429) INFO: max output length: 45 +2024-01-16 21:43:00,295 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:00,336 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:00,336 (beam_search:476) INFO: -5.35 * 1.0 = -5.35 for ctc +2024-01-16 21:43:00,336 (beam_search:479) INFO: total log probability: -5.35 +2024-01-16 21:43:00,336 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:00,336 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:00,336 (beam_search:483) INFO: best hypo: PORLYECLWIONRINCSABLE + +2024-01-16 21:43:00,337 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 21:43:00,345 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:43:00,345 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:43:00,345 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:00,403 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:00,403 (beam_search:476) INFO: -8.57 * 1.0 = -8.57 for ctc +2024-01-16 21:43:00,403 (beam_search:479) INFO: total log probability: -8.57 +2024-01-16 21:43:00,403 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 21:43:00,403 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:00,404 (beam_search:483) INFO: best hypo: HCLOWPORTRAYDEFERENT + +2024-01-16 21:43:00,405 (asr_inference:494) INFO: speech length: 44160 +2024-01-16 21:43:00,413 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:43:00,413 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:43:00,413 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:00,458 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:00,458 (beam_search:476) INFO: -3.27 * 1.0 = -3.27 for ctc +2024-01-16 21:43:00,458 (beam_search:479) INFO: total log probability: -3.27 +2024-01-16 21:43:00,458 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:00,458 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:00,458 (beam_search:483) INFO: best hypo: SOVEATDESIDENCES + +2024-01-16 21:43:00,460 (asr_inference:494) INFO: speech length: 55680 +2024-01-16 21:43:00,468 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 21:43:00,468 (beam_search:429) INFO: max output length: 84 +2024-01-16 21:43:00,468 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:00,565 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:00,565 (beam_search:476) INFO: -6.11 * 1.0 = -6.11 for ctc +2024-01-16 21:43:00,565 (beam_search:479) INFO: total log probability: -6.11 +2024-01-16 21:43:00,565 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:00,565 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:00,565 (beam_search:483) INFO: best hypo: SIGNELETRONCETDUCTIONDPOLTWAYES + +2024-01-16 21:43:00,566 (asr_inference:494) INFO: speech length: 24960 +2024-01-16 21:43:00,573 (beam_search:428) INFO: decoder input length: 36 +2024-01-16 21:43:00,573 (beam_search:429) INFO: max output length: 36 +2024-01-16 21:43:00,573 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:00,595 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:00,595 (beam_search:476) INFO: -2.82 * 1.0 = -2.82 for ctc +2024-01-16 21:43:00,595 (beam_search:479) INFO: total log probability: -2.82 +2024-01-16 21:43:00,595 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:00,595 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:00,596 (beam_search:483) INFO: best hypo: NOUBORNMSSI + +2024-01-16 21:43:00,596 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 21:43:00,604 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:43:00,605 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:43:00,605 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:00,663 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:00,663 (beam_search:476) INFO: -4.36 * 1.0 = -4.36 for ctc +2024-01-16 21:43:00,663 (beam_search:479) INFO: total log probability: -4.36 +2024-01-16 21:43:00,663 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:00,663 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:00,663 (beam_search:483) INFO: best hypo: JGENRLYACEPTEDRANERS + +2024-01-16 21:43:00,664 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:43:00,672 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:00,672 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:00,672 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:00,711 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:00,711 (beam_search:476) INFO: -3.80 * 1.0 = -3.80 for ctc +2024-01-16 21:43:00,711 (beam_search:479) INFO: total log probability: -3.80 +2024-01-16 21:43:00,711 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:00,711 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:00,711 (beam_search:483) INFO: best hypo: GILEDAWRDWENHIS + +2024-01-16 21:43:00,712 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:43:00,719 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:00,719 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:00,719 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:00,764 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:00,764 (beam_search:476) INFO: -5.93 * 1.0 = -5.93 for ctc +2024-01-16 21:43:00,764 (beam_search:479) INFO: total log probability: -5.93 +2024-01-16 21:43:00,764 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:00,764 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:00,764 (beam_search:483) INFO: best hypo: SOWEDISHEMAUSICLGRPS + +2024-01-16 21:43:00,765 (asr_inference:494) INFO: speech length: 57600 +2024-01-16 21:43:00,774 (beam_search:428) INFO: decoder input length: 87 +2024-01-16 21:43:00,774 (beam_search:429) INFO: max output length: 87 +2024-01-16 21:43:00,774 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:00,850 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:00,850 (beam_search:476) INFO: -4.70 * 1.0 = -4.70 for ctc +2024-01-16 21:43:00,850 (beam_search:479) INFO: total log probability: -4.70 +2024-01-16 21:43:00,850 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:00,850 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:00,850 (beam_search:483) INFO: best hypo: CHALDEREDORTISIMRATING + +2024-01-16 21:43:00,851 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:43:00,859 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:43:00,859 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:43:00,859 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:00,888 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:00,888 (beam_search:476) INFO: -3.64 * 1.0 = -3.64 for ctc +2024-01-16 21:43:00,888 (beam_search:479) INFO: total log probability: -3.64 +2024-01-16 21:43:00,889 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:43:00,889 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:00,889 (beam_search:483) INFO: best hypo: DOSIGHFORMEMS + +2024-01-16 21:43:00,890 (asr_inference:494) INFO: speech length: 38400 +2024-01-16 21:43:00,898 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:43:00,898 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:43:00,898 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:00,954 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:00,954 (beam_search:476) INFO: -5.98 * 1.0 = -5.98 for ctc +2024-01-16 21:43:00,954 (beam_search:479) INFO: total log probability: -5.98 +2024-01-16 21:43:00,954 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:00,954 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:00,954 (beam_search:483) INFO: best hypo: OHIIOUSTATIONOVORSTITYE + +2024-01-16 21:43:00,955 (asr_inference:494) INFO: speech length: 51840 +2024-01-16 21:43:00,964 (beam_search:428) INFO: decoder input length: 78 +2024-01-16 21:43:00,964 (beam_search:429) INFO: max output length: 78 +2024-01-16 21:43:00,964 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,035 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,035 (beam_search:476) INFO: -6.46 * 1.0 = -6.46 for ctc +2024-01-16 21:43:01,035 (beam_search:479) INFO: total log probability: -6.46 +2024-01-16 21:43:01,035 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:01,035 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,035 (beam_search:483) INFO: best hypo: FORMOSSATHENCEINTERKE + +2024-01-16 21:43:01,036 (asr_inference:494) INFO: speech length: 65280 +2024-01-16 21:43:01,046 (beam_search:428) INFO: decoder input length: 99 +2024-01-16 21:43:01,046 (beam_search:429) INFO: max output length: 99 +2024-01-16 21:43:01,046 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,132 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,132 (beam_search:476) INFO: -7.97 * 1.0 = -7.97 for ctc +2024-01-16 21:43:01,132 (beam_search:479) INFO: total log probability: -7.97 +2024-01-16 21:43:01,132 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:43:01,132 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,132 (beam_search:483) INFO: best hypo: EEEANROCONINWVHNTIONSH + +2024-01-16 21:43:01,133 (asr_inference:494) INFO: speech length: 21120 +2024-01-16 21:43:01,140 (beam_search:428) INFO: decoder input length: 30 +2024-01-16 21:43:01,140 (beam_search:429) INFO: max output length: 30 +2024-01-16 21:43:01,140 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,153 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,153 (beam_search:476) INFO: -4.75 * 1.0 = -4.75 for ctc +2024-01-16 21:43:01,153 (beam_search:479) INFO: total log probability: -4.75 +2024-01-16 21:43:01,153 (beam_search:480) INFO: normalized log probability: -0.43 +2024-01-16 21:43:01,154 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,154 (beam_search:483) INFO: best hypo: EARTESEH + +2024-01-16 21:43:01,155 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:43:01,162 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:43:01,162 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:43:01,162 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,213 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,213 (beam_search:476) INFO: -5.11 * 1.0 = -5.11 for ctc +2024-01-16 21:43:01,213 (beam_search:479) INFO: total log probability: -5.11 +2024-01-16 21:43:01,213 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:01,213 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,213 (beam_search:483) INFO: best hypo: MDENYOUROPEANRASHAH + +2024-01-16 21:43:01,214 (asr_inference:494) INFO: speech length: 55680 +2024-01-16 21:43:01,223 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 21:43:01,223 (beam_search:429) INFO: max output length: 84 +2024-01-16 21:43:01,223 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,283 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,283 (beam_search:476) INFO: -6.10 * 1.0 = -6.10 for ctc +2024-01-16 21:43:01,283 (beam_search:479) INFO: total log probability: -6.10 +2024-01-16 21:43:01,283 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:43:01,284 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,284 (beam_search:483) INFO: best hypo: NSNODLEGPILANTH + +2024-01-16 21:43:01,285 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:43:01,292 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:43:01,292 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:43:01,292 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,341 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,341 (beam_search:476) INFO: -3.20 * 1.0 = -3.20 for ctc +2024-01-16 21:43:01,341 (beam_search:479) INFO: total log probability: -3.20 +2024-01-16 21:43:01,341 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:01,341 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,341 (beam_search:483) INFO: best hypo: BIKFINISHPRDUCTIONS + +2024-01-16 21:43:01,342 (asr_inference:494) INFO: speech length: 23040 +2024-01-16 21:43:01,349 (beam_search:428) INFO: decoder input length: 33 +2024-01-16 21:43:01,349 (beam_search:429) INFO: max output length: 33 +2024-01-16 21:43:01,349 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,366 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,366 (beam_search:476) INFO: -4.33 * 1.0 = -4.33 for ctc +2024-01-16 21:43:01,366 (beam_search:479) INFO: total log probability: -4.33 +2024-01-16 21:43:01,366 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 21:43:01,366 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,366 (beam_search:483) INFO: best hypo: NASTIONLEH + +2024-01-16 21:43:01,367 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:43:01,374 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:43:01,374 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:43:01,374 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,407 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,407 (beam_search:476) INFO: -9.04 * 1.0 = -9.04 for ctc +2024-01-16 21:43:01,407 (beam_search:479) INFO: total log probability: -9.04 +2024-01-16 21:43:01,407 (beam_search:480) INFO: normalized log probability: -0.48 +2024-01-16 21:43:01,407 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,407 (beam_search:483) INFO: best hypo: TRADGIKPOITESES + +2024-01-16 21:43:01,408 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:43:01,415 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:43:01,415 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:43:01,415 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,458 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,458 (beam_search:476) INFO: -2.35 * 1.0 = -2.35 for ctc +2024-01-16 21:43:01,458 (beam_search:479) INFO: total log probability: -2.35 +2024-01-16 21:43:01,458 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:43:01,458 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,458 (beam_search:483) INFO: best hypo: TITILGRICESTATE + +2024-01-16 21:43:01,459 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:43:01,467 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:01,467 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:01,467 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,505 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,505 (beam_search:476) INFO: -2.49 * 1.0 = -2.49 for ctc +2024-01-16 21:43:01,505 (beam_search:479) INFO: total log probability: -2.49 +2024-01-16 21:43:01,505 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:01,505 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,505 (beam_search:483) INFO: best hypo: ASTHENAHADAENE + +2024-01-16 21:43:01,506 (asr_inference:494) INFO: speech length: 44160 +2024-01-16 21:43:01,514 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:43:01,514 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:43:01,514 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,574 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,574 (beam_search:476) INFO: -6.46 * 1.0 = -6.46 for ctc +2024-01-16 21:43:01,574 (beam_search:479) INFO: total log probability: -6.46 +2024-01-16 21:43:01,574 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:01,574 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,574 (beam_search:483) INFO: best hypo: EASTEONYURPEANCOUNTRYES + +2024-01-16 21:43:01,576 (asr_inference:494) INFO: speech length: 55680 +2024-01-16 21:43:01,584 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 21:43:01,584 (beam_search:429) INFO: max output length: 84 +2024-01-16 21:43:01,584 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,677 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,677 (beam_search:476) INFO: -5.76 * 1.0 = -5.76 for ctc +2024-01-16 21:43:01,677 (beam_search:479) INFO: total log probability: -5.76 +2024-01-16 21:43:01,677 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:01,677 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,677 (beam_search:483) INFO: best hypo: CONDEDANORTHRIVSETRONSLATIONS + +2024-01-16 21:43:01,678 (asr_inference:494) INFO: speech length: 19200 +2024-01-16 21:43:01,685 (beam_search:428) INFO: decoder input length: 27 +2024-01-16 21:43:01,685 (beam_search:429) INFO: max output length: 27 +2024-01-16 21:43:01,685 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,701 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,701 (beam_search:476) INFO: -7.39 * 1.0 = -7.39 for ctc +2024-01-16 21:43:01,701 (beam_search:479) INFO: total log probability: -7.39 +2024-01-16 21:43:01,701 (beam_search:480) INFO: normalized log probability: -0.49 +2024-01-16 21:43:01,701 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,701 (beam_search:483) INFO: best hypo: OALWORDETES + +2024-01-16 21:43:01,702 (asr_inference:494) INFO: speech length: 48000 +2024-01-16 21:43:01,710 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 21:43:01,711 (beam_search:429) INFO: max output length: 72 +2024-01-16 21:43:01,711 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,774 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,774 (beam_search:476) INFO: -4.72 * 1.0 = -4.72 for ctc +2024-01-16 21:43:01,774 (beam_search:479) INFO: total log probability: -4.72 +2024-01-16 21:43:01,774 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:01,774 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,774 (beam_search:483) INFO: best hypo: CINASSORMOWNADLENDES + +2024-01-16 21:43:01,775 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:43:01,783 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:01,783 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:01,783 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,814 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,814 (beam_search:476) INFO: -2.24 * 1.0 = -2.24 for ctc +2024-01-16 21:43:01,814 (beam_search:479) INFO: total log probability: -2.24 +2024-01-16 21:43:01,814 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:43:01,814 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,814 (beam_search:483) INFO: best hypo: NOBLESAMITYE + +2024-01-16 21:43:01,815 (asr_inference:494) INFO: speech length: 30720 +2024-01-16 21:43:01,822 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 21:43:01,822 (beam_search:429) INFO: max output length: 45 +2024-01-16 21:43:01,822 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,848 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,848 (beam_search:476) INFO: -4.65 * 1.0 = -4.65 for ctc +2024-01-16 21:43:01,848 (beam_search:479) INFO: total log probability: -4.65 +2024-01-16 21:43:01,848 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:43:01,848 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,848 (beam_search:483) INFO: best hypo: ITWOSAEFOLS + +2024-01-16 21:43:01,849 (asr_inference:494) INFO: speech length: 49920 +2024-01-16 21:43:01,857 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 21:43:01,857 (beam_search:429) INFO: max output length: 75 +2024-01-16 21:43:01,858 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,916 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,916 (beam_search:476) INFO: -3.86 * 1.0 = -3.86 for ctc +2024-01-16 21:43:01,916 (beam_search:479) INFO: total log probability: -3.86 +2024-01-16 21:43:01,916 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:01,916 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,916 (beam_search:483) INFO: best hypo: MOUNTSAINTOVINSENT + +2024-01-16 21:43:01,917 (asr_inference:494) INFO: speech length: 44160 +2024-01-16 21:43:01,925 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:43:01,925 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:43:01,925 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:01,983 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:01,983 (beam_search:476) INFO: -5.69 * 1.0 = -5.69 for ctc +2024-01-16 21:43:01,983 (beam_search:479) INFO: total log probability: -5.69 +2024-01-16 21:43:01,983 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:01,983 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:01,983 (beam_search:483) INFO: best hypo: SITYMRCRPOLITONEAIRAR + +2024-01-16 21:43:01,984 (asr_inference:494) INFO: speech length: 57600 +2024-01-16 21:43:01,993 (beam_search:428) INFO: decoder input length: 87 +2024-01-16 21:43:01,993 (beam_search:429) INFO: max output length: 87 +2024-01-16 21:43:01,993 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,078 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,078 (beam_search:476) INFO: -7.89 * 1.0 = -7.89 for ctc +2024-01-16 21:43:02,078 (beam_search:479) INFO: total log probability: -7.89 +2024-01-16 21:43:02,078 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:43:02,078 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,079 (beam_search:483) INFO: best hypo: ROONLERSHODAIDASCHLDRAN + +2024-01-16 21:43:02,080 (asr_inference:494) INFO: speech length: 38400 +2024-01-16 21:43:02,087 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:43:02,087 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:43:02,087 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,129 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,129 (beam_search:476) INFO: -5.76 * 1.0 = -5.76 for ctc +2024-01-16 21:43:02,129 (beam_search:479) INFO: total log probability: -5.76 +2024-01-16 21:43:02,129 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 21:43:02,129 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,129 (beam_search:483) INFO: best hypo: CHONCESLESVOLEL + +2024-01-16 21:43:02,130 (asr_inference:494) INFO: speech length: 38400 +2024-01-16 21:43:02,138 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:43:02,138 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:43:02,138 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,183 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,183 (beam_search:476) INFO: -2.50 * 1.0 = -2.50 for ctc +2024-01-16 21:43:02,183 (beam_search:479) INFO: total log probability: -2.50 +2024-01-16 21:43:02,183 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:43:02,183 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,183 (beam_search:483) INFO: best hypo: IPEPECKATCINTIRLY + +2024-01-16 21:43:02,184 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:43:02,192 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:02,192 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:02,192 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,232 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,232 (beam_search:476) INFO: -2.95 * 1.0 = -2.95 for ctc +2024-01-16 21:43:02,232 (beam_search:479) INFO: total log probability: -2.95 +2024-01-16 21:43:02,232 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:43:02,232 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,232 (beam_search:483) INFO: best hypo: CINGADWARDSDATH + +2024-01-16 21:43:02,233 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:43:02,240 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:43:02,240 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:43:02,240 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,286 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,286 (beam_search:476) INFO: -5.79 * 1.0 = -5.79 for ctc +2024-01-16 21:43:02,286 (beam_search:479) INFO: total log probability: -5.79 +2024-01-16 21:43:02,286 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:43:02,286 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,286 (beam_search:483) INFO: best hypo: AMERICEARAMERICE + +2024-01-16 21:43:02,287 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:43:02,294 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:43:02,294 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:43:02,294 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,329 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,329 (beam_search:476) INFO: -3.65 * 1.0 = -3.65 for ctc +2024-01-16 21:43:02,329 (beam_search:479) INFO: total log probability: -3.65 +2024-01-16 21:43:02,329 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:02,329 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,329 (beam_search:483) INFO: best hypo: COMRTILSHIPSALD + +2024-01-16 21:43:02,330 (asr_inference:494) INFO: speech length: 38400 +2024-01-16 21:43:02,337 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:43:02,337 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:43:02,337 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,381 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,381 (beam_search:476) INFO: -3.81 * 1.0 = -3.81 for ctc +2024-01-16 21:43:02,381 (beam_search:479) INFO: total log probability: -3.81 +2024-01-16 21:43:02,381 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:02,381 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,381 (beam_search:483) INFO: best hypo: PEPLFOMMAENHAMEM + +2024-01-16 21:43:02,382 (asr_inference:494) INFO: speech length: 30720 +2024-01-16 21:43:02,389 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 21:43:02,389 (beam_search:429) INFO: max output length: 45 +2024-01-16 21:43:02,390 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,417 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,418 (beam_search:476) INFO: -1.37 * 1.0 = -1.37 for ctc +2024-01-16 21:43:02,418 (beam_search:479) INFO: total log probability: -1.37 +2024-01-16 21:43:02,418 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 21:43:02,418 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,418 (beam_search:483) INFO: best hypo: RAILRASHCILD + +2024-01-16 21:43:02,419 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:43:02,426 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:43:02,426 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:43:02,426 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,463 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,463 (beam_search:476) INFO: -3.66 * 1.0 = -3.66 for ctc +2024-01-16 21:43:02,463 (beam_search:479) INFO: total log probability: -3.66 +2024-01-16 21:43:02,463 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:02,463 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,463 (beam_search:483) INFO: best hypo: MUTHALDEFENSTOADY + +2024-01-16 21:43:02,464 (asr_inference:494) INFO: speech length: 28800 +2024-01-16 21:43:02,471 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 21:43:02,471 (beam_search:429) INFO: max output length: 42 +2024-01-16 21:43:02,471 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,506 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,506 (beam_search:476) INFO: -4.32 * 1.0 = -4.32 for ctc +2024-01-16 21:43:02,506 (beam_search:479) INFO: total log probability: -4.32 +2024-01-16 21:43:02,506 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:02,506 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,506 (beam_search:483) INFO: best hypo: MOUDTENCHELDRUOLIS + +2024-01-16 21:43:02,507 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:43:02,514 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:02,514 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:02,514 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,559 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,560 (beam_search:476) INFO: -6.52 * 1.0 = -6.52 for ctc +2024-01-16 21:43:02,560 (beam_search:479) INFO: total log probability: -6.52 +2024-01-16 21:43:02,560 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:43:02,560 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,560 (beam_search:483) INFO: best hypo: MOTESERRIHFHALDEVION + +2024-01-16 21:43:02,561 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:43:02,568 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:02,568 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:02,568 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,610 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,611 (beam_search:476) INFO: -6.21 * 1.0 = -6.21 for ctc +2024-01-16 21:43:02,611 (beam_search:479) INFO: total log probability: -6.21 +2024-01-16 21:43:02,611 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:43:02,611 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,611 (beam_search:483) INFO: best hypo: OUSTRALIONIEFOURSE + +2024-01-16 21:43:02,612 (asr_inference:494) INFO: speech length: 42240 +2024-01-16 21:43:02,620 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 21:43:02,620 (beam_search:429) INFO: max output length: 63 +2024-01-16 21:43:02,620 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,677 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,677 (beam_search:476) INFO: -5.30 * 1.0 = -5.30 for ctc +2024-01-16 21:43:02,677 (beam_search:479) INFO: total log probability: -5.30 +2024-01-16 21:43:02,677 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:02,677 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,677 (beam_search:483) INFO: best hypo: AMERYCENDMOSTRYRITES + +2024-01-16 21:43:02,678 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:43:02,686 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:02,686 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:02,686 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,727 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,727 (beam_search:476) INFO: -4.90 * 1.0 = -4.90 for ctc +2024-01-16 21:43:02,727 (beam_search:479) INFO: total log probability: -4.90 +2024-01-16 21:43:02,727 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:43:02,727 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,727 (beam_search:483) INFO: best hypo: FINLYGROWNDGREFITE + +2024-01-16 21:43:02,728 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:43:02,736 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:02,736 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:02,736 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,778 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,778 (beam_search:476) INFO: -6.81 * 1.0 = -6.81 for ctc +2024-01-16 21:43:02,778 (beam_search:479) INFO: total log probability: -6.81 +2024-01-16 21:43:02,778 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 21:43:02,778 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,778 (beam_search:483) INFO: best hypo: WOLTAMPINESOPMATS + +2024-01-16 21:43:02,779 (asr_inference:494) INFO: speech length: 24960 +2024-01-16 21:43:02,786 (beam_search:428) INFO: decoder input length: 36 +2024-01-16 21:43:02,786 (beam_search:429) INFO: max output length: 36 +2024-01-16 21:43:02,786 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,804 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,804 (beam_search:476) INFO: -3.75 * 1.0 = -3.75 for ctc +2024-01-16 21:43:02,804 (beam_search:479) INFO: total log probability: -3.75 +2024-01-16 21:43:02,804 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:43:02,804 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,804 (beam_search:483) INFO: best hypo: CERIOLINA + +2024-01-16 21:43:02,805 (asr_inference:494) INFO: speech length: 38400 +2024-01-16 21:43:02,813 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:43:02,813 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:43:02,813 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,856 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,857 (beam_search:476) INFO: -1.70 * 1.0 = -1.70 for ctc +2024-01-16 21:43:02,857 (beam_search:479) INFO: total log probability: -1.70 +2024-01-16 21:43:02,857 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 21:43:02,857 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,857 (beam_search:483) INFO: best hypo: MYBOATHNOPERATES + +2024-01-16 21:43:02,858 (asr_inference:494) INFO: speech length: 23040 +2024-01-16 21:43:02,865 (beam_search:428) INFO: decoder input length: 33 +2024-01-16 21:43:02,865 (beam_search:429) INFO: max output length: 33 +2024-01-16 21:43:02,865 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,886 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,886 (beam_search:476) INFO: -3.51 * 1.0 = -3.51 for ctc +2024-01-16 21:43:02,887 (beam_search:479) INFO: total log probability: -3.51 +2024-01-16 21:43:02,887 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:43:02,887 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,887 (beam_search:483) INFO: best hypo: CORTEFORITYES + +2024-01-16 21:43:02,888 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:43:02,895 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:43:02,895 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:43:02,895 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,920 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,920 (beam_search:476) INFO: -5.11 * 1.0 = -5.11 for ctc +2024-01-16 21:43:02,920 (beam_search:479) INFO: total log probability: -5.11 +2024-01-16 21:43:02,920 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 21:43:02,920 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,920 (beam_search:483) INFO: best hypo: MIOROAWNDH + +2024-01-16 21:43:02,922 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:43:02,929 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:02,929 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:02,929 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:02,971 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:02,972 (beam_search:476) INFO: -2.96 * 1.0 = -2.96 for ctc +2024-01-16 21:43:02,972 (beam_search:479) INFO: total log probability: -2.96 +2024-01-16 21:43:02,972 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:02,972 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:02,972 (beam_search:483) INFO: best hypo: COSELETHALREACTIONS + +# Accounting: time=153 threads=1 +# Ended (code 0) at Tue Jan 16 21:43:03 CST 2024, elapsed time 153 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.3.log b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.3.log new file mode 100644 index 0000000000000000000000000000000000000000..ae53249273b24987ae3902ab3b05fbdb059974da --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.3.log @@ -0,0 +1,3022 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.3 --config conf/decode_asr.yaml +# Started at Tue Jan 16 21:43:03 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.3 --config conf/decode_asr.yaml +2024-01-16 21:43:04,798 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml +2024-01-16 21:43:04,816 (asr:523) INFO: Vocabulary size: 30 +2024-01-16 21:43:04,880 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 21:43:04,880 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 21:43:04,992 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 21:43:06,283 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 21:43:07,509 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 21:43:07,509 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 21:43:07,509 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 21:43:07,542 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-16 21:43:07,617 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 21:43:07,730 (asr_inference:494) INFO: speech length: 38400 +2024-01-16 21:43:08,930 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:43:08,930 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:43:08,930 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:08,975 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:08,975 (beam_search:476) INFO: -4.22 * 1.0 = -4.22 for ctc +2024-01-16 21:43:08,975 (beam_search:479) INFO: total log probability: -4.22 +2024-01-16 21:43:08,975 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:08,975 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:08,976 (beam_search:483) INFO: best hypo: INGLOISHPECEOFOUSTS + +2024-01-16 21:43:09,002 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 21:43:09,012 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:43:09,012 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:43:09,012 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:09,070 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:09,070 (beam_search:476) INFO: -2.87 * 1.0 = -2.87 for ctc +2024-01-16 21:43:09,070 (beam_search:479) INFO: total log probability: -2.87 +2024-01-16 21:43:09,070 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:43:09,070 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:09,070 (beam_search:483) INFO: best hypo: YONIGTEDSTATEFEDIRAL + +2024-01-16 21:43:09,071 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:43:09,080 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:09,080 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:09,080 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:09,120 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:09,120 (beam_search:476) INFO: -4.00 * 1.0 = -4.00 for ctc +2024-01-16 21:43:09,120 (beam_search:479) INFO: total log probability: -4.00 +2024-01-16 21:43:09,120 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:09,120 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:09,120 (beam_search:483) INFO: best hypo: FADRLERESEIEOVEACT + +2024-01-16 21:43:09,121 (asr_inference:494) INFO: speech length: 28800 +2024-01-16 21:43:09,129 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 21:43:09,129 (beam_search:429) INFO: max output length: 42 +2024-01-16 21:43:09,129 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:09,162 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:09,162 (beam_search:476) INFO: -3.74 * 1.0 = -3.74 for ctc +2024-01-16 21:43:09,162 (beam_search:479) INFO: total log probability: -3.74 +2024-01-16 21:43:09,162 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:09,162 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:09,163 (beam_search:483) INFO: best hypo: WOLIMHENDRYHERSON + +2024-01-16 21:43:09,164 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:43:09,172 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:43:09,172 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:43:09,172 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:09,203 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:09,203 (beam_search:476) INFO: -3.02 * 1.0 = -3.02 for ctc +2024-01-16 21:43:09,203 (beam_search:479) INFO: total log probability: -3.02 +2024-01-16 21:43:09,203 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:09,203 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:09,203 (beam_search:483) INFO: best hypo: GLAPPLAYCHOT + +2024-01-16 21:43:09,204 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 21:43:09,213 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:43:09,213 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:43:09,213 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:09,275 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:09,275 (beam_search:476) INFO: -5.82 * 1.0 = -5.82 for ctc +2024-01-16 21:43:09,275 (beam_search:479) INFO: total log probability: -5.82 +2024-01-16 21:43:09,275 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:43:09,275 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:09,276 (beam_search:483) INFO: best hypo: PASTONGERRHALDSOVOCESH + +2024-01-16 21:43:09,277 (asr_inference:494) INFO: speech length: 49920 +2024-01-16 21:43:09,285 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 21:43:09,285 (beam_search:429) INFO: max output length: 75 +2024-01-16 21:43:09,285 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:09,362 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:09,362 (beam_search:476) INFO: -7.07 * 1.0 = -7.07 for ctc +2024-01-16 21:43:09,362 (beam_search:479) INFO: total log probability: -7.07 +2024-01-16 21:43:09,362 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:09,362 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:09,362 (beam_search:483) INFO: best hypo: ANCHONMESEDRNTHIONDJDINRLS + +2024-01-16 21:43:09,363 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:43:09,371 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:09,371 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:09,371 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:09,415 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:09,415 (beam_search:476) INFO: -3.87 * 1.0 = -3.87 for ctc +2024-01-16 21:43:09,415 (beam_search:479) INFO: total log probability: -3.87 +2024-01-16 21:43:09,415 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:09,415 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:09,415 (beam_search:483) INFO: best hypo: CRONGACTIONSEAMARE + +2024-01-16 21:43:09,416 (asr_inference:494) INFO: speech length: 44160 +2024-01-16 21:43:09,424 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:43:09,424 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:43:09,424 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:09,486 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:09,487 (beam_search:476) INFO: -8.06 * 1.0 = -8.06 for ctc +2024-01-16 21:43:09,487 (beam_search:479) INFO: total log probability: -8.06 +2024-01-16 21:43:09,487 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:43:09,487 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:09,487 (beam_search:483) INFO: best hypo: GONPOUTEPROPILENTYOSEDT + +2024-01-16 21:43:09,488 (asr_inference:494) INFO: speech length: 38400 +2024-01-16 21:43:09,495 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:43:09,495 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:43:09,495 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:09,545 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:09,545 (beam_search:476) INFO: -6.15 * 1.0 = -6.15 for ctc +2024-01-16 21:43:09,545 (beam_search:479) INFO: total log probability: -6.15 +2024-01-16 21:43:09,545 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:09,545 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:09,545 (beam_search:483) INFO: best hypo: LOWISTDINAGYSTDAGHTH + +2024-01-16 21:43:09,546 (asr_inference:494) INFO: speech length: 24960 +2024-01-16 21:43:09,553 (beam_search:428) INFO: decoder input length: 36 +2024-01-16 21:43:09,553 (beam_search:429) INFO: max output length: 36 +2024-01-16 21:43:09,553 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:09,574 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:09,574 (beam_search:476) INFO: -1.52 * 1.0 = -1.52 for ctc +2024-01-16 21:43:09,574 (beam_search:479) INFO: total log probability: -1.52 +2024-01-16 21:43:09,574 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 21:43:09,574 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:09,574 (beam_search:483) INFO: best hypo: CALNDEYOUROS + +2024-01-16 21:43:09,575 (asr_inference:494) INFO: speech length: 72960 +2024-01-16 21:43:09,585 (beam_search:428) INFO: decoder input length: 111 +2024-01-16 21:43:09,585 (beam_search:429) INFO: max output length: 111 +2024-01-16 21:43:09,585 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:09,703 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:09,703 (beam_search:476) INFO: -6.64 * 1.0 = -6.64 for ctc +2024-01-16 21:43:09,703 (beam_search:479) INFO: total log probability: -6.64 +2024-01-16 21:43:09,703 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:09,703 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:09,703 (beam_search:483) INFO: best hypo: MAEGERINTENOASIONALEAEPORTE + +2024-01-16 21:43:09,705 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:43:09,712 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:09,712 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:09,712 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:09,750 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:09,750 (beam_search:476) INFO: -3.53 * 1.0 = -3.53 for ctc +2024-01-16 21:43:09,750 (beam_search:479) INFO: total log probability: -3.53 +2024-01-16 21:43:09,750 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:09,750 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:09,750 (beam_search:483) INFO: best hypo: TOTLFORSEACTIOM + +2024-01-16 21:43:09,751 (asr_inference:494) INFO: speech length: 42240 +2024-01-16 21:43:09,759 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 21:43:09,759 (beam_search:429) INFO: max output length: 63 +2024-01-16 21:43:09,759 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:09,812 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:09,812 (beam_search:476) INFO: -3.62 * 1.0 = -3.62 for ctc +2024-01-16 21:43:09,812 (beam_search:479) INFO: total log probability: -3.62 +2024-01-16 21:43:09,812 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:43:09,812 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:09,812 (beam_search:483) INFO: best hypo: LOSTILESDATOMPEITION + +2024-01-16 21:43:09,813 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:43:09,820 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:09,820 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:09,821 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:09,849 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:09,849 (beam_search:476) INFO: -10.32 * 1.0 = -10.32 for ctc +2024-01-16 21:43:09,849 (beam_search:479) INFO: total log probability: -10.32 +2024-01-16 21:43:09,849 (beam_search:480) INFO: normalized log probability: -0.65 +2024-01-16 21:43:09,849 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:09,850 (beam_search:483) INFO: best hypo: EGREAKEHDHER + +2024-01-16 21:43:09,851 (asr_inference:494) INFO: speech length: 48000 +2024-01-16 21:43:09,859 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 21:43:09,859 (beam_search:429) INFO: max output length: 72 +2024-01-16 21:43:09,859 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:09,941 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:09,941 (beam_search:476) INFO: -8.66 * 1.0 = -8.66 for ctc +2024-01-16 21:43:09,941 (beam_search:479) INFO: total log probability: -8.66 +2024-01-16 21:43:09,941 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:09,941 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:09,941 (beam_search:483) INFO: best hypo: INDVOIREMENTLPOTICTIONAENSYH + +2024-01-16 21:43:09,942 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:43:09,949 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:43:09,949 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:43:09,949 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:09,993 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:09,993 (beam_search:476) INFO: -6.24 * 1.0 = -6.24 for ctc +2024-01-16 21:43:09,993 (beam_search:479) INFO: total log probability: -6.24 +2024-01-16 21:43:09,993 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:09,993 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:09,993 (beam_search:483) INFO: best hypo: MANYTOEBISCKLSQRESTION + +2024-01-16 21:43:09,994 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 21:43:10,002 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:43:10,002 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:43:10,002 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:10,060 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:10,060 (beam_search:476) INFO: -3.98 * 1.0 = -3.98 for ctc +2024-01-16 21:43:10,060 (beam_search:479) INFO: total log probability: -3.98 +2024-01-16 21:43:10,060 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:10,060 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:10,060 (beam_search:483) INFO: best hypo: ANCTONSITYPOTHUNDERE + +2024-01-16 21:43:10,062 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:43:10,069 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:10,070 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:10,070 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:10,113 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:10,113 (beam_search:476) INFO: -4.28 * 1.0 = -4.28 for ctc +2024-01-16 21:43:10,113 (beam_search:479) INFO: total log probability: -4.28 +2024-01-16 21:43:10,113 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:10,113 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:10,113 (beam_search:483) INFO: best hypo: SMLEAOTHEDOCSINGOG + +2024-01-16 21:43:10,115 (asr_inference:494) INFO: speech length: 61440 +2024-01-16 21:43:10,124 (beam_search:428) INFO: decoder input length: 93 +2024-01-16 21:43:10,124 (beam_search:429) INFO: max output length: 93 +2024-01-16 21:43:10,124 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:10,215 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:10,215 (beam_search:476) INFO: -10.22 * 1.0 = -10.22 for ctc +2024-01-16 21:43:10,215 (beam_search:479) INFO: total log probability: -10.22 +2024-01-16 21:43:10,215 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 21:43:10,215 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:10,215 (beam_search:483) INFO: best hypo: NONDGESMITHOUPILIANAIRIRS + +2024-01-16 21:43:10,216 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 21:43:10,224 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:43:10,224 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:43:10,224 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:10,289 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:10,289 (beam_search:476) INFO: -7.21 * 1.0 = -7.21 for ctc +2024-01-16 21:43:10,289 (beam_search:479) INFO: total log probability: -7.21 +2024-01-16 21:43:10,289 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:43:10,289 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:10,289 (beam_search:483) INFO: best hypo: TITOLERELIGEHARREMONON + +2024-01-16 21:43:10,290 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:43:10,297 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:43:10,297 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:43:10,297 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:10,349 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:10,349 (beam_search:476) INFO: -7.10 * 1.0 = -7.10 for ctc +2024-01-16 21:43:10,349 (beam_search:479) INFO: total log probability: -7.10 +2024-01-16 21:43:10,349 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:10,349 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:10,349 (beam_search:483) INFO: best hypo: EGSANMPLESANTLUDEDHAFHMON + +2024-01-16 21:43:10,350 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:43:10,358 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:43:10,358 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:43:10,358 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:10,400 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:10,401 (beam_search:476) INFO: -6.18 * 1.0 = -6.18 for ctc +2024-01-16 21:43:10,401 (beam_search:479) INFO: total log probability: -6.18 +2024-01-16 21:43:10,401 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:10,401 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:10,401 (beam_search:483) INFO: best hypo: YUNOTTESTATEMINTEAIN + +2024-01-16 21:43:10,402 (asr_inference:494) INFO: speech length: 44160 +2024-01-16 21:43:10,410 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:43:10,410 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:43:10,410 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:10,473 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:10,473 (beam_search:476) INFO: -7.66 * 1.0 = -7.66 for ctc +2024-01-16 21:43:10,473 (beam_search:479) INFO: total log probability: -7.66 +2024-01-16 21:43:10,473 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:43:10,473 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:10,473 (beam_search:483) INFO: best hypo: BOLEDREPRESENCEMECXCIMOR + +2024-01-16 21:43:10,474 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:43:10,482 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:10,482 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:10,482 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:10,523 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:10,523 (beam_search:476) INFO: -3.42 * 1.0 = -3.42 for ctc +2024-01-16 21:43:10,523 (beam_search:479) INFO: total log probability: -3.42 +2024-01-16 21:43:10,523 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:10,523 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:10,524 (beam_search:483) INFO: best hypo: SINEFICTIONORTHIS + +2024-01-16 21:43:10,525 (asr_inference:494) INFO: speech length: 42240 +2024-01-16 21:43:10,533 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 21:43:10,533 (beam_search:429) INFO: max output length: 63 +2024-01-16 21:43:10,533 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:10,599 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:10,599 (beam_search:476) INFO: -5.18 * 1.0 = -5.18 for ctc +2024-01-16 21:43:10,599 (beam_search:479) INFO: total log probability: -5.18 +2024-01-16 21:43:10,599 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:10,599 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:10,599 (beam_search:483) INFO: best hypo: ODENAREDEFRENSHOLAECQWATIONS + +2024-01-16 21:43:10,601 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:43:10,608 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:43:10,608 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:43:10,608 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:10,658 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:10,658 (beam_search:476) INFO: -5.75 * 1.0 = -5.75 for ctc +2024-01-16 21:43:10,658 (beam_search:479) INFO: total log probability: -5.75 +2024-01-16 21:43:10,658 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:10,658 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:10,658 (beam_search:483) INFO: best hypo: DPLMATOFTHEHRDESEW + +2024-01-16 21:43:10,659 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:43:10,667 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:10,667 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:10,667 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:10,702 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:10,702 (beam_search:476) INFO: -5.37 * 1.0 = -5.37 for ctc +2024-01-16 21:43:10,702 (beam_search:479) INFO: total log probability: -5.37 +2024-01-16 21:43:10,702 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 21:43:10,702 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:10,702 (beam_search:483) INFO: best hypo: SILECOLOMMISTRY + +2024-01-16 21:43:10,703 (asr_inference:494) INFO: speech length: 21120 +2024-01-16 21:43:10,710 (beam_search:428) INFO: decoder input length: 30 +2024-01-16 21:43:10,710 (beam_search:429) INFO: max output length: 30 +2024-01-16 21:43:10,710 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:10,731 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:10,731 (beam_search:476) INFO: -6.64 * 1.0 = -6.64 for ctc +2024-01-16 21:43:10,731 (beam_search:479) INFO: total log probability: -6.64 +2024-01-16 21:43:10,731 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-16 21:43:10,731 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:10,731 (beam_search:483) INFO: best hypo: URLMLITRYCOL + +2024-01-16 21:43:10,732 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:43:10,740 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:10,740 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:10,740 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:10,784 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:10,784 (beam_search:476) INFO: -4.19 * 1.0 = -4.19 for ctc +2024-01-16 21:43:10,784 (beam_search:479) INFO: total log probability: -4.19 +2024-01-16 21:43:10,784 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:10,784 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:10,784 (beam_search:483) INFO: best hypo: STLONLYLEDSCIOLISUM + +2024-01-16 21:43:10,785 (asr_inference:494) INFO: speech length: 19200 +2024-01-16 21:43:10,792 (beam_search:428) INFO: decoder input length: 27 +2024-01-16 21:43:10,792 (beam_search:429) INFO: max output length: 27 +2024-01-16 21:43:10,792 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:10,806 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:10,806 (beam_search:476) INFO: -2.57 * 1.0 = -2.57 for ctc +2024-01-16 21:43:10,806 (beam_search:479) INFO: total log probability: -2.57 +2024-01-16 21:43:10,806 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:10,806 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:10,806 (beam_search:483) INFO: best hypo: PRINTEISS + +2024-01-16 21:43:10,807 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:43:10,815 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:10,815 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:10,815 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:10,855 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:10,856 (beam_search:476) INFO: -3.34 * 1.0 = -3.34 for ctc +2024-01-16 21:43:10,856 (beam_search:479) INFO: total log probability: -3.34 +2024-01-16 21:43:10,856 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:10,856 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:10,856 (beam_search:483) INFO: best hypo: NOUTASTHEANDPEPL + +2024-01-16 21:43:10,857 (asr_inference:494) INFO: speech length: 59520 +2024-01-16 21:43:10,866 (beam_search:428) INFO: decoder input length: 90 +2024-01-16 21:43:10,866 (beam_search:429) INFO: max output length: 90 +2024-01-16 21:43:10,866 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:10,972 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:10,972 (beam_search:476) INFO: -7.09 * 1.0 = -7.09 for ctc +2024-01-16 21:43:10,972 (beam_search:479) INFO: total log probability: -7.09 +2024-01-16 21:43:10,972 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:10,972 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:10,972 (beam_search:483) INFO: best hypo: SMOGTCORDBACSEDALCTTRONICKPERS + +2024-01-16 21:43:10,974 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:43:10,981 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:10,981 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:10,982 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:11,019 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:11,019 (beam_search:476) INFO: -4.18 * 1.0 = -4.18 for ctc +2024-01-16 21:43:11,019 (beam_search:479) INFO: total log probability: -4.18 +2024-01-16 21:43:11,019 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:43:11,019 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:11,019 (beam_search:483) INFO: best hypo: STATENMYSOLDGRS + +2024-01-16 21:43:11,020 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:43:11,028 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:11,028 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:11,028 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:11,064 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:11,064 (beam_search:476) INFO: -2.56 * 1.0 = -2.56 for ctc +2024-01-16 21:43:11,064 (beam_search:479) INFO: total log probability: -2.56 +2024-01-16 21:43:11,064 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:11,064 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:11,064 (beam_search:483) INFO: best hypo: LORDEASSCRIST + +2024-01-16 21:43:11,065 (asr_inference:494) INFO: speech length: 23040 +2024-01-16 21:43:11,072 (beam_search:428) INFO: decoder input length: 33 +2024-01-16 21:43:11,072 (beam_search:429) INFO: max output length: 33 +2024-01-16 21:43:11,072 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:11,092 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:11,092 (beam_search:476) INFO: -5.60 * 1.0 = -5.60 for ctc +2024-01-16 21:43:11,092 (beam_search:479) INFO: total log probability: -5.60 +2024-01-16 21:43:11,092 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-16 21:43:11,092 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:11,093 (beam_search:483) INFO: best hypo: LADNBLINGP + +2024-01-16 21:43:11,094 (asr_inference:494) INFO: speech length: 30720 +2024-01-16 21:43:11,101 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 21:43:11,101 (beam_search:429) INFO: max output length: 45 +2024-01-16 21:43:11,101 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:11,137 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:11,137 (beam_search:476) INFO: -5.37 * 1.0 = -5.37 for ctc +2024-01-16 21:43:11,137 (beam_search:479) INFO: total log probability: -5.37 +2024-01-16 21:43:11,137 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:11,137 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:11,137 (beam_search:483) INFO: best hypo: HTELIANNESINLTEM + +2024-01-16 21:43:11,138 (asr_inference:494) INFO: speech length: 51840 +2024-01-16 21:43:11,147 (beam_search:428) INFO: decoder input length: 78 +2024-01-16 21:43:11,147 (beam_search:429) INFO: max output length: 78 +2024-01-16 21:43:11,147 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:11,232 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:11,233 (beam_search:476) INFO: -4.11 * 1.0 = -4.11 for ctc +2024-01-16 21:43:11,233 (beam_search:479) INFO: total log probability: -4.11 +2024-01-16 21:43:11,233 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:11,233 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:11,233 (beam_search:483) INFO: best hypo: ANDTEAGRRECRATIONGROUNDTHEM + +2024-01-16 21:43:11,234 (asr_inference:494) INFO: speech length: 44160 +2024-01-16 21:43:11,242 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:43:11,242 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:43:11,242 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:11,296 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:11,296 (beam_search:476) INFO: -3.29 * 1.0 = -3.29 for ctc +2024-01-16 21:43:11,296 (beam_search:479) INFO: total log probability: -3.29 +2024-01-16 21:43:11,296 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:43:11,296 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:11,296 (beam_search:483) INFO: best hypo: GROCSESSTATPRODACTE + +2024-01-16 21:43:11,298 (asr_inference:494) INFO: speech length: 24960 +2024-01-16 21:43:11,304 (beam_search:428) INFO: decoder input length: 36 +2024-01-16 21:43:11,304 (beam_search:429) INFO: max output length: 36 +2024-01-16 21:43:11,305 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:11,328 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:11,328 (beam_search:476) INFO: -3.79 * 1.0 = -3.79 for ctc +2024-01-16 21:43:11,328 (beam_search:479) INFO: total log probability: -3.79 +2024-01-16 21:43:11,328 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:11,328 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:11,328 (beam_search:483) INFO: best hypo: CINCONDVERSE + +2024-01-16 21:43:11,329 (asr_inference:494) INFO: speech length: 26880 +2024-01-16 21:43:11,336 (beam_search:428) INFO: decoder input length: 39 +2024-01-16 21:43:11,336 (beam_search:429) INFO: max output length: 39 +2024-01-16 21:43:11,336 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:11,353 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:11,353 (beam_search:476) INFO: -5.66 * 1.0 = -5.66 for ctc +2024-01-16 21:43:11,353 (beam_search:479) INFO: total log probability: -5.66 +2024-01-16 21:43:11,353 (beam_search:480) INFO: normalized log probability: -0.51 +2024-01-16 21:43:11,353 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:11,353 (beam_search:483) INFO: best hypo: BIELVIL + +2024-01-16 21:43:11,354 (asr_inference:494) INFO: speech length: 49920 +2024-01-16 21:43:11,362 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 21:43:11,363 (beam_search:429) INFO: max output length: 75 +2024-01-16 21:43:11,363 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:11,449 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:11,449 (beam_search:476) INFO: -8.21 * 1.0 = -8.21 for ctc +2024-01-16 21:43:11,449 (beam_search:479) INFO: total log probability: -8.21 +2024-01-16 21:43:11,449 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:11,449 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:11,449 (beam_search:483) INFO: best hypo: FLEOLGONGSATIONNTHEUNODEDSTATE + +2024-01-16 21:43:11,450 (asr_inference:494) INFO: speech length: 30720 +2024-01-16 21:43:11,457 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 21:43:11,457 (beam_search:429) INFO: max output length: 45 +2024-01-16 21:43:11,457 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:11,496 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:11,496 (beam_search:476) INFO: -6.79 * 1.0 = -6.79 for ctc +2024-01-16 21:43:11,496 (beam_search:479) INFO: total log probability: -6.79 +2024-01-16 21:43:11,496 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 21:43:11,496 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:11,496 (beam_search:483) INFO: best hypo: ITRILTHEFTANCFORSES + +2024-01-16 21:43:11,497 (asr_inference:494) INFO: speech length: 51840 +2024-01-16 21:43:11,506 (beam_search:428) INFO: decoder input length: 78 +2024-01-16 21:43:11,506 (beam_search:429) INFO: max output length: 78 +2024-01-16 21:43:11,506 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:11,573 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:11,573 (beam_search:476) INFO: -3.42 * 1.0 = -3.42 for ctc +2024-01-16 21:43:11,573 (beam_search:479) INFO: total log probability: -3.42 +2024-01-16 21:43:11,573 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:11,573 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:11,573 (beam_search:483) INFO: best hypo: ORDDOMITICKSANDRESEVE + +2024-01-16 21:43:11,574 (asr_inference:494) INFO: speech length: 38400 +2024-01-16 21:43:11,582 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:43:11,582 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:43:11,582 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:11,633 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:11,633 (beam_search:476) INFO: -5.30 * 1.0 = -5.30 for ctc +2024-01-16 21:43:11,633 (beam_search:479) INFO: total log probability: -5.30 +2024-01-16 21:43:11,633 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:11,633 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:11,634 (beam_search:483) INFO: best hypo: BRENSWICKSTHENRALWOYLH + +2024-01-16 21:43:11,634 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:43:11,642 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:43:11,642 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:43:11,642 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:11,680 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:11,681 (beam_search:476) INFO: -4.22 * 1.0 = -4.22 for ctc +2024-01-16 21:43:11,681 (beam_search:479) INFO: total log probability: -4.22 +2024-01-16 21:43:11,681 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:11,681 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:11,681 (beam_search:483) INFO: best hypo: ACTESACATDIMEAWOD + +2024-01-16 21:43:11,682 (asr_inference:494) INFO: speech length: 59520 +2024-01-16 21:43:11,691 (beam_search:428) INFO: decoder input length: 90 +2024-01-16 21:43:11,691 (beam_search:429) INFO: max output length: 90 +2024-01-16 21:43:11,691 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:11,768 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:11,768 (beam_search:476) INFO: -9.00 * 1.0 = -9.00 for ctc +2024-01-16 21:43:11,768 (beam_search:479) INFO: total log probability: -9.00 +2024-01-16 21:43:11,768 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 21:43:11,768 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:11,768 (beam_search:483) INFO: best hypo: PEPLEFOROMETOCKYOATED + +2024-01-16 21:43:11,769 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:43:11,776 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:43:11,776 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:43:11,776 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:11,818 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:11,818 (beam_search:476) INFO: -2.79 * 1.0 = -2.79 for ctc +2024-01-16 21:43:11,818 (beam_search:479) INFO: total log probability: -2.79 +2024-01-16 21:43:11,818 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:43:11,818 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:11,818 (beam_search:483) INFO: best hypo: FORCHALDSSINGOE + +2024-01-16 21:43:11,819 (asr_inference:494) INFO: speech length: 44160 +2024-01-16 21:43:11,827 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:43:11,827 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:43:11,827 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:11,881 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:11,881 (beam_search:476) INFO: -5.56 * 1.0 = -5.56 for ctc +2024-01-16 21:43:11,881 (beam_search:479) INFO: total log probability: -5.56 +2024-01-16 21:43:11,881 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:11,881 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:11,881 (beam_search:483) INFO: best hypo: BEARABLVALFTTARMING + +2024-01-16 21:43:11,882 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:43:11,890 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:11,890 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:11,890 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:11,927 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:11,927 (beam_search:476) INFO: -3.32 * 1.0 = -3.32 for ctc +2024-01-16 21:43:11,927 (beam_search:479) INFO: total log probability: -3.32 +2024-01-16 21:43:11,927 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:11,927 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:11,927 (beam_search:483) INFO: best hypo: SOUTHWAILESFEYES + +2024-01-16 21:43:11,928 (asr_inference:494) INFO: speech length: 42240 +2024-01-16 21:43:11,936 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 21:43:11,936 (beam_search:429) INFO: max output length: 63 +2024-01-16 21:43:11,936 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,004 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,004 (beam_search:476) INFO: -11.99 * 1.0 = -11.99 for ctc +2024-01-16 21:43:12,004 (beam_search:479) INFO: total log probability: -11.99 +2024-01-16 21:43:12,004 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 21:43:12,004 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,004 (beam_search:483) INFO: best hypo: CAOFORDIURSTATTUDOEVORSITY + +2024-01-16 21:43:12,005 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:43:12,012 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:12,012 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:12,012 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,034 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,034 (beam_search:476) INFO: -3.82 * 1.0 = -3.82 for ctc +2024-01-16 21:43:12,034 (beam_search:479) INFO: total log probability: -3.82 +2024-01-16 21:43:12,035 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 21:43:12,035 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,035 (beam_search:483) INFO: best hypo: ELDERODO + +2024-01-16 21:43:12,036 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:43:12,043 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:43:12,043 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:43:12,043 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,098 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,098 (beam_search:476) INFO: -2.75 * 1.0 = -2.75 for ctc +2024-01-16 21:43:12,098 (beam_search:479) INFO: total log probability: -2.75 +2024-01-16 21:43:12,098 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 21:43:12,098 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,098 (beam_search:483) INFO: best hypo: OUTDOREOARINTEDSITY + +2024-01-16 21:43:12,099 (asr_inference:494) INFO: speech length: 49920 +2024-01-16 21:43:12,107 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 21:43:12,107 (beam_search:429) INFO: max output length: 75 +2024-01-16 21:43:12,107 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,184 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,184 (beam_search:476) INFO: -3.13 * 1.0 = -3.13 for ctc +2024-01-16 21:43:12,184 (beam_search:479) INFO: total log probability: -3.13 +2024-01-16 21:43:12,184 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 21:43:12,184 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,184 (beam_search:483) INFO: best hypo: CLAMEDPORSIALRSPONCEABILITY + +2024-01-16 21:43:12,185 (asr_inference:494) INFO: speech length: 28800 +2024-01-16 21:43:12,192 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 21:43:12,192 (beam_search:429) INFO: max output length: 42 +2024-01-16 21:43:12,192 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,220 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,220 (beam_search:476) INFO: -3.60 * 1.0 = -3.60 for ctc +2024-01-16 21:43:12,220 (beam_search:479) INFO: total log probability: -3.60 +2024-01-16 21:43:12,220 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:12,220 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,220 (beam_search:483) INFO: best hypo: CRISHIONTERMES + +2024-01-16 21:43:12,221 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:43:12,229 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:43:12,229 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:43:12,229 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,260 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,260 (beam_search:476) INFO: -4.01 * 1.0 = -4.01 for ctc +2024-01-16 21:43:12,260 (beam_search:479) INFO: total log probability: -4.01 +2024-01-16 21:43:12,260 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:12,260 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,260 (beam_search:483) INFO: best hypo: EVENTTOPLACE + +2024-01-16 21:43:12,261 (asr_inference:494) INFO: speech length: 38400 +2024-01-16 21:43:12,269 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:43:12,269 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:43:12,269 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,320 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,320 (beam_search:476) INFO: -4.20 * 1.0 = -4.20 for ctc +2024-01-16 21:43:12,320 (beam_search:479) INFO: total log probability: -4.20 +2024-01-16 21:43:12,320 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:12,320 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,321 (beam_search:483) INFO: best hypo: CANSAIDDATHESINFRONE + +2024-01-16 21:43:12,322 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:43:12,329 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:12,329 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:12,329 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,367 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,367 (beam_search:476) INFO: -2.82 * 1.0 = -2.82 for ctc +2024-01-16 21:43:12,367 (beam_search:479) INFO: total log probability: -2.82 +2024-01-16 21:43:12,367 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:12,367 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,367 (beam_search:483) INFO: best hypo: HISTRYOFMISHOGON + +2024-01-16 21:43:12,368 (asr_inference:494) INFO: speech length: 30720 +2024-01-16 21:43:12,375 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 21:43:12,375 (beam_search:429) INFO: max output length: 45 +2024-01-16 21:43:12,375 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,408 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,408 (beam_search:476) INFO: -3.56 * 1.0 = -3.56 for ctc +2024-01-16 21:43:12,408 (beam_search:479) INFO: total log probability: -3.56 +2024-01-16 21:43:12,408 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:12,408 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,408 (beam_search:483) INFO: best hypo: ORIGINLYTHEAMEM + +2024-01-16 21:43:12,409 (asr_inference:494) INFO: speech length: 42240 +2024-01-16 21:43:12,417 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 21:43:12,417 (beam_search:429) INFO: max output length: 63 +2024-01-16 21:43:12,417 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,481 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,481 (beam_search:476) INFO: -2.63 * 1.0 = -2.63 for ctc +2024-01-16 21:43:12,481 (beam_search:479) INFO: total log probability: -2.63 +2024-01-16 21:43:12,481 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 21:43:12,481 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,481 (beam_search:483) INFO: best hypo: NATIONSFRAEWRECONVEANTION + +2024-01-16 21:43:12,482 (asr_inference:494) INFO: speech length: 26880 +2024-01-16 21:43:12,489 (beam_search:428) INFO: decoder input length: 39 +2024-01-16 21:43:12,489 (beam_search:429) INFO: max output length: 39 +2024-01-16 21:43:12,489 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,508 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,508 (beam_search:476) INFO: -7.12 * 1.0 = -7.12 for ctc +2024-01-16 21:43:12,508 (beam_search:479) INFO: total log probability: -7.12 +2024-01-16 21:43:12,508 (beam_search:480) INFO: normalized log probability: -0.55 +2024-01-16 21:43:12,508 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,509 (beam_search:483) INFO: best hypo: ENOCKCONEH + +2024-01-16 21:43:12,510 (asr_inference:494) INFO: speech length: 42240 +2024-01-16 21:43:12,517 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 21:43:12,518 (beam_search:429) INFO: max output length: 63 +2024-01-16 21:43:12,518 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,579 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,580 (beam_search:476) INFO: -5.71 * 1.0 = -5.71 for ctc +2024-01-16 21:43:12,580 (beam_search:479) INFO: total log probability: -5.71 +2024-01-16 21:43:12,580 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:12,580 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,580 (beam_search:483) INFO: best hypo: ORSTRNSCOLEICONOMISTES + +2024-01-16 21:43:12,581 (asr_inference:494) INFO: speech length: 42240 +2024-01-16 21:43:12,589 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 21:43:12,589 (beam_search:429) INFO: max output length: 63 +2024-01-16 21:43:12,589 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,644 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,644 (beam_search:476) INFO: -7.49 * 1.0 = -7.49 for ctc +2024-01-16 21:43:12,644 (beam_search:479) INFO: total log probability: -7.49 +2024-01-16 21:43:12,644 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:43:12,644 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,644 (beam_search:483) INFO: best hypo: MAINGRUPCOMEPOUWNSES + +2024-01-16 21:43:12,645 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:43:12,652 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:43:12,652 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:43:12,652 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,691 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,691 (beam_search:476) INFO: -7.30 * 1.0 = -7.30 for ctc +2024-01-16 21:43:12,691 (beam_search:479) INFO: total log probability: -7.30 +2024-01-16 21:43:12,691 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 21:43:12,691 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,691 (beam_search:483) INFO: best hypo: HROSIDCLIBLMTERIALS + +2024-01-16 21:43:12,693 (asr_inference:494) INFO: speech length: 28800 +2024-01-16 21:43:12,700 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 21:43:12,700 (beam_search:429) INFO: max output length: 42 +2024-01-16 21:43:12,700 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,730 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,730 (beam_search:476) INFO: -2.36 * 1.0 = -2.36 for ctc +2024-01-16 21:43:12,730 (beam_search:479) INFO: total log probability: -2.36 +2024-01-16 21:43:12,730 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:12,730 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,731 (beam_search:483) INFO: best hypo: COMEINLOARESTOM + +2024-01-16 21:43:12,732 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:43:12,739 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:12,739 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:12,739 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,776 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,776 (beam_search:476) INFO: -3.71 * 1.0 = -3.71 for ctc +2024-01-16 21:43:12,776 (beam_search:479) INFO: total log probability: -3.71 +2024-01-16 21:43:12,776 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:12,776 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,776 (beam_search:483) INFO: best hypo: BRONKSHIYSCOLE + +2024-01-16 21:43:12,777 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 21:43:12,785 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:43:12,785 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:43:12,785 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,852 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,852 (beam_search:476) INFO: -8.20 * 1.0 = -8.20 for ctc +2024-01-16 21:43:12,852 (beam_search:479) INFO: total log probability: -8.20 +2024-01-16 21:43:12,852 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 21:43:12,852 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,853 (beam_search:483) INFO: best hypo: AMERICENBELITICGALRITERS + +2024-01-16 21:43:12,854 (asr_inference:494) INFO: speech length: 30720 +2024-01-16 21:43:12,861 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 21:43:12,861 (beam_search:429) INFO: max output length: 45 +2024-01-16 21:43:12,861 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,894 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,894 (beam_search:476) INFO: -4.76 * 1.0 = -4.76 for ctc +2024-01-16 21:43:12,894 (beam_search:479) INFO: total log probability: -4.76 +2024-01-16 21:43:12,894 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:12,894 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,894 (beam_search:483) INFO: best hypo: CAMOCALILIAENTSS + +2024-01-16 21:43:12,895 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:43:12,903 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:43:12,903 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:43:12,903 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:12,957 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:12,957 (beam_search:476) INFO: -3.97 * 1.0 = -3.97 for ctc +2024-01-16 21:43:12,957 (beam_search:479) INFO: total log probability: -3.97 +2024-01-16 21:43:12,957 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:12,957 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:12,957 (beam_search:483) INFO: best hypo: DLOBLEINTONNTCOMUNITY + +2024-01-16 21:43:12,958 (asr_inference:494) INFO: speech length: 48000 +2024-01-16 21:43:12,966 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 21:43:12,966 (beam_search:429) INFO: max output length: 72 +2024-01-16 21:43:12,966 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:13,040 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:13,040 (beam_search:476) INFO: -9.32 * 1.0 = -9.32 for ctc +2024-01-16 21:43:13,040 (beam_search:479) INFO: total log probability: -9.32 +2024-01-16 21:43:13,040 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:43:13,040 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:13,040 (beam_search:483) INFO: best hypo: TYOGREFICTMAASIEENMARCHEH + +2024-01-16 21:43:13,041 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:43:13,049 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:13,049 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:13,049 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:13,097 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:13,097 (beam_search:476) INFO: -6.21 * 1.0 = -6.21 for ctc +2024-01-16 21:43:13,097 (beam_search:479) INFO: total log probability: -6.21 +2024-01-16 21:43:13,097 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:13,097 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:13,098 (beam_search:483) INFO: best hypo: WIPSOTHISPROVIGDTDOS + +2024-01-16 21:43:13,099 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:43:13,106 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:13,106 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:13,106 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:13,146 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:13,146 (beam_search:476) INFO: -7.97 * 1.0 = -7.97 for ctc +2024-01-16 21:43:13,146 (beam_search:479) INFO: total log probability: -7.97 +2024-01-16 21:43:13,146 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 21:43:13,146 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:13,146 (beam_search:483) INFO: best hypo: SIEFPCTIONNOBLESE + +2024-01-16 21:43:13,147 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:43:13,155 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:13,155 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:13,155 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:13,194 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:13,194 (beam_search:476) INFO: -6.77 * 1.0 = -6.77 for ctc +2024-01-16 21:43:13,194 (beam_search:479) INFO: total log probability: -6.77 +2024-01-16 21:43:13,194 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 21:43:13,194 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:13,195 (beam_search:483) INFO: best hypo: SINESFICTIONFOLEM + +2024-01-16 21:43:13,196 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 21:43:13,204 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:43:13,204 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:43:13,204 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:13,268 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:13,268 (beam_search:476) INFO: -9.62 * 1.0 = -9.62 for ctc +2024-01-16 21:43:13,268 (beam_search:479) INFO: total log probability: -9.62 +2024-01-16 21:43:13,268 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 21:43:13,268 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:13,268 (beam_search:483) INFO: best hypo: SSOBESITSOMEMPROBLOMN + +2024-01-16 21:43:13,269 (asr_inference:494) INFO: speech length: 51840 +2024-01-16 21:43:13,277 (beam_search:428) INFO: decoder input length: 78 +2024-01-16 21:43:13,277 (beam_search:429) INFO: max output length: 78 +2024-01-16 21:43:13,277 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:13,345 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:13,345 (beam_search:476) INFO: -8.49 * 1.0 = -8.49 for ctc +2024-01-16 21:43:13,345 (beam_search:479) INFO: total log probability: -8.49 +2024-01-16 21:43:13,345 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-16 21:43:13,345 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:13,345 (beam_search:483) INFO: best hypo: ASTEONNORTHEMYRICARE + +2024-01-16 21:43:13,346 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:43:13,354 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:13,354 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:13,354 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:13,396 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:13,396 (beam_search:476) INFO: -2.67 * 1.0 = -2.67 for ctc +2024-01-16 21:43:13,396 (beam_search:479) INFO: total log probability: -2.67 +2024-01-16 21:43:13,396 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:13,396 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:13,397 (beam_search:483) INFO: best hypo: PEPESWATNESTLOTING + +2024-01-16 21:43:13,398 (asr_inference:494) INFO: speech length: 48000 +2024-01-16 21:43:13,406 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 21:43:13,406 (beam_search:429) INFO: max output length: 72 +2024-01-16 21:43:13,406 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:13,482 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:13,483 (beam_search:476) INFO: -6.51 * 1.0 = -6.51 for ctc +2024-01-16 21:43:13,483 (beam_search:479) INFO: total log probability: -6.51 +2024-01-16 21:43:13,483 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:13,483 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:13,483 (beam_search:483) INFO: best hypo: DESTINGNTIOVEFOCKLINSTRMENTD + +2024-01-16 21:43:13,484 (asr_inference:494) INFO: speech length: 55680 +2024-01-16 21:43:13,493 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 21:43:13,493 (beam_search:429) INFO: max output length: 84 +2024-01-16 21:43:13,493 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:13,575 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:13,575 (beam_search:476) INFO: -8.66 * 1.0 = -8.66 for ctc +2024-01-16 21:43:13,575 (beam_search:479) INFO: total log probability: -8.66 +2024-01-16 21:43:13,575 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:43:13,575 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:13,575 (beam_search:483) INFO: best hypo: HTAFOIOCONAMORICENRAPTIS + +2024-01-16 21:43:13,576 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:43:13,583 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:43:13,583 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:43:13,583 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:13,616 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:13,616 (beam_search:476) INFO: -6.10 * 1.0 = -6.10 for ctc +2024-01-16 21:43:13,616 (beam_search:479) INFO: total log probability: -6.10 +2024-01-16 21:43:13,616 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 21:43:13,616 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:13,616 (beam_search:483) INFO: best hypo: PORTOGESCHENLS + +2024-01-16 21:43:13,617 (asr_inference:494) INFO: speech length: 59520 +2024-01-16 21:43:13,626 (beam_search:428) INFO: decoder input length: 90 +2024-01-16 21:43:13,626 (beam_search:429) INFO: max output length: 90 +2024-01-16 21:43:13,626 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:13,716 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:13,716 (beam_search:476) INFO: -4.90 * 1.0 = -4.90 for ctc +2024-01-16 21:43:13,716 (beam_search:479) INFO: total log probability: -4.90 +2024-01-16 21:43:13,716 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:13,716 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:13,716 (beam_search:483) INFO: best hypo: INTENASIONLEAPORTITYAYH + +2024-01-16 21:43:13,718 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 21:43:13,726 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:43:13,726 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:43:13,726 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:13,795 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:13,795 (beam_search:476) INFO: -2.32 * 1.0 = -2.32 for ctc +2024-01-16 21:43:13,795 (beam_search:479) INFO: total log probability: -2.32 +2024-01-16 21:43:13,795 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 21:43:13,795 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:13,795 (beam_search:483) INFO: best hypo: MOUNTONRANGESOFBELIVIEAR + +2024-01-16 21:43:13,796 (asr_inference:494) INFO: speech length: 49920 +2024-01-16 21:43:13,804 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 21:43:13,804 (beam_search:429) INFO: max output length: 75 +2024-01-16 21:43:13,804 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:13,852 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:13,852 (beam_search:476) INFO: -4.84 * 1.0 = -4.84 for ctc +2024-01-16 21:43:13,852 (beam_search:479) INFO: total log probability: -4.84 +2024-01-16 21:43:13,852 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:43:13,852 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:13,852 (beam_search:483) INFO: best hypo: FRINCHAREFOARS + +2024-01-16 21:43:13,853 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:43:13,861 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:13,861 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:13,861 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:13,905 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:13,905 (beam_search:476) INFO: -4.55 * 1.0 = -4.55 for ctc +2024-01-16 21:43:13,905 (beam_search:479) INFO: total log probability: -4.55 +2024-01-16 21:43:13,905 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:13,905 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:13,905 (beam_search:483) INFO: best hypo: SSOPRABLAPEARANCSH + +2024-01-16 21:43:13,906 (asr_inference:494) INFO: speech length: 51840 +2024-01-16 21:43:13,914 (beam_search:428) INFO: decoder input length: 78 +2024-01-16 21:43:13,914 (beam_search:429) INFO: max output length: 78 +2024-01-16 21:43:13,914 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:13,971 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:13,971 (beam_search:476) INFO: -5.26 * 1.0 = -5.26 for ctc +2024-01-16 21:43:13,971 (beam_search:479) INFO: total log probability: -5.26 +2024-01-16 21:43:13,971 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:13,971 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:13,971 (beam_search:483) INFO: best hypo: LONGTRVLINGPAPESS + +2024-01-16 21:43:13,972 (asr_inference:494) INFO: speech length: 49920 +2024-01-16 21:43:13,980 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 21:43:13,980 (beam_search:429) INFO: max output length: 75 +2024-01-16 21:43:13,980 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:14,037 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:14,038 (beam_search:476) INFO: -5.53 * 1.0 = -5.53 for ctc +2024-01-16 21:43:14,038 (beam_search:479) INFO: total log probability: -5.53 +2024-01-16 21:43:14,038 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:14,038 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:14,038 (beam_search:483) INFO: best hypo: DEISTRIKTCORTODGE + +2024-01-16 21:43:14,039 (asr_inference:494) INFO: speech length: 26880 +2024-01-16 21:43:14,046 (beam_search:428) INFO: decoder input length: 39 +2024-01-16 21:43:14,046 (beam_search:429) INFO: max output length: 39 +2024-01-16 21:43:14,046 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:14,072 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:14,072 (beam_search:476) INFO: -4.24 * 1.0 = -4.24 for ctc +2024-01-16 21:43:14,072 (beam_search:479) INFO: total log probability: -4.24 +2024-01-16 21:43:14,072 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:14,072 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:14,072 (beam_search:483) INFO: best hypo: DOONYAENMPIE + +2024-01-16 21:43:14,073 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 21:43:14,081 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:43:14,081 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:43:14,081 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:14,142 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:14,142 (beam_search:476) INFO: -5.21 * 1.0 = -5.21 for ctc +2024-01-16 21:43:14,142 (beam_search:479) INFO: total log probability: -5.21 +2024-01-16 21:43:14,142 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:14,142 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:14,142 (beam_search:483) INFO: best hypo: PROTISINASIONALITYACTH + +2024-01-16 21:43:14,144 (asr_inference:494) INFO: speech length: 26880 +2024-01-16 21:43:14,150 (beam_search:428) INFO: decoder input length: 39 +2024-01-16 21:43:14,151 (beam_search:429) INFO: max output length: 39 +2024-01-16 21:43:14,151 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:14,176 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:14,176 (beam_search:476) INFO: -4.00 * 1.0 = -4.00 for ctc +2024-01-16 21:43:14,176 (beam_search:479) INFO: total log probability: -4.00 +2024-01-16 21:43:14,176 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:14,176 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:14,176 (beam_search:483) INFO: best hypo: ISHODICTAPROL + +2024-01-16 21:43:14,177 (asr_inference:494) INFO: speech length: 82560 +2024-01-16 21:43:14,188 (beam_search:428) INFO: decoder input length: 126 +2024-01-16 21:43:14,188 (beam_search:429) INFO: max output length: 126 +2024-01-16 21:43:14,188 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:14,312 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:14,312 (beam_search:476) INFO: -6.35 * 1.0 = -6.35 for ctc +2024-01-16 21:43:14,312 (beam_search:479) INFO: total log probability: -6.35 +2024-01-16 21:43:14,312 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:14,312 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:14,312 (beam_search:483) INFO: best hypo: POPEBLISITYTRADEDCOMPANYES + +2024-01-16 21:43:14,313 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 21:43:14,321 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:43:14,321 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:43:14,321 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:14,407 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:14,407 (beam_search:476) INFO: -7.26 * 1.0 = -7.26 for ctc +2024-01-16 21:43:14,407 (beam_search:479) INFO: total log probability: -7.26 +2024-01-16 21:43:14,407 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:14,407 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:14,408 (beam_search:483) INFO: best hypo: RUSHONVHCTIMSOFSOVEDREBRESENTATIONS + +2024-01-16 21:43:14,409 (asr_inference:494) INFO: speech length: 40320 +2024-01-16 21:43:14,416 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:43:14,416 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:43:14,416 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:14,476 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:14,476 (beam_search:476) INFO: -5.40 * 1.0 = -5.40 for ctc +2024-01-16 21:43:14,476 (beam_search:479) INFO: total log probability: -5.40 +2024-01-16 21:43:14,476 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:14,476 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:14,477 (beam_search:483) INFO: best hypo: WHISTDANSLEVICKLANGWOGEOS + +2024-01-16 21:43:14,478 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:43:14,485 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:14,485 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:14,485 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:14,534 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:14,534 (beam_search:476) INFO: -7.46 * 1.0 = -7.46 for ctc +2024-01-16 21:43:14,534 (beam_search:479) INFO: total log probability: -7.46 +2024-01-16 21:43:14,534 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:43:14,534 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:14,534 (beam_search:483) INFO: best hypo: TALIONROMENDCATHLEKCES + +2024-01-16 21:43:14,535 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:43:14,542 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:43:14,542 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:43:14,542 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:14,592 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:14,592 (beam_search:476) INFO: -7.43 * 1.0 = -7.43 for ctc +2024-01-16 21:43:14,592 (beam_search:479) INFO: total log probability: -7.43 +2024-01-16 21:43:14,592 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:14,592 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:14,593 (beam_search:483) INFO: best hypo: PRENTHEESTISTRIFTNIMBORS + +2024-01-16 21:43:14,594 (asr_inference:494) INFO: speech length: 65280 +2024-01-16 21:43:14,603 (beam_search:428) INFO: decoder input length: 99 +2024-01-16 21:43:14,603 (beam_search:429) INFO: max output length: 99 +2024-01-16 21:43:14,603 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:14,712 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:14,712 (beam_search:476) INFO: -7.56 * 1.0 = -7.56 for ctc +2024-01-16 21:43:14,712 (beam_search:479) INFO: total log probability: -7.56 +2024-01-16 21:43:14,712 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:14,712 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:14,712 (beam_search:483) INFO: best hypo: PREVINCHALSEMBLESOFUNTORIOAOR + +2024-01-16 21:43:14,713 (asr_inference:494) INFO: speech length: 38400 +2024-01-16 21:43:14,720 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:43:14,720 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:43:14,721 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:14,766 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:14,766 (beam_search:476) INFO: -3.07 * 1.0 = -3.07 for ctc +2024-01-16 21:43:14,766 (beam_search:479) INFO: total log probability: -3.07 +2024-01-16 21:43:14,766 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:14,766 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:14,767 (beam_search:483) INFO: best hypo: ROCKSFOAMINGMOUNTE + +2024-01-16 21:43:14,768 (asr_inference:494) INFO: speech length: 36480 +2024-01-16 21:43:14,775 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:14,775 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:14,775 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:14,816 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:14,816 (beam_search:476) INFO: -6.85 * 1.0 = -6.85 for ctc +2024-01-16 21:43:14,816 (beam_search:479) INFO: total log probability: -6.85 +2024-01-16 21:43:14,816 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 21:43:14,816 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:14,816 (beam_search:483) INFO: best hypo: SESTINATEDMONUKCKS + +2024-01-16 21:43:14,817 (asr_inference:494) INFO: speech length: 55680 +2024-01-16 21:43:14,826 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 21:43:14,826 (beam_search:429) INFO: max output length: 84 +2024-01-16 21:43:14,826 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:14,928 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:14,928 (beam_search:476) INFO: -7.42 * 1.0 = -7.42 for ctc +2024-01-16 21:43:14,928 (beam_search:479) INFO: total log probability: -7.42 +2024-01-16 21:43:14,928 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:14,928 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:14,928 (beam_search:483) INFO: best hypo: INCLAEINEASINOLENONDGOVERMENTLE + +2024-01-16 21:43:14,929 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 21:43:14,936 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:43:14,936 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:43:14,936 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:14,974 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:14,974 (beam_search:476) INFO: -3.88 * 1.0 = -3.88 for ctc +2024-01-16 21:43:14,974 (beam_search:479) INFO: total log probability: -3.88 +2024-01-16 21:43:14,974 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:14,974 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:14,974 (beam_search:483) INFO: best hypo: MITCHCKSPACEAIM + +2024-01-16 21:43:14,975 (asr_inference:494) INFO: speech length: 27360 +2024-01-16 21:43:14,982 (beam_search:428) INFO: decoder input length: 40 +2024-01-16 21:43:14,982 (beam_search:429) INFO: max output length: 40 +2024-01-16 21:43:14,982 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:15,025 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:15,025 (beam_search:476) INFO: -4.22 * 1.0 = -4.22 for ctc +2024-01-16 21:43:15,025 (beam_search:479) INFO: total log probability: -4.22 +2024-01-16 21:43:15,025 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:15,025 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:15,025 (beam_search:483) INFO: best hypo: OREREPERETHEBRAKNTHETACE + +2024-01-16 21:43:15,026 (asr_inference:494) INFO: speech length: 21600 +2024-01-16 21:43:15,033 (beam_search:428) INFO: decoder input length: 31 +2024-01-16 21:43:15,033 (beam_search:429) INFO: max output length: 31 +2024-01-16 21:43:15,033 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:15,054 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:15,054 (beam_search:476) INFO: -4.69 * 1.0 = -4.69 for ctc +2024-01-16 21:43:15,054 (beam_search:479) INFO: total log probability: -4.69 +2024-01-16 21:43:15,054 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:43:15,054 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:15,055 (beam_search:483) INFO: best hypo: ERYHTSOPTENTES + +2024-01-16 21:43:15,056 (asr_inference:494) INFO: speech length: 51040 +2024-01-16 21:43:15,064 (beam_search:428) INFO: decoder input length: 77 +2024-01-16 21:43:15,064 (beam_search:429) INFO: max output length: 77 +2024-01-16 21:43:15,064 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:15,173 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:15,173 (beam_search:476) INFO: -7.64 * 1.0 = -7.64 for ctc +2024-01-16 21:43:15,173 (beam_search:479) INFO: total log probability: -7.64 +2024-01-16 21:43:15,173 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:15,173 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:15,174 (beam_search:483) INFO: best hypo: HISMOSTCOMELYACURSWENETHERIDESABLETO + +2024-01-16 21:43:15,175 (asr_inference:494) INFO: speech length: 51680 +2024-01-16 21:43:15,183 (beam_search:428) INFO: decoder input length: 78 +2024-01-16 21:43:15,183 (beam_search:429) INFO: max output length: 78 +2024-01-16 21:43:15,183 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:15,308 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:15,308 (beam_search:476) INFO: -10.30 * 1.0 = -10.30 for ctc +2024-01-16 21:43:15,308 (beam_search:479) INFO: total log probability: -10.30 +2024-01-16 21:43:15,308 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:15,308 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:15,308 (beam_search:483) INFO: best hypo: GRATBARYIARRETFISMENGEBYTHEGRATBEIAREAFMUREN + +2024-01-16 21:43:15,310 (asr_inference:494) INFO: speech length: 20960 +2024-01-16 21:43:15,316 (beam_search:428) INFO: decoder input length: 30 +2024-01-16 21:43:15,316 (beam_search:429) INFO: max output length: 30 +2024-01-16 21:43:15,316 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:15,341 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:15,341 (beam_search:476) INFO: -4.78 * 1.0 = -4.78 for ctc +2024-01-16 21:43:15,341 (beam_search:479) INFO: total log probability: -4.78 +2024-01-16 21:43:15,341 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:15,342 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:15,342 (beam_search:483) INFO: best hypo: IHATLEASHTTREOTS + +2024-01-16 21:43:15,342 (asr_inference:494) INFO: speech length: 20000 +2024-01-16 21:43:15,349 (beam_search:428) INFO: decoder input length: 29 +2024-01-16 21:43:15,349 (beam_search:429) INFO: max output length: 29 +2024-01-16 21:43:15,349 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:15,370 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:15,370 (beam_search:476) INFO: -3.56 * 1.0 = -3.56 for ctc +2024-01-16 21:43:15,370 (beam_search:479) INFO: total log probability: -3.56 +2024-01-16 21:43:15,370 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:15,370 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:15,370 (beam_search:483) INFO: best hypo: DFIHANTESYESIN + +2024-01-16 21:43:15,371 (asr_inference:494) INFO: speech length: 32000 +2024-01-16 21:43:15,378 (beam_search:428) INFO: decoder input length: 47 +2024-01-16 21:43:15,378 (beam_search:429) INFO: max output length: 47 +2024-01-16 21:43:15,378 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:15,426 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:15,426 (beam_search:476) INFO: -4.05 * 1.0 = -4.05 for ctc +2024-01-16 21:43:15,426 (beam_search:479) INFO: total log probability: -4.05 +2024-01-16 21:43:15,426 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:15,426 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:15,426 (beam_search:483) INFO: best hypo: WLHOEVTDENCEOFHEMRYGINT + +2024-01-16 21:43:15,427 (asr_inference:494) INFO: speech length: 19520 +2024-01-16 21:43:15,434 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 21:43:15,434 (beam_search:429) INFO: max output length: 28 +2024-01-16 21:43:15,434 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:15,456 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:15,456 (beam_search:476) INFO: -4.98 * 1.0 = -4.98 for ctc +2024-01-16 21:43:15,456 (beam_search:479) INFO: total log probability: -4.98 +2024-01-16 21:43:15,456 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:43:15,456 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:15,456 (beam_search:483) INFO: best hypo: FIDENANCSERCRICLY + +2024-01-16 21:43:15,457 (asr_inference:494) INFO: speech length: 54080 +2024-01-16 21:43:15,465 (beam_search:428) INFO: decoder input length: 82 +2024-01-16 21:43:15,465 (beam_search:429) INFO: max output length: 82 +2024-01-16 21:43:15,465 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:15,587 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:15,587 (beam_search:476) INFO: -10.57 * 1.0 = -10.57 for ctc +2024-01-16 21:43:15,587 (beam_search:479) INFO: total log probability: -10.57 +2024-01-16 21:43:15,587 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:43:15,587 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:15,587 (beam_search:483) INFO: best hypo: NABLESDEVYOCIVEANDUNDDEMOCRATCSOTIALPLISYE + +2024-01-16 21:43:15,588 (asr_inference:494) INFO: speech length: 35200 +2024-01-16 21:43:15,596 (beam_search:428) INFO: decoder input length: 52 +2024-01-16 21:43:15,596 (beam_search:429) INFO: max output length: 52 +2024-01-16 21:43:15,596 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:15,657 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:15,657 (beam_search:476) INFO: -6.87 * 1.0 = -6.87 for ctc +2024-01-16 21:43:15,657 (beam_search:479) INFO: total log probability: -6.87 +2024-01-16 21:43:15,657 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:15,657 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:15,658 (beam_search:483) INFO: best hypo: MAEDRESENTHIHLESAILABLONDCOSAI + +2024-01-16 21:43:15,659 (asr_inference:494) INFO: speech length: 35360 +2024-01-16 21:43:15,666 (beam_search:428) INFO: decoder input length: 53 +2024-01-16 21:43:15,666 (beam_search:429) INFO: max output length: 53 +2024-01-16 21:43:15,666 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:15,719 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:15,719 (beam_search:476) INFO: -5.88 * 1.0 = -5.88 for ctc +2024-01-16 21:43:15,719 (beam_search:479) INFO: total log probability: -5.88 +2024-01-16 21:43:15,719 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:15,719 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:15,720 (beam_search:483) INFO: best hypo: DUSTRCTANATINSICSTYSAIC + +2024-01-16 21:43:15,721 (asr_inference:494) INFO: speech length: 22880 +2024-01-16 21:43:15,727 (beam_search:428) INFO: decoder input length: 33 +2024-01-16 21:43:15,727 (beam_search:429) INFO: max output length: 33 +2024-01-16 21:43:15,728 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:15,755 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:15,756 (beam_search:476) INFO: -5.58 * 1.0 = -5.58 for ctc +2024-01-16 21:43:15,756 (beam_search:479) INFO: total log probability: -5.58 +2024-01-16 21:43:15,756 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:15,756 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:15,756 (beam_search:483) INFO: best hypo: LITLANTOFUCPEIRIDY + +2024-01-16 21:43:15,757 (asr_inference:494) INFO: speech length: 35040 +2024-01-16 21:43:15,764 (beam_search:428) INFO: decoder input length: 52 +2024-01-16 21:43:15,764 (beam_search:429) INFO: max output length: 52 +2024-01-16 21:43:15,764 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:15,819 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:15,819 (beam_search:476) INFO: -5.67 * 1.0 = -5.67 for ctc +2024-01-16 21:43:15,819 (beam_search:479) INFO: total log probability: -5.67 +2024-01-16 21:43:15,819 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:15,819 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:15,819 (beam_search:483) INFO: best hypo: AYINTHEGROINANDEDFANCSETHR + +2024-01-16 21:43:15,821 (asr_inference:494) INFO: speech length: 54720 +2024-01-16 21:43:15,829 (beam_search:428) INFO: decoder input length: 83 +2024-01-16 21:43:15,829 (beam_search:429) INFO: max output length: 83 +2024-01-16 21:43:15,829 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:15,967 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:15,967 (beam_search:476) INFO: -14.04 * 1.0 = -14.04 for ctc +2024-01-16 21:43:15,967 (beam_search:479) INFO: total log probability: -14.04 +2024-01-16 21:43:15,967 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:15,967 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:15,967 (beam_search:483) INFO: best hypo: ECNLAGYESADIMPLENTIGTRANSHUMNISCLEOFANHANCSPRFORME + +2024-01-16 21:43:15,968 (asr_inference:494) INFO: speech length: 16320 +2024-01-16 21:43:15,975 (beam_search:428) INFO: decoder input length: 23 +2024-01-16 21:43:15,975 (beam_search:429) INFO: max output length: 23 +2024-01-16 21:43:15,975 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:15,993 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:15,993 (beam_search:476) INFO: -3.66 * 1.0 = -3.66 for ctc +2024-01-16 21:43:15,993 (beam_search:479) INFO: total log probability: -3.66 +2024-01-16 21:43:15,993 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:15,993 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:15,993 (beam_search:483) INFO: best hypo: NDCOATDINGNAPSE + +2024-01-16 21:43:15,994 (asr_inference:494) INFO: speech length: 51200 +2024-01-16 21:43:16,002 (beam_search:428) INFO: decoder input length: 77 +2024-01-16 21:43:16,002 (beam_search:429) INFO: max output length: 77 +2024-01-16 21:43:16,002 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:16,102 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:16,102 (beam_search:476) INFO: -10.12 * 1.0 = -10.12 for ctc +2024-01-16 21:43:16,102 (beam_search:479) INFO: total log probability: -10.12 +2024-01-16 21:43:16,102 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:16,102 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:16,102 (beam_search:483) INFO: best hypo: BYSPANSHTURCHMENLLREMEORASDELOSANA + +2024-01-16 21:43:16,104 (asr_inference:494) INFO: speech length: 18080 +2024-01-16 21:43:16,110 (beam_search:428) INFO: decoder input length: 26 +2024-01-16 21:43:16,110 (beam_search:429) INFO: max output length: 26 +2024-01-16 21:43:16,110 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:16,130 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:16,131 (beam_search:476) INFO: -2.87 * 1.0 = -2.87 for ctc +2024-01-16 21:43:16,131 (beam_search:479) INFO: total log probability: -2.87 +2024-01-16 21:43:16,131 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:43:16,131 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:16,131 (beam_search:483) INFO: best hypo: DVFIHTEDDEMOCRATS + +2024-01-16 21:43:16,132 (asr_inference:494) INFO: speech length: 53760 +2024-01-16 21:43:16,140 (beam_search:428) INFO: decoder input length: 81 +2024-01-16 21:43:16,140 (beam_search:429) INFO: max output length: 81 +2024-01-16 21:43:16,140 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:16,264 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:16,264 (beam_search:476) INFO: -10.66 * 1.0 = -10.66 for ctc +2024-01-16 21:43:16,264 (beam_search:479) INFO: total log probability: -10.66 +2024-01-16 21:43:16,264 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:16,264 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:16,264 (beam_search:483) INFO: best hypo: HEWORLTHANPPENSHIPTHASENCOTROLEBYEFFIDEE + +2024-01-16 21:43:16,265 (asr_inference:494) INFO: speech length: 24960 +2024-01-16 21:43:16,272 (beam_search:428) INFO: decoder input length: 36 +2024-01-16 21:43:16,272 (beam_search:429) INFO: max output length: 36 +2024-01-16 21:43:16,272 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:16,305 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:16,305 (beam_search:476) INFO: -3.15 * 1.0 = -3.15 for ctc +2024-01-16 21:43:16,305 (beam_search:479) INFO: total log probability: -3.15 +2024-01-16 21:43:16,305 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:16,305 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:16,305 (beam_search:483) INFO: best hypo: WHEHETARINGPOSITIONI + +2024-01-16 21:43:16,307 (asr_inference:494) INFO: speech length: 85120 +2024-01-16 21:43:16,317 (beam_search:428) INFO: decoder input length: 130 +2024-01-16 21:43:16,317 (beam_search:429) INFO: max output length: 130 +2024-01-16 21:43:16,317 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:16,617 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:16,617 (beam_search:476) INFO: -13.92 * 1.0 = -13.92 for ctc +2024-01-16 21:43:16,617 (beam_search:479) INFO: total log probability: -13.92 +2024-01-16 21:43:16,617 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:16,617 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:16,617 (beam_search:483) INFO: best hypo: EENCRATEDINEVRYSTATANDTERITRYTOPRTECHTANDRESERVTHECONTRYSOUNAKYCOSCISTEMS + +2024-01-16 21:43:16,619 (asr_inference:494) INFO: speech length: 34880 +2024-01-16 21:43:16,626 (beam_search:428) INFO: decoder input length: 52 +2024-01-16 21:43:16,626 (beam_search:429) INFO: max output length: 52 +2024-01-16 21:43:16,626 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:16,686 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:16,686 (beam_search:476) INFO: -7.39 * 1.0 = -7.39 for ctc +2024-01-16 21:43:16,686 (beam_search:479) INFO: total log probability: -7.39 +2024-01-16 21:43:16,686 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:16,686 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:16,686 (beam_search:483) INFO: best hypo: EACATHOENTOFTHENOSILENDWAREMOIL + +2024-01-16 21:43:16,687 (asr_inference:494) INFO: speech length: 44000 +2024-01-16 21:43:16,695 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:43:16,695 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:43:16,695 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:16,781 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:16,781 (beam_search:476) INFO: -5.04 * 1.0 = -5.04 for ctc +2024-01-16 21:43:16,781 (beam_search:479) INFO: total log probability: -5.04 +2024-01-16 21:43:16,781 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:16,781 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:16,781 (beam_search:483) INFO: best hypo: ALAMEFOMTHERELOUDCUPNYESFORVETIN + +2024-01-16 21:43:16,782 (asr_inference:494) INFO: speech length: 19200 +2024-01-16 21:43:16,788 (beam_search:428) INFO: decoder input length: 27 +2024-01-16 21:43:16,788 (beam_search:429) INFO: max output length: 27 +2024-01-16 21:43:16,789 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:16,809 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:16,809 (beam_search:476) INFO: -1.11 * 1.0 = -1.11 for ctc +2024-01-16 21:43:16,809 (beam_search:479) INFO: total log probability: -1.11 +2024-01-16 21:43:16,809 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-16 21:43:16,809 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:16,809 (beam_search:483) INFO: best hypo: THETONISSPLIT + +2024-01-16 21:43:16,810 (asr_inference:494) INFO: speech length: 51040 +2024-01-16 21:43:16,818 (beam_search:428) INFO: decoder input length: 77 +2024-01-16 21:43:16,818 (beam_search:429) INFO: max output length: 77 +2024-01-16 21:43:16,818 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:16,928 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:16,928 (beam_search:476) INFO: -10.64 * 1.0 = -10.64 for ctc +2024-01-16 21:43:16,928 (beam_search:479) INFO: total log probability: -10.64 +2024-01-16 21:43:16,928 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:16,928 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:16,929 (beam_search:483) INFO: best hypo: OSKEATIDFISHISAPETICILYAEGRECIOFSPATHESN + +2024-01-16 21:43:16,930 (asr_inference:494) INFO: speech length: 35520 +2024-01-16 21:43:16,937 (beam_search:428) INFO: decoder input length: 53 +2024-01-16 21:43:16,937 (beam_search:429) INFO: max output length: 53 +2024-01-16 21:43:16,937 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:17,001 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:17,001 (beam_search:476) INFO: -8.02 * 1.0 = -8.02 for ctc +2024-01-16 21:43:17,001 (beam_search:479) INFO: total log probability: -8.02 +2024-01-16 21:43:17,001 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:43:17,001 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:17,001 (beam_search:483) INFO: best hypo: ANDTHENASTIONLWESEDHEMINSHIPTES + +2024-01-16 21:43:17,002 (asr_inference:494) INFO: speech length: 37600 +2024-01-16 21:43:17,010 (beam_search:428) INFO: decoder input length: 56 +2024-01-16 21:43:17,010 (beam_search:429) INFO: max output length: 56 +2024-01-16 21:43:17,010 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:17,078 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:17,078 (beam_search:476) INFO: -4.91 * 1.0 = -4.91 for ctc +2024-01-16 21:43:17,078 (beam_search:479) INFO: total log probability: -4.91 +2024-01-16 21:43:17,078 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:17,078 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:17,079 (beam_search:483) INFO: best hypo: PROBLOMEASNONTORNINPLYNOMALTIME + +2024-01-16 21:43:17,080 (asr_inference:494) INFO: speech length: 42880 +2024-01-16 21:43:17,088 (beam_search:428) INFO: decoder input length: 64 +2024-01-16 21:43:17,088 (beam_search:429) INFO: max output length: 64 +2024-01-16 21:43:17,088 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:17,164 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:17,164 (beam_search:476) INFO: -9.39 * 1.0 = -9.39 for ctc +2024-01-16 21:43:17,164 (beam_search:479) INFO: total log probability: -9.39 +2024-01-16 21:43:17,164 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:43:17,164 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:17,164 (beam_search:483) INFO: best hypo: LAEJUOIRANDPARCERWHATIENSHARTN + +2024-01-16 21:43:17,166 (asr_inference:494) INFO: speech length: 22560 +2024-01-16 21:43:17,173 (beam_search:428) INFO: decoder input length: 33 +2024-01-16 21:43:17,173 (beam_search:429) INFO: max output length: 33 +2024-01-16 21:43:17,173 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:17,199 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:17,199 (beam_search:476) INFO: -2.60 * 1.0 = -2.60 for ctc +2024-01-16 21:43:17,199 (beam_search:479) INFO: total log probability: -2.60 +2024-01-16 21:43:17,199 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:17,199 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:17,200 (beam_search:483) INFO: best hypo: INNDTINSEVETYTHR + +2024-01-16 21:43:17,201 (asr_inference:494) INFO: speech length: 39520 +2024-01-16 21:43:17,208 (beam_search:428) INFO: decoder input length: 59 +2024-01-16 21:43:17,208 (beam_search:429) INFO: max output length: 59 +2024-01-16 21:43:17,208 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:17,274 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:17,274 (beam_search:476) INFO: -5.19 * 1.0 = -5.19 for ctc +2024-01-16 21:43:17,274 (beam_search:479) INFO: total log probability: -5.19 +2024-01-16 21:43:17,274 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:17,274 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:17,274 (beam_search:483) INFO: best hypo: DELPINGANDYOUSINGSUCHTENALDS + +2024-01-16 21:43:17,276 (asr_inference:494) INFO: speech length: 16320 +2024-01-16 21:43:17,282 (beam_search:428) INFO: decoder input length: 23 +2024-01-16 21:43:17,282 (beam_search:429) INFO: max output length: 23 +2024-01-16 21:43:17,282 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:17,300 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:17,300 (beam_search:476) INFO: -6.46 * 1.0 = -6.46 for ctc +2024-01-16 21:43:17,300 (beam_search:479) INFO: total log probability: -6.46 +2024-01-16 21:43:17,300 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 21:43:17,300 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:17,300 (beam_search:483) INFO: best hypo: ORSOMEWPASTIOND + +2024-01-16 21:43:17,301 (asr_inference:494) INFO: speech length: 19360 +2024-01-16 21:43:17,307 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 21:43:17,307 (beam_search:429) INFO: max output length: 28 +2024-01-16 21:43:17,307 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:17,328 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:17,328 (beam_search:476) INFO: -2.11 * 1.0 = -2.11 for ctc +2024-01-16 21:43:17,328 (beam_search:479) INFO: total log probability: -2.11 +2024-01-16 21:43:17,328 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:43:17,328 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:17,328 (beam_search:483) INFO: best hypo: LAMEOROETHATE + +2024-01-16 21:43:17,329 (asr_inference:494) INFO: speech length: 62400 +2024-01-16 21:43:17,339 (beam_search:428) INFO: decoder input length: 95 +2024-01-16 21:43:17,339 (beam_search:429) INFO: max output length: 95 +2024-01-16 21:43:17,339 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:17,492 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:17,492 (beam_search:476) INFO: -8.52 * 1.0 = -8.52 for ctc +2024-01-16 21:43:17,492 (beam_search:479) INFO: total log probability: -8.52 +2024-01-16 21:43:17,492 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:17,492 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:17,493 (beam_search:483) INFO: best hypo: ABLADACAHTERISOURLYINSERTEDSOMONSOFLOIEDBUMNS + +2024-01-16 21:43:17,494 (asr_inference:494) INFO: speech length: 72640 +2024-01-16 21:43:17,503 (beam_search:428) INFO: decoder input length: 111 +2024-01-16 21:43:17,503 (beam_search:429) INFO: max output length: 111 +2024-01-16 21:43:17,503 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:17,731 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:17,731 (beam_search:476) INFO: -22.04 * 1.0 = -22.04 for ctc +2024-01-16 21:43:17,731 (beam_search:479) INFO: total log probability: -22.04 +2024-01-16 21:43:17,731 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:43:17,731 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:17,732 (beam_search:483) INFO: best hypo: ERNOTIONOFYUJENIKANHASPENTTICNALAGESMIGHUNINTENCHONALYINDCKRRAGE + +2024-01-16 21:43:17,733 (asr_inference:494) INFO: speech length: 73440 +2024-01-16 21:43:17,742 (beam_search:428) INFO: decoder input length: 112 +2024-01-16 21:43:17,743 (beam_search:429) INFO: max output length: 112 +2024-01-16 21:43:17,743 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:17,973 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:17,973 (beam_search:476) INFO: -14.40 * 1.0 = -14.40 for ctc +2024-01-16 21:43:17,973 (beam_search:479) INFO: total log probability: -14.40 +2024-01-16 21:43:17,973 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:17,973 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:17,973 (beam_search:483) INFO: best hypo: ATTHETENIONOFRESURTHERSCABEFOKAUSDMPARTIALSOLUTIONSORSOLUTIONS + +2024-01-16 21:43:17,974 (asr_inference:494) INFO: speech length: 26720 +2024-01-16 21:43:17,981 (beam_search:428) INFO: decoder input length: 39 +2024-01-16 21:43:17,981 (beam_search:429) INFO: max output length: 39 +2024-01-16 21:43:17,981 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:18,020 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:18,020 (beam_search:476) INFO: -5.53 * 1.0 = -5.53 for ctc +2024-01-16 21:43:18,020 (beam_search:479) INFO: total log probability: -5.53 +2024-01-16 21:43:18,020 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:18,020 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:18,020 (beam_search:483) INFO: best hypo: NONENOFFORHNTREDOFYEARS + +2024-01-16 21:43:18,021 (asr_inference:494) INFO: speech length: 28480 +2024-01-16 21:43:18,028 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 21:43:18,028 (beam_search:429) INFO: max output length: 42 +2024-01-16 21:43:18,028 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:18,070 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:18,070 (beam_search:476) INFO: -5.04 * 1.0 = -5.04 for ctc +2024-01-16 21:43:18,070 (beam_search:479) INFO: total log probability: -5.04 +2024-01-16 21:43:18,070 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:18,070 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:18,070 (beam_search:483) INFO: best hypo: NLYMUSUBIALSHAESOFIVETT + +2024-01-16 21:43:18,071 (asr_inference:494) INFO: speech length: 42560 +2024-01-16 21:43:18,079 (beam_search:428) INFO: decoder input length: 64 +2024-01-16 21:43:18,079 (beam_search:429) INFO: max output length: 64 +2024-01-16 21:43:18,079 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:18,174 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:18,174 (beam_search:476) INFO: -9.47 * 1.0 = -9.47 for ctc +2024-01-16 21:43:18,174 (beam_search:479) INFO: total log probability: -9.47 +2024-01-16 21:43:18,174 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:18,174 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:18,174 (beam_search:483) INFO: best hypo: TOHCHALTHEADABLESPACHESOFCRUSTISTHANBELONG + +2024-01-16 21:43:18,175 (asr_inference:494) INFO: speech length: 19360 +2024-01-16 21:43:18,181 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 21:43:18,181 (beam_search:429) INFO: max output length: 28 +2024-01-16 21:43:18,181 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:18,204 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:18,204 (beam_search:476) INFO: -3.87 * 1.0 = -3.87 for ctc +2024-01-16 21:43:18,204 (beam_search:479) INFO: total log probability: -3.87 +2024-01-16 21:43:18,204 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:18,204 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:18,204 (beam_search:483) INFO: best hypo: OLGERTHEMRESURCHE + +2024-01-16 21:43:18,205 (asr_inference:494) INFO: speech length: 87680 +2024-01-16 21:43:18,216 (beam_search:428) INFO: decoder input length: 134 +2024-01-16 21:43:18,216 (beam_search:429) INFO: max output length: 134 +2024-01-16 21:43:18,216 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:18,520 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:18,520 (beam_search:476) INFO: -15.15 * 1.0 = -15.15 for ctc +2024-01-16 21:43:18,520 (beam_search:479) INFO: total log probability: -15.15 +2024-01-16 21:43:18,520 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:18,520 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:18,521 (beam_search:483) INFO: best hypo: NINTAINSICXTDYTWOFILIPESNVENTDTHECOMPACTODIOKESETMEDYHAMFORODIOUSDAORGE + +2024-01-16 21:43:18,522 (asr_inference:494) INFO: speech length: 17760 +2024-01-16 21:43:18,528 (beam_search:428) INFO: decoder input length: 25 +2024-01-16 21:43:18,528 (beam_search:429) INFO: max output length: 25 +2024-01-16 21:43:18,528 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:18,549 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:18,549 (beam_search:476) INFO: -3.55 * 1.0 = -3.55 for ctc +2024-01-16 21:43:18,549 (beam_search:479) INFO: total log probability: -3.55 +2024-01-16 21:43:18,549 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:18,549 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:18,549 (beam_search:483) INFO: best hypo: OSTRACTINFTHELOW + +2024-01-16 21:43:18,550 (asr_inference:494) INFO: speech length: 16640 +2024-01-16 21:43:18,557 (beam_search:428) INFO: decoder input length: 23 +2024-01-16 21:43:18,557 (beam_search:429) INFO: max output length: 23 +2024-01-16 21:43:18,557 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:18,575 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:18,575 (beam_search:476) INFO: -3.24 * 1.0 = -3.24 for ctc +2024-01-16 21:43:18,575 (beam_search:479) INFO: total log probability: -3.24 +2024-01-16 21:43:18,575 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:18,575 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:18,575 (beam_search:483) INFO: best hypo: NDFHIBIENDADREP + +2024-01-16 21:43:18,576 (asr_inference:494) INFO: speech length: 27840 +2024-01-16 21:43:18,584 (beam_search:428) INFO: decoder input length: 41 +2024-01-16 21:43:18,584 (beam_search:429) INFO: max output length: 41 +2024-01-16 21:43:18,584 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:18,624 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:18,624 (beam_search:476) INFO: -7.58 * 1.0 = -7.58 for ctc +2024-01-16 21:43:18,625 (beam_search:479) INFO: total log probability: -7.58 +2024-01-16 21:43:18,625 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:43:18,625 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:18,625 (beam_search:483) INFO: best hypo: WEMENSWHRLDCESTHAMINCHOF + +2024-01-16 21:43:18,626 (asr_inference:494) INFO: speech length: 112320 +2024-01-16 21:43:18,638 (beam_search:428) INFO: decoder input length: 173 +2024-01-16 21:43:18,638 (beam_search:429) INFO: max output length: 173 +2024-01-16 21:43:18,638 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:19,110 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:19,110 (beam_search:476) INFO: -15.26 * 1.0 = -15.26 for ctc +2024-01-16 21:43:19,110 (beam_search:479) INFO: total log probability: -15.26 +2024-01-16 21:43:19,110 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:19,110 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:19,111 (beam_search:483) INFO: best hypo: CONTANEDECRTIONSANDCOMENTARYSOTHETATOFENBEIYSINCEANDEGNALGYASMAERCONTRUDERSTOTHE + +2024-01-16 21:43:19,112 (asr_inference:494) INFO: speech length: 32320 +2024-01-16 21:43:19,119 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:43:19,119 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:43:19,119 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:19,170 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:19,170 (beam_search:476) INFO: -5.51 * 1.0 = -5.51 for ctc +2024-01-16 21:43:19,170 (beam_search:479) INFO: total log probability: -5.51 +2024-01-16 21:43:19,170 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:19,170 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:19,170 (beam_search:483) INFO: best hypo: PEUROELHANTIYORSOTIALTRENT + +2024-01-16 21:43:19,172 (asr_inference:494) INFO: speech length: 36320 +2024-01-16 21:43:19,179 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:19,179 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:19,179 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:19,239 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:19,239 (beam_search:476) INFO: -8.76 * 1.0 = -8.76 for ctc +2024-01-16 21:43:19,239 (beam_search:479) INFO: total log probability: -8.76 +2024-01-16 21:43:19,239 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:43:19,239 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:19,239 (beam_search:483) INFO: best hypo: MOSTCOMPACCSAETWERSOLDBLANK + +2024-01-16 21:43:19,241 (asr_inference:494) INFO: speech length: 20640 +2024-01-16 21:43:19,247 (beam_search:428) INFO: decoder input length: 30 +2024-01-16 21:43:19,247 (beam_search:429) INFO: max output length: 30 +2024-01-16 21:43:19,247 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:19,274 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:19,274 (beam_search:476) INFO: -6.13 * 1.0 = -6.13 for ctc +2024-01-16 21:43:19,274 (beam_search:479) INFO: total log probability: -6.13 +2024-01-16 21:43:19,274 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:43:19,274 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:19,274 (beam_search:483) INFO: best hypo: IOFHERISNOULDGRTHE + +2024-01-16 21:43:19,275 (asr_inference:494) INFO: speech length: 65280 +2024-01-16 21:43:19,284 (beam_search:428) INFO: decoder input length: 99 +2024-01-16 21:43:19,284 (beam_search:429) INFO: max output length: 99 +2024-01-16 21:43:19,284 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:19,465 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:19,465 (beam_search:476) INFO: -13.49 * 1.0 = -13.49 for ctc +2024-01-16 21:43:19,465 (beam_search:479) INFO: total log probability: -13.49 +2024-01-16 21:43:19,465 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:19,465 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:19,465 (beam_search:483) INFO: best hypo: HESOTHENSTRALIENCOSTANDINSABBAENTIECTIYOSTRLIANTERATRYS + +2024-01-16 21:43:19,466 (asr_inference:494) INFO: speech length: 35680 +2024-01-16 21:43:19,474 (beam_search:428) INFO: decoder input length: 53 +2024-01-16 21:43:19,474 (beam_search:429) INFO: max output length: 53 +2024-01-16 21:43:19,474 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:19,533 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:19,533 (beam_search:476) INFO: -5.34 * 1.0 = -5.34 for ctc +2024-01-16 21:43:19,533 (beam_search:479) INFO: total log probability: -5.34 +2024-01-16 21:43:19,533 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:19,533 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:19,534 (beam_search:483) INFO: best hypo: DATRATSFTIPOCLYFIVEHNDEREDTTW + +2024-01-16 21:43:19,535 (asr_inference:494) INFO: speech length: 17920 +2024-01-16 21:43:19,541 (beam_search:428) INFO: decoder input length: 25 +2024-01-16 21:43:19,541 (beam_search:429) INFO: max output length: 25 +2024-01-16 21:43:19,541 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:19,561 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:19,561 (beam_search:476) INFO: -2.70 * 1.0 = -2.70 for ctc +2024-01-16 21:43:19,561 (beam_search:479) INFO: total log probability: -2.70 +2024-01-16 21:43:19,561 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:19,561 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:19,561 (beam_search:483) INFO: best hypo: DERPRIVINGTHEDUC + +2024-01-16 21:43:19,562 (asr_inference:494) INFO: speech length: 31200 +2024-01-16 21:43:19,570 (beam_search:428) INFO: decoder input length: 46 +2024-01-16 21:43:19,570 (beam_search:429) INFO: max output length: 46 +2024-01-16 21:43:19,570 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:19,614 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:19,614 (beam_search:476) INFO: -5.06 * 1.0 = -5.06 for ctc +2024-01-16 21:43:19,614 (beam_search:479) INFO: total log probability: -5.06 +2024-01-16 21:43:19,614 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:19,614 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:19,615 (beam_search:483) INFO: best hypo: NIENPERSENTOFTHETOLCAST + +2024-01-16 21:43:19,616 (asr_inference:494) INFO: speech length: 61440 +2024-01-16 21:43:19,624 (beam_search:428) INFO: decoder input length: 93 +2024-01-16 21:43:19,624 (beam_search:429) INFO: max output length: 93 +2024-01-16 21:43:19,624 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:19,763 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:19,763 (beam_search:476) INFO: -10.23 * 1.0 = -10.23 for ctc +2024-01-16 21:43:19,763 (beam_search:479) INFO: total log probability: -10.23 +2024-01-16 21:43:19,763 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:19,763 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:19,763 (beam_search:483) INFO: best hypo: ANDTHERIRSSORBRATRIYANDANTHEIECOMNCATINGATY + +2024-01-16 21:43:19,764 (asr_inference:494) INFO: speech length: 26880 +2024-01-16 21:43:19,771 (beam_search:428) INFO: decoder input length: 39 +2024-01-16 21:43:19,771 (beam_search:429) INFO: max output length: 39 +2024-01-16 21:43:19,771 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:19,802 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:19,802 (beam_search:476) INFO: -5.75 * 1.0 = -5.75 for ctc +2024-01-16 21:43:19,802 (beam_search:479) INFO: total log probability: -5.75 +2024-01-16 21:43:19,802 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:43:19,802 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:19,802 (beam_search:483) INFO: best hypo: EDNOTIMPASHSHIN + +2024-01-16 21:43:19,803 (asr_inference:494) INFO: speech length: 26720 +2024-01-16 21:43:19,810 (beam_search:428) INFO: decoder input length: 39 +2024-01-16 21:43:19,810 (beam_search:429) INFO: max output length: 39 +2024-01-16 21:43:19,810 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:19,846 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:19,846 (beam_search:476) INFO: -4.12 * 1.0 = -4.12 for ctc +2024-01-16 21:43:19,846 (beam_search:479) INFO: total log probability: -4.12 +2024-01-16 21:43:19,846 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:19,846 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:19,847 (beam_search:483) INFO: best hypo: ENTHEREDEMOCRATICPARTY + +2024-01-16 21:43:19,848 (asr_inference:494) INFO: speech length: 38560 +2024-01-16 21:43:19,855 (beam_search:428) INFO: decoder input length: 58 +2024-01-16 21:43:19,855 (beam_search:429) INFO: max output length: 58 +2024-01-16 21:43:19,855 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:19,923 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:19,924 (beam_search:476) INFO: -6.61 * 1.0 = -6.61 for ctc +2024-01-16 21:43:19,924 (beam_search:479) INFO: total log probability: -6.61 +2024-01-16 21:43:19,924 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:19,924 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:19,924 (beam_search:483) INFO: best hypo: NHESONTOPEOFTHEESETHALINDECATH + +2024-01-16 21:43:19,925 (asr_inference:494) INFO: speech length: 16640 +2024-01-16 21:43:19,931 (beam_search:428) INFO: decoder input length: 23 +2024-01-16 21:43:19,931 (beam_search:429) INFO: max output length: 23 +2024-01-16 21:43:19,931 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:19,949 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:19,949 (beam_search:476) INFO: -7.58 * 1.0 = -7.58 for ctc +2024-01-16 21:43:19,949 (beam_search:479) INFO: total log probability: -7.58 +2024-01-16 21:43:19,949 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-16 21:43:19,949 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:19,949 (beam_search:483) INFO: best hypo: LAEUIATHSSEO + +2024-01-16 21:43:19,950 (asr_inference:494) INFO: speech length: 28480 +2024-01-16 21:43:19,957 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 21:43:19,957 (beam_search:429) INFO: max output length: 42 +2024-01-16 21:43:19,957 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:20,001 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:20,001 (beam_search:476) INFO: -7.35 * 1.0 = -7.35 for ctc +2024-01-16 21:43:20,001 (beam_search:479) INFO: total log probability: -7.35 +2024-01-16 21:43:20,001 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:20,001 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:20,001 (beam_search:483) INFO: best hypo: IANANDTANGEDMRAINSPACHYEST + +2024-01-16 21:43:20,002 (asr_inference:494) INFO: speech length: 20160 +2024-01-16 21:43:20,009 (beam_search:428) INFO: decoder input length: 29 +2024-01-16 21:43:20,009 (beam_search:429) INFO: max output length: 29 +2024-01-16 21:43:20,009 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:20,030 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:20,030 (beam_search:476) INFO: -4.39 * 1.0 = -4.39 for ctc +2024-01-16 21:43:20,030 (beam_search:479) INFO: total log probability: -4.39 +2024-01-16 21:43:20,030 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:20,030 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:20,030 (beam_search:483) INFO: best hypo: ROWNDESIRELECTO + +2024-01-16 21:43:20,031 (asr_inference:494) INFO: speech length: 46720 +2024-01-16 21:43:20,039 (beam_search:428) INFO: decoder input length: 70 +2024-01-16 21:43:20,039 (beam_search:429) INFO: max output length: 70 +2024-01-16 21:43:20,039 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:20,140 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:20,140 (beam_search:476) INFO: -8.33 * 1.0 = -8.33 for ctc +2024-01-16 21:43:20,141 (beam_search:479) INFO: total log probability: -8.33 +2024-01-16 21:43:20,141 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:20,141 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:20,141 (beam_search:483) INFO: best hypo: HISFACDOSANTSAYMUCHABOUTWERTHEPROBLMELS + +2024-01-16 21:43:20,142 (asr_inference:494) INFO: speech length: 26720 +2024-01-16 21:43:20,149 (beam_search:428) INFO: decoder input length: 39 +2024-01-16 21:43:20,149 (beam_search:429) INFO: max output length: 39 +2024-01-16 21:43:20,149 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:20,182 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:20,182 (beam_search:476) INFO: -4.42 * 1.0 = -4.42 for ctc +2024-01-16 21:43:20,182 (beam_search:479) INFO: total log probability: -4.42 +2024-01-16 21:43:20,182 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:20,182 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:20,182 (beam_search:483) INFO: best hypo: COMOCLSSITYBEGANAS + +2024-01-16 21:43:20,184 (asr_inference:494) INFO: speech length: 44640 +2024-01-16 21:43:20,192 (beam_search:428) INFO: decoder input length: 67 +2024-01-16 21:43:20,192 (beam_search:429) INFO: max output length: 67 +2024-01-16 21:43:20,192 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:20,279 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:20,279 (beam_search:476) INFO: -10.52 * 1.0 = -10.52 for ctc +2024-01-16 21:43:20,279 (beam_search:479) INFO: total log probability: -10.52 +2024-01-16 21:43:20,279 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:20,279 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:20,280 (beam_search:483) INFO: best hypo: WITTORISTSREVINGTHESTMEBOTANDTRIAMEN + +2024-01-16 21:43:20,281 (asr_inference:494) INFO: speech length: 30080 +2024-01-16 21:43:20,288 (beam_search:428) INFO: decoder input length: 44 +2024-01-16 21:43:20,288 (beam_search:429) INFO: max output length: 44 +2024-01-16 21:43:20,288 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:20,336 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:20,336 (beam_search:476) INFO: -4.57 * 1.0 = -4.57 for ctc +2024-01-16 21:43:20,336 (beam_search:479) INFO: total log probability: -4.57 +2024-01-16 21:43:20,336 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:20,336 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:20,336 (beam_search:483) INFO: best hypo: FIRSTDILOGBETWENTRANSHUMINIS + +2024-01-16 21:43:20,338 (asr_inference:494) INFO: speech length: 30240 +2024-01-16 21:43:20,345 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 21:43:20,345 (beam_search:429) INFO: max output length: 45 +2024-01-16 21:43:20,345 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:20,388 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:20,388 (beam_search:476) INFO: -7.10 * 1.0 = -7.10 for ctc +2024-01-16 21:43:20,388 (beam_search:479) INFO: total log probability: -7.10 +2024-01-16 21:43:20,388 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:20,388 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:20,389 (beam_search:483) INFO: best hypo: NEERBENPRDOFTHELNPCANDS + +2024-01-16 21:43:20,390 (asr_inference:494) INFO: speech length: 25280 +2024-01-16 21:43:20,397 (beam_search:428) INFO: decoder input length: 37 +2024-01-16 21:43:20,397 (beam_search:429) INFO: max output length: 37 +2024-01-16 21:43:20,397 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:20,430 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:20,430 (beam_search:476) INFO: -2.99 * 1.0 = -2.99 for ctc +2024-01-16 21:43:20,430 (beam_search:479) INFO: total log probability: -2.99 +2024-01-16 21:43:20,430 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:20,430 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:20,430 (beam_search:483) INFO: best hypo: REAGESSFIRNITCERANDTH + +2024-01-16 21:43:20,432 (asr_inference:494) INFO: speech length: 20320 +2024-01-16 21:43:20,438 (beam_search:428) INFO: decoder input length: 29 +2024-01-16 21:43:20,438 (beam_search:429) INFO: max output length: 29 +2024-01-16 21:43:20,438 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:20,459 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:20,459 (beam_search:476) INFO: -4.33 * 1.0 = -4.33 for ctc +2024-01-16 21:43:20,459 (beam_search:479) INFO: total log probability: -4.33 +2024-01-16 21:43:20,459 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:20,459 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:20,459 (beam_search:483) INFO: best hypo: INHILABLTREMENC + +2024-01-16 21:43:20,460 (asr_inference:494) INFO: speech length: 23200 +2024-01-16 21:43:20,467 (beam_search:428) INFO: decoder input length: 34 +2024-01-16 21:43:20,467 (beam_search:429) INFO: max output length: 34 +2024-01-16 21:43:20,467 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:20,495 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:20,495 (beam_search:476) INFO: -5.45 * 1.0 = -5.45 for ctc +2024-01-16 21:43:20,495 (beam_search:479) INFO: total log probability: -5.45 +2024-01-16 21:43:20,495 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:43:20,495 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:20,495 (beam_search:483) INFO: best hypo: TOOCATTHANDURSONM + +2024-01-16 21:43:20,496 (asr_inference:494) INFO: speech length: 18240 +2024-01-16 21:43:20,502 (beam_search:428) INFO: decoder input length: 26 +2024-01-16 21:43:20,502 (beam_search:429) INFO: max output length: 26 +2024-01-16 21:43:20,502 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:20,524 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:20,524 (beam_search:476) INFO: -3.71 * 1.0 = -3.71 for ctc +2024-01-16 21:43:20,524 (beam_search:479) INFO: total log probability: -3.71 +2024-01-16 21:43:20,524 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:20,524 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:20,524 (beam_search:483) INFO: best hypo: ORFHLUCHOCLFREDEM + +2024-01-16 21:43:20,526 (asr_inference:494) INFO: speech length: 24640 +2024-01-16 21:43:20,532 (beam_search:428) INFO: decoder input length: 36 +2024-01-16 21:43:20,532 (beam_search:429) INFO: max output length: 36 +2024-01-16 21:43:20,532 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:20,565 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:20,565 (beam_search:476) INFO: -5.98 * 1.0 = -5.98 for ctc +2024-01-16 21:43:20,565 (beam_search:479) INFO: total log probability: -5.98 +2024-01-16 21:43:20,565 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:20,565 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:20,565 (beam_search:483) INFO: best hypo: NERJETICATACKINGSTOU + +2024-01-16 21:43:20,566 (asr_inference:494) INFO: speech length: 40160 +2024-01-16 21:43:20,574 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:43:20,574 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:43:20,574 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:20,654 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:20,654 (beam_search:476) INFO: -7.84 * 1.0 = -7.84 for ctc +2024-01-16 21:43:20,654 (beam_search:479) INFO: total log probability: -7.84 +2024-01-16 21:43:20,654 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:20,654 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:20,655 (beam_search:483) INFO: best hypo: GACHLYFOREOARSAFERTECONISTDOMWASLATE + +2024-01-16 21:43:20,656 (asr_inference:494) INFO: speech length: 20960 +2024-01-16 21:43:20,662 (beam_search:428) INFO: decoder input length: 30 +2024-01-16 21:43:20,662 (beam_search:429) INFO: max output length: 30 +2024-01-16 21:43:20,662 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:20,689 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:20,689 (beam_search:476) INFO: -3.07 * 1.0 = -3.07 for ctc +2024-01-16 21:43:20,689 (beam_search:479) INFO: total log probability: -3.07 +2024-01-16 21:43:20,689 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:20,689 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:20,689 (beam_search:483) INFO: best hypo: ACETHERECAGNITIONTH + +2024-01-16 21:43:20,690 (asr_inference:494) INFO: speech length: 36960 +2024-01-16 21:43:20,698 (beam_search:428) INFO: decoder input length: 55 +2024-01-16 21:43:20,698 (beam_search:429) INFO: max output length: 55 +2024-01-16 21:43:20,698 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:20,755 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:20,755 (beam_search:476) INFO: -6.03 * 1.0 = -6.03 for ctc +2024-01-16 21:43:20,755 (beam_search:479) INFO: total log probability: -6.03 +2024-01-16 21:43:20,755 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:20,755 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:20,755 (beam_search:483) INFO: best hypo: ORLATRONICTBUTENSORDESPLAY + +2024-01-16 21:43:20,756 (asr_inference:494) INFO: speech length: 33120 +2024-01-16 21:43:20,764 (beam_search:428) INFO: decoder input length: 49 +2024-01-16 21:43:20,764 (beam_search:429) INFO: max output length: 49 +2024-01-16 21:43:20,764 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:20,815 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:20,815 (beam_search:476) INFO: -8.02 * 1.0 = -8.02 for ctc +2024-01-16 21:43:20,815 (beam_search:479) INFO: total log probability: -8.02 +2024-01-16 21:43:20,815 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:43:20,815 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:20,815 (beam_search:483) INFO: best hypo: ISUNNONWTHERPEAECULSANMPY + +2024-01-16 21:43:20,816 (asr_inference:494) INFO: speech length: 30400 +2024-01-16 21:43:20,823 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 21:43:20,823 (beam_search:429) INFO: max output length: 45 +2024-01-16 21:43:20,823 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:20,868 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:20,868 (beam_search:476) INFO: -8.36 * 1.0 = -8.36 for ctc +2024-01-16 21:43:20,868 (beam_search:479) INFO: total log probability: -8.36 +2024-01-16 21:43:20,868 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:43:20,868 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:20,869 (beam_search:483) INFO: best hypo: HICHCOMSFOTHEVERBACQUAR + +2024-01-16 21:43:20,870 (asr_inference:494) INFO: speech length: 56160 +2024-01-16 21:43:20,879 (beam_search:428) INFO: decoder input length: 85 +2024-01-16 21:43:20,879 (beam_search:429) INFO: max output length: 85 +2024-01-16 21:43:20,879 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:21,021 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:21,021 (beam_search:476) INFO: -11.76 * 1.0 = -11.76 for ctc +2024-01-16 21:43:21,021 (beam_search:479) INFO: total log probability: -11.76 +2024-01-16 21:43:21,021 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:21,021 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:21,022 (beam_search:483) INFO: best hypo: DETPRPORTIONTLYOVAILABLTTHAEWTRATERFINANHALRESURES + +2024-01-16 21:43:21,023 (asr_inference:494) INFO: speech length: 41440 +2024-01-16 21:43:21,031 (beam_search:428) INFO: decoder input length: 62 +2024-01-16 21:43:21,031 (beam_search:429) INFO: max output length: 62 +2024-01-16 21:43:21,031 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:21,104 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:21,104 (beam_search:476) INFO: -8.03 * 1.0 = -8.03 for ctc +2024-01-16 21:43:21,104 (beam_search:479) INFO: total log probability: -8.03 +2024-01-16 21:43:21,104 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:21,104 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:21,104 (beam_search:483) INFO: best hypo: MNTHRETTOHSOFIVLOFMANYSPACIE + +2024-01-16 21:43:21,105 (asr_inference:494) INFO: speech length: 17440 +2024-01-16 21:43:21,112 (beam_search:428) INFO: decoder input length: 25 +2024-01-16 21:43:21,112 (beam_search:429) INFO: max output length: 25 +2024-01-16 21:43:21,112 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:21,131 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:21,131 (beam_search:476) INFO: -4.03 * 1.0 = -4.03 for ctc +2024-01-16 21:43:21,131 (beam_search:479) INFO: total log probability: -4.03 +2024-01-16 21:43:21,131 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:43:21,131 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:21,131 (beam_search:483) INFO: best hypo: AVEONMORDETICLE + +2024-01-16 21:43:21,132 (asr_inference:494) INFO: speech length: 38560 +2024-01-16 21:43:21,140 (beam_search:428) INFO: decoder input length: 58 +2024-01-16 21:43:21,140 (beam_search:429) INFO: max output length: 58 +2024-01-16 21:43:21,140 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:21,218 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:21,218 (beam_search:476) INFO: -12.21 * 1.0 = -12.21 for ctc +2024-01-16 21:43:21,218 (beam_search:479) INFO: total log probability: -12.21 +2024-01-16 21:43:21,218 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 21:43:21,218 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:21,218 (beam_search:483) INFO: best hypo: ANDTWETYWENSPEACIESOFUSCREANIAGDLFEON + +2024-01-16 21:43:21,219 (asr_inference:494) INFO: speech length: 23520 +2024-01-16 21:43:21,227 (beam_search:428) INFO: decoder input length: 34 +2024-01-16 21:43:21,227 (beam_search:429) INFO: max output length: 34 +2024-01-16 21:43:21,227 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:21,252 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:21,252 (beam_search:476) INFO: -3.37 * 1.0 = -3.37 for ctc +2024-01-16 21:43:21,252 (beam_search:479) INFO: total log probability: -3.37 +2024-01-16 21:43:21,252 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:21,252 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:21,252 (beam_search:483) INFO: best hypo: AHEVINGPRMOUTION + +2024-01-16 21:43:21,253 (asr_inference:494) INFO: speech length: 21280 +2024-01-16 21:43:21,260 (beam_search:428) INFO: decoder input length: 31 +2024-01-16 21:43:21,260 (beam_search:429) INFO: max output length: 31 +2024-01-16 21:43:21,260 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:21,286 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:21,287 (beam_search:476) INFO: -7.26 * 1.0 = -7.26 for ctc +2024-01-16 21:43:21,287 (beam_search:479) INFO: total log probability: -7.26 +2024-01-16 21:43:21,287 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 21:43:21,287 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:21,287 (beam_search:483) INFO: best hypo: ENDTYMISTESUMTION + +2024-01-16 21:43:21,288 (asr_inference:494) INFO: speech length: 18560 +2024-01-16 21:43:21,294 (beam_search:428) INFO: decoder input length: 26 +2024-01-16 21:43:21,294 (beam_search:429) INFO: max output length: 26 +2024-01-16 21:43:21,294 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:21,315 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:21,315 (beam_search:476) INFO: -1.56 * 1.0 = -1.56 for ctc +2024-01-16 21:43:21,315 (beam_search:479) INFO: total log probability: -1.56 +2024-01-16 21:43:21,315 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 21:43:21,315 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:21,315 (beam_search:483) INFO: best hypo: ONTHEFIRSTBELID + +2024-01-16 21:43:21,316 (asr_inference:494) INFO: speech length: 87520 +2024-01-16 21:43:21,326 (beam_search:428) INFO: decoder input length: 134 +2024-01-16 21:43:21,326 (beam_search:429) INFO: max output length: 134 +2024-01-16 21:43:21,327 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:21,616 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:21,616 (beam_search:476) INFO: -16.40 * 1.0 = -16.40 for ctc +2024-01-16 21:43:21,616 (beam_search:479) INFO: total log probability: -16.40 +2024-01-16 21:43:21,616 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:43:21,616 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:21,617 (beam_search:483) INFO: best hypo: STORYINDECATIEFOFTHERISINGLOABLSRIGNIFINGSOFSOPLISHEISTOEBYDJDEN + +2024-01-16 21:43:21,618 (asr_inference:494) INFO: speech length: 41280 +2024-01-16 21:43:21,625 (beam_search:428) INFO: decoder input length: 62 +2024-01-16 21:43:21,626 (beam_search:429) INFO: max output length: 62 +2024-01-16 21:43:21,626 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:21,700 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:21,700 (beam_search:476) INFO: -7.49 * 1.0 = -7.49 for ctc +2024-01-16 21:43:21,700 (beam_search:479) INFO: total log probability: -7.49 +2024-01-16 21:43:21,700 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:21,700 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:21,700 (beam_search:483) INFO: best hypo: WHCHSBAREASEALYENTRSTNDPOLITICS + +2024-01-16 21:43:21,701 (asr_inference:494) INFO: speech length: 53920 +2024-01-16 21:43:21,709 (beam_search:428) INFO: decoder input length: 82 +2024-01-16 21:43:21,709 (beam_search:429) INFO: max output length: 82 +2024-01-16 21:43:21,709 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:21,828 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:21,828 (beam_search:476) INFO: -9.18 * 1.0 = -9.18 for ctc +2024-01-16 21:43:21,828 (beam_search:479) INFO: total log probability: -9.18 +2024-01-16 21:43:21,828 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:21,828 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:21,828 (beam_search:483) INFO: best hypo: WASCOLLDDOLBEACHACXPROINFOLLANDPATNTI + +2024-01-16 21:43:21,829 (asr_inference:494) INFO: speech length: 28160 +2024-01-16 21:43:21,836 (beam_search:428) INFO: decoder input length: 41 +2024-01-16 21:43:21,836 (beam_search:429) INFO: max output length: 41 +2024-01-16 21:43:21,836 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:21,873 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:21,873 (beam_search:476) INFO: -2.49 * 1.0 = -2.49 for ctc +2024-01-16 21:43:21,873 (beam_search:479) INFO: total log probability: -2.49 +2024-01-16 21:43:21,873 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 21:43:21,873 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:21,873 (beam_search:483) INFO: best hypo: OODSAVEANDFINFILESB + +2024-01-16 21:43:21,874 (asr_inference:494) INFO: speech length: 38720 +2024-01-16 21:43:21,882 (beam_search:428) INFO: decoder input length: 58 +2024-01-16 21:43:21,882 (beam_search:429) INFO: max output length: 58 +2024-01-16 21:43:21,882 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:21,954 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:21,954 (beam_search:476) INFO: -7.43 * 1.0 = -7.43 for ctc +2024-01-16 21:43:21,954 (beam_search:479) INFO: total log probability: -7.43 +2024-01-16 21:43:21,954 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:21,954 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:21,954 (beam_search:483) INFO: best hypo: ASTRLIONSNACEBELONGTOSEVONFAMELYES + +2024-01-16 21:43:21,955 (asr_inference:494) INFO: speech length: 16160 +2024-01-16 21:43:21,962 (beam_search:428) INFO: decoder input length: 23 +2024-01-16 21:43:21,962 (beam_search:429) INFO: max output length: 23 +2024-01-16 21:43:21,962 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:21,977 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:21,977 (beam_search:476) INFO: -4.16 * 1.0 = -4.16 for ctc +2024-01-16 21:43:21,977 (beam_search:479) INFO: total log probability: -4.16 +2024-01-16 21:43:21,977 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:43:21,977 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:21,977 (beam_search:483) INFO: best hypo: TDOLANPYOR + +2024-01-16 21:43:21,978 (asr_inference:494) INFO: speech length: 29440 +2024-01-16 21:43:21,985 (beam_search:428) INFO: decoder input length: 43 +2024-01-16 21:43:21,985 (beam_search:429) INFO: max output length: 43 +2024-01-16 21:43:21,985 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:22,027 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:22,027 (beam_search:476) INFO: -3.23 * 1.0 = -3.23 for ctc +2024-01-16 21:43:22,027 (beam_search:479) INFO: total log probability: -3.23 +2024-01-16 21:43:22,027 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:43:22,027 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:22,027 (beam_search:483) INFO: best hypo: LINDSHARPLYSENCESPEAAND + +2024-01-16 21:43:22,029 (asr_inference:494) INFO: speech length: 46720 +2024-01-16 21:43:22,036 (beam_search:428) INFO: decoder input length: 70 +2024-01-16 21:43:22,037 (beam_search:429) INFO: max output length: 70 +2024-01-16 21:43:22,037 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:22,127 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:22,127 (beam_search:476) INFO: -5.89 * 1.0 = -5.89 for ctc +2024-01-16 21:43:22,127 (beam_search:479) INFO: total log probability: -5.89 +2024-01-16 21:43:22,127 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:43:22,127 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:22,127 (beam_search:483) INFO: best hypo: WASRECOURDEDINTHIRLYONFORTRACOSECTA + +2024-01-16 21:43:22,128 (asr_inference:494) INFO: speech length: 21440 +2024-01-16 21:43:22,135 (beam_search:428) INFO: decoder input length: 31 +2024-01-16 21:43:22,135 (beam_search:429) INFO: max output length: 31 +2024-01-16 21:43:22,135 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:22,161 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:22,161 (beam_search:476) INFO: -4.79 * 1.0 = -4.79 for ctc +2024-01-16 21:43:22,161 (beam_search:479) INFO: total log probability: -4.79 +2024-01-16 21:43:22,161 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:43:22,161 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:22,162 (beam_search:483) INFO: best hypo: NOLASIMPROVENTIN + +2024-01-16 21:43:22,163 (asr_inference:494) INFO: speech length: 24960 +2024-01-16 21:43:22,169 (beam_search:428) INFO: decoder input length: 36 +2024-01-16 21:43:22,170 (beam_search:429) INFO: max output length: 36 +2024-01-16 21:43:22,170 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:22,199 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:22,200 (beam_search:476) INFO: -5.44 * 1.0 = -5.44 for ctc +2024-01-16 21:43:22,200 (beam_search:479) INFO: total log probability: -5.44 +2024-01-16 21:43:22,200 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:22,200 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:22,200 (beam_search:483) INFO: best hypo: RBONANDRULLEGESHTH + +2024-01-16 21:43:22,201 (asr_inference:494) INFO: speech length: 16640 +2024-01-16 21:43:22,207 (beam_search:428) INFO: decoder input length: 23 +2024-01-16 21:43:22,207 (beam_search:429) INFO: max output length: 23 +2024-01-16 21:43:22,207 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:22,223 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:22,223 (beam_search:476) INFO: -2.28 * 1.0 = -2.28 for ctc +2024-01-16 21:43:22,223 (beam_search:479) INFO: total log probability: -2.28 +2024-01-16 21:43:22,223 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:43:22,223 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:22,223 (beam_search:483) INFO: best hypo: ACHPLAYRBGNS + +2024-01-16 21:43:22,224 (asr_inference:494) INFO: speech length: 51200 +2024-01-16 21:43:22,232 (beam_search:428) INFO: decoder input length: 77 +2024-01-16 21:43:22,232 (beam_search:429) INFO: max output length: 77 +2024-01-16 21:43:22,232 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:22,332 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:22,332 (beam_search:476) INFO: -6.90 * 1.0 = -6.90 for ctc +2024-01-16 21:43:22,332 (beam_search:479) INFO: total log probability: -6.90 +2024-01-16 21:43:22,332 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:22,332 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:22,332 (beam_search:483) INFO: best hypo: DJHASTHASNSPHIEMANYCOMNTORILEPOUSLE + +2024-01-16 21:43:22,333 (asr_inference:494) INFO: speech length: 16960 +2024-01-16 21:43:22,340 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 21:43:22,340 (beam_search:429) INFO: max output length: 24 +2024-01-16 21:43:22,340 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:22,358 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:22,358 (beam_search:476) INFO: -4.75 * 1.0 = -4.75 for ctc +2024-01-16 21:43:22,358 (beam_search:479) INFO: total log probability: -4.75 +2024-01-16 21:43:22,358 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:43:22,358 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:22,358 (beam_search:483) INFO: best hypo: ORHUOMANIMITDGE + +2024-01-16 21:43:22,360 (asr_inference:494) INFO: speech length: 21280 +2024-01-16 21:43:22,366 (beam_search:428) INFO: decoder input length: 31 +2024-01-16 21:43:22,366 (beam_search:429) INFO: max output length: 31 +2024-01-16 21:43:22,366 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:22,388 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:22,388 (beam_search:476) INFO: -4.62 * 1.0 = -4.62 for ctc +2024-01-16 21:43:22,388 (beam_search:479) INFO: total log probability: -4.62 +2024-01-16 21:43:22,388 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:43:22,388 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:22,388 (beam_search:483) INFO: best hypo: LASPOIRDEDTAPS + +2024-01-16 21:43:22,390 (asr_inference:494) INFO: speech length: 24320 +2024-01-16 21:43:22,396 (beam_search:428) INFO: decoder input length: 35 +2024-01-16 21:43:22,397 (beam_search:429) INFO: max output length: 35 +2024-01-16 21:43:22,397 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:22,431 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:22,431 (beam_search:476) INFO: -4.29 * 1.0 = -4.29 for ctc +2024-01-16 21:43:22,431 (beam_search:479) INFO: total log probability: -4.29 +2024-01-16 21:43:22,431 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:22,431 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:22,431 (beam_search:483) INFO: best hypo: ANTANDISENCEINGTOTHEOTION + +2024-01-16 21:43:22,432 (asr_inference:494) INFO: speech length: 33920 +2024-01-16 21:43:22,440 (beam_search:428) INFO: decoder input length: 50 +2024-01-16 21:43:22,440 (beam_search:429) INFO: max output length: 50 +2024-01-16 21:43:22,440 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:22,495 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:22,495 (beam_search:476) INFO: -6.04 * 1.0 = -6.04 for ctc +2024-01-16 21:43:22,495 (beam_search:479) INFO: total log probability: -6.04 +2024-01-16 21:43:22,495 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:22,495 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:22,496 (beam_search:483) INFO: best hypo: RESINPROTEMPOROFTHESTATSEND + +2024-01-16 21:43:22,497 (asr_inference:494) INFO: speech length: 30400 +2024-01-16 21:43:22,504 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 21:43:22,504 (beam_search:429) INFO: max output length: 45 +2024-01-16 21:43:22,504 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:22,553 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:22,553 (beam_search:476) INFO: -10.43 * 1.0 = -10.43 for ctc +2024-01-16 21:43:22,553 (beam_search:479) INFO: total log probability: -10.43 +2024-01-16 21:43:22,554 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:43:22,554 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:22,554 (beam_search:483) INFO: best hypo: ICHOPCELMOVEANYNUMBROSCQHERS + +2024-01-16 21:43:22,555 (asr_inference:494) INFO: speech length: 20000 +2024-01-16 21:43:22,561 (beam_search:428) INFO: decoder input length: 29 +2024-01-16 21:43:22,561 (beam_search:429) INFO: max output length: 29 +2024-01-16 21:43:22,561 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:22,587 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:22,587 (beam_search:476) INFO: -2.92 * 1.0 = -2.92 for ctc +2024-01-16 21:43:22,587 (beam_search:479) INFO: total log probability: -2.92 +2024-01-16 21:43:22,587 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:22,587 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:22,587 (beam_search:483) INFO: best hypo: PRETHEINSIDTHESCOL + +2024-01-16 21:43:22,588 (asr_inference:494) INFO: speech length: 21280 +2024-01-16 21:43:22,595 (beam_search:428) INFO: decoder input length: 31 +2024-01-16 21:43:22,595 (beam_search:429) INFO: max output length: 31 +2024-01-16 21:43:22,595 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:22,622 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:22,622 (beam_search:476) INFO: -5.86 * 1.0 = -5.86 for ctc +2024-01-16 21:43:22,622 (beam_search:479) INFO: total log probability: -5.86 +2024-01-16 21:43:22,622 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:22,622 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:22,622 (beam_search:483) INFO: best hypo: LINGCAETSHERNESSWIT + +2024-01-16 21:43:22,623 (asr_inference:494) INFO: speech length: 46720 +2024-01-16 21:43:22,631 (beam_search:428) INFO: decoder input length: 70 +2024-01-16 21:43:22,631 (beam_search:429) INFO: max output length: 70 +2024-01-16 21:43:22,631 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:22,722 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:22,722 (beam_search:476) INFO: -8.35 * 1.0 = -8.35 for ctc +2024-01-16 21:43:22,722 (beam_search:479) INFO: total log probability: -8.35 +2024-01-16 21:43:22,722 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:22,722 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:22,722 (beam_search:483) INFO: best hypo: OUNTRYSOFTHEESTENPALYOARCTICFLYWAY + +2024-01-16 21:43:22,723 (asr_inference:494) INFO: speech length: 40640 +2024-01-16 21:43:22,731 (beam_search:428) INFO: decoder input length: 61 +2024-01-16 21:43:22,731 (beam_search:429) INFO: max output length: 61 +2024-01-16 21:43:22,731 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:22,814 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:22,814 (beam_search:476) INFO: -8.31 * 1.0 = -8.31 for ctc +2024-01-16 21:43:22,814 (beam_search:479) INFO: total log probability: -8.31 +2024-01-16 21:43:22,814 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:22,814 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:22,814 (beam_search:483) INFO: best hypo: ASONLSDTICSTIKEAESTAMATTHEOPELATIONIN + +2024-01-16 21:43:22,816 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 21:43:22,823 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:22,823 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:22,823 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:22,879 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:22,879 (beam_search:476) INFO: -8.20 * 1.0 = -8.20 for ctc +2024-01-16 21:43:22,879 (beam_search:479) INFO: total log probability: -8.20 +2024-01-16 21:43:22,879 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:22,879 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:22,879 (beam_search:483) INFO: best hypo: UNDDESPEUTEDWHRLDCHESSHAPEEN + +2024-01-16 21:43:22,880 (asr_inference:494) INFO: speech length: 27040 +2024-01-16 21:43:22,887 (beam_search:428) INFO: decoder input length: 40 +2024-01-16 21:43:22,887 (beam_search:429) INFO: max output length: 40 +2024-01-16 21:43:22,887 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:22,922 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:22,922 (beam_search:476) INFO: -7.32 * 1.0 = -7.32 for ctc +2024-01-16 21:43:22,922 (beam_search:479) INFO: total log probability: -7.32 +2024-01-16 21:43:22,922 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:43:22,922 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:22,922 (beam_search:483) INFO: best hypo: TAYNRELLCAPTBINCEAR + +2024-01-16 21:43:22,923 (asr_inference:494) INFO: speech length: 133280 +2024-01-16 21:43:22,937 (beam_search:428) INFO: decoder input length: 206 +2024-01-16 21:43:22,937 (beam_search:429) INFO: max output length: 206 +2024-01-16 21:43:22,937 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:23,454 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:23,455 (beam_search:476) INFO: -16.49 * 1.0 = -16.49 for ctc +2024-01-16 21:43:23,455 (beam_search:479) INFO: total log probability: -16.49 +2024-01-16 21:43:23,455 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:23,455 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:23,455 (beam_search:483) INFO: best hypo: ERNACTEDBYTHECNERLASEMBLYHWASAMASERRASIALYSEVGRGAIGTHETATESREROTCOARRS + +2024-01-16 21:43:23,457 (asr_inference:494) INFO: speech length: 21120 +2024-01-16 21:43:23,463 (beam_search:428) INFO: decoder input length: 30 +2024-01-16 21:43:23,464 (beam_search:429) INFO: max output length: 30 +2024-01-16 21:43:23,464 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:23,485 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:23,486 (beam_search:476) INFO: -3.66 * 1.0 = -3.66 for ctc +2024-01-16 21:43:23,486 (beam_search:479) INFO: total log probability: -3.66 +2024-01-16 21:43:23,486 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:23,486 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:23,486 (beam_search:483) INFO: best hypo: WHCHRAPEALMOST + +2024-01-16 21:43:23,487 (asr_inference:494) INFO: speech length: 42880 +2024-01-16 21:43:23,495 (beam_search:428) INFO: decoder input length: 64 +2024-01-16 21:43:23,495 (beam_search:429) INFO: max output length: 64 +2024-01-16 21:43:23,495 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:23,576 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:23,576 (beam_search:476) INFO: -10.50 * 1.0 = -10.50 for ctc +2024-01-16 21:43:23,576 (beam_search:479) INFO: total log probability: -10.50 +2024-01-16 21:43:23,576 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:43:23,576 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:23,577 (beam_search:483) INFO: best hypo: SEACTPRETHCTLDNTIOFFORNERANDPROVIDE + +2024-01-16 21:43:23,578 (asr_inference:494) INFO: speech length: 67680 +2024-01-16 21:43:23,587 (beam_search:428) INFO: decoder input length: 103 +2024-01-16 21:43:23,587 (beam_search:429) INFO: max output length: 103 +2024-01-16 21:43:23,587 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:23,757 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:23,757 (beam_search:476) INFO: -12.07 * 1.0 = -12.07 for ctc +2024-01-16 21:43:23,757 (beam_search:479) INFO: total log probability: -12.07 +2024-01-16 21:43:23,757 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:43:23,757 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:23,757 (beam_search:483) INFO: best hypo: WERASTHEFEMIALSPECILEMISDARCBORONBORDEDWITWHAT + +2024-01-16 21:43:23,758 (asr_inference:494) INFO: speech length: 18560 +2024-01-16 21:43:23,765 (beam_search:428) INFO: decoder input length: 26 +2024-01-16 21:43:23,765 (beam_search:429) INFO: max output length: 26 +2024-01-16 21:43:23,765 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:23,783 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:23,784 (beam_search:476) INFO: -5.37 * 1.0 = -5.37 for ctc +2024-01-16 21:43:23,784 (beam_search:479) INFO: total log probability: -5.37 +2024-01-16 21:43:23,784 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:43:23,784 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:23,784 (beam_search:483) INFO: best hypo: WOTERECENCTLSO + +2024-01-16 21:43:23,785 (asr_inference:494) INFO: speech length: 52640 +2024-01-16 21:43:23,793 (beam_search:428) INFO: decoder input length: 80 +2024-01-16 21:43:23,793 (beam_search:429) INFO: max output length: 80 +2024-01-16 21:43:23,793 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:23,877 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:23,877 (beam_search:476) INFO: -6.06 * 1.0 = -6.06 for ctc +2024-01-16 21:43:23,877 (beam_search:479) INFO: total log probability: -6.06 +2024-01-16 21:43:23,877 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:23,877 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:23,877 (beam_search:483) INFO: best hypo: NHIDNTYANTWELVEFINROSSFIRN + +2024-01-16 21:43:23,878 (asr_inference:494) INFO: speech length: 54880 +2024-01-16 21:43:23,887 (beam_search:428) INFO: decoder input length: 83 +2024-01-16 21:43:23,887 (beam_search:429) INFO: max output length: 83 +2024-01-16 21:43:23,887 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:24,013 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:24,013 (beam_search:476) INFO: -10.10 * 1.0 = -10.10 for ctc +2024-01-16 21:43:24,013 (beam_search:479) INFO: total log probability: -10.10 +2024-01-16 21:43:24,013 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:24,013 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:24,014 (beam_search:483) INFO: best hypo: DIDEDNOISISGENRLMADEWHTHASETESCANDOTHEHEAID + +2024-01-16 21:43:24,015 (asr_inference:494) INFO: speech length: 58080 +2024-01-16 21:43:24,023 (beam_search:428) INFO: decoder input length: 88 +2024-01-16 21:43:24,024 (beam_search:429) INFO: max output length: 88 +2024-01-16 21:43:24,024 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:24,160 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:24,160 (beam_search:476) INFO: -11.58 * 1.0 = -11.58 for ctc +2024-01-16 21:43:24,160 (beam_search:479) INFO: total log probability: -11.58 +2024-01-16 21:43:24,160 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:43:24,160 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:24,160 (beam_search:483) INFO: best hypo: THEFIRSTGENRLYRECONIGCSEWERLDCHAESSTCHAMPPEON + +2024-01-16 21:43:24,162 (asr_inference:494) INFO: speech length: 25440 +2024-01-16 21:43:24,169 (beam_search:428) INFO: decoder input length: 37 +2024-01-16 21:43:24,169 (beam_search:429) INFO: max output length: 37 +2024-01-16 21:43:24,169 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:24,206 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:24,206 (beam_search:476) INFO: -3.82 * 1.0 = -3.82 for ctc +2024-01-16 21:43:24,206 (beam_search:479) INFO: total log probability: -3.82 +2024-01-16 21:43:24,206 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:24,206 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:24,206 (beam_search:483) INFO: best hypo: HEPEYANDSITINGLNWREPART + +2024-01-16 21:43:24,207 (asr_inference:494) INFO: speech length: 43840 +2024-01-16 21:43:24,215 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:43:24,215 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:43:24,215 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:24,296 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:24,296 (beam_search:476) INFO: -4.90 * 1.0 = -4.90 for ctc +2024-01-16 21:43:24,296 (beam_search:479) INFO: total log probability: -4.90 +2024-01-16 21:43:24,296 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:24,296 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:24,296 (beam_search:483) INFO: best hypo: ADRELASTTHERELBUMSBOTHTOSEDEANT + +2024-01-16 21:43:24,297 (asr_inference:494) INFO: speech length: 21120 +2024-01-16 21:43:24,304 (beam_search:428) INFO: decoder input length: 30 +2024-01-16 21:43:24,304 (beam_search:429) INFO: max output length: 30 +2024-01-16 21:43:24,304 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:24,331 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:24,331 (beam_search:476) INFO: -2.93 * 1.0 = -2.93 for ctc +2024-01-16 21:43:24,331 (beam_search:479) INFO: total log probability: -2.93 +2024-01-16 21:43:24,331 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:24,331 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:24,331 (beam_search:483) INFO: best hypo: WATESRONTHECONTINEN + +2024-01-16 21:43:24,332 (asr_inference:494) INFO: speech length: 28960 +2024-01-16 21:43:24,339 (beam_search:428) INFO: decoder input length: 43 +2024-01-16 21:43:24,339 (beam_search:429) INFO: max output length: 43 +2024-01-16 21:43:24,339 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:24,382 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:24,382 (beam_search:476) INFO: -3.00 * 1.0 = -3.00 for ctc +2024-01-16 21:43:24,382 (beam_search:479) INFO: total log probability: -3.00 +2024-01-16 21:43:24,382 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 21:43:24,382 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:24,382 (beam_search:483) INFO: best hypo: FTHERANGHPERSONLSTAIRIOS + +2024-01-16 21:43:24,384 (asr_inference:494) INFO: speech length: 45760 +2024-01-16 21:43:24,392 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:43:24,392 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:43:24,392 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:24,488 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:24,488 (beam_search:476) INFO: -9.30 * 1.0 = -9.30 for ctc +2024-01-16 21:43:24,488 (beam_search:479) INFO: total log probability: -9.30 +2024-01-16 21:43:24,488 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:24,488 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:24,488 (beam_search:483) INFO: best hypo: NDEYOMEDERSANDRECOARTINGLEVLCONTROLSON + +2024-01-16 21:43:24,489 (asr_inference:494) INFO: speech length: 16640 +2024-01-16 21:43:24,496 (beam_search:428) INFO: decoder input length: 23 +2024-01-16 21:43:24,496 (beam_search:429) INFO: max output length: 23 +2024-01-16 21:43:24,496 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:24,510 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:24,511 (beam_search:476) INFO: -3.58 * 1.0 = -3.58 for ctc +2024-01-16 21:43:24,511 (beam_search:479) INFO: total log probability: -3.58 +2024-01-16 21:43:24,511 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:24,511 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:24,511 (beam_search:483) INFO: best hypo: LYNOMULTIME + +2024-01-16 21:43:24,512 (asr_inference:494) INFO: speech length: 33760 +2024-01-16 21:43:24,519 (beam_search:428) INFO: decoder input length: 50 +2024-01-16 21:43:24,519 (beam_search:429) INFO: max output length: 50 +2024-01-16 21:43:24,519 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:24,575 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:24,575 (beam_search:476) INFO: -4.51 * 1.0 = -4.51 for ctc +2024-01-16 21:43:24,575 (beam_search:479) INFO: total log probability: -4.51 +2024-01-16 21:43:24,575 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:24,575 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:24,575 (beam_search:483) INFO: best hypo: ANDITOFHENDESTRDTHEPLABILITY + +2024-01-16 21:43:24,576 (asr_inference:494) INFO: speech length: 27040 +2024-01-16 21:43:24,583 (beam_search:428) INFO: decoder input length: 40 +2024-01-16 21:43:24,583 (beam_search:429) INFO: max output length: 40 +2024-01-16 21:43:24,583 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:24,605 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:24,605 (beam_search:476) INFO: -4.16 * 1.0 = -4.16 for ctc +2024-01-16 21:43:24,606 (beam_search:479) INFO: total log probability: -4.16 +2024-01-16 21:43:24,606 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:43:24,606 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:24,606 (beam_search:483) INFO: best hypo: CONFLAUTION + +2024-01-16 21:43:24,607 (asr_inference:494) INFO: speech length: 55200 +2024-01-16 21:43:24,615 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 21:43:24,615 (beam_search:429) INFO: max output length: 84 +2024-01-16 21:43:24,615 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:24,759 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:24,760 (beam_search:476) INFO: -7.41 * 1.0 = -7.41 for ctc +2024-01-16 21:43:24,760 (beam_search:479) INFO: total log probability: -7.41 +2024-01-16 21:43:24,760 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:24,760 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:24,760 (beam_search:483) INFO: best hypo: EUIVLENTTOTHECESTIONOFWHTHERACXEISAMEMBROFCOMPOUSI + +2024-01-16 21:43:24,761 (asr_inference:494) INFO: speech length: 23680 +2024-01-16 21:43:24,768 (beam_search:428) INFO: decoder input length: 34 +2024-01-16 21:43:24,768 (beam_search:429) INFO: max output length: 34 +2024-01-16 21:43:24,768 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:24,795 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:24,795 (beam_search:476) INFO: -2.78 * 1.0 = -2.78 for ctc +2024-01-16 21:43:24,795 (beam_search:479) INFO: total log probability: -2.78 +2024-01-16 21:43:24,795 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:24,795 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:24,796 (beam_search:483) INFO: best hypo: MOSTOITHLASTERANG + +2024-01-16 21:43:24,797 (asr_inference:494) INFO: speech length: 16640 +2024-01-16 21:43:24,803 (beam_search:428) INFO: decoder input length: 23 +2024-01-16 21:43:24,803 (beam_search:429) INFO: max output length: 23 +2024-01-16 21:43:24,803 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:24,820 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:24,820 (beam_search:476) INFO: -4.24 * 1.0 = -4.24 for ctc +2024-01-16 21:43:24,820 (beam_search:479) INFO: total log probability: -4.24 +2024-01-16 21:43:24,820 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:43:24,820 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:24,820 (beam_search:483) INFO: best hypo: POSTHENDERISTME + +2024-01-16 21:43:24,821 (asr_inference:494) INFO: speech length: 31840 +2024-01-16 21:43:24,828 (beam_search:428) INFO: decoder input length: 47 +2024-01-16 21:43:24,828 (beam_search:429) INFO: max output length: 47 +2024-01-16 21:43:24,828 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:24,876 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:24,876 (beam_search:476) INFO: -5.50 * 1.0 = -5.50 for ctc +2024-01-16 21:43:24,876 (beam_search:479) INFO: total log probability: -5.50 +2024-01-16 21:43:24,876 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:24,876 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:24,876 (beam_search:483) INFO: best hypo: OMPACTCOSSATWUIKLYFOUDOUS + +2024-01-16 21:43:24,877 (asr_inference:494) INFO: speech length: 28320 +2024-01-16 21:43:24,884 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 21:43:24,884 (beam_search:429) INFO: max output length: 42 +2024-01-16 21:43:24,884 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:24,928 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:24,928 (beam_search:476) INFO: -7.80 * 1.0 = -7.80 for ctc +2024-01-16 21:43:24,928 (beam_search:479) INFO: total log probability: -7.80 +2024-01-16 21:43:24,928 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:24,928 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:24,928 (beam_search:483) INFO: best hypo: ORFORHUNDRNDTHARTYTHRYFEE + +2024-01-16 21:43:24,929 (asr_inference:494) INFO: speech length: 40160 +2024-01-16 21:43:24,937 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:43:24,937 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:43:24,937 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:25,015 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:25,015 (beam_search:476) INFO: -10.02 * 1.0 = -10.02 for ctc +2024-01-16 21:43:25,015 (beam_search:479) INFO: total log probability: -10.02 +2024-01-16 21:43:25,015 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:25,015 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:25,015 (beam_search:483) INFO: best hypo: INGSWHCHRESELTINASPECSIOTICTTIHEOPON + +2024-01-16 21:43:25,016 (asr_inference:494) INFO: speech length: 21120 +2024-01-16 21:43:25,023 (beam_search:428) INFO: decoder input length: 30 +2024-01-16 21:43:25,023 (beam_search:429) INFO: max output length: 30 +2024-01-16 21:43:25,023 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:25,049 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:25,050 (beam_search:476) INFO: -5.02 * 1.0 = -5.02 for ctc +2024-01-16 21:43:25,050 (beam_search:479) INFO: total log probability: -5.02 +2024-01-16 21:43:25,050 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:43:25,050 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:25,050 (beam_search:483) INFO: best hypo: BFRNANTIYNANTYSEVEN + +2024-01-16 21:43:25,051 (asr_inference:494) INFO: speech length: 18240 +2024-01-16 21:43:25,057 (beam_search:428) INFO: decoder input length: 26 +2024-01-16 21:43:25,057 (beam_search:429) INFO: max output length: 26 +2024-01-16 21:43:25,057 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:25,079 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:25,079 (beam_search:476) INFO: -4.11 * 1.0 = -4.11 for ctc +2024-01-16 21:43:25,079 (beam_search:479) INFO: total log probability: -4.11 +2024-01-16 21:43:25,079 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:25,079 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:25,079 (beam_search:483) INFO: best hypo: OMYCATIONSANDHELT + +2024-01-16 21:43:25,080 (asr_inference:494) INFO: speech length: 27360 +2024-01-16 21:43:25,087 (beam_search:428) INFO: decoder input length: 40 +2024-01-16 21:43:25,087 (beam_search:429) INFO: max output length: 40 +2024-01-16 21:43:25,087 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:25,126 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:25,126 (beam_search:476) INFO: -5.41 * 1.0 = -5.41 for ctc +2024-01-16 21:43:25,126 (beam_search:479) INFO: total log probability: -5.41 +2024-01-16 21:43:25,126 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:25,126 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:25,126 (beam_search:483) INFO: best hypo: ESAYAICHEINAPERSSONNON + +2024-01-16 21:43:25,127 (asr_inference:494) INFO: speech length: 85440 +2024-01-16 21:43:25,138 (beam_search:428) INFO: decoder input length: 131 +2024-01-16 21:43:25,138 (beam_search:429) INFO: max output length: 131 +2024-01-16 21:43:25,138 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:25,418 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:25,418 (beam_search:476) INFO: -15.75 * 1.0 = -15.75 for ctc +2024-01-16 21:43:25,418 (beam_search:479) INFO: total log probability: -15.75 +2024-01-16 21:43:25,418 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:25,419 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:25,419 (beam_search:483) INFO: best hypo: SOMBRAENEPISOFIETHATTHYAYALLSOEBEOUEDONUTHERNONPORSSMETEIRILS + +2024-01-16 21:43:25,420 (asr_inference:494) INFO: speech length: 28000 +2024-01-16 21:43:25,427 (beam_search:428) INFO: decoder input length: 41 +2024-01-16 21:43:25,427 (beam_search:429) INFO: max output length: 41 +2024-01-16 21:43:25,427 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:25,463 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:25,463 (beam_search:476) INFO: -3.43 * 1.0 = -3.43 for ctc +2024-01-16 21:43:25,463 (beam_search:479) INFO: total log probability: -3.43 +2024-01-16 21:43:25,463 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:43:25,463 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:25,463 (beam_search:483) INFO: best hypo: HEPOSEBLYCONPESIFIC + +2024-01-16 21:43:25,464 (asr_inference:494) INFO: speech length: 16320 +2024-01-16 21:43:25,471 (beam_search:428) INFO: decoder input length: 23 +2024-01-16 21:43:25,471 (beam_search:429) INFO: max output length: 23 +2024-01-16 21:43:25,471 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:25,487 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:25,487 (beam_search:476) INFO: -3.33 * 1.0 = -3.33 for ctc +2024-01-16 21:43:25,487 (beam_search:479) INFO: total log probability: -3.33 +2024-01-16 21:43:25,487 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:25,487 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:25,487 (beam_search:483) INFO: best hypo: MATORSINLACTH + +2024-01-16 21:43:25,488 (asr_inference:494) INFO: speech length: 33440 +2024-01-16 21:43:25,496 (beam_search:428) INFO: decoder input length: 50 +2024-01-16 21:43:25,496 (beam_search:429) INFO: max output length: 50 +2024-01-16 21:43:25,496 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:25,541 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:25,541 (beam_search:476) INFO: -3.02 * 1.0 = -3.02 for ctc +2024-01-16 21:43:25,541 (beam_search:479) INFO: total log probability: -3.02 +2024-01-16 21:43:25,541 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:43:25,541 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:25,541 (beam_search:483) INFO: best hypo: ITHOUTFTYMOLETRINGROL + +2024-01-16 21:43:25,542 (asr_inference:494) INFO: speech length: 40960 +2024-01-16 21:43:25,550 (beam_search:428) INFO: decoder input length: 61 +2024-01-16 21:43:25,550 (beam_search:429) INFO: max output length: 61 +2024-01-16 21:43:25,550 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:25,633 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:25,634 (beam_search:476) INFO: -17.11 * 1.0 = -17.11 for ctc +2024-01-16 21:43:25,634 (beam_search:479) INFO: total log probability: -17.11 +2024-01-16 21:43:25,634 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 21:43:25,634 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:25,634 (beam_search:483) INFO: best hypo: HEEDCHACSEMPOULESRSPETIELYTHOCKUSTOFAGKI + +2024-01-16 21:43:25,635 (asr_inference:494) INFO: speech length: 23360 +2024-01-16 21:43:25,642 (beam_search:428) INFO: decoder input length: 34 +2024-01-16 21:43:25,642 (beam_search:429) INFO: max output length: 34 +2024-01-16 21:43:25,642 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:25,670 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:25,670 (beam_search:476) INFO: -5.43 * 1.0 = -5.43 for ctc +2024-01-16 21:43:25,670 (beam_search:479) INFO: total log probability: -5.43 +2024-01-16 21:43:25,670 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:25,670 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:25,670 (beam_search:483) INFO: best hypo: AVHHOMLETDITSFIRS + +2024-01-16 21:43:25,671 (asr_inference:494) INFO: speech length: 34400 +2024-01-16 21:43:25,678 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:25,678 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:25,679 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:25,743 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:25,743 (beam_search:476) INFO: -6.13 * 1.0 = -6.13 for ctc +2024-01-16 21:43:25,743 (beam_search:479) INFO: total log probability: -6.13 +2024-01-16 21:43:25,743 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:25,743 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:25,743 (beam_search:483) INFO: best hypo: REBLEADINGRSSKRMAINESOFROUNDFOATY + +2024-01-16 21:43:25,744 (asr_inference:494) INFO: speech length: 16960 +2024-01-16 21:43:25,751 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 21:43:25,751 (beam_search:429) INFO: max output length: 24 +2024-01-16 21:43:25,751 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:25,767 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:25,767 (beam_search:476) INFO: -4.05 * 1.0 = -4.05 for ctc +2024-01-16 21:43:25,767 (beam_search:479) INFO: total log probability: -4.05 +2024-01-16 21:43:25,767 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:43:25,767 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:25,767 (beam_search:483) INFO: best hypo: DLRCHACTMATD + +2024-01-16 21:43:25,768 (asr_inference:494) INFO: speech length: 166880 +2024-01-16 21:43:25,784 (beam_search:428) INFO: decoder input length: 258 +2024-01-16 21:43:25,784 (beam_search:429) INFO: max output length: 258 +2024-01-16 21:43:25,784 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:26,835 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:26,835 (beam_search:476) INFO: -29.60 * 1.0 = -29.60 for ctc +2024-01-16 21:43:26,835 (beam_search:479) INFO: total log probability: -29.60 +2024-01-16 21:43:26,835 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:26,835 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:26,835 (beam_search:483) INFO: best hypo: SOMESECILRHUMENISCONSIEVETRANEHUMINISMASANOSPRINGOFTHEUMNISTFRETHUTMOVEMENTANDARGEYTHETRENSHUMINISDIFERTFORMTHEUMONISTMAENSTRMEBYHAVIN + +2024-01-16 21:43:26,837 (asr_inference:494) INFO: speech length: 93120 +2024-01-16 21:43:26,848 (beam_search:428) INFO: decoder input length: 143 +2024-01-16 21:43:26,848 (beam_search:429) INFO: max output length: 143 +2024-01-16 21:43:26,848 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:27,092 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:27,092 (beam_search:476) INFO: -10.73 * 1.0 = -10.73 for ctc +2024-01-16 21:43:27,092 (beam_search:479) INFO: total log probability: -10.73 +2024-01-16 21:43:27,092 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:27,092 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:27,092 (beam_search:483) INFO: best hypo: PINTILENESTDANDCHICKSREVONERBLTPRDATIONBYMAMOLE + +2024-01-16 21:43:27,093 (asr_inference:494) INFO: speech length: 117440 +2024-01-16 21:43:27,106 (beam_search:428) INFO: decoder input length: 181 +2024-01-16 21:43:27,106 (beam_search:429) INFO: max output length: 181 +2024-01-16 21:43:27,106 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:27,647 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:27,647 (beam_search:476) INFO: -18.73 * 1.0 = -18.73 for ctc +2024-01-16 21:43:27,647 (beam_search:479) INFO: total log probability: -18.73 +2024-01-16 21:43:27,647 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:27,647 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:27,648 (beam_search:483) INFO: best hypo: NORTHERNPINTALISONEOFTHESPEASHESTOWHICHTHEAGREMENTONTHECONCERVATIONOFAFRKNIRAISIONMYGRTORYWAERBURD + +2024-01-16 21:43:27,649 (asr_inference:494) INFO: speech length: 31680 +2024-01-16 21:43:27,657 (beam_search:428) INFO: decoder input length: 47 +2024-01-16 21:43:27,657 (beam_search:429) INFO: max output length: 47 +2024-01-16 21:43:27,657 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:27,711 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:27,711 (beam_search:476) INFO: -6.82 * 1.0 = -6.82 for ctc +2024-01-16 21:43:27,711 (beam_search:479) INFO: total log probability: -6.82 +2024-01-16 21:43:27,711 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:27,711 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:27,711 (beam_search:483) INFO: best hypo: ANTISNEOEFERUNDOLYINTASMAGIOR + +2024-01-16 21:43:27,712 (asr_inference:494) INFO: speech length: 64480 +2024-01-16 21:43:27,722 (beam_search:428) INFO: decoder input length: 98 +2024-01-16 21:43:27,722 (beam_search:429) INFO: max output length: 98 +2024-01-16 21:43:27,722 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:27,894 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:27,894 (beam_search:476) INFO: -7.27 * 1.0 = -7.27 for ctc +2024-01-16 21:43:27,894 (beam_search:479) INFO: total log probability: -7.27 +2024-01-16 21:43:27,894 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:27,894 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:27,895 (beam_search:483) INFO: best hypo: ERSPECTIVETHEADEAOFMEINEDOUPLATINGISASRTDTOREPRSENT + +2024-01-16 21:43:27,896 (asr_inference:494) INFO: speech length: 33600 +2024-01-16 21:43:27,903 (beam_search:428) INFO: decoder input length: 50 +2024-01-16 21:43:27,903 (beam_search:429) INFO: max output length: 50 +2024-01-16 21:43:27,903 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:27,955 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:27,955 (beam_search:476) INFO: -5.02 * 1.0 = -5.02 for ctc +2024-01-16 21:43:27,955 (beam_search:479) INFO: total log probability: -5.02 +2024-01-16 21:43:27,955 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:27,955 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:27,955 (beam_search:483) INFO: best hypo: NAVRIGEOFTWENTYHENPERDAY + +2024-01-16 21:43:27,956 (asr_inference:494) INFO: speech length: 28800 +2024-01-16 21:43:27,963 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 21:43:27,963 (beam_search:429) INFO: max output length: 42 +2024-01-16 21:43:27,963 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,007 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,007 (beam_search:476) INFO: -9.17 * 1.0 = -9.17 for ctc +2024-01-16 21:43:28,007 (beam_search:479) INFO: total log probability: -9.17 +2024-01-16 21:43:28,007 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:43:28,007 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,008 (beam_search:483) INFO: best hypo: TENIWOADFLLOTHATPEEECGUL + +2024-01-16 21:43:28,009 (asr_inference:494) INFO: speech length: 30880 +2024-01-16 21:43:28,016 (beam_search:428) INFO: decoder input length: 46 +2024-01-16 21:43:28,016 (beam_search:429) INFO: max output length: 46 +2024-01-16 21:43:28,016 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,065 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,065 (beam_search:476) INFO: -7.15 * 1.0 = -7.15 for ctc +2024-01-16 21:43:28,065 (beam_search:479) INFO: total log probability: -7.15 +2024-01-16 21:43:28,065 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:28,065 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,065 (beam_search:483) INFO: best hypo: NDBLEADINGINTOEERIESCHOMEOS + +2024-01-16 21:43:28,066 (asr_inference:494) INFO: speech length: 31520 +2024-01-16 21:43:28,073 (beam_search:428) INFO: decoder input length: 47 +2024-01-16 21:43:28,073 (beam_search:429) INFO: max output length: 47 +2024-01-16 21:43:28,073 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,121 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,121 (beam_search:476) INFO: -2.38 * 1.0 = -2.38 for ctc +2024-01-16 21:43:28,121 (beam_search:479) INFO: total log probability: -2.38 +2024-01-16 21:43:28,121 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 21:43:28,121 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,121 (beam_search:483) INFO: best hypo: ANLETHETOGLIDBETWEANTRS + +2024-01-16 21:43:28,122 (asr_inference:494) INFO: speech length: 34080 +2024-01-16 21:43:28,129 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:28,129 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:28,129 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,188 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,188 (beam_search:476) INFO: -7.41 * 1.0 = -7.41 for ctc +2024-01-16 21:43:28,188 (beam_search:479) INFO: total log probability: -7.41 +2024-01-16 21:43:28,189 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:28,189 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,189 (beam_search:483) INFO: best hypo: IFTHESPROBOESWRFICINLYSOLVHABL + +2024-01-16 21:43:28,190 (asr_inference:494) INFO: speech length: 16800 +2024-01-16 21:43:28,196 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 21:43:28,196 (beam_search:429) INFO: max output length: 24 +2024-01-16 21:43:28,196 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,211 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,211 (beam_search:476) INFO: -4.15 * 1.0 = -4.15 for ctc +2024-01-16 21:43:28,211 (beam_search:479) INFO: total log probability: -4.15 +2024-01-16 21:43:28,211 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:43:28,211 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,211 (beam_search:483) INFO: best hypo: ALOGHCALTIN + +2024-01-16 21:43:28,212 (asr_inference:494) INFO: speech length: 22240 +2024-01-16 21:43:28,219 (beam_search:428) INFO: decoder input length: 32 +2024-01-16 21:43:28,219 (beam_search:429) INFO: max output length: 32 +2024-01-16 21:43:28,219 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,241 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,241 (beam_search:476) INFO: -2.25 * 1.0 = -2.25 for ctc +2024-01-16 21:43:28,241 (beam_search:479) INFO: total log probability: -2.25 +2024-01-16 21:43:28,241 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:28,241 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,241 (beam_search:483) INFO: best hypo: ROKWHENFLUSHED + +2024-01-16 21:43:28,242 (asr_inference:494) INFO: speech length: 31520 +2024-01-16 21:43:28,249 (beam_search:428) INFO: decoder input length: 47 +2024-01-16 21:43:28,249 (beam_search:429) INFO: max output length: 47 +2024-01-16 21:43:28,249 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,297 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,297 (beam_search:476) INFO: -9.76 * 1.0 = -9.76 for ctc +2024-01-16 21:43:28,297 (beam_search:479) INFO: total log probability: -9.76 +2024-01-16 21:43:28,297 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 21:43:28,297 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,297 (beam_search:483) INFO: best hypo: INCLTDINGJHRMNLTOCETCNALDE + +2024-01-16 21:43:28,298 (asr_inference:494) INFO: speech length: 29440 +2024-01-16 21:43:28,305 (beam_search:428) INFO: decoder input length: 43 +2024-01-16 21:43:28,305 (beam_search:429) INFO: max output length: 43 +2024-01-16 21:43:28,305 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,350 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,350 (beam_search:476) INFO: -6.28 * 1.0 = -6.28 for ctc +2024-01-16 21:43:28,350 (beam_search:479) INFO: total log probability: -6.28 +2024-01-16 21:43:28,350 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:28,350 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,350 (beam_search:483) INFO: best hypo: PERNCOFLETHSHOUGESORBOUT + +2024-01-16 21:43:28,352 (asr_inference:494) INFO: speech length: 21920 +2024-01-16 21:43:28,358 (beam_search:428) INFO: decoder input length: 32 +2024-01-16 21:43:28,358 (beam_search:429) INFO: max output length: 32 +2024-01-16 21:43:28,358 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,383 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,384 (beam_search:476) INFO: -5.37 * 1.0 = -5.37 for ctc +2024-01-16 21:43:28,384 (beam_search:479) INFO: total log probability: -5.37 +2024-01-16 21:43:28,384 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:43:28,384 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,384 (beam_search:483) INFO: best hypo: NDATTINSCTDYTHRE + +2024-01-16 21:43:28,385 (asr_inference:494) INFO: speech length: 37920 +2024-01-16 21:43:28,392 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:43:28,392 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:43:28,392 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,459 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,459 (beam_search:476) INFO: -9.85 * 1.0 = -9.85 for ctc +2024-01-16 21:43:28,459 (beam_search:479) INFO: total log probability: -9.85 +2024-01-16 21:43:28,459 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:43:28,459 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,459 (beam_search:483) INFO: best hypo: ANOFECTERSSHUCAEPRODACEOLSOISOL + +2024-01-16 21:43:28,460 (asr_inference:494) INFO: speech length: 38240 +2024-01-16 21:43:28,468 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:43:28,468 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:43:28,468 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,535 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,535 (beam_search:476) INFO: -9.98 * 1.0 = -9.98 for ctc +2024-01-16 21:43:28,535 (beam_search:479) INFO: total log probability: -9.98 +2024-01-16 21:43:28,535 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:43:28,535 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,535 (beam_search:483) INFO: best hypo: OTHEFIRSTNOANSOLRETCHALNGERSTEN + +2024-01-16 21:43:28,536 (asr_inference:494) INFO: speech length: 29280 +2024-01-16 21:43:28,543 (beam_search:428) INFO: decoder input length: 43 +2024-01-16 21:43:28,543 (beam_search:429) INFO: max output length: 43 +2024-01-16 21:43:28,543 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,586 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,586 (beam_search:476) INFO: -3.33 * 1.0 = -3.33 for ctc +2024-01-16 21:43:28,586 (beam_search:479) INFO: total log probability: -3.33 +2024-01-16 21:43:28,586 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:43:28,586 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,586 (beam_search:483) INFO: best hypo: PONENTHASONLYTHECINGAND + +2024-01-16 21:43:28,587 (asr_inference:494) INFO: speech length: 16480 +2024-01-16 21:43:28,594 (beam_search:428) INFO: decoder input length: 23 +2024-01-16 21:43:28,594 (beam_search:429) INFO: max output length: 23 +2024-01-16 21:43:28,594 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,608 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,608 (beam_search:476) INFO: -1.28 * 1.0 = -1.28 for ctc +2024-01-16 21:43:28,608 (beam_search:479) INFO: total log probability: -1.28 +2024-01-16 21:43:28,608 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 21:43:28,608 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,608 (beam_search:483) INFO: best hypo: MAYNARTICLE + +2024-01-16 21:43:28,609 (asr_inference:494) INFO: speech length: 32320 +2024-01-16 21:43:28,616 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 21:43:28,616 (beam_search:429) INFO: max output length: 48 +2024-01-16 21:43:28,616 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,670 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,670 (beam_search:476) INFO: -6.02 * 1.0 = -6.02 for ctc +2024-01-16 21:43:28,670 (beam_search:479) INFO: total log probability: -6.02 +2024-01-16 21:43:28,670 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:28,670 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,670 (beam_search:483) INFO: best hypo: OWNDSERTNLATHSOUSFULFORFITIN + +2024-01-16 21:43:28,672 (asr_inference:494) INFO: speech length: 46880 +2024-01-16 21:43:28,680 (beam_search:428) INFO: decoder input length: 71 +2024-01-16 21:43:28,680 (beam_search:429) INFO: max output length: 71 +2024-01-16 21:43:28,680 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,781 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,781 (beam_search:476) INFO: -3.97 * 1.0 = -3.97 for ctc +2024-01-16 21:43:28,781 (beam_search:479) INFO: total log probability: -3.97 +2024-01-16 21:43:28,781 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 21:43:28,781 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,781 (beam_search:483) INFO: best hypo: TAPEINTHESAMEFOREFACTDERSTHECOMPACTODIO + +2024-01-16 21:43:28,782 (asr_inference:494) INFO: speech length: 31680 +2024-01-16 21:43:28,790 (beam_search:428) INFO: decoder input length: 47 +2024-01-16 21:43:28,790 (beam_search:429) INFO: max output length: 47 +2024-01-16 21:43:28,790 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,838 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,839 (beam_search:476) INFO: -3.74 * 1.0 = -3.74 for ctc +2024-01-16 21:43:28,839 (beam_search:479) INFO: total log probability: -3.74 +2024-01-16 21:43:28,839 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:28,839 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,839 (beam_search:483) INFO: best hypo: SENTHERWASLATEECSPUNGEDFRO + +2024-01-16 21:43:28,840 (asr_inference:494) INFO: speech length: 20160 +2024-01-16 21:43:28,846 (beam_search:428) INFO: decoder input length: 29 +2024-01-16 21:43:28,846 (beam_search:429) INFO: max output length: 29 +2024-01-16 21:43:28,846 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,869 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,869 (beam_search:476) INFO: -4.14 * 1.0 = -4.14 for ctc +2024-01-16 21:43:28,870 (beam_search:479) INFO: total log probability: -4.14 +2024-01-16 21:43:28,870 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:28,870 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,870 (beam_search:483) INFO: best hypo: RDESECTOACQUALITY + +2024-01-16 21:43:28,871 (asr_inference:494) INFO: speech length: 50080 +2024-01-16 21:43:28,879 (beam_search:428) INFO: decoder input length: 76 +2024-01-16 21:43:28,879 (beam_search:429) INFO: max output length: 76 +2024-01-16 21:43:28,879 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:28,968 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:28,968 (beam_search:476) INFO: -6.41 * 1.0 = -6.41 for ctc +2024-01-16 21:43:28,969 (beam_search:479) INFO: total log probability: -6.41 +2024-01-16 21:43:28,969 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:28,969 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:28,969 (beam_search:483) INFO: best hypo: ISFORTHASENSACHUNTREDBYSCTYFET + +2024-01-16 21:43:28,970 (asr_inference:494) INFO: speech length: 21760 +2024-01-16 21:43:28,977 (beam_search:428) INFO: decoder input length: 31 +2024-01-16 21:43:28,977 (beam_search:429) INFO: max output length: 31 +2024-01-16 21:43:28,977 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:29,000 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:29,000 (beam_search:476) INFO: -5.09 * 1.0 = -5.09 for ctc +2024-01-16 21:43:29,000 (beam_search:479) INFO: total log probability: -5.09 +2024-01-16 21:43:29,000 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:43:29,000 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:29,000 (beam_search:483) INFO: best hypo: NITINSOVENDYTHRE + +2024-01-16 21:43:29,001 (asr_inference:494) INFO: speech length: 38080 +2024-01-16 21:43:29,009 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:43:29,009 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:43:29,009 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:29,072 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:29,072 (beam_search:476) INFO: -6.21 * 1.0 = -6.21 for ctc +2024-01-16 21:43:29,072 (beam_search:479) INFO: total log probability: -6.21 +2024-01-16 21:43:29,072 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:29,072 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:29,072 (beam_search:483) INFO: best hypo: ROLLAPLAIRMALSOLOUEBYRUNIN + +2024-01-16 21:43:29,073 (asr_inference:494) INFO: speech length: 39520 +2024-01-16 21:43:29,081 (beam_search:428) INFO: decoder input length: 59 +2024-01-16 21:43:29,081 (beam_search:429) INFO: max output length: 59 +2024-01-16 21:43:29,081 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:29,149 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:29,149 (beam_search:476) INFO: -7.89 * 1.0 = -7.89 for ctc +2024-01-16 21:43:29,149 (beam_search:479) INFO: total log probability: -7.89 +2024-01-16 21:43:29,149 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:43:29,149 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:29,149 (beam_search:483) INFO: best hypo: BLOKHLHPOFESSERGRAGRYSTOACPOIN + +2024-01-16 21:43:29,151 (asr_inference:494) INFO: speech length: 74240 +2024-01-16 21:43:29,160 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 21:43:29,160 (beam_search:429) INFO: max output length: 113 +2024-01-16 21:43:29,160 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:29,393 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:29,393 (beam_search:476) INFO: -13.61 * 1.0 = -13.61 for ctc +2024-01-16 21:43:29,393 (beam_search:479) INFO: total log probability: -13.61 +2024-01-16 21:43:29,393 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:29,393 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:29,393 (beam_search:483) INFO: best hypo: BROWNASLECTETTHHOUSEOREPERSENTIVSFORTHEREYNONCENSECKITIFTERMS + +2024-01-16 21:43:29,395 (asr_inference:494) INFO: speech length: 17280 +2024-01-16 21:43:29,401 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 21:43:29,401 (beam_search:429) INFO: max output length: 24 +2024-01-16 21:43:29,401 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:29,420 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:29,420 (beam_search:476) INFO: -5.23 * 1.0 = -5.23 for ctc +2024-01-16 21:43:29,420 (beam_search:479) INFO: total log probability: -5.23 +2024-01-16 21:43:29,420 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:43:29,420 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:29,420 (beam_search:483) INFO: best hypo: OREGSISTHAPLYW + +2024-01-16 21:43:29,421 (asr_inference:494) INFO: speech length: 21600 +2024-01-16 21:43:29,428 (beam_search:428) INFO: decoder input length: 31 +2024-01-16 21:43:29,428 (beam_search:429) INFO: max output length: 31 +2024-01-16 21:43:29,428 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:29,456 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:29,456 (beam_search:476) INFO: -4.28 * 1.0 = -4.28 for ctc +2024-01-16 21:43:29,456 (beam_search:479) INFO: total log probability: -4.28 +2024-01-16 21:43:29,456 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:29,456 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:29,456 (beam_search:483) INFO: best hypo: AGRUPONEMLSTHATRAC + +2024-01-16 21:43:29,457 (asr_inference:494) INFO: speech length: 16160 +2024-01-16 21:43:29,463 (beam_search:428) INFO: decoder input length: 23 +2024-01-16 21:43:29,463 (beam_search:429) INFO: max output length: 23 +2024-01-16 21:43:29,463 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:29,481 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:29,481 (beam_search:476) INFO: -2.43 * 1.0 = -2.43 for ctc +2024-01-16 21:43:29,481 (beam_search:479) INFO: total log probability: -2.43 +2024-01-16 21:43:29,481 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:29,481 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:29,481 (beam_search:483) INFO: best hypo: NDTHHLDSLAGEST + +2024-01-16 21:43:29,483 (asr_inference:494) INFO: speech length: 43680 +2024-01-16 21:43:29,490 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:43:29,491 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:43:29,491 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:29,577 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:29,577 (beam_search:476) INFO: -8.04 * 1.0 = -8.04 for ctc +2024-01-16 21:43:29,577 (beam_search:479) INFO: total log probability: -8.04 +2024-01-16 21:43:29,577 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:29,577 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:29,578 (beam_search:483) INFO: best hypo: BREADINGTAKSPLACEBETWENAPROLANDDUN + +2024-01-16 21:43:29,579 (asr_inference:494) INFO: speech length: 26400 +2024-01-16 21:43:29,586 (beam_search:428) INFO: decoder input length: 39 +2024-01-16 21:43:29,586 (beam_search:429) INFO: max output length: 39 +2024-01-16 21:43:29,586 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:29,624 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:29,624 (beam_search:476) INFO: -3.92 * 1.0 = -3.92 for ctc +2024-01-16 21:43:29,624 (beam_search:479) INFO: total log probability: -3.92 +2024-01-16 21:43:29,624 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:43:29,624 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:29,624 (beam_search:483) INFO: best hypo: STRALORISATHESOTHENEIND + +2024-01-16 21:43:29,625 (asr_inference:494) INFO: speech length: 36320 +2024-01-16 21:43:29,633 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 21:43:29,633 (beam_search:429) INFO: max output length: 54 +2024-01-16 21:43:29,633 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:29,699 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:29,699 (beam_search:476) INFO: -6.94 * 1.0 = -6.94 for ctc +2024-01-16 21:43:29,699 (beam_search:479) INFO: total log probability: -6.94 +2024-01-16 21:43:29,699 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:29,699 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:29,699 (beam_search:483) INFO: best hypo: TECNHLOUGHCLSINGEILAIRITYISPOSEABL + +2024-01-16 21:43:29,700 (asr_inference:494) INFO: speech length: 22080 +2024-01-16 21:43:29,707 (beam_search:428) INFO: decoder input length: 32 +2024-01-16 21:43:29,707 (beam_search:429) INFO: max output length: 32 +2024-01-16 21:43:29,707 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:29,732 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:29,732 (beam_search:476) INFO: -4.92 * 1.0 = -4.92 for ctc +2024-01-16 21:43:29,732 (beam_search:479) INFO: total log probability: -4.92 +2024-01-16 21:43:29,732 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:29,732 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:29,732 (beam_search:483) INFO: best hypo: NLDINGTHSPLPYCOD + +2024-01-16 21:43:29,734 (asr_inference:494) INFO: speech length: 56320 +2024-01-16 21:43:29,742 (beam_search:428) INFO: decoder input length: 85 +2024-01-16 21:43:29,742 (beam_search:429) INFO: max output length: 85 +2024-01-16 21:43:29,742 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:29,873 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:29,873 (beam_search:476) INFO: -9.71 * 1.0 = -9.71 for ctc +2024-01-16 21:43:29,873 (beam_search:479) INFO: total log probability: -9.71 +2024-01-16 21:43:29,873 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:29,873 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:29,873 (beam_search:483) INFO: best hypo: ESETYFOREHADAHIGEREGOCATIONOLFECATIONCOMPEDET + +2024-01-16 21:43:29,874 (asr_inference:494) INFO: speech length: 29600 +2024-01-16 21:43:29,881 (beam_search:428) INFO: decoder input length: 44 +2024-01-16 21:43:29,881 (beam_search:429) INFO: max output length: 44 +2024-01-16 21:43:29,881 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:29,927 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:29,927 (beam_search:476) INFO: -8.67 * 1.0 = -8.67 for ctc +2024-01-16 21:43:29,927 (beam_search:479) INFO: total log probability: -8.67 +2024-01-16 21:43:29,927 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 21:43:29,927 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:29,928 (beam_search:483) INFO: best hypo: IACPERSWHETHEBPONISCANGSAN + +2024-01-16 21:43:29,928 (asr_inference:494) INFO: speech length: 24160 +2024-01-16 21:43:29,935 (beam_search:428) INFO: decoder input length: 35 +2024-01-16 21:43:29,935 (beam_search:429) INFO: max output length: 35 +2024-01-16 21:43:29,935 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:29,966 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:29,966 (beam_search:476) INFO: -4.48 * 1.0 = -4.48 for ctc +2024-01-16 21:43:29,966 (beam_search:479) INFO: total log probability: -4.48 +2024-01-16 21:43:29,966 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:29,966 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:29,967 (beam_search:483) INFO: best hypo: CONCEVATIONNOSTRELYAR + +2024-01-16 21:43:29,968 (asr_inference:494) INFO: speech length: 19840 +2024-01-16 21:43:29,974 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 21:43:29,974 (beam_search:429) INFO: max output length: 28 +2024-01-16 21:43:29,974 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:30,000 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:30,000 (beam_search:476) INFO: -5.40 * 1.0 = -5.40 for ctc +2024-01-16 21:43:30,000 (beam_search:479) INFO: total log probability: -5.40 +2024-01-16 21:43:30,000 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:30,000 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:30,000 (beam_search:483) INFO: best hypo: ISTHESELAMANDOFFEICU + +2024-01-16 21:43:30,001 (asr_inference:494) INFO: speech length: 58400 +2024-01-16 21:43:30,010 (beam_search:428) INFO: decoder input length: 89 +2024-01-16 21:43:30,010 (beam_search:429) INFO: max output length: 89 +2024-01-16 21:43:30,010 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:30,150 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:30,150 (beam_search:476) INFO: -8.88 * 1.0 = -8.88 for ctc +2024-01-16 21:43:30,150 (beam_search:479) INFO: total log probability: -8.88 +2024-01-16 21:43:30,150 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:30,150 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:30,150 (beam_search:483) INFO: best hypo: FIRSTSELFDECRIGVETRANHUMONSTHATFORMILYINTHEAL + +# Accounting: time=27 threads=1 +# Ended (code 0) at Tue Jan 16 21:43:30 CST 2024, elapsed time 27 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.4.log b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.4.log new file mode 100644 index 0000000000000000000000000000000000000000..15561274fcb51de97d5b2d76a3d72bdea6b9bb91 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.4.log @@ -0,0 +1,3022 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.4 --config conf/decode_asr.yaml +# Started at Tue Jan 16 21:43:30 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.4 --config conf/decode_asr.yaml +2024-01-16 21:43:31,966 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml +2024-01-16 21:43:31,984 (asr:523) INFO: Vocabulary size: 30 +2024-01-16 21:43:32,045 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 21:43:32,046 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 21:43:32,156 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 21:43:33,444 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 21:43:34,650 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 21:43:34,650 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 21:43:34,650 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 21:43:34,683 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-16 21:43:34,757 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 21:43:34,869 (asr_inference:494) INFO: speech length: 51520 +2024-01-16 21:43:36,066 (beam_search:428) INFO: decoder input length: 78 +2024-01-16 21:43:36,066 (beam_search:429) INFO: max output length: 78 +2024-01-16 21:43:36,066 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:36,186 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:36,186 (beam_search:476) INFO: -9.76 * 1.0 = -9.76 for ctc +2024-01-16 21:43:36,186 (beam_search:479) INFO: total log probability: -9.76 +2024-01-16 21:43:36,186 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:36,186 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:36,187 (beam_search:483) INFO: best hypo: REENTRESURCHEINDECATDTHTFACTERSOTHETHENPACDI + +2024-01-16 21:43:36,211 (asr_inference:494) INFO: speech length: 39520 +2024-01-16 21:43:36,220 (beam_search:428) INFO: decoder input length: 59 +2024-01-16 21:43:36,220 (beam_search:429) INFO: max output length: 59 +2024-01-16 21:43:36,220 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:36,297 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:36,297 (beam_search:476) INFO: -9.50 * 1.0 = -9.50 for ctc +2024-01-16 21:43:36,297 (beam_search:479) INFO: total log probability: -9.50 +2024-01-16 21:43:36,297 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:36,297 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:36,297 (beam_search:483) INFO: best hypo: ANDPRVENTIONANDTREKBENTOFOMPLCATIONS + +2024-01-16 21:43:36,298 (asr_inference:494) INFO: speech length: 19840 +2024-01-16 21:43:36,305 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 21:43:36,305 (beam_search:429) INFO: max output length: 28 +2024-01-16 21:43:36,305 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:36,326 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:36,326 (beam_search:476) INFO: -3.84 * 1.0 = -3.84 for ctc +2024-01-16 21:43:36,326 (beam_search:479) INFO: total log probability: -3.84 +2024-01-16 21:43:36,326 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:36,326 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:36,326 (beam_search:483) INFO: best hypo: IHEREAEDONSAIT + +2024-01-16 21:43:36,327 (asr_inference:494) INFO: speech length: 30560 +2024-01-16 21:43:36,335 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 21:43:36,335 (beam_search:429) INFO: max output length: 45 +2024-01-16 21:43:36,335 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:36,383 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:36,383 (beam_search:476) INFO: -9.40 * 1.0 = -9.40 for ctc +2024-01-16 21:43:36,383 (beam_search:479) INFO: total log probability: -9.40 +2024-01-16 21:43:36,383 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:43:36,383 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:36,383 (beam_search:483) INFO: best hypo: OUTOWWORTHEFONDATIOASBAIE + +2024-01-16 21:43:36,385 (asr_inference:494) INFO: speech length: 108160 +2024-01-16 21:43:36,397 (beam_search:428) INFO: decoder input length: 166 +2024-01-16 21:43:36,397 (beam_search:429) INFO: max output length: 166 +2024-01-16 21:43:36,397 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:36,886 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:36,886 (beam_search:476) INFO: -17.74 * 1.0 = -17.74 for ctc +2024-01-16 21:43:36,886 (beam_search:479) INFO: total log probability: -17.74 +2024-01-16 21:43:36,886 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:36,886 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:36,887 (beam_search:483) INFO: best hypo: NOWADAYSOURLYREAGINLCSPRESTRANDBETNBURNANDHSPEITHTOBRIGANDFRATTRANESCONTINUETORUNOTHMUTONTRALW + +2024-01-16 21:43:36,888 (asr_inference:494) INFO: speech length: 67200 +2024-01-16 21:43:36,898 (beam_search:428) INFO: decoder input length: 102 +2024-01-16 21:43:36,898 (beam_search:429) INFO: max output length: 102 +2024-01-16 21:43:36,898 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:37,091 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:37,091 (beam_search:476) INFO: -9.81 * 1.0 = -9.81 for ctc +2024-01-16 21:43:37,091 (beam_search:479) INFO: total log probability: -9.81 +2024-01-16 21:43:37,091 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:43:37,091 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:37,091 (beam_search:483) INFO: best hypo: THEFAMLYSWETHPTINCRLYGONDWANEANORIGIONINCLDTHERETRPONEDAY + +2024-01-16 21:43:37,092 (asr_inference:494) INFO: speech length: 55680 +2024-01-16 21:43:37,101 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 21:43:37,101 (beam_search:429) INFO: max output length: 84 +2024-01-16 21:43:37,101 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:37,229 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:37,229 (beam_search:476) INFO: -14.13 * 1.0 = -14.13 for ctc +2024-01-16 21:43:37,229 (beam_search:479) INFO: total log probability: -14.13 +2024-01-16 21:43:37,229 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:43:37,229 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:37,229 (beam_search:483) INFO: best hypo: BYANETALINDOAMIKCENDMOACJACCOBISDESESTLES + +2024-01-16 21:43:37,230 (asr_inference:494) INFO: speech length: 18240 +2024-01-16 21:43:37,237 (beam_search:428) INFO: decoder input length: 26 +2024-01-16 21:43:37,237 (beam_search:429) INFO: max output length: 26 +2024-01-16 21:43:37,237 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:37,260 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:37,260 (beam_search:476) INFO: -2.36 * 1.0 = -2.36 for ctc +2024-01-16 21:43:37,260 (beam_search:479) INFO: total log probability: -2.36 +2024-01-16 21:43:37,260 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 21:43:37,260 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:37,260 (beam_search:483) INFO: best hypo: MANDWASNAIEDAFTRT + +2024-01-16 21:43:37,261 (asr_inference:494) INFO: speech length: 20320 +2024-01-16 21:43:37,267 (beam_search:428) INFO: decoder input length: 29 +2024-01-16 21:43:37,268 (beam_search:429) INFO: max output length: 29 +2024-01-16 21:43:37,268 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:37,292 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:37,292 (beam_search:476) INFO: -3.83 * 1.0 = -3.83 for ctc +2024-01-16 21:43:37,292 (beam_search:479) INFO: total log probability: -3.83 +2024-01-16 21:43:37,292 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:37,292 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:37,292 (beam_search:483) INFO: best hypo: ARDOFIHALNTELIGENCS + +2024-01-16 21:43:37,293 (asr_inference:494) INFO: speech length: 27200 +2024-01-16 21:43:37,300 (beam_search:428) INFO: decoder input length: 40 +2024-01-16 21:43:37,301 (beam_search:429) INFO: max output length: 40 +2024-01-16 21:43:37,301 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:37,327 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:37,327 (beam_search:476) INFO: -0.96 * 1.0 = -0.96 for ctc +2024-01-16 21:43:37,328 (beam_search:479) INFO: total log probability: -0.96 +2024-01-16 21:43:37,328 (beam_search:480) INFO: normalized log probability: -0.05 +2024-01-16 21:43:37,328 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:37,328 (beam_search:483) INFO: best hypo: ANDISTHERAING + +2024-01-16 21:43:37,329 (asr_inference:494) INFO: speech length: 19520 +2024-01-16 21:43:37,335 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 21:43:37,335 (beam_search:429) INFO: max output length: 28 +2024-01-16 21:43:37,335 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:37,359 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:37,359 (beam_search:476) INFO: -3.52 * 1.0 = -3.52 for ctc +2024-01-16 21:43:37,359 (beam_search:479) INFO: total log probability: -3.52 +2024-01-16 21:43:37,359 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:37,359 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:37,359 (beam_search:483) INFO: best hypo: PRSEOFTHOPILATION + +2024-01-16 21:43:37,360 (asr_inference:494) INFO: speech length: 38400 +2024-01-16 21:43:37,368 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:43:37,368 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:43:37,368 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:37,424 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:37,424 (beam_search:476) INFO: -8.59 * 1.0 = -8.59 for ctc +2024-01-16 21:43:37,424 (beam_search:479) INFO: total log probability: -8.59 +2024-01-16 21:43:37,424 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:43:37,424 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:37,424 (beam_search:483) INFO: best hypo: CHOEARIESOFSOUPLICHSELS + +2024-01-16 21:43:37,426 (asr_inference:494) INFO: speech length: 17600 +2024-01-16 21:43:37,433 (beam_search:428) INFO: decoder input length: 25 +2024-01-16 21:43:37,433 (beam_search:429) INFO: max output length: 25 +2024-01-16 21:43:37,433 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:37,447 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:37,448 (beam_search:476) INFO: -2.42 * 1.0 = -2.42 for ctc +2024-01-16 21:43:37,448 (beam_search:479) INFO: total log probability: -2.42 +2024-01-16 21:43:37,448 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:37,448 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:37,448 (beam_search:483) INFO: best hypo: INPOSEBYLA + +2024-01-16 21:43:37,449 (asr_inference:494) INFO: speech length: 59680 +2024-01-16 21:43:37,458 (beam_search:428) INFO: decoder input length: 91 +2024-01-16 21:43:37,458 (beam_search:429) INFO: max output length: 91 +2024-01-16 21:43:37,458 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:37,592 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:37,592 (beam_search:476) INFO: -7.57 * 1.0 = -7.57 for ctc +2024-01-16 21:43:37,593 (beam_search:479) INFO: total log probability: -7.57 +2024-01-16 21:43:37,593 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:37,593 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:37,593 (beam_search:483) INFO: best hypo: REFRONCEISESTOTHEROLINGCORLITIONGOVBERMEN + +2024-01-16 21:43:37,594 (asr_inference:494) INFO: speech length: 23680 +2024-01-16 21:43:37,601 (beam_search:428) INFO: decoder input length: 34 +2024-01-16 21:43:37,601 (beam_search:429) INFO: max output length: 34 +2024-01-16 21:43:37,601 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:37,630 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:37,630 (beam_search:476) INFO: -3.95 * 1.0 = -3.95 for ctc +2024-01-16 21:43:37,630 (beam_search:479) INFO: total log probability: -3.95 +2024-01-16 21:43:37,630 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:37,630 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:37,630 (beam_search:483) INFO: best hypo: SPAHESOFGLDINGPOSM + +2024-01-16 21:43:37,631 (asr_inference:494) INFO: speech length: 36800 +2024-01-16 21:43:37,639 (beam_search:428) INFO: decoder input length: 55 +2024-01-16 21:43:37,639 (beam_search:429) INFO: max output length: 55 +2024-01-16 21:43:37,639 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:37,704 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:37,704 (beam_search:476) INFO: -5.73 * 1.0 = -5.73 for ctc +2024-01-16 21:43:37,704 (beam_search:479) INFO: total log probability: -5.73 +2024-01-16 21:43:37,704 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:37,704 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:37,704 (beam_search:483) INFO: best hypo: BACSEDONTHEPREVISSTRADGEYOFPLAY + +2024-01-16 21:43:37,705 (asr_inference:494) INFO: speech length: 28640 +2024-01-16 21:43:37,712 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 21:43:37,712 (beam_search:429) INFO: max output length: 42 +2024-01-16 21:43:37,712 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:37,751 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:37,751 (beam_search:476) INFO: -7.19 * 1.0 = -7.19 for ctc +2024-01-16 21:43:37,751 (beam_search:479) INFO: total log probability: -7.19 +2024-01-16 21:43:37,751 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 21:43:37,751 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:37,751 (beam_search:483) INFO: best hypo: ADIDDULISTCASPERATIONE + +2024-01-16 21:43:37,752 (asr_inference:494) INFO: speech length: 41440 +2024-01-16 21:43:37,760 (beam_search:428) INFO: decoder input length: 62 +2024-01-16 21:43:37,760 (beam_search:429) INFO: max output length: 62 +2024-01-16 21:43:37,760 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:37,838 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:37,838 (beam_search:476) INFO: -6.35 * 1.0 = -6.35 for ctc +2024-01-16 21:43:37,838 (beam_search:479) INFO: total log probability: -6.35 +2024-01-16 21:43:37,838 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:37,838 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:37,839 (beam_search:483) INFO: best hypo: PERFETONLSANDHOMRECOARINGOTHUSIASTS + +2024-01-16 21:43:37,840 (asr_inference:494) INFO: speech length: 16640 +2024-01-16 21:43:37,847 (beam_search:428) INFO: decoder input length: 23 +2024-01-16 21:43:37,847 (beam_search:429) INFO: max output length: 23 +2024-01-16 21:43:37,847 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:37,862 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:37,862 (beam_search:476) INFO: -3.88 * 1.0 = -3.88 for ctc +2024-01-16 21:43:37,862 (beam_search:479) INFO: total log probability: -3.88 +2024-01-16 21:43:37,862 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:37,862 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:37,862 (beam_search:483) INFO: best hypo: HEMTHOLPODAY + +2024-01-16 21:43:37,863 (asr_inference:494) INFO: speech length: 80320 +2024-01-16 21:43:37,874 (beam_search:428) INFO: decoder input length: 123 +2024-01-16 21:43:37,874 (beam_search:429) INFO: max output length: 123 +2024-01-16 21:43:37,874 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:38,117 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:38,117 (beam_search:476) INFO: -19.92 * 1.0 = -19.92 for ctc +2024-01-16 21:43:38,117 (beam_search:479) INFO: total log probability: -19.92 +2024-01-16 21:43:38,117 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:43:38,117 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:38,118 (beam_search:483) INFO: best hypo: NOCORTEOFPEPLWITHEPREAVISESAYAGCHMADDVELOUHIGPOPETHURTRISN + +2024-01-16 21:43:38,119 (asr_inference:494) INFO: speech length: 28480 +2024-01-16 21:43:38,126 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 21:43:38,126 (beam_search:429) INFO: max output length: 42 +2024-01-16 21:43:38,126 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:38,171 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:38,171 (beam_search:476) INFO: -5.63 * 1.0 = -5.63 for ctc +2024-01-16 21:43:38,171 (beam_search:479) INFO: total log probability: -5.63 +2024-01-16 21:43:38,171 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:38,171 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:38,172 (beam_search:483) INFO: best hypo: DIVIRDEDINTOTHRYFAMLYESTHA + +2024-01-16 21:43:38,173 (asr_inference:494) INFO: speech length: 39520 +2024-01-16 21:43:38,180 (beam_search:428) INFO: decoder input length: 59 +2024-01-16 21:43:38,180 (beam_search:429) INFO: max output length: 59 +2024-01-16 21:43:38,180 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:38,245 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:38,246 (beam_search:476) INFO: -5.42 * 1.0 = -5.42 for ctc +2024-01-16 21:43:38,246 (beam_search:479) INFO: total log probability: -5.42 +2024-01-16 21:43:38,246 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:38,246 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:38,246 (beam_search:483) INFO: best hypo: HODSLITINTRESTANRLEACINGOSET + +2024-01-16 21:43:38,247 (asr_inference:494) INFO: speech length: 27200 +2024-01-16 21:43:38,254 (beam_search:428) INFO: decoder input length: 40 +2024-01-16 21:43:38,254 (beam_search:429) INFO: max output length: 40 +2024-01-16 21:43:38,254 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:38,295 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:38,295 (beam_search:476) INFO: -6.96 * 1.0 = -6.96 for ctc +2024-01-16 21:43:38,295 (beam_search:479) INFO: total log probability: -6.96 +2024-01-16 21:43:38,295 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:38,295 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:38,295 (beam_search:483) INFO: best hypo: THATAHRMIRANOUTHAVECOMEN + +2024-01-16 21:43:38,296 (asr_inference:494) INFO: speech length: 16800 +2024-01-16 21:43:38,303 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 21:43:38,303 (beam_search:429) INFO: max output length: 24 +2024-01-16 21:43:38,303 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:38,323 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:38,323 (beam_search:476) INFO: -4.86 * 1.0 = -4.86 for ctc +2024-01-16 21:43:38,323 (beam_search:479) INFO: total log probability: -4.86 +2024-01-16 21:43:38,323 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:38,323 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:38,323 (beam_search:483) INFO: best hypo: ANTOWHUSENDSAICE + +2024-01-16 21:43:38,324 (asr_inference:494) INFO: speech length: 43200 +2024-01-16 21:43:38,332 (beam_search:428) INFO: decoder input length: 65 +2024-01-16 21:43:38,332 (beam_search:429) INFO: max output length: 65 +2024-01-16 21:43:38,332 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:38,409 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:38,409 (beam_search:476) INFO: -7.02 * 1.0 = -7.02 for ctc +2024-01-16 21:43:38,409 (beam_search:479) INFO: total log probability: -7.02 +2024-01-16 21:43:38,409 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:38,409 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:38,410 (beam_search:483) INFO: best hypo: SHWOHINEBORYESARNONASBUTPLIS + +2024-01-16 21:43:38,411 (asr_inference:494) INFO: speech length: 44160 +2024-01-16 21:43:38,419 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:43:38,419 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:43:38,419 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:38,506 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:38,506 (beam_search:476) INFO: -7.67 * 1.0 = -7.67 for ctc +2024-01-16 21:43:38,506 (beam_search:479) INFO: total log probability: -7.67 +2024-01-16 21:43:38,507 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:38,507 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:38,507 (beam_search:483) INFO: best hypo: THEOSEISRUHTHEROFASERIBLEANOURISTM + +2024-01-16 21:43:38,508 (asr_inference:494) INFO: speech length: 35360 +2024-01-16 21:43:38,515 (beam_search:428) INFO: decoder input length: 53 +2024-01-16 21:43:38,515 (beam_search:429) INFO: max output length: 53 +2024-01-16 21:43:38,515 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:38,580 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:38,581 (beam_search:476) INFO: -10.32 * 1.0 = -10.32 for ctc +2024-01-16 21:43:38,581 (beam_search:479) INFO: total log probability: -10.32 +2024-01-16 21:43:38,581 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:43:38,581 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:38,581 (beam_search:483) INFO: best hypo: MOSTOTHEMAGRYOUESTSMUSICOMNYES + +2024-01-16 21:43:38,582 (asr_inference:494) INFO: speech length: 107520 +2024-01-16 21:43:38,594 (beam_search:428) INFO: decoder input length: 165 +2024-01-16 21:43:38,594 (beam_search:429) INFO: max output length: 165 +2024-01-16 21:43:38,594 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:39,053 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:39,053 (beam_search:476) INFO: -18.13 * 1.0 = -18.13 for ctc +2024-01-16 21:43:39,053 (beam_search:479) INFO: total log probability: -18.13 +2024-01-16 21:43:39,053 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:39,053 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:39,054 (beam_search:483) INFO: best hypo: WENSTARRIOPPAIRORWNEMONOFONICTRACKISPLAEORRECORTEDHNTHETHAPESMOVINGANONDURECTIONANT + +2024-01-16 21:43:39,055 (asr_inference:494) INFO: speech length: 23520 +2024-01-16 21:43:39,062 (beam_search:428) INFO: decoder input length: 34 +2024-01-16 21:43:39,062 (beam_search:429) INFO: max output length: 34 +2024-01-16 21:43:39,062 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:39,090 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:39,090 (beam_search:476) INFO: -5.22 * 1.0 = -5.22 for ctc +2024-01-16 21:43:39,090 (beam_search:479) INFO: total log probability: -5.22 +2024-01-16 21:43:39,090 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:39,090 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:39,090 (beam_search:483) INFO: best hypo: HITCDEARLYFORMEAN + +2024-01-16 21:43:39,091 (asr_inference:494) INFO: speech length: 20160 +2024-01-16 21:43:39,098 (beam_search:428) INFO: decoder input length: 29 +2024-01-16 21:43:39,098 (beam_search:429) INFO: max output length: 29 +2024-01-16 21:43:39,098 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:39,124 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:39,124 (beam_search:476) INFO: -8.07 * 1.0 = -8.07 for ctc +2024-01-16 21:43:39,124 (beam_search:479) INFO: total log probability: -8.07 +2024-01-16 21:43:39,124 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-16 21:43:39,124 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:39,124 (beam_search:483) INFO: best hypo: STERTEAGIKFOLOSSOVER + +2024-01-16 21:43:39,125 (asr_inference:494) INFO: speech length: 30240 +2024-01-16 21:43:39,132 (beam_search:428) INFO: decoder input length: 45 +2024-01-16 21:43:39,132 (beam_search:429) INFO: max output length: 45 +2024-01-16 21:43:39,132 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:39,183 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:39,183 (beam_search:476) INFO: -7.79 * 1.0 = -7.79 for ctc +2024-01-16 21:43:39,183 (beam_search:479) INFO: total log probability: -7.79 +2024-01-16 21:43:39,183 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:39,183 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:39,184 (beam_search:483) INFO: best hypo: OSISIONGTBANTGCHESTDRNTHEGAE + +2024-01-16 21:43:39,185 (asr_inference:494) INFO: speech length: 16160 +2024-01-16 21:43:39,191 (beam_search:428) INFO: decoder input length: 23 +2024-01-16 21:43:39,191 (beam_search:429) INFO: max output length: 23 +2024-01-16 21:43:39,191 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:39,206 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:39,206 (beam_search:476) INFO: -4.45 * 1.0 = -4.45 for ctc +2024-01-16 21:43:39,206 (beam_search:479) INFO: total log probability: -4.45 +2024-01-16 21:43:39,206 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:43:39,206 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:39,206 (beam_search:483) INFO: best hypo: DOSEAUTWHELS + +2024-01-16 21:43:39,207 (asr_inference:494) INFO: speech length: 40800 +2024-01-16 21:43:39,215 (beam_search:428) INFO: decoder input length: 61 +2024-01-16 21:43:39,215 (beam_search:429) INFO: max output length: 61 +2024-01-16 21:43:39,215 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:39,287 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:39,287 (beam_search:476) INFO: -6.64 * 1.0 = -6.64 for ctc +2024-01-16 21:43:39,287 (beam_search:479) INFO: total log probability: -6.64 +2024-01-16 21:43:39,287 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:39,287 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:39,287 (beam_search:483) INFO: best hypo: ESPOSLOVERHISONBYILOUGICALATER + +2024-01-16 21:43:39,288 (asr_inference:494) INFO: speech length: 60320 +2024-01-16 21:43:39,297 (beam_search:428) INFO: decoder input length: 92 +2024-01-16 21:43:39,297 (beam_search:429) INFO: max output length: 92 +2024-01-16 21:43:39,297 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:39,467 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:39,467 (beam_search:476) INFO: -14.24 * 1.0 = -14.24 for ctc +2024-01-16 21:43:39,467 (beam_search:479) INFO: total log probability: -14.24 +2024-01-16 21:43:39,467 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:43:39,467 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:39,467 (beam_search:483) INFO: best hypo: REPEROUCDTOFRIHTESHOCSARTUNDOPRESTHERSONPROSPECTEDPARNC + +2024-01-16 21:43:39,468 (asr_inference:494) INFO: speech length: 19360 +2024-01-16 21:43:39,475 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 21:43:39,475 (beam_search:429) INFO: max output length: 28 +2024-01-16 21:43:39,475 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:39,499 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:39,499 (beam_search:476) INFO: -3.71 * 1.0 = -3.71 for ctc +2024-01-16 21:43:39,499 (beam_search:479) INFO: total log probability: -3.71 +2024-01-16 21:43:39,499 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:39,499 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:39,499 (beam_search:483) INFO: best hypo: TILANCHANDYNORGEON + +2024-01-16 21:43:39,500 (asr_inference:494) INFO: speech length: 26400 +2024-01-16 21:43:39,508 (beam_search:428) INFO: decoder input length: 39 +2024-01-16 21:43:39,508 (beam_search:429) INFO: max output length: 39 +2024-01-16 21:43:39,508 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:39,540 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:39,541 (beam_search:476) INFO: -3.44 * 1.0 = -3.44 for ctc +2024-01-16 21:43:39,541 (beam_search:479) INFO: total log probability: -3.44 +2024-01-16 21:43:39,541 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:43:39,541 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:39,541 (beam_search:483) INFO: best hypo: RASTPOFOLOUSHADGON + +2024-01-16 21:43:39,542 (asr_inference:494) INFO: speech length: 18240 +2024-01-16 21:43:39,548 (beam_search:428) INFO: decoder input length: 26 +2024-01-16 21:43:39,548 (beam_search:429) INFO: max output length: 26 +2024-01-16 21:43:39,548 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:39,568 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:39,568 (beam_search:476) INFO: -5.47 * 1.0 = -5.47 for ctc +2024-01-16 21:43:39,568 (beam_search:479) INFO: total log probability: -5.47 +2024-01-16 21:43:39,568 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:43:39,568 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:39,568 (beam_search:483) INFO: best hypo: NDTOTHISANDTO + +2024-01-16 21:43:39,569 (asr_inference:494) INFO: speech length: 34880 +2024-01-16 21:43:39,577 (beam_search:428) INFO: decoder input length: 52 +2024-01-16 21:43:39,577 (beam_search:429) INFO: max output length: 52 +2024-01-16 21:43:39,577 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:39,633 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:39,633 (beam_search:476) INFO: -8.09 * 1.0 = -8.09 for ctc +2024-01-16 21:43:39,633 (beam_search:479) INFO: total log probability: -8.09 +2024-01-16 21:43:39,633 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:39,633 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:39,634 (beam_search:483) INFO: best hypo: TFORENTAPLFTHEPLARHASONLD + +2024-01-16 21:43:39,635 (asr_inference:494) INFO: speech length: 57760 +2024-01-16 21:43:39,643 (beam_search:428) INFO: decoder input length: 88 +2024-01-16 21:43:39,643 (beam_search:429) INFO: max output length: 88 +2024-01-16 21:43:39,643 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:39,799 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:39,799 (beam_search:476) INFO: -12.54 * 1.0 = -12.54 for ctc +2024-01-16 21:43:39,799 (beam_search:479) INFO: total log probability: -12.54 +2024-01-16 21:43:39,799 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:39,799 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:39,799 (beam_search:483) INFO: best hypo: SUERDASUBRAKNODHEMIRGHAVECOLDNICOINMPAREMENTTHATOFET + +2024-01-16 21:43:39,800 (asr_inference:494) INFO: speech length: 24160 +2024-01-16 21:43:39,807 (beam_search:428) INFO: decoder input length: 35 +2024-01-16 21:43:39,807 (beam_search:429) INFO: max output length: 35 +2024-01-16 21:43:39,807 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:39,837 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:39,837 (beam_search:476) INFO: -5.44 * 1.0 = -5.44 for ctc +2024-01-16 21:43:39,837 (beam_search:479) INFO: total log probability: -5.44 +2024-01-16 21:43:39,837 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:39,837 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:39,838 (beam_search:483) INFO: best hypo: ROVIDEDRONOSTINDATEA + +2024-01-16 21:43:39,839 (asr_inference:494) INFO: speech length: 35680 +2024-01-16 21:43:39,846 (beam_search:428) INFO: decoder input length: 53 +2024-01-16 21:43:39,846 (beam_search:429) INFO: max output length: 53 +2024-01-16 21:43:39,846 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:39,908 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:39,909 (beam_search:476) INFO: -6.13 * 1.0 = -6.13 for ctc +2024-01-16 21:43:39,909 (beam_search:479) INFO: total log probability: -6.13 +2024-01-16 21:43:39,909 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:39,909 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:39,909 (beam_search:483) INFO: best hypo: OHADANURISNSDETECTIDBYTHEMANES + +2024-01-16 21:43:39,910 (asr_inference:494) INFO: speech length: 38880 +2024-01-16 21:43:39,918 (beam_search:428) INFO: decoder input length: 58 +2024-01-16 21:43:39,918 (beam_search:429) INFO: max output length: 58 +2024-01-16 21:43:39,918 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:39,991 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:39,991 (beam_search:476) INFO: -8.27 * 1.0 = -8.27 for ctc +2024-01-16 21:43:39,991 (beam_search:479) INFO: total log probability: -8.27 +2024-01-16 21:43:39,991 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:39,991 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:39,992 (beam_search:483) INFO: best hypo: LIHSTILDESINETIMPROEHELTHANNGEVITY + +2024-01-16 21:43:39,993 (asr_inference:494) INFO: speech length: 38880 +2024-01-16 21:43:40,000 (beam_search:428) INFO: decoder input length: 58 +2024-01-16 21:43:40,000 (beam_search:429) INFO: max output length: 58 +2024-01-16 21:43:40,000 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:40,075 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:40,075 (beam_search:476) INFO: -4.55 * 1.0 = -4.55 for ctc +2024-01-16 21:43:40,075 (beam_search:479) INFO: total log probability: -4.55 +2024-01-16 21:43:40,075 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:43:40,075 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:40,076 (beam_search:483) INFO: best hypo: HADMORSOFHISTICATEDANDOFTAYPRODCTION + +2024-01-16 21:43:40,077 (asr_inference:494) INFO: speech length: 20160 +2024-01-16 21:43:40,083 (beam_search:428) INFO: decoder input length: 29 +2024-01-16 21:43:40,083 (beam_search:429) INFO: max output length: 29 +2024-01-16 21:43:40,083 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:40,105 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:40,105 (beam_search:476) INFO: -3.92 * 1.0 = -3.92 for ctc +2024-01-16 21:43:40,105 (beam_search:479) INFO: total log probability: -3.92 +2024-01-16 21:43:40,105 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:40,105 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:40,105 (beam_search:483) INFO: best hypo: DEHOUMENISATION + +2024-01-16 21:43:40,107 (asr_inference:494) INFO: speech length: 36960 +2024-01-16 21:43:40,114 (beam_search:428) INFO: decoder input length: 55 +2024-01-16 21:43:40,114 (beam_search:429) INFO: max output length: 55 +2024-01-16 21:43:40,114 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:40,175 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:40,175 (beam_search:476) INFO: -12.37 * 1.0 = -12.37 for ctc +2024-01-16 21:43:40,175 (beam_search:479) INFO: total log probability: -12.37 +2024-01-16 21:43:40,176 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-16 21:43:40,176 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:40,176 (beam_search:483) INFO: best hypo: PACHYSINLDFREHWOATRLAMPROACES + +2024-01-16 21:43:40,177 (asr_inference:494) INFO: speech length: 18560 +2024-01-16 21:43:40,183 (beam_search:428) INFO: decoder input length: 26 +2024-01-16 21:43:40,183 (beam_search:429) INFO: max output length: 26 +2024-01-16 21:43:40,183 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:40,202 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:40,202 (beam_search:476) INFO: -3.46 * 1.0 = -3.46 for ctc +2024-01-16 21:43:40,202 (beam_search:479) INFO: total log probability: -3.46 +2024-01-16 21:43:40,202 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:40,202 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:40,202 (beam_search:483) INFO: best hypo: FOSTANDYOUGREAM + +2024-01-16 21:43:40,204 (asr_inference:494) INFO: speech length: 25760 +2024-01-16 21:43:40,211 (beam_search:428) INFO: decoder input length: 38 +2024-01-16 21:43:40,211 (beam_search:429) INFO: max output length: 38 +2024-01-16 21:43:40,211 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:40,242 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:40,242 (beam_search:476) INFO: -4.97 * 1.0 = -4.97 for ctc +2024-01-16 21:43:40,242 (beam_search:479) INFO: total log probability: -4.97 +2024-01-16 21:43:40,242 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:40,242 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:40,242 (beam_search:483) INFO: best hypo: HEFRYINSDKLPEADIAAT + +2024-01-16 21:43:40,243 (asr_inference:494) INFO: speech length: 35840 +2024-01-16 21:43:40,251 (beam_search:428) INFO: decoder input length: 53 +2024-01-16 21:43:40,251 (beam_search:429) INFO: max output length: 53 +2024-01-16 21:43:40,251 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:40,310 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:40,310 (beam_search:476) INFO: -7.93 * 1.0 = -7.93 for ctc +2024-01-16 21:43:40,310 (beam_search:479) INFO: total log probability: -7.93 +2024-01-16 21:43:40,310 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:40,310 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:40,310 (beam_search:483) INFO: best hypo: THEFORMETICLINAGEINGISDENRL + +2024-01-16 21:43:40,311 (asr_inference:494) INFO: speech length: 68160 +2024-01-16 21:43:40,321 (beam_search:428) INFO: decoder input length: 104 +2024-01-16 21:43:40,321 (beam_search:429) INFO: max output length: 104 +2024-01-16 21:43:40,321 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:40,493 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:40,493 (beam_search:476) INFO: -10.20 * 1.0 = -10.20 for ctc +2024-01-16 21:43:40,493 (beam_search:479) INFO: total log probability: -10.20 +2024-01-16 21:43:40,493 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:40,493 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:40,493 (beam_search:483) INFO: best hypo: PEASIISTTHECLUIONOFNONCHOUMENANDPARTHUMANANAMLS + +2024-01-16 21:43:40,494 (asr_inference:494) INFO: speech length: 47040 +2024-01-16 21:43:40,503 (beam_search:428) INFO: decoder input length: 71 +2024-01-16 21:43:40,503 (beam_search:429) INFO: max output length: 71 +2024-01-16 21:43:40,503 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:40,606 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:40,606 (beam_search:476) INFO: -8.56 * 1.0 = -8.56 for ctc +2024-01-16 21:43:40,606 (beam_search:479) INFO: total log probability: -8.56 +2024-01-16 21:43:40,606 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:40,606 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:40,606 (beam_search:483) INFO: best hypo: NDPEABLHOADRVISLYSUFEDASUBRACNOTHEMRIG + +2024-01-16 21:43:40,607 (asr_inference:494) INFO: speech length: 59520 +2024-01-16 21:43:40,616 (beam_search:428) INFO: decoder input length: 90 +2024-01-16 21:43:40,616 (beam_search:429) INFO: max output length: 90 +2024-01-16 21:43:40,616 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:40,755 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:40,755 (beam_search:476) INFO: -9.94 * 1.0 = -9.94 for ctc +2024-01-16 21:43:40,755 (beam_search:479) INFO: total log probability: -9.94 +2024-01-16 21:43:40,755 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:40,755 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:40,755 (beam_search:483) INFO: best hypo: LSIFIDASTHEINDANGEDORTHRETONDANDOTHEYPEBE + +2024-01-16 21:43:40,756 (asr_inference:494) INFO: speech length: 43520 +2024-01-16 21:43:40,764 (beam_search:428) INFO: decoder input length: 65 +2024-01-16 21:43:40,764 (beam_search:429) INFO: max output length: 65 +2024-01-16 21:43:40,764 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:40,846 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:40,846 (beam_search:476) INFO: -8.44 * 1.0 = -8.44 for ctc +2024-01-16 21:43:40,846 (beam_search:479) INFO: total log probability: -8.44 +2024-01-16 21:43:40,846 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:43:40,846 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:40,846 (beam_search:483) INFO: best hypo: AATERNYJENERLPARCERWATCOENSHARDN + +2024-01-16 21:43:40,848 (asr_inference:494) INFO: speech length: 17120 +2024-01-16 21:43:40,854 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 21:43:40,854 (beam_search:429) INFO: max output length: 24 +2024-01-16 21:43:40,854 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:40,870 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:40,870 (beam_search:476) INFO: -1.89 * 1.0 = -1.89 for ctc +2024-01-16 21:43:40,870 (beam_search:479) INFO: total log probability: -1.89 +2024-01-16 21:43:40,870 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:40,870 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:40,870 (beam_search:483) INFO: best hypo: BUTTIPICKLY + +2024-01-16 21:43:40,871 (asr_inference:494) INFO: speech length: 41440 +2024-01-16 21:43:40,879 (beam_search:428) INFO: decoder input length: 62 +2024-01-16 21:43:40,879 (beam_search:429) INFO: max output length: 62 +2024-01-16 21:43:40,879 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:40,966 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:40,966 (beam_search:476) INFO: -6.88 * 1.0 = -6.88 for ctc +2024-01-16 21:43:40,966 (beam_search:479) INFO: total log probability: -6.88 +2024-01-16 21:43:40,966 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:40,966 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:40,966 (beam_search:483) INFO: best hypo: HICHINTREFEDTHESINLTOTHEHEADOTHECOSSA + +2024-01-16 21:43:40,967 (asr_inference:494) INFO: speech length: 44800 +2024-01-16 21:43:40,976 (beam_search:428) INFO: decoder input length: 67 +2024-01-16 21:43:40,976 (beam_search:429) INFO: max output length: 67 +2024-01-16 21:43:40,976 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:41,072 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:41,072 (beam_search:476) INFO: -7.67 * 1.0 = -7.67 for ctc +2024-01-16 21:43:41,072 (beam_search:479) INFO: total log probability: -7.67 +2024-01-16 21:43:41,072 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:41,072 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:41,073 (beam_search:483) INFO: best hypo: HTHINTHERONCONVENIONLYECSPECTEDLIFETIMEMS + +2024-01-16 21:43:41,074 (asr_inference:494) INFO: speech length: 19840 +2024-01-16 21:43:41,080 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 21:43:41,080 (beam_search:429) INFO: max output length: 28 +2024-01-16 21:43:41,080 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:41,101 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:41,101 (beam_search:476) INFO: -5.69 * 1.0 = -5.69 for ctc +2024-01-16 21:43:41,101 (beam_search:479) INFO: total log probability: -5.69 +2024-01-16 21:43:41,101 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:43:41,101 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:41,101 (beam_search:483) INFO: best hypo: OBSTANHELSTRAIEN + +2024-01-16 21:43:41,102 (asr_inference:494) INFO: speech length: 48640 +2024-01-16 21:43:41,111 (beam_search:428) INFO: decoder input length: 73 +2024-01-16 21:43:41,111 (beam_search:429) INFO: max output length: 73 +2024-01-16 21:43:41,111 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:41,208 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:41,208 (beam_search:476) INFO: -7.84 * 1.0 = -7.84 for ctc +2024-01-16 21:43:41,208 (beam_search:479) INFO: total log probability: -7.84 +2024-01-16 21:43:41,208 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:41,208 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:41,208 (beam_search:483) INFO: best hypo: TWENYHAITHSENTRYCONTUCYCONGRSMENJOAN + +2024-01-16 21:43:41,210 (asr_inference:494) INFO: speech length: 20960 +2024-01-16 21:43:41,218 (beam_search:428) INFO: decoder input length: 30 +2024-01-16 21:43:41,218 (beam_search:429) INFO: max output length: 30 +2024-01-16 21:43:41,218 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:41,245 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:41,245 (beam_search:476) INFO: -5.68 * 1.0 = -5.68 for ctc +2024-01-16 21:43:41,245 (beam_search:479) INFO: total log probability: -5.68 +2024-01-16 21:43:41,245 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:43:41,245 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:41,245 (beam_search:483) INFO: best hypo: NOTHYPOSENTORNDIMA + +2024-01-16 21:43:41,247 (asr_inference:494) INFO: speech length: 37440 +2024-01-16 21:43:41,255 (beam_search:428) INFO: decoder input length: 56 +2024-01-16 21:43:41,255 (beam_search:429) INFO: max output length: 56 +2024-01-16 21:43:41,255 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:41,302 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:41,302 (beam_search:476) INFO: -4.31 * 1.0 = -4.31 for ctc +2024-01-16 21:43:41,302 (beam_search:479) INFO: total log probability: -4.31 +2024-01-16 21:43:41,302 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:41,302 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:41,302 (beam_search:483) INFO: best hypo: HINTINGWITLEDSHOG + +2024-01-16 21:43:41,304 (asr_inference:494) INFO: speech length: 16960 +2024-01-16 21:43:41,311 (beam_search:428) INFO: decoder input length: 24 +2024-01-16 21:43:41,311 (beam_search:429) INFO: max output length: 24 +2024-01-16 21:43:41,311 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:41,326 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:41,326 (beam_search:476) INFO: -4.47 * 1.0 = -4.47 for ctc +2024-01-16 21:43:41,326 (beam_search:479) INFO: total log probability: -4.47 +2024-01-16 21:43:41,326 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 21:43:41,326 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:41,326 (beam_search:483) INFO: best hypo: WENYTHRTAEN + +2024-01-16 21:43:41,327 (asr_inference:494) INFO: speech length: 52800 +2024-01-16 21:43:41,335 (beam_search:428) INFO: decoder input length: 80 +2024-01-16 21:43:41,336 (beam_search:429) INFO: max output length: 80 +2024-01-16 21:43:41,336 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:41,464 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:41,464 (beam_search:476) INFO: -10.92 * 1.0 = -10.92 for ctc +2024-01-16 21:43:41,464 (beam_search:479) INFO: total log probability: -10.92 +2024-01-16 21:43:41,464 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:41,464 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:41,465 (beam_search:483) INFO: best hypo: ORTHYSEVONPRSENTOFTHELDPATSPACHESLVINUSTRALIA + +2024-01-16 21:43:41,466 (asr_inference:494) INFO: speech length: 49440 +2024-01-16 21:43:41,474 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 21:43:41,475 (beam_search:429) INFO: max output length: 75 +2024-01-16 21:43:41,475 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:41,563 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:41,563 (beam_search:476) INFO: -10.30 * 1.0 = -10.30 for ctc +2024-01-16 21:43:41,563 (beam_search:479) INFO: total log probability: -10.30 +2024-01-16 21:43:41,563 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 21:43:41,563 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:41,564 (beam_search:483) INFO: best hypo: URPOESRAINFINLYANDINANTENADYFV + +2024-01-16 21:43:41,565 (asr_inference:494) INFO: speech length: 49440 +2024-01-16 21:43:41,573 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 21:43:41,573 (beam_search:429) INFO: max output length: 75 +2024-01-16 21:43:41,573 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:41,661 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:41,661 (beam_search:476) INFO: -5.71 * 1.0 = -5.71 for ctc +2024-01-16 21:43:41,661 (beam_search:479) INFO: total log probability: -5.71 +2024-01-16 21:43:41,661 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:41,661 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:41,661 (beam_search:483) INFO: best hypo: WILSOETREHUMINISTIKANAPSTRCT + +2024-01-16 21:43:41,662 (asr_inference:494) INFO: speech length: 33760 +2024-01-16 21:43:41,669 (beam_search:428) INFO: decoder input length: 50 +2024-01-16 21:43:41,669 (beam_search:429) INFO: max output length: 50 +2024-01-16 21:43:41,669 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:41,712 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:41,712 (beam_search:476) INFO: -3.99 * 1.0 = -3.99 for ctc +2024-01-16 21:43:41,712 (beam_search:479) INFO: total log probability: -3.99 +2024-01-16 21:43:41,712 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:41,712 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:41,712 (beam_search:483) INFO: best hypo: PANTHRERIATPRTECTION + +2024-01-16 21:43:41,714 (asr_inference:494) INFO: speech length: 64640 +2024-01-16 21:43:41,724 (beam_search:428) INFO: decoder input length: 98 +2024-01-16 21:43:41,724 (beam_search:429) INFO: max output length: 98 +2024-01-16 21:43:41,724 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:41,917 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:41,917 (beam_search:476) INFO: -12.88 * 1.0 = -12.88 for ctc +2024-01-16 21:43:41,917 (beam_search:479) INFO: total log probability: -12.88 +2024-01-16 21:43:41,917 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:41,917 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:41,918 (beam_search:483) INFO: best hypo: GRAYCSMORFISEPROBLEISTHECOMPETATINTPROBLEOFTHETERMININGWHTH + +2024-01-16 21:43:41,919 (asr_inference:494) INFO: speech length: 25760 +2024-01-16 21:43:41,926 (beam_search:428) INFO: decoder input length: 38 +2024-01-16 21:43:41,926 (beam_search:429) INFO: max output length: 38 +2024-01-16 21:43:41,926 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:41,966 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:41,966 (beam_search:476) INFO: -5.90 * 1.0 = -5.90 for ctc +2024-01-16 21:43:41,966 (beam_search:479) INFO: total log probability: -5.90 +2024-01-16 21:43:41,966 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:41,966 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:41,966 (beam_search:483) INFO: best hypo: ORTHERESTICTAURCONCSEPTO + +2024-01-16 21:43:41,967 (asr_inference:494) INFO: speech length: 29440 +2024-01-16 21:43:41,974 (beam_search:428) INFO: decoder input length: 43 +2024-01-16 21:43:41,974 (beam_search:429) INFO: max output length: 43 +2024-01-16 21:43:41,974 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:42,017 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:42,017 (beam_search:476) INFO: -8.51 * 1.0 = -8.51 for ctc +2024-01-16 21:43:42,017 (beam_search:479) INFO: total log probability: -8.51 +2024-01-16 21:43:42,017 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:43:42,017 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:42,017 (beam_search:483) INFO: best hypo: HEHOOFBIEOUSTPTLISATION + +2024-01-16 21:43:42,018 (asr_inference:494) INFO: speech length: 74880 +2024-01-16 21:43:42,028 (beam_search:428) INFO: decoder input length: 114 +2024-01-16 21:43:42,028 (beam_search:429) INFO: max output length: 114 +2024-01-16 21:43:42,028 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:42,262 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:42,262 (beam_search:476) INFO: -13.80 * 1.0 = -13.80 for ctc +2024-01-16 21:43:42,262 (beam_search:479) INFO: total log probability: -13.80 +2024-01-16 21:43:42,262 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:42,262 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:42,262 (beam_search:483) INFO: best hypo: SOMEPRTECTIONOFUNERTANSIGNIFICENCEISONFIRDBYCOLKCAIANATHNICITY + +2024-01-16 21:43:42,264 (asr_inference:494) INFO: speech length: 20320 +2024-01-16 21:43:42,270 (beam_search:428) INFO: decoder input length: 29 +2024-01-16 21:43:42,270 (beam_search:429) INFO: max output length: 29 +2024-01-16 21:43:42,270 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:42,288 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:42,288 (beam_search:476) INFO: -4.27 * 1.0 = -4.27 for ctc +2024-01-16 21:43:42,288 (beam_search:479) INFO: total log probability: -4.27 +2024-01-16 21:43:42,288 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:43:42,288 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:42,288 (beam_search:483) INFO: best hypo: OASTALAGONS + +2024-01-16 21:43:42,289 (asr_inference:494) INFO: speech length: 22080 +2024-01-16 21:43:42,296 (beam_search:428) INFO: decoder input length: 32 +2024-01-16 21:43:42,296 (beam_search:429) INFO: max output length: 32 +2024-01-16 21:43:42,296 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:42,325 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:42,325 (beam_search:476) INFO: -7.17 * 1.0 = -7.17 for ctc +2024-01-16 21:43:42,325 (beam_search:479) INFO: total log probability: -7.17 +2024-01-16 21:43:42,325 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:43:42,325 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:42,325 (beam_search:483) INFO: best hypo: NDCOWGDTIVEINHANCSE + +2024-01-16 21:43:42,326 (asr_inference:494) INFO: speech length: 45440 +2024-01-16 21:43:42,334 (beam_search:428) INFO: decoder input length: 68 +2024-01-16 21:43:42,334 (beam_search:429) INFO: max output length: 68 +2024-01-16 21:43:42,334 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:42,428 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:42,428 (beam_search:476) INFO: -8.81 * 1.0 = -8.81 for ctc +2024-01-16 21:43:42,428 (beam_search:479) INFO: total log probability: -8.81 +2024-01-16 21:43:42,429 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:42,429 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:42,429 (beam_search:483) INFO: best hypo: VANCSTDTHATHRANKANDBEPREMOUDTDTOANLO + +2024-01-16 21:43:42,430 (asr_inference:494) INFO: speech length: 31680 +2024-01-16 21:43:42,437 (beam_search:428) INFO: decoder input length: 47 +2024-01-16 21:43:42,437 (beam_search:429) INFO: max output length: 47 +2024-01-16 21:43:42,437 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:42,492 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:42,492 (beam_search:476) INFO: -6.59 * 1.0 = -6.59 for ctc +2024-01-16 21:43:42,492 (beam_search:479) INFO: total log probability: -6.59 +2024-01-16 21:43:42,492 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:42,492 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:42,492 (beam_search:483) INFO: best hypo: DRBACKOFCOILINGISTHEPSEAILITY + +2024-01-16 21:43:42,493 (asr_inference:494) INFO: speech length: 31360 +2024-01-16 21:43:42,501 (beam_search:428) INFO: decoder input length: 46 +2024-01-16 21:43:42,501 (beam_search:429) INFO: max output length: 46 +2024-01-16 21:43:42,501 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:42,550 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:42,550 (beam_search:476) INFO: -8.82 * 1.0 = -8.82 for ctc +2024-01-16 21:43:42,550 (beam_search:479) INFO: total log probability: -8.82 +2024-01-16 21:43:42,550 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 21:43:42,550 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:42,550 (beam_search:483) INFO: best hypo: INDECATESSUBRACNOLDHEMRIGE + +2024-01-16 21:43:42,551 (asr_inference:494) INFO: speech length: 17920 +2024-01-16 21:43:42,557 (beam_search:428) INFO: decoder input length: 25 +2024-01-16 21:43:42,557 (beam_search:429) INFO: max output length: 25 +2024-01-16 21:43:42,557 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:42,577 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:42,577 (beam_search:476) INFO: -4.49 * 1.0 = -4.49 for ctc +2024-01-16 21:43:42,577 (beam_search:479) INFO: total log probability: -4.49 +2024-01-16 21:43:42,577 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:42,577 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:42,577 (beam_search:483) INFO: best hypo: DEAMISETPORTION + +2024-01-16 21:43:42,578 (asr_inference:494) INFO: speech length: 39200 +2024-01-16 21:43:42,586 (beam_search:428) INFO: decoder input length: 59 +2024-01-16 21:43:42,586 (beam_search:429) INFO: max output length: 59 +2024-01-16 21:43:42,586 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:42,664 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:42,664 (beam_search:476) INFO: -11.09 * 1.0 = -11.09 for ctc +2024-01-16 21:43:42,664 (beam_search:479) INFO: total log probability: -11.09 +2024-01-16 21:43:42,664 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:43:42,664 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:42,665 (beam_search:483) INFO: best hypo: NDOPTIOOFYJEDIKANDHANCSMENTECHALHES + +2024-01-16 21:43:42,666 (asr_inference:494) INFO: speech length: 24800 +2024-01-16 21:43:42,673 (beam_search:428) INFO: decoder input length: 36 +2024-01-16 21:43:42,673 (beam_search:429) INFO: max output length: 36 +2024-01-16 21:43:42,673 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:42,709 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:42,709 (beam_search:476) INFO: -3.88 * 1.0 = -3.88 for ctc +2024-01-16 21:43:42,709 (beam_search:479) INFO: total log probability: -3.88 +2024-01-16 21:43:42,709 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:42,709 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:42,709 (beam_search:483) INFO: best hypo: PLISHENHISHOUREANWAGON + +2024-01-16 21:43:42,710 (asr_inference:494) INFO: speech length: 18240 +2024-01-16 21:43:42,716 (beam_search:428) INFO: decoder input length: 26 +2024-01-16 21:43:42,717 (beam_search:429) INFO: max output length: 26 +2024-01-16 21:43:42,717 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:42,738 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:42,738 (beam_search:476) INFO: -5.11 * 1.0 = -5.11 for ctc +2024-01-16 21:43:42,738 (beam_search:479) INFO: total log probability: -5.11 +2024-01-16 21:43:42,738 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:43:42,738 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:42,738 (beam_search:483) INFO: best hypo: NTHEECTHARMBBEN + +2024-01-16 21:43:42,739 (asr_inference:494) INFO: speech length: 24960 +2024-01-16 21:43:42,746 (beam_search:428) INFO: decoder input length: 36 +2024-01-16 21:43:42,746 (beam_search:429) INFO: max output length: 36 +2024-01-16 21:43:42,746 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:42,777 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:42,777 (beam_search:476) INFO: -6.11 * 1.0 = -6.11 for ctc +2024-01-16 21:43:42,777 (beam_search:479) INFO: total log probability: -6.11 +2024-01-16 21:43:42,777 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:43:42,777 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:42,778 (beam_search:483) INFO: best hypo: RTHROFOFERALISUCLY + +2024-01-16 21:43:42,779 (asr_inference:494) INFO: speech length: 27200 +2024-01-16 21:43:42,786 (beam_search:428) INFO: decoder input length: 40 +2024-01-16 21:43:42,786 (beam_search:429) INFO: max output length: 40 +2024-01-16 21:43:42,786 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:42,827 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:42,827 (beam_search:476) INFO: -6.71 * 1.0 = -6.71 for ctc +2024-01-16 21:43:42,827 (beam_search:479) INFO: total log probability: -6.71 +2024-01-16 21:43:42,827 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:42,827 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:42,827 (beam_search:483) INFO: best hypo: LEDTHAPINHANCTINTWORNYON + +2024-01-16 21:43:42,828 (asr_inference:494) INFO: speech length: 54560 +2024-01-16 21:43:42,837 (beam_search:428) INFO: decoder input length: 83 +2024-01-16 21:43:42,837 (beam_search:429) INFO: max output length: 83 +2024-01-16 21:43:42,837 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:42,962 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:42,962 (beam_search:476) INFO: -8.69 * 1.0 = -8.69 for ctc +2024-01-16 21:43:42,962 (beam_search:479) INFO: total log probability: -8.69 +2024-01-16 21:43:42,962 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:42,962 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:42,963 (beam_search:483) INFO: best hypo: SOUCHASONTHMCLPETATIONANDRANDIMYSDLLGRTHEMS + +2024-01-16 21:43:42,964 (asr_inference:494) INFO: speech length: 17440 +2024-01-16 21:43:42,970 (beam_search:428) INFO: decoder input length: 25 +2024-01-16 21:43:42,970 (beam_search:429) INFO: max output length: 25 +2024-01-16 21:43:42,970 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:42,988 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:42,988 (beam_search:476) INFO: -6.08 * 1.0 = -6.08 for ctc +2024-01-16 21:43:42,988 (beam_search:479) INFO: total log probability: -6.08 +2024-01-16 21:43:42,988 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 21:43:42,988 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:42,988 (beam_search:483) INFO: best hypo: ATYNHADENOIEN + +2024-01-16 21:43:42,989 (asr_inference:494) INFO: speech length: 75520 +2024-01-16 21:43:42,999 (beam_search:428) INFO: decoder input length: 115 +2024-01-16 21:43:42,999 (beam_search:429) INFO: max output length: 115 +2024-01-16 21:43:42,999 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:43,234 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:43,234 (beam_search:476) INFO: -13.07 * 1.0 = -13.07 for ctc +2024-01-16 21:43:43,234 (beam_search:479) INFO: total log probability: -13.07 +2024-01-16 21:43:43,234 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:43,234 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:43,234 (beam_search:483) INFO: best hypo: SHONEBYLADNERTHATIFPEAISNOTYCOLDANPEYTHENTHEREXISTPROVBLEMS + +2024-01-16 21:43:43,236 (asr_inference:494) INFO: speech length: 19680 +2024-01-16 21:43:43,242 (beam_search:428) INFO: decoder input length: 28 +2024-01-16 21:43:43,242 (beam_search:429) INFO: max output length: 28 +2024-01-16 21:43:43,242 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:43,264 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:43,264 (beam_search:476) INFO: -3.71 * 1.0 = -3.71 for ctc +2024-01-16 21:43:43,264 (beam_search:479) INFO: total log probability: -3.71 +2024-01-16 21:43:43,264 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:43,264 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:43,264 (beam_search:483) INFO: best hypo: HECOMPACTDESTFRL + +2024-01-16 21:43:43,265 (asr_inference:494) INFO: speech length: 19200 +2024-01-16 21:43:43,271 (beam_search:428) INFO: decoder input length: 27 +2024-01-16 21:43:43,271 (beam_search:429) INFO: max output length: 27 +2024-01-16 21:43:43,271 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:43,292 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:43,292 (beam_search:476) INFO: -8.39 * 1.0 = -8.39 for ctc +2024-01-16 21:43:43,292 (beam_search:479) INFO: total log probability: -8.39 +2024-01-16 21:43:43,292 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-16 21:43:43,292 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:43,292 (beam_search:483) INFO: best hypo: GRAYGOUOSONAERIL + +2024-01-16 21:43:43,293 (asr_inference:494) INFO: speech length: 43520 +2024-01-16 21:43:43,301 (beam_search:428) INFO: decoder input length: 65 +2024-01-16 21:43:43,301 (beam_search:429) INFO: max output length: 65 +2024-01-16 21:43:43,301 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:43,357 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:43,357 (beam_search:476) INFO: -3.77 * 1.0 = -3.77 for ctc +2024-01-16 21:43:43,357 (beam_search:479) INFO: total log probability: -3.77 +2024-01-16 21:43:43,357 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:43,357 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:43,357 (beam_search:483) INFO: best hypo: WASWENDEREDASIADGEAS + +2024-01-16 21:43:43,359 (asr_inference:494) INFO: speech length: 29440 +2024-01-16 21:43:43,366 (beam_search:428) INFO: decoder input length: 43 +2024-01-16 21:43:43,366 (beam_search:429) INFO: max output length: 43 +2024-01-16 21:43:43,366 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:43,408 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:43,408 (beam_search:476) INFO: -5.76 * 1.0 = -5.76 for ctc +2024-01-16 21:43:43,408 (beam_search:479) INFO: total log probability: -5.76 +2024-01-16 21:43:43,408 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:43,408 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:43,408 (beam_search:483) INFO: best hypo: SAYAECHORDTOANOTHECOLS + +2024-01-16 21:43:43,409 (asr_inference:494) INFO: speech length: 24960 +2024-01-16 21:43:43,416 (beam_search:428) INFO: decoder input length: 36 +2024-01-16 21:43:43,416 (beam_search:429) INFO: max output length: 36 +2024-01-16 21:43:43,416 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:43,448 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:43,448 (beam_search:476) INFO: -7.96 * 1.0 = -7.96 for ctc +2024-01-16 21:43:43,448 (beam_search:479) INFO: total log probability: -7.96 +2024-01-16 21:43:43,448 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 21:43:43,448 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:43,448 (beam_search:483) INFO: best hypo: COCTITUHNCYOFFAVISIOM + +2024-01-16 21:43:43,449 (asr_inference:494) INFO: speech length: 76000 +2024-01-16 21:43:43,459 (beam_search:428) INFO: decoder input length: 116 +2024-01-16 21:43:43,459 (beam_search:429) INFO: max output length: 116 +2024-01-16 21:43:43,459 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:43,642 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:43,642 (beam_search:476) INFO: -8.84 * 1.0 = -8.84 for ctc +2024-01-16 21:43:43,642 (beam_search:479) INFO: total log probability: -8.84 +2024-01-16 21:43:43,642 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:43,642 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:43,643 (beam_search:483) INFO: best hypo: THEFORTHNFITDAYGESPASTEWOHOUTANYELIMENTCS + +2024-01-16 21:43:43,644 (asr_inference:494) INFO: speech length: 58539 +2024-01-16 21:43:43,653 (beam_search:428) INFO: decoder input length: 89 +2024-01-16 21:43:43,653 (beam_search:429) INFO: max output length: 89 +2024-01-16 21:43:43,653 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:43,724 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:43,724 (beam_search:476) INFO: -1.74 * 1.0 = -1.74 for ctc +2024-01-16 21:43:43,724 (beam_search:479) INFO: total log probability: -1.74 +2024-01-16 21:43:43,724 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 21:43:43,724 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:43,725 (beam_search:483) INFO: best hypo: TTHEYNOTHEREPORT + +2024-01-16 21:43:43,726 (asr_inference:494) INFO: speech length: 72000 +2024-01-16 21:43:43,735 (beam_search:428) INFO: decoder input length: 110 +2024-01-16 21:43:43,736 (beam_search:429) INFO: max output length: 110 +2024-01-16 21:43:43,736 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:43,884 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:43,884 (beam_search:476) INFO: -6.14 * 1.0 = -6.14 for ctc +2024-01-16 21:43:43,884 (beam_search:479) INFO: total log probability: -6.14 +2024-01-16 21:43:43,884 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:43:43,884 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:43,884 (beam_search:483) INFO: best hypo: SOCHTHINGHAACORDBEFEORHETOLDFILAP + +2024-01-16 21:43:43,885 (asr_inference:494) INFO: speech length: 104448 +2024-01-16 21:43:43,897 (beam_search:428) INFO: decoder input length: 161 +2024-01-16 21:43:43,897 (beam_search:429) INFO: max output length: 161 +2024-01-16 21:43:43,897 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:44,151 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:44,151 (beam_search:476) INFO: -6.81 * 1.0 = -6.81 for ctc +2024-01-16 21:43:44,151 (beam_search:479) INFO: total log probability: -6.81 +2024-01-16 21:43:44,151 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:44,151 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:44,152 (beam_search:483) INFO: best hypo: THEYONLYHADALEITLETHIRDYTHOSENDOLAERFIER + +2024-01-16 21:43:44,153 (asr_inference:494) INFO: speech length: 65366 +2024-01-16 21:43:44,162 (beam_search:428) INFO: decoder input length: 100 +2024-01-16 21:43:44,162 (beam_search:429) INFO: max output length: 100 +2024-01-16 21:43:44,162 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:44,266 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:44,266 (beam_search:476) INFO: -5.97 * 1.0 = -5.97 for ctc +2024-01-16 21:43:44,266 (beam_search:479) INFO: total log probability: -5.97 +2024-01-16 21:43:44,266 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:44,266 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:44,266 (beam_search:483) INFO: best hypo: IAMEGOINGTOGETATOUWDETH + +2024-01-16 21:43:44,267 (asr_inference:494) INFO: speech length: 76000 +2024-01-16 21:43:44,277 (beam_search:428) INFO: decoder input length: 116 +2024-01-16 21:43:44,277 (beam_search:429) INFO: max output length: 116 +2024-01-16 21:43:44,277 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:44,466 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:44,466 (beam_search:476) INFO: -8.50 * 1.0 = -8.50 for ctc +2024-01-16 21:43:44,466 (beam_search:479) INFO: total log probability: -8.50 +2024-01-16 21:43:44,466 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:44,466 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:44,467 (beam_search:483) INFO: best hypo: OUDDODLYHEMAIENTANEDACORMEANDSMILINGASSPECT + +2024-01-16 21:43:44,468 (asr_inference:494) INFO: speech length: 80000 +2024-01-16 21:43:44,478 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 21:43:44,478 (beam_search:429) INFO: max output length: 122 +2024-01-16 21:43:44,478 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:44,648 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:44,648 (beam_search:476) INFO: -11.71 * 1.0 = -11.71 for ctc +2024-01-16 21:43:44,648 (beam_search:479) INFO: total log probability: -11.71 +2024-01-16 21:43:44,648 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 21:43:44,648 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:44,648 (beam_search:483) INFO: best hypo: JONLOKRIIUMPFENTLYATSHELDENHOBOURD + +2024-01-16 21:43:44,649 (asr_inference:494) INFO: speech length: 70000 +2024-01-16 21:43:44,659 (beam_search:428) INFO: decoder input length: 107 +2024-01-16 21:43:44,659 (beam_search:429) INFO: max output length: 107 +2024-01-16 21:43:44,659 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:44,767 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:44,767 (beam_search:476) INFO: -9.87 * 1.0 = -9.87 for ctc +2024-01-16 21:43:44,767 (beam_search:479) INFO: total log probability: -9.87 +2024-01-16 21:43:44,767 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 21:43:44,767 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:44,767 (beam_search:483) INFO: best hypo: COMONHDILDMARTTALENCTST + +2024-01-16 21:43:44,768 (asr_inference:494) INFO: speech length: 98000 +2024-01-16 21:43:44,780 (beam_search:428) INFO: decoder input length: 151 +2024-01-16 21:43:44,780 (beam_search:429) INFO: max output length: 151 +2024-01-16 21:43:44,780 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:45,061 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:45,062 (beam_search:476) INFO: -5.28 * 1.0 = -5.28 for ctc +2024-01-16 21:43:45,062 (beam_search:479) INFO: total log probability: -5.28 +2024-01-16 21:43:45,062 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 21:43:45,062 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:45,062 (beam_search:483) INFO: best hypo: ITWASBEATINGANDWATINGINTHEAMBOSHOFTHOSPLACKEPITS + +2024-01-16 21:43:45,063 (asr_inference:494) INFO: speech length: 58000 +2024-01-16 21:43:45,072 (beam_search:428) INFO: decoder input length: 88 +2024-01-16 21:43:45,072 (beam_search:429) INFO: max output length: 88 +2024-01-16 21:43:45,072 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:45,163 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:45,163 (beam_search:476) INFO: -8.70 * 1.0 = -8.70 for ctc +2024-01-16 21:43:45,164 (beam_search:479) INFO: total log probability: -8.70 +2024-01-16 21:43:45,164 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 21:43:45,164 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:45,164 (beam_search:483) INFO: best hypo: ALTHEGOEOTANDEAPWIMYBOS + +2024-01-16 21:43:45,165 (asr_inference:494) INFO: speech length: 98000 +2024-01-16 21:43:45,176 (beam_search:428) INFO: decoder input length: 151 +2024-01-16 21:43:45,176 (beam_search:429) INFO: max output length: 151 +2024-01-16 21:43:45,176 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:45,480 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:45,480 (beam_search:476) INFO: -14.99 * 1.0 = -14.99 for ctc +2024-01-16 21:43:45,480 (beam_search:479) INFO: total log probability: -14.99 +2024-01-16 21:43:45,480 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:45,480 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:45,480 (beam_search:483) INFO: best hypo: HEOWEINEDDONINWEINSDREMNYSARCTIONGTHESHAHDOSOFPOLSORS + +2024-01-16 21:43:45,481 (asr_inference:494) INFO: speech length: 92000 +2024-01-16 21:43:45,492 (beam_search:428) INFO: decoder input length: 141 +2024-01-16 21:43:45,492 (beam_search:429) INFO: max output length: 141 +2024-01-16 21:43:45,492 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:45,730 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:45,730 (beam_search:476) INFO: -9.92 * 1.0 = -9.92 for ctc +2024-01-16 21:43:45,730 (beam_search:479) INFO: total log probability: -9.92 +2024-01-16 21:43:45,730 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:45,730 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:45,731 (beam_search:483) INFO: best hypo: IOSTDOAPRESHATTWITOUTBEAETOCPESEMYFELINGS + +2024-01-16 21:43:45,732 (asr_inference:494) INFO: speech length: 66390 +2024-01-16 21:43:45,742 (beam_search:428) INFO: decoder input length: 101 +2024-01-16 21:43:45,742 (beam_search:429) INFO: max output length: 101 +2024-01-16 21:43:45,742 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:45,872 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:45,872 (beam_search:476) INFO: -4.83 * 1.0 = -4.83 for ctc +2024-01-16 21:43:45,872 (beam_search:479) INFO: total log probability: -4.83 +2024-01-16 21:43:45,872 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:45,872 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:45,872 (beam_search:483) INFO: best hypo: SHEDOSANTNOWHATHEASTOKINGABOUWT + +2024-01-16 21:43:45,874 (asr_inference:494) INFO: speech length: 58000 +2024-01-16 21:43:45,882 (beam_search:428) INFO: decoder input length: 88 +2024-01-16 21:43:45,882 (beam_search:429) INFO: max output length: 88 +2024-01-16 21:43:45,882 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:45,982 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:45,982 (beam_search:476) INFO: -3.93 * 1.0 = -3.93 for ctc +2024-01-16 21:43:45,982 (beam_search:479) INFO: total log probability: -3.93 +2024-01-16 21:43:45,982 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:43:45,982 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:45,982 (beam_search:483) INFO: best hypo: YORFARTHERSFIFTCOMANDHENATED + +2024-01-16 21:43:45,983 (asr_inference:494) INFO: speech length: 76000 +2024-01-16 21:43:45,992 (beam_search:428) INFO: decoder input length: 116 +2024-01-16 21:43:45,993 (beam_search:429) INFO: max output length: 116 +2024-01-16 21:43:45,993 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:46,077 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:46,077 (beam_search:476) INFO: -6.39 * 1.0 = -6.39 for ctc +2024-01-16 21:43:46,078 (beam_search:479) INFO: total log probability: -6.39 +2024-01-16 21:43:46,078 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:43:46,078 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:46,078 (beam_search:483) INFO: best hypo: EDONOSEYIHAEYOE + +2024-01-16 21:43:46,079 (asr_inference:494) INFO: speech length: 94082 +2024-01-16 21:43:46,090 (beam_search:428) INFO: decoder input length: 145 +2024-01-16 21:43:46,090 (beam_search:429) INFO: max output length: 145 +2024-01-16 21:43:46,090 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:46,395 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:46,395 (beam_search:476) INFO: -10.25 * 1.0 = -10.25 for ctc +2024-01-16 21:43:46,395 (beam_search:479) INFO: total log probability: -10.25 +2024-01-16 21:43:46,395 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:43:46,395 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:46,395 (beam_search:483) INFO: best hypo: ALITLEWORMEBUTNOTALSTONISHEDEEATINGMELNSANDTHOINGTHRINDABOUT + +2024-01-16 21:43:46,396 (asr_inference:494) INFO: speech length: 60246 +2024-01-16 21:43:46,405 (beam_search:428) INFO: decoder input length: 92 +2024-01-16 21:43:46,405 (beam_search:429) INFO: max output length: 92 +2024-01-16 21:43:46,405 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:46,481 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:46,481 (beam_search:476) INFO: -3.31 * 1.0 = -3.31 for ctc +2024-01-16 21:43:46,481 (beam_search:479) INFO: total log probability: -3.31 +2024-01-16 21:43:46,481 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:46,481 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:46,481 (beam_search:483) INFO: best hypo: THISEISAGRATPORDYE + +2024-01-16 21:43:46,482 (asr_inference:494) INFO: speech length: 62000 +2024-01-16 21:43:46,491 (beam_search:428) INFO: decoder input length: 94 +2024-01-16 21:43:46,491 (beam_search:429) INFO: max output length: 94 +2024-01-16 21:43:46,491 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:46,584 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:46,584 (beam_search:476) INFO: -5.63 * 1.0 = -5.63 for ctc +2024-01-16 21:43:46,584 (beam_search:479) INFO: total log probability: -5.63 +2024-01-16 21:43:46,584 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:46,584 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:46,585 (beam_search:483) INFO: best hypo: THEBOYGROANDPROSPERETTO + +2024-01-16 21:43:46,586 (asr_inference:494) INFO: speech length: 106401 +2024-01-16 21:43:46,598 (beam_search:428) INFO: decoder input length: 164 +2024-01-16 21:43:46,598 (beam_search:429) INFO: max output length: 164 +2024-01-16 21:43:46,598 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:47,001 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:47,001 (beam_search:476) INFO: -9.82 * 1.0 = -9.82 for ctc +2024-01-16 21:43:47,001 (beam_search:479) INFO: total log probability: -9.82 +2024-01-16 21:43:47,001 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:43:47,002 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:47,002 (beam_search:483) INFO: best hypo: ANDLSSUCHLETERSBEPATENTTHATTHEYAYBREDTOTHEMANWHITHLESTELEHORTESTIFIGED + +2024-01-16 21:43:47,003 (asr_inference:494) INFO: speech length: 71202 +2024-01-16 21:43:47,013 (beam_search:428) INFO: decoder input length: 109 +2024-01-16 21:43:47,013 (beam_search:429) INFO: max output length: 109 +2024-01-16 21:43:47,013 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:47,184 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:47,184 (beam_search:476) INFO: -8.99 * 1.0 = -8.99 for ctc +2024-01-16 21:43:47,184 (beam_search:479) INFO: total log probability: -8.99 +2024-01-16 21:43:47,184 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:47,184 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:47,185 (beam_search:483) INFO: best hypo: HOWCOLDAWOMENDERTOVENTEWERSOANYEXTPORARS + +2024-01-16 21:43:47,186 (asr_inference:494) INFO: speech length: 58000 +2024-01-16 21:43:47,194 (beam_search:428) INFO: decoder input length: 88 +2024-01-16 21:43:47,194 (beam_search:429) INFO: max output length: 88 +2024-01-16 21:43:47,194 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:47,282 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:47,282 (beam_search:476) INFO: -5.64 * 1.0 = -5.64 for ctc +2024-01-16 21:43:47,282 (beam_search:479) INFO: total log probability: -5.64 +2024-01-16 21:43:47,282 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:47,282 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:47,282 (beam_search:483) INFO: best hypo: HEREADEHIFRAGINCEALOAED + +2024-01-16 21:43:47,283 (asr_inference:494) INFO: speech length: 38834 +2024-01-16 21:43:47,291 (beam_search:428) INFO: decoder input length: 58 +2024-01-16 21:43:47,291 (beam_search:429) INFO: max output length: 58 +2024-01-16 21:43:47,291 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:47,351 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:47,351 (beam_search:476) INFO: -2.37 * 1.0 = -2.37 for ctc +2024-01-16 21:43:47,351 (beam_search:479) INFO: total log probability: -2.37 +2024-01-16 21:43:47,351 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 21:43:47,351 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:47,351 (beam_search:483) INFO: best hypo: BUTHOWEARYOUGOINGTODEIT + +2024-01-16 21:43:47,353 (asr_inference:494) INFO: speech length: 76288 +2024-01-16 21:43:47,362 (beam_search:428) INFO: decoder input length: 117 +2024-01-16 21:43:47,362 (beam_search:429) INFO: max output length: 117 +2024-01-16 21:43:47,362 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:47,495 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:47,495 (beam_search:476) INFO: -5.20 * 1.0 = -5.20 for ctc +2024-01-16 21:43:47,495 (beam_search:479) INFO: total log probability: -5.20 +2024-01-16 21:43:47,495 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:43:47,495 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:47,496 (beam_search:483) INFO: best hypo: HOWDOYOUWONTOGETWAYWITHISE + +2024-01-16 21:43:47,497 (asr_inference:494) INFO: speech length: 52000 +2024-01-16 21:43:47,505 (beam_search:428) INFO: decoder input length: 79 +2024-01-16 21:43:47,505 (beam_search:429) INFO: max output length: 79 +2024-01-16 21:43:47,505 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:47,571 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:47,571 (beam_search:476) INFO: -4.71 * 1.0 = -4.71 for ctc +2024-01-16 21:43:47,571 (beam_search:479) INFO: total log probability: -4.71 +2024-01-16 21:43:47,571 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:47,571 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:47,571 (beam_search:483) INFO: best hypo: WILWEAVERFOMGETIT + +2024-01-16 21:43:47,573 (asr_inference:494) INFO: speech length: 78000 +2024-01-16 21:43:47,583 (beam_search:428) INFO: decoder input length: 119 +2024-01-16 21:43:47,583 (beam_search:429) INFO: max output length: 119 +2024-01-16 21:43:47,583 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:47,765 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:47,765 (beam_search:476) INFO: -7.80 * 1.0 = -7.80 for ctc +2024-01-16 21:43:47,765 (beam_search:479) INFO: total log probability: -7.80 +2024-01-16 21:43:47,765 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:47,765 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:47,765 (beam_search:483) INFO: best hypo: FRMYARLISRECLECTIONMYSLEWSPERGATDOFHERE + +2024-01-16 21:43:47,767 (asr_inference:494) INFO: speech length: 94000 +2024-01-16 21:43:47,778 (beam_search:428) INFO: decoder input length: 144 +2024-01-16 21:43:47,778 (beam_search:429) INFO: max output length: 144 +2024-01-16 21:43:47,778 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:47,985 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:47,985 (beam_search:476) INFO: -14.13 * 1.0 = -14.13 for ctc +2024-01-16 21:43:47,985 (beam_search:479) INFO: total log probability: -14.13 +2024-01-16 21:43:47,985 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:43:47,985 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:47,985 (beam_search:483) INFO: best hypo: MYOISOSHERWHIYEDOLONYOUWLLSHAKDGANM + +2024-01-16 21:43:47,987 (asr_inference:494) INFO: speech length: 74000 +2024-01-16 21:43:47,996 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 21:43:47,996 (beam_search:429) INFO: max output length: 113 +2024-01-16 21:43:47,996 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:48,121 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:48,121 (beam_search:476) INFO: -13.84 * 1.0 = -13.84 for ctc +2024-01-16 21:43:48,121 (beam_search:479) INFO: total log probability: -13.84 +2024-01-16 21:43:48,121 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-16 21:43:48,121 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:48,121 (beam_search:483) INFO: best hypo: IDEAVTTHENEADISTREOFUGHYEWII + +2024-01-16 21:43:48,122 (asr_inference:494) INFO: speech length: 77824 +2024-01-16 21:43:48,132 (beam_search:428) INFO: decoder input length: 119 +2024-01-16 21:43:48,132 (beam_search:429) INFO: max output length: 119 +2024-01-16 21:43:48,132 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:48,299 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:48,299 (beam_search:476) INFO: -5.04 * 1.0 = -5.04 for ctc +2024-01-16 21:43:48,299 (beam_search:479) INFO: total log probability: -5.04 +2024-01-16 21:43:48,299 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:43:48,299 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:48,299 (beam_search:483) INFO: best hypo: HISSLIMEHANECREPTTHEEADGESOFTHETABL + +2024-01-16 21:43:48,300 (asr_inference:494) INFO: speech length: 50000 +2024-01-16 21:43:48,309 (beam_search:428) INFO: decoder input length: 76 +2024-01-16 21:43:48,309 (beam_search:429) INFO: max output length: 76 +2024-01-16 21:43:48,309 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:48,387 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:48,387 (beam_search:476) INFO: -4.66 * 1.0 = -4.66 for ctc +2024-01-16 21:43:48,387 (beam_search:479) INFO: total log probability: -4.66 +2024-01-16 21:43:48,387 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:48,387 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:48,387 (beam_search:483) INFO: best hypo: WHDLAORNSAIDMISMORTOARM + +2024-01-16 21:43:48,388 (asr_inference:494) INFO: speech length: 56000 +2024-01-16 21:43:48,397 (beam_search:428) INFO: decoder input length: 85 +2024-01-16 21:43:48,397 (beam_search:429) INFO: max output length: 85 +2024-01-16 21:43:48,397 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:48,491 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:48,491 (beam_search:476) INFO: -7.87 * 1.0 = -7.87 for ctc +2024-01-16 21:43:48,491 (beam_search:479) INFO: total log probability: -7.87 +2024-01-16 21:43:48,491 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:48,491 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:48,491 (beam_search:483) INFO: best hypo: ITOKHIHAVEAWTORCHHEADOIT + +2024-01-16 21:43:48,492 (asr_inference:494) INFO: speech length: 75000 +2024-01-16 21:43:48,502 (beam_search:428) INFO: decoder input length: 115 +2024-01-16 21:43:48,502 (beam_search:429) INFO: max output length: 115 +2024-01-16 21:43:48,502 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:48,669 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:48,669 (beam_search:476) INFO: -9.22 * 1.0 = -9.22 for ctc +2024-01-16 21:43:48,669 (beam_search:479) INFO: total log probability: -9.22 +2024-01-16 21:43:48,669 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:48,669 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:48,670 (beam_search:483) INFO: best hypo: MARTHEAWERDSTANDTHCONTRACTIOUALEITSHOS + +2024-01-16 21:43:48,671 (asr_inference:494) INFO: speech length: 74722 +2024-01-16 21:43:48,680 (beam_search:428) INFO: decoder input length: 114 +2024-01-16 21:43:48,680 (beam_search:429) INFO: max output length: 114 +2024-01-16 21:43:48,680 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:48,881 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:48,881 (beam_search:476) INFO: -10.26 * 1.0 = -10.26 for ctc +2024-01-16 21:43:48,881 (beam_search:479) INFO: total log probability: -10.26 +2024-01-16 21:43:48,881 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:48,881 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:48,881 (beam_search:483) INFO: best hypo: ASTOBENDESTINGICHABLEFROMTHEVEASTWHYTPLAINEDROWND + +2024-01-16 21:43:48,883 (asr_inference:494) INFO: speech length: 76000 +2024-01-16 21:43:48,892 (beam_search:428) INFO: decoder input length: 116 +2024-01-16 21:43:48,892 (beam_search:429) INFO: max output length: 116 +2024-01-16 21:43:48,892 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:49,043 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:49,043 (beam_search:476) INFO: -7.49 * 1.0 = -7.49 for ctc +2024-01-16 21:43:49,043 (beam_search:479) INFO: total log probability: -7.49 +2024-01-16 21:43:49,043 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:49,043 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:49,043 (beam_search:483) INFO: best hypo: HEWODDESTROYALTHINGSHATERFICTDDT + +2024-01-16 21:43:49,044 (asr_inference:494) INFO: speech length: 80000 +2024-01-16 21:43:49,054 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 21:43:49,054 (beam_search:429) INFO: max output length: 122 +2024-01-16 21:43:49,054 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:49,250 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:49,251 (beam_search:476) INFO: -6.77 * 1.0 = -6.77 for ctc +2024-01-16 21:43:49,251 (beam_search:479) INFO: total log probability: -6.77 +2024-01-16 21:43:49,251 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:49,251 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:49,251 (beam_search:483) INFO: best hypo: THERUSIONUSIKPLEAERTHECONTWASHEROBEDINSLAV + +2024-01-16 21:43:49,252 (asr_inference:494) INFO: speech length: 86000 +2024-01-16 21:43:49,262 (beam_search:428) INFO: decoder input length: 132 +2024-01-16 21:43:49,263 (beam_search:429) INFO: max output length: 132 +2024-01-16 21:43:49,263 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:49,485 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:49,485 (beam_search:476) INFO: -7.64 * 1.0 = -7.64 for ctc +2024-01-16 21:43:49,485 (beam_search:479) INFO: total log probability: -7.64 +2024-01-16 21:43:49,485 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:49,485 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:49,486 (beam_search:483) INFO: best hypo: TOHISSUPRIHSEHERANTEWASFLATANDUNCOMPROMIYSING + +2024-01-16 21:43:49,487 (asr_inference:494) INFO: speech length: 68608 +2024-01-16 21:43:49,496 (beam_search:428) INFO: decoder input length: 105 +2024-01-16 21:43:49,496 (beam_search:429) INFO: max output length: 105 +2024-01-16 21:43:49,496 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:49,593 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:49,593 (beam_search:476) INFO: -3.30 * 1.0 = -3.30 for ctc +2024-01-16 21:43:49,593 (beam_search:479) INFO: total log probability: -3.30 +2024-01-16 21:43:49,593 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:49,593 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:49,593 (beam_search:483) INFO: best hypo: TTHISSOTBEINTEROSTING + +2024-01-16 21:43:49,594 (asr_inference:494) INFO: speech length: 77142 +2024-01-16 21:43:49,604 (beam_search:428) INFO: decoder input length: 118 +2024-01-16 21:43:49,604 (beam_search:429) INFO: max output length: 118 +2024-01-16 21:43:49,604 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:49,744 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:49,745 (beam_search:476) INFO: -6.78 * 1.0 = -6.78 for ctc +2024-01-16 21:43:49,745 (beam_search:479) INFO: total log probability: -6.78 +2024-01-16 21:43:49,745 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:49,745 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:49,745 (beam_search:483) INFO: best hypo: IAMEAFRADEIDONTHAVEMUCHTIME + +2024-01-16 21:43:49,746 (asr_inference:494) INFO: speech length: 76000 +2024-01-16 21:43:49,756 (beam_search:428) INFO: decoder input length: 116 +2024-01-16 21:43:49,756 (beam_search:429) INFO: max output length: 116 +2024-01-16 21:43:49,756 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:49,970 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:49,970 (beam_search:476) INFO: -7.11 * 1.0 = -7.11 for ctc +2024-01-16 21:43:49,970 (beam_search:479) INFO: total log probability: -7.11 +2024-01-16 21:43:49,970 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:43:49,970 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:49,971 (beam_search:483) INFO: best hypo: CRSMISISANEASYPROBLOMECOMPRDWTHEPOLNATIONGIVINGFEAST + +2024-01-16 21:43:49,972 (asr_inference:494) INFO: speech length: 82000 +2024-01-16 21:43:49,982 (beam_search:428) INFO: decoder input length: 126 +2024-01-16 21:43:49,982 (beam_search:429) INFO: max output length: 126 +2024-01-16 21:43:49,982 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:50,152 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:50,152 (beam_search:476) INFO: -6.15 * 1.0 = -6.15 for ctc +2024-01-16 21:43:50,152 (beam_search:479) INFO: total log probability: -6.15 +2024-01-16 21:43:50,152 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:43:50,152 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:50,153 (beam_search:483) INFO: best hypo: THEPLANTOSORARDYCENSIDERINTHATERHE + +2024-01-16 21:43:50,154 (asr_inference:494) INFO: speech length: 90000 +2024-01-16 21:43:50,164 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 21:43:50,164 (beam_search:429) INFO: max output length: 138 +2024-01-16 21:43:50,164 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:50,305 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:50,305 (beam_search:476) INFO: -14.54 * 1.0 = -14.54 for ctc +2024-01-16 21:43:50,305 (beam_search:479) INFO: total log probability: -14.54 +2024-01-16 21:43:50,305 (beam_search:480) INFO: normalized log probability: -0.45 +2024-01-16 21:43:50,305 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:50,305 (beam_search:483) INFO: best hypo: JONRIREDEWITSHINAINGEYSEH + +2024-01-16 21:43:50,306 (asr_inference:494) INFO: speech length: 80000 +2024-01-16 21:43:50,316 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 21:43:50,316 (beam_search:429) INFO: max output length: 122 +2024-01-16 21:43:50,316 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:50,461 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:50,462 (beam_search:476) INFO: -3.77 * 1.0 = -3.77 for ctc +2024-01-16 21:43:50,462 (beam_search:479) INFO: total log probability: -3.77 +2024-01-16 21:43:50,462 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 21:43:50,462 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:50,462 (beam_search:483) INFO: best hypo: WOVERLIVEDONTHERANCHDIDTHATD + +2024-01-16 21:43:50,463 (asr_inference:494) INFO: speech length: 82262 +2024-01-16 21:43:50,473 (beam_search:428) INFO: decoder input length: 126 +2024-01-16 21:43:50,473 (beam_search:429) INFO: max output length: 126 +2024-01-16 21:43:50,473 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:50,647 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:50,647 (beam_search:476) INFO: -4.16 * 1.0 = -4.16 for ctc +2024-01-16 21:43:50,647 (beam_search:479) INFO: total log probability: -4.16 +2024-01-16 21:43:50,647 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 21:43:50,647 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:50,647 (beam_search:483) INFO: best hypo: WELEVETHEVFVENCUOALITYTOTIMEANDLORL + +2024-01-16 21:43:50,648 (asr_inference:494) INFO: speech length: 104000 +2024-01-16 21:43:50,660 (beam_search:428) INFO: decoder input length: 160 +2024-01-16 21:43:50,660 (beam_search:429) INFO: max output length: 160 +2024-01-16 21:43:50,660 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:50,976 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:50,976 (beam_search:476) INFO: -8.57 * 1.0 = -8.57 for ctc +2024-01-16 21:43:50,976 (beam_search:479) INFO: total log probability: -8.57 +2024-01-16 21:43:50,976 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:50,976 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:50,977 (beam_search:483) INFO: best hypo: AATTHESAMETINGSPEARSANDEROSBEGANTOFALLEAMONGHINBATERS + +2024-01-16 21:43:50,978 (asr_inference:494) INFO: speech length: 59392 +2024-01-16 21:43:50,987 (beam_search:428) INFO: decoder input length: 90 +2024-01-16 21:43:50,987 (beam_search:429) INFO: max output length: 90 +2024-01-16 21:43:50,987 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:51,089 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:51,089 (beam_search:476) INFO: -5.58 * 1.0 = -5.58 for ctc +2024-01-16 21:43:51,089 (beam_search:479) INFO: total log probability: -5.58 +2024-01-16 21:43:51,089 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:51,089 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:51,089 (beam_search:483) INFO: best hypo: ITISMEYTHESIMPALSOUPELITIFEF + +2024-01-16 21:43:51,090 (asr_inference:494) INFO: speech length: 78000 +2024-01-16 21:43:51,100 (beam_search:428) INFO: decoder input length: 119 +2024-01-16 21:43:51,100 (beam_search:429) INFO: max output length: 119 +2024-01-16 21:43:51,100 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:51,271 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:51,271 (beam_search:476) INFO: -7.26 * 1.0 = -7.26 for ctc +2024-01-16 21:43:51,271 (beam_search:479) INFO: total log probability: -7.26 +2024-01-16 21:43:51,271 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:51,271 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:51,271 (beam_search:483) INFO: best hypo: INDSTAIDHEARIGVEONTHNOTOFTESOCONDAY + +2024-01-16 21:43:51,272 (asr_inference:494) INFO: speech length: 134000 +2024-01-16 21:43:51,287 (beam_search:428) INFO: decoder input length: 207 +2024-01-16 21:43:51,287 (beam_search:429) INFO: max output length: 207 +2024-01-16 21:43:51,287 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:51,684 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:51,684 (beam_search:476) INFO: -7.12 * 1.0 = -7.12 for ctc +2024-01-16 21:43:51,684 (beam_search:479) INFO: total log probability: -7.12 +2024-01-16 21:43:51,684 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:43:51,684 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:51,685 (beam_search:483) INFO: best hypo: INHISANGSITYANDSOLICSITOEDEANDLOVEFTTHEDIDNOTCOWNT + +2024-01-16 21:43:51,686 (asr_inference:494) INFO: speech length: 66000 +2024-01-16 21:43:51,696 (beam_search:428) INFO: decoder input length: 101 +2024-01-16 21:43:51,696 (beam_search:429) INFO: max output length: 101 +2024-01-16 21:43:51,696 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:51,844 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:51,844 (beam_search:476) INFO: -7.98 * 1.0 = -7.98 for ctc +2024-01-16 21:43:51,844 (beam_search:479) INFO: total log probability: -7.98 +2024-01-16 21:43:51,844 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:51,844 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:51,844 (beam_search:483) INFO: best hypo: DGODBLESSOMIHOPLILGOANDSINGTHEMFOREVER + +2024-01-16 21:43:51,846 (asr_inference:494) INFO: speech length: 42000 +2024-01-16 21:43:51,854 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 21:43:51,854 (beam_search:429) INFO: max output length: 63 +2024-01-16 21:43:51,854 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:51,895 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:51,895 (beam_search:476) INFO: -2.88 * 1.0 = -2.88 for ctc +2024-01-16 21:43:51,895 (beam_search:479) INFO: total log probability: -2.88 +2024-01-16 21:43:51,895 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:51,895 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:51,895 (beam_search:483) INFO: best hypo: YOWERINGOAGED + +2024-01-16 21:43:51,896 (asr_inference:494) INFO: speech length: 126000 +2024-01-16 21:43:51,909 (beam_search:428) INFO: decoder input length: 194 +2024-01-16 21:43:51,909 (beam_search:429) INFO: max output length: 194 +2024-01-16 21:43:51,909 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:52,338 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:52,339 (beam_search:476) INFO: -16.88 * 1.0 = -16.88 for ctc +2024-01-16 21:43:52,339 (beam_search:479) INFO: total log probability: -16.88 +2024-01-16 21:43:52,339 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:52,339 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:52,339 (beam_search:483) INFO: best hypo: THERLATSESWASOFATELICKETIVHERYCOLOEREFRIAINTOBTINTINWHITEAL + +2024-01-16 21:43:52,340 (asr_inference:494) INFO: speech length: 114000 +2024-01-16 21:43:52,352 (beam_search:428) INFO: decoder input length: 176 +2024-01-16 21:43:52,352 (beam_search:429) INFO: max output length: 176 +2024-01-16 21:43:52,352 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:52,632 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:52,632 (beam_search:476) INFO: -6.57 * 1.0 = -6.57 for ctc +2024-01-16 21:43:52,632 (beam_search:479) INFO: total log probability: -6.57 +2024-01-16 21:43:52,632 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:52,632 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:52,632 (beam_search:483) INFO: best hypo: ITWATHESAMEWHAYWITOLREVFALVEORSANDRIFALS + +2024-01-16 21:43:52,633 (asr_inference:494) INFO: speech length: 41441 +2024-01-16 21:43:52,641 (beam_search:428) INFO: decoder input length: 62 +2024-01-16 21:43:52,641 (beam_search:429) INFO: max output length: 62 +2024-01-16 21:43:52,641 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:52,717 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:52,717 (beam_search:476) INFO: -7.24 * 1.0 = -7.24 for ctc +2024-01-16 21:43:52,717 (beam_search:479) INFO: total log probability: -7.24 +2024-01-16 21:43:52,717 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:52,717 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:52,718 (beam_search:483) INFO: best hypo: HECINGHADRMISTINCQUIREINTOTHATER + +2024-01-16 21:43:52,719 (asr_inference:494) INFO: speech length: 52000 +2024-01-16 21:43:52,727 (beam_search:428) INFO: decoder input length: 79 +2024-01-16 21:43:52,727 (beam_search:429) INFO: max output length: 79 +2024-01-16 21:43:52,727 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:52,792 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:52,792 (beam_search:476) INFO: -7.58 * 1.0 = -7.58 for ctc +2024-01-16 21:43:52,792 (beam_search:479) INFO: total log probability: -7.58 +2024-01-16 21:43:52,793 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:43:52,793 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:52,793 (beam_search:483) INFO: best hypo: AEDOSTENLOKGODTEDT + +2024-01-16 21:43:52,794 (asr_inference:494) INFO: speech length: 130000 +2024-01-16 21:43:52,807 (beam_search:428) INFO: decoder input length: 201 +2024-01-16 21:43:52,807 (beam_search:429) INFO: max output length: 201 +2024-01-16 21:43:52,807 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:53,159 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:53,159 (beam_search:476) INFO: -7.52 * 1.0 = -7.52 for ctc +2024-01-16 21:43:53,159 (beam_search:479) INFO: total log probability: -7.52 +2024-01-16 21:43:53,159 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:43:53,159 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:53,160 (beam_search:483) INFO: best hypo: FORTHEFIRSTTIMEINHISLIFEHEWASYURNINGFORSGRAP + +2024-01-16 21:43:53,161 (asr_inference:494) INFO: speech length: 107861 +2024-01-16 21:43:53,172 (beam_search:428) INFO: decoder input length: 166 +2024-01-16 21:43:53,173 (beam_search:429) INFO: max output length: 166 +2024-01-16 21:43:53,173 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:53,487 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:53,487 (beam_search:476) INFO: -10.53 * 1.0 = -10.53 for ctc +2024-01-16 21:43:53,487 (beam_search:479) INFO: total log probability: -10.53 +2024-01-16 21:43:53,487 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:53,487 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:53,487 (beam_search:483) INFO: best hypo: IDEFIGANYMANTOGETASOLHAMWOTILENCESOREINCELYFORNIER + +2024-01-16 21:43:53,488 (asr_inference:494) INFO: speech length: 66000 +2024-01-16 21:43:53,498 (beam_search:428) INFO: decoder input length: 101 +2024-01-16 21:43:53,498 (beam_search:429) INFO: max output length: 101 +2024-01-16 21:43:53,498 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:53,646 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:53,647 (beam_search:476) INFO: -9.44 * 1.0 = -9.44 for ctc +2024-01-16 21:43:53,647 (beam_search:479) INFO: total log probability: -9.44 +2024-01-16 21:43:53,647 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:53,647 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:53,647 (beam_search:483) INFO: best hypo: HERISSMULTTRUATHIMASHECAMEOFTHEBANGK + +2024-01-16 21:43:53,648 (asr_inference:494) INFO: speech length: 42000 +2024-01-16 21:43:53,656 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 21:43:53,656 (beam_search:429) INFO: max output length: 63 +2024-01-16 21:43:53,656 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:53,714 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:53,714 (beam_search:476) INFO: -8.47 * 1.0 = -8.47 for ctc +2024-01-16 21:43:53,714 (beam_search:479) INFO: total log probability: -8.47 +2024-01-16 21:43:53,714 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:43:53,714 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:53,715 (beam_search:483) INFO: best hypo: ATDYWAYTNONSLELIGTDER + +2024-01-16 21:43:53,716 (asr_inference:494) INFO: speech length: 80000 +2024-01-16 21:43:53,726 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 21:43:53,726 (beam_search:429) INFO: max output length: 122 +2024-01-16 21:43:53,726 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:53,864 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:53,864 (beam_search:476) INFO: -8.36 * 1.0 = -8.36 for ctc +2024-01-16 21:43:53,864 (beam_search:479) INFO: total log probability: -8.36 +2024-01-16 21:43:53,864 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:43:53,864 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:53,865 (beam_search:483) INFO: best hypo: MINHINDEURITCOLTLINGDEATH + +2024-01-16 21:43:53,866 (asr_inference:494) INFO: speech length: 86000 +2024-01-16 21:43:53,876 (beam_search:428) INFO: decoder input length: 132 +2024-01-16 21:43:53,876 (beam_search:429) INFO: max output length: 132 +2024-01-16 21:43:53,876 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:54,052 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:54,052 (beam_search:476) INFO: -8.91 * 1.0 = -8.91 for ctc +2024-01-16 21:43:54,052 (beam_search:479) INFO: total log probability: -8.91 +2024-01-16 21:43:54,052 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:43:54,052 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:54,052 (beam_search:483) INFO: best hypo: METOLSONOHOSEDETHISBOKCEPERRODGERS + +2024-01-16 21:43:54,053 (asr_inference:494) INFO: speech length: 74000 +2024-01-16 21:43:54,063 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 21:43:54,063 (beam_search:429) INFO: max output length: 113 +2024-01-16 21:43:54,063 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:54,168 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:54,168 (beam_search:476) INFO: -5.37 * 1.0 = -5.37 for ctc +2024-01-16 21:43:54,168 (beam_search:479) INFO: total log probability: -5.37 +2024-01-16 21:43:54,168 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:54,168 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:54,168 (beam_search:483) INFO: best hypo: IONLYATDTHEFORTATIONS + +2024-01-16 21:43:54,169 (asr_inference:494) INFO: speech length: 95195 +2024-01-16 21:43:54,180 (beam_search:428) INFO: decoder input length: 146 +2024-01-16 21:43:54,180 (beam_search:429) INFO: max output length: 146 +2024-01-16 21:43:54,180 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:54,482 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:54,482 (beam_search:476) INFO: -12.72 * 1.0 = -12.72 for ctc +2024-01-16 21:43:54,482 (beam_search:479) INFO: total log probability: -12.72 +2024-01-16 21:43:54,482 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:54,482 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:54,482 (beam_search:483) INFO: best hypo: TEREWASPERDEVISIONOFLAVBRINTHEWALTHEYINDEVRIGDELYPOFPOMENTD + +2024-01-16 21:43:54,483 (asr_inference:494) INFO: speech length: 70000 +2024-01-16 21:43:54,493 (beam_search:428) INFO: decoder input length: 107 +2024-01-16 21:43:54,493 (beam_search:429) INFO: max output length: 107 +2024-01-16 21:43:54,493 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:54,639 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:54,639 (beam_search:476) INFO: -9.47 * 1.0 = -9.47 for ctc +2024-01-16 21:43:54,639 (beam_search:479) INFO: total log probability: -9.47 +2024-01-16 21:43:54,639 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:54,639 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:54,639 (beam_search:483) INFO: best hypo: IOLPEYOUTHELABRAONDSAIDTHRICTFACE + +2024-01-16 21:43:54,640 (asr_inference:494) INFO: speech length: 80555 +2024-01-16 21:43:54,650 (beam_search:428) INFO: decoder input length: 123 +2024-01-16 21:43:54,650 (beam_search:429) INFO: max output length: 123 +2024-01-16 21:43:54,650 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:54,839 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:54,839 (beam_search:476) INFO: -9.59 * 1.0 = -9.59 for ctc +2024-01-16 21:43:54,839 (beam_search:479) INFO: total log probability: -9.59 +2024-01-16 21:43:54,839 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:43:54,839 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:54,840 (beam_search:483) INFO: best hypo: ISOFMISTORPIGNODISHEADGRIMLYINEROCASTICLY + +2024-01-16 21:43:54,841 (asr_inference:494) INFO: speech length: 52000 +2024-01-16 21:43:54,849 (beam_search:428) INFO: decoder input length: 79 +2024-01-16 21:43:54,849 (beam_search:429) INFO: max output length: 79 +2024-01-16 21:43:54,849 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:54,947 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:54,947 (beam_search:476) INFO: -6.18 * 1.0 = -6.18 for ctc +2024-01-16 21:43:54,947 (beam_search:479) INFO: total log probability: -6.18 +2024-01-16 21:43:54,947 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:54,947 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:54,947 (beam_search:483) INFO: best hypo: THERINGOFTHEBIGBILEAROASTDINDN + +2024-01-16 21:43:54,948 (asr_inference:494) INFO: speech length: 113761 +2024-01-16 21:43:54,960 (beam_search:428) INFO: decoder input length: 175 +2024-01-16 21:43:54,960 (beam_search:429) INFO: max output length: 175 +2024-01-16 21:43:54,960 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:55,396 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:55,397 (beam_search:476) INFO: -16.51 * 1.0 = -16.51 for ctc +2024-01-16 21:43:55,397 (beam_search:479) INFO: total log probability: -16.51 +2024-01-16 21:43:55,397 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:55,397 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:55,397 (beam_search:483) INFO: best hypo: ORTHESRACHOFAPINONAANSHEADVEASTREAGOSOFTHEURSURFISSREMAEJELOUGICLYUDNON + +2024-01-16 21:43:55,398 (asr_inference:494) INFO: speech length: 92000 +2024-01-16 21:43:55,409 (beam_search:428) INFO: decoder input length: 141 +2024-01-16 21:43:55,409 (beam_search:429) INFO: max output length: 141 +2024-01-16 21:43:55,409 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:55,655 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:55,655 (beam_search:476) INFO: -10.61 * 1.0 = -10.61 for ctc +2024-01-16 21:43:55,656 (beam_search:479) INFO: total log probability: -10.61 +2024-01-16 21:43:55,656 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:55,656 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:55,656 (beam_search:483) INFO: best hypo: THEADBADILYANTERDIDESWHENTHESOGDTHEGLOEOFAFFIR + +2024-01-16 21:43:55,657 (asr_inference:494) INFO: speech length: 38000 +2024-01-16 21:43:55,665 (beam_search:428) INFO: decoder input length: 57 +2024-01-16 21:43:55,665 (beam_search:429) INFO: max output length: 57 +2024-01-16 21:43:55,665 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:55,718 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:55,718 (beam_search:476) INFO: -4.09 * 1.0 = -4.09 for ctc +2024-01-16 21:43:55,718 (beam_search:479) INFO: total log probability: -4.09 +2024-01-16 21:43:55,718 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:55,718 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:55,718 (beam_search:483) INFO: best hypo: CHANSCHARSTHYLITCOMAND + +2024-01-16 21:43:55,719 (asr_inference:494) INFO: speech length: 116000 +2024-01-16 21:43:55,732 (beam_search:428) INFO: decoder input length: 179 +2024-01-16 21:43:55,732 (beam_search:429) INFO: max output length: 179 +2024-01-16 21:43:55,732 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:56,006 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:56,006 (beam_search:476) INFO: -10.17 * 1.0 = -10.17 for ctc +2024-01-16 21:43:56,006 (beam_search:479) INFO: total log probability: -10.17 +2024-01-16 21:43:56,006 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:56,006 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:56,006 (beam_search:483) INFO: best hypo: ITWASJENSAININGSOFHELYOERBEYONTHEROCKCE + +2024-01-16 21:43:56,007 (asr_inference:494) INFO: speech length: 84000 +2024-01-16 21:43:56,018 (beam_search:428) INFO: decoder input length: 129 +2024-01-16 21:43:56,018 (beam_search:429) INFO: max output length: 129 +2024-01-16 21:43:56,018 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:56,150 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:56,150 (beam_search:476) INFO: -6.99 * 1.0 = -6.99 for ctc +2024-01-16 21:43:56,150 (beam_search:479) INFO: total log probability: -6.99 +2024-01-16 21:43:56,150 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:43:56,150 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:56,150 (beam_search:483) INFO: best hypo: OFLINGAROLBOSTBETWENOSED + +2024-01-16 21:43:56,151 (asr_inference:494) INFO: speech length: 119979 +2024-01-16 21:43:56,164 (beam_search:428) INFO: decoder input length: 185 +2024-01-16 21:43:56,164 (beam_search:429) INFO: max output length: 185 +2024-01-16 21:43:56,164 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:56,549 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:56,549 (beam_search:476) INFO: -8.60 * 1.0 = -8.60 for ctc +2024-01-16 21:43:56,549 (beam_search:479) INFO: total log probability: -8.60 +2024-01-16 21:43:56,549 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:43:56,549 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:56,549 (beam_search:483) INFO: best hypo: HATRITANDMURDERANDLOSTFORREVENGCHTHEYPOSSESTTOOFERFLOYING + +2024-01-16 21:43:56,550 (asr_inference:494) INFO: speech length: 69602 +2024-01-16 21:43:56,560 (beam_search:428) INFO: decoder input length: 106 +2024-01-16 21:43:56,560 (beam_search:429) INFO: max output length: 106 +2024-01-16 21:43:56,560 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:56,697 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:56,697 (beam_search:476) INFO: -7.75 * 1.0 = -7.75 for ctc +2024-01-16 21:43:56,697 (beam_search:479) INFO: total log probability: -7.75 +2024-01-16 21:43:56,697 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:56,697 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:56,698 (beam_search:483) INFO: best hypo: THATOCODHEARALLUPEDDONTELIMPOPOE + +2024-01-16 21:43:56,699 (asr_inference:494) INFO: speech length: 34000 +2024-01-16 21:43:56,706 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 21:43:56,706 (beam_search:429) INFO: max output length: 51 +2024-01-16 21:43:56,706 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:56,750 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:56,750 (beam_search:476) INFO: -2.61 * 1.0 = -2.61 for ctc +2024-01-16 21:43:56,750 (beam_search:479) INFO: total log probability: -2.61 +2024-01-16 21:43:56,750 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 21:43:56,750 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:56,750 (beam_search:483) INFO: best hypo: ITWASMYADDETOATE + +2024-01-16 21:43:56,751 (asr_inference:494) INFO: speech length: 55638 +2024-01-16 21:43:56,760 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 21:43:56,760 (beam_search:429) INFO: max output length: 84 +2024-01-16 21:43:56,760 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:56,830 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:56,830 (beam_search:476) INFO: -4.60 * 1.0 = -4.60 for ctc +2024-01-16 21:43:56,830 (beam_search:479) INFO: total log probability: -4.60 +2024-01-16 21:43:56,830 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:56,830 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:56,830 (beam_search:483) INFO: best hypo: SHEDOSANTWONTOWEIN + +2024-01-16 21:43:56,831 (asr_inference:494) INFO: speech length: 81750 +2024-01-16 21:43:56,841 (beam_search:428) INFO: decoder input length: 125 +2024-01-16 21:43:56,841 (beam_search:429) INFO: max output length: 125 +2024-01-16 21:43:56,841 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:57,017 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:57,017 (beam_search:476) INFO: -7.53 * 1.0 = -7.53 for ctc +2024-01-16 21:43:57,017 (beam_search:479) INFO: total log probability: -7.53 +2024-01-16 21:43:57,017 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:43:57,017 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:57,018 (beam_search:483) INFO: best hypo: SHETHINGEAITISBECOSHEWONCESOMTHNGLE + +2024-01-16 21:43:57,019 (asr_inference:494) INFO: speech length: 82000 +2024-01-16 21:43:57,029 (beam_search:428) INFO: decoder input length: 126 +2024-01-16 21:43:57,029 (beam_search:429) INFO: max output length: 126 +2024-01-16 21:43:57,029 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:57,232 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:57,232 (beam_search:476) INFO: -12.72 * 1.0 = -12.72 for ctc +2024-01-16 21:43:57,232 (beam_search:479) INFO: total log probability: -12.72 +2024-01-16 21:43:57,232 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:57,232 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:57,232 (beam_search:483) INFO: best hypo: HSHEPOLDEANDTTHELOCCREASETDONTOBRAKHISBAC + +2024-01-16 21:43:57,233 (asr_inference:494) INFO: speech length: 85443 +2024-01-16 21:43:57,244 (beam_search:428) INFO: decoder input length: 131 +2024-01-16 21:43:57,244 (beam_search:429) INFO: max output length: 131 +2024-01-16 21:43:57,244 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:57,492 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:57,492 (beam_search:476) INFO: -11.32 * 1.0 = -11.32 for ctc +2024-01-16 21:43:57,493 (beam_search:479) INFO: total log probability: -11.32 +2024-01-16 21:43:57,493 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:57,493 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:57,493 (beam_search:483) INFO: best hypo: THATHESOCOLDFORESATWORKINLITEHEALCTRISIDYANDMANGNTISM + +2024-01-16 21:43:57,494 (asr_inference:494) INFO: speech length: 110000 +2024-01-16 21:43:57,506 (beam_search:428) INFO: decoder input length: 169 +2024-01-16 21:43:57,506 (beam_search:429) INFO: max output length: 169 +2024-01-16 21:43:57,506 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:57,820 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:57,820 (beam_search:476) INFO: -14.68 * 1.0 = -14.68 for ctc +2024-01-16 21:43:57,820 (beam_search:479) INFO: total log probability: -14.68 +2024-01-16 21:43:57,820 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:43:57,820 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:57,820 (beam_search:483) INFO: best hypo: WETWIONSHAOBPEINTANDPIAICETGRAGSINANCOSTHETHIHVBLE + +2024-01-16 21:43:57,822 (asr_inference:494) INFO: speech length: 44000 +2024-01-16 21:43:57,830 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:43:57,830 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:43:57,830 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:57,891 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:57,891 (beam_search:476) INFO: -4.86 * 1.0 = -4.86 for ctc +2024-01-16 21:43:57,891 (beam_search:479) INFO: total log probability: -4.86 +2024-01-16 21:43:57,891 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:57,891 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:57,891 (beam_search:483) INFO: best hypo: ALSOEIWONTANFORMATION + +2024-01-16 21:43:57,892 (asr_inference:494) INFO: speech length: 76203 +2024-01-16 21:43:57,902 (beam_search:428) INFO: decoder input length: 117 +2024-01-16 21:43:57,902 (beam_search:429) INFO: max output length: 117 +2024-01-16 21:43:57,902 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:58,072 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:58,072 (beam_search:476) INFO: -5.27 * 1.0 = -5.27 for ctc +2024-01-16 21:43:58,072 (beam_search:479) INFO: total log probability: -5.27 +2024-01-16 21:43:58,072 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:43:58,072 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:58,072 (beam_search:483) INFO: best hypo: THESICTDAYHESPENTINTHECAVONWHGRAGSON + +2024-01-16 21:43:58,073 (asr_inference:494) INFO: speech length: 126401 +2024-01-16 21:43:58,086 (beam_search:428) INFO: decoder input length: 195 +2024-01-16 21:43:58,087 (beam_search:429) INFO: max output length: 195 +2024-01-16 21:43:58,087 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:58,669 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:58,669 (beam_search:476) INFO: -18.85 * 1.0 = -18.85 for ctc +2024-01-16 21:43:58,669 (beam_search:479) INFO: total log probability: -18.85 +2024-01-16 21:43:58,669 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:43:58,669 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:58,670 (beam_search:483) INFO: best hypo: IONTHISYPOTHIESTHEHAMERNGOFTHELTERMUNDINGCRPUCLEONDTHEBOBCOFIREITKCENATICKNRGYONTHONHAND + +2024-01-16 21:43:58,671 (asr_inference:494) INFO: speech length: 96961 +2024-01-16 21:43:58,682 (beam_search:428) INFO: decoder input length: 149 +2024-01-16 21:43:58,682 (beam_search:429) INFO: max output length: 149 +2024-01-16 21:43:58,682 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:59,030 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:59,030 (beam_search:476) INFO: -9.54 * 1.0 = -9.54 for ctc +2024-01-16 21:43:59,030 (beam_search:479) INFO: total log probability: -9.54 +2024-01-16 21:43:59,030 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:43:59,030 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:59,030 (beam_search:483) INFO: best hypo: NOWEAFIRNYWILSTREMEMANDEVERANANONYOUAMURGEFROMALTHEGROVESANDFLOURS + +2024-01-16 21:43:59,031 (asr_inference:494) INFO: speech length: 83362 +2024-01-16 21:43:59,042 (beam_search:428) INFO: decoder input length: 128 +2024-01-16 21:43:59,042 (beam_search:429) INFO: max output length: 128 +2024-01-16 21:43:59,042 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:59,305 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:59,305 (beam_search:476) INFO: -10.89 * 1.0 = -10.89 for ctc +2024-01-16 21:43:59,305 (beam_search:479) INFO: total log probability: -10.89 +2024-01-16 21:43:59,305 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:59,305 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:59,305 (beam_search:483) INFO: best hypo: WITHHOTITTHEMOSTDENCELYPOPILAEDREAGENSOFMOHENTURIPANDAMORICA + +2024-01-16 21:43:59,307 (asr_inference:494) INFO: speech length: 43691 +2024-01-16 21:43:59,315 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:43:59,315 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:43:59,315 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:59,368 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:59,368 (beam_search:476) INFO: -4.20 * 1.0 = -4.20 for ctc +2024-01-16 21:43:59,368 (beam_search:479) INFO: total log probability: -4.20 +2024-01-16 21:43:59,368 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:43:59,368 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:59,368 (beam_search:483) INFO: best hypo: TOMSPINKHASHARPON + +2024-01-16 21:43:59,369 (asr_inference:494) INFO: speech length: 61200 +2024-01-16 21:43:59,378 (beam_search:428) INFO: decoder input length: 93 +2024-01-16 21:43:59,378 (beam_search:429) INFO: max output length: 93 +2024-01-16 21:43:59,378 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:59,517 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:59,517 (beam_search:476) INFO: -9.98 * 1.0 = -9.98 for ctc +2024-01-16 21:43:59,517 (beam_search:479) INFO: total log probability: -9.98 +2024-01-16 21:43:59,517 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:43:59,517 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:59,517 (beam_search:483) INFO: best hypo: HEWNTEDGTHEFISHETTHISFOLEALREYSOFAGON + +2024-01-16 21:43:59,518 (asr_inference:494) INFO: speech length: 80000 +2024-01-16 21:43:59,528 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 21:43:59,528 (beam_search:429) INFO: max output length: 122 +2024-01-16 21:43:59,528 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:59,746 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:59,746 (beam_search:476) INFO: -9.41 * 1.0 = -9.41 for ctc +2024-01-16 21:43:59,746 (beam_search:479) INFO: total log probability: -9.41 +2024-01-16 21:43:59,746 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:43:59,746 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:59,746 (beam_search:483) INFO: best hypo: LAAFLASHEHELONCHEHIMSELFINTTHEFETHEDMASOTHHOUHL + +2024-01-16 21:43:59,748 (asr_inference:494) INFO: speech length: 80214 +2024-01-16 21:43:59,758 (beam_search:428) INFO: decoder input length: 123 +2024-01-16 21:43:59,758 (beam_search:429) INFO: max output length: 123 +2024-01-16 21:43:59,758 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:59,901 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:59,901 (beam_search:476) INFO: -3.29 * 1.0 = -3.29 for ctc +2024-01-16 21:43:59,901 (beam_search:479) INFO: total log probability: -3.29 +2024-01-16 21:43:59,901 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 21:43:59,901 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:59,901 (beam_search:483) INFO: best hypo: ITCONTAEATOTLEOFTWENTYANTRES + +2024-01-16 21:43:59,902 (asr_inference:494) INFO: speech length: 56491 +2024-01-16 21:43:59,911 (beam_search:428) INFO: decoder input length: 86 +2024-01-16 21:43:59,911 (beam_search:429) INFO: max output length: 86 +2024-01-16 21:43:59,911 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:43:59,997 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:43:59,997 (beam_search:476) INFO: -4.30 * 1.0 = -4.30 for ctc +2024-01-16 21:43:59,997 (beam_search:479) INFO: total log probability: -4.30 +2024-01-16 21:43:59,997 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:43:59,997 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:43:59,997 (beam_search:483) INFO: best hypo: IHAVEFHELTMORECOMFORABLE + +2024-01-16 21:43:59,998 (asr_inference:494) INFO: speech length: 40000 +2024-01-16 21:44:00,006 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 21:44:00,006 (beam_search:429) INFO: max output length: 60 +2024-01-16 21:44:00,006 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:00,066 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:00,067 (beam_search:476) INFO: -5.86 * 1.0 = -5.86 for ctc +2024-01-16 21:44:00,067 (beam_search:479) INFO: total log probability: -5.86 +2024-01-16 21:44:00,067 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:44:00,067 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:00,067 (beam_search:483) INFO: best hypo: THEDPOESTOMNTHVEATELITY + +2024-01-16 21:44:00,068 (asr_inference:494) INFO: speech length: 74000 +2024-01-16 21:44:00,078 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 21:44:00,078 (beam_search:429) INFO: max output length: 113 +2024-01-16 21:44:00,078 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:00,238 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:00,238 (beam_search:476) INFO: -7.36 * 1.0 = -7.36 for ctc +2024-01-16 21:44:00,238 (beam_search:479) INFO: total log probability: -7.36 +2024-01-16 21:44:00,238 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:44:00,238 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:00,239 (beam_search:483) INFO: best hypo: THEWLLEDOGKTHRSTHISGONTMUSLETORDHIMN + +2024-01-16 21:44:00,240 (asr_inference:494) INFO: speech length: 65536 +2024-01-16 21:44:00,249 (beam_search:428) INFO: decoder input length: 100 +2024-01-16 21:44:00,249 (beam_search:429) INFO: max output length: 100 +2024-01-16 21:44:00,249 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:00,384 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:00,384 (beam_search:476) INFO: -9.43 * 1.0 = -9.43 for ctc +2024-01-16 21:44:00,384 (beam_search:479) INFO: total log probability: -9.43 +2024-01-16 21:44:00,384 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:44:00,384 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:00,384 (beam_search:483) INFO: best hypo: THEGAEBILEVORSCEOFHESEMERIYRANGOUT + +2024-01-16 21:44:00,386 (asr_inference:494) INFO: speech length: 90000 +2024-01-16 21:44:00,396 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 21:44:00,396 (beam_search:429) INFO: max output length: 138 +2024-01-16 21:44:00,396 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:00,637 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:00,637 (beam_search:476) INFO: -8.26 * 1.0 = -8.26 for ctc +2024-01-16 21:44:00,637 (beam_search:479) INFO: total log probability: -8.26 +2024-01-16 21:44:00,637 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:44:00,637 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:00,637 (beam_search:483) INFO: best hypo: ITWASAIRIVERANMMURGINGLAKOURSELSFOMTEGRATSOMP + +2024-01-16 21:44:00,638 (asr_inference:494) INFO: speech length: 76161 +2024-01-16 21:44:00,648 (beam_search:428) INFO: decoder input length: 117 +2024-01-16 21:44:00,648 (beam_search:429) INFO: max output length: 117 +2024-01-16 21:44:00,648 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:00,889 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:00,890 (beam_search:476) INFO: -9.66 * 1.0 = -9.66 for ctc +2024-01-16 21:44:00,890 (beam_search:479) INFO: total log probability: -9.66 +2024-01-16 21:44:00,890 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:44:00,890 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:00,890 (beam_search:483) INFO: best hypo: SAIDTHEMOLLPULINGHIMSELFTOGETHWITHANEFERTOMUSTTHINGMEVERYROD + +2024-01-16 21:44:00,891 (asr_inference:494) INFO: speech length: 118272 +2024-01-16 21:44:00,904 (beam_search:428) INFO: decoder input length: 182 +2024-01-16 21:44:00,904 (beam_search:429) INFO: max output length: 182 +2024-01-16 21:44:00,904 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:01,299 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:01,299 (beam_search:476) INFO: -10.91 * 1.0 = -10.91 for ctc +2024-01-16 21:44:01,299 (beam_search:479) INFO: total log probability: -10.91 +2024-01-16 21:44:01,299 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:44:01,299 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:01,299 (beam_search:483) INFO: best hypo: INWHATBYUCOLLICKSCOOOFFENCEHEHADBEDTORTTHWASBEYONDIMADGENING + +2024-01-16 21:44:01,301 (asr_inference:494) INFO: speech length: 77122 +2024-01-16 21:44:01,310 (beam_search:428) INFO: decoder input length: 118 +2024-01-16 21:44:01,310 (beam_search:429) INFO: max output length: 118 +2024-01-16 21:44:01,310 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:01,545 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:01,546 (beam_search:476) INFO: -10.75 * 1.0 = -10.75 for ctc +2024-01-16 21:44:01,546 (beam_search:479) INFO: total log probability: -10.75 +2024-01-16 21:44:01,546 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:44:01,546 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:01,546 (beam_search:483) INFO: best hypo: HADNOTINABLEDINVESTIGADERSTOOPETAINACOMPERITIVFLYLITLCLOUST + +2024-01-16 21:44:01,547 (asr_inference:494) INFO: speech length: 66000 +2024-01-16 21:44:01,556 (beam_search:428) INFO: decoder input length: 101 +2024-01-16 21:44:01,556 (beam_search:429) INFO: max output length: 101 +2024-01-16 21:44:01,556 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:01,685 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:01,685 (beam_search:476) INFO: -8.42 * 1.0 = -8.42 for ctc +2024-01-16 21:44:01,685 (beam_search:479) INFO: total log probability: -8.42 +2024-01-16 21:44:01,685 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:44:01,685 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:01,685 (beam_search:483) INFO: best hypo: ITTRIKLOFFRESHBLODRANOVERSFACE + +2024-01-16 21:44:01,686 (asr_inference:494) INFO: speech length: 66000 +2024-01-16 21:44:01,696 (beam_search:428) INFO: decoder input length: 101 +2024-01-16 21:44:01,696 (beam_search:429) INFO: max output length: 101 +2024-01-16 21:44:01,696 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:01,809 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:01,809 (beam_search:476) INFO: -10.46 * 1.0 = -10.46 for ctc +2024-01-16 21:44:01,809 (beam_search:479) INFO: total log probability: -10.46 +2024-01-16 21:44:01,809 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:44:01,809 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:01,809 (beam_search:483) INFO: best hypo: DITWASACUORESCONSITEANTSESE + +2024-01-16 21:44:01,810 (asr_inference:494) INFO: speech length: 78000 +2024-01-16 21:44:01,820 (beam_search:428) INFO: decoder input length: 119 +2024-01-16 21:44:01,820 (beam_search:429) INFO: max output length: 119 +2024-01-16 21:44:01,820 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:01,945 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:01,945 (beam_search:476) INFO: -5.99 * 1.0 = -5.99 for ctc +2024-01-16 21:44:01,945 (beam_search:479) INFO: total log probability: -5.99 +2024-01-16 21:44:01,945 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:44:01,945 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:01,945 (beam_search:483) INFO: best hypo: ITISTHEFIRPARTLYSHESAINE + +2024-01-16 21:44:01,946 (asr_inference:494) INFO: speech length: 74000 +2024-01-16 21:44:01,956 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 21:44:01,956 (beam_search:429) INFO: max output length: 113 +2024-01-16 21:44:01,956 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:02,108 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:02,108 (beam_search:476) INFO: -7.48 * 1.0 = -7.48 for ctc +2024-01-16 21:44:02,108 (beam_search:479) INFO: total log probability: -7.48 +2024-01-16 21:44:02,108 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:44:02,108 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:02,109 (beam_search:483) INFO: best hypo: THEYGOSTLAYEOFTHEBOSHANDPOKDAWAYAN + +2024-01-16 21:44:02,110 (asr_inference:494) INFO: speech length: 72000 +2024-01-16 21:44:02,119 (beam_search:428) INFO: decoder input length: 110 +2024-01-16 21:44:02,119 (beam_search:429) INFO: max output length: 110 +2024-01-16 21:44:02,119 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:02,271 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:02,271 (beam_search:476) INFO: -5.40 * 1.0 = -5.40 for ctc +2024-01-16 21:44:02,271 (beam_search:479) INFO: total log probability: -5.40 +2024-01-16 21:44:02,271 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:44:02,271 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:02,271 (beam_search:483) INFO: best hypo: INOTHATOUREINCHARDETHEREANDJENOSE + +2024-01-16 21:44:02,272 (asr_inference:494) INFO: speech length: 72000 +2024-01-16 21:44:02,282 (beam_search:428) INFO: decoder input length: 110 +2024-01-16 21:44:02,282 (beam_search:429) INFO: max output length: 110 +2024-01-16 21:44:02,282 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:02,455 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:02,455 (beam_search:476) INFO: -5.13 * 1.0 = -5.13 for ctc +2024-01-16 21:44:02,455 (beam_search:479) INFO: total log probability: -5.13 +2024-01-16 21:44:02,455 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 21:44:02,455 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:02,455 (beam_search:483) INFO: best hypo: FORTIETHEECSATINGTHILEOFHISADVENTHEWASGON + +2024-01-16 21:44:02,456 (asr_inference:494) INFO: speech length: 60000 +2024-01-16 21:44:02,465 (beam_search:428) INFO: decoder input length: 91 +2024-01-16 21:44:02,465 (beam_search:429) INFO: max output length: 91 +2024-01-16 21:44:02,465 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:02,595 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:02,595 (beam_search:476) INFO: -9.65 * 1.0 = -9.65 for ctc +2024-01-16 21:44:02,595 (beam_search:479) INFO: total log probability: -9.65 +2024-01-16 21:44:02,595 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:44:02,595 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:02,595 (beam_search:483) INFO: best hypo: FAEDLYHISFINGRSCLOSTHADLYOVETHEANGOCIF + +2024-01-16 21:44:02,596 (asr_inference:494) INFO: speech length: 102000 +2024-01-16 21:44:02,607 (beam_search:428) INFO: decoder input length: 157 +2024-01-16 21:44:02,607 (beam_search:429) INFO: max output length: 157 +2024-01-16 21:44:02,607 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:02,900 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:02,900 (beam_search:476) INFO: -6.90 * 1.0 = -6.90 for ctc +2024-01-16 21:44:02,900 (beam_search:479) INFO: total log probability: -6.90 +2024-01-16 21:44:02,900 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:44:02,900 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:02,900 (beam_search:483) INFO: best hypo: DEARSIREYOURSECONTVICTDOMHASFOLLONONDSCHADGDULETIME + +2024-01-16 21:44:02,901 (asr_inference:494) INFO: speech length: 46000 +2024-01-16 21:44:02,909 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:44:02,909 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:44:02,909 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:02,963 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:02,963 (beam_search:476) INFO: -5.44 * 1.0 = -5.44 for ctc +2024-01-16 21:44:02,963 (beam_search:479) INFO: total log probability: -5.44 +2024-01-16 21:44:02,963 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:44:02,963 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:02,963 (beam_search:483) INFO: best hypo: HCONCARFHMSELFE + +2024-01-16 21:44:02,964 (asr_inference:494) INFO: speech length: 44000 +2024-01-16 21:44:02,972 (beam_search:428) INFO: decoder input length: 66 +2024-01-16 21:44:02,972 (beam_search:429) INFO: max output length: 66 +2024-01-16 21:44:02,972 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:03,057 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:03,057 (beam_search:476) INFO: -6.79 * 1.0 = -6.79 for ctc +2024-01-16 21:44:03,057 (beam_search:479) INFO: total log probability: -6.79 +2024-01-16 21:44:03,057 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:44:03,057 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:03,057 (beam_search:483) INFO: best hypo: ACHINSILTATETOTHEVALYUOTHECLAME + +2024-01-16 21:44:03,059 (asr_inference:494) INFO: speech length: 89440 +2024-01-16 21:44:03,070 (beam_search:428) INFO: decoder input length: 137 +2024-01-16 21:44:03,070 (beam_search:429) INFO: max output length: 137 +2024-01-16 21:44:03,070 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:03,360 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:03,360 (beam_search:476) INFO: -9.98 * 1.0 = -9.98 for ctc +2024-01-16 21:44:03,361 (beam_search:479) INFO: total log probability: -9.98 +2024-01-16 21:44:03,361 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:44:03,361 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:03,361 (beam_search:483) INFO: best hypo: THEITMABETRANCSFORMEDINTOANYOOFTHFORMSOFWHCHENRGYISECEPTABL + +2024-01-16 21:44:03,362 (asr_inference:494) INFO: speech length: 161792 +2024-01-16 21:44:03,378 (beam_search:428) INFO: decoder input length: 250 +2024-01-16 21:44:03,378 (beam_search:429) INFO: max output length: 250 +2024-01-16 21:44:03,378 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:04,067 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:04,067 (beam_search:476) INFO: -17.71 * 1.0 = -17.71 for ctc +2024-01-16 21:44:04,067 (beam_search:479) INFO: total log probability: -17.71 +2024-01-16 21:44:04,067 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:44:04,067 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:04,067 (beam_search:483) INFO: best hypo: MESEIDESSCREAMEDGRIDLOVFIADNDMANYIFESTEDTHHIRIARDICKAEBONDDENNMENTOFHISTAIAR + +2024-01-16 21:44:04,069 (asr_inference:494) INFO: speech length: 100000 +2024-01-16 21:44:04,080 (beam_search:428) INFO: decoder input length: 154 +2024-01-16 21:44:04,080 (beam_search:429) INFO: max output length: 154 +2024-01-16 21:44:04,080 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:04,267 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:04,267 (beam_search:476) INFO: -9.69 * 1.0 = -9.69 for ctc +2024-01-16 21:44:04,267 (beam_search:479) INFO: total log probability: -9.69 +2024-01-16 21:44:04,267 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 21:44:04,267 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:04,268 (beam_search:483) INFO: best hypo: IWHNTONOHOWTALTHISISPOSTABLE + +2024-01-16 21:44:04,269 (asr_inference:494) INFO: speech length: 102881 +2024-01-16 21:44:04,280 (beam_search:428) INFO: decoder input length: 158 +2024-01-16 21:44:04,281 (beam_search:429) INFO: max output length: 158 +2024-01-16 21:44:04,281 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:04,668 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:04,668 (beam_search:476) INFO: -14.01 * 1.0 = -14.01 for ctc +2024-01-16 21:44:04,668 (beam_search:479) INFO: total log probability: -14.01 +2024-01-16 21:44:04,668 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:44:04,668 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:04,669 (beam_search:483) INFO: best hypo: RESENTINGASEMPLANNSTRCTIEILOSTRATIONOFTHESTROGOFORLIVEFAMNGTHERIVLESPEACES + +2024-01-16 21:44:04,670 (asr_inference:494) INFO: speech length: 78000 +2024-01-16 21:44:04,680 (beam_search:428) INFO: decoder input length: 119 +2024-01-16 21:44:04,680 (beam_search:429) INFO: max output length: 119 +2024-01-16 21:44:04,680 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:04,851 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:04,851 (beam_search:476) INFO: -6.73 * 1.0 = -6.73 for ctc +2024-01-16 21:44:04,851 (beam_search:479) INFO: total log probability: -6.73 +2024-01-16 21:44:04,851 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:44:04,851 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:04,852 (beam_search:483) INFO: best hypo: HILNEVEDOATAPOFWERKTHEHOLVORIANDGCH + +2024-01-16 21:44:04,853 (asr_inference:494) INFO: speech length: 68750 +2024-01-16 21:44:04,862 (beam_search:428) INFO: decoder input length: 105 +2024-01-16 21:44:04,862 (beam_search:429) INFO: max output length: 105 +2024-01-16 21:44:04,862 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:04,990 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:04,990 (beam_search:476) INFO: -7.25 * 1.0 = -7.25 for ctc +2024-01-16 21:44:04,990 (beam_search:479) INFO: total log probability: -7.25 +2024-01-16 21:44:04,990 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:44:04,990 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:04,990 (beam_search:483) INFO: best hypo: IHAEHNTEALONTISRIGEREPLADFLIP + +2024-01-16 21:44:04,991 (asr_inference:494) INFO: speech length: 48000 +2024-01-16 21:44:05,000 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 21:44:05,000 (beam_search:429) INFO: max output length: 72 +2024-01-16 21:44:05,000 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:05,083 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:05,083 (beam_search:476) INFO: -6.31 * 1.0 = -6.31 for ctc +2024-01-16 21:44:05,083 (beam_search:479) INFO: total log probability: -6.31 +2024-01-16 21:44:05,083 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:44:05,083 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:05,084 (beam_search:483) INFO: best hypo: LORDBUTINGLADTOSEYOAGINFIL + +2024-01-16 21:44:05,085 (asr_inference:494) INFO: speech length: 55979 +2024-01-16 21:44:05,093 (beam_search:428) INFO: decoder input length: 85 +2024-01-16 21:44:05,093 (beam_search:429) INFO: max output length: 85 +2024-01-16 21:44:05,093 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:05,194 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:05,194 (beam_search:476) INFO: -8.04 * 1.0 = -8.04 for ctc +2024-01-16 21:44:05,194 (beam_search:479) INFO: total log probability: -8.04 +2024-01-16 21:44:05,194 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:44:05,194 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:05,194 (beam_search:483) INFO: best hypo: COWELINLYIWENDATDIDTHATFISDEA + +2024-01-16 21:44:05,196 (asr_inference:494) INFO: speech length: 82000 +2024-01-16 21:44:05,205 (beam_search:428) INFO: decoder input length: 126 +2024-01-16 21:44:05,205 (beam_search:429) INFO: max output length: 126 +2024-01-16 21:44:05,205 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:05,399 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:05,399 (beam_search:476) INFO: -8.72 * 1.0 = -8.72 for ctc +2024-01-16 21:44:05,399 (beam_search:479) INFO: total log probability: -8.72 +2024-01-16 21:44:05,399 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:44:05,399 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:05,399 (beam_search:483) INFO: best hypo: THEARNOTREAGILEOSTERPIRATSNCKLESCOTNUDED + +2024-01-16 21:44:05,400 (asr_inference:494) INFO: speech length: 126000 +2024-01-16 21:44:05,413 (beam_search:428) INFO: decoder input length: 194 +2024-01-16 21:44:05,413 (beam_search:429) INFO: max output length: 194 +2024-01-16 21:44:05,413 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:05,856 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:05,856 (beam_search:476) INFO: -15.92 * 1.0 = -15.92 for ctc +2024-01-16 21:44:05,856 (beam_search:479) INFO: total log probability: -15.92 +2024-01-16 21:44:05,856 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:44:05,856 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:05,857 (beam_search:483) INFO: best hypo: TEMUSTBEHURINGFORBISESSBUTITHTYOUMIGTWNTTOCEKALOKATHERSITD + +2024-01-16 21:44:05,858 (asr_inference:494) INFO: speech length: 46000 +2024-01-16 21:44:05,866 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 21:44:05,866 (beam_search:429) INFO: max output length: 69 +2024-01-16 21:44:05,866 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:05,956 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:05,956 (beam_search:476) INFO: -6.53 * 1.0 = -6.53 for ctc +2024-01-16 21:44:05,956 (beam_search:479) INFO: total log probability: -6.53 +2024-01-16 21:44:05,956 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:44:05,956 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:05,957 (beam_search:483) INFO: best hypo: DERASNOCHANCETOFIREWITOUTHIDINGHIM + +2024-01-16 21:44:05,958 (asr_inference:494) INFO: speech length: 102000 +2024-01-16 21:44:05,969 (beam_search:428) INFO: decoder input length: 157 +2024-01-16 21:44:05,969 (beam_search:429) INFO: max output length: 157 +2024-01-16 21:44:05,969 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:06,261 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:06,261 (beam_search:476) INFO: -13.09 * 1.0 = -13.09 for ctc +2024-01-16 21:44:06,261 (beam_search:479) INFO: total log probability: -13.09 +2024-01-16 21:44:06,261 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:44:06,261 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:06,261 (beam_search:483) INFO: best hypo: ASFORHIMSELFWONTTHSTREATREALWAYARNINGNKESINGSETLY + +2024-01-16 21:44:06,262 (asr_inference:494) INFO: speech length: 74000 +2024-01-16 21:44:06,272 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 21:44:06,272 (beam_search:429) INFO: max output length: 113 +2024-01-16 21:44:06,272 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:06,406 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:06,406 (beam_search:476) INFO: -8.90 * 1.0 = -8.90 for ctc +2024-01-16 21:44:06,406 (beam_search:479) INFO: total log probability: -8.90 +2024-01-16 21:44:06,406 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:44:06,406 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:06,407 (beam_search:483) INFO: best hypo: DONHMCONYORBOYOLONGWODESSYE + +2024-01-16 21:44:06,408 (asr_inference:494) INFO: speech length: 56406 +2024-01-16 21:44:06,416 (beam_search:428) INFO: decoder input length: 86 +2024-01-16 21:44:06,416 (beam_search:429) INFO: max output length: 86 +2024-01-16 21:44:06,416 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:06,494 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:06,495 (beam_search:476) INFO: -6.28 * 1.0 = -6.28 for ctc +2024-01-16 21:44:06,495 (beam_search:479) INFO: total log probability: -6.28 +2024-01-16 21:44:06,495 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:44:06,495 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:06,495 (beam_search:483) INFO: best hypo: CODBAIYPEARHESHOTED + +2024-01-16 21:44:06,496 (asr_inference:494) INFO: speech length: 116000 +2024-01-16 21:44:06,508 (beam_search:428) INFO: decoder input length: 179 +2024-01-16 21:44:06,508 (beam_search:429) INFO: max output length: 179 +2024-01-16 21:44:06,508 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:06,849 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:06,850 (beam_search:476) INFO: -12.28 * 1.0 = -12.28 for ctc +2024-01-16 21:44:06,850 (beam_search:479) INFO: total log probability: -12.28 +2024-01-16 21:44:06,850 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:44:06,850 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:06,850 (beam_search:483) INFO: best hypo: BUTSUCHDEVERGENSOFPINIONWLDCONSTITUTNOMENANCETOSITY + +2024-01-16 21:44:06,851 (asr_inference:494) INFO: speech length: 136000 +2024-01-16 21:44:06,866 (beam_search:428) INFO: decoder input length: 210 +2024-01-16 21:44:06,866 (beam_search:429) INFO: max output length: 210 +2024-01-16 21:44:06,866 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:07,212 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:07,212 (beam_search:476) INFO: -15.96 * 1.0 = -15.96 for ctc +2024-01-16 21:44:07,212 (beam_search:479) INFO: total log probability: -15.96 +2024-01-16 21:44:07,212 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 21:44:07,212 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:07,212 (beam_search:483) INFO: best hypo: IBIEWASONTHANCEEANDOLYONWOESAVINGHONTDLD + +2024-01-16 21:44:07,213 (asr_inference:494) INFO: speech length: 88000 +2024-01-16 21:44:07,224 (beam_search:428) INFO: decoder input length: 135 +2024-01-16 21:44:07,224 (beam_search:429) INFO: max output length: 135 +2024-01-16 21:44:07,224 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:07,372 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:07,372 (beam_search:476) INFO: -8.56 * 1.0 = -8.56 for ctc +2024-01-16 21:44:07,372 (beam_search:479) INFO: total log probability: -8.56 +2024-01-16 21:44:07,372 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:44:07,373 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:07,373 (beam_search:483) INFO: best hypo: EIOCANOTFOALEYOWSHESAINDNE + +2024-01-16 21:44:07,374 (asr_inference:494) INFO: speech length: 89429 +2024-01-16 21:44:07,384 (beam_search:428) INFO: decoder input length: 137 +2024-01-16 21:44:07,384 (beam_search:429) INFO: max output length: 137 +2024-01-16 21:44:07,384 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:07,616 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:07,616 (beam_search:476) INFO: -7.83 * 1.0 = -7.83 for ctc +2024-01-16 21:44:07,616 (beam_search:479) INFO: total log probability: -7.83 +2024-01-16 21:44:07,616 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:44:07,616 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:07,617 (beam_search:483) INFO: best hypo: ONTHEFARCOARNEROFTHECOMPOWNDFENTEAHOAKBREADED + +2024-01-16 21:44:07,618 (asr_inference:494) INFO: speech length: 108000 +2024-01-16 21:44:07,630 (beam_search:428) INFO: decoder input length: 166 +2024-01-16 21:44:07,630 (beam_search:429) INFO: max output length: 166 +2024-01-16 21:44:07,630 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:07,894 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:07,894 (beam_search:476) INFO: -9.62 * 1.0 = -9.62 for ctc +2024-01-16 21:44:07,894 (beam_search:479) INFO: total log probability: -9.62 +2024-01-16 21:44:07,894 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:44:07,894 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:07,894 (beam_search:483) INFO: best hypo: TENAGINTOERHADSOCHANIERSTATINGWAYAOBOTHM + +2024-01-16 21:44:07,896 (asr_inference:494) INFO: speech length: 91520 +2024-01-16 21:44:07,907 (beam_search:428) INFO: decoder input length: 140 +2024-01-16 21:44:07,907 (beam_search:429) INFO: max output length: 140 +2024-01-16 21:44:07,907 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:08,232 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:08,232 (beam_search:476) INFO: -13.04 * 1.0 = -13.04 for ctc +2024-01-16 21:44:08,233 (beam_search:479) INFO: total log probability: -13.04 +2024-01-16 21:44:08,233 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:44:08,233 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:08,233 (beam_search:483) INFO: best hypo: WEALLNOOMANASASECESTOLSTABLCUNTRYAROALMORTHEFORTHATFORTHEHOLREAGON + +2024-01-16 21:44:08,234 (asr_inference:494) INFO: speech length: 175657 +2024-01-16 21:44:08,250 (beam_search:428) INFO: decoder input length: 272 +2024-01-16 21:44:08,250 (beam_search:429) INFO: max output length: 272 +2024-01-16 21:44:08,250 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:09,386 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:09,386 (beam_search:476) INFO: -25.99 * 1.0 = -25.99 for ctc +2024-01-16 21:44:09,386 (beam_search:479) INFO: total log probability: -25.99 +2024-01-16 21:44:09,386 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:44:09,386 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:09,386 (beam_search:483) INFO: best hypo: THERFORITSHIGHTIMEOUCOMEFORBOUGDTHETEPREPORSLFORREVEUBEDENOPRATINALSBRATSONOFTEOARDITANDNONOALDITSRVISIESANDERADIDLACTEWUSOBOTISON + +2024-01-16 21:44:09,388 (asr_inference:494) INFO: speech length: 192959 +2024-01-16 21:44:09,406 (beam_search:428) INFO: decoder input length: 299 +2024-01-16 21:44:09,406 (beam_search:429) INFO: max output length: 299 +2024-01-16 21:44:09,406 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:10,673 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:10,673 (beam_search:476) INFO: -25.35 * 1.0 = -25.35 for ctc +2024-01-16 21:44:10,673 (beam_search:479) INFO: total log probability: -25.35 +2024-01-16 21:44:10,673 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:44:10,673 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:10,674 (beam_search:483) INFO: best hypo: ITISCEEARTHATWEHAVENOTIMETOWASTTHENURESSOULTSHOFTHEYEIDPEASIESIEREOARDINSIENTDIFICKBASIESOFGLIMINTTJAINCELENOROUMFORHESITDACSEON + +2024-01-16 21:44:10,675 (asr_inference:494) INFO: speech length: 121910 +2024-01-16 21:44:10,688 (beam_search:428) INFO: decoder input length: 188 +2024-01-16 21:44:10,688 (beam_search:429) INFO: max output length: 188 +2024-01-16 21:44:10,688 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:11,165 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:11,165 (beam_search:476) INFO: -14.00 * 1.0 = -14.00 for ctc +2024-01-16 21:44:11,165 (beam_search:479) INFO: total log probability: -14.00 +2024-01-16 21:44:11,165 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:44:11,165 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:11,166 (beam_search:483) INFO: best hypo: SENTSOINTHECONTAENORHIHAEVERAVENTUCHEDCOMESLAVESCONTOFETGODSDROGESITHETR + +2024-01-16 21:44:11,167 (asr_inference:494) INFO: speech length: 184299 +2024-01-16 21:44:11,184 (beam_search:428) INFO: decoder input length: 285 +2024-01-16 21:44:11,184 (beam_search:429) INFO: max output length: 285 +2024-01-16 21:44:11,184 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:12,321 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:12,321 (beam_search:476) INFO: -30.02 * 1.0 = -30.02 for ctc +2024-01-16 21:44:12,321 (beam_search:479) INFO: total log probability: -30.02 +2024-01-16 21:44:12,321 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:44:12,321 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:12,322 (beam_search:483) INFO: best hypo: IHOPETHATCOMIONSMBITINISHESINISHIFIVSWWNTCARAKTTHEDACXTPROBLOEMBUTWILBEANANCSEFOREEISTINGCHOLINGERSOFTHEROBPTANCSPBORDSECTAR + +2024-01-16 21:44:12,323 (asr_inference:494) INFO: speech length: 350399 +2024-01-16 21:44:12,355 (beam_search:428) INFO: decoder input length: 545 +2024-01-16 21:44:12,355 (beam_search:429) INFO: max output length: 545 +2024-01-16 21:44:12,355 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:16,933 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:16,933 (beam_search:476) INFO: -71.65 * 1.0 = -71.65 for ctc +2024-01-16 21:44:16,933 (beam_search:479) INFO: total log probability: -71.65 +2024-01-16 21:44:16,933 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:44:16,933 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:16,935 (beam_search:483) INFO: best hypo: ITEOUASIWASDESIONTAINARLYBYONPERSONTHEORMEPESIDEDOTHENIDESTACESAGANSETHEARTICILATEDMCRATICKDRMNDURITYOFTEUSCONGRSSBYALLOFITSREPOBLKENSOFICSTDEMERCRATICTDEMRTMBERSITWASNAGREMENTWITHOUTANYBIDNDINGOBLIGATIONSASTHELEADERSOFIRAUNVERYOUPENTLYINPRESIDHMAEPLYNTHEERYDATHESOCLDDELWASPOLISHED + +2024-01-16 21:44:16,937 (asr_inference:494) INFO: speech length: 133421 +2024-01-16 21:44:16,951 (beam_search:428) INFO: decoder input length: 206 +2024-01-16 21:44:16,951 (beam_search:429) INFO: max output length: 206 +2024-01-16 21:44:16,951 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:17,614 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:17,614 (beam_search:476) INFO: -15.34 * 1.0 = -15.34 for ctc +2024-01-16 21:44:17,614 (beam_search:479) INFO: total log probability: -15.34 +2024-01-16 21:44:17,614 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:44:17,614 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:17,615 (beam_search:483) INFO: best hypo: FRESPEACGEISSSENCIALYAECECTINGTATPEPLURFREETOSAYTHINGSWEDONOTLIEKENOTMELYFREETOSAYTHINGSWEDOLIEK + +2024-01-16 21:44:17,616 (asr_inference:494) INFO: speech length: 25905 +2024-01-16 21:44:17,623 (beam_search:428) INFO: decoder input length: 38 +2024-01-16 21:44:17,623 (beam_search:429) INFO: max output length: 38 +2024-01-16 21:44:17,623 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:17,655 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:17,655 (beam_search:476) INFO: -4.32 * 1.0 = -4.32 for ctc +2024-01-16 21:44:17,655 (beam_search:479) INFO: total log probability: -4.32 +2024-01-16 21:44:17,655 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:44:17,655 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:17,656 (beam_search:483) INFO: best hypo: LATASLARNDFOMTHIE + +2024-01-16 21:44:17,657 (asr_inference:494) INFO: speech length: 289909 +2024-01-16 21:44:17,683 (beam_search:428) INFO: decoder input length: 450 +2024-01-16 21:44:17,683 (beam_search:429) INFO: max output length: 450 +2024-01-16 21:44:17,683 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:20,804 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:20,804 (beam_search:476) INFO: -48.73 * 1.0 = -48.73 for ctc +2024-01-16 21:44:20,804 (beam_search:479) INFO: total log probability: -48.73 +2024-01-16 21:44:20,804 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:44:20,804 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:20,805 (beam_search:483) INFO: best hypo: BESINGHATHEANEVIMENTAEEAFECTOFPRDUCSMUSTBEAVERYINMPRTANTISHUINGTEREUANDHEOLIGTDEAROTHEIECOLABRNEVSAVERYOUSOLORIANTATIONFORTHECOSTSUMEMISOFCOURSHEIACULABERSOODGIVENTOTHEMOSTANDVIREMENTAFFENDYPORDUCTTHEIFOREMITIONSHOLBECLEAREANDOE + +2024-01-16 21:44:20,807 (asr_inference:494) INFO: speech length: 165099 +2024-01-16 21:44:20,823 (beam_search:428) INFO: decoder input length: 255 +2024-01-16 21:44:20,823 (beam_search:429) INFO: max output length: 255 +2024-01-16 21:44:20,823 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:21,855 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:21,855 (beam_search:476) INFO: -26.02 * 1.0 = -26.02 for ctc +2024-01-16 21:44:21,855 (beam_search:479) INFO: total log probability: -26.02 +2024-01-16 21:44:21,855 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:44:21,855 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:21,856 (beam_search:483) INFO: best hypo: HOWEVERTHECIRENDRIGIEMENITSTOBBETERSALORTTOTHDIGIDLINFVEIRNENTEDENSHOURFARRIMINERATIONTOGREATDURSANHTOOFOMETOONSOMEREXAPECTATIONS + +2024-01-16 21:44:21,857 (asr_inference:494) INFO: speech length: 165120 +2024-01-16 21:44:21,873 (beam_search:428) INFO: decoder input length: 255 +2024-01-16 21:44:21,873 (beam_search:429) INFO: max output length: 255 +2024-01-16 21:44:21,873 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:22,928 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:22,928 (beam_search:476) INFO: -35.31 * 1.0 = -35.31 for ctc +2024-01-16 21:44:22,928 (beam_search:479) INFO: total log probability: -35.31 +2024-01-16 21:44:22,928 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:44:22,928 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:22,929 (beam_search:483) INFO: best hypo: ADCOLSBOTEOMIONANDMEMBRSTATHOANHANCDHERSEPORTTORECENCSIYATIONTOSECURPECSANDSTOBLITYANARLENDIWILTHERFORARESUCALIGETOPEESSUPORTHISMENDEN + +2024-01-16 21:44:22,931 (asr_inference:494) INFO: speech length: 310354 +2024-01-16 21:44:22,959 (beam_search:428) INFO: decoder input length: 482 +2024-01-16 21:44:22,959 (beam_search:429) INFO: max output length: 482 +2024-01-16 21:44:22,959 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:26,213 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:26,213 (beam_search:476) INFO: -50.46 * 1.0 = -50.46 for ctc +2024-01-16 21:44:26,213 (beam_search:479) INFO: total log probability: -50.46 +2024-01-16 21:44:26,213 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:44:26,213 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:26,214 (beam_search:483) INFO: best hypo: TRATIGIGCHOICISABOUTERETOLEWESTMUSTBEMADNOWLTOAKINNECOUNEANETOFASEOUTFORSILFULSUPSITDSBUTTEKTHEGASASFORSOFYUITCANBEAHELPFULBRIGINGRNSICONARYMEADIUMTOBEUSINMEMINMANYMEBERSTHASIEONTOEDCHIFOVERANMBISHISLIMIGTARGETS + +2024-01-16 21:44:26,216 (asr_inference:494) INFO: speech length: 242240 +2024-01-16 21:44:26,237 (beam_search:428) INFO: decoder input length: 376 +2024-01-16 21:44:26,237 (beam_search:429) INFO: max output length: 376 +2024-01-16 21:44:26,237 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:27,807 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:27,807 (beam_search:476) INFO: -34.54 * 1.0 = -34.54 for ctc +2024-01-16 21:44:27,807 (beam_search:479) INFO: total log probability: -34.54 +2024-01-16 21:44:27,807 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:44:27,807 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:27,808 (beam_search:483) INFO: best hypo: EWEAEDPOSILYAOFERISCOPEWECANCOOTHTOPOROSUETHEYSAMEPLISESINHSAMEMANERNONINGTHATWELLEDTOHEAMRSOTSTHERSOLSESTTHATWENODRDA + +2024-01-16 21:44:27,809 (asr_inference:494) INFO: speech length: 18556 +2024-01-16 21:44:27,816 (beam_search:428) INFO: decoder input length: 26 +2024-01-16 21:44:27,816 (beam_search:429) INFO: max output length: 26 +2024-01-16 21:44:27,816 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:27,835 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:27,835 (beam_search:476) INFO: -3.37 * 1.0 = -3.37 for ctc +2024-01-16 21:44:27,835 (beam_search:479) INFO: total log probability: -3.37 +2024-01-16 21:44:27,835 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:44:27,835 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:27,835 (beam_search:483) INFO: best hypo: UTERISNOPTIONB + +2024-01-16 21:44:27,836 (asr_inference:494) INFO: speech length: 68800 +2024-01-16 21:44:27,846 (beam_search:428) INFO: decoder input length: 105 +2024-01-16 21:44:27,846 (beam_search:429) INFO: max output length: 105 +2024-01-16 21:44:27,846 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:27,993 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:27,993 (beam_search:476) INFO: -9.66 * 1.0 = -9.66 for ctc +2024-01-16 21:44:27,993 (beam_search:479) INFO: total log probability: -9.66 +2024-01-16 21:44:27,993 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:44:27,993 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:27,993 (beam_search:483) INFO: best hypo: WEALLSOENEADATHANGEHINORHRDOLIDGTYI + +2024-01-16 21:44:27,994 (asr_inference:494) INFO: speech length: 356118 +2024-01-16 21:44:28,025 (beam_search:428) INFO: decoder input length: 554 +2024-01-16 21:44:28,025 (beam_search:429) INFO: max output length: 554 +2024-01-16 21:44:28,025 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:32,103 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:32,103 (beam_search:476) INFO: -53.02 * 1.0 = -53.02 for ctc +2024-01-16 21:44:32,103 (beam_search:479) INFO: total log probability: -53.02 +2024-01-16 21:44:32,103 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:44:32,103 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:32,104 (beam_search:483) INFO: best hypo: ILAGHBUTOFTHERESONOFCOURSEISILIGLFISINGKANDTHEOFOMALTWEDOUNOFENBYHALAMVESESWHICHARLEAGISTERETOCOUNTRIESHICHLAUCETHEILOFTHRRSUREISTWANFORSTTINTHENATIONEAGEMENCSNOMONTOFTESABEITYMASERSORDECXTRPPERWARUILEDRESETHEPROBLOMEOFRETIUSING + +2024-01-16 21:44:32,106 (asr_inference:494) INFO: speech length: 247325 +2024-01-16 21:44:32,129 (beam_search:428) INFO: decoder input length: 384 +2024-01-16 21:44:32,129 (beam_search:429) INFO: max output length: 384 +2024-01-16 21:44:32,129 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:34,376 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:34,376 (beam_search:476) INFO: -43.63 * 1.0 = -43.63 for ctc +2024-01-16 21:44:34,376 (beam_search:479) INFO: total log probability: -43.63 +2024-01-16 21:44:34,376 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:44:34,376 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:34,377 (beam_search:483) INFO: best hypo: THEOMPRMICEOLLSOINCLUDSCLEARERUASTOHEFINEWHICHMBRSTATDASHURSTICTIONANTHOPRATIOMTYEMBRSTATESCONCSERDFORCROSBOTHECACESASHEHATHENEDTOEIMVOLLEFYOURJUSTHENYOFORWORKANDPLEACEWOESUPRTTOMROHISEIRECIF + +2024-01-16 21:44:34,379 (asr_inference:494) INFO: speech length: 239040 +2024-01-16 21:44:34,400 (beam_search:428) INFO: decoder input length: 371 +2024-01-16 21:44:34,400 (beam_search:429) INFO: max output length: 371 +2024-01-16 21:44:34,400 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:36,523 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:36,523 (beam_search:476) INFO: -36.24 * 1.0 = -36.24 for ctc +2024-01-16 21:44:36,523 (beam_search:479) INFO: total log probability: -36.24 +2024-01-16 21:44:36,523 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:44:36,523 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:36,524 (beam_search:483) INFO: best hypo: ENOTHEGRENSWIODTHAVSBLEHATHISURBADBEESCRIMINALBEESDELIBRTLYCOTAMINATINGHUDYWHTHEDANGRUSINGREADIENTBUTINFACINFACHEDINGHEHUYBESARLAVLALLRSDOUNMIHITOCARYPOLONBACTTHERHIVSTODTOFETHERYOUNG + +2024-01-16 21:44:36,526 (asr_inference:494) INFO: speech length: 48000 +2024-01-16 21:44:36,535 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 21:44:36,535 (beam_search:429) INFO: max output length: 72 +2024-01-16 21:44:36,535 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:36,641 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:36,641 (beam_search:476) INFO: -5.70 * 1.0 = -5.70 for ctc +2024-01-16 21:44:36,641 (beam_search:479) INFO: total log probability: -5.70 +2024-01-16 21:44:36,641 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:44:36,641 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:36,642 (beam_search:483) INFO: best hypo: BUTITWASTHECOUNTRYITDHELDBENGMORECAPRABL + +2024-01-16 21:44:36,643 (asr_inference:494) INFO: speech length: 74868 +2024-01-16 21:44:36,652 (beam_search:428) INFO: decoder input length: 114 +2024-01-16 21:44:36,652 (beam_search:429) INFO: max output length: 114 +2024-01-16 21:44:36,652 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:36,886 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:36,886 (beam_search:476) INFO: -12.24 * 1.0 = -12.24 for ctc +2024-01-16 21:44:36,886 (beam_search:479) INFO: total log probability: -12.24 +2024-01-16 21:44:36,886 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:44:36,886 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:36,886 (beam_search:483) INFO: best hypo: RINTOTHEPRTFOLIAOOFTHENUCOMISIONREDALINGWITFAUNDEMENTERIGHTE + +2024-01-16 21:44:36,887 (asr_inference:494) INFO: speech length: 47679 +2024-01-16 21:44:36,896 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 21:44:36,896 (beam_search:429) INFO: max output length: 72 +2024-01-16 21:44:36,896 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:36,998 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:36,998 (beam_search:476) INFO: -9.15 * 1.0 = -9.15 for ctc +2024-01-16 21:44:36,998 (beam_search:479) INFO: total log probability: -9.15 +2024-01-16 21:44:36,998 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:44:36,998 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:36,998 (beam_search:483) INFO: best hypo: EMESIGETATTHEYUDOUTATHAVEANYNUSOLTIONE + +2024-01-16 21:44:36,999 (asr_inference:494) INFO: speech length: 142379 +2024-01-16 21:44:37,013 (beam_search:428) INFO: decoder input length: 220 +2024-01-16 21:44:37,013 (beam_search:429) INFO: max output length: 220 +2024-01-16 21:44:37,013 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:37,661 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:37,661 (beam_search:476) INFO: -19.42 * 1.0 = -19.42 for ctc +2024-01-16 21:44:37,661 (beam_search:479) INFO: total log probability: -19.42 +2024-01-16 21:44:37,661 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:44:37,661 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:37,662 (beam_search:483) INFO: best hypo: ARYUWILINGTOACTINERVFATHEREFORTHESOSIALDEMENTIONTOBEINLODEDINTHEEOUCOMPETENSYESASPREPUS + +2024-01-16 21:44:37,663 (asr_inference:494) INFO: speech length: 83513 +2024-01-16 21:44:37,673 (beam_search:428) INFO: decoder input length: 128 +2024-01-16 21:44:37,673 (beam_search:429) INFO: max output length: 128 +2024-01-16 21:44:37,674 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:37,950 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:37,950 (beam_search:476) INFO: -17.39 * 1.0 = -17.39 for ctc +2024-01-16 21:44:37,950 (beam_search:479) INFO: total log probability: -17.39 +2024-01-16 21:44:37,950 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:44:37,950 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:37,951 (beam_search:483) INFO: best hypo: AERNCTHEONDPESPECTRUPOLIYESTAKINGWITHEREFORMEOFOURTELICONTHFRAMWR + +2024-01-16 21:44:37,952 (asr_inference:494) INFO: speech length: 195190 +2024-01-16 21:44:37,970 (beam_search:428) INFO: decoder input length: 302 +2024-01-16 21:44:37,970 (beam_search:429) INFO: max output length: 302 +2024-01-16 21:44:37,970 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:39,257 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:39,257 (beam_search:476) INFO: -28.56 * 1.0 = -28.56 for ctc +2024-01-16 21:44:39,257 (beam_search:479) INFO: total log probability: -28.56 +2024-01-16 21:44:39,257 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:44:39,257 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:39,258 (beam_search:483) INFO: best hypo: IBELEHISREMARCESWERAINEXTPLIITELYRACISCTEDANDSENAFOBICKEANDPRMOTEDRAILINTOLRANCINWHAYTHISNOUCETABLEORALAOUEDINTEONTOICTUTIOFTHISHOUSE + +2024-01-16 21:44:39,260 (asr_inference:494) INFO: speech length: 95040 +2024-01-16 21:44:39,271 (beam_search:428) INFO: decoder input length: 146 +2024-01-16 21:44:39,271 (beam_search:429) INFO: max output length: 146 +2024-01-16 21:44:39,271 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:39,625 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:39,625 (beam_search:476) INFO: -18.71 * 1.0 = -18.71 for ctc +2024-01-16 21:44:39,625 (beam_search:479) INFO: total log probability: -18.71 +2024-01-16 21:44:39,625 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:44:39,625 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:39,625 (beam_search:483) INFO: best hypo: REALIGHEGSAMPLSHOLTHATSOVINGIESRLATETOABOCATIONFEYULDSTRONCOMNITYDEVELPMENT + +2024-01-16 21:44:39,626 (asr_inference:494) INFO: speech length: 154228 +2024-01-16 21:44:39,642 (beam_search:428) INFO: decoder input length: 238 +2024-01-16 21:44:39,642 (beam_search:429) INFO: max output length: 238 +2024-01-16 21:44:39,642 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:40,538 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:40,538 (beam_search:476) INFO: -32.03 * 1.0 = -32.03 for ctc +2024-01-16 21:44:40,538 (beam_search:479) INFO: total log probability: -32.03 +2024-01-16 21:44:40,538 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:44:40,538 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:40,539 (beam_search:483) INFO: best hypo: SIHOLPETHAHISWLHAEMFORUSHEASWHELANTATRUSHECONLSANDISIGTDNCSTREMEMSCCESSTARYAFTRTHESEGTWISIGIFICENDATINORGSTTHISOURB + +2024-01-16 21:44:40,540 (asr_inference:494) INFO: speech length: 249565 +2024-01-16 21:44:40,563 (beam_search:428) INFO: decoder input length: 387 +2024-01-16 21:44:40,563 (beam_search:429) INFO: max output length: 387 +2024-01-16 21:44:40,563 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:42,502 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:42,502 (beam_search:476) INFO: -28.92 * 1.0 = -28.92 for ctc +2024-01-16 21:44:42,502 (beam_search:479) INFO: total log probability: -28.92 +2024-01-16 21:44:42,502 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:44:42,502 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:42,503 (beam_search:483) INFO: best hypo: SHEECEPTOTHEFACTTHATSITISIENHIPISAYNASIONLBOURTOFTHENOSIONOGRISDICTIONBUTHYOURLSOSAIDTHATACORDIGTOTHEMASTRICKTREATYANDHEASRIGHTDTHEASTOBEADIRECLING + +2024-01-16 21:44:42,505 (asr_inference:494) INFO: speech length: 343360 +2024-01-16 21:44:42,535 (beam_search:428) INFO: decoder input length: 534 +2024-01-16 21:44:42,535 (beam_search:429) INFO: max output length: 534 +2024-01-16 21:44:42,535 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:45,972 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:45,972 (beam_search:476) INFO: -47.23 * 1.0 = -47.23 for ctc +2024-01-16 21:44:45,972 (beam_search:479) INFO: total log probability: -47.23 +2024-01-16 21:44:45,972 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:44:45,972 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:45,974 (beam_search:483) INFO: best hypo: EOFAILDESPESIOLYEINTHEMSTHRATINGAYULIFIDEDANDTAFISHENTATPRORCHTOOLIMITCAENGCHTREATMENTASELEASINSTRANGTHANINGITSELEADINGPOLITICKLCOSITIONINDESUGENDERICOSITHERETHEFORTAKINGISRESOLUIONANACTOFUTMORSTIMPORTANS + +2024-01-16 21:44:45,975 (asr_inference:494) INFO: speech length: 68479 +2024-01-16 21:44:45,985 (beam_search:428) INFO: decoder input length: 104 +2024-01-16 21:44:45,985 (beam_search:429) INFO: max output length: 104 +2024-01-16 21:44:45,985 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:46,179 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:46,179 (beam_search:476) INFO: -8.12 * 1.0 = -8.12 for ctc +2024-01-16 21:44:46,179 (beam_search:479) INFO: total log probability: -8.12 +2024-01-16 21:44:46,179 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:44:46,179 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:46,180 (beam_search:483) INFO: best hypo: THEUNIGTESSTATESOFYUROVILBAFACTWITSWEDONASPROVIDENCS + +2024-01-16 21:44:46,181 (asr_inference:494) INFO: speech length: 124799 +2024-01-16 21:44:46,194 (beam_search:428) INFO: decoder input length: 192 +2024-01-16 21:44:46,194 (beam_search:429) INFO: max output length: 192 +2024-01-16 21:44:46,194 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:46,770 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:46,771 (beam_search:476) INFO: -14.86 * 1.0 = -14.86 for ctc +2024-01-16 21:44:46,771 (beam_search:479) INFO: total log probability: -14.86 +2024-01-16 21:44:46,771 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 21:44:46,771 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:46,771 (beam_search:483) INFO: best hypo: ITDMUSBETHECAPBITLEOFBOTTHATSANDWEMUSSRECONISEPOLSTINISSTHATASPROVIDIDFOREINTHEOFLOGREENCS + +2024-01-16 21:44:46,773 (asr_inference:494) INFO: speech length: 234841 +2024-01-16 21:44:46,793 (beam_search:428) INFO: decoder input length: 364 +2024-01-16 21:44:46,793 (beam_search:429) INFO: max output length: 364 +2024-01-16 21:44:46,793 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:48,570 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:48,570 (beam_search:476) INFO: -33.51 * 1.0 = -33.51 for ctc +2024-01-16 21:44:48,570 (beam_search:479) INFO: total log probability: -33.51 +2024-01-16 21:44:48,570 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:44:48,570 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:48,571 (beam_search:483) INFO: best hypo: TYUKRANESFASETWITDWONEOFCRUSIALCHALINGESEINICGHISTARYITWULDBEFIUNTHEMENTALYRONGKTOPRESTHENATIONNOWEWITALTIBESOFORESTRICTIONSPOPELIDALCOALEDOSTERITEPOLI + +2024-01-16 21:44:48,573 (asr_inference:494) INFO: speech length: 77119 +2024-01-16 21:44:48,583 (beam_search:428) INFO: decoder input length: 118 +2024-01-16 21:44:48,583 (beam_search:429) INFO: max output length: 118 +2024-01-16 21:44:48,583 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:48,791 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:48,791 (beam_search:476) INFO: -6.69 * 1.0 = -6.69 for ctc +2024-01-16 21:44:48,791 (beam_search:479) INFO: total log probability: -6.69 +2024-01-16 21:44:48,791 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 21:44:48,791 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:48,792 (beam_search:483) INFO: best hypo: MORRULSANDREAGILATIONWILLNOTINMPROVETHESSITUATIO + +2024-01-16 21:44:48,793 (asr_inference:494) INFO: speech length: 82879 +2024-01-16 21:44:48,803 (beam_search:428) INFO: decoder input length: 127 +2024-01-16 21:44:48,803 (beam_search:429) INFO: max output length: 127 +2024-01-16 21:44:48,803 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:49,073 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:49,073 (beam_search:476) INFO: -9.79 * 1.0 = -9.79 for ctc +2024-01-16 21:44:49,073 (beam_search:479) INFO: total log probability: -9.79 +2024-01-16 21:44:49,073 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 21:44:49,073 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:49,073 (beam_search:483) INFO: best hypo: ATLEASTBEWUDLIKETONOLTHESORSEOFTHEMUNYANDTHEPOSIBLEMORTHIFS + +2024-01-16 21:44:49,074 (asr_inference:494) INFO: speech length: 315519 +2024-01-16 21:44:49,103 (beam_search:428) INFO: decoder input length: 490 +2024-01-16 21:44:49,103 (beam_search:429) INFO: max output length: 490 +2024-01-16 21:44:49,103 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:52,373 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:52,373 (beam_search:476) INFO: -50.85 * 1.0 = -50.85 for ctc +2024-01-16 21:44:52,373 (beam_search:479) INFO: total log probability: -50.85 +2024-01-16 21:44:52,373 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:44:52,373 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:52,375 (beam_search:483) INFO: best hypo: TOWEAVETHOUSEYURPENWALLANGWOACESINTOTHEISGLABLISEDWERLDTISINTTOTHEAISGOABLISECONMYINDISGOBEVILIAGEWHICHISCOTIALYCONOMICKSOSIALEELNBPOLITICOLBPWITSEAEMOSTVELABLEEASTHEITFROMTHEINTIREEOUGTHATWEMUSTTHAKFOLACOUNSANDT + +2024-01-16 21:44:52,376 (asr_inference:494) INFO: speech length: 115826 +2024-01-16 21:44:52,389 (beam_search:428) INFO: decoder input length: 178 +2024-01-16 21:44:52,389 (beam_search:429) INFO: max output length: 178 +2024-01-16 21:44:52,389 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:52,889 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:52,889 (beam_search:476) INFO: -16.24 * 1.0 = -16.24 for ctc +2024-01-16 21:44:52,889 (beam_search:479) INFO: total log probability: -16.24 +2024-01-16 21:44:52,889 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:44:52,889 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:52,890 (beam_search:483) INFO: best hypo: EAETOREBETTHATALLHEAYANNOTBEYOUSTOFINCSSIGURITEXPANCESBARTHERSCONTROLORMLITRYSOUPORN + +2024-01-16 21:44:52,891 (asr_inference:494) INFO: speech length: 77749 +2024-01-16 21:44:52,901 (beam_search:428) INFO: decoder input length: 119 +2024-01-16 21:44:52,901 (beam_search:429) INFO: max output length: 119 +2024-01-16 21:44:52,901 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:53,144 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:53,144 (beam_search:476) INFO: -11.55 * 1.0 = -11.55 for ctc +2024-01-16 21:44:53,144 (beam_search:479) INFO: total log probability: -11.55 +2024-01-16 21:44:53,144 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 21:44:53,144 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:53,144 (beam_search:483) INFO: best hypo: THIGHEINTIFIKREPORTSBCALEMARMOREURDENTMORLARMINGANDMORSHOCKING + +2024-01-16 21:44:53,146 (asr_inference:494) INFO: speech length: 316143 +2024-01-16 21:44:53,174 (beam_search:428) INFO: decoder input length: 491 +2024-01-16 21:44:53,174 (beam_search:429) INFO: max output length: 491 +2024-01-16 21:44:53,174 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:55,571 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:55,572 (beam_search:476) INFO: -39.77 * 1.0 = -39.77 for ctc +2024-01-16 21:44:55,572 (beam_search:479) INFO: total log probability: -39.77 +2024-01-16 21:44:55,572 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:44:55,572 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:55,572 (beam_search:483) INFO: best hypo: FINOLYIMWHEWHAEHINKINGKABOUNTHEREINOVATIFEFFINSIONINSTRMENTSWHNKUTHEBOLTHFORORSELFSTOUGSOUPORTOUOUERACONOMYSBUTAOSSOTOLSOPORKTTHOSCOHERINEAE + +2024-01-16 21:44:55,574 (asr_inference:494) INFO: speech length: 49600 +2024-01-16 21:44:55,583 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 21:44:55,583 (beam_search:429) INFO: max output length: 75 +2024-01-16 21:44:55,583 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:55,681 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:55,681 (beam_search:476) INFO: -6.65 * 1.0 = -6.65 for ctc +2024-01-16 21:44:55,681 (beam_search:479) INFO: total log probability: -6.65 +2024-01-16 21:44:55,681 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:44:55,681 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:55,682 (beam_search:483) INFO: best hypo: THATGIVEAESORYUNIKETOULINESMAKING + +2024-01-16 21:44:55,683 (asr_inference:494) INFO: speech length: 49279 +2024-01-16 21:44:55,691 (beam_search:428) INFO: decoder input length: 74 +2024-01-16 21:44:55,691 (beam_search:429) INFO: max output length: 74 +2024-01-16 21:44:55,691 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:55,767 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:55,767 (beam_search:476) INFO: -8.47 * 1.0 = -8.47 for ctc +2024-01-16 21:44:55,767 (beam_search:479) INFO: total log probability: -8.47 +2024-01-16 21:44:55,767 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 21:44:55,767 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:55,767 (beam_search:483) INFO: best hypo: DPAPERAVERYHLWEEKPROPOSIL + +2024-01-16 21:44:55,768 (asr_inference:494) INFO: speech length: 106873 +2024-01-16 21:44:55,780 (beam_search:428) INFO: decoder input length: 164 +2024-01-16 21:44:55,780 (beam_search:429) INFO: max output length: 164 +2024-01-16 21:44:55,780 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:56,160 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:56,160 (beam_search:476) INFO: -16.81 * 1.0 = -16.81 for ctc +2024-01-16 21:44:56,160 (beam_search:479) INFO: total log probability: -16.81 +2024-01-16 21:44:56,160 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:44:56,160 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:56,160 (beam_search:483) INFO: best hypo: SRUSHASOLYSBEVERYPROUDNATIONWITHICHCLDCHUEREWITINVENTIONSWITHANESCE + +2024-01-16 21:44:56,161 (asr_inference:494) INFO: speech length: 213759 +2024-01-16 21:44:56,181 (beam_search:428) INFO: decoder input length: 331 +2024-01-16 21:44:56,181 (beam_search:429) INFO: max output length: 331 +2024-01-16 21:44:56,181 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:57,789 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:57,790 (beam_search:476) INFO: -31.12 * 1.0 = -31.12 for ctc +2024-01-16 21:44:57,790 (beam_search:479) INFO: total log probability: -31.12 +2024-01-16 21:44:57,790 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:44:57,790 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:57,791 (beam_search:483) INFO: best hypo: ARTACTAITIONNVHENAMODICEOFTACSAITIONNSOMECACESMIYJUSTHELPESEMTODOWATIHEAREDYSHEGESTEDANDHONOSMAKTHECACEFORTHEETERSPECTOFBANKRECAPDLIATIONTHATWENEVERSOL + +2024-01-16 21:44:57,792 (asr_inference:494) INFO: speech length: 212480 +2024-01-16 21:44:57,812 (beam_search:428) INFO: decoder input length: 329 +2024-01-16 21:44:57,812 (beam_search:429) INFO: max output length: 329 +2024-01-16 21:44:57,812 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:44:59,427 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:44:59,427 (beam_search:476) INFO: -40.00 * 1.0 = -40.00 for ctc +2024-01-16 21:44:59,427 (beam_search:479) INFO: total log probability: -40.00 +2024-01-16 21:44:59,427 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 21:44:59,427 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:44:59,428 (beam_search:483) INFO: best hypo: THEULOPBEANHSIDLOMSUPORTOFICEMOROVERASAMONGITCSTHIUSTOPROMOUHTFESILYTHATANDCOURDINATECSCHANGESOFINFORMATIONANDUTHEACTEVITYESERLATYTOLELCATIONBDINTEYUNION + +2024-01-16 21:44:59,430 (asr_inference:494) INFO: speech length: 163826 +2024-01-16 21:44:59,445 (beam_search:428) INFO: decoder input length: 253 +2024-01-16 21:44:59,445 (beam_search:429) INFO: max output length: 253 +2024-01-16 21:44:59,445 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:00,411 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:00,411 (beam_search:476) INFO: -32.24 * 1.0 = -32.24 for ctc +2024-01-16 21:45:00,411 (beam_search:479) INFO: total log probability: -32.24 +2024-01-16 21:45:00,411 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:45:00,411 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:00,412 (beam_search:483) INFO: best hypo: HECONLSNOFTHERAMEBORKAGEMENTPROVIEALIGLYBINDINGKINSTRMENTTOOBVGIRATANDSTRANTNEUOSTRLIRBELITHERIATSIOSANDTOINCEESCOPERATION + +2024-01-16 21:45:00,413 (asr_inference:494) INFO: speech length: 95678 +2024-01-16 21:45:00,424 (beam_search:428) INFO: decoder input length: 147 +2024-01-16 21:45:00,424 (beam_search:429) INFO: max output length: 147 +2024-01-16 21:45:00,424 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:00,816 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:00,816 (beam_search:476) INFO: -23.21 * 1.0 = -23.21 for ctc +2024-01-16 21:45:00,816 (beam_search:479) INFO: total log probability: -23.21 +2024-01-16 21:45:00,816 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:45:00,816 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:00,817 (beam_search:483) INFO: best hypo: EREFRUREWEASINTHECOUNSELASGLMITIONTORENTACASBARITHAULDBEDTHESESTENTOFTEBACTOTHRISI + +2024-01-16 21:45:00,819 (asr_inference:494) INFO: speech length: 151339 +2024-01-16 21:45:00,834 (beam_search:428) INFO: decoder input length: 234 +2024-01-16 21:45:00,834 (beam_search:429) INFO: max output length: 234 +2024-01-16 21:45:00,834 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:01,569 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:01,569 (beam_search:476) INFO: -17.45 * 1.0 = -17.45 for ctc +2024-01-16 21:45:01,569 (beam_search:479) INFO: total log probability: -17.45 +2024-01-16 21:45:01,569 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:45:01,569 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:01,570 (beam_search:483) INFO: best hypo: AINOTHEWERDSTHEOBJECTIONISNOTWHETHEMUNYISPAEDORNOTTHEOPBEBJECTIONIISWETHETHEYSADIDECTLINKORENO + +2024-01-16 21:45:01,571 (asr_inference:494) INFO: speech length: 105263 +2024-01-16 21:45:01,583 (beam_search:428) INFO: decoder input length: 162 +2024-01-16 21:45:01,583 (beam_search:429) INFO: max output length: 162 +2024-01-16 21:45:01,583 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:02,036 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:02,036 (beam_search:476) INFO: -19.33 * 1.0 = -19.33 for ctc +2024-01-16 21:45:02,036 (beam_search:479) INFO: total log probability: -19.33 +2024-01-16 21:45:02,036 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:45:02,036 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:02,036 (beam_search:483) INFO: best hypo: TOHESTINGISHIESTHETWOMAEDOSHEARYOUMERITSAEYOUSEBYTHECARNTGORENTANDTHEDANIONNUKLEPROGDHME + +2024-01-16 21:45:02,038 (asr_inference:494) INFO: speech length: 161261 +2024-01-16 21:45:02,053 (beam_search:428) INFO: decoder input length: 249 +2024-01-16 21:45:02,053 (beam_search:429) INFO: max output length: 249 +2024-01-16 21:45:02,053 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:02,887 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:02,887 (beam_search:476) INFO: -24.62 * 1.0 = -24.62 for ctc +2024-01-16 21:45:02,887 (beam_search:479) INFO: total log probability: -24.62 +2024-01-16 21:45:02,887 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:45:02,887 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:02,887 (beam_search:483) INFO: best hypo: ESSMETHEBDRONMHENKACTERESECTIONHERASDENTISHEFORMOVILANCEANDITITHEMOSTETREANFORMOFGHNTRBASEDESCRMINATI + +2024-01-16 21:45:02,889 (asr_inference:494) INFO: speech length: 79671 +2024-01-16 21:45:02,899 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 21:45:02,899 (beam_search:429) INFO: max output length: 122 +2024-01-16 21:45:02,899 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:03,165 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:03,165 (beam_search:476) INFO: -16.31 * 1.0 = -16.31 for ctc +2024-01-16 21:45:03,165 (beam_search:479) INFO: total log probability: -16.31 +2024-01-16 21:45:03,165 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:45:03,165 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:03,166 (beam_search:483) INFO: best hypo: WECANLOKTOSOMEEIRNNINEUMEMBORSORGODEXSANPLEAREGARDEDTHENOLAGES + +2024-01-16 21:45:03,167 (asr_inference:494) INFO: speech length: 72640 +2024-01-16 21:45:03,177 (beam_search:428) INFO: decoder input length: 111 +2024-01-16 21:45:03,177 (beam_search:429) INFO: max output length: 111 +2024-01-16 21:45:03,177 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:03,362 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:03,362 (beam_search:476) INFO: -13.61 * 1.0 = -13.61 for ctc +2024-01-16 21:45:03,362 (beam_search:479) INFO: total log probability: -13.61 +2024-01-16 21:45:03,362 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 21:45:03,362 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:03,362 (beam_search:483) INFO: best hypo: INMVLVEDSFORHEPOSITEIVEANDCESTTACTEIVEABROATC + +2024-01-16 21:45:03,363 (asr_inference:494) INFO: speech length: 105599 +2024-01-16 21:45:03,375 (beam_search:428) INFO: decoder input length: 162 +2024-01-16 21:45:03,375 (beam_search:429) INFO: max output length: 162 +2024-01-16 21:45:03,375 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:03,792 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:03,793 (beam_search:476) INFO: -19.68 * 1.0 = -19.68 for ctc +2024-01-16 21:45:03,793 (beam_search:479) INFO: total log probability: -19.68 +2024-01-16 21:45:03,793 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:45:03,793 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:03,793 (beam_search:483) INFO: best hypo: OIHOPETHATISILBECOMPEATETAIRINHAFORSIVILEFOUOCHERTHATDMANSEGADBETOAFREMUNS + +2024-01-16 21:45:03,794 (asr_inference:494) INFO: speech length: 128960 +2024-01-16 21:45:03,808 (beam_search:428) INFO: decoder input length: 199 +2024-01-16 21:45:03,808 (beam_search:429) INFO: max output length: 199 +2024-01-16 21:45:03,808 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:04,491 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:04,491 (beam_search:476) INFO: -16.74 * 1.0 = -16.74 for ctc +2024-01-16 21:45:04,491 (beam_search:479) INFO: total log probability: -16.74 +2024-01-16 21:45:04,491 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:45:04,491 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:04,492 (beam_search:483) INFO: best hypo: ORFORDERANDCORISHETHEYOUANDETFHURHTOBRINGAMNGPESINOFGNISTANANDTOOVERCOMETOFFRESILSICURITYNVEIREMENTINTHECONTY + +2024-01-16 21:45:04,493 (asr_inference:494) INFO: speech length: 52160 +2024-01-16 21:45:04,502 (beam_search:428) INFO: decoder input length: 79 +2024-01-16 21:45:04,502 (beam_search:429) INFO: max output length: 79 +2024-01-16 21:45:04,502 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:04,606 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:04,606 (beam_search:476) INFO: -4.88 * 1.0 = -4.88 for ctc +2024-01-16 21:45:04,606 (beam_search:479) INFO: total log probability: -4.88 +2024-01-16 21:45:04,606 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 21:45:04,606 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:04,607 (beam_search:483) INFO: best hypo: BEANDTHESTANTTHATSOMEPEPLOARANGRY + +2024-01-16 21:45:04,608 (asr_inference:494) INFO: speech length: 27200 +2024-01-16 21:45:04,615 (beam_search:428) INFO: decoder input length: 40 +2024-01-16 21:45:04,615 (beam_search:429) INFO: max output length: 40 +2024-01-16 21:45:04,615 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:04,651 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:04,651 (beam_search:476) INFO: -7.23 * 1.0 = -7.23 for ctc +2024-01-16 21:45:04,651 (beam_search:479) INFO: total log probability: -7.23 +2024-01-16 21:45:04,651 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 21:45:04,651 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:04,651 (beam_search:483) INFO: best hypo: OENTOHEMORSTPONCIVLD + +2024-01-16 21:45:04,652 (asr_inference:494) INFO: speech length: 154526 +2024-01-16 21:45:04,667 (beam_search:428) INFO: decoder input length: 239 +2024-01-16 21:45:04,667 (beam_search:429) INFO: max output length: 239 +2024-01-16 21:45:04,667 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:05,465 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:05,465 (beam_search:476) INFO: -23.94 * 1.0 = -23.94 for ctc +2024-01-16 21:45:05,465 (beam_search:479) INFO: total log probability: -23.94 +2024-01-16 21:45:05,465 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:45:05,465 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:05,466 (beam_search:483) INFO: best hypo: EMUSTEDACTIFIHITHTHISSUTIATIONANDHASETHECOMIONTOCONSIDERTHEMOSTEDICKETGCOMINSATIONMESHERSFORLOLWPESENGES + +2024-01-16 21:45:05,467 (asr_inference:494) INFO: speech length: 233600 +2024-01-16 21:45:05,488 (beam_search:428) INFO: decoder input length: 362 +2024-01-16 21:45:05,488 (beam_search:429) INFO: max output length: 362 +2024-01-16 21:45:05,488 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:07,446 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:07,446 (beam_search:476) INFO: -36.02 * 1.0 = -36.02 for ctc +2024-01-16 21:45:07,446 (beam_search:479) INFO: total log probability: -36.02 +2024-01-16 21:45:07,446 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:45:07,446 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:07,447 (beam_search:483) INFO: best hypo: THECOMITIONINGBISHETHEYUOPIONTPORLAMENTINTHEUPCOMINKREVISIONTOOPENISPOSITIONONDTHISMATHEREWHICHREALYCONCSERDACESETOLUSTICSINOUROPANDTHEINFORSTMENTOFRICESGRANTEDBYHEYUROPIUNRYUNDLO + +2024-01-16 21:45:07,449 (asr_inference:494) INFO: speech length: 103040 +2024-01-16 21:45:07,461 (beam_search:428) INFO: decoder input length: 158 +2024-01-16 21:45:07,461 (beam_search:429) INFO: max output length: 158 +2024-01-16 21:45:07,461 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:07,885 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:07,886 (beam_search:476) INFO: -20.37 * 1.0 = -20.37 for ctc +2024-01-16 21:45:07,886 (beam_search:479) INFO: total log probability: -20.37 +2024-01-16 21:45:07,886 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 21:45:07,886 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:07,886 (beam_search:483) INFO: best hypo: ILMERYMUCHTHERSOUNTIOOFTOCKTENTHEASRALYANDPOLISTINIONSANDENCIRLYHOPTHATHEWLDSUCCED + +2024-01-16 21:45:07,888 (asr_inference:494) INFO: speech length: 109440 +2024-01-16 21:45:07,900 (beam_search:428) INFO: decoder input length: 168 +2024-01-16 21:45:07,900 (beam_search:429) INFO: max output length: 168 +2024-01-16 21:45:07,900 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:08,372 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:08,372 (beam_search:476) INFO: -23.48 * 1.0 = -23.48 for ctc +2024-01-16 21:45:08,372 (beam_search:479) INFO: total log probability: -23.48 +2024-01-16 21:45:08,372 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 21:45:08,372 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:08,372 (beam_search:483) INFO: best hypo: LWEHAEECUMELATIONOFPROBLANCSRESILTINGFROMTHEARTIFISHALUNDTHEBAGEITINGKANDVERETPRIVUSYUS + +2024-01-16 21:45:08,374 (asr_inference:494) INFO: speech length: 73584 +2024-01-16 21:45:08,383 (beam_search:428) INFO: decoder input length: 112 +2024-01-16 21:45:08,383 (beam_search:429) INFO: max output length: 112 +2024-01-16 21:45:08,384 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:08,585 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:08,585 (beam_search:476) INFO: -10.91 * 1.0 = -10.91 for ctc +2024-01-16 21:45:08,586 (beam_search:479) INFO: total log probability: -10.91 +2024-01-16 21:45:08,586 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:45:08,586 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:08,586 (beam_search:483) INFO: best hypo: ELETASTNOTBETHEMANOFOUSTADYITISBETODASINSTITUTIO + +2024-01-16 21:45:08,587 (asr_inference:494) INFO: speech length: 287020 +2024-01-16 21:45:08,613 (beam_search:428) INFO: decoder input length: 446 +2024-01-16 21:45:08,613 (beam_search:429) INFO: max output length: 446 +2024-01-16 21:45:08,613 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:11,016 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:11,016 (beam_search:476) INFO: -44.49 * 1.0 = -44.49 for ctc +2024-01-16 21:45:11,016 (beam_search:479) INFO: total log probability: -44.49 +2024-01-16 21:45:11,016 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 21:45:11,016 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:11,017 (beam_search:483) INFO: best hypo: EIGODARLSEOENTOBECOMEAMBSHETESOTHEYEAREMAKINGITAYDEIRSADACTIVITHISWOWWHIDLYNONEAMONGSHTTOYUOPEASITIESEANDPUTPICIPATINGNHVBENTSEBETTATYUROPEIONNASHONLFORLOKALEL + +2024-01-16 21:45:11,019 (asr_inference:494) INFO: speech length: 141760 +2024-01-16 21:45:11,033 (beam_search:428) INFO: decoder input length: 219 +2024-01-16 21:45:11,034 (beam_search:429) INFO: max output length: 219 +2024-01-16 21:45:11,034 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:11,776 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:11,776 (beam_search:476) INFO: -22.73 * 1.0 = -22.73 for ctc +2024-01-16 21:45:11,776 (beam_search:479) INFO: total log probability: -22.73 +2024-01-16 21:45:11,776 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 21:45:11,776 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:11,777 (beam_search:483) INFO: best hypo: DSARTDLYSUCHINPACTSESTMENTCOLDPREAMTSERTANPROBLOMSSUCHASTHOSPOSEDBYTHEELCTRNIKIDEDTFICATIONOFSHEPANDSCOTEND + +2024-01-16 21:45:11,778 (asr_inference:494) INFO: speech length: 204471 +2024-01-16 21:45:11,796 (beam_search:428) INFO: decoder input length: 317 +2024-01-16 21:45:11,796 (beam_search:429) INFO: max output length: 317 +2024-01-16 21:45:11,796 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:13,301 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:13,301 (beam_search:476) INFO: -27.37 * 1.0 = -27.37 for ctc +2024-01-16 21:45:13,301 (beam_search:479) INFO: total log probability: -27.37 +2024-01-16 21:45:13,301 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:45:13,301 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:13,302 (beam_search:483) INFO: best hypo: THEORTISCONTENTTOSETHATHITSWORKHASINFORMETHEDESHARGHROUESANDHASONTEBEUTEDTOPROPOSOLSEFORIMPROVINGTHEFINCHALMANAHENTOFEYOUSPENDINGANDBETETORKATINGOFYOFNCS + +2024-01-16 21:45:13,303 (asr_inference:494) INFO: speech length: 110394 +2024-01-16 21:45:13,316 (beam_search:428) INFO: decoder input length: 170 +2024-01-16 21:45:13,316 (beam_search:429) INFO: max output length: 170 +2024-01-16 21:45:13,316 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:13,739 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:13,739 (beam_search:476) INFO: -16.99 * 1.0 = -16.99 for ctc +2024-01-16 21:45:13,739 (beam_search:479) INFO: total log probability: -16.99 +2024-01-16 21:45:13,739 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 21:45:13,739 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:13,739 (beam_search:483) INFO: best hypo: RECGUAIHRYGLAITHEANDSERTENTYIASNEADETDFORTHEOBLIKESECTORANDFORTHINDUSTRY + +2024-01-16 21:45:13,741 (asr_inference:494) INFO: speech length: 109428 +2024-01-16 21:45:13,753 (beam_search:428) INFO: decoder input length: 168 +2024-01-16 21:45:13,753 (beam_search:429) INFO: max output length: 168 +2024-01-16 21:45:13,753 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:14,209 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:14,209 (beam_search:476) INFO: -16.92 * 1.0 = -16.92 for ctc +2024-01-16 21:45:14,209 (beam_search:479) INFO: total log probability: -16.92 +2024-01-16 21:45:14,209 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 21:45:14,209 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:14,209 (beam_search:483) INFO: best hypo: ISITDRELYNOTPOSEABLRTOOUEAATHERHOUSINFESILIDESWIHEPROPETHRESEPTIONCODIOSINTHEMENTIME + +2024-01-16 21:45:14,211 (asr_inference:494) INFO: speech length: 77120 +2024-01-16 21:45:14,221 (beam_search:428) INFO: decoder input length: 118 +2024-01-16 21:45:14,221 (beam_search:429) INFO: max output length: 118 +2024-01-16 21:45:14,221 (beam_search:430) INFO: min output length: 0 +2024-01-16 21:45:14,404 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 21:45:14,404 (beam_search:476) INFO: -7.70 * 1.0 = -7.70 for ctc +2024-01-16 21:45:14,404 (beam_search:479) INFO: total log probability: -7.70 +2024-01-16 21:45:14,404 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 21:45:14,404 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 21:45:14,404 (beam_search:483) INFO: best hypo: WELYOUTEAKEACSIONATDLASTIFNOTTHEINWEINDE + +# Accounting: time=105 threads=1 +# Ended (code 0) at Tue Jan 16 21:45:15 CST 2024, elapsed time 105 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/calculate_rtf.log b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/calculate_rtf.log new file mode 100644 index 0000000000000000000000000000000000000000..cbfa996a175d0bdd10c2a7f21d2fc89d4aad30ed --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/calculate_rtf.log @@ -0,0 +1,9 @@ +# pyscripts/utils/calculate_rtf.py --log-dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir --log-name asr_inference --input-shift 0.0625 --start-times-marker "speech length" --end-times-marker "best hypo" --inf-num 1 +# Started at Tue Jan 16 21:45:15 CST 2024 +# +Total audio duration: 5412.381 [sec] +Total decoding time: 368.691 [sec] +RTF: 0.068 +Latency: 337.629 [ms/sentence] +# Accounting: time=0 threads=1 +# Ended (code 0) at Tue Jan 16 21:45:15 CST 2024, elapsed time 0 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.1.scp b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.1.scp new file mode 100644 index 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tensor(-13.1875) +cv_eng_000758 tensor(-9.8615) +cv_eng_000759 tensor(-7.3957) +cv_eng_000760 tensor(-18.7863) +cv_eng_000761 tensor(-8.8880) +cv_eng_000762 tensor(-9.9649) +cv_eng_000763 tensor(-6.2189) +cv_eng_000764 tensor(-9.8710) +cv_eng_000765 tensor(-9.6176) +cv_eng_000766 tensor(-11.3689) +cv_eng_000767 tensor(-8.6894) +cv_eng_000768 tensor(-20.5561) +cv_eng_000769 tensor(-14.6378) +cv_eng_000770 tensor(-16.9226) +cv_eng_000771 tensor(-21.3771) +cv_eng_000772 tensor(-29.1006) +cv_eng_000773 tensor(-12.5294) +cv_eng_000774 tensor(-15.7872) +cv_eng_000775 tensor(-9.8315) +cv_eng_000776 tensor(-14.2749) +cv_eng_000777 tensor(-15.6115) +cv_eng_000778 tensor(-11.6327) +cv_eng_000779 tensor(-23.4547) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.1/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.1/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..2574ec5dd0b40659ba4cb12fbcba3d5b1bf23767 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.1/1best_recog/text @@ -0,0 +1,273 @@ +LAD_eng_000254 HE REMAED WEL CAMPION ANTIL NINTIN SICTY FIVE A YAR IN WHCHE SUFHED A TERABL ACXITENT +LAD_eng_000255 AY LIBRL CON ERVETIVE HE AS DEFEATED IN ATY N ATY TWO +LAD_eng_000256 WON ROVED LAR CODRE TWO ROUDE AT WHANCE +LAD_eng_000257 SOME O THE OUNTIS HAE SERVAYIS FOR MALTBLE YEARS +LAD_eng_000258 BOTHOF THEVIRSINS FEATHE THE SONG HAPY HOLIDAY +LAD_eng_000259 SHAKSPIER MANY REFRNCES UR MAD TO SHEENDS INTR ACTIOND OR CARICTES FOM VERIUT PLAYES +LAD_eng_000260 IF NDY THE PROGRAM CULDBRAKE OUT GUST LITL FOME IT TWO FOMELIAR APROUCH +LAD_eng_000261 THE HALBUM WAS RELESE INO STRALIAR ARN NIN TINT OAGIST TWO THOUSENT AN E LEVON +LAD_eng_000262 HE NOW PLACE FOR A STRALIN TLOBE PEIRT GLOURY +LAD_eng_000263 ITIT NOT NONE HOW 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SICXTH AORGIST TWO THAUSEND AND FORE LASTING FRA TOUTL OF SEVENTY OND DAYS +LAD_eng_000277 HE HAS ALSOD CONTRIBETE TO THE NUN YOURC RE EU OF BOKS +LAD_eng_000278 BY PLACING SMAL ART OBDGECT THRO OUT THE ILME +LAD_eng_000279 IT I FOUNED IN BRESIL +LAD_eng_000280 IT WA THE SID OF THE CAMLY I IDENTIFIED MORLE WIFH +LAD_eng_000281 ECANDED IT SIGHTHE MUST LLSO SOD MIT A WORK PLAN +LAD_eng_000282 DUNDEY WON THE MACH THRE TWOE +LAD_eng_000283 HOWEVER THE VILIG REMANED ICILAT DT NTIL THE RIVUL OF TE FIRST NOUS PAPER SECOND RE POUBLICK +LAD_eng_000284 THE FIRST ERVI I THENU CHARC WAS HELD NINTN FIFTY ON AL THO THE BILDIG WAS NOT FULY FINISHED +LAD_eng_000285 THE AVRIGH HOUSEHLD SIES WAS TWO PONT TWO SEVON ND THE AVRIGH FAMLY SIES WAS THRE POENTESIARO SIARO +LAD_eng_000286 IT WAS FIRSTE RARD CAST ON THRED GANIOURY TWO HOUSEND AND TEN +LAD_eng_000287 THE WINGS WE NOW AD IN A SINGLE PRESING +LAD_eng_000288 TE DOCTE OFOLOIFY IN ENDENYEARIG MANAGEMENT +LAD_eng_000289 THISE O WAY THE MAEN ARKGUMEN OF SAIFTDY RISSKS +LAD_eng_000290 HE WAS ALLSO AD A LIFH MEMBR OF SCOUND THORPYUNITED +LAD_eng_000291 SHE FHEIRS THE L GAT DEFORSE BUT THIE NEVER HAPENS +LAD_eng_000292 FOT DROPS NABLE T HAD TH FOT SRAT ACROUSE +LAD_eng_000293 WHETH THE AR FLOY IS FREY OR FOURST CN FEC THE ENDGY OFIENCY OF TH WHNDO +LAD_eng_000294 AFTR GETIG HE IT MESERENT THE MAD TH NOU DORS +LAD_eng_000295 FRAGMNTE ON ACH FACE RE MARET WTH LTERS AY BE SE +LAD_eng_000296 FROM TH FIRSTD MINITE BOTH TEMES SHOD THE DESIRE TO COMPEET WI THEGREIOF A PROCERS +LAD_eng_000297 FISICL HERIBY ECERSIDSES MAY HELP TH PATIONTE TO MAIN TAIN MUSL STRINGTH +LAD_eng_000298 HOWEVER THE TOWNE HE LIVS IN NO UND WONT TO HEARABOUT HER +LAD_eng_000299 A DISRIES AEPOENTDENT O AN ACTING CHIVE JUSTISS OR JOUDGE OF THESOPREME CORT +LAD_eng_000300 THE SORY BES OUT CUVERING IS TEN REMOVEDT AND TE BENDS AR PARTHALY COCKET +LAD_eng_000301 THIS NASTIAL MOVENT WHCHE BEGON WTH SO UH HOP CAMETO A SAD EAND +LAD_eng_000302 HIS A SEOSIATE OUSUALY CALD HIM TE 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+cv_eng_000761 A I COLED AN TOPESORIN AT IT EER +cv_eng_000762 FORS INPLITY GUR INCHESD IS NORMILYARDOUNDED TO HE NERESH HOL NOMBER +cv_eng_000763 IOF WE ACTILY DEO ON IS SOLD IT WIL BEF +cv_eng_000764 THEI FO OF H HIC TRY S APL SHAPEDT +cv_eng_000765 THEREIE ACTSHANGE IS NO WOBLY +cv_eng_000766 A WHAT OU EAT TO DAY WHEASK AND TORKS TO MOROE +cv_eng_000767 A THE WOTED AN FLOS OUT OF TE SCONMNTS AS HE LO OAPL E RIVER +cv_eng_000768 AM HEWHIY ID DION YOESAY SOM THINKCDHEADCD +cv_eng_000769 T AVEYOSE NO MARNM EETDEEEEE +cv_eng_000770 I COTD GO ONEFREDAIS ABOU THE DIDIOS WONS THE DUAST IN HIS PARTOF THE WERETD +cv_eng_000771 THO SOEO LAD DEVFTHEAR INCOREIRERAE NINGD INSITOPCEYOT THE YOARE +cv_eng_000772 AA COAS EVES OPB DCECT IS ESER GLWNDSH EECLDELDARDL +cv_eng_000773 THE SWEEDS WERANABL TO YOUS HR VEACALS WHIH ER TOCE IN TH MODE +cv_eng_000774 I THE ACKT ID NOT POR HE BIT BAYING AE REPEOSENTIP TO E BEAR AN THE CORTLIS +cv_eng_000775 CHONWRPLESTELEPINROULT IDN +cv_eng_000776 HE WA CON 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I T L I N G L E N D B E Y O N D W A I L E S I T H A S B E E A E N C H R L Y I N G L I S H S P E A K I N G F O R N I N H U N T R E D O E A R S +LAD_eng_000317 H E P L A D W T H T E N P L A R S F O R H A R V F W A S A G A N E T H T R D I T I O N I N D D E A S S P E +LAD_eng_000318 T H E R E I D I N G G J O U D G W A S W E B S T O F A I R H O W A S A L E A D Y A S I E D T O T H E C O R T B E F O R E T H I S C A C E W A S S H E D U L T +LAD_eng_000319 B G G R A T H E F I V E W A S T H E T H I R D O T H E M A I N S E R I S T O F E C H E A L I V E L O U N C H +LAD_eng_000320 I T S M O T O I S H O E V E Y O U A R A N D W H E R E V E R Y O U A R E O N T H E D I R N Y O F F A I F Y O A E W E L C O M H E R +LAD_eng_000321 R O B E T A M I L E A S C O T H W I L T S O N +LAD_eng_000322 A F T R E W N Y U A R B R A K S I R A O D E G R E W A S H E F L L I N G V E N C H E R +LAD_eng_000323 A Y A M T E Y M A N U F A C T E D A M O R T L C I T O F T H E A D S A I D A R D R A C S T E R +LAD_eng_000324 T H E E S E S S A Y A M E D T O B I L E D A L E F T W I N G O L T E R N I T I F T O N O U L A B E R A N D T H E E S S A N D P E +LAD_eng_000325 H E L I V S L I K H E A S Y O N G P R S O N +LAD_eng_000326 M A S T E O F S I N D I N E N D E N E A R I G M A N I G E N T +LAD_eng_000327 S H E F A I L E D T O A K T H E T O P T H R E A T H E C E N I O N D J U N I E A R T R A C T R I L E S T H A T D U N +LAD_eng_000328 A T O A R E F O L O U D I N S U P O R T +LAD_eng_000329 T H E E S T A B L I S E N A T E N S E V E N T Y O N A N D E W E O T H E L D E S T C L O P S I N T H E S O U T H O F I N G L E N D +LAD_eng_000330 H E W S A M E M B R O F T H E G E A S S C O T L E N D A D F I S E R Y B O R D +LAD_eng_000331 T W O T H O U S E N D N D F I V E G E N T L M E N +LAD_eng_000332 A O U R E F I L E A D A S T O N G R E C E P T I O N I N U R A P A D C H V E D E S T O B E U T I O N T U T T H A T W A S N O T T H E C A C E H E R +LAD_eng_000333 B L T H O I S S T D E T H E S P O S T E R I E R A N G A L S T R O 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E R Y F A V T F U L E T O T H E R I D I O N L N O V H L +LAD_eng_000342 T H I S P R S A U M P T I O N I S N O T F U L E F I L D W O N H A S T O N O T L E A T E T W O C O N G E T D I A M I T E S +LAD_eng_000343 N O T A L E T I T L E S I N L D E D G O L D A N A C S T H E R E V E N G O F D E A T A D E R R A D M O B I L O U T R U N E R S A N D S A K G R S O N I C T H E H E A G H O G K +LAD_eng_000344 T H E N I N T N N I N T Y N I N D J U G M E N T N O T D T A T T H E I N T F L O N C O T H E F A R T H E R O F T H E C U S D H I S B E E T H E R +LAD_eng_000345 M O K D A F S W E A R S R V E N G E H A N D J O I N S F O U R S E S I T M U L K C M T O O V E R T R O M M O K B E T H +LAD_eng_000346 T H E E A D Y A V L E V I L I G E C O R T W A S A L L A S A N C H O U S T O C A P T H E E N E R O U N T H E V I L I G E G A P L E S +LAD_eng_000347 T H E A S A N I N R A N K S I S T E M E A C H R A N C A V I G M O R E P O U E R T H T H E L O E R A N K +LAD_eng_000348 T H E A S T A B L I S C H E D D E P L I M A T I C R L A T I O N D S O N D E P T O M E R N I N T N T H N I N T N S E V E N T Y T W O +LAD_eng_000349 T H I A S F I R T H C S T E N D E D T O I N C L D M O R E Y O U C A Y D A T E I N D E C S E M B E R T W O T H O U S E D N D F O R T E N +LAD_eng_000350 T H E U C H G O V E N T I S C I R N T L Y E S A M I N G T H E L E A K L E C O N C I C Q E N C E S F H E R O L I N G +LAD_eng_000351 F R O M N I N T I N T H E R T Y T H R E E T O N I N T I N F O A R T Y N I N T H E M E R I C E D L E E W O N D W E L V E A U T O T H E F I R S T S I C S T E N +LAD_eng_000352 T H E A I R H E F E L E S I C K W T T I V F O S H I M S E L F +LAD_eng_000353 S I C X T T E M E S H A E E D E V E I D E D I N T O T W O G R U P S O F T H R E T E M E M S A C H +LAD_eng_000354 T H E F I R S T S E S O N R E M I A D O N D W E L T H D U N T W O T H O U S E N D A N F I F D E E N +LAD_eng_000355 I T S A C E D T H E W E I H B O L R D A N D S I S T A M T W E N T Y F O R E C O M B I N G F E A C E S F R O M B O T H +LAD_eng_000356 V E L Y U E T W O O H N U M B R S W O N T W O A N D T H R E +LAD_eng_000357 T H E L O R P A T O F M E N S D R E S E S W E M U C H H O U R T I N L E A N G T H N T H O S F R W I E I N +LAD_eng_000358 T H E I G O A L T H S I N T E R N W E R C E A D E D B Y T H E M U L E R S +LAD_eng_000359 J O S O F H I Y S C O L A V E R Y W E K O F T H S C O L H E A R +LAD_eng_000360 A S R S I L O F A L T H E A R G U M E N T G E T I G T O H E R +LAD_eng_000361 I T H A D Q U A R T E R S A R I N S H E F E I L D O U N I G T E D C I N G D O M +LAD_eng_000362 L A Y L L S O F I H A L Y S I D E T H E C O N T R A C T O N S T A G E W H T H E D I R E C T E A N D P R O D O U S E S O F T H E G O L D A N I Y S +LAD_eng_000363 F I S I C L E F E R I A P Y C O N H E L P A T I E N T E T O A R N H O T O W A E K W T H E F O T D R O U P E +LAD_eng_000364 I T E N T O N T O S E L T H R E Y H U N D R E D T H O U S E D Y A U N I T S A C H E V E F I V E F N O +LAD_eng_000365 T H E N A M E M S T D U C K A F E R T H A T +LAD_eng_000366 T H E I L B O M L A T E R B R A C T H E D I M E N D R E C O R D O N D C O K C U M U S I C K +LAD_eng_000367 I T E D E A T O R I A L W E S O U B M I T A N D I T O R T H E A P O L I T O P R I S E +LAD_eng_000368 D J O S O F P L A Y E S A U R F E T C E D I E A C H W E E O T H S H O +LAD_eng_000369 T H E W A T F R A T I M E M B I L D I N G U T T H E F O R S E S B E I G I N G T O O A N D R I F T H I S E A V L R E A L Y A E X S I S T S +LAD_eng_000370 B R E A E M E N C I O N O F T H C O N V I C T I O N A P E R D O N P A G E T H R E O F T H E N O U Y O U O K T I M E S +LAD_eng_000371 O R D E D B Y P E S I O N O N P I C H F R M B A K R I H T T O F R U N T L E F T E T +LAD_eng_000372 H E A S M E M B E R O F T H E C O R T O T H E R I L C O L I G O F A R T L U N D E N Y O U C A Y +LAD_eng_000373 D E R I N T H E C O U R S E O F H E C A M P A I N F I R G E A N D V I S I T E D L L T H E R T Y N I N W A S I G T A N S T A T C O N T Y I S +LAD_eng_000374 A S T R I P O F P A P E R O F L E A N T H +LAD_eng_000375 S A T O H A D F R E K U N T L Y W E R K T O G E T H W T H Y O U K Y Y A M E A R O N P R E V I U S P O G E C T S +LAD_eng_000376 S H E W S B O R E O N D S C R E N D U I N G T H E E P S O D B R U R D C A S T O N F O R T H N O V E M B E R N I N T E N N I N T Y F O R +M-AILABS_eng_000159 A H T U R E D R O U W N E D S H E H A D C O M I N S O G E N T E L Y T H A T H E H A D E V E H E R D H E R +M-AILABS_eng_000160 A T O B E S H O U O R H W E M U S T C E O U R D O R S S H O T W E M U S L A T N O W O U N I N H A +M-AILABS_eng_000161 A C I D E S B E N E B E G A N M O K I N G L Y Y O U M A H V E W E D E W H I I C A L D O T R O U S W H N I C O O D J O S T A S W I L L H E D E S T R E Y O U T H A T I D O T A D O A N C E R H I M +M-AILABS_eng_000162 T H E P E S E N T T I R H I M S E F A P O N H I M A N D B O U N E D H I S F O R L A K E D H T L Y S O A T E C O N O T M O E +M-AILABS_eng_000163 N O R M U S T T H O U S O L I M I T H T H E O L Y O N O F I S R I L A S T O T H I N H H A T H B U T W N E N W A Y I N W H C H C N G O R I F Y H I M S E L F B Y T H E +M-AILABS_eng_000164 T H E L D C O M P R S O N D B E T W E N T H I M P A L S O F E X S E A K I T I V E A N D T H E L I T B R L E A R T U S M A N H W W O D L E U R N D T H A T H E R E O N L Y O N E E R T O P O S I E D E C I S I O S O A L A B L E I N A L L T H E W E R L O T H I N K I N G +M-AILABS_eng_000165 A V F T R T H I C S P E R I A N C E T H E E N D V A D E R S W E R C A I R F L T O C E P E A S A K F E D I S T E N C E F R O M T H E W A L L +M-AILABS_eng_000166 M A O N O U B E A E R S M I N G F I R T H E R I T H I N Y O A T N O I T I H A V E H E R A M O S T M S T E R I A U S T E L P R G R O M E M E S E A S E W H A T I S I T E I S S H E D A D N O I T I S N O T U B O U T H E R Y +M-AILABS_eng_000167 D N O W H L E M I S T O R T H U N T O N S A D D I E T H E B A S K T O M E I L T A K I T W +M-AILABS_eng_000168 A N A R A B I O A N N I H T E C S C L A M E T R O G T W H I T H A T W A S A M A G I A K N I G H T W O A S I N I T H E R S D I F R N T S O R T S A N I G H T S M A T S A I D T H E S A L R A N D T H E N I G T B U T N U B I G H T M E N E S A T T H E S A M E N I G H T Y O M E A N +M-AILABS_eng_000169 I V E T U R E D O B F E O U P W A R D O H U D R D O M Y B E S T E H A N D S F O R N O O T H E R F L T T H E M F A L W I N G O U A N D S U C H A S Y O U A N D Y T H I N K I L L T A K E Y O U O N +M-AILABS_eng_000170 G U H E W O H E S E H I M H E R H A R T L A P E D U B B N A P R O H E N T I O N A T E V E Y R I N G O F H E D O R B L T +M-AILABS_eng_000171 A A T H E A S B O K S S D I C S O N I W L C E A L T H E R E S T W E O U S E N T O M S T R B E L T H E A R O F A C I N T H T H E W L A V O U Y O F O R T H I M S E L E S A S W E L A S F R P O S S A Y D +M-AILABS_eng_000172 B U T I N G L W A S N O T I T L S H O U R T H A T H E Y C O U L D N O T G E D I N T H E G A T S O P E D I N O R D A N D T H R E H A V Y B O A R R S W E R H E L D I N P L A C E B Y M E N E S O F S T O U T S T A P L E S R I V I D T E T O T H E S H E T E S O F S T D E L +M-AILABS_eng_000173 A I W O A N T T H E I L S I D O D O N C O L D L Y I H E N T A D O S O N H O R E S I W N U N T M E N T O B R I G E T H E W I T M H E P U S H E I S W A Y F O R E D W I C H W A Y T O T H E S T A B L S +M-AILABS_eng_000174 I R I S L E I T W A C O C A N D D E V F R T H E F I R S T I M E Y O U A N T E A N S H O U S E A N D B E S I G D E S T H A T W A S N O T I M E T O A R O U S E S P I O N I T H M I N D S O F A N Y W O N +M-AILABS_eng_000175 D O U N O T R E M E M E R T H A T E S A S T H Y D E M O N T H A T D T H E S P I R I T H I C H C K E A P E S T H E I S N O B L C O R E A G E S C E H A I U N M A U C H O A B L +M-AILABS_eng_000176 A T M I S T R B E L E O A C A N H E N O O F C O N H E E L I V I N G A L A S Y L I F N A D D R O U S Y C O L I D G E H A +M-AILABS_eng_000177 A N D T E C I T O N F O L L O W E D E M U R L Y A T T H E R H E A L S +M-AILABS_eng_000178 T H E F I R S T T U H W O D C O S A N E C S P L O I O N I N W H I C H A M O G S U C H H U N D R E D S O F I N F E R A T E D M E N A N D R E C K L S S B O R Y S +M-AILABS_eng_000179 W O N T E G A T P L E S E R S O F M A R G R E T L I E A T T H I S T I M E W A S I N E A D E S B O Y +M-AILABS_eng_000180 T H T H I N A S G O N U N D L O N N O F R S O N E O R B I G A C X I T D E N T W E S H A L H A V E T O C O M B E R M Y I S W I T H E I N E R I V E R N C E R Y O N T H W E R K C O I N L +M-AILABS_eng_000181 A A Y O U R L A T S A I D S H E W E L S H E H E D H E R B R E A T H O T H E A N C S R H A L +M-AILABS_eng_000182 R O H T T O L E T H E G I R L S T A H E M U S C O H T H E R F O T H E R T O L I V N G I P K G E S S I S L E S L T L E O L D C A B O N A N H N T H E H E R D T H S R E D F U L D E C R E +M-AILABS_eng_000183 M A R G I T S A T D O W N T H E R O G K G P A R T L Y T O W O R M E H R S E L F F O T H E D A N P N E S S O T H E E A V N I N G H U N G O U T H E R R E S A N D O V E F I T E A D M A D H E R C H I L Y +M-AILABS_eng_000184 O N O W Y O A R M S T A K E N B O U T T H A T E L I D T H E C I N G T H E A R N O T M Y P R E S N E R S B U T M Y S L A V E S H O M I Y P R C E U S T F R O M T H E C I N G O F E V E +M-AILABS_eng_000185 H E R F A T H E T O U T H E O M E R S A T I O N +M-AILABS_eng_000186 I N A C O U R E R W A S A S O U R D O F D R E I N G T A B L E I N W H I C H L Y A C O M E A N D B R U S H C E N I D Y S E E D M U C H I N T E R S T D I N T H E T A B L E A N D W A S A E X S A M I N G A T H E T H E G U E R U R E T R N +M-AILABS_eng_000187 I A V E S O M E T I M T A U H T T A T M Y S E F S H E A G R E E D B U T O F C O R I O T N O E S T I L I H V E T B E P R T Y C A R F U L S O M E W E N I S L L I S O V E R E B Y M Y D E S C O R L O I N G O V E R H E A R +M-AILABS_eng_000188 I S H A L S T A Y E R E P L D T H O N G A N F O R I M E N T O S T C O F R E +M-AILABS_eng_000189 W H A T D Y O U D E O A S T T H E S O R S E R E R +M-AILABS_eng_000190 W H I Y T H E R A R A N I M E S Y O U R S H O R T H I N E S N O T A N Y M O R E R E P L I D E S R O U H T I M Q U E T H E P I N K E S N D H M L S O Q U E O T H E L S S O I W O N T H A V E M Y P E B L E Q U A R L I N G +M-AILABS_eng_000191 T I P R A E R S E C L I C I N G C L P I N G A R B N G S N I P D O T F C U G S T A C O F N O U S P E R A N D P A S E I N A I N L A R G S R A B O C S S U R K I L E R S R B E N G F O L D A N D A D E D R A D Y T O M A L F O T H E F I N L A P E L +M-AILABS_eng_000192 I T W A S F O R E D A Y S A F E D T H E S U P R I Y S O F L H E R S H O R S E W H N T H E S T R A N G R S L E A F T H E S T D A T T T H E C A I R O F R O G E D O L D F O R S T R H I R M O N +M-AILABS_eng_000193 M P O R E T E M P L T O N H E S A I D I O U S T N O H A M M A N Y U A R S G O W E N W E B O R Y S E M N T O S C O U W I T H M N D N T A L H A T S O R O F H I N U N O E B U T A N D T I L I E R A N C R O U S H M O R E +M-AILABS_eng_000194 I F O N D T E I N T H E F O A R R S T N D B R U G T H E E A R A P R E S N E R E P L Y D T H E C A P T O N +M-AILABS_eng_000195 O M A B E C O M P I T E N T I D T H E F R O M P E R S O N L C S P E I R I N C E O R T H E E C S P E R I N C E O F O T H E R S T O A N C S E R T W H T M O E O R L E S S C O R A C T K N E S O R A T L E A S T E N I T E M T O H +M-AILABS_eng_000196 L W N N I N T E T O L A T S T R E D S A I D H O A K G O N B U Y D I G O F H I S O G A R +M-AILABS_eng_000197 R A T W A S S U R P R I S T O F I N E H E C O L D S E S O P L A I L Y T H R T H E H I Y W A L O W O A H T E R U B O F H E R R B U T T H E S N D W A S A B L T O S H O O T I T S B E M E S T R A T D O N T H O R T H E A R A E N S P E I R N T +M-AILABS_eng_000198 T H E S P A T E I D S P R O N G O P E +M-AILABS_eng_000199 G C O M E D E A N I L W I T H E G A V E S O C H A S U P O S I T I O N +M-AILABS_eng_000200 Y O S E A N D T I L T H E S C H L P I L E S R I N G E N T E D W E W A S T T L A T O F T I M I N S T D A D Y T H A T N O W M A B E B E T E R I M P L O Y E D A N M P R C T S C I N G A T H L E T I C +M-AILABS_eng_000201 Y O V E D N I T H A N W D E C L A R E D D A R T H Y T H E S T E N C E A R J U S T O E N D E R F O L +M-AILABS_eng_000202 E M F O R T W E N I N G T A N F I V E F T H E R E E T W O E T H E I N O W A S B E R L Y T W O E N Y M Y W S A W A Y W H N H O D I O N F I R D H S R O C K I T S T H E M D A C O L O S T O L C L O W E O A P E R I N A M T Y N E S E +M-AILABS_eng_000203 T H E P A D N O A T E N C I O N T O T H E F A C T H A T G I P K G E S C I S I L D I D N O T O N T O M A R Y A N Y O F T H E M E M F O R H E H A D E T E R M E N D T H A T H N I T W A S E G R E E D W H O S H O D H A V H I M +M-AILABS_eng_000204 W A T D Y O U T H I O F T H A T H E C R I D O P E N G A C O P B Y O T H E R E C K E D A N D L A N G T F L A T O T H E L I B R Y T A B L E L +M-AILABS_eng_000205 I T L E C O P I E R U T A S O R T T I M E +M-AILABS_eng_000206 A N D L A S T T H E R O U D O V E I G E T D A B L E P E P L E H O H A D N O H A R T S A N D O U O D N I T H E R S M I L E N O R F R O W N +M-AILABS_eng_000207 T H E I N Y O L L C A C H I T E S A I T H I C H +M-AILABS_eng_000208 W H T I S I T I Q U E R E D N O T F I E L I N G S R E N B U T T H A T T W A S A E V A L D A T E M D T O S E C E R L I T L F R E A D R T Y S I N G F O T H E A N D E O V E R +M-AILABS_eng_000209 S O E G A V E T H E L I R C T H E T H R D U N D E D O L O S F O R B O K S A N D A C A S K O F G O D O L D A L F R P E T E R T H E C L R K R A N T H E A I L H I M S E L F A N D G A V E H E C A F M I W +M-AILABS_eng_000210 A T L I E K T H A T A N A L S I N N E D E R L A N T D W I T H M E R L Y G R I N T H T F A T E D A W A Y C H A N G I N G I N T O A L I N K S W H I C I N T R E T O S P E R D F O L O W E D B Y A N U N O N R E C E W I T S O U R T N O U S A N D P O N T E D E R S +M-AILABS_eng_000211 A S H E C O L D N O T D E O E M A R G R I L A N C D U N C O N H O U S L Y A T H E U N G K L E D O R N E R O F H E O M E S H E U O H A R T H N D E R T A K A S U R V I N C S P L A C E C O O S H E +M-AILABS_eng_000212 A D N O H E S H E R E P L I D E D W I T I N I S E N K 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U O N E D S A E B A N D O A R I T H Y +M-AILABS_eng_000218 G S R D N L Y I M A S I N T E R T E D N T H E C A C E S O A R B U T I C A N A K H E D S R T A L S O F T I R E P L I D +M-AILABS_eng_000219 O R A N Y M I C E O R E V E N G G R A S H O P E R S +M-AILABS_eng_000220 A N D T H E T H E P A S I O D O N T H E T E L Y O U W H A E T O D O R W H A T A N N O T T O D E H W E T H E M U N Y T H E Y G I V E Y O U A N J U S T P A M E N T F O Y O U P A I N S I N T H E R E C S T C A N G E L I C +M-AILABS_eng_000221 W H T D I S T A T M E A N A S T T H E R I N C E S +M-AILABS_eng_000222 D E H A D B E E D R O N E D H E W A S F L O A O D I N G N A S I O F L I T A N D N O W T H E D S H I N I N G L T E F I S I E S S W H E A M I N C Q U I S I T I E L Y E O U P T O H I E N D S T A R +M-AILABS_eng_000223 B U D O L D G N H D A R I K T W O L E F T D R E M E M E T H E T A I L I R E D T O Y O U I T H T H O N E R M O A B L T H E T H E F I R S T T H E B R O N S T E N D E T H E W R L D O F O A P L W E R S O U L 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9 4 6 7 20 2 22 6 5 4 10 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.2/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.2/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..eb8e9a29364b933824b3c81f60cd583ae49a31bd --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.2/1best_recog/score @@ -0,0 +1,273 @@ +cv_eng_000780 tensor(-10.9459) +cv_eng_000781 tensor(-18.0919) +cv_eng_000782 tensor(-20.5933) +cv_eng_000783 tensor(-6.4430) +cv_eng_000784 tensor(-27.6893) +cv_eng_000785 tensor(-6.6201) +cv_eng_000786 tensor(-11.2411) +cv_eng_000787 tensor(-20.7268) +cv_eng_000788 tensor(-21.3413) +cv_eng_000789 tensor(-14.9628) +cv_eng_000790 tensor(-4.0572) +cv_eng_000791 tensor(-13.1523) +cv_eng_000792 tensor(-11.9374) 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C S T P E R T I O N C C U R E C T L Y +fleurs_eng_000448 T H E S E S C I A L F O R I D Y O F T H C H U R H O U I E B E N I N M R O M F O R O V E R A T H O U S E I N Y O E A R S A N D D I S C O N C O N T R T I O N F P O U E R I M U N Y W E A D T O M A Y T O C E S T I O N W E T H E R I C T E N E N T W A S B E N G M A T +fleurs_eng_000449 T H E S U N D D O A R B O N S A R T H E A R G E I S T T H E T E O R A L M A N G R O E B E L I N T H E W E R O E D S T R C H I N G A T Y C O L O M E T E R S F I F T Y M I L E S I N T O T H E B A N G W L D E A S H E A N A N I N D I N H I N T E R L A N T F O M T H A O W C O S T +fleurs_eng_000450 R E A G U L R N O N C T H N T H E M A T H O L A R M A E D O N L Y N C O T L I N B U T N E T E N D I S T R U T O N E R N O U C D B Y A N O T I M A T E I S S I S M I N T W A R T V E U R I T Y O F L N G W I G D E S I N K E U T I N G P A N I H I N G L S H F R I N C H E R I B I C K A N D O A P N E S +fleurs_eng_000451 E R W N P R T I T B A T I N S T S I D Y A N O U S I S T R E N S P R T I N C S I S T A E N C A L O S T E R W N O M P L A N E O B O U T R E C D P R T I O N S I S T O M +fleurs_eng_000452 L A T N H A D A S F R T H A N G E S O T H E O N S R T E S M F V A R M I N L B I L E D R I G H E E A I N G W T H E E A U M A S I N G F R Y T H I R L A N C O M P E T R E R I G D T I N G O T H E K O S I R V E T H I S P A R Y I N F B I R A M A N A L D I L L +fleurs_eng_000453 A N W N H A S L N T O R T H A T H A T L I H T A T U E S A R O V E R M U E N P A S T H A D E N S A I D E T H E P O S E I L I T Y O F S N O E I C E O R F R E S I N G T E M B T E R S +fleurs_eng_000454 H S L E A E I N T E R U T I O N I S H E P R A S T I S O F H E B O U E S A Y W A K I N D I R I N G Y O R N O M O S T S L E P E R E A D A N D F L I N G A S L E S H O U R T I M E L A T E R E N T O S I C T D E I N T S T I T +fleurs_eng_000455 B L I S W M U R L E T H E T W O D R I Y P P O U R E R S T O G E T H E A N D T E N W I T G Q U E N G W E A T H A N D S S C U E E T H E I N T O A B O L W R I +fleurs_eng_000456 F O T H E S P R I N G B O A C K E I D A N D E D A F I V E D M A C H T H E S I N G S T R E +fleurs_eng_000457 Y U O S T L K T H O N E X P U R T D A P O L N T E A R T H C O S I N G T I D H D S O T E S A M L B Y W Y E X S E R T O F F O R S O T H E E D G I T A R I Y A U S A L A C S Y +fleurs_eng_000458 T H O R W T H E N I H T T H E T W E N H E R D E N F I T Y A N D T O H E R E C O P Y S W E R M A D N O N N O N A S B U M E L A P B O R O D S I D E S +fleurs_eng_000459 F I R S E A M L N G I S E I N Y A T R E C A M N D A T I O N D S T H A T A N N O D T I B L M A T I K N I S H I T D I O F S H D B E T E K H O M B O F O R T H E E N D O T H I C Y E A R T O S E C U R A R A C S P O R E R S E G N S T H O S T I L I N T E R V E N T I O N S A N D T O R E S T A B L I S T D I P L M A T I C R E L A T I O N S W I I T S N A V E R S +fleurs_eng_000460 S A N E T E R S B R C R E S I S I N L T I M E N T O W N W H O H T A S I N G E S A R C S E N T I F F R M R E S E R R E C U R I E N C S C H O C T H E T R N E N S +fleurs_eng_000461 A O C O R D I N G T O E P A N S N O G E L R A G E A N S Y W R D Y L A C T I O F C A E S I O M N D I A D I N H A S E N D E N I F I E T A T H E P L A N T +fleurs_eng_000462 S A E G O K A T I O N A N D R E C O M O N A T I O N S H O L F T L V E R Y A T I O N B A K O N D F O R T H H B E U T W E N D T H E T W O P A L L S E W I T E A C H E G E N E R Y T I O N +fleurs_eng_000463 E L A M N T L Y C O U L S T H U M A N D P O T A S H I M R C O N S E T D M U T L E S O F P O R E S R A L S O M T L E L A S I V E R A N D G O L D +fleurs_eng_000464 T H E O R L T I O B T W E N B R A N P O T H O A L A G E Y A N E H A V E Y O U R S S P R T S I N C S I S T S A N D T H E R E S R C G E +fleurs_eng_000465 A N C I O N C H A I N E N T H A D O U N E A K C W A Y O F S H O I N G D I F R E N T T I M E M P E R E A T D S E A C H E S T D A K E O F C H I N E O R E E C H F A M I L Y T H A T W A S I M P O U R E R W A S H A E D E S T I N T O F D I N I S T Y +fleurs_eng_000466 A H E M B L P O P B E L E D I M E R N H H S T E S H L Y T D R I N T H E S U M E R I S P A A M A L Y B U D W H T L V O I L T O M E T D N A N Y O V L A L A B E C O N T I N C T I C H E S C H E E S T O U O F I S H I T D S E T E R +fleurs_eng_000467 T H E A N O N C T H N T W A S M A D E A V E R T R N M A T Y F O N O M E R S A T I O N W H T T O R K S H S P R D O D E N T R E S E P T T H E Y E A P A E R O R D O U N +fleurs_eng_000468 P E R Y S A T E T A T H H E O D R E T E N T O T E C X E I C T O S T S E U T O R E S U L T S O F T O N G H C S C O K I S C D E D E R M O N W H T H E H E R S A P P A S F O R D F R M Y S E L F N T H S R A C E S B U E L E T E R S T T H O W O W L R E M A E I N T H E R A I D A N D G B P E N O T I G E N R Y T W E W N S O U T E I R L I N O P R M I A R Y +fleurs_eng_000469 E H E W A E L S O I N G A G E I N G G R A I N G B A K N O L T D S F O R M I N Y O U N T R E S R S O N I N G S E A P L E S O F W H I S E R K I N G C L E E D T H E H E A M P R I E N M E N I N I N I S R E A L P R T E R E D S O N T H E F I R S T F R O F H O N D T H F R U N T O F T H E N O O C O N A D Y I N O F L O E D L E R I N W O H N D E R D L D E L +fleurs_eng_000470 H M O R T R D I N H R C H E S O N T A N H O E T H E N E A S T E R R I C I L N S A T T E D Y N G H T T U R I N T H E E S T R W E E N D B U T H E C O G R E W T I O N S O T I M B R A K I N I N T O S E L E B R A T I O N A T T H E S R O C O F M I N T H T O S O L B R A K C R I C E S R E S U R E C T I O N +fleurs_eng_000471 F I L N I A G R A T B O D I N G D U S T E N A T I O N T H E L A N D O F A T H O U S E N L A K H E S T O U S E O F I L E N C D S T W O O A N D T H E L A K A N N T H E C O S T A O E A R K Y P E L O G O E S +fleurs_eng_000472 E R N T T E N T E R A N D A R G H I N C S I N F R S L A D Y C E S T D E N O F R N D I S A C E R S I O N R A N O E S R P E S I N A C H L C A N D I S O U S T R D A Y A V E N G N L H P L A T H A T A S T A D Y F O F T D Y O L O M E I T E R S T H E R T Y W N M I L S A W Y F R O M W N O L S I D I S +fleurs_eng_000473 S V E R W E T H E R I H E E N A R N C T E R E F O R N Y D A N D E R S W H T H E F O N A M O N A N H W H T H E P O T I N C L T O C O S D A M I A G E S I R I S C S O S I O L D I S T R U P T I O N O R L O S O F H M E N L I F E +fleurs_eng_000474 F O R E S A M B L T H E M O S T C O M E N S T H L I M I N G E F H O T O C K R F E Y F O R M U T I T H E H O R A L D I S T H R Y F I R E I L N M E A E R W H C H W A S T H E D O M I N E N T F I E L M E M S I G E S A T H E C L O E S O F T H E A N I L O G F I L E A R A +fleurs_eng_000475 I T I S R E L A T E D T O B U T Y U S E L Y N O T I B L T I N G O L P I N G T I L E S K E T O R I N G A R M O U T E N E R I N G T H E L A T E R W O E S D O U N E I N S D E C T E R I N G A N D R E C R I R I N G M U S H T H I F R S K E E S A N D B O T S +fleurs_eng_000476 I A R N I N G D A M K C L O A S E S C A N H E P T H E D R I Y I H M A N Y H O T E L S H V E N I A R N A N D I R N I N G B O R O V A L A B L E F R L O N E V E N I F O N H S N O T P R E S E N T I N T H E R O M +mls_eng_000283 E E V E D N Y U N C E R D H O R S L Y S H E E S U H E R C H A R E U N L E I T H E G L A O S E H E O T H F I R E R A N D S C R E D E H R H A N D S O U T O T H E B L A S E S T E R E W A S N O O T H E L I H T I N T E O N M M Y T H E T I M N T H E W I N W I D N O T O H O R D E D I S M R L Y S T I L +mls_eng_000284 E M Y D E A R M A R L D E E A E R W H I D O O U D N O T D E S I S T F O M T H E S I L Y P E R S S O U O T O F E A N A D N M A N D G I N A R Y T H E A D S E R W H A T I S T H E T H E O L Y O U O F M U N Y W E A R S P B A N U R D S N O T S H U R T S L E V E D M E R S S I N A R Y P P E G S O F A M A I Y C E N D S +mls_eng_000285 T E R I T C L T U B P T E I S T A T O T E S I N G L I A S E T H E R M E L I N W C H P E S E S E P O N T I E I N F L E A T I O N T H O A R S E N D T I N G E N T T H E R S T I A L E R E S U H E R S C A V L L I E A T H T W O C O A L D N E S O F T H I S P O N T B E N P L E T I O N +mls_eng_000286 M U C H L I K A N F O U N E U S N D O F O R M E I T Y A N T O H A T M O N S T E R H O M T H E T H E A B O N N I H T T H E F A R T H E R O F T H A T F A T L P R O D G I N Y M A D E D C I L H E R S E L F F O R V E R Y H A R T S T E S P I H T T H A T H E H A D R A H E R R I D L W H I C H N O W I H T C O U O T E V E R L O U S H A T S U F E R D D E D L Y D E +mls_eng_000287 H I M A S H E M A S E R W I H P R S I O N G R E S I E S M U N T I G T O H R E T H O U L S E N A N S T I E S A N D A L S O T H E R Y S M A L V L L I M S T H A D O C K I E P Y D E B T H E F L N T D M U E N D E C O E S I E R A T I O N T H I S L A S E M A S E N T W H C H N O U E S E I T A T E N U M E R S C O R A T I N D I S M O S T D E L I K E U P U T H E O P R A T I O N +mls_eng_000288 W H I S H U L I T H A E B E N D E M E D N E C R O M A N C S Y T O I N D E V E R O O N B I N G T H E S F A S T O I V F O L I L V E B Y G I A R F U L E L M I N A T I O N A N D C H A N G E T O T H E P E R V E C T F O D +mls_eng_000289 D N A Y T H O V E R A S E S B E Y M Y B E A D T Y E A T I A M R I G E C E L O V E S A I D B U T H O U T A R E G U D L I H V E T H R I C E F O M N D A R T H O U L E T O Y E L T H E S O V E R A N G I F T S O F A R T H T H E V I E T O R S O R O R D T H E L O R A L E T B R O L O R V I N T H I N G K S O F L I L E W E R R T +mls_eng_000290 B O C K E S T O H V E B E N A K E N O L A C T R O L T H O H A M P E D B Y I L E H E L T H F A D G R A T P I N T O N I S F A V E R I S H A T E S C R I G E D O N L Y T H U C E P L A N S H W I T H H A T D C O M U N D E R I S O N P E R S I N L O P S O V A T I O N +mls_eng_000291 H A D R A T H E R S R N G O P A N D H A D N O T C H A I N S E D I N T O N E I M S T H E I S H Y F E L D I N T H E S T A M S C O V E R I N T H E M U P A G A N A N D T H E Y A P E R E D A S P E R F E C T D I N S A C T S I N T H E M A Y O F T H E F O L N I N H E A R +mls_eng_000292 N O T H I N G S A Y W O O B D E C E A N D H O A T S O F B U T Y M O U D P R E S V E N D T H E M S A E S T O T H E U N D E R S T A N D I N G O F T H E F O R T I E L E T B O R S O N H O U E A R T O K O F I T T H E I S B E A T S W I C H O H A E B R U G T O M E T R A N S L A T D A R C O N S E R E W I T T H I S S O U P E S T I O N +mls_eng_000293 N O U S M E I N C U P T I T Y A N D H E N E R V E H I M S E L F A G A I N S I T H I S F A I S W A L S O R D O S O V E F L U S H D H E W A S T I M I D E V E I N T O R R O U D N E S +mls_eng_000294 T B E C O M E M O L L I U G H E L I K C K E A S T H E S H I E X S F L U S H T H E A S R E A R W O A I M G E F I N O E I N T E M O R D I N G T H E R D H A F T I S P R I N G S O L T I D I T L O N G A O U R S O F A L L R E A D I N G A N D P E R S T E D H A R T B Y N E V E R S E A S I N G R I M E M S E S T I C U N O N D E R S T A N D I T +mls_eng_000295 W O N F T H E H O W I N G R G D E R S A I D T H E A L L P E H E A O V O I S A P O S E N S H O L F I S H T H E S E R B I T R A N D D E D L Y A N D C O E B E Y O U S E I N P U I T I N G E N I N M Y S T O D E A +mls_eng_000296 T H E B E U T E A S R O U P E S O F H A V I O N A S L O N T O A D U R I H T E A R A N D C O L E T A R H E L O K E I N B O W N L I S M A G H S T Y R A B R O U R D T O U C H I N G T H G R E N L E V E S A L L A T R E M B L I T G O U G L L I H T +mls_eng_000297 I A D D U N O R M O R T H N T H A T I U N D T L T H I S N A T A R E H I S A P E L U D L Y S E T E D E T H E E W O R T H M T H E L I F I T E L F T O E M W H S T R B L L B U R S M E D A N O R Y I S T S A O R L Y H E P R T E S T E I T O U L N O T G O D A S T M E T O W A T T H R A M U N C E A N D T I L I A T A E X A M I N B O N O F T H E I S E S +mls_eng_000298 R O S C O N G R E S F O U N D A T I O N R E S I O N A N D T I T E T H A T O R G N I H E T H E S A N T P E T E R S B U R I G I N T E R N A S I O N L E E C E N O M I C F O R M R O U S N E F T R U S I O N D S T A D O N E D O I L E N A N E R G Y C O M B P A N Y W +mls_eng_000299 H O W G L A T E D I N S P A C A L T H E D E L I C E A T F R O S T W E K Y O U E A T R A C T D E D N O E D O U T A M A V E R D A T H E D I N Y T R A C S O M S B U T F E W U E O V A S H A E R E A L Y H A D A N O P R T N I T Y T O S T A N D Y T H E D E T E A L O T H I S F R U S T E S I N E S M Y N U T L Y O R V C O N S I D E D H A T T H E E R E O R H I N T H R E Y U R F O R D E S I N E A T M O S T +mls_eng_000300 T H E A H A N T H O F E S I N T R I N G T O I N F I K G I U O N T H E M E N K I L V H E O F E N D E R O R W E N H I M M O R V H A N T H E I N T E N D E N T O D W A N D T H I S B E C O M E A C C U S F U L E A N U H E R D S O H A T H E P R I M I T I V E L I G E S L A T E R S W H E R E C E F U L I N O R E C Q U I U R I N G T H E R I E T A L I T I O N T O B E D L M I T E D T O A N I Y F O R A N O I +mls_eng_000301 A T S I R E S S W O R D T H E J U O S E R E T E R E N T H E C O M P O N Y T H A T G O E B G O S H I C E S B E G O N W I T M R T A M O M E I S H E N D E R E D B Y T H E F O B U T W H N C E A G A I N T H E W O R K G O S O N B Y L I E N S F R O M D R I A S A S R E I S S E N T W I T H R O I L E D G R A N T A N D G I F T S F R Y O S E S P I S S +mls_eng_000302 A N T P R O D K E Y H A I E I N A N D Y E A R O U T E S V I N H U D R T F R O N C E S H O E L V E D N I T H O V E N O S O B A D L Y B R E U L A C S P M L A I N M U R T Y I S O C K U P Y H T E O R E B O H O U E S +mls_eng_000303 T E D T H I S E I S A L L Y O U R A N C T S E R T I S T W O F E A I R E F O R O N E O F H I S O L I N T C S A N D Y W O R E Y O U O W E T H A T T H I S P L A C E N O M O R S E Y O U A N G S I T A N T E R D E F L E R A N S T H E E S T I S E S T H E I S M O R E C R O U N D T O M E D A M A N D R A V E N D G O N O N I S E F A R A C S T H E A T S M Y A M E I N D E D +mls_eng_000304 W H E N I R E T U R N E D A T O T H E H O U S E S W H R E H A D B E E N H A P Y C H I L E D O N D L Y A E P I L O F A S H I E S W R A Y T H A D S T O D A I W E P T L O N G K A N D T O F O R O G E T M Y W E P I N G I S A I D O U T U N D E V E A S C O M S E O N D T H E S W O R T E R S I N A S T H R S I U G F Y A E R N I G T I D P L A Y E M Y F L T T O T H E S M E M O N +mls_eng_000305 T E D O O U N O T S E E W H A U T L E S E R I T D G I V E S M E W E H A V E G R O N O U P O G E T E R I N T H I S H O U S E S I N H E W O R S A B O Y H I S S E I M P L Y A N O R B E A R E A S O U C A N D T H E I H T O F T H S M Y G L D L E V I N H I S F H A C E B O R D E A R H E H A S N O A M U S M E N E C E P T H I C B L A N G A T D T H S H O P S K S C P I N G +mls_eng_000306 I T I S D E V I E S S B O Y R E V E A L D I N G N Y E L P I C A L O R G R E Y U W O N G A T I O N I L O V E L O V E D L O V E I L N O T B E T H E W O N D O F C U P I T B U T H E A D I F T H S T A T I O N O F E G E V E R S L E R D U C T O E I S T I N C E S +mls_eng_000307 S H O R P L Y A S H E S H O K H A N S I T H E R O G O D B E S Y U M A Y D Y A T C H A I T H E B I S H O P S A I D W E N S H E C E S E H I M A N D H I S L I P S M O R D O F T E R W O R D F O R E S O M E S I C K E N T S A S I F H E W E R I N P R E A R E I U D M U T H E O F O L L O R E H E R E O U L O F T H O M A N D T I E N S I L A N S E T E L +mls_eng_000308 F O L L A E D H I M S T E A E L T H E L Y H E A N D H E W W A S A N A S T P I N G P O S R E R F I L I N G H I S B O U C K E A T C A M E U P T B E H E I E D H I M A N D P L U N C H E D A N D L O N G N I F N T O O I S N A C K +mls_eng_000309 T S A I S T H C K E R S I A S D O U S T N O T J U P E T E R D I S T R I B U E T T O H E G O G D T H E P R E P O R S T I O N A N D D I V I D E N T S P A R I N G L Y A N D S E V E R A L L Y A S A G M E N D I T O H I S C O M A N D R S W H E N H I S G E A S T S T R A N G T O O N E A N O T H E R I V F O C K O U R S I U S Q U L S K L E D E M I U S A S Y O U N E R R A T +mls_eng_000310 E A N W H R N O N H U L E R R S T R A N A T O C A M E T A G A I N I N F B H O H T S O T H I S N O O U S E N W E P I N G B E R C H E R U L S P I R T S T I L E N E V E R D O U T H E F A T I S C E P I N G P U C T E R G O O D F O R P R E S E N I L +mls_eng_000311 A N D O B E C O M E T H E R E C K E R D O F W H A T P E P L A E D O N I T H E R M O R A M I U B L E M O E N T S T H E R E C K E R D O F T H C O N C Q U E S T S A T P E S E H O W M E N D H A V E L I V E D A N D L E A V E R D D O U G A T B I L T U N A N L I E R E D G A R D I E D A T R E A F O R E R S T +mls_eng_000312 T H E O F L I N G O T E S O L A S P E T O C K I N S R A I N A S W I L A T N Y N S E S I N A B L E D A N S I N G O F M I G I S I N T H E E V E I N I N G S O C O N S O N D E F E T A N D I N G O R T I S I N G T H E O I N S A D D I O F U L R E C O I R S S I S S T H E L E S T A T A L L A T R M B L E B O E F O R T H A T P R O W C E T U N D E R +mls_eng_000313 W A S S T O R E M N G E J E N R L E T A M P E A R E W A S C I L L G E N E R L C O S T I E N G W A S B L A M E D A N I N D E D E S N O B C O M T O P A R I S T H D E V I C X S E L N A T I O N S A G I N S E A L L W H I H T H E M O U N T O N A N H E T R O T I O U S M O E A R M U S T D E V O N M A K E H A L A S H E C A N +mls_eng_000314 T H E M O M E N W A S F E A V F U L A M I T Y O F O H A D N E V E R S W H U N G T H E B U T L L A C K E O V E R H I M E N B U T T H E H O B E N E R V E D H I S A N E F O R A D E S P R E T B L O A N D T E C U O M S E R U L E P R O S T R A I T B E F O R H I M +mls_eng_000315 T H I N T H E W I N S T O U T T H E G L E R S T A N D D R K H N D N I G C A M E O N L A E I N G K M Y O L D C O T I O N D C O U I L T W A S C O L L D A S I N M Y S W E T S U N T O U S T I N H I S S C E E +mls_eng_000316 Y O U M A Y D A S O U P L E T O W E R E O F O U R I R T A T I O N T O C E P P Y O U R F E N A T T I I S M Y O U H E W E L A F Y O U N E D N O T M I D T H E C O U S T T H E P A R E D U N O T W O N T E S T A N D I N Y O U R W A Y B U T Y O I N S I S T D O N T H E S O I M I T I N G O R C O M P A L T I O N +mls_eng_000317 W E W A S B R E D B Y A E R E V E R N T E R Y A C S N I H T E T B E I N G B Y O T H E M E N E S C X S F O R T O T O U H I D I C K L Y W A S B O N E A N M A C H A T I N S E V E N T Y N I N A N D H W A S T H E O N L Y S O F L I V E R O F E L E T E R O F F I F T E N I T W A S N T H I C O U N D T T H A T H E A S O U R D S A I F A N D C O L R A N D M A R C K I N G S +mls_eng_000318 E A N D W H A T H A S T E I T M A K S O F O L I N T O T E S E C I O N T T H E R B Y T H I H T I M E D I A F H A N D E R S N E S E R S E S G I U M O S T A D M R A B L S E A K C K R E T O N T H E C O N T R Y I T S T A R S M E N O T A W I T W I C H M O S T C O D S O R E S I T +mls_eng_000319 T H E R D L Y T H A L S A I D W H E R T H E I T I S I N C E A R E N E T H E R E T O R E A C E H N O R T O P O R E F O R T H I L Y A N A C O S I S S A I D W H E R E T H O I N A L L O T H E R E S P E C T E T H E Y O A R R C O L I H A T V E R E T O H S M I N A R A D V A N C E D A N D V E S I O U S P E R S O E N T D E G R A D E D +mls_eng_000320 T H E C I N D L Y F R A N G I S S I M P T H E T I N K E V R Y D A Y H E P A S N O T A S B E T W E N U S A N D T R I Y T O N C K E R I R G S R U S T L H E W L I M P R O V E I A S U O R H I M H I S T I M E I S S O U O U R T A N D F R E A C H A I R A N L I B U R T Y W L L S O O N R E S T A O R H I M +mls_eng_000321 T H I S C R E S T I O N S I T I S N O L E V I D E N T M A Y F R E K C E N T L Y B E U N C T E R E D W E S E Q U L L P R O P R I T Y I N O P S I T W A S E S A N D I F T H E R B E A N Y A C A I N S U N G W H I C H T H E Y C A N D B E U N C T E R E D O N L Y I N O N E N W A Y T H E U N C S E R W I L D E P E N D A P O N T H E N A T E R O F T H E O E C A T I O N +mls_eng_000322 I N H I S N O H T B O R T H E I N S T R L S Y S E C K N E D I O N A T Y O N O W A T E S C O U T S E S T H B A L E D W A S T C E K I N D D O W N R M A N O L D O M E N S F R C I T A T I O N A T T H L S O N M O R L E A D M I N S B Y T H E A G E N D T H E R A N D C I E N T B Y H M T O S E R T E A S +nchlt_eng_001588 C R E S T I O N T T H E O L I G E O N S H +nchlt_eng_001589 O P T A D E E A G O F I T H E S +nchlt_eng_001590 A L A M E N T O S P E S I A L F O N T I O N S +nchlt_eng_001591 T O R D E W A S I N A N N U N E V O R E I T Y +nchlt_eng_001592 S I E S F I C T I O N N O V L E S P R V H A N +nchlt_eng_001593 C O S T D H I B P O P +nchlt_eng_001594 I N D V E R S L E A T B L A Y E T R O N S F O R M E +nchlt_eng_001595 F R I N G H P R O T I S T A N C E S +nchlt_eng_001596 O F E G O U N A Y F O R S S H H D K E +nchlt_eng_001597 H E A R O S I N M O S O L I G Y A N D L E A G E N D +nchlt_eng_001598 B U I S N S C L A S S E T N D N E +nchlt_eng_001599 C L A I D P L A Y C H O R T E E +nchlt_eng_001600 P O S I Y T R I N S W E R E R O P O R T E D +nchlt_eng_001601 A L D V I C K T H E A T E R +nchlt_eng_001602 O R T H E D O C K S M O N O C K S E +nchlt_eng_001603 N A T I O N S W M E M B R S T A T E S +nchlt_eng_001604 F H E T H O W I L D C O P +nchlt_eng_001605 C R O S E R I C S K Y U E F E I T S +nchlt_eng_001606 A C T H O L F O L M E M A R C K E S C O P I T Y +nchlt_eng_001607 M O U O S I C L G R U P S R E A S T A B L A S H E D +nchlt_eng_001608 P R O M I S E N E R E P A C E S E +nchlt_eng_001609 F O L N D S I K N E K S +nchlt_eng_001610 T O E L A V I O N S E R Y S B A S T +nchlt_eng_001611 H N O E P O L I T I O C A O E P O R T Y H E E E +nchlt_eng_001612 A N C H O N T D E A G O P A C H E V E D +nchlt_eng_001613 F L A T M U S I G N T R O L +nchlt_eng_001614 A M R I C O N T I C N O L I D T O I N O L I D Y R A T E S +nchlt_eng_001615 D O A T E S O F V A R I N S +nchlt_eng_001616 P O P I L E I T W R E I S T A C T I O N S +nchlt_eng_001617 D U C H E W I S T I N D E A R +nchlt_eng_001618 G O L D M A T L R E S P I E N S E +nchlt_eng_001619 R E A S H I O N S O S I A L D E M O C R E T I C K E H +nchlt_eng_001620 A M I R I Y C O N F L M E P R O D U S E S +nchlt_eng_001621 F R E E S O F T E R Y F U N D A T I O N +nchlt_eng_001622 R I L E R M A T I O C T H E A T +nchlt_eng_001623 I T A B L E M O L O S K S H +nchlt_eng_001624 F E A T C H E S I N T L W D B E A C H E R S +nchlt_eng_001625 O C F O R D I C T I O N R Y C H A N G E T +nchlt_eng_001626 S A L C O W P I R I S I O N D R Y H U N D +nchlt_eng_001627 P R O W N M O N I S T E R C I V E N +nchlt_eng_001628 L A N G E S O F Y U R O C K E +nchlt_eng_001629 S O T H E A S T I N G L O N D T +nchlt_eng_001630 N O U R L I N E D S E N O M A R T H +nchlt_eng_001631 E C O U L K R E D I T O P O T O N N T S Y +nchlt_eng_001632 S O U T H E A S T I N G L A N D +nchlt_eng_001633 M A Y W E H T E +nchlt_eng_001634 R E C O L R D H A T A T E O M E E S C R I P E S +nchlt_eng_001635 M U S I C A L G R E P E S F O M C A L O F O R N I E A H +nchlt_eng_001636 M A I N B E T L E T I N C S +nchlt_eng_001637 P O R D L I S E H M U S I O C T A L E I N S T R M E N T S +nchlt_eng_001638 L A N W I G E S O F S A D Y E A R R O A V I A R E +nchlt_eng_001639 C O L D O R T I N T I O N S E H +nchlt_eng_001640 D O B E H I M H S +nchlt_eng_001641 A N D Y P O P K L I M I N T H +nchlt_eng_001642 G I T H E Y C O N P O I V E I T +nchlt_eng_001643 C I N F E I A N A N D +nchlt_eng_001644 I L E C T O N I C K M E U S O C K L I N S T R O N C S +nchlt_eng_001645 A G E M O L D T O O R T E R N +nchlt_eng_001646 L O R A N C T L V E M O R N A S T I O N L E +nchlt_eng_001647 L E G B A C S P L P L A Y A R S +nchlt_eng_001648 B O D I S O M E A N T H E A N C I O N T M E D E T R A N I O N +nchlt_eng_001649 O U N I G T I D S T A T S R E K O C O N S E D +nchlt_eng_001650 P R O P O S I O N L F E L A T Y E S E +nchlt_eng_001651 S P E T I A L A C O N O M N G E S O R O N D S +nchlt_eng_001652 M A N S T R M W I S T +nchlt_eng_001653 E V E N G R U C H H L S +nchlt_eng_001654 B Y T H E D I O N S T O K K +nchlt_eng_001655 N D A R T I O C K E A H A S N O +nchlt_eng_001656 W A S T I N M U S I C L E S +nchlt_eng_001657 C O N E V I T O F D U D A Y S A M R E G O R T +nchlt_eng_001658 O P P I C K M E M B R S T A T D S +nchlt_eng_001659 P R I M I N I S S A I D J O N +nchlt_eng_001660 R O A C K S F O R M I N G M O U N T +nchlt_eng_001661 M A D G E R L E A K T L M N S +nchlt_eng_001662 P O L A N A T I O N M A N A G H E N T T +nchlt_eng_001663 F R A N C H E F I S I S S T +nchlt_eng_001664 H I A R E C O M P R E I T I O N D R A T I O +nchlt_eng_001665 R E C O A R D N G I N D E S T Y S O U C H A T I O N +nchlt_eng_001666 T E A P A D E O U N L I N M A G O S I A N +nchlt_eng_001667 H I P O P E R E C U L D P O R O T O U C S E N S S +nchlt_eng_001668 F I N I G T S T A T M U S H E N E N S +nchlt_eng_001669 W H I D L Y S U S E D L O C K A L D +nchlt_eng_001670 N O R H E M A Y C O D C O N T I N E N T +nchlt_eng_001671 A F R C O N M E R I C O N R E P E S +nchlt_eng_001672 T H R E T E N D M E L I G T R A C T I O N S +nchlt_eng_001673 U H T T H E W O R D F I N I N T N E E E E E E +nchlt_eng_001674 T H E T O M I K M L E I L E N O P T O C A L F I S I C E +nchlt_eng_001675 E T O N E +nchlt_eng_001676 M Y R S O L +nchlt_eng_001677 C O N S T R C T N O U O H R A L G A G E +nchlt_eng_001678 P O R L Y E C L W I O N R I N C S A B L E +nchlt_eng_001679 H C L O W P O R T R A Y D E F E R E N T +nchlt_eng_001680 S O V E A T D E S I D E N C E S +nchlt_eng_001681 S I G N E L E T R O N C E T D U C T I O N D P O L T W A Y E S +nchlt_eng_001682 N O U B O R N M S S I +nchlt_eng_001683 J G E N R L Y A C E P T E D R A N E R S +nchlt_eng_001684 G I L E D A W R D W E N H I S +nchlt_eng_001685 S O W E D I S H E M A U S I C L G R P S +nchlt_eng_001686 C H A L D E R E D O R T I S I M R A T I N G +nchlt_eng_001687 D O S I G H F O R M E M S +nchlt_eng_001688 O H I I O U S T A T I O N O V O R S T I T Y E +nchlt_eng_001689 F O R M O S S A T H E N C E I N T E R K E +nchlt_eng_001690 E E E A N R O C O N I N W V H N T I O N S H +nchlt_eng_001691 E A R T E S E H +nchlt_eng_001692 M D E N Y O U R O P E A N R A S H A H +nchlt_eng_001693 N S N O D L E G P I L A N T H +nchlt_eng_001694 B I K F I N I S H P R D U C T I O N S +nchlt_eng_001695 N A S T I O N L E H +nchlt_eng_001696 T R A D G I K P O I T E S E S +nchlt_eng_001697 T I T I L G R I C E S T A T E +nchlt_eng_001698 A S T H E N A H A D A E N E +nchlt_eng_001699 E A S T E O N Y U R P E A N C O U N T R Y E S +nchlt_eng_001700 C O N D E D A N O R T H R I V S E T R O N S L A T I O N S +nchlt_eng_001701 O A L W O R D E T E S +nchlt_eng_001702 C I N A S S O R M O W N A D L E N D E S +nchlt_eng_001703 N O B L E S A M I T Y E +nchlt_eng_001704 I T W O S A E F O L S +nchlt_eng_001705 M O U N T S A I N T O V I N S E N T +nchlt_eng_001706 S I T Y M R C R P O L I T O N E A I R A R +nchlt_eng_001707 R O O N L E R S H O D A I D A S C H L D R A N +nchlt_eng_001708 C H O N C E S L E S V O L E L +nchlt_eng_001709 I P E P E C K A T C I N T I R L Y +nchlt_eng_001710 C I N G A D W A R D S D A T H +nchlt_eng_001711 A M E R I C E A R A M E R I C E +nchlt_eng_001712 C O M R T I L S H I P S A L D +nchlt_eng_001713 P E P L F O M M A E N H A M E M +nchlt_eng_001714 R A I L R A S H C I L D +nchlt_eng_001715 M U T H A L D E F E N S T O A D Y +nchlt_eng_001716 M O U D T E N C H E L D R U O L I S +nchlt_eng_001717 M O T E S E R R I H F H A L D E V I O N +nchlt_eng_001718 O U S T R A L I O N I E F O U R S E +nchlt_eng_001719 A M E R Y C E N D M O S T R Y R I T E S +nchlt_eng_001720 F I N L Y G R O W N D G R E F I T E +nchlt_eng_001721 W O L T A M P I N E S O P M A T S +nchlt_eng_001722 C E R I O L I N A +nchlt_eng_001723 M Y B O A T H N O P E R A T E S +nchlt_eng_001724 C O R T E F O R I T Y E S +nchlt_eng_001725 M I O R O A W N D H +nchlt_eng_001726 C O S E L E T H A L R E A C T I O N S diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.2/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.2/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..3c8aa6a7cdea461a69e6612fcb597d9b3ee6ad74 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.2/1best_recog/token_int @@ -0,0 +1,273 @@ +cv_eng_000780 2 17 10 3 5 11 2 5 2 5 15 3 2 22 3 16 17 3 7 2 15 19 2 18 13 14 16 24 2 5 7 12 2 15 4 10 2 22 14 4 3 11 9 8 14 11 3 2 4 10 3 2 22 6 13 19 2 4 6 9 +cv_eng_000781 2 4 10 8 2 18 5 13 8 19 3 11 2 10 5 9 4 2 13 3 4 2 4 6 2 8 16 25 9 4 12 5 3 7 2 6 14 13 22 13 3 7 16 12 2 10 5 12 10 2 8 7 9 3 11 12 5 19 9 3 2 6 18 2 16 6 13 13 9 4 6 +cv_eng_000782 5 12 12 14 4 2 19 2 5 9 3 9 10 2 10 2 10 2 10 2 12 2 3 7 +cv_eng_000783 2 17 10 8 19 2 6 8 2 4 10 5 4 2 21 13 5 8 7 12 2 16 5 3 21 3 2 20 6 8 7 2 6 23 3 11 +cv_eng_000784 3 3 3 2 10 5 19 8 4 2 5 6 12 2 12 6 7 12 8 9 4 3 3 2 4 10 3 2 18 6 11 2 17 5 6 2 18 6 11 9 8 6 13 2 22 6 9 3 2 17 8 6 18 2 6 2 11 9 6 13 4 3 9 3 +cv_eng_000785 2 4 3 21 13 6 16 5 4 8 6 7 2 17 5 9 2 21 14 4 2 5 2 21 11 6 23 3 2 8 4 2 8 7 2 18 5 11 8 22 11 5 3 13 19 +cv_eng_000786 2 10 3 7 11 19 2 4 6 11 13 12 4 6 7 2 9 4 8 13 3 9 3 2 17 10 3 11 2 10 3 2 10 5 12 2 4 2 3 2 9 6 14 7 12 3 12 2 11 6 7 8 7 20 2 8 7 2 13 4 8 7 +cv_eng_000787 2 8 4 2 17 5 9 2 4 10 8 9 2 16 6 7 4 8 7 14 3 12 2 12 2 4 6 2 9 16 3 4 10 11 13 8 7 20 2 16 6 7 18 13 8 16 2 5 7 12 18 23 13 23 3 12 2 5 7 12 2 13 6 9 3 2 9 8 9 2 11 3 4 8 11 7 2 4 6 11 2 3 2 4 6 2 3 11 3 9 4 11 8 14 13 3 2 11 3 22 11 5 12 8 14 6 +cv_eng_000788 12 3 12 10 2 10 3 11 2 18 5 7 15 3 13 19 2 10 2 17 6 5 9 2 18 6 15 3 2 21 11 3 6 10 6 7 9 5 3 2 3 12 12 14 12 2 19 +cv_eng_000789 3 2 17 6 17 7 4 2 8 12 8 2 3 5 24 3 2 18 6 11 12 8 7 15 7 2 22 10 2 21 4 +cv_eng_000790 2 4 10 5 4 2 17 5 9 2 15 19 2 12 11 3 2 4 6 2 9 8 7 9 3 +cv_eng_000791 2 10 3 2 9 2 20 6 9 13 5 8 11 3 7 4 2 5 2 15 14 9 4 3 11 3 2 6 18 12 2 9 10 3 5 11 6 9 4 2 16 6 13 6 2 +cv_eng_000792 2 4 10 3 7 2 13 8 7 2 4 6 11 7 9 2 4 6 2 4 10 3 2 16 10 6 11 8 9 10 3 2 6 18 2 9 10 8 7 20 9 2 10 5 4 2 4 2 22 13 4 3 11 2 5 7 12 3 9 21 3 14 3 2 2 8 4 3 +cv_eng_000793 2 17 14 12 2 7 6 4 2 4 10 6 9 3 11 2 8 7 2 10 3 11 3 5 16 2 5 12 11 4 +cv_eng_000794 2 4 6 3 13 16 8 6 13 9 3 2 8 7 16 4 3 7 12 18 3 9 4 2 4 10 3 2 5 8 19 10 +cv_eng_000795 2 15 19 2 7 3 9 4 2 16 6 7 2 10 3 13 21 3 2 6 17 2 8 4 2 4 10 5 8 4 9 +cv_eng_000796 2 4 10 5 4 9 2 5 2 16 6 14 12 10 8 9 4 2 6 4 2 13 19 2 6 7 +cv_eng_000797 10 6 3 2 18 6 11 2 4 10 3 2 22 3 9 4 2 5 7 12 2 21 6 2 22 3 5 11 3 18 6 11 2 4 10 3 2 15 13 9 4 +cv_eng_000798 16 2 8 7 8 9 10 3 13 19 2 4 10 3 2 17 10 21 12 19 6 9 6 9 2 10 4 2 8 7 9 4 11 6 16 24 19 6 2 22 6 2 12 8 16 4 +cv_eng_000799 2 5 13 13 2 17 3 2 6 7 3 12 2 22 19 2 4 10 3 2 3 23 3 11 8 4 2 15 6 11 2 9 6 7 2 8 24 16 3 4 10 +cv_eng_000800 5 5 4 10 3 4 10 3 6 3 7 2 20 2 4 10 3 2 17 8 13 5 9 3 11 8 7 2 4 6 2 15 6 11 6 7 15 7 3 2 8 7 13 7 12 2 3 3 4 10 3 11 +cv_eng_000801 2 3 2 12 6 2 22 21 22 8 9 4 12 2 8 11 8 16 15 2 15 8 7 12 13 8 22 21 22 21 4 10 +cv_eng_000802 5 4 2 6 2 9 3 8 6 13 3 2 21 5 4 11 19 2 10 3 2 4 6 2 10 3 11 2 21 13 5 3 2 5 9 2 5 16 4 2 8 7 20 2 6 11 3 16 4 3 11 10 3 10 12 +cv_eng_000803 2 4 3 2 22 3 23 3 11 13 19 2 17 13 3 22 3 18 13 19 2 5 7 4 3 9 2 4 10 3 2 8 3 9 2 9 3 7 4 3 11 2 21 14 2 6 21 2 4 2 4 6 7 2 9 10 8 15 +cv_eng_000804 2 4 10 3 2 4 11 5 16 3 2 11 3 11 23 8 9 4 8 7 20 2 17 5 9 2 5 13 9 6 2 16 6 15 21 3 4 3 12 +cv_eng_000805 2 10 5 4 12 2 15 5 11 9 10 2 17 5 9 2 5 2 17 10 5 11 2 6 18 2 4 10 3 2 8 15 21 11 4 7 9 2 6 18 2 8 13 16 2 4 11 15 11 19 2 16 6 9 24 6 15 21 19 3 2 8 7 2 22 19 2 13 6 14 20 8 16 24 13 2 11 3 9 11 16 10 3 +cv_eng_000806 2 9 8 7 2 10 3 2 17 5 9 2 22 6 11 7 19 2 14 12 2 4 10 3 2 10 6 22 6 14 3 +cv_eng_000807 2 4 10 8 9 2 17 3 8 3 7 16 3 10 2 3 5 9 3 2 5 7 2 6 18 8 4 8 13 19 2 4 10 3 11 2 10 3 6 4 6 2 5 9 3 2 15 5 16 2 11 3 4 6 5 12 2 17 8 7 4 10 2 15 19 2 16 6 13 8 9 4 8 6 7 2 18 6 5 9 3 9 2 6 21 3 11 5 4 12 8 7 2 15 8 13 +cv_eng_000808 2 8 4 2 8 9 2 11 3 9 21 6 7 3 5 14 13 3 2 18 6 11 2 17 5 4 3 11 2 9 6 2 21 13 8 2 5 7 12 2 15 5 7 3 15 3 7 4 2 6 18 2 17 6 4 3 11 2 11 3 9 6 14 11 9 3 9 2 5 7 12 2 15 6 2 10 6 14 9 4 11 5 +cv_eng_000809 2 12 8 9 3 9 2 4 10 6 2 18 3 6 11 3 9 2 18 5 19 3 2 6 18 2 4 10 3 2 20 10 6 11 23 3 2 10 3 2 9 5 12 3 12 +fleurs_eng_000413 2 4 10 3 2 20 8 9 8 5 21 2 21 13 5 4 6 10 2 6 11 2 20 8 9 9 5 2 2 7 16 11 5 6 13 2 21 6 13 8 9 2 8 2 4 10 3 7 20 8 6 7 2 23 5 6 13 19 2 6 18 2 4 10 3 2 12 3 5 12 2 16 6 7 4 2 4 5 7 20 2 9 3 23 11 13 2 21 3 11 15 8 7 12 9 2 6 18 2 17 10 8 16 10 2 4 10 3 20 11 5 4 2 21 3 11 15 3 7 4 2 8 9 2 4 10 3 2 13 5 11 4 3 8 9 2 9 3 9 3 23 3 11 13 3 2 9 15 13 2 4 6 7 9 2 9 6 23 11 13 3 2 4 3 15 21 13 3 9 2 5 7 12 2 4 10 3 2 20 11 5 4 2 9 21 5 7 24 9 +fleurs_eng_000414 2 4 17 6 11 3 2 10 3 2 8 7 12 2 6 18 2 4 3 2 15 8 13 3 2 5 20 3 9 2 17 3 9 4 6 11 7 2 19 14 11 6 2 22 3 20 5 7 4 6 2 12 3 13 4 2 4 3 11 2 6 7 2 9 4 8 13 2 6 7 3 2 6 18 2 4 10 3 2 22 8 20 8 9 4 2 6 3 13 8 7 9 2 6 18 2 4 10 3 2 4 8 15 3 2 5 9 2 11 3 9 8 14 13 4 2 6 18 2 4 10 3 2 11 3 14 16 9 5 8 9 2 21 3 21 22 13 2 22 3 20 5 7 2 4 6 2 6 14 9 3 9 2 22 14 4 3 7 9 2 4 6 2 18 5 9 4 6 7 2 11 13 23 12 8 7 20 2 8 8 2 8 2 5 +fleurs_eng_000415 2 8 18 9 2 19 6 14 2 6 7 13 19 2 20 6 13 2 9 2 10 6 11 3 2 6 14 9 8 7 20 2 9 10 8 21 2 6 11 2 16 2 9 16 24 12 11 8 6 7 12 9 2 19 6 14 13 2 7 6 4 2 3 2 5 2 9 3 21 11 4 2 23 3 9 5 2 5 9 2 5 2 4 17 6 2 18 10 6 14 9 2 8 7 2 7 6 8 20 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+swc_eng_001887 AN LE THE TO GLID BETWEAN TRS +swc_eng_001888 IF THES PROBOES WR FICINLY SOLVHABL +swc_eng_001889 ALOGHCAL TIN +swc_eng_001890 ROK WHEN FLUSHED +swc_eng_001891 INCLTDING JHRMNL TOCETC NALDE +swc_eng_001892 PERNC OF LE TH SHOUGES OR BOUT +swc_eng_001893 ND ATTIN SCTDY THRE +swc_eng_001894 ANOFECTERS SHU CAE PRODACE OLSOI SOL +swc_eng_001895 O THE FIRST NOAN SOLRET CHALNGERSTEN +swc_eng_001896 PONENT HAS ONLY THE CING AND +swc_eng_001897 MAYN ARTICLE +swc_eng_001898 OWND SERTN LATHS OUSFUL FOR FITIN +swc_eng_001899 TAPE IN THE SAME FORE FACTDERS THE COMPACT ODIO +swc_eng_001900 SENTHER WAS LATE ECSPUNGED FRO +swc_eng_001901 R DESECTO ACQUALITY +swc_eng_001902 IS FOR THASEN SACHUNTRED BY SCTY FET +swc_eng_001903 NITIN SOVENDY THRE +swc_eng_001904 ROLL A PLAIR MAL SO LOUE BY RUNIN +swc_eng_001905 BLOK HLH POFESSER GRAGRY STOAC POIN +swc_eng_001906 BROWN AS LECTE T TH HOUSE O REPERSENTIVS FOR THEREY NON CEN SECKITIF TERMS +swc_eng_001907 OR EGSIST HAPLY W +swc_eng_001908 A GRUP O NEMLS THAT RAC +swc_eng_001909 ND TH HLDS LAGEST +swc_eng_001910 BREADING TAK S PLACE BETWEN APROL AND DUN +swc_eng_001911 STRALOR IS ATHE SOTHEN EIND +swc_eng_001912 TEC NHLOUGHCL SINGEILAIRITY IS POSEABL +swc_eng_001913 NLDING TH SPLPY COD +swc_eng_001914 ESETY FORE HAD A HIGEREGOCATION OLFECATION COMPEDET +swc_eng_001915 IACPERS WHE THEBPONIS CANG SAN +swc_eng_001916 CONCEVATION NOSTRELYAR +swc_eng_001917 IS THE SELAMANDOF FEICU +swc_eng_001918 FIRST SELF DECRIGVE TRAN HUMONST HAT FORMILY INTHE AL diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.3/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.3/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..5e89b0db56831719d8ae64d7d80b9f402e2f7e3b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.3/1best_recog/token @@ -0,0 +1,273 @@ +nchlt_eng_001727 I N G L O I S H P E C E O F O U S T S +nchlt_eng_001728 Y O N I G T E D S T A T E F E D I R A L +nchlt_eng_001729 F A D R L E R E S E I E O V E A C T +nchlt_eng_001730 W O L I M H E N D R Y H E R S O N +nchlt_eng_001731 G L A P P L A Y C H O T +nchlt_eng_001732 P A S T O N G E R R H A L D S O V O C E S H +nchlt_eng_001733 A N C H O N M E S E D R N T H I O N D J D I N R L S +nchlt_eng_001734 C R O N G A C T I O N S E A M A R E +nchlt_eng_001735 G O N P O U T E P R O P I L E N T Y O S E D T +nchlt_eng_001736 L O W I S T D I N A G Y S T D A G H T H +nchlt_eng_001737 C A L N D E Y O U R O S +nchlt_eng_001738 M A E G E R I N T E N O A S I O N A L E A E P O R T E +nchlt_eng_001739 T O T L F O R S E A C T I O M +nchlt_eng_001740 L O S T I L E S D A T O M P E I T I O N +nchlt_eng_001741 E G R E A K E H D H E R +nchlt_eng_001742 I N D V O I R E M E N T L P O T I C T I O N A E N S Y H +nchlt_eng_001743 M A N Y T O E B I S C K L S Q R E S T I O N +nchlt_eng_001744 A N C T O N S I T Y P O T H U N D E R E +nchlt_eng_001745 S M L E A O T H E D O C S I N G O G +nchlt_eng_001746 N O N D G E S M I T H O U P I L I A N A I R I R S +nchlt_eng_001747 T I T O L E R E L I G E H A R R E M O N O N +nchlt_eng_001748 E G S A N M P L E S A N T L U D E D H A F H M O N +nchlt_eng_001749 Y U N O T T E S T A T E M I N T E A I N +nchlt_eng_001750 B O L E D R E P R E S E N C E M E C X C I M O R +nchlt_eng_001751 S I N E F I C T I O N O R T H I S +nchlt_eng_001752 O D E N A R E D E F R E N S H O L A E C Q W A T I O N S +nchlt_eng_001753 D P L M A T O F T H E H R D E S E W +nchlt_eng_001754 S I L E C O L O M M I S T R Y +nchlt_eng_001755 U R L M L I T R Y C O L +nchlt_eng_001756 S T L O N L Y L E D S C I O L I S U M +nchlt_eng_001757 P R I N T E I S S +nchlt_eng_001758 N O U T A S T H E A N D P E P L +nchlt_eng_001759 S M O G T C O R D B A C S E D A L C T T R O N I C K P E R S +nchlt_eng_001760 S T A T E N M Y S O L D G R S +nchlt_eng_001761 L O R D E A S S C R I S T +nchlt_eng_001762 L A D N B L I N G P +nchlt_eng_001763 H T E L I A N N E S I N L T E M +nchlt_eng_001764 A N D T E A G R R E C R A T I O N G R O U N D T H E M +nchlt_eng_001765 G R O C S E S S T A T P R O D A C T E +nchlt_eng_001766 C I N C O N D V E R S E +nchlt_eng_001767 B I E L V I L +nchlt_eng_001768 F L E O L G O N G S A T I O N N T H E U N O D E D S T A T E +nchlt_eng_001769 I T R I L T H E F T A N C F O R S E S +nchlt_eng_001770 O R D D O M I T I C K S A N D R E S E V E +nchlt_eng_001771 B R E N S W I C K S T H E N R A L W O Y L H +nchlt_eng_001772 A C T E S A C A T D I M E A W O D +nchlt_eng_001773 P E P L E F O R O M E T O C K Y O A T E D +nchlt_eng_001774 F O R C H A L D S S I N G O E +nchlt_eng_001775 B E A R A B L V A L F T T A R M I N G +nchlt_eng_001776 S O U T H W A I L E S F E Y E S +nchlt_eng_001777 C A O F O R D I U R S T A T T U D O E V O R S I T Y +nchlt_eng_001778 E L D E R O D O +nchlt_eng_001779 O U T D O R E O A R I N T E D S I T Y +nchlt_eng_001780 C L A M E D P O R S I A L R S P O N C E A B I L I T Y +nchlt_eng_001781 C R I S H I O N T E R M E S +nchlt_eng_001782 E V E N T T O P L A C E +nchlt_eng_001783 C A N S A I D D A T H E S I N F R O N E +nchlt_eng_001784 H I S T R Y O F M I S H O G O N +nchlt_eng_001785 O R I G I N L Y T H E A M E M +nchlt_eng_001786 N A T I O N S F R A E W R E C O N V E A N T I O N +nchlt_eng_001787 E N O C K C O N E H +nchlt_eng_001788 O R S T R N S C O L E I C O N O M I S T E S +nchlt_eng_001789 M A I N G R U P C O M E P O U W N S E S +nchlt_eng_001790 H R O S I D C L I B L M T E R I A L S +nchlt_eng_001791 C O M E I N L O A R E S T O M +nchlt_eng_001792 B R O N K S H I Y S C O L E +nchlt_eng_001793 A M E R I C E N B E L I T I C G A L R I T E R S +nchlt_eng_001794 C A M O C A L I L I A E N T S S +nchlt_eng_001795 D L O B L E I N T O N N T C O M U N I T Y +nchlt_eng_001796 T Y O G R E F I C T M A A S I E E N M A R C H E H +nchlt_eng_001797 W I P S O T H I S P R O V I G D T D O S +nchlt_eng_001798 S I E F P C T I O N N O B L E S E +nchlt_eng_001799 S I N E S F I C T I O N F O L E M +nchlt_eng_001800 S S O B E S I T S O M E M P R O B L O M N +nchlt_eng_001801 A S T E O N N O R T H E M Y R I C A R E +nchlt_eng_001802 P E P E S W A T N E S T L O T I N G +nchlt_eng_001803 D E S T I N G N T I O V E F O C K L I N S T R M E N T D +nchlt_eng_001804 H T A F O I O C O N A M O R I C E N R A P T I S +nchlt_eng_001805 P O R T O G E S C H E N L S +nchlt_eng_001806 I N T E N A S I O N L E A P O R T I T Y A Y H +nchlt_eng_001807 M O U N T O N R A N G E S O F B E L I V I E A R +nchlt_eng_001808 F R I N C H A R E F O A R S +nchlt_eng_001809 S S O P R A B L A P E A R A N C S H +nchlt_eng_001810 L O N G T R V L I N G P A P E S S +nchlt_eng_001811 D E I S T R I K T C O R T O D G E +nchlt_eng_001812 D O O N Y A E N M P I E +nchlt_eng_001813 P R O T I S I N A S I O N A L I T Y A C T H 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M U R E N +swc_eng_001748 I H A T L E A S H T T R E O T S +swc_eng_001749 D F I H A N T E S Y E S I N +swc_eng_001750 W L H O E V T D E N C E O F H E M R Y G I N T +swc_eng_001751 F I D E N A N C S E R C R I C L Y +swc_eng_001752 N A B L E S D E V Y O C I V E A N D U N D D E M O C R A T C S O T I A L P L I S Y E +swc_eng_001753 M A E D R E S E N T H I H L E S A I L A B L O N D C O S A I +swc_eng_001754 D U S T R C T A N A T I N S I C S T Y S A I C +swc_eng_001755 L I T L A N T O F U C P E I R I D Y +swc_eng_001756 A Y I N T H E G R O I N A N D E D F A N C S E T H R +swc_eng_001757 E C N L A G Y E S A D I M P L E N T I G T R A N S H U M N I S C L E O F A N H A N C S P R F O R M E +swc_eng_001758 N D C O A T D I N G N A P S E +swc_eng_001759 B Y S P A N S H T U R C H M E N L L R E M E O R A S D E L O S A N A +swc_eng_001760 D V F I H T E D D E M O C R A T S +swc_eng_001761 H E W O R L T H A N P P E N S H I P T H A S E N C O T R O L E B Y E F F I D E E +swc_eng_001762 W H E H E T A R I N G P O S I T I O N I +swc_eng_001763 E E N C R A T E D I N E V R Y S T A T A N D T E R I T R Y T O P R T E C H T A N D R E S E R V T H E C O N T R Y S O U N A K Y C O S C I S T E M S +swc_eng_001764 E A C A T H O E N T O F T H E N O S I L E N D W A R E M O I L +swc_eng_001765 A L A M E F O M T H E R E L O U D C U P N Y E S F O R V E T I N +swc_eng_001766 T H E T O N I S S P L I T +swc_eng_001767 O S K E A T I D F I S H I S A P E T I C I L Y A E G R E C I O F S P A T H E S N +swc_eng_001768 A N D T H E N A S T I O N L W E S E D H E M I N S H I P T E S +swc_eng_001769 P R O B L O M E A S N O N T O R N I N P L Y N O M A L T I M E +swc_eng_001770 L A E J U O I R A N D P A R C E R W H A T I E N S H A R T N +swc_eng_001771 I N N D T I N S E V E T Y T H R +swc_eng_001772 D E L P I N G A N D Y O U S I N G S U C H T E N A L D S +swc_eng_001773 O R S O M E W P A S T I O N D +swc_eng_001774 L A M E O R O E T H A T E +swc_eng_001775 A B L A D A C A H T E R I S O U R L Y I N S E R T E D S O M O N S O F L O I E D B U M N S +swc_eng_001776 E R N O T I O N O F Y U J E N I K A N H A S P E N T T I C N A L A G E S M I G H U N I N T E N C H O N A L Y I N D C K R R A G E +swc_eng_001777 A T T H E T E N I O N O F R E S U R T H E R S C A B E F O K A U S D M P A R T I A L S O L U T I O N S O R S O L U T I O N S +swc_eng_001778 N O N E N O F F O R H N T R E D O F Y E A R S +swc_eng_001779 N L Y M U S U B I A L S H A E S O F I V E T T +swc_eng_001780 T O H C H A L T H E A D A B L E S P A C H E S O F C R U S T I S T H A N B E L O N G +swc_eng_001781 O L G E R T H E M R E S U R C H E +swc_eng_001782 N I N T A I N S I C X T D Y T W O F I L I P E S N V E N T D T H E C O M P A C T O D I O K E S E T M E D Y H A M F O R O D I O U S D A O R G E +swc_eng_001783 O S T R A C T I N F T H E L O W +swc_eng_001784 N D F H I B I E N D A D R E P +swc_eng_001785 W E M E N S W H R L D C E S T H A M I N C H O F +swc_eng_001786 C O N T A N E D E C R T I O N S A N D C O M E N T A R Y S O T H E T A T O F E N B E I Y S I N C E A N D E G N A L G Y A S M A E R C O N T R U D E R S T O T H E +swc_eng_001787 P E U R O E L H A N T I Y O R S O T I A L T R E N T +swc_eng_001788 M O S T C O M P A C C S A E T W E R S O L D B L A N K +swc_eng_001789 I O F H E R I S N O U L D G R T H E +swc_eng_001790 H E S O T H E N S T R A L I E N C O S T A N D I N S A B B A E N T I E C T I Y O S T R L I A N T E R A T R Y S +swc_eng_001791 D A T R A T S F T I P O C L Y F I V E H N D E R E D T T W +swc_eng_001792 D E R P R I V I N G T H E D U C +swc_eng_001793 N I E N P E R S E N T O F T H E T O L C A S T +swc_eng_001794 A N D T H E R I R S S O R B R A T R I Y A N D A N T H E I E C O M N C A T I N G A T Y +swc_eng_001795 E D N O T I M P A S H S H I N +swc_eng_001796 E N T H E R E D E M O C R A T I C P A R T Y +swc_eng_001797 N H E S O N T O P E O F T H E E S E T H A L I N D E C A T H +swc_eng_001798 L A E U I A T H S S E O +swc_eng_001799 I A N A N D T A N G E D M R A I N S P A C H Y E S T +swc_eng_001800 R O W N D E S I R 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PLACKE PITS +voxforge_eng_000885 ALTHE GOE OT AND EAPWI MY BOS +voxforge_eng_000886 HE OWEINED DON IN WEINS DREMN Y SARCTIONG THE SHAHDOS OF POL SORS +voxforge_eng_000887 I OST DOA PRESHAT TWIT OUT BE AE TO CPESE MY FELINGS +voxforge_eng_000888 SHE DOSANT NO WHAT HE AS TOKING ABOUWT +voxforge_eng_000889 YOR FARTHERS FIFT COMAND HENATED +voxforge_eng_000890 E DON OSEY I HAE YOE +voxforge_eng_000891 ALITLE WORME BUT NOT AL STONISHEDE EATING MELNS AND THOING THRIND ABOUT +voxforge_eng_000892 THISE IS A GRAT PORDYE +voxforge_eng_000893 THE BOY GRO AND PROSPERET TO +voxforge_eng_000894 AND LS SUCH LETERS BE PATENT THAT THEY AY B RED TO THEM AN WHITH LE STELE HOR TESTIFIGED +voxforge_eng_000895 HOW COLD A WOMEN DER TO VENTE WER SO ANY EXTPORARS +voxforge_eng_000896 HE READE HI FRAGINCE ALOAED +voxforge_eng_000897 BUT HOWE AR YOU GOING TO DE IT +voxforge_eng_000898 HOW DO YOU WON TO GET WAY WITHISE +voxforge_eng_000899 WIL WE AVER FOM GET IT +voxforge_eng_000900 FR MY ARLIS RECLECTION MY SLE WS PERGATD OF HERE +voxforge_eng_000901 MY O IS OSHER WHIY E DOLON YOU WLL SHAKD GANM +voxforge_eng_000902 IDEAVT THE NEADIST REOFUG HYEWII +voxforge_eng_000903 HIS SLIME HANE CREPT THE EADGES OF THE TABL +voxforge_eng_000904 WHD LA ORN SAID MIS MORTO ARM +voxforge_eng_000905 IT OK HI HAVEA WTO RCH HEAD O IT +voxforge_eng_000906 MARTHEA WER D STAND TH CONTRACTIOUALE ITSHOS +voxforge_eng_000907 AS TO BE NDESTINGICHABLE FROM THE VEAST WHYT PLAINED ROWND +voxforge_eng_000908 HE WOD DE STROY AL THINGS HATER FICTDDT +voxforge_eng_000909 THE RUSION USIK PLEAER THE CONT WAS HERO BEDIN SLAV +voxforge_eng_000910 TO HIS SUPRIHSE HER ANTE WAS FLAT AND UN COMPROMIYSING +voxforge_eng_000911 T THIS SOT BE INTEROSTING +voxforge_eng_000912 I AME A FRADE I DONT HAVE MUCH TIME +voxforge_eng_000913 CRSMIS IS AN EASY PROBLOME COMPRD W THE POLNATION GIVING FEAST +voxforge_eng_000914 THE PLANTOS OR ARDY CEN SIDERIN TH ATERHE +voxforge_eng_000915 JON RIREDE WIT SHINAING EYSEH +voxforge_eng_000916 WO VER LIVED ON THE RANCH DID THATD +voxforge_eng_000917 WE LEVE THE VFVENCUOALITY TO TIME AND LORL +voxforge_eng_000918 A AT THE SAME TING SPEARS AND EROS BEGANTO FALLE AMONG H IN BATERS +voxforge_eng_000920 IT IS MEY THE SIMPAL SOUPELITIFEF +voxforge_eng_000921 IND STAID HE ARIGVE ON TH NOT OFTE SOCON DAY +voxforge_eng_000922 IN HIS ANGSITY AND SOLICSITOED E AND LOVEFT THE DID NOT COWNT +voxforge_eng_000923 DGOD BLESSOM I HOPL IL GO AND SING THEM FOREVER +voxforge_eng_000924 YO WER IN GOAGED +voxforge_eng_000925 THER LATSES WAS OF A TELICKET IV HERY COLOERE FRIAIN TOB TINTIN WHIT EAL +voxforge_eng_000927 IT WA THE SAME WHAY WIT OL REVFALVEORS AND RIFALS +voxforge_eng_000928 HE CING HAD RMIST INCQUIREINTO TH ATER +voxforge_eng_000929 AE DOS TEN LOK GODTED T +voxforge_eng_000930 FOR THE FIRST TIME IN HIS LIFE HE WAS YURNING FOR SGRAP +voxforge_eng_000931 I DEFIG ANY MAN TO GET A SOLHAMWOT ILENCE SORE IN CELYFORNIER +voxforge_eng_000932 HER IS SMULT TRU AT HIM AS HE CAME OF THE BANGK 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LIMPOPOE +voxforge_eng_000951 IT WAS MY AD DE TO ATE +voxforge_eng_000952 SHE DOSANT WON TO WEIN +voxforge_eng_000953 SHE THINGE AITIS BECOS HE WONCE SOM THNG LE +voxforge_eng_000954 HSHE POLD E AND T THE LOC CREASETDON TO BRAK HIS BAC +voxforge_eng_000955 THA THE SOCOLD FORES AT WORK IN LITE HE ALCTRISIDY AND MANGNTISM +voxforge_eng_000956 WE TWION SHAOBPEINT AND PIAICET GRAGSIN AN COS THE THIHVBLE +voxforge_eng_000957 AL SOE I WONT ANFORMATION +voxforge_eng_000958 THE SICT DAY HE SPENT IN THE CAVON WH GRAGSON +voxforge_eng_000959 ION THIS Y POTHIES THE HAMERNG OF THE LTER MUNDING CRPUCLE OND THE BOB COFIRE ITK CENATICK NRGY ON TH ON HAND +voxforge_eng_000960 NOWE A FIRNY WIL STREMEM AND EVER AN ANON YOU A MURGE FROM AL THE GROVES AND FLOURS +voxforge_eng_000961 WITH HOT IT THE MOST DENCELY POPILAED REAGENS OF MOHEN TURIP ANDAMORICA +voxforge_eng_000962 TOM SPINK HAS HAR PON +voxforge_eng_000963 HE WNTED G THE FISHE T THIS FOLE ALREY SOF AGON +voxforge_eng_000964 LA A FLASHE HE LONCHE HIMSELF INT THE FETHED MAS O TH HOUHL +voxforge_eng_000965 IT CONTAE A TOTLE OF TWENTY ANTRES +voxforge_eng_000966 I HAVE FHELT MORE COMFORABLE +voxforge_eng_000967 THED POES TO MNTH VEATELITY +voxforge_eng_000968 THE WLLE DOGK THRST HIS GONT MUSLE TORD HIMN +voxforge_eng_000971 THE GAEBILE VORSCE OF HE SEMERIY RANG OUT +voxforge_eng_000972 IT WAS AI RIVER ANM MURGING LAK OURSELS FOM TE GRAT SOMP +voxforge_eng_000973 SAID THE MOLL PULING HIMSELF TOGETH WITHAN EFERT O MUST THING ME VERY ROD +voxforge_eng_000974 IN WHAT BYUCOLLICK SCOO OF FENCE HE HAD BED TORT THWAS BEYOND IMADGENING +voxforge_eng_000975 HAD NOT INABLED IN VESTIGADERS TO OPE TAIN A COMPERITIVFLY LITL CLOUST +voxforge_eng_000976 IT TRIKL OF FRESH BLOD RAN OVE RS FACE +voxforge_eng_000977 D IT WAS A CUORES CONSI TEANTSESE +voxforge_eng_000978 IT IS THE FIR PARTLY SHE SAINE +voxforge_eng_000979 THEY GOST LAYE OF THEBOSH AND POKD AWAY AN +voxforge_eng_000980 I NO THAT OURE IN CHARDE THERE AND JE NOSE +voxforge_eng_000981 FOR TIE THE ECSATING THILE OF HIS ADVENTHE WAS GON +voxforge_eng_000982 FAEDLY HIS FINGRS CLOS THADLY OVE THE ANGOCIF +voxforge_eng_000983 DEAR SIRE YOUR SECONT VICTDOM HAS FOLLON OND SCHADGDULE TIME +voxforge_eng_000984 H CON CAR F HMSELF E +voxforge_eng_000985 ACH INSILT ATE TO THE VALYU O THE CLAME +voxforge_eng_000986 THE IT MA BE TRANCS FORMED INTO ANY O OF TH FORMS OF WHCH ENRGY ISECEPTABL +voxforge_eng_000987 MESEIDES SCREAMED GRID LOVF IADND MANYIFESTED TH HIRIARD ICK AE BOND DEN N MENT OF HISTAIAR +voxforge_eng_000988 I WHN TO NO HOWT AL THIS IS POSTABLE +voxforge_eng_000989 RESENTING A SEMPL AN NSTRCTIE ILOSTRATION OF THE STROGOFOR LIVEF AMNG THERIVLE SPEACES +voxforge_eng_000990 HIL NEVE DO A TAP OF WERK THE HOL VORIANDGCH +voxforge_eng_000991 I HAE HNTE ALON TIS RIGE REPLAD FLIP +voxforge_eng_000992 LORD BUT IN GLAD TO SE YO AGIN FIL +voxforge_eng_000993 COWELINLY I WEN DATDID THATF IS DEA +voxforge_eng_000994 THE AR NOT REAGILE OSTER PIRATS NCKLES CO TNUDED 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FOM THIE +voxpopuli_eng_000502 BESING HAT HE ANEVI MENTAE EAFECT OF PRDUCS MUST BE AVERY INMPRTANT ISHU ING TER EU AND HE OL IG TDEAR O THE IECOLABR NEVSA VER YOUSOL ORIANTATION FOR THECOSTSUMEMIS OF COURS HE IACULABER SOOD GIVEN TO THE MOST AND VIREMENT AF FENDY PORDUCT THE IFOREMITIONSHOL BECLEARE AND OE +voxpopuli_eng_000503 HOWEVER THE CIRENDRIGIEME NITS TO B BETER SALORT TO TH DIGIDL INFVEIRNENT EDE NSHOUR FAR RIMINERATION TO GREATDURS ANH TO OFOME TO ONSOMER EXAPECTATIONS +voxpopuli_eng_000504 AD COLS BO TE OMION AND MEMBRSTATH O ANHANCD HERSEPORT TO RECENCSIYATION TO SECUR PECS AND STOBLITY AN ARLEND IWIL THER FOR ARE SU CALIGE TO PEES SUPORTHIS MENDEN +voxpopuli_eng_000505 TRATIGIG CHOICIS ABOUT ERE TO LE WEST MUST BE MAD NOWL TOAKIN NE COUNE ANE TO FASE OUT FORSILFUL SUPSITDS BUT TEK THE GAS AS FORSOFYU IT CAN BE A HELP FUL BRIGING RNSICONARY MEADIUM TO BE US IN MEMIN MANY MEBERSTHAS I E ON TO EDCHIF OVER ANMBISHIS LIMIGTARGETS +voxpopuli_eng_000506 EWE AED POSILY AOFERIS COPE WE CAN COOTH TO POROSUE THEY SAME PLISES IN H SAME MANER NONING THAT WEL LED TO HE AMRSOTS THE RSOLSEST THAT WE NO DRDA +voxpopuli_eng_000507 U TERIS NOPTION B +voxpopuli_eng_000508 WE ALL SOE NEAD A THANGEH INOR HR DOLIDGTYI +voxpopuli_eng_000509 I LAGH BUT OF THE RESON OF COURSE IS ILIGL FISINGK AND THE OFOM AL TWE DOUN OFEN BY HALAM VESES WHICH AR LEAGISTERE TO COUNTRIES HICH LAUCE THE IL OF THR RSUREIS TW AN FORST T IN THENATIONE AGEMENCS NO MONT OF TESABEITY MASERS ORD ECXTRP PER WAR UILEDRESE THE PROBLOME OF RETIUSING +voxpopuli_eng_000510 THE OMPRMICE OLLSO IN CLUD SCLEARE RUASTO HEFINE WHICH MBRSTATD AS HURSTICTION AN TH OPRATIOM TYEMBRSTATES CONCSERD FOR CROS BO THE CACES AS HEHA THE NED TO EIMVOLLEF YOUR JUST HEN YOF OR WORK AND PLEACE WOESUPRT TO MRO HIS EIRECIF +voxpopuli_eng_000511 ENO THEGRENS WIOD T HAV S BLE HATHIS UR BAD BEES CRIMINAL BEES DELIBRTLY COTAMINATING HUDY WH THE DANGRUSINGREADIENT BUTIN FAC IN FAC HE DINGHE HUY BES AR L AVLALLRS DOUNM IHI TO CARY POLON BAC T THER HIVSTOD TO FE THER YOUNG +voxpopuli_eng_000512 BUT IT WAS THE COUNTRY ITD HELD BENG MORE CAPRABL +voxpopuli_eng_000513 R IN TO THE PRT FOLIAO OF THE NU COMISION RE DALING WIT FAUNDEMENTERIGHTE +voxpopuli_eng_000514 E MESIGE TAT THE YU DOUT AT HAVE ANYNUSOLTIONE +voxpopuli_eng_000515 AR YU WILING TO ACT INERVFATHEREFOR THE SOSIAL DE MENTION TO BE INLODED INTHE EOU COMPETENSYES AS PREPUS +voxpopuli_eng_000516 AERNCTHE OND PESPECTRU POLIYES TAKING WI THEREFORME OF OUR TELICON TH FRAM WR +voxpopuli_eng_000517 I BELE HIS REMARC ES WERA INEXTPLIITELY RACISCTED AND SENAFOBICKE AND PRMOTED RAIL INTOLRANC IN WHAY TH IS NO UCETABLE OR ALAOUED IN TE ONTOICTUTIOF THIS HOUSE +voxpopuli_eng_000518 REALIGH EGSAMPL SHOL THAT SOVING IES RLATETO A BOCATION FEYULD STRON COMNITY DEVELPMENT +voxpopuli_eng_000519 SI HOLPE THA HISWLHAE MFORUSHEAS WHEL AN TAT RUSHE CON LS AND ISIGT D N CSTREMEM SCCESSTARY AFTR THES EG TWISIGIFICEND AT IN ORGST THIS OUR B +voxpopuli_eng_000520 SHE ECEPTO THE FACT THAT 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POSIBLE MORTHIFS +voxpopuli_eng_000527 TO WEAVE THOUSE YURPEN WAL LANGWOACES IN TO THEIS GLABLISED WERLDT ISINT TO THEAIS GOABLIS ECONMY IN DIS GOBE VILIAGE WHICH IS COTIALY CONOMICK SOSIALE ELNBPOLITICOLBPW ITSE AE MOST VELABLE EASTHEIT FROM THEINTIRE E OUG THAT WE MUST THAK FOLACOUNS AND T +voxpopuli_eng_000528 EAE TOREBET THAT ALL HE AY AN NOT BE YOUS TO FINCS SIGURIT EXPANCES BARTHERS CONTROL OR MLITRY SOU PORN +voxpopuli_eng_000529 THIG HE INTIFIK REPORTS BCALE MAR MORE URDENTMORLARMING AND MOR SHOCKING +voxpopuli_eng_000530 FINOLY IM WHE WHAE HINKINGK ABOUN THERE INOVATIFEF FI NSION INSTRMENTS WHN KU THE BOLTH FOR ORSELFS TOUG SOUPORT OUOUER A CONOMYS BUT AOS SO TOL SOPORKT THOS COHER INEAE +voxpopuli_eng_000531 THAT GIVE AE SOR YUNIKE TOUL IN ES MAKING +voxpopuli_eng_000532 D PAPER A VERYHL WEEK PROPOSIL +voxpopuli_eng_000533 SRUSHAS OLYS BE VERY PROUDNATION WITH ICH CLDCHUERE WI T INVENTIONS WITHAN ES CE +voxpopuli_eng_000534 AR TACTAITION NVHEN A MODICE OF TACSAITION N SOME CACES MIY JUST HELPES EM TO DO WAT IHEAREDY SHE GESTED AND HO NOS MAK THE CACE FOR THE ETERSPECT OF BANKRE CAPDLIATION THAT WE NEVERSOL +voxpopuli_eng_000535 THEULOPBE AN HSIDLOM SUPORTOF ICE MOR OVER AS A MONG ITCS TH IUS TO PROMOUHT FESILY THAT AND COURDINAT ECSCHANGES OF INFORMATION AND UTHE ACTEVITYES ER LATY TO LELCATION BD IN TEYUNION +voxpopuli_eng_000536 HECONLSNOF THE RAMEBORK AGEMENT PROVIE A LIGLY BINDINGK INSTRMENT TO OBV GIRAT AND STRANTN EU OSTRLIR BE LITHERI ATSIOS AND TO INCEESCOPERATION +voxpopuli_eng_000537 EREFRURE WE AS INTHE COUNSEL AS GLMITION TO RENTA CAS BARI THA ULDBED THE SESTENT OF TEBACT O TH RISI +voxpopuli_eng_000538 AINOTHE WERDS THEOBJECTION IS NOT WHETHE MUNY ISPAED OR NOT THEOPBEBJECTION IIS WETHE THEY S A DIDECTLINK ORENO +voxpopuli_eng_000539 TO HESTINGISHIES THE TWO MAE DOS HEAR YOUMER IT SAE YOUSE BY THECAR NT GORENT AND THE DANION NUKLEPROGDHME +voxpopuli_eng_000540 ESS METHEBDRONM HENKACTERE SECTION HERASD ENT IS HE FORM O VILANCE AND IT I THE MOST ETREAN FORM OF GHNTR BASEDESCRMINATI +voxpopuli_eng_000541 WE CAN LOK TO SOME EIRN NIN E U MEMBORS OR GOD EXSANPLE A REGARDED THE NOLAGES +voxpopuli_eng_000542 INM VLVEDS FOR HE POSITEIVE AND CESTTACTEIVE A BROATC +voxpopuli_eng_000543 O I HOPE THAT IS IL BE COMPEATET AIR IN HAFORSIVILE FOUOCHER THATD MANSE GAD BE TO A FRE MUNS +voxpopuli_eng_000544 OR FORDER ANDCORISHETHE YOU AND ETFHURH TO BRING A MNG PES IN OF GNISTAN ANDTO OVER COME T OF FRESILSICURITY N VEIREMENT IN THECONTY +voxpopuli_eng_000545 BEANDTHE STANT THAT SOME PEPL OAR ANGRY +voxpopuli_eng_000546 OEN TO HE MORSTPONCIVLD +voxpopuli_eng_000547 E MUST EDACTIFIHITH THIS SUTIATION AND H ASE THECOMION TO CONSIDER THE MOST EDICKET GCOMINSATION MESHERS FORLOLW PESENGES +voxpopuli_eng_000548 THE COMITION INGBISHE THE YUOPIONT PORLAMENT IN THE UPCOMIN KREVISION TO OPEN IS POSITION OND THIS MATHERE WHICHREALY CONCSERD ACESE TOL USTICS INOUROP AND THE INFORSTMENT OFRICES GRANTED BY HE YUROPIUNR YUND LO +voxpopuli_eng_000549 I L M ERY MUCH THERSOUNTIO OF TOCK TEN THEAS RALY AND POLISTINIONS AND ENCIRLY HOP THAT HE WLD SUCCED +voxpopuli_eng_000550 L WE HAE ECUMELATION OF PROBLANCS RESILTING FROM THE AR TIFISHAL UND THE BAGEITINGK AND VERETPRIVUS YUS +voxpopuli_eng_000551 ELET AST NOT BE THE MAN OF OUSTADY IT IS BE TODAS INSTITUTIO +voxpopuli_eng_000552 E I GOD ARLSEOEN TO BECOME AMBSHETES O THE YEARE MAKING IT AY DEIRS AD ACTIVITHIS WOW WHIDLY NONE A MONGSHT TO YUOPEASITIESE AND PUTPICIPATING N HVBENTSE BET TAT YUROPEION NASHONL FOR LOK ALEL +voxpopuli_eng_000553 DSARTDLY SUCH INPACT SESTMENT COLD PREAMT SERTAN PROBLOMS SUCH AS THOS POSED BY THE ELCTRNIK IDEDTFICATION OF SHEP AND SCOTEND +voxpopuli_eng_000554 THE ORT IS CONTENT TO SE THATHITS WORK HAS INFORME THE DESHARGH ROUES AND HAS ONTEBEUTEDTO PROPOSOLSE FOR IM PROVING THE FINCHAL MANAHENT OFE YOUSPENDING AND BETETORKATING OF YOFNCS +voxpopuli_eng_000555 RECGUAI HRY GLAI THE AND SERTENTY IASNEADETD FOR THE OBLIKE SECTOR AND FOR TH INDUSTRY 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A N T E R D I D E S W H E N T H E S O G D T H E G L O E O F A F F I R +voxforge_eng_000946 C H A N S C H A R S T H Y L I T C O M A N D +voxforge_eng_000947 I T W A S J E N S A I N I N G S O F H E L Y O E R B E Y O N T H E R O C K C E +voxforge_eng_000948 O F L I N G A R O L B O S T B E T W E N O S E D +voxforge_eng_000949 H A T R I T A N D M U R D E R A N D L O S T F O R R E V E N G C H T H E Y P O S S E S T T O O F E R F L O Y I N G +voxforge_eng_000950 T H A T O C O D H E A R A L L U P E D D O N T E L I M P O P O E +voxforge_eng_000951 I T W A S M Y A D D E T O A T E +voxforge_eng_000952 S H E D O S A N T W O N T O W E I N +voxforge_eng_000953 S H E T H I N G E A I T I S B E C O S H E W O N C E S O M T H N G L E +voxforge_eng_000954 H S H E P O L D E A N D T T H E L O C C R E A S E T D O N T O B R A K H I S B A C +voxforge_eng_000955 T H A T H E S O C O L D F O R E S A T W O R K I N L I T E H E A L C T R I S I D Y A N D M A N G N T I S M +voxforge_eng_000956 W E T W I O N S H A O B P E I N T A N D P I A I C E T G R A G S I N A N C O S T H E T H I H V B L E +voxforge_eng_000957 A L S O E I W O N T A N F O R M A T I O N +voxforge_eng_000958 T H E S I C T D A Y H E S P E N T I N T H E C A V O N W H G R A G S O N +voxforge_eng_000959 I O N T H I S Y P O T H I E S T H E H A M E R N G O F T H E L T E R M U N D I N G C R P U C L E O N D T H E B O B C O F I R E I T K C E N A T I C K N R G Y O N T H O N H A N D +voxforge_eng_000960 N O W E A F I R N Y W I L S T R E M E M A N D E V E R A N A N O N Y O U A M U R G E F R O M A L T H E G R O V E S A N D F L O U R S +voxforge_eng_000961 W I T H H O T I T T H E M O S T D E N C E L Y P O P I L A E D R E A G E N S O F M O H E N T U R I P A N D A M O R I C A +voxforge_eng_000962 T O M S P I N K H A S H A R P O N +voxforge_eng_000963 H E W N T E D G T H E F I S H E T T H I S F O L E A L R E Y S O F A G O N +voxforge_eng_000964 L A A F L A S H E H E L O N C H E H I M S E L F I N T T H E F E T H E D M A S O T H H O U H L +voxforge_eng_000965 I T C O N T A E A T O T L E O F T W E N T Y A N T R E S +voxforge_eng_000966 I H A V E F H E L T M O R E C O M F O R A B L E +voxforge_eng_000967 T H E D P O E S T O M N T H V E A T E L I T Y +voxforge_eng_000968 T H E W L L E D O G K T H R S T H I S G O N T M U S L E T O R D H I M N +voxforge_eng_000971 T H E G A E B I L E V O R S C E O F H E S E M E R I Y R A N G O U T +voxforge_eng_000972 I T W A S A I R I V E R A N M M U R G I N G L A K O U R S E L S F O M T E G R A T S O M P +voxforge_eng_000973 S A I D T H E M O L L P U L I N G H I M S E L F T O G E T H W I T H A N E F E R T O M U S T T H I N G M E V E R Y R O D +voxforge_eng_000974 I N W H A T B Y U C O L L I C K S C O O O F F E N C E H E H A D B E D T O R T T H W A S B E Y O N D I M A D G E N I N G +voxforge_eng_000975 H A D N O T I N A B L E D I N V E S T I G A D E R S T O O P E T A I N A C O M P E R I T I V F L Y L I T L C L O U S T +voxforge_eng_000976 I T T R I K L O F F R E S H B L O D R A N O V E R S F A C E +voxforge_eng_000977 D I T W A S A C U O R E S C O N S I T E A N T S E S E +voxforge_eng_000978 I T I S T H E F I R P A R T L Y S H E S A I N E +voxforge_eng_000979 T H E Y G O S T L A Y E O F T H E B O S H A N D P O K D A W A Y A N +voxforge_eng_000980 I N O T H A T O U R E I N C H A R D E T H E R E A N D J E N O S E +voxforge_eng_000981 F O R T I E T H E E C S A T I N G T H I L E O F H I S A D V E N T H E W A S G O N +voxforge_eng_000982 F A E D L Y H I S F I N G R S C L O S T H A D L Y O V E T H E A N G O C I F +voxforge_eng_000983 D E A R S I R E Y O U R S E C O N T V I C T D O M H A S F O L L O N O N D S C H A D G D U L E T I M E +voxforge_eng_000984 H C O N C A R F H M S E L F E +voxforge_eng_000985 A C H I N S I L T A T E T O T H E V A L Y U O T H E C L A M E +voxforge_eng_000986 T H E I T M A B E T R A N C S F O R M E D I N T O A N Y O O F T H F O R M S O F W H C H E N R G Y I S E C E P T A B L +voxforge_eng_000987 M E S E I D E S S C R E A M E D G R I D L O V F I A D N D M A N Y I F E S T E D T H H I R I A R D I C K A E B O N D D E N N M E N T O F H I S T A I A R +voxforge_eng_000988 I W H N T O N O H O W T A L T H I S I S P O S T A B L E +voxforge_eng_000989 R E S E N T I N G A S E M P L A N N S T R C T I E I L O S T R A T I O N O F T H E S T R O G O F O R L I V E F A M N G T H E R I V L E S P E A C E S +voxforge_eng_000990 H I L N E V E D O A T A P O F W E R K T H E H O L V O R I A N D G C H +voxforge_eng_000991 I H A E H N T E A L O N T I S R I G E R E P L A D F L I P +voxforge_eng_000992 L O R D B U T I N G L A D T O S E Y O A G I N F I L +voxforge_eng_000993 C O W E L I N L Y I W E N D A T D I D T H A T F I S D E A +voxforge_eng_000994 T H E A R N O T R E A G I L E O S T E R P I R A T S N C K L E S C O T N U D E D +voxforge_eng_000995 T E M U S T B E H U R I N G F O R B I S E S S B U T I T H T Y O U M I G T W N T T O C E K A L O K A T H E R S I T D +voxforge_eng_000996 D E R A S N O C H A N C E T O F I R E W I T O U T H I D I N G H I M +voxforge_eng_000997 A S F O R H I M S E L F W O N T T H S T R E A T R E A L W A Y A R N I N G N K E S I N G S E T L Y +voxforge_eng_000998 D O N H M C O N Y O R B O Y O L O N G W O D E S S Y E +voxforge_eng_000999 C O D B A I Y P E A R H E S H O T E D +voxforge_eng_001000 B U T S U C H D E V E R G E N S O F P I N I O N W L D C O N S T I T U T N O M E N A N C E T O S I T Y +voxforge_eng_001001 I B I E W A S O N T H A N C E E A N D O L Y O N W O E S A V I N G H O N T D L D +voxforge_eng_001002 E I O C A N O T F O A L E Y O W S H E S A I N D N E +voxforge_eng_001003 O N T H E F A R C O A R N E R O F T H E C O M P O W N D F E N T E A H O A K B R E A D E D +voxforge_eng_001004 T E N A G I N T O E R H A D S O C H A N I E R S T A T I N G W A Y A O B O T H M +voxpopuli_eng_000494 W E A L L N O O M A N A S A S E C E S T O L S T A B L C U N T R Y A R O A L M O R T H E F O R T H A T F O R T H E H O L R E A G O N +voxpopuli_eng_000495 T H E R F O R I T S H I G H T I M E O U C O M E F O R B O U G D T H E T E P R E P O R S L F O R R E V E U B E D E N O P R A T I N A L S B R A T S O N O F T E O A R D I T A N D N O N O A L D I T S R V I S I E S A N D E R A D I D L A C T E W U S O B O T I S O N +voxpopuli_eng_000496 I T I S C E E A R T H A T W E H A V E N O T I M E T O W A S T T H E N U R E S S O U L T S H O F T H E Y E I D P E A S I E S I E R E O A R D I N S I E N T D I F I C K B A S I E S O F G L I M I N T T J A I N C E L E N O R O U M F O R H E S I T D A C S E O N +voxpopuli_eng_000497 S E N T S O I N T H E C O N T A E N O R H I H A E V E R A V E N T U C H E D C O M E S L A V E S C O N T O F E T G O D S D R O G E S I T H E T R +voxpopuli_eng_000498 I H O P E T H A T C O M I O N S M B I T I N I S H E S I N I S H I F I V S W W N T C A R A K T T H E D A C X T P R O B L O E M B U T W I L B E A N A N C S E F O R E E I S T I N G C H O L I N G E R S O F T H E R O B P T A N C S P B O R D S E C T A R +voxpopuli_eng_000499 I T E O U A S I W A S D E S I O N T A I N A R L Y B Y O N P E R S O N T H E O R M E P E S I D E D O T H E N I D E S T A C E S A G A N S E T H E A R T I C I L A T E D M C R A T I C K D R M N D U R I T Y O F T E U S C O N G R S S B Y A L L O F I T S R E P O B L K E N S O F I C S T D E M E R C R A T I C T D E M R T M B E R S I T W A S N A G R E M E N T W I T H O U T A N Y B I D N D I N G O B L I G A T I O N S A S T H E L E A D E R S O F I R A U N V E R Y O U P E N T L Y I N P R E S I D H M A E P L Y N T H E E R Y D A T H E S O C L D D E L W A S P O L I S H E D +voxpopuli_eng_000500 F R E S P E A C G E I S S S E N C I A L Y A E C E C T I N G T A T P E P L U R F R E E T O S A Y T H I N G S W E D O N O T L I E K E N O T M E L Y F R E E T O S A Y T H I N G S W E D O L I E K +voxpopuli_eng_000501 L A T A S L A R N D F O M T H I E +voxpopuli_eng_000502 B E S I N G H A T H E A N E V I M E N T A E E A F E C T O F P R D U C S M U S T B E A V E R Y I N M P R T A N T I S H U I N G T E R E U A N D H E O L I G T D E A R O T H E I E C O L A B R N E V S A V E R Y O U S O L O R I A N T A T I O N F O R T H E C O S T S U M E M I S O F C O U R S H E I A C U L A B E R S O O D G I V E N T O T H E M O S T A N D V I R E M E N T A F F E N D Y P O R D U C T T H E I F O R E M I T I O N S H O L B E C L E A R E A N D O E +voxpopuli_eng_000503 H O W E V E R T H E C I R E N D R I G I E M E N I T S T O B B E T E R S A L O R T T O T H D I G I D L I N F V E I R N E N T E D E N S H O U R F A R R I M I N E R A T I O N T O G R E A T D U R S A N H T O O F O M E T O O N S O M E R E X A P E C T A T I O N S +voxpopuli_eng_000504 A D C O L S B O T E O M I O N A N D M E M B R S T A T H O A N H A N C D H E R S E P O R T T O R E C E N C S I Y A T I O N T O S E C U R P E C S A N D S T O B L I T Y A N A R L E N D I W I L T H E R F O R A R E S U C A L I G E T O P E E S S U P O R T H I S M E N D E N +voxpopuli_eng_000505 T R A T I G I G C H O I C I S A B O U T E R E T O L E W E S T M U S T B E M A D N O W L T O A K I N N E C O U N E A N E T O F A S E O U T F O R S I L F U L S U P S I T D S B U T T E K T H E G A S A S F O R S O F Y U I T C A N B E A H E L P F U L B R I G I N G R N S I C O N A R Y M E A D I U M T O B E U S I N M E M I N M A N Y M E B E R S T H A S I E O N T O E D C H I F O V E R A N M B I S H I S L I M I G T A R G E T S +voxpopuli_eng_000506 E W E A E D P O S I L Y A O F E R I S C O P E W E C A N C O O T H T O P O R O S U E T H E Y S A M E P L I S E S I N H S A M E M A N E R N O N I N G T H A T W E L L E D T O H E A M R S O T S T H E R S O L S E S T T H A T W E N O D R D A +voxpopuli_eng_000507 U T E R I S N O P T I O N B +voxpopuli_eng_000508 W E A L L S O E N E A D A T H A N G E H I N O R H R D O L I D G T Y I +voxpopuli_eng_000509 I L A G H B U T O F T H E R E S O N O F C O U R S E I S I L I G L F I S I N G K A N D T H E O F O M A L T W E D O U N O F E N B Y H A L A M V E S E S W H I C H A R L E A G I S T E R E T O C O U N T R I E S H I C H L A U C E T H E I L O F T H R R S U R E I S T W A N F O R S T T I N T H E N A T I O N E A G E M E N C S N O M O N T O F T E S A B E I T Y M A S E R S O R D E C X T R P P E R W A R U I L E D R E S E T H E P R O B L O M E O F R E T I U S I N G +voxpopuli_eng_000510 T H E O M P R M I C E O L L S O I N C L U D S C L E A R E R U A S T O H E F I N E W H I C H M B R S T A T D A S H U R S T I C T I O N A N T H O P R A T I O M T Y E M B R S T A T E S C O N C S E R D F O R C R O S B O T H E C A C E S A S H E H A T H E N E D T O E I M V O L L E F Y O U R J U S T H E N Y O F O R W O R K A N D P L E A C E W O E S U P R T T O M R O H I S E I R E C I F +voxpopuli_eng_000511 E N O T H E G R E N S W I O D T H A V S B L E H A T H I S U R B A D B E E S C R I M I N A L B E E S D E L I B R T L Y C O T A M I N A T I N G H U D Y W H T H E D A N G R U S I N G R E A D I E N T B U T I N F A C I N F A C H E D I N G H E H U Y B E S A R L A V L A L L R S D O U N M I H I T O C A R Y P O L O N B A C T T H E R H I V S T O D T O F E T H E R Y O U N G +voxpopuli_eng_000512 B U T I T W A S T H E C O U N T R Y I T D H E L D B E N G M O R E C A P R A B L +voxpopuli_eng_000513 R I N T O T H E P R T F O L I A O O F T H E N U C O M I S I O N R E D A L I N G W I T F A U N D E M E N T E R I G H T E +voxpopuli_eng_000514 E M E S I G E T A T T H E Y U D O U T A T H A V E A N Y N U S O L T I O N E +voxpopuli_eng_000515 A R Y U W I L I N G T O A C T I N E R V F A T H E R E F O R T H E S O S I A L D E M E N T I O N T O B E I N L O D E D I N T H E E O U C O M P E T E N S Y E S A S P R E P U S +voxpopuli_eng_000516 A E R N C T H E O N D P E S P E C T R U P O L I Y E S T A K I N G W I T H E R E F O R M E O F O U R T E L I C O N T H F R A M W R +voxpopuli_eng_000517 I B E L E H I S R E M A R C E S W E R A I N E X T P L I I T E L Y R A C I S C T E D A N D S E N A F O B I C K E A N D P R M O T E D R A I L I N T O L R A N C I N W H A Y T H I S N O U C E T A B L E O R A L A O U E D I N T E O N T O I C T U T I O F T H I S H O U S E +voxpopuli_eng_000518 R E A L I G H E G S A M P L S H O L T H A T S O V I N G I E S R L A T E T O A B O C A T I O N F E Y U L D S T R O N C O M N I T Y D E V E L P M E N T +voxpopuli_eng_000519 S I H O L P E T H A H I S W L H A E M F O R U S H E A S W H E L A N T A T R U S H E C O N L S A N D I S I G T D N C S T R E M E M S C C E S S T A R Y A F T R T H E S E G T W I S I G I F I C E N D A T I N O R G S T T H I S O U R B +voxpopuli_eng_000520 S H E E C E P T O T H E F A C T T H A T S I T I S I E N H I P I S A Y N A S I O N L B O U R T O F T H E N O S I O N O G R I S D I C T I O N B U T H Y O U R L S O S A I D T H A T A C O R D I G T O T H E M A S T R I C K T R E A T Y A N D H E A S R I G H T D T H E A S T O B E A D I R E C L I N G +voxpopuli_eng_000521 E O F A I L D E S P E S I O L Y E I N T H E M S T H R A T I N G A Y U L I F I D E D A N D T A F I S H E N T A T P R O R C H T O O L I M I T C A E N G C H T R E A T M E N T A S E L E A S I N S T R A N G T H A N I N G I T S E L E A D I N G P O L I T I C K L C O S I T I O N I N D E S U G E N D E R I C O S I T H E R E T H E F O R T A K I N G I S R E S O L U I O N A N A C T O F U T M O R S T I M P O R T A N S +voxpopuli_eng_000522 T H E U N I G T E S S T A T E S O F Y U R O V I L B A F A C T W I T S W E D O N A S P R O V I D E N C S +voxpopuli_eng_000523 I T D M U S B E T H E C A P B I T L E O F B O T T H A T S A N D W E M U S S R E C O N I S E P O L S T I N I S S T H A T A S P R O V I D I D F O R E I N T H E O F L O G R E E N C S +voxpopuli_eng_000524 T Y U K R A N E S F A S E T W I T D W O N E O F C R U S I A L C H A L I N G E S E I N I C G H I S T A R Y I T W U L D B E F I U N T H E M E N T A L Y R O N G K T O P R E S T H E N A T I O N N O W E W I T A L T I B E S O F O R E S T R I C T I O N S P O P E L I D A L C O A L E D O S T E R I T E P O L I +voxpopuli_eng_000525 M O R R U L S A N D R E A G I L A T I O N W I L L N O T I N M P R O V E T H E S S I T U A T I O +voxpopuli_eng_000526 A T L E A S T B E W U D L I K E T O N O L T H E S O R S E O F T H E M U N Y A N D T H E P O S I B L E M O R T H I F S +voxpopuli_eng_000527 T O W E A V E T H O U S E Y U R P E N W A L L A N G W O A C E S I N T O T H E I S G L A B L I S E D W E R L D T I S I N T T O T H E A I S G O A B L I S E C O N M Y I N D I S G O B E V I L I A G E W H I C H I S C O T I A L Y C O N O M I C K S O S I A L E E L N B P O L I T I C O L B P W I T S E A E M O S T V E L A B L E E A S T H E I T F R O M T H E I N T I R E E O U G T H A T W E M U S T T H A K F O L A C O U N S A N D T +voxpopuli_eng_000528 E A E T O R E B E T T H A T A L L H E A Y A N N O T B E Y O U S T O F I N C S S I G U R I T E X P A N C E S B A R T H E R S C O N T R O L O R M L I T R Y S O U P O R N +voxpopuli_eng_000529 T H I G H E I N T I F I K R E P O R T S B C A L E M A R M O R E U R D E N T M O R L A R M I N G A N D M O R S H O C K I N G +voxpopuli_eng_000530 F I N O L Y I M W H E W H A E H I N K I N G K A B O U N T H E R E I N O V A T I F E F F I N S I O N I N S T R M E N T S W H N K U T H E B O L T H F O R O R S E L F S T O U G S O U P O R T O U O U E R A C O N O M Y S B U T A O S S O T O L S O P O R K T T H O S C O H E R I N E A E +voxpopuli_eng_000531 T H A T G I V E A E S O R Y U N I K E T O U L I N E S M A K I N G +voxpopuli_eng_000532 D P A P E R A V E R Y H L W E E K P R O P O S I L +voxpopuli_eng_000533 S R U S H A S O L Y S B E V E R Y P R O U D N A T I O N W I T H I C H C L D C H U E R E W I T I N V E N T I O N S W I T H A N E S C E +voxpopuli_eng_000534 A R T A C T A I T I O N N V H E N A M O D I C E O F T A C S A I T I O N N S O M E C A C E S M I Y J U S T H E L P E S E M T O D O W A T I H E A R E D Y S H E G E S T E D A N D H O N O S M A K T H E C A C E F O R T H E E T E R S P E C T O F B A N K R E C A P D L I A T I O N T H A T W E N E V E R S O L +voxpopuli_eng_000535 T H E U L O P B E A N H S I D L O M S U P O R T O F I C E M O R O V E R A S A M O N G I T C S T H I U S T O P R O M O U H T F E S I L Y T H A T A N D C O U R D I N A T E C S C H A N G E S O F I N F O R M A T I O N A N D U T H E A C T E V I T Y E S E R L 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(LAD_eng_000255-LAD_eng_000255) +W O N R O V E D L A R C O D R E T W O R O U D E A T W H A N C E (LAD_eng_000256-LAD_eng_000256) +S O M E O T H E O U N T I S H A E S E R V A Y I S F O R M A L T B L E Y E A R S (LAD_eng_000257-LAD_eng_000257) +B O T H O F T H E V I R S I N S F E A T H E T H E S O N G H A P Y H O L I D A Y (LAD_eng_000258-LAD_eng_000258) +S H A K S P I E R M A N Y R E F R N C E S U R M A D T O S H E E N D S I N T R A C T I O N D O R C A R I C T E S F O M V E R I U T P L A Y E S (LAD_eng_000259-LAD_eng_000259) +I F N D Y T H E P R O G R A M C U L D B R A K E O U T G U S T L I T L F O M E I T T W O F O M E L I A R A P R O U C H (LAD_eng_000260-LAD_eng_000260) +T H E H A L B U M W A S R E L E S E I N O S T R A L I A R A R N N I N T I N T O A G I S T T W O T H O U S E N T A N E L E V O N (LAD_eng_000261-LAD_eng_000261) +H E N O W P L A C E F O R A S T R A L I N T L O B E P E I R T G L O U R Y (LAD_eng_000262-LAD_eng_000262) +I T I T N O T N O N E H O W M U C H E I F A N Y O F H E R T L A M E M S A R E T R O (LAD_eng_000263-LAD_eng_000263) +A S M L L B I S N E S S O N E R B R A U R D O P R A T E D H I S W E A A N H A P F A R M E F O R S I C T E N E A R S F R O T H E A G O F W E N T Y T W O (LAD_eng_000264-LAD_eng_000264) +I N T H E N I N T H S E N C T R Y H E W A S A N I R I S H P O A T (LAD_eng_000265-LAD_eng_000265) +T H E Y A R M A U E C E T B Y S T R O N G N (LAD_eng_000266-LAD_eng_000266) +T H E L A L L E I S T H E R E F O R V O U L E D (LAD_eng_000267-LAD_eng_000267) +I N T H E A R L Y S T A G E S C A M E C L O S E T O U A S L E P (LAD_eng_000268-LAD_eng_000268) +R U N I N G E V E R Y T H A R T Y M I N I T T H O U T S E R V I S T I M E M S (LAD_eng_000269-LAD_eng_000269) +A S R E S I L T W H E N T H E C O L I G E R E O P E N D I T W H A S A S A A L L M A L E C O L I G E (LAD_eng_000270-LAD_eng_000270) +T H E T I M E B E T W E N T H E S P O N T I S V E R R A B L E A N D C A N A C E R A N Y W H E F R O A I N I T T O M U C H L O N G E R (LAD_eng_000271-LAD_eng_000271) +W E A R K O N T H E E E E A E A S S T D A R T E D I N M A R H C H T W O T H A U S E D A N D S E V O N A T C O S T O F F I V E M I L I O N D D O L L E R S (LAD_eng_000272-LAD_eng_000272) +H O W E V E R T E W A S S O M E D E C A G R E M E N T O E T H E E N D I N G T H E M E M W I C H O M O R Y A N D Y O S H I M O R Y D E C S K U S T E A T L E A N G T H O V E R E M O U L (LAD_eng_000273-LAD_eng_000273) +T H E C A P L E H A D N O C H I L D R O N (LAD_eng_000274-LAD_eng_000274) +T H E F I T I A L S I N G L F T H A T D E B Y U A L T H M P A R I S S C O L I N G H A D A E L A B R T M U S I C V I D E A O (LAD_eng_000275-LAD_eng_000275) +T H E S E R I S E N D E D O N S I C X T H A O R G I S T T W O T H A U S E N D A N D F O R E L A S T I N G F R A T O U T L O F S E V E N T Y O N D D A Y S (LAD_eng_000276-LAD_eng_000276) +H E H A S A L S O D C O N T R I B E T E T O T H E N U N Y O U R C R E E U O F B O K S (LAD_eng_000277-LAD_eng_000277) +B Y P L A C I N G S M A L A R T O B D G E C T T H R O O U T T H E I L M E (LAD_eng_000278-LAD_eng_000278) +I T I F O U N E D I N B R E S I L (LAD_eng_000279-LAD_eng_000279) +I T W A T H E S I D O F T H E C A M L Y I I D E N T I F I E D M O R L E W I F H (LAD_eng_000280-LAD_eng_000280) +E C A N D E D I T S I G H T H E M U S T L L S O S O D M I T A W O R K P L A N (LAD_eng_000281-LAD_eng_000281) +D U N D E Y W O N T H E M A C H T H R E T W O E (LAD_eng_000282-LAD_eng_000282) +H O W E V E R T H E V I L I G R E M A N E D I C I L A T D T N T I L T H E R I V U L O F T E F I R S T N O U S P A P E R S E C O N D R E P O U B L I C K (LAD_eng_000283-LAD_eng_000283) +T H E F I R S T E R V I I T H E N U C H A R C W A S H E L D N I N T N F I F T Y O N A L T H O T H E B I L D I G W A S N O T F U L Y F I N I S H E D (LAD_eng_000284-LAD_eng_000284) +T H E A V R I G H H O U S E H L D S I E S W A S T W O P O N T T W O S E V O N N D T H E A V R I G H F A M L Y S I E S W A S T H R E P O E N T E S I A R O S I A R O (LAD_eng_000285-LAD_eng_000285) +I T W A S F I R S T E R A R D C A S T O N T H R E D G A N I O U R Y T W O H O U S E N D A N D T E N (LAD_eng_000286-LAD_eng_000286) +T H E W I N G S W E N O W A D I N A S I N G L E P R E S I N G (LAD_eng_000287-LAD_eng_000287) +T E D O C T E O F O L O I F Y I N E N D E N Y E A R I G M A N A G E M E N T (LAD_eng_000288-LAD_eng_000288) +T H I S E O W A Y T H E M A E N A R K G U M E N O F S A I F T D Y R I S S K S (LAD_eng_000289-LAD_eng_000289) +H E W A S A L L S O A D A L I F H M E M B R O F S C O U N D T H O R P Y U N I T E D (LAD_eng_000290-LAD_eng_000290) +S H E F H E I R S T H E L G A T D E F O R S E B U T T H I E N E V E R H A P E N S (LAD_eng_000291-LAD_eng_000291) +F O T D R O P S N A B L E T H A D T H F O T S R A T A C R O U S E (LAD_eng_000292-LAD_eng_000292) +W H E T H T H E A R F L O Y I S F R E Y O R F O U R S T C N F E C T H E E N D G Y O F I E N C Y O F T H W H N D O (LAD_eng_000293-LAD_eng_000293) +A F T R G E T I G H E I T M E S E R E N T T H E M A D T H N O U D O R S (LAD_eng_000294-LAD_eng_000294) +F R A G M N T E O N A C H F A C E R E M A R E T W T H L T E R S A Y B E S E (LAD_eng_000295-LAD_eng_000295) +F R O M T H F I R S T D M I N I T E B O T H T E M E S S H O D T H E D E S I R E T O C O M P E E T W I T H E G R E I O F A P R O C E R S (LAD_eng_000296-LAD_eng_000296) +F I S I C L H E R I B Y E C E R S I D S E S M A Y H E L P T H P A T I O N T E T O M A I N T A I N M U S L S T R I N G T H (LAD_eng_000297-LAD_eng_000297) +H O W E V E R T H E T O W N E H E L I V S I N N O U N D W O N T T O H E A R A B O U T H E R (LAD_eng_000298-LAD_eng_000298) +A D I S R I E S A E P O E N T D E N T O A N A C T I N G C H I V E J U S T I S S O R J O U D G E O F T H E S O P R E M E C O R T (LAD_eng_000299-LAD_eng_000299) +T H E S O R Y B E S O U T C U V E R I N G I S T E N R E M O V E D T A N D T E B E N D S A R P A R T H A L Y C O C K E T (LAD_eng_000300-LAD_eng_000300) +T H I S N A S T I A L M O V E N T W H C H E B E G O N W T H S O U H H O P C A M E T O A S A D E A N D (LAD_eng_000301-LAD_eng_000301) +H I S A S E O S I A T E O U S U A L Y C A L D H I M T E O R E T H E G O O D L O K I N G G I Y (LAD_eng_000302-LAD_eng_000302) +I T S M A E N O F H I C E S W E R I N L U N D E N W E T H E S E C E N D O F I S S B E L F A S T (LAD_eng_000303-LAD_eng_000303) +A C T U L Y I H A D N E R B E E N T O A V I L I D G E B E F O R T H A T (LAD_eng_000304-LAD_eng_000304) +H E A S C H A G E I T H P L A D I N G T O S E T O F B O M S I N U R A P A N D T H E U N I G T E T A T E (LAD_eng_000305-LAD_eng_000305) +M A K I N G M R A R S I S T H E T H I R D S T U D I U R A L B A E B Y B E L D E N A S T R A L I A N A R T I S T G O T I E A Y (LAD_eng_000306-LAD_eng_000306) +H E T H E N M O V E D T O W O A S I N G T O D D E S I A N D W A S A P A R T N E I T W O R D B R O W N E A N D T I L L N I N T E N W E N T Y N I N (LAD_eng_000307-LAD_eng_000307) +J O S O F H I Y S C O L E A D T H E S C O L E S T H E Y C O M P E T G A N E D I N A L S P O R T S (LAD_eng_000308-LAD_eng_000308) +W E L F P L U S O N M A C H B A N D E R C O A U R D (LAD_eng_000309-LAD_eng_000309) +I T H I N K I M I G H T B E N O T H I N G (LAD_eng_000310-LAD_eng_000310) +T H E H O M A S B I L T A N D L I V E D I N B Y A N D R J A C A N D C A N I D Y D E P E T Y C O L E C T O T H E I N T E R N L R E V I N U S E R V I S (LAD_eng_000311-LAD_eng_000311) +I N N I N T A N S I C E Y E F O R E H E W E N T B A K T O O M S E K A N D E N T E T H E A C T O S C H O U L O F O A M S K (LAD_eng_000312-LAD_eng_000312) +T H E B A N K I S J U I N T L Y O N E D B Y H I M A N D H I S B R O U V E R A N D R E L I T I V S (LAD_eng_000313-LAD_eng_000313) +H E S O P E I C E N T L Y W A N T O C O L I N B R E I S T A L (LAD_eng_000314-LAD_eng_000314) +W O N T H A U S E N D A T H U N D E D F O R T Y S I C K C S F O A R T H I D I T I O N (LAD_eng_000315-LAD_eng_000315) +A P A T O F L I T L I N G L E N D B E Y O N D W A I L E S I T H A S B E E A E N C H R L Y I N G L I S H S P E A K I N G F O R N I N H U N T R E D O E A R S (LAD_eng_000316-LAD_eng_000316) +H E P L A D W T H T E N P L A R S F O R H A R V F W A S A G A N E T H T R D I T I O N I N D D E A S S P E (LAD_eng_000317-LAD_eng_000317) +T H E R E I D I N G G J O U D G W A S W E B S T O F A I R H O W A S A L E A D Y A S I E D T O T H E C O R T B E F O R E T H I S C A C E W A S S H E D U L T (LAD_eng_000318-LAD_eng_000318) +B G G R A T H E F I V E W A S T H E T H I R D O T H E M A I N S E R I S T O F E C H E A L I V E L O U N C H (LAD_eng_000319-LAD_eng_000319) +I T S M O T O I S H O E V E Y O U A R A N D W H E R E V E R Y O U A R E O N T H E D I R N Y O F F A I F Y O A E W E L C O M H E R (LAD_eng_000320-LAD_eng_000320) +R O B E T A M I L E A S C O T H W I L T S O N (LAD_eng_000321-LAD_eng_000321) +A F T R E W N Y U A R B R A K S I R A O D E G R E W A S H E F L L I N G V E N C H E R (LAD_eng_000322-LAD_eng_000322) +A Y A M T E Y M A N U F A C T E D A M O R T L C I T O F T H E A D S A I D A R D R A C S T E R (LAD_eng_000323-LAD_eng_000323) +T H E E S E S S A Y A M E D T O B I L E D A L E F T W I N G O L T E R N I T I F T O N O U L A B E R A N D T H E E S S A N D P E (LAD_eng_000324-LAD_eng_000324) +H E L I V S L I K H E A S Y O N G P R S O N (LAD_eng_000325-LAD_eng_000325) +M A S T E O F S I N D I N E N D E N E A R I G M A N I G E N T (LAD_eng_000326-LAD_eng_000326) +S H E F A I L E D T O A K T H E T O P T H R E A T H E C E N I O N D J U N I E A R T R A C T R I L E S T H A T D U N (LAD_eng_000327-LAD_eng_000327) +A T O A R E F O L O U D I N S U P O R T (LAD_eng_000328-LAD_eng_000328) +T H E E S T A B L I S E N A T E N S E V E N T Y O N A N D E W E O T H E L D E S T C L O P S I N T H E S O U T H O F I N G L E N D (LAD_eng_000329-LAD_eng_000329) +H E W S A M E M B R O F T H E G E A S S C O T L E N D A D F I S E R Y B O R D (LAD_eng_000330-LAD_eng_000330) +T W O T H O U S E N D N D F I V E G E N T L M E N (LAD_eng_000331-LAD_eng_000331) +A O U R E F I L E A D A S T O N G R E C E P T I O N I N U R A P A D C H V E D E S T O B E U T I O N T U T T H A T W A S N O T T H E C A C E H E R (LAD_eng_000332-LAD_eng_000332) +B L T H O I S S T D E T H E S P O S T E R I E R A N G A L S T R O C T H E S (LAD_eng_000333-LAD_eng_000333) +H E W A S A L L S O A T H R E T I M E F R E N C H E N A S I N L H A M B P I A N N I N T I N N I N T Y N I N T E N I T Y F O R E T W O H O U S E D A N W O N (LAD_eng_000334-LAD_eng_000334) +T H E I L I G E S T R U C T H E R S H O W N I N I S M A P I S T A G R A T E X S T E N T U N C H A G E T D A Y (LAD_eng_000335-LAD_eng_000335) +R U H A R I S R E C A G N I S E D I T N U K L E R D E S A U S T E R E C P O R T E S A N D O T H E S A F T Y O F I T S T E N O L A G Y (LAD_eng_000336-LAD_eng_000336) +A S O F T W O T H O U S E N D O D F O R T E N A E M T Y V E E I S A V A L A B L E W I T H I N T H E U N I G T E D C I N G D M O N V E R G E I N M E D I E R A N D S C G I Y (LAD_eng_000337-LAD_eng_000337) +N O Y O U R K P E A N G I N R A N D M H O U S E (LAD_eng_000338-LAD_eng_000338) +T H E D U T C H Y W A S E C U R E D I T E O U T C O M E O F T H E O F I C T W A L R (LAD_eng_000339-LAD_eng_000339) +W I T G O D P A C E S D A R T E T H E M A C H H I H B O T H T E M E M S O L T E N A T I N G S O P R E M I S Y (LAD_eng_000340-LAD_eng_000340) +T H I S V R T I O N I S N O N T E D O R B E I N E R Y F A V T F U L E T O T H E R I D I O N L N O V H L (LAD_eng_000341-LAD_eng_000341) +T H I S P R S A U M P T I O N I S N O T F U L E F I L D W O N H A S T O N O T L E A T E T W O C O N G E T D I A M I T E S (LAD_eng_000342-LAD_eng_000342) +N O T A L E T I T L E S I N L D E D G O L D A N A C S T H E R E V E N G O F D E A T A D E R R A D M O B I L O U T R U N E R S A N D S A K G R S O N I C T H E H E A G H O G K (LAD_eng_000343-LAD_eng_000343) +T H E N I N T N N I N T Y N I N D J U G M E N T N O T D T A T T H E I N T F L O N C O T H E F A R T H E R O F T H E C U S D H I S B E E T H E R (LAD_eng_000344-LAD_eng_000344) +M O K D A F S W E A R S R V E N G E H A N D J O I N S F O U R S E S I T M U L K C M T O O V E R T R O M M O K B E T H (LAD_eng_000345-LAD_eng_000345) +T H E E A D Y A V L E V I L I G E C O R T W A S A L L A S A N C H O U S T O C A P T H E E N E R O U N T H E V I L I G E G A P L E S (LAD_eng_000346-LAD_eng_000346) +T H E A S A N I N R A N K S I S T E M E A C H R A N C A V I G M O R E P O U E R T H T H E L O E R A N K (LAD_eng_000347-LAD_eng_000347) +T H E A S T A B L I S C H E D D E P L I M A T I C R L A T I O N D S O N D E P T O M E R N I N T N T H N I N T N S E V E N T Y T W O (LAD_eng_000348-LAD_eng_000348) +T H I A S F I R T H C S T E N D E D T O I N C L D M O R E Y O U C A Y D A T E I N D E C S E M B E R T W O T H O U S E D N D F O R T E N (LAD_eng_000349-LAD_eng_000349) +T H E U C H G O V E N T I S C I R N T L Y E S A M I N G T H E L E A K L E C O N C I C Q E N C E S F H E R O L I N G (LAD_eng_000350-LAD_eng_000350) +F R O M N I N T I N T H E R T Y T H R E E T O N I N T I N F O A R T Y N I N T H E M E R I C E D L E E W O N D W E L V E A U T O T H E F I R S T S I C S T E N (LAD_eng_000351-LAD_eng_000351) +T H E A I R H E F E L E S I C K W T T I V F O S H I M S E L F (LAD_eng_000352-LAD_eng_000352) +S I C X T T E M E S H A E E D E V E I D E D I N T O T W O G R U P S O F T H R E T E M E M S A C H (LAD_eng_000353-LAD_eng_000353) +T H E F I R S T S E S O N R E M I A D O N D W E L T H D U N T W O T H O U S E N D A N F I F D E E N (LAD_eng_000354-LAD_eng_000354) +I T S A C E D T H E W E I H B O L R D A N D S I S T A M T W E N T Y F O R E C O M B I N G F E A C E S F R O M B O T H (LAD_eng_000355-LAD_eng_000355) +V E L Y U E T W O O H N U M B R S W O N T W O A N D T H R E (LAD_eng_000356-LAD_eng_000356) +T H E L O R P A T O F M E N S D R E S E S W E M U C H H O U R T I N L E A N G T H N T H O S F R W I E I N (LAD_eng_000357-LAD_eng_000357) +T H E I G O A L T H S I N T E R N W E R C E A D E D B Y T H E M U L E R S (LAD_eng_000358-LAD_eng_000358) +J O S O F H I Y S C O L A V E R Y W E K O F T H S C O L H E A R (LAD_eng_000359-LAD_eng_000359) +A S R S I L O F A L T H E A R G U M E N T G E T I G T O H E R (LAD_eng_000360-LAD_eng_000360) +I T H A D Q U A R T E R S A R I N S H E F E I L D O U N I G T E D C I N G D O M (LAD_eng_000361-LAD_eng_000361) +L A Y L L S O F I H A L Y S I D E T H E C O N T R A C T O N S T A G E W H T H E D I R E C T E A N D P R O D O U S E S O F T H E G O L D A N I Y S (LAD_eng_000362-LAD_eng_000362) +F I S I C L E F E R I A P Y C O N H E L P A T I E N T E T O A R N H O T O W A E K W T H E F O T D R O U P E (LAD_eng_000363-LAD_eng_000363) +I T E N T O N T O S E L T H R E Y H U N D R E D T H O U S E D Y A U N I T S A C H E V E F I V E F N O (LAD_eng_000364-LAD_eng_000364) +T H E N A M E M S T D U C K A F E R T H A T (LAD_eng_000365-LAD_eng_000365) +T H E I L B O M L A T E R B R A C T H E D I M E N D R E C O R D O N D C O K C U M U S I C K (LAD_eng_000366-LAD_eng_000366) +I T E D E A T O R I A L W E S O U B M I T A N D I T O R T H E A P O L I T O P R I S E (LAD_eng_000367-LAD_eng_000367) +D J O S O F P L A Y E S A U R F E T C E D I E A C H W E E O T H S H O (LAD_eng_000368-LAD_eng_000368) +T H E W A T F R A T I M E M B I L D I N G U T T H E F O R S E S B E I G I N G T O O A N D R I F T H I S E A V L R E A L Y A E X S I S T S (LAD_eng_000369-LAD_eng_000369) +B R E A E M E N C I O N O F T H C O N V I C T I O N A P E R D O N P A G E T H R E O F T H E N O U Y O U O K T I M E S (LAD_eng_000370-LAD_eng_000370) +O R D E D B Y P E S I O N O N P I C H F R M B A K R I H T T O F R U N T L E F T E T (LAD_eng_000371-LAD_eng_000371) +H E A S M E M B E R O F T H E C O R T O T H E R I L C O L I G O F A R T L U N D E N Y O U C A Y (LAD_eng_000372-LAD_eng_000372) +D E R I N T H E C O U R S E O F H E C A M P A I N F I R G E A N D V I S I T E D L L T H E R T Y N I N W A S I G T A N S T A T C O N T Y I S (LAD_eng_000373-LAD_eng_000373) +A S T R I P O F P A P E R O F L E A N T H (LAD_eng_000374-LAD_eng_000374) +S A T O H A D F R E K U N T L Y W E R K T O G E T H W T H Y O U K Y Y A M E A R O N P R E V I U S P O G E C T S (LAD_eng_000375-LAD_eng_000375) +S H E W S B O R E O N D S C R E N D U I N G T H E E P S O D B R U R D C A S T O N F O R T H N O V E M B E R N I N T E N N I N T Y F O R (LAD_eng_000376-LAD_eng_000376) +A H T U R E D R O U W N E D S H E H A D C O M I N S O G E N T E L Y T H A T H E H A D E V E H E R D H E R (M-AILABS_eng_000159-M-AILABS_eng_000159) +A T O B E S H O U O R H W E M U S T C E O U R D O R S S H O T W E M U S L A T N O W O U N I N H A (M-AILABS_eng_000160-M-AILABS_eng_000160) +A C I D E S B E N E B E G A N M O K I N G L Y Y O U M A H V E W E D E W H I I C A L D O T R O U S W H N I C O O D J O S T A S W I L L H E D E S T R E Y O U T H A T I D O T A D O A N C E R H I M (M-AILABS_eng_000161-M-AILABS_eng_000161) +T H E P E S E N T T I R H I M S E F A P O N H I M A N D B O U N E D H I S F O R L A K E D H T L Y S O A T E C O N O T M O E (M-AILABS_eng_000162-M-AILABS_eng_000162) +N O R M U S T T H O U S O L I M I T H T H E O L Y O N O F I S R I L A S T O T H I N H H A T H B U T W N E N W A Y I N W H C H C N G O R I F Y H I M S E L F B Y T H E (M-AILABS_eng_000163-M-AILABS_eng_000163) +T H E L D C O M P R S O N D B E T W E N T H I M P A L S O F E X S E A K I T I V E A N D T H E L I T B R L E A R T U S M A N H W W O D L E U R N D T H A T H E R E O N L Y O N E E R T O P O S I E D E C I S I O S O A L A B L E I N A L L T H E W E R L O T H I N K I N G (M-AILABS_eng_000164-M-AILABS_eng_000164) +A V F T R T H I C S P E R I A N C E T H E E N D V A D E R S W E R C A I R F L T O C E P E A S A K F E D I S T E N C E F R O M T H E W A L L (M-AILABS_eng_000165-M-AILABS_eng_000165) +M A O N O U B E A E R S M I N G F I R T H E R I T H I N Y O A T N O I T I H A V E H E R A M O S T M S T E R I A U S T E L P R G R O M E M E S E A S E W H A T I S I T E I S S H E D A D N O I T I S N O T U B O U T H E R Y (M-AILABS_eng_000166-M-AILABS_eng_000166) +D N O W H L E M I S T O R T H U N T O N S A D D I E T H E B A S K T O M E I L T A K I T W (M-AILABS_eng_000167-M-AILABS_eng_000167) +A N A R A B I O A N N I H T E C S C L A M E T R O G T W H I T H A T W A S A M A G I A K N I G H T W O A S I N I T H E R S D I F R N T S O R T S A N I G H T S M A T S A I D T H E S A L R A N D T H E N I G T B U T N U B I G H T M E N E S A T T H E S A M E N I G H T Y O M E A N (M-AILABS_eng_000168-M-AILABS_eng_000168) +I V E T U R E D O B F E O U P W A R D O H U D R D O M Y B E S T E H A N D S F O R N O O T H E R F L T T H E M F A L W I N G O U A N D S U C H A S Y O U A N D Y T H I N K I L L T A K E Y O U O N (M-AILABS_eng_000169-M-AILABS_eng_000169) +G U H E W O H E S E H I M H E R H A R T L A P E D U B B N A P R O H E N T I O N A T E V E Y R I N G O F H E D O R B L T (M-AILABS_eng_000170-M-AILABS_eng_000170) +A A T H E A S B O K S S D I C S O N I W L C E A L T H E R E S T W E O U S E N T O M S T R B E L T H E A R O F A C I N T H T H E W L A V O U Y O F O R T H I M S E L E S A S W E L A S F R P O S S A Y D (M-AILABS_eng_000171-M-AILABS_eng_000171) +B U T I N G L W A S N O T I T L S H O U R T H A T H E Y C O U L D N O T G E D I N T H E G A T S O P E D I N O R D A N D T H R E H A V Y B O A R R S W E R H E L D I N P L A C E B Y M E N E S O F S T O U T S T A P L E S R I V I D T E T O T H E S H E T E S O F S T D E L (M-AILABS_eng_000172-M-AILABS_eng_000172) +A I W O A N T T H E I L S I D O D O N C O L D L Y I H E N T A D O S O N H O R E S I W N U N T M E N T O B R I G E T H E W I T M H E P U S H E I S W A Y F O R E D W I C H W A Y T O T H E S T A B L S (M-AILABS_eng_000173-M-AILABS_eng_000173) +I R I S L E I T W A C O C A N D D E V F R T H E F I R S T I M E Y O U A N T E A N S H O U S E A N D B E S I G D E S T H A T W A S N O T I M E T O A R O U S E S P I O N I T H M I N D S O F A N Y W O N (M-AILABS_eng_000174-M-AILABS_eng_000174) +D O U N O T R E M E M E R T H A T E S A S T H Y D E M O N T H A T D T H E S P I R I T H I C H C K E A P E S T H E I S N O B L C O R E A G E S C E H A I U N M A U C H O A B L (M-AILABS_eng_000175-M-AILABS_eng_000175) +A T M I S T R B E L E O A C A N H E N O O F C O N H E E L I V I N G A L A S Y L I F N A D D R O U S Y C O L I D G E H A (M-AILABS_eng_000176-M-AILABS_eng_000176) +A N D T E C I T O N F O L L O W E D E M U R L Y A T T H E R H E A L S (M-AILABS_eng_000177-M-AILABS_eng_000177) +T H E F I R S T T U H W O D C O S A N E C S P L O I O N I N W H I C H A M O G S U C H H U N D R E D S O F I N F E R A T E D M E N A N D R E C K L S S B O R Y S (M-AILABS_eng_000178-M-AILABS_eng_000178) +W O N T E G A T P L E S E R S O F M A R G R E T L I E A T T H I S T I M E W A S I N E A D E S B O Y (M-AILABS_eng_000179-M-AILABS_eng_000179) +T H T H I N A S G O N U N D L O N N O F R S O N E O R B I G A C X I T D E N T W E S H A L H A V E T O C O M B E R M Y I S W I T H E I N E R I V E R N C E R Y O N T H W E R K C O I N L (M-AILABS_eng_000180-M-AILABS_eng_000180) +A A Y O U R L A T S A I D S H E W E L S H E H E D H E R B R E A T H O T H E A N C S R H A L (M-AILABS_eng_000181-M-AILABS_eng_000181) +R O H T T O L E T H E G I R L S T A H E M U S C O H T H E R F O T H E R T O L I V N G I P K G E S S I S L E S L T L E O L D C A B O N A N H N T H E H E R D T H S R E D F U L D E C R E (M-AILABS_eng_000182-M-AILABS_eng_000182) +M A R G I T S A T D O W N T H E R O G K G P A R T L Y T O W O R M E H R S E L F F O T H E D A N P N E S S O T H E E A V N I N G H U N G O U T H E R R E S A N D O V E F I T E A D M A D H E R C H I L Y (M-AILABS_eng_000183-M-AILABS_eng_000183) +O N O W Y O A R M S T A K E N B O U T T H A T E L I D T H E C I N G T H E A R N O T M Y P R E S N E R S B U T M Y S L A V E S H O M I Y P R C E U S T F R O M T H E C I N G O F E V E (M-AILABS_eng_000184-M-AILABS_eng_000184) +H E R F A T H E T O U T H E O M E R S A T I O N (M-AILABS_eng_000185-M-AILABS_eng_000185) +I N A C O U R E R W A S A S O U R D O F D R E I N G T A B L E I N W H I C H L Y A C O M E A N D B R U S H C E N I D Y S E E D M U C H I N T E R S T D I N T H E T A B L E A N D W A S A E X S A M I N G A T H E T H E G U E R U R E T R N (M-AILABS_eng_000186-M-AILABS_eng_000186) +I A V E S O M E T I M T A U H T T A T M Y S E F S H E A G R E E D B U T O F C O R I O T N O E S T I L I H V E T B E P R T Y C A R F U L S O M E W E N I S L L I S O V E R E B Y M Y D E S C O R L O I N G O V E R H E A R (M-AILABS_eng_000187-M-AILABS_eng_000187) +I S H A L S T A Y E R E P L D T H O N G A N F O R I M E N T O S T C O F R E (M-AILABS_eng_000188-M-AILABS_eng_000188) +W H A T D Y O U D E O A S T T H E S O R S E R E R (M-AILABS_eng_000189-M-AILABS_eng_000189) +W H I Y T H E R A R A N I M E S Y O U R S H O R T H I N E S N O T A N Y M O R E R E P L I D E S R O U H T I M Q U E T H E P I N K E S N D H M L S O Q U E O T H E L S S O I W O N T H A V E M Y P E B L E Q U A R L I N G (M-AILABS_eng_000190-M-AILABS_eng_000190) +T I P R A E R S E C L I C I N G C L P I N G A R B N G S N I P D O T F C U G S T A C O F N O U S P E R A N D P A S E I N A I N L A R G S R A B O C S S U R K I L E R S R B E N G F O L D A N D A D E D R A D Y T O M A L F O T H E F I N L A P E L (M-AILABS_eng_000191-M-AILABS_eng_000191) +I T W A S F O R E D A Y S A F E D T H E S U P R I Y S O F L H E R S H O R S E W H N T H E S T R A N G R S L E A F T H E S T D A T T T H E C A I R O F R O G E D O L D F O R S T R H I R M O N (M-AILABS_eng_000192-M-AILABS_eng_000192) +M P O R E T E M P L T O N H E S A I D I O U S T N O H A M M A N Y U A R S G O W E N W E B O R Y S E M N T O S C O U W I T H M N D N T A L H A T S O R O F H I N U N O E B U T A N D T I L I E R A N C R O U S H M O R E (M-AILABS_eng_000193-M-AILABS_eng_000193) +I F O N D T E I N T H E F O A R R S T N D B R U G T H E E A R A P R E S N E R E P L Y D T H E C A P T O N (M-AILABS_eng_000194-M-AILABS_eng_000194) +O M A B E C O M P I T E N T I D T H E F R O M P E R S O N L C S P E I R I N C E O R T H E E C S P E R I N C E O F O T H E R S T O A N C S E R T W H T M O E O R L E S S C O R A C T K N E S O R A T L E A S T E N I T E M T O H (M-AILABS_eng_000195-M-AILABS_eng_000195) +L W N N I N T E T O L A T S T R E D S A I D H O A K G O N B U Y D I G O F H I S O G A R (M-AILABS_eng_000196-M-AILABS_eng_000196) +R A T W A S S U R P R I S T O F I N E H E C O L D S E S O P L A I L Y T H R T H E H I Y W A L O W O A H T E R U B O F H E R R B U T T H E S N D W A S A B L T O S H O O T I T S B E M E S T R A T D O N T H O R T H E A R A E N S P E I R N T (M-AILABS_eng_000197-M-AILABS_eng_000197) +T H E S P A T E I D S P R O N G O P E (M-AILABS_eng_000198-M-AILABS_eng_000198) +G C O M E D E A N I L W I T H E G A V E S O C H A S U P O S I T I O N (M-AILABS_eng_000199-M-AILABS_eng_000199) +Y O S E A N D T I L T H E S C H L P I L E S R I N G E N T E D W E W A S T T L A T O F T I M I N S T D A D Y T H A T N O W M A B E B E T E R I M P L O Y E D A N M P R C T S C I N G A T H L E T I C (M-AILABS_eng_000200-M-AILABS_eng_000200) +Y O V E D N I T H A N W D E C L A R E D D A R T H Y T H E S T E N C E A R J U S T O E N D E R F O L (M-AILABS_eng_000201-M-AILABS_eng_000201) +E M F O R T W E N I N G T A N F I V E F T H E R E E T W O E T H E I N O W A S B E R L Y T W O E N Y M Y W S A W A Y W H N H O D I O N F I R D H S R O C K I T S T H E M D A C O L O S T O L C L O W E O A P E R I N A M T Y N E S E (M-AILABS_eng_000202-M-AILABS_eng_000202) +T H E P A D N O A T E N C I O N T O T H E F A C T H A T G I P K G E S C I S I L D I D N O T O N T O M A R Y A N Y O F T H E M E M F O R H E H A D E T E R M E N D T H A T H N I T W A S E G R E E D W H O S H O D H A V H I M (M-AILABS_eng_000203-M-AILABS_eng_000203) +W A T D Y O U T H I O F T H A T H E C R I D O P E N G A C O P B Y O T H E R E C K E D A N D L A N G T F L A T O T H E L I B R Y T A B L E L (M-AILABS_eng_000204-M-AILABS_eng_000204) +I T L E C O P I E R U T A S O R T T I M E (M-AILABS_eng_000205-M-AILABS_eng_000205) +A N D L A S T T H E R O U D O V E I G E T D A B L E P E P L E H O H A D N O H A R T S A N D O U O D N I T H E R S M I L E N O R F R O W N (M-AILABS_eng_000206-M-AILABS_eng_000206) +T H E I N Y O L L C A C H I T E S A I T H I C H (M-AILABS_eng_000207-M-AILABS_eng_000207) +W H T I S I T I Q U E R E D N O T F I E L I N G S R E N B U T T H A T T W A S A E V A L D A T E M D T O S E C E R L I T L F R E A D R T Y S I N G F O T H E A N D E O V E R (M-AILABS_eng_000208-M-AILABS_eng_000208) +S O E G A V E T H E L I R C T H E T H R D U N D E D O L O S F O R B O K S A N D A C A S K O F G O D O L D A L F R P E T E R T H E C L R K R A N T H E A I L H I M S E L F A N D G A V E H E C A F M I W (M-AILABS_eng_000209-M-AILABS_eng_000209) +A T L I E K T H A T A N A L S I N N E D E R L A N T D W I T H M E R L Y G R I N T H T F A T E D A W A Y C H A N G I N G I N T O A L I N K S W H I C I N T R E T O S P E R D F O L O W E D B Y A N U N O N R E C E W I T S O U R T N O U S A N D P O N T E D E R S (M-AILABS_eng_000210-M-AILABS_eng_000210) +A S H E C O L D N O T D E O E M A R G R I L A N C D U N C O N H O U S L Y A T H E U N G K L E D O R N E R O F H E O M E S H E U O H A R T H N D E R T A K A S U R V I N C S P L A C E C O O S H E (M-AILABS_eng_000211-M-AILABS_eng_000211) +A D N O H E S H E R E P L I D E D W I T I N I S E N K E R Y O U S I T Y D I D I G I V F T H E T O Y O U W H A L (M-AILABS_eng_000212-M-AILABS_eng_000212) +M A R B O R M I L E S A N T H E A G A C E N T D E L I N G W R H E L D U N D E R L O N G L E E S T H E M U S T I F P O S A B L T B E R E L E T (M-AILABS_eng_000213-M-AILABS_eng_000213) +D A C O P W A E O S T U N I S T H E L A T E M (M-AILABS_eng_000214-M-AILABS_eng_000214) +I T D B O N D E D H E A R A N D T H A I R A B O U T H E C I O K O N H O U S A N A T F I R S T D O R T H Y C O U L D N O T T E L H A T I T W O S W H I L T H E S P E A C I N G O F T H E C H I O C I O N S N E R L Y D E F E N D H E R (M-AILABS_eng_000215-M-AILABS_eng_000215) +T H E S O L D E R G A V E Y A L H A T E R O U S E D A S C H U A R O F H I S C O M R A D A N D B R G T T H E T U M B L I N G I N T O T H S T R E T W H E N T H E Y S A W H O T H E O L R S P R E S C I S E P R I S N E R W A S S C A P I N G (M-AILABS_eng_000216-M-AILABS_eng_000216) +G J I M H A D R E F E U S E T O L E T H E F E L D O F G R A S W H E R H E W A S I N G A G E D A N D B I S I L Y E A T I N G S O T H E W I S U R D G U G T O U T O T H B O U G Y A N D J U O N E D S A E B A N D O A R I T H Y (M-AILABS_eng_000217-M-AILABS_eng_000217) +G S R D N L Y I M A S I N T E R T E D N T H E C A C E S O A R B U T I C A N A K H E D S R T A L S O F T I R E P L I D (M-AILABS_eng_000218-M-AILABS_eng_000218) +O R A N Y M I C E O R E V E N G G R A S H O P E R S (M-AILABS_eng_000219-M-AILABS_eng_000219) +A N D T H E T H E P A S I O D O N T H E T E L Y O U W H A E T O D O R W H A T A N N O T T O D E H W E T H E M U N Y T H E Y G I V E Y O U A N J U S T P A M E N T F O Y O U P A I N S I N T H E R E C S T C A N G E L I C (M-AILABS_eng_000220-M-AILABS_eng_000220) +W H T D I S T A T M E A N A S T T H E R I N C E S (M-AILABS_eng_000221-M-AILABS_eng_000221) +D E H A D B E E D R O N E D H E W A S F L O A O D I N G N A S I O F L I T A N D N O W T H E D S H I N I N G L T E F I S I E S S W H E A M I N C Q U I S I T I E L Y E O U P T O H I E N D S T A R (M-AILABS_eng_000222-M-AILABS_eng_000222) +B U D O L D G N H D A R I K T W O L E F T D R E M E M E T H E T A I L I R E D T O Y O U I T H T H O N E R M O A B L T H E T H E F I R S T T H E B R O N S T E N D E T H E W R L D O F O A P L W E R S O U L D G R S S I N T F R M S O M E B L A S T I D P L A N T N O U T E R S P A C S F I E A N O H O M (M-AILABS_eng_000223-M-AILABS_eng_000223) +A P P E W I E O S P E A T T H E M E N A N D G T H E M T O G O W A Y S H E C A N T B R E E T H P O R T H I N G W I T T H I S R O W D O B O U T T E R H L (M-AILABS_eng_000224-M-AILABS_eng_000224) +A W H E N I Y T O K T H I S C A C E H E S A I D I B U L E V E D D O N E I N D M Y H A R T D I C S O N W A S I N S E N T I S T O B E L E I T B U T M Y F A T H A S P B E N R O U T L Y S H A C (M-AILABS_eng_000225-M-AILABS_eng_000225) +A D H A P T R S I C K O V E F T H E P I T O O R S E A T S E (M-AILABS_eng_000226-M-AILABS_eng_000226) +R E M E M E R T H E C A N N O T T O U C H U S (M-AILABS_eng_000227-M-AILABS_eng_000227) +I V M E T I M E A S O U R G I V E M E T I M E I O F T E I R S A N Y T H N G I H A T I T S A H U R Y I V E A N Y D E A E Y O U R M A D G E S T Y A N D O N C E T T H E S I C T T H S N O B E N O S P R O N C E S (M-AILABS_eng_000228-M-AILABS_eng_000228) +T O N O F T R O A H T D E C L E A R E T H E S A L E R M A N (M-AILABS_eng_000229-M-AILABS_eng_000229) +A A S F O R T H A T S A I D M A R G R I T R T H E H O T I L Y I H O L D I T H I S O H O N Y S O I T C Q U E E M U L D E P E N S A Y (M-AILABS_eng_000230-M-AILABS_eng_000230) +W E H E T H E S W O R D S T H E C I N G W O S H E D W A S F U L F T H E P I N C E S N E V E R T A P E T O N C Q P H I R I F T H E Y C O D B E T R O A N D S M E R E D H I M S E L F O V E R W I T H F A T A N D S P R A N G I N T T H E O V E I N T (M-AILABS_eng_000231-M-AILABS_eng_000231) +Y O S H O L B A L Y G E T P A R C E F R O M Y O U R E R O M E V I O N D R E S E V E R E I L L H A V E S O M T O U O L S G I V E N O U T H E N A D T E D D E P O M A S H E H A S T O N D E R S T A N T H T I N G S A C E T O L O F E N C E S H (M-AILABS_eng_000232-M-AILABS_eng_000232) +B Y T H E T I M T H E F R O U S T A D S A D I N T H E S H O B E F O A R E W A Y F O M H E L S T D O N (M-AILABS_eng_000233-M-AILABS_eng_000233) +O E N T H I G W O E N T O E S A Y B E G A N D C E N I T Y (M-AILABS_eng_000234-M-AILABS_eng_000234) +T H I M P O R T N T R A C H I C W A S O N F I D E T O N O O A M B T H E R E A L P R P R I T E R (M-AILABS_eng_000235-M-AILABS_eng_000235) +U N N W H W E A I D E D A B B L A S E D O N T D B A S K O D O V E M Y T H I S T I M G C G O (cv_eng_000707-cv_eng_000707) +I D T D A T A S E P R T S U P S E C T I O N W H I C H D L S W I T H I S A S P E C T D (cv_eng_000708-cv_eng_000708) +O P R A T I O N O F T H E F R O N T L A N C O N T D N U R E D O N T H E W O D E N D T T E S S E I L S (cv_eng_000709-cv_eng_000709) +M N I S O N F H L O R I D I S T W E N C E P E R E N T O V E R N C T R I M L Y E W H I D R A N G O F A V O M I N G S (cv_eng_000710-cv_eng_000710) +F O R J I N T B E C K I N G K S H I T S S T O R E T H F R E S H P B O C K T B U T D E A D E S N D E L E V E T H E M U N T O E R L E R O L D C A S (cv_eng_000711-cv_eng_000711) +T H E O T H E F O R T I N G C O U M P O S E S A R D T O W O Y U R E C A M P S R E F I R T T O C O L E C T I V E L Y A S T H E Y U N E R S T D E C O L N G E (cv_eng_000712-cv_eng_000712) +I T S T W O B H A D T H E D W E H A E C U I C K L E G O R N G T O F R G E T M Y T A E T D (cv_eng_000713-cv_eng_000713) +W O N E N O T O R E I N T E G E L O R Y S H O H H O U R D H E G I N T L A I D I R E D I S T A T Y A T F O A L A D G R O R T C O N T O N (cv_eng_000714-cv_eng_000714) +E A N M P E R I A L D I I A T (cv_eng_000715-cv_eng_000715) +T H E E S E L D I N C O M P N Y H T D A S H U D T A K E S C K O R I T Y O O T P R A T I O N (cv_eng_000716-cv_eng_000716) +T E C O I N G M I Y N I N G C A N B E D O N W I T G D O F H I S C O A R T S A O R E I T E S P E S I O L I S T H O R D L Y (cv_eng_000717-cv_eng_000717) +G T H E L S O L E T H E N O U S I O N A L R A N K I N G N (cv_eng_000718-cv_eng_000718) +T T A R W R S G R A N E S B I S H O P E O F N H I M O R E C K E (cv_eng_000719-cv_eng_000719) +A A I O U N D E R T H A R T T H I T O L H I M A N D H E O K M Y P L A S E S P D (cv_eng_000720-cv_eng_000720) +I T H O R G T I D G I V E T H E C I T I T C S A T D R E A E T E (cv_eng_000721-cv_eng_000721) +A S T I E W V E T L D E N I H T O M N T H E R I C T O E S (cv_eng_000722-cv_eng_000722) +H O E D Y O U R N O S T H T O C A E D T H I T M A Y F O M T H E F A B L I N G H O R M O T O F O N T I O N (cv_eng_000723-cv_eng_000723) +D T H A T S O N E D T S L A K E T H E R E P O L O M E A I I E D E I (cv_eng_000724-cv_eng_000724) +I H I S R I N C U L I G O R W A S N O T P L A R L Y D E F I D E B O W N D G R E A I N T H E S P I T D O F T E C H A R I B E U N P A N I N S T O L A E (cv_eng_000725-cv_eng_000725) +M A R S I A L S H A V E R O F S L A S H F I L N M E D G A V E T H E F I L M E A N D A T O U T O F C A N (cv_eng_000726-cv_eng_000726) +H O W P E R D Y I T I T A T E (cv_eng_000727-cv_eng_000727) +H I S T T D I L E B E G A N E T O E S A E M B L E T E M Y I K C L E T D E M O S K I N O S E S (cv_eng_000728-cv_eng_000728) +H I S A L L C A P A B L F I N G L Y T I N B L T W O E A M E N T E D E S R U P T O F P O E R (cv_eng_000729-cv_eng_000729) +T H E F L A M E T O W I A K C S C I N I N G L I N I R M L Y I N I N G S A S F P O L H E L I S T E A T O N D U I L R L N D A S E L A N D D T T R Y (cv_eng_000730-cv_eng_000730) +S H E D E T E R O U S H E L Y T O R O W O A T A H (cv_eng_000731-cv_eng_000731) +A E M T T H E O R G O N Y S E O R S O F T H E R O T E S E A N D A G R E D C R E A T T W O W O R K I N G R U M S (cv_eng_000732-cv_eng_000732) +A T H E B O N S T R O C T T H O F F H A L D P O R D W H I L E O A B O F H I G R E N O N S T O R D (cv_eng_000733-cv_eng_000733) +O N L Y C A M E D O N T O M E S G A R I T A N D G L D F I L D S O U I S E I L B A C K E A R W E R U N D C O N T I S T E D (cv_eng_000734-cv_eng_000734) +B P I T H I S A D C H I R D Y S C O L E W H O S F E S A N C O U C K I L A D I N O N A I N M E A N S T E S T (cv_eng_000735-cv_eng_000735) +S O M E W E N T W A Y W H L O U W A S E R A N D O T H E P E P E C A M E M (cv_eng_000736-cv_eng_000736) +D A D T T H A N T H A H T D T C T C D N C N (cv_eng_000737-cv_eng_000737) +T H A T C U R A C O N O T Y W A S L O K C A D M A N L Y T H A T H I S T O R I C L E A N D D E O G R E F O C L E R E G I O N O F C U R E (cv_eng_000738-cv_eng_000738) +T E L V A T I O A T H E S I H T I S A M O F S I L E V E L E I G (cv_eng_000739-cv_eng_000739) +A T O B A S T R I D E T O A N C H E C T T C O N P T E M E T E T E D I N T O H I S T O N (cv_eng_000740-cv_eng_000740) +I H E A V E T O W O R K E T H I S S I T O D L Y (cv_eng_000741-cv_eng_000741) +U T E D R A T H E R O N W O S F E N M W A S K I L A G E A G L E A D W L A F G L A T I N G O N T E R E N O N E S (cv_eng_000742-cv_eng_000742) +W H E O T H E B I L I N G D O S T H E S E L E D R B E I T E T H E B O Y T R O M B L E D T D A T W H A H E S O (cv_eng_000743-cv_eng_000743) +D E I O C R A T A N E B R H I N D D B A K E I E R W O N I T T H E O P E N S E O (cv_eng_000744-cv_eng_000744) +L W O R Y O U E T W O R D I N O G E A T H A B L Y T H E O O D I N I N H E C U L D Y U S E O A N A T L Y S I N D T O L R O U M N P (cv_eng_000745-cv_eng_000745) +T R A N T H W W A S B O R N I N B E L Y E S E S I T E D I N D B R I T O C S P O N D R E A S S (cv_eng_000746-cv_eng_000746) +D E R I R D Y F A S E O F L I C F M O M E S F A S T (cv_eng_000747-cv_eng_000747) +A A A T N O T E T H D E E (cv_eng_000748-cv_eng_000748) +S O R V E W I N T O L T D L U E T O E E (cv_eng_000749-cv_eng_000749) +A T O E T E R E L U R L E N S T E Y E O R D F O M B R A K G B E S T A T I O N I N S O E N I F R E N D E D E C T I O N S (cv_eng_000750-cv_eng_000750) +A A C H A E C R E P U P B L I K A N T E R E D T W O S H O U T E R S I N T O T H E P A R R O R O L E N P O G C O M P O T I S I O (cv_eng_000751-cv_eng_000751) +T I D H E R W I L I M S R O E T H E S K A N G P L A Y H A N T S H A R E D S T O R Y R E D I T H T T H O P R E I P I T (cv_eng_000752-cv_eng_000752) +T I S F A S T O F E L L E D W O R D E S T O A F B E T E R D N C H E I R R I T Y F L I N D E R A T N Y S A I D E O F O I D Y E T H E R A U R T (cv_eng_000753-cv_eng_000753) +O T H E S E E N X T R A G O A R S T S W E N E S E U R N L T T H E D O N G O N L E A L N M T O F O A C K G A L F T E R A Y H A T H E I R H A R D T S (cv_eng_000754-cv_eng_000754) +A I P I H A N D E R O N D B A K C T T O E S T R L I O W M (cv_eng_000755-cv_eng_000755) +A L P R E M I T M E T T O I N T E R D U S E S Y O U T O H E R M O D E S T I D C Q R E N (cv_eng_000756-cv_eng_000756) +E N O R G E I O N H E R L E N W A S B O S T O Y T H E N O N D A D E T D O F M O R F H E N S P S T O T D (cv_eng_000757-cv_eng_000757) +E T S H E I S O F M A K C O C O N D E S E S N T H (cv_eng_000758-cv_eng_000758) +G D I M E S O R E T H E I L E S T N O G T O N D I S T (cv_eng_000759-cv_eng_000759) +A A O T H O S A N D O N T E L A N C T L O L T H E S H R E Y I G D L O L D P O R A B E D I T O N O (cv_eng_000760-cv_eng_000760) +A I C O L E D A N T O P E S O R I N A T I T E E R (cv_eng_000761-cv_eng_000761) +F O R S I N P L I T Y G U R I N C H E S D I S N O R M I L Y A R D O U N D E D T O H E N E R E S H H O L N O M B E R (cv_eng_000762-cv_eng_000762) +I O F W E A C T I L Y D E O O N I S S O L D I T W I L B E F (cv_eng_000763-cv_eng_000763) +T H E I F O O F H H I C T R Y S A P L S H A P E D T (cv_eng_000764-cv_eng_000764) +T H E R E I E A C T S H A N G E I S N O W O B L Y (cv_eng_000765-cv_eng_000765) +A W H A T O U E A T T O D A Y W H E A S K A N D T O R K S T O M O R O E (cv_eng_000766-cv_eng_000766) +A T H E W O T E D A N F L O S O U T O F T E S C O N M N T S A S H E L O O A P L E R I V E R (cv_eng_000767-cv_eng_000767) +A M H E W H I Y I D D I O N Y O E S A Y S O M T H I N K C D H E A D C D (cv_eng_000768-cv_eng_000768) +T A V E Y O S E N O M A R N M E E T D E E E E E (cv_eng_000769-cv_eng_000769) +I C O T D G O O N E F R E D A I S A B O U T H E D I D I O S W O N S T H E D U A S T I N H I S P A R T O F T H E W E R E T D (cv_eng_000770-cv_eng_000770) +T H O S O E O L A D D E V F T H E A R I N C O R E I R E R A E N I N G D I N S I T O P C E Y O T T H E Y O A R E (cv_eng_000771-cv_eng_000771) +A A C O A S E V E S O P B D C E C T I S E S E R G L W N D S H E E C L D E L D A R D L (cv_eng_000772-cv_eng_000772) +T H E S W E E D S W E R A N A B L T O Y O U S H R V E A C A L S W H I H E R T O C E I N T H M O D E (cv_eng_000773-cv_eng_000773) +I T H E A C K T I D N O T P O R H E B I T B A Y I N G A E R E P E O S E N T I P T O E B E A R A N T H E C O R T L I S (cv_eng_000774-cv_eng_000774) +C H O N W R P L E S T E L E P I N R O U L T I D N (cv_eng_000775-cv_eng_000775) +H E W A C O N V I C T D E D A M B A N I S O S I P R S F O R S O V I O N H O R S R P N I S M E N T (cv_eng_000776-cv_eng_000776) +T H E C O P L O F T W O C H O L D O N A D A T E R S O F E A U R R S E L E N D E A A N T H E S I N F M U T H A L B R V E R Y (cv_eng_000777-cv_eng_000777) +N O F T H T H R E R E R E N D E M S R E C H H E C U A R A M O F H E M O D G U R I T Y O F H E S I N T I C T L D T (cv_eng_000778-cv_eng_000778) +I E T I T E R P E N S A E X C E A D E D I N D E R A S T S O M E R R O S I P C A I R A I T I I N H O S O L E U N E R S Y O T H O R A P E I R D O S T O N G B O A T H (cv_eng_000779-cv_eng_000779) +W H E A R A A M E B E C W E N M Y F L U C K A N D M T H B U T E R S I U R E T H E B O L Y T O S (cv_eng_000780-cv_eng_000780) +T H I F A L I Y E R H A S T L E T T O I C X S T D A E N O U L B L E N C D H A D H I N S E R D A Y S E O F C O L L S T O (cv_eng_000781-cv_eng_000781) +A D D U T Y A S E S H H H H D E N (cv_eng_000782-cv_eng_000782) +W H I Y O I T H A T P L A I N D C A E P E G O I N O V E R (cv_eng_000783-cv_eng_000783) +E E E H A Y I T A O D D O N D I S T E E T H E F O R W A O F O R S I O L B O S E W I O F O R S O L T E S E (cv_eng_000784-cv_eng_000784) +T E P L O C A T I O N W A S P U T A P R O V E I T I N F A R I B R A E L Y (cv_eng_000785-cv_eng_000785) +H E N R Y T O R L D T O N S T I L E S E W H E R H E H A D T E S O U N D E D R O N I N G I N L T I N (cv_eng_000786-cv_eng_000786) +I T W A S T H I S C O N T I N U E D D T O S C E T H R L I N G C O N F L I C A N D F V L V E D A N D L O S E S I S R E T I R N T O R E T O E R E S T R I U L E R E B R A D I U O (cv_eng_000787-cv_eng_000787) +D E D H H E R F A N M E L Y H W O A S F O M E P R E O H O N S A E E D D U D Y (cv_eng_000788-cv_eng_000788) +E W O W N T I D I E A K E F O R D I N M N B H P T (cv_eng_000789-cv_eng_000789) +T H A T W A S M Y D R E T O S I N S E (cv_eng_000790-cv_eng_000790) +H E S G O S L A I R E N T A M U S T E R E O F D S H E A R O S T C O L O (cv_eng_000791-cv_eng_000791) +T H E N L I N T O R N S T O T H E C H O R I S H E O F S H I N G S H A T T B L T E R A N D E S P E U E I T E (cv_eng_000792-cv_eng_000792) +W U D N O T T H O S E R I N H E R E A C A D R T (cv_eng_000793-cv_eng_000793) +T O E L C I O L S E I N C T E N D F E S T T H E A I Y H (cv_eng_000794-cv_eng_000794) +M Y N E S T C O N H E L P E O W I T T H A I T S (cv_eng_000795-cv_eng_000795) +T H A T S A C O U D H I S T O T L Y O N (cv_eng_000796-cv_eng_000796) +H O E F O R T H E B E S T A N D P O B E A R E F O R T H E M L S T (cv_eng_000797-cv_eng_000797) +C I N I S H E L Y T H E W H P D Y O S O S H T I N S T R O C K Y O B O D I C T (cv_eng_000798-cv_eng_000798) +A L L W E O N E D B Y T H E E V E R I T M O R S O N I K C E T H (cv_eng_000799-cv_eng_000799) +A A T H E T H E O E N G T H E W I L A S E R I N T O M O R O N M N E I N L N D E E T H E R (cv_eng_000800-cv_eng_000800) +E D O B P B I S T D I R I C M M I N D L I B P B P T H (cv_eng_000801-cv_eng_000801) +A T O S E I O L E P A T R Y H E T O H E R P L A E A S A C T I N G O R E C T E R H E H D (cv_eng_000802-cv_eng_000802) +T E B E V E R L Y W L E B E F L Y A N T E S T H E I E S S E N T E R P U O P T T O N S H I M (cv_eng_000803-cv_eng_000803) +T H E T R A C E R E R V I S T I N G W A S A L S O C O M P E T E D (cv_eng_000804-cv_eng_000804) +H A T D M A R S H W A S A W H A R O F T H E I M P R T N S O F I L C T R M R Y C O S K O M P Y E I N B Y L O U G I C K L R E S R C H E (cv_eng_000805-cv_eng_000805) +S I N H E W A S B O R N Y U D T H E H O B O U E (cv_eng_000806-cv_eng_000806) +T H I S W E I E N C E H E A S E A N O F I T I L Y T H E R H E O T O A S E M A C R E T O A D W I N T H M Y C O L I S T I O N F O A S E S O P E R A T D I N M I L (cv_eng_000807-cv_eng_000807) +I T I S R E S P O N E A U L E F O R W A T E R S O P L I A N D M A N E M E N T O F W O T E R R E S O U R S E S A N D M O H O U S T R A (cv_eng_000808-cv_eng_000808) +D I S E S T H O F E O R E S F A Y E O F T H E G H O R V E H E S A D E D (cv_eng_000809-cv_eng_000809) +T H E G I S I A P P L A T O H O R G I S S A N C R A O L P O L I S I T H E N G I O N V A O L Y O F T H E D E A D C O N T T A N G S E V R L P E R M I N D S O F W H I C H T H E G R A T P E R M E N T I S T H E L A R T E I S S E S E V E R L E S M L T O N S S O V R L E T E M P L E S A N D T H E G R A T S P A N K S (fleurs_eng_000413-fleurs_eng_000413) +T W O R E H E I N D O F T E M I L E A G E S W E S T O R N Y U R O B E G A N T O D E L T T E R O N S T I L O N E O F T H E B I G I S T O E L I N S O F T H E T I M E A S R E S I U L T O F T H E R E U C S A I S P E P B L B E G A N T O O U S E S B U T E N S T O F A S T O N R L V D I N G I I I A (fleurs_eng_000414-fleurs_eng_000414) +I F S Y O U O N L Y G O L S H O R E O U S I N G S H I P O R C S C K D R I O N D S Y O U L N O T E A S E P R T V E S A A S A T W O F H O U S I N N O I G (fleurs_eng_000415-fleurs_eng_000415) +B D O B A L H U I S M A R E W I H T O A D L C I O R E I O N I D N O T B Y W A B I N I M P R E S I O N N M I L E R T O H O E T H E T A R Y W A S R E L A T E D (fleurs_eng_000416-fleurs_eng_000416) +T E R D E C T P L N D D E F I E N C E B O L E H A D L I N G S C I L S A N E C S L N T I E W O R D M A Y T H E S T A N D O U T A N W A S C L E R H A T H I S W A T H E T E M T O B E (fleurs_eng_000417-fleurs_eng_000417) +T H E D E A S S C A I R E D B Y P I A K G E W I C H T H N M Y G R E T T O C U M E N T O R O M O S K E T O S (fleurs_eng_000418-fleurs_eng_000418) +F O R T H S P R I N G B O G C K E H I D I N D E D O A F F L I V E N A T H L O I N G S T R A K E (fleurs_eng_000419-fleurs_eng_000419) +T H E U S T H E H I N S L W A S G O F R I E N D M A N Y B E B L L A D Y K I N G O U (fleurs_eng_000420-fleurs_eng_000420) +T H E Y U S E O F E A O R E C O R I N G H A S L A D T O I N P O R N E D D E C S C O R V E R E S I N T H E I N T E R P R O T A T I O N O F M Y G C R L R E C T P R E S T I O N S F A S I A L M O V E E N S W H I C H L A S E A F Y O M I L S S I C K E N S (fleurs_eng_000421-fleurs_eng_000421) +L S A T T H E N O R T H I S T T H E G R A T S A N C H U R Y O F R L A T D Y O F A T H Y M U S H R I N G A E L E C E A F W O R L D G H T F I M I S M E R I O N A P B R I S T I O N S (fleurs_eng_000422-fleurs_eng_000422) +E I F O N E B Y C L O S O T H E A C T I O N Y G O R H E T O W W O W G I T I N E A L Y W T O T E C A P I N G S I H T C L O S T O T H M O U S I C K E (fleurs_eng_000423-fleurs_eng_000423) +M T Y A G U S K U R E R I S B L Y H F A R E T H E B I G I S T N D A C O N T I N T O N I D S O N W H N A C O M S T O W H O U L D L I F T (fleurs_eng_000424-fleurs_eng_000424) +W E M E N I T I S R E C M E N E T H A T A N Y W H E M E N T R O V L I R S A Y T H E A R M A R D R E G A R L E S O F A C H U L M A R T L S T A T T I S T (fleurs_eng_000425-fleurs_eng_000425) +C O U O A O M O F I F T Y T H R E B E G A N H F G O V E R M E N T G O V E R S H I P E R I L E T H E S Y E A R A N D S I N E A B I L L A S T M U N C H L E G L I D S I N G S A N S E C X S M A R A G E (fleurs_eng_000426-fleurs_eng_000426) +A S L I H P L T I O N N T H R H A Y D Y W A S N O T T H E I N D O F P O L O M T I S T O D Y T H E R U E L Y L O C K A T E D I N S I Y E S O R A C A N P S E S E S I U R T O R E S I O N T H O S B U L A N M O T N T I M S (fleurs_eng_000427-fleurs_eng_000427) +T H E Y O U E L Y H A V E S P E T I L F O R N K N I N E R T A M E O P E R S T O C A E G I S A N A G O M O D N C A E T H I M A T T H E P R M I S (fleurs_eng_000428-fleurs_eng_000428) +O H E T E R H E A N D I S E A N D S N O W C O E D I O N S A R N O R M A E A N D M A N Y C U N T R Y E S I H A N D T R A P F I T O S O N M O S T E L Y U N I N T E R U P T E D A L L E Y E R R O U N E D (fleurs_eng_000429-fleurs_eng_000429) +B E C A R F L N O T T O A E L O U L F A B O I C T O B E C M E T O H I D W H I C H C A N C O S S T R A N K A D G E O R I N A S T R E N C A S C E S S S Q O A R T C H E (fleurs_eng_000430-fleurs_eng_000430) +F E I R L T H L L D E R N A Y H A V C T P E I N C E O V E R C H U L D O B E U S O R T R M O H B H E F O R B I N G A B E N D I N R N G W A Y (fleurs_eng_000431-fleurs_eng_000431) +B E B L M A N O T N T I C H I P A T T H A T P E T I O N C A N D N D E R S T A N I N G R A L O N E S A R Y F O R T R V L U R S R E T R N I N G H O M E (fleurs_eng_000432-fleurs_eng_000432) +O N O T E R T H E O P R I K O F O U S T I L I T Y E S B R I N I N A N T S H E A T E D A D N A V B L B O K A Y E O F T I R M I N Y (fleurs_eng_000433-fleurs_eng_000433) +H T H E V E N R S O F P I S S A D N I A T T I E N O F T H I N D E R D W E R E P L E O F H S A R S (fleurs_eng_000434-fleurs_eng_000434) +Y U S I N H I P S T T R E S P B U R T D G O O D A S B Y F A R T H E M O S T O F I E N T W A Y H M O E L A R G E M U T O P E B E A N D G O O C R O U S O A T I O N S (fleurs_eng_000435-fleurs_eng_000435) +L E B R L R E T I S O H M O T H E R E C O N S T R C T I O N V E R N H A S P O A K E A S O T H A O R D I N G O F R E C O N S R C T I N G C O N C H A C T T O R S T H E D W A U T I N G A N I N S I H E R S (fleurs_eng_000436-fleurs_eng_000436) +U A S B O D O E B O D A M R S E C K L T A C E Y T O G E T E R O N D G O M E A T H E N O R M E A W L A C A L P R I C E I S F I V E H U N D R E D C O N D L E S F R O U S F O R T H E M S H O R E T R I I I (fleurs_eng_000437-fleurs_eng_000437) +T H E T H R E C I N G D M E S W A S O N E O F T H E B L T B L U D I E S T A R S A N A N G I O N T C H I N E S H I S T R E T H O U S E N C S O F P E B L E D I D E D F I D I N G T O S I I T H H I H I S S E I T H E G R A N P A L E S A T S I A N N (fleurs_eng_000438-fleurs_eng_000438) +T H E I S C O U P L E S M A Y C H O S T O M A K A N D A N D O U S I O N P L A N D F O R T H E R E B A V Y (fleurs_eng_000439-fleurs_eng_000439) +N O T H I N C A N B E F E N U T H E H A T H E C P E R B E U T O F L S C K I A B O V E A N D N T H E M A N Y S U R W U N I N G M U N D S B E R Y L I T L O T H S W L L T A N B E F E E N R H E R F R O M I N S I T H E C A V E (fleurs_eng_000440-fleurs_eng_000440) +H E W A S O E I C E N L Y R E L O K C A T E T O A D T E N B R O K S H O S T P T L A N D C A M B R I G E (fleurs_eng_000441-fleurs_eng_000441) +D T H A N T I A C A N S T A I D Y P U L E A T I O N I S E R O U D I N H E R N T H I T E T H I I S M O S T I N P E N E C O E N T R E T H E W H E R A L D N T H P O P L I U L E A T I O N (fleurs_eng_000442-fleurs_eng_000442) +B R D G L R A L O U N C S T H E N C T H E P M A T H R A R M A E O L Y N C O U T I L O N B U T N P L E N E D E S T R P T I O N S A R N U C D B Y A N O D T I M T E D S T O M I N O W A O V E R I T Y O F L I N W I C H G E S I N C L T I N G S B A N I S H A N G L S H F R E N C H E R B I C A N D A P N S (fleurs_eng_000443-fleurs_eng_000443) +T H I S O P R S G D P R T E N D E T O S H T H E O A R E R E A B O R I L E S A S T H E S C A I W L B E D A R C M O R E L E S T O R O N T H E C O (fleurs_eng_000444-fleurs_eng_000444) +F I R E S C O U C R E O S O V E N C I L Y D U S T H F I E R B Y L V I O N T H R T Y F I V E P E A M (fleurs_eng_000445-fleurs_eng_000445) +H I S C L O C O M I C A L S P E C H E E O N M Y A N I N D E C A T E N O U S I N G R E D C A B I G H E D D O U S E (fleurs_eng_000446-fleurs_eng_000446) +A N P R T I C K I L E I T I S L E T H A T O N C E N D E T E C W E T H E A P E R S O N I S L I N G B Y A N T E R P R I N G M Y G R L C S T P E R T I O N C C U R E C T L Y (fleurs_eng_000447-fleurs_eng_000447) +T H E S E S C I A L F O R I D Y O F T H C H U R H O U I E B E N I N M R O M F O R O V E R A T H O U S E I N Y O E A R S A N D D I S C O N C O N T R T I O N F P O U E R I M U N Y W E A D T O M A Y T O C E S T I O N W E T H E R I C T E N E N T W A S B E N G M A T (fleurs_eng_000448-fleurs_eng_000448) +T H E S U N D D O A R B O N S A R T H E A R G E I S T T H E T E O R A L M A N G R O E B E L I N T H E W E R O E D S T R C H I N G A T Y C O L O M E T E R S F I F T Y M I L E S I N T O T H E B A N G W L D E A S H E A N A N I N D I N H I N T E R L A N T F O M T H A O W C O S T (fleurs_eng_000449-fleurs_eng_000449) +R E A G U L R N O N C T H N T H E M A T H O L A R M A E D O N L Y N C O T L I N B U T N E T E N D I S T R U T O N E R N O U C D B Y A N O T I M A T E I S S I S M I N T W A R T V E U R I T Y O F L N G W I G D E S I N K E U T I N G P A N I H I N G L S H F R I N C H E R I B I C K A N D O A P N E S (fleurs_eng_000450-fleurs_eng_000450) +E R W N P R T I T B A T I N S T S I D Y A N O U S I S T R E N S P R T I N C S I S T A E N C A L O S T E R W N O M P L A N E O B O U T R E C D P R T I O N S I S T O M (fleurs_eng_000451-fleurs_eng_000451) +L A T N H A D A S F R T H A N G E S O T H E O N S R T E S M F V A R M I N L B I L E D R I G H E E A I N G W T H E E A U M A S I N G F R Y T H I R L A N C O M P E T R E R I G D T I N G O T H E K O S I R V E T H I S P A R Y I N F B I R A M A N A L D I L L (fleurs_eng_000452-fleurs_eng_000452) +A N W N H A S L N T O R T H A T H A T L I H T A T U E S A R O V E R M U E N P A S T H A D E N S A I D E T H E P O S E I L I T Y O F S N O E I C E O R F R E S I N G T E M B T E R S (fleurs_eng_000453-fleurs_eng_000453) +H S L E A E I N T E R U T I O N I S H E P R A S T I S O F H E B O U E S A Y W A K I N D I R I N G Y O R N O M O S T S L E P E R E A D A N D F L I N G A S L E S H O U R T I M E L A T E R E N T O S I C T D E I N T S T I T (fleurs_eng_000454-fleurs_eng_000454) +B L I S W M U R L E T H E T W O D R I Y P P O U R E R S T O G E T H E A N D T E N W I T G Q U E N G W E A T H A N D S S C U E E T H E I N T O A B O L W R I (fleurs_eng_000455-fleurs_eng_000455) +F O T H E S P R I N G B O A C K E I D A N D E D A F I V E D M A C H T H E S I N G S T R E (fleurs_eng_000456-fleurs_eng_000456) +Y U O S T L K T H O N E X P U R T D A P O L N T E A R T H C O S I N G T I D H D S O T E S A M L B Y W Y E X S E R T O F F O R S O T H E E D G I T A R I Y A U S A L A C S Y (fleurs_eng_000457-fleurs_eng_000457) +T H O R W T H E N I H T T H E T W E N H E R D E N F I T Y A N D T O H E R E C O P Y S W E R M A D N O N N O N A S B U M E L A P B O R O D S I D E S (fleurs_eng_000458-fleurs_eng_000458) +F I R S E A M L N G I S E I N Y A T R E C A M N D A T I O N D S T H A T A N N O D T I B L M A T I K N I S H I T D I O F S H D B E T E K H O M B O F O R T H E E N D O T H I C Y E A R T O S E C U R A R A C S P O R E R S E G N S T H O S T I L I N T E R V E N T I O N S A N D T O R E S T A B L I S T D I P L M A T I C R E L A T I O N S W I I T S N A V E R S (fleurs_eng_000459-fleurs_eng_000459) +S A N E T E R S B R C R E S I S I N L T I M E N T O W N W H O H T A S I N G E S A R C S E N T I F F R M R E S E R R E C U R I E N C S C H O C T H E T R N E N S (fleurs_eng_000460-fleurs_eng_000460) +A O C O R D I N G T O E P A N S N O G E L R A G E A N S Y W R D Y L A C T I O F C A E S I O M N D I A D I N H A S E N D E N I F I E T A T H E P L A N T (fleurs_eng_000461-fleurs_eng_000461) +S A E G O K A T I O N A N D R E C O M O N A T I O N S H O L F T L V E R Y A T I O N B A K O N D F O R T H H B E U T W E N D T H E T W O P A L L S E W I T E A C H E G E N E R Y T I O N (fleurs_eng_000462-fleurs_eng_000462) +E L A M N T L Y C O U L S T H U M A N D P O T A S H I M R C O N S E T D M U T L E S O F P O R E S R A L S O M T L E L A S I V E R A N D G O L D (fleurs_eng_000463-fleurs_eng_000463) +T H E O R L T I O B T W E N B R A N P O T H O A L A G E Y A N E H A V E Y O U R S S P R T S I N C S I S T S A N D T H E R E S R C G E (fleurs_eng_000464-fleurs_eng_000464) +A N C I O N C H A I N E N T H A D O U N E A K C W A Y O F S H O I N G D I F R E N T T I M E M P E R E A T D S E A C H E S T D A K E O F C H I N E O R E E C H F A M I L Y T H A T W A S I M P O U R E R W A S H A E D E S T I N T O F D I N I S T Y (fleurs_eng_000465-fleurs_eng_000465) +A H E M B L P O P B E L E D I M E R N H H S T E S H L Y T D R I N T H E S U M E R I S P A A M A L Y B U D W H T L V O I L T O M E T D N A N Y O V L A L A B E C O N T I N C T I C H E S C H E E S T O U O F I S H I T D S E T E R (fleurs_eng_000466-fleurs_eng_000466) +T H E A N O N C T H N T W A S M A D E A V E R T R N M A T Y F O N O M E R S A T I O N W H T T O R K S H S P R D O D E N T R E S E P T T H E Y E A P A E R O R D O U N (fleurs_eng_000467-fleurs_eng_000467) +P E R Y S A T E T A T H H E O D R E T E N T O T E C X E I C T O S T S E U T O R E S U L T S O F T O N G H C S C O K I S C D E D E R M O N W H T H E H E R S A P P A S F O R D F R M Y S E L F N T H S R A C E S B U E L E T E R S T T H O W O W L R E M A E I N T H E R A I D A N D G B P E N O T I G E N R Y T W E W N S O U T E I R L I N O P R M I A R Y (fleurs_eng_000468-fleurs_eng_000468) +E H E W A E L S O I N G A G E I N G G R A I N G B A K N O L T D S F O R M I N Y O U N T R E S R S O N I N G S E A P L E S O F W H I S E R K I N G C L E E D T H E H E A M P R I E N M E N I N I N I S R E A L P R T E R E D S O N T H E F I R S T F R O F H O N D T H F R U N T O F T H E N O O C O N A D Y I N O F L O E D L E R I N W O H N D E R D L D E L (fleurs_eng_000469-fleurs_eng_000469) +H M O R T R D I N H R C H E S O N T A N H O E T H E N E A S T E R R I C I L N S A T T E D Y N G H T T U R I N T H E E S T R W E E N D B U T H E C O G R E W T I O N S O T I M B R A K I N I N T O S E L E B R A T I O N A T T H E S R O C O F M I N T H T O S O L B R A K C R I C E S R E S U R E C T I O N (fleurs_eng_000470-fleurs_eng_000470) +F I L N I A G R A T B O D I N G D U S T E N A T I O N T H E L A N D O F A T H O U S E N L A K H E S T O U S E O F I L E N C D S T W O O A N D T H E L A K A N N T H E C O S T A O E A R K Y P E L O G O E S (fleurs_eng_000471-fleurs_eng_000471) +E R N T T E N T E R A N D A R G H I N C S I N F R S L A D Y C E S T D E N O F R N D I S A C E R S I O N R A N O E S R P E S I N A C H L C A N D I S O U S T R D A Y A V E N G N L H P L A T H A T A S T A D Y F O F T D Y O L O M E I T E R S T H E R T Y W N M I L S A W Y F R O M W N O L S I D I S (fleurs_eng_000472-fleurs_eng_000472) +S V E R W E T H E R I H E E N A R N C T E R E F O R N Y D A N D E R S W H T H E F O N A M O N A N H W H T H E P O T I N C L T O C O S D A M I A G E S I R I S C S O S I O L D I S T R U P T I O N O R L O S O F H M E N L I F E (fleurs_eng_000473-fleurs_eng_000473) +F O R E S A M B L T H E M O S T C O M E N S T H L I M I N G E F H O T O C K R F E Y F O R M U T I T H E H O R A L D I S T H R Y F I R E I L N M E A E R W H C H W A S T H E D O M I N E N T F I E L M E M S I G E S A T H E C L O E S O F T H E A N I L O G F I L E A R A (fleurs_eng_000474-fleurs_eng_000474) +I T I S R E L A T E D T O B U T Y U S E L Y N O T I B L T I N G O L P I N G T I L E S K E T O R I N G A R M O U T E N E R I N G T H E L A T E R W O E S D O U N E I N S D E C T E R I N G A N D R E C R I R I N G M U S H T H I F R S K E E S A N D B O T S (fleurs_eng_000475-fleurs_eng_000475) +I A R N I N G D A M K C L O A S E S C A N H E P T H E D R I Y I H M A N Y H O T E L S H V E N I A R N A N D I R N I N G B O R O V A L A B L E F R L O N E V E N I F O N H S N O T P R E S E N T I N T H E R O M (fleurs_eng_000476-fleurs_eng_000476) +E E V E D N Y U N C E R D H O R S L Y S H E E S U H E R C H A R E U N L E I T H E G L A O S E H E O T H F I R E R A N D S C R E D E H R H A N D S O U T O T H E B L A S E S T E R E W A S N O O T H E L I H T I N T E O N M M Y T H E T I M N T H E W I N W I D N O T O H O R D E D I S M R L Y S T I L (mls_eng_000283-mls_eng_000283) +E M Y D E A R M A R L D E E A E R W H I D O O U D N O T D E S I S T F O M T H E S I L Y P E R S S O U O T O F E A N A D N M A N D G I N A R Y T H E A D S E R W H A T I S T H E T H E O L Y O U O F M U N Y W E A R S P B A N U R D S N O T S H U R T S L E V E D M E R S S I N A R Y P P E G S O F A M A I Y C E N D S (mls_eng_000284-mls_eng_000284) +T E R I T C L T U B P T E I S T A T O T E S I N G L I A S E T H E R M E L I N W C H P E S E S E P O N T I E I N F L E A T I O N T H O A R S E N D T I N G E N T T H E R S T I A L E R E S U H E R S C A V L L I E A T H T W O C O A L D N E S O F T H I S P O N T B E N P L E T I O N (mls_eng_000285-mls_eng_000285) +M U C H L I K A N F O U N E U S N D O F O R M E I T Y A N T O H A T M O N S T E R H O M T H E T H E A B O N N I H T T H E F A R T H E R O F T H A T F A T L P R O D G I N Y M A D E D C I L H E R S E L F F O R V E R Y H A R T S T E S P I H T T H A T H E H A D R A H E R R I D L W H I C H N O W I H T C O U O T E V E R L O U S H A T S U F E R D D E D L Y D E (mls_eng_000286-mls_eng_000286) +H I M A S H E M A S E R W I H P R S I O N G R E S I E S M U N T I G T O H R E T H O U L S E N A N S T I E S A N D A L S O T H E R Y S M A L V L L I M S T H A D O C K I E P Y D E B T H E F L N T D M U E N D E C O E S I E R A T I O N T H I S L A S E M A S E N T W H C H N O U E S E I T A T E N U M E R S C O R A T I N D I S M O S T D E L I K E U P U T H E O P R A T I O N (mls_eng_000287-mls_eng_000287) +W H I S H U L I T H A E B E N D E M E D N E C R O M A N C S Y T O I N D E V E R O O N B I N G T H E S F A S T O I V F O L I L V E B Y G I A R F U L E L M I N A T I O N A N D C H A N G E T O T H E P E R V E C T F O D (mls_eng_000288-mls_eng_000288) +D N A Y T H O V E R A S E S B E Y M Y B E A D T Y E A T I A M R I G E C E L O V E S A I D B U T H O U T A R E G U D L I H V E T H R I C E F O M N D A R T H O U L E T O Y E L T H E S O V E R A N G I F T S O F A R T H T H E V I E T O R S O R O R D T H E L O R A L E T B R O L O R V I N T H I N G K S O F L I L E W E R R T (mls_eng_000289-mls_eng_000289) +B O C K E S T O H V E B E N A K E N O L A C T R O L T H O H A M P E D B Y I L E H E L T H F A D G R A T P I N T O N I S F A V E R I S H A T E S C R I G E D O N L Y T H U C E P L A N S H W I T H H A T D C O M U N D E R I S O N P E R S I N L O P S O V A T I O N (mls_eng_000290-mls_eng_000290) +H A D R A T H E R S R N G O P A N D H A D N O T C H A I N S E D I N T O N E I M S T H E I S H Y F E L D I N T H E S T A M S C O V E R I N T H E M U P A G A N A N D T H E Y A P E R E D A S P E R F E C T D I N S A C T S I N T H E M A Y O F T H E F O L N I N H E A R (mls_eng_000291-mls_eng_000291) +N O T H I N G S A Y W O O B D E C E A N D H O A T S O F B U T Y M O U D P R E S V E N D T H E M S A E S T O T H E U N D E R S T A N D I N G O F T H E F O R T I E L E T B O R S O N H O U E A R T O K O F I T T H E I S B E A T S W I C H O H A E B R U G T O M E T R A N S L A T D A R C O N S E R E W I T T H I S S O U P E S T I O N (mls_eng_000292-mls_eng_000292) +N O U S M E I N C U P T I T Y A N D H E N E R V E H I M S E L F A G A I N S I T H I S F A I S W A L S O R D O S O V E F L U S H D H E W A S T I M I D E V E I N T O R R O U D N E S (mls_eng_000293-mls_eng_000293) +T B E C O M E M O L L I U G H E L I K C K E A S T H E S H I E X S F L U S H T H E A S R E A R W O A I M G E F I N O E I N T E M O R D I N G T H E R D H A F T I S P R I N G S O L T I D I T L O N G A O U R S O F A L L R E A D I N G A N D P E R S T E D H A R T B Y N E V E R S E A S I N G R I M E M S E S T I C U N O N D E R S T A N D I T (mls_eng_000294-mls_eng_000294) +W O N F T H E H O W I N G R G D E R S A I D T H E A L L P E H E A O V O I S A P O S E N S H O L F I S H T H E S E R B I T R A N D D E D L Y A N D C O E B E Y O U S E I N P U I T I N G E N I N M Y S T O D E A (mls_eng_000295-mls_eng_000295) +T H E B E U T E A S R O U P E S O F H A V I O N A S L O N T O A D U R I H T E A R A N D C O L E T A R H E L O K E I N B O W N L I S M A G H S T Y R A B R O U R D T O U C H I N G T H G R E N L E V E S A L L A T R E M B L I T G O U G L L I H T (mls_eng_000296-mls_eng_000296) +I A D D U N O R M O R T H N T H A T I U N D T L T H I S N A T A R E H I S A P E L U D L Y S E T E D E T H E E W O R T H M T H E L I F I T E L F T O E M W H S T R B L L B U R S M E D A N O R Y I S T S A O R L Y H E P R T E S T E I T O U L N O T G O D A S T M E T O W A T T H R A M U N C E A N D T I L I A T A E X A M I N B O N O F T H E I S E S (mls_eng_000297-mls_eng_000297) +R O S C O N G R E S F O U N D A T I O N R E S I O N A N D T I T E T H A T O R G N I H E T H E S A N T P E T E R S B U R I G I N T E R N A S I O N L E E C E N O M I C F O R M R O U S N E F T R U S I O N D S T A D O N E D O I L E N A N E R G Y C O M B P A N Y W (mls_eng_000298-mls_eng_000298) +H O W G L A T E D I N S P A C A L T H E D E L I C E A T F R O S T W E K Y O U E A T R A C T D E D N O E D O U T A M A V E R D A T H E D I N Y T R A C S O M S B U T F E W U E O V A S H A E R E A L Y H A D A N O P R T N I T Y T O S T A N D Y T H E D E T E A L O T H I S F R U S T E S I N E S M Y N U T L Y O R V C O N S I D E D H A T T H E E R E O R H I N T H R E Y U R F O R D E S I N E A T M O S T (mls_eng_000299-mls_eng_000299) +T H E A H A N T H O F E S I N T R I N G T O I N F I K G I U O N T H E M E N K I L V H E O F E N D E R O R W E N H I M M O R V H A N T H E I N T E N D E N T O D W A N D T H I S B E C O M E A C C U S F U L E A N U H E R D S O H A T H E P R I M I T I V E L I G E S L A T E R S W H E R E C E F U L I N O R E C Q U I U R I N G T H E R I E T A L I T I O N T O B E D L M I T E D T O A N I Y F O R A N O I (mls_eng_000300-mls_eng_000300) +A T S I R E S S W O R D T H E J U O S E R E T E R E N T H E C O M P O N Y T H A T G O E B G O S H I C E S B E G O N W I T M R T A M O M E I S H E N D E R E D B Y T H E F O B U T W H N C E A G A I N T H E W O R K G O S O N B Y L I E N S F R O M D R I A S A S R E I S S E N T W I T H R O I L E D G R A N T A N D G I F T S F R Y O S E S P I S S (mls_eng_000301-mls_eng_000301) +A N T P R O D K E Y H A I E I N A N D Y E A R O U T E S V I N H U D R T F R O N C E S H O E L V E D N I T H O V E N O S O B A D L Y B R E U L A C S P M L A I N M U R T Y I S O C K U P Y H T E O R E B O H O U E S (mls_eng_000302-mls_eng_000302) +T E D T H I S E I S A L L Y O U R A N C T S E R T I S T W O F E A I R E F O R O N E O F H I S O L I N T C S A N D Y W O R E Y O U O W E T H A T T H I S P L A C E N O M O R S E Y O U A N G S I T A N T E R D E F L E R A N S T H E E S T I S E S T H E I S M O R E C R O U N D T O M E D A M A N D R A V E N D G O N O N I S E F A R A C S T H E A T S M Y A M E I N D E D (mls_eng_000303-mls_eng_000303) +W H E N I R E T U R N E D A T O T H E H O U S E S W H R E H A D B E E N H A P Y C H I L E D O N D L Y A E P I L O F A S H I E S W R A Y T H A D S T O D A I W E P T L O N G K A N D T O F O R O G E T M Y W E P I N G I S A I D O U T U N D E V E A S C O M S E O N D T H E S W O R T E R S I N A S T H R S I U G F Y A E R N I G T I D P L A Y E M Y F L T T O T H E S M E M O N (mls_eng_000304-mls_eng_000304) +T E D O O U N O T S E E W H A U T L E S E R I T D G I V E S M E W E H A V E G R O N O U P O G E T E R I N T H I S H O U S E S I N H E W O R S A B O Y H I S S E I M P L Y A N O R B E A R E A S O U C A N D T H E I H T O F T H S M Y G L D L E V I N H I S F H A C E B O R D E A R H E H A S N O A M U S M E N E C E P T H I C B L A N G A T D T H S H O P S K S C P I N G (mls_eng_000305-mls_eng_000305) +I T I S D E V I E S S B O Y R E V E A L D I N G N Y E L P I C A L O R G R E Y U W O N G A T I O N I L O V E L O V E D L O V E I L N O T B E T H E W O N D O F C U P I T B U T H E A D I F T H S T A T I O N O F E G E V E R S L E R D U C T O E I S T I N C E S (mls_eng_000306-mls_eng_000306) +S H O R P L Y A S H E S H O K H A N S I T H E R O G O D B E S Y U M A Y D Y A T C H A I T H E B I S H O P S A I D W E N S H E C E S E H I M A N D H I S L I P S M O R D O F T E R W O R D F O R E S O M E S I C K E N T S A S I F H E W E R I N P R E A R E I U D M U T H E O F O L L O R E H E R E O U L O F T H O M A N D T I E N S I L A N S E T E L (mls_eng_000307-mls_eng_000307) +F O L L A E D H I M S T E A E L T H E L Y H E A N D H E W W A S A N A S T P I N G P O S R E R F I L I N G H I S B O U C K E A T C A M E U P T B E H E I E D H I M A N D P L U N C H E D A N D L O N G N I F N T O O I S N A C K (mls_eng_000308-mls_eng_000308) +T S A I S T H C K E R S I A S D O U S T N O T J U P E T E R D I S T R I B U E T T O H E G O G D T H E P R E P O R S T I O N A N D D I V I D E N T S P A R I N G L Y A N D S E V E R A L L Y A S A G M E N D I T O H I S C O M A N D R S W H E N H I S G E A S T S T R A N G T O O N E A N O T H E R I V F O C K O U R S I U S Q U L S K L E D E M I U S A S Y O U N E R R A T (mls_eng_000309-mls_eng_000309) +E A N W H R N O N H U L E R R S T R A N A T O C A M E T A G A I N I N F B H O H T S O T H I S N O O U S E N W E P I N G B E R C H E R U L S P I R T S T I L E N E V E R D O U T H E F A T I S C E P I N G P U C T E R G O O D F O R P R E S E N I L (mls_eng_000310-mls_eng_000310) +A N D O B E C O M E T H E R E C K E R D O F W H A T P E P L A E D O N I T H E R M O R A M I U B L E M O E N T S T H E R E C K E R D O F T H C O N C Q U E S T S A T P E S E H O W M E N D H A V E L I V E D A N D L E A V E R D D O U G A T B I L T U N A N L I E R E D G A R D I E D A T R E A F O R E R S T (mls_eng_000311-mls_eng_000311) +T H E O F L I N G O T E S O L A S P E T O C K I N S R A I N A S W I L A T N Y N S E S I N A B L E D A N S I N G O F M I G I S I N T H E E V E I N I N G S O C O N S O N D E F E T A N D I N G O R T I S I N G T H E O I N S A D D I O F U L R E C O I R S S I S S T H E L E S T A T A L L A T R M B L E B O E F O R T H A T P R O W C E T U N D E R (mls_eng_000312-mls_eng_000312) +W A S S T O R E M N G E J E N R L E T A M P E A R E W A S C I L L G E N E R L C O S T I E N G W A S B L A M E D A N I N D E D E S N O B C O M T O P A R I S T H D E V I C X S E L N A T I O N S A G I N S E A L L W H I H T H E M O U N T O N A N H E T R O T I O U S M O E A R M U S T D E V O N M A K E H A L A S H E C A N (mls_eng_000313-mls_eng_000313) +T H E M O M E N W A S F E A V F U L A M I T Y O F O H A D N E V E R S W H U N G T H E B U T L L A C K E O V E R H I M E N B U T T H E H O B E N E R V E D H I S A N E F O R A D E S P R E T B L O A N D T E C U O M S E R U L E P R O S T R A I T B E F O R H I M (mls_eng_000314-mls_eng_000314) +T H I N T H E W I N S T O U T T H E G L E R S T A N D D R K H N D N I G C A M E O N L A E I N G K M Y O L D C O T I O N D C O U I L T W A S C O L L D A S I N M Y S W E T S U N T O U S T I N H I S S C E E (mls_eng_000315-mls_eng_000315) +Y O U M A Y D A S O U P L E T O W E R E O F O U R I R T A T I O N T O C E P P Y O U R F E N A T T I I S M Y O U H E W E L A F Y O U N E D N O T M I D T H E C O U S T T H E P A R E D U N O T W O N T E S T A N D I N Y O U R W A Y B U T Y O I N S I S T D O N T H E S O I M I T I N G O R C O M P A L T I O N (mls_eng_000316-mls_eng_000316) +W E W A S B R E D B Y A E R E V E R N T E R Y A C S N I H T E T B E I N G B Y O T H E M E N E S C X S F O R T O T O U H I D I C K L Y W A S B O N E A N M A C H A T I N S E V E N T Y N I N A N D H W A S T H E O N L Y S O F L I V E R O F E L E T E R O F F I F T E N I T W A S N T H I C O U N D T T H A T H E A S O U R D S A I F A N D C O L R A N D M A R C K I N G S (mls_eng_000317-mls_eng_000317) +E A N D W H A T H A S T E I T M A K S O F O L I N T O T E S E C I O N T T H E R B Y T H I H T I M E D I A F H A N D E R S N E S E R S E S G I U M O S T A D M R A B L S E A K C K R E T O N T H E C O N T R Y I T S T A R S M E N O T A W I T W I C H M O S T C O D S O R E S I T (mls_eng_000318-mls_eng_000318) +T H E R D L Y T H A L S A I D W H E R T H E I T I S I N C E A R E N E T H E R E T O R E A C E H N O R T O P O R E F O R T H I L Y A N A C O S I S S A I D W H E R E T H O I N A L L O T H E R E S P E C T E T H E Y O A R R C O L I H A T V E R E T O H S M I N A R A D V A N C E D A N D V E S I O U S P E R S O E N T D E G R A D E D (mls_eng_000319-mls_eng_000319) +T H E C I N D L Y F R A N G I S S I M P T H E T I N K E V R Y D A Y H E P A S N O T A S B E T W E N U S A N D T R I Y T O N C K E R I R G S R U S T L H E W L I M P R O V E I A S U O R H I M H I S T I M E I S S O U O U R T A N D F R E A C H A I R A N L I B U R T Y W L L S O O N R E S T A O R H I M (mls_eng_000320-mls_eng_000320) +T H I S C R E S T I O N S I T I S N O L E V I D E N T M A Y F R E K C E N T L Y B E U N C T E R E D W E S E Q U L L P R O P R I T Y I N O P S I T W A S E S A N D I F T H E R B E A N Y A C A I N S U N G W H I C H T H E Y C A N D B E U N C T E R E D O N L Y I N O N E N W A Y T H E U N C S E R W I L D E P E N D A P O N T H E N A T E R O F T H E O E C A T I O N (mls_eng_000321-mls_eng_000321) +I N H I S N O H T B O R T H E I N S T R L S Y S E C K N E D I O N A T Y O N O W A T E S C O U T S E S T H B A L E D W A S T C E K I N D D O W N R M A N O L D O M E N S F R C I T A T I O N A T T H L S O N M O R L E A D M I N S B Y T H E A G E N D T H E R A N D C I E N T B Y H M T O S E R T E A S (mls_eng_000322-mls_eng_000322) +C R E S T I O N T T H E O L I G E O N S H (nchlt_eng_001588-nchlt_eng_001588) +O P T A D E E A G O F I T H E S (nchlt_eng_001589-nchlt_eng_001589) +A L A M E N T O S P E S I A L F O N T I O N S (nchlt_eng_001590-nchlt_eng_001590) +T O R D E W A S I N A N N U N E V O R E I T Y (nchlt_eng_001591-nchlt_eng_001591) +S I E S F I C T I O N N O V L E S P R V H A N (nchlt_eng_001592-nchlt_eng_001592) +C O S T D H I B P O P (nchlt_eng_001593-nchlt_eng_001593) +I N D V E R S L E A T B L A Y E T R O N S F O R M E (nchlt_eng_001594-nchlt_eng_001594) +F R I N G H P R O T I S T A N C E S (nchlt_eng_001595-nchlt_eng_001595) +O F E G O U N A Y F O R S S H H D K E (nchlt_eng_001596-nchlt_eng_001596) +H E A R O S I N M O S O L I G Y A N D L E A G E N D (nchlt_eng_001597-nchlt_eng_001597) +B U I S N S C L A S S E T N D N E (nchlt_eng_001598-nchlt_eng_001598) +C L A I D P L A Y C H O R T E E (nchlt_eng_001599-nchlt_eng_001599) +P O S I Y T R I N S W E R E R O P O R T E D (nchlt_eng_001600-nchlt_eng_001600) +A L D V I C K T H E A T E R (nchlt_eng_001601-nchlt_eng_001601) +O R T H E D O C K S M O N O C K S E (nchlt_eng_001602-nchlt_eng_001602) +N A T I O N S W M E M B R S T A T E S (nchlt_eng_001603-nchlt_eng_001603) +F H E T H O W I L D C O P (nchlt_eng_001604-nchlt_eng_001604) +C R O S E R I C S K Y U E F E I T S (nchlt_eng_001605-nchlt_eng_001605) +A C T H O L F O L M E M A R C K E S C O P I T Y (nchlt_eng_001606-nchlt_eng_001606) +M O U O S I C L G R U P S R E A S T A B L A S H E D (nchlt_eng_001607-nchlt_eng_001607) +P R O M I S E N E R E P A C E S E (nchlt_eng_001608-nchlt_eng_001608) +F O L N D S I K N E K S (nchlt_eng_001609-nchlt_eng_001609) +T O E L A V I O N S E R Y S B A S T (nchlt_eng_001610-nchlt_eng_001610) +H N O E P O L I T I O C A O E P O R T Y H E E E (nchlt_eng_001611-nchlt_eng_001611) +A N C H O N T D E A G O P A C H E V E D (nchlt_eng_001612-nchlt_eng_001612) +F L A T M U S I G N T R O L (nchlt_eng_001613-nchlt_eng_001613) +A M R I C O N T I C N O L I D T O I N O L I D Y R A T E S (nchlt_eng_001614-nchlt_eng_001614) +D O A T E S O F V A R I N S (nchlt_eng_001615-nchlt_eng_001615) +P O P I L E I T W R E I S T A C T I O N S (nchlt_eng_001616-nchlt_eng_001616) +D U C H E W I S T I N D E A R (nchlt_eng_001617-nchlt_eng_001617) +G O L D M A T L R E S P I E N S E (nchlt_eng_001618-nchlt_eng_001618) +R E A S H I O N S O S I A L D E M O C R E T I C K E H (nchlt_eng_001619-nchlt_eng_001619) +A M I R I Y C O N F L M E P R O D U S E S (nchlt_eng_001620-nchlt_eng_001620) +F R E E S O F T E R Y F U N D A T I O N (nchlt_eng_001621-nchlt_eng_001621) +R I L E R M A T I O C T H E A T (nchlt_eng_001622-nchlt_eng_001622) +I T A B L E M O L O S K S H 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A N D E D F A N C S E T H R (swc_eng_001756-swc_eng_001756) +E C N L A G Y E S A D I M P L E N T I G T R A N S H U M N I S C L E O F A N H A N C S P R F O R M E (swc_eng_001757-swc_eng_001757) +N D C O A T D I N G N A P S E (swc_eng_001758-swc_eng_001758) +B Y S P A N S H T U R C H M E N L L R E M E O R A S D E L O S A N A (swc_eng_001759-swc_eng_001759) +D V F I H T E D D E M O C R A T S (swc_eng_001760-swc_eng_001760) +H E W O R L T H A N P P E N S H I P T H A S E N C O T R O L E B Y E F F I D E E (swc_eng_001761-swc_eng_001761) +W H E H E T A R I N G P O S I T I O N I (swc_eng_001762-swc_eng_001762) +E E N C R A T E D I N E V R Y S T A T A N D T E R I T R Y T O P R T E C H T A N D R E S E R V T H E C O N T R Y S O U N A K Y C O S C I S T E M S (swc_eng_001763-swc_eng_001763) +E A C A T H O E N T O F T H E N O S I L E N D W A R E M O I L (swc_eng_001764-swc_eng_001764) +A L A M E F O M T H E R E L O U D C U P N Y E S F O R V E T I N (swc_eng_001765-swc_eng_001765) +T H E T O N I S S 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T H E T E N I O N O F R E S U R T H E R S C A B E F O K A U S D M P A R T I A L S O L U T I O N S O R S O L U T I O N S (swc_eng_001777-swc_eng_001777) +N O N E N O F F O R H N T R E D O F Y E A R S (swc_eng_001778-swc_eng_001778) +N L Y M U S U B I A L S H A E S O F I V E T T (swc_eng_001779-swc_eng_001779) +T O H C H A L T H E A D A B L E S P A C H E S O F C R U S T I S T H A N B E L O N G (swc_eng_001780-swc_eng_001780) +O L G E R T H E M R E S U R C H E (swc_eng_001781-swc_eng_001781) +N I N T A I N S I C X T D Y T W O F I L I P E S N V E N T D T H E C O M P A C T O D I O K E S E T M E D Y H A M F O R O D I O U S D A O R G E (swc_eng_001782-swc_eng_001782) +O S T R A C T I N F T H E L O W (swc_eng_001783-swc_eng_001783) +N D F H I B I E N D A D R E P (swc_eng_001784-swc_eng_001784) +W E M E N S W H R L D C E S T H A M I N C H O F (swc_eng_001785-swc_eng_001785) +C O N T A N E D E C R T I O N S A N D C O M E N T A R Y S O T H E T A T O F E N B E I Y S I N C E A N D E G N A L G Y A S M A E R C O N T R U D E R S T O T H E (swc_eng_001786-swc_eng_001786) +P E U R O E L H A N T I Y O R S O T I A L T R E N T (swc_eng_001787-swc_eng_001787) +M O S T C O M P A C C S A E T W E R S O L D B L A N K (swc_eng_001788-swc_eng_001788) +I O F H E R I S N O U L D G R T H E (swc_eng_001789-swc_eng_001789) +H E S O T H E N S T R A L I E N C O S T A N D I N S A B B A E N T I E C T I Y O S T R L I A N T E R A T R Y S (swc_eng_001790-swc_eng_001790) +D A T R A T S F T I P O C L Y F I V E H N D E R E D T T W (swc_eng_001791-swc_eng_001791) +D E R P R I V I N G T H E D U C (swc_eng_001792-swc_eng_001792) +N I E N P E R S E N T O F T H E T O L C A S T (swc_eng_001793-swc_eng_001793) +A N D T H E R I R S S O R B R A T R I Y A N D A N T H E I E C O M N C A T I N G A T Y (swc_eng_001794-swc_eng_001794) +E D N O T I M P A S H S H I N (swc_eng_001795-swc_eng_001795) +E N T H E R E D E M O C R A T I C P A R T Y (swc_eng_001796-swc_eng_001796) +N H E S O N T O P E O F T H E E S E T H A L I N D E C A T H (swc_eng_001797-swc_eng_001797) +L A E U I A T H S S E O (swc_eng_001798-swc_eng_001798) +I A N A N D T A N G E D M R A I N S P A C H Y E S T (swc_eng_001799-swc_eng_001799) +R O W N D E S I R E L E C T O (swc_eng_001800-swc_eng_001800) +H I S F A C D O S A N T S A Y M U C H A B O U T W E R T H E P R O B L M E L S (swc_eng_001801-swc_eng_001801) +C O M O C L S S I T Y B E G A N A S (swc_eng_001802-swc_eng_001802) +W I T T O R I S T S R E V I N G T H E S T M E B O T A N D T R I A M E N (swc_eng_001803-swc_eng_001803) +F I R S T D I L O G B E T W E N T R A N S H U M I N I S (swc_eng_001804-swc_eng_001804) +N E E R B E N P R D O F T H E L N P C A N D S (swc_eng_001805-swc_eng_001805) +R E A G E S S F I R N I T C E R A N D T H (swc_eng_001806-swc_eng_001806) +I N H I L A B L T R E M E N C (swc_eng_001807-swc_eng_001807) +T O O C A T T H A N D U R S O N M (swc_eng_001808-swc_eng_001808) +O R F H L U C H O C L F R E D E M (swc_eng_001809-swc_eng_001809) +N E R J E T I C A T A C K I N 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F I R S T B E L I D (swc_eng_001822-swc_eng_001822) +S T O R Y I N D E C A T I E F O F T H E R I S I N G L O A B L S R I G N I F I N G S O F S O P L I S H E I S T O E B Y D J D E N (swc_eng_001823-swc_eng_001823) +W H C H S B A R E A S E A L Y E N T R S T N D P O L I T I C S (swc_eng_001824-swc_eng_001824) +W A S C O L L D D O L B E A C H A C X P R O I N F O L L A N D P A T N T I (swc_eng_001825-swc_eng_001825) +O O D S A V E A N D F I N F I L E S B (swc_eng_001826-swc_eng_001826) +A S T R L I O N S N A C E B E L O N G T O S E V O N F A M E L Y E S (swc_eng_001827-swc_eng_001827) +T D O L A N P Y O R (swc_eng_001828-swc_eng_001828) +L I N D S H A R P L Y S E N C E S P E A A N D (swc_eng_001829-swc_eng_001829) +W A S R E C O U R D E D I N T H I R L Y O N F O R T R A C O S E C T A (swc_eng_001830-swc_eng_001830) +N O L A S I M P R O V E N T I N (swc_eng_001831-swc_eng_001831) +R B O N A N D R U L L E G E S H T H (swc_eng_001832-swc_eng_001832) +A C H P L A Y R B G N S (swc_eng_001833-swc_eng_001833) +D J H A S T H A S N S P H I E M A N Y C O M N T O R I L E P O U S L E (swc_eng_001834-swc_eng_001834) +O R H U O M A N I M I T D G E (swc_eng_001835-swc_eng_001835) +L A S P O I R D E D T A P S (swc_eng_001836-swc_eng_001836) +A N T A N D I S E N C E I N G T O T H E O T I O N (swc_eng_001837-swc_eng_001837) +R E S I N P R O T E M P O R O F T H E S T A T S E N D (swc_eng_001838-swc_eng_001838) +I C H O P C E L M O V E A N Y N U M B R O S C Q H E R S (swc_eng_001839-swc_eng_001839) +P R E T H E I N S I D T H E S C O L (swc_eng_001840-swc_eng_001840) +L I N G C A E T S H E R N E S S W I T (swc_eng_001841-swc_eng_001841) +O U N T R Y S O F T H E E S T E N P A L Y O A R C T I C F L Y W A Y (swc_eng_001842-swc_eng_001842) +A S O N L S D T I C S T I K E A E S T A M A T T H E O P E L A T I O N I N (swc_eng_001843-swc_eng_001843) +U N D D E S P E U T E D W H R L D C H E S S H A P E E N (swc_eng_001844-swc_eng_001844) +T A Y N R E L L C A P T B I N C E A R (swc_eng_001845-swc_eng_001845) +E R N A C T E D B Y T H E C N E R L A S E M B L Y H W A S A M A S E R R A S I A L Y S E V G R G A I G T H E T A T E S R E R O T C O A R R S (swc_eng_001846-swc_eng_001846) +W H C H R A P E A L M O S T (swc_eng_001847-swc_eng_001847) +S E A C T P R E T H C T L D N T I O F F O R N E R A N D P R O V I D E (swc_eng_001848-swc_eng_001848) +W E R A S T H E F E M I A L S P E C I L E M I S D A R C B O R O N B O R D E D W I T W H A T (swc_eng_001849-swc_eng_001849) +W O T E R E C E N C T L S O (swc_eng_001850-swc_eng_001850) +N H I D N T Y A N T W E L V E F I N R O S S F I R N (swc_eng_001851-swc_eng_001851) +D I D E D N O I S I S G E N R L M A D E W H T H A S E T E S C A N D O T H E H E A I D (swc_eng_001852-swc_eng_001852) +T H E F I R S T G E N R L Y R E C O N I G C S E W E R L D C H A E S S T C H A M P P E O N (swc_eng_001853-swc_eng_001853) +H E P E Y A N D S I T I N G L N W R E P A R T (swc_eng_001854-swc_eng_001854) +A D R E L A S T T H E R E L B U M S B O T H T O S E D E A N T (swc_eng_001855-swc_eng_001855) +W A T E S R O N T H E C O N T I N E N (swc_eng_001856-swc_eng_001856) +F T H E R A N G H P E R S O N L S T A I R I O S (swc_eng_001857-swc_eng_001857) +N D E Y O M E D E R S A N D R E C O A R T I N G L E V L C O N T R O L S O N (swc_eng_001858-swc_eng_001858) +L Y N O M U L T I M E (swc_eng_001859-swc_eng_001859) +A N D I T O F H E N D E S T R D T H E P L A B I L I T Y (swc_eng_001860-swc_eng_001860) +C O N F L A U T I O N (swc_eng_001861-swc_eng_001861) +E U I V L E N T T O T H E C E S T I O N O F W H T H E R A C X E I S A M E M B R O F C O M P O U S I (swc_eng_001862-swc_eng_001862) +M O S T O I T H L A S T E R A N G (swc_eng_001863-swc_eng_001863) +P O S T H E N D E R I S T M E (swc_eng_001864-swc_eng_001864) +O M P A C T C O S S A T W U I K L Y F O U D O U S (swc_eng_001865-swc_eng_001865) +O R F O R H U N D R N D T H A R T Y T H R Y F E E (swc_eng_001866-swc_eng_001866) +I N G S W H C H R E S E L T I N A S P E C S I O T I C T T I H E O P O N (swc_eng_001867-swc_eng_001867) +B F R N A N T I Y N A N T Y S E V E N (swc_eng_001868-swc_eng_001868) +O M Y C A T I O N S A N D H E L T (swc_eng_001869-swc_eng_001869) +E S A Y A I C H E I N A P E R S S O N N O N (swc_eng_001870-swc_eng_001870) +S O M B R A E N E P I S O F I E T H A T T H Y A Y A L L S O E B E O U E D O N U T H E R N O N P O R S S M E T E I R I L S (swc_eng_001871-swc_eng_001871) +H E P O S E B L Y C O N P E S I F I C (swc_eng_001872-swc_eng_001872) +M A T O R S I N L A C T H (swc_eng_001873-swc_eng_001873) +I T H O U T F T Y M O L E T R I N G R O L (swc_eng_001874-swc_eng_001874) +H E E D C H A C S E M P O U L E S R S P E T I E L Y T H O C K U S T O F A G K I (swc_eng_001875-swc_eng_001875) +A V H H O M L E T D I T S F I R S (swc_eng_001876-swc_eng_001876) +R E B L E A D I N G R S S K R M A I N E S O F R O U N D F O A T Y (swc_eng_001877-swc_eng_001877) +D L R C H A C T M A T D (swc_eng_001878-swc_eng_001878) +S O M E S E C I L R H U M E N I S C O N S I E V E T R A N E H U M I N I S M A S A N O S P R I N G O F T H E U M N I S T F R E T H U T M O V E M E N T A N D A R G E Y T H E T R E N S H U M I N I S D I F E R T F O R M T H E U M O N I S T M A E N S T R M E B Y H A V I N (swc_eng_001879-swc_eng_001879) +P I N T I L E N E S T D A N D C H I C K S R E V O N E R B L T P R D A T I O N B Y M A M O L E (swc_eng_001880-swc_eng_001880) +N O R T H E R N P I N T A L I S O N E O F T H E S P E A S H E S T O W H I C H T H E A G R E M E N T O N T H E C O N C E R V A T I O N O F A F R K N I R A I S I O N M Y G R T O R Y W A E R B U R D (swc_eng_001881-swc_eng_001881) +A N T I S N E O E F E R U N D O L Y I N T A S M A G I O R (swc_eng_001882-swc_eng_001882) +E R S P E C T I V E T H E A D E A O F M E I N E D O U P L A T I N G I S A S R T D T O R E P R S E N T (swc_eng_001883-swc_eng_001883) +N A V R I G E O F T W E N T Y H E N P E R D A Y (swc_eng_001884-swc_eng_001884) +T E N I W O A D F L L O T H A T P E E E C G U L (swc_eng_001885-swc_eng_001885) +N D B L E A D I N G I N T O E E R I E S C H O M E O S (swc_eng_001886-swc_eng_001886) +A N L E T H E T O G L I D B E T W E A N T R S (swc_eng_001887-swc_eng_001887) +I F T H E S P R O B O E S W R F I C I N L Y S O L V H A B L (swc_eng_001888-swc_eng_001888) +A L O G H C A L T I N (swc_eng_001889-swc_eng_001889) +R O K W H E N F L U S H E D (swc_eng_001890-swc_eng_001890) +I N C L T D I N G J H R M N L T O C E T C N A L D E (swc_eng_001891-swc_eng_001891) +P E R N C O F L E T H S H O U G E S O R B O U T (swc_eng_001892-swc_eng_001892) +N D A T T I N S C T D Y T H R E (swc_eng_001893-swc_eng_001893) +A N O F E C T E R S S H U C A E P R O D A C E O L S O I S O L (swc_eng_001894-swc_eng_001894) +O T H E F I R S T N O A N S O L R E T C H A L N G E R S T E N (swc_eng_001895-swc_eng_001895) +P O N E N T H A S O N L Y T H E C I N G A N D (swc_eng_001896-swc_eng_001896) +M A Y N A R T I C L E (swc_eng_001897-swc_eng_001897) +O W N D S E R T N L A T H S O U S F U 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E A D I N G T A K S P L A C E B E T W E N A P R O L A N D D U N (swc_eng_001910-swc_eng_001910) +S T R A L O R I S A T H E S O T H E N E I N D (swc_eng_001911-swc_eng_001911) +T E C N H L O U G H C L S I N G E I L A I R I T Y I S P O S E A B L (swc_eng_001912-swc_eng_001912) +N L D I N G T H S P L P Y C O D (swc_eng_001913-swc_eng_001913) +E S E T Y F O R E H A D A H I G E R E G O C A T I O N O L F E C A T I O N C O M P E D E T (swc_eng_001914-swc_eng_001914) +I A C P E R S W H E T H E B P O N I S C A N G S A N (swc_eng_001915-swc_eng_001915) +C O N C E V A T I O N N O S T R E L Y A R (swc_eng_001916-swc_eng_001916) +I S T H E S E L A M A N D O F F E I C U (swc_eng_001917-swc_eng_001917) +F I R S T S E L F D E C R I G V E T R A N H U M O N S T H A T F O R M I L Y I N T H E A L (swc_eng_001918-swc_eng_001918) +R E E N T R E S U R C H E I N D E C A T D T H T F A C T E R S O T H E T H E N P A C D I (swc_eng_001919-swc_eng_001919) +A N D P R V E N T I O N A N D T R E K B E N T O F O M P L C A T I O N S (swc_eng_001920-swc_eng_001920) +I H E R E A E D O N S A I T (swc_eng_001921-swc_eng_001921) +O U T O W W O R T H E F O N D A T I O A S B A I E (swc_eng_001922-swc_eng_001922) +N O W A D A Y S O U R L Y R E A G I N L C S P R E S T R A N D B E T N B U R N A N D H S P E I T H T O B R I G A N D F R A T T R A N E S C O N T I N U E T O R U N O T H M U T O N T R A L W (swc_eng_001923-swc_eng_001923) +T H E F A M L Y S W E T H P T I N C R L Y G O N D W A N E A N O R I G I O N I N C L D T H E R E T R P O N E D A Y (swc_eng_001924-swc_eng_001924) +B Y A N E T A L I N D O A M I K C E N D M O A C J A C C O B I S D E S E S T L E S (swc_eng_001925-swc_eng_001925) +M A N D W A S N A I E D A F T R T (swc_eng_001926-swc_eng_001926) +A R D O F I H A L N T E L I G E N C S (swc_eng_001927-swc_eng_001927) +A N D I S T H E R A I N G (swc_eng_001928-swc_eng_001928) +P R S E O F T H O P I L A T I O N (swc_eng_001929-swc_eng_001929) +C H O E A R I E S O F S O U P L I C H S E L S (swc_eng_001930-swc_eng_001930) +I N P O S E B Y L A (swc_eng_001931-swc_eng_001931) +R E F R O N C E I S E S T O T H E R O L I N G C O R L I T I O N G O V B E R M E N (swc_eng_001932-swc_eng_001932) +S P A H E S O F G L D I N G P O S M (swc_eng_001933-swc_eng_001933) +B A C S E D O N T H E P R E V I S S T R A D G E Y O F P L A Y (swc_eng_001934-swc_eng_001934) +A D I D D U L I S T C A S P E R A T I O N E (swc_eng_001935-swc_eng_001935) +P E R F E T O N L S A N D H O M R E C O A R I N G O T H U S I A S T S (swc_eng_001936-swc_eng_001936) +H E M T H O L P O D A Y (swc_eng_001937-swc_eng_001937) +N O C O R T E O F P E P L W I T H E P R E A V I S E S A Y A G C H M A D D V E L O U H I G P O P E T H U R T R I S N (swc_eng_001938-swc_eng_001938) +D I V I R D E D I N T O T H R Y F A M L Y E S T H A (swc_eng_001939-swc_eng_001939) +H O D S L I T I N T R E S T A N R L E A C I N G O S E T (swc_eng_001940-swc_eng_001940) +T H A T A H R M I R A N O U T H A V E C O M E N (swc_eng_001941-swc_eng_001941) +A N T O W H U S E N D S A I C E (swc_eng_001942-swc_eng_001942) +S H W O H I N E B O R Y E S A R N O N A S B U T P L I S (swc_eng_001943-swc_eng_001943) +T H E O S E I S R U H T H E R O F A S E R I B L E A N O U R I S T M (swc_eng_001944-swc_eng_001944) +M O S T O T H E M A G R Y O U E S T S M U S I C O M N Y E S (swc_eng_001945-swc_eng_001945) +W E N S T A R R I O P P A I R O R W N E M O N O F O N I C T R A C K I S P L A E O R R E C O R T E D H N T H E T H A P E S M O V I N G A N O N D U R E C T I O N A N T (swc_eng_001946-swc_eng_001946) +H I T C D E A R L Y F O R M E A N (swc_eng_001947-swc_eng_001947) +S T E R T E A G I K F O L O S S O V E R (swc_eng_001948-swc_eng_001948) +O S I S I O N G T B A N T G C H E S T D R N T H E G A E (swc_eng_001949-swc_eng_001949) +D O S E A U T W H E L S (swc_eng_001950-swc_eng_001950) +E S P O S L O V E R H I S O N B Y I L O U G I C A L A T E R (swc_eng_001951-swc_eng_001951) +R E P E R O U C D T O F R I H T E S H O C 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O A C E S (swc_eng_001963-swc_eng_001963) +F O S T A N D Y O U G R E A M (swc_eng_001964-swc_eng_001964) +H E F R Y I N S D K L P E A D I A A T (swc_eng_001965-swc_eng_001965) +T H E F O R M E T I C L I N A G E I N G I S D E N R L (swc_eng_001966-swc_eng_001966) +P E A S I I S T T H E C L U I O N O F N O N C H O U M E N A N D P A R T H U M A N A N A M L S (swc_eng_001967-swc_eng_001967) +N D P E A B L H O A D R V I S L Y S U F E D A S U B R A C N O T H E M R I G (swc_eng_001968-swc_eng_001968) +L S I F I D A S T H E I N D A N G E D O R T H R E T O N D A N D O T H E Y P E B E (swc_eng_001969-swc_eng_001969) +A A T E R N Y J E N E R L P A R C E R W A T C O E N S H A R D N (swc_eng_001970-swc_eng_001970) +B U T T I P I C K L Y (swc_eng_001971-swc_eng_001971) +H I C H I N T R E F E D T H E S I N L T O T H E H E A D O T H E C O S S A (swc_eng_001972-swc_eng_001972) +H T H I N T H E R O N C O N V E N I O N L Y E C S P E C T E D L I F E T I M E M S (swc_eng_001973-swc_eng_001973) +O B S T A N H E L S T R A I E N (swc_eng_001974-swc_eng_001974) +T W E N Y H A I T H S E N T R Y C O N T U C Y C O N G R S M E N J O A N (swc_eng_001975-swc_eng_001975) +N O T H Y P O S E N T O R N D I M A (swc_eng_001976-swc_eng_001976) +H I N T I N G W I T L E D S H O G (swc_eng_001977-swc_eng_001977) +W E N Y T H R T A E N (swc_eng_001978-swc_eng_001978) +O R T H Y S E V O N P R S E N T O F T H E L D P A T S P A C H E S L V I N U S T R A L I A (swc_eng_001979-swc_eng_001979) +U R P O E S R A I N F I N L Y A N D I N A N T E N A D Y F V (swc_eng_001980-swc_eng_001980) +W I L S O E T R E H U M I N I S T I K A N A P S T R C T (swc_eng_001981-swc_eng_001981) +P A N T H R E R I A T P R T E C T I O N (swc_eng_001982-swc_eng_001982) +G R A Y C S M O R F I S E P R O B L E I S T H E C O M P E T A T I N T P R O B L E O F T H E T E R M I N I N G W H T H (swc_eng_001983-swc_eng_001983) +O R T H E R E S T I C T A U R C O N C S E P T O (swc_eng_001984-swc_eng_001984) +H E H O O F B I E O U S T P T L I S A T I 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(voxforge_eng_001003-voxforge_eng_001003) +T E N A G I N T O E R H A D S O C H A N I E R S T A T I N G W A Y A O B O T H M (voxforge_eng_001004-voxforge_eng_001004) +W E A L L N O O M A N A S A S E C E S T O L S T A B L C U N T R Y A R O A L M O R T H E F O R T H A T F O R T H E H O L R E A G O N (voxpopuli_eng_000494-voxpopuli_eng_000494) +T H E R F O R I T S H I G H T I M E O U C O M E F O R B O U G D T H E T E P R E P O R S L F O R R E V E U B E D E N O P R A T I N A L S B R A T S O N O F T E O A R D I T A N D N O N O A L D I T S R V I S I E S A N D E R A D I D L A C T E W U S O B O T I S O N (voxpopuli_eng_000495-voxpopuli_eng_000495) +I T I S C E E A R T H A T W E H A V E N O T I M E T O W A S T T H E N U R E S S O U L T S H O F T H E Y E I D P E A S I E S I E R E O A R D I N S I E N T D I F I C K B A S I E S O F G L I M I N T T J A I N C E L E N O R O U M F O R H E S I T D A C S E O N (voxpopuli_eng_000496-voxpopuli_eng_000496) +S E N T S O I N T H E C O N T A E N O R H I H A E V E R A V E N T U C H E D C O M E S L A V E S C O N T O F E T G O D S D R O G E S I T H E T R (voxpopuli_eng_000497-voxpopuli_eng_000497) +I H O P E T H A T C O M I O N S M B I T I N I S H E S I N I S H I F I V S W W N T C A R A K T T H E D A C X T P R O B L O E M B U T W I L B E A N A N C S E F O R E E I S T I N G C H O L I N G E R S O F T H E R O B P T A N C S P B O R D S E C T A R (voxpopuli_eng_000498-voxpopuli_eng_000498) +I T E O U A S I W A S D E S I O N T A I N A R L Y B Y O N P E R S O N T H E O R M E P E S I D E D O T H E N I D E S T A C E S A G A N S E T H E A R T I C I L A T E D M C R A T I C K D R M N D U R I T Y O F T E U S C O N G R S S B Y A L L O F I T S R E P O B L K E N S O F I C S T D E M E R C R A T I C T D E M R T M B E R S I T W A S N A G R E M E N T W I T H O U T A N Y B I D N D I N G O B L I G A T I O N S A S T H E L E A D E R S O F I R A U N V E R Y O U P E N T L Y I N P R E S I D H M A E P L Y N T H E E R Y D A T H E S O C L D D E L W A S P O L I S H E D (voxpopuli_eng_000499-voxpopuli_eng_000499) +F R E S P E A C G E I S S S E N C I A L Y A E C E C T I N G T A T P E P L U R F R E E T O S A Y T H I N G S W E D O N O T L I E K E N O T M E L Y F R E E T O S A Y T H I N G S W E D O L I E K (voxpopuli_eng_000500-voxpopuli_eng_000500) +L A T A S L A R N D F O M T H I E (voxpopuli_eng_000501-voxpopuli_eng_000501) +B E S I N G H A T H E A N E V I M E N T A E E A F E C T O F P R D U C S M U S T B E A V E R Y I N M P R T A N T I S H U I N G T E R E U A N D H E O L I G T D E A R O T H E I E C O L A B R N E V S A V E R Y O U S O L O R I A N T A T I O N F O R T H E C O S T S U M E M I S O F C O U R S H E I A C U L A B E R S O O D G I V E N T O T H E M O S T A N D V I R E M E N T A F F E N D Y P O R D U C T T H E I F O R E M I T I O N S H O L B E C L E A R E A N D O E (voxpopuli_eng_000502-voxpopuli_eng_000502) +H O W E V E R T H E C I R E N D R I G I E M E N I T S T O B B E T E R S A L O R T T O T H D I G I D L I N F V E I R N E N T E D E N S H O U R F A R R I M I N E R A T I O N T O G R E A T D U R S A N H T O O F O M E T O O N S O M E R E X A P E C T A T I O N S (voxpopuli_eng_000503-voxpopuli_eng_000503) +A D C O L S B O T E O M I O N A N D M E M B R S T A T H O A N H A N C D H E R S E P O R T T O R E C E N C S I Y A T I O N T O S E C U R P E C S A N D S T O B L I T Y A N A R L E N D I W I L T H E R F O R A R E S U C A L I G E T O P E E S S U P O R T H I S M E N D E N (voxpopuli_eng_000504-voxpopuli_eng_000504) +T R A T I G I G C H O I C I S A B O U T E R E T O L E W E S T M U S T B E M A D N O W L T O A K I N N E C O U N E A N E T O F A S E O U T F O R S I L F U L S U P S I T D S B U T T E K T H E G A S A S F O R S O F Y U I T C A N B E A H E L P F U L B R I G I N G R N S I C O N A R Y M E A D I U M T O B E U S I N M E M I N M A N Y M E B E R S T H A S I E O N T O E D C H I F O V E R A N M B I S H I S L I M I G T A R G E T S (voxpopuli_eng_000505-voxpopuli_eng_000505) +E W E A E D P O S I L Y A O F E R I S C O P E W E C A N C O O T H T O P O R O S U E T H E Y S A M E P L I S E S I N H S A M E M A N E R N O N I N G T H A T W E L L E D T O H E A M R S O T S T H E R S O L S E S T T H A T W E N O D R D A (voxpopuli_eng_000506-voxpopuli_eng_000506) +U T E R I S N O P T I O N B (voxpopuli_eng_000507-voxpopuli_eng_000507) +W E A L L S O E N E A D A T H A N G E H I N O R H R D O L I D G T Y I (voxpopuli_eng_000508-voxpopuli_eng_000508) +I L A G H B U T O F T H E R E S O N O F C O U R S E I S I L I G L F I S I N G K A N D T H E O F O M A L T W E D O U N O F E N B Y H A L A M V E S E S W H I C H A R L E A G I S T E R E T O C O U N T R I E S H I C H L A U C E T H E I L O F T H R R S U R E I S T W A N F O R S T T I N T H E N A T I O N E A G E M E N C S N O M O N T O F T E S A B E I T Y M A S E R S O R D E C X T R P P E R W A R U I L E D R E S E T H E P R O B L O M E O F R E T I U S I N G (voxpopuli_eng_000509-voxpopuli_eng_000509) +T H E O M P R M I C E O L L S O I N C L U D S C L E A R E R U A S T O H E F I N E W H I C H M B R S T A T D A S H U R S T I C T I O N A N T H O P R A T I O M T Y E M B R S T A T E S C O N C S E R D F O R C R O S B O T H E C A C E S A S H E H A T H E N E D T O E I M V O L L E F Y O U R J U S T H E N Y O F O R W O R K A N D P L E A C E W O E S U P R T T O M R O H I S E I R E C I F (voxpopuli_eng_000510-voxpopuli_eng_000510) +E N O T H E G R E N S W I O D T H A V S B L E H A T H I S U R B A D B E E S C R I M I N A L B E E S D E L I B R T L Y C O T A M I N A T I N G H U D Y W H T H E D A N G R U S I N G R E A D I E N T B U T I N F A C I N F A C H E D I N G H E H U Y B E S A R L A V L A L L R S D O U N M I H I T O C A R Y P O L O N B A C T T H E R H I V S T O D T O F E T H E R Y O U N G (voxpopuli_eng_000511-voxpopuli_eng_000511) +B U T I T W A S T H E C O U N T R Y I T D H E L D B E N G M O R E C A P R A B L (voxpopuli_eng_000512-voxpopuli_eng_000512) +R I N T O T H E P R T F O L I A O O F T H E N U C O M I S I O N R E D A L I N G W I T F A U N D E M E N T E R I G H T E (voxpopuli_eng_000513-voxpopuli_eng_000513) +E M E S I G E T A T T H E Y U D O U T A T H A V E A N Y N U S O L T I O N E (voxpopuli_eng_000514-voxpopuli_eng_000514) +A R Y U W I L I N G T O A C T I N E R V F A T H E R E F O R T H E S O S I A L D E M E N T I O N T O B E I N L O D E D I N T H E E O U C O M P E T E N S Y E S A S P R E P U S (voxpopuli_eng_000515-voxpopuli_eng_000515) +A E R N C T H E O N D P E S P E C T R U P O L I Y E S T A K I N G W I T H E R E F O R M E O F O U R T E L I C O N T H F R A M W R (voxpopuli_eng_000516-voxpopuli_eng_000516) +I B E L E H I S R E M A R C E S W E R A I N E X T P L I I T E L Y R A C I S C T E D A N D S E N A F O B I C K E A N D P R M O T E D R A I L I N T O L R A N C I N W H A Y T H I S N O U C E T A B L E O R A L A O U E D I N T E O N T O I C T U T I O F T H I S H O U S E (voxpopuli_eng_000517-voxpopuli_eng_000517) +R E A L I G H E G S A M P L S H O L T H A T S O V I N G I E S R L A T E T O A B O C A T I O N F E Y U L D S T R O N C O M N I T Y D E V E L P M E N T (voxpopuli_eng_000518-voxpopuli_eng_000518) +S I H O L P E T H A H I S W L H A E M F O R U S H E A S W H E L A N T A T R U S H E C O N L S A N D I S I G T D N C S T R E M E M S C C E S S T A R Y A F T R T H E S E G T W I S I G I F I C E N D A T I N O R G S T T H I S O U R B (voxpopuli_eng_000519-voxpopuli_eng_000519) +S H E E C E P T O T H E F A C T T H A T S I T I S I E N H I P I S A Y N A S I O N L B O U R T O F T H E N O S I O N O G R I S D I C T I O N B U T H Y O U R L S O S A I D T H A T A C O R D I G T O T H E M A S T R I C K T R E A T Y A N D H E A S R I G H T D T H E A S T O B E A D I R E C L I N G (voxpopuli_eng_000520-voxpopuli_eng_000520) +E O F A I L D E S P E S I O L Y E I N T H E M S T H R A T I N G A Y U L I F I D E D A N D T A F I S H E N T A T P R O R C H T O O L I M I T C A E N G C H T R E A T M E N T A S E L E A S I N S T R A N G T H A N I N G I T S E L E A D I N G P O L I T I C K L C O S I T I O N I N D E S U G E N D E R I C O S I T H E R E T H E F O R T A K I N G I S R E S O L U I O N A N A C T O F U T M O R S T I M P O R T A N S (voxpopuli_eng_000521-voxpopuli_eng_000521) +T H E U N I G T E S S T A T E S O F Y U R O V I L B A F A C T W I T S W E D O N A S P R O V I D E N C S (voxpopuli_eng_000522-voxpopuli_eng_000522) +I T D M U S B E T H E C A P B I T L E O F B O T T H A T S A N D W E M U S S R E C O N I S E P O L S T I N I S S T H A T A S P R O V I D I D F O R E I N T H E O F L O G R E E N C S (voxpopuli_eng_000523-voxpopuli_eng_000523) +T Y U K R A N E S F A S E T W I T D W O N E O F C R U S I A L C H A L I N G E S E I N I C G H I S T A R Y I T W U L D B E F I U N T H E M E N T A L Y R O N G K T O P R E S T H E N A T I O N N O W E W I T A L T I B E S O F O R E S T R I C T I O N S P O P E L I D A L C O A L E D O S T E R I T E P O L I (voxpopuli_eng_000524-voxpopuli_eng_000524) +M O R R U L S A N D R E A G I L A T I O N W I L L N O T I N M P R O V E T H E S S I T U A T I O (voxpopuli_eng_000525-voxpopuli_eng_000525) +A T L E A S T B E W U D L I K E T O N O L T H E S O R S E O F T H E M U N Y A N D T H E P O S I B L E M O R T H I F S (voxpopuli_eng_000526-voxpopuli_eng_000526) +T O W E A V E T H O U S E Y U R P E N W A L L A N G W O A C E S I N T O T H E I S G L A B L I S E D W E R L D T I S I N T T O T H E A I S G O A B L I S E C O N M Y I N D I S G O B E V I L I A G E W H I C H I S C O T I A L Y C O N O M I C K S O S I A L E E L N B P O L I T I C O L B P W I T S E A E M O S T V E L A B L E E A S T H E I T F R O M T H E I N T I R E E O U G T H A T W E M U S T T H A K F O L A C O U N S A N D T (voxpopuli_eng_000527-voxpopuli_eng_000527) +E A E T O R E B E T T H A T A L L H E A Y A N N O T B E Y O U S T O F I N C S S I G U R I T E X P A N C E S B A R T H E R S C O N T R O L O R M L I T R Y S O U P O R N (voxpopuli_eng_000528-voxpopuli_eng_000528) +T H I G H E I N T I F I K R E P O R T S B C A L E M A R M O R E U R D E N T M O R L A R M I N G A N D M O R S H O C K I N G (voxpopuli_eng_000529-voxpopuli_eng_000529) +F I N O L Y I M W H E W H A E H I N K I N G K A B O U N T H E R E I N O V A T I F E F F I N S I O N I N S T R M E N T S W H N K U T H E B O L T H F O R O R S E L F S T O U G S O U P O R T O U O U E R A C O N O M Y S B U T A O S S O T O L S O P O R K T T H O S C O H E R I N E A E (voxpopuli_eng_000530-voxpopuli_eng_000530) +T H A T G I V E A E S O R Y U N I K E T O U L I N E S M A K I N G (voxpopuli_eng_000531-voxpopuli_eng_000531) +D P A P E R A V E R Y H L W E E K P R O P O S I L (voxpopuli_eng_000532-voxpopuli_eng_000532) +S R U S H A S O L Y S B E V E R Y P R O U D N A T I O N W I T H I C H C L D C H U E R E W I T I N V E N T I O N S W I T H A N E S C E (voxpopuli_eng_000533-voxpopuli_eng_000533) +A R T A C T A I T I O N N V H E N A M O D I C E O F T A C S A I T I O N N S O M E C A C E S M I Y J U S T H E L P E S E M T O D O W A T I H E A R E D Y S H E G E S T E D A N D H O N O S M A K T H E C A C E F O R T H E E T E R S P E C T O F B A N K R E C A P D L I A T I O N T H A T W E N E V E R S O L (voxpopuli_eng_000534-voxpopuli_eng_000534) +T H E U L O P B E A N H S I D L O M S U P O R T O F I C E M O R O V E R A S A M O N G I T C S T H I U S T O P R O M O U H T F E S I L Y T H A T A N D C O U R D I N A T E C S C H A N G E S O F I N F O R M A T I O N A N D U T H E A C T E V I T Y E S E R L A T Y T O L E L C A T I O N B D I N T E Y U N I O N (voxpopuli_eng_000535-voxpopuli_eng_000535) +H E C O N L S N O F T H E R A M E B O R K A G E M E N T P R O V I E A L I G L Y B I N D I N G K I N S T R M E N T T O O B V G I R A T A N D S T R A N T N E U O S T R L I R B E L I T H E R I A T S I O S A N D T O I N C E E S C O P E R A T I O N (voxpopuli_eng_000536-voxpopuli_eng_000536) +E R E F R U R E W E A S I N T H E C O U N S E L A S G L M I T I O N T O R E N T A C A S B A R I T H A U L D B E D T H E S E S T E N T O F T E B A C T O T H R I S I (voxpopuli_eng_000537-voxpopuli_eng_000537) +A I N O T H E W E R D S T H E O B J E C T I O N I S N O T W H E T H E M U N Y I S P A E D O R N O T T H E O P B E B J E C T I O N I I S W E T H E T H E Y S A D I D E C T L I N K O R E N O (voxpopuli_eng_000538-voxpopuli_eng_000538) +T O H E S T I N G I S H I E S T H E T W O M A E D O S H E A R Y O U M E R I T S A E Y O U S E B Y T H E C A R N T G O R E N T A N D T H E D A N I O N N U K L E P R O G D H M E (voxpopuli_eng_000539-voxpopuli_eng_000539) +E S S M E T H E B D R O N M H E N K A C T E R E S E C T I O N H E R A S D E N T I S H E F O R M O V I L A N C E A N D I T I T H E M O S T E T R E A N F O R M O F G H N T R B A S E D E S C R M I N A T I (voxpopuli_eng_000540-voxpopuli_eng_000540) +W E C A N L O K T O S O M E E I R N N I N E U M E M B O R S O R G O D E X S A N P L E A R E G A R D E D T H E N O L A G E S (voxpopuli_eng_000541-voxpopuli_eng_000541) +I N M V L V E D S F O R H E P O S I T E I V E A N D C E S T T A C T E I V E A B R O A T C (voxpopuli_eng_000542-voxpopuli_eng_000542) +O I H O P E T H A T I S I L B E C O M P E A T E T A I R I N H A F O R S I V I L E F O U O C H E R T H A T D M A N S E G A D B E T O A F R E M U N S (voxpopuli_eng_000543-voxpopuli_eng_000543) +O R F O R D E R A N D C O R I S H E T H E Y O U A N D E T F H U R H T O B R I N G A M N G P E S I N O F G N I S T A N A N D T O O V E R C O M E T O F F R E S I L S I C U R I T Y N V E I R E M E N T I N T H E C O N T Y (voxpopuli_eng_000544-voxpopuli_eng_000544) +B E A N D T H E S T A N T T H A T S O M E P E P L O A R A N G R Y (voxpopuli_eng_000545-voxpopuli_eng_000545) +O E N T O H E M O R S T P O N C I V L D (voxpopuli_eng_000546-voxpopuli_eng_000546) +E M U S T E D A C T I F I H I T H T H I S S U T I A T I O N A N D H A S E T H E C O M I O N T O C O N S I D E R T H E M O S T E D I C K E T G C O M I N S A T I O N M E S H E R S F O R L O L W P E S E N G E S (voxpopuli_eng_000547-voxpopuli_eng_000547) +T H E C O M I T I O N I N G B I S H E T H E Y U O P I O N T P O R L A M E N T I N T H E U P C O M I N K R E V I S I O N T O O P E N I S P O S I T I O N O N D T H I S M A T H E R E W H I C H R E A L Y C O N C S E R D A C E S E T O L U S T I C S I N O U R O P A N D T H E I N F O R S T M E N T O F R I C E S G R A N T E D B Y H E Y U R O P I U N R Y U N D L O (voxpopuli_eng_000548-voxpopuli_eng_000548) +I L M E R Y M U C H T H E R S O U N T I O O F T O C K T E N T H E A S R A L Y A N D P O L I S T I N I O N S A N D E N C I R L Y H O P T H A T H E W L D S U C C E D (voxpopuli_eng_000549-voxpopuli_eng_000549) +L W E H A E E C U M E L A T I O N O F P R O B L A N C S R E S I L T I N G F R O M T H E A R T I F I S H A L U N D T H E B A G E I T I N G K A N D V E R E T P R I V U S Y U S (voxpopuli_eng_000550-voxpopuli_eng_000550) +E L E T A S T N O T B E T H E M A N O F O U S T A D Y I T I S B E T O D A S I N S T I T U T I O (voxpopuli_eng_000551-voxpopuli_eng_000551) +E I G O D A R L S E O E N T O B E C O M E A M B S H E T E S O T H E Y E A R E M A K I N G I T A Y D E I R S A D A C T I V I T H I S W O W W H I D L Y N O N E A M O N G S H T T O Y U O P E A S I T I E S E A N D P U T P I C I P A T I N G N H V B E N T S E B E T T A T Y U R O P E I O N N A S H O N L F O R L O K A L E L (voxpopuli_eng_000552-voxpopuli_eng_000552) +D S A R T D L Y S U C H I N P A C T S E S T M E N T C O L D P R E A M T S E R T A N P R O B L O M S S U C H A S T H O S P O S E D B Y T H E E L C T R N I K I D E D T F I C A T I O N O F S H E P A N D S C O T E N D (voxpopuli_eng_000553-voxpopuli_eng_000553) +T H E O R T I S C O N T E N T T O S E T H A T H I T S W O R K H A S I N F O R M E T H E D E S H A R G H R O U E S A N D H A S O N T E B E U T E D T O P R O P O S O L S E F O R I M P R O V I N G T H E F I N C H A L M A N A H E N T O F E Y O U S P E N D I N G A N D B E T E T O R K A T I N G O F Y O F N C S (voxpopuli_eng_000554-voxpopuli_eng_000554) +R E C G U A I H R Y G L A I T H E A N D S E R T E N T Y I A S N E A D E T D F O R T H E O B L I K E S E C T O R A N D F O R T H I N D U S T R Y (voxpopuli_eng_000555-voxpopuli_eng_000555) +I S I T D R E L Y N O T P O S E A B L R T O O U E A A T H E R H O U S I N F E S I L I D E S W I H E P R O P E T H R E S E P T I O N C O D I O S I N T H E M E N T I M E (voxpopuli_eng_000556-voxpopuli_eng_000556) +W E L Y O U T E A K E A C S I O N A T D L A S T I F N O T T H E I N W E I N D E (voxpopuli_eng_000557-voxpopuli_eng_000557) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..14522cd1c63d67fcbe0fbb003753962155889b3a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/ref.trn @@ -0,0 +1,1092 @@ +H E R E M A I N E D W O R L D C H A M P I O N U N T I L N I N E T E E N S I X T Y F I V E A Y E A R I N W H I C H H E S U F F E R E D A T E R R I B L E A C C I D E N T (LAD_eng_000254-LAD_eng_000254) +A L I B E R A L C O N S E R V A T I V E H E W A S D E F E A T E D I N E I G H T E E N E I G H T Y T W O (LAD_eng_000255-LAD_eng_000255) +O N E R O A D L A Y E R C A N D R A W T W O R O A D S A T O N C E (LAD_eng_000256-LAD_eng_000256) +S O M E O F T H E C O U N T R I E S H A V E S U R V E Y S F O R M U L T I P L E Y E A R S (LAD_eng_000257-LAD_eng_000257) +B O T H O F T H E V E R S I O N S F E A T U R E T H E S O N G H A P P Y H O L I D A Y (LAD_eng_000258-LAD_eng_000258) +S H A K E S P E A R E M A N Y R E F E R E N C E S A R E M A D E T O S C E N E S I N T E R A C T I O N S O R C H A R A C T E R S F R O M V A R I O U S P L A Y S (LAD_eng_000259-LAD_eng_000259) +I F O N L Y T H E P R O G R A M C O U L D B R E A K O U T J U S T A L I T T L E F R O M I T S T O O F A M I L I A R A P P R O A C H (LAD_eng_000260-LAD_eng_000260) +T H E A L B U M W A S R E L E A S E D I N A U S T R A L I A O N N I N E T E E N T H A U G U S T T W O T H O U S A N D A N D E L E V E N (LAD_eng_000261-LAD_eng_000261) +H E N O W P L A Y S F O R A U S T R A L I A N C L U B P E R T H G L O R Y (LAD_eng_000262-LAD_eng_000262) +I T I S N O T K N O W N H O W M U C H I F A N Y O F H E R C L A I M S A R E T R U E (LAD_eng_000263-LAD_eng_000263) +A S M A L L B U S I N E S S O W N E R B R O A D O P E R A T E D H I S W H E A T A N D S H E E P F A R M F O R S I X T E E N Y E A R S F R O M T H E A G E O F T W E N T Y T W O (LAD_eng_000264-LAD_eng_000264) +I N T H E N I N T H C E N T U R Y H E W A S A N I R I S H P O E T (LAD_eng_000265-LAD_eng_000265) +T H E Y A R E M A R K E D B Y S T R O N G (LAD_eng_000266-LAD_eng_000266) +T H E L A W I S T H E R E F O R E V A L I D (LAD_eng_000267-LAD_eng_000267) +I N T H E E A R L Y S T A G E S C A M E C L O S E T O U S A S L E E P (LAD_eng_000268-LAD_eng_000268) +R U N N I N G E V E R Y T H I R T Y M I N U T E S T H R O U G H O U T S E R V I C E T I M E S (LAD_eng_000269-LAD_eng_000269) +A S A R E S U L T W H E N T H E C O L L E G E R E O P E N E D I T W A S A S A N A L L M A L E C O L L E G E (LAD_eng_000270-LAD_eng_000270) +T H E T I M E B E T W E E N T H E S E P O I N T S I S V A R I A B L E A N D C A N O C C U R A N Y W H E R E F R O M A M I N U T E T O M U C H L O N G E R (LAD_eng_000271-LAD_eng_000271) +W O R K O N T H E E E S S T A R T E D I N M A R C H T W O T H O U S A N D A N D S E V E N A T A C O S T O F F I V E M I L L I O N D O L L A R S (LAD_eng_000272-LAD_eng_000272) +H O W E V E R T H E R E W A S S O M E D I S A G R E E M E N T O V E R T H E E N D I N G T H E M E W H I C H O M O R I A N D Y O S H I M O R I D I S C U S S E D A T L E N G T H O V E R E M A I L (LAD_eng_000273-LAD_eng_000273) +T H E C O U P L E H A D N O C H I L D R E N (LAD_eng_000274-LAD_eng_000274) +T H E O F F I C I A L S I N G L E O F T H A T D E B U T A L B U M P A R I S C A L L I N G H A D A N E L A B O R A T E M U S I C V I D E O (LAD_eng_000275-LAD_eng_000275) +T H E S E R I E S E N D E D O N S I X T H A U G U S T T W O T H O U S A N D A N D F O U R L A S T I N G F O R A T O T A L O F S E V E N T Y O N E D A Y S (LAD_eng_000276-LAD_eng_000276) +H E H A S A L S O C O N T R I B U T E D T O T H E N E W Y O R K R E V I E W O F B O O K S (LAD_eng_000277-LAD_eng_000277) +B Y P L A C I N G S M A L L A R T O B J E C T S T H R O U G H O U T T H E F I L M (LAD_eng_000278-LAD_eng_000278) +I T I S F O U N D I N B R A Z I L (LAD_eng_000279-LAD_eng_000279) +I T W A S T H E S I D E O F T H E F A M I L Y I I D E N T I F I E D M O R E W I T H (LAD_eng_000280-LAD_eng_000280) +C A N D I D A T E S I T E S M U S T A L S O S U B M I T A W O R K P L A N (LAD_eng_000281-LAD_eng_000281) +D U N D E E W O N T H E M A T C H T H R E E T W O (LAD_eng_000282-LAD_eng_000282) +H O W E V E R T H E V I L L A G E R E M A I N E D I S O L A T E D U N T I L T H E A R R I V A L O F T H E F I R S T N E W S P A P E R S E C O N D R E P U B L I C (LAD_eng_000283-LAD_eng_000283) +T H E F I R S T S E R V I C E I N T H E N E W C H U R C H W A S H E L D I N N I N E T E E N F I F T Y O N E A L T H O U G H T H E B U I L D I N G W A S N O T F U L L Y F I N I S H E D (LAD_eng_000284-LAD_eng_000284) +T H E A V E R A G E H O U S E H O L D S I Z E W A S T W O P O I N T T W O S E V E N A N D T H E A V E R A G E F A M I L Y S I Z E W A S T H R E E P O I N T Z E R O Z E R O (LAD_eng_000285-LAD_eng_000285) +I T W A S F I R S T B R O A D C A S T O N T H I R D J A N U A R Y T W O T H O U S A N D A N D T E N (LAD_eng_000286-LAD_eng_000286) +T H E W I N G S W E R E N O W M A D E I N A S I N G L E P R E S S I N G (LAD_eng_000287-LAD_eng_000287) +D O C T O R O F P H I L O S O P H Y I N E N G I N E E R I N G M A N A G E M E N T (LAD_eng_000288-LAD_eng_000288) +T H I S T O O K A W A Y T H E M A I N A R G U M E N T O F S A F E T Y R I S K S (LAD_eng_000289-LAD_eng_000289) +H E W A S A L S O M A D E A L I F E M E M B E R O F S C U N T H O R P E U N I T E D (LAD_eng_000290-LAD_eng_000290) +S H E F E A R S T H E Y W I L L G E T A D I V O R C E B U T T H I S N E V E R H A P P E N S (LAD_eng_000291-LAD_eng_000291) +F O O T D R O P S U N A B L E T O H O L D T H E F O O T S T R A I G H T A C R O S S (LAD_eng_000292-LAD_eng_000292) +W H E T H E R T H E A I R F L O W I S F R E E O R F O R C E D C A N A F F E C T T H E E N E R G Y E F F I C I E N C Y O F T H E W I N D O W (LAD_eng_000293-LAD_eng_000293) +A F T E R G E T T I N G T H E R I G H T M E A S U R E M E N T S T H E Y M A D E T H E N E W D O O R S (LAD_eng_000294-LAD_eng_000294) +F R A G M E N T S O N E A C H F A C E A R E M A R K E D W I T H L E T T E R S A B C (LAD_eng_000295-LAD_eng_000295) +F R O M T H E F I R S T M I N U T E S B O T H T E A M S S H O W E D T H E I R D E S I R E T O C O M P E T E W I T H A G G R E S S I V E A P P R O A C H E S (LAD_eng_000296-LAD_eng_000296) +P H Y S I C A L T H E R A P Y E X E R C I S E S M A Y H E L P P A T I E N T S T O M A I N T A I N M U S C L E S T R E N G T H (LAD_eng_000297-LAD_eng_000297) +H O W E V E R T H E T O W N S H E L I V E S I N N O O N E W A N T S T O H E A R A B O U T H E R (LAD_eng_000298-LAD_eng_000298) +D E S C R I B E S A P P O I N T M E N T S O F A N A C T I N G C H I E F J U S T I C E O R J U D G E O F T H E S U P R E M E C O U R T (LAD_eng_000299-LAD_eng_000299) +T H E S O Y B E A N S O U T E R C O V E R I N G I S T H E N R E M O V E D A N D T H E B E A N S A R E P A R T I A L L Y C O O K E D (LAD_eng_000300-LAD_eng_000300) +T H I S N A T I O N A L M O V E M E N T W H I C H H A D B E G U N W I T H S O M U C H H O P E C A M E T O A S A D E N D (LAD_eng_000301-LAD_eng_000301) +H I S A S S O C I A T E S U S U A L L Y C A L L E D H I M T O R T H E G O O D L O O K I N G G U Y (LAD_eng_000302-LAD_eng_000302) +I T S M A I N O F F I C E S W E R E I N L O N D O N W I T H A S E C O N D O F F I C E B E L F A S T (LAD_eng_000303-LAD_eng_000303) +A C T U A L L Y I H A D N E V E R B E E N T O A V I L L A G E B E F O R E T H A T (LAD_eng_000304-LAD_eng_000304) +H E W A S C H A R G E D W I T H P L A N N I N G T O S E T O F F B O M B S I N E U R O P E A N D T H E U N I T E D S T A T E S (LAD_eng_000305-LAD_eng_000305) +M A K I N G M I R R O R S I S T H E T H I R D S T U D I O A L B U M B Y B E L G I A N A U S T R A L I A N A R T I S T G O T Y E (LAD_eng_000306-LAD_eng_000306) +H E T H E N M O V E D T O W A S H I N G T O N D C A N D W A S A P A R T N E R W I T H W A R D B R O W N U N T I L N I N E T E E N T W E N T Y N I N E (LAD_eng_000307-LAD_eng_000307) +J O S E P H H I G H S C H O O L A N D T H E S C H O O L S T H E Y C O M P E T E A G A I N S T I N A L L S P O R T S (LAD_eng_000308-LAD_eng_000308) +T W E L V E P L U S O N E M A T C H B A N P E R C A R D (LAD_eng_000309-LAD_eng_000309) +I T H I N K I M I G H T B E N O T H I N G (LAD_eng_000310-LAD_eng_000310) +T H E H O M E W A S B U I L T A N D L I V E D I N B Y A N D R E W J A C K S O N K E N N E D Y D E P U T Y C O L L E C T O R F O R T H E I N T E R N A L R E V E N U E S E R V I C E (LAD_eng_000311-LAD_eng_000311) +I N N I N E T E E N S I X T Y F O U R H E W E N T B A C K T O O M S K A N D E N T E R E D T H E A C T O R S S C H O O L O F O M S K (LAD_eng_000312-LAD_eng_000312) +T H E B A N K I S J O I N T L Y O W N E D B Y H I M A N D H I S B R O T H E R S A N D R E L A T I V E S (LAD_eng_000313-LAD_eng_000313) +H E S U B S E Q U E N T L Y W E N T T O S C H O O L I N B R I S T O L (LAD_eng_000314-LAD_eng_000314) +O N E T H O U S A N D E I G H T H U N D R E D A N D F O R T Y S I X F O U R T H E D I T I O N (LAD_eng_000315-LAD_eng_000315) +A P A R T O F L I T T L E E N G L A N D B E Y O N D W A L E S I T H A S B E E N E S S E N T I A L L Y E N G L I S H S P E A K I N G F O R N I N E H U N D R E D Y E A R S (LAD_eng_000316-LAD_eng_000316) +H E P L A Y E D W I T H T E N P L A Y E R S F O R H A L F W A S A G A I N S T T H E T R A D I T I O N I N G S P (LAD_eng_000317-LAD_eng_000317) +T H E P R E S I D I N G J U D G E W A S W E B S T E R T H A Y E R W H O W A S A L R E A D Y A S S I G N E D T O T H E C O U R T B E F O R E T H I S C A S E W A S S C H E D U L E D (LAD_eng_000318-LAD_eng_000318) +B I G B R O T H E R F I V E W A S T H E T H I R D O F T H E M A I N S E R I E S T O F E A T U R E A L I V E L A U N C H (LAD_eng_000319-LAD_eng_000319) +I T S M O T T O I S W H O E V E R Y O U A R E A N D W H E R E V E R Y O U A R E O N T H E J O U R N E Y O F F A I T H Y O U A R E W E L C O M E H E R E (LAD_eng_000320-LAD_eng_000320) +R O B E R T E M I L L E R A S C O A C H W I L S O N (LAD_eng_000321-LAD_eng_000321) +A F T E R A O N E Y E A R B R E A K Z E R O D E G R E E W A S H E R F O L L O W I N G V E N T U R E (LAD_eng_000322-LAD_eng_000322) +A M T M A N U F A C T U R E D A M O D E L K I T O F T H E Z Z R D R A G S T E R (LAD_eng_000323-LAD_eng_000323) +T H E S S A A I M E D T O B U I L D A L E F T W I N G A L T E R N A T I V E T O N E W L A B O U R A N D T H E S N P (LAD_eng_000324-LAD_eng_000324) +H E L I V E S L I K E H E I S A Y O U N G P E R S O N (LAD_eng_000325-LAD_eng_000325) +M A S T E R O F S C I E N C E I N E N G I N E E R I N G M A N A G E M E N T (LAD_eng_000326-LAD_eng_000326) +S H E F A I L E D T O M A K E T H E T O P T H R E E A T T H E K E N Y A N J U N I O R T R A C K T R I A L S T H A T J U N E (LAD_eng_000327-LAD_eng_000327) +A T O U R F O L L O W E D I N S U P P O R T (LAD_eng_000328-LAD_eng_000328) +T H E Y W E R E E S T A B L I S H E D I N E I G H T E E N S E V E N T Y O N E A N D A R E O N E O F T H E O L D E S T C L U B S I N T H E S O U T H O F E N G L A N D (LAD_eng_000329-LAD_eng_000329) +H E W A S A M E M B E R O F T H E Y E S S C O T L A N D A D V I S O R Y B O A R D (LAD_eng_000330-LAD_eng_000330) +T W O T H O U S A N D A N D F I V E G E N T L E M A N (LAD_eng_000331-LAD_eng_000331) +O U R F I L M H A D A S T R O N G R E C E P T I O N I N E U R O P E A N D A C H I E V E D D I S T R I B U T I O N B U T T H A T W A S N O T T H E C A S E H E R E (LAD_eng_000332-LAD_eng_000332) +O R T H O S I S S T R E T C H E S P O S T E R I O R A N K L E S T R U C T U R E S (LAD_eng_000333-LAD_eng_000333) +H E W A S A L S O A T H R E E T I M E F R E N C H N A T I O N A L C H A M P I O N N I N E T E E N N I N E T Y N I N E T E E N N I N E T Y F O U R T W O T H O U S A N D A N D O N E (LAD_eng_000334-LAD_eng_000334) +T H E V I L L A G E S T R U C T U R E S H O W N I N H I S M A P I S T O A G R E A T E X T E N T U N C H A N G E D T O D A Y (LAD_eng_000335-LAD_eng_000335) +R U S S I A I S R E C O G N I Z E D F O R I T S N U C L E A R D I S A S T E R E X P E R T I S E A N D F O R T H E S A F E T Y O F I T S T E C H N O L O G Y (LAD_eng_000336-LAD_eng_000336) +A S O F T W O T H O U S A N D A N D F O U R T E E N M T V I S A V A I L A B L E W I T H I N T H E U N I T E D K I N G D O M O N V I R G I N M E D I A A N D S K Y (LAD_eng_000337-LAD_eng_000337) +N E W Y O R K P E N G U I N R A N D O M H O U S E (LAD_eng_000338-LAD_eng_000338) +T H E D U C H Y W A S S E C U R E D I N T H E O U T C O M E O F T H E G O T H I C W A R (LAD_eng_000339-LAD_eng_000339) +W I T H G O O D P A C E S T A R T E D T H E M A T C H W I T H B O T H T E A M S A L T E R N A T I N G S U P R E M A C Y (LAD_eng_000340-LAD_eng_000340) +T H I S V E R S I O N I S N O T E D F O R B E I N G V E R Y F A I T H F U L T O T H E O R I G I N A L N O V E L (LAD_eng_000341-LAD_eng_000341) +T H I S P R E S U M P T I O N I S N O T F U L F I L L E D O N E H A S T O K N O W A T L E A S T T W O C O N J U G A T E D I A M E T E R S (LAD_eng_000342-LAD_eng_000342) +N O T A B L E T I T L E S I N C L U D E D G O L D E N A X E T H E R E V E N G E O F D E A T H A D D E R R A D M O B I L E O U T R U N N E R S A N D S E G A S O N I C T H E H E D G E H O G (LAD_eng_000343-LAD_eng_000343) +T H E N I N E T E E N N I N E T Y N I N E J U D G M E N T N O T E D T H A T T H E I N F L U E N C E O F T H E F A T H E R O F T H E A C C U S E D H A S B E E N T H E R E (LAD_eng_000344-LAD_eng_000344) +M A C D U F F S W E A R S R E V E N G E A N D J O I N S F O R C E S W I T H M A L C O L M T O O V E R T H R O W M A C B E T H (LAD_eng_000345-LAD_eng_000345) +T H E M E D I A E V A L V I L L A G E C O U R T W A S A L W A Y S A N X I O U S T O K E E P T H E F E N C E A R O U N D T H E V I L L A G E G A P L E S S (LAD_eng_000346-LAD_eng_000346) +T H E R E W A S A N I N E R A N K S Y S T E M E A C H R A N K H A V I N G M O R E P O W E R T H A N T H E L O W E R R A N K (LAD_eng_000347-LAD_eng_000347) +T H E Y E S T A B L I S H E D D I P L O M A T I C R E L A T I O N S O N S E P T E M B E R N I N E T E E N T H N I N E T E E N S E V E N T Y T W O (LAD_eng_000348-LAD_eng_000348) +T H I S W A S F U R T H E R E X T E N D E D T O I N C L U D E M O R E U K D A T E S I N D E C E M B E R T W O T H O U S A N D A N D F O U R T E E N (LAD_eng_000349-LAD_eng_000349) +T H E D U T C H G O V E R N M E N T I S C U R R E N T L Y E X A M I N I N G T H E L E G A L C O N S E Q U E N C E S O F T H E R U L I N G (LAD_eng_000350-LAD_eng_000350) +F R O M N I N E T E E N T H I R T Y T H R E E T O N I N E T E E N F O R T Y N I N E T H E A M E R I C A N L E A G U E W O N T W E L V E O U T O F T H E F I R S T S I X T E E N (LAD_eng_000351-LAD_eng_000351) +T H E R E H E F E L L S I C K W I T H T Y P H U S H I M S E L F (LAD_eng_000352-LAD_eng_000352) +S I X T E A M S H A V E B E E N D I V I D E D I N T W O G R O U P S O F T H R E E T E A M S E A C H (LAD_eng_000353-LAD_eng_000353) +T H E F I R S T S E A S O N P R E M I E R E D O N T W E L F T H J U N E T W O T H O U S A N D A N D F I F T E E N (LAD_eng_000354-LAD_eng_000354) +I T S U C C E E D E D T H E Y B O A R D A N D S Y S T E M T W E N T Y F O U R C O M B I N I N G F E A T U R E S F R O M B O T H (LAD_eng_000355-LAD_eng_000355) +V O L U M E T W O N U M B E R S O N E T W O A N D T H R E E (LAD_eng_000356-LAD_eng_000356) +T H E L O W E R P A R T O F M E N S D R E S S E S W E R E M U C H S H O R T E R I N L E N G T H T H A N T H O S E F O R W O M E N (LAD_eng_000357-LAD_eng_000357) +T H E V I S I G O T H S I N T U R N W E R E S U C C E E D E D B Y T H E M O O R S (LAD_eng_000358-LAD_eng_000358) +J O S E P H H I G H S C H O O L E V E R Y W E E K O F T H E S C H O O L Y E A R (LAD_eng_000359-LAD_eng_000359) +A S A R E S U L T O F A L L T H E A R G U M E N T S G E T T I N G T O H E R (LAD_eng_000360-LAD_eng_000360) +I T S H E A D Q U A R T E R S A R E I N S H E F F I E L D U N I T E D K I N G D O M (LAD_eng_000361-LAD_eng_000361) +L A Y A L S O O F F I C I A L L Y S I G N E D T H E C O N T R A C T O N S T A G E W I T H T H E D I R E C T O R A N D P R O D U C E R S O F T H E G O L D E N E Y E S (LAD_eng_000362-LAD_eng_000362) +P H Y S I C A L T H E R A P Y C A N H E L P P A T I E N T S T O L E A R N H O W T O W A L K W I T H A F O O T D R O P (LAD_eng_000363-LAD_eng_000363) +I T W E N T O N T O S E L L T H R E E H U N D R E D T H O U S A N D U N I T S A C H I E V E F I V E N O (LAD_eng_000364-LAD_eng_000364) +T H E N A M E S T U C K A F T E R T H A T (LAD_eng_000365-LAD_eng_000365) +T H E A L B U M L A T E R B R O K E T H E D I A M O N D R E C O R D O N Q Q M U S I C (LAD_eng_000366-LAD_eng_000366) +I T S E D I T O R I A L W E S U B M I T E A R N E D I T S A U T H O R A P U L I T Z E R P R I Z E (LAD_eng_000367-LAD_eng_000367) +J O S E P H P L A Y S A R E F E A T U R E D E A C H W E E K O N T H E S H O W (LAD_eng_000368-LAD_eng_000368) +T H E Y W A I T F O R A T I M E B U I L D I N G U P T H E I R F O R C E S B E G I N N I N G T O W O N D E R I F T H I S E V I L R E A L L Y E X I S T S (LAD_eng_000369-LAD_eng_000369) +B R I E F M E N T I O N O F T H E C O N V I C T I O N A P P E A R E D O N P A G E T H R E E O F T H E N E W Y O R K T I M E S (LAD_eng_000370-LAD_eng_000370) +O R D E R E D B Y P O S I T I O N O N P I T C H F R O M B A C K R I G H T T O F R O N T L E F T (LAD_eng_000371-LAD_eng_000371) +H E I S M E M B E R O F T H E C O U R T O F T H E R O Y A L C O L L E G E O F A R T L O N D O N U K (LAD_eng_000372-LAD_eng_000372) +D U R I N G T H E C O U R S E O F T H E C A M P A I G N F E R G U S O N V I S I T E D A L L T H I R T Y N I N E W A S H I N G T O N S T A T E C O U N T I E S (LAD_eng_000373-LAD_eng_000373) +A S T R I P O F P A P E R O F L E N G T H (LAD_eng_000374-LAD_eng_000374) +S A T O U H A D F R E Q U E N T L Y W O R K E D T O G E T H E R W I T H Y O K O Y A M A O N P R E V I O U S P R O J E C T S (LAD_eng_000375-LAD_eng_000375) +S H E W A S B O R N O N S C R E E N D U R I N G T H E E P I S O D E B R O A D C A S T O N F O U R T H N O V E M B E R N I N E T E E N N I N E T Y F O U R (LAD_eng_000376-LAD_eng_000376) +H E T U R N E D R O U N D S H E H A D C O M E I N S O G E N T L Y T H A T H E H A D N E V E R H E A R D H E R (M-AILABS_eng_000159-M-AILABS_eng_000159) +A H T O B E S U R E W E M U S T K E E P O U R D O O R S S H U T W E M U S T L E T N O O N E I N (M-AILABS_eng_000160-M-AILABS_eng_000160) +K I N S M E N H E B E G A N M O C K I N G L Y Y O U M A Y H A V E W O N D E R E D W H Y I C A L L E D A T R U C E W H E N I C O U L D J U S T A S W E L L H A V E D E S T R O Y E D Y O U T H A T I D O U B T A T O A N S W E R E D H I M (M-AILABS_eng_000161-M-AILABS_eng_000161) +T H E P E A S A N T T H R E W H I M S E L F U P O N H I M A N D B O U N D H I S F O U R L E G S T I G H T L Y S O T H A T H E C O U L D N O T M O V E (M-AILABS_eng_000162-M-AILABS_eng_000162) +N O R M U S T T H O U S O L I M I T T H E H O L Y O N E O F I S R A E L A S T O T H I N K H E H A T H B U T O N E W A Y I N W H I C H H E C A N G L O R I F Y H I M S E L F B Y T H E E (M-AILABS_eng_000163-M-AILABS_eng_000163) +T H E O L D C O M P A R I S O N B E T W E E N T H E I M P U L S I V E E X E C U T I V E A N D T H E L I B E R A L A R T S M A N W H O H A S L E A R N E D T H A T T H E R E A R E O N L Y O N E O R T W O P O S I T I V E D E C I S I O N S A V A I L A B L E I N A L L T H E W O R L D O F T H I N K I N G (M-AILABS_eng_000164-M-AILABS_eng_000164) +A F T E R T H I S E X P E R I E N C E T H E I N V A D E R S W E R E C A R E F U L T O K E E P A S A F E D I S T A N C E F R O M T H E W A L L (M-AILABS_eng_000165-M-AILABS_eng_000165) +C A N Y O U B E A R S O M E T H I N G F U R T H E R I T H I N K Y O U O U G H T T O K N O W I T I H A V E H E R E A M O S T M Y S T E R I O U S T E L E P A G R A M Y E S W H A T I S I T I S S H E D E A D N O I T I S N O T A B O U T H E R (M-AILABS_eng_000166-M-AILABS_eng_000166) +N O M I S T E R T H O R N T O N S A I D G I V E T H E B A S K E T T O M E I L L T A K E I T (M-AILABS_eng_000167-M-AILABS_eng_000167) +A N A R A B I A N N I G H T E X C L A I M E D T R O T W H Y T H A T W A S A M A G I C N I G H T W A S N T I T T H E R E S D I F F E R E N T S O R T S O F N I G H T S M A T E S A I D T H E S A I L O R A N D T H E K N I G H T B U T T O N B R I G H T M E A N S A I N T T H E S A M E N I G H T Y O U M E A N (M-AILABS_eng_000168-M-AILABS_eng_000168) +I V E T U R N E D O F F U P W A R D S O F A H U N D R E D O F M Y B E S T H A N D S F O R N O O T H E R F A U L T T H A N F O L L O W I N G Y O U A N D S U C H A S Y O U A N D D Y E T H I N K I L L T A K E Y O U O N (M-AILABS_eng_000169-M-AILABS_eng_000169) +B U T W H E N S H O U L D S H E S E E H I M H E R H E A R T L E A P E D U P I N A P P R E H E N S I O N A T E V E R Y R I N G O F T H E D O O R B E L L (M-AILABS_eng_000170-M-AILABS_eng_000170) +T H E S E B O O K S D I X O N I W I L L K E E P A L L T H E R E S T W I L L Y O U S E N D T O M I S T E R B E L L T H E Y A R E O F A K I N D T H A T H E W I L L V A L U E F O R T H E M S E L V E S A S W E L L A S F O R P A P A S S A K E (M-AILABS_eng_000171-M-AILABS_eng_000171) +B U T I N G A W A S N O T A T A L L S U R E T H E Y C O U L D N O T G E T I N T H E G A T E S O P E N E D I N W A R D A N D T H R E E H E A V Y B A R S W E R E H E L D I N P L A C E B Y M E A N S O F S T O U T S T A P L E S R I V E T E D T O T H E S H E E T S O F S T E E L (M-AILABS_eng_000172-M-AILABS_eng_000172) +I W A N T T H A L S A I D H O D D A N C O L D L Y I W A N T A D O Z E N H O R S E S I W A N T M E N T O R I D E T H E M W I T H M E H E P U S H E D H I S W A Y F O R W A R D W H I C H W A Y T O T H E S T A B L E S (M-AILABS_eng_000173-M-AILABS_eng_000173) +T H E R E I S A L I M I T T O W H A T Y O U C A N D O T H E F I R S T T I M E Y O U E N T E R A M A N S H O U S E A N D B E S I D E S T H A T W A S N O T I M E T O A R O U S E S U S P I C I O N I N T H E M I N D O F A N Y O N E (M-AILABS_eng_000174-M-AILABS_eng_000174) +D O Y O U N O T R E M E M B E R T H A T H E S A Y S T H Y D E M O N T H A T S T H Y S P I R I T W H I C H K E E P S T H E E I S N O B L E C O U R A G E O U S H I G H U N M A T C H A B L E (M-AILABS_eng_000175-M-AILABS_eng_000175) +M I S T E R B E L L W H A T C A N H E K N O W O F J O H N H E L I V I N G A L A Z Y L I F E I N A D R O W S Y C O L L E G E (M-AILABS_eng_000176-M-AILABS_eng_000176) +A N D T H E K I T T E N F O L L O W E D D E M U R E L Y A T T H E I R H E E L S (M-AILABS_eng_000177-M-AILABS_eng_000177) +T H E F I R S T T O U C H W O U L D C A U S E A N E X P L O S I O N I N W H I C H A M O N G S U C H H U N D R E D S O F I N F U R I A T E D M E N A N D R E C K L E S S B O Y S (M-AILABS_eng_000178-M-AILABS_eng_000178) +O N E O F T H E G R E A T P L E A S U R E S O F M A R G A R E T S L I F E A T T H I S T I M E W A S I N E D I T H S B O Y (M-AILABS_eng_000179-M-AILABS_eng_000179) +T H E T H I N G H A S G O N E O N L O N G E N O U G H I F T H E R E I S O N E M O R E B I G A C C I D E N T W E S H A L L H A V E T O C O M P R O M I S E W I T H T H E I N T E R R I V E R A N D C A R R Y O N T H E W O R K J O I N T L Y (M-AILABS_eng_000180-M-AILABS_eng_000180) +Y O U A R E L A T E S A I D S H E W E L L S H E H E L D H E R B R E A T H F O R T H E A N S W E R (M-AILABS_eng_000181-M-AILABS_eng_000181) +T R O T T O L D T H E G I R L S T H A T T H E Y M U S T G O W I T H T H E I R F A T H E R T O L I V E I N G H I P G H I S I Z Z L E S L I T T L E O L D C A B I N A N D W H E N T H E Y H E A R D T H I S D R E A D F U L D E C R E E (M-AILABS_eng_000182-M-AILABS_eng_000182) +M A R G A R E T S A T D O W N O N T H E R U G P A R T L Y T O W A R M H E R S E L F F O R T H E D A M P N E S S O F T H E E V E N I N G H U N G A B O U T H E R D R E S S A N D O V E R F A T I G U E H A D M A D E H E R C H I L L Y (M-AILABS_eng_000183-M-AILABS_eng_000183) +O H N O Y O U A R E M I S T A K E N A B O U T T H A T R E P L I E D T H E K I N G T H E Y A R E N O T M Y P R I S O N E R S B U T M Y S L A V E S W H O M I P U R C H A S E D F R O M T H E K I N G O F E V (M-AILABS_eng_000184-M-AILABS_eng_000184) +H E R F A T H E R T O O K U P T H E C O N V E R S A T I O N (M-AILABS_eng_000185-M-AILABS_eng_000185) +I N A C O R N E R W A S A S O R T O F D R E S S I N G T A B L E O N W H I C H L A Y A C O M B A N D B R U S H K E N N E D Y S E E M E D M U C H I N T E R E S T E D I N T H E T A B L E A N D W A S E X A M I N I N G I T W H E N T H E G U R U R E T U R N E D (M-AILABS_eng_000186-M-AILABS_eng_000186) +I H A V E S O M E T I M E S T H O U G H T T H A T M Y S E L F S H E A G R E E D B U T O F C O U R S E I D O N T K N O W S T I L L I H A V E T O B E P R E T T Y C A R E F U L S O M E O N E I S A L W A Y S O V E R H E R E B Y M Y D E S K O R L O O K I N G O V E R H E R E (M-AILABS_eng_000187-M-AILABS_eng_000187) +I S H A L L S T A Y R E P L I E D T H E Y O U N G M A N F O R I M E A N T O S E T Y O U F R E E (M-AILABS_eng_000188-M-AILABS_eng_000188) +W H A T D O Y O U D O A S K E D T H E S O R C E R E R (M-AILABS_eng_000189-M-AILABS_eng_000189) +W H Y T H E Y R E O U R E N E M I E S Y O U R S H O R T H I G H N E S S N O T A N Y M O R E R E P L I E D T R O T I M Q U E E N O F T H E P I N K I E S A N D I M A L S O Q U E E N O F T H E B L U E S S O I W O N T H A V E M Y P E O P L E Q U A R R E L I N G (M-AILABS_eng_000190-M-AILABS_eng_000190) +T Y P E W R I T E R S W E R E C L I C K I N G C L I P P I N G S W E R E B E I N G S N I P P E D O U T O F A H U G E S T A C K O F N E W S P A P E R S A N D P A S T E D I N T O L A R G E S C R A P B O O K S C I R C U L A R S W E R E B E I N G F O L D E D A N D M A D E R E A D Y T O M A I L F O R T H E F I N A L A P P E A L (M-AILABS_eng_000191-M-AILABS_eng_000191) +I T W A S F O U R D A Y S A F T E R T H E S U R P R I S E O F A D L E R S H O R S T W H E N T H E S T R A N G E R S L E F T T H E E S T A T E T O T H E C A R E O F R U G G E D O L D F O R S T E R H E R M A N N (M-AILABS_eng_000192-M-AILABS_eng_000192) +P O O R T E M P L E T O N H E S A I D I U S E D T O K N O W H I M Y E A R S A G O W H E N W E W E R E B O Y S W E N T T O S C H O O L W I T H H I M A N D A L L T H A T S O R T O F T H I N G Y O U K N O W B U T U N T I L I R A N A C R O S S H I M (M-AILABS_eng_000193-M-AILABS_eng_000193) +I F O U N D H E R I N T H E F O R E S T A N D B R O U G H T H E R H E R E A P R I S O N E R R E P L I E D T H E C A P T A I N (M-AILABS_eng_000194-M-AILABS_eng_000194) +W H O M A Y B E C O M P E T E N T E I T H E R F R O M P E R S O N A L E X P E R I E N C E O R T H E E X P E R I E N C E O F O T H E R S T O A N S W E R I T W I T H M O R E O R L E S S C O R R E C T N E S S O R A T L E A S T A N A T T E M P T (M-AILABS_eng_000195-M-AILABS_eng_000195) +O N E H U N D R E D N I N E T Y T W O L A Y T E S T R E E T S A I D H O G A N B I T I N G O F F H I S C I G A R (M-AILABS_eng_000196-M-AILABS_eng_000196) +T R O T W A S S U R P R I S E D T O F I N D S H E C O U L D S E E S O P L A I N L Y T H R O U G H T H E H I G H W A L L O F W A T E R A B O V E H E R B U T T H E S U N W A S A B L E T O S H O O T I T S B E A M S S T R A I G H T D O W N T H R O U G H T H E T R A N S P A R E N T S E A (M-AILABS_eng_000197-M-AILABS_eng_000197) +T H E S P O T W H E R E I T H A D S P R U N G U P (M-AILABS_eng_000198-M-AILABS_eng_000198) +C A L M D E N I A L W H I C H S H E G A V E T O S U C H A S U P P O S I T I O N (M-AILABS_eng_000199-M-AILABS_eng_000199) +Y O U S E E U N T I L T H E S E S C H O O L P I L L S W E R E I N V E N T E D W E W A S T E D A L O T O F T I M E I N S T U D Y T H A T M A Y N O W B E B E T T E R E M P L O Y E D I N P R A C T I C I N G A T H L E T I C S (M-AILABS_eng_000200-M-AILABS_eng_000200) +Y O U V E D O N E I T N O W D E C L A R E D D O R O T H Y T H E S E T E N T S A R E J U S T W O N D E R F U L (M-AILABS_eng_000201-M-AILABS_eng_000201) +F O R T W E N T Y T E N F I V E T H R E E T W O T H E L I N E R W A S B A R E L Y T W E N T Y M I L E S A W A Y W H E N H O D D A N F I R E D H I S R O C K E T S T H E Y M A D E A C O L O S S A L C L O U D O F V A P O R I N E M P T I N E S S (M-AILABS_eng_000202-M-AILABS_eng_000202) +T H E Y P A I D N O A T T E N T I O N T O T H E F A C T T H A T G H I P G H I S I Z Z L E D I D N O T W A N T T O M A R R Y A N Y O F T H E M F O R T H E Y H A D D E T E R M I N E D T H A T W H E N I T W A S A G R E E D W H O S H O U L D H A V E H I M (M-AILABS_eng_000203-M-AILABS_eng_000203) +W H A T D O Y O U T H I N K O F T H A T H E C R I E D O P E N I N G A C O P Y O F T H E R E C O R D A N D L A Y I N G I T F L A T O N T H E L I B R A R Y T A B L E (M-AILABS_eng_000204-M-AILABS_eng_000204) +I T W I L L R E Q U I R E B U T A S H O R T T I M E (M-AILABS_eng_000205-M-AILABS_eng_000205) +A N D L A S T T H E C R O W D O F V E G E T A B L E P E O P L E W H O H A D N O H E A R T S A N D C O U L D N E I T H E R S M I L E N O R F R O W N (M-AILABS_eng_000206-M-AILABS_eng_000206) +T H E N Y O U L L C A T C H I T S A I D T H E W I T C H (M-AILABS_eng_000207-M-AILABS_eng_000207) +W H A T I S I T I Q U E R I E D N O T F E E L I N G C E R T A I N B U T T H A T I T W A S A V E I L E D A T T E M P T T O S E C U R E A L I T T L E F R E E A D V E R T I S I N G F O R T H E V A N D E R V E E R (M-AILABS_eng_000208-M-AILABS_eng_000208) +S O H E G A V E T H E C L E R K T H E T H I R D H U N D R E D D O L L A R S F O R B O O K S A N D A C A S K O F G O O D O L D A L E F O R P E T E R T H E C L E R K D R A N K T H E A L E H I M S E L F A N D G A V E T H E C A L F M I L K (M-AILABS_eng_000209-M-AILABS_eng_000209) +L I K E T H A T I N A L I C E I N W O N D E R L A N D W I T H M E R E L Y A G R I N T H A T F A D E D A W A Y C H A N G I N G I N T O A L Y N X W H I C H I N T U R N D I S A P P E A R E D F O L L O W E D B Y A N U N K N O W N C R E A T U R E W I T H S H O R T N O S E A N D P O I N T E D E A R S (M-AILABS_eng_000210-M-AILABS_eng_000210) +S H E C O U L D N O T D O M A R G A R E T G L A N C E D U N C O N S C I O U S L Y A T T H E U N C L E A N E D C O R N E R S O F T H E R O O M S H E C O U L D H A R D L Y U N D E R T A K E A S E R V A N T S P L A C E C O U L D S H E (M-AILABS_eng_000211-M-AILABS_eng_000211) +N O S H E R E P L I E D W I T H I N N O C E N T C U R I O S I T Y D I D I G I V E T H E M T O Y O U (M-AILABS_eng_000212-M-AILABS_eng_000212) +M A R L B O R O U G H M I L L S A N D T H E A D J A C E N T D W E L L I N G W E R E H E L D U N D E R A L O N G L E A S E T H E Y M U S T I F P O S S I B L E B E R E L E T (M-AILABS_eng_000213-M-AILABS_eng_000213) +A C O P W A V E D A S T U N P I S T O L A T H I M (M-AILABS_eng_000214-M-AILABS_eng_000214) +I T B O U N D E D H E R E A N D T H E R E A B O U T T H E C H I C K E N H O U S E A N D A T F I R S T D O R O T H Y C O U L D N O T T E L L W H A T I T W A S W H I L E T H E S C R E E C H I N G O F T H E C H I C K E N S N E A R L Y D E A F E N E D H E R (M-AILABS_eng_000215-M-AILABS_eng_000215) +T H E S O L D I E R G A V E A Y E L L T H A T A R O U S E D A S C O R E O F H I S C O M R A D E S A N D B R O U G H T T H E M T U M B L I N G I N T O T H E S T R E E T W H E N T H E Y S A W H O W T H E B O O L O O R O O S P R E C I O U S P R I S O N E R W A S E S C A P I N G (M-AILABS_eng_000216-M-AILABS_eng_000216) +J I M H A D R E F U S E D T O L E A V E T H E F I E L D O F G R A S S W H E R E H E W A S E N G A G E D I N B U S I L Y E A T I N G S O T H E W I Z A R D G O T O U T O F T H E B U G G Y A N D J O I N E D Z E B A N D D O R O T H Y (M-AILABS_eng_000217-M-AILABS_eng_000217) +C E R T A I N L Y I A M A S I N T E R E S T E D I N T H E C A S E A S Y O U A R E B U T I C A N T M A K E H E A D S O R T A I L S O F I T I R E P L I E D (M-AILABS_eng_000218-M-AILABS_eng_000218) +O R A N Y M I C E O R E V E N G R A S S H O P P E R S (M-AILABS_eng_000219-M-AILABS_eng_000219) +A N D T H E M T H A T P A Y S Y O D U N T H E Y T E L L Y O W H A T T E N T O D O O R W H A T T E N N O T T O D O W I T H E M O N E Y T H E Y G I V E S Y O U I N J U S T P A Y M E N T F O R Y O U R P A I N S I N F A I R E X C H A N G E L I K E (M-AILABS_eng_000220-M-AILABS_eng_000220) +W H A T D O E S T H A T M E A N A S K E D T H E P R I N C E S S (M-AILABS_eng_000221-M-AILABS_eng_000221) +H E H A D B E E N D R O W N E D H E W A S F L O A T I N G I N A S E A O F L I G H T A N D N O W A N D T H E N S H I N I N G L I T T L E F I S H E S S W A M I N Q U I S I T I V E L Y U P T O H I M A N D S T A R E D (M-AILABS_eng_000222-M-AILABS_eng_000222) +B U T O L D G U N N A R H A D A T R I C K O R T W O L E F T R E M E M B E R T H E T A L E T H A T I R E A D T O Y O U I N T H E T H R O N E R O O M O F B A L D A R T H E F I R S T O F T H E B R O N S T O E N T E R T H E W O R L D O F O P A L W E R E S O L D I E R S S E N T F R O M S O M E B L A S T E D P L A N E T I N O U T E R S P A C E T O F I N D A N E W H O M E (M-AILABS_eng_000223-M-AILABS_eng_000223) +P A P A W I L L Y O U S P E A K T O T H E M E N A N D G E T T H E M T O G O A W A Y S H E C A N N O T B R E A T H E P O O R T H I N G W I T H T H I S C R O W D A B O U T H E R (M-AILABS_eng_000224-M-AILABS_eng_000224) +W H E N I T O O K T H I S C A S E H E S A I D I B E L I E V E D D O W N I N M Y H E A R T T H A T D I X O N W A S I N N O C E N T I S T I L L B E L I E V E I T B U T M Y F A I T H H A S B E E N R U D E L Y S H A K E N (M-AILABS_eng_000225-M-AILABS_eng_000225) +C H A P T E R S I X O F T H E P I R A T E S O F E R S A T Z (M-AILABS_eng_000226-M-AILABS_eng_000226) +R E M E M B E R T H E Y C A N N O T T O U C H U S (M-AILABS_eng_000227-M-AILABS_eng_000227) +G I V E M E T I M E A Z U R E G I V E M E T I M E I F T H E R E S A N Y T H I N G I H A T E I T S A H U R R Y I V E A N I D E A Y O U R M A J E S T Y A N N O U N C E D T H E S I X T H S N U B N O S E D P R I N C E S S (M-AILABS_eng_000228-M-AILABS_eng_000228) +T R U E E N O U G H T R O T D E C L A R E D T H E S A I L O R M A N (M-AILABS_eng_000229-M-AILABS_eng_000229) +A S F O R T H A T S A I D M A R G A R E T R A T H E R H A U G H T I L Y I H O L D I T I S H O N I S O I T Q U I M A L Y P E N S E (M-AILABS_eng_000230-M-AILABS_eng_000230) +W H E N H E H E A R D T H E S E W O R D S T H E K I N G W H O S E H E A D W A S F U L L O F T H E P R I N C E S S N E V E R S T O P P E D T O I N Q U I R E I F T H E Y C O U L D B E T R U E A N D S M E A R E D H I M S E L F O V E R W I T H F A T A N D S P R A N G I N T O T H E O V E N (M-AILABS_eng_000231-M-AILABS_eng_000231) +Y O U S H O U L D B E A B L E T O G E T P A R T S F R O M Y O U R R O O M V I S I O N R E C E I V E R I L L H A V E S O M E T O O L S G I V E N Y O U T H E N H E A D D E D D I P L O M A C Y H A S T O U N D E R S T A N D T H E T H I N G S T H A T C O N T R O L E V E N T S (M-AILABS_eng_000232-M-AILABS_eng_000232) +B Y T H E T I M E T H E F R O S T H A D S E T I N T H E Y S H O U L D B E F A R A W A Y F R O M H E L S T O N E (M-AILABS_eng_000233-M-AILABS_eng_000233) +O N E T H I N G I W A N T T O S A Y B E G A N K E N N E D Y (M-AILABS_eng_000234-M-AILABS_eng_000234) +T H I S I M P O R T A N T T R A F F I C W A S C O N F I D E D T O N O O N E B U T T H E R E A L P R O P R I E T O R (M-AILABS_eng_000235-M-AILABS_eng_000235) +H E W A S R E P L A C E D O N B A S S G U I T A R B Y J U S T I N K L U G (cv_eng_000707-cv_eng_000707) +I D A D D A S E P A R A T E S U B S E C T I O N W H I C H D E A L S W I T H T H I S A S P E C T (cv_eng_000708-cv_eng_000708) +O P E R A T I O N O F T H E T R U N K L I N E C O N T I N U E D O N W O O D E N T R E S T L E S (cv_eng_000709-cv_eng_000709) +M A G N E S I U M F L U O R I D E I S T R A N S P A R E N T O V E R A N E X T R E M E L Y W I D E R A N G E O F W A V E L E N G T H S (cv_eng_000710-cv_eng_000710) +F O U R G I A N T P A C K I N G S H E D S S T O R E D F R E S H P A C K E D P O T A T O E S A N D D E L I V E R E D T H E M O N T O R A I L R O A D C A R S (cv_eng_000711-cv_eng_000711) +T H E O T H E R F O U R T E E N C A M P U S E S A R E T W O Y E A R C A M P U S E S R E F E R R E D T O C O L L E C T I V E L Y A S T H E U N I V E R S I T Y C O L L E G E (cv_eng_000712-cv_eng_000712) +I T S T O O B A D T H A T H E S Q U I C K L Y G O I N G T O F O R G E T M Y N A M E H E T H O U G H T (cv_eng_000713-cv_eng_000713) +O N E P I C T U R E I N T H E G A L L E R Y S H O W S H O W D I L I G E N T S L A V E S E R E C T T H E S T A T U E O F A D M I R A L T H O M P S O N (cv_eng_000714-cv_eng_000714) +I M P E R I A L D I E T (cv_eng_000715-cv_eng_000715) +T H E R E S U L T I N G C O M P A N Y I S S T R A T T E C S E C U R I T Y C O R P O R A T I O N (cv_eng_000716-cv_eng_000716) +B I T C O I N M I N I N G C A N B E D O N E W I T H G R A P H I C S C A R D S O R W I T H S P E C I A L I Z E D H A R D W A R E (cv_eng_000717-cv_eng_000717) +T H E Y A L S O L E A D T H E N A T I O N A L R A N K I N G (cv_eng_000718-cv_eng_000718) +C H A R L E S G R A V E S B I S H O P O F L I M E R I C K (cv_eng_000719-cv_eng_000719) +A N D A T T H A T I T O L D H I M A N D H E T O O K M Y P L A C E (cv_eng_000720-cv_eng_000720) +I T H O U G H T I D G I V E T H E K I D S A T R E A T (cv_eng_000721-cv_eng_000721) +A C E V E D O D E N I E D S H O W I N G T H E P I C T U R E S (cv_eng_000722-cv_eng_000722) +H O L D Y O U R N O S E T O K E E P T H E S M E L L F R O M D I S A B L I N G Y O U R M O T O R F U N C T I O N S (cv_eng_000723-cv_eng_000723) +T H A T S O U N D S L I K E T H E I R P R O B L E M (cv_eng_000724-cv_eng_000724) +H I S T O R I C A L L Y T H E R E W A S N O C L E A R L Y D E F I N E D B O U N D A R Y I N T H I S P A R T O F T H E A R A B I A N P E N I N S U L A (cv_eng_000725-cv_eng_000725) +M A R S H A L L S H A F F E R O F S L A S H F I L M G A V E T H E F I L M A N E I G H T O U T O F T E N (cv_eng_000726-cv_eng_000726) +H O W C A N Y O U S A Y T H A T (cv_eng_000727-cv_eng_000727) +H I S S T Y L E B E G A N T O R E S E M B L E M I C H A E L D A M A S K I N O S (cv_eng_000728-cv_eng_000728) +H E I S A L S O C A P A B L E O F F I R I N G L I G H T N I N G B O L T S W I T H I M M E N S E D E S T R U C T I V E P O W E R (cv_eng_000729-cv_eng_000729) +H E C L A I M E D T W O W I C K E T S I N E N G L A N D S O N L Y I N N I N G S A S B O R D E R W E R E B E A T E N C O M P R E H E N S I V E L Y (cv_eng_000730-cv_eng_000730) +S H E D I D M U C H L I T E R A R Y W O R K (cv_eng_000731-cv_eng_000731) +H E M E T T H E O R G A N I Z E R S O F T H E P R O T E S T S A N D A G R E E D T O C R E A T E T W O W O R K I N G G R O U P S (cv_eng_000732-cv_eng_000732) +T H E B A L L S T R U C K T H E F O U L P O L E W E L L A B O V E T H E G R E E N M O N S T E R (cv_eng_000733-cv_eng_000733) +O N L Y C A M D E N T H O M A S G A R R E T T A N D G O L D F I E L D S S O U T H E Z E K I E L B A K E R W E R E U N C O N T E S T E D (cv_eng_000734-cv_eng_000734) +I T I S A C H A R I T Y S C H O O L W H O S E F E E S A R E C A L C U L A T E D O N A M E A N S T E S T (cv_eng_000735-cv_eng_000735) +S O M E W E N T A W A Y W H I L E I W A S T H E R E A N D O T H E R P E O P L E C A M E (cv_eng_000736-cv_eng_000736) +S E V E N (cv_eng_000737-cv_eng_000737) +T H E K U R A K H A N A T E W A S L O C A T E D M A I N L Y I N T H E H I S T O R I C A L A N D G E O G R A P H I C A L R E G I O N O F K U R A (cv_eng_000738-cv_eng_000738) +T H E E L E V A T I O N A T T H E S I T E I S A B O V E S E A L E V E L (cv_eng_000739-cv_eng_000739) +T O B I A S T R I E D T O I N J E C T C O N T E M P T I N T O H I S T O N E (cv_eng_000740-cv_eng_000740) +I H A V E T O W O R K T H I S S A T U R D A Y (cv_eng_000741-cv_eng_000741) +T H E G R E A T R U L E R S F O U N D T H E S Q U E A K Y G R A T E W A S G R A T I N G O N T H E I R N E R V E S (cv_eng_000742-cv_eng_000742) +W H E N T H E B L I N D I N G D U S T H A D S E T T L E D A B I T T H E B O Y T R E M B L E D A T W H A T H E S A W (cv_eng_000743-cv_eng_000743) +D E M O C R A T A M B E R B A K E R W O N T H E O P E N S E A T (cv_eng_000744-cv_eng_000744) +B O T H A R E P U T T O G E T H E R B Y S T U D E N T S I N T H E C O L L E G E S J O U R N A L I S M P R O G R A M (cv_eng_000745-cv_eng_000745) +T R E N C H W A S B O R N I N B E L I Z E C I T Y I N B R I T I S H H O N D U R A S (cv_eng_000746-cv_eng_000746) +T H E E A R L Y P H A S E O F L I F E M O V E S F A S T (cv_eng_000747-cv_eng_000747) +N O (cv_eng_000748-cv_eng_000748) +S E V E N (cv_eng_000749-cv_eng_000749) +A T O N E T I M E R A I L W A Y L I N E S D I V E R G E D F R O M R U G B Y S T A T I O N I N S E V E N D I F F E R E N T D I R E C T I O N S (cv_eng_000750-cv_eng_000750) +C Z E C H R E P U B L I C E N T E R E D T W O S H O O T E R S I N T O T H E P A R A L Y M P I C C O M P E T I T I O N (cv_eng_000751-cv_eng_000751) +T Y G E R W I L L I A M S W R O T E T H E S C R E E N P L A Y A N D S H A R E D S T O R Y C R E D I T W I T H T H E B R O T H E R S (cv_eng_000752-cv_eng_000752) +T H I S F E S T I V A L W A S T O B E A C H A R I T Y F U N D R A I S E R F O R T H E A R E A (cv_eng_000753-cv_eng_000753) +T H E S E E X T R A C A R D S W E R E I N S E R T E D R A N D O M L Y I N T O P A C K S (cv_eng_000754-cv_eng_000754) +H E N R Y W E N T B A C K T O A U S T R A L I A (cv_eng_000755-cv_eng_000755) +P E R M I T M E T O I N T R O D U C E T O Y O U H E R M A J E S T Y T H E Q U E E N (cv_eng_000756-cv_eng_000756) +I N O R I G I N H E R O I N W A S S U P P O S E D T O B E T H E “ N O N A D D I C T I V E M O R P H I N E S U B S T I T U T E ” (cv_eng_000757-cv_eng_000757) +S H E I S O F M E X I C A N D E S C E N T (cv_eng_000758-cv_eng_000758) +I A M S U R E T H E R E I S N O T O N H I S (cv_eng_000759-cv_eng_000759) +T H O S E W H O D O N T L E A R N F R O M H I S T O R Y A R E D O O M E D T O R E P E A T I T (cv_eng_000760-cv_eng_000760) +I C O U L D N ’ T S T O P S T A R I N G A T I T (cv_eng_000761-cv_eng_000761) +F O R S I M P L I C I T Y G E A R I N C H E S I S N O R M A L L Y R O U N D E D T O T H E N E A R E S T W H O L E N U M B E R (cv_eng_000762-cv_eng_000762) +I F W E A C T U A L L Y D O W A N T I T S O L V E D I T W I L L B E (cv_eng_000763-cv_eng_000763) +T H E F R U I T O F A F I G T R E E I S A P P L E S H A P E D (cv_eng_000764-cv_eng_000764) +F A I R E X C H A N G E I S N O R O B B E R Y (cv_eng_000765-cv_eng_000765) +W H A T Y O U E A T T O D A Y W A L K S A N D T A L K S T O M O R R O W (cv_eng_000766-cv_eng_000766) +T H E W A T E R T H E N F L O W S O U T O F T H E S W A M P S A S T H E L U A P U L A R I V E R (cv_eng_000767-cv_eng_000767) +W H Y D I D N T Y O U S A Y S O M E T H I N G (cv_eng_000768-cv_eng_000768) +H A V E Y O U S E E N O M A R (cv_eng_000769-cv_eng_000769) +I C O U L D G O O N F O R D A Y S A B O U T T H E D E L I C I O U S W I N E S P R O D U C E D I N T H I S P A R T O F T H E W O R L D (cv_eng_000770-cv_eng_000770) +T H E P H I L A D E L P H I A I N Q U I R E R N A M E D H I M C I T Y P L A Y E R O F T H E Y E A R (cv_eng_000771-cv_eng_000771) +B O T S M A Y B E S U B J E C T T O S P E C I A L R U L E S (cv_eng_000772-cv_eng_000772) +T H E S W E D E S W E R E U N A B L E T O U S E T H E I R V E H I C L E S W H I C H W E R E S T U C K I N T H E M U D (cv_eng_000773-cv_eng_000773) +T H E A C T D I D N O T P R O H I B I T P A Y I N G A R E P R E S E N T A T I V E T O A P P E A R I N C O U R T (cv_eng_000774-cv_eng_000774) +C A N W E P L E A S E L E A V E N O W (cv_eng_000775-cv_eng_000775) +H E W A S C O N V I C T E D A N D B A N I S H E D T O C Y P R U S F O R S E V E N Y E A R S F O R P U N I S H M E N T (cv_eng_000776-cv_eng_000776) +T H E C O U P L E H A V E T W O C H I L D R E N A D A U G H T E R S O P H I A R O S A L I N D A A N D A S O N M A T E O B R A V E R Y (cv_eng_000777-cv_eng_000777) +N O N E O F T H E T H R E E R E F E R E N D U M S R E A C H E D T H E Q U O R U M O F T H E M A J O R I T Y O F T H O S E E N T I T L E D (cv_eng_000778-cv_eng_000778) +T U R P I N S U C C E E D E D I N D I R A S A M A R A S E K E R A W H O S A W T H E U N I V E R S I T Y T H R O U G H A P E R I O D O F S T R O N G G R O W T H (cv_eng_000779-cv_eng_000779) +H E R E I A M B E T W E E N M Y F L O C K A N D M Y T R E A S U R E T H E B O Y T H O U G H T (cv_eng_000780-cv_eng_000780) +T H I S F A I L U R E H A S L E D T O S I X T E E N P O W E R P L A N T S H A V I N G Z E R O D A Y S O F C O A L S T O C K (cv_eng_000781-cv_eng_000781) +Y E S (cv_eng_000782-cv_eng_000782) +W H Y D O E S T H A T P L A N E K E E P G O I N G O V E R (cv_eng_000783-cv_eng_000783) +I V E D O N E T H I S B E F O R E W I T H V I R T U A L B O X W I T H G O O D R E S U L T S (cv_eng_000784-cv_eng_000784) +T H E A P P L I C A T I O N W A S A P P R O V E D I N F E B R U A R Y (cv_eng_000785-cv_eng_000785) +H E N R Y T A R L T O N S T I L E S W H E R E H E H A D A S O U N D T R A I N I N G I N L A T I N (cv_eng_000786-cv_eng_000786) +I T W A S D I S C O N T I N U E D D U E T O S C H E D U L I N G C O N F L I C T S I N V O L V E D I N L E W I S S R E T U R N T O T E R R E S T R I A L R A D I O (cv_eng_000787-cv_eng_000787) +H E R F A M I L Y W A S F R O M B R I A N Z A (cv_eng_000788-cv_eng_000788) +W H A T D I D Y O U E A T F O R D I N N E R (cv_eng_000789-cv_eng_000789) +T H A T W A S M Y D R A W T O S C I E N C E (cv_eng_000790-cv_eng_000790) +H E I S C O N S I D E R E D A M A S T E R O F C H I A R O S C U R O (cv_eng_000791-cv_eng_000791) +I T T H E N R E T U R N S T O T H E C H U R C H A S C E N D S A T T H E A L T A R A N D D I S A P P E A R S (cv_eng_000792-cv_eng_000792) +Y O U C A N N O T L O S E W H A T Y O U N E V E R H A D (cv_eng_000793-cv_eng_000793) +T H E J A W S E X T E N D P A S T T H E E Y E (cv_eng_000794-cv_eng_000794) +M Y N I E C E C A N H E L P Y O U W I T H T H A T (cv_eng_000795-cv_eng_000795) +T H A T S T H E K I N D O F S T U F F T H E Y W A N T (cv_eng_000796-cv_eng_000796) +H O P E F O R T H E B E S T A N D P R E P A R E F O R T H E W O R S T (cv_eng_000797-cv_eng_000797) +I N I T I A L L Y T H E W E I G H T L O S S W A S A T T A I N E D S T R I C T L Y B Y D I E T (cv_eng_000798-cv_eng_000798) +A L L W E R E O W N E D B Y T H E E V E R E T T M O O R E S Y N D I C A T E (cv_eng_000799-cv_eng_000799) +W I L L I T R A I N T O M O R R O W (cv_eng_000800-cv_eng_000800) +D U B I S T E W I G M E I N E L I E B E (cv_eng_000801-cv_eng_000801) +L U C I L E P E T R Y T O O K H E R P L A C E A S A C T I N G D I R E C T O R (cv_eng_000802-cv_eng_000802) +T H E B E A V E R R I V E R B R I E F L Y E N T E R S T H E E A S T C E N T R A L P A R T O F T H E T O W N S H I P (cv_eng_000803-cv_eng_000803) +T H E T R A C K R E S U R F A C I N G W A S A L S O C O M P L E T E D (cv_eng_000804-cv_eng_000804) +H I N D M A R S H W A S A W A R E O F T H E I M P O R T A N C E O F E L E C T R O N M I C R O S C O P Y I N B I O L O G I C A L R E S E A R C H (cv_eng_000805-cv_eng_000805) +S I N H A W A S B O R N I N A L L A H A B A D (cv_eng_000806-cv_eng_000806) +T H I S B R I D G E I S U N O F F I C I A L L Y R E F E R R E D T O A S B L A C K W A T E R B R I D G E B Y C O A L I T I O N F O R C E S O P E R A T I N G T H E R E (cv_eng_000807-cv_eng_000807) +I T I S R E S P O N S I B L E F O R W A T E R S U P P L Y A N D M A N A G E M E N T O F W A T E R R E S O U R C E S I N M A H A R A S H T R A (cv_eng_000808-cv_eng_000808) +T H I S I S T H E F I R S T P H A S E O F T H E J O B H E S A I D (cv_eng_000809-cv_eng_000809) +T H E G I Z A P L A T E A U O R G I Z A N E C R O P O L I S I N T H E E G Y P T I A N V A L L E Y O F T H E D E A D C O N T A I N S S E V E R A L P Y R A M I D S O F W H I C H T H E G R E A T P Y R A M I D I S T H E L A R G E S T S E V E R A L S M A L L T O M B S S E V E R A L T E M P L E S A N D T H E G R E A T S P H I N X (fleurs_eng_000413-fleurs_eng_000413) +T O W A R D S T H E E N D O F T H E M I D D L E A G E S W E S T E R N E U R O P E B E G A N T O D E V E L O P T H E I R O W N S T Y L E O N E O F T H E B I G G E S T D E V E L O P M E N T S O F T H E T I M E A S A R E S U L T O F T H E C R U S A D E S P E O P L E B E G A N T O U S E B U T T O N S T O F A S T E N C L O T H I N G (fleurs_eng_000414-fleurs_eng_000414) +I F Y O U O N L Y G O A S H O R E U S I N G S H I P B O A R D E X C U R S I O N S Y O U W I L L N O T N E E D A S E P A R A T E V I S A A S O F 2 0 0 9 (fleurs_eng_000415-fleurs_eng_000415) +D U V A L L W H O I S M A R R I E D W I T H T W O A D U L T C H I L D R E N D I D N O T L E A V E A B I G I M P R E S S I O N O N M I L L E R T O W H O M T H E S T O R Y W A S R E L A T E D (fleurs_eng_000416-fleurs_eng_000416) +T H E I R D I S C I P L I N E D D E F E N C E B A L L H A N D L I N G S K I L L S A N D E X C E L L E N T T E A M W O R K M A D E T H E M S T A N D O U T A N D I T W A S C L E A R T H A T T H I S W A S T H E T E A M T O B E A T (fleurs_eng_000417-fleurs_eng_000417) +T H E D I S E A S E I S C A R R I E D B Y P I G S W H I C H T H E N M I G R A T E S T O H U M A N S T H R O U G H M O S Q U I T O S (fleurs_eng_000418-fleurs_eng_000418) +F O R T H E S P R I N G B O K S I T E N D E D A F I V E M A T C H L O S I N G S T R E A K (fleurs_eng_000419-fleurs_eng_000419) +T H U S T H E P E N C I L W A S A G O O D F R I E N D T O M A N Y P E O P L E W H E N I T C A M E O U T (fleurs_eng_000420-fleurs_eng_000420) +T H E U S E O F V I D E O R E C O R D I N G H A S L E D T O I M P O R T A N T D I S C O V E R I E S I N T H E I N T E R P R E T A T I O N O F M I C R O E X P R E S S I O N S F A C I A L M O V E M E N T S W H I C H L A S T A F E W M I L L I S E C O N D S (fleurs_eng_000421-fleurs_eng_000421) +A L S O T O T H E N O R T H V I S I T T H E G R E A T S A N C T U A R Y O F O U R L A D Y O F F A T I M A S H R I N E A P L A C E O F W O R L D W I D E F A M O U S M A R I A N A P P A R I T I O N S (fleurs_eng_000422-fleurs_eng_000422) +I F Y O U W A N T T O B E C L O S E T O T H E A C T I O N Y O U R E G O I N G T O H A V E T O G E T I N E A R L Y T O G E T A C A M P I N G S I T E C L O S E T O T H E M U S I C (fleurs_eng_000423-fleurs_eng_000423) +M A D A G A S C A R I S B Y F A R T H E B I G G E S T A N D A C O N T I N E N T O N I T S O W N W H E N I T C O M E S T O W I L D L I F E (fleurs_eng_000424-fleurs_eng_000424) +W O M E N I T I S R E C O M M E N D E D T H A T A N Y W O M E N T R A V E L L E R S S A Y T H A T T H E Y A R E M A R R I E D R E G A R D L E S S O F A C T U A L M A R I T A L S T A T U S (fleurs_eng_000425-fleurs_eng_000425) +C U O M O 5 3 B E G A N H I S G O V E R N O R S H I P E A R L I E R T H I S Y E A R A N D S I G N E D A B I L L L A S T M O N T H L E G A L I Z I N G S A M E S E X M A R R I A G E (fleurs_eng_000426-fleurs_eng_000426) +A S L I G H T P O L L U T I O N I N T H E I R H E Y D A Y W A S N O T T H E K I N D O F P R O B L E M I T I S T O D A Y T H E Y A R E U S U A L L Y L O C A T E D I N C I T I E S O R A T C A M P U S E S E A S I E R T O R E A C H T H A N T H O S E B U I L T I N M O D E R N T I M E S (fleurs_eng_000427-fleurs_eng_000427) +T H E Y U S U A L L Y H A V E S P E C I A L F O O D D R I N K A N D E N T E R T A I N M E N T O F F E R S T O K E E P G U E S T S I N A G O O D M O O D A N D K E E P T H E M A T T H E P R E M I S E (fleurs_eng_000428-fleurs_eng_000428) +O N T H E O T H E R H A N D I C Y A N D S N O W Y C O N D I T I O N S A R E N O R M A L I N M A N Y C O U N T R I E S A N D T R A F F I C G O E S O N M O S T L Y U N I N T E R R U P T E D A L L Y E A R R O U N D (fleurs_eng_000429-fleurs_eng_000429) +B E C A R E F U L N O T T O A L L O W F A B R I C T O B E C O M E T O O H O T W H I C H C A N C A U S E S H R I N K A G E O R I N E X T R E M E C A S E S S C O R C H (fleurs_eng_000430-fleurs_eng_000430) +F E R A L C H I L D R E N M A Y H A V E E X P E R I E N C E D S E V E R E C H I L D A B U S E O R T R A U M A B E F O R E B E I N G A B A N D O N E D O R R U N N I N G A W A Y (fleurs_eng_000431-fleurs_eng_000431) +P E O P L E M A Y N O T A N T I C I P A T E T H A T P A T I E N C E A N D U N D E R S T A N D I N G A R E A L S O N E C E S S A R Y F O R T R A V E L L E R S R E T U R N I N G H O M E (fleurs_eng_000432-fleurs_eng_000432) +S O O N A F T E R T H E O U T B R E A K O F H O S T I L I T I E S B R I T A I N I N I T I A T E D A N A V A L B L O C K A D E O F G E R M A N Y (fleurs_eng_000433-fleurs_eng_000433) +T H E G O V E R N O R S O F F I C E S A I D N I N E T E E N O F T H E I N J U R E D W E R E P O L I C E O F F I C E R S (fleurs_eng_000434-fleurs_eng_000434) +U S I N G S H I P S T O T R A N S P O R T G O O D S I S B Y F A R T H E M O S T E F F I C I E N T W A Y T O M O V E L A R G E A M O U N T S O F P E O P L E A N D G O O D S A C R O S S O C E A N S (fleurs_eng_000435-fleurs_eng_000435) +L I B E R A L C R I T I C I S M O F T H E R E C O N S T R U C T I O N E F F O R T H A S F O C U S E D O N T H E A W A R D I N G O F R E C O N S T R U C T I O N C O N T R A C T S T O P E R C E I V E D W A S H I N G T O N I N S I D E R S (fleurs_eng_000436-fleurs_eng_000436) +Y O U C A N U S E B O D A B O D A M O T O R C Y C L E T A X I T O G E T A R O U N D G O M A T H E N O R M A L L O C A L P R I C E I S 5 0 0 C O N G O L E S E F R A N C S F O R T H E S H O R T R I D E (fleurs_eng_000437-fleurs_eng_000437) +T H E T H R E E K I N G D O M S W A S O N E O F T H E B L O O D I E S T E R A S I N A N C I E N T C H I N A S H I S T O R Y T H O U S A N D S O F P E O P L E D I E D F I G H T I N G T O S I T I N T H E H I G H E S T S E A T I N T H E G R A N D P A L A C E A T X I A N (fleurs_eng_000438-fleurs_eng_000438) +T H E S E C O U P L E S M A Y C H O O S E T O M A K E A N A D O P T I O N P L A N F O R T H E I R B A B Y (fleurs_eng_000439-fleurs_eng_000439) +N O T H I N G C A N B E S E E N O T H E R T H A N T H E C L E A R B E A U T I F U L S K Y A B O V E A N D T H E M A N Y S U R R O U N D I N G M O U N T A I N S V E R Y L I T T L E O F T H I S W O R L D C A N B E S E E N O R H E A R D F R O M I N S I D E T H E C A V E (fleurs_eng_000440-fleurs_eng_000440) +H E W A S S U B S E Q U E N T L Y R E L O C A T E D T O A D D E N B R O O K E S H O S P I T A L I N C A M B R I D G E (fleurs_eng_000441-fleurs_eng_000441) +V A T I C A N C I T Y S P O P U L A T I O N I S A R O U N D 8 0 0 I T I S T H E S M A L L E S T I N D E P E N D E N T C O U N T R Y I N T H E W O R L D A N D T H E C O U N T R Y W I T H T H E L O W E S T P O P U L A T I O N (fleurs_eng_000442-fleurs_eng_000442) +R E G U L A R A N N O U N C E M E N T S I N T H E M E T R O A R E M A D E O N L Y I N C A T A L A N B U T U N P L A N N E D D I S R U P T I O N S A R E A N N O U N C E D B Y A N A U T O M A T E D S Y S T E M I N A W I D E V A R I E T Y O F L A N G U A G E S I N C L U D I N G S P A N I S H E N G L I S H F R E N C H A R A B I C A N D J A P A N E S E (fleurs_eng_000443-fleurs_eng_000443) +T H I S O F F E R S A G O O D O P P O R T U N I T Y T O S E E T H E A U R O R A B O R E A L I S A S T H E S K Y W I L L B E D A R K M O R E O R L E S S A R O U N D T H E C L O C K (fleurs_eng_000444-fleurs_eng_000444) +F I R E R E S C U E C R E W S E V E N T U A L L Y D O U S E D T H E F I R E B Y 1 1 3 5 P M (fleurs_eng_000445-fleurs_eng_000445) +T H I S I S C A L L E D A C H E M I C A L S P H Y O U C A N M A K E A N I N D I C A T O R U S I N G R E D C A B B A G E J U I C E (fleurs_eng_000446-fleurs_eng_000446) +I N P A R T I C U L A R I T I S C L A I M E D T H A T O N E C A N D E T E C T W H E T H E R A P E R S O N I S L Y I N G B Y I N T E R P R E T I N G M I C R O E X P R E S S I O N S C O R R E C T L Y (fleurs_eng_000447-fleurs_eng_000447) +T H E C E N T R A L A U T H O R I T Y O F T H E C H U R C H H A D B E E N I N R O M E F O R O V E R A T H O U S A N D Y E A R S A N D T H I S C O N C E N T R A T I O N O F P O W E R A N D M O N E Y L E D M A N Y T O Q U E S T I O N W H E T H E R T H I S T E N E T W A S B E I N G M E T (fleurs_eng_000448-fleurs_eng_000448) +T H E S U N D A R B A N S A R E T H E L A R G E S T L I T T O R A L M A N G R O V E B E L T I N T H E W O R L D S T R E T C H I N G 8 0 K M 5 0 M I I N T O T H E B A N G L A D E S H I A N D I N D I A N H I N T E R L A N D F R O M T H E C O A S T (fleurs_eng_000449-fleurs_eng_000449) +R E G U L A R A N N O U N C E M E N T S I N T H E M E T R O A R E M A D E O N L Y I N C A T A L A N B U T U N P L A N N E D D I S R U P T I O N S A R E A N N O U N C E D B Y A N A U T O M A T E D S Y S T E M I N A W I D E V A R I E T Y O F L A N G U A G E S I N C L U D I N G S P A N I S H E N G L I S H F R E N C H A R A B I C A N D J A P A N E S E (fleurs_eng_000450-fleurs_eng_000450) +E V E R Y O N E P A R T I C I P A T E S I N S O C I E T Y A N D U S E S T R A N S P O R T A T I O N S Y S T E M S A L M O S T E V E R Y O N E C O M P L A I N S A B O U T T R A N S P O R T A T I O N S Y S T E M S (fleurs_eng_000451-fleurs_eng_000451) +L A Y T O N H A D A S K E D F O R C H A N G E S T O T H E C O N S E R V A T I V E S E N V I R O N M E N T A L B I L L D U R I N G T H E M E E T I N G W I T H T H E P M A S K I N G F O R A T H O R O U G H A N D C O M P L E T E R E W R I T I N G O F T H E C O N S E R V A T I V E P A R T Y S E N V I R O N M E N T A L B I L L (fleurs_eng_000452-fleurs_eng_000452) +A N Y O N E W H O S G O I N G T O D R I V E A T H I G H L A T I T U D E S O R O V E R M O U N T A I N P A S S E S S H O U L D C O N S I D E R T H E P O S S I B I L I T Y O F S N O W I C E O R F R E E Z I N G T E M P E R A T U R E S (fleurs_eng_000453-fleurs_eng_000453) +S L E E P I N T E R R U P T I O N I S T H E P R O C E S S O F P U R P O S E F U L L Y A W A K E N I N G D U R I N G Y O U R N O R M A L S L E E P P E R I O D A N D F A L L I N G A S L E E P A S H O R T T I M E L A T E R 1 0 – 6 0 M I N U T E S (fleurs_eng_000454-fleurs_eng_000454) +S W I R L T H E T W O D R Y P O W D E R S T O G E T H E R A N D T H E N W I T H C L E A N W E T H A N D S S Q U E E Z E T H E M I N T O A B A L L (fleurs_eng_000455-fleurs_eng_000455) +F O R T H E S P R I N G B O K S I T E N D E D A F I V E M A T C H L O S I N G S T R E A K (fleurs_eng_000456-fleurs_eng_000456) +J U S T L I K E T H E M O O N E X E R T S A P U L L O N T H E E A R T H C A U S I N G T I D E S S O D O E S T H E M I L K Y W A Y E X E R T A F O R C E O N T H E S A G I T T A R I U S G A L A X Y (fleurs_eng_000457-fleurs_eng_000457) +T H R O U G H T H E N I G H T B E T W E E N 1 5 0 A N D 2 0 0 C O P I E S W E R E M A D E N O W K N O W N A S D U N L A P B R O A D S I D E S (fleurs_eng_000458-fleurs_eng_000458) +F I R S T A M O N G I T S 7 8 R E C O M M E N D A T I O N S I S T H A T A N E W D I P L O M A T I C I N I T I A T I V E S H O U L D B E T A K E N B E F O R E T H E E N D O F T H I S Y E A R T O S E C U R E I R A Q S B O R D E R S A G A I N S T H O S T I L E I N T E R V E N T I O N S A N D T O R E E S T A B L I S H D I P L O M A T I C R E L A T I O N S W I T H I T S N E I G H B O R S (fleurs_eng_000459-fleurs_eng_000459) +S A I N T P E T E R S B U R G C R U I S E S I N C L U D E T I M E I N T O W N C R U I S E P A S S E N G E R S A R E E X E M P T E D F R O M V I S A R E Q U I R E M E N T S C H E C K T H E T E R M S (fleurs_eng_000460-fleurs_eng_000460) +A C C O R D I N G T O J A P A N S N U C L E A R A G E N C Y R A D I O A C T I V E C A E S I U M A N D I O D I N E H A S B E E N I D E N T I F I E D A T T H E P L A N T (fleurs_eng_000461-fleurs_eng_000461) +S E G R E G A T I O N A N D R E C O M B I N A T I O N S H U F F L E V A R I A T I O N B A C K A N D F O R T H B E T W E E N T H E T W O P O O L S W I T H E A C H G E N E R A T I O N (fleurs_eng_000462-fleurs_eng_000462) +E L E M E N T S L I K E C A L C I U M A N D P O T A S S I U M A R E C O N S I D E R E D M E T A L S O F C O U R S E T H E R E A R E A L S O M E T A L S L I K E S I L V E R A N D G O L D (fleurs_eng_000463-fleurs_eng_000463) +T H E C O R R E L A T I O N B E T W E E N B R A I N P A T H O L O G Y A N D B E H A V I O U R S U P P O R T S S C I E N T I S T S I N T H E I R R E S E A R C H (fleurs_eng_000464-fleurs_eng_000464) +A N C I E N T C H I N A H A D A U N I Q U E W A Y O F S H O W I N G D I F F E R E N T T I M E P E R I O D S E A C H S T A G E O F C H I N A O R E A C H F A M I L Y T H A T W A S I N P O W E R W A S A D I S T I N C T I V E D Y N A S T Y (fleurs_eng_000465-fleurs_eng_000465) +A S I M P L E P O P U L A R D I N N E R E S P E C I A L L Y D U R I N G T H E S U M M E R I S T H E P A A M B O L I B R E A D W I T H O L I V E O I L T O M A T O A N D A N Y A V A I L A B L E C O N D I M E N T S S U C H A S C H E E S E T U N A F I S H E T C (fleurs_eng_000466-fleurs_eng_000466) +T H E A N N O U N C E M E N T W A S M A D E A F T E R T R U M P H A D A P H O N E C O N V E R S A T I O N W I T H T U R K I S H P R E S I D E N T R E C E P T A Y Y I P E R D O Ğ A N (fleurs_eng_000467-fleurs_eng_000467) +P E R R Y S T A T E D T H A T H E W O U L D R E T U R N T O T E X A S T O A S S E S S T H E R E S U L T S O F T O N I G H T S C A U C U S D E T E R M I N E W H E T H E R T H E R E I S A P A T H F O R W A R D F O R M Y S E L F I N T H I S R A C E B U T L A T E R S A I D T H A T H E W O U L D R E M A I N I N T H E R A C E A N D C O M P E T E I N T H E J A N U A R Y 2 1 S O U T H C A R O L I N A P R I M A R Y (fleurs_eng_000468-fleurs_eng_000468) +H E W A S A L S O E N G A G E D I N E N G R A V I N G B A N K N O T E S F O R M A N Y C O U N T R I E S R E C E N T E X A M P L E S O F H I S W O R K I N C L U D I N G T H E P R I M E M I N I S T E R I A L P O R T R A I T S O N T H E F R O N T O F T H E N E W C A N A D I A N 5 A N D 1 0 0 B I L L S (fleurs_eng_000469-fleurs_eng_000469) +M O R E T R A D I T I O N A L C H U R C H E S O F T E N H O L D A N E A S T E R V I G I L O N S A T U R D A Y N I G H T D U R I N G T H E E A S T E R W E E K E N D W I T H T H E C O N G R E G A T I O N S O F T E N B R E A K I N G I N T O C E L E B R A T I O N A T T H E S T R O K E O F M I D N I G H T T O C E L E B R A T E C H R I S T S R E S U R R E C T I O N (fleurs_eng_000470-fleurs_eng_000470) +F I N L A N D I S A G R E A T B O A T I N G D E S T I N A T I O N T H E L A N D O F A T H O U S A N D L A K E S H A S T H O U S A N D S O F I S L A N D S T O O I N T H E L A K E S A N D I N T H E C O A S T A L A R C H I P E L A G O S (fleurs_eng_000471-fleurs_eng_000471) +C U R R E N T S E N A T O R A N D A R G E N T I N E F I R S T L A D Y C R I S T I N A F E R N A N D E Z D E K I R C H N E R A N N O U N C E D H E R P R E S I D E N T I A L C A N D I D A C Y Y E S T E R D A Y E V E N I N G I N L A P L A T A A C I T Y 5 0 K I L O M E T E R S 3 1 M I L E S A W A Y F R O M B U E N O S A I R E S (fleurs_eng_000472-fleurs_eng_000472) +S E V E R E W E A T H E R I S T H E G E N E R I C T E R M F O R A N Y D A N G E R O U S W E A T H E R P H E N O M E N O N W I T H T H E P O T E N T I A L T O C A U S E D A M A G E S E R I O U S S O C I A L D I S R U P T I O N O R L O S S O F H U M A N L I F E (fleurs_eng_000473-fleurs_eng_000473) +F O R E X A M P L E T H E M O S T C O M M O N S T I L L I M A G E P H O T O G R A P H Y F O R M A T I N T H E W O R L D I S 3 5 M M W H I C H W A S T H E D O M I N A N T F I L M S I Z E A T T H E C L O S E O F T H E A N A L O G F I L M E R A (fleurs_eng_000474-fleurs_eng_000474) +I T I S R E L A T E D T O B U T U S U A L L Y N O T I N V O L V I N G A L P I N E S T Y L E S K I T O U R I N G O R M O U N T A I N E E R I N G T H E L A T T E R O N E S D O N E I N S T E E P T E R R A I N A N D R E Q U I R I N G M U C H S T I F F E R S K I S A N D B O O T S (fleurs_eng_000475-fleurs_eng_000475) +I R O N I N G D A M P C L O T H E S C A N H E L P T H E M D R Y M A N Y H O T E L S H A V E A N I R O N A N D I R O N I N G B O A R D A V A I L A B L E F O R L O A N E V E N I F O N E I S N O T P R E S E N T I N T H E R O O M (fleurs_eng_000476-fleurs_eng_000476) +E V A D N E A N S W E R E D H O A R S E L Y S H E D R E W H E R C H A I R A L I T T L E C L O S E R T O T H E F I R E A N D S P R E A D H E R H A N D S O U T T O T H E B L A Z E T H E R E W A S N O O T H E R L I G H T I N T H E R O O M B Y T H I S T I M E T H E W I N D W I T H O U T H O W L E D D I S M A L L Y S T I L L (mls_eng_000283-mls_eng_000283) +M Y D E A R M A R I A W H Y D O Y O U N O T D E S I S T F R O M T H I S S I L L Y P U R S U I T O F A N I M A G I N A R Y T R E A S U R E W H A T I S T H E V A L U E O F M O N E Y W E A R E S P A N I A R D S N O T S H I R T S L E E V E D M E R C E N A R Y P I G S O F A M E R I C A N S (mls_eng_000284-mls_eng_000284) +C R I T I C A L T E M P E R A T U R E I S T H A T O F T H E S I N G L E I S O T H E R M A L L I N E W H I C H P R E S E N T S A P O I N T O F I N F L E X I O N A T A H O R I Z O N T A L T A N G E N T T H E C R I T I C A L P R E S S U R E A N D T H E C R I T I C A L V O L U M E A R E T H E T W O C O O R D I N A T E S O F T H I S P O I N T O F I N F L E X I O N (mls_eng_000285-mls_eng_000285) +M U C H L I K E I N F O U L N E S S A N D D E F O R M I T Y U N T O T H A T M O N S T E R W H O M T H E T H E B A N K N I G H T T H E F A T H E R O F T H A T F A T A L P R O G E N Y M A D E K I L L H E R S E L F F O R V E R Y H E A R T S D E S P I T E T H A T H E H A D R E A D H E R R I D D L E W H I C H N O W I G H T C O U L D E V E R L O O S E B U T S U F F E R E D D E A D L Y D U E L (mls_eng_000286-mls_eng_000286) +H E H A S M A N A G E D T O M E A S U R E W I T H P R E C I S I O N P R E S S U R E S A M O U N T I N G T O T H R E E T H O U S A N D A T M O S P H E R E S A N D A L S O T H E V E R Y S M A L L V O L U M E S T H E N O C C U P I E D B Y T H E F L U I D M A S S U N D E R C O N S I D E R A T I O N T H I S L A S T M E A S U R E M E N T W H I C H N E C E S S I T A T E S N U M E R O U S C O R R E C T I O N S I S T H E M O S T D E L I C A T E P A R T O F T H E O P E R A T I O N (mls_eng_000287-mls_eng_000287) +W H Y S H O U L D I T H A V E B E E N D E E M E D N E C R O M A N C Y T O E N D E A V O R T O C O M B I N E T H E S E P A R T S T O E V O L V E B Y C A R E F U L E L I M I N A T I O N A N D C H A N G E T O T H E P E R F E C T F O O D (mls_eng_000288-mls_eng_000288) +N A Y T H O U G H O F R U S H E S B E M Y B E D Y E T I A M R I C H L O V E S A I D B U T A R G U E D L I F E T H R I C E F O N D A R T T H O U T O Y I E L D T H E S O V E R E I G N G I F T S O F E A R T H T H E V I C T O R S W O R D T H E L A U R E L E D B R O W F O R V I S I O N E D T H I N G S O F L I T T L E W O R T H (mls_eng_000289-mls_eng_000289) +B O C K S E E M S T O H A V E B E E N A K E E N C O L L E C T O R A L T H O U G H H A M P E R E D B Y I L L H E A L T H A N D A G R E A T P O I N T I N H I S F A V O U R I S T H A T H E D E S C R I B E D O N L Y T H O S E P L A N T S W H I C H H A D C O M E U N D E R H I S O W N P E R S O N A L O B S E R V A T I O N (mls_eng_000290-mls_eng_000290) +H A D R A T H E R S H R U N K U P A N D H A D N O T C H A N G E D I N T O N Y M P H S T H E S E I L E F T I N T H E S T E M S C O V E R I N G T H E M U P A G A I N A N D T H E Y A P P E A R E D A S P E R F E C T I N S E C T S I N T H E M A Y O F T H E F O L L O W I N G Y E A R (mls_eng_000291-mls_eng_000291) +N O T H I N G S A V E O B J E C T S A N D T H O U G H T S O F B E A U T Y C O U L D P R E S E N T T H E M S E L V E S T O T H E U N D E R S T A N D I N G O F T H E F O R T U N A T E P E R S O N W H O P A R T O O K O F I T T H E S E P A G E S W H I C H Y O U H A V E B R O U G H T T O M E T O T R A N S L A T E A R E C O N C E R N E D W I T H T H I S S U P E R S T I T I O N (mls_eng_000292-mls_eng_000292) +N O W S E E M E D I N S I P I D I T Y A N D H E D N E R V E H I M S E L F A G A I N S T I T H I S F A C E W O R E A S O R T O F S E V E R E F L U S H H E W A S T I M I D E V E N T O R U D E N E S S (mls_eng_000293-mls_eng_000293) +B E C A M E M O R E L I F E L I K E A S T H E C H E E K S F L U S H T H E R E W A S R A R E W A R M T H I N A W I N T E R M O R N I N G T O C H E E R T H E H A L F D E S P A I R I N G S O U L T I R E D A F T E R L O N G H O U R S O F O I L R E A D I N G A N D P I E R C E D T O T H E H E A R T B Y N E V E R C E A S I N G R H Y M E S Y E T I C O U L D N O T U N D E R S T A N D I T (mls_eng_000294-mls_eng_000294) +O N E O F T H E H A W A I I A N W R I T E R S S A I D T H E O P I H I A W A I S A P O I S O N S H E L L F I S H T H E S E A R E B I T T E R A N D D E A D L Y A N D C A N B E U S E D I N P U T T I N G E N E M I E S T O D E A T H (mls_eng_000295-mls_eng_000295) +T H E B E A U T E O U S R O B E S O F H E A V E N A S L A N T T H E D E W B R I G H T E A R T H A N D C O L O U R E D A I R H E L O O K S I N B O U N D L E S S M A J E S T Y A B R O A D T O U C H I N G T H E G R E E N L E A V E S A L L A T R E M B L E W I T H G O L D L I G H T (mls_eng_000296-mls_eng_000296) +I C A N D O N O M O R E T H A N T H A T U N T I L T H I S M A T T E R I S A B S O L U T E L Y S E T T L E D T H E Y A R E W O R T H M O R E T H A N L I F E I T S E L F T O M E M R C O W P E R S E E M E D A N N O Y E D S U R E L Y H E P R O T E S T E D Y O U A R E N O T G O I N G T O A S K M E T O W A I T T H R E E M O N T H S U N T I L I C A N E X A M I N E O N E O F T H E S E (mls_eng_000297-mls_eng_000297) +R O S C O N G R E S S F O U N D A T I O N R U S S I A N E N T I T Y T H A T O R G A N I Z E D T H E S A I N T P E T E R S B U R G I N T E R N A T I O N A L E C O N O M I C F O R U M R O S N E F T R U S S I A N S T A T E O W N E D O I L A N D E N E R G Y C O M P A N Y (mls_eng_000298-mls_eng_000298) +H O W I T G L I T T E R E D A N D S P A R K L E D T H E D E L I C A T E F R O S T W O R K Y O U W E R E A T T R A C T E D N O D O U B T A N D M A R V E L L E D A T T H E D A I N T Y T R A C I N G S B U T F E W O F U S H A V E R E A L L Y H A D A N O P P O R T U N I T Y T O S T U D Y T H E D E T A I L O F T H E S E F R O S T D E S I G N S M I N U T E L Y O R H A V E C O N S I D E R E D T H A T T H E R E W E R E M O R E T H A N T H R E E O R F O U R D E S I G N S A T M O S T (mls_eng_000299-mls_eng_000299) +O T H E R T H A N T H E O F F E N S E I N T R Y I N G T O I N F L I C T A W O U N D T H E Y M A Y K I L L T H E O F F E N D E R O R W O U N D H I M M O R E T H A N T H E Y I N T E N D E D T O D O A N D T H I S B E C O M E S A C A U S E F O R A N E W F E U D S O T H A T T H E P R I M I T I V E L E G I S L A T O R S W E R E C A R E F U L I N R E Q U I R I N G T H E R E T A L I A T I O N T O B E L I M I T E D T O A N E Y E F O R A N E Y E (mls_eng_000300-mls_eng_000300) +A T C Y R U S W O R D T H E J E W S R E T U R N T H E C O M P A N Y T H A T G O G O D S H O U S E B E G U N W I T H M I R T H A N D M O A N I S H I N D E R E D B Y T H E F O E B U T O N C E A G A I N T H E W O R K G O E S O N B Y L I C E N S E F R O M D E R I U S E Z R A I S S E N T W I T H R O Y A L G R A N T A N D G I F T S F O R U S E S P I O U S (mls_eng_000301-mls_eng_000301) +N E T P R O D U C T Y E A R I N A N D Y E A R O U T S E V E N H U N D R E D F R A N C S H E L I V E D I N I T H O W N O T S O B A D L Y W E W I L L E X P L A I N M A R I U S O C C U P I E D I N T H E G O R B E A U H O U S E (mls_eng_000302-mls_eng_000302) +T H E N T H I S I S A L L Y O U R A N S W E R T I S T O O F A I R F O R O N E O F H I S A L L I A N C E A N D I W A R N Y O U T H A T T H I S P L A C E N O M O R E S E E Y O U E X I T E N T E R D E F L O R E S T H E B E S T I S T H E R E I S M O R E G R O U N D T O M E E T A M A N S R E V E N G E O N H O N E S T D E F L O R E S T H A T S M Y N A M E I N D E E D (mls_eng_000303-mls_eng_000303) +W H E N I R E T U R N E D T O T H E H O U S E W H E R E I H A D B E E N A H A P P Y C H I L D O N L Y A P I L E O F A S H E S W H E R E I T H A D S T O O D I W E P T L O N G A N D T O F O R G E T M Y W E E P I N G I S A I L E D O U T O N T H E V A S T C A L M S E A O N T H E S E W A T E R S I N A S T A R S A P P H I R E N I G H T I P L A Y E D M Y F L U T E T O T H E S U M M E R M O O N (mls_eng_000304-mls_eng_000304) +D O Y O U N O T S E E W H A T P L E A S U R E I T G I V E S H I M W E H A V E G R O W N U P T O G E T H E R I N T H I S H O U S E S I N C E H E W A S A B O Y I S I M P L Y C A N N O T B E A R A S Y O U C A N T H E S I G H T O F T H E S M I L E L E A V I N G H I S F A C E P O O R D E A R H E H A S N O A M U S E M E N T E X C E P T T H I S P L A Y I N G A T T H E S H O P K E E P I N G (mls_eng_000305-mls_eng_000305) +I T I S A N E B U L O U S B O D Y R E V O L V I N G I N A N E L L I P T I C A L O R B I T O F G R E A T E L O N G A T I O N L O V E L O V E L O V E W I L L N O T B E T H E W O U N D O F C U P I D B U T T H E M A N I F E S T A T I O N O F U N I V E R S A L R E P R O D U C T I V E I N S T I N C T S (mls_eng_000306-mls_eng_000306) +S H A R P L Y A S H E S H O O K H A N D S W I T H H E R G O D B L E S S Y O U M Y D E A R C H I L D T H E B I S H O P S A I D W H E N S H E K I S S E D H I M A N D H I S L I P S M O V E D A F T E R W A R D F O R S O M E S E C O N D S A S I F H E W E R E I N P R A Y E R H E R M O T H E R F O L L O W E D H E R O U T O F T H E R O O M A N D T H E N S I L E N C E S E T T L E D (mls_eng_000307-mls_eng_000307) +F O L L O W E D H I M S T E A L T H I L Y A N D W H E N H E W A S I N A S T O O P I N G P O S T U R E F I L L I N G H I S B U C K E T C A M E U P B E H I N D H I M A N D P L U N G E D A L O N G K N I F E I N T O H I S N E C K (mls_eng_000308-mls_eng_000308) +S A I T H C H E R S I A S D O E S N O T J U P I T E R D I S T R I B U T E T O T H E G O D S T H E I R P R O P O R T I O N A N D D I V I D E N D S P A R I N G L Y A N D S E V E R A L L Y A S A G A M E M N O N D I D T O H I S C O M M A N D E R S W H E N H I S G U E S T S D R A N K T O O N E A N O T H E R I F C H E R S I A S Q U O T H C L E O D E M U S A S Y O U N A R R A T E (mls_eng_000309-mls_eng_000309) +A N D W H E R E N O N E S H A L L D A R E R E S T R A I N U S W E C A N M E E T A G A I N I N T H O U G H T S O T H E R E S N O U S E I N W E E P I N G B E A R A C H E E R F U L S P I R I T S T I L L N E V E R D O U B T T H A T F A T E I S K E E P I N G F U T U R E G O O D F O R P R E S E N T I L L (mls_eng_000310-mls_eng_000310) +A N D T O B E C O M E T H E R E C O R D O F W H A T P E O P L E H A V E D O N E I N T H E I R M O R E A M I A B L E M O M E N T S T H E R E C O R D O F T H E C O N Q U E S T S O F P E A C E H O W M E N H A V E L I V E D A N D L A B O R E D D U G A N D B U I L T H E W N A N D C L E A R E D G A R D E N E D A N D R E F O R E S T (mls_eng_000311-mls_eng_000311) +T H E L O W F L Y I N G O F T H E S W A L L O W S B E T O K E N S R A I N A S W E L L A S A N Y U N S E A S O N A B L E D A N C I N G O F M I D G E S I N T H E E V E N I N G S O R E C O R N S O N T H E F E E T A N D R H E U M A T I S M I N T H E J O I N T S A R E D I R E F U L P R E C U R S O R S T H E L E A V E S A R E A L L A T R E M B L E B E F O R E T H E A P P R O A C H O F T H U N D E R (mls_eng_000312-mls_eng_000312) +W A S S T O R M E D G E N E R A L D A M P I E R R E W A S K I L L E D G E N E R A L C U S T I N E W A S B L A M E D A N D I N D E E D I S N O W C O M E T O P A R I S T O G I V E E X P L A N A T I O N S A G A I N S T A L L W H I C H T H E M O U N T A I N A N D A T R O C I O U S M A R A T M U S T E V E N M A K E H E A D A S T H E Y C A N (mls_eng_000313-mls_eng_000313) +T H E M O M E N T W A S F E A R F U L A M I G H T I E R F O E H A D N E V E R S W U N G T H E B A T T L E A X E O V E R H I M B U T H O P E N E R V E D H I S A R M F O R A D E S P E R A T E B L O W A N D T E C U M S E H F E L L P R O S T R A T E B E F O R E H I M (mls_eng_000314-mls_eng_000314) +T H E N T H E W I N D S T O P P E D T H E C L O U D S T U R N E D D A R K A N D N I G H T C A M E O N L I K E I N K M Y O L D C O T T O N Q U I L T W A S C O L D A S I R O N M Y S W E E T S O N T O S S E D I N H I S S L E E P (mls_eng_000315-mls_eng_000315) +Y O U M A Y D O A S Y O U P L E A S E T O W O R K O F F Y O U R I R R I T A T I O N T O K E E P U P Y O U R F A N A T I C I S M Y O U A R E W E L L O F F Y O U N E E D N O T M I N D T H E C O S T T H E P O O R D O N O T W A N T T O S T A N D I N Y O U R W A Y B U T Y O U I N S I S T O N T H E I R S U B M I T T I N G T O Y O U R C O M P U L S I O N (mls_eng_000316-mls_eng_000316) +H E W A S B R E D B Y R E V G A S N E Y D B E I N G B Y O T H M A N E S I X F O U R T W O T W O H E D W I G H E W A S B O R N I N M A R C H E I G H T E E N S E V E N T Y N I N E A N D H E W A S T H E O N L Y S U R V I V O R O F A L I T T E R O F F I F T E E N I T W A S O N T H I S A C C O U N T T H A T H E W A S C A L L E D S A F E I N C O L O R A N D M A R K I N G S (mls_eng_000317-mls_eng_000317) +A N D W H A T H A S T E I T M A K E S T O F A L L I N T O T H E S E C O N D T H E R E B Y T H I S T I M E D I A P H A N T A S N E E Z E S A C H O O M O S T A D M I R A B L E S E C R E T O N T H E C O N T R A R Y I T S T I R S M E N O T A W H I T W H I C H M O S T C O N C E R N S I T H A H A H A (mls_eng_000318-mls_eng_000318) +T H I R D L Y T H A L E S S A I D W H E R E T H E C I T I Z E N S A R E N E I T H E R T O O R I C H N O R T O O P O O R F O U R T H L Y A N A C H A R S I S S A I D W H E R E T H O U G H I N A L L O T H E R R E S P E C T S T H E Y A R E E Q U A L Y E T V I R T U O U S M E N A R E A D V A N C E D A N D V I C I O U S P E R S O N D E G R A D E D (mls_eng_000319-mls_eng_000319) +T H E K I N D L Y F R A N K I S S Y M P A T H E T I C E V E R Y D A Y H E P A S S E S N O T E S B E T W E E N U S A N D I T R Y T O E N C O U R A G E R U S S E L L H E W I L L I M P R O V E I A S S U R E H I M H I S T I M E I S S H O R T A N D F R E S H A I R A N D L I B E R T Y W I L L S O O N R E S T O R E H I M (mls_eng_000320-mls_eng_000320) +T H E S E Q U E S T I O N S I T I S N O W E V I D E N T M A Y F R E Q U E N T L Y B E A N S W E R E D W I T H E Q U A L P R O P R I E T Y I N O P P O S I T E W A Y S A N D I F T H E R E B E A N Y O C C A S I O N S O N W H I C H T H E Y C A N B E A N S W E R E D O N L Y I N O N E W A Y T H E A N S W E R W I L L D E P E N D U P O N T H E N A T U R E O F T H E O C C A S I O N (mls_eng_000321-mls_eng_000321) +I N H I S N O T E B O R E T H E M I N S T R E L S Y S E C O N D E D I T I O N E I G H T E E N O H E I G H T S C O T T S A Y S T H E B A L L A D W A S T A K E N D O W N F R O M A N O L D W O M A N S R E C I T A T I O N A T T H E A L S T O N M O O R L E A D M I N E S B Y T H E A G E N T T H E R E A N D S E N T B Y H I M T O S U R T E E S (mls_eng_000322-mls_eng_000322) +C H R I S T I A N T H E O L O G I A N S (nchlt_eng_001588-nchlt_eng_001588) +O B T A I N E A G L E F E A T H E R S (nchlt_eng_001589-nchlt_eng_001589) +E L E M E N T A R Y S P E C I A L F U N C T I O N S (nchlt_eng_001590-nchlt_eng_001590) +G E O R G E W A S H I N G T O N U N I V E R S I T Y (nchlt_eng_001591-nchlt_eng_001591) +S C I E N C E F I C T I O N N O V E L S (nchlt_eng_001592-nchlt_eng_001592) +C O A S T H I P H O P (nchlt_eng_001593-nchlt_eng_001593) +I N V E R S E L A P L A C E T R A N S F O R M (nchlt_eng_001594-nchlt_eng_001594) +F R E N C H P R O T E S T A N T S (nchlt_eng_001595-nchlt_eng_001595) +A F G H A N A I R F O R C E (nchlt_eng_001596-nchlt_eng_001596) +H E R O E S I N M Y T H O L O G Y A N D L E G E N D (nchlt_eng_001597-nchlt_eng_001597) +B U S I N E S S C L A S S S E A T (nchlt_eng_001598-nchlt_eng_001598) +C L U B P L A Y C H A R T (nchlt_eng_001599-nchlt_eng_001599) +P O S I T R O N S W E R E R E P O R T E D (nchlt_eng_001600-nchlt_eng_001600) +O L D V I C T H E A T R E (nchlt_eng_001601-nchlt_eng_001601) +O R T H O D O X M O N A R C H S (nchlt_eng_001602-nchlt_eng_001602) +N A T I O N S M E M B E R S T A T E S (nchlt_eng_001603-nchlt_eng_001603) +F I F A W O R L D C U P (nchlt_eng_001604-nchlt_eng_001604) +C R E W S R E S C U E E F F O R T S (nchlt_eng_001605-nchlt_eng_001605) +A C T U A L F I L M M I C R O S C O P I C A L L Y (nchlt_eng_001606-nchlt_eng_001606) +M U S I C A L G R O U P S R E E S T A B L I S H E D (nchlt_eng_001607-nchlt_eng_001607) +P R I M U S I N T E R P A R E S (nchlt_eng_001608-nchlt_eng_001608) +F I L M T E C H N I Q U E S (nchlt_eng_001609-nchlt_eng_001609) +T E L E V I S I O N S E R I E S B A S E D (nchlt_eng_001610-nchlt_eng_001610) +N E W P O L I T I C A L P A R T Y (nchlt_eng_001611-nchlt_eng_001611) +A N C I E N T E G Y P T A C H I E V E D (nchlt_eng_001612-nchlt_eng_001612) +F L A T M U S I C N A T U R A L (nchlt_eng_001613-nchlt_eng_001613) +A M E R I C A N S T E C H N O L O G Y W R I T E R S (nchlt_eng_001614-nchlt_eng_001614) +D A U G H T E R S O F B A R O N S (nchlt_eng_001615-nchlt_eng_001615) +P O P U L A R T O U R I S T A T T R A C T I O N S (nchlt_eng_001616-nchlt_eng_001616) +D U T C H W E S T I N D I A (nchlt_eng_001617-nchlt_eng_001617) +G O L D M E D A L R E C I P I E N T S (nchlt_eng_001618-nchlt_eng_001618) +R U S S I A N S O C I A L D E M O C R A T I C (nchlt_eng_001619-nchlt_eng_001619) +A M E R I C A N F I L M P R O D U C E R S (nchlt_eng_001620-nchlt_eng_001620) +F R E E S O F T W A R E F O U N D A T I O N (nchlt_eng_001621-nchlt_eng_001621) +R O Y A L D R A M A T I C T H E A T R E (nchlt_eng_001622-nchlt_eng_001622) +E D I B L E M O L L U S C S (nchlt_eng_001623-nchlt_eng_001623) +F E A T U R E S I N C L U D E B E A C H E S (nchlt_eng_001624-nchlt_eng_001624) +O X F O R D D I C T I O N A R Y C H A N G E D (nchlt_eng_001625-nchlt_eng_001625) +S A L U K I P E R S I A N G R E Y H O U N D (nchlt_eng_001626-nchlt_eng_001626) +P R I M E M I N I S T E R K E V I N (nchlt_eng_001627-nchlt_eng_001627) +L A N G U A G E S O F I R A Q (nchlt_eng_001628-nchlt_eng_001628) +S O U T H E A S T E N G L A N D (nchlt_eng_001629-nchlt_eng_001629) +N E W L I N E C I N E M A (nchlt_eng_001630-nchlt_eng_001630) +E Q U A L C R E D I T O P P O R T U N I T Y (nchlt_eng_001631-nchlt_eng_001631) +S O U T H E A S T E N G L A N D 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(swc_eng_001770-swc_eng_001770) +I N N I N E T E E N S E V E N T Y T H R E E (swc_eng_001771-swc_eng_001771) +D E V E L O P I N G A N D U S I N G S U C H T E C H N O L O G I E S (swc_eng_001772-swc_eng_001772) +F O R S O M E Q U E S T I O N S (swc_eng_001773-swc_eng_001773) +C L A I M O F P R O O F T H A T P (swc_eng_001774-swc_eng_001774) +A B L A D D E R C A T H E T E R I S U S U A L L Y I N S E R T E D T O M O N I T O R F L U I D B A L A N C E (swc_eng_001775-swc_eng_001775) +P R O M O T I O N O F E U G E N I C E N H A N C E M E N T T E C H N O L O G I E S M I G H T U N I N T E N T I O N A L L Y E N C O U R A G E (swc_eng_001776-swc_eng_001776) +T H E A T T E N T I O N O F R E S E A R C H E R S C A N B E F O C U S E D O N P A R T I A L S O L U T I O N S O R S O L U T I O N S (swc_eng_001777-swc_eng_001777) +K N O W N O F F O R H U N D R E D S O F Y E A R S (swc_eng_001778-swc_eng_001778) +O N L Y M A R S U P I A L S H A V E S U R V I V E D T O T H E P R E S E N T (swc_eng_001779-swc_eng_001779) +T O W H I C H A L L T H E E D I B L E S P E C I E S O F C R U S T A C E A N B E L O N G (swc_eng_001780-swc_eng_001780) +A L G O R I T H M R E S E A R C H (swc_eng_001781-swc_eng_001781) +N I N E T E E N S I X T Y T W O P H I L I P S I N V E N T E D T H E C O M P A C T A U D I O C A S S E T T E M E D I U M F O R A U D I O S T O R A G E (swc_eng_001782-swc_eng_001782) +O B S T R U C T I O N O F T H E F L O W (swc_eng_001783-swc_eng_001783) +A M P H I B I A N S A N D R E P T I L E S (swc_eng_001784-swc_eng_001784) +W O M E N S W O R L D C H E S S C H A M P I O N S H I P (swc_eng_001785-swc_eng_001785) +C O N T A I N S D E S C R I P T I O N S A N D C O M M E N T A R I E S O N T H E S T A T E O F N B I C S C I E N C E A N D T E C H N O L O G Y B Y M A J O R C O N T R I B U T O R S T O T H E S E F I E L D S (swc_eng_001786-swc_eng_001786) +P U E R I L E F A N T A S Y O R S O C I A L T R E N D (swc_eng_001787-swc_eng_001787) +M O S T C O M P A C T C A S S E T 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(swc_eng_001798-swc_eng_001798) +I S A N E N D A N G E R E D M A R I N E S P E C I E S (swc_eng_001799-swc_eng_001799) +B R O W N D E S I R E D E L E C T I O N (swc_eng_001800-swc_eng_001800) +T H I S F A C T D O E S N T S A Y M U C H A B O U T W H E R E T H E P R O B L E M L I E S (swc_eng_001801-swc_eng_001801) +E C O N O M I C A L S O C I E T Y B E G A N A S A (swc_eng_001802-swc_eng_001802) +W I T H T O U R I S T S A R R I V I N G B Y S T E A M B O A T A N D T R A I N (swc_eng_001803-swc_eng_001803) +F I R S T D I A L O G U E B E T W E E N T R A N S H U M A N I S M (swc_eng_001804-swc_eng_001804) +N E V E R B E E N P A R T O F T H E O L Y M P I C G A M E S (swc_eng_001805-swc_eng_001805) +R E G I S F U R N I T U R E A N D (swc_eng_001806-swc_eng_001806) +I N H I G H L E V E L T O U R N A M E N T S (swc_eng_001807-swc_eng_001807) +T O L O C A T E T H E A N E U R Y S M (swc_eng_001808-swc_eng_001808) +M O R P H O L O G I C A L F R E E D O M (swc_eng_001809-swc_eng_001809) +E N E R G 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(swc_eng_001820-swc_eng_001820) +T R A N S H U M A N I S T A S S U M P T I O N (swc_eng_001821-swc_eng_001821) +O N T H E F I R S T B A L L O T (swc_eng_001822-swc_eng_001822) +S T O R Y I N D I C A T I V E O F T H E R I S E I N G L O B A L S I G N I F I C A N C E O F S H O E P O L I S H I S T O L D B Y J E A N (swc_eng_001823-swc_eng_001823) +W H I C H S P A R K E D H I S E A R L Y I N T E R E S T I N P O L I T I C S (swc_eng_001824-swc_eng_001824) +W A S C A L L E D D O L B Y H X P R O I N F U L L A N D P A T E N T E D (swc_eng_001825-swc_eng_001825) +C O U L D S A V E A N D F I N D F I L E S B Y N U M B E R (swc_eng_001826-swc_eng_001826) +A U S T R A L I A N S N A K E S B E L O N G T O S E V E N F A M I L I E S (swc_eng_001827-swc_eng_001827) +D E V E L O P I N G P L A Y E R S (swc_eng_001828-swc_eng_001828) +D E C L I N E D S H A R P L Y S I N C E I T S P E A K I N T H E L A T E (swc_eng_001829-swc_eng_001829) +W A S R E C O R D E D E N T I R E L Y O N A F O U R T R A C K C A S S 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(swc_eng_001851-swc_eng_001851) +D I A G N O S I S I S G E N E R A L L Y M A D E W I T H A C T S C A N O F T H E H E A D (swc_eng_001852-swc_eng_001852) +F I R S T G E N E R A L L Y R E C O G N I Z E D W O R L D C H E S S C H A M P I O N (swc_eng_001853-swc_eng_001853) +S H E P P E Y A N D S I T T I N G B O U R N E W E R E P A R T (swc_eng_001854-swc_eng_001854) +H A D R E L E A S E D T H E I R A L B U M S B O T H T O C D A N D (swc_eng_001855-swc_eng_001855) +W A T E R S A R O U N D T H E C O N T I N E N T (swc_eng_001856-swc_eng_001856) +T H E R A N G E P E R S O N A L S T E R E O S (swc_eng_001857-swc_eng_001857) +A N D V U M E T E R S A N D R E C O R D I N G L E V E L C O N T R O L S O N (swc_eng_001858-swc_eng_001858) +P O L Y N O M I A L T I M E (swc_eng_001859-swc_eng_001859) +A N D I T O F T E N D E S T R O Y E D T H E P L A Y A B I L I T Y (swc_eng_001860-swc_eng_001860) +C O N F U S I O N (swc_eng_001861-swc_eng_001861) +E Q U I V A L E N T T O T H E Q U E S T I O N O F W H E 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I L I S O N E O F T H E S P E C I E S T O W H I C H T H E A G R E E M E N T O N T H E C O N S E R V A T I O N O F A F R I C A N E U R A S I A N M I G R A T O R Y W A T E R B I R D S (swc_eng_001881-swc_eng_001881) +A N D I S N O W F O U N D O N L Y I N T A S M A N I A (swc_eng_001882-swc_eng_001882) +T H E I D E A O F M I N D U P L O A D I N G I S A S S E R T E D T O R E P R E S E N T (swc_eng_001883-swc_eng_001883) +A N A V E R A G E O F T W E N T Y O N E P E R D A Y (swc_eng_001884-swc_eng_001884) +T H E N I T W O U L D F O L L O W T H A T P E Q U A L S (swc_eng_001885-swc_eng_001885) +A N D B L E E D I N G I N T O V A R I O U S T U M O R S (swc_eng_001886-swc_eng_001886) +A L L O W T H E M T O G L I D E B E T W E E N T R E E S (swc_eng_001887-swc_eng_001887) +I F T H E S E P R O B L E M S W E R E E F F I C I E N T L Y S O L V A B L E (swc_eng_001888-swc_eng_001888) +G E O L O G I C A L T I M E (swc_eng_001889-swc_eng_001889) +W H E N F L U S H E D (swc_eng_001890-swc_eng_001890) +I N C L U D I N G G E R M I N A L C H O I C E T E C H N O L O G Y (swc_eng_001891-swc_eng_001891) +A P P E A R A N C E O F L E A T H E R S H O E S O R B O O T S (swc_eng_001892-swc_eng_001892) +I N E I G H T E E N S I X T Y T H R E E (swc_eng_001893-swc_eng_001893) +M A N U F A C T U R E S H O E C A R E P R O D U C T S A L S O S E L L (swc_eng_001894-swc_eng_001894) +T H E F I R S T N O N S O V I E T C H A L L E N G E R S I N C E (swc_eng_001895-swc_eng_001895) +O P P O N E N T H A S O N L Y T H E K I N G A N D (swc_eng_001896-swc_eng_001896) +M A I N A R T I C L E (swc_eng_001897-swc_eng_001897) +F O U N D C E R T A I N L E N G T H S U S E F U L F O R F I T T I N G (swc_eng_001898-swc_eng_001898) +T A P E I N T H E S A M E F O R M F A C T O R A S T H E C O M P A C T A U D I O (swc_eng_001899-swc_eng_001899) +C E N S U R E W A S L A T E R E X P U N G E D F R O M (swc_eng_001900-swc_eng_001900) +O R D E F A C T O E Q U A L I T Y (swc_eng_001901-swc_eng_001901) +I S F O U R T H O U S A N D 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B L E (swc_eng_001912-swc_eng_001912) +I N C L U D I N G T H E S L E E P Y C O D (swc_eng_001913-swc_eng_001913) +S E V E N T Y F O U R H A D A H I G H E R E D U C A T I O N Q U A L I F I C A T I O N C O M P A R E D (swc_eng_001914-swc_eng_001914) +T H I S O C C U R S W H E N T H E O P P O N E N T S K I N G I S I N C H E C K (swc_eng_001915-swc_eng_001915) +C O N S E R V A T I O N I N A U S T R A L I A (swc_eng_001916-swc_eng_001916) +I S T H E S A L A M A N D E R F I S H (swc_eng_001917-swc_eng_001917) +F I R S T S E L F D E S C R I B E D T R A N S H U M A N I S T S M E T F O R M A L L Y I N T H E E A R L Y (swc_eng_001918-swc_eng_001918) +R E C E N T R E S E A R C H I N D I C A T E S T H A T F A C T O R S O T H E R T H A N P R A C T I C E (swc_eng_001919-swc_eng_001919) +A N D P R E V E N T I O N A N D T R E A T M E N T O F C O M P L I C A T I O N S (swc_eng_001920-swc_eng_001920) +W I T H A R A P I D O N S E T (swc_eng_001921-swc_eng_001921) +U T A H W A R T H E F O U N D A T I O N W A S B U R I E D (swc_eng_001922-swc_eng_001922) +N O W A D A Y S H O U R L Y R E G I O N A L E X P R E S S T R A I N S B E T W E E N B E R N A N D S P I E Z T O B R I G A N D F R E I G H T T R A I N S C O N T I N U E T O R U N O N T H E M O U N T A I N R A I L W A Y (swc_eng_001923-swc_eng_001923) +O T H E R F A M I L I E S W I T H A P O T E N T I A L L Y G O N D W A N A N O R I G I N I N C L U D E T H E R E T R O P I N N I D A E (swc_eng_001924-swc_eng_001924) +B Y A N I T A L I A N D O M I N I C A N M O N K J A C O B U S D E C E S S O L I S (swc_eng_001925-swc_eng_001925) +C O M M A N D W A S N A M E D A F T E R T H E (swc_eng_001926-swc_eng_001926) +A R T I F I C I A L I N T E L L I G E N C E (swc_eng_001927-swc_eng_001927) +A N D I S T H E R E I G N I N G (swc_eng_001928-swc_eng_001928) +P E R C E N T O F T H E P O P U L A T I O N (swc_eng_001929-swc_eng_001929) +C H I E F A R E A S O F S H O E P O L I S H S A L E S (swc_eng_001930-swc_eng_001930) +I M P O S E D B Y L A W (swc_eng_001931-swc_eng_001931) +R E F E R E N C E S T O T H E R U L I N G C O A L I T I O N G O V E R N M E N T (swc_eng_001932-swc_eng_001932) +S P E C I E S O F G L I D I N G P O S S U M (swc_eng_001933-swc_eng_001933) +B A S E D O N T H E P R E V I O U S S T R A T E G Y O F P L A Y (swc_eng_001934-swc_eng_001934) +A N D I D E A L I S T I C A S P I R A T I O N S (swc_eng_001935-swc_eng_001935) +P R O F E S S I O N A L S A N D H O M E R E C O R D I N G E N T H U S I A S T S (swc_eng_001936-swc_eng_001936) +F A M I L Y E L A P I D A E (swc_eng_001937-swc_eng_001937) +T H A N A Q U A R T E R O F P E O P L E W I T H A P R E V I O U S S A H M A Y D E V E L O P H Y P O P I T U I T A R I S M (swc_eng_001938-swc_eng_001938) +D I V I D E D I N T O T H R E E F A M I L I E S T H A T (swc_eng_001939-swc_eng_001939) +S H O W E D S L I G H T I N T E R E S T I N R E L E A S I N G C A S S E T T E S (swc_eng_001940-swc_eng_001940) +F A M I L I A R E N O U G H T O H A V E C O M M O N N A M E S (swc_eng_001941-swc_eng_001941) +I N T W O T H O U S A N D S I X (swc_eng_001942-swc_eng_001942) +S H O E S H I N E B O Y S A R E K N O W N A S B O O T P O L I S H B O Y S (swc_eng_001943-swc_eng_001943) +T H E C A U S E I S R U P T U R E O F A C E R E B R A L A N E U R Y S M (swc_eng_001944-swc_eng_001944) +M O S T O F T H E M A J O R U S M U S I C C O M P A N I E S (swc_eng_001945-swc_eng_001945) +O N E S T E R E O P A I R O R O N E M O N O P H O N I C T R A C K I S P L A Y E D O R R E C O R D E D W H E N T H E T A P E I S M O V I N G I N O N E D I R E C T I O N A N D (swc_eng_001946-swc_eng_001946) +W H E R E I T S E A R L Y F O R M I N (swc_eng_001947-swc_eng_001947) +A S T R A T E G I C P H I L O S O P H E R (swc_eng_001948-swc_eng_001948) +P O S I T I O N I N G A D V A N T A G E S D U R I N G T H E G A M E (swc_eng_001949-swc_eng_001949) +N E W S O U T H W A L E S (swc_eng_001950-swc_eng_001950) +D I S P O S A L O V E R H I S O W N B I O L O G I C A L N A T U R E (swc_eng_001951-swc_eng_001951) +R E P R O D U C T I V E R I G H T S O R E X E R T U N D U E P R E S S U R E S O N P R O S P E C T I V E P A R E N T S (swc_eng_001952-swc_eng_001952) +S T I L L A N C I E N T I N O R I G I N (swc_eng_001953-swc_eng_001953) +R A S T A P O P O U L O S S H I R E D G U N (swc_eng_001954-swc_eng_001954) +I N T W O T H O U S A N D T W O (swc_eng_001955-swc_eng_001955) +F O R E X A M P L E I F T H E P L A Y E R H A S O N L Y (swc_eng_001956-swc_eng_001956) +S U F F E R E D A S U B A R A C H N O I D H E M O R R H A G E H A V E C O G N I T I V E I M P A I R M E N T T H A T A F F E C T S (swc_eng_001957-swc_eng_001957) +P R O V I D E D P R O G N O S T I C D A T A (swc_eng_001958-swc_eng_001958) +W H O H A D A N E U R Y S M S D E T E C T E D B Y O T H E R M E A N S (swc_eng_001959-swc_eng_001959) +L I F E S T Y L E S D E S I G N E D T O I M P R O V E H E A L T H A N D L O N G E V I T Y (swc_eng_001960-swc_eng_001960) +H A D M O R E S O P H I S T I C A T E D E N D O F T A P E P R E D I C T I O N (swc_eng_001961-swc_eng_001961) +D E H U M A N I Z A T I O N (swc_eng_001962-swc_eng_001962) +S P E C I E S I N C L U D E F R E S H W A T E R L A M P R E Y S (swc_eng_001963-swc_eng_001963) +F I R S T A N G I O G R A M (swc_eng_001964-swc_eng_001964) +T H E F R E E E N C Y C L O P E D I A A T (swc_eng_001965-swc_eng_001965) +T H E R E F O R E M E D I C A L I M A G I N G I S G E N E R A L L Y (swc_eng_001966-swc_eng_001966) +S P E C I E S I S T T H E E X C L U S I O N O F N O N H U M A N A N D P A R T H U M A N A N I M A L S (swc_eng_001967-swc_eng_001967) +I N P E O P L E W H O H A D P R E V I O U S L Y S U F F E R E D A S U B A R A C H N O I D H E M O R R H A G E (swc_eng_001968-swc_eng_001968) +C L A S S I F I E D A S E I T H E R E N D A N G E R E D O R T H R E A T E N E D U N D E R T H E E P B C A C T (swc_eng_001969-swc_eng_001969) +A N D A T T O R N E Y G E N E R A L P A R K E R W A T K I N S H A R D I N (swc_eng_001970-swc_eng_001970) +B U T T Y P 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(voxforge_eng_000986-voxforge_eng_000986) +M E R C E D E S S C R E A M E D C R I E D L A U G H E D A N D M A N I F E S T E D T H E C H A O T I C A B A N D O N M E N T O F H Y S T E R I A (voxforge_eng_000987-voxforge_eng_000987) +I W A N T T O K N O W H O W A L L T H I S I S P O S S I B L E (voxforge_eng_000988-voxforge_eng_000988) +P R E S E N T I N G A S I M P L E A N D I N S T R U C T I V E I L L U S T R A T I O N O F T H E S T R U G G L E F O R L I F E A M O N G T H E R I V A L S P E C I E S (voxforge_eng_000989-voxforge_eng_000989) +H E L L N E V E R D O A T A P O F W O R K T H E W H O L E V O Y A G E (voxforge_eng_000990-voxforge_eng_000990) +I H A V E H U N T E D A L O N G T H I S R I D G E R E P L I E D P H I L I P (voxforge_eng_000991-voxforge_eng_000991) +L O R D B U T I M G L A D T O S E E Y O U A G A I N P H I L (voxforge_eng_000992-voxforge_eng_000992) +H O W V A L I A N T L Y I W E N T A T I T T H A T F I R S T D A Y (voxforge_eng_000993-voxforge_eng_000993) +T H E Y A R E N O T R E G U L A R O Y S T E R P I R A T E S N I C H O L A S C O N T I N U E D (voxforge_eng_000994-voxforge_eng_000994) +T H E Y M U S T B E H U R T I N G F O R B U S I N E S S B U T I T H O U G H T Y O U M I G H T W A N T T O T A K E A L O O K A T T H E I R S I T E (voxforge_eng_000995-voxforge_eng_000995) +T H E R E W A S N O C H A N C E T O F I R E W I T H O U T H I T T I N G H I M (voxforge_eng_000996-voxforge_eng_000996) +A S F O R H I M S E L F W E R E N T T H E S T R E E T R A I L W A Y E A R N I N G S I N C R E A S I N G S T E A D I L Y (voxforge_eng_000997-voxforge_eng_000997) +D U N H A M C A N Y O U R B O Y G O A L O N G W I T H J E S S E (voxforge_eng_000998-voxforge_eng_000998) +G O O D B Y E P I E R R E H E S H O U T E D (voxforge_eng_000999-voxforge_eng_000999) +B U T S U C H D I V E R G E N C E O F O P I N I O N W O U L D C O N S T I T U T E N O M E N A C E T O S O C I E T Y (voxforge_eng_001000-voxforge_eng_001000) +T H E R E W A S O N E C H A N C E A N D O N L Y O N E O F S A V I N G J E A N N E (voxforge_eng_001001-voxforge_eng_001001) +I C A N N O T F O L L O W Y O U S H E S A I D (voxforge_eng_001002-voxforge_eng_001002) +O N T H E F A R C O R N E R O F T H E C O M P O U N D F E N C E A H A W K B R O O D E D (voxforge_eng_001003-voxforge_eng_001003) +T H E N A G A I N T U D O R H A D S U C H A N I R R I T A T I N G W A Y A B O U T H I M (voxforge_eng_001004-voxforge_eng_001004) +W E A L L K N O W O M A N A S A S U C C E S S F U L S T A B L E C O U N T R Y A R O L E M O D E L F O R T H E W H O L E R E G I O N (voxpopuli_eng_000494-voxpopuli_eng_000494) +T H E R E F O R E I T S H I G H T I M E T H A T Y O U C O M E F O R W A R D W I T H A P R O P O S A L F O R R E V I E W W I T H A N O P E R A T I O N A L S E P A R A T I O N O F T H E A U D I T A N D N O N A U D I T S E R V I C E S U N D E R A D I R E C T E U S U P E R V I S I O N (voxpopuli_eng_000495-voxpopuli_eng_000495) +I T I S C L E A R T H A T W E H A V E N O T I M E T O W A S T E T H E N E W R E S U L T S O F T H E I P C C R E G A R D I N G T H E S C I E N T I F I C B A S I S O F C L I M A T E C H A N G E L E A V E N O R O O M F O R H E S I T A T I O N (voxpopuli_eng_000496-voxpopuli_eng_000496) +5 S O I N T H E C O N T A I N E R S W H I C H A R E N E V E R E V E N T O U C H E D C O M E S L A V E S C O U N T E R F E I T G O O D S D R U G S E T C (voxpopuli_eng_000497-voxpopuli_eng_000497) +I H O P E T H A T T H E C O M M I S S I O N S M O B I L I T Y I N I T I A T I V E S W I L L N O T C R E A T E T H E N E X T P R O B L E M B U T W I L L B E A N A N S W E R F O R E X I S T I N G C H A L L E N G E S O F T H E R O A D T R A N S P O R T S E C T O R (voxpopuli_eng_000498-voxpopuli_eng_000498) +I N T H E U S I T W A S A D E C I S I O N T A K E N O N L Y B Y O N E P E R S O N T H E F O R M E R P R E S I D E N T O F T H E U N I T E D S T A T E S A G A I N S T T H E A R T I C U L A T E D D E M O C R A T I C M A J O R I T Y O F T H E U S C O N G R E S S B Y A L L O F I T S R E P U B L I C A N A N D S O M E O F I T S D E M O C R A T M E M B E R S I T W A S A N A G R E E M E N T W I T H O U T A N Y B I N D I N G O B L I G A T I O N S A S T H E L E A D E R S O F I R A N V E R Y O P E N L Y A N D P R E C I S E L Y M A D E C L E A R O N T H E V E R Y D A Y T H I S S O C A L L E D D E A L W A S P U B L I S H E D (voxpopuli_eng_000499-voxpopuli_eng_000499) +F R E E S P E E C H I S E S S E N T I A L L Y A C C E P T I N G T H A T P E O P L E A R E F R E E T O S A Y T H I N G S W E D O L I K E N O T M E R E L Y F R E E T O S A Y T H I N G S W E D O L I K E (voxpopuli_eng_000500-voxpopuli_eng_000500) +L E T U S L E A R N F R O M T H I S (voxpopuli_eng_000501-voxpopuli_eng_000501) +W E T H I N K T H A T T H E E N V I R O N M E N T A L E F F E C T O F P R O D U C T S M U S T B E A V E R Y I M P O R T A N T I S S U E I N T H E E U A N D T H E W H O L E I D E A O F A N E C O L A B E L G I V E S A V E R Y U S E F U L O R I E N T A T I O N F O R C O N S U M E R S O F C O U R S E T H E E C O L A B E L S H O U L D B E G I V E N T O T H E M O S T E N V I R O N M E N T A L L Y F R I E N D L Y P R O D U C T S A N D T H E I N F O R M A T I O N S H O U L D B E C L E A R A N D C O R R E C T (voxpopuli_eng_000502-voxpopuli_eng_000502) +H O W E V E R T H E C U R R E N T R E G I M E N E E D S T O B E B E T T E R T A I L O R E D T O T H E D I G I T A L E N V I R O N M E N T I N O R D E R T O E N S U R E F A I R R E M U N E R A T I O N T O C R E A T O R S A N D T O C O N F O R M T O C O N S U M E R E X P E C T A T I O N S (voxpopuli_eng_000503-voxpopuli_eng_000503) +I T C A L L S U P O N T H E C O M M I S S I O N A N D M E M B E R S T A T E S T O E N H A N C E T H E I R S U P P O R T F O R R E C O N C I L I A T I O N T O S E C U R E P E A C E A N D S T A B I L I T Y A N D I R E L A N D I W O U L D T H E R E F O R E U R G E Y O U C O L L E A G U E S T O P L E A S E S U P P O R T T H I S A M E N D M E N T (voxpopuli_eng_000504-voxpopuli_eng_000504) +S T R A T E G I C C 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(voxpopuli_eng_000508-voxpopuli_eng_000508) +A L A R G E P A R T O F T H E R E A S O N I S O F C O U R S E I L L E G A L F I S H I N G M O R E O F T E N T H A N N O T B Y V E S S E L S W H I C H A R E R E G I S T E R E D T O C O U N T R I E S W H I C H L A C K T H E W I L L O R T H E R E S O U R C E S T O E N F O R C E I N T E R N A T I O N A L A G R E E M E N T S N O A M O U N T O F T R A C E A B I L I T Y M E A S U R E S O R E X T R A P A P E R W O R K W I L L A D D R E S S T H E P R O B L E M O F R E D U C I N G (voxpopuli_eng_000509-voxpopuli_eng_000509) +T H E C O M P R O M I S E A L S O I N C L U D E S C L E A R R U L E S T O D E F I N E W H I C H M E M B E R S T A T E H A S J U R I S D I C T I O N A N D T H E C O O P E R A T I O N B E T W E E N M E M B E R S T A T E S C O N C E R N E D I N C R O S S B O R D E R C A S E S A S W E L L A S T H E N E E D T O I N V O L V E E U R O J U S T T H A N K Y O U F O R Y O U R W O R K A N D P L E A S E D O S U P P O R T T H I S D I R E C T I V E (voxpopuli_eng_000510-voxpopuli_eng_000510) +T H E G R E E N S W O U L D H A V E U S B E L I E V E T H A T T H E S E A R E B A D B E E S C R I M I N A L B E E S D E L I B E R A T E L Y C O N T A M I N A T I N G H O N E Y W I T H A D A N G E R O U S I N G R E D I E N T B U T I N F A C T T H E Y A R E D O I N G W H A T H O N E Y B E E S H A V E A L W A Y S D O N E W H I C H I S T O C A R R Y P O L L E N B A C K T O T H E I R H I V E S T O F E E D T H E I R Y O U N G (voxpopuli_eng_000511-voxpopuli_eng_000511) +B U T I T W A S T H E C O U N T R Y I T S E L F B E I N G M O R E C A P A B L E (voxpopuli_eng_000512-voxpopuli_eng_000512) +I N T O T H E P O R T F O L I O O F T H E N E W C O M M I S S I O N E R D E A L I N G W I T H F U N D A M E N T A L R I G H T S (voxpopuli_eng_000513-voxpopuli_eng_000513) +T H E M E S S A G E I S T H A T T H E E U D O E S N O T H A V E A N Y N E W S O L U T I O N S (voxpopuli_eng_000514-voxpopuli_eng_000514) +A R E Y O U W I L L I N G T O A C T I N F A V O U R O F T H E S O C I A L D I M E N S I O N T O B E I N C L U D E D I N T H E E U C O M P E T E N C I E S A S P R O P O S E D (voxpopuli_eng_000515-voxpopuli_eng_000515) +T H E N E X T S T E P O N S P E C T R U M P O L I C Y I S B E I N G T A K E N W I T H T H E R E F O R M O F O U R T E L E C O M F R A M E W O R K (voxpopuli_eng_000516-voxpopuli_eng_000516) +I B E L I E V E H I S R E M A R K S W E R E E X P L I C I T L Y R A C I S T A N D X E N O P H O B I C A N D P R O M O T E D R A C I A L I N T O L E R A N C E I N A W A Y T H A T I S N O T A C C E P T A B L E O R A L L O W E D I N T H E C O N S T I T U T I O N O F T H I S H O U S E (voxpopuli_eng_000517-voxpopuli_eng_000517) +R E A L L I F E E X A M P L E S S H O W T H A T S O L V I N G I S S U E S R E L A T E D T O E D U C A T I O N F U E L S S T R O N G C O M M U N I T Y D E V E L O P M E N T (voxpopuli_eng_000518-voxpopuli_eng_000518) +S O I H O P E T H I S W I L L H A P P E N F O R R U S S I A A S W E L L A N D T H A T R U S S I A C A N A L S O E N V I S A G E A N E X T R E M E S U C C E S S S T O R Y A F T E R T H E S I G N I F I C A N T D A T E I N A U G U S T T H I S Y E A R (voxpopuli_eng_000519-voxpopuli_eng_000519) +S H E A C C E P T E D T H E F A C T T H A T C I T I Z E N S H I P I S S U B J E C T T O N A T I O N A L J U R I S D I C T I O N B U T S H E A L S O S A I D T H A T A C C O R D I N G T O T H E M A A S T R I C H T T R E A T Y A N D S H E I S R I G H T T H E R E H A S T O B E A D I R E C T L I N K (voxpopuli_eng_000520-voxpopuli_eng_000520) +T H E E U F A I L E D E S P E C I A L L Y I N D E M O N S T R A T I N G A U N I F I E D A N D E F F I C I E N T A P P R O A C H T O C L I M A T E C H A N G E T R E A T M E N T A S W E L L A S I N S T R E N G T H E N I N G I T S L E A D I N G P O L I T I C A L P O S I T I O N I N T H I S A G E N D A I C O N S I D E R T H E R E F O R E T A K I N G T H I S R E S O L U T I O N A N A C T O F U T M O S T I M P O R T A N C E (voxpopuli_eng_000521-voxpopuli_eng_000521) +T H E U N I T E D S T A T E S O F E U R O P E W I L L B E A F A C T W I T H S W E D E N A S A P R O V I N C E (voxpopuli_eng_000522-voxpopuli_eng_000522) +I T M U S T B E T H E C A P I T A L O F B O T H S T A T E S A N D W E M U S T R E C O G N I S E P A L E S T I N E A S A S T A T E A S P R O V I D E D F O R I N T H E O S L O A G R E E M E N T S (voxpopuli_eng_000523-voxpopuli_eng_000523) +U K R A I N E I S F A C E D W I T H O N E O F T H E C R U C I A L C H A L L E N G E S I N I T S H I S T O R Y I T W O U L D B E F U N D A M E N T A L L Y W R O N G T O P R E S S T H E N A T I O N N O W W I T H A L L T Y P E S O F R E S T R I C T I O N S P O P U L A R L Y C A L L E D A U S T E R I T Y P O L I C Y (voxpopuli_eng_000524-voxpopuli_eng_000524) +M O R E R U L E S A N D R E G U L A T I O N W I L L N O T I M P R O V E T H E S I T U A T I O N (voxpopuli_eng_000525-voxpopuli_eng_000525) +A T L E A S T W E W O U L D L I K E T O K N O W T H E S O U R C E O F T H E M O N E Y A N D T H E P O S S I B L E M O T I V E S (voxpopuli_eng_000526-voxpopuli_eng_000526) +T O H A V E T H O S E E U R O P E A N W O R L D L A N G U A G E S I N T O D A Y S G L O B A L I S E D W O R L D I N T O D A Y S G L O B A L I S E D E C O N O M Y I N T H I S G L O B A L V I L L A G E W H I C H I S C U L T U R A L E C O N O M I C S O C I A L A N D P O L I T I C A L I S A M O S T V A L U A B L E A S S E T F O R T H E E N T I R E E U W H I C H W E M U S T T A K E F U L L A C C O U N T O F A N D (voxpopuli_eng_000527-voxpopuli_eng_000527) +W E H A V E T O R E P E A T T H A T O D A C A N N O T B E U S E D T O F I N A N C E S E C U R I T Y E X P E N S E S B O R D E R C O N T R O L O R M I L I T A R Y S U P P O R T (voxpopuli_eng_000528-voxpopuli_eng_000528) +I F A N Y T H I N G T H E S C I E N T I F I C R E P O R T S A R E B E C O M I N G M O R E U R G E N T M O R E A L A R M I N G A N D M O R E S H O C K I N G (voxpopuli_eng_000529-voxpopuli_eng_000529) +F I N A L L Y W H E N I T C O M E S T O I N N O V A T I V E F I N A N C I A L I N S T R U M E N T S W E N E E D T H E M B O T H F O R O U R S E L V E S T O S U P P O R T O U R E C O N O M I E S B U T A L S O T O S U P P O R T T H O S E W H O A R E I N N E E D (voxpopuli_eng_000530-voxpopuli_eng_000530) +T H A T G I V E S U S A U N I Q U E T O O L I N P E A C E M A K I N G (voxpopuli_eng_000531-voxpopuli_eng_000531) +P A P E R A V E R Y W E A K P R O P O S A L (voxpopuli_eng_000532-voxpopuli_eng_000532) +R U S S I A H A S A L W A Y S B E E N A V E R Y P R O U D N A T I O N W I T H A R I C H C U L T U R E W I T H I N V E N T I O N S A N D E S P R I T (voxpopuli_eng_000533-voxpopuli_eng_000533) +F A I R T A X A T I O N E V E N A M O D I C U M O F T A X A T I O N I N S O M E C A S E S M I G H T J U S T H E L P U S T O D O W H A T I H A V E A L R E A D Y S U G G E S T E D A N D W H O K N O W S M A K E T H E C A S E F O R T H E R E T R O S P E C T I V E B A N K R E C A P I T A L I S A T I O N T H A T W E N E V E R S A W (voxpopuli_eng_000534-voxpopuli_eng_000534) +T H E E U R O P E A N A S Y L U M S U P P O R T O F F I C E M O R E O V E R H A S A M O N G I T S T A S K S T O P R O M O T E F A C I L I T A T E A N D C O O R D I N A T E E X C H A N G E S O F I N F O R M A T I O N A N D O T H E R A C T I V I T I E S R E L A T E D T O R E L O C A T I O N W I T H I N T H E U N I O N (voxpopuli_eng_000535-voxpopuli_eng_000535) +T H E C O N C L U S I O N O F T H E F R A M E W O R K A G R E E M E N T P R O V I D E S A L E G A L L Y B I N D I N G I N S T R U M E N T T O U P G R A D E A N D S T R E N G T H E N E U A U S T R A L I A B I L A T E R A L R E L A T I O N S A N D T O I N C R E A S E C O O P E R A T I O N (voxpopuli_eng_000536-voxpopuli_eng_000536) +T H E R E F O R E W E A R E A S K I N G T H E C O U N C I L A N D T H E C O M M I S S I O N T O P R E S E N T A T R A N S P A R E N T A N D C O M P L E T E A S S E S S M E N T O F T H E I M P A C T O F T H E C R I S I S (voxpopuli_eng_000537-voxpopuli_eng_000537) +I N O T H E R W O R D S T H E O B J E C T I O N I S N O T W H E T H E R M O N E Y I S P A I D O R N O T T H E O B J E C T I O N I S W H E T H E R T H E R E I S A D I R E C T L I N K O R N O T (voxpopuli_eng_000538-voxpopuli_eng_000538) +I T D I S T I N G U I S H E S T H E T W O M A I N D O S S I E R S H U M A N R I G H T S A B U S E S B Y T H E C U R R E N T G O V E R N M E N T A N D T H E I R A N I A N N U C L E A R P R O G R A M M E (voxpopuli_eng_000539-voxpopuli_eng_000539) +M R P R E S I D E N T S E X U A L H A R A S S M E N T I S A F O R M O F V I O L E N C E A N D I T I S T H E M O S T E X T R E M E F O R M O F G E N D E R — B A S E D D I S C R I M I N A T I O N (voxpopuli_eng_000540-voxpopuli_eng_000540) +W E C A N L O O K T O S O M E N O N E U M E M B E R S F O R G O O D E X A M P L E S A S R E G A R D S T E C H N O L O G I E S (voxpopuli_eng_000541-voxpopuli_eng_000541) +I N V O L V E D F O R T H E I R P O S I T I V E A N D C O N S T R U C T I V E A P P R O A C H (voxpopuli_eng_000542-voxpopuli_eng_000542) +S O I H O P E T H A T T H I S W I L L B E C O M P L E T E D I N T H E F O R E S E E A B L E F U T U R E W H I C H M E A N S M A Y B E T W O O R T H R E E M O N T H S (voxpopuli_eng_000543-voxpopuli_eng_000543) +F U R T H E R E N C O U R A G E T H E U N S E F F O R T S T O B R I N G A B O U T P E A C E I N A F G H A N I S T A N A N D T O O V E R C O M E T H E F R A G I L E S E C U R I T Y E N V I R O N M E N T I N T H E C O U N T R Y (voxpopuli_eng_000544-voxpopuli_eng_000544) +W E U N D E R S T A N D T H A T S O M E P E O P L E A R E A N G R Y (voxpopuli_eng_000545-voxpopuli_eng_000545) +W E W A N T T O B E M O R E R E S P O N S I B L E (voxpopuli_eng_000546-voxpopuli_eng_000546) +W E M U S T R E C T I F Y T H I S S I T U A T I O N A N D W E A S K T H E C O M M I S S I O N T O C O N S I D E R T H E M O S T A D E Q U A T E C O M P E N S A T I O N M E A S U R E S F O R O U R P A S S E N G E R S (voxpopuli_eng_000547-voxpopuli_eng_000547) +T H E C O M M I S S I O N I N V I T E S P A R L I A M E N T I N T H E U P C O M I N G R E V I S I O N T O O P E N I T S P O S I T I O N O N T H I S M A T T E R W H I C H R E A L L Y C O N C E R N S A C C E S S T O J U S T I C E I N E U R O P E A N D T H E E N F O R C E M E N T O F R I G H T S G R A N T E D B Y E U R O P E A N U N I O N L A W (voxpopuli_eng_000548-voxpopuli_eng_000548) +I W E L C O M E V E R Y M U C H T H E R E S U M P T I O N O F T A L K S B E T W E E N T H E I S R A E L I S A N D T H E P A L E S T I N I A N S A N D S I N C E R E L Y H O P E T H A T T H E Y W I L L S U C C E E D (voxpopuli_eng_000549-voxpopuli_eng_000549) +W E H A V E A N A C C U M U L A T I O N O F P R O B L E M S R E S U L T I N G F R O M A R T I F I C I A L U N D E R B U D G E T I N G I N P R E V I O U S Y E A R S (voxpopuli_eng_000550-voxpopuli_eng_000550) +L E T U S N O T B E T H E M A N O F Y E S T E R D A Y L E T U S B E T O D A Y S I N S T I T U T I O N (voxpopuli_eng_000551-voxpopuli_eng_000551) +I W O U L D U R G E Y O U T O B E C O M E A M B A S S A D O R S O F T H E Y E A R B Y M A K I N G I T S I D E A S A N D A C T I V I T I E S W I D E L Y K N O W N A M O N G S T E U R O P E A N C I T I Z E N S A N D P A R T I C I P A T I N G I N E V E N T S B E I T A T E U R O P E A N N A T I O N A L O R L O C A L L E V E L (voxpopuli_eng_000552-voxpopuli_eng_000552) +C E R T A I N L Y S U C H I M P A C T A S S E S S M E N T C O U L D P R E E M P T C E R T A I N P R O B L E M S S U C H A S T H O S E P O S E D B Y T H E E L E C T R O N I C I D E N T I F I C A T I O N O F S H E E P I N S C O T L A N D (voxpopuli_eng_000553-voxpopuli_eng_000553) +T H E C O U R T I S C O N T E N T T O S E E T H A T I T S W O R K H A S I N F O R M E D T H E D I S C H A R G E P R O C E S S A N D H A S C O N T R I B U T E D T O P R O P O S A L S F O R I M P R O V I N G T H E F I N A N C I A L M A N A G E M E N T O F E U S P E N D I N G A N D B E T T E R T A R G E T I N G O F E U F U N D S (voxpopuli_eng_000554-voxpopuli_eng_000554) +R E G U L A T O R Y C L A R I T Y A N D C E R T A I N T Y I S N E E D E D F O R T H E P U B L I C S E C T O R A N D F O R I N D U S T R Y (voxpopuli_eng_000555-voxpopuli_eng_000555) +I S I T R E A L L Y N O T P O S S I B L E T O U S E O T H E R H O U S I N G F A C I L I T I E S W I T H A P P R O P R I A T E R E C E P T I O N C O N D I T I O N S I N T H E M E A N T I M E (voxpopuli_eng_000556-voxpopuli_eng_000556) +W I L L Y O U T A K E A C T I O N A T L A S T I F N O T T H E N W H E N (voxpopuli_eng_000557-voxpopuli_eng_000557) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/result.txt b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..6dacd3c9572c7480e66327aa4471a380b1f5f017 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/result.txt @@ -0,0 +1,12693 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,--------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/hyp.trn| +|--------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000254 | 1 100 | 77.0 8.0 15.0 0.0 23.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000255 | 1 60 | 75.0 10.0 15.0 3.3 28.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000256 | 1 41 | 70.7 14.6 14.6 9.8 39.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000257 | 1 53 | 81.1 7.5 11.3 1.9 20.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000258 | 1 51 | 86.3 3.9 9.8 0.0 13.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000259 | 1 92 | 79.3 7.6 13.0 4.3 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000260 | 1 79 | 78.5 7.6 13.9 3.8 25.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000261 | 1 80 | 81.3 8.8 10.0 7.5 26.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000262 | 1 44 | 84.1 11.4 4.5 6.8 22.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000263 | 1 54 | 85.2 7.4 7.4 5.6 20.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000264 | 1 107 | 82.2 2.8 15.0 2.8 20.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000265 | 1 41 | 90.2 4.9 4.9 2.4 12.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000266 | 1 25 | 84.0 12.0 4.0 8.0 24.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000267 | 1 26 | 84.6 11.5 3.8 15.4 30.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000268 | 1 43 | 90.7 0.0 9.3 2.3 11.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000269 | 1 53 | 77.4 7.5 15.1 1.9 24.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000270 | 1 66 | 84.8 4.5 10.6 4.5 19.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000271 | 1 93 | 82.8 5.4 11.8 1.1 18.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000272 | 1 91 | 90.1 5.5 4.4 8.8 18.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000273 | 1 114 | 84.2 7.9 7.9 5.3 21.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000274 | 1 26 | 88.5 7.7 3.8 0.0 11.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000275 | 1 82 | 81.7 4.9 13.4 4.9 23.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000276 | 1 94 | 89.4 5.3 5.3 4.3 14.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000277 | 1 55 | 83.6 10.9 5.5 3.6 20.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000278 | 1 48 | 85.4 4.2 10.4 4.2 18.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000279 | 1 21 | 85.7 9.5 4.8 4.8 19.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000280 | 1 52 | 90.4 3.8 5.8 1.9 11.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000281 | 1 44 | 84.1 11.4 4.5 13.6 29.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000282 | 1 30 | 90.0 3.3 6.7 3.3 13.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000283 | 1 94 | 85.1 7.4 7.4 5.3 20.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000284 | 1 111 | 80.2 1.8 18.0 0.9 20.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000285 | 1 104 | 80.8 10.6 8.7 3.8 23.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000286 | 1 60 | 88.3 3.3 8.3 10.0 21.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000287 | 1 44 | 88.6 0.0 11.4 0.0 11.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000288 | 1 46 | 69.6 15.2 15.2 8.7 39.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000289 | 1 48 | 85.4 2.1 12.5 10.4 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000290 | 1 51 | 88.2 3.9 7.8 7.8 19.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000291 | 1 56 | 76.8 10.7 12.5 1.8 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000292 | 1 50 | 76.0 4.0 20.0 2.0 26.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000293 | 1 85 | 75.3 8.2 16.5 1.2 25.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000294 | 1 60 | 70.0 5.0 25.0 0.0 30.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000295 | 1 52 | 80.8 5.8 13.5 5.8 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000296 | 1 91 | 79.1 5.5 15.4 5.5 26.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000297 | 1 72 | 80.6 12.5 6.9 6.9 26.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000298 | 1 60 | 88.3 5.0 6.7 1.7 13.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000299 | 1 79 | 82.3 8.9 8.9 5.1 22.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000300 | 1 78 | 83.3 5.1 11.5 5.1 21.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000301 | 1 74 | 79.7 2.7 17.6 2.7 23.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000302 | 1 58 | 84.5 5.2 10.3 8.6 24.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000303 | 1 60 | 80.0 15.0 5.0 3.3 23.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000304 | 1 50 | 84.0 4.0 12.0 0.0 16.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000305 | 1 77 | 81.8 2.6 15.6 1.3 19.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000306 | 1 74 | 85.1 9.5 5.4 5.4 20.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000307 | 1 91 | 85.7 5.5 8.8 8.8 23.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000308 | 1 69 | 75.4 8.7 15.9 2.9 27.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000309 | 1 34 | 82.4 2.9 14.7 8.8 26.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000310 | 1 26 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000311 | 1 107 | 78.5 7.5 14.0 0.9 22.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000312 | 1 81 | 82.7 6.2 11.1 4.9 22.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000313 | 1 63 | 87.3 6.3 6.3 0.0 12.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000314 | 1 41 | 73.2 14.6 12.2 2.4 29.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000315 | 1 55 | 72.7 10.9 16.4 7.3 34.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000316 | 1 100 | 83.0 10.0 7.0 2.0 19.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000317 | 1 70 | 81.4 4.3 14.3 8.6 27.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000318 | 1 107 | 79.4 7.5 13.1 1.9 22.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000319 | 1 74 | 82.4 6.8 10.8 0.0 17.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000320 | 1 94 | 85.1 3.2 11.7 2.1 17.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000321 | 1 31 | 80.6 6.5 12.9 3.2 22.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000322 | 1 59 | 71.2 13.6 15.3 5.1 33.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000323 | 1 52 | 80.8 11.5 7.7 17.3 36.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000324 | 1 75 | 84.0 10.7 5.3 12.0 28.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000325 | 1 34 | 79.4 2.9 17.6 0.0 20.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000326 | 1 43 | 72.1 11.6 16.3 0.0 27.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000327 | 1 76 | 81.6 6.6 11.8 3.9 22.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000328 | 1 26 | 80.8 7.7 11.5 3.8 23.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000329 | 1 101 | 73.3 5.9 20.8 0.0 26.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000330 | 1 50 | 86.0 8.0 6.0 2.0 16.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000331 | 1 31 | 87.1 9.7 3.2 0.0 12.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000332 | 1 98 | 80.6 6.1 13.3 4.1 23.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000333 | 1 45 | 75.6 15.6 8.9 4.4 28.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000334 | 1 107 | 80.4 3.7 15.9 4.7 24.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000335 | 1 75 | 84.0 4.0 12.0 4.0 20.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000336 | 1 92 | 73.9 10.9 15.2 2.2 28.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000337 | 1 100 | 87.0 6.0 7.0 11.0 24.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000338 | 1 29 | 82.8 3.4 13.8 6.9 24.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000339 | 1 54 | 87.0 1.9 11.1 7.4 20.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000340 | 1 70 | 81.4 10.0 8.6 1.4 20.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000341 | 1 67 | 82.1 6.0 11.9 4.5 22.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000342 | 1 82 | 82.9 2.4 14.6 4.9 22.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000343 | 1 111 | 86.5 6.3 7.2 2.7 16.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000344 | 1 102 | 81.4 2.9 15.7 2.0 20.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000345 | 1 73 | 79.5 11.0 9.6 6.8 27.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000346 | 1 91 | 75.8 8.8 15.4 3.3 27.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000347 | 1 76 | 80.3 3.9 15.8 0.0 19.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000348 | 1 82 | 81.7 6.1 12.2 4.9 23.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000349 | 1 89 | 82.0 5.6 12.4 7.9 25.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000350 | 1 80 | 75.0 6.3 18.8 3.8 28.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000351 | 1 105 | 81.0 7.6 11.4 2.9 21.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000352 | 1 38 | 78.9 13.2 7.9 5.3 26.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000353 | 1 61 | 80.3 3.3 16.4 11.5 31.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000354 | 1 67 | 82.1 7.5 10.4 3.0 20.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000355 | 1 76 | 80.3 7.9 11.8 6.6 26.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000356 | 1 36 | 86.1 2.8 11.1 11.1 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000357 | 1 79 | 78.5 1.3 20.3 3.8 25.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000358 | 1 49 | 77.6 8.2 14.3 6.1 28.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000359 | 1 48 | 72.9 12.5 14.6 0.0 27.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000360 | 1 47 | 78.7 2.1 19.1 0.0 21.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000361 | 1 48 | 85.4 4.2 10.4 6.3 20.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000362 | 1 99 | 79.8 7.1 13.1 1.0 21.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000363 | 1 72 | 76.4 11.1 12.5 5.6 29.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000364 | 1 63 | 90.5 3.2 6.3 6.3 15.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000365 | 1 25 | 96.0 0.0 4.0 8.0 12.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000366 | 1 53 | 79.2 17.0 3.8 5.7 26.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000367 | 1 58 | 74.1 10.3 15.5 6.9 32.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000368 | 1 47 | 76.6 8.5 14.9 8.5 31.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000369 | 1 92 | 81.5 3.3 15.2 5.4 23.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000370 | 1 76 | 84.2 6.6 9.2 2.6 18.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000371 | 1 58 | 82.8 3.4 13.8 3.4 20.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000372 | 1 64 | 79.7 9.4 10.9 6.3 26.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000373 | 1 92 | 80.4 7.6 12.0 3.3 22.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000374 | 1 26 | 96.2 0.0 3.8 3.8 7.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000375 | 1 71 | 81.7 5.6 12.7 8.5 26.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000376 | 1 90 | 84.4 3.3 12.2 3.3 18.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| m | 77 8404 | 79.3 5.6 15.1 4.4 25.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000707 | 1 45 | 53.3 35.6 11.1 35.6 82.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000708 | 1 57 | 78.9 5.3 15.8 5.3 26.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000709 | 1 56 | 78.6 10.7 10.7 16.1 37.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000710 | 1 77 | 66.2 16.9 16.9 5.2 39.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000711 | 1 91 | 69.2 19.8 11.0 7.7 38.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000712 | 1 99 | 75.8 10.1 14.1 6.1 30.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000713 | 1 63 | 68.3 12.7 19.0 6.3 38.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000714 | 1 89 | 55.1 29.2 15.7 7.9 52.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000715 | 1 13 | 84.6 15.4 0.0 46.2 61.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000716 | 1 54 | 66.7 18.5 14.8 11.1 44.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000717 | 1 75 | 69.3 21.3 9.3 8.0 38.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000718 | 1 35 | 80.0 5.7 14.3 14.3 34.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000719 | 1 33 | 75.8 24.2 0.0 12.1 36.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000720 | 1 43 | 83.7 7.0 9.3 27.9 44.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000721 | 1 34 | 85.3 8.8 5.9 17.6 32.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000722 | 1 35 | 62.9 22.9 14.3 11.4 48.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000723 | 1 68 | 69.1 19.1 11.8 7.4 38.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000724 | 1 30 | 76.7 16.7 6.7 43.3 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000725 | 1 88 | 68.2 20.5 11.4 10.2 42.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000726 | 1 64 | 84.4 7.8 7.8 9.4 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000727 | 1 20 | 45.0 35.0 20.0 5.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000728 | 1 46 | 82.6 8.7 8.7 26.1 43.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000729 | 1 75 | 60.0 13.3 26.7 0.0 40.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000730 | 1 85 | 56.5 30.6 12.9 16.5 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000731 | 1 26 | 50.0 42.3 7.7 19.2 69.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000732 | 1 77 | 75.3 7.8 16.9 1.3 26.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000733 | 1 58 | 67.2 24.1 8.6 13.8 46.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000734 | 1 78 | 78.2 7.7 14.1 10.3 32.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000735 | 1 64 | 73.4 17.2 9.4 12.5 39.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000736 | 1 54 | 79.6 3.7 16.7 1.9 22.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000737 | 1 5 | 20.0 80.0 0.0 360.0 440.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000738 | 1 85 | 72.9 15.3 11.8 5.9 32.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000739 | 1 44 | 72.7 6.8 20.5 9.1 36.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000740 | 1 45 | 84.4 8.9 6.7 31.1 46.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000741 | 1 28 | 85.7 10.7 3.6 7.1 21.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000742 | 1 68 | 57.4 26.5 16.2 17.6 60.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000743 | 1 72 | 80.6 9.7 9.7 5.6 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000744 | 1 38 | 86.8 7.9 5.3 36.8 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000745 | 1 68 | 48.5 41.2 10.3 22.1 73.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000746 | 1 50 | 80.0 16.0 4.0 14.0 34.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000747 | 1 34 | 70.6 17.6 11.8 2.9 32.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000748 | 1 2 | 100.0 0.0 0.0 650.0 650.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000749 | 1 5 | 80.0 20.0 0.0 320.0 340.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000750 | 1 83 | 65.1 15.7 19.3 4.8 39.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000751 | 1 67 | 80.6 16.4 3.0 13.4 32.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000752 | 1 77 | 68.8 20.8 10.4 7.8 39.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000753 | 1 57 | 68.4 24.6 7.0 50.9 82.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000754 | 1 51 | 76.5 15.7 7.8 80.4 103.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000755 | 1 28 | 64.3 21.4 14.3 35.7 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000756 | 1 51 | 68.6 17.6 13.7 17.6 49.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000757 | 1 74 | 56.8 20.3 23.0 4.1 47.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000758 | 1 25 | 80.0 16.0 4.0 28.0 48.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000759 | 1 29 | 75.9 10.3 13.8 27.6 51.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000760 | 1 57 | 50.9 35.1 14.0 17.5 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000761 | 1 29 | 69.0 17.2 13.8 24.1 55.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000762 | 1 74 | 79.7 8.1 12.2 4.1 24.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000763 | 1 43 | 76.7 7.0 16.3 7.0 30.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000764 | 1 39 | 69.2 12.8 17.9 5.1 35.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000765 | 1 27 | 66.7 25.9 7.4 14.8 48.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000766 | 1 43 | 83.7 9.3 7.0 14.0 30.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000767 | 1 59 | 76.3 13.6 10.2 11.9 35.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000768 | 1 27 | 77.8 14.8 7.4 63.0 85.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000769 | 1 18 | 66.7 11.1 22.2 77.8 111.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000770 | 1 83 | 72.3 16.9 10.8 2.4 30.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000771 | 1 59 | 57.6 32.2 10.2 18.6 61.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000772 | 1 36 | 47.2 44.4 8.3 50.0 102.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000773 | 1 72 | 72.2 6.9 20.8 6.9 34.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000774 | 1 67 | 79.1 11.9 9.0 20.9 41.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000775 | 1 23 | 47.8 34.8 17.4 30.4 82.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000776 | 1 70 | 71.4 10.0 18.6 4.3 32.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000777 | 1 80 | 68.8 17.5 13.8 11.3 42.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000778 | 1 82 | 69.5 11.0 19.5 3.7 34.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000779 | 1 93 | 63.4 22.6 14.0 21.5 58.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000780 | 1 58 | 77.6 10.3 12.1 13.8 36.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000781 | 1 75 | 65.3 17.3 17.3 10.7 45.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000782 | 1 3 | 100.0 0.0 0.0 700.0 700.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000783 | 1 35 | 80.0 11.4 8.6 8.6 28.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000784 | 1 54 | 57.4 25.9 16.7 35.2 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000785 | 1 40 | 75.0 12.5 12.5 22.5 47.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000786 | 1 59 | 88.1 5.1 6.8 10.2 22.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000787 | 1 94 | 83.0 8.5 8.5 20.2 37.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000788 | 1 27 | 77.8 18.5 3.7 77.8 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000789 | 1 27 | 59.3 22.2 18.5 33.3 74.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000790 | 1 27 | 81.5 7.4 11.1 0.0 18.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000791 | 1 40 | 70.0 25.0 5.0 12.5 42.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000792 | 1 65 | 63.1 24.6 12.3 13.8 50.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000793 | 1 34 | 47.1 23.5 29.4 14.7 67.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000794 | 1 28 | 57.1 39.3 3.6 14.3 57.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000795 | 1 31 | 74.2 12.9 12.9 9.7 35.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000796 | 1 33 | 45.5 27.3 27.3 3.0 57.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000797 | 1 43 | 83.7 7.0 9.3 4.7 20.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000798 | 1 55 | 54.5 23.6 21.8 5.5 50.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000799 | 1 44 | 75.0 13.6 11.4 2.3 27.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000800 | 1 21 | 61.9 33.3 4.8 152.4 190.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000801 | 1 24 | 62.5 29.2 8.3 37.5 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000802 | 1 46 | 78.3 13.0 8.7 28.3 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000803 | 1 68 | 64.7 11.8 23.5 7.4 42.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000804 | 1 40 | 82.5 10.0 7.5 2.5 20.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000805 | 1 83 | 75.9 12.0 12.0 10.8 34.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000806 | 1 27 | 63.0 37.0 0.0 11.1 48.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000807 | 1 96 | 60.4 28.1 11.5 10.4 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000808 | 1 83 | 80.7 14.5 4.8 2.4 21.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000809 | 1 42 | 69.0 21.4 9.5 14.3 45.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000413 | 1 195 | 79.0 8.7 12.3 8.2 29.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000414 | 1 201 | 76.6 8.0 15.4 6.5 29.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000415 | 1 93 | 75.3 9.7 15.1 20.4 45.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000416 | 1 116 | 71.6 8.6 19.8 3.4 31.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000417 | 1 138 | 74.6 6.5 18.8 0.7 26.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000418 | 1 78 | 70.5 11.5 17.9 3.8 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000419 | 1 53 | 84.9 7.5 7.5 17.0 32.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000420 | 1 65 | 60.0 16.9 23.1 3.1 43.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000421 | 1 148 | 80.4 12.2 7.4 6.8 26.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000422 | 1 119 | 74.8 12.6 12.6 5.0 30.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000423 | 1 114 | 66.7 8.8 24.6 7.9 41.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000424 | 1 85 | 76.5 12.9 10.6 7.1 30.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000425 | 1 111 | 73.9 5.4 20.7 2.7 28.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000426 | 1 106 | 82.1 8.5 9.4 21.7 39.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000427 | 1 173 | 69.9 6.9 23.1 1.2 31.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000428 | 1 120 | 65.8 10.8 23.3 0.8 35.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000429 | 1 127 | 82.7 6.3 11.0 6.3 23.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000430 | 1 101 | 81.2 10.9 7.9 9.9 28.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000431 | 1 103 | 66.0 10.7 23.3 7.8 41.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000432 | 1 106 | 78.3 4.7 17.0 0.9 22.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000433 | 1 84 | 71.4 13.1 15.5 7.1 35.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000434 | 1 70 | 67.1 14.3 18.6 4.3 37.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000435 | 1 119 | 73.1 8.4 18.5 4.2 31.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000436 | 1 135 | 67.4 16.3 16.3 3.0 35.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000437 | 1 121 | 71.9 13.2 14.9 15.7 43.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000438 | 1 163 | 79.8 9.2 11.0 4.3 24.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000439 | 1 64 | 87.5 4.7 7.8 7.8 20.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000440 | 1 163 | 76.7 6.7 16.6 1.8 25.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000441 | 1 67 | 77.6 7.5 14.9 6.0 28.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000442 | 1 133 | 54.1 15.0 30.8 9.8 55.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000443 | 1 206 | 69.9 13.1 17.0 4.4 34.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000444 | 1 111 | 65.8 12.6 21.6 2.7 36.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000445 | 1 55 | 65.5 14.5 20.0 30.9 65.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000446 | 1 79 | 63.3 19.0 17.7 5.1 41.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000447 | 1 116 | 73.3 12.1 14.7 3.4 30.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000448 | 1 174 | 77.0 10.3 12.6 5.7 28.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000449 | 1 147 | 82.3 10.2 7.5 19.7 37.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000450 | 1 206 | 66.5 13.6 19.9 3.9 37.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000451 | 1 119 | 60.5 19.3 20.2 2.5 42.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000452 | 1 190 | 65.8 11.1 23.2 4.2 38.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000453 | 1 137 | 62.8 16.1 21.2 1.5 38.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000454 | 1 143 | 67.1 16.8 16.1 9.1 42.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000455 | 1 89 | 83.1 9.0 7.9 15.7 32.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000456 | 1 53 | 83.0 9.4 7.5 11.3 28.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000457 | 1 121 | 66.9 12.4 20.7 6.6 39.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000458 | 1 85 | 74.1 15.3 10.6 17.6 43.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000459 | 1 227 | 75.3 13.2 11.5 3.1 27.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000460 | 1 115 | 60.0 19.1 20.9 3.5 43.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000461 | 1 98 | 75.5 13.3 11.2 4.1 28.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000462 | 1 105 | 83.8 11.4 4.8 7.6 23.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000463 | 1 110 | 65.5 10.9 23.6 3.6 38.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000464 | 1 91 | 70.3 6.6 23.1 8.8 38.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000465 | 1 143 | 80.4 13.3 6.3 9.1 28.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000466 | 1 155 | 64.5 15.5 20.0 8.4 43.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000467 | 1 106 | 68.9 16.0 15.1 6.6 37.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000468 | 1 250 | 65.6 12.4 22.0 4.4 38.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000469 | 1 180 | 73.3 15.0 11.7 30.0 56.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000470 | 1 211 | 72.0 10.4 17.5 2.8 30.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000471 | 1 141 | 76.6 9.2 14.2 3.5 27.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000472 | 1 191 | 60.7 18.3 20.9 11.0 50.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000473 | 1 154 | 74.7 11.0 14.3 2.6 27.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000474 | 1 148 | 81.1 13.5 5.4 15.5 34.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000475 | 1 165 | 77.0 9.1 13.9 3.6 26.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000476 | 1 139 | 84.2 3.6 12.2 5.8 21.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000283 | 1 198 | 76.3 11.1 12.6 6.6 30.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000284 | 1 173 | 82.1 11.0 6.9 14.5 32.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000285 | 1 215 | 63.7 11.2 25.1 3.3 39.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000286 | 1 236 | 83.9 5.5 10.6 3.4 19.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000287 | 1 279 | 66.3 9.7 24.0 2.2 35.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000288 | 1 140 | 82.9 6.4 10.7 4.3 21.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000289 | 1 200 | 83.0 6.0 11.0 10.0 27.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000290 | 1 193 | 72.5 6.7 20.7 1.6 29.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000291 | 1 171 | 84.8 7.0 8.2 3.5 18.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000292 | 1 222 | 76.1 8.6 15.3 3.6 27.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000293 | 1 121 | 77.7 6.6 15.7 5.0 27.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000294 | 1 232 | 71.1 9.9 19.0 6.5 35.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000295 | 1 139 | 74.8 10.8 14.4 7.9 33.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000296 | 1 166 | 75.9 9.0 15.1 5.4 29.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000297 | 1 236 | 71.6 11.9 16.5 8.1 36.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000298 | 1 152 | 84.9 6.6 8.6 7.9 23.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000299 | 1 289 | 74.4 6.9 18.7 3.5 29.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000300 | 1 272 | 74.3 9.9 15.8 1.8 27.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000301 | 1 219 | 83.1 10.0 6.8 4.1 21.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000302 | 1 138 | 71.0 13.0 15.9 9.4 38.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000303 | 1 230 | 81.3 9.1 9.6 7.0 25.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000304 | 1 248 | 80.2 6.5 13.3 5.6 25.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000305 | 1 241 | 83.4 4.6 12.0 7.1 23.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000306 | 1 178 | 73.0 10.7 16.3 2.8 29.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000307 | 1 233 | 78.5 9.4 12.0 3.4 24.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000308 | 1 138 | 85.5 5.8 8.7 8.7 23.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000309 | 1 223 | 85.2 6.7 8.1 5.4 20.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000310 | 1 184 | 73.9 6.5 19.6 2.2 28.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000311 | 1 200 | 82.5 6.0 11.5 5.5 23.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000312 | 1 239 | 71.1 10.5 18.4 4.6 33.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000313 | 1 203 | 78.8 11.3 9.9 6.9 28.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000314 | 1 160 | 83.8 7.5 8.8 8.1 24.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000315 | 1 141 | 78.0 10.6 11.3 4.3 26.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000316 | 1 219 | 79.5 6.4 14.2 2.3 22.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000317 | 1 233 | 78.1 10.3 11.6 9.0 30.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000318 | 1 183 | 77.0 8.2 14.8 6.0 29.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000319 | 1 205 | 81.0 8.8 10.2 7.3 26.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000320 | 1 194 | 83.0 6.7 10.3 5.7 22.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000321 | 1 229 | 83.8 10.5 5.7 3.1 19.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000322 | 1 207 | 77.3 7.7 15.0 3.4 26.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001588 | 1 21 | 71.4 23.8 4.8 14.3 42.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001589 | 1 21 | 61.9 23.8 14.3 4.8 42.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001590 | 1 28 | 71.4 17.9 10.7 3.6 32.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001591 | 1 28 | 64.3 25.0 10.7 3.6 39.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001592 | 1 22 | 77.3 4.5 18.2 36.4 59.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001593 | 1 13 | 76.9 15.4 7.7 7.7 30.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001594 | 1 25 | 84.0 12.0 4.0 24.0 40.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001595 | 1 18 | 77.8 22.2 0.0 5.6 27.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001596 | 1 16 | 62.5 31.3 6.3 62.5 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001597 | 1 30 | 83.3 10.0 6.7 6.7 23.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001598 | 1 19 | 73.7 5.3 21.1 31.6 57.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001599 | 1 15 | 80.0 20.0 0.0 33.3 53.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001600 | 1 23 | 91.3 8.7 0.0 13.0 21.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001601 | 1 15 | 86.7 6.7 6.7 13.3 26.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001602 | 1 17 | 70.6 23.5 5.9 29.4 58.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001603 | 1 21 | 90.5 0.0 9.5 14.3 23.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001604 | 1 14 | 57.1 35.7 7.1 21.4 64.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001605 | 1 20 | 60.0 25.0 15.0 15.0 55.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001606 | 1 27 | 63.0 25.9 11.1 11.1 48.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001607 | 1 28 | 85.7 7.1 7.1 7.1 21.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001608 | 1 18 | 72.2 22.2 5.6 16.7 44.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001609 | 1 15 | 33.3 60.0 6.7 6.7 73.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001610 | 1 23 | 69.6 13.0 17.4 4.3 34.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001611 | 1 19 | 84.2 10.5 5.3 47.4 63.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001612 | 1 22 | 77.3 13.6 9.1 13.6 36.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001613 | 1 18 | 77.8 11.1 11.1 0.0 22.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001614 | 1 29 | 65.5 17.2 17.2 34.5 69.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001615 | 1 19 | 68.4 10.5 21.1 5.3 36.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001616 | 1 27 | 63.0 18.5 18.5 7.4 44.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001617 | 1 16 | 81.3 12.5 6.3 12.5 31.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001618 | 1 21 | 76.2 9.5 14.3 9.5 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001619 | 1 25 | 80.0 20.0 0.0 28.0 48.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001620 | 1 23 | 78.3 13.0 8.7 13.0 34.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001621 | 1 24 | 83.3 8.3 8.3 4.2 20.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001622 | 1 22 | 68.2 4.5 27.3 9.1 40.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001623 | 1 15 | 60.0 33.3 6.7 26.7 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001624 | 1 24 | 79.2 16.7 4.2 8.3 29.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001625 | 1 25 | 84.0 8.0 8.0 4.0 20.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001626 | 1 24 | 66.7 20.8 12.5 16.7 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001627 | 1 20 | 65.0 35.0 0.0 0.0 35.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001628 | 1 17 | 64.7 17.6 17.6 23.5 58.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001629 | 1 18 | 83.3 11.1 5.6 11.1 27.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001630 | 1 15 | 66.7 33.3 0.0 33.3 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001631 | 1 24 | 70.8 16.7 12.5 16.7 45.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001632 | 1 18 | 94.4 5.6 0.0 0.0 5.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001633 | 1 3 | 100.0 0.0 0.0 166.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001634 | 1 23 | 87.0 8.7 4.3 30.4 43.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001635 | 1 30 | 86.7 6.7 6.7 13.3 26.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001636 | 1 17 | 76.5 17.6 5.9 0.0 23.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001637 | 1 26 | 96.2 0.0 3.8 23.1 26.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001638 | 1 25 | 72.0 20.0 8.0 20.0 48.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001639 | 1 17 | 70.6 17.6 11.8 17.6 47.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001640 | 1 5 | 40.0 60.0 0.0 100.0 160.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001641 | 1 16 | 56.3 31.3 12.5 25.0 68.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001642 | 1 18 | 50.0 38.9 11.1 16.7 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001643 | 1 14 | 64.3 28.6 7.1 7.1 42.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001644 | 1 30 | 73.3 16.7 10.0 6.7 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001645 | 1 14 | 78.6 21.4 0.0 28.6 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001646 | 1 27 | 70.4 14.8 14.8 7.4 37.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001647 | 1 23 | 65.2 8.7 26.1 4.3 39.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001648 | 1 37 | 73.0 16.2 10.8 5.4 32.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001649 | 1 24 | 79.2 8.3 12.5 16.7 37.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001650 | 1 23 | 69.6 13.0 17.4 8.7 39.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001651 | 1 22 | 68.2 31.8 0.0 13.6 45.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001652 | 1 16 | 68.8 6.3 25.0 0.0 31.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001653 | 1 18 | 66.7 11.1 22.2 0.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001654 | 1 18 | 66.7 16.7 16.7 16.7 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001655 | 1 17 | 82.4 5.9 11.8 23.5 41.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001656 | 1 17 | 76.5 11.8 11.8 5.9 29.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001657 | 1 28 | 60.7 28.6 10.7 7.1 46.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001658 | 1 18 | 83.3 11.1 5.6 16.7 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001659 | 1 19 | 68.4 15.8 15.8 10.5 42.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001660 | 1 18 | 100.0 0.0 0.0 11.1 11.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001661 | 1 18 | 61.1 22.2 16.7 11.1 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001662 | 1 22 | 81.8 9.1 9.1 9.1 27.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001663 | 1 17 | 58.8 23.5 17.6 5.9 47.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001664 | 1 24 | 79.2 16.7 4.2 4.2 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001665 | 1 30 | 80.0 6.7 13.3 10.0 30.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001666 | 1 21 | 61.9 28.6 9.5 23.8 61.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001667 | 1 24 | 70.8 20.8 8.3 25.0 54.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001668 | 1 21 | 76.2 14.3 9.5 14.3 38.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001669 | 1 17 | 94.1 0.0 5.9 23.5 29.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001670 | 1 24 | 70.8 25.0 4.2 4.2 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001671 | 1 24 | 70.8 12.5 16.7 0.0 29.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001672 | 1 27 | 81.5 3.7 14.8 3.7 22.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001673 | 1 8 | 100.0 0.0 0.0 237.5 237.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001674 | 1 36 | 58.3 22.2 19.4 5.6 47.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001675 | 1 4 | 75.0 0.0 25.0 75.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001676 | 1 6 | 50.0 50.0 0.0 16.7 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001677 | 1 23 | 78.3 8.7 13.0 17.4 39.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001678 | 1 25 | 64.0 28.0 8.0 4.0 40.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001679 | 1 21 | 81.0 14.3 4.8 9.5 28.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001680 | 1 19 | 68.4 10.5 21.1 10.5 42.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001681 | 1 28 | 82.1 14.3 3.6 21.4 39.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001682 | 1 16 | 68.8 12.5 18.8 0.0 31.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001683 | 1 25 | 80.0 0.0 20.0 8.0 28.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001684 | 1 19 | 68.4 15.8 15.8 10.5 42.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001685 | 1 22 | 86.4 0.0 13.6 13.6 27.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001686 | 1 23 | 73.9 26.1 0.0 4.3 30.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001687 | 1 12 | 83.3 16.7 0.0 16.7 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001688 | 1 21 | 71.4 23.8 4.8 28.6 57.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001689 | 1 28 | 60.7 25.0 14.3 3.6 42.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001690 | 1 19 | 73.7 26.3 0.0 31.6 57.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001691 | 1 4 | 100.0 0.0 0.0 125.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001692 | 1 22 | 72.7 13.6 13.6 13.6 40.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001693 | 1 23 | 47.8 26.1 26.1 8.7 60.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001694 | 1 22 | 90.9 4.5 4.5 0.0 9.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001695 | 1 8 | 87.5 0.0 12.5 37.5 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001696 | 1 12 | 83.3 16.7 0.0 33.3 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001697 | 1 17 | 70.6 29.4 0.0 0.0 29.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001698 | 1 13 | 100.0 0.0 0.0 30.8 30.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001699 | 1 26 | 84.6 11.5 3.8 0.0 15.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001700 | 1 35 | 71.4 14.3 14.3 5.7 34.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001701 | 1 16 | 56.3 12.5 31.3 12.5 56.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001702 | 1 23 | 52.2 43.5 4.3 8.7 56.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001703 | 1 12 | 75.0 16.7 8.3 16.7 41.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001704 | 1 17 | 35.3 35.3 29.4 5.9 70.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001705 | 1 19 | 94.7 5.3 0.0 5.3 10.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001706 | 1 22 | 72.7 18.2 9.1 13.6 40.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001707 | 1 27 | 81.5 7.4 11.1 11.1 29.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001708 | 1 16 | 62.5 25.0 12.5 18.8 56.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001709 | 1 19 | 73.7 21.1 5.3 10.5 36.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001710 | 1 18 | 83.3 11.1 5.6 5.6 22.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001711 | 1 15 | 93.3 6.7 0.0 26.7 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001712 | 1 22 | 72.7 4.5 22.7 0.0 27.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001713 | 1 20 | 75.0 5.0 20.0 15.0 40.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001714 | 1 17 | 76.5 5.9 17.6 0.0 23.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001715 | 1 21 | 76.2 14.3 9.5 0.0 23.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001716 | 1 19 | 78.9 10.5 10.5 15.8 36.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001717 | 1 20 | 75.0 10.0 15.0 30.0 55.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001718 | 1 20 | 75.0 20.0 5.0 10.0 35.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001719 | 1 24 | 75.0 12.5 12.5 12.5 37.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001720 | 1 22 | 77.3 13.6 9.1 0.0 22.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001721 | 1 24 | 62.5 12.5 25.0 4.2 41.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001722 | 1 8 | 87.5 12.5 0.0 12.5 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001723 | 1 22 | 54.5 27.3 18.2 4.5 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001724 | 1 16 | 50.0 37.5 12.5 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001725 | 1 7 | 85.7 14.3 0.0 57.1 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001726 | 1 22 | 90.9 4.5 4.5 0.0 9.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001727 | 1 17 | 76.5 23.5 0.0 17.6 41.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001728 | 1 21 | 85.7 9.5 4.8 9.5 23.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001729 | 1 19 | 78.9 10.5 10.5 15.8 36.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001730 | 1 22 | 72.7 9.1 18.2 4.5 31.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001731 | 1 15 | 66.7 26.7 6.7 0.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001732 | 1 23 | 73.9 17.4 8.7 17.4 43.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001733 | 1 27 | 59.3 29.6 11.1 18.5 59.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001734 | 1 18 | 72.2 27.8 0.0 16.7 44.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001735 | 1 25 | 68.0 24.0 8.0 12.0 44.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001736 | 1 19 | 73.7 21.1 5.3 26.3 52.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001737 | 1 13 | 61.5 23.1 15.4 15.4 53.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001738 | 1 27 | 77.8 18.5 3.7 18.5 40.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001739 | 1 18 | 77.8 16.7 5.6 0.0 22.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001740 | 1 25 | 72.0 12.0 16.0 4.0 32.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001741 | 1 5 | 80.0 20.0 0.0 180.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001742 | 1 31 | 74.2 16.1 9.7 16.1 41.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001743 | 1 25 | 72.0 16.0 12.0 8.0 36.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001744 | 1 21 | 71.4 23.8 4.8 9.5 38.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001745 | 1 24 | 62.5 16.7 20.8 4.2 41.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001746 | 1 26 | 61.5 30.8 7.7 11.5 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001747 | 1 20 | 75.0 25.0 0.0 25.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001748 | 1 24 | 75.0 25.0 0.0 25.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001749 | 1 23 | 78.3 4.3 17.4 17.4 39.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001750 | 1 22 | 81.8 18.2 0.0 18.2 36.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001751 | 1 23 | 69.6 13.0 17.4 0.0 30.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001752 | 1 31 | 67.7 22.6 9.7 6.5 38.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001753 | 1 25 | 72.0 16.0 12.0 0.0 28.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001754 | 1 21 | 52.4 23.8 23.8 4.8 52.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001755 | 1 22 | 59.1 0.0 40.9 9.1 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001756 | 1 22 | 77.3 9.1 13.6 9.1 31.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001757 | 1 8 | 87.5 12.5 0.0 25.0 37.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001758 | 1 20 | 60.0 25.0 15.0 10.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001759 | 1 33 | 78.8 15.2 6.1 12.1 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001760 | 1 20 | 75.0 10.0 15.0 0.0 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001761 | 1 17 | 82.4 5.9 11.8 5.9 23.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001762 | 1 9 | 55.6 44.4 0.0 33.3 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001763 | 1 21 | 66.7 19.0 14.3 4.8 38.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001764 | 1 31 | 80.6 9.7 9.7 9.7 29.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001765 | 1 19 | 89.5 5.3 5.3 15.8 26.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001766 | 1 12 | 66.7 25.0 8.3 25.0 58.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001767 | 1 9 | 66.7 0.0 33.3 11.1 44.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001768 | 1 39 | 66.7 17.9 15.4 0.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001769 | 1 21 | 61.9 28.6 9.5 14.3 52.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001770 | 1 22 | 63.6 27.3 9.1 13.6 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001771 | 1 26 | 76.9 7.7 15.4 7.7 30.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001772 | 1 21 | 66.7 14.3 19.0 14.3 47.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001773 | 1 17 | 94.1 0.0 5.9 41.2 47.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001774 | 1 18 | 83.3 5.6 11.1 5.6 22.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001775 | 1 21 | 71.4 19.0 9.5 9.5 38.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001776 | 1 19 | 78.9 5.3 15.8 10.5 31.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001777 | 1 27 | 66.7 33.3 0.0 11.1 44.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001778 | 1 8 | 75.0 25.0 0.0 0.0 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001779 | 1 21 | 85.7 4.8 9.5 19.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001780 | 1 30 | 80.0 13.3 6.7 3.3 23.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001781 | 1 15 | 80.0 13.3 6.7 6.7 26.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001782 | 1 17 | 82.4 0.0 17.6 5.9 23.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001783 | 1 23 | 73.9 17.4 8.7 13.0 39.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001784 | 1 19 | 78.9 15.8 5.3 0.0 21.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001785 | 1 19 | 84.2 0.0 15.8 5.3 21.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001786 | 1 28 | 89.3 3.6 7.1 7.1 17.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001787 | 1 5 | 40.0 60.0 0.0 120.0 180.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001788 | 1 26 | 73.1 15.4 11.5 15.4 42.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001789 | 1 20 | 90.0 5.0 5.0 20.0 30.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001790 | 1 20 | 70.0 20.0 10.0 15.0 45.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001791 | 1 18 | 61.1 27.8 11.1 5.6 44.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001792 | 1 17 | 70.6 11.8 17.6 11.8 41.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001793 | 1 26 | 84.6 11.5 3.8 7.7 23.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001794 | 1 17 | 64.7 29.4 5.9 11.8 47.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001795 | 1 25 | 76.0 12.0 12.0 8.0 32.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001796 | 1 25 | 68.0 28.0 4.0 20.0 52.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001797 | 1 21 | 57.1 38.1 4.8 14.3 57.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001798 | 1 22 | 63.6 13.6 22.7 9.1 45.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001799 | 1 20 | 75.0 10.0 15.0 5.0 30.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001800 | 1 18 | 77.8 22.2 0.0 38.9 61.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001801 | 1 21 | 81.0 14.3 4.8 14.3 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001802 | 1 23 | 73.9 13.0 13.0 0.0 26.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001803 | 1 28 | 82.1 14.3 3.6 10.7 28.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001804 | 1 24 | 70.8 25.0 4.2 20.8 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001805 | 1 19 | 63.2 10.5 26.3 10.5 47.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001806 | 1 28 | 67.9 21.4 10.7 10.7 42.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001807 | 1 26 | 88.5 7.7 3.8 7.7 19.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001808 | 1 16 | 75.0 12.5 12.5 12.5 37.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001809 | 1 21 | 66.7 19.0 14.3 14.3 47.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001810 | 1 20 | 80.0 10.0 10.0 10.0 30.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001811 | 1 20 | 80.0 10.0 10.0 5.0 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001812 | 1 14 | 57.1 28.6 14.3 14.3 57.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001813 | 1 23 | 78.3 17.4 4.3 8.7 30.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001814 | 1 16 | 62.5 25.0 12.5 6.3 43.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001815 | 1 25 | 84.0 16.0 0.0 12.0 28.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001816 | 1 39 | 74.4 15.4 10.3 10.3 35.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001817 | 1 21 | 81.0 19.0 0.0 33.3 52.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001818 | 1 22 | 77.3 13.6 9.1 18.2 40.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001819 | 1 25 | 60.0 36.0 4.0 20.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001820 | 1 29 | 79.3 17.2 3.4 13.8 34.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001821 | 1 18 | 94.4 5.6 0.0 16.7 22.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001822 | 1 21 | 66.7 23.8 9.5 0.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001823 | 1 37 | 73.0 13.5 13.5 10.8 37.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001824 | 1 16 | 68.8 31.3 0.0 12.5 43.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001744 | 1 31 | 83.9 6.5 9.7 9.7 25.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001745 | 1 18 | 38.9 50.0 11.1 0.0 61.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001746 | 1 54 | 79.6 5.6 14.8 0.0 20.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001747 | 1 62 | 72.6 14.5 12.9 0.0 27.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001748 | 1 21 | 76.2 0.0 23.8 19.0 42.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001749 | 1 15 | 60.0 26.7 13.3 13.3 53.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001750 | 1 35 | 71.4 8.6 20.0 2.9 31.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001751 | 1 22 | 63.6 13.6 22.7 4.5 40.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001752 | 1 49 | 79.6 12.2 8.2 8.2 28.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001753 | 1 40 | 67.5 15.0 17.5 7.5 40.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001754 | 1 30 | 63.3 20.0 16.7 10.0 46.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001755 | 1 22 | 63.6 18.2 18.2 9.1 45.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001756 | 1 26 | 73.1 7.7 19.2 38.5 65.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001757 | 1 72 | 66.7 11.1 22.2 2.8 36.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001758 | 1 17 | 58.8 23.5 17.6 17.6 58.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001759 | 1 43 | 65.1 23.3 11.6 7.0 41.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001760 | 1 17 | 88.2 5.9 5.9 11.8 23.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001761 | 1 50 | 78.0 8.0 14.0 16.0 38.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001762 | 1 27 | 81.5 0.0 18.5 7.4 25.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001763 | 1 96 | 82.3 4.2 13.5 3.1 20.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001764 | 1 42 | 61.9 14.3 23.8 7.1 45.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001765 | 1 47 | 70.2 14.9 14.9 2.1 31.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001766 | 1 21 | 57.1 0.0 42.9 19.0 61.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001767 | 1 55 | 58.2 23.6 18.2 3.6 45.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001768 | 1 36 | 77.8 11.1 11.1 13.9 36.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001769 | 1 42 | 78.6 7.1 14.3 4.8 26.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001770 | 1 28 | 85.7 10.7 3.6 32.1 46.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001771 | 1 25 | 68.0 12.0 20.0 0.0 32.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001772 | 1 38 | 73.7 7.9 18.4 5.3 31.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001773 | 1 18 | 72.2 22.2 5.6 0.0 27.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001774 | 1 21 | 66.7 9.5 23.8 4.8 38.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001775 | 1 63 | 66.7 15.9 17.5 3.2 36.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001776 | 1 77 | 70.1 23.4 6.5 3.9 33.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001777 | 1 77 | 84.4 5.2 10.4 6.5 22.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001778 | 1 30 | 83.3 6.7 10.0 3.3 20.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001779 | 1 44 | 54.5 9.1 36.4 0.0 45.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001780 | 1 52 | 78.8 13.5 7.7 3.8 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001781 | 1 18 | 72.2 22.2 5.6 11.1 38.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001782 | 1 87 | 74.7 12.6 12.6 8.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001783 | 1 23 | 78.3 4.3 17.4 0.0 21.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001784 | 1 23 | 52.2 21.7 26.1 4.3 52.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001785 | 1 31 | 67.7 22.6 9.7 0.0 32.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001786 | 1 120 | 69.2 9.2 21.7 4.2 35.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001787 | 1 31 | 74.2 16.1 9.7 6.5 32.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001788 | 1 38 | 81.6 5.3 13.2 0.0 18.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001789 | 1 24 | 70.8 8.3 20.8 12.5 41.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001790 | 1 73 | 72.6 12.3 15.1 1.4 28.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001791 | 1 39 | 74.4 7.7 17.9 5.1 30.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001792 | 1 18 | 94.4 0.0 5.6 5.6 11.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001793 | 1 30 | 83.3 3.3 13.3 3.3 20.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001794 | 1 58 | 70.7 5.2 24.1 8.6 37.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001795 | 1 23 | 65.2 13.0 21.7 4.3 39.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001796 | 1 23 | 95.7 4.3 0.0 8.7 13.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001797 | 1 49 | 65.3 6.1 28.6 2.0 36.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001798 | 1 17 | 41.2 41.2 17.6 5.9 64.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001799 | 1 31 | 71.0 9.7 19.4 16.1 45.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001800 | 1 22 | 77.3 0.0 22.7 0.0 22.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001801 | 1 54 | 85.2 0.0 14.8 3.7 18.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001802 | 1 29 | 65.5 6.9 27.6 0.0 34.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001803 | 1 45 | 73.3 11.1 15.6 8.9 35.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001804 | 1 36 | 83.3 2.8 13.9 2.8 19.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001805 | 1 36 | 63.9 11.1 25.0 0.0 36.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001806 | 1 19 | 78.9 15.8 5.3 31.6 52.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001807 | 1 25 | 52.0 16.0 32.0 0.0 48.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001808 | 1 22 | 77.3 4.5 18.2 9.1 31.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001809 | 1 21 | 57.1 23.8 19.0 9.5 52.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001810 | 1 25 | 76.0 12.0 12.0 8.0 32.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001811 | 1 50 | 68.0 16.0 16.0 4.0 36.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001812 | 1 24 | 70.8 8.3 20.8 12.5 41.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001813 | 1 32 | 81.3 9.4 9.4 3.1 21.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001814 | 1 31 | 74.2 12.9 12.9 9.7 35.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001815 | 1 32 | 81.3 3.1 15.6 3.1 21.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001816 | 1 70 | 74.3 7.1 18.6 1.4 27.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001817 | 1 52 | 61.5 5.8 32.7 0.0 38.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001818 | 1 19 | 63.2 21.1 15.8 5.3 42.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001819 | 1 33 | 75.8 21.2 3.0 33.3 57.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001820 | 1 19 | 84.2 5.3 10.5 5.3 21.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001821 | 1 24 | 50.0 37.5 12.5 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001822 | 1 19 | 78.9 15.8 5.3 0.0 21.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001823 | 1 82 | 80.5 4.9 14.6 7.3 26.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001824 | 1 44 | 75.0 6.8 18.2 2.3 27.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001825 | 1 45 | 84.4 11.1 4.4 8.9 24.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001826 | 1 35 | 65.7 2.9 31.4 0.0 34.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001827 | 1 42 | 81.0 11.9 7.1 0.0 19.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001828 | 1 18 | 38.9 27.8 33.3 5.6 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001829 | 1 43 | 55.8 7.0 37.2 0.0 44.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001830 | 1 51 | 70.6 5.9 23.5 3.9 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001831 | 1 23 | 69.6 13.0 17.4 4.3 34.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001832 | 1 27 | 63.0 14.8 22.2 0.0 37.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001833 | 1 18 | 77.8 0.0 22.2 0.0 22.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001834 | 1 45 | 68.9 8.9 22.2 11.1 42.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001835 | 1 17 | 76.5 5.9 17.6 17.6 41.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001836 | 1 21 | 66.7 4.8 28.6 4.8 38.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001837 | 1 30 | 56.7 26.7 16.7 6.7 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001838 | 1 37 | 78.4 2.7 18.9 8.1 29.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001839 | 1 48 | 56.3 10.4 33.3 2.1 45.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001840 | 1 25 | 68.0 16.0 16.0 4.0 36.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001841 | 1 28 | 71.4 7.1 21.4 0.0 28.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001842 | 1 42 | 85.7 4.8 9.5 7.1 21.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001843 | 1 47 | 70.2 14.9 14.9 6.4 36.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001844 | 1 31 | 80.6 12.9 6.5 9.7 29.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001845 | 1 20 | 55.0 40.0 5.0 20.0 65.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001846 | 1 96 | 78.1 7.3 14.6 4.2 26.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001847 | 1 18 | 83.3 5.6 11.1 0.0 16.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001848 | 1 47 | 63.8 19.1 17.0 2.1 38.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001849 | 1 62 | 77.4 8.1 14.5 3.2 25.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001850 | 1 18 | 61.1 22.2 16.7 5.6 44.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001851 | 1 27 | 77.8 18.5 3.7 14.8 37.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001852 | 1 55 | 76.4 10.9 12.7 5.5 29.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001853 | 1 47 | 83.0 6.4 10.6 19.1 36.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001854 | 1 35 | 74.3 5.7 20.0 0.0 25.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001855 | 1 40 | 80.0 10.0 10.0 5.0 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001856 | 1 27 | 81.5 0.0 18.5 0.0 18.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001857 | 1 26 | 84.6 11.5 3.8 11.5 26.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001858 | 1 45 | 86.7 8.9 4.4 6.7 20.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001859 | 1 15 | 73.3 6.7 20.0 6.7 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001860 | 1 38 | 84.2 2.6 13.2 0.0 15.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001861 | 1 9 | 88.9 11.1 0.0 22.2 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001862 | 1 64 | 87.5 1.6 10.9 6.3 18.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001863 | 1 22 | 77.3 9.1 13.6 4.5 27.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001864 | 1 13 | 92.3 7.7 0.0 15.4 23.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001865 | 1 34 | 73.5 8.8 17.6 2.9 29.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001866 | 1 30 | 80.0 10.0 10.0 10.0 30.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001867 | 1 49 | 57.1 10.2 32.7 16.3 59.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001868 | 1 28 | 64.3 14.3 21.4 0.0 35.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001869 | 1 30 | 60.0 3.3 36.7 0.0 40.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001870 | 1 24 | 79.2 0.0 20.8 33.3 54.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001871 | 1 76 | 78.9 10.5 10.5 10.5 31.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001872 | 1 24 | 79.2 16.7 4.2 0.0 20.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001873 | 1 11 | 72.7 18.2 9.1 45.5 72.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001874 | 1 35 | 57.1 14.3 28.6 0.0 42.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001875 | 1 52 | 48.1 32.7 19.2 5.8 57.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001876 | 1 27 | 66.7 11.1 22.2 0.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001877 | 1 36 | 86.1 11.1 2.8 11.1 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001878 | 1 17 | 58.8 17.6 23.5 5.9 47.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001879 | 1 174 | 81.0 6.3 12.6 4.6 23.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001880 | 1 63 | 77.8 7.9 14.3 1.6 23.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001881 | 1 122 | 83.6 7.4 9.0 1.6 18.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001882 | 1 33 | 78.8 15.2 6.1 12.1 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001883 | 1 51 | 86.3 5.9 7.8 27.5 41.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001884 | 1 32 | 84.4 6.3 9.4 3.1 18.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001885 | 1 34 | 73.5 5.9 20.6 8.8 35.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001886 | 1 32 | 71.9 15.6 12.5 9.4 37.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001887 | 1 33 | 75.8 9.1 15.2 3.0 27.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001888 | 1 43 | 76.7 2.3 20.9 2.3 25.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001889 | 1 15 | 60.0 20.0 20.0 0.0 40.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001890 | 1 12 | 100.0 0.0 0.0 33.3 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001891 | 1 36 | 58.3 22.2 19.4 0.0 41.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001892 | 1 36 | 72.2 5.6 22.2 5.6 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001893 | 1 23 | 56.5 17.4 26.1 8.7 52.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001894 | 1 40 | 65.0 22.5 12.5 2.5 37.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001895 | 1 37 | 78.4 8.1 13.5 10.8 32.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001896 | 1 30 | 90.0 3.3 6.7 0.0 10.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001897 | 1 12 | 91.7 8.3 0.0 0.0 8.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001898 | 1 40 | 72.5 7.5 20.0 2.5 30.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001899 | 1 49 | 87.8 6.1 6.1 2.0 14.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001900 | 1 31 | 77.4 12.9 9.7 6.5 29.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001901 | 1 20 | 75.0 15.0 10.0 5.0 30.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001902 | 1 42 | 71.4 14.3 14.3 0.0 28.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001903 | 1 22 | 68.2 13.6 18.2 0.0 31.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001904 | 1 37 | 67.6 8.1 24.3 13.5 45.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001905 | 1 44 | 68.2 9.1 22.7 2.3 34.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001906 | 1 81 | 81.5 4.9 13.6 4.9 23.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001907 | 1 20 | 65.0 5.0 30.0 15.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001908 | 1 29 | 69.0 10.3 20.7 0.0 31.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001909 | 1 22 | 72.7 4.5 22.7 0.0 27.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001910 | 1 43 | 86.0 9.3 4.7 0.0 14.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001911 | 1 32 | 75.0 6.3 18.8 3.1 28.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001912 | 1 37 | 78.4 16.2 5.4 8.1 29.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001913 | 1 24 | 75.0 0.0 25.0 4.2 29.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001914 | 1 58 | 72.4 13.8 13.8 1.7 29.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001915 | 1 47 | 46.8 17.0 36.2 0.0 53.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001916 | 1 25 | 68.0 16.0 16.0 4.0 36.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001917 | 1 21 | 76.2 23.8 0.0 9.5 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001918 | 1 61 | 75.4 9.8 14.8 1.6 26.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001919 | 1 58 | 75.9 10.3 13.8 1.7 25.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001920 | 1 45 | 86.7 6.7 6.7 0.0 13.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001921 | 1 18 | 55.6 22.2 22.2 16.7 61.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001922 | 1 34 | 73.5 11.8 14.7 5.9 32.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001923 | 1 129 | 77.5 6.2 16.3 4.7 27.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001924 | 1 76 | 69.7 10.5 19.7 6.6 36.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001925 | 1 48 | 72.9 18.8 8.3 8.3 35.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001926 | 1 27 | 74.1 3.7 22.2 0.0 25.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001927 | 1 23 | 69.6 17.4 13.0 0.0 30.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001928 | 1 19 | 78.9 5.3 15.8 0.0 21.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001929 | 1 26 | 69.2 7.7 23.1 0.0 30.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001930 | 1 32 | 68.8 12.5 18.8 3.1 34.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001931 | 1 14 | 78.6 7.1 14.3 0.0 21.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001932 | 1 45 | 86.7 6.7 6.7 11.1 24.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001933 | 1 25 | 76.0 8.0 16.0 0.0 24.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001934 | 1 38 | 86.8 2.6 10.5 5.3 18.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001935 | 1 26 | 76.9 15.4 7.7 3.8 26.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001936 | 1 44 | 79.5 4.5 15.9 4.5 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001937 | 1 15 | 40.0 53.3 6.7 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001938 | 1 72 | 65.3 18.1 16.7 12.5 47.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001939 | 1 32 | 84.4 6.3 9.4 3.1 18.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001940 | 1 45 | 66.7 6.7 26.7 0.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001941 | 1 36 | 50.0 16.7 33.3 13.9 63.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001942 | 1 19 | 68.4 15.8 15.8 15.8 47.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001943 | 1 44 | 65.9 4.5 29.5 6.8 40.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001944 | 1 43 | 72.1 16.3 11.6 7.0 34.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001945 | 1 37 | 78.4 5.4 16.2 16.2 37.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001946 | 1 106 | 83.0 8.5 8.5 4.7 21.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001947 | 1 23 | 69.6 8.7 21.7 8.7 39.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001948 | 1 23 | 60.9 21.7 17.4 13.0 52.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001949 | 1 38 | 68.4 10.5 21.1 7.9 39.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001950 | 1 15 | 46.7 26.7 26.7 13.3 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001951 | 1 39 | 79.5 5.1 15.4 5.1 25.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001952 | 1 67 | 71.6 13.4 14.9 10.4 38.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001953 | 1 23 | 60.9 21.7 17.4 8.7 47.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001954 | 1 24 | 66.7 20.8 12.5 4.2 37.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001955 | 1 19 | 73.7 5.3 21.1 10.5 36.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001956 | 1 34 | 76.5 5.9 17.6 8.8 32.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001957 | 1 73 | 69.9 11.0 19.2 4.1 34.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001958 | 1 24 | 83.3 4.2 12.5 4.2 20.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001959 | 1 41 | 78.0 7.3 14.6 2.4 24.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001960 | 1 51 | 72.5 3.9 23.5 2.0 29.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001961 | 1 45 | 82.2 8.9 8.9 0.0 17.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001962 | 1 14 | 85.7 14.3 0.0 21.4 35.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001963 | 1 35 | 71.4 11.4 17.1 11.4 40.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001964 | 1 15 | 73.3 20.0 6.7 13.3 40.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001965 | 1 24 | 62.5 20.8 16.7 4.2 41.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001966 | 1 38 | 71.1 10.5 18.4 2.6 31.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001967 | 1 60 | 81.7 5.0 13.3 5.0 23.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001968 | 1 63 | 65.1 9.5 25.4 1.6 36.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001969 | 1 64 | 65.6 10.9 23.4 3.1 37.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001970 | 1 42 | 73.8 11.9 14.3 4.8 31.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001971 | 1 13 | 76.9 15.4 7.7 0.0 23.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001972 | 1 61 | 68.9 4.9 26.2 0.0 31.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001973 | 1 50 | 84.0 4.0 12.0 6.0 22.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001974 | 1 18 | 72.2 16.7 11.1 5.6 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001975 | 1 43 | 74.4 16.3 9.3 4.7 30.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001976 | 1 27 | 51.9 22.2 25.9 3.7 51.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001977 | 1 22 | 81.8 9.1 9.1 0.0 18.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001978 | 1 15 | 73.3 6.7 20.0 0.0 26.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001979 | 1 68 | 66.2 11.8 22.1 1.5 35.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001980 | 1 51 | 56.9 13.7 29.4 0.0 43.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001981 | 1 42 | 69.0 11.9 19.0 0.0 31.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001982 | 1 16 | 81.3 6.3 12.5 50.0 68.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001983 | 1 77 | 76.6 11.7 11.7 2.6 26.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001984 | 1 31 | 80.6 6.5 12.9 6.5 25.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001985 | 1 33 | 60.6 15.2 24.2 6.1 45.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001986 | 1 77 | 84.4 7.8 7.8 1.3 16.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001987 | 1 15 | 80.0 0.0 20.0 6.7 26.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001988 | 1 25 | 68.0 12.0 20.0 8.0 40.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001989 | 1 51 | 64.7 3.9 31.4 17.6 52.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001990 | 1 38 | 81.6 7.9 10.5 0.0 18.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001991 | 1 35 | 71.4 14.3 14.3 0.0 28.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001992 | 1 15 | 80.0 20.0 0.0 6.7 26.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001993 | 1 44 | 63.6 25.0 11.4 6.8 43.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001994 | 1 29 | 86.2 0.0 13.8 6.9 20.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001995 | 1 21 | 61.9 19.0 19.0 4.8 42.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001996 | 1 31 | 54.8 16.1 29.0 0.0 45.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001997 | 1 34 | 58.8 17.6 23.5 8.8 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001998 | 1 53 | 71.7 15.1 13.2 5.7 34.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001999 | 1 20 | 40.0 20.0 40.0 15.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002000 | 1 64 | 84.4 1.6 14.1 26.6 42.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002001 | 1 21 | 66.7 19.0 14.3 0.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002002 | 1 17 | 70.6 17.6 11.8 11.8 41.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002003 | 1 23 | 78.3 13.0 8.7 8.7 30.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002004 | 1 23 | 82.6 8.7 8.7 26.1 43.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002005 | 1 25 | 72.0 20.0 8.0 0.0 28.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000874 | 1 57 | 75.4 5.3 19.3 5.3 29.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000875 | 1 20 | 90.0 0.0 10.0 10.0 20.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000876 | 1 46 | 73.9 10.9 15.2 2.2 28.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000877 | 1 50 | 86.0 4.0 10.0 6.0 20.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000878 | 1 24 | 95.8 4.2 0.0 20.8 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000879 | 1 49 | 85.7 12.2 2.0 6.1 20.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000880 | 1 45 | 75.6 11.1 13.3 4.4 28.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000883 | 1 26 | 69.2 19.2 11.5 19.2 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000884 | 1 60 | 93.3 3.3 3.3 1.7 8.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000885 | 1 36 | 72.2 2.8 25.0 5.6 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000886 | 1 62 | 77.4 11.3 11.3 16.1 38.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000887 | 1 65 | 69.2 10.8 20.0 0.0 30.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000888 | 1 40 | 85.0 5.0 10.0 5.0 20.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000889 | 1 36 | 80.6 5.6 13.9 2.8 22.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000890 | 1 23 | 69.6 8.7 21.7 8.7 39.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000891 | 1 81 | 84.0 1.2 14.8 2.5 18.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000892 | 1 21 | 85.7 9.5 4.8 9.5 23.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000893 | 1 26 | 88.5 7.7 3.8 11.5 23.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000894 | 1 91 | 85.7 6.6 7.7 4.4 18.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000895 | 1 57 | 80.7 5.3 14.0 1.8 21.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000896 | 1 27 | 77.8 14.8 7.4 7.4 29.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000897 | 1 30 | 93.3 3.3 3.3 3.3 10.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000898 | 1 37 | 83.8 2.7 13.5 2.7 18.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000899 | 1 22 | 86.4 9.1 4.5 4.5 18.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000900 | 1 61 | 70.5 6.6 23.0 1.6 31.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000901 | 1 31 | 77.4 9.7 12.9 58.1 80.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000902 | 1 24 | 75.0 25.0 0.0 33.3 58.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000903 | 1 45 | 82.2 8.9 8.9 4.4 22.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000904 | 1 32 | 65.6 21.9 12.5 3.1 37.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000905 | 1 48 | 58.3 8.3 33.3 0.0 41.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000906 | 1 50 | 74.0 6.0 20.0 8.0 34.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000907 | 1 60 | 85.0 8.3 6.7 3.3 18.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000908 | 1 42 | 78.6 7.1 14.3 7.1 28.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000909 | 1 57 | 82.5 3.5 14.0 3.5 21.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000910 | 1 54 | 92.6 1.9 5.6 5.6 13.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000911 | 1 26 | 80.8 7.7 11.5 7.7 26.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000912 | 1 33 | 97.0 0.0 3.0 9.1 12.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000913 | 1 68 | 79.4 10.3 10.3 1.5 22.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000914 | 1 47 | 74.5 6.4 19.1 6.4 31.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000915 | 1 28 | 85.7 3.6 10.7 14.3 28.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000916 | 1 35 | 94.3 2.9 2.9 2.9 8.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000917 | 1 40 | 87.5 10.0 2.5 7.5 20.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000918 | 1 67 | 85.1 7.5 7.5 6.0 20.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000920 | 1 35 | 80.0 5.7 14.3 8.6 28.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000921 | 1 49 | 77.6 4.1 18.4 8.2 30.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000922 | 1 57 | 91.2 5.3 3.5 10.5 19.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000923 | 1 46 | 89.1 4.3 6.5 8.7 19.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000924 | 1 16 | 81.3 6.3 12.5 12.5 31.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000925 | 1 65 | 78.5 13.8 7.7 18.5 40.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000927 | 1 49 | 87.8 4.1 8.2 8.2 20.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000928 | 1 48 | 72.9 4.2 22.9 2.1 29.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000929 | 1 19 | 68.4 15.8 15.8 36.8 68.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000930 | 1 58 | 91.4 3.4 5.2 0.0 8.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000931 | 1 57 | 84.2 14.0 1.8 8.8 24.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000932 | 1 51 | 80.4 9.8 9.8 2.0 21.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000933 | 1 31 | 51.6 22.6 25.8 6.5 54.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000934 | 1 38 | 73.7 7.9 18.4 5.3 31.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000935 | 1 38 | 76.3 15.8 7.9 13.2 36.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000938 | 1 26 | 80.8 15.4 3.8 3.8 23.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000939 | 1 74 | 78.4 8.1 13.5 8.1 29.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000940 | 1 55 | 61.8 9.1 29.1 3.6 41.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000942 | 1 51 | 74.5 9.8 15.7 9.8 35.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000943 | 1 39 | 76.9 12.8 10.3 5.1 28.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000944 | 1 102 | 72.5 6.9 20.6 6.9 34.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000945 | 1 57 | 75.4 17.5 7.0 7.0 31.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000946 | 1 32 | 68.8 9.4 21.9 3.1 34.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000947 | 1 50 | 84.0 4.0 12.0 6.0 22.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000948 | 1 32 | 62.5 18.8 18.8 9.4 46.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000949 | 1 68 | 86.8 10.3 2.9 2.9 16.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000950 | 1 47 | 78.7 2.1 19.1 2.1 23.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000951 | 1 23 | 82.6 4.3 13.0 8.7 26.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000952 | 1 22 | 86.4 4.5 9.1 9.1 22.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000953 | 1 48 | 72.9 14.6 12.5 2.1 29.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000954 | 1 52 | 80.8 7.7 11.5 11.5 30.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000955 | 1 76 | 76.3 5.3 18.4 2.6 26.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000956 | 1 52 | 71.2 21.2 7.7 21.2 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000957 | 1 23 | 91.3 8.7 0.0 8.7 17.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000958 | 1 48 | 85.4 8.3 6.3 0.0 14.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000959 | 1 118 | 80.5 6.8 12.7 5.1 24.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000960 | 1 87 | 86.2 5.7 8.0 3.4 17.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000961 | 1 74 | 82.4 9.5 8.1 4.1 21.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000962 | 1 23 | 87.0 0.0 13.0 4.3 17.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000963 | 1 60 | 73.3 0.0 26.7 5.0 31.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000964 | 1 67 | 79.1 4.5 16.4 4.5 25.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000965 | 1 37 | 83.8 5.4 10.8 2.7 18.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000966 | 1 25 | 96.0 0.0 4.0 16.0 20.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000967 | 1 31 | 61.3 19.4 19.4 6.5 45.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000968 | 1 47 | 80.9 8.5 10.6 4.3 23.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000971 | 1 41 | 82.9 7.3 9.8 9.8 26.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000972 | 1 61 | 77.0 9.8 13.1 4.9 27.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000973 | 1 81 | 85.2 4.9 9.9 0.0 14.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000974 | 1 71 | 85.9 9.9 4.2 5.6 19.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000975 | 1 68 | 85.3 10.3 4.4 7.4 22.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000976 | 1 42 | 83.3 4.8 11.9 2.4 19.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000977 | 1 28 | 75.0 14.3 10.7 28.6 53.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000978 | 1 30 | 93.3 3.3 3.3 3.3 10.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000979 | 1 46 | 71.7 10.9 17.4 8.7 37.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000980 | 1 52 | 76.9 1.9 21.2 1.9 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000981 | 1 56 | 82.1 5.4 12.5 1.8 19.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000982 | 1 57 | 66.7 10.5 22.8 1.8 35.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000983 | 1 55 | 90.9 9.1 0.0 9.1 18.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000984 | 1 23 | 73.9 4.3 21.7 8.7 34.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000985 | 1 43 | 81.4 4.7 14.0 4.7 23.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000986 | 1 85 | 81.2 3.5 15.3 2.4 21.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000987 | 1 82 | 76.8 13.4 9.8 20.7 43.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000988 | 1 39 | 82.1 7.7 10.3 2.6 20.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000989 | 1 97 | 80.4 5.2 14.4 3.1 22.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000990 | 1 44 | 81.8 9.1 9.1 9.1 27.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000991 | 1 45 | 75.6 4.4 20.0 0.0 24.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000992 | 1 38 | 84.2 5.3 10.5 0.0 15.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000993 | 1 41 | 68.3 9.8 22.0 7.3 39.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000994 | 1 54 | 75.9 9.3 14.8 3.7 27.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000995 | 1 91 | 79.1 4.4 16.5 0.0 20.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000996 | 1 47 | 83.0 4.3 12.8 0.0 17.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000997 | 1 69 | 76.8 5.8 17.4 2.9 26.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000998 | 1 39 | 71.8 10.3 17.9 5.1 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000999 | 1 25 | 72.0 8.0 20.0 16.0 44.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001000 | 1 68 | 85.3 2.9 11.8 1.5 16.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001001 | 1 50 | 72.0 22.0 6.0 8.0 36.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001002 | 1 28 | 82.1 14.3 3.6 21.4 39.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001003 | 1 54 | 90.7 7.4 1.9 3.7 13.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001004 | 1 53 | 83.0 5.7 11.3 3.8 20.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000494 | 1 81 | 77.8 6.2 16.0 17.3 39.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000495 | 1 169 | 69.8 14.8 15.4 7.1 37.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000496 | 1 147 | 82.3 8.8 8.8 14.3 32.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000497 | 1 91 | 78.0 7.7 14.3 9.9 31.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000498 | 1 156 | 75.0 8.3 16.7 10.3 35.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000499 | 1 377 | 77.5 6.9 15.6 5.8 28.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000500 | 1 123 | 86.2 4.9 8.9 6.5 20.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000501 | 1 22 | 77.3 13.6 9.1 4.5 27.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000502 | 1 304 | 74.7 8.6 16.8 7.6 32.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000503 | 1 170 | 75.3 9.4 15.3 4.7 29.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000504 | 1 203 | 68.5 10.8 20.7 0.5 32.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000505 | 1 285 | 76.1 8.1 15.8 7.0 30.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000506 | 1 168 | 67.9 9.5 22.6 10.1 42.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000507 | 1 22 | 68.2 0.0 31.8 9.1 40.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000508 | 1 42 | 71.4 9.5 19.0 21.4 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000509 | 1 284 | 75.0 11.6 13.4 12.3 37.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000510 | 1 261 | 72.4 10.3 17.2 5.7 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000511 | 1 256 | 73.4 7.4 19.1 7.0 33.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000512 | 1 48 | 91.7 4.2 4.2 6.3 14.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000513 | 1 74 | 82.4 6.8 10.8 9.5 27.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000514 | 1 58 | 67.2 12.1 20.7 0.0 32.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000515 | 1 106 | 80.2 13.2 6.6 4.7 24.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000516 | 1 88 | 68.2 10.2 21.6 9.1 40.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000517 | 1 168 | 81.5 4.8 13.7 8.3 26.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000518 | 1 99 | 75.8 8.1 16.2 4.0 28.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000519 | 1 151 | 68.2 14.6 17.2 10.6 42.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000520 | 1 179 | 78.2 12.3 9.5 8.4 30.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000521 | 1 248 | 79.4 10.9 9.7 4.8 25.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000522 | 1 68 | 82.4 7.4 10.3 4.4 22.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000523 | 1 119 | 79.8 5.0 15.1 5.9 26.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000524 | 1 187 | 80.2 9.1 10.7 8.0 27.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000525 | 1 56 | 92.9 1.8 5.4 7.1 14.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000526 | 1 79 | 83.5 6.3 10.1 2.5 19.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000527 | 1 247 | 76.9 12.1 10.9 13.4 36.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000528 | 1 105 | 75.2 11.4 13.3 11.4 36.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000529 | 1 91 | 71.4 5.5 23.1 2.2 30.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000530 | 1 156 | 73.1 15.4 11.5 19.9 46.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000531 | 1 42 | 73.8 16.7 9.5 7.1 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000532 | 1 26 | 92.3 7.7 0.0 15.4 23.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000533 | 1 89 | 74.2 5.6 20.2 10.1 36.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000534 | 1 199 | 79.4 8.5 12.1 5.0 25.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000535 | 1 185 | 80.0 13.0 7.0 6.5 26.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000536 | 1 166 | 74.7 7.2 18.1 4.2 29.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000537 | 1 131 | 58.8 14.5 26.7 3.8 45.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000538 | 1 119 | 84.0 5.0 10.9 4.2 20.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000539 | 1 118 | 69.5 14.4 16.1 5.9 36.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000540 | 1 115 | 73.0 17.4 9.6 14.8 41.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000541 | 1 76 | 82.9 7.9 9.2 11.8 28.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000542 | 1 53 | 79.2 9.4 11.3 11.3 32.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000543 | 1 101 | 66.3 16.8 16.8 8.9 42.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000544 | 1 133 | 72.2 15.8 12.0 11.3 39.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000545 | 1 40 | 80.0 10.0 10.0 7.5 27.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000546 | 1 30 | 56.7 16.7 26.7 3.3 46.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000547 | 1 127 | 76.4 12.6 11.0 6.3 29.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000548 | 1 204 | 82.8 8.8 8.3 11.8 28.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000549 | 1 127 | 68.5 9.4 22.0 1.6 33.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000550 | 1 95 | 76.8 13.7 9.5 17.9 41.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000551 | 1 63 | 82.5 9.5 7.9 3.2 20.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000552 | 1 194 | 74.7 10.3 14.9 13.9 39.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000553 | 1 138 | 81.2 8.7 10.1 1.4 20.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000554 | 1 196 | 82.1 7.7 10.2 2.6 20.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000555 | 1 81 | 82.7 11.1 6.2 12.3 29.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000556 | 1 111 | 74.8 10.8 14.4 3.6 28.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000557 | 1 45 | 91.1 4.4 4.4 13.3 22.2 100.0 | +|====================================================================================================================| +| Sum/Avg | 1092 67334 | 76.2 10.0 13.8 7.2 31.0 99.9 | +|====================================================================================================================| +| Mean | 1.1 66.3 | 75.0 12.2 12.8 12.1 37.1 99.9 | +| S.D. | 2.4 267.1 | 10.4 8.8 7.2 36.6 37.6 3.1 | +| Median | 1.0 40.0 | 76.4 10.3 12.3 6.5 31.6 100.0 | +`--------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,--------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/hyp.trn| +|--------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000254 | 1 100 | 77 8 15 0 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000255 | 1 60 | 45 6 9 2 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000256 | 1 41 | 29 6 6 4 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000257 | 1 53 | 43 4 6 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000258 | 1 51 | 44 2 5 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000259 | 1 92 | 73 7 12 4 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000260 | 1 79 | 62 6 11 3 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000261 | 1 80 | 65 7 8 6 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000262 | 1 44 | 37 5 2 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000263 | 1 54 | 46 4 4 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000264 | 1 107 | 88 3 16 3 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000265 | 1 41 | 37 2 2 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000266 | 1 25 | 21 3 1 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000267 | 1 26 | 22 3 1 4 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000268 | 1 43 | 39 0 4 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000269 | 1 53 | 41 4 8 1 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000270 | 1 66 | 56 3 7 3 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000271 | 1 93 | 77 5 11 1 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000272 | 1 91 | 82 5 4 8 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000273 | 1 114 | 96 9 9 6 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000274 | 1 26 | 23 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000275 | 1 82 | 67 4 11 4 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000276 | 1 94 | 84 5 5 4 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000277 | 1 55 | 46 6 3 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000278 | 1 48 | 41 2 5 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000279 | 1 21 | 18 2 1 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000280 | 1 52 | 47 2 3 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000281 | 1 44 | 37 5 2 6 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000282 | 1 30 | 27 1 2 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000283 | 1 94 | 80 7 7 5 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000284 | 1 111 | 89 2 20 1 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000285 | 1 104 | 84 11 9 4 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000286 | 1 60 | 53 2 5 6 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000287 | 1 44 | 39 0 5 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000288 | 1 46 | 32 7 7 4 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000289 | 1 48 | 41 1 6 5 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000290 | 1 51 | 45 2 4 4 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000291 | 1 56 | 43 6 7 1 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000292 | 1 50 | 38 2 10 1 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000293 | 1 85 | 64 7 14 1 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000294 | 1 60 | 42 3 15 0 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000295 | 1 52 | 42 3 7 3 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000296 | 1 91 | 72 5 14 5 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000297 | 1 72 | 58 9 5 5 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000298 | 1 60 | 53 3 4 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000299 | 1 79 | 65 7 7 4 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000300 | 1 78 | 65 4 9 4 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000301 | 1 74 | 59 2 13 2 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000302 | 1 58 | 49 3 6 5 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000303 | 1 60 | 48 9 3 2 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000304 | 1 50 | 42 2 6 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000305 | 1 77 | 63 2 12 1 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000306 | 1 74 | 63 7 4 4 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000307 | 1 91 | 78 5 8 8 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000308 | 1 69 | 52 6 11 2 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000309 | 1 34 | 28 1 5 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000310 | 1 26 | 26 0 0 0 0 0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000311 | 1 107 | 84 8 15 1 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000312 | 1 81 | 67 5 9 4 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000313 | 1 63 | 55 4 4 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000314 | 1 41 | 30 6 5 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000315 | 1 55 | 40 6 9 4 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000316 | 1 100 | 83 10 7 2 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000317 | 1 70 | 57 3 10 6 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000318 | 1 107 | 85 8 14 2 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000319 | 1 74 | 61 5 8 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000320 | 1 94 | 80 3 11 2 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000321 | 1 31 | 25 2 4 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000322 | 1 59 | 42 8 9 3 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000323 | 1 52 | 42 6 4 9 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000324 | 1 75 | 63 8 4 9 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000325 | 1 34 | 27 1 6 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000326 | 1 43 | 31 5 7 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000327 | 1 76 | 62 5 9 3 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000328 | 1 26 | 21 2 3 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000329 | 1 101 | 74 6 21 0 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000330 | 1 50 | 43 4 3 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000331 | 1 31 | 27 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000332 | 1 98 | 79 6 13 4 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000333 | 1 45 | 34 7 4 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000334 | 1 107 | 86 4 17 5 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000335 | 1 75 | 63 3 9 3 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000336 | 1 92 | 68 10 14 2 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000337 | 1 100 | 87 6 7 11 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000338 | 1 29 | 24 1 4 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000339 | 1 54 | 47 1 6 4 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000340 | 1 70 | 57 7 6 1 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000341 | 1 67 | 55 4 8 3 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000342 | 1 82 | 68 2 12 4 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000343 | 1 111 | 96 7 8 3 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000344 | 1 102 | 83 3 16 2 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000345 | 1 73 | 58 8 7 5 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000346 | 1 91 | 69 8 14 3 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000347 | 1 76 | 61 3 12 0 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000348 | 1 82 | 67 5 10 4 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000349 | 1 89 | 73 5 11 7 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000350 | 1 80 | 60 5 15 3 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000351 | 1 105 | 85 8 12 3 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000352 | 1 38 | 30 5 3 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000353 | 1 61 | 49 2 10 7 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000354 | 1 67 | 55 5 7 2 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000355 | 1 76 | 61 6 9 5 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000356 | 1 36 | 31 1 4 4 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000357 | 1 79 | 62 1 16 3 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000358 | 1 49 | 38 4 7 3 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000359 | 1 48 | 35 6 7 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000360 | 1 47 | 37 1 9 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000361 | 1 48 | 41 2 5 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000362 | 1 99 | 79 7 13 1 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000363 | 1 72 | 55 8 9 4 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000364 | 1 63 | 57 2 4 4 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000365 | 1 25 | 24 0 1 2 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000366 | 1 53 | 42 9 2 3 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000367 | 1 58 | 43 6 9 4 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000368 | 1 47 | 36 4 7 4 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000369 | 1 92 | 75 3 14 5 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000370 | 1 76 | 64 5 7 2 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000371 | 1 58 | 48 2 8 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000372 | 1 64 | 51 6 7 4 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000373 | 1 92 | 74 7 11 3 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000374 | 1 26 | 25 0 1 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000375 | 1 71 | 58 4 9 6 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000376 | 1 90 | 76 3 11 3 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| m | 77 8404 | 6664 467 1273 368 2108 77 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000707 | 1 45 | 24 16 5 16 37 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000708 | 1 57 | 45 3 9 3 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000709 | 1 56 | 44 6 6 9 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000710 | 1 77 | 51 13 13 4 30 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000711 | 1 91 | 63 18 10 7 35 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000712 | 1 99 | 75 10 14 6 30 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000713 | 1 63 | 43 8 12 4 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000714 | 1 89 | 49 26 14 7 47 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000715 | 1 13 | 11 2 0 6 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000716 | 1 54 | 36 10 8 6 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000717 | 1 75 | 52 16 7 6 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000718 | 1 35 | 28 2 5 5 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000719 | 1 33 | 25 8 0 4 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000720 | 1 43 | 36 3 4 12 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000721 | 1 34 | 29 3 2 6 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000722 | 1 35 | 22 8 5 4 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000723 | 1 68 | 47 13 8 5 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000724 | 1 30 | 23 5 2 13 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000725 | 1 88 | 60 18 10 9 37 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000726 | 1 64 | 54 5 5 6 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000727 | 1 20 | 9 7 4 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000728 | 1 46 | 38 4 4 12 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000729 | 1 75 | 45 10 20 0 30 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000730 | 1 85 | 48 26 11 14 51 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000731 | 1 26 | 13 11 2 5 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000732 | 1 77 | 58 6 13 1 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000733 | 1 58 | 39 14 5 8 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000734 | 1 78 | 61 6 11 8 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000735 | 1 64 | 47 11 6 8 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000736 | 1 54 | 43 2 9 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000737 | 1 5 | 1 4 0 18 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000738 | 1 85 | 62 13 10 5 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000739 | 1 44 | 32 3 9 4 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000740 | 1 45 | 38 4 3 14 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000741 | 1 28 | 24 3 1 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000742 | 1 68 | 39 18 11 12 41 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000743 | 1 72 | 58 7 7 4 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000744 | 1 38 | 33 3 2 14 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000745 | 1 68 | 33 28 7 15 50 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000746 | 1 50 | 40 8 2 7 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000747 | 1 34 | 24 6 4 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000748 | 1 2 | 2 0 0 13 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000749 | 1 5 | 4 1 0 16 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000750 | 1 83 | 54 13 16 4 33 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000751 | 1 67 | 54 11 2 9 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000752 | 1 77 | 53 16 8 6 30 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000753 | 1 57 | 39 14 4 29 47 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000754 | 1 51 | 39 8 4 41 53 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000755 | 1 28 | 18 6 4 10 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000756 | 1 51 | 35 9 7 9 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000757 | 1 74 | 42 15 17 3 35 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000758 | 1 25 | 20 4 1 7 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000759 | 1 29 | 22 3 4 8 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000760 | 1 57 | 29 20 8 10 38 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000761 | 1 29 | 20 5 4 7 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000762 | 1 74 | 59 6 9 3 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000763 | 1 43 | 33 3 7 3 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000764 | 1 39 | 27 5 7 2 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000765 | 1 27 | 18 7 2 4 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000766 | 1 43 | 36 4 3 6 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000767 | 1 59 | 45 8 6 7 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000768 | 1 27 | 21 4 2 17 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000769 | 1 18 | 12 2 4 14 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000770 | 1 83 | 60 14 9 2 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000771 | 1 59 | 34 19 6 11 36 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000772 | 1 36 | 17 16 3 18 37 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000773 | 1 72 | 52 5 15 5 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000774 | 1 67 | 53 8 6 14 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000775 | 1 23 | 11 8 4 7 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000776 | 1 70 | 50 7 13 3 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000777 | 1 80 | 55 14 11 9 34 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000778 | 1 82 | 57 9 16 3 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000779 | 1 93 | 59 21 13 20 54 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000780 | 1 58 | 45 6 7 8 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000781 | 1 75 | 49 13 13 8 34 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000782 | 1 3 | 3 0 0 21 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000783 | 1 35 | 28 4 3 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000784 | 1 54 | 31 14 9 19 42 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000785 | 1 40 | 30 5 5 9 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000786 | 1 59 | 52 3 4 6 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000787 | 1 94 | 78 8 8 19 35 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000788 | 1 27 | 21 5 1 21 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000789 | 1 27 | 16 6 5 9 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000790 | 1 27 | 22 2 3 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000791 | 1 40 | 28 10 2 5 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000792 | 1 65 | 41 16 8 9 33 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000793 | 1 34 | 16 8 10 5 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000794 | 1 28 | 16 11 1 4 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000795 | 1 31 | 23 4 4 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000796 | 1 33 | 15 9 9 1 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000797 | 1 43 | 36 3 4 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000798 | 1 55 | 30 13 12 3 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000799 | 1 44 | 33 6 5 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000800 | 1 21 | 13 7 1 32 40 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000801 | 1 24 | 15 7 2 9 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000802 | 1 46 | 36 6 4 13 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000803 | 1 68 | 44 8 16 5 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000804 | 1 40 | 33 4 3 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000805 | 1 83 | 63 10 10 9 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000806 | 1 27 | 17 10 0 3 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000807 | 1 96 | 58 27 11 10 48 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000808 | 1 83 | 67 12 4 2 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000809 | 1 42 | 29 9 4 6 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000413 | 1 195 | 154 17 24 16 57 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000414 | 1 201 | 154 16 31 13 60 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000415 | 1 93 | 70 9 14 19 42 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000416 | 1 116 | 83 10 23 4 37 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000417 | 1 138 | 103 9 26 1 36 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000418 | 1 78 | 55 9 14 3 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000419 | 1 53 | 45 4 4 9 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000420 | 1 65 | 39 11 15 2 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000421 | 1 148 | 119 18 11 10 39 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000422 | 1 119 | 89 15 15 6 36 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000423 | 1 114 | 76 10 28 9 47 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000424 | 1 85 | 65 11 9 6 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000425 | 1 111 | 82 6 23 3 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000426 | 1 106 | 87 9 10 23 42 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000427 | 1 173 | 121 12 40 2 54 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000428 | 1 120 | 79 13 28 1 42 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000429 | 1 127 | 105 8 14 8 30 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000430 | 1 101 | 82 11 8 10 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000431 | 1 103 | 68 11 24 8 43 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000432 | 1 106 | 83 5 18 1 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000433 | 1 84 | 60 11 13 6 30 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000434 | 1 70 | 47 10 13 3 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000435 | 1 119 | 87 10 22 5 37 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000436 | 1 135 | 91 22 22 4 48 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000437 | 1 121 | 87 16 18 19 53 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000438 | 1 163 | 130 15 18 7 40 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000439 | 1 64 | 56 3 5 5 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000440 | 1 163 | 125 11 27 3 41 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000441 | 1 67 | 52 5 10 4 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000442 | 1 133 | 72 20 41 13 74 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000443 | 1 206 | 144 27 35 9 71 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000444 | 1 111 | 73 14 24 3 41 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000445 | 1 55 | 36 8 11 17 36 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000446 | 1 79 | 50 15 14 4 33 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000447 | 1 116 | 85 14 17 4 35 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000448 | 1 174 | 134 18 22 10 50 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000449 | 1 147 | 121 15 11 29 55 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000450 | 1 206 | 137 28 41 8 77 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000451 | 1 119 | 72 23 24 3 50 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000452 | 1 190 | 125 21 44 8 73 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000453 | 1 137 | 86 22 29 2 53 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000454 | 1 143 | 96 24 23 13 60 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000455 | 1 89 | 74 8 7 14 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000456 | 1 53 | 44 5 4 6 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000457 | 1 121 | 81 15 25 8 48 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000458 | 1 85 | 63 13 9 15 37 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000459 | 1 227 | 171 30 26 7 63 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000460 | 1 115 | 69 22 24 4 50 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000461 | 1 98 | 74 13 11 4 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000462 | 1 105 | 88 12 5 8 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000463 | 1 110 | 72 12 26 4 42 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000464 | 1 91 | 64 6 21 8 35 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000465 | 1 143 | 115 19 9 13 41 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000466 | 1 155 | 100 24 31 13 68 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000467 | 1 106 | 73 17 16 7 40 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000468 | 1 250 | 164 31 55 11 97 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000469 | 1 180 | 132 27 21 54 102 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000470 | 1 211 | 152 22 37 6 65 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000471 | 1 141 | 108 13 20 5 38 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000472 | 1 191 | 116 35 40 21 96 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000473 | 1 154 | 115 17 22 4 43 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000474 | 1 148 | 120 20 8 23 51 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000475 | 1 165 | 127 15 23 6 44 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000476 | 1 139 | 117 5 17 8 30 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000283 | 1 198 | 151 22 25 13 60 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000284 | 1 173 | 142 19 12 25 56 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000285 | 1 215 | 137 24 54 7 85 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000286 | 1 236 | 198 13 25 8 46 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000287 | 1 279 | 185 27 67 6 100 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000288 | 1 140 | 116 9 15 6 30 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000289 | 1 200 | 166 12 22 20 54 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000290 | 1 193 | 140 13 40 3 56 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000291 | 1 171 | 145 12 14 6 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000292 | 1 222 | 169 19 34 8 61 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000293 | 1 121 | 94 8 19 6 33 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000294 | 1 232 | 165 23 44 15 82 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000295 | 1 139 | 104 15 20 11 46 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000296 | 1 166 | 126 15 25 9 49 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000297 | 1 236 | 169 28 39 19 86 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000298 | 1 152 | 129 10 13 12 35 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000299 | 1 289 | 215 20 54 10 84 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000300 | 1 272 | 202 27 43 5 75 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000301 | 1 219 | 182 22 15 9 46 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000302 | 1 138 | 98 18 22 13 53 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000303 | 1 230 | 187 21 22 16 59 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000304 | 1 248 | 199 16 33 14 63 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000305 | 1 241 | 201 11 29 17 57 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000306 | 1 178 | 130 19 29 5 53 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000307 | 1 233 | 183 22 28 8 58 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000308 | 1 138 | 118 8 12 12 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000309 | 1 223 | 190 15 18 12 45 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000310 | 1 184 | 136 12 36 4 52 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000311 | 1 200 | 165 12 23 11 46 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000312 | 1 239 | 170 25 44 11 80 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000313 | 1 203 | 160 23 20 14 57 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000314 | 1 160 | 134 12 14 13 39 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000315 | 1 141 | 110 15 16 6 37 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000316 | 1 219 | 174 14 31 5 50 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000317 | 1 233 | 182 24 27 21 72 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000318 | 1 183 | 141 15 27 11 53 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000319 | 1 205 | 166 18 21 15 54 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000320 | 1 194 | 161 13 20 11 44 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000321 | 1 229 | 192 24 13 7 44 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000322 | 1 207 | 160 16 31 7 54 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001588 | 1 21 | 15 5 1 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001589 | 1 21 | 13 5 3 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001590 | 1 28 | 20 5 3 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001591 | 1 28 | 18 7 3 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001592 | 1 22 | 17 1 4 8 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001593 | 1 13 | 10 2 1 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001594 | 1 25 | 21 3 1 6 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001595 | 1 18 | 14 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001596 | 1 16 | 10 5 1 10 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001597 | 1 30 | 25 3 2 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001598 | 1 19 | 14 1 4 6 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001599 | 1 15 | 12 3 0 5 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001600 | 1 23 | 21 2 0 3 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001601 | 1 15 | 13 1 1 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001602 | 1 17 | 12 4 1 5 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001603 | 1 21 | 19 0 2 3 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001604 | 1 14 | 8 5 1 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001605 | 1 20 | 12 5 3 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001606 | 1 27 | 17 7 3 3 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001607 | 1 28 | 24 2 2 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001608 | 1 18 | 13 4 1 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001609 | 1 15 | 5 9 1 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001610 | 1 23 | 16 3 4 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001611 | 1 19 | 16 2 1 9 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001612 | 1 22 | 17 3 2 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001613 | 1 18 | 14 2 2 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001614 | 1 29 | 19 5 5 10 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001615 | 1 19 | 13 2 4 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001616 | 1 27 | 17 5 5 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001617 | 1 16 | 13 2 1 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001618 | 1 21 | 16 2 3 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001619 | 1 25 | 20 5 0 7 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001620 | 1 23 | 18 3 2 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001621 | 1 24 | 20 2 2 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001622 | 1 22 | 15 1 6 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001623 | 1 15 | 9 5 1 4 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001624 | 1 24 | 19 4 1 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001625 | 1 25 | 21 2 2 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001626 | 1 24 | 16 5 3 4 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001627 | 1 20 | 13 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001628 | 1 17 | 11 3 3 4 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001629 | 1 18 | 15 2 1 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001630 | 1 15 | 10 5 0 5 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001631 | 1 24 | 17 4 3 4 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001632 | 1 18 | 17 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001633 | 1 3 | 3 0 0 5 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001634 | 1 23 | 20 2 1 7 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001635 | 1 30 | 26 2 2 4 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001636 | 1 17 | 13 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001637 | 1 26 | 25 0 1 6 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001638 | 1 25 | 18 5 2 5 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001639 | 1 17 | 12 3 2 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001640 | 1 5 | 2 3 0 5 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001641 | 1 16 | 9 5 2 4 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001642 | 1 18 | 9 7 2 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001643 | 1 14 | 9 4 1 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001644 | 1 30 | 22 5 3 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001645 | 1 14 | 11 3 0 4 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001646 | 1 27 | 19 4 4 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001647 | 1 23 | 15 2 6 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001648 | 1 37 | 27 6 4 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001649 | 1 24 | 19 2 3 4 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001650 | 1 23 | 16 3 4 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001651 | 1 22 | 15 7 0 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001652 | 1 16 | 11 1 4 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001653 | 1 18 | 12 2 4 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001654 | 1 18 | 12 3 3 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001655 | 1 17 | 14 1 2 4 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001656 | 1 17 | 13 2 2 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001657 | 1 28 | 17 8 3 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001658 | 1 18 | 15 2 1 3 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001659 | 1 19 | 13 3 3 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001660 | 1 18 | 18 0 0 2 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001661 | 1 18 | 11 4 3 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001662 | 1 22 | 18 2 2 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001663 | 1 17 | 10 4 3 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001664 | 1 24 | 19 4 1 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001665 | 1 30 | 24 2 4 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001666 | 1 21 | 13 6 2 5 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001667 | 1 24 | 17 5 2 6 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001668 | 1 21 | 16 3 2 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001669 | 1 17 | 16 0 1 4 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001670 | 1 24 | 17 6 1 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001671 | 1 24 | 17 3 4 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001672 | 1 27 | 22 1 4 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001673 | 1 8 | 8 0 0 19 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001674 | 1 36 | 21 8 7 2 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001675 | 1 4 | 3 0 1 3 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001676 | 1 6 | 3 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001677 | 1 23 | 18 2 3 4 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001678 | 1 25 | 16 7 2 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001679 | 1 21 | 17 3 1 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001680 | 1 19 | 13 2 4 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001681 | 1 28 | 23 4 1 6 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001682 | 1 16 | 11 2 3 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001683 | 1 25 | 20 0 5 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001684 | 1 19 | 13 3 3 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001685 | 1 22 | 19 0 3 3 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001686 | 1 23 | 17 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001687 | 1 12 | 10 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001688 | 1 21 | 15 5 1 6 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001689 | 1 28 | 17 7 4 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001690 | 1 19 | 14 5 0 6 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001691 | 1 4 | 4 0 0 5 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001692 | 1 22 | 16 3 3 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001693 | 1 23 | 11 6 6 2 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001694 | 1 22 | 20 1 1 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001695 | 1 8 | 7 0 1 3 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001696 | 1 12 | 10 2 0 4 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001697 | 1 17 | 12 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001698 | 1 13 | 13 0 0 4 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001699 | 1 26 | 22 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001700 | 1 35 | 25 5 5 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001701 | 1 16 | 9 2 5 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001702 | 1 23 | 12 10 1 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001703 | 1 12 | 9 2 1 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001704 | 1 17 | 6 6 5 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001705 | 1 19 | 18 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001706 | 1 22 | 16 4 2 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001707 | 1 27 | 22 2 3 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001708 | 1 16 | 10 4 2 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001709 | 1 19 | 14 4 1 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001710 | 1 18 | 15 2 1 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001711 | 1 15 | 14 1 0 4 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001712 | 1 22 | 16 1 5 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001713 | 1 20 | 15 1 4 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001714 | 1 17 | 13 1 3 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001715 | 1 21 | 16 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001716 | 1 19 | 15 2 2 3 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001717 | 1 20 | 15 2 3 6 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001718 | 1 20 | 15 4 1 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001719 | 1 24 | 18 3 3 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001720 | 1 22 | 17 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001721 | 1 24 | 15 3 6 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001722 | 1 8 | 7 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001723 | 1 22 | 12 6 4 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001724 | 1 16 | 8 6 2 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001725 | 1 7 | 6 1 0 4 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001726 | 1 22 | 20 1 1 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001727 | 1 17 | 13 4 0 3 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001728 | 1 21 | 18 2 1 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001729 | 1 19 | 15 2 2 3 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001730 | 1 22 | 16 2 4 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001731 | 1 15 | 10 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001732 | 1 23 | 17 4 2 4 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001733 | 1 27 | 16 8 3 5 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001734 | 1 18 | 13 5 0 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001735 | 1 25 | 17 6 2 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001736 | 1 19 | 14 4 1 5 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001737 | 1 13 | 8 3 2 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001738 | 1 27 | 21 5 1 5 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001739 | 1 18 | 14 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001740 | 1 25 | 18 3 4 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001741 | 1 5 | 4 1 0 9 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001742 | 1 31 | 23 5 3 5 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001743 | 1 25 | 18 4 3 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001744 | 1 21 | 15 5 1 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001745 | 1 24 | 15 4 5 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001746 | 1 26 | 16 8 2 3 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001747 | 1 20 | 15 5 0 5 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001748 | 1 24 | 18 6 0 6 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001749 | 1 23 | 18 1 4 4 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001750 | 1 22 | 18 4 0 4 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001751 | 1 23 | 16 3 4 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001752 | 1 31 | 21 7 3 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001753 | 1 25 | 18 4 3 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001754 | 1 21 | 11 5 5 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001755 | 1 22 | 13 0 9 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001756 | 1 22 | 17 2 3 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001757 | 1 8 | 7 1 0 2 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001758 | 1 20 | 12 5 3 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001759 | 1 33 | 26 5 2 4 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001760 | 1 20 | 15 2 3 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001761 | 1 17 | 14 1 2 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001762 | 1 9 | 5 4 0 3 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001763 | 1 21 | 14 4 3 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001764 | 1 31 | 25 3 3 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001765 | 1 19 | 17 1 1 3 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001766 | 1 12 | 8 3 1 3 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001767 | 1 9 | 6 0 3 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001768 | 1 39 | 26 7 6 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001769 | 1 21 | 13 6 2 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001770 | 1 22 | 14 6 2 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001771 | 1 26 | 20 2 4 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001772 | 1 21 | 14 3 4 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001773 | 1 17 | 16 0 1 7 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001774 | 1 18 | 15 1 2 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001775 | 1 21 | 15 4 2 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001776 | 1 19 | 15 1 3 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001777 | 1 27 | 18 9 0 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001778 | 1 8 | 6 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001779 | 1 21 | 18 1 2 4 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001780 | 1 30 | 24 4 2 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001781 | 1 15 | 12 2 1 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001782 | 1 17 | 14 0 3 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001783 | 1 23 | 17 4 2 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001784 | 1 19 | 15 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001785 | 1 19 | 16 0 3 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001786 | 1 28 | 25 1 2 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001787 | 1 5 | 2 3 0 6 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001788 | 1 26 | 19 4 3 4 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001789 | 1 20 | 18 1 1 4 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001790 | 1 20 | 14 4 2 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001791 | 1 18 | 11 5 2 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001792 | 1 17 | 12 2 3 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001793 | 1 26 | 22 3 1 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001794 | 1 17 | 11 5 1 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001795 | 1 25 | 19 3 3 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001796 | 1 25 | 17 7 1 5 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001797 | 1 21 | 12 8 1 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001798 | 1 22 | 14 3 5 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001799 | 1 20 | 15 2 3 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001800 | 1 18 | 14 4 0 7 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001801 | 1 21 | 17 3 1 3 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001802 | 1 23 | 17 3 3 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001803 | 1 28 | 23 4 1 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001804 | 1 24 | 17 6 1 5 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001805 | 1 19 | 12 2 5 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001806 | 1 28 | 19 6 3 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001807 | 1 26 | 23 2 1 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001808 | 1 16 | 12 2 2 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001809 | 1 21 | 14 4 3 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001810 | 1 20 | 16 2 2 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001811 | 1 20 | 16 2 2 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001812 | 1 14 | 8 4 2 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001813 | 1 23 | 18 4 1 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001814 | 1 16 | 10 4 2 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001815 | 1 25 | 21 4 0 3 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001816 | 1 39 | 29 6 4 4 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001817 | 1 21 | 17 4 0 7 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001818 | 1 22 | 17 3 2 4 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001819 | 1 25 | 15 9 1 5 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001820 | 1 29 | 23 5 1 4 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001821 | 1 18 | 17 1 0 3 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001822 | 1 21 | 14 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001823 | 1 37 | 27 5 5 4 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001824 | 1 16 | 11 5 0 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001744 | 1 31 | 26 2 3 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001745 | 1 18 | 7 9 2 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001746 | 1 54 | 43 3 8 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001747 | 1 62 | 45 9 8 0 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001748 | 1 21 | 16 0 5 4 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001749 | 1 15 | 9 4 2 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001750 | 1 35 | 25 3 7 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001751 | 1 22 | 14 3 5 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001752 | 1 49 | 39 6 4 4 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001753 | 1 40 | 27 6 7 3 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001754 | 1 30 | 19 6 5 3 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001755 | 1 22 | 14 4 4 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001756 | 1 26 | 19 2 5 10 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001757 | 1 72 | 48 8 16 2 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001758 | 1 17 | 10 4 3 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001759 | 1 43 | 28 10 5 3 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001760 | 1 17 | 15 1 1 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001761 | 1 50 | 39 4 7 8 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001762 | 1 27 | 22 0 5 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001763 | 1 96 | 79 4 13 3 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001764 | 1 42 | 26 6 10 3 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001765 | 1 47 | 33 7 7 1 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001766 | 1 21 | 12 0 9 4 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001767 | 1 55 | 32 13 10 2 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001768 | 1 36 | 28 4 4 5 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001769 | 1 42 | 33 3 6 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001770 | 1 28 | 24 3 1 9 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001771 | 1 25 | 17 3 5 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001772 | 1 38 | 28 3 7 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001773 | 1 18 | 13 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001774 | 1 21 | 14 2 5 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001775 | 1 63 | 42 10 11 2 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001776 | 1 77 | 54 18 5 3 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001777 | 1 77 | 65 4 8 5 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001778 | 1 30 | 25 2 3 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001779 | 1 44 | 24 4 16 0 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001780 | 1 52 | 41 7 4 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001781 | 1 18 | 13 4 1 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001782 | 1 87 | 65 11 11 7 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001783 | 1 23 | 18 1 4 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001784 | 1 23 | 12 5 6 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001785 | 1 31 | 21 7 3 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001786 | 1 120 | 83 11 26 5 42 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001787 | 1 31 | 23 5 3 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001788 | 1 38 | 31 2 5 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001789 | 1 24 | 17 2 5 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001790 | 1 73 | 53 9 11 1 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001791 | 1 39 | 29 3 7 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001792 | 1 18 | 17 0 1 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001793 | 1 30 | 25 1 4 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001794 | 1 58 | 41 3 14 5 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001795 | 1 23 | 15 3 5 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001796 | 1 23 | 22 1 0 2 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001797 | 1 49 | 32 3 14 1 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001798 | 1 17 | 7 7 3 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001799 | 1 31 | 22 3 6 5 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001800 | 1 22 | 17 0 5 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001801 | 1 54 | 46 0 8 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001802 | 1 29 | 19 2 8 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001803 | 1 45 | 33 5 7 4 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001804 | 1 36 | 30 1 5 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001805 | 1 36 | 23 4 9 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001806 | 1 19 | 15 3 1 6 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001807 | 1 25 | 13 4 8 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001808 | 1 22 | 17 1 4 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001809 | 1 21 | 12 5 4 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001810 | 1 25 | 19 3 3 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001811 | 1 50 | 34 8 8 2 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001812 | 1 24 | 17 2 5 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001813 | 1 32 | 26 3 3 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001814 | 1 31 | 23 4 4 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001815 | 1 32 | 26 1 5 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001816 | 1 70 | 52 5 13 1 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001817 | 1 52 | 32 3 17 0 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001818 | 1 19 | 12 4 3 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001819 | 1 33 | 25 7 1 11 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001820 | 1 19 | 16 1 2 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001821 | 1 24 | 12 9 3 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001822 | 1 19 | 15 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001823 | 1 82 | 66 4 12 6 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001824 | 1 44 | 33 3 8 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001825 | 1 45 | 38 5 2 4 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001826 | 1 35 | 23 1 11 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001827 | 1 42 | 34 5 3 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001828 | 1 18 | 7 5 6 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001829 | 1 43 | 24 3 16 0 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001830 | 1 51 | 36 3 12 2 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001831 | 1 23 | 16 3 4 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001832 | 1 27 | 17 4 6 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001833 | 1 18 | 14 0 4 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001834 | 1 45 | 31 4 10 5 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001835 | 1 17 | 13 1 3 3 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001836 | 1 21 | 14 1 6 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001837 | 1 30 | 17 8 5 2 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001838 | 1 37 | 29 1 7 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001839 | 1 48 | 27 5 16 1 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001840 | 1 25 | 17 4 4 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001841 | 1 28 | 20 2 6 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001842 | 1 42 | 36 2 4 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001843 | 1 47 | 33 7 7 3 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001844 | 1 31 | 25 4 2 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001845 | 1 20 | 11 8 1 4 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001846 | 1 96 | 75 7 14 4 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001847 | 1 18 | 15 1 2 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001848 | 1 47 | 30 9 8 1 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001849 | 1 62 | 48 5 9 2 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001850 | 1 18 | 11 4 3 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001851 | 1 27 | 21 5 1 4 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001852 | 1 55 | 42 6 7 3 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001853 | 1 47 | 39 3 5 9 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001854 | 1 35 | 26 2 7 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001855 | 1 40 | 32 4 4 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001856 | 1 27 | 22 0 5 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001857 | 1 26 | 22 3 1 3 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001858 | 1 45 | 39 4 2 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001859 | 1 15 | 11 1 3 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001860 | 1 38 | 32 1 5 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001861 | 1 9 | 8 1 0 2 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001862 | 1 64 | 56 1 7 4 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001863 | 1 22 | 17 2 3 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001864 | 1 13 | 12 1 0 2 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001865 | 1 34 | 25 3 6 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001866 | 1 30 | 24 3 3 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001867 | 1 49 | 28 5 16 8 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001868 | 1 28 | 18 4 6 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001869 | 1 30 | 18 1 11 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001870 | 1 24 | 19 0 5 8 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001871 | 1 76 | 60 8 8 8 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001872 | 1 24 | 19 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001873 | 1 11 | 8 2 1 5 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001874 | 1 35 | 20 5 10 0 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001875 | 1 52 | 25 17 10 3 30 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001876 | 1 27 | 18 3 6 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001877 | 1 36 | 31 4 1 4 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001878 | 1 17 | 10 3 4 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001879 | 1 174 | 141 11 22 8 41 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001880 | 1 63 | 49 5 9 1 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001881 | 1 122 | 102 9 11 2 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001882 | 1 33 | 26 5 2 4 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001883 | 1 51 | 44 3 4 14 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001884 | 1 32 | 27 2 3 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001885 | 1 34 | 25 2 7 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001886 | 1 32 | 23 5 4 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001887 | 1 33 | 25 3 5 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001888 | 1 43 | 33 1 9 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001889 | 1 15 | 9 3 3 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001890 | 1 12 | 12 0 0 4 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001891 | 1 36 | 21 8 7 0 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001892 | 1 36 | 26 2 8 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001893 | 1 23 | 13 4 6 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001894 | 1 40 | 26 9 5 1 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001895 | 1 37 | 29 3 5 4 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001896 | 1 30 | 27 1 2 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001897 | 1 12 | 11 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001898 | 1 40 | 29 3 8 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001899 | 1 49 | 43 3 3 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001900 | 1 31 | 24 4 3 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001901 | 1 20 | 15 3 2 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001902 | 1 42 | 30 6 6 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001903 | 1 22 | 15 3 4 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001904 | 1 37 | 25 3 9 5 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001905 | 1 44 | 30 4 10 1 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001906 | 1 81 | 66 4 11 4 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001907 | 1 20 | 13 1 6 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001908 | 1 29 | 20 3 6 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001909 | 1 22 | 16 1 5 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001910 | 1 43 | 37 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001911 | 1 32 | 24 2 6 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001912 | 1 37 | 29 6 2 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001913 | 1 24 | 18 0 6 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001914 | 1 58 | 42 8 8 1 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001915 | 1 47 | 22 8 17 0 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001916 | 1 25 | 17 4 4 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001917 | 1 21 | 16 5 0 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001918 | 1 61 | 46 6 9 1 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001919 | 1 58 | 44 6 8 1 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001920 | 1 45 | 39 3 3 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001921 | 1 18 | 10 4 4 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001922 | 1 34 | 25 4 5 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001923 | 1 129 | 100 8 21 6 35 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001924 | 1 76 | 53 8 15 5 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001925 | 1 48 | 35 9 4 4 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001926 | 1 27 | 20 1 6 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001927 | 1 23 | 16 4 3 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001928 | 1 19 | 15 1 3 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001929 | 1 26 | 18 2 6 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001930 | 1 32 | 22 4 6 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001931 | 1 14 | 11 1 2 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001932 | 1 45 | 39 3 3 5 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001933 | 1 25 | 19 2 4 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001934 | 1 38 | 33 1 4 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001935 | 1 26 | 20 4 2 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001936 | 1 44 | 35 2 7 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001937 | 1 15 | 6 8 1 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001938 | 1 72 | 47 13 12 9 34 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001939 | 1 32 | 27 2 3 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001940 | 1 45 | 30 3 12 0 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001941 | 1 36 | 18 6 12 5 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001942 | 1 19 | 13 3 3 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001943 | 1 44 | 29 2 13 3 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001944 | 1 43 | 31 7 5 3 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001945 | 1 37 | 29 2 6 6 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001946 | 1 106 | 88 9 9 5 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001947 | 1 23 | 16 2 5 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001948 | 1 23 | 14 5 4 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001949 | 1 38 | 26 4 8 3 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001950 | 1 15 | 7 4 4 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001951 | 1 39 | 31 2 6 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001952 | 1 67 | 48 9 10 7 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001953 | 1 23 | 14 5 4 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001954 | 1 24 | 16 5 3 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001955 | 1 19 | 14 1 4 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001956 | 1 34 | 26 2 6 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001957 | 1 73 | 51 8 14 3 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001958 | 1 24 | 20 1 3 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001959 | 1 41 | 32 3 6 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001960 | 1 51 | 37 2 12 1 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001961 | 1 45 | 37 4 4 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001962 | 1 14 | 12 2 0 3 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001963 | 1 35 | 25 4 6 4 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001964 | 1 15 | 11 3 1 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001965 | 1 24 | 15 5 4 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001966 | 1 38 | 27 4 7 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001967 | 1 60 | 49 3 8 3 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001968 | 1 63 | 41 6 16 1 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001969 | 1 64 | 42 7 15 2 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001970 | 1 42 | 31 5 6 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001971 | 1 13 | 10 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001972 | 1 61 | 42 3 16 0 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001973 | 1 50 | 42 2 6 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001974 | 1 18 | 13 3 2 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001975 | 1 43 | 32 7 4 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001976 | 1 27 | 14 6 7 1 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001977 | 1 22 | 18 2 2 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001978 | 1 15 | 11 1 3 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001979 | 1 68 | 45 8 15 1 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001980 | 1 51 | 29 7 15 0 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001981 | 1 42 | 29 5 8 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001982 | 1 16 | 13 1 2 8 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001983 | 1 77 | 59 9 9 2 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001984 | 1 31 | 25 2 4 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001985 | 1 33 | 20 5 8 2 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001986 | 1 77 | 65 6 6 1 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001987 | 1 15 | 12 0 3 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001988 | 1 25 | 17 3 5 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001989 | 1 51 | 33 2 16 9 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001990 | 1 38 | 31 3 4 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001991 | 1 35 | 25 5 5 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001992 | 1 15 | 12 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001993 | 1 44 | 28 11 5 3 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001994 | 1 29 | 25 0 4 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001995 | 1 21 | 13 4 4 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001996 | 1 31 | 17 5 9 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001997 | 1 34 | 20 6 8 3 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001998 | 1 53 | 38 8 7 3 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001999 | 1 20 | 8 4 8 3 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002000 | 1 64 | 54 1 9 17 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002001 | 1 21 | 14 4 3 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002002 | 1 17 | 12 3 2 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002003 | 1 23 | 18 3 2 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002004 | 1 23 | 19 2 2 6 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002005 | 1 25 | 18 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000874 | 1 57 | 43 3 11 3 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000875 | 1 20 | 18 0 2 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000876 | 1 46 | 34 5 7 1 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000877 | 1 50 | 43 2 5 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000878 | 1 24 | 23 1 0 5 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000879 | 1 49 | 42 6 1 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000880 | 1 45 | 34 5 6 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000883 | 1 26 | 18 5 3 5 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000884 | 1 60 | 56 2 2 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000885 | 1 36 | 26 1 9 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000886 | 1 62 | 48 7 7 10 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000887 | 1 65 | 45 7 13 0 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000888 | 1 40 | 34 2 4 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000889 | 1 36 | 29 2 5 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000890 | 1 23 | 16 2 5 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000891 | 1 81 | 68 1 12 2 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000892 | 1 21 | 18 2 1 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000893 | 1 26 | 23 2 1 3 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000894 | 1 91 | 78 6 7 4 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000895 | 1 57 | 46 3 8 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000896 | 1 27 | 21 4 2 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000897 | 1 30 | 28 1 1 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000898 | 1 37 | 31 1 5 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000899 | 1 22 | 19 2 1 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000900 | 1 61 | 43 4 14 1 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000901 | 1 31 | 24 3 4 18 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000902 | 1 24 | 18 6 0 8 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000903 | 1 45 | 37 4 4 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000904 | 1 32 | 21 7 4 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000905 | 1 48 | 28 4 16 0 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000906 | 1 50 | 37 3 10 4 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000907 | 1 60 | 51 5 4 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000908 | 1 42 | 33 3 6 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000909 | 1 57 | 47 2 8 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000910 | 1 54 | 50 1 3 3 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000911 | 1 26 | 21 2 3 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000912 | 1 33 | 32 0 1 3 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000913 | 1 68 | 54 7 7 1 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000914 | 1 47 | 35 3 9 3 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000915 | 1 28 | 24 1 3 4 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000916 | 1 35 | 33 1 1 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000917 | 1 40 | 35 4 1 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000918 | 1 67 | 57 5 5 4 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000920 | 1 35 | 28 2 5 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000921 | 1 49 | 38 2 9 4 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000922 | 1 57 | 52 3 2 6 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000923 | 1 46 | 41 2 3 4 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000924 | 1 16 | 13 1 2 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000925 | 1 65 | 51 9 5 12 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000927 | 1 49 | 43 2 4 4 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000928 | 1 48 | 35 2 11 1 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000929 | 1 19 | 13 3 3 7 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000930 | 1 58 | 53 2 3 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000931 | 1 57 | 48 8 1 5 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000932 | 1 51 | 41 5 5 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000933 | 1 31 | 16 7 8 2 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000934 | 1 38 | 28 3 7 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000935 | 1 38 | 29 6 3 5 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000938 | 1 26 | 21 4 1 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000939 | 1 74 | 58 6 10 6 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000940 | 1 55 | 34 5 16 2 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000942 | 1 51 | 38 5 8 5 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000943 | 1 39 | 30 5 4 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000944 | 1 102 | 74 7 21 7 35 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000945 | 1 57 | 43 10 4 4 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000946 | 1 32 | 22 3 7 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000947 | 1 50 | 42 2 6 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000948 | 1 32 | 20 6 6 3 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000949 | 1 68 | 59 7 2 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000950 | 1 47 | 37 1 9 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000951 | 1 23 | 19 1 3 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000952 | 1 22 | 19 1 2 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000953 | 1 48 | 35 7 6 1 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000954 | 1 52 | 42 4 6 6 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000955 | 1 76 | 58 4 14 2 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000956 | 1 52 | 37 11 4 11 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000957 | 1 23 | 21 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000958 | 1 48 | 41 4 3 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000959 | 1 118 | 95 8 15 6 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000960 | 1 87 | 75 5 7 3 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000961 | 1 74 | 61 7 6 3 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000962 | 1 23 | 20 0 3 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000963 | 1 60 | 44 0 16 3 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000964 | 1 67 | 53 3 11 3 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000965 | 1 37 | 31 2 4 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000966 | 1 25 | 24 0 1 4 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000967 | 1 31 | 19 6 6 2 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000968 | 1 47 | 38 4 5 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000971 | 1 41 | 34 3 4 4 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000972 | 1 61 | 47 6 8 3 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000973 | 1 81 | 69 4 8 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000974 | 1 71 | 61 7 3 4 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000975 | 1 68 | 58 7 3 5 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000976 | 1 42 | 35 2 5 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000977 | 1 28 | 21 4 3 8 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000978 | 1 30 | 28 1 1 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000979 | 1 46 | 33 5 8 4 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000980 | 1 52 | 40 1 11 1 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000981 | 1 56 | 46 3 7 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000982 | 1 57 | 38 6 13 1 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000983 | 1 55 | 50 5 0 5 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000984 | 1 23 | 17 1 5 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000985 | 1 43 | 35 2 6 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000986 | 1 85 | 69 3 13 2 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000987 | 1 82 | 63 11 8 17 36 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000988 | 1 39 | 32 3 4 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000989 | 1 97 | 78 5 14 3 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000990 | 1 44 | 36 4 4 4 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000991 | 1 45 | 34 2 9 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000992 | 1 38 | 32 2 4 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000993 | 1 41 | 28 4 9 3 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000994 | 1 54 | 41 5 8 2 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000995 | 1 91 | 72 4 15 0 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000996 | 1 47 | 39 2 6 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000997 | 1 69 | 53 4 12 2 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000998 | 1 39 | 28 4 7 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000999 | 1 25 | 18 2 5 4 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001000 | 1 68 | 58 2 8 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001001 | 1 50 | 36 11 3 4 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001002 | 1 28 | 23 4 1 6 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001003 | 1 54 | 49 4 1 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001004 | 1 53 | 44 3 6 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000494 | 1 81 | 63 5 13 14 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000495 | 1 169 | 118 25 26 12 63 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000496 | 1 147 | 121 13 13 21 47 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000497 | 1 91 | 71 7 13 9 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000498 | 1 156 | 117 13 26 16 55 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000499 | 1 377 | 292 26 59 22 107 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000500 | 1 123 | 106 6 11 8 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000501 | 1 22 | 17 3 2 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000502 | 1 304 | 227 26 51 23 100 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000503 | 1 170 | 128 16 26 8 50 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000504 | 1 203 | 139 22 42 1 65 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000505 | 1 285 | 217 23 45 20 88 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000506 | 1 168 | 114 16 38 17 71 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000507 | 1 22 | 15 0 7 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000508 | 1 42 | 30 4 8 9 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000509 | 1 284 | 213 33 38 35 106 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000510 | 1 261 | 189 27 45 15 87 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000511 | 1 256 | 188 19 49 18 86 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000512 | 1 48 | 44 2 2 3 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000513 | 1 74 | 61 5 8 7 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000514 | 1 58 | 39 7 12 0 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000515 | 1 106 | 85 14 7 5 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000516 | 1 88 | 60 9 19 8 36 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000517 | 1 168 | 137 8 23 14 45 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000518 | 1 99 | 75 8 16 4 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000519 | 1 151 | 103 22 26 16 64 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000520 | 1 179 | 140 22 17 15 54 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000521 | 1 248 | 197 27 24 12 63 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000522 | 1 68 | 56 5 7 3 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000523 | 1 119 | 95 6 18 7 31 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000524 | 1 187 | 150 17 20 15 52 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000525 | 1 56 | 52 1 3 4 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000526 | 1 79 | 66 5 8 2 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000527 | 1 247 | 190 30 27 33 90 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000528 | 1 105 | 79 12 14 12 38 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000529 | 1 91 | 65 5 21 2 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000530 | 1 156 | 114 24 18 31 73 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000531 | 1 42 | 31 7 4 3 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000532 | 1 26 | 24 2 0 4 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000533 | 1 89 | 66 5 18 9 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000534 | 1 199 | 158 17 24 10 51 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000535 | 1 185 | 148 24 13 12 49 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000536 | 1 166 | 124 12 30 7 49 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000537 | 1 131 | 77 19 35 5 59 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000538 | 1 119 | 100 6 13 5 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000539 | 1 118 | 82 17 19 7 43 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000540 | 1 115 | 84 20 11 17 48 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000541 | 1 76 | 63 6 7 9 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000542 | 1 53 | 42 5 6 6 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000543 | 1 101 | 67 17 17 9 43 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000544 | 1 133 | 96 21 16 15 52 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000545 | 1 40 | 32 4 4 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000546 | 1 30 | 17 5 8 1 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000547 | 1 127 | 97 16 14 8 38 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000548 | 1 204 | 169 18 17 24 59 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000549 | 1 127 | 87 12 28 2 42 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000550 | 1 95 | 73 13 9 17 39 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000551 | 1 63 | 52 6 5 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000552 | 1 194 | 145 20 29 27 76 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000553 | 1 138 | 112 12 14 2 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000554 | 1 196 | 161 15 20 5 40 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000555 | 1 81 | 67 9 5 10 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000556 | 1 111 | 83 12 16 4 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000557 | 1 45 | 41 2 2 6 10 1 | +|====================================================================================================================| +| Sum | 1092 67334 | 51304 6728 9302 4864 20894 1091 | +|====================================================================================================================| +| Mean | 1.1 66.3 | 50.5 6.6 9.2 4.8 20.6 1.1 | +| S.D. | 2.4 267.1 | 211.6 15.6 40.7 12.6 67.7 2.4 | +| Median | 1.0 40.0 | 29.5 4.0 5.0 3.0 12.0 1.0 | +`--------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/hyp.trn + +Speakers: + 0: lad_eng_000254 + 1: lad_eng_000255 + 2: lad_eng_000256 + 3: lad_eng_000257 + 4: lad_eng_000258 + 5: lad_eng_000259 + 6: lad_eng_000260 + 7: lad_eng_000261 + 8: lad_eng_000262 + 9: lad_eng_000263 + 10: lad_eng_000264 + 11: lad_eng_000265 + 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lad_eng_000313 + 60: lad_eng_000314 + 61: lad_eng_000315 + 62: lad_eng_000316 + 63: lad_eng_000317 + 64: lad_eng_000318 + 65: lad_eng_000319 + 66: lad_eng_000320 + 67: lad_eng_000321 + 68: lad_eng_000322 + 69: lad_eng_000323 + 70: lad_eng_000324 + 71: lad_eng_000325 + 72: lad_eng_000326 + 73: lad_eng_000327 + 74: lad_eng_000328 + 75: lad_eng_000329 + 76: lad_eng_000330 + 77: lad_eng_000331 + 78: lad_eng_000332 + 79: lad_eng_000333 + 80: lad_eng_000334 + 81: lad_eng_000335 + 82: lad_eng_000336 + 83: lad_eng_000337 + 84: lad_eng_000338 + 85: lad_eng_000339 + 86: lad_eng_000340 + 87: lad_eng_000341 + 88: lad_eng_000342 + 89: lad_eng_000343 + 90: lad_eng_000344 + 91: lad_eng_000345 + 92: lad_eng_000346 + 93: lad_eng_000347 + 94: lad_eng_000348 + 95: lad_eng_000349 + 96: lad_eng_000350 + 97: lad_eng_000351 + 98: lad_eng_000352 + 99: lad_eng_000353 + 100: lad_eng_000354 + 101: lad_eng_000355 + 102: lad_eng_000356 + 103: lad_eng_000357 + 104: lad_eng_000358 + 105: lad_eng_000359 + 106: 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swc_eng_001980 + 805: swc_eng_001981 + 806: swc_eng_001982 + 807: swc_eng_001983 + 808: swc_eng_001984 + 809: swc_eng_001985 + 810: swc_eng_001986 + 811: swc_eng_001987 + 812: swc_eng_001988 + 813: swc_eng_001989 + 814: swc_eng_001990 + 815: swc_eng_001991 + 816: swc_eng_001992 + 817: swc_eng_001993 + 818: swc_eng_001994 + 819: swc_eng_001995 + 820: swc_eng_001996 + 821: swc_eng_001997 + 822: swc_eng_001998 + 823: swc_eng_001999 + 824: swc_eng_002000 + 825: swc_eng_002001 + 826: swc_eng_002002 + 827: swc_eng_002003 + 828: swc_eng_002004 + 829: swc_eng_002005 + 830: voxforge_eng_000874 + 831: voxforge_eng_000875 + 832: voxforge_eng_000876 + 833: voxforge_eng_000877 + 834: voxforge_eng_000878 + 835: voxforge_eng_000879 + 836: voxforge_eng_000880 + 837: voxforge_eng_000883 + 838: voxforge_eng_000884 + 839: voxforge_eng_000885 + 840: voxforge_eng_000886 + 841: voxforge_eng_000887 + 842: voxforge_eng_000888 + 843: voxforge_eng_000889 + 844: voxforge_eng_000890 + 845: voxforge_eng_000891 + 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883: voxforge_eng_000931 + 884: voxforge_eng_000932 + 885: voxforge_eng_000933 + 886: voxforge_eng_000934 + 887: voxforge_eng_000935 + 888: voxforge_eng_000938 + 889: voxforge_eng_000939 + 890: voxforge_eng_000940 + 891: voxforge_eng_000942 + 892: voxforge_eng_000943 + 893: voxforge_eng_000944 + 894: voxforge_eng_000945 + 895: voxforge_eng_000946 + 896: voxforge_eng_000947 + 897: voxforge_eng_000948 + 898: voxforge_eng_000949 + 899: voxforge_eng_000950 + 900: voxforge_eng_000951 + 901: voxforge_eng_000952 + 902: voxforge_eng_000953 + 903: voxforge_eng_000954 + 904: voxforge_eng_000955 + 905: voxforge_eng_000956 + 906: voxforge_eng_000957 + 907: voxforge_eng_000958 + 908: voxforge_eng_000959 + 909: voxforge_eng_000960 + 910: voxforge_eng_000961 + 911: voxforge_eng_000962 + 912: voxforge_eng_000963 + 913: voxforge_eng_000964 + 914: voxforge_eng_000965 + 915: voxforge_eng_000966 + 916: voxforge_eng_000967 + 917: voxforge_eng_000968 + 918: voxforge_eng_000971 + 919: voxforge_eng_000972 + 920: voxforge_eng_000973 + 921: voxforge_eng_000974 + 922: voxforge_eng_000975 + 923: voxforge_eng_000976 + 924: voxforge_eng_000977 + 925: voxforge_eng_000978 + 926: voxforge_eng_000979 + 927: voxforge_eng_000980 + 928: voxforge_eng_000981 + 929: voxforge_eng_000982 + 930: voxforge_eng_000983 + 931: voxforge_eng_000984 + 932: voxforge_eng_000985 + 933: voxforge_eng_000986 + 934: voxforge_eng_000987 + 935: voxforge_eng_000988 + 936: voxforge_eng_000989 + 937: voxforge_eng_000990 + 938: voxforge_eng_000991 + 939: voxforge_eng_000992 + 940: voxforge_eng_000993 + 941: voxforge_eng_000994 + 942: voxforge_eng_000995 + 943: voxforge_eng_000996 + 944: voxforge_eng_000997 + 945: voxforge_eng_000998 + 946: voxforge_eng_000999 + 947: voxforge_eng_001000 + 948: voxforge_eng_001001 + 949: voxforge_eng_001002 + 950: voxforge_eng_001003 + 951: voxforge_eng_001004 + 952: voxpopuli_eng_000494 + 953: voxpopuli_eng_000495 + 954: voxpopuli_eng_000496 + 955: voxpopuli_eng_000497 + 956: voxpopuli_eng_000498 + 957: voxpopuli_eng_000499 + 958: voxpopuli_eng_000500 + 959: voxpopuli_eng_000501 + 960: voxpopuli_eng_000502 + 961: voxpopuli_eng_000503 + 962: voxpopuli_eng_000504 + 963: voxpopuli_eng_000505 + 964: voxpopuli_eng_000506 + 965: voxpopuli_eng_000507 + 966: voxpopuli_eng_000508 + 967: voxpopuli_eng_000509 + 968: voxpopuli_eng_000510 + 969: voxpopuli_eng_000511 + 970: voxpopuli_eng_000512 + 971: voxpopuli_eng_000513 + 972: voxpopuli_eng_000514 + 973: voxpopuli_eng_000515 + 974: voxpopuli_eng_000516 + 975: voxpopuli_eng_000517 + 976: voxpopuli_eng_000518 + 977: voxpopuli_eng_000519 + 978: voxpopuli_eng_000520 + 979: voxpopuli_eng_000521 + 980: voxpopuli_eng_000522 + 981: voxpopuli_eng_000523 + 982: voxpopuli_eng_000524 + 983: voxpopuli_eng_000525 + 984: voxpopuli_eng_000526 + 985: voxpopuli_eng_000527 + 986: voxpopuli_eng_000528 + 987: voxpopuli_eng_000529 + 988: voxpopuli_eng_000530 + 989: voxpopuli_eng_000531 + 990: voxpopuli_eng_000532 + 991: voxpopuli_eng_000533 + 992: voxpopuli_eng_000534 + 993: voxpopuli_eng_000535 + 994: voxpopuli_eng_000536 + 995: voxpopuli_eng_000537 + 996: voxpopuli_eng_000538 + 997: voxpopuli_eng_000539 + 998: voxpopuli_eng_000540 + 999: voxpopuli_eng_000541 + 1000: voxpopuli_eng_000542 + 1001: voxpopuli_eng_000543 + 1002: voxpopuli_eng_000544 + 1003: voxpopuli_eng_000545 + 1004: voxpopuli_eng_000546 + 1005: voxpopuli_eng_000547 + 1006: voxpopuli_eng_000548 + 1007: voxpopuli_eng_000549 + 1008: voxpopuli_eng_000550 + 1009: voxpopuli_eng_000551 + 1010: voxpopuli_eng_000552 + 1011: voxpopuli_eng_000553 + 1012: voxpopuli_eng_000554 + 1013: voxpopuli_eng_000555 + 1014: voxpopuli_eng_000556 + 1015: voxpopuli_eng_000557 + +Speaker sentences 0: lad_eng_000254 #utts: 1 +id: (lad_eng_000254-lad_eng_000254) +Scores: (#C #S #D #I) 77 8 15 0 +REF: h e r e m a I N e d w O R l D c H a m p i o n U n t i l n i n E t E E n s i X t y f i v e a y E a r i n w h I c H h e s u F f E R e d a t e R r I b l E a c C i D e n t +HYP: h e r e m a * * e d w * E l * c * a m p i o n A n t i l n i n * t * I n s i C t y f i v e a y * a r i n w h * c * ******* h e s u * f * H e d a t e * r A b l * a c X i T e n t +Eval: D D D S D D S D D S S D D D D D D S D S D S S + +Speaker sentences 1: lad_eng_000255 #utts: 1 +id: (lad_eng_000255-lad_eng_000255) +Scores: (#C #S #D #I) 45 6 9 2 +REF: a * l i b E r A l ******* c o n S e r v A t i v e h e W a s d e f e a t e d i n E I G H t E E n E I G H t y t w o +HYP: a Y l i b * r * l c o n e r v E t i v e h e * a s d e f e a t e d i n * * * A t Y n * * * A t y t w o +Eval: I D D I S S D D D D S S S D D D S + +Speaker sentences 2: lad_eng_000256 #utts: 1 +id: (lad_eng_000256-lad_eng_000256) +Scores: (#C #S #D #I) 29 6 6 4 +REF: * o n E r o * A d l a Y E r c A N d r A W t w o r o A d S a t * * O n c e +HYP: W o n * r o V E d l a * * r c * * O d r * E t w o r o U d E a t W H A n c e +Eval: I D I S D D D D S D S S S I I S + +Speaker sentences 3: lad_eng_000257 #utts: 1 +id: (lad_eng_000257-lad_eng_000257) +Scores: (#C #S #D #I) 43 4 6 1 +REF: s o m e o F t h e C o u n t R i E s h a V e s U r v E y * s f o r m U l t I P l e y e a r s +HYP: s o m e o * t h e * o u n t * i * s h a * e s E r v A y I s f o r m A l t * B l e y e a r s +Eval: D D D D D S S I S D S + +Speaker sentences 4: lad_eng_000258 #utts: 1 +id: (lad_eng_000258-lad_eng_000258) +Scores: (#C #S #D #I) 44 2 5 0 +REF: b o t h o f t h e v E r s i O n s f e a t U R e t h e s o n g h a P p y h o l i d a y +HYP: b o t h ******* o f t h e ******* v I r s i * n s f e a t * H e t h e s o n g h a * p y h o l i d a y +Eval: D D S D D S D + +Speaker sentences 5: lad_eng_000259 #utts: 1 +id: (lad_eng_000259-lad_eng_000259) +Scores: (#C #S #D #I) 73 7 12 4 +REF: s h a k E s p * e A r E m a n y r e f E r E n c e s A r E m a d E t o s * C e n E s i n t E r ******* a c t i o n S o r c H a r A c t e R s f R o m v A r i O u S p l a y * s +HYP: s h a k * s p I e * r * m a n y r e f * r * n c e s U r * m a d * t o s H E e n D s i n t * r a c t i o n D o r c * a r I c t e * s f * o m v E r i * u T p l a y E s +Eval: D I D D D D S D D I S S D I S D S D D S D S I + +Speaker sentences 6: lad_eng_000260 #utts: 1 +id: (lad_eng_000260-lad_eng_000260) +Scores: (#C #S #D #I) 62 6 11 3 +REF: i f O n L y t h e p r o g r a m c O u l d b r E a k * o u t J u s t A l i T t l E f R o m * i t S t * o O f A m I l i a r a P p r o A c h +HYP: i f * n D y t h e p r o g r a m c * u l d ******* b r * a k E o u t G u s t ******* * l i * t l * f * o m E i t * t W o f O m E l i a r a * p r o U c h +Eval: D S D D D I S D D D D D I D I S S S D S + +Speaker sentences 7: lad_eng_000261 #utts: 1 +id: (lad_eng_000261-lad_eng_000261) +Scores: (#C #S #D #I) 65 7 8 6 +REF: t h e * a l b u m w a s r e l e A s e D i n * A U s t r a l i a * * O n n i n E t E E n t H * a U g U s t t w o t h o u s A n D a n D e ******* l e v E n +HYP: t h e H a l b u m w a s r e l e * s e * i n O * * s t r a l i a R A R n n i n t * I n t * O a * g I s t t w o t h o u s E n T a n * e l e v O n +Eval: I D D I D D I I S S D S D I D S S S D I S + +Speaker sentences 8: lad_eng_000262 #utts: 1 +id: (lad_eng_000262-lad_eng_000262) +Scores: (#C #S #D #I) 37 5 2 3 +REF: h e n o w p l a Y S f o r a U s t r a l i A n C l U b * p e * r t H g l o * r y +HYP: h e n o w p l a C E f o r a s t r a l i * n T l O b E p e I r t * g l o U r y +Eval: S S S D S S I I D I + +Speaker sentences 9: lad_eng_000263 #utts: 1 +id: (lad_eng_000263-lad_eng_000263) +Scores: (#C #S #D #I) 46 4 4 3 +REF: i t i S n o t K n o W n * h o w m u c h * i f a n y o f h e r C l a * I m s a r e t r U E +HYP: i t ******* i T n o t * n o * n E h o w m u c h E i f a n y o f h e r T l a M E m s a r e t r * O +Eval: D S D D I I S I S D S + +Speaker sentences 10: lad_eng_000264 #utts: 1 +id: (lad_eng_000264-lad_eng_000264) +Scores: (#C #S #D #I) 88 3 16 3 +REF: a s m A l l b U s I n e s s o W n e r b r O a * * d o p E r a t e d h i s w H e a T a n D S h E E p f a r m * f o r s i X t E e n Y e a r s f r o M t h e a g E o f T w e n t y t w o +HYP: a s m * l l b I s * n e s s o * n e r b r * a U R d o p * r a t e d h i s w * e a * a n * * h * A p f a r m E f o r s i C t * e n ******* * e a r s f r o * t h e a g * o f * w e n t y t w o +Eval: D S D D D I I D D D D D D S I S D D D D D D + +Speaker sentences 11: lad_eng_000265 #utts: 1 +id: (lad_eng_000265-lad_eng_000265) +Scores: (#C #S #D #I) 37 2 2 1 +REF: i n t h e n i n t h C e n * t U r y h e w a s a n i r i s h p o E t +HYP: i n t h e ******* n i n t h S e n C t * r y h e w a s a n i r i s h p o A t +Eval: D S I D S + +Speaker sentences 12: lad_eng_000266 #utts: 1 +id: (lad_eng_000266-lad_eng_000266) +Scores: (#C #S #D #I) 21 3 1 2 +REF: t h e y a r E m a * R K e D b y s t r o n g * +HYP: t h e y a r * m a U E C e T b y s t r o n g N +Eval: D I S S S I + +Speaker sentences 13: lad_eng_000267 #utts: 1 +id: (lad_eng_000267-lad_eng_000267) +Scores: (#C #S #D #I) 22 3 1 4 +REF: t h e l a * * W i s t h e r e ******* f o r E v * A l I d +HYP: t h e l a L L E i s t h e r e f o r * v O U l E d +Eval: I I S I D I S S + +Speaker sentences 14: lad_eng_000268 #utts: 1 +id: (lad_eng_000268-lad_eng_000268) +Scores: (#C #S #D #I) 39 0 4 1 +REF: i n t h E e a r l y s t a g e s c a m e c l o s e t o u S a ******* s l E e p +HYP: i n t h * ******* e a r l y s t a g e s c a m e c l o s e t o u * a s l * e p +Eval: D D D I D + +Speaker sentences 15: lad_eng_000269 #utts: 1 +id: (lad_eng_000269-lad_eng_000269) +Scores: (#C #S #D #I) 41 4 8 1 +REF: r u N n i n g e v e r y t h I r t y m i n U t E S t h R o U G H O u t s e r v i C E t i m e * s +HYP: r u * n i n g e v e r y t h A r t y m i n I t * * t h * o * * * u t s e r v i * S t i m e M s +Eval: D S S D D D D D D S D S I + +Speaker sentences 16: lad_eng_000270 #utts: 1 +id: (lad_eng_000270-lad_eng_000270) +Scores: (#C #S #D #I) 56 3 7 3 +REF: a s A r e s U l t w h e n t h e c o L l E g e r e ******* o p e n E d i t w * a s a s a N a l l ******* m a l e c o L l E g e +HYP: a s ******* * r e s I l t w h e n t h e ******* c o * l I g e r e o p e n * d i t w H a s a s a * a l l m a l e c o * l I g e +Eval: D D S D D S I D I D I D S + +Speaker sentences 17: lad_eng_000271 #utts: 1 +id: (lad_eng_000271-lad_eng_000271) +Scores: (#C #S #D #I) 77 5 11 1 +REF: t h e t i m e b e t w E e n t h e s E p o I n t S i s v A r I a b l e a n d c a n O C c U r a n y ******* w h E R e f r o M a M i n U t E t o m u c h l o n g e r +HYP: t h e t i m e b e t w * e n t h e s * p o * n t * i s v E r R a b l e a n d c a n ******* * A c E r a n y w h * * e f r o * a * i n I t * t o m u c h l o n g e r +Eval: D D D D S S D D S S I D D D D S D + +Speaker sentences 18: lad_eng_000272 #utts: 1 +id: (lad_eng_000272-lad_eng_000272) +Scores: (#C #S #D #I) 82 5 4 8 +REF: w * O r k o n t h e e e * * * * s s t * a r t e d i n m a r * c h t w o t h O u s A N d a n d s e v E n a t A c o s t o f f i v e m i L l i o n * d o l l A r s +HYP: w E A r k o n t h e e e E A E A s s t D a r t e d i n m a r H c h t w o t h A u s * E d a n d s e v O n a t ******* * c o s t o f f i v e m i * l i o n D d o l l E r s +Eval: I S I I I I I I S D S S D D D I S + +Speaker sentences 19: lad_eng_000273 #utts: 1 +id: (lad_eng_000273-lad_eng_000273) +Scores: (#C #S #D #I) 96 9 9 6 +REF: h o w e v e r t H E R e w a s s o m e d * I S a ******* g r E e m e n t o V e R t h e e n d i n g t h e m e * w H i c h o ******* m o r I a n d y o s h i m o r I d * I s C u s S e D a t l e * n g t h o v e r e m A I l +HYP: h o w e v e r t * * * e ******* w a s s o m e d E C a g r * e m e n t o * e * t h e e n d i n g t h e m e M w * i c h o m o r Y a n d y o s h i m o r Y d E C s K u s T e * a t l e A n g t h o v e r e m O U l +Eval: D D D D I S S I D D D I D I S S I S S S D I S S + +Speaker sentences 20: lad_eng_000274 #utts: 1 +id: (lad_eng_000274-lad_eng_000274) +Scores: (#C #S #D #I) 23 2 1 0 +REF: t h e c O U p l e h a d n o c h i l d r E n +HYP: t h e c * A p l e h a d n o c h i l d r O n +Eval: D S S + +Speaker sentences 21: lad_eng_000275 #utts: 1 +id: (lad_eng_000275-lad_eng_000275) +Scores: (#C #S #D #I) 67 4 11 4 +REF: t h e O F f i C i a l s i n g l E O f t h a t d e b * u T a l ******* B U m p a r i * s c A L l i n g h a d a N e l a b O r A t E m u s i c v i d e * o +HYP: t h e ******* * * f i T i a l s i n g l * * f t h a t d e b Y u * a l T H m p a r i S s c * O l i n g h a d a * e l a b * r * t * m u s i c v i d e A o +Eval: D D D S D D I D I S S I D S D D D D I + +Speaker sentences 22: lad_eng_000276 #utts: 1 +id: (lad_eng_000276-lad_eng_000276) +Scores: (#C #S #D #I) 84 5 5 4 +REF: t h e s e r i E s e n d e d o n s i * x t h a * U g U s t t w o t h O u s A n d a n d f o U r * l a s t i n g f O r a t o * t A l o f s e v e n t y o n E d a y s +HYP: t h e s e r i * s e n d e d o n s i C x t h a O R g I s t t w o t h A u s E n d a n d f o * r E l a s t i n g f * r ******* a t o U t * l o f s e v e n t y o n D d a y s +Eval: D I I S S S S D I D D I D S + +Speaker sentences 23: lad_eng_000277 #utts: 1 +id: (lad_eng_000277-lad_eng_000277) +Scores: (#C #S #D #I) 46 6 3 2 +REF: h e h a s a l s o * c o n t r i b U t e D t o t h e n E W y o * r K r e V I e W o f b O o k s +HYP: h e h a s a l s o D c o n t r i b E t e * t o t h e n U N y o U r C r e * e U o f b * o k s +Eval: I S D S S I S D S S D + +Speaker sentences 24: lad_eng_000278 #utts: 1 +id: (lad_eng_000278-lad_eng_000278) +Scores: (#C #S #D #I) 41 2 5 2 +REF: b y p l a c i n g s m a L l a r t o b * J e c t S t h r o U G H o u t t h e F i l m * +HYP: b y p l a c i n g s m a * l a r t o b D G e c t * t h r o * * o u t t h e * i l m E +Eval: D I S D D D S D I + +Speaker sentences 25: lad_eng_000279 #utts: 1 +id: (lad_eng_000279-lad_eng_000279) +Scores: (#C #S #D #I) 18 2 1 1 +REF: i t i S f o u n * d i n b r A Z i l +HYP: i t i * f o u n E d i n b r E S i l +Eval: D I S S + +Speaker sentences 26: lad_eng_000280 #utts: 1 +id: (lad_eng_000280-lad_eng_000280) +Scores: (#C #S #D #I) 47 2 3 1 +REF: i t w a S t h e s i d E o f t h e F a m I l y i i d e n t i f i e d m o r * e w i T h +HYP: i t w a * t h e s i d * o f t h e C a m * l y i i d e n t i f i e d m o r L e w i F h +Eval: D D S D I S + +Speaker sentences 27: lad_eng_000281 #utts: 1 +id: (lad_eng_000281-lad_eng_000281) +Scores: (#C #S #D #I) 37 5 2 6 +REF: * c a n d I d ******* A t E s i * * t * e S m u s t A l s o s * U B m i t a w o r k p l a n +HYP: E c a n d E d I t * s i G H t H e * m u s t L l s o s O D m i t a w o r k p l a n +Eval: I S I S D I I I D S I S S + +Speaker sentences 28: lad_eng_000282 #utts: 1 +id: (lad_eng_000282-lad_eng_000282) +Scores: (#C #S #D #I) 27 1 2 1 +REF: d u n d e E w o n t h e m a T c h t h r E e t w o * +HYP: d u n d e Y w o n t h e m a * c h t h r * e t w o E +Eval: S D D I + +Speaker sentences 29: lad_eng_000283 #utts: 1 +id: (lad_eng_000283-lad_eng_000283) +Scores: (#C #S #D #I) 80 7 7 5 +REF: h o w e v e r t h e v i L l A g E r e m a I n e d i S O l a t E d * U n t i l t h e A R r i v A l o f t H e f i r s t n E W s ******* p a p e r s e c o n d r e ******* p * u b l i c * +HYP: h o w e v e r t h e v i * l I g * r e m a * n e d i C I l a t d T * n t i l t h e * * r i v U l o f t * e f i r s t n O U s p a p e r s e c o n d r e p O u b l i c K +Eval: D S D D S S S I D D D S D S S I I I I + +Speaker sentences 30: lad_eng_000284 #utts: 1 +id: (lad_eng_000284-lad_eng_000284) +Scores: (#C #S #D #I) 89 2 20 1 +REF: t h e f i r s t S e r v i C E i N t h e n E W c h U r c H w a s h e l d I N n i n E t E E n f i f t y o n E a l ******* t h o U G H t h e b U i l d i N g w a s n o t f u L l y f i n i s h e d +HYP: t h e f i r s t * e r v i * * i * t h e ******* n * U c h A r c * w a s h e l d ******* * * n i n * t * * n f i f t y o n * a l t h o * * * t h e b * i l d i * g w a s n o t f u * l y f i n i s h e d +Eval: D D D D D D S S D D D D D D D D I D D D D D D + +Speaker sentences 31: lad_eng_000285 #utts: 1 +id: (lad_eng_000285-lad_eng_000285) +Scores: (#C #S #D #I) 84 11 9 4 +REF: t h e a v E r A g E h o u s e h O l d s i Z e * w a s t w o p o I n t t w o s e v E n A n d t h e a v E r A g E f a m I l y s i Z e * w a s t h r E e p o I n t * Z E r o * Z E r o +HYP: t h e a v * r I g H h o u s e h * l d s i * e S w a s t w o p o * n t t w o s e v O n * n d t h e a v * r I g H f a m * l y s i * e S w a s t h r * e p o E n t E S I A r o S I A r o +Eval: D S S D D I D S D D S S D D I D S I S S S I S S + +Speaker sentences 32: lad_eng_000286 #utts: 1 +id: (lad_eng_000286-lad_eng_000286) +Scores: (#C #S #D #I) 53 2 5 6 +REF: i t w a s f i r s t * B r O a * d ******* c a s t o n t h I r * d J a n * * u A r y t w o T h o u s A n d a n d t e n +HYP: i t w a s f i r s t E * r * a R d c a s t o n t h * r E d G a n I O u * r y t w o * h o u s E n d a n d t e n +Eval: I D D I I D I S I I D D S + +Speaker sentences 33: lad_eng_000287 #utts: 1 +id: (lad_eng_000287-lad_eng_000287) +Scores: (#C #S #D #I) 39 0 5 0 +REF: t h e w i n g s w E R e n o w M a d E i n a s i n g l e p r e S s i n g +HYP: t h e w i n g s w * * e n o w * a d * i n a s i n g l e p r e * s i n g +Eval: D D D D D + +Speaker sentences 34: lad_eng_000288 #utts: 1 +id: (lad_eng_000288-lad_eng_000288) +Scores: (#C #S #D #I) 32 7 7 4 +REF: * * ******* d o c t O R o f P H I L o S o P H y i n e n G I n * e E r i N g m a n a g e m e n t +HYP: T E d o c t * E o f ******* * * * * o L o I F y i n e n D E n Y e A r i * g m a n a g e m e n t +Eval: I I I D S D D D D D S S S S S I S D + +Speaker sentences 35: lad_eng_000289 #utts: 1 +id: (lad_eng_000289-lad_eng_000289) +Scores: (#C #S #D #I) 41 1 6 5 +REF: t h i s * T O o K A w a y t h e m a I n a r * g u m e n T o f s a * f E t * y r i * s k s +HYP: t h i s E * * o * * w a y t h e m a E n a r K g u m e n * o f s a I f * t D y r i S s k s +Eval: I D D D D S I D I D I I + +Speaker sentences 36: lad_eng_000290 #utts: 1 +id: (lad_eng_000290-lad_eng_000290) +Scores: (#C #S #D #I) 45 2 4 4 +REF: h e w a s a * l s o M a d E a l i f E m e m b E r o f s c * u n * ******* t h o r p E u n i t e d +HYP: h e w a s a L l s o * a d * a l i f H m e m b * r o f s c O u n D t h o r p * Y u n i t e d +Eval: I D D S D I I I D S + +Speaker sentences 37: lad_eng_000291 #utts: 1 +id: (lad_eng_000291-lad_eng_000291) +Scores: (#C #S #D #I) 43 6 7 1 +REF: s h e f * e A r s t h e Y W I L l g E t A d I V o r C e b u t t h i S n e v e r h a P p e n s +HYP: s h e f H e I r s t h e * * * * l g A t ******* * d E F o r S e b u t t h i E n e v e r h a * p e n s +Eval: I S D D D D S D D S S S S D + +Speaker sentences 38: lad_eng_000292 #utts: 1 +id: (lad_eng_000292-lad_eng_000292) +Scores: (#C #S #D #I) 38 2 10 1 +REF: f O o t d r o p s U n a b l e t O h O L d t h E f O o t s T r a I G H t a c r o * s S +HYP: f * o t d r o p s * n a b l e t * h * A d t h * f * o t s * r a * * * t a c r o U s E +Eval: D D D D S D D D D D D I S + +Speaker sentences 39: lad_eng_000293 #utts: 1 +id: (lad_eng_000293-lad_eng_000293) +Scores: (#C #S #D #I) 64 7 14 1 +REF: w h e t h E R t h e a I r f l o W i s f r e E o r f o * r C E D c A n A F f e c T t h e e n E R g y E F f I C i e n c y o f t h E w I n d o W +HYP: w h e t h * * t h e a * r f l o Y i s f r e Y o r f o U r * S T c * n * * f e c * t h e e n * D g y * O f * * i e n c y o f t h * w H n d o * +Eval: D D D S S I D S S D D D D D S D S D D D S D + +Speaker sentences 40: lad_eng_000294 #utts: 1 +id: (lad_eng_000294-lad_eng_000294) +Scores: (#C #S #D #I) 42 3 15 0 +REF: a f t E r g e T t i N g T h e R i G H t m e A s U R e M e n t S t h e Y m a d E t h E n E W d O o r s +HYP: a f t * r g e * t i * g * h e * i * * t m e * s * * e R e n t * t h e * m a d * t h * n O U d * o r s +Eval: D D D D D D D D D D S D D D D S S D + +Speaker sentences 41: lad_eng_000295 #utts: 1 +id: (lad_eng_000295-lad_eng_000295) +Scores: (#C #S #D #I) 42 3 7 3 +REF: f r a g m E n t S o n E a c h f a c e A r e m a r K e D w I t h l E T t e r s a * b * * C +HYP: f r a g m * n t E o n * a c h f a c e * r e m a r * e T w * t h l * * t e r s a Y b E S E +Eval: D S D D D S D D D I I I S + +Speaker sentences 42: lad_eng_000296 #utts: 1 +id: (lad_eng_000296-lad_eng_000296) +Scores: (#C #S #D #I) 72 5 14 5 +REF: f r o m t h E f i r s t * m i n U t e S b o t h t e A m * s s h o W E d t h e I R d e s i r e t o c o m p e T e * w i ******* t h A G g r e S S i V E a P p r o A c H e * s +HYP: f r o m t h * f i r s t D m i n I t e * b o t h t e * m E s s h o * * d t h e * * d e s i r e t o c o m p e * e T w i t h ******* * E g r e * * i O F a p r o * c * e R s +Eval: D I S D D I D D D D D I I D D S D D S S S D D I + +Speaker sentences 43: lad_eng_000297 #utts: 1 +id: (lad_eng_000297-lad_eng_000297) +Scores: (#C #S #D #I) 58 9 5 5 +REF: P H Y s i c A l T h e r A P y e X e r C i * s e s m a y h e l p ******* * * p a t i E n t S t o m a i n ******* t a i n m u s C l E s t r E n g t h +HYP: * F I s i c * l * h e r I B y e C e r S i D s e s m a y h e l p T H p a t i O n t E t o m a i n t a i n m u s * l * s t r I n g t h +Eval: D S S D D S S S S I I I I S S I D D S + +Speaker sentences 44: lad_eng_000298 #utts: 1 +id: (lad_eng_000298-lad_eng_000298) +Scores: (#C #S #D #I) 53 3 4 1 +REF: h o w e v e r t h e t o w n * S h e l i v E s i n n o O n E w A n t S t o h e a r a b o u t h e r +HYP: h o w e v e r t h e t o w n E * h e l i v * s i n n o U n D w O n t * t o h e a r ******* a b o u t h e r +Eval: I D D S S S D D + +Speaker sentences 45: lad_eng_000299 #utts: 1 +id: (lad_eng_000299-lad_eng_000299) +Scores: (#C #S #D #I) 65 7 7 4 +REF: * ******* d E s C r i B e s a P p o I n t M e n t S o F a n a c t i n g c h i * e F j u s t i C E o r j * u d g e o f t h e s U p r e m e c o U r t +HYP: A d I s * r i * e s a E p o E n t D e n t * o * a n a c t i n g c h i V e * j u s t i S S o r j O u d g e o f t h e ******* s O p r e m e c o * r t +Eval: I I S D D S S S D D I D S S I D S D + +Speaker sentences 46: lad_eng_000300 #utts: 1 +id: (lad_eng_000300-lad_eng_000300) +Scores: (#C #S #D #I) 65 4 9 4 +REF: t h e s o * y ******* b e A N s o u t E R c O v e r i n g i s t H e n r e m o v e d * a n d t H e b e A n * s a r E p a r t I a L l y c o O k e D +HYP: t h e s o R y b e * * s o u t * * c U v e r i n g i s t * e n r e m o v e d T a n d t * e b e * n D s a r * p a r t H a * l y c o C k e T +Eval: I I D D D D S D I D D I D S D S S + +Speaker sentences 47: lad_eng_000301 #utts: 1 +id: (lad_eng_000301-lad_eng_000301) +Scores: (#C #S #D #I) 59 2 13 2 +REF: t h i s n a * t i O N a l m o v E M e n t w h I c H h A D b e g U n w I t h s o M u C h h o p E c a m e t o a s a d e * n d +HYP: t h i s n a S t i * * a l m o v * * e n t w h * c * ******* h * E b e g O n w * t h s o * u * h h o p * c a m e ******* t o a s a d e A n d +Eval: I D D D D D D D D S S D D D D D I + +Speaker sentences 48: lad_eng_000302 #utts: 1 +id: (lad_eng_000302-lad_eng_000302) +Scores: (#C #S #D #I) 49 3 6 5 +REF: h i s a ******* s S o C i a t e S * u s u a L l y c a L l E d h i m t * o r * t h e g o o d ******* l O o k i n g g U y +HYP: h i s a s E o S i a t e * O u s u a * l y c a * l * d h i m t E o r E t h e ******* g o o d l * o k i n g g I y +Eval: I S S D I D D D I I D I D S + +Speaker sentences 49: lad_eng_000303 #utts: 1 +id: (lad_eng_000303-lad_eng_000303) +Scores: (#C #S #D #I) 48 9 3 2 +REF: i t s m a I n o f F i c e s w e r E i n l O n d O n w * I t h A s e c O n d o F f i C E b e l ******* f a s t +HYP: i t s m a E n o f H i c e s w e r * i n l U n d E n w E t h ******* E s e c E n d o * f i S S b e l f a s t +Eval: S S D S S I S D S S D S S I + +Speaker sentences 50: lad_eng_000304 #utts: 1 +id: (lad_eng_000304-lad_eng_000304) +Scores: (#C #S #D #I) 42 2 6 0 +REF: a c t u A L l y i h a d n E V e r b e e n t o a v i l L A g e b e f o r E t h a t +HYP: a c t u * * l y i h a d n * * e r b e e n ******* t o a v i l I D g e b e f o r * t h a t +Eval: D D D D D S S D + +Speaker sentences 51: lad_eng_000305 #utts: 1 +id: (lad_eng_000305-lad_eng_000305) +Scores: (#C #S #D #I) 63 2 12 1 +REF: h e W a s c h a R g e D W i t h p l a N N i n g t o s e t o F f b o m B s i n E u r O p E a n d t h e u n i * t e D S t a t e S +HYP: h e * a s c h a * g e * * i t h p l a * D i n g t o s e t o * f b o m * s i n * u r A p * a n d t h e u n i G t e * * t a t e * +Eval: D D D D D S D D D S D I D D D + +Speaker sentences 52: lad_eng_000306 #utts: 1 +id: (lad_eng_000306-lad_eng_000306) +Scores: (#C #S #D #I) 63 7 4 4 +REF: m a k i n g m I R r O r s i s t h e t h i r d s t u d i * O a l b U M b y b e l G I A n ******* a U s t r a l i a n a r t i s t g o t Y e * * +HYP: m a k i n g m * * r A r s i s t h e t h i r d s t u d i U R a l b A E b y b e l * D E n a * s t r a l i a n a r t i s t g o t I e A Y +Eval: D D S I S S S D S S I D S I I + +Speaker sentences 53: lad_eng_000307 #utts: 1 +id: (lad_eng_000307-lad_eng_000307) +Scores: (#C #S #D #I) 78 5 8 8 +REF: h e t h e n m o v e d t o w * a s H i n g ******* t o N d * ******* * C a n d w a s a p a r t ******* n e R W i t H w A r d b r o w n U n * t i * l n i n E t E e n T w e n t y n i n E +HYP: h e t h e n m o v e d t o w O a s * i n g t o D d E S I a n d w a s a p a r t n e * * i t * w O r d b r o w n E A n D t i L l n i n * t * e n * w e n t y n i n * +Eval: I D I S I I I S I D D D S S S I I D D D D + +Speaker sentences 54: lad_eng_000308 #utts: 1 +id: (lad_eng_000308-lad_eng_000308) +Scores: (#C #S #D #I) 52 6 11 2 +REF: j o s E P H h i G H s c H O o l * a N d t h e s c H O o l * s t h e y c o m p e t E A g a I n S T i n a L l s p o r t s +HYP: j o s O F h i * Y s c * * o l E a * d t h e s c * * o l E s t h e y c o m p e t * * g a * n E D i n ******* a * l s p o r t s +Eval: S S S D S D D I D D D I D D D S S D D + +Speaker sentences 55: lad_eng_000309 #utts: 1 +id: (lad_eng_000309-lad_eng_000309) +Scores: (#C #S #D #I) 28 1 5 3 +REF: T w e l V E p l u s o n E m a T c h b a n * P e r c * a * r d +HYP: * w e l * F p l u s o n * m a * c h b a n D * e r c O a U r d +Eval: D D S D D I D I I + +Speaker sentences 56: lad_eng_000310 #utts: 1 +id: (lad_eng_000310-lad_eng_000310) +Scores: (#C #S #D #I) 26 0 0 0 +REF: i t h i n k i m i g h t b e n o t h i n g +HYP: i t h i n k i m i g h t b e n o t h i n g +Eval: + +Speaker sentences 57: lad_eng_000311 #utts: 1 +id: (lad_eng_000311-lad_eng_000311) +Scores: (#C #S #D #I) 84 8 15 1 +REF: t h e h o m E W a s b U i l t a n d l i v e d i n b y a n d r E W j a c K S O n * K E N n E d y d e p U t y c o L l e c t O R F o R t h e i n t e r n A l r e v E n u E s e r v i C E +HYP: t h e h o m * * a s b * i l t a n d l i v e d i n b y a n d r * * j a c * A n D * C A n I d y d e p E t y c o * l e c t * * * o * t h e i n t e r n * l r e v I n u * s e r v i * S +Eval: D D D D D D S S I D S S S S D D D D D D S D D S + +Speaker sentences 58: lad_eng_000312 #utts: 1 +id: (lad_eng_000312-lad_eng_000312) +Scores: (#C #S #D #I) 67 5 9 4 +REF: i n n i n E t E E n s i * X T y f o U r * h e w e n t b a C k t o o m s * k a n d e n t E R e D t h e a c t o R S s c h o O l o f o * m s k +HYP: i n n i n * t * A n s i C E y E f o * r E h e w e n t b a * k t o o m s E k a n d e n t * * e * t h e a c t o * * s c h o U l o f o A m s k +Eval: D D S I S S S D I D I D D D D D S I + +Speaker sentences 59: lad_eng_000313 #utts: 1 +id: (lad_eng_000313-lad_eng_000313) +Scores: (#C #S #D #I) 55 4 4 0 +REF: t h e b a n k i s j O i n t l y o W n e d b y h i m a n d h i s b r o T H e r S a n d r e l A t i v E s +HYP: t h e ******* b a n k i s j U i n t l y o * n e d b y h i m a n d h i s b r o U V e r * a n d r e l I t i v * s +Eval: D S D S S D S D + +Speaker sentences 60: lad_eng_000314 #utts: 1 +id: (lad_eng_000314-lad_eng_000314) +Scores: (#C #S #D #I) 30 6 5 1 +REF: h e s U B S e Q U e n t l y w E n T t o S c H O o l i n b r * i s t O l +HYP: h e s * O P e I C e n t l y w A n * t o * c * * o l i n b r E i s t A l +Eval: D S S S S S D D D D I S + +Speaker sentences 61: lad_eng_000315 #utts: 1 +id: (lad_eng_000315-lad_eng_000315) +Scores: (#C #S #D #I) 40 6 9 4 +REF: * o n E t h O u s A n d E I G H t h u n d R e D A N d f o r t y s i * * * X f o U r t h E d i t i o n +HYP: W o n * t h A u s E n d * * * A t h u n d * e * ******* * * d f o r t y s i C K C S f o A r t h I d i t i o n +Eval: I D S S D D D S D D D D D I I I S S S + +Speaker sentences 62: lad_eng_000316 #utts: 1 +id: (lad_eng_000316-lad_eng_000316) +Scores: (#C #S #D #I) 83 10 7 2 +REF: a p a R t o f l i T t l E E n g l A n d b e y o n d w a * l e s i t h a s b e e N E S S e n T I A L l y E n g l i s h ******* s p e a k i n g f o r n i n E h u n D r e d Y e a r s +HYP: a p a * t o f l i * t l * I n g l E n d b e y o n d w a I l e s i t h a s b e e * * A e n * C H R l y I n g l i s h s p e a k i n g f o r n i n * h u n T r e d O e a r s +Eval: D D D S S I D D S S D S S S S I D S S + +Speaker sentences 63: lad_eng_000317 #utts: 1 +id: (lad_eng_000317-lad_eng_000317) +Scores: (#C #S #D #I) 57 3 10 6 +REF: h e p l a Y E d w I t h t e n p l a Y E r s f o r h a * L f w a s a g a I n S T t h E t r A d i t i o n i n * * G * * s p * +HYP: h e p l a * * d w * t h t e n p l a * * r s f o r h a R V f w a s a g a * n * * E t h * t r * d i t i o n i n D D E A S s p E +Eval: D D D D D I S D D D S D D I I S I I I + +Speaker sentences 64: lad_eng_000318 #utts: 1 +id: (lad_eng_000318-lad_eng_000318) +Scores: (#C #S #D #I) 85 8 14 2 +REF: t h e P r e S i d i n g * j * u d g E w a s w e b s t E R T H a Y E r W h o w a s a l R e a d y a S s i G N e d t o t h e c o U r t b e f o r e t h i s c a S e w a s s C h e d u l E D +HYP: t h e * r e * i d i n g G j O u d g * w a s w e b s t O * F a * I r * h o w a s ******* a l e a d y a s i * * e d t o ******* t h e c o * r t b e f o r e ******* t h i s c a C e w a s s * h e d u l * T +Eval: D D I I D S S D S D S D D S S D D D D D S D D S + +Speaker sentences 65: lad_eng_000319 #utts: 1 +id: (lad_eng_000319-lad_eng_000319) +Scores: (#C #S #D #I) 61 5 8 0 +REF: b I g B r O t h e R f i v e w a s t h e t h i r d o F t h e m a i n s e r i E s t o f e A T U R e a l i v e l A u n c h +HYP: b * g G r A t h e * f i v e w a s t h e t h i r d o * t h e m a i n s e r i * s ******* t o ******* f e * * C H e a l i v e l O u n c h +Eval: D S S D D D D D D D S S S + +Speaker sentences 66: lad_eng_000320 #utts: 1 +id: (lad_eng_000320-lad_eng_000320) +Scores: (#C #S #D #I) 80 3 11 2 +REF: i t s m o T t o i s W h o ******* e v e R y o u a r E a n d w h e r e v e r y o u a r e o n t h e J O U r n E y o f f a i T H y o U a R e w e l ******* c o m E h e r E +HYP: i t s m o * t o i s * h o e v e * y o u a r * a n d w h e r e v e r y o u a r e o n t h e * D I r n * y o f f a i * F y o * a * e w e l c o m * h e r * +Eval: D D I D D D S S D D S D D I D D + +Speaker sentences 67: lad_eng_000321 #utts: 1 +id: (lad_eng_000321-lad_eng_000321) +Scores: (#C #S #D #I) 25 2 4 1 +REF: r o b e R t E m i L l e R a s c o A C h w i l * s o n +HYP: r o b e * t A m i * l e * a s c o * T h w i l T s o n +Eval: D S D D D S I + +Speaker sentences 68: lad_eng_000322 #utts: 1 +id: (lad_eng_000322-lad_eng_000322) +Scores: (#C #S #D #I) 42 8 9 3 +REF: a f t E r A O n E y E a r b r E a k Z E r ******* * o d e g r E e w a s h e R f O l l O W i n g v e n T U R e * +HYP: a f t * r ******* E W n y U a r b r * a k S I r A o d e g r * e w a s h e * f * l l * * i n g v e n * C H e R +Eval: D D S S S S D S S I I D D D D D D S S I + +Speaker sentences 69: lad_eng_000323 #utts: 1 +id: (lad_eng_000323-lad_eng_000323) +Scores: (#C #S #D #I) 42 6 4 9 +REF: a * * m t * * m a n u f a c t U R e d a m o D E l K i t o f t h e * Z * * * Z * r d r a G s t e r +HYP: a Y A m t E Y m a n u f a c t * * e d a m o R T l C i t o f ******* t h e A D S A I D A r ******* d r a C s t e r +Eval: I I I I D D S S S D I S I I I S I D S + +Speaker sentences 70: lad_eng_000324 #utts: 1 +id: (lad_eng_000324-lad_eng_000324) +Scores: (#C #S #D #I) 63 8 4 9 +REF: t h e * s * s a * a I m e d t o b U i l * d a l e f t ******* w i n g A l t e r n A t i V E t o n E W l a b O U r a n d t h e * * s n * p * +HYP: t h e E s E S s a Y a * m e d t o b * i l E d a l e f t w i n g O l t e r n I t i * F t o n O U l a b * E r a n d t h e E S s A n D p E +Eval: I I S I D D I I S S D S S S D S I I S I I + +Speaker sentences 71: lad_eng_000325 #utts: 1 +id: (lad_eng_000325-lad_eng_000325) +Scores: (#C #S #D #I) 27 1 6 0 +REF: h e l i v E s l i k E h e I s A y o U n g p E r s o n +HYP: h e l i v * s l i k * h e A s ******* * y o * n g p * r s o n +Eval: D D S D D D D + +Speaker sentences 72: lad_eng_000326 #utts: 1 +id: (lad_eng_000326-lad_eng_000326) +Scores: (#C #S #D #I) 31 5 7 0 +REF: m a s t e R o f s C i E n C E i n e n G I n e E r i N g m a n A g E M e n t +HYP: m a s t e * o f s * i * n * D i n e n D E n e A r i * g m a n I g * * e n t +Eval: D D D D S S S S D S D D + +Speaker sentences 73: lad_eng_000327 #utts: 1 +id: (lad_eng_000327-lad_eng_000327) +Scores: (#C #S #D #I) 62 5 9 3 +REF: s h e f a i l e d t o M a k E t h e t o p t h r E e a T t h e K e n Y A n * j u n i * O r t r a c K t r i A l * s t h a t J u n E +HYP: s h e f a i l e d t o * a k * t h e t o p t h r * e a * ******* t h e C e n I O n D j u n i E A r ******* t r a c * t r i * l E s t h a t D u n * +Eval: D D D D D S S S I I S D D D I S D + +Speaker sentences 74: lad_eng_000328 #utts: 1 +id: (lad_eng_000328-lad_eng_000328) +Scores: (#C #S #D #I) 21 2 3 1 +REF: a t o U r * f o L l o W E d i n s u P p o r t +HYP: a t o A r E f o * l o * U d i n s u * p o r t +Eval: S I D D S D + +Speaker sentences 75: lad_eng_000329 #utts: 1 +id: (lad_eng_000329-lad_eng_000329) +Scores: (#C #S #D #I) 74 6 21 0 +REF: t h E Y W E R e e s t a b l i s H e D I n E I G H t E e n s e v e n t y o n E a n d A R e O N e o F t h e O l d e s t c l U B s i n t h e s o u t h o f E n g l A n d +HYP: t h * * ******* * * * e e s t a b l i s * e * * n * * * A t * e n s e v e n t y o n * a n d * * e * W e o * t h e * l d e s t c l O P s ******* i n ******* t h e s o u t h o f I n g l E n d +Eval: D D D D D D D D D D D D S D D D D D S D D S S D D S S + +Speaker sentences 76: lad_eng_000330 #utts: 1 +id: (lad_eng_000330-lad_eng_000330) +Scores: (#C #S #D #I) 43 4 3 1 +REF: h e w A s a m e m b E r o f t h e Y e * s s c o t l A n d a d V i s O r y b o A r d +HYP: h e w * s a m e m b * r o f t h e G e A s s c o t l E n d a d F i s E r y b o * r d +Eval: D D S I S S S D + +Speaker sentences 77: lad_eng_000331 #utts: 1 +id: (lad_eng_000331-lad_eng_000331) +Scores: (#C #S #D #I) 27 3 1 0 +REF: t w o t h o u s A n d A n d f i v e g e n t l E m A n +HYP: t w o t h o u s E n d * n d f i v e g e n t l m E n +Eval: S D S S + +Speaker sentences 78: lad_eng_000332 #utts: 1 +id: (lad_eng_000332-lad_eng_000332) +Scores: (#C #S #D #I) 79 6 13 4 +REF: * o u r * f i l M H a d a s t R o n g r e c e p t i o n i n E u r O P E a * N d A c h I E v e D d I s t R I b * u t i o n B u t t h a t w a s n o t t h e c a S e h e r E +HYP: A o u r E f i l E * a d a s t * o n g r e c e p t i o n i n ******* * u r * * * a P A d * c h * * v e * d E s t * O b E u t i o n T u t t h a t w a s n o t t h e c a C e h e r * +Eval: I I S D D D D D D D I S D D D D S D S I S S D + +Speaker sentences 79: lad_eng_000333 #utts: 1 +id: (lad_eng_000333-lad_eng_000333) +Scores: (#C #S #D #I) 34 7 4 2 +REF: * O R t h o S i s s t R e t C h e s p o s t e r i O r a n * K l E s t r U c t U R e s +HYP: B L t h o * i s s t D e t * h e s p o s t e r i E r a n G A l * s t r O c t * H e s +Eval: I S S D S D S I S D S D S + +Speaker sentences 80: lad_eng_000334 #utts: 1 +id: (lad_eng_000334-lad_eng_000334) +Scores: (#C #S #D #I) 86 4 17 5 +REF: h e w a s a * l s o a t h r E e t i m e f r e n c h * n a T i O n A l C h a m * p i O n n i n E t E E n n i n E t y n i n E t E e N n i N E t y f o U r * t w o T h o u s A N d a n D * o n E +HYP: h e w a s a L l s o a t h r * e t i m e f r e n c h E n a S i * n * l * h a m B p i A n n i n * t * I n n i n * t y n i n * t * e * n i * * t y f o * r E t w o * h o u s * E d a n * W o n * +Eval: I D I S D D D I S D D S D D D D D D D I D D S D I D + +Speaker sentences 81: lad_eng_000335 #utts: 1 +id: (lad_eng_000335-lad_eng_000335) +Scores: (#C #S #D #I) 63 3 9 3 +REF: t h e V i L l A g e s t r u c t U R e * s h o w n i n H i s m a p i s t O a g r E a t e x * t e n t u n ******* c h a N g e D t O d a y +HYP: t h e ******* * i * l I g e s t r u c t * H e R s h o w n i n * i s m a p i s t * a g r * a t e x S t e n t u n c h a * g e * t d a y +Eval: D D D S D S I D D D I I D D S + +Speaker sentences 82: lad_eng_000336 #utts: 1 +id: (lad_eng_000336-lad_eng_000336) +Scores: (#C #S #D #I) 68 10 14 2 +REF: r u S S I a * i s r e c O g n i Z e d F O R i t S n u C l e A r d I s a * s t e r e X p E r t I s E a n d F o R t h e s a f E t y o f i t s t e C H n o l O g y +HYP: r u * * H a R i s r e c A g n i S e d ******* * * * i t * n u K l e * r d E s a U s t e r e C p O r t E s * a n d * o * t h e s a f * t y o f ******* i t s t e * n o l A g y +Eval: D D S I S S D D D D D S D S I S S S D D D D D D S S + +Speaker sentences 83: lad_eng_000337 #utts: 1 +id: (lad_eng_000337-lad_eng_000337) +Scores: (#C #S #D #I) 87 6 7 11 +REF: a s o f t w o t h o u s A n d A N d f o U r ******* t E e n * * m t * v * * i s a v a I l a b l e w i t h i n t h e u n i * t e d K i n g d O m o n v I r g * i n m e d i * A a n d s * * K y +HYP: a s o f t w o t h o u s E n d * O d f o * r t * e n A E m ******* t Y v E E i s a v a * l a b l e w i t h i n t h e ******* u n i G t e d C i n g d * m o n v E r g E i n m e d i E R a n d s C G I y +Eval: S D S D I D I I D I I I D D I S D S I I S I I S + +Speaker sentences 84: lad_eng_000338 #utts: 1 +id: (lad_eng_000338-lad_eng_000338) +Scores: (#C #S #D #I) 24 1 4 2 +REF: n E W y o * r k p e * n g U i n r a n d O m h o u s e +HYP: n * * O y o U r k p e A n g * i n r a n d * m h o u s e +Eval: D D S I I D D + +Speaker sentences 85: lad_eng_000339 #utts: 1 +id: (lad_eng_000339-lad_eng_000339) +Scores: (#C #S #D #I) 47 1 6 4 +REF: t h e d u * c h y w a s S e c u r e d i N t H e o u t ******* c o m e o f t h e G o T H i c * w a * r +HYP: t h e d u T c h y w a s * e c u r e d i * ******* t * e o u t c o m e o f t h e * o * F i c T w a L r +Eval: I D D D D I D D S I I + +Speaker sentences 86: lad_eng_000340 #utts: 1 +id: (lad_eng_000340-lad_eng_000340) +Scores: (#C #S #D #I) 57 7 6 1 +REF: w i t H g O o d p a c e s T a r t e D t h e m a T c h W i T h b o t h t e * A m s A l t e R n a t i n g s U p r e m A C y +HYP: w i t * g * o d p a c e s D a r t e * t h e m a * c h H i * h b o t h t e M E m s O l t e * n a t i n g s O p r e m I S y +Eval: D D S D D S D I S S D S S S + +Speaker sentences 87: lad_eng_000341 #utts: 1 +id: (lad_eng_000341-lad_eng_000341) +Scores: (#C #S #D #I) 55 4 8 3 +REF: t h i s v E r S i o n i s n o * t e d F o r b e i n G V e r y f a I t H f u l * t o t h e O r i G i * n A l n o v E l +HYP: t h i s v * r T i o n i s n o N t e d * o r b e i n * * e r y f a V t * f u l E t o t h e ******* * r i D i O n * l n o v H l +Eval: D S I D D D S D I D D S I D S + +Speaker sentences 88: lad_eng_000342 #utts: 1 +id: (lad_eng_000342-lad_eng_000342) +Scores: (#C #S #D #I) 68 2 12 4 +REF: t h i s p r E s * u m p t i o n i s n o t f u l * f i L l E d * o n E h a s t o K n o W A t l e a S t * t w o c o n J U g A t E d i a m E t e R s +HYP: t h i s p r * s A u m p t i o n i s n o t f u l E f i * l * d W o n * h a s t o * n o * * t l e a * t E t w o c o n * * g E t * d i a m I t e * s +Eval: D I I D D I D D D D D I D D S D S D + +Speaker sentences 89: lad_eng_000343 #utts: 1 +id: (lad_eng_000343-lad_eng_000343) +Scores: (#C #S #D #I) 96 7 8 3 +REF: n o t a B l e t i t l e s i n C l U d e d g o l d E n a X E t h e r e v e n g E o f d e a t H a D d e r r a d m o b i l E o u t ******* r u N n e r s a n d s * E g A s o n i c t h e h e D g E h o g * +HYP: n o t a * l e t i t l e s i n l * d e d g o l d A n a C S t h e r e v e n g * o f d e a t * a * d e r r a d m o b i l * o u t r u * n e r s a n d s A K g R s o n i c t h e h e A g * h o g K +Eval: D S D S S S D D D D I D I S S S D I + +Speaker sentences 90: lad_eng_000344 #utts: 1 +id: (lad_eng_000344-lad_eng_000344) +Scores: (#C #S #D #I) 83 3 16 2 +REF: t h e n i n E t E E n n i n E t y n i n E j u D g m e n t n o t E d t H a t t h e i n * f l U E n c E o F t h e f a * t h e r o f t h e A C c u s E d h A s b e e N t h e r E +HYP: t h e n i n * t * * n n i n * t y n i n D j u * g m e n t n o t * d t * a t t h e i n T f l * O n c * o * t h e f a R t h e r o f t h e ******* * * c u s * d h I s b e e * t h e r * +Eval: D D D D S D D D I D S D D I D D D D S D D + +Speaker sentences 91: lad_eng_000345 #utts: 1 +id: (lad_eng_000345-lad_eng_000345) +Scores: (#C #S #D #I) 58 8 7 5 +REF: m * A C d U F f s w e a r s r E v e n g e * a n d j o i n s f o * r C e s W i t H m A l * c O L m t o o v e r t H r o W m * A C b e t h +HYP: m O K d * A f s w e a r s r * v e n g e H a n d j o i n s f o U r S e s * i t * m U l K c * * m t o o v e r t * r o M m O K b e t h +Eval: I S S D S D I I S D D S I D D D S I S S + +Speaker sentences 92: lad_eng_000346 #utts: 1 +id: (lad_eng_000346-lad_eng_000346) +Scores: (#C #S #D #I) 69 8 14 3 +REF: t h e M e * d * I a E v A l * v i L l A g e c o U r t w a s a l W a Y s a n X I o u s t o K E E p t h e F e n C e A r o u n D t h e v i L l A g e g a p l e S s +HYP: t h e * e A d Y a * v * l E v i * l I g e c o * r t w a s a l L a * s a n C H o u s t o * C A p t h e * e n * e * r o u n * t h e ******* v i * l I g e g a p l e * s +Eval: D I I S D D I D S D S D S S D S S D D D D D D S D + +Speaker sentences 93: lad_eng_000347 #utts: 1 +id: (lad_eng_000347-lad_eng_000347) +Scores: (#C #S #D #I) 61 3 12 0 +REF: t h E R e W a s a n i n E r a n k s Y s t e m e a c h r a n K H a v i N g m o r e p o W e r t h A N t h e l o W e R r a n k +HYP: t h * * e * a s a n i n * r a n k s I s t e m e a c h r a n C * a v i * g ******* m o r e p o U e r t h * * t h e l o * e * ******* r a n k +Eval: D D D D S S D D D S D D D D D + +Speaker sentences 94: lad_eng_000348 #utts: 1 +id: (lad_eng_000348-lad_eng_000348) +Scores: (#C #S #D #I) 67 5 10 4 +REF: t h e Y * E s t a b l i s * h e d d I p l O m a t i c r E l a t i o n * s o n * S e p t E m B e r n i n E t E E n t h n i n E t E E n s e v e n t y t w o +HYP: t h e * A s t a b l i s C h e d d E p l I m a t i c r * l a t i o n D s o n D * e p t O m * e r ******* n i n * t * * n t h n i n * t * n s e v e n t y t w o +Eval: D I S I S S D I I D S D D D D D D D S + +Speaker sentences 95: lad_eng_000349 #utts: 1 +id: (lad_eng_000349-lad_eng_000349) +Scores: (#C #S #D #I) 73 5 11 7 +REF: t h i S W a s f U r t h E R E X t e n d e d t o i n ******* c l U d E m o r e * * u * * K d a t e S i n d e c * e m b e r t w o t h o u s A N d A n d f o U r ******* t E e n +HYP: t h i * * a s f I r t h * * C S t e n d e d t o i n c l * d * m o r e Y O u C A Y d a t e * i n d e c S e m b e r t w o t h o u s * E d * n d f o * r t * e n +Eval: D D S D D S S I D D I I I I S D I D S D D I D + +Speaker sentences 96: lad_eng_000350 #utts: 1 +id: (lad_eng_000350-lad_eng_000350) +Scores: (#C #S #D #I) 60 5 15 3 +REF: t h e D u T c h g o v E R N M e n t i s c U R r E n t l y e X a m I N i n g t h e l e G a * l * c o n * S E q U e n c e s O f T h e r U l i n g +HYP: t h e * u * c h g o v * * * * e n t i s c * I r * n t l y e S a m * * i n g t h e ******* l e * a K l E c o n C I C q * e n c e s * f * h e r O l i n g +Eval: D D D D D D D S D S D D D D I I I S S D D D S + +Speaker sentences 97: lad_eng_000351 #utts: 1 +id: (lad_eng_000351-lad_eng_000351) +Scores: (#C #S #D #I) 85 8 12 3 +REF: f r o m n i n E t E E n t h I r t y t h r e e t o n i n E t E E n f o * r t y n i n E t h e A m e r i c A N l e A G U e w o n * T w e l v e O u t o F t h e f i r s t s i * X t E e n +HYP: f r o m n i n * t * I n t h E r t y t h r e e t o n i n * t * I n f o A r t y n i n * t h e * m e r i c E D l e * * * e w o n D * w e l v e A u t ******* o * t h e f i r s t s i C S t e n +Eval: D D S S D D S I D D S S D D D I D S D D I S S + +Speaker sentences 98: lad_eng_000352 #utts: 1 +id: (lad_eng_000352-lad_eng_000352) +Scores: (#C #S #D #I) 30 5 3 2 +REF: t h e * * r E h e f e l L s i c k w I t H t Y P H U s h i m s e l f +HYP: t h e A I r * h e f e l E s i c k w * t * t I V F O s h i m s e l f +Eval: I I D S D D S S S S + +Speaker sentences 99: lad_eng_000353 #utts: 1 +id: (lad_eng_000353-lad_eng_000353) +Scores: (#C #S #D #I) 49 2 10 7 +REF: s i * x * t e A m * s h a V e B E e N d I v * i d e d i n * * t w o g r O u p s o f t h r E e t e * A m s E a c h +HYP: s i C x T t e * m E s h a * e * * e * ******* d E v E i d e d i n T O t w o g r * u p s ******* o f t h r * e t e M E m s * a c h +Eval: I I D I D D D D D S I I I D D D I S D + +Speaker sentences 100: lad_eng_000354 #utts: 1 +id: (lad_eng_000354-lad_eng_000354) +Scores: (#C #S #D #I) 55 5 7 2 +REF: t h e f i r s t s e A s o n P r e m i E R E d o n * T w e l F t h J u n E t w o t h o u s A n d a n D f i f ******* T e e n +HYP: t h e f i r s t s e * s o n * r e m i * A d o n D * w e l * t h D u n * t w o t h o u s E n d a n * f i f D e e n +Eval: D D D S S I D D S D S D I S + +Speaker sentences 101: lad_eng_000355 #utts: 1 +id: (lad_eng_000355-lad_eng_000355) +Scores: (#C #S #D #I) 61 6 9 5 +REF: i t s U C c E E D e d t h * ******* * e * Y b o A r d a n d s Y s t E m t w e n t y f o U r * c o m b I N i n g f e a T U R e s f r o m b o t h +HYP: i t s * A c * * * e d t h E W e I H b o L r d a n d s I s t A m t w e n t y f o * r E c o m b * * i n g f e a * * C e s f r o m b o t h +Eval: D S D D D I I I I S S S S D I D D D D S + +Speaker sentences 102: lad_eng_000356 #utts: 1 +id: (lad_eng_000356-lad_eng_000356) +Scores: (#C #S #D #I) 31 1 4 4 +REF: v O l * u M e t w * o * n u m b E r s * o n E t w o a n d t h r E e +HYP: v E l Y u * e t w O o H n u m b * r s W o n * t w o a n d t h r * e +Eval: S I D I I D I D D + +Speaker sentences 103: lad_eng_000357 #utts: 1 +id: (lad_eng_000357-lad_eng_000357) +Scores: (#C #S #D #I) 62 1 16 3 +REF: t h e l o W E r p a R t o f m e n s d r e S s e s w E R e m u c h S h o * r t E R i n l e * n g T H t h A n t h o s E f O r w O M e * n +HYP: t h e l o * * r p a * t o f m e n s d r e * s e s w * * e m u c h * h o U r t * * i n l e A n g * * ******* t h * n t h o s * f * r w * I e I n +Eval: D D D D D D D I D D I D D D D D D D S I + +Speaker sentences 104: lad_eng_000358 #utts: 1 +id: (lad_eng_000358-lad_eng_000358) +Scores: (#C #S #D #I) 38 4 7 3 +REF: t h e V I S i g o * * t h s i n t U r n w e r E S U C c e E d e d b y t h e m * O O r s +HYP: t h e * * * i g o A L t h s i n t E r n w e r * * * * c e A d e d b y t h e m U L E r s +Eval: D D D I I S D D D D S I S S + +Speaker sentences 105: lad_eng_000359 #utts: 1 +id: (lad_eng_000359-lad_eng_000359) +Scores: (#C #S #D #I) 35 6 7 0 +REF: j o s E P H h i G H s c H O o l E v e r y w E e k o f t h E s c H O o l Y e a r +HYP: j o s O F h i * Y s c * * o l A v e r y w * e k o f t h * s c * * o l H e a r +Eval: S S S D S D D S D D D D S + +Speaker sentences 106: lad_eng_000360 #utts: 1 +id: (lad_eng_000360-lad_eng_000360) +Scores: (#C #S #D #I) 37 1 9 0 +REF: a s A r E s U l T o f a L l t h e a r g u m e n t S g e T t i N g t o h e r +HYP: a s ******* * r * s I l * o f ******* a * l t h e a r g u m e n t * g e * t i * g t o h e r +Eval: D D D S D D D D D D + +Speaker sentences 107: lad_eng_000361 #utts: 1 +id: (lad_eng_000361-lad_eng_000361) +Scores: (#C #S #D #I) 41 2 5 3 +REF: i t S h E a d ******* q u a r t e r s a r E i n s h e f F i E l d * u n i * t e d K i n g d o m +HYP: i t * h * a d q u a r t e r s a r * ******* i n s h e f E i * l d O u n i G t e d C i n g d o m +Eval: D D I D D S D I I S + +Speaker sentences 108: lad_eng_000362 #utts: 1 +id: (lad_eng_000362-lad_eng_000362) +Scores: (#C #S #D #I) 79 7 13 1 +REF: l a y A l s o O F f i C I a L l y s i G N e D t h e c o n t r a c t o n s t a g e w I T h t h e d i r e c t O R a n d p r o d * u C e R s o f t h e g o l d E n E y E s +HYP: l a y L l s o * * f i * H a * l y s i * D e * t h e c o n t r a c t o n s t a g e w * * h t h e ******* d i r e c t * E a n d p r o d O u S e * s o f ******* t h e g o l d A n I y * s +Eval: S D D D S D D S D D D D D S I S D D S S D + +Speaker sentences 109: lad_eng_000363 #utts: 1 +id: (lad_eng_000363-lad_eng_000363) +Scores: (#C #S #D #I) 55 8 9 4 +REF: P H Y s i c A l * T H e r * a p y c A n h e l P p a t i e n t S t o L E a r n h o W t o w a L k w I t h A f O o t d r o * p * +HYP: * F I s i c * l E * F e r I a p y c O n h e l * p a t i e n t E t o * * a r n h o * t o w a E k w t h ******* E f * o t d r o U p E +Eval: D S S D I D S I S D S D D D S S D S D I I + +Speaker sentences 110: lad_eng_000364 #utts: 1 +id: (lad_eng_000364-lad_eng_000364) +Scores: (#C #S #D #I) 57 2 4 4 +REF: i t W e n t o n t o s e L l t h r e E h u n d r e d t h o u s A N d * * u n i t s a ******* c h I e v e f i v e * n o +HYP: i t * e n t o n t o s e * l t h r e Y h u n d r e d t h o u s * E d Y A u n i t s a c h * e v e f i v e F n o +Eval: D D S D S I I I D I + +Speaker sentences 111: lad_eng_000365 #utts: 1 +id: (lad_eng_000365-lad_eng_000365) +Scores: (#C #S #D #I) 24 0 1 2 +REF: t h e n a m e * s t * u c k a f T e r t h a t +HYP: t h e n a m e M s t D u c k a f * e r t h a t +Eval: I I D + +Speaker sentences 112: lad_eng_000366 #utts: 1 +id: (lad_eng_000366-lad_eng_000366) +Scores: (#C #S #D #I) 42 9 2 3 +REF: t h e A l b U m l a t e r b r O K E t h e d i A m O n d r e c o r d o n * * Q Q m u s i c * +HYP: t h e I l b O m l a t e r b r * A C t h e d i * m E n d r e c o r d o n D C O K C U m u s i c K +Eval: S S D S S D S I I S S S S I + +Speaker sentences 113: lad_eng_000367 #utts: 1 +id: (lad_eng_000367-lad_eng_000367) +Scores: (#C #S #D #I) 43 6 9 4 +REF: i t S e d * I t o r i a l w e s * u b m i t E a R n E d i t S A U t h O R a p U l ******* i t Z E R * p r i Z e +HYP: i t * e d E A t o r i a l w e s O u b m i t * a * n * d i t * O R t h * E a p O l i t * * * O p r i S e +Eval: D I S I D D D D S S D S S I D D D I S + +Speaker sentences 114: lad_eng_000368 #utts: 1 +id: (lad_eng_000368-lad_eng_000368) +Scores: (#C #S #D #I) 36 4 7 4 +REF: * j o s E P H p l a y * s a * r E f e A t U R e d * e a c h w e e K o N t h E s h o W +HYP: D j o s O F p l a y E s a U r * f e * t * C e d I e a c h w e e * o * t h * s h o * +Eval: I S S S I I D D D S I D D D D + +Speaker sentences 115: lad_eng_000369 #utts: 1 +id: (lad_eng_000369-lad_eng_000369) +Scores: (#C #S #D #I) 75 3 14 5 +REF: t h e Y w a I t f O r a t i m e * b U i l d i n g u P t h e I R f o r C e s b e G i N N i n g t o W o * n d E r i f t h i s e * v I l r e a L l y * e x * i s t s +HYP: t h e * w a * t f * r ******* a t i m e M b * i l d i n g u T t h e * * f o r S e s b e * i * G i n g ******* t o * o A n d * r i f t h i s e A v * l r e a * l y A e x S i s t s +Eval: D D D D I D S D D S D D S D D I D I D D I I + +Speaker sentences 116: lad_eng_000370 #utts: 1 +id: (lad_eng_000370-lad_eng_000370) +Scores: (#C #S #D #I) 64 5 7 2 +REF: b r I e * F m e n T i o n o f t h E c o n v i c t i o n a P p e A r E d o n p a g e t h r E e o f t h e n E W y o * R k t i m e s +HYP: b r * e A E m e n C i o n o f t h * c o n v i c t i o n a * p e * r * d o n p a g e t h r * e o f t h e n * O U y o U O k t i m e s +Eval: D I S S D D D D D D S S I S + +Speaker sentences 117: lad_eng_000371 #utts: 1 +id: (lad_eng_000371-lad_eng_000371) +Scores: (#C #S #D #I) 48 2 8 2 +REF: o r d E R e d b y p O s I T i o n o n p i T c h f r O m b a C k r i G h t t o f r O n t l e f * * t +HYP: o r d * * e d b y p E s * * i o n o n p i * c h f r * m b a * k r i * h t t o f r U n t l e f T E t +Eval: D D S D D D D D D S I I + +Speaker sentences 118: lad_eng_000372 #utts: 1 +id: (lad_eng_000372-lad_eng_000372) +Scores: (#C #S #D #I) 51 6 7 4 +REF: h e I s m e m b e r o f t h e c o U r t o F t h e r O Y A l c o L l E g E o f a r t l O n d O n * * u * * K +HYP: h e A s m e m b e r o f t h e ******* c o * r t o * t h e r * * I l c o * l I g * o f a r t l U n d E n Y O u C A Y +Eval: S D D D D D S D S D S S I I I I S + +Speaker sentences 119: lad_eng_000373 #utts: 1 +id: (lad_eng_000373-lad_eng_000373) +Scores: (#C #S #D #I) 74 7 11 3 +REF: d U r i n G t h e c o u r s e o f T h e c a m ******* p a i G n f E r g U S O n * v i s i t e d A l l t h I r t y n i n E w a s H i N g t O n s t a t E c o U n t * i E s +HYP: d E r i n * t h e ******* c o u r s e o f * h e c a m p a i * n f I r g E A n D v i s i t e d * l l t h E r t y n i n * w a s * i * g t A n s t a t * c o * n t Y i * s +Eval: S D D D I D S S S S I D S D D D S D D I D + +Speaker sentences 120: lad_eng_000374 #utts: 1 +id: (lad_eng_000374-lad_eng_000374) +Scores: (#C #S #D #I) 25 0 1 1 +REF: a s t r i p o f p a p e r o f l e * n G t h +HYP: a s t r i p o f p a p e r o f l e A n * t h +Eval: I D + +Speaker sentences 121: lad_eng_000375 #utts: 1 +id: (lad_eng_000375-lad_eng_000375) +Scores: (#C #S #D #I) 58 4 9 6 +REF: s a t o U h a d f r e Q u E n t l y w O r k E D t o ******* g e t h E R w I t h y o * k ******* * O y a m * a * o n p r e v i O u s p R o J e c t s +HYP: s a t o * h a d f r e K u * n t l y w E r k * * t o g e t h * * w * t h y o U k Y y a m E a R o n p r e v i * u s p * o G e c t s +Eval: D S D S D D I D D D I I I S I I D D S + +Speaker sentences 122: lad_eng_000376 #utts: 1 +id: (lad_eng_000376-lad_eng_000376) +Scores: (#C #S #D #I) 76 3 11 3 +REF: s h e w A s b o r N o n * ******* s c r E e n d u R i n g t h e e p I s o d E b r O A d ******* c a s t o n f o U r t h n o v e m b e r n i n E t E e n n i n E t y f o U r +HYP: s h e w * s b o r E o n D s c r * e n d u * i n g ******* t h e e p * s o d * b r U R d c a s t o n f o * r t h n o v e m b e r n i n * t * e n n i n * t y f o * r +Eval: D S I I D D D D D S S I D D D D D + +Speaker sentences 123: m #utts: 77 +id: (m-ailabs_eng_000159-m-ailabs_eng_000159) +Scores: (#C #S #D #I) 63 0 6 4 +REF: * h E t u r N e d r o u * n * d s h e h a d c o m E i n s o g e n t * l y t h a t h e h a d N e v e R h e A r d h e r +HYP: A h * t u r * e d r o u W n E d s h e h a d c o m * i n s o g e n t E l y t h a t h e h a d * e v e * h e * r d h e r +Eval: I D D I I D I D D D + +id: (m-ailabs_eng_000160-m-ailabs_eng_000160) +Scores: (#C #S #D #I) 52 4 6 9 +REF: a H t o b e s * * u * r ******* E w e m u s t K E e P o u r d O o r s s h U t ******* w e m u s T l E t n o * o * n E i n * * +HYP: a * t o b e s H O u O r H w e m u s t * C e * o u r d * o r s s h O t w e m u s * l A t n o W o U n * i n H A +Eval: D I I I I S D S D D S I D S I I D I I + +id: (m-ailabs_eng_000161-m-ailabs_eng_000161) +Scores: (#C #S #D #I) 108 12 24 4 +REF: * ******* K i * N s M e n H e b e g a n m o C k i n g l y y o u m a Y h A v e w O N D e R e D w h Y i c a L l E d A t r * u C E w h E n i c o U L d j U s t a s w E l l h A V e d e s t r O Y e D y o u t h a t i d o U B t a T o a n S W e r E D h i m +HYP: A C i D E s B e n * e b e g a n m o * k i n g l y y o u m a * h * v e w * * * e D e * w h I i c a * l * d O t r O u * S w h * n i c o * O d j O s t a s w I l l ******* h * * e d e s t r * * e * y o u t h a t i d o * * t a D o a n * C e r * * h i m +Eval: I I S I S S D D D D D D D S D S D D S I D S D D S S S D D D D D D D D S D S D D + +id: (m-ailabs_eng_000162-m-ailabs_eng_000162) +Scores: (#C #S #D #I) 70 7 15 1 +REF: t h e p e A s A n t t H r E W h i m s e L f U p o n h i m a n d b o u n * d h i s f o U r l E G S T I G h t l y s o T H a t H e c o U L D n o t m o V e +HYP: t h e p e * s E n t t I r * * h i m s e * f A p o n h i m ******* a n d b o u n E d h i s f o * r l A K E * * D h t l y s o * * a t * e c o * * * n o t m o * e +Eval: D S S D D D S D I D S S S D D S D D D D D D D + +id: (m-ailabs_eng_000163-m-ailabs_eng_000163) +Scores: (#C #S #D #I) 102 2 13 2 +REF: n o r m u s t t h o u s o l i m i t * t h e H o l y o n E o f i s r A E l a s t o t h i n K h E h a t h b u t O n e * w a y i n w h I c H h E c A n g L o r i f y h i m s e l f b y t h E e +HYP: n o r m u s t t h o u s o l i m i t H t h e * o l y o n * o f i s r * I l a s t o t h i n * h * h a t h b u t W n e N w a y i n w h * c * ******* h * c * n ******* g * o r i f y h i m s e l f b y t h * e +Eval: I D D D S D D S I D D D D D D D D + +id: (m-ailabs_eng_000164-m-ailabs_eng_000164) +Scores: (#C #S #D #I) 140 15 25 6 +REF: t h e O l d c o m p A r I s o n * b e t w E e n t h E i m p U l s I V E e x * e * C U t i v e a n d t h e l i * b E r A l * a r t * s m a n W h O H A S l e A r n E d t h a t T h E R e A r e o n l y o n e O r t W o p o s I T i V e d e c i s i o N s A V a I l a b l e i n a l l t h e w O r l D o F t h i n k i n g +HYP: t h e * l d c o m p * r * s o n D b e t w * e n t h * i m p A l s O F e x S e A K I t i v e a n d ******* t h e l i T b * r * l E a r t U s m a n * h W W O D l e U r n * d t h a t * h * * e ******* * r e o n l y o n e E r ******* t * o p o s * * i * e d e c i s i o * s O a * l a b l e i n a l l t h e ******* w E r l * o * t h i n k i n g +Eval: D D D I D D S S S S I I S S D I D D I I D S S S S S D D D D D D S D D D D D D S S D D S D D + +id: (m-ailabs_eng_000165-m-ailabs_eng_000165) +Scores: (#C #S #D #I) 73 6 6 5 +REF: a * f t E r t h i S E X p e r i E n c e t h e I n * v a d e r s w e r E c a * r E f U l t o K E e p * a s a * f e d i s t A n c e f r o m t h e w a l l +HYP: a V f t * r t h i * C S p e r i A n c e t h e E n D v a d e r s w e r * c a I r * f * l t o * C e p E a s a K f e d i s t E n c e f r o m t h e w a l l +Eval: I D D S S S S I D I D D D S I I S + +id: (m-ailabs_eng_000166-m-ailabs_eng_000166) +Scores: (#C #S #D #I) 123 9 18 10 +REF: * C a * n Y o u b e a * r s O m E T H i n g f U r t h e r i t h i n K y o U O U G H T t O K n o W i t i h a v e h e r E a m o s t m Y s t e r i O u s t e l E p A g r * * A m * Y e * s * w h a t i s i t * i s s h e d E a d n o i t i s n o t A b o u t h e r * +HYP: M a O n * o u b e a E r s * m * * i n g f I r t h e r i t h i n * y o * * * * * * A t * * n o * i t i h a v e h e r * a m o s t m * s t e r i A u s t e l * p R g r O M E m E S e A s E w h a t i s i t E i s s h e d * a d n o i t i s n o t U b o u t h e r Y +Eval: I S I D I D D D S S D D D D D D D S D D D D D S D S I I S I S I I I D S I + +id: (m-ailabs_eng_000167-m-ailabs_eng_000167) +Scores: (#C #S #D #I) 44 3 9 9 +REF: * ******* n o * * * * m i s t E r t h O R n t o n s a I d * G i V e t h e b a s k E T t o m e ******* i L l t a k E i t * +HYP: D n o W H L E m i s t O r t h * U n t o n s a * d D i * e ******* t h e b a s k * * ******* t o m e i * l t a k * i t W +Eval: I I I I I I S D S D I S D D D D D I D D I + +id: (m-ailabs_eng_000168-m-ailabs_eng_000168) +Scores: (#C #S #D #I) 158 4 22 8 +REF: a n a r a b i * a n n i G h t e * X c l a I m e D t r o * t w h Y t h a t w a s a m a g i * C n i g h t w * a s * n T i t T h e r E s d i F f E r E n t s o r t s O F n i g h t s m a t E s a i d t h e s a I l O r a n d t h e K n i g H t b u T t O n * b R i g h t m e A n * s a I N t t h e s a m e n i g h t y o U m e a n +HYP: a n a r a b i O a n n i * h t e C S c l a * m e * t r o G t w h I t h a t w a s a m a g i A K n i g h t w O a s I n * i t * h e r * s d i * f * r * n t s o r t s * A n i g h t s m a t * s a i d t h e s a * l * r a n d t h e * n i g * t b u * t * n U b * i g h t m e * n E s a * * t t h e s a m e n i g h t y o * m e a n +Eval: I D I S D D I S I S I I D D D D D D D S D D D D D D D I D D I D D D + +id: (m-ailabs_eng_000169-m-ailabs_eng_000169) +Scores: (#C #S #D #I) 115 4 17 3 +REF: i v e t u r N e d o * f F * u p w a r d S o F A h u N d r E d o F m y b e s t * h a n d s f o r n o o t h e r f A U l t t h A N f O L l O w i n g Y o u a n d s u c h a s y o u a n d D y E t h i n k i l l t a k e y o u o n +HYP: i v e ******* t u r * e d o B f E O u p w a r d * o * ******* * h u * d r * d ******* o * m y b e s t E h a n d s f o r n o o t h e r f * * l t t h E M f * A l * w i n g * o u a n d s u c h a s y o u a n d * y * t h i n k i l l t a k e y o u o n +Eval: D D I S I D D D D D D D D I D D S S D S D D D D + +id: (m-ailabs_eng_000170-m-ailabs_eng_000170) +Scores: (#C #S #D #I) 69 7 17 0 +REF: B u T W h e N S H o U L D S h e s E e h i m h e r h E a r t l E a p e d u P I n a P p r E h e n S i o n a t e v e R y r i n g o f T h e d O o r b E l L +HYP: G u * * h e * * W o * * * * h e s * e h i m h e r h * a r t l * a p e d u B B n a * p r O h e n T i o n a t e v e * y r i n g ******* o f * h e d * o r b * l T +Eval: S D D D D S D D D D D D D S S D S S D D D D D S + +id: (m-ailabs_eng_000171-m-ailabs_eng_000171) +Scores: (#C #S #D #I) 111 11 28 9 +REF: * * ******* t h e * s E b O o k * ******* s d i * X o n i w I L l K E e P a L l t h e r e s t w I L L Y o u s e n D t o m I s t E r b e L l t h e Y a r E o f a K i n D t h A t h e w I L l v A L u * E f o r t h E m ******* s e l V e s a s w e L l a s f O r P A p A s s a K E +HYP: A A t h e A s * b * o k S s d i C S o n i w * * l * C e * a * l t h e ******* r e s t w * * E * o u s e n * t o m * s t * r b e * l t h e * a r * o f a C i n * t h * t h e w * * l A v * O u Y O f o r t h I m s e l * e s a s w e * l ******* a s f * r * * p O s s a Y D +Eval: I I I I D D I I I S D D D S D D D D D S D D D D D D D S D D D D S D S I S S I D D D D D D S S S + +id: (m-ailabs_eng_000172-m-ailabs_eng_000172) +Scores: (#C #S #D #I) 151 8 12 10 +REF: b u t i n g A w a s n o t A t A L l s * * u r ******* * * E t h e y c o u l d n o t g e T i n t h e g a t E s o p E N e d i n W A r d a n d t h r E e h E a v y b * a * r s w e r E h e l d i n p l a c e b y m e A n * s o f s t o u t s t a p l e s r i v * E t e D t o t h e s h E e t * s o f s t E e l +HYP: b u t i n g L w a s n o t I t * * l s H O u r T H A t h e y ******* c o u l d n o t g e D i n t h e g a t * s o p * * e d i n O r d a n d t h r * e h * a v y b O a R r s w e r * h e l d i n p l a c e b y m e * n E s o f s t o u t s t a p l e s r i v I D t e * t o t h e s h * e t E s o f s t D e l +Eval: S S D D I I I I I S D S D D D S S D D I I D D I I S D D I S + +id: (m-ailabs_eng_000173-m-ailabs_eng_000173) +Scores: (#C #S #D #I) 110 10 15 5 +REF: * i w * a n t t h * A l s A i d H o d D A n c o l d l y i W A n t a d o Z E n h o r S e s i w * A n t m e n t o * r i D e t h e M w i t H m E h e p u s h e D H i s w a y f o r W A R d w H i c h w a y t o t h e s t a b l E s +HYP: A i w O a n t t h E I l s * i d * o d O n c o l d l y ******* i H E n t ******* a d o S O n h o r * e s ******* i w N U n t m e n t o B r i G e t h e * w i t * m * h e p u s h e * * i s w a y f o r * * E d w * i c h w a y t o t h e s t a b l * s +Eval: I I I S D D S S D S S D S S D D I S I S D D D D D D D S D D + +id: (m-ailabs_eng_000174-m-ailabs_eng_000174) +Scores: (#C #S #D #I) 110 5 29 9 +REF: T H E r E i s A l I M i T t O w H a T Y o U c a n * d * * ******* * O t h e f i r s T t i m e y o u E n t e R A M a n s h o u s e a n d b e s i * d e s t h a t w a s n o t i m e t o a r o u s e S U s p I C i o n i N t h E m i n d * o f a n y ******* * o n E +HYP: * * I r * i s ******* * l * E i * ******* t * w * a * ******* C o * c a n D d E V F R t h e ******* f i r s * ******* t i m e ******* y o u A n t e * ******* * * a n s h o u s e a n d b e s i G d e s t h a t w a s n o t i m e t o a r o u s e ******* * * s p * * i o n i * t h * m i n d S o f a n y W o n * +Eval: D D S D D D D S D D D D D D S D I I I I I S D D D D S D D D D I D D D D D D D I I I D + +id: (m-ailabs_eng_000175-m-ailabs_eng_000175) +Scores: (#C #S #D #I) 94 7 12 6 +REF: d O Y o u n o t r e m e m B e r t h a t H e s a Y s t h y d e m o n t h a t S t h Y s p i r i t W h i c h * k e E p * s t h E e i s n o b l E c o U r * a g e O U S h * i G H u n ******* m a T c h * a b l E +HYP: d * * o u n o t r e m e m * e r t h a t * e s a * s t h y d e m o n t h a t D t h E s p i r i t * h i c h C k e A p E s t h * e i s n o b l * c o * r E a g e S C E h A i * * u n m a U c h O a b l * +Eval: D D D D D S S D I S I D D D I S S S I D D I S I D + +id: (m-ailabs_eng_000176-m-ailabs_eng_000176) +Scores: (#C #S #D #I) 62 7 9 7 +REF: * ******* * m i s t E r b e l L W H a T c a n h e K n o W o f J o H n h * e l i v i n g a l a Z y l i f E I n a * d r o W s y c o l L E g e * * +HYP: A T m i s t * r b e l E * O a * ******* c a n h e * n o * o f C o * n h E e l i v i n g a l a S y l i f * * n a D d r o U s y c o l I D g e H A +Eval: I I I D S D S D D D D S D I S D D I S S S I I + +id: (m-ailabs_eng_000177-m-ailabs_eng_000177) +Scores: (#C #S #D #I) 38 3 6 0 +REF: a n d t H e K i T t E n f o l l o w e D d e m u r E l y a t t h e I r h e E l s +HYP: a n d t * e ******* C i * t O n f o l l o w e * d e m u r * l y a t t h e * r h e A l s +Eval: D D S D S D D D S + +id: (m-ailabs_eng_000178-m-ailabs_eng_000178) +Scores: (#C #S #D #I) 91 4 10 3 +REF: t h e f i r s t t O u C h w o U L d c A U s E a n e * X p l o S i o n i n w h i c h a ******* m o N g s u c h h u n d r e d s o f i n f U r I a t e d m e n a n d r e c k l E s s b o * y s +HYP: t h e f i r s t t * u h ******* w o * * d c * O s * a n e C S p l o * i o n i n w h i c h a m o * g s u c h h u n d r e d s o f i n f E r * a t e d m e n a n d r e c k l * s s b o R y s +Eval: D S D D D D S D I S D I D S D D I + +id: (m-ailabs_eng_000179-m-ailabs_eng_000179) +Scores: (#C #S #D #I) 58 4 13 1 +REF: O N E o F t H e g R E a t p l e A s U r E s o f m a r g A r e t S l i F e a t t h i s t i m e w a s i n e * d I T H s b o y +HYP: * * W o N t * e g * * a t p l e * s E r * s ******* o f m a r g * r e t * l i * e a t t h i s t i m e w a s i n e A d * * E s b o y +Eval: D D S S D D D D S D D D D D I D D S + +id: (m-ailabs_eng_000180-m-ailabs_eng_000180) +Scores: (#C #S #D #I) 106 6 33 4 +REF: t h E t h i n G H a s g o n E O n * l o n G E n o U G H I f T H E r E I s o n e M o r E b i g a c C i * d e n t w e s h a L l h a v e t o c o m * P r O m * i s E w i T H t h e i n T e R r i v e r A n D c A R r y o n t h E w O r k J o i n T l Y +HYP: t h * t h i n * * a s g o n * U n D l o n * * n o * * * ******* * f * * * r * * s o n e * o r * b i g a c X i T d e n t w e s h a * l ******* h a v e t o c o m B E r * m Y i s * w i * * t h e i n * e * r i v e r * n * c * E r y ******* o n t h * w E r k C o i n * l * +Eval: D D D D S I D D D D D D D D D D D D D D S I D D I S D I D D D D D D D D S D D S S D D + +id: (m-ailabs_eng_000181-m-ailabs_eng_000181) +Scores: (#C #S #D #I) 49 3 9 5 +REF: * * ******* y o u A r E l a t E s a i d s h e w e L l s h e h e L d h e r b r e a t h F o R t h e a n * s * W E R +HYP: A A y o u ******* * r * l a t * s a i d s h e w e * l s h e h e * d h e r ******* b r e a t h * o * t h e a n C s R H A L +Eval: I I I D D D D D D D D D I I S S S + +id: (m-ailabs_eng_000182-m-ailabs_eng_000182) +Scores: (#C #S #D #I) 106 7 26 3 +REF: T r o * t t o l D t h e g i r l s t H a T T h e Y m u s T G o W I T h t h e I r f A t h e r t o l i v E I n g H i p * ******* g H I s i Z Z l e s l I T t l e o l d c a b I n a n D W h E n t h e Y h e A r d t h I s D r e A d f u l d e c r E e +HYP: * r o H t t o l E t h e g i r l s t * a * * h e * m u s * C o * * * h t h e * r f O t h e r t o l i v * * n g * i p K g E S s i * S l e s l * * t l e o l d c a b O n a n * * h * n t h e * ******* h e * r d t h * s * r e * d f u l d e c r * e +Eval: D I S D D D D D S D D D D S D D D I I S S D S D D S D D D D D D D D D D + +id: (m-ailabs_eng_000183-m-ailabs_eng_000183) +Scores: (#C #S #D #I) 115 6 19 4 +REF: m a r g A R E t s a t d o w N O n t h e r * * U g p a r t l y t o w A r m * h E r s e l f f o R t h e d a M p n e s s o F t h e e * v E n i n g h u n g A B o u t h e r D r e S s a n d o v e R f A T i G U e H a d m a d E h e r c h i L l y +HYP: m a r g * * I t s a t d o w * ******* * n t h e r O G K g p a r t l y t o w O r m E h * r s e l f f o * t h e d a N p n e s s o * t h e e A v * n i n g h u n g * * o u t h e r * r e * s a n d o v e f * * i * T e * a d m a d * h e r c h i * l y +Eval: D D S D D D I I S S I D D S D I D D D D D S D D D S D D D + +id: (m-ailabs_eng_000184-m-ailabs_eng_000184) +Scores: (#C #S #D #I) 107 6 14 3 +REF: o H n o * y o U a r E m I s t a k e n A b o u t t h a t R e P l i E d t h e K i n g t h e Y a r E n o t m y p r I s O n e r s b u t m y s l a v e s W h o m i * p U r c H A s E D f r o m t h e K i n g o f e v * +HYP: o * n o W y o * a r * m * s t a k e n * b o u t t h a t * e * l i * d t h e C i n g t h e * a r * n o t m y p r E s * n e r s b u t m y s l a v e s * h o m i Y p * r c E U s * T f r o m t h e C i n g o f e v E +Eval: D I D D D D D D D S D D S D D I D S S D S S I + +id: (m-ailabs_eng_000185-m-ailabs_eng_000185) +Scores: (#C #S #D #I) 28 1 6 0 +REF: h e r f a t h e R t O o K u P t h e C o N V e r s a t i o n +HYP: h e r f a t h e * t * o * u * t h e * o * M e r s a t i o n +Eval: D D D D D D S + +id: (m-ailabs_eng_000186-m-ailabs_eng_000186) +Scores: (#C #S #D #I) 132 6 19 7 +REF: i n a c o * r N e r w a s a s o * r T o f d r e S S i n g ******* t a b l e O n w h i c h l A y a c o m B a n d b r u s h K e N n E d y s E e M e d m u c h i n t e r E s t E d i n t h e t a b l e a n d w a s * e x * a m I N i n g I t W h e N t h e g u * ******* r u r e t U r n E D +HYP: i n a c o U r * e r w a s a s o U r D o f d r e * * i n g t a b l e I n w h i c h l * y a c o m * E a n d b r u s h C e * n I d y s * e * e d m u c h i n t e r * s t * d i n ******* t h e t a b l e a n d ******* w a s A e x S a m * * i n g A t * h e * t h e g u E r u r e t * r n * * +Eval: I D I S D D I S D D S S D S D D D D D D I I D D S D D I I D D D + +id: (m-ailabs_eng_000187-m-ailabs_eng_000187) +Scores: (#C #S #D #I) 131 7 30 3 +REF: i H a v e s o m e ******* t i m E S t H O u G h t t H a t m y ******* s e L f s h e a g r e e d b u t o f c o U r S E i D o N t K n o W s t i L l i h A v e t O b e p r E T t y c a r E f u l s o m e O n E i s A l W A Y s o v e r H E R e b y m y d e s K o r l O o K i n g o v e r h e * r E +HYP: i * a v e s o m e t i m * * t * A u * h t t * a t m y s e * f s h e a g r e e d b u t ******* o f ******* c o * r * * i * o * t * n o E s t i * l i h * v e t * b e p r * * t y c a r * f u l s o m e W E n * i s * l * L I s o v e r * * * e b y m y d e s C o r l * o * i n g o v e r h e A r * +Eval: D I D D D S D D I D D D D D D D D D S D D D D D D S S D D D S S D D D S D D I D + +id: (m-ailabs_eng_000188-m-ailabs_eng_000188) +Scores: (#C #S #D #I) 48 2 11 1 +REF: i s h a L l s t a y * r e p l I E d t h E Y o U n g M a n f o r i m e A n t o s E t Y o U f r E e +HYP: i s h a * l s t a y E r e p l * * d t h * * o * n g * a n f o r i m e n t o s * t ******* C o * f r * e +Eval: D I D D D D D D S D D S D D + +id: (m-ailabs_eng_000189-m-ailabs_eng_000189) +Scores: (#C #S #D #I) 27 2 4 1 +REF: w h a t d O y o u d * o a s K E D t h e s o r C e r e r +HYP: w h a t ******* d * y o u d E o a s * * T t h e s o r S e r e r +Eval: D D I D D S S + +id: (m-ailabs_eng_000190-m-ailabs_eng_000190) +Scores: (#C #S #D #I) 128 6 25 4 +REF: w h * y t h e Y r E O U r E n E m I e s y o u r s h o r t h i G H n e S s n o t a n y m o r e r e p l i E d * T r o * * t i m q u E e N O F t h e p i n k I e s A n d I m A l s o q u E e N o F t h e B l U E s s o i w o n t h a v e m y p e O P l e q u a R r E l i n g +HYP: w h I y t h e * r * * A r A n I m * e s y o u r s h o r t h i * * n e * s n o t a n y m o r e r e p l i * d E S r o U H t i m q u * e * ******* * * t h e p i n k * e s * n d H m * l s o q u * e * o * t h e * l * * s s o i w o n t h a v e m y p e * B l e q u a * r * l i n g +Eval: I D D D S S S D D D D D I S I I D D D D D D D S D D D D D D D D S D D + +id: (m-ailabs_eng_000191-m-ailabs_eng_000191) +Scores: (#C #S #D #I) 135 17 41 4 +REF: t Y p E W r I T e r s W E R e c l i c K i n g c l I P p i n g S W E r E b E I n g s n i P p E d o U t O f A H u g E s t a c K o f n E W s P A p e r S a n d p a s T e D i n ******* * ******* T O l a r g E s C r a P b O o K s C I r C U l A r s W E r E b e I n g f o l D E d a n d M a d e * r E a d y t o m a I l f o R t h e f i n A l a P p e A l +HYP: t I p * r * A e r s * * * e c l i c * i n g c l * * p i n g * * A r * b * * n g s n i * p * d o * t * f ******* * C u g * s t a c * o f n O U s * p e r * a n d p a s * e * i n A I N l a r g * s * r a b * o C s S U r K I l E r s * * r * b e * n g f o l * * d a n d * a d e D r * a d y t o m a * l f o * t h e f i n * l a * p e * l +Eval: S D S D S D D D D D D D D S D D D D D D D D D S D D S S D S D D D I I I S S D D S D S S S S S S D D D D D D D I D D D D D D + +id: (m-ailabs_eng_000192-m-ailabs_eng_000192) +Scores: (#C #S #D #I) 107 5 16 6 +REF: i t w a s f o U r * d a y s a f T e R t h e s u R p r i * s E o f A D l * e r s h o r s T w h E n t h e s t r a n g E r s l e * f T t h e E s t * a t E t O t h e c a * r E o f r U G g e d o l d f o r s t E r h E r m A N n +HYP: i t w a s f o * r E d a y s a f * e D t h e s u * p r i Y s * o f * * l H e r s h o r s E w h * n t h e s t r a n g * r s l e A f * t h e * s t D a t * t * t h e c a I r * o f r * O g e d o l d f o r s t * r h I r m * O n +Eval: D I D S D I D D D I S D D I D D I D D I D D S D S D S + +id: (m-ailabs_eng_000193-m-ailabs_eng_000193) +Scores: (#C #S #D #I) 108 9 39 16 +REF: * p O o r * t e m p l E t o n h e s a i d i * u s E D t O K n o W h I m * * * y ******* E a r s A g o w H e n W E w E R e b o * y s * W E n T t o s c H O o L w i t H h I m A n d * * a L l T h a t s o r T o f T h i n G Y O u K n o W b u t U n * t i l i r a n A c r o S s h I m ******* * * * +HYP: M p * o r E t e m p l t o n h e s a i d i O u s * * ******* t * * n o * h A m M A N y U a r s * g o w * e n ******* * * ******* w * * e ******* b o R y s E * M n * ******* t o ******* s c * * o U w i t * ******* h * m * n d N T a * l * h a t s o r * ******* o f * h i n * * * u ******* * n o E b u t A n D t i l i E r a n ******* * c r o U s h * m O R E +Eval: I D I S I D D D D D D S I I I I S D D D D D D D D D I I D S D D D D D S D D D D I I D D D D D D D D D D S S I S D D S D I I I I + +id: (m-ailabs_eng_000194-m-ailabs_eng_000194) +Scores: (#C #S #D #I) 58 5 14 2 +REF: i f o U n d H e R i n t h e f o * r E s t A n d b r O u g H t h e R H e * r E a p r I s O n e R r e p l I E d t h e c a p t A I n +HYP: i f o * n d T e * i n t h e ******* f o A r R s t * n d ******* b r * u g * t h e * * e A r * a p r E s * n e * r e p l * Y d t h e c a p t * O n +Eval: D S D D I S D D D D D D I D S D D D S D S + +id: (m-ailabs_eng_000195-m-ailabs_eng_000195) +Scores: (#C #S #D #I) 121 7 18 9 +REF: W H o m a Y b e c o m p E t e n t E i * ******* t h e R f r o m p e r s o n A l E X p e * r i E n c e o r t h e e * X p e r i E n c e o f o t h e r s t o a n * s W e r I t w I t H m o R e o r l e s s c o R r E c t * n e S s o r a t l e a s t * A n A T t e m P t * * +HYP: * * o m a * b e c o m p I t e n t * i D t h e * f r o m p e r s o n * l C S p e I r i * n c e o r t h e e C S p e r i * n c e o f o t h e r s t o a n C s * e r * t ******* w H t * m o * e o r l e s s c o * r A c t K n e * s o r a t l e a s t E * n * I t e m * t O H +Eval: D D D S D I I D D S S I D I S D I D D D S D D D S I D I D D S D I I + +id: (m-ailabs_eng_000196-m-ailabs_eng_000196) +Scores: (#C #S #D #I) 42 9 15 3 +REF: O N E H U n D R E D n i n E t Y t W o l a Y t E s t r E e T s a i d h o * * g A n b * I T i N g o F f h i S C I g a r +HYP: * * L * W n * * * * n i n * t E t * o l a * t * s t r * e D s a i d h o A K g O n b U Y D i * g o * f h i * S O g a r +Eval: D D S D S D D D D D S D D D D S I I S I S S D D D S S + +id: (m-ailabs_eng_000197-m-ailabs_eng_000197) +Scores: (#C #S #D #I) 133 9 32 6 +REF: T r O t w a s s u r p r i s E D t o f i n D S h e c o U l d s E e s o p l a i N l y t h r O U G H t h e h i G H w a L l o F w * a * t e r A b o V E h e * r b u t t h e s U n * w a s a b l E t o s h o o t i t s b e A m S s t r a I G H t d o W n t h R o U G H t h e T r a * n s p * A r E n t S E A +HYP: * r A t w a s s u r p r i s * * t o f i n E * h e c o * l d s * e s o p l a i * l y t h r * * * * t h e h i * Y w a * l ******* o * w O a H t e r U b o * F h e R r b u t t h e s * n D w a s a b l * t o s h o o t i t s b e * m E s t r a * * * t d o * n t h * o * * R t h e ******* A r a E n s p E I r * n t ******* * * * +Eval: D S D D S D D D D D D D D D S D D D I I S D S I D I D D S D D D D D D D S D S I I S D D D D D + +id: (m-ailabs_eng_000198-m-ailabs_eng_000198) +Scores: (#C #S #D #I) 20 3 8 1 +REF: t h e s p O t W H E R e i T H A d s p r U n g U p * +HYP: t h e s p A t * * * * e i * ******* * * d s p r O n g O p E +Eval: S D D D D D D D D S S I + +id: (m-ailabs_eng_000199-m-ailabs_eng_000199) +Scores: (#C #S #D #I) 35 3 10 4 +REF: * c A L m * d e * n i A l w H i C H S h e g a v e T O s U c h a s ******* u P p o s i t i o n +HYP: G c * O m E d e A n i * l w * i * T * h e g a v e ******* * * s O c h a ******* s u * p o s i t i o n +Eval: I D S I I D D D S D D D D S D I D + +id: (m-ailabs_eng_000200-m-ailabs_eng_000200) +Scores: (#C #S #D #I) 106 10 19 5 +REF: y o U s E e U n * t i l t h E S e s c h O O l p i l L s W E r E i n V e n t e d w e w a s t E D A l O t o f t i m E i n s t * U d y t h a t M A Y n o w ******* * * b e b e T t e r E m p l o y e d I n p r A c t I c i n g a t h l e t i c S +HYP: y o * s * e A n D t i l t h * * e s c h * * l p i l E s * * r * i n G e n t e d w e w a s t * * T l A t o f t i m * i n s t D A d y t h a t ******* * * * n o w M A b e b e * t e r I m p l o y e d A n M p r * c t S c i n g a t h l e t i c * +Eval: D D S I D D D D S D D D S D D S S D I S D D D D I I I D S S S D S D + +id: (m-ailabs_eng_000201-m-ailabs_eng_000201) +Scores: (#C #S #D #I) 53 4 8 3 +REF: y o U v e d O n E i t * * n O w d e c l a r e d d O r O t h y t h e s E t e n T S a r E j u s t W o * n d e r f U l +HYP: y o * v e d * n * i t H A n * w d e c l a r e d d A r * t h y t h e s * t e n C E a r * j u s t * o E n d e r f O l +Eval: D D D I I D S D D S S D D I S + +id: (m-ailabs_eng_000202-m-ailabs_eng_000202) +Scores: (#C #S #D #I) 114 18 14 9 +REF: * * f o r t w e n * T Y t E n f i v e * t h * r e e t w o * ******* t h e L i n E R w a s b A r E l y t w * e n T y m I L E s a w a y w h E n h o ******* d D A n f i r E d h I s r o c k E t s t h e Y m A d E a c o l o s S A l c l o U D o F V a p O r i n E m P t I n e s S +HYP: E M f o r t w e n I N G t A n f i v e F t h E r e e t w o E t h e * i n * O w a s b E r * l y t w O e n * y m * Y W s a w a y w h * n h o d I O n f i r * d h * s r o c k I t s t h e * m * d * a c o l o s T O l c l o W E o * * a p E r i n A m * t Y n e s E +Eval: I I I S S S I I I I D D S S D I D D S S D I S S D D S D D D S S S S D D S S D S S + +id: (m-ailabs_eng_000203-m-ailabs_eng_000203) +Scores: (#C #S #D #I) 126 5 24 6 +REF: t h e Y p a I d n o a T t e n T i o n t o t h e f a c T t h a t g H i p ******* * g * * H i s i Z Z l E d i d n o t W A n T t o m a R r y a n y o f t h * * e m f o r T h e Y h a D d e t e r m I n E d t h a t W h E n i t w a s A g r e e d w h o s h o U L d h a v E h i m +HYP: t h e * p a * d n o a * t e n C i o n t o t h e f a c * ******* t h a t g * i p K g E S C i s i * * l * d i d n o t * O n * t o m a * r y a n y o f t h E M e m f o r * h e * ******* h a * ******* d e t e r m E n * d t h a t * h * n i t ******* w a s E g r e e d w h o s h o * * d h a v * h i m +Eval: D D D S D D D I I I I S D D D D S D D I I D D D D D S D D D D S D D D + +id: (m-ailabs_eng_000204-m-ailabs_eng_000204) +Scores: (#C #S #D #I) 83 2 18 2 +REF: w H a t d O y o u t h i N K o f t h a t h e c r i E d o p e N I n g a c o p * y o F t h e r e c O R d a n d l a Y I n g I t f l a t o N t h e l i b R A r y t a b l e * +HYP: w * a t d * ******* y o u ******* t h i * * o f t h a t h e c r i * d o p e * * n g a c o p B y ******* o * ******* t h e r e c K E d a n d l a * * n g * t f l a t o * t h e l i b * * r y t a b l e L +Eval: D D D D D D D D D I D D D S S D D D D D D I + +id: (m-ailabs_eng_000205-m-ailabs_eng_000205) +Scores: (#C #S #D #I) 23 2 7 2 +REF: i t W I L l R e * Q U i R e * B u t a s H o r t t i m e +HYP: i t * * * l * e C O P i * e R * u t a s * o r t t i m e +Eval: D D D D I S S D I D D + +id: (m-ailabs_eng_000206-m-ailabs_eng_000206) +Scores: (#C #S #D #I) 80 2 8 3 +REF: a n d l a s t t h e C r o W d o F v e * g e t * a b l e p e O p l e W h o h a d n o h E a r t s a n d C o u L d n E i ******* t h e r s m i l e n o r f r o w n +HYP: a n d l a s t t h e * r o U d o * v e I g e t D a b l e p e * p l e ******* * h o h a d n o h * a r t s a n d * o u O d n * i t h e r s m i l e n o r f r o w n +Eval: D S D I I D D D D D S D I + +id: (m-ailabs_eng_000207-m-ailabs_eng_000207) +Scores: (#C #S #D #I) 28 0 6 2 +REF: t h e * n y o U l l c a T c h i t * s a i D t h E W i T c h +HYP: t h e I n y o * l l c a * c h i t E s a i * t h * * i * c h +Eval: I D D I D D D D + +id: (m-ailabs_eng_000208-m-ailabs_eng_000208) +Scores: (#C #S #D #I) 96 10 22 1 +REF: w h A t i s i t i q u e r I e d n o t f E e l i n g C E r T A I n b u t t h a t I t w a s a * v E I l E d a T t e m P T t o s e c U R e A l i T t l E f r E e a d V E r t I s i n g f o R t h e V a n d e R v E e r +HYP: w h * t i s i t i q u e r * e d n o t f I e l i n g * S r * * E n b u t t h a t * t ******* w a s a E v * A l * d a t e m * D t o ******* s e c * * e ******* R l i * t l * ******* f r * e a d * * r t Y s i n g f o * t h e * a n d e O v e r +Eval: D D S D S D D S D D I D S D S D S D D D D S D D D D D D S D D S S + +id: (m-ailabs_eng_000209-m-ailabs_eng_000209) +Scores: (#C #S #D #I) 123 4 21 1 +REF: s o H e g a v e t h e C l E r K t h e t h I r d H u n d R e D d o L l A R s f o r b O o k s a n d a c a s k o f g O o d o l d a l E f O r p e t e r t h e c l E r k D r a n K t h e a * l E h i m s e l f a n d g a v e T h e c a L f m i L K +HYP: s o * e g a v e t h e * l I r C t h e t h * r d * u n d * e * ******* d o * l * O s f o r b * o k s a n d a c a s k o f g * o d ******* o l d a l * f * r p e t e r t h e c l * r k * r a n * t h e a I l * h i m s e l f a n d g a v e * h e c a * f m i * W +Eval: D D S S D D D D D D D S D D D D D D D D I D D D D S + +id: (m-ailabs_eng_000210-m-ailabs_eng_000210) +Scores: (#C #S #D #I) 140 9 30 9 +REF: * * ******* l i K e * t h a t I n a l I C E i n W O n * d e r l a n * d w i t h m e r E l y A g r i n t h A t f a D e d a w a y c h a n g i n g i n t o a l Y n * X w h i c H i n t U r N D I s A P p e A r E d f o L l o w e d b y a n u N K n o W n C r e A T U R e w i t H s H o * r t n o * s E a n d p o I n t e d e A r s +HYP: A T l i * e K t h a t A n a l * * S i n * * n E d e r l a n T d ******* w i t h m e r * l y ******* * g r i n t h * t f a T e d a w a y c h a n g i n g i n t o a l I n K S w h i c * i n t * r E T O s * * p e * r * d f o * l o w e d b y a n u * * n o * n * r e * * * C e w i t * s * o U r t n o U s * ******* a n d p o * n t e d e * r s +Eval: I I I D I S D D S D D I I D D D D D S S I S D D S S S D D D D D D D D D D D D S D D I I D D D D + +id: (m-ailabs_eng_000211-m-ailabs_eng_000211) +Scores: (#C #S #D #I) 100 11 25 10 +REF: * ******* s h e c o U l d n o t d * o * ******* m a r g A r E T G l a n c E d u n ******* c o n S C I o u s l y a T t h e u n * C l E A N e d C o r n e r S o f T h e R O o m * ******* s h e C O u L D h a r D L Y U n d e r ******* t a k E a s E r v A n T s p l a c e c o U L D s h e +HYP: A s h e c o * l d n o t d E o E m a r g * r * I * l a n c * d u n c o n * * H o u s l y a * ******* t h e u n G K l * * * e d * o r n e r * o f ******* * h e * * o m E s h e * * u * O h a r T H * n d e r t a k * a s U r v I n C s p l a c e c o * * O s h e +Eval: I I D I I I D D S D D I D D S D D I S D D D D D D D D D I I D D D S S S S D I D S S S D D S + +id: (m-ailabs_eng_000212-m-ailabs_eng_000212) +Scores: (#C #S #D #I) 51 6 4 11 +REF: * ******* * n o * * s h e r e p l i * e d w i t H i N n O C e n T C U r I o * s i t y d i d i g i v E t h e M t o y o u * * * * +HYP: A D n o H E s h e r e p l i D e d w i t * i * n I S e n * K E r Y o U s i t y d i d i g i v F t h e * t o y o u W H A L +Eval: I I I I I I D D S S D S S S I S D I I I I + +id: (m-ailabs_eng_000213-m-ailabs_eng_000213) +Scores: (#C #S #D #I) 81 5 17 1 +REF: m a r L b o r O U G H m i l L s a n D t h e a D J a c e n t d W e L l i n g w E r E h e l d u n d e r A l o n g l e A s E t h e Y m u s t i f p o S s I b l E * b e r e l e t +HYP: m a r * b o r * * * * m i l E s a n * t h e ******* a G a c e n t d * e * l i n g w * r * h e l d u n d e r ******* * l o n g l e E s * t h e * m u s t i f p o * s A b l * T b e r e l e t +Eval: D D D D D S D D S S D D D D D D S D D D S D I + +id: (m-ailabs_eng_000214-m-ailabs_eng_000214) +Scores: (#C #S #D #I) 21 6 4 5 +REF: * ******* a c o p w a V e D A s t ******* u n P i s ******* t O L * a t H I m +HYP: D a c o p w a e * * O s t u n i s t H E L a t ******* * E m +Eval: I I S D D S I S I S S I D D S + +id: (m-ailabs_eng_000215-m-ailabs_eng_000215) +Scores: (#C #S #D #I) 125 8 21 4 +REF: i t * b o U n d e d h e * r E a n d t h * E r E a b o u T t h e c H i C k E n h o u s E a n D a t f i r s t d o r O t h y c o u l d n o t t e L l W h a t i t w A s w h i l E t h e s C R e E c H i n g o f t h e c h i * c K E n s n e A r l y d e A f e n E d h e r +HYP: i t D b o * n d e d h e A r * a n d t h A I r * a b o u * t h e ******* c * i O k O n h o u s * a n * a t f i r s t d o r * t h y ******* c o u l d ******* n o t t e * l * h a t i t w O s ******* w h i l * t h e s * P e A c * i n g o f t h e ******* c h i O c I O n s n e * r l y d e * f e n * d h e r +Eval: I D I D I S D D D D S S D D D D D D D S D D D S S D D I S S D D D + +id: (m-ailabs_eng_000216-m-ailabs_eng_000216) +Scores: (#C #S #D #I) 137 3 26 4 +REF: t h e s o l d I e r g a v e A y E L l T h a t A r o u s e d a s c * * O r E o f h i s c o m r a d E S a n d b r O U g H t t h e M t u m b l i n g i n t o t h E s t r E e t w h e n t h e y s a w h o W t h e B O o l O O r O O s p r e * c i O U s * p r i s O n e r w a s E s c a p i n g +HYP: t h e s o l d * e r g a v e ******* * y * A l * h a t E r o u s e d a s c H U A r * o f h i s c o m r a d * * a n d b r * * g * t t h e * t u m b l i n g i n t o t h * s t r * e t w h e n t h e y s a w ******* h o * t h e * * o l * * r * * s p r e S c i * * s E p r i s * n e r w a s * s c a p i n g +Eval: D D D D S D S I I S D D D D D D D D D D D D D D D D D I D D I D D + +id: (m-ailabs_eng_000217-m-ailabs_eng_000217) +Scores: (#C #S #D #I) 120 8 15 8 +REF: * j i m h a d r e f * u s e D t o l E A V e t h e f I e l d o f g r a S s w h e r E h e w a s E n g a g e d I n * b U s i l y e a t i n g s o t h e w i Z A r d g * O t o u t o F t h E b * u G g y a n d j * o I n e d * Z e b a n D d o * r O t h y +HYP: G j i m h a d r e f E u s e * t o l * * * e t h e f * e l d o f g r a * s ******* w h e r * h e w a s I n g a g e d A n D b I s i l y e a t i n g s o t h e w i S U r d g U G t o u t ******* o * t h * b O u * g y a n d j U o * n e d S A e b a n * ******* d o A r I t h y +Eval: I I D D D D D D D D S S I S S S I S D D D I D I D I S D D I S + +id: (m-ailabs_eng_000218-m-ailabs_eng_000218) +Scores: (#C #S #D #I) 71 4 23 0 +REF: C E r T A I n l y i A m a s i n t e r E S t e d I n t h e c a S e A s Y o U a r E b u t i c a n T M a k E h e A d s O r t a I l s o f I t i r e p l i E d +HYP: G S r * * D n l y i ******* * m ******* a s i n t e r * * t e d * n ******* t h e ******* c a C e ******* * s * o * a r * b u t i c a n * * a k * h e * d s * r t a * l s o f * t i r e p l i * d +Eval: S S D D S D D D D D D D D S D D D D D D D D D D D D D + +id: (m-ailabs_eng_000219-m-ailabs_eng_000219) +Scores: (#C #S #D #I) 30 1 1 1 +REF: o r a n y m i c e o r e v e n * g r a s S h o P p e r s +HYP: o r a n y m i c e o r e v e n G g r a s h o * p e r s +Eval: I S D + +id: (m-ailabs_eng_000220-m-ailabs_eng_000220) +Scores: (#C #S #D #I) 120 15 18 5 +REF: a n d t h e M t h A T p a Y s Y o d U n t h e Y t e L l y o * w h a T T e N t o d O o r w h a t T E n n o t t o d * O w I t h e m O n E y t h e y g i v e S y o u I n j u s t p a Y m e n t f o R y o u R p a i n s ******* i n F A I r e X c * * H a n g e l i K E +HYP: a n d t h e * t h * E p a * s ******* I o d O n t h e * t e * l y o U w h a * e * t o d * o r w h a t A n n o t t o d E H w E t h e m U n * y t h e y g i v e * y o u A n j u s t p a * m e n t f o * y o u * p a i n s i n ******* T H E r ******* e * c S T C a n g e l i * C +Eval: D D S D D S S D D I D S D D S S I S S S D D S D D D I D S S S D D I I S D S + +id: (m-ailabs_eng_000221-m-ailabs_eng_000221) +Scores: (#C #S #D #I) 29 2 7 0 +REF: w h A t d O E s t H a t m e a n a s K E D t h e P r i n c e S s +HYP: w h * t d * I s t * a t m e a n a s * * T t h e * r i n c e * s +Eval: D D S D D D S D D + +id: (m-ailabs_eng_000222-m-ailabs_eng_000222) +Scores: (#C #S #D #I) 109 6 17 7 +REF: H e h a d b e e N d r o W n e d h e w a s f l o a * T i n g I n a s E A o f l i G H t a n d n o w A N D t h e N s h i n i n g l I T t L e f i s H e s s w * * a m i n * q u i s i t i V e l y ******* * * u p t o h i M A n d s t a r E D +HYP: D e h a d b e e * d r o * n e d h e w a s f l o a O D i n g * n a s * I o f l i * * t a n d n o w ******* * * * t h e D s h i n i n g l * * t * e f i s I e s s w H E a m i n C q u i s i t i * e l y E O u p t o h i E * n d s t a r * * +Eval: S D D I S D D S D D D D D D S D D D S I I I D I I I S D D D + +id: (m-ailabs_eng_000223-m-ailabs_eng_000223) +Scores: (#C #S #D #I) 169 12 50 4 +REF: b u T o l d g U N n A R h A d a T r i C k O R t w o l e f t * r e m e m B e R t h e t a * l E T H A T i r e A d t o y o u i N t h E t h R o n e r O O m o F b A l D A R t h e f i r s t O F t h e b r o n s t O e n T e R t h e w O r l d o f o P a * l w e r E s o * l d I E r s s E n t f r O m s o m e b l a s t E d p l a n E t I n o u t e r s p a c E T O f i N D a n E W h o m E +HYP: b u D o l d g * * n * * h * d a ******* * r i * k ******* * * t w o l e f t D r e m e m * e * t h e t a I l * ******* * * * * i r e * d ******* t o y o u i * t h * t h * o n e r * * m o * A b * l T H E t h e f i r s t ******* * * t h e b r o n s t * e n D e * t h e w * r l d o f o * a P l w e r * s o U l d * G r s s I n t ******* f r * m s o m e b l a s t I d p l a n * t * n ******* o u t e r ******* s p a c * ******* * S f i * E a ******* n * O h o m * +Eval: S D D D D D D D D D D D I D D I D D D D D D D D D D D D D D S D S S S D D D D S D D D I D I D S S D D S D D D D D D D S D S D D S D + +id: (m-ailabs_eng_000224-m-ailabs_eng_000224) +Scores: (#C #S #D #I) 88 5 17 3 +REF: P a * p A w i L L Y o U s p e a K t O t h e m e n a n d g E t T h e m t o g o A w a y s h e c a N n O t b r e A t h E p O o r t h i n g w i t H t h i s C r o w d A b o u t H e r * * +HYP: * a P p E w i * E * o * s p e a * t * t h e m e n a n d ******* g * t * h e m ******* t o g o * w a y s h e c a * n * t b r e E t h * p * o r t h i n g w i t * t h i s * r o w d O b o u t T e r H L +Eval: D I S D S D D D D D D D D D D D S D D D D S S I I + +id: (m-ailabs_eng_000225-m-ailabs_eng_000225) +Scores: (#C #S #D #I) 104 8 24 7 +REF: * ******* w h e n i * t O o k t h i s c a S e h e s a i d i b E l I e v e d d o W n * i n * m y h E a r T T H A t d i * X o n w a s i N n O C e n t i s t I L L b e l I E V e i t b u t m y f a I t H h a s b E e n r * u D E l y s h a K E N +HYP: A w h e n i Y t * o k t h i s c a C e h e s a i d i b U l * e v e d d o * n E i n D m y h * a r * ******* * * * t d i C S o n w a s i * n * S e n t i s t * * O b e l * * * e ******* i t b u t m y f a * t * ******* h a s P b * e n r O u * T l y s h a * * C +Eval: I I I D S S D D I I D D D D D D I S D D S D D S D D D D D D D S D I D S D D S + +id: (m-ailabs_eng_000226-m-ailabs_eng_000226) +Scores: (#C #S #D #I) 26 4 6 8 +REF: * ******* C h a p t E r s i * X o * * f t h e p i R A t E S o F E r ******* s * a t * Z +HYP: A D h a p t * r s i C K o V E f t h e p i * * t * * o * O r s E a t S E +Eval: I I S D I S I I D D D D D S I I I S + +id: (m-ailabs_eng_000227-m-ailabs_eng_000227) +Scores: (#C #S #D #I) 27 0 2 2 +REF: r e ******* m e m B e r t h e Y c a n ******* n o t t o u c h u s +HYP: r e m e m * e r t h e * c a n n o t t o u c h u s +Eval: I D D I + +id: (m-ailabs_eng_000228-m-ailabs_eng_000228) +Scores: (#C #S #D #I) 112 8 13 12 +REF: G i v E m e t i m e a * ******* Z u r E g i v e m e t i m e i * f t H e * r E s a n y t h I n g i h a t E i t s a h u R r y i v e a n * I d e a * y o u r m a * J e s t y a n * N o U n c e D t h e s i * * X t h s n U b * ******* n o s E D p r I n c e S s +HYP: * i v * m e t i m e a S O u r * g i v e m e t i m e i O f t * e I r * s a n y t h * n g i h a t * i t s a h u * r y i v e ******* a n Y d e a E y o u r m a D G e s t y a n D o * n c e T t h e s i C T t h s n O b E n o s * * p r O n c e * s +Eval: D D I I S D I D I D D D D D I S I I S I S D S I I S S I I D D S D + +id: (m-ailabs_eng_000229-m-ailabs_eng_000229) +Scores: (#C #S #D #I) 29 3 8 3 +REF: t R U E E n o U G H t r o * * t d e c l * a r e D t h e s a I l O r m a n +HYP: t * * * ******* O n o * * F t r o A H t d e c l E a r e * t h e s a * l E r m a n +Eval: D D D D S D D S I I I D D S + +id: (m-ailabs_eng_000230-m-ailabs_eng_000230) +Scores: (#C #S #D #I) 67 7 7 8 +REF: * a s f o r t h a t s a i d m a r g A r E t r A t h e R h A U G H t i l y i h o l d i t * i s * h o n I s o ******* i t * q u * I m A l * Y p e n s * E +HYP: A a s f o r t h a t s a i d m a r g * r I t r * t h e * h * * * O t i l y i h o l d i t H i s O h o n Y s o i t C q u E E m U l D * E p e n s A Y +Eval: I D S D D D D D S I I S I I I S S I D S I S + +id: (m-ailabs_eng_000231-m-ailabs_eng_000231) +Scores: (#C #S #D #I) 142 4 30 4 +REF: w H E N H e h e A R D t h e s E w o r d s t h e K i n g w H o s E h e A d w a s f u L l O f t h e p R i n c e S s n e v e r S t O P p e D t o I n * q * U i r E i f t h e y c o U L d b e t r U E a n d s m e A r e d h i m s e l f o v e r w i t h f a t a n d s p r a n g i n t O t h e o v e * n * +HYP: w * * * ******* * e h e * * * t h e s * w o r d s t h e C i n g ******* w * o s * h e * d w a s ******* f u * l * f t h e p * i n c e * s n e v e r * t * A p e * t o * n C q P H i r * i f t h e y ******* c o * * d b e t r * O a n d s m e * r e d h i m s e l f o v e r w i t h f a t a n d s p r a n g i n t * t h e ******* o v e I n T +Eval: D D D D D D D D D S D D D D D D D D D D D S D D I I S D D D D D S D D D I I + +id: (m-ailabs_eng_000232-m-ailabs_eng_000232) +Scores: (#C #S #D #I) 122 13 29 11 +REF: y o U s h o U l D b E a B l E T O g e t p a r T S f r o m y o u r * r O o m * v I S i o n * ******* r e C e I v e r * i l l h a v e s o m E t o * o l s g i v e n Y o u t h e n H E a d D e d d I p L o m ******* a * ******* C Y h a s t o U n d e r s t a n D t h E t H i n g s T H a T c O N t R o l E V e n * T s * +HYP: y o * ******* s h o * l * b * a * l * * Y g e t p a r C E f r o m y o u r E r * o m E v * * i o n D r e S e * v e r E i l l h a v e ******* s o m * t o U o l s g i v e n * o u t h e n ******* * * a d T e d d E p * o m a S H E h a s ******* t o * n d e r s t a n * t h * t * i n g s * * a * ******* c E t * o l O F e n C E s H +Eval: D D D D D D D D S S S I D I D D I I S D I D D I D D D D S S D I I I S S D D D D D D D D D S S D S S I S I + +id: (m-ailabs_eng_000233-m-ailabs_eng_000233) +Scores: (#C #S #D #I) 57 2 11 4 +REF: b y t h e t i m E t h e f r o * s t H a d s E T i n t h e Y s h o U L D b e f * a r * A w a y f R o m h e l s t * o n E +HYP: b y t h e t i m * t h e ******* f r o U s t * a d s A D i n t h e * ******* s h o * * * b e f O a r E * w a y f * o m h e l s t D o n * +Eval: D D I D S S D D D D D I I D D I D + +id: (m-ailabs_eng_000234-m-ailabs_eng_000234) +Scores: (#C #S #D #I) 26 4 7 5 +REF: o N e * t h i N g I w * A n T t o * s a y b e ******* g a n * K e N n E D y +HYP: o * e N t h i * g ******* * w O E n * ******* t o E s a y b e g a n D C e * n I T y +Eval: D I D D D I S D D I I I S D S S + +id: (m-ailabs_eng_000235-m-ailabs_eng_000235) +Scores: (#C #S #D #I) 51 5 13 1 +REF: t h i S I m p o r t A n T t r a F F i c w a s C o n ******* f i d e D t o n o o N E b U T t h e r e a l p r O p r i E t O r +HYP: t h i * * m p o r t * n * ******* t r a C H i c w a s * o n f i d e * t o n o o * A M b * * t h e ******* r e a l p r * p r i * t E r +Eval: D D D D D S S D I D D S S D D D D D S + +Speaker sentences 124: cv_eng_000707 #utts: 1 +id: (cv_eng_000707-cv_eng_000707) +Scores: (#C #S #D #I) 24 16 5 16 +REF: * * ******* * ******* * h * e W a * * S * * R E P l a C e d o n * * b a S s G U I T A R B y * J U s t i * N K L U g * +HYP: U N N W h W e * a I D E D A B B l a S e d o n T D b a * s ******* K O D O V E M y T H I s t i M G * * C g O +Eval: I I I I I I I D I I S I I S S S S I I D D S S S S S S S I S S I S D D S I + +Speaker sentences 125: cv_eng_000708 #utts: 1 +id: (cv_eng_000708-cv_eng_000708) +Scores: (#C #S #D #I) 45 3 9 3 +REF: i d A d ******* * D a s e p A r A t E s u B s e c t i o n w h i c h d E A l s w i t H T h i s a s p e c t * +HYP: i d T d A T a s e p * r * t * ******* s u P s e c t i o n w h i c h d * * l s ******* w i t * * h i s a s p e c t D +Eval: S I I S D D D D S D D D D D I + +Speaker sentences 126: cv_eng_000709 #utts: 1 +id: (cv_eng_000709-cv_eng_000709) +Scores: (#C #S #D #I) 44 6 6 9 +REF: o p E r a t i o n o f t h e T r U n K l I n E c o n t I n u * e d o n ******* * * * w O o d e n * * t R e s T L e * * s +HYP: o p * r a t i o n o f t h e F r O n * T l A n * c o n t D n u R e d o n T H E w * o d e n D T t * e s * S e I L s +Eval: D S S D S S D S I I I I I D I I D D S I I + +Speaker sentences 127: cv_eng_000710 #utts: 1 +id: (cv_eng_000710-cv_eng_000710) +Scores: (#C #S #D #I) 51 13 13 4 +REF: m A G n E s I U M f * l U o r i d E i s t R A n * S p A r e n t o v e r A n E X t r E m E l y * w * i d E r a n g E o f W a v E L E n g T H s +HYP: m * n I s * O N f H l * o r i d * i s t W E n C E p E r e n t o v e r * n ******* * C t r I m * l y E w H i d * r a n g * o f * a v O M I n g * * s +Eval: D S S D S S I D D S S I S S D D D S S D I I D D D S S S D D + +Speaker sentences 128: cv_eng_000711 #utts: 1 +id: (cv_eng_000711-cv_eng_000711) +Scores: (#C #S #D #I) 63 18 10 7 +REF: f o U r G i A n t P A c k i n g * s h E D s s t o r e D f r e s h p * A c k E D P O t * * a T O e s A n D d e ******* l I V e R e D t h e m O n t o R A I l * r ******* o A d c a R s +HYP: f o * r J i * n t B E c k i n g K s h I T s s t o r e T H f r e s h p B O c k * T B U t D E a * D e s * n * d e l * * e V e * t h e m U n t o E R l E r o L d c a * s +Eval: D S D S S I S S S S I S D S S S I I D S D D I D D S D S S S S I I S D + +Speaker sentences 129: cv_eng_000712 #utts: 1 +id: (cv_eng_000712-cv_eng_000712) +Scores: (#C #S #D #I) 75 10 14 6 +REF: t h e o t h e R f o U r t E E n * c * A m p U s e s a r E t * w o ******* y E A r * c a m p U S E s r e f E R r E D t o c o L l e c t i v e l y a s t h e * u n I V e r s I t Y c o L l E g e +HYP: t h e o t h e * f o * r t * I n G c O U m p O s e s a r D t O w o y * U r E c a m p * * * s r e f * I r * T t o c o * l e c t i v e l y a s t h e Y u n * * e r s * t D E c o * l N g e +Eval: D D D S I I S S S I I D S I D D D D S D S D I D D D S S D S + +Speaker sentences 130: cv_eng_000713 #utts: 1 +id: (cv_eng_000713-cv_eng_000713) +Scores: (#C #S #D #I) 43 8 12 4 +REF: i t s t O o b * a d t h * * A T h * e S Q u i c k l Y g o I n g t o f O r g e t m y N a M E H e t H O U G H T +HYP: i t s t W o b H a d t h E D W E h A e * C u i c k l E g o R n g ******* t o f * r g e t m y T a * * ******* * e t * * * * * D +Eval: S I I I S S I D S S S D D S D D D D D D D D D S + +Speaker sentences 131: cv_eng_000714 #utts: 1 +id: (cv_eng_000714-cv_eng_000714) +Scores: (#C #S #D #I) 49 26 14 7 +REF: * o n e * P I C t U r e i n t H e g A L l E r y s h o W S h o W d I L I g E n t S l ******* a V E S E r e C T T H E s t a t * ******* U E * o * F a d M I r A L t H o M P S o n +HYP: W o n e N * O t O r e i n t * e g * E l O r y s h o * H h o U R d H E g I n t ******* * l a * I D I r e * D * * * I s t a t Y A T F o A L a d * G r * O R t C o * N T o n +Eval: I I D S S S D D S S D S S S S S S S D D I D S S S D S D D D S I I S S I I S D S D S S S D S S + +Speaker sentences 132: cv_eng_000715 #utts: 1 +id: (cv_eng_000715-cv_eng_000715) +Scores: (#C #S #D #I) 11 2 0 6 +REF: * ******* * I m ******* p e r i a l d * ******* i E t +HYP: E A N m p e r i a l d I i A t +Eval: I I I S I I I S + +Speaker sentences 133: cv_eng_000716 #utts: 1 +id: (cv_eng_000716-cv_eng_000716) +Scores: (#C #S #D #I) 36 10 8 6 +REF: t h e R e s U l T i n G c o m p A n y * * * I s S T R A t * T e C s E c * U r i t y * C o R p O r a t i o n +HYP: t h e * e s E l D i n * c o m p * n y H T D A s H U D t A K e * ******* s * c K O r i t y O * o T p * r a t i o n +Eval: D S S D D I I I S S S S S I S D D D I S I D S D + +Speaker sentences 134: cv_eng_000717 #utts: 1 +id: (cv_eng_000717-cv_eng_000717) +Scores: (#C #S #D #I) 52 16 7 6 +REF: B I T c o i n * m i * n i n g c a n b e d o n E w i t H g * R A P h i C s c * a r D s * o r W i t H s ******* p e C i A l i Z E D h A r d W A R E +HYP: T E c o i n G m i Y n i n g c a n b e d o n * w i t * g D O F h i * s c O a r T s A o r E i t * E s p e S i O l i * S T h O r d * * L Y +Eval: S S S I I D D I S S S D I S I S D S I S S D S S S D D S S + +Speaker sentences 135: cv_eng_000718 #utts: 1 +id: (cv_eng_000718-cv_eng_000718) +Scores: (#C #S #D #I) 28 2 5 5 +REF: * ******* t h e Y A l ******* s o l e A D t h e n * A T i o n a l r a n k i n g * +HYP: G t h e * * l s o l e * * t h e ******* n O U S i o n a l r a n k i n g N +Eval: I I D D I D D D I S S I + +Speaker sentences 136: cv_eng_000719 #utts: 1 +id: (cv_eng_000719-cv_eng_000719) +Scores: (#C #S #D #I) 25 8 0 4 +REF: * C H a r L E s g r a V e s b i s h o p * o f * L i m E r I c k * +HYP: T T a r W R s g r a N e s b i s h o p E o f N H i m O r E c k E +Eval: I S S S S S I I S S S I + +Speaker sentences 137: cv_eng_000720 #utts: 1 +id: (cv_eng_000720-cv_eng_000720) +Scores: (#C #S #D #I) 36 3 4 12 +REF: * a ******* * * * n d A T t h a * t * * i t o l D h i m a n d h e T O o k m y p l a C e * ******* * * +HYP: A a I O U n d ******* E R t h a R t T H i t o l * h i m a n d h e * * o k m y p l a S e S P D +Eval: I I I I I D S S I I I D D D S I I I I + +Speaker sentences 138: cv_eng_000721 #utts: 1 +id: (cv_eng_000721-cv_eng_000721) +Scores: (#C #S #D #I) 29 3 2 6 +REF: i t h o U g H t i d g i v e t h e * * K i * D s a t * r e a * t * +HYP: i t h o R g * t i d g i v e t h e C I T i T C s a ******* t D r e a E t E +Eval: S D I I S I S D I I I + +Speaker sentences 139: cv_eng_000722 #utts: 1 +id: (cv_eng_000722-cv_eng_000722) +Scores: (#C #S #D #I) 22 8 5 4 +REF: a * ******* * C e * v e D O d e n i E D S H o W I n G t h e P i c t U R e s +HYP: a S T I e W v e T L d e n i * H * T o * M n * t h e R i c t * O e s +Eval: I I I S I S S D S D S D S D S D S + +Speaker sentences 140: cv_eng_000723 #utts: 1 +id: (cv_eng_000723-cv_eng_000723) +Scores: (#C #S #D #I) 47 13 8 5 +REF: h o L d y o u r n o s * E t o K E e P t h * E S m E L L f R o m * * D I S a b l i n g Y o U r m o t O R * f U n C t i o n S +HYP: h o E d y o u r n o s T H t o C A e D t h I T * m * A Y f * o m T H E F a b l i n g H o * r m o t * * O f O n * t i o n * +Eval: S I S S S S I S D D S S D I I S S S S D D D I S D D + +Speaker sentences 141: cv_eng_000724 #utts: 1 +id: (cv_eng_000724-cv_eng_000724) +Scores: (#C #S #D #I) 23 5 2 13 +REF: * ******* t h a t s o U n * d * s l I k e t h e I r * p * R o * * ******* * * * B L e ******* M +HYP: D t h a t s o * n E d T s l A k e t h e * r E p O L o M E A I I E D e I +Eval: I I D I I S D I I S I I I I I I S S I S + +Speaker sentences 142: cv_eng_000725 #utts: 1 +id: (cv_eng_000725-cv_eng_000725) +Scores: (#C #S #D #I) 60 18 10 9 +REF: * ******* h i s T O r i * c A L l Y T H E r E w a s n o * C l E a r l y d e f i N e D b o U n d A r Y i n t h I s p A R t * o f t H e * * a r A b I A n p E n i n s * U l a * +HYP: I h i s * r i N c * U l * ******* I G O r * w a s n o T P l * a r l y d e f i D e * b o W n d G r E A i n t h E s p * I t D o f t * e C H a r I b E U n ******* p A n i n s T O l a E +Eval: I I D S I D S D D S S S D I S D S D S S S S S D S I D I I S S S D S I S I + +Speaker sentences 143: cv_eng_000726 #utts: 1 +id: (cv_eng_000726-cv_eng_000726) +Scores: (#C #S #D #I) 54 5 5 6 +REF: m a r ******* s H a L l s h a F F e r o f s l a s h f i l * m * * g a v e t h e f i l m * a n * E I G H t o u t o f T E n +HYP: m a r s I a * l s h a * V e r o f s l a s h f i l N m E D g a v e t h e f i l m E a n D * * * A t o u t o f C A n +Eval: I S D D S I I I I I D D D S S S + +Speaker sentences 144: cv_eng_000727 #utts: 1 +id: (cv_eng_000727-cv_eng_000727) +Scores: (#C #S #D #I) 9 7 4 1 +REF: h o w C A N y O U S A Y t H a t * +HYP: h o w ******* P E R D y * I * T I t * a t E +Eval: D S S S S D S D S S D I + +Speaker sentences 145: cv_eng_000728 #utts: 1 +id: (cv_eng_000728-cv_eng_000728) +Scores: (#C #S #D #I) 38 4 4 12 +REF: h i s * S t ******* * Y l e b e g a n * t o R e s * e m b l * * e m * i * c H A e L * d A m A s k i n o * * s +HYP: h i s T * t D I l e b e g a n E t o * e s A e m b l E T e m Y i K c * L e * T d E m O s k i n o S E s +Eval: I D I I S I D I I I I I D S D I S S I I + +Speaker sentences 146: cv_eng_000729 #utts: 1 +id: (cv_eng_000729-cv_eng_000729) +Scores: (#C #S #D #I) 45 10 20 0 +REF: h E i s a l S O c a p a b l E O F f I R i n g l I G H t N i n G b O l t S w I T H I M m e n S e d e s T r u C t I V E p o W e r +HYP: h * ******* i s a l * L c a p a b l * ******* * * f * * i n g l * * Y t i n * ******* b * l t * w * O E * A m e n T e d e s * r u P t * O F p o * e r +Eval: D D D S D D D D D D D D S S D D D D D S S D S S D S D S S D + +Speaker sentences 147: cv_eng_000730 #utts: 1 +id: (cv_eng_000730-cv_eng_000730) +Scores: (#C #S #D #I) 48 26 11 14 +REF: * h e C l a I m e D t W o w i C k E T s * i n E n g l A n D S * O N l y i N n i n g s a s * B o R D e R W E R E B e a t ******* E n * C O M P r * * * ******* * * e ******* H E n * S I V E L y +HYP: T h e F l a * m e * t * o w i A k C s C i n ******* I n g l I n * * I R M l y i * n i n g s a s F P o L H e * * * L I S T e a t O n D * U I L r L N D A S e L A n D D T T R y +Eval: I S D D D S S S I D S S D D I S S D I S S S D D D S S S S I S I D S S S I I I I I I I S S I S S S S S + +Speaker sentences 148: cv_eng_000731 #utts: 1 +id: (cv_eng_000731-cv_eng_000731) +Scores: (#C #S #D #I) 13 11 2 5 +REF: s h e d * I D M u C h l * I t E R A r Y w o * * ******* R K +HYP: s h e d E T E R O u S h E l Y t * * O r O w o A T A H +Eval: I S S S S S S I S D D S S I I I S S + +Speaker sentences 149: cv_eng_000732 #utts: 1 +id: (cv_eng_000732-cv_eng_000732) +Scores: (#C #S #D #I) 58 6 13 1 +REF: H e m E t t h e o r g A n I Z e * r s o f t h e P r o t e s T S a n d a g r E e d T O c r e a t E t w o w o r k i n G g r O u P s +HYP: A e m * t t h e ******* o r g O n Y S e O r s o f t h e * r o t e s * E a n d ******* a g r * e d ******* * * c r e a t * t w o w o r k i n * ******* g r * u M s +Eval: S D D S S S I D D S D D D D D D D D D S + +Speaker sentences 150: cv_eng_000733 #utts: 1 +id: (cv_eng_000733-cv_eng_000733) +Scores: (#C #S #D #I) 39 14 5 8 +REF: * ******* t h e b A L L s t r U c K t h * E f O U l * p o L E w * E l L * a b ******* o V E T h E g r E e n M o n s t E r * +HYP: A t h e b * O N s t r O c T t h O F f H A l D p o R D w H I l E O a b o * F * h I g r * e n * o n s t O r D +Eval: I I D S S S S I S S S I S S I S S I I D S D S D D S I + +Speaker sentences 151: cv_eng_000734 #utts: 1 +id: (cv_eng_000734-cv_eng_000734) +Scores: (#C #S #D #I) 61 6 11 8 +REF: o n l y c a m * d E n t H o m * A s g a R r E T t a n d g O l d ******* f i E l d S s o u T H * E Z e K i E l b a * k e * r w e r E u n * ******* c o n t E s t e d +HYP: o n l y c a m E d O n t * o m E s g a * r * I t a n d g * l d f i * l d * s o u * * I S e * i * l b a C k e A r w e r * u n D c o n t I s t e d +Eval: I S D I S D D S D I D D D D I S S D D I I D I I S + +Speaker sentences 152: cv_eng_000735 #utts: 1 +id: (cv_eng_000735-cv_eng_000735) +Scores: (#C #S #D #I) 47 11 6 8 +REF: * * ******* i t * i s a ******* * c h A r I T y s c H O o l w h o s E f E e s a R E c A L c * U l a T E D o n a * m e a n s t e s t +HYP: B P i t H i s a D c h I r * D y s c * * o l E w h o s * f * e s a * N c O U c K I l a D I N o n a I N m e a n s t e s t +Eval: I I I I I I S D S D D S D D D S S S I S S S S I S + +Speaker sentences 153: cv_eng_000736 #utts: 1 +id: (cv_eng_000736-cv_eng_000736) +Scores: (#C #S #D #I) 43 2 9 1 +REF: s o m e w e n t A w a y w h I l E I w a s T H e r E a n d o t h e R p e O p L e c a m e * +HYP: s o m e w e n t * w a y w h * l * O U w a s * * e r * a n d o t h e * p e * p * e c a m e M +Eval: D D D S S D D D D D D I + +Speaker sentences 154: cv_eng_000737 #utts: 1 +id: (cv_eng_000737-cv_eng_000737) +Scores: (#C #S #D #I) 1 4 0 18 +REF: * * * * ******* * * * * * * * * * * * * * S E V E n +HYP: D A D T T H A N T H A H T D T C T C D N C n +Eval: I I I I I I I I I I I I I I I I I I S S S S + +Speaker sentences 155: cv_eng_000738 #utts: 1 +id: (cv_eng_000738-cv_eng_000738) +Scores: (#C #S #D #I) 62 13 10 5 +REF: t h * E K u r a K H A n A t E w a s l o * c a T E d m a I n l y I N t h * E h i s t o r i c A l * a n d G e o g r A P H I c A l * r e g i o n o f K u r A +HYP: t h A T C u r a * C O n O t Y w a s l o K c a * * d m a * n l y ******* * * t h A T h i s t o r i c * l E a n d D e o g r * E F O c * l E r e g i o n o f C u r E +Eval: I S S D S S S S I D D D D D D I S D I S D S S S D I S S + +Speaker sentences 156: cv_eng_000739 #utts: 1 +id: (cv_eng_000739-cv_eng_000739) +Scores: (#C #S #D #I) 32 3 9 4 +REF: t H e E l E v a t i o N a T t h e s i * t E i s a B o V E s E A l e v e l * * * +HYP: t * e * l * v a t i o * a * t h e s i H t * i s a M o * F s * * I l e v e l E I G +Eval: D D D D D I D S D S D D S I I I + +Speaker sentences 157: cv_eng_000740 #utts: 1 +id: (cv_eng_000740-cv_eng_000740) +Scores: (#C #S #D #I) 38 4 3 14 +REF: * ******* t o ******* b I a s t r i E d * t o I n * J e c * ******* t c o n * ******* t e m * * P t * * i n ******* t o h i s t o n E +HYP: A t o b a s t r i * d E t o ******* A n C H e c T t c o n P t e m E T E t E D i n t o h i s t o n * +Eval: I I I S D I D S I S I I I I I I S I I I D + +Speaker sentences 158: cv_eng_000741 #utts: 1 +id: (cv_eng_000741-cv_eng_000741) +Scores: (#C #S #D #I) 24 3 1 2 +REF: i h * a v e t o w o r k * t h i s s A t U R d A y +HYP: i h E a v e t o w o r k E t h i s s I t * O d L y +Eval: I I S D S S + +Speaker sentences 159: cv_eng_000742 #utts: 1 +id: (cv_eng_000742-cv_eng_000742) +Scores: (#C #S #D #I) 39 18 11 12 +REF: * ******* t H e * G r E a ******* t * * r * U L E R s f O U n D T H E * * S Q U e a K Y g * R a T E w * a S g R a t i n g o n t H e I r * n E R V e s +HYP: U t * e D * r * a t H E r O N W O s f * E n * M W A S K I L A G e a * * g L E a * D w L a F g L a t i n g o n t * e * r E n * O N e s +Eval: I I D I D D I I I I S S S S D S D S S S S I I S S S D D I S D S I S S D D I D S S + +Speaker sentences 160: cv_eng_000743 #utts: 1 +id: (cv_eng_000743-cv_eng_000743) +Scores: (#C #S #D #I) 58 7 7 4 +REF: w h e N t h e b L i N D i n g d U s t h A D s e T T l e d A b * i t * t h e b o y t r E m b l e * * d a t w h a T h e s A W +HYP: w h e O t h e b * i * L i n g d O s t h * E s e * * l e d R b E i t E t h e b o y t r O m b l e D T d a t w h a * h e s * O +Eval: S D D S S D S D D S I I S I I D D S + +Speaker sentences 161: cv_eng_000744 #utts: 1 +id: (cv_eng_000744-cv_eng_000744) +Scores: (#C #S #D #I) 33 3 2 14 +REF: d e M o c r a t a M B e ******* * r * ******* * * * * b a k * ******* * e r w o n ******* * * t h e o p e n s e A T +HYP: d e I o c r a t a * N e B r H I N D D b a k E I e r w o n I T t h e o p e n s e * O +Eval: S D S I I I I I I I I I I I I I I D S + +Speaker sentences 162: cv_eng_000745 #utts: 1 +id: (cv_eng_000745-cv_eng_000745) +Scores: (#C #S #D #I) 33 28 7 15 +REF: * B o T H A R e * * P U T * T o ******* g e * t h E R b * y S t U D e ******* N T S i n * T h e c O l L E G E s * J o U R n a * l I s M P R o * G r * A m * * +HYP: L W o R Y O U e T W O R D I N o g e A t h * A b L y ******* * t * H e O O D i n I N h e ******* c U l D Y U s E * o * A n a T l Y s I N D T o L r O U m N P +Eval: I S S S S S I I S S S I S I I D S I D D D S I S S S I S D S S S S S I D D S I S S S S S I S I S I I + +Speaker sentences 163: cv_eng_000746 #utts: 1 +id: (cv_eng_000746-cv_eng_000746) +Scores: (#C #S #D #I) 40 8 2 7 +REF: t r E n C h * w a s b o r n i n b e l * I Z e C i t * Y i n * b r i t * I s H H o n d U r * a * s +HYP: t r A n T h W w a s b o r n i n b e l Y E S e S i t E D i n D b r i t O C s * P o n d * r E a S s +Eval: S S I I S S S I S I I S D S D I I + +Speaker sentences 164: cv_eng_000747 #utts: 1 +id: (cv_eng_000747-cv_eng_000747) +Scores: (#C #S #D #I) 24 6 4 1 +REF: T H e E A r L y P H a s e o f l i * f E m o V e s f a s t +HYP: * D e ******* R I r D y * F a s e o f l i C f * m o M e s f a s t +Eval: D S D S S S D S I D S + +Speaker sentences 165: cv_eng_000748 #utts: 1 +id: (cv_eng_000748-cv_eng_000748) +Scores: (#C #S #D #I) 2 0 0 13 +REF: * * * * ******* n o * * * * * * ******* * +HYP: A A A T n o T E T H D E E +Eval: I I I I I I I I I I I I I + +Speaker sentences 166: cv_eng_000749 #utts: 1 +id: (cv_eng_000749-cv_eng_000749) +Scores: (#C #S #D #I) 4 1 0 16 +REF: s * E v e * * n * ******* * * * * * * * * * * * +HYP: s O R v e W I n T O L T D L U E T O E E +Eval: I S I I I I I I I I I I I I I I I + +Speaker sentences 167: cv_eng_000750 #utts: 1 +id: (cv_eng_000750-cv_eng_000750) +Scores: (#C #S #D #I) 54 13 16 4 +REF: a t o N e t I M e r A I l W A Y l I n E s D I V e * r G E d f R o m * r * U g ******* b Y s t a t i o n i n s E V e n D i F f E r e n T d I R e c t i o n s +HYP: a t o * e t * * e r E l * * U R l E n * s T E Y e O r * * d f * o m B r A K g b * E s t a t i o n i n s * O e n * i * f * r e n * ******* d E D e c t i o n s +Eval: D D D S S D D S S S D S S S I D D D I I S I D S D S D D D D D S S + +Speaker sentences 168: cv_eng_000751 #utts: 1 +id: (cv_eng_000751-cv_eng_000751) +Scores: (#C #S #D #I) 54 11 2 9 +REF: * ******* * c * Z e c H r e p u * b l i C E n t e r e d t w o s h o O t e r s i n t o t h e p a * * * r * A l Y M p I C c o m p E t i T i o N +HYP: A A c H A e c * r e p u P b l i K A n t e r e d t w o s h o U t e r s i n t o t h e p a R R O r O l E N p O G c o m p O t i S i o * +Eval: I I I I S D I S S S I I I I S S S S S S S D + +Speaker sentences 169: cv_eng_000752 #utts: 1 +id: (cv_eng_000752-cv_eng_000752) +Scores: (#C #S #D #I) 53 16 8 6 +REF: t ******* * * Y G e r w i L l i A m s W r o T e t h e s C R E E N p l a y * a n ******* D s h a r e d s t o r y C r e d ******* i t W I t H t h E B r O T H E R S +HYP: t I D H e r w i * l i * m s * r o * e t h e s K A N G p l a y H a n T s h a r e d s t o r y * r e d i t * H t * t h O P r * E I P I T +Eval: I I I S S D D D D S S S S S I I S D I D S D S S D S S S S S + +Speaker sentences 170: cv_eng_000753 #utts: 1 +id: (cv_eng_000753-cv_eng_000753) +Scores: (#C #S #D #I) 39 14 4 29 +REF: t H i s f E s t ******* * * I V A l * * w * * * A s t o * * b * * e A c h * * A r i t y f * U n d * r ******* * * * * ******* * a i S e R * f o * * ******* * R t h e a * r E A +HYP: t * i s f A s t O F E L l E D w O R D E s ******* t o A F b E T e R D N c h E I R r i t y f L I n d E r A T N Y S a i D e * O f o I D Y E t h e R a U r * T +Eval: D S I I I S S S I I I I I S D I I I I S S S I I S I S I I I I I I I I S D I I I I I S S I D S + +Speaker sentences 171: cv_eng_000754 #utts: 1 +id: (cv_eng_000754-cv_eng_000754) +Scores: (#C #S #D #I) 39 8 4 41 +REF: * ******* t h e s e e * x t r a * C a r * D s w E R e I n * s e * r * * * ******* t * e d R A n D o M l Y * I n * t ******* o * P a c k * * * * * * ******* * * * ******* * * * * * ******* * * ******* * * * * * s +HYP: O t h e s e e N x t r a G O a r S T s w * * e * n E s e U r N L T t H e d * O n G o N l E A L n M t o F O a c k G A L F T E R A Y H A T H E I R H A R D T s +Eval: I I I I S I S D D D I I I I I I I D S S S S I S I I I S I I I I I I I I I I I I I I I I I I I I I I I I I + +Speaker sentences 172: cv_eng_000755 #utts: 1 +id: (cv_eng_000755-cv_eng_000755) +Scores: (#C #S #D #I) 18 6 4 10 +REF: * * * * ******* h E n * * r Y W E n T b a * c K t o A U s t r A l i * * A +HYP: A I P I h A n D E r * * O n D b a K c T t o * E s t r * l i O W M +Eval: I I I I I S I I D D S S I S D S D I I S + +Speaker sentences 173: cv_eng_000756 #utts: 1 +id: (cv_eng_000756-cv_eng_000756) +Scores: (#C #S #D #I) 35 9 7 9 +REF: * * ******* p E r * m i t m e ******* * t o i n t * r O d u C e T o * Y o U h e r m A J e s t * Y T H E q U E e n +HYP: A L p * r E m i t m e T t o i n t E r * d u S e S Y o U T o * h e r m O D e s t I D * * * C q * R e n +Eval: I I I D I I I I D S S S I S D S S I S D D D S D S + +Speaker sentences 174: cv_eng_000757 #utts: 1 +id: (cv_eng_000757-cv_eng_000757) +Scores: (#C #S #D #I) 42 15 17 3 +REF: I n o r I g * i * n h e r O I n w a S s U P P o s E D t o B E t h e “ n o n A d * d I C t I V E m o r P h I n E s U B s T I t U t E ” +HYP: E n ******* o r * g E i O n h e r L E n w a * s * B o s * * ******* t o ******* * Y t h e *** n o n * d A d * E t D O F m o r F h E n * s * P s * * t O t * D +Eval: S D D I I S S D D S S D D D D D S D D I D S S S S S S D D S D D S D S + +Speaker sentences 175: cv_eng_000758 #utts: 1 +id: (cv_eng_000758-cv_eng_000758) +Scores: (#C #S #D #I) 20 4 1 7 +REF: * * ******* s h e i s o f m * E X I c A n d e s C e * n t ******* * +HYP: E T s h e i s o f m A K C O c O n d e s * e S n t H +Eval: I I I I S S S S D I I I + +Speaker sentences 176: cv_eng_000759 #utts: 1 +id: (cv_eng_000759-cv_eng_000759) +Scores: (#C #S #D #I) 22 3 4 8 +REF: * * ******* i A m * s U r e t h e * R e I s * n o * t o n H i s * +HYP: G D i * m E s O r e t h e I L e ******* * s T n o G t o n ******* D i s T +Eval: I I I D I S I S D D I I D S I + +Speaker sentences 177: cv_eng_000760 #utts: 1 +id: (cv_eng_000760-cv_eng_000760) +Scores: (#C #S #D #I) 29 20 8 10 +REF: T H o S E W h o * * * d ******* o n t * l E a R n F R o M * h I s T O r * y A R E d O o M E d T o * R E P e * A t * I T +HYP: A A o * * T h o S A N d o n t E l * a * n C T L o L T h E s * H r E y * * I G d L o * L d P o R A B e D I t O N O +Eval: S S D D S I I I I I D D S S S S I S D S I D D S S S D S S I S S S I S I S S + +Speaker sentences 178: cv_eng_000761 #utts: 1 +id: (cv_eng_000761-cv_eng_000761) +Scores: (#C #S #D #I) 20 5 4 7 +REF: * ******* i c o U l * d N ’ T S t o p s T A r i n G a t i t ******* * * * +HYP: A i c o * l E d A N * t o p E s * O r i n * a t i t E E R +Eval: I I D I S S S D S D S D I I I I + +Speaker sentences 179: cv_eng_000762 #utts: 1 +id: (cv_eng_000762-cv_eng_000762) +Scores: (#C #S #D #I) 59 6 9 3 +REF: f o r s ******* i M p l I C i t y g E A r i n c h e s * i s n o r m A L l y r * o u n d e d t o T h e n e A r e s T W h o l E n U m b e r +HYP: f o r ******* s i N p l * * i t y g * U r i n c h e s D i s n o r m * I l y A r D o u n d e d t o * h e n e * r e s H * h o l * n O m b e r +Eval: D I S D D D S I D S S I D D S D D S + +Speaker sentences 180: cv_eng_000763 #utts: 1 +id: (cv_eng_000763-cv_eng_000763) +Scores: (#C #S #D #I) 33 3 7 3 +REF: i * f w e a c t U A L l y d * o W A n T i T s o l V E d i t w i L l b e * +HYP: i O f w e a c t * * I l y d E o * O n * i S s o l * * d i t w i * l b e F +Eval: I D D S I D S D S D D D I + +Speaker sentences 181: cv_eng_000764 #utts: 1 +id: (cv_eng_000764-cv_eng_000764) +Scores: (#C #S #D #I) 27 5 7 2 +REF: t h e * f R U I T o f A F i G t r E E I s a P p l E s h a p e d * +HYP: t h e I f * * * O o f H H i C t r * Y * s a * p l * s h a p e d T +Eval: I D D D S S S S D S D D D I + +Speaker sentences 182: cv_eng_000765 #utts: 1 +id: (cv_eng_000765-cv_eng_000765) +Scores: (#C #S #D #I) 18 7 2 4 +REF: F A I r * e ******* X c * * h a n g e i s n o R o B b E R y +HYP: T H E r E I e A c T S h a n g e i s n o W o * b * L y +Eval: S S S I S I S I I S D D S + +Speaker sentences 183: cv_eng_000766 #utts: 1 +id: (cv_eng_000766-cv_eng_000766) +Scores: (#C #S #D #I) 36 4 3 6 +REF: * ******* w h a t Y o u e a t t o ******* d a y w * * a L k S a n d t A L k s t o ******* m o R r o W +HYP: A w h a t * o u e a t t o d a y w H E a S k * a n d t O R k s t o m o * r o E +Eval: I I D I I I S D S S I D S + +Speaker sentences 184: cv_eng_000767 #utts: 1 +id: (cv_eng_000767-cv_eng_000767) +Scores: (#C #S #D #I) 45 8 6 7 +REF: * ******* t h e w A t e R T H E n f l o W s o u t o f t H e s * W A m * P s a s T h e l * ******* U a p U l ******* A r i v e r +HYP: A t h e w O t e D * * A n f l o * s o u t o f t * e s C O N m N T s a s * h e l O O a p * l E r i v e r +Eval: I I S S D D S D D I S S I S D I I S D I S + +Speaker sentences 185: cv_eng_000768 #utts: 1 +id: (cv_eng_000768-cv_eng_000768) +Scores: (#C #S #D #I) 21 4 2 17 +REF: * * ******* * * w h * y ******* * * d i D n T y o U s a y s o m E t h i n * * * * * * * * G +HYP: A M H E w h I y I D d i O n * y o * E s a y s o m t h i n K C D H E A D C D +Eval: I I I I I I I I I S D D S S I I I I I I I I S + +Speaker sentences 186: cv_eng_000769 #utts: 1 +id: (cv_eng_000769-cv_eng_000769) +Scores: (#C #S #D #I) 12 2 4 14 +REF: * H a v e y o U s e E n o ******* m a r * * ******* * * * * * * * * * +HYP: T a v e ******* y o * ******* s e n ******* o m a r N M E E T D E E E E E +Eval: I S D D D S D I I I I I I I I I I I I I + +Speaker sentences 187: cv_eng_000770 #utts: 1 +id: (cv_eng_000770-cv_eng_000770) +Scores: (#C #S #D #I) 60 14 9 2 +REF: i c o U L d g o o n f O r d a Y s a b o u T t h e d E L i C i o U s w I n E s * P R O d u C E D i n T h i s p a r t o f t h e w O r * L d +HYP: i c o * T d g o o n E f * r E d a I s a b o u * t h e d * * i D i o * s w O n * s T H E d u A S T i n * h i s p a r t ******* o f t h e w E r E T d +Eval: D S S D S S D D D S D S D I S S S S S S D D S I S + +Speaker sentences 188: cv_eng_000771 #utts: 1 +id: (cv_eng_000771-cv_eng_000771) +Scores: (#C #S #D #I) 34 19 6 11 +REF: t h E * * P H I l a * ******* d e * L P h I a * i n * * Q U i r e r * * n A M E d H i M C i t Y p L A Y e R o F t h e y E a r * +HYP: t h O S O E O l a D d e V F T h E a R i n C O R E i r e r A E n I N G d * i * N S i t * O p * * C e * Y o T t h e y O a r E +Eval: S I I S S S I I I S S S I I I S S I I S S S D D S S D S D D S D S S S I + +Speaker sentences 189: cv_eng_000772 #utts: 1 +id: (cv_eng_000772-cv_eng_000772) +Scores: (#C #S #D #I) 17 16 3 18 +REF: B O T S * M a Y * B e * S U b ******* * J e c t T O * s P e C I A l * * * * * * R U l * e * * * * * S +HYP: * * A A C O a S E V e S O P b D C e c t I S E s * e R G l W N D S H E E C l D e L D A R D L +Eval: D D S S I S S I S I S S I I S S S I D S S S I I I I I I S S I I I I I I S + +Speaker sentences 190: cv_eng_000773 #utts: 1 +id: (cv_eng_000773-cv_eng_000773) +Scores: (#C #S #D #I) 52 5 15 5 +REF: t h e s w e D e * s w e r E U n a b l E t o * * u s E T h E I r v e H I c * l E s w h i C h W e r E S t U c K i n t h E m U d * +HYP: t h e s w e * e D s w e r * ******* A n a b l * t o Y O u s * * h * * r v e * A c A l * s w h i * h * e r * * t O c E i n t h * m O d E +Eval: D I D D S D I I D D D D D S I D D D D D S S D S I + +Speaker sentences 191: cv_eng_000774 #utts: 1 +id: (cv_eng_000774-cv_eng_000774) +Scores: (#C #S #D #I) 53 8 6 14 +REF: * ******* t h e a c * t D i d n o t p * r O h * I b i t P a y i n g a * r e p R e * s e n T A t i V E t o A P P e a r I n ******* * * * c o U r t * * * +HYP: I t h e a c K t * i d n o t p O r h E b i t B a y i n g a E r e p * e O s e n * * t i * P t o E B e a r A n T H E c o * r t L I S +Eval: I I I D I S I S S I D I D D D S S S S S I I I I D I I I + +Speaker sentences 192: cv_eng_000775 #utts: 1 +id: (cv_eng_000775-cv_eng_000775) +Scores: (#C #S #D #I) 11 8 4 7 +REF: c * A n w E p l e A s * e l e * * * * * A V E N O W +HYP: c H O n ******* w * R p l e * s T e ******* l e P I N R O U L T I D N +Eval: I S D D S D I D I I I I I S S S S S S + +Speaker sentences 193: cv_eng_000776 #utts: 1 +id: (cv_eng_000776-cv_eng_000776) +Scores: (#C #S #D #I) 50 7 13 3 +REF: h e w a S c o n ******* v i c t * e d a N D b a n i s H E D T o C Y p r U s f o r s E v * E n Y E A r s F O r p U n i s H m e n t +HYP: h e w a * c o n v i c t D e d a * M b a n i s * * * * o S I p r * s f o r s O v I O n * H O r s * * r ******* p * n i s * m e n t +Eval: D I I D S D D D D S S D S I S D S S D D D D D + +Speaker sentences 194: cv_eng_000777 #utts: 1 +id: (cv_eng_000777-cv_eng_000777) +Scores: (#C #S #D #I) 55 14 11 9 +REF: t h e c o U p l E H A V E t w o c h I l d R E n a d a U G H t e r s o P H I a * * r O s A l I n d * a a n D * * A s O n ******* * m A t ******* * E O b r A v e r y +HYP: t h e c o * p l * * * O F t w o c h O l d * O n a d a * * * t e r s o F E a U R r * s E l E n d E a a n * T H E s I n F m U t H A L b r * v e r y +Eval: D D D D S S S D S D D D S S S I I D S S I D I I S S I I S I I S S D + +Speaker sentences 195: cv_eng_000778 #utts: 1 +id: (cv_eng_000778-cv_eng_000778) +Scores: (#C #S #D #I) 57 9 16 3 +REF: N O n E o f t h E t h r E e r E F e r e n d U m s r e A c h E D T h e Q u O r U m o f T h e m * A J O r i t y o f T h O s E E n t i * t l E d * +HYP: * * n * o f t h * t h r * e r * * e r e n d E m s r e * c h * * * h e C u A r A m ******* o f * h e m O D G U r i t y o f * h E s * I n t i C t l * d T +Eval: D D D D D D D S D D D D S S S D D I S S S D S D S I D I + +Speaker sentences 196: cv_eng_000779 #utts: 1 +id: (cv_eng_000779-cv_eng_000779) +Scores: (#C #S #D #I) 59 21 13 20 +REF: * * * ******* * t U r p I n s * * U C c e E d e d i n ******* d I r a * * s A m * * A r A s * * ******* E K E r a W H O S A W T h * * * * e u n I V e r s I T y * t h R o U G H a p e R i O d o F s t R o n g G R o W t h +HYP: I E T I t E r p E n s A E X c e A d e d i n d E r a S T s O m E R r O s I P C A I r a I T I * I N * h O S O L e u n * * e r s * * y O t h * o * * R a p e * i R d o * s t * o n g * B o A t h +Eval: I I I I I S S I I S S S I S I I S I I S S I I I S S S S S S D S S D I I I I D D D D I D D D S D S D D D S S + +Speaker sentences 197: cv_eng_000780 #utts: 1 +id: (cv_eng_000780-cv_eng_000780) +Scores: (#C #S #D #I) 45 6 7 8 +REF: * h e * r E I a m * b e T w E e n m y f l O c k a n d m * Y * * t R e A s * u r e t h e b o * y t H o U G H T +HYP: W h e A r * A a m E b e C w * e n m y f l U c k a n d m T H B U t * e R s I u r e t h e b o L y t * o * * * S +Eval: I I D S I S D S I S I I D S I I D D D D S + +Speaker sentences 198: cv_eng_000781 #utts: 1 +id: (cv_eng_000781-cv_eng_000781) +Scores: (#C #S #D #I) 49 13 13 8 +REF: t h i S f a I l U R e * h a s * l e D t o S i * x * t * E e n P o W E R P l A n T S h a * * V i n G Z e r O d a y s * o f c o A l s t o C K +HYP: t h i * f a * l I Y e R h a s T l e T t o * i C x S t D A e n * o * * U L B l E n C D h a D H i n * ******* S e r * ******* d a y s E o f c o * l L s t o * * +Eval: D D S S I I S D I I I S D D D S S S S S S I I S D D S D D I D S D D + +Speaker sentences 199: cv_eng_000782 #utts: 1 +id: (cv_eng_000782-cv_eng_000782) +Scores: (#C #S #D #I) 3 0 0 21 +REF: * * * * * ******* y ******* * * e s * ******* * ******* * ******* * ******* * ******* * * +HYP: A D D U T y A S e s H H H H D E N +Eval: I I I I I I I I I I I I I I I I I I I I I + +Speaker sentences 200: cv_eng_000783 #utts: 1 +id: (cv_eng_000783-cv_eng_000783) +Scores: (#C #S #D #I) 28 4 3 3 +REF: w h * y D o E S t h a t p l a * n E K E e p * g o i n G o v e r +HYP: w h I y * o * I t h a t p l a I n D C A e p E g o i n * o v e r +Eval: I D D S I S S S I D + +Speaker sentences 201: cv_eng_000784 #utts: 1 +id: (cv_eng_000784-cv_eng_000784) +Scores: (#C #S #D #I) 31 14 9 19 +REF: * * * ******* * * * i * ******* * V E d o n * * * * * e t h I S B E f o r E w I T H V I r T U A l ******* b o * X w i T H G O o D r E s U l t * s * +HYP: E E E H A Y i T A O D d o n D I S T E e t h * E * * f o r * w * A O F O r S I O l b o S E w i O F * * o * r * s O l t E s E +Eval: I I I I I I I I I I S S I I I I I D S D D D D S S S S S S S I I S S S D D D D S I I + +Speaker sentences 202: cv_eng_000785 #utts: 1 +id: (cv_eng_000785-cv_eng_000785) +Scores: (#C #S #D #I) 30 5 5 9 +REF: t H e A P p l I c a t i o n w a s ******* * * * a P p r o v e ******* * D i n f * * E b r U a * R y +HYP: t * e ******* * * p l O c a t i o n w a s P U T a p r o v e I T i n f A R I b r * a E L y +Eval: D D D D S I I I I S I I S I I S D I S + +Speaker sentences 203: cv_eng_000786 #utts: 1 +id: (cv_eng_000786-cv_eng_000786) +Scores: (#C #S #D #I) 52 3 4 6 +REF: h e n r y t A r l * t o n s t i l e s * w h e r E h e h a d ******* * A s o u n * * d T r A I n i n g i n l A t i n +HYP: h e n r y t O r l D t o n s t i l e s E w h e r * h e h a d T E s o u n D E d * r * O n i n g i n l * t i n +Eval: S I I D I I S I I D D S D + +Speaker sentences 204: cv_eng_000787 #utts: 1 +id: (cv_eng_000787-cv_eng_000787) +Scores: (#C #S #D #I) 78 8 8 19 +REF: i t w a s * D i s ******* c o n t i n u e d d U E t o s c H e * D U l i n g c o n f l i c T S I n * * v O l v e d I n * l * * e W I s * s r e t U r n t o * ******* * t * ******* e R r e s t r i A l * * * * r a d i * o +HYP: i t w a s T H i s c o n t i n u e d d * * t o s c * e T H R l i n g c o n f l i c * * A n D F v * l v e d A n D l O S e * s I s r e t I r n t o R E t O e * r e s t r i U l E R E B r a d i U o +Eval: I S I D D D I S S D D S I I D S I I I D S I S I I I I I D S I I I I I + +Speaker sentences 205: cv_eng_000788 #utts: 1 +id: (cv_eng_000788-cv_eng_000788) +Scores: (#C #S #D #I) 21 5 1 21 +REF: * * * * ******* h e r f a * m I l y ******* * w * a s f R o m * B r * * I A n Z a * ******* * * * * * ******* * +HYP: D E D H h e r f a N m E l y H w O a s f * o m E P r E O H O n S a E E D D U D Y +Eval: I I I I I I S I I I D I S I I S S S I I I I I I I I I + +Speaker sentences 206: cv_eng_000789 #utts: 1 +id: (cv_eng_000789-cv_eng_000789) +Scores: (#C #S #D #I) 16 6 5 9 +REF: * ******* w * H A t D i d Y O U e a * T f o r d i n * n ******* * * ******* E R +HYP: E w O W N t * i d ******* * * I e a K E f o r ******* d i n M n B H P T +Eval: I I I S S D D D D S I S D I I I I I S S + +Speaker sentences 207: cv_eng_000790 #utts: 1 +id: (cv_eng_000790-cv_eng_000790) +Scores: (#C #S #D #I) 22 2 3 0 +REF: t h a t w a s m y d r A W t o s C i E n C e +HYP: t h a t w a s m y d r * E t o s * i * n S e +Eval: D S D D S + +Speaker sentences 208: cv_eng_000791 #utts: 1 +id: (cv_eng_000791-cv_eng_000791) +Scores: (#C #S #D #I) 28 10 2 5 +REF: h e I s C o N s I D E r e * D a m A s t e r * o f * C h I a r o s * ******* c U R o +HYP: h e * s G o * s L A I r e N T a m U s t e r E o f D S h E a r o s T c O L o +Eval: D S D S S S I S S I I S S I I S S + +Speaker sentences 209: cv_eng_000792 #utts: 1 +id: (cv_eng_000792-cv_eng_000792) +Scores: (#C #S #D #I) 41 16 8 9 +REF: I T t h e n * * R E t U r n s t o t h e c h U r * C h * * * A s C E n D s * a t t H E A l t A r a n D d I s A p * P e ******* A R S +HYP: * * ******* t h e n L I N t O r n s t o t h e c h O r I S h E O F s H I n G s H a t t * * B l t E r a n * ******* d E s * p E U e I T E +Eval: D D D I I S S S S I S I I I S S S S I D D S S D D S D I S I S S S + +Speaker sentences 210: cv_eng_000793 #utts: 1 +id: (cv_eng_000793-cv_eng_000793) +Scores: (#C #S #D #I) 16 8 10 5 +REF: Y O u * C A N n o t * L o s e W H A T Y O U N e V e * R H a d * * +HYP: * W u D * * * n o t T H o s e ******* * * * R * I N H e R e A C * a d R T +Eval: D S I D D D I S D D D D S D S S S S I S D I I + +Speaker sentences 211: cv_eng_000794 #utts: 1 +id: (cv_eng_000794-cv_eng_000794) +Scores: (#C #S #D #I) 16 11 1 4 +REF: t H e * J A W s * * E X t e n d P A s t t h e * E y E +HYP: t O e L C I O L s E I N C t e n d ******* F E s t t h e A I y H +Eval: S I S S S S I I S S D S S I S S + +Speaker sentences 212: cv_eng_000795 #utts: 1 +id: (cv_eng_000795-cv_eng_000795) +Scores: (#C #S #D #I) 23 4 4 3 +REF: m y n I e C E c A n h e l p * Y o U W i t H t h a * t * +HYP: m y n * e S T c O n h e l p E * o W * i t * t h a I t S +Eval: D S S S I D S D D I I + +Speaker sentences 213: cv_eng_000796 #utts: 1 +id: (cv_eng_000796-cv_eng_000796) +Scores: (#C #S #D #I) 15 9 9 1 +REF: t h a t s T H E K I N d O F s t U F F * t H E y W A n T +HYP: t h a t s * * A C O U d ******* * H I s t * * * O t L y * O n * +Eval: D D S S S S D D S S D D D I S S D S D + +Speaker sentences 214: cv_eng_000797 #utts: 1 +id: (cv_eng_000797-cv_eng_000797) +Scores: (#C #S #D #I) 36 3 4 2 +REF: h o P e f o r t h e b e s t a n d p * ******* R e P a r e f o r t h e W O R s t +HYP: h o * e f o r t h e b e s t a n d p O B e * a r e ******* f o r t h e * M L s t +Eval: D I I S D D D S S + +Speaker sentences 215: cv_eng_000798 #utts: 1 +id: (cv_eng_000798-cv_eng_000798) +Scores: (#C #S #D #I) 30 13 12 3 +REF: * ******* i n i T I A L l y t h e w E I G h T L o S s W A s A T t A i n E D s t r I c T L y * b Y d i E t +HYP: C i n i * S H E l y t h e w * * * h P D Y o * s ******* * O s * H t i n * * ******* s t r O c * K y O b O d i C t +Eval: I I D S S S D D D S S S D D D S D S S D D D S D S I S S + +Speaker sentences 216: cv_eng_000799 #utts: 1 +id: (cv_eng_000799-cv_eng_000799) +Scores: (#C #S #D #I) 33 6 5 1 +REF: a l l w E R e o W n e d b y t h e e v e r E t T m O o r E s Y n D i * c A t E +HYP: a l l w * * e o * n e d b y t h e e v e r I t m * o r * s O n i K c E t H +Eval: D D D S S D D S S I S S + +Speaker sentences 217: cv_eng_000800 #utts: 1 +id: (cv_eng_000800-cv_eng_000800) +Scores: (#C #S #D #I) 13 7 1 32 +REF: * * * * * * * W I L L * I t * * * * R a * * * i n t o ******* m o R r o * * * * ******* * * * * * ******* * * * * * W +HYP: A A T H E T H E O E N G t H E W I L a S E R i n t o m o * r o N M N E I N L N D E E T H E R +Eval: I I I I I I I S S S S I S I I I I S I I I I D I I I I I I I I I I I I I I I I S + +Speaker sentences 218: cv_eng_000801 #utts: 1 +id: (cv_eng_000801-cv_eng_000801) +Scores: (#C #S #D #I) 15 7 2 9 +REF: * ******* d U * * b i s t * E W i * G m E i n E l i * E b * * E +HYP: E d O B P b i s t D I R i C M m * i n * D l i B P b P T H +Eval: I I S I I I S S I S D D S I S I I S + +Speaker sentences 219: cv_eng_000802 #utts: 1 +id: (cv_eng_000802-cv_eng_000802) +Scores: (#C #S #D #I) 36 6 4 13 +REF: * * ******* * L U C i * l e p E t r y ******* * * t O o K h e r p l a C e a s a c t ******* i n g D I r e c t O r * * * * +HYP: A T O S E i O l e p A t r y H E t * o * h e r p l a * e a s a c t i n g * O r e c t E r H E H D +Eval: I I I I S S S I S I I I D D D I D S S I I I I + +Speaker sentences 220: cv_eng_000803 #utts: 1 +id: (cv_eng_000803-cv_eng_000803) +Scores: (#C #S #D #I) 44 8 16 5 +REF: t H e b e A v e r * * R I V e R b R I e f l y E n t e R s t h e * e A s T C e n t * r A L p A R T o F t H E t o W n ******* s h i P +HYP: t * e b e * v e r L Y * W L e * ******* b * * e f l y A n t e * s t h e I e * s S e n t E r * * p * * U o P t * * t o * n s h i M +Eval: D D I I D S S D D D D S D I D S S I D D D D S S D D D I S + +Speaker sentences 221: cv_eng_000804 #utts: 1 +id: (cv_eng_000804-cv_eng_000804) +Scores: (#C #S #D #I) 33 4 3 1 +REF: t h e t r a c K r e S U r * F A C i n g w a s a l s o c o m p L e t e d +HYP: t h e t r a c E r e * * r V I S T i n g w a s a l s o c o m p * e t e d +Eval: S D D I S S S D + +Speaker sentences 222: cv_eng_000805 #utts: 1 +id: (cv_eng_000805-cv_eng_000805) +Scores: (#C #S #D #I) 63 10 10 9 +REF: h I N d ******* m a r s h w a s a ******* w * a r E o f t h e i m p O r t A n C E o f E l E c ******* t r * O N M I c R o s C o * p y * i n b I O l o * g i c A l r e s E A r c h * +HYP: h A T d m a r s h w a s a w H a r * o f t h e i m p * r t * n * S o f I l * c t r M R Y * * c * o s K o M p y E i n b Y l o U g i c K l r e s * * r c h E +Eval: S S I I I D D D D S S D I I S S D D D S I I S S I S D D I + +Speaker sentences 223: cv_eng_000806 #utts: 1 +id: (cv_eng_000806-cv_eng_000806) +Scores: (#C #S #D #I) 17 10 0 3 +REF: s i n ******* h A w a s b o r n * I N A L L A h A b * A D +HYP: s i n h E w a s b o r n Y U D T H E h O b O U E +Eval: I S I S S S S S S S I S S + +Speaker sentences 224: cv_eng_000807 #utts: 1 +id: (cv_eng_000807-cv_eng_000807) +Scores: (#C #S #D #I) 58 27 11 10 +REF: t h i s B R i * D G e * * I s * U n ******* o F f i C i A L l y R E F e r ******* R e D t o a s * B L a c K W A t * E R B R i D G E B y c o A l i * t i o n f o R C e s o p e r a t * i n G T H E R E +HYP: t h i s W E i E N C e H E A s E A n o * f i T i * * l y * T H e r H e * O t o a s E * M a c R E t O A D * W i N T H M y c o * l i S t i o n f o A S e s o p e r a t D i n * * * M I L +Eval: S S I S S I I S I S I D S D D D S S I S D S I D S S S S I S S D S S S S S D I S S I D D D S S S + +Speaker sentences 225: cv_eng_000808 #utts: 1 +id: (cv_eng_000808-cv_eng_000808) +Scores: (#C #S #D #I) 67 12 4 2 +REF: i t i s r e s p o n S I B l e f o r w a t e r s U P p l Y a n d m a n A G e m e n t o f w A t e r r e s o u r C e s I n * m * A h A R A s H t r a +HYP: i t i s r e s p o n E A U l e f o r w a t e r s O p l I a n d m a n * * e m e n t o f w O t e r r e s o u r S e s A n D m O h * O U s * t r a +Eval: S S S S S S D D S S S I I S D S S D + +Speaker sentences 226: cv_eng_000809 #utts: 1 +id: (cv_eng_000809-cv_eng_000809) +Scores: (#C #S #D #I) 29 9 4 6 +REF: T H i s I s t h E f * I r * s T P H a S e o f t h e * J o * * B h e s a * I d +HYP: * D i s ******* E s t h O f E O r E s * * F a Y e o f t h e G H o R V E h e s a D E d +Eval: D S D S S I S I D D S S I S I I S I S + +Speaker sentences 227: fleurs_eng_000413 #utts: 1 +id: (fleurs_eng_000413-fleurs_eng_000413) +Scores: (#C #S #D #I) 154 17 24 16 +REF: t h e g i * Z a * p l a t E A U o r g i * Z a n E c r * o * ******* p o l i s i N t h e E g Y P T i A n v a L l E y o f t h e d e a d c o n * ******* t a I n S s e v E r A l p Y r A m i * d s o f w h i c h t h e g r E a t p Y r A m * I D i s t h e l a r G e * s T * * s e v e r A l * s m A L l t o M B s s E v E r A l * t e m p l e s a n d t h e g r E a t s p H I n * X +HYP: t h e g i S I a P p l a t * O H o r g i S S a n * c r A o L p o l i s i * t h e ******* N g * * * i O n v a O l * y o f t h e d e a d c o n T t a * n G s e v * r * l p E r * m i N d s o f w h i c h t h e ******* g r * a t p E r * m E N T i s t h e l a r T e I s * S E s e v e r * l E s m * * l t o * N s s O v * r * l E t e m p l e s a n d t h e g r * a t s p * A n K S +Eval: I S I D S S I S D I I I D D S D D D S S D I I D S D D S D I D D S D I S S S I D I I D I D D D S S D D I D D S I S + +Speaker sentences 228: fleurs_eng_000414 #utts: 1 +id: (fleurs_eng_000414-fleurs_eng_000414) +Scores: (#C #S #D #I) 154 16 31 13 +REF: t O w A r D S T h e E n d o f t H e m i D D l e a g e s w e s t E r n E u r o P E b e g a n t o d E V e l O P t H e I r o W n s t Y l E o n e o f t h e b i G g E s t D E V e l O P M E n T s o f t h e t i m e a s A r e s * u l t o f t h e C r * u * s a D E s p e O p * l E b e g a n t o * u s e * b u T t O n s t o f a s t E n C l O T H i n g ******* * * ******* * ******* * +HYP: t * w O r * E * h e I n d o f t * e m i * * l e a g e s w e s t O r n Y u r o * * b e g a n ******* t o d * * e l * T t * e * r o * n s t I l * o n e o f t h e b i * g I s t * * O e l * * * I n * s o f t h e t i m e a s ******* * r e s I u l t o f t h e * r E u C s a * I s p e * p B l * b e g a n t o O u s e S b u * t E n s t o f a s t O n R l * V D i n g I I I A +Eval: D S D S D S D D D S S D D D D D D S D D D S D D S D D S D D D S D D D I D I I D S D I D I I D S S S D S S I I I I I I I + +Speaker sentences 229: fleurs_eng_000415 #utts: 1 +id: (fleurs_eng_000415-fleurs_eng_000415) +Scores: (#C #S #D #I) 70 9 14 19 +REF: i f * y o u o n l y g o * A s ******* h o r e * u s i n g s h i p B o A r D * E X c * U r S i o n * s y o u W I L l n o t N E e D a s e p A r A t E v I s a a s ******* * * * o ******* f * * * * ******* * * 2 0 0 9 +HYP: i f S y o u o n l y g o L * s h o r e O u s i n g s h i p o * r * C S c K D r * i o n D s y o u ******* * * * l n o t * * e * a s e p * r * t * v E s a a s A T W o f H O U S I N N O I G +Eval: I I D I I S D D I S S I S D I D D D D D D D D D D S I I I I I I I I I I I I S S S S + +Speaker sentences 230: fleurs_eng_000416 #utts: 1 +id: (fleurs_eng_000416-fleurs_eng_000416) +Scores: (#C #S #D #I) 83 10 23 4 +REF: * d U V a L l W h O i s m a R r I e D w i T h t W o a ******* d U l T c H i L D r e * * n D i d n o t L E A V E a b i G i m p r e S s i o n O n m i L l e r t o W h o M t h e S t O r y w a s r e l a t e d +HYP: B d O B a * l ******* * h * U i s ******* m a * r * e * w i * h t * o a d * l * c * i * O r e I O n * i d n o t ******* * * B Y W a b i N i m p r e * s i o n * n m i * l e r t o * h o E t h e * t A r y w a s r e l a t e d +Eval: I S S D D D D S D D D D D D I D D D D S I I D D D D S S S S D D D D S D S + +Speaker sentences 231: fleurs_eng_000417 #utts: 1 +id: (fleurs_eng_000417-fleurs_eng_000417) +Scores: (#C #S #D #I) 103 9 26 1 +REF: t H e I r d I S c I p l I n E d d e f * e n c e b A l L h a N d l i n g s K i L l s a n D e X c E L l E n t T e A M w o r K m a D E t h e M s t a n d o u t a n D I T w a s c l e A r T h a T t h i s w a S t h e t e A m t o b e A T +HYP: t * e * r d * E c T p l * n * d d e f I e n c e b O l E h a * d l i n g s C i * l s a n * e * c * S l * n t I e * * w o r D m a * Y t h e * s t a n d o u t a n * ******* * * w a s c l e * r * h a * t h i s w a * t h e t e * m t o b e * * +Eval: D D D S S D D I S S D S D D D D S D S D D S D S D D D D D D D D D D D D + +Speaker sentences 232: fleurs_eng_000418 #utts: 1 +id: (fleurs_eng_000418-fleurs_eng_000418) +Scores: (#C #S #D #I) 55 9 14 3 +REF: t h e d I S e a s E I s c a R r I e d b y p i * * g S w H i c h t h E n m * I g r A T e S t o H u m A n S t H r o U G H m o s Q U I t o s +HYP: t h e d * * e a s * * s c a I r * e d b y p i A K g E w * i c h t h * n m Y g r * * e T t o C u m E n * t O r o * * * m o s * K E t o s +Eval: D D D D S D I I S D D I S D D S S S D S D D D D S S + +Speaker sentences 233: fleurs_eng_000419 #utts: 1 +id: (fleurs_eng_000419-fleurs_eng_000419) +Scores: (#C #S #D #I) 45 4 4 9 +REF: f o r t h E s p r i n g ******* b o * * k S * i T E n d e d * a * f * i v e ******* M a t C h l o S i n g s t r E a k * +HYP: f o r t h * s p r i n g b o G C k E H i D I n d e d O a F f L i v e N a t * h l o * i n g s t r * a k E +Eval: D I I I S I S S I I I I S D D D I + +Speaker sentences 234: fleurs_eng_000420 #utts: 1 +id: (fleurs_eng_000420-fleurs_eng_000420) +Scores: (#C #S #D #I) 39 11 15 2 +REF: t h * u s t h e P E n C I l w a s A g O o D f r i e n d T O m a n y P e O P l E * W H E N i T C A M E o u T +HYP: t h E u s t h e H I n * S l w a s ******* * g * o * f r i e n d ******* * * m a n y B e * B l * L A D Y K i * ******* * * N G o u * +Eval: I S S D S D D D D D D D S D S D I S S S S D D D D S S D + +Speaker sentences 235: fleurs_eng_000421 #utts: 1 +id: (fleurs_eng_000421-fleurs_eng_000421) +Scores: (#C #S #D #I) 119 18 11 10 +REF: t h e * u s e o f V I D e * o r e c o r D i n g h a s l E d t o i M p o r T A n * T d * I s c o * v e r I e s i n t h e i n t e r p r E t a t i o n o f m * I c * * r O e * X p r e s S i o n s f a C i a l m o v e M e n T s w h i c h l a s T a f E W m i l L I s E c * O n D s +HYP: t h e Y u s e o f * * * e A o r e c o r * i n g h a s l A d t o i N p o r * * n E D d E C s c o R v e r * e s ******* i n t h e i n t e r p r O t a t i o n o f m Y G c R L r e C T p r e s T i o n s f a S i a l m o v e e n * s w h i c h l a s E a ******* f Y O m i l S s I c K E n * s +Eval: I D D D I D S S D D I S I S I D D S I S I I S I S S S S D S D S S S S S I S D + +Speaker sentences 236: fleurs_eng_000422 #utts: 1 +id: (fleurs_eng_000422-fleurs_eng_000422) +Scores: (#C #S #D #I) 89 15 15 6 +REF: A l s O * t O t h e n o r t h V i s I t t h e g r E a t s a n c T u A r y o f O U r l a * d y o f F a t * * I m A s h r i n E a * P l A c e O f w o r l d W I D E f A m O U s m A r i A n a P p A r i * t i o n s +HYP: * l s * A t * t h e n o r t h * i s * t t h e ******* g r * a t s a n c H u * r y o f * * r l a T d y o f * a t H Y m * U s h r i n G a E * l E c e A f w o r l d G H T f I m * I s m E r i O n a * p B r i S t i o n s +Eval: D D I D D D D D S D D D I D I I S D S S I D S S S S S S S D S S S D S I + +Speaker sentences 237: fleurs_eng_000423 #utts: 1 +id: (fleurs_eng_000423-fleurs_eng_000423) +Scores: (#C #S #D #I) 76 10 28 9 +REF: * ******* i f Y o U W A n T T O b E c l o s E T o t h e a c t i o n y O U R E g o I N G t o H A V E T o * g E t i n e a R l y ******* * t o G E t A c a M p i n g s i * t E c l o s E t o t h E m * u s i c * * +HYP: E i f * o * * * n * ******* * E b Y c l o s * ******* * o t h e ******* a c t i o n y * * * * ******* g o R H E t o * W W * o W g I t i n e a * l y W t o * * t ******* E c a * p i n g ******* s i H t * c l o s * ******* t o t h * m O u s i c K E +Eval: I I D D D D D D D S S D D D D D D D D D S S S D S S S D I S D I I D D D S D D I D D D D I I I + +Speaker sentences 238: fleurs_eng_000424 #utts: 1 +id: (fleurs_eng_000424-fleurs_eng_000424) +Scores: (#C #S #D #I) 65 11 9 6 +REF: m A D a g A s C A r * i s b * y * f a r * t h e b i G g E s t A n d a c o n t i N E n t o n i T s o W n w h E n I T c o m E s t o w * * I l d l i f E +HYP: m T Y a g U s K U r E R i s b L y H f a r E t h e b i * g I s t * n d a c o n t i * * n t o n i D s o * n w h * n * A c o m * s ******* t o w H O U l d l i f T +Eval: S S S S S I S I I I D S D D D S D D D S D D I I S S + +Speaker sentences 239: fleurs_eng_000425 #utts: 1 +id: (fleurs_eng_000425-fleurs_eng_000425) +Scores: (#C #S #D #I) 82 6 23 3 +REF: w O m e n i t i s r e c O M m e n D e D t h a t a n y w * O m e n t r A v E L l E r S s a y T H A T t h e Y a r E m a R r I E d r e g a r D l e S s o f a c T u A l m a r I t A l s t a * t U s * +HYP: w E m e n i t i s r e c * * m e n * e * t h a t a n y w H E m e n ******* t r O v * * l I r * s a y ******* * * * * t h e * a r * m a * r * * d r e g a r * l e * s o f a c H u * l m a r * t * l s t a T t I s T +Eval: S D D D D I S D S D D S D D D D D D D D D D D D D S D D D I S I + +Speaker sentences 240: fleurs_eng_000426 #utts: 1 +id: (fleurs_eng_000426-fleurs_eng_000426) +Scores: (#C #S #D #I) 87 9 10 23 +REF: c * u * ******* * o m o ******* * * * * * * * 5 3 b e g a n h I S g o v e r * * n * ******* * o * * r s h i p e A r L i * e R t h I s y e a r a n d s i G n e D a b i L l l a s t m O n T h l e g A l i * Z i n g s a M E s e * x * m a R r I a g e +HYP: c O u O A o m o F I F T Y T H R E b e g a n h * F g o v e r M E n T G o V E r s h i p e * r * i L e * t h E s y e a r a n d s i * n e * a b i * l l a s t m U n C h l e g * l i D S i n g s a N s e C x S m a * r * a g e +Eval: I I I I I I I I I I I I S S D S I I I I I I I D D I D S D D D S S D I S S S I I D D + +Speaker sentences 241: fleurs_eng_000427 #utts: 1 +id: (fleurs_eng_000427-fleurs_eng_000427) +Scores: (#C #S #D #I) 121 12 40 2 +REF: a s l i G h T p O L l U t i o n I n t h E I r h E y d A y w a s n o t t h e K i n d o f p R o B l E m I t i s t o ******* d A y t h e Y A r E U S u A L l y l o c * a t e d i n C i T I e s o r a T c a M p U s e s e A s i E r t o r e A C H T H A n t h o s E b u I l T I n m o D E R n t i m E s +HYP: a s l i * h * ******* p * * l * t i o n * n t h * * r h A y d * y w a s n o t t h e * i n d o f p * o * l O m ******* * t ******* i s t o d * y t h e * ******* * r * * * u * E l y l o c K a t e d i n S i * Y e s o r a * c a N p * s e s e * s i U r t o ******* r e * * * ******* S I O n t h o s * b u * l * A n ******* m o * * T n t i m * s +Eval: D D D D D D D D D S D D D D S D D D I D D D D D D D D S I S D S D S D D S D D D D D S S S D D D S D D D S D + +Speaker sentences 242: fleurs_eng_000428 #utts: 1 +id: (fleurs_eng_000428-fleurs_eng_000428) +Scores: (#C #S #D #I) 79 13 28 1 +REF: t h e Y U S u A L l y h a v e s p e C i A l f O o D D r I n k A n D E n T e r ******* t a I N m e N T o F F e r s t o K E e P g U E S T s I n a g O o D m O o d A n D K E e P t h E m a t t h e p r E m i s E +HYP: t h e * Y O u * E l y h a v e s p e T i * l f * o * * r * n k ******* * n * I n * e r t a * * m e * * o * P e r s t o C A e * g * * * I s A n a g * o * m * o d * n * C A e * t h I m a t t h e p r * m i s * +Eval: D S S D S S D D D D D D D D S D I D D D D D S S S D D D D S S D D D D D S S D S D D + +Speaker sentences 243: fleurs_eng_000429 #utts: 1 +id: (fleurs_eng_000429-fleurs_eng_000429) +Scores: (#C #S #D #I) 105 8 14 8 +REF: o N T h e O t H e r h * a n d i C Y a n d s n o w Y c o N d I T i o n s a r E n o r m a L I n * m a n y c O u n t r I e s * * a n d t r a F f i C G o E s o n m o s t * l y u n ******* i n t e R r u p t e d a l l * y e A r r o u n * d +HYP: o * * h e ******* * t * e r h E a n d i S E a n d s n o w * c o E d * * i o n s a r * n o r m a E A n D m a n y c * u n t r Y e s I H a n d t r a P f i T * o * s o n m o s t E l y u n i n t e * r u p t e d a l l E y e * r r o u n E d +Eval: D D D D D I S S D S D D D S S I D S I I S S D D I I D I D I + +Speaker sentences 244: fleurs_eng_000430 #utts: 1 +id: (fleurs_eng_000430-fleurs_eng_000430) +Scores: (#C #S #D #I) 82 11 8 10 +REF: b e c a r E f U l n o t t o a * L l o * W f a b R i c t o b e c O m e t O o h O T w h i c h c a n c A U s E s H r I n k a * g e o r i n * E X t r e M E c a s * e * ******* s s C o * r * c h * +HYP: b e c a r * f * l n o t t o a E l o U L f a b O i c t o b e c * m e t * o h I D w h i c h c a n c * O s * s T r A n k a D g e o r i n A * S t r e * N c a s C e S s s Q o A r T c h E +Eval: D D I S I S S D D S S D S D S S I I D S D S I I I S I I I + +Speaker sentences 245: fleurs_eng_000431 #utts: 1 +id: (fleurs_eng_000431-fleurs_eng_000431) +Scores: (#C #S #D #I) 68 11 24 8 +REF: f e * r A l C h I l d R e * n M a y h a v E E X p e R i E n c e D S E v e r E c h I l d * A b * u s E o r t r A U m * A b * e ******* f o r E b E i n g a ******* b A n d O n E D O R r U N N I n g A w a y +HYP: f e I r * l T h L l d * e R n * a y h a v * C T p e * i * n c e * O v e r * c h U l d O b E u s * ******* o r t r * * m O H b H e f o r * b * i n g a b E n d I n * * ******* * * r * * * * n g * w a y +Eval: I D S S D I D D S S D D D S S D S I S I D D D D I S I I D D I S S D D D D D D D D D D + +Speaker sentences 246: fleurs_eng_000432 #utts: 1 +id: (fleurs_eng_000432-fleurs_eng_000432) +Scores: (#C #S #D #I) 83 5 18 1 +REF: P e O P l E m a Y n o t A n t i c * i p a t E t h a t p A t i E n c E a n d U n d e r s t a n D i n g A r E a l S o n E C e S s a r y f o r t r A v E L l E r s r e t U r n i n g h o m e +HYP: B e * B l * m a * n o t * n t i c H i p a t * t h a t p E t i O n c * a n d * n d e r s t a n * i n g * r * a l * o n * * e * s a r y f o r t r * v * * l U r s r e t * r n i n g h o m e +Eval: S D S D D D I D S S D D D D D D D D D D D D S D + +Speaker sentences 247: fleurs_eng_000433 #utts: 1 +id: (fleurs_eng_000433-fleurs_eng_000433) +Scores: (#C #S #D #I) 60 11 13 6 +REF: S O o n A F t e r t h e o U T B r E A k o f H o * s t i l i t I e s b r I T A i n i n * I t ******* * * I a t e d a * n a v A l b L o C k a D e o f G E r m A n y +HYP: * * o n ******* * O t e r t h e o * * P r * I k o f * o U s t i l i t Y e s b r * * * i n i n A N t S H E a t e d a D n a v B l b * o * k a Y e o f T I r m I n y +Eval: D D D D S D D S D S D I S D D D I S I I I S I S D D S S S S + +Speaker sentences 248: fleurs_eng_000434 #utts: 1 +id: (fleurs_eng_000434-fleurs_eng_000434) +Scores: (#C #S #D #I) 47 10 13 3 +REF: * ******* t h e G O v e R n O r s o f F i C E s a I d n i * N E t E e n o f t h E i n J U r E d w e r e p O l I C e o F f I C E r s +HYP: H t h e * * v e * n * r s o f P i * S s a * d n i A T t I e n o f ******* t h * i n D E r * d w e r e p * l * * e o * f H S A r s +Eval: I I D D D D S D S D I S S S D D S S D D D D D S S S + +Speaker sentences 249: fleurs_eng_000435 #utts: 1 +id: (fleurs_eng_000435-fleurs_eng_000435) +Scores: (#C #S #D #I) 87 10 22 5 +REF: * u s i n G S h i p s t O t r A N s p * O r t * g o o d S I s b y f a r t h e m o s t E F f I C i e n t w a y T O m o V e l a r g e A m O u N t S o F p e O P L e a n d g o ******* o D S A c r o S s o * C E A n s +HYP: Y u s i n * * h i p s ******* t * t r * E s p B U r t D g o o d * A s b y f a r t h e m o s t * O f * * i e n t w a y * H m o * e l a r g e * m * u * t * o * p e * * B e a n d ******* g o o * * * c r o U s o A T I O n s +Eval: I D D D D D S I S I D S D S D D D S D D D D D D D D S D I D D D S I S S S + +Speaker sentences 250: fleurs_eng_000436 #utts: 1 +id: (fleurs_eng_000436-fleurs_eng_000436) +Scores: (#C #S #D #I) 91 22 22 4 +REF: l I b E r A l C r I t i C I S m o F t h e r e c o n s t r U c t i o n * e F F O r T h a s F o C U S e D o N t h E a W A r d i n g o f r e ******* c o n s T r U c t i O n * c o n T R a c t S t o P E r C E I V e ******* d w a S H i n g T O n i n s i D e r s +HYP: l E b * r * l * r E t i S O H m o * t h e ******* r e c o n s t r * c t i o n V e * * * r N h a s ******* P o * A K e A S o * t h * ******* a * O r d i n g o f r e c o n s * r * c t i * n G c o n C H a c t * t o * * r * S T H e d w a U T i n g A n i n s i H e r s +Eval: S D D D S S S S D D D I D D D S D S D S S S S D D D D S I D D D I S S D D D D S S S I S S S S S + +Speaker sentences 251: fleurs_eng_000437 #utts: 1 +id: (fleurs_eng_000437-fleurs_eng_000437) +Scores: (#C #S #D #I) 87 16 18 19 +REF: Y O u C a N U s E b o d * * A b o d ******* a m O T O r C Y c * l E t a * X I t o g e t A r o U n d g o m * a t h e n o r m * a L l O c a l p r i c e i s ******* * * * * * * * * 5 0 0 c o n G O l e s E f r A N C s f o r t h e * s h o r * t r * i D E +HYP: * * u ******* * a * ******* * s * b o d O E b o d a m * * * r S E c K l * t a C E Y t o g e t ******* E r o * n d g o m E a t h e n o r m E a * W l A c a l p r i c e i s F I V E H U N D R E D c o n * D l e s * f r * O U s f o r t h e M s h o r E t r I i I +Eval: D D D D D D D D I I S I D D D S S I D I S S D S D I I D S S I I I I I I I I I S S S D S D D S S I I I S S + +Speaker sentences 252: fleurs_eng_000438 #utts: 1 +id: (fleurs_eng_000438-fleurs_eng_000438) +Scores: (#C #S #D #I) 130 15 18 7 +REF: t h e t h r E e K i n g d O m * s w a s o n e o f t h e b l * ******* * O O d i e s t E r A s I n a n C i E n t c h i n A s h i s t O r Y t h o u s A n D s o f p e O P l e d i * e d f i G H T i n g t o s i T i N t h E h i G h E s T s e A T i N t h e g r a n D p a l A C e * a t X i a * n +HYP: t h e t h r * e C i n g d * m E s w a s o n e o f t h e b l T B L U d i e s t A r * s A n a n G i O n t c h i n E s h i s t * r E t h o u s E n C s o f p e * B l e d i D e d f i * * D i n g t o s i * i * t h * h i * h I s * s e * * i * t h e g r a n * p a l * * e S a t S i a N n +Eval: D S D I I I I S S S D S S S S D S S S D S I D D S D D D D S D D D D D D D I S I + +Speaker sentences 253: fleurs_eng_000439 #utts: 1 +id: (fleurs_eng_000439-fleurs_eng_000439) +Scores: (#C #S #D #I) 56 3 5 5 +REF: t h e * s E c o u p l e s m a y c h O o s E t o m a k E a n * a * d o P T i o n p l a n * f o r t h e I r * b a B y +HYP: t h e I s * c o u p l e s m a y c h * o s * t o m a k * a n D a N d o U S i o n p l a n D f o r t h e * r E b a V y +Eval: I D D D D I I S S I D I S + +Speaker sentences 254: fleurs_eng_000440 #utts: 1 +id: (fleurs_eng_000440-fleurs_eng_000440) +Scores: (#C #S #D #I) 125 11 27 3 +REF: n o t h i n G c a n b e S E e n O t h e R T h a N t h e c L e A r b e A u t I f U l s * k Y a b o v e a n d * t h e m a n y s u R r O u n D i n g m O u n T A I N s V e r y l i T t l E o F t h I s w O R l D C a n b e S e e n O r h e A r D f r o m i n ******* s i D E t h e c a v e +HYP: n o t h i n * c a n b e * F e n U t h e * * h a * t h e c P e * r b e * u t O f * l s C k I a b o v e a n d N t h e m a n y s u * r W u n * i n g m * u n * * * D s B e r y l i * t l * o * t h * s ******* w * * l * L T a n b e F e e n * r h e * r * f r o m i n s i * * t h e c a v e +Eval: D D S S D D D S D D S D I S I D S D D D D D S S D D D D D D D D S S S D D D I D D + +Speaker sentences 255: fleurs_eng_000441 #utts: 1 +id: (fleurs_eng_000441-fleurs_eng_000441) +Scores: (#C #S #D #I) 52 5 10 4 +REF: h e w a s S U B S e Q U e n T l y r e l o * c a t e D t o a d D e n ******* b r O o k E s h o s * p I t A l I n * c a m b r i D g e +HYP: h e w a s * * * O e I C e n * l y r e l o K c a t e * t o a d T e n b r * o k * s h o s T p * t * l A n D c a m b r i * g e +Eval: D D D S S S D I D S I D D I D D S I D + +Speaker sentences 256: fleurs_eng_000442 #utts: 1 +id: (fleurs_eng_000442-fleurs_eng_000442) +Scores: (#C #S #D #I) 72 20 41 13 +REF: * * V a * t i * c a n * * C i T y S P O p u l * a t i o n i s A r o u N d * 8 0 0 I t i * S t h E * s ******* m A L L E s t i n D E p e n D e N T c o U n t r Y I N t h e w * O r L D a N d T H E C O U n T R Y W I T H t h E L o W E S T p O P u l * a t i o n +HYP: D T H a N t i A c a n S T A i D y * * * p u l E a t i o n i s ******* E r o u * d I N H E R N t H i T E t h I I s m * * * O s t i n * * p e n * e * * ******* c o E n t r * ******* * E t h e w H E r * * ******* a L d ******* * * * * * * n * * * ******* * * * * t h * P o * * * * ******* p L I u l E a t i o n +Eval: I I S I I I I S S D D D I D S D I S S S S S S I S S I I D D D S D D D D D D S D D D S I S D D D S D D D D D D D D D D D D D D D D S D D D D D S S I + +Speaker sentences 257: fleurs_eng_000443 #utts: 1 +id: (fleurs_eng_000443-fleurs_eng_000443) +Scores: (#C #S #D #I) 144 27 35 9 +REF: * r E g U l A r a N N o u n c * E M e n T S I N t h e ******* * m E t * r O a r E m a D e o N l y I n c * A t A l A n b u t U n ******* p l A N n e D d I s * r U p t i o n s a r E A N n O u N c E d b y a n A U t O m A t e d S Y s t E m i n A w I D E v A r i E t y o f l A n * G U A g e s i n c l U D i n g s P a n i s h E n g l I s h f r e n c h A r A b i c a n d J a p A n E s E +HYP: B r D g * l * r ******* a * L o u n c S T H e n * * ******* * C t h e P m A t H r * a r * m a * e o * l y * n c O U t I l O n b u t * n p l * E n e * d E s T r * p t i o n s a r * * * n * u * c * d b y a n O D t I m * t e d * * s t O m i n O w * A O v E r i * t y o f l I n W I C H g e s i n c l * T i n g s B a n i s h A n g l * s h f r e n c h E r * b i c a n d * a p * n * s * +Eval: I S D D D D S I S S D D D D S I I S I D D D D D I S S S D I D S D S I D D D D D D D S S S D D D S S D S S S D S I S S S D S S S D S D D D D D + +Speaker sentences 258: fleurs_eng_000444 #utts: 1 +id: (fleurs_eng_000444-fleurs_eng_000444) +Scores: (#C #S #D #I) 73 14 24 3 +REF: t h i s o F F E r s A g O O d O P p O r t U n I T Y t o s E E t h e * a U r O r * a b o r E A l I s a s t h e s * K Y w I L l b e d a r K m o r e O R l e S s A r o U n D t h e c L o C K +HYP: t h i s o * * P r s ******* * g * * d * * p * r t E n * D E t o s * H t h e O a r E r E a ******* b o r * I l E s a s t h e s C A I w * * l b e d a r C m o r e ******* * * l e * s T O r o * n * t h e c * o * * +Eval: D D S D D D D D D D S D S S D S I S S I D D S S I S S D D S D D D D S S D D D D D + +Speaker sentences 259: fleurs_eng_000445 #utts: 1 +id: (fleurs_eng_000445-fleurs_eng_000445) +Scores: (#C #S #D #I) 36 8 11 17 +REF: f i R E r e s c * u E c r e W s E v e n T U A L l y d O u s E D t h E f i R e * b * ******* * * * * * ******* * * * * y 1 1 3 5 p * ******* * m +HYP: f i * * ******* r e s c O u * c r e O s O v e n * * C I l y d * u s * * t h * f i * e R b Y L V I O N T H R T y F I V E p E A m +Eval: D D D I D S S D D S S D D D D D I I I I I I I I I I I I I S S S S I I I + +Speaker sentences 260: fleurs_eng_000446 #utts: 1 +id: (fleurs_eng_000446-fleurs_eng_000446) +Scores: (#C #S #D #I) 50 15 14 4 +REF: T h I S i s c A L l E D A c H E m i c a l s p H Y O U C A n m A K E a n i n d I c a t O R * u s i n g r e d c a B b A g * e * * J u I C e +HYP: * h * * ******* i s c * * l * * O c * O m i c a l s p E C H E E O n ******* m * * Y a n i n d E c a t E N O u s i n g r e d c a * b I g H e D D O u * S e +Eval: D D D D D D D D S D S S S S S S S D D D S S S S I D S I I I S D S + +Speaker sentences 261: fleurs_eng_000447 #utts: 1 +id: (fleurs_eng_000447-fleurs_eng_000447) +Scores: (#C #S #D #I) 85 14 17 4 +REF: I n p A r t i c * U l A R i t i s C l A I M e D t h a t o n E c A n d e t e c T w H e t h e R a p e r s o n i s l Y i n g b y I n t e r p r E T i n g m * I C r * ******* O E X p R e S S i o n S c O R r e c t l y +HYP: A n p * r t i c K I l * E i t i s * l * * * e * t h a t o n * c E n d e t e c * w * e t h e * ******* a p e r s o n i s l * i n g b y A n t e r p r * * i n g m Y G r L C S T p * e R T i o n C c * U r e c t l y +Eval: S D I S D S D D D D D D S D D D D D S D D I S S I I S S S D S S S D S + +Speaker sentences 262: fleurs_eng_000448 #utts: 1 +id: (fleurs_eng_000448-fleurs_eng_000448) +Scores: (#C #S #D #I) 134 18 22 10 +REF: t h e C e N T R a l A U T H o r i T y o f t h E c h u r C h * * H A D b E e n i n * r o m E f o r o v e r a t h o u s * A n D y * e a r s a n d T H i s c o n c E n t r A t i o n O f p o W e r A N D m O n E y L e * d ******* * * m a N y t o Q U e s t i o n w H e t h e r T H i S t e n e * t w a s b e I n g m E t +HYP: t h e S e S C I a l * * * F o r i D y o f t h * c h u r * h O U * I E b * e n i n M r o m * f o r o v e r a t h o u s E I n * y O e a r s a n d * D i s c o n c O n t r * t i o n * f p o U e r * * I m U n * y W e A d T O m a * y t o * C e s t i o n w * e t h e r * * i * C t e n e N t w a s b e * n g m A t +Eval: S S S S D D D S S D D I I D S S D I D I S D I D S S D D S D D S S D S I I I I D D S D D D D S I D S + +Speaker sentences 263: fleurs_eng_000449 #utts: 1 +id: (fleurs_eng_000449-fleurs_eng_000449) +Scores: (#C #S #D #I) 121 15 11 29 +REF: t h e s u n * ******* d * a r b A n s a r E t h e L a r g e * s t * * L I t T o r a l m a n ******* g r o V e b e l T i n t h e w O r * L d s t r E T c h i n g * 8 0 * * * K m * * * * * * * * 5 0 m i * * * i n t o t h e b a n g * l A d e * s h I a n ******* * D i n d i A n h i n t e r l a n D f R o m t h * * E c o A s t +HYP: t h e s u n D d O a r b O n s a r * t h e * a r g e I s t T H E t E o r a l m a n g r o * e b e l * i n t h e w E r O E d s t r * * c h i n g A T Y C O L O m E T E R S F I F T Y m i L E S i n t o t h e b a n g W l * d e A s h E a n A N i n d i * n ******* h i n t e r l a n T f * o m t h A O W c o * s t +Eval: I I I S D D I I I S S S I D D S I S D D I S S I I I S I I I I I I I I S S I I I I D I S I I S D D S D I I S D + +Speaker sentences 264: fleurs_eng_000450 #utts: 1 +id: (fleurs_eng_000450-fleurs_eng_000450) +Scores: (#C #S #D #I) 137 28 41 8 +REF: r e * g u l A r A N n o U n c E M E N t S I n t h e m E t R o * a r E m a D e * o n l y I n c A t A l A n b u t U n P L A N N e D d i s * r u P t I o n S A R e A N n o u N c E d b y a n A U t O m a t e * D s Y s T E m i n A w I D E v * A r i E t y o f l A n g U A g * e s i n C L u D i n g S p a n i S h E n g l I s h f r E n c h A r A b i c * a n d J a p A n e s E +HYP: r e A g u l * r * * n o * n c * * * * t * ******* H n t h e m A t H o L a r * m a * e D o n l y * n c O t * l I n b u t * n * * * E T e * N d i s T r u * t * o n * * * e ******* * R n o u * c * d ******* b y a n * O t I m a t e I S s I s * * m i n * T w * A R T v E U r i * t y o f l * n g W I g D e s i n K E u T i n g * p a n i * h I n g l * s h f r I n c h E r I b i c K a n d O a p * n e s * +Eval: I D D D D D D D D D D S S S I D D I D S D S D D D D S S D S I D D D D D D D S D D D D S S I S S D D D S D S S S I S D D S S I S S S D D S D S S S I S D D + +Speaker sentences 265: fleurs_eng_000451 #utts: 1 +id: (fleurs_eng_000451-fleurs_eng_000451) +Scores: (#C #S #D #I) 72 23 24 3 +REF: E V e r Y O n E p A r t i C I P a t E S i n s O C i E T y a n D * u s E s t r A n s p O r T A t i O n s Y s t * e M S a l M o s t E V e r Y O n E C o m p l a I n S A b o u T t r * A N S p O r T A t i o n s Y s t E m S +HYP: * * e r W n * p * r t i * T B a t * * i n s T S i * D y a n * O u s I s ******* t r E n s p * r * * t i * n C s I s t A e N C a l o s t * * e r W n * * o m p l a * n E O b o u * t r E C D p * r * * t i o n s I s t O m * +Eval: D D S S D D D S S D D S S D S D I S D S D D D D S S I S S S D D S S D D D S S D I S S S D D D S S D + +Speaker sentences 266: fleurs_eng_000452 #utts: 1 +id: (fleurs_eng_000452-fleurs_eng_000452) +Scores: (#C #S #D #I) 125 21 44 8 +REF: l a Y t O n h a d a s K E D f O r C h a n g e s T o t h e C o n ******* s E r V A t I V e s E N v I r O N m E n T A l b i l L d U r i N g T h e M E e T i n g w I T H t h e * * P m a s K i n g f O r A t h O r O U G H a n D c o m p L e t E r e W r i * * t i n g o F t h e C o N s E r v A t * i V E p a r T y S E n * V i r O N m E n T a l * B i l l +HYP: l a * t * n h a d a s * * * ******* f * r ******* T h a n g e s * o t h e * o n s * r * * t * * e s M F v A r * * m I n * * l b i l E d * r i * g * h e * * e A i n g ******* w * * * t h e E A U m a s * i n g f * r ******* Y t h I r * * * L a n * c o m p * e t * r e r i G D t i n g o * t h e K o s I r v E t H i * S p a r * y * I n F B i r * A m A n * a l D * i l l +Eval: D D D D D D D D S D D I D D D D D S S S D D S D D S D D D D D S D D D D I I S D D D S S D D D S D D D S I I D S S S S I D S D D S I S D S S D I D + +Speaker sentences 267: fleurs_eng_000453 #utts: 1 +id: (fleurs_eng_000453-fleurs_eng_000453) +Scores: (#C #S #D #I) 86 22 29 2 +REF: a N Y O n E W h O s G O I n G t o D r I V E a t h I G H l * A t I t u D e s O r o v e r m O u N T A I n p a S S E s S h O U L d C O n s * i d e R t h e p o S s I B i l i t y o f s n o W i c e o r f r E e Z i n g t e m P E R A t U r E s +HYP: a * * * n W N h A s * * L n * t o * r * T H a t h * A T l I H t A t u * e s A r o v e r m * u * * E n p a * * * s ******* T h * * A d * E n s A i d e * t h e p o * s * E i l i t y o f s n o E i c e o r ******* f r * e S i n g t e m * * * B t E r * s +Eval: D D D S S S D D S D D D S S S D S S I S S D S D D D S S D D D D S D D S D S I D D D S S D D S D D D S S D + +Speaker sentences 268: fleurs_eng_000454 #utts: 1 +id: (fleurs_eng_000454-fleurs_eng_000454) +Scores: (#C #S #D #I) 96 24 23 13 +REF: * ******* s l e * e P i n t e R r u P t i o n i s T h e p r O C E S s o f P U R P o S e F U L L y A w a k E N i n G d U r i n g y o U r n o R m A L s l E e P p e r I O d a n d f A L l i n g a ******* s l E e P A s h o * r T t i m e l a t e r 1 0 – 6 0 M i N U t * e ******* * * * s * * * +HYP: H s l e A e * i n t e * r u * t i o n i s * h e p r A S T I s o f H E B o U e * * S A y ******* * w a k * * i n * d I r i n g y o * r n o * m O S T s l * e * p e r E A d a n d f * * l i n g a s l * e * ******* * s h o U r * t i m e l a t e r E N T O S i * C t D e I N T s T I T +Eval: I I I D D D D S S S S S S S S S D D S S D D D D D S D D S S S D D S S D D I D D D D I D S S S S S S D S I I I I I I I I + +Speaker sentences 269: fleurs_eng_000455 #utts: 1 +id: (fleurs_eng_000455-fleurs_eng_000455) +Scores: (#C #S #D #I) 74 8 7 14 +REF: * * * ******* s w * I r l * t h e t w o d r * y * p o W D e r s t o g e t h e R a n d t H e n w i t H * C L e A n * w e * t h a n d s s Q u E e Z e t h e M i n t o a b A l * * ******* L +HYP: B L I s w M U r l E t h e t w o d r I y P p o U R e r s t o g e t h e * a n d t * e n w i t * G Q U e * n G w e A t h a n d s s C u * e * e t h e * i n t o a b O l W R I +Eval: I I I I I S I I I S S D D D I S S D I I S D D D S I I I S + +Speaker sentences 270: fleurs_eng_000456 #utts: 1 +id: (fleurs_eng_000456-fleurs_eng_000456) +Scores: (#C #S #D #I) 44 5 4 6 +REF: f o R t h e s p r i n g ******* b o * * k S i T E n d e d a f i v e * ******* m a T c h * L O s i n g s t r e A K +HYP: f o * t h e s p r i n g b o A C k E i D A n d e d a f i v e D m a * c h T H E s i n g s t r e * * +Eval: D I I I S S S I I D I S S D D + +Speaker sentences 271: fleurs_eng_000457 #utts: 1 +id: (fleurs_eng_000457-fleurs_eng_000457) +Scores: (#C #S #D #I) 81 15 25 8 +REF: J u * s t l I k E t h E M O o n e x ******* * E r t S a p U L l O n t H e E a r t h c A U s i n g t i d E S s o D O e s T H E m I l K y w A y e x * e r t * A f o r C E o N t h e S A g i T t a r i * * u s G a l a * X y +HYP: Y u O s t ******* l * k * t h * * * o n ******* e x P U r t D a ******* p * O l ******* * n t * e * a r t h c * O s i n g t i d H D s o ******* * T e s ******* * * A m * l B y w * y e x S e r t O F f o r * S o * t h e E D g i * t a r i Y A u s * a l a C S y +Eval: S I D D D D D D D I I S S D D S D D D D D S S S D D S D D D S D S D I I S D S D S S D I I D I S + +Speaker sentences 272: fleurs_eng_000458 #utts: 1 +id: (fleurs_eng_000458-fleurs_eng_000458) +Scores: (#C #S #D #I) 63 13 9 15 +REF: t h R o U G H t h e n i G h t * B e ******* t w * * ******* * e * * e n * 1 5 0 a n d * * * 2 0 0 c o p I E s w e r E m a d E n o W K n o W n a s D u * * N l a p b * r o A d s i d e s +HYP: t h * o * R W t h e n i * h t T H e t w E N H e R D e n F I T Y a n d T O H E R E c o p * Y s w e r * m a d * n o N * n o * n a s B u M E l a p b O r o * d s i d e s +Eval: D D S S D I S I I I I I I I I S S S I I I S S S D S D D S D D S I I S I D + +Speaker sentences 273: fleurs_eng_000459 #utts: 1 +id: (fleurs_eng_000459-fleurs_eng_000459) +Scores: (#C #S #D #I) 171 30 26 7 +REF: f i r s T a m O n g i T s * * * 7 8 r e c O M m E n d a t i o n S I s t h a t a * n E W D i P l O m a t i C I n i * T i A t * i V E s h O U L d b e t A k ******* E N b E f o r E t h e e n d o F t h i S y e a r t o s e c u r E I r a Q s B o r D e r s A g A I n s t h o s t i l E i n t e r v e n t i o n s a n d t o r E e s t a b l i s H d i p l O m a t i c r e l a t i o n s w i T H i t s n E I G H B O r s +HYP: f i r s * E a m L n g i s E I N Y A T r e c * A m * n d a t i o n D * s t h a t a N n O D T i B l * m a t i K * n i S H i * t D i O F s h * * * d b e t E k H O M b O f o r * t h e e n d ******* o * t h i C y e a r t o s e c u r * A r a C s ******* P o r * e r s E g * * n s t h o s t i l * i n t e r v e n t i o n s a n d t o r * e s t a b l i s T d i p l * m a t i c r e l a t i o n s w i * * i t s n * * * A V E r s +Eval: D S S S I I I S S S D S D S D I S S S S D S D I S D I S S D D D S I S S S S D D D S D S S D S D S D D D D S D D D D D D S S S + +Speaker sentences 274: fleurs_eng_000460 #utts: 1 +id: (fleurs_eng_000460-fleurs_eng_000460) +Scores: (#C #S #D #I) 69 22 24 4 +REF: s a I n T P e t e r s ******* b U r G c r U I s E s i n C l U D E t i m e I n t o w n C R U I S E P a S s E n g e R s a r E E X e M P t E D f r O m V I s * A r e Q u I r E M e n T s c h E c K t h e t E r * * M s +HYP: s a * n * * e t e r s b * r * ******* c r * E s I s i n l * * * ******* t i m e * n ******* t o w n * * W H O H T a * s I n g e * s a r * C S e * N t I F f r * m R E s E R r e C u * r * I e n C s c h O c * t h e t * r N E N s +Eval: D D D I D D D D S S S D D D D D D D D S S S S S D S D D S S D S S S D S S I S S D D S S S D D I I S + +Speaker sentences 275: fleurs_eng_000461 #utts: 1 +id: (fleurs_eng_000461-fleurs_eng_000461) +Scores: (#C #S #D #I) 74 13 11 4 +REF: a C c o r d i n g t o J A p a n s n U C L e A r a g e * n C y * r A d * I O a c t i V E c a e s i U m A n d i ******* O d i n E h a s B E e n I d e n T i f i e D a T t h e p l a n t +HYP: a O c o r d i n g t o * E p a n s n * O G e L r a g e A n S y W r * d Y L a c t i O F c a e s i O m * n d i A d i n * ******* h a s * * e n * d e n * i f i e T a * t h e p l a n t +Eval: S D S D S S S I S I D I S S S S S D I S D D D D D D S D + +Speaker sentences 276: fleurs_eng_000462 #utts: 1 +id: (fleurs_eng_000462-fleurs_eng_000462) +Scores: (#C #S #D #I) 88 12 5 8 +REF: s * e g R E G a t i o n a n d r e c o m B I n a t i o n s h * U f F l E v A r I a t i o n b a C k A n d f o r t * ******* h b e * t w E e n * t h e t w o p O O l s * w i t H e a c h * g e n e r A t i o n +HYP: s A e g O K a t i o n a n d r e c o m * O n a t i o n s h O L f T l * v E r Y a t i o n b a * k O n d f o r t H h b e U t w * e n D t h e t w o p A L l s E w i t * e a c h E g e n e r Y t i o n +Eval: I S S S D S I S S D S S D S I I I D I S S I D I S + +Speaker sentences 277: fleurs_eng_000463 #utts: 1 +id: (fleurs_eng_000463-fleurs_eng_000463) +Scores: (#C #S #D #I) 72 12 26 4 +REF: e l E m E n t S l I K E c * A l * C I u m a n d p o t a s S i U m A r E c o n ******* s I D e R E d m E t A l * s o f C o U r S E T H e R E A r E a l s o m E t A l S l I K E s i L v e r a n d g o l d +HYP: e l A m * n t * l * * Y c O U l S T H u m a n d p o t a s H i * m * r * c o n s * * e * T d m U t * l E s o f P o * r * * ******* * * e * S * r * a l s o m * t * l E l * * A s i * v e r a n d ******* g o l d +Eval: S D D D D S I S I S S S D D D I D D D S S D I S D D D D D D D S D D D D S D D S D D + +Speaker sentences 278: fleurs_eng_000464 #utts: 1 +id: (fleurs_eng_000464-fleurs_eng_000464) +Scores: (#C #S #D #I) 64 6 21 8 +REF: t h e C o R r E l A t i o N b E t w E e n b r a I n p A t h o * l O g * y a n D B e ******* h a v * I o u r * s U P p O r t S s C i E n * T i s t s I n * t h e I R r e s E A r c * H +HYP: t h e * o * r * l * t i o * b * t w * e n b r a * n p O t h o A l A g E y a n * * e h a v E Y o u r S s * * p * r t * s * i * n C S i s t s A n D t h e * * ******* r e s * * r c G E +Eval: D D D D D D D D S I S I D D I I S I D D D D D D I S S I D D D D D I S + +Speaker sentences 279: fleurs_eng_000465 #utts: 1 +id: (fleurs_eng_000465-fleurs_eng_000465) +Scores: (#C #S #D #I) 115 19 9 13 +REF: a n c i E n T c h * i * * n A h a d A u ******* n I Q U E w a y o f s h o W i n g d i F f E r e n t t i m e * p e r * I O d s e a c h * s t * a G e o f c h i n A o r * e A c h f a m i l y t h a t w a s i N p o * W e r w a s * a * d * I s t i n C t I V E d Y n A s t y +HYP: a n c i O n * c h A i N E n T h a d * O u n E A K C w a y o f s h o * i n g d i * f * r e n t t i m e M p e r E A T d s e a c h E s t D a K e o f c h i n E o r E e * c h f a m i l y ******* t h a t w a s i * M p o U R e r w a s H a E d E s t i n * t O F d I n I s t y +Eval: S D I I I S D S I S S S S D D D I I S S I I S S I D D D S I S I I I S D S S S S S + +Speaker sentences 280: fleurs_eng_000466 #utts: 1 +id: (fleurs_eng_000466-fleurs_eng_000466) +Scores: (#C #S #D #I) 100 24 31 13 +REF: a S I m P l E p o p * U l A R d i N N e r * * E s P e C I A L l y * d U r i n G t h e s u M m e r i s T H E p a a m B O l I b R E A d w I t H O l I v E o i l t o ******* m A t O A n D a n y A v * a I l a b L e c o n D i M E n T S S U c h A s c h e e s E t * u N A f i s h ******* * * * * e t * C +HYP: a H E m B l * p o p B E l * E d i * M e r N H H s T e * * S H l y T d * r i n * t h e s u * m e r i s ******* * * * p a a m * A l Y b * * U d w H t * * l * v * ******* o i l t o m E t D * n * a n y O v L a * l a b * e c o n T i * * n * C T I c h ******* E s ******* c h e e s * t O u * O f i s h I T D S e t E R +Eval: S S S D I S D S D S I I S S D D S S I D D D D D D D D S S D D S S D D D D D I S S D D S I D D S D D D S S S D S D D I D S I I I I I I S + +Speaker sentences 281: fleurs_eng_000467 #utts: 1 +id: (fleurs_eng_000467-fleurs_eng_000467) +Scores: (#C #S #D #I) 73 17 16 7 +REF: t h e a N n o U n c E M E n t w a s m a d e a F T e r t r U m P H a D A P H o n E C o N V e r s a t i o n w I t H t U r k I s h * p r E S I d e n t r e C e p * t * * A y Y I p * e * * r d o Ğ A n +HYP: t h e ******* a * n o * n c * T H n t w a s m a d e a * V e r t r N m * * a * T Y * F o n * * o * M e r s a t i o n w H t * t O r k * s h S p r * D O d e n t r e S e p T t H E y E A p A e R O r d o ** U n +Eval: D D D D S S D S S D D D S S D S D D D S S D S D I D S S S I I I S S S I I I D S + +Speaker sentences 282: fleurs_eng_000468 #utts: 1 +id: (fleurs_eng_000468-fleurs_eng_000468) +Scores: (#C #S #D #I) 164 31 55 11 +REF: p e R r y s T a t e D t H a t * h e W o U L d r e t U R n t o t e * x A S t o A s * s e S S t H E r e s u l t s o f t o n I g h T s c A U C U s * d e ******* T e r m I n E w h E t h e R T h e r E I s a * p a T H f o R W A r d f O r m y s e l f I n t h I s r a c e * b u T l A t e r s A I D t h A T H E w o U l D r e m a I N i n t h e r a C E a n d C O M p E T e I n t * H e J A n U A r y * * 2 1 s o u t H C A r O l i n A p r I m * a r y +HYP: p e * r y s * a t e * ******* t * a t H h e * o * * d r e t * E n ******* t o t e C x E I C t o * s T s e * * U t * O r e s u l t s o f t o n * g h C s c * O K I s C d e D e r m O n * w h * t h e * * h e r * ******* * s a P p a * * S f o * * * r d ******* f * r m y s e l f * n t h * s ******* r a c e S b u E l E t e r s * * T t h * * * * O w o W l * r e m a * E i n ******* t h e r a I D a n d * G B p * * e ******* * n O t I G e ******* * * n * * r y T W E W N s o u t * E I r * l i n O p r * m I a r y +Eval: D D D D D I D D D D S D I S S S D I D D S D S D S D S S S I I S S D D D D D D D I D D S D D D D D D D D I S S D D S D D D D S S D D S D S S D S S D D D D S I S D D D D D I I S S S D S S D S D I + +Speaker sentences 283: fleurs_eng_000469 #utts: 1 +id: (fleurs_eng_000469-fleurs_eng_000469) +Scores: (#C #S #D #I) 132 27 21 54 +REF: * ******* h e w a S A l s o E n g a g e D i n * E N g r a V i n g b a N k ******* n o * t E s f o r m A n y C o u n t r I e s r * E C E n T e X a M p l e s o f * h i s W O r k i n * c l * U d I N G t * * h e * * p r i * * m e * ******* * M i n i s T E r I a l p O r t * r A I T s o n t h e ******* * * * * * f r * * ******* * o * * ******* * * ******* * * * n t o f t h e n E W c A n a d * ******* i A n 5 * * A N d * * * * * 1 0 0 * * * * * B I l * * l S +HYP: E h e w a * ******* E l s o I n g a g e * i n G * * g r a * i n g b a * k n o L t D s f o r m I n y * o u n t r * e s r S O N I n G S e * a * p l e s o f W h i s * E r k ******* i n G c l E E d * * * t H E h e A M p r i E N m e N I N i n i s * r E a l p * r t E r * E D s o n t h e F I R S T f r O F H o N D T H F R U n t o f t h e n O O c O n a d Y i * n O F L O E d L E R I N W O H N D E R D l D E l * +Eval: I I D D S S D I D D D D I I S S D D I S S S S S D D I D S D I I S D D D I I I I I I I I I S D S S D I D S S I I I I I I I I I I I I I I I I I I I S S S I I D S I I S S I I I I I S S S I I I I I S S I I D + +Speaker sentences 284: fleurs_eng_000470 #utts: 1 +id: (fleurs_eng_000470-fleurs_eng_000470) +Scores: (#C #S #D #I) 152 22 37 6 +REF: * ******* m o r E t r A d I T i O n A L C h U r c h e s o F t ******* E n h o L D * * A n e a s t e r V i G i l O n s a t U R d A y n I g h t D u r i n G t h e e A s t E r w E e K e n d W I T H t h e c o N g r e G A t i o n s o F t ******* E N b r E a k i n G i n t o C e l e b r a t i o n a t t h e s T r o K E o f m i D n I G H t t o C E l E b r a T E c H r i S T s r e s u R r e c t i o n +HYP: H m o r * t r * d * * i * n * * * h * r c h e s o N t A n h o * E T H E n e a s t e r R i C i l * n s a t T E d * y n * g h t T u r i n * t h e e * s t * r w * e * e n d * * B U t h e c o * g r e * W t i o n s o * t I M b r * a k i n * i n t o ******* S e l e b r a t i o n a t t h e s * r o * C o f m i * n * * * t H t o S O l * b r a * K c * r i C E s ******* r e s u * r e c t i o n +Eval: I I D D D D D D D D D S I S D S I I S S S D S S D D S D D D D D D D S S D D S D I S S D D D S D D S D D D D S S S D D S D S S D D + +Speaker sentences 285: fleurs_eng_000471 #utts: 1 +id: (fleurs_eng_000471-fleurs_eng_000471) +Scores: (#C #S #D #I) 108 13 20 5 +REF: f i N l A n D i S a g r E a t b o A T i n g d E s t I n a t i o n t h e l a n d o f a t h o u s A n D l a k E S h A s t H o u s A N D S o f i S l A n * d s t * o o I n * t h e l a k E S a n D I n t h e c o A s t a * L a r C H I p e l A g o * s +HYP: f i * l * n * ******* i * a g r * a t b o * D i n g d U s t E n a t i o n t h e l a n d o f a t h o u s E n * l a k * * h E s t * o u s * * * E o f i * l E n C d s t W o o A n D t h e l a k * * a n * * n t h e c o * s t a O E a r K Y p e l O g o E s +Eval: D D D D D D D S S S S D D D S D D D D S D S I I S I D D D D D I S S S S S I + +Speaker sentences 286: fleurs_eng_000472 #utts: 1 +id: (fleurs_eng_000472-fleurs_eng_000472) +Scores: (#C #S #D #I) 116 35 40 21 +REF: C U R r E n t S e n A t O r a n d a r g * E n * T i n E f I r s T l a d y c R I s t * I n A f E r N A n d E Z D E K I r * C H n E r a N n o U N C e D H E r p R e s i D E n T I A l c a n d ******* i D A C Y Y E s t E r d a y E v e N I n g I n l A p l a t * a * a * C I T y * * * * 5 0 K I l o m e * t e r s ******* * * * * * * 3 1 m i l E s a w A y f r o m B U E n o * s A i R E s +HYP: * * E r * n t T e n * t E r a n d a r g H I n C S i n * f * r s * ******* l a d y c * E s t D E n * O f * r * * n d I S * A C E r S I O n * r a * n o * * * e S * * r p * e s i * * n A C H l c a n d i * * * S O U s t * r d a y A v e * * n g * n ******* l * H p l a t H a T a S T A D y F O F T D Y * O l o m e I t e r s T H E R T Y W N m i l * s a w * y f r o m * * W n o L s ******* * i D I s +Eval: D D S D S D S I S I S D D D D D S I S D S D D D S S D S S S I S S D D D D D S D D D D D S S S I D D D S S S D S D D D D D S I I I S S S I I I I S S D S I I I I I I I I S S D D D D S I D D S S + +Speaker sentences 287: fleurs_eng_000473 #utts: 1 +id: (fleurs_eng_000473-fleurs_eng_000473) +Scores: (#C #S #D #I) 115 17 22 4 +REF: s E v e r E w e A t h e r i S T h e G e n E r I c t e r M f o r A n y d a n G e r O U s w E A t h e R P H E n O m E n O n * w I T h t h e p o t E n T I A l t o c A U s E d a m * a g e s E r i O U s * s o C i A l d i s * r u p t i o n o r l o S s o f h U m A n l i f e +HYP: s * v e r * w e * t h e r i * * h e * e n A r N c t e r E f o r * n y d a n D e r * * s w * H t h e * * F O n A m O n A n H w * * h t h e p o t I n * * C l t o c * O s * d a m I a g e s I r i * * s C s o S i O l d i s T r u p t i o n o r l o * s o f h * m E n l i f e +Eval: D D D D D D S S S D S D D D S D D S S S S S I D D S D D S D S D I S D D I S S I D D S + +Speaker sentences 288: fleurs_eng_000474 #utts: 1 +id: (fleurs_eng_000474-fleurs_eng_000474) +Scores: (#C #S #D #I) 120 20 8 23 +REF: f o r e * X a m P l E t h e m o s t c o M m O n s t I L l i m * A g e P h o ******* t o * G r A P H y f o r m A t i N t h e W o r * l d i s ******* * * * * ******* * * * * * 3 5 m * * M w h I c h w a s t h e d o m i n A n t f i * l * * m s i Z e * a T t h e c l o S e * o f t h e a n A l o g f i l M E r a +HYP: f o r e S a m B l * t h e m o s t c o * m E n s t * H l i m I N g e F h o t o C K r * F E y f o r m U t i * t h e H o r A l d i s T H R Y F I R E I L N m E A E R w h * c h w a s t h e d o m i n E n t f i E l M E m s i G e S a * t h e c l o * e S o f t h e a n I l o g f i l E A r a +Eval: I S S D D S D S I S S I I S D S S S D S I I I I I I I I I I I I S S I I S S D S I I I S I D D I S S S + +Speaker sentences 289: fleurs_eng_000475 #utts: 1 +id: (fleurs_eng_000475-fleurs_eng_000475) +Scores: (#C #S #D #I) 127 15 23 6 +REF: i t i s r e l a t e d t o b u t * u s U A L l y n o t i N V O l V i n g A l p i n E S t Y l e s k I t o U r i n g O r m o u N t A I n E e r i n g t h e l a T t e r * o N e s d o * n e i n s T E e P t e R r A i n * a n d r e Q U i r i n g m u C h S t * i F f E r s k * I s a n d b O o t s +HYP: i t i s r e l a t e d ******* t o b u t Y u s * * E l y n o t i * * B l T i n g O l p i n G * t I l e ******* s k * E t o * r i n g A r m o u * t * E n * e r i n g t h e l a * t e r W o * e s d o U n e i n s * D e * C t e * r * i n G a n d r e C R i r i n g m u S h * t H i * f * r ******* s k E E s a n d b * o t s +Eval: D I D D S D D S S S S D S D D S D S D D S D D I D I D S D S D D I S S S D I D D D I S D + +Speaker sentences 290: fleurs_eng_000476 #utts: 1 +id: (fleurs_eng_000476-fleurs_eng_000476) +Scores: (#C #S #D #I) 117 5 17 8 +REF: i * r O n i n g d a m P c l o T H e s c a n h e L p t h e M d r * y * ******* * m a n y h o ******* t e l s h A v e A n i * r O n a n d i r O n i n g b o A r D A v a I l a b l e f O r l o A n e ******* v e n i f o n E I s n o t p r e s e n t i n t h e r O o m +HYP: i A r * n i n g d a m K c l o A S e s ******* c a n ******* h e * p t h e * d r I y I H m a n y h o t e l s h * v e ******* * n i A r * n a n d i r * n i n g b o * r * O v a * l a b l e f * r l o * n e v e n i f o n * H s n o t p r e s e n t i n t h e r * o m +Eval: I D S S S D D D D I I I I I D D D I D D D D S D D D I D S D + +Speaker sentences 291: mls_eng_000283 #utts: 1 +id: (mls_eng_000283-mls_eng_000283) +Scores: (#C #S #D #I) 151 22 25 13 +REF: * e ******* v A d n E A n S W e r E d h o A r s E l y s h e D R e * W h e r c h a I r * A l * i T t L e C l * o s e * R T o t h E f i r e * a n d s P r e A d h E r h a n d ******* s o u T t o t h e b l a Z e * t H e r e w a s n o o ******* t h e R l i G h t i n t H e R o O m B y t h I S t i m E t h e w i n D w i T H o U t * h o * W L e d D i s m A L l y s t i L l +HYP: E e v E d n Y U n * C e r * d h o * r s * l y s h e * * e S U h e r c h a * r E U N l E i * t H e G l A o s e H E * o t h * f i r e R a n d s C r e * d E h * r h a n d s o u * ******* t o t h e b l a S e S t * e r e w a s n o o t h e * l i * h t i n t * e * o N m M y t h * E t i m N t h e w i n * w i D N o * t O h o R D e d * i s m * R l y s t i * l +Eval: I I S S S D S D D D D D I S D I S S I D S S I I S D D I S D S D I D D S I D I D D D D S S D S S D S S D I I S S D D S D + +Speaker sentences 292: mls_eng_000284 #utts: 1 +id: (mls_eng_000284-mls_eng_000284) +Scores: (#C #S #D #I) 142 19 12 25 +REF: * m y d e a r m a r * * * I a * * w h Y d o Y o u * n o t d e s i s t f R o m t h I S s i L l y p U r * ******* s * u I t o f * a n * I m a * * g i n a r y t R e a * s U r E w h a t i s t h e * * V A l * * u E o f m O n E y w e a r E s p * a n I A r d s n o t s h I r t s l E e v e d m e r * C E n a r y * p I g s o f a ******* m E R i * c A n * s +HYP: E m y d e a r m a r L D E E a E R w h I d o * o u D n o t d e s i s t f * o m t h * E s i * l y p E r S s O u O t o f E a n A D N m a N D g i n a r y t H e a D s E r * w h a t i s t h e T H E O l Y O u * o f m U n * y w e a r * ******* s p B a n * U r d s n o t s h U r t s l * e v e d m e r S S I n a r y P p E g s o f a m * A i Y c E n D s +Eval: I I I I S I I S D I D D S D S I I I S I I S S I I S I S D I I S S I I D S D D D I D S S D I S S I S I D S I S I + +Speaker sentences 293: mls_eng_000285 #utts: 1 +id: (mls_eng_000285-mls_eng_000285) +Scores: (#C #S #D #I) 137 24 54 7 +REF: * C r i t I c A l t ******* E M p E R A t U R e i s t H a t o F t H e s i n g l E i * s * O t h e r m A L l i n E w H I c h p R e s e N T s A p o I n t O F i n f l e * X i o n A t A h o * r I Z O n T A L t A n g e n t t h e C r I t i C a l * P r e S s u R E A N D T h e C r I T I c a L v O l U M e a R E t h E t w o c o O R d I n A T e s o f t h i s p o I n t O F I n F l e X i o n +HYP: T E r i t * c * l t U B p * * * t * * e i s t * a t o * t * e s i n g l * i A s E t h e r m * E l i n * w * * c h p * e s e * * s ******* E p o * n t ******* I E i n f l e A T i o n ******* * t ******* * h o A r S E n * * D t I n g e n t t h e ******* * r S t i * a l E * r e * s u * * ******* * * * * h e * r * * S c a * v * l L I e a * * ******* t h * t w o c o A L d * n * * e s o f t h i s p o * n t * * B E n P l e T i o n +Eval: I S D D I S S D D D D D D D D D I I S D S D D D D D D D S D D S S I S D D D D I S S S D D S S D D S D I D D D D D D D D D D D D S D D S S D D D D S S D D D D D D S S S S + +Speaker sentences 294: mls_eng_000286 #utts: 1 +id: (mls_eng_000286-mls_eng_000286) +Scores: (#C #S #D #I) 198 13 25 8 +REF: m u c h l i k E I n f o u L n e S s A n d D E f o r m * i t y U n ******* t o T h a t m o n s t e r W h o m t h e t h e * b A n K n i G h t t h e f a * t h e r o f t h a t f a t A l p r o * g E n y m a d e * K i L l h e r ******* s e l f f o r v e r y h E a r t s D e s p i * t E t h a t h e h a d r E a D h e r r i D d l E w h i c h n o w i G h t c o u L D e v e r l o O s E B U t s u F f e r E d d e A d l y d U e L +HYP: m u c h l i k * A n f o u * n e U s * n d * O f o r m E i t y A n t o * h a t m o n s t e r * h o m t h e t h e A b O n * n i * h t t h e f a R t h e r o f t h a t f a t * l p r o D g I n y m a d e D C i * l h e r s e l f f o r ******* v e r y h * a r t s T e s p i H t * ******* t h a t h e h a d r * a * h e r r i * d l * w h i c h n o w i * h t c o u O T e v e r l o U s * H A t s u * f e r * d d e * d l y d * e * +Eval: D S D S D D S I S I D D I S D D I D I S I S D I D D S I D D D D D D D S S S D S S D D D D D + +Speaker sentences 295: mls_eng_000287 #utts: 1 +id: (mls_eng_000287-mls_eng_000287) +Scores: (#C #S #D #I) 185 27 67 6 +REF: H E h A S m a N A G e D T O m E a s U r E w i T h p r E C I s i o n * P r e S s U R e s A m O u n t i N g t o T h r E e t h o u * s A n D a T M O s P H E R e s a n d a l s o t h E V e r y s m a L l v O l U m E s t h E N o C c U P i e * * d * b Y t h e f l U I d m A S S U n d e R c o N s i D e r a t i o n t h i s l a s T m E a s U R E M e n t w h I c h n E C e s S i t a t e S n u m e r O U s c o R r E C t i O n S i s T H E m o s t d e l i C A T e * p A R t O F T h e o p E r a t i o n +HYP: * * ******* h * I m a * S H e * ******* * * m * a s E r * w i * h p r * * * s i o n G * r e * s * I e s * m * u n t i * g t o * h r * e t h o u L s E n * ******* a * * N s * * T I e s a n d a l s o t h * ******* * e r y s m a * l v L l I m * s t h A D o * c * K i e P Y d E b * t h e f l N T d m * U E * n d e * c o E s i * e r a t i o n t h i s l a s E m * a s * * * * e n t w h * c h n O U e s E i t a t e * n u m e r * * s ******* c o * r * A t i * n D i s ******* * * * m o s t d e l i * * K e U p * U t ******* * * * h e ******* o p * r a t i o n +Eval: D D D D S D S S D D D D D S D D D D D I D D D S D D D D D I S D D D D S D D S S D D D D S S D S S D D S I I I D S S D S S D D S D S D D D D D D S S S D D D D D D S D S D D D D D D S I D S D D D D D D + +Speaker sentences 296: mls_eng_000288 #utts: 1 +id: (mls_eng_000288-mls_eng_000288) +Scores: (#C #S #D #I) 116 9 15 6 +REF: w h Y s h O u l D i t h a V e b E e n d E e m e d n e ******* c r o m a n c * y t o E n d e A v O r T o C o M b i n E t h e s E P a R T s t o E v * o * * l v e b y * C a r E f u l e l I m i n a t i o n a n d c h a n g e t o t h e p e r F e c t f O o d +HYP: w h I s h * u l * i t ******* h a * e b * e n d * e m e d n e c r o m a n c S y t o I n d e * v E r * o * o N b i n G t h e s * F a * * s t o I v F o L I l v e b y G I a r * f u l e l * m i n a t i o n a n d c h a n g e t o t h e p e r V e c t f * o d +Eval: S D D D D D D I I S D S D D S S D S D D S I I I I S D D S D + +Speaker sentences 297: mls_eng_000289 #utts: 1 +id: (mls_eng_000289-mls_eng_000289) +Scores: (#C #S #D #I) 166 12 22 20 +REF: * n a y t H O U G h o * F r U s H e s b e * m y b e * d * y e * t i a m r i * * c H l o v e s a i d b * * * * u t a r * g u E d l i * F e t h r i c e f o * n d a r T t h o u * * t o y I e l D t h e s o v e r E I G n g i f t s o f E a r t h t h e v i C t o r s * W o r d t h e l A U r E l e D b r o W F o r v I S i O n E D t h i n g * s o f l i T T l e w * O r t H +HYP: D n a y t * * * * h o V E r A s * e s b e Y m y b e A d T y e A t i a m r i G E c E l o v e s a i d b U T H O u t a r E g u * d l i H V e t h r i c e f o M n d a r * t h o u L E t o y * e l * t h e s o v e r * * A n g i f t s o f * a r t h t h e v i E t o r s O R o r d t h e l * O r A l e T b r o L * o r v * * i * n * * t h i n g K s o f l i * * l e w E R r t * +Eval: I D D D D I S S D I I I I I I S I I I I I D I S I D I I D D D D S D S I S D S S S S D D D D D D I D D I S D + +Speaker sentences 298: mls_eng_000290 #utts: 1 +id: (mls_eng_000290-mls_eng_000290) +Scores: (#C #S #D #I) 140 13 40 3 +REF: b o c k S E e M s t o h A v e b E e n a k E e n C o L l E c t O r A l t h o U G H h a m p E R e d b y i l L h e A l t h * a N d A g r E a t p O i n t I n H i s f a v O U r i s T h a t H E D e s c r i B e d o n l y t h O S e p l a n T s w H i * C h h a * d c o m E u n d e r H i s o W n p e r s O n A l o B s E R v a t i o n +HYP: b o c k * * e * s ******* t o h * v e b * e n a k * e n * o * l A c t * r O l t h o * * * h a m p * * e d b y i l E h e * l t h F a * d ******* * g r * a t p * i n t ******* O n ******* * i s f a v * E r ******* i s ******* * h a t ******* * * * e s c r i G e d o n l y t h U C e p l a n * s H w * i T h h a T d c o m * u n d e r ******* * i s o * n p e r s I n * l o P s * O v a t i o n +Eval: D D D D D D D D D S D S D D D D D S D I D D D D D D S D D D S D D D D D D D S S S D S D I S I D D D D S D S D S + +Speaker sentences 299: mls_eng_000291 #utts: 1 +id: (mls_eng_000291-mls_eng_000291) +Scores: (#C #S #D #I) 145 12 14 6 +REF: h a d r a t h e r s H r U n K U p a n d h a d n o t c h a * n G e d i n ******* t o n * Y m P H s t h e * s E * I L e F T i n t h e s t E m s c o v e r i n G t h e m u p a g a I n a n d t h e y a P p e A r e d a s p e r f e c t * i n s E c t s i n t h e m a y o f t h e f o L l O W i n G Y e a r +HYP: h a d ******* r a t h e r s * r * n G O p a n d h a d n o t c h a I n S e d i n t o ******* n E I m * * s t h e I s * H Y F e L D i n t h e s t A m s c o v e r i n * t h e m u p a g a * n a n d t h e y a * p e * r e d a s p e r f e c t D i n s A c t s i n t h e m a y o f t h e f o * l * N i n * H e a r +Eval: D D D S S I S I D I S D D I D I S S S S S D D D D I S D D S D S + +Speaker sentences 300: mls_eng_000292 #utts: 1 +id: (mls_eng_000292-mls_eng_000292) +Scores: (#C #S #D #I) 169 19 34 8 +REF: n o t h i n g s a * V E o b J e c T S a n d T h o U G H t s o f b E A u t y C o u L d p r e s * e n T t h e m s E L V e s t o t h e u n d e r s t a n d i n g o f t h e f o r t U N A T e * P E r s o n W h o * * P a r t O o k o f i t t h e * s E * P a G E s w H i c H Y o U h a V e b r O u g H T t o m e T O t r a n s l a t E a r E c o n C e r N e D w i t H t h i s s * u p e R s T I t i o n +HYP: n o t h i n g s a Y W O o b D e c * E a n d * h o * * A t s ******* o f b * * u t y M o u * d p r e s V e n D t h e m s * * A e s t o t h e u n d e r s t a n d i n g o f t h e f o r t * I E L e T B O r s o n * h o U E * a r t * o k o f i t t h e I s * B E a T s w * i c * H o * ******* h a * e b r * u g * * ******* t o m e ******* * * t r a n s l a t D a r * c o n S e r * e * w i t * t h i s s O u p e * s * * t i o n +Eval: I S S S D S D D D S D D D S D I S D D S D S S S I S S D I I D D I D I S S S D D S D D D D D D D D D D S D S D D D I D D D + +Speaker sentences 301: mls_eng_000293 #utts: 1 +id: (mls_eng_000293-mls_eng_000293) +Scores: (#C #S #D #I) 94 8 19 6 +REF: n o W s E E m e D i n S I p I D i t y a n d h e D n e r v e h i m s e l f a g a i n s T i t h i s f a C E w O R E a * s o r T o F s E v E R e f ******* l u s h * h e w a s t i m i d e v e * n t o * r * u d E n e S s +HYP: n o U s * * m e * ******* i n C U p * T i t y ******* a n d h e * n e r v e h i m s e l f a g a i n s * i t h i s f a I S w * * * ******* a L s o r D o * s O v * * e f l u s h D h e w a s ******* t i m i d e v e I n ******* t o R r O u d * n e * s +Eval: S D D D D S S D S D D D S S D D D D I S D S D D I I D I D I I D D + +Speaker sentences 302: mls_eng_000294 #utts: 1 +id: (mls_eng_000294-mls_eng_000294) +Scores: (#C #S #D #I) 165 23 44 15 +REF: * b e c A m e m o R E l i * * F e ******* l i * * k e a s t h e C h E e K s f l u s h t h E R e W a s r * a r E w * a R m T H * i n A W i n t e R m o r N i n g t O C h E e r T H E h a L f * D E s ******* p A I r i n g s o U l t i R E d A F t E R l o n g H o u r s o f O I l r e a d i n g a n d p I e r C E D T O t H e * h E a r t b y n e v e r C e a s i n g r H Y m e * s Y e * t i c O u L D n O T U n d e r ******* s t a n d i t +HYP: T b e c O m e m o * L l i U G H e l i K C k e a s ******* t h e S h I e X s f l u s h t h * * e * a s r E a r * w O a I m G E F i n ******* * O E i n t e * m o r D i n g ******* t * * h * e r ******* * * D h a * f T I s p * * r i n g s o * l t i * * d * I t * * l o n g A o u r s o f A L l r e a d i n g a n d p * e r * * * ******* * * S t * e D h * a r t b y n e v e r S e a s i n g r * I m e M s * e S t i ******* c * u * * ******* n * * O n d e r s t a n d ******* i t +Eval: I S D S I I S I I I D S S S D D D I D I S S S I D D S S D S D D D D D D D S D I S S I D D D D D D S D D S S S D D D D D D D S D I D S D S I D I D D D D D D D S I D + +Speaker sentences 303: mls_eng_000295 #utts: 1 +id: (mls_eng_000295-mls_eng_000295) +Scores: (#C #S #D #I) 104 15 20 11 +REF: * o n E O f t h e h A w A I i A n * W r I T e r S s a i d t h e * * O p * I h * I a * * W A i s a p o I s O n s h E L l f i s h t h e s e A r E b i T t E r a n d d e A d l y a n d c A N b e * * u s e D i n p u T t i n g e n * E m I E s t o d e a T H +HYP: W o n * * f t h e h O w * * i * n G * r G D e r * s a i d t h e A L L p E h E a O V O i s a p o * s E n s h * O l f i s h t h e s e ******* * r * b i * t * r a n d d e * d l y a n d c O E b e Y O u s e * i n p u I t i n g e n I N m * Y s ******* t o d e a * * +Eval: I D D S D D D I D S S D I I S I S I S I I S S D S D S D D D D D D S S I I D S I S D S D D D + +Speaker sentences 304: mls_eng_000296 #utts: 1 +id: (mls_eng_000296-mls_eng_000296) +Scores: (#C #S #D #I) 126 15 25 9 +REF: t h e b e A u t ******* e O U s r o * B e s o f h E a v * E n a s ******* l A n T t H E d E W B r i G h t e a r T H a n d c o l O U R e D a I r h e l O o k S i n b o U n D l E S s m a ******* J E s t y a b r o * A d t o u c h i n g t h E g r E e n l e A v e s a l l a ******* t r e m b l E W i t H g o * * l D l i G h t +HYP: t h e b e * u t e * A s r o U P e s o f h * a v I O n a s l O n * t O A d * U * r i * h t e a r * * a n d ******* c o l * * * e T a * r h e l * o k E i n b o W n * l * I s m a G H s t y R a b r o U R d t o u c h i n g t h * g r * e n l e * v e s a l l a t r e m b l * * i t * g o U G l * l i * h t +Eval: D I D S I S D I S I S D S S D S D D D D D D D D S D D S S D D S I S S S I S D D D I D D D I I D D + +Speaker sentences 305: mls_eng_000297 #utts: 1 +id: (mls_eng_000297-mls_eng_000297) +Scores: (#C #S #D #I) 169 28 39 19 +REF: i C a N d O n o * m o r E t h A n t h a t * u n * t I l t h i s M a T t E r * * i s a B S O l u T E l y s e T t L e d * t h e Y A R e w o r t h m O R E t h A N l i f E i t S e l f t o M e * * * * M r C O W P E r s E E m e d a N n o * y * E D s * U r E l y h e p r O t e s t e * D Y o u A R E n o t g o I N G T O a s K m e t o w a I t t h r E E m O n T H S U n * t i l i C a N * e x a m i n E * o n E o f t h e * s e * +HYP: i * a D d U n o R m o r * t h * n t h a t I u n D t * l t h i s N a * t A r E H i s a * P E l u * D l y s e * t * e d E t h e * * * e w o r t h m * * * t h * E l i f * i t e l f t o * e M W H S T r B L L B U r s * * m e d a * n o R y I S T s A O r * l y h e p r * t e s t e I T * o u ******* * * * L n o t g o * * * ******* * D a s T m e t o w a * t t h r * A m U n * C E A n D t i l i * a T A e x a m i n * B o n * o f t h e I s e S +Eval: D S S I D D I I D S D S I I D S S D S D D I D D D D D D D S D S D I I I I S S S S S S D D D I I S S I S D D I S D D D D D S D D D D D S S D D S S D S S S I D S I D I D I I + +Speaker sentences 306: mls_eng_000298 #utts: 1 +id: (mls_eng_000298-mls_eng_000298) +Scores: (#C #S #D #I) 129 10 13 12 +REF: r o s ******* c o n g r e S s f o u n d a t i o n r U S s i A n E n * t i t Y t h a t o r g A n i Z e D t h e s a I n t p e t e r s ******* b u r * g i n ******* t e r n a T i o n A l * e c O n o m i c f o r U m r o * s ******* n e f t r u S s i A n * s t a T E o W n e d o i l * A n D E n e r g y c o m * p a n y * +HYP: r o s c o n g r e * s f o u n d a t i o n r * E s i O n A n D t i t E t h a t o r g * n i H e * t h e s a * n t p e t e r s b u r I g ******* i n t e r n a S i o n * l E e c E n o m i c f o r * m r o U s n e f t r u * s i O n D s t a * D o * n e d o i l E * n * A n e r g y c o m B p a n y W +Eval: I D D S S S I S D S D D I I D I S D I S D I I D S I D S D I D D S I I + +Speaker sentences 307: mls_eng_000299 #utts: 1 +id: (mls_eng_000299-mls_eng_000299) +Scores: (#C #S #D #I) 215 20 54 10 +REF: h o w I T g l I T t E R e d A n D s p a R K l E D t h e d e l i c * a t E f r o s t ******* w O R k y o u W E R e a T t r a c t * e d n o d o u B t a N D m a R v e L L E d a T t h e d A i n T y t r a c I N G s b u t f e w * * o F U s h a V e r e a L l y h a d a n o P p O r t U n i t y t o s t * U d y t h e d e t * a I l o F t h E s E f r O s t D e s i G n * s m I n u t E l y o r H A v E c o n ******* s i d E R e d T h a t t h E R e W e r e M o r E T h A n t h r E e * O r f o U r d e s i G n S a t m o s t +HYP: h o w ******* * * g l * A t * * e d I n * s p a C A l * * t h e d e l i c E a t * f r o s t w * E k y o u ******* * * * e a t r a c t D e d n o E d o u * t a * * m a * v e * * R d a * t h e d * i n * y t r a c S O M s b u t f e w U E o V A s h a * e r e a * l y h a d a n ******* o * p * r t * n i t y t o s t A N d y t h e d e t E a * l o * t h I s * f r U s t ******* * e s i * n E s m Y n u t * l y o r * * v * c o n s i d * * e d * h a t t h * * e * e r e * o r * * h I n t h r * e Y U r ******* f o * r ******* d e s i * n E a t m o s t +Eval: D D D D S D D S D S S D D I D I D S D D D D S I S D D D D D D S D D D S S S I I S S D D D D D D I S I D D S D S D D D I S D D D D I D D D D D D D D D S D I S D D D D S + +Speaker sentences 308: mls_eng_000300 #utts: 1 +id: (mls_eng_000300-mls_eng_000300) +Scores: (#C #S #D #I) 202 27 43 5 +REF: O t h e R T h a n t h E o F f e N s E i n t r Y i n g t o i n f L i C T A W o U n D t h e Y m A Y k i L l T h e o f F e n d e r o r w O U n D h i m m o r E T h a n t h e Y i n t e n d e D t o d O a n d t h i s b e c o m e S a c A u s E f * O R a n E W F e U d s o T h a T t h e p r i m i t i v e l E g I s l a t O r s w * e r e c A R e f u l i n r e * q u i * r i n g t h e r * e t a l i A t i o n t o b e l I m i t e d t o a n E y E f o r a n E Y E +HYP: * t h e * ******* A h a n ******* t h * o * f e * s * i n t r * i n g t o i n f * i * * K G I U o * n * t h e * m * E N k i * l V h e o f e n d e r o r w * E n * h i m m o r * V h a n t h e * i n t e n d e N t o d * W a n d t h i s ******* b e c o m e * a ******* c C u s * ******* f U L E a ******* n * * U H e R d s o * h a * ******* t h e p r i m i t i v e l I g E s l a t E r s ******* w H e r e c * * e f u l i n O r e C q u i U r i n g ******* t h e ******* r I e t a l i * t i o n t o b e D l * m i t e d t o a n ******* I y * f o r a n * O I +Eval: D D D S D D D D D D D D D S S S S D D D D S S D S S D S D D S D S D S D D D S D D I S S D D D S S S D D D S S S D I D D S I I D D I D S D D S D D S S + +Speaker sentences 309: mls_eng_000301 #utts: 1 +id: (mls_eng_000301-mls_eng_000301) +Scores: (#C #S #D #I) 182 22 15 9 +REF: a t C Y r * U s w o r d t h e j E W s * r e t U r * n t h e c o m p A n y t h a t g o * * g o D s h O U S e * b e g U n w i t H m I r t H a N D m o A N i s h I n d e r e d b y t h e f o E b u t * O n c e a g a i n t h e w o r k g o E s o n b y l i C e n s E f r o m d E r i U s E Z r A i s s e n t w i t h r o Y A l * * g r a n t a n d g i f t s f O r U s e s p i O U s +HYP: a t S I r E S s w o r d t h e j U O s E r e t E r E n t h e c o m p O n y t h a t g o E B g o * s h * I C e S b e g O n w i t * m * r t * a * * m o M E i s h E n d e r e d b y t h e f o * b u t W H n c e a g a i n t h e w o r k g o * s o n b y l i * e n s * f r o m d * r i A s A S r E i s s e n t w i t h r o * I l E D g r a n t a n d g i f t s f * r Y O s e s p i * S s +Eval: S S I S S S I S I S I I D D S S I S D D D D D S S S D I S D D D D S S S S D S I I D S S D S + +Speaker sentences 310: mls_eng_000302 #utts: 1 +id: (mls_eng_000302-mls_eng_000302) +Scores: (#C #S #D #I) 98 18 22 13 +REF: * n E t p r o d U C T y E a * R i n a n d y e a r o u t * s E v E n h u N d r E D f r A n c * s h * e l I v e d I n i t h o * W n o T s o b a d l y * W e W I L l * E X p * l a i n m A r * * ******* i U s o c C u p I E D I N t H e G o r B e A U h o u S e * +HYP: A n * t p r o d * K E y H a I E i n ******* a n d y e a r o u t E s * v I n ******* h u * d r * T f r O n c E s h O e ******* l * v e d * n i t h o V E n o * s o b a d l y B R e ******* * * U l A C S p M l a i n m U r T Y i * s o c K u p * * Y * H t * e * o r * e B O h o u * e S +Eval: I D D S S S I S D I D S D D D S S I I D D D I S D I S D D D S I S S I S I I I D S D D S D S D D D S S D I + +Speaker sentences 311: mls_eng_000303 #utts: 1 +id: (mls_eng_000303-mls_eng_000303) +Scores: (#C #S #D #I) 187 21 22 16 +REF: t H e N t h i s * i s a l l y o u r a n * * s W e r t i s t O o f * a i r f o r o n e o f h i s A L l i A n * c E a n d I w A r N y o u * * * t h a t t h i s p l a c e n o m o r E s E e y o u * * E X i t E n t e r d e f l O r * E s t h e B e s t i * * s t h E R e i s m o r e G r o u n d t o m E e T a m a n S r E v e n G E * o n H o n E s T D e f L O r * E s t h * a t s m y N a m e i n d E e d +HYP: t * e D t h i s E i s a l l y o u r ******* a n C T s * e r t i s t W o f E a i r E f o r o n e o f h i s * O l i * n T c S a n d Y w O r E y o u O W E t h a t t h i s p l a c e n o m o r * s * e y o u A N G S i t A n t e r d e ******* f l E r A N s t h e * e s t i S E s t h * * e i s m o r e C r o u n d t o m * e D a ******* m a n D r A v e n * D G o n * o n I s * * e ******* f * A r A C s t h E a t s m y * a m e ******* i n d * e d +Eval: D S I D I I D S I S D S D I S S S S I I I D D I I S S S D S I S D I I D D S D S D S S D S I D S D D D D S I S I D D D + +Speaker sentences 312: mls_eng_000304 #utts: 1 +id: (mls_eng_000304-mls_eng_000304) +Scores: (#C #S #D #I) 199 16 33 14 +REF: w h e n i r e t u r n e d * t o t h e h o u s e w h E r e I h a d b e e n A h a P p y c h i l * d o n * l y a * p i l E o f a s h * e s w H E r E I t h a d s t O o d i w e p t l o n g * a n d t o f o r * g e t m y w E e p i n g i s a i L E d o u t O n T H e v * a s T c A L m s e A o n * t h e s E w * A t e r s i n a s ******* t A r * S A P P H I R e * n i g H t i * p l a y e D m y f l U t E t o t h e s U M m e R m O o n +HYP: w h e n i r e t u r n e d A t o t h e h o u s e S w h * r e ******* * h a d b e e n ******* * h a * p y c h i l E d o n D l y a E p i l * ******* o f a s h I e s ******* w * * r * A Y t h a d s t * o d A i w e p t l o n g K a n d t o f o r O g e t m y w * e p i n g i s a i * * d o u t U n ******* * D e ******* v E a s * c * O m s e * o n D t h e s * w O R t e r s i n ******* a ******* s t H r S I U G F Y A e R n i g * t i D p l a y e * m y f l * t * t o t h e s * * m e * m * o n +Eval: I S D D D D D D I I I D D I D D D D S S D S I I D D D S D D S D I D D S D I D I S D D I S I S S S S S S S I D I D D D D D D D + +Speaker sentences 313: mls_eng_000305 #utts: 1 +id: (mls_eng_000305-mls_eng_000305) +Scores: (#C #S #D #I) 201 11 29 17 +REF: * * d o Y o u n o t s e e w h a * t P l e A s U r E i t g i v e s H I m * w e h a v e g r o W n * u p T o g e t H e r i n t h i s h o u s e s i n C E h e w * A s a b o y * i * s * i m p l y C a N n o T b e a r * a s Y o u c a n * t h e S i G h t o f t h E s m * I l E l e A v i n G h i s f * a c e P O o r d e a r h e h a s n o a m u s E m e n T e X c e p T t h i S P l a Y I n g a t * t h E s h o p ******* * ******* k E E p i n g +HYP: T E d o * o u n o t s e e w h a U t * l e * s E r * i t D g i v e s * * m E w e h a v e g r o * n O u p * o g e t * e r i n t h i s h o u s e s i n * * h e w O R s a ******* b o y H i S s E i m p l y * a * n o R b e a r E a s * o u c a n D t h e * i * h t o f t h * s m Y G l D l e * v i n * h i s f H a c e * B o r d e a r h e h a s n o a m u s * m e n * e * c e p * ******* t h i C B l a * * n g a t D t h * s h o p S k S C p i n g +Eval: I I D I D D S D S D D I D I D D D D I S D I I I D D S I D I D D D I S S D D I D S D D D D D S S D D I D I I I S S + +Speaker sentences 314: mls_eng_000306 #utts: 1 +id: (mls_eng_000306-mls_eng_000306) +Scores: (#C #S #D #I) 130 19 29 5 +REF: i t i s A N e B U L O U s b o D y r e v * O l V i n g I n A N e L l I p T i c a l o r B I T O F g r e A T E L o n g a t i o n * l o v e l o v e * l o v e W i L l n o t b e t h e w o U n d o f c u p i D b u T t h e M a N i f * E s t a t i o n o f * U N I v e r s A l R e P r O d u c t I V e i N s t i n c T s +HYP: i t i s ******* * D e * V I E S s b o * y r e v E A l D i n g * n * * Y e * l * p * i c a l o r * * * ******* * * g r e * Y U W o n g a t i o n I l o v e l o v e D l o v e ******* * i * l n o t b e t h e w o * n d o f c u p i T b u * t h e * a D i f T H s t a t i o n o f E G E v e r s * l * e * r * d u c t * O e i * s t i n c E s +Eval: D D S D S S S S D I S S D D D S D D D D D D D D D D S S S I I D D D D S D D S I S I S S S D D D D D S D S + +Speaker sentences 315: mls_eng_000307 #utts: 1 +id: (mls_eng_000307-mls_eng_000307) +Scores: (#C #S #D #I) 183 22 28 8 +REF: s h A r p l y a s h e s h O o k h a n D s W i t H h e r * g o d b L e S s y O u m * y d E a R c h * i L D t h e b i s h o p s a i d w H e n s h e K I S s e D h i m a n d h i s l i p s m o V E d A f ******* t e r w A r d f o r * s o m e s E c * O n D s a s i f h e w e r E i n p r * a Y e R H E R m O t h e R f o l l o W e D h e r * o u T o f t h E R O o m a n d t H e n s i l E n C E s e T t L e D +HYP: s h O r p l y a s h e s h * o k h a n * s * i t * h e r O g o d b * e * s y * u ******* m A y d Y a T c h A i * * t h e b i s h o p ******* s a i d w * e n s h e * C E s e * h i m a n d h i s l i p s m o * R d O f t e r w O r d f o r E s o m e s I c K E n T s a s ******* i f h e w e r * i n ******* p r E a R e * I U D m U t h e * O f o l l o R e * h e r E o u L o f t h * * * o m a n d t I e n s i l A n * * s e * t * e L +Eval: S D D D D I D D D D I S S I D D D D D S S D D S S I S I S I S S D D D I S D S S S S D S S D I S D D D S S D D D D S + +Speaker sentences 316: mls_eng_000308 #utts: 1 +id: (mls_eng_000308-mls_eng_000308) +Scores: (#C #S #D #I) 118 8 12 12 +REF: f o l l O W e d h i m s t e a * l t h I l y * * a n d W h e N H E w a s I n a s t O O p i n g p o s T U r e * f i L l i n g h i s b * u c k e * t c a m e u p * b e h * i N d h i m a n d p l u n * G e d a * * l o n g K n i f E I n ******* t o H i s n E c k +HYP: f o l l * A e d h i m s t e a E l t h E l y H E a n d * h e * * W w a s A n a s t * * p i n g p o s * * r e R f i * l i n g h i s b O u c k e A t c a m e u p T b e h E i E d h i m a n d p l u n C H e d a N D l o n g * n i f * * n t o O i s n A c k +Eval: D S I S I I D D D S S D D D D I D I I I I S I S I I D D D I S S + +Speaker sentences 317: mls_eng_000309 #utts: 1 +id: (mls_eng_000309-mls_eng_000309) +Scores: (#C #S #D #I) 190 15 18 12 +REF: * s a i * t h c H e r s i a s d o E s * n o t j u p I t e r d i s t r i b u T e * t o T h e g o * d S t h e I R p r O p o r * t i o n a n d d i v i d e n D s p a r i n g l y a n d s e v e r a l l y a s a g A m e M N O n d i D t o h i s c o M m a n d E r s w h e n h i s g U e * s t s D r a n K t o o n e a n o t h e r i * f ******* * c * H E r s i A s q u O T H C l e O d e m * u s a s y o u n A r r a t E +HYP: T s a i S t h c K e r s i a s d o U s T n o t j u p E t e r d i s t r i b u * e T t o * h e g o G d * t h e * * p r E p o r S t i o n a n d d i v i d e n T s p a r i n g l y a n d s e v e r a l l y a s a g m e * * * n ******* d i * ******* t o h i s c o * m a n d * r s w h e n h i s g * e A s t s T r a n G t o o n e a n o t h e r i V f O c K O U r s i U s q u * L S K l e * d e m I u s a s y o u ******* n E r r a t * +Eval: I I S S I S D I D I D D D S I S S D D D D D D D D D I S S I I I I S S S D S S S D I D S D + +Speaker sentences 318: mls_eng_000310 #utts: 1 +id: (mls_eng_000310-mls_eng_000310) +Scores: (#C #S #D #I) 136 12 36 4 +REF: * a n D w h E r E n o n E S h A L l D A r E r E s t r a I n U S W E c a N m E e t a g a i n i n * T h o U G h t s o t h E R E s n o * u s e I n w E e p i n g b e A r A c h E e r F u l s p i r I t s t i l L n e v e r d o u B T t h A T f a t E i s K E e p i n g F u * t U r E g o o d f o r p r e s e n T i L l +HYP: E a n * w h * r * n o n * * h * U l * E r * r * s t r a * n * A T O c a * ******* m * e t a g a i n i n F B h o * * h t s o t h * * I s n o O u s e ******* * n w * e p i n g b e * r ******* * c h * e r * u l s p i r * t s t i l E n e v e r d o u * * t h * E f a t * ******* i s * C e p i n g P u C t E r * g o o d f o r p r e s e n * i * l +Eval: I D D D D D D S D S D D D D S S S D D D I S D D D D S I D D D D D D D D D S D D D S D D D S S I S D D D + +Speaker sentences 319: mls_eng_000311 #utts: 1 +id: (mls_eng_000311-mls_eng_000311) +Scores: (#C #S #D #I) 165 12 23 11 +REF: a n d T o b e ******* c o m e t h e r e c * O r d o f w h a t p e O p l E H a V e d o n E i N t h e I r m o r E a m i A b l e m o M e n t s t h e r e c * O r d o f t h E c o n * q u e s t ******* s O F p e A C e h o w m e n * h a v e l i v e d a n d l * a B O r E d d * u g a N D b U i l t H E W n a n D C l * e A r e d g a r d E N e d a N D r e * f o r e * s t +HYP: a n d * o b e c o m e t h e r e c K E r d o f w h a t p e * p l * * a * e d o n * i * t h e * r m o r * a m i U b l e m o * e n t s t h e r e c K E r d o f ******* t h * c o n C q u e s t s A T p e * S e h o w m e n D h a v e l i v e d a n d l E a V E r * d d O u g a * T b * i l t * * U n a n * * l I e * r e d g a r d * I e d a * T r e A f o r e R s t +Eval: D I I S D D D D D D D D S D I S D D I I S S D S I I S S D I D S D D D S D D I D D S D S I I + +Speaker sentences 320: mls_eng_000312 #utts: 1 +id: (mls_eng_000312-mls_eng_000312) +Scores: (#C #S #D #I) 170 25 44 11 +REF: t h e L o W f l Y i n g o F t H e s W A L l O W s B e t o * k E n s r a i n a s w E L l a S A n y U n ******* s e A s O n a b l e d a n C i n g o f m i D g E s i n t h e e v e * n i n g s o R E c o R n S * o n T H e f E e t a n d R H E U M A t i s M i n * t h e J o i n T s a R E d i R E f u l P r e c * U r * s O R s t h e l E A V e s a R E a l l a ******* t r E m b l e b * e ******* f o r E t h E a * P p r o A c H O F t H u n d e r +HYP: t h e * o * f l * i n g o * t * e s * * O l * A s P e t o C k I n s r a i n a s w * I l ******* a T * n y * n s e * s I n a b l e d a n S i n g o f m i * g I s i n ******* t h e e v e I n i n g s o * * ******* c o * n * S o n ******* * D e f * e t a n d ******* * I N G O R t i s * ******* i n G t h e * o i n * s a * D d i * O f u l * r e c O I r S s I S s t h e l * * * e s T a * T a l l a t r * m b l e b O e f o r * t h * ******* a T p r o W c * ******* * E t * u n d e r +Eval: D D D D D D D S D S S I S D S D S D D I D S S D S D I D D D D D I D D S D D D S S S S S D D I D D D S D S D I S I S S D D D S D S I D I I D D D I S S D D D S D + +Speaker sentences 321: mls_eng_000313 #utts: 1 +id: (mls_eng_000313-mls_eng_000313) +Scores: (#C #S #D #I) 160 23 20 14 +REF: w a s s t o r * m * * e D G e n E r A l * D a m ******* p I e ******* R r e w a s K i l l E D g e n e r A l c U s ******* t i * n E w a s b l a m e d a n D i n ******* d E e d I s n o W c o m E t o p a r i s t O G I v E E x * P l A n a t i o n s a g A i n s T a l l w h i C h t h e m o u n t A I n a n D * * A t r o C i o u s m * A R a T m u s t * e v E n m a k e h E a D a s T h e Y c a n +HYP: w a s s t o r E m N G e * J e n * r * l E T a m p * e A r e w a s C i l l * * g e n e r * l c O s t i E n G w a s b l a m e d a n * i n d * e d E s n o B c o m * t o p a r i s ******* t H D E v * I C x S E l * n a t i o n s a g * i n s E a l l w h i * h t h e m o u n t * O n a n * H E t r o T i o u s m O E a R m u s t D e v O n m a k e h * a L a s * h e * c a n +Eval: I I I D S D D I S I D I S S D D D S I I S D I D S S D D S S S D S S I S D D S D D S D I I S S I S S S I S D S D D + +Speaker sentences 322: mls_eng_000314 #utts: 1 +id: (mls_eng_000314-mls_eng_000314) +Scores: (#C #S #D #I) 134 12 14 13 +REF: t h e m o m e n T w a s f e a R f u l a m i G H t I E R * f o E h a d n e v e r s w * u n g t h e b A T t l ******* E a * X e o v e r h i m * * b u * ******* t * * h o P e n e r v e d h i s a R M f o r a d e s p E r A t E b l o W a n d t e ******* c u * m s e H F E l L p r o s t r a * t E b e f o r E h i m +HYP: t h e m o m e n * w a s f e a V f u l a m i * * t * * Y O f o * h a d n e v e r s w H u n g t h e b * U t l L a C K e o v e r h i m E N b u T t H E h o B e n e r v e d h i s a N E f o r a d e s p * r E t * b l o * a n d t e c u O m s e * ******* R U l E p r o s t r a I t * b e f o r * h i m +Eval: D S D D D D S I D I D S I S I S I I I I I I S S S D S D D I I D D S S S I D D + +Speaker sentences 323: mls_eng_000315 #utts: 1 +id: (mls_eng_000315-mls_eng_000315) +Scores: (#C #S #D #I) 110 15 16 6 +REF: t h E n t h e w i n D s t o P P E D t h e C l O U D s t U R n E d d A r k * A n d n i g H T c a m e o n l I K e i n * k m y o l d c o t ******* T o n * * Q u i l t w a s c o * l d a s i R O n m y s w E e t s O n t o S s E D i n h i s s L e e P +HYP: t h I n t h e w i n * s t o * * U T t h e G l * E R s ******* t * A n * d d * r k H n d n i g * * c a m e o n l * A e i n G k m y o l d c o t I o n D C O u i l t w a s c o L l d a s i * * n m y s w * e t s U n t o U s * T i n h i s s C e e * +Eval: S D D D S S S D S S D D S D D I S D D D S I I S I I S I D D D S S D S S D + +Speaker sentences 324: mls_eng_000316 #utts: 1 +id: (mls_eng_000316-mls_eng_000316) +Scores: (#C #S #D #I) 174 14 31 5 +REF: y o u m a y d O a s Y o u p l E A S e t o w O r K o F f Y o u r i R r I t a t i o n t o K E e p U p y o u r f A n a * t i C i s m y o u A R e w e L l O F f y o u n E e d n o t m i N d t h e c o * s t t h e p O O r * d O n o t w A n T t O s t a n d i n y o u r w a y b u t y o U i n ******* s i s t * o n t h e I R s U B m i T t i n g T O Y o U r c o m p U l S i o n +HYP: y o u m a y d * a s * o u p l * * * e ******* t o w E r E o * f * o u r i * r * t a t i o n t o * C e p * p y o u r f E n a T t i * i s m y o u * H e w e * l * A f y o u n * e d n o t m i * d t h e c o U s t t h e p * A r E d U n o t w O n * ******* t E s t a n d i n ******* y o u r w a y b u t y o * i n s i s t D o n t h e * * s O I m i * t i n g ******* * * * o * r c o m p A l T i o n +Eval: D D D D D D S S D D D D D S D S I D D S D D S D D I D S I S S D D S D D I I D D S S D D D D D D S S + +Speaker sentences 325: mls_eng_000317 #utts: 1 +id: (mls_eng_000317-mls_eng_000317) +Scores: (#C #S #D #I) 182 24 27 21 +REF: H e w a s b r e d b y * * r e v * * * * * G a s ******* n * * * e Y D b e i n g b y o t h * m A n e s I x * f o U r t W o t W o * h E d W i * G H E w a s b o R n * I n m a R c h E I G H t E E n s e v e n t y ******* n i n E a n d h E w a s t h e o n l y s U R V i v O r o f A l I T t e r o f f i f t E e n i t w a s O n t h i S A C c o u n * t t h a t h e W a s C A L L E d s a * f E I n * c o l O r a n d m a r * k i n g s +HYP: W e w a s b r e d b y A E r e v E R N T E R Y a C s n I H T e * T b e i n g b y o t h E m E n e ******* s C x S f o * r t * o t * o U h I d * i C K L Y w a s b o * n E A n m a * c h * * * A t * I n s e v e n t y n i n * a n d h * w a s t h e ******* o n l y s O F L i v E r o f ******* E l * E t e r o f f i f t * e n i t w a s * n t h i * * * c o u n D t t h a t h e * a s * * O U R d s a I f * A n D c o l * r a n d m a r C k i n g s +Eval: S I I I I I I I S S S I I I I D S I S D S I D D D I S D I S S S D I S D D D D S D S I D D D S S S S D S D S D D D D D I D D D S S S I D S I D I + +Speaker sentences 326: mls_eng_000318 #utts: 1 +id: (mls_eng_000318-mls_eng_000318) +Scores: (#C #S #D #I) 141 15 27 11 +REF: * a n d w h a t h a s t e i t m a k E s T o f A L l i n t o t H e s e c * o n D t h e r E b y t h i S t i m e d i ******* a P h a n * T A s n e * e * * Z e s A C H O O m o s t a d m I r a b l E s e * * c * r e t o n t h e c o n t R A r y i t s t I r s m e n o t a w H i t w H i c h m o s t c o * N C E r N s i t H A H A H A +HYP: E a n d w h a t h a s t e i t ******* m a k * s * o ******* f * O l ******* i n t o t * e s e c I o n T t h e r * b y t h i H t i m e d i a F h a n D E R s n e S e R S e s * * G I U m o s t a d m * r a b l * s e A K c K r e t o n t h e ******* c o n t * * r y i t s t A r s m e n o t a ******* w * i t w * i c h m o s t c o D S O r E s i t ******* * * ******* * * ******* * * +Eval: I D D D D D S D D I S D S I S I S S I I I S D D S S S D D I I I D D D S D D D I S S S S D D D D D D D D D + +Speaker sentences 327: mls_eng_000319 #utts: 1 +id: (mls_eng_000319-mls_eng_000319) +Scores: (#C #S #D #I) 166 18 21 15 +REF: t h I r d l y t h a l E S s a i d w h e r E t h e C i t i Z E n * S a r e n e I t h e r * t O o r * I c * h n o r t O o p O o r * f o U r t h * l y a n ******* a c H A R s ******* i s s a i d w h e r e t h o U G H i n a l l o t h e R r e s p e c t S t h e ******* y a r E E Q U A l * Y E t v I r * ******* t U o ******* U s m E n a r E a d v a n c e d a n d v I C i o u s p e r s o * n * d e g r a d e d +HYP: t h E r d l y t h a l * * s a i d w h e r * t h e * i t i S I n C E a r e n e * t h e r E t * o ******* r E A c E h n o r t * o p * o r E f o * r t h I l y a n a c * * O s i s s a i d w h e r e t h o * * * i n a l l o t h e * ******* r e s p e c t E t h e y O a r R * * C O l I H A t v E r E t * o H s m I n a r * a d v a n c e d a n d v E S i o u s p e r s o E n T d e g r a d e d +Eval: S D D D D S S I S D I D D I S I D D I D I I D D S I D D D D D S I S S D D S S I S S S I I D I S S D S S I I + +Speaker sentences 328: mls_eng_000320 #utts: 1 +id: (mls_eng_000320-mls_eng_000320) +Scores: (#C #S #D #I) 161 13 20 11 +REF: t h e K i n d l y f r a n K i s s Y m p A t h e t i * C e v E r y d a y h e p a S S E s n o t E s b e t w E e n u s a n d I t r * y t o E n ******* c O U r * A g E r u S s E L l h e w I L l i m ******* p r o v e i a S s u * r E h i m h i s t i m e i s s * H o * r t a n d f r e * S h a i r a n D l i b E r t y w I l l s o o n r e ******* s t * o r E h i m +HYP: t h e C i n d l y f r a n G i s s I m p * t h e t i N K e v * r y d a y h e p a * * * s n o t A s b e t w * e n ******* u s a n d ******* * t r I y t o * n c K E r I R g S r u * s * T l h e w * * l i m p r o v e i a * s u O r * h i m h i s t i m e i s s O U o U r t a n d f r e A C h a i r ******* a n * l i b U r t y w * l l s o o n r e s t A o r * h i m +Eval: S S S D I S D D D D S D D D D I D I S S I S S D D S D D I D I D I S I I S D D S D I I D + +Speaker sentences 329: mls_eng_000321 #utts: 1 +id: (mls_eng_000321-mls_eng_000321) +Scores: (#C #S #D #I) 192 24 13 7 +REF: t h E s E Q U e s t i o n s i t i s n o W e v i d e n t m a y f r e ******* Q U e n t l y b e A n S W e r e d w I T H e q u A l p r o p r i E t y i n o P p O s ******* i t E w a * Y s a n d i f t h e r E b e a n y O C c a S i O n s O n * w h i c h t h e y c a n * b e A n S W e r e d o n l y i n o n e * w a y t h e A n * s W e r w i L l d e p e n d U p o n t h e n a t U r E o f t h e o C c a S i o n +HYP: t h I s * C R e s t i o n s i t i s n o L e v i d e n t m a y f r e K C e n t l y b e U n C T e r e d w * E S e q u L l p r o p r i * t y i n ******* o * p * s i t * w a S E s a n d i f t h e r * b e a n y A c a * i * n s U n G w h i c h t h e y c a n D b e U n C T e r e d o n l y i n o n e N w a y t h e U n C s * e r w i * l d e p e n d A p o n t h e n a t E r * o f t h e o E c a T i o n +Eval: S D S S S I S S S S S D S S S D D D D I D I S D S S D D S I I S S S I S I D D S S D S S + +Speaker sentences 330: mls_eng_000322 #utts: 1 +id: (mls_eng_000322-mls_eng_000322) +Scores: (#C #S #D #I) 160 16 31 7 +REF: i n h i s n o * t E b o r E t h e M i n s t r E l s y s e c O n D e d I T i o n E I G H t E E n o H E I G H t * s c o T t s A Y s t h E b a L l A d w a s t * A k E n * d o w n F r O m a n o l d W o m A n s * r E c i t a t i o n a t t h E A l s T o n m O o r l e a d m i n E s b y t h e a g e n T t h e r E a n d * S e n t b y h I m t o s U r t ******* e E s +HYP: i n h i s n o H t * b o r * t h e * i n s t r * l s y s e c K n * ******* e d * * i o n * * * A t Y O n ******* o W * * * A t E s c o U t s * E s t h * b a * l E d w a s t C E k I n D d o w n * r * m ******* a n o l d * o m E n s F r * c i t a t i o n a t t h * * l s * o n m * o r l e a d ******* m i n * s b y t h e a g e n D t h e r * a n d C I e n t b y h * m t o s E r t e A s +Eval: I D D D D S D D D D D D D S S S D S D D D S I S D S D D S I S S I D D D D S I D D D D D D D S D I S D S I S + +Speaker sentences 331: nchlt_eng_001588 #utts: 1 +id: (nchlt_eng_001588-nchlt_eng_001588) +Scores: (#C #S #D #I) 15 5 1 3 +REF: c H r I s t i A n * t h e o l O g I A n ******* s * +HYP: c * r E s t i O n T t h e o l I g E O n s H +Eval: D S S I S S S I I + +Speaker sentences 332: nchlt_eng_001589 #utts: 1 +id: (nchlt_eng_001589-nchlt_eng_001589) +Scores: (#C #S #D #I) 13 5 3 1 +REF: o * B t a I N e a g L E f E A t h e R s +HYP: o P t a D E e a g * O f * I t h e * s +Eval: I S S S D S D S D + +Speaker sentences 333: nchlt_eng_001590 #utts: 1 +id: (nchlt_eng_001590-nchlt_eng_001590) +Scores: (#C #S #D #I) 20 5 3 1 +REF: * E l E m e n t A R Y s p e C i a l f U n C t i o n s +HYP: A l A m e n t * * O s p e S i a l f O n * t i o n s +Eval: I S S D D S S S D + +Speaker sentences 334: nchlt_eng_001591 #utts: 1 +id: (nchlt_eng_001591-nchlt_eng_001591) +Scores: (#C #S #D #I) 18 7 3 1 +REF: G E o r G e w a s H i n G T O n * u n I v E r S i t y +HYP: * T o r D e w a s * i n * A n N u n E v O r E i t y +Eval: D S S D D S S I S S S + +Speaker sentences 335: nchlt_eng_001592 #utts: 1 +id: (nchlt_eng_001592-nchlt_eng_001592) +Scores: (#C #S #D #I) 17 1 4 8 +REF: s C i e N C E f i c t i o n n o v E l * s ******* * * * * * * +HYP: s * i e * * S f i c t i o n n o v * l E s P R V H A N +Eval: D D D S D I I I I I I I I + +Speaker sentences 336: nchlt_eng_001593 #utts: 1 +id: (nchlt_eng_001593-nchlt_eng_001593) +Scores: (#C #S #D #I) 10 2 1 1 +REF: c o A s t * h i P H o p +HYP: c o * s t D h i B P o p +Eval: D I S S + +Speaker sentences 337: nchlt_eng_001594 #utts: 1 +id: (nchlt_eng_001594-nchlt_eng_001594) +Scores: (#C #S #D #I) 21 3 1 6 +REF: i n * v e r s E l * a * ******* P l a * C e t r A n s f o r m * +HYP: i n D v e r s * l E a T B l a Y e t r O n s f o r m E +Eval: I D I I I S I S S I + +Speaker sentences 338: nchlt_eng_001595 #utts: 1 +id: (nchlt_eng_001595-nchlt_eng_001595) +Scores: (#C #S #D #I) 14 4 0 1 +REF: f r E n C h p r o t E s t a n * T s +HYP: f r I n G h p r o t I s t a n C E s +Eval: S S S I S + +Speaker sentences 339: nchlt_eng_001596 #utts: 1 +id: (nchlt_eng_001596-nchlt_eng_001596) +Scores: (#C #S #D #I) 10 5 1 10 +REF: A f * ******* g H A n a I R f o r * ******* * * ******* * * ******* C e +HYP: O f E g O U n a * Y f o r S S H H D K e +Eval: S I I S S D S I I I I I I I I S + +Speaker sentences 340: nchlt_eng_001597 #utts: 1 +id: (nchlt_eng_001597-nchlt_eng_001597) +Scores: (#C #S #D #I) 25 3 2 2 +REF: h e * r o E s i n m Y T H o l O g y a n d l e * g e n d +HYP: h e A r o * s i n m * O S o l I g y a n d l e A g e n d +Eval: I D D S S S I + +Speaker sentences 341: nchlt_eng_001598 #utts: 1 +id: (nchlt_eng_001598-nchlt_eng_001598) +Scores: (#C #S #D #I) 14 1 4 6 +REF: b u * s I n E S s c l a S s s e A t ******* * * * * +HYP: b u I s n * * s c l a * s s e * t N D N E +Eval: I S D D D D I I I I I + +Speaker sentences 342: nchlt_eng_001599 #utts: 1 +id: (nchlt_eng_001599-nchlt_eng_001599) +Scores: (#C #S #D #I) 12 3 0 5 +REF: c l * U B p l a y c h ******* A r t * ******* * +HYP: c l A I D p l a y c h O r t E E +Eval: I S S I S I I I + +Speaker sentences 343: nchlt_eng_001600 #utts: 1 +id: (nchlt_eng_001600-nchlt_eng_001600) +Scores: (#C #S #D #I) 21 2 0 3 +REF: p o s i * ******* t r O n s w e r e r * E p o r t e d +HYP: p o s i Y t r I n s w e r e r O p o r t e d +Eval: I I S I S + +Speaker sentences 344: nchlt_eng_001601 #utts: 1 +id: (nchlt_eng_001601-nchlt_eng_001601) +Scores: (#C #S #D #I) 13 1 1 2 +REF: O l d v i c * t h e a t R e * +HYP: A l d v i c K t h e a t * e R +Eval: S I D I + +Speaker sentences 345: nchlt_eng_001602 #utts: 1 +id: (nchlt_eng_001602-nchlt_eng_001602) +Scores: (#C #S #D #I) 12 4 1 5 +REF: o r ******* t h * O d o * * X m o n A R c H s * +HYP: o r t h E d o C K S m o n * O c K s E +Eval: I I S I I S D S S I + +Speaker sentences 346: nchlt_eng_001603 #utts: 1 +id: (nchlt_eng_001603-nchlt_eng_001603) +Scores: (#C #S #D #I) 19 0 2 3 +REF: n a t i o n s ******* * m e m b E r s t a t e ******* s +HYP: n a t i o n s W m e m b * r ******* s t a t e s +Eval: I I D D I + +Speaker sentences 347: nchlt_eng_001604 #utts: 1 +id: (nchlt_eng_001604-nchlt_eng_001604) +Scores: (#C #S #D #I) 8 5 1 3 +REF: f I F A * W o ******* * R l d c U p +HYP: f * H E T H o W I l d c O p +Eval: D S S I S I I S S + +Speaker sentences 348: nchlt_eng_001605 #utts: 1 +id: (nchlt_eng_001605-nchlt_eng_001605) +Scores: (#C #S #D #I) 12 5 3 3 +REF: c r E W s * r * E s * C u E e F f O R t s +HYP: c r * O s E r I C s K Y u * e * f E I t s +Eval: D S I I S I S D D S S + +Speaker sentences 349: nchlt_eng_001606 #utts: 1 +id: (nchlt_eng_001606-nchlt_eng_001606) +Scores: (#C #S #D #I) 17 7 3 3 +REF: a c t U A l f I l m * m * I c R O s ******* c o p i C A L L y +HYP: a c t H O l f O l m E m A R c K E s c o p i * * * T y +Eval: S S S I I S S S I D D D S + +Speaker sentences 350: nchlt_eng_001607 #utts: 1 +id: (nchlt_eng_001607-nchlt_eng_001607) +Scores: (#C #S #D #I) 24 2 2 2 +REF: m * u * s i c A l g r O u p s r e E s t a b l I s h e d +HYP: m O u O s i c * l g r * u p s r e A s t a b l A s h e d +Eval: I I D D S S + +Speaker sentences 351: nchlt_eng_001608 #utts: 1 +id: (nchlt_eng_001608-nchlt_eng_001608) +Scores: (#C #S #D #I) 13 4 1 3 +REF: p r I m U s * I n T e r * p a R e s * +HYP: p r O m I s E * n e r E p a C e s E +Eval: S S I D S I S I + +Speaker sentences 352: nchlt_eng_001609 #utts: 1 +id: (nchlt_eng_001609-nchlt_eng_001609) +Scores: (#C #S #D #I) 5 9 1 1 +REF: f I l * M T E C H n I Q U E s +HYP: f O l N D S I K n * E K s +Eval: S I S S S S S D S S S + +Speaker sentences 353: nchlt_eng_001610 #utts: 1 +id: (nchlt_eng_001610-nchlt_eng_001610) +Scores: (#C #S #D #I) 16 3 4 1 +REF: t * e l E v I S i o n s e r I E s b a s E D +HYP: t O e l A v * * i o n s e r * Y s b a s * T +Eval: I S D D D S D S + +Speaker sentences 354: nchlt_eng_001611 #utts: 1 +id: (nchlt_eng_001611-nchlt_eng_001611) +Scores: (#C #S #D #I) 16 2 1 9 +REF: * ******* n * e W p o l i t i * c a * L p A r t y * * * * +HYP: H n O e * p o l i t i O c a O E p O r t y H E E E +Eval: I I I D I I S S I I I I + +Speaker sentences 355: nchlt_eng_001612 #utts: 1 +id: (nchlt_eng_001612-nchlt_eng_001612) +Scores: (#C #S #D #I) 17 3 2 3 +REF: a n c I E n t * e * g Y p T a ******* c h I e v e d +HYP: a n c H O n t D e A g O p * a c h * e v e d +Eval: S S I I S D I D + +Speaker sentences 356: nchlt_eng_001613 #utts: 1 +id: (nchlt_eng_001613-nchlt_eng_001613) +Scores: (#C #S #D #I) 14 2 2 0 +REF: f l a t m u s i C n A t U r A l +HYP: f l a t m u s i G n * t * r O l +Eval: S D D S + +Speaker sentences 357: nchlt_eng_001614 #utts: 1 +id: (nchlt_eng_001614-nchlt_eng_001614) +Scores: (#C #S #D #I) 19 5 5 10 +REF: a ******* m E r i c A n S t E c H n o l * * ******* * * * * o * * G y W r I t e R s +HYP: a m * r i c O n ******* * t I c n o l I D T O I N o L I D y * r A t e * s +Eval: I D S D D S S I I I I I I I I I S D S D + +Speaker sentences 358: nchlt_eng_001615 #utts: 1 +id: (nchlt_eng_001615-nchlt_eng_001615) +Scores: (#C #S #D #I) 13 2 4 1 +REF: d * a U G H t e R s o f B a r O n s +HYP: d O a * * * t e * s o f V a r I n s +Eval: I D D D D S S + +Speaker sentences 359: nchlt_eng_001616 #utts: 1 +id: (nchlt_eng_001616-nchlt_eng_001616) +Scores: (#C #S #D #I) 17 5 5 2 +REF: p o p U l A R t O U r * ******* i s T A T t R a c t i o n s +HYP: p o p I l * E I t W r E i s * * * t * a c t i o n s +Eval: S D S S S S I I D D D D + +Speaker sentences 360: nchlt_eng_001617 #utts: 1 +id: (nchlt_eng_001617-nchlt_eng_001617) +Scores: (#C #S #D #I) 13 2 1 2 +REF: d u T c h * w E s t i n d I a * +HYP: d u * c h E w I s t i n d E a R +Eval: D I S S I + +Speaker sentences 361: nchlt_eng_001618 #utts: 1 +id: (nchlt_eng_001618-nchlt_eng_001618) +Scores: (#C #S #D #I) 16 2 3 2 +REF: g o l d m E D a * l r e C I p i e n T s * +HYP: g o l d m * * a T l r e S p i e n * s E +Eval: D D I S S D I + +Speaker sentences 362: nchlt_eng_001619 #utts: 1 +id: (nchlt_eng_001619-nchlt_eng_001619) +Scores: (#C #S #D #I) 20 5 0 7 +REF: r * U s S i A n s o C i a l d e m ******* o c r A t ******* i c * ******* * * +HYP: r E A s H i O n s o S i a l d e m o c r E t i c K E H +Eval: I S S S S I S I I I I I + +Speaker sentences 363: nchlt_eng_001620 #utts: 1 +id: (nchlt_eng_001620-nchlt_eng_001620) +Scores: (#C #S #D #I) 18 3 2 3 +REF: a ******* m E r i * c A n f I l m * p r o d u C e R s +HYP: a m I r i Y c O n f * l m E p r o d u S e * s +Eval: I S I S D I S D + +Speaker sentences 364: nchlt_eng_001621 #utts: 1 +id: (nchlt_eng_001621-nchlt_eng_001621) +Scores: (#C #S #D #I) 20 2 2 1 +REF: f r e ******* e s o f t W A r E f O u n d a t i o n +HYP: f r e e s o f t * E r Y f * u n d a t i o n +Eval: I D S S D + +Speaker sentences 365: nchlt_eng_001622 #utts: 1 +id: (nchlt_eng_001622-nchlt_eng_001622) +Scores: (#C #S #D #I) 15 1 6 2 +REF: r O Y A l * D r A m a t i * c t h e a t R E +HYP: r * * I l E * r * m a t i O c t h e a t * * +Eval: D D S I D D I D D + +Speaker sentences 366: nchlt_eng_001623 #utts: 1 +id: (nchlt_eng_001623-nchlt_eng_001623) +Scores: (#C #S #D #I) 9 5 1 4 +REF: * E D I b l e m o L l U s * C s ******* * +HYP: I T A b l e m o * l O s K s H +Eval: I S S S D S I S I I + +Speaker sentences 367: nchlt_eng_001624 #utts: 1 +id: (nchlt_eng_001624-nchlt_eng_001624) +Scores: (#C #S #D #I) 19 4 1 2 +REF: f e a t U R e s i n ******* C l U d E b e a c h e * s +HYP: f e a t C H e s i n T l W d * b e a c h e R s +Eval: S S I S S D I + +Speaker sentences 368: nchlt_eng_001625 #utts: 1 +id: (nchlt_eng_001625-nchlt_eng_001625) +Scores: (#C #S #D #I) 21 2 2 1 +REF: o * X f o r D d i c t i o n A r y c h a n g e D +HYP: o C f o r * d i c t i o n * r y c h a n g e T +Eval: I S D D S + +Speaker sentences 369: nchlt_eng_001626 #utts: 1 +id: (nchlt_eng_001626-nchlt_eng_001626) +Scores: (#C #S #D #I) 16 5 3 4 +REF: s a l ******* U K I p E r * s i A n * G r E y ******* h O u n d +HYP: s a l C O W p I r I s i O n D * r * y h * u n d +Eval: I S S S S I S I D D I D + +Speaker sentences 370: nchlt_eng_001627 #utts: 1 +id: (nchlt_eng_001627-nchlt_eng_001627) +Scores: (#C #S #D #I) 13 7 0 0 +REF: p r I M E m I n i s t e r K E v I n +HYP: p r O W N m O n i s t e r C I v E n +Eval: S S S S S S S + +Speaker sentences 371: nchlt_eng_001628 #utts: 1 +id: (nchlt_eng_001628-nchlt_eng_001628) +Scores: (#C #S #D #I) 11 3 3 4 +REF: l a n G U A g e s o f * I r ******* * * A Q +HYP: l a n * * * g e s o f Y U r O C K E +Eval: D D D I S I I I S S + +Speaker sentences 372: nchlt_eng_001629 #utts: 1 +id: (nchlt_eng_001629-nchlt_eng_001629) +Scores: (#C #S #D #I) 15 2 1 2 +REF: s o U t h e a s t E n g l A n d ******* * +HYP: s o * t h e a s t I n g l O n d T +Eval: D S S I I + +Speaker sentences 373: nchlt_eng_001630 #utts: 1 +id: (nchlt_eng_001630-nchlt_eng_001630) +Scores: (#C #S #D #I) 10 5 0 5 +REF: n * E W l i n e * C I n E m a * * * +HYP: n O U R l i n e D S E n O m a R T H +Eval: I S S I S S S I I I + +Speaker sentences 374: nchlt_eng_001631 #utts: 1 +id: (nchlt_eng_001631-nchlt_eng_001631) +Scores: (#C #S #D #I) 17 4 3 4 +REF: e ******* * Q u A l C r e d i t o P p o R t U n ******* I t * y +HYP: e C O u * l K r e d i t o * p o * t O n N t S y +Eval: I I S D S D D S I S I + +Speaker sentences 375: nchlt_eng_001632 #utts: 1 +id: (nchlt_eng_001632-nchlt_eng_001632) +Scores: (#C #S #D #I) 17 1 0 0 +REF: s o u t h e a s t E n g l a n d +HYP: s o u t h e a s t I n g l a n d +Eval: S + +Speaker sentences 376: nchlt_eng_001633 #utts: 1 +id: (nchlt_eng_001633-nchlt_eng_001633) +Scores: (#C #S #D #I) 3 0 0 5 +REF: m a y * * * * * +HYP: m a y W E H T E +Eval: I I I I I + +Speaker sentences 377: nchlt_eng_001634 #utts: 1 +id: (nchlt_eng_001634-nchlt_eng_001634) +Scores: (#C #S #D #I) 20 2 1 7 +REF: r e c o * r d h E t ******* a t ******* * ******* * m * D e s c r i B e s +HYP: r e c o L r d h A t a t E O m E * e s c r i P e s +Eval: I S I I I I I I D S + +Speaker sentences 378: nchlt_eng_001635 #utts: 1 +id: (nchlt_eng_001635-nchlt_eng_001635) +Scores: (#C #S #D #I) 26 2 2 4 +REF: m u s i c a l g r O U p * s f R o m c a l I f o r n i * a ******* * +HYP: m u s i c a l g r * E p E s f * o m c a l O f o r n i E a H +Eval: D S I D S I I I + +Speaker sentences 379: nchlt_eng_001636 #utts: 1 +id: (nchlt_eng_001636-nchlt_eng_001636) +Scores: (#C #S #D #I) 13 3 1 0 +REF: m a i n b A T t l e t A n K s +HYP: m a i n b * E t l e t I n C s +Eval: D S S S + +Speaker sentences 380: nchlt_eng_001637 #utts: 1 +id: (nchlt_eng_001637-nchlt_eng_001637) +Scores: (#C #S #D #I) 25 0 1 6 +REF: p o * * l i s * h m u s i * c * a l * i n s t r U m e n t s +HYP: p o R D l i s E h m u s i O c T a l E i n s t r * m e n t s +Eval: I I I I I I D + +Speaker sentences 381: nchlt_eng_001638 #utts: 1 +id: (nchlt_eng_001638-nchlt_eng_001638) +Scores: (#C #S #D #I) 18 5 2 5 +REF: l a n G U A g e s o f s a U d I a * ******* r * a B i a * * +HYP: l a n * W I g e s o f s a * d Y E a R r O a V i a R E +Eval: D S S D S S I I I S I I + +Speaker sentences 382: nchlt_eng_001639 #utts: 1 +id: (nchlt_eng_001639-nchlt_eng_001639) +Scores: (#C #S #D #I) 12 3 2 3 +REF: c o l d W A r t E n S i o n s ******* * * +HYP: c o l d * O r ******* t I n T i o n s E H +Eval: D S D S S I I I + +Speaker sentences 383: nchlt_eng_001640 #utts: 1 +id: (nchlt_eng_001640-nchlt_eng_001640) +Scores: (#C #S #D #I) 2 3 0 5 +REF: d U b * ******* * * * B Y +HYP: d O b E H I M H S +Eval: S I I I I I S S + +Speaker sentences 384: nchlt_eng_001641 #utts: 1 +id: (nchlt_eng_001641-nchlt_eng_001641) +Scores: (#C #S #D #I) 9 5 2 4 +REF: a n * ******* T I p o p E C l E m E n t ******* * +HYP: a n D Y p o p * ******* K l I m I n t H +Eval: I I S S D D S S S I I + +Speaker sentences 385: nchlt_eng_001642 #utts: 1 +id: (nchlt_eng_001642-nchlt_eng_001642) +Scores: (#C #S #D #I) 9 7 2 3 +REF: g E t S * * T A K E n p R i v * A t E +HYP: g I t * H E Y C O n p O i v E I t * +Eval: S D I I S S S S S I S D + +Speaker sentences 386: nchlt_eng_001643 #utts: 1 +id: (nchlt_eng_001643-nchlt_eng_001643) +Scores: (#C #S #D #I) 9 4 1 1 +REF: K i n G f e R D I n ******* a n d +HYP: C i n * f e I A n a n d +Eval: S D S S S I + +Speaker sentences 387: nchlt_eng_001644 #utts: 1 +id: (nchlt_eng_001644-nchlt_eng_001644) +Scores: (#C #S #D #I) 22 5 3 2 +REF: E l e c t R o n i c * m * u s I c A l i n s t r U M E n T s +HYP: I l e c t * o n i c K m E u s O c K l i n s t r * * O n C s +Eval: S D I I S S D D S S + +Speaker sentences 388: nchlt_eng_001645 #utts: 1 +id: (nchlt_eng_001645-nchlt_eng_001645) +Scores: (#C #S #D #I) 11 3 0 4 +REF: a g e m E l * ******* t * W A t e r * +HYP: a g e m O l D t O O R t e r N +Eval: S I I I S S I + +Speaker sentences 389: nchlt_eng_001646 #utts: 1 +id: (nchlt_eng_001646-nchlt_eng_001646) +Scores: (#C #S #D #I) 19 4 4 2 +REF: l A W r E n c E l I v e R m o r E n a * t i o n A l * +HYP: l * O r A n c T l * v e m o r * n a S t i o n * l E +Eval: D S S S D S D I D I + +Speaker sentences 390: nchlt_eng_001647 #utts: 1 +id: (nchlt_eng_001647-nchlt_eng_001647) +Scores: (#C #S #D #I) 15 2 6 1 +REF: l e A g U E b a * s E B A L l p l a y E r s +HYP: l e * g * * b a C s * * * P l p l a y A r s +Eval: D D D I D D D S S + +Speaker sentences 391: nchlt_eng_001648 #utts: 1 +id: (nchlt_eng_001648-nchlt_eng_001648) +Scores: (#C #S #D #I) 27 6 4 2 +REF: b U D d H i s * m * I n t h e a n c i E n t m e d I T e R r a n E A n +HYP: b * O d * i s O m E A n t h e a n c i O n t m e d * * e T r a n I O n +Eval: D S D I I S S D D S S S + +Speaker sentences 392: nchlt_eng_001649 #utts: 1 +id: (nchlt_eng_001649-nchlt_eng_001649) +Scores: (#C #S #D #I) 19 2 3 4 +REF: * u n i * t E d s t a t E s r e * * c o G n I Z e d +HYP: O u n i G t I d s t a t * s r e K O c o * n * S e d +Eval: I I S D I I D D S + +Speaker sentences 393: nchlt_eng_001650 #utts: 1 +id: (nchlt_eng_001650-nchlt_eng_001650) +Scores: (#C #S #D #I) 16 3 4 2 +REF: p r o p o s I T i o n A l f A L l a C I e s ******* * +HYP: p r o p o s * * i o n * l f * E l a T Y e s E +Eval: D D D D S S S I I + +Speaker sentences 394: nchlt_eng_001651 #utts: 1 +id: (nchlt_eng_001651-nchlt_eng_001651) +Scores: (#C #S #D #I) 15 7 0 3 +REF: s p e C i a l E c o n o m ******* * * I C Z o n E s +HYP: s p e T i a l A c o n o m N G E S O R o n D s +Eval: S S I I I S S S S S + +Speaker sentences 395: nchlt_eng_001652 #utts: 1 +id: (nchlt_eng_001652-nchlt_eng_001652) +Scores: (#C #S #D #I) 11 1 4 0 +REF: m a I n s t r E A m w E s t +HYP: m a * n ******* s t r * * m w I s t +Eval: D D D D S + +Speaker sentences 396: nchlt_eng_001653 #utts: 1 +id: (nchlt_eng_001653-nchlt_eng_001653) +Scores: (#C #S #D #I) 12 2 4 0 +REF: e v e N I n g r u S h h O U R s +HYP: e v e * * n g r u C h h * * L s +Eval: D D S D D S + +Speaker sentences 397: nchlt_eng_001654 #utts: 1 +id: (nchlt_eng_001654-nchlt_eng_001654) +Scores: (#C #S #D #I) 12 3 3 3 +REF: b * O F h e ******* d I T i o n s t o * O k +HYP: b Y T h ******* e d * * i o n s t o K k +Eval: I S S D I D D I S + +Speaker sentences 398: nchlt_eng_001655 #utts: 1 +id: (nchlt_eng_001655-nchlt_eng_001655) +Scores: (#C #S #D #I) 14 1 2 4 +REF: A n * T a r C t i * c * * a h a s n o +HYP: * n D a r * t i O c K E a h a s n o +Eval: D I S D I I I + +Speaker sentences 399: nchlt_eng_001656 #utts: 1 +id: (nchlt_eng_001656-nchlt_eng_001656) +Scores: (#C #S #D #I) 13 2 2 1 +REF: w E s t E n D m u s i c A l * s +HYP: w A s t I n * m u s i c * l E s +Eval: S S D D I + +Speaker sentences 400: nchlt_eng_001657 #utts: 1 +id: (nchlt_eng_001657-nchlt_eng_001657) +Scores: (#C #S #D #I) 17 8 3 2 +REF: c o n S e R v A t I V E J u d a I s ******* * m r e g A r D S +HYP: c o n * e * v I t O F D u d a Y s A m r e g O r * T +Eval: D D S S S S S S I I S D S + +Speaker sentences 401: nchlt_eng_001658 #utts: 1 +id: (nchlt_eng_001658-nchlt_eng_001658) +Scores: (#C #S #D #I) 15 2 1 3 +REF: o * ******* p E c * m e m b E r s t a t E s +HYP: o P p I c K m e m b * r s t a t D s +Eval: I I S I D S + +Speaker sentences 402: nchlt_eng_001659 #utts: 1 +id: (nchlt_eng_001659-nchlt_eng_001659) +Scores: (#C #S #D #I) 13 3 3 2 +REF: p r i M E m i n i * ******* s T E R j o H n +HYP: p r i * * m i n i S s A I D j o * n +Eval: D D I I S S S D + +Speaker sentences 403: nchlt_eng_001660 #utts: 1 +id: (nchlt_eng_001660-nchlt_eng_001660) +Scores: (#C #S #D #I) 18 0 0 2 +REF: r o * c k s f o r m i n g m o * n t +HYP: r o A c k s f o r m i n g m o U n t +Eval: I I + +Speaker sentences 404: nchlt_eng_001661 #utts: 1 +id: (nchlt_eng_001661-nchlt_eng_001661) +Scores: (#C #S #D #I) 11 4 3 2 +REF: m a * J O r l e a G U E t E A m * s +HYP: m a D G E r l e a * * K t * L m N s +Eval: I S S D D S D S I + +Speaker sentences 405: nchlt_eng_001662 #utts: 1 +id: (nchlt_eng_001662-nchlt_eng_001662) +Scores: (#C #S #D #I) 18 2 2 2 +REF: p o L l I n a t i o n m a n a g E M e n * ******* t +HYP: p o * l A n a t i o n m a n a g * H e n T t +Eval: D S D S I I + +Speaker sentences 406: nchlt_eng_001663 #utts: 1 +id: (nchlt_eng_001663-nchlt_eng_001663) +Scores: (#C #S #D #I) 10 4 3 1 +REF: f r E n c h * P H Y s i C I s t S +HYP: f r A n c h E * F I s i * S s t * +Eval: S I D S S D S D + +Speaker sentences 407: nchlt_eng_001664 #utts: 1 +id: (nchlt_eng_001664-nchlt_eng_001664) +Scores: (#C #S #D #I) 19 4 1 1 +REF: h i G H e R c o m p r e S S i o n * r a t i o +HYP: h i A R e * c o m p r e I T i o n D r a t i o +Eval: S S D S S I + +Speaker sentences 408: nchlt_eng_001665 #utts: 1 +id: (nchlt_eng_001665-nchlt_eng_001665) +Scores: (#C #S #D #I) 24 2 4 3 +REF: r e ******* c o * r d I n g i n d U s t R y A S s o * c I a t i o n +HYP: r e c o A r d * n g i n d E s t * y * * s o U c H a t i o n +Eval: I I D S D D D I S + +Speaker sentences 409: nchlt_eng_001666 #utts: 1 +id: (nchlt_eng_001666-nchlt_eng_001666) +Scores: (#C #S #D #I) 13 6 2 5 +REF: * * D p G S o * n ******* l i n E m a g A Z i * n E +HYP: T E A p A D E o U n l i n * m a g O S i A n * +Eval: I I S S S S I I D S S I D + +Speaker sentences 410: nchlt_eng_001667 #utts: 1 +id: (nchlt_eng_001667-nchlt_eng_001667) +Scores: (#C #S #D #I) 17 5 2 6 +REF: h i p H o p r e c O R d p * r o ******* * D u c * e * * R s +HYP: h i p ******* * o p E r e c U L d p O r o T O u c S e N S s +Eval: D D S S S I I I S I I I S + +Speaker sentences 411: nchlt_eng_001668 #utts: 1 +id: (nchlt_eng_001668-nchlt_eng_001668) +Scores: (#C #S #D #I) 16 3 2 3 +REF: f i ******* n i * t E s t a t E m A C h I n e * s +HYP: f i n i G t * s t a t * m U S h E n e N s +Eval: I I D D S S S I + +Speaker sentences 412: nchlt_eng_001669 #utts: 1 +id: (nchlt_eng_001669-nchlt_eng_001669) +Scores: (#C #S #D #I) 16 0 1 4 +REF: w * i d E l y * u s e d l o c * a l * +HYP: w H i d * l y S u s e d l o c K a l D +Eval: I D I I I + +Speaker sentences 413: nchlt_eng_001670 #utts: 1 +id: (nchlt_eng_001670-nchlt_eng_001670) +Scores: (#C #S #D #I) 17 6 1 1 +REF: n o r T h * A m E R I c A N c o n t i n e n t +HYP: n o r h E * m A Y c O D c o n t i n e n t +Eval: S I D S S S S S + +Speaker sentences 414: nchlt_eng_001671 #utts: 1 +id: (nchlt_eng_001671-nchlt_eng_001671) +Scores: (#C #S #D #I) 17 3 4 0 +REF: a f r I c A n A m e r i c A n r A P p e R s +HYP: a f r * c O n * m e r i c O n r * E p e * s +Eval: D S D S D S D + +Speaker sentences 415: nchlt_eng_001672 #utts: 1 +id: (nchlt_eng_001672-nchlt_eng_001672) +Scores: (#C #S #D #I) 22 1 4 1 +REF: t h r e A t e n E d m I l i * t A r Y a c t i o n s +HYP: t h r e * t e n * d m E l i G t * r * a c t i o n s +Eval: D D S I D D + +Speaker sentences 416: nchlt_eng_001673 #utts: 1 +id: (nchlt_eng_001673-nchlt_eng_001673) +Scores: (#C #S #D #I) 8 0 0 19 +REF: * * * ******* t h e w o r d ******* * ******* * * * * * * * * * * * * +HYP: U H T t h e w o r d F I N I N T N E E E E E E +Eval: I I I I I I I I I I I I I I I I I I I + +Speaker sentences 417: nchlt_eng_001674 #utts: 1 +id: (nchlt_eng_001674-nchlt_eng_001674) +Scores: (#C #S #D #I) 21 8 7 2 +REF: * * A t o m i C m O l e C U l A R A n D o p t I c a l P H Y s i c S +HYP: T H E t o m i K m * l e * I l * * ******* E n * o p t O c a l * F I s i c E +Eval: I I S S D D S D D D S D S D S S S + +Speaker sentences 418: nchlt_eng_001675 #utts: 1 +id: (nchlt_eng_001675-nchlt_eng_001675) +Scores: (#C #S #D #I) 3 0 1 3 +REF: * ******* t o W n * +HYP: E t o * n E +Eval: I I D I + +Speaker sentences 419: nchlt_eng_001676 #utts: 1 +id: (nchlt_eng_001676-nchlt_eng_001676) +Scores: (#C #S #D #I) 3 3 0 1 +REF: m A r ******* C E l +HYP: m Y r S O l +Eval: S I S S + +Speaker sentences 420: nchlt_eng_001677 #utts: 1 +id: (nchlt_eng_001677-nchlt_eng_001677) +Scores: (#C #S #D #I) 18 2 3 4 +REF: c o n ******* s t r U c t n * * E W r a I l ******* g a U g e +HYP: c o n s t r * c t n O U O H r a * l g a * g e +Eval: I D I I S S D I D + +Speaker sentences 421: nchlt_eng_001678 #utts: 1 +id: (nchlt_eng_001678-nchlt_eng_001678) +Scores: (#C #S #D #I) 16 7 2 1 +REF: p A U l I e X c l U S i o n P r i n c * I P l e +HYP: p O R l Y e c l * W i o n * r i n c S A B l e +Eval: S S S S D S D I S S + +Speaker sentences 422: nchlt_eng_001679 #utts: 1 +id: (nchlt_eng_001679-nchlt_eng_001679) +Scores: (#C #S #D #I) 17 3 1 2 +REF: h * * U E p o r t r a y d I F f e r e n t +HYP: h C L O W p o r t r a y d * E f e r e n t +Eval: I I S S D S + +Speaker sentences 423: nchlt_eng_001680 #utts: 1 +id: (nchlt_eng_001680-nchlt_eng_001680) +Scores: (#C #S #D #I) 13 2 4 2 +REF: S s o v I e * t d I S s i d e n * T s +HYP: * ******* s o v * e A t d * E s i d e n C E s +Eval: D D D I D S I S + +Speaker sentences 424: nchlt_eng_001681 #utts: 1 +id: (nchlt_eng_001681-nchlt_eng_001681) +Scores: (#C #S #D #I) 23 4 1 6 +REF: s i g n A l * t r A n * * S d u c t i o n * p * A t H w a y * s +HYP: s i g n E l E t r O n C E T d u c t i o n D p O L t * w a y E s +Eval: S I S I I S I I S D I + +Speaker sentences 425: nchlt_eng_001682 #utts: 1 +id: (nchlt_eng_001682-nchlt_eng_001682) +Scores: (#C #S #D #I) 11 2 3 0 +REF: n E W b o r n m E s s i A H +HYP: n O U b o r n m * s s i * * +Eval: S S D D D + +Speaker sentences 426: nchlt_eng_001683 #utts: 1 +id: (nchlt_eng_001683-nchlt_eng_001683) +Scores: (#C #S #D #I) 20 0 5 2 +REF: * g e n E r A L l y a C c e p t e d r a n G e * s +HYP: J g e n * r * * l y a * c e p t e d r a n * e R s +Eval: I D D D D D I + +Speaker sentences 427: nchlt_eng_001684 #utts: 1 +id: (nchlt_eng_001684-nchlt_eng_001684) +Scores: (#C #S #D #I) 13 3 3 2 +REF: g U i l * d a ******* w A r d w I N n E R s +HYP: g * i l E d a w * r d w * E n H I s +Eval: D I I D D S S S + +Speaker sentences 428: nchlt_eng_001685 #utts: 1 +id: (nchlt_eng_001685-nchlt_eng_001685) +Scores: (#C #S #D #I) 19 0 3 3 +REF: s * w e d i s h * m * u s i c A l g r O U p s +HYP: s O w e d i s h E m A u s i c * l g r * * p s +Eval: I I I D D D + +Speaker sentences 429: nchlt_eng_001686 #utts: 1 +id: (nchlt_eng_001686-nchlt_eng_001686) +Scores: (#C #S #D #I) 17 6 0 1 +REF: c h I l d H O O d A U t i s * m r a t i n g +HYP: c h A l d E R E d O R t i s I m r a t i n g +Eval: S S S S S S I + +Speaker sentences 430: nchlt_eng_001687 #utts: 1 +id: (nchlt_eng_001687-nchlt_eng_001687) +Scores: (#C #S #D #I) 10 2 0 2 +REF: d o s A g E f o r * * m s +HYP: d o s I g H f o r M E m s +Eval: S S I I + +Speaker sentences 431: nchlt_eng_001688 #utts: 1 +id: (nchlt_eng_001688-nchlt_eng_001688) +Scores: (#C #S #D #I) 15 5 1 6 +REF: o ******* h * ******* i o s t a t E U n ******* I v E r s * i t y * +HYP: o h I i o U s t a t * I O n O v O r s T i t y E +Eval: I I I S D S S I S S I I + +Speaker sentences 432: nchlt_eng_001689 #utts: 1 +id: (nchlt_eng_001689-nchlt_eng_001689) +Scores: (#C #S #D #I) 17 7 4 1 +REF: f o r ******* m E R s E T t L E M e n T S i n t U r k e Y +HYP: f o r m O S s * A t * * H e n C E i n t E r k e * +Eval: I S S D S D D S S S S D + +Speaker sentences 433: nchlt_eng_001690 #utts: 1 +id: (nchlt_eng_001690-nchlt_eng_001690) +Scores: (#C #S #D #I) 14 5 0 6 +REF: * * * ******* a M E r I c A n i n * v E n t i o n s * +HYP: E E E a N r O c O n i n W v H n t i o n s H +Eval: I I I I S S S S I S I + +Speaker sentences 434: nchlt_eng_001691 #utts: 1 +id: (nchlt_eng_001691-nchlt_eng_001691) +Scores: (#C #S #D #I) 4 0 0 5 +REF: * ******* a r t * s * * +HYP: E a r t E s E H +Eval: I I I I I + +Speaker sentences 435: nchlt_eng_001692 #utts: 1 +id: (nchlt_eng_001692-nchlt_eng_001692) +Scores: (#C #S #D #I) 16 3 3 3 +REF: m O d e R n * E u r o p e a n r U S s I a ******* * +HYP: m * d e * n Y O u r o p e a n r * A s H a H +Eval: D D I S D S S I I + +Speaker sentences 436: nchlt_eng_001693 #utts: 1 +id: (nchlt_eng_001693-nchlt_eng_001693) +Scores: (#C #S #D #I) 11 6 6 2 +REF: n A T I O n A L l e A g U E p E N N a n t ******* * +HYP: n * * S n O D l e * g * * p * I L a n t H +Eval: D D S S S S D D D D S S I I + +Speaker sentences 437: nchlt_eng_001694 #utts: 1 +id: (nchlt_eng_001694-nchlt_eng_001694) +Scores: (#C #S #D #I) 20 1 1 0 +REF: b i G f i n i s h p r O d u c t i o n s +HYP: b i K f i n i s h p r * d u c t i o n s +Eval: S D + +Speaker sentences 438: nchlt_eng_001695 #utts: 1 +id: (nchlt_eng_001695-nchlt_eng_001695) +Scores: (#C #S #D #I) 7 0 1 3 +REF: n a * t i o n A l * * +HYP: n a S t i o n * l E H +Eval: I D I I + +Speaker sentences 439: nchlt_eng_001696 #utts: 1 +id: (nchlt_eng_001696-nchlt_eng_001696) +Scores: (#C #S #D #I) 10 2 0 4 +REF: t r a * g i C p o * * e * T s +HYP: t r a D g i K p o I T e S E s +Eval: I S I I I S + +Speaker sentences 440: nchlt_eng_001697 #utts: 1 +id: (nchlt_eng_001697-nchlt_eng_001697) +Scores: (#C #S #D #I) 12 5 0 0 +REF: t O t A l g r O S S s t a t e +HYP: t I t I l g r I C E s t a t e +Eval: S S S S S + +Speaker sentences 441: nchlt_eng_001698 #utts: 1 +id: (nchlt_eng_001698-nchlt_eng_001698) +Scores: (#C #S #D #I) 13 0 0 4 +REF: a * ******* t h e n a h a d a * n * +HYP: a S t h e n a h a d a E n E +Eval: I I I I + +Speaker sentences 442: nchlt_eng_001699 #utts: 1 +id: (nchlt_eng_001699-nchlt_eng_001699) +Scores: (#C #S #D #I) 22 3 1 0 +REF: e a s t e R n E u r O p e a n c o u n t r I e s +HYP: e a s t e O n Y u r * p e a n c o u n t r Y e s +Eval: S S D S + +Speaker sentences 443: nchlt_eng_001700 #utts: 1 +id: (nchlt_eng_001700-nchlt_eng_001700) +Scores: (#C #S #D #I) 25 5 5 2 +REF: c o n d E M N e d U n ******* A U t h O r i * Z e D t r A n s l a t i o n s +HYP: c o n d * * * e d A n O R t h * r i V S e * t r O n s l a t i o n s +Eval: D D D S I S S D I S D S + +Speaker sentences 444: nchlt_eng_001701 #utts: 1 +id: (nchlt_eng_001701-nchlt_eng_001701) +Scores: (#C #S #D #I) 9 2 5 2 +REF: C o * l D w A r * L e A D e R s +HYP: * o A l * w O r D * e * T e * s +Eval: D I D S I D D S D + +Speaker sentences 445: nchlt_eng_001702 #utts: 1 +id: (nchlt_eng_001702-nchlt_eng_001702) +Scores: (#C #S #D #I) 12 10 1 2 +REF: K E n * * E s A W m o U n T a I N l A n d I s +HYP: C I n A S s O R m o W n a * D l E n d E s +Eval: S S I I S S S S S D S S S + +Speaker sentences 446: nchlt_eng_001703 #utts: 1 +id: (nchlt_eng_001703-nchlt_eng_001703) +Scores: (#C #S #D #I) 9 2 1 2 +REF: n o b E l * F a m i L y * +HYP: n o b * l E S a m i T y E +Eval: D I S S I + +Speaker sentences 447: nchlt_eng_001704 #utts: 1 +id: (nchlt_eng_001704-nchlt_eng_001704) +Scores: (#C #S #D #I) 6 6 5 1 +REF: * E D w A R D s a I R f o R C E +HYP: I T w * * O s a * * E f o * L S +Eval: I S S D D S D D S D S S + +Speaker sentences 448: nchlt_eng_001705 #utts: 1 +id: (nchlt_eng_001705-nchlt_eng_001705) +Scores: (#C #S #D #I) 18 1 0 1 +REF: m o u n t s a i n t * v i n C e n t +HYP: m o u n t s a i n t O v i n S e n t +Eval: I S + +Speaker sentences 449: nchlt_eng_001706 #utts: 1 +id: (nchlt_eng_001706-nchlt_eng_001706) +Scores: (#C #S #D #I) 16 4 2 3 +REF: C i t y m E T r O p o l i t A n * a * r E a * +HYP: S i t y m R C r * p o l i t O n E a I r * a R +Eval: S S S D S I I D I + +Speaker sentences 450: nchlt_eng_001707 #utts: 1 +id: (nchlt_eng_001707-nchlt_eng_001707) +Scores: (#C #S #D #I) 22 2 3 3 +REF: r * * U l e r s W h o d * i E d a s c h I l d r E n +HYP: r O O N l e r s * h o d A i * d a s c h * l d r A n +Eval: I I S D I D D S + +Speaker sentences 451: nchlt_eng_001708 #utts: 1 +id: (nchlt_eng_001708-nchlt_eng_001708) +Scores: (#C #S #D #I) 10 4 2 3 +REF: c h A n c e * L l O R s ******* v I L l e * +HYP: c h O n c e S l * E s v * O l e L +Eval: S I S D S I D S I + +Speaker sentences 452: nchlt_eng_001709 #utts: 1 +id: (nchlt_eng_001709-nchlt_eng_001709) +Scores: (#C #S #D #I) 14 4 1 2 +REF: i ******* p * p A c k E t S E n t i r E l y +HYP: i p E p E c k A t C I n t i r * l y +Eval: I I S S S S D + +Speaker sentences 453: nchlt_eng_001710 #utts: 1 +id: (nchlt_eng_001710-nchlt_eng_001710) +Scores: (#C #S #D #I) 15 2 1 1 +REF: K i n g E d ******* w a r d s d E a t h +HYP: C i n g A d w a r d s d * a t h +Eval: S S I D + +Speaker sentences 454: nchlt_eng_001711 #utts: 1 +id: (nchlt_eng_001711-nchlt_eng_001711) +Scores: (#C #S #D #I) 14 1 0 4 +REF: a ******* m e r i c * a * a ******* m e r i c A +HYP: a m e r i c E a R a m e r i c E +Eval: I I I I S + +Speaker sentences 455: nchlt_eng_001712 #utts: 1 +id: (nchlt_eng_001712-nchlt_eng_001712) +Scores: (#C #S #D #I) 16 1 5 0 +REF: c o M m E r C i A l s h i p s a I l E d +HYP: c o * m * r T i * l s h i p s a * l * d +Eval: D D S D D D + +Speaker sentences 456: nchlt_eng_001713 #utts: 1 +id: (nchlt_eng_001713-nchlt_eng_001713) +Scores: (#C #S #D #I) 15 1 4 3 +REF: p e O p l E f R o m m a * n N h * * e I m +HYP: p e * p l * f * o m m a E n h A M e * m +Eval: D D D I S I I D + +Speaker sentences 457: nchlt_eng_001714 #utts: 1 +id: (nchlt_eng_001714-nchlt_eng_001714) +Scores: (#C #S #D #I) 13 1 3 0 +REF: r a i l C r a s h K i L l E d +HYP: r a i l * r a s h C i * l * d +Eval: D S D D + +Speaker sentences 458: nchlt_eng_001715 #utts: 1 +id: (nchlt_eng_001715-nchlt_eng_001715) +Scores: (#C #S #D #I) 16 3 2 0 +REF: m u t U a l d e f e n s E t R E a T y +HYP: m u t H a l d e f e n s * t * O a D y +Eval: S D D S S + +Speaker sentences 459: nchlt_eng_001716 #utts: 1 +id: (nchlt_eng_001716-nchlt_eng_001716) +Scores: (#C #S #D #I) 15 2 2 3 +REF: m o * d * e R n c h I l d r u * l E R s +HYP: m o U d T e * n c h E l d r u O l * I s +Eval: I I D S I D S + +Speaker sentences 460: nchlt_eng_001717 #utts: 1 +id: (nchlt_eng_001717-nchlt_eng_001717) +Scores: (#C #S #D #I) 15 2 3 6 +REF: m o t * ******* * O r r i * f * * l E d I v I S i o n +HYP: m o t E S E r r i H f H A l * d E v * * i o n +Eval: I I I S I I I D S D D + +Speaker sentences 461: nchlt_eng_001718 #utts: 1 +id: (nchlt_eng_001718-nchlt_eng_001718) +Scores: (#C #S #D #I) 15 4 1 2 +REF: A u ******* s t r a l i A n A i R f o * r C e +HYP: O u s t r a l i O n * i E f o U r S e +Eval: S I S D S I S + +Speaker sentences 462: nchlt_eng_001719 #utts: 1 +id: (nchlt_eng_001719-nchlt_eng_001719) +Scores: (#C #S #D #I) 18 3 3 3 +REF: a ******* m e r * I c A n * m Y s t E r y W r i t e R s +HYP: a m e r Y c E n D m O s t * r y * r i t e * s +Eval: I I S S I S D D D + +Speaker sentences 463: nchlt_eng_001720 #utts: 1 +id: (nchlt_eng_001720-nchlt_eng_001720) +Scores: (#C #S #D #I) 17 3 2 0 +REF: f i n E l y g r o U n d g r A P H i t e +HYP: f i n * l y g r o W n d g r * E F i t e +Eval: D S D S S + +Speaker sentences 464: nchlt_eng_001721 #utts: 1 +id: (nchlt_eng_001721-nchlt_eng_001721) +Scores: (#C #S #D #I) 15 3 6 1 +REF: w o R l D C H a m p i O n * s H I p m a t C H +HYP: w o * l * * T a m p i * n E s * O p m a t * S +Eval: D D D S D I D S D S + +Speaker sentences 465: nchlt_eng_001722 #utts: 1 +id: (nchlt_eng_001722-nchlt_eng_001722) +Scores: (#C #S #D #I) 7 1 0 1 +REF: c A r * o l i n a +HYP: c E r I o l i n a +Eval: S I + +Speaker sentences 466: nchlt_eng_001723 #utts: 1 +id: (nchlt_eng_001723-nchlt_eng_001723) +Scores: (#C #S #D #I) 12 6 4 1 +REF: m * O b I L E P h O n E o p e r a t O R s +HYP: m Y b * * O A T h n * o p e r a t * E s +Eval: I S D D S S S S D D S + +Speaker sentences 467: nchlt_eng_001724 #utts: 1 +id: (nchlt_eng_001724-nchlt_eng_001724) +Scores: (#C #S #D #I) 8 6 2 0 +REF: Q U A r t Z V A r i E t I e s +HYP: * C O r t E F O r i * t Y e s +Eval: D S S S S S D S + +Speaker sentences 468: nchlt_eng_001725 #utts: 1 +id: (nchlt_eng_001725-nchlt_eng_001725) +Scores: (#C #S #D #I) 6 1 0 4 +REF: m i * D r * a * n d * +HYP: m i O r O a W n d H +Eval: I S I I I + +Speaker sentences 469: nchlt_eng_001726 #utts: 1 +id: (nchlt_eng_001726-nchlt_eng_001726) +Scores: (#C #S #D #I) 20 1 1 0 +REF: c A U s e l e t h a l r e a c t i o n s +HYP: c * O s e l e t h a l r e a c t i o n s +Eval: D S + +Speaker sentences 470: nchlt_eng_001727 #utts: 1 +id: (nchlt_eng_001727-nchlt_eng_001727) +Scores: (#C #S #D #I) 13 4 0 3 +REF: E n g l * i s h p A c * I f * I s t s +HYP: I n g l O i s h p E c E O f O U s t s +Eval: S I S I S I S + +Speaker sentences 471: nchlt_eng_001728 #utts: 1 +id: (nchlt_eng_001728-nchlt_eng_001728) +Scores: (#C #S #D #I) 18 2 1 2 +REF: * U n i * t e d s t a t e S f e d E r a l +HYP: Y O n i G t e d s t a t e * f e d I r a l +Eval: I S I D S + +Speaker sentences 472: nchlt_eng_001729 #utts: 1 +id: (nchlt_eng_001729-nchlt_eng_001729) +Scores: (#C #S #D #I) 15 2 2 3 +REF: f E d E r A l * r e s * * e R v e a c t +HYP: f A d * r * l E r e s E I e O v e a c t +Eval: S D D I I I S + +Speaker sentences 473: nchlt_eng_001730 #utts: 1 +id: (nchlt_eng_001730-nchlt_eng_001730) +Scores: (#C #S #D #I) 16 2 4 1 +REF: w I L l i A m h e n * r y h A R r I s o n +HYP: w * O l i * m h e n D r y h * E r * s o n +Eval: D S D I D S D + +Speaker sentences 474: nchlt_eng_001731 #utts: 1 +id: (nchlt_eng_001731-nchlt_eng_001731) +Scores: (#C #S #D #I) 10 4 1 0 +REF: C l U B p l a y c h A R t +HYP: G l A P p l a y c h * O t +Eval: S S S D S + +Speaker sentences 475: nchlt_eng_001732 #utts: 1 +id: (nchlt_eng_001732-nchlt_eng_001732) +Scores: (#C #S #D #I) 17 4 2 4 +REF: p a s S E n g e r r * a I l * s E R v I c e s ******* * +HYP: p a s T O n g e r r H a * l D s * O v O c e s H +Eval: S S I D I D S S I I + +Speaker sentences 476: nchlt_eng_001733 #utts: 1 +id: (nchlt_eng_001733-nchlt_eng_001733) +Scores: (#C #S #D #I) 16 8 3 5 +REF: a n c I E n T m A C e d O n * * i ******* A n * * G E n E r A l s +HYP: a n c H O n * m E S e d R n T H i O n D J D I n * r * l s +Eval: S S D S S S I I I S I I S S D D + +Speaker sentences 477: nchlt_eng_001734 #utts: 1 +id: (nchlt_eng_001734-nchlt_eng_001734) +Scores: (#C #S #D #I) 13 5 0 3 +REF: * K o n g a c t i o n C I N E m a * * +HYP: C R o n g a c t i o n S E A m a R E +Eval: I S S S S S I I + +Speaker sentences 478: nchlt_eng_001735 #utts: 1 +id: (nchlt_eng_001735-nchlt_eng_001735) +Scores: (#C #S #D #I) 17 6 2 3 +REF: g U n ******* p o W D e R p r o p E L l A n t * U s e d * +HYP: g O n p o U T e * p r o p * I l E n t Y O s e d T +Eval: S I S S D D S S I S I + +Speaker sentences 479: nchlt_eng_001736 #utts: 1 +id: (nchlt_eng_001736-nchlt_eng_001736) +Scores: (#C #S #D #I) 14 4 1 5 +REF: l o w E s t * E n E R g y s t * a * * t ******* E +HYP: l o w I s t D I n * A g y s t D a G H t H +Eval: S I S D S I I I I S + +Speaker sentences 480: nchlt_eng_001737 #utts: 1 +id: (nchlt_eng_001737-nchlt_eng_001737) +Scores: (#C #S #D #I) 8 3 2 2 +REF: c a l E n d A R * * E r A s +HYP: c a l * n d * E Y O U r O s +Eval: D D S I I S S + +Speaker sentences 481: nchlt_eng_001738 #utts: 1 +id: (nchlt_eng_001738-nchlt_eng_001738) +Scores: (#C #S #D #I) 21 5 1 5 +REF: m a ******* * J O r i n t e R n * a T i o n a l * a I R p o r t * +HYP: m a E G E r i n t e n O a S i o n a l E a * E p o r t E +Eval: I I S S S I S I D S I + +Speaker sentences 482: nchlt_eng_001739 #utts: 1 +id: (nchlt_eng_001739-nchlt_eng_001739) +Scores: (#C #S #D #I) 14 3 1 0 +REF: t o t A l f o r C e a c t i N G +HYP: t o t * l f o r S e a c t i O M +Eval: D S S S + +Speaker sentences 483: nchlt_eng_001740 #utts: 1 +id: (nchlt_eng_001740-nchlt_eng_001740) +Scores: (#C #S #D #I) 18 3 4 1 +REF: l o s * S l e S s d a t A C o m p R e S S i o n +HYP: l o s T I l e * s d a t * * o m p * e I T i o n +Eval: I S D D D D S S + +Speaker sentences 484: nchlt_eng_001741 #utts: 1 +id: (nchlt_eng_001741-nchlt_eng_001741) +Scores: (#C #S #D #I) 4 1 0 9 +REF: * ******* g r * * * e * * * e ******* K +HYP: E g r E A K e H D H e R +Eval: I I I I I I I I I S + +Speaker sentences 485: nchlt_eng_001742 #utts: 1 +id: (nchlt_eng_001742-nchlt_eng_001742) +Scores: (#C #S #D #I) 23 5 3 5 +REF: E n * ******* v * i r O N m e n t A l p R o t E c t i o n a G e n C y ******* * +HYP: I n D v O i r * E m e n t * l p * o t I c t i o n a e n S y H +Eval: S I I I D S D D S S S I I + +Speaker sentences 486: nchlt_eng_001743 #utts: 1 +id: (nchlt_eng_001743-nchlt_eng_001743) +Scores: (#C #S #D #I) 18 4 3 2 +REF: m a n * I t o * b A s c H O O l s q U e s t i o n +HYP: m a n Y t o E b * I s c * * K l s q R e s t i o n +Eval: I S I D S D D S S + +Speaker sentences 487: nchlt_eng_001744 #utts: 1 +id: (nchlt_eng_001744-nchlt_eng_001744) +Scores: (#C #S #D #I) 15 5 1 2 +REF: a n c I E n T C i t y p I t h u n d * * A +HYP: a n c T O n * S i t y p O t h u n d E R E +Eval: S S D S S I I S + +Speaker sentences 488: nchlt_eng_001745 #utts: 1 +id: (nchlt_eng_001745-nchlt_eng_001745) +Scores: (#C #S #D #I) 15 4 5 1 +REF: s m A l L * o R t h O d o X s Y n A g o g U E +HYP: s m * l E A o * t h E d o C s I n * g o g * * +Eval: D S I D S S S D D D + +Speaker sentences 489: nchlt_eng_001746 #utts: 1 +id: (nchlt_eng_001746-nchlt_eng_001746) +Scores: (#C #S #D #I) 16 8 2 3 +REF: * L A R g e s T m E t R o * p O l i T a n a * r E A s +HYP: N O N D g e s * m I t H o U p I l i * a n a I r I R s +Eval: I S S S D S S I S D I S S + +Speaker sentences 490: nchlt_eng_001747 #utts: 1 +id: (nchlt_eng_001747-nchlt_eng_001747) +Scores: (#C #S #D #I) 15 5 0 5 +REF: t i t * l e r e l i g * ******* * I O r O m A n * A +HYP: t i t O l e r e l i g E H A R r E m O n O N +Eval: I I I I S S S S I S + +Speaker sentences 491: nchlt_eng_001748 #utts: 1 +id: (nchlt_eng_001748-nchlt_eng_001748) +Scores: (#C #S #D #I) 18 6 0 6 +REF: e * ******* X a * m p l e s I n ******* C l u d e * h U f * F m A n +HYP: e G S a N m p l e s A n T l u d e D h A f H m O n +Eval: I I S I S I S I S I S S + +Speaker sentences 492: nchlt_eng_001749 #utts: 1 +id: (nchlt_eng_001749-nchlt_eng_001749) +Scores: (#C #S #D #I) 18 1 4 4 +REF: * u n * * I t e D s t a t e S m A i n t * a i n S +HYP: Y u n O T t e * s t a t e * m * i n t E a i n * +Eval: I I I S D D D I D + +Speaker sentences 493: nchlt_eng_001750 #utts: 1 +id: (nchlt_eng_001750-nchlt_eng_001750) +Scores: (#C #S #D #I) 18 4 0 4 +REF: b o l * d r e p r e s e n T S m * A x * i m * A +HYP: b o l E d r e p r e s e n C E m E C x C i m O R +Eval: I S S I S I I S + +Speaker sentences 494: nchlt_eng_001751 #utts: 1 +id: (nchlt_eng_001751-nchlt_eng_001751) +Scores: (#C #S #D #I) 16 3 4 0 +REF: s C i E n C e f i c t i o n A U t h O R s +HYP: s * i * n * e f i c t i o n O R t h * I s +Eval: D D D S S D S + +Speaker sentences 495: nchlt_eng_001752 #utts: 1 +id: (nchlt_eng_001752-nchlt_eng_001752) +Scores: (#C #S #D #I) 21 7 3 2 +REF: o R d I n a r Y d I F f E r e n T I A l * e * q U a t i o n s +HYP: o * d E n a r E d * E f * r e n S H O l A e C q W a t i o n s +Eval: D S S D S D S S S I I S + +Speaker sentences 496: nchlt_eng_001753 #utts: 1 +id: (nchlt_eng_001753-nchlt_eng_001753) +Scores: (#C #S #D #I) 18 4 3 0 +REF: d I p l O m a t S o f t h e h O L Y s e E +HYP: d * p l * m a t * o f t h e h R D E s e W +Eval: D D D S S S S + +Speaker sentences 497: nchlt_eng_001754 #utts: 1 +id: (nchlt_eng_001754-nchlt_eng_001754) +Scores: (#C #S #D #I) 11 5 5 1 +REF: s E R i A l * K I L l E R m Y s t E r y +HYP: s * * i * l E * C O l O M m I s t * r y +Eval: D D D I D S S S S S D + +Speaker sentences 498: nchlt_eng_001755 #utts: 1 +id: (nchlt_eng_001755-nchlt_eng_001755) +Scores: (#C #S #D #I) 13 0 9 2 +REF: * r O Y A l m I l ******* i t A r y c o L l E G E +HYP: U r * * * l m * l i t * r y c o * l * * * +Eval: I D D D D I D D D D D + +Speaker sentences 499: nchlt_eng_001756 #utts: 1 +id: (nchlt_eng_001756-nchlt_eng_001756) +Scores: (#C #S #D #I) 17 2 3 2 +REF: s * l o W l y l e A d S s O c i A l i s * m +HYP: s T l o N l y l e * d * s * c i O l i s U m +Eval: I S D D D S I + +Speaker sentences 500: nchlt_eng_001757 #utts: 1 +id: (nchlt_eng_001757-nchlt_eng_001757) +Scores: (#C #S #D #I) 7 1 0 2 +REF: p r i n t e * * R s +HYP: p r i n t e I S s +Eval: I I S + +Speaker sentences 501: nchlt_eng_001758 #utts: 1 +id: (nchlt_eng_001758-nchlt_eng_001758) +Scores: (#C #S #D #I) 12 5 3 2 +REF: n E W t E s t A M e ******* * n T p e O p l E +HYP: n O U t A s t * H e A n D p e * p l * +Eval: S S S D S I I S D D + +Speaker sentences 502: nchlt_eng_001759 #utts: 1 +id: (nchlt_eng_001759-nchlt_eng_001759) +Scores: (#C #S #D #I) 26 5 2 4 +REF: s m A R t c A r d b a * s e d E l E c * ******* t r o n i c * p U r s E +HYP: s m O G t c O r d b a C s e d A l * c T t r o n i c K p E r s * +Eval: S S S I S D I I I S D + +Speaker sentences 503: nchlt_eng_001760 #utts: 1 +id: (nchlt_eng_001760-nchlt_eng_001760) +Scores: (#C #S #D #I) 15 2 3 0 +REF: s t a t e S A R m y s o l d I E r s +HYP: s t a t e * * N m y s o l d * G r s +Eval: D D S D S + +Speaker sentences 504: nchlt_eng_001761 #utts: 1 +id: (nchlt_eng_001761-nchlt_eng_001761) +Scores: (#C #S #D #I) 14 1 2 1 +REF: l o r d J e * s U s c H r i s t +HYP: l o r d * e A s s c * r i s t +Eval: D I S D + +Speaker sentences 505: nchlt_eng_001762 #utts: 1 +id: (nchlt_eng_001762-nchlt_eng_001762) +Scores: (#C #S #D #I) 5 4 0 3 +REF: l Y d E n ******* b * U R g * +HYP: l A d n b L I N g P +Eval: S S I I S S I + +Speaker sentences 506: nchlt_eng_001763 #utts: 1 +id: (nchlt_eng_001763-nchlt_eng_001763) +Scores: (#C #S #D #I) 14 4 3 1 +REF: * I t A l i a n n A T i O n A l t e A m +HYP: H t E l i a n n E S i * n * l t e * m +Eval: I S S S S D D D + +Speaker sentences 507: nchlt_eng_001764 #utts: 1 +id: (nchlt_eng_001764-nchlt_eng_001764) +Scores: (#C #S #D #I) 25 3 3 3 +REF: a n * ******* t * I g U A r e c r E a t i o n g r o u n d t h U m B +HYP: a n D t E A g * R r e c r * a t i o n g r o u n d t h E m * +Eval: I I I S D S D S D + +Speaker sentences 508: nchlt_eng_001765 #utts: 1 +id: (nchlt_eng_001765-nchlt_eng_001765) +Scores: (#C #S #D #I) 17 1 1 3 +REF: g r o * s * s s t a t E p r o d U c t * +HYP: g r o C s E s s t a t * p r o d A c t E +Eval: I I D S I + +Speaker sentences 509: nchlt_eng_001766 #utts: 1 +id: (nchlt_eng_001766-nchlt_eng_001766) +Scores: (#C #S #D #I) 8 3 1 3 +REF: K i n G K o n G v * * s * +HYP: C i n * C o n D v E R s E +Eval: S D S S I I I + +Speaker sentences 510: nchlt_eng_001767 #utts: 1 +id: (nchlt_eng_001767-nchlt_eng_001767) +Scores: (#C #S #D #I) 6 0 3 1 +REF: b * e L l v i L l E +HYP: b I e * l v i * l * +Eval: I D D D + +Speaker sentences 511: nchlt_eng_001768 #utts: 1 +id: (nchlt_eng_001768-nchlt_eng_001768) +Scores: (#C #S #D #I) 26 7 6 0 +REF: f I l M o R g A n I Z a t i o n S I n t h e u n I T e d s t a t e S +HYP: f * l E o L g O n G S a t i o n * * n ******* t h e ******* u n O D e d s t a t e * +Eval: D S S S S S D D D D S S D + +Speaker sentences 512: nchlt_eng_001769 #utts: 1 +id: (nchlt_eng_001769-nchlt_eng_001769) +Scores: (#C #S #D #I) 13 6 2 3 +REF: i S r A E l * D e ******* f * E n S E f o r C e s +HYP: i T r * I l T H e f T A n * C f o r S e s +Eval: S D S I S I I S D S S + +Speaker sentences 513: nchlt_eng_001770 #utts: 1 +id: (nchlt_eng_001770-nchlt_eng_001770) +Scores: (#C #S #D #I) 14 6 2 3 +REF: * * A U T o m A t i c * s E n d r e C e I v e +HYP: O R D D o m I t i c K s A n d ******* r e S e * v e +Eval: I I S S S S I S D S D + +Speaker sentences 514: nchlt_eng_001771 #utts: 1 +id: (nchlt_eng_001771-nchlt_eng_001771) +Scores: (#C #S #D #I) 20 2 4 2 +REF: b r U n s w i c k s O U t h e R n r a I l w A y * * +HYP: b r E n s w i c k s * * t h e * n r a * l w O y L H +Eval: S D D D D S I I + +Speaker sentences 515: nchlt_eng_001772 #utts: 1 +id: (nchlt_eng_001772-nchlt_eng_001772) +Scores: (#C #S #D #I) 14 3 4 3 +REF: a c t R e S s a ******* c a * d E m Y a ******* w A R d +HYP: a c t * e * s a c a T d I m * E a w * O d +Eval: D D I I S D S I D S + +Speaker sentences 516: nchlt_eng_001773 #utts: 1 +id: (nchlt_eng_001773-nchlt_eng_001773) +Scores: (#C #S #D #I) 16 0 1 7 +REF: p e O p l e f * r o m * t o * k y o * * * * +HYP: p e * p l e f O r o m E t o C k y o A T E D +Eval: D I I I I I I I + +Speaker sentences 517: nchlt_eng_001774 #utts: 1 +id: (nchlt_eng_001774-nchlt_eng_001774) +Scores: (#C #S #D #I) 15 1 2 1 +REF: f o r c h a R l E s s i n g * e R +HYP: f o r c h a * l D s s i n g O e * +Eval: D S I D + +Speaker sentences 518: nchlt_eng_001775 #utts: 1 +id: (nchlt_eng_001775-nchlt_eng_001775) +Scores: (#C #S #D #I) 15 4 2 2 +REF: * V a r I a b l E v a l V E t * I m i n g +HYP: B E a r * a b l * v a l F T t A R m i n g +Eval: I S D D S S I S + +Speaker sentences 519: nchlt_eng_001776 #utts: 1 +id: (nchlt_eng_001776-nchlt_eng_001776) +Scores: (#C #S #D #I) 15 1 3 2 +REF: s o u t h w a * l e s V A L L e y * s +HYP: s o u t h w a I l e s * * * F e y E s +Eval: I D D D S I + +Speaker sentences 520: nchlt_eng_001777 #utts: 1 +id: (nchlt_eng_001777-nchlt_eng_001777) +Scores: (#C #S #D #I) 18 9 0 3 +REF: c a L I f o r N i A s t a t ******* E u ******* * N I v E r s i t y +HYP: c a O f o r D i U R s t a t T u D O E v O r s i t y +Eval: S S S S S I S I I S S S + +Speaker sentences 521: nchlt_eng_001778 #utts: 1 +id: (nchlt_eng_001778-nchlt_eng_001778) +Scores: (#C #S #D #I) 6 2 0 0 +REF: e l d O r A d o +HYP: e l d E r O d o +Eval: S S + +Speaker sentences 522: nchlt_eng_001779 #utts: 1 +id: (nchlt_eng_001779-nchlt_eng_001779) +Scores: (#C #S #D #I) 18 1 2 4 +REF: o u t ******* d O o r * o * r ******* i E n t e d C i t y +HYP: o u t d * o r E o A r i * n t e d S i t y +Eval: I D I I I D S + +Speaker sentences 523: nchlt_eng_001780 #utts: 1 +id: (nchlt_eng_001780-nchlt_eng_001780) +Scores: (#C #S #D #I) 24 4 2 1 +REF: c l a I m e d p A r T i a l r E s p o n * S I b i l i t y +HYP: c l a * m e d p O r S i a l r * s p o n C E A b i l i t y +Eval: D S S D I S S + +Speaker sentences 524: nchlt_eng_001781 #utts: 1 +id: (nchlt_eng_001781-nchlt_eng_001781) +Scores: (#C #S #D #I) 12 2 1 1 +REF: c H r i s T i A n t e r m * s +HYP: c * r i s H i O n t e r m E s +Eval: D S S I + +Speaker sentences 525: nchlt_eng_001782 #utts: 1 +id: (nchlt_eng_001782-nchlt_eng_001782) +Scores: (#C #S #D #I) 14 0 3 1 +REF: e ******* v e n t S t O o K p l a c e +HYP: e v e n t * t * o * p l a c e +Eval: I D D D + +Speaker sentences 526: nchlt_eng_001783 #utts: 1 +id: (nchlt_eng_001783-nchlt_eng_001783) +Scores: (#C #S #D #I) 17 4 2 3 +REF: c a n ******* * C E R d E a t h * s i n f r A n C e +HYP: c a n S A I D d * a t h E s i n f r O n * e +Eval: I I S S S D I S D + +Speaker sentences 527: nchlt_eng_001784 #utts: 1 +id: (nchlt_eng_001784-nchlt_eng_001784) +Scores: (#C #S #D #I) 15 3 1 0 +REF: h i s t O r y o f m i C h I g A n +HYP: h i s t * r y o f m i S h O g O n +Eval: D S S S + +Speaker sentences 528: nchlt_eng_001785 #utts: 1 +id: (nchlt_eng_001785-nchlt_eng_001785) +Scores: (#C #S #D #I) 16 0 3 1 +REF: o r i g i n A L l y t h e N a m e * +HYP: o r i g i n * * l y t h e * a m e M +Eval: D D D I + +Speaker sentences 529: nchlt_eng_001786 #utts: 1 +id: (nchlt_eng_001786-nchlt_eng_001786) +Scores: (#C #S #D #I) 25 1 2 2 +REF: n a t i o n s f r a M e ******* w O r K c o n v e * n t i o n +HYP: n a t i o n s f r a * e w * r E c o n v e A n t i o n +Eval: D I D S I + +Speaker sentences 530: nchlt_eng_001787 #utts: 1 +id: (nchlt_eng_001787-nchlt_eng_001787) +Scores: (#C #S #D #I) 2 3 0 6 +REF: * ******* L o * * c * * A L +HYP: E N o C K c O N E H +Eval: I I S I I I I S S + +Speaker sentences 531: nchlt_eng_001788 #utts: 1 +id: (nchlt_eng_001788-nchlt_eng_001788) +Scores: (#C #S #D #I) 19 4 3 4 +REF: * A U s t r I A n s c H O o l * E c o n o m ******* i s t * s +HYP: O R s t r * n s c * * o l E I c o n o m i s t E s +Eval: I S S D S D D I S I I + +Speaker sentences 532: nchlt_eng_001789 #utts: 1 +id: (nchlt_eng_001789-nchlt_eng_001789) +Scores: (#C #S #D #I) 18 1 1 4 +REF: m a i n g r O u p c o m * ******* p o u * n * D s +HYP: m a i n g r * u p c o m E p o u W n S E s +Eval: D I I I I S + +Speaker sentences 533: nchlt_eng_001790 #utts: 1 +id: (nchlt_eng_001790-nchlt_eng_001790) +Scores: (#C #S #D #I) 14 4 2 3 +REF: * r * ******* E C Y c l A b l E m A t e r i a l s +HYP: H r O S I D c l I b l * m * t e r i a l s +Eval: I I I S S S S D D + +Speaker sentences 534: nchlt_eng_001791 #utts: 1 +id: (nchlt_eng_001791-nchlt_eng_001791) +Scores: (#C #S #D #I) 11 5 2 1 +REF: c o m M O n l * a W S Y s t E m S +HYP: c o m E I n l O a R * E s t O m * +Eval: S S I S D S S D + +Speaker sentences 535: nchlt_eng_001792 #utts: 1 +id: (nchlt_eng_001792-nchlt_eng_001792) +Scores: (#C #S #D #I) 12 2 3 2 +REF: b r o n * X h i G H s c H O o l * +HYP: b r o n K S h i * Y s c * * o l E +Eval: I S D S D D I + +Speaker sentences 536: nchlt_eng_001793 #utts: 1 +id: (nchlt_eng_001793-nchlt_eng_001793) +Scores: (#C #S #D #I) 22 3 1 2 +REF: a ******* m e r i c A n P O l i t i c * a l W r i t e r s +HYP: a m e r i c E n B E l i t i c G a l * r i t e r s +Eval: I S S S I D + +Speaker sentences 537: nchlt_eng_001794 #utts: 1 +id: (nchlt_eng_001794-nchlt_eng_001794) +Scores: (#C #S #D #I) 11 5 1 2 +REF: c H E m I c a l E l E M e n t * ******* s +HYP: c * A m O c a l I l I A e n t S s +Eval: D S S S S S I I + +Speaker sentences 538: nchlt_eng_001795 #utts: 1 +id: (nchlt_eng_001795-nchlt_eng_001795) +Scores: (#C #S #D #I) 19 3 3 2 +REF: G l o b A l * i n t E R n ******* E t c o M m u n i t y +HYP: D l o b * l E i n t * O n N t c o * m u n i t y +Eval: S D I D S I S D + +Speaker sentences 539: nchlt_eng_001796 #utts: 1 +id: (nchlt_eng_001796-nchlt_eng_001796) +Scores: (#C #S #D #I) 17 7 1 5 +REF: G E o ******* g r A P H i c * m a G a Z i N e * m a r c * * h +HYP: T Y o g r * E F i c T m a a S i E e N m a r c H E h +Eval: S S I D S S I S S S I I I + +Speaker sentences 540: nchlt_eng_001797 #utts: 1 +id: (nchlt_eng_001797-nchlt_eng_001797) +Scores: (#C #S #D #I) 12 8 1 3 +REF: w E B s * E R V i C E p r o v i * d * E R s +HYP: w I P s O T H i * S p r o v i G d T D O s +Eval: S S I S S S D S I I S S + +Speaker sentences 541: nchlt_eng_001798 #utts: 1 +id: (nchlt_eng_001798-nchlt_eng_001798) +Scores: (#C #S #D #I) 14 3 5 2 +REF: s C i E N C e f I c t i o n n o V E l * s * +HYP: s * i * * e ******* f P c t i o n n o * B l E s E +Eval: D D D S D S D S I I + +Speaker sentences 542: nchlt_eng_001799 #utts: 1 +id: (nchlt_eng_001799-nchlt_eng_001799) +Scores: (#C #S #D #I) 15 2 3 1 +REF: s C i E n C e f i c t i o n f I l * m +HYP: s * i * n * e S f i c t i o n f O l E m +Eval: D D D S S I + +Speaker sentences 543: nchlt_eng_001800 #utts: 1 +id: (nchlt_eng_001800-nchlt_eng_001800) +Scores: (#C #S #D #I) 14 4 0 7 +REF: * ******* s U b * ******* s E t s * * U m p r o b l E m * +HYP: S s O b E s I t s O M E m p r o b l O m N +Eval: I I S I I S I I S S I + +Speaker sentences 544: nchlt_eng_001801 #utts: 1 +id: (nchlt_eng_001801-nchlt_eng_001801) +Scores: (#C #S #D #I) 17 3 1 3 +REF: E a s t e R n n o r t h * A m E r i c a * * +HYP: * a s t e O n n o r t h E m Y r i c a R E +Eval: D S I S S I I + +Speaker sentences 545: nchlt_eng_001802 #utts: 1 +id: (nchlt_eng_001802-nchlt_eng_001802) +Scores: (#C #S #D #I) 17 3 3 0 +REF: p e p Y s w I t n e S s E D l O o t i n g +HYP: p e p E s w A t n e * s * T l * o t i n g +Eval: S S D D S D + +Speaker sentences 546: nchlt_eng_001803 #utts: 1 +id: (nchlt_eng_001803-nchlt_eng_001803) +Scores: (#C #S #D #I) 23 4 1 3 +REF: d I s t i n * C t i * v e V o c A l i n s t r U m e n t * +HYP: d E s t i n G N t i O v e F o c K l i n s t r * m e n t D +Eval: S I S I S S D I + +Speaker sentences 547: nchlt_eng_001804 #utts: 1 +id: (nchlt_eng_001804-nchlt_eng_001804) +Scores: (#C #S #D #I) 17 6 1 5 +REF: * * ******* a f R i * c A n a ******* m E r i c A n r a P p E R s +HYP: H T a f O i O c O n a m O r i c E n r a * p T I s +Eval: I I I S I S I S S D S S + +Speaker sentences 548: nchlt_eng_001805 #utts: 1 +id: (nchlt_eng_001805-nchlt_eng_001805) +Scores: (#C #S #D #I) 12 2 5 2 +REF: p o r ******* t U g U e s E * G e n E R A l s +HYP: p o r t O g * e s * C H e n * * * l s +Eval: I S D D I S D D D + +Speaker sentences 549: nchlt_eng_001806 #utts: 1 +id: (nchlt_eng_001806-nchlt_eng_001806) +Scores: (#C #S #D #I) 19 6 3 3 +REF: i n t e R n a T i o n A l * a I R p o r t S * * I a T A +HYP: i n t e n a S i o n * l E a * * p o r t I T Y a Y H +Eval: S S D I D D S I I S S S + +Speaker sentences 550: nchlt_eng_001807 #utts: 1 +id: (nchlt_eng_001807-nchlt_eng_001807) +Scores: (#C #S #D #I) 23 2 1 2 +REF: m o u n t A I n r a n g e s o f b O l i v i * a * +HYP: m o u n t * O n r a n g e s o f b E l i v i E a R +Eval: D S S I I + +Speaker sentences 551: nchlt_eng_001808 #utts: 1 +id: (nchlt_eng_001808-nchlt_eng_001808) +Scores: (#C #S #D #I) 12 2 2 2 +REF: f r E n c h a I r * f o * r C E +HYP: f r I n c h a * r E f o A r * S +Eval: S D I I D S + +Speaker sentences 552: nchlt_eng_001809 #utts: 1 +id: (nchlt_eng_001809-nchlt_eng_001809) +Scores: (#C #S #D #I) 14 4 3 3 +REF: * ******* s U p E r b O W l a P p e a r a n c * E +HYP: S s O p * r A b * * l a p e a r a n c S H +Eval: I I S D S D D S I S + +Speaker sentences 553: nchlt_eng_001810 #utts: 1 +id: (nchlt_eng_001810-nchlt_eng_001810) +Scores: (#C #S #D #I) 16 2 2 2 +REF: l o n g t r A v E l i n g p a * * I R s +HYP: l o n g t r * v * l i n g p a P E S s +Eval: D D I I S S + +Speaker sentences 554: nchlt_eng_001811 #utts: 1 +id: (nchlt_eng_001811-nchlt_eng_001811) +Scores: (#C #S #D #I) 16 2 2 1 +REF: d * i s t r i C t c o U r t J U d g e +HYP: d E i s t r i K t c o * r t * O d g e +Eval: I S D D S + +Speaker sentences 555: nchlt_eng_001812 #utts: 1 +id: (nchlt_eng_001812-nchlt_eng_001812) +Scores: (#C #S #D #I) 8 4 2 2 +REF: d U R R A n I * e * m p i R e +HYP: d * O O n Y A e N m p i * e +Eval: D S S S S I I D + +Speaker sentences 556: nchlt_eng_001813 #utts: 1 +id: (nchlt_eng_001813-nchlt_eng_001813) +Scores: (#C #S #D #I) 18 4 1 2 +REF: B r I t i s H n a T i o n a l i t y a c t ******* * +HYP: P r O t i s * I n a S i o n a l i t y a c t H +Eval: S S D S S I I + +Speaker sentences 557: nchlt_eng_001814 #utts: 1 +id: (nchlt_eng_001814-nchlt_eng_001814) +Scores: (#C #S #D #I) 10 4 2 1 +REF: i S s U E d * A t E a p r I l +HYP: i * s H O d I C t * a p r O l +Eval: D S S I S D S + +Speaker sentences 558: nchlt_eng_001815 #utts: 1 +id: (nchlt_eng_001815-nchlt_eng_001815) +Scores: (#C #S #D #I) 21 4 0 3 +REF: * * p U b l i * C L y t r a d e d c o m p a n I e s +HYP: P O p E b l i S I T y t r a d e d c o m p a n Y e s +Eval: I I S I S S S + +Speaker sentences 559: nchlt_eng_001816 #utts: 1 +id: (nchlt_eng_001816-nchlt_eng_001816) +Scores: (#C #S #D #I) 29 6 4 4 +REF: r u S s I A n v I c t i m s o f s o v I e T S r e P r e s * * * * S i o n s +HYP: r u * s H O n v H c t i m s o f s o v * e * ******* D r e B r e s E N T A T i o n s +Eval: D S S S D D D S S I I I I S + +Speaker sentences 560: nchlt_eng_001817 #utts: 1 +id: (nchlt_eng_001817-nchlt_eng_001817) +Scores: (#C #S #D #I) 17 4 0 7 +REF: w * E s t * * * s ******* l A v i c * l a n g U A g e * s +HYP: w H I s t D A N s l E v i c K l a n g W O g e O s +Eval: I S I I I I S I S S I + +Speaker sentences 561: nchlt_eng_001818 #utts: 1 +id: (nchlt_eng_001818-nchlt_eng_001818) +Scores: (#C #S #D #I) 17 3 2 4 +REF: I t a l i A n r o m A n * c a t h O l * I c * * +HYP: * t a l i O n r o m E n D c a t h * l E K c E S +Eval: D S S I D I S I I + +Speaker sentences 562: nchlt_eng_001819 #utts: 1 +id: (nchlt_eng_001819-nchlt_eng_001819) +Scores: (#C #S #D #I) 15 9 1 5 +REF: F r e n C h * R e s ******* * i s t ******* A N C E * M E m b E r s +HYP: P r e n T h E * e s T i s t R I F T N I m b O r s +Eval: S S I D I I I S S S S I S S S + +Speaker sentences 563: nchlt_eng_001820 #utts: 1 +id: (nchlt_eng_001820-nchlt_eng_001820) +Scores: (#C #S #D #I) 23 5 1 4 +REF: p r O v i n c I a l s Y m b O l * s o f O n t A r i * * o * +HYP: p r E v i n c H a l s E m b * l E s o f U n t O r i O A o R +Eval: S S S D I S S I I I + +Speaker sentences 564: nchlt_eng_001821 #utts: 1 +id: (nchlt_eng_001821-nchlt_eng_001821) +Scores: (#C #S #D #I) 17 1 0 3 +REF: r o c k ******* s f o R m i n g m o * n t * +HYP: r o c k s f o A m i n g m o U n t E +Eval: I S I I + +Speaker sentences 565: nchlt_eng_001822 #utts: 1 +id: (nchlt_eng_001822-nchlt_eng_001822) +Scores: (#C #S #D #I) 14 5 2 0 +REF: A S s A s S i n a t e d m o n A R c H s +HYP: * * s E s T i n a t e d m o n U K c K s +Eval: D D S S S S S + +Speaker sentences 566: nchlt_eng_001823 #utts: 1 +id: (nchlt_eng_001823-nchlt_eng_001823) +Scores: (#C #S #D #I) 27 5 5 4 +REF: i n c l U D e i n T e R N a T i O n A l * n o n * ******* g o v e r N m e n t A l * +HYP: i n c l * A e i n e * a S i * n O l E n o n D g o v e r * m e n t * l E +Eval: D S S D S S D S I I I D D I + +Speaker sentences 567: nchlt_eng_001824 #utts: 1 +id: (nchlt_eng_001824-nchlt_eng_001824) +Scores: (#C #S #D #I) 11 5 0 2 +REF: m E t * R I c * s p a c e S m +HYP: m I t C H c K s p a c e A I m +Eval: S I S S I S S + +Speaker sentences 568: swc_eng_001744 #utts: 1 +id: (swc_eng_001744-swc_eng_001744) +Scores: (#C #S #D #I) 26 2 3 3 +REF: o r * r e ******* p A I r * t h e b r E a k I n t h e t a P e +HYP: o r E r e p * E r E t h e b r * a k * n t h e t a C e +Eval: I I D S I D D S + +Speaker sentences 569: swc_eng_001745 #utts: 1 +id: (swc_eng_001745-swc_eng_001745) +Scores: (#C #S #D #I) 7 9 2 0 +REF: V A r I O U S s U B S t A n C e s +HYP: * E r * Y H T s O P t E n T e s +Eval: D S D S S S S S S S S + +Speaker sentences 570: swc_eng_001746 #utts: 1 +id: (swc_eng_001746-swc_eng_001746) +Scores: (#C #S #D #I) 43 3 8 0 +REF: T h i s m o s t c o M m O N l y O C c u r s w H e N n e I t h e r S i d e I s a b l e t o +HYP: * h i s m o s t c o * m * E l y * A c u r s w * e * n e t h e r * i d e * s a b l e t o +Eval: D D D S D S D D S D D + +Speaker sentences 571: swc_eng_001747 #utts: 1 +id: (swc_eng_001747-swc_eng_001747) +Scores: (#C #S #D #I) 45 9 8 0 +REF: g r E a t b a r R i E r r e E f i s m A n A g e D b y t h e g r E a t b A R R i E R r e E f m A r I n E +HYP: g r * a t b a r Y i A r r e T f i s m E n * g e * b y t h e g r * a t b * * E i * A r e A f m U r E n * +Eval: D S S S S D D D D D S D S S S S D + +Speaker sentences 572: swc_eng_001748 #utts: 1 +id: (swc_eng_001748-swc_eng_001748) +Scores: (#C #S #D #I) 16 0 5 4 +REF: * ******* * a t l e a s * t t H r E e R o U t E s +HYP: I H a t l e a s H t t * r * e * o * t * s +Eval: I I I I D D D D D + +Speaker sentences 573: swc_eng_001749 #utts: 1 +id: (swc_eng_001749-swc_eng_001749) +Scores: (#C #S #D #I) 9 4 2 2 +REF: d E f i C I E n * * C I e s i n +HYP: d * f i * H A n T E S Y e s i n +Eval: D D S S I I S S + +Speaker sentences 574: swc_eng_001750 #utts: 1 +id: (swc_eng_001750-swc_eng_001750) +Scores: (#C #S #D #I) 25 3 7 1 +REF: w I L l S h o W e v I d e n c e o f h e m O R r H A g E i n * +HYP: w * * l * h o * e v T d e n c e o f h e m * r * Y g * i n T +Eval: D D D D S D S D S D I + +Speaker sentences 575: swc_eng_001751 #utts: 1 +id: (swc_eng_001751-swc_eng_001751) +Scores: (#C #S #D #I) 14 3 5 1 +REF: f i N d A n a n * s W e r Q U i c K l y +HYP: f i * d ******* E n ******* a n C s * e r C R i c * l y +Eval: D D S D I D S S D + +Speaker sentences 576: swc_eng_001752 #utts: 1 +id: (swc_eng_001752-swc_eng_001752) +Scores: (#C #S #D #I) 39 6 4 4 +REF: E n a b l e s d I v * I S i v e a n d u n * ******* d e m ******* o c r a t I c s o C i a l p O l i C I e S +HYP: * n a b l e s d E v Y O C i v e a n d u n D d e m o c r a t * c s o T i a l p * l i S Y e * +Eval: D S I S S I I I D S D S S D + +Speaker sentences 577: swc_eng_001753 #utts: 1 +id: (swc_eng_001753-swc_eng_001753) +Scores: (#C #S #D #I) 27 6 7 3 +REF: m a D e * r e C e n T t * i T l e s a V A i l a b l E o n * c A S s E T T E +HYP: m a * e D r e S e n * t H i H l e s a * i l a b l * o n D c * O s * * A I +Eval: D I S D I S D S D I D S D D S S + +Speaker sentences 578: swc_eng_001754 #utts: 1 +id: (swc_eng_001754-swc_eng_001754) +Scores: (#C #S #D #I) 19 6 5 3 +REF: d I s t r I c t I n E I G H t E E n s i * X t ******* y s * i X +HYP: d U s t r * c t A n * * * A t * I n s i C S t y s A i C +Eval: S D S D D D S D S I S I I S + +Speaker sentences 579: swc_eng_001755 #utts: 1 +id: (swc_eng_001755-swc_eng_001755) +Scores: (#C #S #D #I) 14 4 4 2 +REF: A l i T t l E I n t o f u * * T U r i T y +HYP: * ******* l i * t l * A n t o f u C P E I r i D y +Eval: D D D D S I I S S S + +Speaker sentences 580: swc_eng_001756 #utts: 1 +id: (swc_eng_001756-swc_eng_001756) +Scores: (#C #S #D #I) 19 2 5 10 +REF: * * ******* * * * * * ******* g r o i n a n d A d V a n c * e D t h r O U G H +HYP: A Y I N T H E g r o i n a n d E d F a n c S e * t h r * * * * +Eval: I I I I I I I I I S S I D D D D D + +Speaker sentences 581: swc_eng_001757 #utts: 1 +id: (swc_eng_001757-swc_eng_001757) +Scores: (#C #S #D #I) 48 8 16 2 +REF: T e c H n O l O g I e s I N i m p l E M e n t i N g t r a n s ******* h u m A n i s T G O A l S o f E n ******* h a n c E D p E r f o r m A N C e +HYP: * e c * n * l A g Y e s A D i m p l * * e n t i * g t r a n s h u m * n i s * ******* * * C l E o f A n h a n c * S p * r f o r m * * * e +Eval: D D D S S S S D D D I D D D D D S S S I D S D D D D + +Speaker sentences 582: swc_eng_001758 #utts: 1 +id: (swc_eng_001758-swc_eng_001758) +Scores: (#C #S #D #I) 10 4 3 3 +REF: I n * ******* c * L U d i n g n a p H T H A +HYP: * n D c O A T d i n g n a p * * S E +Eval: D I I I S S D D S S + +Speaker sentences 583: swc_eng_001759 #utts: 1 +id: (swc_eng_001759-swc_eng_001759) +Scores: (#C #S #D #I) 28 10 5 3 +REF: b y s p a n I s h C H u r c h ******* m A n l U I S r * A m * I r E Z d e l U C E n a +HYP: b y s p a n * s h * T u r c h m E n l * * L r E m E O r A S d e ******* l O S A n a +Eval: D D S I S D D S I S I S S S D S S S + +Speaker sentences 584: swc_eng_001760 #utts: 1 +id: (swc_eng_001760-swc_eng_001760) +Scores: (#C #S #D #I) 15 1 1 2 +REF: d I v * i * D e d d e m o c r a t s +HYP: d * v F i H T e d d e m o c r a t s +Eval: D I I S + +Speaker sentences 585: swc_eng_001761 #utts: 1 +id: (swc_eng_001761-swc_eng_001761) +Scores: (#C #S #D #I) 39 4 7 8 +REF: T h e w o r l D C h a * M p I O n ******* s h i p * h a s B E e n c o N t r o L l e D b y * * f i ******* d * ******* e +HYP: * h e w o r l * T h a N P p E n s h i p T h a s * * e n c o * t r o * l e * b y E F f i d E e +Eval: D D S I S S S I I D D D D D I I I I I + +Speaker sentences 586: swc_eng_001762 #utts: 1 +id: (swc_eng_001762-swc_eng_001762) +Scores: (#C #S #D #I) 22 0 5 2 +REF: w h E R e T h e S t a r T i n g p o s i t i o n ******* * +HYP: w h * * e * h e * t a r * i n g p o s i t i o n I +Eval: D D D D D I I + +Speaker sentences 587: swc_eng_001763 #utts: 1 +id: (swc_eng_001763-swc_eng_001763) +Scores: (#C #S #D #I) 79 4 13 3 +REF: B e e n c r E a t e d i n e v E r y s t a t E a n d t e R r i t O r y t o p r O t e c * t a n d P r e s e r v E t h e c o U n t r y s * u n I Q U E E c o s * Y s t e m s +HYP: * e e n ******* c r * a t e d i n e v * r y s t a t * a n d t e * r i t * r y t o p r * t e c H t a n d * r e s e r v * t h e c o * n t r y s O u n * A K Y * c o s C I s t e m s +Eval: D D D D D D D D I D D D I D S S S D I S + +Speaker sentences 588: swc_eng_001764 #utts: 1 +id: (swc_eng_001764-swc_eng_001764) +Scores: (#C #S #D #I) 26 6 10 3 +REF: D e D I c a t I o * n * o f t h e n E W Z E A l A n d w a r M e ******* m o R i A l +HYP: * e * A c a t H o E n T o f t h e ******* n * * ******* O S I l E n d w a r ******* * e m o * i * l +Eval: D D S S I I D D D D S S S S D D I D D + +Speaker sentences 589: swc_eng_001765 #utts: 1 +id: (swc_eng_001765-swc_eng_001765) +Scores: (#C #S #D #I) 33 7 7 1 +REF: a C C l a I m * f R o m t h e r A I l R o A d c O M p A n I e s f o r v e t O i n G +HYP: a * l a * m E f * o m t h e r * E l o U d c * U p * n Y e s f o r v e t i n * +Eval: D S D I D D S S S D S D S S D + +Speaker sentences 590: swc_eng_001766 #utts: 1 +id: (swc_eng_001766-swc_eng_001766) +Scores: (#C #S #D #I) 12 0 9 4 +REF: * * * ******* t o W n i s s p l i T B E t W E E N +HYP: T H E t o * n i s s p l i * ******* * * t * * * * +Eval: I I I I D D D D D D D D D + +Speaker sentences 591: swc_eng_001767 #utts: 1 +id: (swc_eng_001767-swc_eng_001767) +Scores: (#C #S #D #I) 32 13 10 2 +REF: M o s * * Q U i T O f i s h i s a p A R t i c U L A R l y a G g r e S S i V E s p E C I e s K N O W n +HYP: * o s K E A T i D f i s h i s a p * E t i c * * * I l y a E g r e * C i O F s p A T H e s * * * * n +Eval: D I I S S S S D S D D D S S D S S S S S S D D D D + +Speaker sentences 592: swc_eng_001768 #utts: 1 +id: (swc_eng_001768-swc_eng_001768) +Scores: (#C #S #D #I) 28 4 4 5 +REF: a n d t h e n a * t i o n A l C H e s * S C h A m P i O n ******* s h i p * * s +HYP: a n d t h e n a S t i o n * l * W e s E D * h E m i * n s h i p T E s +Eval: I D D S I S D S S D I I I + +Speaker sentences 593: swc_eng_001769 #utts: 1 +id: (swc_eng_001769-swc_eng_001769) +Scores: (#C #S #D #I) 33 3 6 2 +REF: p r o b l E m I s K n o W n t o r U n i n p O l y ******* n o ******* m I a l t i m e +HYP: p r o b l O m E A s * n o * n t o r * n i n ******* p * l y n o m * a l t i m e +Eval: S S S D D D D D I I D + +Speaker sentences 594: swc_eng_001770 #utts: 1 +id: (swc_eng_001770-swc_eng_001770) +Scores: (#C #S #D #I) 24 3 1 9 +REF: * * * ******* j * * * r a n d p a r K e r w * a t K i * n s h a r D I n +HYP: L A E j U O I r a n d p a r C e r w H a t i E n s h a r * T n +Eval: I I I I I I I S I S I D S + +Speaker sentences 595: swc_eng_001771 #utts: 1 +id: (swc_eng_001771-swc_eng_001771) +Scores: (#C #S #D #I) 17 3 5 0 +REF: i n N I n E t E E n s e v e N t y t h r E E +HYP: i n * * n D t I n s e v e * t y t h r * * +Eval: D D S S S D D D + +Speaker sentences 596: swc_eng_001772 #utts: 1 +id: (swc_eng_001772-swc_eng_001772) +Scores: (#C #S #D #I) 28 3 7 2 +REF: d E V e l O p i n g a n d * * u s i n g s u c h t e C H n O l O G I E s +HYP: d * * e l * p i n g a n d Y O u s i n g s u c h t e * n A l * * * D s +Eval: D D D I I D S S D D D S + +Speaker sentences 597: swc_eng_001773 #utts: 1 +id: (swc_eng_001773-swc_eng_001773) +Scores: (#C #S #D #I) 13 4 1 0 +REF: F o r s o m e Q U E s t i o n S +HYP: * o r s o m e W P A s t i o n D +Eval: D S S S S + +Speaker sentences 598: swc_eng_001774 #utts: 1 +id: (swc_eng_001774-swc_eng_001774) +Scores: (#C #S #D #I) 14 2 5 1 +REF: C l a I m * o F P r O o F t h a t P +HYP: * l a * m E o * * r * o E t h a t E +Eval: D D I D D D S S + +Speaker sentences 599: swc_eng_001775 #utts: 1 +id: (swc_eng_001775-swc_eng_001775) +Scores: (#C #S #D #I) 42 10 11 2 +REF: a b l a D d E R c a T h E t e r i s U S u A L l y i n ******* s e r t e d T o m o n I T o R F l U i * d b A L A n C E +HYP: a b l a * d * A c a * h * t e r i s * O u * R l y i n s e r t e d S o m o n * S o F * l O i E d ******* b * U M n * S +Eval: D D S D D D S D S I S D S S D S I D D S S D S + +Speaker sentences 600: swc_eng_001776 #utts: 1 +id: (swc_eng_001776-swc_eng_001776) +Scores: (#C #S #D #I) 54 18 5 3 +REF: P r O M o t i o n o f E u ******* G e n i C E n ******* h a N C E M e n t t E c H n O l O g I e s m i g h T u n i n t e n T I o n a L l y E n * c O U r a g e +HYP: E r N o t i o n o f Y u J e n i K A n h a * * S P e n t t I c n A l A g * e s m i g h * u n i n t e n C H o n a * l y I n D c K R r a g e +Eval: S S S S I S S S I D D S S S S S S D D S S D S I S S + +Speaker sentences 601: swc_eng_001777 #utts: 1 +id: (swc_eng_001777-swc_eng_001777) +Scores: (#C #S #D #I) 65 4 8 5 +REF: * * ******* t h e A T t e n T i o n o f r e ******* s E A r C h e r s c a N b e f o * C u s E d O N p a r t i a l s o l u t i o n s o r s o l u t i o n s +HYP: A T t h e * * t e n * i o n o f r e s * U r T h e r s c a * b e f o K A u s * d * M p a r t i a l ******* s o l u t i o n s o r s o l u t i o n s +Eval: I I I D D D I D S S D I S D D S D + +Speaker sentences 602: swc_eng_001778 #utts: 1 +id: (swc_eng_001778-swc_eng_001778) +Scores: (#C #S #D #I) 25 2 3 1 +REF: K n o * W n o f f o r h U n D r e d S o f y e a r s +HYP: * n o N E n o f f o r h * n T r e d * o f y e a r s +Eval: D I S D S D + +Speaker sentences 603: swc_eng_001779 #utts: 1 +id: (swc_eng_001779-swc_eng_001779) +Scores: (#C #S #D #I) 24 4 16 0 +REF: O n l y m A R s u P i a l s h a V e s U R V i v e D T O t H E P R E S E N t +HYP: * n l y m * U s u B i a l s h a * e s * O F i v e * ******* * * t * * * * * * * * t +Eval: D D S S D D S S D D D D D D D D D D D D + +Speaker sentences 604: swc_eng_001780 #utts: 1 +id: (swc_eng_001780-swc_eng_001780) +Scores: (#C #S #D #I) 41 7 4 2 +REF: t o W h I c h a L l t h e E d I b l e s p E c I e s o f c r u s t ******* * A C E a n b e l o n g +HYP: t o * h * c h a * l t h e ******* A d A b l e s p A c H e s o f c r u s t I S T H a n b e l o n g +Eval: D D D D S S S S I I S S S + +Speaker sentences 605: swc_eng_001781 #utts: 1 +id: (swc_eng_001781-swc_eng_001781) +Scores: (#C #S #D #I) 13 4 1 2 +REF: A l g O r I t h * m r e s E A r c h * +HYP: O l g E r t h E m r e s * U r c h E +Eval: S S S I D S I + +Speaker sentences 606: swc_eng_001782 #utts: 1 +id: (swc_eng_001782-swc_eng_001782) +Scores: (#C #S #D #I) 65 11 11 7 +REF: n i n E t E E n s i * x t * y t w o P H i l i p * s I n ******* v e n t E d t h e c o m p a c t A U d i o C A S s e T t E m e d * I U m f o r A U d i o * * S T o r A g e +HYP: n i n * t A I n s i C x t D y t w o * F i l i p E s * n v e n t * d t h e c o m p a c t * O d i o ******* * K E s e * t * m e d Y H A m f o r * O d i o U S D A o r * g e +Eval: D S S I I D S I D I D D S D D S S D D I S S D S I I S S D + +Speaker sentences 607: swc_eng_001783 #utts: 1 +id: (swc_eng_001783-swc_eng_001783) +Scores: (#C #S #D #I) 18 1 4 0 +REF: o B s t r U c t i O n O f t h e F l o w +HYP: o * s t r A c t i * n * f t h e * l o w +Eval: D S D D D + +Speaker sentences 608: swc_eng_001784 #utts: 1 +id: (swc_eng_001784-swc_eng_001784) +Scores: (#C #S #D #I) 12 5 6 1 +REF: * A M P h i b i A n S a N d r e p T I L E S +HYP: N D F h i b i E n D a * d r e p * * * * * +Eval: I S S S S S D D D D D D + +Speaker sentences 609: swc_eng_001785 #utts: 1 +id: (swc_eng_001785-swc_eng_001785) +Scores: (#C #S #D #I) 21 7 3 0 +REF: w O m e n s w O r l d c H e s S C h a m P i O n S h I P +HYP: w E m e n s w H r l d c * e s T * h a m i * n C h O F +Eval: S S D S D S D S S S + +Speaker sentences 610: swc_eng_001786 #utts: 1 +id: (swc_eng_001786-swc_eng_001786) +Scores: (#C #S #D #I) 83 11 26 5 +REF: c o n t a I n S d e S c r I P t i o n s a n d c o M m e n t a r I E s o N t h e S t a t E o f * n ******* b * ******* i ******* C s C i E n c e a n d T e C H n O l O g y B Y m a J O r c o n t r I B u T O r s t o t h E S E F I e L D S +HYP: c o n t a * n E d e * c r * * t i o n s a n d c o * m e n t a r * Y s o * t h e * t a t * o f E n b E i Y s * i * n c e a n d * e G n A l * g y A S m a * E r c o n t r * * u D E r s ******* t o t h * * * ******* * * e * * * +Eval: D S D D D D D S D D D I I I I I S D D D S S S D S S D S D D S S D D D D D D D D D D + +Speaker sentences 611: swc_eng_001787 #utts: 1 +id: (swc_eng_001787-swc_eng_001787) +Scores: (#C #S #D #I) 23 5 3 2 +REF: p U e * r I L e * F a n t A S y o r s o C i a l t r e n D +HYP: p * e U r * O e L H a n t * I y o r s o T i a l t r e n T +Eval: D I D S I S D S S S + +Speaker sentences 612: swc_eng_001788 #utts: 1 +id: (swc_eng_001788-swc_eng_001788) +Scores: (#C #S #D #I) 31 2 5 0 +REF: m o s t c o m p a c T c A s S e T t E S w e r E s o l d b l a n k +HYP: m o s t c o m p a c * c s A e * t * * w e r * s o l d b l a n k +Eval: D S S D D D D + +Speaker sentences 613: swc_eng_001789 #utts: 1 +id: (swc_eng_001789-swc_eng_001789) +Scores: (#C #S #D #I) 17 2 5 3 +REF: i * f T h e r E i s A n * A l * g O r I t h M +HYP: i O f * h e r * i s * n O U l D g * r * t h E +Eval: I D D D I S I D D S + +Speaker sentences 614: swc_eng_001790 #utts: 1 +id: (swc_eng_001790-swc_eng_001790) +Scores: (#C #S #D #I) 53 9 11 1 +REF: T h e s o U t h e R n A U s t r a l i A n c o A s t a n d i n s U b a * n t A R c t i C A U s t r A l i a n t e R r I t O r I E s +HYP: * h e s o * t h e * n * * s t r a l i E n c o * s t a n d i n s A b B a E n t I E c t i Y * O s t r * l i a n t e * r A t * r * Y s +Eval: D D D D D S D S S I S S S D S D D S D D S + +Speaker sentences 615: swc_eng_001791 #utts: 1 +id: (swc_eng_001791-swc_eng_001791) +Scores: (#C #S #D #I) 29 3 7 2 +REF: d a t A r a t E s O f t Y p I c A L l y f i v e h U n d * r e d * t O +HYP: d a t * ******* r a t * s * f t I p O c * * l y f i v e h * n d E r e d T t W +Eval: D D D D S S D D D I I S + +Speaker sentences 616: swc_eng_001792 #utts: 1 +id: (swc_eng_001792-swc_eng_001792) +Scores: (#C #S #D #I) 17 0 1 1 +REF: d e * p r i v i n g t h e d u c K +HYP: d e R p r i v i n g t h e d u c * +Eval: I D + +Speaker sentences 617: swc_eng_001793 #utts: 1 +id: (swc_eng_001793-swc_eng_001793) +Scores: (#C #S #D #I) 25 1 4 1 +REF: n i N e * p e r C e n t o f t h e t o T A l c a s t +HYP: n i * e N p e r S e n t o f ******* t h e t o * * l c a s t +Eval: D I S D D D + +Speaker sentences 618: swc_eng_001794 #utts: 1 +id: (swc_eng_001794-swc_eng_001794) +Scores: (#C #S #D #I) 41 3 14 5 +REF: a n * t * e r i O r * C E r E b r A L a R t E r * y a n d a n t * e R i O R c o M m U n I c a t i n g a R t E R y +HYP: a n D t H e r i * r S S O r * b r * * a * t * r I y a n d a n t H e * i * E c o * m * n * c a t i n g a * t * * y +Eval: I I D I S S D D D D D I I D D S D D D D D D + +Speaker sentences 619: swc_eng_001795 #utts: 1 +id: (swc_eng_001795-swc_eng_001795) +Scores: (#C #S #D #I) 15 3 5 1 +REF: I T D I d n o t i m ******* p a R T s h i n E +HYP: * E * * d ******* n o t i m p a S H s h i n * +Eval: D S D D D I S S D + +Speaker sentences 620: swc_eng_001796 #utts: 1 +id: (swc_eng_001796-swc_eng_001796) +Scores: (#C #S #D #I) 22 1 0 2 +REF: e n ******* t * I r e d e m o c r a t i c p a r t y +HYP: e n t H E r e d e m o c r a t i c p a r t y +Eval: I I S + +Speaker sentences 621: swc_eng_001797 #utts: 1 +id: (swc_eng_001797-swc_eng_001797) +Scores: (#C #S #D #I) 32 3 14 1 +REF: n O T C h e s o n t o p * o f t h e C A S s e T t E S h E L l i n d I c a T E t h E +HYP: n * * * h e s o n t o p E o f t h e * * E s e * t * ******* * h * A l i n d E c a * * ******* t h * +Eval: D D D I D D S D D D D D S S D D D D + +Speaker sentences 622: swc_eng_001798 #utts: 1 +id: (swc_eng_001798-swc_eng_001798) +Scores: (#C #S #D #I) 7 7 3 1 +REF: A L l O W O N E t * O s H o W +HYP: * * l A E U I A t H S s E o * +Eval: D D S S S S S I S S D + +Speaker sentences 623: swc_eng_001799 #utts: 1 +id: (swc_eng_001799-swc_eng_001799) +Scores: (#C #S #D #I) 22 3 6 5 +REF: i S a n E n ******* d * a n g E R e d m A r * i n E s p E c * I e s * +HYP: i * a n ******* A n d T a n g * * e d m * r A i n * s p A c H Y e s T +Eval: D D S I I D D D I D S I S I + +Speaker sentences 624: swc_eng_001800 #utts: 1 +id: (swc_eng_001800-swc_eng_001800) +Scores: (#C #S #D #I) 17 0 5 0 +REF: B r o w n d e s i r e D E l e c t I o N +HYP: * r o w n d e s i r e * * l e c t * o * +Eval: D D D D D + +Speaker sentences 625: swc_eng_001801 #utts: 1 +id: (swc_eng_001801-swc_eng_001801) +Scores: (#C #S #D #I) 46 0 8 2 +REF: T h i s f a c T d o E s * n t s a y m u c h a b o u t w H e r E t h e p r o b l E m * l I E s +HYP: * h i s f a c * d o * s A n t s a y m u c h a b o u t w * e r * t h e p r o b l * m E l * * s +Eval: D D D I D D D I D D + +Speaker sentences 626: swc_eng_001802 #utts: 1 +id: (swc_eng_001802-swc_eng_001802) +Scores: (#C #S #D #I) 19 2 8 0 +REF: E c O N o m I c A l s O C i E t y b e g a n a s A +HYP: * c * * o m O c * l s * S i * t y b e g a n a s ******* * +Eval: D D D S D D S D D D + +Speaker sentences 627: swc_eng_001803 #utts: 1 +id: (swc_eng_001803-swc_eng_001803) +Scores: (#C #S #D #I) 33 5 7 4 +REF: w i t H t o U r i s t s A R r I v i n g * B Y s t * e A M b o A t a n d t r * a * I n +HYP: w i t * t o * r i s t s ******* * * r E v i n g T H E s t M e * b o * t a n d t r I a M E n +Eval: D D D D D S I S S I D S D I I S + +Speaker sentences 628: swc_eng_001804 #utts: 1 +id: (swc_eng_001804-swc_eng_001804) +Scores: (#C #S #D #I) 30 1 5 1 +REF: f i r s t d i A l o g U E b e t w E e n t r a n s ******* h u m A n i s M +HYP: f i r s t d i * l o g * * b e t w * e n t r a n s h u m I n i s * +Eval: D D D D I S D + +Speaker sentences 629: swc_eng_001805 #utts: 1 +id: (swc_eng_001805-swc_eng_001805) +Scores: (#C #S #D #I) 23 4 9 0 +REF: n e V e r b E e n p A r T o f t h e O l Y M p I c G a M E s +HYP: n e * e r b * e n p * r D o f t h e ******* * l * N p * c ******* * a N D s +Eval: D D D S D D D S D D D S S + +Speaker sentences 630: swc_eng_001806 #utts: 1 +id: (swc_eng_001806-swc_eng_001806) +Scores: (#C #S #D #I) 15 3 1 6 +REF: r e * g * I s f U r n i t U R e * a n d ******* * * +HYP: r e A g E S s f I r n i t * C e R a n d T H +Eval: I I S S D S I I I I + +Speaker sentences 631: swc_eng_001807 #utts: 1 +id: (swc_eng_001807-swc_eng_001807) +Scores: (#C #S #D #I) 13 4 8 0 +REF: i n h i G H l E V E l t O U r N A m e n T S +HYP: i n h i * * ******* l * A B l t * * r * E m e n * C +Eval: D D D D S S D D D S D S + +Speaker sentences 632: swc_eng_001808 #utts: 1 +id: (swc_eng_001808-swc_eng_001808) +Scores: (#C #S #D #I) 17 1 4 2 +REF: t o L o c a t E t h E a n E u r Y s * * m +HYP: t o * o c a t * t h * a n D u r * s O N m +Eval: D D D S D I I + +Speaker sentences 633: swc_eng_001809 #utts: 1 +id: (swc_eng_001809-swc_eng_001809) +Scores: (#C #S #D #I) 12 5 4 2 +REF: M o r ******* P h O l * O G I c A l f r E e d O m +HYP: * o r F h * l U C H O c * l f r * e d E m +Eval: D I S D I S S S D D S + +Speaker sentences 634: swc_eng_001810 #utts: 1 +id: (swc_eng_001810-swc_eng_001810) +Scores: (#C #S #D #I) 19 3 3 2 +REF: E n ******* e r ******* G e t i c a T t a c k i n g s t Y L E +HYP: * n e r J e t i c a * t a c k i n g s t * O U +Eval: D I I S D D S S + +Speaker sentences 635: swc_eng_001811 #utts: 1 +id: (swc_eng_001811-swc_eng_001811) +Scores: (#C #S #D #I) 34 8 8 2 +REF: E X a c T l y f o r T Y Y E a r s a f T e r t H e c o R n E R s t * ******* o N E w a s l a I D +HYP: * G a c H l y f o r * E * O a r s a f * e r t * e c o * n * I s t D o * M w a s l a T E +Eval: D S S D S D S D D D D S I I D S S S + +Speaker sentences 636: swc_eng_001812 #utts: 1 +id: (swc_eng_001812-swc_eng_001812) +Scores: (#C #S #D #I) 17 2 5 3 +REF: B a S e D O N t h e r e c O g n i t i o n ******* * * +HYP: * a C e * ******* * * t h e r e c A g n i t i o n T H +Eval: D S D D D D S I I I + +Speaker sentences 637: swc_eng_001813 #utts: 1 +id: (swc_eng_001813-swc_eng_001813) +Scores: (#C #S #D #I) 26 3 3 1 +REF: o r E l E C t r o n i c * b u T t O n s o r d I s p l a y +HYP: o r * l * A t r o n i c T b u * t E n s o r d E s p l a y +Eval: D D S I D S S + +Speaker sentences 638: swc_eng_001814 #utts: 1 +id: (swc_eng_001814-swc_eng_001814) +Scores: (#C #S #D #I) 23 4 4 3 +REF: i s u n K n o W n w H E t h e r p * e Q u A l s * n p * +HYP: i s u n n o * n w * * t h e r p E A e C u * l s A n M p Y +Eval: S D D D I S S D I S I + +Speaker sentences 639: swc_eng_001815 #utts: 1 +id: (swc_eng_001815-swc_eng_001815) +Scores: (#C #S #D #I) 26 1 5 1 +REF: W h i c h c o m E s f R o M t h e v e r b a c * u E r E +HYP: * h i c h c o m * s f * o * t h e v e r b a c Q u A r * +Eval: D D D D I S D + +Speaker sentences 640: swc_eng_001816 #utts: 1 +id: (swc_eng_001816-swc_eng_001816) +Scores: (#C #S #D #I) 52 5 13 1 +REF: d * I S p r O p o r t i o n A t E l y A v a i l a b l E t O t h O S e w I t H G r E a t e r f i n a n C I a l r e s O u r C e s +HYP: d E T p r * p o r t i o n * t * l y O v a i l a b l * t * t h * A e w * t * * r * a t e r f i n a n * H a l r e s * u r * e s +Eval: I S S D D D S D D D S D D D D D S D D + +Speaker sentences 641: swc_eng_001817 #utts: 1 +id: (swc_eng_001817-swc_eng_001817) +Scores: (#C #S #D #I) 32 3 17 0 +REF: T H E I M m I N E n T t h r e A t S t o T h E s U R V i v A l o f m a n y s p E c i e S +HYP: * * * ******* * * m * * * n * t h r e * t * t o * h * s * O F i v * l o f m a n y s p A c i e * +Eval: D D D D D D D D D D D D D D D S S D S D + +Speaker sentences 642: swc_eng_001818 #utts: 1 +id: (swc_eng_001818-swc_eng_001818) +Scores: (#C #S #D #I) 12 4 3 1 +REF: E v e * n m o r E d I F F i c U l T +HYP: A v e O n m o r * d * E T i c * l E +Eval: S I D D S S D S + +Speaker sentences 643: swc_eng_001819 #utts: 1 +id: (swc_eng_001819-swc_eng_001819) +Scores: (#C #S #D #I) 25 7 1 11 +REF: a n d ******* * * * * * * 2 1 s p e * c i e s o f * O c * e a n i * C d O l P H I n +HYP: a n d T W E T Y W E N s p e A c i e s o f U S c R e a n i A G d * l F E O n +Eval: I I I I I I I S S I I S I I S D S S S + +Speaker sentences 644: swc_eng_001820 #utts: 1 +id: (swc_eng_001820-swc_eng_001820) +Scores: (#C #S #D #I) 16 1 2 1 +REF: a C h I e v i n g p r O m o * t i o n +HYP: a h * e v i n g p r * m o U t i o n +Eval: S D D I + +Speaker sentences 645: swc_eng_001821 #utts: 1 +id: (swc_eng_001821-swc_eng_001821) +Scores: (#C #S #D #I) 12 9 3 0 +REF: T R A n S H U M A N i s t A S s u m P t i o n +HYP: * * E n D T Y M i s t E s u m * t i o n +Eval: D D S S S S S S S S S D + +Speaker sentences 646: swc_eng_001822 #utts: 1 +id: (swc_eng_001822-swc_eng_001822) +Scores: (#C #S #D #I) 15 3 1 0 +REF: o n t h e f i r s t b A L l O T +HYP: o n t h e f i r s t b * E l I D +Eval: D S S S + +Speaker sentences 647: swc_eng_001823 #utts: 1 +id: (swc_eng_001823-swc_eng_001823) +Scores: (#C #S #D #I) 66 4 12 6 +REF: s t o r y i n d I c a t i V e * o f t h e r i s E i n g l o B a * l s * i g n i f i C A n C E o f s H o E p O l i s h * i s t o L D b y * j * e A n +HYP: s t o r y i n d E c a t i * e F o f t h e r i s * ******* i n ******* g l o * a B l s R i g n i f i * * n G S o f s * o * p * l i s h E i s t o * E b y D j D e * n +Eval: S D I D D D D I I D D S S D D D I D S I I D + +Speaker sentences 648: swc_eng_001824 #utts: 1 +id: (swc_eng_001824-swc_eng_001824) +Scores: (#C #S #D #I) 33 3 8 1 +REF: w h I c h s P a r K e D H I s e a R l y I n t E r E s t I n * p o l i t i c s +HYP: w h * c h s B a r * e * * A s e a * l y E n t * r * s t * n D p o l i t i c s +Eval: D S D D D S D S D D D I + +Speaker sentences 649: swc_eng_001825 #utts: 1 +id: (swc_eng_001825-swc_eng_001825) +Scores: (#C #S #D #I) 38 5 2 4 +REF: w a s c A l l E d d o l b Y * * h * * x p r o i n f U l l a n d p a t E n t E D +HYP: w a s c O l l * d d o l b E A C h A C x p r o i n f O l l a n d p a t * n t I +Eval: S D S I I I I S D S S + +Speaker sentences 650: swc_eng_001826 #utts: 1 +id: (swc_eng_001826-swc_eng_001826) +Scores: (#C #S #D #I) 23 1 11 0 +REF: C o U L d s a v e a n d f i n D f i l e s B Y N U M b E R +HYP: * o * O d s a v e a n d f i n * f i l e s ******* * * * * * b * * +Eval: D D S D D D D D D D D D + +Speaker sentences 651: swc_eng_001827 #utts: 1 +id: (swc_eng_001827-swc_eng_001827) +Scores: (#C #S #D #I) 34 5 3 0 +REF: a U s t r A l i A n s n a K e S b e l o n g t o s e v E n f a m I l I e s +HYP: a * s t r * l i O n s n a C e * b e l o n g t o s e v O n f a m E l Y e s +Eval: D D S S D S S S + +Speaker sentences 652: swc_eng_001828 #utts: 1 +id: (swc_eng_001828-swc_eng_001828) +Scores: (#C #S #D #I) 7 5 6 1 +REF: * d E V E l O P I n G p L A y E r S +HYP: T d * O l * A n * p * * y O r * +Eval: I D S S D S S D D D S D + +Speaker sentences 653: swc_eng_001829 #utts: 1 +id: (swc_eng_001829-swc_eng_001829) +Scores: (#C #S #D #I) 24 3 16 0 +REF: D E C l i n E d s h a r p l y s I n c e I T s p e a K I N T H E L a T E +HYP: * * * l i n * d s h a r p l y s E n c e ******* * * s p e a * ******* * * ******* * * * * a N D +Eval: D D D D S D D D D D D D D D D D D S S + +Speaker sentences 654: swc_eng_001830 #utts: 1 +id: (swc_eng_001830-swc_eng_001830) +Scores: (#C #S #D #I) 36 3 12 2 +REF: w a s r e c o * r d e d E n t * i r E l y o n A f o U r t r a c K C A S s E T T e t a P E +HYP: w a s r e c o U r d e d I n t H i r * l y o n ******* * f o * r t r a c * * * O s * * * e C t a * * +Eval: I S I D D D D D D D S D D D S D D + +Speaker sentences 655: swc_eng_001831 #utts: 1 +id: (swc_eng_001831-swc_eng_001831) +Scores: (#C #S #D #I) 16 3 4 1 +REF: E n o R M O U s i m ******* p r o v E M e n t i n +HYP: * n o * L A s i m p r o v * * e n t i n +Eval: D D S S S I D D + +Speaker sentences 656: swc_eng_001832 #utts: 1 +id: (swc_eng_001832-swc_eng_001832) +Scores: (#C #S #D #I) 17 4 6 0 +REF: U r b A n a n d r u R A l l e g I s L A t O R S +HYP: * r b O n a n d r u * * l l e g E s * H t * * H +Eval: D S D D S D S D D S + +Speaker sentences 657: swc_eng_001833 #utts: 1 +id: (swc_eng_001833-swc_eng_001833) +Scores: (#C #S #D #I) 14 0 4 0 +REF: E a c h p l a y E r b E g I n s +HYP: * a c h p l a y * r b * g * n s +Eval: D D D D + +Speaker sentences 658: swc_eng_001834 #utts: 1 +id: (swc_eng_001834-swc_eng_001834) +Scores: (#C #S #D #I) 31 4 10 5 +REF: * C h E S s h a s I n ******* s p * i R e D m a n y c o m B I n A t o r i A l * p * u Z Z l e S +HYP: D J h * A s T h a s * n s p H i * e * m a n y c o m * * n * t o r i * l E p O u * S l e * +Eval: I S D S S D I I D D D D D D I I D S D + +Speaker sentences 659: swc_eng_001835 #utts: 1 +id: (swc_eng_001835-swc_eng_001835) +Scores: (#C #S #D #I) 13 1 3 3 +REF: M o r E h u * m a n E i m * * A g e +HYP: * o r * h u O m a n * i m I T D g e +Eval: D D I D I I S + +Speaker sentences 660: swc_eng_001836 #utts: 1 +id: (swc_eng_001836-swc_eng_001836) +Scores: (#C #S #D #I) 14 1 6 1 +REF: W E L l a s p * i r A T e d t a p E s +HYP: * * * l ******* a s p O i r * D e d t a p * s +Eval: D D D D I D S D + +Speaker sentences 661: swc_eng_001837 #utts: 1 +id: (swc_eng_001837-swc_eng_001837) +Scores: (#C #S #D #I) 17 8 5 2 +REF: T I n t ******* I n d E s C e n D S i n * t o t h e o C E A n +HYP: * A n t A n ******* d I s * e n * C E i n G t o t h e ******* o T I O n +Eval: D S I S D S D D S S I D S S S + +Speaker sentences 662: swc_eng_001838 #utts: 1 +id: (swc_eng_001838-swc_eng_001838) +Scores: (#C #S #D #I) 29 1 7 3 +REF: P r e s i D E n T p r o t e m * * * o f t h e s t a t E s e n A T E +HYP: * r e s i * * n * p r o t e m P O R o f t h e s t a t * s e n * * D +Eval: D D D D I I I D D D S + +Speaker sentences 663: swc_eng_001839 #utts: 1 +id: (swc_eng_001839-swc_eng_001839) +Scores: (#C #S #D #I) 27 5 16 1 +REF: B i S h o p c A N m o v e a n y n u m b E r o F s * q U A r E s D I A G O N A L L Y +HYP: * i C h o p c E L m o v e a n y n u m b * r ******* o * s C q H E r * s ******* * * * * * * * * * * +Eval: D S S S D D D I S S D D D D D D D D D D D D + +Speaker sentences 664: swc_eng_001840 #utts: 1 +id: (swc_eng_001840-swc_eng_001840) +Scores: (#C #S #D #I) 17 4 4 1 +REF: p r e S S U R e i n ******* s i d E t h e s K U L l +HYP: p r e * * T H e i n s i d * t h e s * C O l +Eval: D D S S I D D S S + +Speaker sentences 665: swc_eng_001841 #utts: 1 +id: (swc_eng_001841-swc_eng_001841) +Scores: (#C #S #D #I) 20 2 6 0 +REF: l i n E c O N N e C t S s h E e r n e s s w i t H +HYP: l i n G c * * A e * t * s h * e r n e s s w i t * +Eval: S D D S D D D D + +Speaker sentences 666: swc_eng_001842 #utts: 1 +id: (swc_eng_001842-swc_eng_001842) +Scores: (#C #S #D #I) 36 2 4 3 +REF: C o u n t r I E s o f t h e W e s t e R n p a l * E a r c ******* t i c f l y ******* w a y +HYP: * o u n t r * Y s o f t h e * e s t e * n p a l Y O a r c t i c f l y w a y +Eval: D D S D D I S I I + +Speaker sentences 667: swc_eng_001843 #utts: 1 +id: (swc_eng_001843-swc_eng_001843) +Scores: (#C #S #D #I) 33 7 7 3 +REF: N a T I o n A l s T A t i * s t i * C S * e s t I m a t E D t h e P o p U l a t i o n i n +HYP: * a * S o n * l ******* s D t i C s t i K E A e s t A m a t * * t h e * o p E l a t i o n i n +Eval: D D S D D S S I I S S I S D D D S + +Speaker sentences 668: swc_eng_001844 #utts: 1 +id: (swc_eng_001844-swc_eng_001844) +Scores: (#C #S #D #I) 25 4 2 3 +REF: u n * ******* d I s p * u t e d w O r l d c h e s s C h a M p I O n +HYP: u n D d E s p E u t e d w H r l d c h e s s * h a * p E E n +Eval: I I S I S D D S S + +Speaker sentences 669: swc_eng_001845 #utts: 1 +id: (swc_eng_001845-swc_eng_001845) +Scores: (#C #S #D #I) 11 8 1 4 +REF: J O S E r A U l c a p * A b L A n c * ******* a * +HYP: T A Y N r E L l c a p T b * I n c E a R +Eval: S S S S S S I S D S I I I + +Speaker sentences 670: swc_eng_001846 #utts: 1 +id: (swc_eng_001846-swc_eng_001846) +Scores: (#C #S #D #I) 75 7 14 4 +REF: W e r E E n a c t e d b y t h e G E n e r A l a S s e m b l y * w a s a m E a s U r E r a C i a L l y s e * g r E g a T i N g t h e S t a t e s r A I L r o A D c * a * r s +HYP: * e r * * n a c t e d b y t h e * C n e r * l a s e m b l y H w a s a m * a s E r * r a S i a * l y s e V g r * g a * i * g t h e * t a t e s r * E r o * T c O a R r s +Eval: D D D D S D S I D S D S D I D D D D D S S D S I I + +Speaker sentences 671: swc_eng_001847 #utts: 1 +id: (swc_eng_001847-swc_eng_001847) +Scores: (#C #S #D #I) 15 1 2 0 +REF: w h I c h W r a p S a l m o s t +HYP: w h * c h * r a p E a l m o s t +Eval: D D S + +Speaker sentences 672: swc_eng_001848 #utts: 1 +id: (swc_eng_001848-swc_eng_001848) +Scores: (#C #S #D #I) 30 9 8 1 +REF: T H I s a c t p r O t E c t S A L l n A t i V E f A U n * A a n d p r o v i d e S +HYP: * * * s E a c t p r E t H c t * * * l D n * t i O F f O R n E R a n d p r o v i d e * +Eval: D D D S S S D D D S D S S S S I S D + +Speaker sentences 673: swc_eng_001849 #utts: 1 +id: (swc_eng_001849-swc_eng_001849) +Scores: (#C #S #D #I) 48 5 9 2 +REF: w H e r E a s t h e f e m * a l E S s p e c U l U m i s d a r K b * r o W n b o r d E R e d w i t H w h I t E +HYP: w * e r a s t h e f e m I a l * * s p e c I l E m i s d a r C b O r o * n ******* b o r d * * e d w i t * w h A t * +Eval: D S I D D S S S I D D D D D S D + +Speaker sentences 674: swc_eng_001850 #utts: 1 +id: (swc_eng_001850-swc_eng_001850) +Scores: (#C #S #D #I) 11 4 3 1 +REF: R o t A r Y c O n * t R O l s o R +HYP: W o t E r E c E n C t * * l s o * +Eval: S S S S I D D D + +Speaker sentences 675: swc_eng_001851 #utts: 1 +id: (swc_eng_001851-swc_eng_001851) +Scores: (#C #S #D #I) 21 5 1 4 +REF: n * i * n E t E E n t w e l v e * i n r o * s T H E r n +HYP: n H i D n * t Y A n t w e l v e F i n r o S s F I r n +Eval: I I D S S I I S S S + +Speaker sentences 676: swc_eng_001852 #utts: 1 +id: (swc_eng_001852-swc_eng_001852) +Scores: (#C #S #D #I) 42 6 7 3 +REF: d i * A G n o S i s i s g e n E r A L l Y m a d e w I t h a C t * s c a n o F t h e h e a * d +HYP: d i D E D n o * i s i s g e n * r * * l * m a d e w H t h ******* a S E t E s c a n D o * t h e h e a I d +Eval: I S S D D D D D S D S S I S D I + +Speaker sentences 677: swc_eng_001853 #utts: 1 +id: (swc_eng_001853-swc_eng_001853) +Scores: (#C #S #D #I) 39 3 5 9 +REF: * * * ******* f i r s t g e n E r A L l y r e c o G n i * * Z e D w O r l d c h * e s s * c h a m * p I o n +HYP: T H E f i r s t g e n * r * * l y r e c o * n i G C S e * w E r l d c h A e s s T c h a m P p E o n +Eval: I I I I D D D D I I S D S I I I S + +Speaker sentences 678: swc_eng_001854 #utts: 1 +id: (swc_eng_001854-swc_eng_001854) +Scores: (#C #S #D #I) 26 2 7 0 +REF: S h e P p e y a n d s i T t i n g B O U R n E w E r e p a r t +HYP: * h e * p e y a n d s i * t i n g * * L n * w * r e p a r t +Eval: D D D D D S S D D + +Speaker sentences 679: swc_eng_001855 #utts: 1 +id: (swc_eng_001855-swc_eng_001855) +Scores: (#C #S #D #I) 32 4 4 2 +REF: H a d r e l E a s E D t h e I r A l b u m s b o t h t o * C d * a n D +HYP: * a d r e l * a s * T t h e * r E l b u m s b o t h t o S E d E a n T +Eval: D D D S D S I S I S + +Speaker sentences 680: swc_eng_001856 #utts: 1 +id: (swc_eng_001856-swc_eng_001856) +Scores: (#C #S #D #I) 22 0 5 0 +REF: w a t e R s A r o U n D t h e c o n t i n e n T +HYP: w a t e * s * r o * n * t h e c o n t i n e n * +Eval: D D D D D + +Speaker sentences 681: swc_eng_001857 #utts: 1 +id: (swc_eng_001857-swc_eng_001857) +Scores: (#C #S #D #I) 22 3 1 3 +REF: * ******* t h e r a n g E p e r s o n A l s t * E r E o s +HYP: F t h e r a n g H p e r s o n * l s t A I r I o s +Eval: I I S D I S S + +Speaker sentences 682: swc_eng_001858 #utts: 1 +id: (swc_eng_001858-swc_eng_001858) +Scores: (#C #S #D #I) 39 4 2 3 +REF: A n d ******* * V U m e T e r s a n d r e c o * r D i n g l e v E l c o n t r o l s o n +HYP: * n d E Y O m e D e r s a n d r e c o A r T i n g l e v * l c o n t r o l s o n +Eval: D I I S S S I S D + +Speaker sentences 683: swc_eng_001859 #utts: 1 +id: (swc_eng_001859-swc_eng_001859) +Scores: (#C #S #D #I) 11 1 3 1 +REF: P O l y n o ******* m I A l t i m e +HYP: * * l y n o m * U l t i m e +Eval: D D I D S + +Speaker sentences 684: swc_eng_001860 #utts: 1 +id: (swc_eng_001860-swc_eng_001860) +Scores: (#C #S #D #I) 32 1 5 0 +REF: a n d i t o f T e n d e s t r O Y E d t h e p l A Y a b i l i t y +HYP: a n d i t o f H e n d e s t r * * * d t h e p l * * a b i l i t y +Eval: S D D D D D + +Speaker sentences 685: swc_eng_001861 #utts: 1 +id: (swc_eng_001861-swc_eng_001861) +Scores: (#C #S #D #I) 8 1 0 2 +REF: c o n f * * u S i o n +HYP: c o n f L A u T i o n +Eval: I I S + +Speaker sentences 686: swc_eng_001862 #utts: 1 +id: (swc_eng_001862-swc_eng_001862) +Scores: (#C #S #D #I) 56 1 7 4 +REF: e Q u i v A l e n t t o t h e Q U e s t i o n o f w h E t h e r * * x * i s a m e m b E r o f c o m p o * s i T E +HYP: e * u i v * l e n t t o t h e * C e s t i o n o f w h * t h e r A C x E i s a m e m b * r o f c o m p o U s i * * +Eval: D D D S D I I I D I D D + +Speaker sentences 687: swc_eng_001863 #utts: 1 +id: (swc_eng_001863-swc_eng_001863) +Scores: (#C #S #D #I) 17 2 3 1 +REF: m o V E s t o i t S l a s t * r a n K +HYP: m o * * s ******* t o i t H l a s t E r a n G +Eval: D D D S I S + +Speaker sentences 688: swc_eng_001864 #utts: 1 +id: (swc_eng_001864-swc_eng_001864) +Scores: (#C #S #D #I) 12 1 0 2 +REF: p o s t G e n d e r i s * m * +HYP: p o s t H e n d e r i s T m E +Eval: S I I + +Speaker sentences 689: swc_eng_001865 #utts: 1 +id: (swc_eng_001865-swc_eng_001865) +Scores: (#C #S #D #I) 25 3 6 1 +REF: C o m p a c t c A s s E T t E Q u i C k l y f o u N d * u s E +HYP: * o m p a c t c O s s * A t * W u i * k l y f o u * d O u s * +Eval: D S D S D S D D I D + +Speaker sentences 690: swc_eng_001866 #utts: 1 +id: (swc_eng_001866-swc_eng_001866) +Scores: (#C #S #D #I) 24 3 3 3 +REF: * * ******* f o U r h u n d r E d t h I r t y t h r E E f e e T +HYP: O R f o * r h u n d r N d t h A r t y t h r * Y f e e * +Eval: I I I D S S D S D + +Speaker sentences 691: swc_eng_001867 #utts: 1 +id: (swc_eng_001867-swc_eng_001867) +Scores: (#C #S #D #I) 28 5 16 8 +REF: * * * * ******* w h I c h r e s U l t i n a s p e c * i * F i c * t Y P e o F p A W n S T R U C T U R E +HYP: I N G S w h * c h r e s E l t i n ******* a ******* s p e c S i O T i c T t I H e o * ******* p * O n ******* * * * * * * * * * +Eval: I I I I I D S D D I I S I S S D D D S D D D D D D D D D D + +Speaker sentences 692: swc_eng_001868 #utts: 1 +id: (swc_eng_001868-swc_eng_001868) +Scores: (#C #S #D #I) 18 4 6 0 +REF: b E f O r E n I n E t E E N n I n E t y s e v e n +HYP: b * f * r * n A n * t * I Y n A n * t y s e v e n +Eval: D D D S D D S S S D + +Speaker sentences 693: swc_eng_001869 #utts: 1 +id: (swc_eng_001869-swc_eng_001869) +Scores: (#C #S #D #I) 18 1 11 0 +REF: C o M m U N I c a t i o n s a n d h e A l t H C A R E +HYP: * o * m * * Y c a t i o n s a n d h e * l t * ******* * * * * +Eval: D D D D S D D D D D D D + +Speaker sentences 694: swc_eng_001870 #utts: 1 +id: (swc_eng_001870-swc_eng_001870) +Scores: (#C #S #D #I) 19 0 5 8 +REF: * s * * ******* a * * h * i n a p e r * s o n K n o W n T O +HYP: E s A Y a I C h E i n a p e r S s o n * n o * n ******* * * +Eval: I I I I I I I I D D D D D + +Speaker sentences 695: swc_eng_001871 #utts: 1 +id: (swc_eng_001871-swc_eng_001871) +Scores: (#C #S #D #I) 60 8 8 8 +REF: s o m E b r a * n D S S p * E C I f * Y t h a t t h E y M a y a * l ******* s o * b e * u S e d o n O t h e r n o n p o r O U s m A t e * r i A l s +HYP: s o m * b r a E n * E * p I S O f I E t h a t t h * y * a y a L l s o E b e O u * e d o n U t h e r n o n p o r * S s m E t e I r i * l s +Eval: D I D S D I S S S I S D D I I I I D S D S S I D + +Speaker sentences 696: swc_eng_001872 #utts: 1 +id: (swc_eng_001872-swc_eng_001872) +Scores: (#C #S #D #I) 19 4 1 0 +REF: T h e p o s S I b l y c o n S p e C i f i c +HYP: * h e p o s E b l y c o n p e S i f i c +Eval: D S S S S + +Speaker sentences 697: swc_eng_001873 #utts: 1 +id: (swc_eng_001873-swc_eng_001873) +Scores: (#C #S #D #I) 8 2 1 5 +REF: m * * * * * i n l E N G t h +HYP: m A T O R S i n l * A C t h +Eval: I I I I I D S S + +Speaker sentences 698: swc_eng_001874 #utts: 1 +id: (swc_eng_001874-swc_eng_001874) +Scores: (#C #S #D #I) 20 5 10 0 +REF: W i t h o u t f I V E Z E R O m o V e D r A W i n g r U l E +HYP: * i t h o u t f * * * ******* * * T Y m o L e T r * * i n g r O l * +Eval: D D D D D D D S S S S D D S D + +Speaker sentences 699: swc_eng_001875 #utts: 1 +id: (swc_eng_001875-swc_eng_001875) +Scores: (#C #S #D #I) 25 17 10 3 +REF: T h * I S S I T U a T I O N p A R A L l e L s r E s p e C t i V e l y C Z E c H O s L o * V a * k i A +HYP: * h E E D * * C H a C S E M p * * O U l e * s r * s p e * t i * e l y T H O c K U s T o F a G k i * +Eval: D I S S D D S S S S S S D D S S D D D D S S S S S S I S I D + +Speaker sentences 700: swc_eng_001876 #utts: 1 +id: (swc_eng_001876-swc_eng_001876) +Scores: (#C #S #D #I) 18 3 6 0 +REF: F a v E R S h A m E l e C t E d i t s f i r s T +HYP: * a v * H h O m * l e * t * d i t s f i r s * +Eval: D D S S S D D D D + +Speaker sentences 701: swc_eng_001877 #utts: 1 +id: (swc_eng_001877-swc_eng_001877) +Scores: (#C #S #D #I) 31 4 1 4 +REF: r e ******* b l e E d i n g r I s k r E m a i n * s * * A r o u n d f o R t y +HYP: r e b l e A d i n g r S s k r * m a i n E s O F r o u n d f o A t y +Eval: I S S D I I I S S + +Speaker sentences 702: swc_eng_001878 #utts: 1 +id: (swc_eng_001878-swc_eng_001878) +Scores: (#C #S #D #I) 10 3 4 1 +REF: d E l I V E r c h E c * K m a t E +HYP: d * l * * * r c h A c T m a t D +Eval: D D D D S I S S + +Speaker sentences 703: swc_eng_001879 #utts: 1 +id: (swc_eng_001879-swc_eng_001879) +Scores: (#C #S #D #I) 141 11 22 8 +REF: s o m e s e c U l A r h u m A n i S T s c o n C E i * v e t r a n * S h u m A n i s m a s a n o F F s p r i n g o f t h e H u m A n i s t f r E e t h O u G H t m o v e ******* m e n t a n d a r g U e * t h A T t r A n s ******* h u m A n i S T s d i F f e r * f R o * m t h e H u m A n i s t m a I n ******* s t r E A m b y h a v i n G +HYP: s o m e s e c I l * r h u m E n i * * s c o n * S i E v e t r a n E h u m I n i s m a s a n o * * s p r i n g o f t h e * u m * n i s t f r * e t h * u * * t m o v e m e n t a n d a r g * e Y t h * E t r E n s h u m I n i * * s d i * f e r T f * o R m t h e * u m O n i s t m a E n s t r * * m E b y h a v i n * +Eval: S D S D D D S I I S S D D D D D D D D I D I D S S I S D D D I D I D S S I D D S D + +Speaker sentences 704: swc_eng_001880 #utts: 1 +id: (swc_eng_001880-swc_eng_001880) +Scores: (#C #S #D #I) 49 5 9 1 +REF: p i n t A i l * n e s t S a n d c h i c k S A r e v U L n e r A b l E t O p r E d a t i o n b y m a M m A l S +HYP: p i n t * i l E n e s t D a n d c h i c k * S r e ******* v * O n e r * b l * t * p r * d a t i o n b y m a * m O l E +Eval: D I S D S D D S D D D D D S S + +Speaker sentences 705: swc_eng_001881 #utts: 1 +id: (swc_eng_001881-swc_eng_001881) +Scores: (#C #S #D #I) 102 9 11 2 +REF: n o r t h e r n p i n t a I l i s o n e o f t h e s p e * C I e s t o w h i c h t h e a g r E e m e n t o n t h e c o n S e r v a t i o n o f a f r I C A N E U r a * s i A n m I g r A t o r y w a T e r b I r d S +HYP: n o r t h e r n p i n t a * l i s o n e o f t h e s p e A S H e s ******* t o w h i c h t h e ******* a g r * e m e n t o n t h e ******* c o n C e r v a t i o n o f a f r * * * K N I r a I s i O n m Y g r * t o r y w a * e r b U r d * +Eval: D I S S D D D D S D D D S S S I S S D D S D + +Speaker sentences 706: swc_eng_001882 #utts: 1 +id: (swc_eng_001882-swc_eng_001882) +Scores: (#C #S #D #I) 26 5 2 4 +REF: a n D i s n * o W f * O u n d o N l y i n t a s ******* m a N i * A +HYP: a n T i s n E o E f E R u n d o * l y i n ******* t a s m a G i O R +Eval: S I S I S D D I S I S + +Speaker sentences 707: swc_eng_001883 #utts: 1 +id: (swc_eng_001883-swc_eng_001883) +Scores: (#C #S #D #I) 44 3 4 14 +REF: * * * * * * * * * * ******* t h e I d e a o f m * i n * d * u p l O a D i n g i s a S s E r t E d t o r e p r E s e n t +HYP: E R S P E C T I V E t h e A d e a o f m E i n E d O u p l * a T i n g i s a s * r t * d t o r e p r * s e n t +Eval: I I I I I I I I I I I S I I I D S S D D D + +Speaker sentences 708: swc_eng_001884 #utts: 1 +id: (swc_eng_001884-swc_eng_001884) +Scores: (#C #S #D #I) 27 2 3 1 +REF: A n a v E r A g e o f t w e n t y O N e * p e r d a y +HYP: * n a v * r I g e o f t w e n t y * H e N p e r d a y +Eval: D D S D S I + +Speaker sentences 709: swc_eng_001885 #utts: 1 +id: (swc_eng_001885-swc_eng_001885) +Scores: (#C #S #D #I) 25 2 7 3 +REF: t H e n i T w o U L d f O l l o W t h a t p * * e * Q u A l S +HYP: t * e n i * w o * A d f * l l o * t h a t p E E e C G u * l * +Eval: D D D S D D I I I S D D + +Speaker sentences 710: swc_eng_001886 #utts: 1 +id: (swc_eng_001886-swc_eng_001886) +Scores: (#C #S #D #I) 23 5 4 3 +REF: A n d b l e E d i n g i n t o * V A r i O U s * T U m * o R s +HYP: * n d b l e A d i n g i n t o E * E r i * E s C H O m E o * s +Eval: D S I D S D S I S S I D + +Speaker sentences 711: swc_eng_001887 #utts: 1 +id: (swc_eng_001887-swc_eng_001887) +Scores: (#C #S #D #I) 25 3 5 1 +REF: a * L l O W t h e M t o g l i d E b e t w e E n t r E E s +HYP: a N l * E t h e * t o g l i d * b e t w e A n t r * * s +Eval: I S D S D D S D D + +Speaker sentences 712: swc_eng_001888 #utts: 1 +id: (swc_eng_001888-swc_eng_001888) +Scores: (#C #S #D #I) 33 1 9 1 +REF: i f t h e s E p r o b L e M s w E r E E F f i c i E n T l y s o l v * a b l E +HYP: i f t h e s * p r o b O e * s w * r * * * f i c i * n * l y s o l v H a b l * +Eval: D S D D D D D D D I D + +Speaker sentences 713: swc_eng_001889 #utts: 1 +id: (swc_eng_001889-swc_eng_001889) +Scores: (#C #S #D #I) 9 3 3 0 +REF: G E O l o g I c a l t i M E +HYP: * * A l o g H c a l t i * N +Eval: D D S S D S + +Speaker sentences 714: swc_eng_001890 #utts: 1 +id: (swc_eng_001890-swc_eng_001890) +Scores: (#C #S #D #I) 12 0 0 4 +REF: * * * ******* w h e n f l u s h e d +HYP: R O K w h e n f l u s h e d +Eval: I I I I + +Speaker sentences 715: swc_eng_001891 #utts: 1 +id: (swc_eng_001891-swc_eng_001891) +Scores: (#C #S #D #I) 21 8 7 0 +REF: i n c l U d i n g G E r m I n A l C H o I c e t E c H n O l O G Y +HYP: i n c l T d i n g J H r m * n * l * T o * c e ******* t * c n A l * D E +Eval: S S S D D D S D D D S S D S S + +Speaker sentences 716: swc_eng_001892 #utts: 1 +id: (swc_eng_001892-swc_eng_001892) +Scores: (#C #S #D #I) 26 2 8 2 +REF: A P p e A r A n c E o f l e A t h E R s h o * * e s o r b o O t S +HYP: * * p e * r * n c * o f l e t h * * s h o U G e s o r b o U t * +Eval: D D D D D S D D I I S D + +Speaker sentences 717: swc_eng_001893 #utts: 1 +id: (swc_eng_001893-swc_eng_001893) +Scores: (#C #S #D #I) 13 4 6 2 +REF: I n * E I G H t E E n s I X t * y t h r E e +HYP: * n D * * * A t T I n s * C t D y t h r * e +Eval: D I D D D S S S D S I D + +Speaker sentences 718: swc_eng_001894 #utts: 1 +id: (swc_eng_001894-swc_eng_001894) +Scores: (#C #S #D #I) 26 9 5 1 +REF: M a n U f A c t U r E s h O E c a R e p r o d U c T S A l s o * s E L l +HYP: * a n O f E c t E r S s h * U c a * e p r o d A c * E O l s o I s * O l +Eval: D S S S S D S D S D S S I D S + +Speaker sentences 719: swc_eng_001895 #utts: 1 +id: (swc_eng_001895-swc_eng_001895) +Scores: (#C #S #D #I) 29 3 5 4 +REF: * ******* t h e f i r s t n o * n s o V I e t c h a L l E n g e r s I N C e * +HYP: O t h e f i r s t n o A n s o L R e t c h a * l * n g e r ******* s * * T e N +Eval: I I I S S D D D D D S I + +Speaker sentences 720: swc_eng_001896 #utts: 1 +id: (swc_eng_001896-swc_eng_001896) +Scores: (#C #S #D #I) 27 1 2 0 +REF: O P p o n e n t h a s o n l y t h e K i n g a n d +HYP: * * p o n e n t h a s o n l y t h e C i n g a n d +Eval: D D S + +Speaker sentences 721: swc_eng_001897 #utts: 1 +id: (swc_eng_001897-swc_eng_001897) +Scores: (#C #S #D #I) 11 1 0 0 +REF: m a I n a r t i c l e +HYP: m a Y n a r t i c l e +Eval: S + +Speaker sentences 722: swc_eng_001898 #utts: 1 +id: (swc_eng_001898-swc_eng_001898) +Scores: (#C #S #D #I) 29 3 8 1 +REF: F o U n d C e r t A I n l E N G t h s * u s E f u l f o r f i T t i n G +HYP: * o W n d S e r t * * n l * * A t h s O u s * f u l f o r f i * t i n * +Eval: D S S D D D D S I D D D + +Speaker sentences 723: swc_eng_001899 #utts: 1 +id: (swc_eng_001899-swc_eng_001899) +Scores: (#C #S #D #I) 43 3 3 1 +REF: t a p e i n t h e s a m e f o r M f a c t * O r A s t h e c o m p a c t A U d i o +HYP: t a p e i n t h e s a m e f o r E f a c t D E r ******* * s t h e c o m p a c t * O d i o +Eval: S I S D D D S + +Speaker sentences 724: swc_eng_001900 #utts: 1 +id: (swc_eng_001900-swc_eng_001900) +Scores: (#C #S #D #I) 24 4 3 2 +REF: C e n S U R e * w a s l a t e R e * X p u n g e d f r o M +HYP: S e n * T H e R w a s l a t e * e C S p u n g e d f r o * +Eval: S D S S I D I S D + +Speaker sentences 725: swc_eng_001901 #utts: 1 +id: (swc_eng_001901-swc_eng_001901) +Scores: (#C #S #D #I) 15 3 2 1 +REF: O r d e F A c t o * E q u a l i t y +HYP: * r d e ******* S E c t o A C q u a l i t y +Eval: D D S S I S + +Speaker sentences 726: swc_eng_001902 #utts: 1 +id: (swc_eng_001902-swc_eng_001902) +Scores: (#C #S #D #I) 30 6 6 0 +REF: i s f o U r t h O U s A n D s I X h u n D r e d b y s I X t y f E e t +HYP: i s f o * r t h * A s E n * s * A C h u n T r e d b y s * C t y f * e t +Eval: D D S S D D S S S D S D + +Speaker sentences 727: swc_eng_001903 #utts: 1 +id: (swc_eng_001903-swc_eng_001903) +Scores: (#C #S #D #I) 15 3 4 0 +REF: n i N E t E E n s E v e n T y t h r E e +HYP: n i * * t * I n s O v e n D y t h r * e +Eval: D D D S S S D + +Speaker sentences 728: swc_eng_001904 #utts: 1 +id: (swc_eng_001904-swc_eng_001904) +Scores: (#C #S #D #I) 25 3 9 5 +REF: * * * * ******* a p l a Y E r m a Y A L s o l o S e b y r u N n i n G O U T +HYP: R O L L a p l a * I r m a L * * s o l o U e b y r u * n i n * ******* * * * +Eval: I I I I I D S S D D S D D D D D D + +Speaker sentences 729: swc_eng_001905 #utts: 1 +id: (swc_eng_001905-swc_eng_001905) +Scores: (#C #S #D #I) 30 4 10 1 +REF: P U b l I C h E A l T h p R o f e s s O r g r E g O r y s t o * c K p o i n T S +HYP: * * b l O K h * * l * h p * o f e s s E r g r A g * r y s t o A c * p o i n * * +Eval: D D S S D D D D S S D I D D D + +Speaker sentences 730: swc_eng_001906 #utts: 1 +id: (swc_eng_001906-swc_eng_001906) +Scores: (#C #S #D #I) 66 4 11 4 +REF: b r o w n W a s E l e c t e D t O t h E h o u s e o F r e p R e * s e n T A t i v E s f o r t h * r e E n o n c O n ******* s e c * U t i V E t e r m s +HYP: b r o w n * a s * l e c t e * t * t h * h o u s e o * r e p * e R s e n * * t i v * s f o r t h E r e Y n o n c E n s e c K I t i * F t e r m s +Eval: D D D D D D D I D D D I S S I I S D S + +Speaker sentences 731: swc_eng_001907 #utts: 1 +id: (swc_eng_001907-swc_eng_001907) +Scores: (#C #S #D #I) 13 1 6 3 +REF: C o * ******* e * X i s t h a P p I l y w I T H +HYP: * o R e G S i s t h a * p * l y w * * * +Eval: D I I I S D D D D D + +Speaker sentences 732: swc_eng_001908 #utts: 1 +id: (swc_eng_001908-swc_eng_001908) +Scores: (#C #S #D #I) 20 3 6 0 +REF: a g r O u p o F M A M m A l s t h a t r a I S E +HYP: a g r * u p o * * N E m * l s t h a t r a * * C +Eval: D D D S S D D D S + +Speaker sentences 733: swc_eng_001909 #utts: 1 +id: (swc_eng_001909-swc_eng_001909) +Scores: (#C #S #D #I) 16 1 5 0 +REF: A n d t h E W O R l d s l a R g e s t +HYP: * n d t h * * * H l d s l a * g e s t +Eval: D D D D S D + +Speaker sentences 734: swc_eng_001910 #utts: 1 +id: (swc_eng_001910-swc_eng_001910) +Scores: (#C #S #D #I) 37 4 2 0 +REF: b r e E d i n g t a k E s p l a c e b e t w E e n a p r I l a n d J u n E +HYP: b r e A d i n g t a k s p l a c e b e t w * e n a p r O l a n d D u n * +Eval: S S D S S D + +Speaker sentences 735: swc_eng_001911 #utts: 1 +id: (swc_eng_001911-swc_eng_001911) +Scores: (#C #S #D #I) 24 2 6 1 +REF: A U s t r a l I A i s a T t h e s o U t h e R n e * n d +HYP: * * s t r a l O R i s a * ******* t h e s o * t h e * n e I n d +Eval: D D S S D D D D I + +Speaker sentences 736: swc_eng_001912 #utts: 1 +id: (swc_eng_001912-swc_eng_001912) +Scores: (#C #S #D #I) 29 6 2 3 +REF: t e c H n O l o * g I c A l s i n g * U l a * r i t y i s p o s S I b l E +HYP: t e c n H l o U g H c * l s i n g E I l a I r i t y i s p o s E A b l * +Eval: S S I S D I S I S S D + +Speaker sentences 737: swc_eng_001913 #utts: 1 +id: (swc_eng_001913-swc_eng_001913) +Scores: (#C #S #D #I) 18 0 6 1 +REF: I n C l U d i n g t h E s * l E E p y c o d +HYP: * n * l * d i n g t h * s P l * * p y c o d +Eval: D D D D I D D + +Speaker sentences 738: swc_eng_001914 #utts: 1 +id: (swc_eng_001914-swc_eng_001914) +Scores: (#C #S #D #I) 42 8 8 1 +REF: S e V e N t y f o U r * h a d a h i g H e r e D U c a t i o n Q U A l I f I c a t i o n c o m p A R e D +HYP: * e S e * t y f o * r E h a d a h i g * e r ******* e G O c a t i o n * * O l * f E c a t i o n c o m p E D e T +Eval: D S D D I D D S S D D S D S S S S + +Speaker sentences 739: swc_eng_001915 #utts: 1 +id: (swc_eng_001915-swc_eng_001915) +Scores: (#C #S #D #I) 22 8 17 0 +REF: T H i S O c C U r s w h e N t h e O P p o N E n T s K I n g I s I n C H E C K +HYP: * * i * ******* A c P E r s w h e * t h e ******* * B p o * * n I s C A n g * s ******* A n ******* * * * * * +Eval: D D D D S S S D D D S D D S S S D D S D D D D D D + +Speaker sentences 740: swc_eng_001916 #utts: 1 +id: (swc_eng_001916-swc_eng_001916) +Scores: (#C #S #D #I) 17 4 4 1 +REF: c o n S e R v a t i o n I n A U s t r A l I a * +HYP: c o n C e * v a t i o n * n ******* * O s t r E l Y a R +Eval: S D D D D S S S I + +Speaker sentences 741: swc_eng_001917 #utts: 1 +id: (swc_eng_001917-swc_eng_001917) +Scores: (#C #S #D #I) 16 5 0 2 +REF: i s t h e s A l a m a n d * E R f * i S H +HYP: i s t h e s E l a m a n d O F f E i C U +Eval: S I S S I S S + +Speaker sentences 742: swc_eng_001918 #utts: 1 +id: (swc_eng_001918-swc_eng_001918) +Scores: (#C #S #D #I) 46 6 9 1 +REF: f i r s t s e l f d e S c r i * B e D t r a n S h u m A n I s t S M E t f o r m A L l y i n t h e E a R l Y +HYP: f i r s t s e l f d e * c r i G V e * t r a n h u m O n * s t * H A t f o r m * I l y i n ******* t h e * a * l * +Eval: D I S D S S D D S S D S D D D D + +Speaker sentences 743: swc_eng_001919 #utts: 1 +id: (swc_eng_001919-swc_eng_001919) +Scores: (#C #S #D #I) 44 6 8 1 +REF: r e C e n t r e s E A r c h * i n d I c a t E S t h A t f a c t O r s o t h e R t h A n p R a c T i C E +HYP: r e * e n t r e s * U r c h E i n d E c a t * D t h * t f a c t E r s o t h e * t h E n p * a c D i * * +Eval: D D S I S D S D S D S D S D D + +Speaker sentences 744: swc_eng_001920 #utts: 1 +id: (swc_eng_001920-swc_eng_001920) +Scores: (#C #S #D #I) 39 3 3 0 +REF: a n d p r E v e n t i o n a n d t r e A T M e n t o f C o m p l I c a t i o n s +HYP: a n d p r * v e n t i o n a n d t r e * K B e n t o f * o m p l c a t i o n s +Eval: D D S S D S + +Speaker sentences 745: swc_eng_001921 #utts: 1 +id: (swc_eng_001921-swc_eng_001921) +Scores: (#C #S #D #I) 10 4 4 3 +REF: W i T H A r * a P I d o n ******* s * E t +HYP: * i * * H E r E a * E d o n s A I t +Eval: D D D S S I D S I I S + +Speaker sentences 746: swc_eng_001922 #utts: 1 +id: (swc_eng_001922-swc_eng_001922) +Scores: (#C #S #D #I) 25 4 5 2 +REF: * u ******* t A H w A r t h e f o U n d a t i o N W a s b U R i e D +HYP: O u t O W w O r t h e f o * n d a t i o * * a s b * A i e * +Eval: I I S S S D D D D S D + +Speaker sentences 747: swc_eng_001923 #utts: 1 +id: (swc_eng_001923-swc_eng_001923) +Scores: (#C #S #D #I) 100 8 21 6 +REF: n o w a d a y s H o u r l y r e * g i O n A l E X p r e S s t r a I n S b e t W E E n b E r n a n d * s ******* p * i E Z t o b r i g a n d f r E I G H t t r a I n * s c o n t i n u e t o r u n o N t h E m O u N t A I n * r a I l w A Y +HYP: n o w a d a y s * o u r l y r e A g i * n * l C S p r e * s ******* t r a * n D b e t * * * n b U r n a n d H s p E i T H t o b r i g a n d f r * * * A t t r a * n E s c o n t i n u e t o r u n o * t h * m * u * t * O n T r a * l w * * +Eval: D I D D S S D D D S D D D S I I I S S D D D S D I D D D D D S I D D D + +Speaker sentences 748: swc_eng_001924 #utts: 1 +id: (swc_eng_001924-swc_eng_001924) +Scores: (#C #S #D #I) 53 8 15 5 +REF: O t h e R f a m I l I E s w * I t h A p O t E n T I A L l y g o n d ******* w a n * ******* a n o r i g i * n i n c l U d E t h e r e t r O p I N n I d a E +HYP: * t h e * f a m * l * Y s w E t h ******* * p * t I n * * C R l y g o n d w a n E a n ******* o r i g i O n i n c l * d * t h e ******* r e t r * p * O n E d a Y +Eval: D D D D S I S D D D S D D S S I I I D I D D D D D S S S + +Speaker sentences 749: swc_eng_001925 #utts: 1 +id: (swc_eng_001925-swc_eng_001925) +Scores: (#C #S #D #I) 35 9 4 4 +REF: b y a n I t a l i A n d o * m i N I c A n * m o N K j a * ******* c o b U s d e C e S s O l I s +HYP: b y a n ******* E t a l i * n d o A m i * K c E n D m o A C j a C c o b I s d e S e * s T l E s +Eval: D S D I D S S I S S I I S S D S S + +Speaker sentences 750: swc_eng_001926 #utts: 1 +id: (swc_eng_001926-swc_eng_001926) +Scores: (#C #S #D #I) 20 1 6 0 +REF: C O M m a n d w a s n a M e d a f t E r t H E +HYP: * * * m a n d w a s n a I e d a f t * r t * * +Eval: D D D S D D D + +Speaker sentences 751: swc_eng_001927 #utts: 1 +id: (swc_eng_001927-swc_eng_001927) +Scores: (#C #S #D #I) 16 4 3 0 +REF: a r T I f i C I a l I n t e L l i g e n c E +HYP: a r D O f i * H a l * n t e * l i g e n c S +Eval: S S D S D D S + +Speaker sentences 752: swc_eng_001928 #utts: 1 +id: (swc_eng_001928-swc_eng_001928) +Scores: (#C #S #D #I) 15 1 3 0 +REF: a n d i s t h e r E I G N i n g +HYP: a n d i s t h e r * * * A i n g +Eval: D D D S + +Speaker sentences 753: swc_eng_001929 #utts: 1 +id: (swc_eng_001929-swc_eng_001929) +Scores: (#C #S #D #I) 18 2 6 0 +REF: p E r C e N T o f t h E P o p U l a t i o n +HYP: p * r ******* S e * * o f t h * * o p I l a t i o n +Eval: D D S D D D D S + +Speaker sentences 754: swc_eng_001930 #utts: 1 +id: (swc_eng_001930-swc_eng_001930) +Scores: (#C #S #D #I) 22 4 6 1 +REF: c h I e F a r * e A s o f s H o E p O l i S h s A l E s +HYP: c h O e * a r I e * s o f s * o * U p * l i C h s E l * s +Eval: S D I D D D S D S S D + +Speaker sentences 755: swc_eng_001931 #utts: 1 +id: (swc_eng_001931-swc_eng_001931) +Scores: (#C #S #D #I) 11 1 2 0 +REF: i M p o s e D b y l a W +HYP: i N p o s e * b y l a * +Eval: S D D + +Speaker sentences 756: swc_eng_001932 #utts: 1 +id: (swc_eng_001932-swc_eng_001932) +Scores: (#C #S #D #I) 39 3 3 5 +REF: r e f E r E n c * ******* * * e s t o t h e r U l i n g c o A l i t i o n g o v * e r N m e n T +HYP: r e f * r O n c E I S e s t o t h e r O l i n g c o R l i t i o n g o v B e r * m e n * +Eval: D S I I I I S S I D D + +Speaker sentences 757: swc_eng_001933 #utts: 1 +id: (swc_eng_001933-swc_eng_001933) +Scores: (#C #S #D #I) 19 2 4 0 +REF: s p E C I e s o f g l I d i n g p o S s U m +HYP: s p * A H e s o f g l * d i n g p o * s * m +Eval: D S S D D D + +Speaker sentences 758: swc_eng_001934 #utts: 1 +id: (swc_eng_001934-swc_eng_001934) +Scores: (#C #S #D #I) 33 1 4 2 +REF: b a * s e d o n t h e p r e v i O U s s t r a T E g * y o f p l a y +HYP: b a C s e d o n t h e p r e v i * * s ******* s t r a * D g E y o f p l a y +Eval: I D D D D S I + +Speaker sentences 759: swc_eng_001935 #utts: 1 +id: (swc_eng_001935-swc_eng_001935) +Scores: (#C #S #D #I) 20 4 2 1 +REF: a N d i d ******* E A l i s t I c a s p I r a t i o n S +HYP: a * d i d D U l i s t * c a s p E r a t i o n E +Eval: D I S S D S S + +Speaker sentences 760: swc_eng_001936 #utts: 1 +id: (swc_eng_001936-swc_eng_001936) +Scores: (#C #S #D #I) 35 2 7 2 +REF: p * r O f e S S I o n A l s a n d h o m E r e c o * r D i n g E N t h u s i a s t s +HYP: p E r * f e * * T o n * l s a n d h o m * r e c o A r * i n g * O t h u s i a s t s +Eval: I D D D S D D I D D S + +Speaker sentences 761: swc_eng_001937 #utts: 1 +id: (swc_eng_001937-swc_eng_001937) +Scores: (#C #S #D #I) 6 8 1 0 +REF: F A m I L Y E l A p I d a E +HYP: H E m T H O l * p O d a Y +Eval: S S S S S S D S S + +Speaker sentences 762: swc_eng_001938 #utts: 1 +id: (swc_eng_001938-swc_eng_001938) +Scores: (#C #S #D #I) 47 13 12 9 +REF: T H A n A Q U A r t e R o f p e O p l E w i t h A p r e * v i * O U s * * S a * * h m a Y d E v e l o P h * * Y p o p I t * u I t A r i s M +HYP: * * * n ******* * ******* O C O r t e * o f p e * p l * w i t h ******* E p r e A v i S E s A Y a G C h m a D d * v e l o U h I G p o p E t H u R t * r i s N +Eval: D D D D D D S S S D D D D S I I S S I I S I I S D S I I S S I S D S + +Speaker sentences 763: swc_eng_001939 #utts: 1 +id: (swc_eng_001939-swc_eng_001939) +Scores: (#C #S #D #I) 27 2 3 1 +REF: d i v i * d e d i n t o t h r E E f a m I l I e s t h a T +HYP: d i v i R d e d i n t o t h r * Y f a m * l Y e s t h a * +Eval: I D S D S D + +Speaker sentences 764: swc_eng_001940 #utts: 1 +id: (swc_eng_001940-swc_eng_001940) +Scores: (#C #S #D #I) 30 3 12 0 +REF: S h o W E d s l i G H t i n t E r e s t I n r E l e a S i n g C A S s e T t E S +HYP: * h o * * d s l i * * t i n t * r e s t A n r * l e a C i n g * * O s e * t * * +Eval: D D D D D D S D S D D S D D D + +Speaker sentences 765: swc_eng_001941 #utts: 1 +id: (swc_eng_001941-swc_eng_001941) +Scores: (#C #S #D #I) 18 6 12 5 +REF: * * * * F a ******* M I L i A r E n o u G H t O h a v e c O M M o N N A m e S +HYP: T H A T a H R M i * r A n o u * * ******* t * h a v e c * * * o * ******* * * m e N +Eval: I I I I S I S S S D S D D D D D D D D D D D S + +Speaker sentences 766: swc_eng_001942 #utts: 1 +id: (swc_eng_001942-swc_eng_001942) +Scores: (#C #S #D #I) 13 3 3 3 +REF: I n t W o * T h O u s A n d s * i * X +HYP: A n t * o W * h * u s E n d s A i C E +Eval: S D I D D S I I S + +Speaker sentences 767: swc_eng_001943 #utts: 1 +id: (swc_eng_001943-swc_eng_001943) +Scores: (#C #S #D #I) 29 2 13 3 +REF: s h * o E S h i n e b o * y * s a r E K n o W n a s b O O t p O l i S H B O Y s +HYP: s h W o * h i n e b o R y E s a r * ******* * n o * n a s b * U t p * l i * * ******* * * * s +Eval: I D S I I D D D D D S D D D D D D D + +Speaker sentences 768: swc_eng_001944 #utts: 1 +id: (swc_eng_001944-swc_eng_001944) +Scores: (#C #S #D #I) 31 7 5 3 +REF: t h e C A U s e i s r u P t U R e * o f a C e r E b R A l * a n E u r Y s * m +HYP: t h e * * O s e i s r u H t * H e R o f a S e r I b * * l E a n O u r I s T m +Eval: D D S S D S I S S D D I S S I + +Speaker sentences 769: swc_eng_001945 #utts: 1 +id: (swc_eng_001945-swc_eng_001945) +Scores: (#C #S #D #I) 29 2 6 6 +REF: m o s t o F t h e m a J O r * * u * * * s m u s I C * c o m P A n I e s +HYP: m o s t o * t h e m a * G r Y O u E S T s m u s * * I c o m * * n Y e s +Eval: D D S I I I I I D D I D D S + +Speaker sentences 770: swc_eng_001946 #utts: 1 +id: (swc_eng_001946-swc_eng_001946) +Scores: (#C #S #D #I) 88 9 9 5 +REF: O N e * s t * E r E o * p a i r o r O n e m o n o P H o n i c t r a c k i s p l a Y e D o r r e c o r D e d W h E n t h e t * a p e I s m o v i n g I n o n E d I r e c t i o n a n ******* D +HYP: * W e N s t A R r I o P p a i r o r W n e m o n o * F o n i c t r a c k i s p l a * e * o r r e c o r T e d ******* * h * n t h e t H a p e * s m o v i n g A n o n * d U r e c t i o n a n T +Eval: D S I I S S I S D S D D S D D D I D S D S I S + +Speaker sentences 771: swc_eng_001947 #utts: 1 +id: (swc_eng_001947-swc_eng_001947) +Scores: (#C #S #D #I) 16 2 5 2 +REF: W h E R E i t * S e a r l y f o r m * I n +HYP: * h * * * ******* i t C D e a r l y f o r m E A n +Eval: D D D D D I S I S + +Speaker sentences 772: swc_eng_001948 #utts: 1 +id: (swc_eng_001948-swc_eng_001948) +Scores: (#C #S #D #I) 14 5 4 3 +REF: A s t * r A t e * g i C P H I l o * s o P H e r +HYP: * ******* s t E r t e A g i K * F O l o S s o * V e r +Eval: D D I S I S D S S I D S + +Speaker sentences 773: swc_eng_001949 #utts: 1 +id: (swc_eng_001949-swc_eng_001949) +Scores: (#C #S #D #I) 26 4 8 3 +REF: P o s i T i o N I n g A D V a n t A g * * e s * d U r I n G t h e g a M e +HYP: * o s i S i o * * n g T B a n t * g C H e s T d * r * n * t h e g a * e +Eval: D S D D S S S D I I I D D D D + +Speaker sentences 774: swc_eng_001950 #utts: 1 +id: (swc_eng_001950-swc_eng_001950) +Scores: (#C #S #D #I) 7 4 4 2 +REF: N E W s * O u t H w A L e * s +HYP: * D O s E A u t * ******* w * H e L s +Eval: D S S I S D D D S I + +Speaker sentences 775: swc_eng_001951 #utts: 1 +id: (swc_eng_001951-swc_eng_001951) +Scores: (#C #S #D #I) 31 2 6 2 +REF: D I s p o s A l o v e r h i s o W n b * i O l o * g i c a l N a t U r E +HYP: * E s p o s * l o v e r h i s o * n b Y i * l o U g i c a l * a t E r * +Eval: D S D D I D I D S D + +Speaker sentences 776: swc_eng_001952 #utts: 1 +id: (swc_eng_001952-swc_eng_001952) +Scores: (#C #S #D #I) 48 9 10 7 +REF: r e ******* p * r o D u c * t I V E r i G h t * s * o R E X E r t u n d U E p r e S s U R e * s o n p r o s p e c t I V e * p a r E n T S +HYP: r e p E r o * u c D t * O F r i * h t E s H o * C S A r t u n d * O p r e * s T H e R s o n p r o s p e c t * * e D p a r * n * C +Eval: I I D I D S S D I I D S S S D S D S S I D D I D D S + +Speaker sentences 777: swc_eng_001953 #utts: 1 +id: (swc_eng_001953-swc_eng_001953) +Scores: (#C #S #D #I) 14 5 4 2 +REF: S t i L l a n c I E n T I N * o r I g * I n +HYP: * t i * l a n c H A n D * Y N o r * g E O n +Eval: D D S S S D S I D I S + +Speaker sentences 778: swc_eng_001954 #utts: 1 +id: (swc_eng_001954-swc_eng_001954) +Scores: (#C #S #D #I) 16 5 3 1 +REF: r a s t A p o * P o U l o S s h I R E d g U n +HYP: r a s t p o F o * l o U s h * * A d g O n +Eval: S I S D S D D S S + +Speaker sentences 779: swc_eng_001955 #utts: 1 +id: (swc_eng_001955-swc_eng_001955) +Scores: (#C #S #D #I) 14 1 4 2 +REF: I n * t W o t h O U s ******* a n d t W o +HYP: * n D t * o t h * I s a n d t * o +Eval: D I D D S I D + +Speaker sentences 780: swc_eng_001956 #utts: 1 +id: (swc_eng_001956-swc_eng_001956) +Scores: (#C #S #D #I) 26 2 6 3 +REF: * f o r e * X a M p l E I f t h e p l a Y E r h a s o n l ******* Y +HYP: T f o r ******* e N T a * p l * * f t h e p l a * * r h a s o n l D +Eval: I D I S D D D D D I S + +Speaker sentences 781: swc_eng_001957 #utts: 1 +id: (swc_eng_001957-swc_eng_001957) +Scores: (#C #S #D #I) 51 8 14 3 +REF: s u F F e r E d a s u b A r a C H n o I d h e m O R r H A g E h a v e c o * G n I T i V E i * m p a I r * m e n t t h a t A F f e C t S +HYP: s u * * e r * d a s u b * r a K n o * d h e m * I r * * g * h a v e c o L D n * * i C O i N m p a * r E m e n t t h a t O f e * t * +Eval: D D D D S S D D S D D D I S D D S S I D I S S D D + +Speaker sentences 782: swc_eng_001958 #utts: 1 +id: (swc_eng_001958-swc_eng_001958) +Scores: (#C #S #D #I) 20 1 3 1 +REF: P r o v i d e d P r o G n o s t i C d a t * a +HYP: * r o v i d e d * r o * n o s t i N d a t E a +Eval: D D D S I + +Speaker sentences 783: swc_eng_001959 #utts: 1 +id: (swc_eng_001959-swc_eng_001959) +Scores: (#C #S #D #I) 32 3 6 1 +REF: W H o h a d a n E u r Y s M s d e t e c t E d b y O t h e R m E a n * s +HYP: * * o h a d a n * u r I s N s d e t e c t I d b y * t h e * m * a n E s +Eval: D D D S S S D D D I + +Speaker sentences 784: swc_eng_001960 #utts: 1 +id: (swc_eng_001960-swc_eng_001960) +Scores: (#C #S #D #I) 37 2 12 1 +REF: l i F E s t Y l E S d e s i G n e D t O i m ******* p r o V e h e A l t h a n D L O n g e v i t y +HYP: l i * H s t I l * * d e s i * n e * t * i m p r o * e h e * l t h ******* a n * * * n g e v i t y +Eval: D S S D D D D D I D D D D D D + +Speaker sentences 785: swc_eng_001961 #utts: 1 +id: (swc_eng_001961-swc_eng_001961) +Scores: (#C #S #D #I) 37 4 4 0 +REF: h a d m o r E s o P h i s t i c a t e d E n d o f t a P E p r E d I c t i o n +HYP: h a d m o r * ******* s o F h i s t i c a t e d A n d o f t a * Y p r O d * c t i o n +Eval: D D S S D S S D + +Speaker sentences 786: swc_eng_001962 #utts: 1 +id: (swc_eng_001962-swc_eng_001962) +Scores: (#C #S #D #I) 12 2 0 3 +REF: d e ******* h * u m A n ******* i Z a t i o n +HYP: d e h O u m E n i S a t i o n +Eval: I I S I S + +Speaker sentences 787: swc_eng_001963 #utts: 1 +id: (swc_eng_001963-swc_eng_001963) +Scores: (#C #S #D #I) 25 4 6 4 +REF: S p E c I E s i n C l U d E f r e S h w * a t E r l a m p r * * * e Y s +HYP: * p A c H Y s i n l * d * f r e * h w O a t * r l a m p r O A C e * s +Eval: D S S S S D D D I D I I I D + +Speaker sentences 788: swc_eng_001964 #utts: 1 +id: (swc_eng_001964-swc_eng_001964) +Scores: (#C #S #D #I) 11 3 1 2 +REF: f I R s t a n G I o * g r * a m +HYP: f * O s t a n D Y o U g r E a m +Eval: D S S S I I + +Speaker sentences 789: swc_eng_001965 #utts: 1 +id: (swc_eng_001965-swc_eng_001965) +Scores: (#C #S #D #I) 15 5 4 1 +REF: T h e f r E E E n C Y C l O p e * d i a a t +HYP: * h e f r * * Y I n S D K l * p e A d i a a t +Eval: D D D S S S S S D I + +Speaker sentences 790: swc_eng_001966 #utts: 1 +id: (swc_eng_001966-swc_eng_001966) +Scores: (#C #S #D #I) 27 4 7 1 +REF: t h e R E f o r E m e D i c A l i M a g * i n g i s G e n E r A L l Y +HYP: t h e * f o r * m e T i c * l i N a g E i n g i s D e n * r * * l * +Eval: D S D S D S I S D D D D + +Speaker sentences 791: swc_eng_001967 #utts: 1 +id: (swc_eng_001967-swc_eng_001967) +Scores: (#C #S #D #I) 49 3 8 3 +REF: S p e C I E s * i s t t h E e X c l u S i o n o f n o n * h * u m A n a n d p a r t h u m a n a n I m A l s +HYP: * p e * * A s I i s t t h * ******* e * c l u * i o n o f n o n C h O u m E n a n d p a r t h u m a n a n A m * l s +Eval: D D D S I D D D D I I S S D + +Speaker sentences 792: swc_eng_001968 #utts: 1 +id: (swc_eng_001968-swc_eng_001968) +Scores: (#C #S #D #I) 41 6 16 1 +REF: I n * p e O P l E W h o H a d P r E v i O U s l y s u F f E R e d a s u b A r a c H n o I D h e m O R r H A g E +HYP: * n D p e A B l * * h o * a d * r * v i * * s l y s u * f * * e d a s u b * r a c n o * T h e m * r * I g * +Eval: D I S S D D D D D D D D D D D S D S D S D S D + +Speaker sentences 793: swc_eng_001969 #utts: 1 +id: (swc_eng_001969-swc_eng_001969) +Scores: (#C #S #D #I) 42 7 15 2 +REF: C l A S s i f i E d a s E I t h e R E n ******* d a n g E R e d o r t h r e A t E n E d U n d E R t h e * E p B C A C T +HYP: * l * * s i f i * d a s * * t h e * I n d a n g * * e d o r t h r e * t O n * d A n d * O t h e Y * p * E * B E +Eval: D D D D D D D S I D D D S D S D S I D D S D S S + +Speaker sentences 794: swc_eng_001970 #utts: 1 +id: (swc_eng_001970-swc_eng_001970) +Scores: (#C #S #D #I) 31 5 6 2 +REF: a N D a T t O r n E y G e n e r A l p a r K e r w a t ******* * K I n s h a r d I n +HYP: a * * a * t E r n * y J e n e r * l p a r C e r w a t C O E n s h a r d * n +Eval: D D D S D S D S I I S S D + +Speaker sentences 795: swc_eng_001971 #utts: 1 +id: (swc_eng_001971-swc_eng_001971) +Scores: (#C #S #D #I) 10 2 1 0 +REF: b u t t Y p i c A L l y +HYP: b u t t I p i c * K l y +Eval: S D S + +Speaker sentences 796: swc_eng_001972 #utts: 1 +id: (swc_eng_001972-swc_eng_001972) +Scores: (#C #S #D #I) 42 3 16 0 +REF: W h i c h i n t U r N f e d t h e s i G n A l t o t h e h e a d o F t h e c A s s E T T E D E C K +HYP: * h i c h ******* i n ******* t * r E f e d t h e s i * n * l t o t h e ******* h e a d o * t h e c O s s * * * * ******* * * * A +Eval: D D D D S D D D D S D D D D D D D D S + +Speaker sentences 797: swc_eng_001973 #utts: 1 +id: (swc_eng_001973-swc_eng_001973) +Scores: (#C #S #D #I) 42 2 6 3 +REF: W I t h i n t h e I r o W n c o n v e n T i o n A L l y e * X p e c t e d l i f e ******* t i m e * s +HYP: * H t h i n t h e * r o * n c o n v e n * i o n * * l y e C S p e c t e d l i f e t i m e M s +Eval: D S D D D D D I S I I + +Speaker sentences 798: swc_eng_001974 #utts: 1 +id: (swc_eng_001974-swc_eng_001974) +Scores: (#C #S #D #I) 13 3 2 1 +REF: S U b s t a n T I A l s t r a i * n +HYP: * O b s t a n * H E l s t r a i E n +Eval: D S D S S I + +Speaker sentences 799: swc_eng_001975 #utts: 1 +id: (swc_eng_001975-swc_eng_001975) +Scores: (#C #S #D #I) 32 7 4 2 +REF: t w e n * * T i E t h C e n t U r y K E n t u c K y c o n g r E s S m A n j o H n +HYP: t w e n Y H A i * t h S e n t * r y C O n t u c * y c o n g r * s m E n j o A n +Eval: I I S D S D S S D D S S S + +Speaker sentences 800: swc_eng_001976 #utts: 1 +id: (swc_eng_001976-swc_eng_001976) +Scores: (#C #S #D #I) 14 6 7 1 +REF: N I n E t * y p E R C e n t A r E E n d E m I C +HYP: * * n O t H y p * * O S e n t O r * * n d I m * A +Eval: D D S I D D S S S D D S D S + +Speaker sentences 801: swc_eng_001977 #utts: 1 +id: (swc_eng_001977-swc_eng_001977) +Scores: (#C #S #D #I) 18 2 2 0 +REF: h U n t i n g w i t H l e A d s h o T +HYP: h I n t i n g w i t * l e * d s h o G +Eval: S D D S + +Speaker sentences 802: swc_eng_001978 #utts: 1 +id: (swc_eng_001978-swc_eng_001978) +Scores: (#C #S #D #I) 11 1 3 0 +REF: T w e n T y t h I r t E e n +HYP: * w e n * y t h * r t A e n +Eval: D D D S + +Speaker sentences 803: swc_eng_001979 #utts: 1 +id: (swc_eng_001979-swc_eng_001979) +Scores: (#C #S #D #I) 45 8 15 1 +REF: * A L t h O U G H s e v E n p E r C e n t o f t h e W O R l d S B a t S s p E c I e s l I v E i n A u s t r a l i a +HYP: O R t h * * * Y s e v O n p * r ******* S e n t o f t h e * * * l d * P a t * s p A c H e s l * v * ******* i n ******* * u s t r a l i a +Eval: I S S D D D S S D D S D D D D S D S S D D D D D + +Speaker sentences 804: swc_eng_001980 #utts: 1 +id: (swc_eng_001980-swc_eng_001980) +Scores: (#C #S #D #I) 29 7 15 0 +REF: K A r p o V s r E i G n f i n A L l y E n D E d i N n I n E t E e n E I G H T y f I v E +HYP: * U r p o E s r A i * n f i n * * l y A n * * d i * ******* n A n * t * e n * * * A D y f * v * +Eval: D S S S D D D S D D D D S D D D D D S S D D + +Speaker sentences 805: swc_eng_001981 #utts: 1 +id: (swc_eng_001981-swc_eng_001981) +Scores: (#C #S #D #I) 29 5 8 0 +REF: w H i l E s o M e t r A N S h u m A n i S T s t A k E a n a B s t r A c t +HYP: w * i l * s o * e t r * E h u m I n i * * s t I k * a n a P s t r * c t +Eval: D D D D S S S D D S D S D + +Speaker sentences 806: swc_eng_001982 #utts: 1 +id: (swc_eng_001982-swc_eng_001982) +Scores: (#C #S #D #I) 13 1 2 8 +REF: * * * * * * * W r i * t E p r O t e c t i o n +HYP: P A N T H R E r i A t * p r * t e c t i o n +Eval: I I I I I I I S I D D + +Speaker sentences 807: swc_eng_001983 #utts: 1 +id: (swc_eng_001983-swc_eng_001983) +Scores: (#C #S #D #I) 59 9 9 2 +REF: g r a P H I s O m o r P H i s M p r o b l e M i s t h e c o m p U t a t i O n A L p r o b l e M o f * D e ******* t e r m i n i n g w h E t h E R +HYP: g r a * * Y C s m o r F i s E p r o b l e * i s t h e c o m p E t a t i * n * T p r o b l e * o f T H e t e r m i n i n g w h * t h * * +Eval: D D S S S S S S D S D D S D I S I D D D + +Speaker sentences 808: swc_eng_001984 #utts: 1 +id: (swc_eng_001984-swc_eng_001984) +Scores: (#C #S #D #I) 25 2 4 2 +REF: F U r ******* t h e R r e s t R i c t O u r c o n c * e p t o F +HYP: * O r t h e * r e s t * i c t A u r c o n c S e p t o * +Eval: D S I D D S I D + +Speaker sentences 809: swc_eng_001985 #utts: 1 +id: (swc_eng_001985-swc_eng_001985) +Scores: (#C #S #D #I) 20 5 8 2 +REF: T h O S e W h o S U R V i V e H o * s * p I t A l i Z a t i o n +HYP: * h * * e * h o * O F B i e * o U s T p * t * l i S a t i o n +Eval: D D D D D S S S S D I I D D S + +Speaker sentences 810: swc_eng_001986 #utts: 1 +id: (swc_eng_001986-swc_eng_001986) +Scores: (#C #S #D #I) 65 6 6 1 +REF: s o m e p r O t e c t i o n o f u n C e r t a I n s i g n i f i c A n c e i s C o n f E R r E d b y c * A U c a S i a n E t h n i c i t y +HYP: s o m e p r * t e c t i o n o f u n e r t a * n s i g n i f i c E n c e i s * o n f * I r * d b y c O L K c a * i a n A t h n i c i t y +Eval: D S D S D D S D I S S D S + +Speaker sentences 811: swc_eng_001987 #utts: 1 +id: (swc_eng_001987-swc_eng_001987) +Scores: (#C #S #D #I) 12 0 3 1 +REF: C o a s t a l L a ******* g O o n s +HYP: * o a s t a l * a g * o n s +Eval: D D I D + +Speaker sentences 812: swc_eng_001988 #utts: 1 +id: (swc_eng_001988-swc_eng_001988) +Scores: (#C #S #D #I) 17 3 5 2 +REF: A n d c o * g N I t i v e E n ******* h a n c E M e N T +HYP: * n d c o W g * D t i v e I n h a n c * S e * * +Eval: D I D S S I D S D D + +Speaker sentences 813: swc_eng_001989 #utts: 1 +id: (swc_eng_001989-swc_eng_001989) +Scores: (#C #S #D #I) 33 2 16 9 +REF: * * * * * * * t h E E I G H t h r a n k a n d b e p r O m o * * t E d t o a n A L l o W E D P I E C E +HYP: V A N C S T D t h * * * * A t h r a n k a n d b e p r E m o U D t * d t o a n * * l o * * * ******* * * * * * +Eval: I I I I I I I D D D D S S I I D D D D D D D D D D D D + +Speaker sentences 814: swc_eng_001990 #utts: 1 +id: (swc_eng_001990-swc_eng_001990) +Scores: (#C #S #D #I) 31 3 4 0 +REF: d r A W b a c k o f c o i l i n g i s t h e p O S s I B i l i t y +HYP: d r * b a c k o f c o i l i n g i s t h e ******* p * * s E A i l i t y +Eval: D S D D D S S + +Speaker sentences 815: swc_eng_001991 #utts: 1 +id: (swc_eng_001991-swc_eng_001991) +Scores: (#C #S #D #I) 25 5 5 0 +REF: i n d I c a t e S A s u b A r a c H n o I d h e m O R r H A g e +HYP: i n d E c a t e * S s u b * r a c n o L d h e m * * r * I g e +Eval: S D S D S S D D D S + +Speaker sentences 816: swc_eng_001992 #utts: 1 +id: (swc_eng_001992-swc_eng_001992) +Scores: (#C #S #D #I) 12 3 0 1 +REF: d * a m A G e D p o r t i o n +HYP: d E a m I S e T p o r t i o n +Eval: I S S S + +Speaker sentences 817: swc_eng_001993 #utts: 1 +id: (swc_eng_001993-swc_eng_001993) +Scores: (#C #S #D #I) 28 11 5 3 +REF: A d o p t i o N o f E U G e N i C E n * ******* h a n c E m e n T t e c ******* h N O l O G I e s +HYP: N d o p t i o * o f Y J e D i K A n D h a n c S m e n * ******* t e c h * A l * H e s +Eval: S D S S S S S S I I S D D I D S D S S + +Speaker sentences 818: swc_eng_001994 #utts: 1 +id: (swc_eng_001994-swc_eng_001994) +Scores: (#C #S #D #I) 25 0 4 2 +REF: p O l i s h * O n h i s h o * r S e a n D w a g o n +HYP: p * l i s h E * n h i s h o U r * e a n * w a g o n +Eval: D I D I D D + +Speaker sentences 819: swc_eng_001995 #utts: 1 +id: (swc_eng_001995-swc_eng_001995) +Scores: (#C #S #D #I) 13 4 4 1 +REF: A n D t h e N e X t C h a * m P I O n +HYP: * n * t h e * e C t * h a R m B B E n +Eval: D D D S D I S S S + +Speaker sentences 820: swc_eng_001996 #utts: 1 +id: (swc_eng_001996-swc_eng_001996) +Scores: (#C #S #D #I) 17 5 9 0 +REF: B r O t h E r o f A U T H O r a l D O U s H u X l E y +HYP: * r * t h * r o f * * O F E r a l * * I s * u C l * y +Eval: D D D D D S S S D D S D S D + +Speaker sentences 821: swc_eng_001997 #utts: 1 +id: (swc_eng_001997-swc_eng_001997) +Scores: (#C #S #D #I) 20 6 8 3 +REF: W O R l * d C h a M p ******* i O n N I n E t E E n t w * E n T y o n E +HYP: * * * l E d T h a * p i * n H A n C t * I n t w O R n * y o n * +Eval: D D D I S D I D S S S D S I S D D + +Speaker sentences 822: swc_eng_001998 #utts: 1 +id: (swc_eng_001998-swc_eng_001998) +Scores: (#C #S #D #I) 38 8 7 3 +REF: s * u c h a s Q U A n t U m c O M p U t a t i o n a n d r a n d * O m I Z E d A l g O r I t h * m s +HYP: s O u c h ******* a s * * O n t H m c * L p E t a t i o n a n d r a n d I m * Y S d L l g * r * t h E m s +Eval: I D D D S S D S S I S D S S S D D I + +Speaker sentences 823: swc_eng_001999 #utts: 1 +id: (swc_eng_001999-swc_eng_001999) +Scores: (#C #S #D #I) 8 4 8 3 +REF: E I G H t E E N n * I N e T Y n * i N e * +HYP: * * * A t * * Y n H A D e * * n O i * e N +Eval: D D D S D D S I S S D D I D I + +Speaker sentences 824: swc_eng_002000 #utts: 1 +id: (swc_eng_002000-swc_eng_002000) +Scores: (#C #S #D #I) 54 1 9 17 +REF: W A S s h o W n * b y l a d n e r t h a t i f p * * * ≠ n * * ******* * * * * * * * p * * t h e n t h e r E e x i s t p r o * b l e m s I N +HYP: * * * ******* s h o * n E b y l a d n e r t h a t i f p E A I S n O T Y C O L D A N p E Y t h e n t h e r * e x i s t p r o V b l e m s ******* * * +Eval: D D D D D I I I I S I I I I I I I I I I I I D I D D D + +Speaker sentences 825: swc_eng_002001 #utts: 1 +id: (swc_eng_002001-swc_eng_002001) +Scores: (#C #S #D #I) 14 4 3 0 +REF: T h e c o m p a c t d I s C G r E W +HYP: * h e c o m p a c t d E s * T F r * L +Eval: D S D S S D S + +Speaker sentences 826: swc_eng_002002 #utts: 1 +id: (swc_eng_002002-swc_eng_002002) +Scores: (#C #S #D #I) 12 3 2 2 +REF: g r E y g o * o s C E n a * r i O +HYP: g r A y g o U o ******* s * O n a E r i L +Eval: S I D D S I S + +Speaker sentences 827: swc_eng_002003 #utts: 1 +id: (swc_eng_002003-swc_eng_002003) +Scores: (#C #S #D #I) 18 3 2 2 +REF: w a s R e n d e r e d a s * a J E d R e * Z +HYP: w a s W e n d e r e d a s I a * * d G e A S +Eval: S I D D S I S + +Speaker sentences 828: swc_eng_002004 #utts: 1 +id: (swc_eng_002004-swc_eng_002004) +Scores: (#C #S #D #I) 19 2 2 6 +REF: s * * ******* a * * h o r * t o a n o t h e R c A U s E +HYP: s A Y a E C h o r D t o a n o t h e * c O L s * +Eval: I I I I I I D S S D + +Speaker sentences 829: swc_eng_002005 #utts: 1 +id: (swc_eng_002005-swc_eng_002005) +Scores: (#C #S #D #I) 18 5 2 0 +REF: c o N S t i t u E n c y o f f a v E R s H A m +HYP: c o * C t i t u H n c y o f f a v * I s I O m +Eval: D S S D S S S + +Speaker sentences 830: voxforge_eng_000874 #utts: 1 +id: (voxforge_eng_000874-voxforge_eng_000874) +Scores: (#C #S #D #I) 43 3 11 3 +REF: t h e f o U r t h A n D f i F t H d a y * * s p a s S e D w I T h o u t a n y D E V e l O P m e n t * s +HYP: t h e f o * r t h * n * f i * t * d a y G E s p a s T e * w * O h o u t a n y * * * e l * I m e n t C s +Eval: D D D D D I I S D D S D D D D S I + +Speaker sentences 831: voxforge_eng_000875 #utts: 1 +id: (voxforge_eng_000875-voxforge_eng_000875) +Scores: (#C #S #D #I) 18 0 2 2 +REF: * ******* t h e y K n o W t h e r e p o r t +HYP: T t h e y * n o * t h e r e p o r t +Eval: I I D D + +Speaker sentences 832: voxforge_eng_000876 #utts: 1 +id: (voxforge_eng_000876-voxforge_eng_000876) +Scores: (#C #S #D #I) 34 5 7 1 +REF: s U c h t h i n g S h a D O C c U R r E d b e f * o r E h e t o l d P H i l I p +HYP: s O c h t h i n g * h a * * A c * O r * d b e f E o r * h e t o l d * F i l A p +Eval: S D D D S D S D I D D S S + +Speaker sentences 833: voxforge_eng_000877 #utts: 1 +id: (voxforge_eng_000877-voxforge_eng_000877) +Scores: (#C #S #D #I) 43 2 5 3 +REF: t h e y o n l y h a d a l * i T t l e t h i r T y t h o U s A n D d o L l a * r f i R e * +HYP: t h e y o n l y h a d a l E i * t l e t h i r D y t h o * s E n * d o * l a E r f i * e R +Eval: I D S D S D D I D I + +Speaker sentences 834: voxforge_eng_000878 #utts: 1 +id: (voxforge_eng_000878-voxforge_eng_000878) +Scores: (#C #S #D #I) 23 1 0 5 +REF: i a m * g o i n g t o g e t I t o u * * * t * +HYP: i a m E g o i n g t o g e t A t o u W D E t H +Eval: I S I I I I + +Speaker sentences 835: voxforge_eng_000879 #utts: 1 +id: (voxforge_eng_000879-voxforge_eng_000879) +Scores: (#C #S #D #I) 42 6 1 3 +REF: o u T W A R d l y h e m a i * n t a I n e d a c A L m * a n d s m i l i n g a * s p e c t +HYP: o u D D O d l y h e m a i E n t a * n e d a c O R m E a n d s m i l i n g a S s p e c t +Eval: S S S S I D S S I I + +Speaker sentences 836: voxforge_eng_000880 #utts: 1 +id: (voxforge_eng_000880-voxforge_eng_000880) +Scores: (#C #S #D #I) 34 5 6 2 +REF: j o A n l O o k E D T r * ******* i u m p H A n t l y a t s h e l d O n W h o b o W E d +HYP: j o * n l * o k * * * r I i u m p F E n t l y a t s h e l d E n * h o b o U R d +Eval: D D D D D I I S S S D S S + +Speaker sentences 837: voxforge_eng_000883 #utts: 1 +id: (voxforge_eng_000883-voxforge_eng_000883) +Scores: (#C #S #D #I) 18 5 3 5 +REF: c o m E o n ******* * d E l * m a r * C H a L l e n * G E D +HYP: c o m * o n H d I l D m a r T * T a * l e n C T S T +Eval: D I I S I I D S D I S S S + +Speaker sentences 838: voxforge_eng_000884 #utts: 1 +id: (voxforge_eng_000884-voxforge_eng_000884) +Scores: (#C #S #D #I) 56 2 2 1 +REF: i t w a s b e a t i n g a n d w a I t i n g i n t h e a m b U s h o f t h o s E B l a c k * p i t s +HYP: i t w a s b e a t i n g a n d w a * t i n g i n t h e a m b O s h o f t h o s * P l a c k E p i t s +Eval: D S D S I + +Speaker sentences 839: voxforge_eng_000885 #utts: 1 +id: (voxforge_eng_000885-voxforge_eng_000885) +Scores: (#C #S #D #I) 26 1 9 2 +REF: * l E T t h e M g o * o U t a n d e a T w i T H m y b o Y s +HYP: A l * * ******* t h e * g o E o * t a n d e a * P w i * * m y b o * s +Eval: I D D D D I D D S D D D + +Speaker sentences 840: voxforge_eng_000886 #utts: 1 +id: (voxforge_eng_000886-voxforge_eng_000886) +Scores: (#C #S #D #I) 48 7 7 10 +REF: h e * w e * n * T d o W n i n * M i D s ******* T r e A m * ******* * s E a r c H i * n g t h e s h a * d o W s o f B o T H s H o r E s +HYP: h e O w e I n E D d o * n i n W E i N s D r e * m N Y s * a r c T i O n g t h e s h a H d o * s o f P o * L s * o r * s +Eval: I I I S D I S S I S D I I I D S I I D S D S D D + +Speaker sentences 841: voxforge_eng_000887 #utts: 1 +id: (voxforge_eng_000887-voxforge_eng_000887) +Scores: (#C #S #D #I) 45 7 13 0 +REF: i J U s t d o a P p r e C I a t E I t w i t H o u t b e I N G a B L e t o E X p R e s S m y f E e l i n g s +HYP: i * O s t d o ******* a p r e S H a t * * t ******* w i t o u t b e * * * a * * e t o * C p * e s E m y f * e l i n g s +Eval: D S D S S S D D D S D D D D D D S D S D + +Speaker sentences 842: voxforge_eng_000888 #utts: 1 +id: (voxforge_eng_000888-voxforge_eng_000888) +Scores: (#C #S #D #I) 34 2 4 2 +REF: s h e d o E s * n t K n o W w h a t h e I s t A L k i n g a b o u * t +HYP: s h e d o * s A n t * n o * w h a t h e A s t * O k i n g a b o u W t +Eval: D I D D S D S I + +Speaker sentences 843: voxforge_eng_000889 #utts: 1 +id: (voxforge_eng_000889-voxforge_eng_000889) +Scores: (#C #S #D #I) 29 2 5 1 +REF: y o U r f a * t h e r s f i f t H c o M m a n d h e n O D D e d +HYP: y o * r f a R t h e r s f i f t * c o * m a n d h e ******* n * A T e d +Eval: D I D D D D S S + +Speaker sentences 844: voxforge_eng_000890 #utts: 1 +id: (voxforge_eng_000890-voxforge_eng_000890) +Scores: (#C #S #D #I) 16 2 5 2 +REF: * ******* d o n T Y o U s e E i h a T e y o U +HYP: E d o n * * o * ******* s e Y i h a * e y o E +Eval: I I D D D D S D S + +Speaker sentences 845: voxforge_eng_000891 #utts: 1 +id: (voxforge_eng_000891-voxforge_eng_000891) +Scores: (#C #S #D #I) 68 1 12 2 +REF: a l i T t l e w A r m * b u t n o T A t a L l A s t o n i s h e d * e a t i n g m e l O n s a n d t h R o W i n g t h E r i n d a b o u t +HYP: a ******* l i * t l e w O r m E b u t n o * ******* * t a * l * s t o n i s h e d E e a t i n g m e l * n s a n d t h * o * i n g t h * ******* r i n d a b o u t +Eval: D D S I D D D D D I D D D D D + +Speaker sentences 846: voxforge_eng_000892 #utts: 1 +id: (voxforge_eng_000892-voxforge_eng_000892) +Scores: (#C #S #D #I) 18 2 1 2 +REF: t h i s * i s a g r E a t p A r T y * +HYP: t h i s E i s a g r * a t p O r D y E +Eval: I D S S I + +Speaker sentences 847: voxforge_eng_000893 #utts: 1 +id: (voxforge_eng_000893-voxforge_eng_000893) +Scores: (#C #S #D #I) 23 2 1 3 +REF: t h e b o y g r E W a n d p r o s p e r e * ******* * D +HYP: t h e b o y g r * O a n d p r o s p e r e T T O +Eval: D S I I I S + +Speaker sentences 848: voxforge_eng_000894 #utts: 1 +id: (voxforge_eng_000894-voxforge_eng_000894) +Scores: (#C #S #D #I) 78 6 7 4 +REF: U n L E S s s u c h l e T t e r s b e p a t e n t t h a t t h e y M a y b E r e A d t o t h e m a n D w * i t h A l L s * e A l e D * o r t e s t i f i * e d +HYP: A n D L s s u c h l e * t e r s b e p a t e n t t h a t t h e y * a y b * r e * d t o t h e m a n * w H i t h l E s T e * l e * H o r t e s t i f i G e d +Eval: S S S S D D D D D I S S I D D I I + +Speaker sentences 849: voxforge_eng_000895 #utts: 1 +id: (voxforge_eng_000895-voxforge_eng_000895) +Scores: (#C #S #D #I) 46 3 8 1 +REF: h o w c o U l d a w o m A n d A r E t o v e n t U R e w H e r E s o M a n y e x * p L o r E r s +HYP: h o w c o * l d a w o m E n d E r * t o v e n t * * e w * e r * s o * a n y e x T p * o r A r s +Eval: D S S D D D D D D I D S + +Speaker sentences 850: voxforge_eng_000896 #utts: 1 +id: (voxforge_eng_000896-voxforge_eng_000896) +Scores: (#C #S #D #I) 21 4 2 2 +REF: h e r e a d * h i S f r a g M E n T S a l o * U d +HYP: h e r e a d E h i * f r a g * I n C E a l o A E d +Eval: I D D S S S I S + +Speaker sentences 851: voxforge_eng_000897 #utts: 1 +id: (voxforge_eng_000897-voxforge_eng_000897) +Scores: (#C #S #D #I) 28 1 1 1 +REF: b u t h o w * a r E y o u g o i n g t o d O i t +HYP: b u t h o w E a r * y o u g o i n g t o d E i t +Eval: I D S + +Speaker sentences 852: voxforge_eng_000898 #utts: 1 +id: (voxforge_eng_000898-voxforge_eng_000898) +Scores: (#C #S #D #I) 31 1 5 1 +REF: h o w d o y o u w A n T t o g e t A w a y w i T H t h i s * +HYP: h o w d o y o u w O n * t o g e t * w a y w i * * ******* t h i s E +Eval: S D D D D D I + +Speaker sentences 853: voxforge_eng_000899 #utts: 1 +id: (voxforge_eng_000899-voxforge_eng_000899) +Scores: (#C #S #D #I) 19 2 1 1 +REF: w i L l w e E v e r f o * R g e t i t +HYP: w i * l w e A v e r f o M g e t i t +Eval: D S I S + +Speaker sentences 854: voxforge_eng_000900 #utts: 1 +id: (voxforge_eng_000900-voxforge_eng_000900) +Scores: (#C #S #D #I) 43 4 14 1 +REF: f r O M m y E a r l i E s T r e c O L l e c t i o n m y s l E e P w A s A p e r * I O d o f T e R r O R +HYP: f r * * m y * a r l i * s * r e c * * l e c t i o n m y s l * e * w * s ******* * p e r G A T d o f H e * r * E +Eval: D D D D D D D D D D D D I S S S D D S + +Speaker sentences 855: voxforge_eng_000901 #utts: 1 +id: (voxforge_eng_000901-voxforge_eng_000901) +Scores: (#C #S #D #I) 24 3 4 18 +REF: * * ******* * ******* * * ******* * * * * * ******* w h * y ******* * d o G G o n E y o u A l l s h a k E A g a I n * +HYP: M Y O I S O S H E R w h I y E d o * L o n * y o u W l l s h a k D * g a * n M +Eval: I I I I I I I I I I I I I I I I I D S D S S D D I + +Speaker sentences 856: voxforge_eng_000902 #utts: 1 +id: (voxforge_eng_000902-voxforge_eng_000902) +Scores: (#C #S #D #I) 18 6 0 8 +REF: i * T I S t h e n e a R E s t r e * f u g ******* * * e * * * +HYP: i D E A V T t h e n e a D I s t r e O f u g H Y e W I I +Eval: I S S S S S S I I I I I I I + +Speaker sentences 857: voxforge_eng_000903 #utts: 1 +id: (voxforge_eng_000903-voxforge_eng_000903) +Scores: (#C #S #D #I) 37 4 4 2 +REF: h i s s l i m * h a n D S G r I P p E D t h e e * d g e s o f t h e t a b l E +HYP: h i s s l i m E h a n * E C r * E p * T t h e e A d g e s o f t h e t a b l * +Eval: I D S S D S D S I D + +Speaker sentences 858: voxforge_eng_000904 #utts: 1 +id: (voxforge_eng_000904-voxforge_eng_000904) +Scores: (#C #S #D #I) 21 7 4 1 +REF: w h I T E l E G H o r n S s a i d m R s m o r t I M E r * +HYP: w h * * D l * A o r n * s a i d m I s m o r t O A r M +Eval: D D S D S S D S S S S I + +Speaker sentences 859: voxforge_eng_000905 #utts: 1 +id: (voxforge_eng_000905-voxforge_eng_000905) +Scores: (#C #S #D #I) 28 4 16 0 +REF: i t T O o k h i M h a L F a N H O U R t o r E A c h T h e E d G E o F i t +HYP: i t * * o k h i * h a * V E a * * * * * W t o r * * c h * h e ******* A d * * o * i t +Eval: D D D D S S D D D D D S D D D D S D D D + +Speaker sentences 860: voxforge_eng_000906 #utts: 1 +id: (voxforge_eng_000906-voxforge_eng_000906) +Scores: (#C #S #D #I) 37 3 10 4 +REF: m a r t h * a w H e r E d O W E s t a n d O N t h E c o n t r a c t * * u a l * i S s U E s +HYP: m a r t h E a w * e r * d * ******* * * s t a n d ******* * * t h * c o n t r a c t I O u a l E i T s H O s +Eval: I D D D D D D D D D D I I I S S S + +Speaker sentences 861: voxforge_eng_000907 #utts: 1 +id: (voxforge_eng_000907-voxforge_eng_000907) +Scores: (#C #S #D #I) 51 5 4 2 +REF: a s t o b e U n d I s t i n g U i S h a b l e f r o m t h e v * a s t w h I t E p l a i n * S A r o U n d +HYP: a s t o b e * n d E s t i n g * i C h a b l e f r o m t h e v E a s t w h Y t * p l a i n E D * r o W n d +Eval: D S D S I S D I S D S + +Speaker sentences 862: voxforge_eng_000908 #utts: 1 +id: (voxforge_eng_000908-voxforge_eng_000908) +Scores: (#C #S #D #I) 33 3 6 3 +REF: h e w o U L d d e ******* s t r o y a L l t h i n g s T h a t A r E f i * X E d * +HYP: h e w o * * d d e s t r o y a * l t h i n g s * h a t ******* E r * f i C T D d T +Eval: D D I D D D S D I S S I + +Speaker sentences 863: voxforge_eng_000909 #utts: 1 +id: (voxforge_eng_000909-voxforge_eng_000909) +Scores: (#C #S #D #I) 47 2 8 2 +REF: t h e r u S s i A n M u s i C p l * a Y e r t h e c o U n t w a s h e r o ******* b e d i E n T s l a v E +HYP: t h e r u * s i O n * u s i K p l E a * e r t h e c o * n t w a s h e r ******* o b e d i * n * s l a v * +Eval: D S D S I D D D I D D D + +Speaker sentences 864: voxforge_eng_000910 #utts: 1 +id: (voxforge_eng_000910-voxforge_eng_000910) +Scores: (#C #S #D #I) 50 1 3 3 +REF: t o h i s s u R p r i * s e h e r a n S W e R w a s f l a t a n d u n ******* c o m p r o m i * s i n g +HYP: t o h i s s u * p r i H s e h e r a n * T e * w a s f l a t a n d u n c o m p r o m i Y s i n g +Eval: D I D S D I I + +Speaker sentences 865: voxforge_eng_000911 #utts: 1 +id: (voxforge_eng_000911-voxforge_eng_000911) +Scores: (#C #S #D #I) 21 2 3 2 +REF: * ******* t h i s s H o U L D b e i n t e r E s t i n g +HYP: T t h i s s * o * * T b e i n t e r O s t i n g +Eval: I I D D D S S + +Speaker sentences 866: voxforge_eng_000912 #utts: 1 +id: (voxforge_eng_000912-voxforge_eng_000912) +Scores: (#C #S #D #I) 32 0 1 3 +REF: i a m * a ******* f r a I d * i d o n t h a v e m u c h t i m e +HYP: i a m E a f r a * d E i d o n t h a v e m u c h t i m e +Eval: I I D I + +Speaker sentences 867: voxforge_eng_000913 #utts: 1 +id: (voxforge_eng_000913-voxforge_eng_000913) +Scores: (#C #S #D #I) 54 7 7 1 +REF: c H r I s T m A s i s a n e a s y p r o b l E m * c o m p A r E d w I t h A p o l Y n E S i A n g i v i n g f e a s t +HYP: c * r * s * m I s i s a n e a s y p r o b l O m E c o m p * r * d w t h ******* E p o l * n A T i O n g i v i n g f e a s t +Eval: D D D S S I D D S D S D S S S + +Speaker sentences 868: voxforge_eng_000914 #utts: 1 +id: (voxforge_eng_000914-voxforge_eng_000914) +Scores: (#C #S #D #I) 35 3 9 3 +REF: t h e p l a n t E R s A r E a L r E A d y c O n ******* s i d e r i n G t h E M a T t e r * * +HYP: t h e p l a n t * O s O r * a * r * * d y c E n s i d e r i n * t h * * a * t e r H E +Eval: D S S D D D D S I D D D D I I + +Speaker sentences 869: voxforge_eng_000915 #utts: 1 +id: (voxforge_eng_000915-voxforge_eng_000915) +Scores: (#C #S #D #I) 24 1 3 4 +REF: j o A n C r i * e d * w i t H s h i n * i n g e y * e S +HYP: j o * n * r i R e d E w i t * s h i n A i n g e y S e H +Eval: D D I I D I I S + +Speaker sentences 870: voxforge_eng_000916 #utts: 1 +id: (voxforge_eng_000916-voxforge_eng_000916) +Scores: (#C #S #D #I) 33 1 1 1 +REF: w H o E v e r l i v e d o n t h e r a n c h d i d t h a t * +HYP: w * o v e r l i v e d o n t h e r a n c h d i d t h a t D +Eval: D S I + +Speaker sentences 871: voxforge_eng_000917 #utts: 1 +id: (voxforge_eng_000917-voxforge_eng_000917) +Scores: (#C #S #D #I) 35 4 1 3 +REF: w e l e A v e t h e * E v e n T u * a l i t y t o t i m e a n d l * A W +HYP: w e l e * v e t h e V F v e n C u O a l i t y t o t i m e a n d l O R L +Eval: D I S S I I S S + +Speaker sentences 872: voxforge_eng_000918 #utts: 1 +id: (voxforge_eng_000918-voxforge_eng_000918) +Scores: (#C #S #D #I) 57 5 5 4 +REF: * ******* a t t h e s a m e t i M E s p e a r s a n d A R r o W s b e g a n t o f a l l * a m o n g T h E i n ******* V a D e r s +HYP: A a t t h e s a m e t i N G s p e a r s a n d * E r o * s b e g a n ******* t o f a l l E a m o n g * h * i n B a T e r s +Eval: I I S S D S D D I D D I S S + +Speaker sentences 873: voxforge_eng_000920 #utts: 1 +id: (voxforge_eng_000920-voxforge_eng_000920) +Scores: (#C #S #D #I) 28 2 5 3 +REF: i t i s m E R e L y t h e s i m p * l E s * u p e R l A t i V e * +HYP: i t i s m * * e * y t h e s i m p A l * s O u p e * l I t i F e F +Eval: D D D I D I D S S I + +Speaker sentences 874: voxforge_eng_000921 #utts: 1 +id: (voxforge_eng_000921-voxforge_eng_000921) +Scores: (#C #S #D #I) 38 2 9 4 +REF: i n * ******* s t E a * d h e a R r i * v e D o n t h E n I G H t o f t H e s E c o n D d a y +HYP: i n D s t * a I d h e a * r i G v e * o n t h * n * * O t o f ******* t * e s O c o n * d a y +Eval: I I D I D I D D D D S D D S D + +Speaker sentences 875: voxforge_eng_000922 #utts: 1 +id: (voxforge_eng_000922-voxforge_eng_000922) +Scores: (#C #S #D #I) 52 3 2 6 +REF: i n h i s a n * X i E t y a n d s o l i c * i t * U d ******* e a n d l o v e * * t h e Y d i d n o t c o U n t +HYP: i n h i s a n G S i * t y a n d s o l i c S i t O E d e a n d l o v e F T t h e * d i d n o t c o W n t +Eval: I S D I I S I I I D S + +Speaker sentences 876: voxforge_eng_000923 #utts: 1 +id: (voxforge_eng_000923-voxforge_eng_000923) +Scores: (#C #S #D #I) 41 2 3 4 +REF: * g o d b l e s s * * i h o p E i L l g o O n * s E E i n g t h e m f o r e v e r +HYP: D g o d b l e s s O M i h o p L i * l g o A n D s * * i n g t h e m f o r e v e r +Eval: I I I S D S I D D + +Speaker sentences 877: voxforge_eng_000924 #utts: 1 +id: (voxforge_eng_000924-voxforge_eng_000924) +Scores: (#C #S #D #I) 13 1 2 2 +REF: y o U w e r E E n ******* g * a g e d +HYP: y o * w e r * I n g O a g e d +Eval: D D S I I + +Speaker sentences 878: voxforge_eng_000925 #utts: 1 +id: (voxforge_eng_000925-voxforge_eng_000925) +Scores: (#C #S #D #I) 51 9 5 12 +REF: t h e * l a * C e * w a s o f a D e l i c A T e * i v ******* * O r y c o l o * r * f * * a i n ******* t L Y t i n t E D w * i t H Y e L l O W +HYP: t h e R l a T S e S w a s o f a T e l i c * K e T i v H E r y c o l o E r E f R I a i n t O B t i n t I N w H i t * * e A l * * +Eval: I I S I S D S I I I S I I I I I S S S S I D D S D D + +Speaker sentences 879: voxforge_eng_000927 #utts: 1 +id: (voxforge_eng_000927-voxforge_eng_000927) +Scores: (#C #S #D #I) 43 2 4 4 +REF: i t w a S t h e s a m e w * a y w i t H o U R r e v * O l v e * r s a n d r i f * l E s +HYP: i t w a * t h e s a m e w H a y w i t * o * L r e v F A l v e O r s a n d r i f A l * s +Eval: D I D D S I S I I D + +Speaker sentences 880: voxforge_eng_000928 #utts: 1 +id: (voxforge_eng_000928-voxforge_eng_000928) +Scores: (#C #S #D #I) 35 2 11 1 +REF: T h e K i n g h a d P r O m i s E D t O E n * q u i r e i n t o t h E M a T t e r +HYP: * h e C i n g h a d * r * m i s * * ******* t * I n C q u i r e ******* i n t o t h * * a * t e r +Eval: D S D D D D D D S I D D D D + +Speaker sentences 881: voxforge_eng_000929 #utts: 1 +id: (voxforge_eng_000929-voxforge_eng_000929) +Scores: (#C #S #D #I) 13 3 3 7 +REF: * * ******* d o E s t H A T l O o k g o * * O d ******* * +HYP: A E d o * s t * E N l * o k g o D T E d T +Eval: I I I D D S S D I I S I I + +Speaker sentences 882: voxforge_eng_000930 #utts: 1 +id: (voxforge_eng_000930-voxforge_eng_000930) +Scores: (#C #S #D #I) 53 2 3 0 +REF: f o r t h e f i r s t t i m e i n h i s l i f e h e w a s y E A r n i n g f o r A s C r a p +HYP: f o r t h e f i r s t t i m e i n h i s l i f e h e w a s y * U r n i n g f o r ******* * s G r a p +Eval: D S D D S + +Speaker sentences 883: voxforge_eng_000931 #utts: 1 +id: (voxforge_eng_000931-voxforge_eng_000931) +Scores: (#C #S #D #I) 48 8 1 5 +REF: i d e f * Y a n y m a n t o g e t a s o l * O m * o N i S l A n * D s o r e i n c A l I f o r n i * A +HYP: i d e f I G a n y m a n t o g e t a s o l H A m W o T i * l E n C E s o r e i n c E l Y f o r n i E R +Eval: I S I S I S D S I S S S I S + +Speaker sentences 884: voxforge_eng_000932 #utts: 1 +id: (voxforge_eng_000932-voxforge_eng_000932) +Scores: (#C #S #D #I) 41 5 5 1 +REF: h e r E Y E s s m I l E D t r u T H a t h i m a s h e c a m e U P t h e b a n * k +HYP: h e r * * I s s m U l * T t r u * * a t h i m a s h e c a m e O F t h e b a n G k +Eval: D D S S D S D D S S I + +Speaker sentences 885: voxforge_eng_000933 #utts: 1 +id: (voxforge_eng_000933-voxforge_eng_000933) +Scores: (#C #S #D #I) 16 7 8 2 +REF: a * N y w a y N O * o n E s A W H e R l i K E t H A T +HYP: a T D y w a y * T N o n * s * * ******* L e * l i * * G t D E R +Eval: I S D S I D D D D S D D D S S S S + +Speaker sentences 886: voxforge_eng_000934 #utts: 1 +id: (voxforge_eng_000934-voxforge_eng_000934) +Scores: (#C #S #D #I) 28 3 7 2 +REF: m E n W h O E n ******* d * u r E i t c A L l I t l I V i n g d e a t h +HYP: m I n * h * I n d E u r * i t c * O l * t l * * i n g d e a t h +Eval: S D D S I I D D S D D D + +Speaker sentences 887: voxforge_eng_000935 #utts: 1 +id: (voxforge_eng_000935-voxforge_eng_000935) +Scores: (#C #S #D #I) 29 6 3 5 +REF: m A T t H E W s o n W h o s * * ******* * t h i s b O o k K E e p e r r o * g e r s +HYP: m * E t * O L s o n O h o s E D E t h i s b * o k C e p e r r o D g e r s +Eval: D S D S S S I I I I D S S I + +Speaker sentences 888: voxforge_eng_000938 #utts: 1 +id: (voxforge_eng_000938-voxforge_eng_000938) +Scores: (#C #S #D #I) 21 4 1 1 +REF: i o n l y R E A d t h e Q U o * t a t i o n s +HYP: i o n l y A T d t h e * F o R t a t i o n s +Eval: S S S D S I + +Speaker sentences 889: voxforge_eng_000939 #utts: 1 +id: (voxforge_eng_000939-voxforge_eng_000939) +Scores: (#C #S #D #I) 58 6 10 6 +REF: t H e r e w a s P R O p e r d I v i s i o n o f l a * b O r i n t h e w O R K t h e y i n d I v * i * d U A L l y p E R f * o R m e * * d +HYP: t * e r e w a s * * * p e r d E v i s i o n o f l a V b * r i n t h e w * A L t h e y i n d E v R i G d * * E l y p * O f P o * m e N T d +Eval: D D D D S I D D S S S I I D D S D S I D I I + +Speaker sentences 890: voxforge_eng_000940 #utts: 1 +id: (voxforge_eng_000940-voxforge_eng_000940) +Scores: (#C #S #D #I) 34 5 16 2 +REF: i L l T e L L y o u t h e l I b r ******* a R I A n * s a i d W I t h A B r i G H t E N I N G f a c e +HYP: i O l ******* P e * * y o u t h e l A b r a * * O n D s a i d * * t h ******* * * r i * C t * * * * * f a c e +Eval: S D S D D S I D D S I D D D D D D S D D D D D + +Speaker sentences 891: voxforge_eng_000942 #utts: 1 +id: (voxforge_eng_000942-voxforge_eng_000942) +Scores: (#C #S #D #I) 38 5 8 5 +REF: i s A W m * * * * r p i K E n o d H i s h e a d g r i m l y A n D S A r * c a s t i c A L l y +HYP: i s O F m I S T O r p i * * G n o d * i s h e a d g r i m l y I n * ******* * E r O c a s t i c * * l y +Eval: S S I I I I D D S D S D D D S I D D + +Speaker sentences 892: voxforge_eng_000943 #utts: 1 +id: (voxforge_eng_000943-voxforge_eng_000943) +Scores: (#C #S #D #I) 30 5 4 2 +REF: t h e r I N G i n g o f t h e b i g b E l L a r o U s E d H i * * M +HYP: t h e r * * * i n g o f t h e b i g b I l E a r o A s T d * i N D N +Eval: D D D S S S S D I I S + +Speaker sentences 893: voxforge_eng_000944 #utts: 1 +id: (voxforge_eng_000944-voxforge_eng_000944) +Scores: (#C #S #D #I) 74 7 21 7 +REF: * * ******* t h e s C r a T c h o f a p i n o n a M a n s h e a d v * a s t r e * g I o N s o f t h e E A r T H S s u r f A C E r e ******* m a I N G e O l o * g i c A L l y u N K n o W n B U T +HYP: O R t h e ******* s * r a * c h o f a p i n o n a * a n s h e a d v E a s t r e A g * o * s o f t h e * U r * * * ******* s u r f I S S r e m a * E J e * l o U g i c * * l y u * D n o * n ******* * * * +Eval: I I I D D D D I I D D D S D D D D S S S I D S S D I D D D S D D D D D + +Speaker sentences 894: voxforge_eng_000945 #utts: 1 +id: (voxforge_eng_000945-voxforge_eng_000945) +Scores: (#C #S #D #I) 43 10 4 4 +REF: * ******* h e H a d b a R E l y E n t e r E d T H I s w h e n h e s * A W t h e g l o W o f a * f i r E +HYP: T h e * a d b a D I l y A n t e r * d ******* I D E s w h e n T h e s O G D t h e g l o E o f a F f i r * +Eval: I I D S S S D D S S S S I S S S I D + +Speaker sentences 895: voxforge_eng_000946 #utts: 1 +id: (voxforge_eng_000946-voxforge_eng_000946) +Scores: (#C #S #D #I) 22 3 7 1 +REF: c h a n G E c h a I r s D A y ******* l i G H t c o M m a n D E d +HYP: c h a n * S c h a * r s T H y l i * * t c o * m a n * * d +Eval: D S D S S I D D D D D + +Speaker sentences 896: voxforge_eng_000947 #utts: 1 +id: (voxforge_eng_000947-voxforge_eng_000947) +Scores: (#C #S #D #I) 42 2 6 3 +REF: i t w a s j e A N n E s * i n G i n g s o f * T l y o V e r b e y o n D t h e r o c k * S +HYP: i t w a s j e * * n * s A i n * i n g s o f H E l y o * e r b e y o n * t h e r o c k C E +Eval: D D D I D I S D D I S + +Speaker sentences 897: voxforge_eng_000948 #utts: 1 +id: (voxforge_eng_000948-voxforge_eng_000948) +Scores: (#C #S #D #I) 20 6 6 3 +REF: A f ******* l Y i n g a R r o W P A S s E D b e t w E e n U s * * +HYP: * O f l * i n g a * r o L * B O s * T b e t w * e n O s E D +Eval: D S I D D S D S S D S D S I I + +Speaker sentences 898: voxforge_eng_000949 #utts: 1 +id: (voxforge_eng_000949-voxforge_eng_000949) +Scores: (#C #S #D #I) 59 7 2 2 +REF: h a t r E D a n d m u r d e r a n d l U s t f o r r e v e n g * E t h e y p o s s e S s E D t o o V e r ******* f l o W i n g +HYP: h a t r I T a n d m u r d e r a n d l O s t f o r r e v e n g C H t h e y p o s s e * s * T t o o F e r f l o Y i n g +Eval: S S S I S D D S S I S + +Speaker sentences 899: voxforge_eng_000950 #utts: 1 +id: (voxforge_eng_000950-voxforge_eng_000950) +Scores: (#C #S #D #I) 37 1 9 1 +REF: t h a t Y o U c o U L d h e a r a l l u p A N d d o W n t H e l i m p o p o * +HYP: t h a t * o * ******* c o * * d h e a r a l l u p ******* * E d d o * n t * e l i m p o p o E +Eval: D D D D D D D S D D I + +Speaker sentences 900: voxforge_eng_000951 #utts: 1 +id: (voxforge_eng_000951-voxforge_eng_000951) +Scores: (#C #S #D #I) 19 1 3 2 +REF: i t w a s m y * * I d e A t o a t E e +HYP: i t w a s m y A D d e * t o a ******* t * e +Eval: I I S D D D + +Speaker sentences 901: voxforge_eng_000952 #utts: 1 +id: (voxforge_eng_000952-voxforge_eng_000952) +Scores: (#C #S #D #I) 19 1 2 2 +REF: s h e d o E s * n t w A n T t o w * i n +HYP: s h e d o * s A n t w O n * t o w E i n +Eval: D I S D I + +Speaker sentences 902: voxforge_eng_000953 #utts: 1 +id: (voxforge_eng_000953-voxforge_eng_000953) +Scores: (#C #S #D #I) 35 7 6 1 +REF: s h e t h i n K S * i t i s b e c A U s E h e w A n T S s o m E t h I n g E l S e +HYP: s h e t h i n G E A i t ******* i s b e c * O s * h e w O n C E s o m t h * n g * l * e +Eval: S S I D D S D S S S S D D D + +Speaker sentences 903: voxforge_eng_000954 #utts: 1 +id: (voxforge_eng_000954-voxforge_eng_000954) +Scores: (#C #S #D #I) 42 4 6 6 +REF: * * h e p U l * L e D a n d ******* * t h e l o G c r * a s H e D d o W n t o b r E a k h i s b a c K +HYP: H S h e p O l D e * a n d T t h e l o C c r E a s * e * T d o * n t o b r * a k h i s b a c * +Eval: I I S I S D I I S I D D S D D D + +Speaker sentences 904: voxforge_eng_000955 #utts: 1 +id: (voxforge_eng_000955-voxforge_eng_000955) +Scores: (#C #S #D #I) 58 4 14 2 +REF: t h a T t h e s o c A L l E d f o r C e s a t w o r k i n l i G H t * h e A T E l E c t r i C i T y a n d m a * g n E t i s m I N +HYP: t h a * t h e s o ******* c * O l * d f o r * e s a t w o r k i n l i * * t E h e * * A l * c t r i S i D y a n d m a N g n * t i s m ******* * * +Eval: D D D S D D D D I D D S D S S I D D D D + +Speaker sentences 905: voxforge_eng_000956 #utts: 1 +id: (voxforge_eng_000956-voxforge_eng_000956) +Scores: (#C #S #D #I) 37 11 4 11 +REF: H e t * U R n E D s h a * R p * * L Y a n d * F a * c e D g r E g s O n a * ******* c R o S s t h e t * * * A b l e +HYP: W e t W I O n * * s h a O B p E I N T a n d P I a I c e T g r A g s I n a N c * o * s t h e t H I H V b l e +Eval: S I S S D D I S I I S S I S I S S S I I D D I I I S + +Speaker sentences 906: voxforge_eng_000957 #utts: 1 +id: (voxforge_eng_000957-voxforge_eng_000957) +Scores: (#C #S #D #I) 21 2 0 2 +REF: a l ******* s o * i w A n t I n f o r m a t i o n +HYP: a l s o E i w O n t A n f o r m a t i o n +Eval: I I S S + +Speaker sentences 907: voxforge_eng_000958 #utts: 1 +id: (voxforge_eng_000958-voxforge_eng_000958) +Scores: (#C #S #D #I) 41 4 3 0 +REF: t h e s i X t H d a y h e s p e n t i n t h e c a B I n w I T h g r E g s o n +HYP: t h e s i C t * d a y h e s p e n t i n t h e c a V O n w * * h g r A g s o n +Eval: S D S S D D S + +Speaker sentences 908: voxforge_eng_000959 #utts: 1 +id: (voxforge_eng_000959-voxforge_eng_000959) +Scores: (#C #S #D #I) 95 8 15 6 +REF: * o n t h i s H y ******* p o t h E S i * s t h e h a M m e r I n g o f t h e U l t * r A m u n d A n E c O r p u S c l e S o n * t h e b o b c o N f E r S i t S K I n E t i c * E n E r g y o n t h E o n E h a n d +HYP: I o n t h i s * y p o t h * * i E s t h e h a * m e r * n g o f t h e * l t E r * m u n d I n G c * r p u * c l e * o n D t h e b o b c o * f I r E i t K C E n A t i c K * n * r g y o n t h * o n * h a n d +Eval: I D I D D I D D D I D S S D D D I D S S S S S S I D D D D + +Speaker sentences 909: voxforge_eng_000960 #utts: 1 +id: (voxforge_eng_000960-voxforge_eng_000960) +Scores: (#C #S #D #I) 75 5 7 3 +REF: n o w * a f E r n y w i L l O W Y s t r e * A m a n d e v e r a n D a n o n y o u * E m E r g e f r o m a L l t h e g r o v e s a n d f l o W E r s +HYP: n o w E a f I r n y w i * l * * * s t r e M E m a n d e v e r a n * a n o n y o u A m U r g e f r o m a * l t h e g r o v e s a n d f l o * U r s +Eval: I S D D D D I S D I S S D D S + +Speaker sentences 910: voxforge_eng_000961 #utts: 1 +id: (voxforge_eng_000961-voxforge_eng_000961) +Scores: (#C #S #D #I) 61 7 6 3 +REF: w i t * ******* h o U t i t t h e m o s t d e n S e l y p o p U l a T e d r e * g I O n s o f m o D e R n E u r O p E a n d a m E r i c a +HYP: w i t H h o * t i t t h e m o s t d e n C e l y p o p I l a * e d r e A g * E n s o f m o H e * n T u r I p * a n d ******* a m O r i c a +Eval: I I D S S D I D S S D S S D D S + +Speaker sentences 911: voxforge_eng_000962 #utts: 1 +id: (voxforge_eng_000962-voxforge_eng_000962) +Scores: (#C #S #D #I) 20 0 3 1 +REF: t o m s p i n k h a s A h a r ******* p O o n +HYP: t o m s p i n k h a s ******* * h a r p * o n +Eval: D D I D + +Speaker sentences 912: voxforge_eng_000963 #utts: 1 +id: (voxforge_eng_000963-voxforge_eng_000963) +Scores: (#C #S #D #I) 44 0 16 3 +REF: h e w A n t e d T O g I V E t h e f I N i s h * t O t h i s f o * e a l r e A D y s o f A R * g o n E +HYP: h e w * n t e d ******* * * g * * * t h e f * * i s h E t * t h i s f o L e a l r e * * y s o ******* f * * A g o n * +Eval: D D D D D D D D D I D I D D D D D I D + +Speaker sentences 913: voxforge_eng_000964 #utts: 1 +id: (voxforge_eng_000964-voxforge_eng_000964) +Scores: (#C #S #D #I) 53 3 11 3 +REF: l I K E a f l a s h * h e l A U n c h e D h i m s e l f i n t O t h e f e A t h E R e d m a S s o F t h E * o * W l +HYP: l * * A a f l a s h E h e l * O n c h e * h i m s e l f i n t * t h e f e * t h * * e d m a * s o * t h * H o U H l +Eval: D D S I D S D D D D D D D D I I S + +Speaker sentences 914: voxforge_eng_000965 #utts: 1 +id: (voxforge_eng_000965-voxforge_eng_000965) +Scores: (#C #S #D #I) 31 2 4 1 +REF: i t c o n t a I N S a t o t A l * o f t w e n t y E n t r I e s +HYP: i t c o n t a * * E a t o t * l E o f t w e n t y A n t r * e s +Eval: D D S D I S D + +Speaker sentences 915: voxforge_eng_000966 #utts: 1 +id: (voxforge_eng_000966-voxforge_eng_000966) +Scores: (#C #S #D #I) 24 0 1 4 +REF: i ******* * * v e f * e l t m o r e c o m f o r T a b l e +HYP: i H A v e f H e l t m o r e c o m f o r * a b l e +Eval: I I I I D + +Speaker sentences 916: voxforge_eng_000967 #utts: 1 +id: (voxforge_eng_000967-voxforge_eng_000967) +Scores: (#C #S #D #I) 19 6 6 2 +REF: * D I d I p o S S e S s t O o m U C h v * I t A l i t y +HYP: T H E d ******* * p o * * e * s t * o m N T h v E A t E l i t y +Eval: I S S D D D D D D S S I S S + +Speaker sentences 917: voxforge_eng_000968 #utts: 1 +id: (voxforge_eng_000968-voxforge_eng_000968) +Scores: (#C #S #D #I) 38 4 5 2 +REF: t h e w O l F d o g * t h r U s t h i s g A U n t m u Z Z l e t o W A r d h i m * +HYP: t h e w L l E d o g K t h r * s t h i s g * O n t m u * S l e t o * * r d h i m N +Eval: S S I D D S D S D D I + +Speaker sentences 918: voxforge_eng_000971 #utts: 1 +id: (voxforge_eng_000971-voxforge_eng_000971) +Scores: (#C #S #D #I) 34 3 4 4 +REF: t h e g a * b R i E l * v o * I c e o f T h e s A m U r A i * r a n g o u t +HYP: t h e g a E b * i * l E v o R S c e o f * h e s E m E r * i Y r a n g o u t +Eval: I D D I I S D S S D I + +Speaker sentences 919: voxforge_eng_000972 #utts: 1 +id: (voxforge_eng_000972-voxforge_eng_000972) +Scores: (#C #S #D #I) 47 6 8 3 +REF: i t w a s O U R r i v e r * * * E m E r g i n g l I k E o u r s e l V E s f R o m t H e g r E a t s W A m p +HYP: i t w a s * A I r i v e r A N M m U r g i n g l A k * o u r s e l * * s f * o m t * e g r * a t s * O m p +Eval: D S S I I I S S S D D D D D D D S + +Speaker sentences 920: voxforge_eng_000973 #utts: 1 +id: (voxforge_eng_000973-voxforge_eng_000973) +Scores: (#C #S #D #I) 69 4 8 0 +REF: s a i d t h e m o l E p u L l i n g h i m s e l f t o g e t h E R w i t h a n e F f O r t Y o U m u s t t h i n K m e v e r y r U d E +HYP: s a i d t h e m o l L p u * l i n g h i m s e l f t o g e t h * * w i t h ******* a n e * f E r t * o * m u s t t h i n G m e v e r y r O d * +Eval: S D D D D D S D D S S D + +Speaker sentences 921: voxforge_eng_000974 #utts: 1 +id: (voxforge_eng_000974-voxforge_eng_000974) +Scores: (#C #S #D #I) 61 7 3 4 +REF: i n w h a t b * u c o * l i c * s c H o o L o f f e n c e h e h a d b E e N t A U G H t w a s b e y o n d i m a * g I n i n g +HYP: i n w h a t b Y u c o L l i c K s c * o o * o f f e n c e h e h a d b * e D t O R T t H w a s b e y o n d i m a D g E n i n g +Eval: I I I D D D S S S S S S I S + +Speaker sentences 922: voxforge_eng_000975 #utts: 1 +id: (voxforge_eng_000975-voxforge_eng_000975) +Scores: (#C #S #D #I) 58 7 3 5 +REF: h a d n o t E n a b l e d i n ******* v e s t i g a T O r s t o o * * B t a i n a T c o m p A r A t i v E l y l i T t l E c * o * s t +HYP: h a d n o t I n a b l e d i n v e s t i g a D E r s t o o P E t a i n a * c o m p E r I t i v F l y l i * t l * c L o U s t +Eval: S I S S I I S D S S S D D I I + +Speaker sentences 923: voxforge_eng_000976 #utts: 1 +id: (voxforge_eng_000976-voxforge_eng_000976) +Scores: (#C #S #D #I) 35 2 5 1 +REF: * A t r i C k l E o f f r e s h b l O o d r a n o v e R H I s f a c e +HYP: I T t r i * k l * o f f r e s h b l * o d r a n o v e * * R s f a c e +Eval: I S D D D D D S + +Speaker sentences 924: voxforge_eng_000977 #utts: 1 +id: (voxforge_eng_000977-voxforge_eng_000977) +Scores: (#C #S #D #I) 21 4 3 8 +REF: * ******* i t w a s a c u R I o * U s c o I n C i ******* D e * n * * * C e +HYP: D i t w a s a c u * * o R E s c o * n S i T e A n T S E S e +Eval: I I D D I S D S I S I I I I S + +Speaker sentences 925: voxforge_eng_000978 #utts: 1 +id: (voxforge_eng_000978-voxforge_eng_000978) +Scores: (#C #S #D #I) 28 1 1 1 +REF: i t i s t h e f i r E p a r t l y s h e s a i * D +HYP: i t i s t h e f i r * p a r t l y s h e s a i N E +Eval: D I S + +Speaker sentences 926: voxforge_eng_000979 #utts: 1 +id: (voxforge_eng_000979-voxforge_eng_000979) +Scores: (#C #S #D #I) 33 5 8 4 +REF: t h e y J U s t l a y * o F f I N t h e b U s h a n d p L U G G E d a w a y ******* * * +HYP: t h e y G O s t l a y E o * f ******* * * t h e ******* b O s h a n d p * * * O K d a w a y A N +Eval: S S I D D D D D S D D D S S I I I + +Speaker sentences 927: voxforge_eng_000980 #utts: 1 +id: (voxforge_eng_000980-voxforge_eng_000980) +Scores: (#C #S #D #I) 40 1 11 1 +REF: i K n o W t h a t Y o u A r e i n c h a r G e t h e r e a n d j E A N N e K n o W s * +HYP: i * n o * t h a t * o u ******* * r e i n c h a r D e t h e r e a n d j * * * * e * n o * s E +Eval: D D D D D S D D D D D D I + +Speaker sentences 928: voxforge_eng_000981 #utts: 1 +id: (voxforge_eng_000981-voxforge_eng_000981) +Scores: (#C #S #D #I) 46 3 7 1 +REF: f o r A t i M e t h e e X c * I t i n g t h R i l L o f h i s a d v e n t U R e w a s g o n E +HYP: f o r ******* * t i * e t h e e * c S A t i n g t h * i l E o f h i s a d v e n t * H e w a s g o n * +Eval: D D D D I S D S D S D + +Speaker sentences 929: voxforge_eng_000982 #utts: 1 +id: (voxforge_eng_000982-voxforge_eng_000982) +Scores: (#C #S #D #I) 38 6 13 1 +REF: S U D D e N l y h i s f i n g E r s c l o s E D t I G h * T l y o v e R t h e H a n D K E R c H i E f +HYP: * * F A e D l y h i s f i n g * r s c l o s * * t * * h A D l y o v e * t h e * a n * * G O c * i * f +Eval: D D S S S D D D D D I S D D D D S S D D + +Speaker sentences 930: voxforge_eng_000983 #utts: 1 +id: (voxforge_eng_000983-voxforge_eng_000983) +Scores: (#C #S #D #I) 50 5 0 5 +REF: d e a r s i r * y o u r s e c o n D v i c t * I m h a s f A l l E n o n * s c h * * E d u l e t i m e +HYP: d e a r s i r E y o u r s e c o n T v i c t D O m h a s f O l l O n o n D s c h A D G d u l e t i m e +Eval: I S I S S S I I I S + +Speaker sentences 931: voxforge_eng_000984 #utts: 1 +id: (voxforge_eng_000984-voxforge_eng_000984) +Scores: (#C #S #D #I) 17 1 5 2 +REF: h E c A n c a r E f O R h I m s e l f ******* * +HYP: h * c O n c a r * f * * h * m s e l f E +Eval: D S D D D D I I + +Speaker sentences 932: voxforge_eng_000985 #utts: 1 +id: (voxforge_eng_000985-voxforge_eng_000985) +Scores: (#C #S #D #I) 35 2 6 2 +REF: E a c h i n s U l t a D D e D t o t h e v a l * u E o F t h e c l a I m * +HYP: * a c h i n s I l t a * T e * t o t h e v a l Y u * o * t h e c l a * m E +Eval: D S D S D I D D D I + +Speaker sentences 933: voxforge_eng_000986 #utts: 1 +id: (voxforge_eng_000986-voxforge_eng_000986) +Scores: (#C #S #D #I) 69 3 13 2 +REF: t h O U G H i t m a Y b e t r a n * s ******* f o r m e d i n t o a n y o N E o f t h E f o r m s o f w h I c h e n E r g y i S s U S c e p t I b l E +HYP: t h * * * E i t m a * b e t r a n C s f o r m e d i n t o a n y o * * o f t h * f o r m s o f w h * c h e n * r g y i * ******* s * E c e p t A b l * +Eval: D D D S D I I D D D D D D D D S S D + +Speaker sentences 934: voxforge_eng_000987 #utts: 1 +id: (voxforge_eng_000987-voxforge_eng_000987) +Scores: (#C #S #D #I) 63 11 8 17 +REF: m e R C e * d e s s c r e a m e d C r i E d l A U G H E D * a * n d m a n * i f e s t e d t h E C h * * * a * O T i c * a * ******* b A n * ******* d * * O n ******* m e n t o f h Y s t E R i a * +HYP: m e * S e I d e s s c r e a m e d G r i * d l * * * O V F I a D n d m a n Y i f e s t e d t h * * h I R I a R D i c K a E b O n D d E N n m e n t o f h I s t * A i a R +Eval: D S I S D D D D S S S I I I D D I I I I S S I I I S I I I I S I S D S I + +Speaker sentences 935: voxforge_eng_000988 #utts: 1 +id: (voxforge_eng_000988-voxforge_eng_000988) +Scores: (#C #S #D #I) 32 3 4 1 +REF: i w A n T t o K n o W h o w * a L l t h i s i s p o s S I b l e +HYP: i w H n * t o * n o * h o w T a * l t h i s i s p o s T A b l e +Eval: S D D D I D S S + +Speaker sentences 936: voxforge_eng_000989 #utts: 1 +id: (voxforge_eng_000989-voxforge_eng_000989) +Scores: (#C #S #D #I) 78 5 14 3 +REF: P r e s e n t i n g a s I m p l E a n D I n s t r U c t i V e i L l U s t r a t i o n o f t h e s t r U G g L E f o r l i F e * a m O n g t h e r i v A l * s p e * c I e s +HYP: * r e s e n t i n g a s E m p l * a n * * n s t r * c t i * e i * l O s t r a t i o n o f t h e s t r * O g * * O f o r l i V e F a m * n g t h e ******* r i v * l E s p e A c * e s +Eval: D S D D D D D D S D S D D S S I D D D I I D + +Speaker sentences 937: voxforge_eng_000990 #utts: 1 +id: (voxforge_eng_000990-voxforge_eng_000990) +Scores: (#C #S #D #I) 36 4 4 4 +REF: h E L l n e v e R d o a t a p o f w O r k t h e W h o l E v o * Y a * * g * E +HYP: h * I l n e v e * d o a t a p o f w E r k t h e * h o l * v o R I a N D g C H +Eval: D S D S D D I S I I I S + +Speaker sentences 938: voxforge_eng_000991 #utts: 1 +id: (voxforge_eng_000991-voxforge_eng_000991) +Scores: (#C #S #D #I) 34 2 9 0 +REF: i h a V e h U n t e D a l o n G t H i s r i D g e r e p l I E d P H I l i p +HYP: i h a * e h * n t e * a l o n * t * i s r i * g e r e p l * A d * * F l i p +Eval: D D D D D D D S D D S + +Speaker sentences 939: voxforge_eng_000992 #utts: 1 +id: (voxforge_eng_000992-voxforge_eng_000992) +Scores: (#C #S #D #I) 32 2 4 0 +REF: l o r d b u t i M g l a d t o s E e y o U a g A i n P H i l +HYP: l o r d b u t i N g l a d t o s * e y o * a g * i n * F i l +Eval: S D D D D S + +Speaker sentences 940: voxforge_eng_000993 #utts: 1 +id: (voxforge_eng_000993-voxforge_eng_000993) +Scores: (#C #S #D #I) 28 4 9 3 +REF: H o w V A l i A n T l y i w e n T * a t i T t h a t f ******* i R s T d * a Y +HYP: C o w ******* * E l i * n * l y i w e n * D a t D i D t h a t ******* f i * s * d E a * +Eval: S D D S D D D I S S D I D D I D + +Speaker sentences 941: voxforge_eng_000994 #utts: 1 +id: (voxforge_eng_000994-voxforge_eng_000994) +Scores: (#C #S #D #I) 41 5 8 2 +REF: t h e Y a r E n o t r e * g U l A R o Y s t e r p i r a t E s n I c H O l A s c o N t I n u * e d +HYP: t h e * a r * n o t r e A g I l * E o * s t e r p i r a t * s n * c * K l E s c o t * n u D e d +Eval: D D I S D S D D D D S S S D I + +Speaker sentences 942: voxforge_eng_000995 #utts: 1 +id: (voxforge_eng_000995-voxforge_eng_000995) +Scores: (#C #S #D #I) 72 4 15 0 +REF: t H e Y m u s t b e h u r T i n g f o r b U S i N e s s b u t i t H O U G h t y o u m i g H t w A n t t o T A k E a l O o k a T t h e I r s i t E +HYP: t * e * m u s t b e h u r * i n g f o r b * * i S e s s b u t i t * * * * h t y o u m i g * t w * n t t o C E k * a l * o k a * t h e * r s i t D +Eval: D D D D D S D D D D D D S S D D D D S + +Speaker sentences 943: voxforge_eng_000996 #utts: 1 +id: (voxforge_eng_000996-voxforge_eng_000996) +Scores: (#C #S #D #I) 39 2 6 0 +REF: T H e r E W a s n o c h a n c e t o f i r e w i t H o u t h i T T i n g h i m +HYP: * D e r * ******* * a s n o c h a n c e t o f i r e w i t * o u t h i * D i n g h i m +Eval: D S D D D D D S + +Speaker sentences 944: voxforge_eng_000997 #utts: 1 +id: (voxforge_eng_000997-voxforge_eng_000997) +Scores: (#C #S #D #I) 53 4 12 2 +REF: a s f o r h i m s e l f w E R E n t t h E s t r e E t r * a I l ******* w a y E a r n i n g S I n C R e A s i n g s T e A D I l y +HYP: a s f o r h i m s e l f w * * O n t t h * s t r e A t r E a * l w a y * a r n i n g * * n * K e * s i n g s * e * * T l y +Eval: D D S D S I D I D D D D S D D D D S + +Speaker sentences 945: voxforge_eng_000998 #utts: 1 +id: (voxforge_eng_000998-voxforge_eng_000998) +Scores: (#C #S #D #I) 28 4 7 2 +REF: d U n ******* h A m c A n y o U r b o y G o A l o n g w I T H J e s s * e +HYP: d O n h * m c O n ******* y o * r b o y * o * l o n g w * * O D e s s Y e +Eval: S I D S D D D D D D S S I + +Speaker sentences 946: voxforge_eng_000999 #utts: 1 +id: (voxforge_eng_000999-voxforge_eng_000999) +Scores: (#C #S #D #I) 18 2 5 4 +REF: G O o d ******* b * * y E p I e ******* R r E h e s h o U t e d +HYP: * C o d b A I y * p * e A r * h e s h o * t e d +Eval: D S I I I D D I S D D + +Speaker sentences 947: voxforge_eng_001000 #utts: 1 +id: (voxforge_eng_001000-voxforge_eng_001000) +Scores: (#C #S #D #I) 58 2 8 1 +REF: b u t s u c h d I v e r g e n C E o f O p i n i o n w O U l d c o n s t i t u t E n o m e n a * c e t o s O C i E t y +HYP: b u t s u c h d E v e r g e n * S o f * p i n i o n w * * l d c o n s t i t u t * n o m e n a N c e t o s * * i * t y +Eval: S D S D D D D I D D D + +Speaker sentences 948: voxforge_eng_001001 #utts: 1 +id: (voxforge_eng_001001-voxforge_eng_001001) +Scores: (#C #S #D #I) 36 11 3 4 +REF: T H E R e w a s o n E C h a n c * e a n d o N l y o n E * o F s a v i n g J E A n * * N E +HYP: I B I e w a s o n * T h a n c E e a n d o l y o n * W o E s a v i n g * H O n T D L D +Eval: S S S S D S I S D I S D S S I I S S + +Speaker sentences 949: voxforge_eng_001002 #utts: 1 +id: (voxforge_eng_001002-voxforge_eng_001002) +Scores: (#C #S #D #I) 23 4 1 6 +REF: * ******* i * c a n N o t f o L l O W y o U s h e s a i * d * * +HYP: E i O c a n o t f o A l * E y o W s h e s a i N d N E +Eval: I I I S S D S S I I I + +Speaker sentences 950: voxforge_eng_001003 #utts: 1 +id: (voxforge_eng_001003-voxforge_eng_001003) +Scores: (#C #S #D #I) 49 4 1 2 +REF: o n t h e f a r c o * r n e r o f t h e c o m p o U n d f e n C e a h * a W k b r O O d e d +HYP: o n t h e f a r c o A r n e r o f t h e c o m p o W n d f e n T e a h O a * k b r E A d e d +Eval: I S S I D S S + +Speaker sentences 951: voxforge_eng_001004 #utts: 1 +id: (voxforge_eng_001004-voxforge_eng_001004) +Scores: (#C #S #D #I) 44 3 6 2 +REF: t H e n a g A i n t U D o * r h a d s U c h a n i R r I t a t i n g w a y a * b o U t h I m +HYP: t * e n a g * i n t * * o E r h a d s O c h a n i E r S t a t i n g w a y a O b o * t h * m +Eval: D D D D I S S S I D D + +Speaker sentences 952: voxpopuli_eng_000494 #utts: 1 +id: (voxpopuli_eng_000494-voxpopuli_eng_000494) +Scores: (#C #S #D #I) 63 5 13 14 +REF: w e a l l K n o W o ******* m a n a s a s U C c e S s F U l s t a b l E c O u n t r y a r ******* o * l E m o * ******* * D e * * * ******* * * * L f o r t h e W h o l E r e * g I o n +HYP: w e a l l * n o * o m a n a s a s * E c e * s T O l ******* s t a b l * c * u n t r y a ******* r o A l * m o R T H e F O R T H A T f o r t h e ******* * h o l * r e A g * o n +Eval: D D I D S D S S D D D D I I D I I I S I I I I I I I S D D D I D + +Speaker sentences 953: voxpopuli_eng_000495 #utts: 1 +id: (voxpopuli_eng_000495-voxpopuli_eng_000495) +Scores: (#C #S #D #I) 118 25 26 12 +REF: t h e r E f o r E i t s h i g h t i m e T H A T Y o u c o m e f o r W A R D W I t ******* h * * A p r O p o * s A l f o r r e v I e W W I T H * A n o p E r a t i O n a l s E P A r a t I o n o f t H e * a U d i t a n d n o n * a U d i t s E r v i * C e s U n d e R * a d i * R E c t e U * * s U P E R V i s I o n +HYP: t h e r * f o r * i t s h i g h t i m e ******* * * * * * o u c o m e f o r * B O U G D t h E T E p r E p o R s * l f o r r e v * e U * * B E D E n ******* o p * r a t i * n a l ******* s * * B r a t S o n o f t * e O a R d i t a n d n o n O a L d i t ******* s * r v i S I e s A n d e * R a ******* d i D L A c t e * W U s O B O T i s * o n +Eval: D D D D D D D D D S S S S S S I I I S S I D D S D D S S I S D D D D D D S S D I S I S D D I S S D I D I S S D I I S S S S S D + +Speaker sentences 954: voxpopuli_eng_000496 #utts: 1 +id: (voxpopuli_eng_000496-voxpopuli_eng_000496) +Scores: (#C #S #D #I) 121 13 13 21 +REF: i t i s c L e a r t h a t w e h a v e n o t i m e t o w a s t E t h e n E W r e * s * u l t s * o f t h * * e i * ******* p * * * * * * C C r e G a r d i n G T H E s C i e n t * i f i c * b a s i * s o f C l i m * A t E * C H a * n G e l E A V e n o r o O m f o r h e s i t * a * T I o n +HYP: i t i s ******* c E e a r t h a t w e h a v e n o t i m e t o w a s t * t h e n * U r e S s O u l t s H o f t h E Y e i D p E A S I E S I E r e O a r d i n * ******* * * * s * i e n t D i f i c K b a s i E s o f G l i m I N t * T J a I n C e l * * * e n o r o U m f o r h e s i t D a C S E o n +Eval: D S D D S I I I I I I I I I I I I I S S S D D D D D D I I I S I S D I S S I S D D D S I I S S + +Speaker sentences 955: voxpopuli_eng_000497 #utts: 1 +id: (voxpopuli_eng_000497-voxpopuli_eng_000497) +Scores: (#C #S #D #I) 71 7 13 9 +REF: * * * 5 s o i n t h e c o n ******* t a I n E r S W h i C h a R E N e v e r E v e n t O u c h e d c o m e s l a v e s c o U n t E R f e I t g O o d s d r U g * s ******* * * * e t C +HYP: S E N T s o i n t h e c o n t a E n O r * * h i * h ******* a * * ******* * e v e r A v e n t * u c h e d c o m e s l a v e s c o * n t * O f e * t g * o d s d r O g E s I T H e t R +Eval: I I I S I S S D D D D D D D D S D D D S D D S I I I I I S + +Speaker sentences 956: voxpopuli_eng_000498 #utts: 1 +id: (voxpopuli_eng_000498-voxpopuli_eng_000498) +Scores: (#C #S #D #I) 117 13 26 16 +REF: i h o p e t h a T t H E c o M m I S S i o n s m O b * * i L i * * T Y i n i * T i A T i v E s w I L L * n O t c * r E a * t E t h e * N E x t p r o b l * e m b u t w i L l b e a n a n * s W e R f o r * e X i s t i n g c h A L l E n g e * s o f t h e r ******* o A D t R a n * s p * o r T s e c t O r +HYP: i h o p e t h a * ******* t * * c o * m * * * i o n s m * b I T i N i S H E S i n i S H i * F i v * s w * * * W n * t c A r * a K t * t h e D A C x t p r o b l O e m b u t w i * l b e a n ******* a n C s * e * f o r E e * i s t i n g c h * O l I n g e R s o f t h e ******* r o B P t * a n C s p B o r * D s e c t A r +Eval: D D D D D D D D D I I S I I S S I S D S D D D D I D I D I D I S S I D D I D D I D D S S I D I S S D I I D S S + +Speaker sentences 957: voxpopuli_eng_000499 #utts: 1 +id: (voxpopuli_eng_000499-voxpopuli_eng_000499) +Scores: (#C #S #D #I) 292 26 59 22 +REF: i N t H e * * U s i T w a s A d e C I s i o n t a K E n O N l y b y o n E p e r s o n t h e F o r m e R p R e s i d e N T o F t h e U n i T e D s t a T e s a g a I n s T t h e a r t i c U l a t e D d E m O c r a t i c * * * m ******* A J O r i t y o f t H e u ******* s c o n g r E s s b y a l l o f i t s r e p U b l I C A N A n D s o M E O f i T s * d e m * O c r a * * * ******* t * * m ******* * * E m b e r s i t w a s A n a g r E e m e n t w i t h o u t a n y b i * n d i n g o b l i g a t i o n s a s t h e l e a d e r s o f i r a * n v e r y o * p e n * +HYP: i * t * e O U A s i * w a s ******* * d e * * s i o n t a * I n ******* A R l y b y o n * p e r s o n t h e * o r m e * p * e s i d e * D o * t h e ******* * n i D e * s t a C e s a g a * n s E t h e a r t i c I l a t e * ******* d * m * c r a t i c K D R m N D U r i t y o f t * e ******* u s c o n g r * s s b y a l l o f i t s r e p O b l * * * * K E n * s o * * * f ******* i C s T d e m E R c r a T I C t D E m R T m b e r s i t w a s * n ******* a g r * e m e n t w i t h o u t a n y b i D n d i n g ******* o b l i g a t i o n s a s t h e ******* l e a d e r s o f i r a U n v e r y o U p e n T +Eval: D D I I S D D D D D D S D S S D D D D D S D D D S D S D S S D D D D I I I I S S S D D I D S D D D D S S D D D D D S I I S I I I I I I I I I S D D D I D D I I I + +>> REF: l y A n D p r e C I s E L Y m a D e C l E A R O n t h e V e r y d a Y t h I S s o c A L l E d d e A l w a s p U B l i s h e d +>> HYP: l y I n * ******* p r e * * s I D H m a * e ******* P l * * Y * n ******* t h e * e r y d a * t h * E s o ******* c * * l * d d e * l w a s p * O l i s h e d +>> Eval: S D D D D S S S D D S D D S D D D D D S D D D D D D S + +Speaker sentences 958: voxpopuli_eng_000500 #utts: 1 +id: (voxpopuli_eng_000500-voxpopuli_eng_000500) +Scores: (#C #S #D #I) 106 6 11 8 +REF: f r E e s p e E c * H i s E s ******* s e n T i a L l y a C c e P t i n g t H a t p e O p l E A r E f r e e t o s a y t h i n g s w e d * ******* * o * l i * k e n o t m E R e l y f r e e t o s a y t h i n g s w e d o l i K e * +HYP: f r * e s p e A c G E i s * s s e n C i a * l y a E c e C t i n g ******* t * a t p e * p l * U r * f r e e t o s a y t h i n g s w e d O N o T l i E k e n o t m * * e l y f r e e t o s a y t h i n g s w e d o l i * e K +Eval: D S I S D I S D S S D D D D S D I I I I I D D D I + +Speaker sentences 959: voxpopuli_eng_000501 #utts: 1 +id: (voxpopuli_eng_000501-voxpopuli_eng_000501) +Scores: (#C #S #D #I) 17 3 2 1 +REF: l E t U s l E a r n * f R o m t h i S +HYP: l A t A s l * a r n D f * o m t h i E +Eval: S S D I D S + +Speaker sentences 960: voxpopuli_eng_000502 #utts: 1 +id: (voxpopuli_eng_000502-voxpopuli_eng_000502) +Scores: (#C #S #D #I) 227 26 51 23 +REF: W e T H i n K T h a t T h e E n * v i R O N m e n t a L e F f e c t o f p r O d u c T s m u s t b e a v e r y i * m p O r t a n t i s S u E i n * t H e * e u a n d T h e W H o l E i * ******* * d e a * o F * A N * e c o l a b E L G I v E s a v e r ******* y u s E F U l o r i E n t a t i o n f o r * * * c o * N s u m e * R s o f c o u r s E T h e * E c O l a b e L s H o U L d B E g i v e n t o t h e m o s t E n * ******* v i r O N m e n t ******* a L L Y f R I e n d L y p R o * d u c t S A N D t h e i N f o r * m A t i o n s h o U l D b e c l e a r * a n d C o R R e C T +HYP: B e ******* * S i n G * h a t * h e A n E v i * * m e n t a E e A f e c t o f p r * d u c * s m u s t b e a ******* v e r y i N m p * r t a n t i s H u * i n G t * e R e u a n d * h e * * o l * i G T d e a R o * T H E I e c o l a b * R N E v * s ******* a v e r y O u s * * O l o r i A n t a t i o n f o r T H E c o S T s u m e M I s o f c o u r s * * h e I A c U l a b e R s * o * O d ******* * * g i v e n t o t h e m o s t A n D v i r * E m e n t a * * F f * * e n d * y p * o R d u c t * ******* * * * t h e i * f o r E m I t i o n ******* s h o * l * b e ******* c l e a r E a n d * o * * e * * +Eval: S D D S S D D S I D D S S S D D D I D S D I D I D D D D I I I I D I S S I D S S S D D I S D D S S I I I I S I S D D I S S S D D S D D D S I I D S I D D S D D D D I D D D D D D I S D D D D I D D D D D + +Speaker sentences 961: voxpopuli_eng_000503 #utts: 1 +id: (voxpopuli_eng_000503-voxpopuli_eng_000503) +Scores: (#C #S #D #I) 128 16 26 8 +REF: h o w e v e r t h e c U R r e n T r E g i * m e n E E D s t o b E b e T t e r T a I l o r E D t o t h E d i g i T A l E n * v * i r O n M e n t I N O R d e R T O E n s * * u r E f a I r r E m U n e r a t i o n t o C r e a t * O r s a n D t o C o N f o R m * t o C o n s U m e r e x * p e c t a t i o n s +HYP: h o w e v e r t h e c * I r e n * D r I g i E m e n * I T s t o b * b e * t e r S a * l o r * T t o t h * d i g i * D l I n F v E i r * n * e n t ******* * * * E d e * ******* * * * n s H O u r * f a * r r I m I n e r a t i o n t o G r e a t D U r s a n H t o * o * f o * m E t o * o n s O m e r e x A p e c t a t i o n s +Eval: D S D S S I D S S D D S D D S D D S S I I D D D D D D S D D D D D I I D D S S S I S S D D D I D S I + +Speaker sentences 962: voxpopuli_eng_000504 #utts: 1 +id: (voxpopuli_eng_000504-voxpopuli_eng_000504) +Scores: (#C #S #D #I) 139 22 42 1 +REF: I T c A L l s U P o N t H e C o M m I S S i o n a n d m e m b E r s t a t E S T o E n h a n c E T h e I r s U P p o r t F o R r e c O n c * i L I a t i o n t o s e c u r E p e A c E a n d s t A b I l i t y a n D I r E l A n d i w O U l D t h e r E f o r E U r G e Y O u c O L l E A g U e S t o p L e A s E s u P p o r T t h i s A m e n d M e n T +HYP: A D c * O l s * B o * t * e * o * m * * * i o n a n d m e m b * r ******* s t a t * H * o A n h a n c D * h e * r ******* s * E p o r t T o * r e c E n c S i * Y a t i o n t o s e c u r * p e * c S a n d s t O b * l i t y a n * A r * l E n d i ******* w * I l * t h e r f o r * A r * e * S u c * A l * I g * e * t o p * e E s * s u * p o r * ******* t h i s * m e n d * e n * +Eval: S S D S D S D D D D D D D D D D S D S S D D D D S S D S I D S D D S S D D S D S D D S D S D S D D S D S D S D D D S D D D D D D D + +Speaker sentences 963: voxpopuli_eng_000505 #utts: 1 +id: (voxpopuli_eng_000505-voxpopuli_eng_000505) +Scores: (#C #S #D #I) 217 23 45 20 +REF: S t r a t E g i C c h o i c E s a b o u t W H e r e t o * I N V e s t m u s t b e m a d E n o w * t * a k i n G I n T O A C c o u n T T H e * n E e D t o P H a s e o u t f o S s i l f u E l s u B s i * d I E s b u t t A k ******* * * e g a s a s A f o S s I L f * u E L i t c a n b e a h e l p ******* f u l b r i D g i n g T r A n s i T I o n a r y m e * d i u m t o b e u s ******* E D * * * i n m a n y m e M b e r s t * a T E s i F W e W A n T t o * A c h i E V E o * U r a * m b i * T i O U s C l i m A T E t a r g e t s +HYP: * t r a t I g i G c h o i c I s a b o u t * * e r e t o L E W e s t m u s t b e m a d * n o w L t O a k i n * * n * E * * c o u n * ******* * * e A n * e * t o * F a s e o u t f o R s i l ******* f u * l s u P s i T d * * s b u t t E k T H e g a s a s ******* * f o R s * * O f Y u * * i t c a n b e a h e l p f u l b r i * g i n g * r * n s i * C o n a r y m e A d i u m t o b e u s I N M E M i n m a n y m e * b e r ******* s t H a * * s i * * e * O n * t o E D c h i * * F o V E r a N m b i S H i * * s * l i m * * I G t a r g e t s +Eval: D S S S D D I S S S D I I D D D S D D D D D D I D D D S S D D S I D D S I I I D D S D D S I D D I D D D D S I I S S I I I D D I D D D D D S D I S D D S I S I I S D D D D D S S + +Speaker sentences 964: voxpopuli_eng_000506 #utts: 1 +id: (voxpopuli_eng_000506-voxpopuli_eng_000506) +Scores: (#C #S #D #I) 114 16 38 17 +REF: M I D D L E e A S T w e a R e * p o S s i B l y A T a T H r E s ******* H o L D w e c a n c H o o S E t o p U r * s u e t h e * s a m e p O l i C I e s i n T h E s a m e m a N n e r K n o W i n g t h a T t H E Y w I L l l e A d t o T h e S a m E r E s U L t s t h e r E s U l * T s * t h a t ******* * * ******* * * ******* * * * * +HYP: * * * * * * ******* e * * * ******* w e a * e D p o * s i * l y ******* * * a O F E r I s C o P E w e c a n c * o o T H t o p O r O s u e t h e Y s a m e p * l i * S e s i n * h * s a m e m a * n e r * n o N i n g t h a * ******* t * * * w * E l l e * d t o * h e * a m * ******* r * s * O t s t h e r * s O l S E s T t h a t W E N O D R D A +Eval: D D D D D D D D D D D D I D D D D D S S S S I S S S D S S S I I D D S D D D D S D D D D D D S D D D D D D D S D S I S I I I I I I I I I I I I + +Speaker sentences 965: voxpopuli_eng_000507 #utts: 1 +id: (voxpopuli_eng_000507-voxpopuli_eng_000507) +Scores: (#C #S #D #I) 15 0 7 2 +REF: B u T t H e r E i s A n o p t i o n ******* * +HYP: * u * t * e r * ******* i s * n ******* o p t i o n B +Eval: D D D D D D D I I + +Speaker sentences 966: voxpopuli_eng_000508 #utts: 1 +id: (voxpopuli_eng_000508-voxpopuli_eng_000508) +Scores: (#C #S #D #I) 30 4 8 9 +REF: T H I S w e a * l ******* s o * n e E d a C h a n g e * i n o U r * * I d E o l * O g * y * +HYP: * * * * ******* w e a L l s o E n e A d a T h a n g e H i n ******* o * r H R d * o l I D g T y I +Eval: D D D D D I I I S S I D D I I S D I S I I + +Speaker sentences 967: voxpopuli_eng_000509 #utts: 1 +id: (voxpopuli_eng_000509-voxpopuli_eng_000509) +Scores: (#C #S #D #I) 213 33 38 35 +REF: A l a R g E P A R t o f t h e r e A s o n I S o f c o u r s e i * ******* L l E g A l f i s H i n g * * * * M O R e o f * * ******* * * ******* t * e N T H A n N o * * T b y ******* * * * * * v e S s e L s w h i c h a r E R e * g i s t e r e D t o c o u n t r i e s W h i c h l a * c K t h e W i L l o R t h E r E s O u r C e * s t O E n ******* f o r * * C E i n ******* t * e R n a t i o n A L a g R E e m e n T s n o A m o U n t o f t R A C e * a b I L i t y m E a s U r E s o r * e * x t r A p A p e r ******* w O r K W i L l A D d r e s S t h e p r o b l E m * o f r e * D u C i n g +HYP: I l a * g H * B U t o f t h e r e * s o n ******* * * o f c o u r s e i S I l I g * l f i s * i n g K A N D T H e o f O M A L t W e * D O U n * o F E N b y H A L A M v e * s e * s w h i c h a r * L e A g i s t e r e * t o c o u n t r i e s * h i c h l a U c E t h e * i * l o F t h R r * s * u r * e I s t W A n f o r S T T i n t H e * n a t i o n * E a g * * e m e n C s n o * m o * n t o f t * * * e S a b * E i t y m * a s E r * s o r D e C x t r * ******* p p e r w A r * U i * l ******* * E d r e s E t h e p r o b l O m E o f r e T I u S i n g +Eval: S D S D S S D D D D I I S S D D I I I I S S S I I I I I I I D S S S D I I S I I I I I I D D D S I D D I S D D S S D D D I S S I I I S S I I D D S D D S D D D D D I D S D S D I I D D S I S D S D D D S S S I I S S + +Speaker sentences 968: voxpopuli_eng_000510 #utts: 1 +id: (voxpopuli_eng_000510-voxpopuli_eng_000510) +Scores: (#C #S #D #I) 189 27 45 15 +REF: t h e C o m p r O m i S e * A l s o i n ******* c l u d E s c l e a r * r u L E s t o D e f i n e w h i c h M E m b E r s t a t E H a s J u r I s D i c t i o n a n D t h E C O o p E r a t i o N B E t W E E N M e m b E r s t a t e s c o n c * e r N E d * I N c r o S s b o ******* R D e R c a S e s a s W e L L a S t h e n E e d t o * i N v o l V e * * E u r O j u s t T h A n K y o U f O R Y o U r w o r k a n d p l e a S e D o s u P p O r t t * ******* * * * ******* h i s D i r e c T i V E +HYP: t h e * o m p r * m i C e O L l s o i n c l u d s ******* c l e a r E r u * A s ******* t o H e f i n e w h i c h * * m b * r ******* s t a t D * a s H u r * s T i c t i o n a n * t h * * * o p * r a t i o M * * t * * * * ******* Y e m b * r ******* s t a t e s c o n c S e r * * d F O R c r o * s b o T H e * c a C e s a s H e * * H a * t h e n * e d t o E i M v o l L e F Y O u r j u s t * h E n * y o * ******* f * * * o * r w o r k a n d p l e a C e W o E s u * p * r t t O M R O h i s E i r e c * i * F +Eval: D D S I S I S D I D S D S D D D D S D S D S D D D D D S D D D D D D D S D D I D D I S S D I S S D S S D D S D D I S S I I S S D S D D D D D D D S S S D D I I I I I I S D D S + +Speaker sentences 969: voxpopuli_eng_000511 #utts: 1 +id: (voxpopuli_eng_000511-voxpopuli_eng_000511) +Scores: (#C #S #D #I) 188 19 49 18 +REF: * * * ******* t h e g r E e n s w * o U L D h a v E U s b E l I E V e T h a T t h E s E A r E b a d b e e s c r i m i n a l b e e s d e l i b E r A t E l y c o N t a m i n a t i n g h O N E y w * I t h A d a n g E r O u s i n g r e * d i e n t b u t i n f a c T T H E Y * a * ******* R e d O i n g W h A T h O N E y b E e s H a * V E a * l W a * * Y s d o * n E W H i C h i S t o c a R r y p o L l E n b a c K t O t h e I r h i v E s * * * t o f E e D t h e I r y o u n g +HYP: E N O t h e ******* g r * e n s w I o D T h a v * * s b * l * * * e * h a * ******* t h I s * U r * b a d b e e s c r i m i n a l b e e s d e l i b * r * t * l y c o * t a m i n a t i n g h * U D y w H t h ******* E d a n g * r * u s ******* i n g r e A d i e n t b u t ******* i n f a c * * * I N F a C H e d * i n g ******* * h * E h * * U y b * e s * a R L a V l * a L L R s d o U n M * * i * h ******* i * t o c a * r y p o * l O n b a c * t * t h e * r h i v * s T O D t o f * e * t h e * r y o u n g +Eval: I I I I D D I S S S D D D D D D D D D S D S D D D D D D S S I S D S D D D I D D D D S S I I I S D D D D S D D S D D I S S I D I I S I S D D D D D D D S D D D D I I I D D D + +Speaker sentences 970: voxpopuli_eng_000512 #utts: 1 +id: (voxpopuli_eng_000512-voxpopuli_eng_000512) +Scores: (#C #S #D #I) 44 2 2 3 +REF: b u t i t w a s t h e c o u n t r y i t * ******* S e l F b e I n g m o r e c a p * a b l E +HYP: b u t i t w a s t h e c o u n t r y i t D H e l D b e * n g m o r e c a p R a b l * +Eval: I I S S D I D + +Speaker sentences 971: voxpopuli_eng_000513 #utts: 1 +id: (voxpopuli_eng_000513-voxpopuli_eng_000513) +Scores: (#C #S #D #I) 61 5 8 7 +REF: * ******* i n ******* t o t h e p O r t ******* f o l i * o o f t h e n E W c o M m i S s i o n E r * d E a l i n g w i t H f * u n d A m e n t A L r i g h t S +HYP: R i n t o t h e p * r t f o l i A o o f t h e n * U c o * m i * s i o n r E d * a l i n g w i t * f A u n d E m e n t * * E r i g h t E +Eval: I I I D I I D S D D S I D D I S D D S S + +Speaker sentences 972: voxpopuli_eng_000514 #utts: 1 +id: (voxpopuli_eng_000514-voxpopuli_eng_000514) +Scores: (#C #S #D #I) 39 7 12 0 +REF: T H e m e S s A g e I S t H a t t h e E u d o E S N O t h a v e a n y n E W s o l U t i o n S +HYP: * * e m e * s I g e ******* * * t * a t t h e Y u d o U T * A t h a v e a n y ******* n * * U s o l * t i o n E +Eval: D D D S D D D D S S S D S D D D S D S + +Speaker sentences 973: voxpopuli_eng_000515 #utts: 1 +id: (voxpopuli_eng_000515-voxpopuli_eng_000515) +Scores: (#C #S #D #I) 85 14 7 5 +REF: a r E y O u w i L l i n g t o a c t i n * * f a V O U r * o F t h e s o C i a l d * I m e n S i o n t o b e i n C l U d e d i n t h e e * u c o m p e t e n C I e s a s p r O p O s E D +HYP: a r * y * u w i * l i n g t o a c t i n E R V f a T H E r E F o R t h e s o S i a l d E m e n T i o n t o b e i n * l O d e d i n ******* t h e e O u c o m p e t e n S Y e s a s p r E p U s * * +Eval: D D D I I S S S S I S S S I S S D S D I S S S S D D + +Speaker sentences 974: voxpopuli_eng_000516 #utts: 1 +id: (voxpopuli_eng_000516-voxpopuli_eng_000516) +Scores: (#C #S #D #I) 60 9 19 8 +REF: T H e n E X t S T e P o n * * * s p e c t r u M p o l i C y I S B e I N G t a k E n * w i T H t h e r e f o r m * o f o u r t e l E c o * ******* * M f r a m E w O r K +HYP: * A e R n * C t ******* * H e * o n D P E s p e c t r u * p o l i * y ******* * * ******* * e * * S t a k I n G w i * * t h e ******* r e f o r m E o f o u r t e l I c o N T H f r a m w * r * +Eval: D S S D S D D S D I I I D D D D D D D D D S S I D D D I S I I I S S D D + +Speaker sentences 975: voxpopuli_eng_000517 #utts: 1 +id: (voxpopuli_eng_000517-voxpopuli_eng_000517) +Scores: (#C #S #D #I) 137 8 23 14 +REF: i b e l I E V e h i s r e m a r * ******* K s w e r E * * e x * p l i C i t * l y r a c i s * t * * a n d X e n O P H o b i c * * a n d p r O m o t e d r a C i A l i n t o l E r a n c E i n A w * a y t h A T i s n o T A C c e P t a b l e o r a l L o W e d i n t H e C o n S t * i * t u t i O N o f t h i s h o u s e +HYP: i b e l * * * e h i s r e m a r C E s w e r A I N e x T p l i * i t E l y r a c i s C t E D a n d S e n * A F o b i c K E a n d p r * m o t e d r a * i * l i n t o l * r a n c * i n ******* * w H a y t h * * i s n o * * U c e * t a b l e o r a l A o U e d i n t * e * o n * t O i C t u t i * * ******* o f t h i s h o u s e +Eval: D D D I I S S I I I D I I I I S D S S I I D D D D D D D I D D D D S D S S D D D I I D D D + +Speaker sentences 976: voxpopuli_eng_000518 #utts: 1 +id: (voxpopuli_eng_000518-voxpopuli_eng_000518) +Scores: (#C #S #D #I) 75 8 16 4 +REF: r e a L l i F E e * X a m p l E S s h o W t h a t s o L v i n g i S S U e s r E l a t e D t o * E D U c a t i o n f U e * * l S s t r o n G c o M m U n i t y d e v e l O p m e n t +HYP: r e a * ******* l i G H e G S a m p l * * s h o L t h a t s o * v i n g i * * * e s r * l a t e * ******* t o A B O c a t i o n f * e Y U l D s t r o n * c o * m * n i t y d e v e l * p m e n t +Eval: D D S S I S D D S D D D D D D D I S S S D I I S D D D D + +Speaker sentences 977: voxpopuli_eng_000519 #utts: 1 +id: (voxpopuli_eng_000519-voxpopuli_eng_000519) +Scores: (#C #S #D #I) 103 22 26 16 +REF: s O i h o * p e t h I S W i * L l h a P P e N * f o R r u S s I A a s w * e L l a n D t H a t r u S s I A c A n A l s O E n * V i s A g E * A n E X t r e m e * s U c c e S s s t O r y a f t E r t h * * ******* e * * * * s i g N i f i c A n T D a t E i n A U g U s t t h i s Y E A r ******* * +HYP: s * ******* i h o L p e t h * A H i S W l ******* h a * * e * M f o * ******* r u * s * H E a s w H e * l a n * t * a t r u * s H E c O n * l s * A n D i s I g T D n C S t r e m e M s * c c e * s ******* s t A r y a f t * r t h E S e G T W I s i g * i f i c E n D * a t * i n O R g * s t t h i s * O U r B +Eval: D D I D S S I S D D D D I D D D D S S I D D D D S S S D D S I S S S I S S S I D D D S D I I I I I I I D S S D D S S D D S S I I + +Speaker sentences 978: voxpopuli_eng_000520 #utts: 1 +id: (voxpopuli_eng_000520-voxpopuli_eng_000520) +Scores: (#C #S #D #I) 140 22 17 15 +REF: s h e A C c e p t E D t h e f a c t t h a t C i t i * Z e n S h i p i s ******* * * * * s * * * * U b J E C t * * t * O n A T i o n A L J U r i s d i c t i o n b u t * S H E A l s o s a i d t h a t a C c o r d i N g t o t h e m A a s t r i c H T t r e a t y a n d S h e I s r i g h t * t h E R e H a s t o b e a d i r e c T l i n K +HYP: s h e * E c e p t * O t h e f a c t t h a t S i t i S I e n h i p i s A Y N A s I O N L b O U R t O F t H E n O S i o n * O * G r i s d i c t i o n b u t H * Y O U R l s o s a i d t h a t a c o r d i * g t o t h e m * a s t r i c * K t r e a t y a n d * h e A s r i g h t D t h * * e ******* * a s t o ******* b e ******* a ******* d i r e c * l i n G +Eval: D S D S S I S S I I I I I I I I I S S S S I I I S S S D S D S I D S S S S S D D D S D S I D D D D D D D D S + +Speaker sentences 979: voxpopuli_eng_000521 #utts: 1 +id: (voxpopuli_eng_000521-voxpopuli_eng_000521) +Scores: (#C #S #D #I) 197 27 24 12 +REF: T H e E U f a i l E d e s p e C i A L l y * i n * D e m O N s t * r a t i n g a u N i f i * e d a n d * E F f i C I e n t a * P p r o A c h t o C l i m A t E c H a * n g * E t r e a t m e n t a s W e l L a s i n s t r E n g t h E n i n g i t s l e a d i n g p o l i t i c A l P o s i t i o n i n T H I s A g e n d * A i c o N s i * D e r * t h E R e f o r E t a k i n g T H i s r e s o l u T i o n a n a c t o f u t m o * s t i m p o r t a n C E +HYP: * * e * O f a i l * d e s p e S i * O l y E i n T H e m * * s t H r a t i n g a Y u L i f i D e d a n d T A f i S H e n t a T p r o R c h t o O l i m I t * ******* c * a E n g C H t r e a t m e n t a s * e l E a s i n s t r A n g t h A n i n g i t s E l e a d i n g p o l i t i c K l C o s i t i o n i n ******* * D E s ******* U g e n d E R i c o * s i T H e r E t h * * e f o r * t a k i n g * * i s ******* r e s o l u * i o n a n ******* a c t o f u t m o R s t i m p o r t a n * S +Eval: D D D S D S D S I I S D D I S S I I S S S S I S S S S D D D I I S D S S S S S S D D S S D S I S D I S I D D D D D D D D I D S + +Speaker sentences 980: voxpopuli_eng_000522 #utts: 1 +id: (voxpopuli_eng_000522-voxpopuli_eng_000522) +Scores: (#C #S #D #I) 56 5 7 3 +REF: t h e u n i * t e D s t a t e s o f E u r o P E W i L l b E a f a c t w i t H s w e d E n a s A p r o v i * * n c E +HYP: t h e u n i G t e S s t a t e s o f Y u r o * V * i * l b * a f a c t w i t * s w e d O n a s ******* * p r o v i D E n c S +Eval: I S S D S D D D D S D D I I S + +Speaker sentences 981: voxpopuli_eng_000523 #utts: 1 +id: (voxpopuli_eng_000523-voxpopuli_eng_000523) +Scores: (#C #S #D #I) 95 6 18 7 +REF: i t * m u s T b e t h e c a p * i t A l * o f b o t H S t * a t E s a n d w e m u s T r e c o G n i s e p A l E s t i n E A s A s t * a t E a s p r o v i d E d f o r * i n t h e o * S l o A g r E e M e n T s +HYP: i t D m u s * b e ******* t h e c a p B i t * l E o f b o t * * t H a t * s a n d w e m u s S r e c o * n i s e p O l * s t i n * ******* I s ******* * s t H a t * a s p r o v i d I d f o r E i n ******* t h e o F l o ******* * g r * e * e n C s +Eval: I D D I D I D D I D S D S D D D S D D I D S I D I S D D D D S + +Speaker sentences 982: voxpopuli_eng_000524 #utts: 1 +id: (voxpopuli_eng_000524-voxpopuli_eng_000524) +Scores: (#C #S #D #I) 150 17 20 15 +REF: * * u ******* k r a I n E I s f a C e ******* D w i t H * o n e o f T H E c r u C i a l c h a L l E n g e s * i n i T S h i s t O r y i t w O u l d b e f * u n ******* * * D A m e n t a L l y W r o n g * t o p r e S s t h e n a t i o n n o w * w i t H a L l t Y P e s o f r e s t r i c t i o n s p o p U l * * a R l Y c * a L l e d A U s t e r i t Y p o l i C Y +HYP: T Y u k r a * n * E s f a S e T w i t D W o n e o f ******* * * * c r u S i a l c h a * l I n g e s E i n ******* i C G h i s t A r y i t w * u l d b e f I u n T H E m e n t a * l y * r o n g K t o p r e * s t h e n a t i o n n o w E w i t * a * l t I B e s o f O r e s t r i c t i o n s p o p E l I D a * l * c O a * l e d * O s t e r i t E p o l i * * +Eval: I I I D D S S I S S I D D D D S D S I D S S S D I I I I S S D D I D I D D S S S S I I D D I D D S S D D + +Speaker sentences 983: voxpopuli_eng_000525 #utts: 1 +id: (voxpopuli_eng_000525-voxpopuli_eng_000525) +Scores: (#C #S #D #I) 52 1 3 4 +REF: m o r E r u l E s a n d r e * g U l a t i o n w i l l n o t i * m ******* p r o v e t h e * s i t u a t i o N +HYP: m o r * r u l * s a n d r e A g I l a t i o n w i l l n o t i N m p r o v e t h e S s i t u a t i o * +Eval: D D I S I I I D + +Speaker sentences 984: voxpopuli_eng_000526 #utts: 1 +id: (voxpopuli_eng_000526-voxpopuli_eng_000526) +Scores: (#C #S #D #I) 66 5 8 2 +REF: a t l e a s t W e w O u L d l i k e t o K n o W t h e s o U r C e o f t h e m O n E y a n d t h e p o S s i b l e m o * t * i V E s +HYP: a t l e a s t B e w * u * d l i k e t o * n o L t h e s o * r S e o f t h e m U n * y ******* a n d t h e p o * s i b l e m o R t H i * F s +Eval: S D D D S D S S D D D I I D S + +Speaker sentences 985: voxpopuli_eng_000527 #utts: 1 +id: (voxpopuli_eng_000527-voxpopuli_eng_000527) +Scores: (#C #S #D #I) 190 30 27 33 +REF: t o * H a v e t h o * s e E u r O p e A n w O R l D l a n g * U a G e s i n t o ******* * D A Y s g l O b A l i s e d w O r l d * * * i n * t o ******* * * D a Y s g L o B a * l i s E D e c o n O m y i n T H i s g L o b A L v i l L a g e w h i c h i s c U L t U R a l * E c o n o m i c * s o C i a l * * A n D p o l i t i c A l * * * i * s * a * m o s t v A l U a b l e * a s * S e * t f * o R t h e E n t i r e e ******* * u * W h I C H w e m u s t t * a k E f U L l a C c o u n T O F a n d ******* * +HYP: t o W E a v e t h o U s e Y u r * p e * n w * A l * l a n g W O a C e s i n t o T H E I s g l A b * l i s e d w E r l d T I S i n T t o T H E a I s g * o * a B l i s * * e c o n * m y i n * D i s g * o b * E v i l I a g e w h i c h i s c * O t * I a l Y * c o n o m i c K s o S i a l E E L n * B p o l i t i c O l B P W i T s E a E m o s t v E l * a b l e E a s T H e I t f R o M t h e ******* I n t i r e e O u G T h * A T w e m u s t t H a k * f * O l ******* a * c o u n * ******* * S a n d T +Eval: I S I S D D D S D I S S I I S S S S D S I I I I I I I S S D D I D D D D S D D S S D S D S I D I S I I S D S S I I I I I I S D I I S I I S D S I I I S D S S I D D S D D D D D S I I + +Speaker sentences 986: voxpopuli_eng_000528 #utts: 1 +id: (voxpopuli_eng_000528-voxpopuli_eng_000528) +Scores: (#C #S #D #I) 79 12 14 12 +REF: W e H a V e t o r e P e A t t h a t ******* * * * * O D a * C a n ******* n o t b e * * u s E D t o f i N A n c E s E C u r i t Y e x p E n S e s b O r * D e r * c o n t r o l o r m I l i t A r y s * u P p o r T +HYP: * e ******* * a * e t o ******* r e B e * t t h a t A L L H E a Y * a n n o t b e Y O u s * * t o f i * * n c S s I G u r i t * e x p A n C e s b A r T H e r S c o n t r o l o r m * l i t * r y s O u p o r N +Eval: D D D D D S D I I I I I S S I D I I I D D D D S S S D S S S I S I D D I S S + +Speaker sentences 987: voxpopuli_eng_000529 #utts: 1 +id: (voxpopuli_eng_000529-voxpopuli_eng_000529) +Scores: (#C #S #D #I) 65 5 21 2 +REF: I F A N Y t h i N g T h e S C i E n t i f i C r e p o r t s * * a R e B E C O m I N G m o r e u r G e n t m o r E A l a r m i n g a n d m o r E s h o c k i n g +HYP: * * ******* * * * t h i * g * h e * * i * n t i f i K r e p o r t s B C a L e * * * * m * A R m o r e u r D e n t ******* m o r * ******* * l a r m i n g a n d m o r * s h o c k i n g +Eval: D D D D D D D D D D D S I I S D D D D D S S S D D D D D + +Speaker sentences 988: voxpopuli_eng_000530 #utts: 1 +id: (voxpopuli_eng_000530-voxpopuli_eng_000530) +Scores: (#C #S #D #I) 114 24 18 31 +REF: f i n A L l y ******* * * ******* * * * w h * e ******* * * n i * * T * C o M E S t * * * O i N n o v a t i V e * f i N A n C i A L i n s t r U m e n t s w E n E E D t h e M b o * t h f o r o U r s e l V E s t o * * s * u P p o r t * * o u * r * E c o n o m I E s b u t a * * L s o t o * s U P p o r * t t h o s E W H o A r E i N n e * e D +HYP: f i n * O l y I M W H E w h A e H I n K i N G K A B o * U N t H E R E i * n o v a t i F e F f i * n S i O N i n s t r * m e n t s w * H n K U t h e * b o L t h f o r o * r s e l * F s t o U G s O u * p o r t O U o u E r A c o n o m * Y s b u t a O S s o t o L s * O p o r K t t h o s * * C o H E r * i * ******* n e A e * +Eval: D S I I I I I I I I I I I S I I S I S D S S I I I S D S I D S S S S D D S S S S D I D D S I I I D I I I I S D S I I S I D S I D D S S S D D D I D + +Speaker sentences 989: voxpopuli_eng_000531 #utts: 1 +id: (voxpopuli_eng_000531-voxpopuli_eng_000531) +Scores: (#C #S #D #I) 31 7 4 3 +REF: t h a t g i v e S * * U s A * u n i Q U e t o O l i n P e A C E m a k i n g +HYP: t h a t g i v e * A E s O R Y u n i * K e t o U l i n * e * S m a k i n g +Eval: D I I S S S I D S S D D S S + +Speaker sentences 990: voxpopuli_eng_000532 #utts: 1 +id: (voxpopuli_eng_000532-voxpopuli_eng_000532) +Scores: (#C #S #D #I) 24 2 0 4 +REF: * ******* p a p e r a v e r y * * w e A k p r o p o s A l +HYP: D p a p e r a v e r y H L w e E k p r o p o s I l +Eval: I I I I S S + +Speaker sentences 991: voxpopuli_eng_000533 #utts: 1 +id: (voxpopuli_eng_000533-voxpopuli_eng_000533) +Scores: (#C #S #D #I) 66 5 18 9 +REF: * r u S s I A h a s A l W A y s b E e N A v e r y p r o u d n a t i o n w i t h A R i c h c U l * * T u * r e w i ******* t H i n v e n t i o n s * * * * a n D e s P R I T +HYP: S r u * s * * ******* h a s O l * * y s b * e * ******* * v e r y p r o u d ******* n a t i o n w i t h ******* * * i c h c * l D C H u E r e w i t * i n v e n t i o n s W I T H a n * e s * C E +Eval: I D D D D S D D D D D D D D D D D I I S I I D I I I I D D S S S + +Speaker sentences 992: voxpopuli_eng_000534 #utts: 1 +id: (voxpopuli_eng_000534-voxpopuli_eng_000534) +Scores: (#C #S #D #I) 158 17 24 10 +REF: F a I r t a * X a * t i o n E v * e n a m o d i c U M o f t a * X a * t i o n I n s o m e c a S e s m i G H T j u s t h e l p * * U S t o d o w H a t i h A V e a L r e A d y s * U G g e s t e d a n d W h o K n o W s m a k E t h e c a S e f o r t h e R e t * r O s p e c t I V E b a n k r e ******* c a p I T A l i S a t i o n t h a t w e n e v e r s A W +HYP: * a * r t a C T a I t i o n N v H e n a m o d i c * E o f t a C S a I t i o n * n s o m e c a C e s m i * * Y j u s t h e l p E S E M t o d o w * a t i ******* h * * e ******* a * r e * d y s H E g e s t e d a n d * h o * n o * s m a k * t h e c a C e f o r t h e * e t E r * s p e c t O F b a n k ******* r e c a p * * D l i * a t i o n t h a t w e n e v e r ******* s O L +Eval: D D I S I S I D S I S I D S D D S I I S S D D D D D D D I S S D D D D S D I D S S S D I D D S D D S S + +Speaker sentences 993: voxpopuli_eng_000535 #utts: 1 +id: (voxpopuli_eng_000535-voxpopuli_eng_000535) +Scores: (#C #S #D #I) 148 24 13 12 +REF: t h E e u R o p * e ******* a n A s * Y l U m s u P p o r t o f F i c e m o r E o v e r H a s a ******* m o n g i t * s t * A S K s t o p r o m o * * t E f A C i l * I t * a t E a n d c o O r d i n a t E e * X c h a n g e s o f i n f o r m a t i o n a n d O t h e R a c t I v i t I e s * r E l a t E D t o R e l O c a t i o n W I T H i n t H e u n i o n +HYP: t h * ******* e u L o p B e a n H s I D l O m s u * p o r t ******* o f i c e m o r o v e r * a s a m o n g i t C s t H I U s t o p r o m o U H t * f E S i l Y t H a t * a n d c o U r d i n a t * e C S c h a n g e s o f i n f o r m a t i o n a n d U t h e * a c t E v i t Y e s E r l a t * Y t o L e l * c a t i o n * B D i n t * e Y u n i o n +Eval: D D S I I S I S S D D S S D I I I S S S I I D S S I S I D S D I S S D S S I S D S S D D S S S D S + +Speaker sentences 994: voxpopuli_eng_000536 #utts: 1 +id: (voxpopuli_eng_000536-voxpopuli_eng_000536) +Scores: (#C #S #D #I) 124 12 30 7 +REF: T h e c o n C l U s I O n o f t h e F r a m e W o r k a g R E e m e n t p r o v i D e S a l E g A L l y b i n d i n g * i n s t r U m e n t t o * * U P g * r a D E a n d s t r E n G t H E n e u A U s t r A l i A b * I l A t * e r A L R E L a t * i o N s a n d t o i n c R e A S e c O o p e r a t i o n +HYP: * h e ******* c o n * l * s * * n ******* o f t h e * r a m e B o r k a g * * e m e n t p r o v i * e * a l I g * * l y b i n d i n g K i n s t r * m e n t t o O B V g I r a * T a n d s t r A n * t * * n e u * O s t r * l i R b E l I t H e r * I * * * a t S i o * s a n d t o i n c * e * * e S c * o p e r a t i o n +Eval: D D D D D D D D S D D D D S D D I D I I S S I D S S D D D D S D S I S S I D S D D D I D D D D S D + +Speaker sentences 995: voxpopuli_eng_000537 #utts: 1 +id: (voxpopuli_eng_000537-voxpopuli_eng_000537) +Scores: (#C #S #D #I) 77 19 35 5 +REF: T H e r e f * O r e w E A R e a s K i n G t h e c o u n C I l a N D T H E C O M m i S S i o n t o P r E S e n t a T R a N s ******* P a r E N t a N D C O M P L e * ******* t * e A S s e S s M e n t o f t H e I M P a c t o F t h E C r i s i S +HYP: * * e r e f R U r e w * ******* * * e a s i n * ******* t h e c o u n S E l a * * ******* * * S * G L m i * T i o n t o * r * * e n t ******* a * C a * s B a r I t H a * * * U L D B e D t H e * * s e * s T e n t o f t * e ******* * * B a c t o * t h * * r i s i * +Eval: D D I S D D D D S D D S S D D D D D S D S S D S D D D D D S D I S S S S D D D S S S S I I I D D D S D D D D S D D D D + +Speaker sentences 996: voxpopuli_eng_000538 #utts: 1 +id: (voxpopuli_eng_000538-voxpopuli_eng_000538) +Scores: (#C #S #D #I) 100 6 13 5 +REF: * i n o t h e R w O r d s t h e o b j e c t i o n i s n o t w h e t h e R m O n E y i s p a I d o r n o t t h e o * * * b j e c t i o n * i s w H e t h e R t h e R E I s a d i R e c t l i n k o r n o T +HYP: A i n ******* o t h e * w E r d s t h e ******* o b j e c t i o n i s n o t w h e t h e * m U n * y i s ******* p a E d o r n o t t h e ******* o P B E b j e c t i o n I i s w * e t h e * t h e * Y * s a d i D e c t ******* l i n k o r E n o * +Eval: I D D S D D S D D S D I I I I D D D S D S D S D + +Speaker sentences 997: voxpopuli_eng_000539 #utts: 1 +id: (voxpopuli_eng_000539-voxpopuli_eng_000539) +Scores: (#C #S #D #I) 82 17 19 7 +REF: I t * D I s t i n g U i s h * e s t h e t w o m a I N d o s S I e * r S * H u m A N R i G H t ******* s * * A B u s e S b y t h e c U R r E n t g o V E r N M e n t a n d t h e I R a n i A n n u C l e A R p r o g R A M m e +HYP: * t O H E s t i n g * i s h I e s t h e t w o m a * E d o s H e A r * Y O u m E R * i * * t s A E Y O u s e * b y t h e ******* c * A r n t g o * * r * * e n t a n d t h e * D a n i O n n u K l e * * ******* p r o g * D H m e +Eval: D I S S D I D S S S I D I S S S D D D I I I S S D D D S S D D D D D S S S D D D D S S + +Speaker sentences 998: voxpopuli_eng_000540 #utts: 1 +id: (voxpopuli_eng_000540-voxpopuli_eng_000540) +Scores: (#C #S #D #I) 84 20 11 17 +REF: * M R P R e * * S I d * * * * ******* * e n * * * t * * * s e * X U A L h A r a s S M e n t i s * A f o r m o F v i O l E n c e a n d i t i S t h e m o s t e X t r e M E f o r m o f g E n D E r — b a s e D d I s c r I m i n a t i O N +HYP: E S S * M e T H E B d R O N M H e n K A C t E R E s e C T I O N h E r a s D e n t i s H E f o r m o * v i * l A n c e a n d i t i * t h e m o s t e * t r e A N f o r m o f g H n * T r b a s e * ******* d E s c r * m i n a t i * * +Eval: I S S D S I I S S I I I I I I I I I I I I I S S S S S S S I S D D S D D S S S D S S D D S D D D + +Speaker sentences 999: voxpopuli_eng_000541 #utts: 1 +id: (voxpopuli_eng_000541-voxpopuli_eng_000541) +Scores: (#C #S #D #I) 63 6 7 9 +REF: w e c a n l O o k t o s o m * ******* e * * * n O n e ******* u m e m b E r s F o r g O o d e x * a M p l e S a S r e g a r d * S t * e C H n o l O g I e s +HYP: w e c a n l * o k t o s o m E e I R N n I n e u m e m b O r s * o r g * o d e x S a N p l e * a * r e g a r d E D t H e * n o l A g * e s +Eval: D I I I I I S I S D D I S D D I S I D S S D + +Speaker sentences1000: voxpopuli_eng_000542 #utts: 1 +id: (voxpopuli_eng_000542-voxpopuli_eng_000542) +Scores: (#C #S #D #I) 42 5 6 6 +REF: i n * ******* v O l v e d * f o r T h e I R p o s i t * i v e a n d c O N s t R U c t * i v e a P P r o a * c H +HYP: i n M v * l v e d S f o r * h e * * p o s i t E i v e a n d c * E s t T A c t E i v e a B r o a T c * +Eval: I I D I D D D I D S S S I S S I D + +Speaker sentences1001: voxpopuli_eng_000543 #utts: 1 +id: (voxpopuli_eng_000543-voxpopuli_eng_000543) +Scores: (#C #S #D #I) 67 17 17 9 +REF: S o i h o p e t h a t T H i s W i L l b e c o m p L e * t e * ******* * * D i n T h E f o r E s E E A B l e f * u T U R e * W h I C H m E a n s * M a * Y b e t W o O R T H r E e m O n T H s +HYP: * o i h o p e t h a t * * i s * i * l b e c o m p * e A t e T A I R i n * h * A f o r * s * I V I l e f O u O C H e R T h A T D m * a n s E G a D b e t * o * A * F r * e m U n * * s +Eval: D D D D D D I I I I I S D D S D D S S S I S S S I S S S S D I S I S D D S D S D S D D + +Speaker sentences1002: voxpopuli_eng_000544 #utts: 1 +id: (voxpopuli_eng_000544-voxpopuli_eng_000544) +Scores: (#C #S #D #I) 96 21 16 15 +REF: * * ******* f U r T H e r E n * c o U r * A G e t h e * * u ******* * n S e * f F O r T S t o b r i n g a B O U T p e A C E i n A f ******* g H A n i s t a n a n d t o o v e r ******* c o m e t ******* H E f r A G i l E s E c u r i t y E n ******* v * i r O N m e n t i n t h e c o U n t R y +HYP: O R f O r * D e r A n D c o * r I S H e ******* t h e Y O u A n D e T f H U r * H t o b r i n g a M N G p e * * S i n O f g * * n i s t a n a n d ******* t o o v e r c o m e t O F f r E S i l * ******* s I c u r i t y * n v E i r * E m e n t i n t h e ******* c o * n t * y +Eval: I I I S D S S I D I S S D I I I I S I S S D S S S S S D D S S I D D D I I S S S S D D S D I I D S D D D + +Speaker sentences1003: voxpopuli_eng_000545 #utts: 1 +id: (voxpopuli_eng_000545-voxpopuli_eng_000545) +Scores: (#C #S #D #I) 32 4 4 3 +REF: W e U n d * * e R s t a n D t h a t s o m e p e O p l E * a r E a n g r y +HYP: B e ******* A n d T H e s t a n T t h a t s o m e p e * p l * O a r * a n g r y +Eval: S D S I I S S D D I D + +Speaker sentences1004: voxpopuli_eng_000546 #utts: 1 +id: (voxpopuli_eng_000546-voxpopuli_eng_000546) +Scores: (#C #S #D #I) 17 5 8 1 +REF: W e W A n T t o B e m o R E r E s * p o n S i B l E +HYP: O e ******* * * n * t o H e m o * * ******* r * s T p o n C i V l D +Eval: S D D D D S D D D D I S S S + +Speaker sentences1005: voxpopuli_eng_000547 #utts: 1 +id: (voxpopuli_eng_000547-voxpopuli_eng_000547) +Scores: (#C #S #D #I) 97 16 14 8 +REF: W e m u s t R e * * c t i f * * * * Y t h i s s I t U a t i o n a n d W E a s K t h e c o M m I S S i o n t o c o n s i d e r t h e m o s t A d E Q U A t E * c o m P E n s a t i o n m e A s U R e * s f o r o U R p A S s e n g e R s +HYP: * e m u s t * e D A c t i f I H I T H t h i s s U t I a t i o n a n d * H a s E t h e ******* c o * m * * * i o n t o c o n s i d e r t h e m o s t E d I C K E t * G c o m * I n s a t i o n m e * s * H e R s f o r L o L W p * E s e n g e * s +Eval: D D I I I I I I S S S D S S D D D D D S S S S S D I D S D D S I S S S D S D + +Speaker sentences1006: voxpopuli_eng_000548 #utts: 1 +id: (voxpopuli_eng_000548-voxpopuli_eng_000548) +Scores: (#C #S #D #I) 169 18 17 24 +REF: t h e c o M m i S S i o n i n * V i * * * ******* t * e ******* * * * * * * * S p A r l I a m e n t i n t h e u p c o m i n G * r e v i s i o n t o o p e n i T s p o s i t i o n o n * t h i s m a t T e r * w h i c h r e a L l y c o n c * e r N S a C c e s S t o * J u s t i c E i n E u r o p E a n d t h e E n f o r C E m e n t o f r i G H T s g r a n t e d b y * e ******* * u r o p E A n * * u n I O N l A W +HYP: t h e c o * m i * T i o n i n G B i S H E t H e Y U O P I O N T p O r l * a m e n t i n t h e u p c o m i n * K r e v i s i o n t o o p e n i * s p o s i t i o n o n D t h i s m a t H e r E w h i c h ******* r e a * l y c o n c S e r * D a * c e s E t o L * u s t i c S i n ******* O u r o p * a n d t h e I n f o r S T m e n t o f ******* r i * C E s g r a n t e d b y H e Y u r o p I U n R Y u n * * D l * O +Eval: D D S I S I I I I I I I I I I I I I S S D D I D I S I D D I D S D S I D S D S D S S S D D S S I I I S S I I D D S D S + +Speaker sentences1007: voxpopuli_eng_000549 #utts: 1 +id: (voxpopuli_eng_000549-voxpopuli_eng_000549) +Scores: (#C #S #D #I) 87 12 28 2 +REF: i W E l C O m E V e r y m u c h t h e r E s * u M P t i o N o f t A L k S B E t W E e n t h e I s ******* r a E l I S a n d T H E p A l E s t i n i A n s a n d S I n c E r E l y h o p E t h a t T h e Y w I l L s u c c E e d +HYP: i * * l * m * * e r y m u c h t h e ******* r * s O u * N t i o * o f t O C k * * * t * * e n t h e ******* A s r a * l * Y a n d ******* * * * p O l I s t i n i O n s a n d * E n c I r * l y h o p * t h a t * h e * w * l D s u c c * e d +Eval: D D D S D D D D I D S D S S D D D D D D S I D D S D D D D S S S D S S D D D D D S D + +Speaker sentences1008: voxpopuli_eng_000550 #utts: 1 +id: (voxpopuli_eng_000550-voxpopuli_eng_000550) +Scores: (#C #S #D #I) 73 13 9 17 +REF: * ******* w e h a V e A N A C c u m U l a t i o n o f p r o b l * E M s r e s U l t i n g f r o m ******* * * * a r ******* t i f i C I a l u n d ******* * * e R b U D g e * t i n g * I n * * P r e * * V i O u s y E A R s +HYP: L w e h a * e ******* * * * E c u m E l a t i o n o f p r o b l A N C s r e s I l t i n g f r o m T H E a r t i f i S H a l u n d T H e * b * A g e I t i n g K A n D V E r e T P R i V u s y * * U s +Eval: I I D D D D D S S I S S S I I I I I S S I I I D D S I I S I I S I I S S D D S + +Speaker sentences1009: voxpopuli_eng_000551 #utts: 1 +id: (voxpopuli_eng_000551-voxpopuli_eng_000551) +Scores: (#C #S #D #I) 52 6 5 2 +REF: * l e t U s * n o t b e t h e m a n o f Y E s t E R d A y L E t U s b e t o d a Y s i n s t i t u t i o N +HYP: E l e t A s T n o t b e t h e m a n o f O U s t * A d * y * I t I s b e t o d a * s i n s t i t u t i o * +Eval: I S I S S D S D D S S D D + +Speaker sentences1010: voxpopuli_eng_000552 #utts: 1 +id: (voxpopuli_eng_000552-voxpopuli_eng_000552) +Scores: (#C #S #D #I) 145 20 29 27 +REF: * ******* i W o U L d U r * G e Y o * U t o b e c o m e a m b A S s A D O R s o F t h e y e a r B Y m a k i n g i t S * * I d e * A s a N d a c t i v i t * i E s ******* * * * w * i d E l y K n o W n * a ******* m o n g s * * ******* t * E u R o p e a N C i t i Z e N s * a n d p A R t * i c i p a t i n g I n E v * e n t s * b e I t * a t E u r o p e * A n n a T I o n A l * o r l o C A L * l E V e l +HYP: E i G o * * d A r L S e ******* * o E N t o b e c o m e a m b * * s H E T E s o * t h e y e a r ******* * E m a k i n g i t * A Y d e I R s a * d a c t i v i t H i * s W O W w H i d * l y * n o * n E a m o n g s H T t O Y u * o p e a * ******* S i t i * e * s E a n d p * U t P i c i p a t i n g * n H v B e n t s E b e ******* * t T a t Y u r o p e I O n n a S H o n * l F o r l o * * K A l * * e l +Eval: I I S D D S I S D D I S D D S S S S D D D S D I I S I S D I D I I I I I D D D I I I I I I S D D D S D D I D S I D S I I D D I S I S S S D I D D S I D D + +Speaker sentences1011: voxpopuli_eng_000553 #utts: 1 +id: (voxpopuli_eng_000553-voxpopuli_eng_000553) +Scores: (#C #S #D #I) 112 12 14 2 +REF: * C E r t A I N l y s u c h i M p a c t A S s e s S m e n t c o U l d p r e E m P t C e r t a I n p r o b l E m s s u c h a s t h o s E p o s e d b y t h e e l E c t r O n i C i d e N t I f i c a t i o n o f s h E e p I n * s c o t L A n d +HYP: D S A r t * * D l y s u c h i N p a c t * * s e s T m e n t c o * l d p r e ******* A m * t S e r t a * n p r o b l O m s s u c h a s t h o s * p o s e d b y t h e e l * c t r * n i K i d e D t * f i c a t i o n o f s h * e p A n D s c o t * E n d +Eval: I S S D D S S D D S D D S D S D S D D D S S D D S I D S + +Speaker sentences1012: voxpopuli_eng_000554 #utts: 1 +id: (voxpopuli_eng_000554-voxpopuli_eng_000554) +Scores: (#C #S #D #I) 161 15 20 5 +REF: t h e C o U r t i s c o n t e n t t o s E e t h a t i t s w o r k h a s i n f o r m e D t h e d I s C h a r g E P r o C e S s a n d h a s C o n t R I b * u t e d t o p r o p o s A l s * f o r i m ******* p r o v i n g t h e f i N A n c I a l m a n a G E M e n t o f * * E u s p e n d i n g a n d b e T t e R t A r G E t i n g o f E U f U n D s +HYP: t h e * o * r t i s c o n t e n t t o s * e t h a t H i t s w o r k h a s i n f o r m e * t h e d E s * h a r g H * r o U e * s a n d h a s * o n t * E b E u t e d ******* t o p r o p o s O l s E f o r i m p r o v i n g t h e f i * * n c H a l m a n a * * H e n t o f E Y O u ******* s p e n d i n g a n d b e * t e * ******* t O r K A t i n g o f * Y O f * n C s +Eval: D D D S D S D S D S D D D S I D S I I D D S D D S I I S D D D D S S S D S S D S + +Speaker sentences1013: voxpopuli_eng_000555 #utts: 1 +id: (voxpopuli_eng_000555-voxpopuli_eng_000555) +Scores: (#C #S #D #I) 67 9 5 10 +REF: r e * g u L a * T O r y C l a R i ******* t * Y a n d C e r t A I n t y i * s n e E d e * d f o r t h e P U b l i * C s e c t o r a n d f o r ******* * * i n d u s t r y +HYP: r e C g u * a I H r y G l a * i t H E a n d S e r t * E n t y i A s ******* n e A d e T d f o r t h e * O b l i K E s e c t o r a n d f o r T H i n d u s t r y +Eval: I D I S S S D I I S S D S I D S I D S I S I I I + +Speaker sentences1014: voxpopuli_eng_000556 #utts: 1 +id: (voxpopuli_eng_000556-voxpopuli_eng_000556) +Scores: (#C #S #D #I) 83 12 16 4 +REF: i s i t r e A L l y n o t p o s S I b l E t o * u S e ******* * O t h e r h o u s i n G f A C i l i T I e s w i T h * A P p r o p R I A t E r e C e p t i o n c o N d I T i o N s i n t h e m e A n t i m e +HYP: i s i t D r e * * l y ******* n o t p o s E A b l R t o O u * e A A t h e r h o u s i n * f E S i l i * D e s w i h E * * p r o p * * E t H r e S e p t i o n c o * d * * i o * s i n t h e ******* m e * n t i m e +Eval: S D D D S S S I D I I S D S S D S S I D D D D S S S D D D D D D + +Speaker sentences1015: voxpopuli_eng_000557 #utts: 1 +id: (voxpopuli_eng_000557-voxpopuli_eng_000557) +Scores: (#C #S #D #I) 41 2 2 6 +REF: w I L l y o u t * a k e a c T i o n a t * l a s t i f n o t t h e * n w H e * n * * +HYP: w * E l y o u t E a k e a c S i o n a t D l a s t i f n o t t h e I n w * e I n D E +Eval: D S I S I I D I I I + + diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..240da0ffc4263a37029fe5bfa845eec748a1a1cf --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/hyp.trn @@ -0,0 +1,1092 @@ +HE REMAED WEL CAMPION ANTIL NINTIN SICTY FIVE A YAR IN WHCHE SUFHED A TERABL ACXITENT (LAD_eng_000254-LAD_eng_000254) +AY LIBRL CON ERVETIVE HE AS DEFEATED IN ATY N ATY TWO (LAD_eng_000255-LAD_eng_000255) +WON ROVED LAR CODRE TWO ROUDE AT WHANCE (LAD_eng_000256-LAD_eng_000256) +SOME O THE OUNTIS HAE SERVAYIS FOR MALTBLE YEARS (LAD_eng_000257-LAD_eng_000257) +BOTHOF THEVIRSINS FEATHE THE SONG HAPY HOLIDAY (LAD_eng_000258-LAD_eng_000258) +SHAKSPIER MANY REFRNCES UR MAD TO SHEENDS INTR ACTIOND OR CARICTES FOM VERIUT PLAYES (LAD_eng_000259-LAD_eng_000259) +IF NDY THE PROGRAM CULDBRAKE OUT GUST LITL FOME IT TWO FOMELIAR APROUCH (LAD_eng_000260-LAD_eng_000260) +THE HALBUM WAS RELESE INO STRALIAR ARN NIN TINT OAGIST TWO THOUSENT AN E LEVON (LAD_eng_000261-LAD_eng_000261) +HE NOW PLACE FOR A STRALIN TLOBE PEIRT GLOURY (LAD_eng_000262-LAD_eng_000262) +ITIT NOT NONE HOW MUCHE IF ANY OF HER TLAMEMS ARE TRO (LAD_eng_000263-LAD_eng_000263) +A SMLL BISNESS ONER BRAURD OPRATED HIS WEA AN HAP FARME FOR SICTENEARS FRO THE AG OF WENTY TWO (LAD_eng_000264-LAD_eng_000264) +IN THENINTH SENCTRY HE WAS AN IRISH POAT (LAD_eng_000265-LAD_eng_000265) +THEY AR MAUECET BY STRONGN (LAD_eng_000266-LAD_eng_000266) +THE LALLE IS THERE FOR VOULED (LAD_eng_000267-LAD_eng_000267) +IN THEARLY STAGES CAME CLOSE TO U A SLEP (LAD_eng_000268-LAD_eng_000268) +RUNING EVERY THARTY MINIT THO UT SERVIS TIMEMS (LAD_eng_000269-LAD_eng_000269) +AS RESILT WHEN THECOLIGE RE OPEND IT WHAS AS A ALL MALE COLIGE (LAD_eng_000270-LAD_eng_000270) +THE TIME BETWEN THES PONT IS VERRABLE AND CANACER ANY WHE FRO A INIT TO MUCH LONGER (LAD_eng_000271-LAD_eng_000271) +WEARK ON THE E E EAEAS STDARTED IN MARHCH TWO THAUSED AND SEVON AT COST OF FIVE MILIOND DOLLERS (LAD_eng_000272-LAD_eng_000272) +HOWEVER TEWAS SOME DEC A GREMENT OE THE ENDING THEMEM WICH O MORY AND YOSHIMORY DECSKUSTE AT LEANGTH OVER EMOUL (LAD_eng_000273-LAD_eng_000273) +THE CAPLE HAD NO CHILDRON (LAD_eng_000274-LAD_eng_000274) +THEFITIAL SINGL F THAT DEBYU AL THM PARISS COLING HAD A ELABRT MUSIC VIDEAO (LAD_eng_000275-LAD_eng_000275) +THE SERIS ENDED ON SICXTH AORGIST TWO THAUSEND AND FORE LASTING FRA TOUTL OF SEVENTY OND DAYS (LAD_eng_000276-LAD_eng_000276) +HE HAS ALSOD CONTRIBETE TO THE NUN YOURC RE EU OF BOKS (LAD_eng_000277-LAD_eng_000277) +BY PLACING SMAL ART OBDGECT THRO OUT THE ILME (LAD_eng_000278-LAD_eng_000278) +IT I FOUNED IN BRESIL (LAD_eng_000279-LAD_eng_000279) +IT WA THE SID OF THE CAMLY I IDENTIFIED MORLE WIFH (LAD_eng_000280-LAD_eng_000280) +ECANDED IT SIGHTHE MUST LLSO SOD MIT A WORK PLAN (LAD_eng_000281-LAD_eng_000281) +DUNDEY WON THE MACH THRE TWOE (LAD_eng_000282-LAD_eng_000282) +HOWEVER THE VILIG REMANED ICILAT DT NTIL THE RIVUL OF TE FIRST NOUS PAPER SECOND RE POUBLICK (LAD_eng_000283-LAD_eng_000283) +THE FIRST ERVI I THENU CHARC WAS HELD NINTN FIFTY ON AL THO THE BILDIG WAS NOT FULY FINISHED (LAD_eng_000284-LAD_eng_000284) +THE AVRIGH HOUSEHLD SIES WAS TWO PONT TWO SEVON ND THE AVRIGH FAMLY SIES WAS THRE POENTESIARO SIARO (LAD_eng_000285-LAD_eng_000285) +IT WAS FIRSTE RARD CAST ON THRED GANIOURY TWO HOUSEND AND TEN (LAD_eng_000286-LAD_eng_000286) +THE WINGS WE NOW AD IN A SINGLE PRESING (LAD_eng_000287-LAD_eng_000287) +TE DOCTE OFOLOIFY IN ENDENYEARIG MANAGEMENT (LAD_eng_000288-LAD_eng_000288) +THISE O WAY THE MAEN ARKGUMEN OF SAIFTDY RISSKS (LAD_eng_000289-LAD_eng_000289) +HE WAS ALLSO AD A LIFH MEMBR OF SCOUND THORPYUNITED (LAD_eng_000290-LAD_eng_000290) +SHE FHEIRS THE L GAT DEFORSE BUT THIE NEVER HAPENS (LAD_eng_000291-LAD_eng_000291) +FOT DROPS NABLE T HAD TH FOT SRAT ACROUSE (LAD_eng_000292-LAD_eng_000292) +WHETH THE AR FLOY IS FREY OR FOURST CN FEC THE ENDGY OFIENCY OF TH WHNDO (LAD_eng_000293-LAD_eng_000293) +AFTR GETIG HE IT MESERENT THE MAD TH NOU DORS (LAD_eng_000294-LAD_eng_000294) +FRAGMNTE ON ACH FACE RE MARET WTH LTERS AY BE SE (LAD_eng_000295-LAD_eng_000295) +FROM TH FIRSTD MINITE BOTH TEMES SHOD THE DESIRE TO COMPEET WI THEGREIOF A PROCERS (LAD_eng_000296-LAD_eng_000296) +FISICL HERIBY ECERSIDSES MAY HELP TH PATIONTE TO MAIN TAIN MUSL STRINGTH (LAD_eng_000297-LAD_eng_000297) +HOWEVER THE TOWNE HE LIVS IN NO UND WONT TO HEARABOUT HER (LAD_eng_000298-LAD_eng_000298) +A DISRIES AEPOENTDENT O AN ACTING CHIVE JUSTISS OR JOUDGE OF THESOPREME CORT (LAD_eng_000299-LAD_eng_000299) +THE SORY BES OUT CUVERING IS TEN REMOVEDT AND TE BENDS AR PARTHALY COCKET (LAD_eng_000300-LAD_eng_000300) +THIS NASTIAL MOVENT WHCHE BEGON WTH SO UH HOP CAMETO A SAD EAND (LAD_eng_000301-LAD_eng_000301) +HIS A SEOSIATE OUSUALY CALD HIM TE ORE THEGOOD LOKING GIY (LAD_eng_000302-LAD_eng_000302) +ITS MAEN OFHICES WER IN LUNDEN WE THE SECEND OFISS BEL FAST (LAD_eng_000303-LAD_eng_000303) +ACTULY I HAD NER BEENTO A VILIDGE BEFOR THAT (LAD_eng_000304-LAD_eng_000304) +HE AS CHAGE ITH PLADING TO SET OF BOMS IN URAP AND THE UNIGTE TATE (LAD_eng_000305-LAD_eng_000305) +MAKING MRARS IS THE THIRD STUDIUR ALBAE BY BELDEN ASTRALIAN ARTIST GOTIEAY (LAD_eng_000306-LAD_eng_000306) +HE THEN MOVED TO WOASING TOD DE SI AND WAS A PART NE IT WORD BROWNEANDTILL NINTEN WENTY NIN (LAD_eng_000307-LAD_eng_000307) +JOS OF HIY SCOLE AD THE SCOLES THEY COMPET GANED INAL SPORTS (LAD_eng_000308-LAD_eng_000308) +WELF PLUS ON MACH BAND ER COAURD (LAD_eng_000309-LAD_eng_000309) +I THINK I MIGHT BE NOTHING (LAD_eng_000310-LAD_eng_000310) +THE HOM AS BILT AND LIVED IN BY ANDR JAC AND CANIDY DEPETY COLECT O THE INTERNL REVINU SERVIS (LAD_eng_000311-LAD_eng_000311) +IN NINTAN SICE YEFORE HE WENT BAK TO OMSEK AND ENTE THE ACTO SCHOUL OF OAMSK (LAD_eng_000312-LAD_eng_000312) +THEBANK IS JUINTLY ONED BY HIM AND HIS BROUVER AND RELITIVS (LAD_eng_000313-LAD_eng_000313) +HE SOPEICENTLY WAN TO COL IN BREISTAL (LAD_eng_000314-LAD_eng_000314) +WON THAUSEND AT HUNDED FORTY SICKCS FOARTH IDITION (LAD_eng_000315-LAD_eng_000315) +A PAT OF LITL INGLEND BEYOND WAILES IT HAS BEE A ENCHRLY INGLISH SPEAKING FOR NIN HUNTRED OEARS (LAD_eng_000316-LAD_eng_000316) +HE PLAD WTH TEN PLARS FOR HARVF WAS AGANETH TRDITION IND DE ASS PE (LAD_eng_000317-LAD_eng_000317) +THE REIDING GJOUDG WAS WEBST O FAIR HO WASAL EADY A SIED TOTHE CORT BEFORETHIS CACE WAS SHEDULT (LAD_eng_000318-LAD_eng_000318) +BG GRATHE FIVE WAS THE THIRD O THE MAIN SERISTOFECHE A LIVE LOUNCH (LAD_eng_000319-LAD_eng_000319) +ITS MOTO IS HO EVE YOU AR AND WHEREVER YOU ARE ON THE DIRNY OF FAIF YO AE WEL COM HER (LAD_eng_000320-LAD_eng_000320) +ROBET A MILE AS COTH WILTSON (LAD_eng_000321-LAD_eng_000321) +AFTRE WN YUAR BRAK SIR AO DEGRE WAS HE FLLING VENCHER (LAD_eng_000322-LAD_eng_000322) +AY AM TEY MANUFACTED A MORTL CIT OFTHE AD SAID ARDRACSTER (LAD_eng_000323-LAD_eng_000323) +THE ESESS AY AMED TO BILED A LEFT WING OLTERNITIF TO NOU LABER AND THE ESSAND PE (LAD_eng_000324-LAD_eng_000324) +HE LIVS LIK HE AS YONG PRSON (LAD_eng_000325-LAD_eng_000325) +MASTE OF SIND IN ENDENEARIG MANIGENT (LAD_eng_000326-LAD_eng_000326) +SHE FAILED TO AK THE TOP THRE ATHE CENION DJUNIEARTRAC TRILES THAT DUN (LAD_eng_000327-LAD_eng_000327) +A TOARE FOLOUD IN SUPORT (LAD_eng_000328-LAD_eng_000328) +THE ESTABLISE N ATEN SEVENTY ON AND E WE O THE LDEST CLOPSINTHE SOUTH OF INGLEND (LAD_eng_000329-LAD_eng_000329) +HE WS A MEMBR OF THE GEAS SCOTLEND ADFISERY BORD (LAD_eng_000330-LAD_eng_000330) +TWO THOUSEND ND FIVE GENTL MEN (LAD_eng_000331-LAD_eng_000331) +AOURE FILE AD A STONG RECEPTION INUR APAD CHVE DESTOBEUTION TUT THAT WAS NOT THE CACE HER (LAD_eng_000332-LAD_eng_000332) +BL THOIS STDETHES POSTERIER ANGAL STROCTHES (LAD_eng_000333-LAD_eng_000333) +HE WAS ALLSO A THRE TIME FRENCHE NASINL HAMBPIAN NINTIN NINTY NINTE NITY FORE TWO HOUSED AN WON (LAD_eng_000334-LAD_eng_000334) +THEILIGE STRUCTHER SHOWN IN IS MAP IS T A GRAT EXSTENT UN CHAGE T DAY (LAD_eng_000335-LAD_eng_000335) +RUHAR IS RECAGNISED IT NUKLER DESAUSTER ECPORTES AND O THE SAFTY OFITS TE NOLAGY (LAD_eng_000336-LAD_eng_000336) +AS OF TWO THOUSEND OD FOR TEN AEMTY VEE IS AVALABLE WITHIN THEUNIGTED CINGDM ON VERGEIN MEDIER AND SCGIY (LAD_eng_000337-LAD_eng_000337) +NOYOURK PEANGIN RANDM HOUSE (LAD_eng_000338-LAD_eng_000338) +THE DUTCHY WAS ECURED ITE OUT COME OF THE OFICT WALR (LAD_eng_000339-LAD_eng_000339) +WIT GOD PACE SDARTE THE MACH HIH BOTH TEMEMS OLTENATING SOPREMISY (LAD_eng_000340-LAD_eng_000340) +THIS VRTION IS NONTED OR BEIN ERY 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CIRNTLY ESAMING THELEAKLE CONCICQENCES F HE ROLING (LAD_eng_000350-LAD_eng_000350) +FROM NINTIN THERTY THREE TO NINTIN FOARTY NIN THE MERICED LEE WOND WELVE AUTO THE FIRST SICST EN (LAD_eng_000351-LAD_eng_000351) +THEAIR HE FELE SICK WT TIVFOS HIMSELF (LAD_eng_000352-LAD_eng_000352) +SICXT TEMES HAE EDEVEIDED INTO TWO GRUPSOF THRE TEMEMS ACH (LAD_eng_000353-LAD_eng_000353) +THE FIRST SESON REMI AD OND WELTH DUN TWO THOUSEND AN FIF DEEN (LAD_eng_000354-LAD_eng_000354) +IT SACED THE WEI H BOLRD AND SISTAM TWENTY FORE COMBING FEACES FROM BOTH (LAD_eng_000355-LAD_eng_000355) +VELYUE TWOOH NUMBRS WON TWO AND THRE (LAD_eng_000356-LAD_eng_000356) +THE LOR PAT OF MENS DRESES WE MUCH HOURT IN LEANGTHN THOS FR WIEIN (LAD_eng_000357-LAD_eng_000357) +THE IGOALTHS IN TERN WER CEADED BY THE MULERS (LAD_eng_000358-LAD_eng_000358) +JOS OF HIY SCOL AVERY WEK OF TH SCOL HEAR (LAD_eng_000359-LAD_eng_000359) +AS RSIL OFAL THE ARGUMENT GETIG TO HER (LAD_eng_000360-LAD_eng_000360) +IT HAD QUARTERS ARIN 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WN NINTETO LAT STRED SAID HOAKGON BUYDIG OF HI SOGAR (M-AILABS_eng_000196-M-AILABS_eng_000196) +RAT WAS SURPRIS TO FINE HE COLD SE SO PLAILY THR THE HIY WALO WOAHTER UBOF HERR BUT THE SND WAS ABL TO SHOOT ITS BEME STRAT DON THOR THEARAENSPEIRNT (M-AILABS_eng_000197-M-AILABS_eng_000197) +THE SPAT E ID SPRONG OPE (M-AILABS_eng_000198-M-AILABS_eng_000198) +GCOME DEANIL WIT HE GAVE SOCH AS UPOSITION (M-AILABS_eng_000199-M-AILABS_eng_000199) +YO SE ANDTIL THE SCHL PILES R INGENTED WE WAST T LAT OF TIM IN STDADY THAT NOW MA BE BETER IMPLOYED ANMPRCTSCING ATHLETIC (M-AILABS_eng_000200-M-AILABS_eng_000200) +YOVE DN IT HANW DECLARED DARTHY THES TENCE AR JUST OENDERFOL (M-AILABS_eng_000201-M-AILABS_eng_000201) +EMFOR TWENING TAN FIVEF THEREE TWOE THE INO WAS BERLY TWOENY MYWS AWAY WHN HO DION FIRD HS ROCKITS THE MD A COLOSTOL CLOWE O APER IN AMTYNESE (M-AILABS_eng_000202-M-AILABS_eng_000202) +THE PAD NO ATENCION TO THE FACTHAT GIP KGESCISIL DID NOT ON TO MARY ANY OF THEMEM FOR HEHADETERMEND THAT HN ITWAS EGREED WHO SHOD HAV HIM (M-AILABS_eng_000203-M-AILABS_eng_000203) +WAT DYOUTHI OF THAT HE CRID OPENG A COPBYOTHE RECKED AND LANG T FLAT O THE LIBRY TABLEL (M-AILABS_eng_000204-M-AILABS_eng_000204) +IT L ECOPIER UT A SORT TIME (M-AILABS_eng_000205-M-AILABS_eng_000205) +AND LAST THE ROUD O VEIGETDABLE PEPLEHO HAD NO HARTS AND OUOD NI THER SMILE NOR FROWN (M-AILABS_eng_000206-M-AILABS_eng_000206) +THEIN YOLL CACH ITE SAI TH ICH (M-AILABS_eng_000207-M-AILABS_eng_000207) +WHT IS IT I QUERED NOT FIELING SREN BUT THAT TWAS AE VALD A TEMD TOSECER LITLFRE ADRTYSING FO THE ANDEOV ER (M-AILABS_eng_000208-M-AILABS_eng_000208) +SO E GAVE THE LIRC THE THRD UNDEDOLOS FOR BOKS AND A CASK OF GODOLD AL FR PETER THE CLRK RAN THE AIL HIMSELF AND GAVE HE CAF MIW (M-AILABS_eng_000209-M-AILABS_eng_000209) +AT LIEK THAT AN ALS IN NEDERLANTDWITH MERLY GRIN THT FATED AWAY CHANGING INTO A LINKS WHIC IN TRE TOSPERD FOLOWED BY AN UNON RECE WIT SOURT NOUSAND PONTED ERS (M-AILABS_eng_000210-M-AILABS_eng_000210) +A SHE COLD NOT DEOE MARGRI LANCD UN CONHOUSLY ATHE UNGKLED ORNER OFHE OME SHE UO HAR TH NDER TAK A SURVINCS PLACE COO SHE (M-AILABS_eng_000211-M-AILABS_eng_000211) +A DNOHE SHE REPLIDED WIT INISEN KERYOUSITY DID I GIVF THE TO YOUWHAL (M-AILABS_eng_000212-M-AILABS_eng_000212) +MARBOR MILES AN THEAG ACENT DELING WR HELD UNDER LONG LEES THE MUST IF POSABL TBE RELET (M-AILABS_eng_000213-M-AILABS_eng_000213) +D A COP WA E OST UN IS THE LATEM (M-AILABS_eng_000214-M-AILABS_eng_000214) +ITD BONDED HEAR AND THAIR ABOU THECIOKON HOUS AN AT FIRST DORTHYCOULDNOT TEL HAT IT WOSWHIL THE SPEACING OF THECHIOCIONS NERLY DEFEND HER (M-AILABS_eng_000215-M-AILABS_eng_000215) +THE SOLDER GAVE YAL HAT EROUSED A SCHUAR OF HIS COMRAD AND BRGT THE TUMBLING INTO TH STRET WHEN THEY SAWHO THE OLRS PRESCISE PRISNER WAS SCAPING (M-AILABS_eng_000216-M-AILABS_eng_000216) +GJIM HAD REFEUSE TO LE THE FELD OF GRASWHER HE WAS INGAGED AND BISILY EATING SO THE WISURD GUGT OUTO TH BOUGY AND JUONED SAEB ANDOARITHY (M-AILABS_eng_000217-M-AILABS_eng_000217) +GSRDNLY IMAS INTERTED NTHECACES O AR BUT I CAN AK HEDS R TALS OF T I REPLID (M-AILABS_eng_000218-M-AILABS_eng_000218) +OR ANY MICE OR EVENG GRAS HOPERS (M-AILABS_eng_000219-M-AILABS_eng_000219) +AND THE THE PASIO DON THE TEL YOU WHA E TO D OR WHAT AN NOT TO DEH WE THE MUNY THEY GIVE YOU AN JUST PAMENT FO YOU PAINS INTHERECSTCANGE LIC (M-AILABS_eng_000220-M-AILABS_eng_000220) +WHT DIS TAT MEAN AST THE RINCES (M-AILABS_eng_000221-M-AILABS_eng_000221) +DE HAD BEE DRONED HE WAS FLOAODING N A SI OF LIT AND NOW THED SHINING LTE FISIES SWHEAM INCQUISITIELY E OUP TO HIE ND STAR (M-AILABS_eng_000222-M-AILABS_eng_000222) +BUD OLD GN HD ARIK TWO LEFTD REMEME THE TAIL I REDTO YOU I TH THONERM OABLTHE THE FIRST THE BRONS T ENDE THE WRLD OF OAPL WER SOULDGRS SINTFRM SOME BLASTID PLANT NOUTERSPACS FIE ANO HOM (M-AILABS_eng_000223-M-AILABS_eng_000223) +APPE WIE O SPEA T THE MEN ANDGT HEMTO GO WAY SHE CANT BREETH POR THING WIT THIS ROWD OBOUT TERHL (M-AILABS_eng_000224-M-AILABS_eng_000224) +A WHEN IY TOK THIS CACE HE SAID I BULEVED DONE IND MY HART DICSON WAS INSENT I STO BELEIT BUT MY FATHASPBEN ROUTLY SHAC (M-AILABS_eng_000225-M-AILABS_eng_000225) +A DHAPTR SICK OVEF THE PIT O OR SEATSE (M-AILABS_eng_000226-M-AILABS_eng_000226) +RE MEMER THE CAN NOT TOUCH US (M-AILABS_eng_000227-M-AILABS_eng_000227) +IV ME TIME AS OUR GIVE ME TIME IOF TEIRS ANYTHNG I HAT ITS A HURY IVEAN Y DEAE YOUR MADGESTY AND ONCET THE SICT TH SNOBE NOS PRONCES (M-AILABS_eng_000228-M-AILABS_eng_000228) +TONOF TROAHT DECLEARE THE SALER MAN (M-AILABS_eng_000229-M-AILABS_eng_000229) +AAS FOR THAT SAID MARGRIT RTHE HOTILY I HOLD IT HIS OHONY SO IT CQUEE MULD EPENSAY (M-AILABS_eng_000230-M-AILABS_eng_000230) +WE HE THES WORDS THE CINGWOS HED WASFUL F THE PINCES NEVER TAPE TO NCQPHIR IF THEYCOD BE TRO AND SMERED HIMSELF OVER WITH FAT AND SPRANG INT THEOVEINT (M-AILABS_eng_000231-M-AILABS_eng_000231) +YOSHOL B AL Y GET PARCE FROM YOURE ROME VIOND RESEVERE ILL HAVESOM TOUOLS GIVEN OU THEN ADTED DEPOM AS HE HASTO NDERSTAN TH TINGS ACE TOL OFENCESH (M-AILABS_eng_000232-M-AILABS_eng_000232) +BY THE TIM THEFROUST AD SAD IN THESHO BE FOARE WAY FOM HELSTDON (M-AILABS_eng_000233-M-AILABS_eng_000233) +OEN THIG WOENTOE SAY BE GAND CENITY (M-AILABS_eng_000234-M-AILABS_eng_000234) +THI MPORTNTRACHIC WAS ON FIDE TO NO OAMB THEREAL PRPRITER (M-AILABS_eng_000235-M-AILABS_eng_000235) +UN N WHWE AIDE DAB BLASED ONTD BASKODOVE MY THISTIMG CGO (cv_eng_000707-cv_eng_000707) +ID TD AT A SEPRTSUPSECTION WHICH DLSWIT HIS ASPECTD (cv_eng_000708-cv_eng_000708) +OPRATION OF THE FRONTLAN CONTDNURED ON THE WODENDT TESSEILS (cv_eng_000709-cv_eng_000709) +M NISON FHLORID IS TWENCEPERENT OVER NCTRIMLYE WHID RANG OF AVOMINGS (cv_eng_000710-cv_eng_000710) +FOR JINT BECKINGK SHITS STORETHFRESH PBOCKT BUTDEADES N DE LEVE THEM UNTO E RLER OLD CAS (cv_eng_000711-cv_eng_000711) +THE OTHE FORTING COUMPOSES ARD TOWO YURE CAMPS REFIRT TO COLECTIVELY AS THE YUNERSTDECOLNGE (cv_eng_000712-cv_eng_000712) +ITS TWO BHAD THEDWE HAE CUICKLE GORNGTO FRGET MY TAE TD (cv_eng_000713-cv_eng_000713) +WONEN O TORE IN TE GELORY SHOH HOURDHE GINTL AID IRED ISTATY AT FOAL ADGRORTCONTON (cv_eng_000714-cv_eng_000714) +E ANM PERIAL DI IAT (cv_eng_000715-cv_eng_000715) +THE ESELDIN COMPNYHTD AS HUD TAKESCKORITYO OTPRATION (cv_eng_000716-cv_eng_000716) +TE COING MIYNING CAN BE DON WIT GDOF HIS COARTS AOR EITES PESIOLIST HORDLY (cv_eng_000717-cv_eng_000717) +G THE L SO LE THENOUSIONAL RANKINGN (cv_eng_000718-cv_eng_000718) +T TARWRS GRANES BISHOPE OF NHIMORECKE (cv_eng_000719-cv_eng_000719) +AA IOUNDER THART THI TOL HIM AND HE OK MY PLASES PD (cv_eng_000720-cv_eng_000720) +I THORGT ID GIVE THE CITITCS ATDREAETE (cv_eng_000721-cv_eng_000721) +AS TIEWVETL DENIH TOMN THE RICTOES (cv_eng_000722-cv_eng_000722) +HOED YOUR NOSTH TO CAED THIT MAY FOM THEF ABLING HOR MOT OFONTION (cv_eng_000723-cv_eng_000723) +D THAT SONEDTS LAKE THERE POLOME AIIEDE I (cv_eng_000724-cv_eng_000724) +I HIS RINCULIGOR WAS NO TPLARLY DEFIDE BOWNDGREAIN THES PITD OF TECH ARIBEUNPANINSTOLAE (cv_eng_000725-cv_eng_000725) +MAR SIAL SHAVER OF SLASH FILNME DGAVE THE FILME AND AT OUT OF CAN (cv_eng_000726-cv_eng_000726) +HOWPERDYI TI TATE (cv_eng_000727-cv_eng_000727) +HIST T DILE BEGANE TO ESAEMBLETE MYIKCLE TDEMOSKINOSES (cv_eng_000728-cv_eng_000728) +HIS ALL CAPABL FING LYT INBLT WOE AMENTE DESRUPTOF POER (cv_eng_000729-cv_eng_000729) +THE FLAME TO WIAK CS CININGLIN IRMLY ININGS AS FPOLHE LISTEAT OND UILRLND ASE LANDDT TRY (cv_eng_000730-cv_eng_000730) +SHE DETEROUSHELY TORO WOAT AH (cv_eng_000731-cv_eng_000731) +AE MT THEORGONYSEORS OF THE ROTESE ANDAGRED CREAT TWO WORKINGRUMS (cv_eng_000732-cv_eng_000732) +A THE BON STROCT THOF FHALD PORD WHILE OAB OF HI GREN ONSTORD (cv_eng_000733-cv_eng_000733) +ONLY CAMEDON TOME S GARIT AND GLD FILD SOU IS EIL BACKEAR WER UND CONTISTED (cv_eng_000734-cv_eng_000734) +BP IT HIS A D CHIRDY SCOLEWHOS FES AN COUCKILADIN ON AINMEANS TEST (cv_eng_000735-cv_eng_000735) +SOME WENT WAY WHL OUWAS ER AND OTHE PEPE CAMEM (cv_eng_000736-cv_eng_000736) +DADT THANTHAHTDTCTCDNCN (cv_eng_000737-cv_eng_000737) +THAT CURA CONOTY WAS LOKCAD MANLY THAT HISTORICLE AND DEOGREFOCLE REGION OF CURE (cv_eng_000738-cv_eng_000738) +TE LVATIO A THE SIHT IS AMOF SILEVELEIG (cv_eng_000739-cv_eng_000739) +A TO B AS TRIDE TOANCHECT T CONP TEMETETED IN TO HIS TON (cv_eng_000740-cv_eng_000740) +I HEAVE TO WORKE THIS SITODLY (cv_eng_000741-cv_eng_000741) +U TED RA THE RON WOS FENMWAS KILAGEA GLEAD WLAF GLATING ON TERE NONES (cv_eng_000742-cv_eng_000742) +WHEO THE BILING DOST HE SELED R BEITE THE BOY TROMBLEDTD AT WHA HE SO (cv_eng_000743-cv_eng_000743) +DEIOCRAT ANE BRH IND DBAKE IER WON IT THE OPEN SEO (cv_eng_000744-cv_eng_000744) +LWORY OUET WORD INO GEATHA BLYTHE OOD IN INHECULDY USE OANATLYSINDTOL ROUMNP (cv_eng_000745-cv_eng_000745) +TRANTHW WAS BORN IN BELYESE SITED IND BRITOCS PONDREASS (cv_eng_000746-cv_eng_000746) +DERIRDY FASE OF LICF MOMES FAST (cv_eng_000747-cv_eng_000747) +AAAT NOTETHDE E (cv_eng_000748-cv_eng_000748) +SORVEWINT OLTDLUETOEE (cv_eng_000749-cv_eng_000749) +AT OE TE RE LURLENS TEYEORD FOM BRAKG BESTATION IN SOEN IFRENDEDECTIONS (cv_eng_000750-cv_eng_000750) +A ACHAEC REPUPBLIK ANTERED TWO SHOUTERS INTO THE PARRORO LENPOG COMPOTISIO (cv_eng_000751-cv_eng_000751) +T ID HER WILIMS ROE THE SKANG PLAY HAN T SHARED STORY RED IT HT THO PREIPIT (cv_eng_000752-cv_eng_000752) +TIS FAST OFE LLED WORDESTOAF BETERDNCHEIRRITY FLINDER ATNY SAIDE OFOID YE THERAURT (cv_eng_000753-cv_eng_000753) +O THESE ENXTRA GOARSTS WE NESEURNLT THED ONGONLE ALNMT OF OACKGALFTE RAY HATHE IR HARDTS (cv_eng_000754-cv_eng_000754) +AIPI HANDER OND BAKCT TO ESTRLIOWM (cv_eng_000755-cv_eng_000755) +AL PREMIT ME T TO INTERDUSESYOU TO HER MODESTID CQREN (cv_eng_000756-cv_eng_000756) +ENORGEION HERLEN WA S BOSTOY THE NONDADETDOF MORFHEN SPSTOTD (cv_eng_000757-cv_eng_000757) +ET SHE IS OF MAKCOCON DESESNT H (cv_eng_000758-cv_eng_000758) +GD I ME SORE THEILEST NOGT ONDIST (cv_eng_000759-cv_eng_000759) +AAO THOS AND ONTE LANCTLOL THESHREY IGDLOLD POR A BEDIT ONO (cv_eng_000760-cv_eng_000760) +A I COLED AN TOPESORIN AT IT EER (cv_eng_000761-cv_eng_000761) +FORS INPLITY GUR INCHESD IS NORMILYARDOUNDED TO HE NERESH HOL NOMBER (cv_eng_000762-cv_eng_000762) +IOF WE ACTILY DEO ON IS SOLD IT WIL BEF (cv_eng_000763-cv_eng_000763) +THEI FO OF H HIC TRY S APL SHAPEDT (cv_eng_000764-cv_eng_000764) +THEREIE ACTSHANGE IS NO WOBLY (cv_eng_000765-cv_eng_000765) +A WHAT OU EAT TO DAY WHEASK AND TORKS TO MOROE (cv_eng_000766-cv_eng_000766) +A THE WOTED AN FLOS OUT OF TE SCONMNTS AS HE LO OAPL E RIVER (cv_eng_000767-cv_eng_000767) +AM HEWHIY ID DION YOESAY SOM THINKCDHEADCD (cv_eng_000768-cv_eng_000768) +T AVEYOSE NO MARNM EETDEEEEE (cv_eng_000769-cv_eng_000769) +I COTD GO ONEFREDAIS ABOU THE DIDIOS WONS THE DUAST IN HIS PARTOF THE WERETD (cv_eng_000770-cv_eng_000770) +THO SOEO LAD DEVFTHEAR INCOREIRERAE NINGD INSITOPCEYOT THE YOARE (cv_eng_000771-cv_eng_000771) +AA COAS EVES OPB DCECT IS ESER GLWNDSH EECLDELDARDL (cv_eng_000772-cv_eng_000772) +THE SWEEDS WERANABL TO YOUS HR VEACALS WHIH ER TOCE IN TH MODE (cv_eng_000773-cv_eng_000773) +I THE ACKT ID NOT POR HE BIT BAYING AE REPEOSENTIP TO E BEAR AN THE CORTLIS (cv_eng_000774-cv_eng_000774) +CHONWRPLESTELEPINROULT IDN (cv_eng_000775-cv_eng_000775) +HE WA CON VICTDED AM BANIS O SIPRS FOR SOVION HORS RPNISMENT (cv_eng_000776-cv_eng_000776) +THE COPL OF TWO CHOLDON A DATER SO FEAUR RSELENDEA AN THE SIN F MUT HAL BRVERY (cv_eng_000777-cv_eng_000777) +N OF TH THRE RERENDEMS RECH HE CUARAMOF HE MODGURITY OF HES INTICTLDT (cv_eng_000778-cv_eng_000778) +IET ITERPEN SAEX CEADED IN DERAST SOMER ROSIP CAIRA ITI IN HOSOLE UNERSYO THOR A PEIRD O STONG BOATH (cv_eng_000779-cv_eng_000779) +WHEAR A AME BECWEN MY FLUCK AND MTH BUTERSIURE THE BOLY TOS (cv_eng_000780-cv_eng_000780) +THI FALIYER HAST LET TO ICXSTDAEN OULBLENCD HADH INSERDAYSE OF COLLSTO (cv_eng_000781-cv_eng_000781) +ADDUT Y ASESH H H H D EN (cv_eng_000782-cv_eng_000782) +WHIY OI THAT PLAIND CAEPE GOIN OVER (cv_eng_000783-cv_eng_000783) +EEE HAYIT AOD DONDISTEE THE FOR WAO FORSIOL BOSE WIOF O RSOLTESE (cv_eng_000784-cv_eng_000784) +TEPLOCATION WAS PUT A PROVE IT IN FARIBRAELY (cv_eng_000785-cv_eng_000785) +HENRY TORLDTON STILESE WHER HE HAD T E SOUNDED RONING IN LTIN (cv_eng_000786-cv_eng_000786) +IT WAS THIS CONTINUED D TO SCETHRLING CONFLIC ANDFVLVED AND LOSE SIS RETIRN TOR E TO ERESTRIULE REBRADIUO (cv_eng_000787-cv_eng_000787) +DEDH HER FANMELY H WOAS FOME PREOHONSAE EDDUD Y (cv_eng_000788-cv_eng_000788) +E WOWNT IDI EAKE FORDINMN BH PT (cv_eng_000789-cv_eng_000789) +THAT WAS MY DRE TO SINSE (cv_eng_000790-cv_eng_000790) +HE S GOSLAIRENT A MUSTERE OFD SHEAROST COLO (cv_eng_000791-cv_eng_000791) +THEN LIN TORNS TO THE CHORISHE OF SHINGS HAT T BLTER ANDESPEUE ITE (cv_eng_000792-cv_eng_000792) +WUD NOT THOSER IN HEREAC ADRT (cv_eng_000793-cv_eng_000793) +TOELCIOLSE INCTENDFEST THE AIYH (cv_eng_000794-cv_eng_000794) +MY NEST CON HELPE OW IT THAITS (cv_eng_000795-cv_eng_000795) +THATS A COUDHIST OT LY ON (cv_eng_000796-cv_eng_000796) +HOE FOR THE BEST AND PO BEAREFOR THE MLST (cv_eng_000797-cv_eng_000797) +C INISHELY THE WHPDYOSOS HT INSTROCKYO BO DICT (cv_eng_000798-cv_eng_000798) +ALL WE ONED BY THE EVERIT MOR SON IKCETH (cv_eng_000799-cv_eng_000799) +AATHETHEOEN G THE WILASERIN TO MORONMNE INLND EETHER (cv_eng_000800-cv_eng_000800) +E DO BPBISTD IRICM MINDLIBPBPTH (cv_eng_000801-cv_eng_000801) +AT O SEIOLE PATRY HE TO HER PLAE AS ACT ING ORECTERHEHD (cv_eng_000802-cv_eng_000802) +TE BEVERLY WLEBEFLY ANTES THE IES SENTER PU OP T TON SHIM (cv_eng_000803-cv_eng_000803) +THE TRACE RERVISTING WAS ALSO COMPETED (cv_eng_000804-cv_eng_000804) +HATD MARSH WAS A WHAR OF THE IMPRTNS OF ILC TRMRY COSKOMPYE IN BY LOUGICKL RESRCHE (cv_eng_000805-cv_eng_000805) +SIN HE WAS BORNY UD THE HOBOUE (cv_eng_000806-cv_eng_000806) +THIS WEIENCEH EASE AN OFITILY THER HEOTO ASE MAC RETOAD WINTH MY COLISTION FOASES OPERATDIN MIL (cv_eng_000807-cv_eng_000807) +IT IS RESPONEAULE FOR WATER SO PLI AND MANEMENT OF WOTER RESOURSES AND MO HOUSTRA (cv_eng_000808-cv_eng_000808) +DISES THO FEORES FAYE OF THE GHORVE HE SADED (cv_eng_000809-cv_eng_000809) +THE GISIAP PLATOH OR GISSA NCRAOL POLIS I THENGION VAOLY OF THE DEAD CONT TANG SEVRL PERMINDS OF WHICH THEGRAT PERMENT IS THE LARTEIS SESEVERLE SML TONS SOVRLE TEMPLES AND THE GRAT SPANKS (fleurs_eng_000413-fleurs_eng_000413) +TWORE HE IND OF TE MILE AGES WESTORN YURO BEGANTO DELT TER ON STIL ONE OF THE BIGIST OELINS OF THE TIME AS RESIULT OF THE REUCSAIS PEPBL BEGAN TO OUSES BUTENS TO FASTON RLVDING II I A (fleurs_eng_000414-fleurs_eng_000414) +IFS YOU ONLY GOL S HORE OUSING SHIP OR C SCKDRIONDS YOUL NOT E A SEPRT VESA AS A TWO FHOUS IN NOIG (fleurs_eng_000415-fleurs_eng_000415) +BDOBALHUISMARE WIH TO A DL CIOREION ID NOTBYW A BIN IMPRESION N MILER TO HOE THE TARY WAS RELATED (fleurs_eng_000416-fleurs_eng_000416) +TER DECTPLND DEFIENCE BOLE HADLING SCILS AN ECSLNT IE WORD MAY THE STAND OUT AN WAS CLER HA THIS WA THE TEM TO BE (fleurs_eng_000417-fleurs_eng_000417) +THE DEAS S CAIRED BY PIAKGE WICH THN MY GRET TO CUMEN TORO MOSKETOS (fleurs_eng_000418-fleurs_eng_000418) +FOR TH SPRING BOGCKE HID INDED OAF FLIVE NATH LOING STRAKE (fleurs_eng_000419-fleurs_eng_000419) +THEUS THE HINSL WAS GO FRIEND MANY BEBL LADYK ING OU (fleurs_eng_000420-fleurs_eng_000420) +THE YUSE OF EAO RECORING HAS LAD TO INPORNED DECSCORVERESIN THE INTERPROTATION OF MYGCRLR ECTPRESTIONS FASIAL MOVE ENS WHICH LASE AFYO MILS SICKENS (fleurs_eng_000421-fleurs_eng_000421) +LS AT THE NORTH IST THEGRAT SANCHURY OF R LATDY OF ATHY MUSHRING AE LECE AF WORLD GHT FIMIS MERION APBRISTIONS (fleurs_eng_000422-fleurs_eng_000422) +E IF O NE BY CLOSO THEACTION YGORHE TO W W OW GIT IN EALY W TO TE CAPINGSIHT CLOSTO TH MOUSICKE (fleurs_eng_000423-fleurs_eng_000423) +MTYAGUSKURERIS BLY HFARE THE BIGIST ND 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THER HIVSTOD TO FE THER YOUNG (voxpopuli_eng_000511-voxpopuli_eng_000511) +BUT IT WAS THE COUNTRY ITD HELD BENG MORE CAPRABL (voxpopuli_eng_000512-voxpopuli_eng_000512) +R IN TO THE PRT FOLIAO OF THE NU COMISION RE DALING WIT FAUNDEMENTERIGHTE (voxpopuli_eng_000513-voxpopuli_eng_000513) +E MESIGE TAT THE YU DOUT AT HAVE ANYNUSOLTIONE (voxpopuli_eng_000514-voxpopuli_eng_000514) +AR YU WILING TO ACT INERVFATHEREFOR THE SOSIAL DE MENTION TO BE INLODED INTHE EOU COMPETENSYES AS PREPUS (voxpopuli_eng_000515-voxpopuli_eng_000515) +AERNCTHE OND PESPECTRU POLIYES TAKING WI THEREFORME OF OUR TELICON TH FRAM WR (voxpopuli_eng_000516-voxpopuli_eng_000516) +I BELE HIS REMARC ES WERA INEXTPLIITELY RACISCTED AND SENAFOBICKE AND PRMOTED RAIL INTOLRANC IN WHAY TH IS NO UCETABLE OR ALAOUED IN TE ONTOICTUTIOF THIS HOUSE (voxpopuli_eng_000517-voxpopuli_eng_000517) +REALIGH EGSAMPL SHOL THAT SOVING IES RLATETO A BOCATION FEYULD STRON COMNITY DEVELPMENT (voxpopuli_eng_000518-voxpopuli_eng_000518) +SI HOLPE THA HISWLHAE MFORUSHEAS WHEL AN TAT RUSHE CON LS AND ISIGT D N CSTREMEM SCCESSTARY AFTR THES EG TWISIGIFICEND AT IN ORGST THIS OUR B (voxpopuli_eng_000519-voxpopuli_eng_000519) +SHE ECEPTO THE FACT THAT SITISIEN HIP IS AY NASIONL BOURT OFTHE NOSIONO GRISDICTION BUTH YOURLSO SAID THAT A CORDIG TO THE MASTRICK TREATY AND HE AS RIGHTD THEAS TOBEADIREC LING (voxpopuli_eng_000520-voxpopuli_eng_000520) +E O FAILD ESPESIOLYE IN THEMSTHRATING AYULIFIDED AND T AFISHENT AT PRORCH TO OLIMITCAENGCH TREATMENT AS ELE AS IN STRANGTHANING ITSELEADING POLITICKL COSITION INDESUGENDER I COSITHERE THEFOR TAKING ISRESOLUION ANACT OF UTMORST IMPORTANS (voxpopuli_eng_000521-voxpopuli_eng_000521) +THE UNIGTES STATES OF YUROV IL B A FACT WIT SWEDON AS PROVIDENCS (voxpopuli_eng_000522-voxpopuli_eng_000522) +ITD MUS BETHE CAPBITLE OF BOT THATS AND WE MUSS RECONISE POLSTINIS STHAT AS PROVIDID FORE INTHE OF LOGREENCS (voxpopuli_eng_000523-voxpopuli_eng_000523) +TYU KRAN ES FASE T WITD WONE OF CRUSIAL CHALINGESE INICG HISTARY IT WULD BE FIUN THE MENTALY RONGK TO PRES THE NATION NOWE WIT AL TIBES OFORESTRICTIONS POPELIDAL COALED OSTERITE POLI (voxpopuli_eng_000524-voxpopuli_eng_000524) +MOR RULS AND REAGILATION WILL NOT INM PROVE THES SITUATIO (voxpopuli_eng_000525-voxpopuli_eng_000525) +AT LEAST BE WUD LIKE TO NOL THE SORSE OF THE MUNYAND THE POSIBLE MORTHIFS (voxpopuli_eng_000526-voxpopuli_eng_000526) +TO WEAVE THOUSE YURPEN WAL LANGWOACES IN TO THEIS GLABLISED WERLDT ISINT TO THEAIS GOABLIS ECONMY IN DIS GOBE VILIAGE WHICH IS COTIALY CONOMICK SOSIALE ELNBPOLITICOLBPW ITSE AE MOST VELABLE EASTHEIT FROM THEINTIRE E OUG THAT WE MUST THAK FOLACOUNS AND T (voxpopuli_eng_000527-voxpopuli_eng_000527) +EAE TOREBET THAT ALL HE AY AN NOT BE YOUS TO FINCS SIGURIT EXPANCES BARTHERS CONTROL OR MLITRY SOU PORN (voxpopuli_eng_000528-voxpopuli_eng_000528) +THIG HE INTIFIK REPORTS BCALE MAR MORE URDENTMORLARMING AND MOR SHOCKING (voxpopuli_eng_000529-voxpopuli_eng_000529) +FINOLY IM WHE WHAE HINKINGK ABOUN THERE INOVATIFEF FI NSION INSTRMENTS WHN KU THE BOLTH FOR ORSELFS TOUG SOUPORT OUOUER A CONOMYS BUT AOS SO TOL SOPORKT THOS COHER INEAE (voxpopuli_eng_000530-voxpopuli_eng_000530) +THAT GIVE AE SOR YUNIKE TOUL IN ES MAKING (voxpopuli_eng_000531-voxpopuli_eng_000531) +D PAPER A VERYHL WEEK PROPOSIL (voxpopuli_eng_000532-voxpopuli_eng_000532) +SRUSHAS OLYS BE VERY PROUDNATION WITH ICH CLDCHUERE WI T INVENTIONS WITHAN ES CE (voxpopuli_eng_000533-voxpopuli_eng_000533) +AR TACTAITION NVHEN A MODICE OF TACSAITION N SOME CACES MIY JUST HELPES EM TO DO WAT IHEAREDY SHE GESTED AND HO NOS MAK THE CACE FOR THE ETERSPECT OF BANKRE CAPDLIATION THAT WE NEVERSOL (voxpopuli_eng_000534-voxpopuli_eng_000534) +THEULOPBE AN HSIDLOM SUPORTOF ICE MOR OVER AS A MONG ITCS TH IUS TO PROMOUHT FESILY THAT AND COURDINAT ECSCHANGES OF INFORMATION AND UTHE ACTEVITYES ER LATY TO LELCATION BD IN TEYUNION (voxpopuli_eng_000535-voxpopuli_eng_000535) +HECONLSNOF THE RAMEBORK AGEMENT PROVIE A LIGLY BINDINGK INSTRMENT TO OBV GIRAT AND STRANTN EU OSTRLIR BE LITHERI ATSIOS AND TO INCEESCOPERATION (voxpopuli_eng_000536-voxpopuli_eng_000536) +EREFRURE WE AS INTHE COUNSEL AS GLMITION TO RENTA CAS BARI THA ULDBED THE SESTENT OF TEBACT O TH RISI (voxpopuli_eng_000537-voxpopuli_eng_000537) +AINOTHE WERDS THEOBJECTION IS NOT WHETHE MUNY ISPAED OR NOT THEOPBEBJECTION IIS WETHE THEY S A DIDECTLINK ORENO (voxpopuli_eng_000538-voxpopuli_eng_000538) +TO HESTINGISHIES THE TWO MAE DOS HEAR YOUMER IT SAE YOUSE BY THECAR NT GORENT AND THE DANION NUKLEPROGDHME (voxpopuli_eng_000539-voxpopuli_eng_000539) +ESS METHEBDRONM HENKACTERE SECTION HERASD ENT IS HE FORM O VILANCE AND IT I THE MOST ETREAN FORM OF GHNTR BASEDESCRMINATI (voxpopuli_eng_000540-voxpopuli_eng_000540) +WE CAN LOK TO SOME EIRN NIN E U MEMBORS OR GOD EXSANPLE A REGARDED THE NOLAGES (voxpopuli_eng_000541-voxpopuli_eng_000541) +INM VLVEDS FOR HE POSITEIVE AND CESTTACTEIVE A BROATC (voxpopuli_eng_000542-voxpopuli_eng_000542) +O I HOPE THAT IS IL BE COMPEATET AIR IN HAFORSIVILE FOUOCHER THATD MANSE GAD BE TO A FRE MUNS (voxpopuli_eng_000543-voxpopuli_eng_000543) +OR FORDER ANDCORISHETHE YOU AND ETFHURH TO BRING A MNG PES IN OF GNISTAN ANDTO OVER COME T OF FRESILSICURITY N VEIREMENT IN THECONTY (voxpopuli_eng_000544-voxpopuli_eng_000544) +BEANDTHE STANT THAT SOME PEPL OAR ANGRY (voxpopuli_eng_000545-voxpopuli_eng_000545) +OEN TO HE MORSTPONCIVLD (voxpopuli_eng_000546-voxpopuli_eng_000546) +E MUST EDACTIFIHITH THIS SUTIATION AND H ASE THECOMION TO CONSIDER THE MOST EDICKET GCOMINSATION MESHERS FORLOLW PESENGES (voxpopuli_eng_000547-voxpopuli_eng_000547) +THE COMITION INGBISHE THE YUOPIONT PORLAMENT IN THE UPCOMIN KREVISION TO OPEN IS POSITION OND THIS MATHERE WHICHREALY CONCSERD ACESE TOL USTICS INOUROP AND THE INFORSTMENT OFRICES GRANTED BY HE YUROPIUNR YUND LO (voxpopuli_eng_000548-voxpopuli_eng_000548) +I L M ERY MUCH THERSOUNTIO OF TOCK TEN THEAS RALY AND POLISTINIONS AND ENCIRLY HOP THAT HE WLD SUCCED (voxpopuli_eng_000549-voxpopuli_eng_000549) +L WE HAE ECUMELATION OF PROBLANCS RESILTING FROM THE AR TIFISHAL UND THE BAGEITINGK AND VERETPRIVUS YUS (voxpopuli_eng_000550-voxpopuli_eng_000550) +ELET AST NOT BE THE MAN OF OUSTADY IT IS BE TODAS INSTITUTIO (voxpopuli_eng_000551-voxpopuli_eng_000551) +E I GOD ARLSEOEN TO BECOME AMBSHETES O THE YEARE MAKING IT AY DEIRS AD ACTIVITHIS WOW WHIDLY NONE A MONGSHT TO YUOPEASITIESE AND PUTPICIPATING N HVBENTSE BET TAT YUROPEION NASHONL FOR LOK ALEL (voxpopuli_eng_000552-voxpopuli_eng_000552) +DSARTDLY SUCH INPACT SESTMENT COLD PREAMT SERTAN PROBLOMS SUCH AS THOS POSED BY THE ELCTRNIK IDEDTFICATION OF SHEP AND SCOTEND (voxpopuli_eng_000553-voxpopuli_eng_000553) +THE ORT IS CONTENT TO SE THATHITS WORK HAS INFORME THE DESHARGH ROUES AND HAS ONTEBEUTEDTO PROPOSOLSE FOR IM PROVING THE FINCHAL MANAHENT OFE YOUSPENDING AND BETETORKATING OF YOFNCS (voxpopuli_eng_000554-voxpopuli_eng_000554) +RECGUAI HRY GLAI THE AND SERTENTY IASNEADETD FOR THE OBLIKE SECTOR AND FOR TH INDUSTRY (voxpopuli_eng_000555-voxpopuli_eng_000555) +IS ITDRELYNOT POSEABLR TO OUE A ATHER HOUSIN FESILIDES WI HE PROPETH RESEPTION CODIOS IN THEMENTIME (voxpopuli_eng_000556-voxpopuli_eng_000556) +WEL YOU TEAKE ACSION ATD LAST IF NOT THEIN WEINDE (voxpopuli_eng_000557-voxpopuli_eng_000557) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..2aa20283f1faa12feb6d729258a115e50789476a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/ref.trn @@ -0,0 +1,1092 @@ +HE REMAINED WORLD CHAMPION UNTIL NINETEEN SIXTY FIVE A YEAR IN WHICH HE SUFFERED A TERRIBLE ACCIDENT (LAD_eng_000254-LAD_eng_000254) +A LIBERALCONSERVATIVE HE WAS DEFEATED IN EIGHTEEN EIGHTY TWO (LAD_eng_000255-LAD_eng_000255) +ONE ROAD LAYER CAN DRAW TWO ROADS AT ONCE (LAD_eng_000256-LAD_eng_000256) +SOME OF THE COUNTRIES HAVE SURVEYS FOR MULTIPLE YEARS (LAD_eng_000257-LAD_eng_000257) +BOTH OF THE VERSIONS FEATURE THE SONG HAPPY HOLIDAY (LAD_eng_000258-LAD_eng_000258) +SHAKESPEARE MANY REFERENCES ARE MADE TO SCENES INTERACTIONS OR CHARACTERS FROM VARIOUS PLAYS (LAD_eng_000259-LAD_eng_000259) +IF ONLY THE PROGRAM COULD BREAK OUT JUST A LITTLE FROM ITS TOOFAMILIAR APPROACH (LAD_eng_000260-LAD_eng_000260) +THE ALBUM WAS RELEASED IN AUSTRALIA ON NINETEENTH AUGUST TWO THOUSAND AND ELEVEN (LAD_eng_000261-LAD_eng_000261) +HE NOW PLAYS FOR AUSTRALIAN CLUB PERTH GLORY (LAD_eng_000262-LAD_eng_000262) +IT IS NOT KNOWN HOW MUCH IF ANY OF HER CLAIMS ARE TRUE (LAD_eng_000263-LAD_eng_000263) +A SMALL BUSINESS OWNER BROAD OPERATED HIS WHEAT AND SHEEP FARM FOR SIXTEEN YEARS FROM THE AGE OF TWENTY TWO (LAD_eng_000264-LAD_eng_000264) +IN THE NINTH CENTURY HE WAS AN IRISH POET (LAD_eng_000265-LAD_eng_000265) +THEY ARE MARKED BY STRONG (LAD_eng_000266-LAD_eng_000266) +THE LAW IS THEREFORE VALID (LAD_eng_000267-LAD_eng_000267) +IN THE EARLY STAGES CAME CLOSE TO US ASLEEP (LAD_eng_000268-LAD_eng_000268) +RUNNING EVERY THIRTY MINUTES THROUGHOUT SERVICE TIMES (LAD_eng_000269-LAD_eng_000269) +AS A RESULT WHEN THE COLLEGE REOPENED IT WAS AS AN ALLMALE COLLEGE (LAD_eng_000270-LAD_eng_000270) +THE TIME BETWEEN THESE POINTS IS VARIABLE AND CAN OCCUR ANYWHERE FROM A MINUTE TO MUCH LONGER (LAD_eng_000271-LAD_eng_000271) +WORK ON THE E E S STARTED IN MARCH TWO THOUSAND AND SEVEN AT A COST OF FIVE MILLION DOLLARS (LAD_eng_000272-LAD_eng_000272) +HOWEVER THERE WAS SOME DISAGREEMENT OVER THE ENDING THEME WHICH OMORI AND YOSHIMORI DISCUSSED AT LENGTH OVER EMAIL (LAD_eng_000273-LAD_eng_000273) +THE COUPLE HAD NO CHILDREN (LAD_eng_000274-LAD_eng_000274) +THE OFFICIAL SINGLE OF THAT DEBUT ALBUM PARIS CALLING HAD AN ELABORATE MUSIC VIDEO (LAD_eng_000275-LAD_eng_000275) +THE SERIES ENDED ON SIXTH AUGUST TWO THOUSAND AND FOUR LASTING FOR A TOTAL OF SEVENTY ONE DAYS (LAD_eng_000276-LAD_eng_000276) +HE HAS ALSO CONTRIBUTED TO THE NEW YORK REVIEW OF BOOKS (LAD_eng_000277-LAD_eng_000277) +BY PLACING SMALL ART OBJECTS THROUGHOUT THE FILM (LAD_eng_000278-LAD_eng_000278) +IT IS FOUND IN BRAZIL (LAD_eng_000279-LAD_eng_000279) +IT WAS THE SIDE OF THE FAMILY I IDENTIFIED MORE WITH (LAD_eng_000280-LAD_eng_000280) +CANDIDATE SITES MUST ALSO SUBMIT A WORK PLAN (LAD_eng_000281-LAD_eng_000281) +DUNDEE WON THE MATCH THREE TWO (LAD_eng_000282-LAD_eng_000282) +HOWEVER THE VILLAGE REMAINED ISOLATED UNTIL THE ARRIVAL OF THE FIRST NEWSPAPER SECOND REPUBLIC (LAD_eng_000283-LAD_eng_000283) +THE FIRST SERVICE IN THE NEW CHURCH WAS HELD IN NINETEEN FIFTY ONE ALTHOUGH THE BUILDING WAS NOT FULLY FINISHED (LAD_eng_000284-LAD_eng_000284) +THE AVERAGE HOUSEHOLD SIZE WAS TWO POINT TWO SEVEN AND THE AVERAGE FAMILY SIZE WAS THREE POINT ZERO ZERO (LAD_eng_000285-LAD_eng_000285) +IT WAS FIRST BROADCAST ON THIRD JANUARY TWO THOUSAND AND TEN (LAD_eng_000286-LAD_eng_000286) +THE WINGS WERE NOW MADE IN A SINGLE PRESSING (LAD_eng_000287-LAD_eng_000287) +DOCTOR OF PHILOSOPHY IN ENGINEERING MANAGEMENT (LAD_eng_000288-LAD_eng_000288) +THIS TOOK AWAY THE MAIN ARGUMENT OF SAFETY RISKS (LAD_eng_000289-LAD_eng_000289) +HE WAS ALSO MADE A LIFE MEMBER OF SCUNTHORPE UNITED (LAD_eng_000290-LAD_eng_000290) +SHE FEARS THEY WILL GET A DIVORCE BUT THIS NEVER HAPPENS (LAD_eng_000291-LAD_eng_000291) +FOOT DROPS UNABLE TO HOLD THE FOOT STRAIGHT ACROSS (LAD_eng_000292-LAD_eng_000292) +WHETHER THE AIR FLOW IS FREE OR FORCED CAN AFFECT THE ENERGY EFFICIENCY OF THE WINDOW (LAD_eng_000293-LAD_eng_000293) +AFTER GETTING THE RIGHT MEASUREMENTS THEY MADE THE NEW DOORS (LAD_eng_000294-LAD_eng_000294) +FRAGMENTS ON EACH FACE ARE MARKED WITH LETTERS A B C (LAD_eng_000295-LAD_eng_000295) +FROM THE FIRST MINUTES BOTH TEAMS SHOWED THEIR DESIRE TO COMPETE WITH AGGRESSIVE APPROACHES (LAD_eng_000296-LAD_eng_000296) +PHYSICAL THERAPY EXERCISES MAY HELP PATIENTS TO MAINTAIN MUSCLE STRENGTH (LAD_eng_000297-LAD_eng_000297) +HOWEVER THE TOWN SHE LIVES IN NO ONE WANTS TO HEAR ABOUT HER (LAD_eng_000298-LAD_eng_000298) +DESCRIBES APPOINTMENTS OF AN ACTING CHIEF JUSTICE OR JUDGE OF THE SUPREME COURT (LAD_eng_000299-LAD_eng_000299) +THE SOYBEANS OUTER COVERING IS THEN REMOVED AND THE BEANS ARE PARTIALLY COOKED (LAD_eng_000300-LAD_eng_000300) +THIS NATIONAL MOVEMENT WHICH HAD BEGUN WITH SO MUCH HOPE CAME TO A SAD END (LAD_eng_000301-LAD_eng_000301) +HIS ASSOCIATES USUALLY CALLED HIM T OR THE GOODLOOKING GUY (LAD_eng_000302-LAD_eng_000302) +ITS MAIN OFFICES WERE IN LONDON WITH A SECOND OFFICE BELFAST (LAD_eng_000303-LAD_eng_000303) +ACTUALLY I HAD NEVER BEEN TO A VILLAGE BEFORE THAT (LAD_eng_000304-LAD_eng_000304) +HE WAS CHARGED WITH PLANNING TO SET OFF BOMBS IN EUROPE AND THE UNITED STATES (LAD_eng_000305-LAD_eng_000305) +MAKING MIRRORS IS THE THIRD STUDIO ALBUM BY BELGIANAUSTRALIAN ARTIST GOTYE (LAD_eng_000306-LAD_eng_000306) +HE THEN MOVED TO WASHINGTON DC AND WAS A PARTNER WITH WARD BROWN UNTIL NINETEEN TWENTY NINE (LAD_eng_000307-LAD_eng_000307) +JOSEPH HIGH SCHOOL AND THE SCHOOLS THEY COMPETE AGAINST IN ALL SPORTS (LAD_eng_000308-LAD_eng_000308) +TWELVE PLUS ONE MATCH BAN PER CARD (LAD_eng_000309-LAD_eng_000309) +I THINK I MIGHT BE NOTHING (LAD_eng_000310-LAD_eng_000310) +THE HOME WAS BUILT AND LIVED IN BY ANDREW JACKSON KENNEDY DEPUTY COLLECTOR FOR THE INTERNAL REVENUE SERVICE (LAD_eng_000311-LAD_eng_000311) +IN NINETEEN SIXTY FOUR HE WENT BACK TO OMSK AND ENTERED THE ACTORS SCHOOL OF OMSK (LAD_eng_000312-LAD_eng_000312) +THE BANK IS JOINTLY OWNED BY HIM AND HIS BROTHERS AND RELATIVES (LAD_eng_000313-LAD_eng_000313) +HE SUBSEQUENTLY WENT TO SCHOOL IN BRISTOL (LAD_eng_000314-LAD_eng_000314) +ONE THOUSAND EIGHT HUNDRED AND FORTY SIX FOURTH EDITION (LAD_eng_000315-LAD_eng_000315) +A PART OF LITTLE ENGLAND BEYOND WALES IT HAS BEEN ESSENTIALLY ENGLISHSPEAKING FOR NINE HUNDRED YEARS (LAD_eng_000316-LAD_eng_000316) +HE PLAYED WITH TEN PLAYERS FOR HALF WAS AGAINST THE TRADITION IN G S P (LAD_eng_000317-LAD_eng_000317) +THE PRESIDING JUDGE WAS WEBSTER THAYER WHO WAS ALREADY ASSIGNED TO THE COURT BEFORE THIS CASE WAS SCHEDULED (LAD_eng_000318-LAD_eng_000318) +BIG BROTHER FIVE WAS THE THIRD OF THE MAIN SERIES TO FEATURE A LIVE LAUNCH (LAD_eng_000319-LAD_eng_000319) +ITS MOTTO IS WHOEVER YOU ARE AND WHEREVER YOU ARE ON THE JOURNEY OF FAITH YOU ARE WELCOME HERE (LAD_eng_000320-LAD_eng_000320) +ROBERT E MILLER AS COACH WILSON (LAD_eng_000321-LAD_eng_000321) +AFTER A ONEYEAR BREAK ZERO DEGREE WAS HER FOLLOWING VENTURE (LAD_eng_000322-LAD_eng_000322) +A M T MANUFACTURED A MODEL KIT OF THE Z Z R DRAGSTER (LAD_eng_000323-LAD_eng_000323) +THE S S A AIMED TO BUILD A LEFTWING ALTERNATIVE TO NEW LABOUR AND THE S N P (LAD_eng_000324-LAD_eng_000324) +HE LIVES LIKE HE IS A YOUNG PERSON (LAD_eng_000325-LAD_eng_000325) +MASTER OF SCIENCE IN ENGINEERING MANAGEMENT (LAD_eng_000326-LAD_eng_000326) +SHE FAILED TO MAKE THE TOP THREE AT THE KENYAN JUNIOR TRACK TRIALS THAT JUNE (LAD_eng_000327-LAD_eng_000327) +A TOUR FOLLOWED IN SUPPORT (LAD_eng_000328-LAD_eng_000328) +THEY WERE ESTABLISHED IN EIGHTEEN SEVENTY ONE AND ARE ONE OF THE OLDEST CLUBS IN THE SOUTH OF ENGLAND (LAD_eng_000329-LAD_eng_000329) +HE WAS A MEMBER OF THE YES SCOTLAND ADVISORY BOARD (LAD_eng_000330-LAD_eng_000330) +TWO THOUSAND AND FIVE GENTLEMAN (LAD_eng_000331-LAD_eng_000331) +OUR FILM HAD A STRONG RECEPTION IN EUROPE AND ACHIEVED DISTRIBUTION BUT THAT WAS NOT THE CASE HERE (LAD_eng_000332-LAD_eng_000332) +ORTHOSIS STRETCHES POSTERIOR ANKLE STRUCTURES (LAD_eng_000333-LAD_eng_000333) +HE WAS ALSO A THREE TIME FRENCH NATIONAL CHAMPION NINETEEN NINETY NINETEEN NINETY FOUR TWO THOUSAND AND ONE (LAD_eng_000334-LAD_eng_000334) +THE VILLAGE STRUCTURE SHOWN IN HIS MAP IS TO A GREAT EXTENT UNCHANGED TODAY (LAD_eng_000335-LAD_eng_000335) +RUSSIA IS RECOGNIZED FOR ITS NUCLEAR DISASTER EXPERTISE AND FOR THE SAFETY OF ITS TECHNOLOGY (LAD_eng_000336-LAD_eng_000336) +AS OF TWO THOUSAND AND FOURTEEN M T V IS AVAILABLE WITHIN THE UNITED KINGDOM ON VIRGIN MEDIA AND SKY (LAD_eng_000337-LAD_eng_000337) +NEW YORK PENGUIN RANDOM HOUSE (LAD_eng_000338-LAD_eng_000338) +THE DUCHY WAS SECURED IN THE OUTCOME OF THE GOTHIC WAR (LAD_eng_000339-LAD_eng_000339) +WITH GOOD PACE STARTED THE MATCH WITH BOTH TEAMS ALTERNATING SUPREMACY (LAD_eng_000340-LAD_eng_000340) +THIS VERSION IS NOTED FOR BEING VERY FAITHFUL TO THE ORIGINAL NOVEL (LAD_eng_000341-LAD_eng_000341) +THIS PRESUMPTION IS NOT FULFILLED ONE HAS TO KNOW AT LEAST TWO CONJUGATE DIAMETERS (LAD_eng_000342-LAD_eng_000342) +NOTABLE TITLES INCLUDED GOLDEN AXE THE REVENGE OF DEATH ADDER RAD MOBILE OUTRUNNERS AND SEGA SONIC THE HEDGEHOG (LAD_eng_000343-LAD_eng_000343) +THE NINETEEN NINETY NINE JUDGMENT NOTED THAT THE INFLUENCE OF THE FATHER OF THE ACCUSED HAS BEEN THERE (LAD_eng_000344-LAD_eng_000344) +MACDUFF SWEARS REVENGE AND JOINS FORCES WITH MALCOLM TO OVERTHROW MACBETH (LAD_eng_000345-LAD_eng_000345) +THE MEDIAEVAL VILLAGE COURT WAS ALWAYS ANXIOUS TO KEEP THE FENCE AROUND THE VILLAGE GAPLESS (LAD_eng_000346-LAD_eng_000346) +THERE WAS A NINE RANK SYSTEM EACH RANK HAVING MORE POWER THAN THE LOWER RANK (LAD_eng_000347-LAD_eng_000347) +THEY ESTABLISHED DIPLOMATIC RELATIONS ON SEPTEMBER NINETEENTH NINETEEN SEVENTY TWO (LAD_eng_000348-LAD_eng_000348) +THIS WAS FURTHER EXTENDED TO INCLUDE MORE U K DATES IN DECEMBER TWO THOUSAND AND FOURTEEN (LAD_eng_000349-LAD_eng_000349) +THE DUTCH GOVERNMENT IS CURRENTLY EXAMINING THE LEGAL CONSEQUENCES OF THE RULING (LAD_eng_000350-LAD_eng_000350) +FROM NINETEEN THIRTY THREE TO NINETEEN FORTY NINE THE AMERICAN LEAGUE WON TWELVE OUT OF THE FIRST SIXTEEN (LAD_eng_000351-LAD_eng_000351) +THERE HE FELL SICK WITH TYPHUS HIMSELF (LAD_eng_000352-LAD_eng_000352) +SIX TEAMS HAVE BEEN DIVIDED IN TWO GROUPS OF THREE TEAMS EACH (LAD_eng_000353-LAD_eng_000353) +THE FIRST SEASON PREMIERED ON TWELFTH JUNE TWO THOUSAND AND FIFTEEN (LAD_eng_000354-LAD_eng_000354) +IT SUCCEEDED THE Y BOARD AND SYSTEM TWENTY FOUR COMBINING FEATURES FROM BOTH (LAD_eng_000355-LAD_eng_000355) +VOLUME TWO NUMBERS ONE TWO AND THREE (LAD_eng_000356-LAD_eng_000356) +THE LOWER PART OF MENS DRESSES WERE MUCH SHORTER IN LENGTH THAN THOSE FOR WOMEN (LAD_eng_000357-LAD_eng_000357) +THE VISIGOTHS IN TURN WERE SUCCEEDED BY THE MOORS (LAD_eng_000358-LAD_eng_000358) +JOSEPH HIGH SCHOOL EVERY WEEK OF THE SCHOOL YEAR (LAD_eng_000359-LAD_eng_000359) +AS A RESULT OF ALL THE ARGUMENTS GETTING TO HER (LAD_eng_000360-LAD_eng_000360) +ITS HEADQUARTERS ARE IN SHEFFIELD UNITED KINGDOM (LAD_eng_000361-LAD_eng_000361) +LAY ALSO OFFICIALLY SIGNED THE CONTRACT ON STAGE WITH THE DIRECTOR AND PRODUCERS OF THE GOLDEN EYES (LAD_eng_000362-LAD_eng_000362) +PHYSICAL THERAPY CAN HELP PATIENTS TO LEARN HOW TO WALK WITH A FOOT DROP (LAD_eng_000363-LAD_eng_000363) +IT WENT ON TO SELL THREE HUNDRED THOUSAND UNITS ACHIEVE FIVE NO (LAD_eng_000364-LAD_eng_000364) +THE NAME STUCK AFTER THAT (LAD_eng_000365-LAD_eng_000365) +THE ALBUM LATER BROKE THE DIAMOND RECORD ON Q Q MUSIC (LAD_eng_000366-LAD_eng_000366) +ITS EDITORIAL WE SUBMIT EARNED ITS AUTHOR A PULITZER PRIZE (LAD_eng_000367-LAD_eng_000367) +JOSEPH PLAYS ARE FEATURED EACH WEEK ON THE SHOW (LAD_eng_000368-LAD_eng_000368) +THEY WAIT FOR A TIME BUILDING UP THEIR FORCES BEGINNING TO WONDER IF THIS EVIL REALLY EXISTS (LAD_eng_000369-LAD_eng_000369) +BRIEF MENTION OF THE CONVICTION APPEARED ON PAGE THREE OF THE NEW YORK TIMES (LAD_eng_000370-LAD_eng_000370) +ORDERED BY POSITION ON PITCH FROM BACK RIGHT TO FRONT LEFT (LAD_eng_000371-LAD_eng_000371) +HE IS MEMBER OF THE COURT OF THE ROYAL COLLEGE OF ART LONDON U K (LAD_eng_000372-LAD_eng_000372) +DURING THE COURSE OF THE CAMPAIGN FERGUSON VISITED ALL THIRTY NINE WASHINGTON STATE COUNTIES (LAD_eng_000373-LAD_eng_000373) +A STRIP OF PAPER OF LENGTH (LAD_eng_000374-LAD_eng_000374) +SATOU HAD FREQUENTLY WORKED TOGETHER WITH YOKOYAMA ON PREVIOUS PROJECTS (LAD_eng_000375-LAD_eng_000375) +SHE WAS BORN ONSCREEN DURING THE EPISODE BROADCAST ON FOURTH NOVEMBER NINETEEN NINETY FOUR (LAD_eng_000376-LAD_eng_000376) +HE TURNED ROUND SHE HAD COME IN SO GENTLY THAT HE HAD NEVER HEARD HER (M-AILABS_eng_000159-M-AILABS_eng_000159) +AH TO BE SURE WE MUST KEEP OUR DOORS SHUTWE MUST LET NO ONE IN (M-AILABS_eng_000160-M-AILABS_eng_000160) +KINSMEN HE BEGAN MOCKINGLY YOU MAY HAVE WONDERED WHY I CALLED A TRUCE WHEN I COULD JUST AS WELL HAVE DESTROYED YOU THAT I DOUBT ATO ANSWERED HIM (M-AILABS_eng_000161-M-AILABS_eng_000161) +THE PEASANT THREW HIMSELF UPON HIM AND BOUND HIS FOUR LEGS TIGHTLY SO THAT HE COULD NOT MOVE (M-AILABS_eng_000162-M-AILABS_eng_000162) +NOR MUST THOU SO LIMIT THE HOLY ONE OF ISRAEL AS TO THINK HE HATH BUT ONE WAY IN WHICH HE CAN GLORIFY HIMSELF BY THEE (M-AILABS_eng_000163-M-AILABS_eng_000163) +THE OLD COMPARISON BETWEEN THE IMPULSIVE EXECUTIVE AND THE LIBERAL ARTS MAN WHO HAS LEARNED THAT THERE ARE ONLY ONE OR TWO POSITIVE DECISIONS AVAILABLE IN ALL THE WORLD OF THINKING (M-AILABS_eng_000164-M-AILABS_eng_000164) +AFTER THIS EXPERIENCE THE INVADERS WERE CAREFUL TO KEEP A SAFE DISTANCE FROM THE WALL (M-AILABS_eng_000165-M-AILABS_eng_000165) +CAN YOU BEAR SOMETHING FURTHER I THINK YOU OUGHT TO KNOW IT I HAVE HERE A MOST MYSTERIOUS TELEPAGRAM YES WHAT IS IT IS SHE DEAD NO IT IS NOT ABOUT HER (M-AILABS_eng_000166-M-AILABS_eng_000166) +NO MISTER THORNTON SAID GIVE THE BASKET TO MEILL TAKE IT (M-AILABS_eng_000167-M-AILABS_eng_000167) +AN ARABIAN NIGHT EXCLAIMED TROT WHY THAT WAS A MAGIC NIGHT WASNT IT THERES DIFFERENT SORTS OF NIGHTS MATE SAID THE SAILOR AND THE KNIGHT BUTTONBRIGHT MEANS AINT THE SAME NIGHT YOU MEAN (M-AILABS_eng_000168-M-AILABS_eng_000168) +IVE TURNED OFF UPWARDS OF A HUNDRED OF MY BEST HANDS FOR NO OTHER FAULT THAN FOLLOWING YOU AND SUCH AS YOU AND DYE THINK ILL TAKE YOU ON (M-AILABS_eng_000169-M-AILABS_eng_000169) +BUT WHEN SHOULD SHE SEE HIM HER HEART LEAPED UP IN APPREHENSION AT EVERY RING OF THE DOORBELL (M-AILABS_eng_000170-M-AILABS_eng_000170) +THESE BOOKS DIXON I WILL KEEP ALL THE REST WILL YOU SEND TO MISTER BELL THEY ARE OF A KIND THAT HE WILL VALUE FOR THEMSELVES AS WELL AS FOR PAPAS SAKE (M-AILABS_eng_000171-M-AILABS_eng_000171) +BUT INGA WAS NOT AT ALL SURE THEY COULD NOT GET IN THE GATES OPENED INWARD AND THREE HEAVY BARS WERE HELD IN PLACE BY MEANS OF STOUT STAPLES RIVETED TO THE SHEETS OF STEEL (M-AILABS_eng_000172-M-AILABS_eng_000172) +I WANT THAL SAID HODDAN COLDLY I WANT A DOZEN HORSES I WANT MEN TO RIDE THEM WITH ME HE PUSHED HIS WAY FORWARD WHICH WAY TO THE STABLES (M-AILABS_eng_000173-M-AILABS_eng_000173) +THERE IS A LIMIT TO WHAT YOU CAN DO THE FIRST TIME YOU ENTER A MANS HOUSE AND BESIDES THAT WAS NO TIME TO AROUSE SUSPICION IN THE MIND OF ANYONE (M-AILABS_eng_000174-M-AILABS_eng_000174) +DO YOU NOT REMEMBER THAT HE SAYS THY DEMON THATS THY SPIRIT WHICH KEEPS THEE IS NOBLE COURAGEOUS HIGH UNMATCHABLE (M-AILABS_eng_000175-M-AILABS_eng_000175) +MISTER BELL WHAT CAN HE KNOW OF JOHN HE LIVING A LAZY LIFE IN A DROWSY COLLEGE (M-AILABS_eng_000176-M-AILABS_eng_000176) +AND THE KITTEN FOLLOWED DEMURELY AT THEIR HEELS (M-AILABS_eng_000177-M-AILABS_eng_000177) +THE FIRST TOUCH WOULD CAUSE AN EXPLOSION IN WHICH AMONG SUCH HUNDREDS OF INFURIATED MEN AND RECKLESS BOYS (M-AILABS_eng_000178-M-AILABS_eng_000178) +ONE OF THE GREAT PLEASURES OF MARGARETS LIFE AT THIS TIME WAS IN EDITHS BOY (M-AILABS_eng_000179-M-AILABS_eng_000179) +THE THING HAS GONE ON LONG ENOUGH IF THERE IS ONE MORE BIG ACCIDENT WE SHALL HAVE TO COMPROMISE WITH THE INTERRIVER AND CARRY ON THE WORK JOINTLY (M-AILABS_eng_000180-M-AILABS_eng_000180) +YOU ARE LATE SAID SHE WELL SHE HELD HER BREATH FOR THE ANSWER (M-AILABS_eng_000181-M-AILABS_eng_000181) +TROT TOLD THE GIRLS THAT THEY MUST GO WITH THEIR FATHER TO LIVE IN GHIPGHISIZZLES LITTLE OLD CABIN AND WHEN THEY HEARD THIS DREADFUL DECREE (M-AILABS_eng_000182-M-AILABS_eng_000182) +MARGARET SAT DOWN ON THE RUG PARTLY TO WARM HERSELF FOR THE DAMPNESS OF THE EVENING HUNG ABOUT HER DRESS AND OVERFATIGUE HAD MADE HER CHILLY (M-AILABS_eng_000183-M-AILABS_eng_000183) +OH NO YOU ARE MISTAKEN ABOUT THAT REPLIED THE KING THEY ARE NOT MY PRISONERS BUT MY SLAVES WHOM I PURCHASED FROM THE KING OF EV (M-AILABS_eng_000184-M-AILABS_eng_000184) +HER FATHER TOOK UP THE CONVERSATION (M-AILABS_eng_000185-M-AILABS_eng_000185) +IN A CORNER WAS A SORT OF DRESSINGTABLE ON WHICH LAY A COMB AND BRUSH KENNEDY SEEMED MUCH INTERESTED IN THE TABLE AND WAS EXAMINING IT WHEN THE GURU RETURNED (M-AILABS_eng_000186-M-AILABS_eng_000186) +I HAVE SOMETIMES THOUGHT THAT MYSELF SHE AGREED BUT OF COURSE I DONT KNOW STILL I HAVE TO BE PRETTY CAREFUL SOME ONE IS ALWAYS OVER HERE BY MY DESK OR LOOKING OVER HERE (M-AILABS_eng_000187-M-AILABS_eng_000187) +I SHALL STAY REPLIED THE YOUNG MAN FOR I MEAN TO SET YOU FREE (M-AILABS_eng_000188-M-AILABS_eng_000188) +WHAT DO YOU DO ASKED THE SORCERER (M-AILABS_eng_000189-M-AILABS_eng_000189) +WHY THEYRE OUR ENEMIES YOUR SHORT HIGHNESS NOT ANY MORE REPLIED TROT IM QUEEN OF THE PINKIES AND IM ALSO QUEEN OF THE BLUES SO I WONT HAVE MY PEOPLE QUARRELING (M-AILABS_eng_000190-M-AILABS_eng_000190) +TYPEWRITERS WERE CLICKING CLIPPINGS WERE BEING SNIPPED OUT OF A HUGE STACK OF NEWSPAPERS AND PASTED INTO LARGE SCRAPBOOKS CIRCULARS WERE BEING FOLDED AND MADE READY TO MAIL FOR THE FINAL APPEAL (M-AILABS_eng_000191-M-AILABS_eng_000191) +IT WAS FOUR DAYS AFTER THE SURPRISE OF ADLERS HORST WHEN THE STRANGERS LEFT THE ESTATE TO THE CARE OF RUGGED OLD FORSTER HERMANN (M-AILABS_eng_000192-M-AILABS_eng_000192) +POOR TEMPLETON HE SAID I USED TO KNOW HIM YEARS AGO WHEN WE WERE BOYS WENT TO SCHOOL WITH HIM AND ALL THAT SORT OF THING YOU KNOW BUT UNTIL I RAN ACROSS HIM (M-AILABS_eng_000193-M-AILABS_eng_000193) +I FOUND HER IN THE FOREST AND BROUGHT HER HERE A PRISONER REPLIED THE CAPTAIN (M-AILABS_eng_000194-M-AILABS_eng_000194) +WHO MAY BE COMPETENT EITHER FROM PERSONAL EXPERIENCE OR THE EXPERIENCE OF OTHERS TO ANSWER IT WITH MORE OR LESS CORRECTNESS OR AT LEAST AN ATTEMPT (M-AILABS_eng_000195-M-AILABS_eng_000195) +ONE HUNDRED NINETYTWO LAYTE STREET SAID HOGAN BITING OFF HIS CIGAR (M-AILABS_eng_000196-M-AILABS_eng_000196) +TROT WAS SURPRISED TO FIND SHE COULD SEE SO PLAINLY THROUGH THE HIGH WALL OF WATER ABOVE HER BUT THE SUN WAS ABLE TO SHOOT ITS BEAMS STRAIGHT DOWN THROUGH THE TRANSPARENT SEA (M-AILABS_eng_000197-M-AILABS_eng_000197) +THE SPOT WHERE IT HAD SPRUNG UP (M-AILABS_eng_000198-M-AILABS_eng_000198) +CALM DENIAL WHICH SHE GAVE TO SUCH A SUPPOSITION (M-AILABS_eng_000199-M-AILABS_eng_000199) +YOU SEE UNTIL THESE SCHOOL PILLS WERE INVENTED WE WASTED A LOT OF TIME IN STUDY THAT MAY NOW BE BETTER EMPLOYED IN PRACTICING ATHLETICS (M-AILABS_eng_000200-M-AILABS_eng_000200) +YOUVE DONE IT NOW DECLARED DOROTHY THESE TENTS ARE JUST WONDERFUL (M-AILABS_eng_000201-M-AILABS_eng_000201) +FOR TWENTY TEN FIVE THREE TWOTHE LINER WAS BARELY TWENTY MILES AWAY WHEN HODDAN FIRED HIS ROCKETS THEY MADE A COLOSSAL CLOUD OF VAPOR IN EMPTINESS (M-AILABS_eng_000202-M-AILABS_eng_000202) +THEY PAID NO ATTENTION TO THE FACT THAT GHIPGHISIZZLE DID NOT WANT TO MARRY ANY OF THEM FOR THEY HAD DETERMINED THAT WHEN IT WAS AGREED WHO SHOULD HAVE HIM (M-AILABS_eng_000203-M-AILABS_eng_000203) +WHAT DO YOU THINK OF THAT HE CRIED OPENING A COPY OF THE RECORD AND LAYING IT FLAT ON THE LIBRARY TABLE (M-AILABS_eng_000204-M-AILABS_eng_000204) +IT WILL REQUIRE BUT A SHORT TIME (M-AILABS_eng_000205-M-AILABS_eng_000205) +AND LAST THE CROWD OF VEGETABLE PEOPLE WHO HAD NO HEARTS AND COULD NEITHER SMILE NOR FROWN (M-AILABS_eng_000206-M-AILABS_eng_000206) +THEN YOULL CATCH IT SAID THE WITCH (M-AILABS_eng_000207-M-AILABS_eng_000207) +WHAT IS IT I QUERIED NOT FEELING CERTAIN BUT THAT IT WAS A VEILED ATTEMPT TO SECURE A LITTLE FREE ADVERTISING FOR THE VANDERVEER (M-AILABS_eng_000208-M-AILABS_eng_000208) +SO HE GAVE THE CLERK THE THIRD HUNDRED DOLLARS FOR BOOKS AND A CASK OF GOOD OLD ALE FOR PETER THE CLERK DRANK THE ALE HIMSELF AND GAVE THE CALF MILK (M-AILABS_eng_000209-M-AILABS_eng_000209) +LIKE THAT IN ALICE IN WONDERLAND WITH MERELY A GRIN THAT FADED AWAY CHANGING INTO A LYNX WHICH IN TURN DISAPPEARED FOLLOWED BY AN UNKNOWN CREATURE WITH SHORT NOSE AND POINTED EARS (M-AILABS_eng_000210-M-AILABS_eng_000210) +SHE COULD NOT DOMARGARET GLANCED UNCONSCIOUSLY AT THE UNCLEANED CORNERS OF THE ROOMSHE COULD HARDLY UNDERTAKE A SERVANTS PLACE COULD SHE (M-AILABS_eng_000211-M-AILABS_eng_000211) +NO SHE REPLIED WITH INNOCENT CURIOSITY DID I GIVE THEM TO YOU (M-AILABS_eng_000212-M-AILABS_eng_000212) +MARLBOROUGH MILLS AND THE ADJACENT DWELLING WERE HELD UNDER A LONG LEASE THEY MUST IF POSSIBLE BE RELET (M-AILABS_eng_000213-M-AILABS_eng_000213) +A COP WAVED A STUNPISTOL AT HIM (M-AILABS_eng_000214-M-AILABS_eng_000214) +IT BOUNDED HERE AND THERE ABOUT THE CHICKEN HOUSE AND AT FIRST DOROTHY COULD NOT TELL WHAT IT WAS WHILE THE SCREECHING OF THE CHICKENS NEARLY DEAFENED HER (M-AILABS_eng_000215-M-AILABS_eng_000215) +THE SOLDIER GAVE A YELL THAT AROUSED A SCORE OF HIS COMRADES AND BROUGHT THEM TUMBLING INTO THE STREET WHEN THEY SAW HOW THE BOOLOOROOS PRECIOUS PRISONER WAS ESCAPING (M-AILABS_eng_000216-M-AILABS_eng_000216) +JIM HAD REFUSED TO LEAVE THE FIELD OF GRASS WHERE HE WAS ENGAGED IN BUSILY EATING SO THE WIZARD GOT OUT OF THE BUGGY AND JOINED ZEB AND DOROTHY (M-AILABS_eng_000217-M-AILABS_eng_000217) +CERTAINLY I AM AS INTERESTED IN THE CASE AS YOU ARE BUT I CANT MAKE HEADS OR TAILS OF IT I REPLIED (M-AILABS_eng_000218-M-AILABS_eng_000218) +OR ANY MICE OR EVEN GRASSHOPPERS (M-AILABS_eng_000219-M-AILABS_eng_000219) +AND THEM THAT PAYS YO DUN THEY TELL YO WHATTEN TO DO OR WHATTEN NOT TO DO WI THE MONEY THEY GIVES YOU IN JUST PAYMENT FOR YOUR PAINSIN FAIR EXCHANGE LIKE (M-AILABS_eng_000220-M-AILABS_eng_000220) +WHAT DOES THAT MEAN ASKED THE PRINCESS (M-AILABS_eng_000221-M-AILABS_eng_000221) +HE HAD BEEN DROWNED HE WAS FLOATING IN A SEA OF LIGHT AND NOW AND THEN SHINING LITTLE FISHES SWAM INQUISITIVELY UP TO HIM AND STARED (M-AILABS_eng_000222-M-AILABS_eng_000222) +BUT OLD GUNNAR HAD A TRICK OR TWO LEFT REMEMBER THE TALE THAT I READ TO YOU IN THE THRONEROOM OF BALDAR THE FIRST OF THE BRONS TO ENTER THE WORLD OF OPAL WERE SOLDIERS SENT FROM SOME BLASTED PLANET IN OUTER SPACE TO FIND A NEW HOME (M-AILABS_eng_000223-M-AILABS_eng_000223) +PAPA WILL YOU SPEAK TO THE MEN AND GET THEM TO GO AWAY SHE CANNOT BREATHE POOR THING WITH THIS CROWD ABOUT HER (M-AILABS_eng_000224-M-AILABS_eng_000224) +WHEN I TOOK THIS CASE HE SAID I BELIEVED DOWN IN MY HEART THAT DIXON WAS INNOCENT I STILL BELIEVE IT BUT MY FAITH HAS BEEN RUDELY SHAKEN (M-AILABS_eng_000225-M-AILABS_eng_000225) +CHAPTER SIX OF THE PIRATES OF ERSATZ (M-AILABS_eng_000226-M-AILABS_eng_000226) +REMEMBER THEY CANNOT TOUCH US (M-AILABS_eng_000227-M-AILABS_eng_000227) +GIVE ME TIME AZURE GIVE ME TIME IF THERES ANYTHING I HATE ITS A HURRY IVE AN IDEA YOUR MAJESTY ANNOUNCED THE SIXTH SNUBNOSED PRINCESS (M-AILABS_eng_000228-M-AILABS_eng_000228) +TRUE ENOUGH TROT DECLARED THE SAILOR MAN (M-AILABS_eng_000229-M-AILABS_eng_000229) +AS FOR THAT SAID MARGARET RATHER HAUGHTILY I HOLD IT IS HONI SOIT QUI MAL Y PENSE (M-AILABS_eng_000230-M-AILABS_eng_000230) +WHEN HE HEARD THESE WORDS THE KING WHOSE HEAD WAS FULL OF THE PRINCESS NEVER STOPPED TO INQUIRE IF THEY COULD BE TRUE AND SMEARED HIMSELF OVER WITH FAT AND SPRANG INTO THE OVEN (M-AILABS_eng_000231-M-AILABS_eng_000231) +YOU SHOULD BE ABLE TO GET PARTS FROM YOUR ROOM VISIONRECEIVER ILL HAVE SOME TOOLS GIVEN YOU THEN HE ADDED DIPLOMACY HAS TO UNDERSTAND THE THINGS THAT CONTROL EVENTS (M-AILABS_eng_000232-M-AILABS_eng_000232) +BY THE TIME THE FROST HAD SET IN THEY SHOULD BE FAR AWAY FROM HELSTONE (M-AILABS_eng_000233-M-AILABS_eng_000233) +ONE THING I WANT TO SAY BEGAN KENNEDY (M-AILABS_eng_000234-M-AILABS_eng_000234) +THIS IMPORTANT TRAFFIC WAS CONFIDED TO NO ONE BUT THE REAL PROPRIETOR (M-AILABS_eng_000235-M-AILABS_eng_000235) +HE WAS REPLACED ON BASS GUITAR BY JUSTIN KLUG (cv_eng_000707-cv_eng_000707) +ID ADD A SEPARATE SUBSECTION WHICH DEALS WITH THIS ASPECT (cv_eng_000708-cv_eng_000708) +OPERATION OF THE TRUNK LINE CONTINUED ON WOODEN TRESTLES (cv_eng_000709-cv_eng_000709) +MAGNESIUM FLUORIDE IS TRANSPARENT OVER AN EXTREMELY WIDE RANGE OF WAVELENGTHS (cv_eng_000710-cv_eng_000710) +FOUR GIANT PACKING SHEDS STORED FRESH PACKED POTATOES AND DELIVERED THEM ONTO RAILROAD CARS (cv_eng_000711-cv_eng_000711) +THE OTHER FOURTEEN CAMPUSES ARE TWOYEAR CAMPUSES REFERRED TO COLLECTIVELY AS THE UNIVERSITY COLLEGE (cv_eng_000712-cv_eng_000712) +ITS TOO BAD THAT HES QUICKLY GOING TO FORGET MY NAME HE THOUGHT (cv_eng_000713-cv_eng_000713) +ONE PICTURE IN THE GALLERY SHOWS HOW DILIGENT SLAVES ERECT THE STATUE OF ADMIRAL THOMPSON (cv_eng_000714-cv_eng_000714) +IMPERIAL DIET (cv_eng_000715-cv_eng_000715) +THE RESULTING COMPANY IS STRATTEC SECURITY CORPORATION (cv_eng_000716-cv_eng_000716) +BITCOIN MINING CAN BE DONE WITH GRAPHICS CARDS OR WITH SPECIALIZED HARDWARE (cv_eng_000717-cv_eng_000717) +THEY ALSO LEAD THE NATIONAL RANKING (cv_eng_000718-cv_eng_000718) +CHARLES GRAVES BISHOP OF LIMERICK (cv_eng_000719-cv_eng_000719) +AND AT THAT I TOLD HIM AND HE TOOK MY PLACE (cv_eng_000720-cv_eng_000720) +I THOUGHT ID GIVE THE KIDS A TREAT (cv_eng_000721-cv_eng_000721) +ACEVEDO DENIED SHOWING THE PICTURES (cv_eng_000722-cv_eng_000722) +HOLD YOUR NOSE TO KEEP THE SMELL FROM DISABLING YOUR MOTOR FUNCTIONS (cv_eng_000723-cv_eng_000723) +THAT SOUNDS LIKE THEIR PROBLEM (cv_eng_000724-cv_eng_000724) +HISTORICALLY THERE WAS NO CLEARLY DEFINED BOUNDARY IN THIS PART OF THE ARABIAN PENINSULA (cv_eng_000725-cv_eng_000725) +MARSHALL SHAFFER OF SLASH FILM GAVE THE FILM AN EIGHT OUT OF TEN (cv_eng_000726-cv_eng_000726) +HOW CAN YOU SAY THAT (cv_eng_000727-cv_eng_000727) +HIS STYLE BEGAN TO RESEMBLE MICHAEL DAMASKINOS (cv_eng_000728-cv_eng_000728) +HE IS ALSO CAPABLE OF FIRING LIGHTNING BOLTS WITH IMMENSE DESTRUCTIVE POWER (cv_eng_000729-cv_eng_000729) +HE CLAIMED TWO WICKETS IN ENGLANDS ONLY INNINGS AS BORDER WERE BEATEN COMPREHENSIVELY (cv_eng_000730-cv_eng_000730) +SHE DID MUCH LITERARY WORK (cv_eng_000731-cv_eng_000731) +HE MET THE ORGANIZERS OF THE PROTESTS AND AGREED TO CREATE TWO WORKING GROUPS (cv_eng_000732-cv_eng_000732) +THE BALL STRUCK THE FOUL POLE WELL ABOVE THE GREEN MONSTER (cv_eng_000733-cv_eng_000733) +ONLY CAMDEN THOMAS GARRETT AND GOLDFIELDS SOUTH EZEKIEL BAKER WERE UNCONTESTED (cv_eng_000734-cv_eng_000734) +IT IS A CHARITY SCHOOL WHOSE FEES ARE CALCULATED ON A MEANS TEST (cv_eng_000735-cv_eng_000735) +SOME WENT AWAY WHILE I WAS THERE AND OTHER PEOPLE CAME (cv_eng_000736-cv_eng_000736) +SEVEN (cv_eng_000737-cv_eng_000737) +THE KURA KHANATE WAS LOCATED MAINLY IN THE HISTORICAL AND GEOGRAPHICAL REGION OF KURA (cv_eng_000738-cv_eng_000738) +THE ELEVATION AT THE SITE IS ABOVE SEA LEVEL (cv_eng_000739-cv_eng_000739) +TOBIAS TRIED TO INJECT CONTEMPT INTO HIS TONE (cv_eng_000740-cv_eng_000740) +I HAVE TO WORK THIS SATURDAY (cv_eng_000741-cv_eng_000741) +THE GREAT RULERS FOUND THE SQUEAKY GRATE WAS GRATING ON THEIR NERVES (cv_eng_000742-cv_eng_000742) +WHEN THE BLINDING DUST HAD SETTLED A BIT THE BOY TREMBLED AT WHAT HE SAW (cv_eng_000743-cv_eng_000743) +DEMOCRAT AMBER BAKER WON THE OPEN SEAT (cv_eng_000744-cv_eng_000744) +BOTH ARE PUT TOGETHER BY STUDENTS IN THE COLLEGES JOURNALISM PROGRAM (cv_eng_000745-cv_eng_000745) +TRENCH WAS BORN IN BELIZE CITY IN BRITISH HONDURAS (cv_eng_000746-cv_eng_000746) +THE EARLY PHASE OF LIFE MOVES FAST (cv_eng_000747-cv_eng_000747) +NO (cv_eng_000748-cv_eng_000748) +SEVEN (cv_eng_000749-cv_eng_000749) +AT ONE TIME RAILWAY LINES DIVERGED FROM RUGBY STATION IN SEVEN DIFFERENT DIRECTIONS (cv_eng_000750-cv_eng_000750) +CZECH REPUBLIC ENTERED TWO SHOOTERS INTO THE PARALYMPIC COMPETITION (cv_eng_000751-cv_eng_000751) +TYGER WILLIAMS WROTE THE SCREENPLAY AND SHARED STORY CREDIT WITH THE BROTHERS (cv_eng_000752-cv_eng_000752) +THIS FESTIVAL WAS TO BE A CHARITY FUNDRAISER FOR THE AREA (cv_eng_000753-cv_eng_000753) +THESE EXTRA CARDS WERE INSERTED RANDOMLY INTO PACKS (cv_eng_000754-cv_eng_000754) +HENRY WENT BACK TO AUSTRALIA (cv_eng_000755-cv_eng_000755) +PERMIT ME TO INTRODUCE TO YOU HER MAJESTY THE QUEEN (cv_eng_000756-cv_eng_000756) +IN ORIGIN HEROIN WAS SUPPOSED TO BE THE “NONADDICTIVE MORPHINE SUBSTITUTE” (cv_eng_000757-cv_eng_000757) +SHE IS OF MEXICAN DESCENT (cv_eng_000758-cv_eng_000758) +I AM SURE THERE IS NOT ON HIS (cv_eng_000759-cv_eng_000759) +THOSE WHO DONT LEARN FROM HISTORY ARE DOOMED TO REPEAT IT (cv_eng_000760-cv_eng_000760) +I COULDN’T STOP STARING AT IT (cv_eng_000761-cv_eng_000761) +FOR SIMPLICITY GEAR INCHES IS NORMALLY ROUNDED TO THE NEAREST WHOLE NUMBER (cv_eng_000762-cv_eng_000762) +IF WE ACTUALLY DO WANT IT SOLVED IT WILL BE (cv_eng_000763-cv_eng_000763) +THE FRUIT OF A FIG TREE IS APPLE SHAPED (cv_eng_000764-cv_eng_000764) +FAIR EXCHANGE IS NO ROBBERY (cv_eng_000765-cv_eng_000765) +WHAT YOU EAT TODAY WALKS AND TALKS TOMORROW (cv_eng_000766-cv_eng_000766) +THE WATER THEN FLOWS OUT OF THE SWAMPS AS THE LUAPULA RIVER (cv_eng_000767-cv_eng_000767) +WHY DIDNT YOU SAY SOMETHING (cv_eng_000768-cv_eng_000768) +HAVE YOU SEEN OMAR (cv_eng_000769-cv_eng_000769) +I COULD GO ON FOR DAYS ABOUT THE DELICIOUS WINES PRODUCED IN THIS PART OF THE WORLD (cv_eng_000770-cv_eng_000770) +THE PHILADELPHIA INQUIRER NAMED HIM CITY PLAYER OF THE YEAR (cv_eng_000771-cv_eng_000771) +BOTS MAY BE SUBJECT TO SPECIAL RULES (cv_eng_000772-cv_eng_000772) +THE SWEDES WERE UNABLE TO USE THEIR VEHICLES WHICH WERE STUCK IN THE MUD (cv_eng_000773-cv_eng_000773) +THE ACT DID NOT PROHIBIT PAYING A REPRESENTATIVE TO APPEAR IN COURT (cv_eng_000774-cv_eng_000774) +CAN WE PLEASE LEAVE NOW (cv_eng_000775-cv_eng_000775) +HE WAS CONVICTED AND BANISHED TO CYPRUS FOR SEVEN YEARS FOR PUNISHMENT (cv_eng_000776-cv_eng_000776) +THE COUPLE HAVE TWO CHILDREN A DAUGHTER SOPHIA ROSALINDA AND A SON MATEO BRAVERY (cv_eng_000777-cv_eng_000777) +NONE OF THE THREE REFERENDUMS REACHED THE QUORUM OF THE MAJORITY OF THOSE ENTITLED (cv_eng_000778-cv_eng_000778) +TURPIN SUCCEEDED INDIRA SAMARASEKERA WHO SAW THE UNIVERSITY THROUGH A PERIOD OF STRONG GROWTH (cv_eng_000779-cv_eng_000779) +HERE I AM BETWEEN MY FLOCK AND MY TREASURE THE BOY THOUGHT (cv_eng_000780-cv_eng_000780) +THIS FAILURE HAS LED TO SIXTEEN POWER PLANTS HAVING ZERO DAYS OF COAL STOCK (cv_eng_000781-cv_eng_000781) +YES (cv_eng_000782-cv_eng_000782) +WHY DOES THAT PLANE KEEP GOING OVER (cv_eng_000783-cv_eng_000783) +IVE DONE THIS BEFORE WITH VIRTUALBOX WITH GOOD RESULTS (cv_eng_000784-cv_eng_000784) +THE APPLICATION WAS APPROVED IN FEBRUARY (cv_eng_000785-cv_eng_000785) +HENRY TARLTON STILES WHERE HE HAD A SOUND TRAINING IN LATIN (cv_eng_000786-cv_eng_000786) +IT WAS DISCONTINUED DUE TO SCHEDULING CONFLICTS INVOLVED IN LEWISS RETURN TO TERRESTRIAL RADIO (cv_eng_000787-cv_eng_000787) +HER FAMILY WAS FROM BRIANZA (cv_eng_000788-cv_eng_000788) +WHAT DID YOU EAT FOR DINNER (cv_eng_000789-cv_eng_000789) +THAT WAS MY DRAW TO SCIENCE (cv_eng_000790-cv_eng_000790) +HE IS CONSIDERED A MASTER OF CHIAROSCURO (cv_eng_000791-cv_eng_000791) +IT THEN RETURNS TO THE CHURCH ASCENDS AT THE ALTAR AND DISAPPEARS (cv_eng_000792-cv_eng_000792) +YOU CANNOT LOSE WHAT YOU NEVER HAD (cv_eng_000793-cv_eng_000793) +THE JAWS EXTEND PAST THE EYE (cv_eng_000794-cv_eng_000794) +MY NIECE CAN HELP YOU WITH THAT (cv_eng_000795-cv_eng_000795) +THATS THE KIND OF STUFF THEY WANT (cv_eng_000796-cv_eng_000796) +HOPE FOR THE BEST AND PREPARE FOR THE WORST (cv_eng_000797-cv_eng_000797) +INITIALLY THE WEIGHT LOSS WAS ATTAINED STRICTLY BY DIET (cv_eng_000798-cv_eng_000798) +ALL WERE OWNED BY THE EVERETTMOORE SYNDICATE (cv_eng_000799-cv_eng_000799) +WILL IT RAIN TOMORROW (cv_eng_000800-cv_eng_000800) +DU BIST EWIG MEINE LIEBE (cv_eng_000801-cv_eng_000801) +LUCILE PETRY TOOK HER PLACE AS ACTING DIRECTOR (cv_eng_000802-cv_eng_000802) +THE BEAVER RIVER BRIEFLY ENTERS THE EASTCENTRAL PART OF THE TOWNSHIP (cv_eng_000803-cv_eng_000803) +THE TRACK RESURFACING WAS ALSO COMPLETED (cv_eng_000804-cv_eng_000804) +HINDMARSH WAS AWARE OF THE IMPORTANCE OF ELECTRON MICROSCOPY IN BIOLOGICAL RESEARCH (cv_eng_000805-cv_eng_000805) +SINHA WAS BORN IN ALLAHABAD (cv_eng_000806-cv_eng_000806) +THIS BRIDGE IS UNOFFICIALLY REFERRED TO AS BLACKWATER BRIDGE BY COALITION FORCES OPERATING THERE (cv_eng_000807-cv_eng_000807) +IT IS RESPONSIBLE FOR WATER SUPPLY AND MANAGEMENT OF WATER RESOURCES IN MAHARASHTRA (cv_eng_000808-cv_eng_000808) +THIS IS THE FIRST PHASE OF THE JOB HE SAID (cv_eng_000809-cv_eng_000809) +THE GIZA PLATEAU OR GIZA NECROPOLIS IN THE EGYPTIAN VALLEY OF THE DEAD CONTAINS SEVERAL PYRAMIDS OF WHICH THE GREAT PYRAMID IS THE LARGEST SEVERAL SMALL TOMBS SEVERAL TEMPLES AND THE GREAT SPHINX (fleurs_eng_000413-fleurs_eng_000413) +TOWARDS THE END OF THE MIDDLE AGES WESTERN EUROPE BEGAN TO DEVELOP THEIR OWN STYLE ONE OF THE BIGGEST DEVELOPMENTS OF THE TIME AS A RESULT OF THE CRUSADES PEOPLE BEGAN TO USE BUTTONS TO FASTEN CLOTHING (fleurs_eng_000414-fleurs_eng_000414) +IF YOU ONLY GO ASHORE USING SHIPBOARD EXCURSIONS YOU WILL NOT NEED A SEPARATE VISA AS OF 2009 (fleurs_eng_000415-fleurs_eng_000415) +DUVALL WHO IS MARRIED WITH TWO ADULT CHILDREN DID NOT LEAVE A BIG IMPRESSION ON MILLER TO WHOM THE STORY WAS RELATED (fleurs_eng_000416-fleurs_eng_000416) +THEIR DISCIPLINED DEFENCE BALL HANDLING SKILLS AND EXCELLENT TEAM WORK MADE THEM STAND OUT AND IT WAS CLEAR THAT THIS WAS THE TEAM TO BEAT (fleurs_eng_000417-fleurs_eng_000417) +THE DISEASE IS CARRIED BY PIGS WHICH THEN MIGRATES TO HUMANS THROUGH MOSQUITOS (fleurs_eng_000418-fleurs_eng_000418) +FOR THE SPRINGBOKS IT ENDED A FIVEMATCH LOSING STREAK (fleurs_eng_000419-fleurs_eng_000419) +THUS THE PENCIL WAS A GOOD FRIEND TO MANY PEOPLE WHEN IT CAME OUT (fleurs_eng_000420-fleurs_eng_000420) +THE USE OF VIDEO RECORDING HAS LED TO IMPORTANT DISCOVERIES IN THE INTERPRETATION OF MICROEXPRESSIONS FACIAL MOVEMENTS WHICH LAST A FEW MILLISECONDS (fleurs_eng_000421-fleurs_eng_000421) +ALSO TO THE NORTH VISIT THE GREAT SANCTUARY OF OUR LADY OF FATIMA SHRINE A PLACE OF WORLDWIDE FAMOUS MARIAN APPARITIONS (fleurs_eng_000422-fleurs_eng_000422) +IF YOU WANT TO BE CLOSE TO THE ACTION YOURE GOING TO HAVE TO GET IN EARLY TO GET A CAMPING SITE CLOSE TO THE MUSIC (fleurs_eng_000423-fleurs_eng_000423) +MADAGASCAR IS BY FAR THE BIGGEST AND A CONTINENT ON ITS OWN WHEN IT COMES TO WILDLIFE (fleurs_eng_000424-fleurs_eng_000424) +WOMEN IT IS RECOMMENDED THAT ANY WOMEN TRAVELLERS SAY THAT THEY ARE MARRIED REGARDLESS OF ACTUAL MARITAL STATUS (fleurs_eng_000425-fleurs_eng_000425) +CUOMO 53 BEGAN HIS GOVERNORSHIP EARLIER THIS YEAR AND SIGNED A BILL LAST MONTH LEGALIZING SAMESEX MARRIAGE (fleurs_eng_000426-fleurs_eng_000426) +AS LIGHT POLLUTION IN THEIR HEYDAY WAS NOT THE KIND OF PROBLEM IT IS TODAY THEY ARE USUALLY LOCATED IN CITIES OR AT CAMPUSES EASIER TO REACH THAN THOSE BUILT IN MODERN TIMES (fleurs_eng_000427-fleurs_eng_000427) +THEY USUALLY HAVE SPECIAL FOOD DRINK AND ENTERTAINMENT OFFERS TO KEEP GUESTS IN A GOOD MOOD AND KEEP THEM AT THE PREMISE (fleurs_eng_000428-fleurs_eng_000428) +ON THE OTHER HAND ICY AND SNOWY CONDITIONS ARE NORMAL IN MANY COUNTRIES AND TRAFFIC GOES ON MOSTLY UNINTERRUPTED ALL YEAR ROUND (fleurs_eng_000429-fleurs_eng_000429) +BE CAREFUL NOT TO ALLOW FABRIC TO BECOME TOO HOT WHICH CAN CAUSE SHRINKAGE OR IN EXTREME CASES SCORCH (fleurs_eng_000430-fleurs_eng_000430) +FERAL CHILDREN MAY HAVE EXPERIENCED SEVERE CHILD ABUSE OR TRAUMA BEFORE BEING ABANDONED OR RUNNING AWAY (fleurs_eng_000431-fleurs_eng_000431) +PEOPLE MAY NOT ANTICIPATE THAT PATIENCE AND UNDERSTANDING ARE ALSO NECESSARY FOR TRAVELLERS RETURNING HOME (fleurs_eng_000432-fleurs_eng_000432) +SOON AFTER THE OUTBREAK OF HOSTILITIES BRITAIN INITIATED A NAVAL BLOCKADE OF GERMANY (fleurs_eng_000433-fleurs_eng_000433) +THE GOVERNORS OFFICE SAID NINETEEN OF THE INJURED WERE POLICE OFFICERS (fleurs_eng_000434-fleurs_eng_000434) +USING SHIPS TO TRANSPORT GOODS IS BY FAR THE MOST EFFICIENT WAY TO MOVE LARGE AMOUNTS OF PEOPLE AND GOODS ACROSS OCEANS (fleurs_eng_000435-fleurs_eng_000435) +LIBERAL CRITICISM OF THE RECONSTRUCTION EFFORT HAS FOCUSED ON THE AWARDING OF RECONSTRUCTION CONTRACTS TO PERCEIVED WASHINGTON INSIDERS (fleurs_eng_000436-fleurs_eng_000436) +YOU CAN USE BODABODA MOTORCYCLE TAXI TO GET AROUND GOMA THE NORMAL LOCAL PRICE IS 500 CONGOLESE FRANCS FOR THE SHORT RIDE (fleurs_eng_000437-fleurs_eng_000437) +THE THREE KINGDOMS WAS ONE OF THE BLOODIEST ERAS IN ANCIENT CHINAS HISTORY THOUSANDS OF PEOPLE DIED FIGHTING TO SIT IN THE HIGHEST SEAT IN THE GRAND PALACE AT XIAN (fleurs_eng_000438-fleurs_eng_000438) +THESE COUPLES MAY CHOOSE TO MAKE AN ADOPTION PLAN FOR THEIR BABY (fleurs_eng_000439-fleurs_eng_000439) +NOTHING CAN BE SEEN OTHER THAN THE CLEAR BEAUTIFUL SKY ABOVE AND THE MANY SURROUNDING MOUNTAINS VERY LITTLE OF THIS WORLD CAN BE SEEN OR HEARD FROM INSIDE THE CAVE (fleurs_eng_000440-fleurs_eng_000440) +HE WAS SUBSEQUENTLY RELOCATED TO ADDENBROOKES HOSPITAL IN CAMBRIDGE (fleurs_eng_000441-fleurs_eng_000441) +VATICAN CITYS POPULATION IS AROUND 800 IT IS THE SMALLEST INDEPENDENT COUNTRY IN THE WORLD AND THE COUNTRY WITH THE LOWEST POPULATION (fleurs_eng_000442-fleurs_eng_000442) +REGULAR ANNOUNCEMENTS IN THE METRO ARE MADE ONLY IN CATALAN BUT UNPLANNED DISRUPTIONS ARE ANNOUNCED BY AN AUTOMATED SYSTEM IN A WIDE VARIETY OF LANGUAGES INCLUDING SPANISH ENGLISH FRENCH ARABIC AND JAPANESE (fleurs_eng_000443-fleurs_eng_000443) +THIS OFFERS A GOOD OPPORTUNITY TO SEE THE AURORA BOREALIS AS THE SKY WILL BE DARK MORE OR LESS AROUND THE CLOCK (fleurs_eng_000444-fleurs_eng_000444) +FIRE RESCUE CREWS EVENTUALLY DOUSED THE FIRE BY 1135 PM (fleurs_eng_000445-fleurs_eng_000445) +THIS IS CALLED A CHEMICALS PH YOU CAN MAKE AN INDICATOR USING RED CABBAGE JUICE (fleurs_eng_000446-fleurs_eng_000446) +IN PARTICULAR IT IS CLAIMED THAT ONE CAN DETECT WHETHER A PERSON IS LYING BY INTERPRETING MICROEXPRESSIONS CORRECTLY (fleurs_eng_000447-fleurs_eng_000447) +THE CENTRAL AUTHORITY OF THE CHURCH HAD BEEN IN ROME FOR OVER A THOUSAND YEARS AND THIS CONCENTRATION OF POWER AND MONEY LED MANY TO QUESTION WHETHER THIS TENET WAS BEING MET (fleurs_eng_000448-fleurs_eng_000448) +THE SUNDARBANS ARE THE LARGEST LITTORAL MANGROVE BELT IN THE WORLD STRETCHING 80 KM 50 MI INTO THE BANGLADESHI AND INDIAN HINTERLAND FROM THE COAST (fleurs_eng_000449-fleurs_eng_000449) +REGULAR ANNOUNCEMENTS IN THE METRO ARE MADE ONLY IN CATALAN BUT UNPLANNED DISRUPTIONS ARE ANNOUNCED BY AN AUTOMATED SYSTEM IN A WIDE VARIETY OF LANGUAGES INCLUDING SPANISH ENGLISH FRENCH ARABIC AND JAPANESE (fleurs_eng_000450-fleurs_eng_000450) +EVERYONE PARTICIPATES IN SOCIETY AND USES TRANSPORTATION SYSTEMS ALMOST EVERYONE COMPLAINS ABOUT TRANSPORTATION SYSTEMS (fleurs_eng_000451-fleurs_eng_000451) +LAYTON HAD ASKED FOR CHANGES TO THE CONSERVATIVES ENVIRONMENTAL BILL DURING THE MEETING WITH THE PM ASKING FOR A THOROUGH AND COMPLETE REWRITING OF THE CONSERVATIVE PARTYS ENVIRONMENTAL BILL (fleurs_eng_000452-fleurs_eng_000452) +ANYONE WHOS GOING TO DRIVE AT HIGH LATITUDES OR OVER MOUNTAIN PASSES SHOULD CONSIDER THE POSSIBILITY OF SNOW ICE OR FREEZING TEMPERATURES (fleurs_eng_000453-fleurs_eng_000453) +SLEEP INTERRUPTION IS THE PROCESS OF PURPOSEFULLY AWAKENING DURING YOUR NORMAL SLEEP PERIOD AND FALLING ASLEEP A SHORT TIME LATER 10–60 MINUTES (fleurs_eng_000454-fleurs_eng_000454) +SWIRL THE TWO DRY POWDERS TOGETHER AND THEN WITH CLEAN WET HANDS SQUEEZE THEM INTO A BALL (fleurs_eng_000455-fleurs_eng_000455) +FOR THE SPRINGBOKS IT ENDED A FIVEMATCH LOSING STREAK (fleurs_eng_000456-fleurs_eng_000456) +JUST LIKE THE MOON EXERTS A PULL ON THE EARTH CAUSING TIDES SO DOES THE MILKY WAY EXERT A FORCE ON THE SAGITTARIUS GALAXY (fleurs_eng_000457-fleurs_eng_000457) +THROUGH THE NIGHT BETWEEN 150 AND 200 COPIES WERE MADE NOW KNOWN AS DUNLAP BROADSIDES (fleurs_eng_000458-fleurs_eng_000458) +FIRST AMONG ITS 78 RECOMMENDATIONS IS THAT A NEW DIPLOMATIC INITIATIVE SHOULD BE TAKEN BEFORE THE END OF THIS YEAR TO SECURE IRAQS BORDERS AGAINST HOSTILE INTERVENTIONS AND TO REESTABLISH DIPLOMATIC RELATIONS WITH ITS NEIGHBORS (fleurs_eng_000459-fleurs_eng_000459) +SAINT PETERSBURG CRUISES INCLUDE TIME IN TOWN CRUISE PASSENGERS ARE EXEMPTED FROM VISA REQUIREMENTS CHECK THE TERMS (fleurs_eng_000460-fleurs_eng_000460) +ACCORDING TO JAPANS NUCLEAR AGENCY RADIOACTIVE CAESIUM AND IODINE HAS BEEN IDENTIFIED AT THE PLANT (fleurs_eng_000461-fleurs_eng_000461) +SEGREGATION AND RECOMBINATION SHUFFLE VARIATION BACK AND FORTH BETWEEN THE TWO POOLS WITH EACH GENERATION (fleurs_eng_000462-fleurs_eng_000462) +ELEMENTS LIKE CALCIUM AND POTASSIUM ARE CONSIDERED METALS OF COURSE THERE ARE ALSO METALS LIKE SILVER AND GOLD (fleurs_eng_000463-fleurs_eng_000463) +THE CORRELATION BETWEEN BRAIN PATHOLOGY AND BEHAVIOUR SUPPORTS SCIENTISTS IN THEIR RESEARCH (fleurs_eng_000464-fleurs_eng_000464) +ANCIENT CHINA HAD A UNIQUE WAY OF SHOWING DIFFERENT TIME PERIODS EACH STAGE OF CHINA OR EACH FAMILY THAT WAS IN POWER WAS A DISTINCTIVE DYNASTY (fleurs_eng_000465-fleurs_eng_000465) +A SIMPLE POPULAR DINNER ESPECIALLY DURING THE SUMMER IS THE PA AMB OLI BREAD WITH OLIVE OIL TOMATO AND ANY AVAILABLE CONDIMENTS SUCH AS CHEESE TUNAFISH ETC (fleurs_eng_000466-fleurs_eng_000466) +THE ANNOUNCEMENT WAS MADE AFTER TRUMP HAD A PHONE CONVERSATION WITH TURKISH PRESIDENT RECEP TAYYIP ERDOĞAN (fleurs_eng_000467-fleurs_eng_000467) +PERRY STATED THAT HE WOULD RETURN TO TEXAS TO ASSESS THE RESULTS OF TONIGHTS CAUCUS DETERMINE WHETHER THERE IS A PATH FORWARD FOR MYSELF IN THIS RACE BUT LATER SAID THAT HE WOULD REMAIN IN THE RACE AND COMPETE IN THE JANUARY 21 SOUTH CAROLINA PRIMARY (fleurs_eng_000468-fleurs_eng_000468) +HE WAS ALSO ENGAGED IN ENGRAVING BANKNOTES FOR MANY COUNTRIES RECENT EXAMPLES OF HIS WORK INCLUDING THE PRIME MINISTERIAL PORTRAITS ON THE FRONT OF THE NEW CANADIAN 5 AND 100 BILLS (fleurs_eng_000469-fleurs_eng_000469) +MORE TRADITIONAL CHURCHES OFTEN HOLD AN EASTER VIGIL ON SATURDAY NIGHT DURING THE EASTER WEEKEND WITH THE CONGREGATIONS OFTEN BREAKING INTO CELEBRATION AT THE STROKE OF MIDNIGHT TO CELEBRATE CHRISTS RESURRECTION (fleurs_eng_000470-fleurs_eng_000470) +FINLAND IS A GREAT BOATING DESTINATION THE LAND OF A THOUSAND LAKES HAS THOUSANDS OF ISLANDS TOO IN THE LAKES AND IN THE COASTAL ARCHIPELAGOS (fleurs_eng_000471-fleurs_eng_000471) +CURRENT SENATOR AND ARGENTINE FIRST LADY CRISTINA FERNANDEZ DE KIRCHNER ANNOUNCED HER PRESIDENTIAL CANDIDACY YESTERDAY EVENING IN LA PLATA A CITY 50 KILOMETERS 31 MILES AWAY FROM BUENOS AIRES (fleurs_eng_000472-fleurs_eng_000472) +SEVERE WEATHER IS THE GENERIC TERM FOR ANY DANGEROUS WEATHER PHENOMENON WITH THE POTENTIAL TO CAUSE DAMAGE SERIOUS SOCIAL DISRUPTION OR LOSS OF HUMAN LIFE (fleurs_eng_000473-fleurs_eng_000473) +FOR EXAMPLE THE MOST COMMON STILL IMAGE PHOTOGRAPHY FORMAT IN THE WORLD IS 35MM WHICH WAS THE DOMINANT FILM SIZE AT THE CLOSE OF THE ANALOG FILM ERA (fleurs_eng_000474-fleurs_eng_000474) +IT IS RELATED TO BUT USUALLY NOT INVOLVING ALPINE STYLE SKI TOURING OR MOUNTAINEERING THE LATTER ONES DONE IN STEEP TERRAIN AND REQUIRING MUCH STIFFER SKIS AND BOOTS (fleurs_eng_000475-fleurs_eng_000475) +IRONING DAMP CLOTHES CAN HELP THEM DRY MANY HOTELS HAVE AN IRON AND IRONING BOARD AVAILABLE FOR LOAN EVEN IF ONE IS NOT PRESENT IN THE ROOM (fleurs_eng_000476-fleurs_eng_000476) +EVADNE ANSWERED HOARSELY SHE DREW HER CHAIR A LITTLE CLOSER TO THE FIRE AND SPREAD HER HANDS OUT TO THE BLAZE THERE WAS NO OTHER LIGHT IN THE ROOM BY THIS TIME THE WIND WITHOUT HOWLED DISMALLY STILL (mls_eng_000283-mls_eng_000283) +MY DEAR MARIA WHY DO YOU NOT DESIST FROM THIS SILLY PURSUIT OF AN IMAGINARY TREASURE WHAT IS THE VALUE OF MONEY WE ARE SPANIARDS NOT SHIRTSLEEVED MERCENARY PIGS OF AMERICANS (mls_eng_000284-mls_eng_000284) +CRITICAL TEMPERATURE IS THAT OF THE SINGLE ISOTHERMAL LINE WHICH PRESENTS A POINT OF INFLEXION AT A HORIZONTAL TANGENT THE CRITICAL PRESSURE AND THE CRITICAL VOLUME ARE THE TWO COORDINATES OF THIS POINT OF INFLEXION (mls_eng_000285-mls_eng_000285) +MUCH LIKE IN FOULNESS AND DEFORMITY UNTO THAT MONSTER WHOM THE THEBAN KNIGHT THE FATHER OF THAT FATAL PROGENY MADE KILL HERSELF FOR VERY HEARTS DESPITE THAT HE HAD READ HER RIDDLE WHICH NO WIGHT COULD EVER LOOSE BUT SUFFERED DEADLY DUEL (mls_eng_000286-mls_eng_000286) +HE HAS MANAGED TO MEASURE WITH PRECISION PRESSURES AMOUNTING TO THREE THOUSAND ATMOSPHERES AND ALSO THE VERY SMALL VOLUMES THEN OCCUPIED BY THE FLUID MASS UNDER CONSIDERATION THIS LAST MEASUREMENT WHICH NECESSITATES NUMEROUS CORRECTIONS IS THE MOST DELICATE PART OF THE OPERATION (mls_eng_000287-mls_eng_000287) +WHY SHOULD IT HAVE BEEN DEEMED NECROMANCY TO ENDEAVOR TO COMBINE THESE PARTS TO EVOLVE BY CAREFUL ELIMINATION AND CHANGE TO THE PERFECT FOOD (mls_eng_000288-mls_eng_000288) +NAY THOUGH OF RUSHES BE MY BED YET I AM RICH LOVE SAID BUT ARGUED LIFE THRICE FOND ART THOU TO YIELD THE SOVEREIGN GIFTS OF EARTH THE VICTOR SWORD THE LAURELED BROW FOR VISIONED THINGS OF LITTLE WORTH (mls_eng_000289-mls_eng_000289) +BOCK SEEMS TO HAVE BEEN A KEEN COLLECTOR ALTHOUGH HAMPERED BY ILL HEALTH AND A GREAT POINT IN HIS FAVOUR IS THAT HE DESCRIBED ONLY THOSE PLANTS WHICH HAD COME UNDER HIS OWN PERSONAL OBSERVATION (mls_eng_000290-mls_eng_000290) +HAD RATHER SHRUNK UP AND HAD NOT CHANGED INTO NYMPHS THESE I LEFT IN THE STEMS COVERING THEM UP AGAIN AND THEY APPEARED AS PERFECT INSECTS IN THE MAY OF THE FOLLOWING YEAR (mls_eng_000291-mls_eng_000291) +NOTHING SAVE OBJECTS AND THOUGHTS OF BEAUTY COULD PRESENT THEMSELVES TO THE UNDERSTANDING OF THE FORTUNATE PERSON WHO PARTOOK OF IT THESE PAGES WHICH YOU HAVE BROUGHT TO ME TO TRANSLATE ARE CONCERNED WITH THIS SUPERSTITION (mls_eng_000292-mls_eng_000292) +NOW SEEMED INSIPIDITY AND HED NERVE HIMSELF AGAINST IT HIS FACE WORE A SORT OF SEVERE FLUSH HE WAS TIMID EVEN TO RUDENESS (mls_eng_000293-mls_eng_000293) +BECAME MORE LIFELIKE AS THE CHEEKS FLUSH THERE WAS RARE WARMTH IN A WINTER MORNING TO CHEER THE HALFDESPAIRING SOUL TIRED AFTER LONG HOURS OF OIL READING AND PIERCED TO THE HEART BY NEVER CEASING RHYMES YET I COULD NOT UNDERSTAND IT (mls_eng_000294-mls_eng_000294) +ONE OF THE HAWAIIAN WRITERS SAID THE OPIHIAWA IS A POISON SHELLFISH THESE ARE BITTER AND DEADLY AND CAN BE USED IN PUTTING ENEMIES TO DEATH (mls_eng_000295-mls_eng_000295) +THE BEAUTEOUS ROBES OF HEAVEN ASLANT THE DEW BRIGHT EARTH AND COLOURED AIR HE LOOKS IN BOUNDLESS MAJESTY ABROAD TOUCHING THE GREEN LEAVES ALL ATREMBLE WITH GOLD LIGHT (mls_eng_000296-mls_eng_000296) +I CAN DO NO MORE THAN THAT UNTIL THIS MATTER IS ABSOLUTELY SETTLED THEY ARE WORTH MORE THAN LIFE ITSELF TO ME MR COWPER SEEMED ANNOYED SURELY HE PROTESTED YOU ARE NOT GOING TO ASK ME TO WAIT THREE MONTHS UNTIL I CAN EXAMINE ONE OF THESE (mls_eng_000297-mls_eng_000297) +ROSCONGRESS FOUNDATION RUSSIAN ENTITY THAT ORGANIZED THE SAINT PETERSBURG INTERNATIONAL ECONOMIC FORUM ROSNEFT RUSSIAN STATEOWNED OIL AND ENERGY COMPANY (mls_eng_000298-mls_eng_000298) +HOW IT GLITTERED AND SPARKLED THE DELICATE FROSTWORK YOU WERE ATTRACTED NO DOUBT AND MARVELLED AT THE DAINTY TRACINGS BUT FEW OF US HAVE REALLY HAD AN OPPORTUNITY TO STUDY THE DETAIL OF THESE FROST DESIGNS MINUTELY OR HAVE CONSIDERED THAT THERE WERE MORE THAN THREE OR FOUR DESIGNS AT MOST (mls_eng_000299-mls_eng_000299) +OTHER THAN THE OFFENSE IN TRYING TO INFLICT A WOUND THEY MAY KILL THE OFFENDER OR WOUND HIM MORE THAN THEY INTENDED TO DO AND THIS BECOMES A CAUSE FOR A NEW FEUD SO THAT THE PRIMITIVE LEGISLATORS WERE CAREFUL IN REQUIRING THE RETALIATION TO BE LIMITED TO AN EYE FOR AN EYE (mls_eng_000300-mls_eng_000300) +AT CYRUS WORD THE JEWS RETURN THE COMPANY THAT GO GODS HOUSE BEGUN WITH MIRTH AND MOAN IS HINDERED BY THE FOE BUT ONCE AGAIN THE WORK GOES ON BY LICENSE FROM DERIUS EZRA IS SENT WITH ROYAL GRANT AND GIFTS FOR USES PIOUS (mls_eng_000301-mls_eng_000301) +NET PRODUCT YEAR IN AND YEAR OUT SEVEN HUNDRED FRANCS HE LIVED IN IT HOW NOT SO BADLY WE WILL EXPLAIN MARIUS OCCUPIED IN THE GORBEAU HOUSE (mls_eng_000302-mls_eng_000302) +THEN THIS IS ALL YOUR ANSWER TIS TOO FAIR FOR ONE OF HIS ALLIANCE AND I WARN YOU THAT THIS PLACE NO MORE SEE YOU EXIT ENTER DE FLORES THE BEST IS THERE IS MORE GROUND TO MEET A MANS REVENGE ON HONEST DE FLORES THATS MY NAME INDEED (mls_eng_000303-mls_eng_000303) +WHEN I RETURNED TO THE HOUSE WHERE I HAD BEEN A HAPPY CHILD ONLY A PILE OF ASHES WHERE IT HAD STOOD I WEPT LONG AND TO FORGET MY WEEPING I SAILED OUT ON THE VAST CALM SEA ON THESE WATERS IN A STAR SAPPHIRE NIGHT I PLAYED MY FLUTE TO THE SUMMER MOON (mls_eng_000304-mls_eng_000304) +DO YOU NOT SEE WHAT PLEASURE IT GIVES HIM WE HAVE GROWN UP TOGETHER IN THIS HOUSE SINCE HE WAS A BOY I SIMPLY CANNOT BEAR AS YOU CAN THE SIGHT OF THE SMILE LEAVING HIS FACE POOR DEAR HE HAS NO AMUSEMENT EXCEPT THIS PLAYING AT THE SHOPKEEPING (mls_eng_000305-mls_eng_000305) +IT IS A NEBULOUS BODY REVOLVING IN AN ELLIPTICAL ORBIT OF GREAT ELONGATION LOVE LOVE LOVE WILL NOT BE THE WOUND OF CUPID BUT THE MANIFESTATION OF UNIVERSAL REPRODUCTIVE INSTINCTS (mls_eng_000306-mls_eng_000306) +SHARPLY AS HE SHOOK HANDS WITH HER GOD BLESS YOU MY DEAR CHILD THE BISHOP SAID WHEN SHE KISSED HIM AND HIS LIPS MOVED AFTERWARD FOR SOME SECONDS AS IF HE WERE IN PRAYER HER MOTHER FOLLOWED HER OUT OF THE ROOM AND THEN SILENCE SETTLED (mls_eng_000307-mls_eng_000307) +FOLLOWED HIM STEALTHILY AND WHEN HE WAS IN A STOOPING POSTURE FILLING HIS BUCKET CAME UP BEHIND HIM AND PLUNGED A LONG KNIFE INTO HIS NECK (mls_eng_000308-mls_eng_000308) +SAITH CHERSIAS DOES NOT JUPITER DISTRIBUTE TO THE GODS THEIR PROPORTION AND DIVIDEND SPARINGLY AND SEVERALLY AS AGAMEMNON DID TO HIS COMMANDERS WHEN HIS GUESTS DRANK TO ONE ANOTHER IF CHERSIAS QUOTH CLEODEMUS AS YOU NARRATE (mls_eng_000309-mls_eng_000309) +AND WHERE NONE SHALL DARE RESTRAIN US WE CAN MEET AGAIN IN THOUGHT SO THERES NO USE IN WEEPING BEAR A CHEERFUL SPIRIT STILL NEVER DOUBT THAT FATE IS KEEPING FUTURE GOOD FOR PRESENT ILL (mls_eng_000310-mls_eng_000310) +AND TO BECOME THE RECORD OF WHAT PEOPLE HAVE DONE IN THEIR MORE AMIABLE MOMENTS THE RECORD OF THE CONQUESTS OF PEACE HOW MEN HAVE LIVED AND LABORED DUG AND BUILT HEWN AND CLEARED GARDENED AND REFOREST (mls_eng_000311-mls_eng_000311) +THE LOW FLYING OF THE SWALLOWS BETOKENS RAIN AS WELL AS ANY UNSEASONABLE DANCING OF MIDGES IN THE EVENING SORE CORNS ON THE FEET AND RHEUMATISM IN THE JOINTS ARE DIREFUL PRECURSORS THE LEAVES ARE ALL ATREMBLE BEFORE THE APPROACH OF THUNDER (mls_eng_000312-mls_eng_000312) +WAS STORMED GENERAL DAMPIERRE WAS KILLED GENERAL CUSTINE WAS BLAMED AND INDEED IS NOW COME TO PARIS TO GIVE EXPLANATIONS AGAINST ALL WHICH THE MOUNTAIN AND ATROCIOUS MARAT MUST EVEN MAKE HEAD AS THEY CAN (mls_eng_000313-mls_eng_000313) +THE MOMENT WAS FEARFUL A MIGHTIER FOE HAD NEVER SWUNG THE BATTLEAXE OVER HIM BUT HOPE NERVED HIS ARM FOR A DESPERATE BLOW AND TECUMSEH FELL PROSTRATE BEFORE HIM (mls_eng_000314-mls_eng_000314) +THEN THE WIND STOPPED THE CLOUDS TURNED DARK AND NIGHT CAME ON LIKE INK MY OLD COTTON QUILT WAS COLD AS IRON MY SWEET SON TOSSED IN HIS SLEEP (mls_eng_000315-mls_eng_000315) +YOU MAY DO AS YOU PLEASE TO WORK OFF YOUR IRRITATION TO KEEP UP YOUR FANATICISM YOU ARE WELL OFF YOU NEED NOT MIND THE COST THE POOR DO NOT WANT TO STAND IN YOUR WAY BUT YOU INSIST ON THEIR SUBMITTING TO YOUR COMPULSION (mls_eng_000316-mls_eng_000316) +HE WAS BRED BY REV G A SNEYD BEING BY OTHMAN E SIX FOUR TWO TWO HEDWIG HE WAS BORN IN MARCH EIGHTEEN SEVENTYNINE AND HE WAS THE ONLY SURVIVOR OF A LITTER OF FIFTEEN IT WAS ON THIS ACCOUNT THAT HE WAS CALLED SAFE IN COLOR AND MARKINGS (mls_eng_000317-mls_eng_000317) +AND WHAT HASTE IT MAKES TO FALL INTO THE SECOND THERE BY THIS TIME DIAPHANTA SNEEZES ACHOO MOST ADMIRABLE SECRET ON THE CONTRARY IT STIRS ME NOT A WHIT WHICH MOST CONCERNS IT HA HA HA (mls_eng_000318-mls_eng_000318) +THIRDLY THALES SAID WHERE THE CITIZENS ARE NEITHER TOO RICH NOR TOO POOR FOURTHLY ANACHARSIS SAID WHERE THOUGH IN ALL OTHER RESPECTS THEY ARE EQUAL YET VIRTUOUS MEN ARE ADVANCED AND VICIOUS PERSON DEGRADED (mls_eng_000319-mls_eng_000319) +THE KINDLY FRANK IS SYMPATHETIC EVERY DAY HE PASSES NOTES BETWEEN US AND I TRY TO ENCOURAGE RUSSELL HE WILL IMPROVE I ASSURE HIM HIS TIME IS SHORT AND FRESH AIR AND LIBERTY WILL SOON RESTORE HIM (mls_eng_000320-mls_eng_000320) +THESE QUESTIONS IT IS NOW EVIDENT MAY FREQUENTLY BE ANSWERED WITH EQUAL PROPRIETY IN OPPOSITE WAYS AND IF THERE BE ANY OCCASIONS ON WHICH THEY CAN BE ANSWERED ONLY IN ONE WAY THE ANSWER WILL DEPEND UPON THE NATURE OF THE OCCASION (mls_eng_000321-mls_eng_000321) +IN HIS NOTE BORE THE MINSTRELSY SECOND EDITION EIGHTEEN OH EIGHT SCOTT SAYS THE BALLAD WAS TAKEN DOWN FROM AN OLD WOMANS RECITATION AT THE ALSTON MOOR LEAD MINES BY THE AGENT THERE AND SENT BY HIM TO SURTEES 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GOALS OF ENHANCED PERFORMANCE (swc_eng_001757-swc_eng_001757) +INCLUDING NAPHTHA (swc_eng_001758-swc_eng_001758) +BY SPANISH CHURCHMAN LUIS RAMIREZ DE LUCENA (swc_eng_001759-swc_eng_001759) +DIVIDED DEMOCRATS (swc_eng_001760-swc_eng_001760) +THE WORLD CHAMPIONSHIP HAS BEEN CONTROLLED BY FIDE (swc_eng_001761-swc_eng_001761) +WHERE THE STARTING POSITION (swc_eng_001762-swc_eng_001762) +BEEN CREATED IN EVERY STATE AND TERRITORY TO PROTECT AND PRESERVE THE COUNTRYS UNIQUE ECOSYSTEMS (swc_eng_001763-swc_eng_001763) +DEDICATION OF THE NEW ZEALAND WAR MEMORIAL (swc_eng_001764-swc_eng_001764) +ACCLAIM FROM THE RAILROAD COMPANIES FOR VETOING (swc_eng_001765-swc_eng_001765) +TOWN IS SPLIT BETWEEN (swc_eng_001766-swc_eng_001766) +MOSQUITOFISH IS A PARTICULARLY AGGRESSIVE SPECIES KNOWN (swc_eng_001767-swc_eng_001767) +AND THE NATIONAL CHESS CHAMPIONSHIPS (swc_eng_001768-swc_eng_001768) +PROBLEM IS KNOWN TO RUN IN POLYNOMIAL TIME (swc_eng_001769-swc_eng_001769) +JR AND PARKER WATKINS HARDIN 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PATENTED (swc_eng_001825-swc_eng_001825) +COULD SAVE AND FIND FILES BY NUMBER (swc_eng_001826-swc_eng_001826) +AUSTRALIAN SNAKES BELONG TO SEVEN FAMILIES (swc_eng_001827-swc_eng_001827) +DEVELOPING PLAYERS (swc_eng_001828-swc_eng_001828) +DECLINED SHARPLY SINCE ITS PEAK IN THE LATE (swc_eng_001829-swc_eng_001829) +WAS RECORDED ENTIRELY ON A FOUR TRACK CASSETTE TAPE (swc_eng_001830-swc_eng_001830) +ENORMOUS IMPROVEMENT IN (swc_eng_001831-swc_eng_001831) +URBAN AND RURAL LEGISLATORS (swc_eng_001832-swc_eng_001832) +EACH PLAYER BEGINS (swc_eng_001833-swc_eng_001833) +CHESS HAS INSPIRED MANY COMBINATORIAL PUZZLES (swc_eng_001834-swc_eng_001834) +MORE HUMANE IMAGE (swc_eng_001835-swc_eng_001835) +WELL AS PIRATED TAPES (swc_eng_001836-swc_eng_001836) +TINTIN DESCENDS INTO THE OCEAN (swc_eng_001837-swc_eng_001837) +PRESIDENT PRO TEM OF THE STATE SENATE (swc_eng_001838-swc_eng_001838) +BISHOP CAN MOVE ANY NUMBER OF SQUARES DIAGONALLY (swc_eng_001839-swc_eng_001839) +PRESSURE INSIDE THE SKULL 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PRODUCTS ALSO SELL (swc_eng_001894-swc_eng_001894) +THE FIRST NON SOVIET CHALLENGER SINCE (swc_eng_001895-swc_eng_001895) +OPPONENT HAS ONLY THE KING AND (swc_eng_001896-swc_eng_001896) +MAIN ARTICLE (swc_eng_001897-swc_eng_001897) +FOUND CERTAIN LENGTHS USEFUL FOR FITTING (swc_eng_001898-swc_eng_001898) +TAPE IN THE SAME FORM FACTOR AS THE COMPACT AUDIO (swc_eng_001899-swc_eng_001899) +CENSURE WAS LATER EXPUNGED FROM (swc_eng_001900-swc_eng_001900) +OR DE FACTO EQUALITY (swc_eng_001901-swc_eng_001901) +IS FOUR THOUSAND SIX HUNDRED BY SIXTY FEET (swc_eng_001902-swc_eng_001902) +NINETEEN SEVENTY THREE (swc_eng_001903-swc_eng_001903) +A PLAYER MAY ALSO LOSE BY RUNNING OUT (swc_eng_001904-swc_eng_001904) +PUBLIC HEALTH PROFESSOR GREGORY STOCK POINTS (swc_eng_001905-swc_eng_001905) +BROWN WAS ELECTED TO THE HOUSE OF REPRESENTATIVES FOR THREE NON CONSECUTIVE TERMS (swc_eng_001906-swc_eng_001906) +COEXIST HAPPILY WITH (swc_eng_001907-swc_eng_001907) +A GROUP OF MAMMALS THAT RAISE (swc_eng_001908-swc_eng_001908) +AND THE WORLDS LARGEST (swc_eng_001909-swc_eng_001909) +BREEDING TAKES PLACE BETWEEN APRIL AND JUNE (swc_eng_001910-swc_eng_001910) +AUSTRALIA IS AT THE SOUTHERN END (swc_eng_001911-swc_eng_001911) +TECHNOLOGICAL SINGULARITY IS POSSIBLE (swc_eng_001912-swc_eng_001912) +INCLUDING THE SLEEPY COD (swc_eng_001913-swc_eng_001913) +SEVENTY FOUR HAD A HIGHER EDUCATION QUALIFICATION COMPARED (swc_eng_001914-swc_eng_001914) +THIS OCCURS WHEN THE OPPONENTS KING IS IN CHECK (swc_eng_001915-swc_eng_001915) +CONSERVATION IN AUSTRALIA (swc_eng_001916-swc_eng_001916) +IS THE SALAMANDERFISH (swc_eng_001917-swc_eng_001917) +FIRST SELF DESCRIBED TRANSHUMANISTS MET FORMALLY IN THE EARLY (swc_eng_001918-swc_eng_001918) +RECENT RESEARCH INDICATES THAT FACTORS OTHER THAN PRACTICE (swc_eng_001919-swc_eng_001919) +AND PREVENTION AND TREATMENT OF COMPLICATIONS (swc_eng_001920-swc_eng_001920) +WITH A RAPID ONSET (swc_eng_001921-swc_eng_001921) +UTAH WAR THE FOUNDATION WAS BURIED (swc_eng_001922-swc_eng_001922) +NOWADAYS HOURLY REGIONAL EXPRESS TRAINS BETWEEN BERN AND SPIEZ TO BRIG AND FREIGHT TRAINS CONTINUE TO RUN ON THE MOUNTAIN RAILWAY (swc_eng_001923-swc_eng_001923) +OTHER FAMILIES WITH A POTENTIALLY GONDWANAN ORIGIN INCLUDE THE RETROPINNIDAE (swc_eng_001924-swc_eng_001924) +BY AN ITALIAN DOMINICAN MONK JACOBUS DE CESSOLIS (swc_eng_001925-swc_eng_001925) +COMMAND WAS NAMED AFTER THE (swc_eng_001926-swc_eng_001926) +ARTIFICIAL INTELLIGENCE (swc_eng_001927-swc_eng_001927) +AND IS THE REIGNING (swc_eng_001928-swc_eng_001928) +PER CENT OF THE POPULATION (swc_eng_001929-swc_eng_001929) +CHIEF AREAS OF SHOE POLISH SALES (swc_eng_001930-swc_eng_001930) +IMPOSED BY LAW (swc_eng_001931-swc_eng_001931) +REFERENCES TO THE RULING COALITION GOVERNMENT (swc_eng_001932-swc_eng_001932) +SPECIES OF GLIDING POSSUM (swc_eng_001933-swc_eng_001933) +BASED ON THE PREVIOUS STRATEGY OF PLAY (swc_eng_001934-swc_eng_001934) +AND IDEALISTIC ASPIRATIONS (swc_eng_001935-swc_eng_001935) +PROFESSIONALS AND HOME RECORDING ENTHUSIASTS (swc_eng_001936-swc_eng_001936) +FAMILY ELAPIDAE (swc_eng_001937-swc_eng_001937) +THAN A QUARTER OF PEOPLE WITH A PREVIOUS SAH MAY DEVELOP HYPOPITUITARISM (swc_eng_001938-swc_eng_001938) +DIVIDED INTO THREE FAMILIES THAT (swc_eng_001939-swc_eng_001939) +SHOWED SLIGHT INTEREST IN RELEASING CASSETTES (swc_eng_001940-swc_eng_001940) +FAMILIAR ENOUGH TO HAVE COMMON NAMES (swc_eng_001941-swc_eng_001941) +IN TWO THOUSAND SIX (swc_eng_001942-swc_eng_001942) +SHOESHINE BOYS ARE KNOWN AS BOOT POLISH BOYS (swc_eng_001943-swc_eng_001943) +THE CAUSE IS RUPTURE OF A CEREBRAL ANEURYSM (swc_eng_001944-swc_eng_001944) +MOST OF THE MAJOR U S MUSIC COMPANIES (swc_eng_001945-swc_eng_001945) +ONE STEREO PAIR OR ONE MONOPHONIC TRACK IS PLAYED OR RECORDED WHEN THE TAPE IS MOVING IN ONE DIRECTION AND (swc_eng_001946-swc_eng_001946) +WHERE ITS EARLY FORM IN (swc_eng_001947-swc_eng_001947) +A STRATEGIC PHILOSOPHER 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(swc_eng_001962-swc_eng_001962) +SPECIES INCLUDE FRESHWATER LAMPREYS (swc_eng_001963-swc_eng_001963) +FIRST ANGIOGRAM (swc_eng_001964-swc_eng_001964) +THE FREE ENCYCLOPEDIA AT (swc_eng_001965-swc_eng_001965) +THEREFORE MEDICAL IMAGING IS GENERALLY (swc_eng_001966-swc_eng_001966) +SPECIESIST THE EXCLUSION OF NON HUMAN AND PART HUMAN ANIMALS (swc_eng_001967-swc_eng_001967) +IN PEOPLE WHO HAD PREVIOUSLY SUFFERED A SUBARACHNOID HEMORRHAGE (swc_eng_001968-swc_eng_001968) +CLASSIFIED AS EITHER ENDANGERED OR THREATENED UNDER THE EPBC ACT (swc_eng_001969-swc_eng_001969) +AND ATTORNEY GENERAL PARKER WATKINS HARDIN (swc_eng_001970-swc_eng_001970) +BUT TYPICALLY (swc_eng_001971-swc_eng_001971) +WHICH IN TURN FED THE SIGNAL TO THE HEAD OF THE CASSETTE DECK (swc_eng_001972-swc_eng_001972) +WITHIN THEIR OWN CONVENTIONALLY EXPECTED LIFETIMES (swc_eng_001973-swc_eng_001973) +SUBSTANTIAL STRAIN (swc_eng_001974-swc_eng_001974) +TWENTIETH CENTURY KENTUCKY CONGRESSMAN JOHN (swc_eng_001975-swc_eng_001975) 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(swc_eng_001989-swc_eng_001989) +DRAWBACK OF COILING IS THE POSSIBILITY (swc_eng_001990-swc_eng_001990) +INDICATES A SUBARACHNOID HEMORRHAGE (swc_eng_001991-swc_eng_001991) +DAMAGED PORTION (swc_eng_001992-swc_eng_001992) +ADOPTION OF EUGENIC ENHANCEMENT TECHNOLOGIES (swc_eng_001993-swc_eng_001993) +POLISH ON HIS HORSE AND WAGON (swc_eng_001994-swc_eng_001994) +AND THE NEXT CHAMPION (swc_eng_001995-swc_eng_001995) +BROTHER OF AUTHOR ALDOUS HUXLEY (swc_eng_001996-swc_eng_001996) +WORLD CHAMPION NINETEEN TWENTY ONE (swc_eng_001997-swc_eng_001997) +SUCH AS QUANTUM COMPUTATION AND RANDOMIZED ALGORITHMS (swc_eng_001998-swc_eng_001998) +EIGHTEEN NINETY NINE (swc_eng_001999-swc_eng_001999) +WAS SHOWN BY LADNER THAT IF P ≠ N P THEN THERE EXIST PROBLEMS IN (swc_eng_002000-swc_eng_002000) +THE COMPACT DISC GREW (swc_eng_002001-swc_eng_002001) +GREY GOO SCENARIO (swc_eng_002002-swc_eng_002002) +WAS RENDERED AS AJEDREZ (swc_eng_002003-swc_eng_002003) +SAH OR TO ANOTHER CAUSE (swc_eng_002004-swc_eng_002004) +CONSTITUENCY OF FAVERSHAM (swc_eng_002005-swc_eng_002005) +THE FOURTH AND FIFTH DAYS PASSED WITHOUT ANY DEVELOPMENTS (voxforge_eng_000874-voxforge_eng_000874) +THEY KNOW THE REPORT (voxforge_eng_000875-voxforge_eng_000875) +SUCH THINGS HAD OCCURRED BEFORE HE TOLD PHILIP (voxforge_eng_000876-voxforge_eng_000876) +THEY ONLY HAD A LITTLE THIRTY THOUSAND DOLLAR FIRE (voxforge_eng_000877-voxforge_eng_000877) +I AM GOING TO GET IT OUT (voxforge_eng_000878-voxforge_eng_000878) +OUTWARDLY HE MAINTAINED A CALM AND SMILING ASPECT (voxforge_eng_000879-voxforge_eng_000879) +JOAN LOOKED TRIUMPHANTLY AT SHELDON WHO BOWED (voxforge_eng_000880-voxforge_eng_000880) +COME ON DEL MAR CHALLENGED (voxforge_eng_000883-voxforge_eng_000883) +IT WAS BEATING AND WAITING IN THE AMBUSH OF THOSE BLACK PITS (voxforge_eng_000884-voxforge_eng_000884) +LET THEM GO OUT AND EAT WITH MY BOYS (voxforge_eng_000885-voxforge_eng_000885) +HE WENT DOWN IN MIDSTREAM SEARCHING THE SHADOWS OF BOTH 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(voxforge_eng_000897-voxforge_eng_000897) +HOW DO YOU WANT TO GET AWAY WITH THIS (voxforge_eng_000898-voxforge_eng_000898) +WILL WE EVER FORGET IT (voxforge_eng_000899-voxforge_eng_000899) +FROM MY EARLIEST RECOLLECTION MY SLEEP WAS A PERIOD OF TERROR (voxforge_eng_000900-voxforge_eng_000900) +WHY DOGGONE YOU ALL SHAKE AGAIN (voxforge_eng_000901-voxforge_eng_000901) +IT IS THE NEAREST REFUGE (voxforge_eng_000902-voxforge_eng_000902) +HIS SLIM HANDS GRIPPED THE EDGES OF THE TABLE (voxforge_eng_000903-voxforge_eng_000903) +WHITE LEGHORNS SAID MRS MORTIMER (voxforge_eng_000904-voxforge_eng_000904) +IT TOOK HIM HALF AN HOUR TO REACH THE EDGE OF IT (voxforge_eng_000905-voxforge_eng_000905) +MARTHA WHERE DO WE STAND ON THE CONTRACTUAL ISSUES (voxforge_eng_000906-voxforge_eng_000906) +AS TO BE UNDISTINGUISHABLE FROM THE VAST WHITE PLAINS AROUND (voxforge_eng_000907-voxforge_eng_000907) +HE WOULD DESTROY ALL THINGS THAT ARE FIXED (voxforge_eng_000908-voxforge_eng_000908) +THE RUSSIAN MUSIC PLAYER THE COUNT WAS HER OBEDIENT SLAVE (voxforge_eng_000909-voxforge_eng_000909) +TO HIS SURPRISE HER ANSWER WAS FLAT AND UNCOMPROMISING (voxforge_eng_000910-voxforge_eng_000910) +THIS SHOULD BE INTERESTING (voxforge_eng_000911-voxforge_eng_000911) +I AM AFRAID I DONT HAVE MUCH TIME (voxforge_eng_000912-voxforge_eng_000912) +CHRISTMAS IS AN EASY PROBLEM COMPARED WITH A POLYNESIAN GIVING FEAST (voxforge_eng_000913-voxforge_eng_000913) +THE PLANTERS ARE ALREADY CONSIDERING THE MATTER (voxforge_eng_000914-voxforge_eng_000914) +JOAN CRIED WITH SHINING EYES (voxforge_eng_000915-voxforge_eng_000915) +WHOEVER LIVED ON THE RANCH DID THAT (voxforge_eng_000916-voxforge_eng_000916) +WE LEAVE THE EVENTUALITY TO TIME AND LAW (voxforge_eng_000917-voxforge_eng_000917) +AT THE SAME TIME SPEARS AND ARROWS BEGAN TO FALL AMONG THE INVADERS (voxforge_eng_000918-voxforge_eng_000918) +IT IS MERELY THE SIMPLE SUPERLATIVE (voxforge_eng_000920-voxforge_eng_000920) +INSTEAD HE ARRIVED ON THE NIGHT OF THE SECOND DAY (voxforge_eng_000921-voxforge_eng_000921) +IN HIS ANXIETY AND SOLICITUDE AND LOVE THEY DID NOT COUNT (voxforge_eng_000922-voxforge_eng_000922) +GOD BLESS I HOPE ILL GO ON SEEING THEM FOREVER (voxforge_eng_000923-voxforge_eng_000923) +YOU WERE ENGAGED (voxforge_eng_000924-voxforge_eng_000924) +THE LACE WAS OF A DELICATE IVORY COLOR FAINTLY TINTED WITH YELLOW (voxforge_eng_000925-voxforge_eng_000925) +IT WAS THE SAME WAY WITH OUR REVOLVERS AND RIFLES (voxforge_eng_000927-voxforge_eng_000927) +THE KING HAD PROMISED TO ENQUIRE INTO THE MATTER (voxforge_eng_000928-voxforge_eng_000928) +DOES THAT LOOK GOOD (voxforge_eng_000929-voxforge_eng_000929) +FOR THE FIRST TIME IN HIS LIFE HE WAS YEARNING FOR A SCRAP (voxforge_eng_000930-voxforge_eng_000930) +I DEFY ANY MAN TO GET A SOLOMON ISLAND SORE IN CALIFORNIA (voxforge_eng_000931-voxforge_eng_000931) +HER EYES SMILED TRUTH AT HIM AS HE CAME UP THE BANK (voxforge_eng_000932-voxforge_eng_000932) +ANYWAY NO ONE SAW HER LIKE THAT 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WAS JEANNE SINGING SOFTLY OVER BEYOND THE ROCKS (voxforge_eng_000947-voxforge_eng_000947) +A FLYING ARROW PASSED BETWEEN US (voxforge_eng_000948-voxforge_eng_000948) +HATRED AND MURDER AND LUST FOR REVENGE THEY POSSESSED TO OVERFLOWING (voxforge_eng_000949-voxforge_eng_000949) +THAT YOU COULD HEAR ALL UP AND DOWN THE LIMPOPO (voxforge_eng_000950-voxforge_eng_000950) +IT WAS MY IDEA TO A TEE (voxforge_eng_000951-voxforge_eng_000951) +SHE DOESNT WANT TO WIN (voxforge_eng_000952-voxforge_eng_000952) +SHE THINKS IT IS BECAUSE HE WANTS SOMETHING ELSE (voxforge_eng_000953-voxforge_eng_000953) +HE PULLED AND THE LOG CRASHED DOWN TO BREAK HIS BACK (voxforge_eng_000954-voxforge_eng_000954) +THAT THE SO CALLED FORCES AT WORK IN LIGHT HEAT ELECTRICITY AND MAGNETISM IN (voxforge_eng_000955-voxforge_eng_000955) +HE TURNED SHARPLY AND FACED GREGSON ACROSS THE TABLE (voxforge_eng_000956-voxforge_eng_000956) +ALSO I WANT INFORMATION (voxforge_eng_000957-voxforge_eng_000957) +THE SIXTH DAY HE SPENT IN THE CABIN WITH GREGSON (voxforge_eng_000958-voxforge_eng_000958) +ON THIS HYPOTHESIS THE HAMMERING OF THE ULTRA MUNDANE CORPUSCLES ON THE BOB CONFERS ITS KINETIC ENERGY ON THE ONE HAND (voxforge_eng_000959-voxforge_eng_000959) +NOW A FERNY WILLOWY STREAM AND EVER AND ANON YOU EMERGE FROM ALL THE GROVES AND FLOWERS (voxforge_eng_000960-voxforge_eng_000960) +WITHOUT IT THE MOST DENSELY POPULATED REGIONS OF MODERN EUROPE AND AMERICA (voxforge_eng_000961-voxforge_eng_000961) +TOM SPINK HAS A HARPOON (voxforge_eng_000962-voxforge_eng_000962) +HE WANTED TO GIVE THE FINISH TO THIS FOE ALREADY SO FAR GONE (voxforge_eng_000963-voxforge_eng_000963) +LIKE A FLASH HE LAUNCHED HIMSELF INTO THE FEATHERED MASS OF THE OWL (voxforge_eng_000964-voxforge_eng_000964) +IT CONTAINS A TOTAL OF TWENTY ENTRIES (voxforge_eng_000965-voxforge_eng_000965) +IVE FELT MORE COMFORTABLE (voxforge_eng_000966-voxforge_eng_000966) +DID I POSSESS TOO MUCH VITALITY (voxforge_eng_000967-voxforge_eng_000967) +THE WOLF DOG THRUST HIS GAUNT MUZZLE TOWARD HIM (voxforge_eng_000968-voxforge_eng_000968) +THE GABRIEL VOICE OF THE SAMURAI RANG OUT (voxforge_eng_000971-voxforge_eng_000971) +IT WAS OUR RIVER EMERGING LIKE OURSELVES FROM THE GREAT SWAMP (voxforge_eng_000972-voxforge_eng_000972) +SAID THE MOLE PULLING HIMSELF TOGETHER WITH AN EFFORT YOU MUST THINK ME VERY RUDE (voxforge_eng_000973-voxforge_eng_000973) +IN WHAT BUCOLIC SCHOOL OF FENCE HE HAD BEEN TAUGHT WAS BEYOND IMAGINING (voxforge_eng_000974-voxforge_eng_000974) +HAD NOT ENABLED INVESTIGATORS TO OBTAIN AT COMPARATIVELY LITTLE COST (voxforge_eng_000975-voxforge_eng_000975) +A TRICKLE OF FRESH BLOOD RAN OVER HIS FACE (voxforge_eng_000976-voxforge_eng_000976) +IT WAS A CURIOUS COINCIDENCE (voxforge_eng_000977-voxforge_eng_000977) +IT IS THE FIRE PARTLY SHE SAID (voxforge_eng_000978-voxforge_eng_000978) +THEY JUST LAY OFF IN THE BUSH AND PLUGGED AWAY (voxforge_eng_000979-voxforge_eng_000979) +I KNOW THAT YOU ARE IN CHARGE THERE AND JEANNE KNOWS (voxforge_eng_000980-voxforge_eng_000980) +FOR A TIME THE EXCITING THRILL OF HIS ADVENTURE WAS GONE (voxforge_eng_000981-voxforge_eng_000981) +SUDDENLY HIS FINGERS CLOSED TIGHTLY OVER THE HANDKERCHIEF (voxforge_eng_000982-voxforge_eng_000982) +DEAR SIR YOUR SECOND VICTIM HAS FALLEN ON SCHEDULE TIME (voxforge_eng_000983-voxforge_eng_000983) +HE CAN CARE FOR HIMSELF (voxforge_eng_000984-voxforge_eng_000984) +EACH INSULT ADDED TO THE VALUE OF THE CLAIM (voxforge_eng_000985-voxforge_eng_000985) +THOUGH IT MAY BE TRANSFORMED INTO ANY ONE OF THE FORMS OF WHICH ENERGY IS SUSCEPTIBLE (voxforge_eng_000986-voxforge_eng_000986) +MERCEDES SCREAMED CRIED LAUGHED AND MANIFESTED THE CHAOTIC ABANDONMENT OF HYSTERIA (voxforge_eng_000987-voxforge_eng_000987) +I WANT TO KNOW HOW ALL THIS IS POSSIBLE (voxforge_eng_000988-voxforge_eng_000988) +PRESENTING A SIMPLE AND INSTRUCTIVE ILLUSTRATION OF THE STRUGGLE FOR LIFE AMONG THE RIVAL SPECIES (voxforge_eng_000989-voxforge_eng_000989) +HELL NEVER DO A TAP OF WORK THE WHOLE VOYAGE (voxforge_eng_000990-voxforge_eng_000990) +I HAVE HUNTED ALONG THIS RIDGE REPLIED PHILIP (voxforge_eng_000991-voxforge_eng_000991) +LORD BUT IM GLAD TO SEE YOU AGAIN PHIL (voxforge_eng_000992-voxforge_eng_000992) +HOW VALIANTLY I WENT AT IT THAT FIRST DAY (voxforge_eng_000993-voxforge_eng_000993) +THEY ARE NOT REGULAR OYSTER PIRATES NICHOLAS CONTINUED (voxforge_eng_000994-voxforge_eng_000994) +THEY MUST BE HURTING FOR BUSINESS BUT I THOUGHT YOU MIGHT WANT TO TAKE A LOOK AT THEIR SITE (voxforge_eng_000995-voxforge_eng_000995) +THERE WAS NO CHANCE TO FIRE WITHOUT HITTING HIM (voxforge_eng_000996-voxforge_eng_000996) +AS FOR HIMSELF WERENT THE STREET RAILWAY EARNINGS INCREASING STEADILY (voxforge_eng_000997-voxforge_eng_000997) +DUNHAM CAN YOUR BOY GO ALONG WITH JESSE (voxforge_eng_000998-voxforge_eng_000998) +GOODBYE PIERRE HE SHOUTED (voxforge_eng_000999-voxforge_eng_000999) +BUT SUCH DIVERGENCE OF OPINION WOULD CONSTITUTE NO MENACE TO SOCIETY (voxforge_eng_001000-voxforge_eng_001000) +THERE WAS ONE CHANCE AND ONLY ONE OF SAVING JEANNE (voxforge_eng_001001-voxforge_eng_001001) +I CANNOT FOLLOW YOU SHE SAID (voxforge_eng_001002-voxforge_eng_001002) +ON THE FAR CORNER OF THE COMPOUND FENCE A HAWK BROODED (voxforge_eng_001003-voxforge_eng_001003) +THEN AGAIN TUDOR HAD SUCH AN IRRITATING WAY ABOUT HIM (voxforge_eng_001004-voxforge_eng_001004) +WE ALL KNOW OMAN AS A SUCCESSFUL STABLE COUNTRY A ROLE MODEL FOR THE WHOLE REGION (voxpopuli_eng_000494-voxpopuli_eng_000494) +THEREFORE ITS HIGH TIME THAT YOU COME FORWARD WITH A PROPOSAL FOR REVIEW WITH AN OPERATIONAL SEPARATION OF THE AUDIT AND NON AUDIT SERVICES UNDER A DIRECT EU SUPERVISION (voxpopuli_eng_000495-voxpopuli_eng_000495) +IT IS CLEAR THAT WE HAVE NO TIME TO WASTE THE NEW RESULTS OF THE IPCC REGARDING THE SCIENTIFIC BASIS OF CLIMATE CHANGE LEAVE NO ROOM FOR HESITATION (voxpopuli_eng_000496-voxpopuli_eng_000496) +5 SO IN THE CONTAINERS WHICH ARE NEVER EVEN TOUCHED COME SLAVES COUNTERFEIT GOODS DRUGS ETC (voxpopuli_eng_000497-voxpopuli_eng_000497) +I HOPE THAT THE COMMISSIONS MOBILITY INITIATIVES WILL NOT CREATE THE NEXT PROBLEM BUT WILL BE AN ANSWER FOR EXISTING CHALLENGES OF THE ROAD TRANSPORT SECTOR (voxpopuli_eng_000498-voxpopuli_eng_000498) +IN THE US IT WAS A DECISION TAKEN ONLY BY ONE PERSON THE FORMER PRESIDENT OF THE UNITED STATES AGAINST THE ARTICULATED DEMOCRATIC MAJORITY OF THE US CONGRESS BY ALL OF ITS REPUBLICAN AND SOME OF ITS DEMOCRAT MEMBERS IT WAS AN AGREEMENT WITHOUT ANY BINDING OBLIGATIONS AS THE LEADERS OF IRAN VERY OPENLY AND PRECISELY MADE CLEAR ON THE VERY DAY THIS SO CALLED DEAL WAS PUBLISHED (voxpopuli_eng_000499-voxpopuli_eng_000499) +FREE SPEECH IS ESSENTIALLY ACCEPTING THAT PEOPLE ARE FREE TO SAY THINGS WE DO LIKE NOT MERELY FREE TO SAY THINGS WE DO LIKE (voxpopuli_eng_000500-voxpopuli_eng_000500) +LET US LEARN FROM THIS (voxpopuli_eng_000501-voxpopuli_eng_000501) +WE THINK THAT THE ENVIRONMENTAL EFFECT OF PRODUCTS MUST BE A VERY IMPORTANT ISSUE IN THE EU AND THE WHOLE IDEA OF AN ECOLABEL GIVES A VERY USEFUL ORIENTATION FOR CONSUMERS OF COURSE THE ECOLABEL SHOULD BE GIVEN TO THE MOST ENVIRONMENTALLY FRIENDLY PRODUCTS AND THE INFORMATION SHOULD BE CLEAR AND CORRECT (voxpopuli_eng_000502-voxpopuli_eng_000502) +HOWEVER THE CURRENT REGIME NEEDS TO BE BETTER TAILORED TO THE DIGITAL ENVIRONMENT IN ORDER TO ENSURE FAIR REMUNERATION TO CREATORS AND TO CONFORM TO CONSUMER EXPECTATIONS (voxpopuli_eng_000503-voxpopuli_eng_000503) +IT CALLS UPON THE COMMISSION AND MEMBER STATES TO ENHANCE THEIR SUPPORT FOR RECONCILIATION TO SECURE PEACE AND STABILITY AND IRELAND I WOULD THEREFORE URGE YOU COLLEAGUES TO PLEASE SUPPORT THIS AMENDMENT (voxpopuli_eng_000504-voxpopuli_eng_000504) +STRATEGIC CHOICES ABOUT WHERE TO INVEST MUST BE MADE NOW TAKING INTO ACCOUNT THE NEED TO PHASE OUT FOSSIL FUEL SUBSIDIES BUT TAKE GAS AS A FOSSIL FUEL IT CAN BE A HELPFUL BRIDGING TRANSITIONARY MEDIUM TO BE USED IN MANY MEMBER STATES IF WE WANT TO ACHIEVE OUR AMBITIOUS CLIMATE TARGETS (voxpopuli_eng_000505-voxpopuli_eng_000505) +MIDDLE EAST WE ARE POSSIBLY AT A THRESHOLD WE CAN CHOOSE TO PURSUE THE SAME POLICIES IN THE SAME MANNER KNOWING THAT THEY WILL LEAD TO THE SAME RESULTS THE RESULTS THAT (voxpopuli_eng_000506-voxpopuli_eng_000506) +BUT THERE IS AN OPTION (voxpopuli_eng_000507-voxpopuli_eng_000507) +THIS WE ALSO NEED A CHANGE IN OUR IDEOLOGY (voxpopuli_eng_000508-voxpopuli_eng_000508) +A LARGE PART OF THE REASON IS OF COURSE ILLEGAL FISHING MORE OFTEN THAN NOT BY VESSELS WHICH ARE REGISTERED TO COUNTRIES WHICH LACK THE WILL OR THE RESOURCES TO ENFORCE INTERNATIONAL AGREEMENTS NO AMOUNT OF TRACEABILITY MEASURES OR EXTRA PAPERWORK WILL ADDRESS THE PROBLEM OF REDUCING (voxpopuli_eng_000509-voxpopuli_eng_000509) +THE COMPROMISE ALSO INCLUDES CLEAR RULES TO DEFINE WHICH MEMBER STATE HAS JURISDICTION AND THE COOPERATION BETWEEN MEMBER STATES CONCERNED IN CROSS BORDER CASES AS WELL AS THE NEED TO INVOLVE EUROJUST THANK YOU FOR YOUR WORK AND PLEASE DO SUPPORT THIS DIRECTIVE (voxpopuli_eng_000510-voxpopuli_eng_000510) +THE GREENS WOULD HAVE US BELIEVE THAT THESE ARE BAD BEES CRIMINAL BEES DELIBERATELY CONTAMINATING HONEY WITH A DANGEROUS INGREDIENT BUT IN FACT THEY ARE DOING WHAT HONEY BEES HAVE ALWAYS DONE WHICH IS TO CARRY POLLEN BACK TO THEIR HIVES TO FEED THEIR YOUNG (voxpopuli_eng_000511-voxpopuli_eng_000511) +BUT IT WAS THE COUNTRY ITSELF BEING MORE CAPABLE (voxpopuli_eng_000512-voxpopuli_eng_000512) +INTO THE PORTFOLIO OF THE NEW COMMISSIONER DEALING WITH FUNDAMENTAL RIGHTS (voxpopuli_eng_000513-voxpopuli_eng_000513) +THE MESSAGE IS THAT THE EU DOES NOT HAVE ANY NEW SOLUTIONS (voxpopuli_eng_000514-voxpopuli_eng_000514) +ARE YOU WILLING TO ACT IN FAVOUR OF THE SOCIAL DIMENSION TO BE INCLUDED IN THE EU COMPETENCIES AS PROPOSED (voxpopuli_eng_000515-voxpopuli_eng_000515) +THE NEXT STEP ON SPECTRUM POLICY IS BEING TAKEN WITH THE REFORM OF OUR TELECOM FRAMEWORK (voxpopuli_eng_000516-voxpopuli_eng_000516) +I BELIEVE HIS REMARKS WERE EXPLICITLY RACIST AND XENOPHOBIC AND PROMOTED RACIAL INTOLERANCE IN A WAY THAT IS NOT ACCEPTABLE OR ALLOWED IN THE CONSTITUTION OF THIS HOUSE (voxpopuli_eng_000517-voxpopuli_eng_000517) +REAL LIFE EXAMPLES SHOW THAT SOLVING ISSUES RELATED TO EDUCATION FUELS STRONG COMMUNITY DEVELOPMENT (voxpopuli_eng_000518-voxpopuli_eng_000518) +SO I HOPE THIS WILL HAPPEN FOR RUSSIA AS WELL AND THAT RUSSIA CAN ALSO ENVISAGE AN EXTREME SUCCESS STORY AFTER THE SIGNIFICANT DATE IN AUGUST THIS YEAR (voxpopuli_eng_000519-voxpopuli_eng_000519) +SHE ACCEPTED THE FACT THAT CITIZENSHIP IS SUBJECT TO NATIONAL JURISDICTION BUT SHE ALSO SAID THAT ACCORDING TO THE MAASTRICHT TREATY AND SHE IS RIGHT THERE HAS TO BE A DIRECT LINK (voxpopuli_eng_000520-voxpopuli_eng_000520) +THE EU FAILED ESPECIALLY IN DEMONSTRATING A UNIFIED AND EFFICIENT APPROACH TO CLIMATE CHANGE TREATMENT AS WELL AS IN STRENGTHENING ITS LEADING POLITICAL POSITION IN THIS AGENDA I CONSIDER THEREFORE TAKING THIS RESOLUTION AN ACT OF UTMOST IMPORTANCE (voxpopuli_eng_000521-voxpopuli_eng_000521) +THE UNITED STATES OF EUROPE WILL BE A FACT WITH SWEDEN AS A PROVINCE (voxpopuli_eng_000522-voxpopuli_eng_000522) +IT MUST BE THE CAPITAL OF BOTH STATES AND WE MUST RECOGNISE PALESTINE AS A STATE AS PROVIDED FOR IN THE OSLO AGREEMENTS (voxpopuli_eng_000523-voxpopuli_eng_000523) +UKRAINE IS FACED WITH ONE OF THE CRUCIAL CHALLENGES IN ITS HISTORY IT WOULD BE FUNDAMENTALLY WRONG TO PRESS THE NATION NOW WITH ALL TYPES OF RESTRICTIONS POPULARLY CALLED AUSTERITY POLICY (voxpopuli_eng_000524-voxpopuli_eng_000524) +MORE RULES AND REGULATION WILL NOT IMPROVE THE SITUATION (voxpopuli_eng_000525-voxpopuli_eng_000525) +AT LEAST WE WOULD LIKE TO KNOW THE SOURCE OF THE MONEY AND THE POSSIBLE MOTIVES (voxpopuli_eng_000526-voxpopuli_eng_000526) +TO HAVE THOSE EUROPEAN WORLD LANGUAGES IN TODAYS GLOBALISED WORLD IN TODAYS GLOBALISED ECONOMY IN THIS GLOBAL VILLAGE WHICH IS CULTURAL ECONOMIC SOCIAL AND POLITICAL IS A MOST VALUABLE ASSET FOR THE ENTIRE EU WHICH WE MUST TAKE FULL ACCOUNT OF AND (voxpopuli_eng_000527-voxpopuli_eng_000527) +WE HAVE TO REPEAT THAT ODA CANNOT BE USED TO FINANCE SECURITY EXPENSES BORDER CONTROL OR MILITARY SUPPORT (voxpopuli_eng_000528-voxpopuli_eng_000528) +IF ANYTHING THE SCIENTIFIC REPORTS ARE BECOMING MORE URGENT MORE ALARMING AND MORE SHOCKING (voxpopuli_eng_000529-voxpopuli_eng_000529) +FINALLY WHEN IT COMES TO INNOVATIVE FINANCIAL INSTRUMENTS WE NEED THEM BOTH FOR OURSELVES TO SUPPORT OUR ECONOMIES BUT ALSO TO SUPPORT THOSE WHO ARE IN NEED (voxpopuli_eng_000530-voxpopuli_eng_000530) +THAT GIVES US A UNIQUE TOOL IN PEACEMAKING (voxpopuli_eng_000531-voxpopuli_eng_000531) +PAPER A VERY WEAK PROPOSAL (voxpopuli_eng_000532-voxpopuli_eng_000532) +RUSSIA HAS ALWAYS BEEN A VERY PROUD NATION WITH A RICH CULTURE WITH INVENTIONS AND ESPRIT (voxpopuli_eng_000533-voxpopuli_eng_000533) +FAIR TAXATION EVEN A MODICUM OF TAXATION IN SOME CASES MIGHT JUST HELP US TO DO WHAT I HAVE ALREADY SUGGESTED AND WHO KNOWS MAKE THE CASE FOR THE RETROSPECTIVE BANK RECAPITALISATION THAT WE NEVER SAW (voxpopuli_eng_000534-voxpopuli_eng_000534) +THE EUROPEAN ASYLUM SUPPORT OFFICE MOREOVER HAS AMONG ITS TASKS TO PROMOTE FACILITATE AND COORDINATE EXCHANGES OF INFORMATION AND OTHER ACTIVITIES RELATED TO RELOCATION WITHIN THE UNION (voxpopuli_eng_000535-voxpopuli_eng_000535) +THE CONCLUSION OF THE FRAMEWORK AGREEMENT PROVIDES A LEGALLY BINDING INSTRUMENT TO UPGRADE AND STRENGTHEN EU AUSTRALIA BILATERAL RELATIONS AND TO INCREASE COOPERATION (voxpopuli_eng_000536-voxpopuli_eng_000536) +THEREFORE WE ARE ASKING THE COUNCIL AND THE COMMISSION TO PRESENT A TRANSPARENT AND COMPLETE ASSESSMENT OF THE IMPACT OF THE CRISIS (voxpopuli_eng_000537-voxpopuli_eng_000537) +IN OTHER WORDS THE OBJECTION IS NOT WHETHER MONEY IS PAID OR NOT THE OBJECTION IS WHETHER THERE IS A DIRECT LINK OR NOT (voxpopuli_eng_000538-voxpopuli_eng_000538) +IT DISTINGUISHES THE TWO MAIN DOSSIERS HUMAN RIGHTS ABUSES BY THE CURRENT GOVERNMENT AND THE IRANIAN NUCLEAR PROGRAMME (voxpopuli_eng_000539-voxpopuli_eng_000539) +MR PRESIDENT SEXUAL HARASSMENT IS A FORM OF VIOLENCE AND IT IS THE MOST EXTREME FORM OF GENDER—BASED DISCRIMINATION (voxpopuli_eng_000540-voxpopuli_eng_000540) +WE CAN LOOK TO SOME NON EU MEMBERS FOR GOOD EXAMPLES AS REGARDS TECHNOLOGIES (voxpopuli_eng_000541-voxpopuli_eng_000541) +INVOLVED FOR THEIR POSITIVE AND CONSTRUCTIVE APPROACH (voxpopuli_eng_000542-voxpopuli_eng_000542) +SO I HOPE THAT THIS WILL BE COMPLETED IN THE FORESEEABLE FUTURE WHICH MEANS MAYBE TWO OR THREE MONTHS (voxpopuli_eng_000543-voxpopuli_eng_000543) +FURTHER ENCOURAGE THE UNS EFFORTS TO BRING ABOUT PEACE IN AFGHANISTAN AND TO OVERCOME THE FRAGILE SECURITY ENVIRONMENT IN THE COUNTRY (voxpopuli_eng_000544-voxpopuli_eng_000544) +WE UNDERSTAND THAT SOME PEOPLE ARE ANGRY (voxpopuli_eng_000545-voxpopuli_eng_000545) +WE WANT TO BE MORE RESPONSIBLE (voxpopuli_eng_000546-voxpopuli_eng_000546) +WE MUST RECTIFY THIS SITUATION AND WE ASK THE COMMISSION TO CONSIDER THE MOST ADEQUATE COMPENSATION MEASURES FOR OUR PASSENGERS (voxpopuli_eng_000547-voxpopuli_eng_000547) +THE COMMISSION INVITES PARLIAMENT IN THE UPCOMING REVISION TO OPEN ITS POSITION ON THIS MATTER WHICH REALLY CONCERNS ACCESS TO JUSTICE IN EUROPE AND THE ENFORCEMENT OF RIGHTS GRANTED BY EUROPEAN UNION LAW (voxpopuli_eng_000548-voxpopuli_eng_000548) +I WELCOME VERY MUCH THE RESUMPTION OF TALKS BETWEEN THE ISRAELIS AND THE PALESTINIANS AND SINCERELY HOPE THAT THEY WILL SUCCEED (voxpopuli_eng_000549-voxpopuli_eng_000549) +WE HAVE AN ACCUMULATION OF PROBLEMS RESULTING FROM ARTIFICIAL UNDER BUDGETING IN PREVIOUS YEARS (voxpopuli_eng_000550-voxpopuli_eng_000550) +LET US NOT BE THE MAN OF YESTERDAY LET US BE TODAYS INSTITUTION (voxpopuli_eng_000551-voxpopuli_eng_000551) +I WOULD URGE YOU TO BECOME AMBASSADORS OF THE YEAR BY MAKING ITS IDEAS AND ACTIVITIES WIDELY KNOWN AMONGST EUROPEAN CITIZENS AND PARTICIPATING IN EVENTS BE IT AT EUROPEAN NATIONAL OR LOCAL LEVEL (voxpopuli_eng_000552-voxpopuli_eng_000552) +CERTAINLY SUCH IMPACT ASSESSMENT COULD PRE EMPT CERTAIN PROBLEMS SUCH AS THOSE POSED BY THE ELECTRONIC IDENTIFICATION OF SHEEP IN SCOTLAND (voxpopuli_eng_000553-voxpopuli_eng_000553) +THE COURT IS CONTENT TO SEE THAT ITS WORK HAS INFORMED THE DISCHARGE PROCESS AND HAS CONTRIBUTED TO PROPOSALS FOR IMPROVING THE FINANCIAL MANAGEMENT OF EU SPENDING AND BETTER TARGETING OF EU FUNDS (voxpopuli_eng_000554-voxpopuli_eng_000554) +REGULATORY CLARITY AND CERTAINTY IS NEEDED FOR THE PUBLIC SECTOR AND FOR INDUSTRY (voxpopuli_eng_000555-voxpopuli_eng_000555) +IS IT REALLY NOT POSSIBLE TO USE OTHER HOUSING FACILITIES WITH APPROPRIATE RECEPTION CONDITIONS IN THE MEANTIME (voxpopuli_eng_000556-voxpopuli_eng_000556) +WILL YOU TAKE ACTION AT LAST IF NOT THEN WHEN (voxpopuli_eng_000557-voxpopuli_eng_000557) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/result.txt b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..78ec62fec85bffd3d89470ebc966a14b649d479a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/result.txt @@ -0,0 +1,12689 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,--------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/hyp.trn| +|--------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000254 | 1 17 | 29.4 64.7 5.9 0.0 70.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000255 | 1 9 | 44.4 55.6 0.0 33.3 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000256 | 1 9 | 22.2 66.7 11.1 0.0 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000257 | 1 9 | 44.4 55.6 0.0 0.0 55.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000258 | 1 9 | 33.3 44.4 22.2 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000259 | 1 13 | 23.1 76.9 0.0 7.7 84.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000260 | 1 14 | 28.6 64.3 7.1 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000261 | 1 13 | 23.1 76.9 0.0 15.4 92.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000262 | 1 8 | 37.5 62.5 0.0 12.5 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000263 | 1 13 | 53.8 38.5 7.7 0.0 46.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000264 | 1 20 | 30.0 65.0 5.0 0.0 70.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000265 | 1 9 | 55.6 33.3 11.1 0.0 44.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000266 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000267 | 1 5 | 40.0 60.0 0.0 20.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000268 | 1 9 | 55.6 33.3 11.1 11.1 55.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000269 | 1 7 | 14.3 85.7 0.0 14.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000270 | 1 13 | 30.8 61.5 7.7 7.7 76.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000271 | 1 17 | 47.1 52.9 0.0 0.0 52.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000272 | 1 20 | 55.0 40.0 5.0 0.0 45.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000273 | 1 18 | 38.9 55.6 5.6 16.7 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000274 | 1 5 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000275 | 1 14 | 21.4 71.4 7.1 7.1 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000276 | 1 18 | 50.0 44.4 5.6 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000277 | 1 11 | 45.5 54.5 0.0 9.1 63.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000278 | 1 8 | 50.0 50.0 0.0 12.5 62.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000279 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000280 | 1 11 | 54.5 45.5 0.0 0.0 45.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000281 | 1 8 | 50.0 50.0 0.0 25.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000282 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000283 | 1 14 | 42.9 57.1 0.0 21.4 78.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000284 | 1 20 | 45.0 45.0 10.0 5.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000285 | 1 19 | 31.6 63.2 5.3 0.0 68.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000286 | 1 11 | 54.5 45.5 0.0 9.1 54.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000287 | 1 9 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000288 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000289 | 1 9 | 22.2 77.8 0.0 0.0 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000290 | 1 10 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000291 | 1 11 | 27.3 63.6 9.1 0.0 72.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000292 | 1 9 | 11.1 88.9 0.0 0.0 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000293 | 1 16 | 31.3 68.8 0.0 0.0 68.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000294 | 1 10 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000295 | 1 11 | 18.2 81.8 0.0 0.0 81.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000296 | 1 14 | 28.6 71.4 0.0 7.1 78.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000297 | 1 10 | 30.0 70.0 0.0 20.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000298 | 1 13 | 46.2 46.2 7.7 0.0 53.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000299 | 1 13 | 30.8 61.5 7.7 7.7 76.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000300 | 1 13 | 23.1 76.9 0.0 7.7 84.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000301 | 1 15 | 26.7 60.0 13.3 0.0 73.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000302 | 1 10 | 20.0 80.0 0.0 10.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000303 | 1 11 | 18.2 81.8 0.0 9.1 90.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000304 | 1 10 | 40.0 50.0 10.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000305 | 1 15 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000306 | 1 11 | 54.5 45.5 0.0 9.1 54.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000307 | 1 17 | 41.2 58.8 0.0 11.8 70.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000308 | 1 12 | 25.0 66.7 8.3 8.3 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000309 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000310 | 1 6 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000311 | 1 18 | 33.3 66.7 0.0 5.6 72.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000312 | 1 16 | 43.8 56.3 0.0 0.0 56.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000313 | 1 12 | 50.0 41.7 8.3 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000314 | 1 7 | 42.9 57.1 0.0 0.0 57.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000315 | 1 9 | 11.1 77.8 11.1 0.0 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000316 | 1 16 | 37.5 62.5 0.0 12.5 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000317 | 1 15 | 26.7 66.7 6.7 0.0 73.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000318 | 1 18 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000319 | 1 15 | 53.3 33.3 13.3 0.0 46.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000320 | 1 19 | 52.6 47.4 0.0 10.5 57.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000321 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000322 | 1 10 | 10.0 90.0 0.0 10.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000323 | 1 13 | 7.7 76.9 15.4 0.0 92.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000324 | 1 18 | 33.3 55.6 11.1 5.6 72.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000325 | 1 8 | 25.0 62.5 12.5 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000326 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000327 | 1 15 | 40.0 46.7 13.3 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000328 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000329 | 1 19 | 26.3 57.9 15.8 0.0 73.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000330 | 1 10 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000331 | 1 5 | 40.0 60.0 0.0 20.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000332 | 1 18 | 33.3 61.1 5.6 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000333 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000334 | 1 18 | 27.8 72.2 0.0 0.0 72.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000335 | 1 14 | 35.7 57.1 7.1 14.3 78.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000336 | 1 15 | 20.0 73.3 6.7 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000337 | 1 20 | 35.0 60.0 5.0 0.0 65.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000338 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000339 | 1 11 | 36.4 63.6 0.0 0.0 63.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000340 | 1 11 | 27.3 72.7 0.0 0.0 72.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000341 | 1 12 | 25.0 66.7 8.3 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000342 | 1 14 | 42.9 57.1 0.0 0.0 57.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000343 | 1 18 | 38.9 61.1 0.0 11.1 72.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000344 | 1 18 | 22.2 72.2 5.6 0.0 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000345 | 1 11 | 36.4 63.6 0.0 18.2 81.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000346 | 1 15 | 26.7 66.7 6.7 6.7 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000347 | 1 15 | 26.7 60.0 13.3 0.0 73.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000348 | 1 10 | 20.0 80.0 0.0 10.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000349 | 1 16 | 25.0 75.0 0.0 12.5 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000350 | 1 12 | 16.7 75.0 8.3 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000351 | 1 18 | 33.3 61.1 5.6 5.6 72.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000352 | 1 7 | 42.9 57.1 0.0 0.0 57.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000353 | 1 12 | 8.3 75.0 16.7 0.0 91.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000354 | 1 11 | 27.3 72.7 0.0 18.2 90.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000355 | 1 13 | 46.2 53.8 0.0 7.7 61.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000356 | 1 7 | 28.6 71.4 0.0 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000357 | 1 15 | 33.3 60.0 6.7 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000358 | 1 9 | 44.4 55.6 0.0 0.0 55.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000359 | 1 9 | 11.1 88.9 0.0 11.1 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000360 | 1 10 | 40.0 40.0 20.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000361 | 1 7 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000362 | 1 17 | 35.3 52.9 11.8 0.0 64.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000363 | 1 14 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000364 | 1 12 | 41.7 58.3 0.0 8.3 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000365 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000366 | 1 11 | 36.4 45.5 18.2 0.0 63.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000367 | 1 10 | 20.0 80.0 0.0 10.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000368 | 1 9 | 0.0 100.0 0.0 11.1 111.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000369 | 1 17 | 11.8 76.5 11.8 0.0 88.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000370 | 1 14 | 50.0 42.9 7.1 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000371 | 1 11 | 27.3 72.7 0.0 0.0 72.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000372 | 1 15 | 40.0 53.3 6.7 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000373 | 1 14 | 14.3 78.6 7.1 14.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000374 | 1 6 | 83.3 16.7 0.0 0.0 16.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000375 | 1 10 | 20.0 80.0 0.0 30.0 110.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000376 | 1 14 | 21.4 78.6 0.0 7.1 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| m | 77 1634 | 32.1 60.2 7.7 3.4 71.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000707 | 1 9 | 0.0 100.0 0.0 22.2 122.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000708 | 1 10 | 30.0 50.0 20.0 10.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000709 | 1 9 | 33.3 55.6 11.1 11.1 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000710 | 1 11 | 27.3 63.6 9.1 9.1 81.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000711 | 1 14 | 7.1 92.9 0.0 14.3 107.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000712 | 1 14 | 28.6 64.3 7.1 7.1 78.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000713 | 1 13 | 15.4 69.2 15.4 0.0 84.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000714 | 1 15 | 6.7 86.7 6.7 6.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000715 | 1 2 | 0.0 100.0 0.0 150.0 250.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000716 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000717 | 1 12 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000718 | 1 6 | 0.0 100.0 0.0 16.7 116.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000719 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000720 | 1 11 | 36.4 63.6 0.0 9.1 72.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000721 | 1 8 | 50.0 37.5 12.5 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000722 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000723 | 1 12 | 16.7 83.3 0.0 8.3 91.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000724 | 1 5 | 20.0 80.0 0.0 60.0 140.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000725 | 1 14 | 21.4 64.3 14.3 7.1 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000726 | 1 13 | 38.5 61.5 0.0 7.7 69.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000727 | 1 5 | 0.0 60.0 40.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000728 | 1 7 | 14.3 85.7 0.0 14.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000729 | 1 12 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000730 | 1 13 | 7.7 92.3 0.0 23.1 115.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000731 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000732 | 1 14 | 21.4 50.0 28.6 0.0 78.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000733 | 1 11 | 9.1 90.9 0.0 18.2 109.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000734 | 1 11 | 18.2 81.8 0.0 36.4 118.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000735 | 1 13 | 30.8 61.5 7.7 7.7 76.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000736 | 1 11 | 27.3 63.6 9.1 0.0 72.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000737 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000738 | 1 14 | 28.6 64.3 7.1 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000739 | 1 9 | 22.2 66.7 11.1 0.0 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000740 | 1 8 | 25.0 62.5 12.5 75.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000741 | 1 6 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000742 | 1 12 | 16.7 75.0 8.3 25.0 108.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000743 | 1 15 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000744 | 1 7 | 42.9 57.1 0.0 57.1 114.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000745 | 1 11 | 9.1 90.9 0.0 9.1 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000746 | 1 9 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000747 | 1 7 | 28.6 57.1 14.3 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000748 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000749 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000750 | 1 13 | 15.4 76.9 7.7 0.0 84.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000751 | 1 9 | 33.3 66.7 0.0 22.2 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000752 | 1 12 | 25.0 75.0 0.0 41.7 116.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000753 | 1 11 | 0.0 100.0 0.0 9.1 109.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000754 | 1 8 | 12.5 87.5 0.0 87.5 175.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000755 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000756 | 1 10 | 40.0 40.0 20.0 20.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000757 | 1 11 | 9.1 72.7 18.2 0.0 90.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000758 | 1 5 | 60.0 40.0 0.0 40.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000759 | 1 8 | 12.5 62.5 25.0 12.5 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000760 | 1 11 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000761 | 1 6 | 50.0 50.0 0.0 33.3 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000762 | 1 12 | 16.7 75.0 8.3 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000763 | 1 10 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000764 | 1 9 | 11.1 88.9 0.0 0.0 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000765 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000766 | 1 8 | 37.5 62.5 0.0 37.5 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000767 | 1 12 | 41.7 58.3 0.0 25.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000768 | 1 5 | 0.0 100.0 0.0 40.0 140.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000769 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000770 | 1 17 | 29.4 52.9 17.6 5.9 76.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000771 | 1 10 | 10.0 80.0 10.0 0.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000772 | 1 7 | 0.0 100.0 0.0 28.6 128.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000773 | 1 14 | 21.4 71.4 7.1 0.0 78.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000774 | 1 12 | 25.0 75.0 0.0 41.7 116.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000775 | 1 5 | 0.0 40.0 60.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000776 | 1 12 | 16.7 75.0 8.3 8.3 91.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000777 | 1 14 | 21.4 78.6 0.0 21.4 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000778 | 1 14 | 14.3 78.6 7.1 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000779 | 1 14 | 7.1 92.9 0.0 35.7 128.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000780 | 1 12 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000781 | 1 14 | 14.3 64.3 21.4 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000782 | 1 1 | 0.0 100.0 0.0 700.0 800.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000783 | 1 7 | 28.6 71.4 0.0 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000784 | 1 9 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000785 | 1 6 | 33.3 50.0 16.7 50.0 116.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000786 | 1 11 | 36.4 63.6 0.0 9.1 72.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000787 | 1 14 | 28.6 71.4 0.0 28.6 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000788 | 1 5 | 20.0 80.0 0.0 80.0 160.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000789 | 1 6 | 0.0 100.0 0.0 16.7 116.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000790 | 1 6 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000791 | 1 7 | 28.6 71.4 0.0 14.3 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000792 | 1 12 | 25.0 66.7 8.3 16.7 91.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000793 | 1 7 | 0.0 85.7 14.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000794 | 1 6 | 16.7 50.0 33.3 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000795 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000796 | 1 7 | 14.3 71.4 14.3 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000797 | 1 9 | 55.6 44.4 0.0 0.0 44.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000798 | 1 9 | 11.1 66.7 22.2 11.1 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000799 | 1 7 | 42.9 57.1 0.0 28.6 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000800 | 1 4 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000801 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000802 | 1 8 | 25.0 75.0 0.0 50.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000803 | 1 11 | 9.1 81.8 9.1 18.2 109.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000804 | 1 6 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000805 | 1 12 | 41.7 58.3 0.0 33.3 91.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000806 | 1 5 | 20.0 80.0 0.0 40.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000807 | 1 14 | 7.1 92.9 0.0 14.3 107.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000808 | 1 13 | 46.2 53.8 0.0 15.4 69.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000809 | 1 10 | 30.0 60.0 10.0 0.0 70.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000413 | 1 33 | 36.4 60.6 3.0 3.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000414 | 1 37 | 37.8 56.8 5.4 8.1 70.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000415 | 1 18 | 27.8 72.2 0.0 27.8 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000416 | 1 22 | 22.7 63.6 13.6 0.0 77.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000417 | 1 25 | 24.0 72.0 4.0 0.0 76.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000418 | 1 13 | 23.1 76.9 0.0 7.7 84.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000419 | 1 9 | 11.1 88.9 0.0 22.2 111.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000420 | 1 14 | 28.6 50.0 21.4 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000421 | 1 22 | 31.8 63.6 4.5 9.1 77.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000422 | 1 21 | 19.0 76.2 4.8 4.8 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000423 | 1 26 | 15.4 57.7 26.9 11.5 96.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000424 | 1 17 | 17.6 70.6 11.8 0.0 82.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000425 | 1 18 | 33.3 55.6 11.1 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000426 | 1 17 | 29.4 70.6 0.0 23.5 94.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000427 | 1 33 | 21.2 60.6 18.2 0.0 78.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000428 | 1 22 | 22.7 77.3 0.0 0.0 77.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000429 | 1 22 | 13.6 81.8 4.5 4.5 90.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000430 | 1 19 | 36.8 63.2 0.0 10.5 73.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000431 | 1 16 | 0.0 100.0 0.0 12.5 112.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000432 | 1 15 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000433 | 1 13 | 23.1 69.2 7.7 7.7 84.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000434 | 1 11 | 18.2 81.8 0.0 9.1 90.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000435 | 1 22 | 27.3 68.2 4.5 0.0 72.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000436 | 1 18 | 11.1 66.7 22.2 16.7 105.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000437 | 1 22 | 22.7 68.2 9.1 9.1 86.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000438 | 1 30 | 30.0 70.0 0.0 3.3 73.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000439 | 1 12 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000440 | 1 30 | 33.3 60.0 6.7 3.3 70.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000441 | 1 9 | 33.3 66.7 0.0 11.1 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000442 | 1 22 | 9.1 54.5 36.4 0.0 90.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000443 | 1 32 | 25.0 68.8 6.3 6.3 81.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000444 | 1 22 | 36.4 50.0 13.6 0.0 63.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000445 | 1 10 | 10.0 80.0 10.0 30.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000446 | 1 15 | 13.3 73.3 13.3 0.0 86.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000447 | 1 18 | 33.3 61.1 5.6 11.1 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000448 | 1 32 | 28.1 68.8 3.1 3.1 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000449 | 1 25 | 24.0 76.0 0.0 12.0 88.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000450 | 1 32 | 21.9 59.4 18.8 0.0 78.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000451 | 1 14 | 7.1 92.9 0.0 14.3 107.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000452 | 1 29 | 13.8 79.3 6.9 3.4 89.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000453 | 1 22 | 22.7 72.7 4.5 0.0 77.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000454 | 1 22 | 27.3 63.6 9.1 18.2 90.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000455 | 1 17 | 35.3 64.7 0.0 11.8 76.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000456 | 1 9 | 22.2 77.8 0.0 22.2 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000457 | 1 24 | 4.2 75.0 20.8 0.0 95.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000458 | 1 15 | 13.3 86.7 0.0 20.0 106.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000459 | 1 35 | 28.6 62.9 8.6 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000460 | 1 17 | 11.8 76.5 11.8 5.9 94.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000461 | 1 15 | 20.0 80.0 0.0 6.7 86.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000462 | 1 15 | 26.7 73.3 0.0 13.3 86.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000463 | 1 18 | 16.7 72.2 11.1 5.6 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000464 | 1 12 | 8.3 91.7 0.0 0.0 91.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000465 | 1 26 | 23.1 69.2 7.7 7.7 84.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000466 | 1 27 | 18.5 74.1 7.4 0.0 81.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000467 | 1 16 | 12.5 81.3 6.3 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000468 | 1 46 | 10.9 60.9 28.3 0.0 89.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000469 | 1 31 | 22.6 74.2 3.2 29.0 106.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000470 | 1 31 | 22.6 71.0 6.5 6.5 83.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000471 | 1 25 | 32.0 64.0 4.0 4.0 72.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000472 | 1 29 | 6.9 82.8 10.3 0.0 93.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000473 | 1 25 | 24.0 76.0 0.0 0.0 76.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000474 | 1 28 | 35.7 64.3 0.0 10.7 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000475 | 1 28 | 28.6 53.6 17.9 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000476 | 1 27 | 25.9 70.4 3.7 3.7 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000283 | 1 38 | 21.1 73.7 5.3 7.9 86.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000284 | 1 31 | 35.5 61.3 3.2 3.2 67.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000285 | 1 35 | 11.4 71.4 17.1 2.9 91.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000286 | 1 42 | 28.6 69.0 2.4 2.4 73.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000287 | 1 42 | 16.7 64.3 19.0 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000288 | 1 24 | 29.2 70.8 0.0 0.0 70.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000289 | 1 39 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000290 | 1 35 | 11.4 65.7 22.9 0.0 88.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000291 | 1 33 | 45.5 51.5 3.0 0.0 54.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000292 | 1 36 | 30.6 61.1 8.3 0.0 69.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000293 | 1 23 | 21.7 60.9 17.4 0.0 78.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000294 | 1 42 | 16.7 69.0 14.3 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000295 | 1 26 | 34.6 57.7 7.7 11.5 76.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000296 | 1 28 | 21.4 78.6 0.0 7.1 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000297 | 1 47 | 21.3 72.3 6.4 2.1 80.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000298 | 1 19 | 15.8 84.2 0.0 15.8 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000299 | 1 51 | 21.6 68.6 9.8 2.0 80.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000300 | 1 53 | 20.8 47.2 32.1 0.0 79.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000301 | 1 44 | 47.7 50.0 2.3 0.0 52.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000302 | 1 27 | 14.8 74.1 11.1 0.0 85.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000303 | 1 49 | 34.7 53.1 12.2 0.0 65.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000304 | 1 54 | 29.6 53.7 16.7 0.0 70.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000305 | 1 49 | 32.7 63.3 4.1 2.0 69.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000306 | 1 30 | 30.0 56.7 13.3 3.3 73.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000307 | 1 46 | 28.3 60.9 10.9 2.2 73.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000308 | 1 26 | 38.5 57.7 3.8 7.7 69.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000309 | 1 36 | 38.9 55.6 5.6 2.8 63.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000310 | 1 35 | 20.0 68.6 11.4 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000311 | 1 37 | 24.3 75.7 0.0 2.7 78.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000312 | 1 42 | 16.7 69.0 14.3 2.4 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000313 | 1 35 | 28.6 68.6 2.9 14.3 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000314 | 1 29 | 48.3 51.7 0.0 6.9 58.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000315 | 1 29 | 37.9 62.1 0.0 3.4 65.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000316 | 1 45 | 33.3 57.8 8.9 2.2 68.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000317 | 1 49 | 28.6 63.3 8.2 2.0 73.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000318 | 1 36 | 30.6 47.2 22.2 8.3 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000319 | 1 34 | 29.4 67.6 2.9 8.8 79.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000320 | 1 37 | 40.5 51.4 8.1 8.1 67.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000321 | 1 42 | 47.6 52.4 0.0 4.8 57.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000322 | 1 39 | 30.8 59.0 10.3 2.6 71.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001588 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001589 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001590 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001591 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001592 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001593 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001594 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001595 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001596 | 1 3 | 0.0 100.0 0.0 133.3 233.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001597 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001598 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001599 | 1 3 | 33.3 66.7 0.0 66.7 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001600 | 1 3 | 33.3 66.7 0.0 66.7 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001601 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001602 | 1 2 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001603 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001604 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001605 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001606 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001607 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001608 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001609 | 1 2 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001610 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001611 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001612 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001613 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001614 | 1 4 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001615 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001616 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001617 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001618 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001619 | 1 3 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001620 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001621 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001622 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001623 | 1 2 | 0.0 100.0 0.0 150.0 250.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001624 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001625 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001626 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001627 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001628 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001629 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001630 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001631 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001632 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001633 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001634 | 1 3 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001635 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001636 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001637 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001638 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001639 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001640 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001641 | 1 2 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001642 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001643 | 1 2 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001644 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001645 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001646 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001647 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001648 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001649 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001650 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001651 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001652 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001653 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001654 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001655 | 1 3 | 66.7 33.3 0.0 33.3 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001656 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001657 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001658 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001659 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001660 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001661 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001662 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001663 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001664 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001665 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001666 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001667 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001668 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001669 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001670 | 1 3 | 33.3 66.7 0.0 66.7 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001671 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001672 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001673 | 1 2 | 100.0 0.0 0.0 150.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001674 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001675 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001676 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001677 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001678 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001679 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001680 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001681 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001682 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001683 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001684 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001685 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001686 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001687 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001688 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001689 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001690 | 1 2 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001691 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001692 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001693 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001694 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001695 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001696 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001697 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001698 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001699 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001700 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001701 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001702 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001703 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001704 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001705 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001706 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001707 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001708 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001709 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001710 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001711 | 1 2 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001712 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001713 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001714 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001715 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001716 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001717 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001718 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001719 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001720 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001721 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001722 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001723 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001724 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001725 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001726 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001727 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001728 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001729 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001730 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001731 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001732 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001733 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001734 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001735 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001736 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001737 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001738 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001739 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001740 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001741 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001742 | 1 3 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001743 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001744 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001745 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001746 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001747 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001748 | 1 3 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001749 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001750 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001751 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001752 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001753 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001754 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001755 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001756 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001757 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001758 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001759 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001760 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001761 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001762 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001763 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001764 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001765 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001766 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001767 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001768 | 1 6 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001769 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001770 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001771 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001772 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001773 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001774 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001775 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001776 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001777 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001778 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001779 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001780 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001781 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001782 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001783 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001784 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001785 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001786 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001787 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001788 | 1 3 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001789 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001790 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001791 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001792 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001793 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001794 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001795 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001796 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001797 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001798 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001799 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001800 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001801 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001802 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001803 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001804 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001805 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001806 | 1 4 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001807 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001808 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001809 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001810 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001811 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001812 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001813 | 1 3 | 33.3 33.3 33.3 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001814 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001815 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001816 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001817 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001818 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001819 | 1 3 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001820 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001821 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001822 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001823 | 1 3 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001824 | 1 4 | 25.0 50.0 25.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001744 | 1 7 | 28.6 71.4 0.0 14.3 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001745 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001746 | 1 10 | 30.0 70.0 0.0 10.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001747 | 1 11 | 27.3 72.7 0.0 0.0 72.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001748 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001749 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001750 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001751 | 1 4 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001752 | 1 6 | 16.7 83.3 0.0 33.3 116.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001753 | 1 6 | 0.0 100.0 0.0 16.7 116.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001754 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001755 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001756 | 1 4 | 50.0 50.0 0.0 50.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001757 | 1 8 | 12.5 87.5 0.0 12.5 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001758 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001759 | 1 7 | 14.3 85.7 0.0 14.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001760 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001761 | 1 8 | 25.0 75.0 0.0 50.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001762 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001763 | 1 15 | 33.3 60.0 6.7 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001764 | 1 7 | 14.3 57.1 28.6 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001765 | 1 7 | 28.6 71.4 0.0 42.9 114.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001766 | 1 4 | 50.0 25.0 25.0 25.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001767 | 1 7 | 28.6 71.4 0.0 14.3 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001768 | 1 5 | 40.0 60.0 0.0 40.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001769 | 1 8 | 25.0 62.5 12.5 12.5 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001770 | 1 5 | 20.0 80.0 0.0 40.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001771 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001772 | 1 5 | 40.0 60.0 0.0 20.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001773 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001774 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001775 | 1 10 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001776 | 1 8 | 12.5 87.5 0.0 50.0 137.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001777 | 1 12 | 41.7 50.0 8.3 16.7 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001778 | 1 6 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001779 | 1 7 | 0.0 85.7 14.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001780 | 1 9 | 33.3 55.6 11.1 11.1 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001781 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001782 | 1 13 | 30.8 61.5 7.7 7.7 76.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001783 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001784 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001785 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001786 | 1 18 | 22.2 72.2 5.6 16.7 94.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001787 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001788 | 1 6 | 50.0 50.0 0.0 16.7 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001789 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001790 | 1 10 | 20.0 70.0 10.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001791 | 1 7 | 14.3 71.4 14.3 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001792 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001793 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001794 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001795 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001796 | 1 3 | 66.7 33.3 0.0 33.3 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001797 | 1 9 | 33.3 44.4 22.2 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001798 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001799 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001800 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001801 | 1 10 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001802 | 1 5 | 40.0 40.0 20.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001803 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001804 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001805 | 1 7 | 14.3 57.1 28.6 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001806 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001807 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001808 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001809 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001810 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001811 | 1 8 | 12.5 87.5 0.0 12.5 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001812 | 1 4 | 25.0 50.0 25.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001813 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001814 | 1 7 | 14.3 71.4 14.3 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001815 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001816 | 1 8 | 0.0 100.0 0.0 12.5 112.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001817 | 1 9 | 33.3 55.6 11.1 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001818 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001819 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001820 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001821 | 1 2 | 0.0 100.0 0.0 150.0 250.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001822 | 1 4 | 75.0 25.0 0.0 0.0 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001823 | 1 15 | 40.0 46.7 13.3 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001824 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001825 | 1 10 | 40.0 60.0 0.0 10.0 70.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001826 | 1 7 | 42.9 42.9 14.3 0.0 57.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001827 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001828 | 1 2 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001829 | 1 8 | 12.5 50.0 37.5 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001830 | 1 9 | 22.2 55.6 22.2 0.0 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001831 | 1 3 | 33.3 66.7 0.0 66.7 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001832 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001833 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001834 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001835 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001836 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001837 | 1 5 | 0.0 60.0 40.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001838 | 1 7 | 42.9 57.1 0.0 0.0 57.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001839 | 1 8 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001840 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001841 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001842 | 1 6 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001843 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001844 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001845 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001846 | 1 15 | 33.3 66.7 0.0 13.3 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001847 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001848 | 1 8 | 12.5 62.5 25.0 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001849 | 1 10 | 20.0 70.0 10.0 10.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001850 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001851 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001852 | 1 12 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001853 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001854 | 1 5 | 40.0 60.0 0.0 20.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001855 | 1 8 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001856 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001857 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001858 | 1 8 | 37.5 62.5 0.0 12.5 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001859 | 1 2 | 50.0 50.0 0.0 50.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001860 | 1 6 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001861 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001862 | 1 12 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001863 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001864 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001865 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001866 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001867 | 1 9 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001868 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001869 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001870 | 1 6 | 33.3 50.0 16.7 16.7 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001871 | 1 14 | 28.6 71.4 0.0 14.3 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001872 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001873 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001874 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001875 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001876 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001877 | 1 5 | 0.0 100.0 0.0 40.0 140.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001878 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001879 | 1 24 | 29.2 70.8 0.0 12.5 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001880 | 1 10 | 20.0 70.0 10.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001881 | 1 19 | 42.1 42.1 15.8 0.0 57.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001882 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001883 | 1 9 | 44.4 55.6 0.0 22.2 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001884 | 1 7 | 57.1 42.9 0.0 0.0 42.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001885 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001886 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001887 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001888 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001889 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001890 | 1 2 | 100.0 0.0 0.0 50.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001891 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001892 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001893 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001894 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001895 | 1 6 | 33.3 50.0 16.7 16.7 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001896 | 1 6 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001897 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001898 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001899 | 1 10 | 60.0 30.0 10.0 0.0 40.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001900 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001901 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001902 | 1 8 | 25.0 62.5 12.5 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001903 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001904 | 1 8 | 25.0 62.5 12.5 12.5 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001905 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001906 | 1 13 | 38.5 61.5 0.0 7.7 69.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001907 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001908 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001909 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001910 | 1 7 | 28.6 71.4 0.0 14.3 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001911 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001912 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001913 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001914 | 1 8 | 25.0 62.5 12.5 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001915 | 1 9 | 0.0 55.6 44.4 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001916 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001917 | 1 3 | 66.7 33.3 0.0 33.3 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001918 | 1 9 | 22.2 77.8 0.0 0.0 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001919 | 1 8 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001920 | 1 6 | 50.0 50.0 0.0 16.7 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001921 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001922 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001923 | 1 21 | 38.1 57.1 4.8 4.8 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001924 | 1 10 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001925 | 1 8 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001926 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001927 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001928 | 1 4 | 75.0 25.0 0.0 0.0 25.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001929 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001930 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001931 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001932 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001933 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001934 | 1 7 | 57.1 28.6 14.3 0.0 42.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001935 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001936 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001937 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001938 | 1 12 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001939 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001940 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001941 | 1 6 | 16.7 66.7 16.7 16.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001942 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001943 | 1 8 | 12.5 75.0 12.5 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001944 | 1 8 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001945 | 1 8 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001946 | 1 20 | 35.0 60.0 5.0 5.0 70.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001947 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001948 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001949 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001950 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001951 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001952 | 1 9 | 11.1 88.9 0.0 11.1 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001953 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001954 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001955 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001956 | 1 7 | 28.6 57.1 14.3 14.3 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001957 | 1 9 | 33.3 66.7 0.0 22.2 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001958 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001959 | 1 7 | 28.6 71.4 0.0 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001960 | 1 7 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001961 | 1 7 | 28.6 57.1 14.3 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001962 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001963 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001964 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001965 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001966 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001967 | 1 10 | 50.0 40.0 10.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001968 | 1 9 | 11.1 88.9 0.0 22.2 111.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001969 | 1 10 | 20.0 80.0 0.0 10.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001970 | 1 6 | 0.0 100.0 0.0 16.7 116.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001971 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001972 | 1 13 | 30.8 38.5 30.8 0.0 69.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001973 | 1 6 | 0.0 100.0 0.0 16.7 116.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001974 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001975 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001976 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001977 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001978 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001979 | 1 12 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001980 | 1 8 | 0.0 87.5 12.5 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001981 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001982 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001983 | 1 10 | 30.0 70.0 0.0 20.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001984 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001985 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001986 | 1 10 | 40.0 60.0 0.0 10.0 70.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001987 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001988 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001989 | 1 10 | 50.0 40.0 10.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001990 | 1 6 | 50.0 33.3 16.7 16.7 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001991 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001992 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001993 | 1 5 | 20.0 80.0 0.0 60.0 140.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001994 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001995 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001996 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001997 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001998 | 1 7 | 14.3 71.4 14.3 14.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001999 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002000 | 1 15 | 40.0 46.7 13.3 6.7 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002001 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002002 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002003 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002004 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002005 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000874 | 1 9 | 22.2 77.8 0.0 0.0 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000875 | 1 4 | 75.0 25.0 0.0 25.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000876 | 1 8 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000877 | 1 9 | 44.4 55.6 0.0 0.0 55.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000878 | 1 7 | 57.1 42.9 0.0 0.0 42.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000879 | 1 8 | 50.0 50.0 0.0 12.5 62.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000880 | 1 7 | 14.3 85.7 0.0 14.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000883 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000884 | 1 12 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000885 | 1 9 | 22.2 55.6 22.2 0.0 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000886 | 1 11 | 36.4 63.6 0.0 18.2 81.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000887 | 1 12 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000888 | 1 8 | 37.5 62.5 0.0 0.0 62.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000889 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000890 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000891 | 1 15 | 33.3 46.7 20.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000892 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000893 | 1 5 | 60.0 40.0 0.0 20.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000894 | 1 17 | 41.2 58.8 0.0 11.8 70.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000895 | 1 11 | 36.4 63.6 0.0 0.0 63.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000896 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000897 | 1 8 | 62.5 37.5 0.0 0.0 37.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000898 | 1 9 | 55.6 33.3 11.1 0.0 44.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000899 | 1 5 | 40.0 60.0 0.0 20.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000900 | 1 11 | 27.3 63.6 9.1 0.0 72.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000901 | 1 6 | 16.7 83.3 0.0 83.3 166.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000902 | 1 5 | 20.0 60.0 20.0 20.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000903 | 1 9 | 44.4 55.6 0.0 0.0 55.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000904 | 1 5 | 20.0 80.0 0.0 40.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000905 | 1 12 | 16.7 58.3 25.0 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000906 | 1 9 | 11.1 66.7 22.2 0.0 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000907 | 1 10 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000908 | 1 8 | 25.0 62.5 12.5 12.5 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000909 | 1 10 | 30.0 70.0 0.0 0.0 70.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000910 | 1 9 | 66.7 33.3 0.0 11.1 44.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000911 | 1 4 | 50.0 50.0 0.0 25.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000912 | 1 8 | 75.0 25.0 0.0 12.5 37.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000913 | 1 11 | 45.5 54.5 0.0 0.0 54.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000914 | 1 7 | 14.3 85.7 0.0 14.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000915 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000916 | 1 7 | 71.4 28.6 0.0 14.3 42.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000917 | 1 8 | 62.5 37.5 0.0 0.0 37.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000918 | 1 13 | 46.2 46.2 7.7 15.4 69.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000920 | 1 6 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000921 | 1 10 | 30.0 60.0 10.0 10.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000922 | 1 11 | 54.5 45.5 0.0 9.1 54.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000923 | 1 10 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000924 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000925 | 1 12 | 25.0 75.0 0.0 16.7 91.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000927 | 1 10 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000928 | 1 9 | 11.1 66.7 22.2 0.0 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000929 | 1 4 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000930 | 1 13 | 76.9 15.4 7.7 0.0 23.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000931 | 1 12 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000932 | 1 12 | 58.3 41.7 0.0 0.0 41.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000933 | 1 7 | 0.0 71.4 28.6 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000934 | 1 8 | 25.0 75.0 0.0 12.5 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000935 | 1 5 | 20.0 80.0 0.0 40.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000938 | 1 5 | 60.0 40.0 0.0 20.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000939 | 1 12 | 41.7 58.3 0.0 0.0 58.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000940 | 1 10 | 40.0 40.0 20.0 10.0 70.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000942 | 1 10 | 30.0 50.0 20.0 0.0 70.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000943 | 1 8 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000944 | 1 19 | 42.1 52.6 5.3 0.0 57.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000945 | 1 13 | 23.1 61.5 15.4 7.7 84.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000946 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000947 | 1 9 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000948 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000949 | 1 11 | 54.5 45.5 0.0 9.1 54.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000950 | 1 10 | 30.0 50.0 20.0 0.0 70.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000951 | 1 7 | 57.1 28.6 14.3 14.3 57.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000952 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000953 | 1 9 | 22.2 66.7 11.1 11.1 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000954 | 1 11 | 36.4 54.5 9.1 18.2 81.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000955 | 1 14 | 35.7 50.0 14.3 0.0 64.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000956 | 1 9 | 22.2 77.8 0.0 11.1 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000957 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000958 | 1 10 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000959 | 1 21 | 38.1 61.9 0.0 4.8 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000960 | 1 17 | 52.9 47.1 0.0 5.9 52.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000961 | 1 12 | 33.3 58.3 8.3 8.3 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000962 | 1 5 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000963 | 1 13 | 23.1 61.5 15.4 0.0 76.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000964 | 1 13 | 30.8 69.2 0.0 0.0 69.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000965 | 1 7 | 57.1 42.9 0.0 0.0 42.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000966 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000967 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000968 | 1 9 | 22.2 77.8 0.0 0.0 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000971 | 1 8 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000972 | 1 11 | 27.3 72.7 0.0 9.1 81.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000973 | 1 15 | 40.0 53.3 6.7 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000974 | 1 13 | 53.8 46.2 0.0 0.0 46.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000975 | 1 10 | 30.0 70.0 0.0 20.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000976 | 1 9 | 44.4 55.6 0.0 0.0 55.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000977 | 1 5 | 60.0 40.0 0.0 40.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000978 | 1 7 | 71.4 28.6 0.0 0.0 28.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000979 | 1 10 | 30.0 50.0 20.0 10.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000980 | 1 11 | 45.5 45.5 9.1 0.0 54.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000981 | 1 11 | 45.5 45.5 9.1 0.0 54.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000982 | 1 8 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000983 | 1 10 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000984 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000985 | 1 9 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000986 | 1 16 | 43.8 50.0 6.3 6.3 62.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000987 | 1 11 | 18.2 81.8 0.0 45.5 127.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000988 | 1 9 | 44.4 55.6 0.0 0.0 55.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000989 | 1 15 | 20.0 66.7 13.3 0.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000990 | 1 10 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000991 | 1 8 | 12.5 87.5 0.0 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000992 | 1 9 | 44.4 55.6 0.0 0.0 55.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000993 | 1 9 | 11.1 66.7 22.2 0.0 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000994 | 1 8 | 12.5 87.5 0.0 12.5 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000995 | 1 19 | 42.1 57.9 0.0 0.0 57.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000996 | 1 9 | 55.6 33.3 11.1 0.0 44.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000997 | 1 10 | 30.0 70.0 0.0 10.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000998 | 1 8 | 12.5 87.5 0.0 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000999 | 1 4 | 25.0 75.0 0.0 50.0 125.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001000 | 1 11 | 45.5 54.5 0.0 0.0 54.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001001 | 1 10 | 30.0 70.0 0.0 20.0 90.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001002 | 1 6 | 16.7 83.3 0.0 33.3 116.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001003 | 1 11 | 54.5 45.5 0.0 0.0 45.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001004 | 1 10 | 30.0 70.0 0.0 0.0 70.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000494 | 1 16 | 31.3 62.5 6.3 12.5 81.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000495 | 1 29 | 27.6 58.6 13.8 3.4 75.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000496 | 1 28 | 42.9 53.6 3.6 3.6 60.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000497 | 1 16 | 31.3 62.5 6.3 6.3 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000498 | 1 26 | 26.9 61.5 11.5 0.0 73.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000499 | 1 68 | 27.9 57.4 14.7 1.5 73.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000500 | 1 24 | 58.3 41.7 0.0 4.2 45.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000501 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000502 | 1 52 | 25.0 67.3 7.7 1.9 76.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000503 | 1 27 | 25.9 63.0 11.1 0.0 74.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000504 | 1 32 | 12.5 78.1 9.4 0.0 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000505 | 1 52 | 34.6 53.8 11.5 7.7 73.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000506 | 1 32 | 31.3 53.1 15.6 9.4 78.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000507 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000508 | 1 9 | 22.2 66.7 11.1 11.1 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000509 | 1 47 | 27.7 70.2 2.1 17.0 89.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000510 | 1 43 | 16.3 76.7 7.0 2.3 86.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000511 | 1 45 | 17.8 77.8 4.4 0.0 82.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000512 | 1 9 | 66.7 33.3 0.0 11.1 44.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000513 | 1 11 | 27.3 72.7 0.0 27.3 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000514 | 1 12 | 16.7 58.3 25.0 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000515 | 1 20 | 30.0 55.0 15.0 5.0 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000516 | 1 16 | 12.5 56.3 31.3 12.5 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000517 | 1 28 | 35.7 57.1 7.1 3.6 67.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000518 | 1 14 | 7.1 85.7 7.1 0.0 92.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000519 | 1 28 | 10.7 78.6 10.7 7.1 96.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000520 | 1 32 | 34.4 53.1 12.5 9.4 75.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000521 | 1 38 | 26.3 55.3 18.4 5.3 78.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000522 | 1 14 | 42.9 50.0 7.1 0.0 57.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000523 | 1 23 | 17.4 65.2 17.4 0.0 82.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000524 | 1 31 | 16.1 80.6 3.2 6.5 90.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000525 | 1 9 | 33.3 66.7 0.0 11.1 77.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000526 | 1 16 | 50.0 43.8 6.3 0.0 50.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000527 | 1 42 | 21.4 71.4 7.1 7.1 85.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000528 | 1 18 | 22.2 77.8 0.0 11.1 88.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000529 | 1 14 | 28.6 50.0 21.4 0.0 71.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000530 | 1 27 | 7.4 88.9 3.7 14.8 107.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000531 | 1 8 | 25.0 75.0 0.0 12.5 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000532 | 1 5 | 40.0 60.0 0.0 20.0 80.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000533 | 1 16 | 18.8 62.5 18.8 6.3 87.5 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000534 | 1 36 | 33.3 61.1 5.6 2.8 69.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000535 | 1 27 | 22.2 77.8 0.0 18.5 96.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000536 | 1 23 | 30.4 56.5 13.0 8.7 78.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000537 | 1 22 | 18.2 63.6 18.2 9.1 90.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000538 | 1 24 | 20.8 54.2 25.0 0.0 79.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000539 | 1 18 | 27.8 66.7 5.6 11.1 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000540 | 1 19 | 42.1 57.9 0.0 10.5 68.4 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000541 | 1 14 | 28.6 71.4 0.0 21.4 92.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000542 | 1 7 | 28.6 71.4 0.0 28.6 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000543 | 1 19 | 26.3 73.7 0.0 5.3 78.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000544 | 1 21 | 19.0 76.2 4.8 19.0 100.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000545 | 1 7 | 42.9 57.1 0.0 0.0 57.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000546 | 1 6 | 16.7 50.0 33.3 0.0 83.3 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000547 | 1 20 | 35.0 55.0 10.0 0.0 65.0 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000548 | 1 33 | 33.3 57.6 9.1 9.1 75.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000549 | 1 21 | 28.6 61.9 9.5 4.8 76.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000550 | 1 14 | 21.4 71.4 7.1 28.6 107.1 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000551 | 1 13 | 46.2 53.8 0.0 0.0 53.8 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000552 | 1 33 | 18.2 72.7 9.1 12.1 93.9 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000553 | 1 21 | 33.3 61.9 4.8 0.0 66.7 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000554 | 1 33 | 39.4 45.5 15.2 3.0 63.6 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000555 | 1 13 | 53.8 38.5 7.7 23.1 69.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000556 | 1 17 | 11.8 82.4 5.9 0.0 88.2 100.0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000557 | 1 10 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|====================================================================================================================| +| Sum/Avg | 1092 11772 | 27.2 66.1 6.6 7.6 80.4 99.9 | +|====================================================================================================================| +| Mean | 1.1 11.6 | 22.0 73.6 4.4 15.7 93.7 99.9 | +| S.D. | 2.4 51.8 | 18.8 20.4 8.2 34.2 42.4 3.1 | +| Median | 1.0 7.0 | 22.2 71.4 0.0 2.2 85.9 100.0 | +`--------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,--------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/hyp.trn| +|--------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000254 | 1 17 | 5 11 1 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000255 | 1 9 | 4 5 0 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000256 | 1 9 | 2 6 1 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000257 | 1 9 | 4 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000258 | 1 9 | 3 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000259 | 1 13 | 3 10 0 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000260 | 1 14 | 4 9 1 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000261 | 1 13 | 3 10 0 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000262 | 1 8 | 3 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000263 | 1 13 | 7 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000264 | 1 20 | 6 13 1 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000265 | 1 9 | 5 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000266 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000267 | 1 5 | 2 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000268 | 1 9 | 5 3 1 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000269 | 1 7 | 1 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000270 | 1 13 | 4 8 1 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000271 | 1 17 | 8 9 0 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000272 | 1 20 | 11 8 1 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000273 | 1 18 | 7 10 1 3 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000274 | 1 5 | 3 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000275 | 1 14 | 3 10 1 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000276 | 1 18 | 9 8 1 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000277 | 1 11 | 5 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000278 | 1 8 | 4 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000279 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000280 | 1 11 | 6 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000281 | 1 8 | 4 4 0 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000282 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000283 | 1 14 | 6 8 0 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000284 | 1 20 | 9 9 2 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000285 | 1 19 | 6 12 1 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000286 | 1 11 | 6 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000287 | 1 9 | 6 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000288 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000289 | 1 9 | 2 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000290 | 1 10 | 4 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000291 | 1 11 | 3 7 1 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000292 | 1 9 | 1 8 0 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000293 | 1 16 | 5 11 0 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000294 | 1 10 | 0 10 0 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000295 | 1 11 | 2 9 0 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000296 | 1 14 | 4 10 0 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000297 | 1 10 | 3 7 0 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000298 | 1 13 | 6 6 1 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000299 | 1 13 | 4 8 1 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000300 | 1 13 | 3 10 0 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000301 | 1 15 | 4 9 2 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000302 | 1 10 | 2 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000303 | 1 11 | 2 9 0 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000304 | 1 10 | 4 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000305 | 1 15 | 6 9 0 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000306 | 1 11 | 6 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000307 | 1 17 | 7 10 0 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000308 | 1 12 | 3 8 1 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000309 | 1 7 | 1 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000310 | 1 6 | 6 0 0 0 0 0 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000311 | 1 18 | 6 12 0 1 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000312 | 1 16 | 7 9 0 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000313 | 1 12 | 6 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000314 | 1 7 | 3 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000315 | 1 9 | 1 7 1 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000316 | 1 16 | 6 10 0 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000317 | 1 15 | 4 10 1 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000318 | 1 18 | 3 15 0 0 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000319 | 1 15 | 8 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000320 | 1 19 | 10 9 0 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000321 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000322 | 1 10 | 1 9 0 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000323 | 1 13 | 1 10 2 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000324 | 1 18 | 6 10 2 1 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000325 | 1 8 | 2 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000326 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000327 | 1 15 | 6 7 2 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000328 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000329 | 1 19 | 5 11 3 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000330 | 1 10 | 4 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000331 | 1 5 | 2 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000332 | 1 18 | 6 11 1 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000333 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000334 | 1 18 | 5 13 0 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000335 | 1 14 | 5 8 1 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000336 | 1 15 | 3 11 1 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000337 | 1 20 | 7 12 1 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000338 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000339 | 1 11 | 4 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000340 | 1 11 | 3 8 0 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000341 | 1 12 | 3 8 1 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000342 | 1 14 | 6 8 0 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000343 | 1 18 | 7 11 0 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000344 | 1 18 | 4 13 1 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000345 | 1 11 | 4 7 0 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000346 | 1 15 | 4 10 1 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000347 | 1 15 | 4 9 2 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000348 | 1 10 | 2 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000349 | 1 16 | 4 12 0 2 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000350 | 1 12 | 2 9 1 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000351 | 1 18 | 6 11 1 1 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000352 | 1 7 | 3 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000353 | 1 12 | 1 9 2 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000354 | 1 11 | 3 8 0 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000355 | 1 13 | 6 7 0 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000356 | 1 7 | 2 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000357 | 1 15 | 5 9 1 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000358 | 1 9 | 4 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000359 | 1 9 | 1 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000360 | 1 10 | 4 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000361 | 1 7 | 0 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000362 | 1 17 | 6 9 2 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000363 | 1 14 | 2 12 0 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000364 | 1 12 | 5 7 0 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000365 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000366 | 1 11 | 4 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000367 | 1 10 | 2 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000368 | 1 9 | 0 9 0 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000369 | 1 17 | 2 13 2 0 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000370 | 1 14 | 7 6 1 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000371 | 1 11 | 3 8 0 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000372 | 1 15 | 6 8 1 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000373 | 1 14 | 2 11 1 2 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000374 | 1 6 | 5 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000375 | 1 10 | 2 8 0 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| lad_eng_000376 | 1 14 | 3 11 0 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| m | 77 1634 | 525 983 126 56 1165 77 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000707 | 1 9 | 0 9 0 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000708 | 1 10 | 3 5 2 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000709 | 1 9 | 3 5 1 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000710 | 1 11 | 3 7 1 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000711 | 1 14 | 1 13 0 2 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000712 | 1 14 | 4 9 1 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000713 | 1 13 | 2 9 2 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000714 | 1 15 | 1 13 1 1 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000715 | 1 2 | 0 2 0 3 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000716 | 1 7 | 1 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000717 | 1 12 | 2 10 0 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000718 | 1 6 | 0 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000719 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000720 | 1 11 | 4 7 0 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000721 | 1 8 | 4 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000722 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000723 | 1 12 | 2 10 0 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000724 | 1 5 | 1 4 0 3 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000725 | 1 14 | 3 9 2 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000726 | 1 13 | 5 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000727 | 1 5 | 0 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000728 | 1 7 | 1 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000729 | 1 12 | 0 10 2 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000730 | 1 13 | 1 12 0 3 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000731 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000732 | 1 14 | 3 7 4 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000733 | 1 11 | 1 10 0 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000734 | 1 11 | 2 9 0 4 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000735 | 1 13 | 4 8 1 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000736 | 1 11 | 3 7 1 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000737 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000738 | 1 14 | 4 9 1 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000739 | 1 9 | 2 6 1 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000740 | 1 8 | 2 5 1 6 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000741 | 1 6 | 3 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000742 | 1 12 | 2 9 1 3 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000743 | 1 15 | 5 10 0 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000744 | 1 7 | 3 4 0 4 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000745 | 1 11 | 1 10 0 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000746 | 1 9 | 3 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000747 | 1 7 | 2 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000748 | 1 1 | 0 1 0 2 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000749 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000750 | 1 13 | 2 10 1 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000751 | 1 9 | 3 6 0 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000752 | 1 12 | 3 9 0 5 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000753 | 1 11 | 0 11 0 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000754 | 1 8 | 1 7 0 7 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000755 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000756 | 1 10 | 4 4 2 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000757 | 1 11 | 1 8 2 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000758 | 1 5 | 3 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000759 | 1 8 | 1 5 2 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000760 | 1 11 | 0 11 0 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000761 | 1 6 | 3 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000762 | 1 12 | 2 9 1 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000763 | 1 10 | 2 8 0 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000764 | 1 9 | 1 8 0 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000765 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000766 | 1 8 | 3 5 0 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000767 | 1 12 | 5 7 0 3 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000768 | 1 5 | 0 5 0 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000769 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000770 | 1 17 | 5 9 3 1 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000771 | 1 10 | 1 8 1 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000772 | 1 7 | 0 7 0 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000773 | 1 14 | 3 10 1 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000774 | 1 12 | 3 9 0 5 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000775 | 1 5 | 0 2 3 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000776 | 1 12 | 2 9 1 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000777 | 1 14 | 3 11 0 3 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000778 | 1 14 | 2 11 1 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000779 | 1 14 | 1 13 0 5 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000780 | 1 12 | 3 9 0 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000781 | 1 14 | 2 9 3 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000782 | 1 1 | 0 1 0 7 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000783 | 1 7 | 2 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000784 | 1 9 | 0 9 0 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000785 | 1 6 | 2 3 1 3 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000786 | 1 11 | 4 7 0 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000787 | 1 14 | 4 10 0 4 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000788 | 1 5 | 1 4 0 4 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000789 | 1 6 | 0 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000790 | 1 6 | 4 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000791 | 1 7 | 2 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000792 | 1 12 | 3 8 1 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000793 | 1 7 | 0 6 1 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000794 | 1 6 | 1 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000795 | 1 7 | 1 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000796 | 1 7 | 1 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000797 | 1 9 | 5 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000798 | 1 9 | 1 6 2 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000799 | 1 7 | 3 4 0 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000800 | 1 4 | 0 4 0 4 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000801 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000802 | 1 8 | 2 6 0 4 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000803 | 1 11 | 1 9 1 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000804 | 1 6 | 3 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000805 | 1 12 | 5 7 0 4 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000806 | 1 5 | 1 4 0 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000807 | 1 14 | 1 13 0 2 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000808 | 1 13 | 6 7 0 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| cv_eng_000809 | 1 10 | 3 6 1 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000413 | 1 33 | 12 20 1 1 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000414 | 1 37 | 14 21 2 3 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000415 | 1 18 | 5 13 0 5 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000416 | 1 22 | 5 14 3 0 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000417 | 1 25 | 6 18 1 0 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000418 | 1 13 | 3 10 0 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000419 | 1 9 | 1 8 0 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000420 | 1 14 | 4 7 3 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000421 | 1 22 | 7 14 1 2 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000422 | 1 21 | 4 16 1 1 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000423 | 1 26 | 4 15 7 3 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000424 | 1 17 | 3 12 2 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000425 | 1 18 | 6 10 2 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000426 | 1 17 | 5 12 0 4 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000427 | 1 33 | 7 20 6 0 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000428 | 1 22 | 5 17 0 0 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000429 | 1 22 | 3 18 1 1 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000430 | 1 19 | 7 12 0 2 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000431 | 1 16 | 0 16 0 2 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000432 | 1 15 | 5 10 0 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000433 | 1 13 | 3 9 1 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000434 | 1 11 | 2 9 0 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000435 | 1 22 | 6 15 1 0 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000436 | 1 18 | 2 12 4 3 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000437 | 1 22 | 5 15 2 2 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000438 | 1 30 | 9 21 0 1 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000439 | 1 12 | 4 8 0 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000440 | 1 30 | 10 18 2 1 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000441 | 1 9 | 3 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000442 | 1 22 | 2 12 8 0 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000443 | 1 32 | 8 22 2 2 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000444 | 1 22 | 8 11 3 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000445 | 1 10 | 1 8 1 3 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000446 | 1 15 | 2 11 2 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000447 | 1 18 | 6 11 1 2 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000448 | 1 32 | 9 22 1 1 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000449 | 1 25 | 6 19 0 3 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000450 | 1 32 | 7 19 6 0 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000451 | 1 14 | 1 13 0 2 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000452 | 1 29 | 4 23 2 1 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000453 | 1 22 | 5 16 1 0 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000454 | 1 22 | 6 14 2 4 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000455 | 1 17 | 6 11 0 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000456 | 1 9 | 2 7 0 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000457 | 1 24 | 1 18 5 0 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000458 | 1 15 | 2 13 0 3 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000459 | 1 35 | 10 22 3 0 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000460 | 1 17 | 2 13 2 1 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000461 | 1 15 | 3 12 0 1 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000462 | 1 15 | 4 11 0 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000463 | 1 18 | 3 13 2 1 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000464 | 1 12 | 1 11 0 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000465 | 1 26 | 6 18 2 2 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000466 | 1 27 | 5 20 2 0 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000467 | 1 16 | 2 13 1 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000468 | 1 46 | 5 28 13 0 41 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000469 | 1 31 | 7 23 1 9 33 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000470 | 1 31 | 7 22 2 2 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000471 | 1 25 | 8 16 1 1 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000472 | 1 29 | 2 24 3 0 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000473 | 1 25 | 6 19 0 0 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000474 | 1 28 | 10 18 0 3 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000475 | 1 28 | 8 15 5 0 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| fleurs_eng_000476 | 1 27 | 7 19 1 1 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000283 | 1 38 | 8 28 2 3 33 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000284 | 1 31 | 11 19 1 1 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000285 | 1 35 | 4 25 6 1 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000286 | 1 42 | 12 29 1 1 31 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000287 | 1 42 | 7 27 8 0 35 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000288 | 1 24 | 7 17 0 0 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000289 | 1 39 | 13 26 0 0 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000290 | 1 35 | 4 23 8 0 31 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000291 | 1 33 | 15 17 1 0 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000292 | 1 36 | 11 22 3 0 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000293 | 1 23 | 5 14 4 0 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000294 | 1 42 | 7 29 6 0 35 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000295 | 1 26 | 9 15 2 3 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000296 | 1 28 | 6 22 0 2 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000297 | 1 47 | 10 34 3 1 38 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000298 | 1 19 | 3 16 0 3 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000299 | 1 51 | 11 35 5 1 41 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000300 | 1 53 | 11 25 17 0 42 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000301 | 1 44 | 21 22 1 0 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000302 | 1 27 | 4 20 3 0 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000303 | 1 49 | 17 26 6 0 32 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000304 | 1 54 | 16 29 9 0 38 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000305 | 1 49 | 16 31 2 1 34 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000306 | 1 30 | 9 17 4 1 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000307 | 1 46 | 13 28 5 1 34 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000308 | 1 26 | 10 15 1 2 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000309 | 1 36 | 14 20 2 1 23 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000310 | 1 35 | 7 24 4 0 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000311 | 1 37 | 9 28 0 1 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000312 | 1 42 | 7 29 6 1 36 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000313 | 1 35 | 10 24 1 5 30 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000314 | 1 29 | 14 15 0 2 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000315 | 1 29 | 11 18 0 1 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000316 | 1 45 | 15 26 4 1 31 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000317 | 1 49 | 14 31 4 1 36 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000318 | 1 36 | 11 17 8 3 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000319 | 1 34 | 10 23 1 3 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000320 | 1 37 | 15 19 3 3 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000321 | 1 42 | 20 22 0 2 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| mls_eng_000322 | 1 39 | 12 23 4 1 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001588 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001589 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001590 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001591 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001592 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001593 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001594 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001595 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001596 | 1 3 | 0 3 0 4 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001597 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001598 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001599 | 1 3 | 1 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001600 | 1 3 | 1 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001601 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001602 | 1 2 | 0 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001603 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001604 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001605 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001606 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001607 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001608 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001609 | 1 2 | 0 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001610 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001611 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001612 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001613 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001614 | 1 4 | 0 4 0 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001615 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001616 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001617 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001618 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001619 | 1 3 | 0 3 0 3 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001620 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001621 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001622 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001623 | 1 2 | 0 2 0 3 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001624 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001625 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001626 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001627 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001628 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001629 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001630 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001631 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001632 | 1 3 | 2 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001633 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001634 | 1 3 | 0 3 0 3 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001635 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001636 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001637 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001638 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001639 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001640 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001641 | 1 2 | 0 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001642 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001643 | 1 2 | 0 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001644 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001645 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001646 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001647 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001648 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001649 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001650 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001651 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001652 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001653 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001654 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001655 | 1 3 | 2 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001656 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001657 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001658 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001659 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001660 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001661 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001662 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001663 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001664 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001665 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001666 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001667 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001668 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001669 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001670 | 1 3 | 1 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001671 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001672 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001673 | 1 2 | 2 0 0 3 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001674 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001675 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001676 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001677 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001678 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001679 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001680 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001681 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001682 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001683 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001684 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001685 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001686 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001687 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001688 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001689 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001690 | 1 2 | 0 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001691 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001692 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001693 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001694 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001695 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001696 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001697 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001698 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001699 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001700 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001701 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001702 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001703 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001704 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001705 | 1 3 | 2 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001706 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001707 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001708 | 1 1 | 0 1 0 2 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001709 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001710 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001711 | 1 2 | 0 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001712 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001713 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001714 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001715 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001716 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001717 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001718 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001719 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001720 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001721 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001722 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001723 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001724 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001725 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001726 | 1 3 | 2 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001727 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001728 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001729 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001730 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001731 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001732 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001733 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001734 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001735 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001736 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001737 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001738 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001739 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001740 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001741 | 1 1 | 0 1 0 2 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001742 | 1 3 | 0 3 0 3 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001743 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001744 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001745 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001746 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001747 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001748 | 1 3 | 0 3 0 3 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001749 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001750 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001751 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001752 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001753 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001754 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001755 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001756 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001757 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001758 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001759 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001760 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001761 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001762 | 1 1 | 0 1 0 2 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001763 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001764 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001765 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001766 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001767 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001768 | 1 6 | 0 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001769 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001770 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001771 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001772 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001773 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001774 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001775 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001776 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001777 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001778 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001779 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001780 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001781 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001782 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001783 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001784 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001785 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001786 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001787 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001788 | 1 3 | 0 3 0 3 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001789 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001790 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001791 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001792 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001793 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001794 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001795 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001796 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001797 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001798 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001799 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001800 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001801 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001802 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001803 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001804 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001805 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001806 | 1 4 | 0 4 0 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001807 | 1 4 | 2 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001808 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001809 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001810 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001811 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001812 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001813 | 1 3 | 1 1 1 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001814 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001815 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001816 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001817 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001818 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001819 | 1 3 | 0 3 0 3 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001820 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001821 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001822 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001823 | 1 3 | 0 3 0 3 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| nchlt_eng_001824 | 1 4 | 1 2 1 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001744 | 1 7 | 2 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001745 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001746 | 1 10 | 3 7 0 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001747 | 1 11 | 3 8 0 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001748 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001749 | 1 2 | 1 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001750 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001751 | 1 4 | 0 2 2 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001752 | 1 6 | 1 5 0 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001753 | 1 6 | 0 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001754 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001755 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001756 | 1 4 | 2 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001757 | 1 8 | 1 7 0 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001758 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001759 | 1 7 | 1 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001760 | 1 2 | 1 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001761 | 1 8 | 2 6 0 4 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001762 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001763 | 1 15 | 5 9 1 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001764 | 1 7 | 1 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001765 | 1 7 | 2 5 0 3 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001766 | 1 4 | 2 1 1 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001767 | 1 7 | 2 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001768 | 1 5 | 2 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001769 | 1 8 | 2 5 1 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001770 | 1 5 | 1 4 0 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001771 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001772 | 1 5 | 2 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001773 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001774 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001775 | 1 10 | 2 8 0 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001776 | 1 8 | 1 7 0 4 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001777 | 1 12 | 5 6 1 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001778 | 1 6 | 4 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001779 | 1 7 | 0 6 1 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001780 | 1 9 | 3 5 1 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001781 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001782 | 1 13 | 4 8 1 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001783 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001784 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001785 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001786 | 1 18 | 4 13 1 3 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001787 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001788 | 1 6 | 3 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001789 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001790 | 1 10 | 2 7 1 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001791 | 1 7 | 1 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001792 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001793 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001794 | 1 7 | 1 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001795 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001796 | 1 3 | 2 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001797 | 1 9 | 3 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001798 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001799 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001800 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001801 | 1 10 | 4 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001802 | 1 5 | 2 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001803 | 1 7 | 1 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001804 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001805 | 1 7 | 1 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001806 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001807 | 1 4 | 1 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001808 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001809 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001810 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001811 | 1 8 | 1 7 0 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001812 | 1 4 | 1 2 1 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001813 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001814 | 1 7 | 1 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001815 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001816 | 1 8 | 0 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001817 | 1 9 | 3 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001818 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001819 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001820 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001821 | 1 2 | 0 2 0 3 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001822 | 1 4 | 3 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001823 | 1 15 | 6 7 2 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001824 | 1 7 | 1 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001825 | 1 10 | 4 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001826 | 1 7 | 3 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001827 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001828 | 1 2 | 0 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001829 | 1 8 | 1 4 3 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001830 | 1 9 | 2 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001831 | 1 3 | 1 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001832 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001833 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001834 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001835 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001836 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001837 | 1 5 | 0 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001838 | 1 7 | 3 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001839 | 1 8 | 2 4 2 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001840 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001841 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001842 | 1 6 | 2 4 0 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001843 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001844 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001845 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001846 | 1 15 | 5 10 0 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001847 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001848 | 1 8 | 1 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001849 | 1 10 | 2 7 1 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001850 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001851 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001852 | 1 12 | 3 6 3 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001853 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001854 | 1 5 | 2 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001855 | 1 8 | 2 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001856 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001857 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001858 | 1 8 | 3 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001859 | 1 2 | 1 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001860 | 1 6 | 3 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001861 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001862 | 1 12 | 6 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001863 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001864 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001865 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001866 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001867 | 1 9 | 0 6 3 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001868 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001869 | 1 4 | 1 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001870 | 1 6 | 2 3 1 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001871 | 1 14 | 4 10 0 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001872 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001873 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001874 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001875 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001876 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001877 | 1 5 | 0 5 0 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001878 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001879 | 1 24 | 7 17 0 3 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001880 | 1 10 | 2 7 1 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001881 | 1 19 | 8 8 3 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001882 | 1 7 | 1 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001883 | 1 9 | 4 5 0 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001884 | 1 7 | 4 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001885 | 1 7 | 1 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001886 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001887 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001888 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001889 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001890 | 1 2 | 2 0 0 1 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001891 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001892 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001893 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001894 | 1 6 | 0 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001895 | 1 6 | 2 3 1 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001896 | 1 6 | 4 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001897 | 1 2 | 1 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001898 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001899 | 1 10 | 6 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001900 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001901 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001902 | 1 8 | 2 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001903 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001904 | 1 8 | 2 5 1 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001905 | 1 6 | 0 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001906 | 1 13 | 5 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001907 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001908 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001909 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001910 | 1 7 | 2 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001911 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001912 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001913 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001914 | 1 8 | 2 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001915 | 1 9 | 0 5 4 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001916 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001917 | 1 3 | 2 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001918 | 1 9 | 2 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001919 | 1 8 | 0 8 0 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001920 | 1 6 | 3 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001921 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001922 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001923 | 1 21 | 8 12 1 1 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001924 | 1 10 | 0 10 0 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001925 | 1 8 | 2 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001926 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001927 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001928 | 1 4 | 3 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001929 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001930 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001931 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001932 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001933 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001934 | 1 7 | 4 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001935 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001936 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001937 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001938 | 1 12 | 0 12 0 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001939 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001940 | 1 6 | 0 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001941 | 1 6 | 1 4 1 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001942 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001943 | 1 8 | 1 6 1 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001944 | 1 8 | 4 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001945 | 1 8 | 2 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001946 | 1 20 | 7 12 1 1 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001947 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001948 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001949 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001950 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001951 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001952 | 1 9 | 1 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001953 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001954 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001955 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001956 | 1 7 | 2 4 1 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001957 | 1 9 | 3 6 0 2 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001958 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001959 | 1 7 | 2 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001960 | 1 7 | 0 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001961 | 1 7 | 2 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001962 | 1 1 | 0 1 0 2 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001963 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001964 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001965 | 1 4 | 1 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001966 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001967 | 1 10 | 5 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001968 | 1 9 | 1 8 0 2 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001969 | 1 10 | 2 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001970 | 1 6 | 0 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001971 | 1 2 | 1 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001972 | 1 13 | 4 5 4 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001973 | 1 6 | 0 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001974 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001975 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001976 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001977 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001978 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001979 | 1 12 | 2 8 2 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001980 | 1 8 | 0 7 1 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001981 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001982 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001983 | 1 10 | 3 7 0 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001984 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001985 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001986 | 1 10 | 4 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001987 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001988 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001989 | 1 10 | 5 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001990 | 1 6 | 3 2 1 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001991 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001992 | 1 2 | 1 1 0 0 1 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001993 | 1 5 | 1 4 0 3 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001994 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001995 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001996 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001997 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001998 | 1 7 | 1 5 1 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_001999 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002000 | 1 15 | 6 7 2 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002001 | 1 4 | 1 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002002 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002003 | 1 4 | 2 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002004 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| swc_eng_002005 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000874 | 1 9 | 2 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000875 | 1 4 | 3 1 0 1 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000876 | 1 8 | 2 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000877 | 1 9 | 4 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000878 | 1 7 | 4 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000879 | 1 8 | 4 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000880 | 1 7 | 1 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000883 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000884 | 1 12 | 8 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000885 | 1 9 | 2 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000886 | 1 11 | 4 7 0 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000887 | 1 12 | 3 9 0 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000888 | 1 8 | 3 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000889 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000890 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000891 | 1 15 | 5 7 3 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000892 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000893 | 1 5 | 3 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000894 | 1 17 | 7 10 0 2 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000895 | 1 11 | 4 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000896 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000897 | 1 8 | 5 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000898 | 1 9 | 5 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000899 | 1 5 | 2 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000900 | 1 11 | 3 7 1 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000901 | 1 6 | 1 5 0 5 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000902 | 1 5 | 1 3 1 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000903 | 1 9 | 4 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000904 | 1 5 | 1 4 0 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000905 | 1 12 | 2 7 3 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000906 | 1 9 | 1 6 2 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000907 | 1 10 | 5 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000908 | 1 8 | 2 5 1 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000909 | 1 10 | 3 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000910 | 1 9 | 6 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000911 | 1 4 | 2 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000912 | 1 8 | 6 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000913 | 1 11 | 5 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000914 | 1 7 | 1 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000915 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000916 | 1 7 | 5 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000917 | 1 8 | 5 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000918 | 1 13 | 6 6 1 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000920 | 1 6 | 3 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000921 | 1 10 | 3 6 1 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000922 | 1 11 | 6 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000923 | 1 10 | 4 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000924 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000925 | 1 12 | 3 9 0 2 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000927 | 1 10 | 4 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000928 | 1 9 | 1 6 2 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000929 | 1 4 | 0 4 0 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000930 | 1 13 | 10 2 1 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000931 | 1 12 | 8 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000932 | 1 12 | 7 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000933 | 1 7 | 0 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000934 | 1 8 | 2 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000935 | 1 5 | 1 4 0 2 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000938 | 1 5 | 3 2 0 1 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000939 | 1 12 | 5 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000940 | 1 10 | 4 4 2 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000942 | 1 10 | 3 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000943 | 1 8 | 4 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000944 | 1 19 | 8 10 1 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000945 | 1 13 | 3 8 2 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000946 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000947 | 1 9 | 3 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000948 | 1 6 | 0 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000949 | 1 11 | 6 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000950 | 1 10 | 3 5 2 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000951 | 1 7 | 4 2 1 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000952 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000953 | 1 9 | 2 6 1 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000954 | 1 11 | 4 6 1 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000955 | 1 14 | 5 7 2 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000956 | 1 9 | 2 7 0 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000957 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000958 | 1 10 | 6 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000959 | 1 21 | 8 13 0 1 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000960 | 1 17 | 9 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000961 | 1 12 | 4 7 1 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000962 | 1 5 | 3 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000963 | 1 13 | 3 8 2 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000964 | 1 13 | 4 9 0 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000965 | 1 7 | 4 3 0 0 3 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000966 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000967 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000968 | 1 9 | 2 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000971 | 1 8 | 4 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000972 | 1 11 | 3 8 0 1 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000973 | 1 15 | 6 8 1 0 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000974 | 1 13 | 7 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000975 | 1 10 | 3 7 0 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000976 | 1 9 | 4 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000977 | 1 5 | 3 2 0 2 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000978 | 1 7 | 5 2 0 0 2 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000979 | 1 10 | 3 5 2 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000980 | 1 11 | 5 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000981 | 1 11 | 5 5 1 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000982 | 1 8 | 2 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000983 | 1 10 | 4 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000984 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000985 | 1 9 | 3 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000986 | 1 16 | 7 8 1 1 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000987 | 1 11 | 2 9 0 5 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000988 | 1 9 | 4 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000989 | 1 15 | 3 10 2 0 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000990 | 1 10 | 5 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000991 | 1 8 | 1 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000992 | 1 9 | 4 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000993 | 1 9 | 1 6 2 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000994 | 1 8 | 1 7 0 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000995 | 1 19 | 8 11 0 0 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000996 | 1 9 | 5 3 1 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000997 | 1 10 | 3 7 0 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000998 | 1 8 | 1 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_000999 | 1 4 | 1 3 0 2 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001000 | 1 11 | 5 6 0 0 6 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001001 | 1 10 | 3 7 0 2 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001002 | 1 6 | 1 5 0 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001003 | 1 11 | 6 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxforge_eng_001004 | 1 10 | 3 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000494 | 1 16 | 5 10 1 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000495 | 1 29 | 8 17 4 1 22 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000496 | 1 28 | 12 15 1 1 17 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000497 | 1 16 | 5 10 1 1 12 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000498 | 1 26 | 7 16 3 0 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000499 | 1 68 | 19 39 10 1 50 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000500 | 1 24 | 14 10 0 1 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000501 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000502 | 1 52 | 13 35 4 1 40 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000503 | 1 27 | 7 17 3 0 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000504 | 1 32 | 4 25 3 0 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000505 | 1 52 | 18 28 6 4 38 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000506 | 1 32 | 10 17 5 3 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000507 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000508 | 1 9 | 2 6 1 1 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000509 | 1 47 | 13 33 1 8 42 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000510 | 1 43 | 7 33 3 1 37 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000511 | 1 45 | 8 35 2 0 37 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000512 | 1 9 | 6 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000513 | 1 11 | 3 8 0 3 11 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000514 | 1 12 | 2 7 3 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000515 | 1 20 | 6 11 3 1 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000516 | 1 16 | 2 9 5 2 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000517 | 1 28 | 10 16 2 1 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000518 | 1 14 | 1 12 1 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000519 | 1 28 | 3 22 3 2 27 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000520 | 1 32 | 11 17 4 3 24 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000521 | 1 38 | 10 21 7 2 30 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000522 | 1 14 | 6 7 1 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000523 | 1 23 | 4 15 4 0 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000524 | 1 31 | 5 25 1 2 28 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000525 | 1 9 | 3 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000526 | 1 16 | 8 7 1 0 8 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000527 | 1 42 | 9 30 3 3 36 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000528 | 1 18 | 4 14 0 2 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000529 | 1 14 | 4 7 3 0 10 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000530 | 1 27 | 2 24 1 4 29 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000531 | 1 8 | 2 6 0 1 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000532 | 1 5 | 2 3 0 1 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000533 | 1 16 | 3 10 3 1 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000534 | 1 36 | 12 22 2 1 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000535 | 1 27 | 6 21 0 5 26 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000536 | 1 23 | 7 13 3 2 18 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000537 | 1 22 | 4 14 4 2 20 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000538 | 1 24 | 5 13 6 0 19 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000539 | 1 18 | 5 12 1 2 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000540 | 1 19 | 8 11 0 2 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000541 | 1 14 | 4 10 0 3 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000542 | 1 7 | 2 5 0 2 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000543 | 1 19 | 5 14 0 1 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000544 | 1 21 | 4 16 1 4 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000545 | 1 7 | 3 4 0 0 4 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000546 | 1 6 | 1 3 2 0 5 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000547 | 1 20 | 7 11 2 0 13 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000548 | 1 33 | 11 19 3 3 25 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000549 | 1 21 | 6 13 2 1 16 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000550 | 1 14 | 3 10 1 4 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000551 | 1 13 | 6 7 0 0 7 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000552 | 1 33 | 6 24 3 4 31 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000553 | 1 21 | 7 13 1 0 14 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000554 | 1 33 | 13 15 5 1 21 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000555 | 1 13 | 7 5 1 3 9 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000556 | 1 17 | 2 14 1 0 15 1 | +|--------------------------+----------------------+------------------------------------------------------------------| +| voxpopuli_eng_000557 | 1 10 | 4 6 0 0 6 1 | +|====================================================================================================================| +| Sum | 1092 11772 | 3204 7786 782 898 9466 1091 | +|====================================================================================================================| +| Mean | 1.1 11.6 | 3.2 7.7 0.8 0.9 9.3 1.1 | +| S.D. | 2.4 51.8 | 16.7 31.2 4.2 2.1 37.0 2.4 | +| Median | 1.0 7.0 | 2.0 5.0 0.0 1.0 6.0 1.0 | +`--------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/hyp.trn + +Speakers: + 0: lad_eng_000254 + 1: lad_eng_000255 + 2: lad_eng_000256 + 3: lad_eng_000257 + 4: lad_eng_000258 + 5: lad_eng_000259 + 6: lad_eng_000260 + 7: lad_eng_000261 + 8: lad_eng_000262 + 9: lad_eng_000263 + 10: lad_eng_000264 + 11: lad_eng_000265 + 12: lad_eng_000266 + 13: lad_eng_000267 + 14: lad_eng_000268 + 15: lad_eng_000269 + 16: lad_eng_000270 + 17: lad_eng_000271 + 18: lad_eng_000272 + 19: lad_eng_000273 + 20: lad_eng_000274 + 21: lad_eng_000275 + 22: lad_eng_000276 + 23: lad_eng_000277 + 24: lad_eng_000278 + 25: lad_eng_000279 + 26: lad_eng_000280 + 27: lad_eng_000281 + 28: lad_eng_000282 + 29: lad_eng_000283 + 30: lad_eng_000284 + 31: lad_eng_000285 + 32: lad_eng_000286 + 33: lad_eng_000287 + 34: lad_eng_000288 + 35: lad_eng_000289 + 36: lad_eng_000290 + 37: lad_eng_000291 + 38: lad_eng_000292 + 39: lad_eng_000293 + 40: lad_eng_000294 + 41: lad_eng_000295 + 42: lad_eng_000296 + 43: lad_eng_000297 + 44: lad_eng_000298 + 45: lad_eng_000299 + 46: lad_eng_000300 + 47: lad_eng_000301 + 48: lad_eng_000302 + 49: lad_eng_000303 + 50: lad_eng_000304 + 51: lad_eng_000305 + 52: lad_eng_000306 + 53: lad_eng_000307 + 54: lad_eng_000308 + 55: lad_eng_000309 + 56: lad_eng_000310 + 57: lad_eng_000311 + 58: lad_eng_000312 + 59: lad_eng_000313 + 60: lad_eng_000314 + 61: lad_eng_000315 + 62: lad_eng_000316 + 63: lad_eng_000317 + 64: lad_eng_000318 + 65: lad_eng_000319 + 66: lad_eng_000320 + 67: lad_eng_000321 + 68: lad_eng_000322 + 69: lad_eng_000323 + 70: lad_eng_000324 + 71: lad_eng_000325 + 72: lad_eng_000326 + 73: lad_eng_000327 + 74: lad_eng_000328 + 75: lad_eng_000329 + 76: lad_eng_000330 + 77: lad_eng_000331 + 78: lad_eng_000332 + 79: lad_eng_000333 + 80: lad_eng_000334 + 81: lad_eng_000335 + 82: lad_eng_000336 + 83: lad_eng_000337 + 84: lad_eng_000338 + 85: lad_eng_000339 + 86: lad_eng_000340 + 87: lad_eng_000341 + 88: lad_eng_000342 + 89: lad_eng_000343 + 90: lad_eng_000344 + 91: lad_eng_000345 + 92: lad_eng_000346 + 93: lad_eng_000347 + 94: lad_eng_000348 + 95: lad_eng_000349 + 96: lad_eng_000350 + 97: lad_eng_000351 + 98: lad_eng_000352 + 99: lad_eng_000353 + 100: lad_eng_000354 + 101: lad_eng_000355 + 102: lad_eng_000356 + 103: lad_eng_000357 + 104: lad_eng_000358 + 105: lad_eng_000359 + 106: lad_eng_000360 + 107: lad_eng_000361 + 108: lad_eng_000362 + 109: lad_eng_000363 + 110: lad_eng_000364 + 111: lad_eng_000365 + 112: lad_eng_000366 + 113: lad_eng_000367 + 114: lad_eng_000368 + 115: lad_eng_000369 + 116: lad_eng_000370 + 117: lad_eng_000371 + 118: lad_eng_000372 + 119: lad_eng_000373 + 120: lad_eng_000374 + 121: lad_eng_000375 + 122: lad_eng_000376 + 123: m + 124: cv_eng_000707 + 125: cv_eng_000708 + 126: cv_eng_000709 + 127: cv_eng_000710 + 128: cv_eng_000711 + 129: cv_eng_000712 + 130: cv_eng_000713 + 131: cv_eng_000714 + 132: cv_eng_000715 + 133: cv_eng_000716 + 134: cv_eng_000717 + 135: cv_eng_000718 + 136: cv_eng_000719 + 137: cv_eng_000720 + 138: cv_eng_000721 + 139: cv_eng_000722 + 140: cv_eng_000723 + 141: cv_eng_000724 + 142: cv_eng_000725 + 143: cv_eng_000726 + 144: cv_eng_000727 + 145: cv_eng_000728 + 146: cv_eng_000729 + 147: cv_eng_000730 + 148: cv_eng_000731 + 149: cv_eng_000732 + 150: cv_eng_000733 + 151: cv_eng_000734 + 152: cv_eng_000735 + 153: cv_eng_000736 + 154: cv_eng_000737 + 155: cv_eng_000738 + 156: cv_eng_000739 + 157: cv_eng_000740 + 158: cv_eng_000741 + 159: cv_eng_000742 + 160: cv_eng_000743 + 161: cv_eng_000744 + 162: cv_eng_000745 + 163: cv_eng_000746 + 164: cv_eng_000747 + 165: cv_eng_000748 + 166: cv_eng_000749 + 167: cv_eng_000750 + 168: cv_eng_000751 + 169: cv_eng_000752 + 170: cv_eng_000753 + 171: cv_eng_000754 + 172: cv_eng_000755 + 173: cv_eng_000756 + 174: cv_eng_000757 + 175: cv_eng_000758 + 176: cv_eng_000759 + 177: cv_eng_000760 + 178: cv_eng_000761 + 179: cv_eng_000762 + 180: cv_eng_000763 + 181: cv_eng_000764 + 182: cv_eng_000765 + 183: cv_eng_000766 + 184: cv_eng_000767 + 185: cv_eng_000768 + 186: cv_eng_000769 + 187: cv_eng_000770 + 188: cv_eng_000771 + 189: cv_eng_000772 + 190: cv_eng_000773 + 191: cv_eng_000774 + 192: cv_eng_000775 + 193: cv_eng_000776 + 194: cv_eng_000777 + 195: cv_eng_000778 + 196: cv_eng_000779 + 197: cv_eng_000780 + 198: cv_eng_000781 + 199: cv_eng_000782 + 200: cv_eng_000783 + 201: cv_eng_000784 + 202: cv_eng_000785 + 203: cv_eng_000786 + 204: cv_eng_000787 + 205: cv_eng_000788 + 206: cv_eng_000789 + 207: cv_eng_000790 + 208: cv_eng_000791 + 209: cv_eng_000792 + 210: cv_eng_000793 + 211: cv_eng_000794 + 212: cv_eng_000795 + 213: cv_eng_000796 + 214: cv_eng_000797 + 215: cv_eng_000798 + 216: cv_eng_000799 + 217: cv_eng_000800 + 218: cv_eng_000801 + 219: cv_eng_000802 + 220: cv_eng_000803 + 221: cv_eng_000804 + 222: cv_eng_000805 + 223: cv_eng_000806 + 224: cv_eng_000807 + 225: cv_eng_000808 + 226: cv_eng_000809 + 227: fleurs_eng_000413 + 228: fleurs_eng_000414 + 229: fleurs_eng_000415 + 230: fleurs_eng_000416 + 231: fleurs_eng_000417 + 232: fleurs_eng_000418 + 233: fleurs_eng_000419 + 234: fleurs_eng_000420 + 235: fleurs_eng_000421 + 236: fleurs_eng_000422 + 237: fleurs_eng_000423 + 238: fleurs_eng_000424 + 239: fleurs_eng_000425 + 240: fleurs_eng_000426 + 241: fleurs_eng_000427 + 242: fleurs_eng_000428 + 243: fleurs_eng_000429 + 244: fleurs_eng_000430 + 245: fleurs_eng_000431 + 246: fleurs_eng_000432 + 247: fleurs_eng_000433 + 248: fleurs_eng_000434 + 249: fleurs_eng_000435 + 250: fleurs_eng_000436 + 251: fleurs_eng_000437 + 252: fleurs_eng_000438 + 253: fleurs_eng_000439 + 254: fleurs_eng_000440 + 255: fleurs_eng_000441 + 256: fleurs_eng_000442 + 257: fleurs_eng_000443 + 258: fleurs_eng_000444 + 259: fleurs_eng_000445 + 260: fleurs_eng_000446 + 261: fleurs_eng_000447 + 262: fleurs_eng_000448 + 263: fleurs_eng_000449 + 264: fleurs_eng_000450 + 265: fleurs_eng_000451 + 266: fleurs_eng_000452 + 267: fleurs_eng_000453 + 268: fleurs_eng_000454 + 269: fleurs_eng_000455 + 270: fleurs_eng_000456 + 271: fleurs_eng_000457 + 272: fleurs_eng_000458 + 273: fleurs_eng_000459 + 274: fleurs_eng_000460 + 275: fleurs_eng_000461 + 276: fleurs_eng_000462 + 277: fleurs_eng_000463 + 278: fleurs_eng_000464 + 279: fleurs_eng_000465 + 280: fleurs_eng_000466 + 281: fleurs_eng_000467 + 282: fleurs_eng_000468 + 283: fleurs_eng_000469 + 284: fleurs_eng_000470 + 285: fleurs_eng_000471 + 286: fleurs_eng_000472 + 287: fleurs_eng_000473 + 288: fleurs_eng_000474 + 289: fleurs_eng_000475 + 290: fleurs_eng_000476 + 291: mls_eng_000283 + 292: mls_eng_000284 + 293: mls_eng_000285 + 294: mls_eng_000286 + 295: mls_eng_000287 + 296: mls_eng_000288 + 297: mls_eng_000289 + 298: mls_eng_000290 + 299: mls_eng_000291 + 300: mls_eng_000292 + 301: mls_eng_000293 + 302: mls_eng_000294 + 303: mls_eng_000295 + 304: mls_eng_000296 + 305: mls_eng_000297 + 306: mls_eng_000298 + 307: mls_eng_000299 + 308: mls_eng_000300 + 309: mls_eng_000301 + 310: mls_eng_000302 + 311: mls_eng_000303 + 312: mls_eng_000304 + 313: mls_eng_000305 + 314: mls_eng_000306 + 315: mls_eng_000307 + 316: mls_eng_000308 + 317: mls_eng_000309 + 318: mls_eng_000310 + 319: mls_eng_000311 + 320: mls_eng_000312 + 321: mls_eng_000313 + 322: mls_eng_000314 + 323: mls_eng_000315 + 324: mls_eng_000316 + 325: mls_eng_000317 + 326: mls_eng_000318 + 327: mls_eng_000319 + 328: mls_eng_000320 + 329: mls_eng_000321 + 330: mls_eng_000322 + 331: nchlt_eng_001588 + 332: nchlt_eng_001589 + 333: nchlt_eng_001590 + 334: nchlt_eng_001591 + 335: nchlt_eng_001592 + 336: nchlt_eng_001593 + 337: nchlt_eng_001594 + 338: nchlt_eng_001595 + 339: nchlt_eng_001596 + 340: nchlt_eng_001597 + 341: nchlt_eng_001598 + 342: nchlt_eng_001599 + 343: nchlt_eng_001600 + 344: nchlt_eng_001601 + 345: nchlt_eng_001602 + 346: nchlt_eng_001603 + 347: nchlt_eng_001604 + 348: nchlt_eng_001605 + 349: nchlt_eng_001606 + 350: nchlt_eng_001607 + 351: nchlt_eng_001608 + 352: nchlt_eng_001609 + 353: nchlt_eng_001610 + 354: nchlt_eng_001611 + 355: nchlt_eng_001612 + 356: nchlt_eng_001613 + 357: nchlt_eng_001614 + 358: nchlt_eng_001615 + 359: nchlt_eng_001616 + 360: nchlt_eng_001617 + 361: nchlt_eng_001618 + 362: nchlt_eng_001619 + 363: nchlt_eng_001620 + 364: nchlt_eng_001621 + 365: nchlt_eng_001622 + 366: nchlt_eng_001623 + 367: nchlt_eng_001624 + 368: nchlt_eng_001625 + 369: nchlt_eng_001626 + 370: nchlt_eng_001627 + 371: nchlt_eng_001628 + 372: nchlt_eng_001629 + 373: nchlt_eng_001630 + 374: nchlt_eng_001631 + 375: nchlt_eng_001632 + 376: nchlt_eng_001633 + 377: nchlt_eng_001634 + 378: nchlt_eng_001635 + 379: nchlt_eng_001636 + 380: nchlt_eng_001637 + 381: nchlt_eng_001638 + 382: nchlt_eng_001639 + 383: nchlt_eng_001640 + 384: nchlt_eng_001641 + 385: nchlt_eng_001642 + 386: nchlt_eng_001643 + 387: nchlt_eng_001644 + 388: nchlt_eng_001645 + 389: nchlt_eng_001646 + 390: nchlt_eng_001647 + 391: nchlt_eng_001648 + 392: nchlt_eng_001649 + 393: nchlt_eng_001650 + 394: nchlt_eng_001651 + 395: nchlt_eng_001652 + 396: nchlt_eng_001653 + 397: nchlt_eng_001654 + 398: nchlt_eng_001655 + 399: nchlt_eng_001656 + 400: nchlt_eng_001657 + 401: nchlt_eng_001658 + 402: nchlt_eng_001659 + 403: nchlt_eng_001660 + 404: nchlt_eng_001661 + 405: nchlt_eng_001662 + 406: nchlt_eng_001663 + 407: nchlt_eng_001664 + 408: nchlt_eng_001665 + 409: nchlt_eng_001666 + 410: nchlt_eng_001667 + 411: nchlt_eng_001668 + 412: nchlt_eng_001669 + 413: nchlt_eng_001670 + 414: nchlt_eng_001671 + 415: nchlt_eng_001672 + 416: nchlt_eng_001673 + 417: nchlt_eng_001674 + 418: nchlt_eng_001675 + 419: nchlt_eng_001676 + 420: nchlt_eng_001677 + 421: nchlt_eng_001678 + 422: nchlt_eng_001679 + 423: nchlt_eng_001680 + 424: nchlt_eng_001681 + 425: nchlt_eng_001682 + 426: nchlt_eng_001683 + 427: nchlt_eng_001684 + 428: nchlt_eng_001685 + 429: nchlt_eng_001686 + 430: nchlt_eng_001687 + 431: nchlt_eng_001688 + 432: nchlt_eng_001689 + 433: nchlt_eng_001690 + 434: nchlt_eng_001691 + 435: nchlt_eng_001692 + 436: nchlt_eng_001693 + 437: nchlt_eng_001694 + 438: nchlt_eng_001695 + 439: nchlt_eng_001696 + 440: nchlt_eng_001697 + 441: nchlt_eng_001698 + 442: nchlt_eng_001699 + 443: nchlt_eng_001700 + 444: nchlt_eng_001701 + 445: nchlt_eng_001702 + 446: nchlt_eng_001703 + 447: nchlt_eng_001704 + 448: nchlt_eng_001705 + 449: nchlt_eng_001706 + 450: nchlt_eng_001707 + 451: nchlt_eng_001708 + 452: nchlt_eng_001709 + 453: nchlt_eng_001710 + 454: nchlt_eng_001711 + 455: nchlt_eng_001712 + 456: nchlt_eng_001713 + 457: nchlt_eng_001714 + 458: nchlt_eng_001715 + 459: nchlt_eng_001716 + 460: nchlt_eng_001717 + 461: nchlt_eng_001718 + 462: nchlt_eng_001719 + 463: nchlt_eng_001720 + 464: nchlt_eng_001721 + 465: nchlt_eng_001722 + 466: nchlt_eng_001723 + 467: nchlt_eng_001724 + 468: nchlt_eng_001725 + 469: nchlt_eng_001726 + 470: nchlt_eng_001727 + 471: nchlt_eng_001728 + 472: nchlt_eng_001729 + 473: nchlt_eng_001730 + 474: nchlt_eng_001731 + 475: nchlt_eng_001732 + 476: nchlt_eng_001733 + 477: nchlt_eng_001734 + 478: nchlt_eng_001735 + 479: nchlt_eng_001736 + 480: nchlt_eng_001737 + 481: nchlt_eng_001738 + 482: nchlt_eng_001739 + 483: nchlt_eng_001740 + 484: nchlt_eng_001741 + 485: nchlt_eng_001742 + 486: nchlt_eng_001743 + 487: nchlt_eng_001744 + 488: nchlt_eng_001745 + 489: nchlt_eng_001746 + 490: nchlt_eng_001747 + 491: nchlt_eng_001748 + 492: nchlt_eng_001749 + 493: nchlt_eng_001750 + 494: nchlt_eng_001751 + 495: nchlt_eng_001752 + 496: nchlt_eng_001753 + 497: nchlt_eng_001754 + 498: nchlt_eng_001755 + 499: nchlt_eng_001756 + 500: nchlt_eng_001757 + 501: nchlt_eng_001758 + 502: nchlt_eng_001759 + 503: nchlt_eng_001760 + 504: nchlt_eng_001761 + 505: nchlt_eng_001762 + 506: nchlt_eng_001763 + 507: nchlt_eng_001764 + 508: nchlt_eng_001765 + 509: nchlt_eng_001766 + 510: nchlt_eng_001767 + 511: nchlt_eng_001768 + 512: nchlt_eng_001769 + 513: nchlt_eng_001770 + 514: nchlt_eng_001771 + 515: nchlt_eng_001772 + 516: nchlt_eng_001773 + 517: nchlt_eng_001774 + 518: nchlt_eng_001775 + 519: nchlt_eng_001776 + 520: nchlt_eng_001777 + 521: nchlt_eng_001778 + 522: nchlt_eng_001779 + 523: nchlt_eng_001780 + 524: nchlt_eng_001781 + 525: nchlt_eng_001782 + 526: nchlt_eng_001783 + 527: nchlt_eng_001784 + 528: nchlt_eng_001785 + 529: nchlt_eng_001786 + 530: nchlt_eng_001787 + 531: nchlt_eng_001788 + 532: nchlt_eng_001789 + 533: nchlt_eng_001790 + 534: nchlt_eng_001791 + 535: nchlt_eng_001792 + 536: nchlt_eng_001793 + 537: nchlt_eng_001794 + 538: nchlt_eng_001795 + 539: nchlt_eng_001796 + 540: nchlt_eng_001797 + 541: nchlt_eng_001798 + 542: nchlt_eng_001799 + 543: nchlt_eng_001800 + 544: nchlt_eng_001801 + 545: nchlt_eng_001802 + 546: nchlt_eng_001803 + 547: nchlt_eng_001804 + 548: nchlt_eng_001805 + 549: nchlt_eng_001806 + 550: nchlt_eng_001807 + 551: nchlt_eng_001808 + 552: nchlt_eng_001809 + 553: nchlt_eng_001810 + 554: nchlt_eng_001811 + 555: nchlt_eng_001812 + 556: nchlt_eng_001813 + 557: nchlt_eng_001814 + 558: nchlt_eng_001815 + 559: nchlt_eng_001816 + 560: nchlt_eng_001817 + 561: nchlt_eng_001818 + 562: nchlt_eng_001819 + 563: nchlt_eng_001820 + 564: nchlt_eng_001821 + 565: nchlt_eng_001822 + 566: nchlt_eng_001823 + 567: nchlt_eng_001824 + 568: swc_eng_001744 + 569: swc_eng_001745 + 570: swc_eng_001746 + 571: swc_eng_001747 + 572: swc_eng_001748 + 573: swc_eng_001749 + 574: swc_eng_001750 + 575: swc_eng_001751 + 576: swc_eng_001752 + 577: swc_eng_001753 + 578: swc_eng_001754 + 579: swc_eng_001755 + 580: swc_eng_001756 + 581: swc_eng_001757 + 582: swc_eng_001758 + 583: swc_eng_001759 + 584: swc_eng_001760 + 585: swc_eng_001761 + 586: swc_eng_001762 + 587: swc_eng_001763 + 588: swc_eng_001764 + 589: swc_eng_001765 + 590: swc_eng_001766 + 591: swc_eng_001767 + 592: swc_eng_001768 + 593: swc_eng_001769 + 594: swc_eng_001770 + 595: swc_eng_001771 + 596: swc_eng_001772 + 597: swc_eng_001773 + 598: swc_eng_001774 + 599: swc_eng_001775 + 600: swc_eng_001776 + 601: swc_eng_001777 + 602: swc_eng_001778 + 603: swc_eng_001779 + 604: swc_eng_001780 + 605: swc_eng_001781 + 606: swc_eng_001782 + 607: swc_eng_001783 + 608: swc_eng_001784 + 609: swc_eng_001785 + 610: swc_eng_001786 + 611: swc_eng_001787 + 612: swc_eng_001788 + 613: swc_eng_001789 + 614: swc_eng_001790 + 615: swc_eng_001791 + 616: swc_eng_001792 + 617: swc_eng_001793 + 618: swc_eng_001794 + 619: swc_eng_001795 + 620: swc_eng_001796 + 621: swc_eng_001797 + 622: swc_eng_001798 + 623: swc_eng_001799 + 624: swc_eng_001800 + 625: swc_eng_001801 + 626: swc_eng_001802 + 627: swc_eng_001803 + 628: swc_eng_001804 + 629: swc_eng_001805 + 630: swc_eng_001806 + 631: swc_eng_001807 + 632: 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***** A LIBERALCONSERVATIVE he WAS defeated in *** EIGHTEEN EIGHTY two +HYP: AY LIBRL CON ERVETIVE he AS defeated in ATY N ATY two +Eval: I I S S S I S S + +Speaker sentences 2: lad_eng_000256 #utts: 1 +id: (lad_eng_000256-lad_eng_000256) +Scores: (#C #S #D #I) 2 6 1 0 +REF: ONE ROAD LAYER CAN DRAW two ROADS at ONCE +HYP: *** WON ROVED LAR CODRE two ROUDE at WHANCE +Eval: D S S S S S S + +Speaker sentences 3: lad_eng_000257 #utts: 1 +id: (lad_eng_000257-lad_eng_000257) +Scores: (#C #S #D #I) 4 5 0 0 +REF: some OF the COUNTRIES HAVE SURVEYS for MULTIPLE years +HYP: some O the OUNTIS HAE SERVAYIS for MALTBLE years +Eval: S S S S S + +Speaker sentences 4: lad_eng_000258 #utts: 1 +id: (lad_eng_000258-lad_eng_000258) +Scores: (#C #S #D #I) 3 4 2 0 +REF: BOTH OF THE VERSIONS FEATURE the song HAPPY holiday +HYP: **** ** BOTHOF THEVIRSINS FEATHE the song HAPY holiday +Eval: D D S S S S + +Speaker sentences 5: lad_eng_000259 #utts: 1 +id: (lad_eng_000259-lad_eng_000259) +Scores: (#C #S #D #I) 3 10 0 1 +REF: SHAKESPEARE many REFERENCES ARE MADE to ******* SCENES INTERACTIONS or CHARACTERS FROM VARIOUS PLAYS +HYP: SHAKSPIER many REFRNCES UR MAD to SHEENDS INTR ACTIOND or CARICTES FOM VERIUT PLAYES +Eval: S S S S I S S S S S S + +Speaker sentences 6: lad_eng_000260 #utts: 1 +id: (lad_eng_000260-lad_eng_000260) +Scores: (#C #S #D #I) 4 9 1 0 +REF: if ONLY the program COULD BREAK out JUST A LITTLE FROM ITS TOOFAMILIAR APPROACH +HYP: if NDY the program ***** CULDBRAKE out GUST LITL FOME IT TWO FOMELIAR APROUCH +Eval: S D S S S S S S S S + +Speaker sentences 7: lad_eng_000261 #utts: 1 +id: (lad_eng_000261-lad_eng_000261) +Scores: (#C #S #D #I) 3 10 0 2 +REF: the ALBUM was ****** RELEASED IN AUSTRALIA ON NINETEENTH AUGUST two ******** THOUSAND AND ELEVEN +HYP: the HALBUM was RELESE INO STRALIAR ARN NIN TINT OAGIST two THOUSENT AN E LEVON +Eval: S I S S S S S S I S S S + +Speaker sentences 8: lad_eng_000262 #utts: 1 +id: (lad_eng_000262-lad_eng_000262) +Scores: (#C #S #D #I) 3 5 0 1 +REF: he now PLAYS for * AUSTRALIAN CLUB PERTH GLORY +HYP: he now PLACE for A STRALIN TLOBE PEIRT GLOURY +Eval: S I S S S S + +Speaker sentences 9: lad_eng_000263 #utts: 1 +id: (lad_eng_000263-lad_eng_000263) +Scores: (#C #S #D #I) 7 5 1 0 +REF: IT IS not KNOWN how MUCH if any of her CLAIMS are TRUE +HYP: ** ITIT not NONE how MUCHE if any of her TLAMEMS are TRO +Eval: D S S S S S + +Speaker sentences 10: lad_eng_000264 #utts: 1 +id: (lad_eng_000264-lad_eng_000264) +Scores: (#C #S #D #I) 6 13 1 0 +REF: a SMALL BUSINESS OWNER BROAD OPERATED his WHEAT AND SHEEP FARM for SIXTEEN YEARS FROM the AGE of TWENTY two +HYP: a SMLL BISNESS ONER BRAURD OPRATED his WEA AN HAP FARME for ******* SICTENEARS FRO the AG of WENTY two +Eval: S S S S S S S S S D S S S S + +Speaker sentences 11: lad_eng_000265 #utts: 1 +id: (lad_eng_000265-lad_eng_000265) +Scores: (#C #S #D #I) 5 3 1 0 +REF: in THE NINTH CENTURY he was an irish POET +HYP: in *** THENINTH SENCTRY he was an irish POAT +Eval: D S S S + +Speaker sentences 12: lad_eng_000266 #utts: 1 +id: (lad_eng_000266-lad_eng_000266) +Scores: (#C #S #D #I) 2 3 0 0 +REF: they ARE MARKED by STRONG +HYP: they AR MAUECET by STRONGN +Eval: S S S + +Speaker sentences 13: lad_eng_000267 #utts: 1 +id: (lad_eng_000267-lad_eng_000267) +Scores: (#C #S #D #I) 2 3 0 1 +REF: the LAW is ***** THEREFORE VALID +HYP: the LALLE is THERE FOR VOULED +Eval: S I S S + +Speaker sentences 14: lad_eng_000268 #utts: 1 +id: (lad_eng_000268-lad_eng_000268) +Scores: (#C #S #D #I) 5 3 1 1 +REF: in THE EARLY stages came close to * US ASLEEP +HYP: in *** THEARLY stages came close to U A SLEP +Eval: D S I S S + +Speaker sentences 15: lad_eng_000269 #utts: 1 +id: (lad_eng_000269-lad_eng_000269) +Scores: (#C #S #D #I) 1 6 0 1 +REF: RUNNING every ****** THIRTY MINUTES THROUGHOUT SERVICE TIMES +HYP: RUNING every THARTY MINIT THO UT SERVIS TIMEMS +Eval: S I S S S S S + +Speaker sentences 16: lad_eng_000270 #utts: 1 +id: (lad_eng_000270-lad_eng_000270) +Scores: (#C #S #D #I) 4 8 1 1 +REF: as A RESULT when THE COLLEGE REOPENED it WAS as * AN ALLMALE COLLEGE +HYP: as * RESILT when THECOLIGE RE OPEND it WHAS as A ALL MALE COLIGE +Eval: D S S S S S I S S S + +Speaker sentences 17: lad_eng_000271 #utts: 1 +id: (lad_eng_000271-lad_eng_000271) +Scores: (#C #S #D #I) 8 9 0 0 +REF: the time BETWEEN THESE POINTS is VARIABLE and CAN OCCUR ANYWHERE FROM a MINUTE to much longer +HYP: the time BETWEN THES PONT is VERRABLE and CANACER ANY WHE FRO a INIT to much longer +Eval: S S S S S S S S S + +Speaker sentences 18: lad_eng_000272 #utts: 1 +id: (lad_eng_000272-lad_eng_000272) +Scores: (#C #S #D #I) 11 8 1 0 +REF: WORK on the e e S STARTED in MARCH two THOUSAND and SEVEN at A cost of five MILLION DOLLARS +HYP: WEARK on the e e EAEAS STDARTED in MARHCH two THAUSED and SEVON at * cost of five MILIOND DOLLERS +Eval: S S S S S S D S S + +Speaker sentences 19: lad_eng_000273 #utts: 1 +id: (lad_eng_000273-lad_eng_000273) +Scores: (#C #S #D #I) 7 10 1 3 +REF: however THERE WAS some *** * DISAGREEMENT OVER the ending ****** THEME WHICH OMORI and YOSHIMORI DISCUSSED at LENGTH over EMAIL +HYP: however ***** TEWAS some DEC A GREMENT OE the ending THEMEM WICH O MORY and YOSHIMORY DECSKUSTE at LEANGTH over EMOUL +Eval: D S I I S S I S S S S S S S + +Speaker sentences 20: lad_eng_000274 #utts: 1 +id: (lad_eng_000274-lad_eng_000274) +Scores: (#C #S #D #I) 3 2 0 0 +REF: the COUPLE had no CHILDREN +HYP: the CAPLE had no CHILDRON +Eval: S S + +Speaker sentences 21: lad_eng_000275 #utts: 1 +id: (lad_eng_000275-lad_eng_000275) +Scores: (#C #S #D #I) 3 10 1 1 +REF: THE OFFICIAL SINGLE OF that ***** DEBUT ALBUM PARIS CALLING had AN ELABORATE music VIDEO +HYP: *** THEFITIAL SINGL F that DEBYU AL THM PARISS COLING had A ELABRT music VIDEAO +Eval: D S S S I S S S S S S S + +Speaker sentences 22: lad_eng_000276 #utts: 1 +id: (lad_eng_000276-lad_eng_000276) +Scores: (#C #S #D #I) 9 8 1 0 +REF: the SERIES ended on SIXTH AUGUST two THOUSAND and FOUR lasting FOR A TOTAL of seventy ONE days +HYP: the SERIS ended on SICXTH AORGIST two THAUSEND and FORE lasting *** FRA TOUTL of seventy OND days +Eval: S S S S S D S S S + +Speaker sentences 23: lad_eng_000277 #utts: 1 +id: (lad_eng_000277-lad_eng_000277) +Scores: (#C #S #D #I) 5 6 0 1 +REF: he has ALSO CONTRIBUTED to the *** NEW YORK REVIEW of BOOKS +HYP: he has ALSOD CONTRIBETE to the NUN YOURC RE EU of BOKS +Eval: S S I S S S S + +Speaker sentences 24: lad_eng_000278 #utts: 1 +id: (lad_eng_000278-lad_eng_000278) +Scores: (#C #S #D #I) 4 4 0 1 +REF: by placing SMALL art ******* OBJECTS THROUGHOUT the FILM +HYP: by placing SMAL art OBDGECT THRO OUT the ILME +Eval: S I S S S + +Speaker sentences 25: lad_eng_000279 #utts: 1 +id: (lad_eng_000279-lad_eng_000279) +Scores: (#C #S #D #I) 2 3 0 0 +REF: it IS FOUND in BRAZIL +HYP: it I FOUNED in BRESIL +Eval: S S S + +Speaker sentences 26: lad_eng_000280 #utts: 1 +id: (lad_eng_000280-lad_eng_000280) +Scores: (#C #S #D #I) 6 5 0 0 +REF: it WAS the SIDE of the FAMILY i identified MORE WITH +HYP: it WA the SID of the CAMLY i identified MORLE WIFH +Eval: S S S S S + +Speaker sentences 27: lad_eng_000281 #utts: 1 +id: (lad_eng_000281-lad_eng_000281) +Scores: (#C #S #D #I) 4 4 0 2 +REF: ******* CANDIDATE SITES must **** ALSO SUBMIT a work plan +HYP: ECANDED IT SIGHTHE must LLSO SOD MIT a work plan +Eval: I S S I S S + +Speaker sentences 28: lad_eng_000282 #utts: 1 +id: (lad_eng_000282-lad_eng_000282) +Scores: (#C #S #D #I) 2 4 0 0 +REF: DUNDEE won the MATCH THREE TWO +HYP: DUNDEY won the MACH THRE TWOE +Eval: S S S S + +Speaker sentences 29: lad_eng_000283 #utts: 1 +id: (lad_eng_000283-lad_eng_000283) +Scores: (#C #S #D #I) 6 8 0 3 +REF: however the ***** VILLAGE REMAINED ISOLATED UNTIL the ARRIVAL of THE first **** NEWSPAPER second ** REPUBLIC +HYP: however the VILIG REMANED ICILAT DT NTIL the RIVUL of TE first NOUS PAPER second RE POUBLICK +Eval: I S S S S S S I S I S + +Speaker sentences 30: lad_eng_000284 #utts: 1 +id: (lad_eng_000284-lad_eng_000284) +Scores: (#C #S #D #I) 9 9 2 1 +REF: the first SERVICE IN THE NEW CHURCH was held IN NINETEEN fifty ** ONE ALTHOUGH the BUILDING was not FULLY finished +HYP: the first ******* ERVI I THENU CHARC was held ** NINTN fifty ON AL THO the BILDIG was not FULY finished +Eval: D S S S S D S I S S S S + +Speaker sentences 31: lad_eng_000285 #utts: 1 +id: (lad_eng_000285-lad_eng_000285) +Scores: (#C #S #D #I) 6 12 1 0 +REF: the AVERAGE HOUSEHOLD SIZE was two POINT two SEVEN AND the AVERAGE FAMILY SIZE was THREE POINT ZERO ZERO +HYP: the AVRIGH HOUSEHLD SIES was two PONT two SEVON ND the AVRIGH FAMLY SIES was ***** THRE POENTESIARO SIARO +Eval: S S S S S S S S S D S S S + +Speaker sentences 32: lad_eng_000286 #utts: 1 +id: (lad_eng_000286-lad_eng_000286) +Scores: (#C #S #D #I) 6 5 0 1 +REF: it was ****** FIRST BROADCAST on THIRD JANUARY two THOUSAND and ten +HYP: it was FIRSTE RARD CAST on THRED GANIOURY two HOUSEND and ten +Eval: I S S S S S + +Speaker sentences 33: lad_eng_000287 #utts: 1 +id: (lad_eng_000287-lad_eng_000287) +Scores: (#C #S #D #I) 6 3 0 0 +REF: the wings WERE now MADE in a single PRESSING +HYP: the wings WE now AD in a single PRESING +Eval: S S S + +Speaker sentences 34: lad_eng_000288 #utts: 1 +id: (lad_eng_000288-lad_eng_000288) +Scores: (#C #S #D #I) 2 4 0 0 +REF: DOCTOR OF PHILOSOPHY in ENGINEERING management +HYP: TE DOCTE OFOLOIFY in ENDENYEARIG management +Eval: S S S S + +Speaker sentences 35: lad_eng_000289 #utts: 1 +id: (lad_eng_000289-lad_eng_000289) +Scores: (#C #S #D #I) 2 7 0 0 +REF: THIS TOOK AWAY the MAIN ARGUMENT of SAFETY RISKS +HYP: THISE O WAY the MAEN ARKGUMEN of SAIFTDY RISSKS +Eval: S S S S S S S + +Speaker sentences 36: lad_eng_000290 #utts: 1 +id: (lad_eng_000290-lad_eng_000290) +Scores: (#C #S #D #I) 4 6 0 0 +REF: he was ALSO MADE a LIFE MEMBER of SCUNTHORPE UNITED +HYP: he was ALLSO AD a LIFH MEMBR of SCOUND THORPYUNITED +Eval: S S S S S S + +Speaker sentences 37: lad_eng_000291 #utts: 1 +id: (lad_eng_000291-lad_eng_000291) +Scores: (#C #S #D #I) 3 7 1 0 +REF: she FEARS THEY WILL GET A DIVORCE but THIS never HAPPENS +HYP: she ***** FHEIRS THE L GAT DEFORSE but THIE never HAPENS +Eval: D S S S S S S S + +Speaker sentences 38: lad_eng_000292 #utts: 1 +id: (lad_eng_000292-lad_eng_000292) +Scores: (#C #S #D #I) 1 8 0 0 +REF: FOOT drops UNABLE TO HOLD THE FOOT STRAIGHT ACROSS +HYP: FOT drops NABLE T HAD TH FOT SRAT ACROUSE +Eval: S S S S S S S S + +Speaker sentences 39: lad_eng_000293 #utts: 1 +id: (lad_eng_000293-lad_eng_000293) +Scores: (#C #S #D #I) 5 11 0 0 +REF: WHETHER the AIR FLOW is FREE or FORCED CAN AFFECT the ENERGY EFFICIENCY of THE WINDOW +HYP: WHETH the AR FLOY is FREY or FOURST CN FEC the ENDGY OFIENCY of TH WHNDO +Eval: S S S S S S S S S S S + +Speaker sentences 40: lad_eng_000294 #utts: 1 +id: (lad_eng_000294-lad_eng_000294) +Scores: (#C #S #D #I) 0 10 0 0 +REF: AFTER GETTING THE RIGHT MEASUREMENTS THEY MADE THE NEW DOORS +HYP: AFTR GETIG HE IT MESERENT THE MAD TH NOU DORS +Eval: S S S S S S S S S S + +Speaker sentences 41: lad_eng_000295 #utts: 1 +id: (lad_eng_000295-lad_eng_000295) +Scores: (#C #S #D #I) 2 9 0 0 +REF: FRAGMENTS on EACH face ARE MARKED WITH LETTERS A B C +HYP: FRAGMNTE on ACH face RE MARET WTH LTERS AY BE SE +Eval: S S S S S S S S S + +Speaker sentences 42: lad_eng_000296 #utts: 1 +id: (lad_eng_000296-lad_eng_000296) +Scores: (#C #S #D #I) 4 10 0 1 +REF: from THE FIRST MINUTES both TEAMS SHOWED THEIR desire to ******* COMPETE WITH AGGRESSIVE APPROACHES +HYP: from TH FIRSTD MINITE both TEMES SHOD THE desire to COMPEET WI THEGREIOF A PROCERS +Eval: S S S S S S I S S S S + +Speaker sentences 43: lad_eng_000297 #utts: 1 +id: (lad_eng_000297-lad_eng_000297) +Scores: (#C #S #D #I) 3 7 0 2 +REF: PHYSICAL THERAPY EXERCISES may help ** PATIENTS to **** MAINTAIN MUSCLE STRENGTH +HYP: FISICL HERIBY ECERSIDSES may help TH PATIONTE to MAIN TAIN MUSL STRINGTH +Eval: S S S I S I S S S + +Speaker sentences 44: lad_eng_000298 #utts: 1 +id: (lad_eng_000298-lad_eng_000298) +Scores: (#C #S #D #I) 6 6 1 0 +REF: however the TOWN SHE LIVES in no ONE WANTS to HEAR ABOUT her +HYP: however the TOWNE HE LIVS in no UND WONT to **** HEARABOUT her +Eval: S S S S S D S + +Speaker sentences 45: lad_eng_000299 #utts: 1 +id: (lad_eng_000299-lad_eng_000299) +Scores: (#C #S #D #I) 4 8 1 1 +REF: * DESCRIBES APPOINTMENTS OF an acting CHIEF JUSTICE or JUDGE of THE SUPREME COURT +HYP: A DISRIES AEPOENTDENT O an acting CHIVE JUSTISS or JOUDGE of *** THESOPREME CORT +Eval: I S S S S S S D S S + +Speaker sentences 46: lad_eng_000300 #utts: 1 +id: (lad_eng_000300-lad_eng_000300) +Scores: (#C #S #D #I) 3 10 0 1 +REF: the **** SOYBEANS OUTER COVERING is THEN REMOVED and THE BEANS ARE PARTIALLY COOKED +HYP: the SORY BES OUT CUVERING is TEN REMOVEDT and TE BENDS AR PARTHALY COCKET +Eval: I S S S S S S S S S S + +Speaker sentences 47: lad_eng_000301 #utts: 1 +id: (lad_eng_000301-lad_eng_000301) +Scores: (#C #S #D #I) 4 9 2 0 +REF: this NATIONAL MOVEMENT WHICH HAD BEGUN WITH so MUCH HOPE CAME TO a sad END +HYP: this ******** NASTIAL MOVENT WHCHE BEGON WTH so **** UH HOP CAMETO a sad EAND +Eval: D S S S S S D S S S S + +Speaker sentences 48: lad_eng_000302 #utts: 1 +id: (lad_eng_000302-lad_eng_000302) +Scores: (#C #S #D #I) 2 8 0 1 +REF: his * ASSOCIATES USUALLY CALLED him T OR THE GOODLOOKING GUY +HYP: his A SEOSIATE OUSUALY CALD him TE ORE THEGOOD LOKING GIY +Eval: I S S S S S S S S + +Speaker sentences 49: lad_eng_000303 #utts: 1 +id: (lad_eng_000303-lad_eng_000303) +Scores: (#C #S #D #I) 2 9 0 1 +REF: its MAIN OFFICES WERE in ****** LONDON WITH A SECOND OFFICE BELFAST +HYP: its MAEN OFHICES WER in LUNDEN WE THE SECEND OFISS BEL FAST +Eval: S S S I S S S S S S + +Speaker sentences 50: lad_eng_000304 #utts: 1 +id: (lad_eng_000304-lad_eng_000304) +Scores: (#C #S #D #I) 4 5 1 0 +REF: ACTUALLY i had NEVER BEEN TO a VILLAGE BEFORE that +HYP: ACTULY i had ***** NER BEENTO a VILIDGE BEFOR that +Eval: S D S S S S + +Speaker sentences 51: lad_eng_000305 #utts: 1 +id: (lad_eng_000305-lad_eng_000305) +Scores: (#C #S #D #I) 6 9 0 0 +REF: he WAS CHARGED WITH PLANNING to set OFF BOMBS in EUROPE and the UNITED STATES +HYP: he AS CHAGE ITH PLADING to set OF BOMS in URAP and the UNIGTE TATE +Eval: S S S S S S S S S + +Speaker sentences 52: lad_eng_000306 #utts: 1 +id: (lad_eng_000306-lad_eng_000306) +Scores: (#C #S #D #I) 6 5 0 1 +REF: making MIRRORS is the third STUDIO ALBUM by ****** BELGIANAUSTRALIAN artist GOTYE +HYP: making MRARS is the third STUDIUR ALBAE by BELDEN ASTRALIAN artist GOTIEAY +Eval: S S S I S S + +Speaker sentences 53: lad_eng_000307 #utts: 1 +id: (lad_eng_000307-lad_eng_000307) +Scores: (#C #S #D #I) 7 10 0 2 +REF: he then moved to ******* *** WASHINGTON DC and was a PARTNER WITH WARD BROWN UNTIL NINETEEN TWENTY NINE +HYP: he then moved to WOASING TOD DE SI and was a PART NE IT WORD BROWNEANDTILL NINTEN WENTY NIN +Eval: I I S S S S S S S S S S + +Speaker sentences 54: lad_eng_000308 #utts: 1 +id: (lad_eng_000308-lad_eng_000308) +Scores: (#C #S #D #I) 3 8 1 1 +REF: *** JOSEPH HIGH SCHOOL AND the SCHOOLS they COMPETE AGAINST IN ALL sports +HYP: JOS OF HIY SCOLE AD the SCOLES they ******* COMPET GANED INAL sports +Eval: I S S S S S D S S S + +Speaker sentences 55: lad_eng_000309 #utts: 1 +id: (lad_eng_000309-lad_eng_000309) +Scores: (#C #S #D #I) 1 6 0 0 +REF: TWELVE plus ONE MATCH BAN PER CARD +HYP: WELF plus ON MACH BAND ER COAURD +Eval: S S S S S S + +Speaker sentences 56: lad_eng_000310 #utts: 1 +id: (lad_eng_000310-lad_eng_000310) +Scores: (#C #S #D #I) 6 0 0 0 +REF: i think i might be nothing +HYP: i think i might be nothing +Eval: + +Speaker sentences 57: lad_eng_000311 #utts: 1 +id: (lad_eng_000311-lad_eng_000311) +Scores: (#C #S #D #I) 6 12 0 1 +REF: the HOME WAS BUILT and lived in by **** ANDREW JACKSON KENNEDY DEPUTY COLLECTOR FOR the INTERNAL REVENUE SERVICE +HYP: the HOM AS BILT and lived in by ANDR JAC AND CANIDY DEPETY COLECT O the INTERNL REVINU SERVIS +Eval: S S S I S S S S S S S S S + +Speaker sentences 58: lad_eng_000312 #utts: 1 +id: (lad_eng_000312-lad_eng_000312) +Scores: (#C #S #D #I) 7 9 0 0 +REF: in NINETEEN SIXTY FOUR he went BACK to OMSK and ENTERED the ACTORS SCHOOL of OMSK +HYP: in NINTAN SICE YEFORE he went BAK to OMSEK and ENTE the ACTO SCHOUL of OAMSK +Eval: S S S S S S S S S + +Speaker sentences 59: lad_eng_000313 #utts: 1 +id: (lad_eng_000313-lad_eng_000313) +Scores: (#C #S #D #I) 6 5 1 0 +REF: THE BANK is JOINTLY OWNED by him and his BROTHERS and RELATIVES +HYP: *** THEBANK is JUINTLY ONED by him and his BROUVER and RELITIVS +Eval: D S S S S S + +Speaker sentences 60: lad_eng_000314 #utts: 1 +id: (lad_eng_000314-lad_eng_000314) +Scores: (#C #S #D #I) 3 4 0 0 +REF: he SUBSEQUENTLY WENT to SCHOOL in BRISTOL +HYP: he SOPEICENTLY WAN to COL in BREISTAL +Eval: S S S S + +Speaker sentences 61: lad_eng_000315 #utts: 1 +id: (lad_eng_000315-lad_eng_000315) +Scores: (#C #S #D #I) 1 7 1 0 +REF: ONE THOUSAND EIGHT HUNDRED AND forty SIX FOURTH EDITION +HYP: *** WON THAUSEND AT HUNDED forty SICKCS FOARTH IDITION +Eval: D S S S S S S S + +Speaker sentences 62: lad_eng_000316 #utts: 1 +id: (lad_eng_000316-lad_eng_000316) +Scores: (#C #S #D #I) 6 10 0 2 +REF: a PART of LITTLE ENGLAND beyond WALES it has *** * BEEN ESSENTIALLY ENGLISHSPEAKING for NINE HUNDRED YEARS +HYP: a PAT of LITL INGLEND beyond WAILES it has BEE A ENCHRLY INGLISH SPEAKING for NIN HUNTRED OEARS +Eval: S S S S I I S S S S S S + +Speaker sentences 63: lad_eng_000317 #utts: 1 +id: (lad_eng_000317-lad_eng_000317) +Scores: (#C #S #D #I) 4 10 1 0 +REF: he PLAYED WITH ten PLAYERS for HALF was AGAINST THE TRADITION IN G S P +HYP: he PLAD WTH ten PLARS for HARVF was ******* AGANETH TRDITION IND DE ASS PE +Eval: S S S S D S S S S S S + +Speaker sentences 64: lad_eng_000318 #utts: 1 +id: (lad_eng_000318-lad_eng_000318) +Scores: (#C #S #D #I) 3 15 0 0 +REF: the PRESIDING JUDGE was WEBSTER THAYER WHO WAS ALREADY ASSIGNED TO THE COURT BEFORE THIS CASE was SCHEDULED +HYP: the REIDING GJOUDG was WEBST O FAIR HO WASAL EADY A SIED TOTHE CORT BEFORETHIS CACE was SHEDULT +Eval: S S S S S S S S S S S S S S S + +Speaker sentences 65: lad_eng_000319 #utts: 1 +id: (lad_eng_000319-lad_eng_000319) +Scores: (#C #S #D #I) 8 5 2 0 +REF: BIG BROTHER five was the third OF the main SERIES TO FEATURE a live LAUNCH +HYP: BG GRATHE five was the third O the main ****** ** SERISTOFECHE a live LOUNCH +Eval: S S S D D S S + +Speaker sentences 66: lad_eng_000320 #utts: 1 +id: (lad_eng_000320-lad_eng_000320) +Scores: (#C #S #D #I) 10 9 0 2 +REF: its MOTTO is ** WHOEVER you ARE and wherever you are on the JOURNEY of **** FAITH YOU ARE WELCOME HERE +HYP: its MOTO is HO EVE you AR and wherever you are on the DIRNY of FAIF YO AE WEL COM HER +Eval: S I S S S I S S S S S + +Speaker sentences 67: lad_eng_000321 #utts: 1 +id: (lad_eng_000321-lad_eng_000321) +Scores: (#C #S #D #I) 1 5 0 0 +REF: ROBERT E MILLER as COACH WILSON +HYP: ROBET A MILE as COTH WILTSON +Eval: S S S S S + +Speaker sentences 68: lad_eng_000322 #utts: 1 +id: (lad_eng_000322-lad_eng_000322) +Scores: (#C #S #D #I) 1 9 0 1 +REF: ***** AFTER A ONEYEAR BREAK ZERO DEGREE was HER FOLLOWING VENTURE +HYP: AFTRE WN YUAR BRAK SIR AO DEGRE was HE FLLING VENCHER +Eval: I S S S S S S S S S + +Speaker sentences 69: lad_eng_000323 #utts: 1 +id: (lad_eng_000323-lad_eng_000323) +Scores: (#C #S #D #I) 1 10 2 0 +REF: A M T MANUFACTURED a MODEL KIT OF THE Z Z R DRAGSTER +HYP: AY AM TEY MANUFACTED a ***** *** MORTL CIT OFTHE AD SAID ARDRACSTER +Eval: S S S S D D S S S S S S + +Speaker sentences 70: lad_eng_000324 #utts: 1 +id: (lad_eng_000324-lad_eng_000324) +Scores: (#C #S #D #I) 6 10 2 1 +REF: the S S A AIMED to BUILD a **** LEFTWING ALTERNATIVE to NEW LABOUR and the S N P +HYP: the * ESESS AY AMED to BILED a LEFT WING OLTERNITIF to NOU LABER and the * ESSAND PE +Eval: D S S S S I S S S S D S S + +Speaker sentences 71: lad_eng_000325 #utts: 1 +id: (lad_eng_000325-lad_eng_000325) +Scores: (#C #S #D #I) 2 5 1 0 +REF: he LIVES LIKE he IS A YOUNG PERSON +HYP: he LIVS LIK he ** AS YONG PRSON +Eval: S S D S S S + +Speaker sentences 72: lad_eng_000326 #utts: 1 +id: (lad_eng_000326-lad_eng_000326) +Scores: (#C #S #D #I) 2 4 0 0 +REF: MASTER of SCIENCE in ENGINEERING MANAGEMENT +HYP: MASTE of SIND in ENDENEARIG MANIGENT +Eval: S S S S + +Speaker sentences 73: lad_eng_000327 #utts: 1 +id: (lad_eng_000327-lad_eng_000327) +Scores: (#C #S #D #I) 6 7 2 0 +REF: she failed to MAKE the top THREE AT THE KENYAN JUNIOR TRACK TRIALS that JUNE +HYP: she failed to AK the top ***** ** THRE ATHE CENION DJUNIEARTRAC TRILES that DUN +Eval: S D D S S S S S S + +Speaker sentences 74: lad_eng_000328 #utts: 1 +id: (lad_eng_000328-lad_eng_000328) +Scores: (#C #S #D #I) 2 3 0 0 +REF: a TOUR FOLLOWED in SUPPORT +HYP: a TOARE FOLOUD in SUPORT +Eval: S S S + +Speaker sentences 75: lad_eng_000329 #utts: 1 +id: (lad_eng_000329-lad_eng_000329) +Scores: (#C #S #D #I) 5 11 3 0 +REF: THEY WERE ESTABLISHED IN EIGHTEEN seventy ONE and ARE ONE OF the OLDEST CLUBS IN THE south of ENGLAND +HYP: **** THE ESTABLISE N ATEN seventy ON and E WE O the ****** ***** LDEST CLOPSINTHE south of INGLEND +Eval: D S S S S S S S S D D S S S + +Speaker sentences 76: lad_eng_000330 #utts: 1 +id: (lad_eng_000330-lad_eng_000330) +Scores: (#C #S #D #I) 4 6 0 0 +REF: he WAS a MEMBER of the YES SCOTLAND ADVISORY BOARD +HYP: he WS a MEMBR of the GEAS SCOTLEND ADFISERY BORD +Eval: S S S S S S + +Speaker sentences 77: lad_eng_000331 #utts: 1 +id: (lad_eng_000331-lad_eng_000331) +Scores: (#C #S #D #I) 2 3 0 1 +REF: two THOUSAND AND five ***** GENTLEMAN +HYP: two THOUSEND ND five GENTL MEN +Eval: S S I S + +Speaker sentences 78: lad_eng_000332 #utts: 1 +id: (lad_eng_000332-lad_eng_000332) +Scores: (#C #S #D #I) 6 11 1 0 +REF: OUR FILM HAD a STRONG reception IN EUROPE AND ACHIEVED DISTRIBUTION BUT that was not the CASE HERE +HYP: AOURE FILE AD a STONG reception ** INUR APAD CHVE DESTOBEUTION TUT that was not the CACE HER +Eval: S S S S D S S S S S S S + +Speaker sentences 79: lad_eng_000333 #utts: 1 +id: (lad_eng_000333-lad_eng_000333) +Scores: (#C #S #D #I) 0 5 0 1 +REF: ** ORTHOSIS STRETCHES POSTERIOR ANKLE STRUCTURES +HYP: BL THOIS STDETHES POSTERIER ANGAL STROCTHES +Eval: I S S S S S + +Speaker sentences 80: lad_eng_000334 #utts: 1 +id: (lad_eng_000334-lad_eng_000334) +Scores: (#C #S #D #I) 5 13 0 0 +REF: he was ALSO a THREE time FRENCH NATIONAL CHAMPION NINETEEN NINETY NINETEEN NINETY FOUR two THOUSAND AND ONE +HYP: he was ALLSO a THRE time FRENCHE NASINL HAMBPIAN NINTIN NINTY NINTE NITY FORE two HOUSED AN WON +Eval: S S S S S S S S S S S S S + +Speaker sentences 81: lad_eng_000335 #utts: 1 +id: (lad_eng_000335-lad_eng_000335) +Scores: (#C #S #D #I) 5 8 1 2 +REF: THE VILLAGE STRUCTURE shown in HIS map is TO a **** ******* GREAT EXTENT UNCHANGED TODAY +HYP: *** THEILIGE STRUCTHER shown in IS map is T a GRAT EXSTENT UN CHAGE T DAY +Eval: D S S S S I I S S S S + +Speaker sentences 82: lad_eng_000336 #utts: 1 +id: (lad_eng_000336-lad_eng_000336) +Scores: (#C #S #D #I) 3 11 1 0 +REF: RUSSIA is RECOGNIZED FOR ITS NUCLEAR DISASTER EXPERTISE and FOR the SAFETY OF ITS TECHNOLOGY +HYP: RUHAR is ********** RECAGNISED IT NUKLER DESAUSTER ECPORTES and O the SAFTY OFITS TE NOLAGY +Eval: S D S S S S S S S S S S + +Speaker sentences 83: lad_eng_000337 #utts: 1 +id: (lad_eng_000337-lad_eng_000337) +Scores: (#C #S #D #I) 7 12 1 0 +REF: as of two THOUSAND AND FOURTEEN M T V is AVAILABLE within THE UNITED KINGDOM on VIRGIN MEDIA and SKY +HYP: as of two THOUSEND OD FOR TEN AEMTY VEE is AVALABLE within *** THEUNIGTED CINGDM on VERGEIN MEDIER and SCGIY +Eval: S S S S S S S D S S S S S + +Speaker sentences 84: lad_eng_000338 #utts: 1 +id: (lad_eng_000338-lad_eng_000338) +Scores: (#C #S #D #I) 1 3 1 0 +REF: NEW YORK PENGUIN RANDOM house +HYP: *** NOYOURK PEANGIN RANDM house +Eval: D S S S + +Speaker sentences 85: lad_eng_000339 #utts: 1 +id: (lad_eng_000339-lad_eng_000339) +Scores: (#C #S #D #I) 4 7 0 0 +REF: the DUCHY was SECURED IN THE OUTCOME of the GOTHIC WAR +HYP: the DUTCHY was ECURED ITE OUT COME of the OFICT WALR +Eval: S S S S S S S + +Speaker sentences 86: lad_eng_000340 #utts: 1 +id: (lad_eng_000340-lad_eng_000340) +Scores: (#C #S #D #I) 3 8 0 0 +REF: WITH GOOD pace STARTED the MATCH WITH both TEAMS ALTERNATING SUPREMACY +HYP: WIT GOD pace SDARTE the MACH HIH both TEMEMS OLTENATING SOPREMISY +Eval: S S S S S S S S + +Speaker sentences 87: lad_eng_000341 #utts: 1 +id: (lad_eng_000341-lad_eng_000341) +Scores: (#C #S #D #I) 3 8 1 0 +REF: this VERSION is NOTED FOR BEING VERY FAITHFUL to THE ORIGINAL NOVEL +HYP: this VRTION is NONTED OR BEIN ERY FAVTFULE to *** THERIDIONL NOVHL +Eval: S S S S S S D S S + +Speaker sentences 88: lad_eng_000342 #utts: 1 +id: (lad_eng_000342-lad_eng_000342) +Scores: (#C #S #D #I) 6 8 0 0 +REF: this PRESUMPTION is not FULFILLED ONE has to KNOW AT LEAST two CONJUGATE DIAMETERS +HYP: this PRSAUMPTION is not FULEFILD WON has to NO T LEATE two CONGET DIAMITES +Eval: S S S S S S S S + +Speaker sentences 89: lad_eng_000343 #utts: 1 +id: (lad_eng_000343-lad_eng_000343) +Scores: (#C #S #D #I) 7 11 0 2 +REF: NOTABLE titles ** INCLUDED GOLDEN AXE the REVENGE of DEATH ADDER rad ***** MOBILE OUTRUNNERS and SEGA sonic the HEDGEHOG +HYP: NOTALE titles IN LDED GOLDAN ACS the REVENG of DEAT ADER rad MOBIL OUT RUNERS and SAKGR sonic the HEAGHOGK +Eval: S I S S S S S S I S S S S + +Speaker sentences 90: lad_eng_000344 #utts: 1 +id: (lad_eng_000344-lad_eng_000344) +Scores: (#C #S #D #I) 4 13 1 0 +REF: the NINETEEN NINETY NINE JUDGMENT NOTED THAT the INFLUENCE OF the FATHER of THE ACCUSED HAS BEEN THERE +HYP: the NINTN NINTY NIND JUGMENT NOTD TAT the INTFLONC O the FARTHER of *** THECUSD HIS BEE THER +Eval: S S S S S S S S S D S S S S + +Speaker sentences 91: lad_eng_000345 #utts: 1 +id: (lad_eng_000345-lad_eng_000345) +Scores: (#C #S #D #I) 4 7 0 2 +REF: *** MACDUFF swears REVENGE and joins FORCES WITH MALCOLM to ******** OVERTHROW MACBETH +HYP: MOK DAF swears RVENGEH and joins FOURSES IT MULKCM to OVERTROM MOK BETH +Eval: I S S S S S I S S + +Speaker sentences 92: lad_eng_000346 #utts: 1 +id: (lad_eng_000346-lad_eng_000346) +Scores: (#C #S #D #I) 4 10 1 1 +REF: the **** MEDIAEVAL VILLAGE COURT was ALWAYS ANXIOUS to KEEP the FENCE AROUND THE VILLAGE GAPLESS +HYP: the EADY AVLE VILIGE CORT was ALLAS ANCHOUS to CAP the ***** ENE ROUN THEVILIGE GAPLES +Eval: I S S S S S S D S S S S + +Speaker sentences 93: lad_eng_000347 #utts: 1 +id: (lad_eng_000347-lad_eng_000347) +Scores: (#C #S #D #I) 4 9 2 0 +REF: THERE WAS a NINE rank SYSTEM each RANK HAVING MORE POWER THAN the LOWER RANK +HYP: THE AS a NIN rank SISTEM each **** RANC AVIGMORE POUER TH the ***** LOERANK +Eval: S S S S D S S S S D S + +Speaker sentences 94: lad_eng_000348 #utts: 1 +id: (lad_eng_000348-lad_eng_000348) +Scores: (#C #S #D #I) 2 8 0 1 +REF: *** THEY ESTABLISHED DIPLOMATIC RELATIONS ON SEPTEMBER NINETEENTH NINETEEN seventy two +HYP: THE A STABLISCHED DEPLIMATIC RLATIONDS OND EPTOMERNINTNTH NINT N seventy two +Eval: I S S S S S S S S + +Speaker sentences 95: lad_eng_000349 #utts: 1 +id: (lad_eng_000349-lad_eng_000349) +Scores: (#C #S #D #I) 4 12 0 2 +REF: THIS WAS FURTHER EXTENDED to ** INCLUDE more U K DATES in DECEMBER two ******* THOUSAND AND FOURTEEN +HYP: THI AS FIRTH CSTENDED to IN CLD more YOU CAY DATE in DECSEMBER two THOUSED ND FOR TEN +Eval: S S S S I S S S S S I S S S + +Speaker sentences 96: lad_eng_000350 #utts: 1 +id: (lad_eng_000350-lad_eng_000350) +Scores: (#C #S #D #I) 2 9 1 0 +REF: the DUTCH GOVERNMENT is CURRENTLY EXAMINING THE LEGAL CONSEQUENCES OF THE RULING +HYP: the UCH GOVENT is ********* CIRNTLY ESAMING THELEAKLE CONCICQENCES F HE ROLING +Eval: S S D S S S S S S S + +Speaker sentences 97: lad_eng_000351 #utts: 1 +id: (lad_eng_000351-lad_eng_000351) +Scores: (#C #S #D #I) 6 11 1 1 +REF: from NINETEEN THIRTY three to NINETEEN FORTY NINE the AMERICAN LEAGUE WON TWELVE OUT OF the first ***** SIXTEEN +HYP: from NINTIN THERTY three to NINTIN FOARTY NIN the ******** MERICED LEE WOND WELVE AUTO the first SICST EN +Eval: S S S S S D S S S S S I S + +Speaker sentences 98: lad_eng_000352 #utts: 1 +id: (lad_eng_000352-lad_eng_000352) +Scores: (#C #S #D #I) 3 4 0 0 +REF: THERE he FELL sick WITH TYPHUS himself +HYP: THEAIR he FELE sick WT TIVFOS himself +Eval: S S S S + +Speaker sentences 99: lad_eng_000353 #utts: 1 +id: (lad_eng_000353-lad_eng_000353) +Scores: (#C #S #D #I) 1 9 2 0 +REF: SIX TEAMS HAVE BEEN DIVIDED IN two GROUPS OF THREE TEAMS EACH +HYP: *** SICXT TEMES HAE EDEVEIDED INTO two ****** GRUPSOF THRE TEMEMS ACH +Eval: D S S S S S D S S S S + +Speaker sentences 100: lad_eng_000354 #utts: 1 +id: (lad_eng_000354-lad_eng_000354) +Scores: (#C #S #D #I) 3 8 0 2 +REF: the first ***** SEASON PREMIERED ON TWELFTH JUNE two ******** THOUSAND AND FIFTEEN +HYP: the first SESON REMI AD OND WELTH DUN two THOUSEND AN FIF DEEN +Eval: I S S S S S I S S S + +Speaker sentences 101: lad_eng_000355 #utts: 1 +id: (lad_eng_000355-lad_eng_000355) +Scores: (#C #S #D #I) 6 7 0 1 +REF: it SUCCEEDED the *** Y BOARD and SYSTEM twenty FOUR COMBINING FEATURES from both +HYP: it SACED the WEI H BOLRD and SISTAM twenty FORE COMBING FEACES from both +Eval: S I S S S S S S + +Speaker sentences 102: lad_eng_000356 #utts: 1 +id: (lad_eng_000356-lad_eng_000356) +Scores: (#C #S #D #I) 2 5 0 0 +REF: VOLUME TWO NUMBERS ONE two and THREE +HYP: VELYUE TWOOH NUMBRS WON two and THRE +Eval: S S S S S + +Speaker sentences 103: lad_eng_000357 #utts: 1 +id: (lad_eng_000357-lad_eng_000357) +Scores: (#C #S #D #I) 5 9 1 0 +REF: the LOWER PART of mens DRESSES WERE much SHORTER in LENGTH THAN THOSE FOR WOMEN +HYP: the LOR PAT of mens DRESES WE much HOURT in ****** LEANGTHN THOS FR WIEIN +Eval: S S S S S D S S S S + +Speaker sentences 104: lad_eng_000358 #utts: 1 +id: (lad_eng_000358-lad_eng_000358) +Scores: (#C #S #D #I) 4 5 0 0 +REF: the VISIGOTHS in TURN WERE SUCCEEDED by the MOORS +HYP: the IGOALTHS in TERN WER CEADED by the MULERS +Eval: S S S S S + +Speaker sentences 105: lad_eng_000359 #utts: 1 +id: (lad_eng_000359-lad_eng_000359) +Scores: (#C #S #D #I) 1 8 0 1 +REF: *** JOSEPH HIGH SCHOOL EVERY WEEK of THE SCHOOL YEAR +HYP: JOS OF HIY SCOL AVERY WEK of TH SCOL HEAR +Eval: I S S S S S S S S + +Speaker sentences 106: lad_eng_000360 #utts: 1 +id: (lad_eng_000360-lad_eng_000360) +Scores: (#C #S #D #I) 4 4 2 0 +REF: as A RESULT OF ALL the ARGUMENTS GETTING to her +HYP: as * ****** RSIL OFAL the ARGUMENT GETIG to her +Eval: D D S S S S + +Speaker sentences 107: lad_eng_000361 #utts: 1 +id: (lad_eng_000361-lad_eng_000361) +Scores: (#C #S #D #I) 0 7 0 0 +REF: ITS HEADQUARTERS ARE IN SHEFFIELD UNITED KINGDOM +HYP: IT HAD QUARTERS ARIN SHEFEILD OUNIGTED CINGDOM +Eval: S S S S S S S + +Speaker sentences 108: lad_eng_000362 #utts: 1 +id: (lad_eng_000362-lad_eng_000362) +Scores: (#C #S #D #I) 6 9 2 0 +REF: lay ALSO OFFICIALLY SIGNED the contract on stage WITH THE DIRECTOR and PRODUCERS OF THE GOLDEN EYES +HYP: lay LLSO FIHALY SIDE the contract on stage **** WH THEDIRECTE and ********* PRODOUSES OFTHE GOLDAN IYS +Eval: S S S D S S D S S S S + +Speaker sentences 109: lad_eng_000363 #utts: 1 +id: (lad_eng_000363-lad_eng_000363) +Scores: (#C #S #D #I) 2 12 0 0 +REF: PHYSICAL THERAPY CAN HELP PATIENTS to LEARN HOW to WALK WITH A FOOT DROP +HYP: FISICLE FERIAPY CON HEL PATIENTE to ARN HO to WAEK W THE FOT DROUPE +Eval: S S S S S S S S S S S S + +Speaker sentences 110: lad_eng_000364 #utts: 1 +id: (lad_eng_000364-lad_eng_000364) +Scores: (#C #S #D #I) 5 7 0 1 +REF: it WENT on to SELL THREE hundred ******* THOUSAND UNITS ACHIEVE FIVE no +HYP: it ENT on to SEL THREY hundred THOUSED YAUNITS A CHEVE FIVEF no +Eval: S S S I S S S S + +Speaker sentences 111: lad_eng_000365 #utts: 1 +id: (lad_eng_000365-lad_eng_000365) +Scores: (#C #S #D #I) 2 3 0 0 +REF: the NAME STUCK AFTER that +HYP: the NAMEM STDUCK AFER that +Eval: S S S + +Speaker sentences 112: lad_eng_000366 #utts: 1 +id: (lad_eng_000366-lad_eng_000366) +Scores: (#C #S #D #I) 4 5 2 0 +REF: the ALBUM later BROKE the DIAMOND record ON Q Q MUSIC +HYP: the ILBOM later BRAC the DIMEND record ** * OND COKCUMUSICK +Eval: S S S D D S S + +Speaker sentences 113: lad_eng_000367 #utts: 1 +id: (lad_eng_000367-lad_eng_000367) +Scores: (#C #S #D #I) 2 8 0 1 +REF: ITS EDITORIAL we SUBMIT EARNED ITS AUTHOR a *** PULITZER PRIZE +HYP: IT EDEATORIAL we SOUBMIT AND IT ORTHE a POL IT OPRISE +Eval: S S S S S S I S S + +Speaker sentences 114: lad_eng_000368 #utts: 1 +id: (lad_eng_000368-lad_eng_000368) +Scores: (#C #S #D #I) 0 9 0 1 +REF: **** JOSEPH PLAYS ARE FEATURED EACH WEEK ON THE SHOW +HYP: DJOS OF PLAYES AUR FETCED IEACH WEE O TH SHO +Eval: I S S S S S S S S S + +Speaker sentences 115: lad_eng_000369 #utts: 1 +id: (lad_eng_000369-lad_eng_000369) +Scores: (#C #S #D #I) 2 13 2 0 +REF: THEY WAIT FOR A TIME BUILDING UP THEIR FORCES BEGINNING TO WONDER if this EVIL REALLY EXISTS +HYP: **** **** THE WAT FRA TIMEM BILDING UT THE FORSES BEIGINGTO OANDR if this EAVL REALY AEXSISTS +Eval: D D S S S S S S S S S S S S S + +Speaker sentences 116: lad_eng_000370 #utts: 1 +id: (lad_eng_000370-lad_eng_000370) +Scores: (#C #S #D #I) 7 6 1 0 +REF: BRIEF MENTION of THE conviction APPEARED on page THREE of the NEW YORK times +HYP: BREAE MENCION of TH conviction APERD on page THRE of the *** NOUYOUOK times +Eval: S S S S S D S + +Speaker sentences 117: lad_eng_000371 #utts: 1 +id: (lad_eng_000371-lad_eng_000371) +Scores: (#C #S #D #I) 3 8 0 0 +REF: ORDERED by POSITION on PITCH FROM BACK RIGHT to FRONT LEFT +HYP: ORDED by PESION on PICH FRM BAK RIHT to FRUNT LEFTET +Eval: S S S S S S S S + +Speaker sentences 118: lad_eng_000372 #utts: 1 +id: (lad_eng_000372-lad_eng_000372) +Scores: (#C #S #D #I) 6 8 1 0 +REF: he IS member of THE COURT OF the ROYAL COLLEGE of art LONDON U K +HYP: he AS member of *** THECORT O the RIL COLIG of art LUNDEN YOU CAY +Eval: S D S S S S S S S + +Speaker sentences 119: lad_eng_000373 #utts: 1 +id: (lad_eng_000373-lad_eng_000373) +Scores: (#C #S #D #I) 2 11 1 2 +REF: DURING THE COURSE of ** *** THE CAMPAIGN FERGUSON visited ALL THIRTY NINE WASHINGTON STATE COUNTIES +HYP: ****** DERIN THECOURSE of HE CAM PAIN FIRGE AND visited LL THERTY NIN WASIGTAN STAT CONTYIS +Eval: D S S I I S S S S S S S S S + +Speaker sentences 120: lad_eng_000374 #utts: 1 +id: (lad_eng_000374-lad_eng_000374) +Scores: (#C #S #D #I) 5 1 0 0 +REF: a strip of paper of LENGTH +HYP: a strip of paper of LEANTH +Eval: S + +Speaker sentences 121: lad_eng_000375 #utts: 1 +id: (lad_eng_000375-lad_eng_000375) +Scores: (#C #S #D #I) 2 8 0 3 +REF: SATOU had ********* **** ** FREQUENTLY WORKED TOGETHER WITH YOKOYAMA on PREVIOUS PROJECTS +HYP: SATO had FREKUNTLY WERK TO GETH WTH YOUK Y YAMEAR on PREVIUS POGECTS +Eval: S I I I S S S S S S S + +Speaker sentences 122: lad_eng_000376 #utts: 1 +id: (lad_eng_000376-lad_eng_000376) +Scores: (#C #S #D #I) 3 11 0 1 +REF: she ** WAS BORN ONSCREEN DURING THE EPISODE BROADCAST on FOURTH november NINETEEN NINETY FOUR +HYP: she WS BORE OND SCREN DUINGTHE EPSOD BRURD CAST on FORTH november NINTEN NINTY FOR +Eval: I S S S S S S S S S S S + +Speaker sentences 123: m #utts: 77 +id: (m-ailabs_eng_000159-m-ailabs_eng_000159) +Scores: (#C #S #D #I) 8 7 0 0 +REF: HE TURNED ROUND she had COME in so GENTLY that he had NEVER HEARD her +HYP: AH TURED ROUWNED she had COM in so GENTELY that he had EVE HERD her +Eval: S S S S S S S + +id: (m-ailabs_eng_000160-m-ailabs_eng_000160) +Scores: (#C #S #D #I) 6 9 0 2 +REF: AH to be ****** SURE we must KEEP our **** DOORS SHUTWE MUST LET no ONE IN +HYP: A to be SHOUOR H we must CE our DORS SHOT WE MUS LAT no WOUN INHA +Eval: S I S S I S S S S S S + +id: (m-ailabs_eng_000161-m-ailabs_eng_000161) +Scores: (#C #S #D #I) 9 18 1 1 +REF: * KINSMEN HE began MOCKINGLY you MAY HAVE WONDERED WHY i CALLED A TRUCE WHEN i COULD JUST as WELL HAVE DESTROYED you that i DOUBT ATO ANSWERED him +HYP: A CIDESBEN E began MOKINGLY you MA HVE WEDE WHI i CALD O TROUS WHN i COOD JOST as **** WILLHE DESTRE you that i DOT ADO ANCER him +Eval: I S S S S S S S S S S S S S D S S S S S + +id: (m-ailabs_eng_000162-m-ailabs_eng_000162) +Scores: (#C #S #D #I) 4 13 1 0 +REF: the PEASANT THREW HIMSELF UPON HIM AND BOUND his FOUR LEGS TIGHTLY so THAT HE COULD not MOVE +HYP: the ******* PESENT TIR HIMSEF APON HIMAND BOUNED his FOR LAKE DHTLY so AT E CO not MOE +Eval: D S S S S S S S S S S S S S + +id: (m-ailabs_eng_000163-m-ailabs_eng_000163) +Scores: (#C #S #D #I) 14 10 2 0 +REF: nor must thou so LIMIT the HOLY ONE of ISRAEL as to THINK HE hath but ONE way in WHICH HE CAN GLORIFY himself by THEE +HYP: nor must thou so LIMITH the OLY ON of ISRIL as to THIN H hath but WNEN way in ***** ** WHCH CNGORIFY himself by THE +Eval: S S S S S S S D D S S S + +id: (m-ailabs_eng_000164-m-ailabs_eng_000164) +Scores: (#C #S #D #I) 8 21 2 0 +REF: the OLD COMPARISON BETWEEN THE IMPULSIVE EXECUTIVE AND THE LIBERAL ARTS man WHO HAS LEARNED that THERE ARE only one OR TWO POSITIVE DECISIONS AVAILABLE in all THE WORLD OF thinking +HYP: the LD COMPRSOND BETWEN TH IMPALS OF EXSEAKITIVE ANDTHE LITBRLE ARTUS man HW WOD LEURND that ***** HERE only one ERTO POSIE DECISIOS O ALABLE in all *** THEWERL O thinking +Eval: S S S S S S S S S S S S S D S S S S S S D S S + +id: (m-ailabs_eng_000165-m-ailabs_eng_000165) +Scores: (#C #S #D #I) 6 9 0 0 +REF: AFTER THIS EXPERIENCE the INVADERS WERE CAREFUL to KEEP a SAFE DISTANCE from the wall +HYP: AVFTR THI CSPERIANCE the ENDVADERS WER CAIRFL to CEPE a SAKFE DISTENCE from the wall +Eval: S S S S S S S S S + +id: (m-ailabs_eng_000166-m-ailabs_eng_000166) +Scores: (#C #S #D #I) 14 17 1 2 +REF: * *** CAN YOU BEAR SOMETHING FURTHER i THINK YOU OUGHT TO KNOW it i have HERE a most MYSTERIOUS TELEPAGRAM YES what is IT is she DEAD no it is not ABOUT HER +HYP: M AON OU BEAER SM ING FIRTHER i ***** THIN YO AT NO it i have HER a most MSTERIAUS TELPRGROMEM ESEASE what is ITE is she DAD no it is not UBOUT HERY +Eval: I I S S S S S D S S S S S S S S S S S S + +id: (m-ailabs_eng_000167-m-ailabs_eng_000167) +Scores: (#C #S #D #I) 0 11 0 1 +REF: * NO MISTER THORNTON SAID GIVE THE BASKET TO MEILL TAKE IT +HYP: D NOWHLE MISTOR THUNTON SAD D IETHE BASKTO ME IL TAK ITW +Eval: I S S S S S S S S S S S + +id: (m-ailabs_eng_000168-m-ailabs_eng_000168) +Scores: (#C #S #D #I) 16 17 0 0 +REF: an ARABIAN NIGHT EXCLAIMED TROT WHY that was a MAGIC night WASNT it THERES DIFFERENT sorts OF nights MATE said the SAILOR and the KNIGHT BUTTONBRIGHT MEANS AINT the same night YOU mean +HYP: an ARABIOAN NIHT ECSCLAME TROGT WHI that was a MAGIAK night WOASIN it HERS DIFRNT sorts A nights MAT said the SALR and the NIGT BUTNUBIGHT MENES AT the same night YO mean +Eval: S S S S S S S S S S S S S S S S S + +id: (m-ailabs_eng_000169-m-ailabs_eng_000169) +Scores: (#C #S #D #I) 15 11 3 0 +REF: IVE TURNED OFF UPWARDS OF A HUNDRED OF my BEST hands for no other FAULT THAN FOLLOWING YOU and such as you and DYE think ill take you on +HYP: *** ****** *** IVETURED OBFE OUPWARD O HUDRDO my BESTE hands for no other FLT THEM FALWING OU and such as you and Y think ill take you on +Eval: D D D S S S S S S S S S S S + +id: (m-ailabs_eng_000170-m-ailabs_eng_000170) +Scores: (#C #S #D #I) 3 14 1 0 +REF: BUT WHEN SHOULD SHE SEE him her HEART LEAPED UP IN APPREHENSION at EVERY RING OF THE DOORBELL +HYP: GU HE WO HE SE him her HART LAPED UB BN APROHENTION at ***** EVEY RINGOF HE DORBLT +Eval: S S S S S S S S S S D S S S S + +id: (m-ailabs_eng_000171-m-ailabs_eng_000171) +Scores: (#C #S #D #I) 7 22 3 3 +REF: ** ***** THESE BOOKS DIXON i WILL KEEP ALL THE REST WILL YOU SEND to MISTER BELL THEY ARE of a KIND THAT he WILL VALUE for **** THEMSELVES as WELL AS FOR PAPAS SAKE +HYP: AA THEAS BOKS S DICSON i **** WL CE AL THEREST WE OU SEN to MSTR BEL THE AR of a CIN THT he **** WLAVOUYO for THIM SELES as **** WELAS FR POS SAYD +Eval: I I S S S D S S S S S S S S S S S S S D S I S D S S S S + +id: (m-ailabs_eng_000172-m-ailabs_eng_000172) +Scores: (#C #S #D #I) 17 18 0 1 +REF: but INGA was not AT ALL SURE THEY COULD not GET in the **** GATES OPENED INWARD and THREE HEAVY BARS WERE held in place by MEANS of stout staples RIVETED to the SHEETS of STEEL +HYP: but INGL was not IT L SHOUR THA THEYCOULD not GED in the GATS OPED IN ORD and THRE HAVY BOARRS WER held in place by MENES of stout staples RIVIDTE to the SHETES of STDEL +Eval: S S S S S S S I S S S S S S S S S S S + +id: (m-ailabs_eng_000173-m-ailabs_eng_000173) +Scores: (#C #S #D #I) 7 20 2 0 +REF: I WANT THAL SAID HODDAN COLDLY I WANT A DOZEN HORSES I WANT men to RIDE THEM WITH ME he PUSHED HIS way FORWARD WHICH way to the STABLES +HYP: * **** AI WOANT THEIL SID OD ON COLDLYI HENTA DOSON HORESI WNUNT men to BRIGE THE WIT M he PUSHE IS way FORED WICH way to the STABLS +Eval: D D S S S S S S S S S S S S S S S S S S S S + +id: (m-ailabs_eng_000174-m-ailabs_eng_000174) +Scores: (#C #S #D #I) 9 15 7 1 +REF: THERE is A LIMIT TO WHAT YOU CAN DO THE FIRST TIME YOU ENTER A MANS house and BESIDES that was no time to AROUSE SUSPICION IN THE MIND of *** ANYONE +HYP: IR is * ***** ** **** *** *** LEIT WACO CAND DEV FR THEFIRSTIMEYOU ANTE ANS house and BESIGDES that was no time to ****** AROUSESPION I TH MINDS of ANY WON +Eval: S D D D D D D S S S S S S S S S D S S S S I S + +id: (m-ailabs_eng_000175-m-ailabs_eng_000175) +Scores: (#C #S #D #I) 6 14 0 1 +REF: DO YOU not REMEMBER that HE SAYS thy demon THATS THY spirit WHICH KEEPS THEE is **** NOBLE COURAGEOUS HIGH UNMATCHABLE +HYP: D OU not REMEMER that E SAS thy demon THATD THE spirit HICH CKEAPES THE is NOBL COREAGESCE HAI UN MAUCHOABL +Eval: S S S S S S S S S S I S S S S + +id: (m-ailabs_eng_000176-m-ailabs_eng_000176) +Scores: (#C #S #D #I) 4 13 0 0 +REF: MISTER BELL WHAT CAN he KNOW of JOHN HE living a LAZY LIFE IN A DROWSY COLLEGE +HYP: A TMISTR BELE OACAN he NO of CON HEE living a LASY LIF N AD DROUSY COLIDGEHA +Eval: S S S S S S S S S S S S S + +id: (m-ailabs_eng_000177-m-ailabs_eng_000177) +Scores: (#C #S #D #I) 2 5 1 0 +REF: and THE KITTEN FOLLOWED DEMURELY at THEIR HEELS +HYP: and *** TECITON FOLLOWE DEMURLY at THER HEALS +Eval: D S S S S S + +id: (m-ailabs_eng_000178-m-ailabs_eng_000178) +Scores: (#C #S #D #I) 10 8 0 1 +REF: the first TOUCH WOULD CAUSE an EXPLOSION in which * AMONG such hundreds of INFURIATED men and RECKLESS BOYS +HYP: the first TU HWOD COS an ECSPLOION in which A MOG such hundreds of INFERATED men and RECKLSS BORYS +Eval: S S S S I S S S S + +id: (m-ailabs_eng_000179-m-ailabs_eng_000179) +Scores: (#C #S #D #I) 6 8 1 0 +REF: ONE OF THE GREAT PLEASURES OF MARGARETS LIFE at this time was in EDITHS boy +HYP: *** W ON TE GAT PLESERSOF MARGRET LIE at this time was in EADES boy +Eval: D S S S S S S S S + +id: (m-ailabs_eng_000180-m-ailabs_eng_000180) +Scores: (#C #S #D #I) 5 20 3 0 +REF: THE THING HAS GONE ON LONG ENOUGH IF THERE IS one MORE big ACCIDENT we SHALL HAVE to COMPROMISE WITH the INTERRIVER AND CARRY ON THE WORK JOINTLY +HYP: *** TH THIN AS GON UND LON NOF R S one OR big ACXITDENT we ***** SHALHAVE to COMBERMYIS WI the ********** INERIVER N CERYON TH WERK COINL +Eval: D S S S S S S S S S S S D S S S D S S S S S S + +id: (m-ailabs_eng_000181-m-ailabs_eng_000181) +Scores: (#C #S #D #I) 4 8 1 0 +REF: YOU ARE LATE said she WELL she HELD HER BREATH FOR the ANSWER +HYP: AA YOUR LAT said she WEL she **** HED HERBREATH O the ANCSRHAL +Eval: S S S S D S S S S + +id: (m-ailabs_eng_000182-m-ailabs_eng_000182) +Scores: (#C #S #D #I) 4 20 1 1 +REF: TROT TOLD the girls THAT THEY MUST GO WITH THEIR FATHER to *** LIVE IN GHIPGHISIZZLES LITTLE old CABIN AND WHEN THEY HEARD THIS DREADFUL DECREE +HYP: ROHT TOLE the girls TA HE MUS CO H THER FOTHER to LIV N GIPK GESSISLES LTLE old ***** CABON AN HN THEHERD THS REDFUL DECRE +Eval: S S S S S S S S S I S S S S D S S S S S S S + +id: (m-ailabs_eng_000183-m-ailabs_eng_000183) +Scores: (#C #S #D #I) 11 14 1 1 +REF: MARGARET sat down ON the RUG partly to WARM HERSELF FOR the DAMPNESS OF the EVENING hung ABOUT her DRESS and *** OVERFATIGUE HAD MADE her CHILLY +HYP: MARGIT sat down ** the ROGKG partly to WORME HRSELF FO the DANPNESS O the EAVNING hung OUT her RES and OVE FITE AD MAD her CHILY +Eval: S D S S S S S S S S S I S S S S + +id: (m-ailabs_eng_000184-m-ailabs_eng_000184) +Scores: (#C #S #D #I) 10 16 0 0 +REF: OH NO YOU ARE MISTAKEN ABOUT that REPLIED the KING THEY ARE not my PRISONERS but my slaves WHOM I PURCHASED from the KING of EV +HYP: O NOW YO AR MSTAKEN BOUT that ELID the CING THE AR not my PRESNERS but my slaves HOM IY PRCEUST from the CING of EVE +Eval: S S S S S S S S S S S S S S S S + +id: (m-ailabs_eng_000185-m-ailabs_eng_000185) +Scores: (#C #S #D #I) 2 4 0 0 +REF: her FATHER TOOK UP the CONVERSATION +HYP: her FATHE TO U the OMERSATION +Eval: S S S S + +id: (m-ailabs_eng_000186-m-ailabs_eng_000186) +Scores: (#C #S #D #I) 11 16 3 2 +REF: in a CORNER was a SORT of ****** DRESSINGTABLE ON which LAY a COMB AND brush KENNEDY SEEMED much INTERESTED IN THE table AND WAS EXAMINING IT WHEN the *** GURU RETURNED +HYP: in a COURER was a SOURD of DREING TABLE IN which LY a **** COMEAND brush CENIDY SEED much ********** INTERSTD INTHE table *** ANDWAS AEXSAMING AT HE the GUE RU RETRN +Eval: S S I S S S D S S S D S S D S S S S I S S + +id: (m-ailabs_eng_000187-m-ailabs_eng_000187) +Scores: (#C #S #D #I) 12 19 3 2 +REF: i *** **** HAVE SOMETIMES THOUGHT THAT MYSELF she agreed BUT OF COURSE i DONT KNOW STILL i HAVE TO be PRETTY CAREFUL SOME ONE is ALWAYS over HERE by my DESK or LOOKING over HERE +HYP: i AVE SOME TIM TAUHT TAT MY SEF she agreed *** ** BUTOFCOR i OT NOE STIL i HVE T be ****** PRTY CARFUL SOMEWEN is LLIS over E by my DESC or LOING over HEAR +Eval: I I S S S S S D D S S S S S S D S S S S S S S S + +id: (m-ailabs_eng_000188-m-ailabs_eng_000188) +Scores: (#C #S #D #I) 5 8 1 1 +REF: i SHALL stay REPLIED THE YOUNG MAN for i ** MEAN to SET YOU FREE +HYP: i SHAL stay EREPLD TH ONG AN for i ME N to *** STCO FRE +Eval: S S S S S I S D S S + +id: (m-ailabs_eng_000189-m-ailabs_eng_000189) +Scores: (#C #S #D #I) 2 4 1 0 +REF: WHAT DO you DO ASKED the SORCERER +HYP: **** WHATD you DEO AST the SORSERER +Eval: D S S S S + +id: (m-ailabs_eng_000190-m-ailabs_eng_000190) +Scores: (#C #S #D #I) 13 17 1 0 +REF: WHY THEYRE OUR ENEMIES your short HIGHNESS not any more REPLIED TROT im QUEEN OF the PINKIES AND IM ALSO QUEEN OF the BLUES so i wont have my PEOPLE QUARRELING +HYP: WHIY THER AR ANIMES your short HINES not any more REPLIDE SROUHT im ***** QUE the PINKES ND HM LSO QUE O the LS so i wont have my PEBLE QUARLING +Eval: S S S S S S S D S S S S S S S S S S + +id: (m-ailabs_eng_000191-m-ailabs_eng_000191) +Scores: (#C #S #D #I) 5 27 0 4 +REF: TYPEWRITERS WERE CLICKING CLIPPINGS WERE BEING SNIPPED OUT OF A HUGE STACK of **** NEWSPAPERS and **** ** * PASTED INTO LARGE SCRAPBOOKS CIRCULARS WERE BEING FOLDED and MADE READY to MAIL FOR the FINAL APPEAL +HYP: TIP RAERS E CLICING CLPING AR BNG SNIPD OT F CUG STAC of NOUS PER and PASE IN A IN LARG SRA BOCS SURKILERS R BENG FOLD and ADED RADY to MAL FO the FINL APEL +Eval: S S S S S S S S S S S S I S I I I S S S S S S S S S S S S S S + +id: (m-ailabs_eng_000192-m-ailabs_eng_000192) +Scores: (#C #S #D #I) 10 14 0 0 +REF: it was FOUR days AFTER the SURPRISE of ADLERS HORST WHEN the STRANGERS LEFT the ESTATE TO the CARE of RUGGED old FORSTER HERMANN +HYP: it was FORE days AFED the SUPRIYS of LHERS HORSE WHN the STRANGRS LEAF the STDAT T the CAIR of ROGED old FORSTR HIRMON +Eval: S S S S S S S S S S S S S S + +id: (m-ailabs_eng_000193-m-ailabs_eng_000193) +Scores: (#C #S #D #I) 4 21 9 1 +REF: ***** POOR TEMPLETON he said i USED TO KNOW HIM YEARS AGO WHEN WE WERE BOYS WENT TO SCHOOL WITH HIM AND ALL THAT SORT OF THING YOU KNOW but UNTIL I RAN ACROSS HIM +HYP: MPORE TEMPL TON he said i **** ** **** *** ***** *** **** ** OUST NO HAM MANY UARS GO WENWEBORYSE MNTOSCOU WITHM NDNT AL HAT SOROF HIN UNOE but ***** ANDTIL IERANCROUS HM ORE +Eval: I S S D D D D D D D D S S S S S S S S S S S S S S S D S S S S + +id: (m-ailabs_eng_000194-m-ailabs_eng_000194) +Scores: (#C #S #D #I) 4 9 2 0 +REF: i FOUND HER in THE FOREST AND BROUGHT HER HERE a PRISONER REPLIED the CAPTAIN +HYP: i FOND TE in *** ****** THEFOARRST NDBRUGT HE EAR a PRESNE REPLYD the CAPTON +Eval: S S D D S S S S S S S + +id: (m-ailabs_eng_000195-m-ailabs_eng_000195) +Scores: (#C #S #D #I) 11 14 1 1 +REF: WHO MAY be ********* COMPETENT EITHER from PERSONAL EXPERIENCE or the EXPERIENCE of others to ANSWER IT WITH MORE or less CORRECTNESS or at LEAST AN ATTEMPT +HYP: O MA be COMPITENT ID THE from PERSONL CSPEIRINCE or the ECSPERINCE of others to ****** ANCSER TWHT MOE or less CORACTKNES or at LEASTE N ITEMTOH +Eval: S S I S S S S S D S S S S S S S + +id: (m-ailabs_eng_000196-m-ailabs_eng_000196) +Scores: (#C #S #D #I) 1 10 0 0 +REF: ONE HUNDRED NINETYTWO LAYTE STREET said HOGAN BITING OFF HIS CIGAR +HYP: L WN NINTETO LAT STRED said HOAKGON BUYDIG OF HI SOGAR +Eval: S S S S S S S S S S + +id: (m-ailabs_eng_000197-m-ailabs_eng_000197) +Scores: (#C #S #D #I) 10 20 3 0 +REF: TROT was SURPRISED to FIND SHE COULD SEE so PLAINLY THROUGH the HIGH WALL OF WATER ABOVE HER but the SUN was ABLE to shoot its BEAMS STRAIGHT DOWN THROUGH THE TRANSPARENT SEA +HYP: RAT was SURPRIS to FINE HE COLD SE so PLAILY THR the **** HIY WALO WOAHTER UBOF HERR but the SND was ABL to shoot its ***** ******** BEME STRAT DON THOR THEARAENSPEIRNT +Eval: S S S S S S S S D S S S S S S S D D S S S S S + +id: (m-ailabs_eng_000198-m-ailabs_eng_000198) +Scores: (#C #S #D #I) 1 5 1 0 +REF: the SPOT WHERE IT HAD SPRUNG UP +HYP: the **** SPAT E ID SPRONG OPE +Eval: D S S S S S + +id: (m-ailabs_eng_000199-m-ailabs_eng_000199) +Scores: (#C #S #D #I) 1 7 1 0 +REF: CALM DENIAL WHICH SHE gave TO SUCH A SUPPOSITION +HYP: GCOME DEANIL WIT HE gave ** SOCH AS UPOSITION +Eval: S S S S D S S S + +id: (m-ailabs_eng_000200-m-ailabs_eng_000200) +Scores: (#C #S #D #I) 6 17 2 1 +REF: YOU SEE UNTIL THESE SCHOOL PILLS WERE INVENTED we WASTED A LOT of TIME in STUDY that MAY now ** be BETTER EMPLOYED IN PRACTICING ATHLETICS +HYP: YO SE ANDTIL THE SCHL PILES R INGENTED we WAST T LAT of TIM in STDADY that *** now MA be ****** BETER IMPLOYED ANMPRCTSCING ATHLETIC +Eval: S S S S S S S S S S S S S D I D S S S S + +id: (m-ailabs_eng_000201-m-ailabs_eng_000201) +Scores: (#C #S #D #I) 3 8 0 0 +REF: YOUVE DONE it NOW declared DOROTHY THESE TENTS ARE just WONDERFUL +HYP: YOVE DN it HANW declared DARTHY THES TENCE AR just OENDERFOL +Eval: S S S S S S S S + +id: (m-ailabs_eng_000202-m-ailabs_eng_000202) +Scores: (#C #S #D #I) 4 22 0 2 +REF: ***** FOR TWENTY TEN FIVE THREE TWOTHE LINER was BARELY TWENTY MILES away *** WHEN HODDAN FIRED HIS ROCKETS THEY MADE a COLOSSAL CLOUD OF VAPOR in EMPTINESS +HYP: EMFOR TWENING TAN FIVEF THEREE TWOE THE INO was BERLY TWOENY MYWS away WHN HO DION FIRD HS ROCKITS THE MD a COLOSTOL CLOWE O APER in AMTYNESE +Eval: I S S S S S S S S S S I S S S S S S S S S S S S + +id: (m-ailabs_eng_000203-m-ailabs_eng_000203) +Scores: (#C #S #D #I) 12 15 3 0 +REF: THEY PAID no ATTENTION to the FACT THAT GHIPGHISIZZLE did not WANT to MARRY any of THEM for THEY HAD DETERMINED that WHEN IT WAS AGREED who SHOULD HAVE him +HYP: THE PAD no ATENCION to the FACTHAT GIP KGESCISIL did not ON to MARY any of THEMEM for **** *** HEHADETERMEND that **** HN ITWAS EGREED who SHOD HAV him +Eval: S S S S S S S S S D D S D S S S S S + +id: (m-ailabs_eng_000204-m-ailabs_eng_000204) +Scores: (#C #S #D #I) 7 11 4 0 +REF: WHAT DO YOU THINK of that he CRIED OPENING a COPY OF THE RECORD and LAYING IT flat ON the LIBRARY TABLE +HYP: **** ** WAT DYOUTHI of that he CRID OPENG a **** ** COPBYOTHE RECKED and LANG T flat O the LIBRY TABLEL +Eval: D D S S S S D D S S S S S S S + +id: (m-ailabs_eng_000205-m-ailabs_eng_000205) +Scores: (#C #S #D #I) 3 4 0 0 +REF: it WILL REQUIRE BUT a SHORT time +HYP: it L ECOPIER UT a SORT time +Eval: S S S S + +id: (m-ailabs_eng_000206-m-ailabs_eng_000206) +Scores: (#C #S #D #I) 9 7 1 1 +REF: and last the CROWD OF VEGETABLE PEOPLE WHO had no HEARTS and **** COULD NEITHER smile nor frown +HYP: and last the ***** ROUD O VEIGETDABLE PEPLEHO had no HARTS and OUOD NI THER smile nor frown +Eval: D S S S S S I S S + +id: (m-ailabs_eng_000207-m-ailabs_eng_000207) +Scores: (#C #S #D #I) 0 7 0 0 +REF: THEN YOULL CATCH IT SAID THE WITCH +HYP: THEIN YOLL CACH ITE SAI TH ICH +Eval: S S S S S S S + +id: (m-ailabs_eng_000208-m-ailabs_eng_000208) +Scores: (#C #S #D #I) 8 12 4 2 +REF: WHAT is it i QUERIED not FEELING CERTAIN but that **** IT WAS a VEILED ATTEMPT TO SECURE A LITTLE FREE ADVERTISING FOR the ****** VANDERVEER +HYP: WHT is it i QUERED not FIELING SREN but that TWAS AE VALD a ****** ******* ** ****** TEMD TOSECER LITLFRE ADRTYSING FO the ANDEOV ER +Eval: S S S S I S S D D D D S S S S S I S + +id: (m-ailabs_eng_000209-m-ailabs_eng_000209) +Scores: (#C #S #D #I) 15 14 2 0 +REF: so HE gave the CLERK the THIRD HUNDRED DOLLARS for BOOKS and a cask of GOOD OLD ALE FOR peter the CLERK DRANK the ALE himself and gave THE CALF MILK +HYP: so E gave the LIRC the ***** THRD UNDEDOLOS for BOKS and a cask of **** GODOLD AL FR peter the CLRK RAN the AIL himself and gave HE CAF MIW +Eval: S S D S S S D S S S S S S S S S + +id: (m-ailabs_eng_000210-m-ailabs_eng_000210) +Scores: (#C #S #D #I) 10 19 3 1 +REF: ** LIKE that IN ALICE in WONDERLAND WITH MERELY A grin THAT FADED away changing into a LYNX WHICH in TURN DISAPPEARED FOLLOWED by an UNKNOWN CREATURE WITH SHORT NOSE AND POINTED EARS +HYP: AT LIEK that AN ALS in ********** **** NEDERLANTDWITH MERLY grin THT FATED away changing into a LINKS WHIC in TRE TOSPERD FOLOWED by an ******* UNON RECE WIT SOURT NOUSAND PONTED ERS +Eval: I S S S D D S S S S S S S S S D S S S S S S S + +id: (m-ailabs_eng_000211-m-ailabs_eng_000211) +Scores: (#C #S #D #I) 5 16 0 4 +REF: * she COULD not **** ****** ***** DOMARGARET GLANCED UNCONSCIOUSLY AT THE UNCLEANED CORNERS OF THE ROOMSHE COULD HARDLY UNDERTAKE a SERVANTS place COULD she +HYP: A she COLD not DEOE MARGRI LANCD UN CONHOUSLY ATHE UNGKLED ORNER OFHE OME SHE UO HAR TH NDER TAK a SURVINCS place COO she +Eval: I S I I I S S S S S S S S S S S S S S S + +id: (m-ailabs_eng_000212-m-ailabs_eng_000212) +Scores: (#C #S #D #I) 4 8 0 1 +REF: * NO she REPLIED WITH INNOCENT CURIOSITY did i GIVE THEM to YOU +HYP: A DNOHE she REPLIDED WIT INISEN KERYOUSITY did i GIVF THE to YOUWHAL +Eval: I S S S S S S S S + +id: (m-ailabs_eng_000213-m-ailabs_eng_000213) +Scores: (#C #S #D #I) 6 11 1 0 +REF: MARLBOROUGH MILLS AND THE ADJACENT DWELLING WERE held under A long LEASE THEY must if POSSIBLE BE relet +HYP: MARBOR MILES AN THEAG ACENT DELING WR held under * long LEES THE must if POSABL TBE relet +Eval: S S S S S S S D S S S S + +id: (m-ailabs_eng_000214-m-ailabs_eng_000214) +Scores: (#C #S #D #I) 2 5 0 3 +REF: * a cop ** * WAVED A STUNPISTOL AT HIM +HYP: D a cop WA E OST UN IS THE LATEM +Eval: I I I S S S S S + +id: (m-ailabs_eng_000215-m-ailabs_eng_000215) +Scores: (#C #S #D #I) 7 16 5 0 +REF: IT BOUNDED HERE and THERE ABOUT THE CHICKEN HOUSE AND at first DOROTHY COULD NOT TELL WHAT it WAS WHILE the SCREECHING of THE CHICKENS NEARLY DEAFENED her +HYP: ITD BONDED HEAR and ***** THAIR ABOU THECIOKON HOUS AN at first ******* ***** DORTHYCOULDNOT TEL HAT it *** WOSWHIL the SPEACING of *** THECHIOCIONS NERLY DEFEND her +Eval: S S S D S S S S S D D S S S D S S D S S S + +id: (m-ailabs_eng_000216-m-ailabs_eng_000216) +Scores: (#C #S #D #I) 12 15 2 0 +REF: the SOLDIER gave A YELL THAT AROUSED a SCORE of his COMRADES and BROUGHT THEM tumbling into THE STREET when they SAW HOW the BOOLOOROOS PRECIOUS PRISONER was ESCAPING +HYP: the SOLDER gave * YAL HAT EROUSED a SCHUAR of his COMRAD and BRGT THE tumbling into TH STRET when they *** SAWHO the OLRS PRESCISE PRISNER was SCAPING +Eval: S D S S S S S S S S S D S S S S S + +id: (m-ailabs_eng_000217-m-ailabs_eng_000217) +Scores: (#C #S #D #I) 10 16 3 0 +REF: JIM had REFUSED to LEAVE the FIELD of GRASS WHERE he was ENGAGED IN BUSILY eating so the WIZARD GOT OUT OF THE BUGGY and JOINED ZEB AND DOROTHY +HYP: GJIM had REFEUSE to LE the FELD of ***** GRASWHER he was INGAGED AND BISILY eating so the ****** WISURD GUGT OUTO TH BOUGY and ****** JUONED SAEB ANDOARITHY +Eval: S S S S D S S S S D S S S S S D S S S + +id: (m-ailabs_eng_000218-m-ailabs_eng_000218) +Scores: (#C #S #D #I) 4 13 5 0 +REF: CERTAINLY I AM AS INTERESTED IN THE CASE AS YOU ARE but i CANT MAKE HEADS OR TAILS of IT i REPLIED +HYP: ********* * ** ** ********** GSRDNLY IMAS INTERTED NTHECACES O AR but i CAN AK HEDS R TALS of T i REPLID +Eval: D D D D D S S S S S S S S S S S S S + +id: (m-ailabs_eng_000219-m-ailabs_eng_000219) +Scores: (#C #S #D #I) 4 2 0 1 +REF: or any mice or ***** EVEN GRASSHOPPERS +HYP: or any mice or EVENG GRAS HOPERS +Eval: I S S + +id: (m-ailabs_eng_000220-m-ailabs_eng_000220) +Scores: (#C #S #D #I) 9 22 1 1 +REF: and THEM THAT PAYS YO DUN THEY TELL YO WHATTEN to DO or **** WHATTEN not to DO WI the MONEY they GIVES you IN just PAYMENT FOR YOUR PAINSIN FAIR EXCHANGE LIKE +HYP: and THE THE PASIO DON THE TEL YOU WHA E to D or WHAT AN not to DEH WE the MUNY they GIVE you AN just ******* PAMENT FO YOU PAINS INTHERECSTCANGE LIC +Eval: S S S S S S S S S S I S S S S S S D S S S S S S + +id: (m-ailabs_eng_000221-m-ailabs_eng_000221) +Scores: (#C #S #D #I) 2 5 0 0 +REF: WHAT DOES THAT mean ASKED the PRINCESS +HYP: WHT DIS TAT mean AST the RINCES +Eval: S S S S S + +id: (m-ailabs_eng_000222-m-ailabs_eng_000222) +Scores: (#C #S #D #I) 9 16 1 1 +REF: HE had BEEN DROWNED he was FLOATING IN a SEA of LIGHT and now AND THEN shining *** LITTLE FISHES SWAM INQUISITIVELY UP to HIM AND STARED +HYP: DE had BEE DRONED he was FLOAODING N a SI of LIT and now *** THED shining LTE FISIES SWHEAM INCQUISITIELY E OUP to HIE ND STAR +Eval: S S S S S S S D S I S S S S S S S S + +id: (m-ailabs_eng_000223-m-ailabs_eng_000223) +Scores: (#C #S #D #I) 12 25 11 0 +REF: BUT old GUNNAR HAD A TRICK OR two LEFT REMEMBER the TALE THAT i READ TO you IN THE THRONEROOM OF BALDAR the first OF the brons TO ENTER the WORLD of OPAL WERE SOLDIERS SENT FROM some BLASTED PLANET IN OUTER SPACE TO FIND A NEW HOME +HYP: BUD old ****** *** GN HD ARIK two LEFTD REMEME the **** TAIL i **** REDTO you ** I TH THONERM OABLTHE the first ** the brons T ENDE the WRLD of **** OAPL WER SOULDGRS SINTFRM some ******* ****** ** ***** BLASTID PLANT NOUTERSPACS FIE ANO HOM +Eval: S D D S S S S S D S D S D S S S S D S S S D S S S S D D D D S S S S S S + +id: (m-ailabs_eng_000224-m-ailabs_eng_000224) +Scores: (#C #S #D #I) 6 15 2 0 +REF: PAPA WILL YOU SPEAK TO the men AND GET THEM TO go AWAY she CANNOT BREATHE POOR thing WITH this CROWD ABOUT HER +HYP: APPE WIE O SPEA T the men *** *** ANDGT HEMTO go WAY she CANT BREETH POR thing WIT this ROWD OBOUT TERHL +Eval: S S S S S D D S S S S S S S S S S + +id: (m-ailabs_eng_000225-m-ailabs_eng_000225) +Scores: (#C #S #D #I) 10 14 4 1 +REF: * when I TOOK this CASE he said i BELIEVED DOWN IN my HEART THAT DIXON was INNOCENT i STILL BELIEVE IT but my FAITH HAS BEEN RUDELY SHAKEN +HYP: A when IY TOK this CACE he said i BULEVED DONE IND my ***** HART DICSON was INSENT i ***** STO BELEIT but my ***** *** FATHASPBEN ROUTLY SHAC +Eval: I S S S S S S D S S S D S S D D S S S + +id: (m-ailabs_eng_000226-m-ailabs_eng_000226) +Scores: (#C #S #D #I) 1 6 0 2 +REF: * CHAPTER SIX OF the *** PIRATES OF ERSATZ +HYP: A DHAPTR SICK OVEF the PIT O OR SEATSE +Eval: I S S S I S S S + +id: (m-ailabs_eng_000227-m-ailabs_eng_000227) +Scores: (#C #S #D #I) 2 3 0 2 +REF: ** ***** REMEMBER THEY CANNOT touch us +HYP: RE MEMER THE CAN NOT touch us +Eval: I I S S S + +id: (m-ailabs_eng_000228-m-ailabs_eng_000228) +Scores: (#C #S #D #I) 10 15 0 4 +REF: GIVE me time ** AZURE give me time IF THERES ANYTHING i HATE its a HURRY IVE AN IDEA your ******** MAJESTY ANNOUNCED the **** ** SIXTH SNUBNOSED PRINCESS +HYP: IV me time AS OUR give me time IOF TEIRS ANYTHNG i HAT its a HURY IVEAN Y DEAE your MADGESTY AND ONCET the SICT TH SNOBE NOS PRONCES +Eval: S I S S S S S S S S S I S S I I S S S + +id: (m-ailabs_eng_000229-m-ailabs_eng_000229) +Scores: (#C #S #D #I) 2 4 1 0 +REF: TRUE ENOUGH TROT DECLARED the SAILOR man +HYP: **** TONOF TROAHT DECLEARE the SALER man +Eval: D S S S S + +id: (m-ailabs_eng_000230-m-ailabs_eng_000230) +Scores: (#C #S #D #I) 6 11 0 0 +REF: AS for that said MARGARET RATHER HAUGHTILY i hold it IS HONI SOIT QUI MAL Y PENSE +HYP: AAS for that said MARGRIT RTHE HOTILY i hold it HIS OHONY SO IT CQUEE MULD EPENSAY +Eval: S S S S S S S S S S S + +id: (m-ailabs_eng_000231-m-ailabs_eng_000231) +Scores: (#C #S #D #I) 15 14 5 0 +REF: WHEN he HEARD THESE words the KING WHOSE HEAD WAS FULL OF the PRINCESS never STOPPED to INQUIRE if THEY COULD be TRUE and SMEARED himself over with fat and sprang INTO THE OVEN +HYP: WE he ***** THES words the **** ***** CINGWOS HED WASFUL F the PINCES never TAPE to NCQPHIR if **** THEYCOD be TRO and SMERED himself over with fat and sprang **** INT THEOVEINT +Eval: S D S D D S S S S S S S D S S S D S S + +id: (m-ailabs_eng_000232-m-ailabs_eng_000232) +Scores: (#C #S #D #I) 5 22 2 1 +REF: YOU SHOULD BE ABLE TO get PARTS from ***** YOUR ROOM VISIONRECEIVER ill HAVE SOME TOOLS given YOU then HE ADDED DIPLOMACY HAS TO UNDERSTAND THE THINGS THAT CONTROL EVENTS +HYP: *** YOSHOL B AL Y get PARCE from YOURE ROME VIOND RESEVERE ill **** HAVESOM TOUOLS given OU then ADTED DEPOM AS HE HASTO NDERSTAN TH TINGS ACE TOL OFENCESH +Eval: D S S S S S I S S S D S S S S S S S S S S S S S S + +id: (m-ailabs_eng_000233-m-ailabs_eng_000233) +Scores: (#C #S #D #I) 4 9 2 0 +REF: by the TIME THE FROST HAD SET in THEY SHOULD be FAR AWAY FROM HELSTONE +HYP: by the **** TIM THEFROUST AD SAD in **** THESHO be FOARE WAY FOM HELSTDON +Eval: D S S S S D S S S S S + +id: (m-ailabs_eng_000234-m-ailabs_eng_000234) +Scores: (#C #S #D #I) 1 5 2 1 +REF: ONE THING I WANT TO say ** BEGAN KENNEDY +HYP: *** ***** OEN THIG WOENTOE say BE GAND CENITY +Eval: D D S S S I S S + +id: (m-ailabs_eng_000235-m-ailabs_eng_000235) +Scores: (#C #S #D #I) 3 6 3 1 +REF: THIS IMPORTANT TRAFFIC was ** CONFIDED to no ONE BUT THE REAL PROPRIETOR +HYP: **** THI MPORTNTRACHIC was ON FIDE to no *** *** OAMB THEREAL PRPRITER +Eval: D S S I S D D S S S + +Speaker sentences 124: cv_eng_000707 #utts: 1 +id: (cv_eng_000707-cv_eng_000707) +Scores: (#C #S #D #I) 0 9 0 2 +REF: ** * HE WAS REPLACED ON BASS GUITAR BY JUSTIN KLUG +HYP: UN N WHWE AIDE DAB BLASED ONTD BASKODOVE MY THISTIMG CGO +Eval: I I S S S S S S S S S + +Speaker sentences 125: cv_eng_000708 #utts: 1 +id: (cv_eng_000708-cv_eng_000708) +Scores: (#C #S #D #I) 3 5 2 1 +REF: id ** ADD a SEPARATE SUBSECTION which DEALS WITH THIS ASPECT +HYP: id TD AT a ******** SEPRTSUPSECTION which ***** DLSWIT HIS ASPECTD +Eval: I S D S D S S S + +Speaker sentences 126: cv_eng_000709 #utts: 1 +id: (cv_eng_000709-cv_eng_000709) +Scores: (#C #S #D #I) 3 5 1 1 +REF: OPERATION of the TRUNK LINE CONTINUED on *** WOODEN TRESTLES +HYP: OPRATION of the ***** FRONTLAN CONTDNURED on THE WODENDT TESSEILS +Eval: S D S S I S S + +Speaker sentences 127: cv_eng_000710 #utts: 1 +id: (cv_eng_000710-cv_eng_000710) +Scores: (#C #S #D #I) 3 7 1 1 +REF: * MAGNESIUM FLUORIDE is TRANSPARENT over AN EXTREMELY WIDE RANGE of WAVELENGTHS +HYP: M NISON FHLORID is TWENCEPERENT over ** NCTRIMLYE WHID RANG of AVOMINGS +Eval: I S S S D S S S S + +Speaker sentences 128: cv_eng_000711 #utts: 1 +id: (cv_eng_000711-cv_eng_000711) +Scores: (#C #S #D #I) 1 13 0 2 +REF: FOUR GIANT PACKING SHEDS STORED FRESH PACKED POTATOES AND DELIVERED them **** * ONTO RAILROAD CARS +HYP: FOR JINT BECKINGK SHITS STORETHFRESH PBOCKT BUTDEADES N DE LEVE them UNTO E RLER OLD CAS +Eval: S S S S S S S S S S I I S S S + +Speaker sentences 129: cv_eng_000712 #utts: 1 +id: (cv_eng_000712-cv_eng_000712) +Scores: (#C #S #D #I) 4 9 1 1 +REF: the **** OTHER FOURTEEN CAMPUSES ARE TWOYEAR CAMPUSES REFERRED to COLLECTIVELY as the UNIVERSITY COLLEGE +HYP: the OTHE FORTING COUMPOSES ARD TOWO YURE CAMPS REFIRT to COLECTIVELY as the ********** YUNERSTDECOLNGE +Eval: I S S S S S S S S D S + +Speaker sentences 130: cv_eng_000713 #utts: 1 +id: (cv_eng_000713-cv_eng_000713) +Scores: (#C #S #D #I) 2 9 2 0 +REF: its TOO BAD THAT HES QUICKLY GOING TO FORGET my NAME HE THOUGHT +HYP: its *** TWO BHAD THEDWE HAE CUICKLE GORNGTO FRGET my **** TAE TD +Eval: D S S S S S S S D S S + +Speaker sentences 131: cv_eng_000714 #utts: 1 +id: (cv_eng_000714-cv_eng_000714) +Scores: (#C #S #D #I) 1 13 1 1 +REF: ***** ONE PICTURE in THE GALLERY SHOWS HOW DILIGENT SLAVES ERECT THE STATUE OF ADMIRAL THOMPSON +HYP: WONEN O TORE in *** TE GELORY SHOH HOURDHE GINTL AID IRED ISTATY AT FOAL ADGRORTCONTON +Eval: I S S D S S S S S S S S S S S + +Speaker sentences 132: cv_eng_000715 #utts: 1 +id: (cv_eng_000715-cv_eng_000715) +Scores: (#C #S #D #I) 0 2 0 3 +REF: * *** ****** IMPERIAL DIET +HYP: E ANM PERIAL DI IAT +Eval: I I I S S + +Speaker sentences 133: cv_eng_000716 #utts: 1 +id: (cv_eng_000716-cv_eng_000716) +Scores: (#C #S #D #I) 1 6 0 0 +REF: the RESULTING COMPANY IS STRATTEC SECURITY CORPORATION +HYP: the ESELDIN COMPNYHTD AS HUD TAKESCKORITYO OTPRATION +Eval: S S S S S S + +Speaker sentences 134: cv_eng_000717 #utts: 1 +id: (cv_eng_000717-cv_eng_000717) +Scores: (#C #S #D #I) 2 10 0 2 +REF: ** BITCOIN MINING can be *** DONE WITH GRAPHICS CARDS OR WITH SPECIALIZED HARDWARE +HYP: TE COING MIYNING can be DON WIT GDOF HIS COARTS AOR EITES PESIOLIST HORDLY +Eval: I S S I S S S S S S S S + +Speaker sentences 135: cv_eng_000718 #utts: 1 +id: (cv_eng_000718-cv_eng_000718) +Scores: (#C #S #D #I) 0 6 0 1 +REF: * THEY ALSO LEAD THE NATIONAL RANKING +HYP: G THE L SO LE THENOUSIONAL RANKINGN +Eval: I S S S S S S + +Speaker sentences 136: cv_eng_000719 #utts: 1 +id: (cv_eng_000719-cv_eng_000719) +Scores: (#C #S #D #I) 1 4 0 1 +REF: * CHARLES GRAVES BISHOP of LIMERICK +HYP: T TARWRS GRANES BISHOPE of NHIMORECKE +Eval: I S S S S + +Speaker sentences 137: cv_eng_000720 #utts: 1 +id: (cv_eng_000720-cv_eng_000720) +Scores: (#C #S #D #I) 4 7 0 1 +REF: AND AT THAT I TOLD him and he TOOK my ****** PLACE +HYP: AA IOUNDER THART THI TOL him and he OK my PLASES PD +Eval: S S S S S S I S + +Speaker sentences 138: cv_eng_000721 #utts: 1 +id: (cv_eng_000721-cv_eng_000721) +Scores: (#C #S #D #I) 4 3 1 0 +REF: i THOUGHT id give the KIDS A TREAT +HYP: i THORGT id give the **** CITITCS ATDREAETE +Eval: S D S S + +Speaker sentences 139: cv_eng_000722 #utts: 1 +id: (cv_eng_000722-cv_eng_000722) +Scores: (#C #S #D #I) 1 4 0 1 +REF: ** ACEVEDO DENIED SHOWING the PICTURES +HYP: AS TIEWVETL DENIH TOMN the RICTOES +Eval: I S S S S + +Speaker sentences 140: cv_eng_000723 #utts: 1 +id: (cv_eng_000723-cv_eng_000723) +Scores: (#C #S #D #I) 2 10 0 1 +REF: HOLD your NOSE to **** KEEP THE SMELL FROM DISABLING YOUR MOTOR FUNCTIONS +HYP: HOED your NOSTH to CAED THIT MAY FOM THEF ABLING HOR MOT OFONTION +Eval: S S I S S S S S S S S + +Speaker sentences 141: cv_eng_000724 #utts: 1 +id: (cv_eng_000724-cv_eng_000724) +Scores: (#C #S #D #I) 1 4 0 3 +REF: * that ******* **** SOUNDS LIKE THEIR PROBLEM +HYP: D that SONEDTS LAKE THERE POLOME AIIEDE I +Eval: I I I S S S S + +Speaker sentences 142: cv_eng_000725 #utts: 1 +id: (cv_eng_000725-cv_eng_000725) +Scores: (#C #S #D #I) 3 9 2 1 +REF: * HISTORICALLY THERE was no CLEARLY DEFINED BOUNDARY IN THIS PART of THE ARABIAN PENINSULA +HYP: I HIS RINCULIGOR was no ******* TPLARLY DEFIDE BOWNDGREAIN THES PITD of *** TECH ARIBEUNPANINSTOLAE +Eval: I S S D S S S S S D S S + +Speaker sentences 143: cv_eng_000726 #utts: 1 +id: (cv_eng_000726-cv_eng_000726) +Scores: (#C #S #D #I) 5 8 0 1 +REF: *** MARSHALL SHAFFER of slash FILM GAVE the FILM AN EIGHT out of TEN +HYP: MAR SIAL SHAVER of slash FILNME DGAVE the FILME AND AT out of CAN +Eval: I S S S S S S S S + +Speaker sentences 144: cv_eng_000727 #utts: 1 +id: (cv_eng_000727-cv_eng_000727) +Scores: (#C #S #D #I) 0 3 2 0 +REF: HOW CAN YOU SAY THAT +HYP: *** *** HOWPERDYI TI TATE +Eval: D D S S S + +Speaker sentences 145: cv_eng_000728 #utts: 1 +id: (cv_eng_000728-cv_eng_000728) +Scores: (#C #S #D #I) 1 6 0 1 +REF: **** HIS STYLE BEGAN to RESEMBLE MICHAEL DAMASKINOS +HYP: HIST T DILE BEGANE to ESAEMBLETE MYIKCLE TDEMOSKINOSES +Eval: I S S S S S S + +Speaker sentences 146: cv_eng_000729 #utts: 1 +id: (cv_eng_000729-cv_eng_000729) +Scores: (#C #S #D #I) 0 10 2 0 +REF: HE IS ALSO CAPABLE OF FIRING LIGHTNING BOLTS WITH IMMENSE DESTRUCTIVE POWER +HYP: ** ** HIS ALL CAPABL FING LYT INBLT WOE AMENTE DESRUPTOF POER +Eval: D D S S S S S S S S S S + +Speaker sentences 147: cv_eng_000730 #utts: 1 +id: (cv_eng_000730-cv_eng_000730) +Scores: (#C #S #D #I) 1 12 0 3 +REF: HE CLAIMED TWO WICKETS IN ENGLANDS ONLY INNINGS as ****** ******* *** BORDER WERE BEATEN COMPREHENSIVELY +HYP: THE FLAME TO WIAK CS CININGLIN IRMLY ININGS as FPOLHE LISTEAT OND UILRLND ASE LANDDT TRY +Eval: S S S S S S S S I I I S S S S + +Speaker sentences 148: cv_eng_000731 #utts: 1 +id: (cv_eng_000731-cv_eng_000731) +Scores: (#C #S #D #I) 1 4 0 0 +REF: she DID MUCH LITERARY WORK +HYP: she DETEROUSHELY TORO WOAT AH +Eval: S S S S + +Speaker sentences 149: cv_eng_000732 #utts: 1 +id: (cv_eng_000732-cv_eng_000732) +Scores: (#C #S #D #I) 3 7 4 0 +REF: HE MET THE ORGANIZERS of the PROTESTS AND AGREED TO CREATE two WORKING GROUPS +HYP: ** AE MT THEORGONYSEORS of the ******** *** ROTESE ANDAGRED CREAT two ******* WORKINGRUMS +Eval: D S S S D D S S S D S + +Speaker sentences 150: cv_eng_000733 #utts: 1 +id: (cv_eng_000733-cv_eng_000733) +Scores: (#C #S #D #I) 1 10 0 2 +REF: * the *** BALL STRUCK THE FOUL POLE WELL ABOVE THE GREEN MONSTER +HYP: A the BON STROCT THOF FHALD PORD WHILE OAB OF HI GREN ONSTORD +Eval: I I S S S S S S S S S S + +Speaker sentences 151: cv_eng_000734 #utts: 1 +id: (cv_eng_000734-cv_eng_000734) +Scores: (#C #S #D #I) 2 9 0 4 +REF: only ******* CAMDEN THOMAS GARRETT and *** **** *** GOLDFIELDS SOUTH EZEKIEL BAKER WERE UNCONTESTED +HYP: only CAMEDON TOME S GARIT and GLD FILD SOU IS EIL BACKEAR WER UND CONTISTED +Eval: I S S S I I I S S S S S S + +Speaker sentences 152: cv_eng_000735 #utts: 1 +id: (cv_eng_000735-cv_eng_000735) +Scores: (#C #S #D #I) 4 8 1 1 +REF: ** it IS a CHARITY SCHOOL WHOSE FEES ARE CALCULATED on A MEANS test +HYP: BP it HIS a D CHIRDY SCOLEWHOS FES AN COUCKILADIN on * AINMEANS test +Eval: I S S S S S S S D S + +Speaker sentences 153: cv_eng_000736 #utts: 1 +id: (cv_eng_000736-cv_eng_000736) +Scores: (#C #S #D #I) 3 7 1 0 +REF: some went AWAY WHILE I WAS THERE and OTHER PEOPLE CAME +HYP: some went **** WAY WHL OUWAS ER and OTHE PEPE CAMEM +Eval: D S S S S S S S + +Speaker sentences 154: cv_eng_000737 #utts: 1 +id: (cv_eng_000737-cv_eng_000737) +Scores: (#C #S #D #I) 0 1 0 1 +REF: **** SEVEN +HYP: DADT THANTHAHTDTCTCDNCN +Eval: I S + +Speaker sentences 155: cv_eng_000738 #utts: 1 +id: (cv_eng_000738-cv_eng_000738) +Scores: (#C #S #D #I) 4 9 1 0 +REF: THE KURA KHANATE was LOCATED MAINLY IN THE HISTORICAL and GEOGRAPHICAL region of KURA +HYP: THAT CURA CONOTY was ******* LOKCAD MANLY THAT HISTORICLE and DEOGREFOCLE region of CURE +Eval: S S S D S S S S S S + +Speaker sentences 156: cv_eng_000739 #utts: 1 +id: (cv_eng_000739-cv_eng_000739) +Scores: (#C #S #D #I) 2 6 1 0 +REF: THE ELEVATION AT the SITE is ABOVE SEA LEVEL +HYP: TE LVATIO A the SIHT is ***** AMOF SILEVELEIG +Eval: S S S S D S S + +Speaker sentences 157: cv_eng_000740 #utts: 1 +id: (cv_eng_000740-cv_eng_000740) +Scores: (#C #S #D #I) 2 5 1 6 +REF: TOBIAS TRIED to * ** ***** ********* * **** INJECT CONTEMPT INTO his TONE +HYP: ****** A to B AS TRIDE TOANCHECT T CONP TEMETETED IN TO his TON +Eval: D S I I I I I I S S S S + +Speaker sentences 158: cv_eng_000741 #utts: 1 +id: (cv_eng_000741-cv_eng_000741) +Scores: (#C #S #D #I) 3 3 0 0 +REF: i HAVE to WORK this SATURDAY +HYP: i HEAVE to WORKE this SITODLY +Eval: S S S + +Speaker sentences 159: cv_eng_000742 #utts: 1 +id: (cv_eng_000742-cv_eng_000742) +Scores: (#C #S #D #I) 2 9 1 3 +REF: * *** ** the GREAT RULERS FOUND THE SQUEAKY GRATE WAS GRATING on THEIR NERVES +HYP: U TED RA the ***** RON WOS FENMWAS KILAGEA GLEAD WLAF GLATING on TERE NONES +Eval: I I I D S S S S S S S S S + +Speaker sentences 160: cv_eng_000743 #utts: 1 +id: (cv_eng_000743-cv_eng_000743) +Scores: (#C #S #D #I) 5 10 0 0 +REF: WHEN the BLINDING DUST HAD SETTLED A BIT the boy TREMBLED at WHAT he SAW +HYP: WHEO the BILING DOST HE SELED R BEITE the boy TROMBLEDTD at WHA he SO +Eval: S S S S S S S S S S + +Speaker sentences 161: cv_eng_000744 #utts: 1 +id: (cv_eng_000744-cv_eng_000744) +Scores: (#C #S #D #I) 3 4 0 4 +REF: ******** *** *** DEMOCRAT AMBER BAKER won ** the open SEAT +HYP: DEIOCRAT ANE BRH IND DBAKE IER won IT the open SEO +Eval: I I I S S S I S + +Speaker sentences 162: cv_eng_000745 #utts: 1 +id: (cv_eng_000745-cv_eng_000745) +Scores: (#C #S #D #I) 1 10 0 1 +REF: ***** BOTH ARE PUT TOGETHER BY STUDENTS in THE COLLEGES JOURNALISM PROGRAM +HYP: LWORY OUET WORD INO GEATHA BLYTHE OOD in INHECULDY USE OANATLYSINDTOL ROUMNP +Eval: I S S S S S S S S S S + +Speaker sentences 163: cv_eng_000746 #utts: 1 +id: (cv_eng_000746-cv_eng_000746) +Scores: (#C #S #D #I) 3 6 0 0 +REF: TRENCH was born in BELIZE CITY IN BRITISH HONDURAS +HYP: TRANTHW was born in BELYESE SITED IND BRITOCS PONDREASS +Eval: S S S S S S + +Speaker sentences 164: cv_eng_000747 #utts: 1 +id: (cv_eng_000747-cv_eng_000747) +Scores: (#C #S #D #I) 2 4 1 0 +REF: THE EARLY PHASE of LIFE MOVES fast +HYP: *** DERIRDY FASE of LICF MOMES fast +Eval: D S S S S + +Speaker sentences 165: cv_eng_000748 #utts: 1 +id: (cv_eng_000748-cv_eng_000748) +Scores: (#C #S #D #I) 0 1 0 2 +REF: **** ******** NO +HYP: AAAT NOTETHDE E +Eval: I I S + +Speaker sentences 166: cv_eng_000749 #utts: 1 +id: (cv_eng_000749-cv_eng_000749) +Scores: (#C #S #D #I) 0 1 0 1 +REF: ********* SEVEN +HYP: SORVEWINT OLTDLUETOEE +Eval: I S + +Speaker sentences 167: cv_eng_000750 #utts: 1 +id: (cv_eng_000750-cv_eng_000750) +Scores: (#C #S #D #I) 2 10 1 0 +REF: at ONE TIME RAILWAY LINES DIVERGED FROM RUGBY STATION in SEVEN DIFFERENT DIRECTIONS +HYP: at OE TE RE LURLENS TEYEORD FOM BRAKG BESTATION in ***** SOEN IFRENDEDECTIONS +Eval: S S S S S S S S D S S + +Speaker sentences 168: cv_eng_000751 #utts: 1 +id: (cv_eng_000751-cv_eng_000751) +Scores: (#C #S #D #I) 3 6 0 2 +REF: * CZECH REPUBLIC ENTERED two SHOOTERS into the ******* PARALYMPIC COMPETITION +HYP: A ACHAEC REPUPBLIK ANTERED two SHOUTERS into the PARRORO LENPOG COMPOTISIO +Eval: I S S S S I S S + +Speaker sentences 169: cv_eng_000752 #utts: 1 +id: (cv_eng_000752-cv_eng_000752) +Scores: (#C #S #D #I) 3 9 0 5 +REF: * ** TYGER WILLIAMS WROTE the ***** **** SCREENPLAY AND shared story *** CREDIT WITH THE BROTHERS +HYP: T ID HER WILIMS ROE the SKANG PLAY HAN T shared story RED IT HT THO PREIPIT +Eval: I I S S S I I S S I S S S S + +Speaker sentences 170: cv_eng_000753 #utts: 1 +id: (cv_eng_000753-cv_eng_000753) +Scores: (#C #S #D #I) 0 11 0 1 +REF: *** THIS FESTIVAL WAS TO BE A CHARITY FUNDRAISER FOR THE AREA +HYP: TIS FAST OFE LLED WORDESTOAF BETERDNCHEIRRITY FLINDER ATNY SAIDE OFOID YE THERAURT +Eval: I S S S S S S S S S S S + +Speaker sentences 171: cv_eng_000754 #utts: 1 +id: (cv_eng_000754-cv_eng_000754) +Scores: (#C #S #D #I) 1 7 0 7 +REF: * these ****** ******* ** ********* **** ******* EXTRA CARDS WERE INSERTED RANDOMLY INTO PACKS +HYP: O these ENXTRA GOARSTS WE NESEURNLT THED ONGONLE ALNMT OF OACKGALFTE RAY HATHE IR HARDTS +Eval: I I I I I I I S S S S S S S + +Speaker sentences 172: cv_eng_000755 #utts: 1 +id: (cv_eng_000755-cv_eng_000755) +Scores: (#C #S #D #I) 1 4 0 1 +REF: **** HENRY WENT BACK to AUSTRALIA +HYP: AIPI HANDER OND BAKCT to ESTRLIOWM +Eval: I S S S S + +Speaker sentences 173: cv_eng_000756 #utts: 1 +id: (cv_eng_000756-cv_eng_000756) +Scores: (#C #S #D #I) 4 4 2 2 +REF: ** PERMIT me * to INTRODUCE to YOU her MAJESTY THE QUEEN +HYP: AL PREMIT me T to INTERDUSESYOU to *** her ******* MODESTID CQREN +Eval: I S I S D D S S + +Speaker sentences 174: cv_eng_000757 #utts: 1 +id: (cv_eng_000757-cv_eng_000757) +Scores: (#C #S #D #I) 1 8 2 0 +REF: IN ORIGIN HEROIN WAS SUPPOSED TO BE the “NONADDICTIVE MORPHINE SUBSTITUTE” +HYP: ** ****** ENORGEION HERLEN WA S BOSTOY the NONDADETDOF MORFHEN SPSTOTD +Eval: D D S S S S S S S S + +Speaker sentences 175: cv_eng_000758 #utts: 1 +id: (cv_eng_000758-cv_eng_000758) +Scores: (#C #S #D #I) 3 2 0 2 +REF: ** she is of ******** MEXICAN DESCENT +HYP: ET she is of MAKCOCON DESESNT H +Eval: I I S S + +Speaker sentences 176: cv_eng_000759 #utts: 1 +id: (cv_eng_000759-cv_eng_000759) +Scores: (#C #S #D #I) 1 5 2 1 +REF: ** i AM SURE THERE IS NOT ON HIS +HYP: GD i ** **** ME SORE THEILEST NOGT ONDIST +Eval: I D D S S S S S + +Speaker sentences 177: cv_eng_000760 #utts: 1 +id: (cv_eng_000760-cv_eng_000760) +Scores: (#C #S #D #I) 0 11 0 0 +REF: THOSE WHO DONT LEARN FROM HISTORY ARE DOOMED TO REPEAT IT +HYP: AAO THOS AND ONTE LANCTLOL THESHREY IGDLOLD POR A BEDIT ONO +Eval: S S S S S S S S S S S + +Speaker sentences 178: cv_eng_000761 #utts: 1 +id: (cv_eng_000761-cv_eng_000761) +Scores: (#C #S #D #I) 3 3 0 2 +REF: * i COULDN’T STOP STARING at it *** +HYP: A i COLED AN TOPESORIN at it EER +Eval: I S S S I + +Speaker sentences 179: cv_eng_000762 #utts: 1 +id: (cv_eng_000762-cv_eng_000762) +Scores: (#C #S #D #I) 2 9 1 0 +REF: FOR SIMPLICITY GEAR INCHES is NORMALLY ROUNDED to THE NEAREST WHOLE NUMBER +HYP: FORS INPLITY GUR INCHESD is ******** NORMILYARDOUNDED to HE NERESH HOL NOMBER +Eval: S S S S D S S S S S + +Speaker sentences 180: cv_eng_000763 #utts: 1 +id: (cv_eng_000763-cv_eng_000763) +Scores: (#C #S #D #I) 2 8 0 0 +REF: IF we ACTUALLY DO WANT IT SOLVED it WILL BE +HYP: IOF we ACTILY DEO ON IS SOLD it WIL BEF +Eval: S S S S S S S S + +Speaker sentences 181: cv_eng_000764 #utts: 1 +id: (cv_eng_000764-cv_eng_000764) +Scores: (#C #S #D #I) 1 8 0 0 +REF: THE FRUIT of A FIG TREE IS APPLE SHAPED +HYP: THEI FO of H HIC TRY S APL SHAPEDT +Eval: S S S S S S S S + +Speaker sentences 182: cv_eng_000765 #utts: 1 +id: (cv_eng_000765-cv_eng_000765) +Scores: (#C #S #D #I) 2 3 0 0 +REF: FAIR EXCHANGE is no ROBBERY +HYP: THEREIE ACTSHANGE is no WOBLY +Eval: S S S + +Speaker sentences 183: cv_eng_000766 #utts: 1 +id: (cv_eng_000766-cv_eng_000766) +Scores: (#C #S #D #I) 3 5 0 3 +REF: * what YOU eat ** TODAY WALKS and ***** TALKS TOMORROW +HYP: A what OU eat TO DAY WHEASK and TORKS TO MOROE +Eval: I S I S S I S S + +Speaker sentences 184: cv_eng_000767 #utts: 1 +id: (cv_eng_000767-cv_eng_000767) +Scores: (#C #S #D #I) 5 7 0 3 +REF: * the WATER THEN FLOWS out of THE SWAMPS as ** ** THE LUAPULA river +HYP: A the WOTED AN FLOS out of TE SCONMNTS as HE LO OAPL E river +Eval: I S S S S S I I S S + +Speaker sentences 185: cv_eng_000768 #utts: 1 +id: (cv_eng_000768-cv_eng_000768) +Scores: (#C #S #D #I) 0 5 0 2 +REF: ** ****** WHY DIDNT YOU SAY SOMETHING +HYP: AM HEWHIY ID DION YOESAY SOM THINKCDHEADCD +Eval: I I S S S S S + +Speaker sentences 186: cv_eng_000769 #utts: 1 +id: (cv_eng_000769-cv_eng_000769) +Scores: (#C #S #D #I) 0 4 0 1 +REF: * HAVE YOU SEEN OMAR +HYP: T AVEYOSE NO MARNM EETDEEEEE +Eval: I S S S S + +Speaker sentences 187: cv_eng_000770 #utts: 1 +id: (cv_eng_000770-cv_eng_000770) +Scores: (#C #S #D #I) 5 9 3 1 +REF: i COULD go ON FOR DAYS ABOUT the ****** DELICIOUS WINES PRODUCED in THIS PART OF the WORLD +HYP: i COTD go ** *** ONEFREDAIS ABOU the DIDIOS WONS THE DUAST in **** HIS PARTOF the WERETD +Eval: S D D S S I S S S D S S S + +Speaker sentences 188: cv_eng_000771 #utts: 1 +id: (cv_eng_000771-cv_eng_000771) +Scores: (#C #S #D #I) 1 8 1 0 +REF: THE PHILADELPHIA INQUIRER NAMED HIM CITY PLAYER OF the YEAR +HYP: *** THO SOEO LAD DEVFTHEAR INCOREIRERAE NINGD INSITOPCEYOT the YOARE +Eval: D S S S S S S S S + +Speaker sentences 189: cv_eng_000772 #utts: 1 +id: (cv_eng_000772-cv_eng_000772) +Scores: (#C #S #D #I) 0 7 0 2 +REF: ** **** BOTS MAY BE SUBJECT TO SPECIAL RULES +HYP: AA COAS EVES OPB DCECT IS ESER GLWNDSH EECLDELDARDL +Eval: I I S S S S S S S + +Speaker sentences 190: cv_eng_000773 #utts: 1 +id: (cv_eng_000773-cv_eng_000773) +Scores: (#C #S #D #I) 3 10 1 0 +REF: the SWEDES WERE UNABLE to USE THEIR VEHICLES WHICH WERE STUCK in THE MUD +HYP: the ****** SWEEDS WERANABL to YOUS HR VEACALS WHIH ER TOCE in TH MODE +Eval: D S S S S S S S S S S + +Speaker sentences 191: cv_eng_000774 #utts: 1 +id: (cv_eng_000774-cv_eng_000774) +Scores: (#C #S #D #I) 3 9 0 5 +REF: * the ACT DID not *** ** PROHIBIT PAYING A REPRESENTATIVE to * **** APPEAR IN COURT +HYP: I the ACKT ID not POR HE BIT BAYING AE REPEOSENTIP to E BEAR AN THE CORTLIS +Eval: I S S I I S S S S I I S S S + +Speaker sentences 192: cv_eng_000775 #utts: 1 +id: (cv_eng_000775-cv_eng_000775) +Scores: (#C #S #D #I) 0 2 3 0 +REF: CAN WE PLEASE LEAVE NOW +HYP: *** ** ****** CHONWRPLESTELEPINROULT IDN +Eval: D D D S S + +Speaker sentences 193: cv_eng_000776 #utts: 1 +id: (cv_eng_000776-cv_eng_000776) +Scores: (#C #S #D #I) 2 9 1 1 +REF: he ** WAS CONVICTED AND BANISHED TO CYPRUS for SEVEN YEARS FOR PUNISHMENT +HYP: he WA CON VICTDED AM BANIS O SIPRS for ***** SOVION HORS RPNISMENT +Eval: I S S S S S S D S S S + +Speaker sentences 194: cv_eng_000777 #utts: 1 +id: (cv_eng_000777-cv_eng_000777) +Scores: (#C #S #D #I) 3 11 0 3 +REF: the COUPLE HAVE two CHILDREN a ***** ** ***** DAUGHTER SOPHIA ROSALINDA AND A SON MATEO BRAVERY +HYP: the COPL OF two CHOLDON a DATER SO FEAUR RSELENDEA AN THE SIN F MUT HAL BRVERY +Eval: S S S I I I S S S S S S S S + +Speaker sentences 195: cv_eng_000778 #utts: 1 +id: (cv_eng_000778-cv_eng_000778) +Scores: (#C #S #D #I) 2 11 1 0 +REF: NONE of THE THREE REFERENDUMS REACHED THE QUORUM OF THE MAJORITY of THOSE ENTITLED +HYP: N of *** TH THRE RERENDEMS RECH HE CUARAMOF HE MODGURITY of HES INTICTLDT +Eval: S D S S S S S S S S S S + +Speaker sentences 196: cv_eng_000779 #utts: 1 +id: (cv_eng_000779-cv_eng_000779) +Scores: (#C #S #D #I) 1 13 0 5 +REF: *** ******* **** ****** ** TURPIN SUCCEEDED INDIRA SAMARASEKERA WHO SAW THE UNIVERSITY THROUGH a PERIOD OF STRONG GROWTH +HYP: IET ITERPEN SAEX CEADED IN DERAST SOMER ROSIP CAIRA ITI IN HOSOLE UNERSYO THOR a PEIRD O STONG BOATH +Eval: I I I I I S S S S S S S S S S S S S + +Speaker sentences 197: cv_eng_000780 #utts: 1 +id: (cv_eng_000780-cv_eng_000780) +Scores: (#C #S #D #I) 3 9 0 0 +REF: HERE I AM BETWEEN my FLOCK and MY TREASURE the BOY THOUGHT +HYP: WHEAR A AME BECWEN my FLUCK and MTH BUTERSIURE the BOLY TOS +Eval: S S S S S S S S S + +Speaker sentences 198: cv_eng_000781 #utts: 1 +id: (cv_eng_000781-cv_eng_000781) +Scores: (#C #S #D #I) 2 9 3 0 +REF: THIS FAILURE HAS LED to SIXTEEN POWER PLANTS HAVING ZERO DAYS of COAL STOCK +HYP: THI FALIYER HAST LET to ******* ***** ICXSTDAEN OULBLENCD HADH INSERDAYSE of **** COLLSTO +Eval: S S S S D D S S S S D S + +Speaker sentences 199: cv_eng_000782 #utts: 1 +id: (cv_eng_000782-cv_eng_000782) +Scores: (#C #S #D #I) 0 1 0 7 +REF: ***** * ***** * * * * YES +HYP: ADDUT Y ASESH H H H D EN +Eval: I I I I I I I S + +Speaker sentences 200: cv_eng_000783 #utts: 1 +id: (cv_eng_000783-cv_eng_000783) +Scores: (#C #S #D #I) 2 5 0 0 +REF: WHY DOES that PLANE KEEP GOING over +HYP: WHIY OI that PLAIND CAEPE GOIN over +Eval: S S S S S + +Speaker sentences 201: cv_eng_000784 #utts: 1 +id: (cv_eng_000784-cv_eng_000784) +Scores: (#C #S #D #I) 0 9 0 3 +REF: *** ***** *** IVE DONE THIS BEFORE WITH VIRTUALBOX WITH GOOD RESULTS +HYP: EEE HAYIT AOD DONDISTEE THE FOR WAO FORSIOL BOSE WIOF O RSOLTESE +Eval: I I I S S S S S S S S S + +Speaker sentences 202: cv_eng_000785 #utts: 1 +id: (cv_eng_000785-cv_eng_000785) +Scores: (#C #S #D #I) 2 3 1 3 +REF: THE APPLICATION was *** * ***** APPROVED in FEBRUARY +HYP: *** TEPLOCATION was PUT A PROVE IT in FARIBRAELY +Eval: D S I I I S S + +Speaker sentences 203: cv_eng_000786 #utts: 1 +id: (cv_eng_000786-cv_eng_000786) +Scores: (#C #S #D #I) 4 7 0 1 +REF: henry TARLTON STILES WHERE he had * A SOUND TRAINING in LATIN +HYP: henry TORLDTON STILESE WHER he had T E SOUNDED RONING in LTIN +Eval: S S S I S S S S + +Speaker sentences 204: cv_eng_000787 #utts: 1 +id: (cv_eng_000787-cv_eng_000787) +Scores: (#C #S #D #I) 4 10 0 4 +REF: it was **** DISCONTINUED DUE to ********** ******* ********* SCHEDULING CONFLICTS INVOLVED IN LEWISS RETURN to TERRESTRIAL RADIO +HYP: it was THIS CONTINUED D to SCETHRLING CONFLIC ANDFVLVED AND LOSE SIS RETIRN TOR E to ERESTRIULE REBRADIUO +Eval: I S S I I I S S S S S S S S + +Speaker sentences 205: cv_eng_000788 #utts: 1 +id: (cv_eng_000788-cv_eng_000788) +Scores: (#C #S #D #I) 1 4 0 4 +REF: **** her ******* * **** FAMILY WAS FROM BRIANZA +HYP: DEDH her FANMELY H WOAS FOME PREOHONSAE EDDUD Y +Eval: I I I I S S S S + +Speaker sentences 206: cv_eng_000789 #utts: 1 +id: (cv_eng_000789-cv_eng_000789) +Scores: (#C #S #D #I) 0 6 0 1 +REF: * WHAT DID YOU EAT FOR DINNER +HYP: E WOWNT IDI EAKE FORDINMN BH PT +Eval: I S S S S S S + +Speaker sentences 207: cv_eng_000790 #utts: 1 +id: (cv_eng_000790-cv_eng_000790) +Scores: (#C #S #D #I) 4 2 0 0 +REF: that was my DRAW to SCIENCE +HYP: that was my DRE to SINSE +Eval: S S + +Speaker sentences 208: cv_eng_000791 #utts: 1 +id: (cv_eng_000791-cv_eng_000791) +Scores: (#C #S #D #I) 2 5 0 1 +REF: he IS CONSIDERED a ******* MASTER OF CHIAROSCURO +HYP: he S GOSLAIRENT a MUSTERE OFD SHEAROST COLO +Eval: S S I S S S + +Speaker sentences 209: cv_eng_000792 #utts: 1 +id: (cv_eng_000792-cv_eng_000792) +Scores: (#C #S #D #I) 3 8 1 2 +REF: IT then *** RETURNS to the ******** CHURCH ASCENDS AT THE ALTAR AND DISAPPEARS +HYP: ** then LIN TORNS to the CHORISHE OF SHINGS HAT T BLTER ANDESPEUE ITE +Eval: D I S I S S S S S S S + +Speaker sentences 210: cv_eng_000793 #utts: 1 +id: (cv_eng_000793-cv_eng_000793) +Scores: (#C #S #D #I) 0 6 1 0 +REF: YOU CANNOT LOSE WHAT YOU NEVER HAD +HYP: *** WUD NOT THOSER IN HEREAC ADRT +Eval: D S S S S S S + +Speaker sentences 211: cv_eng_000794 #utts: 1 +id: (cv_eng_000794-cv_eng_000794) +Scores: (#C #S #D #I) 1 3 2 0 +REF: THE JAWS EXTEND PAST the EYE +HYP: *** **** TOELCIOLSE INCTENDFEST the AIYH +Eval: D D S S S + +Speaker sentences 212: cv_eng_000795 #utts: 1 +id: (cv_eng_000795-cv_eng_000795) +Scores: (#C #S #D #I) 1 6 0 0 +REF: my NIECE CAN HELP YOU WITH THAT +HYP: my NEST CON HELPE OW IT THAITS +Eval: S S S S S S + +Speaker sentences 213: cv_eng_000796 #utts: 1 +id: (cv_eng_000796-cv_eng_000796) +Scores: (#C #S #D #I) 1 5 1 0 +REF: thats THE KIND OF STUFF THEY WANT +HYP: thats *** A COUDHIST OT LY ON +Eval: D S S S S S + +Speaker sentences 214: cv_eng_000797 #utts: 1 +id: (cv_eng_000797-cv_eng_000797) +Scores: (#C #S #D #I) 5 4 0 0 +REF: HOPE for the best and PREPARE FOR the WORST +HYP: HOE for the best and PO BEAREFOR the MLST +Eval: S S S S + +Speaker sentences 215: cv_eng_000798 #utts: 1 +id: (cv_eng_000798-cv_eng_000798) +Scores: (#C #S #D #I) 1 6 2 1 +REF: * INITIALLY the WEIGHT LOSS WAS ATTAINED STRICTLY BY DIET +HYP: C INISHELY the ****** **** WHPDYOSOS HT INSTROCKYO BO DICT +Eval: I S D D S S S S S + +Speaker sentences 216: cv_eng_000799 #utts: 1 +id: (cv_eng_000799-cv_eng_000799) +Scores: (#C #S #D #I) 3 4 0 2 +REF: all WERE OWNED by the ****** *** EVERETTMOORE SYNDICATE +HYP: all WE ONED by the EVERIT MOR SON IKCETH +Eval: S S I I S S + +Speaker sentences 217: cv_eng_000800 #utts: 1 +id: (cv_eng_000800-cv_eng_000800) +Scores: (#C #S #D #I) 0 4 0 4 +REF: *********** * *** ********* WILL IT RAIN TOMORROW +HYP: AATHETHEOEN G THE WILASERIN TO MORONMNE INLND EETHER +Eval: I I I I S S S S + +Speaker sentences 218: cv_eng_000801 #utts: 1 +id: (cv_eng_000801-cv_eng_000801) +Scores: (#C #S #D #I) 0 5 0 0 +REF: DU BIST EWIG MEINE LIEBE +HYP: E DO BPBISTD IRICM MINDLIBPBPTH +Eval: S S S S S + +Speaker sentences 219: cv_eng_000802 #utts: 1 +id: (cv_eng_000802-cv_eng_000802) +Scores: (#C #S #D #I) 2 6 0 4 +REF: ** * ****** LUCILE PETRY TOOK her PLACE as *** ACTING DIRECTOR +HYP: AT O SEIOLE PATRY HE TO her PLAE as ACT ING ORECTERHEHD +Eval: I I I S S S S I S S + +Speaker sentences 220: cv_eng_000803 #utts: 1 +id: (cv_eng_000803-cv_eng_000803) +Scores: (#C #S #D #I) 1 9 1 2 +REF: THE BEAVER RIVER BRIEFLY ENTERS the *** ****** EASTCENTRAL PART OF THE TOWNSHIP +HYP: *** TE BEVERLY WLEBEFLY ANTES the IES SENTER PU OP T TON SHIM +Eval: D S S S S I I S S S S S + +Speaker sentences 221: cv_eng_000804 #utts: 1 +id: (cv_eng_000804-cv_eng_000804) +Scores: (#C #S #D #I) 3 3 0 0 +REF: the TRACK RESURFACING was also COMPLETED +HYP: the TRACE RERVISTING was also COMPETED +Eval: S S S + +Speaker sentences 222: cv_eng_000805 #utts: 1 +id: (cv_eng_000805-cv_eng_000805) +Scores: (#C #S #D #I) 5 7 0 4 +REF: **** HINDMARSH was * AWARE of the IMPORTANCE of *** ELECTRON MICROSCOPY in ** BIOLOGICAL RESEARCH +HYP: HATD MARSH was A WHAR of the IMPRTNS of ILC TRMRY COSKOMPYE in BY LOUGICKL RESRCHE +Eval: I S I S S I S S I S S + +Speaker sentences 223: cv_eng_000806 #utts: 1 +id: (cv_eng_000806-cv_eng_000806) +Scores: (#C #S #D #I) 1 4 0 2 +REF: *** SINHA was ***** BORN IN ALLAHABAD +HYP: SIN HE was BORNY UD THE HOBOUE +Eval: I S I S S S + +Speaker sentences 224: cv_eng_000807 #utts: 1 +id: (cv_eng_000807-cv_eng_000807) +Scores: (#C #S #D #I) 1 13 0 2 +REF: this ******** **** BRIDGE IS UNOFFICIALLY REFERRED TO AS BLACKWATER BRIDGE BY COALITION FORCES OPERATING THERE +HYP: this WEIENCEH EASE AN OFITILY THER HEOTO ASE MAC RETOAD WINTH MY COLISTION FOASES OPERATDIN MIL +Eval: I I S S S S S S S S S S S S S + +Speaker sentences 225: cv_eng_000808 #utts: 1 +id: (cv_eng_000808-cv_eng_000808) +Scores: (#C #S #D #I) 6 7 0 2 +REF: it is RESPONSIBLE for water ** SUPPLY and MANAGEMENT of ***** WATER RESOURCES IN MAHARASHTRA +HYP: it is RESPONEAULE for water SO PLI and MANEMENT of WOTER RESOURSES AND MO HOUSTRA +Eval: S I S S I S S S S + +Speaker sentences 226: cv_eng_000809 #utts: 1 +id: (cv_eng_000809-cv_eng_000809) +Scores: (#C #S #D #I) 3 6 1 0 +REF: THIS IS THE FIRST PHASE of the JOB he SAID +HYP: **** DISES THO FEORES FAYE of the GHORVE he SADED +Eval: D S S S S S S + +Speaker sentences 227: fleurs_eng_000413 #utts: 1 +id: (fleurs_eng_000413-fleurs_eng_000413) +Scores: (#C #S #D #I) 12 20 1 1 +REF: the GIZA PLATEAU or GIZA NECROPOLIS IN THE EGYPTIAN VALLEY of the dead **** CONTAINS SEVERAL PYRAMIDS of which THE GREAT PYRAMID is the LARGEST SEVERAL SMALL TOMBS SEVERAL temples and the GREAT SPHINX +HYP: the GISIAP PLATOH or GISSA NCRAOL POLIS I THENGION VAOLY of the dead CONT TANG SEVRL PERMINDS of which *** THEGRAT PERMENT is the LARTEIS SESEVERLE SML TONS SOVRLE temples and the GRAT SPANKS +Eval: S S S S S S S S I S S S D S S S S S S S S S + +Speaker sentences 228: fleurs_eng_000414 #utts: 1 +id: (fleurs_eng_000414-fleurs_eng_000414) +Scores: (#C #S #D #I) 14 21 2 3 +REF: TOWARDS THE END of THE MIDDLE ages WESTERN EUROPE BEGAN TO DEVELOP THEIR OWN STYLE one of the BIGGEST DEVELOPMENTS of the time as A RESULT of the CRUSADES PEOPLE began to USE BUTTONS to ****** ******* ** FASTEN CLOTHING +HYP: TWORE HE IND of TE MILE ages ******* WESTORN YURO BEGANTO DELT TER ON STIL one of the BIGIST OELINS of the time as * RESIULT of the REUCSAIS PEPBL began to OUSES BUTENS to FASTON RLVDING II I A +Eval: S S S S S D S S S S S S S S S D S S S S S I I I S S + +Speaker sentences 229: fleurs_eng_000415 #utts: 1 +id: (fleurs_eng_000415-fleurs_eng_000415) +Scores: (#C #S #D #I) 5 13 0 5 +REF: IF you only *** * GO ASHORE USING SHIPBOARD EXCURSIONS YOU WILL not NEED a SEPARATE VISA as * *** ***** OF 2009 +HYP: IFS you only GOL S HORE OUSING SHIP OR C SCKDRIONDS YOUL not E a SEPRT VESA as A TWO FHOUS IN NOIG +Eval: S I I S S S S S S S S S S I I I S S + +Speaker sentences 230: fleurs_eng_000416 #utts: 1 +id: (fleurs_eng_000416-fleurs_eng_000416) +Scores: (#C #S #D #I) 5 14 3 0 +REF: DUVALL WHO IS MARRIED WITH TWO ADULT CHILDREN DID NOT LEAVE a BIG IMPRESSION ON MILLER to WHOM the STORY was related +HYP: ****** *** ** BDOBALHUISMARE WIH TO A DL CIOREION ID NOTBYW a BIN IMPRESION N MILER to HOE the TARY was related +Eval: D D D S S S S S S S S S S S S S S + +Speaker sentences 231: fleurs_eng_000417 #utts: 1 +id: (fleurs_eng_000417-fleurs_eng_000417) +Scores: (#C #S #D #I) 6 18 1 0 +REF: THEIR DISCIPLINED DEFENCE BALL HANDLING SKILLS AND EXCELLENT TEAM WORK MADE THEM stand out AND IT was CLEAR THAT this WAS the TEAM to BEAT +HYP: TER DECTPLND DEFIENCE BOLE HADLING SCILS AN ECSLNT IE WORD MAY THE stand out *** AN was CLER HA this WA the TEM to BE +Eval: S S S S S S S S S S S S D S S S S S S + +Speaker sentences 232: fleurs_eng_000418 #utts: 1 +id: (fleurs_eng_000418-fleurs_eng_000418) +Scores: (#C #S #D #I) 3 10 0 1 +REF: the DISEASE IS CARRIED by ****** PIGS WHICH THEN MIGRATES to HUMANS THROUGH MOSQUITOS +HYP: the DEAS S CAIRED by PIAKGE WICH THN MY GRET to CUMEN TORO MOSKETOS +Eval: S S S I S S S S S S S + +Speaker sentences 233: fleurs_eng_000419 #utts: 1 +id: (fleurs_eng_000419-fleurs_eng_000419) +Scores: (#C #S #D #I) 1 8 0 2 +REF: for ** ****** THE SPRINGBOKS IT ENDED A FIVEMATCH LOSING STREAK +HYP: for TH SPRING BOGCKE HID INDED OAF FLIVE NATH LOING STRAKE +Eval: I I S S S S S S S S + +Speaker sentences 234: fleurs_eng_000420 #utts: 1 +id: (fleurs_eng_000420-fleurs_eng_000420) +Scores: (#C #S #D #I) 4 7 3 0 +REF: THUS the PENCIL was A GOOD friend TO many PEOPLE WHEN IT CAME OUT +HYP: THEUS the HINSL was * GO friend ** many ****** BEBL LADYK ING OU +Eval: S S D S D D S S S S + +Speaker sentences 235: fleurs_eng_000421 #utts: 1 +id: (fleurs_eng_000421-fleurs_eng_000421) +Scores: (#C #S #D #I) 7 14 1 2 +REF: the USE of VIDEO RECORDING has LED to IMPORTANT DISCOVERIES IN the INTERPRETATION of ******* ************ MICROEXPRESSIONS FACIAL MOVEMENTS which LAST A FEW MILLISECONDS +HYP: the YUSE of EAO RECORING has LAD to ********* INPORNED DECSCORVERESIN the INTERPROTATION of MYGCRLR ECTPRESTIONS FASIAL MOVE ENS which LASE AFYO MILS SICKENS +Eval: S S S S D S S S I I S S S S S S S + +Speaker sentences 236: fleurs_eng_000422 #utts: 1 +id: (fleurs_eng_000422-fleurs_eng_000422) +Scores: (#C #S #D #I) 4 16 1 1 +REF: ALSO TO the north VISIT THE GREAT SANCTUARY of OUR LADY of **** FATIMA SHRINE A PLACE OF WORLDWIDE FAMOUS MARIAN APPARITIONS +HYP: LS AT the north ***** IST THEGRAT SANCHURY of R LATDY of ATHY MUSHRING AE LECE AF WORLD GHT FIMIS MERION APBRISTIONS +Eval: S S D S S S S S I S S S S S S S S S + +Speaker sentences 237: fleurs_eng_000423 #utts: 1 +id: (fleurs_eng_000423-fleurs_eng_000423) +Scores: (#C #S #D #I) 4 15 7 3 +REF: * if * YOU WANT TO BE CLOSE to THE ACTION YOURE GOING TO HAVE TO GET in **** EARLY to GET A CAMPING SITE CLOSE TO THE MUSIC +HYP: E if O NE BY CLOSO THEACTION YGORHE to *** ****** ***** ***** W W OW GIT in EALY W to *** * ******* TE CAPINGSIHT CLOSTO TH MOUSICKE +Eval: I I S S S S S D D D D S S S S I S D D D S S S S S + +Speaker sentences 238: fleurs_eng_000424 #utts: 1 +id: (fleurs_eng_000424-fleurs_eng_000424) +Scores: (#C #S #D #I) 3 12 2 0 +REF: MADAGASCAR IS BY FAR the BIGGEST AND a CONTINENT on ITS OWN WHEN IT COMES TO WILDLIFE +HYP: ********** MTYAGUSKURERIS BLY HFARE the BIGIST ND a CONTINT on *** IDS ON WHN A COMSTO WHOULDLIFT +Eval: D S S S S S S D S S S S S S + +Speaker sentences 239: fleurs_eng_000425 #utts: 1 +id: (fleurs_eng_000425-fleurs_eng_000425) +Scores: (#C #S #D #I) 6 10 2 0 +REF: WOMEN it is RECOMMENDED that any WOMEN TRAVELLERS say THAT THEY ARE MARRIED REGARDLESS of ACTUAL MARITAL STATUS +HYP: WEMEN it is RECMENE that any ***** WHEMENTROVLIR say **** THE AR MARD REGARLES of ACHUL MARTL STATTIST +Eval: S S D S D S S S S S S S + +Speaker sentences 240: fleurs_eng_000426 #utts: 1 +id: (fleurs_eng_000426-fleurs_eng_000426) +Scores: (#C #S #D #I) 5 12 0 4 +REF: **** **** CUOMO 53 began ** HIS GOVERNORSHIP EARLIER THIS year and SIGNED a BILL last ***** MONTH LEGALIZING SAMESEX MARRIAGE +HYP: COUO AOMO FIFTY THRE began HF GOVERMENT GOVERSHIP ERILE THES year and SINE a BIL last MUNCH LEGLIDSING SAN SECXS MARAGE +Eval: I I S S I S S S S S S I S S S S + +Speaker sentences 241: fleurs_eng_000427 #utts: 1 +id: (fleurs_eng_000427-fleurs_eng_000427) +Scores: (#C #S #D #I) 7 20 6 0 +REF: as LIGHT POLLUTION IN THEIR HEYDAY was not the KIND of PROBLEM IT IS TODAY THEY ARE USUALLY LOCATED in CITIES or AT CAMPUSES EASIER TO REACH THAN THOSE BUILT IN MODERN TIMES +HYP: as ***** LIHPLTION N THR HAYDY was not the IND of ******* ** POLOMTIS TO DY THER UELY LOCKATED in SIYES or ** ******** ****** A CANPSES ESIUR TORESION THOS BUL ANMOTN TIMS +Eval: D S S S S S D D S S S S S S S D D D S S S S S S S S + +Speaker sentences 242: fleurs_eng_000428 #utts: 1 +id: (fleurs_eng_000428-fleurs_eng_000428) +Scores: (#C #S #D #I) 5 17 0 0 +REF: THEY USUALLY have SPECIAL FOOD DRINK AND ENTERTAINMENT OFFERS to KEEP GUESTS IN a GOOD MOOD AND KEEP THEM at the PREMISE +HYP: THE YOUELY have SPETIL FO RNKN INER TAME OPERS to CAE GIS AN a GO MOD N CAE THIM at the PRMIS +Eval: S S S S S S S S S S S S S S S S S + +Speaker sentences 243: fleurs_eng_000429 #utts: 1 +id: (fleurs_eng_000429-fleurs_eng_000429) +Scores: (#C #S #D #I) 3 18 1 1 +REF: ON THE OTHER HAND ICY and SNOWY CONDITIONS ARE NORMAL IN many COUNTRIES AND TRAFFIC GOES on ******* MOSTLY UNINTERRUPTED ALL YEAR ROUND +HYP: ** O HETER HEAND ISE and SNOW COEDIONS AR NORMAE AND many CUNTRYES IHAND TRAPFIT OS on MOSTELY UN INTERUPTED ALLE YER ROUNED +Eval: D S S S S S S S S S S S S S I S S S S S + +Speaker sentences 244: fleurs_eng_000430 #utts: 1 +id: (fleurs_eng_000430-fleurs_eng_000430) +Scores: (#C #S #D #I) 7 12 0 2 +REF: be CAREFUL not to ** ALLOW FABRIC to BECOME TOO HOT which can CAUSE SHRINKAGE or *** IN EXTREME CASES SCORCH +HYP: be CARFL not to AE LOUL FABOIC to BECME TO HID which can COS STRANKADGE or INA STREN CASCES S SQOARTCHE +Eval: S I S S S S S S S I S S S S + +Speaker sentences 245: fleurs_eng_000431 #utts: 1 +id: (fleurs_eng_000431-fleurs_eng_000431) +Scores: (#C #S #D #I) 0 16 0 2 +REF: ***** ******** FERAL CHILDREN MAY HAVE EXPERIENCED SEVERE CHILD ABUSE OR TRAUMA BEFORE BEING ABANDONED OR RUNNING AWAY +HYP: FEIRL THLLDERN AY HAV CTPEINCE O VER CHULD O BEUSOR TRMOH BHE FOR BING A BENDIN RNG WAY +Eval: I I S S S S S S S S S S S S S S S S + +Speaker sentences 246: fleurs_eng_000432 #utts: 1 +id: (fleurs_eng_000432-fleurs_eng_000432) +Scores: (#C #S #D #I) 5 10 0 0 +REF: PEOPLE MAY not ANTICIPATE that PATIENCE and UNDERSTANDING ARE ALSO NECESSARY for TRAVELLERS RETURNING home +HYP: BEBL MA not NTICHIPAT that PETIONC and NDERSTANING R ALO NESARY for TRVLURS RETRNING home +Eval: S S S S S S S S S S + +Speaker sentences 247: fleurs_eng_000433 #utts: 1 +id: (fleurs_eng_000433-fleurs_eng_000433) +Scores: (#C #S #D #I) 3 9 1 1 +REF: SOON AFTER the OUTBREAK of *********** HOSTILITIES BRITAIN INITIATED A NAVAL BLOCKADE of GERMANY +HYP: **** ONOTER the OPRIK of OUSTILITYES BRIN INANT SHEATED AD NAVBL BOKAYE of TIRMINY +Eval: D S S I S S S S S S S + +Speaker sentences 248: fleurs_eng_000434 #utts: 1 +id: (fleurs_eng_000434-fleurs_eng_000434) +Scores: (#C #S #D #I) 2 9 0 1 +REF: * the GOVERNORS OFFICE SAID NINETEEN OF THE INJURED were POLICE OFFICERS +HYP: H the VENRS OFPIS SAD NIAT TIEN OFTH INDERD were PLE OFHSARS +Eval: I S S S S S S S S S + +Speaker sentences 249: fleurs_eng_000435 #utts: 1 +id: (fleurs_eng_000435-fleurs_eng_000435) +Scores: (#C #S #D #I) 6 15 1 0 +REF: USING SHIPS TO TRANSPORT GOODS IS by far the most EFFICIENT way TO MOVE large AMOUNTS OF PEOPLE AND GOODS ACROSS OCEANS +HYP: ***** YUSIN HIPST TRESPBURTD GOOD AS by far the most OFIENT way H MOE large MUT O PEBE ANDGO O CROUS OATIONS +Eval: D S S S S S S S S S S S S S S S + +Speaker sentences 250: fleurs_eng_000436 #utts: 1 +id: (fleurs_eng_000436-fleurs_eng_000436) +Scores: (#C #S #D #I) 2 12 4 3 +REF: LIBERAL CRITICISM OF THE RECONSTRUCTION EFFORT HAS FOCUSED ON THE AWARDING of ** RECONSTRUCTION CONTRACTS to ***** * PERCEIVED WASHINGTON INSIDERS +HYP: ******* ********* ** *** LEBRL RETISOHM O THERECONSTRCTION VERN HASPOAKEASO THAORDING of RE CONSRCTING CONCHACT to RSTHE D WAUTING AN INSIHERS +Eval: D D D D S S S S S S S I S S I I S S S + +Speaker sentences 251: fleurs_eng_000437 #utts: 1 +id: (fleurs_eng_000437-fleurs_eng_000437) +Scores: (#C #S #D #I) 5 15 2 2 +REF: YOU CAN USE BODABODA MOTORCYCLE TAXI to GET AROUND GOMA the NORMAL LOCAL price is **** 500 CONGOLESE FRANCS for **** THE SHORT RIDE +HYP: UAS BODOE BOD A MRSECKL TACEY to *** GETEROND GOMEA the ****** NORMEAWLACAL price is FIVE HUNDRED CONDLES FROUS for THEM SHORET RII I +Eval: S S S S S S D S S D S I S S S I S S S + +Speaker sentences 252: fleurs_eng_000438 #utts: 1 +id: (fleurs_eng_000438-fleurs_eng_000438) +Scores: (#C #S #D #I) 9 21 0 1 +REF: the THREE KINGDOMS was one of the *** BLOODIEST ERAS IN ANCIENT CHINAS HISTORY THOUSANDS of PEOPLE DIED FIGHTING to SIT IN THE HIGHEST SEAT IN the GRAND PALACE at XIAN +HYP: the THRE CINGDMES was one of the BLT BLUDIEST ARS AN ANGIONT CHINES HISTRE THOUSENCS of PEBLE DIDED FIDING to SI I TH HIHIS SE I the GRAN PALES at SIANN +Eval: S S I S S S S S S S S S S S S S S S S S S S + +Speaker sentences 253: fleurs_eng_000439 #utts: 1 +id: (fleurs_eng_000439-fleurs_eng_000439) +Scores: (#C #S #D #I) 4 8 0 0 +REF: THESE couples may CHOOSE to MAKE AN ADOPTION PLAN for THEIR BABY +HYP: THEIS couples may CHOS to MAK AND ANDOUSION PLAND for THERE BAVY +Eval: S S S S S S S S + +Speaker sentences 254: fleurs_eng_000440 #utts: 1 +id: (fleurs_eng_000440-fleurs_eng_000440) +Scores: (#C #S #D #I) 10 18 2 1 +REF: NOTHING can be SEEN OTHER THAN the CLEAR BEAUTIFUL SKY above AND the many SURROUNDING MOUNTAINS VERY LITTLE OF THIS WORLD CAN be SEEN OR HEARD from ** INSIDE the cave +HYP: NOTHIN can be FEN UTHE HA the CPER BEUTOFL SCKI above ANDN the many *********** ********* SURWUNING MUNDS BERY LITL O THSWLLTAN be FEEN R HER from IN SI the cave +Eval: S S S S S S S S D D S S S S S S S S S I S + +Speaker sentences 255: fleurs_eng_000441 #utts: 1 +id: (fleurs_eng_000441-fleurs_eng_000441) +Scores: (#C #S #D #I) 3 6 0 1 +REF: he was SUBSEQUENTLY RELOCATED to ***** ADDENBROOKES HOSPITAL IN CAMBRIDGE +HYP: he was OEICENLY RELOKCATE to ADTEN BROKS HOSTPTL AND CAMBRIGE +Eval: S S I S S S S + +Speaker sentences 256: fleurs_eng_000442 #utts: 1 +id: (fleurs_eng_000442-fleurs_eng_000442) +Scores: (#C #S #D #I) 2 12 8 0 +REF: VATICAN CITYS POPULATION IS AROUND 800 IT is THE SMALLEST INDEPENDENT COUNTRY IN THE WORLD AND the COUNTRY WITH THE LOWEST POPULATION +HYP: ******* DTHANTIACAN STAIDY PULEATION ISEROUD INHERNTHITE THI is *** ******** *********** ******* ** *** MOST INPENECOENTRE the ******* WHERALD N TH POPLIULEATION +Eval: D S S S S S S D D D D D D S S D S S S S + +Speaker sentences 257: fleurs_eng_000443 #utts: 1 +id: (fleurs_eng_000443-fleurs_eng_000443) +Scores: (#C #S #D #I) 8 22 2 2 +REF: REGULAR ANNOUNCEMENTS IN the * METRO ARE MADE ONLY IN CATALAN but * UNPLANNED DISRUPTIONS ARE ANNOUNCED by an AUTOMATED SYSTEM in A WIDE VARIETY of LANGUAGES INCLUDING SPANISH ENGLISH french ARABIC and JAPANESE +HYP: ******* ************* BRDGLRALOUNCSTHENC the P MATHR AR MAE OLY N COUTILON but N PLENE DESTRPTIONS AR NUCD by an ODTIMTED STOM in O WAO VERITY of LINWICHGES INCLTING SBANISH ANGLSH french ERBIC and APNS +Eval: D D S I S S S S S S I S S S S S S S S S S S S S S S + +Speaker sentences 258: fleurs_eng_000444 #utts: 1 +id: (fleurs_eng_000444-fleurs_eng_000444) +Scores: (#C #S #D #I) 8 11 3 0 +REF: this OFFERS A GOOD OPPORTUNITY to SEE the AURORA BOREALIS as the SKY WILL be DARK more OR LESS AROUND the CLOCK +HYP: this ****** OPRS GD PRTENDE to SH the OA REREABORILES as the SCAI WL be DARC more ** **** LESTORON the CO +Eval: D S S S S S S S S S D D S S + +Speaker sentences 259: fleurs_eng_000445 #utts: 1 +id: (fleurs_eng_000445-fleurs_eng_000445) +Scores: (#C #S #D #I) 1 8 1 3 +REF: FIRE RESCUE CREWS EVENTUALLY DOUSED THE FIRE by ***** ***** **** 1135 PM +HYP: **** FIRESCOU CREOS OVENCILY DUS TH FIER by LVION THRTY FIVE PE AM +Eval: D S S S S S S I I I S S + +Speaker sentences 260: fleurs_eng_000446 #utts: 1 +id: (fleurs_eng_000446-fleurs_eng_000446) +Scores: (#C #S #D #I) 2 11 2 0 +REF: THIS IS CALLED A CHEMICALS PH YOU CAN MAKE an INDICATOR USING red CABBAGE JUICE +HYP: **** ** HIS CL O COMICALS PE CHE EONMY an INDECATEN OUSING red CABIGHED DOUSE +Eval: D D S S S S S S S S S S S + +Speaker sentences 261: fleurs_eng_000447 #utts: 1 +id: (fleurs_eng_000447-fleurs_eng_000447) +Scores: (#C #S #D #I) 6 11 1 2 +REF: IN PARTICULAR it is CLAIMED that ONE CAN DETECT WHETHER A person is LYING by ********** ** INTERPRETING MICROEXPRESSIONS CORRECTLY +HYP: AN PRTICKILE it is LE that *** ON CEN DETEC WETHEA person is LING by ANTERPRING MY GRL CSTPERTIONC CURECTLY +Eval: S S S D S S S S S I I S S S + +Speaker sentences 262: fleurs_eng_000448 #utts: 1 +id: (fleurs_eng_000448-fleurs_eng_000448) +Scores: (#C #S #D #I) 9 22 1 1 +REF: the CENTRAL AUTHORITY of THE CHURCH HAD BEEN in ROME for over a THOUSAND YEARS and THIS CONCENTRATION OF POWER AND MONEY LED MANY to *** QUESTION WHETHER THIS TENET was BEING MET +HYP: the SESCIAL FORIDY of TH CHURHOU IE BEN in MROM for over a THOUSEIN YOEARS and **** DIS CONCONTRTION F POUER I MUNY WEAD to MAY TO CESTION WETHER ICTENENT was BENG MAT +Eval: S S S S S S S S S D S S S S S S S I S S S S S S + +Speaker sentences 263: fleurs_eng_000449 #utts: 1 +id: (fleurs_eng_000449-fleurs_eng_000449) +Scores: (#C #S #D #I) 6 19 0 3 +REF: the **** ******** ** SUNDARBANS ARE the LARGEST LITTORAL MANGROVE BELT in the WORLD STRETCHING 80 KM 50 MI into the BANGLADESHI AND INDIAN HINTERLAND FROM THE COAST +HYP: the SUND DOARBONS AR THE ARGEIST the TEORAL MAN GROE BEL in the WEROED STRCHING ATY COLOMETERS FIFTY MILES into the BANGWLDEASHE AN AN INDINHINTERLANT FOM THAOW COST +Eval: I I I S S S S S S S S S S S S S S S S S S S + +Speaker sentences 264: fleurs_eng_000450 #utts: 1 +id: (fleurs_eng_000450-fleurs_eng_000450) +Scores: (#C #S #D #I) 7 19 6 0 +REF: REGULAR ANNOUNCEMENTS IN the METRO ARE MADE only IN CATALAN but UNPLANNED DISRUPTIONS ARE ANNOUNCED BY an AUTOMATED SYSTEM in A WIDE VARIETY of LANGUAGES INCLUDING SPANISH ENGLISH FRENCH ARABIC and JAPANESE +HYP: ******* REAGULR NONCTHN the MATHOL AR MAED only N COTLIN but ********* *********** *** NETENDISTRUTON ERNOUCDBY an OTIMATEIS SISM in * **** TWARTVEURITY of LNGWIGDES INKEUTING PANIH INGLSH FRINCH ERIBICK and OAPNES +Eval: D S S S S S S S D D D S S S S D D S S S S S S S S + +Speaker sentences 265: fleurs_eng_000451 #utts: 1 +id: (fleurs_eng_000451-fleurs_eng_000451) +Scores: (#C #S #D #I) 1 13 0 2 +REF: ** EVERYONE PARTICIPATES in ****** SOCIETY AND USES TRANSPORTATION SYSTEMS ALMOST EVERYONE COMPLAINS ABOUT TRANSPORTATION SYSTEMS +HYP: ER WN PRTITBAT in STSIDY AN OUSISTRENSPRTINCSISTAENC AL OST ER WN OMPLANE OBOU TRECD PRTION SISTOM +Eval: I S S I S S S S S S S S S S S + +Speaker sentences 266: fleurs_eng_000452 #utts: 1 +id: (fleurs_eng_000452-fleurs_eng_000452) +Scores: (#C #S #D #I) 4 23 2 1 +REF: LAYTON had ASKED FOR CHANGES TO the CONSERVATIVES ENVIRONMENTAL BILL DURING THE MEETING WITH the PM ASKING FOR A THOROUGH AND COMPLETE REWRITING OF the ** CONSERVATIVE PARTYS ENVIRONMENTAL BILL +HYP: LATN had ***** *** ASFRTHANGES O the ON SRTES MFVARMINL BILE DRIG HE EAINGW the EAUM ASING FRY THIRL AN COMPET RE RIGDTING O the KO SIRVETHIS PARY INFBIRAMANALD ILL +Eval: S D D S S S S S S S S S S S S S S S S S S I S S S S + +Speaker sentences 267: fleurs_eng_000453 #utts: 1 +id: (fleurs_eng_000453-fleurs_eng_000453) +Scores: (#C #S #D #I) 5 16 1 0 +REF: ANYONE WHOS GOING to DRIVE AT HIGH LATITUDES OR over MOUNTAIN PASSES SHOULD CONSIDER the POSSIBILITY of SNOW ice OR FREEZING TEMPERATURES +HYP: ANW NHAS LN to R THAT HAT LIHTATUES AR over MU EN PASTHAD ENSAIDE the POSEILITY of SNOE ice ** ORFRESING TEMBTERS +Eval: S S S S S S S S S S S S S S D S S + +Speaker sentences 268: fleurs_eng_000454 #utts: 1 +id: (fleurs_eng_000454-fleurs_eng_000454) +Scores: (#C #S #D #I) 6 14 2 4 +REF: * SLEEP INTERRUPTION is THE PROCESS of PURPOSEFULLY AWAKENING DURING YOUR NORMAL SLEEP PERIOD and FALLING ASLEEP a *** SHORT time later ** ** 10–60 MINUTES +HYP: H SLEAE INTERUTION is HE PRASTIS of ************ HE BOUESAYWAKIN DIRING YOR NOMOSTSLE PEREAD and ******* FLING a SLE SHOUR time later EN TO SICTDE INTSTIT +Eval: I S S S S D S S S S S S D S I S I I S S + +Speaker sentences 269: fleurs_eng_000455 #utts: 1 +id: (fleurs_eng_000455-fleurs_eng_000455) +Scores: (#C #S #D #I) 6 11 0 2 +REF: *** SWIRL the two DRY POWDERS TOGETHER and THEN WITH CLEAN WET hands SQUEEZE THEM into a ***** BALL +HYP: BLI SWMURLE the two DRIYP POURERS TOGETHE and TEN WIT GQUENG WEAT hands SCUEE THE into a BOLWR I +Eval: I S S S S S S S S S S I S + +Speaker sentences 270: fleurs_eng_000456 #utts: 1 +id: (fleurs_eng_000456-fleurs_eng_000456) +Scores: (#C #S #D #I) 2 7 0 2 +REF: FOR the ****** SPRINGBOKS IT ENDED a ***** FIVEMATCH LOSING STREAK +HYP: FO the SPRING BOACKE ID ANDED a FIVED MACH THESING STRE +Eval: S I S S S I S S S + +Speaker sentences 271: fleurs_eng_000457 #utts: 1 +id: (fleurs_eng_000457-fleurs_eng_000457) +Scores: (#C #S #D #I) 1 18 5 0 +REF: JUST LIKE THE MOON EXERTS A PULL ON THE EARTH CAUSING TIDES SO DOES THE MILKY WAY EXERT A FORCE ON the SAGITTARIUS GALAXY +HYP: **** **** *** **** ****** YUOSTLK TH ONEX PURTD APOLN TE ARTH COSING TIDHD SOTESA MLBY WY EXSERT OF FORS O the EDGITARIYAUS ALACSY +Eval: D D D D D S S S S S S S S S S S S S S S S S S + +Speaker sentences 272: fleurs_eng_000458 #utts: 1 +id: (fleurs_eng_000458-fleurs_eng_000458) +Scores: (#C #S #D #I) 2 13 0 3 +REF: ***** *** THROUGH the NIGHT BETWEEN 150 AND 200 COPIES WERE MADE NOW KNOWN as **** DUNLAP BROADSIDES +HYP: THORW THE NIHT the TWEN HERDEN FITY ANDTO HERE COPYS WER MAD NON NON as BUME LAP BORODSIDES +Eval: I I S S S S S S S S S S S I S S + +Speaker sentences 273: fleurs_eng_000459 #utts: 1 +id: (fleurs_eng_000459-fleurs_eng_000459) +Scores: (#C #S #D #I) 10 22 3 0 +REF: FIRST AMONG ITS 78 RECOMMENDATIONS IS that A NEW DIPLOMATIC INITIATIVE SHOULD be TAKEN BEFORE the END OF THIS year to SECURE IRAQS BORDERS AGAINST HOSTILE interventions and to REESTABLISH DIPLOMATIC relations WITH its NEIGHBORS +HYP: ***** FIRSEAMLNG I SEINYAT RECAMNDATIOND S that AN NOD TIBLMATIK NISHITDIOF SHD be TEK HOMBOFOR the *** ENDO THIC year to ****** SECUR ARACSPORERS EGNST HOSTIL interventions and to RESTABLIST DIPLMATIC relations WI its NAVERS +Eval: D S S S S S S S S S S S S D S S D S S S S S S S S + +Speaker sentences 274: fleurs_eng_000460 #utts: 1 +id: (fleurs_eng_000460-fleurs_eng_000460) +Scores: (#C #S #D #I) 2 13 2 1 +REF: SAINT PETERSBURG CRUISES INCLUDE TIME in ***** TOWN CRUISE PASSENGERS ARE EXEMPTED FROM VISA REQUIREMENTS CHECK the TERMS +HYP: ***** ********** SAN ETERS BRCRESIS in LTIME NTOWN WHOH TASINGES AR CSENTIF FRM RESER RECURIENCS CHOC the TRNENS +Eval: D D S S S I S S S S S S S S S S + +Speaker sentences 275: fleurs_eng_000461 #utts: 1 +id: (fleurs_eng_000461-fleurs_eng_000461) +Scores: (#C #S #D #I) 3 12 0 1 +REF: ACCORDING to ***** JAPANS NUCLEAR AGENCY RADIOACTIVE CAESIUM AND IODINE HAS BEEN IDENTIFIED AT the plant +HYP: AOCORDING to EPANS NOGELR AGEANSY WRDYL ACTIOF CAESIOM ND I ADINHAS EN DENIFIET A the plant +Eval: S I S S S S S S S S S S S + +Speaker sentences 276: fleurs_eng_000462 #utts: 1 +id: (fleurs_eng_000462-fleurs_eng_000462) +Scores: (#C #S #D #I) 4 11 0 2 +REF: ****** SEGREGATION and RECOMBINATION SHUFFLE VARIATION BACK AND forth * BETWEEN the two POOLS WITH EACH GENERATION +HYP: SAEGOK ATION and RECOMONATION SHOLFTL VERYATION BAK OND forth H BEUTWEND the two PALLSE WIT EACHE GENERYTION +Eval: I S S S S S S I S S S S S + +Speaker sentences 277: fleurs_eng_000463 #utts: 1 +id: (fleurs_eng_000463-fleurs_eng_000463) +Scores: (#C #S #D #I) 3 13 2 1 +REF: ELEMENTS LIKE CALCIUM and ******** POTASSIUM ARE CONSIDERED METALS of COURSE THERE ARE also METALS LIKE SILVER AND GOLD +HYP: ELAMNT LY COULSTHUM and POTASHIM R CON SETD MUTLES of ****** PORES R also ****** MTLE LA SIVER ANDGOLD +Eval: S S S I S S S S D S S D S S S S + +Speaker sentences 278: fleurs_eng_000464 #utts: 1 +id: (fleurs_eng_000464-fleurs_eng_000464) +Scores: (#C #S #D #I) 1 11 0 0 +REF: the CORRELATION BETWEEN BRAIN PATHOLOGY AND BEHAVIOUR SUPPORTS SCIENTISTS IN THEIR RESEARCH +HYP: the ORLTIO BTWEN BRAN POTHOALAGEY AN E HAVEYOURS SPRT SINCSISTS AND THERESRCGE +Eval: S S S S S S S S S S S + +Speaker sentences 279: fleurs_eng_000465 #utts: 1 +id: (fleurs_eng_000465-fleurs_eng_000465) +Scores: (#C #S #D #I) 6 18 2 2 +REF: ANCIENT CHINA had A UNIQUE way of SHOWING DIFFERENT TIME PERIODS EACH STAGE of CHINA OR EACH FAMILY THAT was IN POWER was *** ** A DISTINCTIVE DYNASTY +HYP: ANCION CHAINENT had OU NEAKC way of SHOING DIFRENT TIMEM PEREATDS EACHE STDAKE of ***** CHINE ORE ECH FAMILYTHAT was ** IMPOURER was HAE DE STINT OF DINISTY +Eval: S S S S S S S S S S D S S S S D S I I S S S + +Speaker sentences 280: fleurs_eng_000466 #utts: 1 +id: (fleurs_eng_000466-fleurs_eng_000466) +Scores: (#C #S #D #I) 5 20 2 0 +REF: a SIMPLE POPULAR DINNER ESPECIALLY DURING the SUMMER is THE pa AMB OLI BREAD WITH OLIVE OIL TOMATO AND any AVAILABLE CONDIMENTS SUCH AS CHEESE TUNAFISH ETC +HYP: a HEMBL POPBELE DIMERNH HSTESHLY TDRIN the SUMER is *** pa AM ALY BUD WHT LVOIL TO METD N any ********* OVLALABE CONTINC TICHESCHEES TOUOFISH ITD SETER +Eval: S S S S S S D S S S S S S S S D S S S S S S + +Speaker sentences 281: fleurs_eng_000467 #utts: 1 +id: (fleurs_eng_000467-fleurs_eng_000467) +Scores: (#C #S #D #I) 2 13 1 0 +REF: THE ANNOUNCEMENT was made AFTER TRUMP HAD A PHONE CONVERSATION WITH TURKISH PRESIDENT RECEP TAYYIP ERDOĞAN +HYP: *** THEANONCTHNT was made AVER TRNM ATY FON OMERSATION WHT TORKSHS PRDODENT RESEPT THE YEAP AERORDOUN +Eval: D S S S S S S S S S S S S S + +Speaker sentences 282: fleurs_eng_000468 #utts: 1 +id: (fleurs_eng_000468-fleurs_eng_000468) +Scores: (#C #S #D #I) 5 28 13 0 +REF: PERRY STATED THAT he WOULD RETURN TO TEXAS TO ASSESS THE results of TONIGHTS CAUCUS DETERMINE WHETHER THERE IS A PATH FORWARD FOR myself IN THIS RACE BUT LATER SAID THAT HE WOULD REMAIN IN THE RACE and COMPETE IN THE JANUARY 21 SOUTH CAROLINA PRIMARY +HYP: ***** PERY SATETATH he ***** ****** ** OD RETENTO TECXEICTO STSEUTO results of ******** ****** TONGHCS COKISC DE DERMON WHTHE HERS AP PASFORDFR myself ** **** **** N THSRACES BUE LETER ST TH OWOWL REMAE INTHE RAID and ******* ** *** ******* GBPENOTIGENRYTWEWN SOUT EIRLINO PRMIARY +Eval: D S S D D D S S S S D D S S S S S S S S D D D S S S S S S S S S S D D D D S S S S + +Speaker sentences 283: fleurs_eng_000469 #utts: 1 +id: (fleurs_eng_000469-fleurs_eng_000469) +Scores: (#C #S #D #I) 7 23 1 9 +REF: * he WAS ALSO ENGAGED IN ENGRAVING BANKNOTES for MANY COUNTRIES RECENT EXAMPLES of **** HIS WORK INCLUDING THE PRIME MINISTERIAL PORTRAITS on the ***** **** **** ** FRONT of the *** ****** ** NEW CANADIAN 5 AND 100 BILLS +HYP: E he WAELSO INGAGE ING GRAING BAK NOLTDS for **** MINY OUNTRES RSONINGSEAPLES of WHIS ERKINGCLEED THEHE AMPRIENMEN I NINIS REAL PRTEREDS on the FIRST FROF HOND TH FRUNT of the NOO CONADY IN O FLOEDLER IN WO HNDER DLDEL +Eval: I S S S S S S D S S S I S S S S S S S I I I I S I I I S S S S S S + +Speaker sentences 284: fleurs_eng_000470 #utts: 1 +id: (fleurs_eng_000470-fleurs_eng_000470) +Scores: (#C #S #D #I) 7 22 2 2 +REF: MORE TRADITIONAL CHURCHES OFTEN HOLD an *** **** easter VIGIL ON SATURDAY NIGHT DURING the EASTER WEEKEND WITH the CONGREGATIONS OFTEN BREAKING INTO CELEBRATION at the STROKE of MIDNIGHT TO CELEBRATE CHRISTS RESURRECTION +HYP: H MOR TRDIN HRCHES ONT an HOE THEN easter RICIL N SATTEDY NGHT TURIN the ESTR WEEND BU the COGREWTIONS OT IM BRAKIN INTOSELEBRATION at the SROC of ******** ** MINTHTO SOLBRAK CRICESRESURECTION +Eval: S S S S S I I S S S S S S S S S S S S S S D D S S S + +Speaker sentences 285: fleurs_eng_000471 #utts: 1 +id: (fleurs_eng_000471-fleurs_eng_000471) +Scores: (#C #S #D #I) 8 16 1 1 +REF: FINLAND IS a GREAT BOATING DESTINATION the land of a THOUSAND LAKES HAS THOUSANDS of ISLANDS TOO IN the LAKES AND IN the ******* COASTAL ARCHIPELAGOS +HYP: ******* FILNI a GRAT BODING DUSTENATION the land of a THOUSEN LAK HES TOUSE of ILENCDS TWOO AND the LAK AN N the COSTAOE ARKY PELOGOES +Eval: D S S S S S S S S S S S S S S I S S + +Speaker sentences 286: fleurs_eng_000472 #utts: 1 +id: (fleurs_eng_000472-fleurs_eng_000472) +Scores: (#C #S #D #I) 2 24 3 0 +REF: CURRENT SENATOR and ARGENTINE FIRST LADY CRISTINA FERNANDEZ DE KIRCHNER ANNOUNCED HER PRESIDENTIAL CANDIDACY YESTERDAY EVENING IN LA PLATA A CITY 50 KILOMETERS 31 MILES AWAY from BUENOS AIRES +HYP: ERNT TENTER and ********* ***** ARGHINCSIN FRSLADY CESTDENOFRNDIS A CERSIONR ANOES R PESINACHL CAND IS OUSTRDAY AVENG NLHPLATHAT AS TADY FOFTDY OLOMEITERS THERTY WN MILS AWY from ****** WNOLSIDIS +Eval: S S D D S S S S S S S S S S S S S S S S S S S S S D S + +Speaker sentences 287: fleurs_eng_000473 #utts: 1 +id: (fleurs_eng_000473-fleurs_eng_000473) +Scores: (#C #S #D #I) 6 19 0 0 +REF: SEVERE WEATHER IS THE GENERIC TERM for ANY DANGEROUS WEATHER PHENOMENON WITH the POTENTIAL to CAUSE DAMAGE SERIOUS SOCIAL DISRUPTION or LOSS of HUMAN life +HYP: SVER WETHER I HE ENARNC TERE for NY DANDERS WHTHE FONAMONANH WH the POTINCL to COS DAMIAGE SIRISC SOSIOL DISTRUPTION or LOS of HMEN life +Eval: S S S S S S S S S S S S S S S S S S S + +Speaker sentences 288: fleurs_eng_000474 #utts: 1 +id: (fleurs_eng_000474-fleurs_eng_000474) +Scores: (#C #S #D #I) 10 18 0 3 +REF: for ** EXAMPLE the most ***** COMMON STILL IMAGE PHOTOGRAPHY FORMAT IN the WORLD is **** 35MM WHICH was the DOMINANT FILM SIZE AT the CLOSE of the ANALOG FILM ERA +HYP: for ES AMBL the most COMEN STHL IMINGE FHO TOCKRFEY FORMUT I the HORALD is THRY FIRE ILNMEAERWHCH was the DOMINENT FIELMEM SIGES A the CLOES of the ANILOG FILE ARA +Eval: I S I S S S S S S S I S S S S S S S S S S + +Speaker sentences 289: fleurs_eng_000475 #utts: 1 +id: (fleurs_eng_000475-fleurs_eng_000475) +Scores: (#C #S #D #I) 8 15 5 0 +REF: it is RELATED TO but USUALLY not INVOLVING ALPINE STYLE SKI TOURING OR MOUNTAINEERING the LATTER ONES DONE in STEEP TERRAIN and REQUIRING MUCH STIFFER SKIS and BOOTS +HYP: it is ******* RELATEDTO but YUSELY not ********* ****** IBLTING OLPING TILESKETORING AR MOUTENERING the LATER WOES DOUNE in ***** SDECTERING and ********* RECRIRING MUSH THIFRSKEES and BOTS +Eval: D S S D D S S S S S S S S D S D S S S S + +Speaker sentences 290: fleurs_eng_000476 #utts: 1 +id: (fleurs_eng_000476-fleurs_eng_000476) +Scores: (#C #S #D #I) 7 19 1 1 +REF: IRONING DAMP CLOTHES CAN HELP THEM DRY many HOTELS HAVE AN IRON and ****** IRONING BOARD AVAILABLE FOR LOAN EVEN if ONE IS not present in the ROOM +HYP: ******* IARNING DAMK CLOASESCANHEP THE DRIYI H many HO TELS HVEN IARN and IRNING BOR OVALABLE FR LON E VEN if ON HS not present in the ROM +Eval: D S S S S S S S S S S I S S S S S S S S S + +Speaker sentences 291: mls_eng_000283 #utts: 1 +id: (mls_eng_000283-mls_eng_000283) +Scores: (#C #S #D #I) 8 28 2 3 +REF: ** EVADNE ANSWERED HOARSELY she DREW her CHAIR A LITTLE CLOSER TO THE FIRE and SPREAD HER HANDS OUT TO the BLAZE THERE was no * OTHER LIGHT in THE ROOM BY THIS TIME the *** WIND WITHOUT HOWLED DISMALLY STILL +HYP: EE VEDNY UNCERD HORSLY she ESU her ***** CHARE UNLEITHE GLAOSEHE O TH FIRER and ****** SCREDEHR HAND S OUTO the BLASES TERE was no O THE LIHT in TE ONM MY THE TIMN the WIN WIDNOT OHORD ED ISMRLY STIL +Eval: I S S S S D S S S S S S D S S S S S S I S S S S S S S I S S S S S + +Speaker sentences 292: mls_eng_000284 #utts: 1 +id: (mls_eng_000284-mls_eng_000284) +Scores: (#C #S #D #I) 11 19 1 1 +REF: MY dear MARIA WHY do YOU not desist FROM THIS SILLY PURSUIT OF AN IMAGINARY TREASURE what is the VALUE of MONEY we ARE SPANIARDS not SHIRTSLEEVED MERCENARY PIGS of * AMERICANS +HYP: EMY dear MARLDEEAER WHI do OUD not desist FOM THE SILY PERS SOUOT OFE ANADNMANDGINARY THEADSER what is the THEOLYOU of MUNY we *** ARSPBANURDS not SHURTSLEVED MERSSINARY PPEGS of A MAIYCENDS +Eval: S S S S S S S S S S S S S S D S S S S I S + +Speaker sentences 293: mls_eng_000285 #utts: 1 +id: (mls_eng_000285-mls_eng_000285) +Scores: (#C #S #D #I) 4 25 6 1 +REF: ******* CRITICAL TEMPERATURE is THAT OF THE SINGLE ISOTHERMAL LINE WHICH PRESENTS A POINT OF INFLEXION AT A HORIZONTAL TANGENT THE CRITICAL PRESSURE AND THE CRITICAL VOLUME ARE THE two COORDINATES of this POINT OF INFLEXION +HYP: TERITCL T UBPTE is **** ** *** ****** ********** TAT O TE SINGL IASE THERME LIN WCH PESESE PONTIE INFLEATIONT HOARS END TINGENT THERSTIALE RESU HE RSCA VLLIE ATH two COALDNES of this ***** PONT BENPLETION +Eval: I S S D D D D D S S S S S S S S S S S S S S S S S S S S S D S S + +Speaker sentences 294: mls_eng_000286 #utts: 1 +id: (mls_eng_000286-mls_eng_000286) +Scores: (#C #S #D #I) 12 29 1 1 +REF: much *** LIKE IN FOULNESS AND DEFORMITY UNTO THAT monster WHOM the THEBAN KNIGHT the FATHER of that FATAL PROGENY MADE KILL HERSELF FOR VERY HEARTS DESPITE THAT he had READ her RIDDLE which no WIGHT COULD ever LOOSE BUT SUFFERED DEADLY DUEL +HYP: much LIK AN FOUNEUS ND OFORMEITY AN TO HAT monster HOM the THEABON NIHT the FARTHER of that ***** FATL PRODGINY MADED CIL HER SELF FORVERY HARTS TESPIHTTHAT he had RA her RIDL which no WIHT COUOT ever LOUS HAT SUFERD DEDLY DE +Eval: I S S S S S S S S S S S D S S S S S S S S S S S S S S S S S S + +Speaker sentences 295: mls_eng_000287 #utts: 1 +id: (mls_eng_000287-mls_eng_000287) +Scores: (#C #S #D #I) 7 27 8 0 +REF: HE HAS MANAGED TO MEASURE WITH PRECISION PRESSURES AMOUNTING to THREE THOUSAND ATMOSPHERES and also THE VERY SMALL VOLUMES THEN OCCUPIED BY the FLUID MASS UNDER CONSIDERATION this LAST MEASUREMENT WHICH NECESSITATES NUMEROUS CORRECTIONS is THE most DELICATE PART OF THE OPERATION +HYP: ** *** HI MASHE MASER WIH PRSIONG RESIES MUNTIG to ***** HRE THOULSENANSTIES and also *** THERY SMAL VLLIMS THAD OCKIEPYDE B the FLNTD MUE NDE COESIERATION this **** LASE MASENT WHCH NOUESEITATE NUMERSCORATIND is *** most ******** **** DELIKEU PUT HEOPRATION +Eval: D D S S S S S S S D S S D S S S S S S S S S S D S S S S S D D D S S S + +Speaker sentences 296: mls_eng_000288 #utts: 1 +id: (mls_eng_000288-mls_eng_000288) +Scores: (#C #S #D #I) 7 17 0 0 +REF: WHY SHOULD IT HAVE BEEN DEEMED NECROMANCY to ENDEAVOR TO COMBINE THESE PARTS to EVOLVE by CAREFUL ELIMINATION and change to the PERFECT FOOD +HYP: WHI SHUL ITHAE BEN DEMED NE CROMANCSY to INDEVER O ONBING THES FAS to IVFOLILVE by GIARFUL ELMINATION and change to the PERVECT FOD +Eval: S S S S S S S S S S S S S S S S S + +Speaker sentences 297: mls_eng_000289 #utts: 1 +id: (mls_eng_000289-mls_eng_000289) +Scores: (#C #S #D #I) 13 26 0 0 +REF: NAY THOUGH OF RUSHES BE my BED YET i am RICH love said BUT ARGUED LIFE thrice FOND ART THOU to YIELD the SOVEREIGN gifts of EARTH the VICTOR SWORD the LAURELED BROW FOR VISIONED THINGS of LITTLE WORTH +HYP: DNAY TH OVE RASES BEY my BEADT YEAT i am RIGECE love said BUTHOUT AREGUD LIHVE thrice FOMND AR THOULE to YEL the SOVERAN gifts of ARTH the VIETOR SORORD the LORALET BROL OR VIN THINGKS of LILE WERRT +Eval: S S S S S S S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 298: mls_eng_000290 #utts: 1 +id: (mls_eng_000290-mls_eng_000290) +Scores: (#C #S #D #I) 4 23 8 0 +REF: bock SEEMS TO HAVE BEEN a KEEN COLLECTOR ALTHOUGH HAMPERED by ILL HEALTH AND A GREAT POINT IN HIS FAVOUR IS THAT HE DESCRIBED only THOSE PLANTS WHICH HAD COME UNDER HIS OWN PERSONAL OBSERVATION +HYP: bock ***** ESTO HVE BEN a KEN OLACTR OLTHO HAMPED by *** ****** *** * ***** ***** ILE HELTHF AD GRAT PINTONIS FAVERISHAT ESCRIGED only ***** THUCE PLANSHWIT H HATD COM UNDERIS ON PERSINL OPSOVATION +Eval: D S S S S S S S D D D D D D S S S S S S S D S S S S S S S S S + +Speaker sentences 299: mls_eng_000291 #utts: 1 +id: (mls_eng_000291-mls_eng_000291) +Scores: (#C #S #D #I) 15 17 1 0 +REF: HAD RATHER SHRUNK UP and had not CHANGED INTO NYMPHS THESE I LEFT in the STEMS COVERING them up AGAIN and they APPEARED as PERFECT INSECTS in the may of the FOLLOWING YEAR +HYP: *** HADRATHER SRNG OP and had not CHAINSED IN TONEIMS THEIS HY FELD in the STAMS COVERIN them up AGAN and they APERED as PERFECTD INSACTS in the may of the FOLNIN HEAR +Eval: D S S S S S S S S S S S S S S S S S + +Speaker sentences 300: mls_eng_000292 #utts: 1 +id: (mls_eng_000292-mls_eng_000292) +Scores: (#C #S #D #I) 11 22 3 0 +REF: nothing SAVE OBJECTS and THOUGHTS OF BEAUTY COULD PRESENT THEMSELVES to the understanding of the FORTUNATE PERSON WHO PARTOOK of it THESE PAGES WHICH YOU HAVE BROUGHT TO me TO TRANSLATE ARE CONCERNED WITH this SUPERSTITION +HYP: nothing SAYWO OBDECE and ******** HOATSOF BUTY MOUD PRESVEND THEMSAES to the understanding of the FORTIELET BORSON HOUE ARTOK of it ***** THEIS BEAT S WIC HOHAE BRUGTO me ** TRANSLATD AR CONSERE WIT this SOUPESTION +Eval: S S D S S S S S S S S S D S S S S S S D S S S S S + +Speaker sentences 301: mls_eng_000293 #utts: 1 +id: (mls_eng_000293-mls_eng_000293) +Scores: (#C #S #D #I) 5 14 4 0 +REF: NOW SEEMED INSIPIDITY AND HED nerve himself AGAINST it his FACE WORE A SORT OF SEVERE FLUSH he WAS TIMID EVEN TO RUDENESS +HYP: *** ****** NOU SMEINCUPTITYAND HE nerve himself AGAINS it his FAIS WAL SORD O SOVE F LUSHD he *** ***** WASTIMID EVEINTOR ROUDNES +Eval: D D S S S S S S S S S S S D D S S S + +Speaker sentences 302: mls_eng_000294 #utts: 1 +id: (mls_eng_000294-mls_eng_000294) +Scores: (#C #S #D #I) 7 29 6 0 +REF: BECAME MORE LIFELIKE AS THE CHEEKS flush THERE WAS RARE WARMTH IN A WINTER MORNING TO CHEER THE HALFDESPAIRING SOUL TIRED AFTER long HOURS of OIL reading and PIERCED TO THE HEART by never CEASING RHYMES YET I COULD NOT UNDERSTAND IT +HYP: TBECOME MOL LIUGHE LIKCKE ASTHE SHIEXS flush ***** *** THE AS REAR WOAIMGE FINOEINTE MORDINGT HERD HAFT IS PRING SOL TID IT long AOURS of ALL reading and ******* ** PERSTED HART by never ******* ****** SEASING RIMEMS EST ICUN ONDER STANDIT +Eval: S S S S S S D D S S S S S S S S S S S S S S S D D S S D D S S S S S S + +Speaker sentences 303: mls_eng_000295 #utts: 1 +id: (mls_eng_000295-mls_eng_000295) +Scores: (#C #S #D #I) 9 15 2 3 +REF: ONE OF the HAWAIIAN WRITERS said the ***** ** *** OPIHIAWA is a POISON SHELLFISH THESE ARE BITTER and DEADLY and CAN be USED in PUTTING ENEMIES TO DEATH +HYP: WON F the HOWING RGDER said the ALLPE HE AOV O is a ****** POSEN SHOLFISH THESER BITR and DEDLY and COE be YOUSE in ******* PUITING ENINMYSTO DEA +Eval: S S S S I I I S D S S S S S S S D S S S + +Speaker sentences 304: mls_eng_000296 #utts: 1 +id: (mls_eng_000296-mls_eng_000296) +Scores: (#C #S #D #I) 6 22 0 2 +REF: the **** BEAUTEOUS ROBES of HEAVEN ASLANT THE DEW BRIGHT EARTH AND COLOURED AIR he LOOKS in BOUNDLESS MAJESTY ABROAD touching THE GREEN LEAVES all * ATREMBLE WITH GOLD LIGHT +HYP: the BEUT EAS ROUPES of HAVION AS LON TOA DU RIHT EAR ANDCOLET AR he LOKE in BOWNLIS MA GHSTYRABROURD touching TH GREN LEVES all A TREMBL IT GOUGL LIHT +Eval: I S S S S S S S S S S S S S S S S S S I S S S S + +Speaker sentences 305: mls_eng_000297 #utts: 1 +id: (mls_eng_000297-mls_eng_000297) +Scores: (#C #S #D #I) 10 34 3 1 +REF: i CAN DO NO MORE THAN that UNTIL this MATTER IS ABSOLUTELY SETTLED THEY ARE worth * MORE THAN LIFE ITSELF to ME MR COWPER SEEMED ANNOYED SURELY he PROTESTED YOU ARE NOT GOING TO ASK me to WAIT THREE MONTHS UNTIL i CAN EXAMINE ONE of THESE +HYP: i AD DU NOR MOR THN that IUNDTL this NATARE HIS APELUDLY SETEDE THE E worth M THE LIF IT ELF to EM WHSTR BLLBUR SMED ANORYIST SAORLY he ********* *** *** PRTESTEIT OULNOT GOD AST me to WAT THRA MUNCE ANDTIL i AT AEXAMIN BON of THEISES +Eval: S S S S S S S S S S S S I S S S S S S S S S S D D D S S S S S S S S S S S S + +Speaker sentences 306: mls_eng_000298 #utts: 1 +id: (mls_eng_000298-mls_eng_000298) +Scores: (#C #S #D #I) 3 16 0 3 +REF: *** ROSCONGRESS foundation RUSSIAN ENTITY that ORGANIZED the **** ****** SAINT PETERSBURG INTERNATIONAL ECONOMIC FORUM ROSNEFT RUSSIAN STATEOWNED OIL AND ENERGY COMPANY +HYP: ROS CONGRES foundation RESION ANDTITE that ORGNIHE the SANT PETERS BURIGIN TERNASIONLE ECENOMIC FORM ROUS NEFT RUSIOND STADONED OILE N ANERGY COMBPANYW +Eval: I S S S S I I S S S S S S S S S S S S + +Speaker sentences 307: mls_eng_000299 #utts: 1 +id: (mls_eng_000299-mls_eng_000299) +Scores: (#C #S #D #I) 11 35 5 1 +REF: how IT GLITTERED AND SPARKLED the DELICATE FROSTWORK YOU WERE ATTRACTED NO DOUBT AND MARVELLED AT the DAINTY TRACINGS but FEW OF US HAVE REALLY had AN OPPORTUNITY to STUDY the DETAIL OF THESE FROST DESIGNS MINUTELY or HAVE CONSIDERED THAT THERE WERE MORE THAN THREE or *** FOUR DESIGNS at most +HYP: how ** GLATED IN SPACAL the DELICEAT FROST WEK YOUE A TRACTDED NOEDOUT A MAVERD A the DINY TRACSOMS but FEWUE OV AS HAE REALY had ** ANOPRTNITY to STANDY the ****** DETEAL O THIS FRUSTESINES MYNUTLY or **** ********** V CON SIDED HAT THE ERE or HIN THRE YURFORDESINE at most +Eval: D S S S S S S S S S S S S S S S S S S S S D S S D S S S S S D D S S S S S S I S S + +Speaker sentences 308: mls_eng_000300 #utts: 1 +id: (mls_eng_000300-mls_eng_000300) +Scores: (#C #S #D #I) 11 25 17 0 +REF: OTHER THAN THE OFFENSE in TRYING to INFLICT A WOUND THEY MAY KILL THE OFFENDER or WOUND him MORE THAN THEY INTENDED to DO AND THIS BECOMES A CAUSE FOR A NEW FEUD so THAT THE primitive LEGISLATORS WERE CAREFUL IN REQUIRING THE RETALIATION to BE LIMITED to AN EYE for an EYE +HYP: ***** **** THEAHANTH OFES in TRING to ******* * INFIKGIUON THE MENKIL VHE OF ENDER or WEN him MOR VHAN THE INTENDEN to ** *** **** ******* * ***** DWAND THISBECOME ACCUSFULE ANUHERD so **** HATHE primitive *********** **** ******* ** LIGESLATERSWHERE CEFUL INORECQUIURINGTHERIETALITION to ** BEDLMITED to ** ANIY for an OI +Eval: D D S S S D D S S S S S S S S S S S D D D D D D S S S S D S D D D D S S S D S D S S + +Speaker sentences 309: mls_eng_000301 #utts: 1 +id: (mls_eng_000301-mls_eng_000301) +Scores: (#C #S #D #I) 21 22 1 0 +REF: at CYRUS word the JEWS RETURN the COMPANY that GO GODS HOUSE BEGUN WITH MIRTH AND MOAN is HINDERED by the FOE but ONCE again the work GOES on by LICENSE from DERIUS EZRA is sent with ROYAL grant and gifts FOR USES PIOUS +HYP: at SIRESS word the JUOSE RETEREN the COMPONY that GOE BGOS HICES BEGON WIT MRT A MOME is HENDERED by the FO but WHNCE again the work GOS on by LIENS from DRIAS ASRE is sent with ROILED grant and gifts *** FRYOSES PISS +Eval: S S S S S S S S S S S S S S S S S S S S D S S + +Speaker sentences 310: mls_eng_000302 #utts: 1 +id: (mls_eng_000302-mls_eng_000302) +Scores: (#C #S #D #I) 4 20 3 0 +REF: NET PRODUCT YEAR IN AND year OUT SEVEN HUNDRED FRANCS HE LIVED IN it HOW NOT so badly WE WILL EXPLAIN MARIUS OCCUPIED IN THE GORBEAU HOUSE +HYP: *** ANT PRODKE YHAIE INAND year *** ***** OUTE SVINHUDRT FRONCES HOELVED N it HOVE NO so badly BREUL ACSPMLAIN MURTY IS OCKUPY H TE OREBO HOUES +Eval: D S S S S D D S S S S S S S S S S S S S S S S + +Speaker sentences 311: mls_eng_000303 #utts: 1 +id: (mls_eng_000303-mls_eng_000303) +Scores: (#C #S #D #I) 17 26 6 0 +REF: THEN THIS is all YOUR ANSWER tis TOO FAIR FOR one of his ALLIANCE and I WARN YOU that this place no MORE SEE you EXIT ENTER DE FLORES the BEST IS THERE is more GROUND to MEET A MANS REVENGE ON HONEST DE FLORES THATS my NAME INDEED +HYP: TED THISE is all **** YOURANCTSER tis *** TWO FEAIREFOR one of his OLINTCS and Y WORE YOUOWE that this place no MOR SE you **** ANGSIT ANTER DEFLERANS the EST ISES THE is more CROUND to **** * MED AMAND RAVEND GON ONIS EFARACS THEATS my **** AMEINDED +Eval: S S D S D S S S S S S S S D S S S S S S S D D S S S S S S S D S + +Speaker sentences 312: mls_eng_000304 #utts: 1 +id: (mls_eng_000304-mls_eng_000304) +Scores: (#C #S #D #I) 16 29 9 0 +REF: when i RETURNED to the HOUSE WHERE I had been A HAPPY CHILD ONLY A PILE OF ASHES WHERE IT had STOOD I wept LONG and to FORGET my WEEPING i SAILED out ON THE VAST CALM SEA ON THESE WATERS IN A STAR SAPPHIRE NIGHT I PLAYED my FLUTE to the SUMMER MOON +HYP: when i RETURNEDA to the ***** ***** HOUSESWHRE had been * ***** ***** **** HAPY CHILED ONDLY AE PILOF ASHIESWRAYT had ***** STODAI wept LONGK and to FOROGET my WEPING i SAID out ** *** UNDEVEAS COM SE OND THES WORTERS INAS THRS IUGFY AER NIGT ID PLAYE my FLT to the SME MON +Eval: S D D S D D D D S S S S S S D S S S S S D D S S S S S S S S S S S S S S S S + +Speaker sentences 313: mls_eng_000305 #utts: 1 +id: (mls_eng_000305-mls_eng_000305) +Scores: (#C #S #D #I) 16 31 2 1 +REF: DO YOU not see WHAT PLEASURE IT GIVES HIM we have GROWN UP TOGETHER in this house SINCE he WAS A BOY I SIMPLY CANNOT BEAR as YOU CAN the SIGHT of THE SMILE LEAVING his FACE POOR dear he has no ******* AMUSEMENT EXCEPT THIS PLAYING AT THE SHOPKEEPING +HYP: TEDO OU not see **** WHAUT LESER ITDGIVES ME we have GRON OUP OGETER in this house SIN he *** WORS ABOYH IS SEIMPLY ANOR BEARE as OU CAND the IHT of TH SMYGLD LEVIN his FHACE BOR dear he has no AMUSMEN ECEPTHIC BLANG ATD TH SHOP S KSCPING +Eval: S S D S S S S S S S S D S S S S S S S S S S S S S S I S S S S S S S + +Speaker sentences 314: mls_eng_000306 #utts: 1 +id: (mls_eng_000306-mls_eng_000306) +Scores: (#C #S #D #I) 9 17 4 1 +REF: it is A NEBULOUS BODY REVOLVING IN AN ELLIPTICAL ORBIT OF GREAT ELONGATION LOVE love LOVE WILL not be the WOUND of CUPID BUT the MANIFESTATION of * UNIVERSAL REPRODUCTIVE INSTINCTS +HYP: it is * ******** **** ********* DEVIESS BOY REVEALDING N YELPICAL OR GREY UWONGATIONI love LOVED LOVEIL not be the WOND of CUPIT BU the ADIFTHSTATION of E GEVERSL ERDUCTOE ISTINCES +Eval: D D D D S S S S S S S S S S S S S S I S S S + +Speaker sentences 315: mls_eng_000307 #utts: 1 +id: (mls_eng_000307-mls_eng_000307) +Scores: (#C #S #D #I) 13 28 5 1 +REF: SHARPLY as he SHOOK HANDS WITH her GOD BLESS YOU MY DEAR CHILD the BISHOP SAID WHEN she KISSED him and his lips **** MOVED AFTERWARD FOR some SECONDS AS IF he WERE IN PRAYER HER MOTHER FOLLOWED HER OUT of THE ROOM and THEN SILENCE SETTLED +HYP: SHORPLY as he SHOK HANS IT her *** OGOD BES YUMAY DYAT CHAI the ****** BISHOPSAID WEN she CESE him and his lips MORD OF TERWORD FORE some ******* SICKENTS ASIF he **** ** WER INPREARE IUD MUTHEOFOLLORE HERE OUL of TH OM and TIEN SILAN SETEL +Eval: S S S S D S S S S S D S S S I S S S D S S D D S S S S S S S S S S S + +Speaker sentences 316: mls_eng_000308 #utts: 1 +id: (mls_eng_000308-mls_eng_000308) +Scores: (#C #S #D #I) 10 15 1 2 +REF: FOLLOWED him STEALTHILY and WHEN he * was IN a STOOPING POSTURE FILLING his BUCKET came UP BEHIND him and PLUNGED A long *** KNIFE INTO HIS NECK +HYP: FOLLAED him STEAELTHELYHE and **** he W was AN a STPING POSRER FILING his BOUCKEAT came UPT BEHEIED him and PLUNCHED AND long NIF N TO OIS NACK +Eval: S S D I S S S S S S S S S I S S S S + +Speaker sentences 317: mls_eng_000309 #utts: 1 +id: (mls_eng_000309-mls_eng_000309) +Scores: (#C #S #D #I) 14 20 2 1 +REF: SAITH CHERSIAS DOES not JUPITER DISTRIBUTE to THE GODS THEIR PROPORTION and DIVIDEND sparingly and severally as AGAMEMNON DID TO his COMMANDERS when his GUESTS DRANK to one another *** IF CHERSIAS QUOTH CLEODEMUS as YOU NARRATE +HYP: TSAISTH CKERSIAS DOUST not JUPETER DISTRIBUET to HE GOGD THE PREPORSTION and DIVIDENT sparingly and severally as ********* AG MENDITO his COMANDRS when his GEASTS TRANG to one another IVF O CKOURSIUS QULS KLEDEMIUS as *** YOUNERRAT +Eval: S S S S S S S S S S D S S S S S I S S S S D S + +Speaker sentences 318: mls_eng_000310 #utts: 1 +id: (mls_eng_000310-mls_eng_000310) +Scores: (#C #S #D #I) 7 24 4 0 +REF: AND WHERE NONE SHALL DARE RESTRAIN US WE CAN MEET again in THOUGHT so THERES no USE IN WEEPING BEAR A CHEERFUL SPIRIT STILL never DOUBT THAT FATE IS KEEPING FUTURE good for PRESENT ILL +HYP: *** EAN WHR NON HUL ER RSTRAN A TO CAMET again in FBHOHT so THIS no *** ** OUSEN WEPING BER CHERUL SPIRT STILE never ***** DOU THE FATIS CEPING PUCTER good for PRESEN IL +Eval: D S S S S S S S S S S S D D S S S S S S D S S S S S S S + +Speaker sentences 319: mls_eng_000311 #utts: 1 +id: (mls_eng_000311-mls_eng_000311) +Scores: (#C #S #D #I) 9 28 0 1 +REF: and * TO BECOME the RECORD of what PEOPLE HAVE DONE IN THEIR MORE AMIABLE MOMENTS the RECORD OF THE CONQUESTS OF PEACE how MEN have lived and LABORED DUG AND BUILT HEWN AND CLEARED GARDENED AND REFOREST +HYP: and O BE COME the RECKERD of what PEPL AE DON I THER MOR AMIUBLE MOENTS the RECKERD OFTH CONCQUEST S AT PESE how MEND have lived and LEAVERD DOUG AT BILT UN AN LIERED GARDIED AT REAFORERST +Eval: I S S S S S S S S S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 320: mls_eng_000312 #utts: 1 +id: (mls_eng_000312-mls_eng_000312) +Scores: (#C #S #D #I) 7 29 6 1 +REF: the LOW FLYING OF THE SWALLOWS BETOKENS rain as WELL AS ANY UNSEASONABLE DANCING of MIDGES IN THE EVENING SORE CORNS ON THE FEET AND RHEUMATISM IN the JOINTS ARE DIREFUL PRECURSORS the LEAVES ARE all * ATREMBLE BEFORE THE APPROACH OF THUNDER +HYP: the O FLING O TE SOLAS PETOCKINS rain as WILAT NY N SESINABLE DANSING of ****** ** *** ******* **** MIGIS INTHE EVEINING SOCON SONDE FET ANDINGORTISING the OINS AD DIOFUL RECOIRSSISS the ****** LESTAT all A TRMBLE BOE FOR THAT PROWCE TUNDER +Eval: S S S S S S S S S S S D D D D D S S S S S S S S S S S D S I S S S S S S + +Speaker sentences 321: mls_eng_000313 #utts: 1 +id: (mls_eng_000313-mls_eng_000313) +Scores: (#C #S #D #I) 10 24 1 5 +REF: was ********* ****** STORMED GENERAL DAMPIERRE was **** KILLED GENERAL CUSTINE was blamed AND INDEED IS NOW COME TO PARIS to GIVE EXPLANATIONS AGAINST all WHICH the ******* ** MOUNTAIN AND ATROCIOUS MARAT MUST EVEN make HEAD as THEY can +HYP: was STOREMNGE JENRLE TAM PE ARE was CILL GENERL COS TIENG was blamed *** AN IN DED ES NOB COM to PARISTH DEVICXSELNATIONS AGINSE all WHIH the MOUNTON AN HE TROTIOUS MOE AR MUSTD EVON make HAL as HE can +Eval: I I S S S I S S S D S S S S S S S S S S I I S S S S S S S S + +Speaker sentences 322: mls_eng_000314 #utts: 1 +id: (mls_eng_000314-mls_eng_000314) +Scores: (#C #S #D #I) 14 15 0 2 +REF: the MOMENT was FEARFUL a MIGHTIER FOE had never SWUNG the **** BATTLEAXE over HIM but *** HOPE nerved his ARM for a DESPERATE BLOW and TECUMSEH FELL PROSTRATE BEFORE him +HYP: the MOMEN was FEAVFUL a MITY OFO had never SWHUNG the BUTL LACKE over HIMEN but THE HOBE nerved his ANE for a DESPRET BLO and TE CUOMSERULE PROSTRAIT BEFOR him +Eval: S S S S S I S S I S S S S S S S S + +Speaker sentences 323: mls_eng_000315 #utts: 1 +id: (mls_eng_000315-mls_eng_000315) +Scores: (#C #S #D #I) 11 18 0 1 +REF: THEN the WIND STOPPED the CLOUDS TURNED DARK AND NIGHT came on LIKE INK my old *** COTTON QUILT was COLD as IRON my SWEET SON TOSSED in his SLEEP +HYP: THIN the WIN STOUT the GLERSTAND DRK H ND NIG came on LAE INGK my old COT IOND COUILT was COLLD as IN my SWET SUN TOUST in his SCEE +Eval: S S S S S S S S S S I S S S S S S S S + +Speaker sentences 324: mls_eng_000316 #utts: 1 +id: (mls_eng_000316-mls_eng_000316) +Scores: (#C #S #D #I) 15 26 4 1 +REF: you may DO as YOU PLEASE TO WORK OFF YOUR IRRITATION to KEEP UP your FANATICISM you ARE WELL OFF you NEED not MIND the COST the POOR DO not WANT TO stand IN YOUR way but ** YOU INSIST on THEIR SUBMITTING TO YOUR COMPULSION +HYP: you may D as *** OU PLETO WERE OF OUR IRTATION to CEP P your FENATTIISM you HE WEL AF you NED not MID the COUST the PARE DU not **** WONTE stand ** INYOUR way but YO IN SISTD on ***** THE SOIMITING OR COMPALTION +Eval: S D S S S S S S S S S S S S S S S S S D S D S I S S D S S S S + +Speaker sentences 325: mls_eng_000317 #utts: 1 +id: (mls_eng_000317-mls_eng_000317) +Scores: (#C #S #D #I) 14 31 4 1 +REF: HE was bred by REV G A SNEYD being by OTHMAN E SIX FOUR TWO TWO HEDWIG HE was **** BORN IN MARCH EIGHTEEN SEVENTYNINE and HE was THE ONLY SURVIVOR OF A LITTER of FIFTEEN it was ON THIS ACCOUNT that he WAS CALLED SAFE IN COLOR and MARKINGS +HYP: WE was bred by *** AEREVERNTERY ACS NIHTET being by ****** OTHEMEN ESCXS FOR TO TOU HIDICK LY was BONE AN MACH ATIN SEVENTY NIN and H was *** **** THEONLY SOFLIVER OFE LETER of FIFTEN it was N THI COUNDT that he AS OURD SAIF AND COLR and MARCKINGS +Eval: S D S S S D S S S S S S S I S S S S S S D D S S S S S S S S S S S S S S + +Speaker sentences 326: mls_eng_000318 #utts: 1 +id: (mls_eng_000318-mls_eng_000318) +Scores: (#C #S #D #I) 11 17 8 3 +REF: AND what haste IT MAKES TO FALL INTO THE SECOND THERE by THIS time ** ******** DIAPHANTA SNEEZES ACHOO most ADMIRABLE SECRET on THE CONTRARY it STIRS me not A WHIT WHICH most *** CONCERNS it HA HA HA +HYP: EAND what haste ** ***** ** ITMAKS OFOLINTO TE SECIONT THER by THIH time DI AFHANDER SNESERS ES GIU most ADMRABL SEAKCKRET on *** THECONTRY it STARS me not * AWIT WICH most COD SORES it ** ** ** +Eval: S D D D S S S S S S I I S S S S S D S S D S S I S D D D + +Speaker sentences 327: mls_eng_000319 #utts: 1 +id: (mls_eng_000319-mls_eng_000319) +Scores: (#C #S #D #I) 10 23 1 3 +REF: THIRDLY THALES said WHERE the CITIZENS are NEITHER TOO RICH nor ** **** TOO POOR FOURTHLY ANACHARSIS said where THOUGH in all ************ OTHER RESPECTS THEY ARE EQUAL YET VIRTUOUS MEN ARE advanced and VICIOUS PERSON DEGRADED +HYP: THERDLY THAL said WHER the ITISINCE are ******* NETHERE TOREACEH nor TO PORE FORTHILY AN ACOS IS said where THO in all OTHERESPECTE THE YOARR COL IHAT VERE TO HS MIN AR advanced and VESIOUS PERSOEN TDEGRADED +Eval: S S S S D S S I I S S S S S I S S S S S S S S S S S S + +Speaker sentences 328: mls_eng_000320 #utts: 1 +id: (mls_eng_000320-mls_eng_000320) +Scores: (#C #S #D #I) 15 19 3 3 +REF: the KINDLY FRANK is SYMPATHETIC EVERY day he PASSES NOTES BETWEEN US and I TRY to * ENCOURAGE RUSSELL he ** WILL IMPROVE i ASSURE him his time is SHORT and FRESH AIR AND LIBERTY WILL soon ** RESTORE him +HYP: the CINDLY FRANG is SIMPTHETINK EVRY day he ****** PAS NOTAS BETWENUS and * TRIY to N CKERIRGS RUSTL he WL IM PROVE i ASUOR him his time is SOUOURT and ***** FREACH AIRAN LIBURTY WLL soon RE STAOR him +Eval: S S S S D S S S D S I S S I S S S S D S S S S I S + +Speaker sentences 329: mls_eng_000321 #utts: 1 +id: (mls_eng_000321-mls_eng_000321) +Scores: (#C #S #D #I) 20 22 0 2 +REF: THESE QUESTIONS it is NOW evident may *** FREQUENTLY be ANSWERED WITH EQUAL PROPRIETY IN OPPOSITE WAYS and if THERE be any * OCCASIONS ON which they CAN be ANSWERED only in ONE way the ANSWER WILL depend UPON the NATURE of the OCCASION +HYP: THIS CRESTIONS it is NOL evident may FRE KCENTLY be UNCTERED WES EQULL PROPRITY INOPS IT WASES and if THER be any A CAINS UNG which they CAND be UNCTERED only in ONEN way the UNCSER WIL depend APON the NATER of the OECATION +Eval: S S S I S S S S S S S S S I S S S S S S S S S S + +Speaker sentences 330: mls_eng_000322 #utts: 1 +id: (mls_eng_000322-mls_eng_000322) +Scores: (#C #S #D #I) 12 23 4 1 +REF: in his NOTE BORE the MINSTRELSY SECOND EDITION EIGHTEEN OH EIGHT SCOTT SAYS THE BALLAD was TAKEN down FROM AN old WOMANS RECITATION at THE ALSTON MOOR LEAD MINES by the AGENT THERE and SENT by HIM to **** SURTEES +HYP: in his NOHT BOR the ********** ****** INSTRLSY SECKNEDION ATYONOW AT ESCOUT SES TH BALED was TCEKIND down **** RMAN old OMENS FRCITATION at *** TH LSON MOR LEADMINS by the AGEND THER and CIENT by HM to SERT EAS +Eval: S S D D S S S S S S S S S D S S S D S S S S S S S S I S + +Speaker sentences 331: nchlt_eng_001588 #utts: 1 +id: (nchlt_eng_001588-nchlt_eng_001588) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ********* CHRISTIAN THEOLOGIANS +HYP: CRESTIONT THEOLIGEON SH +Eval: I S S + +Speaker sentences 332: nchlt_eng_001589 #utts: 1 +id: (nchlt_eng_001589-nchlt_eng_001589) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** OBTAIN EAGLE FEATHERS +HYP: OP TADE EAGO FITHES +Eval: I S S S + +Speaker sentences 333: nchlt_eng_001590 #utts: 1 +id: (nchlt_eng_001590-nchlt_eng_001590) +Scores: (#C #S #D #I) 0 3 0 1 +REF: * ELEMENTARY SPECIAL FUNCTIONS +HYP: A LAMENTO SPESIAL FONTIONS +Eval: I S S S + +Speaker sentences 334: nchlt_eng_001591 #utts: 1 +id: (nchlt_eng_001591-nchlt_eng_001591) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ***** GEORGE WASHINGTON UNIVERSITY +HYP: TORDE WASIN AN NUNEVOREITY +Eval: I S S S + +Speaker sentences 335: nchlt_eng_001592 #utts: 1 +id: (nchlt_eng_001592-nchlt_eng_001592) +Scores: (#C #S #D #I) 1 2 0 1 +REF: SCIENCE fiction ****** NOVELS +HYP: SIES fiction NOVLES PRVHAN +Eval: S I S + +Speaker sentences 336: nchlt_eng_001593 #utts: 1 +id: (nchlt_eng_001593-nchlt_eng_001593) +Scores: (#C #S #D #I) 0 3 0 0 +REF: COAST HIP HOP +HYP: COSTD HIB POP +Eval: S S S + +Speaker sentences 337: nchlt_eng_001594 #utts: 1 +id: (nchlt_eng_001594-nchlt_eng_001594) +Scores: (#C #S #D #I) 0 3 0 2 +REF: ******* **** INVERSE LAPLACE TRANSFORM +HYP: INDVERS LEAT BLAY E TRONSFORME +Eval: I I S S S + +Speaker sentences 338: nchlt_eng_001595 #utts: 1 +id: (nchlt_eng_001595-nchlt_eng_001595) +Scores: (#C #S #D #I) 0 2 0 0 +REF: FRENCH PROTESTANTS +HYP: FRINGH PROTISTANCES +Eval: S S + +Speaker sentences 339: nchlt_eng_001596 #utts: 1 +id: (nchlt_eng_001596-nchlt_eng_001596) +Scores: (#C #S #D #I) 0 3 0 4 +REF: *** **** ** **** AFGHAN AIR FORCE +HYP: OFE GOUN AY FORS SH HD KE +Eval: I I I I S S S + +Speaker sentences 340: nchlt_eng_001597 #utts: 1 +id: (nchlt_eng_001597-nchlt_eng_001597) +Scores: (#C #S #D #I) 2 3 0 0 +REF: HEROES in MYTHOLOGY and LEGEND +HYP: HEAROS in MOSOLIGY and LEAGEND +Eval: S S S + +Speaker sentences 341: nchlt_eng_001598 #utts: 1 +id: (nchlt_eng_001598-nchlt_eng_001598) +Scores: (#C #S #D #I) 0 3 0 2 +REF: **** ** BUSINESS CLASS SEAT +HYP: BUIS NS CLAS SET NDNE +Eval: I I S S S + +Speaker sentences 342: nchlt_eng_001599 #utts: 1 +id: (nchlt_eng_001599-nchlt_eng_001599) +Scores: (#C #S #D #I) 1 2 0 2 +REF: CLUB play ** **** CHART +HYP: CLAID play CH ORTE E +Eval: S I I S + +Speaker sentences 343: nchlt_eng_001600 #utts: 1 +id: (nchlt_eng_001600-nchlt_eng_001600) +Scores: (#C #S #D #I) 1 2 0 2 +REF: ***** POSITRONS were ** REPORTED +HYP: POSIY TRINS were RO PORTED +Eval: I S I S + +Speaker sentences 344: nchlt_eng_001601 #utts: 1 +id: (nchlt_eng_001601-nchlt_eng_001601) +Scores: (#C #S #D #I) 0 3 0 0 +REF: OLD VIC THEATRE +HYP: ALD VICK THEATER +Eval: S S S + +Speaker sentences 345: nchlt_eng_001602 #utts: 1 +id: (nchlt_eng_001602-nchlt_eng_001602) +Scores: (#C #S #D #I) 0 2 0 2 +REF: ** *** ORTHODOX MONARCHS +HYP: OR THE DOCKS MONOCKSE +Eval: I I S S + +Speaker sentences 346: nchlt_eng_001603 #utts: 1 +id: (nchlt_eng_001603-nchlt_eng_001603) +Scores: (#C #S #D #I) 1 2 0 1 +REF: nations * MEMBER STATES +HYP: nations W MEMBRSTATE S +Eval: I S S + +Speaker sentences 347: nchlt_eng_001604 #utts: 1 +id: (nchlt_eng_001604-nchlt_eng_001604) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** FIFA WORLD CUP +HYP: FHE THO WILD COP +Eval: I S S S + +Speaker sentences 348: nchlt_eng_001605 #utts: 1 +id: (nchlt_eng_001605-nchlt_eng_001605) +Scores: (#C #S #D #I) 0 3 0 0 +REF: CREWS RESCUE EFFORTS +HYP: CROSE RICSKYU EFEITS +Eval: S S S + +Speaker sentences 349: nchlt_eng_001606 #utts: 1 +id: (nchlt_eng_001606-nchlt_eng_001606) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ****** ACTUAL FILM MICROSCOPICALLY +HYP: ACTHOL FOLME MARCKES COPITY +Eval: I S S S + +Speaker sentences 350: nchlt_eng_001607 #utts: 1 +id: (nchlt_eng_001607-nchlt_eng_001607) +Scores: (#C #S #D #I) 0 3 0 0 +REF: MUSICAL GROUPS REESTABLISHED +HYP: MOUOSICL GRUPS REASTABLASHED +Eval: S S S + +Speaker sentences 351: nchlt_eng_001608 #utts: 1 +id: (nchlt_eng_001608-nchlt_eng_001608) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ******* PRIMUS INTER PARES +HYP: PROMISE N ERE PACESE +Eval: I S S S + +Speaker sentences 352: nchlt_eng_001609 #utts: 1 +id: (nchlt_eng_001609-nchlt_eng_001609) +Scores: (#C #S #D #I) 0 2 0 2 +REF: ***** *** FILM TECHNIQUES +HYP: FOLND SIK NEK S +Eval: I I S S + +Speaker sentences 353: nchlt_eng_001610 #utts: 1 +id: (nchlt_eng_001610-nchlt_eng_001610) +Scores: (#C #S #D #I) 0 3 0 0 +REF: TELEVISION SERIES BASED +HYP: TOELAVION SERYS BAST +Eval: S S S + +Speaker sentences 354: nchlt_eng_001611 #utts: 1 +id: (nchlt_eng_001611-nchlt_eng_001611) +Scores: (#C #S #D #I) 0 3 0 1 +REF: * NEW POLITICAL PARTY +HYP: H NOE POLITIOCAOE PORTYHEEE +Eval: I S S S + +Speaker sentences 355: nchlt_eng_001612 #utts: 1 +id: (nchlt_eng_001612-nchlt_eng_001612) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ******** ANCIENT EGYPT ACHIEVED +HYP: ANCHONTD EAGOP A CHEVED +Eval: I S S S + +Speaker sentences 356: nchlt_eng_001613 #utts: 1 +id: (nchlt_eng_001613-nchlt_eng_001613) +Scores: (#C #S #D #I) 1 2 0 0 +REF: flat MUSIC NATURAL +HYP: flat MUSIG NTROL +Eval: S S + +Speaker sentences 357: nchlt_eng_001614 #utts: 1 +id: (nchlt_eng_001614-nchlt_eng_001614) +Scores: (#C #S #D #I) 0 4 0 2 +REF: * ****** AMERICAN S TECHNOLOGY WRITERS +HYP: A MRICON TIC NOLID TOINOLIDY RATES +Eval: I I S S S S + +Speaker sentences 358: nchlt_eng_001615 #utts: 1 +id: (nchlt_eng_001615-nchlt_eng_001615) +Scores: (#C #S #D #I) 1 2 0 0 +REF: DAUGHTERS of BARONS +HYP: DOATES of VARINS +Eval: S S + +Speaker sentences 359: nchlt_eng_001616 #utts: 1 +id: (nchlt_eng_001616-nchlt_eng_001616) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ******** POPULAR TOURIST ATTRACTIONS +HYP: POPILEIT WRE IS TACTIONS +Eval: I S S S + +Speaker sentences 360: nchlt_eng_001617 #utts: 1 +id: (nchlt_eng_001617-nchlt_eng_001617) +Scores: (#C #S #D #I) 0 3 0 0 +REF: DUTCH WEST INDIA +HYP: DUCHE WIST INDEAR +Eval: S S S + +Speaker sentences 361: nchlt_eng_001618 #utts: 1 +id: (nchlt_eng_001618-nchlt_eng_001618) +Scores: (#C #S #D #I) 1 2 0 1 +REF: gold **** MEDAL RECIPIENTS +HYP: gold MATL RE SPIENSE +Eval: I S S + +Speaker sentences 362: nchlt_eng_001619 #utts: 1 +id: (nchlt_eng_001619-nchlt_eng_001619) +Scores: (#C #S #D #I) 0 3 0 3 +REF: ******** ****** *** RUSSIAN SOCIAL DEMOCRATIC +HYP: REASHION SOSIAL DEM OCRET ICK EH +Eval: I I I S S S + +Speaker sentences 363: nchlt_eng_001620 #utts: 1 +id: (nchlt_eng_001620-nchlt_eng_001620) +Scores: (#C #S #D #I) 0 3 0 1 +REF: * AMERICAN FILM PRODUCERS +HYP: A MIRIYCON FLME PRODUSES +Eval: I S S S + +Speaker sentences 364: nchlt_eng_001621 #utts: 1 +id: (nchlt_eng_001621-nchlt_eng_001621) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** FREE SOFTWARE FOUNDATION +HYP: FRE E SOFTERY FUNDATION +Eval: I S S S + +Speaker sentences 365: nchlt_eng_001622 #utts: 1 +id: (nchlt_eng_001622-nchlt_eng_001622) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ROYAL DRAMATIC THEATRE +HYP: RILE RMATIOC THEAT +Eval: S S S + +Speaker sentences 366: nchlt_eng_001623 #utts: 1 +id: (nchlt_eng_001623-nchlt_eng_001623) +Scores: (#C #S #D #I) 0 2 0 3 +REF: ** **** ****** EDIBLE MOLLUSCS +HYP: IT ABLE MOLOSK S H +Eval: I I I S S + +Speaker sentences 367: nchlt_eng_001624 #utts: 1 +id: (nchlt_eng_001624-nchlt_eng_001624) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ******** FEATURES INCLUDE BEACHES +HYP: FEATCHES IN TLWD BEACHERS +Eval: I S S S + +Speaker sentences 368: nchlt_eng_001625 #utts: 1 +id: (nchlt_eng_001625-nchlt_eng_001625) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** OXFORD DICTIONARY CHANGED +HYP: OC FOR DICTIONRY CHANGET +Eval: I S S S + +Speaker sentences 369: nchlt_eng_001626 #utts: 1 +id: (nchlt_eng_001626-nchlt_eng_001626) +Scores: (#C #S #D #I) 0 3 0 2 +REF: *** *** SALUKI PERSIAN GREYHOUND +HYP: SAL COW PIRISIOND RY HUND +Eval: I I S S S + +Speaker sentences 370: nchlt_eng_001627 #utts: 1 +id: (nchlt_eng_001627-nchlt_eng_001627) +Scores: (#C #S #D #I) 0 3 0 0 +REF: PRIME MINISTER KEVIN +HYP: PROWN MONISTER CIVEN +Eval: S S S + +Speaker sentences 371: nchlt_eng_001628 #utts: 1 +id: (nchlt_eng_001628-nchlt_eng_001628) +Scores: (#C #S #D #I) 1 2 0 1 +REF: LANGUAGES of *** IRAQ +HYP: LANGES of YUR OCKE +Eval: S I S + +Speaker sentences 372: nchlt_eng_001629 #utts: 1 +id: (nchlt_eng_001629-nchlt_eng_001629) +Scores: (#C #S #D #I) 1 2 0 1 +REF: SOUTH east ******* ENGLAND +HYP: SOTH east INGLOND T +Eval: S I S + +Speaker sentences 373: nchlt_eng_001630 #utts: 1 +id: (nchlt_eng_001630-nchlt_eng_001630) +Scores: (#C #S #D #I) 0 3 0 0 +REF: NEW LINE CINEMA +HYP: NOUR LINED SENOMARTH +Eval: S S S + +Speaker sentences 374: nchlt_eng_001631 #utts: 1 +id: (nchlt_eng_001631-nchlt_eng_001631) +Scores: (#C #S #D #I) 0 3 0 2 +REF: * **** EQUAL CREDIT OPPORTUNITY +HYP: E COUL KREDIT OPOTON NTSY +Eval: I I S S S + +Speaker sentences 375: nchlt_eng_001632 #utts: 1 +id: (nchlt_eng_001632-nchlt_eng_001632) +Scores: (#C #S #D #I) 2 1 0 0 +REF: south east ENGLAND +HYP: south east INGLAND +Eval: S + +Speaker sentences 376: nchlt_eng_001633 #utts: 1 +id: (nchlt_eng_001633-nchlt_eng_001633) +Scores: (#C #S #D #I) 0 1 0 0 +REF: MAY +HYP: MAYWEHTE +Eval: S + +Speaker sentences 377: nchlt_eng_001634 #utts: 1 +id: (nchlt_eng_001634-nchlt_eng_001634) +Scores: (#C #S #D #I) 0 3 0 3 +REF: ******* *** ** RECORD HETATM DESCRIBES +HYP: RECOLRD HAT AT E OME ESCRIPES +Eval: I I I S S S + +Speaker sentences 378: nchlt_eng_001635 #utts: 1 +id: (nchlt_eng_001635-nchlt_eng_001635) +Scores: (#C #S #D #I) 1 3 0 1 +REF: musical ****** GROUPS FROM CALIFORNIA +HYP: musical GREPES FOM CALOFORNIEA H +Eval: I S S S + +Speaker sentences 379: nchlt_eng_001636 #utts: 1 +id: (nchlt_eng_001636-nchlt_eng_001636) +Scores: (#C #S #D #I) 1 2 0 0 +REF: main BATTLE TANKS +HYP: main BETLE TINCS +Eval: S S + +Speaker sentences 380: nchlt_eng_001637 #utts: 1 +id: (nchlt_eng_001637-nchlt_eng_001637) +Scores: (#C #S #D #I) 0 3 0 0 +REF: POLISH MUSICAL INSTRUMENTS +HYP: PORDLISEH MUSIOCTALE INSTRMENTS +Eval: S S S + +Speaker sentences 381: nchlt_eng_001638 #utts: 1 +id: (nchlt_eng_001638-nchlt_eng_001638) +Scores: (#C #S #D #I) 1 3 0 0 +REF: LANGUAGES of SAUDI ARABIA +HYP: LANWIGES of SADYEAR ROAVIARE +Eval: S S S + +Speaker sentences 382: nchlt_eng_001639 #utts: 1 +id: (nchlt_eng_001639-nchlt_eng_001639) +Scores: (#C #S #D #I) 1 2 0 0 +REF: cold WAR TENSIONS +HYP: cold ORTINTIONS EH +Eval: S S + +Speaker sentences 383: nchlt_eng_001640 #utts: 1 +id: (nchlt_eng_001640-nchlt_eng_001640) +Scores: (#C #S #D #I) 0 1 0 1 +REF: **** DUBBY +HYP: DOBE HIMHS +Eval: I S + +Speaker sentences 384: nchlt_eng_001641 #utts: 1 +id: (nchlt_eng_001641-nchlt_eng_001641) +Scores: (#C #S #D #I) 0 2 0 2 +REF: *** * ANTIPOPE CLEMENT +HYP: AND Y POPKLIMINT H +Eval: I I S S + +Speaker sentences 385: nchlt_eng_001642 #utts: 1 +id: (nchlt_eng_001642-nchlt_eng_001642) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** GETS TAKEN PRIVATE +HYP: GIT HEY CON POIVEIT +Eval: I S S S + +Speaker sentences 386: nchlt_eng_001643 #utts: 1 +id: (nchlt_eng_001643-nchlt_eng_001643) +Scores: (#C #S #D #I) 0 2 0 2 +REF: *** *** KING FERDINAND +HYP: CIN FEI AN AND +Eval: I I S S + +Speaker sentences 387: nchlt_eng_001644 #utts: 1 +id: (nchlt_eng_001644-nchlt_eng_001644) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ELECTRONIC MUSICAL INSTRUMENTS +HYP: ILECTONICK MEUSOCKL INSTRONCS +Eval: S S S + +Speaker sentences 388: nchlt_eng_001645 #utts: 1 +id: (nchlt_eng_001645-nchlt_eng_001645) +Scores: (#C #S #D #I) 1 2 0 1 +REF: age **** MELT WATER +HYP: age MOLD TO ORTERN +Eval: I S S + +Speaker sentences 389: nchlt_eng_001646 #utts: 1 +id: (nchlt_eng_001646-nchlt_eng_001646) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ******* LAWRENCE LIVERMORE NATIONAL +HYP: LORANCT LVE MOR NASTIONLE +Eval: I S S S + +Speaker sentences 390: nchlt_eng_001647 #utts: 1 +id: (nchlt_eng_001647-nchlt_eng_001647) +Scores: (#C #S #D #I) 0 3 0 0 +REF: LEAGUE BASEBALL PLAYERS +HYP: LEG BACSPL PLAYARS +Eval: S S S + +Speaker sentences 391: nchlt_eng_001648 #utts: 1 +id: (nchlt_eng_001648-nchlt_eng_001648) +Scores: (#C #S #D #I) 1 4 0 0 +REF: BUDDHISM IN the ANCIENT MEDITERRANEAN +HYP: BODISOME AN the ANCIONT MEDETRANION +Eval: S S S S + +Speaker sentences 392: nchlt_eng_001649 #utts: 1 +id: (nchlt_eng_001649-nchlt_eng_001649) +Scores: (#C #S #D #I) 0 3 0 0 +REF: UNITED STATES RECOGNIZED +HYP: OUNIGTID STATS REKOCONSED +Eval: S S S + +Speaker sentences 393: nchlt_eng_001650 #utts: 1 +id: (nchlt_eng_001650-nchlt_eng_001650) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ********** PROPOSITIONAL FALLACIES +HYP: PROPOSIONL FELATYES E +Eval: I S S + +Speaker sentences 394: nchlt_eng_001651 #utts: 1 +id: (nchlt_eng_001651-nchlt_eng_001651) +Scores: (#C #S #D #I) 0 3 0 0 +REF: SPECIAL ECONOMIC ZONES +HYP: SPETIAL ACONOM NGESORONDS +Eval: S S S + +Speaker sentences 395: nchlt_eng_001652 #utts: 1 +id: (nchlt_eng_001652-nchlt_eng_001652) +Scores: (#C #S #D #I) 0 2 1 0 +REF: MAIN STREAM WEST +HYP: **** MANSTRM WIST +Eval: D S S + +Speaker sentences 396: nchlt_eng_001653 #utts: 1 +id: (nchlt_eng_001653-nchlt_eng_001653) +Scores: (#C #S #D #I) 0 3 0 0 +REF: EVENING RUSH HOURS +HYP: EVENG RUCH HLS +Eval: S S S + +Speaker sentences 397: nchlt_eng_001654 #utts: 1 +id: (nchlt_eng_001654-nchlt_eng_001654) +Scores: (#C #S #D #I) 0 3 0 2 +REF: ** *** BOFH EDITIONS TOOK +HYP: BY THE DIONS TOK K +Eval: I I S S S + +Speaker sentences 398: nchlt_eng_001655 #utts: 1 +id: (nchlt_eng_001655-nchlt_eng_001655) +Scores: (#C #S #D #I) 2 1 0 1 +REF: ** ANTARCTICA has no +HYP: ND ARTIOCKEA has no +Eval: I S + +Speaker sentences 399: nchlt_eng_001656 #utts: 1 +id: (nchlt_eng_001656-nchlt_eng_001656) +Scores: (#C #S #D #I) 0 3 0 0 +REF: WEST END MUSICALS +HYP: WAST IN MUSICLES +Eval: S S S + +Speaker sentences 400: nchlt_eng_001657 #utts: 1 +id: (nchlt_eng_001657-nchlt_eng_001657) +Scores: (#C #S #D #I) 0 3 0 2 +REF: ******* ** CONSERVATIVE JUDAISM REGARDS +HYP: CONEVIT OF DUDAYS AM REGORT +Eval: I I S S S + +Speaker sentences 401: nchlt_eng_001658 #utts: 1 +id: (nchlt_eng_001658-nchlt_eng_001658) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** OPEC MEMBER STATES +HYP: OP PICK MEMBR STATDS +Eval: I S S S + +Speaker sentences 402: nchlt_eng_001659 #utts: 1 +id: (nchlt_eng_001659-nchlt_eng_001659) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** PRIME MINISTER JOHN +HYP: PRI MINIS SAID JON +Eval: I S S S + +Speaker sentences 403: nchlt_eng_001660 #utts: 1 +id: (nchlt_eng_001660-nchlt_eng_001660) +Scores: (#C #S #D #I) 1 2 0 0 +REF: ROCKS forming MONT +HYP: ROACKS forming MOUNT +Eval: S S + +Speaker sentences 404: nchlt_eng_001661 #utts: 1 +id: (nchlt_eng_001661-nchlt_eng_001661) +Scores: (#C #S #D #I) 0 3 0 0 +REF: MAJOR LEAGUE TEAMS +HYP: MADGER LEAK TLMNS +Eval: S S S + +Speaker sentences 405: nchlt_eng_001662 #utts: 1 +id: (nchlt_eng_001662-nchlt_eng_001662) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ********** POLLINATION MANAGEMENT +HYP: POLANATION MANAGHENT T +Eval: I S S + +Speaker sentences 406: nchlt_eng_001663 #utts: 1 +id: (nchlt_eng_001663-nchlt_eng_001663) +Scores: (#C #S #D #I) 0 2 0 0 +REF: FRENCH PHYSICISTS +HYP: FRANCHE FISISST +Eval: S S + +Speaker sentences 407: nchlt_eng_001664 #utts: 1 +id: (nchlt_eng_001664-nchlt_eng_001664) +Scores: (#C #S #D #I) 1 2 0 0 +REF: HIGHER COMPRESSION ratio +HYP: HIARE COMPREITIOND ratio +Eval: S S + +Speaker sentences 408: nchlt_eng_001665 #utts: 1 +id: (nchlt_eng_001665-nchlt_eng_001665) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** RECORDING INDUSTRY ASSOCIATION +HYP: RE COARDNG INDESTY SOUCHATION +Eval: I S S S + +Speaker sentences 409: nchlt_eng_001666 #utts: 1 +id: (nchlt_eng_001666-nchlt_eng_001666) +Scores: (#C #S #D #I) 0 4 0 0 +REF: DPG S ONLINE MAGAZINE +HYP: TEAPADE OUN LIN MAGOSIAN +Eval: S S S S + +Speaker sentences 410: nchlt_eng_001667 #utts: 1 +id: (nchlt_eng_001667-nchlt_eng_001667) +Scores: (#C #S #D #I) 0 4 0 0 +REF: HIP HOP RECORD PRODUCERS +HYP: HIPOPERECULD PORO TOUCSENS S +Eval: S S S S + +Speaker sentences 411: nchlt_eng_001668 #utts: 1 +id: (nchlt_eng_001668-nchlt_eng_001668) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** FINITE STATE MACHINES +HYP: FI NIGT STAT MUSHENENS +Eval: I S S S + +Speaker sentences 412: nchlt_eng_001669 #utts: 1 +id: (nchlt_eng_001669-nchlt_eng_001669) +Scores: (#C #S #D #I) 0 3 0 0 +REF: WIDELY USED LOCAL +HYP: WHIDLY SUSED LOCKALD +Eval: S S S + +Speaker sentences 413: nchlt_eng_001670 #utts: 1 +id: (nchlt_eng_001670-nchlt_eng_001670) +Scores: (#C #S #D #I) 1 2 0 2 +REF: *** ** NORTH AMERICAN continent +HYP: NOR HE MAY COD continent +Eval: I I S S + +Speaker sentences 414: nchlt_eng_001671 #utts: 1 +id: (nchlt_eng_001671-nchlt_eng_001671) +Scores: (#C #S #D #I) 0 3 0 0 +REF: AFRICAN AMERICAN RAPPERS +HYP: AFRCON MERICON REPES +Eval: S S S + +Speaker sentences 415: nchlt_eng_001672 #utts: 1 +id: (nchlt_eng_001672-nchlt_eng_001672) +Scores: (#C #S #D #I) 1 2 0 0 +REF: THREATENED MILITARY actions +HYP: THRETEND MELIGTR actions +Eval: S S + +Speaker sentences 416: nchlt_eng_001673 #utts: 1 +id: (nchlt_eng_001673-nchlt_eng_001673) +Scores: (#C #S #D #I) 2 0 0 3 +REF: *** the word * ************ +HYP: UHT the word F ININTNEEEEEE +Eval: I I I + +Speaker sentences 417: nchlt_eng_001674 #utts: 1 +id: (nchlt_eng_001674-nchlt_eng_001674) +Scores: (#C #S #D #I) 0 4 1 0 +REF: ATOMIC MOLECULAR AND OPTICAL PHYSICS +HYP: ****** THETOMIK MLEILEN OPTOCAL FISICE +Eval: D S S S S + +Speaker sentences 418: nchlt_eng_001675 #utts: 1 +id: (nchlt_eng_001675-nchlt_eng_001675) +Scores: (#C #S #D #I) 0 1 0 1 +REF: * TOWN +HYP: E TONE +Eval: I S + +Speaker sentences 419: nchlt_eng_001676 #utts: 1 +id: (nchlt_eng_001676-nchlt_eng_001676) +Scores: (#C #S #D #I) 0 1 0 1 +REF: *** MARCEL +HYP: MYR SOL +Eval: I S + +Speaker sentences 420: nchlt_eng_001677 #utts: 1 +id: (nchlt_eng_001677-nchlt_eng_001677) +Scores: (#C #S #D #I) 0 3 0 2 +REF: *** ***** CONSTRUCT NEW RAILGAUGE +HYP: CON STRCT NOUOH RAL GAGE +Eval: I I S S S + +Speaker sentences 421: nchlt_eng_001678 #utts: 1 +id: (nchlt_eng_001678-nchlt_eng_001678) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ***** PAULI EXCLUSION PRINCIPLE +HYP: PORLY E CLWION RINCSABLE +Eval: I S S S + +Speaker sentences 422: nchlt_eng_001679 #utts: 1 +id: (nchlt_eng_001679-nchlt_eng_001679) +Scores: (#C #S #D #I) 1 2 0 0 +REF: HUE portray DIFFERENT +HYP: HCLOW portray DEFERENT +Eval: S S + +Speaker sentences 423: nchlt_eng_001680 #utts: 1 +id: (nchlt_eng_001680-nchlt_eng_001680) +Scores: (#C #S #D #I) 0 2 1 0 +REF: S SOVIET DISSIDENTS +HYP: * SOVEAT DESIDENCES +Eval: D S S + +Speaker sentences 424: nchlt_eng_001681 #utts: 1 +id: (nchlt_eng_001681-nchlt_eng_001681) +Scores: (#C #S #D #I) 0 3 0 0 +REF: SIGNAL TRANSDUCTION PATHWAYS +HYP: SIGNELE TRONCETDUCTIOND POLTWAYES +Eval: S S S + +Speaker sentences 425: nchlt_eng_001682 #utts: 1 +id: (nchlt_eng_001682-nchlt_eng_001682) +Scores: (#C #S #D #I) 1 2 0 0 +REF: NEW born MESSIAH +HYP: NOU born MSSI +Eval: S S + +Speaker sentences 426: nchlt_eng_001683 #utts: 1 +id: (nchlt_eng_001683-nchlt_eng_001683) +Scores: (#C #S #D #I) 0 3 0 0 +REF: GENERALLY ACCEPTED RANGES +HYP: JGENRLY ACEPTED RANERS +Eval: S S S + +Speaker sentences 427: nchlt_eng_001684 #utts: 1 +id: (nchlt_eng_001684-nchlt_eng_001684) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ***** GUILD AWARD WINNERS +HYP: GILED A WRD WENHIS +Eval: I S S S + +Speaker sentences 428: nchlt_eng_001685 #utts: 1 +id: (nchlt_eng_001685-nchlt_eng_001685) +Scores: (#C #S #D #I) 0 3 0 0 +REF: SWEDISH MUSICAL GROUPS +HYP: SOWEDISHE MAUSICL GRPS +Eval: S S S + +Speaker sentences 429: nchlt_eng_001686 #utts: 1 +id: (nchlt_eng_001686-nchlt_eng_001686) +Scores: (#C #S #D #I) 1 2 0 0 +REF: CHILDHOOD AUTISM rating +HYP: CHALDERED ORTISIM rating +Eval: S S + +Speaker sentences 430: nchlt_eng_001687 #utts: 1 +id: (nchlt_eng_001687-nchlt_eng_001687) +Scores: (#C #S #D #I) 0 2 0 0 +REF: DOSAGE FORMS +HYP: DOSIGH FORMEMS +Eval: S S + +Speaker sentences 431: nchlt_eng_001688 #utts: 1 +id: (nchlt_eng_001688-nchlt_eng_001688) +Scores: (#C #S #D #I) 0 3 0 1 +REF: * OHIO STATE UNIVERSITY +HYP: O HI IOUSTATION OVORSTITYE +Eval: I S S S + +Speaker sentences 432: nchlt_eng_001689 #utts: 1 +id: (nchlt_eng_001689-nchlt_eng_001689) +Scores: (#C #S #D #I) 1 3 0 1 +REF: *** FORMER SETTLEMENTS in TURKEY +HYP: FOR MOS SATHENCE in TERKE +Eval: I S S S + +Speaker sentences 433: nchlt_eng_001690 #utts: 1 +id: (nchlt_eng_001690-nchlt_eng_001690) +Scores: (#C #S #D #I) 0 2 0 2 +REF: *** ** AMERICAN INVENTIONS +HYP: EEE AN ROCON INWVHNTIONSH +Eval: I I S S + +Speaker sentences 434: nchlt_eng_001691 #utts: 1 +id: (nchlt_eng_001691-nchlt_eng_001691) +Scores: (#C #S #D #I) 0 1 0 1 +REF: * ARTS +HYP: E ARTESEH +Eval: I S + +Speaker sentences 435: nchlt_eng_001692 #utts: 1 +id: (nchlt_eng_001692-nchlt_eng_001692) +Scores: (#C #S #D #I) 0 3 0 1 +REF: **** MODERN EUROPEAN RUSSIA +HYP: MDEN YOUROPEAN RASHA H +Eval: I S S S + +Speaker sentences 436: nchlt_eng_001693 #utts: 1 +id: (nchlt_eng_001693-nchlt_eng_001693) +Scores: (#C #S #D #I) 0 3 0 2 +REF: ** *** NATIONAL LEAGUE PENNANT +HYP: NS NOD LEG PILANT H +Eval: I I S S S + +Speaker sentences 437: nchlt_eng_001694 #utts: 1 +id: (nchlt_eng_001694-nchlt_eng_001694) +Scores: (#C #S #D #I) 1 2 0 0 +REF: BIG finish PRODUCTIONS +HYP: BIK finish PRDUCTIONS +Eval: S S + +Speaker sentences 438: nchlt_eng_001695 #utts: 1 +id: (nchlt_eng_001695-nchlt_eng_001695) +Scores: (#C #S #D #I) 0 1 0 0 +REF: NATIONAL +HYP: NASTIONLEH +Eval: S + +Speaker sentences 439: nchlt_eng_001696 #utts: 1 +id: (nchlt_eng_001696-nchlt_eng_001696) +Scores: (#C #S #D #I) 0 2 0 0 +REF: TRAGIC POETS +HYP: TRADGIK POITESES +Eval: S S + +Speaker sentences 440: nchlt_eng_001697 #utts: 1 +id: (nchlt_eng_001697-nchlt_eng_001697) +Scores: (#C #S #D #I) 1 2 0 0 +REF: TOTAL GROSS state +HYP: TITIL GRICE state +Eval: S S + +Speaker sentences 441: nchlt_eng_001698 #utts: 1 +id: (nchlt_eng_001698-nchlt_eng_001698) +Scores: (#C #S #D #I) 1 2 0 1 +REF: ** ATHENA had AN +HYP: AS THENA had AENE +Eval: I S S + +Speaker sentences 442: nchlt_eng_001699 #utts: 1 +id: (nchlt_eng_001699-nchlt_eng_001699) +Scores: (#C #S #D #I) 0 3 0 0 +REF: EASTERN EUROPEAN COUNTRIES +HYP: EASTEON YURPEAN COUNTRYES +Eval: S S S + +Speaker sentences 443: nchlt_eng_001700 #utts: 1 +id: (nchlt_eng_001700-nchlt_eng_001700) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ****** CONDEMNED UNAUTHORIZED TRANSLATIONS +HYP: CONDED AN ORTHRIVSE TRONSLATIONS +Eval: I S S S + +Speaker sentences 444: nchlt_eng_001701 #utts: 1 +id: (nchlt_eng_001701-nchlt_eng_001701) +Scores: (#C #S #D #I) 0 3 0 0 +REF: COLD WAR LEADERS +HYP: OAL WORD ETES +Eval: S S S + +Speaker sentences 445: nchlt_eng_001702 #utts: 1 +id: (nchlt_eng_001702-nchlt_eng_001702) +Scores: (#C #S #D #I) 0 3 0 2 +REF: ***** *** KENESAW MOUNTAIN LANDIS +HYP: CINAS SOR MOWN AD LENDES +Eval: I I S S S + +Speaker sentences 446: nchlt_eng_001703 #utts: 1 +id: (nchlt_eng_001703-nchlt_eng_001703) +Scores: (#C #S #D #I) 0 2 0 0 +REF: NOBEL FAMILY +HYP: NOBLE SAMITYE +Eval: S S + +Speaker sentences 447: nchlt_eng_001704 #utts: 1 +id: (nchlt_eng_001704-nchlt_eng_001704) +Scores: (#C #S #D #I) 0 3 0 0 +REF: EDWARDS AIR FORCE +HYP: IT WOS AEFOLS +Eval: S S S + +Speaker sentences 448: nchlt_eng_001705 #utts: 1 +id: (nchlt_eng_001705-nchlt_eng_001705) +Scores: (#C #S #D #I) 2 1 0 0 +REF: mount saint VINCENT +HYP: mount saint OVINSENT +Eval: S + +Speaker sentences 449: nchlt_eng_001706 #utts: 1 +id: (nchlt_eng_001706-nchlt_eng_001706) +Scores: (#C #S #D #I) 0 3 0 0 +REF: CITY METROPOLITAN AREA +HYP: SITY MRCRPOLITON EAIRAR +Eval: S S S + +Speaker sentences 450: nchlt_eng_001707 #utts: 1 +id: (nchlt_eng_001707-nchlt_eng_001707) +Scores: (#C #S #D #I) 1 4 0 0 +REF: RULERS WHO DIED as CHILDREN +HYP: ROONLERS HO DAID as CHLDRAN +Eval: S S S S + +Speaker sentences 451: nchlt_eng_001708 #utts: 1 +id: (nchlt_eng_001708-nchlt_eng_001708) +Scores: (#C #S #D #I) 0 1 0 2 +REF: ******* *** CHANCELLORSVILLE +HYP: CHONCES LES VOLEL +Eval: I I S + +Speaker sentences 452: nchlt_eng_001709 #utts: 1 +id: (nchlt_eng_001709-nchlt_eng_001709) +Scores: (#C #S #D #I) 0 3 0 1 +REF: * IP PACKETS ENTIRELY +HYP: I PE PECKATC INTIRLY +Eval: I S S S + +Speaker sentences 453: nchlt_eng_001710 #utts: 1 +id: (nchlt_eng_001710-nchlt_eng_001710) +Scores: (#C #S #D #I) 0 3 0 1 +REF: **** KING EDWARDS DEATH +HYP: CING AD WARDS DATH +Eval: I S S S + +Speaker sentences 454: nchlt_eng_001711 #utts: 1 +id: (nchlt_eng_001711-nchlt_eng_001711) +Scores: (#C #S #D #I) 0 2 0 2 +REF: * ******** AMERICA AMERICA +HYP: A MERICEAR A MERICE +Eval: I I S S + +Speaker sentences 455: nchlt_eng_001712 #utts: 1 +id: (nchlt_eng_001712-nchlt_eng_001712) +Scores: (#C #S #D #I) 1 2 0 0 +REF: COMMERCIAL ship SAILED +HYP: COMRTIL ship SALD +Eval: S S + +Speaker sentences 456: nchlt_eng_001713 #utts: 1 +id: (nchlt_eng_001713-nchlt_eng_001713) +Scores: (#C #S #D #I) 0 3 0 1 +REF: **** PEOPLE FROM MANNHEIM +HYP: PEPL FOM MAEN HAMEM +Eval: I S S S + +Speaker sentences 457: nchlt_eng_001714 #utts: 1 +id: (nchlt_eng_001714-nchlt_eng_001714) +Scores: (#C #S #D #I) 1 2 0 0 +REF: rail CRASH KILLED +HYP: rail RASH CILD +Eval: S S + +Speaker sentences 458: nchlt_eng_001715 #utts: 1 +id: (nchlt_eng_001715-nchlt_eng_001715) +Scores: (#C #S #D #I) 0 3 0 0 +REF: MUTUAL DEFENSE TREATY +HYP: MUTHAL DEFENS TOADY +Eval: S S S + +Speaker sentences 459: nchlt_eng_001716 #utts: 1 +id: (nchlt_eng_001716-nchlt_eng_001716) +Scores: (#C #S #D #I) 0 3 0 0 +REF: MODERN CHILD RULERS +HYP: MOUDTEN CHELD RUOLIS +Eval: S S S + +Speaker sentences 460: nchlt_eng_001717 #utts: 1 +id: (nchlt_eng_001717-nchlt_eng_001717) +Scores: (#C #S #D #I) 0 3 0 1 +REF: **** MOTOR RIFLE DIVISION +HYP: MOTE SER RIHFHAL DEVION +Eval: I S S S + +Speaker sentences 461: nchlt_eng_001718 #utts: 1 +id: (nchlt_eng_001718-nchlt_eng_001718) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** AUSTRALIAN AIR FORCE +HYP: OU STRALION IE FOURSE +Eval: I S S S + +Speaker sentences 462: nchlt_eng_001719 #utts: 1 +id: (nchlt_eng_001719-nchlt_eng_001719) +Scores: (#C #S #D #I) 0 3 0 2 +REF: * **** AMERICAN MYSTERY WRITERS +HYP: A MERY CEND MOSTRY RITES +Eval: I I S S S + +Speaker sentences 463: nchlt_eng_001720 #utts: 1 +id: (nchlt_eng_001720-nchlt_eng_001720) +Scores: (#C #S #D #I) 0 3 0 0 +REF: FINELY GROUND GRAPHITE +HYP: FINLY GROWND GREFITE +Eval: S S S + +Speaker sentences 464: nchlt_eng_001721 #utts: 1 +id: (nchlt_eng_001721-nchlt_eng_001721) +Scores: (#C #S #D #I) 0 3 0 0 +REF: WORLD CHAMPIONSHIP MATCH +HYP: WOL TAMPINESOP MATS +Eval: S S S + +Speaker sentences 465: nchlt_eng_001722 #utts: 1 +id: (nchlt_eng_001722-nchlt_eng_001722) +Scores: (#C #S #D #I) 0 1 0 0 +REF: CAROLINA +HYP: CERIOLINA +Eval: S + +Speaker sentences 466: nchlt_eng_001723 #utts: 1 +id: (nchlt_eng_001723-nchlt_eng_001723) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** MOBILE PHONE OPERATORS +HYP: MY BOATH N OPERATES +Eval: I S S S + +Speaker sentences 467: nchlt_eng_001724 #utts: 1 +id: (nchlt_eng_001724-nchlt_eng_001724) +Scores: (#C #S #D #I) 0 2 0 0 +REF: QUARTZ VARIETIES +HYP: CORTE FORITYES +Eval: S S + +Speaker sentences 468: nchlt_eng_001725 #utts: 1 +id: (nchlt_eng_001725-nchlt_eng_001725) +Scores: (#C #S #D #I) 0 1 0 1 +REF: *** MIDRAND +HYP: MIO ROAWNDH +Eval: I S + +Speaker sentences 469: nchlt_eng_001726 #utts: 1 +id: (nchlt_eng_001726-nchlt_eng_001726) +Scores: (#C #S #D #I) 2 1 0 0 +REF: CAUSE lethal reactions +HYP: COSE lethal reactions +Eval: S + +Speaker sentences 470: nchlt_eng_001727 #utts: 1 +id: (nchlt_eng_001727-nchlt_eng_001727) +Scores: (#C #S #D #I) 0 2 0 0 +REF: ENGLISH PACIFISTS +HYP: INGLOISH PECEOFOUSTS +Eval: S S + +Speaker sentences 471: nchlt_eng_001728 #utts: 1 +id: (nchlt_eng_001728-nchlt_eng_001728) +Scores: (#C #S #D #I) 0 3 0 0 +REF: UNITED STATES FEDERAL +HYP: YONIGTED STATE FEDIRAL +Eval: S S S + +Speaker sentences 472: nchlt_eng_001729 #utts: 1 +id: (nchlt_eng_001729-nchlt_eng_001729) +Scores: (#C #S #D #I) 1 2 0 0 +REF: FEDERAL RESERVE act +HYP: FADRLE RESEIEOVE act +Eval: S S + +Speaker sentences 473: nchlt_eng_001730 #utts: 1 +id: (nchlt_eng_001730-nchlt_eng_001730) +Scores: (#C #S #D #I) 0 3 0 0 +REF: WILLIAM HENRY HARRISON +HYP: WOLIM HENDRY HERSON +Eval: S S S + +Speaker sentences 474: nchlt_eng_001731 #utts: 1 +id: (nchlt_eng_001731-nchlt_eng_001731) +Scores: (#C #S #D #I) 1 2 0 0 +REF: CLUB play CHART +HYP: GLAP play CHOT +Eval: S S + +Speaker sentences 475: nchlt_eng_001732 #utts: 1 +id: (nchlt_eng_001732-nchlt_eng_001732) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ********* PASSENGER RAIL SERVICES +HYP: PASTONGER RHALD SOVOCES H +Eval: I S S S + +Speaker sentences 476: nchlt_eng_001733 #utts: 1 +id: (nchlt_eng_001733-nchlt_eng_001733) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ****** ANCIENT MACEDONIAN GENERALS +HYP: ANCHON MESEDRNTHI OND JDINRLS +Eval: I S S S + +Speaker sentences 477: nchlt_eng_001734 #utts: 1 +id: (nchlt_eng_001734-nchlt_eng_001734) +Scores: (#C #S #D #I) 1 2 0 1 +REF: KONG action ** CINEMA +HYP: CRONG action SE AMARE +Eval: S I S + +Speaker sentences 478: nchlt_eng_001735 #utts: 1 +id: (nchlt_eng_001735-nchlt_eng_001735) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** GUNPOWDER PROPELLANT USED +HYP: GON POUTE PROPILENT YOSEDT +Eval: I S S S + +Speaker sentences 479: nchlt_eng_001736 #utts: 1 +id: (nchlt_eng_001736-nchlt_eng_001736) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ******* LOWEST ENERGY STATE +HYP: LOWISTD INAGY STDAGHT H +Eval: I S S S + +Speaker sentences 480: nchlt_eng_001737 #utts: 1 +id: (nchlt_eng_001737-nchlt_eng_001737) +Scores: (#C #S #D #I) 0 2 0 0 +REF: CALENDAR ERAS +HYP: CALNDE YOUROS +Eval: S S + +Speaker sentences 481: nchlt_eng_001738 #utts: 1 +id: (nchlt_eng_001738-nchlt_eng_001738) +Scores: (#C #S #D #I) 0 3 0 2 +REF: ** **** MAJOR INTERNATIONAL AIRPORT +HYP: MA EGER INTE NOASIONALE AEPORTE +Eval: I I S S S + +Speaker sentences 482: nchlt_eng_001739 #utts: 1 +id: (nchlt_eng_001739-nchlt_eng_001739) +Scores: (#C #S #D #I) 0 3 0 0 +REF: TOTAL FORCE ACTING +HYP: TOTL FORSE ACTIOM +Eval: S S S + +Speaker sentences 483: nchlt_eng_001740 #utts: 1 +id: (nchlt_eng_001740-nchlt_eng_001740) +Scores: (#C #S #D #I) 0 3 0 0 +REF: LOSSLESS DATA COMPRESSION +HYP: LOSTILES DAT OMPEITION +Eval: S S S + +Speaker sentences 484: nchlt_eng_001741 #utts: 1 +id: (nchlt_eng_001741-nchlt_eng_001741) +Scores: (#C #S #D #I) 0 1 0 2 +REF: * ********** GREEK +HYP: E GREAKEHDHE R +Eval: I I S + +Speaker sentences 485: nchlt_eng_001742 #utts: 1 +id: (nchlt_eng_001742-nchlt_eng_001742) +Scores: (#C #S #D #I) 0 3 0 3 +REF: *** ********** ********* ENVIRONMENTAL PROTECTION AGENCY +HYP: IND VOIREMENTL POTICTION A ENSY H +Eval: I I I S S S + +Speaker sentences 486: nchlt_eng_001743 #utts: 1 +id: (nchlt_eng_001743-nchlt_eng_001743) +Scores: (#C #S #D #I) 0 3 0 0 +REF: MANITOBA SCHOOLS QUESTION +HYP: MANY TOEBISCKLS QRESTION +Eval: S S S + +Speaker sentences 487: nchlt_eng_001744 #utts: 1 +id: (nchlt_eng_001744-nchlt_eng_001744) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ANCIENT CITY PITHUNDA +HYP: ANCTON SITY POTHUNDERE +Eval: S S S + +Speaker sentences 488: nchlt_eng_001745 #utts: 1 +id: (nchlt_eng_001745-nchlt_eng_001745) +Scores: (#C #S #D #I) 0 3 0 0 +REF: SMALL ORTHODOX SYNAGOGUE +HYP: SMLE AOTHEDOC SINGOG +Eval: S S S + +Speaker sentences 489: nchlt_eng_001746 #utts: 1 +id: (nchlt_eng_001746-nchlt_eng_001746) +Scores: (#C #S #D #I) 0 3 0 0 +REF: LARGEST METROPOLITAN AREAS +HYP: NONDGES MITHOUPILIAN AIRIRS +Eval: S S S + +Speaker sentences 490: nchlt_eng_001747 #utts: 1 +id: (nchlt_eng_001747-nchlt_eng_001747) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ****** TITLE RELIGIO ROMANA +HYP: TITOLE RELIGE HAR REMONON +Eval: I S S S + +Speaker sentences 491: nchlt_eng_001748 #utts: 1 +id: (nchlt_eng_001748-nchlt_eng_001748) +Scores: (#C #S #D #I) 0 3 0 3 +REF: ** ******** ** EXAMPLES INCLUDE HUFFMAN +HYP: EG SANMPLES AN TLUDED HAFH MON +Eval: I I I S S S + +Speaker sentences 492: nchlt_eng_001749 #utts: 1 +id: (nchlt_eng_001749-nchlt_eng_001749) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ***** UNITED STATES MAINTAINS +HYP: YUNOT TE STATE MINTEAIN +Eval: I S S S + +Speaker sentences 493: nchlt_eng_001750 #utts: 1 +id: (nchlt_eng_001750-nchlt_eng_001750) +Scores: (#C #S #D #I) 0 3 0 0 +REF: BOLD REPRESENTS MAXIMA +HYP: BOLED REPRESENCE MECXCIMOR +Eval: S S S + +Speaker sentences 494: nchlt_eng_001751 #utts: 1 +id: (nchlt_eng_001751-nchlt_eng_001751) +Scores: (#C #S #D #I) 1 2 0 0 +REF: SCIENCE fiction AUTHORS +HYP: SINE fiction ORTHIS +Eval: S S + +Speaker sentences 495: nchlt_eng_001752 #utts: 1 +id: (nchlt_eng_001752-nchlt_eng_001752) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ORDINARY DIFFERENTIAL EQUATIONS +HYP: ODENARE DEFRENSHOL AECQWATIONS +Eval: S S S + +Speaker sentences 496: nchlt_eng_001753 #utts: 1 +id: (nchlt_eng_001753-nchlt_eng_001753) +Scores: (#C #S #D #I) 2 3 0 0 +REF: DIPLOMATS of the HOLY SEE +HYP: DPLMAT of the HRDE SEW +Eval: S S S + +Speaker sentences 497: nchlt_eng_001754 #utts: 1 +id: (nchlt_eng_001754-nchlt_eng_001754) +Scores: (#C #S #D #I) 0 3 0 0 +REF: SERIAL KILLER MYSTERY +HYP: SILE COLOM MISTRY +Eval: S S S + +Speaker sentences 498: nchlt_eng_001755 #utts: 1 +id: (nchlt_eng_001755-nchlt_eng_001755) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** ROYAL MILITARY COLLEGE +HYP: URL ML ITRY COL +Eval: I S S S + +Speaker sentences 499: nchlt_eng_001756 #utts: 1 +id: (nchlt_eng_001756-nchlt_eng_001756) +Scores: (#C #S #D #I) 0 3 0 0 +REF: SLOWLY LEADS SOCIALISM +HYP: STLONLY LED SCIOLISUM +Eval: S S S + +Speaker sentences 500: nchlt_eng_001757 #utts: 1 +id: (nchlt_eng_001757-nchlt_eng_001757) +Scores: (#C #S #D #I) 0 1 0 1 +REF: ******** PRINTERS +HYP: PRINTEIS S +Eval: I S + +Speaker sentences 501: nchlt_eng_001758 #utts: 1 +id: (nchlt_eng_001758-nchlt_eng_001758) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** NEW TESTAMENT PEOPLE +HYP: NOU TASTHE AND PEPL +Eval: I S S S + +Speaker sentences 502: nchlt_eng_001759 #utts: 1 +id: (nchlt_eng_001759-nchlt_eng_001759) +Scores: (#C #S #D #I) 0 5 0 1 +REF: ***** SMART CARD BASED ELECTRONIC PURSE +HYP: SMOGT CORD BACSED ALCT TRONICK PERS +Eval: I S S S S S + +Speaker sentences 503: nchlt_eng_001760 #utts: 1 +id: (nchlt_eng_001760-nchlt_eng_001760) +Scores: (#C #S #D #I) 0 3 0 0 +REF: STATES ARMY SOLDIERS +HYP: STATE NMY SOLDGRS +Eval: S S S + +Speaker sentences 504: nchlt_eng_001761 #utts: 1 +id: (nchlt_eng_001761-nchlt_eng_001761) +Scores: (#C #S #D #I) 1 2 0 1 +REF: lord *** JESUS CHRIST +HYP: lord EAS S CRIST +Eval: I S S + +Speaker sentences 505: nchlt_eng_001762 #utts: 1 +id: (nchlt_eng_001762-nchlt_eng_001762) +Scores: (#C #S #D #I) 0 1 0 2 +REF: *** * LYDENBURG +HYP: LAD N BLINGP +Eval: I I S + +Speaker sentences 506: nchlt_eng_001763 #utts: 1 +id: (nchlt_eng_001763-nchlt_eng_001763) +Scores: (#C #S #D #I) 0 3 0 1 +REF: * ITALIAN NATIONAL TEAM +HYP: H TELIAN NESINL TEM +Eval: I S S S + +Speaker sentences 507: nchlt_eng_001764 #utts: 1 +id: (nchlt_eng_001764-nchlt_eng_001764) +Scores: (#C #S #D #I) 1 3 0 1 +REF: *** ANTIGUA RECREATION ground THUMB +HYP: AND TEAGR RECRATION ground THEM +Eval: I S S S + +Speaker sentences 508: nchlt_eng_001765 #utts: 1 +id: (nchlt_eng_001765-nchlt_eng_001765) +Scores: (#C #S #D #I) 0 3 0 0 +REF: GROSS STATE PRODUCT +HYP: GROCSES STAT PRODACTE +Eval: S S S + +Speaker sentences 509: nchlt_eng_001766 #utts: 1 +id: (nchlt_eng_001766-nchlt_eng_001766) +Scores: (#C #S #D #I) 0 3 0 0 +REF: KING KONG VS +HYP: CIN COND VERSE +Eval: S S S + +Speaker sentences 510: nchlt_eng_001767 #utts: 1 +id: (nchlt_eng_001767-nchlt_eng_001767) +Scores: (#C #S #D #I) 0 1 0 0 +REF: BELLVILLE +HYP: BIELVIL +Eval: S + +Speaker sentences 511: nchlt_eng_001768 #utts: 1 +id: (nchlt_eng_001768-nchlt_eng_001768) +Scores: (#C #S #D #I) 0 4 2 0 +REF: FILM ORGANIZATIONS IN THE UNITED STATES +HYP: **** ************* FLE OLGONGSATION NTHEUNODED STATE +Eval: D D S S S S + +Speaker sentences 512: nchlt_eng_001769 #utts: 1 +id: (nchlt_eng_001769-nchlt_eng_001769) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ***** ISRAEL DEFENSE FORCES +HYP: ITRIL THE FTANC FORSES +Eval: I S S S + +Speaker sentences 513: nchlt_eng_001770 #utts: 1 +id: (nchlt_eng_001770-nchlt_eng_001770) +Scores: (#C #S #D #I) 0 3 0 0 +REF: AUTOMATIC SEND RECEIVE +HYP: ORD DOMITICK SANDRESEVE +Eval: S S S + +Speaker sentences 514: nchlt_eng_001771 #utts: 1 +id: (nchlt_eng_001771-nchlt_eng_001771) +Scores: (#C #S #D #I) 0 3 0 0 +REF: BRUNSWICK SOUTHERN RAILWAY +HYP: BRENSWICK STHEN RALWOYLH +Eval: S S S + +Speaker sentences 515: nchlt_eng_001772 #utts: 1 +id: (nchlt_eng_001772-nchlt_eng_001772) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ***** ACTRESS ACADEMY AWARD +HYP: ACTES A CATDIMEA WOD +Eval: I S S S + +Speaker sentences 516: nchlt_eng_001773 #utts: 1 +id: (nchlt_eng_001773-nchlt_eng_001773) +Scores: (#C #S #D #I) 0 3 0 0 +REF: PEOPLE FROM TOKYO +HYP: PEPLE FOROME TOCKYOATED +Eval: S S S + +Speaker sentences 517: nchlt_eng_001774 #utts: 1 +id: (nchlt_eng_001774-nchlt_eng_001774) +Scores: (#C #S #D #I) 1 2 0 0 +REF: for CHARLES SINGER +HYP: for CHALDS SINGOE +Eval: S S + +Speaker sentences 518: nchlt_eng_001775 #utts: 1 +id: (nchlt_eng_001775-nchlt_eng_001775) +Scores: (#C #S #D #I) 0 3 0 0 +REF: VARIABLE VALVE TIMING +HYP: BEARABL VALFT TARMING +Eval: S S S + +Speaker sentences 519: nchlt_eng_001776 #utts: 1 +id: (nchlt_eng_001776-nchlt_eng_001776) +Scores: (#C #S #D #I) 1 2 0 0 +REF: south WALES VALLEYS +HYP: south WAILES FEYES +Eval: S S + +Speaker sentences 520: nchlt_eng_001777 #utts: 1 +id: (nchlt_eng_001777-nchlt_eng_001777) +Scores: (#C #S #D #I) 0 3 0 2 +REF: ** ************ CALIFORNIA STATE UNIVERSITY +HYP: CA OFORDIURSTAT T U DOEVORSITY +Eval: I I S S S + +Speaker sentences 521: nchlt_eng_001778 #utts: 1 +id: (nchlt_eng_001778-nchlt_eng_001778) +Scores: (#C #S #D #I) 0 1 0 0 +REF: ELDORADO +HYP: ELDERODO +Eval: S + +Speaker sentences 522: nchlt_eng_001779 #utts: 1 +id: (nchlt_eng_001779-nchlt_eng_001779) +Scores: (#C #S #D #I) 0 3 0 2 +REF: *** **** OUTDOOR ORIENTED CITY +HYP: OUT DORE OAR INTED SITY +Eval: I I S S S + +Speaker sentences 523: nchlt_eng_001780 #utts: 1 +id: (nchlt_eng_001780-nchlt_eng_001780) +Scores: (#C #S #D #I) 0 3 0 0 +REF: CLAIMED PARTIAL RESPONSIBILITY +HYP: CLAMED PORSIAL RSPONCEABILITY +Eval: S S S + +Speaker sentences 524: nchlt_eng_001781 #utts: 1 +id: (nchlt_eng_001781-nchlt_eng_001781) +Scores: (#C #S #D #I) 0 2 0 0 +REF: CHRISTIAN TERMS +HYP: CRISHION TERMES +Eval: S S + +Speaker sentences 525: nchlt_eng_001782 #utts: 1 +id: (nchlt_eng_001782-nchlt_eng_001782) +Scores: (#C #S #D #I) 1 2 0 1 +REF: * EVENTS TOOK place +HYP: E VENT TO place +Eval: I S S + +Speaker sentences 526: nchlt_eng_001783 #utts: 1 +id: (nchlt_eng_001783-nchlt_eng_001783) +Scores: (#C #S #D #I) 1 3 0 1 +REF: *** CANCER DEATHS in FRANCE +HYP: CAN SAID DATHES in FRONE +Eval: I S S S + +Speaker sentences 527: nchlt_eng_001784 #utts: 1 +id: (nchlt_eng_001784-nchlt_eng_001784) +Scores: (#C #S #D #I) 1 2 0 0 +REF: HISTORY of MICHIGAN +HYP: HISTRY of MISHOGON +Eval: S S + +Speaker sentences 528: nchlt_eng_001785 #utts: 1 +id: (nchlt_eng_001785-nchlt_eng_001785) +Scores: (#C #S #D #I) 1 2 0 0 +REF: ORIGINALLY the NAME +HYP: ORIGINLY the AMEM +Eval: S S + +Speaker sentences 529: nchlt_eng_001786 #utts: 1 +id: (nchlt_eng_001786-nchlt_eng_001786) +Scores: (#C #S #D #I) 1 2 0 1 +REF: nations **** FRAMEWORK CONVENTION +HYP: nations FRAE WRE CONVEANTION +Eval: I S S + +Speaker sentences 530: nchlt_eng_001787 #utts: 1 +id: (nchlt_eng_001787-nchlt_eng_001787) +Scores: (#C #S #D #I) 0 1 0 1 +REF: * LOCAL +HYP: E NOCKCONEH +Eval: I S + +Speaker sentences 531: nchlt_eng_001788 #utts: 1 +id: (nchlt_eng_001788-nchlt_eng_001788) +Scores: (#C #S #D #I) 0 3 0 3 +REF: ** *** * AUSTRIAN SCHOOL ECONOMISTS +HYP: OR STR N SCOLE ICONOM ISTES +Eval: I I I S S S + +Speaker sentences 532: nchlt_eng_001789 #utts: 1 +id: (nchlt_eng_001789-nchlt_eng_001789) +Scores: (#C #S #D #I) 1 2 0 1 +REF: main **** GROUP COMPOUNDS +HYP: main GRUP COME POUWNSES +Eval: I S S + +Speaker sentences 533: nchlt_eng_001790 #utts: 1 +id: (nchlt_eng_001790-nchlt_eng_001790) +Scores: (#C #S #D #I) 0 2 0 1 +REF: *** RECYCLABLE MATERIALS +HYP: HRO SIDCLIBL MTERIALS +Eval: I S S + +Speaker sentences 534: nchlt_eng_001791 #utts: 1 +id: (nchlt_eng_001791-nchlt_eng_001791) +Scores: (#C #S #D #I) 0 3 0 0 +REF: COMMON LAW SYSTEMS +HYP: COMEIN LOAR ESTOM +Eval: S S S + +Speaker sentences 535: nchlt_eng_001792 #utts: 1 +id: (nchlt_eng_001792-nchlt_eng_001792) +Scores: (#C #S #D #I) 0 3 0 0 +REF: BRONX HIGH SCHOOL +HYP: BRONKS HIY SCOLE +Eval: S S S + +Speaker sentences 536: nchlt_eng_001793 #utts: 1 +id: (nchlt_eng_001793-nchlt_eng_001793) +Scores: (#C #S #D #I) 0 3 0 1 +REF: * AMERICAN POLITICAL WRITERS +HYP: A MERICEN BELITICGAL RITERS +Eval: I S S S + +Speaker sentences 537: nchlt_eng_001794 #utts: 1 +id: (nchlt_eng_001794-nchlt_eng_001794) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ******* CHEMICAL ELEMENTS +HYP: CAMOCAL ILIAENTS S +Eval: I S S + +Speaker sentences 538: nchlt_eng_001795 #utts: 1 +id: (nchlt_eng_001795-nchlt_eng_001795) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ****** GLOBAL INTERNET COMMUNITY +HYP: DLOBLE INTON NT COMUNITY +Eval: I S S S + +Speaker sentences 539: nchlt_eng_001796 #utts: 1 +id: (nchlt_eng_001796-nchlt_eng_001796) +Scores: (#C #S #D #I) 0 3 0 2 +REF: *** ******* GEOGRAPHIC MAGAZINE MARCH +HYP: TYO GREFICT MA ASIEEN MARCHEH +Eval: I I S S S + +Speaker sentences 540: nchlt_eng_001797 #utts: 1 +id: (nchlt_eng_001797-nchlt_eng_001797) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** WEB SERVICE PROVIDERS +HYP: WIP SO THIS PROVIGDTDOS +Eval: I S S S + +Speaker sentences 541: nchlt_eng_001798 #utts: 1 +id: (nchlt_eng_001798-nchlt_eng_001798) +Scores: (#C #S #D #I) 0 3 0 0 +REF: SCIENCE FICTION NOVELS +HYP: SI EFPCTION NOBLESE +Eval: S S S + +Speaker sentences 542: nchlt_eng_001799 #utts: 1 +id: (nchlt_eng_001799-nchlt_eng_001799) +Scores: (#C #S #D #I) 0 2 1 0 +REF: SCIENCE FICTION FILM +HYP: ******* SINESFICTION FOLEM +Eval: D S S + +Speaker sentences 543: nchlt_eng_001800 #utts: 1 +id: (nchlt_eng_001800-nchlt_eng_001800) +Scores: (#C #S #D #I) 0 3 0 2 +REF: * **** SUBSET SUM PROBLEM +HYP: S SOBE SIT SOMEM PROBLOMN +Eval: I I S S S + +Speaker sentences 544: nchlt_eng_001801 #utts: 1 +id: (nchlt_eng_001801-nchlt_eng_001801) +Scores: (#C #S #D #I) 1 2 0 1 +REF: EASTERN north * AMERICA +HYP: ASTEON north E MYRICARE +Eval: S I S + +Speaker sentences 545: nchlt_eng_001802 #utts: 1 +id: (nchlt_eng_001802-nchlt_eng_001802) +Scores: (#C #S #D #I) 0 3 0 0 +REF: PEPYS WITNESSED LOOTING +HYP: PEPES WATNEST LOTING +Eval: S S S + +Speaker sentences 546: nchlt_eng_001803 #utts: 1 +id: (nchlt_eng_001803-nchlt_eng_001803) +Scores: (#C #S #D #I) 0 3 0 0 +REF: DISTINCTIVE VOCAL INSTRUMENT +HYP: DESTINGNTIOVE FOCKL INSTRMENTD +Eval: S S S + +Speaker sentences 547: nchlt_eng_001804 #utts: 1 +id: (nchlt_eng_001804-nchlt_eng_001804) +Scores: (#C #S #D #I) 0 3 0 2 +REF: ** ******** AFRICAN AMERICAN RAPPERS +HYP: HT AFOIOCON A MORICEN RAPTIS +Eval: I I S S S + +Speaker sentences 548: nchlt_eng_001805 #utts: 1 +id: (nchlt_eng_001805-nchlt_eng_001805) +Scores: (#C #S #D #I) 0 2 0 1 +REF: *** PORTUGUESE GENERALS +HYP: POR TOGES CHENLS +Eval: I S S + +Speaker sentences 549: nchlt_eng_001806 #utts: 1 +id: (nchlt_eng_001806-nchlt_eng_001806) +Scores: (#C #S #D #I) 0 4 0 2 +REF: **** ******** INTERNATIONAL AIRPORT S IATA +HYP: INTE NASIONLE APORT I TY AYH +Eval: I I S S S S + +Speaker sentences 550: nchlt_eng_001807 #utts: 1 +id: (nchlt_eng_001807-nchlt_eng_001807) +Scores: (#C #S #D #I) 2 2 0 0 +REF: MOUNTAIN ranges of BOLIVIA +HYP: MOUNTON ranges of BELIVIEAR +Eval: S S + +Speaker sentences 551: nchlt_eng_001808 #utts: 1 +id: (nchlt_eng_001808-nchlt_eng_001808) +Scores: (#C #S #D #I) 0 3 0 0 +REF: FRENCH AIR FORCE +HYP: FRINCH ARE FOARS +Eval: S S S + +Speaker sentences 552: nchlt_eng_001809 #utts: 1 +id: (nchlt_eng_001809-nchlt_eng_001809) +Scores: (#C #S #D #I) 0 3 0 1 +REF: * SUPER BOWL APPEARANCE +HYP: S SOPRABL A PEARANCSH +Eval: I S S S + +Speaker sentences 553: nchlt_eng_001810 #utts: 1 +id: (nchlt_eng_001810-nchlt_eng_001810) +Scores: (#C #S #D #I) 1 2 0 1 +REF: long ******* TRAVELING PAIRS +HYP: long TRVLING PAPES S +Eval: I S S + +Speaker sentences 554: nchlt_eng_001811 #utts: 1 +id: (nchlt_eng_001811-nchlt_eng_001811) +Scores: (#C #S #D #I) 0 3 0 0 +REF: DISTRICT COURT JUDGE +HYP: DEISTRIKT CORT ODGE +Eval: S S S + +Speaker sentences 555: nchlt_eng_001812 #utts: 1 +id: (nchlt_eng_001812-nchlt_eng_001812) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ** DURRANI EMPIRE +HYP: DO ONY AENMPIE +Eval: I S S + +Speaker sentences 556: nchlt_eng_001813 #utts: 1 +id: (nchlt_eng_001813-nchlt_eng_001813) +Scores: (#C #S #D #I) 1 1 1 1 +REF: BRITISH NATIONALITY act * +HYP: ******* PROTISINASIONALITY act H +Eval: D S I + +Speaker sentences 557: nchlt_eng_001814 #utts: 1 +id: (nchlt_eng_001814-nchlt_eng_001814) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ISSUE DATE APRIL +HYP: ISHO DICT APROL +Eval: S S S + +Speaker sentences 558: nchlt_eng_001815 #utts: 1 +id: (nchlt_eng_001815-nchlt_eng_001815) +Scores: (#C #S #D #I) 1 2 0 0 +REF: PUBLICLY traded COMPANIES +HYP: POPEBLISITY traded COMPANYES +Eval: S S + +Speaker sentences 559: nchlt_eng_001816 #utts: 1 +id: (nchlt_eng_001816-nchlt_eng_001816) +Scores: (#C #S #D #I) 1 4 1 0 +REF: RUSSIAN VICTIMS of SOVIET S REPRESSIONS +HYP: RUSHON VHCTIMS of ****** SOVED REBRESENTATIONS +Eval: S S D S S + +Speaker sentences 560: nchlt_eng_001817 #utts: 1 +id: (nchlt_eng_001817-nchlt_eng_001817) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ******** WEST SLAVIC LANGUAGES +HYP: WHISTDAN S LEVICK LANGWOGEOS +Eval: I S S S + +Speaker sentences 561: nchlt_eng_001818 #utts: 1 +id: (nchlt_eng_001818-nchlt_eng_001818) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ITALIAN ROMAN CATHOLIC +HYP: TALION ROMEND CATHLEKCES +Eval: S S S + +Speaker sentences 562: nchlt_eng_001819 #utts: 1 +id: (nchlt_eng_001819-nchlt_eng_001819) +Scores: (#C #S #D #I) 0 3 0 3 +REF: ******* ** **** FRENCH RESISTANCE MEMBERS +HYP: PRENTHE ES TIST RIFT N IMBORS +Eval: I I I S S S + +Speaker sentences 563: nchlt_eng_001820 #utts: 1 +id: (nchlt_eng_001820-nchlt_eng_001820) +Scores: (#C #S #D #I) 1 3 0 0 +REF: PROVINCIAL SYMBOLS of ONTARIO +HYP: PREVINCHAL SEMBLES of UNTORIOAOR +Eval: S S S + +Speaker sentences 564: nchlt_eng_001821 #utts: 1 +id: (nchlt_eng_001821-nchlt_eng_001821) +Scores: (#C #S #D #I) 0 3 0 1 +REF: **** ROCKS FORMING MONT +HYP: ROCK S FOAMING MOUNTE +Eval: I S S S + +Speaker sentences 565: nchlt_eng_001822 #utts: 1 +id: (nchlt_eng_001822-nchlt_eng_001822) +Scores: (#C #S #D #I) 0 2 0 0 +REF: ASSASSINATED MONARCHS +HYP: SESTINATED MONUKCKS +Eval: S S + +Speaker sentences 566: nchlt_eng_001823 #utts: 1 +id: (nchlt_eng_001823-nchlt_eng_001823) +Scores: (#C #S #D #I) 0 3 0 3 +REF: ****** ** * INCLUDE INTERNATIONAL NONGOVERNMENTAL +HYP: INCLAE IN E ASINOLE NOND GOVERMENTLE +Eval: I I I S S S + +Speaker sentences 567: nchlt_eng_001824 #utts: 1 +id: (nchlt_eng_001824-nchlt_eng_001824) +Scores: (#C #S #D #I) 1 2 1 1 +REF: ***** METRIC space S M +HYP: MITCH CK space * AIM +Eval: I S D S + +Speaker sentences 568: swc_eng_001744 #utts: 1 +id: (swc_eng_001744-swc_eng_001744) +Scores: (#C #S #D #I) 2 5 0 1 +REF: *** OR REPAIR the BREAK IN the TAPE +HYP: ORE RE PERE the BRAK N the TACE +Eval: I S S S S S + +Speaker sentences 569: swc_eng_001745 #utts: 1 +id: (swc_eng_001745-swc_eng_001745) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ***** VARIOUS SUBSTANCES +HYP: ERYHT SOP TENTES +Eval: I S S + +Speaker sentences 570: swc_eng_001746 #utts: 1 +id: (swc_eng_001746-swc_eng_001746) +Scores: (#C #S #D #I) 3 7 0 1 +REF: THIS most ****** COMMONLY OCCURS WHEN NEITHER SIDE IS able to +HYP: HIS most COMELY ACURS WE NE THER IDE S able to +Eval: S I S S S S S S + +Speaker sentences 571: swc_eng_001747 #utts: 1 +id: (swc_eng_001747-swc_eng_001747) +Scores: (#C #S #D #I) 3 8 0 0 +REF: GREAT BARRIER REEF is MANAGED by the GREAT BARRIER REEF MARINE +HYP: GRAT BARYIAR RETF is MENGE by the GRAT BEIA REAF MUREN +Eval: S S S S S S S S + +Speaker sentences 572: swc_eng_001748 #utts: 1 +id: (swc_eng_001748-swc_eng_001748) +Scores: (#C #S #D #I) 0 4 0 1 +REF: * AT LEAST THREE ROUTES +HYP: I HAT LEASHT TRE OTS +Eval: I S S S S + +Speaker sentences 573: swc_eng_001749 #utts: 1 +id: (swc_eng_001749-swc_eng_001749) +Scores: (#C #S #D #I) 1 1 0 0 +REF: DEFICIENCIES in +HYP: DFIHANTESYES in +Eval: S + +Speaker sentences 574: swc_eng_001750 #utts: 1 +id: (swc_eng_001750-swc_eng_001750) +Scores: (#C #S #D #I) 1 5 0 1 +REF: WILL SHOW EVIDENCE of *** HEMORRHAGE IN +HYP: WL HO EVTDENCE of HEM RYG INT +Eval: S S S I S S + +Speaker sentences 575: swc_eng_001751 #utts: 1 +id: (swc_eng_001751-swc_eng_001751) +Scores: (#C #S #D #I) 0 2 2 0 +REF: FIND AN ANSWER QUICKLY +HYP: **** ** FIDENANCSER CRICLY +Eval: D D S S + +Speaker sentences 576: swc_eng_001752 #utts: 1 +id: (swc_eng_001752-swc_eng_001752) +Scores: (#C #S #D #I) 1 5 0 2 +REF: ENABLES DIVISIVE and *** *** UNDEMOCRATIC SOCIAL POLICIES +HYP: NABLES DEVYOCIVE and UND DEM OCRATC SOTIAL PLISYE +Eval: S S I I S S S + +Speaker sentences 577: swc_eng_001753 #utts: 1 +id: (swc_eng_001753-swc_eng_001753) +Scores: (#C #S #D #I) 0 6 0 1 +REF: **** MADE RECENT TITLES AVAILABLE ON CASSETTE +HYP: MAED RESEN THIHLES A ILABL OND COSAI +Eval: I S S S S S S + +Speaker sentences 578: swc_eng_001754 #utts: 1 +id: (swc_eng_001754-swc_eng_001754) +Scores: (#C #S #D #I) 0 5 0 1 +REF: ******* DISTRICT IN EIGHTEEN SIXTY SIX +HYP: DUSTRCT AN ATIN SICST Y SAIC +Eval: I S S S S S + +Speaker sentences 579: swc_eng_001755 #utts: 1 +id: (swc_eng_001755-swc_eng_001755) +Scores: (#C #S #D #I) 0 3 1 0 +REF: A LITTLE INTO FUTURITY +HYP: * LITL ANTO FUCPEIRIDY +Eval: D S S S + +Speaker sentences 580: swc_eng_001756 #utts: 1 +id: (swc_eng_001756-swc_eng_001756) +Scores: (#C #S #D #I) 2 2 0 2 +REF: ** ***** groin and ADVANCED THROUGH +HYP: AY INTHE groin and EDFANCSE THR +Eval: I I S S + +Speaker sentences 581: swc_eng_001757 #utts: 1 +id: (swc_eng_001757-swc_eng_001757) +Scores: (#C #S #D #I) 1 7 0 1 +REF: TECHNOLOGIES IN IMPLEMENTING TRANSHUMANIST GOALS of ** ENHANCED PERFORMANCE +HYP: ECNLAGYES AD IMPLENTIG TRANS HUMNISCLE of AN HANCS PRFORME +Eval: S S S S S I S S + +Speaker sentences 582: swc_eng_001758 #utts: 1 +id: (swc_eng_001758-swc_eng_001758) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ** INCLUDING NAPHTHA +HYP: ND COATDING NAPSE +Eval: I S S + +Speaker sentences 583: swc_eng_001759 #utts: 1 +id: (swc_eng_001759-swc_eng_001759) +Scores: (#C #S #D #I) 1 6 0 1 +REF: by ****** SPANISH CHURCHMAN LUIS RAMIREZ DE LUCENA +HYP: by SPANSH TURCH MEN LL RE MEORAS DELOSANA +Eval: I S S S S S S + +Speaker sentences 584: swc_eng_001760 #utts: 1 +id: (swc_eng_001760-swc_eng_001760) +Scores: (#C #S #D #I) 1 1 0 0 +REF: DIVIDED democrats +HYP: DVFIHTED democrats +Eval: S + +Speaker sentences 585: swc_eng_001761 #utts: 1 +id: (swc_eng_001761-swc_eng_001761) +Scores: (#C #S #D #I) 2 6 0 4 +REF: ** **** THE WORLD CHAMPIONSHIP has BEEN CONTROLLED by **** ** FIDE +HYP: HE WORL THANPPE N SHIPT has EN COTROLE by EFFI DE E +Eval: I I S S S S S I I S + +Speaker sentences 586: swc_eng_001762 #utts: 1 +id: (swc_eng_001762-swc_eng_001762) +Scores: (#C #S #D #I) 1 3 0 1 +REF: WHERE THE STARTING position * +HYP: WHE HE TARING position I +Eval: S S S I + +Speaker sentences 587: swc_eng_001763 #utts: 1 +id: (swc_eng_001763-swc_eng_001763) +Scores: (#C #S #D #I) 5 9 1 0 +REF: BEEN CREATED in EVERY STATE and TERRITORY to PROTECT and PRESERVE the COUNTRYS UNIQUE ECOSYSTEMS +HYP: **** EENCRATED in EVRY STAT and TERITRY to PRTECHT and RESERV the CONTRYS OUNAKY COSCISTEMS +Eval: D S S S S S S S S S + +Speaker sentences 588: swc_eng_001764 #utts: 1 +id: (swc_eng_001764-swc_eng_001764) +Scores: (#C #S #D #I) 1 4 2 0 +REF: DEDICATION of THE NEW ZEALAND WAR MEMORIAL +HYP: EACATHOENT of *** *** THENOSILEND WARE MOIL +Eval: S D D S S S + +Speaker sentences 589: swc_eng_001765 #utts: 1 +id: (swc_eng_001765-swc_eng_001765) +Scores: (#C #S #D #I) 2 5 0 3 +REF: * ACCLAIM FROM the *** RAILROAD COMPANIES for *** VETOING +HYP: A LAME FOM the REL OUD CUPNYES for VET IN +Eval: I S S I S S I S + +Speaker sentences 590: swc_eng_001766 #utts: 1 +id: (swc_eng_001766-swc_eng_001766) +Scores: (#C #S #D #I) 2 1 1 1 +REF: *** TOWN is split BETWEEN +HYP: THE TON is split ******* +Eval: I S D + +Speaker sentences 591: swc_eng_001767 #utts: 1 +id: (swc_eng_001767-swc_eng_001767) +Scores: (#C #S #D #I) 2 5 0 1 +REF: ******** MOSQUITOFISH is a PARTICULARLY AGGRESSIVE SPECIES KNOWN +HYP: OSKEATID FISH is a PETICILY AEGRECIOF SPATHES N +Eval: I S S S S S + +Speaker sentences 592: swc_eng_001768 #utts: 1 +id: (swc_eng_001768-swc_eng_001768) +Scores: (#C #S #D #I) 2 3 0 2 +REF: and the ******** ***** NATIONAL CHESS CHAMPIONSHIPS +HYP: and the NASTIONL WESED HEM IN SHIPTES +Eval: I I S S S + +Speaker sentences 593: swc_eng_001769 #utts: 1 +id: (swc_eng_001769-swc_eng_001769) +Scores: (#C #S #D #I) 2 5 1 1 +REF: PROBLEM IS KNOWN to ** RUN IN POLYNOMIAL time +HYP: ******* PROBLOMEAS NON to RN INPLY NO MAL time +Eval: D S S I S S S + +Speaker sentences 594: swc_eng_001770 #utts: 1 +id: (swc_eng_001770-swc_eng_001770) +Scores: (#C #S #D #I) 1 4 0 2 +REF: *** JR and ****** PARKER WATKINS HARDIN +HYP: LAE JUOIR and PARCER WHAT IENS HARTN +Eval: I S I S S S + +Speaker sentences 595: swc_eng_001771 #utts: 1 +id: (swc_eng_001771-swc_eng_001771) +Scores: (#C #S #D #I) 1 3 0 1 +REF: in *** NINETEEN SEVENTY THREE +HYP: in NDT IN SEVETY THR +Eval: I S S S + +Speaker sentences 596: swc_eng_001772 #utts: 1 +id: (swc_eng_001772-swc_eng_001772) +Scores: (#C #S #D #I) 2 3 0 1 +REF: DEVELOPING and USING such ** TECHNOLOGIES +HYP: DELPING and YOUSING such TE NALDS +Eval: S S I S + +Speaker sentences 597: swc_eng_001773 #utts: 1 +id: (swc_eng_001773-swc_eng_001773) +Scores: (#C #S #D #I) 1 2 0 0 +REF: FOR some QUESTIONS +HYP: OR some WPASTIOND +Eval: S S + +Speaker sentences 598: swc_eng_001774 #utts: 1 +id: (swc_eng_001774-swc_eng_001774) +Scores: (#C #S #D #I) 1 4 0 0 +REF: CLAIM OF PROOF that P +HYP: LAME O ROE that E +Eval: S S S S + +Speaker sentences 599: swc_eng_001775 #utts: 1 +id: (swc_eng_001775-swc_eng_001775) +Scores: (#C #S #D #I) 2 8 0 0 +REF: a BLADDER CATHETER is USUALLY INSERTED TO MONITOR FLUID BALANCE +HYP: a BLADA CAHTER is OURLY IN SERTED SO MONSOF LOIEDBUMNS +Eval: S S S S S S S S + +Speaker sentences 600: swc_eng_001776 #utts: 1 +id: (swc_eng_001776-swc_eng_001776) +Scores: (#C #S #D #I) 1 7 0 4 +REF: ** PROMOTION of ** ***** ** EUGENIC ENHANCEMENT TECHNOLOGIES MIGHT UNINTENTIONALLY ENCOURAGE +HYP: ER NOTION of YU JENIK AN HASPENT TIC NALAGES MIGH UNINTENCHONALY INDCKRRAGE +Eval: I S I I I S S S S S S + +Speaker sentences 601: swc_eng_001777 #utts: 1 +id: (swc_eng_001777-swc_eng_001777) +Scores: (#C #S #D #I) 5 6 1 2 +REF: ** the ATTENTION of ** RESEARCHERS CAN be FOCUSED ON PARTIAL SOLUTIONS or solutions +HYP: AT the TENION of RE SURTHERS CA be ******* FOKAUSD M PARTIALSOLUTIONS or solutions +Eval: I S I S S D S S S + +Speaker sentences 602: swc_eng_001778 #utts: 1 +id: (swc_eng_001778-swc_eng_001778) +Scores: (#C #S #D #I) 4 2 0 0 +REF: KNOWN of for HUNDREDS of years +HYP: NONEN of for HNTRED of years +Eval: S S + +Speaker sentences 603: swc_eng_001779 #utts: 1 +id: (swc_eng_001779-swc_eng_001779) +Scores: (#C #S #D #I) 0 6 1 0 +REF: ONLY MARSUPIALS HAVE SURVIVED TO THE PRESENT +HYP: **** NLY MUSUBIALS HAE SOFIVE T T +Eval: D S S S S S S + +Speaker sentences 604: swc_eng_001780 #utts: 1 +id: (swc_eng_001780-swc_eng_001780) +Scores: (#C #S #D #I) 3 5 1 1 +REF: to WHICH ALL THE EDIBLE SPECIES of ***** CRUSTACEAN belong +HYP: to ***** HCH AL THEADABLE SPACHES of CRUST ISTHAN belong +Eval: D S S S S I S + +Speaker sentences 605: swc_eng_001781 #utts: 1 +id: (swc_eng_001781-swc_eng_001781) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ***** ALGORITHM RESEARCH +HYP: OLGER THEM RESURCHE +Eval: I S S + +Speaker sentences 606: swc_eng_001782 #utts: 1 +id: (swc_eng_001782-swc_eng_001782) +Scores: (#C #S #D #I) 4 8 1 1 +REF: NINETEEN SIXTY two ******* PHILIPS INVENTED the compact AUDIO CASSETTE MEDIUM for AUDIO STORAGE +HYP: NINTAIN SICXTDY two FILIPES N VENTD the compact ***** ODIOKESET MEDYHAM for ODIOUS DAORGE +Eval: S S I S S D S S S S + +Speaker sentences 607: swc_eng_001783 #utts: 1 +id: (swc_eng_001783-swc_eng_001783) +Scores: (#C #S #D #I) 1 3 0 0 +REF: OBSTRUCTION OF the FLOW +HYP: OSTRACTIN F the LOW +Eval: S S S + +Speaker sentences 608: swc_eng_001784 #utts: 1 +id: (swc_eng_001784-swc_eng_001784) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** AMPHIBIANS AND REPTILES +HYP: ND FHIBIEND AD REP +Eval: I S S S + +Speaker sentences 609: swc_eng_001785 #utts: 1 +id: (swc_eng_001785-swc_eng_001785) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ****** WOMENS WORLD CHESS CHAMPIONSHIP +HYP: WEMENS WHRLD CEST HAM INCHOF +Eval: I S S S S + +Speaker sentences 610: swc_eng_001786 #utts: 1 +id: (swc_eng_001786-swc_eng_001786) +Scores: (#C #S #D #I) 4 13 1 3 +REF: CONTAINS DESCRIPTIONS and COMMENTARIES ON the STATE of ** ** * NBIC SCIENCE and TECHNOLOGY BY MAJOR CONTRIBUTORS TO THESE FIELDS +HYP: CONTANE DECRTIONS and COMENTARYS O the TAT of EN BE I Y SINCE and ********** EG NALGY AS MAER CONTRUDERSTO THE +Eval: S S S S S I I I S S D S S S S S S + +Speaker sentences 611: swc_eng_001787 #utts: 1 +id: (swc_eng_001787-swc_eng_001787) +Scores: (#C #S #D #I) 1 4 0 0 +REF: PUERILE FANTASY or SOCIAL TREND +HYP: PEUROEL HANTIY or SOTIAL TRENT +Eval: S S S S + +Speaker sentences 612: swc_eng_001788 #utts: 1 +id: (swc_eng_001788-swc_eng_001788) +Scores: (#C #S #D #I) 3 3 0 1 +REF: most ****** COMPACT CASSETTES WERE sold blank +HYP: most COMPAC C SAET WER sold blank +Eval: I S S S + +Speaker sentences 613: swc_eng_001789 #utts: 1 +id: (swc_eng_001789-swc_eng_001789) +Scores: (#C #S #D #I) 1 4 0 0 +REF: IF THERE is AN ALGORITHM +HYP: IOF HER is N OULDGRTHE +Eval: S S S S + +Speaker sentences 614: swc_eng_001790 #utts: 1 +id: (swc_eng_001790-swc_eng_001790) +Scores: (#C #S #D #I) 2 7 1 0 +REF: THE SOUTHERN AUSTRALIAN COAST and in SUB ANTARCTIC AUSTRALIAN TERRITORIES +HYP: HE SOTHEN STRALIEN COST and in *** SABBAENTIECTIY OSTRLIAN TERATRYS +Eval: S S S S D S S S + +Speaker sentences 615: swc_eng_001791 #utts: 1 +id: (swc_eng_001791-swc_eng_001791) +Scores: (#C #S #D #I) 1 5 1 0 +REF: DATA RATES OF TYPICALLY five HUNDRED TO +HYP: **** DATRATS F TIPOCLY five HNDEREDT TW +Eval: D S S S S S + +Speaker sentences 616: swc_eng_001792 #utts: 1 +id: (swc_eng_001792-swc_eng_001792) +Scores: (#C #S #D #I) 1 2 0 0 +REF: DEPRIVING the DUCK +HYP: DERPRIVING the DUC +Eval: S S + +Speaker sentences 617: swc_eng_001793 #utts: 1 +id: (swc_eng_001793-swc_eng_001793) +Scores: (#C #S #D #I) 1 4 1 0 +REF: NINE PERCENT OF THE TOTAL cast +HYP: **** NIEN PERSENT OFTHE TOL cast +Eval: D S S S S + +Speaker sentences 618: swc_eng_001794 #utts: 1 +id: (swc_eng_001794-swc_eng_001794) +Scores: (#C #S #D #I) 1 6 0 0 +REF: ANTERIOR CEREBRAL ARTERY and ANTERIOR COMMUNICATING ARTERY +HYP: ANDTHERIRS SORBR ATRIY and ANTHEIE COMNCATING ATY +Eval: S S S S S S + +Speaker sentences 619: swc_eng_001795 #utts: 1 +id: (swc_eng_001795-swc_eng_001795) +Scores: (#C #S #D #I) 0 5 0 0 +REF: IT DID NOT IMPART SHINE +HYP: E DNOT IM PASH SHIN +Eval: S S S S S + +Speaker sentences 620: swc_eng_001796 #utts: 1 +id: (swc_eng_001796-swc_eng_001796) +Scores: (#C #S #D #I) 2 1 0 1 +REF: ** ENTIRE democratic party +HYP: EN THERE democratic party +Eval: I S + +Speaker sentences 621: swc_eng_001797 #utts: 1 +id: (swc_eng_001797-swc_eng_001797) +Scores: (#C #S #D #I) 3 4 2 0 +REF: NOTCHES on TOP of the CASSETTE SHELL INDICATE THE +HYP: NHES on TOPE of the ******** ***** ESETHAL INDECATH +Eval: S S D D S S + +Speaker sentences 622: swc_eng_001798 #utts: 1 +id: (swc_eng_001798-swc_eng_001798) +Scores: (#C #S #D #I) 0 4 0 0 +REF: ALLOW ONE TO SHOW +HYP: LAE UIA THS SEO +Eval: S S S S + +Speaker sentences 623: swc_eng_001799 #utts: 1 +id: (swc_eng_001799-swc_eng_001799) +Scores: (#C #S #D #I) 0 5 0 0 +REF: IS AN ENDANGERED MARINE SPECIES +HYP: I ANAN DTANGED MRAIN SPACHYEST +Eval: S S S S S + +Speaker sentences 624: swc_eng_001800 #utts: 1 +id: (swc_eng_001800-swc_eng_001800) +Scores: (#C #S #D #I) 0 3 0 0 +REF: BROWN DESIRED ELECTION +HYP: ROWN DESIRE LECTO +Eval: S S S + +Speaker sentences 625: swc_eng_001801 #utts: 1 +id: (swc_eng_001801-swc_eng_001801) +Scores: (#C #S #D #I) 4 6 0 0 +REF: THIS FACT DOESNT say much about WHERE the PROBLEM LIES +HYP: HIS FAC DOSANT say much about WER the PROBLME LS +Eval: S S S S S S + +Speaker sentences 626: swc_eng_001802 #utts: 1 +id: (swc_eng_001802-swc_eng_001802) +Scores: (#C #S #D #I) 2 2 1 0 +REF: ECONOMICAL SOCIETY began as A +HYP: COMOCL SSITY began as * +Eval: S S D + +Speaker sentences 627: swc_eng_001803 #utts: 1 +id: (swc_eng_001803-swc_eng_001803) +Scores: (#C #S #D #I) 1 6 0 0 +REF: WITH TOURISTS ARRIVING BY STEAMBOAT and TRAIN +HYP: WIT TORISTSREVING THE STME BOT and TRIAMEN +Eval: S S S S S S + +Speaker sentences 628: swc_eng_001804 #utts: 1 +id: (swc_eng_001804-swc_eng_001804) +Scores: (#C #S #D #I) 1 3 0 1 +REF: first ***** DIALOGUE BETWEEN TRANSHUMANISM +HYP: first DILOG BETWEN TRANS HUMINIS +Eval: I S S S + +Speaker sentences 629: swc_eng_001805 #utts: 1 +id: (swc_eng_001805-swc_eng_001805) +Scores: (#C #S #D #I) 1 4 2 0 +REF: NEVER BEEN PART of THE OLYMPIC GAMES +HYP: NEER BEN PRD of *** ******* THELNPCANDS +Eval: S S S D D S + +Speaker sentences 630: swc_eng_001806 #utts: 1 +id: (swc_eng_001806-swc_eng_001806) +Scores: (#C #S #D #I) 1 2 0 1 +REF: REGIS FURNITURE and ** +HYP: REAGESS FIRNITCER and TH +Eval: S S I + +Speaker sentences 631: swc_eng_001807 #utts: 1 +id: (swc_eng_001807-swc_eng_001807) +Scores: (#C #S #D #I) 1 2 1 0 +REF: in HIGH LEVEL TOURNAMENTS +HYP: in **** HILABL TREMENC +Eval: D S S + +Speaker sentences 632: swc_eng_001808 #utts: 1 +id: (swc_eng_001808-swc_eng_001808) +Scores: (#C #S #D #I) 1 3 0 0 +REF: to LOCATE THE ANEURYSM +HYP: to OCAT TH ANDURSONM +Eval: S S S + +Speaker sentences 633: swc_eng_001809 #utts: 1 +id: (swc_eng_001809-swc_eng_001809) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ** MORPHOLOGICAL FREEDOM +HYP: OR FHLUCHOCL FREDEM +Eval: I S S + +Speaker sentences 634: swc_eng_001810 #utts: 1 +id: (swc_eng_001810-swc_eng_001810) +Scores: (#C #S #D #I) 0 3 0 2 +REF: * ** ENERGETIC ATTACKING STYLE +HYP: N ER JETIC ATACKING STOU +Eval: I I S S S + +Speaker sentences 635: swc_eng_001811 #utts: 1 +id: (swc_eng_001811-swc_eng_001811) +Scores: (#C #S #D #I) 1 7 0 1 +REF: ****** EXACTLY FORTY YEARS AFTER THE CORNERSTONE was LAID +HYP: GACHLY FORE OARS AFER TE CONISTD OM was LATE +Eval: I S S S S S S S + +Speaker sentences 636: swc_eng_001812 #utts: 1 +id: (swc_eng_001812-swc_eng_001812) +Scores: (#C #S #D #I) 1 2 1 1 +REF: BASED ON the *********** RECOGNITION +HYP: ***** ACE the RECAGNITION TH +Eval: D S I S + +Speaker sentences 637: swc_eng_001813 #utts: 1 +id: (swc_eng_001813-swc_eng_001813) +Scores: (#C #S #D #I) 2 3 0 0 +REF: or ELECTRONIC BUTTONS or DISPLAY +HYP: or LATRONICT BUTENS or DESPLAY +Eval: S S S + +Speaker sentences 638: swc_eng_001814 #utts: 1 +id: (swc_eng_001814-swc_eng_001814) +Scores: (#C #S #D #I) 1 5 1 0 +REF: is UNKNOWN WHETHER P EQUALS N P +HYP: is ******* UN NON WTHER PEAECULS ANMPY +Eval: D S S S S S + +Speaker sentences 639: swc_eng_001815 #utts: 1 +id: (swc_eng_001815-swc_eng_001815) +Scores: (#C #S #D #I) 2 4 0 0 +REF: WHICH COMES FROM the verb ACUERE +HYP: HICH COMS FO the verb ACQUAR +Eval: S S S S + +Speaker sentences 640: swc_eng_001816 #utts: 1 +id: (swc_eng_001816-swc_eng_001816) +Scores: (#C #S #D #I) 0 8 0 1 +REF: *** DISPROPORTIONATELY AVAILABLE TO THOSE WITH GREATER FINANCIAL RESOURCES +HYP: DET PRPORTIONTLY OVAILABL T THAE WT RATER FINANHAL RESURES +Eval: I S S S S S S S S + +Speaker sentences 641: swc_eng_001817 #utts: 1 +id: (swc_eng_001817-swc_eng_001817) +Scores: (#C #S #D #I) 3 5 1 0 +REF: THE IMMINENT THREATS to THE SURVIVAL of many SPECIES +HYP: *** MN THRET to H SOFIVL of many SPACIE +Eval: D S S S S S + +Speaker sentences 642: swc_eng_001818 #utts: 1 +id: (swc_eng_001818-swc_eng_001818) +Scores: (#C #S #D #I) 0 3 0 0 +REF: EVEN MORE DIFFICULT +HYP: AVEON MOR DETICLE +Eval: S S S + +Speaker sentences 643: swc_eng_001819 #utts: 1 +id: (swc_eng_001819-swc_eng_001819) +Scores: (#C #S #D #I) 2 4 0 1 +REF: and ***** 21 SPECIES of OCEANIC DOLPHIN +HYP: and TWETY WEN SPEACIES of USCREANIAG DLFEON +Eval: I S S S S + +Speaker sentences 644: swc_eng_001820 #utts: 1 +id: (swc_eng_001820-swc_eng_001820) +Scores: (#C #S #D #I) 0 2 0 1 +REF: * ACHIEVING PROMOTION +HYP: A HEVING PRMOUTION +Eval: I S S + +Speaker sentences 645: swc_eng_001821 #utts: 1 +id: (swc_eng_001821-swc_eng_001821) +Scores: (#C #S #D #I) 0 2 0 3 +REF: *** ** **** TRANSHUMANIST ASSUMPTION +HYP: END TY MIST E SUMTION +Eval: I I I S S + +Speaker sentences 646: swc_eng_001822 #utts: 1 +id: (swc_eng_001822-swc_eng_001822) +Scores: (#C #S #D #I) 3 1 0 0 +REF: on the first BALLOT +HYP: on the first BELID +Eval: S + +Speaker sentences 647: swc_eng_001823 #utts: 1 +id: (swc_eng_001823-swc_eng_001823) +Scores: (#C #S #D #I) 6 7 2 0 +REF: story INDICATIVE of the RISE IN GLOBAL SIGNIFICANCE of SHOE POLISH is TOLD by JEAN +HYP: story INDECATIEF of the **** ** RISINGLOABL SRIGNIFINGS of SO PLISHE is TOE by DJDEN +Eval: S D D S S S S S S + +Speaker sentences 648: swc_eng_001824 #utts: 1 +id: (swc_eng_001824-swc_eng_001824) +Scores: (#C #S #D #I) 1 6 0 0 +REF: WHICH SPARKED HIS EARLY INTEREST IN politics +HYP: WHCH SBARE AS EALY ENTRST ND politics +Eval: S S S S S S + +Speaker sentences 649: swc_eng_001825 #utts: 1 +id: (swc_eng_001825-swc_eng_001825) +Scores: (#C #S #D #I) 4 6 0 1 +REF: was CALLED DOLBY H X pro in FULL and ***** PATENTED +HYP: was COLLD DOLBE ACH ACX pro in FOLL and PATNT I +Eval: S S S S S I S + +Speaker sentences 650: swc_eng_001826 #utts: 1 +id: (swc_eng_001826-swc_eng_001826) +Scores: (#C #S #D #I) 3 3 1 0 +REF: COULD save and FIND files BY NUMBER +HYP: OOD save and FIN files ** B +Eval: S S D S + +Speaker sentences 651: swc_eng_001827 #utts: 1 +id: (swc_eng_001827-swc_eng_001827) +Scores: (#C #S #D #I) 2 4 0 0 +REF: AUSTRALIAN SNAKES belong to SEVEN FAMILIES +HYP: ASTRLION SNACE belong to SEVON FAMELYES +Eval: S S S S + +Speaker sentences 652: swc_eng_001828 #utts: 1 +id: (swc_eng_001828-swc_eng_001828) +Scores: (#C #S #D #I) 0 2 0 2 +REF: ** ** DEVELOPING PLAYERS +HYP: TD OL AN PYOR +Eval: I I S S + +Speaker sentences 653: swc_eng_001829 #utts: 1 +id: (swc_eng_001829-swc_eng_001829) +Scores: (#C #S #D #I) 1 4 3 0 +REF: DECLINED sharply SINCE ITS PEAK IN THE LATE +HYP: LIND sharply ***** *** **** SENCES PEA AND +Eval: S D D D S S S + +Speaker sentences 654: swc_eng_001830 #utts: 1 +id: (swc_eng_001830-swc_eng_001830) +Scores: (#C #S #D #I) 2 5 2 0 +REF: was RECORDED ENTIRELY on A FOUR TRACK CASSETTE TAPE +HYP: was RECOURDED INTHIRLY on * **** FOR TRAC OSECTA +Eval: S S D D S S S + +Speaker sentences 655: swc_eng_001831 #utts: 1 +id: (swc_eng_001831-swc_eng_001831) +Scores: (#C #S #D #I) 1 2 0 2 +REF: *** ** ENORMOUS IMPROVEMENT in +HYP: NOL AS IM PROVENT in +Eval: I I S S + +Speaker sentences 656: swc_eng_001832 #utts: 1 +id: (swc_eng_001832-swc_eng_001832) +Scores: (#C #S #D #I) 1 3 0 0 +REF: URBAN and RURAL LEGISLATORS +HYP: RBON and RUL LEGESHTH +Eval: S S S + +Speaker sentences 657: swc_eng_001833 #utts: 1 +id: (swc_eng_001833-swc_eng_001833) +Scores: (#C #S #D #I) 0 3 0 0 +REF: EACH PLAYER BEGINS +HYP: ACH PLAYR BGNS +Eval: S S S + +Speaker sentences 658: swc_eng_001834 #utts: 1 +id: (swc_eng_001834-swc_eng_001834) +Scores: (#C #S #D #I) 1 5 0 0 +REF: CHESS HAS INSPIRED many COMBINATORIAL PUZZLES +HYP: DJHASTHAS N SPHIE many COMNTORILE POUSLE +Eval: S S S S S + +Speaker sentences 659: swc_eng_001835 #utts: 1 +id: (swc_eng_001835-swc_eng_001835) +Scores: (#C #S #D #I) 0 3 0 0 +REF: MORE HUMANE IMAGE +HYP: OR HUOMAN IMITDGE +Eval: S S S + +Speaker sentences 660: swc_eng_001836 #utts: 1 +id: (swc_eng_001836-swc_eng_001836) +Scores: (#C #S #D #I) 0 3 1 0 +REF: WELL AS PIRATED TAPES +HYP: **** LAS POIRDED TAPS +Eval: D S S S + +Speaker sentences 661: swc_eng_001837 #utts: 1 +id: (swc_eng_001837-swc_eng_001837) +Scores: (#C #S #D #I) 0 3 2 0 +REF: TINTIN DESCENDS INTO THE OCEAN +HYP: ****** ******** ANT ANDISENCEINGTO THEOTION +Eval: D D S S S + +Speaker sentences 662: swc_eng_001838 #utts: 1 +id: (swc_eng_001838-swc_eng_001838) +Scores: (#C #S #D #I) 3 4 0 0 +REF: PRESIDENT pro TEM of the STATE SENATE +HYP: RESIN pro TEMPOR of the STAT SEND +Eval: S S S S + +Speaker sentences 663: swc_eng_001839 #utts: 1 +id: (swc_eng_001839-swc_eng_001839) +Scores: (#C #S #D #I) 2 4 2 0 +REF: BISHOP CAN move any NUMBER OF SQUARES DIAGONALLY +HYP: ICHOP CEL move any ****** ** NUMBRO SCQHERS +Eval: S S D D S S + +Speaker sentences 664: swc_eng_001840 #utts: 1 +id: (swc_eng_001840-swc_eng_001840) +Scores: (#C #S #D #I) 1 3 0 1 +REF: ****** PRESSURE INSIDE the SKULL +HYP: PRETHE IN SID the SCOL +Eval: I S S S + +Speaker sentences 665: swc_eng_001841 #utts: 1 +id: (swc_eng_001841-swc_eng_001841) +Scores: (#C #S #D #I) 0 4 0 0 +REF: LINE CONNECTS SHEERNESS WITH +HYP: LING CAET SHERNESS WIT +Eval: S S S S + +Speaker sentences 666: swc_eng_001842 #utts: 1 +id: (swc_eng_001842-swc_eng_001842) +Scores: (#C #S #D #I) 2 4 0 2 +REF: COUNTRIES of the ***** ******** WESTERN PALEARCTIC FLYWAY +HYP: OUNTRYS of the ESTEN PALYOARC TIC FLY WAY +Eval: S I I S S S + +Speaker sentences 667: swc_eng_001843 #utts: 1 +id: (swc_eng_001843-swc_eng_001843) +Scores: (#C #S #D #I) 2 4 0 1 +REF: ****** NATIONAL STATISTICS ESTIMATED the POPULATION in +HYP: ASONLS DTICSTIK E AESTAMAT the OPELATION in +Eval: I S S S S + +Speaker sentences 668: swc_eng_001844 #utts: 1 +id: (swc_eng_001844-swc_eng_001844) +Scores: (#C #S #D #I) 1 3 0 1 +REF: *** UNDISPUTED WORLD chess CHAMPION +HYP: UND DESPEUTED WHRLD chess HAPEEN +Eval: I S S S + +Speaker sentences 669: swc_eng_001845 #utts: 1 +id: (swc_eng_001845-swc_eng_001845) +Scores: (#C #S #D #I) 0 3 0 2 +REF: **** **** JOSE RAUL CAPABLANCA +HYP: TAYN RELL CAPT BINCE AR +Eval: I I S S S + +Speaker sentences 670: swc_eng_001846 #utts: 1 +id: (swc_eng_001846-swc_eng_001846) +Scores: (#C #S #D #I) 5 10 0 2 +REF: WERE ENACTED by the ***** GENERAL ASSEMBLY was a MEASURE RACIALLY SEGREGATING the ***** STATES RAILROAD CARS +HYP: ER NACTED by the CNERL A SEMBLYH was a MASER RASIALY SEVGRGAIG the TATES RE ROT COARRS +Eval: S S I S S S S S I S S S + +Speaker sentences 671: swc_eng_001847 #utts: 1 +id: (swc_eng_001847-swc_eng_001847) +Scores: (#C #S #D #I) 1 2 0 0 +REF: WHICH WRAPS almost +HYP: WHCH RAPE almost +Eval: S S + +Speaker sentences 672: swc_eng_001848 #utts: 1 +id: (swc_eng_001848-swc_eng_001848) +Scores: (#C #S #D #I) 1 5 2 0 +REF: THIS ACT PROTECTS ALL NATIVE FAUNA and PROVIDES +HYP: **** *** SEACT PRETHCT LDNTIOF FORNER and PROVIDE +Eval: D D S S S S S + +Speaker sentences 673: swc_eng_001849 #utts: 1 +id: (swc_eng_001849-swc_eng_001849) +Scores: (#C #S #D #I) 2 7 1 1 +REF: *** WHEREAS the FEMALES SPECULUM is DARK BROWN BORDERED WITH WHITE +HYP: WER AS the FEMIAL SPECILEM is **** DARC BORONBORDED WIT WHAT +Eval: I S S S D S S S S + +Speaker sentences 674: swc_eng_001850 #utts: 1 +id: (swc_eng_001850-swc_eng_001850) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ROTARY CONTROLS OR +HYP: WOTERE CENCTLS O +Eval: S S S + +Speaker sentences 675: swc_eng_001851 #utts: 1 +id: (swc_eng_001851-swc_eng_001851) +Scores: (#C #S #D #I) 1 3 0 1 +REF: NINETEEN TWELVE in **** ROSTHERN +HYP: NHIDNTYAN TWELVEF in ROSS FIRN +Eval: S S I S + +Speaker sentences 676: swc_eng_001852 #utts: 1 +id: (swc_eng_001852-swc_eng_001852) +Scores: (#C #S #D #I) 3 6 3 0 +REF: DIAGNOSIS is GENERALLY made WITH A C T SCAN OF the HEAD +HYP: DIDEDNOIS is GENRL made **** * * WHTHA SETE SCANDO the HEAID +Eval: S S D D D S S S S + +Speaker sentences 677: swc_eng_001853 #utts: 1 +id: (swc_eng_001853-swc_eng_001853) +Scores: (#C #S #D #I) 1 5 0 1 +REF: *** first GENERALLY RECOGNIZED WORLD CHESS CHAMPION +HYP: THE first GENRLY RECONIGCSE WERLD CHAESST CHAMPPEON +Eval: I S S S S S + +Speaker sentences 678: swc_eng_001854 #utts: 1 +id: (swc_eng_001854-swc_eng_001854) +Scores: (#C #S #D #I) 2 3 0 1 +REF: SHEPPEY and ****** SITTINGBOURNE WERE part +HYP: HEPEY and SITING LN WRE part +Eval: S I S S + +Speaker sentences 679: swc_eng_001855 #utts: 1 +id: (swc_eng_001855-swc_eng_001855) +Scores: (#C #S #D #I) 2 6 0 0 +REF: HAD RELEASED THEIR ALBUMS both to CD AND +HYP: AD RELAST THER ELBUMS both to SEDE ANT +Eval: S S S S S S + +Speaker sentences 680: swc_eng_001856 #utts: 1 +id: (swc_eng_001856-swc_eng_001856) +Scores: (#C #S #D #I) 1 3 0 0 +REF: WATERS AROUND the CONTINENT +HYP: WATES RON the CONTINEN +Eval: S S S + +Speaker sentences 681: swc_eng_001857 #utts: 1 +id: (swc_eng_001857-swc_eng_001857) +Scores: (#C #S #D #I) 1 3 0 1 +REF: * the RANGE PERSONAL STEREOS +HYP: F the RANGH PERSONL STAIRIOS +Eval: I S S S + +Speaker sentences 682: swc_eng_001858 #utts: 1 +id: (swc_eng_001858-swc_eng_001858) +Scores: (#C #S #D #I) 3 5 0 1 +REF: ** AND VU METERS and RECORDING LEVEL controls on +HYP: ND E YO MEDERS and RECOARTING LEVL controls on +Eval: I S S S S S + +Speaker sentences 683: swc_eng_001859 #utts: 1 +id: (swc_eng_001859-swc_eng_001859) +Scores: (#C #S #D #I) 1 1 0 1 +REF: **** POLYNOMIAL time +HYP: LYNO MUL time +Eval: I S + +Speaker sentences 684: swc_eng_001860 #utts: 1 +id: (swc_eng_001860-swc_eng_001860) +Scores: (#C #S #D #I) 3 3 0 0 +REF: and it OFTEN DESTROYED the PLAYABILITY +HYP: and it OFHEN DESTRD the PLABILITY +Eval: S S S + +Speaker sentences 685: swc_eng_001861 #utts: 1 +id: (swc_eng_001861-swc_eng_001861) +Scores: (#C #S #D #I) 0 1 0 0 +REF: CONFUSION +HYP: CONFLAUTION +Eval: S + +Speaker sentences 686: swc_eng_001862 #utts: 1 +id: (swc_eng_001862-swc_eng_001862) +Scores: (#C #S #D #I) 6 6 0 0 +REF: EQUIVALENT to the QUESTION of WHETHER X is a MEMBER of COMPOSITE +HYP: EUIVLENT to the CESTION of WHTHER ACXE is a MEMBR of COMPOUSI +Eval: S S S S S S + +Speaker sentences 687: swc_eng_001863 #utts: 1 +id: (swc_eng_001863-swc_eng_001863) +Scores: (#C #S #D #I) 0 4 1 0 +REF: MOVES TO ITS LAST RANK +HYP: ***** MOSTO ITH LASTE RANG +Eval: D S S S S + +Speaker sentences 688: swc_eng_001864 #utts: 1 +id: (swc_eng_001864-swc_eng_001864) +Scores: (#C #S #D #I) 0 1 0 0 +REF: POSTGENDERISM +HYP: POSTHENDERISTME +Eval: S + +Speaker sentences 689: swc_eng_001865 #utts: 1 +id: (swc_eng_001865-swc_eng_001865) +Scores: (#C #S #D #I) 0 5 0 0 +REF: COMPACT CASSETTE QUICKLY FOUND USE +HYP: OMPACT COSSAT WUIKLY FOUD OUS +Eval: S S S S S + +Speaker sentences 690: swc_eng_001866 #utts: 1 +id: (swc_eng_001866-swc_eng_001866) +Scores: (#C #S #D #I) 0 5 0 1 +REF: ** FOUR HUNDRED THIRTY THREE FEET +HYP: OR FOR HUNDRND THARTY THRY FEE +Eval: I S S S S S + +Speaker sentences 691: swc_eng_001867 #utts: 1 +id: (swc_eng_001867-swc_eng_001867) +Scores: (#C #S #D #I) 0 6 3 0 +REF: WHICH RESULT IN A SPECIFIC TYPE OF PAWN STRUCTURE +HYP: ***** ****** ** INGS WHCH RESELT INASPECSIOTICT TIHE OPON +Eval: D D D S S S S S S + +Speaker sentences 692: swc_eng_001868 #utts: 1 +id: (swc_eng_001868-swc_eng_001868) +Scores: (#C #S #D #I) 1 3 0 0 +REF: BEFORE NINETEEN NINETY seven +HYP: BFR NANTIY NANTY seven +Eval: S S S + +Speaker sentences 693: swc_eng_001869 #utts: 1 +id: (swc_eng_001869-swc_eng_001869) +Scores: (#C #S #D #I) 1 2 1 0 +REF: COMMUNICATIONS and HEALTH CARE +HYP: OMYCATIONS and ****** HELT +Eval: S D S + +Speaker sentences 694: swc_eng_001870 #utts: 1 +id: (swc_eng_001870-swc_eng_001870) +Scores: (#C #S #D #I) 2 3 1 1 +REF: **** SAH in a PERSON KNOWN TO +HYP: ESAY AICHE in a ****** PERSSON NON +Eval: I S D S S + +Speaker sentences 695: swc_eng_001871 #utts: 1 +id: (swc_eng_001871-swc_eng_001871) +Scores: (#C #S #D #I) 4 10 0 2 +REF: *** SOME BRANDS SPECIFY that *** THEY MAY ALSO be USED on OTHER non POROUS MATERIALS +HYP: SOM BRAENE PIS OFIE that THY AY ALL SOE be OUED on UTHER non PORSS METEIRILS +Eval: I S S S I S S S S S S S + +Speaker sentences 696: swc_eng_001872 #utts: 1 +id: (swc_eng_001872-swc_eng_001872) +Scores: (#C #S #D #I) 0 3 0 2 +REF: ** **** THE POSSIBLY CONSPECIFIC +HYP: HE POSE BLY CON PESIFIC +Eval: I I S S S + +Speaker sentences 697: swc_eng_001873 #utts: 1 +id: (swc_eng_001873-swc_eng_001873) +Scores: (#C #S #D #I) 1 2 0 0 +REF: M in LENGTH +HYP: MATORS in LACTH +Eval: S S + +Speaker sentences 698: swc_eng_001874 #utts: 1 +id: (swc_eng_001874-swc_eng_001874) +Scores: (#C #S #D #I) 0 5 1 0 +REF: WITHOUT FIVE ZERO MOVE DRAWING RULE +HYP: ******* ITHOUT FTY MOLE TRING ROL +Eval: D S S S S S + +Speaker sentences 699: swc_eng_001875 #utts: 1 +id: (swc_eng_001875-swc_eng_001875) +Scores: (#C #S #D #I) 0 5 0 1 +REF: **** THIS SITUATION PARALLELS RESPECTIVELY CZECHOSLOVAKIA +HYP: HEED CHACSEM POULES RSPETIELY THOCKUSTOF AGKI +Eval: I S S S S S + +Speaker sentences 700: swc_eng_001876 #utts: 1 +id: (swc_eng_001876-swc_eng_001876) +Scores: (#C #S #D #I) 1 3 0 1 +REF: *** FAVERSHAM ELECTED its FIRST +HYP: AVH HOM LETD its FIRS +Eval: I S S S + +Speaker sentences 701: swc_eng_001877 #utts: 1 +id: (swc_eng_001877-swc_eng_001877) +Scores: (#C #S #D #I) 0 5 0 2 +REF: ** ******** REBLEEDING RISK REMAINS AROUND FORTY +HYP: RE BLEADING RSSK RMAINES OF ROUND FOATY +Eval: I I S S S S S + +Speaker sentences 702: swc_eng_001878 #utts: 1 +id: (swc_eng_001878-swc_eng_001878) +Scores: (#C #S #D #I) 0 2 0 1 +REF: *** DELIVER CHECKMATE +HYP: DLR CHACT MATD +Eval: I S S + +Speaker sentences 703: swc_eng_001879 #utts: 1 +id: (swc_eng_001879-swc_eng_001879) +Scores: (#C #S #D #I) 7 17 0 3 +REF: some ****** SECULAR HUMANISTS CONCEIVE TRANSHUMANISM as an OFFSPRING of the ****** HUMANIST FREETHOUGHT MOVEMENT and ***** ARGUE THAT TRANSHUMANISTS DIFFER FROM the HUMANIST MAINSTREAM BY HAVING +HYP: some SECILR HUMENIS CONSIEVE TRANE HUMINISM as an OSPRING of the UMNIST FRETHUT MOVE MENT and ARGEY THE TRENS HUMINIS DIFER TFORM the UMONIST MAEN STRMEBY HAVIN +Eval: I S S S S S I S S S I S S S S S S S S S + +Speaker sentences 704: swc_eng_001880 #utts: 1 +id: (swc_eng_001880-swc_eng_001880) +Scores: (#C #S #D #I) 2 7 1 0 +REF: PINTAIL NESTS and CHICKS ARE VULNERABLE TO PREDATION by MAMMALS +HYP: PINTILE NESTD and ****** CHICK SREVONERBL T PRDATION by MAMOLE +Eval: S S D S S S S S + +Speaker sentences 705: swc_eng_001881 #utts: 1 +id: (swc_eng_001881-swc_eng_001881) +Scores: (#C #S #D #I) 8 8 3 0 +REF: northern PINTAIL is one of the SPECIES TO which THE AGREEMENT on THE CONSERVATION of AFRICAN EURASIAN MIGRATORY WATERBIRDS +HYP: northern PINTAL is one of the ******* SPEASHESTO which *** THEAGREMENT on *** THECONCERVATION of AFRK NIRAISION MYGRTORY WAERBURD +Eval: S D S D S D S S S S S + +Speaker sentences 706: swc_eng_001882 #utts: 1 +id: (swc_eng_001882-swc_eng_001882) +Scores: (#C #S #D #I) 1 6 0 0 +REF: AND is NOW FOUND ONLY IN TASMANIA +HYP: ANT is NEOE FERUND OLY INTAS MAGIOR +Eval: S S S S S S + +Speaker sentences 707: swc_eng_001883 #utts: 1 +id: (swc_eng_001883-swc_eng_001883) +Scores: (#C #S #D #I) 4 5 0 2 +REF: ********** the IDEA of MIND UPLOADING is * ASSERTED to REPRESENT +HYP: ERSPECTIVE the ADEA of MEINED OUPLATING is A SRTD to REPRSENT +Eval: I S S S I S S + +Speaker sentences 708: swc_eng_001884 #utts: 1 +id: (swc_eng_001884-swc_eng_001884) +Scores: (#C #S #D #I) 4 3 0 0 +REF: AN AVERAGE of twenty ONE per day +HYP: N AVRIGE of twenty HEN per day +Eval: S S S + +Speaker sentences 709: swc_eng_001885 #utts: 1 +id: (swc_eng_001885-swc_eng_001885) +Scores: (#C #S #D #I) 1 6 0 0 +REF: THEN IT WOULD FOLLOW that P EQUALS +HYP: TEN I WOAD FLLO that PEE ECGUL +Eval: S S S S S S + +Speaker sentences 710: swc_eng_001886 #utts: 1 +id: (swc_eng_001886-swc_eng_001886) +Scores: (#C #S #D #I) 0 5 0 0 +REF: AND BLEEDING INTO VARIOUS TUMORS +HYP: ND BLEADING INTOE ERIES CHOMEOS +Eval: S S S S S + +Speaker sentences 711: swc_eng_001887 #utts: 1 +id: (swc_eng_001887-swc_eng_001887) +Scores: (#C #S #D #I) 1 5 0 1 +REF: ** ALLOW THEM to GLIDE BETWEEN TREES +HYP: AN LE THE to GLID BETWEAN TRS +Eval: I S S S S S + +Speaker sentences 712: swc_eng_001888 #utts: 1 +id: (swc_eng_001888-swc_eng_001888) +Scores: (#C #S #D #I) 1 5 0 0 +REF: if THESE PROBLEMS WERE EFFICIENTLY SOLVABLE +HYP: if THES PROBOES WR FICINLY SOLVHABL +Eval: S S S S S + +Speaker sentences 713: swc_eng_001889 #utts: 1 +id: (swc_eng_001889-swc_eng_001889) +Scores: (#C #S #D #I) 0 2 0 0 +REF: GEOLOGICAL TIME +HYP: ALOGHCAL TIN +Eval: S S + +Speaker sentences 714: swc_eng_001890 #utts: 1 +id: (swc_eng_001890-swc_eng_001890) +Scores: (#C #S #D #I) 2 0 0 1 +REF: *** when flushed +HYP: ROK when flushed +Eval: I + +Speaker sentences 715: swc_eng_001891 #utts: 1 +id: (swc_eng_001891-swc_eng_001891) +Scores: (#C #S #D #I) 0 4 0 0 +REF: INCLUDING GERMINAL CHOICE TECHNOLOGY +HYP: INCLTDING JHRMNL TOCETC NALDE +Eval: S S S S + +Speaker sentences 716: swc_eng_001892 #utts: 1 +id: (swc_eng_001892-swc_eng_001892) +Scores: (#C #S #D #I) 2 4 0 1 +REF: APPEARANCE of ** LEATHER SHOES or BOOTS +HYP: PERNC of LE TH SHOUGES or BOUT +Eval: S I S S S + +Speaker sentences 717: swc_eng_001893 #utts: 1 +id: (swc_eng_001893-swc_eng_001893) +Scores: (#C #S #D #I) 0 4 0 0 +REF: IN EIGHTEEN SIXTY THREE +HYP: ND ATTIN SCTDY THRE +Eval: S S S S + +Speaker sentences 718: swc_eng_001894 #utts: 1 +id: (swc_eng_001894-swc_eng_001894) +Scores: (#C #S #D #I) 0 6 0 0 +REF: MANUFACTURE SHOE CARE PRODUCTS ALSO SELL +HYP: ANOFECTERS SHU CAE PRODACE OLSOI SOL +Eval: S S S S S S + +Speaker sentences 719: swc_eng_001895 #utts: 1 +id: (swc_eng_001895-swc_eng_001895) +Scores: (#C #S #D #I) 2 3 1 1 +REF: * the first NON SOVIET CHALLENGER SINCE +HYP: O the first *** NOAN SOLRET CHALNGERSTEN +Eval: I D S S S + +Speaker sentences 720: swc_eng_001896 #utts: 1 +id: (swc_eng_001896-swc_eng_001896) +Scores: (#C #S #D #I) 4 2 0 0 +REF: OPPONENT has only the KING and +HYP: PONENT has only the CING and +Eval: S S + +Speaker sentences 721: swc_eng_001897 #utts: 1 +id: (swc_eng_001897-swc_eng_001897) +Scores: (#C #S #D #I) 1 1 0 0 +REF: MAIN article +HYP: MAYN article +Eval: S + +Speaker sentences 722: swc_eng_001898 #utts: 1 +id: (swc_eng_001898-swc_eng_001898) +Scores: (#C #S #D #I) 1 5 0 0 +REF: FOUND CERTAIN LENGTHS USEFUL for FITTING +HYP: OWND SERTN LATHS OUSFUL for FITIN +Eval: S S S S S + +Speaker sentences 723: swc_eng_001899 #utts: 1 +id: (swc_eng_001899-swc_eng_001899) +Scores: (#C #S #D #I) 6 3 1 0 +REF: tape in the same FORM FACTOR AS the compact AUDIO +HYP: tape in the same **** FORE FACTDERS the compact ODIO +Eval: D S S S + +Speaker sentences 724: swc_eng_001900 #utts: 1 +id: (swc_eng_001900-swc_eng_001900) +Scores: (#C #S #D #I) 1 4 0 0 +REF: CENSURE was LATER EXPUNGED FROM +HYP: SENTHER was LATE ECSPUNGED FRO +Eval: S S S S + +Speaker sentences 725: swc_eng_001901 #utts: 1 +id: (swc_eng_001901-swc_eng_001901) +Scores: (#C #S #D #I) 0 3 1 0 +REF: OR DE FACTO EQUALITY +HYP: ** R DESECTO ACQUALITY +Eval: D S S S + +Speaker sentences 726: swc_eng_001902 #utts: 1 +id: (swc_eng_001902-swc_eng_001902) +Scores: (#C #S #D #I) 2 5 1 0 +REF: is FOUR THOUSAND SIX HUNDRED by SIXTY FEET +HYP: is **** FOR THASEN SACHUNTRED by SCTY FET +Eval: D S S S S S + +Speaker sentences 727: swc_eng_001903 #utts: 1 +id: (swc_eng_001903-swc_eng_001903) +Scores: (#C #S #D #I) 0 3 0 0 +REF: NINETEEN SEVENTY THREE +HYP: NITIN SOVENDY THRE +Eval: S S S + +Speaker sentences 728: swc_eng_001904 #utts: 1 +id: (swc_eng_001904-swc_eng_001904) +Scores: (#C #S #D #I) 2 5 1 1 +REF: **** a PLAYER MAY ALSO LOSE by RUNNING OUT +HYP: ROLL a PLAIR MAL SO LOUE by ******* RUNIN +Eval: I S S S S D S + +Speaker sentences 729: swc_eng_001905 #utts: 1 +id: (swc_eng_001905-swc_eng_001905) +Scores: (#C #S #D #I) 0 6 0 0 +REF: PUBLIC HEALTH PROFESSOR GREGORY STOCK POINTS +HYP: BLOK HLH POFESSER GRAGRY STOAC POIN +Eval: S S S S S S + +Speaker sentences 730: swc_eng_001906 #utts: 1 +id: (swc_eng_001906-swc_eng_001906) +Scores: (#C #S #D #I) 5 8 0 1 +REF: brown WAS ELECTED TO THE house OF REPRESENTATIVES for THREE non *** CONSECUTIVE terms +HYP: brown AS LECTE T TH house O REPERSENTIVS for THEREY non CEN SECKITIF terms +Eval: S S S S S S S I S + +Speaker sentences 731: swc_eng_001907 #utts: 1 +id: (swc_eng_001907-swc_eng_001907) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** COEXIST HAPPILY WITH +HYP: OR EGSIST HAPLY W +Eval: I S S S + +Speaker sentences 732: swc_eng_001908 #utts: 1 +id: (swc_eng_001908-swc_eng_001908) +Scores: (#C #S #D #I) 2 4 0 0 +REF: a GROUP OF MAMMALS that RAISE +HYP: a GRUP O NEMLS that RAC +Eval: S S S S + +Speaker sentences 733: swc_eng_001909 #utts: 1 +id: (swc_eng_001909-swc_eng_001909) +Scores: (#C #S #D #I) 0 4 0 0 +REF: AND THE WORLDS LARGEST +HYP: ND TH HLDS LAGEST +Eval: S S S S + +Speaker sentences 734: swc_eng_001910 #utts: 1 +id: (swc_eng_001910-swc_eng_001910) +Scores: (#C #S #D #I) 2 5 0 1 +REF: ******** BREEDING TAKES place BETWEEN APRIL and JUNE +HYP: BREADING TAK S place BETWEN APROL and DUN +Eval: I S S S S S + +Speaker sentences 735: swc_eng_001911 #utts: 1 +id: (swc_eng_001911-swc_eng_001911) +Scores: (#C #S #D #I) 1 4 1 0 +REF: AUSTRALIA is AT THE SOUTHERN END +HYP: STRALOR is ** ATHE SOTHEN EIND +Eval: S D S S S + +Speaker sentences 736: swc_eng_001912 #utts: 1 +id: (swc_eng_001912-swc_eng_001912) +Scores: (#C #S #D #I) 1 3 0 1 +REF: *** TECHNOLOGICAL SINGULARITY is POSSIBLE +HYP: TEC NHLOUGHCL SINGEILAIRITY is POSEABL +Eval: I S S S + +Speaker sentences 737: swc_eng_001913 #utts: 1 +id: (swc_eng_001913-swc_eng_001913) +Scores: (#C #S #D #I) 1 3 0 0 +REF: INCLUDING THE SLEEPY cod +HYP: NLDING TH SPLPY cod +Eval: S S S + +Speaker sentences 738: swc_eng_001914 #utts: 1 +id: (swc_eng_001914-swc_eng_001914) +Scores: (#C #S #D #I) 2 5 1 0 +REF: SEVENTY FOUR had a HIGHER EDUCATION QUALIFICATION COMPARED +HYP: ESETY FORE had a ****** HIGEREGOCATION OLFECATION COMPEDET +Eval: S S D S S S + +Speaker sentences 739: swc_eng_001915 #utts: 1 +id: (swc_eng_001915-swc_eng_001915) +Scores: (#C #S #D #I) 0 5 4 0 +REF: THIS OCCURS WHEN THE OPPONENTS KING IS IN CHECK +HYP: **** ****** **** *** IACPERS WHE THEBPONIS CANG SAN +Eval: D D D D S S S S S + +Speaker sentences 740: swc_eng_001916 #utts: 1 +id: (swc_eng_001916-swc_eng_001916) +Scores: (#C #S #D #I) 0 2 1 0 +REF: CONSERVATION IN AUSTRALIA +HYP: ************ CONCEVATION NOSTRELYAR +Eval: D S S + +Speaker sentences 741: swc_eng_001917 #utts: 1 +id: (swc_eng_001917-swc_eng_001917) +Scores: (#C #S #D #I) 2 1 0 1 +REF: is the ********** SALAMANDERFISH +HYP: is the SELAMANDOF FEICU +Eval: I S + +Speaker sentences 742: swc_eng_001918 #utts: 1 +id: (swc_eng_001918-swc_eng_001918) +Scores: (#C #S #D #I) 2 7 0 0 +REF: first self DESCRIBED TRANSHUMANISTS MET FORMALLY IN THE EARLY +HYP: first self DECRIGVE TRAN HUMONST HAT FORMILY INTHE AL +Eval: S S S S S S S + +Speaker sentences 743: swc_eng_001919 #utts: 1 +id: (swc_eng_001919-swc_eng_001919) +Scores: (#C #S #D #I) 0 8 0 0 +REF: RECENT RESEARCH INDICATES THAT FACTORS OTHER THAN PRACTICE +HYP: REENT RESURCHE INDECATD THT FACTERS OTHE THEN PACDI +Eval: S S S S S S S S + +Speaker sentences 744: swc_eng_001920 #utts: 1 +id: (swc_eng_001920-swc_eng_001920) +Scores: (#C #S #D #I) 3 3 0 1 +REF: and PREVENTION and TREATMENT of **** COMPLICATIONS +HYP: and PRVENTION and TREKBENT of OMPL CATIONS +Eval: S S I S + +Speaker sentences 745: swc_eng_001921 #utts: 1 +id: (swc_eng_001921-swc_eng_001921) +Scores: (#C #S #D #I) 0 4 0 0 +REF: WITH A RAPID ONSET +HYP: I HEREAED ON SAIT +Eval: S S S S + +Speaker sentences 746: swc_eng_001922 #utts: 1 +id: (swc_eng_001922-swc_eng_001922) +Scores: (#C #S #D #I) 1 5 0 1 +REF: ** UTAH WAR the FOUNDATION WAS BURIED +HYP: OU TOW WOR the FONDATIO AS BAIE +Eval: I S S S S S + +Speaker sentences 747: swc_eng_001923 #utts: 1 +id: (swc_eng_001923-swc_eng_001923) +Scores: (#C #S #D #I) 8 12 1 1 +REF: nowadays HOURLY REGIONAL EXPRESS TRAINS BETWEEN BERN and ** SPIEZ to brig and FREIGHT TRAINS continue to run ON THE MOUNTAIN RAILWAY +HYP: nowadays ****** OURLY REAGINL CSPRESTRAND BETN BURN and HS PEITH to brig and FRAT TRANES continue to run O TH MUTONT RALW +Eval: D S S S S S I S S S S S S S + +Speaker sentences 748: swc_eng_001924 #utts: 1 +id: (swc_eng_001924-swc_eng_001924) +Scores: (#C #S #D #I) 0 10 0 0 +REF: OTHER FAMILIES WITH A POTENTIALLY GONDWANAN ORIGIN INCLUDE THE RETROPINNIDAE +HYP: THE FAMLYS WE TH PTINCRLY GOND WANE ANORIGION INCLD THERETRPONEDAY +Eval: S S S S S S S S S S + +Speaker sentences 749: swc_eng_001925 #utts: 1 +id: (swc_eng_001925-swc_eng_001925) +Scores: (#C #S #D #I) 2 6 0 0 +REF: by AN ITALIAN DOMINICAN MONK JACOBUS de CESSOLIS +HYP: by ANETALIN DOAMIKCEND MOAC JAC COBIS de SESTLES +Eval: S S S S S S + +Speaker sentences 750: swc_eng_001926 #utts: 1 +id: (swc_eng_001926-swc_eng_001926) +Scores: (#C #S #D #I) 1 4 0 0 +REF: COMMAND was NAMED AFTER THE +HYP: MAND was NAIED AFTR T +Eval: S S S S + +Speaker sentences 751: swc_eng_001927 #utts: 1 +id: (swc_eng_001927-swc_eng_001927) +Scores: (#C #S #D #I) 0 2 0 0 +REF: ARTIFICIAL INTELLIGENCE +HYP: ARDOFIHAL NTELIGENCS +Eval: S S + +Speaker sentences 752: swc_eng_001928 #utts: 1 +id: (swc_eng_001928-swc_eng_001928) +Scores: (#C #S #D #I) 3 1 0 0 +REF: and is the REIGNING +HYP: and is the RAING +Eval: S + +Speaker sentences 753: swc_eng_001929 #utts: 1 +id: (swc_eng_001929-swc_eng_001929) +Scores: (#C #S #D #I) 1 3 1 0 +REF: PER CENT of THE POPULATION +HYP: *** PRSE of TH OPILATION +Eval: D S S S + +Speaker sentences 754: swc_eng_001930 #utts: 1 +id: (swc_eng_001930-swc_eng_001930) +Scores: (#C #S #D #I) 1 4 1 0 +REF: CHIEF AREAS of SHOE POLISH SALES +HYP: CHOE ARIES of **** SOUPLICH SELS +Eval: S S D S S + +Speaker sentences 755: swc_eng_001931 #utts: 1 +id: (swc_eng_001931-swc_eng_001931) +Scores: (#C #S #D #I) 1 2 0 0 +REF: IMPOSED by LAW +HYP: INPOSE by LA +Eval: S S + +Speaker sentences 756: swc_eng_001932 #utts: 1 +id: (swc_eng_001932-swc_eng_001932) +Scores: (#C #S #D #I) 2 4 0 1 +REF: ******** REFERENCES to the RULING COALITION GOVERNMENT +HYP: REFRONCE ISES to the ROLING CORLITION GOVBERMEN +Eval: I S S S S + +Speaker sentences 757: swc_eng_001933 #utts: 1 +id: (swc_eng_001933-swc_eng_001933) +Scores: (#C #S #D #I) 1 3 0 0 +REF: SPECIES of GLIDING POSSUM +HYP: SPAHES of GLDING POSM +Eval: S S S + +Speaker sentences 758: swc_eng_001934 #utts: 1 +id: (swc_eng_001934-swc_eng_001934) +Scores: (#C #S #D #I) 4 2 1 0 +REF: BASED on the PREVIOUS STRATEGY of play +HYP: BACSED on the ******** PREVISSTRADGEY of play +Eval: S D S + +Speaker sentences 759: swc_eng_001935 #utts: 1 +id: (swc_eng_001935-swc_eng_001935) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** AND IDEALISTIC ASPIRATIONS +HYP: AD ID DULISTC ASPERATIONE +Eval: I S S S + +Speaker sentences 760: swc_eng_001936 #utts: 1 +id: (swc_eng_001936-swc_eng_001936) +Scores: (#C #S #D #I) 1 4 0 0 +REF: PROFESSIONALS and HOME RECORDING ENTHUSIASTS +HYP: PERFETONLS and HOM RECOARING OTHUSIASTS +Eval: S S S S + +Speaker sentences 761: swc_eng_001937 #utts: 1 +id: (swc_eng_001937-swc_eng_001937) +Scores: (#C #S #D #I) 0 2 0 1 +REF: *** FAMILY ELAPIDAE +HYP: HEM TH OLPODAY +Eval: I S S + +Speaker sentences 762: swc_eng_001938 #utts: 1 +id: (swc_eng_001938-swc_eng_001938) +Scores: (#C #S #D #I) 0 12 0 0 +REF: THAN A QUARTER OF PEOPLE WITH A PREVIOUS SAH MAY DEVELOP HYPOPITUITARISM +HYP: NOCORTE OF PEPL WITHE PREAVIS ES AY AGCH MAD DVELOU HIG POPETHURTRISN +Eval: S S S S S S S S S S S S + +Speaker sentences 763: swc_eng_001939 #utts: 1 +id: (swc_eng_001939-swc_eng_001939) +Scores: (#C #S #D #I) 1 4 0 0 +REF: DIVIDED into THREE FAMILIES THAT +HYP: DIVIRDED into THRY FAMLYES THA +Eval: S S S S + +Speaker sentences 764: swc_eng_001940 #utts: 1 +id: (swc_eng_001940-swc_eng_001940) +Scores: (#C #S #D #I) 0 6 0 0 +REF: SHOWED SLIGHT INTEREST IN RELEASING CASSETTES +HYP: HOD SLIT INTREST AN RLEACING OSET +Eval: S S S S S S + +Speaker sentences 765: swc_eng_001941 #utts: 1 +id: (swc_eng_001941-swc_eng_001941) +Scores: (#C #S #D #I) 1 4 1 1 +REF: **** FAMILIAR ENOUGH TO have COMMON NAMES +HYP: THAT A HRMIR ANOUT have ****** COMEN +Eval: I S S S D S + +Speaker sentences 766: swc_eng_001942 #utts: 1 +id: (swc_eng_001942-swc_eng_001942) +Scores: (#C #S #D #I) 0 4 0 0 +REF: IN TWO THOUSAND SIX +HYP: AN TOW HUSEND SAICE +Eval: S S S S + +Speaker sentences 767: swc_eng_001943 #utts: 1 +id: (swc_eng_001943-swc_eng_001943) +Scores: (#C #S #D #I) 1 6 1 0 +REF: SHOESHINE BOYS ARE KNOWN as BOOT POLISH BOYS +HYP: SHWO HINE BORYES ARNON as **** BUT PLIS +Eval: S S S S D S S + +Speaker sentences 768: swc_eng_001944 #utts: 1 +id: (swc_eng_001944-swc_eng_001944) +Scores: (#C #S #D #I) 4 4 0 0 +REF: the CAUSE is RUPTURE of a CEREBRAL ANEURYSM +HYP: the OSE is RUHTHER of a SERIBLE ANOURISTM +Eval: S S S S + +Speaker sentences 769: swc_eng_001945 #utts: 1 +id: (swc_eng_001945-swc_eng_001945) +Scores: (#C #S #D #I) 2 6 0 0 +REF: most OF the MAJOR U S MUSIC COMPANIES +HYP: most O the MAGR YOU ESTS MUS ICOMNYES +Eval: S S S S S S + +Speaker sentences 770: swc_eng_001946 #utts: 1 +id: (swc_eng_001946-swc_eng_001946) +Scores: (#C #S #D #I) 7 12 1 1 +REF: ONE STEREO pair or ONE MONOPHONIC track is PLAYED or RECORDED WHEN the TAPE IS moving ** IN ONE DIRECTION AND +HYP: WEN STARRIOP pair or WNE MONOFONIC track is PLAE or ******** RECORTEDHN the THAPE S moving AN ON DURECTION AN T +Eval: S S S S S D S S S I S S S S + +Speaker sentences 771: swc_eng_001947 #utts: 1 +id: (swc_eng_001947-swc_eng_001947) +Scores: (#C #S #D #I) 1 3 1 0 +REF: WHERE ITS early FORM IN +HYP: ***** HITCD early FORME AN +Eval: D S S S + +Speaker sentences 772: swc_eng_001948 #utts: 1 +id: (swc_eng_001948-swc_eng_001948) +Scores: (#C #S #D #I) 0 3 0 0 +REF: A STRATEGIC PHILOSOPHER +HYP: STER TEAGIK FOLOSSOVER +Eval: S S S + +Speaker sentences 773: swc_eng_001949 #utts: 1 +id: (swc_eng_001949-swc_eng_001949) +Scores: (#C #S #D #I) 1 4 0 1 +REF: ******** POSITIONING ADVANTAGES DURING the GAME +HYP: OSISIONG T BANTGCHES TDRN the GAE +Eval: I S S S S + +Speaker sentences 774: swc_eng_001950 #utts: 1 +id: (swc_eng_001950-swc_eng_001950) +Scores: (#C #S #D #I) 0 2 1 0 +REF: NEW SOUTH WALES +HYP: *** DO SEAUTWHELS +Eval: D S S + +Speaker sentences 775: swc_eng_001951 #utts: 1 +id: (swc_eng_001951-swc_eng_001951) +Scores: (#C #S #D #I) 2 4 0 0 +REF: DISPOSAL over his OWN BIOLOGICAL NATURE +HYP: ESPOSL over his ON BYILOUGICAL ATER +Eval: S S S S + +Speaker sentences 776: swc_eng_001952 #utts: 1 +id: (swc_eng_001952-swc_eng_001952) +Scores: (#C #S #D #I) 1 8 0 1 +REF: ** REPRODUCTIVE RIGHTS OR EXERT UNDUE PRESSURES on PROSPECTIVE PARENTS +HYP: RE PEROUCDTOF RIHTES HO CSART UNDO PRESTHERS on PROSPECTED PARNC +Eval: I S S S S S S S S + +Speaker sentences 777: swc_eng_001953 #utts: 1 +id: (swc_eng_001953-swc_eng_001953) +Scores: (#C #S #D #I) 0 4 0 0 +REF: STILL ANCIENT IN ORIGIN +HYP: TIL ANCHAND Y NORGEON +Eval: S S S S + +Speaker sentences 778: swc_eng_001954 #utts: 1 +id: (swc_eng_001954-swc_eng_001954) +Scores: (#C #S #D #I) 0 3 0 2 +REF: **** *** RASTAPOPOULOSS HIRED GUN +HYP: RAST POF OLOUS HAD GON +Eval: I I S S S + +Speaker sentences 779: swc_eng_001955 #utts: 1 +id: (swc_eng_001955-swc_eng_001955) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ** IN TWO THOUSAND TWO +HYP: ND TO THIS AND TO +Eval: I S S S S + +Speaker sentences 780: swc_eng_001956 #utts: 1 +id: (swc_eng_001956-swc_eng_001956) +Scores: (#C #S #D #I) 2 4 1 1 +REF: FOR EXAMPLE IF the PLAYER has *** ONLY +HYP: *** TFORENTAPL F the PLAR has ONL D +Eval: D S S S I S + +Speaker sentences 781: swc_eng_001957 #utts: 1 +id: (swc_eng_001957-swc_eng_001957) +Scores: (#C #S #D #I) 3 6 0 2 +REF: SUFFERED a ****** SUBARACHNOID HEMORRHAGE have COGNITIVE IMPAIRMENT that * AFFECTS +HYP: SUERD a SUBRAK NOD HEMIRG have COLDNICO INMPAREMENT that O FET +Eval: S I S S S S I S + +Speaker sentences 782: swc_eng_001958 #utts: 1 +id: (swc_eng_001958-swc_eng_001958) +Scores: (#C #S #D #I) 0 3 0 0 +REF: PROVIDED PROGNOSTIC DATA +HYP: ROVIDED RONOSTIN DATEA +Eval: S S S + +Speaker sentences 783: swc_eng_001959 #utts: 1 +id: (swc_eng_001959-swc_eng_001959) +Scores: (#C #S #D #I) 2 5 0 0 +REF: WHO had ANEURYSMS DETECTED by OTHER MEANS +HYP: O had ANURISNS DETECTID by THE MANES +Eval: S S S S S + +Speaker sentences 784: swc_eng_001960 #utts: 1 +id: (swc_eng_001960-swc_eng_001960) +Scores: (#C #S #D #I) 0 7 0 0 +REF: LIFESTYLES DESIGNED TO IMPROVE HEALTH AND LONGEVITY +HYP: LIHSTIL DESINE T IM PROE HELTHAN NGEVITY +Eval: S S S S S S S + +Speaker sentences 785: swc_eng_001961 #utts: 1 +id: (swc_eng_001961-swc_eng_001961) +Scores: (#C #S #D #I) 2 4 1 0 +REF: had MORE SOPHISTICATED END of TAPE PREDICTION +HYP: had **** MORSOFHISTICATED AND of TAY PRODCTION +Eval: D S S S S + +Speaker sentences 786: swc_eng_001962 #utts: 1 +id: (swc_eng_001962-swc_eng_001962) +Scores: (#C #S #D #I) 0 1 0 2 +REF: ** ****** DEHUMANIZATION +HYP: DE HOUMEN ISATION +Eval: I I S + +Speaker sentences 787: swc_eng_001963 #utts: 1 +id: (swc_eng_001963-swc_eng_001963) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ****** SPECIES INCLUDE FRESHWATER LAMPREYS +HYP: PACHYS IN LD FREHWOATR LAMPROACES +Eval: I S S S S + +Speaker sentences 788: swc_eng_001964 #utts: 1 +id: (swc_eng_001964-swc_eng_001964) +Scores: (#C #S #D #I) 0 2 0 0 +REF: FIRST ANGIOGRAM +HYP: FOST ANDYOUGREAM +Eval: S S + +Speaker sentences 789: swc_eng_001965 #utts: 1 +id: (swc_eng_001965-swc_eng_001965) +Scores: (#C #S #D #I) 1 2 1 0 +REF: THE FREE ENCYCLOPEDIA at +HYP: *** HE FRYINSDKLPEADIA at +Eval: D S S + +Speaker sentences 790: swc_eng_001966 #utts: 1 +id: (swc_eng_001966-swc_eng_001966) +Scores: (#C #S #D #I) 1 4 0 1 +REF: *** THEREFORE MEDICAL IMAGING is GENERALLY +HYP: THE FOR METICL INAGEING is DENRL +Eval: I S S S S + +Speaker sentences 791: swc_eng_001967 #utts: 1 +id: (swc_eng_001967-swc_eng_001967) +Scores: (#C #S #D #I) 5 4 1 0 +REF: SPECIESIST THE EXCLUSION of non HUMAN and part human ANIMALS +HYP: ********** PEASIIST THECLUION of non CHOUMEN and part human ANAMLS +Eval: D S S S S + +Speaker sentences 792: swc_eng_001968 #utts: 1 +id: (swc_eng_001968-swc_eng_001968) +Scores: (#C #S #D #I) 1 8 0 2 +REF: IN PEOPLE WHO HAD PREVIOUSLY SUFFERED a ****** *** SUBARACHNOID HEMORRHAGE +HYP: ND PEABL HO AD RVISLY SUFED a SUBRAC NOT HEM RIG +Eval: S S S S S S I I S S + +Speaker sentences 793: swc_eng_001969 #utts: 1 +id: (swc_eng_001969-swc_eng_001969) +Scores: (#C #S #D #I) 2 8 0 1 +REF: CLASSIFIED as *** EITHER ENDANGERED or THREATENED UNDER THE EPBC ACT +HYP: LSIFID as THE IN DANGED or THRETOND ANDO THEY PE BE +Eval: S I S S S S S S S + +Speaker sentences 794: swc_eng_001970 #utts: 1 +id: (swc_eng_001970-swc_eng_001970) +Scores: (#C #S #D #I) 0 6 0 1 +REF: * AND ATTORNEY GENERAL PARKER WATKINS HARDIN +HYP: A ATERNY JENERL PARCER WAT COENS HARDN +Eval: I S S S S S S + +Speaker sentences 795: swc_eng_001971 #utts: 1 +id: (swc_eng_001971-swc_eng_001971) +Scores: (#C #S #D #I) 1 1 0 0 +REF: but TYPICALLY +HYP: but TIPICKLY +Eval: S + +Speaker sentences 796: swc_eng_001972 #utts: 1 +id: (swc_eng_001972-swc_eng_001972) +Scores: (#C #S #D #I) 4 5 4 0 +REF: WHICH IN TURN fed the SIGNAL to THE HEAD OF the CASSETTE DECK +HYP: ***** ** HICHINTRE fed the SINL to *** THEHEAD O the ******** COSSA +Eval: D D S S D S S D S + +Speaker sentences 797: swc_eng_001973 #utts: 1 +id: (swc_eng_001973-swc_eng_001973) +Scores: (#C #S #D #I) 0 6 0 1 +REF: ***** WITHIN THEIR OWN CONVENTIONALLY EXPECTED LIFETIMES +HYP: HTHIN THER ON CONVENIONLY ECSPECTED LIFE TIMEMS +Eval: I S S S S S S + +Speaker sentences 798: swc_eng_001974 #utts: 1 +id: (swc_eng_001974-swc_eng_001974) +Scores: (#C #S #D #I) 0 2 0 0 +REF: SUBSTANTIAL STRAIN +HYP: OBSTANHEL STRAIEN +Eval: S S + +Speaker sentences 799: swc_eng_001975 #utts: 1 +id: (swc_eng_001975-swc_eng_001975) +Scores: (#C #S #D #I) 0 5 0 1 +REF: ********** TWENTIETH CENTURY KENTUCKY CONGRESSMAN JOHN +HYP: TWENYHAITH SENTRY CONTUCY CONGRS MEN JOAN +Eval: I S S S S S + +Speaker sentences 800: swc_eng_001976 #utts: 1 +id: (swc_eng_001976-swc_eng_001976) +Scores: (#C #S #D #I) 0 4 1 0 +REF: NINETY PER CENT ARE ENDEMIC +HYP: ****** NOTHY POSENT OR NDIMA +Eval: D S S S S + +Speaker sentences 801: swc_eng_001977 #utts: 1 +id: (swc_eng_001977-swc_eng_001977) +Scores: (#C #S #D #I) 0 4 0 0 +REF: HUNTING WITH LEAD SHOT +HYP: HINTING WIT LED SHOG +Eval: S S S S + +Speaker sentences 802: swc_eng_001978 #utts: 1 +id: (swc_eng_001978-swc_eng_001978) +Scores: (#C #S #D #I) 0 2 0 0 +REF: TWENTY THIRTEEN +HYP: WENY THRTAEN +Eval: S S + +Speaker sentences 803: swc_eng_001979 #utts: 1 +id: (swc_eng_001979-swc_eng_001979) +Scores: (#C #S #D #I) 2 8 2 0 +REF: ALTHOUGH SEVEN PER CENT of the WORLDS BATS SPECIES LIVE IN AUSTRALIA +HYP: OR THY SEVON PRSENT of the ****** **** LD PAT SPACHES LVINUSTRALIA +Eval: S S S S D D S S S S + +Speaker sentences 804: swc_eng_001980 #utts: 1 +id: (swc_eng_001980-swc_eng_001980) +Scores: (#C #S #D #I) 0 7 1 0 +REF: KARPOVS REIGN FINALLY ENDED IN NINETEEN EIGHTY FIVE +HYP: ******* URPOES RAIN FINLY AND INANTEN ADY FV +Eval: D S S S S S S S + +Speaker sentences 805: swc_eng_001981 #utts: 1 +id: (swc_eng_001981-swc_eng_001981) +Scores: (#C #S #D #I) 1 5 0 1 +REF: *** WHILE SOME TRANSHUMANISTS TAKE an ABSTRACT +HYP: WIL SOE TRE HUMINIS TIK an APSTRCT +Eval: I S S S S S + +Speaker sentences 806: swc_eng_001982 #utts: 1 +id: (swc_eng_001982-swc_eng_001982) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ******* WRITE PROTECTION +HYP: PANTHRE RIAT PRTECTION +Eval: I S S + +Speaker sentences 807: swc_eng_001983 #utts: 1 +id: (swc_eng_001983-swc_eng_001983) +Scores: (#C #S #D #I) 3 7 0 2 +REF: ****** GRAPH ISOMORPHISM PROBLEM is the COMPUTATIONAL PROBLEM of *** DETERMINING WHETHER +HYP: GRAYCS MOR FISE PROBLE is the COMPETATINT PROBLE of THE TERMINING WHTH +Eval: I S S S S S I S S + +Speaker sentences 808: swc_eng_001984 #utts: 1 +id: (swc_eng_001984-swc_eng_001984) +Scores: (#C #S #D #I) 0 5 0 1 +REF: ** FURTHER RESTRICT OUR CONCEPT OF +HYP: OR THE RESTICT AUR CONCSEPT O +Eval: I S S S S S + +Speaker sentences 809: swc_eng_001985 #utts: 1 +id: (swc_eng_001985-swc_eng_001985) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ** THOSE WHO SURVIVE HOSPITALIZATION +HYP: HE HO OFBI E OUSTPTLISATION +Eval: I S S S S + +Speaker sentences 810: swc_eng_001986 #utts: 1 +id: (swc_eng_001986-swc_eng_001986) +Scores: (#C #S #D #I) 4 6 0 1 +REF: some PROTECTION of ** UNCERTAIN SIGNIFICANCE is CONFERRED by CAUCASIAN ETHNICITY +HYP: some PRTECTION of UN ERTAN SIGNIFICENCE is ONFIRD by COLKCAIAN ATHNICITY +Eval: S I S S S S S + +Speaker sentences 811: swc_eng_001987 #utts: 1 +id: (swc_eng_001987-swc_eng_001987) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ****** COASTAL LAGOONS +HYP: OASTAL A GONS +Eval: I S S + +Speaker sentences 812: swc_eng_001988 #utts: 1 +id: (swc_eng_001988-swc_eng_001988) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** AND COGNITIVE ENHANCEMENT +HYP: ND COWGDTIVE IN HANCSE +Eval: I S S S + +Speaker sentences 813: swc_eng_001989 #utts: 1 +id: (swc_eng_001989-swc_eng_001989) +Scores: (#C #S #D #I) 5 4 1 0 +REF: THE EIGHTH rank and be PROMOTED to an ALLOWED PIECE +HYP: VANCSTDTH ATH rank and be PREMOUDTD to an ******* LO +Eval: S S S D S + +Speaker sentences 814: swc_eng_001990 #utts: 1 +id: (swc_eng_001990-swc_eng_001990) +Scores: (#C #S #D #I) 3 2 1 1 +REF: ** DRAWBACK of coiling is THE POSSIBILITY +HYP: DR BACK of coiling is *** THEPSEAILITY +Eval: I S D S + +Speaker sentences 815: swc_eng_001991 #utts: 1 +id: (swc_eng_001991-swc_eng_001991) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ******** INDICATES A SUBARACHNOID HEMORRHAGE +HYP: INDECATE S SUBRAC NOLD HEMRIGE +Eval: I S S S S + +Speaker sentences 816: swc_eng_001992 #utts: 1 +id: (swc_eng_001992-swc_eng_001992) +Scores: (#C #S #D #I) 1 1 0 0 +REF: DAMAGED portion +HYP: DEAMISET portion +Eval: S + +Speaker sentences 817: swc_eng_001993 #utts: 1 +id: (swc_eng_001993-swc_eng_001993) +Scores: (#C #S #D #I) 1 4 0 3 +REF: ADOPTION of * ***** *** EUGENIC ENHANCEMENT TECHNOLOGIES +HYP: NDOPTIO of Y JEDIK AND HANCSMENTEC HAL HES +Eval: S I I I S S S + +Speaker sentences 818: swc_eng_001994 #utts: 1 +id: (swc_eng_001994-swc_eng_001994) +Scores: (#C #S #D #I) 2 4 0 0 +REF: POLISH ON his HORSE AND wagon +HYP: PLISHE N his HOURE AN wagon +Eval: S S S S + +Speaker sentences 819: swc_eng_001995 #utts: 1 +id: (swc_eng_001995-swc_eng_001995) +Scores: (#C #S #D #I) 1 3 0 0 +REF: AND the NEXT CHAMPION +HYP: N the ECT HARMBBEN +Eval: S S S + +Speaker sentences 820: swc_eng_001996 #utts: 1 +id: (swc_eng_001996-swc_eng_001996) +Scores: (#C #S #D #I) 1 4 0 0 +REF: BROTHER of AUTHOR ALDOUS HUXLEY +HYP: RTHR of OFER ALIS UCLY +Eval: S S S S + +Speaker sentences 821: swc_eng_001997 #utts: 1 +id: (swc_eng_001997-swc_eng_001997) +Scores: (#C #S #D #I) 0 5 0 1 +REF: *** WORLD CHAMPION NINETEEN TWENTY ONE +HYP: LED THAP IN HANCTIN TWORNY ON +Eval: I S S S S S + +Speaker sentences 822: swc_eng_001998 #utts: 1 +id: (swc_eng_001998-swc_eng_001998) +Scores: (#C #S #D #I) 1 5 1 1 +REF: SUCH AS QUANTUM COMPUTATION and ***** RANDOMIZED ALGORITHMS +HYP: **** SOUCHAS ONTHM CLPETATION and RANDI MYSD LLGRTHEMS +Eval: D S S S I S S + +Speaker sentences 823: swc_eng_001999 #utts: 1 +id: (swc_eng_001999-swc_eng_001999) +Scores: (#C #S #D #I) 0 3 0 0 +REF: EIGHTEEN NINETY NINE +HYP: ATY NHADE NOIEN +Eval: S S S + +Speaker sentences 824: swc_eng_002000 #utts: 1 +id: (swc_eng_002000-swc_eng_002000) +Scores: (#C #S #D #I) 6 7 2 1 +REF: WAS SHOWN by ladner that if ** P ≠ N P then THERE exist PROBLEMS IN +HYP: *** SHONE by ladner that if PE AIS NOT YCOLD ANPEY then THER exist ******** PROVBLEMS +Eval: D S I S S S S S D S + +Speaker sentences 825: swc_eng_002001 #utts: 1 +id: (swc_eng_002001-swc_eng_002001) +Scores: (#C #S #D #I) 1 2 1 0 +REF: THE compact DISC GREW +HYP: HE compact **** DESTFRL +Eval: S D S + +Speaker sentences 826: swc_eng_002002 #utts: 1 +id: (swc_eng_002002-swc_eng_002002) +Scores: (#C #S #D #I) 0 2 1 0 +REF: GREY GOO SCENARIO +HYP: **** GRAY GOUOSONAERIL +Eval: D S S + +Speaker sentences 827: swc_eng_002003 #utts: 1 +id: (swc_eng_002003-swc_eng_002003) +Scores: (#C #S #D #I) 2 2 0 0 +REF: was RENDERED as AJEDREZ +HYP: was WENDERED as IADGEAS +Eval: S S + +Speaker sentences 828: swc_eng_002004 #utts: 1 +id: (swc_eng_002004-swc_eng_002004) +Scores: (#C #S #D #I) 1 4 0 1 +REF: *** SAH OR to ANOTHER CAUSE +HYP: SAY AECH ORD to ANOTHE COLS +Eval: I S S S S + +Speaker sentences 829: swc_eng_002005 #utts: 1 +id: (swc_eng_002005-swc_eng_002005) +Scores: (#C #S #D #I) 1 2 0 0 +REF: CONSTITUENCY of FAVERSHAM +HYP: COCTITUHNCY of FAVISIOM +Eval: S S + +Speaker sentences 830: voxforge_eng_000874 #utts: 1 +id: (voxforge_eng_000874-voxforge_eng_000874) +Scores: (#C #S #D #I) 2 7 0 0 +REF: the FOURTH AND FIFTH DAYS PASSED WITHOUT any DEVELOPMENTS +HYP: the FORTH N FIT DAYGES PASTE WOHOUT any ELIMENTCS +Eval: S S S S S S S + +Speaker sentences 831: voxforge_eng_000875 #utts: 1 +id: (voxforge_eng_000875-voxforge_eng_000875) +Scores: (#C #S #D #I) 3 1 0 1 +REF: * they KNOW the report +HYP: T they NO the report +Eval: I S + +Speaker sentences 832: voxforge_eng_000876 #utts: 1 +id: (voxforge_eng_000876-voxforge_eng_000876) +Scores: (#C #S #D #I) 2 6 0 0 +REF: SUCH THINGS HAD OCCURRED BEFORE he told PHILIP +HYP: SOCH THING HA ACORD BEFEOR he told FILAP +Eval: S S S S S S + +Speaker sentences 833: voxforge_eng_000877 #utts: 1 +id: (voxforge_eng_000877-voxforge_eng_000877) +Scores: (#C #S #D #I) 4 5 0 0 +REF: they only had a LITTLE THIRTY THOUSAND DOLLAR FIRE +HYP: they only had a LEITLE THIRDY THOSEN DOLAER FIER +Eval: S S S S S + +Speaker sentences 834: voxforge_eng_000878 #utts: 1 +id: (voxforge_eng_000878-voxforge_eng_000878) +Scores: (#C #S #D #I) 4 3 0 0 +REF: i AM going to get IT OUT +HYP: i AME going to get AT OUWDETH +Eval: S S S + +Speaker sentences 835: voxforge_eng_000879 #utts: 1 +id: (voxforge_eng_000879-voxforge_eng_000879) +Scores: (#C #S #D #I) 4 4 0 1 +REF: *** OUTWARDLY he MAINTAINED a CALM and smiling ASPECT +HYP: OUD DODLY he MAIENTANED a CORME and smiling ASSPECT +Eval: I S S S S + +Speaker sentences 836: voxforge_eng_000880 #utts: 1 +id: (voxforge_eng_000880-voxforge_eng_000880) +Scores: (#C #S #D #I) 1 6 0 1 +REF: *** JOAN LOOKED TRIUMPHANTLY at SHELDON WHO BOWED +HYP: JON LOK RI IUMPFENTLY at SHELDEN HO BOURD +Eval: I S S S S S S + +Speaker sentences 837: voxforge_eng_000883 #utts: 1 +id: (voxforge_eng_000883-voxforge_eng_000883) +Scores: (#C #S #D #I) 1 4 0 1 +REF: COME on * DEL MAR CHALLENGED +HYP: COM on H DILD MART TALENCTST +Eval: S I S S S + +Speaker sentences 838: voxforge_eng_000884 #utts: 1 +id: (voxforge_eng_000884-voxforge_eng_000884) +Scores: (#C #S #D #I) 8 4 0 0 +REF: it was beating and WAITING in the AMBUSH of THOSE BLACK pits +HYP: it was beating and WATING in the AMBOSH of THOS PLACKE pits +Eval: S S S S + +Speaker sentences 839: voxforge_eng_000885 #utts: 1 +id: (voxforge_eng_000885-voxforge_eng_000885) +Scores: (#C #S #D #I) 2 5 2 0 +REF: LET THEM GO OUT and EAT WITH my BOYS +HYP: *** ALTHE GOE OT and *** EAPWI my BOS +Eval: D S S S D S S + +Speaker sentences 840: voxforge_eng_000886 #utts: 1 +id: (voxforge_eng_000886-voxforge_eng_000886) +Scores: (#C #S #D #I) 4 7 0 2 +REF: he WENT DOWN in ***** ***** MIDSTREAM SEARCHING the SHADOWS of BOTH SHORES +HYP: he OWEINED DON in WEINS DREMN Y SARCTIONG the SHAHDOS of POL SORS +Eval: S S I I S S S S S + +Speaker sentences 841: voxforge_eng_000887 #utts: 1 +id: (voxforge_eng_000887-voxforge_eng_000887) +Scores: (#C #S #D #I) 3 9 0 0 +REF: i JUST DO APPRECIATE IT WITHOUT BEING ABLE to EXPRESS my FEELINGS +HYP: i OST DOA PRESHAT TWIT OUT BE AE to CPESE my FELINGS +Eval: S S S S S S S S S + +Speaker sentences 842: voxforge_eng_000888 #utts: 1 +id: (voxforge_eng_000888-voxforge_eng_000888) +Scores: (#C #S #D #I) 3 5 0 0 +REF: she DOESNT KNOW what he IS TALKING ABOUT +HYP: she DOSANT NO what he AS TOKING ABOUWT +Eval: S S S S S + +Speaker sentences 843: voxforge_eng_000889 #utts: 1 +id: (voxforge_eng_000889-voxforge_eng_000889) +Scores: (#C #S #D #I) 0 5 1 0 +REF: YOUR FATHERS FIFTH COMMAND HE NODDED +HYP: **** YOR FARTHERS FIFT COMAND HENATED +Eval: D S S S S S + +Speaker sentences 844: voxforge_eng_000890 #utts: 1 +id: (voxforge_eng_000890-voxforge_eng_000890) +Scores: (#C #S #D #I) 1 5 0 0 +REF: DONT YOU SEE i HATE YOU +HYP: E DON OSEY i HAE YOE +Eval: S S S S S + +Speaker sentences 845: voxforge_eng_000891 #utts: 1 +id: (voxforge_eng_000891-voxforge_eng_000891) +Scores: (#C #S #D #I) 5 7 3 0 +REF: A LITTLE WARM but not AT ALL ASTONISHED eating MELONS and THROWING THE RIND about +HYP: * ALITLE WORME but not ** AL STONISHEDE eating MELNS and ******** THOING THRIND about +Eval: D S S D S S S D S S + +Speaker sentences 846: voxforge_eng_000892 #utts: 1 +id: (voxforge_eng_000892-voxforge_eng_000892) +Scores: (#C #S #D #I) 2 3 0 0 +REF: THIS is a GREAT PARTY +HYP: THISE is a GRAT PORDYE +Eval: S S S + +Speaker sentences 847: voxforge_eng_000893 #utts: 1 +id: (voxforge_eng_000893-voxforge_eng_000893) +Scores: (#C #S #D #I) 3 2 0 1 +REF: the boy GREW and ********* PROSPERED +HYP: the boy GRO and PROSPERET TO +Eval: S I S + +Speaker sentences 848: voxforge_eng_000894 #utts: 1 +id: (voxforge_eng_000894-voxforge_eng_000894) +Scores: (#C #S #D #I) 7 10 0 2 +REF: *** UNLESS such LETTERS be patent that they MAY BE READ to them ** AND WITHALL SEALED OR TESTIFIED +HYP: AND LS such LETERS be patent that they AY B RED to them AN WHITH LE STELE HOR TESTIFIGED +Eval: I S S S S S I S S S S S + +Speaker sentences 849: voxforge_eng_000895 #utts: 1 +id: (voxforge_eng_000895-voxforge_eng_000895) +Scores: (#C #S #D #I) 4 7 0 0 +REF: how COULD a WOMAN DARE to VENTURE WHERE so MANY EXPLORERS +HYP: how COLD a WOMEN DER to VENTE WER so ANY EXTPORARS +Eval: S S S S S S S + +Speaker sentences 850: voxforge_eng_000896 #utts: 1 +id: (voxforge_eng_000896-voxforge_eng_000896) +Scores: (#C #S #D #I) 1 4 0 0 +REF: he READ HIS FRAGMENTS ALOUD +HYP: he READE HI FRAGINCE ALOAED +Eval: S S S S + +Speaker sentences 851: voxforge_eng_000897 #utts: 1 +id: (voxforge_eng_000897-voxforge_eng_000897) +Scores: (#C #S #D #I) 5 3 0 0 +REF: but HOW ARE you going to DO it +HYP: but HOWE AR you going to DE it +Eval: S S S + +Speaker sentences 852: voxforge_eng_000898 #utts: 1 +id: (voxforge_eng_000898-voxforge_eng_000898) +Scores: (#C #S #D #I) 5 3 1 0 +REF: how do you WANT to get AWAY WITH THIS +HYP: how do you WON to get **** WAY WITHISE +Eval: S D S S + +Speaker sentences 853: voxforge_eng_000899 #utts: 1 +id: (voxforge_eng_000899-voxforge_eng_000899) +Scores: (#C #S #D #I) 2 3 0 1 +REF: WILL we **** EVER FORGET it +HYP: WIL we AVER FOM GET it +Eval: S I S S + +Speaker sentences 854: voxforge_eng_000900 #utts: 1 +id: (voxforge_eng_000900-voxforge_eng_000900) +Scores: (#C #S #D #I) 3 7 1 0 +REF: FROM my EARLIEST RECOLLECTION my SLEEP WAS A PERIOD of TERROR +HYP: FR my ARLIS RECLECTION my ***** SLE WS PERGATD of HERE +Eval: S S S D S S S S + +Speaker sentences 855: voxforge_eng_000901 #utts: 1 +id: (voxforge_eng_000901-voxforge_eng_000901) +Scores: (#C #S #D #I) 1 5 0 5 +REF: ** * ** ***** **** WHY DOGGONE you ALL SHAKE AGAIN +HYP: MY O IS OSHER WHIY E DOLON you WLL SHAKD GANM +Eval: I I I I I S S S S S + +Speaker sentences 856: voxforge_eng_000902 #utts: 1 +id: (voxforge_eng_000902-voxforge_eng_000902) +Scores: (#C #S #D #I) 1 3 1 1 +REF: IT IS the ******* NEAREST REFUGE +HYP: ** IDEAVT the NEADIST REOFUG HYEWII +Eval: D S I S S + +Speaker sentences 857: voxforge_eng_000903 #utts: 1 +id: (voxforge_eng_000903-voxforge_eng_000903) +Scores: (#C #S #D #I) 4 5 0 0 +REF: his SLIM HANDS GRIPPED the EDGES of the TABLE +HYP: his SLIME HANE CREPT the EADGES of the TABL +Eval: S S S S S + +Speaker sentences 858: voxforge_eng_000904 #utts: 1 +id: (voxforge_eng_000904-voxforge_eng_000904) +Scores: (#C #S #D #I) 1 4 0 2 +REF: *** WHITE LEGHORNS said *** MRS MORTIMER +HYP: WHD LA ORN said MIS MORTO ARM +Eval: I S S I S S + +Speaker sentences 859: voxforge_eng_000905 #utts: 1 +id: (voxforge_eng_000905-voxforge_eng_000905) +Scores: (#C #S #D #I) 2 7 3 0 +REF: it TOOK HIM HALF AN HOUR TO REACH THE EDGE OF it +HYP: it **** *** **** OK HI HAVEA WTO RCH HEAD O it +Eval: D D D S S S S S S S + +Speaker sentences 860: voxforge_eng_000906 #utts: 1 +id: (voxforge_eng_000906-voxforge_eng_000906) +Scores: (#C #S #D #I) 1 6 2 0 +REF: MARTHA WHERE DO WE stand ON THE CONTRACTUAL ISSUES +HYP: ****** MARTHEA WER D stand ** TH CONTRACTIOUALE ITSHOS +Eval: D S S S D S S S + +Speaker sentences 861: voxforge_eng_000907 #utts: 1 +id: (voxforge_eng_000907-voxforge_eng_000907) +Scores: (#C #S #D #I) 5 5 0 0 +REF: as to be UNDISTINGUISHABLE from the VAST WHITE PLAINS AROUND +HYP: as to be NDESTINGICHABLE from the VEAST WHYT PLAINED ROWND +Eval: S S S S S + +Speaker sentences 862: voxforge_eng_000908 #utts: 1 +id: (voxforge_eng_000908-voxforge_eng_000908) +Scores: (#C #S #D #I) 2 5 1 1 +REF: he *** WOULD DESTROY ALL things THAT ARE FIXED +HYP: he WOD DE STROY AL things **** HATER FICTDDT +Eval: I S S S D S S + +Speaker sentences 863: voxforge_eng_000909 #utts: 1 +id: (voxforge_eng_000909-voxforge_eng_000909) +Scores: (#C #S #D #I) 3 7 0 0 +REF: the RUSSIAN MUSIC PLAYER the COUNT was HER OBEDIENT SLAVE +HYP: the RUSION USIK PLEAER the CONT was HERO BEDIN SLAV +Eval: S S S S S S S + +Speaker sentences 864: voxforge_eng_000910 #utts: 1 +id: (voxforge_eng_000910-voxforge_eng_000910) +Scores: (#C #S #D #I) 6 3 0 1 +REF: to his SURPRISE her ANSWER was flat and ** UNCOMPROMISING +HYP: to his SUPRIHSE her ANTE was flat and UN COMPROMIYSING +Eval: S S I S + +Speaker sentences 865: voxforge_eng_000911 #utts: 1 +id: (voxforge_eng_000911-voxforge_eng_000911) +Scores: (#C #S #D #I) 2 2 0 1 +REF: * this SHOULD be INTERESTING +HYP: T this SOT be INTEROSTING +Eval: I S S + +Speaker sentences 866: voxforge_eng_000912 #utts: 1 +id: (voxforge_eng_000912-voxforge_eng_000912) +Scores: (#C #S #D #I) 6 2 0 1 +REF: i *** AM AFRAID i dont have much time +HYP: i AME A FRADE i dont have much time +Eval: I S S + +Speaker sentences 867: voxforge_eng_000913 #utts: 1 +id: (voxforge_eng_000913-voxforge_eng_000913) +Scores: (#C #S #D #I) 5 6 0 0 +REF: CHRISTMAS is an easy PROBLEM COMPARED WITH A POLYNESIAN giving feast +HYP: CRSMIS is an easy PROBLOME COMPRD W THE POLNATION giving feast +Eval: S S S S S S + +Speaker sentences 868: voxforge_eng_000914 #utts: 1 +id: (voxforge_eng_000914-voxforge_eng_000914) +Scores: (#C #S #D #I) 1 6 0 1 +REF: the ******* PLANTERS ARE ALREADY CONSIDERING THE MATTER +HYP: the PLANTOS OR ARDY CEN SIDERIN TH ATERHE +Eval: I S S S S S S + +Speaker sentences 869: voxforge_eng_000915 #utts: 1 +id: (voxforge_eng_000915-voxforge_eng_000915) +Scores: (#C #S #D #I) 0 5 0 0 +REF: JOAN CRIED WITH SHINING EYES +HYP: JON RIREDE WIT SHINAING EYSEH +Eval: S S S S S + +Speaker sentences 870: voxforge_eng_000916 #utts: 1 +id: (voxforge_eng_000916-voxforge_eng_000916) +Scores: (#C #S #D #I) 5 2 0 1 +REF: ** WHOEVER lived on the ranch did THAT +HYP: WO VER lived on the ranch did THATD +Eval: I S S + +Speaker sentences 871: voxforge_eng_000917 #utts: 1 +id: (voxforge_eng_000917-voxforge_eng_000917) +Scores: (#C #S #D #I) 5 3 0 0 +REF: we LEAVE the EVENTUALITY to time and LAW +HYP: we LEVE the VFVENCUOALITY to time and LORL +Eval: S S S + +Speaker sentences 872: voxforge_eng_000918 #utts: 1 +id: (voxforge_eng_000918-voxforge_eng_000918) +Scores: (#C #S #D #I) 6 6 1 2 +REF: * at the same TIME spears and ARROWS BEGAN TO FALL among * THE INVADERS +HYP: A at the same TING spears and ****** EROS BEGANTO FALLE among H IN BATERS +Eval: I S D S S S I S S + +Speaker sentences 873: voxforge_eng_000920 #utts: 1 +id: (voxforge_eng_000920-voxforge_eng_000920) +Scores: (#C #S #D #I) 3 3 0 0 +REF: it is MERELY the SIMPLE SUPERLATIVE +HYP: it is MEY the SIMPAL SOUPELITIFEF +Eval: S S S + +Speaker sentences 874: voxforge_eng_000921 #utts: 1 +id: (voxforge_eng_000921-voxforge_eng_000921) +Scores: (#C #S #D #I) 3 6 1 1 +REF: *** INSTEAD he ARRIVED on THE NIGHT OF THE SECOND day +HYP: IND STAID he ARIGVE on *** TH NOT OFTE SOCON day +Eval: I S S D S S S S + +Speaker sentences 875: voxforge_eng_000922 #utts: 1 +id: (voxforge_eng_000922-voxforge_eng_000922) +Scores: (#C #S #D #I) 6 5 0 1 +REF: in his ANXIETY and *********** SOLICITUDE and LOVE THEY did not COUNT +HYP: in his ANGSITY and SOLICSITOED E and LOVEFT THE did not COWNT +Eval: S I S S S S + +Speaker sentences 876: voxforge_eng_000923 #utts: 1 +id: (voxforge_eng_000923-voxforge_eng_000923) +Scores: (#C #S #D #I) 4 6 0 0 +REF: GOD BLESS i HOPE ILL go ON SEEING them forever +HYP: DGOD BLESSOM i HOPL IL go AND SING them forever +Eval: S S S S S S + +Speaker sentences 877: voxforge_eng_000924 #utts: 1 +id: (voxforge_eng_000924-voxforge_eng_000924) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** YOU WERE ENGAGED +HYP: YO WER IN GOAGED +Eval: I S S S + +Speaker sentences 878: voxforge_eng_000925 #utts: 1 +id: (voxforge_eng_000925-voxforge_eng_000925) +Scores: (#C #S #D #I) 3 9 0 2 +REF: THE LACE was of a ******** ** DELICATE IVORY COLOR FAINTLY TINTED WITH YELLOW +HYP: THER LATSES was of a TELICKET IV HERY COLOERE FRIAIN TOB TINTIN WHIT EAL +Eval: S S I I S S S S S S S + +Speaker sentences 879: voxforge_eng_000927 #utts: 1 +id: (voxforge_eng_000927-voxforge_eng_000927) +Scores: (#C #S #D #I) 4 6 0 0 +REF: it WAS the same WAY WITH OUR REVOLVERS and RIFLES +HYP: it WA the same WHAY WIT OL REVFALVEORS and RIFALS +Eval: S S S S S S + +Speaker sentences 880: voxforge_eng_000928 #utts: 1 +id: (voxforge_eng_000928-voxforge_eng_000928) +Scores: (#C #S #D #I) 1 6 2 0 +REF: THE KING had PROMISED TO ENQUIRE INTO THE MATTER +HYP: HE CING had ******** ** RMIST INCQUIREINTO TH ATER +Eval: S S D D S S S S + +Speaker sentences 881: voxforge_eng_000929 #utts: 1 +id: (voxforge_eng_000929-voxforge_eng_000929) +Scores: (#C #S #D #I) 0 4 0 2 +REF: ** *** DOES THAT LOOK GOOD +HYP: AE DOS TEN LOK GODTED T +Eval: I I S S S S + +Speaker sentences 882: voxforge_eng_000930 #utts: 1 +id: (voxforge_eng_000930-voxforge_eng_000930) +Scores: (#C #S #D #I) 10 2 1 0 +REF: for the first time in his life he was YEARNING for A SCRAP +HYP: for the first time in his life he was YURNING for * SGRAP +Eval: S D S + +Speaker sentences 883: voxforge_eng_000931 #utts: 1 +id: (voxforge_eng_000931-voxforge_eng_000931) +Scores: (#C #S #D #I) 8 4 0 0 +REF: i DEFY any man to get a SOLOMON ISLAND sore in CALIFORNIA +HYP: i DEFIG any man to get a SOLHAMWOT ILENCE sore in CELYFORNIER +Eval: S S S S + +Speaker sentences 884: voxforge_eng_000932 #utts: 1 +id: (voxforge_eng_000932-voxforge_eng_000932) +Scores: (#C #S #D #I) 7 5 0 0 +REF: her EYES SMILED TRUTH at him as he came UP the BANK +HYP: her IS SMULT TRU at him as he came OF the BANGK +Eval: S S S S S + +Speaker sentences 885: voxforge_eng_000933 #utts: 1 +id: (voxforge_eng_000933-voxforge_eng_000933) +Scores: (#C #S #D #I) 0 5 2 0 +REF: ANYWAY NO ONE SAW HER LIKE THAT +HYP: ****** ** ATDYWAY T NON SLE LIGTDER +Eval: D D S S S S S + +Speaker sentences 886: voxforge_eng_000934 #utts: 1 +id: (voxforge_eng_000934-voxforge_eng_000934) +Scores: (#C #S #D #I) 2 6 0 1 +REF: *** MEN WHO ENDURE it CALL IT LIVING death +HYP: MIN H IN DEUR it COL T LING death +Eval: I S S S S S S + +Speaker sentences 887: voxforge_eng_000935 #utts: 1 +id: (voxforge_eng_000935-voxforge_eng_000935) +Scores: (#C #S #D #I) 1 4 0 2 +REF: ******** MATTHEWSON WHOS this *** BOOKKEEPER ROGERS +HYP: METOLSON OHOSED E this BOK CEPER RODGERS +Eval: I S S I S S + +Speaker sentences 888: voxforge_eng_000938 #utts: 1 +id: (voxforge_eng_000938-voxforge_eng_000938) +Scores: (#C #S #D #I) 3 2 0 1 +REF: i only ** READ the QUOTATIONS +HYP: i only AT D the FORTATIONS +Eval: I S S + +Speaker sentences 889: voxforge_eng_000939 #utts: 1 +id: (voxforge_eng_000939-voxforge_eng_000939) +Scores: (#C #S #D #I) 5 7 0 0 +REF: THERE was PROPER DIVISION of LABOR in the WORK they INDIVIDUALLY PERFORMED +HYP: TERE was PER DEVISION of LAVBR in the WAL they INDEVRIGDELY POFPOMENTD +Eval: S S S S S S S + +Speaker sentences 890: voxforge_eng_000940 #utts: 1 +id: (voxforge_eng_000940-voxforge_eng_000940) +Scores: (#C #S #D #I) 4 4 2 1 +REF: ILL TELL you the **** LIBRARIAN said WITH A BRIGHTENING face +HYP: *** IOLPE you the LABR AOND said **** TH RICT face +Eval: D S I S D S S + +Speaker sentences 891: voxforge_eng_000942 #utts: 1 +id: (voxforge_eng_000942-voxforge_eng_000942) +Scores: (#C #S #D #I) 3 5 2 0 +REF: i SAW MR PIKE NOD HIS head grimly AND SARCASTICALLY +HYP: i *** SOF MISTOR PIGNOD IS head grimly *** INEROCASTICLY +Eval: D S S S S D S + +Speaker sentences 892: voxforge_eng_000943 #utts: 1 +id: (voxforge_eng_000943-voxforge_eng_000943) +Scores: (#C #S #D #I) 4 4 0 0 +REF: the RINGING of the big BELL AROUSED HIM +HYP: the RING of the big BILE AROASTD INDN +Eval: S S S S + +Speaker sentences 893: voxforge_eng_000944 #utts: 1 +id: (voxforge_eng_000944-voxforge_eng_000944) +Scores: (#C #S #D #I) 8 10 1 0 +REF: THE SCRATCH of a pin on a MANS head VAST REGIONS of the EARTHS SURFACE REMAIN GEOLOGICALLY UNKNOWN BUT +HYP: OR THESRACH of a pin on a ANS head VEAST REAGOS of the ****** URSURFISS RE MAE JELOUGICLY UDNON +Eval: S S S S S D S S S S S + +Speaker sentences 894: voxforge_eng_000945 #utts: 1 +id: (voxforge_eng_000945-voxforge_eng_000945) +Scores: (#C #S #D #I) 3 8 2 1 +REF: * he HAD BARELY ENTERED THIS WHEN HE SAW the GLOW of A FIRE +HYP: T he *** ****** AD BADILY ANTERDIDES WHENTHE SOGD the GLOE of AF FIR +Eval: I D D S S S S S S S S + +Speaker sentences 895: voxforge_eng_000946 #utts: 1 +id: (voxforge_eng_000946-voxforge_eng_000946) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ***** CHANGE CHAIRS DAYLIGHT COMMANDED +HYP: CHANS CHARS THY LIT COMAND +Eval: I S S S S + +Speaker sentences 896: voxforge_eng_000947 #utts: 1 +id: (voxforge_eng_000947-voxforge_eng_000947) +Scores: (#C #S #D #I) 3 6 0 0 +REF: it was JEANNE SINGING SOFTLY OVER BEYOND the ROCKS +HYP: it was JEN SAINING SOFHELY OER BEYON the ROCKCE +Eval: S S S S S S + +Speaker sentences 897: voxforge_eng_000948 #utts: 1 +id: (voxforge_eng_000948-voxforge_eng_000948) +Scores: (#C #S #D #I) 0 6 0 0 +REF: A FLYING ARROW PASSED BETWEEN US +HYP: OF LING AROL BOST BETWEN OSED +Eval: S S S S S S + +Speaker sentences 898: voxforge_eng_000949 #utts: 1 +id: (voxforge_eng_000949-voxforge_eng_000949) +Scores: (#C #S #D #I) 6 5 0 1 +REF: HATRED and murder and LUST for REVENGE they POSSESSED to **** OVERFLOWING +HYP: HATRIT and murder and LOST for REVENGCH they POSSEST to OFER FLOYING +Eval: S S S S I S + +Speaker sentences 899: voxforge_eng_000950 #utts: 1 +id: (voxforge_eng_000950-voxforge_eng_000950) +Scores: (#C #S #D #I) 3 5 2 0 +REF: that YOU COULD hear all UP AND DOWN THE LIMPOPO +HYP: that *** OCOD hear all ** UPED DON TE LIMPOPOE +Eval: D S D S S S S + +Speaker sentences 900: voxforge_eng_000951 #utts: 1 +id: (voxforge_eng_000951-voxforge_eng_000951) +Scores: (#C #S #D #I) 4 2 1 1 +REF: it was my ** IDEA to A TEE +HYP: it was my AD DE to * ATE +Eval: I S D S + +Speaker sentences 901: voxforge_eng_000952 #utts: 1 +id: (voxforge_eng_000952-voxforge_eng_000952) +Scores: (#C #S #D #I) 2 3 0 0 +REF: she DOESNT WANT to WIN +HYP: she DOSANT WON to WEIN +Eval: S S S + +Speaker sentences 902: voxforge_eng_000953 #utts: 1 +id: (voxforge_eng_000953-voxforge_eng_000953) +Scores: (#C #S #D #I) 2 6 1 1 +REF: she THINKS IT IS BECAUSE he ***** WANTS SOMETHING ELSE +HYP: she ****** THINGE AITIS BECOS he WONCE SOM THNG LE +Eval: D S S S I S S S + +Speaker sentences 903: voxforge_eng_000954 #utts: 1 +id: (voxforge_eng_000954-voxforge_eng_000954) +Scores: (#C #S #D #I) 4 6 1 2 +REF: **** HE PULLED and * the LOG CRASHED DOWN to BREAK his BACK +HYP: HSHE POLD E and T the *** LOC CREASETDON to BRAK his BAC +Eval: I S S I D S S S S + +Speaker sentences 904: voxforge_eng_000955 #utts: 1 +id: (voxforge_eng_000955-voxforge_eng_000955) +Scores: (#C #S #D #I) 5 7 2 0 +REF: THAT the SO CALLED FORCES at work in LIGHT HEAT ELECTRICITY and MAGNETISM IN +HYP: THA the ** SOCOLD FORES at work in LITE HE ALCTRISIDY and ********* MANGNTISM +Eval: S D S S S S S D S + +Speaker sentences 905: voxforge_eng_000956 #utts: 1 +id: (voxforge_eng_000956-voxforge_eng_000956) +Scores: (#C #S #D #I) 2 7 0 1 +REF: HE TURNED SHARPLY and ******* FACED GREGSON ACROSS the TABLE +HYP: WE TWION SHAOBPEINT and PIAICET GRAGSIN AN COS the THIHVBLE +Eval: S S S I S S S S + +Speaker sentences 906: voxforge_eng_000957 #utts: 1 +id: (voxforge_eng_000957-voxforge_eng_000957) +Scores: (#C #S #D #I) 1 3 0 1 +REF: ** ALSO i WANT INFORMATION +HYP: AL SOE i WONT ANFORMATION +Eval: I S S S + +Speaker sentences 907: voxforge_eng_000958 #utts: 1 +id: (voxforge_eng_000958-voxforge_eng_000958) +Scores: (#C #S #D #I) 6 4 0 0 +REF: the SIXTH day he spent in the CABIN WITH GREGSON +HYP: the SICT day he spent in the CAVON WH GRAGSON +Eval: S S S S + +Speaker sentences 908: voxforge_eng_000959 #utts: 1 +id: (voxforge_eng_000959-voxforge_eng_000959) +Scores: (#C #S #D #I) 8 13 0 1 +REF: ON this * HYPOTHESIS the HAMMERING of the ULTRA MUNDANE CORPUSCLES ON the bob CONFERS ITS KINETIC ENERGY on THE ONE hand +HYP: ION this Y POTHIES the HAMERNG of the LTER MUNDING CRPUCLE OND the bob COFIRE ITK CENATICK NRGY on TH ON hand +Eval: S I S S S S S S S S S S S S + +Speaker sentences 909: voxforge_eng_000960 #utts: 1 +id: (voxforge_eng_000960-voxforge_eng_000960) +Scores: (#C #S #D #I) 9 8 0 1 +REF: NOW a FERNY WILLOWY STREAM and ever AND anon you * EMERGE from ALL the groves and FLOWERS +HYP: NOWE a FIRNY WIL STREMEM and ever AN anon you A MURGE from AL the groves and FLOURS +Eval: S S S S S I S S S + +Speaker sentences 910: voxforge_eng_000961 #utts: 1 +id: (voxforge_eng_000961-voxforge_eng_000961) +Scores: (#C #S #D #I) 4 7 1 1 +REF: **** WITHOUT it the most DENSELY POPULATED REGIONS of MODERN EUROPE AND AMERICA +HYP: WITH HOT it the most DENCELY POPILAED REAGENS of ****** MOHEN TURIP ANDAMORICA +Eval: I S S S S D S S S + +Speaker sentences 911: voxforge_eng_000962 #utts: 1 +id: (voxforge_eng_000962-voxforge_eng_000962) +Scores: (#C #S #D #I) 3 2 0 0 +REF: tom spink has A HARPOON +HYP: tom spink has HAR PON +Eval: S S + +Speaker sentences 912: voxforge_eng_000963 #utts: 1 +id: (voxforge_eng_000963-voxforge_eng_000963) +Scores: (#C #S #D #I) 3 8 2 0 +REF: he WANTED TO GIVE the FINISH TO this FOE ALREADY SO FAR GONE +HYP: he ****** WNTED G the FISHE T this *** FOLE ALREY SOF AGON +Eval: D S S S S D S S S S + +Speaker sentences 913: voxforge_eng_000964 #utts: 1 +id: (voxforge_eng_000964-voxforge_eng_000964) +Scores: (#C #S #D #I) 4 9 0 0 +REF: LIKE a FLASH he LAUNCHED himself INTO the FEATHERED MASS OF THE OWL +HYP: LA a FLASHE he LONCHE himself INT the FETHED MAS O TH HOUHL +Eval: S S S S S S S S S + +Speaker sentences 914: voxforge_eng_000965 #utts: 1 +id: (voxforge_eng_000965-voxforge_eng_000965) +Scores: (#C #S #D #I) 4 3 0 0 +REF: it CONTAINS a TOTAL of twenty ENTRIES +HYP: it CONTAE a TOTLE of twenty ANTRES +Eval: S S S + +Speaker sentences 915: voxforge_eng_000966 #utts: 1 +id: (voxforge_eng_000966-voxforge_eng_000966) +Scores: (#C #S #D #I) 1 3 0 1 +REF: * IVE FELT more COMFORTABLE +HYP: I HAVE FHELT more COMFORABLE +Eval: I S S S + +Speaker sentences 916: voxforge_eng_000967 #utts: 1 +id: (voxforge_eng_000967-voxforge_eng_000967) +Scores: (#C #S #D #I) 0 5 1 0 +REF: DID I POSSESS TOO MUCH VITALITY +HYP: *** THED POES TO MNTH VEATELITY +Eval: D S S S S S + +Speaker sentences 917: voxforge_eng_000968 #utts: 1 +id: (voxforge_eng_000968-voxforge_eng_000968) +Scores: (#C #S #D #I) 2 7 0 0 +REF: the WOLF DOG THRUST his GAUNT MUZZLE TOWARD HIM +HYP: the WLLE DOGK THRST his GONT MUSLE TORD HIMN +Eval: S S S S S S S + +Speaker sentences 918: voxforge_eng_000971 #utts: 1 +id: (voxforge_eng_000971-voxforge_eng_000971) +Scores: (#C #S #D #I) 4 4 0 0 +REF: the GABRIEL VOICE of THE SAMURAI rang out +HYP: the GAEBILE VORSCE of HE SEMERIY rang out +Eval: S S S S + +Speaker sentences 919: voxforge_eng_000972 #utts: 1 +id: (voxforge_eng_000972-voxforge_eng_000972) +Scores: (#C #S #D #I) 3 8 0 1 +REF: it was OUR river *** EMERGING LIKE OURSELVES FROM THE GREAT SWAMP +HYP: it was AI river ANM MURGING LAK OURSELS FOM TE GRAT SOMP +Eval: S I S S S S S S S + +Speaker sentences 920: voxforge_eng_000973 #utts: 1 +id: (voxforge_eng_000973-voxforge_eng_000973) +Scores: (#C #S #D #I) 6 8 1 0 +REF: said the MOLE PULLING himself TOGETHER WITH AN EFFORT YOU must THINK me very RUDE +HYP: said the MOLL PULING himself ******** TOGETH WITHAN EFERT O must THING me very ROD +Eval: S S D S S S S S S + +Speaker sentences 921: voxforge_eng_000974 #utts: 1 +id: (voxforge_eng_000974-voxforge_eng_000974) +Scores: (#C #S #D #I) 7 6 0 0 +REF: in what BUCOLIC SCHOOL of fence he had BEEN TAUGHT WAS beyond IMAGINING +HYP: in what BYUCOLLICK SCOO of fence he had BED TORT THWAS beyond IMADGENING +Eval: S S S S S S + +Speaker sentences 922: voxforge_eng_000975 #utts: 1 +id: (voxforge_eng_000975-voxforge_eng_000975) +Scores: (#C #S #D #I) 3 7 0 2 +REF: had not ******* ENABLED INVESTIGATORS to *** OBTAIN AT COMPARATIVELY LITTLE COST +HYP: had not INABLED IN VESTIGADERS to OPE TAIN A COMPERITIVFLY LITL CLOUST +Eval: I S S I S S S S S + +Speaker sentences 923: voxforge_eng_000976 #utts: 1 +id: (voxforge_eng_000976-voxforge_eng_000976) +Scores: (#C #S #D #I) 4 5 0 0 +REF: A TRICKLE of fresh BLOOD ran OVER HIS face +HYP: IT TRIKL of fresh BLOD ran OVE RS face +Eval: S S S S S + +Speaker sentences 924: voxforge_eng_000977 #utts: 1 +id: (voxforge_eng_000977-voxforge_eng_000977) +Scores: (#C #S #D #I) 3 2 0 2 +REF: * it was a ****** CURIOUS COINCIDENCE +HYP: D it was a CUORES CONSI TEANTSESE +Eval: I I S S + +Speaker sentences 925: voxforge_eng_000978 #utts: 1 +id: (voxforge_eng_000978-voxforge_eng_000978) +Scores: (#C #S #D #I) 5 2 0 0 +REF: it is the FIRE partly she SAID +HYP: it is the FIR partly she SAINE +Eval: S S + +Speaker sentences 926: voxforge_eng_000979 #utts: 1 +id: (voxforge_eng_000979-voxforge_eng_000979) +Scores: (#C #S #D #I) 3 5 2 1 +REF: they JUST LAY OFF IN THE BUSH and PLUGGED away ** +HYP: they **** *** GOST LAYE OF THEBOSH and POKD away AN +Eval: D D S S S S S I + +Speaker sentences 927: voxforge_eng_000980 #utts: 1 +id: (voxforge_eng_000980-voxforge_eng_000980) +Scores: (#C #S #D #I) 5 5 1 0 +REF: i KNOW that YOU ARE in CHARGE there and JEANNE KNOWS +HYP: i NO that *** OURE in CHARDE there and JE NOSE +Eval: S D S S S S + +Speaker sentences 928: voxforge_eng_000981 #utts: 1 +id: (voxforge_eng_000981-voxforge_eng_000981) +Scores: (#C #S #D #I) 5 5 1 0 +REF: for A TIME the EXCITING THRILL of his ADVENTURE was GONE +HYP: for * TIE the ECSATING THILE of his ADVENTHE was GON +Eval: D S S S S S + +Speaker sentences 929: voxforge_eng_000982 #utts: 1 +id: (voxforge_eng_000982-voxforge_eng_000982) +Scores: (#C #S #D #I) 2 6 0 0 +REF: SUDDENLY his FINGERS CLOSED TIGHTLY OVER the HANDKERCHIEF +HYP: FAEDLY his FINGRS CLOS THADLY OVE the ANGOCIF +Eval: S S S S S S + +Speaker sentences 930: voxforge_eng_000983 #utts: 1 +id: (voxforge_eng_000983-voxforge_eng_000983) +Scores: (#C #S #D #I) 4 6 0 0 +REF: dear SIR your SECOND VICTIM has FALLEN ON SCHEDULE time +HYP: dear SIRE your SECONT VICTDOM has FOLLON OND SCHADGDULE time +Eval: S S S S S S + +Speaker sentences 931: voxforge_eng_000984 #utts: 1 +id: (voxforge_eng_000984-voxforge_eng_000984) +Scores: (#C #S #D #I) 0 5 0 1 +REF: * HE CAN CARE FOR HIMSELF +HYP: H CON CAR F HMSELF E +Eval: I S S S S S + +Speaker sentences 932: voxforge_eng_000985 #utts: 1 +id: (voxforge_eng_000985-voxforge_eng_000985) +Scores: (#C #S #D #I) 3 6 0 0 +REF: EACH INSULT ADDED to the VALUE OF the CLAIM +HYP: ACH INSILT ATE to the VALYU O the CLAME +Eval: S S S S S S + +Speaker sentences 933: voxforge_eng_000986 #utts: 1 +id: (voxforge_eng_000986-voxforge_eng_000986) +Scores: (#C #S #D #I) 7 8 1 1 +REF: THOUGH it MAY be ****** TRANSFORMED into any ONE of THE forms of WHICH ENERGY IS SUSCEPTIBLE +HYP: THE it MA be TRANCS FORMED into any O of TH forms of ***** WHCH ENRGY ISECEPTABL +Eval: S S I S S S D S S S + +Speaker sentences 934: voxforge_eng_000987 #utts: 1 +id: (voxforge_eng_000987-voxforge_eng_000987) +Scores: (#C #S #D #I) 2 9 0 5 +REF: MERCEDES screamed **** **** ***** *********** ** CRIED LAUGHED AND MANIFESTED THE CHAOTIC ABANDONMENT of HYSTERIA +HYP: MESEIDES screamed GRID LOVF IADND MANYIFESTED TH HIRIARD ICK AE BOND DEN N MENT of HISTAIAR +Eval: S I I I I I S S S S S S S S + +Speaker sentences 935: voxforge_eng_000988 #utts: 1 +id: (voxforge_eng_000988-voxforge_eng_000988) +Scores: (#C #S #D #I) 4 5 0 0 +REF: i WANT to KNOW HOW ALL this is POSSIBLE +HYP: i WHN to NO HOWT AL this is POSTABLE +Eval: S S S S S + +Speaker sentences 936: voxforge_eng_000989 #utts: 1 +id: (voxforge_eng_000989-voxforge_eng_000989) +Scores: (#C #S #D #I) 3 10 2 0 +REF: PRESENTING a SIMPLE AND INSTRUCTIVE ILLUSTRATION of the STRUGGLE FOR LIFE AMONG THE RIVAL SPECIES +HYP: RESENTING a SEMPL AN NSTRCTIE ILOSTRATION of the ******** *** STROGOFOR LIVEF AMNG THERIVLE SPEACES +Eval: S S S S S D D S S S S S + +Speaker sentences 937: voxforge_eng_000990 #utts: 1 +id: (voxforge_eng_000990-voxforge_eng_000990) +Scores: (#C #S #D #I) 5 5 0 0 +REF: HELL NEVER do a tap of WORK the WHOLE VOYAGE +HYP: HIL NEVE do a tap of WERK the HOL VORIANDGCH +Eval: S S S S S + +Speaker sentences 938: voxforge_eng_000991 #utts: 1 +id: (voxforge_eng_000991-voxforge_eng_000991) +Scores: (#C #S #D #I) 1 7 0 0 +REF: i HAVE HUNTED ALONG THIS RIDGE REPLIED PHILIP +HYP: i HAE HNTE ALON TIS RIGE REPLAD FLIP +Eval: S S S S S S S + +Speaker sentences 939: voxforge_eng_000992 #utts: 1 +id: (voxforge_eng_000992-voxforge_eng_000992) +Scores: (#C #S #D #I) 4 5 0 0 +REF: lord but IM glad to SEE YOU AGAIN PHIL +HYP: lord but IN glad to SE YO AGIN FIL +Eval: S S S S S + +Speaker sentences 940: voxforge_eng_000993 #utts: 1 +id: (voxforge_eng_000993-voxforge_eng_000993) +Scores: (#C #S #D #I) 1 6 2 0 +REF: HOW VALIANTLY i WENT AT IT THAT FIRST DAY +HYP: *** COWELINLY i **** WEN DATDID THATF IS DEA +Eval: D S D S S S S S + +Speaker sentences 941: voxforge_eng_000994 #utts: 1 +id: (voxforge_eng_000994-voxforge_eng_000994) +Scores: (#C #S #D #I) 1 7 0 1 +REF: THEY ARE not ******* REGULAR OYSTER PIRATES NICHOLAS CONTINUED +HYP: THE AR not REAGILE OSTER PIRATS NCKLES CO TNUDED +Eval: S S I S S S S S + +Speaker sentences 942: voxforge_eng_000995 #utts: 1 +id: (voxforge_eng_000995-voxforge_eng_000995) +Scores: (#C #S #D #I) 8 11 0 0 +REF: THEY must be HURTING for BUSINESS but i THOUGHT you MIGHT WANT to TAKE a LOOK AT THEIR SITE +HYP: TE must be HURING for BISESS but i THT you MIGT WNT to CEK a LOK A THER SITD +Eval: S S S S S S S S S S S + +Speaker sentences 943: voxforge_eng_000996 #utts: 1 +id: (voxforge_eng_000996-voxforge_eng_000996) +Scores: (#C #S #D #I) 5 3 1 0 +REF: THERE WAS no chance to fire WITHOUT HITTING him +HYP: ***** DERAS no chance to fire WITOUT HIDING him +Eval: D S S S + +Speaker sentences 944: voxforge_eng_000997 #utts: 1 +id: (voxforge_eng_000997-voxforge_eng_000997) +Scores: (#C #S #D #I) 3 7 0 1 +REF: as for himself **** WERENT THE STREET RAILWAY EARNINGS INCREASING STEADILY +HYP: as for himself WONT TH STREAT REAL WAY ARNING NKESING SETLY +Eval: I S S S S S S S + +Speaker sentences 945: voxforge_eng_000998 #utts: 1 +id: (voxforge_eng_000998-voxforge_eng_000998) +Scores: (#C #S #D #I) 1 7 0 0 +REF: DUNHAM CAN YOUR boy GO ALONG WITH JESSE +HYP: DON HM CONYOR boy O LONG WO DESSYE +Eval: S S S S S S S + +Speaker sentences 946: voxforge_eng_000999 #utts: 1 +id: (voxforge_eng_000999-voxforge_eng_000999) +Scores: (#C #S #D #I) 1 3 0 2 +REF: *** **** GOODBYE PIERRE he SHOUTED +HYP: COD BAIY PE AR he SHOTED +Eval: I I S S S + +Speaker sentences 947: voxforge_eng_001000 #utts: 1 +id: (voxforge_eng_001000-voxforge_eng_001000) +Scores: (#C #S #D #I) 5 6 0 0 +REF: but such DIVERGENCE of OPINION WOULD CONSTITUTE no MENACE to SOCIETY +HYP: but such DEVERGENS of PINION WLD CONSTITUT no MENANCE to SITY +Eval: S S S S S S + +Speaker sentences 948: voxforge_eng_001001 #utts: 1 +id: (voxforge_eng_001001-voxforge_eng_001001) +Scores: (#C #S #D #I) 3 7 0 2 +REF: * THERE was ONE CHANCE and * ONLY ONE OF saving JEANNE +HYP: I BIE was ON THANCEE and O LY ON WOE saving HONTDLD +Eval: I S S S I S S S S + +Speaker sentences 949: voxforge_eng_001002 #utts: 1 +id: (voxforge_eng_001002-voxforge_eng_001002) +Scores: (#C #S #D #I) 1 5 0 2 +REF: * ** I CANNOT FOLLOW YOU she SAID +HYP: E IO CAN OT FOALE YOW she SAINDNE +Eval: I I S S S S S + +Speaker sentences 950: voxforge_eng_001003 #utts: 1 +id: (voxforge_eng_001003-voxforge_eng_001003) +Scores: (#C #S #D #I) 6 5 0 0 +REF: on the far CORNER of the COMPOUND FENCE a HAWK BROODED +HYP: on the far COARNER of the COMPOWND FENTE a HOAK BREADED +Eval: S S S S S + +Speaker sentences 951: voxforge_eng_001004 #utts: 1 +id: (voxforge_eng_001004-voxforge_eng_001004) +Scores: (#C #S #D #I) 3 7 0 0 +REF: THEN AGAIN TUDOR had SUCH an IRRITATING way ABOUT HIM +HYP: TEN AGIN TOER had SOCH an IERSTATING way AOBOT HM +Eval: S S S S S S S + +Speaker sentences 952: voxpopuli_eng_000494 #utts: 1 +id: (voxpopuli_eng_000494-voxpopuli_eng_000494) +Scores: (#C #S #D #I) 5 10 1 2 +REF: we all ** KNOW OMAN as a ************* SUCCESSFUL STABLE COUNTRY A ROLE MODEL for THE WHOLE REGION +HYP: we all NO O MAN as a SECESTOLSTABL CUNTRY AR OAL MOR THEFOR THAT for *** THEHOL REAGON +Eval: I S S I S S S S S S D S S + +Speaker sentences 953: voxpopuli_eng_000495 #utts: 1 +id: (voxpopuli_eng_000495-voxpopuli_eng_000495) +Scores: (#C #S #D #I) 8 17 4 1 +REF: THEREFORE its high time THAT YOU come *** FORWARD WITH A PROPOSAL for REVIEW WITH AN OPERATIONAL SEPARATION of THE AUDIT and non AUDIT SERVICES UNDER A DIRECT EU SUPERVISION +HYP: THERFOR its high time **** OU come FOR BOUGDT HE TE PREPORSL for ****** **** REVEU BE DENOPRATINALSBRATSON of TE OARDIT and non ***** OALDITSRVISIES ANDE RADIDLACT E WUS OBOTISON +Eval: S D S I S S S S D D S S S S S D S S S S S S + +Speaker sentences 954: voxpopuli_eng_000496 #utts: 1 +id: (voxpopuli_eng_000496-voxpopuli_eng_000496) +Scores: (#C #S #D #I) 12 15 1 1 +REF: it IS CLEAR that we have no time to WASTE the NEW RESULTS of THE IPCC REGARDING THE SCIENTIFIC BASIS of ******* CLIMATE CHANGE LEAVE no ROOM for HESITATION +HYP: it ** ISCEEAR that we have no time to WAST the NU RESSOULTSH of THEYE ID PEASIESIE REOARDIN SIENTDIFICK BASIES of GLIMINT T JAINCE LE no ROUM for HESITDACSEON +Eval: D S S S S S S S S S S I S S S S S + +Speaker sentences 955: voxpopuli_eng_000497 #utts: 1 +id: (voxpopuli_eng_000497-voxpopuli_eng_000497) +Scores: (#C #S #D #I) 5 10 1 1 +REF: 5 so in the CONTAINERS WHICH ARE NEVER EVEN TOUCHED come slaves ******** COUNTERFEIT GOODS DRUGS ETC +HYP: SENT so in the ********** CON TAENOR HIHAEVER AVEN TUCHED come slaves CONTOFET GODS DROGES IT HETR +Eval: S D S S S S S I S S S S + +Speaker sentences 956: voxpopuli_eng_000498 #utts: 1 +id: (voxpopuli_eng_000498-voxpopuli_eng_000498) +Scores: (#C #S #D #I) 7 16 3 0 +REF: i hope that THE COMMISSIONS MOBILITY INITIATIVES WILL NOT CREATE the NEXT PROBLEM but WILL be AN ANSWER FOR EXISTING CHALLENGES of THE ROAD TRANSPORT SECTOR +HYP: i hope that *** COMIONS MBITINISHES INISHIFIVS W WNT CARAKT the DACXT PROBLOEM but WIL be ** ANANCSE FORE EISTING CHOLINGERS of *** THER OBP TANCSPBORDSECTAR +Eval: D S S S S S S S S S D S S S S D S S S + +Speaker sentences 957: voxpopuli_eng_000499 #utts: 1 +id: (voxpopuli_eng_000499-voxpopuli_eng_000499) +Scores: (#C #S #D #I) 19 39 10 1 +REF: IN THE US IT was A DECISION TAKEN ONLY by ONE person the FORMER PRESIDENT OF THE UNITED STATES AGAINST the ARTICULATED DEMOCRATIC MAJORITY of THE US CONGRESS by all of its ********* REPUBLICAN AND SOME OF ITS DEMOCRAT MEMBERS it was AN AGREEMENT without any BINDING OBLIGATIONS as THE LEADERS of IRAN very OPENLY AND PRECISELY MADE CLEAR ON the VERY DAY THIS SO CALLED DEAL was PUBLISHED +HYP: I TEOU AS I was * ******** DESION TAINARLY by ON person the ****** ORME PESIDED O THENIDE STACES AGANSE the ARTICILATEDMCRATICK DRM NDURITY of TEU S CONGRSS by all of its REPOBLKEN SO FICS TDEMERCRATIC T DEM RT MBERS it was ** NAGREMENT without any ******* BIDNDINGOBLIGATIONS as *** THELEADERS of IRAUN very OUPENTLY INPRESIDH MAEPLY NTHE ERY DA the **** *** **** ** SOCLD DEL was POLISHED +Eval: S S S S D D S S S D S S S S S S S S S S S S I S S S S S S S D S D S D S S S S S S S S D D D D S S S + +Speaker sentences 958: voxpopuli_eng_000500 #utts: 1 +id: (voxpopuli_eng_000500-voxpopuli_eng_000500) +Scores: (#C #S #D #I) 14 10 0 1 +REF: FREE SPEECH is ESSENTIALLY ACCEPTING THAT PEOPLE ARE free to say things we do *** LIKE not MERELY free to say things we do LIKE +HYP: FRE SPEACGE is S SENCIALY AECECTINGTAT PEPL UR free to say things we do NOT LIEKE not MELY free to say things we do LIEK +Eval: S S S S S S S I S S S + +Speaker sentences 959: voxpopuli_eng_000501 #utts: 1 +id: (voxpopuli_eng_000501-voxpopuli_eng_000501) +Scores: (#C #S #D #I) 0 5 0 0 +REF: LET US LEARN FROM THIS +HYP: LAT AS LARND FOM THIE +Eval: S S S S S + +Speaker sentences 960: voxpopuli_eng_000502 #utts: 1 +id: (voxpopuli_eng_000502-voxpopuli_eng_000502) +Scores: (#C #S #D #I) 13 35 4 1 +REF: WE THINK THAT THE ENVIRONMENTAL EFFECT of PRODUCTS must be A VERY IMPORTANT ISSUE IN THE eu and THE WHOLE IDEA OF AN ECOLABEL GIVES A VERY USEFUL ORIENTATION for CONSUMERS of COURSE THE ECOLABEL SHOULD BE given to the most *** ENVIRONMENTALLY FRIENDLY PRODUCTS AND the INFORMATION SHOULD BE CLEAR and CORRECT +HYP: BESING HAT HE ANEVI MENTAE EAFECT of PRDUCS must be * AVERY INMPRTANT ISHU ING TER eu and HE OL IG TDEAR O THE IECOLABR NEVSA VER YOUSOL ORIANTATION for THECOSTSUMEMIS of ****** COURS HE IACULABER SOOD given to the most AND VIREMENT AF FENDY PORDUCT the *********** ****** IFOREMITIONSHOL BECLEARE and OE +Eval: S S S S S S S D S S S S S S S S S S S S S S S S S D S S S S I S S S S D D S S S + +Speaker sentences 961: voxpopuli_eng_000503 #utts: 1 +id: (voxpopuli_eng_000503-voxpopuli_eng_000503) +Scores: (#C #S #D #I) 7 17 3 0 +REF: however the CURRENT REGIME NEEDS to BE BETTER TAILORED to THE DIGITAL ENVIRONMENT IN ORDER TO ENSURE FAIR REMUNERATION to CREATORS AND to CONFORM to CONSUMER EXPECTATIONS +HYP: however the ******* CIRENDRIGIEME NITS to B BETER SALORT to *** ******* TH DIGIDL INFVEIRNENT EDE NSHOUR FAR RIMINERATION to GREATDURS ANH to OFOME to ONSOMER EXAPECTATIONS +Eval: D S S S S S D D S S S S S S S S S S S S + +Speaker sentences 962: voxpopuli_eng_000504 #utts: 1 +id: (voxpopuli_eng_000504-voxpopuli_eng_000504) +Scores: (#C #S #D #I) 4 25 3 0 +REF: IT CALLS UPON THE COMMISSION and MEMBER STATES TO ENHANCE THEIR SUPPORT FOR RECONCILIATION to SECURE PEACE and STABILITY AND IRELAND I WOULD THEREFORE URGE YOU COLLEAGUES to PLEASE SUPPORT THIS AMENDMENT +HYP: AD COLS BO TE OMION and ****** ****** MEMBRSTATH O ANHANCD HERSEPORT TO RECENCSIYATION to SECUR PECS and STOBLITY AN ARLEND IWIL THER FOR ARE SU CALIGE to ****** PEES SUPORTHIS MENDEN +Eval: S S S S S D D S S S S S S S S S S S S S S S S S D S S S + +Speaker sentences 963: voxpopuli_eng_000505 #utts: 1 +id: (voxpopuli_eng_000505-voxpopuli_eng_000505) +Scores: (#C #S #D #I) 18 28 6 4 +REF: STRATEGIC CHOICES about WHERE to ** INVEST must be MADE NOW TAKING INTO ACCOUNT THE NEED to PHASE out FOSSIL FUEL SUBSIDIES but *** TAKE gas as A FOSSIL FUEL it can be a **** HELPFUL BRIDGING TRANSITIONARY MEDIUM to be USED in ***** many MEMBER STATES IF WE WANT to ACHIEVE OUR AMBITIOUS CLIMATE TARGETS +HYP: TRATIGIG CHOICIS about ERE to LE WEST must be **** MAD NOWL TOAKIN NE COUNE ANE to FASE out ****** FORSILFUL SUPSITDS but TEK THE gas as * ****** FORSOFYU it can be a HELP FUL BRIGING RNSICONARY MEADIUM to be US in MEMIN many ****** MEBERSTHAS I E ON to ******* EDCHIF OVER ANMBISHIS LIMIGTARGETS +Eval: S S S I S D S S S S S S S D S S I S D D S I S S S S S I D S S S S D S S S S + +Speaker sentences 964: voxpopuli_eng_000506 #utts: 1 +id: (voxpopuli_eng_000506-voxpopuli_eng_000506) +Scores: (#C #S #D #I) 10 17 5 3 +REF: MIDDLE EAST WE ARE POSSIBLY AT A THRESHOLD we can CHOOSE to PURSUE THE same POLICIES in THE same MANNER KNOWING that THEY WILL LEAD to THE SAME RESULTS the RESULTS that ** ** **** +HYP: ****** **** ** EWE AED POSILY AOFERIS COPE we can COOTH to POROSUE THEY same PLISES in H same MANER NONING that **** WEL LED to *** HE AMRSOTS the RSOLSEST that WE NO DRDA +Eval: D D D S S S S S S S S S S S S D S S D S S S I I I + +Speaker sentences 965: voxpopuli_eng_000507 #utts: 1 +id: (voxpopuli_eng_000507-voxpopuli_eng_000507) +Scores: (#C #S #D #I) 0 4 1 0 +REF: BUT THERE IS AN OPTION +HYP: *** U TERIS NOPTION B +Eval: D S S S S + +Speaker sentences 966: voxpopuli_eng_000508 #utts: 1 +id: (voxpopuli_eng_000508-voxpopuli_eng_000508) +Scores: (#C #S #D #I) 2 6 1 1 +REF: THIS we *** ALSO NEED a CHANGE IN OUR IDEOLOGY +HYP: **** we ALL SOE NEAD a THANGEH INOR HR DOLIDGTYI +Eval: D I S S S S S S + +Speaker sentences 967: voxpopuli_eng_000509 #utts: 1 +id: (voxpopuli_eng_000509-voxpopuli_eng_000509) +Scores: (#C #S #D #I) 13 33 1 8 +REF: A LARGE PART of the REASON IS of course ** ***** ******* *** ILLEGAL FISHING MORE OFTEN THAN NOT by ***** VESSELS which ARE REGISTERED to countries WHICH LACK the ** ** *** WILL OR THE RESOURCES TO ENFORCE INTERNATIONAL AGREEMENTS no AMOUNT of TRACEABILITY MEASURES OR EXTRA PAPERWORK WILL ADDRESS the PROBLEM of REDUCING +HYP: I LAGH BUT of the ****** RESON of course IS ILIGL FISINGK AND THE OFOM AL TWE DOUN OFEN by HALAM VESES which AR LEAGISTERE to countries HICH LAUCE the IL OF THR RSUREIS TW AN FORST T IN THENATIONE AGEMENCS no MONT of TESABEITY MASERS ORD ECXTRP PER WAR UILEDRESE the PROBLOME of RETIUSING +Eval: S S S D S I I I I S S S S S S I S S S S S I I I S S S S S S S S S S S S S S S S S S + +Speaker sentences 968: voxpopuli_eng_000510 #utts: 1 +id: (voxpopuli_eng_000510-voxpopuli_eng_000510) +Scores: (#C #S #D #I) 7 33 3 1 +REF: the COMPROMISE ALSO INCLUDES CLEAR RULES TO DEFINE which MEMBER STATE HAS JURISDICTION AND THE COOPERATION BETWEEN MEMBER STATES CONCERNED IN CROSS BORDER CASES as WELL AS the NEED to INVOLVE EUROJUST THANK YOU FOR YOUR work and ****** PLEASE DO SUPPORT THIS DIRECTIVE +HYP: the OMPRMICE OLLSO IN CLUD SCLEARE RUASTO HEFINE which ****** ***** MBRSTATD AS HURSTICTION AN TH OPRATIOM TYEMBRSTATES CONCSERD FOR CROS BO THE CACES as **** HEHA the NED to EIMVOLLEF YOUR JUST HEN YOF OR work and PLEACE WOESUPRT TO MRO HIS EIRECIF +Eval: S S S S S S S D D S S S S S S S S S S S S S D S S S S S S S S I S S S S S + +Speaker sentences 969: voxpopuli_eng_000511 #utts: 1 +id: (voxpopuli_eng_000511-voxpopuli_eng_000511) +Scores: (#C #S #D #I) 8 35 2 0 +REF: THE GREENS WOULD HAVE US BELIEVE THAT THESE ARE bad bees criminal bees DELIBERATELY CONTAMINATING HONEY WITH A DANGEROUS INGREDIENT BUT in FACT THEY ARE DOING WHAT HONEY BEES HAVE ALWAYS DONE WHICH IS to CARRY POLLEN BACK TO THEIR HIVES to FEED THEIR young +HYP: ENO THEGRENS WIOD T HAV S BLE HATHIS UR bad bees criminal bees DELIBRTLY COTAMINATING HUDY WH THE DANGRUSINGREADIENT BUTIN FAC in **** **** FAC HE DINGHE HUY BES AR L AVLALLRS DOUNM IHI to CARY POLON BAC T THER HIVSTOD to FE THER young +Eval: S S S S S S S S S S S S S S S S S D D S S S S S S S S S S S S S S S S S S + +Speaker sentences 970: voxpopuli_eng_000512 #utts: 1 +id: (voxpopuli_eng_000512-voxpopuli_eng_000512) +Scores: (#C #S #D #I) 6 3 0 1 +REF: but it was the country *** ITSELF BEING more CAPABLE +HYP: but it was the country ITD HELD BENG more CAPRABL +Eval: I S S S + +Speaker sentences 971: voxpopuli_eng_000513 #utts: 1 +id: (voxpopuli_eng_000513-voxpopuli_eng_000513) +Scores: (#C #S #D #I) 3 8 0 3 +REF: * ** INTO the *** PORTFOLIO of the NEW COMMISSIONER DEALING WITH FUNDAMENTAL RIGHTS +HYP: R IN TO the PRT FOLIAO of the NU COMISION RE DALING WIT FAUNDEMENTERIGHTE +Eval: I I S I S S S S S S S + +Speaker sentences 972: voxpopuli_eng_000514 #utts: 1 +id: (voxpopuli_eng_000514-voxpopuli_eng_000514) +Scores: (#C #S #D #I) 2 7 3 0 +REF: THE MESSAGE IS THAT the EU DOES NOT have ANY NEW SOLUTIONS +HYP: *** E MESIGE TAT the YU DOUT AT have *** *** ANYNUSOLTIONE +Eval: D S S S S S S D D S + +Speaker sentences 973: voxpopuli_eng_000515 #utts: 1 +id: (voxpopuli_eng_000515-voxpopuli_eng_000515) +Scores: (#C #S #D #I) 6 11 3 1 +REF: ARE YOU WILLING to act IN FAVOUR OF the ****** SOCIAL DIMENSION to be INCLUDED IN THE EU COMPETENCIES as PROPOSED +HYP: AR YU WILING to act ** ****** INERVFATHEREFOR the SOSIAL DE MENTION to be ******** INLODED INTHE EOU COMPETENSYES as PREPUS +Eval: S S S D D S I S S D S S S S S + +Speaker sentences 974: voxpopuli_eng_000516 #utts: 1 +id: (voxpopuli_eng_000516-voxpopuli_eng_000516) +Scores: (#C #S #D #I) 2 9 5 2 +REF: THE NEXT STEP ON SPECTRUM POLICY IS BEING TAKEN WITH THE REFORM of our ******* ** TELECOM FRAMEWORK +HYP: *** **** **** ** ******** AERNCTHE OND PESPECTRU POLIYES TAKING WI THEREFORME of our TELICON TH FRAM WR +Eval: D D D D D S S S S S S S I I S S + +Speaker sentences 975: voxpopuli_eng_000517 #utts: 1 +id: (voxpopuli_eng_000517-voxpopuli_eng_000517) +Scores: (#C #S #D #I) 10 16 2 1 +REF: i BELIEVE his ****** REMARKS WERE EXPLICITLY RACIST and XENOPHOBIC and PROMOTED RACIAL INTOLERANCE in A WAY THAT is NOT ACCEPTABLE or ALLOWED in THE CONSTITUTION OF this house +HYP: i BELE his REMARC ES WERA INEXTPLIITELY RACISCTED and SENAFOBICKE and PRMOTED RAIL INTOLRANC in * WHAY TH is NO UCETABLE or ALAOUED in *** TE ONTOICTUTIOF this house +Eval: S I S S S S S S S S D S S S S S D S S + +Speaker sentences 976: voxpopuli_eng_000518 #utts: 1 +id: (voxpopuli_eng_000518-voxpopuli_eng_000518) +Scores: (#C #S #D #I) 1 12 1 0 +REF: REAL LIFE EXAMPLES SHOW that SOLVING ISSUES RELATED TO EDUCATION FUELS STRONG COMMUNITY DEVELOPMENT +HYP: **** REALIGH EGSAMPL SHOL that SOVING IES RLATETO A BOCATION FEYULD STRON COMNITY DEVELPMENT +Eval: D S S S S S S S S S S S S + +Speaker sentences 977: voxpopuli_eng_000519 #utts: 1 +id: (voxpopuli_eng_000519-voxpopuli_eng_000519) +Scores: (#C #S #D #I) 3 22 3 2 +REF: ** SO I HOPE THIS WILL HAPPEN FOR RUSSIA AS WELL and THAT RUSSIA CAN ALSO ENVISAGE AN EXTREME SUCCESS STORY AFTER THE SIGNIFICANT DATE in AUGUST this *** YEAR +HYP: SI HOLPE THA HISWLHAE MFORUSHEAS WHEL AN TAT RUSHE CON LS and **** ****** *** ISIGT D N CSTREMEM SCCESSTARY AFTR THES EG TWISIGIFICEND AT in ORGST this OUR B +Eval: I S S S S S S S S S S D D D S S S S S S S S S S S I S + +Speaker sentences 978: voxpopuli_eng_000520 #utts: 1 +id: (voxpopuli_eng_000520-voxpopuli_eng_000520) +Scores: (#C #S #D #I) 11 17 4 3 +REF: she ACCEPTED the fact that ******** CITIZENSHIP is ** SUBJECT TO NATIONAL JURISDICTION BUT SHE ALSO said that * ACCORDING to the MAASTRICHT treaty and SHE IS RIGHT THERE HAS TO BE A DIRECT LINK +HYP: she ECEPTO the fact that SITISIEN HIP is AY NASIONL BOURT OFTHE NOSIONO GRISDICTION BUTH YOURLSO said that A CORDIG to the MASTRICK treaty and *** ** ***** ***** HE AS RIGHTD THEAS TOBEADIREC LING +Eval: S I S I S S S S S S S I S S D D D D S S S S S S + +Speaker sentences 979: voxpopuli_eng_000521 #utts: 1 +id: (voxpopuli_eng_000521-voxpopuli_eng_000521) +Scores: (#C #S #D #I) 10 21 7 2 +REF: THE EU FAILED ESPECIALLY in DEMONSTRATING A UNIFIED and * ******** EFFICIENT APPROACH to CLIMATE CHANGE treatment as WELL as in STRENGTHENING ITS LEADING POLITICAL POSITION IN THIS AGENDA i CONSIDER THEREFORE taking THIS RESOLUTION AN ACT of UTMOST IMPORTANCE +HYP: E O FAILD ESPESIOLYE in ************* THEMSTHRATING AYULIFIDED and T AFISHENT AT PRORCH to ******* OLIMITCAENGCH treatment as ELE as in ************* *** ******* STRANGTHANING ITSELEADING POLITICKL COSITION INDESUGENDER i COSITHERE THEFOR taking **** ********** ISRESOLUION ANACT of UTMORST IMPORTANS +Eval: S S S S D S S I I S S D S S D D D S S S S S S S D D S S S S + +Speaker sentences 980: voxpopuli_eng_000522 #utts: 1 +id: (voxpopuli_eng_000522-voxpopuli_eng_000522) +Scores: (#C #S #D #I) 6 7 1 0 +REF: the UNITED states of EUROPE WILL BE a fact WITH SWEDEN as A PROVINCE +HYP: the UNIGTES states of YUROV IL B a fact WIT SWEDON as * PROVIDENCS +Eval: S S S S S S D S + +Speaker sentences 981: voxpopuli_eng_000523 #utts: 1 +id: (voxpopuli_eng_000523-voxpopuli_eng_000523) +Scores: (#C #S #D #I) 4 15 4 0 +REF: IT MUST BE THE CAPITAL of BOTH STATES and we MUST RECOGNISE PALESTINE AS A STATE as PROVIDED FOR IN THE OSLO AGREEMENTS +HYP: ** ITD MUS BETHE CAPBITLE of BOT THATS and we **** ********* MUSS RECONISE POLSTINIS STHAT as ******** PROVIDID FORE INTHE OF LOGREENCS +Eval: D S S S S S S D D S S S S D S S S S S + +Speaker sentences 982: voxpopuli_eng_000524 #utts: 1 +id: (voxpopuli_eng_000524-voxpopuli_eng_000524) +Scores: (#C #S #D #I) 5 25 1 2 +REF: UKRAINE IS FACED WITH ONE OF THE CRUCIAL CHALLENGES IN ITS HISTORY it WOULD be **** *** FUNDAMENTALLY WRONG to PRESS the nation NOW WITH ALL TYPES OF RESTRICTIONS POPULARLY CALLED AUSTERITY POLICY +HYP: TYU KRAN ES FASE T WITD WONE OF CRUSIAL CHALINGESE INICG HISTARY it WULD be FIUN THE MENTALY RONGK to PRES the nation *** NOWE WIT AL TIBES OFORESTRICTIONS POPELIDAL COALED OSTERITE POLI +Eval: S S S S S S S S S S S S S I I S S S D S S S S S S S S S + +Speaker sentences 983: voxpopuli_eng_000525 #utts: 1 +id: (voxpopuli_eng_000525-voxpopuli_eng_000525) +Scores: (#C #S #D #I) 3 6 0 1 +REF: MORE RULES and REGULATION will not *** IMPROVE THE SITUATION +HYP: MOR RULS and REAGILATION will not INM PROVE THES SITUATIO +Eval: S S S I S S S + +Speaker sentences 984: voxpopuli_eng_000526 #utts: 1 +id: (voxpopuli_eng_000526-voxpopuli_eng_000526) +Scores: (#C #S #D #I) 8 7 1 0 +REF: at least WE WOULD like to KNOW the SOURCE of the MONEY AND the POSSIBLE MOTIVES +HYP: at least BE WUD like to NOL the SORSE of the ***** MUNYAND the POSIBLE MORTHIFS +Eval: S S S S D S S S + +Speaker sentences 985: voxpopuli_eng_000527 #utts: 1 +id: (voxpopuli_eng_000527-voxpopuli_eng_000527) +Scores: (#C #S #D #I) 9 30 3 3 +REF: to HAVE THOSE EUROPEAN WORLD LANGUAGES in ** ***** TODAYS GLOBALISED WORLD IN TODAYS GLOBALISED ECONOMY in THIS GLOBAL VILLAGE which is CULTURAL ECONOMIC SOCIAL AND POLITICAL IS A most VALUABLE ASSET FOR THE ENTIRE EU WHICH we must TAKE FULL ACCOUNT OF and * +HYP: to WEAVE THOUSE YURPEN WAL LANGWOACES in TO THEIS GLABLISED WERLDT ISINT TO THEAIS GOABLIS ECONMY in DIS GOBE VILIAGE which is ******** COTIALY CONOMICK SOSIALE ELNBPOLITICOLBPW ITSE AE most VELABLE EASTHEIT FROM THEINTIRE E OUG THAT we must **** **** THAK FOLACOUNS and T +Eval: S S S S S I I S S S S S S S S S S D S S S S S S S S S S S S S D D S S I + +Speaker sentences 986: voxpopuli_eng_000528 #utts: 1 +id: (voxpopuli_eng_000528-voxpopuli_eng_000528) +Scores: (#C #S #D #I) 4 14 0 2 +REF: *** WE HAVE TO REPEAT THAT ODA CANNOT be USED to FINANCE SECURITY EXPENSES BORDER control or ****** MILITARY SUPPORT +HYP: EAE TOREBET THAT ALL HE AY AN NOT be YOUS to FINCS SIGURIT EXPANCES BARTHERS control or MLITRY SOU PORN +Eval: I S S S S S S S S S S S S I S S + +Speaker sentences 987: voxpopuli_eng_000529 #utts: 1 +id: (voxpopuli_eng_000529-voxpopuli_eng_000529) +Scores: (#C #S #D #I) 4 7 3 0 +REF: IF ANYTHING THE SCIENTIFIC reports ARE BECOMING MORE URGENT more ALARMING and MORE shocking +HYP: ** THIG HE INTIFIK reports *** ******** BCALE MAR more URDENTMORLARMING and MOR shocking +Eval: D S S S D D S S S S + +Speaker sentences 988: voxpopuli_eng_000530 #utts: 1 +id: (voxpopuli_eng_000530-voxpopuli_eng_000530) +Scores: (#C #S #D #I) 2 24 1 4 +REF: ****** ** *** FINALLY WHEN IT COMES TO INNOVATIVE FINANCIAL INSTRUMENTS WE NEED THEM BOTH for ******* OURSELVES TO SUPPORT OUR ECONOMIES but ALSO TO SUPPORT THOSE WHO ARE IN NEED +HYP: FINOLY IM WHE WHAE HINKINGK ABOUN THERE INOVATIFEF FI NSION INSTRMENTS WHN KU THE BOLTH for ORSELFS TOUG SOUPORT OUOUER A CONOMYS but **** AOS SO TOL SOPORKT THOS COHER INEAE +Eval: I I I S S S S S S S S S S S S I S S S S S D S S S S S S S + +Speaker sentences 989: voxpopuli_eng_000531 #utts: 1 +id: (voxpopuli_eng_000531-voxpopuli_eng_000531) +Scores: (#C #S #D #I) 2 6 0 1 +REF: that GIVES US A UNIQUE TOOL in ** PEACEMAKING +HYP: that GIVE AE SOR YUNIKE TOUL in ES MAKING +Eval: S S S S S I S + +Speaker sentences 990: voxpopuli_eng_000532 #utts: 1 +id: (voxpopuli_eng_000532-voxpopuli_eng_000532) +Scores: (#C #S #D #I) 2 3 0 1 +REF: * paper a VERY WEAK PROPOSAL +HYP: D paper a VERYHL WEEK PROPOSIL +Eval: I S S S + +Speaker sentences 991: voxpopuli_eng_000533 #utts: 1 +id: (voxpopuli_eng_000533-voxpopuli_eng_000533) +Scores: (#C #S #D #I) 3 10 3 1 +REF: RUSSIA HAS ALWAYS BEEN A very PROUD NATION with A RICH CULTURE WITH inventions ****** AND ESPRIT +HYP: ****** *** SRUSHAS OLYS BE very ***** PROUDNATION with ICH CLDCHUERE WI T inventions WITHAN ES CE +Eval: D D S S S D S S S S S I S S + +Speaker sentences 992: voxpopuli_eng_000534 #utts: 1 +id: (voxpopuli_eng_000534-voxpopuli_eng_000534) +Scores: (#C #S #D #I) 12 22 2 1 +REF: FAIR TAXATION EVEN a MODICUM of TAXATION IN some CASES MIGHT just HELP US to do WHAT I HAVE ALREADY SUGGESTED and WHO KNOWS MAKE the CASE for the ********* RETROSPECTIVE BANK RECAPITALISATION that we NEVER SAW +HYP: AR TACTAITION NVHEN a MODICE of TACSAITION N some CACES MIY just HELPES EM to do **** WAT IHEAREDY SHE GESTED and HO NOS MAK the CACE for the ETERSPECT OF BANKRE CAPDLIATION that we ***** NEVERSOL +Eval: S S S S S S S S S S D S S S S S S S S I S S S D S + +Speaker sentences 993: voxpopuli_eng_000535 #utts: 1 +id: (voxpopuli_eng_000535-voxpopuli_eng_000535) +Scores: (#C #S #D #I) 6 21 0 5 +REF: ********* ** ******* THE EUROPEAN ASYLUM SUPPORT OFFICE MOREOVER HAS AMONG ITS TASKS to ******** PROMOTE FACILITATE and COORDINATE EXCHANGES of information and **** OTHER ACTIVITIES RELATED to RELOCATION WITHIN THE UNION +HYP: THEULOPBE AN HSIDLOM SUPORTOF ICE MOR OVER AS A MONG ITCS TH IUS to PROMOUHT FESILY THAT and COURDINAT ECSCHANGES of information and UTHE ACTEVITYES ER LATY to LELCATION BD IN TEYUNION +Eval: I I I S S S S S S S S S S I S S S S I S S S S S S S + +Speaker sentences 994: voxpopuli_eng_000536 #utts: 1 +id: (voxpopuli_eng_000536-voxpopuli_eng_000536) +Scores: (#C #S #D #I) 7 13 3 2 +REF: THE CONCLUSION OF the FRAMEWORK AGREEMENT PROVIDES a LEGALLY BINDING INSTRUMENT to *** UPGRADE and STRENGTHEN eu ******* AUSTRALIA BILATERAL RELATIONS and to INCREASE COOPERATION +HYP: *** ********** HECONLSNOF the RAMEBORK AGEMENT PROVIE a LIGLY BINDINGK INSTRMENT to OBV GIRAT and STRANTN eu OSTRLIR BE LITHERI ATSIOS and to ******** INCEESCOPERATION +Eval: D D S S S S S S S I S S I S S S D S + +Speaker sentences 995: voxpopuli_eng_000537 #utts: 1 +id: (voxpopuli_eng_000537-voxpopuli_eng_000537) +Scores: (#C #S #D #I) 4 14 4 2 +REF: THEREFORE we ARE ASKING THE COUNCIL AND THE COMMISSION to PRESENT A TRANSPARENT AND COMPLETE ASSESSMENT OF the IMPACT of ****** * THE CRISIS +HYP: EREFRURE we *** ****** AS INTHE COUNSEL AS GLMITION to ******* * RENTA CAS BARI THA ULDBED the SESTENT of TEBACT O TH RISI +Eval: S D D S S S S S D D S S S S S S I I S S + +Speaker sentences 996: voxpopuli_eng_000538 #utts: 1 +id: (voxpopuli_eng_000538-voxpopuli_eng_000538) +Scores: (#C #S #D #I) 5 13 6 0 +REF: IN OTHER WORDS THE OBJECTION is not WHETHER MONEY IS PAID or not THE OBJECTION IS WHETHER THERE IS a DIRECT LINK OR NOT +HYP: ** ***** AINOTHE WERDS THEOBJECTION is not ******* WHETHE MUNY ISPAED or not *** THEOPBEBJECTION IIS WETHE THEY S a ****** **** DIDECTLINK ORENO +Eval: D D S S S D S S S D S S S S S D D S S + +Speaker sentences 997: voxpopuli_eng_000539 #utts: 1 +id: (voxpopuli_eng_000539-voxpopuli_eng_000539) +Scores: (#C #S #D #I) 5 12 1 2 +REF: IT DISTINGUISHES the two *** *** MAIN DOSSIERS HUMAN RIGHTS ABUSES by THE CURRENT GOVERNMENT and the IRANIAN NUCLEAR PROGRAMME +HYP: TO HESTINGISHIES the two MAE DOS HEAR YOUMER IT SAE YOUSE by THECAR NT GORENT and the ******* DANION NUKLEPROGDHME +Eval: S S I I S S S S S S S S D S S + +Speaker sentences 998: voxpopuli_eng_000540 #utts: 1 +id: (voxpopuli_eng_000540-voxpopuli_eng_000540) +Scores: (#C #S #D #I) 8 11 0 2 +REF: *** *********** MR PRESIDENT SEXUAL HARASSMENT is A form OF VIOLENCE and it IS the most EXTREME form of GENDER—BASED DISCRIMINATION +HYP: ESS METHEBDRONM HENKACTERE SECTION HERASD ENT is HE form O VILANCE and it I the most ETREAN form of GHNTR BASEDESCRMINATI +Eval: I I S S S S S S S S S S S + +Speaker sentences 999: voxpopuli_eng_000541 #utts: 1 +id: (voxpopuli_eng_000541-voxpopuli_eng_000541) +Scores: (#C #S #D #I) 4 10 0 3 +REF: we can LOOK to some **** *** * NON EU MEMBERS FOR GOOD EXAMPLES AS REGARDS TECHNOLOGIES +HYP: we can LOK to some EIRN NIN E U MEMBORS OR GOD EXSANPLE A REGARDED THE NOLAGES +Eval: S I I I S S S S S S S S S + +Speaker sentences1000: voxpopuli_eng_000542 #utts: 1 +id: (voxpopuli_eng_000542-voxpopuli_eng_000542) +Scores: (#C #S #D #I) 2 5 0 2 +REF: *** INVOLVED for THEIR POSITIVE and ************ CONSTRUCTIVE APPROACH +HYP: INM VLVEDS for HE POSITEIVE and CESTTACTEIVE A BROATC +Eval: I S S S I S S + +Speaker sentences1001: voxpopuli_eng_000543 #utts: 1 +id: (voxpopuli_eng_000543-voxpopuli_eng_000543) +Scores: (#C #S #D #I) 5 14 0 1 +REF: SO i hope that THIS WILL be ********* COMPLETED in THE FORESEEABLE FUTURE WHICH MEANS MAYBE TWO OR THREE MONTHS +HYP: O i hope that IS IL be COMPEATET AIR in HAFORSIVILE FOUOCHER THATD MANSE GAD BE TO A FRE MUNS +Eval: S S S I S S S S S S S S S S S + +Speaker sentences1002: voxpopuli_eng_000544 #utts: 1 +id: (voxpopuli_eng_000544-voxpopuli_eng_000544) +Scores: (#C #S #D #I) 4 16 1 4 +REF: ** FURTHER ENCOURAGE THE UNS EFFORTS to bring * ABOUT PEACE in ** ******* AFGHANISTAN AND TO OVERCOME THE FRAGILE SECURITY ENVIRONMENT in THE COUNTRY +HYP: OR FORDER ANDCORISHETHE YOU AND ETFHURH to bring A MNG PES in OF GNISTAN ANDTO OVER COME T OF FRESILSICURITY N VEIREMENT in *** THECONTY +Eval: I S S S S S I S S I I S S S S S S S S D S + +Speaker sentences1003: voxpopuli_eng_000545 #utts: 1 +id: (voxpopuli_eng_000545-voxpopuli_eng_000545) +Scores: (#C #S #D #I) 3 4 0 0 +REF: WE UNDERSTAND that some PEOPLE ARE angry +HYP: BEANDTHE STANT that some PEPL OAR angry +Eval: S S S S + +Speaker sentences1004: voxpopuli_eng_000546 #utts: 1 +id: (voxpopuli_eng_000546-voxpopuli_eng_000546) +Scores: (#C #S #D #I) 1 3 2 0 +REF: WE WANT to BE MORE RESPONSIBLE +HYP: ** OEN to ** HE MORSTPONCIVLD +Eval: D S D S S + +Speaker sentences1005: voxpopuli_eng_000547 #utts: 1 +id: (voxpopuli_eng_000547-voxpopuli_eng_000547) +Scores: (#C #S #D #I) 7 11 2 0 +REF: WE must RECTIFY this SITUATION and WE ASK THE COMMISSION to consider the most ADEQUATE COMPENSATION MEASURES FOR OUR PASSENGERS +HYP: E must EDACTIFIHITH this SUTIATION and ** H ASE THECOMION to consider the most ******** EDICKET GCOMINSATION MESHERS FORLOLW PESENGES +Eval: S S S D S S S D S S S S S + +Speaker sentences1006: voxpopuli_eng_000548 #utts: 1 +id: (voxpopuli_eng_000548-voxpopuli_eng_000548) +Scores: (#C #S #D #I) 11 19 3 3 +REF: the ******** ******** COMMISSION INVITES PARLIAMENT in the UPCOMING REVISION to open ITS position ON this MATTER WHICH REALLY CONCERNS ACCESS TO JUSTICE IN EUROPE and the ENFORCEMENT OF RIGHTS granted by ** EUROPEAN UNION LAW +HYP: the COMITION INGBISHE THE YUOPIONT PORLAMENT in the UPCOMIN KREVISION to open IS position OND this ****** ***** MATHERE WHICHREALY CONCSERD ACESE TOL USTICS INOUROP and the *********** INFORSTMENT OFRICES granted by HE YUROPIUNR YUND LO +Eval: I I S S S S S S S D D S S S S S S S D S S I S S S + +Speaker sentences1007: voxpopuli_eng_000549 #utts: 1 +id: (voxpopuli_eng_000549-voxpopuli_eng_000549) +Scores: (#C #S #D #I) 6 13 2 1 +REF: i * WELCOME VERY much THE RESUMPTION of TALKS BETWEEN THE ISRAELIS and THE PALESTINIANS and SINCERELY HOPE that THEY WILL SUCCEED +HYP: i L M ERY much *** THERSOUNTIO of TOCK TEN THEAS RALY and *** POLISTINIONS and ENCIRLY HOP that HE WLD SUCCED +Eval: I S S D S S S S S D S S S S S S + +Speaker sentences1008: voxpopuli_eng_000550 #utts: 1 +id: (voxpopuli_eng_000550-voxpopuli_eng_000550) +Scores: (#C #S #D #I) 3 10 1 4 +REF: * we HAVE AN ACCUMULATION of PROBLEMS RESULTING from *** ** ******** ARTIFICIAL UNDER BUDGETING IN PREVIOUS YEARS +HYP: L we **** HAE ECUMELATION of PROBLANCS RESILTING from THE AR TIFISHAL UND THE BAGEITINGK AND VERETPRIVUS YUS +Eval: I D S S S S I I I S S S S S S + +Speaker sentences1009: voxpopuli_eng_000551 #utts: 1 +id: (voxpopuli_eng_000551-voxpopuli_eng_000551) +Scores: (#C #S #D #I) 6 7 0 0 +REF: LET US not be the man of YESTERDAY LET US be TODAYS INSTITUTION +HYP: ELET AST not be the man of OUSTADY IT IS be TODAS INSTITUTIO +Eval: S S S S S S S + +Speaker sentences1010: voxpopuli_eng_000552 #utts: 1 +id: (voxpopuli_eng_000552-voxpopuli_eng_000552) +Scores: (#C #S #D #I) 6 24 3 4 +REF: * i WOULD URGE YOU to become AMBASSADORS OF the YEAR BY making ** ** ***** ITS IDEAS AND ACTIVITIES WIDELY KNOWN AMONGST EUROPEAN CITIZENS and PARTICIPATING IN EVENTS BE IT AT EUROPEAN NATIONAL OR LOCAL LEVEL +HYP: E i ***** GOD ARLSEOEN to become AMBSHETES O the **** YEARE making IT AY DEIRS AD ACTIVITHIS WOW WHIDLY NONE A MONGSHT TO YUOPEASITIESE and ************* PUTPICIPATING N HVBENTSE BET TAT YUROPEION NASHONL FOR LOK ALEL +Eval: I D S S S S D S I I I S S S S S S S S S D S S S S S S S S S S + +Speaker sentences1011: voxpopuli_eng_000553 #utts: 1 +id: (voxpopuli_eng_000553-voxpopuli_eng_000553) +Scores: (#C #S #D #I) 7 13 1 0 +REF: CERTAINLY such IMPACT ASSESSMENT COULD PRE EMPT CERTAIN PROBLEMS such as THOSE posed by the ELECTRONIC IDENTIFICATION of SHEEP IN SCOTLAND +HYP: DSARTDLY such ****** INPACT SESTMENT COLD PREAMT SERTAN PROBLOMS such as THOS posed by the ELCTRNIK IDEDTFICATION of SHEP AND SCOTEND +Eval: S D S S S S S S S S S S S S + +Speaker sentences1012: voxpopuli_eng_000554 #utts: 1 +id: (voxpopuli_eng_000554-voxpopuli_eng_000554) +Scores: (#C #S #D #I) 13 15 5 1 +REF: the COURT is content to SEE THAT ITS work has INFORMED the DISCHARGE PROCESS and has CONTRIBUTED TO PROPOSALS for ** IMPROVING the FINANCIAL MANAGEMENT OF EU SPENDING and BETTER TARGETING of EU FUNDS +HYP: the ORT is content to *** SE THATHITS work has INFORME the DESHARGH ROUES and has *********** ONTEBEUTEDTO PROPOSOLSE for IM PROVING the ********* FINCHAL MANAHENT OFE YOUSPENDING and ****** BETETORKATING of ** YOFNCS +Eval: S D S S S S S D S S I S D S S S S D S D S + +Speaker sentences1013: voxpopuli_eng_000555 #utts: 1 +id: (voxpopuli_eng_000555-voxpopuli_eng_000555) +Scores: (#C #S #D #I) 7 5 1 3 +REF: ******* *** REGULATORY CLARITY and CERTAINTY IS NEEDED for the PUBLIC sector and for ** industry +HYP: RECGUAI HRY GLAI THE and ********* SERTENTY IASNEADETD for the OBLIKE sector and for TH industry +Eval: I I S S D S S S I + +Speaker sentences1014: voxpopuli_eng_000556 #utts: 1 +id: (voxpopuli_eng_000556-voxpopuli_eng_000556) +Scores: (#C #S #D #I) 2 14 1 0 +REF: is IT REALLY NOT POSSIBLE TO USE OTHER HOUSING FACILITIES WITH APPROPRIATE RECEPTION CONDITIONS in THE MEANTIME +HYP: is ITDRELYNOT POSEABLR TO OUE A ATHER HOUSIN FESILIDES WI HE PROPETH RESEPTION CODIOS in *** THEMENTIME +Eval: S S S S S S S S S S S S S D S + +Speaker sentences1015: voxpopuli_eng_000557 #utts: 1 +id: (voxpopuli_eng_000557-voxpopuli_eng_000557) +Scores: (#C #S #D #I) 4 6 0 0 +REF: WILL you TAKE ACTION AT last if not THEN WHEN +HYP: WEL you TEAKE ACSION ATD last if not THEIN WEINDE +Eval: S S S S S S + + diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/text b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/text new file mode 100644 index 0000000000000000000000000000000000000000..0866ef67f00fdf592c794a4496113e383fb92b72 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/text @@ -0,0 +1,1092 @@ +LAD_eng_000254 HE REMAED WEL CAMPION ANTIL NINTIN SICTY FIVE A YAR IN WHCHE SUFHED A TERABL ACXITENT +LAD_eng_000255 AY LIBRL CON ERVETIVE HE AS DEFEATED IN ATY N ATY TWO +LAD_eng_000256 WON ROVED LAR CODRE TWO ROUDE AT WHANCE +LAD_eng_000257 SOME O THE OUNTIS HAE SERVAYIS FOR MALTBLE YEARS +LAD_eng_000258 BOTHOF THEVIRSINS FEATHE THE SONG HAPY HOLIDAY +LAD_eng_000259 SHAKSPIER MANY REFRNCES UR MAD TO SHEENDS INTR ACTIOND OR CARICTES FOM VERIUT PLAYES +LAD_eng_000260 IF NDY THE PROGRAM CULDBRAKE OUT GUST LITL FOME IT TWO FOMELIAR APROUCH +LAD_eng_000261 THE HALBUM WAS RELESE INO STRALIAR ARN NIN TINT OAGIST TWO THOUSENT AN E LEVON +LAD_eng_000262 HE NOW PLACE FOR A STRALIN TLOBE PEIRT GLOURY +LAD_eng_000263 ITIT NOT NONE HOW MUCHE IF ANY OF HER TLAMEMS ARE TRO +LAD_eng_000264 A SMLL BISNESS ONER BRAURD OPRATED HIS WEA AN HAP FARME FOR SICTENEARS FRO THE AG OF WENTY TWO +LAD_eng_000265 IN THENINTH SENCTRY HE WAS AN IRISH POAT +LAD_eng_000266 THEY AR MAUECET BY STRONGN +LAD_eng_000267 THE LALLE IS THERE FOR VOULED +LAD_eng_000268 IN THEARLY STAGES CAME CLOSE TO U A SLEP +LAD_eng_000269 RUNING EVERY THARTY MINIT THO UT SERVIS TIMEMS +LAD_eng_000270 AS RESILT WHEN THECOLIGE RE OPEND IT WHAS AS A ALL MALE COLIGE +LAD_eng_000271 THE TIME BETWEN THES PONT IS VERRABLE AND CANACER ANY WHE FRO A INIT TO MUCH LONGER +LAD_eng_000272 WEARK ON THE E E EAEAS STDARTED IN MARHCH TWO THAUSED AND SEVON AT COST OF FIVE MILIOND DOLLERS +LAD_eng_000273 HOWEVER TEWAS SOME DEC A GREMENT OE THE ENDING THEMEM WICH O MORY AND YOSHIMORY DECSKUSTE AT LEANGTH OVER EMOUL +LAD_eng_000274 THE CAPLE HAD NO CHILDRON +LAD_eng_000275 THEFITIAL SINGL F THAT DEBYU AL THM PARISS COLING HAD A ELABRT MUSIC VIDEAO +LAD_eng_000276 THE SERIS ENDED ON SICXTH AORGIST TWO THAUSEND AND FORE LASTING FRA TOUTL OF SEVENTY OND DAYS +LAD_eng_000277 HE HAS ALSOD CONTRIBETE TO THE NUN YOURC RE EU OF BOKS +LAD_eng_000278 BY PLACING SMAL ART OBDGECT THRO OUT THE ILME +LAD_eng_000279 IT I FOUNED IN BRESIL +LAD_eng_000280 IT WA THE SID OF THE CAMLY I IDENTIFIED MORLE WIFH +LAD_eng_000281 ECANDED IT SIGHTHE MUST LLSO SOD MIT A WORK PLAN +LAD_eng_000282 DUNDEY WON THE MACH THRE TWOE +LAD_eng_000283 HOWEVER THE VILIG REMANED ICILAT DT NTIL THE RIVUL OF TE FIRST NOUS PAPER SECOND RE POUBLICK +LAD_eng_000284 THE FIRST ERVI I THENU CHARC WAS HELD NINTN FIFTY ON AL THO THE BILDIG WAS NOT FULY FINISHED +LAD_eng_000285 THE AVRIGH HOUSEHLD SIES WAS TWO PONT TWO SEVON ND THE AVRIGH FAMLY SIES WAS THRE POENTESIARO SIARO +LAD_eng_000286 IT WAS FIRSTE RARD CAST ON THRED GANIOURY TWO HOUSEND AND TEN +LAD_eng_000287 THE WINGS WE NOW AD IN A SINGLE PRESING +LAD_eng_000288 TE DOCTE OFOLOIFY IN ENDENYEARIG MANAGEMENT +LAD_eng_000289 THISE O WAY THE MAEN ARKGUMEN OF SAIFTDY RISSKS +LAD_eng_000290 HE WAS ALLSO AD A LIFH MEMBR OF SCOUND THORPYUNITED +LAD_eng_000291 SHE FHEIRS THE L GAT DEFORSE BUT THIE NEVER HAPENS +LAD_eng_000292 FOT DROPS NABLE T HAD TH FOT SRAT ACROUSE +LAD_eng_000293 WHETH THE AR FLOY IS FREY OR FOURST CN FEC THE ENDGY OFIENCY OF TH WHNDO +LAD_eng_000294 AFTR GETIG HE IT MESERENT THE MAD TH NOU DORS +LAD_eng_000295 FRAGMNTE ON ACH FACE RE MARET WTH LTERS AY BE SE +LAD_eng_000296 FROM TH FIRSTD MINITE BOTH TEMES SHOD THE DESIRE 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THE GLOE OF AF FIR +voxforge_eng_000946 CHANS CHARS THY LIT COMAND +voxforge_eng_000947 IT WAS JEN SAINING SOFHELY OER BEYON THE ROCKCE +voxforge_eng_000948 OF LING AROL BOST BETWEN OSED +voxforge_eng_000949 HATRIT AND MURDER AND LOST FOR REVENGCH THEY POSSEST TO OFER FLOYING +voxforge_eng_000950 THAT OCOD HEAR ALL UPED DON TE LIMPOPOE +voxforge_eng_000951 IT WAS MY AD DE TO ATE +voxforge_eng_000952 SHE DOSANT WON TO WEIN +voxforge_eng_000953 SHE THINGE AITIS BECOS HE WONCE SOM THNG LE +voxforge_eng_000954 HSHE POLD E AND T THE LOC CREASETDON TO BRAK HIS BAC +voxforge_eng_000955 THA THE SOCOLD FORES AT WORK IN LITE HE ALCTRISIDY AND MANGNTISM +voxforge_eng_000956 WE TWION SHAOBPEINT AND PIAICET GRAGSIN AN COS THE THIHVBLE +voxforge_eng_000957 AL SOE I WONT ANFORMATION +voxforge_eng_000958 THE SICT DAY HE SPENT IN THE CAVON WH GRAGSON +voxforge_eng_000959 ION THIS Y POTHIES THE HAMERNG OF THE LTER MUNDING CRPUCLE OND THE BOB COFIRE ITK CENATICK NRGY ON TH ON HAND +voxforge_eng_000960 NOWE A FIRNY WIL STREMEM AND EVER AN ANON YOU A MURGE FROM AL THE GROVES AND FLOURS +voxforge_eng_000961 WITH HOT IT THE MOST DENCELY POPILAED REAGENS OF MOHEN TURIP ANDAMORICA +voxforge_eng_000962 TOM SPINK HAS HAR PON +voxforge_eng_000963 HE WNTED G THE FISHE T THIS FOLE ALREY SOF AGON +voxforge_eng_000964 LA A FLASHE HE LONCHE HIMSELF INT THE FETHED MAS O TH HOUHL +voxforge_eng_000965 IT CONTAE A TOTLE OF TWENTY ANTRES +voxforge_eng_000966 I HAVE FHELT MORE COMFORABLE +voxforge_eng_000967 THED POES TO MNTH VEATELITY +voxforge_eng_000968 THE WLLE DOGK THRST HIS GONT MUSLE TORD HIMN +voxforge_eng_000971 THE GAEBILE VORSCE OF HE SEMERIY RANG OUT +voxforge_eng_000972 IT WAS AI RIVER ANM MURGING LAK OURSELS FOM TE GRAT SOMP +voxforge_eng_000973 SAID THE MOLL PULING HIMSELF TOGETH WITHAN EFERT O MUST THING ME VERY ROD +voxforge_eng_000974 IN WHAT BYUCOLLICK SCOO OF FENCE HE HAD BED TORT THWAS BEYOND IMADGENING +voxforge_eng_000975 HAD NOT INABLED IN VESTIGADERS TO OPE TAIN A COMPERITIVFLY LITL CLOUST +voxforge_eng_000976 IT TRIKL OF FRESH BLOD RAN OVE RS FACE +voxforge_eng_000977 D IT WAS A CUORES CONSI TEANTSESE +voxforge_eng_000978 IT IS THE FIR PARTLY SHE SAINE +voxforge_eng_000979 THEY GOST LAYE OF THEBOSH AND POKD AWAY AN +voxforge_eng_000980 I NO THAT OURE IN CHARDE THERE AND JE NOSE +voxforge_eng_000981 FOR TIE THE ECSATING THILE OF HIS ADVENTHE WAS GON +voxforge_eng_000982 FAEDLY HIS FINGRS CLOS THADLY OVE THE ANGOCIF +voxforge_eng_000983 DEAR SIRE YOUR SECONT VICTDOM HAS FOLLON OND SCHADGDULE TIME +voxforge_eng_000984 H CON CAR F HMSELF E +voxforge_eng_000985 ACH INSILT ATE TO THE VALYU O THE CLAME +voxforge_eng_000986 THE IT MA BE TRANCS FORMED INTO ANY O OF TH FORMS OF WHCH ENRGY ISECEPTABL +voxforge_eng_000987 MESEIDES SCREAMED GRID LOVF IADND MANYIFESTED TH HIRIARD ICK AE BOND DEN N MENT OF HISTAIAR +voxforge_eng_000988 I WHN TO NO HOWT AL THIS IS POSTABLE +voxforge_eng_000989 RESENTING A SEMPL AN NSTRCTIE ILOSTRATION OF THE STROGOFOR LIVEF AMNG THERIVLE SPEACES +voxforge_eng_000990 HIL NEVE DO A TAP OF WERK THE HOL VORIANDGCH +voxforge_eng_000991 I HAE HNTE ALON TIS RIGE REPLAD FLIP +voxforge_eng_000992 LORD BUT IN GLAD TO SE YO AGIN FIL +voxforge_eng_000993 COWELINLY I WEN DATDID THATF IS DEA +voxforge_eng_000994 THE AR NOT REAGILE OSTER PIRATS NCKLES CO TNUDED +voxforge_eng_000995 TE MUST BE HURING FOR BISESS BUT I THT YOU MIGT WNT TO CEK A LOK A THER SITD +voxforge_eng_000996 DERAS NO CHANCE TO FIRE WITOUT HIDING HIM +voxforge_eng_000997 AS FOR HIMSELF WONT TH STREAT REAL WAY ARNING NKESING SETLY +voxforge_eng_000998 DON HM CONYOR BOY O LONG WO DESSYE +voxforge_eng_000999 COD BAIY PE AR HE SHOTED +voxforge_eng_001000 BUT SUCH DEVERGENS OF PINION WLD CONSTITUT NO MENANCE TO SITY +voxforge_eng_001001 I BIE WAS ON THANCEE AND O LY ON WOE SAVING HONTDLD +voxforge_eng_001002 E IO CAN OT FOALE YOW SHE SAINDNE +voxforge_eng_001003 ON THE FAR COARNER OF THE COMPOWND FENTE A HOAK BREADED +voxforge_eng_001004 TEN AGIN TOER HAD SOCH AN IERSTATING WAY AOBOT HM +voxpopuli_eng_000494 WE ALL NO O MAN AS A SECESTOLSTABL CUNTRY AR OAL MOR THEFOR THAT FOR THEHOL REAGON +voxpopuli_eng_000495 THERFOR ITS HIGH TIME OU COME FOR BOUGDT HE TE PREPORSL FOR REVEU BE DENOPRATINALSBRATSON OF TE OARDIT AND NON OALDITSRVISIES ANDE RADIDLACT E WUS OBOTISON +voxpopuli_eng_000496 IT ISCEEAR THAT WE HAVE NO TIME TO WAST THE NU RESSOULTSH OF THEYE ID PEASIESIE REOARDIN SIENTDIFICK BASIES OF GLIMINT T JAINCE LE NO ROUM FOR HESITDACSEON +voxpopuli_eng_000497 SENT SO IN THE CON TAENOR HIHAEVER AVEN TUCHED COME SLAVES CONTOFET GODS DROGES IT HETR +voxpopuli_eng_000498 I HOPE THAT COMIONS MBITINISHES INISHIFIVS W WNT CARAKT THE DACXT PROBLOEM BUT WIL BE ANANCSE FORE EISTING CHOLINGERS OF THER OBP TANCSPBORDSECTAR +voxpopuli_eng_000499 I TEOU AS I WAS DESION TAINARLY BY ON PERSON THE ORME PESIDED O THENIDE STACES AGANSE THE ARTICILATEDMCRATICK DRM NDURITY OF TEU S CONGRSS BY ALL OF ITS REPOBLKEN SO FICS TDEMERCRATIC T DEM RT MBERS IT WAS NAGREMENT WITHOUT ANY BIDNDINGOBLIGATIONS AS THELEADERS OF IRAUN VERY OUPENTLY INPRESIDH MAEPLY NTHE ERY DA THE SOCLD DEL WAS POLISHED +voxpopuli_eng_000500 FRE SPEACGE IS S SENCIALY AECECTINGTAT PEPL UR FREE TO SAY THINGS WE DO NOT LIEKE NOT MELY FREE TO SAY THINGS WE DO LIEK +voxpopuli_eng_000501 LAT AS LARND FOM THIE +voxpopuli_eng_000502 BESING HAT HE ANEVI MENTAE EAFECT OF PRDUCS MUST BE AVERY INMPRTANT ISHU ING TER EU AND HE OL IG TDEAR O THE IECOLABR NEVSA VER YOUSOL ORIANTATION FOR THECOSTSUMEMIS OF COURS HE IACULABER SOOD GIVEN TO THE MOST AND VIREMENT AF FENDY PORDUCT THE IFOREMITIONSHOL BECLEARE AND OE +voxpopuli_eng_000503 HOWEVER THE CIRENDRIGIEME NITS TO B BETER SALORT TO TH DIGIDL INFVEIRNENT EDE NSHOUR FAR RIMINERATION TO GREATDURS ANH TO OFOME TO ONSOMER EXAPECTATIONS +voxpopuli_eng_000504 AD COLS BO TE OMION AND MEMBRSTATH O ANHANCD HERSEPORT TO RECENCSIYATION TO SECUR PECS AND STOBLITY AN ARLEND IWIL THER FOR ARE SU CALIGE TO PEES SUPORTHIS MENDEN +voxpopuli_eng_000505 TRATIGIG CHOICIS ABOUT ERE TO LE WEST MUST BE MAD NOWL TOAKIN NE COUNE ANE TO FASE OUT FORSILFUL SUPSITDS BUT TEK THE GAS AS FORSOFYU IT CAN BE A HELP FUL BRIGING RNSICONARY MEADIUM TO BE US IN MEMIN MANY MEBERSTHAS I E ON TO EDCHIF OVER ANMBISHIS LIMIGTARGETS +voxpopuli_eng_000506 EWE AED POSILY AOFERIS COPE WE CAN COOTH TO POROSUE THEY SAME PLISES IN H SAME MANER NONING THAT WEL LED TO HE AMRSOTS THE RSOLSEST THAT WE NO DRDA +voxpopuli_eng_000507 U TERIS NOPTION B +voxpopuli_eng_000508 WE ALL SOE NEAD A THANGEH INOR HR DOLIDGTYI +voxpopuli_eng_000509 I LAGH BUT OF THE RESON OF COURSE IS ILIGL FISINGK AND THE OFOM AL TWE DOUN OFEN BY HALAM VESES WHICH AR LEAGISTERE TO COUNTRIES HICH LAUCE THE IL OF THR RSUREIS TW AN FORST T IN THENATIONE AGEMENCS NO MONT OF TESABEITY MASERS ORD ECXTRP PER WAR UILEDRESE THE PROBLOME OF RETIUSING +voxpopuli_eng_000510 THE OMPRMICE OLLSO IN CLUD SCLEARE RUASTO HEFINE WHICH MBRSTATD AS HURSTICTION AN TH OPRATIOM TYEMBRSTATES CONCSERD FOR CROS BO THE CACES AS HEHA THE NED TO EIMVOLLEF YOUR JUST HEN YOF OR WORK AND PLEACE WOESUPRT TO MRO HIS EIRECIF +voxpopuli_eng_000511 ENO THEGRENS WIOD T HAV S BLE HATHIS UR BAD BEES CRIMINAL BEES DELIBRTLY COTAMINATING HUDY WH THE DANGRUSINGREADIENT BUTIN FAC IN FAC HE DINGHE HUY BES AR L AVLALLRS DOUNM IHI TO CARY POLON BAC T THER HIVSTOD TO FE THER YOUNG +voxpopuli_eng_000512 BUT IT WAS THE COUNTRY ITD HELD BENG MORE CAPRABL +voxpopuli_eng_000513 R IN TO THE PRT FOLIAO OF THE NU COMISION RE DALING WIT FAUNDEMENTERIGHTE +voxpopuli_eng_000514 E MESIGE TAT THE YU DOUT AT HAVE ANYNUSOLTIONE +voxpopuli_eng_000515 AR YU WILING TO ACT INERVFATHEREFOR THE SOSIAL DE MENTION TO BE INLODED INTHE EOU COMPETENSYES AS PREPUS +voxpopuli_eng_000516 AERNCTHE OND PESPECTRU POLIYES TAKING WI THEREFORME OF OUR TELICON TH FRAM WR +voxpopuli_eng_000517 I BELE HIS REMARC ES WERA INEXTPLIITELY RACISCTED AND SENAFOBICKE AND PRMOTED RAIL INTOLRANC IN WHAY TH IS NO UCETABLE OR ALAOUED IN TE ONTOICTUTIOF THIS HOUSE +voxpopuli_eng_000518 REALIGH EGSAMPL SHOL THAT SOVING IES RLATETO A BOCATION FEYULD STRON COMNITY DEVELPMENT +voxpopuli_eng_000519 SI HOLPE THA HISWLHAE MFORUSHEAS WHEL AN TAT RUSHE CON LS AND ISIGT D N CSTREMEM SCCESSTARY AFTR THES EG TWISIGIFICEND AT IN ORGST THIS OUR B +voxpopuli_eng_000520 SHE ECEPTO THE FACT THAT SITISIEN HIP IS AY NASIONL BOURT OFTHE NOSIONO GRISDICTION BUTH YOURLSO SAID THAT A CORDIG TO THE MASTRICK TREATY AND HE AS RIGHTD THEAS TOBEADIREC LING +voxpopuli_eng_000521 E O FAILD ESPESIOLYE IN THEMSTHRATING AYULIFIDED AND T AFISHENT AT PRORCH TO OLIMITCAENGCH TREATMENT AS ELE AS IN STRANGTHANING ITSELEADING POLITICKL COSITION INDESUGENDER I COSITHERE THEFOR TAKING ISRESOLUION ANACT OF UTMORST IMPORTANS +voxpopuli_eng_000522 THE UNIGTES STATES OF YUROV IL B A FACT WIT SWEDON AS PROVIDENCS +voxpopuli_eng_000523 ITD MUS BETHE CAPBITLE OF BOT THATS AND WE MUSS RECONISE POLSTINIS STHAT AS PROVIDID FORE INTHE OF LOGREENCS +voxpopuli_eng_000524 TYU KRAN ES FASE T WITD WONE OF CRUSIAL CHALINGESE INICG HISTARY IT WULD BE FIUN THE MENTALY RONGK TO PRES THE NATION NOWE WIT AL TIBES OFORESTRICTIONS POPELIDAL COALED OSTERITE POLI +voxpopuli_eng_000525 MOR RULS AND REAGILATION WILL NOT INM PROVE THES SITUATIO +voxpopuli_eng_000526 AT LEAST BE WUD LIKE TO NOL THE SORSE OF THE MUNYAND THE POSIBLE MORTHIFS +voxpopuli_eng_000527 TO WEAVE THOUSE YURPEN WAL LANGWOACES IN TO THEIS GLABLISED WERLDT ISINT TO THEAIS GOABLIS ECONMY IN DIS GOBE VILIAGE WHICH IS COTIALY CONOMICK SOSIALE ELNBPOLITICOLBPW ITSE AE MOST VELABLE EASTHEIT FROM THEINTIRE E OUG THAT WE MUST THAK FOLACOUNS AND T +voxpopuli_eng_000528 EAE TOREBET THAT ALL HE AY AN NOT BE YOUS TO FINCS SIGURIT EXPANCES BARTHERS CONTROL OR MLITRY SOU PORN +voxpopuli_eng_000529 THIG HE INTIFIK REPORTS BCALE MAR MORE URDENTMORLARMING AND MOR SHOCKING +voxpopuli_eng_000530 FINOLY IM WHE WHAE HINKINGK ABOUN THERE INOVATIFEF FI NSION INSTRMENTS WHN KU THE BOLTH FOR ORSELFS TOUG SOUPORT OUOUER A CONOMYS BUT AOS SO TOL SOPORKT THOS COHER INEAE +voxpopuli_eng_000531 THAT GIVE AE SOR YUNIKE TOUL IN ES MAKING +voxpopuli_eng_000532 D PAPER A VERYHL WEEK PROPOSIL +voxpopuli_eng_000533 SRUSHAS OLYS BE VERY PROUDNATION WITH ICH CLDCHUERE WI T INVENTIONS WITHAN ES CE +voxpopuli_eng_000534 AR TACTAITION NVHEN A MODICE OF TACSAITION N SOME CACES MIY JUST HELPES EM TO DO WAT IHEAREDY SHE GESTED AND HO NOS MAK THE CACE FOR THE ETERSPECT OF BANKRE CAPDLIATION THAT WE NEVERSOL +voxpopuli_eng_000535 THEULOPBE AN HSIDLOM SUPORTOF ICE MOR OVER AS A MONG ITCS TH IUS TO PROMOUHT FESILY THAT AND COURDINAT ECSCHANGES OF INFORMATION AND UTHE ACTEVITYES ER LATY TO LELCATION BD IN TEYUNION +voxpopuli_eng_000536 HECONLSNOF THE RAMEBORK AGEMENT PROVIE A LIGLY BINDINGK INSTRMENT TO OBV GIRAT AND STRANTN EU OSTRLIR BE LITHERI ATSIOS AND TO INCEESCOPERATION +voxpopuli_eng_000537 EREFRURE WE AS INTHE COUNSEL AS GLMITION TO RENTA CAS BARI THA ULDBED THE SESTENT OF TEBACT O TH RISI +voxpopuli_eng_000538 AINOTHE WERDS THEOBJECTION IS NOT WHETHE MUNY ISPAED OR NOT THEOPBEBJECTION IIS WETHE THEY S A DIDECTLINK ORENO +voxpopuli_eng_000539 TO HESTINGISHIES THE TWO MAE DOS HEAR YOUMER IT SAE YOUSE BY THECAR NT GORENT AND THE DANION NUKLEPROGDHME +voxpopuli_eng_000540 ESS METHEBDRONM HENKACTERE SECTION HERASD ENT IS HE FORM O VILANCE AND IT I THE MOST ETREAN FORM OF GHNTR BASEDESCRMINATI +voxpopuli_eng_000541 WE CAN LOK TO SOME EIRN NIN E U MEMBORS OR GOD EXSANPLE A REGARDED THE NOLAGES +voxpopuli_eng_000542 INM VLVEDS FOR HE POSITEIVE AND CESTTACTEIVE A BROATC +voxpopuli_eng_000543 O I HOPE THAT IS IL BE COMPEATET AIR IN HAFORSIVILE FOUOCHER THATD MANSE GAD BE TO A FRE MUNS +voxpopuli_eng_000544 OR FORDER ANDCORISHETHE YOU AND ETFHURH TO BRING A MNG PES IN OF GNISTAN ANDTO OVER COME T OF FRESILSICURITY N VEIREMENT IN THECONTY +voxpopuli_eng_000545 BEANDTHE STANT THAT SOME PEPL OAR ANGRY +voxpopuli_eng_000546 OEN TO HE MORSTPONCIVLD +voxpopuli_eng_000547 E MUST EDACTIFIHITH THIS SUTIATION AND H ASE THECOMION TO CONSIDER THE MOST EDICKET GCOMINSATION MESHERS FORLOLW PESENGES +voxpopuli_eng_000548 THE COMITION INGBISHE THE YUOPIONT PORLAMENT IN THE UPCOMIN KREVISION TO OPEN IS POSITION OND THIS MATHERE WHICHREALY CONCSERD ACESE TOL USTICS INOUROP AND THE INFORSTMENT OFRICES GRANTED BY HE YUROPIUNR YUND LO +voxpopuli_eng_000549 I L M ERY MUCH THERSOUNTIO OF TOCK TEN THEAS RALY AND POLISTINIONS AND ENCIRLY HOP THAT HE WLD SUCCED +voxpopuli_eng_000550 L WE HAE ECUMELATION OF PROBLANCS RESILTING FROM THE AR TIFISHAL UND THE BAGEITINGK AND VERETPRIVUS YUS +voxpopuli_eng_000551 ELET AST NOT BE THE MAN OF OUSTADY IT IS BE TODAS INSTITUTIO +voxpopuli_eng_000552 E I GOD ARLSEOEN TO BECOME AMBSHETES O THE YEARE MAKING IT AY DEIRS AD ACTIVITHIS WOW WHIDLY NONE A MONGSHT TO YUOPEASITIESE AND PUTPICIPATING N HVBENTSE BET TAT YUROPEION NASHONL FOR LOK ALEL +voxpopuli_eng_000553 DSARTDLY SUCH INPACT SESTMENT COLD PREAMT SERTAN PROBLOMS SUCH AS THOS POSED BY THE ELCTRNIK IDEDTFICATION OF SHEP AND SCOTEND +voxpopuli_eng_000554 THE ORT IS CONTENT TO SE THATHITS WORK HAS INFORME THE DESHARGH ROUES AND HAS ONTEBEUTEDTO PROPOSOLSE FOR IM PROVING THE FINCHAL MANAHENT OFE YOUSPENDING AND BETETORKATING OF YOFNCS +voxpopuli_eng_000555 RECGUAI HRY GLAI THE AND SERTENTY IASNEADETD FOR THE OBLIKE SECTOR AND FOR TH INDUSTRY +voxpopuli_eng_000556 IS ITDRELYNOT POSEABLR TO OUE A ATHER HOUSIN FESILIDES WI HE PROPETH RESEPTION CODIOS IN THEMENTIME +voxpopuli_eng_000557 WEL YOU TEAKE ACSION ATD LAST IF NOT THEIN WEINDE diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/token b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/token new file mode 100644 index 0000000000000000000000000000000000000000..ba224d2d6b843aea3d40a04a481180f2dd1c6af2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/token @@ -0,0 +1,1092 @@ +LAD_eng_000254 H E R E M A E D W E L C A M P I O N A N T I L N I N T I N S I C T Y F I V E A Y A R I N W H C H E S U F H E D A T E R A B L A C X I T E N T +LAD_eng_000255 A Y L I B R L C O N E R V E T I V E H E A S D E F E A T E D I N A T Y N A T Y T W O +LAD_eng_000256 W O N R O V E D L A R C O D R E T W O R O U D E A T W H A N C E +LAD_eng_000257 S O M E O T H E O U N T I S H A E S E R V A Y I S F O R M A L T B L E Y E A R S +LAD_eng_000258 B O T H O F T H E V I R S I N S F E A T H E T H E S O N G H A P Y H O L I D A Y +LAD_eng_000259 S H A K S P I E R M A N Y R E F R N C E S U R M A D T O S H E E N D S I N T R A C T I O N D O R C A R I C T E S F O M V E R I U T P L A Y E S +LAD_eng_000260 I F N D Y T H E P R O G R A M C U L D B R A K E O U T G U S T L I T L F O M E I T T W O F O M E L I A R A P R O U C H +LAD_eng_000261 T H E H A L B U M W A S R E L E S E I N O S T R A L I A R A R N N I N T I N T O A G I S T T W O T H O U S E N T A N E L E V O N +LAD_eng_000262 H E N O W P L A C E F O R A S T R A L I N T L O B E P E I R T G L O U R Y +LAD_eng_000263 I T I T N O T N O N E H O W M U C H E I F A N Y O F H E R T L A M E M S A R E T R O +LAD_eng_000264 A S M L L B I S N E S S O N E R B R A U R D O P R A T E D H I S W E A A N H A P F A R M E F O R S I C T E N E A R S F R O T H E A G O F W E N T Y T W O +LAD_eng_000265 I N T H E N I N T H S E N C T R Y H E W A S A N I R I S H P O A T +LAD_eng_000266 T H E Y A R M A U E C E T B Y S T R O N G N +LAD_eng_000267 T H E L A L L E I S T H E R E F O R V O U L E D +LAD_eng_000268 I N T H E A R L Y S T A G E S C A M E C L O S E T O U A S L E P +LAD_eng_000269 R U N I N G E V E R Y T H A R T Y M I N I T T H O U T S E R V I S T I M E M S +LAD_eng_000270 A S R E S I L T W H E N T H E C O L I G E R E O P E N D I T W H A S A S A A L L M A L E C O L I G E +LAD_eng_000271 T H E T I M E B E T W E N T H E S P O N T I S V E R R A B L E A N D C A N A C E R A N Y W H E F R O A I N I T T O M U C H L O N G E R +LAD_eng_000272 W E A R K O N T H E E E E A E A S S T D A R T E D I N M A R H C H T W O T H A U S E D A N D S E V O N A T C O S T O F F I V E M I L I O N D D O L L E R S +LAD_eng_000273 H O W E V E R T E W A S S O M E D E C A G R E M E N T O E T H E E N D I N G T H E M E M W I C H O M O R Y A N D Y O S H I M O R Y D E C S K U S T E A T L E A N G T H O V E R E M O U L +LAD_eng_000274 T H E C A P L E H A D N O C H I L D R O N +LAD_eng_000275 T H E F I T I A L S I N G L F T H A T D E B Y U A L T H M P A R I S S C O L I N G H A D A E L A B R T M U S I C V I D E A O +LAD_eng_000276 T H E S E R I S E N D E D O N S I C X T H A O R G I S T T W O T H A U S E N D A N D F O R E L A S T I N G F R A T O U T L O F S E V E N T Y O N D D A Y S +LAD_eng_000277 H E H A S A L S O D C O N T R I B E T E T O T H E N U N Y O U R C R E E U O F B O K S +LAD_eng_000278 B Y P L A C I N G S M A L A R T O B D G E C T T H R O O U T T H E I L M E +LAD_eng_000279 I T I F O U N E D I N B R E S I L +LAD_eng_000280 I T W A T H E S I D O F T H E C A M L Y I I D E N T I F I E D M O R L E W I F H +LAD_eng_000281 E C A N D E D I T S I G H T H E M U S T L L S O S O D M I T A W O R K P L A N +LAD_eng_000282 D U N D E Y W O N T H E M A C H T H R E T W O E +LAD_eng_000283 H O W E V E R T H E V I L I G R E M A N E D I C I L A T D T N T I L T H E R I V U L O F T E F I R S T N O U S P A P E R S E C O N D R E P O U B L I C K +LAD_eng_000284 T H E F I R S T E R V I I T H E N U C H A R C W A S H E L D N I N T N F I F T Y O N A L T H O T H E B I L D I G W A S N O T F U L Y F I N I S H E D +LAD_eng_000285 T H E A V R I G H H O U S E H L D S I E S W A S T W O P O N T T W O S E V O N N D T H E A V R I G H F A M L Y S I E S W A S T H R E P O E N T E S I A R O S I A R O +LAD_eng_000286 I T W A S F I R S T E R A R D C A S T O N T H R E D G A N I O U R Y T W O H O U S E N D A N D T E N +LAD_eng_000287 T H E W I N G S W E N O W A D I N A S I N G L E P R E S I N G +LAD_eng_000288 T E D O C T E O F O L O I F Y I N E N D E N Y E A R I G M A N A G E M E N T +LAD_eng_000289 T H I S E O W A Y T H E M A E N A R K G U M E N O F S A I F T D Y R I S S K S +LAD_eng_000290 H E W A S A L L S O A D A L I F H M E M B R O F S C O U N D T H O R P Y U N I T E D +LAD_eng_000291 S H E F H E I R S T H E L G A T D E F O R S E B U T T H I E N E V E R H A P E N S +LAD_eng_000292 F O T D R O P S N A B L E T H A D T H F O T S R A T A C R O U S E +LAD_eng_000293 W H E T H T H E A R F L O Y I S F R E Y O R F O U R S T C N F E C T H E E N D G Y O F I E N C Y O F T H W H N D O +LAD_eng_000294 A F T R G E T I G H E I T M E S E R E N T T H E M A D T H N O U D O R S +LAD_eng_000295 F R A G M N T E O N A C H F A C E R E M A R E T W T H L T E R S A Y B E S E +LAD_eng_000296 F R O M T H F I R S T D M I N I T E B O T H T E M E S S H O D T H E D E S I R E T O C O M P E E T W I T H E G R E I O F A P R O C E R S +LAD_eng_000297 F I S I C L H E R I B Y E C E R S I D S E S M A Y H E L P T H P A T I O N T E T O M A I N T A I N M U S L S T R I N G T H +LAD_eng_000298 H O W E V E R T H E T O W N E H E L I V S I N N O U N D W O N T T O H E A R A B O U T H E R +LAD_eng_000299 A D I S R I E S A E P O E N T D E N T O A N A C T I N G C H I V E J U S T I S S O R J O U D G E O F T H E S O P R E M E C O R T +LAD_eng_000300 T H E S O R Y B E S O U T C U V E R I N G I S T E N R E M O V E D T A N D T E B E N D S A R P A R T H A L Y C O C K E T +LAD_eng_000301 T H I S N A S T I A L M O V E N T W H C H E B E G O N W T H S O U H H O P C A M E T O A S A D E A N D +LAD_eng_000302 H I S A S E O S I A T E O U S U A L Y C A L D H I M T E O R E T H E G O O D L O K I N G G I Y +LAD_eng_000303 I T S M A E N O F H I C E S W E R I N L U N D E N W E T H E S E C E N D O F I S S B E L F A S T +LAD_eng_000304 A C T U L Y I H A D N E R B E E N T O A V I L I D G E B E F O R T H A T +LAD_eng_000305 H E A S C H A G E I T H P L A D I N G T O S E T O F B O M S I N U R A P A N D T H E U N I G T E T A T E +LAD_eng_000306 M A K I N G M R A R S I S T H E T H I R D S T U D I U R A L B A E B Y B E L D E N A S T R A L I A N A R T I S T G O T I E A Y +LAD_eng_000307 H E T H E N M O V E D T O W O A S I N G T O D D E S I A N D W A S A P A R T N E I T W O R D B R O W N E A N D T I L L N I N T E N W E N T Y N I N +LAD_eng_000308 J O S O F H I Y S C O L E A D T H E S C O L E S T H E Y C O M P E T G A N E D I N A L S P O R T S +LAD_eng_000309 W E L F P L U S O N M A C H B A N D E R C O A U R D +LAD_eng_000310 I T H I N K I M I G H T B E N O T H I N G +LAD_eng_000311 T H E H O M A S B I L T A N D L I V E D I N B Y A N D R J A C A N D C A N I D Y D E P E T Y C O L E C T O T H E I N T E R N L R E V I N U S E R V I S +LAD_eng_000312 I N N I N T A N S I C E Y E F O R E H E W E N T B A K T O O M S E K A N D E N T E T H E A C T O S C H O U L O F O A M S K +LAD_eng_000313 T H E B A N K I S J U I N T L Y O N E D B Y H I M A N D H I S B R O U V E R A N D R E L I T I V S +LAD_eng_000314 H E S O P E I C E N T L Y W A N T O C O L I N B R E I S T A L +LAD_eng_000315 W O N T H A U S E N D A T H U N D E D F O R T Y S I C K C S F O A R T H I D I T I O N +LAD_eng_000316 A P A T O F L I T L I N G L E N D B E Y O N D W A I L E S I T H A S B E E A E N C H R L Y I N G L I S H S P E A K I N G F O R N I N H U N T R E D O E A R S +LAD_eng_000317 H E P L A D W T H T E N P L A R S F O R H A R V F W A S A G A N E T H T R D I T I O N I N D D E A S S P E +LAD_eng_000318 T H E R E I D I N G G J O U D G W A S W E B S T O F A I R H O W A S A L E A D Y A S I E D T O T H E C O R T B E F O R E T H I S C A C E W A S S H E D U L T +LAD_eng_000319 B G G R A T H E F I V E W A S T H E T H I R D O T H E M A I N S E R I S T O F E C H E A L I V E L O U N C H +LAD_eng_000320 I T S M O T O I S H O E V E Y O U A R A N D W H E R E V E R Y O U A R E O N T H E D I R N Y O F F A I F Y O A E W E L C O M H E R +LAD_eng_000321 R O B E T A M I L E A S C O T H W I L T S O N +LAD_eng_000322 A F T R E W N Y U A R B R A K S I R A O D E G R E W A S H E F L L I N G V E N C H E R +LAD_eng_000323 A Y A M T E Y M A N U F A C T E D A M O R T L C I T O F T H E A D S A I D A R D R A C S T E R +LAD_eng_000324 T H E E S E S S A Y A M E D T O B I L E D A L E F T W I N G O L T E R N I T I F T O N O U L A B E R A N D T H E E S S A N D P E +LAD_eng_000325 H E L I V S L I K H E A S Y O N G P R S O N +LAD_eng_000326 M A S T E O F S I N D I N E N D E N E A R I G M A N I G E N T +LAD_eng_000327 S H E F A I L E D T O A K T H E T O P T H R E A T H E C E N I O N D J U N I E A R T R A C T R I L E S T H A T D U N +LAD_eng_000328 A T O A R E F O L O U D I N S U P O R T +LAD_eng_000329 T H E E S T A B L I S E N A T E N S E V E N T Y O N A N D E W E O T H E L D E S T C L O P S I N T H E S O U T H O F I N G L E N D +LAD_eng_000330 H E W S A M E M B R O F T H E G E A S S C O T L E N D A D F I S E R Y B O R D +LAD_eng_000331 T W O T H O U S E N D N D F I V E G E N T L M E N +LAD_eng_000332 A O U R E F I L E A D A S T O N G R E C E P T I O N I N U R A P A D C H V E D E S T O B E U T I O N T U T T H A T W A S N O T T H E C A C E H E R +LAD_eng_000333 B L T H O I S S T D E T H E S P O S T E R I E R A N G A L S T R O C T H E S +LAD_eng_000334 H E W A S A L L S O A T H R E T I M E F R E N C H E N A S I N L H A M B P I A N N I N T I N N I N T Y N I N T E N I T Y F O R E T W O H O U S E D A N W O N +LAD_eng_000335 T H E I L I G E S T R U C T H E R S H O W N I N I S M A P I S T A G R A T E X S T E N T U N C H A G E T D A Y +LAD_eng_000336 R U H A R I S R E C A G N I S E D I T N U K L E R D E S A U S T E R E C P O R T E S A N D O T H E S A F T Y O F I T S T E N O L A G Y +LAD_eng_000337 A S O F T W O T H O U S E N D O D F O R T E N A E M T Y V E E I S A V A L A B L E W I T H I N T H E U N I G T E D C I N G D M O N V E R G E I N M E D I E R A N D S C G I Y +LAD_eng_000338 N O Y O U R K P E A N G I N R A N D M H O U S E +LAD_eng_000339 T H E D U T C H Y W A S E C U R E D I T E O U T C O M E O F T H E O F I C T W A L R +LAD_eng_000340 W I T G O D P A C E S D A R T E T H E M A C H H I H B O T H T E M E M S O L T E N A T I N G S O P R E M I S Y +LAD_eng_000341 T H I S V R T I O N I S N O N T E D O R B E I N E R Y F A V T F U L E T O T H E R I D I O N L N O V H L +LAD_eng_000342 T H I S P R S A U M P T I O N I S N O T F U L E F I L D W O N H A S T O N O T L E A T E T W O C O N G E T D I A M I T E S +LAD_eng_000343 N O T A L E T I T L E S I N L D E D G O L D A N A C S T H E R E V E N G O F D E A T A D E R R A D M O B I L O U T R U N E R S A N D S A K G R S O N I C T H E H E A G H O G K +LAD_eng_000344 T H E N I N T N N I N T Y N I N D J U G M E N T N O T D T A T T H E I N T F L O N C O T H E F A R T H E R O F T H E C U S D H I S B E E T H E R +LAD_eng_000345 M O K D A F S W E A R S R V E N G E H A N D J O I N S F O U R S E S I T M U L K C M T O O V E R T R O M M O K B E T H +LAD_eng_000346 T H E E A D Y A V L E V I L I G E C O R T W A S A L L A S A N C H O U S T O C A P T H E E N E R O U N T H E V I L I G E G A P L E S +LAD_eng_000347 T H E A S A N I N R A N K S I S T E M E A C H R A N C A V I G M O R E P O U E R T H T H E L O E R A N K +LAD_eng_000348 T H E A S T A B L I S C H E D D E P L I M A T I C R L A T I O N D S O N D E P T O M E R N I N T N T H N I N T N S E V E N T Y T W O +LAD_eng_000349 T H I A S F I R T H C S T E N D E D T O I N C L D M O R E Y O U C A Y D A T E I N D E C S E M B E R T W O T H O U S E D N D F O R T E N +LAD_eng_000350 T H E U C H G O V E N T I S C I R N T L Y E S A M I N G T H E L E A K L E C O N C I C Q E N C E S F H E R O L I N G +LAD_eng_000351 F R O M N I N T I N T H E R T Y T H R E E T O N I N T I N F O A R T Y N I N T H E M E R I C E D L E E W O N D W E L V E A U T O T H E F I R S T S I C S T E N +LAD_eng_000352 T H E A I R H E F E L E S I C K W T T I V F O S H I M S E L F +LAD_eng_000353 S I C X T T E M E S H A E E D E V E I D E D I N T O T W O G R U P S O F T H R E T E M E M S A C H +LAD_eng_000354 T H E F I R S T S E S O N R E M I A D O N D W E L T H D U N T W O T H O U S E N D A N F I F D E E N +LAD_eng_000355 I T S A C E D T H E W E I H B O L R D A N D S I S T A M T W E N T Y F O R E C O M B I N G F E A C E S F R O M B O T H +LAD_eng_000356 V E L Y U E T W O O H N U M B R S W O N T W O A N D T H R E +LAD_eng_000357 T H E L O R P A T O F M E N S D R E S E S W E M U C H H O U R T I N L E A N G T H N T H O S F R W I E I N +LAD_eng_000358 T H E I G O A L T H S I N T E R N W E R C E A D E D B Y T H E M U L E R S +LAD_eng_000359 J O S O F H I Y S C O L A V E R Y W E K O F T H S C O L H E A R +LAD_eng_000360 A S R S I L O F A L T H E A R G U M E N T G E T I G T O H E R +LAD_eng_000361 I T H A D Q U A R T E R S A R I N S H E F E I L D O U N I G T E D C I N G D O M +LAD_eng_000362 L A Y L L S O F I H A L Y S I D E T H E C O N T R A C T O N S T A G E W H T H E D I R E C T E A N D P R O D O U S E S O F T H E G O L D A N I Y S +LAD_eng_000363 F I S I C L E F E R I A P Y C O N H E L P A T I E N T E T O A R N H O T O W A E K W T H E F O T D R O U P E +LAD_eng_000364 I T E N T O N T O S E L T H R E Y H U N D R E D T H O U S E D Y A U N I T S A C H E V E F I V E F N O +LAD_eng_000365 T H E N A M E M S T D U C K A F E R T H A T +LAD_eng_000366 T H E I L B O M L A T E R B R A C T H E D I M E N D R E C O R D O N D C O K C U M U S I C K +LAD_eng_000367 I T E D E A T O R I A L W E S O U B M I T A N D I T O R T H E A P O L I T O P R I S E +LAD_eng_000368 D J O S O F P L A Y E S A U R F E T C E D I E A C H W E E O T H S H O +LAD_eng_000369 T H E W A T F R A T I M E M B I L D I N G U T T H E F O R S E S B E I G I N G T O O A N D R I F T H I S E A V L R E A L Y A E X S I S T S +LAD_eng_000370 B R E A E M E N C I O N O F T H C O N V I C T I O N A P E R D O N P A G E T H R E O F T H E N O U Y O U O K T I M E S +LAD_eng_000371 O R D E D B Y P E S I O N O N P I C H F R M B A K R I H T T O F R U N T L E F T E T +LAD_eng_000372 H E A S M E M B E R O F T H E C O R T O T H E R I L C O L I G O F A R T L U N D E N Y O U C A Y +LAD_eng_000373 D E R I N T H E C O U R S E O F H E C A M P A I N F I R G E A N D V I S I T E D L L T H E R T Y N I N W A S I G T A N S T A T C O N T Y I S +LAD_eng_000374 A S T R I P O F P A P E R O F L E A N T H +LAD_eng_000375 S A T O H A D F R E K U N T L Y W E R K T O G E T H W T H Y O U K Y Y A M E A R O N P R E V I U S P O G E C T S +LAD_eng_000376 S H E W S B O R E O N D S C R E N D U I N G T H E E P S O D B R U R D C A S T O N F O R T H N O V E M B E R N I N T E N N I N T Y F O R +M-AILABS_eng_000159 A H T U R E D R O U W N E D S H E H A D C O M I N S O G E N T E L Y T H A T H E H A D E V E H E R D H E R +M-AILABS_eng_000160 A T O B E S H O U O R H W E M U S T C E O U R D O R S S H O T W E M U S L A T N O W O U N I N H A +M-AILABS_eng_000161 A C I D E S B E N E B E G A N M O K I N G L Y Y O U M A H V E W E D E W H I I C A L D O T R O U S W H N I C O O D J O S T A S W I L L H E D E S T R E Y O U T H A T I D O T A D O A N C E R H I M +M-AILABS_eng_000162 T H E P E S E N T T I R H I M S E F A P O N H I M A N D B O U N E D H I S F O R L A K E D H T L Y S O A T E C O N O T M O E +M-AILABS_eng_000163 N O R M U S T T H O U S O L I M I T H T H E O L Y O N O F I S R I L A S T O T H I N H H A T H B U T W N E N W A Y I N W H C H C N G O R I F Y H I M S E L F B Y T H E +M-AILABS_eng_000164 T H E L D C O M P R S O N D B E T W E N T H I M P A L S O F E X S E A K I T I V E A N D T H E L I T B R L E A R T U S M A N H W W O D L E U R N D T H A T H E R E O N L Y O N E E R T O P O S I E D E C I S I O S O A L A B L E I N A L L T H E W E R L O T H I N K I N G +M-AILABS_eng_000165 A V F T R T H I C S P E R I A N C E T H E E N D V A D E R S W E R C A I R F L T O C E P E A S A K F E D I S T E N C E F R O M T H E W A L L +M-AILABS_eng_000166 M A O N O U B E A E R S M I N G F I R T H E R I T H I N Y O A T N O I T I H A V E H E R A M O S T M S T E R I A U S T E L P R G R O M E M E S E A S E W H A T I S I T E I S S H E D A D N O I T I S N O T U B O U T H E R Y +M-AILABS_eng_000167 D N O W H L E M I S T O R T H U N T O N S A D D I E T H E B A S K T O M E I L T A K I T W +M-AILABS_eng_000168 A N A R A B I O A N N I H T E C S C L A M E T R O G T W H I T H A T W A S A M A G I A K N I G H T W O A S I N I T H E R S D I F R N T S O R T S A N I G H T S M A T S A I D T H E S A L R A N D T H E N I G T B U T N U B I G H T M E N E S A T T H E S A M E N I G H T Y O M E A N +M-AILABS_eng_000169 I V E T U R E D O B F E O U P W A R D O H U D R D O M Y B E S T E H A N D S F O R N O O T H E R F L T T H E M F A L W I N G O U A N D S U C H A S Y O U A N D Y T H I N K I L L T A K E Y O U O N +M-AILABS_eng_000170 G U H E W O H E S E H I M H E R H A R T L A P E D U B B N A P R O H E N T I O N A T E V E Y R I N G O F H E D O R B L T +M-AILABS_eng_000171 A A T H E A S B O K S S D I C S O N I W L C E A L T H E R E S T W E O U S E N T O M S T R B E L T H E A R O F A C I N T H T H E W L A V O U Y O F O R T H I M S E L E S A S W E L A S F R P O S S A Y D +M-AILABS_eng_000172 B U T I N G L W A S N O T I T L S H O U R T H A T H E Y C O U L D N O T G E D I N T H E G A T S O P E D I N O R D A N D T H R E H A V Y B O A R R S W E R H E L D I N P L A C E B Y M E N E S O F S T O U T S T A P L E S R I V I D T E T O T H E S H E T E S O F S T D E L +M-AILABS_eng_000173 A I W O A N T T H E I L S I D O D O N C O L D L Y I H E N T A D O S O N H O R E S I W N U N T M E N T O B R I G E T H E W I T M H E P U S H E I S W A Y F O R E D W I C H W A Y T O T H E S T A B L S +M-AILABS_eng_000174 I R I S L E I T W A C O C A N D D E V F R T H E F I R S T I M E Y O U A N T E A N S H O U S E A N D B E S I G D E S T H A T W A S N O T I M E T O A R O U S E S P I O N I T H M I N D S O F A N Y W O N +M-AILABS_eng_000175 D O U N O T R E M E M E R T H A T E S A S T H Y D E M O N T H A T D T H E S P I R I T H I C H C K E A P E S T H E I S N O B L C O R E A G E S C E H A I U N M A U C H O A B L +M-AILABS_eng_000176 A T M I S T R B E L E O A C A N H E N O O F C O N H E E L I V I N G A L A S Y L I F N A D D R O U S Y C O L I D G E H A +M-AILABS_eng_000177 A N D T E C I T O N F O L L O W E D E M U R L Y A T T H E R H E A L S +M-AILABS_eng_000178 T H E F I R S T T U H W O D C O S A N E C S P L O I O N I N W H I C H A M O G S U C H H U N D R E D S O F I N F E R A T E D M E N A N D R E C K L S S B O R Y S +M-AILABS_eng_000179 W O N T E G A T P L E S E R S O F M A R G R E T L I E A T T H I S T I M E W A S I N E A D E S B O Y +M-AILABS_eng_000180 T H T H I N A S G O N U N D L O N N O F R S O N E O R B I G A C X I T D E N T W E S H A L H A V E T O C O M B E R M Y I S W I T H E I N E R I V E R N C E R Y O N T H W E R K C O I N L +M-AILABS_eng_000181 A A Y O U R L A T S A I D S H E W E L S H E H E D H E R B R E A T H O T H E A N C S R H A L +M-AILABS_eng_000182 R O H T T O L E T H E G I R L S T A H E M U S C O H T H E R F O T H E R T O L I V N G I P K G E S S I S L E S L T L E O L D C A B O N A N H N T H E H E R D T H S R E D F U L D E C R E +M-AILABS_eng_000183 M A R G I T S A T D O W N T H E R O G K G P A R T L Y T O W O R M E H R S E L F F O T H E D A N P N E S S O T H E E A V N I N G H U N G O U T H E R R E S A N D O V E F I T E A D M A D H E R C H I L Y +M-AILABS_eng_000184 O N O W Y O A R M S T A K E N B O U T T H A T E L I D T H E C I N G T H E A R N O T M Y P R E S N E R S B U T M Y S L A V E S H O M I Y P R C E U S T F R O M T H E C I N G O F E V E +M-AILABS_eng_000185 H E R F A T H E T O U T H E O M E R S A T I O N +M-AILABS_eng_000186 I N A C O U R E R W A S A S O U R D O F D R E I N G T A B L E I N W H I C H L Y A C O M E A N D B R U S H C E N I D Y S E E D M U C H I N T E R S T D I N T H E T A B L E A N D W A S A E X S A M I N G A T H E T H E G U E R U R E T R N +M-AILABS_eng_000187 I A V E S O M E T I M T A U H T T A T M Y S E F S H E A G R E E D B U T O F C O R I O T N O E S T I L I H V E T B E P R T Y C A R F U L S O M E W E N I S L L I S O V E R E B Y M Y D E S C O R L O I N G O V E R H E A R +M-AILABS_eng_000188 I S H A L S T A Y E R E P L D T H O N G A N F O R I M E N T O S T C O F R E +M-AILABS_eng_000189 W H A T D Y O U D E O A S T T H E S O R S E R E R +M-AILABS_eng_000190 W H I Y T H E R A R A N I M E S Y O U R S H O R T H I N E S N O T A N Y M O R E R E P L I D E S R O U H T I M Q U E T H E P I N K E S N D H M L S O Q U E O T H E L S S O I W O N T H A V E M Y P E B L E Q U A R L I N G +M-AILABS_eng_000191 T I P R A E R S E C L I C I N G C L P I N G A R B N G S N I P D O T F C U G S T A C O F N O U S P E R A N D P A S E I N A I N L A R G S R A B O C S S U R K I L E R S R B E N G F O L D A N D A D E D R A D Y T O M A L F O T H E F I N L A P E L +M-AILABS_eng_000192 I T W A S F O R E D A Y S A F E D T H E S U P R I Y S O F L H E R S H O R S E W H N T H E S T R A N G R S L E A F T H E S T D A T T T H E C A I R O F R O G E D O L D F O R S T R H I R M O N +M-AILABS_eng_000193 M P O R E T E M P L T O N H E S A I D I O U S T N O H A M M A N Y U A R S G O W E N W E B O R Y S E M N T O S C O U W I T H M N D N T A L H A T S O R O F H I N U N O E B U T A N D T I L I E R A N C R O U S H M O R E +M-AILABS_eng_000194 I F O N D T E I N T H E F O A R R S T N D B R U G T H E E A R A P R E S N E R E P L Y D T H E C A P T O N +M-AILABS_eng_000195 O M A B E C O M P I T E N T I D T H E F R O M P E R S O N L C S P E I R I N C E O R T H E E C S P E R I N C E O F O T H E R S T O A N C S E R T W H T M O E O R L E S S C O R A C T K N E S O R A T L E A S T E N I T E M T O H +M-AILABS_eng_000196 L W N N I N T E T O L A T S T R E D S A I D H O A K G O N B U Y D I G O F H I S O G A R +M-AILABS_eng_000197 R A T W A S S U R P R I S T O F I N E H E C O L D S E S O P L A I L Y T H R T H E H I Y W A L O W O A H T E R U B O F H E R R B U T T H E S N D W A S A B L T O S H O O T I T S B E M E S T R A T D O N T H O R T H E A R A E N S P E I R N T +M-AILABS_eng_000198 T H E S P A T E I D S P R O N G O P E +M-AILABS_eng_000199 G C O M E D E A N I L W I T H E G A V E S O C H A S U P O S I T I O N +M-AILABS_eng_000200 Y O S E A N D T I L T H E S C H L P I L E S R I N G E N T E D W E W A S T T L A T O F T I M I N S T D A D Y T H A T N O W M A B E B E T E R I M P L O Y E D A N M P R C T S C I N G A T H L E T I C +M-AILABS_eng_000201 Y O V E D N I T H A N W D E C L A R E D D A R T H Y T H E S T E N C E A R J U S T O E N D E R F O L +M-AILABS_eng_000202 E M F O R T W E N I N G T A N F I V E F T H E R E E T W O E T H E I N O W A S B E R L Y T W O E N Y M Y W S A W A Y W H N H O D I O N F I R D H S R O C K I T S T H E M D A C O L O S T O L C L O W E O A P E R I N A M T Y N E S E +M-AILABS_eng_000203 T H E P A D N O A T E N C I O N T O T H E F A C T H A T G I P K G E S C I S I L D I D N O T O N T O M A R Y A N Y O F T H E M E M F O R H E H A D E T E R M E N D T H A T H N I T W A S E G R E E D W H O S H O D H A V H I M +M-AILABS_eng_000204 W A T D Y O U T H I O F T H A T H E C R I D O P E N G A C O P B Y O T H E R E C K E D A N D L A N G T F L A T O T H E L I B R Y T A B L E L +M-AILABS_eng_000205 I T L E C O P I E R U T A S O R T T I M E +M-AILABS_eng_000206 A N D L A S T T H E R O U D O V E I G E T D A B L E P E P L E H O H A D N O H A R T S A N D O U O D N I T H E R S M I L E N O R F R O W N +M-AILABS_eng_000207 T H E I N Y O L L C A C H I T E S A I T H I C H +M-AILABS_eng_000208 W H T I S I T I Q U E R E D N O T F I E L I N G S R E N B U T T H A T T W A S A E V A L D A T E M D T O S E C E R L I T L F R E A D R T Y S I N G F O T H E A N D E O V E R +M-AILABS_eng_000209 S O E G A V E T H E L I R C T H E T H R D U N D E D O L O S F O R B O K S A N D A C A S K O F G O D O L D A L F R P E T E R T H E C L R K R A N T H E A I L H I M S E L F A N D G A V E H E C A F M I W +M-AILABS_eng_000210 A T L I E K T H A T A N A L S I N N E D E R L A N T D W I T H M E R L Y G R I N T H T F A T E D A W A Y C H A N G I N G I N T O A L I N K S W H I C I N T R E T O S P E R D F O L O W E D B Y A N U N O N R E C E W I T S O U R T N O U S A N D P O N T E D E R S +M-AILABS_eng_000211 A S H E C O L D N O T D E O E M A R G R I L A N C D U N C O N H O U S L Y A T H E U N G K L E D O R N E R O F H E O M E S H E U O H A R T H N D E R T A K A S U R V I N C S P L A C E C O O S H E +M-AILABS_eng_000212 A D N O H E S H E R E P L I D E D W I T I N I S E N K E R Y O U S I T Y D I D I G I V F T H E T O Y O U W H A L +M-AILABS_eng_000213 M A R B O R M I L E S A N T H E A G A C E N T D E L I N G W R H E L D U N D E R L O N G L E E S T H E M U S T I F P O S A B L T B E R E L E T +M-AILABS_eng_000214 D A C O P W A E O S T U N I S T H E L A T E M +M-AILABS_eng_000215 I T D B O N D E D H E A R A N D T H A I R A B O U T H E C I O K O N H O U S A N A T F I R S T D O R T H Y C O U L D N O T T E L H A T I T W O S W H I L T H E S P E A C I N G O F T H E C H I O C I O N S N E R L Y D E F E N D H E R +M-AILABS_eng_000216 T H E S O L D E R G A V E Y A L H A T E R O U S E D A S C H U A R O F H I S C O M R A D A N D B R G T T H E T U M B L I N G I N T O T H S T R E T W H E N T H E Y S A W H O T H E O L R S P R E S C I S E P R I S N E R W A S S C A P I N G +M-AILABS_eng_000217 G J I M H A D R E F E U S E T O L E T H E F E L D O F G R A S W H E R H E W A S I N G A G E D A N D B I S I L Y E A T I N G S O T H E W I S U R D G U G T O U T O T H B O U G Y A N D J U O N E D S A E B A N D O A R I T H Y +M-AILABS_eng_000218 G S R D N L Y I M A S I N T E R T E D N T H E C A C E S O A R B U T I C A N A K H E D S R T A L S O F T I R E P L I D +M-AILABS_eng_000219 O R A N Y M I C E O R E V E N G G R A S H O P E R S +M-AILABS_eng_000220 A N D T H E T H E P A S I O D O N T H E T E L Y O U W H A E T O D O R W H A T A N N O T T O D E H W E T H E M U N Y T H E Y G I V E Y O U A N J U S T P A M E N T F O Y O U P A I N S I N T H E R E C S T C A N G E L I C +M-AILABS_eng_000221 W H T D I S T A T M E A N A S T T H E R I N C E S +M-AILABS_eng_000222 D E H A D B E E D R O N E D H E W A S F L O A O D I N G N A S I O F L I T A N D N O W T H E D S H I N I N G L T E F I S I E S S W H E A M I N C Q U I S I T I E L Y E O U P T O H I E N D S T A R +M-AILABS_eng_000223 B U D O L D G N H D A R I K T W O L E F T D R E M E M E T H E T A I L I R E D T O Y O U I T H T H O N E R M O A B L T H E T H E F I R S T T H E B R O N S T E N D E T H E W R L D O F O A P L W E R S O U L D G R S S I N T F R M S O M E B L A S T I D P L A N T N O U T E R S P A C S F I E A N O H O M +M-AILABS_eng_000224 A P P E W I E O S P E A T T H E M E N A N D G T H E M T O G O W A Y S H E C A N T B R E E T H P O R T H I N G W I T T H I S R O W D O B O U T T E R H L +M-AILABS_eng_000225 A W H E N I Y T O K T H I S C A C E H E S A I D I B U L E V E D D O N E I N D M Y H A R T D I C S O N W A S I N S E N T I S T O B E L E I T B U T M Y F A T H A S P B E N R O U T L Y S H A C +M-AILABS_eng_000226 A D H A P T R S I C K O V E F T H E P I T O O R S E A T S E +M-AILABS_eng_000227 R E M E M E R T H E C A N N O T T O U C H U S +M-AILABS_eng_000228 I V M E T I M E A S O U R G I V E M E T I M E I O F T E I R S A N Y T H N G I H A T I T S A H U R Y I V E A N Y D E A E Y O U R M A D G E S T Y A N D O N C E T T H E S I C T T H S N O B E N O S P R O N C E S +M-AILABS_eng_000229 T O N O F T R O A H T D E C L E A R E T H E S A L E R M A N +M-AILABS_eng_000230 A A S F O R T H A T S A I D M A R G R I T R T H E H O T I L Y I H O L D I T H I S O H O N Y S O I T C Q U E E M U L D E P E N S A Y +M-AILABS_eng_000231 W E H E T H E S W O R D S T H E C I N G W O S H E D W A S F U L F T H E P I N C E S N E V E R T A P E T O N C Q P H I R I F T H E Y C O D B E T R O A N D S M E R E D H I M S E L F O V E R W I T H F A T A N D S P R A N G I N T T H E O V E I N T +M-AILABS_eng_000232 Y O S H O L B A L Y G E T P A R C E F R O M Y O U R E R O M E V I O N D R E S E V E R E I L L H A V E S O M T O U O L S G I V E N O U T H E N A D T E D D E P O M A S H E H A S T O N D E R S T A N T H T I N G S A C E T O L O F E N C E S H +M-AILABS_eng_000233 B Y T H E T I M T H E F R O U S T A D S A D I N T H E S H O B E F O A R E W A Y F O M H E L S T D O N +M-AILABS_eng_000234 O E N T H I G W O E N T O E S A Y B E G A N D C E N I T Y +M-AILABS_eng_000235 T H I M P O R T N T R A C H I C W A S O N F I D E T O N O O A M B T H E R E A L P R P R I T E R +cv_eng_000707 U N N W H W E A I D E D A B B L A S E D O N T D B A S K O D O V E M Y T H I S T I M G C G O +cv_eng_000708 I D T D A T A S E P R T S U P S E C T I O N W H I C H D L S W I T H I S A S P E C T D +cv_eng_000709 O P R A T I O N O F T H E F R O N T L A N C O N T D N U R E D O N T H E W O D E N D T T E S S E I L S +cv_eng_000710 M N I S O N F H L O R I D I S T W E N C E P E R E N T O V E R N C T R I M L Y E W H I D R A N G O F A V O M I N G S +cv_eng_000711 F O R J I N T B E C K I N G K S H I T S S T O R E T H F R E S H P B O C K T B U T D E A D E S N D E L E V E T H E M U N T O E R L E R O L D C A S +cv_eng_000712 T H E O T H E F O R T I N G C O U M P O S E S A R D T O W O Y U R E C A M P S R E F I R T T O C O L E C T I V E L Y A S T H E Y U N E R S T D E C O L N G E +cv_eng_000713 I T S T W O B H A D T H E D W E H A E C U I C K L E G O R N G T O F R G E T M Y T A E T D +cv_eng_000714 W O N E N O T O R E I N T E G E L O R Y S H O H H O U R D H E G I N T L A I D I R E D I S T A T Y A T F O A L A D G R O R T C O N T O N +cv_eng_000715 E A N M P E R I A L D I I A T +cv_eng_000716 T H E E S E L D I N C O M P N Y H T D A S H U D T A K E S C K O R I T Y O O T P R A T I O N +cv_eng_000717 T E C O I N G M I Y N I N G C A N B E D O N W I T G D O F H I S C O A R T S A O R E I T E S P E S I O L I S T H O R D L Y +cv_eng_000718 G T H E L S O L E T H E N O U S I O N A L R A N K I N G N +cv_eng_000719 T T A R W R S G R A N E S B I S H O P E O F N H I M O R E C K E +cv_eng_000720 A A I O U N D E R T H A R T T H I T O L H I M A N D H E O K M Y P L A S E S P D +cv_eng_000721 I T H O R G T I D G I V E T H E C I T I T C S A T D R E A E T E +cv_eng_000722 A S T I E W V E T L D E N I H T O M N T H E R I C T O E S +cv_eng_000723 H O E D Y O U R N O S T H T O C A E D T H I T M A Y F O M T H E F A B L I N G H O R M O T O F O N T I O N +cv_eng_000724 D T H A T S O N E D T S L A K E T H E R E P O L O M E A I I E D E I +cv_eng_000725 I H I S R I N C U L I G O R W A S N O T P L A R L Y D E F I D E B O W N D G R E A I N T H E S P I T D O F T E C H A R I B E U N P A N I N S T O L A E +cv_eng_000726 M A R S I A L S H A V E R O F S L A S H F I L N M E D G A V E T H E F I L M E A N D A T O U T O F C A N +cv_eng_000727 H O W P E R D Y I T I T A T E +cv_eng_000728 H I S T T D I L E B E G A N E T O E S A E M B L E T E M Y I K C L E T D E M O S K I N O S E S +cv_eng_000729 H I S A L L C A P A B L F I N G L Y T I N B L T W O E A M E N T E D E S R U P T O F P O E R +cv_eng_000730 T H E F L A M E T O W I A K C S C I N I N G L I N I R M L Y I N I N G S A S F P O L H E L I S T E A T O N D U I L R L N D A S E L A N D D T T R Y +cv_eng_000731 S H E D E T E R O U S H E L Y T O R O W O A T A H +cv_eng_000732 A E M T T H E O R G O N Y S E O R S O F T H E R O T E S E A N D A G R E D C R E A T T W O W O R K I N G R U M S +cv_eng_000733 A T H E B O N S T R O C T T H O F F H A L D P O R D W H I L E O A B O F H I G R E N O N S T O R D +cv_eng_000734 O N L Y C A M E D O N T O M E S G A R I T A N D G L D F I L D S O U I S E I L B A C K E A R W E R U N D C O N T I S T E D +cv_eng_000735 B P I T H I S A D C H I R D Y S C O L E W H O S F E S A N C O U C K I L A D I N O N A I N M E A N S T E S T +cv_eng_000736 S O M E W E N T W A Y W H L O U W A S E R A N D O T H E P E P E C A M E M +cv_eng_000737 D A D T T H A N T H A H T D T C T C D N C N +cv_eng_000738 T H A T C U R A C O N O T Y W A S L O K C A D M A N L Y T H A T H I S T O R I C L E A N D D E O G R E F O C L E R E G I O N O F C U R E +cv_eng_000739 T E L V A T I O A T H E S I H T I S A M O F S I L E V E L E I G +cv_eng_000740 A T O B A S T R I D E T O A N C H E C T T C O N P T E M E T E T E D I N T O H I S T O N +cv_eng_000741 I H E A V E T O W O R K E T H I S S I T O D L Y +cv_eng_000742 U T E D R A T H E R O N W O S F E N M W A S K I L A G E A G L E A D W L A F G L A T I N G O N T E R E N O N E S +cv_eng_000743 W H E O T H E B I L I N G D O S T H E S E L E D R B E I T E T H E B O Y T R O M B L E D T D A T W H A H E S O +cv_eng_000744 D E I O C R A T A N E B R H I N D D B A K E I E R W O N I T T H E O P E N S E O +cv_eng_000745 L W O R Y O U E T W O R D I N O G E A T H A B L Y T H E O O D I N I N H E C U L D Y U S E O A N A T L Y S I N D T O L R O U M N P +cv_eng_000746 T R A N T H W W A S B O R N I N B E L Y E S E S I T E D I N D B R I T O C S P O N D R E A S S +cv_eng_000747 D E R I R D Y F A S E O F L I C F M O M E S F A S T +cv_eng_000748 A A A T N O T E T H D E E +cv_eng_000749 S O R V E W I N T O L T D L U E T O E E +cv_eng_000750 A T O E T E R E L U R L E N S T E Y E O R D F O M B R A K G B E S T A T I O N I N S O E N I F R E N D E D E C T I O N S +cv_eng_000751 A A C H A E C R E P U P B L I K A N T E R E D T W O S H O U T E R S I N T O T H E P A R R O R O L E N P O G C O M P O T I S I O +cv_eng_000752 T I D H E R W I L I M S R O E T H E S K A N G P L A Y H A N T S H A R E D S T O R Y R E D I T H T T H O P R E I P I T +cv_eng_000753 T I S F A S T O F E L L E D W O R D E S T O A F B E T E R D N C H E I R R I T Y F L I N D E R A T N Y S A I D E O F O I D Y E T H E R A U R T +cv_eng_000754 O T H E S E E N X T R A G O A R S T S W E N E S E U R N L T T H E D O N G O N L E A L N M T O F O A C K G A L F T E R A Y H A T H E I R H A R D T S +cv_eng_000755 A I P I H A N D E R O N D B A K C T T O E S T R L I O W M +cv_eng_000756 A L P R E M I T M E T T O I N T E R D U S E S Y O U T O H E R M O D E S T I D C Q R E N +cv_eng_000757 E N O R G E I O N H E R L E N W A S B O S T O Y T H E N O N D A D E T D O F M O R F H E N S P S T O T D +cv_eng_000758 E T S H E I S O F M A K C O C O N D E S E S N T H +cv_eng_000759 G D I M E S O R E T H E I L E S T N O G T O N D I S T +cv_eng_000760 A A O T H O S A N D O N T E L A N C T L O L T H E S H R E Y I G D L O L D P O R A B E D I T O N O +cv_eng_000761 A I C O L E D A N T O P E S O R I N A T I T E E R +cv_eng_000762 F O R S I N P L I T Y G U R I N C H E S D I S N O R M I L Y A R D O U N D E D T O H E N E R E S H H O L N O M B E R +cv_eng_000763 I O F W E A C T I L Y D E O O N I S S O L D I T W I L B E F +cv_eng_000764 T H E I F O O F H H I C T R Y S A P L S H A P E D T +cv_eng_000765 T H E R E I E A C T S H A N G E I S N O W O B L Y +cv_eng_000766 A W H A T O U E A T T O D A Y W H E A S K A N D T O R K S T O M O R O E +cv_eng_000767 A T H E W O T E D A N F L O S O U T O F T E S C O N M N T S A S H E L O O A P L E R I V E R +cv_eng_000768 A M H E W H I Y I D D I O N Y O E S A Y S O M T H I N K C D H E A D C D +cv_eng_000769 T A V E Y O S E N O M A R N M E E T D E E E E E +cv_eng_000770 I C O T D G O O N E F R E D A I S A B O U T H E D I D I O S W O N S T H E D U A S T I N H I S P A R T O F T H E W E R E T D +cv_eng_000771 T H O S O E O L A D D E V F T H E A R I N C O R E I R E R A E N I N G D I N S I T O P C E Y O T T H E Y O A R E +cv_eng_000772 A A C O A S E V E S O P B D C E C T I S E S E R G L W N D S H E E C L D E L D A R D L +cv_eng_000773 T H E S W E E D S W E R A N A B L T O Y O U S H R V E A C A L S W H I H E R T O C E I N T H M O D E +cv_eng_000774 I T H E A C K T I D N O T P O R H E B I T B A Y I N G A E R E P E O S E N T I P T O E B E A R A N T H E C O R T L I S +cv_eng_000775 C H O N W R P L E S T E L E P I N R O U L T I D N +cv_eng_000776 H E W A C O N V I C T D E D A M B A N I S O S I P R S F O R S O V I O N H O R S R P N I S M E N T +cv_eng_000777 T H E C O P L O F T W O C H O L D O N A D A T E R S O F E A U R R S E L E N D E A A N T H E S I N F M U T H A L B R V E R Y +cv_eng_000778 N O F T H T H R E R E R E N D E M S R E C H H E C U A R A M O F H E M O D G U R I T Y O F H E S I N T I C T L D T +cv_eng_000779 I E T I T E R P E N S A E X C E A D E D I N D E R A S T S O M E R R O S I P C A I R A I T I I N H O S O L E U N E R S Y O T H O R A P E I R D O S T O N G B O A T H +cv_eng_000780 W H E A R A A M E B E C W E N M Y F L U C K A N D M T H B U T E R S I U R E T H E B O L Y T O S +cv_eng_000781 T H I F A L I Y E R H A S T L E T T O I C X S T D A E N O U L B L E N C D H A D H I N S E R D A Y S E O F C O L L S T O +cv_eng_000782 A D D U T Y A S E S H H H H D E N +cv_eng_000783 W H I Y O I T H A T P L A I N D C A E P E G O I N O V E R +cv_eng_000784 E E E H A Y I T A O D D O N D I S T E E T H E F O R W A O F O R S I O L B O S E W I O F O R S O L T E S E +cv_eng_000785 T E P L O C A T I O N W A S P U T A P R O V E I T I N F A R I B R A E L Y +cv_eng_000786 H E N R Y T O R L D T O N S T I L E S E W H E R H E H A D T E S O U N D E D R O N I N G I N L T I N +cv_eng_000787 I T W A S T H I S C O N T I N U E D D T O S C E T H R L I N G C O N F L I C A N D F V L V E D A N D L O S E S I S R E T I R N T O R E T O E R E S T R I U L E R E B R A D I U O +cv_eng_000788 D E D H H E R F A N M E L Y H W O A S F O M E P R E O H O N S A E E D D U D Y +cv_eng_000789 E W O W N T I D I E A K E F O R D I N M N B H P T +cv_eng_000790 T H A T W A S M Y D R E T O S I N S E +cv_eng_000791 H E S G O S L A I R E N T A M U S T E R E O F D S H E A R O S T C O L O +cv_eng_000792 T H E N L I N T O R N S T O T H E C H O R I S H E O F S H I N G S H A T T B L T E R A N D E S P E U E I T E +cv_eng_000793 W U D N O T T H O S E R I N H E R E A C A D R T +cv_eng_000794 T O E L C I O L S E I N C T E N D F E S T T H E A I Y H +cv_eng_000795 M Y N E S T C O N H E L P E O W I T T H A I T S +cv_eng_000796 T H A T S A C O U D H I S T O T L Y O N +cv_eng_000797 H O E F O R T H E B E S T A N D P O B E A R E F O R T H E M L S T +cv_eng_000798 C I N I S H E L Y T H E W H P D Y O S O S H T I N S T R O C K Y O B O D I C T +cv_eng_000799 A L L W E O N E D B Y T H E E V E R I T M O R S O N I K C E T H +cv_eng_000800 A A T H E T H E O E N G T H E W I L A S E R I N T O M O R O N M N E I N L N D E E T H E R +cv_eng_000801 E D O B P B I S T D I R I C M M I N D L I B P B P T H +cv_eng_000802 A T O S E I O L E P A T R Y H E T O H E R P L A E A S A C T I N G O R E C T E R H E H D +cv_eng_000803 T E B E V E R L Y W L E B E F L Y A N T E S T H E I E S S E N T E R P U O P T T O N S H I M +cv_eng_000804 T H E T R A C E R E R V I S T I N G W A S A L S O C O M P E T E D +cv_eng_000805 H A T D M A R S H W A S A W H A R O F T H E I M P R T N S O F I L C T R M R Y C O S K O M P Y E I N B Y L O U G I C K L R E S R C H E +cv_eng_000806 S I N H E W A S B O R N Y U D T H E H O B O U E +cv_eng_000807 T H I S W E I E N C E H E A S E A N O F I T I L Y T H E R H E O T O A S E M A C R E T O A D W I N T H M Y C O L I S T I O N F O A S E S O P E R A T D I N M I L +cv_eng_000808 I T I S R E S P O N E A U L E F O R W A T E R S O P L I A N D M A N E M E N T O F W O T E R R E S O U R S E S A N D M O H O U S T R A +cv_eng_000809 D I S E S T H O F E O R E S F A Y E O F T H E G H O R V E H E S A D E D +fleurs_eng_000413 T H E G I S I A P P L A T O H O R G I S S A N C R A O L P O L I S I T H E N G I O N V A O L Y O F T H E D E A D C O N T T A N G S E V R L P E R M I N D S O F W H I C H T H E G R A T P E R M E N T I S T H E L A R T E I S S E S E V E R L E S M L T O N S S O V R L E T E M P L E S A N D T H E G R A T S P A N K S +fleurs_eng_000414 T W O R E H E I N D O F T E M I L E A G E S W E S T O R N Y U R O B E G A N T O D E L T T E R O N S T I L O N E O F T H E B I G I S T O E L I N S O F T H E T I M E A S R E S I U L T O F T H E R E U C S A I S P E P B L B E G A N T O O U S E S B U T E N S T O F A S T O N R L V D I N G I I I A +fleurs_eng_000415 I F S Y O U O N L Y G O L S H O R E O U S I N G S H I P O R C S C K D R I O N D S Y O U L N O T E A S E P R T V E S A A S A T W O F H O U S I N N O I G +fleurs_eng_000416 B D O B A L H U I S M A R E W I H T O A D L C I O R E I O N I D N O T B Y W A B I N I M P R E S I O N N M I L E R T O H O E T H E T A R Y W A S R E L A T E D +fleurs_eng_000417 T E R D E C T P L N D D E F I E N C E B O L E H A D L I N G S C I L S A N E C S L N T I E W O R D M A Y T H E S T A N D O U T A N W A S C L E R H A T H I S W A T H E T E M T O B E +fleurs_eng_000418 T H E D E A S S C A I R E D B Y P I A K G E W I C H T H N M Y G R E T T O C U M E N T O R O M O S K E T O S +fleurs_eng_000419 F O R T H S P R I N G B O G C K E H I D I N D E D O A F F L I V E N A T H L O I N G S T R A K E +fleurs_eng_000420 T H E U S T H E H I N S L W A S G O F R I E N D M A N Y B E B L L A D Y K I N G O U +fleurs_eng_000421 T H E Y U S E O F E A O R E C O R I N G H A S L A D T O I N P O R N E D D E C S C O R V E R E S I N T H E I N T E R P R O T A T I O N O F M Y G C R L R E C T P R E S T I O N S F A S I A L M O V E E N S W H I C H L A S E A F Y O M I L S S I C K E N S +fleurs_eng_000422 L S A T T H E N O R T H I S T T H E G R A T S A N C H U R Y O F R L A T D Y O F A T H Y M U S H R I N G A E L E C E A F W O R L D G H T F I M I S M E R I O N A P B R I S T I O N S +fleurs_eng_000423 E I F O N E B Y C L O S O T H E A C T I O N Y G O R H E T O W W O W G I T I N E A L Y W T O T E C A P I N G S I H T C L O S T O T H M O U S I C K E +fleurs_eng_000424 M T Y A G U S K U R E R I S B L Y H F A R E T H E B I G I S T N D A C O N T I N T O N I D S O N W H N A C O M S T O W H O U L D L I F T +fleurs_eng_000425 W E M E N I T I S R E C M E N E T H A T A N Y W H E M E N T R O V L I R S A Y T H E A R M A R D R E G A R L E S O F A C H U L M A R T L S T A T T I S T +fleurs_eng_000426 C O U O A O M O F I F T Y T H R E B E G A N H F G O V E R M E N T G O V E R S H I P E R I L E T H E S Y E A R A N D S I N E A B I L L A S T M U N C H L E G L I D S I N G S A N S E C X S M A R A G E +fleurs_eng_000427 A S L I H P L T I O N N T H R H A Y D Y W A S N O T T H E I N D O F P O L O M T I S T O D Y T H E R U E L Y L O C K A T E D I N S I Y E S O R A C A N P S E S E S I U R T O R E S I O N T H O S B U L A N M O T N T I M S +fleurs_eng_000428 T H E Y O U E L Y H A V E S P E T I L F O R N K N I N E R T A M E O P E R S T O C A E G I S A N A G O M O D N C A E T H I M A T T H E P R M I S +fleurs_eng_000429 O H E T E R H E A N D I S E A N D S N O W C O E D I O N S A R N O R M A E A N D M A N Y C U N T R Y E S I H A N D T R A P F I T O S O N M O S T E L Y U N I N T E R U P T E D A L L E Y E R R O U N E D +fleurs_eng_000430 B E C A R F L N O T T O A E L O U L F A B O I C T O B E C M E T O H I D W H I C H C A N C O S S T R A N K A D G E O R I N A S T R E N C A S C E S S S Q O A R T C H E +fleurs_eng_000431 F E I R L T H L L D E R N A Y H A V C T P E I N C E O V E R C H U L D O B E U S O R T R M O H B H E F O R B I N G A B E N D I N R N G W A Y +fleurs_eng_000432 B E B L M A N O T N T I C H I P A T T H A T P E T I O N C A N D N D E R S T A N I N G R A L O N E S A R Y F O R T R V L U R S R E T R N I N G H O M E +fleurs_eng_000433 O N O T E R T H E O P R I K O F O U S T I L I T Y E S B R I N I N A N T S H E A T E D A D N A V B L B O K A Y E O F T I R M I N Y +fleurs_eng_000434 H T H E V E N R S O F P I S S A D N I A T T I E N O F T H I N D E R D W E R E P L E O F H S A R S +fleurs_eng_000435 Y U S I N H I P S T T R E S P B U R T D G O O D A S B Y F A R T H E M O S T O F I E N T W A Y H M O E L A R G E M U T O P E B E A N D G O O C R O U S O A T I O N S +fleurs_eng_000436 L E B R L R E T I S O H M O T H E R E C O N S T R C T I O N V E R N H A S P O A K E A S O T H A O R D I N G O F R E C O N S R C T I N G C O N C H A C T T O R S T H E D W A U T I N G A N I N S I H E R S +fleurs_eng_000437 U A S B O D O E B O D A M R S E C K L T A C E Y T O G E T E R O N D G O M E A T H E N O R M E A W L A C A L P R I C E I S F I V E H U N D R E D C O N D L E S F R O U S F O R T H E M S H O R E T R I I I +fleurs_eng_000438 T H E T H R E C I N G D M E S W A S O N E O F T H E B L T B L U D I E S T A R S A N A N G I O N T C H I N E S H I S T R E T H O U S E N C S O F P E B L E D I D E D F I D I N G T O S I I T H H I H I S S E I T H E G R A N P A L E S A T S I A N N +fleurs_eng_000439 T H E I S C O U P L E S M A Y C H O S T O M A K A N D A N D O U S I O N P L A N D F O R T H E R E B A V Y +fleurs_eng_000440 N O T H I N C A N B E F E N U T H E H A T H E C P E R B E U T O F L S C K I A B O V E A N D N T H E M A N Y S U R W U N I N G M U N D S B E R Y L I T L O T H S W L L T A N B E F E E N R H E R F R O M I N S I T H E C A V E +fleurs_eng_000441 H E W A S O E I C E N L Y R E L O K C A T E T O A D T E N B R O K S H O S T P T L A N D C A M B R I G E +fleurs_eng_000442 D T H A N T I A C A N S T A I D Y P U L E A T I O N I S E R O U D I N H E R N T H I T E T H I I S M O S T I N P E N E C O E N T R E T H E W H E R A L D N T H P O P L I U L E A T I O N +fleurs_eng_000443 B R D G L R A L O U N C S T H E N C T H E P M A T H R A R M A E O L Y N C O U T I L O N B U T N P L E N E D E S T R P T I O N S A R N U C D B Y A N O D T I M T E D S T O M I N O W A O V E R I T Y O F L I N W I C H G E S I N C L T I N G S B A N I S H A N G L S H F R E N C H E R B I C A N D A P N S +fleurs_eng_000444 T H I S O P R S G D P R T E N D E T O S H T H E O A R E R E A B O R I L E S A S T H E S C A I W L B E D A R C M O R E L E S T O R O N T H E C O +fleurs_eng_000445 F I R E S C O U C R E O S O V E N C I L Y D U S T H F I E R B Y L V I O N T H R T Y F I V E P E A M +fleurs_eng_000446 H I S C L O C O M I C A L S P E C H E E O N M Y A N I N D E C A T E N O U S I N G R E D C A B I G H E D D O U S E +fleurs_eng_000447 A N P R T I C K I L E I T I S L E T H A T O N C E N D E T E C W E T H E A P E R S O N I S L I N G B Y A N T E R P R I N G M Y G R L C S T P E R T I O N C C U R E C T L Y +fleurs_eng_000448 T H E S E S C I A L F O R I D Y O F T H C H U R H O U I E B E N I N M R O M F O R O V E R A T H O U S E I N Y O E A R S A N D D I S C O N C O N T R T I O N F P O U E R I M U N Y W E A D T O M A Y T O C E S T I O N W E T H E R I C T E N E N T W A S B E N G M A T +fleurs_eng_000449 T H E S U N D D O A R B O N S A R T H E A R G E I S T T H E T E O R A L M A N G R O E B E L I N T H E W E R O E D S T R C H I N G A T Y C O L O M E T E R S F I F T Y M I L E S I N T O T H E B A N G W L D E A S H E A N A N I N D I N H I N T E R L A N T F O M T H A O W C O S T +fleurs_eng_000450 R E A G U L R N O N C T H N T H E M A T H O L A R M A E D O N L Y N C O T L I N B U T N E T E N D I S T R U T O N E R N O U C D B Y A N O T I M A T E I S S I S M I N T W A R T V E U R I T Y O F L N G W I G D E S I N K E U T I N G P A N I H I N G L S H F R I N C H E R I B I C K A N D O A P N E S +fleurs_eng_000451 E R W N P R T I T B A T I N S T S I D Y A N O U S I S T R E N S P R T I N C S I S T A E N C A L O S T E R W N O M P L A N E O B O U T R E C D P R T I O N S I S T O M +fleurs_eng_000452 L A T N H A D A S F R T H A N G E S O T H E O N S R T E S M F V A R M I N L B I L E D R I G H E E A I N G W T H E E A U M A S I N G F R Y T H I R L A N C O M P E T R E R I G D T I N G O T H E K O S I R V E T H I S P A R Y I N F B I R A M A N A L D I L L +fleurs_eng_000453 A N W N H A S L N T O R T H A T H A T L I H T A T U E S A R O V E R M U E N P A S T H A D E N S A I D E T H E P O S E I L I T Y O F S N O E I C E O R F R E S I N G T E M B T E R S +fleurs_eng_000454 H S L E A E I N T E R U T I O N I S H E P R A S T I S O F H E B O U E S A Y W A K I N D I R I N G Y O R N O M O S T S L E P E R E A D A N D F L I N G A S L E S H O U R T I M E L A T E R E N T O S I C T D E I N T S T I T +fleurs_eng_000455 B L I S W M U R L E T H E T W O D R I Y P P O U R E R S T O G E T H E A N D T E N W I T G Q U E N G W E A T H A N D S S C U E E T H E I N T O A B O L W R I +fleurs_eng_000456 F O T H E S P R I N G B O A C K E I D A N D E D A F I V E D M A C H T H E S I N G S T R E +fleurs_eng_000457 Y U O S T L K T H O N E X P U R T D A P O L N T E A R T H C O S I N G T I D H D S O T E S A M L B Y W Y E X S E R T O F F O R S O T H E E D G I T A R I Y A U S A L A C S Y +fleurs_eng_000458 T H O R W T H E N I H T T H E T W E N H E R D E N F I T Y A N D T O H E R E C O P Y S W E R M A D N O N N O N A S B U M E L A P B O R O D S I D E S +fleurs_eng_000459 F I R S E A M L N G I S E I N Y A T R E C A M N D A T I O N D S T H A T A N N O D T I B L M A T I K N I S H I T D I O F S H D B E T E K H O M B O F O R T H E E N D O T H I C Y E A R T O S E C U R A R A C S P O R E R S E G N S T H O S T I L I N T E R V E N T I O N S A N D T O R E S T A B L I S T D I P L M A T I C R E L A T I O N S W I I T S N A V E R S +fleurs_eng_000460 S A N E T E R S B R C R E S I S I N L T I M E N T O W N W H O H T A S I N G E S A R C S E N T I F F R M R E S E R R E C U R I E N C S C H O C T H E T R N E N S +fleurs_eng_000461 A O C O R D I N G T O E P A N S N O G E L R A G E A N S Y W R D Y L A C T I O F C A E S I O M N D I A D I N H A S E N D E N I F I E T A T H E P L A N T +fleurs_eng_000462 S A E G O K A T I O N A N D R E C O M O N A T I O N S H O L F T L V E R Y A T I O N B A K O N D F O R T H H B E U T W E N D T H E T W O P A L L S E W I T E A C H E G E N E R Y T I O N +fleurs_eng_000463 E L A M N T L Y C O U L S T H U M A N D P O T A S H I M R C O N S E T D M U T L E S O F P O R E S R A L S O M T L E L A S I V E R A N D G O L D +fleurs_eng_000464 T H E O R L T I O B T W E N B R A N P O T H O A L A G E Y A N E H A V E Y O U R S S P R T S I N C S I S T S A N D T H E R E S R C G E +fleurs_eng_000465 A N C I O N C H A I N E N T H A D O U N E A K C W A Y O F S H O I N G D I F R E N T T I M E M P E R E A T D S E A C H E S T D A K E O F C H I N E O R E E C H F A M I L Y T H A T W A S I M P O U R E R W A S H A E D E S T I N T O F D I N I S T Y +fleurs_eng_000466 A H E M B L P O P B E L E D I M E R N H H S T E S H L Y T D R I N T H E S U M E R I S P A A M A L Y B U D W H T L V O I L T O M E T D N A N Y O V L A L A B E C O N T I N C T I C H E S C H E E S T O U O F I S H I T D S E T E R +fleurs_eng_000467 T H E A N O N C T H N T W A S M A D E A V E R T R N M A T Y F O N O M E R S A T I O N W H T T O R K S H S P R D O D E N T R E S E P T T H E Y E A P A E R O R D O U N +fleurs_eng_000468 P E R Y S A T E T A T H H E O D R E T E N T O T E C X E I C T O S T S E U T O R E S U L T S O F T O N G H C S C O K I S C D E D E R M O N W H T H E H E R S A P P A S F O R D F R M Y S E L F N T H S R A C E S B U E L E T E R S T T H O W O W L R E M A E I N T H E R A I D A N D G B P E N O T I G E N R Y T W E W N S O U T E I R L I N O P R M I A R Y +fleurs_eng_000469 E H E W A E L S O I N G A G E I N G G R A I N G B A K N O L T D S F O R M I N Y O U N T R E S R S O N I N G S E A P L E S O F W H I S E R K I N G C L E E D T H E H E A M P R I E N M E N I N I N I S R E A L P R T E R E D S O N T H E F I R S T F R O F H O N D T H F R U N T O F T H E N O O C O N A D Y I N O F L O E D L E R I N W O H N D E R D L D E L +fleurs_eng_000470 H M O R T R D I N H R C H E S O N T A N H O E T H E N E A S T E R R I C I L N S A T T E D Y N G H T T U R I N T H E E S T R W E E N D B U T H E C O G R E W T I O N S O T I M B R A K I N I N T O S E L E B R A T I O N A T T H E S R O C O F M I N T H T O S O L B R A K C R I C E S R E S U R E C T I O N +fleurs_eng_000471 F I L N I A G R A T B O D I N G D U S T E N A T I O N T H E L A N D O F A T H O U S E N L A K H E S T O U S E O F I L E N C D S T W O O A N D T H E L A K A N N T H E C O S T A O E A R K Y P E L O G O E S +fleurs_eng_000472 E R N T T E N T E R A N D A R G H I N C S I N F R S L A D Y C E S T D E N O F R N D I S A C E R S I O N R A N O E S R P E S I N A C H L C A N D I S O U S T R D A Y A V E N G N L H P L A T H A T A S T A D Y F O F T D Y O L O M E I T E R S T H E R T Y W N M I L S A W Y F R O M W N O L S I D I S +fleurs_eng_000473 S V E R W E T H E R I H E E N A R N C T E R E F O R N Y D A N D E R S W H T H E F O N A M O N A N H W H T H E P O T I N C L T O C O S D A M I A G E S I R I S C S O S I O L D I S T R U P T I O N O R L O S O F H M E N L I F E +fleurs_eng_000474 F O R E S A M B L T H E M O S T C O M E N S T H L I M I N G E F H O T O C K R F E Y F O R M U T I T H E H O R A L D I S T H R Y F I R E I L N M E A E R W H C H W A S T H E D O M I N E N T F I E L M E M S I G E S A T H E C L O E S O F T H E A N I L O G F I L E A R A +fleurs_eng_000475 I T I S R E L A T E D T O B U T Y U S E L Y N O T I B L T I N G O L P I N G T I L E S K E T O R I N G A R M O U T E N E R I N G T H E L A T E R W O E S D O U N E I N S D E C T E R I N G A N D R E C R I R I N G M U S H T H I F R S K E E S A N D B O T S +fleurs_eng_000476 I A R N I N G D A M K C L O A S E S C A N H E P T H E D R I Y I H M A N Y H O T E L S H V E N I A R N A N D I R N I N G B O R O V A L A B L E F R L O N E V E N I F O N H S N O T P R E S E N T I N T H E R O M +mls_eng_000283 E E V E D N Y U N C E R D H O R S L Y S H E E S U H E R C H A R E U N L E I T H E G L A O S E H E O T H F I R E R A N D S C R E D E H R H A N D S O U T O T H E B L A S E S T E R E W A S N O O T H E L I H T I N T E O N M M Y T H E T I M N T H E W I N W I D N O T O H O R D E D I S M R L Y S T I L +mls_eng_000284 E M Y D E A R M A R L D E E A E R W H I D O O U D N O T D E S I S T F O M T H E S I L Y P E R S S O U O T O F E A N A D N M A N D G I N A R Y T H E A D S E R W H A T I S T H E T H E O L Y O U O F M U N Y W E A R S P B A N U R D S N O T S H U R T S L E V E D M E R S S I N A R Y P P E G S O F A M A I Y C E N D S +mls_eng_000285 T E R I T C L T U B P T E I S T A T O T E S I N G L I A S E T H E R M E L I N W C H P E S E S E P O N T I E I N F L E A T I O N T H O A R S E N D T I N G E N T T H E R S T I A L E R E S U H E R S C A V L L I E A T H T W O C O A L D N E S O F T H I S P O N T B E N P L E T I O N +mls_eng_000286 M U C H L I K A N F O U N E U S N D O F O R M E I T Y A N T O H A T M O N S T E R H O M T H E T H E A B O N N I H T T H E F A R T H E R O F T H A T F A T L P R O D G I N Y M A D E D C I L H E R S E L F F O R V E R Y H A R T S T E S P I H T T H A T H E H A D R A H E R R I D L W H I C H N O W I H T C O U O T E V E R L O U S H A T S U F E R D D E D L Y D E +mls_eng_000287 H I M A S H E M A S E R W I H P R S I O N G R E S I E S M U N T I G T O H R E T H O U L S E N A N S T I E S A N D A L S O T H E R Y S M A L V L L I M S T H A D O C K I E P Y D E B T H E F L N T D M U E N D E C O E S I E R A T I O N T H I S L A S E M A S E N T W H C H N O U E S E I T A T E N U M E R S C O R A T I N D I S M O S T D E L I K E U P U T H E O P R A T I O N +mls_eng_000288 W H I S H U L I T H A E B E N D E M E D N E C R O M A N C S Y T O I N D E V E R O O N B I N G T H E S F A S T O I V F O L I L V E B Y G I A R F U L E L M I N A T I O N A N D C H A N G E T O T H E P E R V E C T F O D +mls_eng_000289 D N A Y T H O V E R A S E S B E Y M Y B E A D T Y E A T I A M R I G E C E L O V E S A I D B U T H O U T A R E G U D L I H V E T H R I C E F O M N D A R T H O U L E T O Y E L T H E S O V E R A N G I F T S O F A R T H T H E V I E T O R S O R O R D T H E L O R A L E T B R O L O R V I N T H I N G K S O F L I L E W E R R T +mls_eng_000290 B O C K E S T O H V E B E N A K E N O L A C T R O L T H O H A M P E D B Y I L E H E L T H F A D G R A T P I N T O N I S F A V E R I S H A T E S C R I G E D O N L Y T H U C E P L A N S H W I T H H A T D C O M U N D E R I S O N P E R S I N L O P S O V A T I O N +mls_eng_000291 H A D R A T H E R S R N G O P A N D H A D N O T C H A I N S E D I N T O N E I M S T H E I S H Y F E L D I N T H E S T A M S C O V E R I N T H E M U P A G A N A N D T H E Y A P E R E D A S P E R F E C T D I N S A C T S I N T H E M A Y O F T H E F O L N I N H E A R +mls_eng_000292 N O T H I N G S A Y W O O B D E C E A N D H O A T S O F B U T Y M O U D P R E S V E N D T H E M S A E S T O T H E U N D E R S T A N D I N G O F T H E F O R T I E L E T B O R S O N H O U E A R T O K O F I T T H E I S B E A T S W I C H O H A E B R U G T O M E T R A N S L A T D A R C O N S E R E W I T T H I S S O U P E S T I O N +mls_eng_000293 N O U S M E I N C U P T I T Y A N D H E N E R V E H I M S E L F A G A I N S I T H I S F A I S W A L S O R D O S O V E F L U S H D H E W A S T I M I D E V E I N T O R R O U D N E S +mls_eng_000294 T B E C O M E M O L L I U G H E L I K C K E A S T H E S H I E X S F L U S H T H E A S R E A R W O A I M G E F I N O E I N T E M O R D I N G T H E R D H A F T I S P R I N G S O L T I D I T L O N G A O U R S O F A L L R E A D I N G A N D P E R S T E D H A R T B Y N E V E R S E A S I N G R I M E M S E S T I C U N O N D E R S T A N D I T +mls_eng_000295 W O N F T H E H O W I N G R G D E R S A I D T H E A L L P E H E A O V O I S A P O S E N S H O L F I S H T H E S E R B I T R A N D D E D L Y A N D C O E B E Y O U S E I N P U I T I N G E N I N M Y S T O D E A +mls_eng_000296 T H E B E U T E A S R O U P E S O F H A V I O N A S L O N T O A D U R I H T E A R A N D C O L E T A R H E L O K E I N B O W N L I S M A G H S T Y R A B R O U R D T O U C H I N G T H G R E N L E V E S A L L A T R E M B L I T G O U G L L I H T +mls_eng_000297 I A D D U N O R M O R T H N T H A T I U N D T L T H I S N A T A R E H I S A P E L U D L Y S E T E D E T H E E W O R T H M T H E L I F I T E L F T O E M W H S T R B L L B U R S M E D A N O R Y I S T S A O R L Y H E P R T E S T E I T O U L N O T G O D A S T M E T O W A T T H R A M U N C E A N D T I L I A T A E X A M I N B O N O F T H E I S E S +mls_eng_000298 R O S C O N G R E S F O U N D A T I O N R E S I O N A N D T I T E T H A T O R G N I H E T H E S A N T P E T E R S B U R I G I N T E R N A S I O N L E E C E N O M I C F O R M R O U S N E F T R U S I O N D S T A D O N E D O I L E N A N E R G Y C O M B P A N Y W +mls_eng_000299 H O W G L A T E D I N S P A C A L T H E D E L I C E A T F R O S T W E K Y O U E A T R A C T D E D N O E D O U T A M A V E R D A T H E D I N Y T R A C S O M S B U T F E W U E O V A S H A E R E A L Y H A D A N O P R T N I T Y T O S T A N D Y T H E D E T E A L O T H I S F R U S T E S I N E S M Y N U T L Y O R V C O N S I D E D H A T T H E E R E O R H I N T H R E Y U R F O R D E S I N E A T M O S T +mls_eng_000300 T H E A H A N T H O F E S I N T R I N G T O I N F I K G I U O N T H E M E N K I L V H E O F E N D E R O R W E N H I M M O R V H A N T H E I N T E N D E N T O D W A N D T H I S B E C O M E A C C U S F U L E A N U H E R D S O H A T H E P R I M I T I V E L I G E S L A T E R S W H E R E C E F U L I N O R E C Q U I U R I N G T H E R I E T A L I T I O N T O B E D L M I T E D T O A N I Y F O R A N O I +mls_eng_000301 A T S I R E S S W O R D T H E J U O S E R E T E R E N T H E C O M P O N Y T H A T G O E B G O S H I C E S B E G O N W I T M R T A M O M E I S H E N D E R E D B Y T H E F O B U T W H N C E A G A I N T H E W O R K G O S O N B Y L I E N S F R O M D R I A S A S R E I S S E N T W I T H R O I L E D G R A N T A N D G I F T S F R Y O S E S P I S S +mls_eng_000302 A N T P R O D K E Y H A I E I N A N D Y E A R O U T E S V I N H U D R T F R O N C E S H O E L V E D N I T H O V E N O S O B A D L Y B R E U L A C S P M L A I N M U R T Y I S O C K U P Y H T E O R E B O H O U E S +mls_eng_000303 T E D T H I S E I S A L L Y O U R A N C T S E R T I S T W O F E A I R E F O R O N E O F H I S O L I N T C S A N D Y W O R E Y O U O W E T H A T T H I S P L A C E N O M O R S E Y O U A N G S I T A N T E R D E F L E R A N S T H E E S T I S E S T H E I S M O R E C R O U N D T O M E D A M A N D R A V E N D G O N O N I S E F A R A C S T H E A T S M Y A M E I N D E D +mls_eng_000304 W H E N I R E T U R N E D A T O T H E H O U S E S W H R E H A D B E E N H A P Y C H I L E D O N D L Y A E P I L O F A S H I E S W R A Y T H A D S T O D A I W E P T L O N G K A N D T O F O R O G E T M Y W E P I N G I S A I D O U T U N D E V E A S C O M S E O N D T H E S W O R T E R S I N A S T H R S I U G F Y A E R N I G T I D P L A Y E M Y F L T T O T H E S M E M O N +mls_eng_000305 T E D O O U N O T S E E W H A U T L E S E R I T D G I V E S M E W E H A V E G R O N O U P O G E T E R I N T H I S H O U S E S I N H E W O R S A B O Y H I S S E I M P L Y A N O R B E A R E A S O U C A N D T H E I H T O F T H S M Y G L D L E V I N H I S F H A C E B O R D E A R H E H A S N O A M U S M E N E C E P T H I C B L A N G A T D T H S H O P S K S C P I N G +mls_eng_000306 I T I S D E V I E S S B O Y R E V E A L D I N G N Y E L P I C A L O R G R E Y U W O N G A T I O N I L O V E L O V E D L O V E I L N O T B E T H E W O N D O F C U P I T B U T H E A D I F T H S T A T I O N O F E G E V E R S L E R D U C T O E I S T I N C E S +mls_eng_000307 S H O R P L Y A S H E S H O K H A N S I T H E R O G O D B E S Y U M A Y D Y A T C H A I T H E B I S H O P S A I D W E N S H E C E S E H I M A N D H I S L I P S M O R D O F T E R W O R D F O R E S O M E S I C K E N T S A S I F H E W E R I N P R E A R E I U D M U T H E O F O L L O R E H E R E O U L O F T H O M A N D T I E N S I L A N S E T E L +mls_eng_000308 F O L L A E D H I M S T E A E L T H E L Y H E A N D H E W W A S A N A S T P I N G P O S R E R F I L I N G H I S B O U C K E A T C A M E U P T B E H E I E D H I M A N D P L U N C H E D A N D L O N G N I F N T O O I S N A C K +mls_eng_000309 T S A I S T H C K E R S I A S D O U S T N O T J U P E T E R D I S T R I B U E T T O H E G O G D T H E P R E P O R S T I O N A N D D I V I D E N T S P A R I N G L Y A N D S E V E R A L L Y A S A G M E N D I T O H I S C O M A N D R S W H E N H I S G E A S T S T R A N G T O O N E A N O T H E R I V F O C K O U R S I U S Q U L S K L E D E M I U S A S Y O U N E R R A T +mls_eng_000310 E A N W H R N O N H U L E R R S T R A N A T O C A M E T A G A I N I N F B H O H T S O T H I S N O O U S E N W E P I N G B E R C H E R U L S P I R T S T I L E N E V E R D O U T H E F A T I S C E P I N G P U C T E R G O O D F O R P R E S E N I L +mls_eng_000311 A N D O B E C O M E T H E R E C K E R D O F W H A T P E P L A E D O N I T H E R M O R A M I U B L E M O E N T S T H E R E C K E R D O F T H C O N C Q U E S T S A T P E S E H O W M E N D H A V E L I V E D A N D L E A V E R D D O U G A T B I L T U N A N L I E R E D G A R D I E D A T R E A F O R E R S T +mls_eng_000312 T H E O F L I N G O T E S O L A S P E T O C K I N S R A I N A S W I L A T N Y N S E S I N A B L E D A N S I N G O F M I G I S I N T H E E V E I N I N G S O C O N S O N D E F E T A N D I N G O R T I S I N G T H E O I N S A D D I O F U L R E C O I R S S I S S T H E L E S T A T A L L A T R M B L E B O E F O R T H A T P R O W C E T U N D E R +mls_eng_000313 W A S S T O R E M N G E J E N R L E T A M P E A R E W A S C I L L G E N E R L C O S T I E N G W A S B L A M E D A N I N D E D E S N O B C O M T O P A R I S T H D E V I C X S E L N A T I O N S A G I N S E A L L W H I H T H E M O U N T O N A N H E T R O T I O U S M O E A R M U S T D E V O N M A K E H A L A S H E C A N +mls_eng_000314 T H E M O M E N W A S F E A V F U L A M I T Y O F O H A D N E V E R S W H U N G T H E B U T L L A C K E O V E R H I M E N B U T T H E H O B E N E R V E D H I S A N E F O R A D E S P R E T B L O A N D T E C U O M S E R U L E P R O S T R A I T B E F O R H I M +mls_eng_000315 T H I N T H E W I N S T O U T T H E G L E R S T A N D D R K H N D N I G C A M E O N L A E I N G K M Y O L D C O T I O N D C O U I L T W A S C O L L D A S I N M Y S W E T S U N T O U S T I N H I S S C E E +mls_eng_000316 Y O U M A Y D A S O U P L E T O W E R E O F O U R I R T A T I O N T O C E P P Y O U R F E N A T T I I S M Y O U H E W E L A F Y O U N E D N O T M I D T H E C O U S T T H E P A R E D U N O T W O N T E S T A N D I N Y O U R W A Y B U T Y O I N S I S T D O N T H E S O I M I T I N G O R C O M P A L T I O N +mls_eng_000317 W E W A S B R E D B Y A E R E V E R N T E R Y A C S N I H T E T B E I N G B Y O T H E M E N E S C X S F O R T O T O U H I D I C K L Y W A S B O N E A N M A C H A T I N S E V E N T Y N I N A N D H W A S T H E O N L Y S O F L I V E R O F E L E T E R O F F I F T E N I T W A S N T H I C O U N D T T H A T H E A S O U R D S A I F A N D C O L R A N D M A R C K I N G S +mls_eng_000318 E A N D W H A T H A S T E I T M A K S O F O L I N T O T E S E C I O N T T H E R B Y T H I H T I M E D I A F H A N D E R S N E S E R S E S G I U M O S T A D M R A B L S E A K C K R E T O N T H E C O N T R Y I T S T A R S M E N O T A W I T W I C H M O S T C O D S O R E S I T +mls_eng_000319 T H E R D L Y T H A L S A I D W H E R T H E I T I S I N C E A R E N E T H E R E T O R E A C E H N O R T O P O R E F O R T H I L Y A N A C O S I S S A I D W H E R E T H O I N A L L O T H E R E S P E C T E T H E Y O A R R C O L I H A T V E R E T O H S M I N A R A D V A N C E D A N D V E S I O U S P E R S O E N T D E G R A D E D +mls_eng_000320 T H E C I N D L Y F R A N G I S S I M P T H E T I N K E V R Y D A Y H E P A S N O T A S B E T W E N U S A N D T R I Y T O N C K E R I R G S R U S T L H E W L I M P R O V E I A S U O R H I M H I S T I M E I S S O U O U R T A N D F R E A C H A I R A N L I B U R T Y W L L S O O N R E S T A O R H I M +mls_eng_000321 T H I S C R E S T I O N S I T I S N O L E V I D E N T M A Y F R E K C E N T L Y B E U N C T E R E D W E S E Q U L L P R O P R I T Y I N O P S I T W A S E S A N D I F T H E R B E A N Y A C A I N S U N G W H I C H T H E Y C A N D B E U N C T E R E D O N L Y I N O N E N W A Y T H E U N C S E R W I L D E P E N D A P O N T H E N A T E R O F T H E O E C A T I O N +mls_eng_000322 I N H I S N O H T B O R T H E I N S T R L S Y S E C K N E D I O N A T Y O N O W A T E S C O U T S E S T H B A L E D W A S T C E K I N D D O W N R M A N O L D O M E N S F R C I T A T I O N A T T H L S O N M O R L E A D M I N S B Y T H E A G E N D T H E R A N D C I E N T B Y H M T O S E R T E A S +nchlt_eng_001588 C R E S T I O N T T H E O L I G E O N S H +nchlt_eng_001589 O P T A D E E A G O F I T H E S +nchlt_eng_001590 A L A M E N T O S P E S I A L F O N T I O N S +nchlt_eng_001591 T O R D E W A S I N A N N U N E V O R E I T Y +nchlt_eng_001592 S I E S F I C T I O N N O V L E S P R V H A N +nchlt_eng_001593 C O S T D H I B P O P +nchlt_eng_001594 I N D V E R S L E A T B L A Y E T R O N S F O R M E +nchlt_eng_001595 F R I N G H P R O T I S T A N C E S +nchlt_eng_001596 O F E G O U N A Y F O R S S H H D K E +nchlt_eng_001597 H E A R O S I N M O S O L I G Y A N D L E A G E N D +nchlt_eng_001598 B U I S N S C L A S S E T N D N E +nchlt_eng_001599 C L A I D P L A Y C H O R T E E +nchlt_eng_001600 P O S I Y T R I N S W E R E R O P O R T E D +nchlt_eng_001601 A L D V I C K T H E A T E R +nchlt_eng_001602 O R T H E D O C K S M O N O C K S E +nchlt_eng_001603 N A T I O N S W M E M B R S T A T E S +nchlt_eng_001604 F H E T H O W I L D C O P +nchlt_eng_001605 C R O S E R I C S K Y U E F E I T S +nchlt_eng_001606 A C T H O L F O L M E M A R C K E S C O P I T Y +nchlt_eng_001607 M O U O S I C L G R U P S R E A S T A B L A S H E D +nchlt_eng_001608 P R O M I S E N E R E P A C E S E +nchlt_eng_001609 F O L N D S I K N E K S +nchlt_eng_001610 T O E L A V I O N S E R Y S B A S T +nchlt_eng_001611 H N O E P O L I T I O C A O E P O R T Y H E E E +nchlt_eng_001612 A N C H O N T D E A G O P A C H E V E D +nchlt_eng_001613 F L A T M U S I G N T R O L +nchlt_eng_001614 A M R I C O N T I C N O L I D T O I N O L I D Y R A T E S +nchlt_eng_001615 D O A T E S O F V A R I N S +nchlt_eng_001616 P O P I L E I T W R E I S T A C T I O N S +nchlt_eng_001617 D U C H E W I S T I N D E A R +nchlt_eng_001618 G O L D M A T L R E S P I E N S E +nchlt_eng_001619 R E A S H I O N S O S I A L D E M O C R E T I C K E H +nchlt_eng_001620 A M I R I Y C O N F L M E P R O D U S E S +nchlt_eng_001621 F R E E S O F T E R Y F U N D A T I O N +nchlt_eng_001622 R I L E R M A T I O C T H E A T +nchlt_eng_001623 I T A B L E M O L O S K S H +nchlt_eng_001624 F E A T C H E S I N T L W D B E A C H E R S +nchlt_eng_001625 O C F O R D I C T I O N R Y C H A N G E T +nchlt_eng_001626 S A L C O W P I R I S I O N D R Y H U N D +nchlt_eng_001627 P R O W N M O N I S T E R C I V E N +nchlt_eng_001628 L A N G E S O F Y U R O C K E +nchlt_eng_001629 S O T H E A S T I N G L O N D T +nchlt_eng_001630 N O U R L I N E D S E N O M A R T H +nchlt_eng_001631 E C O U L K R E D I T O P O T O N N T S Y +nchlt_eng_001632 S O U T H E A S T I N G L A N D +nchlt_eng_001633 M A Y W E H T E +nchlt_eng_001634 R E C O L R D H A T A T E O M E E S C R I P E S +nchlt_eng_001635 M U S I C A L G R E P E S F O M C A L O F O R N I E A H +nchlt_eng_001636 M A I N B E T L E T I N C S +nchlt_eng_001637 P O R D L I S E H M U S I O C T A L E I N S T R M E N T S +nchlt_eng_001638 L A N W I G E S O F S A D Y E A R R O A V I A R E +nchlt_eng_001639 C O L D O R T I N T I O N S E H +nchlt_eng_001640 D O B E H I M H S +nchlt_eng_001641 A N D Y P O P K L I M I N T H +nchlt_eng_001642 G I T H E Y C O N P O I V E I T +nchlt_eng_001643 C I N F E I A N A N D +nchlt_eng_001644 I L E C T O N I C K M E U S O C K L I N S T R O N C S +nchlt_eng_001645 A G E M O L D T O O R T E R N +nchlt_eng_001646 L O R A N C T L V E M O R N A S T I O N L E +nchlt_eng_001647 L E G B A C S P L P L A Y A R S +nchlt_eng_001648 B O D I S O M E A N T H E A N C I O N T M E D E T R A N I O N +nchlt_eng_001649 O U N I G T I D S T A T S R E K O C O N S E D +nchlt_eng_001650 P R O P O S I O N L F E L A T Y E S E +nchlt_eng_001651 S P E T I A L A C O N O M N G E S O R O N D S +nchlt_eng_001652 M A N S T R M W I S T +nchlt_eng_001653 E V E N G R U C H H L S +nchlt_eng_001654 B Y T H E D I O N S T O K K +nchlt_eng_001655 N D A R T I O C K E A H A S N O +nchlt_eng_001656 W A S T I N M U S I C L E S +nchlt_eng_001657 C O N E V I T O F D U D A Y S A M R E G O R T +nchlt_eng_001658 O P P I C K M E M B R S T A T D S +nchlt_eng_001659 P R I M I N I S S A I D J O N +nchlt_eng_001660 R O A C K S F O R M I N G M O U N T +nchlt_eng_001661 M A D G E R L E A K T L M N S +nchlt_eng_001662 P O L A N A T I O N M A N A G H E N T T +nchlt_eng_001663 F R A N C H E F I S I S S T +nchlt_eng_001664 H I A R E C O M P R E I T I O N D R A T I O +nchlt_eng_001665 R E C O A R D N G I N D E S T Y S O U C H A T I O N +nchlt_eng_001666 T E A P A D E O U N L I N M A G O S I A N +nchlt_eng_001667 H I P O P E R E C U L D P O R O T O U C S E N S S +nchlt_eng_001668 F I N I G T S T A T M U S H E N E N S +nchlt_eng_001669 W H I D L Y S U S E D L O C K A L D +nchlt_eng_001670 N O R H E M A Y C O D C O N T I N E N T +nchlt_eng_001671 A F R C O N M E R I C O N R E P E S +nchlt_eng_001672 T H R E T E N D M E L I G T R A C T I O N S +nchlt_eng_001673 U H T T H E W O R D F I N I N T N E E E E E E +nchlt_eng_001674 T H E T O M I K M L E I L E N O P T O C A L F I S I C E +nchlt_eng_001675 E T O N E +nchlt_eng_001676 M Y R S O L +nchlt_eng_001677 C O N S T R C T N O U O H R A L G A G E +nchlt_eng_001678 P O R L Y E C L W I O N R I N C S A B L E +nchlt_eng_001679 H C L O W P O R T R A Y D E F E R E N T +nchlt_eng_001680 S O V E A T D E S I D E N C E S +nchlt_eng_001681 S I G N E L E T R O N C E T D U C T I O N D P O L T W A Y E S +nchlt_eng_001682 N O U B O R N M S S I +nchlt_eng_001683 J G E N R L Y A C E P T E D R A N E R S +nchlt_eng_001684 G I L E D A W R D W E N H I S +nchlt_eng_001685 S O W E D I S H E M A U S I C L G R P S +nchlt_eng_001686 C H A L D E R E D O R T I S I M R A T I N G +nchlt_eng_001687 D O S I G H F O R M E M S +nchlt_eng_001688 O H I I O U S T A T I O N O V O R S T I T Y E +nchlt_eng_001689 F O R M O S S A T H E N C E I N T E R K E +nchlt_eng_001690 E E E A N R O C O N I N W V H N T I O N S H +nchlt_eng_001691 E A R T E S E H +nchlt_eng_001692 M D E N Y O U R O P E A N R A S H A H +nchlt_eng_001693 N S N O D L E G P I L A N T H +nchlt_eng_001694 B I K F I N I S H P R D U C T I O N S +nchlt_eng_001695 N A S T I O N L E H +nchlt_eng_001696 T R A D G I K P O I T E S E S +nchlt_eng_001697 T I T I L G R I C E S T A T E +nchlt_eng_001698 A S T H E N A H A D A E N E +nchlt_eng_001699 E A S T E O N Y U R P E A N C O U N T R Y E S +nchlt_eng_001700 C O N D E D A N O R T H R I V S E T R O N S L A T I O N S +nchlt_eng_001701 O A L W O R D E T E S +nchlt_eng_001702 C I N A S S O R M O W N A D L E N D E S +nchlt_eng_001703 N O B L E S A M I T Y E +nchlt_eng_001704 I T W O S A E F O L S +nchlt_eng_001705 M O U N T S A I N T O V I N S E N T +nchlt_eng_001706 S I T Y M R C R P O L I T O N E A I R A R +nchlt_eng_001707 R O O N L E R S H O D A I D A S C H L D R A N +nchlt_eng_001708 C H O N C E S L E S V O L E L +nchlt_eng_001709 I P E P E C K A T C I N T I R L Y +nchlt_eng_001710 C I N G A D W A R D S D A T H +nchlt_eng_001711 A M E R I C E A R A M E R I C E +nchlt_eng_001712 C O M R T I L S H I P S A L D +nchlt_eng_001713 P E P L F O M M A E N H A M E M +nchlt_eng_001714 R A I L R A S H C I L D +nchlt_eng_001715 M U T H A L D E F E N S T O A D Y +nchlt_eng_001716 M O U D T E N C H E L D R U O L I S +nchlt_eng_001717 M O T E S E R R I H F H A L D E V I O N +nchlt_eng_001718 O U S T R A L I O N I E F O U R S E +nchlt_eng_001719 A M E R Y C E N D M O S T R Y R I T E S +nchlt_eng_001720 F I N L Y G R O W N D G R E F I T E +nchlt_eng_001721 W O L T A M P I N E S O P M A T S +nchlt_eng_001722 C E R I O L I N A +nchlt_eng_001723 M Y B O A T H N O P E R A T E S +nchlt_eng_001724 C O R T E F O R I T Y E S +nchlt_eng_001725 M I O R O A W N D H +nchlt_eng_001726 C O S E L E T H A L R E A C T I O N S +nchlt_eng_001727 I N G L O I S H P E C E O F O U S T S +nchlt_eng_001728 Y O N I G T E D S T A T E F E D I R A L +nchlt_eng_001729 F A D R L E R E S E I E O V E A C T +nchlt_eng_001730 W O L I M H E N D R Y H E R S O N +nchlt_eng_001731 G L A P P L A Y C H O T +nchlt_eng_001732 P A S T O N G E R R H A L D S O V O C E S H +nchlt_eng_001733 A N C H O N M E S E D R N T H I O N D J D I N R L S +nchlt_eng_001734 C R O N G A C T I O N S E A M A R E +nchlt_eng_001735 G O N P O U T E P R O P I L E N T Y O S E D T +nchlt_eng_001736 L O W I S T D I N A G Y S T D A G H T H +nchlt_eng_001737 C A L N D E Y O U R O S +nchlt_eng_001738 M A E G E R I N T E N O A S I O N A L E A E P O R T E +nchlt_eng_001739 T O T L F O R S E A C T I O M +nchlt_eng_001740 L O S T I L E S D A T O M P E I T I O N +nchlt_eng_001741 E G R E A K E H D H E R +nchlt_eng_001742 I N D V O I R E M E N T L P O T I C T I O N A E N S Y H +nchlt_eng_001743 M A N Y T O E B I S C K L S Q R E S T I O N +nchlt_eng_001744 A N C T O N S I T Y P O T H U N D E R E +nchlt_eng_001745 S M L E A O T H E D O C S I N G O G +nchlt_eng_001746 N O N D G E S M I T H O U P I L I A N A I R I R S +nchlt_eng_001747 T I T O L E R E L I G E H A R R E M O N O N +nchlt_eng_001748 E G S A N M P L E S A N T L U D E D H A F H M O N +nchlt_eng_001749 Y U N O T T E S T A T E M I N T E A I N +nchlt_eng_001750 B O L E D R E P R E S E N C E M E C X C I M O R +nchlt_eng_001751 S I N E F I C T I O N O R T H I S +nchlt_eng_001752 O D E N A R E D E F R E N S H O L A E C Q W A T I O N S +nchlt_eng_001753 D P L M A T O F T H E H R D E S E W +nchlt_eng_001754 S I L E C O L O M M I S T R Y +nchlt_eng_001755 U R L M L I T R Y C O L +nchlt_eng_001756 S T L O N L Y L E D S C I O L I S U M +nchlt_eng_001757 P R I N T E I S S +nchlt_eng_001758 N O U T A S T H E A N D P E P L +nchlt_eng_001759 S M O G T C O R D B A C S E D A L C T T R O N I C K P E R S +nchlt_eng_001760 S T A T E N M Y S O L D G R S +nchlt_eng_001761 L O R D E A S S C R I S T +nchlt_eng_001762 L A D N B L I N G P +nchlt_eng_001763 H T E L I A N N E S I N L T E M +nchlt_eng_001764 A N D T E A G R R E C R A T I O N G R O U N D T H E M +nchlt_eng_001765 G R O C S E S S T A T P R O D A C T E +nchlt_eng_001766 C I N C O N D V E R S E +nchlt_eng_001767 B I E L V I L +nchlt_eng_001768 F L E O L G O N G S A T I O N N T H E U N O D E D S T A T E +nchlt_eng_001769 I T R I L T H E F T A N C F O R S E S +nchlt_eng_001770 O R D D O M I T I C K S A N D R E S E V E +nchlt_eng_001771 B R E N S W I C K S T H E N R A L W O Y L H +nchlt_eng_001772 A C T E S A C A T D I M E A W O D +nchlt_eng_001773 P E P L E F O R O M E T O C K Y O A T E D +nchlt_eng_001774 F O R C H A L D S S I N G O E +nchlt_eng_001775 B E A R A B L V A L F T T A R M I N G +nchlt_eng_001776 S O U T H W A I L E S F E Y E S +nchlt_eng_001777 C A O F O R D I U R S T A T T U D O E V O R S I T Y +nchlt_eng_001778 E L D E R O D O +nchlt_eng_001779 O U T D O R E O A R I N T E D S I T Y +nchlt_eng_001780 C L A M E D P O R S I A L R S P O N C E A B I L I T Y +nchlt_eng_001781 C R I S H I O N T E R M E S +nchlt_eng_001782 E V E N T T O P L A C E +nchlt_eng_001783 C A N S A I D D A T H E S I N F R O N E +nchlt_eng_001784 H I S T R Y O F M I S H O G O N +nchlt_eng_001785 O R I G I N L Y T H E A M E M +nchlt_eng_001786 N A T I O N S F R A E W R E C O N V E A N T I O N +nchlt_eng_001787 E N O C K C O N E H +nchlt_eng_001788 O R S T R N S C O L E I C O N O M I S T E S +nchlt_eng_001789 M A I N G R U P C O M E P O U W N S E S +nchlt_eng_001790 H R O S I D C L I B L M T E R I A L S +nchlt_eng_001791 C O M E I N L O A R E S T O M +nchlt_eng_001792 B R O N K S H I Y S C O L E +nchlt_eng_001793 A M E R I C E N B E L I T I C G A L R I T E R S +nchlt_eng_001794 C A M O C A L I L I A E N T S S +nchlt_eng_001795 D L O B L E I N T O N N T C O M U N I T Y +nchlt_eng_001796 T Y O G R E F I C T M A A S I E E N M A R C H E H +nchlt_eng_001797 W I P S O T H I S P R O V I G D T D O S +nchlt_eng_001798 S I E F P C T I O N N O B L E S E +nchlt_eng_001799 S I N E S F I C T I O N F O L E M +nchlt_eng_001800 S S O B E S I T S O M E M P R O B L O M N +nchlt_eng_001801 A S T E O N N O R T H E M Y R I C A R E +nchlt_eng_001802 P E P E S W A T N E S T L O T I N G +nchlt_eng_001803 D E S T I N G N T I O V E F O C K L I N S T R M E N T D +nchlt_eng_001804 H T A F O I O C O N A M O R I C E N R A P T I S +nchlt_eng_001805 P O R T O G E S C H E N L S +nchlt_eng_001806 I N T E N A S I O N L E A P O R T I T Y A Y H +nchlt_eng_001807 M O U N T O N R A N G E S O F B E L I V I E A R +nchlt_eng_001808 F R I N C H A R E F O A R S +nchlt_eng_001809 S S O P R A B L A P E A R A N C S H +nchlt_eng_001810 L O N G T R V L I N G P A P E S S +nchlt_eng_001811 D E I S T R I K T C O R T O D G E +nchlt_eng_001812 D O O N Y A E N M P I E +nchlt_eng_001813 P R O T I S I N A S I O N A L I T Y A C T H +nchlt_eng_001814 I S H O D I C T A P R O L +nchlt_eng_001815 P O P E B L I S I T Y T R A D E D C O M P A N Y E S +nchlt_eng_001816 R U S H O N V H C T I M S O F S O V E D R E B R E S E N T A T I O N S +nchlt_eng_001817 W H I S T D A N S L E V I C K L A N G W O G E O S +nchlt_eng_001818 T A L I O N R O M E N D C A T H L E K C E S +nchlt_eng_001819 P R E N T H E E S T I S T R I F T N I M B O R S +nchlt_eng_001820 P R E V I N C H A L S E M B L E S O F U N T O R I O A O R +nchlt_eng_001821 R O C K S F O A M I N G M O U N T E +nchlt_eng_001822 S E S T I N A T E D M O N U K C K S +nchlt_eng_001823 I N C L A E I N E A S I N O L E N O N D G O V E R M E N T L E +nchlt_eng_001824 M I T C H C K S P A C E A I M +swc_eng_001744 O R E R E P E R E T H E B R A K N T H E T A C E +swc_eng_001745 E R Y H T S O P T E N T E S +swc_eng_001746 H I S M O S T C O M E L Y A C U R S W E N E T H E R I D E S A B L E T O +swc_eng_001747 G R A T B A R Y I A R R E T F I S M E N G E B Y T H E G R A T B E I A R E A F M U R E N +swc_eng_001748 I H A T L E A S H T T R E O T S +swc_eng_001749 D F I H A N T E S Y E S I N +swc_eng_001750 W L H O E V T D E N C E O F H E M R Y G I N T +swc_eng_001751 F I D E N A N C S E R C R I C L Y +swc_eng_001752 N A B L E S D E V Y O C I V E A N D U N D D E M O C R A T C S O T I A L P L I S Y E +swc_eng_001753 M A E D R E S E N T H I H L E S A I L A B L O N D C O S A I +swc_eng_001754 D U S T R C T A N A T I N S I C S T Y S A I C +swc_eng_001755 L I T L A N T O F U C P E I R I D Y +swc_eng_001756 A Y I N T H E G R O I N A N D E D F A N C S E T H R +swc_eng_001757 E C N L A G Y E S A D I M P L E N T I G T R A N S H U M N I S C L E O F A N H A N C S P R F O R M E +swc_eng_001758 N D C O A T D I N G N A P S E +swc_eng_001759 B Y S P A N S H T U R C H M E N L L R E M E O R A S D E L O S A N A +swc_eng_001760 D V F I H T E D D E M O C R A T S +swc_eng_001761 H E W O R L T H A N P P E N S H I P T H A S E N C O T R O L E B Y E F F I D E E +swc_eng_001762 W H E H E T A R I N G P O S I T I O N I +swc_eng_001763 E E N C R A T E D I N E V R Y S T A T A N D T E R I T R Y T O P R T E C H T A N D R E S E R V T H E C O N T R Y S O U N A K Y C O S C I S T E M S +swc_eng_001764 E A C A T H O E N T O F T H E N O S I L E N D W A R E M O I L +swc_eng_001765 A L A M E F O M T H E R E L O U D C U P N Y E S F O R V E T I N +swc_eng_001766 T H E T O N I S S P L I T +swc_eng_001767 O S K E A T I D F I S H I S A P E T I C I L Y A E G R E C I O F S P A T H E S N +swc_eng_001768 A N D T H E N A S T I O N L W E S E D H E M I N S H I P T E S +swc_eng_001769 P R O B L O M E A S N O N T O R N I N P L Y N O M A L T I M E +swc_eng_001770 L A E J U O I R A N D P A R C E R W H A T I E N S H A R T N +swc_eng_001771 I N N D T I N S E V E T Y T H R +swc_eng_001772 D E L P I N G A N D Y O U S I N G S U C H T E N A L D S +swc_eng_001773 O R S O M E W P A S T I O N D +swc_eng_001774 L A M E O R O E T H A T E +swc_eng_001775 A B L A D A C A H T E R I S O U R L Y I N S E R T E D S O M O N S O F L O I E D B U M N S +swc_eng_001776 E R N O T I O N O F Y U J E N I K A N H A S P E N T T I C N A L A G E S M I G H U N I N T E N C H O N A L Y I N D C K R R A G E +swc_eng_001777 A T T H E T E N I O N O F R E S U R T H E R S C A B E F O K A U S D M P A R T I A L S O L U T I O N S O R S O L U T I O N S +swc_eng_001778 N O N E N O F F O R H N T R E D O F Y E A R S +swc_eng_001779 N L Y M U S U B I A L S H A E S O F I V E T T +swc_eng_001780 T O H C H A L T H E A D A B L E S P A C H E S O F C R U S T I S T H A N B E L O N G +swc_eng_001781 O L G E R T H E M R E S U R C H E +swc_eng_001782 N I N T A I N S I C X T D Y T W O F I L I P E S N V E N T D T H E C O M P A C T O D I O K E S E T M E D Y H A M F O R O D I O U S D A O R G E +swc_eng_001783 O S T R A C T I N F T H E L O W +swc_eng_001784 N D F H I B I E N D A D R E P +swc_eng_001785 W E M E N S W H R L D C E S T H A M I N C H O F +swc_eng_001786 C O N T A N E D E C R T I O N S A N D C O M E N T A R Y S O T H E T A T O F E N B E I Y S I N C E A N D E G N A L G Y A S M A E R C O N T R U D E R S T O T H E +swc_eng_001787 P E U R O E L H A N T I Y O R S O T I A L T R E N T +swc_eng_001788 M O S T C O M P A C C S A E T W E R S O L D B L A N K +swc_eng_001789 I O F H E R I S N O U L D G R T H E +swc_eng_001790 H E S O T H E N S T R A L I E N C O S T A N D I N S A B B A E N T I E C T I Y O S T R L I A N T E R A T R Y S +swc_eng_001791 D A T R A T S F T I P O C L Y F I V E H N D E R E D T T W +swc_eng_001792 D E R P R I V I N G T H E D U C +swc_eng_001793 N I E N P E R S E N T O F T H E T O L C A S T +swc_eng_001794 A N D T H E R I R S S O R B R A T R I Y A N D A N T H E I E C O M N C A T I N G A T Y +swc_eng_001795 E D N O T I M P A S H S H I N +swc_eng_001796 E N T H E R E D E M O C R A T I C P A R T Y +swc_eng_001797 N H E S O N T O P E O F T H E E S E T H A L I N D E C A T H +swc_eng_001798 L A E U I A T H S S E O +swc_eng_001799 I A N A N D T A N G E D M R A I N S P A C H Y E S T +swc_eng_001800 R O W N D E S I R E L E C T O +swc_eng_001801 H I S F A C D O S A N T S A Y M U C H A B O U T W E R T H E P R O B L M E L S +swc_eng_001802 C O M O C L S S I T Y B E G A N A S +swc_eng_001803 W I T T O R I S T S R E V I N G T H E S T M E B O T A N D T R I A M E N +swc_eng_001804 F I R S T D I L O G B E T W E N T R A N S H U M I N I S +swc_eng_001805 N E E R B E N P R D O F T H E L N P C A N D S +swc_eng_001806 R E A G E S S F I R N I T C E R A N D T H +swc_eng_001807 I N H I L A B L T R E M E N C +swc_eng_001808 T O O C A T T H A N D U R S O N M +swc_eng_001809 O R F H L U C H O C L F R E D E M +swc_eng_001810 N E R J E T I C A T A C K I N G S T O U +swc_eng_001811 G A C H L Y F O R E O A R S A F E R T E C O N I S T D O M W A S L A T E +swc_eng_001812 A C E T H E R E C A G N I T I O N T H +swc_eng_001813 O R L A T R O N I C T B U T E N S O R D E S P L A Y +swc_eng_001814 I S U N N O N W T H E R P E A E C U L S A N M P Y +swc_eng_001815 H I C H C O M S F O T H E V E R B A C Q U A R +swc_eng_001816 D E T P R P O R T I O N T L Y O V A I L A B L T T H A E W T R A T E R F I N A N H A L R E S U R E S +swc_eng_001817 M N T H R E T T O H S O F I V L O F M A N Y S P A C I E +swc_eng_001818 A V E O N M O R D E T I C L E +swc_eng_001819 A N D T W E T Y W E N S P E A C I E S O F U S C R E A N I A G D L F E O N +swc_eng_001820 A H E V I N G P R M O U T I O N +swc_eng_001821 E N D T Y M I S T E S U M T I O N +swc_eng_001822 O N T H E F I R S T B E L I D +swc_eng_001823 S T O R Y I N D E C A T I E F O F T H E R I S I N G L O A B L S R I G N I F I N G S O F S O P L I S H E I S T O E B Y D J D E N +swc_eng_001824 W H C H S B A R E A S E A L Y E N T R S T N D P O L I T I C S +swc_eng_001825 W A S C O L L D D O L B E A C H A C X P R O I N F O L L A N D P A T N T I +swc_eng_001826 O O D S A V E A N D F I N F I L E S B +swc_eng_001827 A S T R L I O N S N A C E B E L O N G T O S E V O N F A M E L Y E S +swc_eng_001828 T D O L A N P Y O R +swc_eng_001829 L I N D S H A R P L Y S E N C E S P E A A N D +swc_eng_001830 W A S R E C O U R D E D I N T H I R L Y O N F O R T R A C O S E C T A +swc_eng_001831 N O L A S I M P R O V E N T I N +swc_eng_001832 R B O N A N D R U L L E G E S H T H +swc_eng_001833 A C H P L A Y R B G N S +swc_eng_001834 D J H A S T H A S N S P H I E M A N Y C O M N T O R I L E P O U S L E +swc_eng_001835 O R H U O M A N I M I T D G E +swc_eng_001836 L A S P O I R D E D T A P S +swc_eng_001837 A N T A N D I S E N C E I N G T O T H E O T I O N +swc_eng_001838 R E S I N P R O T E M P O R O F T H E S T A T S E N D +swc_eng_001839 I C H O P C E L M O V E A N Y N U M B R O S C Q H E R S +swc_eng_001840 P R E T H E I N S I D T H E S C O L +swc_eng_001841 L I N G C A E T S H E R N E S S W I T +swc_eng_001842 O U N T R Y S O F T H E E S T E N P A L Y O A R C T I C F L Y W A Y +swc_eng_001843 A S O N L S D T I C S T I K E A E S T A M A T T H E O P E L A T I O N I N +swc_eng_001844 U N D D E S P E U T E D W H R L D C H E S S H A P E E N +swc_eng_001845 T A Y N R E L L C A P T B I N C E A R +swc_eng_001846 E R N A C T E D B Y T H E C N E R L A S E M B L Y H W A S A M A S E R R A S I A L Y S E V G R G A I G T H E T A T E S R E R O T C O A R R S +swc_eng_001847 W H C H R A P E A L M O S T +swc_eng_001848 S E A C T P R E T H C T L D N T I O F F O R N E R A N D P R O V I D E +swc_eng_001849 W E R A S T H E F E M I A L S P E C I L E M I S D A R C B O R O N B O R D E D W I T W H A T +swc_eng_001850 W O T E R E C E N C T L S O +swc_eng_001851 N H I D N T Y A N T W E L V E F I N R O S S F I R N +swc_eng_001852 D I D E D N O I S I S G E N R L M A D E W H T H A S E T E S C A N D O T H E H E A I D +swc_eng_001853 T H E F I R S T G E N R L Y R E C O N I G C S E W E R L D C H A E S S T C H A M P P E O N +swc_eng_001854 H E P E Y A N D S I T I N G L N W R E P A R T +swc_eng_001855 A D R E L A S T T H E R E L B U M S B O T H T O S E D E A N T +swc_eng_001856 W A T E S R O N T H E C O N T I N E N +swc_eng_001857 F T H E R A N G H P E R S O N L S T A I R I O S +swc_eng_001858 N D E Y O M E D E R S A N D R E C O A R T I N G L E V L C O N T R O L S O N +swc_eng_001859 L Y N O M U L T I M E +swc_eng_001860 A N D I T O F H E N D E S T R D T H E P L A B I L I T Y +swc_eng_001861 C O N F L A U T I O N +swc_eng_001862 E U I V L E N T T O T H E C E S T I O N O F W H T H E R A C X E I S A M E M B R O F C O M P O U S I +swc_eng_001863 M O S T O I T H L A S T E R A N G +swc_eng_001864 P O S T H E N D E R I S T M E +swc_eng_001865 O M P A C T C O S S A T W U I K L Y F O U D O U S +swc_eng_001866 O R F O R H U N D R N D T H A R T Y T H R Y F E E +swc_eng_001867 I N G S W H C H R E S E L T I N A S P E C S I O T I C T T I H E O P O N +swc_eng_001868 B F R N A N T I Y N A N T Y S E V E N +swc_eng_001869 O M Y C A T I O N S A N D H E L T +swc_eng_001870 E S A Y A I C H E I N A P E R S S O N N O N +swc_eng_001871 S O M B R A E N E P I S O F I E T H A T T H Y A Y A L L S O E B E O U E D O N U T H E R N O N P O R S S M E T E I R I L S +swc_eng_001872 H E P O S E B L Y C O N P E S I F I C +swc_eng_001873 M A T O R S I N L A C T H +swc_eng_001874 I T H O U T F T Y M O L E T R I N G R O L +swc_eng_001875 H E E D C H A C S E M P O U L E S R S P E T I E L Y T H O C K U S T O F A G K I +swc_eng_001876 A V H H O M L E T D I T S F I R S +swc_eng_001877 R E B L E A D I N G R S S K R M A I N E S O F R O U N D F O A T Y +swc_eng_001878 D L R C H A C T M A T D +swc_eng_001879 S O M E S E C I L R H U M E N I S C O N S I E V E T R A N E H U M I N I S M A S A N O S P R I N G O F T H E U M N I S T F R E T H U T M O V E M E N T A N D A R G E Y T H E T R E N S H U M I N I S D I F E R T F O R M T H E U M O N I S T M A E N S T R M E B Y H A V I N +swc_eng_001880 P I N T I L E N E S T D A N D C H I C K S R E V O N E R B L T P R D A T I O N B Y M A M O L E +swc_eng_001881 N O R T H E R N P I N T A L I S O N E O F T H E S P E A S H E S T O W H I C H T H E A G R E M E N T O N T H E C O N C E R V A T I O N O F A F R K N I R A I S I O N M Y G R T O R Y W A E R B U R D +swc_eng_001882 A N T I S N E O E F E R U N D O L Y I N T A S M A G I O R +swc_eng_001883 E R S P E C T I V E T H E A D E A O F M E I N E D O U P L A T I N G I S A S R T D T O R E P R S E N T +swc_eng_001884 N A V R I G E O F T W E N T Y H E N P E R D A Y +swc_eng_001885 T E N I W O A D F L L O T H A T P E E E C G U L +swc_eng_001886 N D B L E A D I N G I N T O E E R I E S C H O M E O S +swc_eng_001887 A N L E T H E T O G L I D B E T W E A N T R S +swc_eng_001888 I F T H E S P R O B O E S W R F I C I N L Y S O L V H A B L +swc_eng_001889 A L O G H C A L T I N 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E B Y L A +swc_eng_001932 R E F R O N C E I S E S T O T H E R O L I N G C O R L I T I O N G O V B E R M E N +swc_eng_001933 S P A H E S O F G L D I N G P O S M +swc_eng_001934 B A C S E D O N T H E P R E V I S S T R A D G E Y O F P L A Y +swc_eng_001935 A D I D D U L I S T C A S P E R A T I O N E +swc_eng_001936 P E R F E T O N L S A N D H O M R E C O A R I N G O T H U S I A S T S +swc_eng_001937 H E M T H O L P O D A Y +swc_eng_001938 N O C O R T E O F P E P L W I T H E P R E A V I S E S A Y A G C H M A D D V E L O U H I G P O P E T H U R T R I S N +swc_eng_001939 D I V I R D E D I N T O T H R Y F A M L Y E S T H A +swc_eng_001940 H O D S L I T I N T R E S T A N R L E A C I N G O S E T +swc_eng_001941 T H A T A H R M I R A N O U T H A V E C O M E N +swc_eng_001942 A N T O W H U S E N D S A I C E +swc_eng_001943 S H W O H I N E B O R Y E S A R N O N A S B U T P L I S +swc_eng_001944 T H E O S E I S R U H T H E R O F A S E R I B L E A N O U R I S T M +swc_eng_001945 M O S T O T H E M A G R Y O U E S T S M U S I C O M N Y E S +swc_eng_001946 W E N S T A R R I O P P A I R O R W N E M O N O F O N I C T R A C K I S P L A E O R R E C O R T E D H N T H E T H A P E S M O V I N G A N O N D U R E C T I O N A N T +swc_eng_001947 H I T C D E A R L Y F O R M E A N +swc_eng_001948 S T E R T E A G I K F O L O S S O V E R +swc_eng_001949 O S I S I O N G T B A N T G C H E S T D R N T H E G A E +swc_eng_001950 D O S E A U T W H E L S +swc_eng_001951 E S P O S L O V E R H I S O N B Y I L O U G I C A L A T E R +swc_eng_001952 R E P E R O U C D T O F R I H T E S H O C S A R T U N D O P R E S T H E R S O N P R O S P E C T E D P A R N C +swc_eng_001953 T I L A N C H A N D Y N O R G E O N +swc_eng_001954 R A S T P O F O L O U S H A D G O N +swc_eng_001955 N D T O T H I S A N D T O +swc_eng_001956 T F O R E N T A P L F T H E P L A R H A S O N L D +swc_eng_001957 S U E R D A S U B R A K N O D H E M I R G H A V E C O L D N I C O I N M P A R E M E N T T H A T O F E T +swc_eng_001958 R O V I D E D R O N O S T I N D A T E A +swc_eng_001959 O H A D A N U R I S N S D E T E C T I D B Y T H E M A N E S +swc_eng_001960 L I H S T I L D E S I N E T I M P R O E H E L T H A N N G E V I T Y +swc_eng_001961 H A D M O R S O F H I S T I C A T E D A N D O F T A Y P R O D C T I O N +swc_eng_001962 D E H O U M E N I S A T I O N +swc_eng_001963 P A C H Y S I N L D F R E H W O A T R L A M P R O A C E S +swc_eng_001964 F O S T A N D Y O U G R E A M +swc_eng_001965 H E F R Y I N S D K L P E A D I A A T +swc_eng_001966 T H E F O R M E T I C L I N A G E I N G I S D E N R L +swc_eng_001967 P E A S I I S T T H E C L U I O N O F N O N C H O U M E N A N D P A R T H U M A N A N A M L S +swc_eng_001968 N D P E A B L H O A D R V I S L Y S U F E D A S U B R A C N O T H E M R I G +swc_eng_001969 L S I F I D A S T H E I N D A N G E D O R T H R E T O N D A N D O T H E Y P E B E +swc_eng_001970 A A T E R N Y J E N E R L P A R C E R W A T C O E N S H A R D N +swc_eng_001971 B U T T I P I C K L Y +swc_eng_001972 H I C H I N T R E F E D T H E S I N L T O T H E H E A D O T H E C O S S A +swc_eng_001973 H T H I N T H E R O N C O N V E N I O N L Y E C S P E C T E D L I F E T I M E M S +swc_eng_001974 O B S T A N H E L S T R A I E N +swc_eng_001975 T W E N Y H A I T H S E N T R Y C O N T U C Y C O N G R S M E N J O A N +swc_eng_001976 N O T H Y P O S E N T O R N D I M A +swc_eng_001977 H I N T I N G W I T L E D S H O G +swc_eng_001978 W E N Y T H R T A E N +swc_eng_001979 O R T H Y S E V O N P R S E N T O F T H E L D P A T S P A C H E S L V I N U S T R A L I A +swc_eng_001980 U R P O E S R A I N F I N L Y A N D I N A N T E N A D Y F V +swc_eng_001981 W I L S O E T R E H U M I N I S T I K A N A P S T R C T +swc_eng_001982 P A N T H R E R I A T P R T E C T I O N +swc_eng_001983 G R A Y C S M O R F I S E P R O B L E I S T H E C O M P E T A T I N T P R O B L E O F T H E T E R M I N I N G W H T H +swc_eng_001984 O R T H E R E S T I C T A U R C O N C S E P T O +swc_eng_001985 H E H O O F B I E O U S T P T L I S A T I O N +swc_eng_001986 S O M E P R T E C T I O N O F U N E R T A N S I G N I F I C E N C E I S O N F I R D B Y C O L K C A I A N A T H N I C I T Y +swc_eng_001987 O A S T A L A G O N S +swc_eng_001988 N D C O W G D T I V E I N H A N C S E +swc_eng_001989 V A N C S T D T H A T H R A N K A N D B E P R E M O U D T D T O A N L O +swc_eng_001990 D R B A C K O F C O I L I N G I S T H E P S E A I L I T Y +swc_eng_001991 I N D E C A T E S S U B R A C N O L D H E M R I G E +swc_eng_001992 D E A M I S E T P O R T I O N +swc_eng_001993 N D O P T I O O F Y J E D I K A N D H A N C S M E N T E C H A L H E S +swc_eng_001994 P L I S H E N H I S H O U R E A N W A G O N +swc_eng_001995 N T H E E C T H A R M B B E N +swc_eng_001996 R T H R O F O F E R A L I S U C L Y +swc_eng_001997 L E D T H A P I N H A N C T I N T W O R N Y O N +swc_eng_001998 S O U C H A S O N T H M C L P E T A T I O N A N D R A N D I M Y S D L L G R T H E M S +swc_eng_001999 A T Y N H A D E N O I E N 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D I D T H A T D +voxforge_eng_000917 W E L E V E T H E V F V E N C U O A L I T Y T O T I M E A N D L O R L +voxforge_eng_000918 A A T T H E S A M E T I N G S P E A R S A N D E R O S B E G A N T O F A L L E A M O N G H I N B A T E R S +voxforge_eng_000920 I T I S M E Y T H E S I M P A L S O U P E L I T I F E F +voxforge_eng_000921 I N D S T A I D H E A R I G V E O N T H N O T O F T E S O C O N D A Y +voxforge_eng_000922 I N H I S A N G S I T Y A N D S O L I C S I T O E D E A N D L O V E F T T H E D I D N O T C O W N T +voxforge_eng_000923 D G O D B L E S S O M I H O P L I L G O A N D S I N G T H E M F O R E V E R +voxforge_eng_000924 Y O W E R I N G O A G E D +voxforge_eng_000925 T H E R L A T S E S W A S O F A T E L I C K E T I V H E R Y C O L O E R E F R I A I N T O B T I N T I N W H I T E A L +voxforge_eng_000927 I T W A T H E S A M E W H A Y W I T O L R E V F A L V E O R S A N D R I F A L S +voxforge_eng_000928 H E C I N G H A D R M I S T I N C Q U I R E I N T O T H A T E R +voxforge_eng_000929 A E D O S T E N L O K G O D T E D T +voxforge_eng_000930 F O R T H E F I R S T T I M E I N H I S L I F E H E W A S Y U R N I N G F O R S G R A P +voxforge_eng_000931 I D E F I G A N Y M A N T O G E T A S O L H A M W O T I L E N C E S O R E I N C E L Y F O R N I E R +voxforge_eng_000932 H E R I S S M U L T T R U A T H I M A S H E C A M E O F T H E B A N G K +voxforge_eng_000933 A T D Y W A Y T N O N S L E L I G T D E R +voxforge_eng_000934 M I N H I N D E U R I T C O L T L I N G D E A T H +voxforge_eng_000935 M E T O L S O N O H O S E D E T H I S B O K C E P E R R O D G E R S +voxforge_eng_000938 I O N L Y A T D T H E F O R T A T I O N S +voxforge_eng_000939 T E R E W A S P E R D E V I S I O N O F L A V B R I N T H E W A L T H E Y I N D E V R I G D E L Y P O F P O M E N T D +voxforge_eng_000940 I O L P E Y O U T H E L A B R A O N D S A I D T H R I C T F A C E +voxforge_eng_000942 I S O F M I S T O R P I G N O D I S H E A D G R I M L Y I N E R O C A S T I C L Y +voxforge_eng_000943 T H E R I N G O F T H E B I G B I L E A R O A S T D I N D N +voxforge_eng_000944 O R T H E S R A C H O F A P I N O N A A N S H E A D V E A S T R E A G O S O F T H E U R S U R F I S S R E M A E J E L O U G I C L Y U D N O N +voxforge_eng_000945 T H E A D B A D I L Y A N T E R D I D E S W H E N T H E S O G D T H E G L O E O F A F F I R +voxforge_eng_000946 C H A N S C H A R S T H Y L I T C O M A N D +voxforge_eng_000947 I T W A S J E N S A I N I N G S O F H E L Y O E R B E Y O N T H E R O C K C E +voxforge_eng_000948 O F L I N G A R O L B O S T B E T W E N O S E D +voxforge_eng_000949 H A T R I T A N D M U R D E R A N D L O S T F O R R E V E N G C H T H E Y P O S S E S T T O O F E R F L O Y I N G +voxforge_eng_000950 T H A T O C O D H E A R A L L U P E D D O N T E L I M P O P O E +voxforge_eng_000951 I T W A S M Y A D D E T O A T E +voxforge_eng_000952 S H E D O S A N T W O N T O W E I N +voxforge_eng_000953 S H E T H I N G E A I T I S B E C O S H E W O N C E S O M T H N G L E +voxforge_eng_000954 H S H E P O L D E A N D T T H E L O C C R E A S E T D O N T O B R A K H I S B A C +voxforge_eng_000955 T H A T H E S O C O L D F O R E S A T W O R K I N L I T E H E A L C T R I S I D Y A N D M A N G N T I S M +voxforge_eng_000956 W E T W I O N S H A O B P E I N T A N D P I A I C E T G R A G S I N A N C O S T H E T H I H V B L E +voxforge_eng_000957 A L S O E I W O N T A N F O R M A T I O N +voxforge_eng_000958 T H E S I C T D A Y H E S P E N T I N T H E C A V O N W H G R A G S O N +voxforge_eng_000959 I O N T H I S Y P O T H I E S T H E H A M E R N G O F T H E L T E R M U N D I N G C R P U C L E O N D T H E B O B C O F I R E I T K C E N A T I C K N R G Y O N T H O N H A N D +voxforge_eng_000960 N O W E A F I R N Y W I L S T R E M E M A N D E V E R A N A N O N Y O U A M U R G E F R O M A L T H E G R O V E S A N D F L O U R S +voxforge_eng_000961 W I T H H O T I T T H E M O S T D E N C E L Y P O P I L A E D R E A G E N S O F M O H E N T U R I P A N D A M O R I C A +voxforge_eng_000962 T O M S P I N K H A S H A R P O N +voxforge_eng_000963 H E W N T E D G T H E F I S H E T T H I S F O L E A L R E Y S O F A G O N +voxforge_eng_000964 L A A F L A S H E H E L O N C H E H I M S E L F I N T T H E F E T H E D M A S O T H H O U H L +voxforge_eng_000965 I T C O N T A E A T O T L E O F T W E N T Y A N T R E S +voxforge_eng_000966 I H A V E F H E L T M O R E C O M F O R A B L E +voxforge_eng_000967 T H E D P O E S T O M N T H V E A T E L I T Y +voxforge_eng_000968 T H E W L L E D O G K T H R S T H I S G O N T M U S L E T O R D H I M N +voxforge_eng_000971 T H E G A E B I L E V O R S C E O F H E S E M E R I Y R A N G O U T +voxforge_eng_000972 I T W A S A I R I V E R A N M M U R G I N G L A K O U R S E L S F O M T E G R A T S O M P +voxforge_eng_000973 S A I D T H E M O L L P U L I N G H I M S E L F T O G E T H W I T H A N E F E R T O M U S T T H I N G M E V E R Y R O D +voxforge_eng_000974 I N W H A T B Y U C O L L I C K S C O O O F F E N C E H E H A D B E D T O R T T H W A S B E Y O N D I M A D G E N I N G +voxforge_eng_000975 H A D N O T I N A B L E D I N V E S T I G A D E R S T O O P E T A I N A C O M P E R I T I V F L Y L I T L C L O U S T +voxforge_eng_000976 I T T R I K L O F F R E S H B L O D R A N O V E R S F A C E +voxforge_eng_000977 D I T W A S A C U O R E S C O N S I T E A N T S E S E +voxforge_eng_000978 I T I S T H E F I R P A R T L Y S H E S A I N E +voxforge_eng_000979 T H E Y G O S T L A Y E O F T H E B O S H A N D P O K D A W A Y A N +voxforge_eng_000980 I N O T H A T O U R E I N C H A R D E T H E R E A N D J E N O S E +voxforge_eng_000981 F O R T I E T H E E C S A T I N G T H I L E O F H I S A D V E N T H E W A S G O N +voxforge_eng_000982 F A E D L Y H I S F I N G R S C L O S T H A D L Y O V E T H E A N G O C I F +voxforge_eng_000983 D E A R S I R E Y O U R S E C O N T V I C T D O M H A S F O L L O N O N D S C H A D G D U L E T I M E +voxforge_eng_000984 H C O N C A R F H M S E L F E +voxforge_eng_000985 A C H I N S I L T A T E T O T H E V A L Y U O T H E C L A M E +voxforge_eng_000986 T H E I T M A B E T R A N C S F O R M E D I N T O A N Y O O F T H F O R M S O F W H C H E N R G Y I S E C E P T A B L +voxforge_eng_000987 M E S E I D E S S C R E A M E D G R I D L O V F I A D N D M A N Y I F E S T E D T H H I R I A R D I C K A E B O N D D E N N M E N T O F H I S T A I A R +voxforge_eng_000988 I W H N T O N O H O W T A L T H I S I S P O S T A B L E +voxforge_eng_000989 R E S E N T I N G A S E M P L A N N S T R C T I E I L O S T R A T I O N O F T H E S T R O G O F O R L I V E F A M N G T H E R I V L E S P E A C E S +voxforge_eng_000990 H I L N E V E D O A T A P O F W E R K T H E H O L V O R I A N D G C H +voxforge_eng_000991 I H A E H N T E A L O N T I S R I G E R E P L A D F L I P +voxforge_eng_000992 L O R D B U T I N G L A D T O S E Y O A G I N F I L +voxforge_eng_000993 C O W E L I N L Y I W E N D A T D I D T H A T F I S D E A +voxforge_eng_000994 T H E A R N O T R E A G I L E O S T E R P I R A T S N C K L E S C O T N U D E D +voxforge_eng_000995 T E M U S T B E H U R I N G F O R B I S E S S B U T I T H T Y O U M I G T W N T T O C E K A L O K A T H E R S I T D +voxforge_eng_000996 D E R A S N O C H A N C E T O F I R E W I T O U T H I D I N G H I M +voxforge_eng_000997 A S F O R H I M S E L F W O N T T H S T R E A T R E A L W A Y A R N I N G N K E S I N G S E T L Y +voxforge_eng_000998 D O N H M C O N Y O R B O Y O L O N G W O D E S S Y E +voxforge_eng_000999 C O D B A I Y P E A R H E S H O T E D +voxforge_eng_001000 B U T S U C H D E V E R G E N S O F P I N I O N W L D C O N S T I T U T N O M E N A N C E T O S I T Y +voxforge_eng_001001 I B I E W A S O N T H A N C E E A N D O L Y O N W O E S A V I N G H O N T D L D +voxforge_eng_001002 E I O C A N O T F O A L E Y O W S H E S A I N D N E +voxforge_eng_001003 O N T H E F A R C O A R N E R O F T H E C O M P O W N D F E N T E A H O A K B R E A D E D +voxforge_eng_001004 T E N A G I N T O E R H A D S O C H A N I E R S T A T I N G W A Y A O B O T H M +voxpopuli_eng_000494 W E A L L N O O M A N A S A S E C E S T O L S T A B L C U N T R Y A R O A L M O R T H E F O R T H A T F O R T H E H O L R E A G O N +voxpopuli_eng_000495 T H E R F O R I T S H I G H T I M E O U C O M E F O R B O U G D T H E T E P R E P O R S L F O R R E V E U B E D E N O P R A T I N A L S B R A T S O N O F T E O A R D I T A N D N O N O A L D I T S R V I S I E S A N D E R A D I D L A C T E W U S O B O T I S O N +voxpopuli_eng_000496 I T I S C E E A R T H A T W E H A V E N O T I M E T O W A S T T H E N U R E S S O U L T S H O F T H E Y E I D P E A S I E S I E R E O A R D I N S I E N T D I F I C K B A S I E S O F G L I M I N T T J A I N C E L E N O R O U M F O R H E S I T D A C S E O N +voxpopuli_eng_000497 S E N T S O I N T H E C O N T A E N O R H I H A E V E R A V E N T U C H E D C O M E S L A V E S C O N T O F E T G O D S D R O G E S I T H E T R +voxpopuli_eng_000498 I H O P E T H A T C O M I O N S M B I T I N I S H E S I N I S H I F I V S W W N T C A R A K T T H E D A C X T P R O B L O E M B U T W I L B E A N A N C S E F O R E E I S T I N G C H O L I N G E R S O F T H E R O B P T A N C S P B O R D S E C T A R +voxpopuli_eng_000499 I T E O U A S I W A S D E S I O N T A I N A R L Y B Y O N P E R S O N T H E O R M E P E S I D E D O T H E N I D E S T A C E S A G A N S E T H E A R T I C I L A T E D M C R A T I C K D R M N D U R I T Y O F T E U S C O N G R S S B Y A L L O F I T S R E P O B L K E N S O F I C S T D E M E R C R A T I C T D E M R T M B E R S I T W A S N A G R E M E N T W I T H O U T A N Y B I D N D I N G O B L I G A T I O N S A S T H E L E A D E R S O F I R A U N V E R Y O U P E N T L Y I N P R E S I D H M A E P L Y N T H E E R Y D A T H E S O C L D D E L W A S P O L I S H E D +voxpopuli_eng_000500 F R E S P E A C G E I S S S E N C I A L Y A E C E C T I N G T A T P E P L U R F R E E T O S A Y T H I N G S W E D O N O T L I E K E N O T M E L Y F R E E T O S A Y T H I N G S W E D O L I E K +voxpopuli_eng_000501 L A T A S L A R N D F O M T H I E +voxpopuli_eng_000502 B E S I N G H A T H E A N E V I M E N T A E E A F E C T O F P R D U C S M U S T B E A V E R Y I N M P R T A N T I S H U I N G T E R E U A N D H E O L I G T D E A R O T H E I E C O L A B R N E V S A V E R Y O U S O L O R I A N T A T I O N F O R T H E C O S T S U M E M I S O F C O U R S H E I A C U L A B E R S O O D G I V E N T O T H E M O S T A N D V I R E M E N T A F F E N D Y P O R D U C T T H E I F O R E M I T I O N S H O L B E C L E A R E A N D O E +voxpopuli_eng_000503 H O W E V E R T H E C I R E N D R I G I E M E N I T S T O B B E T E R S A L O R T T O T H D I G I D L I N F V E I R N E N T E D E N S H O U R F A R R I M I N E R A T I O N T O G R E A T D U R S A N H T O O F O M E T O O N S O M E R E X A P E C T A T I O N S +voxpopuli_eng_000504 A D C O L S B O T E O M I O N A N D M E M B R S T A T H O A N H A N C D H E R S E P O R T T O R E C E N C S I Y A T I O N T O S E C U R P E C S A N D S T O B L I T Y A N A R L E N D I W I L T H E R F O R A R E S U C A L I G E T O P E E S S U P O R T H I S M E N D E N +voxpopuli_eng_000505 T R A T I G I G C H O I C I S A B O U T E R E T O L E W E S T M U S T B E M A D N O W L T O A K I N N E C O U N E A N E T O F A S E O U T F O R S I L F U L S U P S I T D S B U T T E K T H E G A S A S F O R S O F Y U I T C A N B E A H E L P F U L B R I G I N G R N S I C O N A R Y M E A D I U M T O B E U S I N M E M I N M A N Y M E B E R S T H A S I E O N T O E D C H I F O V E R A N M B I S H I S L I M I G T A R G E T S +voxpopuli_eng_000506 E W E A E D P O S I L Y A O F E R I S C O P E W E C A N C O O T H T O P O R O S U E T H E Y S A M E P L I S E S I N H S A M E M A N E R N O N I N G T H A T W E L L E D T O H E A M R S O T S T H E R S O L S E S T T H A T W E N O D R D A +voxpopuli_eng_000507 U T E R I S N O P T I O N B +voxpopuli_eng_000508 W E A L L S O E N E A D A T H A N G E H I N O R H R D O L I D G T Y I +voxpopuli_eng_000509 I L A G H B U T O F T H E R E S O N O F C O U R S E I S I L I G L F I S I N G K A N D T H E O F O M A L T W E D O U N O F E N B Y H A L A M V E S E S W H I C H A R L E A G I S T E R E T O C O U N T R I E S H I C H L A U C E T H E I L O F T H R R S U R E I S T W A N F O R S T T I N T H E N A T I O N E A G E M E N C S N O M O N T O F T E S A B E I T Y M A S E R S O R D E C X T R P P E R W A R U I L E D R E S E T H E P R O B L O M E O F R E T I U S I N G +voxpopuli_eng_000510 T H E O M P R M I C E O L L S O I N C L U D S C L E A R E R U A S T O H E F I N E W H I C H M B R S T A T D A S H U R S T I C T I O N A N T H O P R A T I O M T Y E M B R S T A T E S C O N C S E R D F O R C R O S B O T H E C A C E S A S H E H A T H E N E D T O E I M V O L L E F Y O U R J U S T H E N Y O F O R W O R K A N D P L E A C E W O E S U P R T T O M R O H I S E I R E C I F +voxpopuli_eng_000511 E N O T H E G R E N S W I O D T H A V S B L E H A T H I S U R B A D B E E S C R I M I N A L B E E S D E L I B R T L Y C O T A M I N A T I N G H U D Y W H T H E D A N G R U S I N G R E A D I E N T B U T I N F A C I N F A C H E D I N G H E H U Y B E S A R L A V L A L L R S D O U N M I H I T O C A R Y P O L O N B A C T T H E R H I V S T O D T O F E T H E R Y O U N G +voxpopuli_eng_000512 B U T I T W A S T H E C O U N T R Y I T D H E L D B E N G M O R E C A P R A B L +voxpopuli_eng_000513 R I N T O T H E P R T F O L I A O O F T H E N U C O M I S I O N R E D A L I N G W I T F A U N D E M E N T E R I G H T E +voxpopuli_eng_000514 E M E S I G E T A T T H E Y U D O U T A T H A V E A N Y N U S O L T I O N E +voxpopuli_eng_000515 A R Y U W I L I N G T O A C T I N E R V F A T H E R E F O R T H E S O S I A L D E M E N T I O N T O B E I N L O D E D I N T H E E O U C O M P E T E N S Y E S A S P R E P U S +voxpopuli_eng_000516 A E R N C T H E O N D P E S P E C T R U P O L I Y E S T A K I N G W I T H E R E F O R M E O F O U R T E L I C O N T H F R A M W R +voxpopuli_eng_000517 I B E L E H I S R E M A R C E S W E R A I N E X T P L I I T E L Y R A C I S C T E D A N D S E N A F O B I C K E A N D P R M O T E D R A I L I N T O L R A N C I N W H A Y T H I S N O U C E T A B L E O R A L A O U E D I N T E O N T O I C T U T I O F T H I S H O U S E +voxpopuli_eng_000518 R E A L I G H E G S A M P L S H O L T H A T S O V I N G I E S R L A T E T O A B O C A T I O N F E Y U L D S T R O N C O M N I T Y D E V E L P M E N T +voxpopuli_eng_000519 S I H O L P E T H A H I S W L H A E M F O R U S H E A S W H E L A N T A T R U S H E C O N L S A N D I S I G T D N C S T R E M E M S C C E S S T A R Y A F T R T H E S E G T W I S I G I F I C E N D A T I N O R G S T T H I S O U R B +voxpopuli_eng_000520 S H E E C E P T O T H E F A C T T H A T S I T I S I E N H I P I S A Y N A S I O N L B O U R T O F T H E N O S I O N O G R I S D I C T I O N B U T H Y O U R L S O S A I D T H A T A C O R D I G T O T H E M A S T R I C K T R E A T Y A N D H E A S R I G H T D T H E A S T O B E A D I R E C L I N G +voxpopuli_eng_000521 E O F A I L D E S P E S I O L Y E I N T H E M S T H R A T I N G A Y U L I F I D E D A N D T A F I S H E N T A T P R O R C H T O O L I M I T C A E N G C H T R E A T M E N T A S E L E A S I N S T R A N G T H A N I N G I T S E L E A D I N G P O L I T I C K L C O S I T I O N I N D E S U G E N D E R I C O S I T H E R E T H E F O R T A K I N G I S R E S O L U I O N A N A C T O F U T M O R S T I M P O R T A N S +voxpopuli_eng_000522 T H E U N I G T E S S T A T E S O F Y U R O V I L B A F A C T W I T S W E D O N A S P R O V I D E N C S +voxpopuli_eng_000523 I T D M U S B E T H E C A P B I T L E O F B O T T H A T S A N D W E M U S S R E C O N I S E P O L S T I N I S S T H A T A S P R O V I D I D F O R E I N T H E O F L O G R E E N C S +voxpopuli_eng_000524 T Y U K R A N E S F A S E T W I T D W O N E O F C R U S I A L C H A L I N G E S E I N I C G H I S T A R Y I T W U L D B E F I U N T H E M E N T A L Y R O N G K T O P R E S T H E N A T I O N N O W E W I T A L T I B E S O F O R E S T R I C T I O N S P O P E L I D A L C O A L E D O S T E R I T E P O L I +voxpopuli_eng_000525 M O R R U L S A N D R E A G I L A T I O N W I L L N O T I N M P R O V E T H E S S I T U A T I O +voxpopuli_eng_000526 A T L E A S T B E W U D L I K E T O N O L T H E S O R S E O F T H E M U N Y A N D T H E P O S I B L E M O R T H I F S +voxpopuli_eng_000527 T O W E A V E T H O U S E Y U R P E N W A L L A N G W O A C E S I N T O T H E I S G L A B L I S E D W E R L D T I S I N T T O T H E A I S G O A B L I S E C O N M Y I N D I S G O B E V I L I A G E W H I C H I S C O T I A L Y C O N O M I C K S O S I A L E E L N B P O L I T I C O L B P W I T S E A E M O S T V E L A B L E E A S T H E I T F R O M T H E I N T I R E E O U G T H A T W E M U S T T H A K F O L A C O U N S A N D T +voxpopuli_eng_000528 E A E T O R E B E T T H A T A L L H E A Y A N N O T B E Y O U S T O F I N C S S I G U R I T E X P A N C E S B A R T H E R S C O N T R O L O R M L I T R Y S O U P O R N +voxpopuli_eng_000529 T H I G H E I N T I F I K R E P O R T S B C A L E M A R M O R E U R D E N T M O R L A R M I N G A N D M O R S H O C K I N G +voxpopuli_eng_000530 F I N O L Y I M W H E W H A E H I N K I N G K A B O U N T H E R E I N O V A T I F E F F I N S I O N I N S T R M E N T S W H N K U T H E B O L T H F O R O R S E L F S T O U G S O U P O R T O U O U E R A C O N O M Y S B U T A O S S O T O L S O P O R K T T H O S C O H E R I N E A E +voxpopuli_eng_000531 T H A T G I V E A E S O R Y U N I K E T O U L I N E S M A K I N G +voxpopuli_eng_000532 D P A P E R A V E R Y H L W E E K P R O P O S I L +voxpopuli_eng_000533 S R U S H A S O L Y S B E V E R Y P R O U D N A T I O N W I T H I C H C L D C H U E R E W I T I N V E N T I O N S W I T H A N E S C E +voxpopuli_eng_000534 A R T A C T A I T I O N N V H E N A M O D I C E O F T A C S A I T I O N N S O M E C A C E S M I Y J U S T H E L P E S E M T O D O W A T I H E A R E D Y S H E G E S T E D A N D H O N O S M A K T H E C A C E F O R T H E E T E R S P E C T O F B A N K R E C A P D L I A T I O N T H A T W E N E V E R S O L +voxpopuli_eng_000535 T H E U L O P B E A N H S I D L O M S U P O R T O F I C E M O R O V E R A S A M O N G I T C S T H I U S T O P R O M O U H T F E S I L Y T H A T A N D C O U R D I N A T E C S C H A N G E S O F I N F O R M A T I O N A N D U T H E A C T E V I T Y E S E R L A T Y T O L E L C A T I O N B D I N T E Y U N I O N +voxpopuli_eng_000536 H E C O N L S N O F T H E R A M E B O R K A G E M E N T P R O V I E A L I G L Y B I N D I N G K I N S T R M E N T T O O B V G I R A T A N D S T R A N T N E U O S T R L I R B E L I T H E R I A T S I O S A N D T O I N C E E S C O P E R A T I O N +voxpopuli_eng_000537 E R E F R U R E W E A S I N T H E C O U N S E L A S G L M I T I O N T O R E N T A C A S B A R I T H A U L D B E D T H E S E S T E N T O F T E B A C T O T H R I S I +voxpopuli_eng_000538 A I N O T H E W E R D S T H E O B J E C T I O N I S N O T W H E T H E M U N Y I S P A E D O R N O T T H E O P B E B J E C T I O N I I S W E T H E T H E Y S A D I D E C T L I N K O R E N O +voxpopuli_eng_000539 T O H E S T I N G I S H I E S T H E T W O M A E D O S H E A R Y O U M E R I T S A E Y O U S E B Y T H E C A R N T G O R E N T A N D T H E D A N I O N N U K L E P R O G D H M E +voxpopuli_eng_000540 E S S M E T H E B D R O N M H E N K A C T E R E S E C T I O N H E R A S D E N T I S H E F O R M O V I L A N C E A N D I T I T H E M O S T E T R E A N F O R M O F G H N T R B A S E D E S C R M I N A T I +voxpopuli_eng_000541 W E C A N L O K T O S O M E E I R N N I N E U M E M B O R S O R G O D E X S A N P L E A R E G A R D E D T H E N O L A G E S +voxpopuli_eng_000542 I N M V L V E D S F O R H E P O S I T E I V E A N D C E S T T A C T E I V E A B R O A T C +voxpopuli_eng_000543 O I H O P E T H A T I S I L B E C O M P E A T E T A I R I N H A F O R S I V I L E F O U O C H E R T H A T D M A N S E G A D B E T O A F R E M U N S +voxpopuli_eng_000544 O R F O R D E R A N D C O R I S H E T H E Y O U A N D E T F H U R H T O B R I N G A M N G P E S I N O F G N I S T A N A N D T O O V E R C O M E T O F F R E S I L S I C U R I T Y N V E I R E M E N T I N T H E C O N T Y +voxpopuli_eng_000545 B E A N D T H E S T A N T T H A T S O M E P E P L O A R A N G R Y +voxpopuli_eng_000546 O E N T O H E M O R S T P O N C I V L D +voxpopuli_eng_000547 E M U S T E D A C T I F I H I T H T H I S S U T I A T I O N A N D H A S E T H E C O M I O N T O C O N S I D E R T H E M O S T E D I C K E T G C O M I N S A T I O N M E S H E R S F O R L O L W P E S E N G E S +voxpopuli_eng_000548 T H E C O M I T I O N I N G B I S H E T H E Y U O P I O N T P O R L A M E N T I N T H E U P C O M I N K R E V I S I O N T O O P E N I S P O S I T I O N O N D T H I S M A T H E R E W H I C H R E A L Y C O N C S E R D A C E S E T O L U S T I C S I N O U R O P A N D T H E I N F O R S T M E N T O F R I C E S G R A N T E D B Y H E Y U R O P I U N R Y U N D L O +voxpopuli_eng_000549 I L M E R Y M U C H T H E R S O U N T I O O F T O C K T E N T H E A S R A L Y A N D P O L I S T I N I O N S A N D E N C I R L Y H O P T H A T H E W L D S U C C E D +voxpopuli_eng_000550 L W E H A E E C U M E L A T I O N O F P R O B L A N C S R E S I L T I N G F R O M T H E A R T I F I S H A L U N D T H E B A G E I T I N G K A N D V E R E T P R I V U S Y U S +voxpopuli_eng_000551 E L E T A S T N O T B E T H E M A N O F O U S T A D Y I T I S B E T O D A S I N S T I T U T I O +voxpopuli_eng_000552 E I G O D A R L S E O E N T O B E C O M E A M B S H E T E S O T H E Y E A R E M A K I N G I T A Y D E I R S A D A C T I V I T H I S W O W W H I D L Y N O N E A M O N G S H T T O Y U O P E A S I T I E S E A N D P U T P I C I P A T I N G N H V B E N T S E B E T T A T Y U R O P E I O N N A S H O N L F O R L O K A L E L +voxpopuli_eng_000553 D S A R T D L Y S U C H I N P A C T S E S T M E N T C O L D P R E A M T S E R T A N P R O B L O M S S U C H A S T H O S P O S E D B Y T H E E L C T R N I K I D E D T F I C A T I O N O F S H E P A N D S C O T E N D +voxpopuli_eng_000554 T H E O R T I S C O N T E N T T O S E T H A T H I T S W O R K H A S I N F O R M E T H E D E S H A R G H R O U E S A N D H A S O N T E B E U T E D T O P R O P O S O L S E F O R I M P R O V I N G T H E F I N C H A L M A N A H E N T O F E Y O U S P E N D I N G A N D B E T E T O R K A T I N G O F Y O F N C S +voxpopuli_eng_000555 R E C G U A I H R Y G L A I T H E A N D S E R T E N T Y I A S N E A D E T D F O R T H E O B L I K E S E C T O R A N D F O R T H I N D U S T R Y +voxpopuli_eng_000556 I S I T D R E L Y N O T P O S E A B L R T O O U E A A T H E R H O U S I N F E S I L I D E S W I H E P R O P E T H R E S E P T I O N C O D I O S I N T H E M E N T I M E +voxpopuli_eng_000557 W E L Y O U T E A K E A C S I O N A T D L A S T I F N O T T H E I N W E I N D E diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/token_int b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/token_int new file mode 100644 index 0000000000000000000000000000000000000000..c381940091d5d093b5da09a020d7f46cccbacd62 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/token_int @@ -0,0 +1,1092 @@ +LAD_eng_000254 2 10 3 2 11 3 15 5 3 12 2 17 3 13 2 16 5 15 21 8 6 7 2 5 7 4 8 13 2 7 8 7 4 8 7 2 9 8 16 4 19 2 18 8 23 3 2 5 2 19 5 11 2 8 7 2 17 10 16 10 3 2 9 14 18 10 3 12 2 5 2 4 3 11 5 22 13 2 5 16 25 8 4 3 7 4 +LAD_eng_000255 2 5 19 2 13 8 22 11 13 2 16 6 7 2 3 11 23 3 4 8 23 3 2 10 3 2 5 9 2 12 3 18 3 5 4 3 12 2 8 7 2 5 4 19 2 7 2 5 4 19 2 4 17 6 +LAD_eng_000256 2 17 6 7 2 11 6 23 3 12 2 13 5 11 2 16 6 12 11 3 2 4 17 6 2 11 6 14 12 3 2 5 4 2 17 10 5 7 16 3 +LAD_eng_000257 2 9 6 15 3 2 6 2 4 10 3 2 6 14 7 4 8 9 2 10 5 3 2 9 3 11 23 5 19 8 9 2 18 6 11 2 15 5 13 4 22 13 3 2 19 3 5 11 9 +LAD_eng_000258 2 22 6 4 10 6 18 2 4 10 3 23 8 11 9 8 7 9 2 18 3 5 4 10 3 2 4 10 3 2 9 6 7 20 2 10 5 21 19 2 10 6 13 8 12 5 19 +LAD_eng_000259 2 9 10 5 24 9 21 8 3 11 2 15 5 7 19 2 11 3 18 11 7 16 3 9 2 14 11 2 15 5 12 2 4 6 2 9 10 3 3 7 12 9 2 8 7 4 11 2 5 16 4 8 6 7 12 2 6 11 2 16 5 11 8 16 4 3 9 2 18 6 15 2 23 3 11 8 14 4 2 21 13 5 19 3 9 +LAD_eng_000260 2 8 18 2 7 12 19 2 4 10 3 2 21 11 6 20 11 5 15 2 16 14 13 12 22 11 5 24 3 2 6 14 4 2 20 14 9 4 2 13 8 4 13 2 18 6 15 3 2 8 4 2 4 17 6 2 18 6 15 3 13 8 5 11 2 5 21 11 6 14 16 10 +LAD_eng_000261 2 4 10 3 2 10 5 13 22 14 15 2 17 5 9 2 11 3 13 3 9 3 2 8 7 6 2 9 4 11 5 13 8 5 11 2 5 11 7 2 7 8 7 2 4 8 7 4 2 6 5 20 8 9 4 2 4 17 6 2 4 10 6 14 9 3 7 4 2 5 7 2 3 2 13 3 23 6 7 +LAD_eng_000262 2 10 3 2 7 6 17 2 21 13 5 16 3 2 18 6 11 2 5 2 9 4 11 5 13 8 7 2 4 13 6 22 3 2 21 3 8 11 4 2 20 13 6 14 11 19 +LAD_eng_000263 8 4 8 4 2 7 6 4 2 7 6 7 3 2 10 6 17 2 15 14 16 10 3 2 8 18 2 5 7 19 2 6 18 2 10 3 11 2 4 13 5 15 3 15 9 2 5 11 3 2 4 11 6 +LAD_eng_000264 2 5 2 9 15 13 13 2 22 8 9 7 3 9 9 2 6 7 3 11 2 22 11 5 14 11 12 2 6 21 11 5 4 3 12 2 10 8 9 2 17 3 5 2 5 7 2 10 5 21 2 18 5 11 15 3 2 18 6 11 2 9 8 16 4 3 7 3 5 11 9 2 18 11 6 2 4 10 3 2 5 20 2 6 18 2 17 3 7 4 19 2 4 17 6 +LAD_eng_000265 2 8 7 2 4 10 3 7 8 7 4 10 2 9 3 7 16 4 11 19 2 10 3 2 17 5 9 2 5 7 2 8 11 8 9 10 2 21 6 5 4 +LAD_eng_000266 2 4 10 3 19 2 5 11 2 15 5 14 3 16 3 4 2 22 19 2 9 4 11 6 7 20 7 +LAD_eng_000267 2 4 10 3 2 13 5 13 13 3 2 8 9 2 4 10 3 11 3 2 18 6 11 2 23 6 14 13 3 12 +LAD_eng_000268 2 8 7 2 4 10 3 5 11 13 19 2 9 4 5 20 3 9 2 16 5 15 3 2 16 13 6 9 3 2 4 6 2 14 2 5 2 9 13 3 21 +LAD_eng_000269 2 11 14 7 8 7 20 2 3 23 3 11 19 2 4 10 5 11 4 19 2 15 8 7 8 4 2 4 10 6 2 14 4 2 9 3 11 23 8 9 2 4 8 15 3 15 9 +LAD_eng_000270 2 5 9 2 11 3 9 8 13 4 2 17 10 3 7 2 4 10 3 16 6 13 8 20 3 2 11 3 2 6 21 3 7 12 2 8 4 2 17 10 5 9 2 5 9 2 5 2 5 13 13 2 15 5 13 3 2 16 6 13 8 20 3 +LAD_eng_000271 4 10 3 2 4 8 15 3 2 22 3 4 17 3 7 2 4 10 3 9 2 21 6 7 4 2 8 9 2 23 3 11 11 5 22 13 3 2 5 7 12 2 16 5 7 5 16 3 11 2 5 7 19 2 17 10 3 2 18 11 6 2 5 2 8 7 8 4 2 4 6 2 15 14 16 10 2 13 6 7 20 3 11 +LAD_eng_000272 2 17 3 5 11 24 2 6 7 2 4 10 3 2 3 2 3 2 3 5 3 5 9 2 9 4 12 5 11 4 3 12 2 8 7 2 15 5 11 10 16 10 2 4 17 6 2 4 10 5 14 9 3 12 2 5 7 12 2 9 3 23 6 7 2 5 4 2 16 6 9 4 2 6 18 2 18 8 23 3 2 15 8 13 8 6 7 12 2 12 6 13 13 3 11 9 +LAD_eng_000273 2 10 6 17 3 23 3 11 2 4 3 17 5 9 2 9 6 15 3 2 12 3 16 2 5 2 20 11 3 15 3 7 4 2 6 3 2 4 10 3 2 3 7 12 8 7 20 2 4 10 3 15 3 15 2 17 8 16 10 2 6 2 15 6 11 19 2 5 7 12 2 19 6 9 10 8 15 6 11 19 2 12 3 16 9 24 14 9 4 3 2 5 4 2 13 3 5 7 20 4 10 2 6 23 3 11 2 3 15 6 14 13 +LAD_eng_000274 2 4 10 3 2 16 5 21 13 3 2 10 5 12 2 7 6 2 16 10 8 13 12 11 6 7 +LAD_eng_000275 2 4 10 3 18 8 4 8 5 13 2 9 8 7 20 13 2 18 2 4 10 5 4 2 12 3 22 19 14 2 5 13 2 4 10 15 2 21 5 11 8 9 9 2 16 6 13 8 7 20 2 10 5 12 2 5 2 3 13 5 22 11 4 2 15 14 9 8 16 2 23 8 12 3 5 6 +LAD_eng_000276 2 4 10 3 2 9 3 11 8 9 2 3 7 12 3 12 2 6 7 2 9 8 16 25 4 10 2 5 6 11 20 8 9 4 2 4 17 6 2 4 10 5 14 9 3 7 12 2 5 7 12 2 18 6 11 3 2 13 5 9 4 8 7 20 2 18 11 5 2 4 6 14 4 13 2 6 18 2 9 3 23 3 7 4 19 2 6 7 12 2 12 5 19 9 +LAD_eng_000277 2 10 3 2 10 5 9 2 5 13 9 6 12 2 16 6 7 4 11 8 22 3 4 3 2 4 6 2 4 10 3 2 7 14 7 2 19 6 14 11 16 2 11 3 2 3 14 2 6 18 2 22 6 24 9 +LAD_eng_000278 2 22 19 2 21 13 5 16 8 7 20 2 9 15 5 13 2 5 11 4 2 6 22 12 20 3 16 4 2 4 10 11 6 2 6 14 4 2 4 10 3 2 8 13 15 3 +LAD_eng_000279 2 8 4 2 8 2 18 6 14 7 3 12 2 8 7 2 22 11 3 9 8 13 +LAD_eng_000280 2 8 4 2 17 5 2 4 10 3 2 9 8 12 2 6 18 2 4 10 3 2 16 5 15 13 19 2 8 2 8 12 3 7 4 8 18 8 3 12 2 15 6 11 13 3 2 17 8 18 10 +LAD_eng_000281 2 3 16 5 7 12 3 12 2 8 4 2 9 8 20 10 4 10 3 2 15 14 9 4 2 13 13 9 6 2 9 6 12 2 15 8 4 2 5 2 17 6 11 24 2 21 13 5 7 +LAD_eng_000282 2 12 14 7 12 3 19 2 17 6 7 2 4 10 3 2 15 5 16 10 2 4 10 11 3 2 4 17 6 3 +LAD_eng_000283 2 10 6 17 3 23 3 11 2 4 10 3 2 23 8 13 8 20 2 11 3 15 5 7 3 12 2 8 16 8 13 5 4 2 12 4 2 7 4 8 13 2 4 10 3 2 11 8 23 14 13 2 6 18 2 4 3 2 18 8 11 9 4 2 7 6 14 9 2 21 5 21 3 11 2 9 3 16 6 7 12 2 11 3 2 21 6 14 22 13 8 16 24 +LAD_eng_000284 2 4 10 3 2 18 8 11 9 4 2 3 11 23 8 2 8 2 4 10 3 7 14 2 16 10 5 11 16 2 17 5 9 2 10 3 13 12 2 7 8 7 4 7 2 18 8 18 4 19 2 6 7 2 5 13 2 4 10 6 2 4 10 3 2 22 8 13 12 8 20 2 17 5 9 2 7 6 4 2 18 14 13 19 2 18 8 7 8 9 10 3 12 +LAD_eng_000285 2 4 10 3 2 5 23 11 8 20 10 2 10 6 14 9 3 10 13 12 2 9 8 3 9 2 17 5 9 2 4 17 6 2 21 6 7 4 2 4 17 6 2 9 3 23 6 7 2 7 12 2 4 10 3 2 5 23 11 8 20 10 2 18 5 15 13 19 2 9 8 3 9 2 17 5 9 2 4 10 11 3 2 21 6 3 7 4 3 9 8 5 11 6 2 9 8 5 11 6 +LAD_eng_000286 2 8 4 2 17 5 9 2 18 8 11 9 4 3 2 11 5 11 12 2 16 5 9 4 2 6 7 2 4 10 11 3 12 2 20 5 7 8 6 14 11 19 2 4 17 6 2 10 6 14 9 3 7 12 2 5 7 12 2 4 3 7 +LAD_eng_000287 2 4 10 3 2 17 8 7 20 9 2 17 3 2 7 6 17 2 5 12 2 8 7 2 5 2 9 8 7 20 13 3 2 21 11 3 9 8 7 20 +LAD_eng_000288 2 4 3 2 12 6 16 4 3 2 6 18 6 13 6 8 18 19 2 8 7 2 3 7 12 3 7 19 3 5 11 8 20 2 15 5 7 5 20 3 15 3 7 4 +LAD_eng_000289 2 4 10 8 9 3 2 6 2 17 5 19 2 4 10 3 2 15 5 3 7 2 5 11 24 20 14 15 3 7 2 6 18 2 9 5 8 18 4 12 19 2 11 8 9 9 24 9 +LAD_eng_000290 2 10 3 2 17 5 9 2 5 13 13 9 6 2 5 12 2 5 2 13 8 18 10 2 15 3 15 22 11 2 6 18 2 9 16 6 14 7 12 2 4 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a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/run.sh b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/run.sh new file mode 100755 index 0000000000000000000000000000000000000000..e2e1517d9ba432f43f7754d4965b17457e4155aa --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/run.sh @@ -0,0 +1 @@ +./asr.sh --ngpu 1 --stage 11 --stop_stage 13 --nj 32 --inference_nj 4 --gpu_inference true --lang eng1 --inference_asr_model valid.loss.ave.pth --local_data_opts '--duration 10min --lid false --multilingual false --single_lang eng1' --use_lm false --token_type char --feats_type raw --feats_normalize utterance_mvn --asr_config conf/tuning/train_asr_s3prl_houlsby.yaml --inference_config conf/decode_asr.yaml --train_set train_10min_eng1 --valid_set dev_10min_eng1 --test_sets 'dev_10min_eng1 test_10min_eng1' --asr_tag train_asr_s3prl_houlsby_eng1_10min --expdir test_pr --asr_stats_dir test_pr/asr_stats_eng1_10min --local_score_opts 'false false monolingual' --stage 11 "$@"; exit $? diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/tensorboard/train/events.out.tfevents.1705224312.stan.216119.0 b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/tensorboard/train/events.out.tfevents.1705224312.stan.216119.0 new file mode 100644 index 0000000000000000000000000000000000000000..323de15ef33542e398ac68aeffc18ab4afe90012 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/tensorboard/train/events.out.tfevents.1705224312.stan.216119.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ddf444947e7fa77277c2d0ff3c6a1b5e9d6294bb9e699a27d86ab878c6d7b9d0 +size 57087286 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/tensorboard/train/events.out.tfevents.1705404409.stan.806879.0 b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/tensorboard/train/events.out.tfevents.1705404409.stan.806879.0 new file mode 100644 index 0000000000000000000000000000000000000000..d5daeb3ee47feabb513173868ed4ecfb71f24b65 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/tensorboard/train/events.out.tfevents.1705404409.stan.806879.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:56738b54fa21ac00c256d308f4a024ab854877c9754b428a0b16ff2e7fd341d6 +size 56988804 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/tensorboard/valid/events.out.tfevents.1705224312.stan.216119.1 b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/tensorboard/valid/events.out.tfevents.1705224312.stan.216119.1 new file mode 100644 index 0000000000000000000000000000000000000000..42be76acde7e2e818f97b2914ddf4f8a7a248853 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/tensorboard/valid/events.out.tfevents.1705224312.stan.216119.1 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a7d20c3efaf2064398e23085ba90c386af030303fd5ad0406d2f78e26dfe6945 +size 11190 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/tensorboard/valid/events.out.tfevents.1705404409.stan.806879.1 b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/tensorboard/valid/events.out.tfevents.1705404409.stan.806879.1 new file mode 100644 index 0000000000000000000000000000000000000000..f337678d9efeaa0be2f8102bab31970b1a2d1789 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/tensorboard/valid/events.out.tfevents.1705404409.stan.806879.1 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4fa569e8c71b76de6b413fd6de1d95d66182762f1ecdfd534278fa4efa9c80cc +size 11190 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/train.1.log b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/train.1.log new file mode 100644 index 0000000000000000000000000000000000000000..9ea0d60a32e9ca1773f76efcdd3cbfb846c5297c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/train.1.log @@ -0,0 +1,1406 @@ +# python3 -m espnet2.bin.asr_train --use_preprocessor true --bpemodel none --token_type char --token_list data/eng1_token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_eng1/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_eng1_10min/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_10min_eng1/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_eng1_10min/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_10min_eng1/text,text,text --train_shape_file test_pr/asr_stats_eng1_10min/train/text_shape.char --valid_data_path_and_name_and_type dump/raw/dev_10min_eng1/text,text,text --valid_shape_file test_pr/asr_stats_eng1_10min/valid/text_shape.char --ngpu 1 --multiprocessing_distributed True +# Started at Sun Jan 14 17:25:08 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_train.py --use_preprocessor true --bpemodel none --token_type char --token_list data/eng1_token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_eng1/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_eng1_10min/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_10min_eng1/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_eng1_10min/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_10min_eng1/text,text,text --train_shape_file test_pr/asr_stats_eng1_10min/train/text_shape.char --valid_data_path_and_name_and_type dump/raw/dev_10min_eng1/text,text,text --valid_shape_file test_pr/asr_stats_eng1_10min/valid/text_shape.char --ngpu 1 --multiprocessing_distributed True +[stan] 2024-01-14 17:25:09,266 (asr:523) INFO: Vocabulary size: 30 +[stan] 2024-01-14 17:25:09,328 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +[stan] 2024-01-14 17:25:09,328 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +[stan] 2024-01-14 17:25:09,438 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +[stan] 2024-01-14 17:25:10,736 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +[stan] 2024-01-14 17:25:11,571 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.mask_emb.requires_grad = False +[stan] 2024-01-14 17:25:11,571 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.label_embs_concat.requires_grad = False +[stan] 2024-01-14 17:25:11,571 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.0.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,571 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,571 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,571 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.1.0.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,571 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.2.0.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,571 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.3.0.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,571 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.4.0.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,571 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.5.0.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,571 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.6.0.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,571 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,571 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,571 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,571 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_g.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_v.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,572 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,573 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,574 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,574 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,574 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,574 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,574 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,574 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,574 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,574 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,574 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,574 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,574 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,574 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.weight.requires_grad = False +[stan] 2024-01-14 17:25:11,574 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.bias.requires_grad = False +[stan] 2024-01-14 17:25:11,975 (abs_task:1275) INFO: pytorch.version=1.12.0+cu113, cuda.available=True, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True +[stan] 2024-01-14 17:25:11,977 (abs_task:1276) INFO: Model structure: +ESPnetASRModel( + (frontend): S3prlFrontend( + (upstream): S3PRLUpstream( + (upstream): UpstreamExpert( + (model): HubertModel( + (feature_extractor): ConvFeatureExtractionModel( + (conv_layers): ModuleList( + (0): Sequential( + (0): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): Fp32GroupNorm(512, 512, eps=1e-05, affine=True) + (3): GELU(approximate=none) + ) + (1): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (2): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (3): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (4): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (5): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (6): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + ) + ) + (post_extract_proj): Linear(in_features=512, out_features=768, bias=True) + (dropout_input): Dropout(p=0.1, inplace=False) + (dropout_features): Dropout(p=0.1, inplace=False) + (encoder): TransformerEncoder( + (pos_conv): Sequential( + (0): Conv1d(768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16) + (1): SamePad() + (2): GELU(approximate=none) + ) + (layers): ModuleList( + (0): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (1): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (2): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (3): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (4): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (5): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (6): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (7): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (8): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (9): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (10): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (11): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + ) + (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (final_proj): Linear(in_features=768, out_features=256, bias=True) + ) + ) + ) + (featurizer): Featurizer() + ) + (specaug): SpecAug( + (time_warp): TimeWarp(window=5, mode=bicubic) + (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq) + (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time) + ) + (normalize): UtteranceMVN(norm_means=True, norm_vars=False) + (preencoder): LinearProjection( + (linear_out): Linear(in_features=768, out_features=80, bias=True) + (dropout): Dropout(p=0.0, inplace=False) + ) + (encoder): TransformerEncoder( + (embed): Conv2dSubsampling2( + (conv): Sequential( + (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU() + (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) + (3): ReLU() + ) + (out): Sequential( + (0): Linear(in_features=9472, out_features=256, bias=True) + (1): PositionalEncoding( + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + ) + (encoders): MultiSequential( + (0): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (1): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + ) + (criterion_att): LabelSmoothingLoss( + (criterion): KLDivLoss() + ) + (ctc): CTC( + (ctc_lo): Linear(in_features=256, out_features=30, bias=True) + (ctc_loss): CTCLoss() + ) +) + +Model summary: + Class Name: ESPnetASRModel + Total Number of model parameters: 99.96 M + Number of trainable parameters: 5.27 M (5.3%) + Size: 21.07 MB + Type: torch.float32 +[stan] 2024-01-14 17:25:11,977 (abs_task:1279) INFO: Optimizer: +Adam ( +Parameter Group 0 + amsgrad: False + betas: (0.9, 0.999) + capturable: False + eps: 1e-08 + foreach: None + lr: 0.0001 + maximize: False + weight_decay: 1e-06 +) +[stan] 2024-01-14 17:25:11,977 (abs_task:1280) INFO: Scheduler: None +[stan] 2024-01-14 17:25:11,977 (abs_task:1289) INFO: Saving the configuration in test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml +[stan] 2024-01-14 17:25:12,128 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-14 17:25:12,170 (abs_task:1665) INFO: [train] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/train_10min_eng1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/train_10min_eng1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-14 17:25:12,170 (abs_task:1666) INFO: [train] Batch sampler: SortedBatchSampler(N-batch=5, batch_size=8, shape_file=test_pr/asr_stats_eng1_10min/train/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-14 17:25:12,170 (abs_task:1667) INFO: [train] mini-batch sizes summary: N-batch=5, mean=8.2, min=8, max=9 +[stan] 2024-01-14 17:25:12,181 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-14 17:25:12,181 (abs_task:1665) INFO: [valid] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_eng1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_eng1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-14 17:25:12,181 (abs_task:1666) INFO: [valid] Batch sampler: SortedBatchSampler(N-batch=5, batch_size=8, shape_file=test_pr/asr_stats_eng1_10min/valid/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-14 17:25:12,181 (abs_task:1667) INFO: [valid] mini-batch sizes summary: N-batch=5, mean=8.0, min=8, max=8 +[stan] 2024-01-14 17:25:12,182 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-14 17:25:12,192 (abs_task:1665) INFO: [plot_att] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_eng1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_eng1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-14 17:25:12,192 (abs_task:1666) INFO: [plot_att] Batch sampler: UnsortedBatchSampler(N-batch=40, batch_size=1, key_file=test_pr/asr_stats_eng1_10min/valid/speech_shape, +[stan] 2024-01-14 17:25:12,192 (abs_task:1667) INFO: [plot_att] mini-batch sizes summary: N-batch=3, mean=1.0, min=1, max=1 +[stan] 2024-01-14 17:25:12,224 (trainer:300) INFO: 1/30epoch started +[stan] 2024-01-14 17:25:26,454 (trainer:763) INFO: 1epoch:train:1-40batch: iter_time=0.003, forward_time=0.188, loss_ctc=159.631, loss=159.631, backward_time=0.026, grad_norm=1.236e+03, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.419 +[stan] 2024-01-14 17:25:39,057 (trainer:763) INFO: 1epoch:train:41-80batch: iter_time=5.720e-05, forward_time=0.157, loss_ctc=147.257, loss=147.257, backward_time=0.024, grad_norm=278.816, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.261 +[stan] 2024-01-14 17:25:51,709 (trainer:763) INFO: 1epoch:train:81-120batch: iter_time=5.226e-05, forward_time=0.158, loss_ctc=146.473, loss=146.473, backward_time=0.024, grad_norm=154.573, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.265 +[stan] 2024-01-14 17:26:04,384 (trainer:763) INFO: 1epoch:train:121-160batch: iter_time=5.434e-05, forward_time=0.158, loss_ctc=146.274, loss=146.274, backward_time=0.024, grad_norm=287.924, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.267 +[stan] 2024-01-14 17:26:17,012 (trainer:763) INFO: 1epoch:train:161-200batch: iter_time=5.426e-05, forward_time=0.158, loss_ctc=145.215, loss=145.215, backward_time=0.024, grad_norm=246.590, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.263 +[stan] 2024-01-14 17:26:29,652 (trainer:763) INFO: 1epoch:train:201-240batch: iter_time=5.368e-05, forward_time=0.158, loss_ctc=140.779, loss=140.779, backward_time=0.024, grad_norm=311.857, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.264 +[stan] 2024-01-14 17:26:42,313 (trainer:763) INFO: 1epoch:train:241-280batch: iter_time=5.225e-05, forward_time=0.158, loss_ctc=130.541, loss=130.541, backward_time=0.024, grad_norm=492.781, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.266 +[stan] 2024-01-14 17:26:55,002 (trainer:763) INFO: 1epoch:train:281-320batch: iter_time=5.466e-05, forward_time=0.158, loss_ctc=117.417, loss=117.417, backward_time=0.024, grad_norm=273.981, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-14 17:27:07,717 (trainer:763) INFO: 1epoch:train:321-360batch: iter_time=5.422e-05, forward_time=0.159, loss_ctc=104.683, loss=104.683, backward_time=0.024, grad_norm=259.568, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-14 17:27:20,447 (trainer:763) INFO: 1epoch:train:361-400batch: iter_time=5.526e-05, forward_time=0.159, loss_ctc=95.435, loss=95.435, backward_time=0.024, grad_norm=266.606, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 17:27:33,194 (trainer:763) INFO: 1epoch:train:401-440batch: iter_time=5.535e-05, forward_time=0.159, loss_ctc=88.586, loss=88.586, backward_time=0.025, grad_norm=242.033, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:27:45,945 (trainer:763) INFO: 1epoch:train:441-480batch: iter_time=5.537e-05, forward_time=0.159, loss_ctc=82.780, loss=82.780, backward_time=0.025, grad_norm=297.252, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:27:58,697 (trainer:763) INFO: 1epoch:train:481-520batch: iter_time=5.595e-05, forward_time=0.159, loss_ctc=78.576, loss=78.576, backward_time=0.024, grad_norm=235.843, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:28:11,453 (trainer:763) INFO: 1epoch:train:521-560batch: iter_time=5.480e-05, forward_time=0.159, loss_ctc=74.575, loss=74.575, backward_time=0.025, grad_norm=283.879, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:28:24,210 (trainer:763) INFO: 1epoch:train:561-600batch: iter_time=5.302e-05, forward_time=0.159, loss_ctc=70.947, loss=70.947, backward_time=0.025, grad_norm=331.637, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:28:36,974 (trainer:763) INFO: 1epoch:train:601-640batch: iter_time=5.276e-05, forward_time=0.159, loss_ctc=68.118, loss=68.118, backward_time=0.025, grad_norm=257.616, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:28:49,735 (trainer:763) INFO: 1epoch:train:641-680batch: iter_time=5.591e-05, forward_time=0.159, loss_ctc=64.535, loss=64.535, backward_time=0.025, grad_norm=258.798, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:29:02,497 (trainer:763) INFO: 1epoch:train:681-720batch: iter_time=5.403e-05, forward_time=0.159, loss_ctc=61.829, loss=61.829, backward_time=0.024, grad_norm=323.256, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:29:15,249 (trainer:763) INFO: 1epoch:train:721-760batch: iter_time=5.585e-05, forward_time=0.159, loss_ctc=59.229, loss=59.229, backward_time=0.024, grad_norm=324.721, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:29:28,000 (trainer:763) INFO: 1epoch:train:761-800batch: iter_time=5.486e-05, forward_time=0.159, loss_ctc=56.683, loss=56.683, backward_time=0.025, grad_norm=299.725, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-14 17:29:33,119 (trainer:354) INFO: 1epoch results: [train] iter_time=2.096e-04, forward_time=0.160, loss_ctc=101.978, loss=101.978, backward_time=0.024, grad_norm=333.148, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.279, time=4 minutes and 15.82 seconds, total_count=800, gpu_max_cached_mem_GB=17.303, [valid] loss_ctc=270.316, cer_ctc=0.323, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=270.316, time=1.15 seconds, total_count=5, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.91 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 17:29:34,157 (trainer:417) INFO: The best model has been updated: valid.loss +[stan] 2024-01-14 17:29:34,157 (trainer:288) INFO: 2/30epoch started. Estimated time to finish: 2 hours, 6 minutes and 36.04 seconds +[stan] 2024-01-14 17:29:47,182 (trainer:763) INFO: 2epoch:train:1-40batch: iter_time=0.003, forward_time=0.159, loss_ctc=54.661, loss=54.661, backward_time=0.024, grad_norm=324.008, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.301 +[stan] 2024-01-14 17:29:59,927 (trainer:763) INFO: 2epoch:train:41-80batch: iter_time=5.586e-05, forward_time=0.159, loss_ctc=51.973, loss=51.973, backward_time=0.024, grad_norm=300.209, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:30:12,681 (trainer:763) INFO: 2epoch:train:81-120batch: iter_time=5.365e-05, forward_time=0.159, loss_ctc=50.507, loss=50.507, backward_time=0.025, grad_norm=363.854, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:30:25,436 (trainer:763) INFO: 2epoch:train:121-160batch: iter_time=5.387e-05, forward_time=0.159, loss_ctc=49.403, loss=49.403, backward_time=0.024, grad_norm=327.420, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:30:38,196 (trainer:763) INFO: 2epoch:train:161-200batch: iter_time=5.673e-05, forward_time=0.159, loss_ctc=47.370, loss=47.370, backward_time=0.024, grad_norm=369.800, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:30:50,952 (trainer:763) INFO: 2epoch:train:201-240batch: iter_time=5.469e-05, forward_time=0.159, loss_ctc=45.676, loss=45.676, backward_time=0.024, grad_norm=412.661, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:31:03,711 (trainer:763) INFO: 2epoch:train:241-280batch: iter_time=5.707e-05, forward_time=0.159, loss_ctc=44.199, loss=44.199, backward_time=0.024, grad_norm=398.081, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:31:16,468 (trainer:763) INFO: 2epoch:train:281-320batch: iter_time=5.665e-05, forward_time=0.159, loss_ctc=43.072, loss=43.072, backward_time=0.025, grad_norm=345.409, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:31:29,223 (trainer:763) INFO: 2epoch:train:321-360batch: iter_time=5.387e-05, forward_time=0.159, loss_ctc=42.246, loss=42.246, backward_time=0.024, grad_norm=322.597, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:31:41,973 (trainer:763) INFO: 2epoch:train:361-400batch: iter_time=5.298e-05, forward_time=0.159, loss_ctc=41.199, loss=41.199, backward_time=0.025, grad_norm=412.952, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:31:54,729 (trainer:763) INFO: 2epoch:train:401-440batch: iter_time=5.735e-05, forward_time=0.159, loss_ctc=40.724, loss=40.724, backward_time=0.024, grad_norm=370.251, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:32:07,481 (trainer:763) INFO: 2epoch:train:441-480batch: iter_time=5.751e-05, forward_time=0.159, loss_ctc=39.522, loss=39.522, backward_time=0.024, grad_norm=335.598, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:32:20,227 (trainer:763) INFO: 2epoch:train:481-520batch: iter_time=5.609e-05, forward_time=0.159, loss_ctc=38.747, loss=38.747, backward_time=0.024, grad_norm=346.564, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:32:32,976 (trainer:763) INFO: 2epoch:train:521-560batch: iter_time=5.398e-05, forward_time=0.159, loss_ctc=37.865, loss=37.865, backward_time=0.024, grad_norm=357.525, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:32:45,726 (trainer:763) INFO: 2epoch:train:561-600batch: iter_time=6.266e-05, forward_time=0.159, loss_ctc=37.783, loss=37.783, backward_time=0.025, grad_norm=387.424, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:32:58,471 (trainer:763) INFO: 2epoch:train:601-640batch: iter_time=5.520e-05, forward_time=0.159, loss_ctc=36.352, loss=36.352, backward_time=0.024, grad_norm=319.809, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:33:11,224 (trainer:763) INFO: 2epoch:train:641-680batch: iter_time=5.734e-05, forward_time=0.159, loss_ctc=36.211, loss=36.211, backward_time=0.024, grad_norm=398.238, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:33:23,975 (trainer:763) INFO: 2epoch:train:681-720batch: iter_time=5.735e-05, forward_time=0.159, loss_ctc=35.421, loss=35.421, backward_time=0.024, grad_norm=359.115, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:33:36,718 (trainer:763) INFO: 2epoch:train:721-760batch: iter_time=5.698e-05, forward_time=0.159, loss_ctc=34.812, loss=34.812, backward_time=0.024, grad_norm=337.414, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:33:49,457 (trainer:763) INFO: 2epoch:train:761-800batch: iter_time=5.431e-05, forward_time=0.159, loss_ctc=34.603, loss=34.603, backward_time=0.024, grad_norm=359.212, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-14 17:33:54,435 (trainer:354) INFO: 2epoch results: [train] iter_time=2.161e-04, forward_time=0.159, loss_ctc=42.117, loss=42.117, backward_time=0.024, grad_norm=357.407, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.38 seconds, total_count=1600, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=324.103, cer_ctc=0.315, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=324.103, time=1.16 seconds, total_count=10, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.74 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 17:33:55,300 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 17:33:55,301 (trainer:288) INFO: 3/30epoch started. Estimated time to finish: 2 hours, 2 minutes and 3.06 seconds +[stan] 2024-01-14 17:34:08,320 (trainer:763) INFO: 3epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=34.656, loss=34.656, backward_time=0.024, grad_norm=361.022, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.301 +[stan] 2024-01-14 17:34:21,053 (trainer:763) INFO: 3epoch:train:41-80batch: iter_time=5.542e-05, forward_time=0.159, loss_ctc=34.170, loss=34.170, backward_time=0.024, grad_norm=374.599, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 17:34:33,794 (trainer:763) INFO: 3epoch:train:81-120batch: iter_time=5.740e-05, forward_time=0.159, loss_ctc=33.661, loss=33.661, backward_time=0.024, grad_norm=320.638, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:34:46,539 (trainer:763) INFO: 3epoch:train:121-160batch: iter_time=5.609e-05, forward_time=0.159, loss_ctc=33.136, loss=33.136, backward_time=0.024, grad_norm=362.516, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:34:59,291 (trainer:763) INFO: 3epoch:train:161-200batch: iter_time=5.706e-05, forward_time=0.159, loss_ctc=33.551, loss=33.551, backward_time=0.024, grad_norm=371.488, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:35:12,053 (trainer:763) INFO: 3epoch:train:201-240batch: iter_time=5.684e-05, forward_time=0.159, loss_ctc=32.960, loss=32.960, backward_time=0.025, grad_norm=364.642, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:35:24,814 (trainer:763) INFO: 3epoch:train:241-280batch: iter_time=5.764e-05, forward_time=0.159, loss_ctc=31.634, loss=31.634, backward_time=0.024, grad_norm=374.865, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:35:37,579 (trainer:763) INFO: 3epoch:train:281-320batch: iter_time=5.581e-05, forward_time=0.159, loss_ctc=31.042, loss=31.042, backward_time=0.024, grad_norm=324.531, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:35:50,343 (trainer:763) INFO: 3epoch:train:321-360batch: iter_time=5.527e-05, forward_time=0.159, loss_ctc=32.272, loss=32.272, backward_time=0.025, grad_norm=364.420, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:36:03,110 (trainer:763) INFO: 3epoch:train:361-400batch: iter_time=5.702e-05, forward_time=0.159, loss_ctc=32.263, loss=32.263, backward_time=0.025, grad_norm=323.100, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.277 +[stan] 2024-01-14 17:36:15,876 (trainer:763) INFO: 3epoch:train:401-440batch: iter_time=5.547e-05, forward_time=0.159, loss_ctc=31.505, loss=31.505, backward_time=0.025, grad_norm=333.303, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.277 +[stan] 2024-01-14 17:36:28,639 (trainer:763) INFO: 3epoch:train:441-480batch: iter_time=5.464e-05, forward_time=0.159, loss_ctc=30.790, loss=30.790, backward_time=0.025, grad_norm=292.661, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:36:41,399 (trainer:763) INFO: 3epoch:train:481-520batch: iter_time=5.416e-05, forward_time=0.159, loss_ctc=30.215, loss=30.215, backward_time=0.025, grad_norm=328.859, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:36:54,148 (trainer:763) INFO: 3epoch:train:521-560batch: iter_time=5.695e-05, forward_time=0.159, loss_ctc=31.172, loss=31.172, backward_time=0.024, grad_norm=411.121, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:37:06,901 (trainer:763) INFO: 3epoch:train:561-600batch: iter_time=5.718e-05, forward_time=0.159, loss_ctc=30.236, loss=30.236, backward_time=0.025, grad_norm=330.921, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:37:19,646 (trainer:763) INFO: 3epoch:train:601-640batch: iter_time=5.694e-05, forward_time=0.159, loss_ctc=31.156, loss=31.156, backward_time=0.024, grad_norm=358.956, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:37:32,391 (trainer:763) INFO: 3epoch:train:641-680batch: iter_time=5.512e-05, forward_time=0.159, loss_ctc=30.503, loss=30.503, backward_time=0.024, grad_norm=327.911, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:37:45,135 (trainer:763) INFO: 3epoch:train:681-720batch: iter_time=5.656e-05, forward_time=0.159, loss_ctc=29.606, loss=29.606, backward_time=0.024, grad_norm=434.105, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:37:57,872 (trainer:763) INFO: 3epoch:train:721-760batch: iter_time=5.528e-05, forward_time=0.159, loss_ctc=30.188, loss=30.188, backward_time=0.024, grad_norm=362.816, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:38:10,610 (trainer:763) INFO: 3epoch:train:761-800batch: iter_time=5.294e-05, forward_time=0.159, loss_ctc=29.752, loss=29.752, backward_time=0.024, grad_norm=328.365, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:38:15,567 (trainer:354) INFO: 3epoch results: [train] iter_time=2.375e-04, forward_time=0.159, loss_ctc=31.723, loss=31.723, backward_time=0.024, grad_norm=352.542, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.39 seconds, total_count=2400, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=352.934, cer_ctc=0.305, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=352.934, time=1.17 seconds, total_count=15, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.71 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 17:38:16,520 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 17:38:16,520 (trainer:288) INFO: 4/30epoch started. Estimated time to finish: 1 hour, 57 minutes and 38.66 seconds +[stan] 2024-01-14 17:38:29,531 (trainer:763) INFO: 4epoch:train:1-40batch: iter_time=0.003, forward_time=0.159, loss_ctc=28.277, loss=28.277, backward_time=0.024, grad_norm=295.366, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.300 +[stan] 2024-01-14 17:38:42,264 (trainer:763) INFO: 4epoch:train:41-80batch: iter_time=5.266e-05, forward_time=0.159, loss_ctc=29.257, loss=29.257, backward_time=0.024, grad_norm=383.382, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 17:38:55,005 (trainer:763) INFO: 4epoch:train:81-120batch: iter_time=5.247e-05, forward_time=0.159, loss_ctc=28.779, loss=28.779, backward_time=0.024, grad_norm=330.101, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:39:07,755 (trainer:763) INFO: 4epoch:train:121-160batch: iter_time=5.476e-05, forward_time=0.159, loss_ctc=29.427, loss=29.427, backward_time=0.024, grad_norm=347.749, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:39:20,510 (trainer:763) INFO: 4epoch:train:161-200batch: iter_time=5.514e-05, forward_time=0.159, loss_ctc=29.301, loss=29.301, backward_time=0.025, grad_norm=369.458, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:39:33,262 (trainer:763) INFO: 4epoch:train:201-240batch: iter_time=5.627e-05, forward_time=0.159, loss_ctc=29.135, loss=29.135, backward_time=0.024, grad_norm=428.854, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:39:46,018 (trainer:763) INFO: 4epoch:train:241-280batch: iter_time=5.422e-05, forward_time=0.159, loss_ctc=29.441, loss=29.441, backward_time=0.024, grad_norm=325.963, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:39:58,777 (trainer:763) INFO: 4epoch:train:281-320batch: iter_time=5.472e-05, forward_time=0.159, loss_ctc=28.892, loss=28.892, backward_time=0.025, grad_norm=396.839, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:40:11,532 (trainer:763) INFO: 4epoch:train:321-360batch: iter_time=5.546e-05, forward_time=0.159, loss_ctc=28.546, loss=28.546, backward_time=0.024, grad_norm=369.415, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:40:24,284 (trainer:763) INFO: 4epoch:train:361-400batch: iter_time=5.519e-05, forward_time=0.159, loss_ctc=27.364, loss=27.364, backward_time=0.024, grad_norm=307.187, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:40:37,036 (trainer:763) INFO: 4epoch:train:401-440batch: iter_time=5.285e-05, forward_time=0.159, loss_ctc=28.099, loss=28.099, backward_time=0.024, grad_norm=301.115, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:40:49,787 (trainer:763) INFO: 4epoch:train:441-480batch: iter_time=5.558e-05, forward_time=0.159, loss_ctc=27.972, loss=27.972, backward_time=0.024, grad_norm=301.923, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:41:02,538 (trainer:763) INFO: 4epoch:train:481-520batch: iter_time=5.371e-05, forward_time=0.159, loss_ctc=27.799, loss=27.799, backward_time=0.024, grad_norm=318.502, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:41:15,283 (trainer:763) INFO: 4epoch:train:521-560batch: iter_time=5.402e-05, forward_time=0.159, loss_ctc=27.578, loss=27.578, backward_time=0.024, grad_norm=285.443, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:41:28,027 (trainer:763) INFO: 4epoch:train:561-600batch: iter_time=5.395e-05, forward_time=0.159, loss_ctc=27.414, loss=27.414, backward_time=0.024, grad_norm=317.800, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:41:40,781 (trainer:763) INFO: 4epoch:train:601-640batch: iter_time=5.276e-05, forward_time=0.159, loss_ctc=27.498, loss=27.498, backward_time=0.024, grad_norm=358.067, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:41:53,526 (trainer:763) INFO: 4epoch:train:641-680batch: iter_time=5.261e-05, forward_time=0.159, loss_ctc=27.234, loss=27.234, backward_time=0.024, grad_norm=355.161, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:42:06,263 (trainer:763) INFO: 4epoch:train:681-720batch: iter_time=5.531e-05, forward_time=0.159, loss_ctc=27.389, loss=27.389, backward_time=0.024, grad_norm=322.102, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:42:19,003 (trainer:763) INFO: 4epoch:train:721-760batch: iter_time=5.394e-05, forward_time=0.159, loss_ctc=26.683, loss=26.683, backward_time=0.024, grad_norm=341.436, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:42:31,745 (trainer:763) INFO: 4epoch:train:761-800batch: iter_time=4.935e-05, forward_time=0.159, loss_ctc=27.228, loss=27.228, backward_time=0.024, grad_norm=332.779, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:42:36,616 (trainer:354) INFO: 4epoch results: [train] iter_time=2.219e-04, forward_time=0.159, loss_ctc=28.166, loss=28.166, backward_time=0.024, grad_norm=339.432, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.3 seconds, total_count=3200, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=364.447, cer_ctc=0.300, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=364.447, time=1.15 seconds, total_count=20, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.64 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 17:42:37,507 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 17:42:37,507 (trainer:288) INFO: 5/30epoch started. Estimated time to finish: 1 hour, 53 minutes and 14.34 seconds +[stan] 2024-01-14 17:42:50,520 (trainer:763) INFO: 5epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=26.500, loss=26.500, backward_time=0.024, grad_norm=294.455, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.300 +[stan] 2024-01-14 17:43:03,253 (trainer:763) INFO: 5epoch:train:41-80batch: iter_time=5.775e-05, forward_time=0.159, loss_ctc=26.174, loss=26.174, backward_time=0.025, grad_norm=312.609, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 17:43:16,000 (trainer:763) INFO: 5epoch:train:81-120batch: iter_time=5.121e-05, forward_time=0.159, loss_ctc=26.636, loss=26.636, backward_time=0.024, grad_norm=314.863, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:43:28,745 (trainer:763) INFO: 5epoch:train:121-160batch: iter_time=5.370e-05, forward_time=0.159, loss_ctc=26.071, loss=26.071, backward_time=0.024, grad_norm=292.735, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:43:41,505 (trainer:763) INFO: 5epoch:train:161-200batch: iter_time=5.433e-05, forward_time=0.159, loss_ctc=25.660, loss=25.660, backward_time=0.024, grad_norm=289.286, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:43:54,262 (trainer:763) INFO: 5epoch:train:201-240batch: iter_time=5.437e-05, forward_time=0.159, loss_ctc=26.747, loss=26.747, backward_time=0.024, grad_norm=333.294, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:44:07,022 (trainer:763) INFO: 5epoch:train:241-280batch: iter_time=5.633e-05, forward_time=0.159, loss_ctc=26.087, loss=26.087, backward_time=0.024, grad_norm=303.198, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:44:19,784 (trainer:763) INFO: 5epoch:train:281-320batch: iter_time=5.382e-05, forward_time=0.159, loss_ctc=25.677, loss=25.677, backward_time=0.024, grad_norm=310.883, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:44:32,544 (trainer:763) INFO: 5epoch:train:321-360batch: iter_time=5.531e-05, forward_time=0.159, loss_ctc=25.489, loss=25.489, backward_time=0.024, grad_norm=291.400, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:44:45,300 (trainer:763) INFO: 5epoch:train:361-400batch: iter_time=5.449e-05, forward_time=0.159, loss_ctc=25.846, loss=25.846, backward_time=0.024, grad_norm=276.324, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:44:58,056 (trainer:763) INFO: 5epoch:train:401-440batch: iter_time=5.150e-05, forward_time=0.159, loss_ctc=25.362, loss=25.362, backward_time=0.025, grad_norm=292.634, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:45:10,808 (trainer:763) INFO: 5epoch:train:441-480batch: iter_time=5.621e-05, forward_time=0.159, loss_ctc=25.904, loss=25.904, backward_time=0.024, grad_norm=304.686, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:45:23,562 (trainer:763) INFO: 5epoch:train:481-520batch: iter_time=5.289e-05, forward_time=0.159, loss_ctc=24.716, loss=24.716, backward_time=0.025, grad_norm=303.248, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:45:36,319 (trainer:763) INFO: 5epoch:train:521-560batch: iter_time=5.503e-05, forward_time=0.159, loss_ctc=24.512, loss=24.512, backward_time=0.024, grad_norm=294.838, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:45:49,064 (trainer:763) INFO: 5epoch:train:561-600batch: iter_time=5.381e-05, forward_time=0.159, loss_ctc=24.827, loss=24.827, backward_time=0.024, grad_norm=281.584, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:46:01,812 (trainer:763) INFO: 5epoch:train:601-640batch: iter_time=5.177e-05, forward_time=0.159, loss_ctc=25.162, loss=25.162, backward_time=0.025, grad_norm=284.235, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:46:14,551 (trainer:763) INFO: 5epoch:train:641-680batch: iter_time=5.533e-05, forward_time=0.159, loss_ctc=24.229, loss=24.229, backward_time=0.024, grad_norm=273.725, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:46:27,301 (trainer:763) INFO: 5epoch:train:681-720batch: iter_time=5.193e-05, forward_time=0.159, loss_ctc=24.833, loss=24.833, backward_time=0.024, grad_norm=294.635, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:46:40,042 (trainer:763) INFO: 5epoch:train:721-760batch: iter_time=5.508e-05, forward_time=0.159, loss_ctc=24.592, loss=24.592, backward_time=0.024, grad_norm=321.670, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:46:52,776 (trainer:763) INFO: 5epoch:train:761-800batch: iter_time=5.086e-05, forward_time=0.159, loss_ctc=24.622, loss=24.622, backward_time=0.025, grad_norm=302.970, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 17:46:57,596 (trainer:354) INFO: 5epoch results: [train] iter_time=2.309e-04, forward_time=0.159, loss_ctc=25.482, loss=25.482, backward_time=0.024, grad_norm=298.664, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.35 seconds, total_count=4000, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=375.469, cer_ctc=0.297, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=375.469, time=1.16 seconds, total_count=25, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.58 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 17:46:58,490 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 17:46:58,490 (trainer:288) INFO: 6/30epoch started. Estimated time to finish: 1 hour, 48 minutes and 51.33 seconds +[stan] 2024-01-14 17:47:11,544 (trainer:763) INFO: 6epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=24.126, loss=24.126, backward_time=0.024, grad_norm=313.533, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.304 +[stan] 2024-01-14 17:47:24,277 (trainer:763) INFO: 6epoch:train:41-80batch: iter_time=5.468e-05, forward_time=0.159, loss_ctc=24.088, loss=24.088, backward_time=0.024, grad_norm=305.240, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 17:47:37,020 (trainer:763) INFO: 6epoch:train:81-120batch: iter_time=5.579e-05, forward_time=0.159, loss_ctc=23.525, loss=23.525, backward_time=0.024, grad_norm=301.159, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:47:49,767 (trainer:763) INFO: 6epoch:train:121-160batch: iter_time=5.476e-05, forward_time=0.159, loss_ctc=23.583, loss=23.583, backward_time=0.024, grad_norm=320.006, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:48:02,518 (trainer:763) INFO: 6epoch:train:161-200batch: iter_time=5.114e-05, forward_time=0.159, loss_ctc=23.946, loss=23.946, backward_time=0.024, grad_norm=291.209, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:48:15,274 (trainer:763) INFO: 6epoch:train:201-240batch: iter_time=5.439e-05, forward_time=0.159, loss_ctc=23.818, loss=23.818, backward_time=0.024, grad_norm=287.606, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:48:28,037 (trainer:763) INFO: 6epoch:train:241-280batch: iter_time=5.521e-05, forward_time=0.159, loss_ctc=23.594, loss=23.594, backward_time=0.024, grad_norm=325.459, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:48:40,793 (trainer:763) INFO: 6epoch:train:281-320batch: iter_time=5.467e-05, forward_time=0.159, loss_ctc=23.183, loss=23.183, backward_time=0.025, grad_norm=296.163, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:48:53,548 (trainer:763) INFO: 6epoch:train:321-360batch: iter_time=5.266e-05, forward_time=0.159, loss_ctc=22.739, loss=22.739, backward_time=0.024, grad_norm=295.015, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:49:06,301 (trainer:763) INFO: 6epoch:train:361-400batch: iter_time=5.233e-05, forward_time=0.159, loss_ctc=22.980, loss=22.980, backward_time=0.024, grad_norm=310.144, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:49:19,055 (trainer:763) INFO: 6epoch:train:401-440batch: iter_time=5.236e-05, forward_time=0.159, loss_ctc=22.937, loss=22.937, backward_time=0.025, grad_norm=301.028, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:49:31,807 (trainer:763) INFO: 6epoch:train:441-480batch: iter_time=5.480e-05, forward_time=0.159, loss_ctc=23.139, loss=23.139, backward_time=0.024, grad_norm=299.996, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:49:44,555 (trainer:763) INFO: 6epoch:train:481-520batch: iter_time=5.440e-05, forward_time=0.159, loss_ctc=22.954, loss=22.954, backward_time=0.024, grad_norm=298.688, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:49:57,294 (trainer:763) INFO: 6epoch:train:521-560batch: iter_time=5.337e-05, forward_time=0.159, loss_ctc=23.202, loss=23.202, backward_time=0.024, grad_norm=289.928, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:50:10,031 (trainer:763) INFO: 6epoch:train:561-600batch: iter_time=5.305e-05, forward_time=0.159, loss_ctc=22.217, loss=22.217, backward_time=0.024, grad_norm=285.988, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:50:22,772 (trainer:763) INFO: 6epoch:train:601-640batch: iter_time=5.568e-05, forward_time=0.159, loss_ctc=22.437, loss=22.437, backward_time=0.024, grad_norm=302.222, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:50:35,509 (trainer:763) INFO: 6epoch:train:641-680batch: iter_time=5.498e-05, forward_time=0.159, loss_ctc=22.189, loss=22.189, backward_time=0.024, grad_norm=293.745, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:50:48,246 (trainer:763) INFO: 6epoch:train:681-720batch: iter_time=5.545e-05, forward_time=0.159, loss_ctc=22.684, loss=22.684, backward_time=0.024, grad_norm=315.207, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:51:00,984 (trainer:763) INFO: 6epoch:train:721-760batch: iter_time=5.410e-05, forward_time=0.159, loss_ctc=22.209, loss=22.209, backward_time=0.024, grad_norm=296.830, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:51:13,719 (trainer:763) INFO: 6epoch:train:761-800batch: iter_time=4.946e-05, forward_time=0.159, loss_ctc=21.978, loss=21.978, backward_time=0.024, grad_norm=297.847, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 17:51:18,545 (trainer:354) INFO: 6epoch results: [train] iter_time=2.727e-04, forward_time=0.159, loss_ctc=23.076, loss=23.076, backward_time=0.024, grad_norm=301.351, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.31 seconds, total_count=4800, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=378.794, cer_ctc=0.296, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=378.794, time=1.17 seconds, total_count=30, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.57 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 17:51:19,564 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 17:51:19,565 (trainer:288) INFO: 7/30epoch started. Estimated time to finish: 1 hour, 44 minutes and 29.36 seconds +[stan] 2024-01-14 17:51:32,575 (trainer:763) INFO: 7epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=21.910, loss=21.910, backward_time=0.024, grad_norm=295.117, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.300 +[stan] 2024-01-14 17:51:45,304 (trainer:763) INFO: 7epoch:train:41-80batch: iter_time=5.176e-05, forward_time=0.159, loss_ctc=21.745, loss=21.745, backward_time=0.024, grad_norm=291.743, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 17:51:58,041 (trainer:763) INFO: 7epoch:train:81-120batch: iter_time=5.090e-05, forward_time=0.159, loss_ctc=21.816, loss=21.816, backward_time=0.024, grad_norm=278.436, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:52:10,783 (trainer:763) INFO: 7epoch:train:121-160batch: iter_time=5.371e-05, forward_time=0.159, loss_ctc=21.464, loss=21.464, backward_time=0.024, grad_norm=309.225, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:52:23,530 (trainer:763) INFO: 7epoch:train:161-200batch: iter_time=5.294e-05, forward_time=0.159, loss_ctc=21.129, loss=21.129, backward_time=0.024, grad_norm=293.883, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:52:36,282 (trainer:763) INFO: 7epoch:train:201-240batch: iter_time=5.181e-05, forward_time=0.159, loss_ctc=20.613, loss=20.613, backward_time=0.024, grad_norm=286.570, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:52:49,035 (trainer:763) INFO: 7epoch:train:241-280batch: iter_time=5.319e-05, forward_time=0.159, loss_ctc=20.886, loss=20.886, backward_time=0.024, grad_norm=274.998, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:53:01,787 (trainer:763) INFO: 7epoch:train:281-320batch: iter_time=5.292e-05, forward_time=0.159, loss_ctc=20.970, loss=20.970, backward_time=0.025, grad_norm=295.151, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:53:14,538 (trainer:763) INFO: 7epoch:train:321-360batch: iter_time=5.474e-05, forward_time=0.159, loss_ctc=21.453, loss=21.453, backward_time=0.025, grad_norm=299.621, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:53:27,294 (trainer:763) INFO: 7epoch:train:361-400batch: iter_time=5.394e-05, forward_time=0.159, loss_ctc=20.541, loss=20.541, backward_time=0.024, grad_norm=270.335, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:53:40,046 (trainer:763) INFO: 7epoch:train:401-440batch: iter_time=5.358e-05, forward_time=0.159, loss_ctc=20.603, loss=20.603, backward_time=0.024, grad_norm=290.834, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:53:52,802 (trainer:763) INFO: 7epoch:train:441-480batch: iter_time=5.394e-05, forward_time=0.159, loss_ctc=20.556, loss=20.556, backward_time=0.024, grad_norm=275.971, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:54:05,552 (trainer:763) INFO: 7epoch:train:481-520batch: iter_time=5.297e-05, forward_time=0.159, loss_ctc=20.091, loss=20.091, backward_time=0.024, grad_norm=279.474, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:54:18,304 (trainer:763) INFO: 7epoch:train:521-560batch: iter_time=5.175e-05, forward_time=0.159, loss_ctc=20.213, loss=20.213, backward_time=0.024, grad_norm=310.725, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:54:31,049 (trainer:763) INFO: 7epoch:train:561-600batch: iter_time=5.125e-05, forward_time=0.159, loss_ctc=20.138, loss=20.138, backward_time=0.024, grad_norm=289.105, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:54:43,796 (trainer:763) INFO: 7epoch:train:601-640batch: iter_time=5.419e-05, forward_time=0.159, loss_ctc=20.078, loss=20.078, backward_time=0.024, grad_norm=312.236, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:54:56,546 (trainer:763) INFO: 7epoch:train:641-680batch: iter_time=5.216e-05, forward_time=0.159, loss_ctc=19.428, loss=19.428, backward_time=0.024, grad_norm=305.102, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:55:09,289 (trainer:763) INFO: 7epoch:train:681-720batch: iter_time=5.388e-05, forward_time=0.159, loss_ctc=20.723, loss=20.723, backward_time=0.024, grad_norm=305.646, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:55:22,031 (trainer:763) INFO: 7epoch:train:721-760batch: iter_time=5.137e-05, forward_time=0.159, loss_ctc=19.890, loss=19.890, backward_time=0.024, grad_norm=297.649, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:55:34,771 (trainer:763) INFO: 7epoch:train:761-800batch: iter_time=4.916e-05, forward_time=0.159, loss_ctc=19.951, loss=19.951, backward_time=0.024, grad_norm=296.660, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:55:39,544 (trainer:354) INFO: 7epoch results: [train] iter_time=2.314e-04, forward_time=0.159, loss_ctc=20.710, loss=20.710, backward_time=0.024, grad_norm=292.924, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.29 seconds, total_count=5600, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=387.327, cer_ctc=0.301, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=387.327, time=1.16 seconds, total_count=35, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.53 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 17:55:40,573 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 17:55:40,574 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/6epoch.pth +[stan] 2024-01-14 17:55:40,574 (trainer:288) INFO: 8/30epoch started. Estimated time to finish: 1 hour, 40 minutes and 7.43 seconds +[stan] 2024-01-14 17:55:53,584 (trainer:763) INFO: 8epoch:train:1-40batch: iter_time=0.003, forward_time=0.159, loss_ctc=19.070, loss=19.070, backward_time=0.024, grad_norm=272.690, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.300 +[stan] 2024-01-14 17:56:06,319 (trainer:763) INFO: 8epoch:train:41-80batch: iter_time=5.118e-05, forward_time=0.159, loss_ctc=19.164, loss=19.164, backward_time=0.024, grad_norm=301.116, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 17:56:19,059 (trainer:763) INFO: 8epoch:train:81-120batch: iter_time=5.068e-05, forward_time=0.159, loss_ctc=19.388, loss=19.388, backward_time=0.025, grad_norm=297.100, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:56:31,807 (trainer:763) INFO: 8epoch:train:121-160batch: iter_time=5.285e-05, forward_time=0.159, loss_ctc=19.537, loss=19.537, backward_time=0.024, grad_norm=291.777, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:56:44,559 (trainer:763) INFO: 8epoch:train:161-200batch: iter_time=5.389e-05, forward_time=0.159, loss_ctc=19.201, loss=19.201, backward_time=0.024, grad_norm=281.086, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:56:57,315 (trainer:763) INFO: 8epoch:train:201-240batch: iter_time=5.121e-05, forward_time=0.159, loss_ctc=18.820, loss=18.820, backward_time=0.025, grad_norm=279.859, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:57:10,072 (trainer:763) INFO: 8epoch:train:241-280batch: iter_time=5.215e-05, forward_time=0.159, loss_ctc=18.874, loss=18.874, backward_time=0.025, grad_norm=293.278, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:57:22,825 (trainer:763) INFO: 8epoch:train:281-320batch: iter_time=5.327e-05, forward_time=0.159, loss_ctc=18.845, loss=18.845, backward_time=0.025, grad_norm=298.028, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:57:35,584 (trainer:763) INFO: 8epoch:train:321-360batch: iter_time=5.370e-05, forward_time=0.159, loss_ctc=18.715, loss=18.715, backward_time=0.024, grad_norm=300.893, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:57:48,347 (trainer:763) INFO: 8epoch:train:361-400batch: iter_time=5.353e-05, forward_time=0.159, loss_ctc=17.741, loss=17.741, backward_time=0.024, grad_norm=284.847, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:58:01,097 (trainer:763) INFO: 8epoch:train:401-440batch: iter_time=5.320e-05, forward_time=0.159, loss_ctc=18.285, loss=18.285, backward_time=0.024, grad_norm=290.654, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:58:13,854 (trainer:763) INFO: 8epoch:train:441-480batch: iter_time=5.456e-05, forward_time=0.159, loss_ctc=18.206, loss=18.206, backward_time=0.024, grad_norm=295.799, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 17:58:26,603 (trainer:763) INFO: 8epoch:train:481-520batch: iter_time=5.425e-05, forward_time=0.159, loss_ctc=18.387, loss=18.387, backward_time=0.024, grad_norm=279.256, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:58:39,345 (trainer:763) INFO: 8epoch:train:521-560batch: iter_time=5.224e-05, forward_time=0.159, loss_ctc=18.225, loss=18.225, backward_time=0.024, grad_norm=281.472, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:58:52,090 (trainer:763) INFO: 8epoch:train:561-600batch: iter_time=5.413e-05, forward_time=0.159, loss_ctc=17.776, loss=17.776, backward_time=0.024, grad_norm=270.481, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:59:04,842 (trainer:763) INFO: 8epoch:train:601-640batch: iter_time=5.396e-05, forward_time=0.159, loss_ctc=17.785, loss=17.785, backward_time=0.024, grad_norm=280.259, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:59:17,590 (trainer:763) INFO: 8epoch:train:641-680batch: iter_time=5.395e-05, forward_time=0.159, loss_ctc=17.518, loss=17.518, backward_time=0.024, grad_norm=299.905, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:59:30,337 (trainer:763) INFO: 8epoch:train:681-720batch: iter_time=5.253e-05, forward_time=0.159, loss_ctc=17.551, loss=17.551, backward_time=0.024, grad_norm=266.328, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 17:59:43,078 (trainer:763) INFO: 8epoch:train:721-760batch: iter_time=5.164e-05, forward_time=0.159, loss_ctc=17.342, loss=17.342, backward_time=0.024, grad_norm=262.099, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 17:59:55,824 (trainer:763) INFO: 8epoch:train:761-800batch: iter_time=4.866e-05, forward_time=0.159, loss_ctc=17.216, loss=17.216, backward_time=0.024, grad_norm=283.222, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:00:00,581 (trainer:354) INFO: 8epoch results: [train] iter_time=2.184e-04, forward_time=0.159, loss_ctc=18.382, loss=18.382, backward_time=0.024, grad_norm=285.507, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.33 seconds, total_count=6400, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=393.145, cer_ctc=0.302, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=393.145, time=1.16 seconds, total_count=40, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.52 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 18:00:01,538 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 18:00:01,538 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/7epoch.pth +[stan] 2024-01-14 18:00:01,538 (trainer:288) INFO: 9/30epoch started. Estimated time to finish: 1 hour, 35 minutes and 45.61 seconds +[stan] 2024-01-14 18:00:14,565 (trainer:763) INFO: 9epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=17.412, loss=17.412, backward_time=0.024, grad_norm=275.429, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.301 +[stan] 2024-01-14 18:00:27,294 (trainer:763) INFO: 9epoch:train:41-80batch: iter_time=5.307e-05, forward_time=0.159, loss_ctc=17.289, loss=17.289, backward_time=0.024, grad_norm=283.833, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:00:40,040 (trainer:763) INFO: 9epoch:train:81-120batch: iter_time=5.452e-05, forward_time=0.159, loss_ctc=17.168, loss=17.168, backward_time=0.024, grad_norm=288.746, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:00:52,791 (trainer:763) INFO: 9epoch:train:121-160batch: iter_time=5.257e-05, forward_time=0.159, loss_ctc=17.173, loss=17.173, backward_time=0.024, grad_norm=295.925, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:01:05,548 (trainer:763) INFO: 9epoch:train:161-200batch: iter_time=5.294e-05, forward_time=0.159, loss_ctc=17.509, loss=17.509, backward_time=0.024, grad_norm=302.167, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:01:18,303 (trainer:763) INFO: 9epoch:train:201-240batch: iter_time=5.390e-05, forward_time=0.159, loss_ctc=16.726, loss=16.726, backward_time=0.025, grad_norm=263.858, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:01:31,065 (trainer:763) INFO: 9epoch:train:241-280batch: iter_time=5.368e-05, forward_time=0.159, loss_ctc=16.924, loss=16.924, backward_time=0.024, grad_norm=258.484, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:01:43,826 (trainer:763) INFO: 9epoch:train:281-320batch: iter_time=5.227e-05, forward_time=0.159, loss_ctc=16.687, loss=16.687, backward_time=0.024, grad_norm=279.055, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:01:56,582 (trainer:763) INFO: 9epoch:train:321-360batch: iter_time=5.410e-05, forward_time=0.159, loss_ctc=16.576, loss=16.576, backward_time=0.024, grad_norm=281.530, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:02:09,336 (trainer:763) INFO: 9epoch:train:361-400batch: iter_time=5.227e-05, forward_time=0.159, loss_ctc=16.502, loss=16.502, backward_time=0.024, grad_norm=281.593, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:02:22,090 (trainer:763) INFO: 9epoch:train:401-440batch: iter_time=5.189e-05, forward_time=0.159, loss_ctc=16.580, loss=16.580, backward_time=0.025, grad_norm=286.921, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:02:34,843 (trainer:763) INFO: 9epoch:train:441-480batch: iter_time=5.355e-05, forward_time=0.159, loss_ctc=16.043, loss=16.043, backward_time=0.024, grad_norm=268.568, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:02:47,601 (trainer:763) INFO: 9epoch:train:481-520batch: iter_time=5.595e-05, forward_time=0.159, loss_ctc=15.999, loss=15.999, backward_time=0.024, grad_norm=280.490, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:03:00,353 (trainer:763) INFO: 9epoch:train:521-560batch: iter_time=5.189e-05, forward_time=0.159, loss_ctc=15.666, loss=15.666, backward_time=0.024, grad_norm=261.518, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:03:13,095 (trainer:763) INFO: 9epoch:train:561-600batch: iter_time=5.190e-05, forward_time=0.159, loss_ctc=15.649, loss=15.649, backward_time=0.024, grad_norm=271.119, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:03:25,830 (trainer:763) INFO: 9epoch:train:601-640batch: iter_time=5.233e-05, forward_time=0.159, loss_ctc=15.777, loss=15.777, backward_time=0.024, grad_norm=268.025, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:03:38,565 (trainer:763) INFO: 9epoch:train:641-680batch: iter_time=5.486e-05, forward_time=0.159, loss_ctc=15.724, loss=15.724, backward_time=0.024, grad_norm=288.607, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:03:51,311 (trainer:763) INFO: 9epoch:train:681-720batch: iter_time=5.259e-05, forward_time=0.159, loss_ctc=15.229, loss=15.229, backward_time=0.024, grad_norm=268.745, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:04:04,049 (trainer:763) INFO: 9epoch:train:721-760batch: iter_time=5.231e-05, forward_time=0.159, loss_ctc=15.457, loss=15.457, backward_time=0.024, grad_norm=271.835, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:04:16,786 (trainer:763) INFO: 9epoch:train:761-800batch: iter_time=4.995e-05, forward_time=0.159, loss_ctc=15.874, loss=15.874, backward_time=0.025, grad_norm=310.847, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:04:21,546 (trainer:354) INFO: 9epoch results: [train] iter_time=2.454e-04, forward_time=0.159, loss_ctc=16.398, loss=16.398, backward_time=0.024, grad_norm=279.365, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.33 seconds, total_count=7200, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=402.689, cer_ctc=0.304, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=402.689, time=1.17 seconds, total_count=45, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.5 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 18:04:22,611 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 18:04:22,611 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/8epoch.pth +[stan] 2024-01-14 18:04:22,611 (trainer:288) INFO: 10/30epoch started. Estimated time to finish: 1 hour, 31 minutes and 24.24 seconds +[stan] 2024-01-14 18:04:35,623 (trainer:763) INFO: 10epoch:train:1-40batch: iter_time=0.003, forward_time=0.159, loss_ctc=15.068, loss=15.068, backward_time=0.024, grad_norm=289.295, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.300 +[stan] 2024-01-14 18:04:48,356 (trainer:763) INFO: 10epoch:train:41-80batch: iter_time=5.077e-05, forward_time=0.159, loss_ctc=15.319, loss=15.319, backward_time=0.024, grad_norm=292.218, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:05:01,100 (trainer:763) INFO: 10epoch:train:81-120batch: iter_time=5.141e-05, forward_time=0.159, loss_ctc=15.164, loss=15.164, backward_time=0.024, grad_norm=341.919, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:05:13,849 (trainer:763) INFO: 10epoch:train:121-160batch: iter_time=5.231e-05, forward_time=0.159, loss_ctc=14.743, loss=14.743, backward_time=0.024, grad_norm=282.014, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:05:26,603 (trainer:763) INFO: 10epoch:train:161-200batch: iter_time=5.233e-05, forward_time=0.159, loss_ctc=15.279, loss=15.279, backward_time=0.024, grad_norm=307.273, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:05:39,370 (trainer:763) INFO: 10epoch:train:201-240batch: iter_time=5.263e-05, forward_time=0.159, loss_ctc=14.954, loss=14.954, backward_time=0.025, grad_norm=283.687, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.277 +[stan] 2024-01-14 18:05:52,131 (trainer:763) INFO: 10epoch:train:241-280batch: iter_time=5.443e-05, forward_time=0.159, loss_ctc=14.934, loss=14.934, backward_time=0.025, grad_norm=287.230, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:06:04,888 (trainer:763) INFO: 10epoch:train:281-320batch: iter_time=5.738e-05, forward_time=0.159, loss_ctc=14.585, loss=14.585, backward_time=0.024, grad_norm=271.477, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:06:17,650 (trainer:763) INFO: 10epoch:train:321-360batch: iter_time=5.510e-05, forward_time=0.159, loss_ctc=14.916, loss=14.916, backward_time=0.025, grad_norm=286.849, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:06:30,405 (trainer:763) INFO: 10epoch:train:361-400batch: iter_time=5.510e-05, forward_time=0.159, loss_ctc=14.368, loss=14.368, backward_time=0.024, grad_norm=287.682, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:06:43,166 (trainer:763) INFO: 10epoch:train:401-440batch: iter_time=5.573e-05, forward_time=0.159, loss_ctc=14.784, loss=14.784, backward_time=0.025, grad_norm=272.521, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:06:55,917 (trainer:763) INFO: 10epoch:train:441-480batch: iter_time=5.436e-05, forward_time=0.159, loss_ctc=14.483, loss=14.483, backward_time=0.024, grad_norm=268.134, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:07:08,667 (trainer:763) INFO: 10epoch:train:481-520batch: iter_time=5.611e-05, forward_time=0.159, loss_ctc=14.506, loss=14.506, backward_time=0.024, grad_norm=288.709, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:07:21,414 (trainer:763) INFO: 10epoch:train:521-560batch: iter_time=5.207e-05, forward_time=0.159, loss_ctc=14.575, loss=14.575, backward_time=0.025, grad_norm=273.624, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:07:34,161 (trainer:763) INFO: 10epoch:train:561-600batch: iter_time=5.379e-05, forward_time=0.159, loss_ctc=13.969, loss=13.969, backward_time=0.024, grad_norm=270.502, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:07:46,910 (trainer:763) INFO: 10epoch:train:601-640batch: iter_time=5.489e-05, forward_time=0.159, loss_ctc=14.137, loss=14.137, backward_time=0.024, grad_norm=264.619, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:07:59,659 (trainer:763) INFO: 10epoch:train:641-680batch: iter_time=5.474e-05, forward_time=0.159, loss_ctc=13.578, loss=13.578, backward_time=0.024, grad_norm=261.395, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:08:12,401 (trainer:763) INFO: 10epoch:train:681-720batch: iter_time=5.447e-05, forward_time=0.159, loss_ctc=13.818, loss=13.818, backward_time=0.024, grad_norm=277.896, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:08:25,142 (trainer:763) INFO: 10epoch:train:721-760batch: iter_time=5.474e-05, forward_time=0.159, loss_ctc=13.699, loss=13.699, backward_time=0.024, grad_norm=275.384, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:08:37,891 (trainer:763) INFO: 10epoch:train:761-800batch: iter_time=4.765e-05, forward_time=0.159, loss_ctc=13.661, loss=13.661, backward_time=0.024, grad_norm=273.879, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:08:42,624 (trainer:354) INFO: 10epoch results: [train] iter_time=2.239e-04, forward_time=0.159, loss_ctc=14.527, loss=14.527, backward_time=0.024, grad_norm=282.815, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.36 seconds, total_count=8000, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=411.898, cer_ctc=0.307, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=411.898, time=1.17 seconds, total_count=50, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.49 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 18:08:43,709 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 18:08:43,709 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/9epoch.pth +[stan] 2024-01-14 18:08:43,709 (trainer:288) INFO: 11/30epoch started. Estimated time to finish: 1 hour, 27 minutes and 2.97 seconds +[stan] 2024-01-14 18:08:56,730 (trainer:763) INFO: 11epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=14.199, loss=14.199, backward_time=0.024, grad_norm=268.338, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.301 +[stan] 2024-01-14 18:09:09,475 (trainer:763) INFO: 11epoch:train:41-80batch: iter_time=5.416e-05, forward_time=0.159, loss_ctc=13.927, loss=13.927, backward_time=0.024, grad_norm=256.128, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:09:22,226 (trainer:763) INFO: 11epoch:train:81-120batch: iter_time=5.547e-05, forward_time=0.159, loss_ctc=13.320, loss=13.320, backward_time=0.025, grad_norm=275.215, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:09:34,979 (trainer:763) INFO: 11epoch:train:121-160batch: iter_time=5.406e-05, forward_time=0.159, loss_ctc=13.454, loss=13.454, backward_time=0.024, grad_norm=276.082, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:09:47,730 (trainer:763) INFO: 11epoch:train:161-200batch: iter_time=5.259e-05, forward_time=0.159, loss_ctc=13.285, loss=13.285, backward_time=0.024, grad_norm=290.127, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:10:00,486 (trainer:763) INFO: 11epoch:train:201-240batch: iter_time=5.174e-05, forward_time=0.159, loss_ctc=13.421, loss=13.421, backward_time=0.025, grad_norm=260.475, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:10:13,241 (trainer:763) INFO: 11epoch:train:241-280batch: iter_time=5.203e-05, forward_time=0.159, loss_ctc=13.557, loss=13.557, backward_time=0.024, grad_norm=264.219, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:10:25,992 (trainer:763) INFO: 11epoch:train:281-320batch: iter_time=5.230e-05, forward_time=0.159, loss_ctc=13.278, loss=13.278, backward_time=0.025, grad_norm=264.126, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:10:38,743 (trainer:763) INFO: 11epoch:train:321-360batch: iter_time=5.388e-05, forward_time=0.159, loss_ctc=13.153, loss=13.153, backward_time=0.024, grad_norm=250.293, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:10:51,497 (trainer:763) INFO: 11epoch:train:361-400batch: iter_time=5.157e-05, forward_time=0.159, loss_ctc=13.457, loss=13.457, backward_time=0.024, grad_norm=285.765, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:11:04,242 (trainer:763) INFO: 11epoch:train:401-440batch: iter_time=5.283e-05, forward_time=0.159, loss_ctc=12.933, loss=12.933, backward_time=0.024, grad_norm=279.017, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:11:16,980 (trainer:763) INFO: 11epoch:train:441-480batch: iter_time=5.522e-05, forward_time=0.159, loss_ctc=12.795, loss=12.795, backward_time=0.024, grad_norm=257.562, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:11:29,714 (trainer:763) INFO: 11epoch:train:481-520batch: iter_time=5.187e-05, forward_time=0.159, loss_ctc=12.905, loss=12.905, backward_time=0.024, grad_norm=288.402, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:11:42,457 (trainer:763) INFO: 11epoch:train:521-560batch: iter_time=5.452e-05, forward_time=0.159, loss_ctc=12.509, loss=12.509, backward_time=0.024, grad_norm=298.665, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:11:55,206 (trainer:763) INFO: 11epoch:train:561-600batch: iter_time=5.257e-05, forward_time=0.159, loss_ctc=12.778, loss=12.778, backward_time=0.024, grad_norm=273.846, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:12:07,941 (trainer:763) INFO: 11epoch:train:601-640batch: iter_time=5.262e-05, forward_time=0.159, loss_ctc=11.934, loss=11.934, backward_time=0.024, grad_norm=260.342, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:12:20,676 (trainer:763) INFO: 11epoch:train:641-680batch: iter_time=5.534e-05, forward_time=0.159, loss_ctc=12.590, loss=12.590, backward_time=0.024, grad_norm=254.814, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:12:33,412 (trainer:763) INFO: 11epoch:train:681-720batch: iter_time=5.386e-05, forward_time=0.159, loss_ctc=12.448, loss=12.448, backward_time=0.024, grad_norm=252.695, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:12:46,148 (trainer:763) INFO: 11epoch:train:721-760batch: iter_time=5.235e-05, forward_time=0.159, loss_ctc=12.626, loss=12.626, backward_time=0.024, grad_norm=287.126, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:12:58,883 (trainer:763) INFO: 11epoch:train:761-800batch: iter_time=5.044e-05, forward_time=0.159, loss_ctc=12.146, loss=12.146, backward_time=0.024, grad_norm=269.285, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:13:03,617 (trainer:354) INFO: 11epoch results: [train] iter_time=2.268e-04, forward_time=0.159, loss_ctc=13.036, loss=13.036, backward_time=0.024, grad_norm=270.626, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.26 seconds, total_count=8800, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=411.706, cer_ctc=0.306, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=411.706, time=1.17 seconds, total_count=55, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.48 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 18:13:04,519 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 18:13:04,519 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/10epoch.pth +[stan] 2024-01-14 18:13:04,520 (trainer:288) INFO: 12/30epoch started. Estimated time to finish: 1 hour, 22 minutes and 41.24 seconds +[stan] 2024-01-14 18:13:17,532 (trainer:763) INFO: 12epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=12.477, loss=12.477, backward_time=0.024, grad_norm=258.832, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.300 +[stan] 2024-01-14 18:13:30,256 (trainer:763) INFO: 12epoch:train:41-80batch: iter_time=5.201e-05, forward_time=0.159, loss_ctc=11.968, loss=11.968, backward_time=0.024, grad_norm=258.207, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-14 18:13:42,992 (trainer:763) INFO: 12epoch:train:81-120batch: iter_time=5.184e-05, forward_time=0.159, loss_ctc=12.443, loss=12.443, backward_time=0.024, grad_norm=300.795, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:13:55,738 (trainer:763) INFO: 12epoch:train:121-160batch: iter_time=5.424e-05, forward_time=0.159, loss_ctc=12.263, loss=12.263, backward_time=0.024, grad_norm=256.120, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:14:08,485 (trainer:763) INFO: 12epoch:train:161-200batch: iter_time=5.521e-05, forward_time=0.159, loss_ctc=11.843, loss=11.843, backward_time=0.024, grad_norm=269.174, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:14:21,236 (trainer:763) INFO: 12epoch:train:201-240batch: iter_time=5.449e-05, forward_time=0.159, loss_ctc=12.154, loss=12.154, backward_time=0.024, grad_norm=257.686, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:14:33,986 (trainer:763) INFO: 12epoch:train:241-280batch: iter_time=5.213e-05, forward_time=0.159, loss_ctc=11.898, loss=11.898, backward_time=0.024, grad_norm=256.397, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:14:46,736 (trainer:763) INFO: 12epoch:train:281-320batch: iter_time=5.353e-05, forward_time=0.159, loss_ctc=11.723, loss=11.723, backward_time=0.024, grad_norm=252.688, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:14:59,487 (trainer:763) INFO: 12epoch:train:321-360batch: iter_time=5.452e-05, forward_time=0.159, loss_ctc=11.733, loss=11.733, backward_time=0.024, grad_norm=255.462, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:15:12,241 (trainer:763) INFO: 12epoch:train:361-400batch: iter_time=5.634e-05, forward_time=0.159, loss_ctc=11.951, loss=11.951, backward_time=0.024, grad_norm=272.984, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:15:24,995 (trainer:763) INFO: 12epoch:train:401-440batch: iter_time=5.427e-05, forward_time=0.159, loss_ctc=12.372, loss=12.372, backward_time=0.024, grad_norm=254.980, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:15:37,741 (trainer:763) INFO: 12epoch:train:441-480batch: iter_time=5.142e-05, forward_time=0.159, loss_ctc=11.160, loss=11.160, backward_time=0.024, grad_norm=250.690, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:15:50,487 (trainer:763) INFO: 12epoch:train:481-520batch: iter_time=5.515e-05, forward_time=0.159, loss_ctc=11.426, loss=11.426, backward_time=0.024, grad_norm=271.378, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:16:03,232 (trainer:763) INFO: 12epoch:train:521-560batch: iter_time=5.162e-05, forward_time=0.159, loss_ctc=11.672, loss=11.672, backward_time=0.024, grad_norm=247.384, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:16:15,975 (trainer:763) INFO: 12epoch:train:561-600batch: iter_time=5.191e-05, forward_time=0.159, loss_ctc=11.294, loss=11.294, backward_time=0.024, grad_norm=243.344, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:16:28,715 (trainer:763) INFO: 12epoch:train:601-640batch: iter_time=5.145e-05, forward_time=0.159, loss_ctc=11.371, loss=11.371, backward_time=0.024, grad_norm=284.143, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:16:41,453 (trainer:763) INFO: 12epoch:train:641-680batch: iter_time=5.412e-05, forward_time=0.159, loss_ctc=10.851, loss=10.851, backward_time=0.024, grad_norm=266.546, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:16:54,201 (trainer:763) INFO: 12epoch:train:681-720batch: iter_time=5.413e-05, forward_time=0.159, loss_ctc=11.129, loss=11.129, backward_time=0.024, grad_norm=252.558, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:17:06,940 (trainer:763) INFO: 12epoch:train:721-760batch: iter_time=5.457e-05, forward_time=0.159, loss_ctc=11.107, loss=11.107, backward_time=0.024, grad_norm=251.329, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:17:19,671 (trainer:763) INFO: 12epoch:train:761-800batch: iter_time=5.103e-05, forward_time=0.159, loss_ctc=11.077, loss=11.077, backward_time=0.024, grad_norm=252.534, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:17:24,413 (trainer:354) INFO: 12epoch results: [train] iter_time=2.337e-04, forward_time=0.159, loss_ctc=11.696, loss=11.696, backward_time=0.024, grad_norm=260.661, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.23 seconds, total_count=9600, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=419.715, cer_ctc=0.306, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=419.715, time=1.17 seconds, total_count=60, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.49 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 18:17:25,435 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 18:17:25,436 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/11epoch.pth +[stan] 2024-01-14 18:17:25,436 (trainer:288) INFO: 13/30epoch started. Estimated time to finish: 1 hour, 18 minutes and 19.82 seconds +[stan] 2024-01-14 18:17:38,432 (trainer:763) INFO: 13epoch:train:1-40batch: iter_time=0.003, forward_time=0.159, loss_ctc=11.016, loss=11.016, backward_time=0.024, grad_norm=239.256, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.298 +[stan] 2024-01-14 18:17:51,161 (trainer:763) INFO: 13epoch:train:41-80batch: iter_time=5.124e-05, forward_time=0.159, loss_ctc=11.082, loss=11.082, backward_time=0.024, grad_norm=250.288, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:18:03,895 (trainer:763) INFO: 13epoch:train:81-120batch: iter_time=5.379e-05, forward_time=0.159, loss_ctc=10.778, loss=10.778, backward_time=0.024, grad_norm=239.420, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:18:16,636 (trainer:763) INFO: 13epoch:train:121-160batch: iter_time=5.150e-05, forward_time=0.159, loss_ctc=10.933, loss=10.933, backward_time=0.024, grad_norm=256.847, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:18:29,379 (trainer:763) INFO: 13epoch:train:161-200batch: iter_time=5.436e-05, forward_time=0.159, loss_ctc=11.263, loss=11.263, backward_time=0.024, grad_norm=277.973, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:18:42,132 (trainer:763) INFO: 13epoch:train:201-240batch: iter_time=5.422e-05, forward_time=0.159, loss_ctc=10.907, loss=10.907, backward_time=0.024, grad_norm=247.688, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:18:54,893 (trainer:763) INFO: 13epoch:train:241-280batch: iter_time=5.155e-05, forward_time=0.159, loss_ctc=10.921, loss=10.921, backward_time=0.024, grad_norm=257.533, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:19:07,644 (trainer:763) INFO: 13epoch:train:281-320batch: iter_time=5.413e-05, forward_time=0.159, loss_ctc=10.725, loss=10.725, backward_time=0.024, grad_norm=257.604, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:19:20,402 (trainer:763) INFO: 13epoch:train:321-360batch: iter_time=5.591e-05, forward_time=0.159, loss_ctc=10.676, loss=10.676, backward_time=0.024, grad_norm=272.898, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:19:33,162 (trainer:763) INFO: 13epoch:train:361-400batch: iter_time=5.491e-05, forward_time=0.159, loss_ctc=10.831, loss=10.831, backward_time=0.024, grad_norm=267.467, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:19:45,911 (trainer:763) INFO: 13epoch:train:401-440batch: iter_time=5.172e-05, forward_time=0.159, loss_ctc=10.712, loss=10.712, backward_time=0.024, grad_norm=249.900, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:19:58,662 (trainer:763) INFO: 13epoch:train:441-480batch: iter_time=5.175e-05, forward_time=0.159, loss_ctc=10.470, loss=10.470, backward_time=0.025, grad_norm=255.599, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:20:11,401 (trainer:763) INFO: 13epoch:train:481-520batch: iter_time=5.382e-05, forward_time=0.159, loss_ctc=10.326, loss=10.326, backward_time=0.025, grad_norm=255.209, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:20:24,144 (trainer:763) INFO: 13epoch:train:521-560batch: iter_time=5.203e-05, forward_time=0.159, loss_ctc=10.567, loss=10.567, backward_time=0.024, grad_norm=237.411, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:20:36,886 (trainer:763) INFO: 13epoch:train:561-600batch: iter_time=5.522e-05, forward_time=0.159, loss_ctc=10.656, loss=10.656, backward_time=0.024, grad_norm=229.895, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:20:49,622 (trainer:763) INFO: 13epoch:train:601-640batch: iter_time=5.202e-05, forward_time=0.159, loss_ctc=10.406, loss=10.406, backward_time=0.024, grad_norm=239.819, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:21:02,361 (trainer:763) INFO: 13epoch:train:641-680batch: iter_time=5.223e-05, forward_time=0.159, loss_ctc=10.430, loss=10.430, backward_time=0.024, grad_norm=235.376, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:21:15,106 (trainer:763) INFO: 13epoch:train:681-720batch: iter_time=5.483e-05, forward_time=0.159, loss_ctc=10.457, loss=10.457, backward_time=0.024, grad_norm=271.808, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:21:27,843 (trainer:763) INFO: 13epoch:train:721-760batch: iter_time=5.210e-05, forward_time=0.159, loss_ctc=10.534, loss=10.534, backward_time=0.024, grad_norm=293.839, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:21:40,574 (trainer:763) INFO: 13epoch:train:761-800batch: iter_time=4.805e-05, forward_time=0.159, loss_ctc=9.603, loss=9.603, backward_time=0.024, grad_norm=251.471, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:21:45,298 (trainer:354) INFO: 13epoch results: [train] iter_time=2.118e-04, forward_time=0.159, loss_ctc=10.665, loss=10.665, backward_time=0.024, grad_norm=254.365, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.22 seconds, total_count=10400, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=425.295, cer_ctc=0.306, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=425.295, time=1.17 seconds, total_count=65, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.48 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 18:21:46,304 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 18:21:46,304 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/12epoch.pth +[stan] 2024-01-14 18:21:46,304 (trainer:288) INFO: 14/30epoch started. Estimated time to finish: 1 hour, 13 minutes and 58.41 seconds +[stan] 2024-01-14 18:21:59,321 (trainer:763) INFO: 14epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=9.914, loss=9.914, backward_time=0.024, grad_norm=249.380, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.300 +[stan] 2024-01-14 18:22:12,047 (trainer:763) INFO: 14epoch:train:41-80batch: iter_time=5.223e-05, forward_time=0.159, loss_ctc=9.897, loss=9.897, backward_time=0.024, grad_norm=237.067, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-14 18:22:24,785 (trainer:763) INFO: 14epoch:train:81-120batch: iter_time=5.321e-05, forward_time=0.159, loss_ctc=10.233, loss=10.233, backward_time=0.024, grad_norm=242.940, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:22:37,521 (trainer:763) INFO: 14epoch:train:121-160batch: iter_time=5.103e-05, forward_time=0.159, loss_ctc=10.323, loss=10.323, backward_time=0.024, grad_norm=251.429, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:22:50,268 (trainer:763) INFO: 14epoch:train:161-200batch: iter_time=5.378e-05, forward_time=0.159, loss_ctc=10.029, loss=10.029, backward_time=0.025, grad_norm=235.202, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:23:03,014 (trainer:763) INFO: 14epoch:train:201-240batch: iter_time=5.240e-05, forward_time=0.159, loss_ctc=10.184, loss=10.184, backward_time=0.024, grad_norm=229.149, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:23:15,761 (trainer:763) INFO: 14epoch:train:241-280batch: iter_time=5.461e-05, forward_time=0.159, loss_ctc=9.789, loss=9.789, backward_time=0.024, grad_norm=237.926, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:23:28,511 (trainer:763) INFO: 14epoch:train:281-320batch: iter_time=5.194e-05, forward_time=0.159, loss_ctc=9.865, loss=9.865, backward_time=0.025, grad_norm=232.384, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:23:41,257 (trainer:763) INFO: 14epoch:train:321-360batch: iter_time=5.181e-05, forward_time=0.159, loss_ctc=9.726, loss=9.726, backward_time=0.024, grad_norm=236.577, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:23:54,005 (trainer:763) INFO: 14epoch:train:361-400batch: iter_time=5.234e-05, forward_time=0.159, loss_ctc=9.513, loss=9.513, backward_time=0.024, grad_norm=232.350, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:24:06,758 (trainer:763) INFO: 14epoch:train:401-440batch: iter_time=5.399e-05, forward_time=0.159, loss_ctc=9.867, loss=9.867, backward_time=0.025, grad_norm=249.856, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:24:19,507 (trainer:763) INFO: 14epoch:train:441-480batch: iter_time=5.497e-05, forward_time=0.159, loss_ctc=9.636, loss=9.636, backward_time=0.024, grad_norm=238.747, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:24:32,248 (trainer:763) INFO: 14epoch:train:481-520batch: iter_time=5.203e-05, forward_time=0.159, loss_ctc=9.518, loss=9.518, backward_time=0.024, grad_norm=251.276, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:24:44,990 (trainer:763) INFO: 14epoch:train:521-560batch: iter_time=5.456e-05, forward_time=0.159, loss_ctc=9.792, loss=9.792, backward_time=0.024, grad_norm=254.927, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:24:57,735 (trainer:763) INFO: 14epoch:train:561-600batch: iter_time=5.312e-05, forward_time=0.159, loss_ctc=9.185, loss=9.185, backward_time=0.024, grad_norm=236.665, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:25:10,476 (trainer:763) INFO: 14epoch:train:601-640batch: iter_time=5.237e-05, forward_time=0.159, loss_ctc=9.417, loss=9.417, backward_time=0.024, grad_norm=239.410, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:25:23,217 (trainer:763) INFO: 14epoch:train:641-680batch: iter_time=5.388e-05, forward_time=0.159, loss_ctc=9.564, loss=9.564, backward_time=0.024, grad_norm=230.497, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:25:35,953 (trainer:763) INFO: 14epoch:train:681-720batch: iter_time=5.663e-05, forward_time=0.159, loss_ctc=9.057, loss=9.057, backward_time=0.024, grad_norm=226.225, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:25:48,690 (trainer:763) INFO: 14epoch:train:721-760batch: iter_time=5.197e-05, forward_time=0.159, loss_ctc=9.190, loss=9.190, backward_time=0.024, grad_norm=233.081, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:26:01,428 (trainer:763) INFO: 14epoch:train:761-800batch: iter_time=5.106e-05, forward_time=0.159, loss_ctc=9.498, loss=9.498, backward_time=0.024, grad_norm=298.781, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:26:06,121 (trainer:354) INFO: 14epoch results: [train] iter_time=2.269e-04, forward_time=0.159, loss_ctc=9.710, loss=9.710, backward_time=0.024, grad_norm=242.194, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275, time=4 minutes and 15.2 seconds, total_count=11200, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=434.760, cer_ctc=0.306, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=434.760, time=1.16 seconds, total_count=70, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.46 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 18:26:07,053 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 18:26:07,053 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/13epoch.pth +[stan] 2024-01-14 18:26:07,054 (trainer:288) INFO: 15/30epoch started. Estimated time to finish: 1 hour, 9 minutes and 36.95 seconds +[stan] 2024-01-14 18:26:20,071 (trainer:763) INFO: 15epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=9.531, loss=9.531, backward_time=0.024, grad_norm=245.453, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.300 +[stan] 2024-01-14 18:26:32,799 (trainer:763) INFO: 15epoch:train:41-80batch: iter_time=7.412e-05, forward_time=0.159, loss_ctc=9.250, loss=9.250, backward_time=0.024, grad_norm=227.936, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:26:45,533 (trainer:763) INFO: 15epoch:train:81-120batch: iter_time=5.441e-05, forward_time=0.159, loss_ctc=8.638, loss=8.638, backward_time=0.024, grad_norm=213.340, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:26:58,271 (trainer:763) INFO: 15epoch:train:121-160batch: iter_time=5.231e-05, forward_time=0.159, loss_ctc=9.114, loss=9.114, backward_time=0.024, grad_norm=224.325, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:27:11,018 (trainer:763) INFO: 15epoch:train:161-200batch: iter_time=5.098e-05, forward_time=0.159, loss_ctc=9.140, loss=9.140, backward_time=0.024, grad_norm=217.893, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:27:23,766 (trainer:763) INFO: 15epoch:train:201-240batch: iter_time=5.506e-05, forward_time=0.159, loss_ctc=8.916, loss=8.916, backward_time=0.024, grad_norm=219.637, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:27:36,518 (trainer:763) INFO: 15epoch:train:241-280batch: iter_time=5.436e-05, forward_time=0.159, loss_ctc=9.014, loss=9.014, backward_time=0.024, grad_norm=220.609, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:27:49,262 (trainer:763) INFO: 15epoch:train:281-320batch: iter_time=5.233e-05, forward_time=0.159, loss_ctc=8.888, loss=8.888, backward_time=0.024, grad_norm=223.318, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:28:02,014 (trainer:763) INFO: 15epoch:train:321-360batch: iter_time=5.519e-05, forward_time=0.159, loss_ctc=8.656, loss=8.656, backward_time=0.024, grad_norm=227.215, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:28:14,758 (trainer:763) INFO: 15epoch:train:361-400batch: iter_time=5.385e-05, forward_time=0.159, loss_ctc=8.760, loss=8.760, backward_time=0.024, grad_norm=214.162, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:28:27,502 (trainer:763) INFO: 15epoch:train:401-440batch: iter_time=5.229e-05, forward_time=0.159, loss_ctc=8.896, loss=8.896, backward_time=0.025, grad_norm=239.483, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:28:40,241 (trainer:763) INFO: 15epoch:train:441-480batch: iter_time=5.635e-05, forward_time=0.159, loss_ctc=8.890, loss=8.890, backward_time=0.024, grad_norm=222.851, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:28:52,985 (trainer:763) INFO: 15epoch:train:481-520batch: iter_time=5.223e-05, forward_time=0.159, loss_ctc=8.936, loss=8.936, backward_time=0.024, grad_norm=218.184, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:29:05,723 (trainer:763) INFO: 15epoch:train:521-560batch: iter_time=5.348e-05, forward_time=0.159, loss_ctc=8.704, loss=8.704, backward_time=0.024, grad_norm=223.495, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:29:18,461 (trainer:763) INFO: 15epoch:train:561-600batch: iter_time=5.181e-05, forward_time=0.159, loss_ctc=8.449, loss=8.449, backward_time=0.024, grad_norm=218.702, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:29:31,201 (trainer:763) INFO: 15epoch:train:601-640batch: iter_time=5.557e-05, forward_time=0.159, loss_ctc=8.563, loss=8.563, backward_time=0.024, grad_norm=214.081, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:29:43,940 (trainer:763) INFO: 15epoch:train:641-680batch: iter_time=5.544e-05, forward_time=0.159, loss_ctc=8.697, loss=8.697, backward_time=0.024, grad_norm=216.033, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:29:56,680 (trainer:763) INFO: 15epoch:train:681-720batch: iter_time=5.406e-05, forward_time=0.159, loss_ctc=8.140, loss=8.140, backward_time=0.024, grad_norm=212.086, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:30:09,420 (trainer:763) INFO: 15epoch:train:721-760batch: iter_time=6.049e-05, forward_time=0.159, loss_ctc=8.667, loss=8.667, backward_time=0.024, grad_norm=250.205, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:30:22,147 (trainer:763) INFO: 15epoch:train:761-800batch: iter_time=4.887e-05, forward_time=0.159, loss_ctc=8.831, loss=8.831, backward_time=0.024, grad_norm=237.676, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:30:26,887 (trainer:354) INFO: 15epoch results: [train] iter_time=2.387e-04, forward_time=0.159, loss_ctc=8.834, loss=8.834, backward_time=0.024, grad_norm=224.334, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275, time=4 minutes and 15.17 seconds, total_count=12000, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=436.702, cer_ctc=0.311, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=436.702, time=1.17 seconds, total_count=75, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.49 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 18:30:27,906 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 18:30:27,906 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/14epoch.pth +[stan] 2024-01-14 18:30:27,906 (trainer:288) INFO: 16/30epoch started. Estimated time to finish: 1 hour, 5 minutes and 15.68 seconds +[stan] 2024-01-14 18:30:40,920 (trainer:763) INFO: 16epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=8.607, loss=8.607, backward_time=0.024, grad_norm=271.244, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.300 +[stan] 2024-01-14 18:30:53,647 (trainer:763) INFO: 16epoch:train:41-80batch: iter_time=5.508e-05, forward_time=0.159, loss_ctc=8.569, loss=8.569, backward_time=0.024, grad_norm=226.478, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:31:06,385 (trainer:763) INFO: 16epoch:train:81-120batch: iter_time=5.317e-05, forward_time=0.159, loss_ctc=8.577, loss=8.577, backward_time=0.025, grad_norm=226.949, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:31:19,131 (trainer:763) INFO: 16epoch:train:121-160batch: iter_time=5.169e-05, forward_time=0.159, loss_ctc=8.320, loss=8.320, backward_time=0.024, grad_norm=228.708, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:31:31,882 (trainer:763) INFO: 16epoch:train:161-200batch: iter_time=5.547e-05, forward_time=0.159, loss_ctc=8.158, loss=8.158, backward_time=0.024, grad_norm=233.918, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:31:44,634 (trainer:763) INFO: 16epoch:train:201-240batch: iter_time=5.359e-05, forward_time=0.159, loss_ctc=8.139, loss=8.139, backward_time=0.024, grad_norm=211.633, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:31:57,386 (trainer:763) INFO: 16epoch:train:241-280batch: iter_time=5.494e-05, forward_time=0.159, loss_ctc=8.298, loss=8.298, backward_time=0.025, grad_norm=218.213, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:32:10,139 (trainer:763) INFO: 16epoch:train:281-320batch: iter_time=5.370e-05, forward_time=0.159, loss_ctc=8.381, loss=8.381, backward_time=0.024, grad_norm=219.822, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:32:22,890 (trainer:763) INFO: 16epoch:train:321-360batch: iter_time=5.389e-05, forward_time=0.159, loss_ctc=8.245, loss=8.245, backward_time=0.024, grad_norm=222.000, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:32:35,638 (trainer:763) INFO: 16epoch:train:361-400batch: iter_time=5.479e-05, forward_time=0.159, loss_ctc=8.149, loss=8.149, backward_time=0.024, grad_norm=231.139, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:32:48,385 (trainer:763) INFO: 16epoch:train:401-440batch: iter_time=5.479e-05, forward_time=0.159, loss_ctc=8.279, loss=8.279, backward_time=0.024, grad_norm=204.910, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:33:01,132 (trainer:763) INFO: 16epoch:train:441-480batch: iter_time=5.344e-05, forward_time=0.159, loss_ctc=7.919, loss=7.919, backward_time=0.024, grad_norm=213.456, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:33:13,877 (trainer:763) INFO: 16epoch:train:481-520batch: iter_time=5.516e-05, forward_time=0.159, loss_ctc=7.950, loss=7.950, backward_time=0.024, grad_norm=209.820, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:33:26,627 (trainer:763) INFO: 16epoch:train:521-560batch: iter_time=5.206e-05, forward_time=0.159, loss_ctc=8.096, loss=8.096, backward_time=0.024, grad_norm=219.077, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:33:39,370 (trainer:763) INFO: 16epoch:train:561-600batch: iter_time=5.416e-05, forward_time=0.159, loss_ctc=8.126, loss=8.126, backward_time=0.024, grad_norm=230.945, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:33:52,108 (trainer:763) INFO: 16epoch:train:601-640batch: iter_time=5.479e-05, forward_time=0.159, loss_ctc=7.855, loss=7.855, backward_time=0.024, grad_norm=210.309, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:34:04,848 (trainer:763) INFO: 16epoch:train:641-680batch: iter_time=5.619e-05, forward_time=0.159, loss_ctc=7.717, loss=7.717, backward_time=0.024, grad_norm=198.907, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:34:17,582 (trainer:763) INFO: 16epoch:train:681-720batch: iter_time=5.204e-05, forward_time=0.159, loss_ctc=7.457, loss=7.457, backward_time=0.024, grad_norm=208.807, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:34:30,311 (trainer:763) INFO: 16epoch:train:721-760batch: iter_time=5.314e-05, forward_time=0.159, loss_ctc=7.934, loss=7.934, backward_time=0.024, grad_norm=237.217, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:34:43,044 (trainer:763) INFO: 16epoch:train:761-800batch: iter_time=4.789e-05, forward_time=0.159, loss_ctc=7.553, loss=7.553, backward_time=0.024, grad_norm=219.808, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:34:47,746 (trainer:354) INFO: 16epoch results: [train] iter_time=2.332e-04, forward_time=0.159, loss_ctc=8.117, loss=8.117, backward_time=0.024, grad_norm=222.168, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.21 seconds, total_count=12800, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=450.117, cer_ctc=0.319, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=450.117, time=1.17 seconds, total_count=80, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.46 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 18:34:48,694 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 18:34:48,694 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/15epoch.pth +[stan] 2024-01-14 18:34:48,694 (trainer:288) INFO: 17/30epoch started. Estimated time to finish: 1 hour and 54.41 seconds +[stan] 2024-01-14 18:35:01,718 (trainer:763) INFO: 17epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=7.774, loss=7.774, backward_time=0.024, grad_norm=209.151, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.301 +[stan] 2024-01-14 18:35:14,445 (trainer:763) INFO: 17epoch:train:41-80batch: iter_time=5.349e-05, forward_time=0.159, loss_ctc=7.798, loss=7.798, backward_time=0.024, grad_norm=211.000, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:35:27,185 (trainer:763) INFO: 17epoch:train:81-120batch: iter_time=5.297e-05, forward_time=0.159, loss_ctc=7.530, loss=7.530, backward_time=0.025, grad_norm=207.897, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:35:39,927 (trainer:763) INFO: 17epoch:train:121-160batch: iter_time=5.613e-05, forward_time=0.159, loss_ctc=7.595, loss=7.595, backward_time=0.024, grad_norm=209.544, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:35:52,776 (trainer:763) INFO: 17epoch:train:161-200batch: iter_time=5.221e-05, forward_time=0.162, loss_ctc=7.797, loss=7.797, backward_time=0.024, grad_norm=214.928, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.285 +[stan] 2024-01-14 18:36:05,522 (trainer:763) INFO: 17epoch:train:201-240batch: iter_time=5.284e-05, forward_time=0.159, loss_ctc=7.555, loss=7.555, backward_time=0.025, grad_norm=209.472, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:36:18,284 (trainer:763) INFO: 17epoch:train:241-280batch: iter_time=5.493e-05, forward_time=0.159, loss_ctc=7.309, loss=7.309, backward_time=0.025, grad_norm=218.022, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:36:31,042 (trainer:763) INFO: 17epoch:train:281-320batch: iter_time=5.288e-05, forward_time=0.159, loss_ctc=7.800, loss=7.800, backward_time=0.025, grad_norm=220.081, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:36:43,795 (trainer:763) INFO: 17epoch:train:321-360batch: iter_time=5.412e-05, forward_time=0.159, loss_ctc=7.404, loss=7.404, backward_time=0.024, grad_norm=214.091, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:36:56,549 (trainer:763) INFO: 17epoch:train:361-400batch: iter_time=5.308e-05, forward_time=0.159, loss_ctc=7.514, loss=7.514, backward_time=0.024, grad_norm=228.429, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:37:09,306 (trainer:763) INFO: 17epoch:train:401-440batch: iter_time=5.475e-05, forward_time=0.159, loss_ctc=7.439, loss=7.439, backward_time=0.024, grad_norm=214.795, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:37:22,057 (trainer:763) INFO: 17epoch:train:441-480batch: iter_time=5.299e-05, forward_time=0.159, loss_ctc=7.342, loss=7.342, backward_time=0.025, grad_norm=217.244, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:37:34,811 (trainer:763) INFO: 17epoch:train:481-520batch: iter_time=5.562e-05, forward_time=0.159, loss_ctc=7.375, loss=7.375, backward_time=0.025, grad_norm=214.587, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:37:47,556 (trainer:763) INFO: 17epoch:train:521-560batch: iter_time=5.197e-05, forward_time=0.159, loss_ctc=7.011, loss=7.011, backward_time=0.024, grad_norm=204.542, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:38:00,302 (trainer:763) INFO: 17epoch:train:561-600batch: iter_time=5.191e-05, forward_time=0.159, loss_ctc=7.209, loss=7.209, backward_time=0.024, grad_norm=199.277, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:38:13,054 (trainer:763) INFO: 17epoch:train:601-640batch: iter_time=5.463e-05, forward_time=0.159, loss_ctc=7.234, loss=7.234, backward_time=0.024, grad_norm=198.371, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:38:25,800 (trainer:763) INFO: 17epoch:train:641-680batch: iter_time=5.357e-05, forward_time=0.159, loss_ctc=7.220, loss=7.220, backward_time=0.024, grad_norm=209.283, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:38:38,536 (trainer:763) INFO: 17epoch:train:681-720batch: iter_time=5.470e-05, forward_time=0.159, loss_ctc=7.324, loss=7.324, backward_time=0.024, grad_norm=235.590, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:38:51,271 (trainer:763) INFO: 17epoch:train:721-760batch: iter_time=5.165e-05, forward_time=0.159, loss_ctc=7.121, loss=7.121, backward_time=0.024, grad_norm=220.735, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:39:04,001 (trainer:763) INFO: 17epoch:train:761-800batch: iter_time=5.195e-05, forward_time=0.159, loss_ctc=7.115, loss=7.115, backward_time=0.024, grad_norm=219.214, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:39:08,703 (trainer:354) INFO: 17epoch results: [train] iter_time=2.515e-04, forward_time=0.159, loss_ctc=7.423, loss=7.423, backward_time=0.024, grad_norm=213.813, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.39 seconds, total_count=13600, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=450.156, cer_ctc=0.315, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=450.156, time=1.16 seconds, total_count=85, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.46 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 18:39:09,653 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 18:39:09,653 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/16epoch.pth +[stan] 2024-01-14 18:39:09,653 (trainer:288) INFO: 18/30epoch started. Estimated time to finish: 56 minutes and 33.33 seconds +[stan] 2024-01-14 18:39:22,675 (trainer:763) INFO: 18epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=7.187, loss=7.187, backward_time=0.024, grad_norm=209.350, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.301 +[stan] 2024-01-14 18:39:35,401 (trainer:763) INFO: 18epoch:train:41-80batch: iter_time=5.168e-05, forward_time=0.159, loss_ctc=7.093, loss=7.093, backward_time=0.024, grad_norm=223.867, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:39:48,137 (trainer:763) INFO: 18epoch:train:81-120batch: iter_time=5.303e-05, forward_time=0.159, loss_ctc=7.219, loss=7.219, backward_time=0.024, grad_norm=210.685, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:40:00,885 (trainer:763) INFO: 18epoch:train:121-160batch: iter_time=5.182e-05, forward_time=0.159, loss_ctc=6.787, loss=6.787, backward_time=0.024, grad_norm=196.816, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:40:13,634 (trainer:763) INFO: 18epoch:train:161-200batch: iter_time=5.280e-05, forward_time=0.159, loss_ctc=7.024, loss=7.024, backward_time=0.024, grad_norm=201.985, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:40:26,378 (trainer:763) INFO: 18epoch:train:201-240batch: iter_time=5.432e-05, forward_time=0.159, loss_ctc=7.093, loss=7.093, backward_time=0.024, grad_norm=205.820, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:40:39,126 (trainer:763) INFO: 18epoch:train:241-280batch: iter_time=5.284e-05, forward_time=0.159, loss_ctc=7.102, loss=7.102, backward_time=0.025, grad_norm=196.291, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:40:51,874 (trainer:763) INFO: 18epoch:train:281-320batch: iter_time=5.331e-05, forward_time=0.159, loss_ctc=6.673, loss=6.673, backward_time=0.024, grad_norm=196.642, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:41:04,626 (trainer:763) INFO: 18epoch:train:321-360batch: iter_time=5.161e-05, forward_time=0.159, loss_ctc=6.965, loss=6.965, backward_time=0.024, grad_norm=197.932, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:41:17,376 (trainer:763) INFO: 18epoch:train:361-400batch: iter_time=5.206e-05, forward_time=0.159, loss_ctc=6.743, loss=6.743, backward_time=0.024, grad_norm=198.855, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:41:30,125 (trainer:763) INFO: 18epoch:train:401-440batch: iter_time=5.610e-05, forward_time=0.159, loss_ctc=6.990, loss=6.990, backward_time=0.024, grad_norm=220.212, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:41:42,872 (trainer:763) INFO: 18epoch:train:441-480batch: iter_time=5.515e-05, forward_time=0.159, loss_ctc=6.562, loss=6.562, backward_time=0.024, grad_norm=207.031, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:41:55,616 (trainer:763) INFO: 18epoch:train:481-520batch: iter_time=5.326e-05, forward_time=0.159, loss_ctc=6.703, loss=6.703, backward_time=0.024, grad_norm=196.580, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:42:08,360 (trainer:763) INFO: 18epoch:train:521-560batch: iter_time=5.268e-05, forward_time=0.159, loss_ctc=6.907, loss=6.907, backward_time=0.024, grad_norm=201.358, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:42:21,098 (trainer:763) INFO: 18epoch:train:561-600batch: iter_time=5.244e-05, forward_time=0.159, loss_ctc=6.741, loss=6.741, backward_time=0.024, grad_norm=193.288, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:42:33,835 (trainer:763) INFO: 18epoch:train:601-640batch: iter_time=5.432e-05, forward_time=0.159, loss_ctc=6.729, loss=6.729, backward_time=0.024, grad_norm=198.878, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:42:46,566 (trainer:763) INFO: 18epoch:train:641-680batch: iter_time=5.153e-05, forward_time=0.159, loss_ctc=6.732, loss=6.732, backward_time=0.024, grad_norm=209.704, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:42:59,306 (trainer:763) INFO: 18epoch:train:681-720batch: iter_time=5.216e-05, forward_time=0.159, loss_ctc=6.824, loss=6.824, backward_time=0.024, grad_norm=217.009, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:43:12,046 (trainer:763) INFO: 18epoch:train:721-760batch: iter_time=5.528e-05, forward_time=0.159, loss_ctc=6.672, loss=6.672, backward_time=0.024, grad_norm=197.161, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:43:24,778 (trainer:763) INFO: 18epoch:train:761-800batch: iter_time=4.779e-05, forward_time=0.159, loss_ctc=6.655, loss=6.655, backward_time=0.024, grad_norm=218.813, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:43:29,524 (trainer:354) INFO: 18epoch results: [train] iter_time=2.378e-04, forward_time=0.159, loss_ctc=6.870, loss=6.870, backward_time=0.024, grad_norm=204.914, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275, time=4 minutes and 15.21 seconds, total_count=14400, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=453.310, cer_ctc=0.322, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=453.310, time=1.18 seconds, total_count=90, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.48 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 18:43:30,606 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 18:43:30,606 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/17epoch.pth +[stan] 2024-01-14 18:43:30,606 (trainer:288) INFO: 19/30epoch started. Estimated time to finish: 52 minutes and 12.25 seconds +[stan] 2024-01-14 18:43:43,613 (trainer:763) INFO: 19epoch:train:1-40batch: iter_time=0.003, forward_time=0.159, loss_ctc=6.923, loss=6.923, backward_time=0.024, grad_norm=220.121, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.299 +[stan] 2024-01-14 18:43:56,348 (trainer:763) INFO: 19epoch:train:41-80batch: iter_time=5.379e-05, forward_time=0.159, loss_ctc=6.790, loss=6.790, backward_time=0.024, grad_norm=207.351, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:44:09,091 (trainer:763) INFO: 19epoch:train:81-120batch: iter_time=5.353e-05, forward_time=0.159, loss_ctc=6.730, loss=6.730, backward_time=0.025, grad_norm=210.207, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:44:21,836 (trainer:763) INFO: 19epoch:train:121-160batch: iter_time=5.183e-05, forward_time=0.159, loss_ctc=6.367, loss=6.367, backward_time=0.025, grad_norm=197.282, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:44:34,587 (trainer:763) INFO: 19epoch:train:161-200batch: iter_time=5.499e-05, forward_time=0.159, loss_ctc=6.406, loss=6.406, backward_time=0.024, grad_norm=201.823, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:44:47,340 (trainer:763) INFO: 19epoch:train:201-240batch: iter_time=5.444e-05, forward_time=0.159, loss_ctc=6.296, loss=6.296, backward_time=0.024, grad_norm=198.268, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:45:00,095 (trainer:763) INFO: 19epoch:train:241-280batch: iter_time=5.237e-05, forward_time=0.159, loss_ctc=6.344, loss=6.344, backward_time=0.024, grad_norm=207.688, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:45:12,844 (trainer:763) INFO: 19epoch:train:281-320batch: iter_time=5.508e-05, forward_time=0.159, loss_ctc=6.213, loss=6.213, backward_time=0.025, grad_norm=189.713, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:45:25,599 (trainer:763) INFO: 19epoch:train:321-360batch: iter_time=5.209e-05, forward_time=0.159, loss_ctc=6.545, loss=6.545, backward_time=0.024, grad_norm=237.435, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:45:38,354 (trainer:763) INFO: 19epoch:train:361-400batch: iter_time=5.433e-05, forward_time=0.159, loss_ctc=6.672, loss=6.672, backward_time=0.025, grad_norm=206.960, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:45:51,104 (trainer:763) INFO: 19epoch:train:401-440batch: iter_time=5.125e-05, forward_time=0.159, loss_ctc=6.345, loss=6.345, backward_time=0.025, grad_norm=187.811, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:46:03,853 (trainer:763) INFO: 19epoch:train:441-480batch: iter_time=5.235e-05, forward_time=0.159, loss_ctc=6.529, loss=6.529, backward_time=0.024, grad_norm=199.594, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:46:16,610 (trainer:763) INFO: 19epoch:train:481-520batch: iter_time=5.502e-05, forward_time=0.159, loss_ctc=6.173, loss=6.173, backward_time=0.024, grad_norm=191.807, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:46:29,359 (trainer:763) INFO: 19epoch:train:521-560batch: iter_time=5.408e-05, forward_time=0.159, loss_ctc=6.019, loss=6.019, backward_time=0.024, grad_norm=189.411, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:46:42,109 (trainer:763) INFO: 19epoch:train:561-600batch: iter_time=5.206e-05, forward_time=0.159, loss_ctc=6.215, loss=6.215, backward_time=0.024, grad_norm=208.658, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:46:54,854 (trainer:763) INFO: 19epoch:train:601-640batch: iter_time=5.236e-05, forward_time=0.159, loss_ctc=6.397, loss=6.397, backward_time=0.024, grad_norm=191.988, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:47:07,599 (trainer:763) INFO: 19epoch:train:641-680batch: iter_time=5.326e-05, forward_time=0.159, loss_ctc=6.025, loss=6.025, backward_time=0.024, grad_norm=193.959, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:47:20,342 (trainer:763) INFO: 19epoch:train:681-720batch: iter_time=5.179e-05, forward_time=0.159, loss_ctc=6.097, loss=6.097, backward_time=0.024, grad_norm=200.783, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:47:33,080 (trainer:763) INFO: 19epoch:train:721-760batch: iter_time=5.283e-05, forward_time=0.159, loss_ctc=5.905, loss=5.905, backward_time=0.025, grad_norm=187.201, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:47:45,817 (trainer:763) INFO: 19epoch:train:761-800batch: iter_time=4.894e-05, forward_time=0.159, loss_ctc=6.382, loss=6.382, backward_time=0.024, grad_norm=195.921, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:47:50,482 (trainer:354) INFO: 19epoch results: [train] iter_time=2.194e-04, forward_time=0.159, loss_ctc=6.369, loss=6.369, backward_time=0.024, grad_norm=201.199, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.29 seconds, total_count=15200, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=455.237, cer_ctc=0.323, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=455.237, time=1.15 seconds, total_count=95, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.43 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 18:47:51,446 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 18:47:51,447 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/18epoch.pth +[stan] 2024-01-14 18:47:51,447 (trainer:288) INFO: 20/30epoch started. Estimated time to finish: 47 minutes and 51.13 seconds +[stan] 2024-01-14 18:48:04,477 (trainer:763) INFO: 20epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=5.864, loss=5.864, backward_time=0.024, grad_norm=203.253, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.302 +[stan] 2024-01-14 18:48:17,214 (trainer:763) INFO: 20epoch:train:41-80batch: iter_time=5.210e-05, forward_time=0.159, loss_ctc=6.044, loss=6.044, backward_time=0.024, grad_norm=190.714, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:48:29,952 (trainer:763) INFO: 20epoch:train:81-120batch: iter_time=5.235e-05, forward_time=0.159, loss_ctc=6.004, loss=6.004, backward_time=0.024, grad_norm=187.959, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:48:42,701 (trainer:763) INFO: 20epoch:train:121-160batch: iter_time=5.228e-05, forward_time=0.159, loss_ctc=6.197, loss=6.197, backward_time=0.024, grad_norm=204.473, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:48:55,455 (trainer:763) INFO: 20epoch:train:161-200batch: iter_time=5.419e-05, forward_time=0.159, loss_ctc=6.269, loss=6.269, backward_time=0.024, grad_norm=200.132, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:49:08,216 (trainer:763) INFO: 20epoch:train:201-240batch: iter_time=5.249e-05, forward_time=0.159, loss_ctc=6.129, loss=6.129, backward_time=0.024, grad_norm=212.366, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:49:20,972 (trainer:763) INFO: 20epoch:train:241-280batch: iter_time=5.240e-05, forward_time=0.159, loss_ctc=5.994, loss=5.994, backward_time=0.024, grad_norm=216.055, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:49:33,725 (trainer:763) INFO: 20epoch:train:281-320batch: iter_time=5.249e-05, forward_time=0.159, loss_ctc=6.060, loss=6.060, backward_time=0.024, grad_norm=190.558, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:49:46,479 (trainer:763) INFO: 20epoch:train:321-360batch: iter_time=5.460e-05, forward_time=0.159, loss_ctc=5.965, loss=5.965, backward_time=0.025, grad_norm=199.609, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:49:59,238 (trainer:763) INFO: 20epoch:train:361-400batch: iter_time=5.156e-05, forward_time=0.159, loss_ctc=5.841, loss=5.841, backward_time=0.024, grad_norm=176.716, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:50:11,995 (trainer:763) INFO: 20epoch:train:401-440batch: iter_time=5.502e-05, forward_time=0.159, loss_ctc=6.068, loss=6.068, backward_time=0.024, grad_norm=193.031, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:50:24,743 (trainer:763) INFO: 20epoch:train:441-480batch: iter_time=5.239e-05, forward_time=0.159, loss_ctc=6.168, loss=6.168, backward_time=0.024, grad_norm=194.246, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:50:37,497 (trainer:763) INFO: 20epoch:train:481-520batch: iter_time=5.280e-05, forward_time=0.159, loss_ctc=6.055, loss=6.055, backward_time=0.024, grad_norm=249.975, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:50:50,248 (trainer:763) INFO: 20epoch:train:521-560batch: iter_time=5.473e-05, forward_time=0.159, loss_ctc=5.989, loss=5.989, backward_time=0.025, grad_norm=199.562, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:51:03,000 (trainer:763) INFO: 20epoch:train:561-600batch: iter_time=5.583e-05, forward_time=0.159, loss_ctc=5.927, loss=5.927, backward_time=0.024, grad_norm=199.849, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:51:15,753 (trainer:763) INFO: 20epoch:train:601-640batch: iter_time=5.543e-05, forward_time=0.159, loss_ctc=5.750, loss=5.750, backward_time=0.025, grad_norm=184.812, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:51:28,504 (trainer:763) INFO: 20epoch:train:641-680batch: iter_time=5.389e-05, forward_time=0.159, loss_ctc=5.678, loss=5.678, backward_time=0.024, grad_norm=189.884, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:51:41,255 (trainer:763) INFO: 20epoch:train:681-720batch: iter_time=5.346e-05, forward_time=0.159, loss_ctc=5.712, loss=5.712, backward_time=0.024, grad_norm=190.929, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:51:53,998 (trainer:763) INFO: 20epoch:train:721-760batch: iter_time=5.383e-05, forward_time=0.159, loss_ctc=5.753, loss=5.753, backward_time=0.024, grad_norm=189.614, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:52:06,745 (trainer:763) INFO: 20epoch:train:761-800batch: iter_time=5.361e-05, forward_time=0.159, loss_ctc=5.665, loss=5.665, backward_time=0.024, grad_norm=200.377, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:52:11,426 (trainer:354) INFO: 20epoch results: [train] iter_time=2.604e-04, forward_time=0.159, loss_ctc=5.957, loss=5.957, backward_time=0.024, grad_norm=198.706, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.38 seconds, total_count=16000, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=463.075, cer_ctc=0.320, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=463.075, time=1.16 seconds, total_count=100, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.44 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 18:52:12,348 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 18:52:12,349 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/19epoch.pth +[stan] 2024-01-14 18:52:12,349 (trainer:288) INFO: 21/30epoch started. Estimated time to finish: 43 minutes and 30.06 seconds +[stan] 2024-01-14 18:52:25,374 (trainer:763) INFO: 21epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=5.786, loss=5.786, backward_time=0.024, grad_norm=191.906, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.301 +[stan] 2024-01-14 18:52:38,103 (trainer:763) INFO: 21epoch:train:41-80batch: iter_time=5.150e-05, forward_time=0.159, loss_ctc=5.706, loss=5.706, backward_time=0.024, grad_norm=178.875, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:52:50,849 (trainer:763) INFO: 21epoch:train:81-120batch: iter_time=5.409e-05, forward_time=0.159, loss_ctc=5.810, loss=5.810, backward_time=0.024, grad_norm=206.505, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:53:03,597 (trainer:763) INFO: 21epoch:train:121-160batch: iter_time=5.410e-05, forward_time=0.159, loss_ctc=5.970, loss=5.970, backward_time=0.024, grad_norm=203.621, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:53:16,346 (trainer:763) INFO: 21epoch:train:161-200batch: iter_time=5.558e-05, forward_time=0.159, loss_ctc=5.583, loss=5.583, backward_time=0.025, grad_norm=176.764, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:53:29,105 (trainer:763) INFO: 21epoch:train:201-240batch: iter_time=5.422e-05, forward_time=0.159, loss_ctc=5.691, loss=5.691, backward_time=0.024, grad_norm=172.494, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:53:41,860 (trainer:763) INFO: 21epoch:train:241-280batch: iter_time=5.567e-05, forward_time=0.159, loss_ctc=5.606, loss=5.606, backward_time=0.024, grad_norm=190.977, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:53:54,614 (trainer:763) INFO: 21epoch:train:281-320batch: iter_time=5.314e-05, forward_time=0.159, loss_ctc=5.466, loss=5.466, backward_time=0.024, grad_norm=187.884, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:54:07,369 (trainer:763) INFO: 21epoch:train:321-360batch: iter_time=5.278e-05, forward_time=0.159, loss_ctc=5.832, loss=5.832, backward_time=0.024, grad_norm=194.024, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:54:20,116 (trainer:763) INFO: 21epoch:train:361-400batch: iter_time=5.132e-05, forward_time=0.159, loss_ctc=5.406, loss=5.406, backward_time=0.024, grad_norm=190.843, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:54:32,861 (trainer:763) INFO: 21epoch:train:401-440batch: iter_time=5.236e-05, forward_time=0.159, loss_ctc=5.729, loss=5.729, backward_time=0.024, grad_norm=217.047, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:54:45,611 (trainer:763) INFO: 21epoch:train:441-480batch: iter_time=5.694e-05, forward_time=0.159, loss_ctc=5.484, loss=5.484, backward_time=0.024, grad_norm=198.837, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:54:58,357 (trainer:763) INFO: 21epoch:train:481-520batch: iter_time=5.233e-05, forward_time=0.159, loss_ctc=5.602, loss=5.602, backward_time=0.024, grad_norm=189.480, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:55:11,101 (trainer:763) INFO: 21epoch:train:521-560batch: iter_time=5.501e-05, forward_time=0.159, loss_ctc=5.766, loss=5.766, backward_time=0.024, grad_norm=183.376, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:55:23,846 (trainer:763) INFO: 21epoch:train:561-600batch: iter_time=5.472e-05, forward_time=0.159, loss_ctc=5.521, loss=5.521, backward_time=0.024, grad_norm=181.645, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:55:36,586 (trainer:763) INFO: 21epoch:train:601-640batch: iter_time=5.287e-05, forward_time=0.159, loss_ctc=5.576, loss=5.576, backward_time=0.024, grad_norm=184.707, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:55:49,321 (trainer:763) INFO: 21epoch:train:641-680batch: iter_time=5.217e-05, forward_time=0.159, loss_ctc=5.601, loss=5.601, backward_time=0.024, grad_norm=199.400, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:56:02,064 (trainer:763) INFO: 21epoch:train:681-720batch: iter_time=5.380e-05, forward_time=0.159, loss_ctc=5.592, loss=5.592, backward_time=0.024, grad_norm=184.602, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:56:14,804 (trainer:763) INFO: 21epoch:train:721-760batch: iter_time=5.372e-05, forward_time=0.159, loss_ctc=5.556, loss=5.556, backward_time=0.024, grad_norm=174.591, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:56:27,539 (trainer:763) INFO: 21epoch:train:761-800batch: iter_time=4.939e-05, forward_time=0.159, loss_ctc=5.422, loss=5.422, backward_time=0.024, grad_norm=179.223, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:56:32,297 (trainer:354) INFO: 21epoch results: [train] iter_time=2.349e-04, forward_time=0.159, loss_ctc=5.635, loss=5.635, backward_time=0.024, grad_norm=189.340, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.27 seconds, total_count=16800, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=468.342, cer_ctc=0.323, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=468.342, time=1.18 seconds, total_count=105, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.5 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 18:56:33,334 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 18:56:33,335 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/20epoch.pth +[stan] 2024-01-14 18:56:33,335 (trainer:288) INFO: 22/30epoch started. Estimated time to finish: 39 minutes and 9.05 seconds +[stan] 2024-01-14 18:56:46,350 (trainer:763) INFO: 22epoch:train:1-40batch: iter_time=0.003, forward_time=0.159, loss_ctc=5.642, loss=5.642, backward_time=0.024, grad_norm=178.631, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.300 +[stan] 2024-01-14 18:56:59,082 (trainer:763) INFO: 22epoch:train:41-80batch: iter_time=5.050e-05, forward_time=0.159, loss_ctc=5.464, loss=5.464, backward_time=0.024, grad_norm=177.347, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 18:57:11,828 (trainer:763) INFO: 22epoch:train:81-120batch: iter_time=5.321e-05, forward_time=0.159, loss_ctc=5.221, loss=5.221, backward_time=0.024, grad_norm=179.199, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:57:24,578 (trainer:763) INFO: 22epoch:train:121-160batch: iter_time=5.557e-05, forward_time=0.159, loss_ctc=5.286, loss=5.286, backward_time=0.024, grad_norm=179.685, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:57:37,333 (trainer:763) INFO: 22epoch:train:161-200batch: iter_time=5.201e-05, forward_time=0.159, loss_ctc=5.285, loss=5.285, backward_time=0.024, grad_norm=190.252, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:57:50,087 (trainer:763) INFO: 22epoch:train:201-240batch: iter_time=5.426e-05, forward_time=0.159, loss_ctc=5.681, loss=5.681, backward_time=0.024, grad_norm=182.504, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:58:02,842 (trainer:763) INFO: 22epoch:train:241-280batch: iter_time=5.390e-05, forward_time=0.159, loss_ctc=5.214, loss=5.214, backward_time=0.024, grad_norm=181.005, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:58:15,601 (trainer:763) INFO: 22epoch:train:281-320batch: iter_time=5.288e-05, forward_time=0.159, loss_ctc=5.184, loss=5.184, backward_time=0.025, grad_norm=186.869, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:58:28,358 (trainer:763) INFO: 22epoch:train:321-360batch: iter_time=5.158e-05, forward_time=0.159, loss_ctc=5.199, loss=5.199, backward_time=0.024, grad_norm=180.429, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:58:41,122 (trainer:763) INFO: 22epoch:train:361-400batch: iter_time=5.459e-05, forward_time=0.159, loss_ctc=5.293, loss=5.293, backward_time=0.024, grad_norm=207.641, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 18:58:53,877 (trainer:763) INFO: 22epoch:train:401-440batch: iter_time=5.385e-05, forward_time=0.159, loss_ctc=5.234, loss=5.234, backward_time=0.024, grad_norm=194.347, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:59:06,627 (trainer:763) INFO: 22epoch:train:441-480batch: iter_time=5.492e-05, forward_time=0.159, loss_ctc=4.843, loss=4.843, backward_time=0.024, grad_norm=205.120, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:59:19,371 (trainer:763) INFO: 22epoch:train:481-520batch: iter_time=5.413e-05, forward_time=0.159, loss_ctc=4.966, loss=4.966, backward_time=0.024, grad_norm=175.688, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 18:59:32,122 (trainer:763) INFO: 22epoch:train:521-560batch: iter_time=5.277e-05, forward_time=0.159, loss_ctc=5.198, loss=5.198, backward_time=0.024, grad_norm=185.185, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:59:44,869 (trainer:763) INFO: 22epoch:train:561-600batch: iter_time=5.332e-05, forward_time=0.159, loss_ctc=5.097, loss=5.097, backward_time=0.024, grad_norm=186.528, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 18:59:57,606 (trainer:763) INFO: 22epoch:train:601-640batch: iter_time=5.194e-05, forward_time=0.159, loss_ctc=4.849, loss=4.849, backward_time=0.025, grad_norm=201.304, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:00:10,352 (trainer:763) INFO: 22epoch:train:641-680batch: iter_time=5.829e-05, forward_time=0.159, loss_ctc=4.834, loss=4.834, backward_time=0.024, grad_norm=175.286, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:00:23,092 (trainer:763) INFO: 22epoch:train:681-720batch: iter_time=5.169e-05, forward_time=0.159, loss_ctc=5.243, loss=5.243, backward_time=0.024, grad_norm=194.009, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:00:35,837 (trainer:763) INFO: 22epoch:train:721-760batch: iter_time=5.348e-05, forward_time=0.159, loss_ctc=5.227, loss=5.227, backward_time=0.025, grad_norm=179.216, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:00:48,580 (trainer:763) INFO: 22epoch:train:761-800batch: iter_time=5.053e-05, forward_time=0.159, loss_ctc=5.225, loss=5.225, backward_time=0.024, grad_norm=170.677, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:00:53,274 (trainer:354) INFO: 22epoch results: [train] iter_time=2.253e-04, forward_time=0.159, loss_ctc=5.209, loss=5.209, backward_time=0.024, grad_norm=185.546, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.32 seconds, total_count=17600, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=469.417, cer_ctc=0.336, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=469.417, time=1.16 seconds, total_count=110, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.46 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 19:00:54,229 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:00:54,229 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/21epoch.pth +[stan] 2024-01-14 19:00:54,229 (trainer:288) INFO: 23/30epoch started. Estimated time to finish: 34 minutes and 48 seconds +[stan] 2024-01-14 19:01:07,254 (trainer:763) INFO: 23epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=5.219, loss=5.219, backward_time=0.024, grad_norm=175.903, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.301 +[stan] 2024-01-14 19:01:19,998 (trainer:763) INFO: 23epoch:train:41-80batch: iter_time=5.284e-05, forward_time=0.159, loss_ctc=5.062, loss=5.062, backward_time=0.024, grad_norm=188.476, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:01:32,737 (trainer:763) INFO: 23epoch:train:81-120batch: iter_time=5.443e-05, forward_time=0.159, loss_ctc=5.134, loss=5.134, backward_time=0.024, grad_norm=197.144, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:01:45,491 (trainer:763) INFO: 23epoch:train:121-160batch: iter_time=5.381e-05, forward_time=0.159, loss_ctc=5.220, loss=5.220, backward_time=0.024, grad_norm=190.969, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:01:58,242 (trainer:763) INFO: 23epoch:train:161-200batch: iter_time=5.135e-05, forward_time=0.159, loss_ctc=4.690, loss=4.690, backward_time=0.024, grad_norm=173.542, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:02:11,004 (trainer:763) INFO: 23epoch:train:201-240batch: iter_time=5.637e-05, forward_time=0.159, loss_ctc=4.933, loss=4.933, backward_time=0.025, grad_norm=174.947, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:02:23,767 (trainer:763) INFO: 23epoch:train:241-280batch: iter_time=5.317e-05, forward_time=0.159, loss_ctc=4.988, loss=4.988, backward_time=0.024, grad_norm=167.819, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:02:36,528 (trainer:763) INFO: 23epoch:train:281-320batch: iter_time=5.222e-05, forward_time=0.159, loss_ctc=4.863, loss=4.863, backward_time=0.024, grad_norm=179.577, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:02:49,293 (trainer:763) INFO: 23epoch:train:321-360batch: iter_time=5.262e-05, forward_time=0.159, loss_ctc=4.895, loss=4.895, backward_time=0.025, grad_norm=171.411, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:03:02,051 (trainer:763) INFO: 23epoch:train:361-400batch: iter_time=5.604e-05, forward_time=0.159, loss_ctc=4.851, loss=4.851, backward_time=0.024, grad_norm=165.511, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:03:14,804 (trainer:763) INFO: 23epoch:train:401-440batch: iter_time=5.167e-05, forward_time=0.159, loss_ctc=4.771, loss=4.771, backward_time=0.024, grad_norm=175.907, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:03:27,562 (trainer:763) INFO: 23epoch:train:441-480batch: iter_time=5.415e-05, forward_time=0.159, loss_ctc=4.889, loss=4.889, backward_time=0.025, grad_norm=177.952, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:03:40,317 (trainer:763) INFO: 23epoch:train:481-520batch: iter_time=5.477e-05, forward_time=0.159, loss_ctc=5.066, loss=5.066, backward_time=0.025, grad_norm=167.194, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:03:53,069 (trainer:763) INFO: 23epoch:train:521-560batch: iter_time=5.407e-05, forward_time=0.159, loss_ctc=5.142, loss=5.142, backward_time=0.025, grad_norm=171.288, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:04:05,823 (trainer:763) INFO: 23epoch:train:561-600batch: iter_time=5.255e-05, forward_time=0.159, loss_ctc=4.818, loss=4.818, backward_time=0.024, grad_norm=173.759, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:04:18,565 (trainer:763) INFO: 23epoch:train:601-640batch: iter_time=5.266e-05, forward_time=0.159, loss_ctc=5.104, loss=5.104, backward_time=0.025, grad_norm=185.550, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:04:31,310 (trainer:763) INFO: 23epoch:train:641-680batch: iter_time=5.511e-05, forward_time=0.159, loss_ctc=4.664, loss=4.664, backward_time=0.024, grad_norm=181.919, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:04:44,053 (trainer:763) INFO: 23epoch:train:681-720batch: iter_time=5.516e-05, forward_time=0.159, loss_ctc=4.869, loss=4.869, backward_time=0.024, grad_norm=174.367, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:04:56,796 (trainer:763) INFO: 23epoch:train:721-760batch: iter_time=5.375e-05, forward_time=0.159, loss_ctc=4.963, loss=4.963, backward_time=0.024, grad_norm=168.665, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:05:09,541 (trainer:763) INFO: 23epoch:train:761-800batch: iter_time=5.024e-05, forward_time=0.159, loss_ctc=4.710, loss=4.710, backward_time=0.024, grad_norm=165.085, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:05:14,253 (trainer:354) INFO: 23epoch results: [train] iter_time=2.464e-04, forward_time=0.159, loss_ctc=4.943, loss=4.943, backward_time=0.024, grad_norm=176.349, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.39 seconds, total_count=18400, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=472.073, cer_ctc=0.318, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=472.073, time=1.15 seconds, total_count=115, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.48 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 19:05:15,232 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:05:15,232 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/22epoch.pth +[stan] 2024-01-14 19:05:15,232 (trainer:288) INFO: 24/30epoch started. Estimated time to finish: 30 minutes and 27 seconds +[stan] 2024-01-14 19:05:28,254 (trainer:763) INFO: 24epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=4.742, loss=4.742, backward_time=0.024, grad_norm=182.716, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.301 +[stan] 2024-01-14 19:05:40,991 (trainer:763) INFO: 24epoch:train:41-80batch: iter_time=5.406e-05, forward_time=0.159, loss_ctc=4.740, loss=4.740, backward_time=0.024, grad_norm=178.042, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:05:53,732 (trainer:763) INFO: 24epoch:train:81-120batch: iter_time=5.270e-05, forward_time=0.159, loss_ctc=4.742, loss=4.742, backward_time=0.024, grad_norm=167.095, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:06:06,478 (trainer:763) INFO: 24epoch:train:121-160batch: iter_time=5.457e-05, forward_time=0.159, loss_ctc=4.883, loss=4.883, backward_time=0.024, grad_norm=180.241, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:06:19,231 (trainer:763) INFO: 24epoch:train:161-200batch: iter_time=5.568e-05, forward_time=0.159, loss_ctc=4.594, loss=4.594, backward_time=0.024, grad_norm=162.764, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:06:31,983 (trainer:763) INFO: 24epoch:train:201-240batch: iter_time=5.116e-05, forward_time=0.159, loss_ctc=4.869, loss=4.869, backward_time=0.024, grad_norm=173.558, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:06:44,738 (trainer:763) INFO: 24epoch:train:241-280batch: iter_time=5.231e-05, forward_time=0.159, loss_ctc=4.579, loss=4.579, backward_time=0.025, grad_norm=169.341, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:06:57,500 (trainer:763) INFO: 24epoch:train:281-320batch: iter_time=5.470e-05, forward_time=0.159, loss_ctc=4.861, loss=4.861, backward_time=0.024, grad_norm=166.265, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:07:10,260 (trainer:763) INFO: 24epoch:train:321-360batch: iter_time=5.275e-05, forward_time=0.159, loss_ctc=4.896, loss=4.896, backward_time=0.024, grad_norm=178.988, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:07:23,016 (trainer:763) INFO: 24epoch:train:361-400batch: iter_time=5.470e-05, forward_time=0.159, loss_ctc=4.628, loss=4.628, backward_time=0.024, grad_norm=167.444, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:07:35,761 (trainer:763) INFO: 24epoch:train:401-440batch: iter_time=5.734e-05, forward_time=0.159, loss_ctc=4.804, loss=4.804, backward_time=0.024, grad_norm=168.335, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:07:48,512 (trainer:763) INFO: 24epoch:train:441-480batch: iter_time=5.578e-05, forward_time=0.159, loss_ctc=4.815, loss=4.815, backward_time=0.024, grad_norm=173.202, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:08:01,259 (trainer:763) INFO: 24epoch:train:481-520batch: iter_time=5.304e-05, forward_time=0.159, loss_ctc=4.756, loss=4.756, backward_time=0.024, grad_norm=168.671, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:08:14,004 (trainer:763) INFO: 24epoch:train:521-560batch: iter_time=5.276e-05, forward_time=0.159, loss_ctc=4.910, loss=4.910, backward_time=0.024, grad_norm=172.946, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:08:26,748 (trainer:763) INFO: 24epoch:train:561-600batch: iter_time=5.221e-05, forward_time=0.159, loss_ctc=4.778, loss=4.778, backward_time=0.024, grad_norm=171.720, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:08:39,487 (trainer:763) INFO: 24epoch:train:601-640batch: iter_time=5.535e-05, forward_time=0.159, loss_ctc=4.578, loss=4.578, backward_time=0.024, grad_norm=160.660, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:08:52,228 (trainer:763) INFO: 24epoch:train:641-680batch: iter_time=5.566e-05, forward_time=0.159, loss_ctc=4.523, loss=4.523, backward_time=0.024, grad_norm=174.803, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:09:04,966 (trainer:763) INFO: 24epoch:train:681-720batch: iter_time=5.160e-05, forward_time=0.159, loss_ctc=4.615, loss=4.615, backward_time=0.024, grad_norm=173.457, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:09:17,702 (trainer:763) INFO: 24epoch:train:721-760batch: iter_time=5.555e-05, forward_time=0.159, loss_ctc=4.761, loss=4.761, backward_time=0.024, grad_norm=171.551, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:09:30,441 (trainer:763) INFO: 24epoch:train:761-800batch: iter_time=4.956e-05, forward_time=0.159, loss_ctc=4.580, loss=4.580, backward_time=0.024, grad_norm=181.202, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:09:35,165 (trainer:354) INFO: 24epoch results: [train] iter_time=2.400e-04, forward_time=0.159, loss_ctc=4.733, loss=4.733, backward_time=0.024, grad_norm=172.150, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.29 seconds, total_count=19200, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=479.682, cer_ctc=0.333, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=479.682, time=1.18 seconds, total_count=120, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.46 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 19:09:36,241 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:09:36,242 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/23epoch.pth +[stan] 2024-01-14 19:09:36,242 (trainer:288) INFO: 25/30epoch started. Estimated time to finish: 26 minutes and 6 seconds +[stan] 2024-01-14 19:09:49,254 (trainer:763) INFO: 25epoch:train:1-40batch: iter_time=0.003, forward_time=0.159, loss_ctc=4.441, loss=4.441, backward_time=0.024, grad_norm=166.431, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.300 +[stan] 2024-01-14 19:10:01,983 (trainer:763) INFO: 25epoch:train:41-80batch: iter_time=5.333e-05, forward_time=0.159, loss_ctc=4.875, loss=4.875, backward_time=0.024, grad_norm=181.341, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 19:10:14,729 (trainer:763) INFO: 25epoch:train:81-120batch: iter_time=5.345e-05, forward_time=0.159, loss_ctc=4.751, loss=4.751, backward_time=0.024, grad_norm=171.277, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:10:27,474 (trainer:763) INFO: 25epoch:train:121-160batch: iter_time=5.113e-05, forward_time=0.159, loss_ctc=4.306, loss=4.306, backward_time=0.024, grad_norm=174.587, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:10:40,227 (trainer:763) INFO: 25epoch:train:161-200batch: iter_time=5.139e-05, forward_time=0.159, loss_ctc=4.402, loss=4.402, backward_time=0.025, grad_norm=164.768, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:10:52,981 (trainer:763) INFO: 25epoch:train:201-240batch: iter_time=5.628e-05, forward_time=0.159, loss_ctc=4.462, loss=4.462, backward_time=0.024, grad_norm=170.153, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:11:05,738 (trainer:763) INFO: 25epoch:train:241-280batch: iter_time=5.277e-05, forward_time=0.159, loss_ctc=4.393, loss=4.393, backward_time=0.025, grad_norm=166.592, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:11:18,498 (trainer:763) INFO: 25epoch:train:281-320batch: iter_time=5.345e-05, forward_time=0.159, loss_ctc=4.450, loss=4.450, backward_time=0.025, grad_norm=161.351, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:11:31,255 (trainer:763) INFO: 25epoch:train:321-360batch: iter_time=5.509e-05, forward_time=0.159, loss_ctc=4.486, loss=4.486, backward_time=0.025, grad_norm=162.684, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:11:44,007 (trainer:763) INFO: 25epoch:train:361-400batch: iter_time=5.623e-05, forward_time=0.159, loss_ctc=4.209, loss=4.209, backward_time=0.024, grad_norm=161.084, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:11:56,756 (trainer:763) INFO: 25epoch:train:401-440batch: iter_time=5.176e-05, forward_time=0.159, loss_ctc=4.084, loss=4.084, backward_time=0.024, grad_norm=155.677, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:12:09,511 (trainer:763) INFO: 25epoch:train:441-480batch: iter_time=5.854e-05, forward_time=0.159, loss_ctc=4.509, loss=4.509, backward_time=0.024, grad_norm=172.670, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:12:22,263 (trainer:763) INFO: 25epoch:train:481-520batch: iter_time=5.555e-05, forward_time=0.159, loss_ctc=4.455, loss=4.455, backward_time=0.024, grad_norm=163.423, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:12:35,006 (trainer:763) INFO: 25epoch:train:521-560batch: iter_time=5.433e-05, forward_time=0.159, loss_ctc=4.270, loss=4.270, backward_time=0.024, grad_norm=157.802, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:12:47,752 (trainer:763) INFO: 25epoch:train:561-600batch: iter_time=5.494e-05, forward_time=0.159, loss_ctc=4.437, loss=4.437, backward_time=0.024, grad_norm=160.673, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:13:00,493 (trainer:763) INFO: 25epoch:train:601-640batch: iter_time=5.187e-05, forward_time=0.159, loss_ctc=4.243, loss=4.243, backward_time=0.024, grad_norm=159.899, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:13:13,247 (trainer:763) INFO: 25epoch:train:641-680batch: iter_time=5.523e-05, forward_time=0.159, loss_ctc=4.290, loss=4.290, backward_time=0.024, grad_norm=172.165, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:13:25,988 (trainer:763) INFO: 25epoch:train:681-720batch: iter_time=5.284e-05, forward_time=0.159, loss_ctc=4.447, loss=4.447, backward_time=0.024, grad_norm=167.293, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:13:38,725 (trainer:763) INFO: 25epoch:train:721-760batch: iter_time=5.210e-05, forward_time=0.159, loss_ctc=4.363, loss=4.363, backward_time=0.024, grad_norm=179.281, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:13:51,463 (trainer:763) INFO: 25epoch:train:761-800batch: iter_time=5.210e-05, forward_time=0.159, loss_ctc=4.130, loss=4.130, backward_time=0.024, grad_norm=183.956, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:13:56,136 (trainer:354) INFO: 25epoch results: [train] iter_time=2.260e-04, forward_time=0.159, loss_ctc=4.400, loss=4.400, backward_time=0.024, grad_norm=167.655, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.3 seconds, total_count=20000, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=482.101, cer_ctc=0.325, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=482.101, time=1.16 seconds, total_count=125, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.43 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 19:13:57,203 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:13:57,203 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/24epoch.pth +[stan] 2024-01-14 19:13:57,203 (trainer:288) INFO: 26/30epoch started. Estimated time to finish: 21 minutes and 45 seconds +[stan] 2024-01-14 19:14:10,205 (trainer:763) INFO: 26epoch:train:1-40batch: iter_time=0.003, forward_time=0.159, loss_ctc=4.211, loss=4.211, backward_time=0.024, grad_norm=189.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.299 +[stan] 2024-01-14 19:14:22,942 (trainer:763) INFO: 26epoch:train:41-80batch: iter_time=5.378e-05, forward_time=0.159, loss_ctc=4.268, loss=4.268, backward_time=0.024, grad_norm=179.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:14:35,686 (trainer:763) INFO: 26epoch:train:81-120batch: iter_time=5.455e-05, forward_time=0.159, loss_ctc=4.036, loss=4.036, backward_time=0.024, grad_norm=160.933, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:14:48,443 (trainer:763) INFO: 26epoch:train:121-160batch: iter_time=5.248e-05, forward_time=0.159, loss_ctc=4.331, loss=4.331, backward_time=0.024, grad_norm=167.107, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:15:01,201 (trainer:763) INFO: 26epoch:train:161-200batch: iter_time=5.431e-05, forward_time=0.159, loss_ctc=4.488, loss=4.488, backward_time=0.025, grad_norm=190.293, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:15:13,961 (trainer:763) INFO: 26epoch:train:201-240batch: iter_time=5.251e-05, forward_time=0.159, loss_ctc=4.327, loss=4.327, backward_time=0.024, grad_norm=162.506, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:15:26,721 (trainer:763) INFO: 26epoch:train:241-280batch: iter_time=5.443e-05, forward_time=0.159, loss_ctc=4.349, loss=4.349, backward_time=0.024, grad_norm=165.602, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:15:39,480 (trainer:763) INFO: 26epoch:train:281-320batch: iter_time=5.358e-05, forward_time=0.159, loss_ctc=4.331, loss=4.331, backward_time=0.025, grad_norm=173.601, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:15:52,241 (trainer:763) INFO: 26epoch:train:321-360batch: iter_time=5.427e-05, forward_time=0.159, loss_ctc=4.232, loss=4.232, backward_time=0.024, grad_norm=167.588, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:16:05,001 (trainer:763) INFO: 26epoch:train:361-400batch: iter_time=5.462e-05, forward_time=0.159, loss_ctc=4.198, loss=4.198, backward_time=0.024, grad_norm=163.604, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:16:17,761 (trainer:763) INFO: 26epoch:train:401-440batch: iter_time=5.535e-05, forward_time=0.159, loss_ctc=4.312, loss=4.312, backward_time=0.024, grad_norm=166.729, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:16:30,519 (trainer:763) INFO: 26epoch:train:441-480batch: iter_time=5.484e-05, forward_time=0.159, loss_ctc=4.079, loss=4.079, backward_time=0.024, grad_norm=172.632, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:16:43,266 (trainer:763) INFO: 26epoch:train:481-520batch: iter_time=5.436e-05, forward_time=0.159, loss_ctc=4.337, loss=4.337, backward_time=0.024, grad_norm=152.747, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:16:56,016 (trainer:763) INFO: 26epoch:train:521-560batch: iter_time=5.460e-05, forward_time=0.159, loss_ctc=4.552, loss=4.552, backward_time=0.024, grad_norm=166.427, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:17:08,759 (trainer:763) INFO: 26epoch:train:561-600batch: iter_time=5.401e-05, forward_time=0.159, loss_ctc=4.414, loss=4.414, backward_time=0.025, grad_norm=159.378, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:17:21,507 (trainer:763) INFO: 26epoch:train:601-640batch: iter_time=5.531e-05, forward_time=0.159, loss_ctc=4.598, loss=4.598, backward_time=0.024, grad_norm=170.942, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:17:34,250 (trainer:763) INFO: 26epoch:train:641-680batch: iter_time=5.356e-05, forward_time=0.159, loss_ctc=4.354, loss=4.354, backward_time=0.024, grad_norm=175.547, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:17:46,998 (trainer:763) INFO: 26epoch:train:681-720batch: iter_time=5.468e-05, forward_time=0.159, loss_ctc=4.249, loss=4.249, backward_time=0.024, grad_norm=166.508, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:17:59,743 (trainer:763) INFO: 26epoch:train:721-760batch: iter_time=5.242e-05, forward_time=0.159, loss_ctc=4.067, loss=4.067, backward_time=0.024, grad_norm=165.276, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:18:12,480 (trainer:763) INFO: 26epoch:train:761-800batch: iter_time=4.923e-05, forward_time=0.159, loss_ctc=4.142, loss=4.142, backward_time=0.024, grad_norm=165.496, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:18:17,170 (trainer:354) INFO: 26epoch results: [train] iter_time=2.076e-04, forward_time=0.159, loss_ctc=4.294, loss=4.294, backward_time=0.024, grad_norm=169.071, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.35 seconds, total_count=20800, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=483.606, cer_ctc=0.322, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=483.606, time=1.16 seconds, total_count=130, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.45 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 19:18:18,162 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:18:18,163 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/25epoch.pth +[stan] 2024-01-14 19:18:18,163 (trainer:288) INFO: 27/30epoch started. Estimated time to finish: 17 minutes and 23.99 seconds +[stan] 2024-01-14 19:18:31,200 (trainer:763) INFO: 27epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=4.013, loss=4.013, backward_time=0.024, grad_norm=158.102, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.302 +[stan] 2024-01-14 19:18:43,934 (trainer:763) INFO: 27epoch:train:41-80batch: iter_time=5.318e-05, forward_time=0.159, loss_ctc=4.050, loss=4.050, backward_time=0.024, grad_norm=155.916, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 19:18:56,680 (trainer:763) INFO: 27epoch:train:81-120batch: iter_time=5.464e-05, forward_time=0.159, loss_ctc=4.093, loss=4.093, backward_time=0.024, grad_norm=157.822, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:19:09,437 (trainer:763) INFO: 27epoch:train:121-160batch: iter_time=5.536e-05, forward_time=0.159, loss_ctc=4.065, loss=4.065, backward_time=0.024, grad_norm=152.186, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:19:22,190 (trainer:763) INFO: 27epoch:train:161-200batch: iter_time=5.647e-05, forward_time=0.159, loss_ctc=4.096, loss=4.096, backward_time=0.024, grad_norm=150.238, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:19:34,951 (trainer:763) INFO: 27epoch:train:201-240batch: iter_time=5.488e-05, forward_time=0.159, loss_ctc=4.181, loss=4.181, backward_time=0.024, grad_norm=156.775, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:19:47,702 (trainer:763) INFO: 27epoch:train:241-280batch: iter_time=5.474e-05, forward_time=0.159, loss_ctc=4.009, loss=4.009, backward_time=0.024, grad_norm=151.656, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:20:00,463 (trainer:763) INFO: 27epoch:train:281-320batch: iter_time=5.268e-05, forward_time=0.159, loss_ctc=4.145, loss=4.145, backward_time=0.024, grad_norm=154.737, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:20:13,219 (trainer:763) INFO: 27epoch:train:321-360batch: iter_time=5.401e-05, forward_time=0.159, loss_ctc=4.046, loss=4.046, backward_time=0.024, grad_norm=159.431, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:20:25,972 (trainer:763) INFO: 27epoch:train:361-400batch: iter_time=5.284e-05, forward_time=0.159, loss_ctc=3.930, loss=3.930, backward_time=0.024, grad_norm=156.018, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:20:38,720 (trainer:763) INFO: 27epoch:train:401-440batch: iter_time=5.269e-05, forward_time=0.159, loss_ctc=4.049, loss=4.049, backward_time=0.025, grad_norm=153.619, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:20:51,464 (trainer:763) INFO: 27epoch:train:441-480batch: iter_time=5.423e-05, forward_time=0.159, loss_ctc=3.948, loss=3.948, backward_time=0.024, grad_norm=160.522, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:21:04,210 (trainer:763) INFO: 27epoch:train:481-520batch: iter_time=5.577e-05, forward_time=0.159, loss_ctc=4.262, loss=4.262, backward_time=0.025, grad_norm=162.188, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:21:16,952 (trainer:763) INFO: 27epoch:train:521-560batch: iter_time=5.299e-05, forward_time=0.159, loss_ctc=4.056, loss=4.056, backward_time=0.024, grad_norm=154.569, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:21:29,697 (trainer:763) INFO: 27epoch:train:561-600batch: iter_time=5.607e-05, forward_time=0.159, loss_ctc=4.019, loss=4.019, backward_time=0.025, grad_norm=160.934, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:21:42,446 (trainer:763) INFO: 27epoch:train:601-640batch: iter_time=5.314e-05, forward_time=0.159, loss_ctc=4.017, loss=4.017, backward_time=0.024, grad_norm=170.616, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:21:55,191 (trainer:763) INFO: 27epoch:train:641-680batch: iter_time=5.269e-05, forward_time=0.159, loss_ctc=4.168, loss=4.168, backward_time=0.025, grad_norm=154.512, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:22:07,935 (trainer:763) INFO: 27epoch:train:681-720batch: iter_time=5.295e-05, forward_time=0.159, loss_ctc=4.048, loss=4.048, backward_time=0.024, grad_norm=151.017, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:22:20,669 (trainer:763) INFO: 27epoch:train:721-760batch: iter_time=5.426e-05, forward_time=0.159, loss_ctc=4.053, loss=4.053, backward_time=0.024, grad_norm=145.435, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 19:22:33,404 (trainer:763) INFO: 27epoch:train:761-800batch: iter_time=4.872e-05, forward_time=0.159, loss_ctc=4.056, loss=4.056, backward_time=0.024, grad_norm=151.962, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 19:22:38,141 (trainer:354) INFO: 27epoch results: [train] iter_time=2.517e-04, forward_time=0.159, loss_ctc=4.065, loss=4.065, backward_time=0.024, grad_norm=155.913, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.32 seconds, total_count=21600, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=480.715, cer_ctc=0.329, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=480.715, time=1.18 seconds, total_count=135, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.48 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 19:22:39,226 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:22:39,227 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/26epoch.pth +[stan] 2024-01-14 19:22:39,227 (trainer:288) INFO: 28/30epoch started. Estimated time to finish: 13 minutes and 3 seconds +[stan] 2024-01-14 19:22:52,257 (trainer:763) INFO: 28epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=3.956, loss=3.956, backward_time=0.024, grad_norm=149.657, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.302 +[stan] 2024-01-14 19:23:04,997 (trainer:763) INFO: 28epoch:train:41-80batch: iter_time=5.220e-05, forward_time=0.159, loss_ctc=3.751, loss=3.751, backward_time=0.024, grad_norm=148.835, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:23:17,743 (trainer:763) INFO: 28epoch:train:81-120batch: iter_time=5.232e-05, forward_time=0.159, loss_ctc=4.057, loss=4.057, backward_time=0.024, grad_norm=159.136, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:23:30,495 (trainer:763) INFO: 28epoch:train:121-160batch: iter_time=5.269e-05, forward_time=0.159, loss_ctc=3.765, loss=3.765, backward_time=0.024, grad_norm=164.098, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:23:43,250 (trainer:763) INFO: 28epoch:train:161-200batch: iter_time=5.399e-05, forward_time=0.159, loss_ctc=3.855, loss=3.855, backward_time=0.024, grad_norm=151.875, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:23:56,011 (trainer:763) INFO: 28epoch:train:201-240batch: iter_time=5.434e-05, forward_time=0.159, loss_ctc=3.877, loss=3.877, backward_time=0.024, grad_norm=155.923, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:24:08,767 (trainer:763) INFO: 28epoch:train:241-280batch: iter_time=5.431e-05, forward_time=0.159, loss_ctc=3.942, loss=3.942, backward_time=0.025, grad_norm=155.220, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:24:21,525 (trainer:763) INFO: 28epoch:train:281-320batch: iter_time=5.303e-05, forward_time=0.159, loss_ctc=4.194, loss=4.194, backward_time=0.024, grad_norm=165.092, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:24:34,290 (trainer:763) INFO: 28epoch:train:321-360batch: iter_time=5.520e-05, forward_time=0.159, loss_ctc=3.742, loss=3.742, backward_time=0.024, grad_norm=155.825, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:24:47,043 (trainer:763) INFO: 28epoch:train:361-400batch: iter_time=5.561e-05, forward_time=0.159, loss_ctc=4.062, loss=4.062, backward_time=0.024, grad_norm=148.150, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:24:59,792 (trainer:763) INFO: 28epoch:train:401-440batch: iter_time=5.189e-05, forward_time=0.159, loss_ctc=4.182, loss=4.182, backward_time=0.024, grad_norm=158.490, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:25:12,543 (trainer:763) INFO: 28epoch:train:441-480batch: iter_time=5.187e-05, forward_time=0.159, loss_ctc=3.968, loss=3.968, backward_time=0.025, grad_norm=163.180, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:25:25,290 (trainer:763) INFO: 28epoch:train:481-520batch: iter_time=5.205e-05, forward_time=0.159, loss_ctc=3.771, loss=3.771, backward_time=0.024, grad_norm=142.257, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:25:38,037 (trainer:763) INFO: 28epoch:train:521-560batch: iter_time=5.200e-05, forward_time=0.159, loss_ctc=3.890, loss=3.890, backward_time=0.024, grad_norm=150.156, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:25:50,779 (trainer:763) INFO: 28epoch:train:561-600batch: iter_time=5.399e-05, forward_time=0.159, loss_ctc=3.870, loss=3.870, backward_time=0.024, grad_norm=154.630, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:26:03,527 (trainer:763) INFO: 28epoch:train:601-640batch: iter_time=5.114e-05, forward_time=0.159, loss_ctc=3.725, loss=3.725, backward_time=0.024, grad_norm=153.895, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:26:16,270 (trainer:763) INFO: 28epoch:train:641-680batch: iter_time=5.258e-05, forward_time=0.159, loss_ctc=3.886, loss=3.886, backward_time=0.024, grad_norm=152.166, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:26:29,014 (trainer:763) INFO: 28epoch:train:681-720batch: iter_time=5.262e-05, forward_time=0.159, loss_ctc=4.112, loss=4.112, backward_time=0.024, grad_norm=158.013, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:26:41,757 (trainer:763) INFO: 28epoch:train:721-760batch: iter_time=5.552e-05, forward_time=0.159, loss_ctc=3.765, loss=3.765, backward_time=0.024, grad_norm=151.853, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:26:54,494 (trainer:763) INFO: 28epoch:train:761-800batch: iter_time=5.098e-05, forward_time=0.159, loss_ctc=3.568, loss=3.568, backward_time=0.024, grad_norm=157.630, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:26:59,190 (trainer:354) INFO: 28epoch results: [train] iter_time=2.260e-04, forward_time=0.159, loss_ctc=3.897, loss=3.897, backward_time=0.024, grad_norm=154.804, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.35 seconds, total_count=22400, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=488.759, cer_ctc=0.332, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=488.759, time=1.16 seconds, total_count=140, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.46 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 19:27:00,281 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:27:00,281 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/27epoch.pth +[stan] 2024-01-14 19:27:00,281 (trainer:288) INFO: 29/30epoch started. Estimated time to finish: 8 minutes and 42 seconds +[stan] 2024-01-14 19:27:13,309 (trainer:763) INFO: 29epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=3.683, loss=3.683, backward_time=0.024, grad_norm=154.912, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.301 +[stan] 2024-01-14 19:27:26,044 (trainer:763) INFO: 29epoch:train:41-80batch: iter_time=5.089e-05, forward_time=0.159, loss_ctc=4.127, loss=4.127, backward_time=0.024, grad_norm=167.301, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 19:27:38,790 (trainer:763) INFO: 29epoch:train:81-120batch: iter_time=5.150e-05, forward_time=0.159, loss_ctc=3.995, loss=3.995, backward_time=0.025, grad_norm=162.176, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:27:51,543 (trainer:763) INFO: 29epoch:train:121-160batch: iter_time=5.134e-05, forward_time=0.159, loss_ctc=3.463, loss=3.463, backward_time=0.024, grad_norm=149.039, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:28:04,296 (trainer:763) INFO: 29epoch:train:161-200batch: iter_time=5.146e-05, forward_time=0.159, loss_ctc=3.899, loss=3.899, backward_time=0.025, grad_norm=150.437, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:28:17,060 (trainer:763) INFO: 29epoch:train:201-240batch: iter_time=5.484e-05, forward_time=0.159, loss_ctc=3.693, loss=3.693, backward_time=0.025, grad_norm=148.947, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:28:29,827 (trainer:763) INFO: 29epoch:train:241-280batch: iter_time=5.223e-05, forward_time=0.159, loss_ctc=3.722, loss=3.722, backward_time=0.025, grad_norm=147.028, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.277 +[stan] 2024-01-14 19:28:42,588 (trainer:763) INFO: 29epoch:train:281-320batch: iter_time=5.156e-05, forward_time=0.159, loss_ctc=3.848, loss=3.848, backward_time=0.024, grad_norm=146.411, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:28:55,352 (trainer:763) INFO: 29epoch:train:321-360batch: iter_time=5.445e-05, forward_time=0.159, loss_ctc=3.564, loss=3.564, backward_time=0.024, grad_norm=149.318, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:29:08,103 (trainer:763) INFO: 29epoch:train:361-400batch: iter_time=5.272e-05, forward_time=0.159, loss_ctc=3.902, loss=3.902, backward_time=0.024, grad_norm=156.936, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:29:20,852 (trainer:763) INFO: 29epoch:train:401-440batch: iter_time=5.246e-05, forward_time=0.159, loss_ctc=3.646, loss=3.646, backward_time=0.024, grad_norm=145.956, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:29:33,606 (trainer:763) INFO: 29epoch:train:441-480batch: iter_time=5.549e-05, forward_time=0.159, loss_ctc=3.894, loss=3.894, backward_time=0.024, grad_norm=157.284, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:29:46,348 (trainer:763) INFO: 29epoch:train:481-520batch: iter_time=5.435e-05, forward_time=0.159, loss_ctc=3.710, loss=3.710, backward_time=0.024, grad_norm=163.634, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:29:59,098 (trainer:763) INFO: 29epoch:train:521-560batch: iter_time=5.223e-05, forward_time=0.159, loss_ctc=3.653, loss=3.653, backward_time=0.024, grad_norm=149.811, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:30:11,841 (trainer:763) INFO: 29epoch:train:561-600batch: iter_time=5.383e-05, forward_time=0.159, loss_ctc=3.483, loss=3.483, backward_time=0.024, grad_norm=151.770, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:30:24,588 (trainer:763) INFO: 29epoch:train:601-640batch: iter_time=5.455e-05, forward_time=0.159, loss_ctc=3.676, loss=3.676, backward_time=0.024, grad_norm=153.058, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:30:37,333 (trainer:763) INFO: 29epoch:train:641-680batch: iter_time=5.192e-05, forward_time=0.159, loss_ctc=3.499, loss=3.499, backward_time=0.024, grad_norm=146.924, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:30:50,081 (trainer:763) INFO: 29epoch:train:681-720batch: iter_time=5.431e-05, forward_time=0.159, loss_ctc=3.764, loss=3.764, backward_time=0.024, grad_norm=158.409, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:31:02,818 (trainer:763) INFO: 29epoch:train:721-760batch: iter_time=5.386e-05, forward_time=0.159, loss_ctc=3.954, loss=3.954, backward_time=0.024, grad_norm=161.067, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:31:15,556 (trainer:763) INFO: 29epoch:train:761-800batch: iter_time=5.282e-05, forward_time=0.159, loss_ctc=3.942, loss=3.942, backward_time=0.024, grad_norm=158.413, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:31:20,253 (trainer:354) INFO: 29epoch results: [train] iter_time=2.394e-04, forward_time=0.159, loss_ctc=3.756, loss=3.756, backward_time=0.024, grad_norm=153.941, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.35 seconds, total_count=23200, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=485.213, cer_ctc=0.333, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=485.213, time=1.16 seconds, total_count=145, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.45 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 19:31:21,214 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:31:21,215 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/28epoch.pth +[stan] 2024-01-14 19:31:21,215 (trainer:288) INFO: 30/30epoch started. Estimated time to finish: 4 minutes and 21 seconds +[stan] 2024-01-14 19:31:34,228 (trainer:763) INFO: 30epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=3.798, loss=3.798, backward_time=0.024, grad_norm=161.306, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.300 +[stan] 2024-01-14 19:31:46,956 (trainer:763) INFO: 30epoch:train:41-80batch: iter_time=5.339e-05, forward_time=0.159, loss_ctc=3.754, loss=3.754, backward_time=0.024, grad_norm=148.501, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 19:31:59,693 (trainer:763) INFO: 30epoch:train:81-120batch: iter_time=5.283e-05, forward_time=0.159, loss_ctc=3.763, loss=3.763, backward_time=0.024, grad_norm=146.061, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:32:12,445 (trainer:763) INFO: 30epoch:train:121-160batch: iter_time=5.404e-05, forward_time=0.159, loss_ctc=3.636, loss=3.636, backward_time=0.024, grad_norm=150.022, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:32:25,192 (trainer:763) INFO: 30epoch:train:161-200batch: iter_time=5.273e-05, forward_time=0.159, loss_ctc=3.850, loss=3.850, backward_time=0.025, grad_norm=150.781, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:32:37,944 (trainer:763) INFO: 30epoch:train:201-240batch: iter_time=5.425e-05, forward_time=0.159, loss_ctc=3.528, loss=3.528, backward_time=0.024, grad_norm=145.496, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:32:50,699 (trainer:763) INFO: 30epoch:train:241-280batch: iter_time=5.471e-05, forward_time=0.159, loss_ctc=3.513, loss=3.513, backward_time=0.024, grad_norm=146.224, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:33:03,457 (trainer:763) INFO: 30epoch:train:281-320batch: iter_time=5.638e-05, forward_time=0.159, loss_ctc=3.409, loss=3.409, backward_time=0.024, grad_norm=141.399, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:33:16,214 (trainer:763) INFO: 30epoch:train:321-360batch: iter_time=5.274e-05, forward_time=0.159, loss_ctc=3.510, loss=3.510, backward_time=0.024, grad_norm=155.978, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276 +[stan] 2024-01-14 19:33:28,964 (trainer:763) INFO: 30epoch:train:361-400batch: iter_time=5.500e-05, forward_time=0.159, loss_ctc=3.618, loss=3.618, backward_time=0.024, grad_norm=160.648, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:33:41,717 (trainer:763) INFO: 30epoch:train:401-440batch: iter_time=5.284e-05, forward_time=0.159, loss_ctc=3.520, loss=3.520, backward_time=0.024, grad_norm=140.554, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275 +[stan] 2024-01-14 19:33:54,462 (trainer:763) INFO: 30epoch:train:441-480batch: iter_time=5.347e-05, forward_time=0.159, loss_ctc=3.674, loss=3.674, backward_time=0.024, grad_norm=144.705, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:34:07,201 (trainer:763) INFO: 30epoch:train:481-520batch: iter_time=5.575e-05, forward_time=0.159, loss_ctc=3.483, loss=3.483, backward_time=0.024, grad_norm=146.432, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:34:19,939 (trainer:763) INFO: 30epoch:train:521-560batch: iter_time=5.244e-05, forward_time=0.159, loss_ctc=3.837, loss=3.837, backward_time=0.024, grad_norm=146.557, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:34:32,679 (trainer:763) INFO: 30epoch:train:561-600batch: iter_time=5.459e-05, forward_time=0.159, loss_ctc=3.458, loss=3.458, backward_time=0.024, grad_norm=146.401, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:34:45,419 (trainer:763) INFO: 30epoch:train:601-640batch: iter_time=5.286e-05, forward_time=0.159, loss_ctc=3.289, loss=3.289, backward_time=0.024, grad_norm=139.401, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:34:58,156 (trainer:763) INFO: 30epoch:train:641-680batch: iter_time=5.159e-05, forward_time=0.159, loss_ctc=3.367, loss=3.367, backward_time=0.024, grad_norm=142.467, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.274 +[stan] 2024-01-14 19:35:10,890 (trainer:763) INFO: 30epoch:train:681-720batch: iter_time=5.498e-05, forward_time=0.159, loss_ctc=3.445, loss=3.445, backward_time=0.024, grad_norm=143.181, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 19:35:23,624 (trainer:763) INFO: 30epoch:train:721-760batch: iter_time=5.574e-05, forward_time=0.159, loss_ctc=3.379, loss=3.379, backward_time=0.024, grad_norm=138.923, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 19:35:36,356 (trainer:763) INFO: 30epoch:train:761-800batch: iter_time=5.059e-05, forward_time=0.159, loss_ctc=3.536, loss=3.536, backward_time=0.024, grad_norm=146.904, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-14 19:35:41,082 (trainer:354) INFO: 30epoch results: [train] iter_time=2.278e-04, forward_time=0.159, loss_ctc=3.568, loss=3.568, backward_time=0.024, grad_norm=147.097, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.276, time=4 minutes and 15.22 seconds, total_count=24000, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=491.532, cer_ctc=0.337, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=491.532, time=1.19 seconds, total_count=150, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.46 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-14 19:35:42,123 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 19:35:42,123 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/29epoch.pth +[stan] 2024-01-14 19:35:42,123 (trainer:489) INFO: The training was finished at 30 epochs +[stan] 2024-01-14 19:35:42,139 (average_nbest_models:69) INFO: Averaging 5best models: criterion="valid.loss": test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave_5best.pth +# Accounting: time=7834 threads=1 +# Ended (code 0) at Sun Jan 14 19:35:42 CST 2024, elapsed time 7834 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_10min/train.log b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/train.log new file mode 100644 index 0000000000000000000000000000000000000000..5e9c94d05b962777483c40214939b57ce61f58b2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_10min/train.log @@ -0,0 +1,1406 @@ +# python3 -m espnet2.bin.asr_train --use_preprocessor true --bpemodel none --token_type char --token_list data/eng1_token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_eng1/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_eng1_10min/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_10min_eng1/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_eng1_10min/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_10min_eng1/text,text,text --train_shape_file test_pr/asr_stats_eng1_10min/train/text_shape.char --valid_data_path_and_name_and_type dump/raw/dev_10min_eng1/text,text,text --valid_shape_file test_pr/asr_stats_eng1_10min/valid/text_shape.char --ngpu 1 --multiprocessing_distributed True +# Started at Tue Jan 16 19:26:45 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_train.py --use_preprocessor true --bpemodel none --token_type char --token_list data/eng1_token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_eng1/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_eng1_10min/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_10min --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_10min_eng1/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_eng1_10min/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_10min_eng1/text,text,text --train_shape_file test_pr/asr_stats_eng1_10min/train/text_shape.char --valid_data_path_and_name_and_type dump/raw/dev_10min_eng1/text,text,text --valid_shape_file test_pr/asr_stats_eng1_10min/valid/text_shape.char --ngpu 1 --multiprocessing_distributed True +[stan] 2024-01-16 19:26:47,059 (asr:523) INFO: Vocabulary size: 30 +[stan] 2024-01-16 19:26:47,126 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +[stan] 2024-01-16 19:26:47,126 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +[stan] 2024-01-16 19:26:47,240 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +[stan] 2024-01-16 19:26:48,535 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +[stan] 2024-01-16 19:26:49,350 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.mask_emb.requires_grad = False +[stan] 2024-01-16 19:26:49,350 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.label_embs_concat.requires_grad = False +[stan] 2024-01-16 19:26:49,350 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.0.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,350 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,350 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,350 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.1.0.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.2.0.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.3.0.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.4.0.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.5.0.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.6.0.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_g.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_v.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,351 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,352 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,353 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,353 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,353 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,353 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,353 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,353 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,353 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,353 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,353 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,353 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,353 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,353 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.weight.requires_grad = False +[stan] 2024-01-16 19:26:49,353 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.bias.requires_grad = False +[stan] 2024-01-16 19:26:49,734 (abs_task:1275) INFO: pytorch.version=1.12.0+cu113, cuda.available=True, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True +[stan] 2024-01-16 19:26:49,736 (abs_task:1276) INFO: Model structure: +ESPnetASRModel( + (frontend): S3prlFrontend( + (upstream): S3PRLUpstream( + (upstream): UpstreamExpert( + (model): HubertModel( + (feature_extractor): ConvFeatureExtractionModel( + (conv_layers): ModuleList( + (0): Sequential( + (0): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): Fp32GroupNorm(512, 512, eps=1e-05, affine=True) + (3): GELU(approximate=none) + ) + (1): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (2): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (3): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (4): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (5): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (6): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + ) + ) + (post_extract_proj): Linear(in_features=512, out_features=768, bias=True) + (dropout_input): Dropout(p=0.1, inplace=False) + (dropout_features): Dropout(p=0.1, inplace=False) + (encoder): TransformerEncoder( + (pos_conv): Sequential( + (0): Conv1d(768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16) + (1): SamePad() + (2): GELU(approximate=none) + ) + (layers): ModuleList( + (0): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (1): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (2): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (3): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (4): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (5): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (6): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (7): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (8): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (9): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (10): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (11): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + ) + (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (final_proj): Linear(in_features=768, out_features=256, bias=True) + ) + ) + ) + (featurizer): Featurizer() + ) + (specaug): SpecAug( + (time_warp): TimeWarp(window=5, mode=bicubic) + (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq) + (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time) + ) + (normalize): UtteranceMVN(norm_means=True, norm_vars=False) + (preencoder): LinearProjection( + (linear_out): Linear(in_features=768, out_features=80, bias=True) + (dropout): Dropout(p=0.0, inplace=False) + ) + (encoder): TransformerEncoder( + (embed): Conv2dSubsampling2( + (conv): Sequential( + (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU() + (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) + (3): ReLU() + ) + (out): Sequential( + (0): Linear(in_features=9472, out_features=256, bias=True) + (1): PositionalEncoding( + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + ) + (encoders): MultiSequential( + (0): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (1): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + ) + (criterion_att): LabelSmoothingLoss( + (criterion): KLDivLoss() + ) + (ctc): CTC( + (ctc_lo): Linear(in_features=256, out_features=30, bias=True) + (ctc_loss): CTCLoss() + ) +) + +Model summary: + Class Name: ESPnetASRModel + Total Number of model parameters: 99.96 M + Number of trainable parameters: 5.27 M (5.3%) + Size: 21.07 MB + Type: torch.float32 +[stan] 2024-01-16 19:26:49,736 (abs_task:1279) INFO: Optimizer: +Adam ( +Parameter Group 0 + amsgrad: False + betas: (0.9, 0.999) + capturable: False + eps: 1e-08 + foreach: None + lr: 0.0001 + maximize: False + weight_decay: 1e-06 +) +[stan] 2024-01-16 19:26:49,736 (abs_task:1280) INFO: Scheduler: None +[stan] 2024-01-16 19:26:49,736 (abs_task:1289) INFO: Saving the configuration in test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/config.yaml +[stan] 2024-01-16 19:26:49,892 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-16 19:26:49,934 (abs_task:1665) INFO: [train] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/train_10min_eng1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/train_10min_eng1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-16 19:26:49,934 (abs_task:1666) INFO: [train] Batch sampler: SortedBatchSampler(N-batch=5, batch_size=8, shape_file=test_pr/asr_stats_eng1_10min/train/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-16 19:26:49,934 (abs_task:1667) INFO: [train] mini-batch sizes summary: N-batch=5, mean=8.2, min=8, max=9 +[stan] 2024-01-16 19:26:49,945 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-16 19:26:49,945 (abs_task:1665) INFO: [valid] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_eng1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_eng1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-16 19:26:49,945 (abs_task:1666) INFO: [valid] Batch sampler: SortedBatchSampler(N-batch=5, batch_size=8, shape_file=test_pr/asr_stats_eng1_10min/valid/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-16 19:26:49,946 (abs_task:1667) INFO: [valid] mini-batch sizes summary: N-batch=5, mean=8.0, min=8, max=8 +[stan] 2024-01-16 19:26:49,946 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-16 19:26:49,957 (abs_task:1665) INFO: [plot_att] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_eng1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_eng1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-16 19:26:49,957 (abs_task:1666) INFO: [plot_att] Batch sampler: UnsortedBatchSampler(N-batch=40, batch_size=1, key_file=test_pr/asr_stats_eng1_10min/valid/speech_shape, +[stan] 2024-01-16 19:26:49,957 (abs_task:1667) INFO: [plot_att] mini-batch sizes summary: N-batch=3, mean=1.0, min=1, max=1 +[stan] 2024-01-16 19:26:49,989 (trainer:303) INFO: 1/30epoch started +[stan] 2024-01-16 19:27:04,158 (trainer:762) INFO: 1epoch:train:1-40batch: iter_time=0.003, forward_time=0.187, loss_ctc=159.631, loss=159.631, backward_time=0.026, grad_norm=1.236e+03, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.412 +[stan] 2024-01-16 19:27:16,769 (trainer:762) INFO: 1epoch:train:41-80batch: iter_time=5.153e-05, forward_time=0.157, loss_ctc=147.258, loss=147.258, backward_time=0.024, grad_norm=279.474, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.262 +[stan] 2024-01-16 19:27:29,378 (trainer:762) INFO: 1epoch:train:81-120batch: iter_time=4.881e-05, forward_time=0.157, loss_ctc=146.474, loss=146.474, backward_time=0.024, grad_norm=154.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.261 +[stan] 2024-01-16 19:27:41,960 (trainer:762) INFO: 1epoch:train:121-160batch: iter_time=4.973e-05, forward_time=0.157, loss_ctc=146.285, loss=146.285, backward_time=0.024, grad_norm=291.721, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.258 +[stan] 2024-01-16 19:27:54,562 (trainer:762) INFO: 1epoch:train:161-200batch: iter_time=5.253e-05, forward_time=0.157, loss_ctc=145.227, loss=145.227, backward_time=0.024, grad_norm=242.138, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.260 +[stan] 2024-01-16 19:28:07,193 (trainer:762) INFO: 1epoch:train:201-240batch: iter_time=5.003e-05, forward_time=0.158, loss_ctc=140.814, loss=140.814, backward_time=0.024, grad_norm=313.094, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.263 +[stan] 2024-01-16 19:28:19,847 (trainer:762) INFO: 1epoch:train:241-280batch: iter_time=5.288e-05, forward_time=0.158, loss_ctc=130.591, loss=130.591, backward_time=0.024, grad_norm=491.917, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.265 +[stan] 2024-01-16 19:28:32,519 (trainer:762) INFO: 1epoch:train:281-320batch: iter_time=4.987e-05, forward_time=0.158, loss_ctc=117.422, loss=117.422, backward_time=0.024, grad_norm=272.166, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.267 +[stan] 2024-01-16 19:28:45,212 (trainer:762) INFO: 1epoch:train:321-360batch: iter_time=4.908e-05, forward_time=0.158, loss_ctc=104.634, loss=104.634, backward_time=0.024, grad_norm=256.726, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 19:28:57,906 (trainer:762) INFO: 1epoch:train:361-400batch: iter_time=4.870e-05, forward_time=0.158, loss_ctc=95.439, loss=95.439, backward_time=0.024, grad_norm=275.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 19:29:10,606 (trainer:762) INFO: 1epoch:train:401-440batch: iter_time=5.246e-05, forward_time=0.158, loss_ctc=88.660, loss=88.660, backward_time=0.024, grad_norm=238.245, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:29:23,319 (trainer:762) INFO: 1epoch:train:441-480batch: iter_time=4.964e-05, forward_time=0.159, loss_ctc=82.888, loss=82.888, backward_time=0.024, grad_norm=298.569, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:29:36,042 (trainer:762) INFO: 1epoch:train:481-520batch: iter_time=4.907e-05, forward_time=0.159, loss_ctc=78.719, loss=78.719, backward_time=0.024, grad_norm=247.141, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:29:48,763 (trainer:762) INFO: 1epoch:train:521-560batch: iter_time=5.249e-05, forward_time=0.159, loss_ctc=74.812, loss=74.812, backward_time=0.024, grad_norm=308.334, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:30:01,488 (trainer:762) INFO: 1epoch:train:561-600batch: iter_time=4.928e-05, forward_time=0.159, loss_ctc=70.989, loss=70.989, backward_time=0.024, grad_norm=274.306, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:30:14,204 (trainer:762) INFO: 1epoch:train:601-640batch: iter_time=4.927e-05, forward_time=0.159, loss_ctc=67.922, loss=67.922, backward_time=0.024, grad_norm=237.402, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:30:26,928 (trainer:762) INFO: 1epoch:train:641-680batch: iter_time=4.979e-05, forward_time=0.159, loss_ctc=64.122, loss=64.122, backward_time=0.024, grad_norm=238.884, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:30:39,637 (trainer:762) INFO: 1epoch:train:681-720batch: iter_time=4.998e-05, forward_time=0.159, loss_ctc=61.271, loss=61.271, backward_time=0.024, grad_norm=300.473, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:30:52,343 (trainer:762) INFO: 1epoch:train:721-760batch: iter_time=4.988e-05, forward_time=0.159, loss_ctc=58.571, loss=58.571, backward_time=0.024, grad_norm=268.337, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:31:05,045 (trainer:762) INFO: 1epoch:train:761-800batch: iter_time=4.577e-05, forward_time=0.158, loss_ctc=55.895, loss=55.895, backward_time=0.024, grad_norm=291.514, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-16 19:31:10,129 (trainer:357) INFO: 1epoch results: [train] iter_time=1.998e-04, forward_time=0.160, loss_ctc=101.881, loss=101.881, backward_time=0.024, grad_norm=325.765, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.275, time=4 minutes and 15.09 seconds, total_count=800, gpu_max_cached_mem_GB=17.303, [valid] loss_ctc=271.836, cer_ctc=0.323, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=271.836, time=1.16 seconds, total_count=5, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.88 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 19:31:11,228 (trainer:416) INFO: The best model has been updated: valid.loss +[stan] 2024-01-16 19:31:11,228 (trainer:291) INFO: 2/30epoch started. Estimated time to finish: 2 hours, 6 minutes and 15.94 seconds +[stan] 2024-01-16 19:31:24,221 (trainer:762) INFO: 2epoch:train:1-40batch: iter_time=0.003, forward_time=0.159, loss_ctc=53.649, loss=53.649, backward_time=0.024, grad_norm=254.950, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.298 +[stan] 2024-01-16 19:31:36,942 (trainer:762) INFO: 2epoch:train:41-80batch: iter_time=5.290e-05, forward_time=0.159, loss_ctc=51.126, loss=51.126, backward_time=0.024, grad_norm=388.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:31:49,659 (trainer:762) INFO: 2epoch:train:81-120batch: iter_time=4.952e-05, forward_time=0.159, loss_ctc=49.673, loss=49.673, backward_time=0.024, grad_norm=356.891, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:32:02,357 (trainer:762) INFO: 2epoch:train:121-160batch: iter_time=4.982e-05, forward_time=0.158, loss_ctc=48.805, loss=48.805, backward_time=0.024, grad_norm=376.281, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:32:15,065 (trainer:762) INFO: 2epoch:train:161-200batch: iter_time=5.407e-05, forward_time=0.159, loss_ctc=46.742, loss=46.742, backward_time=0.024, grad_norm=311.739, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:32:27,777 (trainer:762) INFO: 2epoch:train:201-240batch: iter_time=5.253e-05, forward_time=0.159, loss_ctc=45.040, loss=45.040, backward_time=0.024, grad_norm=363.285, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:32:40,499 (trainer:762) INFO: 2epoch:train:241-280batch: iter_time=5.526e-05, forward_time=0.159, loss_ctc=43.688, loss=43.688, backward_time=0.024, grad_norm=391.915, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:32:53,209 (trainer:762) INFO: 2epoch:train:281-320batch: iter_time=5.429e-05, forward_time=0.159, loss_ctc=42.482, loss=42.482, backward_time=0.024, grad_norm=328.597, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:33:06,148 (trainer:762) INFO: 2epoch:train:321-360batch: iter_time=5.732e-05, forward_time=0.162, loss_ctc=41.739, loss=41.739, backward_time=0.024, grad_norm=366.493, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.294 +[stan] 2024-01-16 19:33:18,866 (trainer:762) INFO: 2epoch:train:361-400batch: iter_time=5.688e-05, forward_time=0.159, loss_ctc=40.674, loss=40.674, backward_time=0.024, grad_norm=416.568, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:33:31,575 (trainer:762) INFO: 2epoch:train:401-440batch: iter_time=5.231e-05, forward_time=0.159, loss_ctc=40.280, loss=40.280, backward_time=0.024, grad_norm=343.736, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:33:44,282 (trainer:762) INFO: 2epoch:train:441-480batch: iter_time=5.217e-05, forward_time=0.159, loss_ctc=39.213, loss=39.213, backward_time=0.024, grad_norm=353.047, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:33:56,988 (trainer:762) INFO: 2epoch:train:481-520batch: iter_time=5.006e-05, forward_time=0.159, loss_ctc=38.384, loss=38.384, backward_time=0.024, grad_norm=342.350, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:34:09,690 (trainer:762) INFO: 2epoch:train:521-560batch: iter_time=5.104e-05, forward_time=0.158, loss_ctc=37.701, loss=37.701, backward_time=0.024, grad_norm=431.843, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:34:22,393 (trainer:762) INFO: 2epoch:train:561-600batch: iter_time=5.300e-05, forward_time=0.159, loss_ctc=37.451, loss=37.451, backward_time=0.024, grad_norm=361.714, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:34:35,092 (trainer:762) INFO: 2epoch:train:601-640batch: iter_time=4.984e-05, forward_time=0.158, loss_ctc=36.229, loss=36.229, backward_time=0.024, grad_norm=355.214, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:34:47,790 (trainer:762) INFO: 2epoch:train:641-680batch: iter_time=5.152e-05, forward_time=0.158, loss_ctc=35.975, loss=35.975, backward_time=0.024, grad_norm=322.497, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:35:00,490 (trainer:762) INFO: 2epoch:train:681-720batch: iter_time=5.239e-05, forward_time=0.159, loss_ctc=35.264, loss=35.264, backward_time=0.024, grad_norm=326.818, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:35:13,186 (trainer:762) INFO: 2epoch:train:721-760batch: iter_time=5.033e-05, forward_time=0.158, loss_ctc=34.701, loss=34.701, backward_time=0.024, grad_norm=349.556, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:35:25,878 (trainer:762) INFO: 2epoch:train:761-800batch: iter_time=4.919e-05, forward_time=0.158, loss_ctc=34.543, loss=34.543, backward_time=0.024, grad_norm=393.418, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-16 19:35:30,850 (trainer:357) INFO: 2epoch results: [train] iter_time=2.099e-04, forward_time=0.159, loss_ctc=41.668, loss=41.668, backward_time=0.024, grad_norm=356.751, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273, time=4 minutes and 14.72 seconds, total_count=1600, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=325.328, cer_ctc=0.317, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=325.328, time=1.14 seconds, total_count=10, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.75 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 19:35:31,747 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 19:35:31,748 (trainer:291) INFO: 3/30epoch started. Estimated time to finish: 2 hours, 1 minute and 44.62 seconds +[stan] 2024-01-16 19:35:44,738 (trainer:762) INFO: 3epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=34.503, loss=34.503, backward_time=0.024, grad_norm=378.340, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.298 +[stan] 2024-01-16 19:35:57,451 (trainer:762) INFO: 3epoch:train:41-80batch: iter_time=5.611e-05, forward_time=0.159, loss_ctc=34.030, loss=34.030, backward_time=0.024, grad_norm=360.496, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:36:10,174 (trainer:762) INFO: 3epoch:train:81-120batch: iter_time=5.773e-05, forward_time=0.159, loss_ctc=33.537, loss=33.537, backward_time=0.024, grad_norm=313.431, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:36:22,877 (trainer:762) INFO: 3epoch:train:121-160batch: iter_time=5.566e-05, forward_time=0.159, loss_ctc=33.078, loss=33.078, backward_time=0.024, grad_norm=364.454, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:36:35,587 (trainer:762) INFO: 3epoch:train:161-200batch: iter_time=5.715e-05, forward_time=0.159, loss_ctc=33.552, loss=33.552, backward_time=0.024, grad_norm=385.919, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:36:48,299 (trainer:762) INFO: 3epoch:train:201-240batch: iter_time=5.571e-05, forward_time=0.159, loss_ctc=32.870, loss=32.870, backward_time=0.024, grad_norm=352.974, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:37:01,016 (trainer:762) INFO: 3epoch:train:241-280batch: iter_time=5.371e-05, forward_time=0.159, loss_ctc=31.512, loss=31.512, backward_time=0.024, grad_norm=367.848, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:37:13,728 (trainer:762) INFO: 3epoch:train:281-320batch: iter_time=5.068e-05, forward_time=0.159, loss_ctc=30.973, loss=30.973, backward_time=0.024, grad_norm=342.174, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:37:26,440 (trainer:762) INFO: 3epoch:train:321-360batch: iter_time=5.542e-05, forward_time=0.159, loss_ctc=32.141, loss=32.141, backward_time=0.024, grad_norm=330.812, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:37:39,152 (trainer:762) INFO: 3epoch:train:361-400batch: iter_time=5.477e-05, forward_time=0.159, loss_ctc=32.291, loss=32.291, backward_time=0.024, grad_norm=350.080, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:37:51,867 (trainer:762) INFO: 3epoch:train:401-440batch: iter_time=5.306e-05, forward_time=0.159, loss_ctc=31.430, loss=31.430, backward_time=0.024, grad_norm=334.660, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:38:04,572 (trainer:762) INFO: 3epoch:train:441-480batch: iter_time=5.240e-05, forward_time=0.159, loss_ctc=30.768, loss=30.768, backward_time=0.024, grad_norm=326.388, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:38:17,278 (trainer:762) INFO: 3epoch:train:481-520batch: iter_time=5.365e-05, forward_time=0.159, loss_ctc=30.269, loss=30.269, backward_time=0.024, grad_norm=389.877, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:38:29,978 (trainer:762) INFO: 3epoch:train:521-560batch: iter_time=5.352e-05, forward_time=0.158, loss_ctc=31.037, loss=31.037, backward_time=0.024, grad_norm=399.740, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:38:42,676 (trainer:762) INFO: 3epoch:train:561-600batch: iter_time=5.375e-05, forward_time=0.158, loss_ctc=30.171, loss=30.171, backward_time=0.024, grad_norm=327.401, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:38:55,376 (trainer:762) INFO: 3epoch:train:601-640batch: iter_time=5.424e-05, forward_time=0.158, loss_ctc=31.171, loss=31.171, backward_time=0.024, grad_norm=371.294, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:39:08,073 (trainer:762) INFO: 3epoch:train:641-680batch: iter_time=5.353e-05, forward_time=0.158, loss_ctc=30.450, loss=30.450, backward_time=0.024, grad_norm=328.089, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:39:20,773 (trainer:762) INFO: 3epoch:train:681-720batch: iter_time=5.199e-05, forward_time=0.158, loss_ctc=29.689, loss=29.689, backward_time=0.024, grad_norm=485.004, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:39:33,469 (trainer:762) INFO: 3epoch:train:721-760batch: iter_time=5.308e-05, forward_time=0.158, loss_ctc=30.151, loss=30.151, backward_time=0.024, grad_norm=335.994, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 19:39:46,165 (trainer:762) INFO: 3epoch:train:761-800batch: iter_time=4.767e-05, forward_time=0.158, loss_ctc=29.883, loss=29.883, backward_time=0.024, grad_norm=363.079, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:39:51,096 (trainer:357) INFO: 3epoch results: [train] iter_time=2.271e-04, forward_time=0.159, loss_ctc=31.675, loss=31.675, backward_time=0.024, grad_norm=360.403, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.49 seconds, total_count=2400, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=351.409, cer_ctc=0.306, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=351.409, time=1.17 seconds, total_count=15, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.69 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 19:39:52,099 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 19:39:52,099 (trainer:291) INFO: 4/30epoch started. Estimated time to finish: 1 hour, 57 minutes and 18.99 seconds +[stan] 2024-01-16 19:40:05,077 (trainer:762) INFO: 4epoch:train:1-40batch: iter_time=0.003, forward_time=0.159, loss_ctc=28.306, loss=28.306, backward_time=0.024, grad_norm=288.418, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.297 +[stan] 2024-01-16 19:40:17,789 (trainer:762) INFO: 4epoch:train:41-80batch: iter_time=5.141e-05, forward_time=0.159, loss_ctc=29.277, loss=29.277, backward_time=0.024, grad_norm=384.805, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:40:30,504 (trainer:762) INFO: 4epoch:train:81-120batch: iter_time=5.194e-05, forward_time=0.159, loss_ctc=28.706, loss=28.706, backward_time=0.024, grad_norm=299.589, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:40:43,204 (trainer:762) INFO: 4epoch:train:121-160batch: iter_time=5.111e-05, forward_time=0.158, loss_ctc=29.484, loss=29.484, backward_time=0.024, grad_norm=363.057, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:40:55,916 (trainer:762) INFO: 4epoch:train:161-200batch: iter_time=5.056e-05, forward_time=0.159, loss_ctc=29.283, loss=29.283, backward_time=0.024, grad_norm=362.584, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:41:08,627 (trainer:762) INFO: 4epoch:train:201-240batch: iter_time=5.263e-05, forward_time=0.159, loss_ctc=29.016, loss=29.016, backward_time=0.024, grad_norm=375.624, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:41:21,338 (trainer:762) INFO: 4epoch:train:241-280batch: iter_time=5.166e-05, forward_time=0.159, loss_ctc=29.575, loss=29.575, backward_time=0.024, grad_norm=374.429, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:41:34,047 (trainer:762) INFO: 4epoch:train:281-320batch: iter_time=5.278e-05, forward_time=0.159, loss_ctc=28.804, loss=28.804, backward_time=0.024, grad_norm=362.847, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:41:46,757 (trainer:762) INFO: 4epoch:train:321-360batch: iter_time=5.399e-05, forward_time=0.159, loss_ctc=28.526, loss=28.526, backward_time=0.024, grad_norm=383.078, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:41:59,463 (trainer:762) INFO: 4epoch:train:361-400batch: iter_time=5.080e-05, forward_time=0.159, loss_ctc=27.311, loss=27.311, backward_time=0.024, grad_norm=286.203, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:42:12,170 (trainer:762) INFO: 4epoch:train:401-440batch: iter_time=5.099e-05, forward_time=0.159, loss_ctc=28.145, loss=28.145, backward_time=0.024, grad_norm=309.247, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:42:24,876 (trainer:762) INFO: 4epoch:train:441-480batch: iter_time=5.830e-05, forward_time=0.159, loss_ctc=28.033, loss=28.033, backward_time=0.024, grad_norm=333.763, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:42:37,577 (trainer:762) INFO: 4epoch:train:481-520batch: iter_time=5.078e-05, forward_time=0.159, loss_ctc=27.745, loss=27.745, backward_time=0.024, grad_norm=324.980, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:42:50,279 (trainer:762) INFO: 4epoch:train:521-560batch: iter_time=5.396e-05, forward_time=0.159, loss_ctc=27.599, loss=27.599, backward_time=0.024, grad_norm=310.816, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:43:02,967 (trainer:762) INFO: 4epoch:train:561-600batch: iter_time=5.111e-05, forward_time=0.158, loss_ctc=27.459, loss=27.459, backward_time=0.024, grad_norm=327.735, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 19:43:15,662 (trainer:762) INFO: 4epoch:train:601-640batch: iter_time=5.090e-05, forward_time=0.158, loss_ctc=27.454, loss=27.454, backward_time=0.024, grad_norm=341.222, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 19:43:28,357 (trainer:762) INFO: 4epoch:train:641-680batch: iter_time=5.366e-05, forward_time=0.158, loss_ctc=27.431, loss=27.431, backward_time=0.024, grad_norm=409.542, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 19:43:41,050 (trainer:762) INFO: 4epoch:train:681-720batch: iter_time=5.111e-05, forward_time=0.158, loss_ctc=27.466, loss=27.466, backward_time=0.024, grad_norm=338.066, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 19:43:53,746 (trainer:762) INFO: 4epoch:train:721-760batch: iter_time=5.755e-05, forward_time=0.158, loss_ctc=26.723, loss=26.723, backward_time=0.024, grad_norm=357.759, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 19:44:06,433 (trainer:762) INFO: 4epoch:train:761-800batch: iter_time=4.902e-05, forward_time=0.158, loss_ctc=27.296, loss=27.296, backward_time=0.024, grad_norm=325.933, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 19:44:11,331 (trainer:357) INFO: 4epoch results: [train] iter_time=2.150e-04, forward_time=0.159, loss_ctc=28.182, loss=28.182, backward_time=0.024, grad_norm=342.985, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.41 seconds, total_count=3200, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=364.533, cer_ctc=0.300, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=364.533, time=1.16 seconds, total_count=20, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.66 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 19:44:12,253 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 19:44:12,253 (trainer:291) INFO: 5/30epoch started. Estimated time to finish: 1 hour, 52 minutes and 54.72 seconds +[stan] 2024-01-16 19:44:25,261 (trainer:762) INFO: 5epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=26.581, loss=26.581, backward_time=0.024, grad_norm=304.758, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.299 +[stan] 2024-01-16 19:44:37,968 (trainer:762) INFO: 5epoch:train:41-80batch: iter_time=4.874e-05, forward_time=0.159, loss_ctc=26.266, loss=26.266, backward_time=0.024, grad_norm=317.563, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:44:50,678 (trainer:762) INFO: 5epoch:train:81-120batch: iter_time=5.383e-05, forward_time=0.159, loss_ctc=26.725, loss=26.725, backward_time=0.024, grad_norm=313.939, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:45:03,381 (trainer:762) INFO: 5epoch:train:121-160batch: iter_time=5.567e-05, forward_time=0.159, loss_ctc=26.107, loss=26.107, backward_time=0.024, grad_norm=293.328, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:45:16,085 (trainer:762) INFO: 5epoch:train:161-200batch: iter_time=5.034e-05, forward_time=0.159, loss_ctc=25.661, loss=25.661, backward_time=0.024, grad_norm=277.370, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:45:28,792 (trainer:762) INFO: 5epoch:train:201-240batch: iter_time=5.090e-05, forward_time=0.159, loss_ctc=26.679, loss=26.679, backward_time=0.024, grad_norm=307.745, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:45:41,500 (trainer:762) INFO: 5epoch:train:241-280batch: iter_time=5.031e-05, forward_time=0.159, loss_ctc=26.201, loss=26.201, backward_time=0.024, grad_norm=318.793, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:45:54,211 (trainer:762) INFO: 5epoch:train:281-320batch: iter_time=5.384e-05, forward_time=0.159, loss_ctc=25.746, loss=25.746, backward_time=0.024, grad_norm=346.465, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:46:06,928 (trainer:762) INFO: 5epoch:train:321-360batch: iter_time=5.719e-05, forward_time=0.159, loss_ctc=25.462, loss=25.462, backward_time=0.024, grad_norm=298.107, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:46:19,640 (trainer:762) INFO: 5epoch:train:361-400batch: iter_time=5.236e-05, forward_time=0.159, loss_ctc=25.994, loss=25.994, backward_time=0.024, grad_norm=328.198, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:46:32,361 (trainer:762) INFO: 5epoch:train:401-440batch: iter_time=5.459e-05, forward_time=0.159, loss_ctc=25.437, loss=25.437, backward_time=0.024, grad_norm=307.265, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:46:45,066 (trainer:762) INFO: 5epoch:train:441-480batch: iter_time=5.348e-05, forward_time=0.159, loss_ctc=26.064, loss=26.064, backward_time=0.024, grad_norm=324.961, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:46:57,771 (trainer:762) INFO: 5epoch:train:481-520batch: iter_time=5.383e-05, forward_time=0.159, loss_ctc=24.778, loss=24.778, backward_time=0.024, grad_norm=301.663, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:47:10,479 (trainer:762) INFO: 5epoch:train:521-560batch: iter_time=5.384e-05, forward_time=0.159, loss_ctc=24.627, loss=24.627, backward_time=0.024, grad_norm=297.571, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:47:23,178 (trainer:762) INFO: 5epoch:train:561-600batch: iter_time=5.180e-05, forward_time=0.158, loss_ctc=24.947, loss=24.947, backward_time=0.024, grad_norm=307.368, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:47:35,878 (trainer:762) INFO: 5epoch:train:601-640batch: iter_time=5.405e-05, forward_time=0.158, loss_ctc=25.203, loss=25.203, backward_time=0.024, grad_norm=297.987, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:47:48,576 (trainer:762) INFO: 5epoch:train:641-680batch: iter_time=5.215e-05, forward_time=0.158, loss_ctc=24.409, loss=24.409, backward_time=0.024, grad_norm=303.053, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:48:01,273 (trainer:762) INFO: 5epoch:train:681-720batch: iter_time=5.133e-05, forward_time=0.158, loss_ctc=24.957, loss=24.957, backward_time=0.024, grad_norm=299.673, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:48:13,971 (trainer:762) INFO: 5epoch:train:721-760batch: iter_time=5.329e-05, forward_time=0.158, loss_ctc=24.572, loss=24.572, backward_time=0.024, grad_norm=295.348, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:48:26,661 (trainer:762) INFO: 5epoch:train:761-800batch: iter_time=5.220e-05, forward_time=0.158, loss_ctc=24.749, loss=24.749, backward_time=0.024, grad_norm=302.911, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 19:48:31,473 (trainer:357) INFO: 5epoch results: [train] iter_time=2.533e-04, forward_time=0.159, loss_ctc=25.558, loss=25.558, backward_time=0.024, grad_norm=307.203, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.49 seconds, total_count=4000, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=375.528, cer_ctc=0.298, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=375.528, time=1.14 seconds, total_count=25, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.58 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 19:48:32,420 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 19:48:32,420 (trainer:291) INFO: 6/30epoch started. Estimated time to finish: 1 hour, 48 minutes and 32.16 seconds +[stan] 2024-01-16 19:48:45,425 (trainer:762) INFO: 6epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=24.216, loss=24.216, backward_time=0.024, grad_norm=301.671, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.299 +[stan] 2024-01-16 19:48:58,132 (trainer:762) INFO: 6epoch:train:41-80batch: iter_time=5.207e-05, forward_time=0.159, loss_ctc=24.191, loss=24.191, backward_time=0.024, grad_norm=301.862, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:49:10,847 (trainer:762) INFO: 6epoch:train:81-120batch: iter_time=5.123e-05, forward_time=0.159, loss_ctc=23.568, loss=23.568, backward_time=0.024, grad_norm=291.537, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:49:23,546 (trainer:762) INFO: 6epoch:train:121-160batch: iter_time=5.381e-05, forward_time=0.158, loss_ctc=23.640, loss=23.640, backward_time=0.024, grad_norm=311.419, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:49:36,250 (trainer:762) INFO: 6epoch:train:161-200batch: iter_time=5.312e-05, forward_time=0.159, loss_ctc=24.013, loss=24.013, backward_time=0.024, grad_norm=292.530, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:49:48,959 (trainer:762) INFO: 6epoch:train:201-240batch: iter_time=5.258e-05, forward_time=0.159, loss_ctc=23.825, loss=23.825, backward_time=0.024, grad_norm=283.832, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:50:01,667 (trainer:762) INFO: 6epoch:train:241-280batch: iter_time=5.386e-05, forward_time=0.159, loss_ctc=23.668, loss=23.668, backward_time=0.024, grad_norm=323.246, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:50:14,379 (trainer:762) INFO: 6epoch:train:281-320batch: iter_time=5.605e-05, forward_time=0.159, loss_ctc=23.204, loss=23.204, backward_time=0.024, grad_norm=294.071, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:50:27,096 (trainer:762) INFO: 6epoch:train:321-360batch: iter_time=5.193e-05, forward_time=0.159, loss_ctc=22.769, loss=22.769, backward_time=0.024, grad_norm=296.356, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:50:39,818 (trainer:762) INFO: 6epoch:train:361-400batch: iter_time=5.445e-05, forward_time=0.159, loss_ctc=23.078, loss=23.078, backward_time=0.024, grad_norm=324.393, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:50:52,529 (trainer:762) INFO: 6epoch:train:401-440batch: iter_time=5.334e-05, forward_time=0.159, loss_ctc=22.946, loss=22.946, backward_time=0.024, grad_norm=309.160, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:51:05,230 (trainer:762) INFO: 6epoch:train:441-480batch: iter_time=5.334e-05, forward_time=0.159, loss_ctc=23.211, loss=23.211, backward_time=0.024, grad_norm=294.787, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:51:17,935 (trainer:762) INFO: 6epoch:train:481-520batch: iter_time=5.289e-05, forward_time=0.159, loss_ctc=23.020, loss=23.020, backward_time=0.024, grad_norm=297.122, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:51:30,637 (trainer:762) INFO: 6epoch:train:521-560batch: iter_time=5.202e-05, forward_time=0.159, loss_ctc=23.384, loss=23.384, backward_time=0.024, grad_norm=295.925, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:51:43,338 (trainer:762) INFO: 6epoch:train:561-600batch: iter_time=5.128e-05, forward_time=0.158, loss_ctc=22.335, loss=22.335, backward_time=0.024, grad_norm=290.928, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:51:56,041 (trainer:762) INFO: 6epoch:train:601-640batch: iter_time=5.178e-05, forward_time=0.159, loss_ctc=22.561, loss=22.561, backward_time=0.024, grad_norm=309.222, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:52:08,741 (trainer:762) INFO: 6epoch:train:641-680batch: iter_time=5.082e-05, forward_time=0.158, loss_ctc=22.295, loss=22.295, backward_time=0.024, grad_norm=277.934, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:52:21,442 (trainer:762) INFO: 6epoch:train:681-720batch: iter_time=5.150e-05, forward_time=0.158, loss_ctc=22.755, loss=22.755, backward_time=0.024, grad_norm=300.060, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:52:34,145 (trainer:762) INFO: 6epoch:train:721-760batch: iter_time=5.330e-05, forward_time=0.158, loss_ctc=22.382, loss=22.382, backward_time=0.024, grad_norm=307.621, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:52:46,837 (trainer:762) INFO: 6epoch:train:761-800batch: iter_time=5.251e-05, forward_time=0.158, loss_ctc=22.129, loss=22.129, backward_time=0.024, grad_norm=309.578, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 19:52:51,626 (trainer:357) INFO: 6epoch results: [train] iter_time=2.522e-04, forward_time=0.159, loss_ctc=23.160, loss=23.160, backward_time=0.024, grad_norm=300.663, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.49 seconds, total_count=4800, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=376.937, cer_ctc=0.292, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=376.937, time=1.16 seconds, total_count=30, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.55 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 19:52:52,675 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 19:52:52,676 (trainer:291) INFO: 7/30epoch started. Estimated time to finish: 1 hour, 44 minutes and 10.75 seconds +[stan] 2024-01-16 19:53:05,674 (trainer:762) INFO: 7epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=22.023, loss=22.023, backward_time=0.024, grad_norm=296.448, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.299 +[stan] 2024-01-16 19:53:18,388 (trainer:762) INFO: 7epoch:train:41-80batch: iter_time=5.340e-05, forward_time=0.159, loss_ctc=21.824, loss=21.824, backward_time=0.024, grad_norm=297.192, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:53:31,112 (trainer:762) INFO: 7epoch:train:81-120batch: iter_time=4.985e-05, forward_time=0.159, loss_ctc=21.938, loss=21.938, backward_time=0.024, grad_norm=283.043, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:53:43,815 (trainer:762) INFO: 7epoch:train:121-160batch: iter_time=5.292e-05, forward_time=0.159, loss_ctc=21.441, loss=21.441, backward_time=0.024, grad_norm=294.314, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:53:56,525 (trainer:762) INFO: 7epoch:train:161-200batch: iter_time=5.073e-05, forward_time=0.159, loss_ctc=21.144, loss=21.144, backward_time=0.024, grad_norm=284.042, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:54:09,235 (trainer:762) INFO: 7epoch:train:201-240batch: iter_time=5.086e-05, forward_time=0.159, loss_ctc=20.743, loss=20.743, backward_time=0.024, grad_norm=294.671, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:54:21,946 (trainer:762) INFO: 7epoch:train:241-280batch: iter_time=5.234e-05, forward_time=0.159, loss_ctc=20.968, loss=20.968, backward_time=0.024, grad_norm=294.080, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:54:34,666 (trainer:762) INFO: 7epoch:train:281-320batch: iter_time=5.364e-05, forward_time=0.159, loss_ctc=21.077, loss=21.077, backward_time=0.024, grad_norm=303.828, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:54:47,378 (trainer:762) INFO: 7epoch:train:321-360batch: iter_time=5.078e-05, forward_time=0.159, loss_ctc=21.506, loss=21.506, backward_time=0.024, grad_norm=314.706, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:55:00,093 (trainer:762) INFO: 7epoch:train:361-400batch: iter_time=5.385e-05, forward_time=0.159, loss_ctc=20.717, loss=20.717, backward_time=0.024, grad_norm=307.246, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:55:12,806 (trainer:762) INFO: 7epoch:train:401-440batch: iter_time=5.398e-05, forward_time=0.159, loss_ctc=20.741, loss=20.741, backward_time=0.024, grad_norm=302.516, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:55:25,517 (trainer:762) INFO: 7epoch:train:441-480batch: iter_time=5.559e-05, forward_time=0.159, loss_ctc=20.611, loss=20.611, backward_time=0.024, grad_norm=270.586, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:55:38,224 (trainer:762) INFO: 7epoch:train:481-520batch: iter_time=5.224e-05, forward_time=0.159, loss_ctc=20.178, loss=20.178, backward_time=0.024, grad_norm=262.623, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:55:50,923 (trainer:762) INFO: 7epoch:train:521-560batch: iter_time=5.516e-05, forward_time=0.158, loss_ctc=20.214, loss=20.214, backward_time=0.024, grad_norm=270.295, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:56:03,629 (trainer:762) INFO: 7epoch:train:561-600batch: iter_time=5.055e-05, forward_time=0.159, loss_ctc=20.223, loss=20.223, backward_time=0.024, grad_norm=284.682, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:56:16,332 (trainer:762) INFO: 7epoch:train:601-640batch: iter_time=5.069e-05, forward_time=0.159, loss_ctc=20.218, loss=20.218, backward_time=0.024, grad_norm=305.101, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:56:29,031 (trainer:762) INFO: 7epoch:train:641-680batch: iter_time=5.478e-05, forward_time=0.158, loss_ctc=19.558, loss=19.558, backward_time=0.024, grad_norm=309.076, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:56:41,739 (trainer:762) INFO: 7epoch:train:681-720batch: iter_time=5.090e-05, forward_time=0.159, loss_ctc=20.811, loss=20.811, backward_time=0.024, grad_norm=313.288, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:56:54,435 (trainer:762) INFO: 7epoch:train:721-760batch: iter_time=5.155e-05, forward_time=0.158, loss_ctc=20.021, loss=20.021, backward_time=0.024, grad_norm=309.815, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:57:07,132 (trainer:762) INFO: 7epoch:train:761-800batch: iter_time=4.856e-05, forward_time=0.158, loss_ctc=20.013, loss=20.013, backward_time=0.024, grad_norm=294.944, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 19:57:11,896 (trainer:357) INFO: 7epoch results: [train] iter_time=2.284e-04, forward_time=0.159, loss_ctc=20.799, loss=20.799, backward_time=0.024, grad_norm=294.625, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.52 seconds, total_count=5600, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=386.033, cer_ctc=0.297, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=386.033, time=1.16 seconds, total_count=35, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.53 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 19:57:12,884 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 19:57:12,886 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/6epoch.pth +[stan] 2024-01-16 19:57:12,886 (trainer:291) INFO: 8/30epoch started. Estimated time to finish: 1 hour, 39 minutes and 49.52 seconds +[stan] 2024-01-16 19:57:25,914 (trainer:762) INFO: 8epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=19.155, loss=19.155, backward_time=0.024, grad_norm=285.745, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.302 +[stan] 2024-01-16 19:57:38,631 (trainer:762) INFO: 8epoch:train:41-80batch: iter_time=5.601e-05, forward_time=0.159, loss_ctc=19.198, loss=19.198, backward_time=0.024, grad_norm=289.458, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:57:51,344 (trainer:762) INFO: 8epoch:train:81-120batch: iter_time=5.310e-05, forward_time=0.159, loss_ctc=19.555, loss=19.555, backward_time=0.024, grad_norm=301.798, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:58:04,052 (trainer:762) INFO: 8epoch:train:121-160batch: iter_time=5.185e-05, forward_time=0.159, loss_ctc=19.640, loss=19.640, backward_time=0.024, grad_norm=280.279, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:58:16,769 (trainer:762) INFO: 8epoch:train:161-200batch: iter_time=5.116e-05, forward_time=0.159, loss_ctc=19.314, loss=19.314, backward_time=0.024, grad_norm=280.067, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:58:29,485 (trainer:762) INFO: 8epoch:train:201-240batch: iter_time=5.351e-05, forward_time=0.159, loss_ctc=18.940, loss=18.940, backward_time=0.024, grad_norm=304.582, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:58:42,204 (trainer:762) INFO: 8epoch:train:241-280batch: iter_time=5.320e-05, forward_time=0.159, loss_ctc=18.973, loss=18.973, backward_time=0.024, grad_norm=296.228, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:58:54,923 (trainer:762) INFO: 8epoch:train:281-320batch: iter_time=5.209e-05, forward_time=0.159, loss_ctc=18.956, loss=18.956, backward_time=0.024, grad_norm=299.360, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:59:07,638 (trainer:762) INFO: 8epoch:train:321-360batch: iter_time=5.204e-05, forward_time=0.159, loss_ctc=18.827, loss=18.827, backward_time=0.024, grad_norm=305.653, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:59:20,363 (trainer:762) INFO: 8epoch:train:361-400batch: iter_time=5.156e-05, forward_time=0.159, loss_ctc=17.781, loss=17.781, backward_time=0.024, grad_norm=290.268, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:59:33,078 (trainer:762) INFO: 8epoch:train:401-440batch: iter_time=5.229e-05, forward_time=0.159, loss_ctc=18.261, loss=18.261, backward_time=0.024, grad_norm=277.146, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 19:59:45,799 (trainer:762) INFO: 8epoch:train:441-480batch: iter_time=5.192e-05, forward_time=0.159, loss_ctc=18.261, loss=18.261, backward_time=0.024, grad_norm=289.653, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 19:59:58,514 (trainer:762) INFO: 8epoch:train:481-520batch: iter_time=5.465e-05, forward_time=0.159, loss_ctc=18.447, loss=18.447, backward_time=0.024, grad_norm=294.775, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:00:11,230 (trainer:762) INFO: 8epoch:train:521-560batch: iter_time=5.190e-05, forward_time=0.159, loss_ctc=18.249, loss=18.249, backward_time=0.024, grad_norm=275.459, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:00:23,940 (trainer:762) INFO: 8epoch:train:561-600batch: iter_time=5.360e-05, forward_time=0.159, loss_ctc=17.861, loss=17.861, backward_time=0.024, grad_norm=275.848, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:00:36,656 (trainer:762) INFO: 8epoch:train:601-640batch: iter_time=5.274e-05, forward_time=0.159, loss_ctc=17.859, loss=17.859, backward_time=0.024, grad_norm=289.452, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:00:49,361 (trainer:762) INFO: 8epoch:train:641-680batch: iter_time=5.181e-05, forward_time=0.159, loss_ctc=17.555, loss=17.555, backward_time=0.024, grad_norm=313.219, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:01:02,068 (trainer:762) INFO: 8epoch:train:681-720batch: iter_time=5.399e-05, forward_time=0.159, loss_ctc=17.627, loss=17.627, backward_time=0.024, grad_norm=275.797, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:01:14,772 (trainer:762) INFO: 8epoch:train:721-760batch: iter_time=5.283e-05, forward_time=0.159, loss_ctc=17.440, loss=17.440, backward_time=0.024, grad_norm=276.308, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:01:27,475 (trainer:762) INFO: 8epoch:train:761-800batch: iter_time=4.925e-05, forward_time=0.159, loss_ctc=17.196, loss=17.196, backward_time=0.024, grad_norm=276.824, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:01:32,171 (trainer:357) INFO: 8epoch results: [train] iter_time=2.665e-04, forward_time=0.159, loss_ctc=18.455, loss=18.455, backward_time=0.024, grad_norm=288.896, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273, time=4 minutes and 14.67 seconds, total_count=6400, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=393.644, cer_ctc=0.296, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=393.644, time=1.14 seconds, total_count=40, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.47 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 20:01:33,180 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 20:01:33,181 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/7epoch.pth +[stan] 2024-01-16 20:01:33,181 (trainer:291) INFO: 9/30epoch started. Estimated time to finish: 1 hour, 35 minutes and 28.78 seconds +[stan] 2024-01-16 20:01:46,197 (trainer:762) INFO: 9epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=17.540, loss=17.540, backward_time=0.024, grad_norm=284.673, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.300 +[stan] 2024-01-16 20:01:58,908 (trainer:762) INFO: 9epoch:train:41-80batch: iter_time=5.367e-05, forward_time=0.159, loss_ctc=17.287, loss=17.287, backward_time=0.024, grad_norm=277.949, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:02:11,612 (trainer:762) INFO: 9epoch:train:81-120batch: iter_time=5.337e-05, forward_time=0.159, loss_ctc=17.192, loss=17.192, backward_time=0.024, grad_norm=278.794, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:02:24,319 (trainer:762) INFO: 9epoch:train:121-160batch: iter_time=5.112e-05, forward_time=0.159, loss_ctc=17.185, loss=17.185, backward_time=0.024, grad_norm=290.239, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:02:37,032 (trainer:762) INFO: 9epoch:train:161-200batch: iter_time=5.014e-05, forward_time=0.159, loss_ctc=17.671, loss=17.671, backward_time=0.024, grad_norm=311.878, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:02:49,751 (trainer:762) INFO: 9epoch:train:201-240batch: iter_time=5.094e-05, forward_time=0.159, loss_ctc=16.848, loss=16.848, backward_time=0.024, grad_norm=294.296, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:03:02,472 (trainer:762) INFO: 9epoch:train:241-280batch: iter_time=5.204e-05, forward_time=0.159, loss_ctc=17.066, loss=17.066, backward_time=0.024, grad_norm=270.638, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:03:15,183 (trainer:762) INFO: 9epoch:train:281-320batch: iter_time=5.402e-05, forward_time=0.159, loss_ctc=16.769, loss=16.769, backward_time=0.024, grad_norm=271.670, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:03:27,900 (trainer:762) INFO: 9epoch:train:321-360batch: iter_time=5.191e-05, forward_time=0.159, loss_ctc=16.644, loss=16.644, backward_time=0.024, grad_norm=291.664, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:03:40,613 (trainer:762) INFO: 9epoch:train:361-400batch: iter_time=5.147e-05, forward_time=0.159, loss_ctc=16.717, loss=16.717, backward_time=0.024, grad_norm=299.984, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:03:53,335 (trainer:762) INFO: 9epoch:train:401-440batch: iter_time=5.304e-05, forward_time=0.159, loss_ctc=16.656, loss=16.656, backward_time=0.024, grad_norm=291.506, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:04:06,056 (trainer:762) INFO: 9epoch:train:441-480batch: iter_time=5.177e-05, forward_time=0.159, loss_ctc=16.110, loss=16.110, backward_time=0.024, grad_norm=273.457, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:04:18,765 (trainer:762) INFO: 9epoch:train:481-520batch: iter_time=5.357e-05, forward_time=0.159, loss_ctc=16.077, loss=16.077, backward_time=0.024, grad_norm=277.848, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:04:31,475 (trainer:762) INFO: 9epoch:train:521-560batch: iter_time=5.435e-05, forward_time=0.159, loss_ctc=15.770, loss=15.770, backward_time=0.024, grad_norm=260.857, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:04:44,181 (trainer:762) INFO: 9epoch:train:561-600batch: iter_time=5.422e-05, forward_time=0.159, loss_ctc=15.841, loss=15.841, backward_time=0.024, grad_norm=285.074, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:04:56,887 (trainer:762) INFO: 9epoch:train:601-640batch: iter_time=5.057e-05, forward_time=0.159, loss_ctc=15.926, loss=15.926, backward_time=0.024, grad_norm=274.899, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:05:09,587 (trainer:762) INFO: 9epoch:train:641-680batch: iter_time=5.141e-05, forward_time=0.158, loss_ctc=15.735, loss=15.735, backward_time=0.024, grad_norm=290.255, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:05:22,298 (trainer:762) INFO: 9epoch:train:681-720batch: iter_time=5.325e-05, forward_time=0.159, loss_ctc=15.329, loss=15.329, backward_time=0.024, grad_norm=286.684, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:05:34,998 (trainer:762) INFO: 9epoch:train:721-760batch: iter_time=5.209e-05, forward_time=0.158, loss_ctc=15.633, loss=15.633, backward_time=0.024, grad_norm=288.328, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:05:47,695 (trainer:762) INFO: 9epoch:train:761-800batch: iter_time=5.025e-05, forward_time=0.158, loss_ctc=16.025, loss=16.025, backward_time=0.024, grad_norm=309.789, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:05:52,442 (trainer:357) INFO: 9epoch results: [train] iter_time=2.541e-04, forward_time=0.159, loss_ctc=16.501, loss=16.501, backward_time=0.024, grad_norm=285.524, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.59 seconds, total_count=7200, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=403.449, cer_ctc=0.298, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=403.449, time=1.16 seconds, total_count=45, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.5 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 20:05:53,548 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 20:05:53,550 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/8epoch.pth +[stan] 2024-01-16 20:05:53,550 (trainer:291) INFO: 10/30epoch started. Estimated time to finish: 1 hour, 31 minutes and 8.31 seconds +[stan] 2024-01-16 20:06:06,554 (trainer:762) INFO: 10epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=15.199, loss=15.199, backward_time=0.024, grad_norm=280.883, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.299 +[stan] 2024-01-16 20:06:19,274 (trainer:762) INFO: 10epoch:train:41-80batch: iter_time=4.999e-05, forward_time=0.159, loss_ctc=15.363, loss=15.363, backward_time=0.024, grad_norm=286.315, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:06:31,978 (trainer:762) INFO: 10epoch:train:81-120batch: iter_time=5.188e-05, forward_time=0.159, loss_ctc=15.337, loss=15.337, backward_time=0.024, grad_norm=328.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:06:44,702 (trainer:762) INFO: 10epoch:train:121-160batch: iter_time=4.997e-05, forward_time=0.159, loss_ctc=14.845, loss=14.845, backward_time=0.024, grad_norm=272.668, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:06:57,429 (trainer:762) INFO: 10epoch:train:161-200batch: iter_time=5.188e-05, forward_time=0.159, loss_ctc=15.292, loss=15.292, backward_time=0.024, grad_norm=276.255, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273 +[stan] 2024-01-16 20:07:10,152 (trainer:762) INFO: 10epoch:train:201-240batch: iter_time=5.143e-05, forward_time=0.159, loss_ctc=15.046, loss=15.046, backward_time=0.024, grad_norm=276.068, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:07:22,873 (trainer:762) INFO: 10epoch:train:241-280batch: iter_time=5.016e-05, forward_time=0.159, loss_ctc=14.979, loss=14.979, backward_time=0.024, grad_norm=279.642, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:07:35,594 (trainer:762) INFO: 10epoch:train:281-320batch: iter_time=5.296e-05, forward_time=0.159, loss_ctc=14.703, loss=14.703, backward_time=0.024, grad_norm=274.228, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:07:48,315 (trainer:762) INFO: 10epoch:train:321-360batch: iter_time=5.122e-05, forward_time=0.159, loss_ctc=15.065, loss=15.065, backward_time=0.024, grad_norm=282.338, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:08:01,038 (trainer:762) INFO: 10epoch:train:361-400batch: iter_time=5.399e-05, forward_time=0.159, loss_ctc=14.462, loss=14.462, backward_time=0.024, grad_norm=272.155, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:08:13,760 (trainer:762) INFO: 10epoch:train:401-440batch: iter_time=5.371e-05, forward_time=0.159, loss_ctc=14.941, loss=14.941, backward_time=0.024, grad_norm=284.530, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:08:26,476 (trainer:762) INFO: 10epoch:train:441-480batch: iter_time=5.112e-05, forward_time=0.159, loss_ctc=14.562, loss=14.562, backward_time=0.024, grad_norm=272.122, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:08:39,190 (trainer:762) INFO: 10epoch:train:481-520batch: iter_time=5.100e-05, forward_time=0.159, loss_ctc=14.490, loss=14.490, backward_time=0.024, grad_norm=281.628, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:08:51,914 (trainer:762) INFO: 10epoch:train:521-560batch: iter_time=5.052e-05, forward_time=0.159, loss_ctc=14.597, loss=14.597, backward_time=0.024, grad_norm=266.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:09:04,626 (trainer:762) INFO: 10epoch:train:561-600batch: iter_time=5.380e-05, forward_time=0.159, loss_ctc=14.113, loss=14.113, backward_time=0.024, grad_norm=275.259, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:09:17,337 (trainer:762) INFO: 10epoch:train:601-640batch: iter_time=5.048e-05, forward_time=0.159, loss_ctc=14.270, loss=14.270, backward_time=0.024, grad_norm=272.593, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:09:30,050 (trainer:762) INFO: 10epoch:train:641-680batch: iter_time=5.173e-05, forward_time=0.159, loss_ctc=13.712, loss=13.712, backward_time=0.024, grad_norm=269.533, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:09:42,758 (trainer:762) INFO: 10epoch:train:681-720batch: iter_time=5.319e-05, forward_time=0.159, loss_ctc=13.889, loss=13.889, backward_time=0.024, grad_norm=279.121, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:09:55,461 (trainer:762) INFO: 10epoch:train:721-760batch: iter_time=5.025e-05, forward_time=0.159, loss_ctc=13.762, loss=13.762, backward_time=0.024, grad_norm=269.291, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:10:08,169 (trainer:762) INFO: 10epoch:train:761-800batch: iter_time=4.935e-05, forward_time=0.159, loss_ctc=13.780, loss=13.780, backward_time=0.024, grad_norm=282.635, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:10:12,892 (trainer:357) INFO: 10epoch results: [train] iter_time=2.244e-04, forward_time=0.159, loss_ctc=14.620, loss=14.620, backward_time=0.024, grad_norm=279.088, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273, time=4 minutes and 14.69 seconds, total_count=8000, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=411.306, cer_ctc=0.305, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=411.306, time=1.17 seconds, total_count=50, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.48 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 20:10:14,015 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 20:10:14,017 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/9epoch.pth +[stan] 2024-01-16 20:10:14,017 (trainer:291) INFO: 11/30epoch started. Estimated time to finish: 1 hour, 26 minutes and 48.06 seconds +[stan] 2024-01-16 20:10:27,040 (trainer:762) INFO: 11epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=14.270, loss=14.270, backward_time=0.024, grad_norm=256.112, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.301 +[stan] 2024-01-16 20:10:39,757 (trainer:762) INFO: 11epoch:train:41-80batch: iter_time=5.036e-05, forward_time=0.159, loss_ctc=13.991, loss=13.991, backward_time=0.024, grad_norm=271.661, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:10:52,468 (trainer:762) INFO: 11epoch:train:81-120batch: iter_time=5.203e-05, forward_time=0.159, loss_ctc=13.402, loss=13.402, backward_time=0.024, grad_norm=288.951, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:11:05,187 (trainer:762) INFO: 11epoch:train:121-160batch: iter_time=5.199e-05, forward_time=0.159, loss_ctc=13.478, loss=13.478, backward_time=0.024, grad_norm=278.949, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:11:17,909 (trainer:762) INFO: 11epoch:train:161-200batch: iter_time=5.283e-05, forward_time=0.159, loss_ctc=13.295, loss=13.295, backward_time=0.024, grad_norm=272.325, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:11:30,633 (trainer:762) INFO: 11epoch:train:201-240batch: iter_time=5.321e-05, forward_time=0.159, loss_ctc=13.654, loss=13.654, backward_time=0.024, grad_norm=278.453, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:11:43,355 (trainer:762) INFO: 11epoch:train:241-280batch: iter_time=5.348e-05, forward_time=0.159, loss_ctc=13.644, loss=13.644, backward_time=0.024, grad_norm=287.242, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:11:56,077 (trainer:762) INFO: 11epoch:train:281-320batch: iter_time=5.332e-05, forward_time=0.159, loss_ctc=13.339, loss=13.339, backward_time=0.024, grad_norm=279.438, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:12:08,796 (trainer:762) INFO: 11epoch:train:321-360batch: iter_time=5.192e-05, forward_time=0.159, loss_ctc=13.207, loss=13.207, backward_time=0.024, grad_norm=264.025, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:12:21,514 (trainer:762) INFO: 11epoch:train:361-400batch: iter_time=5.073e-05, forward_time=0.159, loss_ctc=13.431, loss=13.431, backward_time=0.024, grad_norm=285.138, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:12:34,235 (trainer:762) INFO: 11epoch:train:401-440batch: iter_time=5.354e-05, forward_time=0.159, loss_ctc=12.958, loss=12.958, backward_time=0.024, grad_norm=273.144, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:12:46,945 (trainer:762) INFO: 11epoch:train:441-480batch: iter_time=5.191e-05, forward_time=0.159, loss_ctc=12.801, loss=12.801, backward_time=0.024, grad_norm=251.677, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:12:59,661 (trainer:762) INFO: 11epoch:train:481-520batch: iter_time=5.262e-05, forward_time=0.159, loss_ctc=13.029, loss=13.029, backward_time=0.024, grad_norm=277.376, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:13:12,383 (trainer:762) INFO: 11epoch:train:521-560batch: iter_time=5.175e-05, forward_time=0.159, loss_ctc=12.544, loss=12.544, backward_time=0.024, grad_norm=266.696, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:13:25,102 (trainer:762) INFO: 11epoch:train:561-600batch: iter_time=5.118e-05, forward_time=0.159, loss_ctc=12.877, loss=12.877, backward_time=0.024, grad_norm=269.089, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:13:37,808 (trainer:762) INFO: 11epoch:train:601-640batch: iter_time=5.306e-05, forward_time=0.159, loss_ctc=12.066, loss=12.066, backward_time=0.024, grad_norm=273.269, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:13:50,513 (trainer:762) INFO: 11epoch:train:641-680batch: iter_time=5.087e-05, forward_time=0.159, loss_ctc=12.705, loss=12.705, backward_time=0.024, grad_norm=253.392, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:14:03,216 (trainer:762) INFO: 11epoch:train:681-720batch: iter_time=5.472e-05, forward_time=0.158, loss_ctc=12.512, loss=12.512, backward_time=0.024, grad_norm=264.838, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:14:15,921 (trainer:762) INFO: 11epoch:train:721-760batch: iter_time=5.054e-05, forward_time=0.159, loss_ctc=12.706, loss=12.706, backward_time=0.024, grad_norm=275.953, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:14:28,622 (trainer:762) INFO: 11epoch:train:761-800batch: iter_time=4.780e-05, forward_time=0.158, loss_ctc=12.240, loss=12.240, backward_time=0.024, grad_norm=266.749, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:14:33,358 (trainer:357) INFO: 11epoch results: [train] iter_time=2.507e-04, forward_time=0.159, loss_ctc=13.107, loss=13.107, backward_time=0.024, grad_norm=271.724, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273, time=4 minutes and 14.69 seconds, total_count=8800, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=415.381, cer_ctc=0.305, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=415.381, time=1.15 seconds, total_count=55, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.5 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 20:14:34,316 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 20:14:34,317 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/10epoch.pth +[stan] 2024-01-16 20:14:34,317 (trainer:291) INFO: 12/30epoch started. Estimated time to finish: 1 hour, 22 minutes and 27.48 seconds +[stan] 2024-01-16 20:14:47,318 (trainer:762) INFO: 12epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=12.591, loss=12.591, backward_time=0.024, grad_norm=263.668, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.299 +[stan] 2024-01-16 20:15:00,027 (trainer:762) INFO: 12epoch:train:41-80batch: iter_time=5.229e-05, forward_time=0.159, loss_ctc=12.123, loss=12.123, backward_time=0.024, grad_norm=261.519, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:15:12,735 (trainer:762) INFO: 12epoch:train:81-120batch: iter_time=5.757e-05, forward_time=0.159, loss_ctc=12.566, loss=12.566, backward_time=0.024, grad_norm=281.796, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:15:25,438 (trainer:762) INFO: 12epoch:train:121-160batch: iter_time=5.146e-05, forward_time=0.158, loss_ctc=12.327, loss=12.327, backward_time=0.024, grad_norm=270.314, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:15:38,160 (trainer:762) INFO: 12epoch:train:161-200batch: iter_time=5.147e-05, forward_time=0.159, loss_ctc=11.842, loss=11.842, backward_time=0.024, grad_norm=251.051, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:15:50,874 (trainer:762) INFO: 12epoch:train:201-240batch: iter_time=5.404e-05, forward_time=0.159, loss_ctc=12.202, loss=12.202, backward_time=0.024, grad_norm=252.882, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:16:03,587 (trainer:762) INFO: 12epoch:train:241-280batch: iter_time=5.138e-05, forward_time=0.159, loss_ctc=11.942, loss=11.942, backward_time=0.024, grad_norm=267.117, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:16:16,301 (trainer:762) INFO: 12epoch:train:281-320batch: iter_time=5.094e-05, forward_time=0.159, loss_ctc=11.775, loss=11.775, backward_time=0.024, grad_norm=256.724, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:16:29,020 (trainer:762) INFO: 12epoch:train:321-360batch: iter_time=5.062e-05, forward_time=0.159, loss_ctc=11.762, loss=11.762, backward_time=0.024, grad_norm=258.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:16:41,739 (trainer:762) INFO: 12epoch:train:361-400batch: iter_time=5.438e-05, forward_time=0.159, loss_ctc=11.997, loss=11.997, backward_time=0.024, grad_norm=273.929, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:16:54,445 (trainer:762) INFO: 12epoch:train:401-440batch: iter_time=5.346e-05, forward_time=0.159, loss_ctc=12.435, loss=12.435, backward_time=0.024, grad_norm=268.707, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:17:07,153 (trainer:762) INFO: 12epoch:train:441-480batch: iter_time=5.407e-05, forward_time=0.159, loss_ctc=11.217, loss=11.217, backward_time=0.024, grad_norm=245.572, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:17:19,862 (trainer:762) INFO: 12epoch:train:481-520batch: iter_time=5.157e-05, forward_time=0.159, loss_ctc=11.460, loss=11.460, backward_time=0.024, grad_norm=257.061, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:17:32,577 (trainer:762) INFO: 12epoch:train:521-560batch: iter_time=5.137e-05, forward_time=0.159, loss_ctc=11.796, loss=11.796, backward_time=0.024, grad_norm=256.531, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:17:45,280 (trainer:762) INFO: 12epoch:train:561-600batch: iter_time=5.409e-05, forward_time=0.159, loss_ctc=11.376, loss=11.376, backward_time=0.024, grad_norm=258.290, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:17:57,987 (trainer:762) INFO: 12epoch:train:601-640batch: iter_time=5.129e-05, forward_time=0.159, loss_ctc=11.500, loss=11.500, backward_time=0.024, grad_norm=260.589, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:18:10,694 (trainer:762) INFO: 12epoch:train:641-680batch: iter_time=5.215e-05, forward_time=0.159, loss_ctc=10.867, loss=10.867, backward_time=0.024, grad_norm=248.171, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:18:23,395 (trainer:762) INFO: 12epoch:train:681-720batch: iter_time=5.417e-05, forward_time=0.159, loss_ctc=11.144, loss=11.144, backward_time=0.024, grad_norm=235.074, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:18:36,104 (trainer:762) INFO: 12epoch:train:721-760batch: iter_time=5.134e-05, forward_time=0.159, loss_ctc=11.178, loss=11.178, backward_time=0.024, grad_norm=243.200, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:18:48,799 (trainer:762) INFO: 12epoch:train:761-800batch: iter_time=4.877e-05, forward_time=0.158, loss_ctc=11.190, loss=11.190, backward_time=0.024, grad_norm=258.470, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 20:18:53,498 (trainer:357) INFO: 12epoch results: [train] iter_time=2.256e-04, forward_time=0.159, loss_ctc=11.764, loss=11.764, backward_time=0.024, grad_norm=258.439, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.56 seconds, total_count=9600, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=422.953, cer_ctc=0.306, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=422.953, time=1.15 seconds, total_count=60, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.47 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 20:18:54,486 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 20:18:54,488 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/11epoch.pth +[stan] 2024-01-16 20:18:54,488 (trainer:291) INFO: 13/30epoch started. Estimated time to finish: 1 hour, 18 minutes and 6.75 seconds +[stan] 2024-01-16 20:19:07,490 (trainer:762) INFO: 13epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=11.123, loss=11.123, backward_time=0.024, grad_norm=240.424, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.299 +[stan] 2024-01-16 20:19:20,192 (trainer:762) INFO: 13epoch:train:41-80batch: iter_time=5.018e-05, forward_time=0.159, loss_ctc=11.099, loss=11.099, backward_time=0.024, grad_norm=232.233, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:19:32,894 (trainer:762) INFO: 13epoch:train:81-120batch: iter_time=5.374e-05, forward_time=0.158, loss_ctc=10.741, loss=10.741, backward_time=0.024, grad_norm=239.637, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:19:45,603 (trainer:762) INFO: 13epoch:train:121-160batch: iter_time=5.091e-05, forward_time=0.159, loss_ctc=10.991, loss=10.991, backward_time=0.024, grad_norm=250.403, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:19:58,319 (trainer:762) INFO: 13epoch:train:161-200batch: iter_time=5.288e-05, forward_time=0.159, loss_ctc=11.397, loss=11.397, backward_time=0.024, grad_norm=264.201, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:20:11,029 (trainer:762) INFO: 13epoch:train:201-240batch: iter_time=5.197e-05, forward_time=0.159, loss_ctc=11.008, loss=11.008, backward_time=0.024, grad_norm=271.243, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:20:23,738 (trainer:762) INFO: 13epoch:train:241-280batch: iter_time=5.167e-05, forward_time=0.159, loss_ctc=10.990, loss=10.990, backward_time=0.024, grad_norm=269.605, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:20:36,451 (trainer:762) INFO: 13epoch:train:281-320batch: iter_time=5.019e-05, forward_time=0.159, loss_ctc=10.820, loss=10.820, backward_time=0.024, grad_norm=265.337, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:20:49,160 (trainer:762) INFO: 13epoch:train:321-360batch: iter_time=5.404e-05, forward_time=0.159, loss_ctc=10.717, loss=10.717, backward_time=0.024, grad_norm=266.674, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:21:01,865 (trainer:762) INFO: 13epoch:train:361-400batch: iter_time=5.289e-05, forward_time=0.159, loss_ctc=10.846, loss=10.846, backward_time=0.024, grad_norm=244.840, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:21:14,571 (trainer:762) INFO: 13epoch:train:401-440batch: iter_time=5.389e-05, forward_time=0.159, loss_ctc=10.723, loss=10.723, backward_time=0.024, grad_norm=238.510, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:21:27,278 (trainer:762) INFO: 13epoch:train:441-480batch: iter_time=5.150e-05, forward_time=0.159, loss_ctc=10.549, loss=10.549, backward_time=0.024, grad_norm=250.727, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:21:39,982 (trainer:762) INFO: 13epoch:train:481-520batch: iter_time=5.084e-05, forward_time=0.158, loss_ctc=10.423, loss=10.423, backward_time=0.024, grad_norm=240.759, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:21:52,684 (trainer:762) INFO: 13epoch:train:521-560batch: iter_time=5.438e-05, forward_time=0.158, loss_ctc=10.632, loss=10.632, backward_time=0.024, grad_norm=230.436, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:22:05,380 (trainer:762) INFO: 13epoch:train:561-600batch: iter_time=5.341e-05, forward_time=0.158, loss_ctc=10.643, loss=10.643, backward_time=0.024, grad_norm=244.634, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 20:22:18,081 (trainer:762) INFO: 13epoch:train:601-640batch: iter_time=5.294e-05, forward_time=0.159, loss_ctc=10.498, loss=10.498, backward_time=0.024, grad_norm=242.463, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:22:30,780 (trainer:762) INFO: 13epoch:train:641-680batch: iter_time=5.493e-05, forward_time=0.158, loss_ctc=10.580, loss=10.580, backward_time=0.024, grad_norm=245.461, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:22:43,486 (trainer:762) INFO: 13epoch:train:681-720batch: iter_time=5.141e-05, forward_time=0.159, loss_ctc=10.415, loss=10.415, backward_time=0.024, grad_norm=246.944, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:22:56,188 (trainer:762) INFO: 13epoch:train:721-760batch: iter_time=4.966e-05, forward_time=0.158, loss_ctc=10.593, loss=10.593, backward_time=0.024, grad_norm=294.493, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:23:08,884 (trainer:762) INFO: 13epoch:train:761-800batch: iter_time=5.039e-05, forward_time=0.158, loss_ctc=9.795, loss=9.795, backward_time=0.024, grad_norm=265.714, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:23:13,626 (trainer:357) INFO: 13epoch results: [train] iter_time=2.296e-04, forward_time=0.159, loss_ctc=10.729, loss=10.729, backward_time=0.024, grad_norm=252.237, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.47 seconds, total_count=10400, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=431.839, cer_ctc=0.310, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=431.839, time=1.17 seconds, total_count=65, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.5 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 20:23:14,669 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 20:23:14,671 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/12epoch.pth +[stan] 2024-01-16 20:23:14,671 (trainer:291) INFO: 14/30epoch started. Estimated time to finish: 1 hour, 13 minutes and 46.12 seconds +[stan] 2024-01-16 20:23:27,668 (trainer:762) INFO: 14epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=10.011, loss=10.011, backward_time=0.024, grad_norm=240.395, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.298 +[stan] 2024-01-16 20:23:40,372 (trainer:762) INFO: 14epoch:train:41-80batch: iter_time=5.028e-05, forward_time=0.159, loss_ctc=10.075, loss=10.075, backward_time=0.024, grad_norm=259.803, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:23:53,071 (trainer:762) INFO: 14epoch:train:81-120batch: iter_time=4.993e-05, forward_time=0.158, loss_ctc=10.370, loss=10.370, backward_time=0.024, grad_norm=251.676, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:24:05,781 (trainer:762) INFO: 14epoch:train:121-160batch: iter_time=5.011e-05, forward_time=0.159, loss_ctc=10.411, loss=10.411, backward_time=0.024, grad_norm=267.803, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:24:18,494 (trainer:762) INFO: 14epoch:train:161-200batch: iter_time=5.077e-05, forward_time=0.159, loss_ctc=10.148, loss=10.148, backward_time=0.024, grad_norm=255.299, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:24:31,209 (trainer:762) INFO: 14epoch:train:201-240batch: iter_time=4.985e-05, forward_time=0.159, loss_ctc=10.181, loss=10.181, backward_time=0.024, grad_norm=241.160, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:24:43,932 (trainer:762) INFO: 14epoch:train:241-280batch: iter_time=5.258e-05, forward_time=0.159, loss_ctc=9.827, loss=9.827, backward_time=0.024, grad_norm=240.569, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:24:56,651 (trainer:762) INFO: 14epoch:train:281-320batch: iter_time=5.069e-05, forward_time=0.159, loss_ctc=9.879, loss=9.879, backward_time=0.024, grad_norm=232.075, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:25:09,370 (trainer:762) INFO: 14epoch:train:321-360batch: iter_time=5.150e-05, forward_time=0.159, loss_ctc=9.809, loss=9.809, backward_time=0.024, grad_norm=238.205, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:25:22,081 (trainer:762) INFO: 14epoch:train:361-400batch: iter_time=5.287e-05, forward_time=0.159, loss_ctc=9.546, loss=9.546, backward_time=0.024, grad_norm=233.319, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:25:34,788 (trainer:762) INFO: 14epoch:train:401-440batch: iter_time=5.128e-05, forward_time=0.159, loss_ctc=9.977, loss=9.977, backward_time=0.024, grad_norm=255.252, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:25:47,502 (trainer:762) INFO: 14epoch:train:441-480batch: iter_time=5.194e-05, forward_time=0.159, loss_ctc=9.645, loss=9.645, backward_time=0.024, grad_norm=255.313, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:26:00,224 (trainer:762) INFO: 14epoch:train:481-520batch: iter_time=5.060e-05, forward_time=0.159, loss_ctc=9.516, loss=9.516, backward_time=0.024, grad_norm=244.483, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:26:12,934 (trainer:762) INFO: 14epoch:train:521-560batch: iter_time=5.084e-05, forward_time=0.159, loss_ctc=9.726, loss=9.726, backward_time=0.024, grad_norm=263.694, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:26:25,643 (trainer:762) INFO: 14epoch:train:561-600batch: iter_time=5.013e-05, forward_time=0.159, loss_ctc=9.181, loss=9.181, backward_time=0.024, grad_norm=229.159, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:26:38,344 (trainer:762) INFO: 14epoch:train:601-640batch: iter_time=5.107e-05, forward_time=0.158, loss_ctc=9.472, loss=9.472, backward_time=0.024, grad_norm=233.208, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:26:51,039 (trainer:762) INFO: 14epoch:train:641-680batch: iter_time=5.020e-05, forward_time=0.158, loss_ctc=9.585, loss=9.585, backward_time=0.024, grad_norm=231.222, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 20:27:03,735 (trainer:762) INFO: 14epoch:train:681-720batch: iter_time=5.107e-05, forward_time=0.158, loss_ctc=9.094, loss=9.094, backward_time=0.024, grad_norm=231.699, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 20:27:16,434 (trainer:762) INFO: 14epoch:train:721-760batch: iter_time=5.134e-05, forward_time=0.158, loss_ctc=9.252, loss=9.252, backward_time=0.024, grad_norm=247.531, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:27:29,128 (trainer:762) INFO: 14epoch:train:761-800batch: iter_time=4.759e-05, forward_time=0.158, loss_ctc=9.578, loss=9.578, backward_time=0.024, grad_norm=316.545, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 20:27:33,826 (trainer:357) INFO: 14epoch results: [train] iter_time=2.277e-04, forward_time=0.159, loss_ctc=9.764, loss=9.764, backward_time=0.024, grad_norm=248.421, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.53 seconds, total_count=11200, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=436.935, cer_ctc=0.312, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=436.935, time=1.16 seconds, total_count=70, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.46 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 20:27:34,812 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 20:27:34,814 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/13epoch.pth +[stan] 2024-01-16 20:27:34,814 (trainer:291) INFO: 15/30epoch started. Estimated time to finish: 1 hour, 9 minutes and 25.51 seconds +[stan] 2024-01-16 20:27:47,822 (trainer:762) INFO: 15epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=9.658, loss=9.658, backward_time=0.024, grad_norm=271.202, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.300 +[stan] 2024-01-16 20:28:00,528 (trainer:762) INFO: 15epoch:train:41-80batch: iter_time=4.945e-05, forward_time=0.159, loss_ctc=9.274, loss=9.274, backward_time=0.024, grad_norm=236.128, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:28:13,230 (trainer:762) INFO: 15epoch:train:81-120batch: iter_time=5.228e-05, forward_time=0.158, loss_ctc=8.741, loss=8.741, backward_time=0.024, grad_norm=216.660, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:28:25,934 (trainer:762) INFO: 15epoch:train:121-160batch: iter_time=5.389e-05, forward_time=0.159, loss_ctc=9.084, loss=9.084, backward_time=0.024, grad_norm=218.809, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:28:38,653 (trainer:762) INFO: 15epoch:train:161-200batch: iter_time=5.078e-05, forward_time=0.159, loss_ctc=9.248, loss=9.248, backward_time=0.024, grad_norm=221.628, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:28:51,370 (trainer:762) INFO: 15epoch:train:201-240batch: iter_time=5.016e-05, forward_time=0.159, loss_ctc=8.928, loss=8.928, backward_time=0.024, grad_norm=231.963, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:29:04,094 (trainer:762) INFO: 15epoch:train:241-280batch: iter_time=5.196e-05, forward_time=0.159, loss_ctc=9.071, loss=9.071, backward_time=0.024, grad_norm=243.252, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:29:16,810 (trainer:762) INFO: 15epoch:train:281-320batch: iter_time=5.123e-05, forward_time=0.159, loss_ctc=8.953, loss=8.953, backward_time=0.024, grad_norm=232.578, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:29:29,524 (trainer:762) INFO: 15epoch:train:321-360batch: iter_time=5.044e-05, forward_time=0.159, loss_ctc=8.716, loss=8.716, backward_time=0.024, grad_norm=230.528, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:29:42,239 (trainer:762) INFO: 15epoch:train:361-400batch: iter_time=5.345e-05, forward_time=0.159, loss_ctc=8.989, loss=8.989, backward_time=0.024, grad_norm=245.050, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:29:54,952 (trainer:762) INFO: 15epoch:train:401-440batch: iter_time=5.050e-05, forward_time=0.159, loss_ctc=8.875, loss=8.875, backward_time=0.024, grad_norm=241.593, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:30:07,662 (trainer:762) INFO: 15epoch:train:441-480batch: iter_time=5.415e-05, forward_time=0.159, loss_ctc=8.875, loss=8.875, backward_time=0.024, grad_norm=237.199, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:30:20,376 (trainer:762) INFO: 15epoch:train:481-520batch: iter_time=5.141e-05, forward_time=0.159, loss_ctc=8.941, loss=8.941, backward_time=0.024, grad_norm=236.089, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:30:33,083 (trainer:762) INFO: 15epoch:train:521-560batch: iter_time=4.976e-05, forward_time=0.159, loss_ctc=8.814, loss=8.814, backward_time=0.024, grad_norm=217.899, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:30:45,801 (trainer:762) INFO: 15epoch:train:561-600batch: iter_time=5.101e-05, forward_time=0.159, loss_ctc=8.490, loss=8.490, backward_time=0.024, grad_norm=213.608, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:30:58,512 (trainer:762) INFO: 15epoch:train:601-640batch: iter_time=5.162e-05, forward_time=0.159, loss_ctc=8.698, loss=8.698, backward_time=0.024, grad_norm=224.382, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:31:11,216 (trainer:762) INFO: 15epoch:train:641-680batch: iter_time=5.042e-05, forward_time=0.159, loss_ctc=8.761, loss=8.761, backward_time=0.024, grad_norm=221.668, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:31:23,921 (trainer:762) INFO: 15epoch:train:681-720batch: iter_time=5.369e-05, forward_time=0.159, loss_ctc=8.300, loss=8.300, backward_time=0.024, grad_norm=212.129, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:31:36,622 (trainer:762) INFO: 15epoch:train:721-760batch: iter_time=5.327e-05, forward_time=0.158, loss_ctc=8.589, loss=8.589, backward_time=0.024, grad_norm=221.839, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:31:49,317 (trainer:762) INFO: 15epoch:train:761-800batch: iter_time=5.146e-05, forward_time=0.158, loss_ctc=8.804, loss=8.804, backward_time=0.024, grad_norm=214.099, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 20:31:53,974 (trainer:357) INFO: 15epoch results: [train] iter_time=2.406e-04, forward_time=0.159, loss_ctc=8.891, loss=8.891, backward_time=0.024, grad_norm=229.415, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.57 seconds, total_count=12000, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=441.370, cer_ctc=0.318, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=441.370, time=1.15 seconds, total_count=75, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.44 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 20:31:54,951 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 20:31:54,952 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/14epoch.pth +[stan] 2024-01-16 20:31:54,952 (trainer:291) INFO: 16/30epoch started. Estimated time to finish: 1 hour, 5 minutes and 4.96 seconds +[stan] 2024-01-16 20:32:07,962 (trainer:762) INFO: 16epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=8.596, loss=8.596, backward_time=0.024, grad_norm=238.602, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.300 +[stan] 2024-01-16 20:32:20,668 (trainer:762) INFO: 16epoch:train:41-80batch: iter_time=5.248e-05, forward_time=0.159, loss_ctc=8.673, loss=8.673, backward_time=0.024, grad_norm=219.301, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:32:33,365 (trainer:762) INFO: 16epoch:train:81-120batch: iter_time=5.262e-05, forward_time=0.158, loss_ctc=8.698, loss=8.698, backward_time=0.024, grad_norm=235.042, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:32:46,067 (trainer:762) INFO: 16epoch:train:121-160batch: iter_time=5.194e-05, forward_time=0.158, loss_ctc=8.385, loss=8.385, backward_time=0.024, grad_norm=213.862, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:32:58,773 (trainer:762) INFO: 16epoch:train:161-200batch: iter_time=5.050e-05, forward_time=0.159, loss_ctc=8.124, loss=8.124, backward_time=0.024, grad_norm=204.193, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:33:11,484 (trainer:762) INFO: 16epoch:train:201-240batch: iter_time=5.447e-05, forward_time=0.159, loss_ctc=8.335, loss=8.335, backward_time=0.024, grad_norm=219.084, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:33:24,191 (trainer:762) INFO: 16epoch:train:241-280batch: iter_time=5.085e-05, forward_time=0.159, loss_ctc=8.438, loss=8.438, backward_time=0.024, grad_norm=229.228, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:33:36,898 (trainer:762) INFO: 16epoch:train:281-320batch: iter_time=5.144e-05, forward_time=0.159, loss_ctc=8.365, loss=8.365, backward_time=0.024, grad_norm=233.678, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:33:49,602 (trainer:762) INFO: 16epoch:train:321-360batch: iter_time=5.212e-05, forward_time=0.159, loss_ctc=8.204, loss=8.204, backward_time=0.024, grad_norm=227.454, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:34:02,311 (trainer:762) INFO: 16epoch:train:361-400batch: iter_time=5.532e-05, forward_time=0.159, loss_ctc=8.135, loss=8.135, backward_time=0.024, grad_norm=233.055, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:34:15,022 (trainer:762) INFO: 16epoch:train:401-440batch: iter_time=5.425e-05, forward_time=0.159, loss_ctc=8.244, loss=8.244, backward_time=0.024, grad_norm=214.328, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:34:27,723 (trainer:762) INFO: 16epoch:train:441-480batch: iter_time=5.371e-05, forward_time=0.158, loss_ctc=7.958, loss=7.958, backward_time=0.024, grad_norm=208.580, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:34:40,427 (trainer:762) INFO: 16epoch:train:481-520batch: iter_time=5.146e-05, forward_time=0.158, loss_ctc=7.961, loss=7.961, backward_time=0.024, grad_norm=210.298, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:34:53,129 (trainer:762) INFO: 16epoch:train:521-560batch: iter_time=5.342e-05, forward_time=0.159, loss_ctc=8.157, loss=8.157, backward_time=0.024, grad_norm=209.227, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:35:05,829 (trainer:762) INFO: 16epoch:train:561-600batch: iter_time=5.143e-05, forward_time=0.158, loss_ctc=8.151, loss=8.151, backward_time=0.024, grad_norm=214.474, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:35:18,527 (trainer:762) INFO: 16epoch:train:601-640batch: iter_time=5.271e-05, forward_time=0.158, loss_ctc=7.925, loss=7.925, backward_time=0.024, grad_norm=225.089, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:35:31,224 (trainer:762) INFO: 16epoch:train:641-680batch: iter_time=5.149e-05, forward_time=0.158, loss_ctc=7.845, loss=7.845, backward_time=0.024, grad_norm=223.385, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:35:43,924 (trainer:762) INFO: 16epoch:train:681-720batch: iter_time=5.496e-05, forward_time=0.158, loss_ctc=7.447, loss=7.447, backward_time=0.024, grad_norm=207.730, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:35:56,617 (trainer:762) INFO: 16epoch:train:721-760batch: iter_time=5.311e-05, forward_time=0.158, loss_ctc=7.975, loss=7.975, backward_time=0.024, grad_norm=214.773, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 20:36:09,307 (trainer:762) INFO: 16epoch:train:761-800batch: iter_time=4.915e-05, forward_time=0.158, loss_ctc=7.692, loss=7.692, backward_time=0.024, grad_norm=219.113, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 20:36:14,032 (trainer:357) INFO: 16epoch results: [train] iter_time=2.468e-04, forward_time=0.158, loss_ctc=8.165, loss=8.165, backward_time=0.024, grad_norm=220.025, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.43 seconds, total_count=12800, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=450.862, cer_ctc=0.317, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=450.862, time=1.17 seconds, total_count=80, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.48 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 20:36:15,140 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 20:36:15,141 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/15epoch.pth +[stan] 2024-01-16 20:36:15,141 (trainer:291) INFO: 17/30epoch started. Estimated time to finish: 1 hour and 44.51 seconds +[stan] 2024-01-16 20:36:28,143 (trainer:762) INFO: 17epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=7.845, loss=7.845, backward_time=0.024, grad_norm=209.747, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.299 +[stan] 2024-01-16 20:36:40,851 (trainer:762) INFO: 17epoch:train:41-80batch: iter_time=5.150e-05, forward_time=0.159, loss_ctc=7.807, loss=7.807, backward_time=0.024, grad_norm=220.673, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:36:53,554 (trainer:762) INFO: 17epoch:train:81-120batch: iter_time=5.278e-05, forward_time=0.158, loss_ctc=7.639, loss=7.639, backward_time=0.024, grad_norm=218.074, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:37:06,264 (trainer:762) INFO: 17epoch:train:121-160batch: iter_time=5.240e-05, forward_time=0.159, loss_ctc=7.714, loss=7.714, backward_time=0.024, grad_norm=220.865, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:37:18,970 (trainer:762) INFO: 17epoch:train:161-200batch: iter_time=5.023e-05, forward_time=0.159, loss_ctc=7.764, loss=7.764, backward_time=0.024, grad_norm=248.221, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:37:31,678 (trainer:762) INFO: 17epoch:train:201-240batch: iter_time=5.679e-05, forward_time=0.159, loss_ctc=7.733, loss=7.733, backward_time=0.024, grad_norm=253.848, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:37:44,383 (trainer:762) INFO: 17epoch:train:241-280batch: iter_time=5.377e-05, forward_time=0.159, loss_ctc=7.286, loss=7.286, backward_time=0.024, grad_norm=218.182, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:37:57,090 (trainer:762) INFO: 17epoch:train:281-320batch: iter_time=5.382e-05, forward_time=0.159, loss_ctc=7.769, loss=7.769, backward_time=0.024, grad_norm=210.868, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:38:09,801 (trainer:762) INFO: 17epoch:train:321-360batch: iter_time=5.257e-05, forward_time=0.159, loss_ctc=7.375, loss=7.375, backward_time=0.024, grad_norm=210.087, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:38:22,511 (trainer:762) INFO: 17epoch:train:361-400batch: iter_time=5.716e-05, forward_time=0.159, loss_ctc=7.516, loss=7.516, backward_time=0.024, grad_norm=245.475, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:38:35,218 (trainer:762) INFO: 17epoch:train:401-440batch: iter_time=5.127e-05, forward_time=0.159, loss_ctc=7.541, loss=7.541, backward_time=0.024, grad_norm=262.427, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:38:47,922 (trainer:762) INFO: 17epoch:train:441-480batch: iter_time=5.330e-05, forward_time=0.159, loss_ctc=7.402, loss=7.402, backward_time=0.024, grad_norm=229.521, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:39:00,630 (trainer:762) INFO: 17epoch:train:481-520batch: iter_time=5.712e-05, forward_time=0.159, loss_ctc=7.502, loss=7.502, backward_time=0.024, grad_norm=205.465, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:39:13,336 (trainer:762) INFO: 17epoch:train:521-560batch: iter_time=5.184e-05, forward_time=0.159, loss_ctc=7.074, loss=7.074, backward_time=0.024, grad_norm=196.146, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:39:26,034 (trainer:762) INFO: 17epoch:train:561-600batch: iter_time=5.079e-05, forward_time=0.158, loss_ctc=7.300, loss=7.300, backward_time=0.024, grad_norm=208.215, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:39:38,733 (trainer:762) INFO: 17epoch:train:601-640batch: iter_time=5.102e-05, forward_time=0.158, loss_ctc=7.240, loss=7.240, backward_time=0.024, grad_norm=203.794, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:39:51,431 (trainer:762) INFO: 17epoch:train:641-680batch: iter_time=5.399e-05, forward_time=0.158, loss_ctc=7.275, loss=7.275, backward_time=0.024, grad_norm=204.340, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:40:04,127 (trainer:762) INFO: 17epoch:train:681-720batch: iter_time=5.226e-05, forward_time=0.158, loss_ctc=7.392, loss=7.392, backward_time=0.024, grad_norm=231.772, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:40:16,839 (trainer:762) INFO: 17epoch:train:721-760batch: iter_time=5.541e-05, forward_time=0.159, loss_ctc=7.275, loss=7.275, backward_time=0.024, grad_norm=232.178, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:40:29,528 (trainer:762) INFO: 17epoch:train:761-800batch: iter_time=4.971e-05, forward_time=0.158, loss_ctc=7.126, loss=7.126, backward_time=0.024, grad_norm=209.383, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 20:40:34,201 (trainer:357) INFO: 17epoch results: [train] iter_time=2.373e-04, forward_time=0.159, loss_ctc=7.479, loss=7.479, backward_time=0.024, grad_norm=221.964, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.47 seconds, total_count=13600, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=451.452, cer_ctc=0.315, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=451.452, time=1.15 seconds, total_count=85, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.44 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 20:40:35,289 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 20:40:35,291 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/16epoch.pth +[stan] 2024-01-16 20:40:35,291 (trainer:291) INFO: 18/30epoch started. Estimated time to finish: 56 minutes and 24.05 seconds +[stan] 2024-01-16 20:40:48,279 (trainer:762) INFO: 18epoch:train:1-40batch: iter_time=0.003, forward_time=0.159, loss_ctc=7.150, loss=7.150, backward_time=0.024, grad_norm=211.341, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.297 +[stan] 2024-01-16 20:41:00,998 (trainer:762) INFO: 18epoch:train:41-80batch: iter_time=5.022e-05, forward_time=0.159, loss_ctc=7.121, loss=7.121, backward_time=0.024, grad_norm=211.512, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:41:13,703 (trainer:762) INFO: 18epoch:train:81-120batch: iter_time=5.279e-05, forward_time=0.159, loss_ctc=7.255, loss=7.255, backward_time=0.024, grad_norm=215.898, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:41:26,415 (trainer:762) INFO: 18epoch:train:121-160batch: iter_time=5.303e-05, forward_time=0.159, loss_ctc=6.908, loss=6.908, backward_time=0.024, grad_norm=204.964, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:41:39,132 (trainer:762) INFO: 18epoch:train:161-200batch: iter_time=5.337e-05, forward_time=0.159, loss_ctc=7.204, loss=7.204, backward_time=0.024, grad_norm=225.271, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:41:51,853 (trainer:762) INFO: 18epoch:train:201-240batch: iter_time=5.396e-05, forward_time=0.159, loss_ctc=7.167, loss=7.167, backward_time=0.024, grad_norm=211.704, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:42:04,571 (trainer:762) INFO: 18epoch:train:241-280batch: iter_time=5.025e-05, forward_time=0.159, loss_ctc=7.153, loss=7.153, backward_time=0.024, grad_norm=209.387, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:42:17,287 (trainer:762) INFO: 18epoch:train:281-320batch: iter_time=5.617e-05, forward_time=0.159, loss_ctc=6.664, loss=6.664, backward_time=0.024, grad_norm=195.684, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:42:30,000 (trainer:762) INFO: 18epoch:train:321-360batch: iter_time=5.313e-05, forward_time=0.159, loss_ctc=7.035, loss=7.035, backward_time=0.024, grad_norm=227.521, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:42:42,715 (trainer:762) INFO: 18epoch:train:361-400batch: iter_time=5.245e-05, forward_time=0.159, loss_ctc=6.765, loss=6.765, backward_time=0.024, grad_norm=200.203, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:42:55,438 (trainer:762) INFO: 18epoch:train:401-440batch: iter_time=5.347e-05, forward_time=0.159, loss_ctc=7.010, loss=7.010, backward_time=0.024, grad_norm=214.033, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:43:08,145 (trainer:762) INFO: 18epoch:train:441-480batch: iter_time=5.336e-05, forward_time=0.159, loss_ctc=6.601, loss=6.601, backward_time=0.024, grad_norm=196.245, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:43:20,861 (trainer:762) INFO: 18epoch:train:481-520batch: iter_time=5.424e-05, forward_time=0.159, loss_ctc=6.696, loss=6.696, backward_time=0.024, grad_norm=197.063, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:43:33,563 (trainer:762) INFO: 18epoch:train:521-560batch: iter_time=5.129e-05, forward_time=0.158, loss_ctc=6.911, loss=6.911, backward_time=0.024, grad_norm=199.468, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:43:46,263 (trainer:762) INFO: 18epoch:train:561-600batch: iter_time=5.186e-05, forward_time=0.158, loss_ctc=6.880, loss=6.880, backward_time=0.024, grad_norm=198.831, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:43:58,970 (trainer:762) INFO: 18epoch:train:601-640batch: iter_time=5.416e-05, forward_time=0.159, loss_ctc=6.855, loss=6.855, backward_time=0.024, grad_norm=194.525, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:44:11,672 (trainer:762) INFO: 18epoch:train:641-680batch: iter_time=5.201e-05, forward_time=0.158, loss_ctc=6.792, loss=6.792, backward_time=0.024, grad_norm=195.061, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:44:24,381 (trainer:762) INFO: 18epoch:train:681-720batch: iter_time=5.352e-05, forward_time=0.159, loss_ctc=6.915, loss=6.915, backward_time=0.024, grad_norm=194.850, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:44:37,085 (trainer:762) INFO: 18epoch:train:721-760batch: iter_time=5.381e-05, forward_time=0.159, loss_ctc=6.858, loss=6.858, backward_time=0.024, grad_norm=202.974, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:44:49,788 (trainer:762) INFO: 18epoch:train:761-800batch: iter_time=4.775e-05, forward_time=0.158, loss_ctc=6.763, loss=6.763, backward_time=0.024, grad_norm=203.727, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:44:54,466 (trainer:357) INFO: 18epoch results: [train] iter_time=2.229e-04, forward_time=0.159, loss_ctc=6.935, loss=6.935, backward_time=0.024, grad_norm=205.513, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.57 seconds, total_count=14400, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=460.126, cer_ctc=0.320, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=460.126, time=1.15 seconds, total_count=90, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.46 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 20:44:55,503 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 20:44:55,504 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/17epoch.pth +[stan] 2024-01-16 20:44:55,504 (trainer:291) INFO: 19/30epoch started. Estimated time to finish: 52 minutes and 3.68 seconds +[stan] 2024-01-16 20:45:08,516 (trainer:762) INFO: 19epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=6.944, loss=6.944, backward_time=0.024, grad_norm=236.661, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.300 +[stan] 2024-01-16 20:45:21,223 (trainer:762) INFO: 19epoch:train:41-80batch: iter_time=5.349e-05, forward_time=0.159, loss_ctc=6.878, loss=6.878, backward_time=0.024, grad_norm=210.103, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:45:33,921 (trainer:762) INFO: 19epoch:train:81-120batch: iter_time=5.092e-05, forward_time=0.158, loss_ctc=6.737, loss=6.737, backward_time=0.024, grad_norm=214.352, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:45:46,627 (trainer:762) INFO: 19epoch:train:121-160batch: iter_time=5.104e-05, forward_time=0.159, loss_ctc=6.392, loss=6.392, backward_time=0.024, grad_norm=200.672, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:45:59,338 (trainer:762) INFO: 19epoch:train:161-200batch: iter_time=5.326e-05, forward_time=0.159, loss_ctc=6.478, loss=6.478, backward_time=0.024, grad_norm=211.100, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:46:12,048 (trainer:762) INFO: 19epoch:train:201-240batch: iter_time=5.037e-05, forward_time=0.159, loss_ctc=6.329, loss=6.329, backward_time=0.024, grad_norm=195.708, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:46:24,765 (trainer:762) INFO: 19epoch:train:241-280batch: iter_time=5.105e-05, forward_time=0.159, loss_ctc=6.437, loss=6.437, backward_time=0.024, grad_norm=207.189, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:46:37,482 (trainer:762) INFO: 19epoch:train:281-320batch: iter_time=5.191e-05, forward_time=0.159, loss_ctc=6.402, loss=6.402, backward_time=0.024, grad_norm=220.474, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:46:50,195 (trainer:762) INFO: 19epoch:train:321-360batch: iter_time=5.347e-05, forward_time=0.159, loss_ctc=6.502, loss=6.502, backward_time=0.024, grad_norm=224.268, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:47:02,908 (trainer:762) INFO: 19epoch:train:361-400batch: iter_time=5.387e-05, forward_time=0.159, loss_ctc=6.638, loss=6.638, backward_time=0.024, grad_norm=223.707, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:47:15,625 (trainer:762) INFO: 19epoch:train:401-440batch: iter_time=5.455e-05, forward_time=0.159, loss_ctc=6.387, loss=6.387, backward_time=0.024, grad_norm=189.395, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:47:28,339 (trainer:762) INFO: 19epoch:train:441-480batch: iter_time=5.120e-05, forward_time=0.159, loss_ctc=6.598, loss=6.598, backward_time=0.024, grad_norm=231.493, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:47:41,048 (trainer:762) INFO: 19epoch:train:481-520batch: iter_time=5.131e-05, forward_time=0.159, loss_ctc=6.233, loss=6.233, backward_time=0.024, grad_norm=203.467, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:47:53,755 (trainer:762) INFO: 19epoch:train:521-560batch: iter_time=5.090e-05, forward_time=0.159, loss_ctc=6.072, loss=6.072, backward_time=0.024, grad_norm=191.146, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:48:06,461 (trainer:762) INFO: 19epoch:train:561-600batch: iter_time=5.049e-05, forward_time=0.159, loss_ctc=6.201, loss=6.201, backward_time=0.024, grad_norm=205.659, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:48:19,157 (trainer:762) INFO: 19epoch:train:601-640batch: iter_time=5.458e-05, forward_time=0.158, loss_ctc=6.480, loss=6.480, backward_time=0.024, grad_norm=211.354, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:48:31,860 (trainer:762) INFO: 19epoch:train:641-680batch: iter_time=5.296e-05, forward_time=0.159, loss_ctc=6.083, loss=6.083, backward_time=0.024, grad_norm=200.495, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:48:44,564 (trainer:762) INFO: 19epoch:train:681-720batch: iter_time=5.107e-05, forward_time=0.159, loss_ctc=6.176, loss=6.176, backward_time=0.024, grad_norm=204.069, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:48:57,265 (trainer:762) INFO: 19epoch:train:721-760batch: iter_time=5.587e-05, forward_time=0.158, loss_ctc=6.038, loss=6.038, backward_time=0.024, grad_norm=194.522, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:49:09,962 (trainer:762) INFO: 19epoch:train:761-800batch: iter_time=5.007e-05, forward_time=0.158, loss_ctc=6.339, loss=6.339, backward_time=0.024, grad_norm=181.321, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:49:14,706 (trainer:357) INFO: 19epoch results: [train] iter_time=2.542e-04, forward_time=0.159, loss_ctc=6.417, loss=6.417, backward_time=0.024, grad_norm=207.858, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.53 seconds, total_count=15200, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=454.762, cer_ctc=0.317, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=454.762, time=1.17 seconds, total_count=95, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.49 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 20:49:15,819 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 20:49:15,821 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/18epoch.pth +[stan] 2024-01-16 20:49:15,821 (trainer:291) INFO: 20/30epoch started. Estimated time to finish: 47 minutes and 43.38 seconds +[stan] 2024-01-16 20:49:28,822 (trainer:762) INFO: 20epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=5.938, loss=5.938, backward_time=0.024, grad_norm=190.751, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.299 +[stan] 2024-01-16 20:49:41,531 (trainer:762) INFO: 20epoch:train:41-80batch: iter_time=5.026e-05, forward_time=0.159, loss_ctc=6.033, loss=6.033, backward_time=0.024, grad_norm=198.949, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:49:54,238 (trainer:762) INFO: 20epoch:train:81-120batch: iter_time=4.918e-05, forward_time=0.159, loss_ctc=5.974, loss=5.974, backward_time=0.024, grad_norm=191.261, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:50:06,954 (trainer:762) INFO: 20epoch:train:121-160batch: iter_time=5.298e-05, forward_time=0.159, loss_ctc=6.140, loss=6.140, backward_time=0.024, grad_norm=209.740, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:50:19,671 (trainer:762) INFO: 20epoch:train:161-200batch: iter_time=4.974e-05, forward_time=0.159, loss_ctc=6.290, loss=6.290, backward_time=0.024, grad_norm=217.381, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:50:32,389 (trainer:762) INFO: 20epoch:train:201-240batch: iter_time=4.989e-05, forward_time=0.159, loss_ctc=6.127, loss=6.127, backward_time=0.024, grad_norm=219.848, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:50:45,108 (trainer:762) INFO: 20epoch:train:241-280batch: iter_time=4.956e-05, forward_time=0.159, loss_ctc=6.042, loss=6.042, backward_time=0.024, grad_norm=192.190, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:50:57,832 (trainer:762) INFO: 20epoch:train:281-320batch: iter_time=5.180e-05, forward_time=0.159, loss_ctc=6.102, loss=6.102, backward_time=0.024, grad_norm=190.611, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:51:10,556 (trainer:762) INFO: 20epoch:train:321-360batch: iter_time=5.053e-05, forward_time=0.159, loss_ctc=6.117, loss=6.117, backward_time=0.024, grad_norm=193.371, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:51:23,275 (trainer:762) INFO: 20epoch:train:361-400batch: iter_time=5.333e-05, forward_time=0.159, loss_ctc=5.851, loss=5.851, backward_time=0.024, grad_norm=181.936, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:51:35,988 (trainer:762) INFO: 20epoch:train:401-440batch: iter_time=5.148e-05, forward_time=0.159, loss_ctc=6.186, loss=6.186, backward_time=0.024, grad_norm=214.474, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:51:48,698 (trainer:762) INFO: 20epoch:train:441-480batch: iter_time=5.349e-05, forward_time=0.159, loss_ctc=6.253, loss=6.253, backward_time=0.024, grad_norm=196.805, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:52:01,403 (trainer:762) INFO: 20epoch:train:481-520batch: iter_time=5.085e-05, forward_time=0.159, loss_ctc=6.004, loss=6.004, backward_time=0.024, grad_norm=197.399, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:52:14,106 (trainer:762) INFO: 20epoch:train:521-560batch: iter_time=5.125e-05, forward_time=0.159, loss_ctc=6.041, loss=6.041, backward_time=0.024, grad_norm=189.176, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:52:26,813 (trainer:762) INFO: 20epoch:train:561-600batch: iter_time=5.161e-05, forward_time=0.159, loss_ctc=5.935, loss=5.935, backward_time=0.024, grad_norm=200.691, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:52:39,519 (trainer:762) INFO: 20epoch:train:601-640batch: iter_time=5.011e-05, forward_time=0.159, loss_ctc=5.800, loss=5.800, backward_time=0.024, grad_norm=187.941, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:52:52,219 (trainer:762) INFO: 20epoch:train:641-680batch: iter_time=5.046e-05, forward_time=0.158, loss_ctc=5.751, loss=5.751, backward_time=0.024, grad_norm=189.863, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:53:04,926 (trainer:762) INFO: 20epoch:train:681-720batch: iter_time=5.482e-05, forward_time=0.159, loss_ctc=5.817, loss=5.817, backward_time=0.024, grad_norm=193.835, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:53:17,623 (trainer:762) INFO: 20epoch:train:721-760batch: iter_time=5.460e-05, forward_time=0.158, loss_ctc=5.846, loss=5.846, backward_time=0.024, grad_norm=184.085, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:53:30,325 (trainer:762) INFO: 20epoch:train:761-800batch: iter_time=4.877e-05, forward_time=0.158, loss_ctc=5.769, loss=5.769, backward_time=0.024, grad_norm=205.838, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:53:34,993 (trainer:357) INFO: 20epoch results: [train] iter_time=2.312e-04, forward_time=0.159, loss_ctc=6.001, loss=6.001, backward_time=0.024, grad_norm=197.307, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.58 seconds, total_count=16000, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=462.094, cer_ctc=0.323, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=462.094, time=1.15 seconds, total_count=100, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.44 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 20:53:36,050 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 20:53:36,052 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/19epoch.pth +[stan] 2024-01-16 20:53:36,052 (trainer:291) INFO: 21/30epoch started. Estimated time to finish: 43 minutes and 23.03 seconds +[stan] 2024-01-16 20:53:49,037 (trainer:762) INFO: 21epoch:train:1-40batch: iter_time=0.003, forward_time=0.159, loss_ctc=5.713, loss=5.713, backward_time=0.024, grad_norm=178.036, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.297 +[stan] 2024-01-16 20:54:01,737 (trainer:762) INFO: 21epoch:train:41-80batch: iter_time=4.990e-05, forward_time=0.158, loss_ctc=5.791, loss=5.791, backward_time=0.024, grad_norm=190.997, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:54:14,447 (trainer:762) INFO: 21epoch:train:81-120batch: iter_time=5.291e-05, forward_time=0.159, loss_ctc=5.800, loss=5.800, backward_time=0.024, grad_norm=199.538, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:54:27,153 (trainer:762) INFO: 21epoch:train:121-160batch: iter_time=5.208e-05, forward_time=0.159, loss_ctc=5.959, loss=5.959, backward_time=0.024, grad_norm=240.040, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:54:39,866 (trainer:762) INFO: 21epoch:train:161-200batch: iter_time=5.186e-05, forward_time=0.159, loss_ctc=5.755, loss=5.755, backward_time=0.024, grad_norm=191.544, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:54:52,584 (trainer:762) INFO: 21epoch:train:201-240batch: iter_time=5.329e-05, forward_time=0.159, loss_ctc=5.826, loss=5.826, backward_time=0.024, grad_norm=198.677, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:55:05,304 (trainer:762) INFO: 21epoch:train:241-280batch: iter_time=5.053e-05, forward_time=0.159, loss_ctc=5.614, loss=5.614, backward_time=0.024, grad_norm=182.665, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:55:18,026 (trainer:762) INFO: 21epoch:train:281-320batch: iter_time=5.077e-05, forward_time=0.159, loss_ctc=5.539, loss=5.539, backward_time=0.024, grad_norm=201.013, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:55:30,747 (trainer:762) INFO: 21epoch:train:321-360batch: iter_time=5.361e-05, forward_time=0.159, loss_ctc=5.859, loss=5.859, backward_time=0.024, grad_norm=189.953, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:55:43,461 (trainer:762) INFO: 21epoch:train:361-400batch: iter_time=5.076e-05, forward_time=0.159, loss_ctc=5.553, loss=5.553, backward_time=0.024, grad_norm=184.241, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:55:56,181 (trainer:762) INFO: 21epoch:train:401-440batch: iter_time=5.074e-05, forward_time=0.159, loss_ctc=5.691, loss=5.691, backward_time=0.024, grad_norm=186.145, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:56:08,898 (trainer:762) INFO: 21epoch:train:441-480batch: iter_time=5.117e-05, forward_time=0.159, loss_ctc=5.571, loss=5.571, backward_time=0.024, grad_norm=189.801, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:56:21,616 (trainer:762) INFO: 21epoch:train:481-520batch: iter_time=5.335e-05, forward_time=0.159, loss_ctc=5.603, loss=5.603, backward_time=0.024, grad_norm=179.244, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:56:34,329 (trainer:762) INFO: 21epoch:train:521-560batch: iter_time=5.101e-05, forward_time=0.159, loss_ctc=5.719, loss=5.719, backward_time=0.024, grad_norm=190.120, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:56:47,040 (trainer:762) INFO: 21epoch:train:561-600batch: iter_time=5.105e-05, forward_time=0.159, loss_ctc=5.665, loss=5.665, backward_time=0.024, grad_norm=226.885, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:56:59,746 (trainer:762) INFO: 21epoch:train:601-640batch: iter_time=5.087e-05, forward_time=0.159, loss_ctc=5.578, loss=5.578, backward_time=0.024, grad_norm=185.379, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:57:12,452 (trainer:762) INFO: 21epoch:train:641-680batch: iter_time=5.147e-05, forward_time=0.159, loss_ctc=5.537, loss=5.537, backward_time=0.024, grad_norm=185.703, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:57:25,152 (trainer:762) INFO: 21epoch:train:681-720batch: iter_time=5.232e-05, forward_time=0.159, loss_ctc=5.632, loss=5.632, backward_time=0.024, grad_norm=178.495, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:57:37,856 (trainer:762) INFO: 21epoch:train:721-760batch: iter_time=5.133e-05, forward_time=0.159, loss_ctc=5.675, loss=5.675, backward_time=0.024, grad_norm=179.053, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:57:50,555 (trainer:762) INFO: 21epoch:train:761-800batch: iter_time=4.820e-05, forward_time=0.158, loss_ctc=5.413, loss=5.413, backward_time=0.024, grad_norm=180.372, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:57:55,265 (trainer:357) INFO: 21epoch results: [train] iter_time=2.117e-04, forward_time=0.159, loss_ctc=5.675, loss=5.675, backward_time=0.024, grad_norm=191.895, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.57 seconds, total_count=16800, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=470.010, cer_ctc=0.330, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=470.010, time=1.15 seconds, total_count=105, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.48 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 20:57:56,269 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 20:57:56,270 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/20epoch.pth +[stan] 2024-01-16 20:57:56,270 (trainer:291) INFO: 22/30epoch started. Estimated time to finish: 39 minutes and 2.69 seconds +[stan] 2024-01-16 20:58:09,274 (trainer:762) INFO: 22epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=5.652, loss=5.652, backward_time=0.024, grad_norm=177.802, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.299 +[stan] 2024-01-16 20:58:21,982 (trainer:762) INFO: 22epoch:train:41-80batch: iter_time=4.983e-05, forward_time=0.159, loss_ctc=5.522, loss=5.522, backward_time=0.024, grad_norm=175.500, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:58:34,685 (trainer:762) INFO: 22epoch:train:81-120batch: iter_time=5.233e-05, forward_time=0.159, loss_ctc=5.282, loss=5.282, backward_time=0.024, grad_norm=193.314, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:58:47,388 (trainer:762) INFO: 22epoch:train:121-160batch: iter_time=5.193e-05, forward_time=0.158, loss_ctc=5.413, loss=5.413, backward_time=0.024, grad_norm=190.572, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 20:59:00,102 (trainer:762) INFO: 22epoch:train:161-200batch: iter_time=5.136e-05, forward_time=0.159, loss_ctc=5.290, loss=5.290, backward_time=0.024, grad_norm=173.493, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:59:12,819 (trainer:762) INFO: 22epoch:train:201-240batch: iter_time=5.107e-05, forward_time=0.159, loss_ctc=5.678, loss=5.678, backward_time=0.024, grad_norm=189.679, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:59:25,537 (trainer:762) INFO: 22epoch:train:241-280batch: iter_time=5.194e-05, forward_time=0.159, loss_ctc=5.276, loss=5.276, backward_time=0.024, grad_norm=186.437, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 20:59:38,251 (trainer:762) INFO: 22epoch:train:281-320batch: iter_time=5.086e-05, forward_time=0.159, loss_ctc=5.104, loss=5.104, backward_time=0.024, grad_norm=188.165, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 20:59:50,970 (trainer:762) INFO: 22epoch:train:321-360batch: iter_time=4.972e-05, forward_time=0.159, loss_ctc=5.264, loss=5.264, backward_time=0.024, grad_norm=178.487, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:00:03,696 (trainer:762) INFO: 22epoch:train:361-400batch: iter_time=5.196e-05, forward_time=0.159, loss_ctc=5.382, loss=5.382, backward_time=0.024, grad_norm=211.919, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:00:16,409 (trainer:762) INFO: 22epoch:train:401-440batch: iter_time=5.264e-05, forward_time=0.159, loss_ctc=5.232, loss=5.232, backward_time=0.024, grad_norm=206.878, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:00:29,121 (trainer:762) INFO: 22epoch:train:441-480batch: iter_time=5.276e-05, forward_time=0.159, loss_ctc=4.901, loss=4.901, backward_time=0.024, grad_norm=198.154, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:00:41,829 (trainer:762) INFO: 22epoch:train:481-520batch: iter_time=4.969e-05, forward_time=0.159, loss_ctc=4.973, loss=4.973, backward_time=0.024, grad_norm=180.218, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:00:54,541 (trainer:762) INFO: 22epoch:train:521-560batch: iter_time=5.088e-05, forward_time=0.159, loss_ctc=5.299, loss=5.299, backward_time=0.024, grad_norm=185.222, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:01:07,255 (trainer:762) INFO: 22epoch:train:561-600batch: iter_time=5.354e-05, forward_time=0.159, loss_ctc=5.170, loss=5.170, backward_time=0.024, grad_norm=189.456, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:01:19,966 (trainer:762) INFO: 22epoch:train:601-640batch: iter_time=4.911e-05, forward_time=0.159, loss_ctc=4.827, loss=4.827, backward_time=0.024, grad_norm=165.821, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:01:32,667 (trainer:762) INFO: 22epoch:train:641-680batch: iter_time=5.422e-05, forward_time=0.159, loss_ctc=4.973, loss=4.973, backward_time=0.024, grad_norm=175.536, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:01:45,360 (trainer:762) INFO: 22epoch:train:681-720batch: iter_time=5.229e-05, forward_time=0.158, loss_ctc=5.208, loss=5.208, backward_time=0.024, grad_norm=176.855, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 21:01:58,056 (trainer:762) INFO: 22epoch:train:721-760batch: iter_time=5.329e-05, forward_time=0.158, loss_ctc=5.196, loss=5.196, backward_time=0.024, grad_norm=172.113, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 21:02:10,753 (trainer:762) INFO: 22epoch:train:761-800batch: iter_time=5.001e-05, forward_time=0.158, loss_ctc=5.316, loss=5.316, backward_time=0.024, grad_norm=178.255, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:02:15,477 (trainer:357) INFO: 22epoch results: [train] iter_time=2.354e-04, forward_time=0.159, loss_ctc=5.248, loss=5.248, backward_time=0.024, grad_norm=184.694, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.56 seconds, total_count=17600, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=479.061, cer_ctc=0.335, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=479.061, time=1.17 seconds, total_count=110, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.47 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 21:02:16,558 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:02:16,559 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/21epoch.pth +[stan] 2024-01-16 21:02:16,560 (trainer:291) INFO: 23/30epoch started. Estimated time to finish: 34 minutes and 42.39 seconds +[stan] 2024-01-16 21:02:29,555 (trainer:762) INFO: 23epoch:train:1-40batch: iter_time=0.003, forward_time=0.159, loss_ctc=5.209, loss=5.209, backward_time=0.024, grad_norm=175.422, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.298 +[stan] 2024-01-16 21:02:42,257 (trainer:762) INFO: 23epoch:train:41-80batch: iter_time=5.031e-05, forward_time=0.159, loss_ctc=5.064, loss=5.064, backward_time=0.024, grad_norm=174.599, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:02:54,955 (trainer:762) INFO: 23epoch:train:81-120batch: iter_time=5.324e-05, forward_time=0.158, loss_ctc=5.183, loss=5.183, backward_time=0.024, grad_norm=176.670, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:03:07,668 (trainer:762) INFO: 23epoch:train:121-160batch: iter_time=5.341e-05, forward_time=0.159, loss_ctc=5.226, loss=5.226, backward_time=0.024, grad_norm=195.973, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:03:20,384 (trainer:762) INFO: 23epoch:train:161-200batch: iter_time=5.102e-05, forward_time=0.159, loss_ctc=4.769, loss=4.769, backward_time=0.024, grad_norm=173.998, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:03:33,095 (trainer:762) INFO: 23epoch:train:201-240batch: iter_time=5.204e-05, forward_time=0.159, loss_ctc=4.949, loss=4.949, backward_time=0.024, grad_norm=182.475, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:03:45,816 (trainer:762) INFO: 23epoch:train:241-280batch: iter_time=5.060e-05, forward_time=0.159, loss_ctc=5.061, loss=5.061, backward_time=0.024, grad_norm=170.719, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:03:58,539 (trainer:762) INFO: 23epoch:train:281-320batch: iter_time=5.187e-05, forward_time=0.159, loss_ctc=4.908, loss=4.908, backward_time=0.024, grad_norm=185.267, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:04:11,264 (trainer:762) INFO: 23epoch:train:321-360batch: iter_time=5.462e-05, forward_time=0.159, loss_ctc=5.059, loss=5.059, backward_time=0.024, grad_norm=184.731, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:04:23,980 (trainer:762) INFO: 23epoch:train:361-400batch: iter_time=5.105e-05, forward_time=0.159, loss_ctc=4.856, loss=4.856, backward_time=0.024, grad_norm=180.849, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:04:36,696 (trainer:762) INFO: 23epoch:train:401-440batch: iter_time=5.170e-05, forward_time=0.159, loss_ctc=4.810, loss=4.810, backward_time=0.024, grad_norm=176.316, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:04:49,410 (trainer:762) INFO: 23epoch:train:441-480batch: iter_time=5.102e-05, forward_time=0.159, loss_ctc=4.890, loss=4.890, backward_time=0.024, grad_norm=188.579, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:05:02,121 (trainer:762) INFO: 23epoch:train:481-520batch: iter_time=5.495e-05, forward_time=0.159, loss_ctc=5.022, loss=5.022, backward_time=0.024, grad_norm=173.305, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:05:14,835 (trainer:762) INFO: 23epoch:train:521-560batch: iter_time=5.414e-05, forward_time=0.159, loss_ctc=5.168, loss=5.168, backward_time=0.024, grad_norm=183.620, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:05:27,548 (trainer:762) INFO: 23epoch:train:561-600batch: iter_time=5.219e-05, forward_time=0.159, loss_ctc=4.773, loss=4.773, backward_time=0.024, grad_norm=168.027, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:05:40,254 (trainer:762) INFO: 23epoch:train:601-640batch: iter_time=5.350e-05, forward_time=0.159, loss_ctc=5.085, loss=5.085, backward_time=0.024, grad_norm=183.395, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:05:52,963 (trainer:762) INFO: 23epoch:train:641-680batch: iter_time=5.178e-05, forward_time=0.159, loss_ctc=4.542, loss=4.542, backward_time=0.024, grad_norm=166.734, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:06:05,666 (trainer:762) INFO: 23epoch:train:681-720batch: iter_time=5.150e-05, forward_time=0.159, loss_ctc=4.871, loss=4.871, backward_time=0.024, grad_norm=177.332, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:06:18,367 (trainer:762) INFO: 23epoch:train:721-760batch: iter_time=5.274e-05, forward_time=0.158, loss_ctc=5.057, loss=5.057, backward_time=0.024, grad_norm=173.153, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:06:31,065 (trainer:762) INFO: 23epoch:train:761-800batch: iter_time=5.003e-05, forward_time=0.158, loss_ctc=4.774, loss=4.774, backward_time=0.024, grad_norm=175.997, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:06:35,741 (trainer:357) INFO: 23epoch results: [train] iter_time=2.214e-04, forward_time=0.159, loss_ctc=4.964, loss=4.964, backward_time=0.024, grad_norm=178.358, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.58 seconds, total_count=18400, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=474.974, cer_ctc=0.333, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=474.974, time=1.15 seconds, total_count=115, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.45 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 21:06:36,842 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:06:36,844 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/22epoch.pth +[stan] 2024-01-16 21:06:36,844 (trainer:291) INFO: 24/30epoch started. Estimated time to finish: 30 minutes and 22.09 seconds +[stan] 2024-01-16 21:06:49,830 (trainer:762) INFO: 24epoch:train:1-40batch: iter_time=0.003, forward_time=0.159, loss_ctc=4.806, loss=4.806, backward_time=0.024, grad_norm=179.828, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.297 +[stan] 2024-01-16 21:07:02,530 (trainer:762) INFO: 24epoch:train:41-80batch: iter_time=5.088e-05, forward_time=0.159, loss_ctc=4.728, loss=4.728, backward_time=0.024, grad_norm=198.897, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:07:15,231 (trainer:762) INFO: 24epoch:train:81-120batch: iter_time=5.271e-05, forward_time=0.158, loss_ctc=4.829, loss=4.829, backward_time=0.024, grad_norm=198.506, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:07:27,936 (trainer:762) INFO: 24epoch:train:121-160batch: iter_time=5.394e-05, forward_time=0.159, loss_ctc=4.879, loss=4.879, backward_time=0.024, grad_norm=175.813, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:07:40,646 (trainer:762) INFO: 24epoch:train:161-200batch: iter_time=5.047e-05, forward_time=0.159, loss_ctc=4.647, loss=4.647, backward_time=0.024, grad_norm=166.415, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:07:53,358 (trainer:762) INFO: 24epoch:train:201-240batch: iter_time=5.058e-05, forward_time=0.159, loss_ctc=4.919, loss=4.919, backward_time=0.024, grad_norm=169.873, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:08:06,081 (trainer:762) INFO: 24epoch:train:241-280batch: iter_time=5.097e-05, forward_time=0.159, loss_ctc=4.561, loss=4.561, backward_time=0.024, grad_norm=183.906, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:08:18,800 (trainer:762) INFO: 24epoch:train:281-320batch: iter_time=5.078e-05, forward_time=0.159, loss_ctc=4.790, loss=4.790, backward_time=0.024, grad_norm=174.161, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:08:31,513 (trainer:762) INFO: 24epoch:train:321-360batch: iter_time=5.341e-05, forward_time=0.159, loss_ctc=4.836, loss=4.836, backward_time=0.024, grad_norm=170.665, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:08:44,226 (trainer:762) INFO: 24epoch:train:361-400batch: iter_time=5.028e-05, forward_time=0.159, loss_ctc=4.609, loss=4.609, backward_time=0.024, grad_norm=171.075, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:08:56,942 (trainer:762) INFO: 24epoch:train:401-440batch: iter_time=5.158e-05, forward_time=0.159, loss_ctc=4.763, loss=4.763, backward_time=0.024, grad_norm=172.448, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:09:09,657 (trainer:762) INFO: 24epoch:train:441-480batch: iter_time=5.423e-05, forward_time=0.159, loss_ctc=4.739, loss=4.739, backward_time=0.024, grad_norm=176.344, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:09:22,370 (trainer:762) INFO: 24epoch:train:481-520batch: iter_time=5.274e-05, forward_time=0.159, loss_ctc=4.747, loss=4.747, backward_time=0.024, grad_norm=169.716, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:09:35,080 (trainer:762) INFO: 24epoch:train:521-560batch: iter_time=5.273e-05, forward_time=0.159, loss_ctc=4.904, loss=4.904, backward_time=0.024, grad_norm=188.838, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:09:47,792 (trainer:762) INFO: 24epoch:train:561-600batch: iter_time=5.262e-05, forward_time=0.159, loss_ctc=4.789, loss=4.789, backward_time=0.024, grad_norm=165.604, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:10:00,508 (trainer:762) INFO: 24epoch:train:601-640batch: iter_time=5.215e-05, forward_time=0.159, loss_ctc=4.552, loss=4.552, backward_time=0.024, grad_norm=161.190, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:10:13,219 (trainer:762) INFO: 24epoch:train:641-680batch: iter_time=5.093e-05, forward_time=0.159, loss_ctc=4.578, loss=4.578, backward_time=0.024, grad_norm=164.923, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:10:25,925 (trainer:762) INFO: 24epoch:train:681-720batch: iter_time=5.367e-05, forward_time=0.159, loss_ctc=4.654, loss=4.654, backward_time=0.024, grad_norm=169.481, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:10:38,629 (trainer:762) INFO: 24epoch:train:721-760batch: iter_time=5.177e-05, forward_time=0.159, loss_ctc=4.837, loss=4.837, backward_time=0.024, grad_norm=177.594, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:10:51,330 (trainer:762) INFO: 24epoch:train:761-800batch: iter_time=5.031e-05, forward_time=0.158, loss_ctc=4.703, loss=4.703, backward_time=0.024, grad_norm=184.209, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:10:55,986 (trainer:357) INFO: 24epoch results: [train] iter_time=2.125e-04, forward_time=0.159, loss_ctc=4.743, loss=4.743, backward_time=0.024, grad_norm=175.974, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.56 seconds, total_count=19200, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=476.607, cer_ctc=0.332, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=476.607, time=1.15 seconds, total_count=120, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.43 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 21:10:57,012 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:10:57,014 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/23epoch.pth +[stan] 2024-01-16 21:10:57,014 (trainer:291) INFO: 25/30epoch started. Estimated time to finish: 26 minutes and 1.76 seconds +[stan] 2024-01-16 21:11:10,030 (trainer:762) INFO: 25epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=4.502, loss=4.502, backward_time=0.024, grad_norm=168.270, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.300 +[stan] 2024-01-16 21:11:22,733 (trainer:762) INFO: 25epoch:train:41-80batch: iter_time=5.371e-05, forward_time=0.159, loss_ctc=4.861, loss=4.861, backward_time=0.024, grad_norm=162.869, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:11:35,431 (trainer:762) INFO: 25epoch:train:81-120batch: iter_time=5.523e-05, forward_time=0.158, loss_ctc=4.875, loss=4.875, backward_time=0.024, grad_norm=172.243, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:11:48,141 (trainer:762) INFO: 25epoch:train:121-160batch: iter_time=5.106e-05, forward_time=0.159, loss_ctc=4.373, loss=4.373, backward_time=0.024, grad_norm=168.902, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:12:00,852 (trainer:762) INFO: 25epoch:train:161-200batch: iter_time=5.371e-05, forward_time=0.159, loss_ctc=4.459, loss=4.459, backward_time=0.024, grad_norm=164.752, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:12:13,568 (trainer:762) INFO: 25epoch:train:201-240batch: iter_time=5.159e-05, forward_time=0.159, loss_ctc=4.505, loss=4.505, backward_time=0.024, grad_norm=170.042, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:12:26,283 (trainer:762) INFO: 25epoch:train:241-280batch: iter_time=5.263e-05, forward_time=0.159, loss_ctc=4.390, loss=4.390, backward_time=0.024, grad_norm=164.943, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:12:38,999 (trainer:762) INFO: 25epoch:train:281-320batch: iter_time=5.174e-05, forward_time=0.159, loss_ctc=4.466, loss=4.466, backward_time=0.024, grad_norm=159.956, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:12:51,716 (trainer:762) INFO: 25epoch:train:321-360batch: iter_time=5.355e-05, forward_time=0.159, loss_ctc=4.493, loss=4.493, backward_time=0.024, grad_norm=157.597, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:13:04,431 (trainer:762) INFO: 25epoch:train:361-400batch: iter_time=5.098e-05, forward_time=0.159, loss_ctc=4.279, loss=4.279, backward_time=0.024, grad_norm=167.786, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:13:17,143 (trainer:762) INFO: 25epoch:train:401-440batch: iter_time=5.377e-05, forward_time=0.159, loss_ctc=4.129, loss=4.129, backward_time=0.024, grad_norm=162.151, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:13:29,856 (trainer:762) INFO: 25epoch:train:441-480batch: iter_time=5.344e-05, forward_time=0.159, loss_ctc=4.513, loss=4.513, backward_time=0.024, grad_norm=169.934, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:13:42,575 (trainer:762) INFO: 25epoch:train:481-520batch: iter_time=5.389e-05, forward_time=0.159, loss_ctc=4.418, loss=4.418, backward_time=0.024, grad_norm=183.071, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:13:55,285 (trainer:762) INFO: 25epoch:train:521-560batch: iter_time=5.513e-05, forward_time=0.159, loss_ctc=4.304, loss=4.304, backward_time=0.024, grad_norm=185.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:14:07,995 (trainer:762) INFO: 25epoch:train:561-600batch: iter_time=5.174e-05, forward_time=0.159, loss_ctc=4.353, loss=4.353, backward_time=0.024, grad_norm=164.561, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:14:20,706 (trainer:762) INFO: 25epoch:train:601-640batch: iter_time=5.481e-05, forward_time=0.159, loss_ctc=4.254, loss=4.254, backward_time=0.024, grad_norm=159.928, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:14:33,412 (trainer:762) INFO: 25epoch:train:641-680batch: iter_time=5.322e-05, forward_time=0.159, loss_ctc=4.153, loss=4.153, backward_time=0.024, grad_norm=155.742, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:14:46,122 (trainer:762) INFO: 25epoch:train:681-720batch: iter_time=5.223e-05, forward_time=0.159, loss_ctc=4.470, loss=4.470, backward_time=0.024, grad_norm=157.809, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:14:58,829 (trainer:762) INFO: 25epoch:train:721-760batch: iter_time=5.347e-05, forward_time=0.159, loss_ctc=4.389, loss=4.389, backward_time=0.024, grad_norm=167.033, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:15:11,526 (trainer:762) INFO: 25epoch:train:761-800batch: iter_time=5.114e-05, forward_time=0.158, loss_ctc=4.239, loss=4.239, backward_time=0.024, grad_norm=169.811, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:15:16,252 (trainer:357) INFO: 25epoch results: [train] iter_time=2.472e-04, forward_time=0.159, loss_ctc=4.421, loss=4.421, backward_time=0.024, grad_norm=166.627, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.59 seconds, total_count=20000, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=486.159, cer_ctc=0.330, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=486.159, time=1.18 seconds, total_count=125, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.47 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 21:15:17,348 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:15:17,349 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/24epoch.pth +[stan] 2024-01-16 21:15:17,349 (trainer:291) INFO: 26/30epoch started. Estimated time to finish: 21 minutes and 41.47 seconds +[stan] 2024-01-16 21:15:30,342 (trainer:762) INFO: 26epoch:train:1-40batch: iter_time=0.003, forward_time=0.159, loss_ctc=4.283, loss=4.283, backward_time=0.024, grad_norm=174.466, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.298 +[stan] 2024-01-16 21:15:43,049 (trainer:762) INFO: 26epoch:train:41-80batch: iter_time=5.014e-05, forward_time=0.159, loss_ctc=4.376, loss=4.376, backward_time=0.024, grad_norm=173.489, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:15:55,754 (trainer:762) INFO: 26epoch:train:81-120batch: iter_time=5.377e-05, forward_time=0.159, loss_ctc=4.089, loss=4.089, backward_time=0.024, grad_norm=160.282, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:16:08,463 (trainer:762) INFO: 26epoch:train:121-160batch: iter_time=5.102e-05, forward_time=0.159, loss_ctc=4.368, loss=4.368, backward_time=0.024, grad_norm=166.044, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:16:21,172 (trainer:762) INFO: 26epoch:train:161-200batch: iter_time=4.943e-05, forward_time=0.159, loss_ctc=4.430, loss=4.430, backward_time=0.024, grad_norm=172.870, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:16:33,892 (trainer:762) INFO: 26epoch:train:201-240batch: iter_time=5.345e-05, forward_time=0.159, loss_ctc=4.439, loss=4.439, backward_time=0.024, grad_norm=168.450, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:16:46,604 (trainer:762) INFO: 26epoch:train:241-280batch: iter_time=5.041e-05, forward_time=0.159, loss_ctc=4.422, loss=4.422, backward_time=0.024, grad_norm=171.649, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:16:59,318 (trainer:762) INFO: 26epoch:train:281-320batch: iter_time=5.048e-05, forward_time=0.159, loss_ctc=4.316, loss=4.316, backward_time=0.024, grad_norm=163.641, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:17:12,035 (trainer:762) INFO: 26epoch:train:321-360batch: iter_time=5.143e-05, forward_time=0.159, loss_ctc=4.145, loss=4.145, backward_time=0.024, grad_norm=160.083, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:17:24,753 (trainer:762) INFO: 26epoch:train:361-400batch: iter_time=5.004e-05, forward_time=0.159, loss_ctc=4.261, loss=4.261, backward_time=0.024, grad_norm=164.674, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:17:37,465 (trainer:762) INFO: 26epoch:train:401-440batch: iter_time=5.379e-05, forward_time=0.159, loss_ctc=4.301, loss=4.301, backward_time=0.024, grad_norm=166.364, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:17:50,179 (trainer:762) INFO: 26epoch:train:441-480batch: iter_time=5.386e-05, forward_time=0.159, loss_ctc=4.159, loss=4.159, backward_time=0.024, grad_norm=168.136, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:18:02,893 (trainer:762) INFO: 26epoch:train:481-520batch: iter_time=5.140e-05, forward_time=0.159, loss_ctc=4.315, loss=4.315, backward_time=0.024, grad_norm=153.139, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:18:15,601 (trainer:762) INFO: 26epoch:train:521-560batch: iter_time=5.137e-05, forward_time=0.159, loss_ctc=4.556, loss=4.556, backward_time=0.024, grad_norm=158.917, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:18:28,310 (trainer:762) INFO: 26epoch:train:561-600batch: iter_time=5.365e-05, forward_time=0.159, loss_ctc=4.316, loss=4.316, backward_time=0.024, grad_norm=168.809, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:18:41,015 (trainer:762) INFO: 26epoch:train:601-640batch: iter_time=5.498e-05, forward_time=0.159, loss_ctc=4.622, loss=4.622, backward_time=0.024, grad_norm=177.948, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:18:53,719 (trainer:762) INFO: 26epoch:train:641-680batch: iter_time=5.047e-05, forward_time=0.159, loss_ctc=4.374, loss=4.374, backward_time=0.024, grad_norm=161.576, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:19:06,419 (trainer:762) INFO: 26epoch:train:681-720batch: iter_time=5.157e-05, forward_time=0.158, loss_ctc=4.281, loss=4.281, backward_time=0.024, grad_norm=162.438, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:19:19,120 (trainer:762) INFO: 26epoch:train:721-760batch: iter_time=5.219e-05, forward_time=0.158, loss_ctc=4.181, loss=4.181, backward_time=0.024, grad_norm=175.313, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:19:31,816 (trainer:762) INFO: 26epoch:train:761-800batch: iter_time=4.878e-05, forward_time=0.158, loss_ctc=4.113, loss=4.113, backward_time=0.024, grad_norm=161.685, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:19:36,463 (trainer:357) INFO: 26epoch results: [train] iter_time=2.197e-04, forward_time=0.159, loss_ctc=4.317, loss=4.317, backward_time=0.024, grad_norm=166.499, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.54 seconds, total_count=20800, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=487.120, cer_ctc=0.336, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=487.120, time=1.16 seconds, total_count=130, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.41 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 21:19:37,582 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:19:37,583 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/25epoch.pth +[stan] 2024-01-16 21:19:37,584 (trainer:291) INFO: 27/30epoch started. Estimated time to finish: 17 minutes and 21.17 seconds +[stan] 2024-01-16 21:19:50,588 (trainer:762) INFO: 27epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=3.969, loss=3.969, backward_time=0.024, grad_norm=156.783, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.299 +[stan] 2024-01-16 21:20:03,289 (trainer:762) INFO: 27epoch:train:41-80batch: iter_time=4.997e-05, forward_time=0.159, loss_ctc=4.107, loss=4.107, backward_time=0.024, grad_norm=161.337, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:20:15,982 (trainer:762) INFO: 27epoch:train:81-120batch: iter_time=4.997e-05, forward_time=0.158, loss_ctc=4.157, loss=4.157, backward_time=0.024, grad_norm=157.449, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 21:20:28,691 (trainer:762) INFO: 27epoch:train:121-160batch: iter_time=5.358e-05, forward_time=0.159, loss_ctc=4.086, loss=4.086, backward_time=0.024, grad_norm=168.225, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:20:41,404 (trainer:762) INFO: 27epoch:train:161-200batch: iter_time=5.276e-05, forward_time=0.159, loss_ctc=4.178, loss=4.178, backward_time=0.024, grad_norm=159.395, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:20:54,118 (trainer:762) INFO: 27epoch:train:201-240batch: iter_time=5.121e-05, forward_time=0.159, loss_ctc=4.221, loss=4.221, backward_time=0.024, grad_norm=162.437, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:21:06,835 (trainer:762) INFO: 27epoch:train:241-280batch: iter_time=5.396e-05, forward_time=0.159, loss_ctc=4.169, loss=4.169, backward_time=0.024, grad_norm=163.571, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:21:19,554 (trainer:762) INFO: 27epoch:train:281-320batch: iter_time=5.054e-05, forward_time=0.159, loss_ctc=4.158, loss=4.158, backward_time=0.024, grad_norm=155.427, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:21:32,269 (trainer:762) INFO: 27epoch:train:321-360batch: iter_time=5.395e-05, forward_time=0.159, loss_ctc=3.995, loss=3.995, backward_time=0.024, grad_norm=150.309, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:21:44,985 (trainer:762) INFO: 27epoch:train:361-400batch: iter_time=5.069e-05, forward_time=0.159, loss_ctc=4.021, loss=4.021, backward_time=0.024, grad_norm=160.028, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:21:57,701 (trainer:762) INFO: 27epoch:train:401-440batch: iter_time=5.090e-05, forward_time=0.159, loss_ctc=3.969, loss=3.969, backward_time=0.024, grad_norm=152.097, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:22:10,415 (trainer:762) INFO: 27epoch:train:441-480batch: iter_time=5.369e-05, forward_time=0.159, loss_ctc=4.040, loss=4.040, backward_time=0.024, grad_norm=173.048, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:22:23,125 (trainer:762) INFO: 27epoch:train:481-520batch: iter_time=5.322e-05, forward_time=0.159, loss_ctc=4.343, loss=4.343, backward_time=0.024, grad_norm=177.214, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:22:35,838 (trainer:762) INFO: 27epoch:train:521-560batch: iter_time=5.414e-05, forward_time=0.159, loss_ctc=4.052, loss=4.052, backward_time=0.024, grad_norm=162.222, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:22:48,555 (trainer:762) INFO: 27epoch:train:561-600batch: iter_time=5.540e-05, forward_time=0.159, loss_ctc=4.059, loss=4.059, backward_time=0.024, grad_norm=164.111, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:23:01,261 (trainer:762) INFO: 27epoch:train:601-640batch: iter_time=5.157e-05, forward_time=0.159, loss_ctc=3.983, loss=3.983, backward_time=0.024, grad_norm=175.015, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:23:13,962 (trainer:762) INFO: 27epoch:train:641-680batch: iter_time=5.309e-05, forward_time=0.158, loss_ctc=4.163, loss=4.163, backward_time=0.024, grad_norm=156.641, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:23:26,671 (trainer:762) INFO: 27epoch:train:681-720batch: iter_time=5.426e-05, forward_time=0.159, loss_ctc=4.074, loss=4.074, backward_time=0.024, grad_norm=151.937, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:23:39,373 (trainer:762) INFO: 27epoch:train:721-760batch: iter_time=5.109e-05, forward_time=0.158, loss_ctc=4.147, loss=4.147, backward_time=0.024, grad_norm=148.741, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:23:52,070 (trainer:762) INFO: 27epoch:train:761-800batch: iter_time=4.955e-05, forward_time=0.158, loss_ctc=4.020, loss=4.020, backward_time=0.024, grad_norm=159.513, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:23:56,735 (trainer:357) INFO: 27epoch results: [train] iter_time=2.361e-04, forward_time=0.159, loss_ctc=4.096, loss=4.096, backward_time=0.024, grad_norm=160.775, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.56 seconds, total_count=21600, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=483.309, cer_ctc=0.334, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=483.309, time=1.16 seconds, total_count=135, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.43 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 21:23:57,763 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:23:57,765 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/26epoch.pth +[stan] 2024-01-16 21:23:57,765 (trainer:291) INFO: 28/30epoch started. Estimated time to finish: 13 minutes and 0.86 seconds +[stan] 2024-01-16 21:24:10,784 (trainer:762) INFO: 28epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=3.995, loss=3.995, backward_time=0.024, grad_norm=162.527, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.301 +[stan] 2024-01-16 21:24:23,491 (trainer:762) INFO: 28epoch:train:41-80batch: iter_time=5.286e-05, forward_time=0.159, loss_ctc=3.666, loss=3.666, backward_time=0.024, grad_norm=151.133, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:24:36,194 (trainer:762) INFO: 28epoch:train:81-120batch: iter_time=5.263e-05, forward_time=0.159, loss_ctc=4.094, loss=4.094, backward_time=0.024, grad_norm=160.161, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:24:48,910 (trainer:762) INFO: 28epoch:train:121-160batch: iter_time=5.244e-05, forward_time=0.159, loss_ctc=3.805, loss=3.805, backward_time=0.024, grad_norm=153.665, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:25:01,619 (trainer:762) INFO: 28epoch:train:161-200batch: iter_time=5.189e-05, forward_time=0.159, loss_ctc=3.954, loss=3.954, backward_time=0.024, grad_norm=150.544, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:25:14,342 (trainer:762) INFO: 28epoch:train:201-240batch: iter_time=5.135e-05, forward_time=0.159, loss_ctc=3.888, loss=3.888, backward_time=0.024, grad_norm=148.854, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:25:27,060 (trainer:762) INFO: 28epoch:train:241-280batch: iter_time=4.990e-05, forward_time=0.159, loss_ctc=4.015, loss=4.015, backward_time=0.024, grad_norm=161.328, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:25:39,776 (trainer:762) INFO: 28epoch:train:281-320batch: iter_time=5.089e-05, forward_time=0.159, loss_ctc=4.267, loss=4.267, backward_time=0.024, grad_norm=166.673, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:25:52,495 (trainer:762) INFO: 28epoch:train:321-360batch: iter_time=5.118e-05, forward_time=0.159, loss_ctc=3.814, loss=3.814, backward_time=0.024, grad_norm=150.285, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:26:05,220 (trainer:762) INFO: 28epoch:train:361-400batch: iter_time=5.320e-05, forward_time=0.159, loss_ctc=4.086, loss=4.086, backward_time=0.024, grad_norm=155.097, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:26:17,939 (trainer:762) INFO: 28epoch:train:401-440batch: iter_time=5.102e-05, forward_time=0.159, loss_ctc=4.119, loss=4.119, backward_time=0.024, grad_norm=158.432, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:26:30,647 (trainer:762) INFO: 28epoch:train:441-480batch: iter_time=5.082e-05, forward_time=0.159, loss_ctc=3.901, loss=3.901, backward_time=0.024, grad_norm=152.234, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:26:43,354 (trainer:762) INFO: 28epoch:train:481-520batch: iter_time=5.086e-05, forward_time=0.159, loss_ctc=3.811, loss=3.811, backward_time=0.024, grad_norm=147.559, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:26:56,063 (trainer:762) INFO: 28epoch:train:521-560batch: iter_time=5.391e-05, forward_time=0.159, loss_ctc=3.893, loss=3.893, backward_time=0.024, grad_norm=148.152, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:27:08,771 (trainer:762) INFO: 28epoch:train:561-600batch: iter_time=5.092e-05, forward_time=0.159, loss_ctc=3.843, loss=3.843, backward_time=0.024, grad_norm=145.627, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:27:21,479 (trainer:762) INFO: 28epoch:train:601-640batch: iter_time=5.375e-05, forward_time=0.159, loss_ctc=3.715, loss=3.715, backward_time=0.024, grad_norm=149.208, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:27:34,185 (trainer:762) INFO: 28epoch:train:641-680batch: iter_time=5.164e-05, forward_time=0.159, loss_ctc=4.004, loss=4.004, backward_time=0.024, grad_norm=159.669, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:27:46,889 (trainer:762) INFO: 28epoch:train:681-720batch: iter_time=5.106e-05, forward_time=0.159, loss_ctc=4.159, loss=4.159, backward_time=0.024, grad_norm=159.874, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:27:59,587 (trainer:762) INFO: 28epoch:train:721-760batch: iter_time=5.464e-05, forward_time=0.158, loss_ctc=3.770, loss=3.770, backward_time=0.024, grad_norm=155.563, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:28:12,287 (trainer:762) INFO: 28epoch:train:761-800batch: iter_time=4.948e-05, forward_time=0.158, loss_ctc=3.640, loss=3.640, backward_time=0.024, grad_norm=155.400, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:28:17,031 (trainer:357) INFO: 28epoch results: [train] iter_time=2.542e-04, forward_time=0.159, loss_ctc=3.922, loss=3.922, backward_time=0.024, grad_norm=154.599, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.6 seconds, total_count=22400, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=487.944, cer_ctc=0.333, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=487.944, time=1.17 seconds, total_count=140, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.49 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 21:28:18,159 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:28:18,161 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/27epoch.pth +[stan] 2024-01-16 21:28:18,161 (trainer:291) INFO: 29/30epoch started. Estimated time to finish: 8 minutes and 40.58 seconds +[stan] 2024-01-16 21:28:31,183 (trainer:762) INFO: 29epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=3.677, loss=3.677, backward_time=0.024, grad_norm=164.602, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.301 +[stan] 2024-01-16 21:28:43,881 (trainer:762) INFO: 29epoch:train:41-80batch: iter_time=5.195e-05, forward_time=0.158, loss_ctc=4.031, loss=4.031, backward_time=0.024, grad_norm=153.192, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:28:56,588 (trainer:762) INFO: 29epoch:train:81-120batch: iter_time=5.372e-05, forward_time=0.159, loss_ctc=4.021, loss=4.021, backward_time=0.024, grad_norm=150.166, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:29:09,303 (trainer:762) INFO: 29epoch:train:121-160batch: iter_time=5.364e-05, forward_time=0.159, loss_ctc=3.540, loss=3.540, backward_time=0.024, grad_norm=149.336, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:29:22,022 (trainer:762) INFO: 29epoch:train:161-200batch: iter_time=5.035e-05, forward_time=0.159, loss_ctc=3.972, loss=3.972, backward_time=0.024, grad_norm=157.129, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:29:34,745 (trainer:762) INFO: 29epoch:train:201-240batch: iter_time=5.282e-05, forward_time=0.159, loss_ctc=3.708, loss=3.708, backward_time=0.024, grad_norm=156.570, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:29:47,471 (trainer:762) INFO: 29epoch:train:241-280batch: iter_time=5.325e-05, forward_time=0.159, loss_ctc=3.717, loss=3.717, backward_time=0.024, grad_norm=145.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:30:00,192 (trainer:762) INFO: 29epoch:train:281-320batch: iter_time=5.131e-05, forward_time=0.159, loss_ctc=3.889, loss=3.889, backward_time=0.024, grad_norm=149.739, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:30:12,917 (trainer:762) INFO: 29epoch:train:321-360batch: iter_time=5.596e-05, forward_time=0.159, loss_ctc=3.558, loss=3.558, backward_time=0.024, grad_norm=152.226, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:30:25,634 (trainer:762) INFO: 29epoch:train:361-400batch: iter_time=5.384e-05, forward_time=0.159, loss_ctc=3.994, loss=3.994, backward_time=0.024, grad_norm=154.540, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:30:38,347 (trainer:762) INFO: 29epoch:train:401-440batch: iter_time=5.403e-05, forward_time=0.159, loss_ctc=3.698, loss=3.698, backward_time=0.024, grad_norm=151.616, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:30:51,059 (trainer:762) INFO: 29epoch:train:441-480batch: iter_time=5.393e-05, forward_time=0.159, loss_ctc=3.939, loss=3.939, backward_time=0.024, grad_norm=163.658, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:31:03,776 (trainer:762) INFO: 29epoch:train:481-520batch: iter_time=5.097e-05, forward_time=0.159, loss_ctc=3.682, loss=3.682, backward_time=0.024, grad_norm=165.688, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:31:16,488 (trainer:762) INFO: 29epoch:train:521-560batch: iter_time=5.517e-05, forward_time=0.159, loss_ctc=3.649, loss=3.649, backward_time=0.024, grad_norm=149.283, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:31:29,193 (trainer:762) INFO: 29epoch:train:561-600batch: iter_time=5.371e-05, forward_time=0.159, loss_ctc=3.371, loss=3.371, backward_time=0.024, grad_norm=151.912, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:31:41,900 (trainer:762) INFO: 29epoch:train:601-640batch: iter_time=5.345e-05, forward_time=0.159, loss_ctc=3.623, loss=3.623, backward_time=0.024, grad_norm=143.959, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:31:54,606 (trainer:762) INFO: 29epoch:train:641-680batch: iter_time=5.215e-05, forward_time=0.159, loss_ctc=3.573, loss=3.573, backward_time=0.024, grad_norm=153.049, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:32:07,301 (trainer:762) INFO: 29epoch:train:681-720batch: iter_time=5.451e-05, forward_time=0.158, loss_ctc=3.811, loss=3.811, backward_time=0.024, grad_norm=159.724, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 21:32:19,999 (trainer:762) INFO: 29epoch:train:721-760batch: iter_time=5.081e-05, forward_time=0.158, loss_ctc=3.963, loss=3.963, backward_time=0.024, grad_norm=155.473, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:32:32,694 (trainer:762) INFO: 29epoch:train:761-800batch: iter_time=4.979e-05, forward_time=0.158, loss_ctc=3.872, loss=3.872, backward_time=0.024, grad_norm=146.248, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 21:32:37,399 (trainer:357) INFO: 29epoch results: [train] iter_time=2.459e-04, forward_time=0.159, loss_ctc=3.764, loss=3.764, backward_time=0.024, grad_norm=153.661, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.273, time=4 minutes and 14.61 seconds, total_count=23200, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=485.580, cer_ctc=0.332, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=485.580, time=1.16 seconds, total_count=145, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.47 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 21:32:38,395 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:32:38,396 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/28epoch.pth +[stan] 2024-01-16 21:32:38,396 (trainer:291) INFO: 30/30epoch started. Estimated time to finish: 4 minutes and 20.29 seconds +[stan] 2024-01-16 21:32:51,397 (trainer:762) INFO: 30epoch:train:1-40batch: iter_time=0.004, forward_time=0.159, loss_ctc=3.678, loss=3.678, backward_time=0.024, grad_norm=149.409, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.299 +[stan] 2024-01-16 21:33:04,098 (trainer:762) INFO: 30epoch:train:41-80batch: iter_time=5.038e-05, forward_time=0.159, loss_ctc=3.799, loss=3.799, backward_time=0.024, grad_norm=154.978, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:33:16,805 (trainer:762) INFO: 30epoch:train:81-120batch: iter_time=5.335e-05, forward_time=0.159, loss_ctc=3.770, loss=3.770, backward_time=0.024, grad_norm=146.396, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:33:29,509 (trainer:762) INFO: 30epoch:train:121-160batch: iter_time=5.322e-05, forward_time=0.159, loss_ctc=3.607, loss=3.607, backward_time=0.024, grad_norm=144.017, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:33:42,221 (trainer:762) INFO: 30epoch:train:161-200batch: iter_time=5.347e-05, forward_time=0.159, loss_ctc=3.920, loss=3.920, backward_time=0.024, grad_norm=153.309, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:33:54,930 (trainer:762) INFO: 30epoch:train:201-240batch: iter_time=5.412e-05, forward_time=0.159, loss_ctc=3.600, loss=3.600, backward_time=0.024, grad_norm=146.320, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:34:07,646 (trainer:762) INFO: 30epoch:train:241-280batch: iter_time=5.522e-05, forward_time=0.159, loss_ctc=3.523, loss=3.523, backward_time=0.024, grad_norm=147.689, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:34:20,357 (trainer:762) INFO: 30epoch:train:281-320batch: iter_time=5.375e-05, forward_time=0.159, loss_ctc=3.454, loss=3.454, backward_time=0.024, grad_norm=140.024, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:34:33,075 (trainer:762) INFO: 30epoch:train:321-360batch: iter_time=5.112e-05, forward_time=0.159, loss_ctc=3.511, loss=3.511, backward_time=0.024, grad_norm=149.316, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-16 21:34:45,786 (trainer:762) INFO: 30epoch:train:361-400batch: iter_time=5.139e-05, forward_time=0.159, loss_ctc=3.527, loss=3.527, backward_time=0.024, grad_norm=152.632, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:34:58,496 (trainer:762) INFO: 30epoch:train:401-440batch: iter_time=5.364e-05, forward_time=0.159, loss_ctc=3.517, loss=3.517, backward_time=0.024, grad_norm=145.084, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:35:11,211 (trainer:762) INFO: 30epoch:train:441-480batch: iter_time=5.402e-05, forward_time=0.159, loss_ctc=3.703, loss=3.703, backward_time=0.024, grad_norm=147.978, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:35:23,922 (trainer:762) INFO: 30epoch:train:481-520batch: iter_time=5.308e-05, forward_time=0.159, loss_ctc=3.554, loss=3.554, backward_time=0.024, grad_norm=147.049, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:35:36,635 (trainer:762) INFO: 30epoch:train:521-560batch: iter_time=5.492e-05, forward_time=0.159, loss_ctc=3.794, loss=3.794, backward_time=0.024, grad_norm=154.993, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:35:49,340 (trainer:762) INFO: 30epoch:train:561-600batch: iter_time=5.059e-05, forward_time=0.159, loss_ctc=3.550, loss=3.550, backward_time=0.024, grad_norm=147.457, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:36:02,042 (trainer:762) INFO: 30epoch:train:601-640batch: iter_time=5.137e-05, forward_time=0.159, loss_ctc=3.363, loss=3.363, backward_time=0.024, grad_norm=149.531, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:36:14,753 (trainer:762) INFO: 30epoch:train:641-680batch: iter_time=5.488e-05, forward_time=0.159, loss_ctc=3.290, loss=3.290, backward_time=0.024, grad_norm=151.150, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.271 +[stan] 2024-01-16 21:36:27,451 (trainer:762) INFO: 30epoch:train:681-720batch: iter_time=5.085e-05, forward_time=0.158, loss_ctc=3.323, loss=3.323, backward_time=0.024, grad_norm=140.727, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:36:40,152 (trainer:762) INFO: 30epoch:train:721-760batch: iter_time=5.076e-05, forward_time=0.158, loss_ctc=3.358, loss=3.358, backward_time=0.024, grad_norm=143.031, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-16 21:36:52,847 (trainer:762) INFO: 30epoch:train:761-800batch: iter_time=4.875e-05, forward_time=0.158, loss_ctc=3.521, loss=3.521, backward_time=0.024, grad_norm=145.360, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 21:36:57,499 (trainer:357) INFO: 30epoch results: [train] iter_time=2.346e-04, forward_time=0.159, loss_ctc=3.568, loss=3.568, backward_time=0.024, grad_norm=147.822, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272, time=4 minutes and 14.53 seconds, total_count=24000, gpu_max_cached_mem_GB=18.760, [valid] loss_ctc=491.835, cer_ctc=0.337, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=491.835, time=1.15 seconds, total_count=150, gpu_max_cached_mem_GB=18.760, [att_plot] time=3.42 seconds, total_count=0, gpu_max_cached_mem_GB=18.760 +[stan] 2024-01-16 21:36:58,489 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 21:36:58,491 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/29epoch.pth +[stan] 2024-01-16 21:36:58,491 (trainer:488) INFO: The training was finished at 30 epochs +[stan] 2024-01-16 21:36:58,507 (average_nbest_models:69) INFO: Averaging 5best models: criterion="valid.loss": test_pr/asr_train_asr_s3prl_houlsby_eng1_10min/valid.loss.ave_5best.pth +# Accounting: time=7814 threads=1 +# Ended (code 0) at Tue Jan 16 21:36:59 CST 2024, elapsed time 7814 seconds diff --git 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sha256:ea9281ae0011fc237f3f33da785f6091e4dffb66cf87bb3d9c3b211ecd9ce35a +size 21123854 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/RESULTS.md b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/RESULTS.md new file mode 100644 index 0000000000000000000000000000000000000000..c0b11745feb7c6063006bc453033c651671667b1 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/RESULTS.md @@ -0,0 +1,45 @@ +[INFO] /home/stan/Desktop/espnet/egs2/ml_superb/asr1/../../../tools/activate_python.sh is not present + +# RESULTS +## Environments +- date: `Wed Jan 17 01:24:03 CST 2024` +- python version: `3.8.10 (default, Jun 4 2021, 15:09:15) [GCC 7.5.0]` +- espnet version: `espnet 202310` +- pytorch version: `pytorch 1.12.0+cu113` +- Git hash: `aa855dffb81937a097ee03089926a0d5256426e2` + - Commit date: `Tue Jan 16 19:36:29 2024 +0800` + +## test_pr/asr_train_asr_s3prl_houlsby_eng1_1h +### WER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|decode_asr_asr_model_valid.loss.ave/test_10min_eng1|1092|11772|39.1|55.9|5.1|5.6|66.5|99.6| + +### CER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|decode_asr_asr_model_valid.loss.ave/test_10min_eng1|1092|67334|81.1|7.0|11.9|4.7|23.6|99.6| + +### TER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +## test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave +### WER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|org/dev_10min_eng1|40|1535|46.0|48.9|5.1|2.3|56.3|100.0| + +### CER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|org/dev_10min_eng1|40|8254|84.0|5.6|10.3|2.6|18.6|100.0| + +### TER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/att_ws/mls_eng_000243/encoder.encoders.0.self_attn.10ep.png 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0000000000000000000000000000000000000000..7f1059aeb7a857bbad57826d4abd7fa0d3666dfe --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/checkpoint.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:381385756a4898e241e4c283687b649db46044d365ce1118a996b2249924081f +size 63363609 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a9a95687492fabbcae663edd5fbc060dc6d37655 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml @@ -0,0 +1,237 @@ +config: conf/tuning/train_asr_s3prl_houlsby.yaml +print_config: false +log_level: INFO +drop_last_iter: false +dry_run: false +iterator_type: sequence +valid_iterator_type: null +output_dir: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h +ngpu: 1 +seed: 0 +num_workers: 4 +num_att_plot: 3 +dist_backend: nccl +dist_init_method: env:// +dist_world_size: null +dist_rank: null +local_rank: 0 +dist_master_addr: null +dist_master_port: null +dist_launcher: null +multiprocessing_distributed: false +unused_parameters: true +sharded_ddp: false +cudnn_enabled: true +cudnn_benchmark: false +cudnn_deterministic: true +collect_stats: false +write_collected_feats: false +max_epoch: 30 +patience: null +val_scheduler_criterion: +- valid +- loss +early_stopping_criterion: +- valid +- loss +- min +best_model_criterion: +- - valid + - loss + - min +keep_nbest_models: 5 +nbest_averaging_interval: 0 +grad_clip: 5.0 +grad_clip_type: 2.0 +grad_noise: false +accum_grad: 4 +no_forward_run: false +resume: true +train_dtype: float32 +use_amp: false +log_interval: null +use_matplotlib: true +use_tensorboard: true +create_graph_in_tensorboard: false +use_wandb: false +wandb_project: null +wandb_id: null +wandb_entity: null +wandb_name: null +wandb_model_log_interval: -1 +detect_anomaly: false +use_adapter: true +adapter: houlsby +save_adapter_only: true +adapter_conf: + bottleneck: 32 +pretrain_path: null +init_param: [] +ignore_init_mismatch: false +freeze_param: +- frontend.upstream +num_iters_per_epoch: 800 +batch_size: 8 +valid_batch_size: null +batch_bins: 1000000 +valid_batch_bins: null +train_shape_file: +- test_pr/asr_stats_eng1_1h/train/speech_shape +- test_pr/asr_stats_eng1_1h/train/text_shape.char +valid_shape_file: +- test_pr/asr_stats_eng1_1h/valid/speech_shape +- test_pr/asr_stats_eng1_1h/valid/text_shape.char +batch_type: sorted +valid_batch_type: null +fold_length: +- 80000 +- 150 +sort_in_batch: descending +shuffle_within_batch: false +sort_batch: descending +multiple_iterator: false +chunk_length: 500 +chunk_shift_ratio: 0.5 +num_cache_chunks: 1024 +chunk_excluded_key_prefixes: [] +chunk_default_fs: null +train_data_path_and_name_and_type: +- - dump/raw/train_1h_eng1/wav.scp + - speech + - sound +- - dump/raw/train_1h_eng1/text + - text + - text +valid_data_path_and_name_and_type: +- - dump/raw/dev_10min_eng1/wav.scp + - speech + - sound +- - dump/raw/dev_10min_eng1/text + - text + - text +allow_variable_data_keys: false +max_cache_size: 0.0 +max_cache_fd: 32 +allow_multi_rates: false +valid_max_cache_size: null +exclude_weight_decay: false +exclude_weight_decay_conf: {} +optim: adam +optim_conf: + lr: 0.0001 + weight_decay: 1.0e-06 +scheduler: null +scheduler_conf: {} +token_list: +- +- +- +- E +- T +- A +- O +- N +- I +- S +- H +- R +- D +- L +- U +- M +- C +- W +- F +- Y +- G +- P +- B +- V +- K +- X +- J +- Q +- Z +- +init: null +input_size: null +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: null + zero_infinity: true + brctc_risk_strategy: exp + brctc_group_strategy: end + brctc_risk_factor: 0.0 +joint_net_conf: null +use_preprocessor: true +use_lang_prompt: false +use_nlp_prompt: false +token_type: char +bpemodel: null +non_linguistic_symbols: null +cleaner: null +g2p: null +speech_volume_normalize: null +rir_scp: null +rir_apply_prob: 1.0 +noise_scp: null +noise_apply_prob: 1.0 +noise_db_range: '13_15' +short_noise_thres: 0.5 +aux_ctc_tasks: [] +frontend: s3prl +frontend_conf: + frontend_conf: + upstream: hubert_base + download_dir: ./hub + multilayer_feature: true + fs: 16k +specaug: specaug +specaug_conf: + apply_time_warp: true + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 27 + num_freq_mask: 2 + apply_time_mask: true + time_mask_width_ratio_range: + - 0.0 + - 0.05 + num_time_mask: 10 +normalize: utterance_mvn +normalize_conf: {} +model: espnet +model_conf: + ctc_weight: 1.0 + extract_feats_in_collect_stats: false +preencoder: linear +preencoder_conf: + input_size: 768 + output_size: 80 +encoder: transformer +encoder_conf: + output_size: 256 + attention_heads: 8 + linear_units: 1024 + num_blocks: 2 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: conv2d2 + normalize_before: true +postencoder: null +postencoder_conf: {} +decoder: null +decoder_conf: {} +preprocessor: default +preprocessor_conf: {} +required: +- output_dir +- token_list +version: '202310' +distributed: false diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.1.log b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.1.log new file mode 100644 index 0000000000000000000000000000000000000000..777abefa216c4124fb861d26b48cb14d2159a356 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.1.log @@ -0,0 +1,129 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1 --config conf/decode_asr.yaml +# Started at Wed Jan 17 01:15:41 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1 --config conf/decode_asr.yaml +2024-01-17 01:15:42,705 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml +2024-01-17 01:15:42,723 (asr:523) INFO: Vocabulary size: 30 +2024-01-17 01:15:42,786 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 01:15:42,786 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 01:15:42,896 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 01:15:44,192 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 01:15:45,450 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 01:15:45,450 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 01:15:45,450 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 01:15:45,483 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-17 01:15:45,558 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 01:15:45,674 (asr_inference:494) INFO: speech length: 228000 +2024-01-17 01:15:46,898 (beam_search:428) INFO: decoder input length: 354 +2024-01-17 01:15:46,898 (beam_search:429) INFO: max output length: 354 +2024-01-17 01:15:46,898 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:15:48,672 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:15:48,672 (beam_search:476) INFO: -36.81 * 1.0 = -36.81 for ctc +2024-01-17 01:15:48,672 (beam_search:479) INFO: total log probability: -36.81 +2024-01-17 01:15:48,672 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:15:48,672 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:15:48,673 (beam_search:483) INFO: best hypo: IAMTHIRUDOFTAKNOLDESFOMYOUSHESADHASTALYISANOWTHEITISIMPOSIBLETHAVEFATHINOUISENOWTHEITISUSESTOEXPECTANYRETERNFROMOUFORALEIHAVEDONIWANTNOMOREOFYOU + +2024-01-17 01:15:48,697 (asr_inference:494) INFO: speech length: 256160 +2024-01-17 01:15:48,721 (beam_search:428) INFO: decoder input length: 398 +2024-01-17 01:15:48,721 (beam_search:429) INFO: max output length: 398 +2024-01-17 01:15:48,721 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:15:50,532 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:15:50,532 (beam_search:476) INFO: -26.43 * 1.0 = -26.43 for ctc +2024-01-17 01:15:50,532 (beam_search:479) INFO: total log probability: -26.43 +2024-01-17 01:15:50,532 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:15:50,532 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:15:50,533 (beam_search:483) INFO: best hypo: THATHEMAYSOMTIMESBEELIKEOTHERCHELDRENLANINGTESIEMYNEORPLAINGPRATLINSIKINGFORHELESCOMSTOMYHEARTATSSINFULORDANMSPEAKINGHOWGODTHOART + +2024-01-17 01:15:50,534 (asr_inference:494) INFO: speech length: 317440 +2024-01-17 01:15:50,563 (beam_search:428) INFO: decoder input length: 493 +2024-01-17 01:15:50,563 (beam_search:429) INFO: max output length: 493 +2024-01-17 01:15:50,563 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:15:53,638 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:15:53,638 (beam_search:476) INFO: -41.98 * 1.0 = -41.98 for ctc +2024-01-17 01:15:53,638 (beam_search:479) INFO: total log probability: -41.98 +2024-01-17 01:15:53,638 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:15:53,638 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:15:53,639 (beam_search:483) INFO: best hypo: TRANSENTHINGSOFALSORSEASITHEGENRALEOUTBURSTOFMULTITODNSPASIONARHUTEDTOGETHERTHELITRCRISNAYTHEREDICULOSWITTHEHERABLEFAROVERTHEILWEYSEOFHEDSMAYBESENRESCALITYCAPRIYOLINGONFORSESFOMTHEROIALSTOD + +2024-01-17 01:15:53,641 (asr_inference:494) INFO: speech length: 226080 +2024-01-17 01:15:53,661 (beam_search:428) INFO: decoder input length: 351 +2024-01-17 01:15:53,662 (beam_search:429) INFO: max output length: 351 +2024-01-17 01:15:53,662 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:15:55,358 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:15:55,358 (beam_search:476) INFO: -31.14 * 1.0 = -31.14 for ctc +2024-01-17 01:15:55,358 (beam_search:479) INFO: total log probability: -31.14 +2024-01-17 01:15:55,358 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:15:55,358 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:15:55,359 (beam_search:483) INFO: best hypo: ITMAYHAVEBENTHTTHEBONSWEREAFOLDEDTOGETHERANDNONASONAHPELBONSFULDEDANDLAIEAWAYFORTHEPRPICESOFINCENTATIONSUCHBUNDLESOFBONSWEPUTTHRUAPROSESOFPRARS + +2024-01-17 01:15:55,361 (asr_inference:494) INFO: speech length: 211200 +2024-01-17 01:15:55,381 (beam_search:428) INFO: decoder input length: 327 +2024-01-17 01:15:55,381 (beam_search:429) INFO: max output length: 327 +2024-01-17 01:15:55,381 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:15:56,811 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:15:56,811 (beam_search:476) INFO: -23.58 * 1.0 = -23.58 for ctc +2024-01-17 01:15:56,811 (beam_search:479) INFO: total log probability: -23.58 +2024-01-17 01:15:56,811 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:15:56,811 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:15:56,812 (beam_search:483) INFO: best hypo: MASAILSNEVEREXPERINEDTHOSGREATTRANSIONSFROMLONESTOGRANDURTHISWASOWINGTOTHEPRUDANTCONDUCTOTHATREPUBLICKWHIHALWAYSPRESERVEDHERPRINCIPLES + +2024-01-17 01:15:56,813 (asr_inference:494) INFO: speech length: 304480 +2024-01-17 01:15:56,840 (beam_search:428) INFO: decoder input length: 473 +2024-01-17 01:15:56,840 (beam_search:429) INFO: max output length: 473 +2024-01-17 01:15:56,840 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:00,201 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:00,201 (beam_search:476) INFO: -55.63 * 1.0 = -55.63 for ctc +2024-01-17 01:16:00,201 (beam_search:479) INFO: total log probability: -55.63 +2024-01-17 01:16:00,201 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:16:00,201 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:00,203 (beam_search:483) INFO: best hypo: ATASMALBEAINGSETIONONTHEMORINGOFUTOBERHRTYONCONDUCTEDITHEATMSFEROFCONSPIRACYANDINTENDEDBYBRODYWEERETOLHATTHENORALAGANCEREQUIRMETFORREVUORDAPROVELEHADBENWAVEDTHATNORMALAPROVEOFTHEMAYROFWASINGTONANDSERTNGOVERSWOLBEHANDLEDINFORMILY + +2024-01-17 01:16:00,205 (asr_inference:494) INFO: speech length: 234080 +2024-01-17 01:16:00,225 (beam_search:428) INFO: decoder input length: 363 +2024-01-17 01:16:00,225 (beam_search:429) INFO: max output length: 363 +2024-01-17 01:16:00,225 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:01,928 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:01,928 (beam_search:476) INFO: -35.83 * 1.0 = -35.83 for ctc +2024-01-17 01:16:01,928 (beam_search:479) INFO: total log probability: -35.83 +2024-01-17 01:16:01,928 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:16:01,928 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:01,929 (beam_search:483) INFO: best hypo: THEBODISTLATDYINCHINERWHODONOTHASTATETOLTAKELIHFORTHERPERFERSOFODSAFFTHERCONSHENCSFROMTACKTOTIMBYBYINGVERSFASCSHEISISSETDERANDLETINTHEGO + +2024-01-17 01:16:01,930 (asr_inference:494) INFO: speech length: 299360 +2024-01-17 01:16:01,957 (beam_search:428) INFO: decoder input length: 465 +2024-01-17 01:16:01,957 (beam_search:429) INFO: max output length: 465 +2024-01-17 01:16:01,957 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:04,995 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:04,995 (beam_search:476) INFO: -31.08 * 1.0 = -31.08 for ctc +2024-01-17 01:16:04,995 (beam_search:479) INFO: total log probability: -31.08 +2024-01-17 01:16:04,995 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:16:04,995 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:04,996 (beam_search:483) INFO: best hypo: THISAGANISSOFENDADTEMPERDBYASIMPLFATHINTHESUPROMICYOFLOVEOVERFEERANDUNBOUNTEDHUMANITYANDCHARITYFORTHEPORANDHELPLESANDUNCONDINALFORGIVENESSOFTHEDIRESTINJURIESWHICHISTHENOTOFTHENOBLEAGENEROSITYANDLIBURALITY + +2024-01-17 01:16:04,998 (asr_inference:494) INFO: speech length: 201120 +2024-01-17 01:16:05,016 (beam_search:428) INFO: decoder input length: 312 +2024-01-17 01:16:05,016 (beam_search:429) INFO: max output length: 312 +2024-01-17 01:16:05,016 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:06,471 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:06,471 (beam_search:476) INFO: -31.01 * 1.0 = -31.01 for ctc +2024-01-17 01:16:06,471 (beam_search:479) INFO: total log probability: -31.01 +2024-01-17 01:16:06,471 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:16:06,471 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:06,472 (beam_search:483) INFO: best hypo: THESECANDMAEFOLOEDANTHECOUPLEOFTESEMERSMENROTTHEAORTHEBARKBEFORCUCHINGAROPETHEYHENTOWARKTSURCHTHESHIPTHELIFTDTHEHACESANDFOUNDTHERHOLDFULOFCARGO + +2024-01-17 01:16:06,473 (asr_inference:494) INFO: speech length: 200480 +2024-01-17 01:16:06,491 (beam_search:428) INFO: decoder input length: 311 +2024-01-17 01:16:06,491 (beam_search:429) INFO: max output length: 311 +2024-01-17 01:16:06,491 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:07,890 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:07,890 (beam_search:476) INFO: -23.76 * 1.0 = -23.76 for ctc +2024-01-17 01:16:07,890 (beam_search:479) INFO: total log probability: -23.76 +2024-01-17 01:16:07,890 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:16:07,890 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:07,891 (beam_search:483) INFO: best hypo: FONDOFISCOMRADSANDRESPECTFULETOHISPASTERSANDMASTERSEVENSCOLMASTERSASTHELADHEPREPARSFORMANWOUDWITHWILLANDTHISTRAININGORKUPEHIMTHROUTYUTHTID + +# Accounting: time=27 threads=1 +# Ended (code 0) at Wed Jan 17 01:16:08 CST 2024, elapsed time 27 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.2.log b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.2.log new file mode 100644 index 0000000000000000000000000000000000000000..6610d6f6d9700ec6252ae7d2b55cc17128c0d863 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.2.log @@ -0,0 +1,129 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2 --config conf/decode_asr.yaml +# Started at Wed Jan 17 01:16:08 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2 --config conf/decode_asr.yaml +2024-01-17 01:16:09,713 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml +2024-01-17 01:16:09,731 (asr:523) INFO: Vocabulary size: 30 +2024-01-17 01:16:09,793 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 01:16:09,793 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 01:16:09,904 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 01:16:11,206 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 01:16:12,430 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 01:16:12,430 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 01:16:12,430 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 01:16:12,463 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-17 01:16:12,538 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 01:16:12,652 (asr_inference:494) INFO: speech length: 167040 +2024-01-17 01:16:13,885 (beam_search:428) INFO: decoder input length: 258 +2024-01-17 01:16:13,885 (beam_search:429) INFO: max output length: 258 +2024-01-17 01:16:13,885 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:14,673 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:14,673 (beam_search:476) INFO: -21.03 * 1.0 = -21.03 for ctc +2024-01-17 01:16:14,673 (beam_search:479) INFO: total log probability: -21.03 +2024-01-17 01:16:14,673 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:16:14,673 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:14,674 (beam_search:483) INFO: best hypo: ASWHENHERICESQUECARECEDTERSIXTINTYEARSHEBECAMEBLINEDHERLARGESOFTBRONICESHADOLIDINTHEM + +2024-01-17 01:16:14,698 (asr_inference:494) INFO: speech length: 249280 +2024-01-17 01:16:14,721 (beam_search:428) INFO: decoder input length: 387 +2024-01-17 01:16:14,721 (beam_search:429) INFO: max output length: 387 +2024-01-17 01:16:14,721 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:16,919 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:16,919 (beam_search:476) INFO: -35.70 * 1.0 = -35.70 for ctc +2024-01-17 01:16:16,919 (beam_search:479) INFO: total log probability: -35.70 +2024-01-17 01:16:16,919 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:16:16,919 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:16,920 (beam_search:483) INFO: best hypo: ALMOSTALLDAYTHEBATARRANGEDBETWETHETOMENBACKINFORTTHEYFORSEEACHOTHEROVERTHELOVABEDSTHECHIFESWELOILEDBODYWASVERYDIFICULTFORTHEOLOHAYTOGRASBRUSEDANDBLEADINGFROMREPEATEDFALLSONTHERUHLAVA + +2024-01-17 01:16:16,922 (asr_inference:494) INFO: speech length: 199840 +2024-01-17 01:16:16,940 (beam_search:428) INFO: decoder input length: 310 +2024-01-17 01:16:16,940 (beam_search:429) INFO: max output length: 310 +2024-01-17 01:16:16,940 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:17,992 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:17,992 (beam_search:476) INFO: -21.89 * 1.0 = -21.89 for ctc +2024-01-17 01:16:17,992 (beam_search:479) INFO: total log probability: -21.89 +2024-01-17 01:16:17,992 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:16:17,992 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:17,993 (beam_search:483) INFO: best hypo: POSYITREITTERRARANDAWARPPANTINGTHEWINMONTHRUOUTHETREESOFTEGARDINANDFROMTIMETOATIMESEEASIFTOA + +2024-01-17 01:16:17,994 (asr_inference:494) INFO: speech length: 175680 +2024-01-17 01:16:18,011 (beam_search:428) INFO: decoder input length: 272 +2024-01-17 01:16:18,011 (beam_search:429) INFO: max output length: 272 +2024-01-17 01:16:18,011 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:19,043 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:19,043 (beam_search:476) INFO: -21.79 * 1.0 = -21.79 for ctc +2024-01-17 01:16:19,044 (beam_search:479) INFO: total log probability: -21.79 +2024-01-17 01:16:19,044 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:16:19,044 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:19,044 (beam_search:483) INFO: best hypo: HISMENTALTORPITITYFOUNDIAPONFISICALINDILENCERENDERSAMEDIAACTIONNALLMANEOFEXERTIONDISTASFULHISCONCHOUSWEKNESSSHOWEITSELF + +2024-01-17 01:16:19,046 (asr_inference:494) INFO: speech length: 256480 +2024-01-17 01:16:19,069 (beam_search:428) INFO: decoder input length: 398 +2024-01-17 01:16:19,069 (beam_search:429) INFO: max output length: 398 +2024-01-17 01:16:19,069 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:21,009 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:21,009 (beam_search:476) INFO: -35.29 * 1.0 = -35.29 for ctc +2024-01-17 01:16:21,009 (beam_search:479) INFO: total log probability: -35.29 +2024-01-17 01:16:21,009 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:16:21,009 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:21,010 (beam_search:483) INFO: best hypo: NORTHALLHOWGLADTHECUINGMOTHERWASNORHOLGREATWARTHEREJOICESINGOFTHEPEOPLETORHOMMENIFISSENTWASTHEROIALBANQUEDTHATGOODCQINGPAMAREAATENDEDTBITHALHERCORT + +2024-01-17 01:16:21,012 (asr_inference:494) INFO: speech length: 238400 +2024-01-17 01:16:21,033 (beam_search:428) INFO: decoder input length: 370 +2024-01-17 01:16:21,033 (beam_search:429) INFO: max output length: 370 +2024-01-17 01:16:21,033 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:22,819 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:22,819 (beam_search:476) INFO: -33.11 * 1.0 = -33.11 for ctc +2024-01-17 01:16:22,819 (beam_search:479) INFO: total log probability: -33.11 +2024-01-17 01:16:22,819 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:16:22,819 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:22,820 (beam_search:483) INFO: best hypo: ANDTHECHANCEOFTHERBEANGSUCHAONUNGANDIMISHIESBYWERYPRAPEDPROSESSMAMELUKASAHORSWASOFECUEANECQULITYREASNINGNOTABOUTHISODERSBUTABOUGTTHEWAYTODOTHE + +2024-01-17 01:16:22,822 (asr_inference:494) INFO: speech length: 295040 +2024-01-17 01:16:22,849 (beam_search:428) INFO: decoder input length: 458 +2024-01-17 01:16:22,849 (beam_search:429) INFO: max output length: 458 +2024-01-17 01:16:22,849 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:25,744 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:25,744 (beam_search:476) INFO: -37.47 * 1.0 = -37.47 for ctc +2024-01-17 01:16:25,744 (beam_search:479) INFO: total log probability: -37.47 +2024-01-17 01:16:25,744 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:16:25,744 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:25,745 (beam_search:483) INFO: best hypo: SHENOKEDBUTSNODROPKOKDOTOFTHEEINDOANDSAIDIDEARENOTOPNTHEDOORFORTHEDORSHAVETOLDMETOLETNOWENINTHATISHARDFORMESAIDTHEWOMANFORIMUSTTAKBACKMYAPLSBUTTHEREISONEWHICHIWILLGIVEYOUANDSHEHELDUPANAAL + +2024-01-17 01:16:25,747 (asr_inference:494) INFO: speech length: 185440 +2024-01-17 01:16:25,765 (beam_search:428) INFO: decoder input length: 287 +2024-01-17 01:16:25,765 (beam_search:429) INFO: max output length: 287 +2024-01-17 01:16:25,765 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:26,894 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:26,894 (beam_search:476) INFO: -29.27 * 1.0 = -29.27 for ctc +2024-01-17 01:16:26,894 (beam_search:479) INFO: total log probability: -29.27 +2024-01-17 01:16:26,894 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:16:26,894 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:26,895 (beam_search:483) INFO: best hypo: HAVIYMORYSPOKACTHELSHOURANDHORCEFINLYSPOKNITTOMYPECEWASWHYFEBYAREYUCOMESOSOONWHERERYOUBARYCHILDANDOTHANARDILSPOKASAMEON + +2024-01-17 01:16:26,897 (asr_inference:494) INFO: speech length: 234400 +2024-01-17 01:16:26,917 (beam_search:428) INFO: decoder input length: 364 +2024-01-17 01:16:26,917 (beam_search:429) INFO: max output length: 364 +2024-01-17 01:16:26,917 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:28,901 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:28,901 (beam_search:476) INFO: -53.75 * 1.0 = -53.75 for ctc +2024-01-17 01:16:28,901 (beam_search:479) INFO: total log probability: -53.75 +2024-01-17 01:16:28,901 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:16:28,901 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:28,902 (beam_search:483) INFO: best hypo: INEVERGEWANYONHOLIETOTTURCHASMCHASSGRANMOHRDOUSSHEHASHEWOUDRAVERBADORCEPRINTHEHASOFORGODTHNTDWELNTHETENSOFWIKEDNESTHEYDONHAEWOMENDORKEPERSANDINNOWSHEWOLDTTWELMININANATENT + +2024-01-17 01:16:28,904 (asr_inference:494) INFO: speech length: 163200 +2024-01-17 01:16:28,920 (beam_search:428) INFO: decoder input length: 252 +2024-01-17 01:16:28,920 (beam_search:429) INFO: max output length: 252 +2024-01-17 01:16:28,920 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:29,819 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:29,819 (beam_search:476) INFO: -23.98 * 1.0 = -23.98 for ctc +2024-01-17 01:16:29,819 (beam_search:479) INFO: total log probability: -23.98 +2024-01-17 01:16:29,819 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:16:29,819 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:29,819 (beam_search:483) INFO: best hypo: THEDUKWASSUPRISETOSEHIMWOHTBRINGSYOUOUTSOIALYABLEARDDEMANDHEOWYOURGRAICETSREPLIDTEBUTLERGASPINGFORATERENCS + +# Accounting: time=22 threads=1 +# Ended (code 0) at Wed Jan 17 01:16:30 CST 2024, elapsed time 22 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.3.log b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.3.log new file mode 100644 index 0000000000000000000000000000000000000000..03fff53f92678e4c707e65a27db35c326e3690b7 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.3.log @@ -0,0 +1,129 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3 --config conf/decode_asr.yaml +# Started at Wed Jan 17 01:16:30 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3 --config conf/decode_asr.yaml +2024-01-17 01:16:31,643 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml +2024-01-17 01:16:31,662 (asr:523) INFO: Vocabulary size: 30 +2024-01-17 01:16:31,725 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 01:16:31,725 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 01:16:31,837 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 01:16:33,140 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 01:16:34,379 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 01:16:34,379 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 01:16:34,379 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 01:16:34,412 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-17 01:16:34,487 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 01:16:34,606 (asr_inference:494) INFO: speech length: 236480 +2024-01-17 01:16:35,829 (beam_search:428) INFO: decoder input length: 367 +2024-01-17 01:16:35,829 (beam_search:429) INFO: max output length: 367 +2024-01-17 01:16:35,829 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:37,755 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:37,755 (beam_search:476) INFO: -36.79 * 1.0 = -36.79 for ctc +2024-01-17 01:16:37,755 (beam_search:479) INFO: total log probability: -36.79 +2024-01-17 01:16:37,755 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:16:37,755 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:37,756 (beam_search:483) INFO: best hypo: FOREYIETSEMINGRONSMUTATIONSOFCALEMAYBEMADEDWHEREHERISANYMIXTROFDEVRSSOUTSOFRAISEFORINSUCHMIXTERSTHEMPONCALESAPENOTTBYTHEMUTALALAINGHACHETHECONSTITUTEAMIDLINGCOLER + +2024-01-17 01:16:37,781 (asr_inference:494) INFO: speech length: 273120 +2024-01-17 01:16:37,807 (beam_search:428) INFO: decoder input length: 424 +2024-01-17 01:16:37,807 (beam_search:429) INFO: max output length: 424 +2024-01-17 01:16:37,807 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:40,488 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:40,489 (beam_search:476) INFO: -48.21 * 1.0 = -48.21 for ctc +2024-01-17 01:16:40,489 (beam_search:479) INFO: total log probability: -48.21 +2024-01-17 01:16:40,489 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:16:40,489 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:40,490 (beam_search:483) INFO: best hypo: ANDALMUSTHESAMEINSTENTCINSASANDYSIDSTEPINTOASHAPDORWAYHEWATEDTHERANTLLCINCAMEUPKINSTOPENDPRETENDEDOSTARTHRTHELASATTHDESPLAYOFHARDWARINTOULSWEREHECONTINEDTOWATCHBARICKYOUSEWHATISEESAMBESTEAIDCINNOTED + +2024-01-17 01:16:40,492 (asr_inference:494) INFO: speech length: 292800 +2024-01-17 01:16:40,519 (beam_search:428) INFO: decoder input length: 455 +2024-01-17 01:16:40,519 (beam_search:429) INFO: max output length: 455 +2024-01-17 01:16:40,519 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:43,224 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:43,224 (beam_search:476) INFO: -42.56 * 1.0 = -42.56 for ctc +2024-01-17 01:16:43,224 (beam_search:479) INFO: total log probability: -42.56 +2024-01-17 01:16:43,224 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:16:43,224 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:43,225 (beam_search:483) INFO: best hypo: THENMTHETHIEKGREANSTAVFLOWDOWETHEHALLEBILDINGANDTHERWASNOTHINGTOBESEENTHEREBUTDEMIOWDOFSOFTFLOWINGGRAYGREANSTAVETHEDROUSHEDONNOWWITTHESWEIFTNESOLLHEWEINKALOOKEDAPINTOBEARSSFAICE + +2024-01-17 01:16:43,227 (asr_inference:494) INFO: speech length: 301760 +2024-01-17 01:16:43,255 (beam_search:428) INFO: decoder input length: 469 +2024-01-17 01:16:43,255 (beam_search:429) INFO: max output length: 469 +2024-01-17 01:16:43,255 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:45,963 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:45,964 (beam_search:476) INFO: -38.26 * 1.0 = -38.26 for ctc +2024-01-17 01:16:45,964 (beam_search:479) INFO: total log probability: -38.26 +2024-01-17 01:16:45,964 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:16:45,964 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:45,965 (beam_search:483) INFO: best hypo: IIHAVEMAESACROFICSTOUROFEWHENINEWTHTHEWENOTMYHAPNESIWASAFTERISALTHATHADSTPEDAFTERISALTHATYOURTENDERNESSHADTERNDTOCAULCULATIONAFTERISAWTHATYOUCARDFORYOURSELFONLYNOTFORME + +2024-01-17 01:16:45,967 (asr_inference:494) INFO: speech length: 249120 +2024-01-17 01:16:45,989 (beam_search:428) INFO: decoder input length: 387 +2024-01-17 01:16:45,989 (beam_search:429) INFO: max output length: 387 +2024-01-17 01:16:45,989 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:47,701 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:47,702 (beam_search:476) INFO: -25.03 * 1.0 = -25.03 for ctc +2024-01-17 01:16:47,702 (beam_search:479) INFO: total log probability: -25.03 +2024-01-17 01:16:47,702 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:16:47,702 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:47,702 (beam_search:483) INFO: best hypo: YETTHATHANDEARNEVERROALSINWINTERISEYACROLWOARINGROUNDANDROUNBEFORITALIHTTHESNNOTHINGUNDERTHEFIRDTREESBUTINOESOMETHINGMUSTBETHEAR + +2024-01-17 01:16:47,704 (asr_inference:494) INFO: speech length: 268480 +2024-01-17 01:16:47,729 (beam_search:428) INFO: decoder input length: 417 +2024-01-17 01:16:47,729 (beam_search:429) INFO: max output length: 417 +2024-01-17 01:16:47,729 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:49,886 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:49,886 (beam_search:476) INFO: -40.41 * 1.0 = -40.41 for ctc +2024-01-17 01:16:49,886 (beam_search:479) INFO: total log probability: -40.41 +2024-01-17 01:16:49,886 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:16:49,886 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:49,887 (beam_search:483) INFO: best hypo: ITISBEYONDTHOUTTHATSOMEPEOBLEHAEMENISETOSAFEMANYTHINGSANOFCOURSETHEJURMANTHASASUMAIIEDASSMAUCHTOORTHREDAYSEAFTERHAFIRSPROCOCITIONSTHEYDROPETINIUNNAWARE + +2024-01-17 01:16:49,889 (asr_inference:494) INFO: speech length: 255840 +2024-01-17 01:16:49,912 (beam_search:428) INFO: decoder input length: 397 +2024-01-17 01:16:49,912 (beam_search:429) INFO: max output length: 397 +2024-01-17 01:16:49,912 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:52,091 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:52,091 (beam_search:476) INFO: -27.23 * 1.0 = -27.23 for ctc +2024-01-17 01:16:52,091 (beam_search:479) INFO: total log probability: -27.23 +2024-01-17 01:16:52,091 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:16:52,091 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:52,092 (beam_search:483) INFO: best hypo: SOONAMANCAMEOUTTOMEHIMTHISMANWASOLOHAABEARDLESSMANBELONGIGTOALALLSROBERCLANWICHINFESTEDTHEDISTRICTPOSEBLYASSISTINTHEMANHUNTERSOFTHEEMPLEINSECURINGVICTOMSFORTHEEMPLEALTRS + +2024-01-17 01:16:52,094 (asr_inference:494) INFO: speech length: 166400 +2024-01-17 01:16:52,110 (beam_search:428) INFO: decoder input length: 257 +2024-01-17 01:16:52,110 (beam_search:429) INFO: max output length: 257 +2024-01-17 01:16:52,111 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:52,820 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:52,820 (beam_search:476) INFO: -17.79 * 1.0 = -17.79 for ctc +2024-01-17 01:16:52,820 (beam_search:479) INFO: total log probability: -17.79 +2024-01-17 01:16:52,820 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:16:52,820 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:52,821 (beam_search:483) INFO: best hypo: WEARNOTLOVERASYOUANDIYAPONTHISSUNYLAINBUTCHILDENWHOEVENEVERNONLOVESJORYORPE + +2024-01-17 01:16:52,822 (asr_inference:494) INFO: speech length: 283040 +2024-01-17 01:16:52,848 (beam_search:428) INFO: decoder input length: 440 +2024-01-17 01:16:52,848 (beam_search:429) INFO: max output length: 440 +2024-01-17 01:16:52,848 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:55,285 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:55,285 (beam_search:476) INFO: -52.92 * 1.0 = -52.92 for ctc +2024-01-17 01:16:55,285 (beam_search:479) INFO: total log probability: -52.92 +2024-01-17 01:16:55,285 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:16:55,285 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:55,286 (beam_search:483) INFO: best hypo: WASMURDEDONHESORSTEFOFISOLEHOUSESWASIAOLMANASDANDURACOMNLYMSREGUODIARYOUTAKASIFHHADDIEDINHISBEAETYOUARNOVENEISIONHAYOUCANONDESTANDWHTITMINSHENANIQUITITYISMERDEDT + +2024-01-17 01:16:55,288 (asr_inference:494) INFO: speech length: 284480 +2024-01-17 01:16:55,314 (beam_search:428) INFO: decoder input length: 442 +2024-01-17 01:16:55,314 (beam_search:429) INFO: max output length: 442 +2024-01-17 01:16:55,314 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:16:57,887 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:16:57,887 (beam_search:476) INFO: -46.95 * 1.0 = -46.95 for ctc +2024-01-17 01:16:57,887 (beam_search:479) INFO: total log probability: -46.95 +2024-01-17 01:16:57,887 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:16:57,887 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:16:57,888 (beam_search:483) INFO: best hypo: BYGODHESAIDTHOYWONTMEMUBUSNSLEIDSMEINANDOUTOMANYHOUSESORWHATDOICARFOTHESICRESTTHATMAYBHIDINTHEREHOWEVERICANOTPLINTHISPEPEFORTHEWACHFOLNESTHEBLUTHOUSOFHISSFNORIARAYINEVERESTREET + +# Accounting: time=28 threads=1 +# Ended (code 0) at Wed Jan 17 01:16:58 CST 2024, elapsed time 28 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.4.log b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.4.log new file mode 100644 index 0000000000000000000000000000000000000000..02fcbabd3d61b4dc98e3ba8532bacedc39ca89ad --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/asr_inference.4.log @@ -0,0 +1,129 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4 --config conf/decode_asr.yaml +# Started at Wed Jan 17 01:16:58 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4 --config conf/decode_asr.yaml +2024-01-17 01:16:59,730 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml +2024-01-17 01:16:59,748 (asr:523) INFO: Vocabulary size: 30 +2024-01-17 01:16:59,810 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 01:16:59,810 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 01:16:59,922 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 01:17:01,229 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 01:17:02,450 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 01:17:02,450 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 01:17:02,450 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 01:17:02,483 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-17 01:17:02,559 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 01:17:02,674 (asr_inference:494) INFO: speech length: 216320 +2024-01-17 01:17:03,898 (beam_search:428) INFO: decoder input length: 335 +2024-01-17 01:17:03,898 (beam_search:429) INFO: max output length: 335 +2024-01-17 01:17:03,898 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:05,639 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:05,639 (beam_search:476) INFO: -50.30 * 1.0 = -50.30 for ctc +2024-01-17 01:17:05,639 (beam_search:479) INFO: total log probability: -50.30 +2024-01-17 01:17:05,639 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:17:05,639 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:05,640 (beam_search:483) INFO: best hypo: HARYBLDETWASPULINTNOTASTIKNORASTONWASINDRITOFHEHANDANTHEPIYLSGRAGSEULDONENONGSHREKUBOFWLLTHERWAROFWATTHEOLESHESTROEDOUODONOUEANDGASETHEGOUNDBUTONLYFILTHERSEVEGINCASTHOR + +2024-01-17 01:17:05,665 (asr_inference:494) INFO: speech length: 303680 +2024-01-17 01:17:05,693 (beam_search:428) INFO: decoder input length: 472 +2024-01-17 01:17:05,693 (beam_search:429) INFO: max output length: 472 +2024-01-17 01:17:05,693 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:08,350 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:08,350 (beam_search:476) INFO: -32.56 * 1.0 = -32.56 for ctc +2024-01-17 01:17:08,350 (beam_search:479) INFO: total log probability: -32.56 +2024-01-17 01:17:08,350 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:17:08,350 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:08,351 (beam_search:483) INFO: best hypo: ANDAFALYOFEITWASTHATANNOTHEWOMENTOFVESERNYWHICHFORNOTHINGBETEDTENTOGOSINTOMYSISTRANDFATHERASSENTAWAYSHESAIDISHULRATHERGOWITHMIHAVENOMINETOSTAYHEARALONITMYTOBABES + +2024-01-17 01:17:08,353 (asr_inference:494) INFO: speech length: 185120 +2024-01-17 01:17:08,371 (beam_search:428) INFO: decoder input length: 287 +2024-01-17 01:17:08,371 (beam_search:429) INFO: max output length: 287 +2024-01-17 01:17:08,371 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:09,395 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:09,395 (beam_search:476) INFO: -22.18 * 1.0 = -22.18 for ctc +2024-01-17 01:17:09,395 (beam_search:479) INFO: total log probability: -22.18 +2024-01-17 01:17:09,395 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:17:09,395 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:09,395 (beam_search:483) INFO: best hypo: EINIDITACORDINGLYWONDERINGHATELITEMANWOULDBEATANDHEPEKEDTOOFTHESTOUTISRUSHESHECOULDFINDWITEALITALEBUNHOFB + +2024-01-17 01:17:09,397 (asr_inference:494) INFO: speech length: 259520 +2024-01-17 01:17:09,420 (beam_search:428) INFO: decoder input length: 403 +2024-01-17 01:17:09,420 (beam_search:429) INFO: max output length: 403 +2024-01-17 01:17:09,420 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:11,706 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:11,706 (beam_search:476) INFO: -41.76 * 1.0 = -41.76 for ctc +2024-01-17 01:17:11,706 (beam_search:479) INFO: total log probability: -41.76 +2024-01-17 01:17:11,706 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:17:11,706 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:11,708 (beam_search:483) INFO: best hypo: ITISNOTHTDREDFULNAECOMSONHODISMAASTHEPLEINWERTHERUSIONSANTHENGLISHFONDTBOVFTENTHOUSNDSLAINBRAVEWELINGTONADBLOKERBOTHMOSNOBLYDROVETHERFOURSANDBONUPARTSAMPEALCROWNWASTAKENATWATEL + +2024-01-17 01:17:11,709 (asr_inference:494) INFO: speech length: 259200 +2024-01-17 01:17:11,733 (beam_search:428) INFO: decoder input length: 402 +2024-01-17 01:17:11,733 (beam_search:429) INFO: max output length: 402 +2024-01-17 01:17:11,733 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:13,963 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:13,964 (beam_search:476) INFO: -37.41 * 1.0 = -37.41 for ctc +2024-01-17 01:17:13,964 (beam_search:479) INFO: total log probability: -37.41 +2024-01-17 01:17:13,964 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:17:13,964 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:13,965 (beam_search:483) INFO: best hypo: SOMEYEASAGOHAFTERMAKINGOURARANGENSFORTHEINCAMPENTATNIGTWHEDCONSTANTLYHADOFAPEASFULRESROKGONBYATRIBOFROUNMOUNCKESTHEEIDENTETHUGHTTHATLONGPOSTIONHADGIENTHEAPRIARLAMTOTHEGL + +2024-01-17 01:17:13,966 (asr_inference:494) INFO: speech length: 294240 +2024-01-17 01:17:13,993 (beam_search:428) INFO: decoder input length: 457 +2024-01-17 01:17:13,993 (beam_search:429) INFO: max output length: 457 +2024-01-17 01:17:13,993 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:16,610 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:16,610 (beam_search:476) INFO: -34.11 * 1.0 = -34.11 for ctc +2024-01-17 01:17:16,610 (beam_search:479) INFO: total log probability: -34.11 +2024-01-17 01:17:16,610 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:17:16,610 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:16,611 (beam_search:483) INFO: best hypo: TOFLASHINATHOMECLEMERINGFORHERMADBETWEENMSSWANESTANSPARTYANDTHEOPRARIFONLYFORMINITSUERTONLYTWASMORTHANAMINTTHATSIMONREMAINEDATTHEFIERHOUSEAFTERBEANGDRODBACKEFTERDINERINTHTACX + +2024-01-17 01:17:16,613 (asr_inference:494) INFO: speech length: 303680 +2024-01-17 01:17:16,640 (beam_search:428) INFO: decoder input length: 472 +2024-01-17 01:17:16,640 (beam_search:429) INFO: max output length: 472 +2024-01-17 01:17:16,640 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:19,890 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:19,890 (beam_search:476) INFO: -66.81 * 1.0 = -66.81 for ctc +2024-01-17 01:17:19,890 (beam_search:479) INFO: total log probability: -66.81 +2024-01-17 01:17:19,890 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:17:19,890 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:19,891 (beam_search:483) INFO: best hypo: ANDINDICIONARYANDHENWEHADCALSTHENICXSWECOTHROUAGEAEMANYFIGERSANDSINKEALIFEOFMHEOUTIONWAVEWHTFERYLIKMUSICKTILDOVETHESCELIGELYROWLIGTELYROWORETHEGLASYWAYEWULGOANDOCOMECOMEAWAYANDOTHERSONGSMSJUDHETALORODWONSSONGANPERPISFORHUS + +2024-01-17 01:17:19,893 (asr_inference:494) INFO: speech length: 272480 +2024-01-17 01:17:19,918 (beam_search:428) INFO: decoder input length: 423 +2024-01-17 01:17:19,918 (beam_search:429) INFO: max output length: 423 +2024-01-17 01:17:19,918 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:22,445 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:22,445 (beam_search:476) INFO: -46.18 * 1.0 = -46.18 for ctc +2024-01-17 01:17:22,445 (beam_search:479) INFO: total log probability: -46.18 +2024-01-17 01:17:22,445 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:17:22,445 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:22,446 (beam_search:483) INFO: best hypo: THATWHICHPATSESAHISTRYINURSCOULSARGOVERMENTLYFABRICATEBUTSOMEHISTRYISAFOURGURYADMRPESENTATIONOFEVENCESLIKETHELDRAMARESENTRINGUPONTHEMPOSIBLEFIGUEOFTHEEROWITHTHEJUSTICULATINGCROWDINTHEBACKROND + +2024-01-17 01:17:22,448 (asr_inference:494) INFO: speech length: 233920 +2024-01-17 01:17:22,469 (beam_search:428) INFO: decoder input length: 363 +2024-01-17 01:17:22,469 (beam_search:429) INFO: max output length: 363 +2024-01-17 01:17:22,469 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:24,082 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:24,082 (beam_search:476) INFO: -25.93 * 1.0 = -25.93 for ctc +2024-01-17 01:17:24,082 (beam_search:479) INFO: total log probability: -25.93 +2024-01-17 01:17:24,082 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:17:24,082 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:24,083 (beam_search:483) INFO: best hypo: HNTHECALEVFHURDTHISHESAIDOLJEUFARHOWGOODLYISTHATVOICENDTHEVASIEREPLIDOOURLORDNEVERSMORTMYHEARINGARTTRETEREORGODLYAETHANTHESINGING + +2024-01-17 01:17:24,085 (asr_inference:494) INFO: speech length: 305760 +2024-01-17 01:17:24,112 (beam_search:428) INFO: decoder input length: 475 +2024-01-17 01:17:24,112 (beam_search:429) INFO: max output length: 475 +2024-01-17 01:17:24,112 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:26,807 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:26,807 (beam_search:476) INFO: -37.87 * 1.0 = -37.87 for ctc +2024-01-17 01:17:26,807 (beam_search:479) INFO: total log probability: -37.87 +2024-01-17 01:17:26,807 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:17:26,807 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:26,808 (beam_search:483) INFO: best hypo: EVIDENTLYTHELURNIDBARIENHADNOTSTODEDSUCHWORKOFTHETOTACAHNEORPERITCHATWHICHNOTABLYTRANSLATEDBYNUSHABYYFROMTHESANSGRITSOKUSEPTATHASNOBECOMEASORTHODOCXICLYMUSLOMEASTHENIHTES + +# Accounting: time=29 threads=1 +# Ended (code 0) at Wed Jan 17 01:17:27 CST 2024, elapsed time 29 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/calculate_rtf.log b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/calculate_rtf.log new file mode 100644 index 0000000000000000000000000000000000000000..b53a00225b3c261c2c268d4a1c32ff7a32ab77bc --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/calculate_rtf.log @@ -0,0 +1,9 @@ +# pyscripts/utils/calculate_rtf.py --log-dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir --log-name asr_inference --input-shift 0.0625 --start-times-marker "speech length" --end-times-marker "best hypo" --inf-num 1 +# Started at Wed Jan 17 01:17:27 CST 2024 +# +Total audio duration: 618.040 [sec] +Total decoding time: 86.646 [sec] +RTF: 0.140 +Latency: 2166.150 [ms/sentence] +# Accounting: time=0 threads=1 +# Ended (code 0) at Wed Jan 17 01:17:27 CST 2024, elapsed time 0 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.1.scp b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.1.scp new file mode 100644 index 0000000000000000000000000000000000000000..3461e51141d223181d58a9832ae835adb929dce5 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.1.scp @@ -0,0 +1,10 @@ +mls_eng_000243 dump/raw/org/dev_10min_eng1/data/format.1/mls_eng_000243.flac +mls_eng_000244 dump/raw/org/dev_10min_eng1/data/format.1/mls_eng_000244.flac +mls_eng_000245 dump/raw/org/dev_10min_eng1/data/format.2/mls_eng_000245.flac +mls_eng_000246 dump/raw/org/dev_10min_eng1/data/format.2/mls_eng_000246.flac +mls_eng_000247 dump/raw/org/dev_10min_eng1/data/format.3/mls_eng_000247.flac +mls_eng_000248 dump/raw/org/dev_10min_eng1/data/format.3/mls_eng_000248.flac +mls_eng_000249 dump/raw/org/dev_10min_eng1/data/format.4/mls_eng_000249.flac +mls_eng_000250 dump/raw/org/dev_10min_eng1/data/format.4/mls_eng_000250.flac +mls_eng_000251 dump/raw/org/dev_10min_eng1/data/format.5/mls_eng_000251.flac +mls_eng_000252 dump/raw/org/dev_10min_eng1/data/format.5/mls_eng_000252.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.2.scp b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.2.scp new file mode 100644 index 0000000000000000000000000000000000000000..57dfd47f98c07f1c55c2f2d31b815483d6aeadef --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.2.scp @@ -0,0 +1,10 @@ +mls_eng_000253 dump/raw/org/dev_10min_eng1/data/format.6/mls_eng_000253.flac +mls_eng_000254 dump/raw/org/dev_10min_eng1/data/format.6/mls_eng_000254.flac +mls_eng_000255 dump/raw/org/dev_10min_eng1/data/format.7/mls_eng_000255.flac +mls_eng_000256 dump/raw/org/dev_10min_eng1/data/format.7/mls_eng_000256.flac +mls_eng_000257 dump/raw/org/dev_10min_eng1/data/format.8/mls_eng_000257.flac +mls_eng_000258 dump/raw/org/dev_10min_eng1/data/format.8/mls_eng_000258.flac +mls_eng_000259 dump/raw/org/dev_10min_eng1/data/format.9/mls_eng_000259.flac +mls_eng_000260 dump/raw/org/dev_10min_eng1/data/format.10/mls_eng_000260.flac +mls_eng_000261 dump/raw/org/dev_10min_eng1/data/format.11/mls_eng_000261.flac +mls_eng_000262 dump/raw/org/dev_10min_eng1/data/format.12/mls_eng_000262.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.3.scp b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.3.scp new file mode 100644 index 0000000000000000000000000000000000000000..1a36277b8bd4955924328f1ea89a248c0fa35056 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.3.scp @@ -0,0 +1,10 @@ +mls_eng_000263 dump/raw/org/dev_10min_eng1/data/format.13/mls_eng_000263.flac +mls_eng_000264 dump/raw/org/dev_10min_eng1/data/format.14/mls_eng_000264.flac +mls_eng_000265 dump/raw/org/dev_10min_eng1/data/format.15/mls_eng_000265.flac +mls_eng_000266 dump/raw/org/dev_10min_eng1/data/format.16/mls_eng_000266.flac +mls_eng_000267 dump/raw/org/dev_10min_eng1/data/format.17/mls_eng_000267.flac +mls_eng_000268 dump/raw/org/dev_10min_eng1/data/format.18/mls_eng_000268.flac +mls_eng_000269 dump/raw/org/dev_10min_eng1/data/format.19/mls_eng_000269.flac +mls_eng_000270 dump/raw/org/dev_10min_eng1/data/format.20/mls_eng_000270.flac +mls_eng_000271 dump/raw/org/dev_10min_eng1/data/format.21/mls_eng_000271.flac +mls_eng_000272 dump/raw/org/dev_10min_eng1/data/format.22/mls_eng_000272.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.4.scp b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.4.scp new file mode 100644 index 0000000000000000000000000000000000000000..cbd32313e1d13010a429ca6206e3d80b77030b6b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/keys.4.scp @@ -0,0 +1,10 @@ +mls_eng_000273 dump/raw/org/dev_10min_eng1/data/format.23/mls_eng_000273.flac +mls_eng_000274 dump/raw/org/dev_10min_eng1/data/format.24/mls_eng_000274.flac +mls_eng_000275 dump/raw/org/dev_10min_eng1/data/format.25/mls_eng_000275.flac +mls_eng_000276 dump/raw/org/dev_10min_eng1/data/format.26/mls_eng_000276.flac +mls_eng_000277 dump/raw/org/dev_10min_eng1/data/format.27/mls_eng_000277.flac +mls_eng_000278 dump/raw/org/dev_10min_eng1/data/format.28/mls_eng_000278.flac +mls_eng_000279 dump/raw/org/dev_10min_eng1/data/format.29/mls_eng_000279.flac +mls_eng_000280 dump/raw/org/dev_10min_eng1/data/format.30/mls_eng_000280.flac +mls_eng_000281 dump/raw/org/dev_10min_eng1/data/format.31/mls_eng_000281.flac +mls_eng_000282 dump/raw/org/dev_10min_eng1/data/format.32/mls_eng_000282.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..9ca76af91965dc7d7f84adfa94ef8938636baa32 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/score @@ -0,0 +1,10 @@ +mls_eng_000243 tensor(-36.8144) +mls_eng_000244 tensor(-26.4316) +mls_eng_000245 tensor(-41.9824) +mls_eng_000246 tensor(-31.1372) +mls_eng_000247 tensor(-23.5815) +mls_eng_000248 tensor(-55.6310) +mls_eng_000249 tensor(-35.8302) +mls_eng_000250 tensor(-31.0784) +mls_eng_000251 tensor(-31.0069) +mls_eng_000252 tensor(-23.7646) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..bdd49214b7f95e7627a1f1620b5a70647d0d4468 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/text @@ -0,0 +1,10 @@ +mls_eng_000243 I AMTHIR UD OF TAKN OLDES FOM YOU SHE SAD HASTALY I SA NOW THE IT IS IMPOSIBLE T HAVE FATHIN OU I SE NOW THE IT IS USESTO EXPECT ANY RETERN FROM OU FOR ALE I HAVE DON I WANT NO MORE OF YOU +mls_eng_000244 THAT HE MAY SOMTIMES BEE LIKE OTHER CHELDREN LANING TE SIE MY NE OR PLAING PRATLIN SIKING FOR HELES COMS TO MY HEART ATS SINFUL ORD ANM SPEAKING HOW GOD THO ART +mls_eng_000245 TRANSENTHINGS OF AL SORSE AS I THE GENRALE OUTBURST OF MULTITODNS PASION AR HUTED TOGETHER THE LITRCRIS NAY THE REDICULOS WIT THE HERABLE FAR OVER THE ILWEY SE OF HEDS MAY BE SEN RESCALITY CAPRIYOLING ON FORSES FOM THE ROIAL STOD +mls_eng_000246 IT MAY HAVE BEN THT THE BONS WERE A FOLDED TOGETHER AND NON AS ONA HPELBONS FULDED AND LAIE AWAY FOR THE PRPICES OF INCENTATION SUCH BUNDLES OF BONS WE PUT THRU APROSES OF PRARS +mls_eng_000247 MASAILS NEVER EXPERINED THOS GREAT TRANSIONS FROM LONES TO GRANDUR THIS WAS OWING TO THE PRUDANT CONDUCT O THAT REPUBLICK WHIH ALWAYS PRESERVED HER PRINCIPLES +mls_eng_000248 AT A SMAL BEAING SETION ON THE MORING OF UTOBER HRTY ON CONDUCTED I THE ATMSFER OF CONSPIRACY AND INTENDED BY BRODY WE ERETOL HAT THE NORAL AGANCE REQUIRMET FOR REVU ORDAPROVELE HAD BEN WAVED THAT NORMAL APROVE OF THE MAYR OF WASINGTON AND SERTN GOVERS WOL BE HANDLED IN FORMILY +mls_eng_000249 THE BODIST LATDY IN CHINER WHO DO NOT HASTATE TOL TAKE LIH FOR THER PERFERS OFOD SAFF THER CONSHENCS FROM TACK TO TIM BY BYING VERS FASCSHEIS IS SETDER AND LETIN THE GO +mls_eng_000250 THIS AGAN IS SOFEND AD TEMPERD BY A SIMPL FATH IN THE SUPROMICY OF LOVE OVER FEER AND UN BOUNTED HUMANITY AND CHARITY FOR THE POR AND HELPLESAND UN CONDINAL FORGIVENESS OF THE DIREST INJURIES WHICH IS THE NOT OF THE NOBLE A GENEROSITY AND LIBURALITY +mls_eng_000251 THE SECAND MAE FOLOED AN THE COUPLE OF TE SEMERSMEN ROT THE AOR THE BARK BEFOR CUCHING A ROPE THEY HENTO WARK T SURCH THE SHIP THE LIFTD THE HACES AND FOUND THER HOLD FUL OF CARGO +mls_eng_000252 FOND OF IS COMRADS AND RESPECTFULE TO HIS PASTERS AND MASTERS EVEN SCOL MASTERS AS THE LAD HE PREPARS FOR MANWOUD WITH WILL AND THIS TRAINING ORKUPE HIM THROUT YUTH TID diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..c2a2496d575af7922670c181a04610808b1fcf7c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/token @@ -0,0 +1,10 @@ +mls_eng_000243 I A M T H I R U D O F T A K N O L D E S F O M Y O U S H E S A D H A S T A L Y I S A N O W T H E I T I S I M P O S I B L E T H A V E F A T H I N O U I S E N O W T H E I T I S U S E S T O E X P E C T A N Y R E T E R N F R O M O U F O R A L E I H A V E D O N I W A N T N O M O R E O F Y O U +mls_eng_000244 T H A T H E M A Y S O M T I M E S B E E L I K E O T H E R C H E L D R E N L A N I N G T E S I E M Y N E O R P L A I N G P R A T L I N S I K I N G F O R H E L E S C O M S T O M Y H E A R T A T S S I N F U L O R D A N M S P E A K I N G H O W G O D T H O A R T +mls_eng_000245 T R A N S E N T H I N G S O F A L S O R S E A S I T H E G E N R A L E O U T B U R S T O F M U L T I T O D N S P A S I O N A R H U T E D T O G E T H E R T H E L I T R C R I S N A Y T H E R E D I C U L O S W I T T H E H E R A B L E F A R O V E R T H E I L W E Y S E O F H E D S M A Y B E S E N R E S C A L I T Y C A P R I Y O L I N G O N F O R S E S F O M T H E R O I A L S T O D +mls_eng_000246 I T M A Y H A V E B E N T H T T H E B O N S W E R E A F O L D E D T O G E T H E R A N D N O N A S O N A H P E L B O N S F U L D E D A N D L A I E A W A Y F O R T H E P R P I C E S O F I N C E N T A T I O N S U C H B U N D L E S O F B O N S W E P U T T H R U A P R O S E S O F P R A R S +mls_eng_000247 M A S A I L S N E V E R E X P E R I N E D T H O S G R E A T T R A N S I O N S F R O M L O N E S T O G R A N D U R T H I S W A S O W I N G T O T H E P R U D A N T C O N D U C T O T H A T R E P U B L I C K W H I H A L W A Y S P R E S E R V E D H E R P R I N C I P L E S +mls_eng_000248 A T A S M A L B E A I N G S E T I O N O N T H E M O R I N G O F U T O B E R H R T Y O N C O N D U C T E D I T H E A T M S F E R O F C O N S P I R A C Y A N D I N T E N D E D B Y B R O D Y W E E R E T O L H A T T H E N O R A L A G A N C E R E Q U I R M E T F O R R E V U O R D A P R O V E L E H A D B E N W A V E D T H A T N O R M A L A P R O V E O F T H E M A Y R O F W A S I N G T O N A N D S E R T N G O V E R S W O L B E H A N D L E D I N F O R M I L Y +mls_eng_000249 T H E B O D I S T L A T D Y I N C H I N E R W H O D O N O T H A S T A T E T O L T A K E L I H F O R T H E R P E R F E R S O F O D S A F F T H E R C O N S H E N C S F R O M T A C K T O T I M B Y B Y I N G V E R S F A S C S H E I S I S S E T D E R A N D L E T I N T H E G O +mls_eng_000250 T H I S A G A N I S S O F E N D A D T E M P E R D B Y A S I M P L F A T H I N T H E S U P R O M I C Y O F L O V E O V E R F E E R A N D U N B O U N T E D H U M A N I T Y A N D C H A R I T Y F O R T H E P O R A N D H E L P L E S A N D U N C O N D I N A L F O R G I V E N E S S O F T H E D I R E S T I N J U R I E S W H I C H I S T H E N O T O F T H E N O B L E A G E N E R O S I T Y A N D L I B U R A L I T Y +mls_eng_000251 T H E S E C A N D M A E F O L O E D A N T H E C O U P L E O F T E S E M E R S M E N R O T T H E A O R T H E B A R K B E F O R C U C H I N G A R O P E T H E Y H E N T O W A R K T S U R C H T H E S H I P T H E L I F T D T H E H A C E S A N D F O U N D T H E R H O L D F U L O F C A R G O +mls_eng_000252 F O N D O F I S C O M R A D S A N D R E S P E C T F U L E T O H I S P A S T E R S A N D M A S T E R S E V E N S C O L M A S T E R S A S T H E L A D H E P R E P A R S F O R M A N W O U D W I T H W I L L A N D T H I S T R A I N I N G O R K U P E H I M T H R O U T Y U T H T I D diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..7f34dd9f59f466367f75bf2715da7364c1c10b4c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.1/1best_recog/token_int @@ -0,0 +1,10 @@ +mls_eng_000243 8 2 5 15 4 10 8 11 2 14 12 2 6 18 2 4 5 24 7 2 6 13 12 3 9 2 18 6 15 2 19 6 14 2 9 10 3 2 9 5 12 2 10 5 9 4 5 13 19 2 8 2 9 5 2 7 6 17 2 4 10 3 2 8 4 2 8 9 2 8 15 21 6 9 8 22 13 3 2 4 2 10 5 23 3 2 18 5 4 10 8 7 2 6 14 2 8 2 9 3 2 7 6 17 2 4 10 3 2 8 4 2 8 9 2 14 9 3 9 4 6 2 3 25 21 3 16 4 2 5 7 19 2 11 3 4 3 11 7 2 18 11 6 15 2 6 14 2 18 6 11 2 5 13 3 2 8 2 10 5 23 3 2 12 6 7 2 8 2 17 5 7 4 2 7 6 2 15 6 11 3 2 6 18 2 19 6 14 +mls_eng_000244 4 10 5 4 2 10 3 2 15 5 19 2 9 6 15 4 8 15 3 9 2 22 3 3 2 13 8 24 3 2 6 4 10 3 11 2 16 10 3 13 12 11 3 7 2 13 5 7 8 7 20 2 4 3 2 9 8 3 2 15 19 2 7 3 2 6 11 2 21 13 5 8 7 20 2 21 11 5 4 13 8 7 2 9 8 24 8 7 20 2 18 6 11 2 10 3 13 3 9 2 16 6 15 9 2 4 6 2 15 19 2 10 3 5 11 4 2 5 4 9 2 9 8 7 18 14 13 2 6 11 12 2 5 7 15 2 9 21 3 5 24 8 7 20 2 10 6 17 2 20 6 12 2 4 10 6 2 5 11 4 +mls_eng_000245 4 11 5 7 9 3 7 4 10 8 7 20 9 2 6 18 2 5 13 2 9 6 11 9 3 2 5 9 2 8 2 4 10 3 2 20 3 7 11 5 13 3 2 6 14 4 22 14 11 9 4 2 6 18 2 15 14 13 4 8 4 6 12 7 9 2 21 5 9 8 6 7 2 5 11 2 10 14 4 3 12 2 4 6 20 3 4 10 3 11 2 4 10 3 2 13 8 4 11 16 11 8 9 2 7 5 19 2 4 10 3 2 11 3 12 8 16 14 13 6 9 2 17 8 4 2 4 10 3 2 10 3 11 5 22 13 3 2 18 5 11 2 6 23 3 11 2 4 10 3 2 8 13 17 3 19 2 9 3 2 6 18 2 10 3 12 9 2 15 5 19 2 22 3 2 9 3 7 2 11 3 9 16 5 13 8 4 19 2 16 5 21 11 8 19 6 13 8 7 20 2 6 7 2 18 6 11 9 3 9 2 18 6 15 2 4 10 3 2 11 6 8 5 13 2 9 4 6 12 +mls_eng_000246 8 4 2 15 5 19 2 10 5 23 3 2 22 3 7 2 4 10 4 2 4 10 3 2 22 6 7 9 2 17 3 11 3 2 5 2 18 6 13 12 3 12 2 4 6 20 3 4 10 3 11 2 5 7 12 2 7 6 7 2 5 9 2 6 7 5 2 10 21 3 13 22 6 7 9 2 18 14 13 12 3 12 2 5 7 12 2 13 5 8 3 2 5 17 5 19 2 18 6 11 2 4 10 3 2 21 11 21 8 16 3 9 2 6 18 2 8 7 16 3 7 4 5 4 8 6 7 2 9 14 16 10 2 22 14 7 12 13 3 9 2 6 18 2 22 6 7 9 2 17 3 2 21 14 4 2 4 10 11 14 2 5 21 11 6 9 3 9 2 6 18 2 21 11 5 11 9 +mls_eng_000247 15 5 9 5 8 13 9 2 7 3 23 3 11 2 3 25 21 3 11 8 7 3 12 2 4 10 6 9 2 20 11 3 5 4 2 4 11 5 7 9 8 6 7 9 2 18 11 6 15 2 13 6 7 3 9 2 4 6 2 20 11 5 7 12 14 11 2 4 10 8 9 2 17 5 9 2 6 17 8 7 20 2 4 6 2 4 10 3 2 21 11 14 12 5 7 4 2 16 6 7 12 14 16 4 2 6 2 4 10 5 4 2 11 3 21 14 22 13 8 16 24 2 17 10 8 10 2 5 13 17 5 19 9 2 21 11 3 9 3 11 23 3 12 2 10 3 11 2 21 11 8 7 16 8 21 13 3 9 +mls_eng_000248 5 4 2 5 2 9 15 5 13 2 22 3 5 8 7 20 2 9 3 4 8 6 7 2 6 7 2 4 10 3 2 15 6 11 8 7 20 2 6 18 2 14 4 6 22 3 11 2 10 11 4 19 2 6 7 2 16 6 7 12 14 16 4 3 12 2 8 2 4 10 3 2 5 4 15 9 18 3 11 2 6 18 2 16 6 7 9 21 8 11 5 16 19 2 5 7 12 2 8 7 4 3 7 12 3 12 2 22 19 2 22 11 6 12 19 2 17 3 2 3 11 3 4 6 13 2 10 5 4 2 4 10 3 2 7 6 11 5 13 2 5 20 5 7 16 3 2 11 3 27 14 8 11 15 3 4 2 18 6 11 2 11 3 23 14 2 6 11 12 5 21 11 6 23 3 13 3 2 10 5 12 2 22 3 7 2 17 5 23 3 12 2 4 10 5 4 2 7 6 11 15 5 13 2 5 21 11 6 23 3 2 6 18 2 4 10 3 2 15 5 19 11 2 6 18 2 17 5 9 8 7 20 4 6 7 2 5 7 12 2 9 3 11 4 7 2 20 6 23 3 11 9 2 17 6 13 2 22 3 2 10 5 7 12 13 3 12 2 8 7 2 18 6 11 15 8 13 19 +mls_eng_000249 4 10 3 2 22 6 12 8 9 4 2 13 5 4 12 19 2 8 7 2 16 10 8 7 3 11 2 17 10 6 2 12 6 2 7 6 4 2 10 5 9 4 5 4 3 2 4 6 13 2 4 5 24 3 2 13 8 10 2 18 6 11 2 4 10 3 11 2 21 3 11 18 3 11 9 2 6 18 6 12 2 9 5 18 18 2 4 10 3 11 2 16 6 7 9 10 3 7 16 9 2 18 11 6 15 2 4 5 16 24 2 4 6 2 4 8 15 2 22 19 2 22 19 8 7 20 2 23 3 11 9 2 18 5 9 16 9 10 3 8 9 2 8 9 2 9 3 4 12 3 11 2 5 7 12 2 13 3 4 8 7 2 4 10 3 2 20 6 +mls_eng_000250 4 10 8 9 2 5 20 5 7 2 8 9 2 9 6 18 3 7 12 2 5 12 2 4 3 15 21 3 11 12 2 22 19 2 5 2 9 8 15 21 13 2 18 5 4 10 2 8 7 2 4 10 3 2 9 14 21 11 6 15 8 16 19 2 6 18 2 13 6 23 3 2 6 23 3 11 2 18 3 3 11 2 5 7 12 2 14 7 2 22 6 14 7 4 3 12 2 10 14 15 5 7 8 4 19 2 5 7 12 2 16 10 5 11 8 4 19 2 18 6 11 2 4 10 3 2 21 6 11 2 5 7 12 2 10 3 13 21 13 3 9 5 7 12 2 14 7 2 16 6 7 12 8 7 5 13 2 18 6 11 20 8 23 3 7 3 9 9 2 6 18 2 4 10 3 2 12 8 11 3 9 4 2 8 7 26 14 11 8 3 9 2 17 10 8 16 10 2 8 9 2 4 10 3 2 7 6 4 2 6 18 2 4 10 3 2 7 6 22 13 3 2 5 2 20 3 7 3 11 6 9 8 4 19 2 5 7 12 2 13 8 22 14 11 5 13 8 4 19 +mls_eng_000251 4 10 3 2 9 3 16 5 7 12 2 15 5 3 2 18 6 13 6 3 12 2 5 7 2 4 10 3 2 16 6 14 21 13 3 2 6 18 2 4 3 2 9 3 15 3 11 9 15 3 7 2 11 6 4 2 4 10 3 2 5 6 11 2 4 10 3 2 22 5 11 24 2 22 3 18 6 11 2 16 14 16 10 8 7 20 2 5 2 11 6 21 3 2 4 10 3 19 2 10 3 7 4 6 2 17 5 11 24 2 4 2 9 14 11 16 10 2 4 10 3 2 9 10 8 21 2 4 10 3 2 13 8 18 4 12 2 4 10 3 2 10 5 16 3 9 2 5 7 12 2 18 6 14 7 12 2 4 10 3 11 2 10 6 13 12 2 18 14 13 2 6 18 2 16 5 11 20 6 +mls_eng_000252 18 6 7 12 2 6 18 2 8 9 2 16 6 15 11 5 12 9 2 5 7 12 2 11 3 9 21 3 16 4 18 14 13 3 2 4 6 2 10 8 9 2 21 5 9 4 3 11 9 2 5 7 12 2 15 5 9 4 3 11 9 2 3 23 3 7 2 9 16 6 13 2 15 5 9 4 3 11 9 2 5 9 2 4 10 3 2 13 5 12 2 10 3 2 21 11 3 21 5 11 9 2 18 6 11 2 15 5 7 17 6 14 12 2 17 8 4 10 2 17 8 13 13 2 5 7 12 2 4 10 8 9 2 4 11 5 8 7 8 7 20 2 6 11 24 14 21 3 2 10 8 15 2 4 10 11 6 14 4 2 19 14 4 10 2 4 8 12 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..3b999548c3dcb53f3baf7c2e1c571bfcbb7f5678 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/score @@ -0,0 +1,10 @@ +mls_eng_000253 tensor(-21.0281) +mls_eng_000254 tensor(-35.7001) +mls_eng_000255 tensor(-21.8933) +mls_eng_000256 tensor(-21.7890) +mls_eng_000257 tensor(-35.2909) +mls_eng_000258 tensor(-33.1090) +mls_eng_000259 tensor(-37.4670) +mls_eng_000260 tensor(-29.2699) +mls_eng_000261 tensor(-53.7500) +mls_eng_000262 tensor(-23.9817) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..8ea28acff35d21eb7134feccf137c001e8e4a4fd --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/text @@ -0,0 +1,10 @@ +mls_eng_000253 AS WHEN HE RICES QUECA RECED TER SIXTINT YEAR SHE BECAME BLINED HER LARGE SOFT BRON ICES HAD O LIDIN THEM +mls_eng_000254 ALMOST ALL DAY THE BATAR RANGED BETWE THE TO MEN BACK IN FORT THEY FORSE EACHOTHER OVER THE LOV ABEDS THE CHIFES WEL OILED BODY WAS VERY DIFICULT FOR THEOLO HAY TO GRAS BRUSED AND BLEADING FROM REPEATED FALLS ON THE RUH LAVA +mls_eng_000255 POSY I TRE IT TERRAR AND A WARP PANTING THE WIN MON THRUOU THE TREES OF TE GARDIN AND FROM TIME TOA TIME SEE AS IF TO A +mls_eng_000256 HIS MENTAL TORPITITY FOUNDI APON FISICAL INDILENCE RENDERS AMEDIA ACTION N ALL MANE OF EXERTION DIS TASFUL HIS CONCHOUS WEKNESS SHOWE IT SELF +mls_eng_000257 NOR THALL HOW GLAD THE CUING MOTHER WAS NOR HOL GREAT WAR THE REJOICESING OF THE PEOPLE TOR HOM MENIFISSENT WAS THE ROIAL BANQUED THAT GOOD CQING PAMAREA ATENDEDT BITHAL HER CORT +mls_eng_000258 AND THE CHANCE OF THER BEANG SUCH A ON UNGAN DIMISHIES BY WERY PRAPED PROSESS MAMELUK AS A HORS WAS OF ECUEANE CQULITY REASNING NOT ABOUT HIS ODERS BUT ABOUGT THE WAY TO DO THE +mls_eng_000259 SHE NOKED BUT SNO DROPK OKDOT OF THE EINDO AND SAID I DEARE NOT OPN THE DOOR FOR THE DORS HAVE TOLD ME TO LET NO WEN IN THAT IS HARD FOR ME SAID THE WOMAN FOR I MUST TAK BACK MY APLS BUT THERE IS ONE WHICH I WILL GIVE YOU AND SHE HELD UP AN AAL +mls_eng_000260 HAV IY MORY SPOK AC THELSHOUR AND HORCE FINLYSPOKNIT TO MY PECEWAS WHY FEBY ARE YU COME SO SOON WHERER YOU BARY CHILD AND O THAN ARDILSPOK ASAME ON +mls_eng_000261 I NEVER GEW ANY ON HO LIE TOT TURCHAS MCH ASSGRANMOHR DOUS SHE HAS HEWOUD RAVER B A DOR CEPRINTHE HAS OF OR GOD THN T DWEL NTHE TENS OF WIKEDNES THEY DON HAE WOMEN DOR KEPERS AND I NNOW SHEWOLD T TWEL MININ AN ATENT +mls_eng_000262 THE DUK WAS SUPRISE TO SE HIM WOHT BRINGS YOU OUT SO IALY ABLEARD DEMAND HE OW YOUR GRAICETS REPLID TE BUTLER GASPING FOR ATERENCS diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..219681e8bf4462745cbfa81f3e33469318a27946 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/token @@ -0,0 +1,10 @@ +mls_eng_000253 A S W H E N H E R I C E S Q U E C A R E C E D T E R S I X T I N T Y E A R S H E B E C A M E B L I N E D H E R L A R G E S O F T B R O N I C E S H A D O L I D I N T H E M +mls_eng_000254 A L M O S T A L L D A Y T H E B A T A R R A N G E D B E T W E T H E T O M E N B A C K I N F O R T T H E Y F O R S E E A C H O T H E R O V E R T H E L O V A B E D S T H E C H I F E S W E L O I L E D B O D Y W A S V E R Y D I F I C U L T F O R T H E O L O H A Y T O G R A S B R U S E D A N D B L E A D I N G F R O M R E P E A T E D F A L L S O N T H E R U H L A V A +mls_eng_000255 P O S Y I T R E I T T E R R A R A N D A W A R P P A N T I N G T H E W I N M O N T H R U O U T H E T R E E S O F T E G A R D I N A N D F R O M T I M E T O A T I M E S E E A S I F T O A +mls_eng_000256 H I S M E N T A L T O R P I T I T Y F O U N D I A P O N F I S I C A L I N D I L E N C E R E N D E R S A M E D I A A C T I O N N A L L M A N E O F E X E R T I O N D I S T A S F U L H I S C O N C H O U S W E K N E S S S H O W E I T S E L F +mls_eng_000257 N O R T H A L L H O W G L A D T H E C U I N G M O T H E R W A S N O R H O L G R E A T W A R T H E R E J O I C E S I N G O F T H E P E O P L E T O R H O M M E N I F I S S E N T W A S T H E R O I A L B A N Q U E D T H A T G O O D C Q I N G P A M A R E A A T E N D E D T B I T H A L H E R C O R T +mls_eng_000258 A N D T H E C H A N C E O F T H E R B E A N G S U C H A O N U N G A N D I M I S H I E S B Y W E R Y P R A P E D P R O S E S S M A M E L U K A S A H O R S W A S O F E C U E A N E C Q U L I T Y R E A S N I N G N O T A B O U T H I S O D E R S B U T A B O U G T T H E W A Y T O D O T H E +mls_eng_000259 S H E N O K E D B U T S N O D R O P K O K D O T O F T H E E I N D O A N D S A I D I D E A R E N O T O P N T H E D O O R F O R T H E D O R S H A V E T O L D M E T O L E T N O W E N I N T H A T I S H A R D F O R M E S A I D T H E W O M A N F O R I M U S T T A K B A C K M Y A P L S B U T T H E R E I S O N E W H I C H I W I L L G I V E Y O U A N D S H E H E L D U P A N A A L +mls_eng_000260 H A V I Y M O R Y S P O K A C T H E L S H O U R A N D H O R C E F I N L Y S P O K N I T T O M Y P E C E W A S W H Y F E B Y A R E Y U C O M E S O S O O N W H E R E R Y O U B A R Y C H I L D A N D O T H A N A R D I L S P O K A S A M E O N +mls_eng_000261 I N E V E R G E W A N Y O N H O L I E T O T T U R C H A S M C H A S S G R A N M O H R D O U S S H E H A S H E W O U D R A V E R B A D O R C E P R I N T H E H A S O F O R G O D T H N T D W E L N T H E T E N S O F W I K E D N E S T H E Y D O N H A E W O M E N D O R K E P E R S A N D I N N O W S H E W O L D T T W E L M I N I N A N A T E N T +mls_eng_000262 T H E D U K W A S S U P R I S E T O S E H I M W O H T B R I N G S Y O U O U T S O I A L Y A B L E A R D D E M A N D H E O W Y O U R G R A I C E T S R E P L I D T E B U T L E R G A S P I N G F O R A T E R E N C S diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..a9f1d80442936fa9ade80f3d132a67cc63fe3f57 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.2/1best_recog/token_int @@ -0,0 +1,10 @@ +mls_eng_000253 5 9 2 17 10 3 7 2 10 3 2 11 8 16 3 9 2 27 14 3 16 5 2 11 3 16 3 12 2 4 3 11 2 9 8 25 4 8 7 4 2 19 3 5 11 2 9 10 3 2 22 3 16 5 15 3 2 22 13 8 7 3 12 2 10 3 11 2 13 5 11 20 3 2 9 6 18 4 2 22 11 6 7 2 8 16 3 9 2 10 5 12 2 6 2 13 8 12 8 7 2 4 10 3 15 +mls_eng_000254 5 13 15 6 9 4 2 5 13 13 2 12 5 19 2 4 10 3 2 22 5 4 5 11 2 11 5 7 20 3 12 2 22 3 4 17 3 2 4 10 3 2 4 6 2 15 3 7 2 22 5 16 24 2 8 7 2 18 6 11 4 2 4 10 3 19 2 18 6 11 9 3 2 3 5 16 10 6 4 10 3 11 2 6 23 3 11 2 4 10 3 2 13 6 23 2 5 22 3 12 9 2 4 10 3 2 16 10 8 18 3 9 2 17 3 13 2 6 8 13 3 12 2 22 6 12 19 2 17 5 9 2 23 3 11 19 2 12 8 18 8 16 14 13 4 2 18 6 11 2 4 10 3 6 13 6 2 10 5 19 2 4 6 2 20 11 5 9 2 22 11 14 9 3 12 2 5 7 12 2 22 13 3 5 12 8 7 20 2 18 11 6 15 2 11 3 21 3 5 4 3 12 2 18 5 13 13 9 2 6 7 2 4 10 3 2 11 14 10 2 13 5 23 5 +mls_eng_000255 21 6 9 19 2 8 2 4 11 3 2 8 4 2 4 3 11 11 5 11 2 5 7 12 2 5 2 17 5 11 21 2 21 5 7 4 8 7 20 2 4 10 3 2 17 8 7 2 15 6 7 2 4 10 11 14 6 14 2 4 10 3 2 4 11 3 3 9 2 6 18 2 4 3 2 20 5 11 12 8 7 2 5 7 12 2 18 11 6 15 2 4 8 15 3 2 4 6 5 2 4 8 15 3 2 9 3 3 2 5 9 2 8 18 2 4 6 2 5 +mls_eng_000256 10 8 9 2 15 3 7 4 5 13 2 4 6 11 21 8 4 8 4 19 2 18 6 14 7 12 8 2 5 21 6 7 2 18 8 9 8 16 5 13 2 8 7 12 8 13 3 7 16 3 2 11 3 7 12 3 11 9 2 5 15 3 12 8 5 2 5 16 4 8 6 7 2 7 2 5 13 13 2 15 5 7 3 2 6 18 2 3 25 3 11 4 8 6 7 2 12 8 9 2 4 5 9 18 14 13 2 10 8 9 2 16 6 7 16 10 6 14 9 2 17 3 24 7 3 9 9 2 9 10 6 17 3 2 8 4 2 9 3 13 18 +mls_eng_000257 7 6 11 2 4 10 5 13 13 2 10 6 17 2 20 13 5 12 2 4 10 3 2 16 14 8 7 20 2 15 6 4 10 3 11 2 17 5 9 2 7 6 11 2 10 6 13 2 20 11 3 5 4 2 17 5 11 2 4 10 3 2 11 3 26 6 8 16 3 9 8 7 20 2 6 18 2 4 10 3 2 21 3 6 21 13 3 2 4 6 11 2 10 6 15 2 15 3 7 8 18 8 9 9 3 7 4 2 17 5 9 2 4 10 3 2 11 6 8 5 13 2 22 5 7 27 14 3 12 2 4 10 5 4 2 20 6 6 12 2 16 27 8 7 20 2 21 5 15 5 11 3 5 2 5 4 3 7 12 3 12 4 2 22 8 4 10 5 13 2 10 3 11 2 16 6 11 4 +mls_eng_000258 5 7 12 2 4 10 3 2 16 10 5 7 16 3 2 6 18 2 4 10 3 11 2 22 3 5 7 20 2 9 14 16 10 2 5 2 6 7 2 14 7 20 5 7 2 12 8 15 8 9 10 8 3 9 2 22 19 2 17 3 11 19 2 21 11 5 21 3 12 2 21 11 6 9 3 9 9 2 15 5 15 3 13 14 24 2 5 9 2 5 2 10 6 11 9 2 17 5 9 2 6 18 2 3 16 14 3 5 7 3 2 16 27 14 13 8 4 19 2 11 3 5 9 7 8 7 20 2 7 6 4 2 5 22 6 14 4 2 10 8 9 2 6 12 3 11 9 2 22 14 4 2 5 22 6 14 20 4 2 4 10 3 2 17 5 19 2 4 6 2 12 6 2 4 10 3 +mls_eng_000259 9 10 3 2 7 6 24 3 12 2 22 14 4 2 9 7 6 2 12 11 6 21 24 2 6 24 12 6 4 2 6 18 2 4 10 3 2 3 8 7 12 6 2 5 7 12 2 9 5 8 12 2 8 2 12 3 5 11 3 2 7 6 4 2 6 21 7 2 4 10 3 2 12 6 6 11 2 18 6 11 2 4 10 3 2 12 6 11 9 2 10 5 23 3 2 4 6 13 12 2 15 3 2 4 6 2 13 3 4 2 7 6 2 17 3 7 2 8 7 2 4 10 5 4 2 8 9 2 10 5 11 12 2 18 6 11 2 15 3 2 9 5 8 12 2 4 10 3 2 17 6 15 5 7 2 18 6 11 2 8 2 15 14 9 4 2 4 5 24 2 22 5 16 24 2 15 19 2 5 21 13 9 2 22 14 4 2 4 10 3 11 3 2 8 9 2 6 7 3 2 17 10 8 16 10 2 8 2 17 8 13 13 2 20 8 23 3 2 19 6 14 2 5 7 12 2 9 10 3 2 10 3 13 12 2 14 21 2 5 7 2 5 5 13 +mls_eng_000260 10 5 23 2 8 19 2 15 6 11 19 2 9 21 6 24 2 5 16 2 4 10 3 13 9 10 6 14 11 2 5 7 12 2 10 6 11 16 3 2 18 8 7 13 19 9 21 6 24 7 8 4 2 4 6 2 15 19 2 21 3 16 3 17 5 9 2 17 10 19 2 18 3 22 19 2 5 11 3 2 19 14 2 16 6 15 3 2 9 6 2 9 6 6 7 2 17 10 3 11 3 11 2 19 6 14 2 22 5 11 19 2 16 10 8 13 12 2 5 7 12 2 6 2 4 10 5 7 2 5 11 12 8 13 9 21 6 24 2 5 9 5 15 3 2 6 7 +mls_eng_000261 8 2 7 3 23 3 11 2 20 3 17 2 5 7 19 2 6 7 2 10 6 2 13 8 3 2 4 6 4 2 4 14 11 16 10 5 9 2 15 16 10 2 5 9 9 20 11 5 7 15 6 10 11 2 12 6 14 9 2 9 10 3 2 10 5 9 2 10 3 17 6 14 12 2 11 5 23 3 11 2 22 2 5 2 12 6 11 2 16 3 21 11 8 7 4 10 3 2 10 5 9 2 6 18 2 6 11 2 20 6 12 2 4 10 7 2 4 2 12 17 3 13 2 7 4 10 3 2 4 3 7 9 2 6 18 2 17 8 24 3 12 7 3 9 2 4 10 3 19 2 12 6 7 2 10 5 3 2 17 6 15 3 7 2 12 6 11 2 24 3 21 3 11 9 2 5 7 12 2 8 2 7 7 6 17 2 9 10 3 17 6 13 12 2 4 2 4 17 3 13 2 15 8 7 8 7 2 5 7 2 5 4 3 7 4 +mls_eng_000262 4 10 3 2 12 14 24 2 17 5 9 2 9 14 21 11 8 9 3 2 4 6 2 9 3 2 10 8 15 2 17 6 10 4 2 22 11 8 7 20 9 2 19 6 14 2 6 14 4 2 9 6 2 8 5 13 19 2 5 22 13 3 5 11 12 2 12 3 15 5 7 12 2 10 3 2 6 17 2 19 6 14 11 2 20 11 5 8 16 3 4 9 2 11 3 21 13 8 12 2 4 3 2 22 14 4 13 3 11 2 20 5 9 21 8 7 20 2 18 6 11 2 5 4 3 11 3 7 16 9 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..cab832207b3f6c7a1a6022966fa2d6a66245409a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/score @@ -0,0 +1,10 @@ +mls_eng_000263 tensor(-36.7919) +mls_eng_000264 tensor(-48.2109) +mls_eng_000265 tensor(-42.5578) +mls_eng_000266 tensor(-38.2632) +mls_eng_000267 tensor(-25.0349) +mls_eng_000268 tensor(-40.4126) +mls_eng_000269 tensor(-27.2266) +mls_eng_000270 tensor(-17.7903) +mls_eng_000271 tensor(-52.9218) +mls_eng_000272 tensor(-46.9524) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..94b1f96c93b1fe4622c15881c59208ed99961b58 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/text @@ -0,0 +1,10 @@ +mls_eng_000263 FOREYIET SEMING RONSMUTATIONS OF CALE MAY BE MADED WHERE HER IS ANY MIXTR OF DEVRS SOUTS OF RAISE FOR IN SUCH MIXTERS THE MPON CALES A PE NOT TBY THE MUTAL ALAING HACHETHE CONSTITUTE A MIDLING COLER +mls_eng_000264 AND ALMUSTHE SAME INSTENT CINSA SANDY SID STEP INTO ASHAP DORWAY HE WATED THER ANTLL CIN CAME UP KIN STOPE ND PRETENDED O STAR THR THE LAS AT TH DESPLAY OF HARD WAR IN TOULS WERE HE CONTINED TO WATCH BARICK YOU SE WHAT I SEE SAMBE STEAID CIN NOTED +mls_eng_000265 THENM THE THIEK GREAN STAV FLOWD OWE THE HALLE BILDING AND THER WAS NOTHING TO BE SEEN THERE BUT DE MIOWD OF SOFT FLOWING GRAY GREAN STAVE THE D ROUSHED ON NOW WIT THE SWEIFTNES OLL HE WEINK A LOOKED AP INTO BEARSS FAICE +mls_eng_000266 II HAVE MAE SACROFICS TO UR OFE WHEN I NEW THTHE WE NOT MY HAPNES IWAS AFTER I SAL THAT HAD STPED AFTER I SAL THAT YOUR TENDERNESS HAD TERND TO CAULCULATION AFTER I SAW THAT YOU CARD FOR YOURSELF ONLY NOT FOR ME +mls_eng_000267 YET THA THANDEAR NEVER ROALS IN WINTER I SEY A CROL WOARING ROUND AND ROUN BEFOR IT A LIHT THESN NOTHING UNDER THE FIRD TREES BUT I NOE SOMETHING MUST BE THEAR +mls_eng_000268 IT IS BEYOND THOUT THAT SOME PEOBLE HAE MENISE TO SA FE MANY THINGS AN OF COURSE THE JURMANT HAS A SUMAIIED AS S MAUCH TO OR THRE DAYSE AFTER HA FIRS PROCOCITIONS THEY DROPET IN IUN NAWARE +mls_eng_000269 SOON A MAN CAME OUT TO ME HIM THIS MAN WAS OLOHA A BEARDLESS MAN BELONGIG TO A LALLS ROBERCLAN WICH INFESTED THE DISTRICT POSEBLY ASSISTIN THE MAN HUNTERS OF THE EMPLE IN SECURING VICTOMS FOR THE EMPLE ALTRS +mls_eng_000270 WE AR NOT LOVERAS YOU AND IY APON THIS SUNY LAIN BUT CHILDEN WHOE VE NEVER NON LOVES JORY ORPE +mls_eng_000271 WAS MURDED ON HESORSTE F OFIS OLE HOUSES WAS I A OL MAN ASD ANDUR A COMNLY MSRE GUODIAR YOU TAK ASIF H HAD DIED IN HIS BEAET YOU AR NOVENEISION HA YOU CANO NDESTAND WHT IT MINS HEN A NIQUITITY IS MERDEDT +mls_eng_000272 BY GOD HE SAID THOY WONT ME MU BUSNS LEIDS ME IN AND OUTO MANY HOUSES OR WHAT DO I CAR FO THE SICREST THAT MAYB HIDIN THERE HOWEVER I CANOT PLIN THIS PEPEFOR THE WACHFOLNES THE BLUT HOUS OF HIS SFNORIAR AY IN EVERESTREET diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..8c29d8b7b95cf568dcf6ffd32ac5d8cdd2df9bc9 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/token @@ -0,0 +1,10 @@ +mls_eng_000263 F O R E Y I E T S E M I N G R O N S M U T A T I O N S O F C A L E M A Y B E M A D E D W H E R E H E R I S A N Y M I X T R O F D E V R S S O U T S O F R A I S E F O R I N S U C H M I X T E R S T H E M P O N C A L E S A P E N O T T B Y T H E M U T A L A L A I N G H A C H E T H E C O N S T I T U T E A M I D L I N G C O L E R +mls_eng_000264 A N D A L M U S T H E S A M E I N S T E N T C I N S A S A N D Y S I D S T E P I N T O A S H A P D O R W A Y H E W A T E D T H E R A N T L L C I N C A M E U P K I N S T O P E N D P R E T E N D E D O S T A R T H R T H E L A S A T T H D E S P L A Y O F H A R D W A R I N T O U L S W E R E H E C O N T I N E D T O W A T C H B A R I C K Y O U S E W H A T I S E E S A M B E S T E A I D C I N N O T E D +mls_eng_000265 T H E N M T H E T H I E K G R E A N S T A V F L O W D O W E T H E H A L L E B I L D I N G A N D T H E R W A S N O T H I N G T O B E S E E N T H E R E B U T D E M I O W D O F S O F T F L O W I N G G R A Y G R E A N S T A V E T H E D R O U S H E D O N N O W W I T T H E S W E I F T N E S O L L H E W E I N K A L O O K E D A P I N T O B E A R S S F A I C E +mls_eng_000266 I I H A V E M A E S A C R O F I C S T O U R O F E W H E N I N E W T H T H E W E N O T M Y H A P N E S I W A S A F T E R I S A L T H A T H A D S T P E D A F T E R I S A L T H A T Y O U R T E N D E R N E S S H A D T E R N D T O C A U L C U L A T I O N A F T E R I S A W T H A T Y O U C A R D F O R Y O U R S E L F O N L Y N O T F O R M E +mls_eng_000267 Y E T T H A T H A N D E A R N E V E R R O A L S I N W I N T E R I S E Y A C R O L W O A R I N G R O U N D A N D R O U N B E F O R I T A L I H T T H E S N N O T H I N G U N D E R T H E F I R D T R E E S B U T I N O E S O M E T H I N G M U S T B E T H E A R +mls_eng_000268 I T I S B E Y O N D T H O U T T H A T S O M E P E O B L E H A E M E N I S E T O S A F E M A N Y T H I N G S A N O F C O U R S E T H E J U R M A N T H A S A S U M A I I E D A S S M A U C H T O O R T H R E D A Y S E A F T E R H A F I R S P R O C O C I T I O N S T H E Y D R O P E T I N I U N N A W A R E +mls_eng_000269 S O O N A M A N C A M E O U T T O M E H I M T H I S M A N W A S O L O H A A B E A R D L E S S M A N B E L O N G I G T O A L A L L S R O B E R C L A N W I C H I N F E S T E D T H E D I S T R I C T P O S E B L Y A S S I S T I N T H E M A N H U N T E R S O F T H E E M P L E I N S E C U R I N G V I C T O M S F O R T H E E M P L E A L T R S +mls_eng_000270 W E A R N O T L O V E R A S Y O U A N D I Y A P O N T H I S S U N Y L A I N B U T C H I L D E N W H O E V E N E V E R N O N L O V E S J O R Y O R P E +mls_eng_000271 W A S M U R D E D O N H E S O R S T E F O F I S O L E H O U S E S W A S I A O L M A N A S D A N D U R A C O M N L Y M S R E G U O D I A R Y O U T A K A S I F H H A D D I E D I N H I S B E A E T Y O U A R N O V E N E I S I O N H A Y O U C A N O N D E S T A N D W H T I T M I N S H E N A N I Q U I T I T Y I S M E R D E D T +mls_eng_000272 B Y G O D H E S A I D T H O Y W O N T M E M U B U S N S L E I D S M E I N A N D O U T O M A N Y H O U S E S O R W H A T D O I C A R F O T H E S I C R E S T T H A T M A Y B H I D I N T H E R E H O W E V E R I C A N O T P L I N T H I S P E P E F O R T H E W A C H F O L N E S T H E B L U T H O U S O F H I S S F N O R I A R A Y I N E V E R E S T R E E T diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..8c030e18c0f4bcd84cf19c8375427654f61f2587 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.3/1best_recog/token_int @@ -0,0 +1,10 @@ +mls_eng_000263 18 6 11 3 19 8 3 4 2 9 3 15 8 7 20 2 11 6 7 9 15 14 4 5 4 8 6 7 9 2 6 18 2 16 5 13 3 2 15 5 19 2 22 3 2 15 5 12 3 12 2 17 10 3 11 3 2 10 3 11 2 8 9 2 5 7 19 2 15 8 25 4 11 2 6 18 2 12 3 23 11 9 2 9 6 14 4 9 2 6 18 2 11 5 8 9 3 2 18 6 11 2 8 7 2 9 14 16 10 2 15 8 25 4 3 11 9 2 4 10 3 2 15 21 6 7 2 16 5 13 3 9 2 5 2 21 3 2 7 6 4 2 4 22 19 2 4 10 3 2 15 14 4 5 13 2 5 13 5 8 7 20 2 10 5 16 10 3 4 10 3 2 16 6 7 9 4 8 4 14 4 3 2 5 2 15 8 12 13 8 7 20 2 16 6 13 3 11 +mls_eng_000264 5 7 12 2 5 13 15 14 9 4 10 3 2 9 5 15 3 2 8 7 9 4 3 7 4 2 16 8 7 9 5 2 9 5 7 12 19 2 9 8 12 2 9 4 3 21 2 8 7 4 6 2 5 9 10 5 21 2 12 6 11 17 5 19 2 10 3 2 17 5 4 3 12 2 4 10 3 11 2 5 7 4 13 13 2 16 8 7 2 16 5 15 3 2 14 21 2 24 8 7 2 9 4 6 21 3 2 7 12 2 21 11 3 4 3 7 12 3 12 2 6 2 9 4 5 11 2 4 10 11 2 4 10 3 2 13 5 9 2 5 4 2 4 10 2 12 3 9 21 13 5 19 2 6 18 2 10 5 11 12 2 17 5 11 2 8 7 2 4 6 14 13 9 2 17 3 11 3 2 10 3 2 16 6 7 4 8 7 3 12 2 4 6 2 17 5 4 16 10 2 22 5 11 8 16 24 2 19 6 14 2 9 3 2 17 10 5 4 2 8 2 9 3 3 2 9 5 15 22 3 2 9 4 3 5 8 12 2 16 8 7 2 7 6 4 3 12 +mls_eng_000265 4 10 3 7 15 2 4 10 3 2 4 10 8 3 24 2 20 11 3 5 7 2 9 4 5 23 2 18 13 6 17 12 2 6 17 3 2 4 10 3 2 10 5 13 13 3 2 22 8 13 12 8 7 20 2 5 7 12 2 4 10 3 11 2 17 5 9 2 7 6 4 10 8 7 20 2 4 6 2 22 3 2 9 3 3 7 2 4 10 3 11 3 2 22 14 4 2 12 3 2 15 8 6 17 12 2 6 18 2 9 6 18 4 2 18 13 6 17 8 7 20 2 20 11 5 19 2 20 11 3 5 7 2 9 4 5 23 3 2 4 10 3 2 12 2 11 6 14 9 10 3 12 2 6 7 2 7 6 17 2 17 8 4 2 4 10 3 2 9 17 3 8 18 4 7 3 9 2 6 13 13 2 10 3 2 17 3 8 7 24 2 5 2 13 6 6 24 3 12 2 5 21 2 8 7 4 6 2 22 3 5 11 9 9 2 18 5 8 16 3 +mls_eng_000266 8 8 2 10 5 23 3 2 15 5 3 2 9 5 16 11 6 18 8 16 9 2 4 6 2 14 11 2 6 18 3 2 17 10 3 7 2 8 2 7 3 17 2 4 10 4 10 3 2 17 3 2 7 6 4 2 15 19 2 10 5 21 7 3 9 2 8 17 5 9 2 5 18 4 3 11 2 8 2 9 5 13 2 4 10 5 4 2 10 5 12 2 9 4 21 3 12 2 5 18 4 3 11 2 8 2 9 5 13 2 4 10 5 4 2 19 6 14 11 2 4 3 7 12 3 11 7 3 9 9 2 10 5 12 2 4 3 11 7 12 2 4 6 2 16 5 14 13 16 14 13 5 4 8 6 7 2 5 18 4 3 11 2 8 2 9 5 17 2 4 10 5 4 2 19 6 14 2 16 5 11 12 2 18 6 11 2 19 6 14 11 9 3 13 18 2 6 7 13 19 2 7 6 4 2 18 6 11 2 15 3 +mls_eng_000267 19 3 4 2 4 10 5 2 4 10 5 7 12 3 5 11 2 7 3 23 3 11 2 11 6 5 13 9 2 8 7 2 17 8 7 4 3 11 2 8 2 9 3 19 2 5 2 16 11 6 13 2 17 6 5 11 8 7 20 2 11 6 14 7 12 2 5 7 12 2 11 6 14 7 2 22 3 18 6 11 2 8 4 2 5 2 13 8 10 4 2 4 10 3 9 7 2 7 6 4 10 8 7 20 2 14 7 12 3 11 2 4 10 3 2 18 8 11 12 2 4 11 3 3 9 2 22 14 4 2 8 2 7 6 3 2 9 6 15 3 4 10 8 7 20 2 15 14 9 4 2 22 3 2 4 10 3 5 11 +mls_eng_000268 8 4 2 8 9 2 22 3 19 6 7 12 2 4 10 6 14 4 2 4 10 5 4 2 9 6 15 3 2 21 3 6 22 13 3 2 10 5 3 2 15 3 7 8 9 3 2 4 6 2 9 5 2 18 3 2 15 5 7 19 2 4 10 8 7 20 9 2 5 7 2 6 18 2 16 6 14 11 9 3 2 4 10 3 2 26 14 11 15 5 7 4 2 10 5 9 2 5 2 9 14 15 5 8 8 3 12 2 5 9 2 9 2 15 5 14 16 10 2 4 6 2 6 11 2 4 10 11 3 2 12 5 19 9 3 2 5 18 4 3 11 2 10 5 2 18 8 11 9 2 21 11 6 16 6 16 8 4 8 6 7 9 2 4 10 3 19 2 12 11 6 21 3 4 2 8 7 2 8 14 7 2 7 5 17 5 11 3 +mls_eng_000269 2 9 6 6 7 2 5 2 15 5 7 2 16 5 15 3 2 6 14 4 2 4 6 2 15 3 2 10 8 15 2 4 10 8 9 2 15 5 7 2 17 5 9 2 6 13 6 10 5 2 5 2 22 3 5 11 12 13 3 9 9 2 15 5 7 2 22 3 13 6 7 20 8 20 2 4 6 2 5 2 13 5 13 13 9 2 11 6 22 3 11 16 13 5 7 2 17 8 16 10 2 8 7 18 3 9 4 3 12 2 4 10 3 2 12 8 9 4 11 8 16 4 2 21 6 9 3 22 13 19 2 5 9 9 8 9 4 8 7 2 4 10 3 2 15 5 7 2 10 14 7 4 3 11 9 2 6 18 2 4 10 3 2 3 15 21 13 3 2 8 7 2 9 3 16 14 11 8 7 20 2 23 8 16 4 6 15 9 2 18 6 11 2 4 10 3 2 3 15 21 13 3 2 5 13 4 11 9 +mls_eng_000270 2 17 3 2 5 11 2 7 6 4 2 13 6 23 3 11 5 9 2 19 6 14 2 5 7 12 2 8 19 2 5 21 6 7 2 4 10 8 9 2 9 14 7 19 2 13 5 8 7 2 22 14 4 2 16 10 8 13 12 3 7 2 17 10 6 3 2 23 3 2 7 3 23 3 11 2 7 6 7 2 13 6 23 3 9 2 26 6 11 19 2 6 11 21 3 +mls_eng_000271 17 5 9 2 15 14 11 12 3 12 2 6 7 2 10 3 9 6 11 9 4 3 2 18 2 6 18 8 9 2 6 13 3 2 10 6 14 9 3 9 2 17 5 9 2 8 2 5 2 6 13 2 15 5 7 2 5 9 12 2 5 7 12 14 11 2 5 2 16 6 15 7 13 19 2 15 9 11 3 2 20 14 6 12 8 5 11 2 19 6 14 2 4 5 24 2 5 9 8 18 2 10 2 10 5 12 2 12 8 3 12 2 8 7 2 10 8 9 2 22 3 5 3 4 2 19 6 14 2 5 11 2 7 6 23 3 7 3 8 9 8 6 7 2 10 5 2 19 6 14 2 16 5 7 6 2 7 12 3 9 4 5 7 12 2 17 10 4 2 8 4 2 15 8 7 9 2 10 3 7 2 5 2 7 8 27 14 8 4 8 4 19 2 8 9 2 15 3 11 12 3 12 4 +mls_eng_000272 22 19 2 20 6 12 2 10 3 2 9 5 8 12 2 4 10 6 19 2 17 6 7 4 2 15 3 2 15 14 2 22 14 9 7 9 2 13 3 8 12 9 2 15 3 2 8 7 2 5 7 12 2 6 14 4 6 2 15 5 7 19 2 10 6 14 9 3 9 2 6 11 2 17 10 5 4 2 12 6 2 8 2 16 5 11 2 18 6 2 4 10 3 2 9 8 16 11 3 9 4 2 4 10 5 4 2 15 5 19 22 2 10 8 12 8 7 2 4 10 3 11 3 2 10 6 17 3 23 3 11 2 8 2 16 5 7 6 4 2 21 13 8 7 2 4 10 8 9 2 21 3 21 3 18 6 11 2 4 10 3 2 17 5 16 10 18 6 13 7 3 9 2 4 10 3 2 22 13 14 4 2 10 6 14 9 2 6 18 2 10 8 9 2 9 18 7 6 11 8 5 11 2 5 19 2 8 7 2 3 23 3 11 3 9 4 11 3 3 4 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..740a5c906ed150001e82603705a8861c1652e859 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/score @@ -0,0 +1,10 @@ +mls_eng_000273 tensor(-50.2951) +mls_eng_000274 tensor(-32.5631) +mls_eng_000275 tensor(-22.1818) +mls_eng_000276 tensor(-41.7596) +mls_eng_000277 tensor(-37.4146) +mls_eng_000278 tensor(-34.1106) +mls_eng_000279 tensor(-66.8122) +mls_eng_000280 tensor(-46.1822) +mls_eng_000281 tensor(-25.9304) +mls_eng_000282 tensor(-37.8664) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..eb48e7fff5e78342f559c7c098599d2694e50e62 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/text @@ -0,0 +1,10 @@ +mls_eng_000273 HARYBL DET WAS PULIN T NOT ASTIK NORASTON WAS INDRITOF HE HAND AN THE PIYLS GRAGS EULD ONE NONG SHREK UBOFWLL THE RWAR OFWAT THE OLE SHE STROED OUODON OUE AND GASE THE GOUNDBUT ONLY FILT HERSEVEGIN CASTHOR +mls_eng_000274 AND A FALY OFE IT WAS THAT AN NOTHE WOMENT OFVE SERNY WHICH FOR NOTHING BETEDTEN TO GO SINTO MY SISTR AND FATHER AS SENT AWAY SHE SAID I SHULRATHER GO WITHM I HAVE NO MINE TO STAY HEAR ALON IT MY TO BABES +mls_eng_000275 EINIDIT ACORDINGLY WONDERING HA TE LITE MAN WOULD BE AT AND HE PEKED TO OF THESTOUT IS RUSHES HECOULD FIND WITEALITALE BUNH OF B +mls_eng_000276 IT IS NO THT DREDFUL NAE COMS ON HO DISMA AS THE PLEIN WER THE RUSIONS AN THE NGLISH FOND T BOVF TEN THOUSND SLAIN BRAVE WELINGTON AD BLOKER BOTH MOS NOBLY DROVE THER FOURS AND BONUPARTS AMPEAL CROWN WAS TAKEN AT WATEL +mls_eng_000277 SOME YEAS AGO HAFTER MAKING OUR ARANGENS FOR THEINCAMPENT AT NIGT WHED CONSTANTLY HAD OF A PEASFUL RES ROKGON BY A TRIB OF ROUN MOUNCKES THE EIDENTE THUGHT THAT LONG POSTION HAD GIEN THE A PRIAR LAM TO THE GL +mls_eng_000278 TO FLASH IN AT HOME CLEMERING FOR HER MAD BETWEEN MSS WAN ESTANS PARTY AND THE OPRAR IF ONLY FOR MINIT SUERTONLY T WAS MOR THAN AMINT THAT SIMON REMAINED AT THE FIER HOUSE AFTER BEANG DROD BACKE FTER DINERINTHTACX +mls_eng_000279 AND INDICIONARY AND HEN WE HAD CALSTHENICXS WE CO THROU A GEAEMANY FIGERS ANDSINKE A LIFE OFMHEOUTION WAVE WHT FERY LIK MUSICK TILD OVE THE SCE LIGELY ROW LIGTELY ROW ORE THE GLASY WAYE WUL GO AND O COME COME AWAY AND OTHERSONGS MS JUDHE TALOROD WONS SONG AN PERPISFOR HUS +mls_eng_000280 THAT WHICH PATSES A HISTRYIN UR SCOULS AR GOVERMENTLY FABRICATE BUT SOME HISTRY IS A FOURGURY AD MRPESENTATION OF E VENCES LIKE THE LDRAMARE SENTRING UPON THEMPOSIBLE FIGUE OF THE ERO WITH THE JUSTICULATING CROWD INTHE BACKROND +mls_eng_000281 HN THE CALEVF HURD THIS HE SAID OL JEUFAR HOW GOODLY IS THAT VOICE ND THE VASIE REPLID O OUR LORD NEVER SMORT MY HEARING ART TRETERE OR GODLYAE THAN THE SINGING +mls_eng_000282 EVIDENTLY THE LURNID BARIEN HAD NOT STODED SUCH WORK OF THE TOTA CAHNE OR PERIT CHAT WHICH NOTABLY TRANSLATED BY NUSHABYY FROM THE SANSGRIT SOKUSEPTAT HAS NO BECOME AS ORTHODOCXICLY MUSLOME AS THE NIHTES diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..8ecba7920f184e6af8f3e8eccfb7d7ee0f4501fa --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/token @@ -0,0 +1,10 @@ +mls_eng_000273 H A R Y B L D E T W A S P U L I N T N O T A S T I K N O R A S T O N W A S I N D R I T O F H E H A N D A N T H E P I Y L S G R A G S E U L D O N E N O N G S H R E K U B O F W L L T H E R W A R O F W A T T H E O L E S H E S T R O E D O U O D O N O U E A N D G A S E T H E G O U N D B U T O N L Y F I L T H E R S E V E G I N C A S T H O R +mls_eng_000274 A N D A F A L Y O F E I T W A S T H A T A N N O T H E W O M E N T O F V E S E R N Y W H I C H F O R N O T H I N G B E T E D T E N T O G O S I N T O M Y S I S T R A N D F A T H E R A S S E N T A W A Y S H E S A I D I S H U L R A T H E R G O W I T H M I H A V E N O M I N E T O S T A Y H E A R A L O N I T M Y T O B A B E S +mls_eng_000275 E I N I D I T A C O R D I N G L Y W O N D E R I N G H A T E L I T E M A N W O U L D B E A T A N D H E P E K E D T O O F T H E S T O U T I S R U S H E S H E C O U L D F I N D W I T E A L I T A L E B U N H O F B +mls_eng_000276 I T I S N O T H T D R E D F U L N A E C O M S O N H O D I S M A A S T H E P L E I N W E R T H E R U S I O N S A N T H E N G L I S H F O N D T B O V F T E N T H O U S N D S L A I N B R A V E W E L I N G T O N A D B L O K E R B O T H M O S N O B L Y D R O V E T H E R F O U R S A N D B O N U P A R T S A M P E A L C R O W N W A S T A K E N A T W A T E L +mls_eng_000277 S O M E Y E A S A G O H A F T E R M A K I N G O U R A R A N G E N S F O R T H E I N C A M P E N T A T N I G T W H E D C O N S T A N T L Y H A D O F A P E A S F U L R E S R O K G O N B Y A T R I B O F R O U N M O U N C K E S T H E E I D E N T E T H U G H T T H A T L O N G P O S T I O N H A D G I E N T H E A P R I A R L A M T O T H E G L +mls_eng_000278 T O F L A S H I N A T H O M E C L E M E R I N G F O R H E R M A D B E T W E E N M S S W A N E S T A N S P A R T Y A N D T H E O P R A R I F O N L Y F O R M I N I T S U E R T O N L Y T W A S M O R T H A N A M I N T T H A T S I M O N R E M A I N E D A T T H E F I E R H O U S E A F T E R B E A N G D R O D B A C K E F T E R D I N E R I N T H T A C X +mls_eng_000279 A N D I N D I C I O N A R Y A N D H E N W E H A D C A L S T H E N I C X S W E C O T H R O U A G E A E M A N Y F I G E R S A N D S I N K E A L I F E O F M H E O U T I O N W A V E W H T F E R Y L I K M U S I C K T I L D O V E T H E S C E L I G E L Y R O W L I G T E L Y R O W O R E T H E G L A S Y W A Y E W U L G O A N D O C O M E C O M E A W A Y A N D O T H E R S O N G S M S J U D H E T A L O R O D W O N S S O N G A N P E R P I S F O R H U S +mls_eng_000280 T H A T W H I C H P A T S E S A H I S T R Y I N U R S C O U L S A R G O V E R M E N T L Y F A B R I C A T E B U T S O M E H I S T R Y I S A F O U R G U R Y A D M R P E S E N T A T I O N O F E V E N C E S L I K E T H E L D R A M A R E S E N T R I N G U P O N T H E M P O S I B L E F I G U E O F T H E E R O W I T H T H E J U S T I C U L A T I N G C R O W D I N T H E B A C K R O N D +mls_eng_000281 H N T H E C A L E V F H U R D T H I S H E S A I D O L J E U F A R H O W G O O D L Y I S T H A T V O I C E N D T H E V A S I E R E P L I D O O U R L O R D N E V E R S M O R T M Y H E A R I N G A R T T R E T E R E O R G O D L Y A E T H A N T H E S I N G I N G +mls_eng_000282 E V I D E N T L Y T H E L U R N I D B A R I E N H A D N O T S T O D E D S U C H W O R K O F T H E T O T A C A H N E O R P E R I T C H A T W H I C H N O T A B L Y T R A N S L A T E D B Y N U S H A B Y Y F R O M T H E S A N S G R I T S O K U S E P T A T H A S N O B E C O M E A S O R T H O D O C X I C L Y M U S L O M E A S T H E N I H T E S diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..ed8da6624b075d27da1d3b7e7a06999ff67a2e33 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/logdir/output.4/1best_recog/token_int @@ -0,0 +1,10 @@ +mls_eng_000273 10 5 11 19 22 13 2 12 3 4 2 17 5 9 2 21 14 13 8 7 2 4 2 7 6 4 2 5 9 4 8 24 2 7 6 11 5 9 4 6 7 2 17 5 9 2 8 7 12 11 8 4 6 18 2 10 3 2 10 5 7 12 2 5 7 2 4 10 3 2 21 8 19 13 9 2 20 11 5 20 9 2 3 14 13 12 2 6 7 3 2 7 6 7 20 2 9 10 11 3 24 2 14 22 6 18 17 13 13 2 4 10 3 2 11 17 5 11 2 6 18 17 5 4 2 4 10 3 2 6 13 3 2 9 10 3 2 9 4 11 6 3 12 2 6 14 6 12 6 7 2 6 14 3 2 5 7 12 2 20 5 9 3 2 4 10 3 2 20 6 14 7 12 22 14 4 2 6 7 13 19 2 18 8 13 4 2 10 3 11 9 3 23 3 20 8 7 2 16 5 9 4 10 6 11 +mls_eng_000274 5 7 12 2 5 2 18 5 13 19 2 6 18 3 2 8 4 2 17 5 9 2 4 10 5 4 2 5 7 2 7 6 4 10 3 2 17 6 15 3 7 4 2 6 18 23 3 2 9 3 11 7 19 2 17 10 8 16 10 2 18 6 11 2 7 6 4 10 8 7 20 2 22 3 4 3 12 4 3 7 2 4 6 2 20 6 2 9 8 7 4 6 2 15 19 2 9 8 9 4 11 2 5 7 12 2 18 5 4 10 3 11 2 5 9 2 9 3 7 4 2 5 17 5 19 2 9 10 3 2 9 5 8 12 2 8 2 9 10 14 13 11 5 4 10 3 11 2 20 6 2 17 8 4 10 15 2 8 2 10 5 23 3 2 7 6 2 15 8 7 3 2 4 6 2 9 4 5 19 2 10 3 5 11 2 5 13 6 7 2 8 4 2 15 19 2 4 6 2 22 5 22 3 9 +mls_eng_000275 3 8 7 8 12 8 4 2 5 16 6 11 12 8 7 20 13 19 2 17 6 7 12 3 11 8 7 20 2 10 5 2 4 3 2 13 8 4 3 2 15 5 7 2 17 6 14 13 12 2 22 3 2 5 4 2 5 7 12 2 10 3 2 21 3 24 3 12 2 4 6 2 6 18 2 4 10 3 9 4 6 14 4 2 8 9 2 11 14 9 10 3 9 2 10 3 16 6 14 13 12 2 18 8 7 12 2 17 8 4 3 5 13 8 4 5 13 3 2 22 14 7 10 2 6 18 2 22 +mls_eng_000276 8 4 2 8 9 2 7 6 2 4 10 4 2 12 11 3 12 18 14 13 2 7 5 3 2 16 6 15 9 2 6 7 2 10 6 2 12 8 9 15 5 2 5 9 2 4 10 3 2 21 13 3 8 7 2 17 3 11 2 4 10 3 2 11 14 9 8 6 7 9 2 5 7 2 4 10 3 2 7 20 13 8 9 10 2 18 6 7 12 2 4 2 22 6 23 18 2 4 3 7 2 4 10 6 14 9 7 12 2 9 13 5 8 7 2 22 11 5 23 3 2 17 3 13 8 7 20 4 6 7 2 5 12 2 22 13 6 24 3 11 2 22 6 4 10 2 15 6 9 2 7 6 22 13 19 2 12 11 6 23 3 2 4 10 3 11 2 18 6 14 11 9 2 5 7 12 2 22 6 7 14 21 5 11 4 9 2 5 15 21 3 5 13 2 16 11 6 17 7 2 17 5 9 2 4 5 24 3 7 2 5 4 2 17 5 4 3 13 +mls_eng_000277 9 6 15 3 2 19 3 5 9 2 5 20 6 2 10 5 18 4 3 11 2 15 5 24 8 7 20 2 6 14 11 2 5 11 5 7 20 3 7 9 2 18 6 11 2 4 10 3 8 7 16 5 15 21 3 7 4 2 5 4 2 7 8 20 4 2 17 10 3 12 2 16 6 7 9 4 5 7 4 13 19 2 10 5 12 2 6 18 2 5 2 21 3 5 9 18 14 13 2 11 3 9 2 11 6 24 20 6 7 2 22 19 2 5 2 4 11 8 22 2 6 18 2 11 6 14 7 2 15 6 14 7 16 24 3 9 2 4 10 3 2 3 8 12 3 7 4 3 2 4 10 14 20 10 4 2 4 10 5 4 2 13 6 7 20 2 21 6 9 4 8 6 7 2 10 5 12 2 20 8 3 7 2 4 10 3 2 5 2 21 11 8 5 11 2 13 5 15 2 4 6 2 4 10 3 2 20 13 +mls_eng_000278 4 6 2 18 13 5 9 10 2 8 7 2 5 4 2 10 6 15 3 2 16 13 3 15 3 11 8 7 20 2 18 6 11 2 10 3 11 2 15 5 12 2 22 3 4 17 3 3 7 2 15 9 9 2 17 5 7 2 3 9 4 5 7 9 2 21 5 11 4 19 2 5 7 12 2 4 10 3 2 6 21 11 5 11 2 8 18 2 6 7 13 19 2 18 6 11 2 15 8 7 8 4 2 9 14 3 11 4 6 7 13 19 2 4 2 17 5 9 2 15 6 11 2 4 10 5 7 2 5 15 8 7 4 2 4 10 5 4 2 9 8 15 6 7 2 11 3 15 5 8 7 3 12 2 5 4 2 4 10 3 2 18 8 3 11 2 10 6 14 9 3 2 5 18 4 3 11 2 22 3 5 7 20 2 12 11 6 12 2 22 5 16 24 3 2 18 4 3 11 2 12 8 7 3 11 8 7 4 10 4 5 16 25 +mls_eng_000279 5 7 12 2 8 7 12 8 16 8 6 7 5 11 19 2 5 7 12 2 10 3 7 2 17 3 2 10 5 12 2 16 5 13 9 4 10 3 7 8 16 25 9 2 17 3 2 16 6 2 4 10 11 6 14 2 5 2 20 3 5 3 15 5 7 19 2 18 8 20 3 11 9 2 5 7 12 9 8 7 24 3 2 5 2 13 8 18 3 2 6 18 15 10 3 6 14 4 8 6 7 2 17 5 23 3 2 17 10 4 2 18 3 11 19 2 13 8 24 2 15 14 9 8 16 24 2 4 8 13 12 2 6 23 3 2 4 10 3 2 9 16 3 2 13 8 20 3 13 19 2 11 6 17 2 13 8 20 4 3 13 19 2 11 6 17 2 6 11 3 2 4 10 3 2 20 13 5 9 19 2 17 5 19 3 2 17 14 13 2 20 6 2 5 7 12 2 6 2 16 6 15 3 2 16 6 15 3 2 5 17 5 19 2 5 7 12 2 6 4 10 3 11 9 6 7 20 9 2 15 9 2 26 14 12 10 3 2 4 5 13 6 11 6 12 2 17 6 7 9 2 9 6 7 20 2 5 7 2 21 3 11 21 8 9 18 6 11 2 10 14 9 +mls_eng_000280 4 10 5 4 2 17 10 8 16 10 2 21 5 4 9 3 9 2 5 2 10 8 9 4 11 19 8 7 2 14 11 2 9 16 6 14 13 9 2 5 11 2 20 6 23 3 11 15 3 7 4 13 19 2 18 5 22 11 8 16 5 4 3 2 22 14 4 2 9 6 15 3 2 10 8 9 4 11 19 2 8 9 2 5 2 18 6 14 11 20 14 11 19 2 5 12 2 15 11 21 3 9 3 7 4 5 4 8 6 7 2 6 18 2 3 2 23 3 7 16 3 9 2 13 8 24 3 2 4 10 3 2 13 12 11 5 15 5 11 3 2 9 3 7 4 11 8 7 20 2 14 21 6 7 2 4 10 3 15 21 6 9 8 22 13 3 2 18 8 20 14 3 2 6 18 2 4 10 3 2 3 11 6 2 17 8 4 10 2 4 10 3 2 26 14 9 4 8 16 14 13 5 4 8 7 20 2 16 11 6 17 12 2 8 7 4 10 3 2 22 5 16 24 11 6 7 12 +mls_eng_000281 10 7 2 4 10 3 2 16 5 13 3 23 18 2 10 14 11 12 2 4 10 8 9 2 10 3 2 9 5 8 12 2 6 13 2 26 3 14 18 5 11 2 10 6 17 2 20 6 6 12 13 19 2 8 9 2 4 10 5 4 2 23 6 8 16 3 2 7 12 2 4 10 3 2 23 5 9 8 3 2 11 3 21 13 8 12 2 6 2 6 14 11 2 13 6 11 12 2 7 3 23 3 11 2 9 15 6 11 4 2 15 19 2 10 3 5 11 8 7 20 2 5 11 4 2 4 11 3 4 3 11 3 2 6 11 2 20 6 12 13 19 5 3 2 4 10 5 7 2 4 10 3 2 9 8 7 20 8 7 20 +mls_eng_000282 3 23 8 12 3 7 4 13 19 2 4 10 3 2 13 14 11 7 8 12 2 22 5 11 8 3 7 2 10 5 12 2 7 6 4 2 9 4 6 12 3 12 2 9 14 16 10 2 17 6 11 24 2 6 18 2 4 10 3 2 4 6 4 5 2 16 5 10 7 3 2 6 11 2 21 3 11 8 4 2 16 10 5 4 2 17 10 8 16 10 2 7 6 4 5 22 13 19 2 4 11 5 7 9 13 5 4 3 12 2 22 19 2 2 7 14 9 10 5 22 19 19 2 18 11 6 15 2 4 10 3 2 9 5 7 9 20 11 8 4 2 9 6 24 14 9 3 21 4 5 4 2 10 5 9 2 7 6 2 22 3 16 6 15 3 2 5 9 2 6 11 4 10 6 12 6 16 25 8 16 13 19 2 15 14 9 13 6 15 3 2 5 9 2 4 10 3 2 7 8 10 4 3 9 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score new file mode 100644 index 0000000000000000000000000000000000000000..a2dee0657a8176fb1fd1c35ce9f79601778d14ed --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score @@ -0,0 +1,40 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+mls_eng_000273 tensor(-50.2951) +mls_eng_000274 tensor(-32.5631) +mls_eng_000275 tensor(-22.1818) +mls_eng_000276 tensor(-41.7596) +mls_eng_000277 tensor(-37.4146) +mls_eng_000278 tensor(-34.1106) +mls_eng_000279 tensor(-66.8122) +mls_eng_000280 tensor(-46.1822) +mls_eng_000281 tensor(-25.9304) +mls_eng_000282 tensor(-37.8664) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..1c7c505e5f383e517e5b8a2570ccc7a490fa900c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/hyp.trn @@ -0,0 +1,40 @@ +I A M T H I R U D O F T A K N O L D E S F O M Y O U S H E S A D H A S T A L Y I S A N O W T H E I T I S I M P O S I B L E T H A V E F A T H I N O U I S E N O W T H E I T I S U S E S T O E X P E C T A N Y R E T E R N F R O M O U F O R A L E I H A V E D O N I W A N T N O M O R E O F Y O U (mls_eng_000243-mls_eng_000243) +T H A T H E M A Y S O M T I M E S B E E L I K E O T H E R C H E L D R E N L A N I N G T E S I E M Y N E O R P L A I N G P R A T L I N S I K I N G F O R H E L E S C O M S T O M Y H E A R T A T S S I N F U L O R D A N M S P E A K I N G H O W G O D T H O A R T (mls_eng_000244-mls_eng_000244) +T R A N S E N T H I N G S O F A L S O R S E A S I T H E G E N R A L E O U T B U R S T O F M U L T I T O D N S P A S I O N A R H U T E D T O G E T H E R T H E L I T R C R I S N A Y T H E R E D I C U L O S W I T T H E H E R A B L E F A R O V E R T H E I L W E Y S E O F H E D S M A Y B E S E N R E S C A L I T Y C A P R I Y O L I N G O N F O R S E S F O M T H E R O I A L S T O D (mls_eng_000245-mls_eng_000245) +I T M A Y H A V E B E N T H T T H E B O N S W E R E A F O L D E D T O G E T H E R A N D N O N A S O N A H P E L B O N S F U L D E D A N D L A I E A W A Y F O R T H E P R P I C E S O F I N C E N T A T I O N S U C H B U N D L E S O F B O N S W E P U T T H R U A P R O S E S O F P R A R S (mls_eng_000246-mls_eng_000246) +M A S A I L S N E V E R E X P E R I N E D T H O S G R E A T T R A N S I O N S F R O M L O N E S T O G R A N D U R T H I S W A S O W I N G T O T H E P R U D A N T C O N D U C T O T H A T R E P U B L I C K W H I H A L W A Y S P R E S E R V E D H E R P R I N C I P L E S (mls_eng_000247-mls_eng_000247) +A T A S M A L B E A I N G S E T I O N O N T H E M O R I N G O F U T O B E R H R T Y O N C O N D U C T E D I T H E A T M S F E R O F C O N S P I R A C Y A N D I N T E N D E D B Y B R O D Y W E E R E T O L H A T T H E N O R A L A G A N C E R E Q U I R M E T F O R R E V U O R D A P R O V E L E H A D B E N W A V E D T H A T N O R M A L A P R O V E O F T H E M A Y R O F W A S I N G T O N A N D S E R T N G O V E R S W O L B E H A N D L E D I N F O R M I L Y (mls_eng_000248-mls_eng_000248) +T H E B O D I S T L A T D Y I N C H I N E R W H O D O N O T H A S T A T E T O L T A K E L I H F O R T H E R P E R F E R S O F O D S A F F T H E R C O N S H E N C S F R O M T A C K T O T I M B Y B Y I N G V E R S F A S C S H E I S I S S E T D E R A N D L E T I N T H E G O (mls_eng_000249-mls_eng_000249) +T H I S A G A N I S S O F E N D A D T E M P E R D B Y A S I M P L F A T H I N T H E S U P R O M I C Y O F L O V E O V E R F E E R A N D U N B O U N T E D H U M A N I T Y A N D C H A R I T Y F O R T H E P O R A N D H E L P L E S A N D U N C O N D I N A L F O R G I V E N E S S O F T H E D I R E S T I N J U R I E S W H I C H I S T H E N O T O F T H E N O B L E A G E N E R O S I T Y A N D L I B U R A L I T Y (mls_eng_000250-mls_eng_000250) +T H E S E C A N D M A E F O L O E D A N T H E C O U P L E O F T E S E M E R S M E N R O T T H E A O R T H E B A R K B E F O R C U C H I N G A R O P E T H E Y H E N T O W A R K T S U R C H T H E S H I P T H E L I F T D T H E H A C E S A N D F O U N D T H E R H O L D F U L O F C A R G O (mls_eng_000251-mls_eng_000251) +F O N D O F I S C O M R A D S A N D R E S P E C T F U L E T O H I S P A S T E R S A N D M A S T E R S E V E N S C O L M A S T E R S A S T H E L A D H E P R E P A R S F O R M A N W O U D W I T H W I L L A N D T H I S T R A I N I N G O R K U P E H I M T H R O U T Y U T H T I D (mls_eng_000252-mls_eng_000252) +A S W H E N H E R I C E S Q U E C A R E C E D T E R S I X T I N T Y E A R S H E B E C A M E B L I N E D H E R L A R G E S O F T B R O N I C E S H A D O L I D I N T H E M (mls_eng_000253-mls_eng_000253) +A L M O S T A L L D A Y T H E B A T A R R A N G E D B E T W E T H E T O M E N B A C K I N F O R T T H E Y F O R S E E A C H O T H E R O V E R T H E L O V A B E D S T H E C H I F E S W E L O I L E D B O D Y W A S V E R Y D I F I C U L T F O R T H E O L O H A Y T O G R A S B R U S E D A N D B L E A D I N G F R O M R E P E A T E D F A L L S O N T H E R U H L A V A (mls_eng_000254-mls_eng_000254) +P O S Y I T R E I T T E R R A R A N D A W A R P P A N T I N G T H E W I N M O N T H R U O U T H E T R E E S O F T E G A R D I N A N D F R O M T I M E T O A T I M E S E E A S I F T O A (mls_eng_000255-mls_eng_000255) +H I S M E N T A L T O R P I T I T Y F O U N D I A P O N F I S I C A L I N D I L E N C E R E N D E R S A M E D I A A C T I O N N A L L M A N E O F E X E R T I O N D I S T A S F U L H I S C O N C H O U S W E K N E S S S H O W E I T S E L F (mls_eng_000256-mls_eng_000256) +N O R T H A L L H O W G L A D T H E C U I N G M O T H E R W A S N O R H O L G R E A T W A R T H E R E J O I C E S I N G O F T H E P E O P L E T O R H O M M E N I F I S S E N T W A S T H E R O I A L B A N Q U E D T H A T G O O D C Q I N G P A M A R E A A T E N D E D T B I T H A L H E R C O R T (mls_eng_000257-mls_eng_000257) +A N D T H E C H A N C E O F T H E R B E A N G S U C H A O N U N G A N D I M I S H I E S B Y W E R Y P R A P E D P R O S E S S M A M E L U K A S A H O R S W A S O F E C U E A N E C Q U L I T Y R E A S N I N G N O T A B O U T H I S O D E R S B U T A B O U G T T H E W A Y T O D O T H E (mls_eng_000258-mls_eng_000258) +S H E N O K E D B U T S N O D R O P K O K D O T O F T H E E I N D O A N D S A I D I D E A R E N O T O P N T H E D O O R F O R T H E D O R S H A V E T O L D M E T O L E T N O W E N I N T H A T I S H A R D F O R M E S A I D T H E W O M A N F O R I M U S T T A K B A C K M Y A P L S B U T T H E R E I S O N E W H I C H I W I L L G I V E Y O U A N D S H E H E L D U P A N A A L (mls_eng_000259-mls_eng_000259) +H A V I Y M O R Y S P O K A C T H E L S H O U R A N D H O R C E F I N L Y S P O K N I T T O M Y P E C E W A S W H Y F E B Y A R E Y U C O M E S O S O O N W H E R E R Y O U B A R Y C H I L D A N D O T H A N A R D I L S P O K A S A M E O N (mls_eng_000260-mls_eng_000260) +I N E V E R G E W A N Y O N H O L I E T O T T U R C H A S M C H A S S G R A N M O H R D O U S S H E H A S H E W O U D R A V E R B A D O R C E P R I N T H E H A S O F O R G O D T H N T D W E L N T H E T E N S O F W I K E D N E S T H E Y D O N H A E W O M E N D O R K E P E R S A N D I N N O W S H E W O L D T T W E L M I N I N A N A T E N T (mls_eng_000261-mls_eng_000261) +T H E D U K W A S S U P R I S E T O S E H I M W O H T B R I N G S Y O U O U T S O I A L Y A B L E A R D D E M A N D H E O W Y O U R G R A I C E T S R E P L I D T E B U T L E R G A S P I N G F O R A T E R E N C S (mls_eng_000262-mls_eng_000262) +F O R E Y I E T S E M I N G R O N S M U T A T I O N S O F C A L E M A Y B E M A D E D W H E R E H E R I S A N Y M I X T R O F D E V R S S O U T S O F R A I S E F O R I N S U C H M I X T E R S T H E M P O N C A L E S A P E N O T T B Y T H E M U T A L A L A I N G H A C H E T H E C O N S T I T U T E A M I D L I N G C O L E R (mls_eng_000263-mls_eng_000263) +A N D A L M U S T H E S A M E I N S T E N T C I N S A S A N D Y S I D S T E P I N T O A S H A P D O R W A Y H E W A T E D T H E R A N T L L C I N C A M E U P K I N S T O P E N D P R E T E N D E D O S T A R T H R T H E L A S A T T H D E S P L A Y O F H A R D W A R I N T O U L S W E R E H E C O N T I N E D T O W A T C H B A R I C K Y O U S E W H A T I S E E S A M B E S T E A I D C I N N O T E D (mls_eng_000264-mls_eng_000264) +T H E N M T H E T H I E K G R E A N S T A V F L O W D O W E T H E H A L L E B I L D I N G A N D T H E R W A S N O T H I N G T O B E S E E N T H E R E B U T D E M I O W D O F S O F T F L O W I N G G R A Y G R E A N S T A V E T H E D R O U S H E D O N N O W W I T T H E S W E I F T N E S O L L H E W E I N K A L O O K E D A P I N T O B E A R S S F A I C E (mls_eng_000265-mls_eng_000265) +I I H A V E M A E S A C R O F I C S T O U R O F E W H E N I N E W T H T H E W E N O T M Y H A P N E S I W A S A F T E R I S A L T H A T H A D S T P E D A F T E R I S A L T H A T Y O U R T E N D E R N E S S H A D T E R N D T O C A U L C U L A T I O N A F T E R I S A W T H A T Y O U C A R D F O R Y O U R S E L F O N L Y N O T F O R M E (mls_eng_000266-mls_eng_000266) +Y E T T H A T H A N D E A R N E V E R R O A L S I N W I N T E R I S E Y A C R O L W O A R I N G R O U N D A N D R O U N B E F O R I T A L I H T T H E S N N O T H I N G U N D E R T H E F I R D T R E E S B U T I N O E S O M E T H I N G M U S T B E T H E A R (mls_eng_000267-mls_eng_000267) +I T I S B E Y O N D T H O U T T H A T S O M E P E O B L E H A E M E N I S E T O S A F E M A N Y T H I N G S A N O F C O U R S E T H E J U R M A N T H A S A S U M A I I E D A S S M A U C H T O O R T H R E D A Y S E A F T E R H A F I R S P R O C O C I T I O N S T H E Y D R O P E T I N I U N N A W A R E (mls_eng_000268-mls_eng_000268) +S O O N A M A N C A M E O U T T O M E H I M T H I S M A N W A S O L O H A A B E A R D L E S S M A N B E L O N G I G T O A L A L L S R O B E R C L A N W I C H I N F E S T E D T H E D I S T R I C T P O S E B L Y A S S I S T I N T H E M A N H U N T E R S O F T H E E M P L E I N S E C U R I N G V I C T O M S F O R T H E E M P L E A L T R S (mls_eng_000269-mls_eng_000269) +W E A R N O T L O V E R A S Y O U A N D I Y A P O N T H I S S U N Y L A I N B U T C H I L D E N W H O E V E N E V E R N O N L O V E S J O R Y O R P E (mls_eng_000270-mls_eng_000270) +W A S M U R D E D O N H E S O R S T E F O F I S O L E H O U S E S W A S I A O L M A N A S D A N D U R A C O M N L Y M S R E G U O D I A R Y O U T A K A S I F H H A D D I E D I N H I S B E A E T Y O U A R N O V E N E I S I O N H A Y O U C A N O N D E S T A N D W H T I T M I N S H E N A N I Q U I T I T Y I S M E R D E D T (mls_eng_000271-mls_eng_000271) +B Y G O D H E S A I D T H O Y W O N T M E M U B U S N S L E I D S M E I N A N D O U T O M A N Y H O U S E S O R W H A T D O I C A R F O T H E S I C R E S T T H A T M A Y B H I D I N T H E R E H O W E V E R I C A N O T P L I N T H I S P E P E F O R T H E W A C H F O L N E S T H E B L U T H O U S O F H I S S F N O R I A R A Y I N E V E R E S T R E E T (mls_eng_000272-mls_eng_000272) +H A R Y B L D E T W A S P U L I N T N O T A S T I K N O R A S T O N W A S I N D R I T O F H E H A N D A N T H E P I Y L S G R A G S E U L D O N E N O N G S H R E K U B O F W L L T H E R W A R O F W A T T H E O L E S H E S T R O E D O U O D O N O U E A N D G A S E T H E G O U N D B U T O N L Y F I L T H E R S E V E G I N C A S T H O R (mls_eng_000273-mls_eng_000273) +A N D A F A L Y O F E I T W A S T H A T A N N O T H E W O M E N T O F V E S E R N Y W H I C H F O R N O T H I N G B E T E D T E N T O G O S I N T O M Y S I S T R A N D F A T H E R A S S E N T A W A Y S H E S A I D I S H U L R A T H E R G O W I T H M I H A V E N O M I N E T O S T A Y H E A R A L O N I T M Y T O B A B E S (mls_eng_000274-mls_eng_000274) +E I N I D I T A C O R D I N G L Y W O N D E R I N G H A T E L I T E M A N W O U L D B E A T A N D H E P E K E D T O O F T H E S T O U T I S R U S H E S H E C O U L D F I N D W I T E A L I T A L E B U N H O F B (mls_eng_000275-mls_eng_000275) +I T I S N O T H T D R E D F U L N A E C O M S O N H O D I S M A A S T H E P L E I N W E R T H E R U S I O N S A N T H E N G L I S H F O N D T B O V F T E N T H O U S N D S L A I N B R A V E W E L I N G T O N A D B L O K E R B O T H M O S N O B L Y D R O V E T H E R F O U R S A N D B O N U P A R T S A M P E A L C R O W N W A S T A K E N A T W A T E L (mls_eng_000276-mls_eng_000276) +S O M E Y E A S A G O H A F T E R M A K I N G O U R A R A N G E N S F O R T H E I N C A M P E N T A T N I G T W H E D C O N S T A N T L Y H A D O F A P E A S F U L R E S R O K G O N B Y A T R I B O F R O U N M O U N C K E S T H E E I D E N T E T H U G H T T H A T L O N G P O S T I O N H A D G I E N T H E A P R I A R L A M T O T H E G L (mls_eng_000277-mls_eng_000277) +T O F L A S H I N A T H O M E C L E M E R I N G F O R H E R M A D B E T W E E N M S S W A N E S T A N S P A R T Y A N D T H E O P R A R I F O N L Y F O R M I N I T S U E R T O N L Y T W A S M O R T H A N A M I N T T H A T S I M O N R E M A I N E D A T T H E F I E R H O U S E A F T E R B E A N G D R O D B A C K E F T E R D I N E R I N T H T A C X (mls_eng_000278-mls_eng_000278) +A N D I N D I C I O N A R Y A N D H E N W E H A D C A L S T H E N I C X S 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F T A K I N G O R D E R S F R O M Y O U S H E S A I D H A S T I L Y I S E E N O W T H A T I T I S I M P O S S I B L E T O H A V E F A I T H I N Y O U I S E E N O W T H A T I T I S U S E L E S S T O E X P E C T A N Y R E T U R N F R O M Y O U F O R A L L I H A V E D O N E I W A N T N O M O R E O F Y O U (mls_eng_000243-mls_eng_000243) +T H A T H E M A Y S O M E T I M E S B E L I K E O T H E R C H I L D R E N L E A R N I N G B E S I D E M Y K N E E O R P L A Y I N G P R A T T L I N G S E E K I N G F O R H E L P C O M E S T O M Y H E A R T A H S I N F U L L O R D I M S P E A K I N G H O W G O O D T H O U A R T (mls_eng_000244-mls_eng_000244) +T R A N S C E N D T H I N G S O F A L L S O R T S A S I N T H E G E N E R A L O U T B U R S T O F M U L T I T U D I N O U S P A S S I O N A R E H U D D L E D T O G E T H E R T H E L U D I C R O U S N A Y T H E R I D I C U L O U S W I T H T H E H O R R I B L E F A R O V E R T H E B I L L O W Y S E A O F H E A D S M A Y B E S E E N R A S C A L I T Y C A P R I O L I N G O N H O R S E S F R O M T H E R O Y A L S T U D (mls_eng_000245-mls_eng_000245) +I T M A Y H A V E B E E N T H A T T H E B O N E S W E R E F O L D E D T O G E T H E R A N D K N O W N A S U N I H I P I L I B O N E S F O L D E D A N D L A I D A W A Y F O R P U R P O S E S O F I N C A N T A T I O N S U C H B U N D L E S O F B O N E S W E R E P U T T H R O U G H A P R O C E S S O F P R A Y E R S (mls_eng_000246-mls_eng_000246) +M A R S E I L L E S N E V E R E X P E R I E N C E D T H O S E G R E A T T R A N S I T I O N S F R O M L O W N E S S T O G R A N D E U R T H I S W A S O W I N G T O T H E P R U D E N T C O N D U C T T H A T R E P U B L I C W H I C H A L W A Y S P R E S E R V E D H E R P R I N C I P L E S (mls_eng_000247-mls_eng_000247) +A T A S M A L L B R I E F I N G S E S S I O N O N T H E M O R N I N G O F O C T O B E R T H I R T Y O N E C O N D U C T E D I N T H E A T M O S P H E R E O F C O N S P I R A C Y A N D A T T E N D E D B Y B R O D Y W E W E R E T O L D T H A T T H E N O R M A L A G E N C Y R E Q U I R E M E N T F O R R E V I E W B O A R D A P P R O V A L H A D B E E N W A I V E D T H A T N O R M A L A P P R O V A L O F T H E M A Y O R O F W A S H I N G T O N A N D C E R T A I N G O V E R N O R S W O U L D B E H A N D L E D I N F O R M A L L Y (mls_eng_000248-mls_eng_000248) +T H E B U D D H I S T L A I T Y I N C H I N A W H O D O N O T H E S I T A T E T O T A K E L I F E F O R T H E P U R P O S E O F F O O D S A L V E T H E I R C O N S C I E N C E F R O M T I M E T O T I M E B Y B U Y I N G B I R D S F I S H E S E T C E T E R A A N D L E T T I N G T H E M G O (mls_eng_000249-mls_eng_000249) +T H I S A G A I N I S S O F T E N E D A N D T E M P E R E D B Y A S I M P L E F A I T H I N T H E S U P R E M A C Y O F L O V E O V E R F E A R A N U N B O U N D E D H U M A N I T Y A N D C H A R I T Y F O R T H E P O O R A N D H E L P L E S S A N U N C O N D I T I O N A L F O R G I V E N E S S O F T H E D I R E S T I N J U R I E S W H I C H I S T H E N O T E O F T H E N O B L E A G E N E R O S I T Y A N D L I B E R A L I T Y (mls_eng_000250-mls_eng_000250) +T H E S E C O N D M A T E F O L L O W E D A N D A C O U P L E O F T H E S T E A M E R S M E N R O W E D T H E M A B O A R D T H E B A R Q U E B E F O R E T O U C H I N G A R O P E T H E Y W E N T T O W O R K T O S E A R C H T H E S H I P T H E Y L I F T E D T H E H A T C H E S A N D F O U N D T H E H O L D F U L L O F C A R G O (mls_eng_000251-mls_eng_000251) +F O N D O F H I S C O M R A D E S A N D R E S P E C T F U L T O H I S P A S T O R S A N D M A S T E R S E V E N S C H O O L M A S T E R S A S A L A D H E P R E P A R E S F O R M A N H O O D W I T H A W I L L A N D T H I S T R A I N I N G O C C U P I E S H I M T H R O U G H O U T Y O U T H T I D E (mls_eng_000252-mls_eng_000252) +W H E N T H E P R I N C E S S C O E C A R E A C H E D H E R S I X T E E N T H Y E A R S H E B E C A M E B L I N D H E R L A R G E S O F T B R O W N E Y E S H A D N O L I G H T I N T H E M (mls_eng_000253-mls_eng_000253) +A L M O S T A L L D A Y T H E B A T T L E R A G E D B E T W E E N T H E T W O M E N B A C K A N D F O R T H T H E Y F O R C E D E A C H O T H E R O V E R T H E L A V A B E D S T H E C H I E F S W E L L O I L E D B O D Y W A S V E R Y D I F F I C U L T F O R T H E O L O H E T O G R A S P B R U I S E D A N D B L E E D I N G F R O M R E P E A T E D F A L L S O N T H E R O U G H L A V A (mls_eng_000254-mls_eng_000254) +P O S Y I S H R I E K E D W I T H T E R R O R A N D I A W O K E P A N T I N G T H E W I N D M O A N E D T H R O U G H T H E T R E E S O F T H E G A R D E N A N D F R O M T I M E T O T I M E C E A S E D A S I F (mls_eng_000255-mls_eng_000255) +H I S M E N T A L T O R P I D I T Y F O U N D E D U P O N P H Y S I C A L I N D O L E N C E R E N D E R S I M M E D I A T E A C T I O N A N D A L L M A N N E R O F E X E R T I O N D I S T A S T E F U L H I S C O N S C I O U S W E A K N E S S S H O W S I T S E L F (mls_eng_000256-mls_eng_000256) +N O R T E L L H O W G L A D T H E Q U E E N M O T H E R W A S N O R H O W G R E A T W E R E T H E R E J O I C I N G O F T H E P E O P L E N O R H O W M A G N I F I C E N T W A S T H E R O Y A L B A N Q U E T T H A T G O O D Q U E E N P O M A R E A A T T E N D E D W I T H A L L H E R C O U R T (mls_eng_000257-mls_eng_000257) +A N D T H E C H A N C E O F T H E R E B E I N G S U C H A O N E A G A I N D I M I N I S H E S B Y V E R Y R A P I D P R O C E S S M A R M A D U K E A S A H O R S E W A S O F E Q U A L Q U A L I T Y R E A S O N I N G N O T A B O U T H I S O R D E R S B U T A B O U T T H E W A Y T O D O T H E M (mls_eng_000258-mls_eng_000258) +S H E K N O C K E D B U T S N O W D R O P L O O K E D O U T O F T H E W I N D O W A N D S A I D I D A R E N O T O P E N T H E D O O R F O R T H E D W A R F S H A V E T O L D M E T O L E T N O O N E I N T H A T I S H A R D F O R M E S A I D T H E W O M A N F O R I M U S T T A K E B A C K M Y A P P L E S B U T T H E R E I S O N E W H I C H I W I L L G I V E Y O U A N D S H E H E L D U P A N A P P L E (mls_eng_000259-mls_eng_000259) +A L B E R T M U R R A Y S P O K E E X C E L S I O R A N D H O R A C E F I N L E Y S P O K E N I C E T O O M Y P I E C E W A S W H Y P H O E B E A R E Y O U C O M E S O S O O N W H E R E A R E Y O U R B E R R I E S C H I L D E M M A V A N A R S D A L E S P O K E T H E S A M E O N E (mls_eng_000260-mls_eng_000260) +I N E V E R K N E W A N Y O N E W H O L I K E D T O G O T O C H U R C H A S M U C H A S G R A N D M O T H E R D O E S S H E S A Y S S H E W O U L D R A T H E R B E A D O O R K E E P E R I N T H E H O U S E O F O U R G O D T H A N T O D W E L L I N T H E T E N T S O F W I C K E D N E S S T H E Y D O N T H A V E W O M E N D O O R K E E P E R S A N D I K N O W S H E W O U L D N O T D W E L L A M I N U T E I N A T E N T (mls_eng_000261-mls_eng_000261) +T H E D U K E W A S S U R P R I S E D T O S E E H I M W H A T B R I N G S Y O U O U T S O E A R L Y A B E L A R D D E M A N D E D H E O H Y O U R G R A C E R E P L I E D T H E B U T L E R G A S P I N G F O R U T T E R A N C E (mls_eng_000262-mls_eng_000262) +F O U R Y E T S E E M I N G T R A N S M U T A T I O N S O F C O L O U R M A Y B E M A D E W H E R E T H E R E I S A N Y M I X T U R E O F D I V E R S E S O R T S O F R A Y S F O R I N S U C H M I X T U R E S T H E C O M P O N E N T C O L O U R S A P P E A R N O T B U T B Y T H E I R M U T U A L A L L A Y I N G E A C H O T H E R C O N S T I T U T E A M I D D L I N G C O L O U R (mls_eng_000263-mls_eng_000263) +I N A L M O S T T H E S A M E I N S T A N T K E N S A W S A N D Y S I D E S T E P I N T O A S H O P D O O R W A Y H E W A I T E D T H E R E U N T I L K E N C A M E U P K E N S T O P P E D A N D P R E T E N D E D T O S T A R E T H R O U G H T H E G L A S S A T T H E D I S P L A Y O F H A R D W A R E A N D T O O L S W H E R E H E C O N T I N U E D T O W A T C H B A R R A C K Y O U S E E W H A T I S E E S A N D Y S A I D K E N N O D D E D (mls_eng_000264-mls_eng_000264) +T H E N T H E T H I C K G R E E N S T U F F F L O W E D O V E R T H E W H O L E B U I L D I N G A N D T H E R E W A S N O T H I N G T O B E S E E N T H E R E B U T A M O U N D O F S O F T F L O W I N G G R A Y G R E E N S T U F F T H A T R U S H E D O N N O W W I T H T H E S W I F T N E S S O F T H E W I N D I L O O K E D U P I N T O B A R R Y S F A C E (mls_eng_000265-mls_eng_000265) +I H A V E M A D E S A C R I F I C E S T O O B U T I T W A S W H E N I K N E W T H A T T H E Y W E R E N O T M Y H A P P I N E S S I T W A S A F T E R I S A W T H A T I H A D S T O O P E D A F T E R I S A W T H A T Y O U R T E N D E R N E S S H A D T U R N E D T O C A L C U L A T I O N A F T E R I S A W T H A T Y O U C A R E D F O R Y O U R S E L F O N L Y N O T F O R M E (mls_eng_000266-mls_eng_000266) +Y E T T H E T H U N D E R N E V E R R O A R S I N W I N T E R I S E E A C R O W W H I R L I N G R O U N D A N D R O U N D B E F O R E I T A L I G H T T H E R E I S N O T H I N G U N D E R T H E F I R T R E E S B U T I K N O W S O M E T H I N G M U S T B E T H E R E (mls_eng_000267-mls_eng_000267) +I T I S B E Y O N D D O U B T T H A T S O M E P E O P L E H A D M A N A G E D T O S A V E M A N Y T H I N G S A N D O F C O U R S E T H E G E R M A N S H A D S U R M I S E D A S M U C H T W O O R T H R E E D A Y S A F T E R T H E F I R S T P E R Q U I S I T I O N S T H E Y D R O P P E D I N U N A W A R E S (mls_eng_000268-mls_eng_000268) +S O O N A M A N C A M E O U T T O M E E T H I M T H I S M A N W A S O L O H E A B E A R D L E S S M A N B E L O N G I N G T O A L A W L E S S R O B B E R C L A N W H I C H I N F E S T E D T H E D I S T R I C T P O S S I B L Y A S S I S T I N G T H E M A N H U N T E R S O F T H E T E M P L E I N S E C U R I N G V I C T I M S F O R T H E T E M P L E A L T A R S (mls_eng_000269-mls_eng_000269) +W E A R E N O T L O V E R S Y O U A N D I U P O N T H I S S U N N Y L A N E B U T C H I L D R E N W H O H A V E N E V E R K N O W N L O V E S J O Y O R P A I N (mls_eng_000270-mls_eng_000270) +W A S M U R D E R E D O N T H E D O O R S T E P O F H I S O W N H O U S E W A S T H I S A N O L D M A N A S K E D A N D R E A C A L M L Y M I S E R I C O R D I A Y O U T A L K A S I F H E H A D D I E D I N H I S B E D Y O U A R E N O V E N E T I A N A N D Y O U C A N N O T U N D E R S T A N D W H A T I T M E A N S W H E N A N I N Q U I S I T O R I S M U R D E R E D (mls_eng_000271-mls_eng_000271) +B Y G O D H E S A I D T H E Y W R O N G M E M Y B U S I N E S S L E A D S M E I N A N D O U T O F M A N Y H O U S E S B U T W H A T D O I C A R E F O R T H E S E C R E T S T H A T M A Y B E H I D D E N T H E R E H O W E V E R I C A N N O T B L A M E T H E S E P E O P L E F O R T H E I R W A T C H F U L N E S S T H E B L O O D H O U N D S O F T H E S I G N O R I A A R E I N E V E R Y S T R E E T (mls_eng_000272-mls_eng_000272) +H O R R I B L E D E A T H W A S P U L L I N G A T H E R N O T A S T I C K N O R A S T O N E W A S I N R E A C H O F H E R H A N D S A N D T H E P I T I L E S S C R A G S E C H O E D O N E L O N G S H R I E K A B O V E A L L T H E R O A R O F T H E W A T E R F A L L S H E S T R O V E T O T U R N O V E R A N D G R A S P T H E G R O U N D B U T O N L Y F E L T H E R S E L F G O I N G F A S T E R (mls_eng_000273-mls_eng_000273) +A N D T H E F O L L Y O F I T W A S T H A T A N O T H E R W O M A N O F C E R N Y W I S H E D F O R N O T H I N G B E T T E R T H A N T O G O S I N C E M Y S I S T E R A N D F A T H E R A R E S E N T A W A Y S H E S A I D I S H O U L D R A T H E R G O W I T H T H E M I H A V E N O M I N D T O S T A Y H E R E A L O N E W I T H M Y T W O B A B I E S (mls_eng_000274-mls_eng_000274) +B I L L Y D I D A C C O R D I N G L Y W O N D E R I N G W H A T T H E L I T T L E M A N W O U L D B E A T A N D H E P I C K E D T W O O F T H E S T O U T E S T R U S H E S H E C O U L D F I N D W I T H A L I T T L E B U N C H O F (mls_eng_000275-mls_eng_000275) +I T I S N O W T H E D R E A D F U L N I G H T C O M E S O N H O W D I S M A L I S T H E P L A I N F O R T H E P R U S S I A N S A N D T H E E N G L I S H F O U N D A B O V E T E N T H O U S A N D S L A I N B R A V E W E L L I N G T O N A N D B L U C H E R B O T H M O S T N O B L Y D R O V E T H E I R F O E S A N D B U O N A P A R T E S I M P E R I A L C R O W N W A S T A K E N A T W A T E R L O O (mls_eng_000276-mls_eng_000276) +S O M E Y E A R S A G O A F T E R M A K I N G O U R A R R A N G E M E N T S F O R T H E E N C A M P M E N T A T N I G H T W E C O N S T A N T L Y H A D O U R P E A C E F U L R E S T B R O K E N B Y A T R I B E O F B R O W N M O N K E Y S T H E Y E V I D E N T L Y T H O U G H T T H A T L O N G P O S S E S S I O N H A D G I V E N T H E M A P R I O R C L A I M T O T H E G R O V E (mls_eng_000277-mls_eng_000277) +T O F L A S H I N A T H O M E C L A M O U R I N G F O R H E R M A I D B E T W E E N M R S V A N E S T E N S P A R T Y A N D T H E O P E R A I F O N L Y F O R A M I N U T E C E R T A I N L Y I T W A S M O R E T H A N A M I N U T E T H A T S I M O N E R E M A I N E D A T T H E P H A Y R E H O U S E A F T E R B E I N G B R O U G H T B A C K A F T E R D I N N E R I N T A X I (mls_eng_000278-mls_eng_000278) +A N D I N D I C T I O N A R Y A N D T H E N W E H A D C A L I S T H E N I C S W E G O T H R O U G H A G R E A T M A N Y F I G U R E S A N D S I N G A L I F E O N T H E O C E A N W A V E W H A T F A I R Y L I K E M U S I C S T E A L S O V E R T H E S E A L I G H T L Y R O W L I G H T L Y R O W O E R T H E G L A S S Y W A V E S W E G O A N D O H C O M E C O M E A W A Y A N D O T H E R S O N G S M R S J U D G E T A Y L O R W R O T E O N E S O N G O N P U R P O S E F O R U S (mls_eng_000279-mls_eng_000279) +T H A T W H I C H P A S S E S A S H I S T O R Y I N O U R S C H O O L S O R G O V E R N M E N T A L L Y F A B R I C A T E D B O O K S O N H I S T O R Y I S A F O R G E R Y A M I S R E P R E S E N T A T I O N O F E V E N T S L I K E T H E O L D D R A M A C E N T E R I N G U P O N T H E I M P O S S I B L E F I G U R E O F T H E H E R O W I T H A G E S T I C U L A T I N G C R O W D I N T H E B A C K G R O U N D (mls_eng_000280-mls_eng_000280) +W H E N T H E C A L I P H H E A R D T H I S H E S A I D O J A A F A R H O W G O O D L Y I S T H A T V O I C E A N D T H E W A Z I R R E P L I E D O O U R L O R D N E V E R S M O T E M Y H E A R I N G A U G H T S W E E T E R O R G O O D L I E R T H A N T H I S S I N G I N G (mls_eng_000281-mls_eng_000281) +E V I D E N T L Y T H E L E A R N E D B A R O N H A D N O T S T U D I E D S U C H W O R K S O F T H E T O T K A H N I O R P A R R O T C H A T W H I C H N O T A B L Y T R A N S L A T E D B Y N A K H S H A B I F R O M T H E S A N S K R I T S U K A S A P T A T I H A S N O W B E C O M E A S O R T H O D O X I C A L L Y M U S L I M A S T H E N I G H T S (mls_eng_000282-mls_eng_000282) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/result.txt b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..cf8555f8ccd1062f34306858c811ebe1f1a05cf3 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/result.txt @@ -0,0 +1,521 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,--------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/hyp.trn| +|--------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000243 | 1 207 | 86.0 3.9 10.1 1.0 15.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000244 | 1 169 | 88.2 3.6 8.3 3.0 14.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000245 | 1 250 | 84.8 5.2 10.0 1.6 16.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000246 | 1 190 | 84.7 4.7 10.5 3.7 18.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000247 | 1 167 | 91.6 1.2 7.2 1.8 10.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000248 | 1 316 | 83.2 4.1 12.7 0.6 17.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000249 | 1 178 | 78.1 11.2 10.7 5.1 27.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000250 | 1 259 | 92.7 1.9 5.4 1.5 8.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000251 | 1 203 | 82.8 3.9 13.3 1.5 18.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000252 | 1 178 | 88.2 3.4 8.4 2.8 14.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000253 | 1 114 | 82.5 6.1 11.4 3.5 21.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000254 | 1 235 | 89.8 3.4 6.8 2.1 12.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000255 | 1 130 | 80.0 6.2 13.8 5.4 25.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000256 | 1 152 | 85.5 5.9 8.6 1.3 15.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000257 | 1 179 | 87.2 7.8 5.0 4.5 17.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000258 | 1 181 | 88.4 5.0 6.6 3.9 15.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000259 | 1 258 | 91.5 1.9 6.6 1.2 9.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000260 | 1 172 | 70.3 11.0 18.6 4.1 33.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000261 | 1 263 | 76.0 4.2 19.8 1.5 25.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000262 | 1 137 | 87.6 3.6 8.8 3.6 16.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000263 | 1 228 | 79.4 6.1 14.5 1.3 21.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000264 | 1 270 | 81.9 8.1 10.0 1.5 19.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000265 | 1 219 | 86.3 8.7 5.0 5.5 19.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000266 | 1 234 | 85.9 3.4 10.7 0.9 15.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000267 | 1 164 | 89.0 4.9 6.1 3.0 14.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000268 | 1 187 | 84.5 9.6 5.9 6.4 21.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000269 | 1 219 | 92.2 1.8 5.9 0.5 8.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000270 | 1 100 | 87.0 2.0 11.0 5.0 18.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000271 | 1 227 | 77.1 7.0 15.9 5.3 28.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000272 | 1 246 | 80.1 8.1 11.8 1.2 21.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000273 | 1 245 | 70.6 11.0 18.4 2.0 31.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000274 | 1 220 | 84.1 5.0 10.9 3.6 19.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000275 | 1 141 | 83.0 5.7 11.3 2.1 19.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000276 | 1 241 | 83.4 6.2 10.4 0.8 17.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000277 | 1 229 | 83.4 4.4 12.2 3.1 19.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000278 | 1 230 | 83.9 6.5 9.6 2.2 18.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000279 | 1 295 | 81.4 7.1 11.5 3.7 22.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000280 | 1 245 | 83.7 5.3 11.0 3.7 20.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000281 | 1 168 | 84.5 8.3 7.1 2.4 17.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000282 | 1 208 | 87.0 7.2 5.8 3.4 16.3 100.0 | +|====================================================================================================================| +| Sum/Avg | 40 8254 | 84.0 5.6 10.3 2.6 18.6 100.0 | +|====================================================================================================================| +| Mean | 1.0 206.4 | 84.2 5.6 10.2 2.8 18.6 100.0 | +| S.D. | 0.0 48.8 | 5.1 2.6 3.7 1.6 5.6 0.0 | +| Median | 1.0 213.5 | 84.5 5.3 10.3 2.6 18.1 100.0 | +`--------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,--------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/hyp.trn| +|--------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000243 | 1 207 | 178 8 21 2 31 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000244 | 1 169 | 149 6 14 5 25 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000245 | 1 250 | 212 13 25 4 42 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000246 | 1 190 | 161 9 20 7 36 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000247 | 1 167 | 153 2 12 3 17 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000248 | 1 316 | 263 13 40 2 55 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000249 | 1 178 | 139 20 19 9 48 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000250 | 1 259 | 240 5 14 4 23 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000251 | 1 203 | 168 8 27 3 38 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000252 | 1 178 | 157 6 15 5 26 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000253 | 1 114 | 94 7 13 4 24 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000254 | 1 235 | 211 8 16 5 29 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000255 | 1 130 | 104 8 18 7 33 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000256 | 1 152 | 130 9 13 2 24 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000257 | 1 179 | 156 14 9 8 31 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000258 | 1 181 | 160 9 12 7 28 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000259 | 1 258 | 236 5 17 3 25 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000260 | 1 172 | 121 19 32 7 58 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000261 | 1 263 | 200 11 52 4 67 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000262 | 1 137 | 120 5 12 5 22 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000263 | 1 228 | 181 14 33 3 50 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000264 | 1 270 | 221 22 27 4 53 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000265 | 1 219 | 189 19 11 12 42 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000266 | 1 234 | 201 8 25 2 35 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000267 | 1 164 | 146 8 10 5 23 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000268 | 1 187 | 158 18 11 12 41 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000269 | 1 219 | 202 4 13 1 18 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000270 | 1 100 | 87 2 11 5 18 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000271 | 1 227 | 175 16 36 12 64 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000272 | 1 246 | 197 20 29 3 52 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000273 | 1 245 | 173 27 45 5 77 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000274 | 1 220 | 185 11 24 8 43 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000275 | 1 141 | 117 8 16 3 27 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000276 | 1 241 | 201 15 25 2 42 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000277 | 1 229 | 191 10 28 7 45 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000278 | 1 230 | 193 15 22 5 42 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000279 | 1 295 | 240 21 34 11 66 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000280 | 1 245 | 205 13 27 9 49 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000281 | 1 168 | 142 14 12 4 30 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000282 | 1 208 | 181 15 12 7 34 1 | +|====================================================================================================================| +| Sum | 40 8254 | 6937 465 852 216 1533 40 | +|====================================================================================================================| +| Mean | 1.0 206.4 | 173.4 11.6 21.3 5.4 38.3 1.0 | +| S.D. | 0.0 48.8 | 41.2 5.9 10.4 3.0 15.0 0.0 | +| Median | 1.0 213.5 | 176.5 10.5 18.5 5.0 35.5 1.0 | +`--------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_cer/hyp.trn + +Speakers: + 0: mls_eng_000243 + 1: mls_eng_000244 + 2: mls_eng_000245 + 3: mls_eng_000246 + 4: mls_eng_000247 + 5: mls_eng_000248 + 6: mls_eng_000249 + 7: mls_eng_000250 + 8: mls_eng_000251 + 9: mls_eng_000252 + 10: mls_eng_000253 + 11: mls_eng_000254 + 12: mls_eng_000255 + 13: mls_eng_000256 + 14: mls_eng_000257 + 15: mls_eng_000258 + 16: mls_eng_000259 + 17: mls_eng_000260 + 18: mls_eng_000261 + 19: mls_eng_000262 + 20: mls_eng_000263 + 21: mls_eng_000264 + 22: mls_eng_000265 + 23: mls_eng_000266 + 24: mls_eng_000267 + 25: mls_eng_000268 + 26: mls_eng_000269 + 27: mls_eng_000270 + 28: mls_eng_000271 + 29: mls_eng_000272 + 30: mls_eng_000273 + 31: mls_eng_000274 + 32: mls_eng_000275 + 33: mls_eng_000276 + 34: mls_eng_000277 + 35: mls_eng_000278 + 36: mls_eng_000279 + 37: mls_eng_000280 + 38: mls_eng_000281 + 39: mls_eng_000282 + +Speaker sentences 0: mls_eng_000243 #utts: 1 +id: (mls_eng_000243-mls_eng_000243) +Scores: (#C #S #D #I) 178 8 21 2 +REF: i a m t * i r ******* E d o f t a k I n G o R d e R s f R o m y o u s h e s a I d h a s t I l y i s E E n o w t h A T i t i s i m p o S s i b l e t O h a v e f a I t h i n Y o u i s E e n o w t h A T i t i s u s E L e S s t o e x p e c t a n y r e t U r n f r o m Y o u f o r a l L i h a v e d o n E i w a n t n o m o r e o f y o u +HYP: i a m ******* t H i r U d o f t a k * n * o L d e * s f * o m y o u s h e s a * d h a s t A l y i s * A n o w t h * E i t i s i m p o * s i b l e t * h a v e f a * t h ******* i n * o u i s * e n o w t h * E i t i s u s * * e * s ******* t o e x p e c t a n y r e t E r n f r o m * o u f o r a l E i h a v e d o n * i w a n t n o m o r e o f y o u +Eval: D I I S D D S D D D S D S D S D D D D D D D S D D D D S D S D + +Speaker sentences 1: mls_eng_000244 #utts: 1 +id: (mls_eng_000244-mls_eng_000244) +Scores: (#C #S #D #I) 149 6 14 5 +REF: t h a t h e m a y s o m E t i m e s b * e l i k e o t h e r c h I l d r e n l E a R n i n g B e ******* s i D e m y K n E e o r p l a Y i n g p r a T t l i n G s E E k i n g f o r h e l * P c o m E s t o m y h e a r t a * H s i n f u l L o r d * I m s p e a k i n g h o w g O o d t h o U a r t +HYP: t h a t h e m a y s o m * t i m e s b E e l i k e o t h e r c h E l d r e n l * a * n i n g T e s i * e m y * n * e o r p l a * i n g p r a * t l i n * s * I k i n g f o r h e l E S c o m * s t o m y h e a r t a T S s i n f u l * o r d A N m s p e a k i n g h o w g * o d t h o * a r t +Eval: D I S D D S I D D D D D D D S I S D I S D I S D D + +Speaker sentences 2: mls_eng_000245 #utts: 1 +id: (mls_eng_000245-mls_eng_000245) +Scores: (#C #S #D #I) 212 13 25 4 +REF: t r a n s C e n D t h i n g s o f a L l s o r T s * a s i N t h e g e n E r a l * o u t b u r s t o f m u l t i t U d I n O U s p a S s i o n a r E h u D D L e d t o g e t h e r t h e l U D I c r O U s n a y t h e r I d i c u l o U s w i t H t h e h O R r I b l e f a r o v e r t h e B i L l O w * y s e A o f h e A d s m a y b e s E e n r A s c a l i t y c a p r i * o l i n g o n H o r s e s f R o m t h e r o Y a l s t U d +HYP: t r a n s * e n * ******* t h i n g s o f a * l s o r * s E a s i * t h e g e n * r a l E o u t b u r s t o f m u l t i t O d * n * * s p a * s i o n a r * h u * * T e d t o g e t h e r t h e l I T R c r * I s n a y t h e r E d i c u l o * s w i t * t h e h * E r A b l e f a r o v e r t h e * i * l * w E y s e * o f h e * d s m a y b e s * e n r E s c a l i t y c a p r i Y o l i n g o n F o r s e s f * o m t h e r o I a l s t O d +Eval: D D D D D I D D I S D D D D D D D S S S S D S S D D D S S D D D I D D D S I S D S S + +Speaker sentences 3: mls_eng_000246 #utts: 1 +id: (mls_eng_000246-mls_eng_000246) +Scores: (#C #S #D #I) 161 9 20 7 +REF: i t m a y h a v e b E e n t h A t t h e b o n E s w e r e ******* * f o l d e d t o g e t h e r a n d K n o W n a s U n * I h I p I l I b o n E s f O l d e d a n d l a i D a w a y f o r ******* * * * p U r p O S e s o f i n c A n t a t i o n s u c h b u n d l e s o f b o n E s w E R e p u t t h r O u G H a p r o C e S s o f p r a Y E r s +HYP: i t m a y h a v e b * e n t h * t t h e b o n * s w e r e A f o l d e d t o g e t h e r a n d * n o * n a s O n A h * p E l * ******* b o n * s f U l d e d a n d l a i E a w a y f o r T H E p * r p I C e s o f i n c E n t a t i o n s u c h b u n d l e s o f b o n * s w * * e p u t t h r * u * * a ******* p r o S e * s o f p r a * * r s +Eval: D D D I I D D S I S D S D D D S S I I I I D S S S D D D D D D D S D D D + +Speaker sentences 4: mls_eng_000247 #utts: 1 +id: (mls_eng_000247-mls_eng_000247) +Scores: (#C #S #D #I) 153 2 12 3 +REF: m a R s E i L l E s n e v e r e x p e r i E n C e d t h o s E g r e a t t r a n s I T i o n s f r o m l o W n e S s t o g r a n d E u r t h i s w a s o w i n g t o t h e p r u d E n t c o n d u c t ******* * t h a t r e p u b l i c * w h i C h a l w a y s p r e s e r v e d h e r p r i n c i p l e s +HYP: m a * s A i * l * s n e v e r e x p e r i * n * e d t h o s * g r e a t t r a n s * * i o n s f r o m l o * n e * s t o g r a n d * u r t h i s w a s o w i n g t o t h e p r u d A n t c o n d u c t O t h a t r e p u b l i c K w h i * h a l w a y s p r e s e r v e d h e r p r i n c i p l e s +Eval: D S D D D D D D D D D D S I I I D + +Speaker sentences 5: mls_eng_000248 #utts: 1 +id: (mls_eng_000248-mls_eng_000248) +Scores: (#C #S #D #I) 263 13 40 2 +REF: a t a s m a L l b R I e F i n g s e S S i o n o n t h e m o r N i n g o f O C t o b e r T h I r t y o n E c o n d u c t e d i N t h e a t m O s P H e r E o f c o n s p i r a c y a n d A T t e n d e d b y b r o d y w e W e r e t o l D T h a t t h e n o r M a l a g E n c Y r e q u i r E m e N t f o r r e v I E W B o A r d a P p r o v A l * h a d b E e n w a I v e d t h a t n o r m a l a P p r o v A L o f t h e m a y O r o f w a s H i n g t o n a n d C e r t A I n g o v e R N O r s w o U l D b e h a n d l e d i n ******* f o r m A L l y +HYP: a t a s m a * l b * * e A i n g s e * T i o n o n t h e m o r * i n g o f * U t o b e r * h * r t y o n * c o n d u c t e d i * t h e a t m * s * F e r * o f c o n s p i r a c y a n d I N t e n d e d b y b r o d y w e * e r e ******* t o l * * h a t t h e n o r * a l a g A n c E r e q u i r * m e * t f o r r e v * * U * o * r d ******* a * p r o v E l E h a d b * e n w a * v e d t h a t n o r m a l a * p r o v * E o f t h e m a y * r o f w a s * i n g t o n a n d S e r t * * n g o v e * * * r s w o * l * b e h a n d l e d i n f o r m * I l y +Eval: D D D S D S D D S D D D D D D S D S S D D D D D S S D D D D S D D D D S I D D D D S D D S D D D D D D D I D S + +Speaker sentences 6: mls_eng_000249 #utts: 1 +id: (mls_eng_000249-mls_eng_000249) +Scores: (#C #S #D #I) 139 20 19 9 +REF: t h e b U D d H i s t l a I t * y i n c h i n * A w h o d o n o t h E s I t a t e t o * t a k e l i F E f o r t h e * p U r * P O s E o F f O o d s a L V E t h e I r c o n s C I e n c E f r o m t I M E t o t i m E b y b U y i n g B I r D s f * * I s h e * s E T C e t * e r A a n d l e T t i n G t h e M g o +HYP: t h e b * O d * i s t l a * t D y i n c h i n E R w h o d o n o t h A s * t a t e t o L t a k e l i * H f o r t h e R p E r F E R s * o * ******* f * o d s a * F F t h e * r c o n s * H e n c S f r o m t A C K t o t i m * b y b * y i n g V E r * s f A S C s h e I s I S S e t D e r * a n d l e * t i n * t h e * g o +Eval: D S D D I I S S D I D S I S I S S D D D D D S S D D S S S S S D D S S D I I S I S S S I D D D D + +Speaker sentences 7: mls_eng_000250 #utts: 1 +id: (mls_eng_000250-mls_eng_000250) +Scores: (#C #S #D #I) 240 5 14 4 +REF: t h i s a g a I n i s s o f T e n E d a N d t e m p e r E d b y a s i m p l E f a I t h i n t h e s u p r E m A c y o f l o v e o v e r f e A r a n * u n ******* b o u n D e d h u m a n i t y a n d c h a r i t y f o r t h e p O o r a n d h e l p l e S s a n * u n ******* c o n d I T i O n a l f o r g i v e n e s s o f t h e d i r e s t i n j u r i e s w h i c h i s t h e n o t E o f t h e n o b l e a g e n e r o s i t y a n d l i b E r a l i t y +HYP: t h i s a g a * n i s s o f * e n * d a * d t e m p e r * d b y a s i m p l * f a * t h i n t h e s u p r O m I c y o f l o v e o v e r f e E r a n D u n b o u n T e d h u m a n i t y a n d c h a r i t y f o r t h e p * o r a n d h e l p l e * s ******* a n D u n c o n d * * i * n a l f o r g i v e n e s s o f t h e d i r e s t i n j u r i e s w h i c h i s t h e n o t * o f t h e n o b l e a g e n e r o s i t y a n d l i b U r a l i t y +Eval: D D D D D D D S S S I I S D D D I I D D D D S + +Speaker sentences 8: mls_eng_000251 #utts: 1 +id: (mls_eng_000251-mls_eng_000251) +Scores: (#C #S #D #I) 168 8 27 3 +REF: t h e s e c O n d m a T e f o L l o W e d a n D * * A c o u p l e o f t H e s T e A m e r s m e n r o W E D t h e M a B o A r D t h e b a r Q U E b e f o r E T O u c h i n g a r o p e t h e y W e n T t o w O r k t O s E A r c h t h e s h i p t h e Y l i f t E d t h e h a T c H e s a n d f o u n d t h e * h o l d f u L l o f c a r g o +HYP: t h e s e c A n d m a * e f o * l o * e d a n * T H E c o u p l e o f t * e s * e * m e r s ******* m e n r o * * T t h e * a * o * r * t h e b a r * * K b e f o r * * C u c h i n g a r o p e t h e y H e n * ******* t o w A r k t * s * U r c h t h e s h i p t h e * l i f t * d t h e h a * c * e s a n d f o u n d t h e R h o l d f u * l o f c a r g o +Eval: S D D D D I I S D D D D D D S D D D D D D S D D S S D D S D D S D D D D I D + +Speaker sentences 9: mls_eng_000252 #utts: 1 +id: (mls_eng_000252-mls_eng_000252) +Scores: (#C #S #D #I) 157 6 15 5 +REF: f o n d o f H i s c o m r a d E s a n d r e s p e c t f u l * t o h i s p a s t O r s a n d m a s t e r s e v e n s c H O o l ******* m a s t e r s a s * * A l a d h e p r e p a r E s f o r m a n H o O d w i t h A w i l l a n d t h i s t r a i n i n g o C C u p I e S h i m t h r O U G H o u t y O u t h ******* t i d E +HYP: f o n d o f * i s c o m r a d * s a n d r e s p e c t f u l E t o h i s p a s t E r s a n d m a s t e r s e v e n s c * * o l m a s t e r s a s T H E l a d h e p r e p a r * s f o r m a n W o U d w i t h ******* * w i l l a n d t h i s t r a i n i n g o R K u p * e * h i m t h r * * * * o u t y * u t h t i d * +Eval: D D I S D D I I I S D S S D D S S D D D D D D D I D + +Speaker sentences 10: mls_eng_000253 #utts: 1 +id: (mls_eng_000253-mls_eng_000253) +Scores: (#C #S #D #I) 94 7 13 4 +REF: * * ******* w h e n T h e P r i N c e S s C O e c a r e A c H e d H e r s i x t E E n t H y e a r s h e b e c a m e b l i n * d h e r l a r g e s o f t b r o W n E Y e s h a d N o l i G H T i n t h e m +HYP: A S w h e n * h e * r i * c e * s Q U e c a r e * c * e d T e r s i x t * I n t * y e a r s h e b e c a m e b l i n E d h e r l a r g e s o f t b r o * n I C e s h a d * o l i * * * D i n t h e m +Eval: I I I D D D D S S D D S D S D I D S S D D D D S + +Speaker sentences 11: mls_eng_000254 #utts: 1 +id: (mls_eng_000254-mls_eng_000254) +Scores: (#C #S #D #I) 211 8 16 5 +REF: a l m o s t a l l d a y t h e b a T t L E r a * g e d b e t w E e N t h e t W o m e n b a c k A n D f o r t H t h e y f o r C e D e a c h o t h e r o v e r t h e l A v A * b e d s t h e c h i E f * s w e l L o i l e d b o d y w a s v e r y d i F f i c u l t f o r t h e o l o ******* h * E t o g r a s P b r u I s e d a n d b l e E d i n g f r o m r e p e a t e d f a l l s o n t h e r O u G h l a v a +HYP: a l m o s t a l l d a y t h e b a * t A R r a N g e d b e t w * e * t h e t * o m e n b a c k I n * f o r t * t h e y f o r S e * e a c h ******* o t h e r o v e r t h e l O v * A b e d s t h e c h i * f E s w e l o i l e d b o d y w a s v e r y d i * f i c u l t f o r t h e ******* o l o h A Y t o g r a s * b r u * s e d a n d b l e A d i n g f r o m r e p e a t e d f a l l s o n t h e r * u * h l a v a +Eval: D S S I D D D S D D S D D S D I D I S D D I I S D D S D D + +Speaker sentences 12: mls_eng_000255 #utts: 1 +id: (mls_eng_000255-mls_eng_000255) +Scores: (#C #S #D #I) 104 8 18 7 +REF: p o s y i S H r I E K e D W i t H t e r r O r a n d I A w O K E p a n t i n g t h e w i n D m o A n E D t h r * o u G H t h e t r e e s o f t H e g a r d E n a n d f r o m t i m e t o * t i m e C E A s e D a s i f ******* * * ******* * +HYP: p o s y i * T r * * * e * * i t * t e r r A r a n d A * w A R P p a n t i n g t h e w i n * m o * n * * t h r U o u * * t h e t r e e s o f t * e g a r d I n a n d f r o m t i m e t o A t i m e * * * s e E a s i f T O A +Eval: D S D D D D D D S S D S S S D D D D I D D D S I D D D S I I I I I + +Speaker sentences 13: mls_eng_000256 #utts: 1 +id: (mls_eng_000256-mls_eng_000256) +Scores: (#C #S #D #I) 130 9 13 2 +REF: h i s m e n t a l t o r p i D i t y f o u n d E D U p o n P H Y s i c a l i n d O l e n c e r e n d e r s I M m e d i a T E a c t i o n A n D a l l m a N n e R o f e x e r t i o n d i s ******* t a s T E f u l h i s c o n S c I o u s w e A k n e s s s h o w S i t ******* s e l f +HYP: h i s m e n t a l t o r p i T i t y f o u n d * I A p o n * F I s i c a l i n d I l e n c e r e n d e r s * A m e d i a * * a c t i o n * n * a l l m a * n e * o f e x e r t i o n d i s t a s * * f u l h i s c o n * c H o u s w e * k n e s s s h o w E i t s e l f +Eval: S D S S D S S S D S D D D D D D I D D D S D S I + +Speaker sentences 14: mls_eng_000257 #utts: 1 +id: (mls_eng_000257-mls_eng_000257) +Scores: (#C #S #D #I) 156 14 9 8 +REF: n o r t * E l l h o w g l a d t h e Q u E E n * m o t h e r w a s n o r h o W g r e a t w E r E t h e r e j o i c * * i n g o f t h e p e o p l e N o r h o W m A G n i f i * C e n t w a s t h e r o Y a l b a n q u e T t h a t g o o d * q U E E n * p O m a r e a a T t e n d e d * W i t h a L l h e r c o U r t +HYP: n o r t H A l l h o w g l a d t h e C u * I n G m o t h e r w a s n o r h o L g r e a t w A r * t h e r e j o i c E S i n g o f t h e p e o p l e T o r h o M m * E n i f i S S e n t w a s t h e r o I a l b a n q u e D t h a t g o o d C q * * I n G p A m a r e a a * t e n d e d T B i t h ******* a * l h e r c o * r t +Eval: I S S D S I S S D I I S S D S I S S S I D D S I S D I S D D D + +Speaker sentences 15: mls_eng_000258 #utts: 1 +id: (mls_eng_000258-mls_eng_000258) +Scores: (#C #S #D #I) 160 9 12 7 +REF: a n d t h e c h a n c e o f t h e r E b e I n g s u c h a o n E * A g a I n d i m I N i s h * e s b y V e r y * r a p I d p r o C e s s m a R m A D u k E a s a h o r s E w a s o f e Q u * a * L * q u A l i t y r e a s O n i n g n o t a b o u t h i s o R d e r s b u t a b o u * t t h e w a y t o d o t h e M +HYP: a n d t h e c h a n c e o f t h e r * b e A n g s u c h a o n * U N g a * n d i m * * i s h I e s b y W e r y P r a p E d p r o S e s s m a * m E L u k * a s a h o r s * w a s o f e C u E a N E C q u * l i t y r e a s * n i n g n o t a b o u t h i s o * d e r s b u t a b o u G t t h e w a y t o d o t h e * +Eval: D S D I S D D D I S I S S D S S D D S I I S I D D D I D + +Speaker sentences 16: mls_eng_000259 #utts: 1 +id: (mls_eng_000259-mls_eng_000259) +Scores: (#C #S #D #I) 236 5 17 3 +REF: s h e K n o C k e d b u t s n o W d r o p * L O o k E d o U t o f t h e W i n d o W a n d s a i d i d * a r e n o t o p E n t h e d o o r f o r t h e d W A r F s h a v e t o l d m e t o l e t n o O N e * i n t h a t i s h a r d f o r m e s a i d t h e w o m a n f o r i m u s t t a k E b a c k m y a P p l E s b u t t h e r e i s o n e w h i c h i w i l l g i v e y o u a n d s h e h e l d u p a n a P P l E +HYP: s h e * n o * k e d b u t s n o d r o p K * * o k * d ******* o * t o f t h e E i n d o * a n d s a i d i d E a r e n o t o p * n t h e d o o r f o r t h e d * O r * s h a v e t o l d m e t o l e t n o * W e N i n t h a t i s h a r d f o r m e s a i d t h e w o m a n f o r i m u s t t a k * b a c k m y a * p l * s b u t t h e r e i s o n e w h i c h i w i l l g i v e y o u a n d s h e h e l d u p a n a * A l * +Eval: D D S I D D D D D S D I D D S D D S I D D D D S D + +Speaker sentences 17: mls_eng_000260 #utts: 1 +id: (mls_eng_000260-mls_eng_000260) +Scores: (#C #S #D #I) 121 19 32 7 +REF: * a L B E R T m U R r A y s p o k E E X c ******* * * e l s I o * r a n d h o r A c e f i n l E y s p o k E n i C E t O o m y p I e c e w a s w h y P H O e b E a r e y O u c o m e s o s o o n w h e r e A r E y o u R b E R r I E S c h i l d * E M M A * V a n a r S d A l E s p o k E T H E s a m e o n E +HYP: H a * V I Y m * O r * y s p o k * * A c T H e l s H o U r a n d h o r * c e f i n l * y ******* s p o k * ******* n i * T t * o m y p * e c e ******* w a s w h y * * F e b Y a r e y * u c o m e s o s o o n w h e r e ******* * r * y o u * b * A r * * Y c h i l d A N D O T H a n a r * d I l * ******* s p o k * * * * A s a m e o n * +Eval: I D S S S S D S D D D S I I I S I D D D D D D S D D D D D S S D D D D D D S D D S I S S S S I S D S D D D D D D S D + +Speaker sentences 18: mls_eng_000261 #utts: 1 +id: (mls_eng_000261-mls_eng_000261) +Scores: (#C #S #D #I) 200 11 52 4 +REF: i n e v e r K N e w a n y ******* o n E W h o l i K e D t O G o t O C H u r c h a s m U c h a s g r a n D m o T h E r d o E s s h e S a Y s S h e w o u L d r a T H e r b E a d O o r K E e p E r i n t h e h O U s E o f o U r g o d t h A n t O d w e L l I n t h e t e n T s o f w i C k e d n e S s t h e y d o n T h a V e w o m e n d O o r ******* k E e p e r s a n d i K n o w s h e w o U l d N O t D w e L l A m i n U T E i n a * * t e n t +HYP: i n e v e r * G e w a n y o n * * h o l i * e * t * ******* * o ******* t * * T u r c h ******* a s m * c h a s S g r a n * m o * h * r d o U s s h e H a * s * h e ******* w o u * d r a * V e r b * a d * o r C e p * r ******* i n ******* t h e h * A s * o f o * r g o d t h * n t * d w e * l * n ******* t h e t e n * s o f w i * k e d n e * s t h e y d o n * h a * e w o m e n d * o r k * e p e r s a n d i N n o w s h e ******* w o * l d * * t T w e * l ******* * m i n * * * ******* i n a N A t e n t +Eval: D S I D D D D D D D D D D S D D S D D D S S D D D D D S D D S S D D D D S D D D D D D D D D D D D D I D S D D D D S D D D D D D D I I + +Speaker sentences 19: mls_eng_000262 #utts: 1 +id: (mls_eng_000262-mls_eng_000262) +Scores: (#C #S #D #I) 120 5 12 5 +REF: t h e d u k E w a s s u R p r i s e D t o s E e h i m w * h A t b r i n g s y o u o u t s o E a R l y a b E l * a r d d e m a n D E d h e o H y o u r g r a * c e * * r e p l i E d t H e b u t l e r g a s p i n g f o r U T t e r A n c E +HYP: t h e d u k * w a s s u * p r i s e * t o s * e h i m w O h * t b r i n g s y o u o u t s o I a * l y a b * l E a r d d e m a n * * d h e o W y o u r g r a I c e T S r e p l i * d t * e b u t l e r g a s p i n g f o r * A t e r E n c S +Eval: D D D D I D S D D I D D S I I I D D D S S S + +Speaker sentences 20: mls_eng_000263 #utts: 1 +id: (mls_eng_000263-mls_eng_000263) +Scores: (#C #S #D #I) 181 14 33 3 +REF: f o U r y * e t s E e m i n g T r A n s m u t a t i o n s o f c O l O U R m a y b e m a d e * w h e r e T h e r E i s a n y m i x t U r E o f d I v E r s E s o R t s o f r a Y s * f o r i n s u c h m i x t U r E s t h e C O m p o N E n T c O l O U R s a P p e A R n o t B U t b y t h e I R m u t U a l a L l a Y i n g E a c h O t h e R c o n s t i t u t e a m i D d l i n g c o l O U r +HYP: f o * r E y I e t s * e m i n g * r O n s m u t a t i o n s o f c A l * * E m a y b e m a d e D w h e r e * h e r * i s a n y m i x t * r * o f d E v * r s * s o U t s o f r a I s E f o r i n s u c h m i x t E r * s t h e * * m p o * * n * c A l * * E s a p e * * n o t * * t ******* b y t h e * * m u t * a l a * l a * i n g H a c h ******* E t h e * c o n s t i t u t e a m i * d l i n g c o l * E r +Eval: D S I D D S S D D S I D D D D S D D S S I S D D D D D D S D D S S D D D D D D D D D D S D S D D D S + +Speaker sentences 21: mls_eng_000264 #utts: 1 +id: (mls_eng_000264-mls_eng_000264) +Scores: (#C #S #D #I) 221 22 27 4 +REF: I n * a l m O s T t h e s a m e i n s t A n t K E n s a W s a n d y s i d E s t e p i n t o a s h O p d O o r w a y h e w a I t e d t h e r E U n t I l K E n c a m e u p k E n s t o P p e D A n d p r e t e n d e d T o s t a r E t h r O U G H t h e G l a S s a t t h E d I s p l a y o f h a r d ******* w a r E A n D t o O l s w H e r e h e c o n t i n U e d t o w a t c h b a R r A c k y o u s E e w h a t i s e e s a N D Y s * * a i d K E n n o D D e d +HYP: A n D a l m U s * ******* t h e s a m e i n s t E n t C I n ******* s a * s a n d y s i d s t e p i n t o a ******* s h A p d * o r w a y h e w a * t e d t h e r * A n t L l C I n c a m e u p k I n s t o * p e * * n d p r e t e n d e d * o s t a r * t h r * * * * t h e * l a * s a t t h * d E s p l a y o f h a r d w a r * I n * t o U l s w * e r e h e c o n t i n * e d t o w a t c h b a * r I c k y o u s * e w h a t i s e e s a M B E s T E a i d C I n n o * T e d +Eval: S I S D D S S S D D S D S D D D S S S S S D D D D D D D D D D D D S I D S D S D D D S D S S S I I S S D S + +Speaker sentences 22: mls_eng_000265 #utts: 1 +id: (mls_eng_000265-mls_eng_000265) +Scores: (#C #S #D #I) 189 19 11 12 +REF: t h e n * t h e t h i C k g r e E n s t U F F f l o w E d o V e R t h e W h * O l e b U i l d i n g a n d t h e r E w a s n o t h i n g t o b e s e e n t h e r e b u t * A m * o U N d o f s o f t f l o w i n g g r a y ******* g r e E n s t U F F t h * A T r * u s h e d o n n o w w i t H t h e s w * i f t n e S s o * F T h e w * i n D I l o o k e d U p i n t o b * a R r Y s f a * c e +HYP: t h e n M t h e t h i E k g r e A n s t * A V f l o w * d o W e * t h e * h A L l e b * i l d i n g a n d t h e r * w a s n o t h i n g t o b e s e e n t h e r e b u t D E m I o * W d o f s o f t f l o w i n g g r a y g r e A n s t A V E t h E D r O u s h e d o n n o w w i t * t h e s w E i f t n e * s o L L * h e w E i n K A l o o k e d A p i n t o b E a * r S s f a I c e +Eval: I S S D S S D S D D I S D D I S I D S I S S S S I S S I D I D I S D I S S S I D S I + +Speaker sentences 23: mls_eng_000266 #utts: 1 +id: (mls_eng_000266-mls_eng_000266) +Scores: (#C #S #D #I) 201 8 25 2 +REF: * i h a v e m a D e s a c r I f i c E s t O o B u T I T W A S w h e n i K n e w t h A T t h e Y w E R e n o t m y h a P p I n e S s i T w a s a f t e r i s a W t h a t I h a d s t O O p e d a f t e r i s a W t h a t y o u r t e n d e r n e s s h a d t U r n E d t o c a * l c u l a t i o n a f t e r i s a w t h a t y o u c a r E d f o r y o u r s e l f o n l y n o t f o r m e +HYP: I i h a v e m a * e s a c r O f i c * s t * o * u * ******* * R O F E w h e n i * n e w t h * * ******* t h e * w * * e n o t m y h a * p * n e * s i * ******* w a s a f t e r i s a L t h a t ******* * h a d s t * * p e d a f t e r i s a L t h a t y o u r t e n d e r n e s s h a d t E r n * d t o c a U l c u l a t i o n a f t e r i s a w t h a t y o u c a r * d f o r y o u r s e l f o n l y n o t f o r m e +Eval: I D S D D D D D D S S S S D D D D D D D D D D D D S D D D D S S D I D + +Speaker sentences 24: mls_eng_000267 #utts: 1 +id: (mls_eng_000267-mls_eng_000267) +Scores: (#C #S #D #I) 146 8 10 5 +REF: y e t t h E t h U n d e * r n e v e r r o a R s i n w i n t e r i s e E a c r o W w H I r L i n g r o u n d a n d r o u n D b e f o r E i t a ******* l i G h t t h E R e I s * n o t h i n g u n d e r t h e f i r * t r e e s b u t i K n o W s o m e t h i n g m u s t b e t h e * r E +HYP: y e t t h A t h A n d e A r n e v e r r o a L s i n w i n t e r i s e Y a c r o L w O A r * i n g r o u n d a n d r o u n * b e f o r * i t a l i * h t t h * * e ******* * s N n o t h i n g u n d e r t h e f i r D t r e e s b u t i * n o E s o m e t h i n g m u s t b e t h e A r * +Eval: S S I S S S S S D D D I D D D D D I I D S I D + +Speaker sentences 25: mls_eng_000268 #utts: 1 +id: (mls_eng_000268-mls_eng_000268) +Scores: (#C #S #D #I) 158 18 11 12 +REF: i t i s b e y o n d * D o u B t t h a t s o m e p e o P l e h a D m A n A G e D t o s a ******* V e m a n y t h i n g s a n D o f c o u r s e t h e G E r m a n S h a * ******* D s u R m * i S e d a * ******* s m * u c h t W o o r t h r E e d a y s * a f t e r T h E f i r s T p E r Q U I S i t i o n s t h e y d r o P p e D i n * u * ******* n a w a r e S +HYP: i t i s b e y o n d T H o u * t t h a t s o m e p e o B l e h a E m E n I S e * t o s a F e m a n y t h i n g s a n * o f c o u r s e t h e J U r m a n T h a S A s u * m A i I e d a S s m A u c h t * o o r t h r * e d a y s E a f t e r * h A f i r s * p * r O C O C i t i o n s t h e y d r o * p e T i n I u N n a w a r e * +Eval: I S D S S S S S D I S D S S S I I S D I S I I I D D I D S D D S S S S D S I I I D + +Speaker sentences 26: mls_eng_000269 #utts: 1 +id: (mls_eng_000269-mls_eng_000269) +Scores: (#C #S #D #I) 202 4 13 1 +REF: s o o n a m a n c a m e o u t t o m E e T h i m t h i s m a n w a s o l o h E a b e a r d l e s s m a n b e l o n g i N g t o a l a W l E S s r o B b e r c l a n w H i c h i n f e s t e d t h e d i s t r i c t p o S s I b l y a s s i s t i n G t h e m a n ******* h u n t e r s o f t h e T e m p l e i n s e c u r i n g v i c t I m s f o r t h e T e m p l e a l t A r s +HYP: s o o n a m a n c a m e o u t t o m * e * h i m t h i s m a n w a s o l o h A a b e a r d l e s s m a n b e l o n g i * g t o a l a * l * L s r o * b e r ******* c l a n w * i c h i n f e s t e d t h e d i s t r i c t p o * s E b l y a s s i s t i n * t h e m a n h u n t e r s o f t h e * e m p l e i n s e c u r i n g v i c t O m s f o r t h e * e m p l e a l t * r s +Eval: D D S D D D S D D D D S D I D S D D + +Speaker sentences 27: mls_eng_000270 #utts: 1 +id: (mls_eng_000270-mls_eng_000270) +Scores: (#C #S #D #I) 87 2 11 5 +REF: w e a r E n o t l o v e r * s y o u a n d i * U p o n t h i s s u N n y l a * n E b u t c h i l d R e n w h o * H A v e n e v e r K n o W n l o v e s j o * y o r p A I N +HYP: w e a r * n o t l o v e r A s y o u a n d i Y A p o n t h i s s u * n y l a I n * b u t c h i l d * e n w h o E * * v e n e v e r * n o * n l o v e s j o R y o r ******* p * * E +Eval: D I I S D I D D I D D D D I D D D S + +Speaker sentences 28: mls_eng_000271 #utts: 1 +id: (mls_eng_000271-mls_eng_000271) +Scores: (#C #S #D #I) 175 16 36 12 +REF: w a s m u r d E R e d o n T h e D O o r s t e P O f * H i s o W N h o u s e * w a s T H i S a N o l D m a n a s K E d a n d * r E a c A L m * l y m I s * e R I C o R d i a * y o u t a L k a s i f h E h a d d i e d i n h i s b * * e D y o u a r E n o v e n e * T i A n * a N D y o u c a N n o T U n d e R s t a n d w h A t i t m E A n s W h e n a N I n * q u i S i t O R i s m U r d E R e d * +HYP: w a s m u r d * * e d o n * h e ******* * S o r s t e * * f O F i s o L E h o u s e S w a s * * i * a * o l * m a n a s * * d a n d U r a c * O m N l y m * s R e G U o * d i a R y o u t a * k a s ******* i f h * h a d d i e d i n h i s b E A e T y o u a r * n o ******* v e n e I S i O n H a * * y o u c a * n o * * n d e * s t a n d w h * t i t m * I n s * h e n a * * n I q u i T i t * Y i s m E r d * * e d T +Eval: D D D D D S D D I S S S I D D D D D D D I S D S I D I S S S D I D D D I I S D D I S S I D D D D D D D D S D D D I S D S S D D I + +Speaker sentences 29: mls_eng_000272 #utts: 1 +id: (mls_eng_000272-mls_eng_000272) +Scores: (#C #S #D #I) 197 20 29 3 +REF: b y g o d h e s a i d t h E y w R o n G m e m Y b u s I n E S s l e A d s m e i n a n d o u t o F m a n y h o u s e s B U T w h a t d o i c a r E f o R t h e s E c r e T s * t h a t m a y b E h i D d E n t h e r e h o w e v e r i c a N n o t B l A M E t h E s E p e O p L e f o r t h e I R w a T c h f U l n e S s t h e b l O O D h o u N D s o f T h * E s I G n o r i a * a R E i n e v e r Y s t r e e t +HYP: b y g o d h e s a i d t h O y w * o n T m e m U b u s * n * * s l e I d s m e i n a n d o u t ******* o * m a n y h o u s e s * O R w h a t d o i c a r * f o * t h e s I c r e * s T t h a t m a y ******* b * h i * d I n t h e r e h o w e v e r i c a * n o t P l * I N t h I s * p e * p * e ******* f o r t h e * * w a * c h f O l n e * s t h e b l U T h o u * * s o f * h I S s * F n o r i a R a * Y i n e v e r * E s t r e e t +Eval: S D S S D D D S D D D S S D D S D I D D D S D S D S S S D D D D D D D S D S S S D D D I S D S I D S D S + +Speaker sentences 30: mls_eng_000273 #utts: 1 +id: (mls_eng_000273-mls_eng_000273) +Scores: (#C #S #D #I) 173 27 45 5 +REF: h O R r I b l E d e A t H w a s p u L l i n G A t H E R n o t a s t i C k n o r a s t o n E w a s i n r E A C H o f h e R h a n d S a n D t h e p i T I l E S s C r a g s e C H O E d o n e L o n g s h r I e k A b o V E A l l t h e r O a r o f * * * t h e W A T E R F A l L s h e s t r o V e * T o T U R n o V e R a n d g R a s P t h e g R o u n d b u t o n l y f E l t h e r s e L F g O i n G F a s t * E r +HYP: h * A r Y b l * d e * t * w a s p u * l i n * * t ******* * * * n o t a ******* s t i * k n o r ******* a ******* s t o n * w a s i n D r * * * I T o f h e * h a n d * a n * t h e p i * Y l * * s G r a g s e * * U L d o n e N o n g s h r * e k U b o * * F W l l t h e r W a r o f W A T t h e * * * * * * O l E s h e s t r o * e D * o U O D O n o U e * a n d g * a s E t h e g * o u n d ******* b u t o n l y f I l t h e r s e * V E g * i n * C a s t H O r +Eval: D S S D D D D D D D D D D D D D D D S D D D S S D D D D S D D S D D S S S D S D D S S S I I I D D D D D D S S D I D S S S S S D D S D D S D S S D D S I S + +Speaker sentences 31: mls_eng_000274 #utts: 1 +id: (mls_eng_000274-mls_eng_000274) +Scores: (#C #S #D #I) 185 11 24 8 +REF: a n d T H E f O L l y o f * i t w a s t h a t a * ******* n o t h e R w o m A n * o f * * C e r n y w * i S h E D f o r n o t h i n g b e T t e R t H A n t o g o s i n C E m y s i s t E r a n d f a t h e r a R E s e n t a w a y s h e s a i d i s h O u l D r a t h e r g o w i T H t h E m i h a v e n o m i n D t o s t a y h e * r E a l o n E W i t H m y t W o b a b I e s +HYP: a n d * * A f * A l y o f E i t w a s t h a t a N n o t h e * w o m E n T o f V E S e r n y w H i C h * * f o r n o t h i n g b e * t e * D t * E n t o g o s i n T O m y s i s t * r a n d f a t h e r a * S s e n t a w a y s h e s a i d i s h * u l * ******* r a t h e r g o w i * * ******* t h * m i h a v e n o m i n E t o s t a y h e A r * a l o n * * i t * m y t * o b a b * e s +Eval: D D S D S I I I D S I I I S I S D D D D S D S S S D D S D D D D D D D S I D D D D D D + +Speaker sentences 32: mls_eng_000275 #utts: 1 +id: (mls_eng_000275-mls_eng_000275) +Scores: (#C #S #D #I) 117 8 16 3 +REF: B i L L Y d i D a C c o r d i n g l y w o n d e r i n g W h a T t H e l i T t L e m a n w o u l d b e a t a n d h e p I C k e d t W o o f t h e s t o u t ******* E s T r u s h e s h e c o u l d f i n d w i t H a l i t T l e b u n C h o f ******* * +HYP: E i * * N I d i T a * c o r d i n g l y w o n d e r i n g * h a * t * e l i * t * e m a n w o u l d b e a t a n d h e p * E k e d t * o o f t h e ******* s t o u t I s * r u s h e s h e ******* c o u l d f i n d w i t * E a ******* l i t A l e b u n * h o f B +Eval: S D D S S S D D D D D D D S D D I S D D D S D S D I I + +Speaker sentences 33: mls_eng_000276 #utts: 1 +id: (mls_eng_000276-mls_eng_000276) +Scores: (#C #S #D #I) 201 15 25 2 +REF: i t i s n o W t h E d r e A d f u l n I G H T c o m E s o n h o W d i s m a L I s t h e p l A i n F O r t h e P r u S s i A n s a n D t h e E n g l i s h f o U n d * A b o v E t e n t h o u s A n d s l a i n b r a v e w e L l i n g t o n a N d b l U C H e r b o t h m o s T n o b l y d r o v e t h e I r f o * E s a n d b U o n A p a r t E s I m p e R I a l c r o w n w a s t a k e n a t w a t e R l O O +HYP: i t i s n o * t h T d r e * d f u l n * * A E c o m * s o n h o * d i s m a * A s t h e p l E i n W E r t h e * r u * s i O n s a n * t h e * n g l i s h f o * n d T b o v F t e n t h o u s * n d s l a i n b r a v e w e * l i n g t o n a * d b l * O K e r b o t h m o s * n o b l y d r o v e t h e * r f o U R s a n d b * o n U p a r t * s A m p e * * a l c r o w n w a s t a k e n a t w a t e * l * * +Eval: D S D D D S S D D D S S S S D D S D D D I S S D D D D S S D D I S D S D S D D D D D + +Speaker sentences 34: mls_eng_000277 #utts: 1 +id: (mls_eng_000277-mls_eng_000277) +Scores: (#C #S #D #I) 191 10 28 7 +REF: s o m e y e a R s a g o * a f t e r m a k i n g o u r a R r a n g E M e n T s f o r t h e E n c a m p M e n t a t n i g H t w * e * c o n s t a n t l y h a d o * U R p e a C E f u l r e s T B r o k * E n b y a t r i b E o f B r o W n m o * n * k e Y s t h e Y e V i d e n t L Y t h O u g h t t h a t l o n g p o S S E s S i o n h a d g i V e n t h e M a p r i O r C l a I m t o t h e g R O V E +HYP: s o m e y e a * s a g o H a f t e r m a k i n g o u r a * r a n g * * e n * s f o r t h e ******* I n c a m p * e n t a t n i g * t w H e D c o n s t a n t l y h a d o F A p e a * S f u l r e s * * r o k G O n b y a t r i b * o f * r o U n m o U n C k e * s t h e * e * i d e n t * E t h * u g h t t h a t l o n g p o * * * s T i o n h a d g i * e n t h e * a p r i A r * l a * m t o t h e g * * * L +Eval: D I D D D D D S D D I I I S S D S D D I S D D S I I D D D D S D D D D S D D S D D D D D S + +Speaker sentences 35: mls_eng_000278 #utts: 1 +id: (mls_eng_000278-mls_eng_000278) +Scores: (#C #S #D #I) 193 15 22 5 +REF: t o f l a s h i n a t h o m e c l A m O U r i n g f o r h e r m a I d b e t w e e n m R s V a n e s t E n s p a r t y a n d t h e o p E r a * i f o n l y f o r A m i n U t E * C e r t A I n l y I t w a s m o r E t h a n a m i n U t E t h a t s i m o n E r e m a i n e d a t t h e P H A Y r E h o u s e a f t e r b e I n g B r o U G H T b a c k * A f t e r d i N n e r i n * t a * x I +HYP: t o f l a s h i n a t h o m e c l E m * E r i n g f o r h e r m a * d b e t w e e n m S s W a n e s t A n s p a r t y a n d t h e o p * r a R i f o n l y f o r ******* * m i n I t * S U e r t * O n l y * t w a s m o r * t h a n a ******* m i n * t * t h a t s i m o n * r e m a i n e d a t t h e * F I E r * h o u s e a f t e r b e A n g D r o * * * D b a c k E * f t e r d i * n e r ******* i n T H t a C x * +Eval: S D S D S S S D I D D S D I S D S D D D D D D D S S S D S S D D D S I D D D I S I D + +Speaker sentences 36: mls_eng_000279 #utts: 1 +id: (mls_eng_000279-mls_eng_000279) +Scores: (#C #S #D #I) 240 21 34 11 +REF: a n d i n d i c T i o n a r y a n d T h e n w e h a d c a l I s t h e n i c * s w e G o t h r o u G H a g R e a T m a n y f i g U r E s a n d s i n * G a l i f e o N T h e o * C E A n w a v e w h A t f A I r y ******* l i k E m u s i c * S t E A l S o v e R t h e s * e A l i g H T l y r o w l i g H t * l y r o w o E r * t h e g l a S s y w a V e S w * E g o a n d o H c o m e c o m e a w a y a n d o t h e r s o n g s m R s j u d G e t a Y l o R W r o T E * o n E s o n g O n p U r p O s E f o r * u s +HYP: a n d i n ******* d i c * i o n a r y a n d * h e n w e h a d c a l * s t h e n i c X s w e C o t h r o u * * a g * e a * E m a n y f i g E r * s a n d ******* s i n K E a l i f e o * F M h e ******* o U T I O n w a v e w h * t f * E r y l i k * m u s i c K * t * I l D o v e * t h e s C e * l i g * E l y r o w l i g * t E l y r o w o * r E t h e g l a * s y w a Y e * w U L g o a n d o * c o m e c o m e a w a y a n d o t h e r ******* s o n g s m * s j u d H e t a * l o * ******* * r o * D W o n S s o n g A n p E r p I s * ******* f o r H u s +Eval: D D D D I S D D D D S S D D I S D S S D I S S S D D S I D I D D S S D I D D S D I D I D S D I S D D D S D D D D D S I S S S S D D I + +Speaker sentences 37: mls_eng_000280 #utts: 1 +id: (mls_eng_000280-mls_eng_000280) +Scores: (#C #S #D #I) 205 13 27 9 +REF: t h a t w h i c h p a S s e s a S h i s t O r y i n O u r s c H o O l s O r g o v e r N m e n t A L l y f a b r i c a t e D b O O K s o * N h i s t O r y i s a f o * r g E r y a * m I S r E p R e s e n t a t i o n o f e ******* v e n * T s l i k e t h e O l D d r a m a * * C e n t E r i n g u p o n t h e I m p o S s i b l e f i g u R e o f t h e H e r o w i t h * * A G E s t i c u l a t i n g c r o w d i n t h e b a c k G r o U n d +HYP: t h a t w h i c h p a T s e s a * h i s t * r y ******* i n * u r s c * o U l s A r g o v e r * m e n t * * l y f a b r i c a t e * b U T s ******* o M E h i s t * r y i s a f o U r g U r y a D m * * r * p * e s e n t a t i o n o f e v e n C E s l i k e t h e * l * ******* d r a m a R E S e n t * r i n g u p o n t h e ******* * m p o * s i b l e f i g u * e o f t h e * e r o w i t h T H E J U s t i c u l a t i n g c r o w d i n ******* t h e b a c k * r o * n d +Eval: S D D D D D S S D D D D S S S D I S D I S I D D D D I I S D D D I I S D D D D D D I I S S S D D D + +Speaker sentences 38: mls_eng_000281 #utts: 1 +id: (mls_eng_000281-mls_eng_000281) +Scores: (#C #S #D #I) 142 14 12 4 +REF: W h E n t h e c a l I P H h E A r d t h i s h e s a i d o * j A A f a r h o w g o o d l y i s t h a t v o i c e A n d t h e W a Z i R r e p l i E d o o u r l o r d n e v e r s m o * t E m y h e a r i n g a U G H t S W E e t e r * o r g O o d l * I e R t h a n t h I S s i n g i n g +HYP: * h * n t h e c a l E V F h * U r d t h i s h e s a i d o L j E U f a r h o w g o o d l y i s t h a t v o i c e * n d t h e V a S i E r e p l i * d o o u r l o r d n e v e r s m o R t * m y h e a r i n g a * * R t * T R e t e r E o r g * o d l Y A e * t h a n t h * E s i n g i n g +Eval: D D S S S D S I S S D S S S D I D D D S D S S I D I S D D S + +Speaker sentences 39: mls_eng_000282 #utts: 1 +id: (mls_eng_000282-mls_eng_000282) +Scores: (#C #S #D #I) 181 15 12 7 +REF: e v i d e n t l y t h e l E A r n E d b a r * O n h a d n o t s t U d I e d s u c h w o r k S o f t h e t o t * ******* K a h n I o r p A R r O t c h a t w h i c h n o t a b l y t r a n s l a t e d b y n A K H s h a b * I f r o m t h e s a n s K r i t s U k A s A p t a t I h a s n o W b e c o m e a s o r t h o d o * x i c A L l y m u s l I m * a s t h e n i G h t * s +HYP: e v i d e n t l y t h e l * U r n I d b a r I E n h a d n o t s t O d * e d s u c h w o r k * o f t h e t o t A C a h n E o r p * E r I t c h a t w h i c h n o t a b l y t r a n s l a t e d b y n * * U s h a b Y Y f r o m t h e s a n s G r i t s O k * U s E p t a t * h a s n o * b e c o m e a s o r t h o d o C x i c * * l y m u s l O m E a s t h e n i * h t E s +Eval: D S S I S S D D I I S S D S S D D S I S S S D S S D D I D D S I D I + + diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..01a642d29ecda9907f1b61d66bdc11dd9c81c377 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/hyp.trn @@ -0,0 +1,40 @@ +I AMTHIR UD OF TAKN OLDES FOM YOU SHE SAD HASTALY I SA NOW THE IT IS IMPOSIBLE T HAVE FATHIN OU I SE NOW THE IT IS USESTO EXPECT ANY RETERN FROM OU FOR ALE I HAVE DON I WANT NO MORE OF YOU (mls_eng_000243-mls_eng_000243) +THAT HE MAY SOMTIMES BEE LIKE OTHER CHELDREN LANING TE SIE MY NE OR PLAING PRATLIN SIKING FOR HELES COMS TO MY HEART ATS SINFUL ORD ANM SPEAKING HOW GOD THO ART (mls_eng_000244-mls_eng_000244) +TRANSENTHINGS OF AL SORSE AS I THE GENRALE OUTBURST OF MULTITODNS PASION AR HUTED TOGETHER THE LITRCRIS NAY THE REDICULOS WIT THE HERABLE FAR OVER THE ILWEY SE OF HEDS MAY BE SEN RESCALITY CAPRIYOLING ON FORSES FOM THE ROIAL STOD (mls_eng_000245-mls_eng_000245) +IT MAY HAVE BEN THT THE BONS WERE A FOLDED TOGETHER AND NON AS ONA HPELBONS FULDED AND LAIE AWAY FOR THE PRPICES OF INCENTATION SUCH BUNDLES OF BONS WE PUT THRU APROSES OF PRARS (mls_eng_000246-mls_eng_000246) +MASAILS NEVER EXPERINED THOS GREAT TRANSIONS FROM LONES TO GRANDUR THIS WAS OWING TO THE PRUDANT CONDUCT O THAT REPUBLICK WHIH ALWAYS PRESERVED HER PRINCIPLES (mls_eng_000247-mls_eng_000247) +AT A SMAL BEAING SETION ON THE MORING OF UTOBER HRTY ON CONDUCTED I THE ATMSFER OF CONSPIRACY AND INTENDED BY BRODY WE ERETOL HAT THE NORAL AGANCE REQUIRMET FOR REVU ORDAPROVELE HAD BEN WAVED THAT NORMAL APROVE OF THE MAYR OF WASINGTON AND SERTN GOVERS WOL BE HANDLED IN FORMILY (mls_eng_000248-mls_eng_000248) +THE BODIST LATDY IN CHINER WHO DO NOT HASTATE TOL TAKE LIH FOR THER PERFERS OFOD SAFF THER CONSHENCS FROM TACK TO TIM BY BYING VERS FASCSHEIS IS SETDER AND LETIN THE GO (mls_eng_000249-mls_eng_000249) +THIS AGAN IS SOFEND AD TEMPERD BY A SIMPL FATH IN THE SUPROMICY OF LOVE OVER FEER AND UN BOUNTED HUMANITY AND CHARITY FOR THE POR AND HELPLESAND UN CONDINAL FORGIVENESS OF THE DIREST INJURIES WHICH IS THE NOT OF THE NOBLE A GENEROSITY AND LIBURALITY (mls_eng_000250-mls_eng_000250) +THE SECAND MAE FOLOED AN THE COUPLE OF TE SEMERSMEN ROT THE AOR THE BARK BEFOR CUCHING A ROPE THEY HENTO WARK T SURCH THE SHIP THE LIFTD THE HACES AND FOUND THER HOLD FUL OF CARGO (mls_eng_000251-mls_eng_000251) +FOND OF IS COMRADS AND RESPECTFULE TO HIS PASTERS AND MASTERS EVEN SCOL MASTERS AS THE LAD HE PREPARS FOR MANWOUD WITH WILL AND THIS TRAINING ORKUPE HIM THROUT YUTH TID (mls_eng_000252-mls_eng_000252) +AS WHEN HE RICES QUECA RECED TER SIXTINT YEAR SHE BECAME BLINED HER LARGE SOFT BRON ICES HAD O LIDIN THEM (mls_eng_000253-mls_eng_000253) +ALMOST ALL DAY THE BATAR RANGED BETWE THE TO MEN BACK IN FORT THEY FORSE EACHOTHER OVER THE LOV ABEDS THE CHIFES WEL OILED BODY WAS VERY DIFICULT FOR THEOLO HAY TO GRAS BRUSED AND BLEADING FROM REPEATED FALLS ON THE RUH LAVA (mls_eng_000254-mls_eng_000254) +POSY I TRE IT TERRAR AND A WARP PANTING THE WIN MON THRUOU THE TREES OF TE GARDIN AND FROM TIME TOA TIME SEE AS IF TO A (mls_eng_000255-mls_eng_000255) +HIS MENTAL TORPITITY FOUNDI APON FISICAL INDILENCE RENDERS AMEDIA ACTION N ALL MANE OF EXERTION DIS TASFUL HIS CONCHOUS WEKNESS SHOWE IT SELF (mls_eng_000256-mls_eng_000256) +NOR THALL HOW GLAD THE CUING MOTHER WAS NOR HOL GREAT WAR THE REJOICESING OF THE PEOPLE TOR HOM MENIFISSENT WAS THE ROIAL BANQUED THAT GOOD CQING PAMAREA ATENDEDT BITHAL HER CORT (mls_eng_000257-mls_eng_000257) +AND THE CHANCE OF THER BEANG SUCH A ON UNGAN DIMISHIES BY WERY PRAPED PROSESS MAMELUK AS A HORS WAS OF ECUEANE CQULITY REASNING NOT ABOUT HIS ODERS BUT ABOUGT THE WAY TO DO THE (mls_eng_000258-mls_eng_000258) +SHE NOKED BUT SNO DROPK OKDOT OF THE EINDO AND SAID I DEARE NOT OPN THE DOOR FOR THE DORS HAVE TOLD ME TO LET NO WEN IN THAT IS HARD FOR ME SAID THE WOMAN FOR I MUST TAK BACK MY APLS BUT THERE IS ONE WHICH I WILL GIVE YOU AND SHE HELD UP AN AAL (mls_eng_000259-mls_eng_000259) +HAV IY MORY SPOK AC THELSHOUR AND HORCE FINLYSPOKNIT TO MY PECEWAS WHY FEBY ARE YU COME SO SOON WHERER YOU BARY CHILD AND O THAN ARDILSPOK ASAME ON (mls_eng_000260-mls_eng_000260) +I NEVER GEW ANY ON HO LIE TOT TURCHAS MCH ASSGRANMOHR DOUS SHE HAS HEWOUD RAVER B A DOR CEPRINTHE HAS OF OR GOD THN T DWEL NTHE TENS OF WIKEDNES THEY DON HAE WOMEN DOR KEPERS AND I NNOW SHEWOLD T TWEL MININ AN ATENT (mls_eng_000261-mls_eng_000261) +THE DUK WAS SUPRISE TO SE HIM WOHT BRINGS YOU OUT SO IALY ABLEARD DEMAND HE OW YOUR GRAICETS REPLID TE BUTLER GASPING FOR ATERENCS (mls_eng_000262-mls_eng_000262) +FOREYIET SEMING RONSMUTATIONS OF CALE MAY BE MADED WHERE HER IS ANY MIXTR OF DEVRS SOUTS OF RAISE FOR IN SUCH MIXTERS THE MPON CALES A PE NOT TBY THE MUTAL ALAING HACHETHE CONSTITUTE A MIDLING COLER (mls_eng_000263-mls_eng_000263) +AND ALMUSTHE SAME INSTENT CINSA SANDY SID STEP INTO ASHAP DORWAY HE WATED THER ANTLL CIN CAME UP KIN STOPE ND PRETENDED O STAR THR THE LAS AT TH DESPLAY OF HARD WAR IN TOULS WERE HE CONTINED TO WATCH BARICK YOU SE WHAT I SEE SAMBE STEAID CIN NOTED (mls_eng_000264-mls_eng_000264) +THENM THE THIEK GREAN STAV FLOWD OWE THE HALLE BILDING AND THER WAS NOTHING TO BE SEEN THERE BUT DE MIOWD OF SOFT FLOWING GRAY GREAN STAVE THE D ROUSHED ON NOW WIT THE SWEIFTNES OLL HE WEINK A LOOKED AP INTO BEARSS FAICE (mls_eng_000265-mls_eng_000265) +II HAVE MAE SACROFICS TO UR OFE WHEN I NEW THTHE WE NOT MY HAPNES IWAS AFTER I SAL THAT HAD STPED AFTER I SAL THAT YOUR TENDERNESS HAD TERND TO CAULCULATION AFTER I SAW THAT YOU CARD FOR YOURSELF ONLY NOT FOR ME (mls_eng_000266-mls_eng_000266) +YET THA THANDEAR NEVER ROALS IN WINTER I SEY A CROL WOARING ROUND AND ROUN BEFOR IT A LIHT THESN NOTHING UNDER THE FIRD TREES BUT I NOE SOMETHING MUST BE THEAR (mls_eng_000267-mls_eng_000267) +IT IS BEYOND THOUT THAT SOME PEOBLE HAE MENISE TO SA FE MANY THINGS AN OF COURSE THE JURMANT HAS A SUMAIIED AS S MAUCH TO OR THRE DAYSE AFTER HA FIRS PROCOCITIONS THEY DROPET IN IUN NAWARE (mls_eng_000268-mls_eng_000268) +SOON A MAN CAME OUT TO ME HIM THIS MAN WAS OLOHA A BEARDLESS MAN BELONGIG TO A LALLS ROBERCLAN WICH INFESTED THE DISTRICT POSEBLY ASSISTIN THE MAN HUNTERS OF THE EMPLE IN SECURING VICTOMS FOR THE EMPLE ALTRS (mls_eng_000269-mls_eng_000269) +WE AR NOT LOVERAS YOU AND IY APON THIS SUNY LAIN BUT CHILDEN WHOE VE NEVER NON LOVES JORY ORPE (mls_eng_000270-mls_eng_000270) +WAS MURDED ON HESORSTE F OFIS OLE HOUSES WAS I A OL MAN ASD ANDUR A COMNLY MSRE GUODIAR YOU TAK ASIF H HAD DIED IN HIS BEAET YOU AR NOVENEISION HA YOU CANO NDESTAND WHT IT MINS HEN A NIQUITITY IS MERDEDT (mls_eng_000271-mls_eng_000271) +BY GOD HE SAID THOY WONT ME MU BUSNS LEIDS ME IN AND OUTO MANY HOUSES OR WHAT DO I CAR FO THE SICREST THAT MAYB HIDIN THERE HOWEVER I CANOT PLIN THIS PEPEFOR THE WACHFOLNES THE BLUT HOUS OF HIS SFNORIAR AY IN EVERESTREET (mls_eng_000272-mls_eng_000272) +HARYBL DET WAS PULIN T NOT ASTIK NORASTON WAS INDRITOF HE HAND AN THE PIYLS GRAGS EULD ONE NONG SHREK UBOFWLL THE RWAR OFWAT THE OLE SHE STROED OUODON OUE AND GASE THE GOUNDBUT ONLY FILT HERSEVEGIN CASTHOR (mls_eng_000273-mls_eng_000273) +AND A FALY OFE IT WAS THAT AN NOTHE WOMENT OFVE SERNY WHICH FOR NOTHING BETEDTEN TO GO SINTO MY SISTR AND FATHER AS SENT AWAY SHE SAID I SHULRATHER GO WITHM I HAVE NO MINE TO STAY HEAR ALON IT MY TO BABES (mls_eng_000274-mls_eng_000274) +EINIDIT ACORDINGLY WONDERING HA TE LITE MAN WOULD BE AT AND HE PEKED TO OF THESTOUT IS RUSHES HECOULD FIND WITEALITALE BUNH OF B (mls_eng_000275-mls_eng_000275) +IT IS NO THT DREDFUL NAE COMS ON HO DISMA AS THE PLEIN WER THE RUSIONS AN THE NGLISH FOND T BOVF TEN THOUSND SLAIN BRAVE WELINGTON AD BLOKER BOTH MOS NOBLY DROVE THER FOURS AND BONUPARTS AMPEAL CROWN WAS TAKEN AT WATEL (mls_eng_000276-mls_eng_000276) +SOME YEAS AGO HAFTER MAKING OUR ARANGENS FOR THEINCAMPENT AT NIGT WHED CONSTANTLY HAD OF A PEASFUL RES ROKGON BY A TRIB OF ROUN MOUNCKES THE EIDENTE THUGHT THAT LONG POSTION HAD GIEN THE A PRIAR LAM TO THE GL (mls_eng_000277-mls_eng_000277) +TO FLASH IN AT HOME CLEMERING FOR HER MAD BETWEEN MSS WAN ESTANS PARTY AND THE OPRAR IF ONLY FOR MINIT SUERTONLY T WAS MOR THAN AMINT THAT SIMON REMAINED AT THE FIER HOUSE AFTER BEANG DROD BACKE FTER DINERINTHTACX (mls_eng_000278-mls_eng_000278) +AND INDICIONARY AND HEN WE HAD CALSTHENICXS WE CO THROU A GEAEMANY FIGERS ANDSINKE A LIFE OFMHEOUTION WAVE WHT FERY LIK MUSICK TILD OVE THE SCE LIGELY ROW LIGTELY ROW ORE THE GLASY WAYE WUL GO AND O COME COME AWAY AND OTHERSONGS MS JUDHE TALOROD WONS SONG AN PERPISFOR HUS (mls_eng_000279-mls_eng_000279) +THAT WHICH PATSES A HISTRYIN UR SCOULS AR GOVERMENTLY FABRICATE BUT SOME HISTRY IS A FOURGURY AD MRPESENTATION OF E VENCES LIKE THE LDRAMARE SENTRING UPON THEMPOSIBLE FIGUE OF THE ERO WITH THE JUSTICULATING CROWD INTHE BACKROND (mls_eng_000280-mls_eng_000280) +HN THE CALEVF HURD THIS HE SAID OL JEUFAR HOW GOODLY IS THAT VOICE ND THE VASIE REPLID O OUR LORD NEVER SMORT MY HEARING ART TRETERE OR GODLYAE THAN THE SINGING (mls_eng_000281-mls_eng_000281) +EVIDENTLY THE LURNID BARIEN HAD NOT STODED SUCH WORK OF THE TOTA CAHNE OR PERIT CHAT WHICH NOTABLY TRANSLATED BY NUSHABYY FROM THE SANSGRIT SOKUSEPTAT HAS NO BECOME AS ORTHODOCXICLY MUSLOME AS THE NIHTES (mls_eng_000282-mls_eng_000282) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..e6e203ca8e5cd5f06e81647944ebbf3a39aabc4e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/ref.trn @@ -0,0 +1,40 @@ +I AM TIRED OF TAKING ORDERS FROM YOU SHE SAID HASTILY I SEE NOW THAT IT IS IMPOSSIBLE TO HAVE FAITH IN YOU I SEE NOW THAT IT IS USELESS TO EXPECT ANY RETURN FROM YOU FOR ALL I HAVE DONE I WANT NO MORE OF YOU (mls_eng_000243-mls_eng_000243) +THAT HE MAY SOMETIMES BE LIKE OTHER CHILDREN LEARNING BESIDE MY KNEE OR PLAYING PRATTLING SEEKING FOR HELP COMES TO MY HEART AH SINFUL LORD IM SPEAKING HOW GOOD THOU ART (mls_eng_000244-mls_eng_000244) +TRANSCEND THINGS OF ALL SORTS AS IN THE GENERAL OUTBURST OF MULTITUDINOUS PASSION ARE HUDDLED TOGETHER THE LUDICROUS NAY THE RIDICULOUS WITH THE HORRIBLE FAR OVER THE BILLOWY SEA OF HEADS MAY BE SEEN RASCALITY CAPRIOLING ON HORSES FROM THE ROYAL STUD (mls_eng_000245-mls_eng_000245) +IT MAY HAVE BEEN THAT THE BONES WERE FOLDED TOGETHER AND KNOWN AS UNIHIPILI BONES FOLDED AND LAID AWAY FOR PURPOSES OF INCANTATION SUCH BUNDLES OF BONES WERE PUT THROUGH A PROCESS OF PRAYERS (mls_eng_000246-mls_eng_000246) +MARSEILLES NEVER EXPERIENCED THOSE GREAT TRANSITIONS FROM LOWNESS TO GRANDEUR THIS WAS OWING TO THE PRUDENT CONDUCT THAT REPUBLIC WHICH ALWAYS PRESERVED HER PRINCIPLES (mls_eng_000247-mls_eng_000247) +AT A SMALL BRIEFING SESSION ON THE MORNING OF OCTOBER THIRTY ONE CONDUCTED IN THE ATMOSPHERE OF CONSPIRACY AND ATTENDED BY BRODY WE WERE TOLD THAT THE NORMAL AGENCY REQUIREMENT FOR REVIEW BOARD APPROVAL HAD BEEN WAIVED THAT NORMAL APPROVAL OF THE MAYOR OF WASHINGTON AND CERTAIN GOVERNORS WOULD BE HANDLED INFORMALLY (mls_eng_000248-mls_eng_000248) +THE BUDDHIST LAITY IN CHINA WHO DO NOT HESITATE TO TAKE LIFE FOR THE PURPOSE OF FOOD SALVE THEIR CONSCIENCE FROM TIME TO TIME BY BUYING BIRDS FISHES ET CETERA AND LETTING THEM GO (mls_eng_000249-mls_eng_000249) +THIS AGAIN IS SOFTENED AND TEMPERED BY A SIMPLE FAITH IN THE SUPREMACY OF LOVE OVER FEAR AN UNBOUNDED HUMANITY AND CHARITY FOR THE POOR AND HELPLESS AN UNCONDITIONAL FORGIVENESS OF THE DIREST INJURIES WHICH IS THE NOTE OF THE NOBLE A GENEROSITY AND LIBERALITY (mls_eng_000250-mls_eng_000250) +THE SECOND MATE FOLLOWED AND A COUPLE OF THE STEAMERS MEN ROWED THEM ABOARD THE BARQUE BEFORE TOUCHING A ROPE THEY WENT TO WORK TO SEARCH THE SHIP THEY LIFTED THE HATCHES AND FOUND THE HOLD FULL OF CARGO (mls_eng_000251-mls_eng_000251) +FOND OF HIS COMRADES AND RESPECTFUL TO HIS PASTORS AND MASTERS EVEN SCHOOLMASTERS AS A LAD HE PREPARES FOR MANHOOD WITH A WILL AND THIS TRAINING OCCUPIES HIM THROUGHOUT YOUTHTIDE (mls_eng_000252-mls_eng_000252) +WHEN THE PRINCESS COECA REACHED HER SIXTEENTH YEAR SHE BECAME BLIND HER LARGE SOFT BROWN EYES HAD NO LIGHT IN THEM (mls_eng_000253-mls_eng_000253) +ALMOST ALL DAY THE BATTLE RAGED BETWEEN THE TWO MEN BACK AND FORTH THEY FORCED EACH OTHER OVER THE LAVA BEDS THE CHIEFS WELLOILED BODY WAS VERY DIFFICULT FOR THE OLOHE TO GRASP BRUISED AND BLEEDING FROM REPEATED FALLS ON THE ROUGH LAVA (mls_eng_000254-mls_eng_000254) +POSY I SHRIEKED WITH TERROR AND I AWOKE PANTING THE WIND MOANED THROUGH THE TREES OF THE GARDEN AND FROM TIME TO TIME CEASED AS IF (mls_eng_000255-mls_eng_000255) +HIS MENTAL TORPIDITY FOUNDED UPON PHYSICAL INDOLENCE RENDERS IMMEDIATE ACTION AND ALL MANNER OF EXERTION DISTASTEFUL HIS CONSCIOUS WEAKNESS SHOWS ITSELF (mls_eng_000256-mls_eng_000256) +NOR TELL HOW GLAD THE QUEEN MOTHER WAS NOR HOW GREAT WERE THE REJOICING OF THE PEOPLE NOR HOW MAGNIFICENT WAS THE ROYAL BANQUET THAT GOOD QUEEN POMAREA ATTENDED WITH ALL HER COURT (mls_eng_000257-mls_eng_000257) +AND THE CHANCE OF THERE BEING SUCH A ONE AGAIN DIMINISHES BY VERY RAPID PROCESS MARMADUKE AS A HORSE WAS OF EQUAL QUALITY REASONING NOT ABOUT HIS ORDERS BUT ABOUT THE WAY TO DO THEM (mls_eng_000258-mls_eng_000258) +SHE KNOCKED BUT SNOWDROP LOOKED OUT OF THE WINDOW AND SAID I DARE NOT OPEN THE DOOR FOR THE DWARFS HAVE TOLD ME TO LET NO ONE IN THAT IS HARD FOR ME SAID THE WOMAN FOR I MUST TAKE BACK MY APPLES BUT THERE IS ONE WHICH I WILL GIVE YOU AND SHE HELD UP AN APPLE (mls_eng_000259-mls_eng_000259) +ALBERT MURRAY SPOKE EXCELSIOR AND HORACE FINLEY SPOKE NICE TOO MY PIECE WAS WHY PHOEBE ARE YOU COME SO SOON WHERE ARE YOUR BERRIES CHILD EMMA VAN ARSDALE SPOKE THE SAME ONE (mls_eng_000260-mls_eng_000260) +I NEVER KNEW ANYONE WHO LIKED TO GO TO CHURCH AS MUCH AS GRANDMOTHER DOES SHE SAYS SHE WOULD RATHER BE A DOORKEEPER IN THE HOUSE OF OUR GOD THAN TO DWELL IN THE TENTS OF WICKEDNESS THEY DONT HAVE WOMEN DOORKEEPERS AND I KNOW SHE WOULD NOT DWELL A MINUTE IN A TENT (mls_eng_000261-mls_eng_000261) +THE DUKE WAS SURPRISED TO SEE HIM WHAT BRINGS YOU OUT SO EARLY ABELARD DEMANDED HE OH YOUR GRACE REPLIED THE BUTLER GASPING FOR UTTERANCE (mls_eng_000262-mls_eng_000262) +FOUR YET SEEMING TRANSMUTATIONS OF COLOUR MAY BE MADE WHERE THERE IS ANY MIXTURE OF DIVERSE SORTS OF RAYS FOR IN SUCH MIXTURES THE COMPONENT COLOURS APPEAR NOT BUT BY THEIR MUTUAL ALLAYING EACH OTHER CONSTITUTE A MIDDLING COLOUR (mls_eng_000263-mls_eng_000263) +IN ALMOST THE SAME INSTANT KEN SAW SANDY SIDESTEP INTO A SHOP DOORWAY HE WAITED THERE UNTIL KEN CAME UP KEN STOPPED AND PRETENDED TO STARE THROUGH THE GLASS AT THE DISPLAY OF HARDWARE AND TOOLS WHERE HE CONTINUED TO WATCH BARRACK YOU SEE WHAT I SEE SANDY SAID KEN NODDED (mls_eng_000264-mls_eng_000264) +THEN THE THICK GREEN STUFF FLOWED OVER THE WHOLE BUILDING AND THERE WAS NOTHING TO BE SEEN THERE BUT A MOUND OF SOFT FLOWING GRAYGREEN STUFF THAT RUSHED ON NOW WITH THE SWIFTNESS OF THE WIND I LOOKED UP INTO BARRYS FACE (mls_eng_000265-mls_eng_000265) +I HAVE MADE SACRIFICES TOO BUT IT WAS WHEN I KNEW THAT THEY WERE NOT MY HAPPINESS IT WAS AFTER I SAW THAT I HAD STOOPED AFTER I SAW THAT YOUR TENDERNESS HAD TURNED TO CALCULATION AFTER I SAW THAT YOU CARED FOR YOURSELF ONLY NOT FOR ME (mls_eng_000266-mls_eng_000266) +YET THE THUNDER NEVER ROARS IN WINTER I SEE A CROW WHIRLING ROUND AND ROUND BEFORE IT ALIGHT THERE IS NOTHING UNDER THE FIR TREES BUT I KNOW SOMETHING MUST BE THERE (mls_eng_000267-mls_eng_000267) +IT IS BEYOND DOUBT THAT SOME PEOPLE HAD MANAGED TO SAVE MANY THINGS AND OF COURSE THE GERMANS HAD SURMISED AS MUCH TWO OR THREE DAYS AFTER THE FIRST PERQUISITIONS THEY DROPPED IN UNAWARES (mls_eng_000268-mls_eng_000268) +SOON A MAN CAME OUT TO MEET HIM THIS MAN WAS OLOHE A BEARDLESS MAN BELONGING TO A LAWLESS ROBBER CLAN WHICH INFESTED THE DISTRICT POSSIBLY ASSISTING THE MANHUNTERS OF THE TEMPLE IN SECURING VICTIMS FOR THE TEMPLE ALTARS (mls_eng_000269-mls_eng_000269) +WE ARE NOT LOVERS YOU AND I UPON THIS SUNNY LANE BUT CHILDREN WHO HAVE NEVER KNOWN LOVES JOY OR PAIN (mls_eng_000270-mls_eng_000270) +WAS MURDERED ON THE DOORSTEP OF HIS OWN HOUSE WAS THIS AN OLD MAN ASKED ANDREA CALMLY MISERICORDIA YOU TALK AS IF HE HAD DIED IN HIS BED YOU ARE NO VENETIAN AND YOU CANNOT UNDERSTAND WHAT IT MEANS WHEN AN INQUISITOR IS MURDERED (mls_eng_000271-mls_eng_000271) +BY GOD HE SAID THEY WRONG ME MY BUSINESS LEADS ME IN AND OUT OF MANY HOUSES BUT WHAT DO I CARE FOR THE SECRETS THAT MAY BE HIDDEN THERE HOWEVER I CANNOT BLAME THESE PEOPLE FOR THEIR WATCHFULNESS THE BLOODHOUNDS OF THE SIGNORIA ARE IN EVERY STREET (mls_eng_000272-mls_eng_000272) +HORRIBLE DEATH WAS PULLING AT HER NOT A STICK NOR A STONE WAS IN REACH OF HER HANDS AND THE PITILESS CRAGS ECHOED ONE LONG SHRIEK ABOVE ALL THE ROAR OF THE WATERFALL SHE STROVE TO TURN OVER AND GRASP THE GROUND BUT ONLY FELT HERSELF GOING FASTER (mls_eng_000273-mls_eng_000273) +AND THE FOLLY OF IT WAS THAT ANOTHER WOMAN OF CERNY WISHED FOR NOTHING BETTER THAN TO GO SINCE MY SISTER AND FATHER ARE SENT AWAY SHE SAID I SHOULD RATHER GO WITH THEM I HAVE NO MIND TO STAY HERE ALONE WITH MY TWO BABIES (mls_eng_000274-mls_eng_000274) +BILLY DID ACCORDINGLY WONDERING WHAT THE LITTLE MAN WOULD BE AT AND HE PICKED TWO OF THE STOUTEST RUSHES HE COULD FIND WITH A LITTLE BUNCH OF (mls_eng_000275-mls_eng_000275) +IT IS NOW THE DREADFUL NIGHT COMES ON HOW DISMAL IS THE PLAIN FOR THE PRUSSIANS AND THE ENGLISH FOUND ABOVE TEN THOUSAND SLAIN BRAVE WELLINGTON AND BLUCHER BOTH MOST NOBLY DROVE THEIR FOES AND BUONAPARTES IMPERIAL CROWN WAS TAKEN AT WATERLOO (mls_eng_000276-mls_eng_000276) +SOME YEARS AGO AFTER MAKING OUR ARRANGEMENTS FOR THE ENCAMPMENT AT NIGHT WE CONSTANTLY HAD OUR PEACEFUL REST BROKEN BY A TRIBE OF BROWN MONKEYS THEY EVIDENTLY THOUGHT THAT LONG POSSESSION HAD GIVEN THEM A PRIOR CLAIM TO THE GROVE (mls_eng_000277-mls_eng_000277) +TO FLASH IN AT HOME CLAMOURING FOR HER MAID BETWEEN MRS VAN ESTENS PARTY AND THE OPERA IF ONLY FOR A MINUTE CERTAINLY IT WAS MORE THAN A MINUTE THAT SIMONE REMAINED AT THE PHAYRE HOUSE AFTER BEING BROUGHT BACK AFTER DINNER IN TAXI (mls_eng_000278-mls_eng_000278) +AND IN DICTIONARY AND THEN WE HAD CALISTHENICS WE GO THROUGH A GREAT MANY FIGURES AND SING A LIFE ON THE OCEAN WAVE WHAT FAIRYLIKE MUSIC STEALS OVER THE SEA LIGHTLY ROW LIGHTLY ROW OER THE GLASSY WAVES WE GO AND OH COME COME AWAY AND OTHER SONGS MRS JUDGE TAYLOR WROTE ONE SONG ON PURPOSE FOR US (mls_eng_000279-mls_eng_000279) +THAT WHICH PASSES AS HISTORY IN OUR SCHOOLS OR GOVERNMENTALLY FABRICATED BOOKS ON HISTORY IS A FORGERY A MISREPRESENTATION OF EVENTS LIKE THE OLD DRAMA CENTERING UPON THE IMPOSSIBLE FIGURE OF THE HERO WITH A GESTICULATING CROWD IN THE BACKGROUND (mls_eng_000280-mls_eng_000280) +WHEN THE CALIPH HEARD THIS HE SAID O JAAFAR HOW GOODLY IS THAT VOICE AND THE WAZIR REPLIED O OUR LORD NEVER SMOTE MY HEARING AUGHT SWEETER OR GOODLIER THAN THIS SINGING (mls_eng_000281-mls_eng_000281) +EVIDENTLY THE LEARNED BARON HAD NOT STUDIED SUCH WORKS OF THE TOTKAHNI OR PARROT CHAT WHICH NOTABLY TRANSLATED BY NAKHSHABI FROM THE SANSKRIT SUKA SAPTATI HAS NOW BECOME AS ORTHODOXICALLY MUSLIM AS THE NIGHTS (mls_eng_000282-mls_eng_000282) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/result.txt b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..7e2ecef30ce4052645c9e84566b3afa7df744d2b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/result.txt @@ -0,0 +1,521 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,--------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/hyp.trn| +|--------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000243 | 1 47 | 53.2 42.6 4.3 0.0 46.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000244 | 1 31 | 48.4 51.6 0.0 3.2 54.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000245 | 1 42 | 42.9 54.8 2.4 0.0 57.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000246 | 1 34 | 52.9 44.1 2.9 5.9 52.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000247 | 1 24 | 62.5 37.5 0.0 4.2 41.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000248 | 1 52 | 46.2 50.0 3.8 1.9 55.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000249 | 1 34 | 35.3 61.8 2.9 0.0 64.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000250 | 1 45 | 64.4 35.6 0.0 2.2 37.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000251 | 1 39 | 38.5 56.4 5.1 0.0 61.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000252 | 1 30 | 60.0 36.7 3.3 6.7 46.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000253 | 1 21 | 42.9 52.4 4.8 4.8 61.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000254 | 1 43 | 53.5 44.2 2.3 2.3 48.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000255 | 1 26 | 53.8 46.2 0.0 7.7 53.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000256 | 1 21 | 38.1 61.9 0.0 9.5 71.4 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000257 | 1 33 | 51.5 45.5 3.0 0.0 48.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000258 | 1 35 | 54.3 45.7 0.0 0.0 45.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000259 | 1 58 | 79.3 20.7 0.0 0.0 20.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000260 | 1 32 | 21.9 68.8 9.4 0.0 78.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000261 | 1 54 | 20.4 63.0 16.7 1.9 81.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000262 | 1 25 | 52.0 48.0 0.0 0.0 48.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000263 | 1 39 | 38.5 53.8 7.7 2.6 64.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000264 | 1 51 | 33.3 60.8 5.9 3.9 70.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000265 | 1 42 | 42.9 57.1 0.0 4.8 61.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000266 | 1 48 | 56.3 35.4 8.3 0.0 43.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000267 | 1 32 | 56.3 43.8 0.0 0.0 43.8 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000268 | 1 34 | 47.1 52.9 0.0 11.8 64.7 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000269 | 1 39 | 64.1 33.3 2.6 2.6 38.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000270 | 1 21 | 38.1 57.1 4.8 0.0 61.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000271 | 1 44 | 29.5 63.6 6.8 4.5 75.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000272 | 1 48 | 43.8 47.9 8.3 2.1 58.3 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000273 | 1 48 | 22.9 56.3 20.8 0.0 77.1 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000274 | 1 46 | 50.0 43.5 6.5 2.2 52.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000275 | 1 27 | 40.7 44.4 14.8 3.7 63.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000276 | 1 42 | 40.5 59.5 0.0 2.4 61.9 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000277 | 1 40 | 42.5 55.0 2.5 2.5 60.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000278 | 1 44 | 50.0 40.9 9.1 0.0 50.0 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000279 | 1 58 | 34.5 51.7 13.8 1.7 67.2 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000280 | 1 40 | 30.0 60.0 10.0 2.5 72.5 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000281 | 1 32 | 59.4 40.6 0.0 0.0 40.6 100.0 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000282 | 1 34 | 58.8 38.2 2.9 2.9 44.1 100.0 | +|====================================================================================================================| +| Sum/Avg | 40 1535 | 46.0 48.9 5.1 2.3 56.3 100.0 | +|====================================================================================================================| +| Mean | 1.0 38.4 | 46.3 49.1 4.6 2.5 56.2 100.0 | +| S.D. | 0.0 10.0 | 12.6 10.2 5.1 2.8 12.9 0.0 | +| Median | 1.0 39.0 | 46.6 49.0 3.0 2.2 56.5 100.0 | +`--------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,--------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/hyp.trn| +|--------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000243 | 1 47 | 25 20 2 0 22 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000244 | 1 31 | 15 16 0 1 17 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000245 | 1 42 | 18 23 1 0 24 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000246 | 1 34 | 18 15 1 2 18 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000247 | 1 24 | 15 9 0 1 10 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000248 | 1 52 | 24 26 2 1 29 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000249 | 1 34 | 12 21 1 0 22 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000250 | 1 45 | 29 16 0 1 17 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000251 | 1 39 | 15 22 2 0 24 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000252 | 1 30 | 18 11 1 2 14 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000253 | 1 21 | 9 11 1 1 13 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000254 | 1 43 | 23 19 1 1 21 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000255 | 1 26 | 14 12 0 2 14 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000256 | 1 21 | 8 13 0 2 15 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000257 | 1 33 | 17 15 1 0 16 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000258 | 1 35 | 19 16 0 0 16 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000259 | 1 58 | 46 12 0 0 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000260 | 1 32 | 7 22 3 0 25 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000261 | 1 54 | 11 34 9 1 44 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000262 | 1 25 | 13 12 0 0 12 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000263 | 1 39 | 15 21 3 1 25 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000264 | 1 51 | 17 31 3 2 36 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000265 | 1 42 | 18 24 0 2 26 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000266 | 1 48 | 27 17 4 0 21 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000267 | 1 32 | 18 14 0 0 14 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000268 | 1 34 | 16 18 0 4 22 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000269 | 1 39 | 25 13 1 1 15 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000270 | 1 21 | 8 12 1 0 13 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000271 | 1 44 | 13 28 3 2 33 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000272 | 1 48 | 21 23 4 1 28 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000273 | 1 48 | 11 27 10 0 37 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000274 | 1 46 | 23 20 3 1 24 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000275 | 1 27 | 11 12 4 1 17 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000276 | 1 42 | 17 25 0 1 26 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000277 | 1 40 | 17 22 1 1 24 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000278 | 1 44 | 22 18 4 0 22 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000279 | 1 58 | 20 30 8 1 39 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000280 | 1 40 | 12 24 4 1 29 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000281 | 1 32 | 19 13 0 0 13 1 | +|---------------------+-----------------------+----------------------------------------------------------------------| +| mls_eng_000282 | 1 34 | 20 13 1 1 15 1 | +|====================================================================================================================| +| Sum | 40 1535 | 706 750 79 35 864 40 | +|====================================================================================================================| +| Mean | 1.0 38.4 | 17.7 18.8 2.0 0.9 21.6 1.0 | +| S.D. | 0.0 10.0 | 7.0 6.3 2.5 0.9 8.2 0.0 | +| Median | 1.0 39.0 | 17.0 18.0 1.0 1.0 21.5 1.0 | +`--------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/score_wer/hyp.trn + +Speakers: + 0: mls_eng_000243 + 1: mls_eng_000244 + 2: mls_eng_000245 + 3: mls_eng_000246 + 4: mls_eng_000247 + 5: mls_eng_000248 + 6: mls_eng_000249 + 7: mls_eng_000250 + 8: mls_eng_000251 + 9: mls_eng_000252 + 10: mls_eng_000253 + 11: mls_eng_000254 + 12: mls_eng_000255 + 13: mls_eng_000256 + 14: mls_eng_000257 + 15: mls_eng_000258 + 16: mls_eng_000259 + 17: mls_eng_000260 + 18: mls_eng_000261 + 19: mls_eng_000262 + 20: mls_eng_000263 + 21: mls_eng_000264 + 22: mls_eng_000265 + 23: mls_eng_000266 + 24: mls_eng_000267 + 25: mls_eng_000268 + 26: mls_eng_000269 + 27: mls_eng_000270 + 28: mls_eng_000271 + 29: mls_eng_000272 + 30: mls_eng_000273 + 31: mls_eng_000274 + 32: mls_eng_000275 + 33: mls_eng_000276 + 34: mls_eng_000277 + 35: mls_eng_000278 + 36: mls_eng_000279 + 37: mls_eng_000280 + 38: mls_eng_000281 + 39: mls_eng_000282 + +Speaker sentences 0: mls_eng_000243 #utts: 1 +id: (mls_eng_000243-mls_eng_000243) +Scores: (#C #S #D #I) 25 20 2 0 +REF: i AM TIRED of TAKING ORDERS FROM you she SAID HASTILY i SEE now THAT it is IMPOSSIBLE TO have FAITH IN YOU i SEE now THAT it is USELESS TO expect any RETURN from YOU for ALL i have DONE i want no more of you +HYP: i AMTHIR UD of TAKN OLDES FOM you she SAD HASTALY i SA now THE it is IMPOSIBLE T have ***** FATHIN OU i SE now THE it is ******* USESTO expect any RETERN from OU for ALE i have DON i want no more of you +Eval: S S S S S S S S S S S D S S S S D S S S S S + +Speaker sentences 1: mls_eng_000244 #utts: 1 +id: (mls_eng_000244-mls_eng_000244) +Scores: (#C #S #D #I) 15 16 0 1 +REF: that he may SOMETIMES BE like other ******** CHILDREN LEARNING BESIDE my KNEE or PLAYING PRATTLING SEEKING for HELP COMES to my heart AH sinful LORD IM speaking how GOOD THOU art +HYP: that he may SOMTIMES BEE like other CHELDREN LANING TE SIE my NE or PLAING PRATLIN SIKING for HELES COMS to my heart ATS sinful ORD ANM speaking how GOD THO art +Eval: S S I S S S S S S S S S S S S S S + +Speaker sentences 2: mls_eng_000245 #utts: 1 +id: (mls_eng_000245-mls_eng_000245) +Scores: (#C #S #D #I) 18 23 1 0 +REF: TRANSCEND THINGS of ALL SORTS as IN the GENERAL outburst of MULTITUDINOUS PASSION ARE HUDDLED together the LUDICROUS nay the RIDICULOUS WITH the HORRIBLE far over the BILLOWY SEA of HEADS may be SEEN RASCALITY CAPRIOLING on HORSES FROM the ROYAL STUD +HYP: ********* TRANSENTHINGS of AL SORSE as I the GENRALE outburst of MULTITODNS PASION AR HUTED together the LITRCRIS nay the REDICULOS WIT the HERABLE far over the ILWEY SE of HEDS may be SEN RESCALITY CAPRIYOLING on FORSES FOM the ROIAL STOD +Eval: D S S S S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 3: mls_eng_000246 #utts: 1 +id: (mls_eng_000246-mls_eng_000246) +Scores: (#C #S #D #I) 18 15 1 2 +REF: it may have BEEN THAT the BONES were * folded together and KNOWN as UNIHIPILI BONES FOLDED and LAID away for *** PURPOSES of INCANTATION such bundles of BONES WERE put THROUGH A PROCESS of PRAYERS +HYP: it may have BEN THT the BONS were A folded together and NON as ONA HPELBONS FULDED and LAIE away for THE PRPICES of INCENTATION such bundles of BONS WE put ******* THRU APROSES of PRARS +Eval: S S S I S S S S S I S S S S D S S S + +Speaker sentences 4: mls_eng_000247 #utts: 1 +id: (mls_eng_000247-mls_eng_000247) +Scores: (#C #S #D #I) 15 9 0 1 +REF: MARSEILLES never EXPERIENCED THOSE great TRANSITIONS from LOWNESS to GRANDEUR this was owing to the PRUDENT conduct * that REPUBLIC WHICH always preserved her principles +HYP: MASAILS never EXPERINED THOS great TRANSIONS from LONES to GRANDUR this was owing to the PRUDANT conduct O that REPUBLICK WHIH always preserved her principles +Eval: S S S S S S S I S S + +Speaker sentences 5: mls_eng_000248 #utts: 1 +id: (mls_eng_000248-mls_eng_000248) +Scores: (#C #S #D #I) 24 26 2 1 +REF: at a SMALL BRIEFING SESSION on the MORNING of OCTOBER THIRTY ONE conducted IN the ATMOSPHERE of conspiracy and ATTENDED by brody we WERE TOLD THAT the NORMAL AGENCY REQUIREMENT for REVIEW BOARD APPROVAL had BEEN WAIVED that normal APPROVAL of the MAYOR of WASHINGTON and CERTAIN GOVERNORS WOULD be handled ** INFORMALLY +HYP: at a SMAL BEAING SETION on the MORING of UTOBER HRTY ON conducted I the ATMSFER of conspiracy and INTENDED by brody we **** ERETOL HAT the NORAL AGANCE REQUIRMET for ****** REVU ORDAPROVELE had BEN WAVED that normal APROVE of the MAYR of WASINGTON and SERTN GOVERS WOL be handled IN FORMILY +Eval: S S S S S S S S S S D S S S S S D S S S S S S S S S S I S + +Speaker sentences 6: mls_eng_000249 #utts: 1 +id: (mls_eng_000249-mls_eng_000249) +Scores: (#C #S #D #I) 12 21 1 0 +REF: the BUDDHIST LAITY in CHINA who do not HESITATE TO take LIFE for THE PURPOSE OF FOOD SALVE THEIR CONSCIENCE from TIME to TIME by BUYING BIRDS FISHES ET CETERA and LETTING THEM go +HYP: the BODIST LATDY in CHINER who do not HASTATE TOL take LIH for *** THER PERFERS OFOD SAFF THER CONSHENCS from TACK to TIM by BYING VERS FASCSHEIS IS SETDER and LETIN THE go +Eval: S S S S S S D S S S S S S S S S S S S S S S + +Speaker sentences 7: mls_eng_000250 #utts: 1 +id: (mls_eng_000250-mls_eng_000250) +Scores: (#C #S #D #I) 29 16 0 1 +REF: this AGAIN is SOFTENED AND TEMPERED by a SIMPLE FAITH in the SUPREMACY of love over **** FEAR AN UNBOUNDED humanity and charity for the POOR and HELPLESS AN UNCONDITIONAL forgiveness of the direst injuries which is the NOTE of the noble a generosity and LIBERALITY +HYP: this AGAN is SOFEND AD TEMPERD by a SIMPL FATH in the SUPROMICY of love over FEER AND UN BOUNTED humanity and charity for the POR and HELPLESAND UN CONDINAL forgiveness of the direst injuries which is the NOT of the noble a generosity and LIBURALITY +Eval: S S S S S S S I S S S S S S S S S + +Speaker sentences 8: mls_eng_000251 #utts: 1 +id: (mls_eng_000251-mls_eng_000251) +Scores: (#C #S #D #I) 15 22 2 0 +REF: the SECOND MATE FOLLOWED AND A couple of THE STEAMERS MEN ROWED THEM ABOARD the BARQUE BEFORE TOUCHING a rope they WENT TO WORK TO SEARCH the ship THEY LIFTED the HATCHES and found THE hold FULL of cargo +HYP: the SECAND MAE FOLOED AN THE couple of *** TE SEMERSMEN ROT THE AOR the BARK BEFOR CUCHING a rope they **** HENTO WARK T SURCH the ship THE LIFTD the HACES and found THER hold FUL of cargo +Eval: S S S S S D S S S S S S S S D S S S S S S S S S + +Speaker sentences 9: mls_eng_000252 #utts: 1 +id: (mls_eng_000252-mls_eng_000252) +Scores: (#C #S #D #I) 18 11 1 2 +REF: fond of HIS COMRADES and RESPECTFUL to his PASTORS and masters even **** SCHOOLMASTERS as A lad he PREPARES for MANHOOD with A will and this training OCCUPIES him ****** THROUGHOUT YOUTHTIDE +HYP: fond of IS COMRADS and RESPECTFULE to his PASTERS and masters even SCOL MASTERS as THE lad he PREPARS for MANWOUD with * will and this training ORKUPE him THROUT YUTH TID +Eval: S S S S I S S S S D S I S S + +Speaker sentences 10: mls_eng_000253 #utts: 1 +id: (mls_eng_000253-mls_eng_000253) +Scores: (#C #S #D #I) 9 11 1 1 +REF: ** when THE PRINCESS COECA REACHED HER SIXTEENTH year she became BLIND her large soft BROWN EYES had NO LIGHT IN them +HYP: AS when HE RICES QUECA RECED TER SIXTINT year she became BLINED her large soft BRON ICES had ** O LIDIN them +Eval: I S S S S S S S S S D S S + +Speaker sentences 11: mls_eng_000254 #utts: 1 +id: (mls_eng_000254-mls_eng_000254) +Scores: (#C #S #D #I) 23 19 1 1 +REF: almost all day the BATTLE RAGED BETWEEN the TWO men back AND FORTH they FORCED EACH OTHER over the LAVA BEDS the ****** CHIEFS WELLOILED body was very DIFFICULT for THE OLOHE to GRASP BRUISED and BLEEDING from repeated falls on the ROUGH lava +HYP: almost all day the BATAR RANGED BETWE the TO men back IN FORT they ****** FORSE EACHOTHER over the LOV ABEDS the CHIFES WEL OILED body was very DIFICULT for THEOLO HAY to GRAS BRUSED and BLEADING from repeated falls on the RUH lava +Eval: S S S S S S D S S S S I S S S S S S S S S + +Speaker sentences 12: mls_eng_000255 #utts: 1 +id: (mls_eng_000255-mls_eng_000255) +Scores: (#C #S #D #I) 14 12 0 2 +REF: posy i SHRIEKED WITH TERROR and I AWOKE panting the WIND MOANED THROUGH the trees of THE GARDEN and from time TO time CEASED as if ** * +HYP: posy i TRE IT TERRAR and A WARP panting the WIN MON THRUOU the trees of TE GARDIN and from time TOA time SEE as if TO A +Eval: S S S S S S S S S S S S I I + +Speaker sentences 13: mls_eng_000256 #utts: 1 +id: (mls_eng_000256-mls_eng_000256) +Scores: (#C #S #D #I) 8 13 0 2 +REF: his mental TORPIDITY FOUNDED UPON PHYSICAL INDOLENCE renders IMMEDIATE action AND all MANNER of exertion *** DISTASTEFUL his ******** CONSCIOUS WEAKNESS SHOWS ITSELF +HYP: his mental TORPITITY FOUNDI APON FISICAL INDILENCE renders AMEDIA action N all MANE of exertion DIS TASFUL his CONCHOUS WEKNESS SHOWE IT SELF +Eval: S S S S S S S S I S I S S S S + +Speaker sentences 14: mls_eng_000257 #utts: 1 +id: (mls_eng_000257-mls_eng_000257) +Scores: (#C #S #D #I) 17 15 1 0 +REF: nor TELL how glad the QUEEN mother was nor HOW great WERE the REJOICING of the people NOR HOW MAGNIFICENT was the ROYAL BANQUET that good QUEEN POMAREA ATTENDED WITH ALL her COURT +HYP: nor THALL how glad the CUING mother was nor HOL great WAR the REJOICESING of the people TOR HOM MENIFISSENT was the ROIAL BANQUED that good ***** CQING PAMAREA ATENDEDT BITHAL her CORT +Eval: S S S S S S S S S S D S S S S S + +Speaker sentences 15: mls_eng_000258 #utts: 1 +id: (mls_eng_000258-mls_eng_000258) +Scores: (#C #S #D #I) 19 16 0 0 +REF: and the chance of THERE BEING such a ONE AGAIN DIMINISHES by VERY RAPID PROCESS MARMADUKE as a HORSE was of EQUAL QUALITY REASONING not about his ORDERS but ABOUT the way to do THEM +HYP: and the chance of THER BEANG such a ON UNGAN DIMISHIES by WERY PRAPED PROSESS MAMELUK as a HORS was of ECUEANE CQULITY REASNING not about his ODERS but ABOUGT the way to do THE +Eval: S S S S S S S S S S S S S S S S + +Speaker sentences 16: mls_eng_000259 #utts: 1 +id: (mls_eng_000259-mls_eng_000259) +Scores: (#C #S #D #I) 46 12 0 0 +REF: she KNOCKED but SNOWDROP LOOKED OUT of the WINDOW and said i DARE not OPEN the door for the DWARFS have told me to let no ONE in that is hard for me said the woman for i must TAKE back my APPLES but there is one which i will give you and she held up an APPLE +HYP: she NOKED but SNO DROPK OKDOT of the EINDO and said i DEARE not OPN the door for the DORS have told me to let no WEN in that is hard for me said the woman for i must TAK back my APLS but there is one which i will give you and she held up an AAL +Eval: S S S S S S S S S S S S + +Speaker sentences 17: mls_eng_000260 #utts: 1 +id: (mls_eng_000260-mls_eng_000260) +Scores: (#C #S #D #I) 7 22 3 0 +REF: ALBERT MURRAY SPOKE EXCELSIOR AND HORACE FINLEY SPOKE NICE TOO my PIECE WAS why PHOEBE are YOU come so soon WHERE ARE YOUR BERRIES child EMMA VAN ARSDALE SPOKE THE SAME ONE +HYP: HAV IY MORY SPOK AC THELSHOUR AND HORCE FINLYSPOKNIT TO my ***** PECEWAS why FEBY are YU come so soon ***** WHERER YOU BARY child **** AND O THAN ARDILSPOK ASAME ON +Eval: S S S S S S S S S S D S S S D S S S D S S S S S S + +Speaker sentences 18: mls_eng_000261 #utts: 1 +id: (mls_eng_000261-mls_eng_000261) +Scores: (#C #S #D #I) 11 34 9 1 +REF: i never KNEW ANYONE WHO LIKED TO GO TO CHURCH AS MUCH AS GRANDMOTHER DOES she SAYS SHE WOULD RATHER BE a DOORKEEPER IN THE HOUSE of OUR god THAN TO DWELL IN THE TENTS of WICKEDNESS they DONT HAVE women *** DOORKEEPERS and i KNOW SHE WOULD NOT DWELL A MINUTE IN A TENT +HYP: i never **** ****** *** GEW ANY ON HO LIE TOT TURCHAS MCH ASSGRANMOHR DOUS she **** HAS HEWOUD RAVER B a ********** DOR CEPRINTHE HAS of OR god **** THN T DWEL NTHE TENS of WIKEDNES they DON HAE women DOR KEPERS and i **** *** ***** NNOW SHEWOLD T TWEL MININ AN ATENT +Eval: D D D S S S S S S S S S S D S S S S D S S S S D S S S S S S S S I S D D D S S S S S S S + +Speaker sentences 19: mls_eng_000262 #utts: 1 +id: (mls_eng_000262-mls_eng_000262) +Scores: (#C #S #D #I) 13 12 0 0 +REF: the DUKE was SURPRISED to SEE him WHAT brings you out so EARLY ABELARD DEMANDED he OH your GRACE REPLIED THE butler gasping for UTTERANCE +HYP: the DUK was SUPRISE to SE him WOHT brings you out so IALY ABLEARD DEMAND he OW your GRAICETS REPLID TE butler gasping for ATERENCS +Eval: S S S S S S S S S S S S + +Speaker sentences 20: mls_eng_000263 #utts: 1 +id: (mls_eng_000263-mls_eng_000263) +Scores: (#C #S #D #I) 15 21 3 1 +REF: FOUR YET SEEMING TRANSMUTATIONS of COLOUR may be MADE where THERE is any MIXTURE of DIVERSE SORTS of RAYS for in such MIXTURES the **** COMPONENT COLOURS APPEAR not BUT BY THEIR MUTUAL ALLAYING EACH OTHER constitute a MIDDLING COLOUR +HYP: **** FOREYIET SEMING RONSMUTATIONS of CALE may be MADED where HER is any MIXTR of DEVRS SOUTS of RAISE for in such MIXTERS the MPON CALES A PE not *** ** TBY THE MUTAL ALAING HACHETHE constitute a MIDLING COLER +Eval: D S S S S S S S S S S S I S S S D D S S S S S S S + +Speaker sentences 21: mls_eng_000264 #utts: 1 +id: (mls_eng_000264-mls_eng_000264) +Scores: (#C #S #D #I) 17 31 3 2 +REF: IN ALMOST THE same INSTANT KEN SAW sandy *** SIDESTEP into A SHOP DOORWAY he WAITED THERE UNTIL KEN came up KEN STOPPED AND pretended TO STARE THROUGH the GLASS at THE DISPLAY of **** HARDWARE AND TOOLS WHERE he CONTINUED to watch BARRACK you SEE what i see SANDY SAID KEN NODDED +HYP: ** AND ALMUSTHE same ******* INSTENT CINSA sandy SID STEP into * ASHAP DORWAY he WATED THER ANTLL CIN came up KIN STOPE ND pretended O STAR THR the LAS at TH DESPLAY of HARD WAR IN TOULS WERE he CONTINED to watch BARICK you SE what i see SAMBE STEAID CIN NOTED +Eval: D S S D S S I S D S S S S S S S S S S S S S S S I S S S S S S S S S S S + +Speaker sentences 22: mls_eng_000265 #utts: 1 +id: (mls_eng_000265-mls_eng_000265) +Scores: (#C #S #D #I) 18 24 0 2 +REF: THEN the THICK GREEN STUFF FLOWED OVER the WHOLE BUILDING and THERE was nothing to be seen there but A MOUND of soft flowing **** ***** GRAYGREEN STUFF THAT RUSHED on now WITH the SWIFTNESS OF THE WIND I looked UP into BARRYS FACE +HYP: THENM the THIEK GREAN STAV FLOWD OWE the HALLE BILDING and THER was nothing to be seen there but DE MIOWD of soft flowing GRAY GREAN STAVE THE D ROUSHED on now WIT the SWEIFTNES OLL HE WEINK A looked AP into BEARSS FAICE +Eval: S S S S S S S S S S S I I S S S S S S S S S S S S S + +Speaker sentences 23: mls_eng_000266 #utts: 1 +id: (mls_eng_000266-mls_eng_000266) +Scores: (#C #S #D #I) 27 17 4 0 +REF: I have MADE SACRIFICES TOO BUT IT WAS when i KNEW THAT THEY WERE not my HAPPINESS IT WAS after i SAW that I had STOOPED after i SAW that your tenderness had TURNED to CALCULATION after i saw that you CARED for yourself only not for me +HYP: II have **** MAE SACROFICS TO UR OFE when i **** NEW THTHE WE not my ********* HAPNES IWAS after i SAL that * had STPED after i SAL that your tenderness had TERND to CAULCULATION after i saw that you CARD for yourself only not for me +Eval: S D S S S S S D S S S D S S S D S S S S S + +Speaker sentences 24: mls_eng_000267 #utts: 1 +id: (mls_eng_000267-mls_eng_000267) +Scores: (#C #S #D #I) 18 14 0 0 +REF: yet THE THUNDER never ROARS in winter i SEE a CROW WHIRLING round and ROUND BEFORE it ALIGHT THERE IS nothing under the FIR trees but i KNOW something must be THERE +HYP: yet THA THANDEAR never ROALS in winter i SEY a CROL WOARING round and ROUN BEFOR it A LIHT THESN nothing under the FIRD trees but i NOE something must be THEAR +Eval: S S S S S S S S S S S S S S + +Speaker sentences 25: mls_eng_000268 #utts: 1 +id: (mls_eng_000268-mls_eng_000268) +Scores: (#C #S #D #I) 16 18 0 4 +REF: it is beyond DOUBT that some PEOPLE HAD MANAGED to ** SAVE many things AND of course the ******* GERMANS HAD SURMISED as * MUCH TWO or THREE DAYS after THE FIRST PERQUISITIONS they DROPPED in *** UNAWARES +HYP: it is beyond THOUT that some PEOBLE HAE MENISE to SA FE many things AN of course the JURMANT HAS A SUMAIIED as S MAUCH TO or THRE DAYSE after HA FIRS PROCOCITIONS they DROPET in IUN NAWARE +Eval: S S S S I S S I S S S I S S S S S S S S I S + +Speaker sentences 26: mls_eng_000269 #utts: 1 +id: (mls_eng_000269-mls_eng_000269) +Scores: (#C #S #D #I) 25 13 1 1 +REF: soon a man came out to MEET him this man was OLOHE a beardless man BELONGING to a LAWLESS ROBBER CLAN WHICH infested the district POSSIBLY ASSISTING the *** MANHUNTERS of the TEMPLE in securing VICTIMS for the TEMPLE ALTARS +HYP: soon a man came out to ME him this man was OLOHA a beardless man BELONGIG to a ******* LALLS ROBERCLAN WICH infested the district POSEBLY ASSISTIN the MAN HUNTERS of the EMPLE in securing VICTOMS for the EMPLE ALTRS +Eval: S S S D S S S S S I S S S S S + +Speaker sentences 27: mls_eng_000270 #utts: 1 +id: (mls_eng_000270-mls_eng_000270) +Scores: (#C #S #D #I) 8 12 1 0 +REF: we ARE not LOVERS you and I UPON this SUNNY LANE but CHILDREN WHO HAVE never KNOWN loves JOY OR PAIN +HYP: we AR not LOVERAS you and IY APON this SUNY LAIN but CHILDEN WHOE VE never NON loves *** JORY ORPE +Eval: S S S S S S S S S S D S S + +Speaker sentences 28: mls_eng_000271 #utts: 1 +id: (mls_eng_000271-mls_eng_000271) +Scores: (#C #S #D #I) 13 28 3 2 +REF: was MURDERED on THE DOORSTEP OF HIS OWN HOUSE was THIS AN OLD man *** ***** ASKED ANDREA CALMLY MISERICORDIA you TALK AS IF HE had died in his BED you ARE NO VENETIAN AND you CANNOT UNDERSTAND WHAT it MEANS WHEN AN INQUISITOR is MURDERED +HYP: was MURDED on *** HESORSTE F OFIS OLE HOUSES was I A OL man ASD ANDUR A COMNLY MSRE GUODIAR you **** TAK ASIF H had died in his BEAET you *** AR NOVENEISION HA you CANO NDESTAND WHT it MINS HEN A NIQUITITY is MERDEDT +Eval: S D S S S S S S S S I I S S S S D S S S S D S S S S S S S S S S S + +Speaker sentences 29: mls_eng_000272 #utts: 1 +id: (mls_eng_000272-mls_eng_000272) +Scores: (#C #S #D #I) 21 23 4 1 +REF: by god he said THEY WRONG me MY BUSINESS LEADS me in and OUT OF many houses BUT what do i CARE FOR the SECRETS that MAY BE HIDDEN there however i CANNOT BLAME THESE PEOPLE FOR THEIR WATCHFULNESS the **** BLOODHOUNDS of THE SIGNORIA ARE in EVERY STREET +HYP: by god he said THOY WONT me MU BUSNS LEIDS me in and *** OUTO many houses OR what do i CAR FO the SICREST that *** MAYB HIDIN there however i ****** CANOT PLIN THIS PEPEFOR THE WACHFOLNES the BLUT HOUS of HIS SFNORIAR AY in ***** EVERESTREET +Eval: S S S S S D S S S S S D S S D S S S S S S I S S S S D S + +Speaker sentences 30: mls_eng_000273 #utts: 1 +id: (mls_eng_000273-mls_eng_000273) +Scores: (#C #S #D #I) 11 27 10 0 +REF: HORRIBLE DEATH was PULLING AT HER not A STICK NOR A STONE was IN REACH OF HER HANDS AND the PITILESS CRAGS ECHOED one LONG SHRIEK ABOVE ALL the ROAR OF the WATERFALL she STROVE TO TURN OVER and GRASP the GROUND BUT only FELT HERSELF GOING FASTER +HYP: HARYBL DET was ******* PULIN T not * ***** *** ASTIK NORASTON was ** ***** INDRITOF HE HAND AN the PIYLS GRAGS EULD one **** NONG SHREK UBOFWLL the RWAR OFWAT the OLE she ****** STROED OUODON OUE and GASE the ****** GOUNDBUT only **** FILT HERSEVEGIN CASTHOR +Eval: S S D S S D D D S S D D S S S S S S S D S S S S S S D S S S S D S D S S S + +Speaker sentences 31: mls_eng_000274 #utts: 1 +id: (mls_eng_000274-mls_eng_000274) +Scores: (#C #S #D #I) 23 20 3 1 +REF: and THE FOLLY OF it was that ** ANOTHER WOMAN OF CERNY WISHED for nothing BETTER THAN to go SINCE my SISTER and father ARE sent away she said i SHOULD RATHER go WITH THEM i have no MIND to stay HERE ALONE WITH my TWO BABIES +HYP: and A FALY OFE it was that AN NOTHE WOMENT OFVE SERNY WHICH for nothing ****** BETEDTEN to go SINTO my SISTR and father AS sent away she said i ****** SHULRATHER go **** WITHM i have no MINE to stay HEAR ALON IT my TO BABES +Eval: S S S I S S S S S D S S S S D S D S S S S S S S + +Speaker sentences 32: mls_eng_000275 #utts: 1 +id: (mls_eng_000275-mls_eng_000275) +Scores: (#C #S #D #I) 11 12 4 1 +REF: BILLY DID ACCORDINGLY wondering WHAT THE LITTLE man would be at and he PICKED TWO of THE STOUTEST rushes HE COULD find WITH A LITTLE BUNCH of * +HYP: ***** EINIDIT ACORDINGLY wondering HA TE LITE man would be at and he PEKED TO of THESTOUT IS rushes ** HECOULD find **** * WITEALITALE BUNH of B +Eval: D S S S S S S S S S D S D D S S I + +Speaker sentences 33: mls_eng_000276 #utts: 1 +id: (mls_eng_000276-mls_eng_000276) +Scores: (#C #S #D #I) 17 25 0 1 +REF: it is NOW THE DREADFUL NIGHT COMES on HOW DISMAL IS the PLAIN FOR the PRUSSIANS AND the ****** ENGLISH FOUND ABOVE ten THOUSAND slain brave WELLINGTON AND BLUCHER both MOST nobly drove THEIR FOES and BUONAPARTES IMPERIAL crown was taken at WATERLOO +HYP: it is NO THT DREDFUL NAE COMS on HO DISMA AS the PLEIN WER the RUSIONS AN the NGLISH FOND T BOVF ten THOUSND slain brave WELINGTON AD BLOKER both MOS nobly drove THER FOURS and BONUPARTS AMPEAL crown was taken at WATEL +Eval: S S S S S S S S S S S S I S S S S S S S S S S S S S + +Speaker sentences 34: mls_eng_000277 #utts: 1 +id: (mls_eng_000277-mls_eng_000277) +Scores: (#C #S #D #I) 17 22 1 1 +REF: some YEARS ago AFTER making our ARRANGEMENTS for THE ENCAMPMENT at NIGHT WE constantly had ** OUR PEACEFUL REST BROKEN by a TRIBE of BROWN MONKEYS THEY EVIDENTLY THOUGHT that long POSSESSION had GIVEN THEM a PRIOR CLAIM to the GROVE +HYP: some YEAS ago HAFTER making our ARANGENS for *** THEINCAMPENT at NIGT WHED constantly had OF A PEASFUL RES ROKGON by a TRIB of ROUN MOUNCKES THE EIDENTE THUGHT that long POSTION had GIEN THE a PRIAR LAM to the GL +Eval: S S S D S S S I S S S S S S S S S S S S S S S S + +Speaker sentences 35: mls_eng_000278 #utts: 1 +id: (mls_eng_000278-mls_eng_000278) +Scores: (#C #S #D #I) 22 18 4 0 +REF: to flash in at home CLAMOURING for her MAID between MRS VAN ESTENS party and the OPERA if only for A MINUTE CERTAINLY IT was MORE than A MINUTE that SIMONE remained at the PHAYRE house after BEING BROUGHT BACK AFTER DINNER IN TAXI +HYP: to flash in at home CLEMERING for her MAD between MSS WAN ESTANS party and the OPRAR if only for * MINIT SUERTONLY T was MOR than * AMINT that SIMON remained at the FIER house after ***** ******* BEANG DROD BACKE FTER DINERINTHTACX +Eval: S S S S S S D S S S S D S S S D D S S S S S + +Speaker sentences 36: mls_eng_000279 #utts: 1 +id: (mls_eng_000279-mls_eng_000279) +Scores: (#C #S #D #I) 20 30 8 1 +REF: and IN DICTIONARY and THEN we had CALISTHENICS we GO THROUGH a GREAT MANY FIGURES AND SING a life ON THE OCEAN wave *** WHAT FAIRYLIKE MUSIC STEALS OVER the SEA LIGHTLY row LIGHTLY row OER the GLASSY WAVES WE go and OH come come away and OTHER SONGS MRS JUDGE TAYLOR WROTE ONE song ON PURPOSE FOR US +HYP: and ** INDICIONARY and HEN we had CALSTHENICXS we CO THROU a ***** **** GEAEMANY FIGERS ANDSINKE a life ** *** OFMHEOUTION wave WHT FERY LIK MUSICK TILD OVE the SCE LIGELY row LIGTELY row ORE the GLASY WAYE WUL go and O come come away and ***** ***** OTHERSONGS MS JUDHE TALOROD WONS song ** AN PERPISFOR HUS +Eval: D S S S S S D D S S S D D S I S S S S S S S S S S S S S D D S S S S S D S S S + +Speaker sentences 37: mls_eng_000280 #utts: 1 +id: (mls_eng_000280-mls_eng_000280) +Scores: (#C #S #D #I) 12 24 4 1 +REF: that which PASSES AS HISTORY IN OUR SCHOOLS OR GOVERNMENTALLY FABRICATED BOOKS ON HISTORY is a FORGERY A MISREPRESENTATION of * EVENTS like the OLD DRAMA CENTERING upon THE IMPOSSIBLE FIGURE of the HERO with A GESTICULATING crowd IN THE BACKGROUND +HYP: that which ****** PATSES A HISTRYIN UR SCOULS AR GOVERMENTLY FABRICATE BUT SOME HISTRY is a FOURGURY AD MRPESENTATION of E VENCES like the *** LDRAMARE SENTRING upon *** THEMPOSIBLE FIGUE of the ERO with THE JUSTICULATING crowd ** INTHE BACKROND +Eval: D S S S S S S S S S S S S S S I S D S S D S S S S S D S S + +Speaker sentences 38: mls_eng_000281 #utts: 1 +id: (mls_eng_000281-mls_eng_000281) +Scores: (#C #S #D #I) 19 13 0 0 +REF: WHEN the CALIPH HEARD this he said O JAAFAR how goodly is that voice AND the WAZIR REPLIED o our lord never SMOTE my hearing AUGHT SWEETER or GOODLIER than THIS singing +HYP: HN the CALEVF HURD this he said OL JEUFAR how goodly is that voice ND the VASIE REPLID o our lord never SMORT my hearing ART TRETERE or GODLYAE than THE singing +Eval: S S S S S S S S S S S S S + +Speaker sentences 39: mls_eng_000282 #utts: 1 +id: (mls_eng_000282-mls_eng_000282) +Scores: (#C #S #D #I) 20 13 1 1 +REF: evidently the LEARNED BARON had not STUDIED such WORKS of the **** TOTKAHNI or PARROT chat which notably translated by NAKHSHABI from the SANSKRIT SUKA SAPTATI has NOW become as ORTHODOXICALLY MUSLIM as the NIGHTS +HYP: evidently the LURNID BARIEN had not STODED such WORK of the TOTA CAHNE or PERIT chat which notably translated by NUSHABYY from the ******** SANSGRIT SOKUSEPTAT has NO become as ORTHODOCXICLY MUSLOME as the NIHTES +Eval: S S S S I S S S D S S S S S S + + diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/text b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/text new file mode 100644 index 0000000000000000000000000000000000000000..9953b249594c4ee0ccab247a33f34db64c13a8d1 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/text @@ -0,0 +1,40 @@ +mls_eng_000243 I AMTHIR UD OF TAKN OLDES FOM YOU SHE SAD HASTALY I SA NOW THE IT IS IMPOSIBLE T HAVE FATHIN OU I SE NOW THE IT IS USESTO EXPECT ANY RETERN FROM OU FOR ALE I HAVE DON I WANT NO MORE OF YOU +mls_eng_000244 THAT HE MAY SOMTIMES BEE LIKE OTHER CHELDREN LANING TE SIE MY NE OR PLAING PRATLIN SIKING FOR HELES COMS TO MY HEART ATS SINFUL ORD ANM SPEAKING HOW GOD THO ART +mls_eng_000245 TRANSENTHINGS OF AL SORSE AS I THE GENRALE OUTBURST OF MULTITODNS PASION AR HUTED TOGETHER THE LITRCRIS NAY THE REDICULOS WIT THE HERABLE FAR OVER THE ILWEY SE OF HEDS MAY BE SEN RESCALITY CAPRIYOLING ON FORSES FOM THE ROIAL STOD +mls_eng_000246 IT MAY HAVE BEN THT THE BONS WERE A FOLDED TOGETHER AND NON AS ONA HPELBONS FULDED AND LAIE AWAY FOR THE PRPICES OF INCENTATION SUCH BUNDLES OF BONS WE PUT THRU APROSES OF PRARS +mls_eng_000247 MASAILS NEVER EXPERINED THOS GREAT TRANSIONS FROM LONES TO GRANDUR THIS WAS OWING TO THE PRUDANT CONDUCT O THAT REPUBLICK WHIH ALWAYS PRESERVED HER PRINCIPLES +mls_eng_000248 AT A SMAL BEAING SETION ON THE MORING OF UTOBER HRTY ON CONDUCTED I THE ATMSFER OF CONSPIRACY AND INTENDED BY BRODY WE ERETOL HAT THE NORAL AGANCE REQUIRMET FOR REVU ORDAPROVELE HAD BEN WAVED THAT NORMAL APROVE OF THE MAYR OF WASINGTON AND SERTN GOVERS WOL BE HANDLED IN FORMILY +mls_eng_000249 THE BODIST LATDY IN CHINER WHO DO NOT HASTATE TOL TAKE LIH FOR THER PERFERS OFOD SAFF THER CONSHENCS FROM TACK TO TIM BY BYING VERS FASCSHEIS IS SETDER AND LETIN THE GO +mls_eng_000250 THIS AGAN IS SOFEND AD TEMPERD BY A SIMPL FATH IN THE SUPROMICY OF LOVE OVER FEER AND UN BOUNTED HUMANITY AND CHARITY FOR THE POR AND HELPLESAND UN CONDINAL FORGIVENESS OF THE DIREST INJURIES WHICH IS THE NOT OF THE NOBLE A GENEROSITY AND LIBURALITY +mls_eng_000251 THE SECAND MAE FOLOED AN THE COUPLE OF TE SEMERSMEN ROT THE AOR THE BARK BEFOR CUCHING A ROPE THEY HENTO WARK T SURCH THE SHIP THE LIFTD THE HACES AND FOUND THER HOLD FUL OF CARGO +mls_eng_000252 FOND OF IS COMRADS AND RESPECTFULE TO HIS PASTERS AND MASTERS EVEN SCOL MASTERS AS THE LAD HE PREPARS FOR MANWOUD WITH WILL AND THIS TRAINING ORKUPE HIM THROUT YUTH TID +mls_eng_000253 AS WHEN HE RICES QUECA RECED TER SIXTINT YEAR SHE BECAME BLINED HER LARGE SOFT BRON ICES HAD O LIDIN THEM +mls_eng_000254 ALMOST ALL DAY THE BATAR RANGED BETWE THE TO MEN BACK IN FORT THEY FORSE EACHOTHER OVER THE LOV ABEDS THE CHIFES WEL OILED BODY WAS VERY DIFICULT FOR THEOLO HAY TO GRAS BRUSED AND BLEADING FROM REPEATED FALLS ON THE RUH LAVA +mls_eng_000255 POSY I TRE IT TERRAR AND A WARP PANTING THE WIN MON THRUOU THE TREES OF TE GARDIN AND FROM TIME TOA TIME SEE AS IF TO A +mls_eng_000256 HIS MENTAL TORPITITY FOUNDI APON FISICAL INDILENCE RENDERS AMEDIA ACTION N ALL MANE OF EXERTION DIS TASFUL HIS CONCHOUS WEKNESS SHOWE IT SELF +mls_eng_000257 NOR THALL HOW GLAD THE CUING MOTHER WAS NOR HOL GREAT WAR THE REJOICESING OF THE PEOPLE TOR HOM MENIFISSENT WAS THE ROIAL BANQUED THAT GOOD CQING PAMAREA ATENDEDT BITHAL HER CORT +mls_eng_000258 AND THE CHANCE OF THER BEANG SUCH A ON UNGAN DIMISHIES BY WERY PRAPED PROSESS MAMELUK AS A HORS WAS OF ECUEANE CQULITY REASNING NOT ABOUT HIS ODERS BUT ABOUGT THE WAY TO DO THE +mls_eng_000259 SHE NOKED BUT SNO DROPK OKDOT OF THE EINDO AND SAID I DEARE NOT OPN THE DOOR FOR THE DORS HAVE TOLD ME TO LET NO WEN IN THAT IS HARD FOR ME SAID THE WOMAN FOR I MUST TAK BACK MY APLS BUT THERE IS ONE WHICH I WILL GIVE YOU AND SHE HELD UP AN AAL +mls_eng_000260 HAV IY MORY SPOK AC THELSHOUR AND HORCE FINLYSPOKNIT TO MY PECEWAS WHY FEBY ARE YU COME SO SOON WHERER YOU BARY CHILD AND O THAN ARDILSPOK ASAME ON +mls_eng_000261 I NEVER GEW ANY ON HO LIE TOT TURCHAS MCH ASSGRANMOHR DOUS SHE HAS HEWOUD RAVER B A DOR CEPRINTHE HAS OF OR GOD THN T DWEL NTHE TENS OF WIKEDNES THEY DON HAE WOMEN DOR KEPERS AND I NNOW SHEWOLD T TWEL MININ AN ATENT +mls_eng_000262 THE DUK WAS SUPRISE TO SE HIM WOHT BRINGS YOU OUT SO IALY ABLEARD DEMAND HE OW YOUR GRAICETS REPLID TE BUTLER GASPING FOR ATERENCS +mls_eng_000263 FOREYIET SEMING RONSMUTATIONS OF CALE MAY BE MADED WHERE HER IS ANY MIXTR OF DEVRS SOUTS OF RAISE FOR IN SUCH MIXTERS THE MPON CALES A PE NOT TBY THE MUTAL ALAING HACHETHE CONSTITUTE A MIDLING COLER +mls_eng_000264 AND ALMUSTHE SAME INSTENT CINSA SANDY SID STEP INTO ASHAP DORWAY HE WATED THER ANTLL CIN CAME UP KIN STOPE ND PRETENDED O STAR THR THE LAS AT TH DESPLAY OF HARD WAR IN TOULS WERE HE CONTINED TO WATCH BARICK YOU SE WHAT I SEE SAMBE STEAID CIN NOTED +mls_eng_000265 THENM THE THIEK GREAN STAV FLOWD OWE THE HALLE BILDING AND THER WAS NOTHING TO BE SEEN THERE BUT DE MIOWD OF SOFT FLOWING GRAY GREAN STAVE THE D ROUSHED ON NOW WIT THE SWEIFTNES OLL HE WEINK A LOOKED AP INTO BEARSS FAICE +mls_eng_000266 II HAVE MAE SACROFICS TO UR OFE WHEN I NEW THTHE WE NOT MY HAPNES IWAS AFTER I SAL THAT HAD STPED AFTER I SAL THAT YOUR TENDERNESS HAD TERND TO CAULCULATION AFTER I SAW THAT YOU CARD FOR YOURSELF ONLY NOT FOR ME +mls_eng_000267 YET THA THANDEAR NEVER ROALS IN WINTER I SEY A CROL WOARING ROUND AND ROUN BEFOR IT A LIHT THESN NOTHING UNDER THE FIRD TREES BUT I NOE SOMETHING MUST BE THEAR +mls_eng_000268 IT IS BEYOND THOUT THAT SOME PEOBLE HAE MENISE TO SA FE MANY THINGS AN OF COURSE THE JURMANT HAS A SUMAIIED AS S MAUCH TO OR THRE DAYSE AFTER HA FIRS PROCOCITIONS THEY DROPET IN IUN NAWARE +mls_eng_000269 SOON A MAN CAME OUT TO ME HIM THIS MAN WAS OLOHA A BEARDLESS MAN BELONGIG TO A LALLS ROBERCLAN WICH INFESTED THE DISTRICT POSEBLY ASSISTIN THE MAN HUNTERS OF THE EMPLE IN SECURING VICTOMS FOR THE EMPLE ALTRS +mls_eng_000270 WE AR NOT LOVERAS YOU AND IY APON THIS SUNY LAIN BUT CHILDEN WHOE VE NEVER NON LOVES JORY ORPE +mls_eng_000271 WAS MURDED ON HESORSTE F OFIS OLE HOUSES WAS I A OL MAN ASD ANDUR A COMNLY MSRE GUODIAR YOU TAK ASIF H HAD DIED IN HIS BEAET YOU AR NOVENEISION HA YOU CANO NDESTAND WHT IT MINS HEN A NIQUITITY IS MERDEDT +mls_eng_000272 BY GOD HE SAID THOY WONT ME MU BUSNS LEIDS ME IN AND OUTO MANY HOUSES OR WHAT DO I CAR FO THE SICREST THAT MAYB HIDIN THERE HOWEVER I CANOT PLIN THIS PEPEFOR THE WACHFOLNES THE BLUT HOUS OF HIS SFNORIAR AY IN EVERESTREET +mls_eng_000273 HARYBL DET WAS PULIN T NOT ASTIK NORASTON WAS INDRITOF HE HAND AN THE PIYLS GRAGS EULD ONE NONG SHREK UBOFWLL THE RWAR OFWAT THE OLE SHE STROED OUODON OUE AND GASE THE GOUNDBUT ONLY FILT HERSEVEGIN CASTHOR +mls_eng_000274 AND A FALY OFE IT WAS THAT AN NOTHE WOMENT OFVE SERNY WHICH FOR NOTHING BETEDTEN TO GO SINTO MY SISTR AND FATHER AS SENT AWAY SHE SAID I SHULRATHER GO WITHM I HAVE NO MINE TO STAY HEAR ALON IT MY TO BABES +mls_eng_000275 EINIDIT ACORDINGLY WONDERING HA TE LITE MAN WOULD BE AT AND HE PEKED TO OF THESTOUT IS RUSHES HECOULD FIND WITEALITALE BUNH OF B +mls_eng_000276 IT IS NO THT DREDFUL NAE COMS ON HO DISMA AS THE PLEIN WER THE RUSIONS AN THE NGLISH FOND T BOVF TEN THOUSND SLAIN BRAVE WELINGTON AD BLOKER BOTH MOS NOBLY DROVE THER FOURS AND BONUPARTS AMPEAL CROWN WAS TAKEN AT WATEL +mls_eng_000277 SOME YEAS AGO HAFTER MAKING OUR ARANGENS FOR THEINCAMPENT AT NIGT WHED CONSTANTLY HAD OF A PEASFUL RES ROKGON BY A TRIB OF ROUN MOUNCKES THE EIDENTE THUGHT THAT LONG POSTION HAD GIEN THE A PRIAR LAM TO THE GL +mls_eng_000278 TO FLASH IN AT HOME CLEMERING FOR HER MAD BETWEEN MSS WAN ESTANS PARTY AND THE OPRAR IF ONLY FOR MINIT SUERTONLY T WAS MOR THAN AMINT THAT SIMON REMAINED AT THE FIER HOUSE AFTER BEANG DROD BACKE FTER DINERINTHTACX +mls_eng_000279 AND INDICIONARY AND HEN WE HAD CALSTHENICXS WE CO THROU A GEAEMANY FIGERS ANDSINKE A LIFE OFMHEOUTION WAVE WHT FERY LIK MUSICK TILD OVE THE SCE LIGELY ROW LIGTELY ROW ORE THE GLASY WAYE WUL GO AND O COME COME AWAY AND OTHERSONGS MS JUDHE TALOROD WONS SONG AN PERPISFOR HUS +mls_eng_000280 THAT WHICH PATSES A HISTRYIN UR SCOULS AR GOVERMENTLY FABRICATE BUT SOME HISTRY IS A FOURGURY AD MRPESENTATION OF E VENCES LIKE THE LDRAMARE SENTRING UPON THEMPOSIBLE FIGUE OF THE ERO WITH THE JUSTICULATING CROWD INTHE BACKROND +mls_eng_000281 HN THE CALEVF HURD THIS HE SAID OL JEUFAR HOW GOODLY IS THAT VOICE ND THE VASIE REPLID O OUR LORD NEVER SMORT MY HEARING ART TRETERE OR GODLYAE THAN THE SINGING +mls_eng_000282 EVIDENTLY THE LURNID BARIEN HAD NOT STODED SUCH WORK OF THE TOTA CAHNE OR PERIT CHAT WHICH NOTABLY TRANSLATED BY NUSHABYY FROM THE SANSGRIT SOKUSEPTAT HAS NO BECOME AS ORTHODOCXICLY MUSLOME AS THE NIHTES diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/token b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/token new file mode 100644 index 0000000000000000000000000000000000000000..71f32c0f1deb871ac1ac06fd0a3c1984435edc9a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/token @@ -0,0 +1,40 @@ +mls_eng_000243 I A M T H I R U D O F T A K N O L D E S F O M Y O U S H E S A D H A S T A L Y I S A N O W T H E I T I S I M P O S I B L E T H A V E F A T H I N O U I S E N O W T H E I T I S U S E S T O E X P E C T A N Y R E T E R N F R O M O U F O R A L E I H A V E D O N I W A N T N O M O R E O F Y O U +mls_eng_000244 T H A T H E M A Y S O M T I M E S B E E L I K E O T H E R C H E L D R E N L A N I N G T E S I E M Y N E O R P L A I N G P R A T L I N S I K I N G F O R H E L E S C O M S T O M Y H E A R T A T S S I N F U L O R D A N M S P E A K I N G H O W G O D T H O A R T +mls_eng_000245 T R A N S E N T H I N G S O F A L S O R S E A S I T H E G E N R A L E O U T B U R S T O F M U L T I T O D N S P A S I O N A R H U T E D T O G E T H E R T H E L I T R C R I S N A Y T H E R E D I C U L O S W I T T H E H E R A B L E F A R O V E R T H E I L W E Y S E O F H E D S M A Y B E S E N R E S C A L I T Y C A P R I Y O L I N G O N F O R S E S F O M T H E R O I A L S T O D +mls_eng_000246 I T M A Y H A V E B E N T H T T H E B O N S W E R E A F O L D E D T O G E T H E R A N D N O N A S O N A H P E L B O N S F U L D E D A N D L A I E A W A Y F O R T H E P R P I C E S O F I N C E N T A T I O N S U C H B U N D L E S O F B O N S W E P U T T H R U A P R O S E S O F P R A R S +mls_eng_000247 M A S A I L S N E V E R E X P E R I N E D T H O S G R E A T T R A N S I O N S F R O M L O N E S T O G R A N D U R T H I S W A S O W I N G T O T H E P R U D A N T C O N D U C T O T H A T R E P U B L I C K W H I H A L W A Y S P R E S E R V E D H E R P R I N C I P L E S +mls_eng_000248 A T A S M A L B E A I N G S E T I O N O N T H E M O R I N G O F U T O B E R H R T Y O N C O N D U C T E D I T H E A T M S F E R O F C O N S P I R A C Y A N D I N T E N D E D B Y B R O D Y W E E R E T O L H A T T H E N O R A L A G A N C E R E Q U I R M E T F O R R E V U O R D A P R O V E L E H A D B E N W A V E D T H A T N O R M A L A P R O V E O F T H E M A Y R O F W A S I N G T O N A N D S E R T N G O V E R S W O L B E H A N D L E D I N F O R M I L Y +mls_eng_000249 T H E B O D I S T L A T D Y I N C H I N E R W H O D O N O T H A S T A T E T O L T A K E L I H F O R T H E R P E R F E R S O F O D S A F F T H E R C O N S H E N C S F R O M T A C K T O T I M B Y B Y I N G V E R S F A S C S H E I S I S S E T D E R A N D L E T I N T H E G O +mls_eng_000250 T H I S A G A N I S S O F E N D A D T E M P E R D B Y A S I M P L F A T H I N T H E S U P R O M I C Y O F L O V E O V E R F E E R A N D U N B O U N T E D H U M A N I T Y A N D C H A R I T Y F O R T H E P O R A N D H E L P L E S A N D U N C O N D I N A L F O R G I V E N E S S O F T H E D I R E S T I N J U R I E S W H I C H I S T H E N O T O F T H E N O B L E A G E N E R O S I T Y A N D L I B U R A L I T Y +mls_eng_000251 T H E S E C A N D M A E F O L O E D A N T H E C O U P L E O F T E S E M E R S M E N R O T T H E A O R T H E B A R K B E F O R C U C H I N G A R O P E T H E Y H E N T O W A R K T S U R C H T H E S H I P T H E L I F T D T H E H A C E S A N D F O U N D T H E R H O L D F U L O F C A R G O +mls_eng_000252 F O N D O F I S C O M R A D S A N D R E S P E C T F U L E T O H I S P A S T E R S A N D M A S T E R S E V E N S C O L M A S T E R S A S T H E L A D H E P R E P A R S F O R M A N W O U D W I T H W I L L A N D T H I S T R A I N I N G O R K U P E H I M T H R O U T Y U T H T I D +mls_eng_000253 A S W H E N H E R I C E S Q U E C A R E C E D T E R S I X T I N T Y E A R S H E B E C A M E B L I N E D H E R L A R G E S O F T B R O N I C E S H A D O L I D I N T H E M +mls_eng_000254 A L M O S T A L L D A Y T H E B A T A R R A N G E D B E T W E T H E T O M E N B A C K I N F O R T T H E Y F O R S E E A C H O T H E R O V E R T H E L O V A B E D S T H E C H I F E S W E L O I L E D B O D Y W A S V E R Y D I F I C U L T F O R T H E O L O H A Y T O G R A S B R U S E D A N D B L E A D I N G F R O M R E P E A T E D F A L L S O N T H E R U H L A V A +mls_eng_000255 P O S Y I T R E I T T E R R A R A N D A W A R P P A N T I N G T H E W I N M O N T H R U O U T H E T R E E S O F T E G A R D I N A N D F R O M T I M E T O A T I M E S E E A S I F T O A +mls_eng_000256 H I S M E N T A L T O R P I T I T Y F O U N D I A P O N F I S I C A L I N D I L E N C E R E N D E R S A M E D I A A C T I O N N A L L M A N E O F E X E R T I O N D I S T A S F U L H I S C O N C H O U S W E K N E S S S H O W E I T S E L F +mls_eng_000257 N O R T H A L L H O W G L A D T H E C U I N G M O T H E R W A S N O R H O L G R E A T W A R T H E R E J O I C E S I N G O F T H E P E O P L E T O R H O M M E N I F I S S E N T W A S T H E R O I A L B A N Q U E D T H A T G O O D C Q I N G P A M A R E A A T E N D E D T B I T H A L H E R C O R T +mls_eng_000258 A N D T H E C H A N C E O F T H E R B E A N G S U C H A O N U N G A N D I M I S H I E S B Y W E R Y P R A P E D P R O S E S S M A M E L U K A S A H O R S W A S O F E C U E A N E C Q U L I T Y R E A S N I N G N O T A B O U T H I S O D E R S B U T A B O U G T T H E W A Y T O D O T H E +mls_eng_000259 S H E N O K E D B U T S N O D R O P K O K D O T O F T H E E I N D O A N D S A I D I D E A R E N O T O P N T H E D O O R F O R T H E D O R S H A V E T O L D M E T O L E T N O W E N I N T H A T I S H A R D F O R M E S A I D T H E W O M A N F O R I M U S T T A K B A C K M Y A P L S B U T T H E R E I S O N E W H I C H I W I L L G I V E Y O U A N D S H E H E L D U P A N A A L +mls_eng_000260 H A V I Y M O R Y S P O K A C T H E L S H O U R A N D H O R C E F I N L Y S P O K N I T T O M Y P E C E W A S W H Y F E B Y A R E Y U C O M E S O S O O N W H E R E R Y O U B A R Y C H I L D A N D O T H A N A R D I L S P O K A S A M E O N +mls_eng_000261 I N E V E R G E W A N Y O N H O L I E T O T T U R C H A S M C H A S S G R A N M O H R D O U S S H E H A S H E W O U D R A V E R B A D O R C E P R I N T H E H A S O F O R G O D T H N T D W E L N T H E T E N S O F W I K E D N E S T H E Y D O N H A E W O M E N D O R K E P E R S A N D I N N O W S H E W O L D T T W E L M I N I N A N A T E N T +mls_eng_000262 T H E D U K W A S S U P R I S E T O S E H I M W O H T B R I N G S Y O U O U T S O I A L Y A B L E A R D D E M A N D H E O W Y O U R G R A I C E T S R E P L I D T E B U T L E R G A S P I N G F O R A T E R E N C S +mls_eng_000263 F O R E Y I E T S E M I N G R O N S M U T A T I O N S O F C A L E M A Y B E M A D E D W H E R E H E R I S A N Y M I X T R O F D E V R S S O U T S O F R A I S E F O R I N S U C H M I X T E R S T H E M P O N C A L E S A P E N O T T B Y T H E M U T A L A L A I N G H A C H E T H E C O N S T I T U T E A M I D L I N G C O L E R +mls_eng_000264 A N D A L M U S T H E S A M E I N S T E N T C I N S A S A N D Y S I D S T E P I N T O A S H A P D O R W A Y H E W A T E D T H E R A N T L L C I N C A M E U P K I N S T O P E N D P R E T E N D E D O S T A R T H R T H E L A S A T T H D E S P L A Y O F H A R D W A R I N T O U L S W E R E H E C O N T I N E D T O W A T C H B A R I C K Y O U S E W H A T I S E E S A M B E S T E A I D C I N N O T E D +mls_eng_000265 T H E N M T H E T H I E K G R E A N S T A V F L O W D O W E T H E H A L L E B I L D I N G A N D T H E R W A S N O T H I N G T O B E S E E N T H E R E B U T D E M I O W D O F S O F T F L O W I N G G R A Y G R E A N S T A V E T H E D R O U S H E D O N N O W W I T T H E S W E I F T N E S O L L H E W E I N K A L O O K E D A P I N T O B E A R S S F A I C E +mls_eng_000266 I I H A V E M A E S A C R O F I C S T O U R O F E W H E N I N E W T H T H E W E N O T M Y H A P N E S I W A S A F T E R I S A L T H A T H A D S T P E D A F T E R I S A L T H A T Y O U R T E N D E R N E S S H A D T E R N D T O C A U L C U L A T I O N A F T E R I S A W T H A T Y O U C A R D F O R Y O U R S E L F O N L Y N O T F O R M E +mls_eng_000267 Y E T T H A T H A N D E A R N E V E R R O A L S I N W I N T E R I S E Y A C R O L W O A R I N G R O U N D A N D R O U N B E F O R I T A L I H T T H E S N N O T H I N G U N D E R T H E F I R D T R E E S B U T I N O E S O M E T H I N G M U S T B E T H E A R +mls_eng_000268 I T I S B E Y O N D T H O U T T H A T S O M E P E O B L E H A E M E N I S E T O S A F E M A N Y T H I N G S A N O F C O U R S E T H E J U R M A N T H A S A S U M A I I E D A S S M A U C H T O O R T H R E D A Y S E A F T E R H A F I R S P R O C O C I T I O N S T H E Y D R O P E T I N I U N N A W A R E +mls_eng_000269 S O O N A M A N C A M E O U T T O M E H I M T H I S M A N W A S O L O H A A B E A R D L E S S M A N B E L O N G I G T O A L A L L S R O B E R C L A N W I C H I N F E S T E D T H E D I S T R I C T P O S E B L Y A S S I S T I N T H E M A N H U N T E R S O F T H E E M P L E I N S E C U R I N G V I C T O M S F O R T H E E M P L E A L T R S +mls_eng_000270 W E A R N O T L O V E R A S Y O U A N D I Y A P O N T H I S S U N Y L A I N B U T C H I L D E N W H O E V E N E V E R N O N L O V E S J O R Y O R P E +mls_eng_000271 W A S M U R D E D O N H E S O R S T E F O F I S O L E H O U S E S W A S I A O L M A N A S D A N D U R A C O M N L Y M S R E G U O D I A R Y O U T A K A S I F H H A D D I E D I N H I S B E A E T Y O U A R N O V E N E I S I O N H A Y O U C A N O N D E S T A N D W H T I T M I N S H E N A N I Q U I T I T Y I S M E R D E D T +mls_eng_000272 B Y G O D H E S A I D T H O Y W O N T M E M U B U S N S L E I D S M E I N A N D O U T O M A N Y H O U S E S O R W H A T D O I C A R F O T H E S I C R E S T T H A T M A Y B H I D I N T H E R E H O W E V E R I C A N O T P L I N T H I S P E P E F O R T H E W A C H F O L N E S T H E B L U T H O U S O F H I S S F N O R I A R A Y I N E V E R E S T R E E T +mls_eng_000273 H A R Y B L D E T W A S P U L I N T N O T A S T I K N O R A S T O N W A S I N D R I T O F H E H A N D A N T H E P I Y L S G R A G S E U L D O N E N O N G S H R E K U B O F W L L T H E R W A R O F W A T T H E O L E S H E S T R O E D O U O D O N O U E A N D G A S E T H E G O U N D B U T O N L Y F I L T H E R S E V E G I N C A S T H O R +mls_eng_000274 A N D A F A L Y O F E I T W A S T H A T A N N O T H E W O M E N T O F V E S E R N Y W H I C H F O R N O T H I N G B E T E D T E N T O G O S I N T O M Y S I S T R A N D F A T H E R A S S E N T A W A Y S H E S A I D I S H U L R A T H E R G O W I T H M I H A V E N O M I N E T O S T A Y H E A R A L O N I T M Y T O B A B E S +mls_eng_000275 E I N I D I T A C O R D I N G L Y W O N D E R I N G H A T E L I T E M A N W O U L D B E A T A N D H E P E K E D T O O F T H E S T O U T I S R U S H E S H E C O U L D F I N D W I T E A L I T A L E B U N H O F B +mls_eng_000276 I T I S N O T H T D R E D F U L N A E C O M S O N H O D I S M A A S T H E P L E I N W E R T H E R U S I O N S A N T H E N G L I S H F O N D T B O V F T E N T H O U S N D S L A I N B R A V E W E L I N G T O N A D B L O K E R B O T H M O S N O B L Y D R O V E T H E R F O U R S A N D B O N U P A R T S A M P E A L C R O W N W A S T A K E N A T W A T E L +mls_eng_000277 S O M E Y E A S A G O H A F T E R M A K I N G O U R A R A N G E N S F O R T H E I N C A M P E N T A T N I G T W H E D C O N S T A N T L Y H A D O F A P E A S F U L R E S R O K G O N B Y A T R I B O F R O U N M O U N C K E S T H E E I D E N T E T H U G H T T H A T L O N G P O S T I O N H A D G I E N T H E A P R I A R L A M T O T H E G L +mls_eng_000278 T O F L A S H I N A T H O M E C L E M E R I N G F O R H E R M A D B E T W E E N M S S W A N E S T A N S P A R T Y A N D T H E O P R A R I F O N L Y F O R M I N I T S U E R T O N L Y T W A S M O R T H A N A M I N T T H A T S I M O N R E M A I N E D A T T H E F I E R H O U S E A F T E R B E A N G D R O D B A C K E F T E R D I N E R I N T H T A C X +mls_eng_000279 A N D I N D I C I O N A R Y A N D H E N W E H A D C A L S T H E N I C X S W E C O T H R O U A G E A E M A N Y F I G E R S A N D S I N K E A L I F E O F M H E O U T I O N W A V E W H T F E R Y L I K M U S I C K T I L D O V E T H E S C E L I G E L Y R O W L I G T E L Y R O W O R E T H E G L A S Y W A Y E W U L G O A N D O C O M E C O M E A W A Y A N D O T H E R S O N G S M S J U D H E T A L O R O D W O N S S O N G A N P E R P I S F O R H U S +mls_eng_000280 T H A T W H I C H P A T S E S A H I S T R Y I N U R S C O U L S A R G O V E R M E N T L Y F A B R I C A T E B U T S O M E H I S T R Y I S A F O U R G U R Y A D M R P E S E N T A T I O N O F E V E N C E S L I K E T H E L D R A M A R E S E N T R I N G U P O N T H E M P O S I B L E F I G U E O F T H E E R O W I T H T H E J U S T I C U L A T I N G C R O W D I N T H E B A C K R O N D +mls_eng_000281 H N T H E C A L E V F H U R D T H I S H E S A I D O L J E U F A R H O W G O O D L Y I S T H A T V O I C E N D T H E V A S I E R E P L I D O O U R L O R D N E V E R S M O R T M Y H E A R I N G A R T T R E T E R E O R G O D L Y A E T H A N T H E S I N G I N G +mls_eng_000282 E V I D E N T L Y T H E L U R N I D B A R I E N H A D N O T S T O D E D S U C H W O R K O F T H E T O T A C A H N E O R P E R I T C H A T W H I C H N O T A B L Y T R A N S L A T E D B Y N U S H A B Y Y F R O M T H E S A N S G R I T S O K U S E P T A T H A S N O B E C O M E A S O R T H O D O C X I C L Y M U S L O M E A S T H E N I H T E S diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/token_int b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/token_int new file mode 100644 index 0000000000000000000000000000000000000000..f5053954faaed510224ebb05bbf1fd1c313d7f18 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_eng1/token_int @@ -0,0 +1,40 @@ +mls_eng_000243 8 2 5 15 4 10 8 11 2 14 12 2 6 18 2 4 5 24 7 2 6 13 12 3 9 2 18 6 15 2 19 6 14 2 9 10 3 2 9 5 12 2 10 5 9 4 5 13 19 2 8 2 9 5 2 7 6 17 2 4 10 3 2 8 4 2 8 9 2 8 15 21 6 9 8 22 13 3 2 4 2 10 5 23 3 2 18 5 4 10 8 7 2 6 14 2 8 2 9 3 2 7 6 17 2 4 10 3 2 8 4 2 8 9 2 14 9 3 9 4 6 2 3 25 21 3 16 4 2 5 7 19 2 11 3 4 3 11 7 2 18 11 6 15 2 6 14 2 18 6 11 2 5 13 3 2 8 2 10 5 23 3 2 12 6 7 2 8 2 17 5 7 4 2 7 6 2 15 6 11 3 2 6 18 2 19 6 14 +mls_eng_000244 4 10 5 4 2 10 3 2 15 5 19 2 9 6 15 4 8 15 3 9 2 22 3 3 2 13 8 24 3 2 6 4 10 3 11 2 16 10 3 13 12 11 3 7 2 13 5 7 8 7 20 2 4 3 2 9 8 3 2 15 19 2 7 3 2 6 11 2 21 13 5 8 7 20 2 21 11 5 4 13 8 7 2 9 8 24 8 7 20 2 18 6 11 2 10 3 13 3 9 2 16 6 15 9 2 4 6 2 15 19 2 10 3 5 11 4 2 5 4 9 2 9 8 7 18 14 13 2 6 11 12 2 5 7 15 2 9 21 3 5 24 8 7 20 2 10 6 17 2 20 6 12 2 4 10 6 2 5 11 4 +mls_eng_000245 4 11 5 7 9 3 7 4 10 8 7 20 9 2 6 18 2 5 13 2 9 6 11 9 3 2 5 9 2 8 2 4 10 3 2 20 3 7 11 5 13 3 2 6 14 4 22 14 11 9 4 2 6 18 2 15 14 13 4 8 4 6 12 7 9 2 21 5 9 8 6 7 2 5 11 2 10 14 4 3 12 2 4 6 20 3 4 10 3 11 2 4 10 3 2 13 8 4 11 16 11 8 9 2 7 5 19 2 4 10 3 2 11 3 12 8 16 14 13 6 9 2 17 8 4 2 4 10 3 2 10 3 11 5 22 13 3 2 18 5 11 2 6 23 3 11 2 4 10 3 2 8 13 17 3 19 2 9 3 2 6 18 2 10 3 12 9 2 15 5 19 2 22 3 2 9 3 7 2 11 3 9 16 5 13 8 4 19 2 16 5 21 11 8 19 6 13 8 7 20 2 6 7 2 18 6 11 9 3 9 2 18 6 15 2 4 10 3 2 11 6 8 5 13 2 9 4 6 12 +mls_eng_000246 8 4 2 15 5 19 2 10 5 23 3 2 22 3 7 2 4 10 4 2 4 10 3 2 22 6 7 9 2 17 3 11 3 2 5 2 18 6 13 12 3 12 2 4 6 20 3 4 10 3 11 2 5 7 12 2 7 6 7 2 5 9 2 6 7 5 2 10 21 3 13 22 6 7 9 2 18 14 13 12 3 12 2 5 7 12 2 13 5 8 3 2 5 17 5 19 2 18 6 11 2 4 10 3 2 21 11 21 8 16 3 9 2 6 18 2 8 7 16 3 7 4 5 4 8 6 7 2 9 14 16 10 2 22 14 7 12 13 3 9 2 6 18 2 22 6 7 9 2 17 3 2 21 14 4 2 4 10 11 14 2 5 21 11 6 9 3 9 2 6 18 2 21 11 5 11 9 +mls_eng_000247 15 5 9 5 8 13 9 2 7 3 23 3 11 2 3 25 21 3 11 8 7 3 12 2 4 10 6 9 2 20 11 3 5 4 2 4 11 5 7 9 8 6 7 9 2 18 11 6 15 2 13 6 7 3 9 2 4 6 2 20 11 5 7 12 14 11 2 4 10 8 9 2 17 5 9 2 6 17 8 7 20 2 4 6 2 4 10 3 2 21 11 14 12 5 7 4 2 16 6 7 12 14 16 4 2 6 2 4 10 5 4 2 11 3 21 14 22 13 8 16 24 2 17 10 8 10 2 5 13 17 5 19 9 2 21 11 3 9 3 11 23 3 12 2 10 3 11 2 21 11 8 7 16 8 21 13 3 9 +mls_eng_000248 5 4 2 5 2 9 15 5 13 2 22 3 5 8 7 20 2 9 3 4 8 6 7 2 6 7 2 4 10 3 2 15 6 11 8 7 20 2 6 18 2 14 4 6 22 3 11 2 10 11 4 19 2 6 7 2 16 6 7 12 14 16 4 3 12 2 8 2 4 10 3 2 5 4 15 9 18 3 11 2 6 18 2 16 6 7 9 21 8 11 5 16 19 2 5 7 12 2 8 7 4 3 7 12 3 12 2 22 19 2 22 11 6 12 19 2 17 3 2 3 11 3 4 6 13 2 10 5 4 2 4 10 3 2 7 6 11 5 13 2 5 20 5 7 16 3 2 11 3 27 14 8 11 15 3 4 2 18 6 11 2 11 3 23 14 2 6 11 12 5 21 11 6 23 3 13 3 2 10 5 12 2 22 3 7 2 17 5 23 3 12 2 4 10 5 4 2 7 6 11 15 5 13 2 5 21 11 6 23 3 2 6 18 2 4 10 3 2 15 5 19 11 2 6 18 2 17 5 9 8 7 20 4 6 7 2 5 7 12 2 9 3 11 4 7 2 20 6 23 3 11 9 2 17 6 13 2 22 3 2 10 5 7 12 13 3 12 2 8 7 2 18 6 11 15 8 13 19 +mls_eng_000249 4 10 3 2 22 6 12 8 9 4 2 13 5 4 12 19 2 8 7 2 16 10 8 7 3 11 2 17 10 6 2 12 6 2 7 6 4 2 10 5 9 4 5 4 3 2 4 6 13 2 4 5 24 3 2 13 8 10 2 18 6 11 2 4 10 3 11 2 21 3 11 18 3 11 9 2 6 18 6 12 2 9 5 18 18 2 4 10 3 11 2 16 6 7 9 10 3 7 16 9 2 18 11 6 15 2 4 5 16 24 2 4 6 2 4 8 15 2 22 19 2 22 19 8 7 20 2 23 3 11 9 2 18 5 9 16 9 10 3 8 9 2 8 9 2 9 3 4 12 3 11 2 5 7 12 2 13 3 4 8 7 2 4 10 3 2 20 6 +mls_eng_000250 4 10 8 9 2 5 20 5 7 2 8 9 2 9 6 18 3 7 12 2 5 12 2 4 3 15 21 3 11 12 2 22 19 2 5 2 9 8 15 21 13 2 18 5 4 10 2 8 7 2 4 10 3 2 9 14 21 11 6 15 8 16 19 2 6 18 2 13 6 23 3 2 6 23 3 11 2 18 3 3 11 2 5 7 12 2 14 7 2 22 6 14 7 4 3 12 2 10 14 15 5 7 8 4 19 2 5 7 12 2 16 10 5 11 8 4 19 2 18 6 11 2 4 10 3 2 21 6 11 2 5 7 12 2 10 3 13 21 13 3 9 5 7 12 2 14 7 2 16 6 7 12 8 7 5 13 2 18 6 11 20 8 23 3 7 3 9 9 2 6 18 2 4 10 3 2 12 8 11 3 9 4 2 8 7 26 14 11 8 3 9 2 17 10 8 16 10 2 8 9 2 4 10 3 2 7 6 4 2 6 18 2 4 10 3 2 7 6 22 13 3 2 5 2 20 3 7 3 11 6 9 8 4 19 2 5 7 12 2 13 8 22 14 11 5 13 8 4 19 +mls_eng_000251 4 10 3 2 9 3 16 5 7 12 2 15 5 3 2 18 6 13 6 3 12 2 5 7 2 4 10 3 2 16 6 14 21 13 3 2 6 18 2 4 3 2 9 3 15 3 11 9 15 3 7 2 11 6 4 2 4 10 3 2 5 6 11 2 4 10 3 2 22 5 11 24 2 22 3 18 6 11 2 16 14 16 10 8 7 20 2 5 2 11 6 21 3 2 4 10 3 19 2 10 3 7 4 6 2 17 5 11 24 2 4 2 9 14 11 16 10 2 4 10 3 2 9 10 8 21 2 4 10 3 2 13 8 18 4 12 2 4 10 3 2 10 5 16 3 9 2 5 7 12 2 18 6 14 7 12 2 4 10 3 11 2 10 6 13 12 2 18 14 13 2 6 18 2 16 5 11 20 6 +mls_eng_000252 18 6 7 12 2 6 18 2 8 9 2 16 6 15 11 5 12 9 2 5 7 12 2 11 3 9 21 3 16 4 18 14 13 3 2 4 6 2 10 8 9 2 21 5 9 4 3 11 9 2 5 7 12 2 15 5 9 4 3 11 9 2 3 23 3 7 2 9 16 6 13 2 15 5 9 4 3 11 9 2 5 9 2 4 10 3 2 13 5 12 2 10 3 2 21 11 3 21 5 11 9 2 18 6 11 2 15 5 7 17 6 14 12 2 17 8 4 10 2 17 8 13 13 2 5 7 12 2 4 10 8 9 2 4 11 5 8 7 8 7 20 2 6 11 24 14 21 3 2 10 8 15 2 4 10 11 6 14 4 2 19 14 4 10 2 4 8 12 +mls_eng_000253 5 9 2 17 10 3 7 2 10 3 2 11 8 16 3 9 2 27 14 3 16 5 2 11 3 16 3 12 2 4 3 11 2 9 8 25 4 8 7 4 2 19 3 5 11 2 9 10 3 2 22 3 16 5 15 3 2 22 13 8 7 3 12 2 10 3 11 2 13 5 11 20 3 2 9 6 18 4 2 22 11 6 7 2 8 16 3 9 2 10 5 12 2 6 2 13 8 12 8 7 2 4 10 3 15 +mls_eng_000254 5 13 15 6 9 4 2 5 13 13 2 12 5 19 2 4 10 3 2 22 5 4 5 11 2 11 5 7 20 3 12 2 22 3 4 17 3 2 4 10 3 2 4 6 2 15 3 7 2 22 5 16 24 2 8 7 2 18 6 11 4 2 4 10 3 19 2 18 6 11 9 3 2 3 5 16 10 6 4 10 3 11 2 6 23 3 11 2 4 10 3 2 13 6 23 2 5 22 3 12 9 2 4 10 3 2 16 10 8 18 3 9 2 17 3 13 2 6 8 13 3 12 2 22 6 12 19 2 17 5 9 2 23 3 11 19 2 12 8 18 8 16 14 13 4 2 18 6 11 2 4 10 3 6 13 6 2 10 5 19 2 4 6 2 20 11 5 9 2 22 11 14 9 3 12 2 5 7 12 2 22 13 3 5 12 8 7 20 2 18 11 6 15 2 11 3 21 3 5 4 3 12 2 18 5 13 13 9 2 6 7 2 4 10 3 2 11 14 10 2 13 5 23 5 +mls_eng_000255 21 6 9 19 2 8 2 4 11 3 2 8 4 2 4 3 11 11 5 11 2 5 7 12 2 5 2 17 5 11 21 2 21 5 7 4 8 7 20 2 4 10 3 2 17 8 7 2 15 6 7 2 4 10 11 14 6 14 2 4 10 3 2 4 11 3 3 9 2 6 18 2 4 3 2 20 5 11 12 8 7 2 5 7 12 2 18 11 6 15 2 4 8 15 3 2 4 6 5 2 4 8 15 3 2 9 3 3 2 5 9 2 8 18 2 4 6 2 5 +mls_eng_000256 10 8 9 2 15 3 7 4 5 13 2 4 6 11 21 8 4 8 4 19 2 18 6 14 7 12 8 2 5 21 6 7 2 18 8 9 8 16 5 13 2 8 7 12 8 13 3 7 16 3 2 11 3 7 12 3 11 9 2 5 15 3 12 8 5 2 5 16 4 8 6 7 2 7 2 5 13 13 2 15 5 7 3 2 6 18 2 3 25 3 11 4 8 6 7 2 12 8 9 2 4 5 9 18 14 13 2 10 8 9 2 16 6 7 16 10 6 14 9 2 17 3 24 7 3 9 9 2 9 10 6 17 3 2 8 4 2 9 3 13 18 +mls_eng_000257 7 6 11 2 4 10 5 13 13 2 10 6 17 2 20 13 5 12 2 4 10 3 2 16 14 8 7 20 2 15 6 4 10 3 11 2 17 5 9 2 7 6 11 2 10 6 13 2 20 11 3 5 4 2 17 5 11 2 4 10 3 2 11 3 26 6 8 16 3 9 8 7 20 2 6 18 2 4 10 3 2 21 3 6 21 13 3 2 4 6 11 2 10 6 15 2 15 3 7 8 18 8 9 9 3 7 4 2 17 5 9 2 4 10 3 2 11 6 8 5 13 2 22 5 7 27 14 3 12 2 4 10 5 4 2 20 6 6 12 2 16 27 8 7 20 2 21 5 15 5 11 3 5 2 5 4 3 7 12 3 12 4 2 22 8 4 10 5 13 2 10 3 11 2 16 6 11 4 +mls_eng_000258 5 7 12 2 4 10 3 2 16 10 5 7 16 3 2 6 18 2 4 10 3 11 2 22 3 5 7 20 2 9 14 16 10 2 5 2 6 7 2 14 7 20 5 7 2 12 8 15 8 9 10 8 3 9 2 22 19 2 17 3 11 19 2 21 11 5 21 3 12 2 21 11 6 9 3 9 9 2 15 5 15 3 13 14 24 2 5 9 2 5 2 10 6 11 9 2 17 5 9 2 6 18 2 3 16 14 3 5 7 3 2 16 27 14 13 8 4 19 2 11 3 5 9 7 8 7 20 2 7 6 4 2 5 22 6 14 4 2 10 8 9 2 6 12 3 11 9 2 22 14 4 2 5 22 6 14 20 4 2 4 10 3 2 17 5 19 2 4 6 2 12 6 2 4 10 3 +mls_eng_000259 9 10 3 2 7 6 24 3 12 2 22 14 4 2 9 7 6 2 12 11 6 21 24 2 6 24 12 6 4 2 6 18 2 4 10 3 2 3 8 7 12 6 2 5 7 12 2 9 5 8 12 2 8 2 12 3 5 11 3 2 7 6 4 2 6 21 7 2 4 10 3 2 12 6 6 11 2 18 6 11 2 4 10 3 2 12 6 11 9 2 10 5 23 3 2 4 6 13 12 2 15 3 2 4 6 2 13 3 4 2 7 6 2 17 3 7 2 8 7 2 4 10 5 4 2 8 9 2 10 5 11 12 2 18 6 11 2 15 3 2 9 5 8 12 2 4 10 3 2 17 6 15 5 7 2 18 6 11 2 8 2 15 14 9 4 2 4 5 24 2 22 5 16 24 2 15 19 2 5 21 13 9 2 22 14 4 2 4 10 3 11 3 2 8 9 2 6 7 3 2 17 10 8 16 10 2 8 2 17 8 13 13 2 20 8 23 3 2 19 6 14 2 5 7 12 2 9 10 3 2 10 3 13 12 2 14 21 2 5 7 2 5 5 13 +mls_eng_000260 10 5 23 2 8 19 2 15 6 11 19 2 9 21 6 24 2 5 16 2 4 10 3 13 9 10 6 14 11 2 5 7 12 2 10 6 11 16 3 2 18 8 7 13 19 9 21 6 24 7 8 4 2 4 6 2 15 19 2 21 3 16 3 17 5 9 2 17 10 19 2 18 3 22 19 2 5 11 3 2 19 14 2 16 6 15 3 2 9 6 2 9 6 6 7 2 17 10 3 11 3 11 2 19 6 14 2 22 5 11 19 2 16 10 8 13 12 2 5 7 12 2 6 2 4 10 5 7 2 5 11 12 8 13 9 21 6 24 2 5 9 5 15 3 2 6 7 +mls_eng_000261 8 2 7 3 23 3 11 2 20 3 17 2 5 7 19 2 6 7 2 10 6 2 13 8 3 2 4 6 4 2 4 14 11 16 10 5 9 2 15 16 10 2 5 9 9 20 11 5 7 15 6 10 11 2 12 6 14 9 2 9 10 3 2 10 5 9 2 10 3 17 6 14 12 2 11 5 23 3 11 2 22 2 5 2 12 6 11 2 16 3 21 11 8 7 4 10 3 2 10 5 9 2 6 18 2 6 11 2 20 6 12 2 4 10 7 2 4 2 12 17 3 13 2 7 4 10 3 2 4 3 7 9 2 6 18 2 17 8 24 3 12 7 3 9 2 4 10 3 19 2 12 6 7 2 10 5 3 2 17 6 15 3 7 2 12 6 11 2 24 3 21 3 11 9 2 5 7 12 2 8 2 7 7 6 17 2 9 10 3 17 6 13 12 2 4 2 4 17 3 13 2 15 8 7 8 7 2 5 7 2 5 4 3 7 4 +mls_eng_000262 4 10 3 2 12 14 24 2 17 5 9 2 9 14 21 11 8 9 3 2 4 6 2 9 3 2 10 8 15 2 17 6 10 4 2 22 11 8 7 20 9 2 19 6 14 2 6 14 4 2 9 6 2 8 5 13 19 2 5 22 13 3 5 11 12 2 12 3 15 5 7 12 2 10 3 2 6 17 2 19 6 14 11 2 20 11 5 8 16 3 4 9 2 11 3 21 13 8 12 2 4 3 2 22 14 4 13 3 11 2 20 5 9 21 8 7 20 2 18 6 11 2 5 4 3 11 3 7 16 9 +mls_eng_000263 18 6 11 3 19 8 3 4 2 9 3 15 8 7 20 2 11 6 7 9 15 14 4 5 4 8 6 7 9 2 6 18 2 16 5 13 3 2 15 5 19 2 22 3 2 15 5 12 3 12 2 17 10 3 11 3 2 10 3 11 2 8 9 2 5 7 19 2 15 8 25 4 11 2 6 18 2 12 3 23 11 9 2 9 6 14 4 9 2 6 18 2 11 5 8 9 3 2 18 6 11 2 8 7 2 9 14 16 10 2 15 8 25 4 3 11 9 2 4 10 3 2 15 21 6 7 2 16 5 13 3 9 2 5 2 21 3 2 7 6 4 2 4 22 19 2 4 10 3 2 15 14 4 5 13 2 5 13 5 8 7 20 2 10 5 16 10 3 4 10 3 2 16 6 7 9 4 8 4 14 4 3 2 5 2 15 8 12 13 8 7 20 2 16 6 13 3 11 +mls_eng_000264 5 7 12 2 5 13 15 14 9 4 10 3 2 9 5 15 3 2 8 7 9 4 3 7 4 2 16 8 7 9 5 2 9 5 7 12 19 2 9 8 12 2 9 4 3 21 2 8 7 4 6 2 5 9 10 5 21 2 12 6 11 17 5 19 2 10 3 2 17 5 4 3 12 2 4 10 3 11 2 5 7 4 13 13 2 16 8 7 2 16 5 15 3 2 14 21 2 24 8 7 2 9 4 6 21 3 2 7 12 2 21 11 3 4 3 7 12 3 12 2 6 2 9 4 5 11 2 4 10 11 2 4 10 3 2 13 5 9 2 5 4 2 4 10 2 12 3 9 21 13 5 19 2 6 18 2 10 5 11 12 2 17 5 11 2 8 7 2 4 6 14 13 9 2 17 3 11 3 2 10 3 2 16 6 7 4 8 7 3 12 2 4 6 2 17 5 4 16 10 2 22 5 11 8 16 24 2 19 6 14 2 9 3 2 17 10 5 4 2 8 2 9 3 3 2 9 5 15 22 3 2 9 4 3 5 8 12 2 16 8 7 2 7 6 4 3 12 +mls_eng_000265 4 10 3 7 15 2 4 10 3 2 4 10 8 3 24 2 20 11 3 5 7 2 9 4 5 23 2 18 13 6 17 12 2 6 17 3 2 4 10 3 2 10 5 13 13 3 2 22 8 13 12 8 7 20 2 5 7 12 2 4 10 3 11 2 17 5 9 2 7 6 4 10 8 7 20 2 4 6 2 22 3 2 9 3 3 7 2 4 10 3 11 3 2 22 14 4 2 12 3 2 15 8 6 17 12 2 6 18 2 9 6 18 4 2 18 13 6 17 8 7 20 2 20 11 5 19 2 20 11 3 5 7 2 9 4 5 23 3 2 4 10 3 2 12 2 11 6 14 9 10 3 12 2 6 7 2 7 6 17 2 17 8 4 2 4 10 3 2 9 17 3 8 18 4 7 3 9 2 6 13 13 2 10 3 2 17 3 8 7 24 2 5 2 13 6 6 24 3 12 2 5 21 2 8 7 4 6 2 22 3 5 11 9 9 2 18 5 8 16 3 +mls_eng_000266 8 8 2 10 5 23 3 2 15 5 3 2 9 5 16 11 6 18 8 16 9 2 4 6 2 14 11 2 6 18 3 2 17 10 3 7 2 8 2 7 3 17 2 4 10 4 10 3 2 17 3 2 7 6 4 2 15 19 2 10 5 21 7 3 9 2 8 17 5 9 2 5 18 4 3 11 2 8 2 9 5 13 2 4 10 5 4 2 10 5 12 2 9 4 21 3 12 2 5 18 4 3 11 2 8 2 9 5 13 2 4 10 5 4 2 19 6 14 11 2 4 3 7 12 3 11 7 3 9 9 2 10 5 12 2 4 3 11 7 12 2 4 6 2 16 5 14 13 16 14 13 5 4 8 6 7 2 5 18 4 3 11 2 8 2 9 5 17 2 4 10 5 4 2 19 6 14 2 16 5 11 12 2 18 6 11 2 19 6 14 11 9 3 13 18 2 6 7 13 19 2 7 6 4 2 18 6 11 2 15 3 +mls_eng_000267 19 3 4 2 4 10 5 2 4 10 5 7 12 3 5 11 2 7 3 23 3 11 2 11 6 5 13 9 2 8 7 2 17 8 7 4 3 11 2 8 2 9 3 19 2 5 2 16 11 6 13 2 17 6 5 11 8 7 20 2 11 6 14 7 12 2 5 7 12 2 11 6 14 7 2 22 3 18 6 11 2 8 4 2 5 2 13 8 10 4 2 4 10 3 9 7 2 7 6 4 10 8 7 20 2 14 7 12 3 11 2 4 10 3 2 18 8 11 12 2 4 11 3 3 9 2 22 14 4 2 8 2 7 6 3 2 9 6 15 3 4 10 8 7 20 2 15 14 9 4 2 22 3 2 4 10 3 5 11 +mls_eng_000268 8 4 2 8 9 2 22 3 19 6 7 12 2 4 10 6 14 4 2 4 10 5 4 2 9 6 15 3 2 21 3 6 22 13 3 2 10 5 3 2 15 3 7 8 9 3 2 4 6 2 9 5 2 18 3 2 15 5 7 19 2 4 10 8 7 20 9 2 5 7 2 6 18 2 16 6 14 11 9 3 2 4 10 3 2 26 14 11 15 5 7 4 2 10 5 9 2 5 2 9 14 15 5 8 8 3 12 2 5 9 2 9 2 15 5 14 16 10 2 4 6 2 6 11 2 4 10 11 3 2 12 5 19 9 3 2 5 18 4 3 11 2 10 5 2 18 8 11 9 2 21 11 6 16 6 16 8 4 8 6 7 9 2 4 10 3 19 2 12 11 6 21 3 4 2 8 7 2 8 14 7 2 7 5 17 5 11 3 +mls_eng_000269 2 9 6 6 7 2 5 2 15 5 7 2 16 5 15 3 2 6 14 4 2 4 6 2 15 3 2 10 8 15 2 4 10 8 9 2 15 5 7 2 17 5 9 2 6 13 6 10 5 2 5 2 22 3 5 11 12 13 3 9 9 2 15 5 7 2 22 3 13 6 7 20 8 20 2 4 6 2 5 2 13 5 13 13 9 2 11 6 22 3 11 16 13 5 7 2 17 8 16 10 2 8 7 18 3 9 4 3 12 2 4 10 3 2 12 8 9 4 11 8 16 4 2 21 6 9 3 22 13 19 2 5 9 9 8 9 4 8 7 2 4 10 3 2 15 5 7 2 10 14 7 4 3 11 9 2 6 18 2 4 10 3 2 3 15 21 13 3 2 8 7 2 9 3 16 14 11 8 7 20 2 23 8 16 4 6 15 9 2 18 6 11 2 4 10 3 2 3 15 21 13 3 2 5 13 4 11 9 +mls_eng_000270 2 17 3 2 5 11 2 7 6 4 2 13 6 23 3 11 5 9 2 19 6 14 2 5 7 12 2 8 19 2 5 21 6 7 2 4 10 8 9 2 9 14 7 19 2 13 5 8 7 2 22 14 4 2 16 10 8 13 12 3 7 2 17 10 6 3 2 23 3 2 7 3 23 3 11 2 7 6 7 2 13 6 23 3 9 2 26 6 11 19 2 6 11 21 3 +mls_eng_000271 17 5 9 2 15 14 11 12 3 12 2 6 7 2 10 3 9 6 11 9 4 3 2 18 2 6 18 8 9 2 6 13 3 2 10 6 14 9 3 9 2 17 5 9 2 8 2 5 2 6 13 2 15 5 7 2 5 9 12 2 5 7 12 14 11 2 5 2 16 6 15 7 13 19 2 15 9 11 3 2 20 14 6 12 8 5 11 2 19 6 14 2 4 5 24 2 5 9 8 18 2 10 2 10 5 12 2 12 8 3 12 2 8 7 2 10 8 9 2 22 3 5 3 4 2 19 6 14 2 5 11 2 7 6 23 3 7 3 8 9 8 6 7 2 10 5 2 19 6 14 2 16 5 7 6 2 7 12 3 9 4 5 7 12 2 17 10 4 2 8 4 2 15 8 7 9 2 10 3 7 2 5 2 7 8 27 14 8 4 8 4 19 2 8 9 2 15 3 11 12 3 12 4 +mls_eng_000272 22 19 2 20 6 12 2 10 3 2 9 5 8 12 2 4 10 6 19 2 17 6 7 4 2 15 3 2 15 14 2 22 14 9 7 9 2 13 3 8 12 9 2 15 3 2 8 7 2 5 7 12 2 6 14 4 6 2 15 5 7 19 2 10 6 14 9 3 9 2 6 11 2 17 10 5 4 2 12 6 2 8 2 16 5 11 2 18 6 2 4 10 3 2 9 8 16 11 3 9 4 2 4 10 5 4 2 15 5 19 22 2 10 8 12 8 7 2 4 10 3 11 3 2 10 6 17 3 23 3 11 2 8 2 16 5 7 6 4 2 21 13 8 7 2 4 10 8 9 2 21 3 21 3 18 6 11 2 4 10 3 2 17 5 16 10 18 6 13 7 3 9 2 4 10 3 2 22 13 14 4 2 10 6 14 9 2 6 18 2 10 8 9 2 9 18 7 6 11 8 5 11 2 5 19 2 8 7 2 3 23 3 11 3 9 4 11 3 3 4 +mls_eng_000273 10 5 11 19 22 13 2 12 3 4 2 17 5 9 2 21 14 13 8 7 2 4 2 7 6 4 2 5 9 4 8 24 2 7 6 11 5 9 4 6 7 2 17 5 9 2 8 7 12 11 8 4 6 18 2 10 3 2 10 5 7 12 2 5 7 2 4 10 3 2 21 8 19 13 9 2 20 11 5 20 9 2 3 14 13 12 2 6 7 3 2 7 6 7 20 2 9 10 11 3 24 2 14 22 6 18 17 13 13 2 4 10 3 2 11 17 5 11 2 6 18 17 5 4 2 4 10 3 2 6 13 3 2 9 10 3 2 9 4 11 6 3 12 2 6 14 6 12 6 7 2 6 14 3 2 5 7 12 2 20 5 9 3 2 4 10 3 2 20 6 14 7 12 22 14 4 2 6 7 13 19 2 18 8 13 4 2 10 3 11 9 3 23 3 20 8 7 2 16 5 9 4 10 6 11 +mls_eng_000274 5 7 12 2 5 2 18 5 13 19 2 6 18 3 2 8 4 2 17 5 9 2 4 10 5 4 2 5 7 2 7 6 4 10 3 2 17 6 15 3 7 4 2 6 18 23 3 2 9 3 11 7 19 2 17 10 8 16 10 2 18 6 11 2 7 6 4 10 8 7 20 2 22 3 4 3 12 4 3 7 2 4 6 2 20 6 2 9 8 7 4 6 2 15 19 2 9 8 9 4 11 2 5 7 12 2 18 5 4 10 3 11 2 5 9 2 9 3 7 4 2 5 17 5 19 2 9 10 3 2 9 5 8 12 2 8 2 9 10 14 13 11 5 4 10 3 11 2 20 6 2 17 8 4 10 15 2 8 2 10 5 23 3 2 7 6 2 15 8 7 3 2 4 6 2 9 4 5 19 2 10 3 5 11 2 5 13 6 7 2 8 4 2 15 19 2 4 6 2 22 5 22 3 9 +mls_eng_000275 3 8 7 8 12 8 4 2 5 16 6 11 12 8 7 20 13 19 2 17 6 7 12 3 11 8 7 20 2 10 5 2 4 3 2 13 8 4 3 2 15 5 7 2 17 6 14 13 12 2 22 3 2 5 4 2 5 7 12 2 10 3 2 21 3 24 3 12 2 4 6 2 6 18 2 4 10 3 9 4 6 14 4 2 8 9 2 11 14 9 10 3 9 2 10 3 16 6 14 13 12 2 18 8 7 12 2 17 8 4 3 5 13 8 4 5 13 3 2 22 14 7 10 2 6 18 2 22 +mls_eng_000276 8 4 2 8 9 2 7 6 2 4 10 4 2 12 11 3 12 18 14 13 2 7 5 3 2 16 6 15 9 2 6 7 2 10 6 2 12 8 9 15 5 2 5 9 2 4 10 3 2 21 13 3 8 7 2 17 3 11 2 4 10 3 2 11 14 9 8 6 7 9 2 5 7 2 4 10 3 2 7 20 13 8 9 10 2 18 6 7 12 2 4 2 22 6 23 18 2 4 3 7 2 4 10 6 14 9 7 12 2 9 13 5 8 7 2 22 11 5 23 3 2 17 3 13 8 7 20 4 6 7 2 5 12 2 22 13 6 24 3 11 2 22 6 4 10 2 15 6 9 2 7 6 22 13 19 2 12 11 6 23 3 2 4 10 3 11 2 18 6 14 11 9 2 5 7 12 2 22 6 7 14 21 5 11 4 9 2 5 15 21 3 5 13 2 16 11 6 17 7 2 17 5 9 2 4 5 24 3 7 2 5 4 2 17 5 4 3 13 +mls_eng_000277 9 6 15 3 2 19 3 5 9 2 5 20 6 2 10 5 18 4 3 11 2 15 5 24 8 7 20 2 6 14 11 2 5 11 5 7 20 3 7 9 2 18 6 11 2 4 10 3 8 7 16 5 15 21 3 7 4 2 5 4 2 7 8 20 4 2 17 10 3 12 2 16 6 7 9 4 5 7 4 13 19 2 10 5 12 2 6 18 2 5 2 21 3 5 9 18 14 13 2 11 3 9 2 11 6 24 20 6 7 2 22 19 2 5 2 4 11 8 22 2 6 18 2 11 6 14 7 2 15 6 14 7 16 24 3 9 2 4 10 3 2 3 8 12 3 7 4 3 2 4 10 14 20 10 4 2 4 10 5 4 2 13 6 7 20 2 21 6 9 4 8 6 7 2 10 5 12 2 20 8 3 7 2 4 10 3 2 5 2 21 11 8 5 11 2 13 5 15 2 4 6 2 4 10 3 2 20 13 +mls_eng_000278 4 6 2 18 13 5 9 10 2 8 7 2 5 4 2 10 6 15 3 2 16 13 3 15 3 11 8 7 20 2 18 6 11 2 10 3 11 2 15 5 12 2 22 3 4 17 3 3 7 2 15 9 9 2 17 5 7 2 3 9 4 5 7 9 2 21 5 11 4 19 2 5 7 12 2 4 10 3 2 6 21 11 5 11 2 8 18 2 6 7 13 19 2 18 6 11 2 15 8 7 8 4 2 9 14 3 11 4 6 7 13 19 2 4 2 17 5 9 2 15 6 11 2 4 10 5 7 2 5 15 8 7 4 2 4 10 5 4 2 9 8 15 6 7 2 11 3 15 5 8 7 3 12 2 5 4 2 4 10 3 2 18 8 3 11 2 10 6 14 9 3 2 5 18 4 3 11 2 22 3 5 7 20 2 12 11 6 12 2 22 5 16 24 3 2 18 4 3 11 2 12 8 7 3 11 8 7 4 10 4 5 16 25 +mls_eng_000279 5 7 12 2 8 7 12 8 16 8 6 7 5 11 19 2 5 7 12 2 10 3 7 2 17 3 2 10 5 12 2 16 5 13 9 4 10 3 7 8 16 25 9 2 17 3 2 16 6 2 4 10 11 6 14 2 5 2 20 3 5 3 15 5 7 19 2 18 8 20 3 11 9 2 5 7 12 9 8 7 24 3 2 5 2 13 8 18 3 2 6 18 15 10 3 6 14 4 8 6 7 2 17 5 23 3 2 17 10 4 2 18 3 11 19 2 13 8 24 2 15 14 9 8 16 24 2 4 8 13 12 2 6 23 3 2 4 10 3 2 9 16 3 2 13 8 20 3 13 19 2 11 6 17 2 13 8 20 4 3 13 19 2 11 6 17 2 6 11 3 2 4 10 3 2 20 13 5 9 19 2 17 5 19 3 2 17 14 13 2 20 6 2 5 7 12 2 6 2 16 6 15 3 2 16 6 15 3 2 5 17 5 19 2 5 7 12 2 6 4 10 3 11 9 6 7 20 9 2 15 9 2 26 14 12 10 3 2 4 5 13 6 11 6 12 2 17 6 7 9 2 9 6 7 20 2 5 7 2 21 3 11 21 8 9 18 6 11 2 10 14 9 +mls_eng_000280 4 10 5 4 2 17 10 8 16 10 2 21 5 4 9 3 9 2 5 2 10 8 9 4 11 19 8 7 2 14 11 2 9 16 6 14 13 9 2 5 11 2 20 6 23 3 11 15 3 7 4 13 19 2 18 5 22 11 8 16 5 4 3 2 22 14 4 2 9 6 15 3 2 10 8 9 4 11 19 2 8 9 2 5 2 18 6 14 11 20 14 11 19 2 5 12 2 15 11 21 3 9 3 7 4 5 4 8 6 7 2 6 18 2 3 2 23 3 7 16 3 9 2 13 8 24 3 2 4 10 3 2 13 12 11 5 15 5 11 3 2 9 3 7 4 11 8 7 20 2 14 21 6 7 2 4 10 3 15 21 6 9 8 22 13 3 2 18 8 20 14 3 2 6 18 2 4 10 3 2 3 11 6 2 17 8 4 10 2 4 10 3 2 26 14 9 4 8 16 14 13 5 4 8 7 20 2 16 11 6 17 12 2 8 7 4 10 3 2 22 5 16 24 11 6 7 12 +mls_eng_000281 10 7 2 4 10 3 2 16 5 13 3 23 18 2 10 14 11 12 2 4 10 8 9 2 10 3 2 9 5 8 12 2 6 13 2 26 3 14 18 5 11 2 10 6 17 2 20 6 6 12 13 19 2 8 9 2 4 10 5 4 2 23 6 8 16 3 2 7 12 2 4 10 3 2 23 5 9 8 3 2 11 3 21 13 8 12 2 6 2 6 14 11 2 13 6 11 12 2 7 3 23 3 11 2 9 15 6 11 4 2 15 19 2 10 3 5 11 8 7 20 2 5 11 4 2 4 11 3 4 3 11 3 2 6 11 2 20 6 12 13 19 5 3 2 4 10 5 7 2 4 10 3 2 9 8 7 20 8 7 20 +mls_eng_000282 3 23 8 12 3 7 4 13 19 2 4 10 3 2 13 14 11 7 8 12 2 22 5 11 8 3 7 2 10 5 12 2 7 6 4 2 9 4 6 12 3 12 2 9 14 16 10 2 17 6 11 24 2 6 18 2 4 10 3 2 4 6 4 5 2 16 5 10 7 3 2 6 11 2 21 3 11 8 4 2 16 10 5 4 2 17 10 8 16 10 2 7 6 4 5 22 13 19 2 4 11 5 7 9 13 5 4 3 12 2 22 19 2 2 7 14 9 10 5 22 19 19 2 18 11 6 15 2 4 10 3 2 9 5 7 9 20 11 8 4 2 9 6 24 14 9 3 21 4 5 4 2 10 5 9 2 7 6 2 22 3 16 6 15 3 2 5 9 2 6 11 4 10 6 12 6 16 25 8 16 13 19 2 15 14 9 13 6 15 3 2 5 9 2 4 10 3 2 7 8 10 4 3 9 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/run.sh b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/run.sh new file mode 100755 index 0000000000000000000000000000000000000000..5bedbce4bb2096c8cb643b4c7afd6cf2b40297ab --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/run.sh @@ -0,0 +1 @@ +./asr.sh --ngpu 1 --stage 11 --stop_stage 13 --nj 32 --inference_nj 4 --gpu_inference true --lang eng1 --inference_asr_model valid.loss.ave.pth --local_data_opts '--duration 1h --lid false --multilingual false --single_lang eng1' --use_lm false --token_type char --feats_type raw --feats_normalize utterance_mvn --asr_config conf/tuning/train_asr_s3prl_houlsby.yaml --inference_config conf/decode_asr.yaml --train_set train_1h_eng1 --valid_set dev_10min_eng1 --test_sets 'dev_10min_eng1 test_10min_eng1' --asr_tag train_asr_s3prl_houlsby_eng1_1h --expdir test_pr --asr_stats_dir test_pr/asr_stats_eng1_1h --local_score_opts 'false false monolingual' --stage 12 "$@"; exit $? diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.1.log b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.1.log new file mode 100644 index 0000000000000000000000000000000000000000..65279612e905d69e81fedcb6d3c036160c5bc5cf --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.1.log @@ -0,0 +1,3022 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.1 --config conf/decode_asr.yaml +# Started at Wed Jan 17 01:17:27 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.1 --config conf/decode_asr.yaml +2024-01-17 01:17:28,776 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml +2024-01-17 01:17:28,794 (asr:523) INFO: Vocabulary size: 30 +2024-01-17 01:17:28,856 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 01:17:28,856 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 01:17:28,967 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 01:17:30,263 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 01:17:31,497 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 01:17:31,497 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 01:17:31,497 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 01:17:31,530 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-17 01:17:31,606 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 01:17:31,720 (asr_inference:494) INFO: speech length: 103766 +2024-01-17 01:17:32,930 (beam_search:428) INFO: decoder input length: 160 +2024-01-17 01:17:32,930 (beam_search:429) INFO: max output length: 160 +2024-01-17 01:17:32,930 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:33,327 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:33,327 (beam_search:476) INFO: -16.73 * 1.0 = -16.73 for ctc +2024-01-17 01:17:33,328 (beam_search:479) INFO: total log probability: -16.73 +2024-01-17 01:17:33,328 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:17:33,328 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:33,328 (beam_search:483) INFO: best hypo: HEREMAIEDWELCHAMPIANANTILNINTENSIXTYFIVEAYEARIWHCHSUFRDATERABLEACXIDENT + +2024-01-17 01:17:33,352 (asr_inference:494) INFO: speech length: 66902 +2024-01-17 01:17:33,362 (beam_search:428) INFO: decoder input length: 102 +2024-01-17 01:17:33,362 (beam_search:429) INFO: max output length: 102 +2024-01-17 01:17:33,362 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:33,529 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:33,529 (beam_search:476) INFO: -8.50 * 1.0 = -8.50 for ctc +2024-01-17 01:17:33,529 (beam_search:479) INFO: total log probability: -8.50 +2024-01-17 01:17:33,529 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:17:33,529 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:33,530 (beam_search:483) INFO: best hypo: AYLIBRALCONSEVITIVEHEWASDEFEATEDINATEINATYTO + +2024-01-17 01:17:33,531 (asr_inference:494) INFO: speech length: 60075 +2024-01-17 01:17:33,540 (beam_search:428) INFO: decoder input length: 91 +2024-01-17 01:17:33,540 (beam_search:429) INFO: max output length: 91 +2024-01-17 01:17:33,540 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:33,658 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:33,658 (beam_search:476) INFO: -8.92 * 1.0 = -8.92 for ctc +2024-01-17 01:17:33,658 (beam_search:479) INFO: total log probability: -8.92 +2024-01-17 01:17:33,658 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:17:33,658 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:33,658 (beam_search:483) INFO: best hypo: ONRODLAARCONDRARTWORODSATWOANCE + +2024-01-17 01:17:33,660 (asr_inference:494) INFO: speech length: 57344 +2024-01-17 01:17:33,669 (beam_search:428) INFO: decoder input length: 87 +2024-01-17 01:17:33,669 (beam_search:429) INFO: max output length: 87 +2024-01-17 01:17:33,669 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:33,802 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:33,803 (beam_search:476) INFO: -9.41 * 1.0 = -9.41 for ctc +2024-01-17 01:17:33,803 (beam_search:479) INFO: total log probability: -9.41 +2024-01-17 01:17:33,803 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:17:33,803 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:33,803 (beam_search:483) INFO: best hypo: SOMEOFTHECONTRESHVESURVAYSFORMALTIPLEYEARS + +2024-01-17 01:17:33,804 (asr_inference:494) INFO: speech length: 62806 +2024-01-17 01:17:33,814 (beam_search:428) INFO: decoder input length: 96 +2024-01-17 01:17:33,814 (beam_search:429) INFO: max output length: 96 +2024-01-17 01:17:33,814 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:33,958 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:33,958 (beam_search:476) INFO: -6.47 * 1.0 = -6.47 for ctc +2024-01-17 01:17:33,958 (beam_search:479) INFO: total log probability: -6.47 +2024-01-17 01:17:33,958 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:17:33,958 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:33,958 (beam_search:483) INFO: best hypo: BOTHOFTHEVRSIONSFEACHRTHESONGHAPYHOLIDAY + +2024-01-17 01:17:33,959 (asr_inference:494) INFO: speech length: 116054 +2024-01-17 01:17:33,972 (beam_search:428) INFO: decoder input length: 179 +2024-01-17 01:17:33,972 (beam_search:429) INFO: max output length: 179 +2024-01-17 01:17:33,972 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:34,415 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:34,415 (beam_search:476) INFO: -14.04 * 1.0 = -14.04 for ctc +2024-01-17 01:17:34,415 (beam_search:479) INFO: total log probability: -14.04 +2024-01-17 01:17:34,415 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:17:34,415 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:34,415 (beam_search:483) INFO: best hypo: SHAKXPIARMANYREFRNCESAREMADETOSENSINTRACTIONSORCARICTESFROMVARIOUSPLAYES + +2024-01-17 01:17:34,416 (asr_inference:494) INFO: speech length: 84651 +2024-01-17 01:17:34,427 (beam_search:428) INFO: decoder input length: 130 +2024-01-17 01:17:34,427 (beam_search:429) INFO: max output length: 130 +2024-01-17 01:17:34,427 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:34,699 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:34,699 (beam_search:476) INFO: -10.47 * 1.0 = -10.47 for ctc +2024-01-17 01:17:34,699 (beam_search:479) INFO: total log probability: -10.47 +2024-01-17 01:17:34,699 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:17:34,699 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:34,699 (beam_search:483) INFO: best hypo: IFONLYTHEROGRAMCULDBRAKEOUTJUSTAITLEFROMITSTOFOMILIARAPROCH + +2024-01-17 01:17:34,701 (asr_inference:494) INFO: speech length: 94208 +2024-01-17 01:17:34,712 (beam_search:428) INFO: decoder input length: 145 +2024-01-17 01:17:34,712 (beam_search:429) INFO: max output length: 145 +2024-01-17 01:17:34,712 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:35,037 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:35,037 (beam_search:476) INFO: -15.49 * 1.0 = -15.49 for ctc +2024-01-17 01:17:35,037 (beam_search:479) INFO: total log probability: -15.49 +2024-01-17 01:17:35,037 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:17:35,037 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:35,037 (beam_search:483) INFO: best hypo: THEHELBEMWASRELEASEDINOSTRALIARONNINTEINTHORGISTTWOTHOUSNDADELEVEN + +2024-01-17 01:17:35,039 (asr_inference:494) INFO: speech length: 64171 +2024-01-17 01:17:35,048 (beam_search:428) INFO: decoder input length: 98 +2024-01-17 01:17:35,048 (beam_search:429) INFO: max output length: 98 +2024-01-17 01:17:35,048 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:35,186 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:35,186 (beam_search:476) INFO: -8.68 * 1.0 = -8.68 for ctc +2024-01-17 01:17:35,186 (beam_search:479) INFO: total log probability: -8.68 +2024-01-17 01:17:35,186 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:17:35,186 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:35,186 (beam_search:483) INFO: best hypo: HENOWPLACEFORASTRALINCLOBEPERTHGLORY + +2024-01-17 01:17:35,187 (asr_inference:494) INFO: speech length: 61440 +2024-01-17 01:17:35,196 (beam_search:428) INFO: decoder input length: 93 +2024-01-17 01:17:35,196 (beam_search:429) INFO: max output length: 93 +2024-01-17 01:17:35,196 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:35,338 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:35,338 (beam_search:476) INFO: -6.35 * 1.0 = -6.35 for ctc +2024-01-17 01:17:35,338 (beam_search:479) INFO: total log probability: -6.35 +2024-01-17 01:17:35,338 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:17:35,338 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:35,338 (beam_search:483) INFO: best hypo: ITISNOTNONHOWMUCHIFEANYOFHECLAMSARTRU + +2024-01-17 01:17:35,339 (asr_inference:494) INFO: speech length: 113323 +2024-01-17 01:17:35,352 (beam_search:428) INFO: decoder input length: 175 +2024-01-17 01:17:35,352 (beam_search:429) INFO: max output length: 175 +2024-01-17 01:17:35,352 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:35,807 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:35,807 (beam_search:476) INFO: -19.43 * 1.0 = -19.43 for ctc +2024-01-17 01:17:35,807 (beam_search:479) INFO: total log probability: -19.43 +2024-01-17 01:17:35,807 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:17:35,807 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:35,807 (beam_search:483) INFO: best hypo: ASMALBISINESSONRBROARDOPRATEDHIWEATADSHEPFAMEFORSICTENYEARSFROTHEAGEOFWENTYTO + +2024-01-17 01:17:35,808 (asr_inference:494) INFO: speech length: 60075 +2024-01-17 01:17:35,817 (beam_search:428) INFO: decoder input length: 91 +2024-01-17 01:17:35,817 (beam_search:429) INFO: max output length: 91 +2024-01-17 01:17:35,817 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:35,935 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:35,935 (beam_search:476) INFO: -4.05 * 1.0 = -4.05 for ctc +2024-01-17 01:17:35,935 (beam_search:479) INFO: total log probability: -4.05 +2024-01-17 01:17:35,935 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 01:17:35,935 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:35,935 (beam_search:483) INFO: best hypo: INTHENINTHSENTURYHEWASANIRISHPOET + +2024-01-17 01:17:35,936 (asr_inference:494) INFO: speech length: 39595 +2024-01-17 01:17:35,944 (beam_search:428) INFO: decoder input length: 59 +2024-01-17 01:17:35,944 (beam_search:429) INFO: max output length: 59 +2024-01-17 01:17:35,944 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:35,999 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:35,999 (beam_search:476) INFO: -3.14 * 1.0 = -3.14 for ctc +2024-01-17 01:17:35,999 (beam_search:479) INFO: total log probability: -3.14 +2024-01-17 01:17:35,999 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 01:17:35,999 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:35,999 (beam_search:483) INFO: best hypo: THEYAREMARKEDBYSTRONG + +2024-01-17 01:17:36,000 (asr_inference:494) INFO: speech length: 43691 +2024-01-17 01:17:36,008 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:17:36,009 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:17:36,009 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:36,070 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:36,070 (beam_search:476) INFO: -4.41 * 1.0 = -4.41 for ctc +2024-01-17 01:17:36,070 (beam_search:479) INFO: total log probability: -4.41 +2024-01-17 01:17:36,070 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:17:36,070 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:36,070 (beam_search:483) INFO: best hypo: THELOWISTHEFORVAOLED + +2024-01-17 01:17:36,071 (asr_inference:494) INFO: speech length: 55979 +2024-01-17 01:17:36,080 (beam_search:428) INFO: decoder input length: 85 +2024-01-17 01:17:36,080 (beam_search:429) INFO: max output length: 85 +2024-01-17 01:17:36,080 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:36,192 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:36,192 (beam_search:476) INFO: -8.23 * 1.0 = -8.23 for ctc +2024-01-17 01:17:36,192 (beam_search:479) INFO: total log probability: -8.23 +2024-01-17 01:17:36,192 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:17:36,192 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:36,193 (beam_search:483) INFO: best hypo: INTHERLYSTAGESCAMECLOSETOUSASLEP + +2024-01-17 01:17:36,194 (asr_inference:494) INFO: speech length: 55979 +2024-01-17 01:17:36,202 (beam_search:428) INFO: decoder input length: 85 +2024-01-17 01:17:36,202 (beam_search:429) INFO: max output length: 85 +2024-01-17 01:17:36,202 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:36,323 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:36,323 (beam_search:476) INFO: -10.88 * 1.0 = -10.88 for ctc +2024-01-17 01:17:36,323 (beam_search:479) INFO: total log probability: -10.88 +2024-01-17 01:17:36,323 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:17:36,323 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:36,324 (beam_search:483) INFO: best hypo: RONINGEVERYTHRTYMINUTTHROATSERVISTIMS + +2024-01-17 01:17:36,325 (asr_inference:494) INFO: speech length: 83286 +2024-01-17 01:17:36,335 (beam_search:428) INFO: decoder input length: 128 +2024-01-17 01:17:36,335 (beam_search:429) INFO: max output length: 128 +2024-01-17 01:17:36,335 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:36,585 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:36,585 (beam_search:476) INFO: -8.88 * 1.0 = -8.88 for ctc +2024-01-17 01:17:36,585 (beam_search:479) INFO: total log probability: -8.88 +2024-01-17 01:17:36,585 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:17:36,585 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:36,585 (beam_search:483) INFO: best hypo: ASARESIULTWHENTHECOLIGEREOPENDITWASASANALLMALECOLIGE + +2024-01-17 01:17:36,587 (asr_inference:494) INFO: speech length: 90112 +2024-01-17 01:17:36,597 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 01:17:36,597 (beam_search:429) INFO: max output length: 138 +2024-01-17 01:17:36,597 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:36,922 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:36,922 (beam_search:476) INFO: -11.91 * 1.0 = -11.91 for ctc +2024-01-17 01:17:36,922 (beam_search:479) INFO: total log probability: -11.91 +2024-01-17 01:17:36,922 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:17:36,922 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:36,922 (beam_search:483) INFO: best hypo: THETIMEBETWEETHESPOINCTISVERIABLANDCANACURANYWHERFROAMINITTOMUCHLONGER + +2024-01-17 01:17:36,924 (asr_inference:494) INFO: speech length: 111958 +2024-01-17 01:17:36,936 (beam_search:428) INFO: decoder input length: 172 +2024-01-17 01:17:36,936 (beam_search:429) INFO: max output length: 172 +2024-01-17 01:17:36,936 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:37,367 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:37,367 (beam_search:476) INFO: -11.70 * 1.0 = -11.70 for ctc +2024-01-17 01:17:37,367 (beam_search:479) INFO: total log probability: -11.70 +2024-01-17 01:17:37,367 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:17:37,367 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:37,368 (beam_search:483) INFO: best hypo: WOARKONTHEEAEESTARTEDINMARCHTWOTHOUSNDANDSEVENATACOSTOFFIVEMILIANDOLERS + +2024-01-17 01:17:37,369 (asr_inference:494) INFO: speech length: 117419 +2024-01-17 01:17:37,381 (beam_search:428) INFO: decoder input length: 181 +2024-01-17 01:17:37,381 (beam_search:429) INFO: max output length: 181 +2024-01-17 01:17:37,381 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:37,913 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:37,914 (beam_search:476) INFO: -16.47 * 1.0 = -16.47 for ctc +2024-01-17 01:17:37,914 (beam_search:479) INFO: total log probability: -16.47 +2024-01-17 01:17:37,914 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:17:37,914 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:37,914 (beam_search:483) INFO: best hypo: HOWEVERTHERWASSOMEDIAGREMENTOVTHENDINGTHEMEWHICHORMORYANDYOHIMORYDISCUSTDATLENGTHOVEREMAL + +2024-01-17 01:17:37,916 (asr_inference:494) INFO: speech length: 38230 +2024-01-17 01:17:37,923 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:17:37,923 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:17:37,923 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:37,975 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:37,975 (beam_search:476) INFO: -3.72 * 1.0 = -3.72 for ctc +2024-01-17 01:17:37,975 (beam_search:479) INFO: total log probability: -3.72 +2024-01-17 01:17:37,975 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:17:37,975 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:37,976 (beam_search:483) INFO: best hypo: THECOPLEHADNOCHILDRAN + +2024-01-17 01:17:37,977 (asr_inference:494) INFO: speech length: 98304 +2024-01-17 01:17:37,988 (beam_search:428) INFO: decoder input length: 151 +2024-01-17 01:17:37,988 (beam_search:429) INFO: max output length: 151 +2024-01-17 01:17:37,988 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:38,309 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:38,309 (beam_search:476) INFO: -13.16 * 1.0 = -13.16 for ctc +2024-01-17 01:17:38,309 (beam_search:479) INFO: total log probability: -13.16 +2024-01-17 01:17:38,309 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:17:38,309 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:38,309 (beam_search:483) INFO: best hypo: THEFIALSINGLOTHATDEBUALBHMPARISCOLINGHADANELABRTMUSICVIDIO + +2024-01-17 01:17:38,311 (asr_inference:494) INFO: speech length: 102400 +2024-01-17 01:17:38,322 (beam_search:428) INFO: decoder input length: 157 +2024-01-17 01:17:38,322 (beam_search:429) INFO: max output length: 157 +2024-01-17 01:17:38,322 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:38,709 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:38,709 (beam_search:476) INFO: -12.66 * 1.0 = -12.66 for ctc +2024-01-17 01:17:38,709 (beam_search:479) INFO: total log probability: -12.66 +2024-01-17 01:17:38,709 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:17:38,709 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:38,710 (beam_search:483) INFO: best hypo: THESERISENDEDONSIXTHORGESTTOTHOUSNDANDFORLASTINGFRATOUTEOFSEVENTYONDAYS + +2024-01-17 01:17:38,711 (asr_inference:494) INFO: speech length: 62806 +2024-01-17 01:17:38,721 (beam_search:428) INFO: decoder input length: 96 +2024-01-17 01:17:38,721 (beam_search:429) INFO: max output length: 96 +2024-01-17 01:17:38,721 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:38,879 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:38,879 (beam_search:476) INFO: -9.90 * 1.0 = -9.90 for ctc +2024-01-17 01:17:38,879 (beam_search:479) INFO: total log probability: -9.90 +2024-01-17 01:17:38,879 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:17:38,879 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:38,879 (beam_search:483) INFO: best hypo: HEHASALSOCONTRIBUTEDTOTHENEWYORKREVIOOFBOOKS + +2024-01-17 01:17:38,880 (asr_inference:494) INFO: speech length: 60075 +2024-01-17 01:17:38,889 (beam_search:428) INFO: decoder input length: 91 +2024-01-17 01:17:38,889 (beam_search:429) INFO: max output length: 91 +2024-01-17 01:17:38,889 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:39,014 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:39,014 (beam_search:476) INFO: -5.50 * 1.0 = -5.50 for ctc +2024-01-17 01:17:39,014 (beam_search:479) INFO: total log probability: -5.50 +2024-01-17 01:17:39,014 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:17:39,014 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:39,014 (beam_search:483) INFO: best hypo: BYPLACINGSMALARTOBJECTTROOUTTHEFILM + +2024-01-17 01:17:39,015 (asr_inference:494) INFO: speech length: 39595 +2024-01-17 01:17:39,023 (beam_search:428) INFO: decoder input length: 59 +2024-01-17 01:17:39,023 (beam_search:429) INFO: max output length: 59 +2024-01-17 01:17:39,023 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:39,071 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:39,071 (beam_search:476) INFO: -2.59 * 1.0 = -2.59 for ctc +2024-01-17 01:17:39,071 (beam_search:479) INFO: total log probability: -2.59 +2024-01-17 01:17:39,071 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 01:17:39,071 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:39,071 (beam_search:483) INFO: best hypo: ITISFOUNDINBRESIL + +2024-01-17 01:17:39,072 (asr_inference:494) INFO: speech length: 61440 +2024-01-17 01:17:39,081 (beam_search:428) INFO: decoder input length: 93 +2024-01-17 01:17:39,081 (beam_search:429) INFO: max output length: 93 +2024-01-17 01:17:39,081 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:39,220 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:39,220 (beam_search:476) INFO: -3.61 * 1.0 = -3.61 for ctc +2024-01-17 01:17:39,220 (beam_search:479) INFO: total log probability: -3.61 +2024-01-17 01:17:39,220 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 01:17:39,220 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:39,220 (beam_search:483) INFO: best hypo: ITWSTHESIDOFTHEFAMLYIIDENTIFIEDMOREWITH + +2024-01-17 01:17:39,222 (asr_inference:494) INFO: speech length: 66902 +2024-01-17 01:17:39,231 (beam_search:428) INFO: decoder input length: 102 +2024-01-17 01:17:39,231 (beam_search:429) INFO: max output length: 102 +2024-01-17 01:17:39,231 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:39,379 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:39,379 (beam_search:476) INFO: -8.35 * 1.0 = -8.35 for ctc +2024-01-17 01:17:39,379 (beam_search:479) INFO: total log probability: -8.35 +2024-01-17 01:17:39,379 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:17:39,379 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:39,380 (beam_search:483) INFO: best hypo: HCANDITSIGHTESMUSTALSORSOBMITAWORKPLAN + +2024-01-17 01:17:39,381 (asr_inference:494) INFO: speech length: 54614 +2024-01-17 01:17:39,389 (beam_search:428) INFO: decoder input length: 83 +2024-01-17 01:17:39,389 (beam_search:429) INFO: max output length: 83 +2024-01-17 01:17:39,389 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:39,469 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:39,469 (beam_search:476) INFO: -3.94 * 1.0 = -3.94 for ctc +2024-01-17 01:17:39,469 (beam_search:479) INFO: total log probability: -3.94 +2024-01-17 01:17:39,469 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:17:39,469 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:39,469 (beam_search:483) INFO: best hypo: DUNDEYWHNTHEMACHTHRETO + +2024-01-17 01:17:39,470 (asr_inference:494) INFO: speech length: 99670 +2024-01-17 01:17:39,481 (beam_search:428) INFO: decoder input length: 153 +2024-01-17 01:17:39,481 (beam_search:429) INFO: max output length: 153 +2024-01-17 01:17:39,481 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:39,874 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:39,874 (beam_search:476) INFO: -13.02 * 1.0 = -13.02 for ctc +2024-01-17 01:17:39,874 (beam_search:479) INFO: total log probability: -13.02 +2024-01-17 01:17:39,874 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:17:39,874 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:39,875 (beam_search:483) INFO: best hypo: HOWEVERTHEVILIGEREMAINDICALATEDANTILTHERIVELOFTHEFIRSTNOUSPAPERSECONDREPOUBLICK + +2024-01-17 01:17:39,876 (asr_inference:494) INFO: speech length: 107862 +2024-01-17 01:17:39,888 (beam_search:428) INFO: decoder input length: 166 +2024-01-17 01:17:39,888 (beam_search:429) INFO: max output length: 166 +2024-01-17 01:17:39,888 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:40,325 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:40,325 (beam_search:476) INFO: -15.73 * 1.0 = -15.73 for ctc +2024-01-17 01:17:40,325 (beam_search:479) INFO: total log probability: -15.73 +2024-01-17 01:17:40,325 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:17:40,325 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:40,326 (beam_search:483) INFO: best hypo: THEFASTSERVISITHEEUCHURCWASHELDININTEFIFTYONALTHOTHEBILDIGWASNOTFULYFINISHED + +2024-01-17 01:17:40,327 (asr_inference:494) INFO: speech length: 113323 +2024-01-17 01:17:40,339 (beam_search:428) INFO: decoder input length: 175 +2024-01-17 01:17:40,339 (beam_search:429) INFO: max output length: 175 +2024-01-17 01:17:40,339 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:40,799 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:40,799 (beam_search:476) INFO: -18.21 * 1.0 = -18.21 for ctc +2024-01-17 01:17:40,799 (beam_search:479) INFO: total log probability: -18.21 +2024-01-17 01:17:40,799 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:17:40,799 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:40,799 (beam_search:483) INFO: best hypo: THEAVERIGEHOUSEHLDSIEWASTWOPOINTTOSEVENNDTHEAVERIGHFAMLYSIEWASTHREPOINTIROSRO + +2024-01-17 01:17:40,801 (asr_inference:494) INFO: speech length: 79190 +2024-01-17 01:17:40,811 (beam_search:428) INFO: decoder input length: 121 +2024-01-17 01:17:40,811 (beam_search:429) INFO: max output length: 121 +2024-01-17 01:17:40,811 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:41,026 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:41,027 (beam_search:476) INFO: -8.04 * 1.0 = -8.04 for ctc +2024-01-17 01:17:41,027 (beam_search:479) INFO: total log probability: -8.04 +2024-01-17 01:17:41,027 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:17:41,027 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:41,027 (beam_search:483) INFO: best hypo: ITWASFIRSTBRADCASTONTHIRDGANIURYTWOTHOUSONDNDTEN + +2024-01-17 01:17:41,028 (asr_inference:494) INFO: speech length: 47787 +2024-01-17 01:17:41,037 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 01:17:41,037 (beam_search:429) INFO: max output length: 72 +2024-01-17 01:17:41,037 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:41,129 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:41,129 (beam_search:476) INFO: -5.11 * 1.0 = -5.11 for ctc +2024-01-17 01:17:41,129 (beam_search:479) INFO: total log probability: -5.11 +2024-01-17 01:17:41,130 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:17:41,130 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:41,130 (beam_search:483) INFO: best hypo: THEWINGSWEROWMADINASINGLEPRESING + +2024-01-17 01:17:41,131 (asr_inference:494) INFO: speech length: 64171 +2024-01-17 01:17:41,140 (beam_search:428) INFO: decoder input length: 98 +2024-01-17 01:17:41,140 (beam_search:429) INFO: max output length: 98 +2024-01-17 01:17:41,140 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:41,280 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:41,281 (beam_search:476) INFO: -12.58 * 1.0 = -12.58 for ctc +2024-01-17 01:17:41,281 (beam_search:479) INFO: total log probability: -12.58 +2024-01-17 01:17:41,281 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:17:41,281 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:41,281 (beam_search:483) INFO: best hypo: HEDOCTROHLOSOFYINENGENEARINGMANAGEMENT + +2024-01-17 01:17:41,282 (asr_inference:494) INFO: speech length: 61440 +2024-01-17 01:17:41,291 (beam_search:428) INFO: decoder input length: 93 +2024-01-17 01:17:41,291 (beam_search:429) INFO: max output length: 93 +2024-01-17 01:17:41,291 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:41,425 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:41,425 (beam_search:476) INFO: -8.25 * 1.0 = -8.25 for ctc +2024-01-17 01:17:41,425 (beam_search:479) INFO: total log probability: -8.25 +2024-01-17 01:17:41,425 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:17:41,425 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:41,426 (beam_search:483) INFO: best hypo: THISTOKWAYTHEMAINARGUMENTOFSAFTYRISSK + +2024-01-17 01:17:41,427 (asr_inference:494) INFO: speech length: 66902 +2024-01-17 01:17:41,436 (beam_search:428) INFO: decoder input length: 102 +2024-01-17 01:17:41,436 (beam_search:429) INFO: max output length: 102 +2024-01-17 01:17:41,436 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:41,600 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:41,600 (beam_search:476) INFO: -7.74 * 1.0 = -7.74 for ctc +2024-01-17 01:17:41,600 (beam_search:479) INFO: total log probability: -7.74 +2024-01-17 01:17:41,600 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:17:41,600 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:41,600 (beam_search:483) INFO: best hypo: HEWASALSOMADALIFEMEMBEROFSGUNTHORPPUNITED + +2024-01-17 01:17:41,601 (asr_inference:494) INFO: speech length: 62806 +2024-01-17 01:17:41,611 (beam_search:428) INFO: decoder input length: 96 +2024-01-17 01:17:41,611 (beam_search:429) INFO: max output length: 96 +2024-01-17 01:17:41,611 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:41,767 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:41,768 (beam_search:476) INFO: -9.07 * 1.0 = -9.07 for ctc +2024-01-17 01:17:41,768 (beam_search:479) INFO: total log probability: -9.07 +2024-01-17 01:17:41,768 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:17:41,768 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:41,768 (beam_search:483) INFO: best hypo: SHEFIARSTHEYWILGETADEVORSEBUTTHISNEVERHAPENS + +2024-01-17 01:17:41,769 (asr_inference:494) INFO: speech length: 64171 +2024-01-17 01:17:41,778 (beam_search:428) INFO: decoder input length: 98 +2024-01-17 01:17:41,778 (beam_search:429) INFO: max output length: 98 +2024-01-17 01:17:41,778 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:41,924 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:41,924 (beam_search:476) INFO: -8.97 * 1.0 = -8.97 for ctc +2024-01-17 01:17:41,924 (beam_search:479) INFO: total log probability: -8.97 +2024-01-17 01:17:41,924 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:17:41,924 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:41,924 (beam_search:483) INFO: best hypo: FOUTDROPSINABLETOHADTHEFOTSTRATACROSE + +2024-01-17 01:17:41,926 (asr_inference:494) INFO: speech length: 87382 +2024-01-17 01:17:41,936 (beam_search:428) INFO: decoder input length: 134 +2024-01-17 01:17:41,936 (beam_search:429) INFO: max output length: 134 +2024-01-17 01:17:41,936 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:42,212 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:42,212 (beam_search:476) INFO: -13.38 * 1.0 = -13.38 for ctc +2024-01-17 01:17:42,212 (beam_search:479) INFO: total log probability: -13.38 +2024-01-17 01:17:42,212 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:17:42,212 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:42,213 (beam_search:483) INFO: best hypo: WHETETHEARFLOISFREYORFORSTCNFECTHEENAGYAFIANCYOFTHEENDO + +2024-01-17 01:17:42,214 (asr_inference:494) INFO: speech length: 57344 +2024-01-17 01:17:42,223 (beam_search:428) INFO: decoder input length: 87 +2024-01-17 01:17:42,223 (beam_search:429) INFO: max output length: 87 +2024-01-17 01:17:42,223 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:42,356 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:42,356 (beam_search:476) INFO: -12.04 * 1.0 = -12.04 for ctc +2024-01-17 01:17:42,356 (beam_search:479) INFO: total log probability: -12.04 +2024-01-17 01:17:42,356 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:17:42,356 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:42,356 (beam_search:483) INFO: best hypo: AFTERGETINHERIHTMASURMENTTHEYMADTHENEWDORS + +2024-01-17 01:17:42,357 (asr_inference:494) INFO: speech length: 72363 +2024-01-17 01:17:42,367 (beam_search:428) INFO: decoder input length: 111 +2024-01-17 01:17:42,367 (beam_search:429) INFO: max output length: 111 +2024-01-17 01:17:42,367 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:42,543 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:42,543 (beam_search:476) INFO: -9.24 * 1.0 = -9.24 for ctc +2024-01-17 01:17:42,543 (beam_search:479) INFO: total log probability: -9.24 +2024-01-17 01:17:42,543 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:17:42,543 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:42,543 (beam_search:483) INFO: best hypo: FRAGMENTSONACHFACEAREMAREWTHLETERSAYBESE + +2024-01-17 01:17:42,544 (asr_inference:494) INFO: speech length: 87382 +2024-01-17 01:17:42,555 (beam_search:428) INFO: decoder input length: 134 +2024-01-17 01:17:42,555 (beam_search:429) INFO: max output length: 134 +2024-01-17 01:17:42,555 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:42,857 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:42,857 (beam_search:476) INFO: -14.98 * 1.0 = -14.98 for ctc +2024-01-17 01:17:42,857 (beam_search:479) INFO: total log probability: -14.98 +2024-01-17 01:17:42,857 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:17:42,857 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:42,857 (beam_search:483) INFO: best hypo: FROMTHEFIRSTMINITSBOTHTEMESSHOWDTHEDISIRETOCMPEETWITHEGEIVEAPROCHES + +2024-01-17 01:17:42,859 (asr_inference:494) INFO: speech length: 81920 +2024-01-17 01:17:42,869 (beam_search:428) INFO: decoder input length: 125 +2024-01-17 01:17:42,869 (beam_search:429) INFO: max output length: 125 +2024-01-17 01:17:42,869 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:43,115 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:43,115 (beam_search:476) INFO: -12.42 * 1.0 = -12.42 for ctc +2024-01-17 01:17:43,115 (beam_search:479) INFO: total log probability: -12.42 +2024-01-17 01:17:43,115 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:17:43,115 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:43,115 (beam_search:483) INFO: best hypo: FISICLHERIPYEXCUSISESMAYHELPPATIONTTOMAINTAINMULESTRINGTH + +2024-01-17 01:17:43,116 (asr_inference:494) INFO: speech length: 75094 +2024-01-17 01:17:43,126 (beam_search:428) INFO: decoder input length: 115 +2024-01-17 01:17:43,126 (beam_search:429) INFO: max output length: 115 +2024-01-17 01:17:43,126 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:43,325 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:43,325 (beam_search:476) INFO: -5.82 * 1.0 = -5.82 for ctc +2024-01-17 01:17:43,325 (beam_search:479) INFO: total log probability: -5.82 +2024-01-17 01:17:43,325 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 01:17:43,325 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:43,325 (beam_search:483) INFO: best hypo: HOWEVERTHETOWNSHELIVSINNOONWANTSTOHERABOUTHER + +2024-01-17 01:17:43,327 (asr_inference:494) INFO: speech length: 95574 +2024-01-17 01:17:43,338 (beam_search:428) INFO: decoder input length: 147 +2024-01-17 01:17:43,338 (beam_search:429) INFO: max output length: 147 +2024-01-17 01:17:43,338 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:43,665 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:43,666 (beam_search:476) INFO: -13.99 * 1.0 = -13.99 for ctc +2024-01-17 01:17:43,666 (beam_search:479) INFO: total log probability: -13.99 +2024-01-17 01:17:43,666 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:17:43,666 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:43,666 (beam_search:483) INFO: best hypo: ANDDISCRIVESAPOINTMETOFANACTINGCHEVEJUSTISORJUDGEOFTHESUPREMECORT + +2024-01-17 01:17:43,667 (asr_inference:494) INFO: speech length: 84651 +2024-01-17 01:17:43,678 (beam_search:428) INFO: decoder input length: 130 +2024-01-17 01:17:43,678 (beam_search:429) INFO: max output length: 130 +2024-01-17 01:17:43,678 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:43,952 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:43,952 (beam_search:476) INFO: -10.68 * 1.0 = -10.68 for ctc +2024-01-17 01:17:43,952 (beam_search:479) INFO: total log probability: -10.68 +2024-01-17 01:17:43,952 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:17:43,952 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:43,953 (beam_search:483) INFO: best hypo: THESOYBENSOUTACOVERINGISTHENREMOVEDANDTHEBENSAREPARTIALYCOOKED + +2024-01-17 01:17:43,954 (asr_inference:494) INFO: speech length: 95574 +2024-01-17 01:17:43,965 (beam_search:428) INFO: decoder input length: 147 +2024-01-17 01:17:43,965 (beam_search:429) INFO: max output length: 147 +2024-01-17 01:17:43,965 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:44,263 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:44,263 (beam_search:476) INFO: -9.48 * 1.0 = -9.48 for ctc +2024-01-17 01:17:44,263 (beam_search:479) INFO: total log probability: -9.48 +2024-01-17 01:17:44,263 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:17:44,263 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:44,264 (beam_search:483) INFO: best hypo: THISNASINALEMOVMENTWHICHADBEGUNWITHSOMUCHHOPCAMETOASADEND + +2024-01-17 01:17:44,265 (asr_inference:494) INFO: speech length: 75094 +2024-01-17 01:17:44,274 (beam_search:428) INFO: decoder input length: 115 +2024-01-17 01:17:44,274 (beam_search:429) INFO: max output length: 115 +2024-01-17 01:17:44,274 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:44,463 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:44,463 (beam_search:476) INFO: -9.63 * 1.0 = -9.63 for ctc +2024-01-17 01:17:44,463 (beam_search:479) INFO: total log probability: -9.63 +2024-01-17 01:17:44,463 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:17:44,463 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:44,463 (beam_search:483) INFO: best hypo: HISASOSCIATYUSUALYCALDHIMTEORTHEODLOKINGGIY + +2024-01-17 01:17:44,464 (asr_inference:494) INFO: speech length: 76459 +2024-01-17 01:17:44,474 (beam_search:428) INFO: decoder input length: 117 +2024-01-17 01:17:44,474 (beam_search:429) INFO: max output length: 117 +2024-01-17 01:17:44,474 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:44,673 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:44,673 (beam_search:476) INFO: -12.85 * 1.0 = -12.85 for ctc +2024-01-17 01:17:44,673 (beam_search:479) INFO: total log probability: -12.85 +2024-01-17 01:17:44,673 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:17:44,673 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:44,674 (beam_search:483) INFO: best hypo: ITSMAINOFICESWERINLUNDANWIEHESECNDOFISBELLFAST + +2024-01-17 01:17:44,675 (asr_inference:494) INFO: speech length: 62806 +2024-01-17 01:17:44,684 (beam_search:428) INFO: decoder input length: 96 +2024-01-17 01:17:44,684 (beam_search:429) INFO: max output length: 96 +2024-01-17 01:17:44,684 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:44,819 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:44,819 (beam_search:476) INFO: -5.70 * 1.0 = -5.70 for ctc +2024-01-17 01:17:44,819 (beam_search:479) INFO: total log probability: -5.70 +2024-01-17 01:17:44,819 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:17:44,819 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:44,819 (beam_search:483) INFO: best hypo: ACTULYIHADNEVERBENTOAVILIGEBEFORTHAT + +2024-01-17 01:17:44,820 (asr_inference:494) INFO: speech length: 81920 +2024-01-17 01:17:44,831 (beam_search:428) INFO: decoder input length: 125 +2024-01-17 01:17:44,831 (beam_search:429) INFO: max output length: 125 +2024-01-17 01:17:44,831 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:45,078 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:45,078 (beam_search:476) INFO: -6.95 * 1.0 = -6.95 for ctc +2024-01-17 01:17:45,078 (beam_search:479) INFO: total log probability: -6.95 +2024-01-17 01:17:45,078 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 01:17:45,078 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:45,078 (beam_search:483) INFO: best hypo: HEASCHARGEDITHPLANINGTOSETOFBOMSINUROPANDTHEUNITEDSTATE + +2024-01-17 01:17:45,079 (asr_inference:494) INFO: speech length: 102400 +2024-01-17 01:17:45,091 (beam_search:428) INFO: decoder input length: 157 +2024-01-17 01:17:45,091 (beam_search:429) INFO: max output length: 157 +2024-01-17 01:17:45,091 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:45,418 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:45,418 (beam_search:476) INFO: -14.82 * 1.0 = -14.82 for ctc +2024-01-17 01:17:45,418 (beam_search:479) INFO: total log probability: -14.82 +2024-01-17 01:17:45,418 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:17:45,418 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:45,419 (beam_search:483) INFO: best hypo: MAKINGMERSISTHEHIRDSTUDORHLBUMBYBELGENASTRALIANARTISTGOTIAY + +2024-01-17 01:17:45,420 (asr_inference:494) INFO: speech length: 109227 +2024-01-17 01:17:45,432 (beam_search:428) INFO: decoder input length: 168 +2024-01-17 01:17:45,432 (beam_search:429) INFO: max output length: 168 +2024-01-17 01:17:45,432 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:45,848 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:45,848 (beam_search:476) INFO: -13.80 * 1.0 = -13.80 for ctc +2024-01-17 01:17:45,848 (beam_search:479) INFO: total log probability: -13.80 +2024-01-17 01:17:45,848 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:17:45,848 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:45,848 (beam_search:483) INFO: best hypo: HETHENMOVEDTOWASINGTONDESEANDWASAPARTNRITHWARDBRONANDTILNINTENTWENTYNIN + +2024-01-17 01:17:45,850 (asr_inference:494) INFO: speech length: 76459 +2024-01-17 01:17:45,859 (beam_search:428) INFO: decoder input length: 117 +2024-01-17 01:17:45,859 (beam_search:429) INFO: max output length: 117 +2024-01-17 01:17:45,859 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:46,067 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:46,068 (beam_search:476) INFO: -10.12 * 1.0 = -10.12 for ctc +2024-01-17 01:17:46,068 (beam_search:479) INFO: total log probability: -10.12 +2024-01-17 01:17:46,068 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:17:46,068 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:46,068 (beam_search:483) INFO: best hypo: JOSOFHIYSCOLEANDTHESCOLESTHECMPEGAINEINALSPORTS + +2024-01-17 01:17:46,069 (asr_inference:494) INFO: speech length: 49152 +2024-01-17 01:17:46,077 (beam_search:428) INFO: decoder input length: 74 +2024-01-17 01:17:46,077 (beam_search:429) INFO: max output length: 74 +2024-01-17 01:17:46,078 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:46,155 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:46,155 (beam_search:476) INFO: -5.44 * 1.0 = -5.44 for ctc +2024-01-17 01:17:46,155 (beam_search:479) INFO: total log probability: -5.44 +2024-01-17 01:17:46,155 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:17:46,155 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:46,155 (beam_search:483) INFO: best hypo: TWELFPLUSONMACHBANPERCARD + +2024-01-17 01:17:46,156 (asr_inference:494) INFO: speech length: 43691 +2024-01-17 01:17:46,164 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:17:46,164 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:17:46,164 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:46,225 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:46,225 (beam_search:476) INFO: -2.50 * 1.0 = -2.50 for ctc +2024-01-17 01:17:46,225 (beam_search:479) INFO: total log probability: -2.50 +2024-01-17 01:17:46,225 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 01:17:46,225 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:46,225 (beam_search:483) INFO: best hypo: IHINKIMIGHTBENOTHING + +2024-01-17 01:17:46,226 (asr_inference:494) INFO: speech length: 118784 +2024-01-17 01:17:46,239 (beam_search:428) INFO: decoder input length: 183 +2024-01-17 01:17:46,239 (beam_search:429) INFO: max output length: 183 +2024-01-17 01:17:46,239 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:46,736 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:46,736 (beam_search:476) INFO: -12.56 * 1.0 = -12.56 for ctc +2024-01-17 01:17:46,736 (beam_search:479) INFO: total log probability: -12.56 +2024-01-17 01:17:46,736 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:17:46,736 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:46,737 (beam_search:483) INFO: best hypo: THEHOEWASBILTANDLIVEDINBYANDRUJACXANDCANDYDEPUTYCLECTEOTHEINTERNALREVINOUSERVIS + +2024-01-17 01:17:46,738 (asr_inference:494) INFO: speech length: 90112 +2024-01-17 01:17:46,749 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 01:17:46,749 (beam_search:429) INFO: max output length: 138 +2024-01-17 01:17:46,749 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:47,036 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:47,036 (beam_search:476) INFO: -11.93 * 1.0 = -11.93 for ctc +2024-01-17 01:17:47,036 (beam_search:479) INFO: total log probability: -11.93 +2024-01-17 01:17:47,036 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:17:47,036 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:47,036 (beam_search:483) INFO: best hypo: INNINTENSIXTYFORHEWENTBACTOOMSKANDENTETHEACTOASCOLOFOMPS + +2024-01-17 01:17:47,038 (asr_inference:494) INFO: speech length: 69632 +2024-01-17 01:17:47,048 (beam_search:428) INFO: decoder input length: 106 +2024-01-17 01:17:47,048 (beam_search:429) INFO: max output length: 106 +2024-01-17 01:17:47,048 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:47,234 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:47,234 (beam_search:476) INFO: -6.11 * 1.0 = -6.11 for ctc +2024-01-17 01:17:47,234 (beam_search:479) INFO: total log probability: -6.11 +2024-01-17 01:17:47,234 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 01:17:47,234 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:47,234 (beam_search:483) INFO: best hypo: THEBANKISJOUNTLYONDBYHIMANDHISBROVERANDRELITIVES + +2024-01-17 01:17:47,235 (asr_inference:494) INFO: speech length: 46422 +2024-01-17 01:17:47,243 (beam_search:428) INFO: decoder input length: 70 +2024-01-17 01:17:47,243 (beam_search:429) INFO: max output length: 70 +2024-01-17 01:17:47,243 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:47,330 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:47,330 (beam_search:476) INFO: -6.74 * 1.0 = -6.74 for ctc +2024-01-17 01:17:47,330 (beam_search:479) INFO: total log probability: -6.74 +2024-01-17 01:17:47,330 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:17:47,330 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:47,331 (beam_search:483) INFO: best hypo: HESUBPSICUNTLYWENTTOCOLINBRISTAL + +2024-01-17 01:17:47,332 (asr_inference:494) INFO: speech length: 69632 +2024-01-17 01:17:47,341 (beam_search:428) INFO: decoder input length: 106 +2024-01-17 01:17:47,341 (beam_search:429) INFO: max output length: 106 +2024-01-17 01:17:47,341 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:47,493 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:47,493 (beam_search:476) INFO: -8.85 * 1.0 = -8.85 for ctc +2024-01-17 01:17:47,493 (beam_search:479) INFO: total log probability: -8.85 +2024-01-17 01:17:47,493 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:17:47,493 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:47,494 (beam_search:483) INFO: best hypo: WONTHOUSANDATHUNDRDFOARTYSICXFORHEDION + +2024-01-17 01:17:47,495 (asr_inference:494) INFO: speech length: 103766 +2024-01-17 01:17:47,506 (beam_search:428) INFO: decoder input length: 160 +2024-01-17 01:17:47,506 (beam_search:429) INFO: max output length: 160 +2024-01-17 01:17:47,506 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:47,914 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:47,914 (beam_search:476) INFO: -12.25 * 1.0 = -12.25 for ctc +2024-01-17 01:17:47,914 (beam_search:479) INFO: total log probability: -12.25 +2024-01-17 01:17:47,914 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:17:47,914 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:47,914 (beam_search:483) INFO: best hypo: APARTOFLITLINGLANDBEYONDWALSITHASBEACENCHALYINGLISHSPEAKINGFORNINHUNDREDYEARS + +2024-01-17 01:17:47,916 (asr_inference:494) INFO: speech length: 87382 +2024-01-17 01:17:47,926 (beam_search:428) INFO: decoder input length: 134 +2024-01-17 01:17:47,926 (beam_search:429) INFO: max output length: 134 +2024-01-17 01:17:47,926 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:48,168 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:48,168 (beam_search:476) INFO: -12.92 * 1.0 = -12.92 for ctc +2024-01-17 01:17:48,168 (beam_search:479) INFO: total log probability: -12.92 +2024-01-17 01:17:48,169 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:17:48,169 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:48,169 (beam_search:483) INFO: best hypo: HEPLADWTHTENPLAYARSFORHARFWASAGAINEATRDIONINJEESP + +2024-01-17 01:17:48,170 (asr_inference:494) INFO: speech length: 109227 +2024-01-17 01:17:48,182 (beam_search:428) INFO: decoder input length: 168 +2024-01-17 01:17:48,182 (beam_search:429) INFO: max output length: 168 +2024-01-17 01:17:48,182 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:48,625 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:48,625 (beam_search:476) INFO: -14.38 * 1.0 = -14.38 for ctc +2024-01-17 01:17:48,625 (beam_search:479) INFO: total log probability: -14.38 +2024-01-17 01:17:48,625 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:17:48,625 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:48,625 (beam_search:483) INFO: best hypo: THERESIDINGJUDGEWASWEBSTAFAIRHOWASALREADYASINDTOTHECORTBEFORETHISCACEWASHEDILD + +2024-01-17 01:17:48,627 (asr_inference:494) INFO: speech length: 77824 +2024-01-17 01:17:48,637 (beam_search:428) INFO: decoder input length: 119 +2024-01-17 01:17:48,637 (beam_search:429) INFO: max output length: 119 +2024-01-17 01:17:48,637 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:48,873 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:48,873 (beam_search:476) INFO: -14.54 * 1.0 = -14.54 for ctc +2024-01-17 01:17:48,873 (beam_search:479) INFO: total log probability: -14.54 +2024-01-17 01:17:48,873 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:17:48,873 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:48,874 (beam_search:483) INFO: best hypo: BIGBRATHERFIVEWASTHEHURDOFHEMAINSARISTOFEACUERALIVELONCH + +2024-01-17 01:17:48,875 (asr_inference:494) INFO: speech length: 109227 +2024-01-17 01:17:48,887 (beam_search:428) INFO: decoder input length: 168 +2024-01-17 01:17:48,887 (beam_search:429) INFO: max output length: 168 +2024-01-17 01:17:48,887 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:49,290 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:49,290 (beam_search:476) INFO: -13.22 * 1.0 = -13.22 for ctc +2024-01-17 01:17:49,290 (beam_search:479) INFO: total log probability: -13.22 +2024-01-17 01:17:49,290 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:17:49,290 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:49,291 (beam_search:483) INFO: best hypo: ITSMOTOISWHOEVEYOUARANDWHEREVEYOUAREONTHEJUNYOFFAIFHYOUAEWELCOMHER + +2024-01-17 01:17:49,292 (asr_inference:494) INFO: speech length: 50518 +2024-01-17 01:17:49,301 (beam_search:428) INFO: decoder input length: 76 +2024-01-17 01:17:49,301 (beam_search:429) INFO: max output length: 76 +2024-01-17 01:17:49,301 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:49,378 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:49,378 (beam_search:476) INFO: -3.67 * 1.0 = -3.67 for ctc +2024-01-17 01:17:49,378 (beam_search:479) INFO: total log probability: -3.67 +2024-01-17 01:17:49,378 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 01:17:49,378 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:49,378 (beam_search:483) INFO: best hypo: ROBATEYMILORASCOCHWILSON + +2024-01-17 01:17:49,379 (asr_inference:494) INFO: speech length: 73728 +2024-01-17 01:17:49,389 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 01:17:49,389 (beam_search:429) INFO: max output length: 113 +2024-01-17 01:17:49,389 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:49,571 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:49,571 (beam_search:476) INFO: -7.71 * 1.0 = -7.71 for ctc +2024-01-17 01:17:49,571 (beam_search:479) INFO: total log probability: -7.71 +2024-01-17 01:17:49,571 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:17:49,571 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:49,571 (beam_search:483) INFO: best hypo: AFTERONYEARBRAKSIRODEGREWASHEFOLOINGVENTHAR + +2024-01-17 01:17:49,573 (asr_inference:494) INFO: speech length: 92843 +2024-01-17 01:17:49,584 (beam_search:428) INFO: decoder input length: 143 +2024-01-17 01:17:49,584 (beam_search:429) INFO: max output length: 143 +2024-01-17 01:17:49,584 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:49,847 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:49,847 (beam_search:476) INFO: -11.54 * 1.0 = -11.54 for ctc +2024-01-17 01:17:49,847 (beam_search:479) INFO: total log probability: -11.54 +2024-01-17 01:17:49,847 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:17:49,847 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:49,847 (beam_search:483) INFO: best hypo: AYAMTEEMANUFACTEDAMORDLCITOFHESEDSEIDARDRACKXSTOR + +2024-01-17 01:17:49,849 (asr_inference:494) INFO: speech length: 94208 +2024-01-17 01:17:49,859 (beam_search:428) INFO: decoder input length: 145 +2024-01-17 01:17:49,860 (beam_search:429) INFO: max output length: 145 +2024-01-17 01:17:49,860 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:50,178 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:50,179 (beam_search:476) INFO: -13.68 * 1.0 = -13.68 for ctc +2024-01-17 01:17:50,179 (beam_search:479) INFO: total log probability: -13.68 +2024-01-17 01:17:50,179 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:17:50,179 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:50,179 (beam_search:483) INFO: best hypo: THEESSESSAYAMEDTOBILDALEFTWINGOLTURNITIVETONOWLABERANDTHEESANPE + +2024-01-17 01:17:50,180 (asr_inference:494) INFO: speech length: 47787 +2024-01-17 01:17:50,188 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 01:17:50,188 (beam_search:429) INFO: max output length: 72 +2024-01-17 01:17:50,188 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:50,269 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:50,269 (beam_search:476) INFO: -5.42 * 1.0 = -5.42 for ctc +2024-01-17 01:17:50,269 (beam_search:479) INFO: total log probability: -5.42 +2024-01-17 01:17:50,269 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:17:50,269 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:50,269 (beam_search:483) INFO: best hypo: HELIVESLIKEHEASAYONGPERSON + +2024-01-17 01:17:50,271 (asr_inference:494) INFO: speech length: 49152 +2024-01-17 01:17:50,279 (beam_search:428) INFO: decoder input length: 74 +2024-01-17 01:17:50,279 (beam_search:429) INFO: max output length: 74 +2024-01-17 01:17:50,279 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:50,367 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:50,367 (beam_search:476) INFO: -8.53 * 1.0 = -8.53 for ctc +2024-01-17 01:17:50,367 (beam_search:479) INFO: total log probability: -8.53 +2024-01-17 01:17:50,367 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:17:50,367 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:50,368 (beam_search:483) INFO: best hypo: MASTEOFSINESINENGENEARIGMANAGENT + +2024-01-17 01:17:50,369 (asr_inference:494) INFO: speech length: 76459 +2024-01-17 01:17:50,378 (beam_search:428) INFO: decoder input length: 117 +2024-01-17 01:17:50,378 (beam_search:429) INFO: max output length: 117 +2024-01-17 01:17:50,379 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:50,608 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:50,608 (beam_search:476) INFO: -11.75 * 1.0 = -11.75 for ctc +2024-01-17 01:17:50,608 (beam_search:479) INFO: total log probability: -11.75 +2024-01-17 01:17:50,608 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:17:50,608 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:50,609 (beam_search:483) INFO: best hypo: SHEFAILEDTOMAKHETOPTHREATTHECANIANJUNIATRACTRILESTHATJON + +2024-01-17 01:17:50,610 (asr_inference:494) INFO: speech length: 42326 +2024-01-17 01:17:50,618 (beam_search:428) INFO: decoder input length: 64 +2024-01-17 01:17:50,618 (beam_search:429) INFO: max output length: 64 +2024-01-17 01:17:50,618 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:50,673 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:50,673 (beam_search:476) INFO: -4.06 * 1.0 = -4.06 for ctc +2024-01-17 01:17:50,673 (beam_search:479) INFO: total log probability: -4.06 +2024-01-17 01:17:50,673 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:17:50,673 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:50,673 (beam_search:483) INFO: best hypo: ATOREFOLOEDINSEPORT + +2024-01-17 01:17:50,674 (asr_inference:494) INFO: speech length: 90112 +2024-01-17 01:17:50,684 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 01:17:50,684 (beam_search:429) INFO: max output length: 138 +2024-01-17 01:17:50,685 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:51,012 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:51,012 (beam_search:476) INFO: -14.99 * 1.0 = -14.99 for ctc +2024-01-17 01:17:51,012 (beam_search:479) INFO: total log probability: -14.99 +2024-01-17 01:17:51,012 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:17:51,012 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:51,013 (beam_search:483) INFO: best hypo: THEYERSTABISHINATENSEVENTYONANDARWNOTHEOLDSTCLOUBSINHESOUTHOFINGLAND + +2024-01-17 01:17:51,014 (asr_inference:494) INFO: speech length: 57344 +2024-01-17 01:17:51,023 (beam_search:428) INFO: decoder input length: 87 +2024-01-17 01:17:51,023 (beam_search:429) INFO: max output length: 87 +2024-01-17 01:17:51,023 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:51,152 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:51,152 (beam_search:476) INFO: -9.09 * 1.0 = -9.09 for ctc +2024-01-17 01:17:51,152 (beam_search:479) INFO: total log probability: -9.09 +2024-01-17 01:17:51,152 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:17:51,152 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:51,153 (beam_search:483) INFO: best hypo: HEASAMEMBEROFTHEGESTSCOTLANDADVISERYBORD + +2024-01-17 01:17:51,154 (asr_inference:494) INFO: speech length: 45056 +2024-01-17 01:17:51,162 (beam_search:428) INFO: decoder input length: 68 +2024-01-17 01:17:51,162 (beam_search:429) INFO: max output length: 68 +2024-01-17 01:17:51,162 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:51,236 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:51,236 (beam_search:476) INFO: -4.20 * 1.0 = -4.20 for ctc +2024-01-17 01:17:51,236 (beam_search:479) INFO: total log probability: -4.20 +2024-01-17 01:17:51,236 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:17:51,236 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:51,236 (beam_search:483) INFO: best hypo: TWOTHOUSANDANDFIVEGENTLEMEN + +2024-01-17 01:17:51,238 (asr_inference:494) INFO: speech length: 96939 +2024-01-17 01:17:51,249 (beam_search:428) INFO: decoder input length: 149 +2024-01-17 01:17:51,249 (beam_search:429) INFO: max output length: 149 +2024-01-17 01:17:51,249 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:51,615 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:51,615 (beam_search:476) INFO: -14.32 * 1.0 = -14.32 for ctc +2024-01-17 01:17:51,615 (beam_search:479) INFO: total log probability: -14.32 +2024-01-17 01:17:51,615 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:17:51,615 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:51,616 (beam_search:483) INFO: best hypo: AOREFILEADSTRONGRESEPTIONINYURUPANDACHIVEDDISTOBUTIONBUTTHATWASNOTTHECACEHER + +2024-01-17 01:17:51,617 (asr_inference:494) INFO: speech length: 55979 +2024-01-17 01:17:51,626 (beam_search:428) INFO: decoder input length: 85 +2024-01-17 01:17:51,626 (beam_search:429) INFO: max output length: 85 +2024-01-17 01:17:51,626 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:51,741 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:51,742 (beam_search:476) INFO: -9.49 * 1.0 = -9.49 for ctc +2024-01-17 01:17:51,742 (beam_search:479) INFO: total log probability: -9.49 +2024-01-17 01:17:51,742 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:17:51,742 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:51,742 (beam_search:483) INFO: best hypo: BOLTHOISSTETCHESPOSTERIARANGCALSTRUCTUES + +2024-01-17 01:17:51,743 (asr_inference:494) INFO: speech length: 117419 +2024-01-17 01:17:51,755 (beam_search:428) INFO: decoder input length: 181 +2024-01-17 01:17:51,755 (beam_search:429) INFO: max output length: 181 +2024-01-17 01:17:51,755 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:52,216 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:52,216 (beam_search:476) INFO: -11.29 * 1.0 = -11.29 for ctc +2024-01-17 01:17:52,216 (beam_search:479) INFO: total log probability: -11.29 +2024-01-17 01:17:52,216 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:17:52,216 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:52,216 (beam_search:483) INFO: best hypo: HEASALSOATHEETIMEFRENCHNASIALCHAMPIANNINTENINTYNINTIENITYFORTWOHOUSNDADWON + +2024-01-17 01:17:52,218 (asr_inference:494) INFO: speech length: 88747 +2024-01-17 01:17:52,229 (beam_search:428) INFO: decoder input length: 136 +2024-01-17 01:17:52,229 (beam_search:429) INFO: max output length: 136 +2024-01-17 01:17:52,229 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:52,508 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:52,508 (beam_search:476) INFO: -9.24 * 1.0 = -9.24 for ctc +2024-01-17 01:17:52,508 (beam_search:479) INFO: total log probability: -9.24 +2024-01-17 01:17:52,508 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:17:52,508 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:52,509 (beam_search:483) INFO: best hypo: THEVILIGESTRUCTURSHOWINHISMAPISTAGREEXTENTUNCHANGEDODAY + +2024-01-17 01:17:52,510 (asr_inference:494) INFO: speech length: 106496 +2024-01-17 01:17:52,522 (beam_search:428) INFO: decoder input length: 164 +2024-01-17 01:17:52,522 (beam_search:429) INFO: max output length: 164 +2024-01-17 01:17:52,522 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:52,912 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:52,912 (beam_search:476) INFO: -18.86 * 1.0 = -18.86 for ctc +2024-01-17 01:17:52,912 (beam_search:479) INFO: total log probability: -18.86 +2024-01-17 01:17:52,913 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:17:52,913 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:52,913 (beam_search:483) INFO: best hypo: RUHAISRECOGNISEDITNUCLARDISARSTTOEXPARTESANDFOTHESAVFTYOITSTECKNOLAGY + +2024-01-17 01:17:52,914 (asr_inference:494) INFO: speech length: 121515 +2024-01-17 01:17:52,927 (beam_search:428) INFO: decoder input length: 187 +2024-01-17 01:17:52,927 (beam_search:429) INFO: max output length: 187 +2024-01-17 01:17:52,927 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:53,446 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:53,446 (beam_search:476) INFO: -16.75 * 1.0 = -16.75 for ctc +2024-01-17 01:17:53,446 (beam_search:479) INFO: total log probability: -16.75 +2024-01-17 01:17:53,446 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:17:53,446 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:53,446 (beam_search:483) INFO: best hypo: ASOFTOTHOUSENDODFORTEENEMTYVEISAVAILABLEWITHINTHEUNITEDCINGDUMONVERGINMEDIARANDSCKIY + +2024-01-17 01:17:53,448 (asr_inference:494) INFO: speech length: 47787 +2024-01-17 01:17:53,456 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 01:17:53,456 (beam_search:429) INFO: max output length: 72 +2024-01-17 01:17:53,456 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:53,529 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:53,529 (beam_search:476) INFO: -6.30 * 1.0 = -6.30 for ctc +2024-01-17 01:17:53,529 (beam_search:479) INFO: total log probability: -6.30 +2024-01-17 01:17:53,529 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:17:53,529 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:53,530 (beam_search:483) INFO: best hypo: NEWYORKPEANGUINRANDMHOUSE + +2024-01-17 01:17:53,531 (asr_inference:494) INFO: speech length: 62806 +2024-01-17 01:17:53,540 (beam_search:428) INFO: decoder input length: 96 +2024-01-17 01:17:53,540 (beam_search:429) INFO: max output length: 96 +2024-01-17 01:17:53,540 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:53,699 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:53,700 (beam_search:476) INFO: -8.58 * 1.0 = -8.58 for ctc +2024-01-17 01:17:53,700 (beam_search:479) INFO: total log probability: -8.58 +2024-01-17 01:17:53,700 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:17:53,700 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:53,700 (beam_search:483) INFO: best hypo: THEDUTCHEYWASSCECUREINTEUTCOMEOFTHEGOFICKWAOR + +2024-01-17 01:17:53,701 (asr_inference:494) INFO: speech length: 79190 +2024-01-17 01:17:53,711 (beam_search:428) INFO: decoder input length: 121 +2024-01-17 01:17:53,711 (beam_search:429) INFO: max output length: 121 +2024-01-17 01:17:53,711 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:53,939 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:53,939 (beam_search:476) INFO: -13.87 * 1.0 = -13.87 for ctc +2024-01-17 01:17:53,939 (beam_search:479) INFO: total log probability: -13.87 +2024-01-17 01:17:53,939 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:17:53,939 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:53,939 (beam_search:483) INFO: best hypo: WIHGODPACESDARTEHEMATCHWITHBOTHTEMESOLTENATINGSUPREMASY + +2024-01-17 01:17:53,940 (asr_inference:494) INFO: speech length: 69632 +2024-01-17 01:17:53,950 (beam_search:428) INFO: decoder input length: 106 +2024-01-17 01:17:53,950 (beam_search:429) INFO: max output length: 106 +2024-01-17 01:17:53,950 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:54,134 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:54,134 (beam_search:476) INFO: -10.14 * 1.0 = -10.14 for ctc +2024-01-17 01:17:54,134 (beam_search:479) INFO: total log probability: -10.14 +2024-01-17 01:17:54,134 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:17:54,134 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:54,134 (beam_search:483) INFO: best hypo: THISVRTIONISNOTEADORBIGVERYFAFULTOTHEARIGINALNOVL + +2024-01-17 01:17:54,136 (asr_inference:494) INFO: speech length: 88747 +2024-01-17 01:17:54,146 (beam_search:428) INFO: decoder input length: 136 +2024-01-17 01:17:54,146 (beam_search:429) INFO: max output length: 136 +2024-01-17 01:17:54,146 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:54,430 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:54,430 (beam_search:476) INFO: -11.34 * 1.0 = -11.34 for ctc +2024-01-17 01:17:54,430 (beam_search:479) INFO: total log probability: -11.34 +2024-01-17 01:17:54,430 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:17:54,430 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:54,430 (beam_search:483) INFO: best hypo: THISPRESUMPTIONISNOTFLEFILEDONHASTONOATLEASTTOCONGATDIAMATES + +2024-01-17 01:17:54,431 (asr_inference:494) INFO: speech length: 152918 +2024-01-17 01:17:54,446 (beam_search:428) INFO: decoder input length: 236 +2024-01-17 01:17:54,446 (beam_search:429) INFO: max output length: 236 +2024-01-17 01:17:54,446 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:55,167 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:55,167 (beam_search:476) INFO: -16.01 * 1.0 = -16.01 for ctc +2024-01-17 01:17:55,167 (beam_search:479) INFO: total log probability: -16.01 +2024-01-17 01:17:55,167 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:17:55,167 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:55,168 (beam_search:483) INFO: best hypo: NOTABLETITLESINCLUDEDGOLDANACXSTHEREVENGOFDETHADERRADMOBILOUTRUNOESANDSAKGARSONICTHEHEGHOG + +2024-01-17 01:17:55,169 (asr_inference:494) INFO: speech length: 109227 +2024-01-17 01:17:55,181 (beam_search:428) INFO: decoder input length: 168 +2024-01-17 01:17:55,181 (beam_search:429) INFO: max output length: 168 +2024-01-17 01:17:55,181 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:55,607 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:55,607 (beam_search:476) INFO: -13.34 * 1.0 = -13.34 for ctc +2024-01-17 01:17:55,607 (beam_search:479) INFO: total log probability: -13.34 +2024-01-17 01:17:55,607 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:17:55,607 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:55,607 (beam_search:483) INFO: best hypo: THENINTENNINTYNINJUGMENTNOTEDTHATTHEINFLONCOFTHFATHEROFTHECUSEDHASBEETHER + +2024-01-17 01:17:55,609 (asr_inference:494) INFO: speech length: 99670 +2024-01-17 01:17:55,620 (beam_search:428) INFO: decoder input length: 153 +2024-01-17 01:17:55,620 (beam_search:429) INFO: max output length: 153 +2024-01-17 01:17:55,620 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:55,941 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:55,941 (beam_search:476) INFO: -16.70 * 1.0 = -16.70 for ctc +2024-01-17 01:17:55,941 (beam_search:479) INFO: total log probability: -16.70 +2024-01-17 01:17:55,941 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:17:55,941 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:55,942 (beam_search:483) INFO: best hypo: MOKDAUFSWARSREVENGEHANDJOINSFORESITHMALCOMTOOVERTROMOKBEATH + +2024-01-17 01:17:55,943 (asr_inference:494) INFO: speech length: 86016 +2024-01-17 01:17:55,953 (beam_search:428) INFO: decoder input length: 132 +2024-01-17 01:17:55,953 (beam_search:429) INFO: max output length: 132 +2024-01-17 01:17:55,953 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:56,251 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:56,251 (beam_search:476) INFO: -16.55 * 1.0 = -16.55 for ctc +2024-01-17 01:17:56,251 (beam_search:479) INFO: total log probability: -16.55 +2024-01-17 01:17:56,251 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:17:56,251 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:56,252 (beam_search:483) INFO: best hypo: THEMEDYEVLVILIGECORTWASALWAYSANIOUSTOCEPETHEFENEARONDTHEILIGEGCAPLES + +2024-01-17 01:17:56,253 (asr_inference:494) INFO: speech length: 75094 +2024-01-17 01:17:56,262 (beam_search:428) INFO: decoder input length: 115 +2024-01-17 01:17:56,262 (beam_search:429) INFO: max output length: 115 +2024-01-17 01:17:56,262 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:56,482 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:56,483 (beam_search:476) INFO: -12.15 * 1.0 = -12.15 for ctc +2024-01-17 01:17:56,483 (beam_search:479) INFO: total log probability: -12.15 +2024-01-17 01:17:56,483 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:17:56,483 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:56,483 (beam_search:483) INFO: best hypo: THERWASANINRANKSISTOMEACHRANKHAVIGMOREPOWETATHELOERANK + +2024-01-17 01:17:56,484 (asr_inference:494) INFO: speech length: 103766 +2024-01-17 01:17:56,496 (beam_search:428) INFO: decoder input length: 160 +2024-01-17 01:17:56,496 (beam_search:429) INFO: max output length: 160 +2024-01-17 01:17:56,496 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:56,837 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:56,837 (beam_search:476) INFO: -10.83 * 1.0 = -10.83 for ctc +2024-01-17 01:17:56,837 (beam_search:479) INFO: total log probability: -10.83 +2024-01-17 01:17:56,838 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:17:56,838 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:56,838 (beam_search:483) INFO: best hypo: THEASTABLISHEDDIPLAMATIRELATIONSONSEPTEMBRNINTENTHNINTENSEVENTYTO + +2024-01-17 01:17:56,839 (asr_inference:494) INFO: speech length: 96939 +2024-01-17 01:17:56,850 (beam_search:428) INFO: decoder input length: 149 +2024-01-17 01:17:56,850 (beam_search:429) INFO: max output length: 149 +2024-01-17 01:17:56,850 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:57,204 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:57,204 (beam_search:476) INFO: -15.01 * 1.0 = -15.01 for ctc +2024-01-17 01:17:57,204 (beam_search:479) INFO: total log probability: -15.01 +2024-01-17 01:17:57,204 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:17:57,204 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:57,204 (beam_search:483) INFO: best hypo: THISWASFIRTHERXTENDEDTOINCLOUDMORUCADATESINDISEMBERTWOTHOUSANDNDFORTEEN + +2024-01-17 01:17:57,206 (asr_inference:494) INFO: speech length: 80555 +2024-01-17 01:17:57,216 (beam_search:428) INFO: decoder input length: 123 +2024-01-17 01:17:57,216 (beam_search:429) INFO: max output length: 123 +2024-01-17 01:17:57,216 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:57,466 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:57,466 (beam_search:476) INFO: -13.67 * 1.0 = -13.67 for ctc +2024-01-17 01:17:57,466 (beam_search:479) INFO: total log probability: -13.67 +2024-01-17 01:17:57,466 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:17:57,466 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:57,466 (beam_search:483) INFO: best hypo: THEUCHGOVERMENTISCARNTLYEXSAMINGTHEEALCONCICUENCESOFTHROLING + +2024-01-17 01:17:57,467 (asr_inference:494) INFO: speech length: 106496 +2024-01-17 01:17:57,479 (beam_search:428) INFO: decoder input length: 164 +2024-01-17 01:17:57,479 (beam_search:429) INFO: max output length: 164 +2024-01-17 01:17:57,479 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:57,903 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:57,903 (beam_search:476) INFO: -18.30 * 1.0 = -18.30 for ctc +2024-01-17 01:17:57,903 (beam_search:479) INFO: total log probability: -18.30 +2024-01-17 01:17:57,903 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:17:57,903 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:57,904 (beam_search:483) INFO: best hypo: FROMNINTENTHURTYTHREETONINTEENFOARTYNINTHEMARICONLEEWONTWELVEOUTOTHEFIRSTSIXTEN + +2024-01-17 01:17:57,905 (asr_inference:494) INFO: speech length: 54614 +2024-01-17 01:17:57,913 (beam_search:428) INFO: decoder input length: 83 +2024-01-17 01:17:57,913 (beam_search:429) INFO: max output length: 83 +2024-01-17 01:17:57,913 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:58,011 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:58,011 (beam_search:476) INFO: -4.83 * 1.0 = -4.83 for ctc +2024-01-17 01:17:58,011 (beam_search:479) INFO: total log probability: -4.83 +2024-01-17 01:17:58,011 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:17:58,011 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:58,011 (beam_search:483) INFO: best hypo: THEARHEFELSICKWITHTIFASHIMSELF + +2024-01-17 01:17:58,012 (asr_inference:494) INFO: speech length: 70998 +2024-01-17 01:17:58,022 (beam_search:428) INFO: decoder input length: 108 +2024-01-17 01:17:58,022 (beam_search:429) INFO: max output length: 108 +2024-01-17 01:17:58,022 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:58,206 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:58,206 (beam_search:476) INFO: -15.32 * 1.0 = -15.32 for ctc +2024-01-17 01:17:58,206 (beam_search:479) INFO: total log probability: -15.32 +2024-01-17 01:17:58,206 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:17:58,206 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:58,206 (beam_search:483) INFO: best hypo: SIXTTEMSAVBEDVIDEDINTOTWOGROUPSOFTHREETEMSEACH + +2024-01-17 01:17:58,207 (asr_inference:494) INFO: speech length: 66902 +2024-01-17 01:17:58,217 (beam_search:428) INFO: decoder input length: 102 +2024-01-17 01:17:58,217 (beam_search:429) INFO: max output length: 102 +2024-01-17 01:17:58,217 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:58,399 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:58,399 (beam_search:476) INFO: -9.02 * 1.0 = -9.02 for ctc +2024-01-17 01:17:58,399 (beam_search:479) INFO: total log probability: -9.02 +2024-01-17 01:17:58,399 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:17:58,399 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:58,400 (beam_search:483) INFO: best hypo: THEFIRSTCEASONPREMIAEDONTWELTHJUONTWOTHOUSNDADFIFTEN + +2024-01-17 01:17:58,401 (asr_inference:494) INFO: speech length: 84651 +2024-01-17 01:17:58,411 (beam_search:428) INFO: decoder input length: 130 +2024-01-17 01:17:58,411 (beam_search:429) INFO: max output length: 130 +2024-01-17 01:17:58,411 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:58,673 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:58,673 (beam_search:476) INFO: -11.06 * 1.0 = -11.06 for ctc +2024-01-17 01:17:58,673 (beam_search:479) INFO: total log probability: -11.06 +2024-01-17 01:17:58,673 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:17:58,673 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:58,673 (beam_search:483) INFO: best hypo: ITSCEEDTHEWHIBOARDANDSISTAMETWENTYFORCOMBINGFEACUESFOMBOTH + +2024-01-17 01:17:58,675 (asr_inference:494) INFO: speech length: 58710 +2024-01-17 01:17:58,683 (beam_search:428) INFO: decoder input length: 89 +2024-01-17 01:17:58,683 (beam_search:429) INFO: max output length: 89 +2024-01-17 01:17:58,683 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:58,783 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:58,783 (beam_search:476) INFO: -6.81 * 1.0 = -6.81 for ctc +2024-01-17 01:17:58,783 (beam_search:479) INFO: total log probability: -6.81 +2024-01-17 01:17:58,783 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:17:58,783 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:58,783 (beam_search:483) INFO: best hypo: VLLIUMETOONUMBERSONTOANDTHRE + +2024-01-17 01:17:58,784 (asr_inference:494) INFO: speech length: 70998 +2024-01-17 01:17:58,794 (beam_search:428) INFO: decoder input length: 108 +2024-01-17 01:17:58,794 (beam_search:429) INFO: max output length: 108 +2024-01-17 01:17:58,794 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:59,001 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:59,001 (beam_search:476) INFO: -14.01 * 1.0 = -14.01 for ctc +2024-01-17 01:17:59,001 (beam_search:479) INFO: total log probability: -14.01 +2024-01-17 01:17:59,001 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:17:59,001 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:59,001 (beam_search:483) INFO: best hypo: THELOWEPARTOFMENSDESESWEMUCHSOURTINLENCTHOTHOSFORWMEN + +2024-01-17 01:17:59,002 (asr_inference:494) INFO: speech length: 64171 +2024-01-17 01:17:59,012 (beam_search:428) INFO: decoder input length: 98 +2024-01-17 01:17:59,012 (beam_search:429) INFO: max output length: 98 +2024-01-17 01:17:59,012 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:59,148 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:59,148 (beam_search:476) INFO: -6.88 * 1.0 = -6.88 for ctc +2024-01-17 01:17:59,148 (beam_search:479) INFO: total log probability: -6.88 +2024-01-17 01:17:59,148 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:17:59,148 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:59,149 (beam_search:483) INFO: best hypo: THEVISIGOTHSINTERNWESCEADEDBYTHEMORS + +2024-01-17 01:17:59,150 (asr_inference:494) INFO: speech length: 68267 +2024-01-17 01:17:59,160 (beam_search:428) INFO: decoder input length: 104 +2024-01-17 01:17:59,160 (beam_search:429) INFO: max output length: 104 +2024-01-17 01:17:59,160 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:59,295 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:59,295 (beam_search:476) INFO: -8.07 * 1.0 = -8.07 for ctc +2024-01-17 01:17:59,295 (beam_search:479) INFO: total log probability: -8.07 +2024-01-17 01:17:59,295 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:17:59,295 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:59,295 (beam_search:483) INFO: best hypo: JOSOFHISCOLEEVERYWEOFTHECOLYEAR + +2024-01-17 01:17:59,297 (asr_inference:494) INFO: speech length: 57344 +2024-01-17 01:17:59,305 (beam_search:428) INFO: decoder input length: 87 +2024-01-17 01:17:59,305 (beam_search:429) INFO: max output length: 87 +2024-01-17 01:17:59,305 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:59,422 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:59,422 (beam_search:476) INFO: -6.29 * 1.0 = -6.29 for ctc +2024-01-17 01:17:59,422 (beam_search:479) INFO: total log probability: -6.29 +2024-01-17 01:17:59,422 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:17:59,422 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:59,422 (beam_search:483) INFO: best hypo: ASTHRSILTOFALTHEARGUMENTGETINGTOHER + +2024-01-17 01:17:59,424 (asr_inference:494) INFO: speech length: 65536 +2024-01-17 01:17:59,433 (beam_search:428) INFO: decoder input length: 100 +2024-01-17 01:17:59,433 (beam_search:429) INFO: max output length: 100 +2024-01-17 01:17:59,433 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:59,584 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:59,584 (beam_search:476) INFO: -8.67 * 1.0 = -8.67 for ctc +2024-01-17 01:17:59,584 (beam_search:479) INFO: total log probability: -8.67 +2024-01-17 01:17:59,584 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:17:59,584 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:59,584 (beam_search:483) INFO: best hypo: ITHADQUARTERSAREINSHEFIALDYOUNITEDCINGDOM + +2024-01-17 01:17:59,585 (asr_inference:494) INFO: speech length: 92843 +2024-01-17 01:17:59,596 (beam_search:428) INFO: decoder input length: 143 +2024-01-17 01:17:59,596 (beam_search:429) INFO: max output length: 143 +2024-01-17 01:17:59,596 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:17:59,939 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:17:59,939 (beam_search:476) INFO: -18.74 * 1.0 = -18.74 for ctc +2024-01-17 01:17:59,939 (beam_search:479) INFO: total log probability: -18.74 +2024-01-17 01:17:59,939 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:17:59,939 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:17:59,939 (beam_search:483) INFO: best hypo: LAYALSOFIALYSINETHECONTRACTONSTAGEWITHEDIRECTERADPREDUSESOFTHEGOULDANEYES + +2024-01-17 01:17:59,941 (asr_inference:494) INFO: speech length: 81920 +2024-01-17 01:17:59,951 (beam_search:428) INFO: decoder input length: 125 +2024-01-17 01:17:59,951 (beam_search:429) INFO: max output length: 125 +2024-01-17 01:17:59,951 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:00,183 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:00,183 (beam_search:476) INFO: -13.79 * 1.0 = -13.79 for ctc +2024-01-17 01:18:00,183 (beam_search:479) INFO: total log probability: -13.79 +2024-01-17 01:18:00,183 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:18:00,183 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:00,184 (beam_search:483) INFO: best hypo: FISICLFERIPYCNHELEPATIONETOLURNHOTOWARKWITHFOTDROP + +2024-01-17 01:18:00,185 (asr_inference:494) INFO: speech length: 86016 +2024-01-17 01:18:00,195 (beam_search:428) INFO: decoder input length: 132 +2024-01-17 01:18:00,195 (beam_search:429) INFO: max output length: 132 +2024-01-17 01:18:00,195 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:00,425 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:00,426 (beam_search:476) INFO: -8.91 * 1.0 = -8.91 for ctc +2024-01-17 01:18:00,426 (beam_search:479) INFO: total log probability: -8.91 +2024-01-17 01:18:00,426 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:18:00,426 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:00,426 (beam_search:483) INFO: best hypo: ITENTONTOSELTHREHUNDREDTHOUSANDUNITSACHEFIVENO + +2024-01-17 01:18:00,427 (asr_inference:494) INFO: speech length: 35499 +2024-01-17 01:18:00,435 (beam_search:428) INFO: decoder input length: 53 +2024-01-17 01:18:00,435 (beam_search:429) INFO: max output length: 53 +2024-01-17 01:18:00,435 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:00,482 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:00,482 (beam_search:476) INFO: -2.94 * 1.0 = -2.94 for ctc +2024-01-17 01:18:00,482 (beam_search:479) INFO: total log probability: -2.94 +2024-01-17 01:18:00,482 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 01:18:00,482 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:00,482 (beam_search:483) INFO: best hypo: THENAMESTOUCKAFERTHAT + +2024-01-17 01:18:00,484 (asr_inference:494) INFO: speech length: 81920 +2024-01-17 01:18:00,493 (beam_search:428) INFO: decoder input length: 125 +2024-01-17 01:18:00,494 (beam_search:429) INFO: max output length: 125 +2024-01-17 01:18:00,494 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:00,696 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:00,696 (beam_search:476) INFO: -13.25 * 1.0 = -13.25 for ctc +2024-01-17 01:18:00,696 (beam_search:479) INFO: total log probability: -13.25 +2024-01-17 01:18:00,696 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:18:00,696 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:00,696 (beam_search:483) INFO: best hypo: THEHLBMLATERBROKTHDIMADRECORDONCUCUOMMUSICK + +2024-01-17 01:18:00,697 (asr_inference:494) INFO: speech length: 83286 +2024-01-17 01:18:00,707 (beam_search:428) INFO: decoder input length: 128 +2024-01-17 01:18:00,707 (beam_search:429) INFO: max output length: 128 +2024-01-17 01:18:00,707 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:00,905 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:00,905 (beam_search:476) INFO: -8.87 * 1.0 = -8.87 for ctc +2024-01-17 01:18:00,905 (beam_search:479) INFO: total log probability: -8.87 +2024-01-17 01:18:00,905 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:18:00,905 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:00,906 (beam_search:483) INFO: best hypo: ITSEDATORIALWESUBMITANDITSOTHRAPOLTOPRIYE + +2024-01-17 01:18:00,907 (asr_inference:494) INFO: speech length: 55979 +2024-01-17 01:18:00,915 (beam_search:428) INFO: decoder input length: 85 +2024-01-17 01:18:00,915 (beam_search:429) INFO: max output length: 85 +2024-01-17 01:18:00,915 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:01,034 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:01,034 (beam_search:476) INFO: -9.31 * 1.0 = -9.31 for ctc +2024-01-17 01:18:01,034 (beam_search:479) INFO: total log probability: -9.31 +2024-01-17 01:18:01,034 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:18:01,034 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:01,034 (beam_search:483) INFO: best hypo: JOSIFPLAYESOURFEATUREDEACHWEEONTHEHO + +2024-01-17 01:18:01,036 (asr_inference:494) INFO: speech length: 102400 +2024-01-17 01:18:01,047 (beam_search:428) INFO: decoder input length: 157 +2024-01-17 01:18:01,047 (beam_search:429) INFO: max output length: 157 +2024-01-17 01:18:01,047 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:01,413 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:01,413 (beam_search:476) INFO: -12.32 * 1.0 = -12.32 for ctc +2024-01-17 01:18:01,413 (beam_search:479) INFO: total log probability: -12.32 +2024-01-17 01:18:01,413 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:18:01,413 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:01,413 (beam_search:483) INFO: best hypo: THEYWATFORATIMEMBILDINGUPTHERFORESBEGINTOONDRIFTHISEAVLREALYEXISTS + +2024-01-17 01:18:01,415 (asr_inference:494) INFO: speech length: 79190 +2024-01-17 01:18:01,425 (beam_search:428) INFO: decoder input length: 121 +2024-01-17 01:18:01,425 (beam_search:429) INFO: max output length: 121 +2024-01-17 01:18:01,425 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:01,676 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:01,676 (beam_search:476) INFO: -12.72 * 1.0 = -12.72 for ctc +2024-01-17 01:18:01,676 (beam_search:479) INFO: total log probability: -12.72 +2024-01-17 01:18:01,676 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:18:01,676 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:01,677 (beam_search:483) INFO: best hypo: BREFEMENTIONOFTHCONVICTIONAPPERDONPAGETHREOFTHENEWYOUOKTIMEMS + +2024-01-17 01:18:01,678 (asr_inference:494) INFO: speech length: 69632 +2024-01-17 01:18:01,688 (beam_search:428) INFO: decoder input length: 106 +2024-01-17 01:18:01,688 (beam_search:429) INFO: max output length: 106 +2024-01-17 01:18:01,688 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:01,858 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:01,858 (beam_search:476) INFO: -6.05 * 1.0 = -6.05 for ctc +2024-01-17 01:18:01,858 (beam_search:479) INFO: total log probability: -6.05 +2024-01-17 01:18:01,858 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 01:18:01,858 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:01,858 (beam_search:483) INFO: best hypo: ODEDBYPOSIONONPICHFROMBACKRIGHTTOFRUNTLEFT + +2024-01-17 01:18:01,859 (asr_inference:494) INFO: speech length: 88747 +2024-01-17 01:18:01,870 (beam_search:428) INFO: decoder input length: 136 +2024-01-17 01:18:01,870 (beam_search:429) INFO: max output length: 136 +2024-01-17 01:18:01,870 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:02,128 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:02,128 (beam_search:476) INFO: -9.07 * 1.0 = -9.07 for ctc +2024-01-17 01:18:02,128 (beam_search:479) INFO: total log probability: -9.07 +2024-01-17 01:18:02,128 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:18:02,128 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:02,128 (beam_search:483) INFO: best hypo: HEISMEMBEROFTHECOURTOTHERILCOLAEOFARTLOUNDONYUCAY + +2024-01-17 01:18:02,129 (asr_inference:494) INFO: speech length: 94208 +2024-01-17 01:18:02,140 (beam_search:428) INFO: decoder input length: 145 +2024-01-17 01:18:02,140 (beam_search:429) INFO: max output length: 145 +2024-01-17 01:18:02,140 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:02,488 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:02,488 (beam_search:476) INFO: -17.14 * 1.0 = -17.14 for ctc +2024-01-17 01:18:02,488 (beam_search:479) INFO: total log probability: -17.14 +2024-01-17 01:18:02,488 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:18:02,488 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:02,488 (beam_search:483) INFO: best hypo: DURIGTHECOURSEOFTECAMPAINFIRGSANDVISITATALLTHERTYNEINWASIGTANSTATECONTES + +2024-01-17 01:18:02,490 (asr_inference:494) INFO: speech length: 43691 +2024-01-17 01:18:02,498 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:18:02,498 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:18:02,498 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:02,558 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:02,558 (beam_search:476) INFO: -3.34 * 1.0 = -3.34 for ctc +2024-01-17 01:18:02,558 (beam_search:479) INFO: total log probability: -3.34 +2024-01-17 01:18:02,558 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:18:02,558 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:02,558 (beam_search:483) INFO: best hypo: ASTRIPOFPAPEROFLENGTH + +2024-01-17 01:18:02,559 (asr_inference:494) INFO: speech length: 87382 +2024-01-17 01:18:02,570 (beam_search:428) INFO: decoder input length: 134 +2024-01-17 01:18:02,570 (beam_search:429) INFO: max output length: 134 +2024-01-17 01:18:02,570 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:02,839 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:02,839 (beam_search:476) INFO: -15.65 * 1.0 = -15.65 for ctc +2024-01-17 01:18:02,839 (beam_search:479) INFO: total log probability: -15.65 +2024-01-17 01:18:02,839 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:18:02,839 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:02,839 (beam_search:483) INFO: best hypo: SATOHADFRECUENTLYWORETOGETHWTHYOUCKAYAMARONPREVIOSPOGJECTS + +2024-01-17 01:18:02,841 (asr_inference:494) INFO: speech length: 109227 +2024-01-17 01:18:02,853 (beam_search:428) INFO: decoder input length: 168 +2024-01-17 01:18:02,853 (beam_search:429) INFO: max output length: 168 +2024-01-17 01:18:02,853 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:03,242 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:03,242 (beam_search:476) INFO: -12.17 * 1.0 = -12.17 for ctc +2024-01-17 01:18:03,242 (beam_search:479) INFO: total log probability: -12.17 +2024-01-17 01:18:03,242 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:18:03,242 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:03,243 (beam_search:483) INFO: best hypo: SHEASBORNONSCREANDUINTHEEPSODBRADCASTONFORHANOVEMBERNINTENINTYFOR + +2024-01-17 01:18:03,244 (asr_inference:494) INFO: speech length: 89597 +2024-01-17 01:18:03,255 (beam_search:428) INFO: decoder input length: 137 +2024-01-17 01:18:03,255 (beam_search:429) INFO: max output length: 137 +2024-01-17 01:18:03,255 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:03,520 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:03,520 (beam_search:476) INFO: -6.32 * 1.0 = -6.32 for ctc +2024-01-17 01:18:03,520 (beam_search:479) INFO: total log probability: -6.32 +2024-01-17 01:18:03,520 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 01:18:03,520 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:03,521 (beam_search:483) INFO: best hypo: HETURNEDROUNDSHHADCOMINSOGENTLYTHATHEHADNEVERHARDHER + +2024-01-17 01:18:03,522 (asr_inference:494) INFO: speech length: 104477 +2024-01-17 01:18:03,534 (beam_search:428) INFO: decoder input length: 161 +2024-01-17 01:18:03,534 (beam_search:429) INFO: max output length: 161 +2024-01-17 01:18:03,534 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:03,837 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:03,837 (beam_search:476) INFO: -10.16 * 1.0 = -10.16 for ctc +2024-01-17 01:18:03,837 (beam_search:479) INFO: total log probability: -10.16 +2024-01-17 01:18:03,837 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:18:03,837 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:03,837 (beam_search:483) INFO: best hypo: ATOBESHOUORANWEMUSTCEOURDORSSHOATWEMUSLATNOONIN + +2024-01-17 01:18:03,838 (asr_inference:494) INFO: speech length: 200157 +2024-01-17 01:18:03,856 (beam_search:428) INFO: decoder input length: 310 +2024-01-17 01:18:03,856 (beam_search:429) INFO: max output length: 310 +2024-01-17 01:18:03,856 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:05,011 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:05,011 (beam_search:476) INFO: -25.13 * 1.0 = -25.13 for ctc +2024-01-17 01:18:05,011 (beam_search:479) INFO: total log probability: -25.13 +2024-01-17 01:18:05,011 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:18:05,011 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:05,012 (beam_search:483) INFO: best hypo: CIDSPMONHEBEGANMOKINGLYYOUMAHVEONDEDWHIYICALDATROUSWHENICOULDJUSASWELLHAVEDISTROREDYOUTHATIDOUTATOANSEDHIM + +2024-01-17 01:18:05,013 (asr_inference:494) INFO: speech length: 87677 +2024-01-17 01:18:05,024 (beam_search:428) INFO: decoder input length: 134 +2024-01-17 01:18:05,024 (beam_search:429) INFO: max output length: 134 +2024-01-17 01:18:05,024 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:05,328 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:05,328 (beam_search:476) INFO: -13.63 * 1.0 = -13.63 for ctc +2024-01-17 01:18:05,328 (beam_search:479) INFO: total log probability: -13.63 +2024-01-17 01:18:05,328 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:18:05,328 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:05,329 (beam_search:483) INFO: best hypo: THEPESNTTHRUWHIMSELFAPONHIMANDBOUNDHISFORLAGSTITLYSOTATHCULDNOTMOVE + +2024-01-17 01:18:05,331 (asr_inference:494) INFO: speech length: 123197 +2024-01-17 01:18:05,343 (beam_search:428) INFO: decoder input length: 190 +2024-01-17 01:18:05,343 (beam_search:429) INFO: max output length: 190 +2024-01-17 01:18:05,343 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:05,897 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:05,897 (beam_search:476) INFO: -14.95 * 1.0 = -14.95 for ctc +2024-01-17 01:18:05,897 (beam_search:479) INFO: total log probability: -14.95 +2024-01-17 01:18:05,897 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:18:05,897 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:05,898 (beam_search:483) INFO: best hypo: NORMUSTTHOUSOLIMETHTHEHLYONOFISRIALASTOTHINKHEHATHBUTONWAYINWHICHCANGORIFIEHMSELFBYTHE + +2024-01-17 01:18:05,899 (asr_inference:494) INFO: speech length: 177917 +2024-01-17 01:18:05,916 (beam_search:428) INFO: decoder input length: 275 +2024-01-17 01:18:05,916 (beam_search:429) INFO: max output length: 275 +2024-01-17 01:18:05,916 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:07,037 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:07,037 (beam_search:476) INFO: -24.76 * 1.0 = -24.76 for ctc +2024-01-17 01:18:07,037 (beam_search:479) INFO: total log probability: -24.76 +2024-01-17 01:18:07,037 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:18:07,037 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:07,038 (beam_search:483) INFO: best hypo: THEOLDCOMPARSONBETWETHEIMPULSIEEXSECTIVEANDTHELIBRALARTSMANWHOWHADLARNEDTHATHEREONLYONRTOPOSITVEDISIONSFALBLEINALTHEWALOHINKING + +2024-01-17 01:18:07,039 (asr_inference:494) INFO: speech length: 95197 +2024-01-17 01:18:07,050 (beam_search:428) INFO: decoder input length: 146 +2024-01-17 01:18:07,050 (beam_search:429) INFO: max output length: 146 +2024-01-17 01:18:07,050 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:07,382 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:07,382 (beam_search:476) INFO: -16.19 * 1.0 = -16.19 for ctc +2024-01-17 01:18:07,382 (beam_search:479) INFO: total log probability: -16.19 +2024-01-17 01:18:07,382 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:18:07,382 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:07,383 (beam_search:483) INFO: best hypo: AFTERTHISEXPERIANCETHENVATORSWERCAIRFULTOCEPEASAVFEDISTNCEFROMTHEAL + +2024-01-17 01:18:07,384 (asr_inference:494) INFO: speech length: 207117 +2024-01-17 01:18:07,404 (beam_search:428) INFO: decoder input length: 321 +2024-01-17 01:18:07,404 (beam_search:429) INFO: max output length: 321 +2024-01-17 01:18:07,404 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:08,599 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:08,599 (beam_search:476) INFO: -21.43 * 1.0 = -21.43 for ctc +2024-01-17 01:18:08,599 (beam_search:479) INFO: total log probability: -21.43 +2024-01-17 01:18:08,599 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:18:08,599 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:08,600 (beam_search:483) INFO: best hypo: ANOUBARSOMTINGFIRTHERITHNYOUATONOITIHAVEHERAMOSTMSTERIOUSTELAPARIGRAMYESWHATISITISHEDIDNOWITISNOTABOUTHER + +2024-01-17 01:18:08,602 (asr_inference:494) INFO: speech length: 90557 +2024-01-17 01:18:08,612 (beam_search:428) INFO: decoder input length: 139 +2024-01-17 01:18:08,612 (beam_search:429) INFO: max output length: 139 +2024-01-17 01:18:08,613 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:08,846 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:08,846 (beam_search:476) INFO: -10.10 * 1.0 = -10.10 for ctc +2024-01-17 01:18:08,846 (beam_search:479) INFO: total log probability: -10.10 +2024-01-17 01:18:08,846 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:18:08,846 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:08,847 (beam_search:483) INFO: best hypo: NOMSTRTOURTANSAIDANDGIETHEASKTTOMEIALTAKEIT + +2024-01-17 01:18:08,848 (asr_inference:494) INFO: speech length: 216957 +2024-01-17 01:18:08,868 (beam_search:428) INFO: decoder input length: 336 +2024-01-17 01:18:08,868 (beam_search:429) INFO: max output length: 336 +2024-01-17 01:18:08,868 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:10,419 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:10,419 (beam_search:476) INFO: -21.88 * 1.0 = -21.88 for ctc +2024-01-17 01:18:10,419 (beam_search:479) INFO: total log probability: -21.88 +2024-01-17 01:18:10,419 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:18:10,419 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:10,420 (beam_search:483) INFO: best hypo: ANDARABIANNIGHTEXCLAMEDTROTWHIYTHATWASAMAGICNIGHTWASNITTHERSDIFRENTSORTSONIGHESMATESAIDTHESALERANDTHENIGHTBUTNBRIGHTMEANSANTTHESAMENIGHTYOUMEAN + +2024-01-17 01:18:10,422 (asr_inference:494) INFO: speech length: 149757 +2024-01-17 01:18:10,437 (beam_search:428) INFO: decoder input length: 231 +2024-01-17 01:18:10,437 (beam_search:429) INFO: max output length: 231 +2024-01-17 01:18:10,437 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:11,220 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:11,220 (beam_search:476) INFO: -16.86 * 1.0 = -16.86 for ctc +2024-01-17 01:18:11,220 (beam_search:479) INFO: total log probability: -16.86 +2024-01-17 01:18:11,220 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:18:11,220 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:11,221 (beam_search:483) INFO: best hypo: IVETRNEDOFUPWARDSOFAHUNDEDFMYBESTDHANDSFORNOOTHERFALTTHEMFOLOINGYOUANDSUCHASYOUANDTHINKILLTAKEYOUAON + +2024-01-17 01:18:11,222 (asr_inference:494) INFO: speech length: 95677 +2024-01-17 01:18:11,233 (beam_search:428) INFO: decoder input length: 147 +2024-01-17 01:18:11,233 (beam_search:429) INFO: max output length: 147 +2024-01-17 01:18:11,233 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:11,562 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:11,562 (beam_search:476) INFO: -17.67 * 1.0 = -17.67 for ctc +2024-01-17 01:18:11,562 (beam_search:479) INFO: total log probability: -17.67 +2024-01-17 01:18:11,562 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:18:11,562 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:11,563 (beam_search:483) INFO: best hypo: BUTWEWIDSHESEHIMHERHARTLEPTUINAPREHENTIONATEVERYRINOFTHDORBIL + +2024-01-17 01:18:11,564 (asr_inference:494) INFO: speech length: 180957 +2024-01-17 01:18:11,580 (beam_search:428) INFO: decoder input length: 280 +2024-01-17 01:18:11,580 (beam_search:429) INFO: max output length: 280 +2024-01-17 01:18:11,580 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:12,627 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:12,627 (beam_search:476) INFO: -26.61 * 1.0 = -26.61 for ctc +2024-01-17 01:18:12,627 (beam_search:479) INFO: total log probability: -26.61 +2024-01-17 01:18:12,627 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:18:12,627 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:12,628 (beam_search:483) INFO: best hypo: THESEBOOKSDICXSONIWLKEPEALTHERESTWEOUSENDTOMSTRBELTHEYAROFACINDTHTHEWLVOULYOUFORTHMSELVESASWELASFORPOPASSAY + +2024-01-17 01:18:12,630 (asr_inference:494) INFO: speech length: 192637 +2024-01-17 01:18:12,647 (beam_search:428) INFO: decoder input length: 298 +2024-01-17 01:18:12,647 (beam_search:429) INFO: max output length: 298 +2024-01-17 01:18:12,647 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:13,923 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:13,923 (beam_search:476) INFO: -22.06 * 1.0 = -22.06 for ctc +2024-01-17 01:18:13,923 (beam_search:479) INFO: total log probability: -22.06 +2024-01-17 01:18:13,923 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:18:13,923 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:13,924 (beam_search:483) INFO: best hypo: UTINGAWASNOTATALSHURTHATTHECOULDNOTGETINTHEGATSOPEDINWARDANDTHREHEVYBARSWEREHELDINPLACEBYMENSOFSTOUTSTAPLESRIVITEDTOTHESHETSOFSTA + +2024-01-17 01:18:13,926 (asr_inference:494) INFO: speech length: 165117 +2024-01-17 01:18:13,942 (beam_search:428) INFO: decoder input length: 255 +2024-01-17 01:18:13,942 (beam_search:429) INFO: max output length: 255 +2024-01-17 01:18:13,942 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:14,823 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:14,823 (beam_search:476) INFO: -16.83 * 1.0 = -16.83 for ctc +2024-01-17 01:18:14,823 (beam_search:479) INFO: total log probability: -16.83 +2024-01-17 01:18:14,823 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:18:14,823 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:14,824 (beam_search:483) INFO: best hypo: IWANTTHOWSAIDHODONCOLDLYIWANADOSONHORSESIWANTMENTOBRIGDTHEWITHMEHEPUSHDHIWAYFORDWHICHWAYTOTHESTABLES + +2024-01-17 01:18:14,825 (asr_inference:494) INFO: speech length: 153437 +2024-01-17 01:18:14,840 (beam_search:428) INFO: decoder input length: 237 +2024-01-17 01:18:14,840 (beam_search:429) INFO: max output length: 237 +2024-01-17 01:18:14,840 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:15,700 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:15,700 (beam_search:476) INFO: -25.76 * 1.0 = -25.76 for ctc +2024-01-17 01:18:15,700 (beam_search:479) INFO: total log probability: -25.76 +2024-01-17 01:18:15,700 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:18:15,700 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:15,701 (beam_search:483) INFO: best hypo: EREISALIMITWHATYUCANDDOFOTHEFIRSTTHIEYOUANTERAMANSHOUSEANDBESIDETHATWASNOTIMETOAROUSSUSPIONNTHEMINDSOFANYWON + +2024-01-17 01:18:15,702 (asr_inference:494) INFO: speech length: 156637 +2024-01-17 01:18:15,718 (beam_search:428) INFO: decoder input length: 242 +2024-01-17 01:18:15,718 (beam_search:429) INFO: max output length: 242 +2024-01-17 01:18:15,718 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:16,433 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:16,434 (beam_search:476) INFO: -15.93 * 1.0 = -15.93 for ctc +2024-01-17 01:18:16,434 (beam_search:479) INFO: total log probability: -15.93 +2024-01-17 01:18:16,434 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:18:16,434 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:16,434 (beam_search:483) INFO: best hypo: DOOUNOTREMEMERTHATHESASTHYDEMANTHATSTHESPIRITWHICHCEPESTHEISNOBLECORAGOUSHIYUNMACHIBL + +2024-01-17 01:18:16,436 (asr_inference:494) INFO: speech length: 141277 +2024-01-17 01:18:16,450 (beam_search:428) INFO: decoder input length: 218 +2024-01-17 01:18:16,450 (beam_search:429) INFO: max output length: 218 +2024-01-17 01:18:16,450 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:16,916 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:16,916 (beam_search:476) INFO: -11.79 * 1.0 = -11.79 for ctc +2024-01-17 01:18:16,916 (beam_search:479) INFO: total log probability: -11.79 +2024-01-17 01:18:16,916 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:18:16,916 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:16,917 (beam_search:483) INFO: best hypo: MSTRBELLWHACANHENOOFJOAONHELIVINGALASYLIFINADROUSYCOLAGE + +2024-01-17 01:18:16,918 (asr_inference:494) INFO: speech length: 56637 +2024-01-17 01:18:16,927 (beam_search:428) INFO: decoder input length: 86 +2024-01-17 01:18:16,927 (beam_search:429) INFO: max output length: 86 +2024-01-17 01:18:16,927 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:17,037 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:17,037 (beam_search:476) INFO: -8.09 * 1.0 = -8.09 for ctc +2024-01-17 01:18:17,037 (beam_search:479) INFO: total log probability: -8.09 +2024-01-17 01:18:17,037 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:18:17,037 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:17,037 (beam_search:483) INFO: best hypo: ANDTHECITNFOLOEDIMUARLYATTHERHEALS + +2024-01-17 01:18:17,039 (asr_inference:494) INFO: speech length: 124477 +2024-01-17 01:18:17,052 (beam_search:428) INFO: decoder input length: 192 +2024-01-17 01:18:17,052 (beam_search:429) INFO: max output length: 192 +2024-01-17 01:18:17,052 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:17,583 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:17,583 (beam_search:476) INFO: -12.62 * 1.0 = -12.62 for ctc +2024-01-17 01:18:17,583 (beam_search:479) INFO: total log probability: -12.62 +2024-01-17 01:18:17,583 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:18:17,583 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:17,584 (beam_search:483) INFO: best hypo: THEFISTTUTCHWOLDCASEANEXPLOSIONINWHICHAMONGSUCHHUNDREDSOFINFERIATEDMENANDRECKLESBOYS + +2024-01-17 01:18:17,585 (asr_inference:494) INFO: speech length: 82077 +2024-01-17 01:18:17,596 (beam_search:428) INFO: decoder input length: 126 +2024-01-17 01:18:17,596 (beam_search:429) INFO: max output length: 126 +2024-01-17 01:18:17,596 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:17,839 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:17,839 (beam_search:476) INFO: -11.76 * 1.0 = -11.76 for ctc +2024-01-17 01:18:17,839 (beam_search:479) INFO: total log probability: -11.76 +2024-01-17 01:18:17,839 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:18:17,839 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:17,840 (beam_search:483) INFO: best hypo: WONFTHGEATPLESUERSOFMARGRATSLIFATTHISTIMEWASINEATSBOY + +2024-01-17 01:18:17,841 (asr_inference:494) INFO: speech length: 132157 +2024-01-17 01:18:17,854 (beam_search:428) INFO: decoder input length: 204 +2024-01-17 01:18:17,854 (beam_search:429) INFO: max output length: 204 +2024-01-17 01:18:17,854 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:18,515 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:18,515 (beam_search:476) INFO: -22.22 * 1.0 = -22.22 for ctc +2024-01-17 01:18:18,515 (beam_search:479) INFO: total log probability: -22.22 +2024-01-17 01:18:18,515 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:18:18,515 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:18,516 (beam_search:483) INFO: best hypo: THTHNGISGONONLONGNOFTHERISONEOREBIAGACXIDENTWESHALHAVETOCOMPRMISEWITTHEINERIVERNDCARYONTHEWORKCUINTLY + +2024-01-17 01:18:18,517 (asr_inference:494) INFO: speech length: 98557 +2024-01-17 01:18:18,528 (beam_search:428) INFO: decoder input length: 151 +2024-01-17 01:18:18,528 (beam_search:429) INFO: max output length: 151 +2024-01-17 01:18:18,528 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:18,772 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:18,772 (beam_search:476) INFO: -11.16 * 1.0 = -11.16 for ctc +2024-01-17 01:18:18,772 (beam_search:479) INFO: total log probability: -11.16 +2024-01-17 01:18:18,772 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:18:18,772 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:18,772 (beam_search:483) INFO: best hypo: YOUARLATSAIDSHEWELSHEHELDHERBRATHOTHEANSR + +2024-01-17 01:18:18,773 (asr_inference:494) INFO: speech length: 142364 +2024-01-17 01:18:18,788 (beam_search:428) INFO: decoder input length: 220 +2024-01-17 01:18:18,788 (beam_search:429) INFO: max output length: 220 +2024-01-17 01:18:18,788 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:19,517 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:19,517 (beam_search:476) INFO: -22.32 * 1.0 = -22.32 for ctc +2024-01-17 01:18:19,517 (beam_search:479) INFO: total log probability: -22.32 +2024-01-17 01:18:19,517 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:18:19,517 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:19,518 (beam_search:483) INFO: best hypo: TRATTOLDTHEGIRLSTHATTHEYMUSGOWITHERFATHERTOLIVANDGIPCUSISILSLITELDCABENANDHENTHEYHERDTHSREDFULDECRE + +2024-01-17 01:18:19,519 (asr_inference:494) INFO: speech length: 143837 +2024-01-17 01:18:19,534 (beam_search:428) INFO: decoder input length: 222 +2024-01-17 01:18:19,534 (beam_search:429) INFO: max output length: 222 +2024-01-17 01:18:19,534 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:20,278 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:20,278 (beam_search:476) INFO: -17.33 * 1.0 = -17.33 for ctc +2024-01-17 01:18:20,278 (beam_search:479) INFO: total log probability: -17.33 +2024-01-17 01:18:20,278 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:18:20,278 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:20,279 (beam_search:483) INFO: best hypo: MARGITSATDONOTHEROGPATLYTOWARMHERSELFFORTHEDAMPNESOTHEEVNINGHUNGBOUTHERDRESANDOVERFITEHADMADHERCHILY + +2024-01-17 01:18:20,280 (asr_inference:494) INFO: speech length: 143197 +2024-01-17 01:18:20,295 (beam_search:428) INFO: decoder input length: 221 +2024-01-17 01:18:20,295 (beam_search:429) INFO: max output length: 221 +2024-01-17 01:18:20,295 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:21,003 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:21,003 (beam_search:476) INFO: -18.52 * 1.0 = -18.52 for ctc +2024-01-17 01:18:21,003 (beam_search:479) INFO: total log probability: -18.52 +2024-01-17 01:18:21,003 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:18:21,003 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:21,003 (beam_search:483) INFO: best hypo: ONOWYOUARMSTAKANABOUTTHATRELIDTHEKINGTHEYARENOTMYPRISONERSBUTMYSLAVESWHOMMYPURCUSEFROMTHECINGOFEV + +2024-01-17 01:18:21,005 (asr_inference:494) INFO: speech length: 40157 +2024-01-17 01:18:21,012 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:18:21,012 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:18:21,013 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:21,073 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:21,073 (beam_search:476) INFO: -5.94 * 1.0 = -5.94 for ctc +2024-01-17 01:18:21,073 (beam_search:479) INFO: total log probability: -5.94 +2024-01-17 01:18:21,073 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:18:21,073 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:21,074 (beam_search:483) INFO: best hypo: HERFATHETOKUTECMBRSATION + +2024-01-17 01:18:21,075 (asr_inference:494) INFO: speech length: 164797 +2024-01-17 01:18:21,091 (beam_search:428) INFO: decoder input length: 255 +2024-01-17 01:18:21,091 (beam_search:429) INFO: max output length: 255 +2024-01-17 01:18:21,091 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:22,061 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:22,061 (beam_search:476) INFO: -24.47 * 1.0 = -24.47 for ctc +2024-01-17 01:18:22,061 (beam_search:479) INFO: total log probability: -24.47 +2024-01-17 01:18:22,061 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:18:22,061 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:22,062 (beam_search:483) INFO: best hypo: INACORNERWASASORTOFDRESINGTABLEONWHICHLAYACOMANDBRUSHCANIDYSEEDMUCHINTRUSTEDINTHETABLEANWASEXAMINGITWHNTHEGORURETERNE + +2024-01-17 01:18:22,064 (asr_inference:494) INFO: speech length: 193597 +2024-01-17 01:18:22,081 (beam_search:428) INFO: decoder input length: 300 +2024-01-17 01:18:22,081 (beam_search:429) INFO: max output length: 300 +2024-01-17 01:18:22,081 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:23,307 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:23,307 (beam_search:476) INFO: -22.37 * 1.0 = -22.37 for ctc +2024-01-17 01:18:23,307 (beam_search:479) INFO: total log probability: -22.37 +2024-01-17 01:18:23,307 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:18:23,307 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:23,308 (beam_search:483) INFO: best hypo: IHAVESOMETIMETHGTTHATMYSELFSHEAGEEDBUTOFCOURSIDONTNOWSTILIHAVETOBEPITYCARFULSOMEONISALWAYSOVERHERBYMYDESSORLOKINGOVERHER + +2024-01-17 01:18:23,309 (asr_inference:494) INFO: speech length: 79197 +2024-01-17 01:18:23,319 (beam_search:428) INFO: decoder input length: 121 +2024-01-17 01:18:23,319 (beam_search:429) INFO: max output length: 121 +2024-01-17 01:18:23,319 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:23,520 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:23,520 (beam_search:476) INFO: -6.21 * 1.0 = -6.21 for ctc +2024-01-17 01:18:23,520 (beam_search:479) INFO: total log probability: -6.21 +2024-01-17 01:18:23,520 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 01:18:23,520 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:23,520 (beam_search:483) INFO: best hypo: ISHLSTAYREPLIDTHEYONGMANFORIMEANTOSITYOFRE + +2024-01-17 01:18:23,522 (asr_inference:494) INFO: speech length: 46877 +2024-01-17 01:18:23,530 (beam_search:428) INFO: decoder input length: 71 +2024-01-17 01:18:23,530 (beam_search:429) INFO: max output length: 71 +2024-01-17 01:18:23,530 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:23,603 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:23,603 (beam_search:476) INFO: -6.72 * 1.0 = -6.72 for ctc +2024-01-17 01:18:23,603 (beam_search:479) INFO: total log probability: -6.72 +2024-01-17 01:18:23,603 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:18:23,603 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:23,604 (beam_search:483) INFO: best hypo: WHATDYODOASDTHESORCERER + +2024-01-17 01:18:23,605 (asr_inference:494) INFO: speech length: 192045 +2024-01-17 01:18:23,622 (beam_search:428) INFO: decoder input length: 298 +2024-01-17 01:18:23,622 (beam_search:429) INFO: max output length: 298 +2024-01-17 01:18:23,622 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:24,775 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:24,775 (beam_search:476) INFO: -23.55 * 1.0 = -23.55 for ctc +2024-01-17 01:18:24,775 (beam_search:479) INFO: total log probability: -23.55 +2024-01-17 01:18:24,775 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:18:24,775 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:24,776 (beam_search:483) INFO: best hypo: WHIYTHEREARENAMESYOURSHORTHINESNOTANYMOREREPLIEDTROATIMQUEOFTHEINKESANDIMALSOQUEOFTHELOSSOIWONTHAVEMYPEPLEQUARLING + +2024-01-17 01:18:24,777 (asr_inference:494) INFO: speech length: 171373 +2024-01-17 01:18:24,793 (beam_search:428) INFO: decoder input length: 265 +2024-01-17 01:18:24,793 (beam_search:429) INFO: max output length: 265 +2024-01-17 01:18:24,793 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:25,912 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:25,913 (beam_search:476) INFO: -35.87 * 1.0 = -35.87 for ctc +2024-01-17 01:18:25,913 (beam_search:479) INFO: total log probability: -35.87 +2024-01-17 01:18:25,913 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:18:25,913 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:25,914 (beam_search:483) INFO: best hypo: TIPRITERWECLICKINGCLIPINGWERBINGSNIPDOTOFAUGETACKOFNOEPERSANDPASEDINANNLARGSCRAPBOKSSERKULERWERBENGFOLDEDANMADREAYTOMALFOTHEFINALAPEL + +2024-01-17 01:18:25,915 (asr_inference:494) INFO: speech length: 141917 +2024-01-17 01:18:25,930 (beam_search:428) INFO: decoder input length: 219 +2024-01-17 01:18:25,930 (beam_search:429) INFO: max output length: 219 +2024-01-17 01:18:25,930 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:26,638 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:26,639 (beam_search:476) INFO: -17.34 * 1.0 = -17.34 for ctc +2024-01-17 01:18:26,639 (beam_search:479) INFO: total log probability: -17.34 +2024-01-17 01:18:26,639 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:18:26,639 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:26,639 (beam_search:483) INFO: best hypo: ITWASFORDAYSAFTERTHESUPRIESOFALTHERSHORSHENTHESTRANGERSLETTHEASTATTOTHECAIROFRUGEDOLDFORSTERHARMEN + +2024-01-17 01:18:26,641 (asr_inference:494) INFO: speech length: 178877 +2024-01-17 01:18:26,657 (beam_search:428) INFO: decoder input length: 277 +2024-01-17 01:18:26,657 (beam_search:429) INFO: max output length: 277 +2024-01-17 01:18:26,657 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:27,685 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:27,685 (beam_search:476) INFO: -30.29 * 1.0 = -30.29 for ctc +2024-01-17 01:18:27,685 (beam_search:479) INFO: total log probability: -30.29 +2024-01-17 01:18:27,685 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:18:27,685 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:27,686 (beam_search:483) INFO: best hypo: BPORTEMPLTONHESAIDIUSTONOWHIMMANYEARSAGOHEWEEBOYSMENTOSCOULWITHMANDALTHATSOUTOFTHNGYONOWBUTANTILIRANCROSHMOR + +2024-01-17 01:18:27,687 (asr_inference:494) INFO: speech length: 75677 +2024-01-17 01:18:27,697 (beam_search:428) INFO: decoder input length: 116 +2024-01-17 01:18:27,697 (beam_search:429) INFO: max output length: 116 +2024-01-17 01:18:27,697 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:27,921 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:27,921 (beam_search:476) INFO: -11.27 * 1.0 = -11.27 for ctc +2024-01-17 01:18:27,921 (beam_search:479) INFO: total log probability: -11.27 +2024-01-17 01:18:27,921 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:18:27,921 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:27,921 (beam_search:483) INFO: best hypo: IFONDHERITHEFARISTANDBOGTHERHERAPRISNEREPLIETHECAPTON + +2024-01-17 01:18:27,923 (asr_inference:494) INFO: speech length: 164797 +2024-01-17 01:18:27,938 (beam_search:428) INFO: decoder input length: 255 +2024-01-17 01:18:27,938 (beam_search:429) INFO: max output length: 255 +2024-01-17 01:18:27,938 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:28,861 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:28,861 (beam_search:476) INFO: -19.24 * 1.0 = -19.24 for ctc +2024-01-17 01:18:28,861 (beam_search:479) INFO: total log probability: -19.24 +2024-01-17 01:18:28,861 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:18:28,861 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:28,862 (beam_search:483) INFO: best hypo: WHOMAYBECOMPITENTITHEFROMPERSINALEXPERIANCEORTHEEXPINSOFOTHERSTOANSERITWITHMORORLESCURECTNESORATLEASTANATTEMTD + +2024-01-17 01:18:28,863 (asr_inference:494) INFO: speech length: 70077 +2024-01-17 01:18:28,873 (beam_search:428) INFO: decoder input length: 107 +2024-01-17 01:18:28,873 (beam_search:429) INFO: max output length: 107 +2024-01-17 01:18:28,873 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:29,049 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:29,049 (beam_search:476) INFO: -12.91 * 1.0 = -12.91 for ctc +2024-01-17 01:18:29,049 (beam_search:479) INFO: total log probability: -12.91 +2024-01-17 01:18:29,049 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:18:29,049 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:29,049 (beam_search:483) INFO: best hypo: ONNINTYTOLATESTRETETSIDHOKGENBITINGOFHISSAGAR + +2024-01-17 01:18:29,051 (asr_inference:494) INFO: speech length: 171837 +2024-01-17 01:18:29,067 (beam_search:428) INFO: decoder input length: 266 +2024-01-17 01:18:29,067 (beam_search:429) INFO: max output length: 266 +2024-01-17 01:18:29,067 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:30,116 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:30,116 (beam_search:476) INFO: -23.56 * 1.0 = -23.56 for ctc +2024-01-17 01:18:30,116 (beam_search:479) INFO: total log probability: -23.56 +2024-01-17 01:18:30,116 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:18:30,116 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:30,116 (beam_search:483) INFO: best hypo: TRATWASRPRIETOFINESHECOUDCESOPLAINLYTHRTHEHIYWALOFWATERABOVEHERBUTTHESONWASABLTOSHUTITSBEMESTRATDOWTHOTHETRANSPARENTSE + +2024-01-17 01:18:30,118 (asr_inference:494) INFO: speech length: 37757 +2024-01-17 01:18:30,126 (beam_search:428) INFO: decoder input length: 56 +2024-01-17 01:18:30,126 (beam_search:429) INFO: max output length: 56 +2024-01-17 01:18:30,126 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:30,177 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:30,177 (beam_search:476) INFO: -4.36 * 1.0 = -4.36 for ctc +2024-01-17 01:18:30,177 (beam_search:479) INFO: total log probability: -4.36 +2024-01-17 01:18:30,177 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:18:30,177 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:30,177 (beam_search:483) INFO: best hypo: THESPATWERIDSPRNGUP + +2024-01-17 01:18:30,178 (asr_inference:494) INFO: speech length: 57277 +2024-01-17 01:18:30,187 (beam_search:428) INFO: decoder input length: 87 +2024-01-17 01:18:30,187 (beam_search:429) INFO: max output length: 87 +2024-01-17 01:18:30,187 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:30,295 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:30,296 (beam_search:476) INFO: -6.37 * 1.0 = -6.37 for ctc +2024-01-17 01:18:30,296 (beam_search:479) INFO: total log probability: -6.37 +2024-01-17 01:18:30,296 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:18:30,296 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:30,296 (beam_search:483) INFO: best hypo: COMEDENILWICSHEGAVESUCHAUPOSION + +2024-01-17 01:18:30,297 (asr_inference:494) INFO: speech length: 143037 +2024-01-17 01:18:30,311 (beam_search:428) INFO: decoder input length: 221 +2024-01-17 01:18:30,311 (beam_search:429) INFO: max output length: 221 +2024-01-17 01:18:30,311 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:31,075 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:31,076 (beam_search:476) INFO: -21.12 * 1.0 = -21.12 for ctc +2024-01-17 01:18:31,076 (beam_search:479) INFO: total log probability: -21.12 +2024-01-17 01:18:31,076 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:18:31,076 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:31,076 (beam_search:483) INFO: best hypo: YOUSEEANDTILTHESCOLPILSWERINVENTEDWEWASTEDALOTOFTIMEINDSTUADYTHATNOWMAYBEBETERIMPLOYEDINMPRACTISINGEATHLATIK + +2024-01-17 01:18:31,078 (asr_inference:494) INFO: speech length: 76125 +2024-01-17 01:18:31,088 (beam_search:428) INFO: decoder input length: 116 +2024-01-17 01:18:31,088 (beam_search:429) INFO: max output length: 116 +2024-01-17 01:18:31,088 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:31,287 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:31,287 (beam_search:476) INFO: -9.97 * 1.0 = -9.97 for ctc +2024-01-17 01:18:31,287 (beam_search:479) INFO: total log probability: -9.97 +2024-01-17 01:18:31,287 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:18:31,287 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:31,287 (beam_search:483) INFO: best hypo: YOVEDONITNOWDICLAREDARTHYTHESTENTSARJUSTWONDERFL + +2024-01-17 01:18:31,288 (asr_inference:494) INFO: speech length: 212317 +2024-01-17 01:18:31,308 (beam_search:428) INFO: decoder input length: 329 +2024-01-17 01:18:31,308 (beam_search:429) INFO: max output length: 329 +2024-01-17 01:18:31,308 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:32,514 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:32,514 (beam_search:476) INFO: -21.72 * 1.0 = -21.72 for ctc +2024-01-17 01:18:32,514 (beam_search:479) INFO: total log probability: -21.72 +2024-01-17 01:18:32,514 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:18:32,514 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:32,515 (beam_search:483) INFO: best hypo: FORTWENINGTENFIVETHRETWOTHEINWASBARLYTWENYMOUSAWAYWHNHODONFIREDHISROCKITSTHEMDECALOSALCLOUDOVAPERINEMTINES + +2024-01-17 01:18:32,516 (asr_inference:494) INFO: speech length: 151997 +2024-01-17 01:18:32,531 (beam_search:428) INFO: decoder input length: 235 +2024-01-17 01:18:32,532 (beam_search:429) INFO: max output length: 235 +2024-01-17 01:18:32,532 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:33,385 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:33,385 (beam_search:476) INFO: -20.40 * 1.0 = -20.40 for ctc +2024-01-17 01:18:33,385 (beam_search:479) INFO: total log probability: -20.40 +2024-01-17 01:18:33,385 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:18:33,385 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:33,386 (beam_search:483) INFO: best hypo: THEYPADNOATENCIONTOTHEFACTHATGIPGUSSISLDIDNOTWNTTOMARYANYOFTHEMFORTHEYHDETERMENDTHATHENITWASAGREEDWHOHOUDHAVEHIM + +2024-01-17 01:18:33,387 (asr_inference:494) INFO: speech length: 116157 +2024-01-17 01:18:33,400 (beam_search:428) INFO: decoder input length: 179 +2024-01-17 01:18:33,400 (beam_search:429) INFO: max output length: 179 +2024-01-17 01:18:33,400 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:33,836 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:33,836 (beam_search:476) INFO: -11.83 * 1.0 = -11.83 for ctc +2024-01-17 01:18:33,836 (beam_search:479) INFO: total log probability: -11.83 +2024-01-17 01:18:33,836 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:18:33,836 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:33,836 (beam_search:483) INFO: best hypo: WHATDOUTINOFTHATHECRIDEOPENGACOPYOHERECARDANDLAIGTFLATONTHELIBRYTABLE + +2024-01-17 01:18:33,838 (asr_inference:494) INFO: speech length: 48045 +2024-01-17 01:18:33,846 (beam_search:428) INFO: decoder input length: 73 +2024-01-17 01:18:33,846 (beam_search:429) INFO: max output length: 73 +2024-01-17 01:18:33,846 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:33,920 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:33,920 (beam_search:476) INFO: -7.01 * 1.0 = -7.01 for ctc +2024-01-17 01:18:33,920 (beam_search:479) INFO: total log probability: -7.01 +2024-01-17 01:18:33,920 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:18:33,920 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:33,921 (beam_search:483) INFO: best hypo: ITLRECUIERBUTASHOURTTIM + +2024-01-17 01:18:33,922 (asr_inference:494) INFO: speech length: 113117 +2024-01-17 01:18:33,934 (beam_search:428) INFO: decoder input length: 174 +2024-01-17 01:18:33,934 (beam_search:429) INFO: max output length: 174 +2024-01-17 01:18:33,934 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:34,366 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:34,366 (beam_search:476) INFO: -10.74 * 1.0 = -10.74 for ctc +2024-01-17 01:18:34,366 (beam_search:479) INFO: total log probability: -10.74 +2024-01-17 01:18:34,366 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:18:34,366 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:34,366 (beam_search:483) INFO: best hypo: ANDLASTTHECROUDOVEGITABLEPEOPLEWHOHADNOHARTSANDCOULDNITHERSMILENORFROWN + +2024-01-17 01:18:34,367 (asr_inference:494) INFO: speech length: 52477 +2024-01-17 01:18:34,376 (beam_search:428) INFO: decoder input length: 79 +2024-01-17 01:18:34,376 (beam_search:429) INFO: max output length: 79 +2024-01-17 01:18:34,376 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:34,457 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:34,457 (beam_search:476) INFO: -4.23 * 1.0 = -4.23 for ctc +2024-01-17 01:18:34,457 (beam_search:479) INFO: total log probability: -4.23 +2024-01-17 01:18:34,457 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:18:34,457 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:34,458 (beam_search:483) INFO: best hypo: THENYOULCACHITSITHEWICH + +2024-01-17 01:18:34,459 (asr_inference:494) INFO: speech length: 124637 +2024-01-17 01:18:34,472 (beam_search:428) INFO: decoder input length: 192 +2024-01-17 01:18:34,472 (beam_search:429) INFO: max output length: 192 +2024-01-17 01:18:34,472 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:35,037 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:35,037 (beam_search:476) INFO: -17.44 * 1.0 = -17.44 for ctc +2024-01-17 01:18:35,038 (beam_search:479) INFO: total log probability: -17.44 +2024-01-17 01:18:35,038 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:18:35,038 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:35,038 (beam_search:483) INFO: best hypo: WHATISITIQUIREDNOTFELINGSERTNBUTTHAIWASAVALEDATEMPTOSECURELITLFREADRTISINGFORTHEANDEOVER + +2024-01-17 01:18:35,040 (asr_inference:494) INFO: speech length: 151517 +2024-01-17 01:18:35,055 (beam_search:428) INFO: decoder input length: 234 +2024-01-17 01:18:35,055 (beam_search:429) INFO: max output length: 234 +2024-01-17 01:18:35,055 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:35,885 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:35,885 (beam_search:476) INFO: -21.82 * 1.0 = -21.82 for ctc +2024-01-17 01:18:35,885 (beam_search:479) INFO: total log probability: -21.82 +2024-01-17 01:18:35,885 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:18:35,885 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:35,886 (beam_search:483) INFO: best hypo: SOHEGAVETHELURKTHATHRDHUNRDOLRSFORBOOKSANDACASKOFGODOLDALFORPETERTHECLURKDRANKTHEAILHIMSELFANDGAVETHECAHMI + +2024-01-17 01:18:35,888 (asr_inference:494) INFO: speech length: 204477 +2024-01-17 01:18:35,906 (beam_search:428) INFO: decoder input length: 317 +2024-01-17 01:18:35,906 (beam_search:429) INFO: max output length: 317 +2024-01-17 01:18:35,906 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:37,303 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:37,303 (beam_search:476) INFO: -27.60 * 1.0 = -27.60 for ctc +2024-01-17 01:18:37,303 (beam_search:479) INFO: total log probability: -27.60 +2024-01-17 01:18:37,303 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:18:37,303 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:37,304 (beam_search:483) INFO: best hypo: ATLIKETHATANALSINWNERLANTWITHMERLYAGRINHATFATEDAWAYCHANGINGINTOALINKXEWHICHINTURNDISOPEREDFOOEDBYANUNONCREATUERWITHSHORTNOWSANDPONEDEARS + +2024-01-17 01:18:37,305 (asr_inference:494) INFO: speech length: 147037 +2024-01-17 01:18:37,320 (beam_search:428) INFO: decoder input length: 227 +2024-01-17 01:18:37,320 (beam_search:429) INFO: max output length: 227 +2024-01-17 01:18:37,320 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:38,068 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:38,068 (beam_search:476) INFO: -25.35 * 1.0 = -25.35 for ctc +2024-01-17 01:18:38,068 (beam_search:479) INFO: total log probability: -25.35 +2024-01-17 01:18:38,068 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:18:38,068 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:38,069 (beam_search:483) INFO: best hypo: SHECOUDNOTDOMARGRITLANSEDUNCONIOUSLYATTHEUNCLECORNERFTHROMSHECOUDHARTHYUDERTAKEASERINTSPLACECOULSHE + +2024-01-17 01:18:38,070 (asr_inference:494) INFO: speech length: 100797 +2024-01-17 01:18:38,082 (beam_search:428) INFO: decoder input length: 155 +2024-01-17 01:18:38,082 (beam_search:429) INFO: max output length: 155 +2024-01-17 01:18:38,082 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:38,366 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:38,366 (beam_search:476) INFO: -10.24 * 1.0 = -10.24 for ctc +2024-01-17 01:18:38,366 (beam_search:479) INFO: total log probability: -10.24 +2024-01-17 01:18:38,366 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:18:38,366 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:38,367 (beam_search:483) INFO: best hypo: NOSHEREPLIDEDWITHINISNCARIOUSITYDIDIGIVETHEMTOYOU + +2024-01-17 01:18:38,368 (asr_inference:494) INFO: speech length: 116797 +2024-01-17 01:18:38,380 (beam_search:428) INFO: decoder input length: 180 +2024-01-17 01:18:38,380 (beam_search:429) INFO: max output length: 180 +2024-01-17 01:18:38,381 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:38,857 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:38,857 (beam_search:476) INFO: -19.34 * 1.0 = -19.34 for ctc +2024-01-17 01:18:38,857 (beam_search:479) INFO: total log probability: -19.34 +2024-01-17 01:18:38,857 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:18:38,857 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:38,858 (beam_search:483) INFO: best hypo: MARBROMILESANTHEAGACSENTDWELINWEREHELDUNDERLONGLEACTSTHEYMUSTIFPOSIBLEBERELEAT + +2024-01-17 01:18:38,859 (asr_inference:494) INFO: speech length: 42557 +2024-01-17 01:18:38,867 (beam_search:428) INFO: decoder input length: 64 +2024-01-17 01:18:38,867 (beam_search:429) INFO: max output length: 64 +2024-01-17 01:18:38,867 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:38,933 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:38,934 (beam_search:476) INFO: -8.31 * 1.0 = -8.31 for ctc +2024-01-17 01:18:38,934 (beam_search:479) INFO: total log probability: -8.31 +2024-01-17 01:18:38,934 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:18:38,934 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:38,934 (beam_search:483) INFO: best hypo: ACAPWAVEOSTONISTHELADOR + +2024-01-17 01:18:38,935 (asr_inference:494) INFO: speech length: 155037 +2024-01-17 01:18:38,950 (beam_search:428) INFO: decoder input length: 240 +2024-01-17 01:18:38,950 (beam_search:429) INFO: max output length: 240 +2024-01-17 01:18:38,950 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:39,848 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:39,848 (beam_search:476) INFO: -21.01 * 1.0 = -21.01 for ctc +2024-01-17 01:18:39,848 (beam_search:479) INFO: total log probability: -21.01 +2024-01-17 01:18:39,848 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:18:39,848 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:39,848 (beam_search:483) INFO: best hypo: ITBOUNDEDHEARANDTHEIRABOTTHECICANHOUSEANDATFIRSTDORTHCOULDNOTTELWHATITWASSWHILTHESCREACINGOFTHECICONSNEARLYDEFENDHER + +2024-01-17 01:18:39,850 (asr_inference:494) INFO: speech length: 162637 +2024-01-17 01:18:39,866 (beam_search:428) INFO: decoder input length: 252 +2024-01-17 01:18:39,866 (beam_search:429) INFO: max output length: 252 +2024-01-17 01:18:39,866 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:40,856 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:40,856 (beam_search:476) INFO: -24.61 * 1.0 = -24.61 for ctc +2024-01-17 01:18:40,856 (beam_search:479) INFO: total log probability: -24.61 +2024-01-17 01:18:40,856 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:18:40,856 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:40,857 (beam_search:483) INFO: best hypo: THESOLDERGAVEAYALTHATAROUWSEDASCOROFHISCOMRADSANDBOGHTTHEMTUMBLINGINTOTHESTREATWENTHESAWHOTHEBOLRSEPRESIOUSPRISNEWASESCAPING + +2024-01-17 01:18:40,858 (asr_inference:494) INFO: speech length: 159677 +2024-01-17 01:18:40,874 (beam_search:428) INFO: decoder input length: 247 +2024-01-17 01:18:40,874 (beam_search:429) INFO: max output length: 247 +2024-01-17 01:18:40,874 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:41,761 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:41,761 (beam_search:476) INFO: -20.52 * 1.0 = -20.52 for ctc +2024-01-17 01:18:41,761 (beam_search:479) INFO: total log probability: -20.52 +2024-01-17 01:18:41,761 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:18:41,761 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:41,762 (beam_search:483) INFO: best hypo: JIMHADREFUSEDTOLEAVETHEFIELDOFGRASSWHEREHEWASNGAGEDNBUSILYEATINGSOTHEWISURDGOTOUTOTHEUGANDJONEDSEBANDDORITHY + +2024-01-17 01:18:41,764 (asr_inference:494) INFO: speech length: 103197 +2024-01-17 01:18:41,775 (beam_search:428) INFO: decoder input length: 159 +2024-01-17 01:18:41,775 (beam_search:429) INFO: max output length: 159 +2024-01-17 01:18:41,775 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:42,129 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:42,129 (beam_search:476) INFO: -15.12 * 1.0 = -15.12 for ctc +2024-01-17 01:18:42,129 (beam_search:479) INFO: total log probability: -15.12 +2024-01-17 01:18:42,129 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:18:42,129 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:42,129 (beam_search:483) INFO: best hypo: SERTNLYIMASINRUTDITHECACESOUARBUTICANMAKHADSRTALSOFITIREPLID + +2024-01-17 01:18:42,131 (asr_inference:494) INFO: speech length: 55197 +2024-01-17 01:18:42,139 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:18:42,139 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:18:42,139 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:42,231 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:42,231 (beam_search:476) INFO: -3.78 * 1.0 = -3.78 for ctc +2024-01-17 01:18:42,231 (beam_search:479) INFO: total log probability: -3.78 +2024-01-17 01:18:42,231 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 01:18:42,231 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:42,231 (beam_search:483) INFO: best hypo: ORANYMICEOREVENGRASHOPERS + +2024-01-17 01:18:42,232 (asr_inference:494) INFO: speech length: 159837 +2024-01-17 01:18:42,248 (beam_search:428) INFO: decoder input length: 247 +2024-01-17 01:18:42,248 (beam_search:429) INFO: max output length: 247 +2024-01-17 01:18:42,248 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:43,161 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:43,161 (beam_search:476) INFO: -24.92 * 1.0 = -24.92 for ctc +2024-01-17 01:18:43,161 (beam_search:479) INFO: total log probability: -24.92 +2024-01-17 01:18:43,161 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:18:43,161 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:43,161 (beam_search:483) INFO: best hypo: ANDTHETHAPASIODONTHYTELYOUWATTODOORWHTINNOTTODOWETHEMONYTHEYGIVEYOUANJUSTPAMENTFOYOURPAINSINTHEREXTANGELIG + +2024-01-17 01:18:43,163 (asr_inference:494) INFO: speech length: 46077 +2024-01-17 01:18:43,171 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:18:43,171 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:18:43,171 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:43,249 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:43,249 (beam_search:476) INFO: -6.13 * 1.0 = -6.13 for ctc +2024-01-17 01:18:43,249 (beam_search:479) INFO: total log probability: -6.13 +2024-01-17 01:18:43,249 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:18:43,249 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:43,249 (beam_search:483) INFO: best hypo: WHATDISTATMEANASTHEPRINCES + +2024-01-17 01:18:43,250 (asr_inference:494) INFO: speech length: 180957 +2024-01-17 01:18:43,267 (beam_search:428) INFO: decoder input length: 280 +2024-01-17 01:18:43,267 (beam_search:429) INFO: max output length: 280 +2024-01-17 01:18:43,267 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:44,245 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:44,245 (beam_search:476) INFO: -17.10 * 1.0 = -17.10 for ctc +2024-01-17 01:18:44,245 (beam_search:479) INFO: total log probability: -17.10 +2024-01-17 01:18:44,245 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:18:44,245 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:44,245 (beam_search:483) INFO: best hypo: HEHADBEDROUNDHEWASFLOTINGINASEOFLIGTANDNOWNTHENSHININGLITLEFIHESSWEAMINCQUISITIVELYUPTOHIMANDSTARE + +2024-01-17 01:18:44,247 (asr_inference:494) INFO: speech length: 197437 +2024-01-17 01:18:44,265 (beam_search:428) INFO: decoder input length: 306 +2024-01-17 01:18:44,265 (beam_search:429) INFO: max output length: 306 +2024-01-17 01:18:44,265 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:45,807 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:45,807 (beam_search:476) INFO: -39.54 * 1.0 = -39.54 for ctc +2024-01-17 01:18:45,807 (beam_search:479) INFO: total log probability: -39.54 +2024-01-17 01:18:45,807 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:18:45,807 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:45,808 (beam_search:483) INFO: best hypo: BUTOLDGUNHADATRCKATOLEFTANDREMEMETHETAILIREDTOYOUITHTHONROMABOTHERTHEFIRSTFTHERAONSTONDTHEWORLDOFOPLWERESOLGERSSENTFROMSOMEBLASTEDPLANITINOUTRSPACETOFINEANWHO + +2024-01-17 01:18:45,810 (asr_inference:494) INFO: speech length: 129437 +2024-01-17 01:18:45,823 (beam_search:428) INFO: decoder input length: 200 +2024-01-17 01:18:45,823 (beam_search:429) INFO: max output length: 200 +2024-01-17 01:18:45,823 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:46,374 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:46,375 (beam_search:476) INFO: -17.14 * 1.0 = -17.14 for ctc +2024-01-17 01:18:46,375 (beam_search:479) INFO: total log probability: -17.14 +2024-01-17 01:18:46,375 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:18:46,375 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:46,375 (beam_search:483) INFO: best hypo: PAPAWILOUSPEKTTHEMENANDGEHEOGOAWAYSHECANOTBREETHPORTHINGWITTHISCROUDOABOUTHER + +2024-01-17 01:18:46,376 (asr_inference:494) INFO: speech length: 162877 +2024-01-17 01:18:46,392 (beam_search:428) INFO: decoder input length: 252 +2024-01-17 01:18:46,392 (beam_search:429) INFO: max output length: 252 +2024-01-17 01:18:46,392 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:47,214 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:47,214 (beam_search:476) INFO: -17.06 * 1.0 = -17.06 for ctc +2024-01-17 01:18:47,214 (beam_search:479) INFO: total log probability: -17.06 +2024-01-17 01:18:47,215 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:18:47,215 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:47,215 (beam_search:483) INFO: best hypo: WHENITOOKTHISCACEHESAIDIBLEVEDOWNINDMYHARTDIXSONWASINSENTISTOBELEITBUTMYFATHASBENRUDTLYSHAKE + +2024-01-17 01:18:47,217 (asr_inference:494) INFO: speech length: 58237 +2024-01-17 01:18:47,226 (beam_search:428) INFO: decoder input length: 88 +2024-01-17 01:18:47,226 (beam_search:429) INFO: max output length: 88 +2024-01-17 01:18:47,226 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:47,334 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:47,334 (beam_search:476) INFO: -10.10 * 1.0 = -10.10 for ctc +2024-01-17 01:18:47,334 (beam_search:479) INFO: total log probability: -10.10 +2024-01-17 01:18:47,334 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:18:47,334 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:47,334 (beam_search:483) INFO: best hypo: CHAPTRSICKOFETHEPIRTOFORSEATS + +2024-01-17 01:18:47,335 (asr_inference:494) INFO: speech length: 45757 +2024-01-17 01:18:47,343 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:18:47,343 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:18:47,343 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:47,410 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:47,411 (beam_search:476) INFO: -4.61 * 1.0 = -4.61 for ctc +2024-01-17 01:18:47,411 (beam_search:479) INFO: total log probability: -4.61 +2024-01-17 01:18:47,411 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:18:47,411 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:47,411 (beam_search:483) INFO: best hypo: REMEMBETHECANNOTTUCHUS + +2024-01-17 01:18:47,412 (asr_inference:494) INFO: speech length: 163357 +2024-01-17 01:18:47,428 (beam_search:428) INFO: decoder input length: 253 +2024-01-17 01:18:47,428 (beam_search:429) INFO: max output length: 253 +2024-01-17 01:18:47,428 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:48,306 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:48,306 (beam_search:476) INFO: -20.54 * 1.0 = -20.54 for ctc +2024-01-17 01:18:48,306 (beam_search:479) INFO: total log probability: -20.54 +2024-01-17 01:18:48,306 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:18:48,306 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:48,307 (beam_search:483) INFO: best hypo: IVEMETIMEASYOURGIVEMETIMEIFHERSANYTHINGIHATITSAHURYIVENIDAYOUMAGUSTYANDOUNCETHESIXTTHESNUBNOSDPRINCES + +2024-01-17 01:18:48,309 (asr_inference:494) INFO: speech length: 49757 +2024-01-17 01:18:48,317 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:18:48,317 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:18:48,317 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:48,402 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:48,403 (beam_search:476) INFO: -7.66 * 1.0 = -7.66 for ctc +2024-01-17 01:18:48,403 (beam_search:479) INFO: total log probability: -7.66 +2024-01-17 01:18:48,403 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:18:48,403 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:48,403 (beam_search:483) INFO: best hypo: TONOFTRATOCLAREDTHESALERMAN + +2024-01-17 01:18:48,404 (asr_inference:494) INFO: speech length: 138397 +2024-01-17 01:18:48,418 (beam_search:428) INFO: decoder input length: 214 +2024-01-17 01:18:48,418 (beam_search:429) INFO: max output length: 214 +2024-01-17 01:18:48,418 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:48,940 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:48,941 (beam_search:476) INFO: -14.15 * 1.0 = -14.15 for ctc +2024-01-17 01:18:48,941 (beam_search:479) INFO: total log probability: -14.15 +2024-01-17 01:18:48,941 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:18:48,941 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:48,941 (beam_search:483) INFO: best hypo: ASFORTHATSAIDMARGRITRETHERHOATALYIHOLDITISHONEYSOITQUEEMALLDEPENSAY + +2024-01-17 01:18:48,942 (asr_inference:494) INFO: speech length: 177709 +2024-01-17 01:18:48,959 (beam_search:428) INFO: decoder input length: 275 +2024-01-17 01:18:48,959 (beam_search:429) INFO: max output length: 275 +2024-01-17 01:18:48,959 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:50,121 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:50,121 (beam_search:476) INFO: -22.10 * 1.0 = -22.10 for ctc +2024-01-17 01:18:50,121 (beam_search:479) INFO: total log probability: -22.10 +2024-01-17 01:18:50,122 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:18:50,122 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:50,122 (beam_search:483) INFO: best hypo: WHENHEHERDTHESWORDSTHEKINGWHOSHADWASFULOFTHPINCESNEVERSTOPETOINQUIRIFTHECOULDBETRUANDSMEAREDHIMSELFOVERWITHFATANDSPRANGINTTHEOVEN + +2024-01-17 01:18:50,124 (asr_inference:494) INFO: speech length: 178989 +2024-01-17 01:18:50,140 (beam_search:428) INFO: decoder input length: 277 +2024-01-17 01:18:50,140 (beam_search:429) INFO: max output length: 277 +2024-01-17 01:18:50,140 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:51,220 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:51,220 (beam_search:476) INFO: -32.64 * 1.0 = -32.64 for ctc +2024-01-17 01:18:51,220 (beam_search:479) INFO: total log probability: -32.64 +2024-01-17 01:18:51,220 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:18:51,220 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:51,221 (beam_search:483) INFO: best hypo: YOSHOULDBEALEGTPARTCEFROMYOURWROMVIONRECEVERILHAVSOMTOULSGIVENOUTHENHEATDDEPLOMASHEHASTONDERSTANDTHTINGSHACNTROLOFVENCS + +2024-01-17 01:18:51,223 (asr_inference:494) INFO: speech length: 71357 +2024-01-17 01:18:51,232 (beam_search:428) INFO: decoder input length: 109 +2024-01-17 01:18:51,233 (beam_search:429) INFO: max output length: 109 +2024-01-17 01:18:51,233 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:51,444 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:51,444 (beam_search:476) INFO: -8.55 * 1.0 = -8.55 for ctc +2024-01-17 01:18:51,444 (beam_search:479) INFO: total log probability: -8.55 +2024-01-17 01:18:51,444 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:18:51,444 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:51,444 (beam_search:483) INFO: best hypo: BYTHETIMTHEFROSTHADSADINTHESHULBEFARWAYFROMHELSTON + +2024-01-17 01:18:51,446 (asr_inference:494) INFO: speech length: 46557 +2024-01-17 01:18:51,454 (beam_search:428) INFO: decoder input length: 70 +2024-01-17 01:18:51,454 (beam_search:429) INFO: max output length: 70 +2024-01-17 01:18:51,454 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:51,539 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:51,539 (beam_search:476) INFO: -3.90 * 1.0 = -3.90 for ctc +2024-01-17 01:18:51,539 (beam_search:479) INFO: total log probability: -3.90 +2024-01-17 01:18:51,539 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 01:18:51,539 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:51,539 (beam_search:483) INFO: best hypo: WONTHINGIWNTTOSAYBEGANDCANITY + +2024-01-17 01:18:51,540 (asr_inference:494) INFO: speech length: 68317 +2024-01-17 01:18:51,550 (beam_search:428) INFO: decoder input length: 104 +2024-01-17 01:18:51,550 (beam_search:429) INFO: max output length: 104 +2024-01-17 01:18:51,550 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:51,740 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:51,740 (beam_search:476) INFO: -12.44 * 1.0 = -12.44 for ctc +2024-01-17 01:18:51,740 (beam_search:479) INFO: total log probability: -12.44 +2024-01-17 01:18:51,740 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:18:51,740 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:51,741 (beam_search:483) INFO: best hypo: THISMPORTNTRAFICWASCONFIGEDTONOONUTTHEEALPROPRITER + +2024-01-17 01:18:51,742 (asr_inference:494) INFO: speech length: 79104 +2024-01-17 01:18:51,752 (beam_search:428) INFO: decoder input length: 121 +2024-01-17 01:18:51,752 (beam_search:429) INFO: max output length: 121 +2024-01-17 01:18:51,752 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:51,948 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:51,948 (beam_search:476) INFO: -16.26 * 1.0 = -16.26 for ctc +2024-01-17 01:18:51,948 (beam_search:479) INFO: total log probability: -16.26 +2024-01-17 01:18:51,948 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:18:51,948 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:51,948 (beam_search:483) INFO: best hypo: INYEOARDOBBLASEDTPONTBASOUDOTMYTHSTIMGGO + +2024-01-17 01:18:51,949 (asr_inference:494) INFO: speech length: 68352 +2024-01-17 01:18:51,959 (beam_search:428) INFO: decoder input length: 104 +2024-01-17 01:18:51,959 (beam_search:429) INFO: max output length: 104 +2024-01-17 01:18:51,959 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:52,134 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:52,135 (beam_search:476) INFO: -10.19 * 1.0 = -10.19 for ctc +2024-01-17 01:18:52,135 (beam_search:479) INFO: total log probability: -10.19 +2024-01-17 01:18:52,135 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:18:52,135 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:52,135 (beam_search:483) INFO: best hypo: IGHTEATAEPRITDSUPSECTIONWHICHDEALSWITISASPECT + +2024-01-17 01:18:52,136 (asr_inference:494) INFO: speech length: 104832 +2024-01-17 01:18:52,148 (beam_search:428) INFO: decoder input length: 161 +2024-01-17 01:18:52,148 (beam_search:429) INFO: max output length: 161 +2024-01-17 01:18:52,148 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:52,446 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:52,446 (beam_search:476) INFO: -15.15 * 1.0 = -15.15 for ctc +2024-01-17 01:18:52,446 (beam_search:479) INFO: total log probability: -15.15 +2024-01-17 01:18:52,446 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:18:52,446 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:52,446 (beam_search:483) INFO: best hypo: OPRATIONOFTHEFRUNTLANGCONTNEDONTHEGOULDANTTRESSELS + +2024-01-17 01:18:52,447 (asr_inference:494) INFO: speech length: 116352 +2024-01-17 01:18:52,460 (beam_search:428) INFO: decoder input length: 179 +2024-01-17 01:18:52,460 (beam_search:429) INFO: max output length: 179 +2024-01-17 01:18:52,460 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:52,832 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:52,832 (beam_search:476) INFO: -16.56 * 1.0 = -16.56 for ctc +2024-01-17 01:18:52,832 (beam_search:479) INFO: total log probability: -16.56 +2024-01-17 01:18:52,832 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:18:52,832 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:52,832 (beam_search:483) INFO: best hypo: MONSIOMFLORIDISTWENSPERENTOVERANEXTRIMLYWHIHDRANGOFAVELINGS + +2024-01-17 01:18:52,834 (asr_inference:494) INFO: speech length: 130944 +2024-01-17 01:18:52,847 (beam_search:428) INFO: decoder input length: 202 +2024-01-17 01:18:52,847 (beam_search:429) INFO: max output length: 202 +2024-01-17 01:18:52,847 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:53,365 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:53,365 (beam_search:476) INFO: -22.66 * 1.0 = -22.66 for ctc +2024-01-17 01:18:53,365 (beam_search:479) INFO: total log probability: -22.66 +2024-01-17 01:18:53,365 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:18:53,365 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:53,365 (beam_search:483) INFO: best hypo: FORJGINTBEAKINKSHEATSSTORTFRESHBPACKETBUTEADESSONDILVEDTHEMUNTORELEROLDGASS + +2024-01-17 01:18:53,367 (asr_inference:494) INFO: speech length: 119424 +2024-01-17 01:18:53,379 (beam_search:428) INFO: decoder input length: 184 +2024-01-17 01:18:53,379 (beam_search:429) INFO: max output length: 184 +2024-01-17 01:18:53,379 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:53,829 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:53,829 (beam_search:476) INFO: -15.18 * 1.0 = -15.18 for ctc +2024-01-17 01:18:53,829 (beam_search:479) INFO: total log probability: -15.18 +2024-01-17 01:18:53,829 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:18:53,829 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:53,829 (beam_search:483) INFO: best hypo: THEOTHEFORTINCAMPASARETOYACAMPSSREFERDTOCOLECTIVELYASTHEYUNERSTICOLAGE + +2024-01-17 01:18:53,831 (asr_inference:494) INFO: speech length: 70272 +2024-01-17 01:18:53,840 (beam_search:428) INFO: decoder input length: 107 +2024-01-17 01:18:53,840 (beam_search:429) INFO: max output length: 107 +2024-01-17 01:18:53,840 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:54,012 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:54,012 (beam_search:476) INFO: -10.08 * 1.0 = -10.08 for ctc +2024-01-17 01:18:54,012 (beam_search:479) INFO: total log probability: -10.08 +2024-01-17 01:18:54,012 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:18:54,012 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:54,013 (beam_search:483) INFO: best hypo: ITSTOTHEARDTHOWHECUICKLEGONTOFRGETMYNAMETH + +2024-01-17 01:18:54,014 (asr_inference:494) INFO: speech length: 113280 +2024-01-17 01:18:54,026 (beam_search:428) INFO: decoder input length: 174 +2024-01-17 01:18:54,026 (beam_search:429) INFO: max output length: 174 +2024-01-17 01:18:54,026 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:54,435 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:54,435 (beam_search:476) INFO: -25.71 * 1.0 = -25.71 for ctc +2024-01-17 01:18:54,435 (beam_search:479) INFO: total log probability: -25.71 +2024-01-17 01:18:54,435 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:18:54,435 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:54,436 (beam_search:483) INFO: best hypo: WONPOTUREINTEGLORSHOTHHOWDHEAGENTLYINTIRENTISTHATHYEALADGRARTTOMPON + +2024-01-17 01:18:54,437 (asr_inference:494) INFO: speech length: 59904 +2024-01-17 01:18:54,446 (beam_search:428) INFO: decoder input length: 91 +2024-01-17 01:18:54,446 (beam_search:429) INFO: max output length: 91 +2024-01-17 01:18:54,446 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:54,503 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:54,503 (beam_search:476) INFO: -5.16 * 1.0 = -5.16 for ctc +2024-01-17 01:18:54,503 (beam_search:479) INFO: total log probability: -5.16 +2024-01-17 01:18:54,503 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:18:54,503 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:54,503 (beam_search:483) INFO: best hypo: AIMPERIALDIYIAT + +2024-01-17 01:18:54,504 (asr_inference:494) INFO: speech length: 93888 +2024-01-17 01:18:54,515 (beam_search:428) INFO: decoder input length: 144 +2024-01-17 01:18:54,515 (beam_search:429) INFO: max output length: 144 +2024-01-17 01:18:54,515 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:54,757 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:54,757 (beam_search:476) INFO: -12.68 * 1.0 = -12.68 for ctc +2024-01-17 01:18:54,757 (beam_search:479) INFO: total log probability: -12.68 +2024-01-17 01:18:54,757 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:18:54,757 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:54,757 (beam_search:483) INFO: best hypo: THEESULTINOMPANYEDASHARTAESICURITYCOTPORATION + +2024-01-17 01:18:54,759 (asr_inference:494) INFO: speech length: 112896 +2024-01-17 01:18:54,771 (beam_search:428) INFO: decoder input length: 174 +2024-01-17 01:18:54,771 (beam_search:429) INFO: max output length: 174 +2024-01-17 01:18:54,771 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:55,136 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:55,137 (beam_search:476) INFO: -14.61 * 1.0 = -14.61 for ctc +2024-01-17 01:18:55,137 (beam_search:479) INFO: total log probability: -14.61 +2024-01-17 01:18:55,137 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:18:55,137 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:55,137 (beam_search:483) INFO: best hypo: BECOINGMININGCANBEDONWITGOFHISCARTSORWITESSPESIALIEDHORDLY + +2024-01-17 01:18:55,138 (asr_inference:494) INFO: speech length: 69696 +2024-01-17 01:18:55,148 (beam_search:428) INFO: decoder input length: 106 +2024-01-17 01:18:55,148 (beam_search:429) INFO: max output length: 106 +2024-01-17 01:18:55,148 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:55,264 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:55,264 (beam_search:476) INFO: -7.17 * 1.0 = -7.17 for ctc +2024-01-17 01:18:55,264 (beam_search:479) INFO: total log probability: -7.17 +2024-01-17 01:18:55,264 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:18:55,264 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:55,264 (beam_search:483) INFO: best hypo: THEYALSOLETHENASIALRANKING + +2024-01-17 01:18:55,265 (asr_inference:494) INFO: speech length: 77184 +2024-01-17 01:18:55,275 (beam_search:428) INFO: decoder input length: 118 +2024-01-17 01:18:55,275 (beam_search:429) INFO: max output length: 118 +2024-01-17 01:18:55,275 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:55,402 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:55,402 (beam_search:476) INFO: -9.80 * 1.0 = -9.80 for ctc +2024-01-17 01:18:55,402 (beam_search:479) INFO: total log probability: -9.80 +2024-01-17 01:18:55,402 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:18:55,402 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:55,402 (beam_search:483) INFO: best hypo: TROWSGRAINSBISHIPOFNIMERICE + +2024-01-17 01:18:55,403 (asr_inference:494) INFO: speech length: 122880 +2024-01-17 01:18:55,416 (beam_search:428) INFO: decoder input length: 189 +2024-01-17 01:18:55,416 (beam_search:429) INFO: max output length: 189 +2024-01-17 01:18:55,416 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:55,687 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:55,687 (beam_search:476) INFO: -12.44 * 1.0 = -12.44 for ctc +2024-01-17 01:18:55,687 (beam_search:479) INFO: total log probability: -12.44 +2024-01-17 01:18:55,687 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:18:55,687 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:55,688 (beam_search:483) INFO: best hypo: IONDEDTHATTHIDORLHIMUNHEOKMYPLASES + +2024-01-17 01:18:55,689 (asr_inference:494) INFO: speech length: 76416 +2024-01-17 01:18:55,699 (beam_search:428) INFO: decoder input length: 117 +2024-01-17 01:18:55,699 (beam_search:429) INFO: max output length: 117 +2024-01-17 01:18:55,699 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:55,828 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:55,828 (beam_search:476) INFO: -5.47 * 1.0 = -5.47 for ctc +2024-01-17 01:18:55,828 (beam_search:479) INFO: total log probability: -5.47 +2024-01-17 01:18:55,828 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:18:55,828 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:55,828 (beam_search:483) INFO: best hypo: ITHOUGTIDGIVETHECITSADREET + +2024-01-17 01:18:55,829 (asr_inference:494) INFO: speech length: 60480 +2024-01-17 01:18:55,839 (beam_search:428) INFO: decoder input length: 92 +2024-01-17 01:18:55,839 (beam_search:429) INFO: max output length: 92 +2024-01-17 01:18:55,839 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:55,937 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:55,937 (beam_search:476) INFO: -7.98 * 1.0 = -7.98 for ctc +2024-01-17 01:18:55,937 (beam_search:479) INFO: total log probability: -7.98 +2024-01-17 01:18:55,937 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:18:55,937 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:55,937 (beam_search:483) INFO: best hypo: ASTHEVITLDINIHTOMTHEPICHES + +2024-01-17 01:18:55,938 (asr_inference:494) INFO: speech length: 99456 +2024-01-17 01:18:55,949 (beam_search:428) INFO: decoder input length: 153 +2024-01-17 01:18:55,949 (beam_search:429) INFO: max output length: 153 +2024-01-17 01:18:55,949 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:56,239 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:56,239 (beam_search:476) INFO: -13.21 * 1.0 = -13.21 for ctc +2024-01-17 01:18:56,239 (beam_search:479) INFO: total log probability: -13.21 +2024-01-17 01:18:56,239 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:18:56,239 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:56,239 (beam_search:483) INFO: best hypo: HOWDYOURNOSTTOCETHISMAYEFROMTHEABLINGYORMOTORFNTION + +2024-01-17 01:18:56,241 (asr_inference:494) INFO: speech length: 135168 +2024-01-17 01:18:56,255 (beam_search:428) INFO: decoder input length: 209 +2024-01-17 01:18:56,255 (beam_search:429) INFO: max output length: 209 +2024-01-17 01:18:56,255 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:56,496 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:56,496 (beam_search:476) INFO: -18.18 * 1.0 = -18.18 for ctc +2024-01-17 01:18:56,496 (beam_search:479) INFO: total log probability: -18.18 +2024-01-17 01:18:56,496 (beam_search:480) INFO: normalized log probability: -0.49 +2024-01-17 01:18:56,496 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:56,496 (beam_search:483) INFO: best hypo: ACTHATSONDSLAKETHEARPROLOMEMIC + +2024-01-17 01:18:56,497 (asr_inference:494) INFO: speech length: 115200 +2024-01-17 01:18:56,510 (beam_search:428) INFO: decoder input length: 177 +2024-01-17 01:18:56,510 (beam_search:429) INFO: max output length: 177 +2024-01-17 01:18:56,510 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:56,917 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:56,917 (beam_search:476) INFO: -17.77 * 1.0 = -17.77 for ctc +2024-01-17 01:18:56,917 (beam_search:479) INFO: total log probability: -17.77 +2024-01-17 01:18:56,917 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:18:56,917 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:56,917 (beam_search:483) INFO: best hypo: HISTRICALIGERWASNOCLEARELYDEFINEBOUNGRYENTHISPITOFTHEARABYENPNINSTOLE + +2024-01-17 01:18:56,918 (asr_inference:494) INFO: speech length: 125568 +2024-01-17 01:18:56,932 (beam_search:428) INFO: decoder input length: 194 +2024-01-17 01:18:56,932 (beam_search:429) INFO: max output length: 194 +2024-01-17 01:18:56,932 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:57,325 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:57,325 (beam_search:476) INFO: -13.51 * 1.0 = -13.51 for ctc +2024-01-17 01:18:57,325 (beam_search:479) INFO: total log probability: -13.51 +2024-01-17 01:18:57,325 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:18:57,325 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:57,325 (beam_search:483) INFO: best hypo: MARSHIALSHAVEROFSLASHFILMEGAVETHEFILLMEANATEOUTOFTEAEN + +2024-01-17 01:18:57,326 (asr_inference:494) INFO: speech length: 41088 +2024-01-17 01:18:57,334 (beam_search:428) INFO: decoder input length: 62 +2024-01-17 01:18:57,334 (beam_search:429) INFO: max output length: 62 +2024-01-17 01:18:57,334 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:57,380 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:57,380 (beam_search:476) INFO: -9.29 * 1.0 = -9.29 for ctc +2024-01-17 01:18:57,380 (beam_search:479) INFO: total log probability: -9.29 +2024-01-17 01:18:57,380 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-17 01:18:57,380 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:57,380 (beam_search:483) INFO: best hypo: AOLPINDIOTITHAT + +2024-01-17 01:18:57,381 (asr_inference:494) INFO: speech length: 117504 +2024-01-17 01:18:57,394 (beam_search:428) INFO: decoder input length: 181 +2024-01-17 01:18:57,394 (beam_search:429) INFO: max output length: 181 +2024-01-17 01:18:57,394 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:57,675 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:57,675 (beam_search:476) INFO: -11.83 * 1.0 = -11.83 for ctc +2024-01-17 01:18:57,675 (beam_search:479) INFO: total log probability: -11.83 +2024-01-17 01:18:57,675 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:18:57,675 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:57,675 (beam_search:483) INFO: best hypo: HISTTDILEBEGANTORESEMBLEMICALTEMASSCKEINOS + +2024-01-17 01:18:57,677 (asr_inference:494) INFO: speech length: 96768 +2024-01-17 01:18:57,688 (beam_search:428) INFO: decoder input length: 149 +2024-01-17 01:18:57,688 (beam_search:429) INFO: max output length: 149 +2024-01-17 01:18:57,688 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:57,985 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:57,985 (beam_search:476) INFO: -15.75 * 1.0 = -15.75 for ctc +2024-01-17 01:18:57,986 (beam_search:479) INFO: total log probability: -15.75 +2024-01-17 01:18:57,986 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:18:57,986 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:57,986 (beam_search:483) INFO: best hypo: HEISALSOLCAPABLOFFIRNGLIGTINMBLEWIFIMENTEDISRUPTIVEPOWER + +2024-01-17 01:18:57,987 (asr_inference:494) INFO: speech length: 133248 +2024-01-17 01:18:58,001 (beam_search:428) INFO: decoder input length: 206 +2024-01-17 01:18:58,001 (beam_search:429) INFO: max output length: 206 +2024-01-17 01:18:58,001 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:58,513 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:58,513 (beam_search:476) INFO: -30.21 * 1.0 = -30.21 for ctc +2024-01-17 01:18:58,513 (beam_search:479) INFO: total log probability: -30.21 +2024-01-17 01:18:58,513 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:18:58,513 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:58,514 (beam_search:483) INFO: best hypo: THECLAMETOWIKEDSCENINGLINPURMALYTININGSASFOWWISEATANTHURONADERASYLADLIRE + +2024-01-17 01:18:58,515 (asr_inference:494) INFO: speech length: 44928 +2024-01-17 01:18:58,523 (beam_search:428) INFO: decoder input length: 68 +2024-01-17 01:18:58,523 (beam_search:429) INFO: max output length: 68 +2024-01-17 01:18:58,523 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:58,583 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:58,583 (beam_search:476) INFO: -10.69 * 1.0 = -10.69 for ctc +2024-01-17 01:18:58,583 (beam_search:479) INFO: total log probability: -10.69 +2024-01-17 01:18:58,583 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:18:58,583 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:58,584 (beam_search:483) INFO: best hypo: SHEEGRUSILYTROWHATH + +2024-01-17 01:18:58,585 (asr_inference:494) INFO: speech length: 95616 +2024-01-17 01:18:58,596 (beam_search:428) INFO: decoder input length: 147 +2024-01-17 01:18:58,596 (beam_search:429) INFO: max output length: 147 +2024-01-17 01:18:58,596 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:58,891 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:58,891 (beam_search:476) INFO: -10.67 * 1.0 = -10.67 for ctc +2024-01-17 01:18:58,891 (beam_search:479) INFO: total log probability: -10.67 +2024-01-17 01:18:58,891 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:18:58,891 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:58,892 (beam_search:483) INFO: best hypo: HEMTTHEORGANISERSOFTHEPROTESANDAGREDDCREATTWOWORKINGROMS + +2024-01-17 01:18:58,893 (asr_inference:494) INFO: speech length: 101952 +2024-01-17 01:18:58,904 (beam_search:428) INFO: decoder input length: 157 +2024-01-17 01:18:58,904 (beam_search:429) INFO: max output length: 157 +2024-01-17 01:18:58,904 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:59,171 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:59,171 (beam_search:476) INFO: -14.40 * 1.0 = -14.40 for ctc +2024-01-17 01:18:59,171 (beam_search:479) INFO: total log probability: -14.40 +2024-01-17 01:18:59,171 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:18:59,171 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:59,171 (beam_search:483) INFO: best hypo: THEBONSTROCTHOFHOLDWOARDWILABOFTHERENONSTORD + +2024-01-17 01:18:59,172 (asr_inference:494) INFO: speech length: 114048 +2024-01-17 01:18:59,185 (beam_search:428) INFO: decoder input length: 176 +2024-01-17 01:18:59,185 (beam_search:429) INFO: max output length: 176 +2024-01-17 01:18:59,185 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:18:59,575 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:18:59,575 (beam_search:476) INFO: -20.27 * 1.0 = -20.27 for ctc +2024-01-17 01:18:59,575 (beam_search:479) INFO: total log probability: -20.27 +2024-01-17 01:18:59,575 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:18:59,575 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:18:59,575 (beam_search:483) INFO: best hypo: INLYCAMDONTOMASGARITANDGOLDFILDSOATISEECHILEBAKCARWERUNCONTESTED + +2024-01-17 01:18:59,577 (asr_inference:494) INFO: speech length: 148224 +2024-01-17 01:18:59,591 (beam_search:428) INFO: decoder input length: 229 +2024-01-17 01:18:59,591 (beam_search:429) INFO: max output length: 229 +2024-01-17 01:18:59,591 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:00,025 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:00,025 (beam_search:476) INFO: -12.94 * 1.0 = -12.94 for ctc +2024-01-17 01:19:00,025 (beam_search:479) INFO: total log probability: -12.94 +2024-01-17 01:19:00,025 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:19:00,025 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:00,026 (beam_search:483) INFO: best hypo: ITISACHRDYSCOLWHOSFESARCOUCULATEDINONINMEANSTEST + +2024-01-17 01:19:00,028 (asr_inference:494) INFO: speech length: 67200 +2024-01-17 01:19:00,038 (beam_search:428) INFO: decoder input length: 102 +2024-01-17 01:19:00,038 (beam_search:429) INFO: max output length: 102 +2024-01-17 01:19:00,038 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:00,195 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:00,195 (beam_search:476) INFO: -8.59 * 1.0 = -8.59 for ctc +2024-01-17 01:19:00,195 (beam_search:479) INFO: total log probability: -8.59 +2024-01-17 01:19:00,195 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:19:00,195 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:00,195 (beam_search:483) INFO: best hypo: SOMEWENTAWAYWHALIOWASTHERANDOTHEPOPLECAM + +2024-01-17 01:19:00,196 (asr_inference:494) INFO: speech length: 26496 +2024-01-17 01:19:00,204 (beam_search:428) INFO: decoder input length: 39 +2024-01-17 01:19:00,204 (beam_search:429) INFO: max output length: 39 +2024-01-17 01:19:00,204 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:00,236 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:00,236 (beam_search:476) INFO: -26.03 * 1.0 = -26.03 for ctc +2024-01-17 01:19:00,236 (beam_search:479) INFO: total log probability: -26.03 +2024-01-17 01:19:00,236 (beam_search:480) INFO: normalized log probability: -1.08 +2024-01-17 01:19:00,236 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:00,237 (beam_search:483) INFO: best hypo: THCSAITHAEDEDUPRE + +2024-01-17 01:19:00,238 (asr_inference:494) INFO: speech length: 130752 +2024-01-17 01:19:00,251 (beam_search:428) INFO: decoder input length: 202 +2024-01-17 01:19:00,251 (beam_search:429) INFO: max output length: 202 +2024-01-17 01:19:00,251 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:00,747 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:00,747 (beam_search:476) INFO: -15.08 * 1.0 = -15.08 for ctc +2024-01-17 01:19:00,747 (beam_search:479) INFO: total log probability: -15.08 +2024-01-17 01:19:00,747 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:19:00,747 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:00,748 (beam_search:483) INFO: best hypo: THATCURACONOTYWASLOKCADEDMANLYITHEHISTORICALEANDJEAGREFICALREAGIONOFCUR + +2024-01-17 01:19:00,749 (asr_inference:494) INFO: speech length: 95040 +2024-01-17 01:19:00,760 (beam_search:428) INFO: decoder input length: 146 +2024-01-17 01:19:00,760 (beam_search:429) INFO: max output length: 146 +2024-01-17 01:19:00,760 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:00,964 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:00,964 (beam_search:476) INFO: -15.07 * 1.0 = -15.07 for ctc +2024-01-17 01:19:00,964 (beam_search:479) INFO: total log probability: -15.07 +2024-01-17 01:19:00,964 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:19:00,964 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:00,965 (beam_search:483) INFO: best hypo: UNCHELOVATIONATHESIGHTISAMOFSULEVBLE + +2024-01-17 01:19:00,966 (asr_inference:494) INFO: speech length: 122496 +2024-01-17 01:19:00,978 (beam_search:428) INFO: decoder input length: 189 +2024-01-17 01:19:00,978 (beam_search:429) INFO: max output length: 189 +2024-01-17 01:19:00,978 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:01,282 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:01,282 (beam_search:476) INFO: -8.15 * 1.0 = -8.15 for ctc +2024-01-17 01:19:01,282 (beam_search:479) INFO: total log probability: -8.15 +2024-01-17 01:19:01,282 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:19:01,282 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:01,282 (beam_search:483) INFO: best hypo: TOBEASTRIEDTONCHECTCONONTEMPTINTOHISTONE + +2024-01-17 01:19:01,284 (asr_inference:494) INFO: speech length: 72960 +2024-01-17 01:19:01,294 (beam_search:428) INFO: decoder input length: 111 +2024-01-17 01:19:01,294 (beam_search:429) INFO: max output length: 111 +2024-01-17 01:19:01,294 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:01,399 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:01,399 (beam_search:476) INFO: -5.41 * 1.0 = -5.41 for ctc +2024-01-17 01:19:01,399 (beam_search:479) INFO: total log probability: -5.41 +2024-01-17 01:19:01,399 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:19:01,399 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:01,399 (beam_search:483) INFO: best hypo: IHAVETOWARLKTHISSATORDY + +2024-01-17 01:19:01,401 (asr_inference:494) INFO: speech length: 124032 +2024-01-17 01:19:01,414 (beam_search:428) INFO: decoder input length: 191 +2024-01-17 01:19:01,414 (beam_search:429) INFO: max output length: 191 +2024-01-17 01:19:01,414 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:01,825 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:01,825 (beam_search:476) INFO: -24.61 * 1.0 = -24.61 for ctc +2024-01-17 01:19:01,825 (beam_search:479) INFO: total log probability: -24.61 +2024-01-17 01:19:01,825 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:19:01,825 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:01,825 (beam_search:483) INFO: best hypo: TDETRATHERONWHSFOUNDTHESCOLEGEGLEADWITHGLATINGONTHERGNONES + +2024-01-17 01:19:01,826 (asr_inference:494) INFO: speech length: 139392 +2024-01-17 01:19:01,841 (beam_search:428) INFO: decoder input length: 215 +2024-01-17 01:19:01,841 (beam_search:429) INFO: max output length: 215 +2024-01-17 01:19:01,841 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:02,305 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:02,305 (beam_search:476) INFO: -12.02 * 1.0 = -12.02 for ctc +2024-01-17 01:19:02,305 (beam_search:479) INFO: total log probability: -12.02 +2024-01-17 01:19:02,305 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:19:02,305 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:02,305 (beam_search:483) INFO: best hypo: WHENTHEBILINGDOSTHESSAELEDFORBITTHEBOYTRMBLEDATWHATHESAW + +2024-01-17 01:19:02,307 (asr_inference:494) INFO: speech length: 100800 +2024-01-17 01:19:02,318 (beam_search:428) INFO: decoder input length: 155 +2024-01-17 01:19:02,318 (beam_search:429) INFO: max output length: 155 +2024-01-17 01:19:02,318 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:02,539 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:02,539 (beam_search:476) INFO: -11.06 * 1.0 = -11.06 for ctc +2024-01-17 01:19:02,539 (beam_search:479) INFO: total log probability: -11.06 +2024-01-17 01:19:02,539 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:19:02,539 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:02,539 (beam_search:483) INFO: best hypo: DEMACRATAMBERANBAKEKHERWONITHEOPONSEE + +2024-01-17 01:19:02,540 (asr_inference:494) INFO: speech length: 109440 +2024-01-17 01:19:02,553 (beam_search:428) INFO: decoder input length: 168 +2024-01-17 01:19:02,553 (beam_search:429) INFO: max output length: 168 +2024-01-17 01:19:02,553 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:02,941 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:02,941 (beam_search:476) INFO: -30.80 * 1.0 = -30.80 for ctc +2024-01-17 01:19:02,941 (beam_search:479) INFO: total log probability: -30.80 +2024-01-17 01:19:02,941 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:19:02,941 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:02,941 (beam_search:483) INFO: best hypo: WORTHAVEORTTEINTOGETHERBAITSOUODENTINHIECUALIEREUSOUNATHESINPOROM + +2024-01-17 01:19:02,943 (asr_inference:494) INFO: speech length: 92736 +2024-01-17 01:19:02,954 (beam_search:428) INFO: decoder input length: 142 +2024-01-17 01:19:02,954 (beam_search:429) INFO: max output length: 142 +2024-01-17 01:19:02,954 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:03,167 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:03,167 (beam_search:476) INFO: -8.10 * 1.0 = -8.10 for ctc +2024-01-17 01:19:03,167 (beam_search:479) INFO: total log probability: -8.10 +2024-01-17 01:19:03,167 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:19:03,167 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:03,168 (beam_search:483) INFO: best hypo: TRINTEWASBORNINBELESSITEDINBRITOSPONDERAS + +2024-01-17 01:19:03,169 (asr_inference:494) INFO: speech length: 52992 +2024-01-17 01:19:03,177 (beam_search:428) INFO: decoder input length: 80 +2024-01-17 01:19:03,177 (beam_search:429) INFO: max output length: 80 +2024-01-17 01:19:03,177 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:03,254 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:03,254 (beam_search:476) INFO: -7.40 * 1.0 = -7.40 for ctc +2024-01-17 01:19:03,254 (beam_search:479) INFO: total log probability: -7.40 +2024-01-17 01:19:03,254 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:19:03,254 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:03,254 (beam_search:483) INFO: best hypo: DORITYFACEOFLIFMOESFAST + +2024-01-17 01:19:03,255 (asr_inference:494) INFO: speech length: 46464 +2024-01-17 01:19:03,264 (beam_search:428) INFO: decoder input length: 70 +2024-01-17 01:19:03,264 (beam_search:429) INFO: max output length: 70 +2024-01-17 01:19:03,264 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:03,295 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:03,295 (beam_search:476) INFO: -10.48 * 1.0 = -10.48 for ctc +2024-01-17 01:19:03,295 (beam_search:479) INFO: total log probability: -10.48 +2024-01-17 01:19:03,295 (beam_search:480) INFO: normalized log probability: -0.81 +2024-01-17 01:19:03,295 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:03,295 (beam_search:483) INFO: best hypo: ANOWHHE + +2024-01-17 01:19:03,296 (asr_inference:494) INFO: speech length: 52992 +2024-01-17 01:19:03,304 (beam_search:428) INFO: decoder input length: 80 +2024-01-17 01:19:03,304 (beam_search:429) INFO: max output length: 80 +2024-01-17 01:19:03,304 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:03,355 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:03,355 (beam_search:476) INFO: -12.80 * 1.0 = -12.80 for ctc +2024-01-17 01:19:03,355 (beam_search:479) INFO: total log probability: -12.80 +2024-01-17 01:19:03,355 (beam_search:480) INFO: normalized log probability: -0.67 +2024-01-17 01:19:03,355 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:03,355 (beam_search:483) INFO: best hypo: SIVEINGORLTDLOL + +2024-01-17 01:19:03,356 (asr_inference:494) INFO: speech length: 102144 +2024-01-17 01:19:03,367 (beam_search:428) INFO: decoder input length: 157 +2024-01-17 01:19:03,367 (beam_search:429) INFO: max output length: 157 +2024-01-17 01:19:03,367 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:03,711 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:03,711 (beam_search:476) INFO: -23.02 * 1.0 = -23.02 for ctc +2024-01-17 01:19:03,711 (beam_search:479) INFO: total log probability: -23.02 +2024-01-17 01:19:03,711 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:19:03,711 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:03,711 (beam_search:483) INFO: best hypo: ATONTMBRYLOUELINSTHEYWARDFROMBRAKGBESTATIONINSONDIFRENERECIONS + +2024-01-17 01:19:03,713 (asr_inference:494) INFO: speech length: 88128 +2024-01-17 01:19:03,724 (beam_search:428) INFO: decoder input length: 135 +2024-01-17 01:19:03,724 (beam_search:429) INFO: max output length: 135 +2024-01-17 01:19:03,724 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:03,994 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:03,994 (beam_search:476) INFO: -11.54 * 1.0 = -11.54 for ctc +2024-01-17 01:19:03,994 (beam_search:479) INFO: total log probability: -11.54 +2024-01-17 01:19:03,994 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:19:03,994 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:03,995 (beam_search:483) INFO: best hypo: CHECKREPUPBLICKENTEDTWOSHOUTERSINTOTHEPAROLIMPIGCOMPATITION + +2024-01-17 01:19:03,996 (asr_inference:494) INFO: speech length: 126336 +2024-01-17 01:19:04,009 (beam_search:428) INFO: decoder input length: 195 +2024-01-17 01:19:04,009 (beam_search:429) INFO: max output length: 195 +2024-01-17 01:19:04,009 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:04,463 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:04,463 (beam_search:476) INFO: -20.15 * 1.0 = -20.15 for ctc +2024-01-17 01:19:04,463 (beam_search:479) INFO: total log probability: -20.15 +2024-01-17 01:19:04,463 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:19:04,463 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:04,464 (beam_search:483) INFO: best hypo: TITERWILIOMSROETHESCGEANGCLAYANDNSSHAREDSTORYRATITTHATTHEPEPIT + +2024-01-17 01:19:04,465 (asr_inference:494) INFO: speech length: 132864 +2024-01-17 01:19:04,479 (beam_search:428) INFO: decoder input length: 205 +2024-01-17 01:19:04,479 (beam_search:429) INFO: max output length: 205 +2024-01-17 01:19:04,479 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:04,959 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:04,959 (beam_search:476) INFO: -26.76 * 1.0 = -26.76 for ctc +2024-01-17 01:19:04,959 (beam_search:479) INFO: total log probability: -26.76 +2024-01-17 01:19:04,959 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:19:04,959 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:04,960 (beam_search:483) INFO: best hypo: TAISFASTOFEALLDWORSTOOFRETERCHHERITYFINTHERADYSADEROFOYDTHERART + +2024-01-17 01:19:04,961 (asr_inference:494) INFO: speech length: 104064 +2024-01-17 01:19:04,972 (beam_search:428) INFO: decoder input length: 160 +2024-01-17 01:19:04,972 (beam_search:429) INFO: max output length: 160 +2024-01-17 01:19:04,972 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:05,316 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:05,316 (beam_search:476) INFO: -26.17 * 1.0 = -26.17 for ctc +2024-01-17 01:19:05,316 (beam_search:479) INFO: total log probability: -26.17 +2024-01-17 01:19:05,316 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:19:05,316 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:05,316 (beam_search:483) INFO: best hypo: OTHESENXTRGARTSWENESURNTTHERNGONLELALMSWOAGALFTERAGWHTTHEALHAT + +2024-01-17 01:19:05,318 (asr_inference:494) INFO: speech length: 67968 +2024-01-17 01:19:05,327 (beam_search:428) INFO: decoder input length: 104 +2024-01-17 01:19:05,327 (beam_search:429) INFO: max output length: 104 +2024-01-17 01:19:05,328 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:05,430 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:05,430 (beam_search:476) INFO: -10.51 * 1.0 = -10.51 for ctc +2024-01-17 01:19:05,430 (beam_search:479) INFO: total log probability: -10.51 +2024-01-17 01:19:05,430 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:19:05,430 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:05,430 (beam_search:483) INFO: best hypo: AHUHNDRONTBACKTOESTRLIOA + +2024-01-17 01:19:05,431 (asr_inference:494) INFO: speech length: 88704 +2024-01-17 01:19:05,442 (beam_search:428) INFO: decoder input length: 136 +2024-01-17 01:19:05,442 (beam_search:429) INFO: max output length: 136 +2024-01-17 01:19:05,442 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:05,659 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:05,659 (beam_search:476) INFO: -12.84 * 1.0 = -12.84 for ctc +2024-01-17 01:19:05,659 (beam_search:479) INFO: total log probability: -12.84 +2024-01-17 01:19:05,659 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:19:05,659 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:05,659 (beam_search:483) INFO: best hypo: PERMITMETOINTRDUSEYOUTOHURRMOGJESTIEDCQEAN + +2024-01-17 01:19:05,660 (asr_inference:494) INFO: speech length: 77568 +2024-01-17 01:19:05,670 (beam_search:428) INFO: decoder input length: 119 +2024-01-17 01:19:05,670 (beam_search:429) INFO: max output length: 119 +2024-01-17 01:19:05,670 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:05,882 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:05,882 (beam_search:476) INFO: -16.14 * 1.0 = -16.14 for ctc +2024-01-17 01:19:05,882 (beam_search:479) INFO: total log probability: -16.14 +2024-01-17 01:19:05,882 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:19:05,882 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:05,883 (beam_search:483) INFO: best hypo: ANORGINHERWONWASSUPOSTOTHENONADICTIVFMORFENSUBSTOT + +2024-01-17 01:19:05,884 (asr_inference:494) INFO: speech length: 86400 +2024-01-17 01:19:05,894 (beam_search:428) INFO: decoder input length: 132 +2024-01-17 01:19:05,894 (beam_search:429) INFO: max output length: 132 +2024-01-17 01:19:05,894 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:06,020 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:06,021 (beam_search:476) INFO: -9.67 * 1.0 = -9.67 for ctc +2024-01-17 01:19:06,021 (beam_search:479) INFO: total log probability: -9.67 +2024-01-17 01:19:06,021 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:19:06,021 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:06,021 (beam_search:483) INFO: best hypo: USHEISOFMEKCICONDESSENT + +2024-01-17 01:19:06,022 (asr_inference:494) INFO: speech length: 77568 +2024-01-17 01:19:06,032 (beam_search:428) INFO: decoder input length: 119 +2024-01-17 01:19:06,032 (beam_search:429) INFO: max output length: 119 +2024-01-17 01:19:06,032 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:06,153 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:06,153 (beam_search:476) INFO: -12.79 * 1.0 = -12.79 for ctc +2024-01-17 01:19:06,153 (beam_search:479) INFO: total log probability: -12.79 +2024-01-17 01:19:06,153 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:19:06,153 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:06,153 (beam_search:483) INFO: best hypo: CSIMSHORTEALESNOTONDIST + +2024-01-17 01:19:06,155 (asr_inference:494) INFO: speech length: 87168 +2024-01-17 01:19:06,165 (beam_search:428) INFO: decoder input length: 134 +2024-01-17 01:19:06,165 (beam_search:429) INFO: max output length: 134 +2024-01-17 01:19:06,165 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:06,413 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:06,413 (beam_search:476) INFO: -24.59 * 1.0 = -24.59 for ctc +2024-01-17 01:19:06,413 (beam_search:479) INFO: total log probability: -24.59 +2024-01-17 01:19:06,413 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:19:06,413 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:06,413 (beam_search:483) INFO: best hypo: IOWHOUSONDONTLONSAONTHESSHREYIVGONTPRAEPEDITOHLONO + +2024-01-17 01:19:06,414 (asr_inference:494) INFO: speech length: 79488 +2024-01-17 01:19:06,424 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 01:19:06,424 (beam_search:429) INFO: max output length: 122 +2024-01-17 01:19:06,424 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:06,548 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:06,548 (beam_search:476) INFO: -7.35 * 1.0 = -7.35 for ctc +2024-01-17 01:19:06,548 (beam_search:479) INFO: total log probability: -7.35 +2024-01-17 01:19:06,548 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:19:06,548 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:06,548 (beam_search:483) INFO: best hypo: ICALEDAONSOPESARLINATIT + +2024-01-17 01:19:06,550 (asr_inference:494) INFO: speech length: 84010 +2024-01-17 01:19:06,560 (beam_search:428) INFO: decoder input length: 129 +2024-01-17 01:19:06,560 (beam_search:429) INFO: max output length: 129 +2024-01-17 01:19:06,560 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:06,806 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:06,806 (beam_search:476) INFO: -9.73 * 1.0 = -9.73 for ctc +2024-01-17 01:19:06,806 (beam_search:479) INFO: total log probability: -9.73 +2024-01-17 01:19:06,806 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:19:06,806 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:06,806 (beam_search:483) INFO: best hypo: FORSIMPLITHYGURINCHESISNORMLYAROUNDEDTOHEERESHOLNOMBER + +2024-01-17 01:19:06,808 (asr_inference:494) INFO: speech length: 79872 +2024-01-17 01:19:06,818 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 01:19:06,818 (beam_search:429) INFO: max output length: 122 +2024-01-17 01:19:06,818 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:06,975 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:06,975 (beam_search:476) INFO: -8.21 * 1.0 = -8.21 for ctc +2024-01-17 01:19:06,975 (beam_search:479) INFO: total log probability: -8.21 +2024-01-17 01:19:06,975 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:19:06,975 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:06,975 (beam_search:483) INFO: best hypo: IFWEACTILYDOONISALEDITWILLBEF + +2024-01-17 01:19:06,976 (asr_inference:494) INFO: speech length: 65664 +2024-01-17 01:19:06,986 (beam_search:428) INFO: decoder input length: 100 +2024-01-17 01:19:06,986 (beam_search:429) INFO: max output length: 100 +2024-01-17 01:19:06,986 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:07,093 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:07,093 (beam_search:476) INFO: -9.51 * 1.0 = -9.51 for ctc +2024-01-17 01:19:07,093 (beam_search:479) INFO: total log probability: -9.51 +2024-01-17 01:19:07,093 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:19:07,093 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:07,093 (beam_search:483) INFO: best hypo: THEFROOFTHICTRYSAPLSHAPED + +2024-01-17 01:19:07,094 (asr_inference:494) INFO: speech length: 63360 +2024-01-17 01:19:07,103 (beam_search:428) INFO: decoder input length: 96 +2024-01-17 01:19:07,103 (beam_search:429) INFO: max output length: 96 +2024-01-17 01:19:07,103 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:07,196 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:07,196 (beam_search:476) INFO: -7.90 * 1.0 = -7.90 for ctc +2024-01-17 01:19:07,196 (beam_search:479) INFO: total log probability: -7.90 +2024-01-17 01:19:07,196 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:19:07,196 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:07,196 (beam_search:483) INFO: best hypo: THEOUTEXTHANGEISNOWOBUY + +2024-01-17 01:19:07,197 (asr_inference:494) INFO: speech length: 112512 +2024-01-17 01:19:07,209 (beam_search:428) INFO: decoder input length: 173 +2024-01-17 01:19:07,210 (beam_search:429) INFO: max output length: 173 +2024-01-17 01:19:07,210 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:07,467 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:07,467 (beam_search:476) INFO: -8.38 * 1.0 = -8.38 for ctc +2024-01-17 01:19:07,467 (beam_search:479) INFO: total log probability: -8.38 +2024-01-17 01:19:07,467 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:19:07,467 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:07,468 (beam_search:483) INFO: best hypo: WHATYOUEAETODAIYWALKSANDTARKSTOMOROW + +2024-01-17 01:19:07,469 (asr_inference:494) INFO: speech length: 82944 +2024-01-17 01:19:07,479 (beam_search:428) INFO: decoder input length: 127 +2024-01-17 01:19:07,479 (beam_search:429) INFO: max output length: 127 +2024-01-17 01:19:07,479 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:07,711 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:07,711 (beam_search:476) INFO: -8.85 * 1.0 = -8.85 for ctc +2024-01-17 01:19:07,711 (beam_search:479) INFO: total log probability: -8.85 +2024-01-17 01:19:07,711 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:19:07,711 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:07,711 (beam_search:483) INFO: best hypo: THEWATEDANFLOSOUTOFTHESWOMPSASTHELOUWOPLARRIVER + +2024-01-17 01:19:07,712 (asr_inference:494) INFO: speech length: 104064 +2024-01-17 01:19:07,724 (beam_search:428) INFO: decoder input length: 160 +2024-01-17 01:19:07,724 (beam_search:429) INFO: max output length: 160 +2024-01-17 01:19:07,724 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:07,914 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:07,914 (beam_search:476) INFO: -11.51 * 1.0 = -11.51 for ctc +2024-01-17 01:19:07,914 (beam_search:479) INFO: total log probability: -11.51 +2024-01-17 01:19:07,914 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:19:07,914 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:07,914 (beam_search:483) INFO: best hypo: AHWHIYIDIDNDYOUSEAESOMEHINGK + +2024-01-17 01:19:07,916 (asr_inference:494) INFO: speech length: 56448 +2024-01-17 01:19:07,924 (beam_search:428) INFO: decoder input length: 86 +2024-01-17 01:19:07,924 (beam_search:429) INFO: max output length: 86 +2024-01-17 01:19:07,924 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:07,977 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:07,978 (beam_search:476) INFO: -7.74 * 1.0 = -7.74 for ctc +2024-01-17 01:19:07,978 (beam_search:479) INFO: total log probability: -7.74 +2024-01-17 01:19:07,978 (beam_search:480) INFO: normalized log probability: -0.43 +2024-01-17 01:19:07,978 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:07,978 (beam_search:483) INFO: best hypo: THAVOUSENOMAR + +2024-01-17 01:19:07,979 (asr_inference:494) INFO: speech length: 110976 +2024-01-17 01:19:07,991 (beam_search:428) INFO: decoder input length: 171 +2024-01-17 01:19:07,991 (beam_search:429) INFO: max output length: 171 +2024-01-17 01:19:07,991 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:08,393 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:08,393 (beam_search:476) INFO: -18.43 * 1.0 = -18.43 for ctc +2024-01-17 01:19:08,393 (beam_search:479) INFO: total log probability: -18.43 +2024-01-17 01:19:08,393 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:19:08,393 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:08,393 (beam_search:483) INFO: best hypo: ICOULDGOANFORDAYSABOUTTHEDADIOUSLONGSPHEDUSEINHISPARTFTHEWEOROED + +2024-01-17 01:19:08,394 (asr_inference:494) INFO: speech length: 99648 +2024-01-17 01:19:08,406 (beam_search:428) INFO: decoder input length: 153 +2024-01-17 01:19:08,406 (beam_search:429) INFO: max output length: 153 +2024-01-17 01:19:08,406 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:08,675 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:08,675 (beam_search:476) INFO: -19.50 * 1.0 = -19.50 for ctc +2024-01-17 01:19:08,675 (beam_search:479) INFO: total log probability: -19.50 +2024-01-17 01:19:08,675 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:19:08,675 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:08,675 (beam_search:483) INFO: best hypo: THOSFHEOLADEOFHEAINCQUIRONNGTINSITICLYOUTTHEYEAR + +2024-01-17 01:19:08,676 (asr_inference:494) INFO: speech length: 88704 +2024-01-17 01:19:08,687 (beam_search:428) INFO: decoder input length: 136 +2024-01-17 01:19:08,687 (beam_search:429) INFO: max output length: 136 +2024-01-17 01:19:08,687 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:08,847 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:08,847 (beam_search:476) INFO: -19.30 * 1.0 = -19.30 for ctc +2024-01-17 01:19:08,847 (beam_search:479) INFO: total log probability: -19.30 +2024-01-17 01:19:08,847 (beam_search:480) INFO: normalized log probability: -0.51 +2024-01-17 01:19:08,847 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:08,848 (beam_search:483) INFO: best hypo: FASLEVEISOFDECTISESSERGLTONSLA + +2024-01-17 01:19:08,849 (asr_inference:494) INFO: speech length: 91584 +2024-01-17 01:19:08,860 (beam_search:428) INFO: decoder input length: 141 +2024-01-17 01:19:08,860 (beam_search:429) INFO: max output length: 141 +2024-01-17 01:19:08,860 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:09,126 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:09,126 (beam_search:476) INFO: -12.14 * 1.0 = -12.14 for ctc +2024-01-17 01:19:09,126 (beam_search:479) INFO: total log probability: -12.14 +2024-01-17 01:19:09,126 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:19:09,126 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:09,127 (beam_search:483) INFO: best hypo: THESWEEDSWERNABLETOOUSERVEICALSWHICHERSTUCKINTHEMOD + +2024-01-17 01:19:09,128 (asr_inference:494) INFO: speech length: 132480 +2024-01-17 01:19:09,141 (beam_search:428) INFO: decoder input length: 204 +2024-01-17 01:19:09,141 (beam_search:429) INFO: max output length: 204 +2024-01-17 01:19:09,141 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:09,586 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:09,586 (beam_search:476) INFO: -15.55 * 1.0 = -15.55 for ctc +2024-01-17 01:19:09,586 (beam_search:479) INFO: total log probability: -15.55 +2024-01-17 01:19:09,586 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:19:09,586 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:09,587 (beam_search:483) INFO: best hypo: THEACKDIDNOTBRORHEBECTBAYINGAREPRESENTIETOAPEARINTHECORICTO + +2024-01-17 01:19:09,588 (asr_inference:494) INFO: speech length: 44544 +2024-01-17 01:19:09,596 (beam_search:428) INFO: decoder input length: 67 +2024-01-17 01:19:09,596 (beam_search:429) INFO: max output length: 67 +2024-01-17 01:19:09,596 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:09,665 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:09,665 (beam_search:476) INFO: -14.72 * 1.0 = -14.72 for ctc +2024-01-17 01:19:09,665 (beam_search:479) INFO: total log probability: -14.72 +2024-01-17 01:19:09,665 (beam_search:480) INFO: normalized log probability: -0.46 +2024-01-17 01:19:09,665 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:09,665 (beam_search:483) INFO: best hypo: CHINGWEREPLISTLOPINGRORALA + +2024-01-17 01:19:09,666 (asr_inference:494) INFO: speech length: 105984 +2024-01-17 01:19:09,678 (beam_search:428) INFO: decoder input length: 163 +2024-01-17 01:19:09,678 (beam_search:429) INFO: max output length: 163 +2024-01-17 01:19:09,678 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:09,989 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:09,989 (beam_search:476) INFO: -9.73 * 1.0 = -9.73 for ctc +2024-01-17 01:19:09,989 (beam_search:479) INFO: total log probability: -9.73 +2024-01-17 01:19:09,989 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:19:09,989 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:09,990 (beam_search:483) INFO: best hypo: HEWASCONVICTEDANBANISDISIPRSHORSEVENYEARSWERPNISMENT + +2024-01-17 01:19:09,991 (asr_inference:494) INFO: speech length: 122496 +2024-01-17 01:19:10,004 (beam_search:428) INFO: decoder input length: 189 +2024-01-17 01:19:10,004 (beam_search:429) INFO: max output length: 189 +2024-01-17 01:19:10,004 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:10,427 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:10,427 (beam_search:476) INFO: -16.76 * 1.0 = -16.76 for ctc +2024-01-17 01:19:10,427 (beam_search:479) INFO: total log probability: -16.76 +2024-01-17 01:19:10,427 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:19:10,427 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:10,427 (beam_search:483) INFO: best hypo: THECUPLOFTOCHALDENADATERSOFEAUROSALENDANDTHESONOMATHYOLBRAVERY + +2024-01-17 01:19:10,429 (asr_inference:494) INFO: speech length: 117504 +2024-01-17 01:19:10,441 (beam_search:428) INFO: decoder input length: 181 +2024-01-17 01:19:10,441 (beam_search:429) INFO: max output length: 181 +2024-01-17 01:19:10,441 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:10,825 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:10,825 (beam_search:476) INFO: -13.25 * 1.0 = -13.25 for ctc +2024-01-17 01:19:10,825 (beam_search:479) INFO: total log probability: -13.25 +2024-01-17 01:19:10,825 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:19:10,825 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:10,825 (beam_search:483) INFO: best hypo: NOFTHHREREFPRENDAMSRECHHEQUARAMOFTHMAGJORITYOFTHOSINTITLED + +2024-01-17 01:19:10,827 (asr_inference:494) INFO: speech length: 160128 +2024-01-17 01:19:10,842 (beam_search:428) INFO: decoder input length: 248 +2024-01-17 01:19:10,842 (beam_search:429) INFO: max output length: 248 +2024-01-17 01:19:10,842 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:11,578 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:11,578 (beam_search:476) INFO: -24.94 * 1.0 = -24.94 for ctc +2024-01-17 01:19:11,578 (beam_search:479) INFO: total log probability: -24.94 +2024-01-17 01:19:11,578 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:19:11,578 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:11,579 (beam_search:483) INFO: best hypo: INTITERPENSECXCEDEDINDEARASTSOMEARRASIPCARAITISWHOSALDOUNERSEYTHRAPEARIEOSTRONGGROTH + +# Accounting: time=105 threads=1 +# Ended (code 0) at Wed Jan 17 01:19:12 CST 2024, elapsed time 105 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.2.log b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.2.log new file mode 100644 index 0000000000000000000000000000000000000000..a37ea037f18554e1d0ce6d29558e77ecc69910b0 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.2.log @@ -0,0 +1,3022 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.2 --config conf/decode_asr.yaml +# Started at Wed Jan 17 01:19:12 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.2 --config conf/decode_asr.yaml +2024-01-17 01:19:13,398 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml +2024-01-17 01:19:13,415 (asr:523) INFO: Vocabulary size: 30 +2024-01-17 01:19:13,478 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 01:19:13,478 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 01:19:13,588 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 01:19:14,882 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 01:19:16,106 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 01:19:16,106 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 01:19:16,106 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 01:19:16,139 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-17 01:19:16,214 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 01:19:16,327 (asr_inference:494) INFO: speech length: 95232 +2024-01-17 01:19:17,538 (beam_search:428) INFO: decoder input length: 146 +2024-01-17 01:19:17,538 (beam_search:429) INFO: max output length: 146 +2024-01-17 01:19:17,538 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:17,789 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:17,789 (beam_search:476) INFO: -11.49 * 1.0 = -11.49 for ctc +2024-01-17 01:19:17,789 (beam_search:479) INFO: total log probability: -11.49 +2024-01-17 01:19:17,789 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:19:17,789 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:17,789 (beam_search:483) INFO: best hypo: HEARIAMBEPTENMYFLOCKANDMIBUTERSUREDTHEBOYTOS + +2024-01-17 01:19:17,814 (asr_inference:494) INFO: speech length: 95040 +2024-01-17 01:19:17,826 (beam_search:428) INFO: decoder input length: 146 +2024-01-17 01:19:17,826 (beam_search:429) INFO: max output length: 146 +2024-01-17 01:19:17,826 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:18,132 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:18,132 (beam_search:476) INFO: -19.26 * 1.0 = -19.26 for ctc +2024-01-17 01:19:18,132 (beam_search:479) INFO: total log probability: -19.26 +2024-01-17 01:19:18,132 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:19:18,132 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:18,132 (beam_search:483) INFO: best hypo: THISFALIAHASTLETTOSICXTEAENPOULBLENCSHADEINSEARDAYSEOFCALESTO + +2024-01-17 01:19:18,134 (asr_inference:494) INFO: speech length: 56448 +2024-01-17 01:19:18,143 (beam_search:428) INFO: decoder input length: 86 +2024-01-17 01:19:18,143 (beam_search:429) INFO: max output length: 86 +2024-01-17 01:19:18,143 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:18,191 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:18,191 (beam_search:476) INFO: -18.69 * 1.0 = -18.69 for ctc +2024-01-17 01:19:18,191 (beam_search:479) INFO: total log probability: -18.69 +2024-01-17 01:19:18,191 (beam_search:480) INFO: normalized log probability: -1.10 +2024-01-17 01:19:18,191 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:18,192 (beam_search:483) INFO: best hypo: AOOYSASDEO + +2024-01-17 01:19:18,193 (asr_inference:494) INFO: speech length: 61440 +2024-01-17 01:19:18,202 (beam_search:428) INFO: decoder input length: 93 +2024-01-17 01:19:18,202 (beam_search:429) INFO: max output length: 93 +2024-01-17 01:19:18,202 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:18,301 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:18,301 (beam_search:476) INFO: -5.56 * 1.0 = -5.56 for ctc +2024-01-17 01:19:18,301 (beam_search:479) INFO: total log probability: -5.56 +2024-01-17 01:19:18,301 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:19:18,301 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:18,301 (beam_search:483) INFO: best hypo: WHIYITHAPLAINCEPEGOINOVER + +2024-01-17 01:19:18,302 (asr_inference:494) INFO: speech length: 178176 +2024-01-17 01:19:18,319 (beam_search:428) INFO: decoder input length: 276 +2024-01-17 01:19:18,319 (beam_search:429) INFO: max output length: 276 +2024-01-17 01:19:18,319 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:18,836 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:18,836 (beam_search:476) INFO: -25.24 * 1.0 = -25.24 for ctc +2024-01-17 01:19:18,836 (beam_search:479) INFO: total log probability: -25.24 +2024-01-17 01:19:18,836 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-17 01:19:18,836 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:18,837 (beam_search:483) INFO: best hypo: ANDNIHAYAEAEDONDOSHEFORWATFIRTIALBOCSWITHORESOULTS + +2024-01-17 01:19:18,838 (asr_inference:494) INFO: speech length: 73152 +2024-01-17 01:19:18,848 (beam_search:428) INFO: decoder input length: 112 +2024-01-17 01:19:18,848 (beam_search:429) INFO: max output length: 112 +2024-01-17 01:19:18,848 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:19,003 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:19,003 (beam_search:476) INFO: -8.12 * 1.0 = -8.12 for ctc +2024-01-17 01:19:19,003 (beam_search:479) INFO: total log probability: -8.12 +2024-01-17 01:19:19,003 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:19:19,003 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:19,004 (beam_search:483) INFO: best hypo: THEPLICATIONWASPUTAPPROVITINFARBRAVY + +2024-01-17 01:19:19,005 (asr_inference:494) INFO: speech length: 109056 +2024-01-17 01:19:19,017 (beam_search:428) INFO: decoder input length: 168 +2024-01-17 01:19:19,017 (beam_search:429) INFO: max output length: 168 +2024-01-17 01:19:19,017 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:19,339 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:19,339 (beam_search:476) INFO: -11.83 * 1.0 = -11.83 for ctc +2024-01-17 01:19:19,339 (beam_search:479) INFO: total log probability: -11.83 +2024-01-17 01:19:19,339 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:19:19,339 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:19,340 (beam_search:483) INFO: best hypo: HENRYTORLEDTONMNSTILSWEARHEHADASOUNDIDRANINGINLITING + +2024-01-17 01:19:19,341 (asr_inference:494) INFO: speech length: 127872 +2024-01-17 01:19:19,354 (beam_search:428) INFO: decoder input length: 197 +2024-01-17 01:19:19,354 (beam_search:429) INFO: max output length: 197 +2024-01-17 01:19:19,354 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:19,894 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:19,894 (beam_search:476) INFO: -18.88 * 1.0 = -18.88 for ctc +2024-01-17 01:19:19,894 (beam_search:479) INFO: total log probability: -18.88 +2024-01-17 01:19:19,894 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:19:19,894 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:19,895 (beam_search:483) INFO: best hypo: ITWASTISCONTINUEDOTOSCETHALINCONFLICSANVLVEDINLOSEHISRETHIRNTORETORESTRIALREBRADIO + +2024-01-17 01:19:19,896 (asr_inference:494) INFO: speech length: 144576 +2024-01-17 01:19:19,911 (beam_search:428) INFO: decoder input length: 223 +2024-01-17 01:19:19,911 (beam_search:429) INFO: max output length: 223 +2024-01-17 01:19:19,911 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:20,170 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:20,170 (beam_search:476) INFO: -10.69 * 1.0 = -10.69 for ctc +2024-01-17 01:19:20,170 (beam_search:479) INFO: total log probability: -10.69 +2024-01-17 01:19:20,170 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:19:20,170 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:20,170 (beam_search:483) INFO: best hypo: ADTTHHERFAMLYWASFROMEBREAHONSA + +2024-01-17 01:19:20,172 (asr_inference:494) INFO: speech length: 74112 +2024-01-17 01:19:20,182 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 01:19:20,182 (beam_search:429) INFO: max output length: 113 +2024-01-17 01:19:20,182 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:20,305 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:20,305 (beam_search:476) INFO: -13.49 * 1.0 = -13.49 for ctc +2024-01-17 01:19:20,305 (beam_search:479) INFO: total log probability: -13.49 +2024-01-17 01:19:20,305 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:19:20,305 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:20,305 (beam_search:483) INFO: best hypo: AWHATDIDYEAEFORINORTHEAPA + +2024-01-17 01:19:20,307 (asr_inference:494) INFO: speech length: 48960 +2024-01-17 01:19:20,315 (beam_search:428) INFO: decoder input length: 74 +2024-01-17 01:19:20,315 (beam_search:429) INFO: max output length: 74 +2024-01-17 01:19:20,315 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:20,381 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:20,381 (beam_search:476) INFO: -3.72 * 1.0 = -3.72 for ctc +2024-01-17 01:19:20,381 (beam_search:479) INFO: total log probability: -3.72 +2024-01-17 01:19:20,381 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:19:20,381 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:20,381 (beam_search:483) INFO: best hypo: THATWASMYDRARTOSINCE + +2024-01-17 01:19:20,382 (asr_inference:494) INFO: speech length: 86400 +2024-01-17 01:19:20,393 (beam_search:428) INFO: decoder input length: 132 +2024-01-17 01:19:20,393 (beam_search:429) INFO: max output length: 132 +2024-01-17 01:19:20,393 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:20,571 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:20,571 (beam_search:476) INFO: -12.15 * 1.0 = -12.15 for ctc +2024-01-17 01:19:20,571 (beam_search:479) INFO: total log probability: -12.15 +2024-01-17 01:19:20,571 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:19:20,572 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:20,572 (beam_search:483) INFO: best hypo: HESCOSEARITAMUSTEREOFSHEAROSEDCOUO + +2024-01-17 01:19:20,573 (asr_inference:494) INFO: speech length: 104064 +2024-01-17 01:19:20,584 (beam_search:428) INFO: decoder input length: 160 +2024-01-17 01:19:20,584 (beam_search:429) INFO: max output length: 160 +2024-01-17 01:19:20,584 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:20,905 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:20,906 (beam_search:476) INFO: -16.93 * 1.0 = -16.93 for ctc +2024-01-17 01:19:20,906 (beam_search:479) INFO: total log probability: -16.93 +2024-01-17 01:19:20,906 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:19:20,906 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:20,906 (beam_search:483) INFO: best hypo: THEHLINTURSTOTHECHURISHOASINESTHATDALTERINDSPEUSEWHETHER + +2024-01-17 01:19:20,907 (asr_inference:494) INFO: speech length: 44160 +2024-01-17 01:19:20,915 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:19:20,915 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:19:20,915 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:20,988 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:20,988 (beam_search:476) INFO: -14.12 * 1.0 = -14.12 for ctc +2024-01-17 01:19:20,988 (beam_search:479) INFO: total log probability: -14.12 +2024-01-17 01:19:20,988 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:19:20,988 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:20,988 (beam_search:483) INFO: best hypo: IOUENOTTHOSWEREINHERACHLEADR + +2024-01-17 01:19:20,989 (asr_inference:494) INFO: speech length: 69696 +2024-01-17 01:19:20,999 (beam_search:428) INFO: decoder input length: 106 +2024-01-17 01:19:20,999 (beam_search:429) INFO: max output length: 106 +2024-01-17 01:19:20,999 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:21,107 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:21,107 (beam_search:476) INFO: -7.98 * 1.0 = -7.98 for ctc +2024-01-17 01:19:21,107 (beam_search:479) INFO: total log probability: -7.98 +2024-01-17 01:19:21,107 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:19:21,107 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:21,107 (beam_search:483) INFO: best hypo: THELJOSIECTENDFESTTHEPIY + +2024-01-17 01:19:21,108 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 01:19:21,116 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:19:21,116 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:19:21,116 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:21,182 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:21,182 (beam_search:476) INFO: -5.19 * 1.0 = -5.19 for ctc +2024-01-17 01:19:21,182 (beam_search:479) INFO: total log probability: -5.19 +2024-01-17 01:19:21,182 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:19:21,182 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:21,182 (beam_search:483) INFO: best hypo: MYNESCANHELPYOITTHATS + +2024-01-17 01:19:21,183 (asr_inference:494) INFO: speech length: 46464 +2024-01-17 01:19:21,192 (beam_search:428) INFO: decoder input length: 70 +2024-01-17 01:19:21,192 (beam_search:429) INFO: max output length: 70 +2024-01-17 01:19:21,192 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:21,250 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:21,250 (beam_search:476) INFO: -7.98 * 1.0 = -7.98 for ctc +2024-01-17 01:19:21,250 (beam_search:479) INFO: total log probability: -7.98 +2024-01-17 01:19:21,250 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:19:21,250 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:21,250 (beam_search:483) INFO: best hypo: BUTSAODHISTOFERYWON + +2024-01-17 01:19:21,251 (asr_inference:494) INFO: speech length: 59136 +2024-01-17 01:19:21,260 (beam_search:428) INFO: decoder input length: 90 +2024-01-17 01:19:21,260 (beam_search:429) INFO: max output length: 90 +2024-01-17 01:19:21,260 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:21,377 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:21,377 (beam_search:476) INFO: -10.14 * 1.0 = -10.14 for ctc +2024-01-17 01:19:21,377 (beam_search:479) INFO: total log probability: -10.14 +2024-01-17 01:19:21,377 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:19:21,377 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:21,377 (beam_search:483) INFO: best hypo: HOWFORTHEBESTANDPRPEAETFUETHEBOST + +2024-01-17 01:19:21,378 (asr_inference:494) INFO: speech length: 89472 +2024-01-17 01:19:21,389 (beam_search:428) INFO: decoder input length: 137 +2024-01-17 01:19:21,389 (beam_search:429) INFO: max output length: 137 +2024-01-17 01:19:21,389 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:21,596 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:21,596 (beam_search:476) INFO: -15.53 * 1.0 = -15.53 for ctc +2024-01-17 01:19:21,596 (beam_search:479) INFO: total log probability: -15.53 +2024-01-17 01:19:21,596 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:19:21,597 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:21,597 (beam_search:483) INFO: best hypo: INISHELYTHEEPLOUSWASHRTENSTRICKLYBYDIT + +2024-01-17 01:19:21,598 (asr_inference:494) INFO: speech length: 86518 +2024-01-17 01:19:21,609 (beam_search:428) INFO: decoder input length: 133 +2024-01-17 01:19:21,609 (beam_search:429) INFO: max output length: 133 +2024-01-17 01:19:21,609 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:21,781 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:21,781 (beam_search:476) INFO: -7.30 * 1.0 = -7.30 for ctc +2024-01-17 01:19:21,781 (beam_search:479) INFO: total log probability: -7.30 +2024-01-17 01:19:21,782 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:19:21,782 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:21,782 (beam_search:483) INFO: best hypo: ALLWEONEDBYTHEEVERITMORESINICIT + +2024-01-17 01:19:21,783 (asr_inference:494) INFO: speech length: 76416 +2024-01-17 01:19:21,793 (beam_search:428) INFO: decoder input length: 117 +2024-01-17 01:19:21,793 (beam_search:429) INFO: max output length: 117 +2024-01-17 01:19:21,793 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:21,961 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:21,961 (beam_search:476) INFO: -16.16 * 1.0 = -16.16 for ctc +2024-01-17 01:19:21,961 (beam_search:479) INFO: total log probability: -16.16 +2024-01-17 01:19:21,961 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:19:21,961 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:21,961 (beam_search:483) INFO: best hypo: BHATHTHENTHEWILASRINGTOMOROMILNTHSEOD + +2024-01-17 01:19:21,963 (asr_inference:494) INFO: speech length: 72000 +2024-01-17 01:19:21,972 (beam_search:428) INFO: decoder input length: 110 +2024-01-17 01:19:21,972 (beam_search:429) INFO: max output length: 110 +2024-01-17 01:19:21,972 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:22,071 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:22,071 (beam_search:476) INFO: -6.82 * 1.0 = -6.82 for ctc +2024-01-17 01:19:22,071 (beam_search:479) INFO: total log probability: -6.82 +2024-01-17 01:19:22,071 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:19:22,071 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:22,071 (beam_search:483) INFO: best hypo: HDORBISTERECKGMMINLIP + +2024-01-17 01:19:22,072 (asr_inference:494) INFO: speech length: 149184 +2024-01-17 01:19:22,087 (beam_search:428) INFO: decoder input length: 231 +2024-01-17 01:19:22,087 (beam_search:429) INFO: max output length: 231 +2024-01-17 01:19:22,087 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:22,414 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:22,414 (beam_search:476) INFO: -14.54 * 1.0 = -14.54 for ctc +2024-01-17 01:19:22,414 (beam_search:479) INFO: total log probability: -14.54 +2024-01-17 01:19:22,414 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:19:22,414 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:22,414 (beam_search:483) INFO: best hypo: OSEIALPETRYTOHERPACEASACTINGDIRECTER + +2024-01-17 01:19:22,416 (asr_inference:494) INFO: speech length: 81792 +2024-01-17 01:19:22,426 (beam_search:428) INFO: decoder input length: 125 +2024-01-17 01:19:22,426 (beam_search:429) INFO: max output length: 125 +2024-01-17 01:19:22,426 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:22,635 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:22,635 (beam_search:476) INFO: -15.82 * 1.0 = -15.82 for ctc +2024-01-17 01:19:22,635 (beam_search:479) INFO: total log probability: -15.82 +2024-01-17 01:19:22,635 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:19:22,635 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:22,635 (beam_search:483) INFO: best hypo: THEBEVERLYWLBIFLYNTESTHEESSENTRPUTOFATONSHIP + +2024-01-17 01:19:22,636 (asr_inference:494) INFO: speech length: 59328 +2024-01-17 01:19:22,645 (beam_search:428) INFO: decoder input length: 90 +2024-01-17 01:19:22,645 (beam_search:429) INFO: max output length: 90 +2024-01-17 01:19:22,645 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:22,764 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:22,764 (beam_search:476) INFO: -7.15 * 1.0 = -7.15 for ctc +2024-01-17 01:19:22,764 (beam_search:479) INFO: total log probability: -7.15 +2024-01-17 01:19:22,764 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:19:22,764 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:22,764 (beam_search:483) INFO: best hypo: THETRACKRESERVSTINGWASALSOCOMPLEATED + +2024-01-17 01:19:22,765 (asr_inference:494) INFO: speech length: 125568 +2024-01-17 01:19:22,778 (beam_search:428) INFO: decoder input length: 194 +2024-01-17 01:19:22,779 (beam_search:429) INFO: max output length: 194 +2024-01-17 01:19:22,779 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:23,240 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:23,240 (beam_search:476) INFO: -21.98 * 1.0 = -21.98 for ctc +2024-01-17 01:19:23,240 (beam_search:479) INFO: total log probability: -21.98 +2024-01-17 01:19:23,240 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:19:23,240 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:23,240 (beam_search:483) INFO: best hypo: HITMARCHWASAWEREOFTHEIMPORTNEOFELECTRMRECOSCMPINBYAELOUGICALRRESERCH + +2024-01-17 01:19:23,242 (asr_inference:494) INFO: speech length: 61056 +2024-01-17 01:19:23,251 (beam_search:428) INFO: decoder input length: 93 +2024-01-17 01:19:23,251 (beam_search:429) INFO: max output length: 93 +2024-01-17 01:19:23,251 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:23,341 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:23,342 (beam_search:476) INFO: -11.35 * 1.0 = -11.35 for ctc +2024-01-17 01:19:23,342 (beam_search:479) INFO: total log probability: -11.35 +2024-01-17 01:19:23,342 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:19:23,342 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:23,342 (beam_search:483) INFO: best hypo: SINHOWBASBORNYANTHEHABAR + +2024-01-17 01:19:23,343 (asr_inference:494) INFO: speech length: 166656 +2024-01-17 01:19:23,359 (beam_search:428) INFO: decoder input length: 258 +2024-01-17 01:19:23,359 (beam_search:429) INFO: max output length: 258 +2024-01-17 01:19:23,359 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:24,074 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:24,074 (beam_search:476) INFO: -33.32 * 1.0 = -33.32 for ctc +2024-01-17 01:19:24,074 (beam_search:479) INFO: total log probability: -33.32 +2024-01-17 01:19:24,074 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:19:24,074 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:24,075 (beam_search:483) INFO: best hypo: NTHISWINCHKEASEANOFIALYHERHODTOASMACKREMPADRINTHBYCOLASINFOUSCSESRAVBERAITINWIL + +2024-01-17 01:19:24,076 (asr_inference:494) INFO: speech length: 114048 +2024-01-17 01:19:24,088 (beam_search:428) INFO: decoder input length: 176 +2024-01-17 01:19:24,088 (beam_search:429) INFO: max output length: 176 +2024-01-17 01:19:24,088 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:24,500 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:24,500 (beam_search:476) INFO: -14.02 * 1.0 = -14.02 for ctc +2024-01-17 01:19:24,500 (beam_search:479) INFO: total log probability: -14.02 +2024-01-17 01:19:24,500 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:19:24,500 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:24,501 (beam_search:483) INFO: best hypo: ITISRESPONSEIPLFORWATESUPLIYANDMANGEMENTOFWATERRESOURSESANDMAHASTRA + +2024-01-17 01:19:24,502 (asr_inference:494) INFO: speech length: 86784 +2024-01-17 01:19:24,513 (beam_search:428) INFO: decoder input length: 133 +2024-01-17 01:19:24,513 (beam_search:429) INFO: max output length: 133 +2024-01-17 01:19:24,513 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:24,705 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:24,705 (beam_search:476) INFO: -14.68 * 1.0 = -14.68 for ctc +2024-01-17 01:19:24,705 (beam_search:479) INFO: total log probability: -14.68 +2024-01-17 01:19:24,705 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:19:24,705 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:24,705 (beam_search:483) INFO: best hypo: JESESTHEFIRSFAICEOFTHEHORVEAHESAYED + +2024-01-17 01:19:24,707 (asr_inference:494) INFO: speech length: 268160 +2024-01-17 01:19:24,732 (beam_search:428) INFO: decoder input length: 416 +2024-01-17 01:19:24,732 (beam_search:429) INFO: max output length: 416 +2024-01-17 01:19:24,732 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:26,903 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:26,903 (beam_search:476) INFO: -36.55 * 1.0 = -36.55 for ctc +2024-01-17 01:19:26,903 (beam_search:479) INFO: total log probability: -36.55 +2024-01-17 01:19:26,903 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:19:26,903 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:26,904 (beam_search:483) INFO: best hypo: THEGISIUPLATOORGIANECRALPOLESINTHEAGIPTIONVAOLYOFTHEDEDCONTAINGSEVERALPERIMENDSOFWHICHTHEGREATEPERMENTISTHELARTESSEVERALESMALTOONSSEVEALTEMPLESANDTHEGREATSPANKS + +2024-01-17 01:19:26,905 (asr_inference:494) INFO: speech length: 249600 +2024-01-17 01:19:26,928 (beam_search:428) INFO: decoder input length: 387 +2024-01-17 01:19:26,928 (beam_search:429) INFO: max output length: 387 +2024-01-17 01:19:26,928 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:28,856 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:28,856 (beam_search:476) INFO: -39.42 * 1.0 = -39.42 for ctc +2024-01-17 01:19:28,856 (beam_search:479) INFO: total log probability: -39.42 +2024-01-17 01:19:28,856 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:19:28,856 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:28,857 (beam_search:483) INFO: best hypo: TWARDTHEINDOFTHMILEAGESWESTERNYURUPBEGANDTODEVELTTHERONSTILONOFTHEBIGESTVELMENSOFTHETIMEASARESULTOFTHECRUCASPEPULBEGANTOUSEBUTENSTOFASTNCLOLTINGRR + +2024-01-17 01:19:28,859 (asr_inference:494) INFO: speech length: 148160 +2024-01-17 01:19:28,874 (beam_search:428) INFO: decoder input length: 229 +2024-01-17 01:19:28,874 (beam_search:429) INFO: max output length: 229 +2024-01-17 01:19:28,874 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:29,494 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:29,494 (beam_search:476) INFO: -17.39 * 1.0 = -17.39 for ctc +2024-01-17 01:19:29,494 (beam_search:479) INFO: total log probability: -17.39 +2024-01-17 01:19:29,494 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:19:29,494 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:29,495 (beam_search:483) INFO: best hypo: IFSYOUONLYGOASHOREUSINGSHIPORISCURIONSYOULNOTNEASEPRTVESAASATWOTHOUSINDNIN + +2024-01-17 01:19:29,496 (asr_inference:494) INFO: speech length: 99840 +2024-01-17 01:19:29,507 (beam_search:428) INFO: decoder input length: 153 +2024-01-17 01:19:29,507 (beam_search:429) INFO: max output length: 153 +2024-01-17 01:19:29,507 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:29,905 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:29,905 (beam_search:476) INFO: -22.82 * 1.0 = -22.82 for ctc +2024-01-17 01:19:29,905 (beam_search:479) INFO: total log probability: -22.82 +2024-01-17 01:19:29,905 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:19:29,905 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:29,906 (beam_search:483) INFO: best hypo: DOUBALHISMAREWIHTOADLCHERENCUDNOTBEWABIGMPRESIONONMILERTOHOMTHESORYWASRELATED + +2024-01-17 01:19:29,907 (asr_inference:494) INFO: speech length: 125760 +2024-01-17 01:19:29,920 (beam_search:428) INFO: decoder input length: 194 +2024-01-17 01:19:29,920 (beam_search:429) INFO: max output length: 194 +2024-01-17 01:19:29,920 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:30,497 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:30,497 (beam_search:476) INFO: -24.01 * 1.0 = -24.01 for ctc +2024-01-17 01:19:30,497 (beam_search:479) INFO: total log probability: -24.01 +2024-01-17 01:19:30,497 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:19:30,497 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:30,497 (beam_search:483) INFO: best hypo: THERDISOPLINDDEFENCSBALHADLINGSCILSANDEXALNTEORKMADTHESTANDOUTANWASCLERHATHISWASTHEEMETOBE + +2024-01-17 01:19:30,499 (asr_inference:494) INFO: speech length: 86400 +2024-01-17 01:19:30,509 (beam_search:428) INFO: decoder input length: 132 +2024-01-17 01:19:30,509 (beam_search:429) INFO: max output length: 132 +2024-01-17 01:19:30,509 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:30,774 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:30,775 (beam_search:476) INFO: -16.02 * 1.0 = -16.02 for ctc +2024-01-17 01:19:30,775 (beam_search:479) INFO: total log probability: -16.02 +2024-01-17 01:19:30,775 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:19:30,775 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:30,775 (beam_search:483) INFO: best hypo: THEDISESISCAREDBYPIGSWHICHHENMYGRETESTOHEUMENSTOROMSCETOS + +2024-01-17 01:19:30,776 (asr_inference:494) INFO: speech length: 79680 +2024-01-17 01:19:30,786 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 01:19:30,786 (beam_search:429) INFO: max output length: 122 +2024-01-17 01:19:30,786 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:30,994 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:30,994 (beam_search:476) INFO: -11.00 * 1.0 = -11.00 for ctc +2024-01-17 01:19:30,994 (beam_search:479) INFO: total log probability: -11.00 +2024-01-17 01:19:30,994 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:19:30,994 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:30,994 (beam_search:483) INFO: best hypo: FORTHESPRINGBOKSITDENDEDAFIVEMATHLOSINGSTREAEK + +2024-01-17 01:19:30,996 (asr_inference:494) INFO: speech length: 63360 +2024-01-17 01:19:31,005 (beam_search:428) INFO: decoder input length: 96 +2024-01-17 01:19:31,005 (beam_search:429) INFO: max output length: 96 +2024-01-17 01:19:31,005 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:31,161 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:31,161 (beam_search:476) INFO: -12.42 * 1.0 = -12.42 for ctc +2024-01-17 01:19:31,161 (beam_search:479) INFO: total log probability: -12.42 +2024-01-17 01:19:31,161 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:19:31,161 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:31,161 (beam_search:483) INFO: best hypo: THESTHEPINSALWITGODFRINDSMANYEOPLEWENICAMEOUT + +2024-01-17 01:19:31,162 (asr_inference:494) INFO: speech length: 184640 +2024-01-17 01:19:31,179 (beam_search:428) INFO: decoder input length: 286 +2024-01-17 01:19:31,179 (beam_search:429) INFO: max output length: 286 +2024-01-17 01:19:31,179 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:32,274 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:32,274 (beam_search:476) INFO: -26.90 * 1.0 = -26.90 for ctc +2024-01-17 01:19:32,274 (beam_search:479) INFO: total log probability: -26.90 +2024-01-17 01:19:32,274 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:19:32,274 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:32,275 (beam_search:483) INFO: best hypo: THEUSEOFVEORECORINGHASLEDTOMPORNDDISCOVERESINTHEINTERPRITATIONOFMYKRLEXPRESTIONSFATIALMOVEMENSWHICHLASAFEUMILESSICKENS + +2024-01-17 01:19:32,277 (asr_inference:494) INFO: speech length: 120960 +2024-01-17 01:19:32,290 (beam_search:428) INFO: decoder input length: 186 +2024-01-17 01:19:32,290 (beam_search:429) INFO: max output length: 186 +2024-01-17 01:19:32,290 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:32,827 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:32,827 (beam_search:476) INFO: -19.27 * 1.0 = -19.27 for ctc +2024-01-17 01:19:32,827 (beam_search:479) INFO: total log probability: -19.27 +2024-01-17 01:19:32,827 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:19:32,827 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:32,827 (beam_search:483) INFO: best hypo: ALSATTHENORTHISTTHEGREATESANCURYOFORLADYOFATHEMUSHRINAPLACEOFWOLDRIGTEFAMSMERIANAVPERIONS + +2024-01-17 01:19:32,829 (asr_inference:494) INFO: speech length: 147840 +2024-01-17 01:19:32,844 (beam_search:428) INFO: decoder input length: 228 +2024-01-17 01:19:32,844 (beam_search:429) INFO: max output length: 228 +2024-01-17 01:19:32,844 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:33,477 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:33,477 (beam_search:476) INFO: -23.73 * 1.0 = -23.73 for ctc +2024-01-17 01:19:33,477 (beam_search:479) INFO: total log probability: -23.73 +2024-01-17 01:19:33,477 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:19:33,477 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:33,477 (beam_search:483) INFO: best hypo: IFYOWNTBECOSEOTHEACTIONYOREHAETOOOGETINEALYTOTACAMPINGSIHTCLOSTOTHEMUSICK + +2024-01-17 01:19:33,479 (asr_inference:494) INFO: speech length: 117120 +2024-01-17 01:19:33,491 (beam_search:428) INFO: decoder input length: 180 +2024-01-17 01:19:33,491 (beam_search:429) INFO: max output length: 180 +2024-01-17 01:19:33,491 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:33,929 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:33,930 (beam_search:476) INFO: -14.98 * 1.0 = -14.98 for ctc +2024-01-17 01:19:33,930 (beam_search:479) INFO: total log probability: -14.98 +2024-01-17 01:19:33,930 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:19:33,930 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:33,930 (beam_search:483) INFO: best hypo: MTAGUSCOARISBYFARETHEBIGESTANDTHECONTINANTONITSONWHENICOMSTOWWILDLIF + +2024-01-17 01:19:33,931 (asr_inference:494) INFO: speech length: 98880 +2024-01-17 01:19:33,942 (beam_search:428) INFO: decoder input length: 152 +2024-01-17 01:19:33,943 (beam_search:429) INFO: max output length: 152 +2024-01-17 01:19:33,943 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:34,337 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:34,337 (beam_search:476) INFO: -19.15 * 1.0 = -19.15 for ctc +2024-01-17 01:19:34,337 (beam_search:479) INFO: total log probability: -19.15 +2024-01-17 01:19:34,337 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:19:34,337 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:34,337 (beam_search:483) INFO: best hypo: WEMENITISRECOMENEDTHATANYWOMENTROVLORSSAYTHEYARMAREDREGARLSTOFACTIUALMARITALSTATIS + +2024-01-17 01:19:34,339 (asr_inference:494) INFO: speech length: 148800 +2024-01-17 01:19:34,354 (beam_search:428) INFO: decoder input length: 230 +2024-01-17 01:19:34,354 (beam_search:429) INFO: max output length: 230 +2024-01-17 01:19:34,354 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:35,085 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:35,085 (beam_search:476) INFO: -19.58 * 1.0 = -19.58 for ctc +2024-01-17 01:19:35,085 (beam_search:479) INFO: total log probability: -19.58 +2024-01-17 01:19:35,085 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:19:35,085 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:35,086 (beam_search:483) INFO: best hypo: UOMOFIFTYTHREBEGANHIGOVERMENTGOVERSHIPERILERTHISYEARANDSINDABILELASTMONTHLEGLISINGSAIMESECXMARAGE + +2024-01-17 01:19:35,087 (asr_inference:494) INFO: speech length: 166080 +2024-01-17 01:19:35,103 (beam_search:428) INFO: decoder input length: 257 +2024-01-17 01:19:35,103 (beam_search:429) INFO: max output length: 257 +2024-01-17 01:19:35,103 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:36,061 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:36,061 (beam_search:476) INFO: -26.15 * 1.0 = -26.15 for ctc +2024-01-17 01:19:36,061 (beam_search:479) INFO: total log probability: -26.15 +2024-01-17 01:19:36,061 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:19:36,061 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:36,062 (beam_search:483) INFO: best hypo: ASLIPLUTIONNHERHADYWASNOTTHECINDOFPROBOMTISTODAYTHERULYLOCATEDINSIIESORACAMPSESEASIERTOREASIONTHOSBILANMOTENTIMS + +2024-01-17 01:19:36,063 (asr_inference:494) INFO: speech length: 119040 +2024-01-17 01:19:36,076 (beam_search:428) INFO: decoder input length: 183 +2024-01-17 01:19:36,076 (beam_search:429) INFO: max output length: 183 +2024-01-17 01:19:36,076 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:36,556 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:36,556 (beam_search:476) INFO: -19.41 * 1.0 = -19.41 for ctc +2024-01-17 01:19:36,556 (beam_search:479) INFO: total log probability: -19.41 +2024-01-17 01:19:36,556 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:19:36,556 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:36,557 (beam_search:483) INFO: best hypo: THEULYHAVESPECIALFODRINKANNRTAMENOPERSTOCEGESANDAGODMODANCETHEMATTHEPRMIS + +2024-01-17 01:19:36,558 (asr_inference:494) INFO: speech length: 193920 +2024-01-17 01:19:36,576 (beam_search:428) INFO: decoder input length: 300 +2024-01-17 01:19:36,576 (beam_search:429) INFO: max output length: 300 +2024-01-17 01:19:36,576 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:37,618 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:37,618 (beam_search:476) INFO: -18.53 * 1.0 = -18.53 for ctc +2024-01-17 01:19:37,618 (beam_search:479) INFO: total log probability: -18.53 +2024-01-17 01:19:37,618 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:19:37,618 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:37,618 (beam_search:483) INFO: best hypo: ONTHEOTHERHANDICESEANDSNOWYCODIONSARNORMAEINMANYCOUNTRESANDTRAFITOESONMOSTLYUNINTRUPTEDALLYEARROUND + +2024-01-17 01:19:37,620 (asr_inference:494) INFO: speech length: 169600 +2024-01-17 01:19:37,636 (beam_search:428) INFO: decoder input length: 262 +2024-01-17 01:19:37,636 (beam_search:429) INFO: max output length: 262 +2024-01-17 01:19:37,636 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:38,407 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:38,407 (beam_search:476) INFO: -20.51 * 1.0 = -20.51 for ctc +2024-01-17 01:19:38,407 (beam_search:479) INFO: total log probability: -20.51 +2024-01-17 01:19:38,407 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:19:38,407 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:38,408 (beam_search:483) INFO: best hypo: BECARFULOTTOALOWFABRICTOBECOMETOHIYEWHICHCANCASESTRANKADGEORINASTRENCASESSQOARTCH + +2024-01-17 01:19:38,409 (asr_inference:494) INFO: speech length: 104640 +2024-01-17 01:19:38,421 (beam_search:428) INFO: decoder input length: 161 +2024-01-17 01:19:38,421 (beam_search:429) INFO: max output length: 161 +2024-01-17 01:19:38,421 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:38,820 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:38,820 (beam_search:476) INFO: -22.04 * 1.0 = -22.04 for ctc +2024-01-17 01:19:38,820 (beam_search:479) INFO: total log probability: -22.04 +2024-01-17 01:19:38,820 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:19:38,820 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:38,821 (beam_search:483) INFO: best hypo: FEIRLCHILDRNMAHAVEXPEIANCSOVERCHILDHBESORTROMMBEFORBINGABANDINRRNGAWAY + +2024-01-17 01:19:38,822 (asr_inference:494) INFO: speech length: 95040 +2024-01-17 01:19:38,833 (beam_search:428) INFO: decoder input length: 146 +2024-01-17 01:19:38,833 (beam_search:429) INFO: max output length: 146 +2024-01-17 01:19:38,833 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:39,194 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:39,194 (beam_search:476) INFO: -17.72 * 1.0 = -17.72 for ctc +2024-01-17 01:19:39,194 (beam_search:479) INFO: total log probability: -17.72 +2024-01-17 01:19:39,194 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:19:39,194 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:39,194 (beam_search:483) INFO: best hypo: PEOPLEMANOTINTICIPATTHAPATIONCSANDNDRESTANGRALSONESERYFORTROVLERSRETRNINGHOM + +2024-01-17 01:19:39,196 (asr_inference:494) INFO: speech length: 87360 +2024-01-17 01:19:39,206 (beam_search:428) INFO: decoder input length: 134 +2024-01-17 01:19:39,206 (beam_search:429) INFO: max output length: 134 +2024-01-17 01:19:39,206 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:39,503 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:39,503 (beam_search:476) INFO: -14.52 * 1.0 = -14.52 for ctc +2024-01-17 01:19:39,503 (beam_search:479) INFO: total log probability: -14.52 +2024-01-17 01:19:39,503 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:19:39,504 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:39,504 (beam_search:483) INFO: best hypo: ONOTERTHEPRIKOFHUSTILITESBRITNINENTSHEATEDANNAVBLEBOCKADEOFHERMANY + +2024-01-17 01:19:39,505 (asr_inference:494) INFO: speech length: 116160 +2024-01-17 01:19:39,518 (beam_search:428) INFO: decoder input length: 179 +2024-01-17 01:19:39,518 (beam_search:429) INFO: max output length: 179 +2024-01-17 01:19:39,518 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:39,840 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:39,840 (beam_search:476) INFO: -13.17 * 1.0 = -13.17 for ctc +2024-01-17 01:19:39,840 (beam_search:479) INFO: total log probability: -13.17 +2024-01-17 01:19:39,840 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:19:39,840 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:39,841 (beam_search:483) INFO: best hypo: THEOENRSOFISSAIDNINTENOFTHEINGUREDWEPLEAESOFISERS + +2024-01-17 01:19:39,842 (asr_inference:494) INFO: speech length: 121920 +2024-01-17 01:19:39,855 (beam_search:428) INFO: decoder input length: 188 +2024-01-17 01:19:39,855 (beam_search:429) INFO: max output length: 188 +2024-01-17 01:19:39,855 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:40,400 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:40,401 (beam_search:476) INFO: -20.04 * 1.0 = -20.04 for ctc +2024-01-17 01:19:40,401 (beam_search:479) INFO: total log probability: -20.04 +2024-01-17 01:19:40,401 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:19:40,401 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:40,401 (beam_search:483) INFO: best hypo: USINGSHIPSTOTRESPORTSGOODSISBYFARTHEMOSOFIENTWAYTOMOELAREMOUTOFPEBLENGODACROSOTIONS + +2024-01-17 01:19:40,403 (asr_inference:494) INFO: speech length: 128640 +2024-01-17 01:19:40,416 (beam_search:428) INFO: decoder input length: 198 +2024-01-17 01:19:40,416 (beam_search:429) INFO: max output length: 198 +2024-01-17 01:19:40,416 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:41,054 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:41,054 (beam_search:476) INFO: -29.99 * 1.0 = -29.99 for ctc +2024-01-17 01:19:41,054 (beam_search:479) INFO: total log probability: -29.99 +2024-01-17 01:19:41,055 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:19:41,055 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:41,055 (beam_search:483) INFO: best hypo: THEBRLCRITISISMOFTHERECONSTRCTINEVERTNHASPOKASOTHEWARDINGOFRECONSTRCINGCONTHACTTORISTEDEWATINGANDINSIERS + +2024-01-17 01:19:41,057 (asr_inference:494) INFO: speech length: 193920 +2024-01-17 01:19:41,074 (beam_search:428) INFO: decoder input length: 300 +2024-01-17 01:19:41,074 (beam_search:429) INFO: max output length: 300 +2024-01-17 01:19:41,074 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:42,152 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:42,152 (beam_search:476) INFO: -30.27 * 1.0 = -30.27 for ctc +2024-01-17 01:19:42,152 (beam_search:479) INFO: total log probability: -30.27 +2024-01-17 01:19:42,152 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:19:42,152 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:42,153 (beam_search:483) INFO: best hypo: UTWEUCUNSBOWDOBBODAMORSECLTACXCYTOGETAROUNDGOMATHENRMAEWICKLEPRICEISFIVEHUNDREDCONDLESFRONSFORTHEMSHOURTR + +2024-01-17 01:19:42,154 (asr_inference:494) INFO: speech length: 190080 +2024-01-17 01:19:42,172 (beam_search:428) INFO: decoder input length: 294 +2024-01-17 01:19:42,172 (beam_search:429) INFO: max output length: 294 +2024-01-17 01:19:42,172 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:43,377 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:43,377 (beam_search:476) INFO: -31.69 * 1.0 = -31.69 for ctc +2024-01-17 01:19:43,377 (beam_search:479) INFO: total log probability: -31.69 +2024-01-17 01:19:43,377 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:19:43,377 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:43,378 (beam_search:483) INFO: best hypo: THETHEKINGDOMSWASONEOFTHEBLTBLUDIASTERASANDANIENTCHINASHISTHRETHOUSONSOFPEOLEDIEDFITINGTOSTITINTHHIEASCEINTHEGRANDPALESATSI + +2024-01-17 01:19:43,379 (asr_inference:494) INFO: speech length: 109440 +2024-01-17 01:19:43,392 (beam_search:428) INFO: decoder input length: 168 +2024-01-17 01:19:43,392 (beam_search:429) INFO: max output length: 168 +2024-01-17 01:19:43,392 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:43,718 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:43,718 (beam_search:476) INFO: -11.37 * 1.0 = -11.37 for ctc +2024-01-17 01:19:43,718 (beam_search:479) INFO: total log probability: -11.37 +2024-01-17 01:19:43,718 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:19:43,718 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:43,719 (beam_search:483) INFO: best hypo: RTHESCOUPLESMAYCHUSETOMAKEKANDADOUSINPLANDFORTHERBAVY + +2024-01-17 01:19:43,720 (asr_inference:494) INFO: speech length: 151680 +2024-01-17 01:19:43,735 (beam_search:428) INFO: decoder input length: 234 +2024-01-17 01:19:43,735 (beam_search:429) INFO: max output length: 234 +2024-01-17 01:19:43,735 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:44,600 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:44,600 (beam_search:476) INFO: -28.99 * 1.0 = -28.99 for ctc +2024-01-17 01:19:44,600 (beam_search:479) INFO: total log probability: -28.99 +2024-01-17 01:19:44,600 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:19:44,600 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:44,601 (beam_search:483) INFO: best hypo: NOTHNGANDBEFENOTHERHNTHECLEAREBUTIFULSCAIYABOVEANDTHEMENYSURUNGMOUNSVERYLITOFTHSWALANBESENORHURDFROMINSIDTHECAVE + +2024-01-17 01:19:44,602 (asr_inference:494) INFO: speech length: 88320 +2024-01-17 01:19:44,613 (beam_search:428) INFO: decoder input length: 135 +2024-01-17 01:19:44,613 (beam_search:429) INFO: max output length: 135 +2024-01-17 01:19:44,613 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:44,874 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:44,874 (beam_search:476) INFO: -11.77 * 1.0 = -11.77 for ctc +2024-01-17 01:19:44,875 (beam_search:479) INFO: total log probability: -11.77 +2024-01-17 01:19:44,875 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:19:44,875 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:44,875 (beam_search:483) INFO: best hypo: HEWASSOBSICUENTLYRELOCATEDTOADINBROKSHOSPITLANCAMBRIAGE + +2024-01-17 01:19:44,876 (asr_inference:494) INFO: speech length: 171840 +2024-01-17 01:19:44,892 (beam_search:428) INFO: decoder input length: 266 +2024-01-17 01:19:44,892 (beam_search:429) INFO: max output length: 266 +2024-01-17 01:19:44,892 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:45,732 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:45,732 (beam_search:476) INFO: -25.12 * 1.0 = -25.12 for ctc +2024-01-17 01:19:45,732 (beam_search:479) INFO: total log probability: -25.12 +2024-01-17 01:19:45,732 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:19:45,732 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:45,733 (beam_search:483) INFO: best hypo: THAICANSTITYPOPULATIONISAROUNDAINHEUNDRIDTHEISTHEMAOSTNTEPENDECONTRNTHEWORALDANDTHEPOPULATION + +2024-01-17 01:19:45,734 (asr_inference:494) INFO: speech length: 209280 +2024-01-17 01:19:45,753 (beam_search:428) INFO: decoder input length: 324 +2024-01-17 01:19:45,753 (beam_search:429) INFO: max output length: 324 +2024-01-17 01:19:45,753 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:47,313 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:47,313 (beam_search:476) INFO: -42.19 * 1.0 = -42.19 for ctc +2024-01-17 01:19:47,313 (beam_search:479) INFO: total log probability: -42.19 +2024-01-17 01:19:47,313 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:19:47,313 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:47,314 (beam_search:483) INFO: best hypo: REGURALOUNSMENTANTHEPMETRUARMAEOLYINCATLONBUTUNPLANEDISTRUPTIONSARNOUSEBYANOTAMAEDSISTOMINAWADVERITYOFLNWICGESNCUTINGSBANISHANGLISHFRENCHERBECKANDHAPONEES + +2024-01-17 01:19:47,316 (asr_inference:494) INFO: speech length: 107520 +2024-01-17 01:19:47,328 (beam_search:428) INFO: decoder input length: 165 +2024-01-17 01:19:47,328 (beam_search:429) INFO: max output length: 165 +2024-01-17 01:19:47,328 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:47,744 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:47,744 (beam_search:476) INFO: -22.90 * 1.0 = -22.90 for ctc +2024-01-17 01:19:47,744 (beam_search:479) INFO: total log probability: -22.90 +2024-01-17 01:19:47,744 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:19:47,744 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:47,744 (beam_search:483) INFO: best hypo: THISOPREAGODPRTUNITITOSETHEOWRABARILESASTHESCKGIWILBEDARKMORLESTRUNTHECLOC + +2024-01-17 01:19:47,746 (asr_inference:494) INFO: speech length: 91520 +2024-01-17 01:19:47,757 (beam_search:428) INFO: decoder input length: 140 +2024-01-17 01:19:47,757 (beam_search:429) INFO: max output length: 140 +2024-01-17 01:19:47,757 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:48,017 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:48,017 (beam_search:476) INFO: -13.86 * 1.0 = -13.86 for ctc +2024-01-17 01:19:48,017 (beam_search:479) INFO: total log probability: -13.86 +2024-01-17 01:19:48,017 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:19:48,017 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:48,017 (beam_search:483) INFO: best hypo: FIRSCKUCROSOVENCIALYDOUSETOFIEBYALEVENTHRTYFIVEPEAM + +2024-01-17 01:19:48,019 (asr_inference:494) INFO: speech length: 72960 +2024-01-17 01:19:48,028 (beam_search:428) INFO: decoder input length: 111 +2024-01-17 01:19:48,028 (beam_search:429) INFO: max output length: 111 +2024-01-17 01:19:48,028 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:48,245 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:48,245 (beam_search:476) INFO: -19.34 * 1.0 = -19.34 for ctc +2024-01-17 01:19:48,245 (beam_search:479) INFO: total log probability: -19.34 +2024-01-17 01:19:48,245 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:19:48,245 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:48,245 (beam_search:483) INFO: best hypo: THISCALTOCMICALSPEEHECANMAKANINDICATEOUSINGREDCABAGHEJOS + +2024-01-17 01:19:48,247 (asr_inference:494) INFO: speech length: 122880 +2024-01-17 01:19:48,260 (beam_search:428) INFO: decoder input length: 189 +2024-01-17 01:19:48,260 (beam_search:429) INFO: max output length: 189 +2024-01-17 01:19:48,260 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:48,804 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:48,804 (beam_search:476) INFO: -18.91 * 1.0 = -18.91 for ctc +2024-01-17 01:19:48,804 (beam_search:479) INFO: total log probability: -18.91 +2024-01-17 01:19:48,804 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:19:48,804 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:48,805 (beam_search:483) INFO: best hypo: INPRTICULRITISLAETHATONECANDETECWETHERAPRSONISLINGBYINTERPRINGMYGROLEXSPRESIONSCORECTLY + +2024-01-17 01:19:48,807 (asr_inference:494) INFO: speech length: 209920 +2024-01-17 01:19:48,826 (beam_search:428) INFO: decoder input length: 325 +2024-01-17 01:19:48,826 (beam_search:429) INFO: max output length: 325 +2024-01-17 01:19:48,826 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:50,248 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:50,248 (beam_search:476) INFO: -25.79 * 1.0 = -25.79 for ctc +2024-01-17 01:19:50,248 (beam_search:479) INFO: total log probability: -25.79 +2024-01-17 01:19:50,248 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:19:50,248 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:50,249 (beam_search:483) INFO: best hypo: THESECHALFORITYOFTHECHUCHODSBENINROMFOROVERATHOUSANYEARSANDTHISCOSONTRATIONAFPOWERANMONYLEDTOMAYTOCUSTIONWHETHERDISTENETWASBENGMET + +2024-01-17 01:19:50,250 (asr_inference:494) INFO: speech length: 195840 +2024-01-17 01:19:50,268 (beam_search:428) INFO: decoder input length: 303 +2024-01-17 01:19:50,268 (beam_search:429) INFO: max output length: 303 +2024-01-17 01:19:50,268 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:51,578 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:51,578 (beam_search:476) INFO: -30.14 * 1.0 = -30.14 for ctc +2024-01-17 01:19:51,578 (beam_search:479) INFO: total log probability: -30.14 +2024-01-17 01:19:51,578 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:19:51,578 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:51,579 (beam_search:483) INFO: best hypo: THESUNDARBONSARTHEARGESTHETORALMANGROEBULINTHEWORLDSTECHINGATYCLAMITERSFIFTYMIESINTOTHEANGLADESHEANDININDIANHINTERLANDFROMTHECOOST + +2024-01-17 01:19:51,581 (asr_inference:494) INFO: speech length: 184320 +2024-01-17 01:19:51,598 (beam_search:428) INFO: decoder input length: 285 +2024-01-17 01:19:51,598 (beam_search:429) INFO: max output length: 285 +2024-01-17 01:19:51,598 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:52,897 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:52,897 (beam_search:476) INFO: -44.72 * 1.0 = -44.72 for ctc +2024-01-17 01:19:52,897 (beam_search:479) INFO: total log probability: -44.72 +2024-01-17 01:19:52,897 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:19:52,897 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:52,898 (beam_search:483) INFO: best hypo: REGULRANONSENCSINHEETHOARMAEONLYINCATALENBUTUNNDISTRUPTINSRNOUNEDBYANOTMATEISSISTMINAWADVERITYOFLIGWINGESINCUDINGSPANTISHINGLSHRENCHERBACKANDJHAPONES + +2024-01-17 01:19:52,900 (asr_inference:494) INFO: speech length: 116160 +2024-01-17 01:19:52,912 (beam_search:428) INFO: decoder input length: 179 +2024-01-17 01:19:52,912 (beam_search:429) INFO: max output length: 179 +2024-01-17 01:19:52,912 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:53,408 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:53,409 (beam_search:476) INFO: -30.97 * 1.0 = -30.97 for ctc +2024-01-17 01:19:53,409 (beam_search:479) INFO: total log probability: -30.97 +2024-01-17 01:19:53,409 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:19:53,409 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:53,409 (beam_search:483) INFO: best hypo: EVERWNPRTITBATINOUCITYANUSISTRNSPRTIONCSISTONCSALMSTERYWNCOMPLAINEOOUTRNSPRTIONSISTOMS + +2024-01-17 01:19:53,411 (asr_inference:494) INFO: speech length: 155520 +2024-01-17 01:19:53,426 (beam_search:428) INFO: decoder input length: 240 +2024-01-17 01:19:53,426 (beam_search:429) INFO: max output length: 240 +2024-01-17 01:19:53,426 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:54,388 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:54,388 (beam_search:476) INFO: -37.56 * 1.0 = -37.56 for ctc +2024-01-17 01:19:54,388 (beam_search:479) INFO: total log probability: -37.56 +2024-01-17 01:19:54,388 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:19:54,388 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:54,389 (beam_search:483) INFO: best hypo: LATNHADASORHANESOTHEONSUIRVETISNVIRMINTLBILDURNTHEMENWTTHEPEMASINGFRYTHURALANDCOMPLETERERIDTINGOFTHECONSERVETHISPARDYINIERMINALIL + +2024-01-17 01:19:54,391 (asr_inference:494) INFO: speech length: 137280 +2024-01-17 01:19:54,405 (beam_search:428) INFO: decoder input length: 212 +2024-01-17 01:19:54,405 (beam_search:429) INFO: max output length: 212 +2024-01-17 01:19:54,405 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:55,054 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:55,054 (beam_search:476) INFO: -28.74 * 1.0 = -28.74 for ctc +2024-01-17 01:19:55,054 (beam_search:479) INFO: total log probability: -28.74 +2024-01-17 01:19:55,054 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:19:55,054 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:55,054 (beam_search:483) INFO: best hypo: INYONHISGONTOTRIEHAHATLITATUDSOROVERMUTNPASSTHRCONCSIDETHEPOSILITYOFSNOICEORFESINGTEMPATURS + +2024-01-17 01:19:55,056 (asr_inference:494) INFO: speech length: 200000 +2024-01-17 01:19:55,074 (beam_search:428) INFO: decoder input length: 310 +2024-01-17 01:19:55,074 (beam_search:429) INFO: max output length: 310 +2024-01-17 01:19:55,074 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:56,275 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:56,275 (beam_search:476) INFO: -38.21 * 1.0 = -38.21 for ctc +2024-01-17 01:19:56,275 (beam_search:479) INFO: total log probability: -38.21 +2024-01-17 01:19:56,275 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:19:56,275 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:56,276 (beam_search:483) INFO: best hypo: HESLEINTRUSTIONISHEPRASTSESOFHEBOUCAYWAKNINBEURINGYOUNORMALESLEEPERIADANDFALINGASLEASHOURTTIMELATERCENTOSICTEMINOTST + +2024-01-17 01:19:56,277 (asr_inference:494) INFO: speech length: 133760 +2024-01-17 01:19:56,291 (beam_search:428) INFO: decoder input length: 206 +2024-01-17 01:19:56,292 (beam_search:429) INFO: max output length: 206 +2024-01-17 01:19:56,292 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:56,850 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:56,850 (beam_search:476) INFO: -25.11 * 1.0 = -25.11 for ctc +2024-01-17 01:19:56,850 (beam_search:479) INFO: total log probability: -25.11 +2024-01-17 01:19:56,850 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:19:56,850 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:56,851 (beam_search:483) INFO: best hypo: OURSWOARLTHETODRIPPOURSTOGETHERANTHEWITHCUENGAWETHANSSCUEAETHEMINTOABALEREH + +2024-01-17 01:19:56,852 (asr_inference:494) INFO: speech length: 64320 +2024-01-17 01:19:56,862 (beam_search:428) INFO: decoder input length: 98 +2024-01-17 01:19:56,862 (beam_search:429) INFO: max output length: 98 +2024-01-17 01:19:56,862 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:57,013 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:57,013 (beam_search:476) INFO: -7.68 * 1.0 = -7.68 for ctc +2024-01-17 01:19:57,013 (beam_search:479) INFO: total log probability: -7.68 +2024-01-17 01:19:57,013 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:19:57,013 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:57,013 (beam_search:483) INFO: best hypo: FORTHESPRNGBOCSITENDEDAFIEMACHLOSINGSTREK + +2024-01-17 01:19:57,014 (asr_inference:494) INFO: speech length: 114240 +2024-01-17 01:19:57,026 (beam_search:428) INFO: decoder input length: 176 +2024-01-17 01:19:57,027 (beam_search:429) INFO: max output length: 176 +2024-01-17 01:19:57,027 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:57,517 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:57,517 (beam_search:476) INFO: -30.28 * 1.0 = -30.28 for ctc +2024-01-17 01:19:57,517 (beam_search:479) INFO: total log probability: -30.28 +2024-01-17 01:19:57,517 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:19:57,517 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:57,518 (beam_search:483) INFO: best hypo: JUSTLIKTHEONEXPURTDSAPULONTHEERTHCASINTHEIDESOTASAMLBYWAYEXERTIFFORTSOFHEEDITARIOUSGALACY + +2024-01-17 01:19:57,519 (asr_inference:494) INFO: speech length: 131520 +2024-01-17 01:19:57,533 (beam_search:428) INFO: decoder input length: 203 +2024-01-17 01:19:57,533 (beam_search:429) INFO: max output length: 203 +2024-01-17 01:19:57,533 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:19:58,077 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:19:58,078 (beam_search:476) INFO: -20.20 * 1.0 = -20.20 for ctc +2024-01-17 01:19:58,078 (beam_search:479) INFO: total log probability: -20.20 +2024-01-17 01:19:58,078 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:19:58,078 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:19:58,078 (beam_search:483) INFO: best hypo: THOROTHENIGHTHETWENHUDERDANFIFTYANTOHUDREDCOPESWEREMADENOWNONASBUNELAPBROLDSIDS + +2024-01-17 01:19:58,080 (asr_inference:494) INFO: speech length: 281280 +2024-01-17 01:19:58,105 (beam_search:428) INFO: decoder input length: 437 +2024-01-17 01:19:58,106 (beam_search:429) INFO: max output length: 437 +2024-01-17 01:19:58,106 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:00,575 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:00,575 (beam_search:476) INFO: -41.37 * 1.0 = -41.37 for ctc +2024-01-17 01:20:00,575 (beam_search:479) INFO: total log probability: -41.37 +2024-01-17 01:20:00,575 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:20:00,575 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:00,576 (beam_search:483) INFO: best hypo: FIRSTAMONGITSEMNDYAERECOMENDATIONSISTHATANNOWDIPLMAIKNISHITIVEHULBETAKEBEFORTHEENDOFTHISYEARTOSECURERRACXPBORERSAGNSHOSTILINTRVENTIONSANDTOREASTABLISEDIPLMAICRELATIONSWITHITSNABERS + +2024-01-17 01:20:00,578 (asr_inference:494) INFO: speech length: 120000 +2024-01-17 01:20:00,591 (beam_search:428) INFO: decoder input length: 185 +2024-01-17 01:20:00,591 (beam_search:429) INFO: max output length: 185 +2024-01-17 01:20:00,591 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:01,112 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:01,112 (beam_search:476) INFO: -32.38 * 1.0 = -32.38 for ctc +2024-01-17 01:20:01,112 (beam_search:479) INFO: total log probability: -32.38 +2024-01-17 01:20:01,112 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:20:01,112 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:01,112 (beam_search:483) INFO: best hypo: SHANTETERSBRCRUSISINCLUDTIMINTOWNWHOTASONGERSARXAENTEDFROMESTERREQUIRIMENTSCHACKTHETRMS + +2024-01-17 01:20:01,114 (asr_inference:494) INFO: speech length: 118080 +2024-01-17 01:20:01,126 (beam_search:428) INFO: decoder input length: 182 +2024-01-17 01:20:01,126 (beam_search:429) INFO: max output length: 182 +2024-01-17 01:20:01,126 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:01,592 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:01,592 (beam_search:476) INFO: -16.06 * 1.0 = -16.06 for ctc +2024-01-17 01:20:01,592 (beam_search:479) INFO: total log probability: -16.06 +2024-01-17 01:20:01,592 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:20:01,592 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:01,593 (beam_search:483) INFO: best hypo: OCORDINTOUPANSNOGULRAGENCSYREDYALACTIVECASEAMEANDIADINHASEIDENIFIEATTHEPLANT + +2024-01-17 01:20:01,594 (asr_inference:494) INFO: speech length: 184320 +2024-01-17 01:20:01,611 (beam_search:428) INFO: decoder input length: 285 +2024-01-17 01:20:01,611 (beam_search:429) INFO: max output length: 285 +2024-01-17 01:20:01,611 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:02,464 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:02,464 (beam_search:476) INFO: -13.05 * 1.0 = -13.05 for ctc +2024-01-17 01:20:02,464 (beam_search:479) INFO: total log probability: -13.05 +2024-01-17 01:20:02,464 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:20:02,464 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:02,465 (beam_search:483) INFO: best hypo: SAGOGATIONANDRECOMONATIONSHUFLVERYIATIONBACKANDFORTHBETWENTHETWOPULESWITHEACHGENERATION + +2024-01-17 01:20:02,466 (asr_inference:494) INFO: speech length: 99840 +2024-01-17 01:20:02,478 (beam_search:428) INFO: decoder input length: 153 +2024-01-17 01:20:02,478 (beam_search:429) INFO: max output length: 153 +2024-01-17 01:20:02,478 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:02,840 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:02,840 (beam_search:476) INFO: -27.12 * 1.0 = -27.12 for ctc +2024-01-17 01:20:02,840 (beam_search:479) INFO: total log probability: -27.12 +2024-01-17 01:20:02,840 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:20:02,840 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:02,840 (beam_search:483) INFO: best hypo: ELAMNTLKCHLTHEMNDPAATIMRCONCSTEDMETLSOFPORSERALSOMETESLKESIVERANDGOLD + +2024-01-17 01:20:02,842 (asr_inference:494) INFO: speech length: 101760 +2024-01-17 01:20:02,853 (beam_search:428) INFO: decoder input length: 156 +2024-01-17 01:20:02,853 (beam_search:429) INFO: max output length: 156 +2024-01-17 01:20:02,853 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:03,201 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:03,201 (beam_search:476) INFO: -16.41 * 1.0 = -16.41 for ctc +2024-01-17 01:20:03,201 (beam_search:479) INFO: total log probability: -16.41 +2024-01-17 01:20:03,201 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:20:03,201 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:03,202 (beam_search:483) INFO: best hypo: THECORLTIONETWEENBRAIPITHOLAGYANDBEHAVYOURSUPORTSINCSANDHERRESURCH + +2024-01-17 01:20:03,203 (asr_inference:494) INFO: speech length: 207040 +2024-01-17 01:20:03,222 (beam_search:428) INFO: decoder input length: 321 +2024-01-17 01:20:03,222 (beam_search:429) INFO: max output length: 321 +2024-01-17 01:20:03,222 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:04,515 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:04,515 (beam_search:476) INFO: -23.02 * 1.0 = -23.02 for ctc +2024-01-17 01:20:04,515 (beam_search:479) INFO: total log probability: -23.02 +2024-01-17 01:20:04,515 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:20:04,515 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:04,516 (beam_search:483) INFO: best hypo: ANCHANCHINAHADAOUNEKWAYOFSHOWINGDIFRENTTIMEPERIADSEACHSTACEOFCHINAEOREEACHFAMILYTHATWASINMPOWERWASTHEDISTINTIFDINISTY + +2024-01-17 01:20:04,517 (asr_inference:494) INFO: speech length: 159360 +2024-01-17 01:20:04,533 (beam_search:428) INFO: decoder input length: 246 +2024-01-17 01:20:04,533 (beam_search:429) INFO: max output length: 246 +2024-01-17 01:20:04,533 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:05,428 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:05,428 (beam_search:476) INFO: -30.48 * 1.0 = -30.48 for ctc +2024-01-17 01:20:05,428 (beam_search:479) INFO: total log probability: -30.48 +2024-01-17 01:20:05,428 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:20:05,428 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:05,429 (beam_search:483) INFO: best hypo: ASIMPLPOPULERDMERHISFECILYDRINTHETUMERISPAAMALDYBREDWIHALVOILETOMATONANYAVIABLECONTMENSSUCHSCHEESETONFISHITSETER + +2024-01-17 01:20:05,431 (asr_inference:494) INFO: speech length: 122880 +2024-01-17 01:20:05,443 (beam_search:428) INFO: decoder input length: 189 +2024-01-17 01:20:05,443 (beam_search:429) INFO: max output length: 189 +2024-01-17 01:20:05,443 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:05,923 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:05,924 (beam_search:476) INFO: -21.99 * 1.0 = -21.99 for ctc +2024-01-17 01:20:05,924 (beam_search:479) INFO: total log probability: -21.99 +2024-01-17 01:20:05,924 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:20:05,924 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:05,924 (beam_search:483) INFO: best hypo: THENOUNSMETWASMADEAFTERTRMPAYFONGCOMERSATIONWITTRKISHPRDIDENTRESEPTEEPERODON + +2024-01-17 01:20:05,926 (asr_inference:494) INFO: speech length: 220800 +2024-01-17 01:20:05,946 (beam_search:428) INFO: decoder input length: 342 +2024-01-17 01:20:05,946 (beam_search:429) INFO: max output length: 342 +2024-01-17 01:20:05,946 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:07,760 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:07,760 (beam_search:476) INFO: -65.48 * 1.0 = -65.48 for ctc +2024-01-17 01:20:07,760 (beam_search:479) INFO: total log probability: -65.48 +2024-01-17 01:20:07,760 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:20:07,760 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:07,761 (beam_search:483) INFO: best hypo: ERYSATETHTHHEWOLDRETENTOTECXSISTOUSESTHERESULTSOFTONIHTESCOKISDIDERMENWHTHERTHERISAPATHFORDFORMYSELFNTHSRACESBUELETERSUTTHAHEWLEREMAININTHERASANHUBPENORIGENRYTWEEWONSOUTARLINOPRIMARY + +2024-01-17 01:20:07,763 (asr_inference:494) INFO: speech length: 294720 +2024-01-17 01:20:07,789 (beam_search:428) INFO: decoder input length: 458 +2024-01-17 01:20:07,789 (beam_search:429) INFO: max output length: 458 +2024-01-17 01:20:07,789 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:10,320 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:10,320 (beam_search:476) INFO: -44.09 * 1.0 = -44.09 for ctc +2024-01-17 01:20:10,320 (beam_search:479) INFO: total log probability: -44.09 +2024-01-17 01:20:10,320 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:20:10,320 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:10,321 (beam_search:483) INFO: best hypo: HEWATALSOINGAGEAINGRAVINGBAKNOTSFORMANYCONTRESRESONINGSEMPLESOFHWRKINCLDTHEMPRIMEMENTMNINISREALPORTRDSONTHEFIRSTFROOUTHEFONTOFTHENEWCANADYANFFIVEDOLRINWONHNDERDLDIL + +2024-01-17 01:20:10,323 (asr_inference:494) INFO: speech length: 203520 +2024-01-17 01:20:10,341 (beam_search:428) INFO: decoder input length: 315 +2024-01-17 01:20:10,341 (beam_search:429) INFO: max output length: 315 +2024-01-17 01:20:10,341 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:11,867 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:11,867 (beam_search:476) INFO: -47.80 * 1.0 = -47.80 for ctc +2024-01-17 01:20:11,867 (beam_search:479) INFO: total log probability: -47.80 +2024-01-17 01:20:11,867 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:20:11,867 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:11,868 (beam_search:483) INFO: best hypo: EMORTRADINALCHURCHESOFTANHALTHENESTRRIGUALNTSATEDYNIGHTTURNTHEESTRWEKGNWERTHECOGREATIONSOTDIMBREAKINGINTOSELEBRATIONATTHESROKOFMINIGTTOSELEBRAECRICESRESEURECTION + +2024-01-17 01:20:11,870 (asr_inference:494) INFO: speech length: 149760 +2024-01-17 01:20:11,885 (beam_search:428) INFO: decoder input length: 231 +2024-01-17 01:20:11,885 (beam_search:429) INFO: max output length: 231 +2024-01-17 01:20:11,885 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:12,635 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:12,635 (beam_search:476) INFO: -24.97 * 1.0 = -24.97 for ctc +2024-01-17 01:20:12,635 (beam_search:479) INFO: total log probability: -24.97 +2024-01-17 01:20:12,635 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:20:12,635 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:12,636 (beam_search:483) INFO: best hypo: FILENISAGREAEBOTINGDESTNATIONTHELANDOFATHOUSONLAEHASTOUSNDOFILENDSTOANTHELAKSANNTHECOSTARKPALOAOS + +2024-01-17 01:20:12,637 (asr_inference:494) INFO: speech length: 193920 +2024-01-17 01:20:12,654 (beam_search:428) INFO: decoder input length: 300 +2024-01-17 01:20:12,655 (beam_search:429) INFO: max output length: 300 +2024-01-17 01:20:12,655 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:14,017 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:14,017 (beam_search:476) INFO: -49.78 * 1.0 = -49.78 for ctc +2024-01-17 01:20:14,017 (beam_search:479) INFO: total log probability: -49.78 +2024-01-17 01:20:14,017 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:20:14,017 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:14,018 (beam_search:483) INFO: best hypo: RANTSHENATERANDARGINCSENFRSTLADECRISTENOFORNDISACURSINRANOULSHERPESONATHOCANDIDUSYOSTRDAYEVENGANLAPLATHAASTADYFIFTCLOMITERSTHERDYWNMILSAWAYFROMENOSIDISTH + +2024-01-17 01:20:14,020 (asr_inference:494) INFO: speech length: 172800 +2024-01-17 01:20:14,036 (beam_search:428) INFO: decoder input length: 267 +2024-01-17 01:20:14,036 (beam_search:429) INFO: max output length: 267 +2024-01-17 01:20:14,036 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:15,026 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:15,026 (beam_search:476) INFO: -26.40 * 1.0 = -26.40 for ctc +2024-01-17 01:20:15,026 (beam_search:479) INFO: total log probability: -26.40 +2024-01-17 01:20:15,026 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:20:15,026 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:15,027 (beam_search:483) INFO: best hypo: SVERWETHERITEUNARICTUNEFORANYDADERUSWTHERFONAMINONWITTHEPATNUALTOCASDAMAGESRIOUSSOSIALDISTRUPTIONORLASOFHUMNLIFE + +2024-01-17 01:20:15,028 (asr_inference:494) INFO: speech length: 222720 +2024-01-17 01:20:15,048 (beam_search:428) INFO: decoder input length: 345 +2024-01-17 01:20:15,049 (beam_search:429) INFO: max output length: 345 +2024-01-17 01:20:15,049 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:16,562 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:16,563 (beam_search:476) INFO: -26.92 * 1.0 = -26.92 for ctc +2024-01-17 01:20:16,563 (beam_search:479) INFO: total log probability: -26.92 +2024-01-17 01:20:16,563 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:20:16,563 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:16,563 (beam_search:483) INFO: best hypo: FOREXSAMPLETHEMOSTCOMEANSTILIMAGEFITOCKRIFYFORMOUTNHEWOLDISTHRTYFIVEMILOMATERWHICHWASTHEDOMINANTFILMESIESATTHECLOSOFTHEANALOGFILMARA + +2024-01-17 01:20:16,565 (asr_inference:494) INFO: speech length: 144000 +2024-01-17 01:20:16,580 (beam_search:428) INFO: decoder input length: 222 +2024-01-17 01:20:16,580 (beam_search:429) INFO: max output length: 222 +2024-01-17 01:20:16,580 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:17,367 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:17,367 (beam_search:476) INFO: -26.39 * 1.0 = -26.39 for ctc +2024-01-17 01:20:17,368 (beam_search:479) INFO: total log probability: -26.39 +2024-01-17 01:20:17,368 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:20:17,368 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:17,368 (beam_search:483) INFO: best hypo: ITISRELATEDTOBUTULYNOTIMVLVINGHLPINSTILSCETORINGORMOTNERINGTHELATERONSDONINSPTURINGANDRECARINGMUSHTHIFRSCESANDBOTES + +2024-01-17 01:20:17,370 (asr_inference:494) INFO: speech length: 203520 +2024-01-17 01:20:17,388 (beam_search:428) INFO: decoder input length: 315 +2024-01-17 01:20:17,388 (beam_search:429) INFO: max output length: 315 +2024-01-17 01:20:17,388 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:18,537 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:18,537 (beam_search:476) INFO: -20.97 * 1.0 = -20.97 for ctc +2024-01-17 01:20:18,537 (beam_search:479) INFO: total log probability: -20.97 +2024-01-17 01:20:18,537 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:20:18,537 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:18,538 (beam_search:483) INFO: best hypo: IARNINGDAMPCLOSCANHEPETHEMDRIYMANYHOATELSHAEANIARNANDIRNINGBORDAVALABLEFORLONGEVENIFONISNOTPRESENINTHEROM + +2024-01-17 01:20:18,539 (asr_inference:494) INFO: speech length: 273760 +2024-01-17 01:20:18,564 (beam_search:428) INFO: decoder input length: 425 +2024-01-17 01:20:18,564 (beam_search:429) INFO: max output length: 425 +2024-01-17 01:20:18,564 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:20,676 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:20,676 (beam_search:476) INFO: -40.80 * 1.0 = -40.80 for ctc +2024-01-17 01:20:20,676 (beam_search:479) INFO: total log probability: -40.80 +2024-01-17 01:20:20,676 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:20:20,676 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:20,677 (beam_search:483) INFO: best hypo: AEVEDNYOUNCERDHORSLYSHEJUWHERCHAREUNTERITEGLOATOTHEFIERANDSCREDHERHANSOUTTOTHELASETHEREWASNOOTHELIGTINTHERONMYTHISTIMETHEWINWIDOTHORDEDMISMALYSTIL + +2024-01-17 01:20:20,678 (asr_inference:494) INFO: speech length: 288160 +2024-01-17 01:20:20,705 (beam_search:428) INFO: decoder input length: 448 +2024-01-17 01:20:20,705 (beam_search:429) INFO: max output length: 448 +2024-01-17 01:20:20,705 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:22,854 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:22,854 (beam_search:476) INFO: -40.27 * 1.0 = -40.27 for ctc +2024-01-17 01:20:22,854 (beam_search:479) INFO: total log probability: -40.27 +2024-01-17 01:20:22,854 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:20:22,854 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:22,855 (beam_search:483) INFO: best hypo: MYDEARMOREAWHIDOYOUNOTDESCISTFOMTHISCILYPESUTOFAANMANDGINARYTLESEURWHATISTHEVOLYOUOFMUONYWEARSPANURDSNOTSHERTSLEVEDMERSINARYPLEAGSOFAMEAICAENS + +2024-01-17 01:20:22,857 (asr_inference:494) INFO: speech length: 196160 +2024-01-17 01:20:22,875 (beam_search:428) INFO: decoder input length: 304 +2024-01-17 01:20:22,875 (beam_search:429) INFO: max output length: 304 +2024-01-17 01:20:22,875 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:24,253 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:24,253 (beam_search:476) INFO: -37.70 * 1.0 = -37.70 for ctc +2024-01-17 01:20:24,253 (beam_search:479) INFO: total log probability: -37.70 +2024-01-17 01:20:24,253 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:20:24,253 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:24,254 (beam_search:483) INFO: best hypo: THECRITIALTRPAURISTATOFTHESINGLASTHERMAELINWHCHPESENSEPOINTOVNEFLECTIONATHORSNDTIENGENTTHERITIALPRESEHERIALVOLIMEATHTWOCULDNESEOFTHISPONTOINPLECTION + +2024-01-17 01:20:24,255 (asr_inference:494) INFO: speech length: 287040 +2024-01-17 01:20:24,282 (beam_search:428) INFO: decoder input length: 446 +2024-01-17 01:20:24,282 (beam_search:429) INFO: max output length: 446 +2024-01-17 01:20:24,282 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:26,844 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:26,844 (beam_search:476) INFO: -31.33 * 1.0 = -31.33 for ctc +2024-01-17 01:20:26,844 (beam_search:479) INFO: total log probability: -31.33 +2024-01-17 01:20:26,844 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:20:26,844 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:26,846 (beam_search:483) INFO: best hypo: MUCHLIKANFOUNUSANDIFORMITYONTOTHATMONSTERWHOMTHEHEBANNIGHTTHEFATHEROFTHATFATLPROGANYMADECILHERSELFFORVERYHARTSTOSPIHTHATHEHADREDHERRILWHICHNOWWIHTCOULDEVERLOSESWUTSUFEREDEADLYD + +2024-01-17 01:20:26,847 (asr_inference:494) INFO: speech length: 241120 +2024-01-17 01:20:26,869 (beam_search:428) INFO: decoder input length: 374 +2024-01-17 01:20:26,869 (beam_search:429) INFO: max output length: 374 +2024-01-17 01:20:26,869 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:29,016 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:29,016 (beam_search:476) INFO: -53.36 * 1.0 = -53.36 for ctc +2024-01-17 01:20:29,016 (beam_search:479) INFO: total log probability: -53.36 +2024-01-17 01:20:29,016 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:20:29,016 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:29,017 (beam_search:483) INFO: best hypo: HISMASEDMESEWIHPEIONPRESIESAMOUNTINGTOTHRETHUSNATNSTFIEANDALSOTHERYSMALVOLIMSTANOCUPIEBYTHEFLOAIDMASENDECONSIDERATIONTHISLASTMESGEMENTWHICHNESESITATESNEUMRUSCOREACTINSISMOSTDELICADPATTEOPORATION + +2024-01-17 01:20:29,019 (asr_inference:494) INFO: speech length: 163840 +2024-01-17 01:20:29,035 (beam_search:428) INFO: decoder input length: 253 +2024-01-17 01:20:29,035 (beam_search:429) INFO: max output length: 253 +2024-01-17 01:20:29,035 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:29,911 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:29,911 (beam_search:476) INFO: -25.45 * 1.0 = -25.45 for ctc +2024-01-17 01:20:29,911 (beam_search:479) INFO: total log probability: -25.45 +2024-01-17 01:20:29,911 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:20:29,911 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:29,912 (beam_search:483) INFO: best hypo: WHIYSHULDITHAEBENDEDNECROMANCYTONDEVERTOONBINTHESFATSTOIVOLVEBYCARFULELMINATIONANDCHANGETOTHEPERFECTFOD + +2024-01-17 01:20:29,913 (asr_inference:494) INFO: speech length: 313440 +2024-01-17 01:20:29,942 (beam_search:428) INFO: decoder input length: 487 +2024-01-17 01:20:29,942 (beam_search:429) INFO: max output length: 487 +2024-01-17 01:20:29,942 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:32,509 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:32,509 (beam_search:476) INFO: -34.52 * 1.0 = -34.52 for ctc +2024-01-17 01:20:32,509 (beam_search:479) INFO: total log probability: -34.52 +2024-01-17 01:20:32,509 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:20:32,509 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:32,510 (beam_search:483) INFO: best hypo: NAYTHOOFRASESBEMYBEDYETIAMRICHLOVESAIDBUTUTARGUDLIVHETHRICEFORNDARETHOWTOYELTHESOVERANGIFTSOFERTHTHEVICTORSORDTHELORALDBROWFORVISIONTHINGKSOFLITLWORTH + +2024-01-17 01:20:32,512 (asr_inference:494) INFO: speech length: 176960 +2024-01-17 01:20:32,529 (beam_search:428) INFO: decoder input length: 274 +2024-01-17 01:20:32,529 (beam_search:429) INFO: max output length: 274 +2024-01-17 01:20:32,529 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:33,709 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:33,709 (beam_search:476) INFO: -34.35 * 1.0 = -34.35 for ctc +2024-01-17 01:20:33,709 (beam_search:479) INFO: total log probability: -34.35 +2024-01-17 01:20:33,709 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:20:33,709 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:33,710 (beam_search:483) INFO: best hypo: BUCKEMETOHAVBENACECULACTERAOTHOHAMPEDBYILHELTHANDAGRETPINTONISFAVERISTHATIDISCRIDEDONLYTHOSEPLANSWITHHADCOMEUNDERISONPERSINALOBSOVATION + +2024-01-17 01:20:33,712 (asr_inference:494) INFO: speech length: 203520 +2024-01-17 01:20:33,730 (beam_search:428) INFO: decoder input length: 315 +2024-01-17 01:20:33,730 (beam_search:429) INFO: max output length: 315 +2024-01-17 01:20:33,730 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:35,110 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:35,111 (beam_search:476) INFO: -22.48 * 1.0 = -22.48 for ctc +2024-01-17 01:20:35,111 (beam_search:479) INFO: total log probability: -22.48 +2024-01-17 01:20:35,111 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:20:35,111 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:35,111 (beam_search:483) INFO: best hypo: HADRATHERSRONGUPANDHADNOTCHAINEDINTONEIMPSTHESHYFELTINTHETEAMSCOVERINTHEMUPAGINANDTHEYUPEAREDASPERFECTINSECTSINTHEMAYOFTHEFOLOINGYEAR + +2024-01-17 01:20:35,113 (asr_inference:494) INFO: speech length: 230880 +2024-01-17 01:20:35,133 (beam_search:428) INFO: decoder input length: 358 +2024-01-17 01:20:35,134 (beam_search:429) INFO: max output length: 358 +2024-01-17 01:20:35,134 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:36,942 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:36,942 (beam_search:476) INFO: -43.03 * 1.0 = -43.03 for ctc +2024-01-17 01:20:36,942 (beam_search:479) INFO: total log probability: -43.03 +2024-01-17 01:20:36,942 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:20:36,942 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:36,943 (beam_search:483) INFO: best hypo: NOTHINGSAYOOBJECSANDTHUTSOFBOUTYHOUDPRESENDTHEMSESTOTHEUNESTANDINGOFTHEFORTIOLUTBURSINWHOPARTOKOFITTHESEBATESWHCYOARBRUGTTOMETRANSALATARONSEUREDWITTHISSOUPOESTION + +2024-01-17 01:20:36,944 (asr_inference:494) INFO: speech length: 160800 +2024-01-17 01:20:36,960 (beam_search:428) INFO: decoder input length: 249 +2024-01-17 01:20:36,960 (beam_search:429) INFO: max output length: 249 +2024-01-17 01:20:36,960 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:37,749 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:37,750 (beam_search:476) INFO: -20.84 * 1.0 = -20.84 for ctc +2024-01-17 01:20:37,750 (beam_search:479) INFO: total log probability: -20.84 +2024-01-17 01:20:37,750 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:20:37,750 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:37,750 (beam_search:483) INFO: best hypo: NOSEMINSOUPITITYANDHEDNERVEHIMSELFAGAINSITHISFAISEWARSORDOFSVEFFLOUSHDHEWASTIMIEDEVENTORUDNES + +2024-01-17 01:20:37,752 (asr_inference:494) INFO: speech length: 247360 +2024-01-17 01:20:37,774 (beam_search:428) INFO: decoder input length: 384 +2024-01-17 01:20:37,774 (beam_search:429) INFO: max output length: 384 +2024-01-17 01:20:37,774 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:39,762 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:39,762 (beam_search:476) INFO: -36.84 * 1.0 = -36.84 for ctc +2024-01-17 01:20:39,762 (beam_search:479) INFO: total log probability: -36.84 +2024-01-17 01:20:39,762 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:20:39,762 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:39,763 (beam_search:483) INFO: best hypo: BECAMEMOLELIFELIKASTECHESFLUSHTHEASRARWARMEFINOFINTEMORNINTOHETHAFTDISPERINGSOLTSIDITLONGOURSOFALLREDINGANTPERSTEDHEARTBYNEVERSEASINGRIMESYETICOULNONDESTANDIT + +2024-01-17 01:20:39,765 (asr_inference:494) INFO: speech length: 176640 +2024-01-17 01:20:39,782 (beam_search:428) INFO: decoder input length: 273 +2024-01-17 01:20:39,782 (beam_search:429) INFO: max output length: 273 +2024-01-17 01:20:39,782 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:40,742 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:40,742 (beam_search:476) INFO: -22.47 * 1.0 = -22.47 for ctc +2024-01-17 01:20:40,742 (beam_search:479) INFO: total log probability: -22.47 +2024-01-17 01:20:40,742 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:20:40,742 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:40,742 (beam_search:483) INFO: best hypo: WONOFTHEOWINRITERSAIDTHEAOPEHEAVAISAPOSONSHALFISHTHESARBITERANDDEDLYANDCADBEOUSEDINPUTINGENAIMESTODEATH + +2024-01-17 01:20:40,744 (asr_inference:494) INFO: speech length: 209280 +2024-01-17 01:20:40,763 (beam_search:428) INFO: decoder input length: 324 +2024-01-17 01:20:40,763 (beam_search:429) INFO: max output length: 324 +2024-01-17 01:20:40,763 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:42,050 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:42,050 (beam_search:476) INFO: -33.21 * 1.0 = -33.21 for ctc +2024-01-17 01:20:42,050 (beam_search:479) INFO: total log probability: -33.21 +2024-01-17 01:20:42,050 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:20:42,050 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:42,051 (beam_search:483) INFO: best hypo: THEBEUTYUSROUBSOFHAVENASLONTADURIHTERSNCOLEITARHELOKSINBOUNLESMAGHUTYABROADTOUCHINGTHGRENLEAVESALLATREMBLITGOLDLIHT + +2024-01-17 01:20:42,053 (asr_inference:494) INFO: speech length: 305760 +2024-01-17 01:20:42,080 (beam_search:428) INFO: decoder input length: 475 +2024-01-17 01:20:42,080 (beam_search:429) INFO: max output length: 475 +2024-01-17 01:20:42,080 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:44,773 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:44,773 (beam_search:476) INFO: -43.20 * 1.0 = -43.20 for ctc +2024-01-17 01:20:44,773 (beam_search:479) INFO: total log probability: -43.20 +2024-01-17 01:20:44,774 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:20:44,774 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:44,775 (beam_search:483) INFO: best hypo: ICANDOUNOMORHANTHATINTLTHISMATERISAPSALUTLYSETEDTHEAWORTHOTHANLIFEITSELFTOMETMSTRBLBURSEMEDANOIEDSURLYHEPROTESTITWOUNOTGODOASTMETOWATETHREMONCSUNTILATEXAMINONEOFTHES + +2024-01-17 01:20:44,776 (asr_inference:494) INFO: speech length: 227520 +2024-01-17 01:20:44,797 (beam_search:428) INFO: decoder input length: 353 +2024-01-17 01:20:44,797 (beam_search:429) INFO: max output length: 353 +2024-01-17 01:20:44,797 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:46,324 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:46,324 (beam_search:476) INFO: -27.52 * 1.0 = -27.52 for ctc +2024-01-17 01:20:46,324 (beam_search:479) INFO: total log probability: -27.52 +2024-01-17 01:20:46,324 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:20:46,324 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:46,325 (beam_search:483) INFO: best hypo: ROSECONGRESFOUNDATIONRUSHINANDTITETHATORGANIEDTHESAITPETERSBURGINTERNASINALEECANOMICFOROMROUSNEFTRUSHIONSTATONDOILEANDANARGYCOMPANY + +2024-01-17 01:20:46,327 (asr_inference:494) INFO: speech length: 265920 +2024-01-17 01:20:46,350 (beam_search:428) INFO: decoder input length: 413 +2024-01-17 01:20:46,350 (beam_search:429) INFO: max output length: 413 +2024-01-17 01:20:46,351 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:48,903 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:48,903 (beam_search:476) INFO: -45.52 * 1.0 = -45.52 for ctc +2024-01-17 01:20:48,903 (beam_search:479) INFO: total log probability: -45.52 +2024-01-17 01:20:48,903 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:20:48,903 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:48,905 (beam_search:483) INFO: best hypo: HOWGLUTEDNSPARCKALETHEDELICATROSTWOKYOUEATRACTEDNODOUTAMARVEDATHEDINTYTRAICSOMSBUTFEOUOFASHAVREALYHADANOPOTUNITYTOSTADYTHEDETALOFTHEFRUSTDESINSMYNUTLYOAVCONSIDEDHATTHEWREORTINTHREYURFORDESINSATMOST + +2024-01-17 01:20:48,907 (asr_inference:494) INFO: speech length: 256160 +2024-01-17 01:20:48,929 (beam_search:428) INFO: decoder input length: 398 +2024-01-17 01:20:48,929 (beam_search:429) INFO: max output length: 398 +2024-01-17 01:20:48,929 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:51,235 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:51,235 (beam_search:476) INFO: -46.41 * 1.0 = -46.41 for ctc +2024-01-17 01:20:51,235 (beam_search:479) INFO: total log probability: -46.41 +2024-01-17 01:20:51,235 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:20:51,235 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:51,237 (beam_search:483) INFO: best hypo: THATHATHEOFENSINTRINGTOINFICKAWONTHEMCKILVEROFHENDERORWNHIMMORTHANTHEINTENDANTODOANDTHISBECOMSECCUSFULANUTHRDSOTATHEPRIMITIVELAGDSLATERSWHECAEFULINORECQUIARINTHERITALITIONTOBELIMITEDTOANYFOANA + +2024-01-17 01:20:51,238 (asr_inference:494) INFO: speech length: 286400 +2024-01-17 01:20:51,265 (beam_search:428) INFO: decoder input length: 445 +2024-01-17 01:20:51,265 (beam_search:429) INFO: max output length: 445 +2024-01-17 01:20:51,265 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:53,763 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:53,763 (beam_search:476) INFO: -35.56 * 1.0 = -35.56 for ctc +2024-01-17 01:20:53,763 (beam_search:479) INFO: total log probability: -35.56 +2024-01-17 01:20:53,763 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:20:53,763 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:53,764 (beam_search:483) INFO: best hypo: ATSIRESWORDTHEJUESWRETERNTHECOMPNYTHATGOGODSHICEBEGONWITHMRTHAMONISHENDEDBYTHEFOBUTWONCEAGAINTHEWRKECOSEONBYLISENSFROMDURIOUSESRAISSENTWITHROILEGRUNTANDGIFTHSFORIUSPIAS + +2024-01-17 01:20:53,766 (asr_inference:494) INFO: speech length: 195680 +2024-01-17 01:20:53,784 (beam_search:428) INFO: decoder input length: 303 +2024-01-17 01:20:53,784 (beam_search:429) INFO: max output length: 303 +2024-01-17 01:20:53,784 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:54,922 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:54,922 (beam_search:476) INFO: -32.02 * 1.0 = -32.02 for ctc +2024-01-17 01:20:54,922 (beam_search:479) INFO: total log probability: -32.02 +2024-01-17 01:20:54,922 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:20:54,922 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:54,922 (beam_search:483) INFO: best hypo: ANTPRODACKYEARINANDYEAROUTSIVINHUNDREFROANCESWHOLIVEDONITHAVENOTSOBADLYWREIULEXPLAINEMORDYISOCUPEDTATHEORBOHOVS + +2024-01-17 01:20:54,924 (asr_inference:494) INFO: speech length: 309280 +2024-01-17 01:20:54,952 (beam_search:428) INFO: decoder input length: 481 +2024-01-17 01:20:54,952 (beam_search:429) INFO: max output length: 481 +2024-01-17 01:20:54,952 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:20:57,761 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:20:57,761 (beam_search:476) INFO: -44.80 * 1.0 = -44.80 for ctc +2024-01-17 01:20:57,761 (beam_search:479) INFO: total log probability: -44.80 +2024-01-17 01:20:57,761 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:20:57,761 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:20:57,762 (beam_search:483) INFO: best hypo: THEANTHISISALLYOURANTARTISTOFARFORONEOFHISALIANCSANDIWOREYOUWTHATTHISPLACENOMORSEYOUAEGXSEITANDTERDFLURANSTHEBESTISTHERISMORCROUDTOETAMANSARYVENJONONSEFORACETHATSMYNAMINDED + +2024-01-17 01:20:57,764 (asr_inference:494) INFO: speech length: 316160 +2024-01-17 01:20:57,792 (beam_search:428) INFO: decoder input length: 491 +2024-01-17 01:20:57,793 (beam_search:429) INFO: max output length: 491 +2024-01-17 01:20:57,793 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:00,768 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:00,768 (beam_search:476) INFO: -36.79 * 1.0 = -36.79 for ctc +2024-01-17 01:21:00,768 (beam_search:479) INFO: total log probability: -36.79 +2024-01-17 01:21:00,768 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:00,768 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:00,769 (beam_search:483) INFO: best hypo: HENIRETURNDTOTHEHOUSEWHEREHADBEAHAPYCHILDONLYAPILOFASHEISWEAYITHADSTUDIWEPTLONGANDTOFOGETMYWEPINGISAIDOUTONDEVATSCAMSSCEONTHESEWATERSINASTARSAFAYANNIGTIPLADEMYFLOTTOTHESUMERMON + +2024-01-17 01:21:00,771 (asr_inference:494) INFO: speech length: 275520 +2024-01-17 01:21:00,797 (beam_search:428) INFO: decoder input length: 428 +2024-01-17 01:21:00,797 (beam_search:429) INFO: max output length: 428 +2024-01-17 01:21:00,797 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:03,330 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:03,330 (beam_search:476) INFO: -36.21 * 1.0 = -36.21 for ctc +2024-01-17 01:21:03,330 (beam_search:479) INFO: total log probability: -36.21 +2024-01-17 01:21:03,330 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:03,330 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:03,331 (beam_search:483) INFO: best hypo: DOYOUNOTSEWHATLESUERITGIVESMEWEHAVEGRONUPTOGETHERINTHISHOUSESINCHEWASABOYISIMPLYCANORBEARASYOUGANTHESIGTOFTHESMILLAVINGHISFACEBOURDEARHEHASNOMUSMENTEXCEPTTHISPLINGATTHSHOPCEPING + +2024-01-17 01:21:03,333 (asr_inference:494) INFO: speech length: 197600 +2024-01-17 01:21:03,351 (beam_search:428) INFO: decoder input length: 306 +2024-01-17 01:21:03,351 (beam_search:429) INFO: max output length: 306 +2024-01-17 01:21:03,351 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:04,663 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:04,663 (beam_search:476) INFO: -32.85 * 1.0 = -32.85 for ctc +2024-01-17 01:21:04,663 (beam_search:479) INFO: total log probability: -32.85 +2024-01-17 01:21:04,663 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:04,663 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:04,664 (beam_search:483) INFO: best hypo: ITISDEVBIUSBOADYREVALINGINYLIPICALORRGREEONGATIONLOVELOVELOVEWALNOTBETHEWONEDOFCUPITBUTHEMANIFISTATIONOFHEERVERSALREPRDUCTIEINSTINCES + +2024-01-17 01:21:04,666 (asr_inference:494) INFO: speech length: 284000 +2024-01-17 01:21:04,692 (beam_search:428) INFO: decoder input length: 441 +2024-01-17 01:21:04,692 (beam_search:429) INFO: max output length: 441 +2024-01-17 01:21:04,692 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:07,183 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:07,183 (beam_search:476) INFO: -42.24 * 1.0 = -42.24 for ctc +2024-01-17 01:21:07,184 (beam_search:479) INFO: total log probability: -42.24 +2024-01-17 01:21:07,184 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:07,184 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:07,185 (beam_search:483) INFO: best hypo: SHOARPLYASHESHOOKHANDWITHERGODBESYUMIGTDEATCHATHEBIHOPSAIDWHENSHECISEDHIMANDHISLIPSMOEDOFTERWARDFORSOMSICKNTSASIFHEWERINPRAARHURMOTHERFOLORDHEROUTOFTHOMANDTHENSILANDCETEL + +2024-01-17 01:21:07,186 (asr_inference:494) INFO: speech length: 180640 +2024-01-17 01:21:07,203 (beam_search:428) INFO: decoder input length: 280 +2024-01-17 01:21:07,203 (beam_search:429) INFO: max output length: 280 +2024-01-17 01:21:07,203 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:08,222 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:08,222 (beam_search:476) INFO: -23.14 * 1.0 = -23.14 for ctc +2024-01-17 01:21:08,222 (beam_search:479) INFO: total log probability: -23.14 +2024-01-17 01:21:08,222 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:08,222 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:08,223 (beam_search:483) INFO: best hypo: FOLOEDHIMSTAELTFULLYANDHEHIWASINASTPINGPOSTHURFILINGHISBOCKETCAMEUPBEHINEDHIMANDPLUNCEDANLONGNIFEINTOHISNECK + +2024-01-17 01:21:08,224 (asr_inference:494) INFO: speech length: 266560 +2024-01-17 01:21:08,248 (beam_search:428) INFO: decoder input length: 414 +2024-01-17 01:21:08,248 (beam_search:429) INFO: max output length: 414 +2024-01-17 01:21:08,248 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:10,600 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:10,600 (beam_search:476) INFO: -37.57 * 1.0 = -37.57 for ctc +2024-01-17 01:21:10,600 (beam_search:479) INFO: total log probability: -37.57 +2024-01-17 01:21:10,600 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:10,600 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:10,601 (beam_search:483) INFO: best hypo: SASTHCURTIESDOUSNOTJUPITERDISTREBUTETOTHEGODSTHEPROPORTIONANDDIVIDENTSPARINGLYANDSEVERALYASAGAMANANDYETOHISCOMANDOERSWHENHISGESTSTRANGKTOONEANOTHERIFVFORCOURIOUSQUTHCLEEDEMOSASYUNERRAT + +2024-01-17 01:21:10,603 (asr_inference:494) INFO: speech length: 207520 +2024-01-17 01:21:10,622 (beam_search:428) INFO: decoder input length: 322 +2024-01-17 01:21:10,622 (beam_search:429) INFO: max output length: 322 +2024-01-17 01:21:10,622 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:11,913 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:11,913 (beam_search:476) INFO: -29.53 * 1.0 = -29.53 for ctc +2024-01-17 01:21:11,913 (beam_search:479) INFO: total log probability: -29.53 +2024-01-17 01:21:11,913 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:11,913 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:11,914 (beam_search:483) INFO: best hypo: ANDHERENONHALDARASTRANTTOAMETAGAININTHOHTSOTHISNOUSEANEPINGBHERECHERULSPIRITSTILNERDOTHEFATISEPINGFEUTURGOODFORPESENIL + +2024-01-17 01:21:11,916 (asr_inference:494) INFO: speech length: 220320 +2024-01-17 01:21:11,936 (beam_search:428) INFO: decoder input length: 342 +2024-01-17 01:21:11,936 (beam_search:429) INFO: max output length: 342 +2024-01-17 01:21:11,936 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:13,561 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:13,561 (beam_search:476) INFO: -30.39 * 1.0 = -30.39 for ctc +2024-01-17 01:21:13,561 (beam_search:479) INFO: total log probability: -30.39 +2024-01-17 01:21:13,561 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:13,561 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:13,562 (beam_search:483) INFO: best hypo: ANDBECOMETHERECARDOFWHATPEPLAVEDONITHERMORAMUBLEMOENTSTHERECARDOFHECONCQUESTSOFPESEHOWMENHAVELIVEDANDLAVERDDUGADBILTHEUONANDCLEREDGARDEDANDREFORST + +2024-01-17 01:21:13,564 (asr_inference:494) INFO: speech length: 254400 +2024-01-17 01:21:13,586 (beam_search:428) INFO: decoder input length: 395 +2024-01-17 01:21:13,586 (beam_search:429) INFO: max output length: 395 +2024-01-17 01:21:13,586 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:15,796 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:15,796 (beam_search:476) INFO: -46.48 * 1.0 = -46.48 for ctc +2024-01-17 01:21:15,796 (beam_search:479) INFO: total log probability: -46.48 +2024-01-17 01:21:15,796 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:15,796 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:15,797 (beam_search:483) INFO: best hypo: THELOFLINGOFTHWLLESPETOKINSRAINASWILASNYANSCASONABLEDANCSINGOFMIGEUSINTHEEVENINGSOACONSANDOFEATANDRIMTISINTHEONCESARDIFULPROCURSSTHELEAVESARALLATRMBLEBEFORTHATPROCHEATTUNDER + +2024-01-17 01:21:15,799 (asr_inference:494) INFO: speech length: 243040 +2024-01-17 01:21:15,820 (beam_search:428) INFO: decoder input length: 377 +2024-01-17 01:21:15,820 (beam_search:429) INFO: max output length: 377 +2024-01-17 01:21:15,820 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:17,819 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:17,819 (beam_search:476) INFO: -38.21 * 1.0 = -38.21 for ctc +2024-01-17 01:21:17,819 (beam_search:479) INFO: total log probability: -38.21 +2024-01-17 01:21:17,819 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:17,819 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:17,820 (beam_search:483) INFO: best hypo: WASASTORMNGHJGENERLDEMPEAREWASCKILEGENERALCOSTIENWASBLAIMEDANINDEDESNOWCOMETOPARISTODEVEXPLONATIONSAGANSETALLWHICHTHEMOUNTONANDATROTIOUSMAUARMUSTEVENDMAKEHAILASTHECAN + +2024-01-17 01:21:17,822 (asr_inference:494) INFO: speech length: 211840 +2024-01-17 01:21:17,842 (beam_search:428) INFO: decoder input length: 328 +2024-01-17 01:21:17,842 (beam_search:429) INFO: max output length: 328 +2024-01-17 01:21:17,842 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:19,238 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:19,238 (beam_search:476) INFO: -24.45 * 1.0 = -24.45 for ctc +2024-01-17 01:21:19,238 (beam_search:479) INFO: total log probability: -24.45 +2024-01-17 01:21:19,238 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:19,238 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:19,239 (beam_search:483) INFO: best hypo: THEMOMENTWASFEFULAMIGTYOFFOHADNEVERSWNGTHEBUTLELACXOVERHIMBUTTHEHOBNERVEDHISARMEFORADESPRATBLOWANDTHCMSERFLEPROSTRAITDBEFORHIM + +2024-01-17 01:21:19,241 (asr_inference:494) INFO: speech length: 208160 +2024-01-17 01:21:19,260 (beam_search:428) INFO: decoder input length: 323 +2024-01-17 01:21:19,260 (beam_search:429) INFO: max output length: 323 +2024-01-17 01:21:19,260 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:20,475 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:20,475 (beam_search:476) INFO: -24.28 * 1.0 = -24.28 for ctc +2024-01-17 01:21:20,475 (beam_search:479) INFO: total log probability: -24.28 +2024-01-17 01:21:20,475 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:20,475 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:20,476 (beam_search:483) INFO: best hypo: THENTHEWINESTOUTTHECLAORSTANDDOARKANDNIGHECAMEONLIKEEINKMYOLDCOTINCUILTWASCAOLDASIANMYSWEETSONTOSTINHISSCLEE + +2024-01-17 01:21:20,478 (asr_inference:494) INFO: speech length: 232640 +2024-01-17 01:21:20,499 (beam_search:428) INFO: decoder input length: 361 +2024-01-17 01:21:20,499 (beam_search:429) INFO: max output length: 361 +2024-01-17 01:21:20,499 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:22,363 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:22,363 (beam_search:476) INFO: -31.97 * 1.0 = -31.97 for ctc +2024-01-17 01:21:22,363 (beam_search:479) INFO: total log probability: -31.97 +2024-01-17 01:21:22,363 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:22,363 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:22,364 (beam_search:483) INFO: best hypo: YOUMAYDOASYUPLEASETOWORKOFORIRITATIONTOKEPYOURFANATICSISMYOUREWELAFFYOUNEDNOTMINDTHECOSTTHEPOREDONOTWANTTSTANDINYOURWAYBUTYUINSISTONTHESUBMITINGTYORCOMPULSION + +2024-01-17 01:21:22,366 (asr_inference:494) INFO: speech length: 258080 +2024-01-17 01:21:22,389 (beam_search:428) INFO: decoder input length: 401 +2024-01-17 01:21:22,389 (beam_search:429) INFO: max output length: 401 +2024-01-17 01:21:22,389 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:24,689 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:24,689 (beam_search:476) INFO: -38.15 * 1.0 = -38.15 for ctc +2024-01-17 01:21:24,689 (beam_search:479) INFO: total log probability: -38.15 +2024-01-17 01:21:24,689 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:24,689 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:24,690 (beam_search:483) INFO: best hypo: HEWASREDBYAREVERNTERYAYSNIGHTBEINGBYOTHMANESIXFOARTOTHIDVICKWLWASBONEANMACHATENSEVENTYNINANDHEWASTHEONLYSOVIVEROFLITEROFFIFTENITWASONTHISACOUNTTHATHWASAUEDSAFEANDCOLORANDMAKINGS + +2024-01-17 01:21:24,692 (asr_inference:494) INFO: speech length: 252160 +2024-01-17 01:21:24,715 (beam_search:428) INFO: decoder input length: 391 +2024-01-17 01:21:24,715 (beam_search:429) INFO: max output length: 391 +2024-01-17 01:21:24,715 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:26,541 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:26,541 (beam_search:476) INFO: -31.62 * 1.0 = -31.62 for ctc +2024-01-17 01:21:26,541 (beam_search:479) INFO: total log probability: -31.62 +2024-01-17 01:21:26,541 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:26,541 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:26,542 (beam_search:483) INFO: best hypo: ANDWHATHASTITAKESOFOLINTOTHESECKANTTHERBYTHISTIMEDIAFHANTEOSNEASERSECGIOMOSTADMRABLESECREITONTHECONTRARYITSTURSMENOTAWITWICHMOSTCONSURNESIT + +2024-01-17 01:21:26,543 (asr_inference:494) INFO: speech length: 262560 +2024-01-17 01:21:26,567 (beam_search:428) INFO: decoder input length: 408 +2024-01-17 01:21:26,567 (beam_search:429) INFO: max output length: 408 +2024-01-17 01:21:26,567 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:28,711 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:28,711 (beam_search:476) INFO: -32.73 * 1.0 = -32.73 for ctc +2024-01-17 01:21:28,711 (beam_search:479) INFO: total log probability: -32.73 +2024-01-17 01:21:28,711 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:28,711 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:28,712 (beam_search:483) INFO: best hypo: THURDLYTHALSAIDWHERTHECITISENSARENEATHERTOREACHNORTOPORFORTHLYANACAUSISSAIDWHERETHOGINALLOTHERESPECTSTHEYARIACULIETVERTOUOSMENARADVANSEDANDVESIOUSPERSONTHEGRADED + +2024-01-17 01:21:28,714 (asr_inference:494) INFO: speech length: 246400 +2024-01-17 01:21:28,737 (beam_search:428) INFO: decoder input length: 382 +2024-01-17 01:21:28,737 (beam_search:429) INFO: max output length: 382 +2024-01-17 01:21:28,737 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:30,595 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:30,596 (beam_search:476) INFO: -25.58 * 1.0 = -25.58 for ctc +2024-01-17 01:21:30,596 (beam_search:479) INFO: total log probability: -25.58 +2024-01-17 01:21:30,596 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:21:30,596 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:30,597 (beam_search:483) INFO: best hypo: THECINDLYFRANKISSIMPOTHATICKAEVERYDAYHEPASNOTESBETWEENUSANDITRIYTOINCURAGERUSSALHEWILIMPROVEIASURHIMHISTIMEISHORTANDFRESHARANDLIORTYWLSONRESTORHIM + +2024-01-17 01:21:30,598 (asr_inference:494) INFO: speech length: 290720 +2024-01-17 01:21:30,625 (beam_search:428) INFO: decoder input length: 452 +2024-01-17 01:21:30,625 (beam_search:429) INFO: max output length: 452 +2024-01-17 01:21:30,625 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:33,220 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:33,220 (beam_search:476) INFO: -31.84 * 1.0 = -31.84 for ctc +2024-01-17 01:21:33,220 (beam_search:479) INFO: total log probability: -31.84 +2024-01-17 01:21:33,220 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:33,220 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:33,222 (beam_search:483) INFO: best hypo: THISCUESTONSITISNOWEVIDENTMAYFRECUENTLYBEANSEREDWASEAQULLPROPRITYINOPSITWASESANDIFTHEREBEANYACASIONSONWHICHTHEYCANBEANSERDONLYINONWAYTHEAUNSERWILDEPENDAPONTHENATUROFTHEACASION + +2024-01-17 01:21:33,223 (asr_inference:494) INFO: speech length: 229280 +2024-01-17 01:21:33,244 (beam_search:428) INFO: decoder input length: 356 +2024-01-17 01:21:33,244 (beam_search:429) INFO: max output length: 356 +2024-01-17 01:21:33,244 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:34,954 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:34,954 (beam_search:476) INFO: -28.11 * 1.0 = -28.11 for ctc +2024-01-17 01:21:34,954 (beam_search:479) INFO: total log probability: -28.11 +2024-01-17 01:21:34,954 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:34,954 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:34,955 (beam_search:483) INFO: best hypo: INHISNOTBORTHEMINSTRILSYSECKNADIONATYNOWATSCOTSESTHEBLEDWASTAKENDOWNFROMAOLDWOMENSREITATIONATTHEHELSAONMORLEDMINESBYTHEAGENTTHERANDSENTBYHIMTOSURTEE + +2024-01-17 01:21:34,956 (asr_inference:494) INFO: speech length: 59520 +2024-01-17 01:21:34,965 (beam_search:428) INFO: decoder input length: 90 +2024-01-17 01:21:34,965 (beam_search:429) INFO: max output length: 90 +2024-01-17 01:21:34,965 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:35,030 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:35,030 (beam_search:476) INFO: -6.15 * 1.0 = -6.15 for ctc +2024-01-17 01:21:35,030 (beam_search:479) INFO: total log probability: -6.15 +2024-01-17 01:21:35,030 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:21:35,030 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:35,030 (beam_search:483) INFO: best hypo: CRISTONTHEOLIGONS + +2024-01-17 01:21:35,032 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:35,039 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:35,039 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:35,039 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:35,081 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:35,081 (beam_search:476) INFO: -3.51 * 1.0 = -3.51 for ctc +2024-01-17 01:21:35,081 (beam_search:479) INFO: total log probability: -3.51 +2024-01-17 01:21:35,081 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:35,081 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:35,081 (beam_search:483) INFO: best hypo: OPTAINEEAGLFHITHERS + +2024-01-17 01:21:35,082 (asr_inference:494) INFO: speech length: 49920 +2024-01-17 01:21:35,090 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:21:35,090 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:21:35,090 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:35,164 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:35,164 (beam_search:476) INFO: -7.56 * 1.0 = -7.56 for ctc +2024-01-17 01:21:35,164 (beam_search:479) INFO: total log probability: -7.56 +2024-01-17 01:21:35,164 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:35,164 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:35,165 (beam_search:483) INFO: best hypo: ELAMENTRYESPCIALEFONGTIONS + +2024-01-17 01:21:35,166 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:35,173 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:35,173 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:35,174 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:35,233 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:35,233 (beam_search:476) INFO: -8.94 * 1.0 = -8.94 for ctc +2024-01-17 01:21:35,233 (beam_search:479) INFO: total log probability: -8.94 +2024-01-17 01:21:35,233 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:21:35,233 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:35,234 (beam_search:483) INFO: best hypo: JORDEWASINGSANNUNVORSTITY + +2024-01-17 01:21:35,235 (asr_inference:494) INFO: speech length: 44160 +2024-01-17 01:21:35,243 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:21:35,243 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:21:35,243 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:35,308 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:35,308 (beam_search:476) INFO: -4.74 * 1.0 = -4.74 for ctc +2024-01-17 01:21:35,308 (beam_search:479) INFO: total log probability: -4.74 +2024-01-17 01:21:35,308 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:35,308 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:35,309 (beam_search:483) INFO: best hypo: SINSFICTIONNOTVESPROVEAN + +2024-01-17 01:21:35,310 (asr_inference:494) INFO: speech length: 28800 +2024-01-17 01:21:35,317 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:21:35,317 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:21:35,317 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:35,339 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:35,339 (beam_search:476) INFO: -3.48 * 1.0 = -3.48 for ctc +2024-01-17 01:21:35,339 (beam_search:479) INFO: total log probability: -3.48 +2024-01-17 01:21:35,339 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:21:35,339 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:35,339 (beam_search:483) INFO: best hypo: COSTDHIPOP + +2024-01-17 01:21:35,340 (asr_inference:494) INFO: speech length: 69120 +2024-01-17 01:21:35,350 (beam_search:428) INFO: decoder input length: 105 +2024-01-17 01:21:35,350 (beam_search:429) INFO: max output length: 105 +2024-01-17 01:21:35,350 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:35,449 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:35,449 (beam_search:476) INFO: -5.24 * 1.0 = -5.24 for ctc +2024-01-17 01:21:35,449 (beam_search:479) INFO: total log probability: -5.24 +2024-01-17 01:21:35,449 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:35,449 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:35,450 (beam_search:483) INFO: best hypo: INVERSLEPBLACETRONSFORME + +2024-01-17 01:21:35,451 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:35,458 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:35,458 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:35,458 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:35,495 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:35,495 (beam_search:476) INFO: -5.04 * 1.0 = -5.04 for ctc +2024-01-17 01:21:35,495 (beam_search:479) INFO: total log probability: -5.04 +2024-01-17 01:21:35,495 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:35,495 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:35,495 (beam_search:483) INFO: best hypo: FRINGHPROTISTANTS + +2024-01-17 01:21:35,497 (asr_inference:494) INFO: speech length: 63360 +2024-01-17 01:21:35,506 (beam_search:428) INFO: decoder input length: 96 +2024-01-17 01:21:35,506 (beam_search:429) INFO: max output length: 96 +2024-01-17 01:21:35,506 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:35,569 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:35,569 (beam_search:476) INFO: -7.64 * 1.0 = -7.64 for ctc +2024-01-17 01:21:35,569 (beam_search:479) INFO: total log probability: -7.64 +2024-01-17 01:21:35,569 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:21:35,569 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:35,570 (beam_search:483) INFO: best hypo: AFGUNAEFORSEST + +2024-01-17 01:21:35,571 (asr_inference:494) INFO: speech length: 74880 +2024-01-17 01:21:35,580 (beam_search:428) INFO: decoder input length: 114 +2024-01-17 01:21:35,581 (beam_search:429) INFO: max output length: 114 +2024-01-17 01:21:35,581 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:35,702 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:35,702 (beam_search:476) INFO: -6.00 * 1.0 = -6.00 for ctc +2024-01-17 01:21:35,702 (beam_search:479) INFO: total log probability: -6.00 +2024-01-17 01:21:35,702 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:35,702 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:35,703 (beam_search:483) INFO: best hypo: HEAROSINMISOLAGYANDLEAGEND + +2024-01-17 01:21:35,704 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:35,711 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:35,711 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:35,711 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:35,753 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:35,753 (beam_search:476) INFO: -10.88 * 1.0 = -10.88 for ctc +2024-01-17 01:21:35,753 (beam_search:479) INFO: total log probability: -10.88 +2024-01-17 01:21:35,753 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:21:35,753 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:35,753 (beam_search:483) INFO: best hypo: BUSNSCLARSSEETTTER + +2024-01-17 01:21:35,754 (asr_inference:494) INFO: speech length: 49920 +2024-01-17 01:21:35,762 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:21:35,762 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:21:35,762 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:35,816 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:35,816 (beam_search:476) INFO: -6.53 * 1.0 = -6.53 for ctc +2024-01-17 01:21:35,816 (beam_search:479) INFO: total log probability: -6.53 +2024-01-17 01:21:35,816 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:21:35,816 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:35,816 (beam_search:483) INFO: best hypo: CLUDBPLAYCHARTET + +2024-01-17 01:21:35,817 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:35,825 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:35,825 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:35,825 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:35,875 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:35,875 (beam_search:476) INFO: -3.83 * 1.0 = -3.83 for ctc +2024-01-17 01:21:35,875 (beam_search:479) INFO: total log probability: -3.83 +2024-01-17 01:21:35,875 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:21:35,875 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:35,876 (beam_search:483) INFO: best hypo: POSYTRONSWERERAPORTED + +2024-01-17 01:21:35,877 (asr_inference:494) INFO: speech length: 28800 +2024-01-17 01:21:35,884 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:21:35,884 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:21:35,884 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:35,915 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:35,915 (beam_search:476) INFO: -3.36 * 1.0 = -3.36 for ctc +2024-01-17 01:21:35,915 (beam_search:479) INFO: total log probability: -3.36 +2024-01-17 01:21:35,915 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:35,915 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:35,915 (beam_search:483) INFO: best hypo: ALLDVICKTHEATAR + +2024-01-17 01:21:35,916 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:35,924 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:35,924 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:35,924 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:35,969 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:35,969 (beam_search:476) INFO: -6.11 * 1.0 = -6.11 for ctc +2024-01-17 01:21:35,969 (beam_search:479) INFO: total log probability: -6.11 +2024-01-17 01:21:35,969 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:21:35,969 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:35,969 (beam_search:483) INFO: best hypo: ORTHEDOCKESMONOUCKS + +2024-01-17 01:21:35,971 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 01:21:35,979 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:21:35,979 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:21:35,979 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:36,031 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:36,032 (beam_search:476) INFO: -3.13 * 1.0 = -3.13 for ctc +2024-01-17 01:21:36,032 (beam_search:479) INFO: total log probability: -3.13 +2024-01-17 01:21:36,032 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:21:36,032 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:36,032 (beam_search:483) INFO: best hypo: NATIONSMBERSTATES + +2024-01-17 01:21:36,033 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:36,040 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:36,040 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:36,040 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:36,077 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:36,077 (beam_search:476) INFO: -3.48 * 1.0 = -3.48 for ctc +2024-01-17 01:21:36,077 (beam_search:479) INFO: total log probability: -3.48 +2024-01-17 01:21:36,077 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:36,077 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:36,077 (beam_search:483) INFO: best hypo: SHEAFHOWILDCOUP + +2024-01-17 01:21:36,078 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:36,086 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:36,086 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:36,086 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:36,130 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:36,130 (beam_search:476) INFO: -6.49 * 1.0 = -6.49 for ctc +2024-01-17 01:21:36,130 (beam_search:479) INFO: total log probability: -6.49 +2024-01-17 01:21:36,130 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:21:36,130 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:36,130 (beam_search:483) INFO: best hypo: CROSRISCKUWFEATS + +2024-01-17 01:21:36,131 (asr_inference:494) INFO: speech length: 53760 +2024-01-17 01:21:36,140 (beam_search:428) INFO: decoder input length: 81 +2024-01-17 01:21:36,140 (beam_search:429) INFO: max output length: 81 +2024-01-17 01:21:36,140 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:36,216 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:36,216 (beam_search:476) INFO: -7.63 * 1.0 = -7.63 for ctc +2024-01-17 01:21:36,216 (beam_search:479) INFO: total log probability: -7.63 +2024-01-17 01:21:36,216 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:21:36,216 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:36,216 (beam_search:483) INFO: best hypo: ACTHALFOMEMOACKRSCOPITE + +2024-01-17 01:21:36,217 (asr_inference:494) INFO: speech length: 51840 +2024-01-17 01:21:36,226 (beam_search:428) INFO: decoder input length: 78 +2024-01-17 01:21:36,226 (beam_search:429) INFO: max output length: 78 +2024-01-17 01:21:36,226 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:36,306 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:36,306 (beam_search:476) INFO: -6.65 * 1.0 = -6.65 for ctc +2024-01-17 01:21:36,306 (beam_search:479) INFO: total log probability: -6.65 +2024-01-17 01:21:36,306 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:36,306 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:36,307 (beam_search:483) INFO: best hypo: MEUSICALGROPSREASTABLISHED + +2024-01-17 01:21:36,308 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 01:21:36,316 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:21:36,316 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:21:36,316 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:36,370 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:36,370 (beam_search:476) INFO: -6.75 * 1.0 = -6.75 for ctc +2024-01-17 01:21:36,370 (beam_search:479) INFO: total log probability: -6.75 +2024-01-17 01:21:36,370 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:21:36,370 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:36,370 (beam_search:483) INFO: best hypo: PROMSCSINSERPAESE + +2024-01-17 01:21:36,371 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:36,379 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:36,379 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:36,379 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:36,410 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:36,410 (beam_search:476) INFO: -5.45 * 1.0 = -5.45 for ctc +2024-01-17 01:21:36,410 (beam_search:479) INFO: total log probability: -5.45 +2024-01-17 01:21:36,410 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:21:36,410 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:36,410 (beam_search:483) INFO: best hypo: FOLNESIKNEKS + +2024-01-17 01:21:36,411 (asr_inference:494) INFO: speech length: 42240 +2024-01-17 01:21:36,419 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:21:36,419 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:21:36,419 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:36,469 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:36,469 (beam_search:476) INFO: -5.35 * 1.0 = -5.35 for ctc +2024-01-17 01:21:36,469 (beam_search:479) INFO: total log probability: -5.35 +2024-01-17 01:21:36,469 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:21:36,469 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:36,469 (beam_search:483) INFO: best hypo: TLVIONSERYESBACET + +2024-01-17 01:21:36,470 (asr_inference:494) INFO: speech length: 72960 +2024-01-17 01:21:36,479 (beam_search:428) INFO: decoder input length: 111 +2024-01-17 01:21:36,479 (beam_search:429) INFO: max output length: 111 +2024-01-17 01:21:36,480 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:36,556 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:36,556 (beam_search:476) INFO: -6.10 * 1.0 = -6.10 for ctc +2024-01-17 01:21:36,556 (beam_search:479) INFO: total log probability: -6.10 +2024-01-17 01:21:36,556 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:21:36,556 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:36,556 (beam_search:483) INFO: best hypo: NEWPOLITICARPATY + +2024-01-17 01:21:36,557 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:36,565 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:36,565 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:36,565 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:36,610 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:36,610 (beam_search:476) INFO: -5.41 * 1.0 = -5.41 for ctc +2024-01-17 01:21:36,610 (beam_search:479) INFO: total log probability: -5.41 +2024-01-17 01:21:36,610 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:21:36,610 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:36,610 (beam_search:483) INFO: best hypo: ANHANTEAGJIPACHEVED + +2024-01-17 01:21:36,611 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:36,618 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:36,618 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:36,618 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:36,653 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:36,653 (beam_search:476) INFO: -2.79 * 1.0 = -2.79 for ctc +2024-01-17 01:21:36,653 (beam_search:479) INFO: total log probability: -2.79 +2024-01-17 01:21:36,653 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:21:36,653 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:36,653 (beam_search:483) INFO: best hypo: FLATMUSIGNATRAL + +2024-01-17 01:21:36,654 (asr_inference:494) INFO: speech length: 48000 +2024-01-17 01:21:36,662 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 01:21:36,662 (beam_search:429) INFO: max output length: 72 +2024-01-17 01:21:36,662 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:36,744 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:36,744 (beam_search:476) INFO: -9.20 * 1.0 = -9.20 for ctc +2024-01-17 01:21:36,744 (beam_search:479) INFO: total log probability: -9.20 +2024-01-17 01:21:36,744 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:21:36,744 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:36,744 (beam_search:483) INFO: best hypo: AMERICANTICNOLADTIGNLEDYRATERS + +2024-01-17 01:21:36,745 (asr_inference:494) INFO: speech length: 42240 +2024-01-17 01:21:36,753 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:21:36,753 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:21:36,753 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:36,794 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:36,795 (beam_search:476) INFO: -2.61 * 1.0 = -2.61 for ctc +2024-01-17 01:21:36,795 (beam_search:479) INFO: total log probability: -2.61 +2024-01-17 01:21:36,795 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:21:36,795 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:36,795 (beam_search:483) INFO: best hypo: DOATESOFBARINS + +2024-01-17 01:21:36,796 (asr_inference:494) INFO: speech length: 49920 +2024-01-17 01:21:36,804 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:21:36,804 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:21:36,804 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:36,875 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:36,876 (beam_search:476) INFO: -5.08 * 1.0 = -5.08 for ctc +2024-01-17 01:21:36,876 (beam_search:479) INFO: total log probability: -5.08 +2024-01-17 01:21:36,876 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:36,876 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:36,876 (beam_search:483) INFO: best hypo: POPULETSWERISATRACTIONS + +2024-01-17 01:21:36,877 (asr_inference:494) INFO: speech length: 28800 +2024-01-17 01:21:36,884 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:21:36,884 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:21:36,884 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:36,913 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:36,913 (beam_search:476) INFO: -2.38 * 1.0 = -2.38 for ctc +2024-01-17 01:21:36,913 (beam_search:479) INFO: total log probability: -2.38 +2024-01-17 01:21:36,913 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:21:36,913 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:36,913 (beam_search:483) INFO: best hypo: DOUCHWASTINDIA + +2024-01-17 01:21:36,914 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:36,922 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:36,922 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:36,922 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:36,971 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:36,971 (beam_search:476) INFO: -6.16 * 1.0 = -6.16 for ctc +2024-01-17 01:21:36,971 (beam_search:479) INFO: total log probability: -6.16 +2024-01-17 01:21:36,971 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:21:36,971 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:36,971 (beam_search:483) INFO: best hypo: GOLDMITAERSIPIENCES + +2024-01-17 01:21:36,972 (asr_inference:494) INFO: speech length: 51840 +2024-01-17 01:21:36,981 (beam_search:428) INFO: decoder input length: 78 +2024-01-17 01:21:36,981 (beam_search:429) INFO: max output length: 78 +2024-01-17 01:21:36,981 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:37,054 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:37,054 (beam_search:476) INFO: -5.69 * 1.0 = -5.69 for ctc +2024-01-17 01:21:37,054 (beam_search:479) INFO: total log probability: -5.69 +2024-01-17 01:21:37,054 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:37,054 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:37,055 (beam_search:483) INFO: best hypo: RESHIONSOCHALDEMACRETICK + +2024-01-17 01:21:37,056 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:37,063 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:37,063 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:37,063 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:37,118 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:37,118 (beam_search:476) INFO: -8.85 * 1.0 = -8.85 for ctc +2024-01-17 01:21:37,118 (beam_search:479) INFO: total log probability: -8.85 +2024-01-17 01:21:37,118 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:21:37,118 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:37,118 (beam_search:483) INFO: best hypo: AMERIKCANFOMEPRODUSES + +2024-01-17 01:21:37,119 (asr_inference:494) INFO: speech length: 42240 +2024-01-17 01:21:37,127 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:21:37,127 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:21:37,127 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:37,184 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:37,184 (beam_search:476) INFO: -4.01 * 1.0 = -4.01 for ctc +2024-01-17 01:21:37,184 (beam_search:479) INFO: total log probability: -4.01 +2024-01-17 01:21:37,184 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:37,184 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:37,184 (beam_search:483) INFO: best hypo: FRESOFTERYAFOUNDATION + +2024-01-17 01:21:37,185 (asr_inference:494) INFO: speech length: 30720 +2024-01-17 01:21:37,192 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:21:37,192 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:21:37,192 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:37,228 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:37,228 (beam_search:476) INFO: -4.68 * 1.0 = -4.68 for ctc +2024-01-17 01:21:37,228 (beam_search:479) INFO: total log probability: -4.68 +2024-01-17 01:21:37,228 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:37,228 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:37,228 (beam_search:483) INFO: best hypo: ROILEDRMATICTHEAT + +2024-01-17 01:21:37,229 (asr_inference:494) INFO: speech length: 38400 +2024-01-17 01:21:37,237 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:21:37,237 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:21:37,237 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:37,276 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:37,276 (beam_search:476) INFO: -6.38 * 1.0 = -6.38 for ctc +2024-01-17 01:21:37,276 (beam_search:479) INFO: total log probability: -6.38 +2024-01-17 01:21:37,276 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:21:37,276 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:37,277 (beam_search:483) INFO: best hypo: ITABLEMOLASCKS + +2024-01-17 01:21:37,278 (asr_inference:494) INFO: speech length: 49920 +2024-01-17 01:21:37,286 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:21:37,286 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:21:37,286 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:37,353 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:37,353 (beam_search:476) INFO: -5.18 * 1.0 = -5.18 for ctc +2024-01-17 01:21:37,353 (beam_search:479) INFO: total log probability: -5.18 +2024-01-17 01:21:37,353 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:21:37,353 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:37,353 (beam_search:483) INFO: best hypo: FEATHERSINCLUDBEACHERS + +2024-01-17 01:21:37,354 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:37,362 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:37,362 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:37,362 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:37,416 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:37,416 (beam_search:476) INFO: -5.93 * 1.0 = -5.93 for ctc +2024-01-17 01:21:37,416 (beam_search:479) INFO: total log probability: -5.93 +2024-01-17 01:21:37,416 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:37,416 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:37,416 (beam_search:483) INFO: best hypo: OCSFORDITIONRYCHANGED + +2024-01-17 01:21:37,417 (asr_inference:494) INFO: speech length: 38400 +2024-01-17 01:21:37,425 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:21:37,425 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:21:37,425 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:37,477 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:37,477 (beam_search:476) INFO: -6.92 * 1.0 = -6.92 for ctc +2024-01-17 01:21:37,477 (beam_search:479) INFO: total log probability: -6.92 +2024-01-17 01:21:37,477 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:21:37,477 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:37,477 (beam_search:483) INFO: best hypo: SALCOOPURSINGRAYHOUND + +2024-01-17 01:21:37,478 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:37,486 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:37,486 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:37,486 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:37,526 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:37,526 (beam_search:476) INFO: -4.40 * 1.0 = -4.40 for ctc +2024-01-17 01:21:37,526 (beam_search:479) INFO: total log probability: -4.40 +2024-01-17 01:21:37,526 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:37,526 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:37,526 (beam_search:483) INFO: best hypo: PRINMNISTERCIVEN + +2024-01-17 01:21:37,528 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:37,535 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:37,535 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:37,535 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:37,574 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:37,574 (beam_search:476) INFO: -3.59 * 1.0 = -3.59 for ctc +2024-01-17 01:21:37,574 (beam_search:479) INFO: total log probability: -3.59 +2024-01-17 01:21:37,574 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:37,574 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:37,574 (beam_search:483) INFO: best hypo: LANGAGESOFYOUROCK + +2024-01-17 01:21:37,575 (asr_inference:494) INFO: speech length: 42240 +2024-01-17 01:21:37,583 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:21:37,583 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:21:37,583 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:37,629 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:37,629 (beam_search:476) INFO: -1.97 * 1.0 = -1.97 for ctc +2024-01-17 01:21:37,629 (beam_search:479) INFO: total log probability: -1.97 +2024-01-17 01:21:37,629 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 01:21:37,629 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:37,629 (beam_search:483) INFO: best hypo: SOUTHEASTINGLAND + +2024-01-17 01:21:37,631 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:37,638 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:37,638 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:37,638 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:37,673 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:37,673 (beam_search:476) INFO: -5.21 * 1.0 = -5.21 for ctc +2024-01-17 01:21:37,673 (beam_search:479) INFO: total log probability: -5.21 +2024-01-17 01:21:37,673 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:21:37,673 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:37,673 (beam_search:483) INFO: best hypo: NEWLINESENAMAR + +2024-01-17 01:21:37,674 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 01:21:37,682 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:21:37,682 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:21:37,682 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:37,745 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:37,745 (beam_search:476) INFO: -11.26 * 1.0 = -11.26 for ctc +2024-01-17 01:21:37,745 (beam_search:479) INFO: total log probability: -11.26 +2024-01-17 01:21:37,745 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-17 01:21:37,745 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:37,745 (beam_search:483) INFO: best hypo: EACULCRADTSOPATUONATY + +2024-01-17 01:21:37,746 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:37,753 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:37,753 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:37,753 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:37,788 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:37,788 (beam_search:476) INFO: -2.51 * 1.0 = -2.51 for ctc +2024-01-17 01:21:37,788 (beam_search:479) INFO: total log probability: -2.51 +2024-01-17 01:21:37,788 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:21:37,788 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:37,788 (beam_search:483) INFO: best hypo: SOUTHESTINGLAND + +2024-01-17 01:21:37,789 (asr_inference:494) INFO: speech length: 24960 +2024-01-17 01:21:37,796 (beam_search:428) INFO: decoder input length: 36 +2024-01-17 01:21:37,796 (beam_search:429) INFO: max output length: 36 +2024-01-17 01:21:37,797 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:37,809 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:37,809 (beam_search:476) INFO: -3.89 * 1.0 = -3.89 for ctc +2024-01-17 01:21:37,809 (beam_search:479) INFO: total log probability: -3.89 +2024-01-17 01:21:37,809 (beam_search:480) INFO: normalized log probability: -0.43 +2024-01-17 01:21:37,809 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:37,810 (beam_search:483) INFO: best hypo: MAYH + +2024-01-17 01:21:37,811 (asr_inference:494) INFO: speech length: 51840 +2024-01-17 01:21:37,819 (beam_search:428) INFO: decoder input length: 78 +2024-01-17 01:21:37,819 (beam_search:429) INFO: max output length: 78 +2024-01-17 01:21:37,819 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:37,899 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:37,899 (beam_search:476) INFO: -8.49 * 1.0 = -8.49 for ctc +2024-01-17 01:21:37,899 (beam_search:479) INFO: total log probability: -8.49 +2024-01-17 01:21:37,899 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:21:37,899 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:37,899 (beam_search:483) INFO: best hypo: RECOLRDHATEATSEMISCRIVES + +2024-01-17 01:21:37,900 (asr_inference:494) INFO: speech length: 67200 +2024-01-17 01:21:37,910 (beam_search:428) INFO: decoder input length: 102 +2024-01-17 01:21:37,910 (beam_search:429) INFO: max output length: 102 +2024-01-17 01:21:37,910 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:38,018 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:38,018 (beam_search:476) INFO: -8.19 * 1.0 = -8.19 for ctc +2024-01-17 01:21:38,018 (beam_search:479) INFO: total log probability: -8.19 +2024-01-17 01:21:38,018 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:21:38,018 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:38,018 (beam_search:483) INFO: best hypo: MUSICALGREPESFROMCALFORNIEA + +2024-01-17 01:21:38,020 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:38,027 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:38,027 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:38,027 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:38,061 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:38,062 (beam_search:476) INFO: -4.65 * 1.0 = -4.65 for ctc +2024-01-17 01:21:38,062 (beam_search:479) INFO: total log probability: -4.65 +2024-01-17 01:21:38,062 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:21:38,062 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:38,062 (beam_search:483) INFO: best hypo: MAINBUTLETINCKS + +2024-01-17 01:21:38,063 (asr_inference:494) INFO: speech length: 57600 +2024-01-17 01:21:38,072 (beam_search:428) INFO: decoder input length: 87 +2024-01-17 01:21:38,072 (beam_search:429) INFO: max output length: 87 +2024-01-17 01:21:38,072 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:38,158 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:38,158 (beam_search:476) INFO: -4.12 * 1.0 = -4.12 for ctc +2024-01-17 01:21:38,158 (beam_search:479) INFO: total log probability: -4.12 +2024-01-17 01:21:38,158 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:21:38,158 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:38,158 (beam_search:483) INFO: best hypo: PODLISHMUSICALINSTRAMENTES + +2024-01-17 01:21:38,159 (asr_inference:494) INFO: speech length: 57600 +2024-01-17 01:21:38,168 (beam_search:428) INFO: decoder input length: 87 +2024-01-17 01:21:38,168 (beam_search:429) INFO: max output length: 87 +2024-01-17 01:21:38,168 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:38,244 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:38,244 (beam_search:476) INFO: -5.28 * 1.0 = -5.28 for ctc +2024-01-17 01:21:38,244 (beam_search:479) INFO: total log probability: -5.28 +2024-01-17 01:21:38,244 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:38,244 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:38,244 (beam_search:483) INFO: best hypo: LANWUGESOFSADIEARAVIA + +2024-01-17 01:21:38,246 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:38,253 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:38,253 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:38,253 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:38,292 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:38,292 (beam_search:476) INFO: -4.51 * 1.0 = -4.51 for ctc +2024-01-17 01:21:38,292 (beam_search:479) INFO: total log probability: -4.51 +2024-01-17 01:21:38,292 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:38,292 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:38,292 (beam_search:483) INFO: best hypo: CALDWARTINTIONS + +2024-01-17 01:21:38,293 (asr_inference:494) INFO: speech length: 28800 +2024-01-17 01:21:38,300 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:21:38,300 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:21:38,300 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:38,320 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:38,320 (beam_search:476) INFO: -8.19 * 1.0 = -8.19 for ctc +2024-01-17 01:21:38,320 (beam_search:479) INFO: total log probability: -8.19 +2024-01-17 01:21:38,320 (beam_search:480) INFO: normalized log probability: -0.68 +2024-01-17 01:21:38,320 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:38,320 (beam_search:483) INFO: best hypo: DOABEWPH + +2024-01-17 01:21:38,321 (asr_inference:494) INFO: speech length: 48000 +2024-01-17 01:21:38,329 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 01:21:38,329 (beam_search:429) INFO: max output length: 72 +2024-01-17 01:21:38,329 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:38,378 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:38,378 (beam_search:476) INFO: -4.48 * 1.0 = -4.48 for ctc +2024-01-17 01:21:38,378 (beam_search:479) INFO: total log probability: -4.48 +2024-01-17 01:21:38,378 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:38,379 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:38,379 (beam_search:483) INFO: best hypo: ANDYPOPECLAMINT + +2024-01-17 01:21:38,380 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:38,387 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:38,387 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:38,387 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:38,424 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:38,424 (beam_search:476) INFO: -3.05 * 1.0 = -3.05 for ctc +2024-01-17 01:21:38,424 (beam_search:479) INFO: total log probability: -3.05 +2024-01-17 01:21:38,424 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:21:38,424 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:38,424 (beam_search:483) INFO: best hypo: GITSTHECNPRIVEAT + +2024-01-17 01:21:38,425 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:38,433 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:38,433 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:38,433 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:38,467 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:38,467 (beam_search:476) INFO: -3.11 * 1.0 = -3.11 for ctc +2024-01-17 01:21:38,467 (beam_search:479) INFO: total log probability: -3.11 +2024-01-17 01:21:38,467 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:38,467 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:38,467 (beam_search:483) INFO: best hypo: CINGFODANEAND + +2024-01-17 01:21:38,469 (asr_inference:494) INFO: speech length: 42240 +2024-01-17 01:21:38,477 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:21:38,477 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:21:38,477 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:38,540 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:38,540 (beam_search:476) INFO: -5.77 * 1.0 = -5.77 for ctc +2024-01-17 01:21:38,540 (beam_search:479) INFO: total log probability: -5.77 +2024-01-17 01:21:38,541 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:21:38,541 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:38,541 (beam_search:483) INFO: best hypo: ILECTRNICMUSICALINSTRMENCS + +2024-01-17 01:21:38,542 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:38,549 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:38,549 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:38,549 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:38,580 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:38,580 (beam_search:476) INFO: -3.79 * 1.0 = -3.79 for ctc +2024-01-17 01:21:38,580 (beam_search:479) INFO: total log probability: -3.79 +2024-01-17 01:21:38,580 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:38,580 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:38,580 (beam_search:483) INFO: best hypo: AGENOUTWATER + +2024-01-17 01:21:38,582 (asr_inference:494) INFO: speech length: 44160 +2024-01-17 01:21:38,590 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:21:38,590 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:21:38,590 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:38,651 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:38,651 (beam_search:476) INFO: -4.90 * 1.0 = -4.90 for ctc +2024-01-17 01:21:38,651 (beam_search:479) INFO: total log probability: -4.90 +2024-01-17 01:21:38,651 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:38,651 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:38,651 (beam_search:483) INFO: best hypo: LORENCELIVEMORNASINALE + +2024-01-17 01:21:38,653 (asr_inference:494) INFO: speech length: 48000 +2024-01-17 01:21:38,661 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 01:21:38,661 (beam_search:429) INFO: max output length: 72 +2024-01-17 01:21:38,661 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:38,719 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:38,719 (beam_search:476) INFO: -4.96 * 1.0 = -4.96 for ctc +2024-01-17 01:21:38,719 (beam_search:479) INFO: total log probability: -4.96 +2024-01-17 01:21:38,719 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:38,719 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:38,719 (beam_search:483) INFO: best hypo: LEAGBACSPALEPLAYERS + +2024-01-17 01:21:38,720 (asr_inference:494) INFO: speech length: 49920 +2024-01-17 01:21:38,728 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:21:38,728 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:21:38,729 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:38,818 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:38,818 (beam_search:476) INFO: -8.95 * 1.0 = -8.95 for ctc +2024-01-17 01:21:38,818 (beam_search:479) INFO: total log probability: -8.95 +2024-01-17 01:21:38,818 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:38,818 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:38,818 (beam_search:483) INFO: best hypo: BUTISOMANTHEANCHANTMEDATRANION + +2024-01-17 01:21:38,820 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:38,827 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:38,827 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:38,827 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:38,880 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:38,880 (beam_search:476) INFO: -6.86 * 1.0 = -6.86 for ctc +2024-01-17 01:21:38,880 (beam_search:479) INFO: total log probability: -6.86 +2024-01-17 01:21:38,880 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:21:38,880 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:38,880 (beam_search:483) INFO: best hypo: OUNITEDSTATSRECOCONIED + +2024-01-17 01:21:38,881 (asr_inference:494) INFO: speech length: 57600 +2024-01-17 01:21:38,890 (beam_search:428) INFO: decoder input length: 87 +2024-01-17 01:21:38,890 (beam_search:429) INFO: max output length: 87 +2024-01-17 01:21:38,890 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:38,955 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:38,955 (beam_search:476) INFO: -6.33 * 1.0 = -6.33 for ctc +2024-01-17 01:21:38,955 (beam_search:479) INFO: total log probability: -6.33 +2024-01-17 01:21:38,955 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:21:38,955 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:38,955 (beam_search:483) INFO: best hypo: PROPASIONALFELACES + +2024-01-17 01:21:38,956 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:38,963 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:38,963 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:38,963 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,003 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,003 (beam_search:476) INFO: -8.23 * 1.0 = -8.23 for ctc +2024-01-17 01:21:39,003 (beam_search:479) INFO: total log probability: -8.23 +2024-01-17 01:21:39,003 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:21:39,003 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,003 (beam_search:483) INFO: best hypo: SPICHALECNOMIEGSOWNS + +2024-01-17 01:21:39,004 (asr_inference:494) INFO: speech length: 38400 +2024-01-17 01:21:39,012 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:21:39,012 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:21:39,012 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,051 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,051 (beam_search:476) INFO: -4.79 * 1.0 = -4.79 for ctc +2024-01-17 01:21:39,051 (beam_search:479) INFO: total log probability: -4.79 +2024-01-17 01:21:39,051 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:39,051 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,051 (beam_search:483) INFO: best hypo: MANSTREAMEWISTD + +2024-01-17 01:21:39,052 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:39,059 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:39,059 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:39,059 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,088 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,088 (beam_search:476) INFO: -2.64 * 1.0 = -2.64 for ctc +2024-01-17 01:21:39,088 (beam_search:479) INFO: total log probability: -2.64 +2024-01-17 01:21:39,088 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:39,088 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,088 (beam_search:483) INFO: best hypo: EVENGRUSHOWS + +2024-01-17 01:21:39,089 (asr_inference:494) INFO: speech length: 30720 +2024-01-17 01:21:39,096 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:21:39,096 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:21:39,096 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,127 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,127 (beam_search:476) INFO: -1.15 * 1.0 = -1.15 for ctc +2024-01-17 01:21:39,127 (beam_search:479) INFO: total log probability: -1.15 +2024-01-17 01:21:39,127 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 01:21:39,127 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,127 (beam_search:483) INFO: best hypo: BYTHEDIONSTOK + +2024-01-17 01:21:39,128 (asr_inference:494) INFO: speech length: 28800 +2024-01-17 01:21:39,135 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:21:39,135 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:21:39,135 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,166 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,166 (beam_search:476) INFO: -4.10 * 1.0 = -4.10 for ctc +2024-01-17 01:21:39,166 (beam_search:479) INFO: total log probability: -4.10 +2024-01-17 01:21:39,166 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:39,166 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,166 (beam_search:483) INFO: best hypo: NDTSARTICKEHASNO + +2024-01-17 01:21:39,167 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:39,175 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:39,175 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:39,175 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,208 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,208 (beam_search:476) INFO: -2.23 * 1.0 = -2.23 for ctc +2024-01-17 01:21:39,208 (beam_search:479) INFO: total log probability: -2.23 +2024-01-17 01:21:39,208 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 01:21:39,208 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,208 (beam_search:483) INFO: best hypo: WEASTINMUSICLES + +2024-01-17 01:21:39,209 (asr_inference:494) INFO: speech length: 49920 +2024-01-17 01:21:39,217 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:21:39,217 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:21:39,217 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,293 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,293 (beam_search:476) INFO: -7.06 * 1.0 = -7.06 for ctc +2024-01-17 01:21:39,293 (beam_search:479) INFO: total log probability: -7.06 +2024-01-17 01:21:39,293 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:39,293 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,294 (beam_search:483) INFO: best hypo: CONSEVITOFJUATAYSOMREGARTS + +2024-01-17 01:21:39,295 (asr_inference:494) INFO: speech length: 51840 +2024-01-17 01:21:39,303 (beam_search:428) INFO: decoder input length: 78 +2024-01-17 01:21:39,303 (beam_search:429) INFO: max output length: 78 +2024-01-17 01:21:39,303 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,352 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,352 (beam_search:476) INFO: -2.81 * 1.0 = -2.81 for ctc +2024-01-17 01:21:39,352 (beam_search:479) INFO: total log probability: -2.81 +2024-01-17 01:21:39,352 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:39,352 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,352 (beam_search:483) INFO: best hypo: OPICKMBERSTATS + +2024-01-17 01:21:39,353 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:39,361 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:39,361 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:39,361 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,393 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,393 (beam_search:476) INFO: -3.57 * 1.0 = -3.57 for ctc +2024-01-17 01:21:39,393 (beam_search:479) INFO: total log probability: -3.57 +2024-01-17 01:21:39,393 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:21:39,393 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,394 (beam_search:483) INFO: best hypo: PRIMINESSAIDJON + +2024-01-17 01:21:39,395 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:39,402 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:39,402 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:39,402 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,444 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,444 (beam_search:476) INFO: -2.03 * 1.0 = -2.03 for ctc +2024-01-17 01:21:39,444 (beam_search:479) INFO: total log probability: -2.03 +2024-01-17 01:21:39,444 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 01:21:39,444 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,445 (beam_search:483) INFO: best hypo: RACKSFOARMINGMOUNT + +2024-01-17 01:21:39,446 (asr_inference:494) INFO: speech length: 30720 +2024-01-17 01:21:39,453 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:21:39,453 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:21:39,453 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,480 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,480 (beam_search:476) INFO: -4.16 * 1.0 = -4.16 for ctc +2024-01-17 01:21:39,480 (beam_search:479) INFO: total log probability: -4.16 +2024-01-17 01:21:39,480 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:39,480 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,480 (beam_search:483) INFO: best hypo: MAGERLEAKTMS + +2024-01-17 01:21:39,481 (asr_inference:494) INFO: speech length: 42240 +2024-01-17 01:21:39,489 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:21:39,489 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:21:39,489 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,535 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,535 (beam_search:476) INFO: -5.21 * 1.0 = -5.21 for ctc +2024-01-17 01:21:39,535 (beam_search:479) INFO: total log probability: -5.21 +2024-01-17 01:21:39,535 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:39,536 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,536 (beam_search:483) INFO: best hypo: POLONATIONMANIGENT + +2024-01-17 01:21:39,537 (asr_inference:494) INFO: speech length: 55680 +2024-01-17 01:21:39,545 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:21:39,545 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:21:39,545 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,593 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,593 (beam_search:476) INFO: -5.73 * 1.0 = -5.73 for ctc +2024-01-17 01:21:39,594 (beam_search:479) INFO: total log probability: -5.73 +2024-01-17 01:21:39,594 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:21:39,594 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,594 (beam_search:483) INFO: best hypo: FRENCHFISIST + +2024-01-17 01:21:39,595 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:39,602 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:39,602 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:39,602 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,655 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,655 (beam_search:476) INFO: -6.92 * 1.0 = -6.92 for ctc +2024-01-17 01:21:39,656 (beam_search:479) INFO: total log probability: -6.92 +2024-01-17 01:21:39,656 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:21:39,656 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,656 (beam_search:483) INFO: best hypo: HIYARCOMPRETSIONRATSIO + +2024-01-17 01:21:39,657 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:39,664 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:39,664 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:39,664 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,721 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,721 (beam_search:476) INFO: -5.65 * 1.0 = -5.65 for ctc +2024-01-17 01:21:39,721 (beam_search:479) INFO: total log probability: -5.65 +2024-01-17 01:21:39,721 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:39,721 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,721 (beam_search:483) INFO: best hypo: RECORDNGINDOUSTRYASOCHATION + +2024-01-17 01:21:39,722 (asr_inference:494) INFO: speech length: 42240 +2024-01-17 01:21:39,730 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:21:39,730 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:21:39,730 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,786 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,786 (beam_search:476) INFO: -4.76 * 1.0 = -4.76 for ctc +2024-01-17 01:21:39,786 (beam_search:479) INFO: total log probability: -4.76 +2024-01-17 01:21:39,786 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:39,786 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,786 (beam_search:483) INFO: best hypo: THEPEAGEONLINMAGASEAN + +2024-01-17 01:21:39,787 (asr_inference:494) INFO: speech length: 53760 +2024-01-17 01:21:39,796 (beam_search:428) INFO: decoder input length: 81 +2024-01-17 01:21:39,796 (beam_search:429) INFO: max output length: 81 +2024-01-17 01:21:39,796 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,878 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,878 (beam_search:476) INFO: -11.52 * 1.0 = -11.52 for ctc +2024-01-17 01:21:39,878 (beam_search:479) INFO: total log probability: -11.52 +2024-01-17 01:21:39,878 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:21:39,878 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,879 (beam_search:483) INFO: best hypo: HIPOPERRECQUADPREGUOSSEONS + +2024-01-17 01:21:39,879 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:39,887 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:39,887 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:39,887 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,926 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,926 (beam_search:476) INFO: -4.86 * 1.0 = -4.86 for ctc +2024-01-17 01:21:39,926 (beam_search:479) INFO: total log probability: -4.86 +2024-01-17 01:21:39,926 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:39,926 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,926 (beam_search:483) INFO: best hypo: FINIGHESTATEMSHENS + +2024-01-17 01:21:39,927 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:39,935 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:39,935 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:39,935 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:39,979 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:39,979 (beam_search:476) INFO: -3.34 * 1.0 = -3.34 for ctc +2024-01-17 01:21:39,979 (beam_search:479) INFO: total log probability: -3.34 +2024-01-17 01:21:39,979 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:39,979 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:39,979 (beam_search:483) INFO: best hypo: WHIDLYOUSEDLOCALE + +2024-01-17 01:21:39,980 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:39,988 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:39,988 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:39,988 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:40,038 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:40,039 (beam_search:476) INFO: -6.27 * 1.0 = -6.27 for ctc +2024-01-17 01:21:40,039 (beam_search:479) INFO: total log probability: -6.27 +2024-01-17 01:21:40,039 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:40,039 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:40,039 (beam_search:483) INFO: best hypo: NORTHEMERYCANCONTINANT + +2024-01-17 01:21:40,040 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:40,048 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:40,048 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:40,048 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:40,093 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:40,093 (beam_search:476) INFO: -7.18 * 1.0 = -7.18 for ctc +2024-01-17 01:21:40,093 (beam_search:479) INFO: total log probability: -7.18 +2024-01-17 01:21:40,093 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:21:40,093 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:40,093 (beam_search:483) INFO: best hypo: AFRCANAMERICANREPAS + +2024-01-17 01:21:40,094 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:40,101 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:40,102 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:40,102 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:40,144 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:40,144 (beam_search:476) INFO: -4.64 * 1.0 = -4.64 for ctc +2024-01-17 01:21:40,144 (beam_search:479) INFO: total log probability: -4.64 +2024-01-17 01:21:40,144 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:40,144 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:40,144 (beam_search:483) INFO: best hypo: THRITONMELIDRYACTIONS + +2024-01-17 01:21:40,146 (asr_inference:494) INFO: speech length: 67200 +2024-01-17 01:21:40,155 (beam_search:428) INFO: decoder input length: 102 +2024-01-17 01:21:40,155 (beam_search:429) INFO: max output length: 102 +2024-01-17 01:21:40,155 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:40,211 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:40,211 (beam_search:476) INFO: -9.64 * 1.0 = -9.64 for ctc +2024-01-17 01:21:40,211 (beam_search:479) INFO: total log probability: -9.64 +2024-01-17 01:21:40,211 (beam_search:480) INFO: normalized log probability: -0.57 +2024-01-17 01:21:40,211 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:40,212 (beam_search:483) INFO: best hypo: ATHEWORDMNN + +2024-01-17 01:21:40,213 (asr_inference:494) INFO: speech length: 42240 +2024-01-17 01:21:40,221 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:21:40,221 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:21:40,221 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:40,296 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:40,296 (beam_search:476) INFO: -8.38 * 1.0 = -8.38 for ctc +2024-01-17 01:21:40,296 (beam_search:479) INFO: total log probability: -8.38 +2024-01-17 01:21:40,296 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:40,296 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:40,296 (beam_search:483) INFO: best hypo: THETOMIKMELIKULANOPTICALFOISICKS + +2024-01-17 01:21:40,297 (asr_inference:494) INFO: speech length: 19200 +2024-01-17 01:21:40,304 (beam_search:428) INFO: decoder input length: 27 +2024-01-17 01:21:40,304 (beam_search:429) INFO: max output length: 27 +2024-01-17 01:21:40,304 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:40,312 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:40,312 (beam_search:476) INFO: -1.97 * 1.0 = -1.97 for ctc +2024-01-17 01:21:40,312 (beam_search:479) INFO: total log probability: -1.97 +2024-01-17 01:21:40,312 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:21:40,312 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:40,312 (beam_search:483) INFO: best hypo: TOWN + +2024-01-17 01:21:40,313 (asr_inference:494) INFO: speech length: 19200 +2024-01-17 01:21:40,319 (beam_search:428) INFO: decoder input length: 27 +2024-01-17 01:21:40,319 (beam_search:429) INFO: max output length: 27 +2024-01-17 01:21:40,319 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:40,330 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:40,330 (beam_search:476) INFO: -2.19 * 1.0 = -2.19 for ctc +2024-01-17 01:21:40,330 (beam_search:479) INFO: total log probability: -2.19 +2024-01-17 01:21:40,330 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:40,330 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:40,330 (beam_search:483) INFO: best hypo: MORSIL + +2024-01-17 01:21:40,331 (asr_inference:494) INFO: speech length: 59520 +2024-01-17 01:21:40,340 (beam_search:428) INFO: decoder input length: 90 +2024-01-17 01:21:40,340 (beam_search:429) INFO: max output length: 90 +2024-01-17 01:21:40,340 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:40,417 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:40,417 (beam_search:476) INFO: -3.99 * 1.0 = -3.99 for ctc +2024-01-17 01:21:40,417 (beam_search:479) INFO: total log probability: -3.99 +2024-01-17 01:21:40,417 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:40,417 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:40,417 (beam_search:483) INFO: best hypo: CONSTRUCTNEWRALGAGEH + +2024-01-17 01:21:40,418 (asr_inference:494) INFO: speech length: 30720 +2024-01-17 01:21:40,425 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:21:40,425 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:21:40,425 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:40,465 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:40,465 (beam_search:476) INFO: -6.83 * 1.0 = -6.83 for ctc +2024-01-17 01:21:40,465 (beam_search:479) INFO: total log probability: -6.83 +2024-01-17 01:21:40,465 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:21:40,465 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:40,466 (beam_search:483) INFO: best hypo: PORLYEXCLUSINRINCSIBL + +2024-01-17 01:21:40,467 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 01:21:40,475 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:21:40,475 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:21:40,475 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:40,535 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:40,535 (beam_search:476) INFO: -6.11 * 1.0 = -6.11 for ctc +2024-01-17 01:21:40,535 (beam_search:479) INFO: total log probability: -6.11 +2024-01-17 01:21:40,535 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:21:40,535 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:40,535 (beam_search:483) INFO: best hypo: HEWOPOURTRAYDIFERENTS + +2024-01-17 01:21:40,536 (asr_inference:494) INFO: speech length: 44160 +2024-01-17 01:21:40,544 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:21:40,544 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:21:40,544 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:40,587 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:40,588 (beam_search:476) INFO: -3.56 * 1.0 = -3.56 for ctc +2024-01-17 01:21:40,588 (beam_search:479) INFO: total log probability: -3.56 +2024-01-17 01:21:40,588 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:21:40,588 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:40,588 (beam_search:483) INFO: best hypo: SOVIATDISIDANCE + +2024-01-17 01:21:40,589 (asr_inference:494) INFO: speech length: 55680 +2024-01-17 01:21:40,597 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:21:40,597 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:21:40,597 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:40,693 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:40,693 (beam_search:476) INFO: -5.94 * 1.0 = -5.94 for ctc +2024-01-17 01:21:40,693 (beam_search:479) INFO: total log probability: -5.94 +2024-01-17 01:21:40,693 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:40,693 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:40,694 (beam_search:483) INFO: best hypo: SIGNALETRONSTDUCTIONPARTHWAYESE + +2024-01-17 01:21:40,695 (asr_inference:494) INFO: speech length: 24960 +2024-01-17 01:21:40,701 (beam_search:428) INFO: decoder input length: 36 +2024-01-17 01:21:40,702 (beam_search:429) INFO: max output length: 36 +2024-01-17 01:21:40,702 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:40,722 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:40,722 (beam_search:476) INFO: -3.68 * 1.0 = -3.68 for ctc +2024-01-17 01:21:40,722 (beam_search:479) INFO: total log probability: -3.68 +2024-01-17 01:21:40,722 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:21:40,722 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:40,722 (beam_search:483) INFO: best hypo: YOUBORNMSI + +2024-01-17 01:21:40,724 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 01:21:40,732 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:21:40,732 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:21:40,732 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:40,795 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:40,795 (beam_search:476) INFO: -6.06 * 1.0 = -6.06 for ctc +2024-01-17 01:21:40,795 (beam_search:479) INFO: total log probability: -6.06 +2024-01-17 01:21:40,795 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:40,795 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:40,795 (beam_search:483) INFO: best hypo: GENERLYACXCEPTEDRANGERS + +2024-01-17 01:21:40,797 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:40,804 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:40,804 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:40,804 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:40,843 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:40,843 (beam_search:476) INFO: -3.50 * 1.0 = -3.50 for ctc +2024-01-17 01:21:40,843 (beam_search:479) INFO: total log probability: -3.50 +2024-01-17 01:21:40,843 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:40,843 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:40,843 (beam_search:483) INFO: best hypo: GILEDAWARDWINIS + +2024-01-17 01:21:40,844 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:40,851 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:40,851 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:40,851 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:40,894 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:40,894 (beam_search:476) INFO: -3.63 * 1.0 = -3.63 for ctc +2024-01-17 01:21:40,894 (beam_search:479) INFO: total log probability: -3.63 +2024-01-17 01:21:40,894 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:40,894 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:40,894 (beam_search:483) INFO: best hypo: SWEDISHMUSICALGROPS + +2024-01-17 01:21:40,895 (asr_inference:494) INFO: speech length: 57600 +2024-01-17 01:21:40,904 (beam_search:428) INFO: decoder input length: 87 +2024-01-17 01:21:40,904 (beam_search:429) INFO: max output length: 87 +2024-01-17 01:21:40,904 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:40,980 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:40,980 (beam_search:476) INFO: -4.03 * 1.0 = -4.03 for ctc +2024-01-17 01:21:40,981 (beam_search:479) INFO: total log probability: -4.03 +2024-01-17 01:21:40,981 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:40,981 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:40,981 (beam_search:483) INFO: best hypo: CHOWDERDOARTISOMRATING + +2024-01-17 01:21:40,982 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:40,989 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:40,989 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:40,989 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,015 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,015 (beam_search:476) INFO: -1.83 * 1.0 = -1.83 for ctc +2024-01-17 01:21:41,015 (beam_search:479) INFO: total log probability: -1.83 +2024-01-17 01:21:41,015 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:21:41,015 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,015 (beam_search:483) INFO: best hypo: DOSIGEFORMS + +2024-01-17 01:21:41,016 (asr_inference:494) INFO: speech length: 38400 +2024-01-17 01:21:41,024 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:21:41,024 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:21:41,024 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,076 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,076 (beam_search:476) INFO: -7.42 * 1.0 = -7.42 for ctc +2024-01-17 01:21:41,076 (beam_search:479) INFO: total log probability: -7.42 +2024-01-17 01:21:41,076 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:21:41,076 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,076 (beam_search:483) INFO: best hypo: OFHIGOSTATUONOVERSTITE + +2024-01-17 01:21:41,078 (asr_inference:494) INFO: speech length: 51840 +2024-01-17 01:21:41,086 (beam_search:428) INFO: decoder input length: 78 +2024-01-17 01:21:41,086 (beam_search:429) INFO: max output length: 78 +2024-01-17 01:21:41,086 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,156 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,156 (beam_search:476) INFO: -5.69 * 1.0 = -5.69 for ctc +2024-01-17 01:21:41,156 (beam_search:479) INFO: total log probability: -5.69 +2024-01-17 01:21:41,156 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:41,156 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,156 (beam_search:483) INFO: best hypo: FORMOSSATOMENTSINTORK + +2024-01-17 01:21:41,158 (asr_inference:494) INFO: speech length: 65280 +2024-01-17 01:21:41,167 (beam_search:428) INFO: decoder input length: 99 +2024-01-17 01:21:41,167 (beam_search:429) INFO: max output length: 99 +2024-01-17 01:21:41,167 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,238 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,238 (beam_search:476) INFO: -5.93 * 1.0 = -5.93 for ctc +2024-01-17 01:21:41,238 (beam_search:479) INFO: total log probability: -5.93 +2024-01-17 01:21:41,238 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:21:41,238 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,238 (beam_search:483) INFO: best hypo: AMERCANINVENTIONS + +2024-01-17 01:21:41,239 (asr_inference:494) INFO: speech length: 21120 +2024-01-17 01:21:41,246 (beam_search:428) INFO: decoder input length: 30 +2024-01-17 01:21:41,246 (beam_search:429) INFO: max output length: 30 +2024-01-17 01:21:41,246 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,255 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,255 (beam_search:476) INFO: -1.51 * 1.0 = -1.51 for ctc +2024-01-17 01:21:41,255 (beam_search:479) INFO: total log probability: -1.51 +2024-01-17 01:21:41,255 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:41,255 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,255 (beam_search:483) INFO: best hypo: ARTS + +2024-01-17 01:21:41,256 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:41,263 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:41,263 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:41,263 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,308 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,308 (beam_search:476) INFO: -6.59 * 1.0 = -6.59 for ctc +2024-01-17 01:21:41,308 (beam_search:479) INFO: total log probability: -6.59 +2024-01-17 01:21:41,308 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:21:41,308 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,308 (beam_search:483) INFO: best hypo: MDONYUROPIANRASHA + +2024-01-17 01:21:41,309 (asr_inference:494) INFO: speech length: 55680 +2024-01-17 01:21:41,318 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:21:41,318 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:21:41,318 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,379 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,379 (beam_search:476) INFO: -5.52 * 1.0 = -5.52 for ctc +2024-01-17 01:21:41,379 (beam_search:479) INFO: total log probability: -5.52 +2024-01-17 01:21:41,379 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:21:41,379 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,379 (beam_search:483) INFO: best hypo: NSTNORLEAEGPINANT + +2024-01-17 01:21:41,380 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:41,387 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:41,388 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:41,388 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,437 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,437 (beam_search:476) INFO: -4.30 * 1.0 = -4.30 for ctc +2024-01-17 01:21:41,437 (beam_search:479) INFO: total log probability: -4.30 +2024-01-17 01:21:41,437 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:41,437 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,437 (beam_search:483) INFO: best hypo: BEAGFINISHPRDUCTIONS + +2024-01-17 01:21:41,439 (asr_inference:494) INFO: speech length: 23040 +2024-01-17 01:21:41,445 (beam_search:428) INFO: decoder input length: 33 +2024-01-17 01:21:41,446 (beam_search:429) INFO: max output length: 33 +2024-01-17 01:21:41,446 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,460 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,460 (beam_search:476) INFO: -2.86 * 1.0 = -2.86 for ctc +2024-01-17 01:21:41,460 (beam_search:479) INFO: total log probability: -2.86 +2024-01-17 01:21:41,460 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:21:41,460 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,460 (beam_search:483) INFO: best hypo: NASINOLE + +2024-01-17 01:21:41,461 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:41,469 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:41,469 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:41,469 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,499 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,499 (beam_search:476) INFO: -4.52 * 1.0 = -4.52 for ctc +2024-01-17 01:21:41,499 (beam_search:479) INFO: total log probability: -4.52 +2024-01-17 01:21:41,499 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:21:41,499 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,499 (beam_search:483) INFO: best hypo: TRADGICGPORTES + +2024-01-17 01:21:41,500 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:41,508 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:41,508 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:41,508 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,549 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,549 (beam_search:476) INFO: -2.66 * 1.0 = -2.66 for ctc +2024-01-17 01:21:41,549 (beam_search:479) INFO: total log probability: -2.66 +2024-01-17 01:21:41,549 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:21:41,549 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,549 (beam_search:483) INFO: best hypo: TITLEGRICESTATE + +2024-01-17 01:21:41,550 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:41,558 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:41,558 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:41,558 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,591 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,591 (beam_search:476) INFO: -3.40 * 1.0 = -3.40 for ctc +2024-01-17 01:21:41,591 (beam_search:479) INFO: total log probability: -3.40 +2024-01-17 01:21:41,591 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:21:41,591 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,591 (beam_search:483) INFO: best hypo: ASTHENAHADEN + +2024-01-17 01:21:41,592 (asr_inference:494) INFO: speech length: 44160 +2024-01-17 01:21:41,600 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:21:41,600 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:21:41,600 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,657 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,657 (beam_search:476) INFO: -6.38 * 1.0 = -6.38 for ctc +2024-01-17 01:21:41,657 (beam_search:479) INFO: total log probability: -6.38 +2024-01-17 01:21:41,657 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:21:41,657 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,657 (beam_search:483) INFO: best hypo: EASTNYURAPIANCONTRYS + +2024-01-17 01:21:41,658 (asr_inference:494) INFO: speech length: 55680 +2024-01-17 01:21:41,666 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:21:41,666 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:21:41,667 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,762 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,762 (beam_search:476) INFO: -5.58 * 1.0 = -5.58 for ctc +2024-01-17 01:21:41,762 (beam_search:479) INFO: total log probability: -5.58 +2024-01-17 01:21:41,762 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:41,762 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,762 (beam_search:483) INFO: best hypo: CONDEMEDANDOTHRIVEDTANSLATIONS + +2024-01-17 01:21:41,763 (asr_inference:494) INFO: speech length: 19200 +2024-01-17 01:21:41,769 (beam_search:428) INFO: decoder input length: 27 +2024-01-17 01:21:41,769 (beam_search:429) INFO: max output length: 27 +2024-01-17 01:21:41,770 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,786 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,786 (beam_search:476) INFO: -6.55 * 1.0 = -6.55 for ctc +2024-01-17 01:21:41,786 (beam_search:479) INFO: total log probability: -6.55 +2024-01-17 01:21:41,786 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:21:41,786 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,786 (beam_search:483) INFO: best hypo: ALTHWARLEDIS + +2024-01-17 01:21:41,787 (asr_inference:494) INFO: speech length: 48000 +2024-01-17 01:21:41,795 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 01:21:41,795 (beam_search:429) INFO: max output length: 72 +2024-01-17 01:21:41,795 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,856 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,856 (beam_search:476) INFO: -7.72 * 1.0 = -7.72 for ctc +2024-01-17 01:21:41,856 (beam_search:479) INFO: total log probability: -7.72 +2024-01-17 01:21:41,856 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:21:41,856 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,856 (beam_search:483) INFO: best hypo: CINASARMOUNTANLENDUS + +2024-01-17 01:21:41,857 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:41,865 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:41,865 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:41,865 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,892 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,892 (beam_search:476) INFO: -2.05 * 1.0 = -2.05 for ctc +2024-01-17 01:21:41,892 (beam_search:479) INFO: total log probability: -2.05 +2024-01-17 01:21:41,892 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:41,892 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,892 (beam_search:483) INFO: best hypo: NOBLSAMITY + +2024-01-17 01:21:41,893 (asr_inference:494) INFO: speech length: 30720 +2024-01-17 01:21:41,900 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:21:41,900 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:21:41,900 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,929 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,929 (beam_search:476) INFO: -5.70 * 1.0 = -5.70 for ctc +2024-01-17 01:21:41,929 (beam_search:479) INFO: total log probability: -5.70 +2024-01-17 01:21:41,929 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:21:41,929 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,929 (beam_search:483) INFO: best hypo: ANDWODSERFURS + +2024-01-17 01:21:41,930 (asr_inference:494) INFO: speech length: 49920 +2024-01-17 01:21:41,939 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:21:41,939 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:21:41,939 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:41,995 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:41,995 (beam_search:476) INFO: -2.78 * 1.0 = -2.78 for ctc +2024-01-17 01:21:41,995 (beam_search:479) INFO: total log probability: -2.78 +2024-01-17 01:21:41,995 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:21:41,995 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:41,996 (beam_search:483) INFO: best hypo: MOUNTSAINTVINCSANT + +2024-01-17 01:21:41,996 (asr_inference:494) INFO: speech length: 44160 +2024-01-17 01:21:42,004 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:21:42,004 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:21:42,004 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,066 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,066 (beam_search:476) INFO: -6.11 * 1.0 = -6.11 for ctc +2024-01-17 01:21:42,066 (beam_search:479) INFO: total log probability: -6.11 +2024-01-17 01:21:42,067 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:42,067 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,067 (beam_search:483) INFO: best hypo: SITYEMERTRUPOLATONEIROA + +2024-01-17 01:21:42,068 (asr_inference:494) INFO: speech length: 57600 +2024-01-17 01:21:42,077 (beam_search:428) INFO: decoder input length: 87 +2024-01-17 01:21:42,077 (beam_search:429) INFO: max output length: 87 +2024-01-17 01:21:42,077 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,165 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,165 (beam_search:476) INFO: -6.02 * 1.0 = -6.02 for ctc +2024-01-17 01:21:42,165 (beam_search:479) INFO: total log probability: -6.02 +2024-01-17 01:21:42,165 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:21:42,165 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,166 (beam_search:483) INFO: best hypo: ROONERSWHODIEDASCHILDREAN + +2024-01-17 01:21:42,167 (asr_inference:494) INFO: speech length: 38400 +2024-01-17 01:21:42,174 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:21:42,174 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:21:42,174 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,208 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,208 (beam_search:476) INFO: -5.06 * 1.0 = -5.06 for ctc +2024-01-17 01:21:42,208 (beam_search:479) INFO: total log probability: -5.06 +2024-01-17 01:21:42,208 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:21:42,208 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,208 (beam_search:483) INFO: best hypo: CHANESLASVOLE + +2024-01-17 01:21:42,209 (asr_inference:494) INFO: speech length: 38400 +2024-01-17 01:21:42,217 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:21:42,217 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:21:42,217 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,263 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,263 (beam_search:476) INFO: -3.89 * 1.0 = -3.89 for ctc +2024-01-17 01:21:42,263 (beam_search:479) INFO: total log probability: -3.89 +2024-01-17 01:21:42,263 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:42,263 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,263 (beam_search:483) INFO: best hypo: IPEEPECATSINTIERLY + +2024-01-17 01:21:42,264 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:42,272 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:42,272 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:42,272 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,311 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,311 (beam_search:476) INFO: -2.71 * 1.0 = -2.71 for ctc +2024-01-17 01:21:42,311 (beam_search:479) INFO: total log probability: -2.71 +2024-01-17 01:21:42,311 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:21:42,311 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,311 (beam_search:483) INFO: best hypo: CINGEDWARDSDEATH + +2024-01-17 01:21:42,312 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:42,319 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:42,320 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:42,320 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,360 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,360 (beam_search:476) INFO: -2.94 * 1.0 = -2.94 for ctc +2024-01-17 01:21:42,360 (beam_search:479) INFO: total log probability: -2.94 +2024-01-17 01:21:42,360 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:42,360 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,360 (beam_search:483) INFO: best hypo: AMERICARAMRICA + +2024-01-17 01:21:42,361 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:42,369 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:42,369 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:42,369 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,405 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,405 (beam_search:476) INFO: -3.98 * 1.0 = -3.98 for ctc +2024-01-17 01:21:42,405 (beam_search:479) INFO: total log probability: -3.98 +2024-01-17 01:21:42,405 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:42,405 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,405 (beam_search:483) INFO: best hypo: COMERHALSHIPSALED + +2024-01-17 01:21:42,407 (asr_inference:494) INFO: speech length: 38400 +2024-01-17 01:21:42,414 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:21:42,414 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:21:42,414 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,454 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,454 (beam_search:476) INFO: -2.92 * 1.0 = -2.92 for ctc +2024-01-17 01:21:42,455 (beam_search:479) INFO: total log probability: -2.92 +2024-01-17 01:21:42,455 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:21:42,455 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,455 (beam_search:483) INFO: best hypo: PEOPLFROMMENHAM + +2024-01-17 01:21:42,456 (asr_inference:494) INFO: speech length: 30720 +2024-01-17 01:21:42,463 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:21:42,463 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:21:42,463 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,493 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,493 (beam_search:476) INFO: -3.76 * 1.0 = -3.76 for ctc +2024-01-17 01:21:42,493 (beam_search:479) INFO: total log probability: -3.76 +2024-01-17 01:21:42,493 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:42,493 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,493 (beam_search:483) INFO: best hypo: RAIALCRASHCILD + +2024-01-17 01:21:42,494 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:42,502 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:42,502 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:42,502 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,538 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,538 (beam_search:476) INFO: -5.39 * 1.0 = -5.39 for ctc +2024-01-17 01:21:42,538 (beam_search:479) INFO: total log probability: -5.39 +2024-01-17 01:21:42,538 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:42,538 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,538 (beam_search:483) INFO: best hypo: MUTHALDEFANSTOUDY + +2024-01-17 01:21:42,539 (asr_inference:494) INFO: speech length: 28800 +2024-01-17 01:21:42,546 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:21:42,546 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:21:42,546 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,578 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,578 (beam_search:476) INFO: -4.71 * 1.0 = -4.71 for ctc +2024-01-17 01:21:42,578 (beam_search:479) INFO: total log probability: -4.71 +2024-01-17 01:21:42,578 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:42,578 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,578 (beam_search:483) INFO: best hypo: MODENCHILDRUOLES + +2024-01-17 01:21:42,579 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:42,586 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:42,586 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:42,586 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,629 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,629 (beam_search:476) INFO: -3.75 * 1.0 = -3.75 for ctc +2024-01-17 01:21:42,629 (beam_search:479) INFO: total log probability: -3.75 +2024-01-17 01:21:42,629 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:42,629 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,629 (beam_search:483) INFO: best hypo: MOTESERRIFALDEVISION + +2024-01-17 01:21:42,630 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:42,638 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:42,638 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:42,638 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,679 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,679 (beam_search:476) INFO: -6.67 * 1.0 = -6.67 for ctc +2024-01-17 01:21:42,679 (beam_search:479) INFO: total log probability: -6.67 +2024-01-17 01:21:42,679 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:21:42,679 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,679 (beam_search:483) INFO: best hypo: OUSTRALIANEAYFORSE + +2024-01-17 01:21:42,680 (asr_inference:494) INFO: speech length: 42240 +2024-01-17 01:21:42,688 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:21:42,688 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:21:42,688 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,746 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,746 (beam_search:476) INFO: -6.29 * 1.0 = -6.29 for ctc +2024-01-17 01:21:42,746 (beam_search:479) INFO: total log probability: -6.29 +2024-01-17 01:21:42,746 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:42,746 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,746 (beam_search:483) INFO: best hypo: AMERYKCANMISTRYRHITERS + +2024-01-17 01:21:42,747 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:42,755 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:42,755 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:42,755 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,800 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,800 (beam_search:476) INFO: -4.84 * 1.0 = -4.84 for ctc +2024-01-17 01:21:42,800 (beam_search:479) INFO: total log probability: -4.84 +2024-01-17 01:21:42,800 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:42,800 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,800 (beam_search:483) INFO: best hypo: FINTHEYGROUNDGREFIHTE + +2024-01-17 01:21:42,802 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:42,809 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:42,809 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:42,809 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,855 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,855 (beam_search:476) INFO: -5.66 * 1.0 = -5.66 for ctc +2024-01-17 01:21:42,855 (beam_search:479) INFO: total log probability: -5.66 +2024-01-17 01:21:42,855 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:21:42,855 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,855 (beam_search:483) INFO: best hypo: WILTEMPINSOFMANTCHE + +2024-01-17 01:21:42,856 (asr_inference:494) INFO: speech length: 24960 +2024-01-17 01:21:42,863 (beam_search:428) INFO: decoder input length: 36 +2024-01-17 01:21:42,863 (beam_search:429) INFO: max output length: 36 +2024-01-17 01:21:42,863 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,878 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,879 (beam_search:476) INFO: -2.52 * 1.0 = -2.52 for ctc +2024-01-17 01:21:42,879 (beam_search:479) INFO: total log probability: -2.52 +2024-01-17 01:21:42,879 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:21:42,879 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,879 (beam_search:483) INFO: best hypo: CARILINA + +2024-01-17 01:21:42,880 (asr_inference:494) INFO: speech length: 38400 +2024-01-17 01:21:42,887 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:21:42,887 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:21:42,887 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,928 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,928 (beam_search:476) INFO: -3.74 * 1.0 = -3.74 for ctc +2024-01-17 01:21:42,928 (beam_search:479) INFO: total log probability: -3.74 +2024-01-17 01:21:42,928 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:42,928 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,928 (beam_search:483) INFO: best hypo: MYBATHINOPERATES + +2024-01-17 01:21:42,929 (asr_inference:494) INFO: speech length: 23040 +2024-01-17 01:21:42,936 (beam_search:428) INFO: decoder input length: 33 +2024-01-17 01:21:42,936 (beam_search:429) INFO: max output length: 33 +2024-01-17 01:21:42,936 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,958 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,958 (beam_search:476) INFO: -3.68 * 1.0 = -3.68 for ctc +2024-01-17 01:21:42,958 (beam_search:479) INFO: total log probability: -3.68 +2024-01-17 01:21:42,958 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:42,958 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,959 (beam_search:483) INFO: best hypo: COURTSVERITIES + +2024-01-17 01:21:42,960 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:42,967 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:42,967 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:42,967 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:42,986 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:42,986 (beam_search:476) INFO: -1.69 * 1.0 = -1.69 for ctc +2024-01-17 01:21:42,986 (beam_search:479) INFO: total log probability: -1.69 +2024-01-17 01:21:42,986 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:42,986 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:42,986 (beam_search:483) INFO: best hypo: MADRAND + +2024-01-17 01:21:42,987 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:42,994 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:42,994 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:42,994 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:43,037 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:43,037 (beam_search:476) INFO: -4.60 * 1.0 = -4.60 for ctc +2024-01-17 01:21:43,037 (beam_search:479) INFO: total log probability: -4.60 +2024-01-17 01:21:43,037 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:43,037 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:43,037 (beam_search:483) INFO: best hypo: CASELITHALEREACTIONS + +# Accounting: time=151 threads=1 +# Ended (code 0) at Wed Jan 17 01:21:43 CST 2024, elapsed time 151 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.3.log b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.3.log new file mode 100644 index 0000000000000000000000000000000000000000..0dd4f021be8651d9b43913a6cb60d059419c64df --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.3.log @@ -0,0 +1,3022 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.3 --config conf/decode_asr.yaml +# Started at Wed Jan 17 01:21:43 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.3 --config conf/decode_asr.yaml +2024-01-17 01:21:44,850 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml +2024-01-17 01:21:44,868 (asr:523) INFO: Vocabulary size: 30 +2024-01-17 01:21:44,930 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 01:21:44,930 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 01:21:45,040 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 01:21:46,337 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 01:21:47,604 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 01:21:47,604 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 01:21:47,604 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 01:21:47,637 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-17 01:21:47,712 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 01:21:47,825 (asr_inference:494) INFO: speech length: 38400 +2024-01-17 01:21:49,037 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:21:49,037 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:21:49,037 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:49,082 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:49,082 (beam_search:476) INFO: -7.51 * 1.0 = -7.51 for ctc +2024-01-17 01:21:49,082 (beam_search:479) INFO: total log probability: -7.51 +2024-01-17 01:21:49,082 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:21:49,082 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:49,082 (beam_search:483) INFO: best hypo: INGLAISHPESCIFOUSTS + +2024-01-17 01:21:49,106 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 01:21:49,116 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:21:49,116 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:21:49,116 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:49,177 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:49,177 (beam_search:476) INFO: -3.59 * 1.0 = -3.59 for ctc +2024-01-17 01:21:49,177 (beam_search:479) INFO: total log probability: -3.59 +2024-01-17 01:21:49,177 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:21:49,177 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:49,177 (beam_search:483) INFO: best hypo: YOUNITEDSTATESFEDERAL + +2024-01-17 01:21:49,178 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:49,186 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:49,186 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:49,186 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:49,227 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:49,227 (beam_search:476) INFO: -4.12 * 1.0 = -4.12 for ctc +2024-01-17 01:21:49,227 (beam_search:479) INFO: total log probability: -4.12 +2024-01-17 01:21:49,227 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:49,227 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:49,227 (beam_search:483) INFO: best hypo: FADRALDRESEROVEACT + +2024-01-17 01:21:49,229 (asr_inference:494) INFO: speech length: 28800 +2024-01-17 01:21:49,236 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:21:49,236 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:21:49,236 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:49,269 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:49,269 (beam_search:476) INFO: -4.30 * 1.0 = -4.30 for ctc +2024-01-17 01:21:49,269 (beam_search:479) INFO: total log probability: -4.30 +2024-01-17 01:21:49,269 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:49,269 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:49,269 (beam_search:483) INFO: best hypo: WILYMHINRYHERASON + +2024-01-17 01:21:49,271 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:49,278 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:49,278 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:49,278 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:49,308 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:49,308 (beam_search:476) INFO: -2.35 * 1.0 = -2.35 for ctc +2024-01-17 01:21:49,308 (beam_search:479) INFO: total log probability: -2.35 +2024-01-17 01:21:49,308 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:21:49,308 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:49,308 (beam_search:483) INFO: best hypo: CLAPPLAYCHOT + +2024-01-17 01:21:49,309 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 01:21:49,317 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:21:49,317 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:21:49,317 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:49,377 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:49,377 (beam_search:476) INFO: -8.77 * 1.0 = -8.77 for ctc +2024-01-17 01:21:49,378 (beam_search:479) INFO: total log probability: -8.77 +2024-01-17 01:21:49,378 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:21:49,378 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:49,378 (beam_search:483) INFO: best hypo: PASSONGERRALESOVICSES + +2024-01-17 01:21:49,379 (asr_inference:494) INFO: speech length: 49920 +2024-01-17 01:21:49,387 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:21:49,387 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:21:49,387 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:49,465 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:49,465 (beam_search:476) INFO: -8.50 * 1.0 = -8.50 for ctc +2024-01-17 01:21:49,465 (beam_search:479) INFO: total log probability: -8.50 +2024-01-17 01:21:49,465 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:21:49,465 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:49,465 (beam_search:483) INFO: best hypo: ANCHANMESSADORNTHIONJENRALS + +2024-01-17 01:21:49,467 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:49,474 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:49,475 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:49,475 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:49,517 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:49,517 (beam_search:476) INFO: -5.44 * 1.0 = -5.44 for ctc +2024-01-17 01:21:49,517 (beam_search:479) INFO: total log probability: -5.44 +2024-01-17 01:21:49,517 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:49,517 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:49,517 (beam_search:483) INFO: best hypo: CONGACTIONDSENTAMA + +2024-01-17 01:21:49,518 (asr_inference:494) INFO: speech length: 44160 +2024-01-17 01:21:49,527 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:21:49,527 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:21:49,527 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:49,590 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:49,590 (beam_search:476) INFO: -6.24 * 1.0 = -6.24 for ctc +2024-01-17 01:21:49,590 (beam_search:479) INFO: total log probability: -6.24 +2024-01-17 01:21:49,590 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:49,590 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:49,590 (beam_search:483) INFO: best hypo: GUNPOUDEAPRPILENTYOUSED + +2024-01-17 01:21:49,591 (asr_inference:494) INFO: speech length: 38400 +2024-01-17 01:21:49,598 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:21:49,599 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:21:49,599 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:49,641 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:49,641 (beam_search:476) INFO: -4.62 * 1.0 = -4.62 for ctc +2024-01-17 01:21:49,641 (beam_search:479) INFO: total log probability: -4.62 +2024-01-17 01:21:49,641 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:49,641 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:49,642 (beam_search:483) INFO: best hypo: LOWSTINAGESTAIGT + +2024-01-17 01:21:49,643 (asr_inference:494) INFO: speech length: 24960 +2024-01-17 01:21:49,650 (beam_search:428) INFO: decoder input length: 36 +2024-01-17 01:21:49,650 (beam_search:429) INFO: max output length: 36 +2024-01-17 01:21:49,650 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:49,672 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:49,672 (beam_search:476) INFO: -2.17 * 1.0 = -2.17 for ctc +2024-01-17 01:21:49,672 (beam_search:479) INFO: total log probability: -2.17 +2024-01-17 01:21:49,672 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:21:49,672 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:49,672 (beam_search:483) INFO: best hypo: CALNDERYUROS + +2024-01-17 01:21:49,673 (asr_inference:494) INFO: speech length: 72960 +2024-01-17 01:21:49,683 (beam_search:428) INFO: decoder input length: 111 +2024-01-17 01:21:49,683 (beam_search:429) INFO: max output length: 111 +2024-01-17 01:21:49,683 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:49,792 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:49,792 (beam_search:476) INFO: -9.57 * 1.0 = -9.57 for ctc +2024-01-17 01:21:49,792 (beam_search:479) INFO: total log probability: -9.57 +2024-01-17 01:21:49,792 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:21:49,792 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:49,792 (beam_search:483) INFO: best hypo: MAGJERINTONASINALEEAPORTES + +2024-01-17 01:21:49,794 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:49,801 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:49,801 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:49,801 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:49,837 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:49,837 (beam_search:476) INFO: -3.59 * 1.0 = -3.59 for ctc +2024-01-17 01:21:49,837 (beam_search:479) INFO: total log probability: -3.59 +2024-01-17 01:21:49,837 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:21:49,837 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:49,837 (beam_search:483) INFO: best hypo: TOTLFORSEACTIM + +2024-01-17 01:21:49,839 (asr_inference:494) INFO: speech length: 42240 +2024-01-17 01:21:49,847 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:21:49,847 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:21:49,847 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:49,903 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:49,903 (beam_search:476) INFO: -4.32 * 1.0 = -4.32 for ctc +2024-01-17 01:21:49,903 (beam_search:479) INFO: total log probability: -4.32 +2024-01-17 01:21:49,903 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:49,903 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:49,904 (beam_search:483) INFO: best hypo: LOSTLESSDATECOMPRITION + +2024-01-17 01:21:49,905 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:49,912 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:49,912 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:49,912 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:49,934 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:49,934 (beam_search:476) INFO: -6.74 * 1.0 = -6.74 for ctc +2024-01-17 01:21:49,934 (beam_search:479) INFO: total log probability: -6.74 +2024-01-17 01:21:49,934 (beam_search:480) INFO: normalized log probability: -0.56 +2024-01-17 01:21:49,934 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:49,934 (beam_search:483) INFO: best hypo: AGREAEKHA + +2024-01-17 01:21:49,935 (asr_inference:494) INFO: speech length: 48000 +2024-01-17 01:21:49,943 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 01:21:49,943 (beam_search:429) INFO: max output length: 72 +2024-01-17 01:21:49,943 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:50,023 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:50,023 (beam_search:476) INFO: -9.28 * 1.0 = -9.28 for ctc +2024-01-17 01:21:50,023 (beam_search:479) INFO: total log probability: -9.28 +2024-01-17 01:21:50,024 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:21:50,024 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:50,024 (beam_search:483) INFO: best hypo: INVORMNTALPROTICTIONADGANCSE + +2024-01-17 01:21:50,025 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:50,032 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:50,032 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:50,032 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:50,073 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:50,073 (beam_search:476) INFO: -4.81 * 1.0 = -4.81 for ctc +2024-01-17 01:21:50,073 (beam_search:479) INFO: total log probability: -4.81 +2024-01-17 01:21:50,073 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:21:50,074 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:50,074 (beam_search:483) INFO: best hypo: MANYTOBISCALSGRITION + +2024-01-17 01:21:50,075 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 01:21:50,083 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:21:50,083 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:21:50,083 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:50,142 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:50,142 (beam_search:476) INFO: -5.08 * 1.0 = -5.08 for ctc +2024-01-17 01:21:50,142 (beam_search:479) INFO: total log probability: -5.08 +2024-01-17 01:21:50,142 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:50,142 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:50,142 (beam_search:483) INFO: best hypo: ANCHOANSITYPITHUNDER + +2024-01-17 01:21:50,143 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:50,151 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:50,151 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:50,151 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:50,196 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:50,196 (beam_search:476) INFO: -4.87 * 1.0 = -4.87 for ctc +2024-01-17 01:21:50,196 (beam_search:479) INFO: total log probability: -4.87 +2024-01-17 01:21:50,196 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:50,196 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:50,196 (beam_search:483) INFO: best hypo: SMALOTHEDACKSINAGOG + +2024-01-17 01:21:50,198 (asr_inference:494) INFO: speech length: 61440 +2024-01-17 01:21:50,207 (beam_search:428) INFO: decoder input length: 93 +2024-01-17 01:21:50,207 (beam_search:429) INFO: max output length: 93 +2024-01-17 01:21:50,207 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:50,287 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:50,287 (beam_search:476) INFO: -4.66 * 1.0 = -4.66 for ctc +2024-01-17 01:21:50,287 (beam_search:479) INFO: total log probability: -4.66 +2024-01-17 01:21:50,287 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:50,287 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:50,287 (beam_search:483) INFO: best hypo: LODGESMTRPILIANERIARS + +2024-01-17 01:21:50,288 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 01:21:50,297 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:21:50,297 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:21:50,297 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:50,355 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:50,355 (beam_search:476) INFO: -7.68 * 1.0 = -7.68 for ctc +2024-01-17 01:21:50,355 (beam_search:479) INFO: total log probability: -7.68 +2024-01-17 01:21:50,355 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:21:50,355 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:50,355 (beam_search:483) INFO: best hypo: TITALRELIGYEOREMONO + +2024-01-17 01:21:50,356 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:50,364 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:50,364 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:50,364 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:50,409 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:50,409 (beam_search:476) INFO: -6.00 * 1.0 = -6.00 for ctc +2024-01-17 01:21:50,409 (beam_search:479) INFO: total log probability: -6.00 +2024-01-17 01:21:50,410 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:50,410 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:50,410 (beam_search:483) INFO: best hypo: EGAMPLESANDCLUDHAFMON + +2024-01-17 01:21:50,411 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:50,418 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:50,418 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:50,418 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:50,461 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:50,461 (beam_search:476) INFO: -7.18 * 1.0 = -7.18 for ctc +2024-01-17 01:21:50,461 (beam_search:479) INFO: total log probability: -7.18 +2024-01-17 01:21:50,461 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:21:50,461 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:50,462 (beam_search:483) INFO: best hypo: YUNOTIDSTATEMANTAINE + +2024-01-17 01:21:50,463 (asr_inference:494) INFO: speech length: 44160 +2024-01-17 01:21:50,471 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:21:50,471 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:21:50,471 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:50,530 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:50,530 (beam_search:476) INFO: -6.63 * 1.0 = -6.63 for ctc +2024-01-17 01:21:50,530 (beam_search:479) INFO: total log probability: -6.63 +2024-01-17 01:21:50,530 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:21:50,530 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:50,530 (beam_search:483) INFO: best hypo: BOLDREPRESENCEMEAXSIMA + +2024-01-17 01:21:50,531 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:50,539 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:50,539 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:50,539 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:50,577 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:50,577 (beam_search:476) INFO: -5.78 * 1.0 = -5.78 for ctc +2024-01-17 01:21:50,577 (beam_search:479) INFO: total log probability: -5.78 +2024-01-17 01:21:50,577 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:21:50,577 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:50,577 (beam_search:483) INFO: best hypo: SINESFCIONORTHES + +2024-01-17 01:21:50,578 (asr_inference:494) INFO: speech length: 42240 +2024-01-17 01:21:50,586 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:21:50,586 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:21:50,586 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:50,650 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:50,650 (beam_search:476) INFO: -6.97 * 1.0 = -6.97 for ctc +2024-01-17 01:21:50,650 (beam_search:479) INFO: total log probability: -6.97 +2024-01-17 01:21:50,650 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:50,650 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:50,650 (beam_search:483) INFO: best hypo: ORDNARYDIFRENSHALECUATIONS + +2024-01-17 01:21:50,651 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:50,659 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:50,659 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:50,659 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:50,714 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:50,715 (beam_search:476) INFO: -6.28 * 1.0 = -6.28 for ctc +2024-01-17 01:21:50,715 (beam_search:479) INFO: total log probability: -6.28 +2024-01-17 01:21:50,715 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:50,715 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:50,715 (beam_search:483) INFO: best hypo: DIPLMATSOFTHEHRDYESE + +2024-01-17 01:21:50,716 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:50,723 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:50,723 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:50,723 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:50,760 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:50,760 (beam_search:476) INFO: -3.84 * 1.0 = -3.84 for ctc +2024-01-17 01:21:50,760 (beam_search:479) INFO: total log probability: -3.84 +2024-01-17 01:21:50,761 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:50,761 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:50,761 (beam_search:483) INFO: best hypo: SIRIALCLOMMISTRY + +2024-01-17 01:21:50,762 (asr_inference:494) INFO: speech length: 21120 +2024-01-17 01:21:50,769 (beam_search:428) INFO: decoder input length: 30 +2024-01-17 01:21:50,769 (beam_search:429) INFO: max output length: 30 +2024-01-17 01:21:50,769 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:50,790 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:50,790 (beam_search:476) INFO: -4.99 * 1.0 = -4.99 for ctc +2024-01-17 01:21:50,790 (beam_search:479) INFO: total log probability: -4.99 +2024-01-17 01:21:50,790 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:21:50,790 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:50,790 (beam_search:483) INFO: best hypo: URELMEITRYCOL + +2024-01-17 01:21:50,791 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:50,798 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:50,799 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:50,799 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:50,842 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:50,842 (beam_search:476) INFO: -4.84 * 1.0 = -4.84 for ctc +2024-01-17 01:21:50,842 (beam_search:479) INFO: total log probability: -4.84 +2024-01-17 01:21:50,842 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:50,842 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:50,843 (beam_search:483) INFO: best hypo: SLOWLYLEDSSCIALISOM + +2024-01-17 01:21:50,844 (asr_inference:494) INFO: speech length: 19200 +2024-01-17 01:21:50,850 (beam_search:428) INFO: decoder input length: 27 +2024-01-17 01:21:50,850 (beam_search:429) INFO: max output length: 27 +2024-01-17 01:21:50,850 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:50,862 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:50,862 (beam_search:476) INFO: -1.30 * 1.0 = -1.30 for ctc +2024-01-17 01:21:50,862 (beam_search:479) INFO: total log probability: -1.30 +2024-01-17 01:21:50,862 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:21:50,862 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:50,862 (beam_search:483) INFO: best hypo: PRINTES + +2024-01-17 01:21:50,863 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:50,871 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:50,871 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:50,871 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:50,911 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:50,911 (beam_search:476) INFO: -5.60 * 1.0 = -5.60 for ctc +2024-01-17 01:21:50,911 (beam_search:479) INFO: total log probability: -5.60 +2024-01-17 01:21:50,911 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:21:50,911 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:50,912 (beam_search:483) INFO: best hypo: NEUTASTHENPEOPL + +2024-01-17 01:21:50,913 (asr_inference:494) INFO: speech length: 59520 +2024-01-17 01:21:50,922 (beam_search:428) INFO: decoder input length: 90 +2024-01-17 01:21:50,922 (beam_search:429) INFO: max output length: 90 +2024-01-17 01:21:50,922 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,026 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,026 (beam_search:476) INFO: -7.69 * 1.0 = -7.69 for ctc +2024-01-17 01:21:51,026 (beam_search:479) INFO: total log probability: -7.69 +2024-01-17 01:21:51,026 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:51,026 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,026 (beam_search:483) INFO: best hypo: SMATCOARDBACETDILECTRONICKPERS + +2024-01-17 01:21:51,027 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:51,034 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:51,034 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:51,034 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,072 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,072 (beam_search:476) INFO: -3.24 * 1.0 = -3.24 for ctc +2024-01-17 01:21:51,072 (beam_search:479) INFO: total log probability: -3.24 +2024-01-17 01:21:51,072 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:51,072 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,072 (beam_search:483) INFO: best hypo: STATEARMYSOLDGERS + +2024-01-17 01:21:51,073 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:51,080 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:51,080 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:51,080 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,111 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,111 (beam_search:476) INFO: -3.38 * 1.0 = -3.38 for ctc +2024-01-17 01:21:51,111 (beam_search:479) INFO: total log probability: -3.38 +2024-01-17 01:21:51,111 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:51,111 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,111 (beam_search:483) INFO: best hypo: LORDJESCRIST + +2024-01-17 01:21:51,112 (asr_inference:494) INFO: speech length: 23040 +2024-01-17 01:21:51,119 (beam_search:428) INFO: decoder input length: 33 +2024-01-17 01:21:51,119 (beam_search:429) INFO: max output length: 33 +2024-01-17 01:21:51,119 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,136 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,136 (beam_search:476) INFO: -2.76 * 1.0 = -2.76 for ctc +2024-01-17 01:21:51,136 (beam_search:479) INFO: total log probability: -2.76 +2024-01-17 01:21:51,136 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:51,136 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,136 (beam_search:483) INFO: best hypo: LADANBIG + +2024-01-17 01:21:51,137 (asr_inference:494) INFO: speech length: 30720 +2024-01-17 01:21:51,145 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:21:51,145 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:21:51,145 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,181 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,181 (beam_search:476) INFO: -6.91 * 1.0 = -6.91 for ctc +2024-01-17 01:21:51,181 (beam_search:479) INFO: total log probability: -6.91 +2024-01-17 01:21:51,181 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:21:51,181 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,181 (beam_search:483) INFO: best hypo: BETAELIANNESINALTEM + +2024-01-17 01:21:51,183 (asr_inference:494) INFO: speech length: 51840 +2024-01-17 01:21:51,191 (beam_search:428) INFO: decoder input length: 78 +2024-01-17 01:21:51,191 (beam_search:429) INFO: max output length: 78 +2024-01-17 01:21:51,191 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,277 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,277 (beam_search:476) INFO: -5.69 * 1.0 = -5.69 for ctc +2024-01-17 01:21:51,277 (beam_search:479) INFO: total log probability: -5.69 +2024-01-17 01:21:51,277 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:51,277 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,277 (beam_search:483) INFO: best hypo: ANDTEAGERRECRIATIONGROUNDTHUM + +2024-01-17 01:21:51,278 (asr_inference:494) INFO: speech length: 44160 +2024-01-17 01:21:51,286 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:21:51,286 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:21:51,286 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,338 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,338 (beam_search:476) INFO: -2.97 * 1.0 = -2.97 for ctc +2024-01-17 01:21:51,338 (beam_search:479) INFO: total log probability: -2.97 +2024-01-17 01:21:51,338 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:21:51,338 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,338 (beam_search:483) INFO: best hypo: GROSESSTATEPRODECT + +2024-01-17 01:21:51,339 (asr_inference:494) INFO: speech length: 24960 +2024-01-17 01:21:51,346 (beam_search:428) INFO: decoder input length: 36 +2024-01-17 01:21:51,346 (beam_search:429) INFO: max output length: 36 +2024-01-17 01:21:51,346 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,369 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,369 (beam_search:476) INFO: -4.39 * 1.0 = -4.39 for ctc +2024-01-17 01:21:51,369 (beam_search:479) INFO: total log probability: -4.39 +2024-01-17 01:21:51,369 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:21:51,369 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,369 (beam_search:483) INFO: best hypo: KINGCONGVERSE + +2024-01-17 01:21:51,370 (asr_inference:494) INFO: speech length: 26880 +2024-01-17 01:21:51,377 (beam_search:428) INFO: decoder input length: 39 +2024-01-17 01:21:51,377 (beam_search:429) INFO: max output length: 39 +2024-01-17 01:21:51,377 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,392 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,392 (beam_search:476) INFO: -7.03 * 1.0 = -7.03 for ctc +2024-01-17 01:21:51,392 (beam_search:479) INFO: total log probability: -7.03 +2024-01-17 01:21:51,392 (beam_search:480) INFO: normalized log probability: -0.70 +2024-01-17 01:21:51,392 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,392 (beam_search:483) INFO: best hypo: BELVAEL + +2024-01-17 01:21:51,393 (asr_inference:494) INFO: speech length: 49920 +2024-01-17 01:21:51,401 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:21:51,401 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:21:51,401 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,490 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,490 (beam_search:476) INFO: -11.60 * 1.0 = -11.60 for ctc +2024-01-17 01:21:51,490 (beam_search:479) INFO: total log probability: -11.60 +2024-01-17 01:21:51,490 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:21:51,490 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,491 (beam_search:483) INFO: best hypo: FLEOLGONISATIONSTHEUNIDEDSTATES + +2024-01-17 01:21:51,492 (asr_inference:494) INFO: speech length: 30720 +2024-01-17 01:21:51,499 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:21:51,499 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:21:51,499 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,536 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,536 (beam_search:476) INFO: -8.25 * 1.0 = -8.25 for ctc +2024-01-17 01:21:51,536 (beam_search:479) INFO: total log probability: -8.25 +2024-01-17 01:21:51,536 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:21:51,536 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,536 (beam_search:483) INFO: best hypo: YSRILTHEFENSFORSES + +2024-01-17 01:21:51,538 (asr_inference:494) INFO: speech length: 51840 +2024-01-17 01:21:51,546 (beam_search:428) INFO: decoder input length: 78 +2024-01-17 01:21:51,546 (beam_search:429) INFO: max output length: 78 +2024-01-17 01:21:51,546 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,611 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,611 (beam_search:476) INFO: -6.27 * 1.0 = -6.27 for ctc +2024-01-17 01:21:51,611 (beam_search:479) INFO: total log probability: -6.27 +2024-01-17 01:21:51,611 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:21:51,611 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,611 (beam_search:483) INFO: best hypo: ORDRMTICKSANDRESEVE + +2024-01-17 01:21:51,612 (asr_inference:494) INFO: speech length: 38400 +2024-01-17 01:21:51,619 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:21:51,619 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:21:51,620 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,667 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,667 (beam_search:476) INFO: -5.12 * 1.0 = -5.12 for ctc +2024-01-17 01:21:51,667 (beam_search:479) INFO: total log probability: -5.12 +2024-01-17 01:21:51,667 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:51,667 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,668 (beam_search:483) INFO: best hypo: BRNDSWICKSETHNRALWAY + +2024-01-17 01:21:51,669 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:51,676 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:51,676 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:51,676 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,713 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,713 (beam_search:476) INFO: -5.34 * 1.0 = -5.34 for ctc +2024-01-17 01:21:51,713 (beam_search:479) INFO: total log probability: -5.34 +2024-01-17 01:21:51,713 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:51,713 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,713 (beam_search:483) INFO: best hypo: ACTRSACATDIMIAWOD + +2024-01-17 01:21:51,714 (asr_inference:494) INFO: speech length: 59520 +2024-01-17 01:21:51,723 (beam_search:428) INFO: decoder input length: 90 +2024-01-17 01:21:51,723 (beam_search:429) INFO: max output length: 90 +2024-01-17 01:21:51,723 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,789 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,789 (beam_search:476) INFO: -6.16 * 1.0 = -6.16 for ctc +2024-01-17 01:21:51,789 (beam_search:479) INFO: total log probability: -6.16 +2024-01-17 01:21:51,789 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:21:51,789 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,789 (beam_search:483) INFO: best hypo: PEPLFROMETOCKIATD + +2024-01-17 01:21:51,790 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:51,798 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:51,798 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:51,798 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,837 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,837 (beam_search:476) INFO: -3.20 * 1.0 = -3.20 for ctc +2024-01-17 01:21:51,837 (beam_search:479) INFO: total log probability: -3.20 +2024-01-17 01:21:51,837 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:51,837 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,838 (beam_search:483) INFO: best hypo: FORCHALDSSINGA + +2024-01-17 01:21:51,839 (asr_inference:494) INFO: speech length: 44160 +2024-01-17 01:21:51,847 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:21:51,847 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:21:51,847 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,903 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,903 (beam_search:476) INFO: -8.43 * 1.0 = -8.43 for ctc +2024-01-17 01:21:51,903 (beam_search:479) INFO: total log probability: -8.43 +2024-01-17 01:21:51,903 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:21:51,903 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,903 (beam_search:483) INFO: best hypo: VEARYIABLVLFHTARMING + +2024-01-17 01:21:51,904 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:51,911 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:51,911 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:51,911 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:51,949 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:51,949 (beam_search:476) INFO: -4.17 * 1.0 = -4.17 for ctc +2024-01-17 01:21:51,949 (beam_search:479) INFO: total log probability: -4.17 +2024-01-17 01:21:51,949 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:51,949 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:51,949 (beam_search:483) INFO: best hypo: SOUTHWALESVELYES + +2024-01-17 01:21:51,950 (asr_inference:494) INFO: speech length: 42240 +2024-01-17 01:21:51,958 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:21:51,958 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:21:51,958 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,026 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,026 (beam_search:476) INFO: -9.96 * 1.0 = -9.96 for ctc +2024-01-17 01:21:52,026 (beam_search:479) INFO: total log probability: -9.96 +2024-01-17 01:21:52,026 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:21:52,026 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,026 (beam_search:483) INFO: best hypo: CALIFORDYEASTATYUTHEVERSITY + +2024-01-17 01:21:52,027 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:52,034 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:52,034 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:52,034 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,055 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,055 (beam_search:476) INFO: -3.27 * 1.0 = -3.27 for ctc +2024-01-17 01:21:52,055 (beam_search:479) INFO: total log probability: -3.27 +2024-01-17 01:21:52,055 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:21:52,055 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,055 (beam_search:483) INFO: best hypo: ELDERODO + +2024-01-17 01:21:52,056 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:52,064 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:52,064 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:52,064 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,115 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,115 (beam_search:476) INFO: -3.37 * 1.0 = -3.37 for ctc +2024-01-17 01:21:52,115 (beam_search:479) INFO: total log probability: -3.37 +2024-01-17 01:21:52,115 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:21:52,115 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,115 (beam_search:483) INFO: best hypo: OUTDOREORINTEDSITY + +2024-01-17 01:21:52,116 (asr_inference:494) INFO: speech length: 49920 +2024-01-17 01:21:52,124 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:21:52,125 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:21:52,125 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,206 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,206 (beam_search:476) INFO: -6.10 * 1.0 = -6.10 for ctc +2024-01-17 01:21:52,206 (beam_search:479) INFO: total log probability: -6.10 +2024-01-17 01:21:52,206 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:52,206 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,206 (beam_search:483) INFO: best hypo: CLAMEDPARSHALRESPONCSABILITY + +2024-01-17 01:21:52,208 (asr_inference:494) INFO: speech length: 28800 +2024-01-17 01:21:52,215 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:21:52,215 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:21:52,215 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,242 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,242 (beam_search:476) INFO: -3.66 * 1.0 = -3.66 for ctc +2024-01-17 01:21:52,242 (beam_search:479) INFO: total log probability: -3.66 +2024-01-17 01:21:52,242 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:52,242 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,242 (beam_search:483) INFO: best hypo: CRISTHONTERMS + +2024-01-17 01:21:52,243 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:52,250 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:52,250 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:52,250 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,281 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,282 (beam_search:476) INFO: -2.86 * 1.0 = -2.86 for ctc +2024-01-17 01:21:52,282 (beam_search:479) INFO: total log probability: -2.86 +2024-01-17 01:21:52,282 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:52,282 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,282 (beam_search:483) INFO: best hypo: EVENSTOKPLACE + +2024-01-17 01:21:52,283 (asr_inference:494) INFO: speech length: 38400 +2024-01-17 01:21:52,290 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:21:52,290 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:21:52,290 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,344 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,344 (beam_search:476) INFO: -5.99 * 1.0 = -5.99 for ctc +2024-01-17 01:21:52,344 (beam_search:479) INFO: total log probability: -5.99 +2024-01-17 01:21:52,344 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:52,344 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,344 (beam_search:483) INFO: best hypo: CENSSARDDITHSINFRONCE + +2024-01-17 01:21:52,345 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:52,352 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:52,352 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:52,352 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,389 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,389 (beam_search:476) INFO: -2.46 * 1.0 = -2.46 for ctc +2024-01-17 01:21:52,389 (beam_search:479) INFO: total log probability: -2.46 +2024-01-17 01:21:52,389 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:21:52,389 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,389 (beam_search:483) INFO: best hypo: HISTRYOFMIHAGAN + +2024-01-17 01:21:52,390 (asr_inference:494) INFO: speech length: 30720 +2024-01-17 01:21:52,397 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:21:52,397 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:21:52,397 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,428 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,428 (beam_search:476) INFO: -2.34 * 1.0 = -2.34 for ctc +2024-01-17 01:21:52,428 (beam_search:479) INFO: total log probability: -2.34 +2024-01-17 01:21:52,428 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:21:52,428 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,428 (beam_search:483) INFO: best hypo: ARIGINLYTHENAM + +2024-01-17 01:21:52,429 (asr_inference:494) INFO: speech length: 42240 +2024-01-17 01:21:52,437 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:21:52,437 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:21:52,437 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,497 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,497 (beam_search:476) INFO: -6.08 * 1.0 = -6.08 for ctc +2024-01-17 01:21:52,497 (beam_search:479) INFO: total log probability: -6.08 +2024-01-17 01:21:52,497 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:52,497 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,497 (beam_search:483) INFO: best hypo: NTIONSFRAIMEWRCONVENTION + +2024-01-17 01:21:52,498 (asr_inference:494) INFO: speech length: 26880 +2024-01-17 01:21:52,505 (beam_search:428) INFO: decoder input length: 39 +2024-01-17 01:21:52,505 (beam_search:429) INFO: max output length: 39 +2024-01-17 01:21:52,505 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,520 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,520 (beam_search:476) INFO: -3.53 * 1.0 = -3.53 for ctc +2024-01-17 01:21:52,520 (beam_search:479) INFO: total log probability: -3.53 +2024-01-17 01:21:52,520 (beam_search:480) INFO: normalized log probability: -0.35 +2024-01-17 01:21:52,520 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,520 (beam_search:483) INFO: best hypo: NOCALE + +2024-01-17 01:21:52,521 (asr_inference:494) INFO: speech length: 42240 +2024-01-17 01:21:52,529 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:21:52,529 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:21:52,529 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,589 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,589 (beam_search:476) INFO: -7.33 * 1.0 = -7.33 for ctc +2024-01-17 01:21:52,589 (beam_search:479) INFO: total log probability: -7.33 +2024-01-17 01:21:52,589 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:21:52,589 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,589 (beam_search:483) INFO: best hypo: OLSTRIANSCOLEECANOMISTS + +2024-01-17 01:21:52,591 (asr_inference:494) INFO: speech length: 42240 +2024-01-17 01:21:52,599 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:21:52,599 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:21:52,599 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,650 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,650 (beam_search:476) INFO: -6.60 * 1.0 = -6.60 for ctc +2024-01-17 01:21:52,650 (beam_search:479) INFO: total log probability: -6.60 +2024-01-17 01:21:52,650 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:21:52,650 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,650 (beam_search:483) INFO: best hypo: MANGRUOPCOMPOUWNDS + +2024-01-17 01:21:52,651 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:52,658 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:52,659 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:52,659 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,696 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,696 (beam_search:476) INFO: -5.09 * 1.0 = -5.09 for ctc +2024-01-17 01:21:52,696 (beam_search:479) INFO: total log probability: -5.09 +2024-01-17 01:21:52,696 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:52,696 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,696 (beam_search:483) INFO: best hypo: RESICLIBLMTERIALS + +2024-01-17 01:21:52,698 (asr_inference:494) INFO: speech length: 28800 +2024-01-17 01:21:52,705 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:21:52,705 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:21:52,705 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,737 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,737 (beam_search:476) INFO: -5.30 * 1.0 = -5.30 for ctc +2024-01-17 01:21:52,737 (beam_search:479) INFO: total log probability: -5.30 +2024-01-17 01:21:52,737 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:21:52,737 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,737 (beam_search:483) INFO: best hypo: COMINLARESESTOM + +2024-01-17 01:21:52,738 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:52,746 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:52,746 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:52,746 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,780 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,780 (beam_search:476) INFO: -5.70 * 1.0 = -5.70 for ctc +2024-01-17 01:21:52,781 (beam_search:479) INFO: total log probability: -5.70 +2024-01-17 01:21:52,781 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:21:52,781 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,781 (beam_search:483) INFO: best hypo: BRONGKSHISCUL + +2024-01-17 01:21:52,782 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 01:21:52,790 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:21:52,790 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:21:52,790 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,857 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,857 (beam_search:476) INFO: -9.09 * 1.0 = -9.09 for ctc +2024-01-17 01:21:52,857 (beam_search:479) INFO: total log probability: -9.09 +2024-01-17 01:21:52,857 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:21:52,857 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,857 (beam_search:483) INFO: best hypo: ANMERCANBELITOGORIHITERS + +2024-01-17 01:21:52,858 (asr_inference:494) INFO: speech length: 30720 +2024-01-17 01:21:52,865 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:21:52,865 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:21:52,865 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,897 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,897 (beam_search:476) INFO: -5.65 * 1.0 = -5.65 for ctc +2024-01-17 01:21:52,897 (beam_search:479) INFO: total log probability: -5.65 +2024-01-17 01:21:52,897 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:21:52,897 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,897 (beam_search:483) INFO: best hypo: CANMICALILAMENTS + +2024-01-17 01:21:52,898 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:52,905 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:52,906 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:52,906 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:52,953 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:52,953 (beam_search:476) INFO: -4.42 * 1.0 = -4.42 for ctc +2024-01-17 01:21:52,953 (beam_search:479) INFO: total log probability: -4.42 +2024-01-17 01:21:52,953 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:21:52,953 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:52,953 (beam_search:483) INFO: best hypo: LOBLEINTONTCMUNITY + +2024-01-17 01:21:52,954 (asr_inference:494) INFO: speech length: 48000 +2024-01-17 01:21:52,962 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 01:21:52,962 (beam_search:429) INFO: max output length: 72 +2024-01-17 01:21:52,962 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:53,032 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:53,032 (beam_search:476) INFO: -7.13 * 1.0 = -7.13 for ctc +2024-01-17 01:21:53,032 (beam_search:479) INFO: total log probability: -7.13 +2024-01-17 01:21:53,032 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:53,032 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:53,032 (beam_search:483) INFO: best hypo: TDYOGREAFICEMAGASENMARCH + +2024-01-17 01:21:53,033 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:53,041 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:53,041 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:53,041 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:53,083 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:53,083 (beam_search:476) INFO: -7.67 * 1.0 = -7.67 for ctc +2024-01-17 01:21:53,083 (beam_search:479) INFO: total log probability: -7.67 +2024-01-17 01:21:53,083 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:21:53,083 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:53,083 (beam_search:483) INFO: best hypo: WIPSOVHISPREVIGDAS + +2024-01-17 01:21:53,085 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:21:53,092 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:21:53,092 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:21:53,092 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:53,129 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:53,129 (beam_search:476) INFO: -7.02 * 1.0 = -7.02 for ctc +2024-01-17 01:21:53,129 (beam_search:479) INFO: total log probability: -7.02 +2024-01-17 01:21:53,130 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:21:53,130 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:53,130 (beam_search:483) INFO: best hypo: SINSFCTIONOBLELES + +2024-01-17 01:21:53,131 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:53,138 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:53,138 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:53,138 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:53,179 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:53,179 (beam_search:476) INFO: -5.58 * 1.0 = -5.58 for ctc +2024-01-17 01:21:53,179 (beam_search:479) INFO: total log probability: -5.58 +2024-01-17 01:21:53,179 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:21:53,179 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:53,179 (beam_search:483) INFO: best hypo: SINESFICTIONFULEM + +2024-01-17 01:21:53,180 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 01:21:53,188 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:21:53,188 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:21:53,188 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:53,241 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:53,241 (beam_search:476) INFO: -3.39 * 1.0 = -3.39 for ctc +2024-01-17 01:21:53,241 (beam_search:479) INFO: total log probability: -3.39 +2024-01-17 01:21:53,241 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:53,241 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:53,242 (beam_search:483) INFO: best hypo: SUBCITSOMEPROBLOM + +2024-01-17 01:21:53,243 (asr_inference:494) INFO: speech length: 51840 +2024-01-17 01:21:53,251 (beam_search:428) INFO: decoder input length: 78 +2024-01-17 01:21:53,251 (beam_search:429) INFO: max output length: 78 +2024-01-17 01:21:53,251 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:53,313 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:53,313 (beam_search:476) INFO: -3.98 * 1.0 = -3.98 for ctc +2024-01-17 01:21:53,313 (beam_search:479) INFO: total log probability: -3.98 +2024-01-17 01:21:53,313 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:53,313 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:53,313 (beam_search:483) INFO: best hypo: EASTONNORTHAMERICKA + +2024-01-17 01:21:53,314 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:53,322 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:53,322 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:53,322 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:53,365 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:53,365 (beam_search:476) INFO: -3.75 * 1.0 = -3.75 for ctc +2024-01-17 01:21:53,365 (beam_search:479) INFO: total log probability: -3.75 +2024-01-17 01:21:53,365 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:53,365 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:53,365 (beam_search:483) INFO: best hypo: PEPESWITNESLOUTING + +2024-01-17 01:21:53,366 (asr_inference:494) INFO: speech length: 48000 +2024-01-17 01:21:53,374 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 01:21:53,374 (beam_search:429) INFO: max output length: 72 +2024-01-17 01:21:53,374 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:53,446 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:53,446 (beam_search:476) INFO: -5.43 * 1.0 = -5.43 for ctc +2024-01-17 01:21:53,446 (beam_search:479) INFO: total log probability: -5.43 +2024-01-17 01:21:53,446 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:53,446 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:53,446 (beam_search:483) INFO: best hypo: DISTINGTIVEVOCALINSTRMENTS + +2024-01-17 01:21:53,448 (asr_inference:494) INFO: speech length: 55680 +2024-01-17 01:21:53,456 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:21:53,456 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:21:53,456 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:53,535 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:53,535 (beam_search:476) INFO: -7.20 * 1.0 = -7.20 for ctc +2024-01-17 01:21:53,535 (beam_search:479) INFO: total log probability: -7.20 +2024-01-17 01:21:53,535 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:21:53,535 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:53,535 (beam_search:483) INFO: best hypo: UAEFRICANAMERICANRAPIES + +2024-01-17 01:21:53,536 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:53,544 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:53,544 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:53,544 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:53,578 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:53,578 (beam_search:476) INFO: -6.41 * 1.0 = -6.41 for ctc +2024-01-17 01:21:53,578 (beam_search:479) INFO: total log probability: -6.41 +2024-01-17 01:21:53,578 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:21:53,578 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:53,578 (beam_search:483) INFO: best hypo: PORTHOGESGENALS + +2024-01-17 01:21:53,579 (asr_inference:494) INFO: speech length: 59520 +2024-01-17 01:21:53,588 (beam_search:428) INFO: decoder input length: 90 +2024-01-17 01:21:53,588 (beam_search:429) INFO: max output length: 90 +2024-01-17 01:21:53,588 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:53,677 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:53,677 (beam_search:476) INFO: -6.60 * 1.0 = -6.60 for ctc +2024-01-17 01:21:53,677 (beam_search:479) INFO: total log probability: -6.60 +2024-01-17 01:21:53,677 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:53,677 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:53,677 (beam_search:483) INFO: best hypo: INTONESINALEIAPORTIDTYAY + +2024-01-17 01:21:53,679 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 01:21:53,687 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:21:53,687 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:21:53,687 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:53,754 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:53,754 (beam_search:476) INFO: -2.12 * 1.0 = -2.12 for ctc +2024-01-17 01:21:53,754 (beam_search:479) INFO: total log probability: -2.12 +2024-01-17 01:21:53,754 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 01:21:53,754 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:53,754 (beam_search:483) INFO: best hypo: MOUNTANRANGESOFBELIVIEAR + +2024-01-17 01:21:53,755 (asr_inference:494) INFO: speech length: 49920 +2024-01-17 01:21:53,763 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:21:53,763 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:21:53,763 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:53,809 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:53,809 (beam_search:476) INFO: -4.69 * 1.0 = -4.69 for ctc +2024-01-17 01:21:53,809 (beam_search:479) INFO: total log probability: -4.69 +2024-01-17 01:21:53,809 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:21:53,809 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:53,809 (beam_search:483) INFO: best hypo: FRENCHAREFOURS + +2024-01-17 01:21:53,810 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:53,818 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:53,818 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:53,818 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:53,860 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:53,861 (beam_search:476) INFO: -6.36 * 1.0 = -6.36 for ctc +2024-01-17 01:21:53,861 (beam_search:479) INFO: total log probability: -6.36 +2024-01-17 01:21:53,861 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:21:53,861 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:53,861 (beam_search:483) INFO: best hypo: SSWOPRABALAPERENCS + +2024-01-17 01:21:53,862 (asr_inference:494) INFO: speech length: 51840 +2024-01-17 01:21:53,870 (beam_search:428) INFO: decoder input length: 78 +2024-01-17 01:21:53,870 (beam_search:429) INFO: max output length: 78 +2024-01-17 01:21:53,870 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:53,924 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:53,924 (beam_search:476) INFO: -4.18 * 1.0 = -4.18 for ctc +2024-01-17 01:21:53,924 (beam_search:479) INFO: total log probability: -4.18 +2024-01-17 01:21:53,924 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:53,924 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:53,925 (beam_search:483) INFO: best hypo: LONGTREVLINGPAIES + +2024-01-17 01:21:53,926 (asr_inference:494) INFO: speech length: 49920 +2024-01-17 01:21:53,934 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:21:53,934 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:21:53,934 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:53,998 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:53,998 (beam_search:476) INFO: -4.17 * 1.0 = -4.17 for ctc +2024-01-17 01:21:53,998 (beam_search:479) INFO: total log probability: -4.17 +2024-01-17 01:21:53,998 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:53,998 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:53,998 (beam_search:483) INFO: best hypo: DISTRICKTCOARTJOUDGEH + +2024-01-17 01:21:53,999 (asr_inference:494) INFO: speech length: 26880 +2024-01-17 01:21:54,006 (beam_search:428) INFO: decoder input length: 39 +2024-01-17 01:21:54,006 (beam_search:429) INFO: max output length: 39 +2024-01-17 01:21:54,006 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:54,029 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:54,029 (beam_search:476) INFO: -6.45 * 1.0 = -6.45 for ctc +2024-01-17 01:21:54,029 (beam_search:479) INFO: total log probability: -6.45 +2024-01-17 01:21:54,029 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:21:54,029 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:54,029 (beam_search:483) INFO: best hypo: YORONYAMPIR + +2024-01-17 01:21:54,030 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 01:21:54,038 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:21:54,039 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:21:54,039 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:54,100 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:54,100 (beam_search:476) INFO: -4.83 * 1.0 = -4.83 for ctc +2024-01-17 01:21:54,100 (beam_search:479) INFO: total log probability: -4.83 +2024-01-17 01:21:54,100 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:54,100 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:54,100 (beam_search:483) INFO: best hypo: BRITISHNNASIONALITYECT + +2024-01-17 01:21:54,102 (asr_inference:494) INFO: speech length: 26880 +2024-01-17 01:21:54,108 (beam_search:428) INFO: decoder input length: 39 +2024-01-17 01:21:54,109 (beam_search:429) INFO: max output length: 39 +2024-01-17 01:21:54,109 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:54,133 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:54,133 (beam_search:476) INFO: -3.38 * 1.0 = -3.38 for ctc +2024-01-17 01:21:54,133 (beam_search:479) INFO: total log probability: -3.38 +2024-01-17 01:21:54,133 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:54,133 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:54,133 (beam_search:483) INFO: best hypo: ISHODATAPRAL + +2024-01-17 01:21:54,134 (asr_inference:494) INFO: speech length: 82560 +2024-01-17 01:21:54,145 (beam_search:428) INFO: decoder input length: 126 +2024-01-17 01:21:54,145 (beam_search:429) INFO: max output length: 126 +2024-01-17 01:21:54,145 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:54,263 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:54,263 (beam_search:476) INFO: -3.73 * 1.0 = -3.73 for ctc +2024-01-17 01:21:54,263 (beam_search:479) INFO: total log probability: -3.73 +2024-01-17 01:21:54,263 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:21:54,263 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:54,263 (beam_search:483) INFO: best hypo: POUBLISITYTRADEDCOMPANES + +2024-01-17 01:21:54,264 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 01:21:54,273 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:21:54,273 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:21:54,273 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:54,354 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:54,354 (beam_search:476) INFO: -6.32 * 1.0 = -6.32 for ctc +2024-01-17 01:21:54,354 (beam_search:479) INFO: total log probability: -6.32 +2024-01-17 01:21:54,354 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:54,354 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:54,355 (beam_search:483) INFO: best hypo: RUSHINVICTIMSOFSOVEDREPENTATIONS + +2024-01-17 01:21:54,356 (asr_inference:494) INFO: speech length: 40320 +2024-01-17 01:21:54,363 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:21:54,363 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:21:54,363 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:54,421 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:54,421 (beam_search:476) INFO: -4.91 * 1.0 = -4.91 for ctc +2024-01-17 01:21:54,421 (beam_search:479) INFO: total log probability: -4.91 +2024-01-17 01:21:54,421 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:54,421 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:54,421 (beam_search:483) INFO: best hypo: WEISTANSLEVICKLANGWAGES + +2024-01-17 01:21:54,422 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:54,430 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:54,430 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:54,430 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:54,477 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:54,477 (beam_search:476) INFO: -4.11 * 1.0 = -4.11 for ctc +2024-01-17 01:21:54,477 (beam_search:479) INFO: total log probability: -4.11 +2024-01-17 01:21:54,477 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:54,477 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:54,477 (beam_search:483) INFO: best hypo: ETALIANROMANCATHLICES + +2024-01-17 01:21:54,478 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:54,485 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:54,485 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:54,485 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:54,528 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:54,528 (beam_search:476) INFO: -9.71 * 1.0 = -9.71 for ctc +2024-01-17 01:21:54,528 (beam_search:479) INFO: total log probability: -9.71 +2024-01-17 01:21:54,528 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-17 01:21:54,528 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:54,528 (beam_search:483) INFO: best hypo: FRENTHSISTRFNIMBERS + +2024-01-17 01:21:54,529 (asr_inference:494) INFO: speech length: 65280 +2024-01-17 01:21:54,539 (beam_search:428) INFO: decoder input length: 99 +2024-01-17 01:21:54,539 (beam_search:429) INFO: max output length: 99 +2024-01-17 01:21:54,539 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:54,637 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:54,637 (beam_search:476) INFO: -4.84 * 1.0 = -4.84 for ctc +2024-01-17 01:21:54,637 (beam_search:479) INFO: total log probability: -4.84 +2024-01-17 01:21:54,637 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:54,637 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:54,637 (beam_search:483) INFO: best hypo: PREVINCHALSIMBLSOFUNTARIO + +2024-01-17 01:21:54,638 (asr_inference:494) INFO: speech length: 38400 +2024-01-17 01:21:54,646 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:21:54,646 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:21:54,646 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:54,687 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:54,687 (beam_search:476) INFO: -3.60 * 1.0 = -3.60 for ctc +2024-01-17 01:21:54,687 (beam_search:479) INFO: total log probability: -3.60 +2024-01-17 01:21:54,687 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:54,687 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:54,687 (beam_search:483) INFO: best hypo: ROCKSFOAMINGMONT + +2024-01-17 01:21:54,688 (asr_inference:494) INFO: speech length: 36480 +2024-01-17 01:21:54,696 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:54,696 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:54,696 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:54,737 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:54,737 (beam_search:476) INFO: -6.89 * 1.0 = -6.89 for ctc +2024-01-17 01:21:54,737 (beam_search:479) INFO: total log probability: -6.89 +2024-01-17 01:21:54,737 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:21:54,737 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:54,737 (beam_search:483) INFO: best hypo: ASASSONATEDMONOCKS + +2024-01-17 01:21:54,738 (asr_inference:494) INFO: speech length: 55680 +2024-01-17 01:21:54,747 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:21:54,747 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:21:54,747 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:54,845 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:54,845 (beam_search:476) INFO: -7.99 * 1.0 = -7.99 for ctc +2024-01-17 01:21:54,846 (beam_search:479) INFO: total log probability: -7.99 +2024-01-17 01:21:54,846 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:54,846 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:54,846 (beam_search:483) INFO: best hypo: INCLUDINTENASHINOLNONGVERMENTOL + +2024-01-17 01:21:54,847 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 01:21:54,854 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:54,854 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:54,854 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:54,887 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:54,887 (beam_search:476) INFO: -3.22 * 1.0 = -3.22 for ctc +2024-01-17 01:21:54,887 (beam_search:479) INFO: total log probability: -3.22 +2024-01-17 01:21:54,887 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:54,887 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:54,887 (beam_search:483) INFO: best hypo: METRICKSPACEIM + +2024-01-17 01:21:54,888 (asr_inference:494) INFO: speech length: 27360 +2024-01-17 01:21:54,895 (beam_search:428) INFO: decoder input length: 40 +2024-01-17 01:21:54,895 (beam_search:429) INFO: max output length: 40 +2024-01-17 01:21:54,895 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:54,936 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:54,936 (beam_search:476) INFO: -4.58 * 1.0 = -4.58 for ctc +2024-01-17 01:21:54,936 (beam_search:479) INFO: total log probability: -4.58 +2024-01-17 01:21:54,936 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:54,936 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:54,936 (beam_search:483) INFO: best hypo: ORREPARETHEBRAKNTHECAPE + +2024-01-17 01:21:54,937 (asr_inference:494) INFO: speech length: 21600 +2024-01-17 01:21:54,944 (beam_search:428) INFO: decoder input length: 31 +2024-01-17 01:21:54,944 (beam_search:429) INFO: max output length: 31 +2024-01-17 01:21:54,944 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:54,963 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:54,963 (beam_search:476) INFO: -4.73 * 1.0 = -4.73 for ctc +2024-01-17 01:21:54,964 (beam_search:479) INFO: total log probability: -4.73 +2024-01-17 01:21:54,964 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:21:54,964 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:54,964 (beam_search:483) INFO: best hypo: ERYHSUPTANTES + +2024-01-17 01:21:54,965 (asr_inference:494) INFO: speech length: 51040 +2024-01-17 01:21:54,973 (beam_search:428) INFO: decoder input length: 77 +2024-01-17 01:21:54,973 (beam_search:429) INFO: max output length: 77 +2024-01-17 01:21:54,973 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:55,087 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:55,087 (beam_search:476) INFO: -5.89 * 1.0 = -5.89 for ctc +2024-01-17 01:21:55,087 (beam_search:479) INFO: total log probability: -5.89 +2024-01-17 01:21:55,087 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:21:55,087 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:55,087 (beam_search:483) INFO: best hypo: HISMOSTCOMELYACURSWHENNETHERSIDEISABLETO + +2024-01-17 01:21:55,088 (asr_inference:494) INFO: speech length: 51680 +2024-01-17 01:21:55,096 (beam_search:428) INFO: decoder input length: 78 +2024-01-17 01:21:55,096 (beam_search:429) INFO: max output length: 78 +2024-01-17 01:21:55,096 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:55,218 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:55,218 (beam_search:476) INFO: -10.73 * 1.0 = -10.73 for ctc +2024-01-17 01:21:55,218 (beam_search:479) INFO: total log probability: -10.73 +2024-01-17 01:21:55,218 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:55,218 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:55,219 (beam_search:483) INFO: best hypo: GRATBARYIARRIFISMANAGEDBYTHEGRETBARIARIFMREN + +2024-01-17 01:21:55,220 (asr_inference:494) INFO: speech length: 20960 +2024-01-17 01:21:55,227 (beam_search:428) INFO: decoder input length: 30 +2024-01-17 01:21:55,227 (beam_search:429) INFO: max output length: 30 +2024-01-17 01:21:55,227 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:55,250 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:55,250 (beam_search:476) INFO: -7.00 * 1.0 = -7.00 for ctc +2024-01-17 01:21:55,250 (beam_search:479) INFO: total log probability: -7.00 +2024-01-17 01:21:55,250 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:21:55,250 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:55,251 (beam_search:483) INFO: best hypo: BYATLEASTHREVOTS + +2024-01-17 01:21:55,252 (asr_inference:494) INFO: speech length: 20000 +2024-01-17 01:21:55,258 (beam_search:428) INFO: decoder input length: 29 +2024-01-17 01:21:55,258 (beam_search:429) INFO: max output length: 29 +2024-01-17 01:21:55,258 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:55,280 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:55,280 (beam_search:476) INFO: -4.41 * 1.0 = -4.41 for ctc +2024-01-17 01:21:55,280 (beam_search:479) INFO: total log probability: -4.41 +2024-01-17 01:21:55,280 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:55,280 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:55,281 (beam_search:483) INFO: best hypo: DEFIHANTCEASEIN + +2024-01-17 01:21:55,282 (asr_inference:494) INFO: speech length: 32000 +2024-01-17 01:21:55,289 (beam_search:428) INFO: decoder input length: 47 +2024-01-17 01:21:55,289 (beam_search:429) INFO: max output length: 47 +2024-01-17 01:21:55,289 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:55,333 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:55,333 (beam_search:476) INFO: -6.03 * 1.0 = -6.03 for ctc +2024-01-17 01:21:55,333 (beam_search:479) INFO: total log probability: -6.03 +2024-01-17 01:21:55,333 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:55,333 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:55,333 (beam_search:483) INFO: best hypo: WLSHOEVDENCEOFHMRGINT + +2024-01-17 01:21:55,335 (asr_inference:494) INFO: speech length: 19520 +2024-01-17 01:21:55,341 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:21:55,341 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:21:55,341 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:55,364 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:55,364 (beam_search:476) INFO: -4.94 * 1.0 = -4.94 for ctc +2024-01-17 01:21:55,364 (beam_search:479) INFO: total log probability: -4.94 +2024-01-17 01:21:55,364 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:55,364 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:55,364 (beam_search:483) INFO: best hypo: INDANANSERHICKLY + +2024-01-17 01:21:55,365 (asr_inference:494) INFO: speech length: 54080 +2024-01-17 01:21:55,374 (beam_search:428) INFO: decoder input length: 82 +2024-01-17 01:21:55,374 (beam_search:429) INFO: max output length: 82 +2024-01-17 01:21:55,374 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:55,496 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:55,496 (beam_search:476) INFO: -10.01 * 1.0 = -10.01 for ctc +2024-01-17 01:21:55,496 (beam_search:479) INFO: total log probability: -10.01 +2024-01-17 01:21:55,496 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:55,496 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:55,496 (beam_search:483) INFO: best hypo: NABLEDEVICSIVEANDUNDEMACRATICSOSHALPOLACYES + +2024-01-17 01:21:55,497 (asr_inference:494) INFO: speech length: 35200 +2024-01-17 01:21:55,505 (beam_search:428) INFO: decoder input length: 52 +2024-01-17 01:21:55,505 (beam_search:429) INFO: max output length: 52 +2024-01-17 01:21:55,505 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:55,564 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:55,564 (beam_search:476) INFO: -8.10 * 1.0 = -8.10 for ctc +2024-01-17 01:21:55,564 (beam_search:479) INFO: total log probability: -8.10 +2024-01-17 01:21:55,564 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:21:55,564 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:55,564 (beam_search:483) INFO: best hypo: MADRESENTTHITLESAILIBLEONCOE + +2024-01-17 01:21:55,565 (asr_inference:494) INFO: speech length: 35360 +2024-01-17 01:21:55,572 (beam_search:428) INFO: decoder input length: 53 +2024-01-17 01:21:55,572 (beam_search:429) INFO: max output length: 53 +2024-01-17 01:21:55,572 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:55,623 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:55,623 (beam_search:476) INFO: -5.93 * 1.0 = -5.93 for ctc +2024-01-17 01:21:55,623 (beam_search:479) INFO: total log probability: -5.93 +2024-01-17 01:21:55,623 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:55,623 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:55,623 (beam_search:483) INFO: best hypo: DISTRICTINATENSICXTYSIC + +2024-01-17 01:21:55,624 (asr_inference:494) INFO: speech length: 22880 +2024-01-17 01:21:55,631 (beam_search:428) INFO: decoder input length: 33 +2024-01-17 01:21:55,631 (beam_search:429) INFO: max output length: 33 +2024-01-17 01:21:55,631 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:55,660 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:55,660 (beam_search:476) INFO: -5.08 * 1.0 = -5.08 for ctc +2024-01-17 01:21:55,660 (beam_search:479) INFO: total log probability: -5.08 +2024-01-17 01:21:55,660 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:55,660 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:55,660 (beam_search:483) INFO: best hypo: LILEINTOFEUCUIRIDY + +2024-01-17 01:21:55,661 (asr_inference:494) INFO: speech length: 35040 +2024-01-17 01:21:55,668 (beam_search:428) INFO: decoder input length: 52 +2024-01-17 01:21:55,669 (beam_search:429) INFO: max output length: 52 +2024-01-17 01:21:55,669 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:55,725 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:55,725 (beam_search:476) INFO: -6.77 * 1.0 = -6.77 for ctc +2024-01-17 01:21:55,725 (beam_search:479) INFO: total log probability: -6.77 +2024-01-17 01:21:55,725 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:55,725 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:55,725 (beam_search:483) INFO: best hypo: AYINTHEGROINANDADVANCEDTHRO + +2024-01-17 01:21:55,726 (asr_inference:494) INFO: speech length: 54720 +2024-01-17 01:21:55,735 (beam_search:428) INFO: decoder input length: 83 +2024-01-17 01:21:55,735 (beam_search:429) INFO: max output length: 83 +2024-01-17 01:21:55,735 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:55,884 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:55,884 (beam_search:476) INFO: -16.01 * 1.0 = -16.01 for ctc +2024-01-17 01:21:55,884 (beam_search:479) INFO: total log probability: -16.01 +2024-01-17 01:21:55,884 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:55,884 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:55,884 (beam_search:483) INFO: best hypo: CNALGYESANDIMPLDMENTIGTRANSHEUMINUSCULSOFANHANSEPERFORMEN + +2024-01-17 01:21:55,886 (asr_inference:494) INFO: speech length: 16320 +2024-01-17 01:21:55,892 (beam_search:428) INFO: decoder input length: 23 +2024-01-17 01:21:55,892 (beam_search:429) INFO: max output length: 23 +2024-01-17 01:21:55,892 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:55,907 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:55,907 (beam_search:476) INFO: -2.90 * 1.0 = -2.90 for ctc +2024-01-17 01:21:55,907 (beam_search:479) INFO: total log probability: -2.90 +2024-01-17 01:21:55,907 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:21:55,907 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:55,907 (beam_search:483) INFO: best hypo: NCOUDINGNAPS + +2024-01-17 01:21:55,908 (asr_inference:494) INFO: speech length: 51200 +2024-01-17 01:21:55,916 (beam_search:428) INFO: decoder input length: 77 +2024-01-17 01:21:55,916 (beam_search:429) INFO: max output length: 77 +2024-01-17 01:21:55,916 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:56,013 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:56,013 (beam_search:476) INFO: -11.46 * 1.0 = -11.46 for ctc +2024-01-17 01:21:56,013 (beam_search:479) INFO: total log probability: -11.46 +2024-01-17 01:21:56,013 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:21:56,013 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:56,014 (beam_search:483) INFO: best hypo: YSPANISHTIRCHMANLOERMERASDELOUCANA + +2024-01-17 01:21:56,015 (asr_inference:494) INFO: speech length: 18080 +2024-01-17 01:21:56,021 (beam_search:428) INFO: decoder input length: 26 +2024-01-17 01:21:56,021 (beam_search:429) INFO: max output length: 26 +2024-01-17 01:21:56,021 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:56,041 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:56,042 (beam_search:476) INFO: -3.54 * 1.0 = -3.54 for ctc +2024-01-17 01:21:56,042 (beam_search:479) INFO: total log probability: -3.54 +2024-01-17 01:21:56,042 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:56,042 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:56,042 (beam_search:483) INFO: best hypo: DEVIHTEDDEMICRATS + +2024-01-17 01:21:56,043 (asr_inference:494) INFO: speech length: 53760 +2024-01-17 01:21:56,051 (beam_search:428) INFO: decoder input length: 81 +2024-01-17 01:21:56,051 (beam_search:429) INFO: max output length: 81 +2024-01-17 01:21:56,051 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:56,171 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:56,171 (beam_search:476) INFO: -8.39 * 1.0 = -8.39 for ctc +2024-01-17 01:21:56,171 (beam_search:479) INFO: total log probability: -8.39 +2024-01-17 01:21:56,171 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:56,171 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:56,171 (beam_search:483) INFO: best hypo: HEWOLDCHANPIANSHIPHASBENCONTROLEBYEIDYE + +2024-01-17 01:21:56,172 (asr_inference:494) INFO: speech length: 24960 +2024-01-17 01:21:56,179 (beam_search:428) INFO: decoder input length: 36 +2024-01-17 01:21:56,179 (beam_search:429) INFO: max output length: 36 +2024-01-17 01:21:56,179 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:56,215 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:56,215 (beam_search:476) INFO: -7.24 * 1.0 = -7.24 for ctc +2024-01-17 01:21:56,215 (beam_search:479) INFO: total log probability: -7.24 +2024-01-17 01:21:56,215 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:21:56,215 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:56,215 (beam_search:483) INFO: best hypo: WHERETESTARINGPOSITIONI + +2024-01-17 01:21:56,216 (asr_inference:494) INFO: speech length: 85120 +2024-01-17 01:21:56,227 (beam_search:428) INFO: decoder input length: 130 +2024-01-17 01:21:56,227 (beam_search:429) INFO: max output length: 130 +2024-01-17 01:21:56,227 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:56,527 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:56,527 (beam_search:476) INFO: -13.20 * 1.0 = -13.20 for ctc +2024-01-17 01:21:56,527 (beam_search:479) INFO: total log probability: -13.20 +2024-01-17 01:21:56,527 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:56,527 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:56,528 (beam_search:483) INFO: best hypo: ECRATEDINEVERYSTATANDTERITRYTOPRTECTANDRESERVTHECONTRESUNAKYCOSISTOMS + +2024-01-17 01:21:56,529 (asr_inference:494) INFO: speech length: 34880 +2024-01-17 01:21:56,536 (beam_search:428) INFO: decoder input length: 52 +2024-01-17 01:21:56,536 (beam_search:429) INFO: max output length: 52 +2024-01-17 01:21:56,536 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:56,593 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:56,593 (beam_search:476) INFO: -11.21 * 1.0 = -11.21 for ctc +2024-01-17 01:21:56,593 (beam_search:479) INFO: total log probability: -11.21 +2024-01-17 01:21:56,593 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:21:56,593 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:56,594 (beam_search:483) INFO: best hypo: EDICATINTOFTHENUSILANDWAREAMO + +2024-01-17 01:21:56,595 (asr_inference:494) INFO: speech length: 44000 +2024-01-17 01:21:56,603 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:21:56,603 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:21:56,603 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:56,692 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:56,692 (beam_search:476) INFO: -10.96 * 1.0 = -10.96 for ctc +2024-01-17 01:21:56,692 (beam_search:479) INFO: total log probability: -10.96 +2024-01-17 01:21:56,692 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:56,692 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:56,692 (beam_search:483) INFO: best hypo: ALAMEFRMTHERELROUDCOUPANESFORVETOIN + +2024-01-17 01:21:56,693 (asr_inference:494) INFO: speech length: 19200 +2024-01-17 01:21:56,700 (beam_search:428) INFO: decoder input length: 27 +2024-01-17 01:21:56,700 (beam_search:429) INFO: max output length: 27 +2024-01-17 01:21:56,700 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:56,720 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:56,720 (beam_search:476) INFO: -2.46 * 1.0 = -2.46 for ctc +2024-01-17 01:21:56,720 (beam_search:479) INFO: total log probability: -2.46 +2024-01-17 01:21:56,720 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:21:56,720 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:56,721 (beam_search:483) INFO: best hypo: THETOWNISSPIT + +2024-01-17 01:21:56,722 (asr_inference:494) INFO: speech length: 51040 +2024-01-17 01:21:56,730 (beam_search:428) INFO: decoder input length: 77 +2024-01-17 01:21:56,730 (beam_search:429) INFO: max output length: 77 +2024-01-17 01:21:56,730 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:56,839 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:56,839 (beam_search:476) INFO: -8.57 * 1.0 = -8.57 for ctc +2024-01-17 01:21:56,839 (beam_search:479) INFO: total log probability: -8.57 +2024-01-17 01:21:56,839 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:56,839 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:56,839 (beam_search:483) INFO: best hypo: MOSKETIYFISHISAPRTICULYAGREIVESPACESNO + +2024-01-17 01:21:56,840 (asr_inference:494) INFO: speech length: 35520 +2024-01-17 01:21:56,848 (beam_search:428) INFO: decoder input length: 53 +2024-01-17 01:21:56,848 (beam_search:429) INFO: max output length: 53 +2024-01-17 01:21:56,848 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:56,910 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:56,911 (beam_search:476) INFO: -6.85 * 1.0 = -6.85 for ctc +2024-01-17 01:21:56,911 (beam_search:479) INFO: total log probability: -6.85 +2024-01-17 01:21:56,911 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:21:56,911 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:56,911 (beam_search:483) INFO: best hypo: ANDTHENAIONALCHESEHEMINSHIPES + +2024-01-17 01:21:56,912 (asr_inference:494) INFO: speech length: 37600 +2024-01-17 01:21:56,920 (beam_search:428) INFO: decoder input length: 56 +2024-01-17 01:21:56,920 (beam_search:429) INFO: max output length: 56 +2024-01-17 01:21:56,920 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:56,990 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:56,991 (beam_search:476) INFO: -9.24 * 1.0 = -9.24 for ctc +2024-01-17 01:21:56,991 (beam_search:479) INFO: total log probability: -9.24 +2024-01-17 01:21:56,991 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:21:56,991 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:56,991 (beam_search:483) INFO: best hypo: ROBLOMISNOWNTORUNINPLYNOMALTIME + +2024-01-17 01:21:56,992 (asr_inference:494) INFO: speech length: 42880 +2024-01-17 01:21:57,000 (beam_search:428) INFO: decoder input length: 64 +2024-01-17 01:21:57,000 (beam_search:429) INFO: max output length: 64 +2024-01-17 01:21:57,000 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:57,080 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:57,080 (beam_search:476) INFO: -8.15 * 1.0 = -8.15 for ctc +2024-01-17 01:21:57,080 (beam_search:479) INFO: total log probability: -8.15 +2024-01-17 01:21:57,080 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:57,080 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:57,081 (beam_search:483) INFO: best hypo: LAYJONIERANDPARCKERWATCONSHEARDN + +2024-01-17 01:21:57,082 (asr_inference:494) INFO: speech length: 22560 +2024-01-17 01:21:57,088 (beam_search:428) INFO: decoder input length: 33 +2024-01-17 01:21:57,088 (beam_search:429) INFO: max output length: 33 +2024-01-17 01:21:57,088 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:57,116 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:57,116 (beam_search:476) INFO: -3.67 * 1.0 = -3.67 for ctc +2024-01-17 01:21:57,116 (beam_search:479) INFO: total log probability: -3.67 +2024-01-17 01:21:57,117 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:21:57,117 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:57,117 (beam_search:483) INFO: best hypo: INNITINSEVENTYTHR + +2024-01-17 01:21:57,118 (asr_inference:494) INFO: speech length: 39520 +2024-01-17 01:21:57,125 (beam_search:428) INFO: decoder input length: 59 +2024-01-17 01:21:57,125 (beam_search:429) INFO: max output length: 59 +2024-01-17 01:21:57,125 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:57,196 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:57,196 (beam_search:476) INFO: -7.90 * 1.0 = -7.90 for ctc +2024-01-17 01:21:57,196 (beam_search:479) INFO: total log probability: -7.90 +2024-01-17 01:21:57,196 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:57,196 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:57,197 (beam_search:483) INFO: best hypo: DEVELOPINGANDOUSINGSUCHTACNALDGE + +2024-01-17 01:21:57,198 (asr_inference:494) INFO: speech length: 16320 +2024-01-17 01:21:57,205 (beam_search:428) INFO: decoder input length: 23 +2024-01-17 01:21:57,205 (beam_search:429) INFO: max output length: 23 +2024-01-17 01:21:57,205 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:57,223 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:57,223 (beam_search:476) INFO: -6.15 * 1.0 = -6.15 for ctc +2024-01-17 01:21:57,223 (beam_search:479) INFO: total log probability: -6.15 +2024-01-17 01:21:57,223 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:21:57,223 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:57,223 (beam_search:483) INFO: best hypo: ORSOMEQUISTIONS + +2024-01-17 01:21:57,224 (asr_inference:494) INFO: speech length: 19360 +2024-01-17 01:21:57,230 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:21:57,230 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:21:57,230 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:57,254 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:57,254 (beam_search:476) INFO: -3.86 * 1.0 = -3.86 for ctc +2024-01-17 01:21:57,254 (beam_search:479) INFO: total log probability: -3.86 +2024-01-17 01:21:57,254 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:57,254 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:57,254 (beam_search:483) INFO: best hypo: LAMEAPROOETHATPE + +2024-01-17 01:21:57,256 (asr_inference:494) INFO: speech length: 62400 +2024-01-17 01:21:57,265 (beam_search:428) INFO: decoder input length: 95 +2024-01-17 01:21:57,265 (beam_search:429) INFO: max output length: 95 +2024-01-17 01:21:57,265 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:57,419 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:57,419 (beam_search:476) INFO: -14.61 * 1.0 = -14.61 for ctc +2024-01-17 01:21:57,419 (beam_search:479) INFO: total log probability: -14.61 +2024-01-17 01:21:57,419 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:21:57,419 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:57,420 (beam_search:483) INFO: best hypo: ABLADECATHATORISOULYINSERTEDSOMONSOFLUIDBONS + +2024-01-17 01:21:57,421 (asr_inference:494) INFO: speech length: 72640 +2024-01-17 01:21:57,430 (beam_search:428) INFO: decoder input length: 111 +2024-01-17 01:21:57,430 (beam_search:429) INFO: max output length: 111 +2024-01-17 01:21:57,430 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:57,660 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:57,660 (beam_search:476) INFO: -16.20 * 1.0 = -16.20 for ctc +2024-01-17 01:21:57,660 (beam_search:479) INFO: total log probability: -16.20 +2024-01-17 01:21:57,660 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:57,660 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:57,661 (beam_search:483) INFO: best hypo: ERNOTIONOFYUJENICANHANSMENTTICNALAGESMIGHUNINTENTINALYINCOURAGE + +2024-01-17 01:21:57,662 (asr_inference:494) INFO: speech length: 73440 +2024-01-17 01:21:57,672 (beam_search:428) INFO: decoder input length: 112 +2024-01-17 01:21:57,672 (beam_search:429) INFO: max output length: 112 +2024-01-17 01:21:57,672 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:57,908 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:57,908 (beam_search:476) INFO: -19.02 * 1.0 = -19.02 for ctc +2024-01-17 01:21:57,908 (beam_search:479) INFO: total log probability: -19.02 +2024-01-17 01:21:57,908 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:21:57,908 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:57,908 (beam_search:483) INFO: best hypo: ATTHEATENINOFRESERTHRSANBEFOCKESEDNMPARTIALSOLUTIONSORSOLTIONS + +2024-01-17 01:21:57,909 (asr_inference:494) INFO: speech length: 26720 +2024-01-17 01:21:57,916 (beam_search:428) INFO: decoder input length: 39 +2024-01-17 01:21:57,916 (beam_search:429) INFO: max output length: 39 +2024-01-17 01:21:57,916 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:57,955 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:57,955 (beam_search:476) INFO: -4.44 * 1.0 = -4.44 for ctc +2024-01-17 01:21:57,955 (beam_search:479) INFO: total log probability: -4.44 +2024-01-17 01:21:57,955 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:57,955 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:57,955 (beam_search:483) INFO: best hypo: NOWNOFFORHNDREDSOFYEARS + +2024-01-17 01:21:57,957 (asr_inference:494) INFO: speech length: 28480 +2024-01-17 01:21:57,964 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:21:57,964 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:21:57,964 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:58,007 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:58,007 (beam_search:476) INFO: -6.23 * 1.0 = -6.23 for ctc +2024-01-17 01:21:58,007 (beam_search:479) INFO: total log probability: -6.23 +2024-01-17 01:21:58,007 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:58,007 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:58,007 (beam_search:483) INFO: best hypo: NLYMAUBIALSHAVESOVIVEDTT + +2024-01-17 01:21:58,009 (asr_inference:494) INFO: speech length: 42560 +2024-01-17 01:21:58,017 (beam_search:428) INFO: decoder input length: 64 +2024-01-17 01:21:58,017 (beam_search:429) INFO: max output length: 64 +2024-01-17 01:21:58,017 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:58,110 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:58,110 (beam_search:476) INFO: -9.51 * 1.0 = -9.51 for ctc +2024-01-17 01:21:58,110 (beam_search:479) INFO: total log probability: -9.51 +2024-01-17 01:21:58,110 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:21:58,110 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:58,110 (beam_search:483) INFO: best hypo: TOWHCHALTHEEDABLESPACESOFCRUSTATIONBELONG + +2024-01-17 01:21:58,111 (asr_inference:494) INFO: speech length: 19360 +2024-01-17 01:21:58,118 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:21:58,118 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:21:58,118 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:58,139 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:58,139 (beam_search:476) INFO: -4.44 * 1.0 = -4.44 for ctc +2024-01-17 01:21:58,139 (beam_search:479) INFO: total log probability: -4.44 +2024-01-17 01:21:58,139 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:21:58,139 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:58,139 (beam_search:483) INFO: best hypo: OGERTHEMRESURCH + +2024-01-17 01:21:58,140 (asr_inference:494) INFO: speech length: 87680 +2024-01-17 01:21:58,151 (beam_search:428) INFO: decoder input length: 134 +2024-01-17 01:21:58,151 (beam_search:429) INFO: max output length: 134 +2024-01-17 01:21:58,151 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:58,450 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:58,450 (beam_search:476) INFO: -20.54 * 1.0 = -20.54 for ctc +2024-01-17 01:21:58,450 (beam_search:479) INFO: total log probability: -20.54 +2024-01-17 01:21:58,450 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:21:58,450 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:58,451 (beam_search:483) INFO: best hypo: NINTEINSICXTYTOFILIPSINVENTEDHECOMPACTODOCASETMEDEOAMFORODIOUSTORGE + +2024-01-17 01:21:58,452 (asr_inference:494) INFO: speech length: 17760 +2024-01-17 01:21:58,459 (beam_search:428) INFO: decoder input length: 25 +2024-01-17 01:21:58,459 (beam_search:429) INFO: max output length: 25 +2024-01-17 01:21:58,459 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:58,478 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:58,478 (beam_search:476) INFO: -4.26 * 1.0 = -4.26 for ctc +2024-01-17 01:21:58,478 (beam_search:479) INFO: total log probability: -4.26 +2024-01-17 01:21:58,478 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:58,478 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:58,478 (beam_search:483) INFO: best hypo: UTRCINFTHELOW + +2024-01-17 01:21:58,479 (asr_inference:494) INFO: speech length: 16640 +2024-01-17 01:21:58,486 (beam_search:428) INFO: decoder input length: 23 +2024-01-17 01:21:58,486 (beam_search:429) INFO: max output length: 23 +2024-01-17 01:21:58,486 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:58,502 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:58,502 (beam_search:476) INFO: -3.66 * 1.0 = -3.66 for ctc +2024-01-17 01:21:58,502 (beam_search:479) INFO: total log probability: -3.66 +2024-01-17 01:21:58,502 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:21:58,502 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:58,503 (beam_search:483) INFO: best hypo: NTFHBIANSADRP + +2024-01-17 01:21:58,503 (asr_inference:494) INFO: speech length: 27840 +2024-01-17 01:21:58,511 (beam_search:428) INFO: decoder input length: 41 +2024-01-17 01:21:58,511 (beam_search:429) INFO: max output length: 41 +2024-01-17 01:21:58,511 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:58,551 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:58,551 (beam_search:476) INFO: -6.83 * 1.0 = -6.83 for ctc +2024-01-17 01:21:58,551 (beam_search:479) INFO: total log probability: -6.83 +2024-01-17 01:21:58,551 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:58,551 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:58,552 (beam_search:483) INFO: best hypo: OMENSWOALDCHESTHAMINCHI + +2024-01-17 01:21:58,553 (asr_inference:494) INFO: speech length: 112320 +2024-01-17 01:21:58,565 (beam_search:428) INFO: decoder input length: 173 +2024-01-17 01:21:58,565 (beam_search:429) INFO: max output length: 173 +2024-01-17 01:21:58,565 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:59,066 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:59,066 (beam_search:476) INFO: -20.01 * 1.0 = -20.01 for ctc +2024-01-17 01:21:59,066 (beam_search:479) INFO: total log probability: -20.01 +2024-01-17 01:21:59,066 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:21:59,066 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:59,066 (beam_search:483) INFO: best hypo: CONTANEDISCRPTIONSANDCOMMANTARIYSONTHESTATOFAENBISINCEANDECNALAGYASMAGERCONTRUTERSTOTHE + +2024-01-17 01:21:59,068 (asr_inference:494) INFO: speech length: 32320 +2024-01-17 01:21:59,075 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:21:59,075 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:21:59,075 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:59,124 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:59,124 (beam_search:476) INFO: -7.27 * 1.0 = -7.27 for ctc +2024-01-17 01:21:59,124 (beam_search:479) INFO: total log probability: -7.27 +2024-01-17 01:21:59,124 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:59,124 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:59,124 (beam_search:483) INFO: best hypo: URHLFHANTIYORSOCIALTRENT + +2024-01-17 01:21:59,126 (asr_inference:494) INFO: speech length: 36320 +2024-01-17 01:21:59,133 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:21:59,133 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:21:59,133 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:59,196 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:59,196 (beam_search:476) INFO: -9.19 * 1.0 = -9.19 for ctc +2024-01-17 01:21:59,196 (beam_search:479) INFO: total log probability: -9.19 +2024-01-17 01:21:59,196 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:21:59,196 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:59,196 (beam_search:483) INFO: best hypo: MOSTCOMPACCASETSWERSOULDBLANK + +2024-01-17 01:21:59,198 (asr_inference:494) INFO: speech length: 20640 +2024-01-17 01:21:59,204 (beam_search:428) INFO: decoder input length: 30 +2024-01-17 01:21:59,204 (beam_search:429) INFO: max output length: 30 +2024-01-17 01:21:59,204 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:59,232 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:59,232 (beam_search:476) INFO: -6.68 * 1.0 = -6.68 for ctc +2024-01-17 01:21:59,232 (beam_search:479) INFO: total log probability: -6.68 +2024-01-17 01:21:59,232 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:21:59,232 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:59,232 (beam_search:483) INFO: best hypo: IFTHERISANOULGERTHE + +2024-01-17 01:21:59,234 (asr_inference:494) INFO: speech length: 65280 +2024-01-17 01:21:59,243 (beam_search:428) INFO: decoder input length: 99 +2024-01-17 01:21:59,243 (beam_search:429) INFO: max output length: 99 +2024-01-17 01:21:59,243 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:59,426 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:59,426 (beam_search:476) INFO: -15.93 * 1.0 = -15.93 for ctc +2024-01-17 01:21:59,426 (beam_search:479) INFO: total log probability: -15.93 +2024-01-17 01:21:59,426 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:59,426 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:59,426 (beam_search:483) INFO: best hypo: HESUTHENOSTRALIANCOSTANDINSUBANTEICTICOUSTRALIANTERITRYS + +2024-01-17 01:21:59,427 (asr_inference:494) INFO: speech length: 35680 +2024-01-17 01:21:59,435 (beam_search:428) INFO: decoder input length: 53 +2024-01-17 01:21:59,435 (beam_search:429) INFO: max output length: 53 +2024-01-17 01:21:59,435 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:59,493 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:59,493 (beam_search:476) INFO: -7.43 * 1.0 = -7.43 for ctc +2024-01-17 01:21:59,493 (beam_search:479) INFO: total log probability: -7.43 +2024-01-17 01:21:59,493 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:59,493 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:59,493 (beam_search:483) INFO: best hypo: AERATSOFTIPICKLYFIVEHUDREDTW + +2024-01-17 01:21:59,495 (asr_inference:494) INFO: speech length: 17920 +2024-01-17 01:21:59,501 (beam_search:428) INFO: decoder input length: 25 +2024-01-17 01:21:59,501 (beam_search:429) INFO: max output length: 25 +2024-01-17 01:21:59,501 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:59,519 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:59,519 (beam_search:476) INFO: -2.77 * 1.0 = -2.77 for ctc +2024-01-17 01:21:59,519 (beam_search:479) INFO: total log probability: -2.77 +2024-01-17 01:21:59,519 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:21:59,519 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:59,519 (beam_search:483) INFO: best hypo: DEPRIVINTHEDOC + +2024-01-17 01:21:59,520 (asr_inference:494) INFO: speech length: 31200 +2024-01-17 01:21:59,528 (beam_search:428) INFO: decoder input length: 46 +2024-01-17 01:21:59,528 (beam_search:429) INFO: max output length: 46 +2024-01-17 01:21:59,528 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:59,574 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:59,574 (beam_search:476) INFO: -7.17 * 1.0 = -7.17 for ctc +2024-01-17 01:21:59,574 (beam_search:479) INFO: total log probability: -7.17 +2024-01-17 01:21:59,574 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:59,574 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:59,574 (beam_search:483) INFO: best hypo: NINEPORSENTOFHETOTLCAST + +2024-01-17 01:21:59,575 (asr_inference:494) INFO: speech length: 61440 +2024-01-17 01:21:59,584 (beam_search:428) INFO: decoder input length: 93 +2024-01-17 01:21:59,584 (beam_search:429) INFO: max output length: 93 +2024-01-17 01:21:59,584 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:59,721 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:59,721 (beam_search:476) INFO: -10.16 * 1.0 = -10.16 for ctc +2024-01-17 01:21:59,721 (beam_search:479) INFO: total log probability: -10.16 +2024-01-17 01:21:59,721 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:59,722 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:59,722 (beam_search:483) INFO: best hypo: ANTERIASSORBRATRYANDANTERIECOMUNCATINATERY + +2024-01-17 01:21:59,723 (asr_inference:494) INFO: speech length: 26880 +2024-01-17 01:21:59,730 (beam_search:428) INFO: decoder input length: 39 +2024-01-17 01:21:59,730 (beam_search:429) INFO: max output length: 39 +2024-01-17 01:21:59,730 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:59,762 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:59,762 (beam_search:476) INFO: -4.55 * 1.0 = -4.55 for ctc +2024-01-17 01:21:59,762 (beam_search:479) INFO: total log probability: -4.55 +2024-01-17 01:21:59,762 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:21:59,762 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:59,762 (beam_search:483) INFO: best hypo: EDNOTIMPARTHSHIN + +2024-01-17 01:21:59,763 (asr_inference:494) INFO: speech length: 26720 +2024-01-17 01:21:59,770 (beam_search:428) INFO: decoder input length: 39 +2024-01-17 01:21:59,770 (beam_search:429) INFO: max output length: 39 +2024-01-17 01:21:59,770 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:59,805 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:59,805 (beam_search:476) INFO: -6.23 * 1.0 = -6.23 for ctc +2024-01-17 01:21:59,805 (beam_search:479) INFO: total log probability: -6.23 +2024-01-17 01:21:59,805 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:21:59,805 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:59,805 (beam_search:483) INFO: best hypo: NTHIERDEMACRATICPARTY + +2024-01-17 01:21:59,807 (asr_inference:494) INFO: speech length: 38560 +2024-01-17 01:21:59,814 (beam_search:428) INFO: decoder input length: 58 +2024-01-17 01:21:59,814 (beam_search:429) INFO: max output length: 58 +2024-01-17 01:21:59,814 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:59,885 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:59,885 (beam_search:476) INFO: -7.27 * 1.0 = -7.27 for ctc +2024-01-17 01:21:59,885 (beam_search:479) INFO: total log probability: -7.27 +2024-01-17 01:21:59,885 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:21:59,885 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:59,885 (beam_search:483) INFO: best hypo: NOHESONTOPOFTHECSETHALINDICATTH + +2024-01-17 01:21:59,886 (asr_inference:494) INFO: speech length: 16640 +2024-01-17 01:21:59,893 (beam_search:428) INFO: decoder input length: 23 +2024-01-17 01:21:59,893 (beam_search:429) INFO: max output length: 23 +2024-01-17 01:21:59,893 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:59,906 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:59,906 (beam_search:476) INFO: -5.47 * 1.0 = -5.47 for ctc +2024-01-17 01:21:59,906 (beam_search:479) INFO: total log probability: -5.47 +2024-01-17 01:21:59,906 (beam_search:480) INFO: normalized log probability: -0.42 +2024-01-17 01:21:59,906 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:59,906 (beam_search:483) INFO: best hypo: LOWATTOSO + +2024-01-17 01:21:59,907 (asr_inference:494) INFO: speech length: 28480 +2024-01-17 01:21:59,914 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:21:59,914 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:21:59,914 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:59,955 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:59,955 (beam_search:476) INFO: -6.02 * 1.0 = -6.02 for ctc +2024-01-17 01:21:59,955 (beam_search:479) INFO: total log probability: -6.02 +2024-01-17 01:21:59,955 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:21:59,955 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:59,955 (beam_search:483) INFO: best hypo: IAINDANGREDMRINSPCHEST + +2024-01-17 01:21:59,956 (asr_inference:494) INFO: speech length: 20160 +2024-01-17 01:21:59,963 (beam_search:428) INFO: decoder input length: 29 +2024-01-17 01:21:59,963 (beam_search:429) INFO: max output length: 29 +2024-01-17 01:21:59,963 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:21:59,987 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:21:59,987 (beam_search:476) INFO: -3.78 * 1.0 = -3.78 for ctc +2024-01-17 01:21:59,987 (beam_search:479) INFO: total log probability: -3.78 +2024-01-17 01:21:59,987 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:21:59,987 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:21:59,987 (beam_search:483) INFO: best hypo: BROWNDESIREALECTIN + +2024-01-17 01:21:59,988 (asr_inference:494) INFO: speech length: 46720 +2024-01-17 01:21:59,997 (beam_search:428) INFO: decoder input length: 70 +2024-01-17 01:21:59,997 (beam_search:429) INFO: max output length: 70 +2024-01-17 01:21:59,997 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:00,103 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:00,103 (beam_search:476) INFO: -8.79 * 1.0 = -8.79 for ctc +2024-01-17 01:22:00,103 (beam_search:479) INFO: total log probability: -8.79 +2024-01-17 01:22:00,103 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:00,103 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:00,103 (beam_search:483) INFO: best hypo: HISFACDOSNTSAYMUCHABOUTWHERETHEPROBLOMLIS + +2024-01-17 01:22:00,104 (asr_inference:494) INFO: speech length: 26720 +2024-01-17 01:22:00,111 (beam_search:428) INFO: decoder input length: 39 +2024-01-17 01:22:00,111 (beam_search:429) INFO: max output length: 39 +2024-01-17 01:22:00,111 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:00,146 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:00,146 (beam_search:476) INFO: -4.88 * 1.0 = -4.88 for ctc +2024-01-17 01:22:00,146 (beam_search:479) INFO: total log probability: -4.88 +2024-01-17 01:22:00,146 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:00,146 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:00,146 (beam_search:483) INFO: best hypo: COMICALSOSITYBGANAS + +2024-01-17 01:22:00,147 (asr_inference:494) INFO: speech length: 44640 +2024-01-17 01:22:00,155 (beam_search:428) INFO: decoder input length: 67 +2024-01-17 01:22:00,155 (beam_search:429) INFO: max output length: 67 +2024-01-17 01:22:00,155 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:00,242 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:00,242 (beam_search:476) INFO: -8.54 * 1.0 = -8.54 for ctc +2024-01-17 01:22:00,242 (beam_search:479) INFO: total log probability: -8.54 +2024-01-17 01:22:00,242 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:00,242 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:00,242 (beam_search:483) INFO: best hypo: ITHTORISTSARVINGTHESTEMEBOTEANDTRIN + +2024-01-17 01:22:00,243 (asr_inference:494) INFO: speech length: 30080 +2024-01-17 01:22:00,250 (beam_search:428) INFO: decoder input length: 44 +2024-01-17 01:22:00,250 (beam_search:429) INFO: max output length: 44 +2024-01-17 01:22:00,250 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:00,298 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:00,298 (beam_search:476) INFO: -6.98 * 1.0 = -6.98 for ctc +2024-01-17 01:22:00,298 (beam_search:479) INFO: total log probability: -6.98 +2024-01-17 01:22:00,298 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:00,298 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:00,298 (beam_search:483) INFO: best hypo: FRSTDILOGBETWEENTRANHUMNIS + +2024-01-17 01:22:00,299 (asr_inference:494) INFO: speech length: 30240 +2024-01-17 01:22:00,307 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:22:00,307 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:22:00,307 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:00,351 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:00,351 (beam_search:476) INFO: -6.04 * 1.0 = -6.04 for ctc +2024-01-17 01:22:00,351 (beam_search:479) INFO: total log probability: -6.04 +2024-01-17 01:22:00,351 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:00,351 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:00,352 (beam_search:483) INFO: best hypo: NERBENPARTOFTHELIPICGANS + +2024-01-17 01:22:00,353 (asr_inference:494) INFO: speech length: 25280 +2024-01-17 01:22:00,360 (beam_search:428) INFO: decoder input length: 37 +2024-01-17 01:22:00,360 (beam_search:429) INFO: max output length: 37 +2024-01-17 01:22:00,360 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:00,392 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:00,392 (beam_search:476) INFO: -3.70 * 1.0 = -3.70 for ctc +2024-01-17 01:22:00,392 (beam_search:479) INFO: total log probability: -3.70 +2024-01-17 01:22:00,392 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:00,392 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:00,392 (beam_search:483) INFO: best hypo: REAGESFURNITCURANDTH + +2024-01-17 01:22:00,393 (asr_inference:494) INFO: speech length: 20320 +2024-01-17 01:22:00,400 (beam_search:428) INFO: decoder input length: 29 +2024-01-17 01:22:00,400 (beam_search:429) INFO: max output length: 29 +2024-01-17 01:22:00,400 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:00,422 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:00,422 (beam_search:476) INFO: -5.47 * 1.0 = -5.47 for ctc +2024-01-17 01:22:00,422 (beam_search:479) INFO: total log probability: -5.47 +2024-01-17 01:22:00,422 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:22:00,422 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:00,422 (beam_search:483) INFO: best hypo: INHILABLTRNMENTS + +2024-01-17 01:22:00,423 (asr_inference:494) INFO: speech length: 23200 +2024-01-17 01:22:00,430 (beam_search:428) INFO: decoder input length: 34 +2024-01-17 01:22:00,430 (beam_search:429) INFO: max output length: 34 +2024-01-17 01:22:00,430 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:00,458 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:00,458 (beam_search:476) INFO: -4.81 * 1.0 = -4.81 for ctc +2024-01-17 01:22:00,458 (beam_search:479) INFO: total log probability: -4.81 +2024-01-17 01:22:00,458 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:00,458 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:00,458 (beam_search:483) INFO: best hypo: OLOCATETHANURISOM + +2024-01-17 01:22:00,459 (asr_inference:494) INFO: speech length: 18240 +2024-01-17 01:22:00,465 (beam_search:428) INFO: decoder input length: 26 +2024-01-17 01:22:00,465 (beam_search:429) INFO: max output length: 26 +2024-01-17 01:22:00,465 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:00,486 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:00,486 (beam_search:476) INFO: -5.26 * 1.0 = -5.26 for ctc +2024-01-17 01:22:00,486 (beam_search:479) INFO: total log probability: -5.26 +2024-01-17 01:22:00,486 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:22:00,486 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:00,486 (beam_search:483) INFO: best hypo: ORHLOUGICALFREDM + +2024-01-17 01:22:00,487 (asr_inference:494) INFO: speech length: 24640 +2024-01-17 01:22:00,494 (beam_search:428) INFO: decoder input length: 36 +2024-01-17 01:22:00,494 (beam_search:429) INFO: max output length: 36 +2024-01-17 01:22:00,494 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:00,526 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:00,526 (beam_search:476) INFO: -5.02 * 1.0 = -5.02 for ctc +2024-01-17 01:22:00,526 (beam_search:479) INFO: total log probability: -5.02 +2024-01-17 01:22:00,526 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:00,527 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:00,527 (beam_search:483) INFO: best hypo: DHERJETICATAKINGSTOW + +2024-01-17 01:22:00,528 (asr_inference:494) INFO: speech length: 40160 +2024-01-17 01:22:00,535 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:22:00,535 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:22:00,535 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:00,615 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:00,615 (beam_search:476) INFO: -8.73 * 1.0 = -8.73 for ctc +2024-01-17 01:22:00,615 (beam_search:479) INFO: total log probability: -8.73 +2024-01-17 01:22:00,615 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:00,616 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:00,616 (beam_search:483) INFO: best hypo: ACTLYFORTYARSAFRTHECARNISHDONWASLATE + +2024-01-17 01:22:00,617 (asr_inference:494) INFO: speech length: 20960 +2024-01-17 01:22:00,624 (beam_search:428) INFO: decoder input length: 30 +2024-01-17 01:22:00,624 (beam_search:429) INFO: max output length: 30 +2024-01-17 01:22:00,624 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:00,651 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:00,651 (beam_search:476) INFO: -4.46 * 1.0 = -4.46 for ctc +2024-01-17 01:22:00,651 (beam_search:479) INFO: total log probability: -4.46 +2024-01-17 01:22:00,652 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:00,652 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:00,652 (beam_search:483) INFO: best hypo: ASEOTHERECAGNITIONTH + +2024-01-17 01:22:00,653 (asr_inference:494) INFO: speech length: 36960 +2024-01-17 01:22:00,660 (beam_search:428) INFO: decoder input length: 55 +2024-01-17 01:22:00,661 (beam_search:429) INFO: max output length: 55 +2024-01-17 01:22:00,661 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:00,716 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:00,716 (beam_search:476) INFO: -5.90 * 1.0 = -5.90 for ctc +2024-01-17 01:22:00,716 (beam_search:479) INFO: total log probability: -5.90 +2024-01-17 01:22:00,716 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:00,716 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:00,716 (beam_search:483) INFO: best hypo: ORLECTRNICKBUTNSORDISPLAY + +2024-01-17 01:22:00,718 (asr_inference:494) INFO: speech length: 33120 +2024-01-17 01:22:00,725 (beam_search:428) INFO: decoder input length: 49 +2024-01-17 01:22:00,725 (beam_search:429) INFO: max output length: 49 +2024-01-17 01:22:00,725 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:00,775 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:00,775 (beam_search:476) INFO: -4.83 * 1.0 = -4.83 for ctc +2024-01-17 01:22:00,775 (beam_search:479) INFO: total log probability: -4.83 +2024-01-17 01:22:00,775 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:00,775 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:00,775 (beam_search:483) INFO: best hypo: ISUNNONWHTHERPEACULSEMPY + +2024-01-17 01:22:00,776 (asr_inference:494) INFO: speech length: 30400 +2024-01-17 01:22:00,783 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:22:00,783 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:22:00,783 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:00,824 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:00,825 (beam_search:476) INFO: -4.72 * 1.0 = -4.72 for ctc +2024-01-17 01:22:00,825 (beam_search:479) INFO: total log probability: -4.72 +2024-01-17 01:22:00,825 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:00,825 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:00,825 (beam_search:483) INFO: best hypo: HICHCOMSFROMTHEVERBA + +2024-01-17 01:22:00,826 (asr_inference:494) INFO: speech length: 56160 +2024-01-17 01:22:00,835 (beam_search:428) INFO: decoder input length: 85 +2024-01-17 01:22:00,835 (beam_search:429) INFO: max output length: 85 +2024-01-17 01:22:00,835 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:00,973 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:00,973 (beam_search:476) INFO: -13.35 * 1.0 = -13.35 for ctc +2024-01-17 01:22:00,973 (beam_search:479) INFO: total log probability: -13.35 +2024-01-17 01:22:00,973 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:22:00,973 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:00,973 (beam_search:483) INFO: best hypo: DISPEPORTINTLYAVALABLTTHOEWIHGEATERINANHALRESORES + +2024-01-17 01:22:00,974 (asr_inference:494) INFO: speech length: 41440 +2024-01-17 01:22:00,982 (beam_search:428) INFO: decoder input length: 62 +2024-01-17 01:22:00,982 (beam_search:429) INFO: max output length: 62 +2024-01-17 01:22:00,982 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:01,058 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:01,058 (beam_search:476) INFO: -9.63 * 1.0 = -9.63 for ctc +2024-01-17 01:22:01,058 (beam_search:479) INFO: total log probability: -9.63 +2024-01-17 01:22:01,058 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:22:01,058 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:01,059 (beam_search:483) INFO: best hypo: YUMNTHRETSTOTHSVIVLOFMANYSPACES + +2024-01-17 01:22:01,060 (asr_inference:494) INFO: speech length: 17440 +2024-01-17 01:22:01,066 (beam_search:428) INFO: decoder input length: 25 +2024-01-17 01:22:01,066 (beam_search:429) INFO: max output length: 25 +2024-01-17 01:22:01,066 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:01,084 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:01,084 (beam_search:476) INFO: -4.87 * 1.0 = -4.87 for ctc +2024-01-17 01:22:01,084 (beam_search:479) INFO: total log probability: -4.87 +2024-01-17 01:22:01,084 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:22:01,084 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:01,085 (beam_search:483) INFO: best hypo: EVENMORDIFCALT + +2024-01-17 01:22:01,086 (asr_inference:494) INFO: speech length: 38560 +2024-01-17 01:22:01,093 (beam_search:428) INFO: decoder input length: 58 +2024-01-17 01:22:01,093 (beam_search:429) INFO: max output length: 58 +2024-01-17 01:22:01,093 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:01,164 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:01,164 (beam_search:476) INFO: -7.88 * 1.0 = -7.88 for ctc +2024-01-17 01:22:01,164 (beam_search:479) INFO: total log probability: -7.88 +2024-01-17 01:22:01,164 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:01,164 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:01,164 (beam_search:483) INFO: best hypo: ANDTWETYWNSPECESOFICEANIGDOLFON + +2024-01-17 01:22:01,165 (asr_inference:494) INFO: speech length: 23520 +2024-01-17 01:22:01,172 (beam_search:428) INFO: decoder input length: 34 +2024-01-17 01:22:01,172 (beam_search:429) INFO: max output length: 34 +2024-01-17 01:22:01,172 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:01,199 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:01,199 (beam_search:476) INFO: -2.47 * 1.0 = -2.47 for ctc +2024-01-17 01:22:01,199 (beam_search:479) INFO: total log probability: -2.47 +2024-01-17 01:22:01,199 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:22:01,199 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:01,199 (beam_search:483) INFO: best hypo: ACHEVINGPRMOTION + +2024-01-17 01:22:01,200 (asr_inference:494) INFO: speech length: 21280 +2024-01-17 01:22:01,207 (beam_search:428) INFO: decoder input length: 31 +2024-01-17 01:22:01,207 (beam_search:429) INFO: max output length: 31 +2024-01-17 01:22:01,207 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:01,234 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:01,235 (beam_search:476) INFO: -10.95 * 1.0 = -10.95 for ctc +2024-01-17 01:22:01,235 (beam_search:479) INFO: total log probability: -10.95 +2024-01-17 01:22:01,235 (beam_search:480) INFO: normalized log probability: -0.46 +2024-01-17 01:22:01,235 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:01,235 (beam_search:483) INFO: best hypo: ERANTEWMINUSEASUMTION + +2024-01-17 01:22:01,236 (asr_inference:494) INFO: speech length: 18560 +2024-01-17 01:22:01,242 (beam_search:428) INFO: decoder input length: 26 +2024-01-17 01:22:01,242 (beam_search:429) INFO: max output length: 26 +2024-01-17 01:22:01,242 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:01,264 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:01,264 (beam_search:476) INFO: -2.93 * 1.0 = -2.93 for ctc +2024-01-17 01:22:01,264 (beam_search:479) INFO: total log probability: -2.93 +2024-01-17 01:22:01,264 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:22:01,264 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:01,264 (beam_search:483) INFO: best hypo: ONTHEFIRSTBELLIT + +2024-01-17 01:22:01,265 (asr_inference:494) INFO: speech length: 87520 +2024-01-17 01:22:01,276 (beam_search:428) INFO: decoder input length: 134 +2024-01-17 01:22:01,276 (beam_search:429) INFO: max output length: 134 +2024-01-17 01:22:01,276 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:01,565 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:01,565 (beam_search:476) INFO: -16.77 * 1.0 = -16.77 for ctc +2024-01-17 01:22:01,565 (beam_search:479) INFO: total log probability: -16.77 +2024-01-17 01:22:01,565 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:01,565 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:01,565 (beam_search:483) INFO: best hypo: STORYNDICATIVEOFTHERISEINGLOBLSIGNINGCSOFSHUPOLISHISTOLDBYJEAN + +2024-01-17 01:22:01,566 (asr_inference:494) INFO: speech length: 41280 +2024-01-17 01:22:01,574 (beam_search:428) INFO: decoder input length: 62 +2024-01-17 01:22:01,574 (beam_search:429) INFO: max output length: 62 +2024-01-17 01:22:01,574 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:01,652 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:01,652 (beam_search:476) INFO: -8.83 * 1.0 = -8.83 for ctc +2024-01-17 01:22:01,652 (beam_search:479) INFO: total log probability: -8.83 +2024-01-17 01:22:01,652 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:01,653 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:01,653 (beam_search:483) INFO: best hypo: WHCHSPAREHISERLYENTRUSTINPOLITICK + +2024-01-17 01:22:01,654 (asr_inference:494) INFO: speech length: 53920 +2024-01-17 01:22:01,662 (beam_search:428) INFO: decoder input length: 82 +2024-01-17 01:22:01,662 (beam_search:429) INFO: max output length: 82 +2024-01-17 01:22:01,662 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:01,780 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:01,781 (beam_search:476) INFO: -10.00 * 1.0 = -10.00 for ctc +2024-01-17 01:22:01,781 (beam_search:479) INFO: total log probability: -10.00 +2024-01-17 01:22:01,781 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:01,781 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:01,781 (beam_search:483) INFO: best hypo: WASCALEDDOLBEACHECXPROWINFULANDPATNTE + +2024-01-17 01:22:01,782 (asr_inference:494) INFO: speech length: 28160 +2024-01-17 01:22:01,789 (beam_search:428) INFO: decoder input length: 41 +2024-01-17 01:22:01,789 (beam_search:429) INFO: max output length: 41 +2024-01-17 01:22:01,789 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:01,827 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:01,827 (beam_search:476) INFO: -4.30 * 1.0 = -4.30 for ctc +2024-01-17 01:22:01,827 (beam_search:479) INFO: total log probability: -4.30 +2024-01-17 01:22:01,827 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:01,827 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:01,827 (beam_search:483) INFO: best hypo: OULDSAVEANDFINEFILSB + +2024-01-17 01:22:01,828 (asr_inference:494) INFO: speech length: 38720 +2024-01-17 01:22:01,836 (beam_search:428) INFO: decoder input length: 58 +2024-01-17 01:22:01,836 (beam_search:429) INFO: max output length: 58 +2024-01-17 01:22:01,836 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:01,905 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:01,905 (beam_search:476) INFO: -9.55 * 1.0 = -9.55 for ctc +2024-01-17 01:22:01,905 (beam_search:479) INFO: total log probability: -9.55 +2024-01-17 01:22:01,905 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:22:01,905 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:01,906 (beam_search:483) INFO: best hypo: ASTRLIANSNAKEBELONGTOSVENFAMLYES + +2024-01-17 01:22:01,907 (asr_inference:494) INFO: speech length: 16160 +2024-01-17 01:22:01,913 (beam_search:428) INFO: decoder input length: 23 +2024-01-17 01:22:01,913 (beam_search:429) INFO: max output length: 23 +2024-01-17 01:22:01,913 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:01,930 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:01,930 (beam_search:476) INFO: -4.57 * 1.0 = -4.57 for ctc +2024-01-17 01:22:01,930 (beam_search:479) INFO: total log probability: -4.57 +2024-01-17 01:22:01,930 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:22:01,930 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:01,930 (beam_search:483) INFO: best hypo: DVELPINPIYARS + +2024-01-17 01:22:01,931 (asr_inference:494) INFO: speech length: 29440 +2024-01-17 01:22:01,938 (beam_search:428) INFO: decoder input length: 43 +2024-01-17 01:22:01,938 (beam_search:429) INFO: max output length: 43 +2024-01-17 01:22:01,938 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:01,979 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:01,979 (beam_search:476) INFO: -4.62 * 1.0 = -4.62 for ctc +2024-01-17 01:22:01,979 (beam_search:479) INFO: total log probability: -4.62 +2024-01-17 01:22:01,979 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:01,979 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:01,979 (beam_search:483) INFO: best hypo: LINDSHARPLYSENCESPEAKAN + +2024-01-17 01:22:01,980 (asr_inference:494) INFO: speech length: 46720 +2024-01-17 01:22:01,988 (beam_search:428) INFO: decoder input length: 70 +2024-01-17 01:22:01,988 (beam_search:429) INFO: max output length: 70 +2024-01-17 01:22:01,988 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:02,080 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:02,080 (beam_search:476) INFO: -7.48 * 1.0 = -7.48 for ctc +2024-01-17 01:22:02,080 (beam_search:479) INFO: total log probability: -7.48 +2024-01-17 01:22:02,081 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:02,081 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:02,081 (beam_search:483) INFO: best hypo: WASRECORDEDINTHIRLYONAFORTRACKCOSET + +2024-01-17 01:22:02,082 (asr_inference:494) INFO: speech length: 21440 +2024-01-17 01:22:02,089 (beam_search:428) INFO: decoder input length: 31 +2024-01-17 01:22:02,089 (beam_search:429) INFO: max output length: 31 +2024-01-17 01:22:02,089 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:02,116 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:02,116 (beam_search:476) INFO: -6.43 * 1.0 = -6.43 for ctc +2024-01-17 01:22:02,116 (beam_search:479) INFO: total log probability: -6.43 +2024-01-17 01:22:02,116 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:22:02,116 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:02,116 (beam_search:483) INFO: best hypo: NORMASIMPROVEMENTIN + +2024-01-17 01:22:02,117 (asr_inference:494) INFO: speech length: 24960 +2024-01-17 01:22:02,124 (beam_search:428) INFO: decoder input length: 36 +2024-01-17 01:22:02,124 (beam_search:429) INFO: max output length: 36 +2024-01-17 01:22:02,124 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:02,155 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:02,155 (beam_search:476) INFO: -10.57 * 1.0 = -10.57 for ctc +2024-01-17 01:22:02,155 (beam_search:479) INFO: total log probability: -10.57 +2024-01-17 01:22:02,155 (beam_search:480) INFO: normalized log probability: -0.44 +2024-01-17 01:22:02,155 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:02,155 (beam_search:483) INFO: best hypo: RBNANDWRORLLEGUSTAC + +2024-01-17 01:22:02,156 (asr_inference:494) INFO: speech length: 16640 +2024-01-17 01:22:02,163 (beam_search:428) INFO: decoder input length: 23 +2024-01-17 01:22:02,163 (beam_search:429) INFO: max output length: 23 +2024-01-17 01:22:02,163 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:02,179 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:02,179 (beam_search:476) INFO: -2.79 * 1.0 = -2.79 for ctc +2024-01-17 01:22:02,179 (beam_search:479) INFO: total log probability: -2.79 +2024-01-17 01:22:02,179 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:02,179 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:02,179 (beam_search:483) INFO: best hypo: ACHPLYERBGINS + +2024-01-17 01:22:02,181 (asr_inference:494) INFO: speech length: 51200 +2024-01-17 01:22:02,189 (beam_search:428) INFO: decoder input length: 77 +2024-01-17 01:22:02,189 (beam_search:429) INFO: max output length: 77 +2024-01-17 01:22:02,189 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:02,294 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:02,294 (beam_search:476) INFO: -9.29 * 1.0 = -9.29 for ctc +2024-01-17 01:22:02,294 (beam_search:479) INFO: total log probability: -9.29 +2024-01-17 01:22:02,294 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:02,294 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:02,294 (beam_search:483) INFO: best hypo: JESTASINSPIREDMANYCOMENATORILEPUSLES + +2024-01-17 01:22:02,295 (asr_inference:494) INFO: speech length: 16960 +2024-01-17 01:22:02,302 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:22:02,302 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:22:02,302 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:02,321 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:02,321 (beam_search:476) INFO: -3.75 * 1.0 = -3.75 for ctc +2024-01-17 01:22:02,321 (beam_search:479) INFO: total log probability: -3.75 +2024-01-17 01:22:02,321 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:02,321 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:02,321 (beam_search:483) INFO: best hypo: ORHEOMANIMADGE + +2024-01-17 01:22:02,323 (asr_inference:494) INFO: speech length: 21280 +2024-01-17 01:22:02,329 (beam_search:428) INFO: decoder input length: 31 +2024-01-17 01:22:02,330 (beam_search:429) INFO: max output length: 31 +2024-01-17 01:22:02,330 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:02,351 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:02,351 (beam_search:476) INFO: -4.60 * 1.0 = -4.60 for ctc +2024-01-17 01:22:02,351 (beam_search:479) INFO: total log probability: -4.60 +2024-01-17 01:22:02,351 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:22:02,351 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:02,351 (beam_search:483) INFO: best hypo: ELASPIRTEDTAPS + +2024-01-17 01:22:02,352 (asr_inference:494) INFO: speech length: 24320 +2024-01-17 01:22:02,359 (beam_search:428) INFO: decoder input length: 35 +2024-01-17 01:22:02,359 (beam_search:429) INFO: max output length: 35 +2024-01-17 01:22:02,359 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:02,393 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:02,394 (beam_search:476) INFO: -8.99 * 1.0 = -8.99 for ctc +2024-01-17 01:22:02,394 (beam_search:479) INFO: total log probability: -8.99 +2024-01-17 01:22:02,394 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:22:02,394 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:02,394 (beam_search:483) INFO: best hypo: ANTANDIESENSNTOTHOTIO + +2024-01-17 01:22:02,395 (asr_inference:494) INFO: speech length: 33920 +2024-01-17 01:22:02,402 (beam_search:428) INFO: decoder input length: 50 +2024-01-17 01:22:02,402 (beam_search:429) INFO: max output length: 50 +2024-01-17 01:22:02,402 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:02,459 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:02,459 (beam_search:476) INFO: -7.81 * 1.0 = -7.81 for ctc +2024-01-17 01:22:02,459 (beam_search:479) INFO: total log probability: -7.81 +2024-01-17 01:22:02,459 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:02,459 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:02,459 (beam_search:483) INFO: best hypo: PRESINPROTEMPOROFTHESTATSENIT + +2024-01-17 01:22:02,460 (asr_inference:494) INFO: speech length: 30400 +2024-01-17 01:22:02,467 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:22:02,467 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:22:02,467 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:02,520 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:02,520 (beam_search:476) INFO: -8.42 * 1.0 = -8.42 for ctc +2024-01-17 01:22:02,520 (beam_search:479) INFO: total log probability: -8.42 +2024-01-17 01:22:02,520 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:02,520 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:02,520 (beam_search:483) INFO: best hypo: ISHOPCANMOVEANYNUMBEROFSQUARES + +2024-01-17 01:22:02,522 (asr_inference:494) INFO: speech length: 20000 +2024-01-17 01:22:02,528 (beam_search:428) INFO: decoder input length: 29 +2024-01-17 01:22:02,528 (beam_search:429) INFO: max output length: 29 +2024-01-17 01:22:02,528 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:02,551 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:02,551 (beam_search:476) INFO: -5.56 * 1.0 = -5.56 for ctc +2024-01-17 01:22:02,551 (beam_search:479) INFO: total log probability: -5.56 +2024-01-17 01:22:02,551 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:22:02,551 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:02,551 (beam_search:483) INFO: best hypo: PEHEINSIDTHESCOL + +2024-01-17 01:22:02,553 (asr_inference:494) INFO: speech length: 21280 +2024-01-17 01:22:02,559 (beam_search:428) INFO: decoder input length: 31 +2024-01-17 01:22:02,559 (beam_search:429) INFO: max output length: 31 +2024-01-17 01:22:02,559 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:02,586 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:02,586 (beam_search:476) INFO: -4.75 * 1.0 = -4.75 for ctc +2024-01-17 01:22:02,586 (beam_search:479) INFO: total log probability: -4.75 +2024-01-17 01:22:02,586 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:02,586 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:02,586 (beam_search:483) INFO: best hypo: INCONACTSHERNESWIT + +2024-01-17 01:22:02,587 (asr_inference:494) INFO: speech length: 46720 +2024-01-17 01:22:02,595 (beam_search:428) INFO: decoder input length: 70 +2024-01-17 01:22:02,595 (beam_search:429) INFO: max output length: 70 +2024-01-17 01:22:02,595 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:02,683 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:02,683 (beam_search:476) INFO: -5.04 * 1.0 = -5.04 for ctc +2024-01-17 01:22:02,683 (beam_search:479) INFO: total log probability: -5.04 +2024-01-17 01:22:02,683 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:22:02,683 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:02,683 (beam_search:483) INFO: best hypo: ONTRESOFTHEESTENPALIARKTICFLYWAY + +2024-01-17 01:22:02,684 (asr_inference:494) INFO: speech length: 40640 +2024-01-17 01:22:02,692 (beam_search:428) INFO: decoder input length: 61 +2024-01-17 01:22:02,692 (beam_search:429) INFO: max output length: 61 +2024-01-17 01:22:02,692 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:02,772 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:02,772 (beam_search:476) INFO: -10.41 * 1.0 = -10.41 for ctc +2024-01-17 01:22:02,772 (beam_search:479) INFO: total log probability: -10.41 +2024-01-17 01:22:02,772 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:22:02,772 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:02,773 (beam_search:483) INFO: best hypo: AINALSDTISTIKSESTMATETHEPOPULATIONIN + +2024-01-17 01:22:02,774 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 01:22:02,781 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:22:02,781 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:22:02,781 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:02,837 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:02,837 (beam_search:476) INFO: -5.32 * 1.0 = -5.32 for ctc +2024-01-17 01:22:02,837 (beam_search:479) INFO: total log probability: -5.32 +2024-01-17 01:22:02,837 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:02,837 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:02,838 (beam_search:483) INFO: best hypo: ONDISPUTEDWORLDCHESTCHAMPIAN + +2024-01-17 01:22:02,839 (asr_inference:494) INFO: speech length: 27040 +2024-01-17 01:22:02,846 (beam_search:428) INFO: decoder input length: 40 +2024-01-17 01:22:02,846 (beam_search:429) INFO: max output length: 40 +2024-01-17 01:22:02,846 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:02,876 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:02,876 (beam_search:476) INFO: -8.34 * 1.0 = -8.34 for ctc +2024-01-17 01:22:02,876 (beam_search:479) INFO: total log probability: -8.34 +2024-01-17 01:22:02,876 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:22:02,876 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:02,877 (beam_search:483) INFO: best hypo: YROULLCAPEABINCEA + +2024-01-17 01:22:02,878 (asr_inference:494) INFO: speech length: 133280 +2024-01-17 01:22:02,891 (beam_search:428) INFO: decoder input length: 206 +2024-01-17 01:22:02,891 (beam_search:429) INFO: max output length: 206 +2024-01-17 01:22:02,891 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:03,418 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:03,418 (beam_search:476) INFO: -14.80 * 1.0 = -14.80 for ctc +2024-01-17 01:22:03,419 (beam_search:479) INFO: total log probability: -14.80 +2024-01-17 01:22:03,419 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:03,419 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:03,419 (beam_search:483) INFO: best hypo: ERINACTEDBYTHEGENRALASEMBLYWASAMESURRATIALYSEVGRGATINGTHESTATESRELROTCARS + +2024-01-17 01:22:03,420 (asr_inference:494) INFO: speech length: 21120 +2024-01-17 01:22:03,427 (beam_search:428) INFO: decoder input length: 30 +2024-01-17 01:22:03,427 (beam_search:429) INFO: max output length: 30 +2024-01-17 01:22:03,427 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:03,448 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:03,448 (beam_search:476) INFO: -4.58 * 1.0 = -4.58 for ctc +2024-01-17 01:22:03,448 (beam_search:479) INFO: total log probability: -4.58 +2024-01-17 01:22:03,448 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:22:03,448 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:03,448 (beam_search:483) INFO: best hypo: WHCHRAPSALMOST + +2024-01-17 01:22:03,450 (asr_inference:494) INFO: speech length: 42880 +2024-01-17 01:22:03,458 (beam_search:428) INFO: decoder input length: 64 +2024-01-17 01:22:03,458 (beam_search:429) INFO: max output length: 64 +2024-01-17 01:22:03,458 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:03,537 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:03,537 (beam_search:476) INFO: -6.83 * 1.0 = -6.83 for ctc +2024-01-17 01:22:03,537 (beam_search:479) INFO: total log probability: -6.83 +2024-01-17 01:22:03,537 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:03,537 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:03,538 (beam_search:483) INFO: best hypo: SECTPRTECSALNATIVEFORNAANDPROVIDS + +2024-01-17 01:22:03,539 (asr_inference:494) INFO: speech length: 67680 +2024-01-17 01:22:03,548 (beam_search:428) INFO: decoder input length: 103 +2024-01-17 01:22:03,548 (beam_search:429) INFO: max output length: 103 +2024-01-17 01:22:03,548 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:03,724 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:03,724 (beam_search:476) INFO: -7.88 * 1.0 = -7.88 for ctc +2024-01-17 01:22:03,724 (beam_search:479) INFO: total log probability: -7.88 +2024-01-17 01:22:03,724 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:22:03,724 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:03,724 (beam_search:483) INFO: best hypo: HEREASTHEFEMALESPECULMISDARKBRONBOREDWITHWHIGHT + +2024-01-17 01:22:03,726 (asr_inference:494) INFO: speech length: 18560 +2024-01-17 01:22:03,732 (beam_search:428) INFO: decoder input length: 26 +2024-01-17 01:22:03,732 (beam_search:429) INFO: max output length: 26 +2024-01-17 01:22:03,732 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:03,750 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:03,750 (beam_search:476) INFO: -5.62 * 1.0 = -5.62 for ctc +2024-01-17 01:22:03,750 (beam_search:479) INFO: total log probability: -5.62 +2024-01-17 01:22:03,750 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:22:03,750 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:03,750 (beam_search:483) INFO: best hypo: OTERYECNTULSO + +2024-01-17 01:22:03,752 (asr_inference:494) INFO: speech length: 52640 +2024-01-17 01:22:03,760 (beam_search:428) INFO: decoder input length: 80 +2024-01-17 01:22:03,760 (beam_search:429) INFO: max output length: 80 +2024-01-17 01:22:03,760 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:03,830 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:03,830 (beam_search:476) INFO: -6.07 * 1.0 = -6.07 for ctc +2024-01-17 01:22:03,830 (beam_search:479) INFO: total log probability: -6.07 +2024-01-17 01:22:03,830 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:03,830 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:03,830 (beam_search:483) INFO: best hypo: NINTEANTWELEINROSFEARN + +2024-01-17 01:22:03,831 (asr_inference:494) INFO: speech length: 54880 +2024-01-17 01:22:03,840 (beam_search:428) INFO: decoder input length: 83 +2024-01-17 01:22:03,840 (beam_search:429) INFO: max output length: 83 +2024-01-17 01:22:03,840 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:03,966 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:03,966 (beam_search:476) INFO: -10.23 * 1.0 = -10.23 for ctc +2024-01-17 01:22:03,966 (beam_search:479) INFO: total log probability: -10.23 +2024-01-17 01:22:03,966 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:03,966 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:03,967 (beam_search:483) INFO: best hypo: DIGNOCISEISGENRLYMADEWIHASETESCANOFTHEHEAD + +2024-01-17 01:22:03,968 (asr_inference:494) INFO: speech length: 58080 +2024-01-17 01:22:03,977 (beam_search:428) INFO: decoder input length: 88 +2024-01-17 01:22:03,977 (beam_search:429) INFO: max output length: 88 +2024-01-17 01:22:03,977 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:04,108 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:04,108 (beam_search:476) INFO: -7.95 * 1.0 = -7.95 for ctc +2024-01-17 01:22:04,108 (beam_search:479) INFO: total log probability: -7.95 +2024-01-17 01:22:04,108 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:04,108 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:04,109 (beam_search:483) INFO: best hypo: THEFIRSTGENRALYRECONISEDWORLDCHESSCHAMPIAN + +2024-01-17 01:22:04,110 (asr_inference:494) INFO: speech length: 25440 +2024-01-17 01:22:04,117 (beam_search:428) INFO: decoder input length: 37 +2024-01-17 01:22:04,117 (beam_search:429) INFO: max output length: 37 +2024-01-17 01:22:04,117 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:04,151 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:04,151 (beam_search:476) INFO: -4.77 * 1.0 = -4.77 for ctc +2024-01-17 01:22:04,151 (beam_search:479) INFO: total log probability: -4.77 +2024-01-17 01:22:04,151 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:04,151 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:04,151 (beam_search:483) INFO: best hypo: HEPPYANDITINGONWEPAR + +2024-01-17 01:22:04,152 (asr_inference:494) INFO: speech length: 43840 +2024-01-17 01:22:04,160 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:22:04,160 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:22:04,160 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:04,243 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:04,243 (beam_search:476) INFO: -6.67 * 1.0 = -6.67 for ctc +2024-01-17 01:22:04,243 (beam_search:479) INFO: total log probability: -6.67 +2024-01-17 01:22:04,243 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:04,243 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:04,243 (beam_search:483) INFO: best hypo: ADRELACETHEIRELBUMSBOTHTOCDYAND + +2024-01-17 01:22:04,244 (asr_inference:494) INFO: speech length: 21120 +2024-01-17 01:22:04,251 (beam_search:428) INFO: decoder input length: 30 +2024-01-17 01:22:04,251 (beam_search:429) INFO: max output length: 30 +2024-01-17 01:22:04,251 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:04,278 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:04,278 (beam_search:476) INFO: -4.61 * 1.0 = -4.61 for ctc +2024-01-17 01:22:04,278 (beam_search:479) INFO: total log probability: -4.61 +2024-01-17 01:22:04,278 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:04,278 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:04,279 (beam_search:483) INFO: best hypo: WATESARONTHECONTINEN + +2024-01-17 01:22:04,280 (asr_inference:494) INFO: speech length: 28960 +2024-01-17 01:22:04,287 (beam_search:428) INFO: decoder input length: 43 +2024-01-17 01:22:04,287 (beam_search:429) INFO: max output length: 43 +2024-01-17 01:22:04,287 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:04,329 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:04,329 (beam_search:476) INFO: -5.54 * 1.0 = -5.54 for ctc +2024-01-17 01:22:04,329 (beam_search:479) INFO: total log probability: -5.54 +2024-01-17 01:22:04,329 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:04,329 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:04,330 (beam_search:483) INFO: best hypo: FTHERAGEPERSINLSTEARIOS + +2024-01-17 01:22:04,331 (asr_inference:494) INFO: speech length: 45760 +2024-01-17 01:22:04,339 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:22:04,339 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:22:04,339 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:04,441 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:04,441 (beam_search:476) INFO: -9.26 * 1.0 = -9.26 for ctc +2024-01-17 01:22:04,441 (beam_search:479) INFO: total log probability: -9.26 +2024-01-17 01:22:04,441 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:04,441 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:04,441 (beam_search:483) INFO: best hypo: NDFEYOUWMETERSANDRECOARINGLEVLCONTROULSON + +2024-01-17 01:22:04,443 (asr_inference:494) INFO: speech length: 16640 +2024-01-17 01:22:04,449 (beam_search:428) INFO: decoder input length: 23 +2024-01-17 01:22:04,449 (beam_search:429) INFO: max output length: 23 +2024-01-17 01:22:04,449 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:04,466 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:04,466 (beam_search:476) INFO: -3.13 * 1.0 = -3.13 for ctc +2024-01-17 01:22:04,466 (beam_search:479) INFO: total log probability: -3.13 +2024-01-17 01:22:04,466 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:04,466 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:04,467 (beam_search:483) INFO: best hypo: POINTOEALTIME + +2024-01-17 01:22:04,468 (asr_inference:494) INFO: speech length: 33760 +2024-01-17 01:22:04,475 (beam_search:428) INFO: decoder input length: 50 +2024-01-17 01:22:04,475 (beam_search:429) INFO: max output length: 50 +2024-01-17 01:22:04,475 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:04,533 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:04,533 (beam_search:476) INFO: -7.90 * 1.0 = -7.90 for ctc +2024-01-17 01:22:04,533 (beam_search:479) INFO: total log probability: -7.90 +2024-01-17 01:22:04,533 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:04,533 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:04,533 (beam_search:483) INFO: best hypo: NDITOFENDESTRORDTHEPLAABILITY + +2024-01-17 01:22:04,535 (asr_inference:494) INFO: speech length: 27040 +2024-01-17 01:22:04,542 (beam_search:428) INFO: decoder input length: 40 +2024-01-17 01:22:04,542 (beam_search:429) INFO: max output length: 40 +2024-01-17 01:22:04,542 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:04,562 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:04,562 (beam_search:476) INFO: -3.75 * 1.0 = -3.75 for ctc +2024-01-17 01:22:04,562 (beam_search:479) INFO: total log probability: -3.75 +2024-01-17 01:22:04,562 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:22:04,562 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:04,562 (beam_search:483) INFO: best hypo: CONFUTION + +2024-01-17 01:22:04,563 (asr_inference:494) INFO: speech length: 55200 +2024-01-17 01:22:04,571 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:22:04,572 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:22:04,572 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:04,715 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:04,715 (beam_search:476) INFO: -10.72 * 1.0 = -10.72 for ctc +2024-01-17 01:22:04,715 (beam_search:479) INFO: total log probability: -10.72 +2024-01-17 01:22:04,715 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:04,715 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:04,715 (beam_search:483) INFO: best hypo: EUIVELENTTOTHEQUSTIONOFWHTHERECXISAMEMBEROFCMPO + +2024-01-17 01:22:04,716 (asr_inference:494) INFO: speech length: 23680 +2024-01-17 01:22:04,723 (beam_search:428) INFO: decoder input length: 34 +2024-01-17 01:22:04,723 (beam_search:429) INFO: max output length: 34 +2024-01-17 01:22:04,723 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:04,753 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:04,753 (beam_search:476) INFO: -3.95 * 1.0 = -3.95 for ctc +2024-01-17 01:22:04,753 (beam_search:479) INFO: total log probability: -3.95 +2024-01-17 01:22:04,753 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:04,753 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:04,753 (beam_search:483) INFO: best hypo: MOSETOITHLASTDRANK + +2024-01-17 01:22:04,754 (asr_inference:494) INFO: speech length: 16640 +2024-01-17 01:22:04,761 (beam_search:428) INFO: decoder input length: 23 +2024-01-17 01:22:04,761 (beam_search:429) INFO: max output length: 23 +2024-01-17 01:22:04,761 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:04,776 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:04,776 (beam_search:476) INFO: -3.03 * 1.0 = -3.03 for ctc +2024-01-17 01:22:04,776 (beam_search:479) INFO: total log probability: -3.03 +2024-01-17 01:22:04,776 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:04,776 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:04,776 (beam_search:483) INFO: best hypo: POSTHENDERISM + +2024-01-17 01:22:04,777 (asr_inference:494) INFO: speech length: 31840 +2024-01-17 01:22:04,785 (beam_search:428) INFO: decoder input length: 47 +2024-01-17 01:22:04,785 (beam_search:429) INFO: max output length: 47 +2024-01-17 01:22:04,785 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:04,834 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:04,834 (beam_search:476) INFO: -7.03 * 1.0 = -7.03 for ctc +2024-01-17 01:22:04,835 (beam_search:479) INFO: total log probability: -7.03 +2024-01-17 01:22:04,835 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:04,835 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:04,835 (beam_search:483) INFO: best hypo: OMPACTCASETQUIKLYFOUNDYOUS + +2024-01-17 01:22:04,836 (asr_inference:494) INFO: speech length: 28320 +2024-01-17 01:22:04,843 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:22:04,843 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:22:04,843 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:04,885 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:04,886 (beam_search:476) INFO: -6.97 * 1.0 = -6.97 for ctc +2024-01-17 01:22:04,886 (beam_search:479) INFO: total log probability: -6.97 +2024-01-17 01:22:04,886 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:22:04,886 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:04,886 (beam_search:483) INFO: best hypo: EFORHUNDRNDTHERDYTHEEFE + +2024-01-17 01:22:04,887 (asr_inference:494) INFO: speech length: 40160 +2024-01-17 01:22:04,894 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:22:04,894 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:22:04,894 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:04,975 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:04,975 (beam_search:476) INFO: -10.21 * 1.0 = -10.21 for ctc +2024-01-17 01:22:04,975 (beam_search:479) INFO: total log probability: -10.21 +2024-01-17 01:22:04,975 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:22:04,975 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:04,975 (beam_search:483) INFO: best hypo: INGSWHICHRESULTINASPESIFICKTHIHEAPON + +2024-01-17 01:22:04,977 (asr_inference:494) INFO: speech length: 21120 +2024-01-17 01:22:04,983 (beam_search:428) INFO: decoder input length: 30 +2024-01-17 01:22:04,983 (beam_search:429) INFO: max output length: 30 +2024-01-17 01:22:04,983 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:05,011 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:05,011 (beam_search:476) INFO: -7.31 * 1.0 = -7.31 for ctc +2024-01-17 01:22:05,011 (beam_search:479) INFO: total log probability: -7.31 +2024-01-17 01:22:05,011 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:22:05,011 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:05,011 (beam_search:483) INFO: best hypo: FOURNINTENHANTYSEVEN + +2024-01-17 01:22:05,012 (asr_inference:494) INFO: speech length: 18240 +2024-01-17 01:22:05,018 (beam_search:428) INFO: decoder input length: 26 +2024-01-17 01:22:05,018 (beam_search:429) INFO: max output length: 26 +2024-01-17 01:22:05,018 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:05,039 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:05,039 (beam_search:476) INFO: -4.30 * 1.0 = -4.30 for ctc +2024-01-17 01:22:05,040 (beam_search:479) INFO: total log probability: -4.30 +2024-01-17 01:22:05,040 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:05,040 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:05,040 (beam_search:483) INFO: best hypo: MICATIONSANDHELF + +2024-01-17 01:22:05,041 (asr_inference:494) INFO: speech length: 27360 +2024-01-17 01:22:05,048 (beam_search:428) INFO: decoder input length: 40 +2024-01-17 01:22:05,048 (beam_search:429) INFO: max output length: 40 +2024-01-17 01:22:05,048 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:05,085 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:05,085 (beam_search:476) INFO: -6.11 * 1.0 = -6.11 for ctc +2024-01-17 01:22:05,085 (beam_search:479) INFO: total log probability: -6.11 +2024-01-17 01:22:05,085 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:22:05,085 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:05,085 (beam_search:483) INFO: best hypo: EAYATCHEINAPEURSONNO + +2024-01-17 01:22:05,086 (asr_inference:494) INFO: speech length: 85440 +2024-01-17 01:22:05,097 (beam_search:428) INFO: decoder input length: 131 +2024-01-17 01:22:05,097 (beam_search:429) INFO: max output length: 131 +2024-01-17 01:22:05,097 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:05,384 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:05,384 (beam_search:476) INFO: -17.30 * 1.0 = -17.30 for ctc +2024-01-17 01:22:05,384 (beam_search:479) INFO: total log probability: -17.30 +2024-01-17 01:22:05,384 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:05,384 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:05,385 (beam_search:483) INFO: best hypo: SOMBREANDSPISIFIDTHATTHEYMAYALSOBEUSEDONOTHERNONPOROSSMIPTERIALS + +2024-01-17 01:22:05,386 (asr_inference:494) INFO: speech length: 28000 +2024-01-17 01:22:05,393 (beam_search:428) INFO: decoder input length: 41 +2024-01-17 01:22:05,393 (beam_search:429) INFO: max output length: 41 +2024-01-17 01:22:05,393 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:05,430 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:05,430 (beam_search:476) INFO: -7.59 * 1.0 = -7.59 for ctc +2024-01-17 01:22:05,430 (beam_search:479) INFO: total log probability: -7.59 +2024-01-17 01:22:05,430 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:22:05,430 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:05,430 (beam_search:483) INFO: best hypo: HEPOSEIBLYCONDPESIFIG + +2024-01-17 01:22:05,431 (asr_inference:494) INFO: speech length: 16320 +2024-01-17 01:22:05,438 (beam_search:428) INFO: decoder input length: 23 +2024-01-17 01:22:05,438 (beam_search:429) INFO: max output length: 23 +2024-01-17 01:22:05,438 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:05,453 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:05,453 (beam_search:476) INFO: -2.75 * 1.0 = -2.75 for ctc +2024-01-17 01:22:05,453 (beam_search:479) INFO: total log probability: -2.75 +2024-01-17 01:22:05,453 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:05,453 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:05,454 (beam_search:483) INFO: best hypo: MATORSINLACX + +2024-01-17 01:22:05,455 (asr_inference:494) INFO: speech length: 33440 +2024-01-17 01:22:05,462 (beam_search:428) INFO: decoder input length: 50 +2024-01-17 01:22:05,462 (beam_search:429) INFO: max output length: 50 +2024-01-17 01:22:05,462 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:05,512 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:05,512 (beam_search:476) INFO: -4.24 * 1.0 = -4.24 for ctc +2024-01-17 01:22:05,512 (beam_search:479) INFO: total log probability: -4.24 +2024-01-17 01:22:05,512 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:22:05,512 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:05,512 (beam_search:483) INFO: best hypo: ITHOUTFIFTYMORETRINGBROA + +2024-01-17 01:22:05,513 (asr_inference:494) INFO: speech length: 40960 +2024-01-17 01:22:05,521 (beam_search:428) INFO: decoder input length: 61 +2024-01-17 01:22:05,521 (beam_search:429) INFO: max output length: 61 +2024-01-17 01:22:05,521 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:05,606 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:05,606 (beam_search:476) INFO: -18.19 * 1.0 = -18.19 for ctc +2024-01-17 01:22:05,607 (beam_search:479) INFO: total log probability: -18.19 +2024-01-17 01:22:05,607 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:22:05,607 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:05,607 (beam_search:483) INFO: best hypo: HISICHACSINPOULESRESPECTIELYTOKASLOFAKI + +2024-01-17 01:22:05,608 (asr_inference:494) INFO: speech length: 23360 +2024-01-17 01:22:05,615 (beam_search:428) INFO: decoder input length: 34 +2024-01-17 01:22:05,615 (beam_search:429) INFO: max output length: 34 +2024-01-17 01:22:05,615 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:05,642 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:05,642 (beam_search:476) INFO: -6.19 * 1.0 = -6.19 for ctc +2024-01-17 01:22:05,642 (beam_search:479) INFO: total log probability: -6.19 +2024-01-17 01:22:05,642 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:22:05,642 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:05,643 (beam_search:483) INFO: best hypo: AVSHONMLETEDISFIR + +2024-01-17 01:22:05,644 (asr_inference:494) INFO: speech length: 34400 +2024-01-17 01:22:05,651 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:22:05,651 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:22:05,651 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:05,714 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:05,714 (beam_search:476) INFO: -5.77 * 1.0 = -5.77 for ctc +2024-01-17 01:22:05,714 (beam_search:479) INFO: total log probability: -5.77 +2024-01-17 01:22:05,714 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:22:05,714 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:05,714 (beam_search:483) INFO: best hypo: EBLEADINGRISTREMAINSOFROUNDFOLTY + +2024-01-17 01:22:05,715 (asr_inference:494) INFO: speech length: 16960 +2024-01-17 01:22:05,722 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:22:05,722 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:22:05,722 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:05,739 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:05,739 (beam_search:476) INFO: -4.24 * 1.0 = -4.24 for ctc +2024-01-17 01:22:05,739 (beam_search:479) INFO: total log probability: -4.24 +2024-01-17 01:22:05,739 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:22:05,739 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:05,739 (beam_search:483) INFO: best hypo: ILERITCHCKMAE + +2024-01-17 01:22:05,740 (asr_inference:494) INFO: speech length: 166880 +2024-01-17 01:22:05,756 (beam_search:428) INFO: decoder input length: 258 +2024-01-17 01:22:05,756 (beam_search:429) INFO: max output length: 258 +2024-01-17 01:22:05,756 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:06,850 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:06,850 (beam_search:476) INFO: -31.83 * 1.0 = -31.83 for ctc +2024-01-17 01:22:06,850 (beam_search:479) INFO: total log probability: -31.83 +2024-01-17 01:22:06,850 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:06,850 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:06,851 (beam_search:483) INFO: best hypo: SOMESECULRHUMINSCONCIVEDRANDSHEUMINISMASANALSPRINGOFTHEEUMNUSTFRETHOUHTMOVEMENTANARGUYTHTRASHUMINISTDIFERFROMTHEEUMINIUSTPAINSTRMBYHAVIN + +2024-01-17 01:22:06,852 (asr_inference:494) INFO: speech length: 93120 +2024-01-17 01:22:06,864 (beam_search:428) INFO: decoder input length: 143 +2024-01-17 01:22:06,864 (beam_search:429) INFO: max output length: 143 +2024-01-17 01:22:06,864 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:07,120 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:07,120 (beam_search:476) INFO: -8.62 * 1.0 = -8.62 for ctc +2024-01-17 01:22:07,120 (beam_search:479) INFO: total log probability: -8.62 +2024-01-17 01:22:07,120 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:22:07,120 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:07,120 (beam_search:483) INFO: best hypo: PINTALENESTANDCHICKAREVONERBLETOPRODATIONBYMAMLE + +2024-01-17 01:22:07,121 (asr_inference:494) INFO: speech length: 117440 +2024-01-17 01:22:07,134 (beam_search:428) INFO: decoder input length: 181 +2024-01-17 01:22:07,134 (beam_search:429) INFO: max output length: 181 +2024-01-17 01:22:07,134 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:07,692 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:07,692 (beam_search:476) INFO: -21.10 * 1.0 = -21.10 for ctc +2024-01-17 01:22:07,692 (beam_search:479) INFO: total log probability: -21.10 +2024-01-17 01:22:07,692 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:07,692 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:07,692 (beam_search:483) INFO: best hypo: NORTHERNPINTALISONOFTHESPECSHESTOWHICHTHEAGRMENTONTHECONSCERVATIONOFAFRACKANURAGIONMOGRITORYWATERBURD + +2024-01-17 01:22:07,694 (asr_inference:494) INFO: speech length: 31680 +2024-01-17 01:22:07,701 (beam_search:428) INFO: decoder input length: 47 +2024-01-17 01:22:07,701 (beam_search:429) INFO: max output length: 47 +2024-01-17 01:22:07,701 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:07,751 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:07,751 (beam_search:476) INFO: -6.71 * 1.0 = -6.71 for ctc +2024-01-17 01:22:07,751 (beam_search:479) INFO: total log probability: -6.71 +2024-01-17 01:22:07,751 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:07,751 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:07,751 (beam_search:483) INFO: best hypo: ANDISNEOEFOUNDONLYINTASMAI + +2024-01-17 01:22:07,752 (asr_inference:494) INFO: speech length: 64480 +2024-01-17 01:22:07,762 (beam_search:428) INFO: decoder input length: 98 +2024-01-17 01:22:07,762 (beam_search:429) INFO: max output length: 98 +2024-01-17 01:22:07,762 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:07,931 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:07,931 (beam_search:476) INFO: -10.37 * 1.0 = -10.37 for ctc +2024-01-17 01:22:07,931 (beam_search:479) INFO: total log probability: -10.37 +2024-01-17 01:22:07,931 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:07,931 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:07,931 (beam_search:483) INFO: best hypo: RPECTIVETHEIDAOFMINDUPLOATINGISASRTEDTOREPROSENT + +2024-01-17 01:22:07,932 (asr_inference:494) INFO: speech length: 33600 +2024-01-17 01:22:07,940 (beam_search:428) INFO: decoder input length: 50 +2024-01-17 01:22:07,940 (beam_search:429) INFO: max output length: 50 +2024-01-17 01:22:07,940 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:07,989 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:07,989 (beam_search:476) INFO: -7.75 * 1.0 = -7.75 for ctc +2024-01-17 01:22:07,989 (beam_search:479) INFO: total log probability: -7.75 +2024-01-17 01:22:07,989 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:22:07,989 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:07,989 (beam_search:483) INFO: best hypo: NAVRIGEOFTWENTONPERDAY + +2024-01-17 01:22:07,990 (asr_inference:494) INFO: speech length: 28800 +2024-01-17 01:22:07,997 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:22:07,997 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:22:07,997 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:08,037 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:08,037 (beam_search:476) INFO: -6.77 * 1.0 = -6.77 for ctc +2024-01-17 01:22:08,037 (beam_search:479) INFO: total log probability: -6.77 +2024-01-17 01:22:08,037 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:22:08,037 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:08,037 (beam_search:483) INFO: best hypo: HANWOLDFALOTHATPEECUL + +2024-01-17 01:22:08,038 (asr_inference:494) INFO: speech length: 30880 +2024-01-17 01:22:08,045 (beam_search:428) INFO: decoder input length: 46 +2024-01-17 01:22:08,045 (beam_search:429) INFO: max output length: 46 +2024-01-17 01:22:08,045 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:08,091 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:08,091 (beam_search:476) INFO: -4.72 * 1.0 = -4.72 for ctc +2024-01-17 01:22:08,091 (beam_search:479) INFO: total log probability: -4.72 +2024-01-17 01:22:08,091 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:08,091 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:08,091 (beam_search:483) INFO: best hypo: NDBLEDINGINTOVERISCHOMERS + +2024-01-17 01:22:08,093 (asr_inference:494) INFO: speech length: 31520 +2024-01-17 01:22:08,100 (beam_search:428) INFO: decoder input length: 47 +2024-01-17 01:22:08,100 (beam_search:429) INFO: max output length: 47 +2024-01-17 01:22:08,100 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:08,144 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:08,144 (beam_search:476) INFO: -4.11 * 1.0 = -4.11 for ctc +2024-01-17 01:22:08,144 (beam_search:479) INFO: total log probability: -4.11 +2024-01-17 01:22:08,144 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:08,144 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:08,144 (beam_search:483) INFO: best hypo: ANDLTHETOGLIDBETWENTRS + +2024-01-17 01:22:08,145 (asr_inference:494) INFO: speech length: 34080 +2024-01-17 01:22:08,152 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:22:08,152 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:22:08,152 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:08,211 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:08,211 (beam_search:476) INFO: -8.99 * 1.0 = -8.99 for ctc +2024-01-17 01:22:08,211 (beam_search:479) INFO: total log probability: -8.99 +2024-01-17 01:22:08,211 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:22:08,211 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:08,212 (beam_search:483) INFO: best hypo: FTHESPROBONSWRFICINTLYSALVEABL + +2024-01-17 01:22:08,213 (asr_inference:494) INFO: speech length: 16800 +2024-01-17 01:22:08,219 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:22:08,219 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:22:08,219 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:08,237 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:08,237 (beam_search:476) INFO: -4.87 * 1.0 = -4.87 for ctc +2024-01-17 01:22:08,237 (beam_search:479) INFO: total log probability: -4.87 +2024-01-17 01:22:08,237 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:22:08,237 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:08,237 (beam_search:483) INFO: best hypo: OALODGICALETIN + +2024-01-17 01:22:08,238 (asr_inference:494) INFO: speech length: 22240 +2024-01-17 01:22:08,245 (beam_search:428) INFO: decoder input length: 32 +2024-01-17 01:22:08,245 (beam_search:429) INFO: max output length: 32 +2024-01-17 01:22:08,245 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:08,267 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:08,267 (beam_search:476) INFO: -2.13 * 1.0 = -2.13 for ctc +2024-01-17 01:22:08,267 (beam_search:479) INFO: total log probability: -2.13 +2024-01-17 01:22:08,267 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:22:08,267 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:08,267 (beam_search:483) INFO: best hypo: ROKWHENFLUSHED + +2024-01-17 01:22:08,268 (asr_inference:494) INFO: speech length: 31520 +2024-01-17 01:22:08,276 (beam_search:428) INFO: decoder input length: 47 +2024-01-17 01:22:08,276 (beam_search:429) INFO: max output length: 47 +2024-01-17 01:22:08,276 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:08,326 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:08,326 (beam_search:476) INFO: -12.65 * 1.0 = -12.65 for ctc +2024-01-17 01:22:08,326 (beam_search:479) INFO: total log probability: -12.65 +2024-01-17 01:22:08,326 (beam_search:480) INFO: normalized log probability: -0.38 +2024-01-17 01:22:08,326 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:08,326 (beam_search:483) INFO: best hypo: INCLUDINGJERMNLTHOICEICNOLGY + +2024-01-17 01:22:08,327 (asr_inference:494) INFO: speech length: 29440 +2024-01-17 01:22:08,334 (beam_search:428) INFO: decoder input length: 43 +2024-01-17 01:22:08,334 (beam_search:429) INFO: max output length: 43 +2024-01-17 01:22:08,334 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:08,376 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:08,376 (beam_search:476) INFO: -9.11 * 1.0 = -9.11 for ctc +2024-01-17 01:22:08,376 (beam_search:479) INFO: total log probability: -9.11 +2024-01-17 01:22:08,376 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:22:08,376 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:08,376 (beam_search:483) INFO: best hypo: PERINEOFLETHESHUSORBOT + +2024-01-17 01:22:08,377 (asr_inference:494) INFO: speech length: 21920 +2024-01-17 01:22:08,384 (beam_search:428) INFO: decoder input length: 32 +2024-01-17 01:22:08,384 (beam_search:429) INFO: max output length: 32 +2024-01-17 01:22:08,384 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:08,407 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:08,407 (beam_search:476) INFO: -2.47 * 1.0 = -2.47 for ctc +2024-01-17 01:22:08,407 (beam_search:479) INFO: total log probability: -2.47 +2024-01-17 01:22:08,407 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:22:08,407 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:08,407 (beam_search:483) INFO: best hypo: NATENSICTYTHR + +2024-01-17 01:22:08,408 (asr_inference:494) INFO: speech length: 37920 +2024-01-17 01:22:08,416 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:22:08,416 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:22:08,416 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:08,485 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:08,485 (beam_search:476) INFO: -13.72 * 1.0 = -13.72 for ctc +2024-01-17 01:22:08,485 (beam_search:479) INFO: total log probability: -13.72 +2024-01-17 01:22:08,485 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:22:08,486 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:08,486 (beam_search:483) INFO: best hypo: MANIUFECTURSSHOCAYPODACKESOLSYSEL + +2024-01-17 01:22:08,487 (asr_inference:494) INFO: speech length: 38240 +2024-01-17 01:22:08,494 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:22:08,494 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:22:08,495 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:08,562 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:08,562 (beam_search:476) INFO: -10.17 * 1.0 = -10.17 for ctc +2024-01-17 01:22:08,562 (beam_search:479) INFO: total log probability: -10.17 +2024-01-17 01:22:08,562 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:22:08,562 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:08,562 (beam_search:483) INFO: best hypo: OTHEFIRSTNONSORIACHALLNGERSINC + +2024-01-17 01:22:08,563 (asr_inference:494) INFO: speech length: 29280 +2024-01-17 01:22:08,570 (beam_search:428) INFO: decoder input length: 43 +2024-01-17 01:22:08,570 (beam_search:429) INFO: max output length: 43 +2024-01-17 01:22:08,570 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:08,614 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:08,614 (beam_search:476) INFO: -5.79 * 1.0 = -5.79 for ctc +2024-01-17 01:22:08,614 (beam_search:479) INFO: total log probability: -5.79 +2024-01-17 01:22:08,614 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:08,614 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:08,614 (beam_search:483) INFO: best hypo: PONENTHASONLYTHECINGAND + +2024-01-17 01:22:08,615 (asr_inference:494) INFO: speech length: 16480 +2024-01-17 01:22:08,622 (beam_search:428) INFO: decoder input length: 23 +2024-01-17 01:22:08,622 (beam_search:429) INFO: max output length: 23 +2024-01-17 01:22:08,622 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:08,635 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:08,635 (beam_search:476) INFO: -2.76 * 1.0 = -2.76 for ctc +2024-01-17 01:22:08,635 (beam_search:479) INFO: total log probability: -2.76 +2024-01-17 01:22:08,635 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:08,635 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:08,636 (beam_search:483) INFO: best hypo: MAINARTICL + +2024-01-17 01:22:08,637 (asr_inference:494) INFO: speech length: 32320 +2024-01-17 01:22:08,644 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 01:22:08,644 (beam_search:429) INFO: max output length: 48 +2024-01-17 01:22:08,644 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:08,699 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:08,699 (beam_search:476) INFO: -7.21 * 1.0 = -7.21 for ctc +2024-01-17 01:22:08,699 (beam_search:479) INFO: total log probability: -7.21 +2024-01-17 01:22:08,699 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:08,699 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:08,699 (beam_search:483) INFO: best hypo: OWNDSERTANLATHSUSFULFORFITIN + +2024-01-17 01:22:08,701 (asr_inference:494) INFO: speech length: 46880 +2024-01-17 01:22:08,709 (beam_search:428) INFO: decoder input length: 71 +2024-01-17 01:22:08,709 (beam_search:429) INFO: max output length: 71 +2024-01-17 01:22:08,709 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:08,812 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:08,812 (beam_search:476) INFO: -5.29 * 1.0 = -5.29 for ctc +2024-01-17 01:22:08,812 (beam_search:479) INFO: total log probability: -5.29 +2024-01-17 01:22:08,812 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 01:22:08,812 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:08,812 (beam_search:483) INFO: best hypo: TAPEINTHESAMEFOREFACTERSTHECOMPACTOIO + +2024-01-17 01:22:08,814 (asr_inference:494) INFO: speech length: 31680 +2024-01-17 01:22:08,821 (beam_search:428) INFO: decoder input length: 47 +2024-01-17 01:22:08,821 (beam_search:429) INFO: max output length: 47 +2024-01-17 01:22:08,821 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:08,870 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:08,870 (beam_search:476) INFO: -5.12 * 1.0 = -5.12 for ctc +2024-01-17 01:22:08,870 (beam_search:479) INFO: total log probability: -5.12 +2024-01-17 01:22:08,870 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:08,870 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:08,870 (beam_search:483) INFO: best hypo: SENTHRWASLATERCXPUNGEFRO + +2024-01-17 01:22:08,871 (asr_inference:494) INFO: speech length: 20160 +2024-01-17 01:22:08,878 (beam_search:428) INFO: decoder input length: 29 +2024-01-17 01:22:08,878 (beam_search:429) INFO: max output length: 29 +2024-01-17 01:22:08,878 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:08,900 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:08,900 (beam_search:476) INFO: -4.08 * 1.0 = -4.08 for ctc +2024-01-17 01:22:08,900 (beam_search:479) INFO: total log probability: -4.08 +2024-01-17 01:22:08,900 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:08,900 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:08,901 (beam_search:483) INFO: best hypo: RDESECTOCQUALITY + +2024-01-17 01:22:08,902 (asr_inference:494) INFO: speech length: 50080 +2024-01-17 01:22:08,910 (beam_search:428) INFO: decoder input length: 76 +2024-01-17 01:22:08,910 (beam_search:429) INFO: max output length: 76 +2024-01-17 01:22:08,910 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:09,002 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:09,002 (beam_search:476) INFO: -9.01 * 1.0 = -9.01 for ctc +2024-01-17 01:22:09,002 (beam_search:479) INFO: total log probability: -9.01 +2024-01-17 01:22:09,002 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:22:09,002 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:09,002 (beam_search:483) INFO: best hypo: ISFORTHOUSNSICHUNDREDBYSIXTFET + +2024-01-17 01:22:09,003 (asr_inference:494) INFO: speech length: 21760 +2024-01-17 01:22:09,010 (beam_search:428) INFO: decoder input length: 31 +2024-01-17 01:22:09,010 (beam_search:429) INFO: max output length: 31 +2024-01-17 01:22:09,010 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:09,034 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:09,034 (beam_search:476) INFO: -4.98 * 1.0 = -4.98 for ctc +2024-01-17 01:22:09,034 (beam_search:479) INFO: total log probability: -4.98 +2024-01-17 01:22:09,034 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:22:09,034 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:09,034 (beam_search:483) INFO: best hypo: NINTINSEVENDYTHR + +2024-01-17 01:22:09,036 (asr_inference:494) INFO: speech length: 38080 +2024-01-17 01:22:09,043 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:22:09,043 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:22:09,043 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:09,108 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:09,108 (beam_search:476) INFO: -7.60 * 1.0 = -7.60 for ctc +2024-01-17 01:22:09,108 (beam_search:479) INFO: total log probability: -7.60 +2024-01-17 01:22:09,108 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:09,108 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:09,108 (beam_search:483) INFO: best hypo: RALAPLAYEMAYALSOLOUSEBYRUN + +2024-01-17 01:22:09,109 (asr_inference:494) INFO: speech length: 39520 +2024-01-17 01:22:09,117 (beam_search:428) INFO: decoder input length: 59 +2024-01-17 01:22:09,117 (beam_search:429) INFO: max output length: 59 +2024-01-17 01:22:09,117 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:09,185 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:09,185 (beam_search:476) INFO: -9.68 * 1.0 = -9.68 for ctc +2024-01-17 01:22:09,185 (beam_search:479) INFO: total log probability: -9.68 +2024-01-17 01:22:09,185 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:22:09,185 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:09,186 (beam_search:483) INFO: best hypo: UBLIKHLTPRFESERGRGRYSTOACKPOIN + +2024-01-17 01:22:09,187 (asr_inference:494) INFO: speech length: 74240 +2024-01-17 01:22:09,197 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 01:22:09,197 (beam_search:429) INFO: max output length: 113 +2024-01-17 01:22:09,197 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:09,421 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:09,421 (beam_search:476) INFO: -13.36 * 1.0 = -13.36 for ctc +2024-01-17 01:22:09,421 (beam_search:479) INFO: total log probability: -13.36 +2024-01-17 01:22:09,421 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:09,421 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:09,421 (beam_search:483) INFO: best hypo: BRONWASALECTEDTTHEHOUSEOREPRSENTIVESFORTHRENONCONSECTIETRMS + +2024-01-17 01:22:09,423 (asr_inference:494) INFO: speech length: 17280 +2024-01-17 01:22:09,429 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:22:09,429 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:22:09,429 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:09,447 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:09,447 (beam_search:476) INFO: -4.05 * 1.0 = -4.05 for ctc +2024-01-17 01:22:09,447 (beam_search:479) INFO: total log probability: -4.05 +2024-01-17 01:22:09,447 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:09,447 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:09,447 (beam_search:483) INFO: best hypo: OEGISTHAPLYWI + +2024-01-17 01:22:09,448 (asr_inference:494) INFO: speech length: 21600 +2024-01-17 01:22:09,455 (beam_search:428) INFO: decoder input length: 31 +2024-01-17 01:22:09,455 (beam_search:429) INFO: max output length: 31 +2024-01-17 01:22:09,455 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:09,484 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:09,484 (beam_search:476) INFO: -6.85 * 1.0 = -6.85 for ctc +2024-01-17 01:22:09,484 (beam_search:479) INFO: total log probability: -6.85 +2024-01-17 01:22:09,485 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:22:09,485 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:09,485 (beam_search:483) INFO: best hypo: AGROPOFMELESTHATRAC + +2024-01-17 01:22:09,486 (asr_inference:494) INFO: speech length: 16160 +2024-01-17 01:22:09,492 (beam_search:428) INFO: decoder input length: 23 +2024-01-17 01:22:09,492 (beam_search:429) INFO: max output length: 23 +2024-01-17 01:22:09,492 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:09,511 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:09,511 (beam_search:476) INFO: -5.17 * 1.0 = -5.17 for ctc +2024-01-17 01:22:09,511 (beam_search:479) INFO: total log probability: -5.17 +2024-01-17 01:22:09,511 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:22:09,511 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:09,511 (beam_search:483) INFO: best hypo: NTHEWOLDSLAGIST + +2024-01-17 01:22:09,512 (asr_inference:494) INFO: speech length: 43680 +2024-01-17 01:22:09,520 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:22:09,520 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:22:09,520 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:09,606 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:09,606 (beam_search:476) INFO: -5.62 * 1.0 = -5.62 for ctc +2024-01-17 01:22:09,606 (beam_search:479) INFO: total log probability: -5.62 +2024-01-17 01:22:09,606 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:22:09,606 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:09,606 (beam_search:483) INFO: best hypo: BREDINGTAKXSPLACEBETWEENAPRLANDJON + +2024-01-17 01:22:09,607 (asr_inference:494) INFO: speech length: 26400 +2024-01-17 01:22:09,614 (beam_search:428) INFO: decoder input length: 39 +2024-01-17 01:22:09,614 (beam_search:429) INFO: max output length: 39 +2024-01-17 01:22:09,614 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:09,654 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:09,654 (beam_search:476) INFO: -7.20 * 1.0 = -7.20 for ctc +2024-01-17 01:22:09,654 (beam_search:479) INFO: total log probability: -7.20 +2024-01-17 01:22:09,654 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:22:09,654 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:09,654 (beam_search:483) INFO: best hypo: STRALORISATTHESOTHENIND + +2024-01-17 01:22:09,655 (asr_inference:494) INFO: speech length: 36320 +2024-01-17 01:22:09,663 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 01:22:09,663 (beam_search:429) INFO: max output length: 54 +2024-01-17 01:22:09,663 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:09,726 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:09,726 (beam_search:476) INFO: -8.13 * 1.0 = -8.13 for ctc +2024-01-17 01:22:09,726 (beam_search:479) INFO: total log probability: -8.13 +2024-01-17 01:22:09,726 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:09,726 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:09,726 (beam_search:483) INFO: best hypo: TECNALOUGICALSINGULARITYISPOSEBL + +2024-01-17 01:22:09,728 (asr_inference:494) INFO: speech length: 22080 +2024-01-17 01:22:09,734 (beam_search:428) INFO: decoder input length: 32 +2024-01-17 01:22:09,734 (beam_search:429) INFO: max output length: 32 +2024-01-17 01:22:09,734 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:09,760 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:09,760 (beam_search:476) INFO: -5.70 * 1.0 = -5.70 for ctc +2024-01-17 01:22:09,760 (beam_search:479) INFO: total log probability: -5.70 +2024-01-17 01:22:09,760 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:22:09,760 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:09,760 (beam_search:483) INFO: best hypo: NLUDINTHELEPYCOD + +2024-01-17 01:22:09,761 (asr_inference:494) INFO: speech length: 56320 +2024-01-17 01:22:09,769 (beam_search:428) INFO: decoder input length: 85 +2024-01-17 01:22:09,770 (beam_search:429) INFO: max output length: 85 +2024-01-17 01:22:09,770 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:09,902 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:09,902 (beam_search:476) INFO: -9.67 * 1.0 = -9.67 for ctc +2024-01-17 01:22:09,902 (beam_search:479) INFO: total log probability: -9.67 +2024-01-17 01:22:09,902 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:09,902 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:09,902 (beam_search:483) INFO: best hypo: SENTYFOREHADAHIYERGICATIONCULFICATIONCOMPEDT + +2024-01-17 01:22:09,903 (asr_inference:494) INFO: speech length: 29600 +2024-01-17 01:22:09,910 (beam_search:428) INFO: decoder input length: 44 +2024-01-17 01:22:09,910 (beam_search:429) INFO: max output length: 44 +2024-01-17 01:22:09,910 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:09,957 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:09,958 (beam_search:476) INFO: -8.69 * 1.0 = -8.69 for ctc +2024-01-17 01:22:09,958 (beam_search:479) INFO: total log probability: -8.69 +2024-01-17 01:22:09,958 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:22:09,958 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:09,958 (beam_search:483) INFO: best hypo: IACKERSWENTHYPONESCINGISAN + +2024-01-17 01:22:09,959 (asr_inference:494) INFO: speech length: 24160 +2024-01-17 01:22:09,966 (beam_search:428) INFO: decoder input length: 35 +2024-01-17 01:22:09,966 (beam_search:429) INFO: max output length: 35 +2024-01-17 01:22:09,966 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:09,994 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:09,994 (beam_search:476) INFO: -5.36 * 1.0 = -5.36 for ctc +2024-01-17 01:22:09,994 (beam_search:479) INFO: total log probability: -5.36 +2024-01-17 01:22:09,994 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:22:09,994 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:09,994 (beam_search:483) INFO: best hypo: ONCEVATIONIUSTRAYA + +2024-01-17 01:22:09,995 (asr_inference:494) INFO: speech length: 19840 +2024-01-17 01:22:10,001 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:22:10,001 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:22:10,001 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:10,022 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:10,022 (beam_search:476) INFO: -6.30 * 1.0 = -6.30 for ctc +2024-01-17 01:22:10,022 (beam_search:479) INFO: total log probability: -6.30 +2024-01-17 01:22:10,022 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:22:10,022 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:10,022 (beam_search:483) INFO: best hypo: ISTHSELAMANDOFF + +2024-01-17 01:22:10,023 (asr_inference:494) INFO: speech length: 58400 +2024-01-17 01:22:10,032 (beam_search:428) INFO: decoder input length: 89 +2024-01-17 01:22:10,032 (beam_search:429) INFO: max output length: 89 +2024-01-17 01:22:10,032 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:10,180 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:10,180 (beam_search:476) INFO: -11.06 * 1.0 = -11.06 for ctc +2024-01-17 01:22:10,180 (beam_search:479) INFO: total log probability: -11.06 +2024-01-17 01:22:10,180 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:10,180 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:10,181 (beam_search:483) INFO: best hypo: FIRSTSELFDISCRIVERANSHUMINESTBEATFORMILYINTHEEAL + +# Accounting: time=27 threads=1 +# Ended (code 0) at Wed Jan 17 01:22:10 CST 2024, elapsed time 27 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.4.log b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.4.log new file mode 100644 index 0000000000000000000000000000000000000000..b70a15eeb17d0de73da2702d59853c8ce496e2c8 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/asr_inference.4.log @@ -0,0 +1,3022 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.4 --config conf/decode_asr.yaml +# Started at Wed Jan 17 01:22:10 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_eng1/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.4 --config conf/decode_asr.yaml +2024-01-17 01:22:12,001 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml +2024-01-17 01:22:12,019 (asr:523) INFO: Vocabulary size: 30 +2024-01-17 01:22:12,081 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 01:22:12,081 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 01:22:12,191 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 01:22:13,494 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 01:22:14,700 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 01:22:14,700 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 01:22:14,700 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 01:22:14,733 (asr_inference:446) INFO: Text tokenizer: CharTokenizer(space_symbol=""non_linguistic_symbols="set()"nonsplit_symbols="set()") +2024-01-17 01:22:14,807 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 01:22:14,919 (asr_inference:494) INFO: speech length: 51520 +2024-01-17 01:22:16,122 (beam_search:428) INFO: decoder input length: 78 +2024-01-17 01:22:16,122 (beam_search:429) INFO: max output length: 78 +2024-01-17 01:22:16,122 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:16,244 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:16,244 (beam_search:476) INFO: -7.72 * 1.0 = -7.72 for ctc +2024-01-17 01:22:16,244 (beam_search:479) INFO: total log probability: -7.72 +2024-01-17 01:22:16,244 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:22:16,244 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:16,244 (beam_search:483) INFO: best hypo: EENTRESURCHINDICATESTATFACTERSOTHETHANPRACTI + +2024-01-17 01:22:16,269 (asr_inference:494) INFO: speech length: 39520 +2024-01-17 01:22:16,278 (beam_search:428) INFO: decoder input length: 59 +2024-01-17 01:22:16,278 (beam_search:429) INFO: max output length: 59 +2024-01-17 01:22:16,278 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:16,356 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:16,356 (beam_search:476) INFO: -9.26 * 1.0 = -9.26 for ctc +2024-01-17 01:22:16,356 (beam_search:479) INFO: total log probability: -9.26 +2024-01-17 01:22:16,356 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:16,356 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:16,356 (beam_search:483) INFO: best hypo: NDPRVENTIONANDTREATMENTOFOMPLCATIONS + +2024-01-17 01:22:16,357 (asr_inference:494) INFO: speech length: 19840 +2024-01-17 01:22:16,365 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:22:16,365 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:22:16,365 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:16,386 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:16,386 (beam_search:476) INFO: -5.01 * 1.0 = -5.01 for ctc +2024-01-17 01:22:16,386 (beam_search:479) INFO: total log probability: -5.01 +2024-01-17 01:22:16,386 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:22:16,386 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:16,386 (beam_search:483) INFO: best hypo: IHRAPIDONSAEIT + +2024-01-17 01:22:16,387 (asr_inference:494) INFO: speech length: 30560 +2024-01-17 01:22:16,394 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:22:16,394 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:22:16,394 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:16,448 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:16,448 (beam_search:476) INFO: -6.48 * 1.0 = -6.48 for ctc +2024-01-17 01:22:16,448 (beam_search:479) INFO: total log probability: -6.48 +2024-01-17 01:22:16,448 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:16,448 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:16,448 (beam_search:483) INFO: best hypo: UTHOWWARTEFOUNDATINWASBARIE + +2024-01-17 01:22:16,450 (asr_inference:494) INFO: speech length: 108160 +2024-01-17 01:22:16,462 (beam_search:428) INFO: decoder input length: 166 +2024-01-17 01:22:16,462 (beam_search:429) INFO: max output length: 166 +2024-01-17 01:22:16,462 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:16,971 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:16,971 (beam_search:476) INFO: -24.48 * 1.0 = -24.48 for ctc +2024-01-17 01:22:16,971 (beam_search:479) INFO: total log probability: -24.48 +2024-01-17 01:22:16,971 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:16,971 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:16,972 (beam_search:483) INFO: best hypo: NOWADAYSOURLYREAGINALXPRESSTRAINSBETEEBURNANDSHPEITSTOBRICGANDFRATTRANESCONTINUETOUNONTHMONTANRILWA + +2024-01-17 01:22:16,974 (asr_inference:494) INFO: speech length: 67200 +2024-01-17 01:22:16,984 (beam_search:428) INFO: decoder input length: 102 +2024-01-17 01:22:16,984 (beam_search:429) INFO: max output length: 102 +2024-01-17 01:22:16,984 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:17,179 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:17,179 (beam_search:476) INFO: -16.97 * 1.0 = -16.97 for ctc +2024-01-17 01:22:17,179 (beam_search:479) INFO: total log probability: -16.97 +2024-01-17 01:22:17,179 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:22:17,179 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:17,179 (beam_search:483) INFO: best hypo: OTHEFAMLYSWITHPTINUALYGONDWANANORIGIONINCLUDTHERETRPONEDAY + +2024-01-17 01:22:17,180 (asr_inference:494) INFO: speech length: 55680 +2024-01-17 01:22:17,189 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:22:17,189 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:22:17,189 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:17,305 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:17,305 (beam_search:476) INFO: -12.92 * 1.0 = -12.92 for ctc +2024-01-17 01:22:17,305 (beam_search:479) INFO: total log probability: -12.92 +2024-01-17 01:22:17,305 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:22:17,305 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:17,305 (beam_search:483) INFO: best hypo: BYANITALINDOMINICANMORKJCOBESDESESLES + +2024-01-17 01:22:17,306 (asr_inference:494) INFO: speech length: 18240 +2024-01-17 01:22:17,313 (beam_search:428) INFO: decoder input length: 26 +2024-01-17 01:22:17,313 (beam_search:429) INFO: max output length: 26 +2024-01-17 01:22:17,313 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:17,335 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:17,335 (beam_search:476) INFO: -4.53 * 1.0 = -4.53 for ctc +2024-01-17 01:22:17,335 (beam_search:479) INFO: total log probability: -4.53 +2024-01-17 01:22:17,335 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:17,335 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:17,335 (beam_search:483) INFO: best hypo: ANDWASNAIEDAFTET + +2024-01-17 01:22:17,337 (asr_inference:494) INFO: speech length: 20320 +2024-01-17 01:22:17,343 (beam_search:428) INFO: decoder input length: 29 +2024-01-17 01:22:17,343 (beam_search:429) INFO: max output length: 29 +2024-01-17 01:22:17,343 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:17,367 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:17,367 (beam_search:476) INFO: -7.18 * 1.0 = -7.18 for ctc +2024-01-17 01:22:17,367 (beam_search:479) INFO: total log probability: -7.18 +2024-01-17 01:22:17,367 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:22:17,367 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:17,368 (beam_search:483) INFO: best hypo: ARTHFIHALNTELIGENCS + +2024-01-17 01:22:17,369 (asr_inference:494) INFO: speech length: 27200 +2024-01-17 01:22:17,376 (beam_search:428) INFO: decoder input length: 40 +2024-01-17 01:22:17,376 (beam_search:429) INFO: max output length: 40 +2024-01-17 01:22:17,376 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:17,402 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:17,402 (beam_search:476) INFO: -1.76 * 1.0 = -1.76 for ctc +2024-01-17 01:22:17,402 (beam_search:479) INFO: total log probability: -1.76 +2024-01-17 01:22:17,402 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 01:22:17,402 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:17,402 (beam_search:483) INFO: best hypo: ANDISTHERAING + +2024-01-17 01:22:17,403 (asr_inference:494) INFO: speech length: 19520 +2024-01-17 01:22:17,410 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:22:17,410 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:22:17,410 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:17,435 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:17,435 (beam_search:476) INFO: -5.06 * 1.0 = -5.06 for ctc +2024-01-17 01:22:17,435 (beam_search:479) INFO: total log probability: -5.06 +2024-01-17 01:22:17,435 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:17,435 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:17,435 (beam_search:483) INFO: best hypo: PRENOFTHEPOPULATION + +2024-01-17 01:22:17,436 (asr_inference:494) INFO: speech length: 38400 +2024-01-17 01:22:17,444 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:22:17,444 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:22:17,444 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:17,499 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:17,499 (beam_search:476) INFO: -8.34 * 1.0 = -8.34 for ctc +2024-01-17 01:22:17,499 (beam_search:479) INFO: total log probability: -8.34 +2024-01-17 01:22:17,499 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:22:17,499 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:17,500 (beam_search:483) INFO: best hypo: CHFEERIRSOFSHUPLIHSAELES + +2024-01-17 01:22:17,501 (asr_inference:494) INFO: speech length: 17600 +2024-01-17 01:22:17,507 (beam_search:428) INFO: decoder input length: 25 +2024-01-17 01:22:17,507 (beam_search:429) INFO: max output length: 25 +2024-01-17 01:22:17,507 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:17,523 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:17,523 (beam_search:476) INFO: -2.59 * 1.0 = -2.59 for ctc +2024-01-17 01:22:17,523 (beam_search:479) INFO: total log probability: -2.59 +2024-01-17 01:22:17,523 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:17,523 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:17,523 (beam_search:483) INFO: best hypo: IMPOSEDBYLA + +2024-01-17 01:22:17,524 (asr_inference:494) INFO: speech length: 59680 +2024-01-17 01:22:17,533 (beam_search:428) INFO: decoder input length: 91 +2024-01-17 01:22:17,533 (beam_search:429) INFO: max output length: 91 +2024-01-17 01:22:17,533 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:17,667 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:17,667 (beam_search:476) INFO: -10.30 * 1.0 = -10.30 for ctc +2024-01-17 01:22:17,667 (beam_search:479) INFO: total log probability: -10.30 +2024-01-17 01:22:17,667 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:17,667 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:17,668 (beam_search:483) INFO: best hypo: RIFRINCESISSTOTHEROLINGCORLAYSHIONDGOVERMEN + +2024-01-17 01:22:17,669 (asr_inference:494) INFO: speech length: 23680 +2024-01-17 01:22:17,676 (beam_search:428) INFO: decoder input length: 34 +2024-01-17 01:22:17,676 (beam_search:429) INFO: max output length: 34 +2024-01-17 01:22:17,676 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:17,705 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:17,705 (beam_search:476) INFO: -3.85 * 1.0 = -3.85 for ctc +2024-01-17 01:22:17,705 (beam_search:479) INFO: total log probability: -3.85 +2024-01-17 01:22:17,705 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:17,705 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:17,705 (beam_search:483) INFO: best hypo: PACHESOFGLIDINGPOSM + +2024-01-17 01:22:17,706 (asr_inference:494) INFO: speech length: 36800 +2024-01-17 01:22:17,714 (beam_search:428) INFO: decoder input length: 55 +2024-01-17 01:22:17,714 (beam_search:429) INFO: max output length: 55 +2024-01-17 01:22:17,714 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:17,776 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:17,776 (beam_search:476) INFO: -6.72 * 1.0 = -6.72 for ctc +2024-01-17 01:22:17,776 (beam_search:479) INFO: total log probability: -6.72 +2024-01-17 01:22:17,776 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:17,776 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:17,776 (beam_search:483) INFO: best hypo: BACEONTHEPREVIOSTRADGYOFPLAY + +2024-01-17 01:22:17,777 (asr_inference:494) INFO: speech length: 28640 +2024-01-17 01:22:17,784 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:22:17,784 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:22:17,784 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:17,824 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:17,824 (beam_search:476) INFO: -5.00 * 1.0 = -5.00 for ctc +2024-01-17 01:22:17,824 (beam_search:479) INFO: total log probability: -5.00 +2024-01-17 01:22:17,824 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:17,824 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:17,824 (beam_search:483) INFO: best hypo: NIDELLISTICKASPERATIONS + +2024-01-17 01:22:17,826 (asr_inference:494) INFO: speech length: 41440 +2024-01-17 01:22:17,834 (beam_search:428) INFO: decoder input length: 62 +2024-01-17 01:22:17,834 (beam_search:429) INFO: max output length: 62 +2024-01-17 01:22:17,834 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:17,914 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:17,914 (beam_search:476) INFO: -12.91 * 1.0 = -12.91 for ctc +2024-01-17 01:22:17,914 (beam_search:479) INFO: total log probability: -12.91 +2024-01-17 01:22:17,914 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:22:17,914 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:17,914 (beam_search:483) INFO: best hypo: PERFECINLSANDWHOMRECOARINGATHOUSIASTS + +2024-01-17 01:22:17,915 (asr_inference:494) INFO: speech length: 16640 +2024-01-17 01:22:17,922 (beam_search:428) INFO: decoder input length: 23 +2024-01-17 01:22:17,922 (beam_search:429) INFO: max output length: 23 +2024-01-17 01:22:17,922 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:17,938 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:17,938 (beam_search:476) INFO: -5.30 * 1.0 = -5.30 for ctc +2024-01-17 01:22:17,938 (beam_search:479) INFO: total log probability: -5.30 +2024-01-17 01:22:17,938 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:22:17,938 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:17,938 (beam_search:483) INFO: best hypo: HEMTHYOLPIDAY + +2024-01-17 01:22:17,940 (asr_inference:494) INFO: speech length: 80320 +2024-01-17 01:22:17,950 (beam_search:428) INFO: decoder input length: 123 +2024-01-17 01:22:17,950 (beam_search:429) INFO: max output length: 123 +2024-01-17 01:22:17,950 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:18,199 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:18,199 (beam_search:476) INFO: -19.89 * 1.0 = -19.89 for ctc +2024-01-17 01:22:18,199 (beam_search:479) INFO: total log probability: -19.89 +2024-01-17 01:22:18,199 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:22:18,199 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:18,200 (beam_search:483) INFO: best hypo: NOCORTEOFPEOPLEWIHAPREVIASESAYACHMADVELOUEHIGPOPITCUITRISM + +2024-01-17 01:22:18,201 (asr_inference:494) INFO: speech length: 28480 +2024-01-17 01:22:18,208 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 01:22:18,208 (beam_search:429) INFO: max output length: 42 +2024-01-17 01:22:18,208 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:18,250 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:18,250 (beam_search:476) INFO: -5.57 * 1.0 = -5.57 for ctc +2024-01-17 01:22:18,250 (beam_search:479) INFO: total log probability: -5.57 +2024-01-17 01:22:18,250 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:18,250 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:18,250 (beam_search:483) INFO: best hypo: DEVIDEDINTOTHREFAMLYSTHA + +2024-01-17 01:22:18,252 (asr_inference:494) INFO: speech length: 39520 +2024-01-17 01:22:18,259 (beam_search:428) INFO: decoder input length: 59 +2024-01-17 01:22:18,259 (beam_search:429) INFO: max output length: 59 +2024-01-17 01:22:18,259 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:18,330 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:18,330 (beam_search:476) INFO: -6.91 * 1.0 = -6.91 for ctc +2024-01-17 01:22:18,330 (beam_search:479) INFO: total log probability: -6.91 +2024-01-17 01:22:18,330 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:18,330 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:18,330 (beam_search:483) INFO: best hypo: HOWDSLIGTINTRESTINRELIAEINGCOET + +2024-01-17 01:22:18,331 (asr_inference:494) INFO: speech length: 27200 +2024-01-17 01:22:18,338 (beam_search:428) INFO: decoder input length: 40 +2024-01-17 01:22:18,338 (beam_search:429) INFO: max output length: 40 +2024-01-17 01:22:18,338 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:18,381 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:18,381 (beam_search:476) INFO: -11.50 * 1.0 = -11.50 for ctc +2024-01-17 01:22:18,381 (beam_search:479) INFO: total log probability: -11.50 +2024-01-17 01:22:18,381 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:22:18,381 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:18,381 (beam_search:483) INFO: best hypo: THATAEMLURNOFTTOHAVECOEN + +2024-01-17 01:22:18,382 (asr_inference:494) INFO: speech length: 16800 +2024-01-17 01:22:18,389 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:22:18,389 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:22:18,389 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:18,407 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:18,407 (beam_search:476) INFO: -6.06 * 1.0 = -6.06 for ctc +2024-01-17 01:22:18,407 (beam_search:479) INFO: total log probability: -6.06 +2024-01-17 01:22:18,407 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:22:18,407 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:18,407 (beam_search:483) INFO: best hypo: NTWOTHUSANSIK + +2024-01-17 01:22:18,408 (asr_inference:494) INFO: speech length: 43200 +2024-01-17 01:22:18,416 (beam_search:428) INFO: decoder input length: 65 +2024-01-17 01:22:18,416 (beam_search:429) INFO: max output length: 65 +2024-01-17 01:22:18,416 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:18,487 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:18,487 (beam_search:476) INFO: -7.13 * 1.0 = -7.13 for ctc +2024-01-17 01:22:18,487 (beam_search:479) INFO: total log probability: -7.13 +2024-01-17 01:22:18,487 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:18,487 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:18,487 (beam_search:483) INFO: best hypo: SHHINBOYSARNONASBOTPOLIS + +2024-01-17 01:22:18,488 (asr_inference:494) INFO: speech length: 44160 +2024-01-17 01:22:18,497 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:22:18,497 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:22:18,497 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:18,583 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:18,583 (beam_search:476) INFO: -9.16 * 1.0 = -9.16 for ctc +2024-01-17 01:22:18,583 (beam_search:479) INFO: total log probability: -9.16 +2024-01-17 01:22:18,583 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:18,583 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:18,584 (beam_search:483) INFO: best hypo: THECOUSEISRUTUROFASERIBLANDURISOM + +2024-01-17 01:22:18,585 (asr_inference:494) INFO: speech length: 35360 +2024-01-17 01:22:18,593 (beam_search:428) INFO: decoder input length: 53 +2024-01-17 01:22:18,593 (beam_search:429) INFO: max output length: 53 +2024-01-17 01:22:18,593 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:18,657 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:18,657 (beam_search:476) INFO: -9.78 * 1.0 = -9.78 for ctc +2024-01-17 01:22:18,657 (beam_search:479) INFO: total log probability: -9.78 +2024-01-17 01:22:18,657 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:22:18,657 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:18,658 (beam_search:483) INFO: best hypo: OSTOFTHEAGJERYOUESSMUSICCOMPNES + +2024-01-17 01:22:18,659 (asr_inference:494) INFO: speech length: 107520 +2024-01-17 01:22:18,671 (beam_search:428) INFO: decoder input length: 165 +2024-01-17 01:22:18,671 (beam_search:429) INFO: max output length: 165 +2024-01-17 01:22:18,671 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:19,112 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:19,112 (beam_search:476) INFO: -15.68 * 1.0 = -15.68 for ctc +2024-01-17 01:22:19,112 (beam_search:479) INFO: total log probability: -15.68 +2024-01-17 01:22:19,112 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:19,112 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:19,113 (beam_search:483) INFO: best hypo: ONCSTEIOPARORONMONOFONICTRACKISPLADORRECORDEDHENTHETAPSMOVINGINONDIRECTIONANDT + +2024-01-17 01:22:19,114 (asr_inference:494) INFO: speech length: 23520 +2024-01-17 01:22:19,121 (beam_search:428) INFO: decoder input length: 34 +2024-01-17 01:22:19,121 (beam_search:429) INFO: max output length: 34 +2024-01-17 01:22:19,121 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:19,147 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:19,148 (beam_search:476) INFO: -2.95 * 1.0 = -2.95 for ctc +2024-01-17 01:22:19,148 (beam_search:479) INFO: total log probability: -2.95 +2024-01-17 01:22:19,148 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:22:19,148 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:19,148 (beam_search:483) INFO: best hypo: EITSEARLYFORMEIN + +2024-01-17 01:22:19,149 (asr_inference:494) INFO: speech length: 20160 +2024-01-17 01:22:19,155 (beam_search:428) INFO: decoder input length: 29 +2024-01-17 01:22:19,155 (beam_search:429) INFO: max output length: 29 +2024-01-17 01:22:19,155 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:19,180 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:19,180 (beam_search:476) INFO: -7.36 * 1.0 = -7.36 for ctc +2024-01-17 01:22:19,180 (beam_search:479) INFO: total log probability: -7.36 +2024-01-17 01:22:19,180 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:22:19,180 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:19,180 (beam_search:483) INFO: best hypo: STRTEAGHCFILOSOVER + +2024-01-17 01:22:19,181 (asr_inference:494) INFO: speech length: 30240 +2024-01-17 01:22:19,188 (beam_search:428) INFO: decoder input length: 45 +2024-01-17 01:22:19,188 (beam_search:429) INFO: max output length: 45 +2024-01-17 01:22:19,188 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:19,239 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:19,239 (beam_search:476) INFO: -8.43 * 1.0 = -8.43 for ctc +2024-01-17 01:22:19,239 (beam_search:479) INFO: total log probability: -8.43 +2024-01-17 01:22:19,239 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:22:19,239 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:19,239 (beam_search:483) INFO: best hypo: OSITIONGTVANAEHESTERNTHEGAME + +2024-01-17 01:22:19,240 (asr_inference:494) INFO: speech length: 16160 +2024-01-17 01:22:19,246 (beam_search:428) INFO: decoder input length: 23 +2024-01-17 01:22:19,247 (beam_search:429) INFO: max output length: 23 +2024-01-17 01:22:19,247 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:19,263 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:19,263 (beam_search:476) INFO: -6.57 * 1.0 = -6.57 for ctc +2024-01-17 01:22:19,263 (beam_search:479) INFO: total log probability: -6.57 +2024-01-17 01:22:19,263 (beam_search:480) INFO: normalized log probability: -0.39 +2024-01-17 01:22:19,263 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:19,263 (beam_search:483) INFO: best hypo: NEOSAUTWHELLS + +2024-01-17 01:22:19,264 (asr_inference:494) INFO: speech length: 40800 +2024-01-17 01:22:19,272 (beam_search:428) INFO: decoder input length: 61 +2024-01-17 01:22:19,272 (beam_search:429) INFO: max output length: 61 +2024-01-17 01:22:19,272 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:19,347 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:19,347 (beam_search:476) INFO: -7.71 * 1.0 = -7.71 for ctc +2024-01-17 01:22:19,347 (beam_search:479) INFO: total log probability: -7.71 +2024-01-17 01:22:19,347 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:19,347 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:19,347 (beam_search:483) INFO: best hypo: DISPOSALOVERHISONBYLOUGICALNATER + +2024-01-17 01:22:19,348 (asr_inference:494) INFO: speech length: 60320 +2024-01-17 01:22:19,357 (beam_search:428) INFO: decoder input length: 92 +2024-01-17 01:22:19,357 (beam_search:429) INFO: max output length: 92 +2024-01-17 01:22:19,357 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:19,519 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:19,519 (beam_search:476) INFO: -14.73 * 1.0 = -14.73 for ctc +2024-01-17 01:22:19,519 (beam_search:479) INFO: total log probability: -14.73 +2024-01-17 01:22:19,519 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:22:19,519 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:19,519 (beam_search:483) INFO: best hypo: EPODUCTIVERIGHTSOREXERTUNDOPRESHORSONPRESPECTIEPAIN + +2024-01-17 01:22:19,520 (asr_inference:494) INFO: speech length: 19360 +2024-01-17 01:22:19,527 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:22:19,527 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:22:19,527 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:19,549 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:19,549 (beam_search:476) INFO: -5.53 * 1.0 = -5.53 for ctc +2024-01-17 01:22:19,549 (beam_search:479) INFO: total log probability: -5.53 +2024-01-17 01:22:19,549 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:22:19,549 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:19,549 (beam_search:483) INFO: best hypo: ILHANCHANTNORGON + +2024-01-17 01:22:19,550 (asr_inference:494) INFO: speech length: 26400 +2024-01-17 01:22:19,557 (beam_search:428) INFO: decoder input length: 39 +2024-01-17 01:22:19,557 (beam_search:429) INFO: max output length: 39 +2024-01-17 01:22:19,557 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:19,590 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:19,590 (beam_search:476) INFO: -5.52 * 1.0 = -5.52 for ctc +2024-01-17 01:22:19,591 (beam_search:479) INFO: total log probability: -5.52 +2024-01-17 01:22:19,591 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:22:19,591 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:19,591 (beam_search:483) INFO: best hypo: RASTAPOPHOLOSHIDGON + +2024-01-17 01:22:19,592 (asr_inference:494) INFO: speech length: 18240 +2024-01-17 01:22:19,598 (beam_search:428) INFO: decoder input length: 26 +2024-01-17 01:22:19,598 (beam_search:429) INFO: max output length: 26 +2024-01-17 01:22:19,598 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:19,617 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:19,617 (beam_search:476) INFO: -4.06 * 1.0 = -4.06 for ctc +2024-01-17 01:22:19,617 (beam_search:479) INFO: total log probability: -4.06 +2024-01-17 01:22:19,617 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:22:19,617 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:19,617 (beam_search:483) INFO: best hypo: NDTOTHUSANDTO + +2024-01-17 01:22:19,618 (asr_inference:494) INFO: speech length: 34880 +2024-01-17 01:22:19,626 (beam_search:428) INFO: decoder input length: 52 +2024-01-17 01:22:19,626 (beam_search:429) INFO: max output length: 52 +2024-01-17 01:22:19,626 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:19,683 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:19,683 (beam_search:476) INFO: -6.36 * 1.0 = -6.36 for ctc +2024-01-17 01:22:19,683 (beam_search:479) INFO: total log probability: -6.36 +2024-01-17 01:22:19,683 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:19,683 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:19,683 (beam_search:483) INFO: best hypo: FORGAMPLFTHEPLAYARHASONLT + +2024-01-17 01:22:19,685 (asr_inference:494) INFO: speech length: 57760 +2024-01-17 01:22:19,693 (beam_search:428) INFO: decoder input length: 88 +2024-01-17 01:22:19,693 (beam_search:429) INFO: max output length: 88 +2024-01-17 01:22:19,693 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:19,837 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:19,837 (beam_search:476) INFO: -16.12 * 1.0 = -16.12 for ctc +2024-01-17 01:22:19,837 (beam_search:479) INFO: total log probability: -16.12 +2024-01-17 01:22:19,837 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:22:19,837 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:19,838 (beam_search:483) INFO: best hypo: SOFEDASUBRACKNODHMERGEHAVECOLNIIVIMPARMENTTHATFECT + +2024-01-17 01:22:19,839 (asr_inference:494) INFO: speech length: 24160 +2024-01-17 01:22:19,846 (beam_search:428) INFO: decoder input length: 35 +2024-01-17 01:22:19,846 (beam_search:429) INFO: max output length: 35 +2024-01-17 01:22:19,846 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:19,876 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:19,876 (beam_search:476) INFO: -8.19 * 1.0 = -8.19 for ctc +2024-01-17 01:22:19,876 (beam_search:479) INFO: total log probability: -8.19 +2024-01-17 01:22:19,876 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:22:19,876 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:19,876 (beam_search:483) INFO: best hypo: PEVIDEIGRONSTICDATER + +2024-01-17 01:22:19,877 (asr_inference:494) INFO: speech length: 35680 +2024-01-17 01:22:19,885 (beam_search:428) INFO: decoder input length: 53 +2024-01-17 01:22:19,885 (beam_search:429) INFO: max output length: 53 +2024-01-17 01:22:19,885 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:19,950 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:19,950 (beam_search:476) INFO: -6.20 * 1.0 = -6.20 for ctc +2024-01-17 01:22:19,950 (beam_search:479) INFO: total log probability: -6.20 +2024-01-17 01:22:19,950 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:19,950 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:19,950 (beam_search:483) INFO: best hypo: HOHADANURISONSDETECTEDBYOTHERMINS + +2024-01-17 01:22:19,951 (asr_inference:494) INFO: speech length: 38880 +2024-01-17 01:22:19,959 (beam_search:428) INFO: decoder input length: 58 +2024-01-17 01:22:19,959 (beam_search:429) INFO: max output length: 58 +2024-01-17 01:22:19,959 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:20,034 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:20,034 (beam_search:476) INFO: -12.28 * 1.0 = -12.28 for ctc +2024-01-17 01:22:20,034 (beam_search:479) INFO: total log probability: -12.28 +2024-01-17 01:22:20,034 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:22:20,034 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:20,034 (beam_search:483) INFO: best hypo: LIOESTILSDISINTOMPROEHELTHANUNCEVITY + +2024-01-17 01:22:20,035 (asr_inference:494) INFO: speech length: 38880 +2024-01-17 01:22:20,043 (beam_search:428) INFO: decoder input length: 58 +2024-01-17 01:22:20,043 (beam_search:429) INFO: max output length: 58 +2024-01-17 01:22:20,043 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:20,115 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:20,115 (beam_search:476) INFO: -6.79 * 1.0 = -6.79 for ctc +2024-01-17 01:22:20,115 (beam_search:479) INFO: total log probability: -6.79 +2024-01-17 01:22:20,116 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:20,116 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:20,116 (beam_search:483) INFO: best hypo: HADMORSUFITICATEDANDOFTAPREDICTIO + +2024-01-17 01:22:20,117 (asr_inference:494) INFO: speech length: 20160 +2024-01-17 01:22:20,123 (beam_search:428) INFO: decoder input length: 29 +2024-01-17 01:22:20,123 (beam_search:429) INFO: max output length: 29 +2024-01-17 01:22:20,123 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:20,144 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:20,144 (beam_search:476) INFO: -3.35 * 1.0 = -3.35 for ctc +2024-01-17 01:22:20,145 (beam_search:479) INFO: total log probability: -3.35 +2024-01-17 01:22:20,145 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:20,145 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:20,145 (beam_search:483) INFO: best hypo: DEHUMANISATION + +2024-01-17 01:22:20,146 (asr_inference:494) INFO: speech length: 36960 +2024-01-17 01:22:20,153 (beam_search:428) INFO: decoder input length: 55 +2024-01-17 01:22:20,153 (beam_search:429) INFO: max output length: 55 +2024-01-17 01:22:20,153 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:20,217 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:20,217 (beam_search:476) INFO: -8.72 * 1.0 = -8.72 for ctc +2024-01-17 01:22:20,217 (beam_search:479) INFO: total log probability: -8.72 +2024-01-17 01:22:20,217 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:22:20,218 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:20,218 (beam_search:483) INFO: best hypo: SPACHESINCLOUDFRESHWATERLAMPRAYE + +2024-01-17 01:22:20,219 (asr_inference:494) INFO: speech length: 18560 +2024-01-17 01:22:20,225 (beam_search:428) INFO: decoder input length: 26 +2024-01-17 01:22:20,225 (beam_search:429) INFO: max output length: 26 +2024-01-17 01:22:20,225 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:20,244 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:20,244 (beam_search:476) INFO: -4.51 * 1.0 = -4.51 for ctc +2024-01-17 01:22:20,244 (beam_search:479) INFO: total log probability: -4.51 +2024-01-17 01:22:20,244 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:22:20,244 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:20,244 (beam_search:483) INFO: best hypo: FOSTANDYUGRIM + +2024-01-17 01:22:20,245 (asr_inference:494) INFO: speech length: 25760 +2024-01-17 01:22:20,252 (beam_search:428) INFO: decoder input length: 38 +2024-01-17 01:22:20,252 (beam_search:429) INFO: max output length: 38 +2024-01-17 01:22:20,252 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:20,285 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:20,285 (beam_search:476) INFO: -4.51 * 1.0 = -4.51 for ctc +2024-01-17 01:22:20,285 (beam_search:479) INFO: total log probability: -4.51 +2024-01-17 01:22:20,285 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:20,285 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:20,285 (beam_search:483) INFO: best hypo: HEFREANSICLOPEDIAAT + +2024-01-17 01:22:20,286 (asr_inference:494) INFO: speech length: 35840 +2024-01-17 01:22:20,294 (beam_search:428) INFO: decoder input length: 53 +2024-01-17 01:22:20,294 (beam_search:429) INFO: max output length: 53 +2024-01-17 01:22:20,294 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:20,354 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:20,354 (beam_search:476) INFO: -7.43 * 1.0 = -7.43 for ctc +2024-01-17 01:22:20,354 (beam_search:479) INFO: total log probability: -7.43 +2024-01-17 01:22:20,354 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:20,354 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:20,355 (beam_search:483) INFO: best hypo: THERFOREMETICALIMAGENGISGENRL + +2024-01-17 01:22:20,356 (asr_inference:494) INFO: speech length: 68160 +2024-01-17 01:22:20,365 (beam_search:428) INFO: decoder input length: 104 +2024-01-17 01:22:20,365 (beam_search:429) INFO: max output length: 104 +2024-01-17 01:22:20,365 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:20,546 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:20,546 (beam_search:476) INFO: -12.90 * 1.0 = -12.90 for ctc +2024-01-17 01:22:20,546 (beam_search:479) INFO: total log probability: -12.90 +2024-01-17 01:22:20,546 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:20,546 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:20,546 (beam_search:483) INFO: best hypo: PEACEISTTHEEXCLUIONOFNONHUMANANDPARTHEUMANANIBLES + +2024-01-17 01:22:20,547 (asr_inference:494) INFO: speech length: 47040 +2024-01-17 01:22:20,556 (beam_search:428) INFO: decoder input length: 71 +2024-01-17 01:22:20,556 (beam_search:429) INFO: max output length: 71 +2024-01-17 01:22:20,556 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:20,661 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:20,661 (beam_search:476) INFO: -12.28 * 1.0 = -12.28 for ctc +2024-01-17 01:22:20,661 (beam_search:479) INFO: total log probability: -12.28 +2024-01-17 01:22:20,661 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:22:20,661 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:20,661 (beam_search:483) INFO: best hypo: NPEOPLHHADPREVIASLYSUFRDASUBRACMOHHEMRIG + +2024-01-17 01:22:20,663 (asr_inference:494) INFO: speech length: 59520 +2024-01-17 01:22:20,672 (beam_search:428) INFO: decoder input length: 90 +2024-01-17 01:22:20,672 (beam_search:429) INFO: max output length: 90 +2024-01-17 01:22:20,672 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:20,816 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:20,816 (beam_search:476) INFO: -13.35 * 1.0 = -13.35 for ctc +2024-01-17 01:22:20,816 (beam_search:479) INFO: total log probability: -13.35 +2024-01-17 01:22:20,817 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:22:20,817 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:20,817 (beam_search:483) INFO: best hypo: LSIFIEDASAITHEINDANGEDORTHRTONDANDTOTHEPEBE + +2024-01-17 01:22:20,818 (asr_inference:494) INFO: speech length: 43520 +2024-01-17 01:22:20,826 (beam_search:428) INFO: decoder input length: 65 +2024-01-17 01:22:20,826 (beam_search:429) INFO: max output length: 65 +2024-01-17 01:22:20,826 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:20,908 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:20,908 (beam_search:476) INFO: -7.45 * 1.0 = -7.45 for ctc +2024-01-17 01:22:20,908 (beam_search:479) INFO: total log probability: -7.45 +2024-01-17 01:22:20,908 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:20,908 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:20,908 (beam_search:483) INFO: best hypo: IANATERNYJENERLPARCKERWATCONSHARDN + +2024-01-17 01:22:20,909 (asr_inference:494) INFO: speech length: 17120 +2024-01-17 01:22:20,916 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:22:20,916 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:22:20,916 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:20,930 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:20,930 (beam_search:476) INFO: -1.91 * 1.0 = -1.91 for ctc +2024-01-17 01:22:20,930 (beam_search:479) INFO: total log probability: -1.91 +2024-01-17 01:22:20,930 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:22:20,930 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:20,930 (beam_search:483) INFO: best hypo: BUTTIPICLY + +2024-01-17 01:22:20,931 (asr_inference:494) INFO: speech length: 41440 +2024-01-17 01:22:20,939 (beam_search:428) INFO: decoder input length: 62 +2024-01-17 01:22:20,939 (beam_search:429) INFO: max output length: 62 +2024-01-17 01:22:20,939 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:21,029 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:21,029 (beam_search:476) INFO: -9.92 * 1.0 = -9.92 for ctc +2024-01-17 01:22:21,029 (beam_search:479) INFO: total log probability: -9.92 +2024-01-17 01:22:21,029 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:21,029 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:21,030 (beam_search:483) INFO: best hypo: WHICHINTURNEFEDTHESIGNLTOTHEHADOFTECOE + +2024-01-17 01:22:21,031 (asr_inference:494) INFO: speech length: 44800 +2024-01-17 01:22:21,039 (beam_search:428) INFO: decoder input length: 67 +2024-01-17 01:22:21,039 (beam_search:429) INFO: max output length: 67 +2024-01-17 01:22:21,039 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:21,133 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:21,133 (beam_search:476) INFO: -6.99 * 1.0 = -6.99 for ctc +2024-01-17 01:22:21,133 (beam_search:479) INFO: total log probability: -6.99 +2024-01-17 01:22:21,133 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:21,133 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:21,133 (beam_search:483) INFO: best hypo: THITHEROWNCONVENIONALYEXPECTEDLIFETIMES + +2024-01-17 01:22:21,134 (asr_inference:494) INFO: speech length: 19840 +2024-01-17 01:22:21,141 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:22:21,141 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:22:21,141 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:21,160 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:21,160 (beam_search:476) INFO: -4.43 * 1.0 = -4.43 for ctc +2024-01-17 01:22:21,160 (beam_search:479) INFO: total log probability: -4.43 +2024-01-17 01:22:21,160 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:22:21,160 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:21,160 (beam_search:483) INFO: best hypo: UBSTANHALSTRIN + +2024-01-17 01:22:21,161 (asr_inference:494) INFO: speech length: 48640 +2024-01-17 01:22:21,169 (beam_search:428) INFO: decoder input length: 73 +2024-01-17 01:22:21,169 (beam_search:429) INFO: max output length: 73 +2024-01-17 01:22:21,169 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:21,262 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:21,262 (beam_search:476) INFO: -8.14 * 1.0 = -8.14 for ctc +2024-01-17 01:22:21,262 (beam_search:479) INFO: total log probability: -8.14 +2024-01-17 01:22:21,262 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:21,262 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:21,263 (beam_search:483) INFO: best hypo: TWENYITHSENTRYCANTUCKYCONGRSMANJOAN + +2024-01-17 01:22:21,264 (asr_inference:494) INFO: speech length: 20960 +2024-01-17 01:22:21,271 (beam_search:428) INFO: decoder input length: 30 +2024-01-17 01:22:21,271 (beam_search:429) INFO: max output length: 30 +2024-01-17 01:22:21,271 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:21,296 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:21,296 (beam_search:476) INFO: -5.98 * 1.0 = -5.98 for ctc +2024-01-17 01:22:21,296 (beam_search:479) INFO: total log probability: -5.98 +2024-01-17 01:22:21,296 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:22:21,296 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:21,296 (beam_search:483) INFO: best hypo: NOTPASINTORINDIMI + +2024-01-17 01:22:21,297 (asr_inference:494) INFO: speech length: 37440 +2024-01-17 01:22:21,305 (beam_search:428) INFO: decoder input length: 56 +2024-01-17 01:22:21,305 (beam_search:429) INFO: max output length: 56 +2024-01-17 01:22:21,305 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:21,351 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:21,351 (beam_search:476) INFO: -4.31 * 1.0 = -4.31 for ctc +2024-01-17 01:22:21,351 (beam_search:479) INFO: total log probability: -4.31 +2024-01-17 01:22:21,351 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:21,351 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:21,351 (beam_search:483) INFO: best hypo: HUNTINGWITHLEDSHOA + +2024-01-17 01:22:21,353 (asr_inference:494) INFO: speech length: 16960 +2024-01-17 01:22:21,359 (beam_search:428) INFO: decoder input length: 24 +2024-01-17 01:22:21,359 (beam_search:429) INFO: max output length: 24 +2024-01-17 01:22:21,359 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:21,374 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:21,374 (beam_search:476) INFO: -4.36 * 1.0 = -4.36 for ctc +2024-01-17 01:22:21,374 (beam_search:479) INFO: total log probability: -4.36 +2024-01-17 01:22:21,375 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:22:21,375 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:21,375 (beam_search:483) INFO: best hypo: WNYTHEIRTEN + +2024-01-17 01:22:21,376 (asr_inference:494) INFO: speech length: 52800 +2024-01-17 01:22:21,384 (beam_search:428) INFO: decoder input length: 80 +2024-01-17 01:22:21,384 (beam_search:429) INFO: max output length: 80 +2024-01-17 01:22:21,384 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:21,513 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:21,513 (beam_search:476) INFO: -10.64 * 1.0 = -10.64 for ctc +2024-01-17 01:22:21,513 (beam_search:479) INFO: total log probability: -10.64 +2024-01-17 01:22:21,513 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:21,513 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:21,514 (beam_search:483) INFO: best hypo: OTHESEVENPRSENTOFTHEWLDSPATSPACHESLIVINUTRALA + +2024-01-17 01:22:21,515 (asr_inference:494) INFO: speech length: 49440 +2024-01-17 01:22:21,523 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:22:21,523 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:22:21,523 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:21,609 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:21,610 (beam_search:476) INFO: -10.04 * 1.0 = -10.04 for ctc +2024-01-17 01:22:21,610 (beam_search:479) INFO: total log probability: -10.04 +2024-01-17 01:22:21,610 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:22:21,610 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:21,610 (beam_search:483) INFO: best hypo: RPOVSRANFINLYENDINNTENHAYFIV + +2024-01-17 01:22:21,611 (asr_inference:494) INFO: speech length: 49440 +2024-01-17 01:22:21,619 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:22:21,619 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:22:21,619 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:21,717 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:21,717 (beam_search:476) INFO: -9.82 * 1.0 = -9.82 for ctc +2024-01-17 01:22:21,717 (beam_search:479) INFO: total log probability: -9.82 +2024-01-17 01:22:21,717 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:22:21,717 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:21,718 (beam_search:483) INFO: best hypo: WILSOMETRENEHUMONISTAKEANABSTRACT + +2024-01-17 01:22:21,719 (asr_inference:494) INFO: speech length: 33760 +2024-01-17 01:22:21,726 (beam_search:428) INFO: decoder input length: 50 +2024-01-17 01:22:21,726 (beam_search:429) INFO: max output length: 50 +2024-01-17 01:22:21,726 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:21,774 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:21,774 (beam_search:476) INFO: -4.20 * 1.0 = -4.20 for ctc +2024-01-17 01:22:21,774 (beam_search:479) INFO: total log probability: -4.20 +2024-01-17 01:22:21,774 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:21,774 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:21,774 (beam_search:483) INFO: best hypo: POINTTHERIHEPRETECTION + +2024-01-17 01:22:21,776 (asr_inference:494) INFO: speech length: 64640 +2024-01-17 01:22:21,785 (beam_search:428) INFO: decoder input length: 98 +2024-01-17 01:22:21,785 (beam_search:429) INFO: max output length: 98 +2024-01-17 01:22:21,785 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:21,981 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:21,981 (beam_search:476) INFO: -14.31 * 1.0 = -14.31 for ctc +2024-01-17 01:22:21,981 (beam_search:479) INFO: total log probability: -14.31 +2024-01-17 01:22:21,981 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:21,981 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:21,981 (beam_search:483) INFO: best hypo: AICSAMORFISMPROBLOMISTHECOMPETATINALPROBLOMOFTHETERMININGWETH + +2024-01-17 01:22:21,983 (asr_inference:494) INFO: speech length: 25760 +2024-01-17 01:22:21,989 (beam_search:428) INFO: decoder input length: 38 +2024-01-17 01:22:21,990 (beam_search:429) INFO: max output length: 38 +2024-01-17 01:22:21,990 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:22,028 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:22,028 (beam_search:476) INFO: -3.48 * 1.0 = -3.48 for ctc +2024-01-17 01:22:22,028 (beam_search:479) INFO: total log probability: -3.48 +2024-01-17 01:22:22,028 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:22:22,028 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:22,029 (beam_search:483) INFO: best hypo: ORTHERESTRICTOURCONSEPTI + +2024-01-17 01:22:22,030 (asr_inference:494) INFO: speech length: 29440 +2024-01-17 01:22:22,037 (beam_search:428) INFO: decoder input length: 43 +2024-01-17 01:22:22,037 (beam_search:429) INFO: max output length: 43 +2024-01-17 01:22:22,037 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:22,081 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:22,081 (beam_search:476) INFO: -7.04 * 1.0 = -7.04 for ctc +2024-01-17 01:22:22,081 (beam_search:479) INFO: total log probability: -7.04 +2024-01-17 01:22:22,081 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:22:22,081 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:22,082 (beam_search:483) INFO: best hypo: HOEHOSARVIEHUSPITALIATION + +2024-01-17 01:22:22,083 (asr_inference:494) INFO: speech length: 74880 +2024-01-17 01:22:22,093 (beam_search:428) INFO: decoder input length: 114 +2024-01-17 01:22:22,093 (beam_search:429) INFO: max output length: 114 +2024-01-17 01:22:22,093 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:22,321 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:22,321 (beam_search:476) INFO: -14.79 * 1.0 = -14.79 for ctc +2024-01-17 01:22:22,321 (beam_search:479) INFO: total log probability: -14.79 +2024-01-17 01:22:22,321 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:22,321 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:22,321 (beam_search:483) INFO: best hypo: SOMEPETECTIONOFUNSERTANGNIFICANCEISONFUREDBYCUCASINATHNITITY + +2024-01-17 01:22:22,323 (asr_inference:494) INFO: speech length: 20320 +2024-01-17 01:22:22,329 (beam_search:428) INFO: decoder input length: 29 +2024-01-17 01:22:22,329 (beam_search:429) INFO: max output length: 29 +2024-01-17 01:22:22,329 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:22,347 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:22,347 (beam_search:476) INFO: -3.43 * 1.0 = -3.43 for ctc +2024-01-17 01:22:22,347 (beam_search:479) INFO: total log probability: -3.43 +2024-01-17 01:22:22,347 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:22:22,347 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:22,347 (beam_search:483) INFO: best hypo: HALSTAEAGONS + +2024-01-17 01:22:22,348 (asr_inference:494) INFO: speech length: 22080 +2024-01-17 01:22:22,355 (beam_search:428) INFO: decoder input length: 32 +2024-01-17 01:22:22,355 (beam_search:429) INFO: max output length: 32 +2024-01-17 01:22:22,355 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:22,380 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:22,380 (beam_search:476) INFO: -6.88 * 1.0 = -6.88 for ctc +2024-01-17 01:22:22,380 (beam_search:479) INFO: total log probability: -6.88 +2024-01-17 01:22:22,380 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:22:22,380 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:22,380 (beam_search:483) INFO: best hypo: COGETIVEINHANSME + +2024-01-17 01:22:22,382 (asr_inference:494) INFO: speech length: 45440 +2024-01-17 01:22:22,390 (beam_search:428) INFO: decoder input length: 68 +2024-01-17 01:22:22,390 (beam_search:429) INFO: max output length: 68 +2024-01-17 01:22:22,390 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:22,480 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:22,480 (beam_search:476) INFO: -7.70 * 1.0 = -7.70 for ctc +2024-01-17 01:22:22,480 (beam_search:479) INFO: total log probability: -7.70 +2024-01-17 01:22:22,480 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:22,480 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:22,480 (beam_search:483) INFO: best hypo: VANSTTHEATHRANKANDBEPRMOTEDTONALO + +2024-01-17 01:22:22,481 (asr_inference:494) INFO: speech length: 31680 +2024-01-17 01:22:22,489 (beam_search:428) INFO: decoder input length: 47 +2024-01-17 01:22:22,489 (beam_search:429) INFO: max output length: 47 +2024-01-17 01:22:22,489 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:22,541 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:22,541 (beam_search:476) INFO: -5.73 * 1.0 = -5.73 for ctc +2024-01-17 01:22:22,541 (beam_search:479) INFO: total log probability: -5.73 +2024-01-17 01:22:22,541 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:22,541 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:22,542 (beam_search:483) INFO: best hypo: DRABACKOFCOILINGISTHEPEBLIT + +2024-01-17 01:22:22,543 (asr_inference:494) INFO: speech length: 31360 +2024-01-17 01:22:22,550 (beam_search:428) INFO: decoder input length: 46 +2024-01-17 01:22:22,550 (beam_search:429) INFO: max output length: 46 +2024-01-17 01:22:22,551 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:22,600 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:22,600 (beam_search:476) INFO: -8.88 * 1.0 = -8.88 for ctc +2024-01-17 01:22:22,600 (beam_search:479) INFO: total log probability: -8.88 +2024-01-17 01:22:22,600 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:22:22,600 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:22,601 (beam_search:483) INFO: best hypo: INDICATESSUBERACKNODHMBRIGE + +2024-01-17 01:22:22,602 (asr_inference:494) INFO: speech length: 17920 +2024-01-17 01:22:22,608 (beam_search:428) INFO: decoder input length: 25 +2024-01-17 01:22:22,608 (beam_search:429) INFO: max output length: 25 +2024-01-17 01:22:22,608 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:22,625 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:22,625 (beam_search:476) INFO: -4.52 * 1.0 = -4.52 for ctc +2024-01-17 01:22:22,625 (beam_search:479) INFO: total log probability: -4.52 +2024-01-17 01:22:22,625 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:22:22,625 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:22,625 (beam_search:483) INFO: best hypo: DAMICHPORTION + +2024-01-17 01:22:22,626 (asr_inference:494) INFO: speech length: 39200 +2024-01-17 01:22:22,634 (beam_search:428) INFO: decoder input length: 59 +2024-01-17 01:22:22,634 (beam_search:429) INFO: max output length: 59 +2024-01-17 01:22:22,634 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:22,706 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:22,706 (beam_search:476) INFO: -13.27 * 1.0 = -13.27 for ctc +2024-01-17 01:22:22,706 (beam_search:479) INFO: total log probability: -13.27 +2024-01-17 01:22:22,706 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:22:22,706 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:22,707 (beam_search:483) INFO: best hypo: NDOPTIOOFYEJEIKANDHANSMNTEKNALJES + +2024-01-17 01:22:22,708 (asr_inference:494) INFO: speech length: 24800 +2024-01-17 01:22:22,714 (beam_search:428) INFO: decoder input length: 36 +2024-01-17 01:22:22,714 (beam_search:429) INFO: max output length: 36 +2024-01-17 01:22:22,714 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:22,751 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:22,751 (beam_search:476) INFO: -6.53 * 1.0 = -6.53 for ctc +2024-01-17 01:22:22,751 (beam_search:479) INFO: total log probability: -6.53 +2024-01-17 01:22:22,751 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:22,751 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:22,751 (beam_search:483) INFO: best hypo: POLISHONHISHORSENDWAGAN + +2024-01-17 01:22:22,752 (asr_inference:494) INFO: speech length: 18240 +2024-01-17 01:22:22,759 (beam_search:428) INFO: decoder input length: 26 +2024-01-17 01:22:22,759 (beam_search:429) INFO: max output length: 26 +2024-01-17 01:22:22,759 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:22,779 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:22,779 (beam_search:476) INFO: -3.47 * 1.0 = -3.47 for ctc +2024-01-17 01:22:22,779 (beam_search:479) INFO: total log probability: -3.47 +2024-01-17 01:22:22,779 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:22,779 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:22,779 (beam_search:483) INFO: best hypo: DTHEEXTHAMPIAN + +2024-01-17 01:22:22,780 (asr_inference:494) INFO: speech length: 24960 +2024-01-17 01:22:22,787 (beam_search:428) INFO: decoder input length: 36 +2024-01-17 01:22:22,787 (beam_search:429) INFO: max output length: 36 +2024-01-17 01:22:22,787 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:22,820 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:22,820 (beam_search:476) INFO: -6.13 * 1.0 = -6.13 for ctc +2024-01-17 01:22:22,820 (beam_search:479) INFO: total log probability: -6.13 +2024-01-17 01:22:22,820 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:22:22,820 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:22,820 (beam_search:483) INFO: best hypo: THEOFAFERALDISHUCXLY + +2024-01-17 01:22:22,822 (asr_inference:494) INFO: speech length: 27200 +2024-01-17 01:22:22,829 (beam_search:428) INFO: decoder input length: 40 +2024-01-17 01:22:22,829 (beam_search:429) INFO: max output length: 40 +2024-01-17 01:22:22,829 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:22,868 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:22,868 (beam_search:476) INFO: -6.94 * 1.0 = -6.94 for ctc +2024-01-17 01:22:22,868 (beam_search:479) INFO: total log probability: -6.94 +2024-01-17 01:22:22,869 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:22:22,869 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:22,869 (beam_search:483) INFO: best hypo: LCHAPINHINCTENTWENYON + +2024-01-17 01:22:22,870 (asr_inference:494) INFO: speech length: 54560 +2024-01-17 01:22:22,879 (beam_search:428) INFO: decoder input length: 83 +2024-01-17 01:22:22,879 (beam_search:429) INFO: max output length: 83 +2024-01-17 01:22:22,879 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:23,006 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:23,007 (beam_search:476) INFO: -8.45 * 1.0 = -8.45 for ctc +2024-01-17 01:22:23,007 (beam_search:479) INFO: total log probability: -8.45 +2024-01-17 01:22:23,007 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:23,007 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:23,007 (beam_search:483) INFO: best hypo: SUCHASQONTIMCOPUTATIONANDRANDMYSEDELGRETHEMS + +2024-01-17 01:22:23,008 (asr_inference:494) INFO: speech length: 17440 +2024-01-17 01:22:23,015 (beam_search:428) INFO: decoder input length: 25 +2024-01-17 01:22:23,015 (beam_search:429) INFO: max output length: 25 +2024-01-17 01:22:23,015 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:23,032 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:23,032 (beam_search:476) INFO: -4.34 * 1.0 = -4.34 for ctc +2024-01-17 01:22:23,032 (beam_search:479) INFO: total log probability: -4.34 +2024-01-17 01:22:23,032 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:22:23,032 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:23,032 (beam_search:483) INFO: best hypo: ATYNHADENOIN + +2024-01-17 01:22:23,033 (asr_inference:494) INFO: speech length: 75520 +2024-01-17 01:22:23,043 (beam_search:428) INFO: decoder input length: 115 +2024-01-17 01:22:23,043 (beam_search:429) INFO: max output length: 115 +2024-01-17 01:22:23,043 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:23,281 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:23,281 (beam_search:476) INFO: -12.82 * 1.0 = -12.82 for ctc +2024-01-17 01:22:23,281 (beam_search:479) INFO: total log probability: -12.82 +2024-01-17 01:22:23,281 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:23,281 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:23,281 (beam_search:483) INFO: best hypo: ASSHONEBYLADNERTHATIFPESNOTACULTOAENPTHANTHEREXISTPROVBLUMS + +2024-01-17 01:22:23,283 (asr_inference:494) INFO: speech length: 19680 +2024-01-17 01:22:23,289 (beam_search:428) INFO: decoder input length: 28 +2024-01-17 01:22:23,289 (beam_search:429) INFO: max output length: 28 +2024-01-17 01:22:23,289 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:23,308 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:23,308 (beam_search:476) INFO: -2.72 * 1.0 = -2.72 for ctc +2024-01-17 01:22:23,308 (beam_search:479) INFO: total log probability: -2.72 +2024-01-17 01:22:23,308 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:23,308 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:23,308 (beam_search:483) INFO: best hypo: HECOMPACTDISO + +2024-01-17 01:22:23,309 (asr_inference:494) INFO: speech length: 19200 +2024-01-17 01:22:23,316 (beam_search:428) INFO: decoder input length: 27 +2024-01-17 01:22:23,316 (beam_search:429) INFO: max output length: 27 +2024-01-17 01:22:23,316 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:23,336 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:23,336 (beam_search:476) INFO: -7.51 * 1.0 = -7.51 for ctc +2024-01-17 01:22:23,336 (beam_search:479) INFO: total log probability: -7.51 +2024-01-17 01:22:23,336 (beam_search:480) INFO: normalized log probability: -0.40 +2024-01-17 01:22:23,336 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:23,336 (beam_search:483) INFO: best hypo: DGREYGOSINERIAL + +2024-01-17 01:22:23,338 (asr_inference:494) INFO: speech length: 43520 +2024-01-17 01:22:23,346 (beam_search:428) INFO: decoder input length: 65 +2024-01-17 01:22:23,346 (beam_search:429) INFO: max output length: 65 +2024-01-17 01:22:23,346 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:23,398 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:23,398 (beam_search:476) INFO: -5.00 * 1.0 = -5.00 for ctc +2024-01-17 01:22:23,398 (beam_search:479) INFO: total log probability: -5.00 +2024-01-17 01:22:23,398 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:23,398 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:23,398 (beam_search:483) INFO: best hypo: WASWENDEREDASIAGES + +2024-01-17 01:22:23,399 (asr_inference:494) INFO: speech length: 29440 +2024-01-17 01:22:23,406 (beam_search:428) INFO: decoder input length: 43 +2024-01-17 01:22:23,406 (beam_search:429) INFO: max output length: 43 +2024-01-17 01:22:23,406 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:23,444 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:23,444 (beam_search:476) INFO: -6.90 * 1.0 = -6.90 for ctc +2024-01-17 01:22:23,444 (beam_search:479) INFO: total log probability: -6.90 +2024-01-17 01:22:23,445 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:22:23,445 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:23,445 (beam_search:483) INFO: best hypo: SAYACHORETONOTHECUS + +2024-01-17 01:22:23,446 (asr_inference:494) INFO: speech length: 24960 +2024-01-17 01:22:23,453 (beam_search:428) INFO: decoder input length: 36 +2024-01-17 01:22:23,453 (beam_search:429) INFO: max output length: 36 +2024-01-17 01:22:23,453 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:23,483 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:23,483 (beam_search:476) INFO: -6.65 * 1.0 = -6.65 for ctc +2024-01-17 01:22:23,483 (beam_search:479) INFO: total log probability: -6.65 +2024-01-17 01:22:23,483 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:22:23,483 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:23,483 (beam_search:483) INFO: best hypo: ONTITUNCEYOFFAVESHOM + +2024-01-17 01:22:23,484 (asr_inference:494) INFO: speech length: 76000 +2024-01-17 01:22:23,494 (beam_search:428) INFO: decoder input length: 116 +2024-01-17 01:22:23,494 (beam_search:429) INFO: max output length: 116 +2024-01-17 01:22:23,494 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:23,681 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:23,681 (beam_search:476) INFO: -11.95 * 1.0 = -11.95 for ctc +2024-01-17 01:22:23,681 (beam_search:479) INFO: total log probability: -11.95 +2024-01-17 01:22:23,681 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:23,681 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:23,681 (beam_search:483) INFO: best hypo: THEFORTHANDFITHDAYSPASEWIHOUTANYDEVELLAMENCE + +2024-01-17 01:22:23,683 (asr_inference:494) INFO: speech length: 58539 +2024-01-17 01:22:23,691 (beam_search:428) INFO: decoder input length: 89 +2024-01-17 01:22:23,691 (beam_search:429) INFO: max output length: 89 +2024-01-17 01:22:23,691 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:23,756 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:23,757 (beam_search:476) INFO: -4.49 * 1.0 = -4.49 for ctc +2024-01-17 01:22:23,757 (beam_search:479) INFO: total log probability: -4.49 +2024-01-17 01:22:23,757 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:23,757 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:23,757 (beam_search:483) INFO: best hypo: THEYNOWTHEREPORT + +2024-01-17 01:22:23,758 (asr_inference:494) INFO: speech length: 72000 +2024-01-17 01:22:23,768 (beam_search:428) INFO: decoder input length: 110 +2024-01-17 01:22:23,768 (beam_search:429) INFO: max output length: 110 +2024-01-17 01:22:23,768 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:23,917 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:23,917 (beam_search:476) INFO: -4.19 * 1.0 = -4.19 for ctc +2024-01-17 01:22:23,917 (beam_search:479) INFO: total log probability: -4.19 +2024-01-17 01:22:23,917 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 01:22:23,917 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:23,918 (beam_search:483) INFO: best hypo: SUCHTHINGSHADACURDBEFORHETOLDFILAP + +2024-01-17 01:22:23,919 (asr_inference:494) INFO: speech length: 104448 +2024-01-17 01:22:23,931 (beam_search:428) INFO: decoder input length: 161 +2024-01-17 01:22:23,931 (beam_search:429) INFO: max output length: 161 +2024-01-17 01:22:23,931 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:24,178 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:24,178 (beam_search:476) INFO: -6.31 * 1.0 = -6.31 for ctc +2024-01-17 01:22:24,178 (beam_search:479) INFO: total log probability: -6.31 +2024-01-17 01:22:24,178 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:22:24,178 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:24,179 (beam_search:483) INFO: best hypo: THEYONLYHADALITLETERDYTHOUSENDDOLERFIER + +2024-01-17 01:22:24,180 (asr_inference:494) INFO: speech length: 65366 +2024-01-17 01:22:24,189 (beam_search:428) INFO: decoder input length: 100 +2024-01-17 01:22:24,189 (beam_search:429) INFO: max output length: 100 +2024-01-17 01:22:24,189 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:24,279 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:24,279 (beam_search:476) INFO: -4.09 * 1.0 = -4.09 for ctc +2024-01-17 01:22:24,279 (beam_search:479) INFO: total log probability: -4.09 +2024-01-17 01:22:24,279 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:24,279 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:24,279 (beam_search:483) INFO: best hypo: IAMGOWINGTOGETITOWD + +2024-01-17 01:22:24,280 (asr_inference:494) INFO: speech length: 76000 +2024-01-17 01:22:24,290 (beam_search:428) INFO: decoder input length: 116 +2024-01-17 01:22:24,290 (beam_search:429) INFO: max output length: 116 +2024-01-17 01:22:24,290 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:24,476 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:24,476 (beam_search:476) INFO: -8.08 * 1.0 = -8.08 for ctc +2024-01-17 01:22:24,476 (beam_search:479) INFO: total log probability: -8.08 +2024-01-17 01:22:24,476 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:24,476 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:24,476 (beam_search:483) INFO: best hypo: HOUDUDLYHEMAINTAINEDACOARMEANDSMILINGASSPECT + +2024-01-17 01:22:24,477 (asr_inference:494) INFO: speech length: 80000 +2024-01-17 01:22:24,487 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 01:22:24,487 (beam_search:429) INFO: max output length: 122 +2024-01-17 01:22:24,487 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:24,655 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:24,655 (beam_search:476) INFO: -8.94 * 1.0 = -8.94 for ctc +2024-01-17 01:22:24,655 (beam_search:479) INFO: total log probability: -8.94 +2024-01-17 01:22:24,655 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:24,655 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:24,655 (beam_search:483) INFO: best hypo: JONLOKETRIUMPFENTLYATSHELDONWHOBOWD + +2024-01-17 01:22:24,656 (asr_inference:494) INFO: speech length: 70000 +2024-01-17 01:22:24,666 (beam_search:428) INFO: decoder input length: 107 +2024-01-17 01:22:24,666 (beam_search:429) INFO: max output length: 107 +2024-01-17 01:22:24,666 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:24,777 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:24,777 (beam_search:476) INFO: -11.97 * 1.0 = -11.97 for ctc +2024-01-17 01:22:24,777 (beam_search:479) INFO: total log probability: -11.97 +2024-01-17 01:22:24,777 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-17 01:22:24,777 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:24,778 (beam_search:483) INFO: best hypo: OMEONENTDILEMARTTALENCST + +2024-01-17 01:22:24,779 (asr_inference:494) INFO: speech length: 98000 +2024-01-17 01:22:24,790 (beam_search:428) INFO: decoder input length: 151 +2024-01-17 01:22:24,790 (beam_search:429) INFO: max output length: 151 +2024-01-17 01:22:24,790 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:25,069 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:25,069 (beam_search:476) INFO: -5.43 * 1.0 = -5.43 for ctc +2024-01-17 01:22:25,069 (beam_search:479) INFO: total log probability: -5.43 +2024-01-17 01:22:25,069 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 01:22:25,069 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:25,070 (beam_search:483) INFO: best hypo: EITWASBEATINGANDWATINGINTHEAMBOSHOFTHOSEBLACKPITS + +2024-01-17 01:22:25,071 (asr_inference:494) INFO: speech length: 58000 +2024-01-17 01:22:25,080 (beam_search:428) INFO: decoder input length: 88 +2024-01-17 01:22:25,080 (beam_search:429) INFO: max output length: 88 +2024-01-17 01:22:25,080 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:25,173 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:25,173 (beam_search:476) INFO: -9.37 * 1.0 = -9.37 for ctc +2024-01-17 01:22:25,173 (beam_search:479) INFO: total log probability: -9.37 +2024-01-17 01:22:25,173 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:22:25,173 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:25,173 (beam_search:483) INFO: best hypo: ITTHEGOOUTANDEWIGMYBOYS + +2024-01-17 01:22:25,174 (asr_inference:494) INFO: speech length: 98000 +2024-01-17 01:22:25,185 (beam_search:428) INFO: decoder input length: 151 +2024-01-17 01:22:25,185 (beam_search:429) INFO: max output length: 151 +2024-01-17 01:22:25,185 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:25,475 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:25,475 (beam_search:476) INFO: -16.48 * 1.0 = -16.48 for ctc +2024-01-17 01:22:25,475 (beam_search:479) INFO: total log probability: -16.48 +2024-01-17 01:22:25,475 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:22:25,475 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:25,475 (beam_search:483) INFO: best hypo: SHEWINEDDOWNINWINESDREMEMSERCHINGTHESHADOSOFBULSHORS + +2024-01-17 01:22:25,477 (asr_inference:494) INFO: speech length: 92000 +2024-01-17 01:22:25,487 (beam_search:428) INFO: decoder input length: 141 +2024-01-17 01:22:25,487 (beam_search:429) INFO: max output length: 141 +2024-01-17 01:22:25,488 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:25,731 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:25,731 (beam_search:476) INFO: -10.15 * 1.0 = -10.15 for ctc +2024-01-17 01:22:25,731 (beam_search:479) INFO: total log probability: -10.15 +2024-01-17 01:22:25,731 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:25,731 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:25,731 (beam_search:483) INFO: best hypo: IJUSTOAPRECSHATEWITHOUTBEAEOEXPRESEMYFELINGS + +2024-01-17 01:22:25,732 (asr_inference:494) INFO: speech length: 66390 +2024-01-17 01:22:25,742 (beam_search:428) INFO: decoder input length: 101 +2024-01-17 01:22:25,742 (beam_search:429) INFO: max output length: 101 +2024-01-17 01:22:25,742 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:25,866 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:25,866 (beam_search:476) INFO: -6.25 * 1.0 = -6.25 for ctc +2024-01-17 01:22:25,866 (beam_search:479) INFO: total log probability: -6.25 +2024-01-17 01:22:25,866 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:25,866 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:25,867 (beam_search:483) INFO: best hypo: SHEDOSNTNOWWHATHEISTAKINGABOUT + +2024-01-17 01:22:25,868 (asr_inference:494) INFO: speech length: 58000 +2024-01-17 01:22:25,876 (beam_search:428) INFO: decoder input length: 88 +2024-01-17 01:22:25,876 (beam_search:429) INFO: max output length: 88 +2024-01-17 01:22:25,876 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:25,976 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:25,976 (beam_search:476) INFO: -3.32 * 1.0 = -3.32 for ctc +2024-01-17 01:22:25,976 (beam_search:479) INFO: total log probability: -3.32 +2024-01-17 01:22:25,976 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 01:22:25,976 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:25,977 (beam_search:483) INFO: best hypo: YOURFATHERSFIFTCOMANDHENOTED + +2024-01-17 01:22:25,978 (asr_inference:494) INFO: speech length: 76000 +2024-01-17 01:22:25,987 (beam_search:428) INFO: decoder input length: 116 +2024-01-17 01:22:25,988 (beam_search:429) INFO: max output length: 116 +2024-01-17 01:22:25,988 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:26,074 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:26,074 (beam_search:476) INFO: -6.88 * 1.0 = -6.88 for ctc +2024-01-17 01:22:26,074 (beam_search:479) INFO: total log probability: -6.88 +2024-01-17 01:22:26,074 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:22:26,074 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:26,074 (beam_search:483) INFO: best hypo: DONOUSEIHAEYOU + +2024-01-17 01:22:26,075 (asr_inference:494) INFO: speech length: 94082 +2024-01-17 01:22:26,086 (beam_search:428) INFO: decoder input length: 145 +2024-01-17 01:22:26,086 (beam_search:429) INFO: max output length: 145 +2024-01-17 01:22:26,086 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:26,403 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:26,403 (beam_search:476) INFO: -12.08 * 1.0 = -12.08 for ctc +2024-01-17 01:22:26,403 (beam_search:479) INFO: total log probability: -12.08 +2024-01-17 01:22:26,403 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:26,403 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:26,403 (beam_search:483) INFO: best hypo: ALITLEWARMEBUTNOTATALSTONISHEDETINGMELENSANDTHROWINGHERINDABOUT + +2024-01-17 01:22:26,404 (asr_inference:494) INFO: speech length: 60246 +2024-01-17 01:22:26,413 (beam_search:428) INFO: decoder input length: 92 +2024-01-17 01:22:26,413 (beam_search:429) INFO: max output length: 92 +2024-01-17 01:22:26,413 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:26,485 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:26,485 (beam_search:476) INFO: -3.65 * 1.0 = -3.65 for ctc +2024-01-17 01:22:26,485 (beam_search:479) INFO: total log probability: -3.65 +2024-01-17 01:22:26,485 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:26,485 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:26,485 (beam_search:483) INFO: best hypo: THISISAGREATPARDY + +2024-01-17 01:22:26,486 (asr_inference:494) INFO: speech length: 62000 +2024-01-17 01:22:26,496 (beam_search:428) INFO: decoder input length: 94 +2024-01-17 01:22:26,496 (beam_search:429) INFO: max output length: 94 +2024-01-17 01:22:26,496 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:26,594 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:26,594 (beam_search:476) INFO: -10.14 * 1.0 = -10.14 for ctc +2024-01-17 01:22:26,595 (beam_search:479) INFO: total log probability: -10.14 +2024-01-17 01:22:26,595 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:22:26,595 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:26,595 (beam_search:483) INFO: best hypo: THEBOYGROINPROSPERCETHOME + +2024-01-17 01:22:26,596 (asr_inference:494) INFO: speech length: 106401 +2024-01-17 01:22:26,608 (beam_search:428) INFO: decoder input length: 164 +2024-01-17 01:22:26,608 (beam_search:429) INFO: max output length: 164 +2024-01-17 01:22:26,608 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:27,012 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:27,012 (beam_search:476) INFO: -8.23 * 1.0 = -8.23 for ctc +2024-01-17 01:22:27,012 (beam_search:479) INFO: total log probability: -8.23 +2024-01-17 01:22:27,012 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 01:22:27,012 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:27,013 (beam_search:483) INFO: best hypo: ANDLESSUCHLETERSBEPATENTTHATTHEYMAYBEREDTOTHEMANDWITHALSEALORTESTIFIED + +2024-01-17 01:22:27,014 (asr_inference:494) INFO: speech length: 71202 +2024-01-17 01:22:27,024 (beam_search:428) INFO: decoder input length: 109 +2024-01-17 01:22:27,024 (beam_search:429) INFO: max output length: 109 +2024-01-17 01:22:27,024 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:27,189 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:27,190 (beam_search:476) INFO: -9.01 * 1.0 = -9.01 for ctc +2024-01-17 01:22:27,190 (beam_search:479) INFO: total log probability: -9.01 +2024-01-17 01:22:27,190 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:27,190 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:27,190 (beam_search:483) INFO: best hypo: HOCOUDAWOMNDEARTOVENTUERWESOMANYEXPORS + +2024-01-17 01:22:27,191 (asr_inference:494) INFO: speech length: 58000 +2024-01-17 01:22:27,200 (beam_search:428) INFO: decoder input length: 88 +2024-01-17 01:22:27,200 (beam_search:429) INFO: max output length: 88 +2024-01-17 01:22:27,200 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:27,288 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:27,288 (beam_search:476) INFO: -4.67 * 1.0 = -4.67 for ctc +2024-01-17 01:22:27,288 (beam_search:479) INFO: total log probability: -4.67 +2024-01-17 01:22:27,288 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:27,288 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:27,288 (beam_search:483) INFO: best hypo: HEREADSHISFRAGMENTEALOWD + +2024-01-17 01:22:27,289 (asr_inference:494) INFO: speech length: 38834 +2024-01-17 01:22:27,297 (beam_search:428) INFO: decoder input length: 58 +2024-01-17 01:22:27,297 (beam_search:429) INFO: max output length: 58 +2024-01-17 01:22:27,297 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:27,355 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:27,355 (beam_search:476) INFO: -3.45 * 1.0 = -3.45 for ctc +2024-01-17 01:22:27,356 (beam_search:479) INFO: total log probability: -3.45 +2024-01-17 01:22:27,356 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 01:22:27,356 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:27,356 (beam_search:483) INFO: best hypo: BUTHOWARYOUGOINGTODOIT + +2024-01-17 01:22:27,357 (asr_inference:494) INFO: speech length: 76288 +2024-01-17 01:22:27,367 (beam_search:428) INFO: decoder input length: 117 +2024-01-17 01:22:27,367 (beam_search:429) INFO: max output length: 117 +2024-01-17 01:22:27,367 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:27,508 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:27,508 (beam_search:476) INFO: -6.74 * 1.0 = -6.74 for ctc +2024-01-17 01:22:27,508 (beam_search:479) INFO: total log probability: -6.74 +2024-01-17 01:22:27,508 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:27,508 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:27,509 (beam_search:483) INFO: best hypo: HOWDOYOUWANTOGETAWAYWITHTHIS + +2024-01-17 01:22:27,510 (asr_inference:494) INFO: speech length: 52000 +2024-01-17 01:22:27,518 (beam_search:428) INFO: decoder input length: 79 +2024-01-17 01:22:27,518 (beam_search:429) INFO: max output length: 79 +2024-01-17 01:22:27,518 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:27,584 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:27,584 (beam_search:476) INFO: -3.10 * 1.0 = -3.10 for ctc +2024-01-17 01:22:27,584 (beam_search:479) INFO: total log probability: -3.10 +2024-01-17 01:22:27,584 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:22:27,584 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:27,584 (beam_search:483) INFO: best hypo: WILWEEVERFORGETIT + +2024-01-17 01:22:27,585 (asr_inference:494) INFO: speech length: 78000 +2024-01-17 01:22:27,595 (beam_search:428) INFO: decoder input length: 119 +2024-01-17 01:22:27,595 (beam_search:429) INFO: max output length: 119 +2024-01-17 01:22:27,595 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:27,783 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:27,783 (beam_search:476) INFO: -10.19 * 1.0 = -10.19 for ctc +2024-01-17 01:22:27,783 (beam_search:479) INFO: total log probability: -10.19 +2024-01-17 01:22:27,783 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:27,783 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:27,783 (beam_search:483) INFO: best hypo: FORMYERLYISRECALECTIONMYSLEWSPERIATOFTHER + +2024-01-17 01:22:27,785 (asr_inference:494) INFO: speech length: 94000 +2024-01-17 01:22:27,796 (beam_search:428) INFO: decoder input length: 144 +2024-01-17 01:22:27,796 (beam_search:429) INFO: max output length: 144 +2024-01-17 01:22:27,796 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:28,016 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:28,016 (beam_search:476) INFO: -16.77 * 1.0 = -16.77 for ctc +2024-01-17 01:22:28,016 (beam_search:479) INFO: total log probability: -16.77 +2024-01-17 01:22:28,016 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:22:28,016 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:28,016 (beam_search:483) INFO: best hypo: TINMISMOSIEARWHIYDOFONYOUOWALLSHAKGEAM + +2024-01-17 01:22:28,017 (asr_inference:494) INFO: speech length: 74000 +2024-01-17 01:22:28,027 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 01:22:28,027 (beam_search:429) INFO: max output length: 113 +2024-01-17 01:22:28,027 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:28,144 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:28,145 (beam_search:476) INFO: -10.28 * 1.0 = -10.28 for ctc +2024-01-17 01:22:28,145 (beam_search:479) INFO: total log probability: -10.28 +2024-01-17 01:22:28,145 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 01:22:28,145 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:28,145 (beam_search:483) INFO: best hypo: EDEVTHTHENEADISTREAFUGYEYI + +2024-01-17 01:22:28,146 (asr_inference:494) INFO: speech length: 77824 +2024-01-17 01:22:28,156 (beam_search:428) INFO: decoder input length: 119 +2024-01-17 01:22:28,156 (beam_search:429) INFO: max output length: 119 +2024-01-17 01:22:28,156 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:28,324 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:28,325 (beam_search:476) INFO: -5.83 * 1.0 = -5.83 for ctc +2024-01-17 01:22:28,325 (beam_search:479) INFO: total log probability: -5.83 +2024-01-17 01:22:28,325 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:22:28,325 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:28,325 (beam_search:483) INFO: best hypo: HISSLIMEHANDSCREPETHEADGESOFTHETABLE + +2024-01-17 01:22:28,326 (asr_inference:494) INFO: speech length: 50000 +2024-01-17 01:22:28,334 (beam_search:428) INFO: decoder input length: 76 +2024-01-17 01:22:28,334 (beam_search:429) INFO: max output length: 76 +2024-01-17 01:22:28,334 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:28,413 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:28,413 (beam_search:476) INFO: -7.01 * 1.0 = -7.01 for ctc +2024-01-17 01:22:28,413 (beam_search:479) INFO: total log probability: -7.01 +2024-01-17 01:22:28,413 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:28,413 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:28,413 (beam_search:483) INFO: best hypo: WHIDLAKHORNSIDMSMORTOMER + +2024-01-17 01:22:28,414 (asr_inference:494) INFO: speech length: 56000 +2024-01-17 01:22:28,423 (beam_search:428) INFO: decoder input length: 85 +2024-01-17 01:22:28,423 (beam_search:429) INFO: max output length: 85 +2024-01-17 01:22:28,423 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:28,516 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:28,516 (beam_search:476) INFO: -10.49 * 1.0 = -10.49 for ctc +2024-01-17 01:22:28,516 (beam_search:479) INFO: total log probability: -10.49 +2024-01-17 01:22:28,516 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-17 01:22:28,516 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:28,517 (beam_search:483) INFO: best hypo: ITOKHMHAFAOUTORCHHEEDOIT + +2024-01-17 01:22:28,518 (asr_inference:494) INFO: speech length: 75000 +2024-01-17 01:22:28,527 (beam_search:428) INFO: decoder input length: 115 +2024-01-17 01:22:28,527 (beam_search:429) INFO: max output length: 115 +2024-01-17 01:22:28,527 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:28,687 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:28,687 (beam_search:476) INFO: -12.00 * 1.0 = -12.00 for ctc +2024-01-17 01:22:28,687 (beam_search:479) INFO: total log probability: -12.00 +2024-01-17 01:22:28,687 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:22:28,687 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:28,687 (beam_search:483) INFO: best hypo: MARTHAWHERDOSTANDOTHEONTRACTULISOUS + +2024-01-17 01:22:28,688 (asr_inference:494) INFO: speech length: 74722 +2024-01-17 01:22:28,698 (beam_search:428) INFO: decoder input length: 114 +2024-01-17 01:22:28,698 (beam_search:429) INFO: max output length: 114 +2024-01-17 01:22:28,698 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:28,898 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:28,898 (beam_search:476) INFO: -7.31 * 1.0 = -7.31 for ctc +2024-01-17 01:22:28,898 (beam_search:479) INFO: total log probability: -7.31 +2024-01-17 01:22:28,898 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:22:28,898 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:28,898 (beam_search:483) INFO: best hypo: ASTOBEUNDISTINGUIHABLEFROMTHEVASTWHIHEPLAINSAROUND + +2024-01-17 01:22:28,899 (asr_inference:494) INFO: speech length: 76000 +2024-01-17 01:22:28,909 (beam_search:428) INFO: decoder input length: 116 +2024-01-17 01:22:28,909 (beam_search:429) INFO: max output length: 116 +2024-01-17 01:22:28,909 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:29,062 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:29,062 (beam_search:476) INFO: -5.65 * 1.0 = -5.65 for ctc +2024-01-17 01:22:29,062 (beam_search:479) INFO: total log probability: -5.65 +2024-01-17 01:22:29,062 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:22:29,062 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:29,062 (beam_search:483) INFO: best hypo: HEWOULDDESTROYALTHINGSTHAERFICXST + +2024-01-17 01:22:29,063 (asr_inference:494) INFO: speech length: 80000 +2024-01-17 01:22:29,073 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 01:22:29,073 (beam_search:429) INFO: max output length: 122 +2024-01-17 01:22:29,073 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:29,256 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:29,256 (beam_search:476) INFO: -10.56 * 1.0 = -10.56 for ctc +2024-01-17 01:22:29,256 (beam_search:479) INFO: total log probability: -10.56 +2024-01-17 01:22:29,256 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:29,256 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:29,256 (beam_search:483) INFO: best hypo: THERUSIONUSICPLARTHECONTWASHROABEDINSLAVE + +2024-01-17 01:22:29,258 (asr_inference:494) INFO: speech length: 86000 +2024-01-17 01:22:29,268 (beam_search:428) INFO: decoder input length: 132 +2024-01-17 01:22:29,268 (beam_search:429) INFO: max output length: 132 +2024-01-17 01:22:29,268 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:29,479 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:29,479 (beam_search:476) INFO: -8.81 * 1.0 = -8.81 for ctc +2024-01-17 01:22:29,479 (beam_search:479) INFO: total log probability: -8.81 +2024-01-17 01:22:29,479 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:29,479 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:29,480 (beam_search:483) INFO: best hypo: TOHISSPRIEHERANTEWASFLATANDUNCOMPROMIZSING + +2024-01-17 01:22:29,481 (asr_inference:494) INFO: speech length: 68608 +2024-01-17 01:22:29,490 (beam_search:428) INFO: decoder input length: 105 +2024-01-17 01:22:29,490 (beam_search:429) INFO: max output length: 105 +2024-01-17 01:22:29,490 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:29,581 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:29,582 (beam_search:476) INFO: -4.81 * 1.0 = -4.81 for ctc +2024-01-17 01:22:29,582 (beam_search:479) INFO: total log probability: -4.81 +2024-01-17 01:22:29,582 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:29,582 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:29,582 (beam_search:483) INFO: best hypo: THISSOULDBEINTRESTING + +2024-01-17 01:22:29,583 (asr_inference:494) INFO: speech length: 77142 +2024-01-17 01:22:29,593 (beam_search:428) INFO: decoder input length: 118 +2024-01-17 01:22:29,593 (beam_search:429) INFO: max output length: 118 +2024-01-17 01:22:29,593 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:29,734 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:29,734 (beam_search:476) INFO: -4.40 * 1.0 = -4.40 for ctc +2024-01-17 01:22:29,734 (beam_search:479) INFO: total log probability: -4.40 +2024-01-17 01:22:29,734 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:22:29,735 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:29,735 (beam_search:483) INFO: best hypo: IAMAFRAIDEIDONTHAVEMUCHTIME + +2024-01-17 01:22:29,736 (asr_inference:494) INFO: speech length: 76000 +2024-01-17 01:22:29,746 (beam_search:428) INFO: decoder input length: 116 +2024-01-17 01:22:29,746 (beam_search:429) INFO: max output length: 116 +2024-01-17 01:22:29,746 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:29,965 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:29,965 (beam_search:476) INFO: -10.87 * 1.0 = -10.87 for ctc +2024-01-17 01:22:29,965 (beam_search:479) INFO: total log probability: -10.87 +2024-01-17 01:22:29,965 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:29,965 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:29,965 (beam_search:483) INFO: best hypo: CRISMSISANEASYPROBLOMECOMPARDWITAPOLNASIONGIVINGFECST + +2024-01-17 01:22:29,967 (asr_inference:494) INFO: speech length: 82000 +2024-01-17 01:22:29,977 (beam_search:428) INFO: decoder input length: 126 +2024-01-17 01:22:29,977 (beam_search:429) INFO: max output length: 126 +2024-01-17 01:22:29,977 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:30,142 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:30,142 (beam_search:476) INFO: -9.59 * 1.0 = -9.59 for ctc +2024-01-17 01:22:30,142 (beam_search:479) INFO: total log probability: -9.59 +2024-01-17 01:22:30,142 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:22:30,142 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:30,143 (beam_search:483) INFO: best hypo: THEPLNTERSARARDYCONSIDERIGTHMATER + +2024-01-17 01:22:30,144 (asr_inference:494) INFO: speech length: 90000 +2024-01-17 01:22:30,155 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 01:22:30,155 (beam_search:429) INFO: max output length: 138 +2024-01-17 01:22:30,155 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:30,283 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:30,283 (beam_search:476) INFO: -7.25 * 1.0 = -7.25 for ctc +2024-01-17 01:22:30,283 (beam_search:479) INFO: total log probability: -7.25 +2024-01-17 01:22:30,283 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:22:30,284 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:30,284 (beam_search:483) INFO: best hypo: JONCRIEDWITHSHINGEIYES + +2024-01-17 01:22:30,285 (asr_inference:494) INFO: speech length: 80000 +2024-01-17 01:22:30,295 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 01:22:30,295 (beam_search:429) INFO: max output length: 122 +2024-01-17 01:22:30,295 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:30,442 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:30,442 (beam_search:476) INFO: -2.42 * 1.0 = -2.42 for ctc +2024-01-17 01:22:30,443 (beam_search:479) INFO: total log probability: -2.42 +2024-01-17 01:22:30,443 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 01:22:30,443 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:30,443 (beam_search:483) INFO: best hypo: WHOEVERLIVEDONTHERANCHDIDTHAT + +2024-01-17 01:22:30,444 (asr_inference:494) INFO: speech length: 82262 +2024-01-17 01:22:30,454 (beam_search:428) INFO: decoder input length: 126 +2024-01-17 01:22:30,454 (beam_search:429) INFO: max output length: 126 +2024-01-17 01:22:30,454 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:30,624 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:30,624 (beam_search:476) INFO: -5.69 * 1.0 = -5.69 for ctc +2024-01-17 01:22:30,624 (beam_search:479) INFO: total log probability: -5.69 +2024-01-17 01:22:30,624 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:22:30,624 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:30,624 (beam_search:483) INFO: best hypo: WELEAVETHEEFVENTUALITYTOTIMEANDLOR + +2024-01-17 01:22:30,626 (asr_inference:494) INFO: speech length: 104000 +2024-01-17 01:22:30,637 (beam_search:428) INFO: decoder input length: 160 +2024-01-17 01:22:30,637 (beam_search:429) INFO: max output length: 160 +2024-01-17 01:22:30,637 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:30,944 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:30,944 (beam_search:476) INFO: -8.77 * 1.0 = -8.77 for ctc +2024-01-17 01:22:30,944 (beam_search:479) INFO: total log probability: -8.77 +2024-01-17 01:22:30,944 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:22:30,944 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:30,944 (beam_search:483) INFO: best hypo: ATTHESAMETINESPIARSNDEROSBEGANTOFALAMONGHIMVATERS + +2024-01-17 01:22:30,946 (asr_inference:494) INFO: speech length: 59392 +2024-01-17 01:22:30,955 (beam_search:428) INFO: decoder input length: 90 +2024-01-17 01:22:30,955 (beam_search:429) INFO: max output length: 90 +2024-01-17 01:22:30,955 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:31,060 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:31,060 (beam_search:476) INFO: -7.83 * 1.0 = -7.83 for ctc +2024-01-17 01:22:31,060 (beam_search:479) INFO: total log probability: -7.83 +2024-01-17 01:22:31,060 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:31,060 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:31,060 (beam_search:483) INFO: best hypo: ITISMEARLYTHESIMPLESOUPELITIF + +2024-01-17 01:22:31,062 (asr_inference:494) INFO: speech length: 78000 +2024-01-17 01:22:31,071 (beam_search:428) INFO: decoder input length: 119 +2024-01-17 01:22:31,071 (beam_search:429) INFO: max output length: 119 +2024-01-17 01:22:31,071 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:31,245 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:31,245 (beam_search:476) INFO: -9.58 * 1.0 = -9.58 for ctc +2024-01-17 01:22:31,245 (beam_search:479) INFO: total log probability: -9.58 +2024-01-17 01:22:31,245 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:31,245 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:31,245 (beam_search:483) INFO: best hypo: INSTEAIDHEARIVEDONTHENIHOFTESECANDAY + +2024-01-17 01:22:31,246 (asr_inference:494) INFO: speech length: 134000 +2024-01-17 01:22:31,261 (beam_search:428) INFO: decoder input length: 207 +2024-01-17 01:22:31,261 (beam_search:429) INFO: max output length: 207 +2024-01-17 01:22:31,261 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:31,635 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:31,635 (beam_search:476) INFO: -9.09 * 1.0 = -9.09 for ctc +2024-01-17 01:22:31,635 (beam_search:479) INFO: total log probability: -9.09 +2024-01-17 01:22:31,635 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:31,635 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:31,635 (beam_search:483) INFO: best hypo: INHISANGSITYANDSULISITUODANDLOVETHEYDIDNOTCOUNT + +2024-01-17 01:22:31,636 (asr_inference:494) INFO: speech length: 66000 +2024-01-17 01:22:31,646 (beam_search:428) INFO: decoder input length: 101 +2024-01-17 01:22:31,646 (beam_search:429) INFO: max output length: 101 +2024-01-17 01:22:31,646 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:31,786 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:31,786 (beam_search:476) INFO: -8.41 * 1.0 = -8.41 for ctc +2024-01-17 01:22:31,786 (beam_search:479) INFO: total log probability: -8.41 +2024-01-17 01:22:31,786 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:31,786 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:31,786 (beam_search:483) INFO: best hypo: GODBLESSHMIHOPELOONSINGTHEMFOREVER + +2024-01-17 01:22:31,787 (asr_inference:494) INFO: speech length: 42000 +2024-01-17 01:22:31,796 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:22:31,796 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:22:31,796 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:31,834 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:31,834 (beam_search:476) INFO: -2.13 * 1.0 = -2.13 for ctc +2024-01-17 01:22:31,834 (beam_search:479) INFO: total log probability: -2.13 +2024-01-17 01:22:31,834 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:22:31,834 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:31,835 (beam_search:483) INFO: best hypo: YOWEREINGAGED + +2024-01-17 01:22:31,836 (asr_inference:494) INFO: speech length: 126000 +2024-01-17 01:22:31,849 (beam_search:428) INFO: decoder input length: 194 +2024-01-17 01:22:31,849 (beam_search:429) INFO: max output length: 194 +2024-01-17 01:22:31,849 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:32,261 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:32,262 (beam_search:476) INFO: -13.50 * 1.0 = -13.50 for ctc +2024-01-17 01:22:32,262 (beam_search:479) INFO: total log probability: -13.50 +2024-01-17 01:22:32,262 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:32,262 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:32,262 (beam_search:483) INFO: best hypo: THERLACESWASOFADELICKEITIVERECALLORFRAINETOMPTINTINWITHEALOL + +2024-01-17 01:22:32,263 (asr_inference:494) INFO: speech length: 114000 +2024-01-17 01:22:32,276 (beam_search:428) INFO: decoder input length: 176 +2024-01-17 01:22:32,276 (beam_search:429) INFO: max output length: 176 +2024-01-17 01:22:32,276 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:32,548 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:32,548 (beam_search:476) INFO: -9.02 * 1.0 = -9.02 for ctc +2024-01-17 01:22:32,548 (beam_search:479) INFO: total log probability: -9.02 +2024-01-17 01:22:32,548 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:32,548 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:32,548 (beam_search:483) INFO: best hypo: ITWATHESAMEWAYWITHOURREVOLVERSANDRIFALS + +2024-01-17 01:22:32,549 (asr_inference:494) INFO: speech length: 41441 +2024-01-17 01:22:32,557 (beam_search:428) INFO: decoder input length: 62 +2024-01-17 01:22:32,557 (beam_search:429) INFO: max output length: 62 +2024-01-17 01:22:32,557 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:32,640 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:32,640 (beam_search:476) INFO: -7.90 * 1.0 = -7.90 for ctc +2024-01-17 01:22:32,640 (beam_search:479) INFO: total log probability: -7.90 +2024-01-17 01:22:32,640 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:32,640 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:32,640 (beam_search:483) INFO: best hypo: HEKINGHADPROMISTOINCQUIREITOTHEMATER + +2024-01-17 01:22:32,641 (asr_inference:494) INFO: speech length: 52000 +2024-01-17 01:22:32,650 (beam_search:428) INFO: decoder input length: 79 +2024-01-17 01:22:32,650 (beam_search:429) INFO: max output length: 79 +2024-01-17 01:22:32,650 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:32,703 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:32,703 (beam_search:476) INFO: -6.72 * 1.0 = -6.72 for ctc +2024-01-17 01:22:32,703 (beam_search:479) INFO: total log probability: -6.72 +2024-01-17 01:22:32,703 (beam_search:480) INFO: normalized log probability: -0.34 +2024-01-17 01:22:32,703 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:32,703 (beam_search:483) INFO: best hypo: DOSTHALOKGOODT + +2024-01-17 01:22:32,704 (asr_inference:494) INFO: speech length: 130000 +2024-01-17 01:22:32,717 (beam_search:428) INFO: decoder input length: 201 +2024-01-17 01:22:32,717 (beam_search:429) INFO: max output length: 201 +2024-01-17 01:22:32,717 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:33,085 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:33,085 (beam_search:476) INFO: -9.03 * 1.0 = -9.03 for ctc +2024-01-17 01:22:33,085 (beam_search:479) INFO: total log probability: -9.03 +2024-01-17 01:22:33,085 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:33,085 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:33,085 (beam_search:483) INFO: best hypo: FORTHEFIRSTTIMEINHISLIFEHEWASYEARNINGFORASCRAP + +2024-01-17 01:22:33,087 (asr_inference:494) INFO: speech length: 107861 +2024-01-17 01:22:33,099 (beam_search:428) INFO: decoder input length: 166 +2024-01-17 01:22:33,099 (beam_search:429) INFO: max output length: 166 +2024-01-17 01:22:33,099 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:33,405 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:33,405 (beam_search:476) INFO: -10.45 * 1.0 = -10.45 for ctc +2024-01-17 01:22:33,405 (beam_search:479) INFO: total log probability: -10.45 +2024-01-17 01:22:33,405 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:33,405 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:33,405 (beam_search:483) INFO: best hypo: IDEFIEANYMANTOGETASOLAMONILENDESSORINCALFORNTHEA + +2024-01-17 01:22:33,407 (asr_inference:494) INFO: speech length: 66000 +2024-01-17 01:22:33,416 (beam_search:428) INFO: decoder input length: 101 +2024-01-17 01:22:33,416 (beam_search:429) INFO: max output length: 101 +2024-01-17 01:22:33,416 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:33,570 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:33,570 (beam_search:476) INFO: -9.81 * 1.0 = -9.81 for ctc +2024-01-17 01:22:33,570 (beam_search:479) INFO: total log probability: -9.81 +2024-01-17 01:22:33,570 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:33,570 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:33,571 (beam_search:483) INFO: best hypo: HERIYSMOULTESTRATHIMASHECAMEOFTHEBANGK + +2024-01-17 01:22:33,572 (asr_inference:494) INFO: speech length: 42000 +2024-01-17 01:22:33,580 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 01:22:33,580 (beam_search:429) INFO: max output length: 63 +2024-01-17 01:22:33,580 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:33,638 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:33,638 (beam_search:476) INFO: -8.86 * 1.0 = -8.86 for ctc +2024-01-17 01:22:33,638 (beam_search:479) INFO: total log probability: -8.86 +2024-01-17 01:22:33,638 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-17 01:22:33,638 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:33,638 (beam_search:483) INFO: best hypo: ETYWAVENONSSALLICGDTHA + +2024-01-17 01:22:33,639 (asr_inference:494) INFO: speech length: 80000 +2024-01-17 01:22:33,649 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 01:22:33,649 (beam_search:429) INFO: max output length: 122 +2024-01-17 01:22:33,649 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:33,795 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:33,795 (beam_search:476) INFO: -7.62 * 1.0 = -7.62 for ctc +2024-01-17 01:22:33,795 (beam_search:479) INFO: total log probability: -7.62 +2024-01-17 01:22:33,795 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:33,795 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:33,795 (beam_search:483) INFO: best hypo: MENWHONDEURITCALATLIVINGDEATH + +2024-01-17 01:22:33,796 (asr_inference:494) INFO: speech length: 86000 +2024-01-17 01:22:33,807 (beam_search:428) INFO: decoder input length: 132 +2024-01-17 01:22:33,807 (beam_search:429) INFO: max output length: 132 +2024-01-17 01:22:33,807 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:33,972 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:33,972 (beam_search:476) INFO: -7.16 * 1.0 = -7.16 for ctc +2024-01-17 01:22:33,972 (beam_search:479) INFO: total log probability: -7.16 +2024-01-17 01:22:33,972 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:33,972 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:33,973 (beam_search:483) INFO: best hypo: MTOSONWHOSEDTHISBOKCEPERRODGERS + +2024-01-17 01:22:33,974 (asr_inference:494) INFO: speech length: 74000 +2024-01-17 01:22:33,983 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 01:22:33,983 (beam_search:429) INFO: max output length: 113 +2024-01-17 01:22:33,983 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:34,113 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:34,113 (beam_search:476) INFO: -9.62 * 1.0 = -9.62 for ctc +2024-01-17 01:22:34,113 (beam_search:479) INFO: total log probability: -9.62 +2024-01-17 01:22:34,113 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:22:34,113 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:34,113 (beam_search:483) INFO: best hypo: IONLYREADTDDHTHEFOARTATIONS + +2024-01-17 01:22:34,114 (asr_inference:494) INFO: speech length: 95195 +2024-01-17 01:22:34,126 (beam_search:428) INFO: decoder input length: 146 +2024-01-17 01:22:34,126 (beam_search:429) INFO: max output length: 146 +2024-01-17 01:22:34,126 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:34,429 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:34,429 (beam_search:476) INFO: -17.03 * 1.0 = -17.03 for ctc +2024-01-17 01:22:34,429 (beam_search:479) INFO: total log probability: -17.03 +2024-01-17 01:22:34,429 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:22:34,429 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:34,429 (beam_search:483) INFO: best hypo: THEWASPOPERDEVISIONOFNAYBERITHEWORETHEINDEVRIGULYPOPOARMED + +2024-01-17 01:22:34,431 (asr_inference:494) INFO: speech length: 70000 +2024-01-17 01:22:34,440 (beam_search:428) INFO: decoder input length: 107 +2024-01-17 01:22:34,440 (beam_search:429) INFO: max output length: 107 +2024-01-17 01:22:34,440 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:34,589 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:34,589 (beam_search:476) INFO: -8.14 * 1.0 = -8.14 for ctc +2024-01-17 01:22:34,589 (beam_search:479) INFO: total log probability: -8.14 +2024-01-17 01:22:34,589 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:22:34,589 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:34,589 (beam_search:483) INFO: best hypo: IALPELYOTHELIBRARINSAIDWTHARIHTFACE + +2024-01-17 01:22:34,591 (asr_inference:494) INFO: speech length: 80555 +2024-01-17 01:22:34,601 (beam_search:428) INFO: decoder input length: 123 +2024-01-17 01:22:34,601 (beam_search:429) INFO: max output length: 123 +2024-01-17 01:22:34,601 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:34,793 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:34,793 (beam_search:476) INFO: -11.99 * 1.0 = -11.99 for ctc +2024-01-17 01:22:34,793 (beam_search:479) INFO: total log probability: -11.99 +2024-01-17 01:22:34,793 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:22:34,793 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:34,793 (beam_search:483) INFO: best hypo: ISWMTRPUIAGNORDHISHEADGRIMLYINSERCASTICLY + +2024-01-17 01:22:34,795 (asr_inference:494) INFO: speech length: 52000 +2024-01-17 01:22:34,803 (beam_search:428) INFO: decoder input length: 79 +2024-01-17 01:22:34,803 (beam_search:429) INFO: max output length: 79 +2024-01-17 01:22:34,803 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:34,896 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:34,896 (beam_search:476) INFO: -5.66 * 1.0 = -5.66 for ctc +2024-01-17 01:22:34,896 (beam_search:479) INFO: total log probability: -5.66 +2024-01-17 01:22:34,896 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:34,896 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:34,897 (beam_search:483) INFO: best hypo: THERINGOFTHEBIGBILEAROUSDHIMN + +2024-01-17 01:22:34,898 (asr_inference:494) INFO: speech length: 113761 +2024-01-17 01:22:34,910 (beam_search:428) INFO: decoder input length: 175 +2024-01-17 01:22:34,910 (beam_search:429) INFO: max output length: 175 +2024-01-17 01:22:34,910 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:35,369 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:35,369 (beam_search:476) INFO: -16.15 * 1.0 = -16.15 for ctc +2024-01-17 01:22:35,369 (beam_search:479) INFO: total log probability: -16.15 +2024-01-17 01:22:35,369 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:35,369 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:35,369 (beam_search:483) INFO: best hypo: ORTHESCRACHOFAPINONAMANSHEADVASTREAGONSOFTHEERTSEIRFISREMAINEJEALOUGICLYUNON + +2024-01-17 01:22:35,371 (asr_inference:494) INFO: speech length: 92000 +2024-01-17 01:22:35,381 (beam_search:428) INFO: decoder input length: 141 +2024-01-17 01:22:35,381 (beam_search:429) INFO: max output length: 141 +2024-01-17 01:22:35,381 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:35,638 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:35,638 (beam_search:476) INFO: -16.66 * 1.0 = -16.66 for ctc +2024-01-17 01:22:35,638 (beam_search:479) INFO: total log probability: -16.66 +2024-01-17 01:22:35,638 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:22:35,638 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:35,638 (beam_search:483) INFO: best hypo: HEHADBURDILYENTERDADDISWHENHESSOUOWDTHEGLOOFAFIR + +2024-01-17 01:22:35,639 (asr_inference:494) INFO: speech length: 38000 +2024-01-17 01:22:35,647 (beam_search:428) INFO: decoder input length: 57 +2024-01-17 01:22:35,647 (beam_search:429) INFO: max output length: 57 +2024-01-17 01:22:35,647 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:35,701 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:35,701 (beam_search:476) INFO: -6.43 * 1.0 = -6.43 for ctc +2024-01-17 01:22:35,701 (beam_search:479) INFO: total log probability: -6.43 +2024-01-17 01:22:35,701 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:35,701 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:35,701 (beam_search:483) INFO: best hypo: THENESCHARSTHELITCOMAND + +2024-01-17 01:22:35,702 (asr_inference:494) INFO: speech length: 116000 +2024-01-17 01:22:35,714 (beam_search:428) INFO: decoder input length: 179 +2024-01-17 01:22:35,715 (beam_search:429) INFO: max output length: 179 +2024-01-17 01:22:35,715 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:35,978 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:35,978 (beam_search:476) INFO: -9.68 * 1.0 = -9.68 for ctc +2024-01-17 01:22:35,978 (beam_search:479) INFO: total log probability: -9.68 +2024-01-17 01:22:35,978 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:35,978 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:35,978 (beam_search:483) INFO: best hypo: ITWASJEANSININGSOFELYOERBEYONTHEOACKS + +2024-01-17 01:22:35,979 (asr_inference:494) INFO: speech length: 84000 +2024-01-17 01:22:35,990 (beam_search:428) INFO: decoder input length: 129 +2024-01-17 01:22:35,990 (beam_search:429) INFO: max output length: 129 +2024-01-17 01:22:35,990 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:36,112 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:36,112 (beam_search:476) INFO: -7.98 * 1.0 = -7.98 for ctc +2024-01-17 01:22:36,112 (beam_search:479) INFO: total log probability: -7.98 +2024-01-17 01:22:36,112 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:22:36,112 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:36,113 (beam_search:483) INFO: best hypo: OFLINGAROWBUSTDBETWENUS + +2024-01-17 01:22:36,114 (asr_inference:494) INFO: speech length: 119979 +2024-01-17 01:22:36,126 (beam_search:428) INFO: decoder input length: 185 +2024-01-17 01:22:36,126 (beam_search:429) INFO: max output length: 185 +2024-01-17 01:22:36,126 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:36,517 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:36,517 (beam_search:476) INFO: -10.69 * 1.0 = -10.69 for ctc +2024-01-17 01:22:36,517 (beam_search:479) INFO: total log probability: -10.69 +2024-01-17 01:22:36,517 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:36,517 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:36,518 (beam_search:483) INFO: best hypo: HATREITANDMURDERANDLOUSTFORREVENCHTHEYPOESESTDTOOFERFLOWING + +2024-01-17 01:22:36,519 (asr_inference:494) INFO: speech length: 69602 +2024-01-17 01:22:36,529 (beam_search:428) INFO: decoder input length: 106 +2024-01-17 01:22:36,529 (beam_search:429) INFO: max output length: 106 +2024-01-17 01:22:36,529 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:36,659 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:36,659 (beam_search:476) INFO: -10.74 * 1.0 = -10.74 for ctc +2024-01-17 01:22:36,660 (beam_search:479) INFO: total log probability: -10.74 +2024-01-17 01:22:36,660 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:22:36,660 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:36,660 (beam_search:483) INFO: best hypo: THTYOUCUDHEREALUPANDONTEIMPOPOE + +2024-01-17 01:22:36,661 (asr_inference:494) INFO: speech length: 34000 +2024-01-17 01:22:36,668 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 01:22:36,669 (beam_search:429) INFO: max output length: 51 +2024-01-17 01:22:36,669 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:36,710 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:36,710 (beam_search:476) INFO: -3.85 * 1.0 = -3.85 for ctc +2024-01-17 01:22:36,710 (beam_search:479) INFO: total log probability: -3.85 +2024-01-17 01:22:36,710 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:36,710 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:36,711 (beam_search:483) INFO: best hypo: ITWASMYADEATOATE + +2024-01-17 01:22:36,712 (asr_inference:494) INFO: speech length: 55638 +2024-01-17 01:22:36,720 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 01:22:36,720 (beam_search:429) INFO: max output length: 84 +2024-01-17 01:22:36,720 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:36,784 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:36,784 (beam_search:476) INFO: -4.38 * 1.0 = -4.38 for ctc +2024-01-17 01:22:36,784 (beam_search:479) INFO: total log probability: -4.38 +2024-01-17 01:22:36,784 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:36,784 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:36,784 (beam_search:483) INFO: best hypo: SHEDOSNTWONTOWIN + +2024-01-17 01:22:36,786 (asr_inference:494) INFO: speech length: 81750 +2024-01-17 01:22:36,796 (beam_search:428) INFO: decoder input length: 125 +2024-01-17 01:22:36,796 (beam_search:429) INFO: max output length: 125 +2024-01-17 01:22:36,796 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:36,975 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:36,975 (beam_search:476) INFO: -9.47 * 1.0 = -9.47 for ctc +2024-01-17 01:22:36,975 (beam_search:479) INFO: total log probability: -9.47 +2024-01-17 01:22:36,975 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:36,975 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:36,975 (beam_search:483) INFO: best hypo: SHEHINKEITISBECASHEWONSESOMTHINGELTE + +2024-01-17 01:22:36,977 (asr_inference:494) INFO: speech length: 82000 +2024-01-17 01:22:36,987 (beam_search:428) INFO: decoder input length: 126 +2024-01-17 01:22:36,987 (beam_search:429) INFO: max output length: 126 +2024-01-17 01:22:36,987 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:37,186 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:37,186 (beam_search:476) INFO: -8.63 * 1.0 = -8.63 for ctc +2024-01-17 01:22:37,186 (beam_search:479) INFO: total log probability: -8.63 +2024-01-17 01:22:37,186 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:37,186 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:37,186 (beam_search:483) INFO: best hypo: HEPULLEDANDTHELOKCRESETDOWNTOBRAKEHISBACK + +2024-01-17 01:22:37,188 (asr_inference:494) INFO: speech length: 85443 +2024-01-17 01:22:37,198 (beam_search:428) INFO: decoder input length: 131 +2024-01-17 01:22:37,198 (beam_search:429) INFO: max output length: 131 +2024-01-17 01:22:37,198 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:37,460 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:37,460 (beam_search:476) INFO: -9.57 * 1.0 = -9.57 for ctc +2024-01-17 01:22:37,460 (beam_search:479) INFO: total log probability: -9.57 +2024-01-17 01:22:37,460 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:22:37,460 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:37,460 (beam_search:483) INFO: best hypo: THATTHESOCALDFORSESATWORKINLIGHTHEEALCTRISITYANDMAGNATISM + +2024-01-17 01:22:37,462 (asr_inference:494) INFO: speech length: 110000 +2024-01-17 01:22:37,474 (beam_search:428) INFO: decoder input length: 169 +2024-01-17 01:22:37,474 (beam_search:429) INFO: max output length: 169 +2024-01-17 01:22:37,474 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:37,757 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:37,757 (beam_search:476) INFO: -13.34 * 1.0 = -13.34 for ctc +2024-01-17 01:22:37,757 (beam_search:479) INFO: total log probability: -13.34 +2024-01-17 01:22:37,757 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:22:37,757 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:37,757 (beam_search:483) INFO: best hypo: HETORNDSHARPLYDANDPICEGRAGSINACOSTTHEPIVELER + +2024-01-17 01:22:37,758 (asr_inference:494) INFO: speech length: 44000 +2024-01-17 01:22:37,766 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:22:37,766 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:22:37,767 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:37,824 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:37,824 (beam_search:476) INFO: -4.39 * 1.0 = -4.39 for ctc +2024-01-17 01:22:37,824 (beam_search:479) INFO: total log probability: -4.39 +2024-01-17 01:22:37,824 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:37,824 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:37,825 (beam_search:483) INFO: best hypo: ALSOIWANTINFRMATION + +2024-01-17 01:22:37,826 (asr_inference:494) INFO: speech length: 76203 +2024-01-17 01:22:37,835 (beam_search:428) INFO: decoder input length: 117 +2024-01-17 01:22:37,836 (beam_search:429) INFO: max output length: 117 +2024-01-17 01:22:37,836 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:38,015 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:38,015 (beam_search:476) INFO: -5.75 * 1.0 = -5.75 for ctc +2024-01-17 01:22:38,015 (beam_search:479) INFO: total log probability: -5.75 +2024-01-17 01:22:38,015 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:22:38,015 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:38,015 (beam_search:483) INFO: best hypo: THESIXTDAYHESPENTINTHECAVENWIHGREAGSON + +2024-01-17 01:22:38,017 (asr_inference:494) INFO: speech length: 126401 +2024-01-17 01:22:38,030 (beam_search:428) INFO: decoder input length: 195 +2024-01-17 01:22:38,030 (beam_search:429) INFO: max output length: 195 +2024-01-17 01:22:38,030 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:38,628 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:38,628 (beam_search:476) INFO: -17.80 * 1.0 = -17.80 for ctc +2024-01-17 01:22:38,628 (beam_search:479) INFO: total log probability: -17.80 +2024-01-17 01:22:38,628 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:38,628 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:38,629 (beam_search:483) INFO: best hypo: ONTHISIPOTHICESTHEHAMERINGOFTHELTRMUNDYINGCORPUSLESONTHEBOBCONFIRSEITSCANATIKNEGYONTHEONHAND + +2024-01-17 01:22:38,630 (asr_inference:494) INFO: speech length: 96961 +2024-01-17 01:22:38,642 (beam_search:428) INFO: decoder input length: 149 +2024-01-17 01:22:38,642 (beam_search:429) INFO: max output length: 149 +2024-01-17 01:22:38,642 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:38,993 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:38,993 (beam_search:476) INFO: -10.27 * 1.0 = -10.27 for ctc +2024-01-17 01:22:38,993 (beam_search:479) INFO: total log probability: -10.27 +2024-01-17 01:22:38,993 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:22:38,993 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:38,994 (beam_search:483) INFO: best hypo: NOWAFIRNYWILWESTREMEANDEVERANANONYOUAMURGEFROMALTHEGROVESANDFLOWERS + +2024-01-17 01:22:38,995 (asr_inference:494) INFO: speech length: 83362 +2024-01-17 01:22:39,006 (beam_search:428) INFO: decoder input length: 128 +2024-01-17 01:22:39,006 (beam_search:429) INFO: max output length: 128 +2024-01-17 01:22:39,006 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:39,264 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:39,265 (beam_search:476) INFO: -9.43 * 1.0 = -9.43 for ctc +2024-01-17 01:22:39,265 (beam_search:479) INFO: total log probability: -9.43 +2024-01-17 01:22:39,265 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:22:39,265 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:39,265 (beam_search:483) INFO: best hypo: WITHOUTITTHEMOSDENSELYPOPULATEDRAGONSOFMOTENYURPANDAMERICA + +2024-01-17 01:22:39,266 (asr_inference:494) INFO: speech length: 43691 +2024-01-17 01:22:39,274 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:22:39,274 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:22:39,274 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:39,333 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:39,333 (beam_search:476) INFO: -4.66 * 1.0 = -4.66 for ctc +2024-01-17 01:22:39,333 (beam_search:479) INFO: total log probability: -4.66 +2024-01-17 01:22:39,333 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:39,333 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:39,333 (beam_search:483) INFO: best hypo: TOMESPINKEHASAHARPOON + +2024-01-17 01:22:39,334 (asr_inference:494) INFO: speech length: 61200 +2024-01-17 01:22:39,343 (beam_search:428) INFO: decoder input length: 93 +2024-01-17 01:22:39,343 (beam_search:429) INFO: max output length: 93 +2024-01-17 01:22:39,343 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:39,472 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:39,472 (beam_search:476) INFO: -10.99 * 1.0 = -10.99 for ctc +2024-01-17 01:22:39,472 (beam_search:479) INFO: total log probability: -10.99 +2024-01-17 01:22:39,472 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:22:39,472 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:39,473 (beam_search:483) INFO: best hypo: HEWNTEGETHEINISHTTHISFOWAREYSOFAGON + +2024-01-17 01:22:39,474 (asr_inference:494) INFO: speech length: 80000 +2024-01-17 01:22:39,484 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 01:22:39,484 (beam_search:429) INFO: max output length: 122 +2024-01-17 01:22:39,484 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:39,696 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:39,696 (beam_search:476) INFO: -12.63 * 1.0 = -12.63 for ctc +2024-01-17 01:22:39,696 (beam_search:479) INFO: total log probability: -12.63 +2024-01-17 01:22:39,696 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:39,696 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:39,696 (beam_search:483) INFO: best hypo: LKEAFLASHELONCEDIMSELFINTTHEFETHEDMASOFTHEHOWL + +2024-01-17 01:22:39,698 (asr_inference:494) INFO: speech length: 80214 +2024-01-17 01:22:39,707 (beam_search:428) INFO: decoder input length: 123 +2024-01-17 01:22:39,708 (beam_search:429) INFO: max output length: 123 +2024-01-17 01:22:39,708 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:39,862 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:39,862 (beam_search:476) INFO: -4.94 * 1.0 = -4.94 for ctc +2024-01-17 01:22:39,862 (beam_search:479) INFO: total log probability: -4.94 +2024-01-17 01:22:39,862 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:22:39,862 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:39,863 (beam_search:483) INFO: best hypo: ITCONTAINESATOTLEOFTWENTYENTRES + +2024-01-17 01:22:39,864 (asr_inference:494) INFO: speech length: 56491 +2024-01-17 01:22:39,872 (beam_search:428) INFO: decoder input length: 86 +2024-01-17 01:22:39,873 (beam_search:429) INFO: max output length: 86 +2024-01-17 01:22:39,873 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:39,955 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:39,955 (beam_search:476) INFO: -4.65 * 1.0 = -4.65 for ctc +2024-01-17 01:22:39,955 (beam_search:479) INFO: total log probability: -4.65 +2024-01-17 01:22:39,955 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:39,955 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:39,955 (beam_search:483) INFO: best hypo: IHAEHELTMORECOMFORTABLE + +2024-01-17 01:22:39,956 (asr_inference:494) INFO: speech length: 40000 +2024-01-17 01:22:39,964 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 01:22:39,964 (beam_search:429) INFO: max output length: 60 +2024-01-17 01:22:39,964 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:40,024 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:40,025 (beam_search:476) INFO: -6.11 * 1.0 = -6.11 for ctc +2024-01-17 01:22:40,025 (beam_search:479) INFO: total log probability: -6.11 +2024-01-17 01:22:40,025 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:40,025 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:40,025 (beam_search:483) INFO: best hypo: THAAPOSSESTOMACHVATELITY + +2024-01-17 01:22:40,026 (asr_inference:494) INFO: speech length: 74000 +2024-01-17 01:22:40,035 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 01:22:40,036 (beam_search:429) INFO: max output length: 113 +2024-01-17 01:22:40,036 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:40,208 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:40,208 (beam_search:476) INFO: -8.75 * 1.0 = -8.75 for ctc +2024-01-17 01:22:40,208 (beam_search:479) INFO: total log probability: -8.75 +2024-01-17 01:22:40,208 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:40,208 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:40,208 (beam_search:483) INFO: best hypo: THEWALFDOGETHRESDHISGONTMUSALETOWARDHIM + +2024-01-17 01:22:40,209 (asr_inference:494) INFO: speech length: 65536 +2024-01-17 01:22:40,219 (beam_search:428) INFO: decoder input length: 100 +2024-01-17 01:22:40,219 (beam_search:429) INFO: max output length: 100 +2024-01-17 01:22:40,219 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:40,347 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:40,347 (beam_search:476) INFO: -4.61 * 1.0 = -4.61 for ctc +2024-01-17 01:22:40,347 (beam_search:479) INFO: total log probability: -4.61 +2024-01-17 01:22:40,347 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 01:22:40,347 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:40,347 (beam_search:483) INFO: best hypo: THEGAVBIALVOICEOFHESMRIYRANGOUT + +2024-01-17 01:22:40,348 (asr_inference:494) INFO: speech length: 90000 +2024-01-17 01:22:40,359 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 01:22:40,359 (beam_search:429) INFO: max output length: 138 +2024-01-17 01:22:40,359 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:40,609 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:40,609 (beam_search:476) INFO: -11.44 * 1.0 = -11.44 for ctc +2024-01-17 01:22:40,609 (beam_search:479) INFO: total log probability: -11.44 +2024-01-17 01:22:40,609 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:40,609 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:40,609 (beam_search:483) INFO: best hypo: ITWASORIVERNDMARGINGLIAKEORSELESFROMTHEREATSWOMP + +2024-01-17 01:22:40,610 (asr_inference:494) INFO: speech length: 76161 +2024-01-17 01:22:40,620 (beam_search:428) INFO: decoder input length: 117 +2024-01-17 01:22:40,620 (beam_search:429) INFO: max output length: 117 +2024-01-17 01:22:40,620 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:40,855 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:40,856 (beam_search:476) INFO: -13.84 * 1.0 = -13.84 for ctc +2024-01-17 01:22:40,856 (beam_search:479) INFO: total log probability: -13.84 +2024-01-17 01:22:40,856 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:40,856 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:40,856 (beam_search:483) INFO: best hypo: SAIDTHEMALPULINGHIMSELFTOGETHEIHAEFARTYOMUSTHINGMEVERYROD + +2024-01-17 01:22:40,857 (asr_inference:494) INFO: speech length: 118272 +2024-01-17 01:22:40,870 (beam_search:428) INFO: decoder input length: 182 +2024-01-17 01:22:40,870 (beam_search:429) INFO: max output length: 182 +2024-01-17 01:22:40,870 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:41,249 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:41,249 (beam_search:476) INFO: -10.06 * 1.0 = -10.06 for ctc +2024-01-17 01:22:41,249 (beam_search:479) INFO: total log probability: -10.06 +2024-01-17 01:22:41,249 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:22:41,249 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:41,250 (beam_search:483) INFO: best hypo: INWHATBEUCOLICKSCOUOOFFENCEHEHADBENTORTWASBEONDIMAGENING + +2024-01-17 01:22:41,251 (asr_inference:494) INFO: speech length: 77122 +2024-01-17 01:22:41,261 (beam_search:428) INFO: decoder input length: 118 +2024-01-17 01:22:41,261 (beam_search:429) INFO: max output length: 118 +2024-01-17 01:22:41,261 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:41,490 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:41,490 (beam_search:476) INFO: -10.96 * 1.0 = -10.96 for ctc +2024-01-17 01:22:41,490 (beam_search:479) INFO: total log probability: -10.96 +2024-01-17 01:22:41,490 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:41,490 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:41,490 (beam_search:483) INFO: best hypo: HADNOTINABLEDINVESTIGATERSTOOBTAINEACOMPRITIVELYLITLCOSET + +2024-01-17 01:22:41,491 (asr_inference:494) INFO: speech length: 66000 +2024-01-17 01:22:41,501 (beam_search:428) INFO: decoder input length: 101 +2024-01-17 01:22:41,501 (beam_search:429) INFO: max output length: 101 +2024-01-17 01:22:41,501 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:41,633 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:41,633 (beam_search:476) INFO: -6.38 * 1.0 = -6.38 for ctc +2024-01-17 01:22:41,633 (beam_search:479) INFO: total log probability: -6.38 +2024-01-17 01:22:41,633 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:41,633 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:41,633 (beam_search:483) INFO: best hypo: ATRICLOFFRESHBLOUDRANOVERHISFACE + +2024-01-17 01:22:41,635 (asr_inference:494) INFO: speech length: 66000 +2024-01-17 01:22:41,644 (beam_search:428) INFO: decoder input length: 101 +2024-01-17 01:22:41,644 (beam_search:429) INFO: max output length: 101 +2024-01-17 01:22:41,644 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:41,741 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:41,741 (beam_search:476) INFO: -6.74 * 1.0 = -6.74 for ctc +2024-01-17 01:22:41,741 (beam_search:479) INFO: total log probability: -6.74 +2024-01-17 01:22:41,741 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:41,741 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:41,741 (beam_search:483) INFO: best hypo: ITWASACURUSCOINEITDANCE + +2024-01-17 01:22:41,742 (asr_inference:494) INFO: speech length: 78000 +2024-01-17 01:22:41,752 (beam_search:428) INFO: decoder input length: 119 +2024-01-17 01:22:41,752 (beam_search:429) INFO: max output length: 119 +2024-01-17 01:22:41,752 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:41,879 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:41,879 (beam_search:476) INFO: -4.94 * 1.0 = -4.94 for ctc +2024-01-17 01:22:41,879 (beam_search:479) INFO: total log probability: -4.94 +2024-01-17 01:22:41,879 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:41,879 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:41,880 (beam_search:483) INFO: best hypo: ITISTHEFIREPARTLYSHESAIDN + +2024-01-17 01:22:41,881 (asr_inference:494) INFO: speech length: 74000 +2024-01-17 01:22:41,890 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 01:22:41,890 (beam_search:429) INFO: max output length: 113 +2024-01-17 01:22:41,890 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:42,051 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:42,051 (beam_search:476) INFO: -6.46 * 1.0 = -6.46 for ctc +2024-01-17 01:22:42,051 (beam_search:479) INFO: total log probability: -6.46 +2024-01-17 01:22:42,051 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:22:42,051 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:42,052 (beam_search:483) INFO: best hypo: THEJUSTLAYOFINTHEOSHANDPLOUKEDAWAYAN + +2024-01-17 01:22:42,053 (asr_inference:494) INFO: speech length: 72000 +2024-01-17 01:22:42,062 (beam_search:428) INFO: decoder input length: 110 +2024-01-17 01:22:42,062 (beam_search:429) INFO: max output length: 110 +2024-01-17 01:22:42,062 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:42,221 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:42,221 (beam_search:476) INFO: -9.75 * 1.0 = -9.75 for ctc +2024-01-17 01:22:42,221 (beam_search:479) INFO: total log probability: -9.75 +2024-01-17 01:22:42,221 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:42,221 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:42,221 (beam_search:483) INFO: best hypo: INOTHATOUWERINCHARDGETHEREANDGEENOSE + +2024-01-17 01:22:42,222 (asr_inference:494) INFO: speech length: 72000 +2024-01-17 01:22:42,232 (beam_search:428) INFO: decoder input length: 110 +2024-01-17 01:22:42,232 (beam_search:429) INFO: max output length: 110 +2024-01-17 01:22:42,232 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:42,407 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:42,407 (beam_search:476) INFO: -7.17 * 1.0 = -7.17 for ctc +2024-01-17 01:22:42,407 (beam_search:479) INFO: total log probability: -7.17 +2024-01-17 01:22:42,407 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:22:42,407 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:42,407 (beam_search:483) INFO: best hypo: FORTIETHEEXSITINGTHRILEOFHISADVENTUEWASGON + +2024-01-17 01:22:42,408 (asr_inference:494) INFO: speech length: 60000 +2024-01-17 01:22:42,417 (beam_search:428) INFO: decoder input length: 91 +2024-01-17 01:22:42,417 (beam_search:429) INFO: max output length: 91 +2024-01-17 01:22:42,417 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:42,560 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:42,560 (beam_search:476) INFO: -13.66 * 1.0 = -13.66 for ctc +2024-01-17 01:22:42,560 (beam_search:479) INFO: total log probability: -13.66 +2024-01-17 01:22:42,560 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:22:42,560 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:42,560 (beam_search:483) INFO: best hypo: FUDNLYHISFINGERSCLOSETHIDLYOVETHEHANGAOCHIF + +2024-01-17 01:22:42,561 (asr_inference:494) INFO: speech length: 102000 +2024-01-17 01:22:42,572 (beam_search:428) INFO: decoder input length: 157 +2024-01-17 01:22:42,573 (beam_search:429) INFO: max output length: 157 +2024-01-17 01:22:42,573 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:42,863 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:42,863 (beam_search:476) INFO: -11.83 * 1.0 = -11.83 for ctc +2024-01-17 01:22:42,863 (beam_search:479) INFO: total log probability: -11.83 +2024-01-17 01:22:42,863 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:42,863 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:42,863 (beam_search:483) INFO: best hypo: DEARSIRYORSECKANTVICTOMHASFOLLONONSCEADGJUALETIME + +2024-01-17 01:22:42,865 (asr_inference:494) INFO: speech length: 46000 +2024-01-17 01:22:42,873 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:22:42,873 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:22:42,873 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:42,926 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:42,926 (beam_search:476) INFO: -5.41 * 1.0 = -5.41 for ctc +2024-01-17 01:22:42,926 (beam_search:479) INFO: total log probability: -5.41 +2024-01-17 01:22:42,926 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:22:42,926 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:42,926 (beam_search:483) INFO: best hypo: HECNCAEFRIMSELF + +2024-01-17 01:22:42,927 (asr_inference:494) INFO: speech length: 44000 +2024-01-17 01:22:42,935 (beam_search:428) INFO: decoder input length: 66 +2024-01-17 01:22:42,935 (beam_search:429) INFO: max output length: 66 +2024-01-17 01:22:42,935 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:43,025 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:43,025 (beam_search:476) INFO: -7.36 * 1.0 = -7.36 for ctc +2024-01-17 01:22:43,025 (beam_search:479) INFO: total log probability: -7.36 +2024-01-17 01:22:43,026 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:43,026 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:43,026 (beam_search:483) INFO: best hypo: EACHINSULTADEDTOTHEVOLOUOFTHECLAIME + +2024-01-17 01:22:43,027 (asr_inference:494) INFO: speech length: 89440 +2024-01-17 01:22:43,038 (beam_search:428) INFO: decoder input length: 137 +2024-01-17 01:22:43,038 (beam_search:429) INFO: max output length: 137 +2024-01-17 01:22:43,038 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:43,341 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:43,341 (beam_search:476) INFO: -10.74 * 1.0 = -10.74 for ctc +2024-01-17 01:22:43,341 (beam_search:479) INFO: total log probability: -10.74 +2024-01-17 01:22:43,341 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:22:43,341 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:43,341 (beam_search:483) INFO: best hypo: THOUITMAYBETRANSFORMEDINTOANYNOFTHEFORMSOFWHCHENRGYISSESEPTIBL + +2024-01-17 01:22:43,343 (asr_inference:494) INFO: speech length: 161792 +2024-01-17 01:22:43,358 (beam_search:428) INFO: decoder input length: 250 +2024-01-17 01:22:43,359 (beam_search:429) INFO: max output length: 250 +2024-01-17 01:22:43,359 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:44,000 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:44,000 (beam_search:476) INFO: -16.70 * 1.0 = -16.70 for ctc +2024-01-17 01:22:44,000 (beam_search:479) INFO: total log probability: -16.70 +2024-01-17 01:22:44,000 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:44,000 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:44,001 (beam_search:483) INFO: best hypo: MESITDOESSCREAMEDGRIEDLOAFIMANYFESTEDTHEHIRADTICKANDBOUNDHENMENTOFHISTADIAR + +2024-01-17 01:22:44,002 (asr_inference:494) INFO: speech length: 100000 +2024-01-17 01:22:44,013 (beam_search:428) INFO: decoder input length: 154 +2024-01-17 01:22:44,013 (beam_search:429) INFO: max output length: 154 +2024-01-17 01:22:44,013 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:44,204 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:44,204 (beam_search:476) INFO: -8.81 * 1.0 = -8.81 for ctc +2024-01-17 01:22:44,204 (beam_search:479) INFO: total log probability: -8.81 +2024-01-17 01:22:44,204 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:44,204 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:44,204 (beam_search:483) INFO: best hypo: IWANTONOHOWALLTHISISPOSEIVBLE + +2024-01-17 01:22:44,206 (asr_inference:494) INFO: speech length: 102881 +2024-01-17 01:22:44,217 (beam_search:428) INFO: decoder input length: 158 +2024-01-17 01:22:44,217 (beam_search:429) INFO: max output length: 158 +2024-01-17 01:22:44,217 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:44,606 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:44,606 (beam_search:476) INFO: -12.92 * 1.0 = -12.92 for ctc +2024-01-17 01:22:44,606 (beam_search:479) INFO: total log probability: -12.92 +2024-01-17 01:22:44,606 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:22:44,606 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:44,607 (beam_search:483) INFO: best hypo: PRENTINGASIMPLANDINSTRUCTIVILUSTRATIONOFTHESTRGLFORLIFEAMNGTHERIVELESPEACES + +2024-01-17 01:22:44,608 (asr_inference:494) INFO: speech length: 78000 +2024-01-17 01:22:44,618 (beam_search:428) INFO: decoder input length: 119 +2024-01-17 01:22:44,618 (beam_search:429) INFO: max output length: 119 +2024-01-17 01:22:44,618 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:44,785 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:44,785 (beam_search:476) INFO: -6.71 * 1.0 = -6.71 for ctc +2024-01-17 01:22:44,785 (beam_search:479) INFO: total log probability: -6.71 +2024-01-17 01:22:44,785 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:44,785 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:44,785 (beam_search:483) INFO: best hypo: HILLNEVERDOATAPOFWORKTHEHOLVOYAGEH + +2024-01-17 01:22:44,786 (asr_inference:494) INFO: speech length: 68750 +2024-01-17 01:22:44,796 (beam_search:428) INFO: decoder input length: 105 +2024-01-17 01:22:44,796 (beam_search:429) INFO: max output length: 105 +2024-01-17 01:22:44,796 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:44,941 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:44,941 (beam_search:476) INFO: -6.60 * 1.0 = -6.60 for ctc +2024-01-17 01:22:44,941 (beam_search:479) INFO: total log probability: -6.60 +2024-01-17 01:22:44,941 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:44,941 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:44,941 (beam_search:483) INFO: best hypo: IHAEHUNTEDALONGTHISRICEREPLIEDFILIP + +2024-01-17 01:22:44,942 (asr_inference:494) INFO: speech length: 48000 +2024-01-17 01:22:44,951 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 01:22:44,951 (beam_search:429) INFO: max output length: 72 +2024-01-17 01:22:44,951 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:45,032 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:45,032 (beam_search:476) INFO: -4.15 * 1.0 = -4.15 for ctc +2024-01-17 01:22:45,032 (beam_search:479) INFO: total log probability: -4.15 +2024-01-17 01:22:45,032 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 01:22:45,032 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:45,032 (beam_search:483) INFO: best hypo: LORDBUTIMGEDTOSEYOAGINFIL + +2024-01-17 01:22:45,033 (asr_inference:494) INFO: speech length: 55979 +2024-01-17 01:22:45,042 (beam_search:428) INFO: decoder input length: 85 +2024-01-17 01:22:45,042 (beam_search:429) INFO: max output length: 85 +2024-01-17 01:22:45,042 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:45,134 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:45,135 (beam_search:476) INFO: -7.42 * 1.0 = -7.42 for ctc +2024-01-17 01:22:45,135 (beam_search:479) INFO: total log probability: -7.42 +2024-01-17 01:22:45,135 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:45,135 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:45,135 (beam_search:483) INFO: best hypo: HOVELINLYIWENDADEDTHAFRSTA + +2024-01-17 01:22:45,136 (asr_inference:494) INFO: speech length: 82000 +2024-01-17 01:22:45,146 (beam_search:428) INFO: decoder input length: 126 +2024-01-17 01:22:45,146 (beam_search:429) INFO: max output length: 126 +2024-01-17 01:22:45,146 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:45,329 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:45,329 (beam_search:476) INFO: -9.18 * 1.0 = -9.18 for ctc +2024-01-17 01:22:45,329 (beam_search:479) INFO: total log probability: -9.18 +2024-01-17 01:22:45,329 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:45,329 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:45,330 (beam_search:483) INFO: best hypo: THEAROTREGULEOSTERPIRETSNICLESCONTNED + +2024-01-17 01:22:45,331 (asr_inference:494) INFO: speech length: 126000 +2024-01-17 01:22:45,344 (beam_search:428) INFO: decoder input length: 194 +2024-01-17 01:22:45,344 (beam_search:429) INFO: max output length: 194 +2024-01-17 01:22:45,344 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:45,791 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:45,791 (beam_search:476) INFO: -19.51 * 1.0 = -19.51 for ctc +2024-01-17 01:22:45,791 (beam_search:479) INFO: total log probability: -19.51 +2024-01-17 01:22:45,791 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:22:45,791 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:45,791 (beam_search:483) INFO: best hypo: THEMSTBEHRDINGFORBUSNESBUTITHUGYOUMIGTWATTTAKELOKTTHERSIGHT + +2024-01-17 01:22:45,793 (asr_inference:494) INFO: speech length: 46000 +2024-01-17 01:22:45,801 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 01:22:45,801 (beam_search:429) INFO: max output length: 69 +2024-01-17 01:22:45,801 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:45,896 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:45,896 (beam_search:476) INFO: -6.44 * 1.0 = -6.44 for ctc +2024-01-17 01:22:45,896 (beam_search:479) INFO: total log probability: -6.44 +2024-01-17 01:22:45,896 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:22:45,896 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:45,896 (beam_search:483) INFO: best hypo: THERWASNOCANCETOFIREWITHOUTHININGHIM + +2024-01-17 01:22:45,898 (asr_inference:494) INFO: speech length: 102000 +2024-01-17 01:22:45,909 (beam_search:428) INFO: decoder input length: 157 +2024-01-17 01:22:45,909 (beam_search:429) INFO: max output length: 157 +2024-01-17 01:22:45,909 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:46,208 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:46,208 (beam_search:476) INFO: -12.44 * 1.0 = -12.44 for ctc +2024-01-17 01:22:46,208 (beam_search:479) INFO: total log probability: -12.44 +2024-01-17 01:22:46,208 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:46,208 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:46,209 (beam_search:483) INFO: best hypo: ASFORHIMSELFWONTTHESTREAERALWAYARNINGSINCREINGSADLY + +2024-01-17 01:22:46,210 (asr_inference:494) INFO: speech length: 74000 +2024-01-17 01:22:46,220 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 01:22:46,220 (beam_search:429) INFO: max output length: 113 +2024-01-17 01:22:46,220 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:46,365 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:46,365 (beam_search:476) INFO: -6.37 * 1.0 = -6.37 for ctc +2024-01-17 01:22:46,365 (beam_search:479) INFO: total log probability: -6.37 +2024-01-17 01:22:46,365 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:46,365 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:46,365 (beam_search:483) INFO: best hypo: DONHIMCANYOURBOYGOLONGWITESSY + +2024-01-17 01:22:46,366 (asr_inference:494) INFO: speech length: 56406 +2024-01-17 01:22:46,375 (beam_search:428) INFO: decoder input length: 86 +2024-01-17 01:22:46,375 (beam_search:429) INFO: max output length: 86 +2024-01-17 01:22:46,375 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:46,449 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:46,449 (beam_search:476) INFO: -4.39 * 1.0 = -4.39 for ctc +2024-01-17 01:22:46,449 (beam_search:479) INFO: total log probability: -4.39 +2024-01-17 01:22:46,449 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:22:46,449 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:46,449 (beam_search:483) INFO: best hypo: GOLDBYPEARHESHOWTED + +2024-01-17 01:22:46,450 (asr_inference:494) INFO: speech length: 116000 +2024-01-17 01:22:46,462 (beam_search:428) INFO: decoder input length: 179 +2024-01-17 01:22:46,462 (beam_search:429) INFO: max output length: 179 +2024-01-17 01:22:46,462 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:46,851 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:46,851 (beam_search:476) INFO: -11.88 * 1.0 = -11.88 for ctc +2024-01-17 01:22:46,851 (beam_search:479) INFO: total log probability: -11.88 +2024-01-17 01:22:46,851 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:46,851 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:46,851 (beam_search:483) INFO: best hypo: BUTSUCHADEVERDGIENSOFAPINIONWOULDCONSTITUTNOMENENCETOSOSCITY + +2024-01-17 01:22:46,853 (asr_inference:494) INFO: speech length: 136000 +2024-01-17 01:22:46,867 (beam_search:428) INFO: decoder input length: 210 +2024-01-17 01:22:46,867 (beam_search:429) INFO: max output length: 210 +2024-01-17 01:22:46,867 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:47,206 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:47,206 (beam_search:476) INFO: -13.34 * 1.0 = -13.34 for ctc +2024-01-17 01:22:47,206 (beam_search:479) INFO: total log probability: -13.34 +2024-01-17 01:22:47,206 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:22:47,206 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:47,206 (beam_search:483) INFO: best hypo: TTHEREWASONECHANCESANDONLYONOFSAVINGJONT + +2024-01-17 01:22:47,208 (asr_inference:494) INFO: speech length: 88000 +2024-01-17 01:22:47,219 (beam_search:428) INFO: decoder input length: 135 +2024-01-17 01:22:47,219 (beam_search:429) INFO: max output length: 135 +2024-01-17 01:22:47,219 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:47,353 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:47,353 (beam_search:476) INFO: -10.38 * 1.0 = -10.38 for ctc +2024-01-17 01:22:47,353 (beam_search:479) INFO: total log probability: -10.38 +2024-01-17 01:22:47,353 (beam_search:480) INFO: normalized log probability: -0.33 +2024-01-17 01:22:47,353 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:47,354 (beam_search:483) INFO: best hypo: IICANOTFOLOEYOSHESAIND + +2024-01-17 01:22:47,355 (asr_inference:494) INFO: speech length: 89429 +2024-01-17 01:22:47,365 (beam_search:428) INFO: decoder input length: 137 +2024-01-17 01:22:47,365 (beam_search:429) INFO: max output length: 137 +2024-01-17 01:22:47,365 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:47,599 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:47,599 (beam_search:476) INFO: -9.28 * 1.0 = -9.28 for ctc +2024-01-17 01:22:47,599 (beam_search:479) INFO: total log probability: -9.28 +2024-01-17 01:22:47,599 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:47,599 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:47,600 (beam_search:483) INFO: best hypo: ONTHEFARCORNEROFTHECOMPOUNDFENTSAWHAOKBREADED + +2024-01-17 01:22:47,601 (asr_inference:494) INFO: speech length: 108000 +2024-01-17 01:22:47,613 (beam_search:428) INFO: decoder input length: 166 +2024-01-17 01:22:47,613 (beam_search:429) INFO: max output length: 166 +2024-01-17 01:22:47,613 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:47,875 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:47,875 (beam_search:476) INFO: -7.86 * 1.0 = -7.86 for ctc +2024-01-17 01:22:47,875 (beam_search:479) INFO: total log probability: -7.86 +2024-01-17 01:22:47,875 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:47,875 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:47,875 (beam_search:483) INFO: best hypo: THENAGINTOTERHADSUCAIRITATINGWAYABOUTHIM + +2024-01-17 01:22:47,876 (asr_inference:494) INFO: speech length: 91520 +2024-01-17 01:22:47,887 (beam_search:428) INFO: decoder input length: 140 +2024-01-17 01:22:47,887 (beam_search:429) INFO: max output length: 140 +2024-01-17 01:22:47,887 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:48,214 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:48,215 (beam_search:476) INFO: -13.73 * 1.0 = -13.73 for ctc +2024-01-17 01:22:48,215 (beam_search:479) INFO: total log probability: -13.73 +2024-01-17 01:22:48,215 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:22:48,215 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:48,215 (beam_search:483) INFO: best hypo: WEALLNOWOMANASASUCESFLESTABLCONTRYAROLMORTHERFORTHATFORTHEHOLREAGON + +2024-01-17 01:22:48,216 (asr_inference:494) INFO: speech length: 175657 +2024-01-17 01:22:48,232 (beam_search:428) INFO: decoder input length: 272 +2024-01-17 01:22:48,232 (beam_search:429) INFO: max output length: 272 +2024-01-17 01:22:48,233 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:49,350 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:49,350 (beam_search:476) INFO: -32.29 * 1.0 = -32.29 for ctc +2024-01-17 01:22:49,350 (beam_search:479) INFO: total log probability: -32.29 +2024-01-17 01:22:49,350 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:22:49,350 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:49,351 (beam_search:483) INFO: best hypo: THEREFORITSHIGHTIMEOUCOMEFORBODETHEPROPOSALFORREVEUBEDANOPRAIONALSUPERACIONOFTHEOARDITANDNONADITSERVISIESUNDERADIECTEAUSOBEITISON + +2024-01-17 01:22:49,353 (asr_inference:494) INFO: speech length: 192959 +2024-01-17 01:22:49,371 (beam_search:428) INFO: decoder input length: 299 +2024-01-17 01:22:49,371 (beam_search:429) INFO: max output length: 299 +2024-01-17 01:22:49,371 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:50,549 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:50,549 (beam_search:476) INFO: -31.81 * 1.0 = -31.81 for ctc +2024-01-17 01:22:50,550 (beam_search:479) INFO: total log probability: -31.81 +2024-01-17 01:22:50,550 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:50,550 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:50,550 (beam_search:483) INFO: best hypo: ITISCKEARETHATWEHAVENOTIMETOWASTTHENUERESOLTSOFTHEEIPEESHERECARDNSIENTIFICBACSESOFGLIMITJAINSELEVENOROUOMEFORHESITDASON + +2024-01-17 01:22:50,552 (asr_inference:494) INFO: speech length: 121910 +2024-01-17 01:22:50,564 (beam_search:428) INFO: decoder input length: 188 +2024-01-17 01:22:50,564 (beam_search:429) INFO: max output length: 188 +2024-01-17 01:22:50,564 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:51,037 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:51,037 (beam_search:476) INFO: -17.47 * 1.0 = -17.47 for ctc +2024-01-17 01:22:51,037 (beam_search:479) INFO: total log probability: -17.47 +2024-01-17 01:22:51,037 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:51,037 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:51,037 (beam_search:483) INFO: best hypo: SENTSOINTHECONTAINERWHIHAEVERAENTUCHEDCOMESLAVESCOUNTEOFETGODSDRUGSITSETR + +2024-01-17 01:22:51,039 (asr_inference:494) INFO: speech length: 184299 +2024-01-17 01:22:51,056 (beam_search:428) INFO: decoder input length: 285 +2024-01-17 01:22:51,056 (beam_search:429) INFO: max output length: 285 +2024-01-17 01:22:51,056 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:52,164 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:52,164 (beam_search:476) INFO: -27.86 * 1.0 = -27.86 for ctc +2024-01-17 01:22:52,164 (beam_search:479) INFO: total log probability: -27.86 +2024-01-17 01:22:52,164 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:22:52,164 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:52,165 (beam_search:483) INFO: best hypo: IHOPETHATCOMIONSMOBITINESHESINISIFIVESHOONTCRATTHENEXTPROBLOMBUTWILLBEAANSERFOREXISTINGCHALINGESOFTHEROUTTANSPOREDSECTO + +2024-01-17 01:22:52,166 (asr_inference:494) INFO: speech length: 350399 +2024-01-17 01:22:52,198 (beam_search:428) INFO: decoder input length: 545 +2024-01-17 01:22:52,198 (beam_search:429) INFO: max output length: 545 +2024-01-17 01:22:52,198 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:56,845 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:56,845 (beam_search:476) INFO: -71.90 * 1.0 = -71.90 for ctc +2024-01-17 01:22:56,845 (beam_search:479) INFO: total log probability: -71.90 +2024-01-17 01:22:56,845 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:22:56,845 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:56,847 (beam_search:483) INFO: best hypo: INTHEWUEITWASADICIONTAGNAULYBYONEPRSONTHEORMERPRESIDENTOTHENIGDEDSTATESAGANCETHEATICULATEDMCRATICDUMAJURITYOTHEUESCONGRESBYALLOFITSREPUBLICKENNDSOMFITSDEMECRATICTDEMACRATMEMBERSITWASANAGREMENTWITHOUTANYBINDNGOBLIGATIONSATHELEDESOFERUNVERYUPANLYANPRESIDHMAPTLYNTHEERYDAYTHESOCALDDELWASPOULISHE + +2024-01-17 01:22:56,849 (asr_inference:494) INFO: speech length: 133421 +2024-01-17 01:22:56,863 (beam_search:428) INFO: decoder input length: 206 +2024-01-17 01:22:56,863 (beam_search:429) INFO: max output length: 206 +2024-01-17 01:22:56,863 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:57,516 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:57,517 (beam_search:476) INFO: -17.73 * 1.0 = -17.73 for ctc +2024-01-17 01:22:57,517 (beam_search:479) INFO: total log probability: -17.73 +2024-01-17 01:22:57,517 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:22:57,517 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:57,517 (beam_search:483) INFO: best hypo: FRESPEACHISASENIUALYAEXETIGTHTPEOPLAREFREETOSAYTHINGSWEDONOTLIKNOTMELYFREETOSAYTHINGSWEDOLIK + +2024-01-17 01:22:57,519 (asr_inference:494) INFO: speech length: 25905 +2024-01-17 01:22:57,526 (beam_search:428) INFO: decoder input length: 38 +2024-01-17 01:22:57,526 (beam_search:429) INFO: max output length: 38 +2024-01-17 01:22:57,526 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:22:57,559 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:22:57,559 (beam_search:476) INFO: -5.22 * 1.0 = -5.22 for ctc +2024-01-17 01:22:57,559 (beam_search:479) INFO: total log probability: -5.22 +2024-01-17 01:22:57,559 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:22:57,559 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:22:57,559 (beam_search:483) INFO: best hypo: HATISLURNEFOMTHIE + +2024-01-17 01:22:57,560 (asr_inference:494) INFO: speech length: 289909 +2024-01-17 01:22:57,587 (beam_search:428) INFO: decoder input length: 450 +2024-01-17 01:22:57,587 (beam_search:429) INFO: max output length: 450 +2024-01-17 01:22:57,587 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:00,681 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:00,681 (beam_search:476) INFO: -55.75 * 1.0 = -55.75 for ctc +2024-01-17 01:23:00,681 (beam_search:479) INFO: total log probability: -55.75 +2024-01-17 01:23:00,681 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:23:00,681 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:00,682 (beam_search:483) INFO: best hypo: BESINTHATTHENVIMENTALEFFECTOFPRODUCSMUSTBEAVRYINMPORTANTISUEINHEREEWUANDTHEWOLIDEAEOTHEECULABERGIVSAVERYUSOULORIANTATIONFORTHEOUSUMERSOFCOUSHEECULABERHOULDGIVENTOTHEMOSTANDVIRMENTAFFANDYPODUCTTHEINFORMATIONSOULDBECLEAREANDCUE + +2024-01-17 01:23:00,684 (asr_inference:494) INFO: speech length: 165099 +2024-01-17 01:23:00,700 (beam_search:428) INFO: decoder input length: 255 +2024-01-17 01:23:00,700 (beam_search:429) INFO: max output length: 255 +2024-01-17 01:23:00,700 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:01,703 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:01,703 (beam_search:476) INFO: -30.10 * 1.0 = -30.10 for ctc +2024-01-17 01:23:01,703 (beam_search:479) INFO: total log probability: -30.10 +2024-01-17 01:23:01,703 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:23:01,703 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:01,704 (beam_search:483) INFO: best hypo: HOWEVERTHECARENDRYGEMNEDESTOBEBETERDALORDTTOTHIGIDALINVIRNMENTTOISHUREFARMINERATIONTOGREATERSAENTOONFOMETOONSUMEREXPECTATIONS + +2024-01-17 01:23:01,706 (asr_inference:494) INFO: speech length: 165120 +2024-01-17 01:23:01,722 (beam_search:428) INFO: decoder input length: 255 +2024-01-17 01:23:01,722 (beam_search:429) INFO: max output length: 255 +2024-01-17 01:23:01,722 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:02,827 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:02,827 (beam_search:476) INFO: -30.32 * 1.0 = -30.32 for ctc +2024-01-17 01:23:02,827 (beam_search:479) INFO: total log probability: -30.32 +2024-01-17 01:23:02,827 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:23:02,827 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:02,828 (beam_search:483) INFO: best hypo: ATCASEBYTHECMIONANDMEMBERSTATTONHANETHERSUPORTTORECONCILIATIONTOSECURPESEANDTIBILITYANDARLANDIWOLTHEREFOREARDYUCALIESTOPLEASESUPORTISAMENMEN + +2024-01-17 01:23:02,830 (asr_inference:494) INFO: speech length: 310354 +2024-01-17 01:23:02,857 (beam_search:428) INFO: decoder input length: 482 +2024-01-17 01:23:02,857 (beam_search:429) INFO: max output length: 482 +2024-01-17 01:23:02,857 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:06,163 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:06,163 (beam_search:476) INFO: -62.10 * 1.0 = -62.10 for ctc +2024-01-17 01:23:06,163 (beam_search:479) INFO: total log probability: -62.10 +2024-01-17 01:23:06,163 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:23:06,163 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:06,165 (beam_search:483) INFO: best hypo: TRATAGICKCHOICESABOUTWHETOEWESTMUTBEMADENOWTAKENINECOUNANETOFASOUTFORSILFULSUPSITESBUTTAKTHEGASASIORSOFYUITCANBEAHELTFULEBRIGINGTRUNSISHONARYMEDIOMTOBEUSEINMEMINMENYMBERSTATIBEONTOEACHIVEOVERAMBISHIOSCLIMITARGITS + +2024-01-17 01:23:06,166 (asr_inference:494) INFO: speech length: 242240 +2024-01-17 01:23:06,187 (beam_search:428) INFO: decoder input length: 376 +2024-01-17 01:23:06,188 (beam_search:429) INFO: max output length: 376 +2024-01-17 01:23:06,188 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:07,729 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:07,729 (beam_search:476) INFO: -38.50 * 1.0 = -38.50 for ctc +2024-01-17 01:23:07,730 (beam_search:479) INFO: total log probability: -38.50 +2024-01-17 01:23:07,730 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-17 01:23:07,730 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:07,730 (beam_search:483) INFO: best hypo: WEAEPOSEILYFRAOLEWECANCUTHTOPRASCUETHESAMEMPOLICESINTHSAMEMANERNOWINGTHATWELEDETOTHISAMPRSOSTHERISAULSTHAWENODEDEA + +2024-01-17 01:23:07,732 (asr_inference:494) INFO: speech length: 18556 +2024-01-17 01:23:07,739 (beam_search:428) INFO: decoder input length: 26 +2024-01-17 01:23:07,739 (beam_search:429) INFO: max output length: 26 +2024-01-17 01:23:07,739 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:07,759 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:07,759 (beam_search:476) INFO: -4.14 * 1.0 = -4.14 for ctc +2024-01-17 01:23:07,759 (beam_search:479) INFO: total log probability: -4.14 +2024-01-17 01:23:07,759 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:23:07,759 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:07,759 (beam_search:483) INFO: best hypo: UTHERSANOPTIONB + +2024-01-17 01:23:07,760 (asr_inference:494) INFO: speech length: 68800 +2024-01-17 01:23:07,770 (beam_search:428) INFO: decoder input length: 105 +2024-01-17 01:23:07,770 (beam_search:429) INFO: max output length: 105 +2024-01-17 01:23:07,770 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:07,906 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:07,906 (beam_search:476) INFO: -11.59 * 1.0 = -11.59 for ctc +2024-01-17 01:23:07,906 (beam_search:479) INFO: total log probability: -11.59 +2024-01-17 01:23:07,906 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:23:07,906 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:07,906 (beam_search:483) INFO: best hypo: WREALLSONEDACHAINGEINORIDOLITIE + +2024-01-17 01:23:07,907 (asr_inference:494) INFO: speech length: 356118 +2024-01-17 01:23:07,939 (beam_search:428) INFO: decoder input length: 554 +2024-01-17 01:23:07,939 (beam_search:429) INFO: max output length: 554 +2024-01-17 01:23:07,939 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:12,034 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:12,034 (beam_search:476) INFO: -59.27 * 1.0 = -59.27 for ctc +2024-01-17 01:23:12,034 (beam_search:479) INFO: total log probability: -59.27 +2024-01-17 01:23:12,035 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:23:12,035 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:12,036 (beam_search:483) INFO: best hypo: ALADEHBATOFTHEREASONOFCOURSEISILIGALFISCINGKANDTHEREOFOMPTDONOFENBYYARRVESESWHICHAREREAGISTERDTOCOUNTRESWHICHLUCKETHEWILOFTHERESURCESTONFORSTINTHENESINALAGREMENSNOMOUNTOFTRESABIITYMESERSOREEXTRPAPREWAREWILADESETHEPROBLOUMEOFREDUSING + +2024-01-17 01:23:12,038 (asr_inference:494) INFO: speech length: 247325 +2024-01-17 01:23:12,060 (beam_search:428) INFO: decoder input length: 384 +2024-01-17 01:23:12,060 (beam_search:429) INFO: max output length: 384 +2024-01-17 01:23:12,060 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:14,322 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:14,322 (beam_search:476) INFO: -50.45 * 1.0 = -50.45 for ctc +2024-01-17 01:23:14,322 (beam_search:479) INFO: total log probability: -50.45 +2024-01-17 01:23:14,322 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:23:14,322 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:14,323 (beam_search:483) INFO: best hypo: THECOMPRMISEALSOINCLDEDKLARERUDSTOTHEFINEWHICHMBERSTATEASHERSTICTIONANDTHEOPRATIONITHIMBERSTATSCONERDFORCRUSBRTHECACESASILATHENEDTOEINVLLFYOURJUSTTHANYOFORWORKANDPLAEOUSEUPORTTOMOHISERECTIV + +2024-01-17 01:23:14,325 (asr_inference:494) INFO: speech length: 239040 +2024-01-17 01:23:14,346 (beam_search:428) INFO: decoder input length: 371 +2024-01-17 01:23:14,346 (beam_search:429) INFO: max output length: 371 +2024-01-17 01:23:14,346 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:16,443 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:16,443 (beam_search:476) INFO: -46.89 * 1.0 = -46.89 for ctc +2024-01-17 01:23:16,443 (beam_search:479) INFO: total log probability: -46.89 +2024-01-17 01:23:16,443 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:23:16,443 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:16,444 (beam_search:483) INFO: best hypo: NOTHERENSWOULDHAVASBELETHATHEARBADBESCRIMINALBESDELIBEATLYCONTAMINATINGHUDYWITHADANEUSNGREDIENTBUTITFACTINFACHEDINGWHTHUNYBESARALHVEALWASDONWIHTOCARYPOLONBACTOTHERHIVESTODTOFEDTHEROUN + +2024-01-17 01:23:16,446 (asr_inference:494) INFO: speech length: 48000 +2024-01-17 01:23:16,454 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 01:23:16,454 (beam_search:429) INFO: max output length: 72 +2024-01-17 01:23:16,454 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:16,551 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:16,551 (beam_search:476) INFO: -4.73 * 1.0 = -4.73 for ctc +2024-01-17 01:23:16,551 (beam_search:479) INFO: total log probability: -4.73 +2024-01-17 01:23:16,551 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 01:23:16,551 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:16,551 (beam_search:483) INFO: best hypo: UTITWASTHECONTRYITSELFBENGMORCAPABL + +2024-01-17 01:23:16,553 (asr_inference:494) INFO: speech length: 74868 +2024-01-17 01:23:16,562 (beam_search:428) INFO: decoder input length: 114 +2024-01-17 01:23:16,562 (beam_search:429) INFO: max output length: 114 +2024-01-17 01:23:16,562 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:16,791 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:16,791 (beam_search:476) INFO: -10.72 * 1.0 = -10.72 for ctc +2024-01-17 01:23:16,791 (beam_search:479) INFO: total log probability: -10.72 +2024-01-17 01:23:16,791 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:23:16,791 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:16,792 (beam_search:483) INFO: best hypo: RINTOTHEPRTFOLIOOFTHENEUWCOMIONARDELINGWITHFUNDEMENTERRITES + +2024-01-17 01:23:16,793 (asr_inference:494) INFO: speech length: 47679 +2024-01-17 01:23:16,801 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 01:23:16,801 (beam_search:429) INFO: max output length: 72 +2024-01-17 01:23:16,801 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:16,911 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:16,911 (beam_search:476) INFO: -15.54 * 1.0 = -15.54 for ctc +2024-01-17 01:23:16,911 (beam_search:479) INFO: total log probability: -15.54 +2024-01-17 01:23:16,911 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-17 01:23:16,911 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:16,911 (beam_search:483) INFO: best hypo: THEMESIYGITATTHEOUDODTNATHAVEANNOURSOLUIONS + +2024-01-17 01:23:16,912 (asr_inference:494) INFO: speech length: 142379 +2024-01-17 01:23:16,926 (beam_search:428) INFO: decoder input length: 220 +2024-01-17 01:23:16,926 (beam_search:429) INFO: max output length: 220 +2024-01-17 01:23:16,927 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:17,583 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:17,583 (beam_search:476) INFO: -18.66 * 1.0 = -18.66 for ctc +2024-01-17 01:23:17,583 (beam_search:479) INFO: total log probability: -18.66 +2024-01-17 01:23:17,583 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:23:17,583 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:17,584 (beam_search:483) INFO: best hypo: ARYOUWILINGTOACTINEREFAVERFORTHESOSIALDEMENTIONTOBEINCLOUDEDINTHEEUCOMPATENCSESASPROPOSE + +2024-01-17 01:23:17,586 (asr_inference:494) INFO: speech length: 83513 +2024-01-17 01:23:17,596 (beam_search:428) INFO: decoder input length: 128 +2024-01-17 01:23:17,596 (beam_search:429) INFO: max output length: 128 +2024-01-17 01:23:17,596 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:17,867 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:17,867 (beam_search:476) INFO: -16.54 * 1.0 = -16.54 for ctc +2024-01-17 01:23:17,867 (beam_search:479) INFO: total log probability: -16.54 +2024-01-17 01:23:17,867 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:23:17,867 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:17,867 (beam_search:483) INFO: best hypo: ANEXTHATONPESPECTRUPOLIESTAKINWITHEEFORMOFOUERTELICONTHFRAMWOR + +2024-01-17 01:23:17,868 (asr_inference:494) INFO: speech length: 195190 +2024-01-17 01:23:17,886 (beam_search:428) INFO: decoder input length: 302 +2024-01-17 01:23:17,886 (beam_search:429) INFO: max output length: 302 +2024-01-17 01:23:17,886 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:19,215 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:19,215 (beam_search:476) INFO: -27.81 * 1.0 = -27.81 for ctc +2024-01-17 01:23:19,215 (beam_search:479) INFO: total log probability: -27.81 +2024-01-17 01:23:19,215 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:23:19,215 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:19,216 (beam_search:483) INFO: best hypo: IBELEVEHISREMARKSWERAEXPLICITLYRACEISTANDTHENAFOBICKANDPRMOTEDRACIALINTOLERANCEINAWAYTHAISNOTCXCEPTIBLEORALOWDINTECONTITUTIONOFTHISHOUS + +2024-01-17 01:23:19,218 (asr_inference:494) INFO: speech length: 95040 +2024-01-17 01:23:19,229 (beam_search:428) INFO: decoder input length: 146 +2024-01-17 01:23:19,229 (beam_search:429) INFO: max output length: 146 +2024-01-17 01:23:19,229 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:19,594 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:19,594 (beam_search:476) INFO: -21.89 * 1.0 = -21.89 for ctc +2024-01-17 01:23:19,594 (beam_search:479) INFO: total log probability: -21.89 +2024-01-17 01:23:19,594 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:23:19,594 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:19,595 (beam_search:483) INFO: best hypo: REALIFEGAMPLSHOTHATSOLVINGITIESRELATETOADUCATIONFEULEDSTRONGCOMINITDEVELOPMENT + +2024-01-17 01:23:19,596 (asr_inference:494) INFO: speech length: 154228 +2024-01-17 01:23:19,612 (beam_search:428) INFO: decoder input length: 238 +2024-01-17 01:23:19,612 (beam_search:429) INFO: max output length: 238 +2024-01-17 01:23:19,612 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:20,487 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:20,487 (beam_search:476) INFO: -33.78 * 1.0 = -33.78 for ctc +2024-01-17 01:23:20,487 (beam_search:479) INFO: total log probability: -33.78 +2024-01-17 01:23:20,487 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:23:20,487 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:20,488 (beam_search:483) INFO: best hypo: SIHOPETHATISILHVPEORUSHAASWELNDTHATRUHACANALTSANDVISAIGNDEXTREMESUCESSTORYAFTERTHSEGTISIGNIFICANDATINORGSTTHISYEARB + +2024-01-17 01:23:20,490 (asr_inference:494) INFO: speech length: 249565 +2024-01-17 01:23:20,512 (beam_search:428) INFO: decoder input length: 387 +2024-01-17 01:23:20,512 (beam_search:429) INFO: max output length: 387 +2024-01-17 01:23:20,512 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:22,423 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:22,423 (beam_search:476) INFO: -35.13 * 1.0 = -35.13 for ctc +2024-01-17 01:23:22,423 (beam_search:479) INFO: total log probability: -35.13 +2024-01-17 01:23:22,423 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:23:22,423 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:22,424 (beam_search:483) INFO: best hypo: SHEECXEPTEDTHEFACTTHATSITISONSHIPISAYNASINALPARTOFTHEOSINOGUDISDICTIONBUTHYOURLSOSAIDTHATACOURDINGTOTHEMASTRICKTREATYANDSHEASRIGHTTHEHASTOBEADIYRECLIN + +2024-01-17 01:23:22,426 (asr_inference:494) INFO: speech length: 343360 +2024-01-17 01:23:22,456 (beam_search:428) INFO: decoder input length: 534 +2024-01-17 01:23:22,456 (beam_search:429) INFO: max output length: 534 +2024-01-17 01:23:22,456 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:25,917 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:25,917 (beam_search:476) INFO: -49.85 * 1.0 = -49.85 for ctc +2024-01-17 01:23:25,917 (beam_search:479) INFO: total log probability: -49.85 +2024-01-17 01:23:25,917 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:23:25,917 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:25,918 (beam_search:483) INFO: best hypo: TDEYWOUFALDESPECIALEANTHEMSTRATINGAUNIFIEDANDTAFFISHENTAPPRORCHTOLIEMITCANGHETREATMENTASWELASINSTRANTHANINGKITSLEDINGPOLITICALCOSIONINDISAGENDERICONSCITHERTHERFORTAKINGTHISRESOLUTIONANACTOFUTMOSTIMPORTANS + +2024-01-17 01:23:25,920 (asr_inference:494) INFO: speech length: 68479 +2024-01-17 01:23:25,930 (beam_search:428) INFO: decoder input length: 104 +2024-01-17 01:23:25,930 (beam_search:429) INFO: max output length: 104 +2024-01-17 01:23:25,930 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:26,130 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:26,130 (beam_search:476) INFO: -9.02 * 1.0 = -9.02 for ctc +2024-01-17 01:23:26,130 (beam_search:479) INFO: total log probability: -9.02 +2024-01-17 01:23:26,130 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 01:23:26,130 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:26,130 (beam_search:483) INFO: best hypo: THEUNIGTEDSTATEOFYURUOWILBEAFACTWITHSWEDONASAPROVIDENC + +2024-01-17 01:23:26,132 (asr_inference:494) INFO: speech length: 124799 +2024-01-17 01:23:26,145 (beam_search:428) INFO: decoder input length: 192 +2024-01-17 01:23:26,145 (beam_search:429) INFO: max output length: 192 +2024-01-17 01:23:26,145 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:26,721 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:26,721 (beam_search:476) INFO: -19.20 * 1.0 = -19.20 for ctc +2024-01-17 01:23:26,721 (beam_search:479) INFO: total log probability: -19.20 +2024-01-17 01:23:26,721 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:23:26,721 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:26,721 (beam_search:483) INFO: best hypo: ITMUSBTHECAPITALEOFBOTTHEATESANDWEMUSRECONISEPOLSTINISTHATASPROVIDEDFORINTHEOVELOGREMENCS + +2024-01-17 01:23:26,723 (asr_inference:494) INFO: speech length: 234841 +2024-01-17 01:23:26,743 (beam_search:428) INFO: decoder input length: 364 +2024-01-17 01:23:26,743 (beam_search:429) INFO: max output length: 364 +2024-01-17 01:23:26,743 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:28,476 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:28,476 (beam_search:476) INFO: -35.36 * 1.0 = -35.36 for ctc +2024-01-17 01:23:28,476 (beam_search:479) INFO: total log probability: -35.36 +2024-01-17 01:23:28,476 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:23:28,476 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:28,477 (beam_search:483) INFO: best hypo: YOUCRAINYSFACETWITHWONEOFCRUSALCHALINGESINITSHISTORYITWOULDBEFUTEMENTARLYRONGKTOPRETHENATIONNOWWITALTHIPESOFRESTRICTIONSPOPELADERLCALEOSTERITEPOLI + +2024-01-17 01:23:28,478 (asr_inference:494) INFO: speech length: 77119 +2024-01-17 01:23:28,488 (beam_search:428) INFO: decoder input length: 118 +2024-01-17 01:23:28,488 (beam_search:429) INFO: max output length: 118 +2024-01-17 01:23:28,488 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:28,687 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:28,687 (beam_search:476) INFO: -6.45 * 1.0 = -6.45 for ctc +2024-01-17 01:23:28,687 (beam_search:479) INFO: total log probability: -6.45 +2024-01-17 01:23:28,687 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 01:23:28,687 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:28,688 (beam_search:483) INFO: best hypo: MORERULSANDREGULATIONWILLNOTIMPROVETHISCITUATIO + +2024-01-17 01:23:28,689 (asr_inference:494) INFO: speech length: 82879 +2024-01-17 01:23:28,699 (beam_search:428) INFO: decoder input length: 127 +2024-01-17 01:23:28,699 (beam_search:429) INFO: max output length: 127 +2024-01-17 01:23:28,699 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:28,958 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:28,959 (beam_search:476) INFO: -12.74 * 1.0 = -12.74 for ctc +2024-01-17 01:23:28,959 (beam_search:479) INFO: total log probability: -12.74 +2024-01-17 01:23:28,959 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:23:28,959 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:28,959 (beam_search:483) INFO: best hypo: ATLEASTWEWOLDLIKETONOWTHESOURSEOFTHEMONYANDTHEPOSIPLMORTIE + +2024-01-17 01:23:28,960 (asr_inference:494) INFO: speech length: 315519 +2024-01-17 01:23:28,989 (beam_search:428) INFO: decoder input length: 490 +2024-01-17 01:23:28,989 (beam_search:429) INFO: max output length: 490 +2024-01-17 01:23:28,989 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:32,150 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:32,150 (beam_search:476) INFO: -59.51 * 1.0 = -59.51 for ctc +2024-01-17 01:23:32,150 (beam_search:479) INFO: total log probability: -59.51 +2024-01-17 01:23:32,150 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-17 01:23:32,150 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:32,151 (beam_search:483) INFO: best hypo: TOWEROFTHOSEYURUPINWALELANGIASHINTOTHESGLUBELICEDWEARLDISINTTOTHEYSGOBELISDECONOMINDHISGOBEVILACHWHICHISGORSTIALYCONOMICKSOSIALELNPLITICOITSARMOSTVELABLEESTHERTFROMTHEINTIREEYOUTHATWEMUSTTHAKFOLACOUNSANDT + +2024-01-17 01:23:32,153 (asr_inference:494) INFO: speech length: 115826 +2024-01-17 01:23:32,166 (beam_search:428) INFO: decoder input length: 178 +2024-01-17 01:23:32,166 (beam_search:429) INFO: max output length: 178 +2024-01-17 01:23:32,166 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:32,670 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:32,670 (beam_search:476) INFO: -21.70 * 1.0 = -21.70 for ctc +2024-01-17 01:23:32,670 (beam_search:479) INFO: total log probability: -21.70 +2024-01-17 01:23:32,670 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:23:32,670 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:32,670 (beam_search:483) INFO: best hypo: WEAVETOREPETETHATALTHEAYANOTBEUSETOFINANSSIURITEXPANCESBARTHERSCONTROLORMLITRYSOPORNT + +2024-01-17 01:23:32,672 (asr_inference:494) INFO: speech length: 77749 +2024-01-17 01:23:32,682 (beam_search:428) INFO: decoder input length: 119 +2024-01-17 01:23:32,682 (beam_search:429) INFO: max output length: 119 +2024-01-17 01:23:32,682 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:32,931 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:32,932 (beam_search:476) INFO: -13.22 * 1.0 = -13.22 for ctc +2024-01-17 01:23:32,932 (beam_search:479) INFO: total log probability: -13.22 +2024-01-17 01:23:32,932 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:23:32,932 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:32,932 (beam_search:483) INFO: best hypo: THNTHESINTIFIREPORTSBECOEMREMOREURGENTORALARMINGANDMORSHOCKING + +2024-01-17 01:23:32,933 (asr_inference:494) INFO: speech length: 316143 +2024-01-17 01:23:32,961 (beam_search:428) INFO: decoder input length: 491 +2024-01-17 01:23:32,962 (beam_search:429) INFO: max output length: 491 +2024-01-17 01:23:32,962 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:35,358 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:35,358 (beam_search:476) INFO: -37.99 * 1.0 = -37.99 for ctc +2024-01-17 01:23:35,358 (beam_search:479) INFO: total log probability: -37.99 +2024-01-17 01:23:35,358 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:23:35,358 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:35,359 (beam_search:483) INFO: best hypo: FINALYMWHENWEATTHINKINGABOUNTHERINOVATIVEFINSIONINSTOUMENTSWHENOUTHEBOLTHFOROURSELSTORSUPOARTOWERACONOMESBUTALSSOTOOESUPORTTHOSHOEREINEAET + +2024-01-17 01:23:35,361 (asr_inference:494) INFO: speech length: 49600 +2024-01-17 01:23:35,369 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 01:23:35,369 (beam_search:429) INFO: max output length: 75 +2024-01-17 01:23:35,369 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:35,460 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:35,460 (beam_search:476) INFO: -9.62 * 1.0 = -9.62 for ctc +2024-01-17 01:23:35,460 (beam_search:479) INFO: total log probability: -9.62 +2024-01-17 01:23:35,460 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:23:35,460 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:35,460 (beam_search:483) INFO: best hypo: THTIVEASOYUNIEKDOLLINPEMAKING + +2024-01-17 01:23:35,461 (asr_inference:494) INFO: speech length: 49279 +2024-01-17 01:23:35,469 (beam_search:428) INFO: decoder input length: 74 +2024-01-17 01:23:35,469 (beam_search:429) INFO: max output length: 74 +2024-01-17 01:23:35,469 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:35,539 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:35,539 (beam_search:476) INFO: -6.64 * 1.0 = -6.64 for ctc +2024-01-17 01:23:35,539 (beam_search:479) INFO: total log probability: -6.64 +2024-01-17 01:23:35,539 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:23:35,539 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:35,539 (beam_search:483) INFO: best hypo: PAPERAVERYDWEEKPROPOSL + +2024-01-17 01:23:35,540 (asr_inference:494) INFO: speech length: 106873 +2024-01-17 01:23:35,552 (beam_search:428) INFO: decoder input length: 164 +2024-01-17 01:23:35,552 (beam_search:429) INFO: max output length: 164 +2024-01-17 01:23:35,552 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:35,940 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:35,940 (beam_search:476) INFO: -14.11 * 1.0 = -14.11 for ctc +2024-01-17 01:23:35,940 (beam_search:479) INFO: total log probability: -14.11 +2024-01-17 01:23:35,940 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:23:35,940 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:35,940 (beam_search:483) INFO: best hypo: SRUSHASALWASBEAVERYPROUDNATIONWITHRICHCOLTCUERWITHINVENTIONSWITHANASPL + +2024-01-17 01:23:35,942 (asr_inference:494) INFO: speech length: 213759 +2024-01-17 01:23:35,962 (beam_search:428) INFO: decoder input length: 331 +2024-01-17 01:23:35,962 (beam_search:429) INFO: max output length: 331 +2024-01-17 01:23:35,962 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:37,598 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:37,598 (beam_search:476) INFO: -30.03 * 1.0 = -30.03 for ctc +2024-01-17 01:23:37,598 (beam_search:479) INFO: total log probability: -30.03 +2024-01-17 01:23:37,598 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:23:37,598 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:37,599 (beam_search:483) INFO: best hypo: ARTACXATINEVENAMODICALOFTACXATIONINSOMECACESMIGHJUSTHELPUSEMTODOWHATIVEAREDYSUEGESTEDANWHONOSEMAKETHECACEFORTHERETRESPECTOFBANKRECAPIDLIZATIONTHATWENEVERSO + +2024-01-17 01:23:37,601 (asr_inference:494) INFO: speech length: 212480 +2024-01-17 01:23:37,621 (beam_search:428) INFO: decoder input length: 329 +2024-01-17 01:23:37,621 (beam_search:429) INFO: max output length: 329 +2024-01-17 01:23:37,621 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:39,135 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:39,135 (beam_search:476) INFO: -35.62 * 1.0 = -35.62 for ctc +2024-01-17 01:23:39,135 (beam_search:479) INFO: total log probability: -35.62 +2024-01-17 01:23:39,135 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:23:39,135 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:39,136 (beam_search:483) INFO: best hypo: THEROPEANASILOMSUPORTOFHISMOROVERASAMONGITSTHASTSTOPRMOUTDFESILYTATANDCOURDINATEXTCANGESOFINFORMATIONANDOTHERACTIVEITESRELATEDOELOCATINWTHINHEUNION + +2024-01-17 01:23:39,137 (asr_inference:494) INFO: speech length: 163826 +2024-01-17 01:23:39,153 (beam_search:428) INFO: decoder input length: 253 +2024-01-17 01:23:39,153 (beam_search:429) INFO: max output length: 253 +2024-01-17 01:23:39,153 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:40,109 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:40,109 (beam_search:476) INFO: -30.83 * 1.0 = -30.83 for ctc +2024-01-17 01:23:40,109 (beam_search:479) INFO: total log probability: -30.83 +2024-01-17 01:23:40,109 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:23:40,109 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:40,110 (beam_search:483) INFO: best hypo: HEONUSOOFTHEFRAMEBORKAGEMENTPROVIDESALIGLYBINDINGINSTRMENTTOOBGRATANDSTRANTNEUOSTRALIABYLITHRRATIONSANDTOINCRESCOPERATION + +2024-01-17 01:23:40,112 (asr_inference:494) INFO: speech length: 95678 +2024-01-17 01:23:40,123 (beam_search:428) INFO: decoder input length: 147 +2024-01-17 01:23:40,123 (beam_search:429) INFO: max output length: 147 +2024-01-17 01:23:40,123 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:40,533 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:40,533 (beam_search:476) INFO: -24.07 * 1.0 = -24.07 for ctc +2024-01-17 01:23:40,533 (beam_search:479) INFO: total log probability: -24.07 +2024-01-17 01:23:40,533 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 01:23:40,533 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:40,534 (beam_search:483) INFO: best hypo: THEREFORWEAEASTINTHECOUSALASGMIONTORESENTHAHASBALETHAOULDBETHESESTMENTOFTHEEBACTOFTHERICIS + +2024-01-17 01:23:40,535 (asr_inference:494) INFO: speech length: 151339 +2024-01-17 01:23:40,550 (beam_search:428) INFO: decoder input length: 234 +2024-01-17 01:23:40,550 (beam_search:429) INFO: max output length: 234 +2024-01-17 01:23:40,550 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:41,272 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:41,272 (beam_search:476) INFO: -15.73 * 1.0 = -15.73 for ctc +2024-01-17 01:23:41,272 (beam_search:479) INFO: total log probability: -15.73 +2024-01-17 01:23:41,272 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 01:23:41,272 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:41,272 (beam_search:483) INFO: best hypo: INOTHEWORDSTHEOBJECTIONISNOTWHETHERMONEYISPADORNOTTHEOBJECTIONISWETHERTERISADIDECTLINKORNO + +2024-01-17 01:23:41,274 (asr_inference:494) INFO: speech length: 105263 +2024-01-17 01:23:41,285 (beam_search:428) INFO: decoder input length: 162 +2024-01-17 01:23:41,286 (beam_search:429) INFO: max output length: 162 +2024-01-17 01:23:41,286 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:41,730 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:41,730 (beam_search:476) INFO: -25.99 * 1.0 = -25.99 for ctc +2024-01-17 01:23:41,730 (beam_search:479) INFO: total log probability: -25.99 +2024-01-17 01:23:41,731 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:23:41,731 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:41,731 (beam_search:483) INFO: best hypo: TOTHSTINGUISHESTHETOMANOEARYOUMERRIGTABBUSEBYTHECADANTGORMENTANDTHEDLANIANNUCLAPROVGDM + +2024-01-17 01:23:41,732 (asr_inference:494) INFO: speech length: 161261 +2024-01-17 01:23:41,748 (beam_search:428) INFO: decoder input length: 249 +2024-01-17 01:23:41,748 (beam_search:429) INFO: max output length: 249 +2024-01-17 01:23:41,748 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:42,586 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:42,586 (beam_search:476) INFO: -28.66 * 1.0 = -28.66 for ctc +2024-01-17 01:23:42,586 (beam_search:479) INFO: total log probability: -28.66 +2024-01-17 01:23:42,586 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:23:42,586 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:42,587 (beam_search:483) INFO: best hypo: YESSMATHMDRUOTHANKATHRSECTIALHERASDMENTISAFORMOFVILANCSANDITISTHEMOSTEXTREAMEFORMOFGNTERBAETHDISCUMINATI + +2024-01-17 01:23:42,588 (asr_inference:494) INFO: speech length: 79671 +2024-01-17 01:23:42,598 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 01:23:42,598 (beam_search:429) INFO: max output length: 122 +2024-01-17 01:23:42,599 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:42,865 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:42,865 (beam_search:476) INFO: -21.64 * 1.0 = -21.64 for ctc +2024-01-17 01:23:42,865 (beam_search:479) INFO: total log probability: -21.64 +2024-01-17 01:23:42,865 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-17 01:23:42,865 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:42,865 (beam_search:483) INFO: best hypo: WECANLOKTOSOMEURANLINOUMEMBERSFOROUODGXAMPLESASREGARDEDTHGNOLIGE + +2024-01-17 01:23:42,867 (asr_inference:494) INFO: speech length: 72640 +2024-01-17 01:23:42,877 (beam_search:428) INFO: decoder input length: 111 +2024-01-17 01:23:42,877 (beam_search:429) INFO: max output length: 111 +2024-01-17 01:23:42,877 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:43,057 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:43,057 (beam_search:476) INFO: -13.08 * 1.0 = -13.08 for ctc +2024-01-17 01:23:43,057 (beam_search:479) INFO: total log probability: -13.08 +2024-01-17 01:23:43,057 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:23:43,057 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:43,057 (beam_search:483) INFO: best hypo: YIMNVALLVEDFORTHERPOSITEVEANDCOSTRACTEIVEABROTCH + +2024-01-17 01:23:43,059 (asr_inference:494) INFO: speech length: 105599 +2024-01-17 01:23:43,070 (beam_search:428) INFO: decoder input length: 162 +2024-01-17 01:23:43,070 (beam_search:429) INFO: max output length: 162 +2024-01-17 01:23:43,070 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:43,469 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:43,469 (beam_search:476) INFO: -15.50 * 1.0 = -15.50 for ctc +2024-01-17 01:23:43,469 (beam_search:479) INFO: total log probability: -15.50 +2024-01-17 01:23:43,469 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:23:43,469 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:43,469 (beam_search:483) INFO: best hypo: OIHOPTHATISWILBECOMPLEATEDEARINHEFACIVILFUTUARTHATMANESMABETOAFREMONS + +2024-01-17 01:23:43,471 (asr_inference:494) INFO: speech length: 128960 +2024-01-17 01:23:43,484 (beam_search:428) INFO: decoder input length: 199 +2024-01-17 01:23:43,484 (beam_search:429) INFO: max output length: 199 +2024-01-17 01:23:43,484 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:44,162 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:44,162 (beam_search:476) INFO: -29.44 * 1.0 = -29.44 for ctc +2024-01-17 01:23:44,162 (beam_search:479) INFO: total log probability: -29.44 +2024-01-17 01:23:44,162 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 01:23:44,162 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:44,162 (beam_search:483) INFO: best hypo: ORFORDERNDCOURDSHTHEYOUHANDEFORTSTOBRINGAMONGKPESINOFGNISTANANTOOVERCOMETHEFFRASILESICUITYANVIRMENTINTHECONTRY + +2024-01-17 01:23:44,164 (asr_inference:494) INFO: speech length: 52160 +2024-01-17 01:23:44,172 (beam_search:428) INFO: decoder input length: 79 +2024-01-17 01:23:44,172 (beam_search:429) INFO: max output length: 79 +2024-01-17 01:23:44,172 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:44,276 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:44,276 (beam_search:476) INFO: -6.34 * 1.0 = -6.34 for ctc +2024-01-17 01:23:44,276 (beam_search:479) INFO: total log probability: -6.34 +2024-01-17 01:23:44,276 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 01:23:44,276 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:44,276 (beam_search:483) INFO: best hypo: BEANDERSTANTTHATSOMEPEOPELARANGRY + +2024-01-17 01:23:44,277 (asr_inference:494) INFO: speech length: 27200 +2024-01-17 01:23:44,284 (beam_search:428) INFO: decoder input length: 40 +2024-01-17 01:23:44,284 (beam_search:429) INFO: max output length: 40 +2024-01-17 01:23:44,284 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:44,318 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:44,318 (beam_search:476) INFO: -5.96 * 1.0 = -5.96 for ctc +2024-01-17 01:23:44,318 (beam_search:479) INFO: total log probability: -5.96 +2024-01-17 01:23:44,318 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 01:23:44,318 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:44,318 (beam_search:483) INFO: best hypo: ONTOBEMRESTPONCIVEL + +2024-01-17 01:23:44,319 (asr_inference:494) INFO: speech length: 154526 +2024-01-17 01:23:44,334 (beam_search:428) INFO: decoder input length: 239 +2024-01-17 01:23:44,334 (beam_search:429) INFO: max output length: 239 +2024-01-17 01:23:44,334 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:45,097 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:45,097 (beam_search:476) INFO: -22.47 * 1.0 = -22.47 for ctc +2024-01-17 01:23:45,097 (beam_search:479) INFO: total log probability: -22.47 +2024-01-17 01:23:45,097 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:23:45,097 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:45,098 (beam_search:483) INFO: best hypo: WEMUSTEDACTIFIETHTHISSUTIATIONANDVEASKTHEOMIONTOCONCSIDERTHEMOSTEDICUITCOMBINSATIONMESESFOWPASNGES + +2024-01-17 01:23:45,099 (asr_inference:494) INFO: speech length: 233600 +2024-01-17 01:23:45,120 (beam_search:428) INFO: decoder input length: 362 +2024-01-17 01:23:45,120 (beam_search:429) INFO: max output length: 362 +2024-01-17 01:23:45,120 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:47,047 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:47,047 (beam_search:476) INFO: -41.03 * 1.0 = -41.03 for ctc +2024-01-17 01:23:47,047 (beam_search:479) INFO: total log probability: -41.03 +2024-01-17 01:23:47,047 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:23:47,047 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:47,048 (beam_search:483) INFO: best hypo: THEOMITIONINBVIHEDTHEYUROPIANTPULAMENTINTHEUPCOMINCREVISIONTOOPENHISPOSITIONONTHISMATERWHICHRELYCONSEDAXESTOSJUSTISINYUROPANDTHEENFORTMENTOFRIESGRANTEDBYHEYUROPIANERYUNANLO + +2024-01-17 01:23:47,050 (asr_inference:494) INFO: speech length: 103040 +2024-01-17 01:23:47,062 (beam_search:428) INFO: decoder input length: 158 +2024-01-17 01:23:47,062 (beam_search:429) INFO: max output length: 158 +2024-01-17 01:23:47,062 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:47,503 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:47,503 (beam_search:476) INFO: -30.62 * 1.0 = -30.62 for ctc +2024-01-17 01:23:47,503 (beam_search:479) INFO: total log probability: -30.62 +2024-01-17 01:23:47,503 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-17 01:23:47,503 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:47,504 (beam_search:483) INFO: best hypo: ILOMVERYMUCHTHRISOUNTIONOFTOKEBETWENTHEOSRALISANPLESTINIONSANDSNCEIRLYHOPTHATHEWILSUCED + +2024-01-17 01:23:47,505 (asr_inference:494) INFO: speech length: 109440 +2024-01-17 01:23:47,517 (beam_search:428) INFO: decoder input length: 168 +2024-01-17 01:23:47,517 (beam_search:429) INFO: max output length: 168 +2024-01-17 01:23:47,517 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:47,966 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:47,966 (beam_search:476) INFO: -19.90 * 1.0 = -19.90 for ctc +2024-01-17 01:23:47,966 (beam_search:479) INFO: total log probability: -19.90 +2024-01-17 01:23:47,967 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:23:47,967 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:47,967 (beam_search:483) INFO: best hypo: WEHAVEACUMILATIONOFPROBLENCSRESULTINGFROMETHEARTIFIHALANDDEBAGEATINGKANDVERPREVIUSYUS + +2024-01-17 01:23:47,968 (asr_inference:494) INFO: speech length: 73584 +2024-01-17 01:23:47,978 (beam_search:428) INFO: decoder input length: 112 +2024-01-17 01:23:47,978 (beam_search:429) INFO: max output length: 112 +2024-01-17 01:23:47,978 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:48,187 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:48,187 (beam_search:476) INFO: -13.16 * 1.0 = -13.16 for ctc +2024-01-17 01:23:48,187 (beam_search:479) INFO: total log probability: -13.16 +2024-01-17 01:23:48,187 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 01:23:48,188 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:48,188 (beam_search:483) INFO: best hypo: LETUSTNOTBETHEMANOFYESTERDYLNTUNBEPOLDAYSINSTHITUTIO + +2024-01-17 01:23:48,189 (asr_inference:494) INFO: speech length: 287020 +2024-01-17 01:23:48,215 (beam_search:428) INFO: decoder input length: 446 +2024-01-17 01:23:48,215 (beam_search:429) INFO: max output length: 446 +2024-01-17 01:23:48,215 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:50,567 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:50,567 (beam_search:476) INFO: -44.72 * 1.0 = -44.72 for ctc +2024-01-17 01:23:50,567 (beam_search:479) INFO: total log probability: -44.72 +2024-01-17 01:23:50,567 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 01:23:50,567 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:50,568 (beam_search:483) INFO: best hypo: TIGOULDERLSUMTOBECOMEAMBASSETHESOTHEYEARMAKNGITSADEARSANDACTIVITHISWOWIDLYNOWNAMONCSHTOYURUPEATITIENSANDPUTPIIPATINGNEVENSBETATYOUROPIANNASIONALLFORLOKALEVL + +2024-01-17 01:23:50,570 (asr_inference:494) INFO: speech length: 141760 +2024-01-17 01:23:50,585 (beam_search:428) INFO: decoder input length: 219 +2024-01-17 01:23:50,585 (beam_search:429) INFO: max output length: 219 +2024-01-17 01:23:50,585 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:51,341 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:51,341 (beam_search:476) INFO: -22.46 * 1.0 = -22.46 for ctc +2024-01-17 01:23:51,341 (beam_search:479) INFO: total log probability: -22.46 +2024-01-17 01:23:51,341 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 01:23:51,342 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:51,342 (beam_search:483) INFO: best hypo: SERTNLYSUCHIMPACESESTMENTCOULDPREMTSERTANPROBLOMSSUCHASTHOSPOSEDBYTHEELECTRONIKIDENTIFICATIONOFSHEPANDSCOTLAND + +2024-01-17 01:23:51,343 (asr_inference:494) INFO: speech length: 204471 +2024-01-17 01:23:51,361 (beam_search:428) INFO: decoder input length: 317 +2024-01-17 01:23:51,362 (beam_search:429) INFO: max output length: 317 +2024-01-17 01:23:51,362 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:52,902 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:52,902 (beam_search:476) INFO: -34.25 * 1.0 = -34.25 for ctc +2024-01-17 01:23:52,903 (beam_search:479) INFO: total log probability: -34.25 +2024-01-17 01:23:52,903 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 01:23:52,903 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:52,903 (beam_search:483) INFO: best hypo: THECORTISCONTENTTOSEETHTITSWORKHASINFORMETHDISCHAGHROSANDHASCONTEBUTEDTOROPOSALSFORIMPROVINGTHEFINANCALMANAGHMENTOFVEYOUSPENDINGANDBETHETARKATINGOFYOUFUNE + +2024-01-17 01:23:52,905 (asr_inference:494) INFO: speech length: 110394 +2024-01-17 01:23:52,917 (beam_search:428) INFO: decoder input length: 170 +2024-01-17 01:23:52,917 (beam_search:429) INFO: max output length: 170 +2024-01-17 01:23:52,917 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:53,343 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:53,343 (beam_search:476) INFO: -14.02 * 1.0 = -14.02 for ctc +2024-01-17 01:23:53,343 (beam_search:479) INFO: total log probability: -14.02 +2024-01-17 01:23:53,343 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 01:23:53,343 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:53,344 (beam_search:483) INFO: best hypo: REGOUTHERECLARIETHEANDSERTANTYISNEDEDFORTHEOBLICKSECTOURANDFORTHEINDUSTRY + +2024-01-17 01:23:53,345 (asr_inference:494) INFO: speech length: 109428 +2024-01-17 01:23:53,357 (beam_search:428) INFO: decoder input length: 168 +2024-01-17 01:23:53,357 (beam_search:429) INFO: max output length: 168 +2024-01-17 01:23:53,357 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:53,845 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:53,845 (beam_search:476) INFO: -20.23 * 1.0 = -20.23 for ctc +2024-01-17 01:23:53,845 (beam_search:479) INFO: total log probability: -20.23 +2024-01-17 01:23:53,845 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:23:53,845 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:53,846 (beam_search:483) INFO: best hypo: ISITREALINOTPOSIBLETOUSAATHERHOUSINGFASCILIDESWITHUPROPREHRESEPTINCONDIONSINTHEMENTIME + +2024-01-17 01:23:53,847 (asr_inference:494) INFO: speech length: 77120 +2024-01-17 01:23:53,858 (beam_search:428) INFO: decoder input length: 118 +2024-01-17 01:23:53,858 (beam_search:429) INFO: max output length: 118 +2024-01-17 01:23:53,858 (beam_search:430) INFO: min output length: 0 +2024-01-17 01:23:54,031 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 01:23:54,032 (beam_search:476) INFO: -9.06 * 1.0 = -9.06 for ctc +2024-01-17 01:23:54,032 (beam_search:479) INFO: total log probability: -9.06 +2024-01-17 01:23:54,032 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 01:23:54,032 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 01:23:54,032 (beam_search:483) INFO: best hypo: WHELYOUTAKEACIONATLASTIFNOTTHENWHEND + +# Accounting: time=104 threads=1 +# Ended (code 0) at Wed Jan 17 01:23:54 CST 2024, elapsed time 104 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/calculate_rtf.log b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/calculate_rtf.log new file mode 100644 index 0000000000000000000000000000000000000000..52cfd1671c8b5ee47eb5025277a4814ca49ca4d3 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/calculate_rtf.log @@ -0,0 +1,9 @@ +# pyscripts/utils/calculate_rtf.py --log-dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir --log-name asr_inference --input-shift 0.0625 --start-times-marker "speech length" --end-times-marker "best hypo" --inf-num 1 +# Started at Wed Jan 17 01:23:54 CST 2024 +# +Total audio duration: 5412.381 [sec] +Total decoding time: 366.568 [sec] +RTF: 0.068 +Latency: 335.685 [ms/sentence] +# Accounting: time=0 threads=1 +# Ended (code 0) at Wed Jan 17 01:23:54 CST 2024, elapsed time 0 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.1.scp b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.1.scp new file mode 100644 index 0000000000000000000000000000000000000000..f4b8622edeb5e70b8c37674021dce38251dac4ce --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/keys.1.scp @@ -0,0 +1,273 @@ +LAD_eng_000254 dump/raw/test_10min_eng1/data/format.1/LAD_eng_000254.flac +LAD_eng_000255 dump/raw/test_10min_eng1/data/format.1/LAD_eng_000255.flac +LAD_eng_000256 dump/raw/test_10min_eng1/data/format.1/LAD_eng_000256.flac +LAD_eng_000257 dump/raw/test_10min_eng1/data/format.1/LAD_eng_000257.flac +LAD_eng_000258 dump/raw/test_10min_eng1/data/format.1/LAD_eng_000258.flac +LAD_eng_000259 dump/raw/test_10min_eng1/data/format.1/LAD_eng_000259.flac +LAD_eng_000260 dump/raw/test_10min_eng1/data/format.1/LAD_eng_000260.flac +LAD_eng_000261 dump/raw/test_10min_eng1/data/format.1/LAD_eng_000261.flac +LAD_eng_000262 dump/raw/test_10min_eng1/data/format.1/LAD_eng_000262.flac +LAD_eng_000263 dump/raw/test_10min_eng1/data/format.1/LAD_eng_000263.flac +LAD_eng_000264 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dump/raw/test_10min_eng1/data/format.32/voxpopuli_eng_000548.flac +voxpopuli_eng_000549 dump/raw/test_10min_eng1/data/format.32/voxpopuli_eng_000549.flac +voxpopuli_eng_000550 dump/raw/test_10min_eng1/data/format.32/voxpopuli_eng_000550.flac +voxpopuli_eng_000551 dump/raw/test_10min_eng1/data/format.32/voxpopuli_eng_000551.flac +voxpopuli_eng_000552 dump/raw/test_10min_eng1/data/format.32/voxpopuli_eng_000552.flac +voxpopuli_eng_000553 dump/raw/test_10min_eng1/data/format.32/voxpopuli_eng_000553.flac +voxpopuli_eng_000554 dump/raw/test_10min_eng1/data/format.32/voxpopuli_eng_000554.flac +voxpopuli_eng_000555 dump/raw/test_10min_eng1/data/format.32/voxpopuli_eng_000555.flac +voxpopuli_eng_000556 dump/raw/test_10min_eng1/data/format.32/voxpopuli_eng_000556.flac +voxpopuli_eng_000557 dump/raw/test_10min_eng1/data/format.32/voxpopuli_eng_000557.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.1/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.1/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..fcb7894b0726754770040260624e0764b7c3c773 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.1/1best_recog/score @@ -0,0 +1,273 @@ +LAD_eng_000254 tensor(-16.7284) +LAD_eng_000255 tensor(-8.5020) +LAD_eng_000256 tensor(-8.9164) +LAD_eng_000257 tensor(-9.4137) +LAD_eng_000258 tensor(-6.4729) +LAD_eng_000259 tensor(-14.0435) +LAD_eng_000260 tensor(-10.4728) +LAD_eng_000261 tensor(-15.4937) +LAD_eng_000262 tensor(-8.6768) +LAD_eng_000263 tensor(-6.3534) +LAD_eng_000264 tensor(-19.4275) +LAD_eng_000265 tensor(-4.0471) +LAD_eng_000266 tensor(-3.1389) +LAD_eng_000267 tensor(-4.4127) +LAD_eng_000268 tensor(-8.2306) +LAD_eng_000269 tensor(-10.8764) +LAD_eng_000270 tensor(-8.8826) +LAD_eng_000271 tensor(-11.9131) +LAD_eng_000272 tensor(-11.6978) +LAD_eng_000273 tensor(-16.4728) +LAD_eng_000274 tensor(-3.7176) +LAD_eng_000275 tensor(-13.1600) +LAD_eng_000276 tensor(-12.6575) +LAD_eng_000277 tensor(-9.8954) +LAD_eng_000278 tensor(-5.5026) +LAD_eng_000279 tensor(-2.5878) +LAD_eng_000280 tensor(-3.6080) +LAD_eng_000281 tensor(-8.3513) +LAD_eng_000282 tensor(-3.9448) +LAD_eng_000283 tensor(-13.0186) +LAD_eng_000284 tensor(-15.7289) +LAD_eng_000285 tensor(-18.2060) +LAD_eng_000286 tensor(-8.0426) +LAD_eng_000287 tensor(-5.1134) +LAD_eng_000288 tensor(-12.5780) +LAD_eng_000289 tensor(-8.2518) +LAD_eng_000290 tensor(-7.7418) +LAD_eng_000291 tensor(-9.0734) +LAD_eng_000292 tensor(-8.9674) +LAD_eng_000293 tensor(-13.3811) +LAD_eng_000294 tensor(-12.0359) +LAD_eng_000295 tensor(-9.2363) +LAD_eng_000296 tensor(-14.9845) +LAD_eng_000297 tensor(-12.4154) +LAD_eng_000298 tensor(-5.8186) +LAD_eng_000299 tensor(-13.9861) +LAD_eng_000300 tensor(-10.6812) +LAD_eng_000301 tensor(-9.4824) +LAD_eng_000302 tensor(-9.6330) 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tensor(-13.5110) +cv_eng_000727 tensor(-9.2937) +cv_eng_000728 tensor(-11.8325) +cv_eng_000729 tensor(-15.7483) +cv_eng_000730 tensor(-30.2122) +cv_eng_000731 tensor(-10.6884) +cv_eng_000732 tensor(-10.6684) +cv_eng_000733 tensor(-14.4019) +cv_eng_000734 tensor(-20.2705) +cv_eng_000735 tensor(-12.9367) +cv_eng_000736 tensor(-8.5871) +cv_eng_000737 tensor(-26.0267) +cv_eng_000738 tensor(-15.0810) +cv_eng_000739 tensor(-15.0724) +cv_eng_000740 tensor(-8.1451) +cv_eng_000741 tensor(-5.4098) +cv_eng_000742 tensor(-24.6127) +cv_eng_000743 tensor(-12.0222) +cv_eng_000744 tensor(-11.0595) +cv_eng_000745 tensor(-30.7971) +cv_eng_000746 tensor(-8.0960) +cv_eng_000747 tensor(-7.3955) +cv_eng_000748 tensor(-10.4823) +cv_eng_000749 tensor(-12.7994) +cv_eng_000750 tensor(-23.0153) +cv_eng_000751 tensor(-11.5414) +cv_eng_000752 tensor(-20.1474) +cv_eng_000753 tensor(-26.7641) +cv_eng_000754 tensor(-26.1737) +cv_eng_000755 tensor(-10.5145) +cv_eng_000756 tensor(-12.8444) +cv_eng_000757 tensor(-16.1441) +cv_eng_000758 tensor(-9.6659) +cv_eng_000759 tensor(-12.7865) +cv_eng_000760 tensor(-24.5888) +cv_eng_000761 tensor(-7.3476) +cv_eng_000762 tensor(-9.7257) +cv_eng_000763 tensor(-8.2150) +cv_eng_000764 tensor(-9.5116) +cv_eng_000765 tensor(-7.9026) +cv_eng_000766 tensor(-8.3831) +cv_eng_000767 tensor(-8.8531) +cv_eng_000768 tensor(-11.5091) +cv_eng_000769 tensor(-7.7393) +cv_eng_000770 tensor(-18.4316) +cv_eng_000771 tensor(-19.5029) +cv_eng_000772 tensor(-19.3033) +cv_eng_000773 tensor(-12.1354) +cv_eng_000774 tensor(-15.5464) +cv_eng_000775 tensor(-14.7220) +cv_eng_000776 tensor(-9.7288) +cv_eng_000777 tensor(-16.7604) +cv_eng_000778 tensor(-13.2494) +cv_eng_000779 tensor(-24.9374) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.1/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.1/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..9a4849ee78dae3732c79cbc3ee440d29bec1bba9 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.1/1best_recog/text @@ -0,0 +1,273 @@ +LAD_eng_000254 HE REMAIED WEL CHAMPIAN ANTIL NINTEN SIXTY FIVE A YEAR I WHCH SUF RD A TERABLE ACXIDENT +LAD_eng_000255 AY LIBRAL CONSEVITIVE HE WAS DEFEATED IN ATEIN ATY TO +LAD_eng_000256 ON ROD LAAR CON DRAR TWO RODS AT WOANCE +LAD_eng_000257 SOME OFTHE CONTRES HVE SURVAYS FOR MALTIPLE YEARS +LAD_eng_000258 BOTH OF THE VRSIONS FEACHR THE SONG HAPY HOLIDAY +LAD_eng_000259 SHAKXPIAR MANY REFRNCES ARE MADE TO SENS INTR ACTIONS OR CARICTES FROM VARIOUS PLAYES +LAD_eng_000260 IF ONLY THE ROGRAM CULDBRAKE OUT JUST A ITLE FROM ITS TO FOMILIAR APROCH +LAD_eng_000261 THE HELBEM WAS RELEASED IN OSTRALIAR ON NINTEINTH ORGIST TWO THOUSND AD ELEVEN +LAD_eng_000262 HE NOW PLACE FOR ASTRALIN CLOBE PERTH GLORY +LAD_eng_000263 IT IS NOT NON HOW MUCH IF EANY OF HE CLAMS AR TRU +LAD_eng_000264 A SMAL BISINESS ONR BROARD OPRATED HI WEAT AD SHEPFAME FOR SICTEN YEARS FRO THE AGE OF WENTY TO +LAD_eng_000265 IN THE NINTH SENTURY HE WAS AN IRISH POET +LAD_eng_000266 THEY ARE MARKED BY STRONG +LAD_eng_000267 THE LOW IS THE FOR VAOLED +LAD_eng_000268 IN THE RLY STAGES CAME CLOSE TO US A SLEP +LAD_eng_000269 RONING EVERY THRTY MINUT THRO AT SERVIS TIMS +LAD_eng_000270 AS A RESIULT WHEN THE COLIGE RE OPEND IT WAS AS AN ALL MALE COLIGE +LAD_eng_000271 THE TIME BETWEE THES POINCT IS VERIABL AND CANACUR ANY WHER FRO A MINIT TO MUCH LONGER +LAD_eng_000272 WOARK ON THE EA E E STARTED IN MARCH TWO THOUSND AND SEVEN AT A COST OF FIVE MILIAN DOLERS +LAD_eng_000273 HOWEVER THER WAS SOME DI AGREMENT OV TH ENDING THEME WHICH OR MORY AND YOHIMORY DISCUSTD AT LENGTH OVER EMAL +LAD_eng_000274 THE COPLE HAD NO CHILDRAN +LAD_eng_000275 THE FIAL SINGL O THAT DEBU AL BHM PARIS COLING HAD AN ELABRT MUSIC VIDIO +LAD_eng_000276 THE SERIS ENDED ON SIXTH ORGEST TO THOUSND AND FOR LASTING FR A TOUTE OF SEVENTY ON DAYS +LAD_eng_000277 HE HAS ALSO CONTRIBUTED TO THE NEW YORK REVIO OF BOOKS +LAD_eng_000278 BY PLACING SMAL ART OBJECT TRO OUT THE FILM +LAD_eng_000279 IT IS FOUND IN BRESIL +LAD_eng_000280 IT WS THE SID OF THE FAMLY I IDENTIFIED MORE WITH +LAD_eng_000281 H CAND IT SIGHTES MUST ALSOR SOBMIT A WORK PLAN +LAD_eng_000282 DUNDEY WHN THE MACH THRE TO +LAD_eng_000283 HOWEVER THE VILIGE REMAIND ICALATED ANTIL THE RIVEL OF THE FIRST NOUS PAPER SECOND REPOUBLICK +LAD_eng_000284 THE FAST SERVIS I THE EU CHURC WAS HELD I NINTE FIFTY ON ALTHO THE BILDIG WAS NOT FULY FINISHED +LAD_eng_000285 THE AVERIGE HOUSEHLD SIE WAS TWO POINT TO SEVEN ND THE AVERIGH FAMLY SIE WAS THRE POINT IRO SRO +LAD_eng_000286 IT WAS FIRST BRAD CAST ON THIRD GANIURY TWO THOUSOND ND TEN +LAD_eng_000287 THE WINGS WER OW MAD IN A SINGLE PRESING +LAD_eng_000288 HE DOCTR O HLOSOFY IN ENGENEARING MANAGEMENT +LAD_eng_000289 THIS TOK WAY THE MAIN ARGUMENT OF SAFTY RISSK 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OR THE OD LOKING GIY +LAD_eng_000303 ITS MAIN OFICES WER IN LUNDAN WIE HE SECND OFIS BELL FAST +LAD_eng_000304 ACTULY I HAD NEVER BEN TO A VILIGE BEFOR THAT +LAD_eng_000305 HE AS CHARGED ITH PLANING TO SET OF BOMS IN UROP AND THE UNITED STATE +LAD_eng_000306 MAKING MERS IS THE HIRD STUDOR HLBUM BY BELGEN ASTRALIAN ARTIST GOTIAY +LAD_eng_000307 HE THEN MOVED TO WASINGTON DE SE AND WAS A PARTNR ITH WARD BRON ANDTIL NINTEN TWENTY NIN +LAD_eng_000308 JOS OF HIY SCOLE AND THE SCOLES THE CMPE GAINE IN AL SPORTS +LAD_eng_000309 TWELF PLUS ON MACH BAN PER CARD +LAD_eng_000310 I HINK I MIGHT BE NOTHING +LAD_eng_000311 THE HOE WAS BILT AND LIVED IN BY ANDRU JACX AND CANDY DEPUTY CLECTE O THE INTERNAL REVINOU SERVIS +LAD_eng_000312 IN NINTEN SIXTY FOR HE WENT BAC TO OMSK AND ENTE THE ACTOA SCOL OF OMPS +LAD_eng_000313 THE BANK IS JOUNTLY OND BY HIM AND HIS BROVER AND RELITIVES +LAD_eng_000314 HE SUBPSICUNTLY WENT TO COL IN BRISTAL +LAD_eng_000315 WON THOUSAND AT HUNDRD FOARTY SICX FORH EDION 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SEPORT +LAD_eng_000329 THEYER STABISH IN ATEN SEVENTY ON AND AR WN O THE OLDST CLOUBS IN HE SOUTH OF INGLAND +LAD_eng_000330 HE AS A MEMBER OF THE GEST SCOTLAND ADVISERY BORD +LAD_eng_000331 TWO THOUSAND AND FIVE GENTLEMEN +LAD_eng_000332 AORE FILE AD STRONG RESEPTION INYURUPAND ACHIVED DISTOBUTION BUT THAT WAS NOT THE CACE HER +LAD_eng_000333 BOLTHOIS STETCHES POSTERIAR ANGCAL STRUCTUES +LAD_eng_000334 HE AS ALSO A THEE TIME FRENCH NASIAL CHAMPIAN NINTE NINTY NINTIE NITY FOR TWO HOUSND AD WON +LAD_eng_000335 THE VILIGE STRUCTUR SHOW IN HIS MAP IS T A GRE EXTENT UN CHANGED O DAY +LAD_eng_000336 RUHA IS RECOGNISED IT NUCLAR DISARST TO EXPARTES AND FO THE SAVFTY O ITS TECKNOLAGY +LAD_eng_000337 AS OF TO THOUSEND OD FORTEEN EMTY VE IS AVAILABLE WITHIN THEUNITED CINGDUM ON VERGIN MEDIAR AND SCKIY +LAD_eng_000338 NEWYORK PEANGUIN RANDM HOUSE +LAD_eng_000339 THE DUTCHEY WAS SCECURE IN TE UT COME OF THE GOFICK WAOR +LAD_eng_000340 WIH GOD PACE SDARTE HE MATCH WITH BOTH TEMES OLTENATING 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TO NINTEEN FOARTY NIN THE MARICON LEE WON TWELVE OUTO THE FIRST SIXTEN +LAD_eng_000352 THEAR HE FEL SICK WITH TIFAS HIMSELF +LAD_eng_000353 SIXT TEMS AVBE DVIDED INTO TWO GROUPS OF THREE TEMS EACH +LAD_eng_000354 THE FIRST CEASON PREMIAED ON TWELTH JUON TWO THOUSND AD FIFTEN +LAD_eng_000355 IT SCEED THE WHI BOARD AND SISTAME TWENTY FOR COMBING FEACUES FOM BOTH +LAD_eng_000356 VLLIUME TOO NUMBERS ON TO AND THRE +LAD_eng_000357 THE LOWE PART OF MENS DESES WE MUCH SOURT IN LENC THO THOS FOR WMEN +LAD_eng_000358 THE VISIGOTHS IN TERN WE SCEADED BY THE MORS +LAD_eng_000359 JOS OF HI SCOLE EVERY WE OF THE COL YEAR +LAD_eng_000360 AS TH RSILT OFAL THE ARGUMENT GETING TO HER +LAD_eng_000361 IT HAD QUARTERS ARE IN SHEFIALD YOUNITED CINGDOM +LAD_eng_000362 LAY ALSO FIALY SINE THE CONTRACT ON STAGE WIT HE DIRECTER AD PREDUSES OFTHE GOULDAN EYES +LAD_eng_000363 FISICL FERIPY CN HELE PATIONE TO LURN HO TO WARK WITH FOT DROP +LAD_eng_000364 IT ENT ON TO SEL THRE HUNDRED THOUSAND UNITS A CHE FIVE NO 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FIRST THIE YOU ANTER A MANS HOUSE AND BESIDE THAT WAS NO TIME TO AROUS SUSPION N THE MINDS OF ANY WON +M-AILABS_eng_000175 DO OU NOT REMEMER THAT HE SAS THY DEMAN THATS THE SPIRIT WHICH CEPES THE IS NOBLE CORAGOUS HIY UN MACHIBL +M-AILABS_eng_000176 MSTR BELL WHACAN HE NO OF JOAON HE LIVING A LASY LIF IN A DROUSY COLAGE +M-AILABS_eng_000177 AND THE CITN FOLOEDIMUARLY AT THER HEALS +M-AILABS_eng_000178 THE FIST TUTCHWOLD CASE AN EXPLOSION IN WHICH AMONG SUCH HUNDREDS OF INFERIATED MEN AND RECKLES BOYS +M-AILABS_eng_000179 WON F TH GEAT PLESUERS OF MARGRATS LIF AT THIS TIME WAS IN EATS BOY +M-AILABS_eng_000180 TH THNG IS GON ON LONG NOF THER IS ONE ORE BIAG ACXIDENT WE SHAL HAVE TO COMPRMISE WIT THEINERIVER ND CARYON THEWORK CUINTLY +M-AILABS_eng_000181 YOUAR LAT SAID SHE WEL SHE HELD HER BRATH O THE ANSR +M-AILABS_eng_000182 TRAT TOLD THE GIRLS THAT THEY MUS GO WIT HER FATHER TO LIV AND GIP CUSISILS LITE LD CABEN AND HEN THEY HERD THS REDFUL DECRE +M-AILABS_eng_000183 MARGIT SAT DON O THE ROG PATLY TO WARM HERSELF FOR THE DAMPNES O THE EVNING HUNG BOUT HER DRES AND OVER FITE HAD MAD HER CHILY +M-AILABS_eng_000184 O NOW YOUAR MSTAKAN ABOUT THAT RELID THE KING THEYARENOT MY PRISONERS BUT MY SLAVES WHOM MY PURCUSE FROM THE CING OF EV +M-AILABS_eng_000185 HER FATHE TOKU TE CMBRSATION +M-AILABS_eng_000186 IN ACORNER WAS A SORT OF DRESING TABLE ON WHICH LAY A COM AND BRUSH CANIDY SEED MUCH INTRUSTED INTHE TABLE AN WAS EXAMING ITWHN THE GORU RETERNE +M-AILABS_eng_000187 I HAVE SOME TIME THGT THAT MYSELF SHE AGEED BUT OFCOURS I DONT NOW STIL I HAVE TO BE PITY CARFUL SOMEON IS ALWAYS OVER HER BY MY DESS OR LOKING OVER HER +M-AILABS_eng_000188 I SHL STAY REPLID THEYONG MAN FOR I MEAN TO SIT YO FRE +M-AILABS_eng_000189 WHAT D YO DO ASD THE SORCERER +M-AILABS_eng_000190 WHIY THERE AR ENAMES YOUR SHORT HINES NOT ANY MORE REPLIED TROAT IM QUE OF THE INKES AND IM ALSO QUE OF THELOS SO I WONT HAVE MY PEPLE QUARLING +M-AILABS_eng_000191 TIPRITER WE CLICKING CLIPING WER BING SNIPD OTOF A UGE TACK OF NOE PERS AND PASED IN AN N LARG SCRAPBOKS SERKULER WER BENG FOLDED AN MAD REAY TO MAL FO THE FINAL APEL +M-AILABS_eng_000192 IT WAS FOR DAYS AFTER THE SUPRIES OF ALTHERS HORS HEN THE STRANGERS LET THE ASTAT TO THE CAIR OF RUGED OLD FORSTER HARMEN +M-AILABS_eng_000193 BPOR TEMPLTON HE SAID I USTONOW HIM MANY EARS AGO HE WE E BOYS MENTO SCOULWITHM AND AL THAT SOUTOFTHNG YONOW BUT AN TIL I RAN CROS HM OR +M-AILABS_eng_000194 I FOND HER I THE FARIST AND BOGT HER HER A PRISNE REPLIE THE CAPTON +M-AILABS_eng_000195 WHO MAY BE COMPITENT ITHE FROM PERSINAL EXPERIANCE OR THE EXPINS OF OTHERS TO ANSER IT WITH MOR OR LES CURECTNES OR AT LEAST AN ATTEMTD +M-AILABS_eng_000196 ON NINTY TO LATESTRETET SID HOKGEN BITING OF HIS SAGAR +M-AILABS_eng_000197 TRAT WA SRPRIE TO FINE SHE COUD CE SO PLAINLY THR THE HIY WAL OF WATER ABOVE HER BUT THE SON WAS ABL TO SHUT ITS BEME STRAT DOW THO THE TRANSPARENT SE +M-AILABS_eng_000198 THE SPAT WER ID SPRNG UP +M-AILABS_eng_000199 COME DENIL WIC 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N N I N T E N I N T Y N I N T I E N I T Y F O R T W O H O U S N D A D W O N +LAD_eng_000335 T H E V I L I G E S T R U C T U R S H O W I N H I S M A P I S T A G R E E X T E N T U N C H A N G E D O D A Y +LAD_eng_000336 R U H A I S R E C O G N I S E D I T N U C L A R D I S A R S T T O E X P A R T E S A N D F O T H E S A V F T Y O I T S T E C K N O L A G Y +LAD_eng_000337 A S O F T O T H O U S E N D O D F O R T E E N E M T Y V E I S A V A I L A B L E W I T H I N T H E U N I T E D C I N G D U M O N V E R G I N M E D I A R A N D S C K I Y +LAD_eng_000338 N E W Y O R K P E A N G U I N R A N D M H O U S E +LAD_eng_000339 T H E D U T C H E Y W A S S C E C U R E I N T E U T C O M E O F T H E G O F I C K W A O R +LAD_eng_000340 W I H G O D P A C E S D A R T E H E M A T C H W I T H B O T H T E M E S O L T E N A T I N G S U P R E M A S Y +LAD_eng_000341 T H I S V R T I O N I S N O T E A D O R B I G V E R Y F A F U L T O T H E A R I G I N A L N O V L +LAD_eng_000342 T H I S P R E S U M P T I O N I S N O T F L E F I L E D O N H A S T O N O A T L E A S T T O C O N G A T D I A M A T E S +LAD_eng_000343 N O T A B L E T I T L E S I N C L U D E D G O L D A N A C X S T H E R E V E N G O F D E T H A D E R R A D M O B I L O U T R U N O E S A N D S A K G A R S O N I C T H E H E G H O G +LAD_eng_000344 T H E N I N T E N N I N T Y N I N J U G M E N T N O T E D T H A T T H E I N F L O N C O F T H F A T H E R O F T H E C U S E D H A S B E E T H E R +LAD_eng_000345 M O K D A U F S W A R S R E V E N G E H A N D J O I N S F O R E S I T H M A L C O M T O O V E R T R O M O K B E A T H +LAD_eng_000346 T H E M E D Y E V L V I L I G E C O R T W A S A L W A Y S A N I O U S T O C E P E T H E F E N E A R O N D T H E I L I G E G C A P L E S +LAD_eng_000347 T H E R W A S A N I N R A N K S I S T O M E A C H R A N K H A V I G M O R E P O W E T A T H E L O E R A N K +LAD_eng_000348 T H E A S T A B L I S H E D D I P L A M A T I R E L A T I O N S O N S E P T E M B R N I N T E N T H N I N T E N S E V E N T Y T O +LAD_eng_000349 T H I S W A S F I R T H E R X T E N D E D T O I N C L O U D M O R U C A D A T E S I N D I S E M B E R T W O T H O U S A N D N D F O R T E E N +LAD_eng_000350 T H E U C H G O V E R M E N T I S C A R N T L Y E X S A M I N G T H E E A L C O N C I C U E N C E S O F T H R O L I N G +LAD_eng_000351 F R O M N I N T E N T H U R T Y T H R E E T O N I N T E E N F O A R T Y N I N T H E M A R I C O N L E E W O N T W E L V E O U T O T H E F I R S T S I X T E N +LAD_eng_000352 T H E A R H E F E L S I C K W I T H T I F A S H I M S E L F +LAD_eng_000353 S I X T T E M S A V B E D V I D E D I N T O T W O G R O U P S O F T H R E E T E M S E A C H +LAD_eng_000354 T H E F I R S T C E A S O N P R E M I A E D O N T W E L T H J U O N T W O T H O U S N D A D F I F T E N +LAD_eng_000355 I T S C E E D T H E W H I B O A R D A N D S I S T A M E T W E N T Y F O R C O M B I N G F E A C U E S F O M B O T H +LAD_eng_000356 V L L I U M E T O O N U M B E R S O N T O A N D T H R E +LAD_eng_000357 T H E L O W E P A R T O F M E N S D E S E S W E M U C H S O U R T I N L E N C T H O T H O S F O R W M E N +LAD_eng_000358 T H E V I S I G O T H S I N T E R N W E S C E A D E D B Y T H E M O R S +LAD_eng_000359 J O S O F H I S C O L E E V E R Y W E O F T H E C O L Y E A R +LAD_eng_000360 A S T H R S I L T O F A L T H E A R G U M E N T G E T I N G T O H E R +LAD_eng_000361 I T H A D Q U A R T E R S A R E I N S H E F I A L D Y O U N I T E D C I N G D O M +LAD_eng_000362 L A Y A L S O F I A L Y S I N E T H E C O N T R A C T O N S T A G E W I T H E D I R E C T E R A D P R E D U S E S O F T H E G O U L D A N E Y E S +LAD_eng_000363 F I S I C L F E R I P Y C N H E L E P A T I O N E T O L U R N H O T O W A R K W I T H F O T D R O P +LAD_eng_000364 I T E N T O N T O S E L T H R E H U N D R E D T H O U S A N D U N I T S A C H E F I V E N O +LAD_eng_000365 T H E N A M E S T O U C K A F E R T H A T +LAD_eng_000366 T H E H L B M L A T E R B R O K T H D I M A D R E C O R D O N C U C U O M M U S I C K +LAD_eng_000367 I T S E D A T O R I A L W E S U B M I T A N D I T S O T H R A P O L T O P R I Y E +LAD_eng_000368 J O S I F P L A Y E S O U R F E A T U R E D E A C H W E E O N T H E H O +LAD_eng_000369 T H E Y W A T F O R A T I M E M B I L D I N G U P T H E R F O R E S B E G I N T O O N D R I F T H I S E A V L R E A L Y E X I S T S +LAD_eng_000370 B R E F E M E N T I O N O F T H C O N V I C T I O N A P P E R D O N P A G E T H R E O F T H E N E W Y O U O K T I M E M S +LAD_eng_000371 O D E D B Y P O S I O N O N P I C H F R O M B A C K R I G H T T O F R U N T L E F T +LAD_eng_000372 H E I S M E M B E R O F T H E C O U R T O T H E R I L C O L A E O F A R T L O U N D O N Y U C A Y +LAD_eng_000373 D U R I G T H E C O U R S E O F T E C A M P A I N F I R G S A N D V I S I T A T A L L T H E R T Y N E I N W A S I G T A N S T A T E C O N T E S +LAD_eng_000374 A S T R I P O F P A P E R O F L E N G T H +LAD_eng_000375 S A T O H A D F R E C U E N T L Y W O R E T O G E T H W T H Y O U C K A Y A M A R O N P R E V I O S P O G J E C T S +LAD_eng_000376 S H E A S B O R N O N S C R E A N D U I N T H E E P S O D B R A D C A S T O N F O R H A N O V E M B E R N I N T E N I N T Y F O R +M-AILABS_eng_000159 H E T U R N E D R O U N D S H H A D C O M I N S O G E N T L Y T H A T H E H A D N E V E R H A R D H E R +M-AILABS_eng_000160 A T O B E S H O U O R A N W E M U S T C E O U R D O R S S H O A T W E M U S L A T N O O N I N +M-AILABS_eng_000161 C I D S P M O N H E B E G A N M O K I N G L Y Y O U M A H V E O N D E D W H I Y I C A L D A T R O U S W H E N I C O U L D J U S A S W E L L H A V E D I S T R O R E D Y O U T H A T I D O U T A T O A N S E D H I M +M-AILABS_eng_000162 T H E P E S N T T H R U W H I M S E L F A P O N H I M A N D B O U N D H I S F O R L A G S T I T L Y S O T A T H C U L D N O T M O V E +M-AILABS_eng_000163 N O R M U S T T H O U S O L I M E T H T H E H L Y O N O F I S R I A L A S T O T H I N K H E H A T H B U T O N W A Y I N W H I C H C A N G O R I F I E H M S E L F B Y T H E +M-AILABS_eng_000164 T H E O L D C O M P A R S O N B E T W E T H E I M P U L S I E E X S E C T I V E A N D T H E L I B R A L A R T S M A N W H O W H A D L A R N E D T H A T H E R E O N L Y O N R T O P O S I T V E D I S I O N S F A L B L E I N A L T H E W A L O H I N K I N G +M-AILABS_eng_000165 A F T E R T H I S E X P E R I A N C E T H E N V A T O R S W E R C A I R F U L T O C E P E A S A V F E D I S T N C E F R O M T H E A L +M-AILABS_eng_000166 A N O U B A R S O M T I N G F I R T H E R I T H N Y O U A T O N O I T I H A V E H E R A M O S T M S T E R I O U S T E L A P A R I G R A M Y E S W H A T I S I T I S H E D I D N O W I T I S N O T A B O U T H E R +M-AILABS_eng_000167 N O M S T R T O U R T A N S A I D A N D G I E T H E A S K T T O M E I A L T A K E I T +M-AILABS_eng_000168 A N D A R A B I A N N I G H T E X C L A M E D T R O T W H I Y T H A T W A S A M A G I C N I G H T W A S N I T T H E R S D I F R E N T S O R T S O N I G H E S M A T E S A I D T H E S A L E R A N D T H E N I G H T B U T N B R I G H T M E A N S A N T T H E S A M E N I G H T Y O U M E A N +M-AILABS_eng_000169 I V E T R N E D O F U P W A R D S O F A H U N D E D F M Y B E S T D H A N D S F O R N O O T H E R F A L T T H E M F O L O I N G Y O U A N D S U C H A S Y O U A N D T H I N K I L L T A K E Y O U A O N +M-AILABS_eng_000170 B U T W E W I D S H E S E H I M H E R H A R T L E P T U I N A P R E H E N T I O N A T E V E R Y R I N O F T H D O R B I L +M-AILABS_eng_000171 T H E S E B O O K S D I C X S O N I W L K E P E A L T H E R E S T W E O U S E N D T O M S T R B E L T H E Y A R O F A C I N D T H T H E W L V O U L Y O U F O R T H M S E L V E S A S W E L A S F O R P O P A S S A Y +M-AILABS_eng_000172 U T I N G A W A S N O T A T A L S H U R T H A T T H E C O U L D N O T G E T I N T H E G A T S O P E D I N W A R D A N D T H R E H E V Y B A R S W E R E H E L D I N P L A C E B Y M E N S O F S T O U T S T A P L E S R I V I T E D T O T H E S H E T S O F S T A +M-AILABS_eng_000173 I W A N T T H O W S A I D H O D O N C O L D L Y I W A N A D O S O N H O R S E S I W A N T M E N T O B R I G D T H E W I T H M E H E P U S H D H I W A Y F O R D W H I C H W A Y T O T H E S T A B L E S +M-AILABS_eng_000174 E R E I S A L I M I T W H A T Y U C A N D D O F O T H E F I R S T T H I E Y O U A N T E R A M A N S H O U S E A N D B E S I D E T H A T W A S N O T I M E T O A R O U S S U S P I O N N T H E M I N D S O F A N Y W O N +M-AILABS_eng_000175 D O O U N O T R E M E M E R T H A T H E S A S T H Y D E M A N T H A T S T H E S P I R I T W H I C H C E P E S T H E I S N O B L E C O R A G O U S H I Y U N M A C H I B L +M-AILABS_eng_000176 M S T R B E L L W H A C A N H E N O O F J O A O N H E L I V I N G A L A S Y L I F I N A D R O U S Y C O L A G E +M-AILABS_eng_000177 A N D T H E C I T N F O L O E D I M U A R L Y A T T H E R H E A L S +M-AILABS_eng_000178 T H E F I S T T U T C H W O L D C A S E A N E X P L O S I O N I N W H I C H A M O N G S U C H H U N D R E D S O F I N F E R I A T E D M E N A N D R E C K L E S B O Y S +M-AILABS_eng_000179 W O N F T H G E A T P L E S U E R S O F M A R G R A T S L I F A T T H I S T I M E W A S I N E A T S B O Y +M-AILABS_eng_000180 T H T H N G I S G O N O N L O N G N O F T H E R I S O N E O R E B I A G A C X I D E N T W E S H A L H A V E T O C O M P R M I S E W I T T H E I N E R I V E R N D C A R Y O N T H E W O R K C U I N T L Y +M-AILABS_eng_000181 Y O U A R L A T S A I D S H E W E L S H E H E L D H E R B R A T H O T H E A N S R +M-AILABS_eng_000182 T R A T T O L D T H E G I R L S T H A T T H E Y M U S G O W I T H E R F A T H E R T O L I V A N D G I P C U S I S I L S L I T E L D C A B E N A N D H E N T H E Y H E R D T H S R E D F U L D E C R E +M-AILABS_eng_000183 M A R G I T S A T D O N O T H E R O G P A T L Y T O W A R M H E R S E L F F O R T H E D A M P N E S O T H E E V N I N G H U N G B O U T H E R D R E S A N D O V E R F I T E H A D M A D H E R C H I L Y +M-AILABS_eng_000184 O N O W Y O U A R M S T A K A N A B O U T T H A T R E L I D T H E K I N G T H E Y A R E N O T M Y P R I S O N E R S B U T M Y S L A V E S W H O M M Y P U R C U S E F R O M T H E C I N G O F E V +M-AILABS_eng_000185 H E R F A T H E T O K U T E C M B R S A T I O N +M-AILABS_eng_000186 I N A C O R N E R W A S A S O R T O F D R E S I N G T A B L E O N W H I C H L A Y A C O M A N D B R U S H C A N I D Y S E E D M U C H I N T R U S T E D I N T H E T A B L E A N W A S E X A M I N G I T W H N T H E G O R U R E T E R N E +M-AILABS_eng_000187 I H A V E S O M E T I M E T H G T T H A T M Y S E L F S H E A G E E D B U T O F C O U R S I D O N T N O W S T I L I H A V E T O B E P I T Y C A R F U L S O M E O N I S A L W A Y S O V E R H E R B Y M Y D E S S O R L O K I N G O V E R H E R +M-AILABS_eng_000188 I S H L S T A Y R E P L I D T H E Y O N G M A N F O R I M E A N T O S I T Y O F R E +M-AILABS_eng_000189 W H A T D Y O D O A S D T H E S O R C E R E R +M-AILABS_eng_000190 W H I Y T H E R E A R E N A M E S Y O U R S H O R T H I N E S N O T A N Y M O R E R E P L I E D T R O A T I M Q U E O F T H E I N K E S A N D I M A L S O Q U E O F T H E L O S S O I W O N T H A V E M Y P E P L E Q U A R L I N G +M-AILABS_eng_000191 T I P R I T E R W E C L I C K I N G C L I P I N G W E R B I N G S N I P D O T O F A U G E T A C K O F N O E P E R S A N D P A S E D I N A N N L A R G S C R A P B O K S S E R K U L E R W E R B E N G F O L D E D A N M A D R E A Y T O M A L F O T H E F I N A L A P E L +M-AILABS_eng_000192 I T W A S F O R D A Y S A F T E R T H E S U P R I E S O F A L T H E R S H O R S H E N T H E S T R A N G E R S L E T T H E A S T A T T O T H E C A I R O F R U G E D O L D F O R S T E R H A R M E N +M-AILABS_eng_000193 B P O R T E M P L T O N H E S A I D I U S T O N O W H I M M A N Y E A R S A G O H E W E E B O Y S M E N T O S C O U L W I T H M A N D A L T H A T S O U T O F T H N G Y O N O W B U T A N T I L I R A N C R O S H M O R +M-AILABS_eng_000194 I F O N D H E R I T H E F A R I S T A N D B O G T H E R H E R A P R I S N E R E P L I E T H E C A P T O N 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HAVE SPECIAL FOD RINKAN NRTAMEN OPERS TO CE GES AND A GOD MOD AN CE THEM AT THE PRMIS +fleurs_eng_000429 ON THE OTHER HAND ICESE AND SNOWY CODIONS AR NORMAE IN MANY COUNTRES AND TRAFIT OES ON MOSTLY UN INTRUPTED ALL YEAR ROUND +fleurs_eng_000430 BE CARFUL OT TO ALOW FABRIC TO BECOME TO HIYE WHICH CAN CASE STRANKADGE OR IN A STREN CASES SQOARTCH +fleurs_eng_000431 FEIRL CHILDRN MA HAV EXPEIANC SO VER CHILD H BES OR TROMM BEFOR BING ABANDIN R RNG AWAY +fleurs_eng_000432 PEOPLE MA NOT INTICIPAT THA PATIONCS AND NDRESTANG R ALSO NESERY FOR TROVLERS RETRNING HOM +fleurs_eng_000433 ON OTER THE PRIK OF HUSTILITES BRITN INENT SHEATED AN NAVBLE BOCKADE OF HERMANY +fleurs_eng_000434 THE OENRS OFIS SAID NINTEN OFTHE INGURED WE PLEAES OFISERS +fleurs_eng_000435 USING SHIPS TO TRESPORTS GOODS IS BY FAR THE MOS OFIENT WAY TO MOE LARE MOUT OF PEBLE N GOD A CROS OTIONS +fleurs_eng_000436 THEBRL CRITISISM OF THE RECONSTRCTIN EVERTN HASPOK AS O THE WARDING OF RECONSTRCING CONTHACT TO RISTE DE WATING AND 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LIFE LIK AS TE CHES FLUSH THE AS RAR WARMEFINOF INTE MORNIN TO HE T HAFTDIS PERING SOL TSID IT LONG OURS OF ALL REDING ANT PERSTED HEART BY NEVER SEASING RIMES YET ICOUL NO NDESTAND IT +mls_eng_000295 WON OF THE OWIN RITER SAID THE AOPE HE AVA IS A POSON SHALFISH THESAR BITER AND DEDLY AND CAD BE OUSED IN PUTING ENAIMES TO DEATH +mls_eng_000296 THE BEUT YUS ROUBS OF HAVEN AS LON T A DU RIHT ERS N COLEIT AR HELOKS IN BOUNLES MAGHUTYABROAD TOUCHING TH GREN LEAVES ALL A TREMBL IT GOLD LIHT +mls_eng_000297 I CAN DOU NO MOR HAN THAT INTL THIS MATER IS APSALUTLY SETED THE A WORTH O THAN LIFE ITSELF TO ME TMSTR BLBUR SEMED ANOIED SURLY HE PROTESTIT WOU NOT GODO AST ME TO WATE THRE MONCS UNTIL AT EXAMIN ONE OF THES +mls_eng_000298 ROSE CONGRES FOUNDATION RUSHIN AND TITE THAT ORGANIED THE SAIT PETERS BURG IN TER NASINALE ECANOMIC FOROM ROUS NEFT RUSHION STATOND OILE AND ANARGY COMPANY +mls_eng_000299 HOW GLUTED N SPARCKALE THE DELICAT ROST WOK YOUE ATRACTED NO DOUT A MARVED A THE DINTY TRAICSOMS BUT FEOU OF AS HAV REALY HAD AN OPOTUNITY TO STADY THE DETAL OFTHE FRUSTDESINS MY NUTLY O AV CONSIDED HAT THE WRE OR TIN THRE YUR FORDESINS AT MOST +mls_eng_000300 THATHA THE OFENS INTRING TO INFICKAWON THE MCKILVER OFHENDER OR WN HIM MOR THAN THE INTENDAN TO DO AND THIS BECOMSECCUSFUL ANUTHRD SO TATHE PRIMITIVELAGDSLATERS WHE CAEFUL IN ORECQUIARIN THERITALITION TO BELIMITED TO AN Y FO ANA +mls_eng_000301 AT SIRES WORD THE JUES WRETERN THE COMPNY THAT GO GODS HICE BEGON WITH MRTH A MON IS HENDED BY THE FO BUT WONCE AGAIN THE WRKE COSE ON BY LISENS FROM DURIOUS ESRA IS SENT WITH ROILE GRUNT AND GIFTHS FORIUS PIAS +mls_eng_000302 ANT PRODACK YEAR IN AND YEAR OUT SIVIN HUNDRE FROANCES WHOLIVED ON IT HAVE NOT SO BADLY WREIUL EXPLAINE MORDYIS OCUPED TA THE ORBOHOVS +mls_eng_000303 THEAN THIS IS ALL YOURANTAR TIS TO FAR FOR ONE OF HIS ALIANCS AND I WORE YOUW THAT THIS PLACE NO MOR SE YOU AEGXSEIT ANDTER DFLURANS THE BEST IS THER IS MOR CROUD TO ET A MANSARYVEN JON ONS EFORACE THATS MY NAM INDED +mls_eng_000304 HEN I RETURND TO THE HOUSE WHERE HAD BE A HAPY CHILD ONLY A PIL OF ASHEIS WEAYIT HAD STUDI WEPT LONG AND TO FOGET MY WEPING I SAID OUT OND E VATS CAMS SCE ON THESE WATERS IN AS TARSAFAY AN NIGT I PLADE MY FLOT TO THE SUMER MON +mls_eng_000305 DO YOU NOT SE WHAT LESUER IT GIVES ME WE HAVE GRON UP TOGETHER IN THIS HOUSE SINC HE WAS ABOY I SIMPLY CANOR BEAR AS YOU GAN THE SIGT OF THE SMIL LAVING HIS FACE BOUR DEAR HE HAS NO MUSMENT EXCEPT THIS PLING AT TH SHOP CEPING +mls_eng_000306 IT IS DEVBIUS BOADY REVALING IN YLIPICAL ORRGRE EONGATION LOVE LOVE LOVEWAL NOT BE THE WONED OF CUPIT BUT HE MANIFISTATION OF HE ERVERSAL REPRDUCTIE INSTINCES +mls_eng_000307 SHOARPLY AS HE SHOOK HAND WIT HER GOD BES YUMIG TDEAT CHA THE BIHOP SAID WHEN SHE CISED HIM AND HIS LIPS MOED OFTERWARD FOR SOM SICKNTS ASIF HE WER INPRAAR HUR MOTHER FOLORD HER OUT OF TH OM AND THEN SILAND CETEL +mls_eng_000308 FOLOED HIM STAELTFULLY AND HE HI WAS IN A STPING POSTHUR FILING HIS BOCKET CAME UP 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DEV EXPLONATIONS AGANSET ALL WHICH THE MOUNTON AND A TROTIOUS MAU AR MUST EVEND MAKE HAIL AS THE CAN +mls_eng_000314 THE MOMENT WAS FEFUL A MIGTY OF FO HAD NEVER SWNG THE BUTLELACX OVER HIM BUT THE HOB NERVED HIS ARME FOR A DESPRAT BLOW AND TH CMSERFLE PROSTRAITD BEFOR HIM +mls_eng_000315 THEN THE WINESTOUT THE CLAORSTAND DOARK AND NIGHE CAME ON LIKE EINK MY OLD COT IN CUILT WAS CAOLD AS IAN MY SWEET SON TOST IN HIS SCLEE +mls_eng_000316 YOU MAY DO AS YU PLEASE TO WORK OF OR IRITATION TO KEP YOUR FANATICSISM YOU RE WEL AFF YOU NED NOT MIND THE COST THE PORE DO NOT WANT T STAND INYOUR WAY BUT YU INSIST ON THE SUBMITING T YOR COMPULSION +mls_eng_000317 HE WAS RED BY A REVERNTERY AYS NIGHT BEING BY OTHMAN ESIX FOAR TO T HIDVICK WL WAS BONE AN MACH ATEN SEVENTY NIN AND HE WAS THE ONLYSOVIVER OF LITER OF FIFTEN IT WASON THIS ACOUNT THAT H WAS AUED SAFE AND COLOR AND MAKINGS +mls_eng_000318 AND WHAT HAST IT AKES O FOL INTO THE SECKANT THER BY THIS TIME DIAFHANTEO SNEASERS ECGIO MOST ADMRABLE SECREIT ON THE CONTRARY IT STURS ME NOT A WIT WICH MOST CON SURNES IT +mls_eng_000319 THURDLY THAL SAID WHER THE CITISENS ARE NEATHER TO REACH NOR TO POR FORTHLY AN A CAUS IS SAID WHERE THOG IN ALL OTHERESPECTS THEYAR IACUL IET VERTOUOS MEN AR ADVANSED AND VESIOUS PERSON THE GRADED +mls_eng_000320 THE CINDLY FRANK IS SIMPOTHATICK AEVERY DAY HE PAS NOTES BETWEEN US AND I TRIY TO INCURAGE RUSSAL HEWIL IMPROVE I ASUR HIM HIS TIME ISHORT AND FRESH ARAND LIORTY WL SON RESTOR HIM +mls_eng_000321 THIS CUESTONS IT IS NOW EVIDENT MAY FRECUENTLY BE ANSERED WAS EAQULL PROPRITY INOPSIT WASES AND IF THERE BE ANY ACASIONS ON WHICH THEY CAN BE ANSERD ONLY IN ON WAY THE AUNSER WIL DEPEND APON THE NATUR OF THE ACASION +mls_eng_000322 IN HIS NOT BOR THE MINSTRILSY SECKNADION ATY NOW AT SCOTSES THE BLED WAS TAKEN DOWN FROMA OLD WOMENS REITATION AT THE HELSAON MOR LEDMINES BY THE AGENT THER AND SENT BY HIM TO SURTEE +nchlt_eng_001588 CRISTON THE OLIGONS +nchlt_eng_001589 OPTAINE EAGL FHITHERS 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WARTINTIONS +nchlt_eng_001640 DOABE WPH +nchlt_eng_001641 AND Y POPECLAMINT +nchlt_eng_001642 GITS THE CN PRIVEAT +nchlt_eng_001643 CING FOD AN EAND +nchlt_eng_001644 ILECTRNIC MUSICAL INSTRMENCS +nchlt_eng_001645 AGE NOUT WATER +nchlt_eng_001646 LORENCE LIVE MOR NASINALE +nchlt_eng_001647 LEAG BACSPALE PLAYERS +nchlt_eng_001648 BUT ISOM AN THE ANCHANT MED ATRANION +nchlt_eng_001649 OUNITED STATS RECOCONIED +nchlt_eng_001650 PROPASIONAL FELACES +nchlt_eng_001651 SPICHAL ECNOMIEGSOWNS +nchlt_eng_001652 MAN STREAME WISTD +nchlt_eng_001653 EVENG RUSH OWS +nchlt_eng_001654 BY THE DIONS TOK +nchlt_eng_001655 NDTS ARTICKE HAS NO +nchlt_eng_001656 WEAST IN MUSICLES +nchlt_eng_001657 CONSEVIT OF JUATAYSOM REGARTS +nchlt_eng_001658 OPICK MBER STATS +nchlt_eng_001659 PRIMINESSAID JON +nchlt_eng_001660 RACKS FOARMING MOUNT +nchlt_eng_001661 MAGER LEAK TMS +nchlt_eng_001662 POLONATION MANIGENT +nchlt_eng_001663 FRENCH FISIST +nchlt_eng_001664 HIYAR COMPRETSION RATSIO +nchlt_eng_001665 RECORD NG INDOUSTRY ASOCHATION +nchlt_eng_001666 THEPEAGE ON LIN MAGASEAN +nchlt_eng_001667 HIPOPER RECQUAD PRE GUOSSEONS +nchlt_eng_001668 FI NIGHE STATE MSHENS +nchlt_eng_001669 WHIDLY OUSED LOCALE +nchlt_eng_001670 NOR THE MERYCAN CONTINANT +nchlt_eng_001671 AFRCAN A MERICAN REPAS +nchlt_eng_001672 THRITON MELIDRY ACTIONS +nchlt_eng_001673 A THE WORD M NN +nchlt_eng_001674 THE TOMIK MELIKULAN OPTICAL FOISICKS +nchlt_eng_001675 TOWN +nchlt_eng_001676 MORSIL +nchlt_eng_001677 CONSTRUCT NEW RAL GAGEH +nchlt_eng_001678 PORLY EXCLUSIN RINCSIBL +nchlt_eng_001679 HEWO POURTRAY DIFERENTS +nchlt_eng_001680 SOVIAT DISIDANCE +nchlt_eng_001681 SIGNALE TRONSTDUCTION PARTHWAYESE +nchlt_eng_001682 YOU BORN MSI +nchlt_eng_001683 GENERLY ACXCEPTED RANGERS +nchlt_eng_001684 GILED A WARD WIN IS +nchlt_eng_001685 SWEDISH MUSICAL GROPS +nchlt_eng_001686 CHOWDERD OARTISOM RATING +nchlt_eng_001687 DOSIGE FORMS +nchlt_eng_001688 OF HIG OSTATUON OVERSTITE +nchlt_eng_001689 FOR MOS SATOMENTS IN TORK +nchlt_eng_001690 AMER CAN INVENTIONS +nchlt_eng_001691 ARTS +nchlt_eng_001692 MDON YUROPIAN RASHA +nchlt_eng_001693 NSTNOR LEAEG PINANT +nchlt_eng_001694 BEAG FINISH PRDUCTIONS +nchlt_eng_001695 NASINOLE +nchlt_eng_001696 TRADGICG PORTES +nchlt_eng_001697 TITLE GRICE STATE +nchlt_eng_001698 AS THENA HAD EN +nchlt_eng_001699 EAST N YURAPIAN CONTRYS +nchlt_eng_001700 CONDEMED AND OTHRIVED TANSLATIONS +nchlt_eng_001701 ALTHWAR LEDIS +nchlt_eng_001702 CINASAR MOUNT AN LENDUS +nchlt_eng_001703 NOBL SAMITY +nchlt_eng_001704 AND WODS ERFURS +nchlt_eng_001705 MOUNT SAINT VINCSANT +nchlt_eng_001706 SITYE MERTRUPOLATON EIROA +nchlt_eng_001707 ROONERS WHO DIED AS CHILDREAN +nchlt_eng_001708 CHANESLAS VOLE +nchlt_eng_001709 I PEE PECATS INTIERLY +nchlt_eng_001710 CING EDWARDS DEATH +nchlt_eng_001711 A MERICAR A MRICA +nchlt_eng_001712 COMERHAL SHIP SALED +nchlt_eng_001713 PEOPL FROM MEN HAM +nchlt_eng_001714 RAIAL CRASH CILD +nchlt_eng_001715 MUTHAL DEFANS TOUDY +nchlt_eng_001716 MODEN CHILD RUOLES +nchlt_eng_001717 MOTESER RIFAL DEVISION +nchlt_eng_001718 OU STRALIAN EAY FORSE +nchlt_eng_001719 A MERYKCAN MISTRY RHITERS +nchlt_eng_001720 FIN THEY GROUND GREFIHTE +nchlt_eng_001721 WIL TEMPINS OF MANTCHE +nchlt_eng_001722 CARILINA +nchlt_eng_001723 MY BATHIN OPERATES +nchlt_eng_001724 COURTS VERITIES +nchlt_eng_001725 MA DRAND +nchlt_eng_001726 CASE LITHALE REACTIONS diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.2/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.2/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..1d637ec662c34f312675b03919a398a4772857b8 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.2/1best_recog/token @@ -0,0 +1,273 @@ +cv_eng_000780 H E A R I A M B E P T E N M Y F L O C K A N D M I B U T E R S U R E D T H E B O Y T O S +cv_eng_000781 T H I S F A L I A H A S T L E T T O S I C X T E A E N P O U L B L E N C S H A D E I N S E A R D A Y S E O F C A L E S T O +cv_eng_000782 A O O Y S A S D E O +cv_eng_000783 W H I Y I T H A P L A I N C E P E G O I N O V E R +cv_eng_000784 A N D N I H A Y A E A E D O N D O S H E F O R W A T F I R T I A L B O C S W I T H O R E S O U L T S +cv_eng_000785 T H E P L I C A T I O N W A S P U T A P P R O V I T I N F A R B R A V Y +cv_eng_000786 H E N R Y T O R L E D T O N M N S T I L S W E A R H E H A D A S O U N D I D R A N I N G I N L I T I N G +cv_eng_000787 I T W A S T I S C O N T I N U E D O T O S C E T H A L I N C O N F L I C S A N V L V E D I N L O S E H I S R E T H I R N T O R E T O R E S T R I A L R E B R A D I O +cv_eng_000788 A D T T H H E R F A M L Y W A S F R O M E B R E A H O N S A +cv_eng_000789 A W H A T D I D Y E A E F O R I N O R T H E A P A +cv_eng_000790 T H A T W A S M Y D R A R T O S I N C E +cv_eng_000791 H E S C O S E A R I T A M U S T E R E O F S H E A R O S E D C O U O +cv_eng_000792 T H E H L I N T U R S T O T H E C H U R I S H O A S I N E S T H A T D A L T E R I N D S P E U S E W H E T H E R +cv_eng_000793 I O U E N O T T H O S W E R E I N H E R A C H L E A D R +cv_eng_000794 T H E L J O S I E C T E N D F E S T T H E P I Y +cv_eng_000795 M Y N E S C A N H E L P Y O I T T H A T S +cv_eng_000796 B U T S A O D H I S T O F E R Y W O N +cv_eng_000797 H O W F O R T H E B E S T A N D P R P E A E T F U E T H E B O S T +cv_eng_000798 I N I S H E L Y T H E E P L O U S W A S H R T E N S T R I C K L Y B Y D I T +cv_eng_000799 A L L W E O N E D B Y T H E E V E R I T M O R E S I N I C I T +cv_eng_000800 B H A T H T H E N T H E W I L A S R I N G T O M O R O M I L N T H S E O D +cv_eng_000801 H D O R B I S T E R E C K G M M I N L I P +cv_eng_000802 O S E I A L P E T R Y T O H E R P A C E A S A C T I N G D I R E C T E R +cv_eng_000803 T H E B E V E R L Y W L B I F L Y N T E S T H E E S S E N T R P U T O F A T O N S H I P +cv_eng_000804 T H E T R A C K R E S E R V S T I N G W A S A L S O C O M P L E A T E D +cv_eng_000805 H I T M A R C H W A S A W E R E O F T H E I M P O R T N E O F E L E C T R M R E C O S C M P I N B Y A E L O U G I C A L R R E S E R C H +cv_eng_000806 S I N H O W B A S B O R N Y A N T H E H A B A R +cv_eng_000807 N T H I S W I N C H K E A S E A N O F I A L Y H E R H O D T O A S M A C K R E M P A D R I N T H B Y C O L A S I N F O U S C S E S R A V B E R A I T I N W I L +cv_eng_000808 I T I S R E S P O N S E I P L F O R W A T E S U P L I Y A N D M A N G E M E N T O F W A T E R R E S O U R S E S A N D M A H A S T R A +cv_eng_000809 J E S E S T H E F I R S F A I C E O F T H E H O R V E A H E S A Y E D +fleurs_eng_000413 T H E G I S I U P L A T O O R G I A N E C R A L P O L E S I N T H E A G I P T I O N V A O L Y O F T H E D E D C O N T A I N G S E V E R A L P E R I M E N D S O F W H I C H T H E G R E A T E P E R M E N T I S T H E L A R T E S S E V E R A L E S M A L T O O N S S E V E A L T E M P L E S A N D T H E G R E A T S P A N K S +fleurs_eng_000414 T W A R D T H E I N D O F T H M I L E A G E S W E S T E R N Y U R U P B E G A N D T O D E V E L T T H E R O N S T I L O N O F T H E B I G E S T V E L M E N S O F T H E T I M E A S A R E S U L T O F T H E C R U C A S P E P U L B E G A N T O U S E B U T E N S T O F A S T N C L O L T I N G R R +fleurs_eng_000415 I F S Y O U O N L Y G O A S H O R E U S I N G S H I P O R I S C U R I O N S Y O U L N O T N E A S E P R T V E S A A S A T W O T H O U S I N D N I N +fleurs_eng_000416 D O U B A L H I S M A R E W I H T O A D L C H E R E N C U D N O T B E W A B I G M P R E S I O N O N M I L E R T O H O M T H E S O R Y W A S R E L A T E D +fleurs_eng_000417 T H E R D I S O P L I N D D E F E N C S B A L H A D L I N G S C I L S A N D E X A L N T E O R K M A D T H E S T A N D O U T A N W A S C L E R H A T H I S W A S T H E E M E T O B E +fleurs_eng_000418 T H E D I S E S I S C A R E D B Y P I G S W H I C H H E N M Y G R E T E S T O H E U M E N S T O R O M S C E T O S +fleurs_eng_000419 F O R T H E S P R I N G B O K S I T D E N D E D A F I V E M A T H L O S I N G S T R E A E K +fleurs_eng_000420 T H E S T H E P I N S A L W I T G O D F R I N D S M A N Y E O P L E W E N I C A M E O U T +fleurs_eng_000421 T H E U S E O F V E O R E C O R I N G H A S L E D T O M P O R N D D I S C O V E R E S I N T H E I N T E R P R I T A T I O N O F M Y K R L E X P R E S T I O N S F A T I A L M O V E M E N S W H I C H L A S A F E U M I L E S S I C K E N S +fleurs_eng_000422 A L S A T T H E N O R T H I S T T H E G R E A T E S A N C U R Y O F O R L A D Y O F A T H E M U S H R I N A P L A C E O F W O L D R I G T E F A M S M E R I A N A V P E R I O N S +fleurs_eng_000423 I F Y O W N T B E C O S E O T H E A C T I O N Y O R E H A E T O O O G E T I N E A L Y T O T A C A M P I N G S I H T C L O S T O T H E M U S I C K +fleurs_eng_000424 M T A G U S C O A R I S B Y F A R E T H E B I G E S T A N D T H E C O N T I N A N T O N I T S O N W H E N I C O M S 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N T R U P T E D A L L Y E A R R O U N D +fleurs_eng_000430 B E C A R F U L O T T O A L O W F A B R I C T O B E C O M E T O H I Y E W H I C H C A N C A S E S T R A N K A D G E O R I N A S T R E N C A S E S S Q O A R T C H +fleurs_eng_000431 F E I R L C H I L D R N M A H A V E X P E I A N C S O V E R C H I L D H B E S O R T R O M M B E F O R B I N G A B A N D I N R R N G A W A Y +fleurs_eng_000432 P E O P L E M A N O T I N T I C I P A T T H A P A T I O N C S A N D N D R E S T A N G R A L S O N E S E R Y F O R T R O V L E R S R E T R N I N G H O M +fleurs_eng_000433 O N O T E R T H E P R I K O F H U S T I L I T E S B R I T N I N E N T S H E A T E D A N N A V B L E B O C K A D E O F H E R M A N Y +fleurs_eng_000434 T H E O E N R S O F I S S A I D N I N T E N O F T H E I N G U R E D W E P L E A E S O F I S E R S +fleurs_eng_000435 U S I N G S H I P S T O T R E S P O R T S G O O D S I S B Y F A R T H E M O S O F I E N T W A Y T O M O E L A R E M O U T O F P E B L E N G O D A C R O S O T I O N S +fleurs_eng_000436 T H E B R L C R I T I S I S M O F T H E R E C O N S T R C T I N E V E R T N H A S P O K A S O T H E W A R D I N G O F R E C O N S T R C I N G C O N T H A C T T O R I S T E D E W A T I N G A N D I N S I E R S +fleurs_eng_000437 U T W E U C U N S B O W D O B B O D A M O R S E C L T A C X C Y T O G E T A R O U N D G O M A T H E N R M A E W I C K L E P R I C E I S F I V E H U N D R E D C O N D L E S F R O N S F O R T H E M S H O U R T R +fleurs_eng_000438 T H E T H E K I N G D O M S W A S O N E O F T H E B L T B L U D I A S T E R A S A N D A N I E N T C H I N A S H I S T H R E T H O U S O N S O F P E O L E D I E D F I T I N G T O S T I T I N T H H I E A S C E I N T H E G R A N D P A L E S A T S I +fleurs_eng_000439 R T H E S C O U P L E S M A Y C H U S E T O M A K E K A N D A D O U S I N P L A N D F O R T H E R B A V Y +fleurs_eng_000440 N O T H N G A N D B E F E N O T H E R H N T H E C L E A R E B U T I F U L S C A I Y A B O V E A N D T H E M E N Y S U R U N G M O U 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Y A L E V E N T H R T Y F I V E P E A M +fleurs_eng_000446 T H I S C A L T O C M I C A L S P E E H E C A N M A K A N I N D I C A T E O U S I N G R E D C A B A G H E J O S +fleurs_eng_000447 I N P R T I C U L R I T I S L A E T H A T O N E C A N D E T E C W E T H E R A P R S O N I S L I N G B Y I N T E R P R I N G M Y G R O L E X S P R E S I O N S C O R E C T L Y +fleurs_eng_000448 T H E S E C H A L F O R I T Y O F T H E C H U C H O D S B E N I N R O M F O R O V E R A T H O U S A N Y E A R S A N D T H I S C O S O N T R A T I O N A F P O W E R A N M O N Y L E D T O M A Y T O C U S T I O N W H E T H E R D I S T E N E T W A S B E N G M E T +fleurs_eng_000449 T H E S U N D A R B O N S A R T H E A R G E S T H E T O R A L M A N G R O E B U L I N T H E W O R L D S T E C H I N G A T Y C L A M I T E R S F I F T Y M I E S I N T O T H E A N G L A D E S H E A N D I N I N D I A N H I N T E R L A N D F R O M T H E C O O S T +fleurs_eng_000450 R E G U L R A N O N S E N C S I N H E E T H O A R M A E O N L Y I N C A T A L E N B U T U N N D I S T R U P T I N S R N O U N E D B Y A N O T M A T E I S S I S T M I N A W A D V E R I T Y O F L I G W I N G E S I N C U D I N G S P A N T I S H I N G L S H R E N C H E R B A C K A N D J H A P O N E S +fleurs_eng_000451 E V E R W N P R T I T B A T I N O U C I T Y A N U S I S T R N S P R T I O N C S I S T O N C S A L M S T E R Y W N C O M P L A I N E O O U T R N S P R T I O N S I S T O M S +fleurs_eng_000452 L A T N H A D A S O R H A N E S O T H E O N S U I R V E T I S N V I R M I N T L B I L D U R N T H E M E N W T T H E P E M A S I N G F R Y T H U R A L A N D C O M P L E T E R E R I D T I N G O F T H E C O N S E R V E T H I S P A R D Y I N I E R M I N A L I L +fleurs_eng_000453 I N Y O N H I S G O N T O T R I E H A H A T L I T A T U D S O R O V E R M U T N P A S S T H R C O N C S I D E T H E P O S I L I T Y O F S N O I C E O R F E S I N G T E M P A T U R S +fleurs_eng_000454 H E S L E I N T R U S T I O N I S H E P R A S T S E S O F H E B O U C A Y W A K N I N B E U R I N G Y O U N O R M A L E S L E E P E R I A D A N D F A L I N G A S L E A S H O U R T T I M E L A T E R C E N T O S I C T E M I N O T S T +fleurs_eng_000455 O U R S W O A R L T H E T O D R I P P O U R S T O G E T H E R A N T H E W I T H C U E N G A W E T H A N S S C U E A E T H E M I N T O A B A L E R E H +fleurs_eng_000456 F O R T H E S P R N G B O C S I T E N D E D A F I E M A C H L O S I N G S T R E K +fleurs_eng_000457 J U S T L I K T H E O N E X P U R T D S A P U L O N T H E E R T H C A S I N T H E I D E S O T A S A M L B Y W A Y E X E R T I F F O R T S O F H E E D I T A R I O U S G A L A C Y +fleurs_eng_000458 T H O R O T H E N I G H T H E T W E N H U D E R D A N F I F T Y A N T O H U D R E D C O P E S W E R E M A D E N O W N O N A S B U N E L A P B R O L D S I D S +fleurs_eng_000459 F I R S T A M O N G I T S E M N D Y A E R E C O M E N D A T I O N S I S T H A T A N N O W D I P L M A I K N I S H I T I V E H U L B E T A K E B E F O R T H E E N D O F T H I S Y E A R T O S E C U R E R R A C X P B O R E R S A G N S H O S T I L I N T R V E N T I O N S A N D T O R E A S T A B L I S E D I P L M A I C R E L A T I O N S W I T H I T S N A B E R S +fleurs_eng_000460 S H A N T E T E R S B R C R U S I S I N C L U D T I M I N T O W N W H O T A S O N G E R S A R X A E N T E D F R O M E S T E R R E Q U I R I M E N T S C H A C K T H E T R M S +fleurs_eng_000461 O C O R D I N T O U P A N S N O G U L R A G E N C S Y R E D Y A L A C T I V E C A S E A M E A N D I A D I N H A S E I D E N I F I E A T T H E P L A N T +fleurs_eng_000462 S A G O G A T I O N A N D R E C O M O N A T I O N S H U F L V E R Y I A T I O N B A C K A N D F O R T H B E T W E N T H E T W O P U L E S W I T H E A C H G E N E R A T I O N +fleurs_eng_000463 E L A M N T L K C H L T H E M N D P A A T I M R C O N C S T E D M E T L S O F P O R S E R A L S O M E T E S L K E S I V E R A N D G O L D +fleurs_eng_000464 T H E C O R L T I O N E T W E E N B R A I P I T H O L A G Y A N D B E H A V Y O U R S U P O R T S I N C S A N D H E R R E S U R C H +fleurs_eng_000465 A N C H A N C H I N A H A D A O U N E K W A Y O F S H O W I N G D I F R E N T T I M E P E R I A D S E A C H S T A C E O F C H I N A E O R E E A C H F A M I L Y T H A T W A S I N M P O W E R W A S T H E D I S T I N T I F D I N I S T Y +fleurs_eng_000466 A S I M P L P O P U L E R D M E R H I S F E C I L Y D R I N T H E T U M E R I S P A A M A L D Y B R E D W I H A L V O I L E T O M A T O N A N Y A V I A B L E C O N T M E N S S U C H S C H E E S E T O N F I S H I T S E T E R +fleurs_eng_000467 T H E N O U N S M E T W A S M A D E A F T E R T R M P A Y F O N G C O M E R S A T I O N W I T T R K I S H P R D I D E N T R E S E P T E E P E R O D O N +fleurs_eng_000468 E R Y S A T E T H T H H E W O L D R E T E N T O T E C X S I S T O U S E S T H E R E S U L T S O F T O N I H T E S C O K I S D I D E R M E N W H T H E R T H E R I S A P A T H F O R D F O R M Y S E L F N T H S R A C E S B U E L E T E R S U T T H A H E W L E R E M A I N I N T H E R A S A N H U B P E N O R I G E N R Y T W E E W O N S O U T A R L I N O P R I M A R Y +fleurs_eng_000469 H E W A T A L S O I N G A G E A I N G R A V I N G B A K N O T S F O R M A N Y C O N T R E S R E S O N I N G S E M P L E S O F H W R K I N C L D T H E M P R I M E M E N T M N I N I S R E A L P O R T R D S O N T H E F I R S T F R O O U T H E F O N T O F T H E N E W C A N A D Y A N F F I V E D O L R I N W O N H N D E R D L D I L +fleurs_eng_000470 E M O R T R A D I N A L C H U R C H E S O F T A N H A L T H E N E S T R R I G U A L N T S A T E D Y N I G H T T U R N T H E E S T R W E K G N W E R T H E C O G R E A T I O N S O T D I M B R E A K I N G I N T O S E L E B R A T I O N A T T H E S R O K O F M I N I G T T O S E L E B R A E C R I C E S R E S E U R E C T I O N +fleurs_eng_000471 F I L E N I S A G R E A E B O T I N G D E S T N A T I O N T H E L A N D O F A T H O U S O N L A E H A S T O U S N D O F I L E N D S T O A N T H E L A K S A N N T H E C O S T A R K P A L O A O S +fleurs_eng_000472 R A N T S H E N A T E R A N D A R G I N C S E N F R S T L A D E C R I S T E N O F O R N D I S A C U R S I N R A N O U L S H E R P E S O N A T H O C A N D I D U S Y O S T R D A Y E V E N G A N L A P L A T H A A S T A D Y F I F T C L O M I T E R S T H E R D Y W N M I L S A W A Y F R O M E N O S I D I S T H +fleurs_eng_000473 S V E R W E T H E R I T E U N A R I C T U N E F O R A N Y D A D E R U S W T H E R F O N A M I N O N W I T T H E P A T N U A L T O C A S D A M A G E S R I O U S S O S I A L D I S T R U P T I O N O R L A S O F H U M N L I F E +fleurs_eng_000474 F O R E X S A M P L E T H E M O S T C O M E A N S T I L I M A G E F I T O C K R I F Y F O R M O U T N H E W O L D I S T H R T Y F I V E M I L O M A T E R W H I C H W A S T H E D O M I N A N T F I L M E S I E S A T T H E C L O S O F T H E A N A L O G F I L M A R A +fleurs_eng_000475 I T I S R E L A T E D T O B U T U L Y N O T I M V L V I N G H L P I N S T I L S C E T O R I N G O R M O T N E R I N G T H E L A T E R O N S D O N I N S P T U R I N G A N D R E C A R I N G M U S H T H I F R S C E S A N D B O T E S +fleurs_eng_000476 I A R N I N G D A M P C L O S C A N H E P E T H E M D R I Y M A N Y H O A T E L S H A E A N I A R N A N D I R N I N G B O R D A V A L A B L E F O R L O N G E V E N I F O N I S N O T P R E S E N I N T H E R O M +mls_eng_000283 A E V E D N Y O U N C E R D H O R S L Y S H E J U W H E R C H A R E U N T E R I T E G L O A T O T H E F I E R A N D S C R E D H E R H A N S O U T T O T H E L A S E T H E R E W A S N O O T H E L I G T I N T H E R O N M Y T H I S T I M E T H E W I N W I D O T H O R D E D M I S M A L Y S T I L +mls_eng_000284 M Y D E A R M O R E A W H I D O Y O U N O T D E S C I S T F O M T H I S C I L Y P E S U T O F A A N M A N D G I N A R Y T L E S E U R W H A T I S T H E V O L Y O U O F M U O N Y W E A R S P A N U R D S N O T S H E R T S L E V E D M E R S I N A R Y P L E A G S O F A M E A I C A E N S +mls_eng_000285 T H E C R I T I A L T R P A U R I S T A T O F T H E S I N G L A S T H E R M A E L I N W H C H P E S E N S E P O I N T O V N E F L E C T I O N A T H O R S N D T I E N G E N T T H E R I T I A L P R E S E H E R I A L V O L I M E A T H T W O C U L D N E S E O F T H I S P O N T O I N P L E C T I O N +mls_eng_000286 M U C H L I K A N F O U N U S A N D I F O R M I T Y O N T O T H A T M O N S T E R W H O M T H E H E B A N N I G H T T H E F A T H E R O F T H A T F A T L P R O G A N Y M A D E C I L H E R S E L F F O R V E R Y H A R T S T O S P I H T H A T H E H A D R E D H E R R I L W H I C H N O W W I H T C O U L D E V E R L O S E S W U T S U F E R E D E A D L Y D +mls_eng_000287 H I S M A S E D M E S E W I H P E I O N P R E S I E S A M O U N T I N G T O T H R E T H U S N A T N S T F I E A N D A L S O T H E R Y S M A L V O L I M S T A N O C U P I E B Y T H E F L O A I D M A S E N D E C O N S I D E R A T I O N T H I S L A S T M E S G E M E N T W H I C H N E S E S I T A T E S N E U M R U S C O R E A C T I N S I S M O S T D E L I C A D P A T T E O P O R A T I O N +mls_eng_000288 W H I Y S H U L D I T H A E B E N D E D N E C R O M A N C Y T O N D E V E R T O O N B I N T H E S F A T S T O I V O L V E B Y C A R F U L E L M I N A T I O N A N D C H A N G E T O T H E P E R F E C T F O D +mls_eng_000289 N A Y T H O O F R A S E S B E M Y B E D Y E T I A M R I C H L O V E S A I D B U T U T A R G U D L I V H E T H R I C E F O R N D A R E T H O W T O Y E L T H E S O V E R A N G I F T S O F E R T H T H E V I C T O R S O R D T H E L O R A L D B R O W F O R V I S I O N T H I N G K S O F L I T L W O R T H +mls_eng_000290 B U C K E M E T O H A V B E N A C E C U L A C T E R A O T H O H A M P E D B Y I L H E L T H A N D A G R E T P I N T O N I S F A V E R I S T H A T I D I S C R I D E D O N L Y T H O S E P L A N S W I T H H A D C O M E U N D E R I S O N P E R S I N A L O B S O V A T I O N +mls_eng_000291 H A D R A T H E R S R O N G U P A N D H A D N O T C H A I N E D I N T O N E I M P S T H E S H Y F E L T I N T H E T E A M S C O V E R I N T H E M U P A G I N A N D T H E Y U P E A R E D A S P E R F E C T I N S E C T S I N T H E M A Y O F T H E F O L O I N G Y E A R +mls_eng_000292 N O T H I N G S A Y O O B J E C S A N D T H U T S O F B O U T Y H O U D P R E S E N D T H E M S E S T O T H E U N E S T A N D I N G O F T H E F O R T I O L U T B U R S I N W H O P A R T O K O F I T T H E S E B A T E S W H C Y O A R B R U G T T O M E T R A N S A L A T A R O N S E U R E D W I T T H I S S O U P O E S T I O N +mls_eng_000293 N O S E M I N S O U P I T I T Y A N D H E D N E R V E H I M S E L F A G A I N S I T H I S F A I S E W A R S O R D O F S V E F F L O U S H D H E W A S T I M I E D E V E N T O R U D N E S +mls_eng_000294 B E C A M E M O L E L I F E L I K A S T E C H E S F L U S H T H E A S R A R W A R M E F I N O F I N T E M O R N I N T O H E T H A F T D I S P E R I N G S O L T S I D I T L O N G O U R S O F A L L R E D I N G A N T P E R S T E D H E A R T B Y N E V E R S E A S I N G R I M E S Y E T I C O U L N O N D E S T A N D I T +mls_eng_000295 W O N O F T H E O W I N R I T E R S A I D T H E A O P E H E A V A I S A P O S O N S H A L F I S H T H E S A R B I T E R A N D D E D L Y A N D C A D B E O U S E D I N P U T I N G E N A I M E S T O D E A T H +mls_eng_000296 T H E B E U T Y U S R O U B S O F H A V E N A S L O N T A D U R I H T E R S N C O L E I T A R H E L O K S I N B O U N L E S M A G H U T Y A B R O A D T O U C H I N G T H G R E N L E A V E S A L L A T R E M B L I T G O L D L I H T +mls_eng_000297 I C A N D O U N O M O R H A N T H A T I N T L T H I S M A T E R I S A P S A L U T L Y S E T E D T H E A W O R T H O T H A N L I F E I T S E L F T O M E T M S T R B L B U R S E M E D A N O I E D S U R L Y H E P R O T E S T I T W O U N O T G O D O A S T M E T O W A T E T H R E M O N C S U N T I L A T E X A M I N O N E O F T H E S +mls_eng_000298 R O S E C O N G R E S F O U N D A T I O N R U S H I N A N D T I T E T H A T O R G A N I E D T H E S A I T P E T E R S B U R G I N T E R N A S I N A L E E C A N O M I C F O R O M R O U S N E F T R U S H I O N S T A T O N D O I L E A N D A N A R G Y C O M P A N Y +mls_eng_000299 H O W G L U T E D N S P A R C K A L E T H E D E L I C A T R O S T W O K Y O U E A T R A C T E D N O D O U T A M A R V E D A T H E D I N T Y T R A I C S O M S B U T F E O U O F A S H A V R E A L Y H A D A N O P O T U N I T Y T O S T A D Y T H E D E T A L O F T H E F R U S T D E S I N S M Y N U T L Y O A V C O N S I D E D H A T T H E W R E O R T I N T H R E Y U R F O R D E S I N S A T M O S T +mls_eng_000300 T H A T H A T H E O F E N S I N T R I N G T O I N F I C K A W O N T H E M C K I L V E R O F H E N D E R O R W N H I M M O R T H A N T H E I N T E N D A N T O D O A N D T H I S B E C O M S E C C U S F U L A N U T H R D S O T A T H E P R I M I T I V E L A G D S L A T E R S W H E C A E F U L I N O R E C Q U I A R I N T H E R I T A L I T I O N T O B E L I M I T E D T O A N Y F O A N A +mls_eng_000301 A T S I R E S W O R D T H E J U E S W R E T E R N T H E C O M P N Y T H A T G O G O D S H I C E B E G O N W I T H M R T H A M O N I S H E N D E D B Y T H E F O B U T W O N C E A G A I N T H E W R K E C O S E O N B Y L I S E N S F R O M D U R I O U S E S R A I S S E N T W I T H R O I L E G R U N T A N D G I F T H S F O R I U S P I A S +mls_eng_000302 A N T P R O D A C K Y E A R I N A N D Y E A R O U T S I V I N H U N D R E F R O A N C E S W H O L I V E D O N I T H A V E N O T S O B A D L Y W R E I U L E X P L A I N E M O R D Y I S O C U P E D T A T H E O R B O H O V S +mls_eng_000303 T H E A N T H I S I S A L L Y O U R A N T A R T I S T O F A R F O R O N E O F H I S A L I A N C S A N D I W O R E Y O U W T H A T T H I S P L A C E N O M O R S E Y O U A E G X S E I T A N D T E R D F L U R A N S T H E B E S T I S T H E R I S M O R C R O U D T O E T A M A N S A R Y V E N J O N O N S E F O R A C E T H A T S M Y N A M I N D E D +mls_eng_000304 H E N I R E T U R N D T O T H E H O U S E W H E R E H A D B E A H A P Y C H I L D O N L Y A P I L O F A S H E I S W E A Y I T H A D S T U D I W E P T L O N G A N D T O F O G E T M Y W E P I N G I S A I D O U T O N D E V A T S C A M S S C E O N T H E S E W A T E R S I N A S T A R S A F A Y A N N I G T I P L A D E M Y F L O T T O T H E S U M E R M O N +mls_eng_000305 D O Y O U N O T S E W H A T L E S U E R I T G I V E S M E W E H A V E G R O N U P T O G E T H E R I N T H I S H O U S E S I N C H E W A S A B O Y I S I M P L Y C A N O R B E A R A S Y O U G A N T H E S I G T O F T H E S M I L L A V I N G H I S F A C E B O U R D E A R H E H A S N O M U S M E N T E X C E P T T H I S P L I N G A T T H S H O P C E P I N G +mls_eng_000306 I T I S D E V B I U S B O A D Y R E V A L I N G I N Y L I P I C A L O R R G R E E O N G A T I O N L O V E L O V E L O V E W A L N O T B E T H E W O N E D O F C U P I T B U T H E M A N I F I S T A T I O N O F H E E R V E R S A L R E P R D U C T I E I N S T I N C E S +mls_eng_000307 S H O A R P L Y A S H E S H O O K H A N D W I T H E R G O D B E S Y U M I G T D E A T C H A T H E B I H O P S A I D W H E N S H E C I S E D H I M A N D H I S L I P S M O E D O F T E R W A R D F O R S O M S I C K N T S A S I F H E W E R I N P R A A R H U R M O T H E R F O L O R D H E R O U T O F T H O M A N D T H E N S I L A N D C E T E L +mls_eng_000308 F O L O E D H I M S T A E L T F U L L Y A N D H E H I W A S I N A S T P I N G P O S T H U R F I L I N G H I S B O C K E T C A M E U P B E H I N E D H I M A N D P L U N C E D A N L O N G N I F E I N T O H I S N E C K +mls_eng_000309 S A S T H C U R T I E S D O U S N O T J U P I T E R D I S T R E B U T E T O T H E G O D S T H E P R O P O R T I O N A N D D I V I D E N T S P A R I N G L Y A N D S E V E R A L Y A S A G A M A N A N D Y E T O H I S C O M A N D O E R S W H E N H I S G E S T S T R A N G K T O O N E A N O T H E R I F V F O R C O U R I O U S Q U T H C L E E D E M O S A S Y U N E R R A T +mls_eng_000310 A N D H E R E N O N H A L D A R A S T R A N T T O A M E T A G A I N I N T H O H T S O T H I S N O U S E A N E P I N G B H E R E C H E R U L S P I R I T S T I L N E R D O T H E F A T I S E P I N G F E U T U R G O O D F O R P E S E N I L +mls_eng_000311 A N D B E C O M E T H E R E C A R D O F W H A T P E P L A V E D O N I T H E R M O R A M U B L E M O E N T S T H E R E C A R D O F H E C O N C Q U E S T S O F P E S E H O W M E N H A V E L I V E D A N D L A V E R D D U G A D B I L T H E U O N A N D C L E R E D G A R D E D A N D R E F O R S T +mls_eng_000312 T H E L O F L I N G O F T H W L L E S P E T O K I N S R A I N A S W I L A S N Y A N S C A S O N A B L E D A N C S I N G O F M I G E U S I N T H E E V E N I N G S O A C O N S A N D O F E A T A N D R I M T I S I N T H E O N C E S A R D I F U L P R O C U R S S T H E L E A V E S A R A L L A T R M B L E B E F O R T H A T P R O C H E A T T U N D E R +mls_eng_000313 W A S A S T O R M N G H J G E N E R L D E M P E A R E W A S C K I L E G E N E R A L C O S T I E N W A S B L A I M E D A N I N D E D E S N O W C O M E T O P A R I S T O D E V E X P L O N A T I O N S A G A N S E T A L L W H I C H T H E M O U N T O N A N D A T R O T I O U S M A U A R M U S T E V E N D M A K E H A I L A S T H E C A N +mls_eng_000314 T H E M O M E N T W A S F E F U L A M I G T Y O F F O H A D N E V E R S W N G T H E B U T L E L A C X O V E R H I M B U T T H E H O B N E R V E D H I S A R M E F O R A D E S P R A T B L O W A N D T H C M S E R F L E P R O S T R A I T D B E F O R H I M +mls_eng_000315 T H E N T H E W I N E S T O U T T H E C L A O R S T A N D D O A R K A N D N I G H E C A M E O N L I K E E I N K M Y O L D C O T I N C U I L T W A S C A O L D A S I A N M Y S W E E T S O N T O S T I N H I S S C L E E +mls_eng_000316 Y O U M A Y D O A S Y U P L E A S E T O W O R K O F O R I R I T A T I O N T O K E P Y O U R F A N A T I C S I S M Y O U R E W E L A F F Y O U N E D N O T M I N D T H E C O S T T H E P O R E D O N O T W A N T T S T A N D I N Y O U R W A Y B U T Y U I N S I S T O N T H E S U B M I T I N G T Y O R C O M P U L S I O N +mls_eng_000317 H E W A S R E D B Y A R E V E R N T E 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U D B E A C H E R S +nchlt_eng_001625 O C S F O R D I T I O N R Y C H A N G E D +nchlt_eng_001626 S A L C O O P U R S I N G R A Y H O U N D +nchlt_eng_001627 P R I N M N I S T E R C I V E N +nchlt_eng_001628 L A N G A G E S O F Y O U R O C K +nchlt_eng_001629 S O U T H E A S T I N G L A N D +nchlt_eng_001630 N E W L I N E S E N A M A R +nchlt_eng_001631 E A C U L C R A D T S O P A T U O N A T Y +nchlt_eng_001632 S O U T H E S T I N G L A N D +nchlt_eng_001633 M A Y H +nchlt_eng_001634 R E C O L R D H A T E A T S E M I S C R I V E S +nchlt_eng_001635 M U S I C A L G R E P E S F R O M C A L F O R N I E A +nchlt_eng_001636 M A I N B U T L E T I N C K S +nchlt_eng_001637 P O D L I S H M U S I C A L I N S T R A M E N T E S +nchlt_eng_001638 L A N W U G E S O F S A D I E A R A V I A +nchlt_eng_001639 C A L D W A R T I N T I O N S +nchlt_eng_001640 D O A B E W P H +nchlt_eng_001641 A N D Y P O P E C L A M I N T +nchlt_eng_001642 G I T S T H E C N P R I V E A T +nchlt_eng_001643 C I N G F O D A N E A N D +nchlt_eng_001644 I L E C T R N I C M U S I C A L I N S T R M E N C S +nchlt_eng_001645 A G E N O U T W A T E R +nchlt_eng_001646 L O R E N C E L I V E M O R N A S I N A L E +nchlt_eng_001647 L E A G B A C S P A L E P L A Y E R S +nchlt_eng_001648 B U T I S O M A N T H E A N C H A N T M E D A T R A N I O N +nchlt_eng_001649 O U N I T E D S T A T S R E C O C O N I E D +nchlt_eng_001650 P R O P A S I O N A L F E L A C E S +nchlt_eng_001651 S P I C H A L E C N O M I E G S O W N S +nchlt_eng_001652 M A N S T R E A M E W I S T D +nchlt_eng_001653 E V E N G R U S H O W S +nchlt_eng_001654 B Y T H E D I O N S T O K +nchlt_eng_001655 N D T S A R T I C K E H A S N O +nchlt_eng_001656 W E A S T I N M U S I C L E S +nchlt_eng_001657 C O N S E V I T O F J U A T A Y S O M R E G A R T S +nchlt_eng_001658 O P I C K M B E R S T A T S +nchlt_eng_001659 P R I M I N E S S A I D J O N +nchlt_eng_001660 R A C K S F O A R M I N G M O U N T +nchlt_eng_001661 M A G E R L E A K T M S +nchlt_eng_001662 P O L O N A T I O N M A N I G E N T +nchlt_eng_001663 F R E N C H F I S I S T +nchlt_eng_001664 H I Y A R C O M P R E T S I O N R A T S I O +nchlt_eng_001665 R E C O R D N G I N D O U S T R Y A S O C H A T I O N +nchlt_eng_001666 T H E P E A G E O N L I N M A G A S E A N +nchlt_eng_001667 H I P O P E R R E C Q U A D P R E G U O S S E O N S +nchlt_eng_001668 F I N I G H E S T A T E M S H E N S +nchlt_eng_001669 W H I D L Y O U S E D L O C A L E +nchlt_eng_001670 N O R T H E M E R Y C A N C O N T I N A N T +nchlt_eng_001671 A F R C A N A M E R I C A N R E P A S +nchlt_eng_001672 T H R I T O N M E L I D R Y A C T I O N S +nchlt_eng_001673 A T H E W O R D M N N +nchlt_eng_001674 T H E T O M I K M E L I K U L A N O P T I C A L F O I S I C K S +nchlt_eng_001675 T O W N +nchlt_eng_001676 M O R S I L +nchlt_eng_001677 C O N S T R U C T N E W R A L G A G E H +nchlt_eng_001678 P O R L Y E X C L U S I N R I N C S I B L +nchlt_eng_001679 H E W O P O U R T R A Y D I F E R E N T S +nchlt_eng_001680 S O V I A T D I S I D A N C E +nchlt_eng_001681 S I G N A L E T R O N S T D U C T I O N P A R T H W A Y E S E +nchlt_eng_001682 Y O U B O R N M S I +nchlt_eng_001683 G E N E R L Y A C X C E P T E D R A N G E R S +nchlt_eng_001684 G I L E D A W A R D W I N I S +nchlt_eng_001685 S W E D I S H M U S I C A L G R O P S +nchlt_eng_001686 C H O W D E R D O A R T I S O M R A T I N G +nchlt_eng_001687 D O S I G E F O R M S +nchlt_eng_001688 O F H I G O S T A T U O N O V E R S T I T E +nchlt_eng_001689 F O R M O S S A T O M E N T S I N T O R K +nchlt_eng_001690 A M E R C A N I N V E N T I O N S +nchlt_eng_001691 A R T S +nchlt_eng_001692 M D O N Y U R O P I A N R A S H A +nchlt_eng_001693 N S T N O R L E A E G P I N A N T +nchlt_eng_001694 B E A G F I N I S H P R D U C T I O N S +nchlt_eng_001695 N A S I N O L E +nchlt_eng_001696 T R A D G I C G P O R T E S +nchlt_eng_001697 T I T L E G R I C E S T A T E +nchlt_eng_001698 A S T H E N A H A D E N +nchlt_eng_001699 E A S T N Y U R A P I A N C O N T R Y S +nchlt_eng_001700 C O N D E M E D A N D O T H R I V E D T A N S L A T I O N S +nchlt_eng_001701 A L T H W A R L E D I S +nchlt_eng_001702 C I N A S A R M O U N T A N L E N D U S +nchlt_eng_001703 N O B L S A M I T Y +nchlt_eng_001704 A N D W O D S E R F U R S +nchlt_eng_001705 M O U N T S A I N T V I N C S A N T +nchlt_eng_001706 S I T Y E M E R T R U P O L A T O N E I R O A +nchlt_eng_001707 R O O N E R S W H O D I E D A S C H I L D R E A N +nchlt_eng_001708 C H A N E S L A S V O L E +nchlt_eng_001709 I P E E P E C A T S I N T I E R L Y +nchlt_eng_001710 C I N G E D W A R D S D E A T H +nchlt_eng_001711 A M E R I C A R A M R I C A +nchlt_eng_001712 C O M E R H A L S H I P S A L E D +nchlt_eng_001713 P E O P L F R O M M E N H A M +nchlt_eng_001714 R A I A L C R A S H C I L D +nchlt_eng_001715 M U T H A L D E F A N S T O U D Y +nchlt_eng_001716 M O D E N C H I L D R U O L E S +nchlt_eng_001717 M O T E S E R R I F A L D E V I S I O N +nchlt_eng_001718 O U S T R A L I A N E A Y F O R S E +nchlt_eng_001719 A M E R Y K C A N M I S T R Y R H I T E R S +nchlt_eng_001720 F I N T H E Y G R O U N D G R E F I H T E +nchlt_eng_001721 W I L T E M P I N S O F M A N T C H E +nchlt_eng_001722 C A R I L I N A +nchlt_eng_001723 M Y B A T H I N O P E R A T E S +nchlt_eng_001724 C O U R T S V E R I T I E S +nchlt_eng_001725 M A D R A N D +nchlt_eng_001726 C A S E L I T H A L E R E A C T I O N S diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.2/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.2/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..e1e83f4c38060d2437a2c8af2ccdcb6abc8493e9 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.2/1best_recog/token_int @@ -0,0 +1,273 @@ +cv_eng_000780 2 10 3 5 11 2 8 2 5 15 2 22 3 21 4 3 7 2 15 19 2 18 13 6 16 24 2 5 7 12 2 15 8 2 22 14 4 3 11 9 14 11 3 12 2 4 10 3 2 22 6 19 2 4 6 9 +cv_eng_000781 2 4 10 8 9 2 18 5 13 8 5 2 10 5 9 4 2 13 3 4 2 4 6 2 9 8 16 25 4 3 5 3 7 2 21 6 14 13 22 13 3 7 16 9 2 10 5 12 3 2 8 7 9 3 5 11 12 5 19 9 3 2 6 18 2 16 5 13 3 9 4 6 +cv_eng_000782 5 6 6 2 19 9 5 9 2 2 2 2 12 3 6 +cv_eng_000783 2 17 10 8 19 2 8 2 4 10 5 2 21 13 5 8 7 2 16 3 21 3 2 20 6 8 7 2 6 23 3 11 +cv_eng_000784 5 7 12 7 8 2 10 5 19 5 3 2 5 3 12 6 7 12 6 9 2 10 3 2 18 6 11 2 17 5 4 2 18 8 11 4 8 5 13 2 22 6 16 9 2 17 8 4 10 2 6 2 11 3 9 6 14 13 4 9 +cv_eng_000785 2 4 10 3 21 13 8 16 5 4 8 6 7 2 17 5 9 2 21 14 4 2 5 21 2 21 11 6 23 2 8 4 2 8 7 2 18 5 11 22 11 5 23 19 +cv_eng_000786 2 10 3 7 11 19 2 4 6 11 13 3 12 2 4 6 7 15 7 9 4 8 13 2 9 2 17 3 5 11 2 10 3 2 10 5 12 2 5 2 9 6 14 7 12 8 12 2 11 5 7 8 7 20 2 8 7 2 13 8 4 8 7 20 +cv_eng_000787 2 8 4 2 17 5 9 2 4 8 9 2 16 6 7 4 8 7 14 3 2 12 6 2 4 6 2 9 16 3 4 10 5 13 8 7 2 16 6 7 18 13 8 16 9 2 5 7 23 13 23 3 12 2 8 7 2 13 6 9 3 2 10 8 9 2 11 3 4 10 8 11 7 2 4 6 11 3 2 4 6 2 11 3 9 4 11 8 5 13 2 11 3 22 11 5 12 8 6 +cv_eng_000788 5 12 4 4 10 2 10 3 11 2 18 5 15 13 19 2 17 5 9 2 18 11 6 15 3 2 22 11 3 5 10 6 7 9 5 +cv_eng_000789 5 2 17 10 5 4 2 12 8 12 19 3 5 3 2 18 6 11 2 8 7 6 11 2 2 4 10 3 5 2 21 5 2 +cv_eng_000790 2 4 10 5 4 2 17 5 9 2 15 19 2 12 11 5 11 4 6 2 9 8 7 16 3 +cv_eng_000791 2 10 3 9 2 16 6 9 3 5 11 8 4 2 5 2 15 14 9 4 3 11 3 2 6 18 2 9 10 3 2 5 11 6 9 3 12 2 16 6 14 6 +cv_eng_000792 2 4 10 3 2 10 13 8 7 4 14 11 9 2 4 6 2 4 10 3 2 16 10 14 11 8 9 10 2 6 5 2 9 8 7 3 9 2 4 10 5 4 2 12 2 5 13 4 3 11 2 8 7 12 9 21 3 14 9 3 2 17 10 3 4 10 3 11 +cv_eng_000793 8 6 14 3 2 7 6 4 2 4 10 6 9 17 3 11 3 8 7 2 10 3 2 11 5 16 10 13 3 2 5 12 11 +cv_eng_000794 2 4 10 3 13 2 26 6 9 2 8 3 16 4 3 7 12 2 18 3 9 4 2 4 10 3 2 21 8 19 +cv_eng_000795 15 19 2 7 3 9 2 16 5 7 2 10 3 13 21 2 19 6 2 8 4 2 4 10 5 4 9 +cv_eng_000796 2 22 14 4 2 9 5 2 6 12 10 8 9 4 6 18 3 11 19 2 17 6 7 +cv_eng_000797 10 6 17 2 18 6 11 2 4 10 3 2 22 3 9 4 2 5 7 12 2 21 11 2 21 3 5 3 4 18 14 3 2 4 10 3 2 22 6 9 4 +cv_eng_000798 2 8 7 8 9 10 3 13 19 2 4 10 3 2 3 21 2 13 6 14 9 17 5 9 2 10 11 4 3 7 2 9 4 11 8 16 24 13 19 2 22 19 2 12 8 4 +cv_eng_000799 2 5 13 13 2 17 3 2 6 7 3 12 2 22 19 2 4 10 3 2 3 23 3 11 8 4 2 15 6 11 3 2 9 8 7 8 16 8 4 +cv_eng_000800 22 10 5 4 10 4 2 10 3 7 2 4 10 3 2 17 8 13 5 9 2 11 8 7 20 2 4 6 2 15 6 11 6 15 2 8 13 7 4 10 2 9 3 6 12 +cv_eng_000801 10 2 12 6 11 2 22 8 9 4 2 3 11 3 16 24 20 15 2 15 8 7 2 13 8 21 +cv_eng_000802 2 6 9 3 8 5 13 2 21 3 4 11 19 2 4 6 2 10 3 11 2 21 5 16 3 2 5 9 2 5 16 4 8 7 20 2 12 8 11 3 16 4 3 11 +cv_eng_000803 2 4 10 3 2 22 3 23 3 11 13 19 2 17 13 22 8 18 13 19 2 7 4 3 9 2 4 10 3 2 3 9 2 9 3 7 4 11 2 21 14 4 2 6 18 2 5 4 6 7 2 9 10 8 21 +cv_eng_000804 4 10 3 2 4 11 5 16 24 2 11 3 9 2 3 11 23 9 4 8 7 20 2 17 5 9 2 5 13 9 6 2 16 6 15 21 13 3 5 4 3 12 +cv_eng_000805 2 10 8 4 2 15 5 11 16 10 2 17 5 9 2 5 17 3 11 3 2 6 18 2 4 10 3 2 8 15 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b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.3/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..c08e04e476e932a32e6f7a8ba40e4bf768bbe393 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.3/1best_recog/score @@ -0,0 +1,273 @@ +nchlt_eng_001727 tensor(-7.5085) +nchlt_eng_001728 tensor(-3.5910) +nchlt_eng_001729 tensor(-4.1190) +nchlt_eng_001730 tensor(-4.2973) +nchlt_eng_001731 tensor(-2.3467) +nchlt_eng_001732 tensor(-8.7665) +nchlt_eng_001733 tensor(-8.5031) +nchlt_eng_001734 tensor(-5.4372) +nchlt_eng_001735 tensor(-6.2434) +nchlt_eng_001736 tensor(-4.6207) +nchlt_eng_001737 tensor(-2.1743) +nchlt_eng_001738 tensor(-9.5656) +nchlt_eng_001739 tensor(-3.5916) +nchlt_eng_001740 tensor(-4.3226) +nchlt_eng_001741 tensor(-6.7438) +nchlt_eng_001742 tensor(-9.2829) +nchlt_eng_001743 tensor(-4.8138) +nchlt_eng_001744 tensor(-5.0810) +nchlt_eng_001745 tensor(-4.8748) +nchlt_eng_001746 tensor(-4.6586) +nchlt_eng_001747 tensor(-7.6794) +nchlt_eng_001748 tensor(-5.9957) +nchlt_eng_001749 tensor(-7.1778) +nchlt_eng_001750 tensor(-6.6336) +nchlt_eng_001751 tensor(-5.7797) +nchlt_eng_001752 tensor(-6.9657) +nchlt_eng_001753 tensor(-6.2754) +nchlt_eng_001754 tensor(-3.8445) +nchlt_eng_001755 tensor(-4.9908) +nchlt_eng_001756 tensor(-4.8407) +nchlt_eng_001757 tensor(-1.3049) +nchlt_eng_001758 tensor(-5.6043) +nchlt_eng_001759 tensor(-7.6949) +nchlt_eng_001760 tensor(-3.2400) +nchlt_eng_001761 tensor(-3.3765) +nchlt_eng_001762 tensor(-2.7638) +nchlt_eng_001763 tensor(-6.9119) +nchlt_eng_001764 tensor(-5.6928) +nchlt_eng_001765 tensor(-2.9747) +nchlt_eng_001766 tensor(-4.3928) +nchlt_eng_001767 tensor(-7.0285) +nchlt_eng_001768 tensor(-11.5998) +nchlt_eng_001769 tensor(-8.2512) +nchlt_eng_001770 tensor(-6.2708) +nchlt_eng_001771 tensor(-5.1152) +nchlt_eng_001772 tensor(-5.3419) +nchlt_eng_001773 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ERLY ENTRUST IN POLITICK +swc_eng_001825 WAS CALED DOLBE ACH ECX PROW IN FUL AND PATNTE +swc_eng_001826 OULD SAVE AND FINE FILS B +swc_eng_001827 ASTRLIAN SNAKE BELONG TO SVEN FAMLYES +swc_eng_001828 DVELP IN PIYARS +swc_eng_001829 LIND SHARPLY SENCES PEAKAN +swc_eng_001830 WAS RECORDED INTHIRLY ON A FOR TRACK COSET +swc_eng_001831 NORMAS IMPROVEMENT IN +swc_eng_001832 RBN AND WRORL LEGUSTAC +swc_eng_001833 ACH PLYER BGINS +swc_eng_001834 JEST AS IN SPIRED MANY COMENATORILE PUSLES +swc_eng_001835 OR HEOMAN IMADGE +swc_eng_001836 ELAS PIRTED TAPS +swc_eng_001837 ANT AND IESENS N TO TH OTIO +swc_eng_001838 PRESIN PROTEMPOR OF THE STAT SENIT +swc_eng_001839 ISHOP CAN MOVE ANY NUMBER OF SQUARES +swc_eng_001840 PEHE INSID THE SCOL +swc_eng_001841 IN CONACT SHERNES WIT +swc_eng_001842 ONTRES OF THE ESTEN PALIARKTIC FLY WAY +swc_eng_001843 AINALS DTISTIKS ESTMATE THE POPULATION IN +swc_eng_001844 ON DISPUTED WORLD CHEST CHAMPIAN +swc_eng_001845 Y ROULL CAPEABINCEA +swc_eng_001846 ER INACTED BY THE GENRAL ASEMBLY WAS A MESUR RATIALY SEVGRGATING THE STATES RELROT CARS +swc_eng_001847 WHCH RAPS ALMOST +swc_eng_001848 SECT PRTECS AL NATIVE FORNA AND PROVIDS +swc_eng_001849 HERE AS THE FEMALE SPECULM IS DARK BRON BORED WITH WHIGHT +swc_eng_001850 OTERYE CNTULS O +swc_eng_001851 NINTEAN TWELE IN ROSFEARN +swc_eng_001852 DIGNOCISE IS GENRLY MADE WIH A SETESCAN OF THE HEAD +swc_eng_001853 THE FIRST GENRALY RECONISED WORLD CHESS CHAMPIAN +swc_eng_001854 HEPPY AND ITINGON WE PAR +swc_eng_001855 AD RELACE THEIR ELBUMS BOTH TO CDY AND +swc_eng_001856 WATES ARON THE CONTINEN +swc_eng_001857 F THE RAGE PERSINL STEARIOS +swc_eng_001858 ND FEYOUW METERS AND RECOARING LEVL CONTROULS ON +swc_eng_001859 POIN TO EAL TIME +swc_eng_001860 ND IT OFEN DESTRORD THE PLAABILITY +swc_eng_001861 CON FUTION +swc_eng_001862 EUIVELENT TO THE QUSTION OF WHTHER ECX IS A MEMBER OF CMPO +swc_eng_001863 MOSE TO ITH LASTD RANK +swc_eng_001864 POSTHENDERISM +swc_eng_001865 OMPACT CASET QUIKLY FOUND YOUS +swc_eng_001866 E FOR HUNDRND THERDY THEE FE +swc_eng_001867 INGS WHICH RESULT IN ASPESIFICK THIHE A PON +swc_eng_001868 FOUR NINTEN HANTY SEVEN +swc_eng_001869 MICATIONS AND HELF +swc_eng_001870 EAY ATCHE IN A PEURSON NO +swc_eng_001871 SOMBREAND SPISIFID THAT THEY MAY AL SO BE USED ON OTHER NON POROSS MIPTERIALS +swc_eng_001872 HE POSEIBLY COND PESIFIG +swc_eng_001873 MATORS IN LACX +swc_eng_001874 ITHOUT FIFTY MORE TRING BROA +swc_eng_001875 HISI CHACSIN POULES RESPECTIELY TOK AS LOF AKI +swc_eng_001876 AVSHONM LETED IS FIR +swc_eng_001877 EBLEADING RIST REMAINS OF ROUND FOLTY +swc_eng_001878 ILERIT CHCK MAE +swc_eng_001879 SOME SECULR HUMINS CONCIVED RANDS HEUMINISM AS AN ALSPRING OF THE EUMNUST FRETHOUHT MOVEMENT AN ARGUYTHT RAS HUMINIST DIFER FROM THE EUMINIUST PAIN STRM BY HAVIN +swc_eng_001880 PINTALE NEST AND CHICK ARE VONERBLE TO PRODATION BY MAMLE +swc_eng_001881 NORTHERN PINTAL IS ON OF THE SPECSHES TO WHICH THE AGRMENT ON THE CONSCERVATION OF AFRACKANURAGION MOGRITORY WATERBURD +swc_eng_001882 AND IS NEOE FOUND ONLY INTASMAI +swc_eng_001883 RPECTIVE THE IDA OF MIND UPLOATING IS A SRTED TO REPROSENT +swc_eng_001884 N AVRIGE OF TWENT ON PER DAY +swc_eng_001885 HAN WOLD FALO THAT PE ECUL +swc_eng_001886 ND BLEDING INTO VERIS CHOMERS +swc_eng_001887 ANDL THE TOGLID BETWEN TRS +swc_eng_001888 F THES PROBONS WR FICINTLY SALVEABL +swc_eng_001889 OALODGICALE T IN +swc_eng_001890 ROK WHEN FLUSHED +swc_eng_001891 INCLUDING JERMNL THOICE ICNOLGY +swc_eng_001892 PERINE OF LE THESHUS OR BOT +swc_eng_001893 N ATEN SICTY THR +swc_eng_001894 MANIUFECTURS SHO CAY PODACKES OL SYSEL +swc_eng_001895 O THE FIRST NON SORIA CHALLNGER SINC +swc_eng_001896 PONENT HAS ONLY THE CING AND +swc_eng_001897 MAIN ARTICL +swc_eng_001898 OWND SERTAN LATH SUSFUL FOR FITIN +swc_eng_001899 TAPE IN THE SAME FORE FACTER S THE COMPACT OIO +swc_eng_001900 SENTHR WAS LATER CXPUNGE FRO +swc_eng_001901 R DESECTO CQUALITY +swc_eng_001902 IS FOR THOUSN SICHUNDRED BY SIXT FET +swc_eng_001903 NINTIN SEVENDY THR +swc_eng_001904 RAL A PLAYE MAYAL SO LOUSE BY RUN +swc_eng_001905 UBLIK HLT PRFESER GRGRY STOACK POIN +swc_eng_001906 BRON WASALECTED T THE HOUSE O REPRSENTIVES FOR THRE NON CONSECTIE TRMS +swc_eng_001907 O EGIST HAPLY WI +swc_eng_001908 A GROP OF MELES THAT RAC +swc_eng_001909 N THE WOLDS LAGIST +swc_eng_001910 BREDING TAKXS PLACE BETWEEN APRL AND JON +swc_eng_001911 STRALOR IS AT THE SOTHEN IND +swc_eng_001912 TECNALOUGICAL SINGULARITY IS POSEBL +swc_eng_001913 NLUDIN THE LEPY COD +swc_eng_001914 SENTY FORE HAD A HIYE RGICATION CULFICATION COMPEDT +swc_eng_001915 IACKERS WEN THY PONES CING ISAN +swc_eng_001916 ONCEVATION I USTRAYA +swc_eng_001917 IS TH SELAMANDOFF +swc_eng_001918 FIRST SELF DISCRIVE RANS HUMINEST BEAT FORMILY IN THE EAL diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.3/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.3/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..8e1ae27df87018f9edf9b1f5a64a282013fbebb5 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.3/1best_recog/token @@ -0,0 +1,273 @@ +nchlt_eng_001727 I N G L A I S H P E S C I F O U S T S +nchlt_eng_001728 Y O U N I T E D S T A T E S F E D E R A L +nchlt_eng_001729 F A D R A L D R E S E R O V E A C T +nchlt_eng_001730 W I L Y M H I N R Y H E R A S O N +nchlt_eng_001731 C L A P P L A Y C H O T +nchlt_eng_001732 P A S S O N G E R R A L E S O V I C S E S +nchlt_eng_001733 A N C H A N M E S S A D O R N T H I O N J E N R A L S +nchlt_eng_001734 C O N G A C T I O N D S E N T A M A +nchlt_eng_001735 G U N P O U D E A P R P I L E N T Y O U S E D +nchlt_eng_001736 L O W S T I N A G E S T A I G T +nchlt_eng_001737 C A L N D E R Y U R O S +nchlt_eng_001738 M A G J E R I N T O N A S I N A L E E A P O R T E S +nchlt_eng_001739 T O T L F O R S E A C T I M +nchlt_eng_001740 L O S T L E S S D A T E C O M P R I T I O N +nchlt_eng_001741 A G R E A E K H A +nchlt_eng_001742 I N V O R M N T A L P R O T I C T I O N A D G A N C S E +nchlt_eng_001743 M A N Y T O B I S C A L S G R I T I O N +nchlt_eng_001744 A N C H O A N S I T Y P I T H U N D E R +nchlt_eng_001745 S M A L O T H E D A C K S I N A G O G +nchlt_eng_001746 L O D G E S M T R P I L I A N E R I A R S +nchlt_eng_001747 T I T A L R E L I G Y E O R E M O N O +nchlt_eng_001748 E G A M P L E S A N D C L U D H A F M O N +nchlt_eng_001749 Y U N O T I D S T A T E M A N T A I N E +nchlt_eng_001750 B O L D R E P R E S E N C E M E A X S I M A +nchlt_eng_001751 S I N E S F C I O N O R T H E S +nchlt_eng_001752 O R D N A R Y D I F R E N S H A L E C U A T I O N S +nchlt_eng_001753 D I P L M A T S O F T H E H R D Y E S E +nchlt_eng_001754 S I R I A L C L O M M I S T R Y +nchlt_eng_001755 U R E L M E I T R Y C O L +nchlt_eng_001756 S L O W L Y L E D S S C I A L I S O M +nchlt_eng_001757 P R I N T E S +nchlt_eng_001758 N E U T A S T H E N P E O P L +nchlt_eng_001759 S M A T C O A R D B A C E T D I L E C T R O N I C K P E R S +nchlt_eng_001760 S T A T E A R M Y S O L D G E R S +nchlt_eng_001761 L O R D J E S C R I S T +nchlt_eng_001762 L A D A N B I G +nchlt_eng_001763 B E T A E L I A N N E S I N A L T E M +nchlt_eng_001764 A N D T E A G E R R E C R I A T I O N G R O U N D T H U M +nchlt_eng_001765 G R O S E S S T A T E P R O D E C T +nchlt_eng_001766 K I N G C O N G V E R S E +nchlt_eng_001767 B E L V A E L +nchlt_eng_001768 F L E O L G O N I S A T I O N S T H E U N I D E D S T A T E S +nchlt_eng_001769 Y S R I L T H E F E N S F O R S E S +nchlt_eng_001770 O R D R M T I C K S A N D R E S E V E +nchlt_eng_001771 B R N D S W I C K S E T H N R A L W A Y +nchlt_eng_001772 A C T R S A C A T D I M I A W O D +nchlt_eng_001773 P E P L F R O M E T O C K I A T D +nchlt_eng_001774 F O R C H A L D S S I N G A +nchlt_eng_001775 V E A R Y I A B L V L F H T A R M I N G +nchlt_eng_001776 S O U T H W A L E S V E L Y E S +nchlt_eng_001777 C A L I F O R D Y E A S T A T Y U T H E V E R S I T Y +nchlt_eng_001778 E L D E R O D O +nchlt_eng_001779 O U T D O R E O R I N T E D S I T Y +nchlt_eng_001780 C L A M E D P A R S H A L R E S P O N C S A B I L I T Y +nchlt_eng_001781 C R I S T H O N T E R M S +nchlt_eng_001782 E V E N S T O K P L A C E +nchlt_eng_001783 C E N S S A R D D I T H S I N F R O N C E +nchlt_eng_001784 H I S T R Y O F M I H A G A N +nchlt_eng_001785 A R I G I N L Y T H E N A M +nchlt_eng_001786 N T I O N S F R A I M E W R C O N V E N T I O N +nchlt_eng_001787 N O C A L E +nchlt_eng_001788 O L S T R I A N S C O L E E C A N O M I S T S +nchlt_eng_001789 M A N G R U O P C O M P O U W N D S +nchlt_eng_001790 R E S I C L I B L M T E R I A L S +nchlt_eng_001791 C O M I N L A R E S E S T O M +nchlt_eng_001792 B R O N G K S H I S C U L +nchlt_eng_001793 A N M E R C A N B E L I T O G O R I H I T E R S +nchlt_eng_001794 C A N M I C A L I L A M E N T S +nchlt_eng_001795 L O B L E I N T O N T C M U N I T Y +nchlt_eng_001796 T D Y O G R E A F I C E M A G A S E N M A R C H +nchlt_eng_001797 W I P S O V H I S P R E V I G D A S +nchlt_eng_001798 S I N S F C T I O N O B L E L E S +nchlt_eng_001799 S I N E S F I C T I O N F U L E M +nchlt_eng_001800 S U B C I T S O M E P R O B L O M +nchlt_eng_001801 E A S T O N N O R T H A M E R I C K A +nchlt_eng_001802 P E P E S W I T N E S L O U T I N G +nchlt_eng_001803 D I S T I N G T I V E V O C A L I N S T R M E N T S +nchlt_eng_001804 U A E F R I C A N A M E R I C A N R A P I E S +nchlt_eng_001805 P O R T H O G E S G E N A L S +nchlt_eng_001806 I N T O N E S I N A L E I A P O R T I D T Y A Y +nchlt_eng_001807 M O U N T A N R A N G E S O F B E L I V I E A R +nchlt_eng_001808 F R E N C H A R E F O U R S +nchlt_eng_001809 S S W O P R A B A L A P E R E N C S +nchlt_eng_001810 L O N G T R E V L I N G P A I E S +nchlt_eng_001811 D I S T R I C K T C O A R T J O U D G E H +nchlt_eng_001812 Y O R O N Y A M P I R +nchlt_eng_001813 B R I T I S H N N A S I O N A L I T Y E C T +nchlt_eng_001814 I S H O D A T A P R A L +nchlt_eng_001815 P O U B L I S I T Y T R A D E D C O M P A N E S +nchlt_eng_001816 R U S H I N V I C T I M S O F S O V E D R E P E N T A T I O N S +nchlt_eng_001817 W E I S T A N S L E V I C K L A N G W A G E S +nchlt_eng_001818 E T A L I A N R O M A N C A T H L I C E S +nchlt_eng_001819 F R E N T H S I S T R F N I M B E R S +nchlt_eng_001820 P R E V I N C H A L S I M B L S O F U N T A R I O +nchlt_eng_001821 R O C K S F O A M I N G M O N T +nchlt_eng_001822 A S A S S O N A T E D M O N O C K S +nchlt_eng_001823 I N C L U D I N T E N A S H I N O L N O N G V E R M E N T O L +nchlt_eng_001824 M E T R I C K S P A C E I M +swc_eng_001744 O R R E P A R E T H E B R A K N T H E C A P E +swc_eng_001745 E R Y H S U P T A N T E S +swc_eng_001746 H I S M O S T C O M E L Y A C U R S W H E N N E T H E R S I D E I S A B L E T O +swc_eng_001747 G R A T B A R Y I A R R I F I S M A N A G E D B Y T H E G R E T B A R I A R I F M R E N +swc_eng_001748 B Y A T L E A S T H R E V O T S +swc_eng_001749 D E F I H A N T C E A S E I N +swc_eng_001750 W L S H O E V D E N C E O F H M R G I N T +swc_eng_001751 I N D A N A N S E R H I C K L Y +swc_eng_001752 N A B L E D E V I C S I V E A N D U N D E M A C R A T I C S O S H A L P O L A C Y E S +swc_eng_001753 M A D R E S E N T T H I T L E S A I L I B L E O N C O E +swc_eng_001754 D I S T R I C T I N A T E N S I C X T Y S I C +swc_eng_001755 L I L E I N T O F E U C U I R I D Y +swc_eng_001756 A Y I N T H E G R O I N A N D A D V A N C E D T H R O +swc_eng_001757 C N A L G Y E S A N D I M P L D M E N T I G T R A N S H E U M I N U S C U L S O F A N H A N S E P E R F O R M E N +swc_eng_001758 N C O U D I N G N A P S +swc_eng_001759 Y S P A N I S H T I R C H M A N L O E R M E R A S D E L O U C A N A +swc_eng_001760 D E V I H T E D D E M I C R A T S +swc_eng_001761 H E W O L D C H A N P I A N S H I P H A S B E N C O N T R O L E B Y E I D Y E +swc_eng_001762 W H E R E T E S T A R I N G P O S I T I O N I +swc_eng_001763 E C R A T E D I N E V E R Y S T A T A N D T E R I T R Y T O P R T E C T A N D R E S E R V T H E C O N T R E S U N A K Y C O S I S T O M S +swc_eng_001764 E D I C A T I N T O F T H E N U S I L A N D W A R E A M O +swc_eng_001765 A L A M E F R M T H E R E L R O U D C O U P A N E S F O R V E T O I N +swc_eng_001766 T H E T O W N I S S P I T +swc_eng_001767 M O S K E T I Y F I S H I S A P R T I C U L Y A G R E I V E S P A C E S N O +swc_eng_001768 A N D T H E N A I O N A L C H E S E H E M I N S H I P E S +swc_eng_001769 R O B L O M I S N O W N T O R U N I N P L Y N O M A L T I M E +swc_eng_001770 L A Y J O N I E R A N D P A R C K E R W A T C O N S H E A R D N +swc_eng_001771 I N N I T I N S E V E N T Y T H R +swc_eng_001772 D E V E L O P I N G A N D O U S I N G S U C H T A C N A L D G E +swc_eng_001773 O R S O M E Q U I S T I O N S +swc_eng_001774 L A M E A P R O O E T H A T P E +swc_eng_001775 A B L A D E C A T H A T O R I S O U L Y I N S E R T E D S O M O N S O F L U I D B O N S +swc_eng_001776 E R N O T I O N O F Y U J E N I C A N H A N S M E N T T I C N A L A G E S M I G H U N I N T E N T I N A L Y I N C O U R A G E +swc_eng_001777 A T T H E A T E N I N O F R E S E R T H R S A N B E F O C K E S E D N M P A R T I A L S O L U T I O N S O R S O L T I O N S +swc_eng_001778 N O W N O F F O R H N D R E D S O F Y E A R S +swc_eng_001779 N L Y M A U B I A L S H A V E S O V I V E D T T +swc_eng_001780 T O W H C H A L T H E E D A B L E S P A C E S O F C R U S T A T I O N B E L O N G +swc_eng_001781 O G E R T H E M R E S U R C H +swc_eng_001782 N I N T E I N S I C X T Y T O F I L I P S I N V E N T E D H E C O M P A C T O D O C A S E T M E D E O A M F O R O D I O U S T O R G E +swc_eng_001783 U T R C I N F T H E L O W +swc_eng_001784 N T F H B I A N S A D R P +swc_eng_001785 O M E N S W O A L D C H E S T H A M I N C H I +swc_eng_001786 C O N T A N E D I S C R P T I O N S A N D C O M M A N T A R I Y S O N T H E S T A T O F A E N B I S I N C E A N D E C N A L A G Y A S M A G E R C O N T R U T E R S T O T H E +swc_eng_001787 U R H L F H A N T I Y O R S O C I A L T R E N T +swc_eng_001788 M O S T C O M P A C C A S E T S W E R S O U L D B L A N K +swc_eng_001789 I F T H E R I S A N O U L G E R T H E +swc_eng_001790 H E S U T H E N O S T R A L I A N C O S T A N D I N S U B A N T E I C T I C O U S T R A L I A N T E R I T R Y S +swc_eng_001791 A E R A T S O F T I P I C K L Y F I V E H U D R E D T W +swc_eng_001792 D E P R I V I N T H E D O C +swc_eng_001793 N I N E P O R S E N T O F H E T O T L C A S T +swc_eng_001794 A N T E R I A S S O R B R A T R Y A N D A N T E R I E C O M U N C A T I N A T E R Y +swc_eng_001795 E D N O T I M P A R T H S H I N +swc_eng_001796 N T H I E R D E M A C R A T I C P A R T Y +swc_eng_001797 N O H E S O N T O P O F T H E C S E T H A L I N D I C A T T H +swc_eng_001798 L O W A T T O S O +swc_eng_001799 I A I N D A N G R E D M R I N S P C H E S T +swc_eng_001800 B R O W N D E S I R E A L E C T I N +swc_eng_001801 H I S F A C D O S N T S A Y M U C H A B O U T W H E R E T H E P R O B L O M L I S +swc_eng_001802 C O M I C A L S O S I T Y B G A N A S +swc_eng_001803 I T H T O R I S T S A R V I N G T H E S T E M E B O T E A N D T R I N +swc_eng_001804 F R S T D I L O G B E T W E E N T R A N H U M N I S +swc_eng_001805 N E R B E N P A R T O F T H E L I P I C G A N S +swc_eng_001806 R E A G E S F U R N I T C U R A N D T H +swc_eng_001807 I N H I L A B L T R N M E N T S +swc_eng_001808 O L O C A T E T H A N U R I S O M +swc_eng_001809 O R H L O U G I C A L F R E D M +swc_eng_001810 D H E R J E T I C A T A K I N G S T O W +swc_eng_001811 A C T L Y F O R T Y A R S A F R T H E C A R N I S H D O N W A S L A T E +swc_eng_001812 A S E O T H E R E C A G N I T I O N T H +swc_eng_001813 O R L E C T R N I C K B U T N S O R D I S P L A Y +swc_eng_001814 I S U N N O N W H T H E R P E A C U L S E M P Y +swc_eng_001815 H I C H C O M S F R O M T H E V E R B A +swc_eng_001816 D I S P E P O R T I N T L Y A V A L A B L T T H O E W I H G E A T E R I N A N H A L R E S O R E S +swc_eng_001817 Y U M N T H R E T S T O T H S V I V L O F M A N Y S P A C E S +swc_eng_001818 E V E N M O R D I F C A L T +swc_eng_001819 A N D T W E T Y W N S P E C E S O F I C E A N I G D O L F O N +swc_eng_001820 A C H E V I N G P R M O T I O N +swc_eng_001821 E R A N T E W M I N U S E A S U M T I O N +swc_eng_001822 O N T H E F I R S T B E L L I T +swc_eng_001823 S T O R Y N D I C A T I V E O F T H E R I S E I N G L O B L S I G N I N G C S O F S H U P O L I S H I S T O L D B Y J E A N +swc_eng_001824 W H C H S P A R E H I S E R L Y E N T R U S T I N P O L I T I C K +swc_eng_001825 W A S C A L E D D O L B E A C H E C X P R O W I N F U L A N D P A T N T E +swc_eng_001826 O U L D S A V E A N D F I N E F I L S B +swc_eng_001827 A S T R L I A N S N A K E B E L O N G T O S V E N F A M L Y E S +swc_eng_001828 D V E L P I N P I Y A R S +swc_eng_001829 L I N D S H A R P L Y S E N C E S P E A K A N +swc_eng_001830 W A S R E C O R D E D I N T H I R L Y O N A F O R T R A C K C O S E T +swc_eng_001831 N O R M A S I M P R O V E M E N T I N +swc_eng_001832 R B N A N D W R O R L L E G U S T A C +swc_eng_001833 A C H P L Y E R B G I N S +swc_eng_001834 J E S T A S I N S P I R E D M A N Y C O M E N A T O R I L E P U S L E S +swc_eng_001835 O R H E O M A N I M A D G E +swc_eng_001836 E L A S P I R T E D T A P S +swc_eng_001837 A N T A N D I E S E N S N T O T H O T I O +swc_eng_001838 P R E S I N P R O T E M P O R O F T H E S T A T S E N I T +swc_eng_001839 I S H O P C A N M O V E A N Y N U M B E R O F S Q U A R E S +swc_eng_001840 P E H E I N S I D T H E S C O L +swc_eng_001841 I N C O N A C T S H E R N E S W I T +swc_eng_001842 O N T R E S O F T H E E S T E N P A L I A R K T I C F L Y W A Y +swc_eng_001843 A I N A L S D T I S T I K S E S T M A T E T H E P O P U L A T I O N I N +swc_eng_001844 O N D I S P U T E D W O R L D C H E S T C H A M P I A N +swc_eng_001845 Y R O U L L C A P E A B I N C E A +swc_eng_001846 E R I N A C T E D B Y T H E G E N R A L A S E M B L Y W A S A M E S U R R A T I A L Y S E V G R G A T I N G T H E S T A T E S R E L R O T C A R S +swc_eng_001847 W H C H R A P S A L M O S T +swc_eng_001848 S E C T P R T E C S A L N A T I V E F O R N A A N D P R O V I D S +swc_eng_001849 H E R E A S T H E F E M A L E S P E C U L M I S D A R K B R O N B O R E D W I T H W H I G H T +swc_eng_001850 O T E R Y E C N T U L S O +swc_eng_001851 N I N T E A N T W E L E I N R O S F E A R N +swc_eng_001852 D I G N O C I S E I S G E N R L Y M A D E W I H A S E T E S C A N O F T H E H E A D +swc_eng_001853 T H E F I R S T G E N R A L Y R E C O N I S E D W O R L D C H E S S C H A M P I A N +swc_eng_001854 H E P P Y A N D I T I N G O N W E P A R +swc_eng_001855 A D R E L A C E T H E I R E L B U M S B O T H T O C D Y A N D +swc_eng_001856 W A T E S A R O N T H E C O N T I N E N +swc_eng_001857 F T H E R A G E P E R S I N L S T E A R I O S +swc_eng_001858 N D F E Y O U W M E T E R S A N D R E C O A R I N G L E V L C O N T R O U L S O N +swc_eng_001859 P O I N T O E A L T I M E +swc_eng_001860 N D I T O F E N D E S T R O R D T H E P L A A B I L I T Y +swc_eng_001861 C O N F U T I O N +swc_eng_001862 E U I V E L E N T T O T H E Q U S T I O N O F W H T H E R E C X I S A M E M B E R O F C M P O +swc_eng_001863 M O S E T O I T H L A S T D R A N K +swc_eng_001864 P O S T H E N D E R I S M +swc_eng_001865 O M P A C T C A S E T Q U I K L Y F O U N D Y O U S +swc_eng_001866 E F O R H U N D R N D T H E R D Y T H E E F E +swc_eng_001867 I N G S W H I C H R E S U L T I N A S P E S I F I C K T H I H E A P O N +swc_eng_001868 F O U R N I N T E N H A N T Y S E V E N +swc_eng_001869 M I C A T I O N S A N D H E L F +swc_eng_001870 E A Y A T C H E I N A P E U R S O N N O +swc_eng_001871 S O M B R E A N D S P I S I F I D T H A T T H E Y M A Y A L S O B E U S E D O N O T H E R N O N P O R O S S M I P T E R I A L S +swc_eng_001872 H E P O S E I B L Y C O N D P E S I F I G +swc_eng_001873 M A T O R S I N L A C X +swc_eng_001874 I T H O U T F I F T Y M O R E T R I N G B R O A +swc_eng_001875 H I S I C H A C S I N P O U L E S R E S P E C T I E L Y T O K A S L O F A K I +swc_eng_001876 A V S H O N M L E T E D I S F I R +swc_eng_001877 E B L E A D I N G R I S T R E M A I N S O F R O U N D F O L T Y +swc_eng_001878 I L E R I T C H C K M A E +swc_eng_001879 S O M E S E C U L R H U M I N S C O N C I V E D R A N D S H E U M I N I S M A S A N A L S P R I N G O F T H E E U M N U S T F R E T H O U H T M O V E M E N T A N A R G U Y T H T R A S H U M I N I S T D I F E R F R O M T H E E U M I N I U S T P A I N S T R M B Y H A V I N +swc_eng_001880 P I N T A L E N E S T A N D C H I C K A R E V O N E R B L E T O P R O D A T I O N B Y M A M L E +swc_eng_001881 N O R T H E R N P I N T A L I S O N O F T H E S P E C S H E S T O W H I C H T H E A G R M E N T O N T H E C O N S C E R V A T I O N O F A F R A C K A N U R A G I O N M O G R I T O R Y W A T E R B U R D +swc_eng_001882 A N D I S N E O E F O U N D O N L Y I N T A S M A I +swc_eng_001883 R P E C T I V E T H E I D A O F M I N D U P L O A T I N G I S A S R T E D T O R E P R O S E N T +swc_eng_001884 N A V R I G E O F T W E N T O N P E R D A Y +swc_eng_001885 H A N W O L D F A L O T H A T P E E C U L +swc_eng_001886 N D B L E D I N G I N T O V E R I S C H O M E R S +swc_eng_001887 A N D L T H E T O G L I D B E T W E N T R S +swc_eng_001888 F T H E S P R O B O N S W R F I C I N T L Y S A L V E A B L +swc_eng_001889 O A L O D G I C A L E T I N +swc_eng_001890 R O K W H E N F L U S H E D +swc_eng_001891 I N C L U D I N G J E R M N L T H O I C E I C N O L G Y +swc_eng_001892 P E R I N E O F L E T H E S H U S O R B O T +swc_eng_001893 N A T E N S I C T Y T H R +swc_eng_001894 M A N I U F E C T U R S S H O C A Y P O D A C K E S O L S Y S E L +swc_eng_001895 O T H E F I R S T N O N S O R I A C H A L L N G E R S I N C +swc_eng_001896 P O N E N T H A S O N L Y T H E C I N G A N D +swc_eng_001897 M A I N A R T I C L +swc_eng_001898 O W N D S E R T A N L A T H S U S F U L F O R F I T I N +swc_eng_001899 T A P E I N T H E S A M E F O R E F A C T E R S T H E C O M P A C T O I O +swc_eng_001900 S E N T H R W A S L A T E R C X P U N G E F R O +swc_eng_001901 R D E S E C T O C Q U A L I T Y +swc_eng_001902 I S F O R T H O U S N S I C H U N D R E D B Y S I X T F E T +swc_eng_001903 N I N T I N S E V E N D Y T H R +swc_eng_001904 R A L A P L A Y E M A Y A L S O L O U S E B Y R U N +swc_eng_001905 U B L I K H L T P R F E S E R G R G R Y S T O A C K P O I N +swc_eng_001906 B R O N W A S A L E C T E D T T H E H O U S E O R E P R S E N T I V E S F O R T H R E N O N C O N S E C T I E T R M S +swc_eng_001907 O E G I S T H A P L Y W I +swc_eng_001908 A G R O P O F M E L E S T H A T R A C +swc_eng_001909 N T H E W O L D S L A G I S T +swc_eng_001910 B R E D I N G T A K X S P L A C E B E T W E E N A P R L A N D J O N +swc_eng_001911 S T R A L O R I S A T T H E S O T H E N I N D +swc_eng_001912 T E C N A L O U G I C A L S I N G U L A R I T Y I S P O S E B L +swc_eng_001913 N L U D I N T H E L E P Y C O D +swc_eng_001914 S E N T Y F O R E H A D A H I Y E R G I C A T I O N C U L F I C A T I O N C O M P E D T +swc_eng_001915 I A C K E R S W E N T H Y P O N E S C I N G I S A N +swc_eng_001916 O N C E V A T I O N I U S T R A Y A +swc_eng_001917 I S T H S E L A M A N D O F F +swc_eng_001918 F I R S T S E L F D I S C R I V E R A N S H U M I N E S T B E A T F O R M I L Y I N T H E E A L diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.3/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.3/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..51a77057e48949d857538b8f3c06e971c7766c0a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.3/1best_recog/token_int @@ -0,0 +1,273 @@ +nchlt_eng_001727 2 8 7 20 13 5 8 9 10 2 21 3 9 16 8 18 6 14 9 4 9 +nchlt_eng_001728 2 19 6 14 7 8 4 3 12 2 9 4 5 4 3 9 2 18 3 12 3 11 5 13 +nchlt_eng_001729 2 18 5 12 11 5 13 12 2 11 3 9 3 11 6 23 3 2 5 16 4 +nchlt_eng_001730 2 17 8 13 19 15 2 10 8 7 11 19 2 10 3 11 5 9 6 7 +nchlt_eng_001731 2 16 13 5 21 2 21 13 5 19 2 16 10 6 4 +nchlt_eng_001732 2 21 5 9 9 6 7 20 3 11 2 11 5 13 3 2 9 6 23 8 16 9 3 9 +nchlt_eng_001733 2 5 7 16 10 5 7 2 15 3 9 9 5 12 6 11 7 4 10 8 6 7 2 26 3 7 11 5 13 9 +nchlt_eng_001734 2 16 6 7 20 2 5 16 4 8 6 7 12 2 9 3 7 4 5 15 5 +nchlt_eng_001735 2 20 14 7 2 21 6 14 12 3 5 2 21 11 21 8 13 3 7 4 2 19 6 14 9 3 12 +nchlt_eng_001736 2 13 6 17 2 9 4 2 8 7 5 20 3 2 9 4 5 8 20 4 +nchlt_eng_001737 2 16 5 13 7 12 3 11 2 19 14 11 6 9 +nchlt_eng_001738 2 15 5 20 26 3 11 2 8 7 4 6 7 5 9 8 7 5 13 3 2 3 5 21 6 11 4 3 9 +nchlt_eng_001739 2 4 6 4 13 2 18 6 11 9 3 2 5 16 4 8 15 +nchlt_eng_001740 2 13 6 9 4 13 3 9 9 2 12 5 4 3 2 16 6 15 21 11 8 4 8 6 7 +nchlt_eng_001741 5 2 20 11 3 5 3 24 10 5 +nchlt_eng_001742 2 8 7 2 23 6 11 2 15 7 4 5 13 2 21 11 6 4 8 16 4 8 6 7 2 5 12 20 5 7 16 9 3 +nchlt_eng_001743 2 15 5 7 19 2 4 6 22 8 9 16 5 13 9 2 20 11 8 4 8 6 7 +nchlt_eng_001744 2 5 7 16 10 6 5 7 2 9 8 4 19 2 21 8 4 10 14 7 12 3 11 +nchlt_eng_001745 2 9 15 5 13 2 6 4 10 3 12 5 16 24 2 9 8 7 5 20 6 20 +nchlt_eng_001746 2 13 6 12 20 3 9 2 15 4 11 21 8 13 8 5 7 2 3 11 8 5 11 9 +nchlt_eng_001747 2 4 8 4 5 13 2 11 3 13 8 20 2 19 3 6 2 11 3 15 6 7 6 +nchlt_eng_001748 2 3 20 5 15 21 13 3 9 2 5 7 12 2 16 13 14 12 2 10 5 18 2 15 6 7 +nchlt_eng_001749 2 19 14 7 6 4 2 8 12 2 9 4 5 4 3 2 15 5 7 4 5 8 7 3 +nchlt_eng_001750 2 22 6 13 12 2 11 3 21 11 3 9 3 7 16 3 2 15 3 5 25 9 8 15 5 +nchlt_eng_001751 2 9 8 7 3 9 18 16 8 6 7 2 6 11 4 10 3 9 +nchlt_eng_001752 2 6 11 12 7 5 11 19 2 12 8 18 11 3 7 9 10 5 13 2 3 16 14 5 4 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6 7 8 9 5 4 8 6 7 9 2 4 10 3 2 14 7 8 12 3 12 2 9 4 5 4 3 9 +nchlt_eng_001769 2 19 9 11 8 13 2 4 10 3 2 18 3 7 9 2 18 6 11 9 3 9 +nchlt_eng_001770 2 6 11 2 12 11 15 4 8 16 24 2 9 5 7 12 2 11 3 9 3 23 3 +nchlt_eng_001771 2 22 11 7 12 9 17 8 16 24 2 9 3 4 10 7 2 11 5 13 17 5 19 +nchlt_eng_001772 2 5 16 4 11 9 2 5 16 5 4 12 8 15 8 5 2 17 6 12 +nchlt_eng_001773 2 21 3 21 13 2 18 11 6 15 3 2 4 6 16 24 8 5 4 12 +nchlt_eng_001774 2 18 6 11 2 16 10 5 13 12 9 2 9 8 7 20 5 +nchlt_eng_001775 2 23 3 5 11 19 8 5 22 13 2 23 13 18 10 2 4 5 11 15 8 7 20 +nchlt_eng_001776 2 9 6 14 4 10 2 17 5 13 3 9 2 23 3 13 19 3 9 +nchlt_eng_001777 2 16 5 13 8 18 6 11 12 19 3 5 2 9 4 5 4 2 19 14 2 4 10 3 23 3 11 9 8 4 19 +nchlt_eng_001778 2 3 13 12 3 11 6 12 6 +nchlt_eng_001779 2 6 14 4 2 12 6 11 3 2 6 11 2 8 7 4 3 12 2 9 8 4 19 +nchlt_eng_001780 2 16 13 5 15 3 12 2 21 5 11 9 10 5 13 2 11 3 9 21 6 7 16 9 5 22 8 13 8 4 19 +nchlt_eng_001781 2 16 11 8 9 4 10 6 7 2 4 3 11 15 9 +nchlt_eng_001782 2 3 23 3 7 9 2 4 6 24 2 21 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23 8 20 12 2 5 9 +nchlt_eng_001798 2 9 8 7 9 18 16 4 8 6 7 2 6 22 13 3 13 3 9 +nchlt_eng_001799 2 9 8 7 3 9 2 18 8 16 4 8 6 7 2 18 14 13 3 15 +nchlt_eng_001800 2 9 14 22 2 16 8 4 2 9 6 15 3 2 21 11 6 22 13 6 15 +nchlt_eng_001801 2 3 5 9 4 6 7 2 7 6 11 4 10 2 5 15 3 11 8 16 24 5 +nchlt_eng_001802 2 21 3 21 3 9 2 17 8 4 7 3 9 2 13 6 14 4 8 7 20 +nchlt_eng_001803 2 12 8 9 4 8 7 20 4 8 23 3 2 23 6 16 5 13 2 8 7 9 4 11 15 3 7 4 9 +nchlt_eng_001804 14 2 5 3 18 11 8 16 5 7 2 5 2 15 3 11 8 16 5 7 2 11 5 21 8 3 9 +nchlt_eng_001805 2 21 6 11 2 4 10 6 20 3 9 2 20 3 7 5 13 9 +nchlt_eng_001806 2 8 7 4 6 2 7 3 9 8 7 5 13 3 2 8 5 21 6 11 4 2 8 12 4 19 2 5 19 +nchlt_eng_001807 2 15 6 14 7 4 5 7 2 11 5 7 20 3 9 2 6 18 2 22 3 13 8 23 8 3 5 11 +nchlt_eng_001808 2 18 11 3 7 16 10 2 5 11 3 18 6 14 11 9 +nchlt_eng_001809 2 9 2 9 17 6 21 11 5 22 5 13 2 5 21 3 11 3 7 16 9 +nchlt_eng_001810 13 6 7 20 2 4 11 3 23 13 8 7 20 2 21 5 8 3 9 +nchlt_eng_001811 2 12 8 9 4 11 8 16 24 4 2 16 6 5 11 4 2 26 6 14 12 20 3 10 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NOTHE CUS +swc_eng_002005 ONTITUNCEY OF FAVESHOM +voxforge_eng_000874 THE FORTH AND FITH DAYS PASE WIHOUT ANY DEVELLAMENCE +voxforge_eng_000875 THEY NOW THE REPORT +voxforge_eng_000876 SUCH THINGS HAD ACURD BEFOR HE TOLD FILAP +voxforge_eng_000877 THEY ONLY HAD A LITLE TERDY THOUSEND DOLER FIER +voxforge_eng_000878 I AM GOWING TO GET IT OWD +voxforge_eng_000879 HOU DUDLY HE MAINTAINED A COARME AND SMILING ASSPECT +voxforge_eng_000880 JON LOKE TRIUMPFENTLY AT SHELDON WHO BOWD +voxforge_eng_000883 OME ONEN T DILE MAR T TALENCST +voxforge_eng_000884 E IT WAS BEATING AND WATING IN THE AMBOSH OF THOSE BLACK PITS +voxforge_eng_000885 IT THE GO OUT AND EWIG MY BOYS +voxforge_eng_000886 SHE WINED DOWN IN WINES DREME M SERCHING THE SHADOS OF BULSHORS +voxforge_eng_000887 I JUS TO APRECSHATE WITH OUT BE AE O EXPRESE MY FELINGS +voxforge_eng_000888 SHE DOSNT NOW WHAT HE IS TAKING ABOUT +voxforge_eng_000889 YOUR FATHERS FIFT COMAND HE NOTED +voxforge_eng_000890 DON OU SE I HAE YOU 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ISOUS +voxforge_eng_000907 AS TO BE UNDISTINGUIHABLE FROM THE VAST WHIHE PLAINS AROUND +voxforge_eng_000908 HE WOULD DESTROY AL THINGS THA ER FICXST +voxforge_eng_000909 THE RUSION USIC PLAR THE CONT WAS HROABEDINSLAVE +voxforge_eng_000910 TO HIS SPRIE HER ANTE WAS FLAT AND UN COMPROMIZSING +voxforge_eng_000911 THIS SOULD BE INTRESTING +voxforge_eng_000912 I AM A FRAIDE I DONT HAVE MUCH TIME +voxforge_eng_000913 CRISMS IS AN EASY PROBLOME COMPARD WIT A POLNASION GIVING FECST +voxforge_eng_000914 THE PLNTERS AR ARDY CONSIDERIG TH MATER +voxforge_eng_000915 JON CRIED WITH SHING EIYES +voxforge_eng_000916 WHO EVER LIVED ON THE RANCH DID THAT +voxforge_eng_000917 WE LEAVE THE EFVENTUALITY TO TIME AND LOR +voxforge_eng_000918 AT THE SAME TINE SPIARS ND EROS BEGAN TO FAL AMONG H IMVATERS +voxforge_eng_000920 IT IS MEARLY THE SIMPLE SOUPELITIF +voxforge_eng_000921 IN STEAID HE ARIVED ON THENIH OF TE SECAN DAY +voxforge_eng_000922 IN HIS ANGSITY AND SULISITUOD AND LOVE THEY DID NOT COUNT +voxforge_eng_000923 GOD BLESSHM I HOPE L O ON SING THEM FOREVER +voxforge_eng_000924 YO WERE INGAGED +voxforge_eng_000925 THER LACES WAS OF A DELICKEIT IVERE CALLOR FRAINETOMPTINTIN WITH EALOL +voxforge_eng_000927 IT WA THE SAME WAY WITH OUR REVOLVERS AND RIFALS +voxforge_eng_000928 HE KING HAD PROMISTO INCQUIRE ITO THE MATER +voxforge_eng_000929 DOS THA LOK GOODT +voxforge_eng_000930 FOR THE FIRST TIME IN HIS LIFE HE WAS YEARNING FOR ASCRAP +voxforge_eng_000931 I DEFIE ANY MAN TO GET A SOLAMON ILENDES SOR IN CALFORNTHEA +voxforge_eng_000932 HER IY SMOULTE STR AT HIM AS HE CAME OF THE BANGK +voxforge_eng_000933 ETY WAVE NONS SAL LICGDTHA +voxforge_eng_000934 MEN WHO NDEURIT CAL AT LIVING DEATH +voxforge_eng_000935 MTO SON WHOSED THIS BOK CEPER RODGERS +voxforge_eng_000938 I ONLY READTD D H THE FOARTATIONS +voxforge_eng_000939 THE WAS POPER DE VISION OF NAYBER I THE WORE THE INDEVRIGULY POPOARMED +voxforge_eng_000940 IALPEL YO THE LIBRARIN SAID WTHA RIHT FACE +voxforge_eng_000942 I SW MTR PUIAGNORD HIS HEAD GRIMLY IN SERCASTICLY +voxforge_eng_000943 THE RING OF THE BIG BILE AROUSD HIMN +voxforge_eng_000944 OR THE SCRACH OF A PIN ON A MANS HEAD VAST REAGONS OF THE ERTSEIRFIS REMAINE JEALOUGICLY UNON +voxforge_eng_000945 HE HAD BURDILY ENTERDAD DIS WHEN HES SOUOWD THE GLO OF A FIR +voxforge_eng_000946 THENES CHARS THE LIT COMAND +voxforge_eng_000947 IT WAS JEAN SINING SOFELY OER BEYON THE OACKS +voxforge_eng_000948 OFLING AROW BUSTD BETWEN US +voxforge_eng_000949 HATREIT AND MURDER AND LOUST FOR REVENCH THEY POESESTD TO OFER FLOWING +voxforge_eng_000950 THT YOUCUD HERE AL UPAN DON TE IMPOPOE +voxforge_eng_000951 IT WAS MY A DEA TO ATE +voxforge_eng_000952 SHE DOSNT WON TO WIN +voxforge_eng_000953 SHE HINKE IT IS BECAS HE WONSE SOMTHING ELTE +voxforge_eng_000954 HE PULLED AND THE LOK CRESET DOWN TO BRAKE HIS BACK +voxforge_eng_000955 THAT THE SOCALD FORSES AT WORK IN LIGHT HEE ALCTRISITY AND MAGNATISM +voxforge_eng_000956 HE TORND SHARPLYD AND PICE GRAGSIN ACOST THE PIVELER +voxforge_eng_000957 AL SO I WANT INFRMATION +voxforge_eng_000958 THE SIXT DAY HE SPENT IN THE CAVEN WIH GREAGSON +voxforge_eng_000959 ON THIS IPOTHICES THE HAMERING OF THE LTR MUNDYING CORPUSLES ON THE BOB CONFIRSE ITS CANATIK NEGY ON THE ON HAND +voxforge_eng_000960 NOW A FIRNY WILWE STREME AND EVER AN ANON YOU AMURGE FROM AL THE GROVES AND FLOWERS +voxforge_eng_000961 WITH OUT IT THE MOS DENSELY POPULATED RAGONS OF MOTEN YURP AND AMERICA +voxforge_eng_000962 TOMESPINKE HAS A HARPOON +voxforge_eng_000963 HE WNTE GE THE INISH T THIS FOW AREYSOFAGON +voxforge_eng_000964 LKE A FLASHE LONCED IMSELF INT THE FETHED MAS OF THE HOWL +voxforge_eng_000965 IT CONTAINES A TOTLE OF T WENTY ENTRES +voxforge_eng_000966 I HAE HELT MORE COMFORTABLE +voxforge_eng_000967 THA A POSSES TO MACH VATELITY +voxforge_eng_000968 THE WALF DOGE THRESD HIS GONT MUSALE TOWARD HIM +voxforge_eng_000971 THE GAVBIAL VOICE OF HE SMRIY RANG OUT +voxforge_eng_000972 IT WAS O RIVER ND MARGING LIAKE ORSELES FROM THE REAT SWOMP +voxforge_eng_000973 SAID THE MAL PULING HIMSELF TOGETHE IHA EFART YO MUSTHING ME VERY ROD +voxforge_eng_000974 IN WHAT BEUCOLICK SCOUO OF FENCE HE HAD BEN TORT WAS BE OND IMAGENING +voxforge_eng_000975 HAD NOT INABLED IN VESTIGATERS TO OB TAINE A COMPRITIVELY LITL COSET +voxforge_eng_000976 A TRICL OF FRESH BLOUD RAN OVER HIS FACE +voxforge_eng_000977 IT WAS A CURUS COINEITDANCE +voxforge_eng_000978 IT IS THE FIRE PARTLY SHE SAIDN +voxforge_eng_000979 THE JUST LAY OF IN THE OSH AND PLOUKED AWAYAN +voxforge_eng_000980 I NO THAT OUWER IN CHARDGE THERE AND GEE NOSE +voxforge_eng_000981 FOR TIE THE EXSITING THRILE OF HIS ADVENTUE WAS GON +voxforge_eng_000982 FUDNLY HIS FINGERS CLOSE THIDLY OVE THE HANGAOCHIF +voxforge_eng_000983 DEAR SIR YOR SECKANT VICTOM HAS FOLLON ON SCEADGJUALE TIME +voxforge_eng_000984 HE CN CAE FR IMSELF +voxforge_eng_000985 EACH INSULT ADED TO THE VOLOU OF THE CLAIME +voxforge_eng_000986 THOU IT MAY BE TRANSFORMED INTO ANY N OF THE FORMS OF WHCH ENRGY IS SESEPTIBL +voxforge_eng_000987 MESITDOES SCREAMED GRIED LOAF I MANYFESTED THE HIRADTICK ANDBOUN DHEN MENT OF HISTADIAR +voxforge_eng_000988 I WAN TO NO HOW ALL THIS IS POSEIVBLE +voxforge_eng_000989 PRENTING A SIMPL AND INSTRUCTIV ILUSTRATION OF THE STRGL FOR LIFE AMNG THE RIVELE SPEACES +voxforge_eng_000990 HILL NEVER DO A TAP OF WORK THE HOL VOYAGEH +voxforge_eng_000991 I HAE HUNTED ALONG THIS RICE REPLIED FILIP +voxforge_eng_000992 LORD BUT IM GED TO SE YO AGIN FIL +voxforge_eng_000993 HOVELINLY I WEN DADED THAF RS TA +voxforge_eng_000994 THE AR OT REGULE OSTER PIRETS NICLES CONTNED +voxforge_eng_000995 THE MST BE HRDING FOR BUSNES BUT I THUG YOU MIGT WAT T TAKE LOK T THER SIGHT +voxforge_eng_000996 THER WAS NO CANCE TO FIRE WITHOUT HINING HIM +voxforge_eng_000997 AS FOR HIMSELF WONT THE STREAE RAL WAY ARNINGS INCREING SADLY +voxforge_eng_000998 DON HIM CAN YOUR BOY GO LONG WIT ESSY +voxforge_eng_000999 GOLD BY PEAR HE SHOWTED +voxforge_eng_001000 BUT SUCH A DEVERDGIENS OF APINION WOULD CONSTITUT NO MENENCE TO SOSCITY +voxforge_eng_001001 T THERE WAS ONE CHANCES AND ONLY ON OF SAVING JONT +voxforge_eng_001002 I I CAN OT FOLOE YO SHE SAIND +voxforge_eng_001003 ON THE FAR CORNER OF THE COMPOUND FENTS A WHAOK BREADED +voxforge_eng_001004 THEN AGIN TOTER HAD SUC A IRITATING WAY ABOUT HIM +voxpopuli_eng_000494 WE ALL NOW OMAN AS A SUCESFLE STABL CONTRY AROL MOR THERFOR THAT FOR THE HOL REAGON +voxpopuli_eng_000495 THEREFOR ITS HIGH TIME OU COME FORBOD E THE PROPOSAL FOR REVEU BE DANOPRAIONAL SUPERACION OF THE OARDIT AND NON ADITSERVISIES UNDER A DIECT EAUS OBEITISON +voxpopuli_eng_000496 IT ISCKEARE THAT WE HAVE NO TIME TO WAST THE NUERESOLTS OF THEE I PEESHE RECARD N SIENTIFIC BACSES OF GLIMIT JAINSE LEVE NO ROUOME FOR HESITDASON +voxpopuli_eng_000497 SENT SO IN THE CONTAINER WHIHAEVER AEN TUCHED COME SLAVES COUNTEOFET GODS DRUGS IT SETR +voxpopuli_eng_000498 I HOPE THAT COMIONS MOBIT INESHES INISIFIVES HO ONT CRAT THE NEXT PROBLOM BUT WILL BE A ANSER FOR EXISTING CHALINGES OF THER OUT TANSPORED SECTO +voxpopuli_eng_000499 IN THEWUE IT WASA DICION TAGNAULY BY ONE PRSON THE ORMER PRESIDENT O THENIGDED STATES AGANCE THE ATICULATED MCRATIC DUMAJURITY O TH EU ES CONGRES BY ALL OF ITS REPUBLICKEN ND SOM FITS DEMECRATIC T DEMACRAT MEMBERSIT WASAN AGREMENT WITHOUT ANY BINDNG OBLIGATIONS AT HE LEDES OF ERUN VERY UPANLY ANPRESIDH MAPTLY NTHE ERY DAY THE SOCALD DEL WAS POULISHE +voxpopuli_eng_000500 FRE SPEACH IS ASENIUALY AEXETIG THT PEOPL ARE FREE TO SAY THINGS WE DO NOT LIK NOT MELY FREE TO SAY THINGS WE DO LIK +voxpopuli_eng_000501 HAT IS LURNE FOM THIE +voxpopuli_eng_000502 BE SIN THAT THE NVIMENTAL EFFECT OF PRODUCS MUST BE AVRY INMPORTANT ISUEIN HER EEWU AND THE WOL I DEAE O THE ECULABER GIVS A VER YUSOULORIANTATION FOR THE OUSUMERS OF COUS HE ECULABER HOULD GIVEN TO THE MOST ANDVIRMENT AF FANDY PODUCT THE INFORMATION SOULD BECLEARE AND CUE +voxpopuli_eng_000503 HOWEVER THE CARENDRYGEM NEDES TO BE BETERD ALORDT TO TH IGIDAL INVIRNMENT TO ISHURE FAR MINERATION TO GREATERS AEN TO ONFOME TO ONSUMER EXPECTATIONS +voxpopuli_eng_000504 AT CASE BY THE CMION AND MEMBER STAT TO NHANE THERSUPORT TO RECONCILIATION TO SECUR PESE AND TIBILITY AND ARLAND IWOL THEREFORE ARD YU CALIES TO PLEASE SUPORT IS AMENMEN +voxpopuli_eng_000505 TRATAGICK CHOICES ABOUT WHE TO E WEST MUT BE MADE NOW TAKEN IN E COUN A NE TO FAS OUT FOR SILFUL SUPSITES BUT TAK THE GAS AS I ORSOFYU IT CAN BE A HELTFULE BRIGING TRUNSISHONARY MEDIOM TO BE USE IN MEMIN MENY MBERSTAT I BE ONTO EACHIVE OVER AMBISHIOS CLIMITARGITS +voxpopuli_eng_000506 WE AE POSEILY FR A OLE WE CAN CUTH TO PRASCUE THE SAMEM POLICES IN TH SAME MANER NOWING THAT WE LEDE TO THISAMPRSOS THE RISAULS THA WENO DEDEA +voxpopuli_eng_000507 UT HER SANOPTION B +voxpopuli_eng_000508 WRE ALL SO NED A CHAINGE IN OR IDOLITIE +voxpopuli_eng_000509 A LADEH BAT OF THE REASON OF COURSE IS ILIGALFISCINGK AND THERE OFOM P TDON OFEN BY YARR VESES WHICH ARE REAGISTERD TO COUNTRES WHICH LUCKE THE WIL OF THE RESURCES TO NFORST INTHE NESINAL AGREMENS NO MOUNT OF TRESABIITY MESERS ORE EXTRPAPREWARE WIL ADESE THE PROBLOUME OF REDUSING +voxpopuli_eng_000510 THE COMPRMISE ALSO INCLDED KLARERUDS TO THE FINE WHICH MBERSTATE AS HERSTICTION AND THE OPRATION ITHIMBERSTATS CONERD FOR CRUSBR THE CACES ASILA THE NED TO EINVLLF YOUR JUST THAN YOF OR WORK AND PLAE OU SEUPORT TO MO HIS ERECTIV +voxpopuli_eng_000511 NO THE RENS WOULD HAV AS BELETHATHE AR BAD BES CRIMINAL BES DELIBEATLY CONTAMINATING HUDY WITHA DANEUS NGREDIENT BUT IT FACT INFAC HE DINGWHT HUNY BES AR AL HVE ALWAS DON WIH TO CARY POLON BAC TOTHER HIVESTOD TO FED THER OUN +voxpopuli_eng_000512 UT IT WAS THE CONTRY ITSELF BENG MOR CAPABL +voxpopuli_eng_000513 R INTO THE PRT FOLIO OF THE NEUW COMIONAR DELING WITH FUNDEMENTER RITES +voxpopuli_eng_000514 THE MESIYGI TAT THE OU DODT NAT HAVE AN NOURSOLUIONS +voxpopuli_eng_000515 AR YOU WILING TO ACT INERE FAVER FOR THE SOSIAL DEMENTION TO BE INCLOUDED IN THE EU COMPATENCSES AS PROPOSE +voxpopuli_eng_000516 A NEXTHAT ON PESPECTRUPOLIES TAKIN WITHE EFORM OF OUER TELICON TH FRAM WOR +voxpopuli_eng_000517 I BELEVE HIS REMARKS WER A EXPLICITLY RACEIST AND THEN AFOBICK AND PRMOTED RACIAL INTOLERANCE IN A WAY THA IS NOT CXCEPTIBLE OR ALOWD IN TE CONTITUTION OF THIS HOUS +voxpopuli_eng_000518 REAL IFE GAMPL SHO THAT SOLVING ITIES RELATE TO ADUCATION FEULED STRONGCOMINIT DEVELOPMENT +voxpopuli_eng_000519 SI HOPE THA TIS ILHVPE ORUSHA AS WEL ND THAT RUHA CAN ALTS AND VISAIG ND EXTREME SUCESS TORY AFTER THS EG TISIGNIFICAND AT IN ORGST THIS YEARB +voxpopuli_eng_000520 SHE ECXEPTED THE FACT THAT SITISON SHIP IS AY NASINAL PART OF THE OSINO GUDISDICTION BUT HYOURLSO SAID THAT ACOURDING TO THE MASTRICK TREATY AND SHE AS RIGHT THE HAS TO BE ADIYREC LIN +voxpopuli_eng_000521 TDEY WOU FALD ESPECIAL EAN THE MST RATING AUNIFIED AND T AFFISHENT APPRORCH TO LIE MITCANGHE TREATMENT ASWEL AS IN STRANTHANINGK ITS LEDING POLITICAL COSION IN DISAGENDER I CONSCITHER THERFOR TAKING THISRESOLUTION AN ACT OF UTMOST IMPORTANS +voxpopuli_eng_000522 THE UNIGTED STATE OF YURUO WIL BE A FACT WITH SWEDON AS A PROVIDENC +voxpopuli_eng_000523 IT MUS B THE CAPITALE OF BOT THEATES AND WE MUS RECONISE POLSTINIS THAT AS PROVIDED FOR IN THE OVE LOGREMENCS +voxpopuli_eng_000524 YOU CRAINYS FACE T WITH WONE OF CRUSAL CHALINGES IN ITS HISTORY IT WOULD BE FU TE MENTARLY RONGK TO PRE THE NATION NOW WIT AL THIPES OF RESTRICTIONS POPELADERL CALE OSTERITE POLI +voxpopuli_eng_000525 MORE RULS AND REGULATION WILL NOT IMPROVE THIS CITUATIO +voxpopuli_eng_000526 AT LEAST WE WOLDLIKE TO NOW THE SOURSE OF THE MONY AND THE POSIPL MORTIE +voxpopuli_eng_000527 TO WEROF THOSE YURUPIN WALE LANGIASH IN TO THES GLUBELICED WEARLD IS INT TO THEYSGOBELISDECONOM IN DHIS GOBE VILACH WHICH IS GORSTIALY CONOMICK SOSIAL ELNPLITICO ITS AR MOST VELABLE ESTHERT FROM THEINTIRE E YOU THAT WE MUST THAK FOL ACOUNS AND T +voxpopuli_eng_000528 WEAVE TO REPETE THAT AL THE AY ANOT BE USE TO FINANS SIURIT EXPANCES BARTHERS CONTROL OR MLITRY SOPORNT +voxpopuli_eng_000529 THN THE SINTIFI REPORTS BECOE MRE MORE URGENT OR ALARMING AND MOR SHOCKING +voxpopuli_eng_000530 FINALYM WHEN WEAT THINKING ABOUN THER INOVATIVE FINSION INSTOUMENTS WHEN OU THE BOLTH FOR OURSELS TOR SUPOART OWER A CONOMES BUT ALS SO TOOE SUPORT THOS HOERE INEAET +voxpopuli_eng_000531 THT IVE A SO YUNIEK DOLL IN PE MAKING +voxpopuli_eng_000532 PAPER A VERYD WEEK PROPOSL +voxpopuli_eng_000533 SRUSHAS ALWAS BE A VERY PROUDNATION WITH RICH COLTCUER WITH INVENTIONS WITHAN AS PL +voxpopuli_eng_000534 ARTACXATIN EVEN A MODICAL OF TACXATION IN SOME CACES MIGH JUST HELPUS EM TO DO WHAT IVEAREDY SUEGESTED AN WHO NOSE MAKE THE CACE FOR THE RETRESPECT OF BANKRE CAPIDLIZATION THAT WE NEVERSO +voxpopuli_eng_000535 THEROPE AN ASILOM SUPORTOFHIS MOR OVER AS AMONG ITS THASTS TO PRMOUTD FESILYTAT AND COURDINAT EXTCANGES OF INFORMATION AND OTHER ACTIVEITES RELATED O ELOCATIN WTH IN HE UNION +voxpopuli_eng_000536 HE ONUSO OF THE FRAMEBORK AGEMENT PROVIDES A LIGLY BINDING INSTRMENT TO OBGRAT AND STRANTN EU OSTRALIA BY LITHRRATIONS AND TO INCRESCOPERATION +voxpopuli_eng_000537 THEREFOR WEAEASTIN THE COUSAL AS GMION TO RESENTHA HAS BALE THA OULD BE THE SESTMENT OF THE EBACT OF THE RICIS +voxpopuli_eng_000538 IN OTHE WORDS THE OBJECTION IS NOT WHETHER MONEY IS PAD OR NOT THE OBJECTION IS WETHER TER IS A DIDECTLINK ORNO +voxpopuli_eng_000539 TO THSTINGUISHES THE TO MAN OEAR YOUMER RIGT AB BUSE BY THE CADANT GORMENT AND THEDLANIAN NUCLAPROVGDM +voxpopuli_eng_000540 YESS MATHMDRUO THANKATHR SECTIAL HERASDMENT IS A FORM OF VILANCS AND IT ISTHE MOST EXTREAME FORM OF GNTERBAETH DISCUMINATI +voxpopuli_eng_000541 WE CAN LOK TO SOME URAN LIN OU MEMBERS FOR OUOD GXAMPLES AS REGARDED THGNOLIGE +voxpopuli_eng_000542 YIMNVALLVED FOR THER POSITEVE AND COSTRACTEIVE ABROTCH +voxpopuli_eng_000543 O I HOP THAT ISWIL BE COMPLEATED EAR IN HE FACIVIL FUTUAR THAT MANES MA BE TO AFRE MONS +voxpopuli_eng_000544 OR FORDER NDCOURDSHTHE YOU HAND EFORTS TO BRING AMONGK PES IN OF GNISTAN AN TO OVERCOME THEF FRASILE SICUITY ANVIRMENT IN THE CONTRY +voxpopuli_eng_000545 BE ANDER STANT THAT SOME PEOPEL AR ANGRY +voxpopuli_eng_000546 ON TO BE MRESTPONCIVEL +voxpopuli_eng_000547 WE MUST EDACTIFIETH THIS SUTIATION AND VEASK THE OMION TO CONCSIDER THE MOST EDICUIT COMBINSATION MESES FOW PASNGES +voxpopuli_eng_000548 THE OMITION INBVIHED THE YUROPIANT PULAMENT IN THE UPCOMINCREVISION TO OPEN HIS POSITION ON THIS MATER WHICH RELY CONSED AXES TO S JUSTIS IN YUROP AND THE ENFORTMENT OF RIES GRANTED BY HE YUROPIANER YUNAN LO +voxpopuli_eng_000549 I L OM VERY MUCH TH RISOUNTION OF TOKE BETWEN THEOS RALIS AN PLESTINIONS AND SNCEIRLY HOP THAT HE WIL SUCED +voxpopuli_eng_000550 WE HAVE ACUMILATION OF PROBLENCS RESULTING FROME THE ARTIFIHAL AND DEBAGEATINGK AND VERPREVIUSYUS +voxpopuli_eng_000551 LET UST NOT BE THE MAN OF YESTERDY LNT UN BEPOL DAYS INSTHITUTIO +voxpopuli_eng_000552 T I GOULD ERLSUM TO BECOME AMBASSETHES O THE YEAR MAKNG ITS A DEARS AND ACTIVITHIS WO WIDLY NOWN A MONCSH TO YURUPEAT ITIENS AND PUTPIIPATING N EVENS BE TAT YOUROPIAN NASIONALL FOR LOK ALEVL +voxpopuli_eng_000553 SERTNLY SUCH IMPACE SESTMENT COULD PREMT SERTAN PROBLOMS SUCH AS THOS POSED BY THE ELECTRONIK IDENTIFICATION OF SHEP AND SCOTLAND +voxpopuli_eng_000554 THE CORTIS CONTENT TO SEE THT ITS WORK HAS INFORME TH DIS CHAGH ROS AND HAS CONTEBUTED TO ROPOSALS FOR IMPROVING THE FINANCAL MANAGHMENT OF VEYOUSPENDING AND BETHE TARKATING OF YOU FUNE +voxpopuli_eng_000555 REGOUTHERE CLARIE THE AND SERTANTY IS NEDED FOR THE OBLICK SECTOUR AND FOR THE INDUSTRY +voxpopuli_eng_000556 IS IT REALINOT POSIBLE TO US A ATHER HOUSING FASCILIDES WITH U PROPRE H RESEPTIN CONDIONS IN THE MEN TIME +voxpopuli_eng_000557 WHEL YOU TAKE ACION AT LAST IF NOT THEN WHEND diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.4/1best_recog/token 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O F T H E L T R M U N D Y I N G C O R P U S L E S O N T H E B O B C O N F I R S E I T S C A N A T I K N E G Y O N T H E O N H A N D +voxforge_eng_000960 N O W A F I R N Y W I L W E S T R E M E A N D E V E R A N A N O N Y O U A M U R G E F R O M A L T H E G R O V E S A N D F L O W E R S +voxforge_eng_000961 W I T H O U T I T T H E M O S D E N S E L Y P O P U L A T E D R A G O N S O F M O T E N Y U R P A N D A M E R I C A +voxforge_eng_000962 T O M E S P I N K E H A S A H A R P O O N +voxforge_eng_000963 H E W N T E G E T H E I N I S H T T H I S F O W A R E Y S O F A G O N +voxforge_eng_000964 L K E A F L A S H E L O N C E D I M S E L F I N T T H E F E T H E D M A S O F T H E H O W L +voxforge_eng_000965 I T C O N T A I N E S A T O T L E O F T W E N T Y E N T R E S +voxforge_eng_000966 I H A E H E L T M O R E C O M F O R T A B L E +voxforge_eng_000967 T H A A P O S S E S T O M A C H V A T E L I T Y +voxforge_eng_000968 T H E W A L F D O G E T H R E S D H I S G O N T M U S A L E T O W A R D H I M +voxforge_eng_000971 T H E G A V B I A L V O I C E O F H E S M R I Y R A N G O U T +voxforge_eng_000972 I T W A S O R I V E R N D M A R G I N G L I A K E O R S E L E S F R O M T H E R E A T S W O M P +voxforge_eng_000973 S A I D T H E M A L P U L I N G H I M S E L F T O G E T H E I H A E F A R T Y O M U S T H I N G M E V E R Y R O D +voxforge_eng_000974 I N W H A T B E U C O L I C K S C O U O O F F E N C E H E H A D B E N T O R T W A S B E O N D I M A G E N I N G +voxforge_eng_000975 H A D N O T I N A B L E D I N V E S T I G A T E R S T O O B T A I N E A C O M P R I T I V E L Y L I T L C O S E T +voxforge_eng_000976 A T R I C L O F F R E S H B L O U D R A N O V E R H I S F A C E +voxforge_eng_000977 I T W A S A C U R U S C O I N E I T D A N C E +voxforge_eng_000978 I T I S T H E F I R E P A R T L Y S H E S A I D N +voxforge_eng_000979 T H E J U S T L A Y O F I N T H E O S H A N D P L O U K E D A W A Y A N +voxforge_eng_000980 I N O T H A T O U W E R I N C H A R D G E T H E R E A N D G E E N O S E +voxforge_eng_000981 F O R T I E T H E E X S I T I N G T H R I L E O F H I S A D V E N T U E W A S G O N +voxforge_eng_000982 F U D N L Y H I S F I N G E R S C L O S E T H I D L Y O V E T H E H A N G A O C H I F +voxforge_eng_000983 D E A R S I R Y O R S E C K A N T V I C T O M H A S F O L L O N O N S C E A D G J U A L E T I M E +voxforge_eng_000984 H E C N C A E F R I M S E L F +voxforge_eng_000985 E A C H I N S U L T A D E D T O T H E V O L O U O F T H E C L A I M E +voxforge_eng_000986 T H O U I T M A Y B E T R A N S F O R M E D I N T O A N Y N O F T H E F O R M S O F W H C H E N R G Y I S S E S E P T I B L +voxforge_eng_000987 M E S I T D O E S S C R E A M E D G R I E D L O A F I M A N Y F E S T E D T H E H I R A D T I C K A N D B O U N D H E N M E N T O F H I S T A D I A R +voxforge_eng_000988 I W A N T O N O H O W A L L T H I S I S P O S E I V B L E +voxforge_eng_000989 P R E N T I N G A S I M P L A N D I N S T R U C T I V I L U S T R A T I O N O F T H E S T R G L F O R L I F E A M N G T H E R I V E L E S P E A C E S +voxforge_eng_000990 H I L L N E V E R D O A T A P O F W O R K T H E H O L V O Y A G E H +voxforge_eng_000991 I H A E H U N T E D A L O N G T H I S R I C E R E P L I E D F I L I P +voxforge_eng_000992 L O R D B U T I M G E D T O S E Y O A G I N F I L +voxforge_eng_000993 H O V E L I N L Y I W E N D A D E D T H A F R S T A +voxforge_eng_000994 T H E A R O T R E G U L E O S T E R P I R E T S N I C L E S C O N T N E D +voxforge_eng_000995 T H E M S T B E H R D I N G F O R B U S N E S B U T I T H U G Y O U M I G T W A T T T A K E L O K T T H E R S I G H T +voxforge_eng_000996 T H E R W A S N O C A N C E T O F I R E W I T H O U T H I N I N G H I M +voxforge_eng_000997 A S F O R H I M S E L F W O N T T H E S T R E A E R A L W A Y A R N I N G S I N C R E I N G S A D L Y +voxforge_eng_000998 D O N H I M C A N Y O U R B O Y G O L O N G W I T E S S Y +voxforge_eng_000999 G O L D B Y P E A R H E S H O W T E D +voxforge_eng_001000 B U T S U C H A D E V E R D G I E N S O F A P I N I O N W O U L D C O N S T I T U T N O M E N E N C E T O S O S C I T Y +voxforge_eng_001001 T T H E R E W A S O N E C H A N C E S A N D O N L Y O N O F S A V I N G J O N T +voxforge_eng_001002 I I C A N O T F O L O E Y O S H E S A I N D +voxforge_eng_001003 O N T H E F A R C O R N E R O F T H E C O M P O U N D F E N T S A W H A O K B R E A D E D +voxforge_eng_001004 T H E N A G I N T O T E R H A D S U C A I R I T A T I N G W A Y A B O U T H I M +voxpopuli_eng_000494 W E A L L N O W O M A N A S A S U C E S F L E S T A B L C O N T R Y A R O L M O R T H E R F O R T H A T F O R T H E H O L R E A G O N +voxpopuli_eng_000495 T H E R E F O R I T S H I G H T I M E O U C O M E F O R B O D E T H E P R O P O S A L F O R R E V E U B E D A N O P R A I O N A L S U P E R A C I O N O F T H E O A R D I T A N D N O N A D I T S E R V I S I E S U N D E R A D I E C T E A U S O B E I T I S O N +voxpopuli_eng_000496 I T I S C K E A R E T H A T W E H A V E N O T I M E T O W A S T T H E N U E R E S O L T S O F T H E E I P E E S H E R E C A R D N S I E N T I F I C B A C S E S O F G L I M I T J A I N S E L E V E N O R O U O M E F O R H E S I T D A S O N +voxpopuli_eng_000497 S E N T S O I N T H E C O N T A I N E R W H I H A E V E R A E N T U C H E D C O M E S L A V E S C O U N T E O F E T G O D S D R U G S I T S E T R +voxpopuli_eng_000498 I H O P E T H A T C O M I O N S M O B I T I N E S H E S I N I S I F I V E S H O O N T C R A T T H E N E X T P R O B L O M B U T W I L L B E A A N S E R F O R E X I S T I N G C H A L I N G E S O F T H E R O U T T A N S P O R E D S E C T O +voxpopuli_eng_000499 I N T H E W U E I T W A S A D I C I O N T A G N A U L Y B Y O N E P R S O N T H E O R M E R P R E S I D E N T O T H E N I G D E D S T A T E S A G A N C E T H E A T I C U L A T E D M C R A T I C D U M A J U R I T Y O T H E U E S C O N G R E S B Y A L L O F I T S R E P U B L I C K E N N D S O M F I T S D E M E C R A T I C T D E M A C R A T M E M B E R S I T W A S A N A G R E M E N T W I T H O U T A N Y B I N D N G O B L I G A T I O N S A T H E L E D E S O F E R U N V E R Y U P A N L Y A N P R E S I D H M A P T L Y N T H E E R Y D A Y T H E S O C A L D D E L W A S P O U L I S H E +voxpopuli_eng_000500 F R E S P E A C H I S A S E N I U A L Y A E X E T I G T H T P E O P L A R E F R E E T O S A Y T H I N G S W E D O N O T L I K N O T M E L Y F R E E T O S A Y T H I N G S W E D O L I K +voxpopuli_eng_000501 H A T I S L U R N E F O M T H I E +voxpopuli_eng_000502 B E S I N T H A T T H E N V I M E N T A L E F F E C T O F P R O D U C S M U S T B E A V R Y I N M P O R T A N T I S U E I N H E R E E W U A N D T H E W O L I D E A E O T H E E C U L A B E R G I V S A V E R Y U S O U L O R I A N T A T I O N F O R T H E O U S U M E R S O F C O U S H E E C U L A B E R H O U L D G I V E N T O T H E M O S T A N D V I R M E N T A F F A N D Y P O D U C T T H E I N F O R M A T I O N S O U L D B E C L E A R E A N D C U E +voxpopuli_eng_000503 H O W E V E R T H E C A R E N D R Y G E M N E D E S T O B E B E T E R D A L O R D T T O T H I G I D A L I N V I R N M E N T T O I S H U R E F A R M I N E R A T I O N T O G R E A T E R S A E N T O O N F O M E T O O N S U M E R E X P E C T A T I O N S +voxpopuli_eng_000504 A T C A S E B Y T H E C M I O N A N D M E M B E R S T A T T O N H A N E T H E R S U P O R T T O R E C O N C I L I A T I O N T O S E C U R P E S E A N D T I B I L I T Y A N D A R L A N D I W O L T H E R E F O R E A R D Y U C A L I E S T O P L E A S E S U P O R T I S A M E N M E N +voxpopuli_eng_000505 T R A T A G I C K C H O I C E S A B O U T W H E T O E W E S T M U T B E M A D E N O W T A K E N I N E C O U N A N E T O F A S O U T F O R S I L F U L S U P S I T E S B U T T A K T H E G A S A S I O R S O F Y U I T C A N B E A H E L T F U L E B R I G I N G T R U N S I S H O N A R Y M E D I O M T O B E U S E I N M E M I N M E N Y M B E R S T A T I B E O N T O E A C H I V E O V E R A M B I S H I O S C L I M I T A R G I T S +voxpopuli_eng_000506 W E A E P O S E I L Y F R A O L E W E C A N C U T H T O P R A S C U E T H E S A M E M P O L I C E S I N T H S A M E M A N E R N O W I N G T H A T W E L E D E T O T H I S A M P R S O S T H E R I S A U L S T H A W E N O D E D E A +voxpopuli_eng_000507 U T H E R S A N O P T I O N B +voxpopuli_eng_000508 W R E A L L S O N E D A C H A I N G E I N O R I D O L I T I E +voxpopuli_eng_000509 A L A D E H B A T O F T H E R E A S O N O F C O U R S E I S I L I G A L F I S C I N G K A N D T H E R E O F O M P T D O N O F E N B Y Y A R R V E S E S W H I C H A R E R E A G I S T E R D T O C O U N T R E S W H I C H L U C K E T H E W I L O F T H E R E S U R C E S T O N F O R S T I N T H E N E S I N A L A G R E M E N S N O M O U N T O F T R E S A B I I T Y M E S E R S O R E E X T R P A P R E W A R E W I L A D E S E T H E P R O B L O U M E O F R E D U S I N G +voxpopuli_eng_000510 T H E C O M P R M I S E A L S O I N C L D E D K L A R E R U D S T O T H E F I N E W H I C H M B E R S T A T E A S H E R S T I C T I O N A N D T H E O P R A T I O N I T H I M B E R S T A T S C O N E R D F O R C R U S B R T H E C A C E S A S I L A T H E N E D T O E I N V L L F Y O U R J U S T T H A N Y O F O R W O R K A N D P L A E O U S E U P O R T T O M O H I S E R E C T I V +voxpopuli_eng_000511 N O T H E R E N S W O U L D H A V A S B E L E T H A T H E A R B A D B E S C R I M I N A L B E S D E L I B E A T L Y C O N T A M I N A T I N G H U D Y W I T H A D A N E U S N G R E D I E N T B U T I T F A C T I N F A C H E D I N G W H T H U N Y B E S A R A L H V E A L W A S D O N W I H T O C A R Y P O L O N B A C T O T H E R H I V E S T O D T O F E D T H E R O U N +voxpopuli_eng_000512 U T I T W A S T H E C O N T R Y I T S E L F B E N G M O R C A P A B L +voxpopuli_eng_000513 R I N T O T H E P R T F O L I O O F T H E N E U W C O M I O N A R D E L I N G W I T H F U N D E M E N T E R R I T E S +voxpopuli_eng_000514 T H E M E S I Y G I T A T T H E O U D O D T N A T H A V E A N N O U R S O L U I O N S +voxpopuli_eng_000515 A R Y O U W I L I N G T O A C T I N E R E F A V E R F O R T H E S O S I A L D E M E N T I O N T O B E I N C L O U D E D I N T H E E U C O M P A T E N C S E S A S P R O P O S E +voxpopuli_eng_000516 A N E X T H A T O N P E S P E C T R U P O L I E S T A K I N W I T H E E F O R M O F O U E R T E L I C O N T H F R A M W O R +voxpopuli_eng_000517 I B E L E V E H I S R E M A R K S W E R A E X P L I C I T L Y R A C E I S T A N D T H E N A F O B I C K A N D P R M O T E D R A C I A L I N T O L E R A N C E I N A W A Y T H A I S N O T C X C E P T I B L E O R A L O W D I N T E C O N T I T U T I O N O F T H I S H O U S +voxpopuli_eng_000518 R E A L I F E G A M P L S H O T H A T S O L V I N G I T I E S R E L A T E T O A D U C A T I O N F E U L E D S T R O N G C O M I N I T D E V E L O P M E N T +voxpopuli_eng_000519 S I H O P E T H A T I S I L H V P E O R U S H A A S W E L N D T H A T R U H A C A N A L T S A N D V I S A I G N D E X T R E M E S U C E S S T O R Y A F T E R T H S E G T I S I G N I F I C A N D A T I N O R G S T T H I S Y E A R B +voxpopuli_eng_000520 S H E E C X E P T E D T H E F A C T T H A T S I T I S O N S H I P I S A Y N A S I N A L P A R T O F T H E O S I N O G U D I S D I C T I O N B U T H Y O U R L S O S A I D T H A T A C O U R D I N G T O T H E M A S T R I C K T R E A T Y A N D S H E A S R I G H T T H E H A S T O B E A D I Y R E C L I N +voxpopuli_eng_000521 T D E Y W O U F A L D E S P E C I A L E A N T H E M S T R A T I N G A U N I F I E D A N D T A F F I S H E N T A P P R O R C H T O L I E M I T C A N G H E T R E A T M E N T A S W E L A S I N S T R A N T H A N I N G K I T S L E D I N G P O L I T I C A L C O S I O N I N D I S A G E N D E R I C O N S C I T H E R T H E R F O R T A K I N G T H I S R E S O L U T I O N A N A C T O F U T M O S T I M P O R T A N S +voxpopuli_eng_000522 T H E U N I G T E D S T A T E O F Y U R U O W I L B E A F A C T W I T H S W E D O N A S A P R O V I D E N C +voxpopuli_eng_000523 I T M U S B T H E C A P I T A L E O F B O T T H E A T E S A N D W E M U S R E C O N I S E P O L S T I N I S T H A T A S P R O V I D E D F O R I N T H E O V E L O G R E M E N C S +voxpopuli_eng_000524 Y O U C R A I N Y S F A C E T W I T H W O N E O F C R U S A L C H A L I N G E S I N I T S H I S T O R Y I T W O U L D B E F U T E M E N T A R L Y R O N G K T O P R E T H E N A T I O N N O W W I T A L T H I P E S O F R E S T R I C T I O N S P O P E L A D E R L C A L E O S T E R I T E P O L I +voxpopuli_eng_000525 M O R E R U L S A N D R E G U L A T I O N W I L L N O T I M P R O V E T H I S C I T U A T I O +voxpopuli_eng_000526 A T L E A S T W E W O L D L I K E T O N O W T H E S O U R S E O F T H E M O N Y A N D T H E P O S I P L M O R T I E +voxpopuli_eng_000527 T O W E R O F T H O S E Y U R U P I N W A L E L A N G I A S H I N T O T H E S G L U B E L I C E D W E A R L D I S I N T T O T H E Y S G O B E L I S D E C O N O M I N D H I S G O B E V I L A C H W H I C H I S G O R S T I A L Y C O N O M I C K S O S I A L E L N P L I T I C O I T S A R M O S T V E L A B L E E S T H E R T F R O M T H E I N T I R E E Y O U T H A T W E M U S T T H A K F O L A C O U N S A N D T +voxpopuli_eng_000528 W E A V E T O R E P E T E T H A T A L T H E A Y A N O T B E U S E T O F I N A N S S I U R I T E X P A N C E S B A R T H E R S C O N T R O L O R M L I T R Y S O P O R N T +voxpopuli_eng_000529 T H N T H E S I N T I F I R E P O R T S B E C O E M R E M O R E U R G E N T O R A L A R M I N G A N D M O R S H O C K I N G +voxpopuli_eng_000530 F I N A L Y M W H E N W E A T T H I N K I N G A B O U N T H E R I N O V A T I V E F I N S I O N I N S T O U M E N T S W H E N O U T H E B O L T H F O R O U R S E L S T O R S U P O A R T O W E R A C O N O M E S B U T A L S S O T O O E S U P O R T T H O S H O E R E I N E A E T +voxpopuli_eng_000531 T H T I V E A S O Y U N I E K D O L L I N P E M A K I N G +voxpopuli_eng_000532 P A P E R A V E R Y D W E E K P R O P O S L +voxpopuli_eng_000533 S R U S H A S A L W A S B E A V E R Y P R O U D N A T I O N W I T H R I C H C O L T C U E R W I T H I N V E N T I O N S W I T H A N A S P L +voxpopuli_eng_000534 A R T A C X A T I N E V E N A M O D I C A L O F T A C X A T I O N I N S O M E C A C E S M I G H J U S T H E L P U S E M T O D O W H A T I V E A R E D Y S U E G E S T E D A N W H O N O S E M A K E T H E C A C E F O R T H E R E T R E S P E C T O F B A N K R E C A P I D L I Z A T I O N T H A T W E N E V E R S O +voxpopuli_eng_000535 T H E R O P E A N A S I L O M S U P O R T O F H I S M O R O V E R A S A M O N G I T S T H A S T S T O P R M O U T D F E S I L Y T A T A N D C O U R D I N A T E X T C A N G E S O F I N F O R M A T I O N A N D O T H E R A C T I V E I T E S R E L A T E D O E L O C A T I N W T H I N H E U N I O N +voxpopuli_eng_000536 H E O N U S O O F T H E F R A M E B O R K A G E M E N T P R O V I D E S A L I G L Y B I N D I N G I N S T R M E N T T O O B G R A T A N D S T R A N T N E U O S T R A L I A B Y L I T H R R A T I O N S A N D T O I N C R E S C O P E R A T I O N +voxpopuli_eng_000537 T H E R E F O R W E A E A S T I N T H E C O U S A L A S G M I O N T O R E S E N T H A H A S B A L E T H A O U L D B E T H E S E S T M E N T O F T H E E B A C T O F T H E R I C I S +voxpopuli_eng_000538 I N O T H E W O R D S T H E O B J E C T I O N I S N O T W H E T H E R M O N E Y I S P A D O R N O T T H E O B J E C T I O N I S W E T H E R T E R I S A D I D E C T L I N K O R N O +voxpopuli_eng_000539 T O T H S T I N G U I S H E S T H E T O M A N O E A R Y O U M E R R I G T A B B U S E B Y T H E C A D A N T G O R M E N T A N D T H E D L A N I A N N U C L A P R O V G D M +voxpopuli_eng_000540 Y E S S M A T H M D R U O T H A N K A T H R S E C T I A L H E R A S D M E N T I S A F O R M O F V I L A N C S A N D I T I S T H E M O S T E X T R E A M E F O R M O F G N T E R B A E T H D I S C U M I N A T I +voxpopuli_eng_000541 W E C A N L O K T O S O M E U R A N L I N O U M E M B E R S F O R O U O D G X A M P L E S A S R E G A R D E D T H G N O L I G E +voxpopuli_eng_000542 Y I M N V A L L V E D F O 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S I T I O N O N T H I S M A T E R W H I C H R E L Y C O N S E D A X E S T O S J U S T I S I N Y U R O P A N D T H E E N F O R T M E N T O F R I E S G R A N T E D B Y H E Y U R O P I A N E R Y U N A N L O +voxpopuli_eng_000549 I L O M V E R Y M U C H T H R I S O U N T I O N O F T O K E B E T W E N T H E O S R A L I S A N P L E S T I N I O N S A N D S N C E I R L Y H O P T H A T H E W I L S U C E D +voxpopuli_eng_000550 W E H A V E A C U M I L A T I O N O F P R O B L E N C S R E S U L T I N G F R O M E T H E A R T I F I H A L A N D D E B A G E A T I N G K A N D V E R P R E V I U S Y U S +voxpopuli_eng_000551 L E T U S T N O T B E T H E M A N O F Y E S T E R D Y L N T U N B E P O L D A Y S I N S T H I T U T I O +voxpopuli_eng_000552 T I G O U L D E R L S U M T O B E C O M E A M B A S S E T H E S O T H E Y E A R M A K N G I T S A D E A R S A N D A C T I V I T H I S W O W I D L Y N O W N A M O N C S H T O Y U R U P E A T I T I E N S A N D P U T P I I P A T I N G N E V E N S B E T A T Y O U 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+voxpopuli_eng_000557 W H E L Y O U T A K E A C I O N A T L A S T I F N O T T H E N W H E N D diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.4/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.4/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..2b3d43f452ac8b803a68990bf3107af404c53c35 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/logdir/output.4/1best_recog/token_int @@ -0,0 +1,273 @@ +swc_eng_001919 3 3 7 4 2 11 3 2 9 14 11 16 10 2 8 7 12 8 16 5 4 3 9 2 4 5 4 2 18 5 16 4 3 11 9 2 6 4 10 3 2 4 10 5 7 2 21 11 5 16 4 8 +swc_eng_001920 7 12 2 21 11 23 3 7 4 8 6 7 2 5 7 12 2 4 11 3 5 4 15 3 7 4 2 6 18 2 6 15 21 13 16 5 4 8 6 7 9 +swc_eng_001921 8 10 2 11 5 21 8 12 2 6 7 2 9 5 3 8 4 +swc_eng_001922 14 2 4 10 6 17 2 17 5 11 2 4 3 2 18 6 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5 7 4 7 2 3 14 2 6 9 4 11 5 13 8 5 2 22 19 2 13 8 4 10 11 11 5 4 8 6 7 9 2 5 7 12 2 4 6 2 8 7 16 11 3 9 16 6 21 3 11 5 4 8 6 7 +voxpopuli_eng_000537 4 10 3 11 3 18 6 11 2 17 3 5 3 5 9 4 8 7 2 4 10 3 2 16 6 14 9 5 13 2 5 9 2 20 15 8 6 7 2 4 6 2 11 3 9 3 7 4 10 5 2 10 5 9 2 22 5 13 3 2 4 10 5 2 6 14 13 12 2 22 3 2 4 10 3 2 9 3 9 4 15 3 7 4 2 6 18 2 4 10 3 2 3 22 5 16 4 2 6 18 2 4 10 3 2 11 8 16 8 9 +voxpopuli_eng_000538 8 7 2 6 4 10 3 2 17 6 11 12 9 2 4 10 3 2 6 22 26 3 16 4 8 6 7 2 8 9 2 7 6 4 2 17 10 3 4 10 3 11 2 15 6 7 3 19 2 8 9 2 21 5 12 2 6 11 2 7 6 4 2 4 10 3 2 6 22 26 3 16 4 8 6 7 2 8 9 2 17 3 4 10 3 11 2 4 3 11 2 8 9 2 5 2 12 8 12 3 16 4 13 8 7 24 2 6 11 7 6 +voxpopuli_eng_000539 2 4 6 2 4 10 9 4 8 7 20 14 8 9 10 3 9 2 4 10 3 2 4 6 2 15 5 7 2 6 3 5 11 2 19 6 14 15 3 11 2 11 8 20 4 2 5 22 2 22 14 9 3 2 22 19 2 4 10 3 2 16 5 12 5 7 4 2 20 6 11 15 3 7 4 2 5 7 12 2 4 10 3 12 13 5 7 8 5 7 2 7 14 16 13 5 21 11 6 23 20 12 15 +voxpopuli_eng_000540 19 3 9 9 2 15 5 4 10 15 12 11 14 6 2 4 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2 8 7 2 6 18 2 20 7 8 9 4 5 7 2 5 7 2 4 6 2 6 23 3 11 16 6 15 3 2 4 10 3 18 2 18 11 5 9 8 13 3 2 9 8 16 14 8 4 19 2 5 7 23 8 11 15 3 7 4 2 8 7 2 4 10 3 2 16 6 7 4 11 19 +voxpopuli_eng_000545 2 22 3 2 5 7 12 3 11 2 9 4 5 7 4 2 4 10 5 4 2 9 6 15 3 2 21 3 6 21 3 13 2 5 11 2 5 7 20 11 19 +voxpopuli_eng_000546 6 7 2 4 6 2 22 3 2 15 11 3 9 4 21 6 7 16 8 23 3 13 +voxpopuli_eng_000547 17 3 2 15 14 9 4 2 3 12 5 16 4 8 18 8 3 4 10 2 4 10 8 9 2 9 14 4 8 5 4 8 6 7 2 5 7 12 2 23 3 5 9 24 2 4 10 3 2 6 15 8 6 7 2 4 6 2 16 6 7 16 9 8 12 3 11 2 4 10 3 2 15 6 9 4 2 3 12 8 16 14 8 4 2 16 6 15 22 8 7 9 5 4 8 6 7 2 15 3 9 3 9 2 18 6 17 2 21 5 9 7 20 3 9 +voxpopuli_eng_000548 4 10 3 2 6 15 8 4 8 6 7 2 8 7 22 23 8 10 3 12 2 4 10 3 2 19 14 11 6 21 8 5 7 4 2 21 14 13 5 15 3 7 4 2 8 7 2 4 10 3 2 14 21 16 6 15 8 7 16 11 3 23 8 9 8 6 7 2 4 6 2 6 21 3 7 2 10 8 9 2 21 6 9 8 4 8 6 7 2 6 7 2 4 10 8 9 2 15 5 4 3 11 2 17 10 8 16 10 2 11 3 13 19 2 16 6 7 9 3 12 2 5 25 3 9 2 4 6 2 9 2 26 14 9 4 8 9 2 8 7 2 19 14 11 6 21 2 5 7 12 2 4 10 3 2 3 7 18 6 11 4 15 3 7 4 2 6 18 2 11 8 3 9 2 20 11 5 7 4 3 12 2 22 19 2 10 3 2 19 14 11 6 21 8 5 7 3 11 2 19 14 7 5 7 2 13 6 +voxpopuli_eng_000549 8 2 13 2 6 15 2 23 3 11 19 2 15 14 16 10 2 4 10 2 11 8 9 6 14 7 4 8 6 7 2 6 18 2 4 6 24 3 2 22 3 4 17 3 7 2 4 10 3 6 9 2 11 5 13 8 9 2 5 7 2 21 13 3 9 4 8 7 8 6 7 9 2 5 7 12 2 9 7 16 3 8 11 13 19 2 10 6 21 2 4 10 5 4 2 10 3 2 17 8 13 2 9 14 16 3 12 +voxpopuli_eng_000550 2 17 3 2 10 5 23 3 2 5 16 14 15 8 13 5 4 8 6 7 2 6 18 2 21 11 6 22 13 3 7 16 9 2 11 3 9 14 13 4 8 7 20 2 18 11 6 15 3 2 4 10 3 2 5 11 4 8 18 8 10 5 13 2 5 7 12 2 12 3 22 5 20 3 5 4 8 7 20 24 2 5 7 12 2 23 3 11 21 11 3 23 8 14 9 19 14 9 +voxpopuli_eng_000551 13 3 4 2 14 9 4 2 7 6 4 2 22 3 2 4 10 3 2 15 5 7 2 6 18 2 19 3 9 4 3 11 12 19 2 13 7 4 2 14 7 2 22 3 21 6 13 2 12 5 19 9 2 8 7 9 4 10 8 4 14 4 8 6 +voxpopuli_eng_000552 4 2 8 2 20 6 14 13 12 2 3 11 13 9 14 15 2 4 6 2 22 3 16 6 15 3 2 5 15 22 5 9 9 3 4 10 3 9 2 6 2 4 10 3 2 19 3 5 11 2 15 5 24 7 20 2 8 4 9 2 5 2 12 3 5 11 9 2 5 7 12 2 5 16 4 8 23 8 4 10 8 9 2 17 6 2 17 8 12 13 19 2 7 6 17 7 2 5 2 15 6 7 16 9 10 2 4 6 2 19 14 11 14 21 3 5 4 2 8 4 8 3 7 9 2 5 7 12 2 21 14 4 21 8 8 21 5 4 8 7 20 2 7 2 3 23 3 7 9 2 22 3 2 4 5 4 2 19 6 14 11 6 21 8 5 7 2 7 5 9 8 6 7 5 13 13 2 18 6 11 2 13 6 24 2 5 13 3 23 13 +voxpopuli_eng_000553 9 3 11 4 7 13 19 2 9 14 16 10 2 8 15 21 5 16 3 2 9 3 9 4 15 3 7 4 2 16 6 14 13 12 2 21 11 3 15 4 2 9 3 11 4 5 7 2 21 11 6 22 13 6 15 9 2 9 14 16 10 2 5 9 2 4 10 6 9 2 21 6 9 3 12 2 22 19 2 4 10 3 2 3 13 3 16 4 11 6 7 8 24 2 8 12 3 7 4 8 18 8 16 5 4 8 6 7 2 6 18 2 9 10 3 21 2 5 7 12 2 9 16 6 4 13 5 7 12 +voxpopuli_eng_000554 4 10 3 2 16 6 11 4 8 9 2 16 6 7 4 3 7 4 2 4 6 2 9 3 3 2 4 10 4 2 8 4 9 2 17 6 11 24 2 10 5 9 2 8 7 18 6 11 15 3 2 4 10 2 12 8 9 2 16 10 5 20 10 2 11 6 9 2 5 7 12 2 10 5 9 2 16 6 7 4 3 22 14 4 3 12 2 4 6 2 11 6 21 6 9 5 13 9 2 18 6 11 2 8 15 21 11 6 23 8 7 20 2 4 10 3 2 18 8 7 5 7 16 5 13 2 15 5 7 5 20 10 15 3 7 4 2 6 18 2 23 3 19 6 14 9 21 3 7 12 8 7 20 2 5 7 12 2 22 3 4 10 3 2 4 5 11 24 5 4 8 7 20 2 6 18 2 19 6 14 2 18 14 7 3 +voxpopuli_eng_000555 11 3 20 6 14 4 10 3 11 3 2 16 13 5 11 8 3 2 4 10 3 2 5 7 12 2 9 3 11 4 5 7 4 19 2 8 9 2 7 3 12 3 12 2 18 6 11 2 4 10 3 2 6 22 13 8 16 24 2 9 3 16 4 6 14 11 2 5 7 12 2 18 6 11 2 4 10 3 2 8 7 12 14 9 4 11 19 +voxpopuli_eng_000556 8 9 2 8 4 2 11 3 5 13 8 7 6 4 2 21 6 9 8 22 13 3 2 4 6 2 14 9 2 5 2 5 4 10 3 11 2 10 6 14 9 8 7 20 2 18 5 9 16 8 13 8 12 3 9 2 17 8 4 10 2 14 2 21 11 6 21 11 3 2 10 2 11 3 9 3 21 4 8 7 2 16 6 7 12 8 6 7 9 2 8 7 2 4 10 3 2 15 3 7 2 4 8 15 3 +voxpopuli_eng_000557 17 10 3 13 2 19 6 14 2 4 5 24 3 2 5 16 8 6 7 2 5 4 2 13 5 9 4 2 8 18 2 7 6 4 2 4 10 3 7 2 17 10 3 7 12 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score new file mode 100644 index 0000000000000000000000000000000000000000..d66b79349a804344ff9f7b7fe045b0f1866d97b4 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score @@ -0,0 +1,1092 @@ +LAD_eng_000254 tensor(-16.7284) +LAD_eng_000255 tensor(-8.5020) +LAD_eng_000256 tensor(-8.9164) +LAD_eng_000257 tensor(-9.4137) +LAD_eng_000258 tensor(-6.4729) +LAD_eng_000259 tensor(-14.0435) +LAD_eng_000260 tensor(-10.4728) +LAD_eng_000261 tensor(-15.4937) +LAD_eng_000262 tensor(-8.6768) +LAD_eng_000263 tensor(-6.3534) +LAD_eng_000264 tensor(-19.4275) +LAD_eng_000265 tensor(-4.0471) +LAD_eng_000266 tensor(-3.1389) +LAD_eng_000267 tensor(-4.4127) +LAD_eng_000268 tensor(-8.2306) +LAD_eng_000269 tensor(-10.8764) +LAD_eng_000270 tensor(-8.8826) +LAD_eng_000271 tensor(-11.9131) +LAD_eng_000272 tensor(-11.6978) +LAD_eng_000273 tensor(-16.4728) +LAD_eng_000274 tensor(-3.7176) +LAD_eng_000275 tensor(-13.1600) +LAD_eng_000276 tensor(-12.6575) +LAD_eng_000277 tensor(-9.8954) 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tensor(-10.5628) +voxforge_eng_000910 tensor(-8.8056) +voxforge_eng_000911 tensor(-4.8098) +voxforge_eng_000912 tensor(-4.4005) +voxforge_eng_000913 tensor(-10.8736) +voxforge_eng_000914 tensor(-9.5853) +voxforge_eng_000915 tensor(-7.2499) +voxforge_eng_000916 tensor(-2.4199) +voxforge_eng_000917 tensor(-5.6937) +voxforge_eng_000918 tensor(-8.7658) +voxforge_eng_000920 tensor(-7.8260) +voxforge_eng_000921 tensor(-9.5812) +voxforge_eng_000922 tensor(-9.0889) +voxforge_eng_000923 tensor(-8.4067) +voxforge_eng_000924 tensor(-2.1278) +voxforge_eng_000925 tensor(-13.4989) +voxforge_eng_000927 tensor(-9.0188) +voxforge_eng_000928 tensor(-7.8969) +voxforge_eng_000929 tensor(-6.7204) +voxforge_eng_000930 tensor(-9.0268) +voxforge_eng_000931 tensor(-10.4453) +voxforge_eng_000932 tensor(-9.8134) +voxforge_eng_000933 tensor(-8.8589) +voxforge_eng_000934 tensor(-7.6220) +voxforge_eng_000935 tensor(-7.1627) +voxforge_eng_000938 tensor(-9.6165) +voxforge_eng_000939 tensor(-17.0330) +voxforge_eng_000940 tensor(-8.1370) +voxforge_eng_000942 tensor(-11.9913) +voxforge_eng_000943 tensor(-5.6641) +voxforge_eng_000944 tensor(-16.1457) +voxforge_eng_000945 tensor(-16.6616) +voxforge_eng_000946 tensor(-6.4343) +voxforge_eng_000947 tensor(-9.6793) +voxforge_eng_000948 tensor(-7.9805) +voxforge_eng_000949 tensor(-10.6947) +voxforge_eng_000950 tensor(-10.7352) +voxforge_eng_000951 tensor(-3.8547) +voxforge_eng_000952 tensor(-4.3844) +voxforge_eng_000953 tensor(-9.4660) +voxforge_eng_000954 tensor(-8.6284) +voxforge_eng_000955 tensor(-9.5721) +voxforge_eng_000956 tensor(-13.3395) +voxforge_eng_000957 tensor(-4.3941) +voxforge_eng_000958 tensor(-5.7501) +voxforge_eng_000959 tensor(-17.8019) +voxforge_eng_000960 tensor(-10.2684) +voxforge_eng_000961 tensor(-9.4255) +voxforge_eng_000962 tensor(-4.6582) +voxforge_eng_000963 tensor(-10.9943) +voxforge_eng_000964 tensor(-12.6315) +voxforge_eng_000965 tensor(-4.9433) +voxforge_eng_000966 tensor(-4.6458) +voxforge_eng_000967 tensor(-6.1126) +voxforge_eng_000968 tensor(-8.7483) +voxforge_eng_000971 tensor(-4.6107) +voxforge_eng_000972 tensor(-11.4366) +voxforge_eng_000973 tensor(-13.8357) +voxforge_eng_000974 tensor(-10.0624) +voxforge_eng_000975 tensor(-10.9551) +voxforge_eng_000976 tensor(-6.3784) +voxforge_eng_000977 tensor(-6.7407) +voxforge_eng_000978 tensor(-4.9433) +voxforge_eng_000979 tensor(-6.4612) +voxforge_eng_000980 tensor(-9.7533) +voxforge_eng_000981 tensor(-7.1694) +voxforge_eng_000982 tensor(-13.6593) +voxforge_eng_000983 tensor(-11.8294) +voxforge_eng_000984 tensor(-5.4099) +voxforge_eng_000985 tensor(-7.3585) +voxforge_eng_000986 tensor(-10.7356) +voxforge_eng_000987 tensor(-16.6986) +voxforge_eng_000988 tensor(-8.8051) +voxforge_eng_000989 tensor(-12.9164) +voxforge_eng_000990 tensor(-6.7095) +voxforge_eng_000991 tensor(-6.6035) +voxforge_eng_000992 tensor(-4.1494) +voxforge_eng_000993 tensor(-7.4153) +voxforge_eng_000994 tensor(-9.1757) +voxforge_eng_000995 tensor(-19.5104) +voxforge_eng_000996 tensor(-6.4448) +voxforge_eng_000997 tensor(-12.4359) +voxforge_eng_000998 tensor(-6.3669) +voxforge_eng_000999 tensor(-4.3860) +voxforge_eng_001000 tensor(-11.8789) +voxforge_eng_001001 tensor(-13.3361) +voxforge_eng_001002 tensor(-10.3776) +voxforge_eng_001003 tensor(-9.2809) +voxforge_eng_001004 tensor(-7.8628) +voxpopuli_eng_000494 tensor(-13.7264) +voxpopuli_eng_000495 tensor(-32.2945) +voxpopuli_eng_000496 tensor(-31.8108) +voxpopuli_eng_000497 tensor(-17.4664) +voxpopuli_eng_000498 tensor(-27.8619) +voxpopuli_eng_000499 tensor(-71.8960) +voxpopuli_eng_000500 tensor(-17.7330) +voxpopuli_eng_000501 tensor(-5.2198) +voxpopuli_eng_000502 tensor(-55.7466) +voxpopuli_eng_000503 tensor(-30.1004) +voxpopuli_eng_000504 tensor(-30.3191) +voxpopuli_eng_000505 tensor(-62.1047) +voxpopuli_eng_000506 tensor(-38.4986) +voxpopuli_eng_000507 tensor(-4.1365) +voxpopuli_eng_000508 tensor(-11.5937) +voxpopuli_eng_000509 tensor(-59.2653) +voxpopuli_eng_000510 tensor(-50.4544) +voxpopuli_eng_000511 tensor(-46.8934) +voxpopuli_eng_000512 tensor(-4.7256) +voxpopuli_eng_000513 tensor(-10.7215) +voxpopuli_eng_000514 tensor(-15.5423) +voxpopuli_eng_000515 tensor(-18.6603) +voxpopuli_eng_000516 tensor(-16.5410) +voxpopuli_eng_000517 tensor(-27.8105) +voxpopuli_eng_000518 tensor(-21.8895) +voxpopuli_eng_000519 tensor(-33.7778) +voxpopuli_eng_000520 tensor(-35.1257) +voxpopuli_eng_000521 tensor(-49.8479) +voxpopuli_eng_000522 tensor(-9.0158) +voxpopuli_eng_000523 tensor(-19.1951) +voxpopuli_eng_000524 tensor(-35.3551) +voxpopuli_eng_000525 tensor(-6.4549) +voxpopuli_eng_000526 tensor(-12.7357) +voxpopuli_eng_000527 tensor(-59.5096) +voxpopuli_eng_000528 tensor(-21.7023) +voxpopuli_eng_000529 tensor(-13.2190) +voxpopuli_eng_000530 tensor(-37.9898) +voxpopuli_eng_000531 tensor(-9.6246) +voxpopuli_eng_000532 tensor(-6.6383) +voxpopuli_eng_000533 tensor(-14.1069) +voxpopuli_eng_000534 tensor(-30.0338) +voxpopuli_eng_000535 tensor(-35.6215) +voxpopuli_eng_000536 tensor(-30.8339) +voxpopuli_eng_000537 tensor(-24.0707) +voxpopuli_eng_000538 tensor(-15.7291) +voxpopuli_eng_000539 tensor(-25.9907) +voxpopuli_eng_000540 tensor(-28.6621) +voxpopuli_eng_000541 tensor(-21.6394) +voxpopuli_eng_000542 tensor(-13.0774) +voxpopuli_eng_000543 tensor(-15.5012) +voxpopuli_eng_000544 tensor(-29.4369) +voxpopuli_eng_000545 tensor(-6.3432) +voxpopuli_eng_000546 tensor(-5.9571) +voxpopuli_eng_000547 tensor(-22.4689) +voxpopuli_eng_000548 tensor(-41.0279) +voxpopuli_eng_000549 tensor(-30.6201) +voxpopuli_eng_000550 tensor(-19.9029) +voxpopuli_eng_000551 tensor(-13.1614) +voxpopuli_eng_000552 tensor(-44.7201) +voxpopuli_eng_000553 tensor(-22.4619) +voxpopuli_eng_000554 tensor(-34.2515) +voxpopuli_eng_000555 tensor(-14.0192) +voxpopuli_eng_000556 tensor(-20.2264) +voxpopuli_eng_000557 tensor(-9.0565) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..3db589b11838ebfeb0fa7be7a091fc1ced1e29d9 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/hyp.trn @@ -0,0 +1,1092 @@ +H E R E M A I E D W E L C H A M P I A N A N T I L N I N T E N S I X T Y F I V E A Y E A R I W H C H S U F R D A T E R A B L E A C X I D E N T (LAD_eng_000254-LAD_eng_000254) +A Y L I B R A L C O N S E V I T I V E H E W A S D E F E A T E D I N A T E I N A T Y T O (LAD_eng_000255-LAD_eng_000255) +O N R O D L A A R C O N D R A R T W O R O D S A T W O A N C E (LAD_eng_000256-LAD_eng_000256) +S O M E O F T H E C O N T R E S H V E S U R V A Y S F O R M A L T I P L E Y E A R S (LAD_eng_000257-LAD_eng_000257) +B O T H O F T H E V R S I O N S F E A C H R T H E S O N G H A P Y H O L I D A Y (LAD_eng_000258-LAD_eng_000258) +S H A K X P I A R M A N Y R E F R N C E S A R E M A D E T O S E N S I N T R A C T I O N S O R C A R I C T E S F R O M V A R I O U S P L A Y E S (LAD_eng_000259-LAD_eng_000259) +I F O N L Y T H E R O G R A M C U L D B R A K E O U T J U S T A I T L E F R O M I T S T O F O M I L I A R A P R O C H (LAD_eng_000260-LAD_eng_000260) +T H E H E L B E M W A S R E L E A S E D I N O S T R A L I A R O N N I N T E I N T H O R G I S T T W O T H O U S N D A D E L E V E N (LAD_eng_000261-LAD_eng_000261) +H E N O W P L A C E F O R A S T R A L I N C L O B E P E R T H G L O R Y (LAD_eng_000262-LAD_eng_000262) +I T I S N O T N O N H O W M U C H I F E A N Y O F H E C L A M S A R T R U (LAD_eng_000263-LAD_eng_000263) +A S M A L B I S I N E S S O N R B R O A R D O P R A T E D H I W E A T A D S H E P F A M E F O R S I C T E N Y E A R S F R O T H E A G E O F W E N T Y T O (LAD_eng_000264-LAD_eng_000264) +I N T H E N I N T H S E N T U R Y H E W A S A N I R I S H P O E T (LAD_eng_000265-LAD_eng_000265) +T H E Y A R E M A R K E D B Y S T R O N G (LAD_eng_000266-LAD_eng_000266) +T H E L O W I S T H E F O R V A O L E D (LAD_eng_000267-LAD_eng_000267) +I N T H E R L Y S T A G E S C A M E C L O S E T O U S A S L E P (LAD_eng_000268-LAD_eng_000268) +R O N I N G E V E R Y T H R T Y M I N U T T H R O A T S E R V I S T I M S (LAD_eng_000269-LAD_eng_000269) +A S A R E S I U L T W H E N T H E C O L I G E R E O P E N D I T W A S A S A N A L L M A L E C O L I G E (LAD_eng_000270-LAD_eng_000270) +T H E T I M E B E T W E E T H E S P O I N C T I S V E R I A B L A N D C A N A C U R A N Y W H E R F R O A M I N I T T O M U C H L O N G E R (LAD_eng_000271-LAD_eng_000271) +W O A R K O N T H E E A E E S T A R T E D I N M A R C H T W O T H O U S N D A N D S E V E N A T A C O S T O F F I V E M I L I A N D O L E R S (LAD_eng_000272-LAD_eng_000272) +H O W E V E R T H E R W A S S O M E D I A G R E M E N T O V T H E N D I N G T H E M E W H I C H O R M O R Y A N D Y O H I M O R Y D I S C U S T D A T L E N G T H O V E R E M A L (LAD_eng_000273-LAD_eng_000273) +T H E C O P L E H A D N O C H I L D R A N (LAD_eng_000274-LAD_eng_000274) +T H E F I A L S I N G L O T H A T D E B U A L B H M P A R I S C O L I N G H A D A N E L A B R T M U S I C V I D I O (LAD_eng_000275-LAD_eng_000275) +T H E S E R I S E N D E D O N S I X T H O R G E S T T O T H O U S N D A N D F O R L A S T I N G F R A T O U T E O F S E V E N T Y O N D A Y S (LAD_eng_000276-LAD_eng_000276) +H E H A S A L S O C O N T R I B U T E D T O T H E N E W Y O R K R E V I O O F B O O K S (LAD_eng_000277-LAD_eng_000277) +B Y P L A C I N G S M A L A R T O B J E C T T R O O U T T H E F I L M (LAD_eng_000278-LAD_eng_000278) +I T I S F O U N D I N B R E S I L (LAD_eng_000279-LAD_eng_000279) +I T W S T H E S I D O F T H E F A M L Y I I D E N T I F I E D M O R E W I T H (LAD_eng_000280-LAD_eng_000280) +H C A N D I T S I G H T E S M U S T A L S O R S O B M I T A W O R K P L A N (LAD_eng_000281-LAD_eng_000281) +D U N D E Y W H N T H E M A C H T H R E T O (LAD_eng_000282-LAD_eng_000282) +H O W E V E R T H E V I L I G E R E M A I N D I C A L A T E D A N T I L T H E R I V E L O F T H E F I R S T N O U S P A P E R S E C O N D R E P O U B L I C K (LAD_eng_000283-LAD_eng_000283) +T H E F A S T S E R V I S I T H E E U C H U R C W A S H E L D I N I N T E F I F T Y O N A L T H O T H E B I L D I G W A S N O T F U L Y F I N I S H E D (LAD_eng_000284-LAD_eng_000284) +T H E A V E R I G E H O U S E H L D S I E W A S T W O P O I N T T O S E V E N N D T H E A V E R I G H F A M L Y S I E W A S T H R E P O I N T I R O S R O (LAD_eng_000285-LAD_eng_000285) +I T W A S F I R S T B R A D C A S T O N T H I R D G A N I U R Y T W O T H O U S O N D N D T E N (LAD_eng_000286-LAD_eng_000286) +T H E W I N G S W E R O W M A D I N A S I N G L E P R E S I N G (LAD_eng_000287-LAD_eng_000287) +H E D O C T R O H L O S O F Y I N E N G E N E A R I N G M A N A G E M E N T (LAD_eng_000288-LAD_eng_000288) +T H I S T O K W A Y T H E M A I N A R G U M E N T O F S A F T Y R I S S K (LAD_eng_000289-LAD_eng_000289) +H E W A S A L S O M A D A L I F E M E M B E R O F S G U N T H O R P P U N I T E D (LAD_eng_000290-LAD_eng_000290) +S H E F I A R S T H E Y W I L G E T A D E V O R S E B U T T H I S N E V E R H A P E N S (LAD_eng_000291-LAD_eng_000291) +F O U T D R O P S I N A B L E T O H A D T H E F O T S T R A T A C R O S E (LAD_eng_000292-LAD_eng_000292) +W H E T E T H E A R F L O I S F R E Y O R F O R S T C N F E C T H E E N A G Y A F I A N C Y O F T H E E N D O (LAD_eng_000293-LAD_eng_000293) +A F T E R G E T I N H E R I H T M A S U R M E N T T H E Y M A D T H E N E W D O R S (LAD_eng_000294-LAD_eng_000294) +F R A G M E N T S O N A C H F A C E A R E M A R E W T H L E T E R S A Y B E S E (LAD_eng_000295-LAD_eng_000295) +F R O M T H E F I R S T M I N I T S B O T H T E M E S S H O W D T H E D I S I R E T O C M P E E T W I T H E G E I V E A P R O C H E S (LAD_eng_000296-LAD_eng_000296) +F I S I C L H E R I P Y E X C U S I S E S M A Y H E L P P A T I O N T T O M A I N T A I N M U L E S T R I N G T H (LAD_eng_000297-LAD_eng_000297) +H O W E V E R T H E T O W N S H E L I V S I N N O O N W A N T S T O H E R A B O U T H E R (LAD_eng_000298-LAD_eng_000298) +A N D D I S C R I V E S A P O I N T M E T O F A N A C T I N G C H E V E J U S T I S O R J U D G E O F T H E S U P R E M E C O R T (LAD_eng_000299-LAD_eng_000299) +T H E S O Y B E N S O U T A C O V E R I N G I S T H E N R E M O V E D A N D T H E B E N S A R E P A R T I A L Y C O O K E D (LAD_eng_000300-LAD_eng_000300) +T H I S N A S I N A L E M O V M E N T W H I C H A D B E G U N W I T H S O M U C H H O P C A M E T O A S A D E N D (LAD_eng_000301-LAD_eng_000301) +H I S A S O S C I A T Y U S U A L Y C A L D H I M T E O R T H E O D L O K I N G G I Y (LAD_eng_000302-LAD_eng_000302) +I T S M A I N O F I C E S W E R I N L U N D A N W I E H E S E C N D O F I S B E L L F A S T (LAD_eng_000303-LAD_eng_000303) +A C T U L Y I H A D N E V E R B E N T O A V I L I G E B E F O R T H A T (LAD_eng_000304-LAD_eng_000304) +H E A S C H A R G E D I T H P L A N I N G T O S E T O F B O M S I N U R O P A N D T H E U N I T E D S T A T E (LAD_eng_000305-LAD_eng_000305) +M A K I N G M E R S I S T H E H I R D S T U D O R H L B U M B Y B E L G E N A S T R A L I A N A R T I S T G O T I A Y (LAD_eng_000306-LAD_eng_000306) +H E T H E N M O V E D T O W A S I N G T O N D E S E A N D W A S A P A R T N R I T H W A R D B R O N A N D T I L N I N T E N T W E N T Y N I N (LAD_eng_000307-LAD_eng_000307) +J O S O F H I Y S C O L E A N D T H E S C O L E S T H E C M P E G A I N E I N A L S P O R T S (LAD_eng_000308-LAD_eng_000308) +T W E L F P L U S O N M A C H B A N P E R C A R D (LAD_eng_000309-LAD_eng_000309) +I H I N K I M I G H T B E N O T H I N G (LAD_eng_000310-LAD_eng_000310) +T H E H O E W A S B I L T A N D L I V E D I N B Y A N D R U J A C X A N D C A N D Y D E P U T Y C L E C T E O T H E I N T E R N A L R E V I N O U S E R V I S (LAD_eng_000311-LAD_eng_000311) +I N N I N T E N S I X T Y F O R H E W E N T B A C T O O M S K A N D E N T E T H E A C T O A S C O L O F O M P S (LAD_eng_000312-LAD_eng_000312) +T H E B A N K I S J O U N T L Y O N D B Y H I M A N D H I S B R O V E R A N D R E L I T I V E S (LAD_eng_000313-LAD_eng_000313) +H E S U B P S I C U N T L Y W E N T T O C O L I N B R I S T A L (LAD_eng_000314-LAD_eng_000314) +W O N T H O U S A N D A T H U N D R D F O A R T Y S I C X F O R H E D I O N (LAD_eng_000315-LAD_eng_000315) +A P A R T O F L I T L I N G L A N D B E Y O N D W A L S I T H A S B E A C E N C H A L Y I N G L I S H S P E A K I N G F O R N I N H U N D R E D Y E A R S (LAD_eng_000316-LAD_eng_000316) +H E P L A D W T H T E N P L A Y A R S F O R H A R F W A S A G A I N E A T R D I O N I N J E E S P (LAD_eng_000317-LAD_eng_000317) +T H E R E S I D I N G J U D G E W A S W E B S T A F A I R H O W A S A L R E A D Y A S I N D T O T H E C O R T B E F O R E T H I S C A C E W A S H E D I L D (LAD_eng_000318-LAD_eng_000318) +B I G B R A T H E R F I V E W A S T H E H U R D O F H E M A I N S A R I S T O F E A C U E R A L I V E L O N C H (LAD_eng_000319-LAD_eng_000319) +I T S M O T O I S W H O E V E Y O U A R A N D W H E R E V E Y O U A R E O N T H E J U N Y O F F A I F H Y O U A E W E L C O M H E R (LAD_eng_000320-LAD_eng_000320) +R O B A T E Y M I L O R A S C O C H W I L S O N (LAD_eng_000321-LAD_eng_000321) +A F T E R O N Y E A R B R A K S I R O D E G R E W A S H E F O L O I N G V E N T H A R (LAD_eng_000322-LAD_eng_000322) +A Y A M T E E M A N U F A C T E D A M O R D L C I T O F H E S E D S E I D A R D R A C K X S T O R (LAD_eng_000323-LAD_eng_000323) +T H E E S S E S S A Y A M E D T O B I L D A L E F T W I N G O L T U R N I T I V E T O N O W L A B E R A N D T H E E S A N P E (LAD_eng_000324-LAD_eng_000324) +H E L I V E S L I K E H E A S A Y O N G P E R S O N (LAD_eng_000325-LAD_eng_000325) +M A S T E O F S I N E S I N E N G E N E A R I G M A N A G E N T (LAD_eng_000326-LAD_eng_000326) +S H E F A I L E D T O M A K H E T O P T H R E A T T H E C A N I A N J U N I A T R A C T R I L E S T H A T J O N (LAD_eng_000327-LAD_eng_000327) +A T O R E F O L O E D I N S E P O R T (LAD_eng_000328-LAD_eng_000328) +T H E Y E R S T A B I S H I N A T E N S E V E N T Y O N A N D A R W N O T H E O L D S T C L O U B S I N H E S O U T H O F I N G L A N D (LAD_eng_000329-LAD_eng_000329) +H E A S A M E M B E R O F T H E G E S T S C O T L A N D A D V I S E R Y B O R D (LAD_eng_000330-LAD_eng_000330) +T W O T H O U S A N D A N D F I V E G E N T L E M E N (LAD_eng_000331-LAD_eng_000331) +A O R E F I L E A D S T R O N G R E S E P T I O N I N Y U R U P A N D A C H I V E D D I S T O B U T I O N B U T T H A T W A S N O T T H E C A C E H E R (LAD_eng_000332-LAD_eng_000332) +B O L T H O I S S T E T C H E S P O S T E R I A R A N G C A L S T R U C T U E S (LAD_eng_000333-LAD_eng_000333) +H E A S A L S O A T H E E T I M E F R E N C H N A S I A L C H A M P I A N N I N T E N I N T Y N I N T I E N I T Y F O R T W O H O U S N D A D W O N (LAD_eng_000334-LAD_eng_000334) +T H E V I L I G E S T R U C T U R S H O W I N H I S M A P I S T A G R E E X T E N T U N C H A N G E D O D A Y (LAD_eng_000335-LAD_eng_000335) +R U H A I S R E C O G N I S E D I T N U C L A R D I S A R S T T O E X P A R T E S A N D F O T H E S A V F T Y O I T S T E C K N O L A G Y (LAD_eng_000336-LAD_eng_000336) +A S O F T O T H O U S E N D O D F O R T E E N E M T Y V E I S A V A I L A B L E W I T H I N T H E U N I T E D C I N G D U M O N V E R G I N M E D I A R A N D S C K I Y (LAD_eng_000337-LAD_eng_000337) +N E W Y O R K P E A N G U I N R A N D M H O U S E (LAD_eng_000338-LAD_eng_000338) +T H E D U T C H E Y W A S S C E C U R E I N T E U T C O M E O F T H E G O F I C K W A O R (LAD_eng_000339-LAD_eng_000339) +W I H G O D P A C E S D A R T E H E M A T C H W I T H B O T H T E M E S O L T E N A T I N G S U P R E M A S Y (LAD_eng_000340-LAD_eng_000340) +T H I S V R T I O N I S N O T E A D O R B I G V E R Y F A F U L T O T H E A R I G I N A L N O V L (LAD_eng_000341-LAD_eng_000341) +T H I S P R E S U M P T I O N I S N O T F L E F I L E D O N H A S T O N O A T L E A S T T O C O N G A T D I A M A T E S (LAD_eng_000342-LAD_eng_000342) +N O T A B L E T I T L E S I N C L U D E D G O L D A N A C X S T H E R E V E N G O F D E T H A D E R R A D M O B I L O U T R U N O E S A N D S A K G A R S O N I C T H E H E G H O G (LAD_eng_000343-LAD_eng_000343) +T H E N I N T E N N I N T Y N I N J U G M E N T N O T E D T H A T T H E I N F L O N C O F T H F A T H E R O F T H E C U S E D H A S B E E T H E R (LAD_eng_000344-LAD_eng_000344) +M O K D A U F S W A R S R E V E N G E H A N D J O I N S F O R E S I T H M A L C O M T O O V E R T R O M O K B E A T H (LAD_eng_000345-LAD_eng_000345) +T H E M E D Y E V L V I L I G E C O R T W A S A L W A Y S A N I O U S T O C E P E T H E F E N E A R O N D T H E I L I G E G C A P L E S (LAD_eng_000346-LAD_eng_000346) +T H E R W A S A N I N R A N K S I S T O M E A C H R A N K H A V I G M O R E P O W E T A T H E L O E R A N K (LAD_eng_000347-LAD_eng_000347) +T H E A S T A B L I S H E D D I P L A M A T I R E L A T I O N S O N S E P T E M B R N I N T E N T H N I N T E N S E V E N T Y T O (LAD_eng_000348-LAD_eng_000348) +T H I S W A S F I R T H E R X T E N D E D T O I N C L O U D M O R U C A D A T E S I N D I S E M B E R T W O T H O U S A N D N D F O R T E E N (LAD_eng_000349-LAD_eng_000349) +T H E U C H G O V E R M E N T I S C A R N T L Y E X S A M I N G T H E E A L C O N C I C U E N C E S O F T H R O L I N G (LAD_eng_000350-LAD_eng_000350) +F R O M N I N T E N T H U R T Y T H R E E T O N I N T E E N F O A R T Y N I N T H E M A R I C O N L E E W O N T W E L V E O U T O T H E F I R S T S I X T E N (LAD_eng_000351-LAD_eng_000351) +T H E A R H E F E L S I C K W I T H T I F A S H I M S E L F (LAD_eng_000352-LAD_eng_000352) +S I X T T E M S A V B E D V I D E D I N T O T W O G R O U P S O F T H R E E T E M S E A C H (LAD_eng_000353-LAD_eng_000353) +T H E F I R S T C E A S O N P R E M I A E D O N T W E L T H J U O N T W O T H O U S N D A D F I F T E N (LAD_eng_000354-LAD_eng_000354) +I T S C E E D T H E W H I B O A R D A N D S I S T A M E T W E N T Y F O R C O M B I N G F E A C U E S F O M B O T H (LAD_eng_000355-LAD_eng_000355) +V L L I U M E T O O N U M B E R S O N T O A N D T H R E (LAD_eng_000356-LAD_eng_000356) +T H E L O W E P A R T O F M E N S D E S E S W E M U C H S O U R T I N L E N C T H O T H O S F O R W M E N (LAD_eng_000357-LAD_eng_000357) +T H E V I S I G O T H S I N T E R N W E S C E A D E D B Y T H E M O R S (LAD_eng_000358-LAD_eng_000358) +J O S O F H I S C O L E E V E R Y W E O F T H E C O L Y E A R (LAD_eng_000359-LAD_eng_000359) +A S T H R S I L T O F A L T H E A R G U M E N T G E T I N G T O H E R (LAD_eng_000360-LAD_eng_000360) +I T H A D Q U A R T E R S A R E I N S H E F I A L D Y O U N I T E D C I N G D O M (LAD_eng_000361-LAD_eng_000361) +L A Y A L S O F I A L Y S I N E T H E C O N T R A C T O N S T A G E W I T H E D I R E C T E R A D P R E D U S E S O F T H E G O U L D A N E Y E S (LAD_eng_000362-LAD_eng_000362) +F I S I C L F E R I P Y C N H E L E P A T I O N E T O L U R N H O T O W A R K W I T H F O T D R O P (LAD_eng_000363-LAD_eng_000363) +I T E N T O N T O S E L T H R E H U N D R E D T H O U S A N D U N I T S A C H E F I V E N O (LAD_eng_000364-LAD_eng_000364) +T H E N A M E S T O U C K A F E R T H A T (LAD_eng_000365-LAD_eng_000365) +T H E H L B M L A T E R B R O K T H D I M A D R E C O R D O N C U C U O M M U S I C K (LAD_eng_000366-LAD_eng_000366) +I T S E D A T O R I A L W E S U B M I T A N D I T S O T H R A P O L T O P R I Y E (LAD_eng_000367-LAD_eng_000367) +J O S I F P L A Y E S O U R F E A T U R E D E A C H W E E O N T H E H O (LAD_eng_000368-LAD_eng_000368) +T H E Y W A T F O R A T I M E M B I L D I N G U P T H E R F O R E S B E G I N T O O N D R I F T H I S E A V L R E A L Y E X I S T S (LAD_eng_000369-LAD_eng_000369) +B R E F E M E N T I O N O F T H C O N V I C T I O N A P P E R D O N P A G E T H R E O F T H E N E W Y O U O K T I M E M S (LAD_eng_000370-LAD_eng_000370) +O D E D B Y P O S I O N O N P I C H F R O M B A C K R I G H T T O F R U N T L E F T (LAD_eng_000371-LAD_eng_000371) +H E I S M E M B E R O F T H E C O U R T O T H E R I L C O L A E O F A R T L O U N D O N Y U C A Y (LAD_eng_000372-LAD_eng_000372) +D U R I G T H E C O U R S E O F T E C A M P A I N F I R G S A N D V I S I T A T A L L T H E R T Y N E I N W A S I G T A N S T A T E C O N T E S (LAD_eng_000373-LAD_eng_000373) +A S T R I P O F P A P E R O F L E N G T H (LAD_eng_000374-LAD_eng_000374) +S A T O H A D F R E C U E N T L Y W O R E T O G E T H W T H Y O U C K A Y A M A R O N P R E V I O S P O G J E C T S (LAD_eng_000375-LAD_eng_000375) +S H E A S B O R N O N S C R E A N D U I N T H E E P S O D B R A D C A S T O N F O R H A N O V E M B E R N I N T E N I N T Y F O R (LAD_eng_000376-LAD_eng_000376) +H E T U R N E D R O U N D S H H A D C O M I N S O G E N T L Y T H A T H E H A D N E V E R H A R D H E R (M-AILABS_eng_000159-M-AILABS_eng_000159) +A T O B E S H O U O R A N W E M U S T C E O U R D O R S S H O A T W E M U S L A T N O O N I N (M-AILABS_eng_000160-M-AILABS_eng_000160) +C I D S P M O N H E B E G A N M O K I N G L Y Y O U M A H V E O N D E D W H I Y I C A L D A T R O U S W H E N I C O U L D J U S A S W E L L H A V E D I S T R O R E D Y O U T H A T I D O U T A T O A N S E D H I M (M-AILABS_eng_000161-M-AILABS_eng_000161) +T H E P E S N T T H R U W H I M S E L F A P O N H I M A N D B O U N D H I S F O R L A G S T I T L Y S O T A T H C U L D N O T M O V E (M-AILABS_eng_000162-M-AILABS_eng_000162) +N O R M U S T T H O U S O L I M E T H T H E H L Y O N O F I S R I A L A S T O T H I N K H E H A T H B U T O N W A Y I N W H I C H C A N G O R I F I E H M S E L F B Y T H E (M-AILABS_eng_000163-M-AILABS_eng_000163) +T H E O L D C O M P A R S O N B E T W E T H E I M P U L S I E E X S E C T I V E A N D T H E L I B R A L A R T S M A N W H O W H A D L A R N E D T H A T H E R E O N L Y O N R T O P O S I T V E D I S I O N S F A L B L E I N A L T H E W A L O H I N K I N G (M-AILABS_eng_000164-M-AILABS_eng_000164) +A F T E R T H I S E X P E R I A N C E T H E N V A T O R S W E R C A I R F U L T O C E P E A S A V F E D I S T N C E F R O M T H E A L (M-AILABS_eng_000165-M-AILABS_eng_000165) +A N O U B A R S O M T I N G F I R T H E R I T H N Y O U A T O N O I T I H A V E H E R A M O S T M S T E R I O U S T E L A P A R I G R A M Y E S W H A T I S I T I S H E D I D N O W I T I S N O T A B O U T H E R (M-AILABS_eng_000166-M-AILABS_eng_000166) +N O M S T R T O U R T A N S A I D A N D G I E T H E A S K T T O M E I A L T A K E I T (M-AILABS_eng_000167-M-AILABS_eng_000167) +A N D A R A B I A N N I G H T E X C L A M E D T R O T W H I Y T H A T W A S A M A G I C N I G H T W A S N I T T H E R S D I F R E N T S O R T S O N I G H E S M A T E S A I D T H E S A L E R A N D T H E N I G H T B U T N B R I G H T M E A N S A N T T H E S A M E N I G H T Y O U M E A N (M-AILABS_eng_000168-M-AILABS_eng_000168) +I V E T R N E D O F U P W A R D S O F A H U N D E D F M Y B E S T D H A N D S F O R N O O T H E R F A L T T H E M F O L O I N G Y O U A N D S U C H A S Y O U A N D T H I N K I L L T A K E Y O U A O N (M-AILABS_eng_000169-M-AILABS_eng_000169) +B U T W E W I D S H E S E H I M H E R H A R T L E P T U I N A P R E H E N T I O N A T E V E R Y R I N O F T H D O R B I L (M-AILABS_eng_000170-M-AILABS_eng_000170) +T H E S E B O O K S D I C X S O N I W L K E P E A L T H E R E S T W E O U S E N D T O M S T R B E L T H E Y A R O F A C I N D T H T H E W L V O U L Y O U F O R T H M S E L V E S A S W E L A S F O R P O P A S S A Y (M-AILABS_eng_000171-M-AILABS_eng_000171) +U T I N G A W A S N O T A T A L S H U R T H A T T H E C O U L D N O T G E T I N T H E G A T S O P E D I N W A R D A N D T H R E H E V Y B A R S W E R E H E L D I N P L A C E B Y M E N S O F S T O U T S T A P L E S R I V I T E D T O T H E S H E T S O F S T A (M-AILABS_eng_000172-M-AILABS_eng_000172) +I W A N T T H O W S A I D H O D O N C O L D L Y I W A N A D O S O N H O R S E S I W A N T M E N T O B R I G D T H E W I T H M E H E P U S H D H I W A Y F O R D W H I C H W A Y T O T H E S T A B L E S (M-AILABS_eng_000173-M-AILABS_eng_000173) +E R E I S A L I M I T W H A T Y U C A N D D O F O T H E F I R S T T H I E Y O U A N T E R A M A N S H O U S E A N D B E S I D E T H A T W A S N O T I M E T O A R O U S S U S P I O N N T H E M I N D S O F A N Y W O N (M-AILABS_eng_000174-M-AILABS_eng_000174) +D O O U N O T R E M E M E R T H A T H E S A S T H Y D E M A N T H A T S T H E S P I R I T W H I C H C E P E S T H E I S N O B L E C O R A G O U S H I Y U N M A C H I B L (M-AILABS_eng_000175-M-AILABS_eng_000175) +M S T R B E L L W H A C A N H E N O O F J O A O N H E L I V I N G A L A S Y L I F I N A D R O U S Y C O L A G E (M-AILABS_eng_000176-M-AILABS_eng_000176) +A N D T H E C I T N F O L O E D I M U A R L Y A T T H E R H E A L S (M-AILABS_eng_000177-M-AILABS_eng_000177) +T H E F I S T T U T C H W O L D C A S E A N E X P L O S I O N I N W H I C H A M O N G S U C H H U N D R E D S O F I N F E R I A T E D M E N A N D R E C K L E S B O Y S (M-AILABS_eng_000178-M-AILABS_eng_000178) +W O N F T H G E A T P L E S U E R S O F M A R G R A T S L I F A T T H I S T I M E W A S I N E A T S B O Y (M-AILABS_eng_000179-M-AILABS_eng_000179) +T H T H N G I S G O N O N L O N G N O F T H E R I S O N E O R E B I A G A C X I D E N T W E S H A L H A V E T O C O M P R M I S E W I T T H E I N E R I V E R N D C A R Y O N T H E W O R K C U I N T L Y (M-AILABS_eng_000180-M-AILABS_eng_000180) +Y O U A R L A T S A I D S H E W E L S H E H E L D H E R B R A T H O T H E A N S R (M-AILABS_eng_000181-M-AILABS_eng_000181) +T R A T T O L D T H E G I R L S T H A T T H E Y M U S G O W I T H E R F A T H E R T O L I V A N D G I P C U S I S I L S L I T E L D C A B E N A N D H E N T H E Y H E R D T H S R E D F U L D E C R E (M-AILABS_eng_000182-M-AILABS_eng_000182) +M A R G I T S A T D O N O T H E R O G P A T L Y T O W A R M H E R S E L F F O R T H E D A M P N E S O T H E E V N I N G H U N G B O U T H E R D R E S A N D O V E R F I T E H A D M A D H E R C H I L Y (M-AILABS_eng_000183-M-AILABS_eng_000183) +O N O W Y O U A R M S T A K A N A B O U T T H A T R E L I D T H E K I N G T H E Y A R E N O T M Y P R I S O N E R S B U T M Y S L A V E S W H O M M Y P U R C U S E F R O M T H E C I N G O F E V (M-AILABS_eng_000184-M-AILABS_eng_000184) +H E R F A T H E T O K U T E C M B R S A T I O N (M-AILABS_eng_000185-M-AILABS_eng_000185) +I N A C O R N E R W A S A S O R T O F D R E S I N G T A B L E O N W H I C H L A Y A C O M A N D B R U S H C A N I D Y S E E D M U C H I N T R U S T E D I N T H E T A B L E A N W A S E X A M I N G I T W H N T H E G O R U R E T E R N E (M-AILABS_eng_000186-M-AILABS_eng_000186) +I H A V E S O M E T I M E T H G T T H A T M Y S E L F S H E A G E E D B U T O F C O U R S I D O N T N O W S T I L I H A V E T O B E P I T Y C A R F U L S O M E O N I S A L W A Y S O V E R H E R B Y M Y D E S S O R L O K I N G O V E R H E R (M-AILABS_eng_000187-M-AILABS_eng_000187) +I S H L S T A Y R E P L I D T H E Y O N G M A N F O R I M E A N T O S I T Y O F R E (M-AILABS_eng_000188-M-AILABS_eng_000188) +W H A T D Y O D O A S D T H E S O R C E R E R (M-AILABS_eng_000189-M-AILABS_eng_000189) +W H I Y T H E R E A R E N A M E S Y O U R S H O R T H I N E S N O T A N Y M O R E R E P L I E D T R O A T I M Q U E O F T H E I N K E S A N D I M A L S O Q U E O F T H E L O S S O I W O N T H A V E M Y P E P L E Q U A R L I N G (M-AILABS_eng_000190-M-AILABS_eng_000190) +T I P R I T E R W E C L I C K I N G C L I P I N G W E R B I N G S N I P D O T O F A U G E T A C K O F N O E P E R S A N D P A S E D I N A N N L A R G S C R A P B O K S S E R K U L E R W E R B E N G F O L D E D A N M A D R E A Y T O M A L F O T H E F I N A L A P E L (M-AILABS_eng_000191-M-AILABS_eng_000191) +I T W A S F O R D A Y S A F T E R T H E S U P R I E S O F A L T H E R S H O R S H E N T H E S T R A N G E R S L E T T H E A S T A T T O T H E C A I R O F R U G E D O L D F O R S T E R H A R M E N (M-AILABS_eng_000192-M-AILABS_eng_000192) +B P O R T E M P L T O N H E S A I D I U S T O N O W H I M M A N Y E A R S A G O H E W E E B O Y S M E N T O S C O U L W I T H M A N D A L T H A T S O U T O F T H N G Y O N O W B U T A N T I L I R A N C R O S H M O R (M-AILABS_eng_000193-M-AILABS_eng_000193) +I F O N D H E R I T H E F A R I S T A N D B O G T H E R H E R A P R I S N E R E P L I E T H E C A P T O N (M-AILABS_eng_000194-M-AILABS_eng_000194) +W H O M A Y B E C O M P I T E N T I T H E F R O M P E R S I N A L E X P E R I A N C E O R T H E E X P I N S O F O T H E R S T O A N S E R I T W I T H M O R O R L E S C U R E C T N E S O R A T L E A S T A N A T T E M T D (M-AILABS_eng_000195-M-AILABS_eng_000195) +O N N I N T Y T O L A T E S T R E T E T S I D H O K G E N B I T I N G O F H I S S A G A R (M-AILABS_eng_000196-M-AILABS_eng_000196) +T R A T W A S R P R I E T O F I N E S H E C O U D C E S O P L A I N L Y T H R T H E H I Y W A L O F W A T E R A B O V E H E R B U T T H E S O N W A S A B L T O S H U T I T S B E M E S T R A T D O W T H O T H E T R A N S P A R E N T S E (M-AILABS_eng_000197-M-AILABS_eng_000197) +T H E S P A T W E R I D S P R N G U P (M-AILABS_eng_000198-M-AILABS_eng_000198) +C O M E D E N I L W I C S H E G A V E S U C H A U P O S I O N (M-AILABS_eng_000199-M-AILABS_eng_000199) +Y O U S E E A N D T I L T H E S C O L P I L S W E R I N V E N T E D W E W A S T E D A L O T O F T I M E I N D S T U A D Y T H A T N O W M A Y B E B E T E R I M P L O Y E D I N M P R A C T I S I N G E A T H L A T I K (M-AILABS_eng_000200-M-AILABS_eng_000200) +Y O V E D O N I T N O W D I C L A R E D A R T H Y T H E S T E N T S A R J U S T W O N D E R F L (M-AILABS_eng_000201-M-AILABS_eng_000201) +F O R T W E N I N G T E N F I V E T H R E T W O T H E I N W A S B A R L Y T W E N Y M O U S A W A Y W H N H O D O N F I R E D H I S R O C K I T S T H E M D E C A L O S A L C L O U D O V A P E R I N E M T I N E S (M-AILABS_eng_000202-M-AILABS_eng_000202) +T H E Y P A D N O A T E N C I O N T O T H E F A C T H A T G I P G U S S I S L D I D N O T W N T T O M A R Y A N Y O F T H E M F O R T H E Y H D E T E R M E N D T H A T H E N I T W A S A G R E E D W H O H O U D H A V E H I M (M-AILABS_eng_000203-M-AILABS_eng_000203) +W H A T D O U T I N O F T H A T H E C R I D E O P E N G A C O P Y O H E R E C A R D A N D L A I G T F L A T O N T H E L I B R Y T A B L E (M-AILABS_eng_000204-M-AILABS_eng_000204) +I T L R E C U I E R B U T A S H O U R T T I M (M-AILABS_eng_000205-M-AILABS_eng_000205) +A N D L A S T T H E C R O U D O V E G I T A B L E P E O P L E W H O H A D N O H A R T S A N D C O U L D N I T H E R S M I L E N O R F R O W N (M-AILABS_eng_000206-M-AILABS_eng_000206) +T H E N Y O U L C A C H I T S I T H E W I C H (M-AILABS_eng_000207-M-AILABS_eng_000207) +W H A T I S I T I Q U I R E D N O T F E L I N G S E R T N B U T T H A I W A S A V A L E D A T E M P T O S E C U R E L I T L F R E A D R T I S I N G F O R T H E A N D E O V E R (M-AILABS_eng_000208-M-AILABS_eng_000208) +S O H E G A V E T H E L U R K T H A T H R D H U N R D O L R S F O R B O O K S A N D A C A S K O F G O D O L D A L F O R P E T E R T H E C L U R K D R A N K T H E A I L H I M S E L F A N D G A V E T H E C A H M I (M-AILABS_eng_000209-M-AILABS_eng_000209) +A T L I K E T H A T A N A L S I N W N E R L A N T W I T H M E R L Y A G R I N H A T F A T E D A W A Y C H A N G I N G I N T O A L I N K X E W H I C H I N T U R N D I S O P E R E D F O O E D B Y A N U N O N C R E A T U E R W I T H S H O R T N O W S A N D P O N E D E A R S (M-AILABS_eng_000210-M-AILABS_eng_000210) +S H E C O U D N O T D O M A R G R I T L A N S E D U N C O N I O U S L Y A T T H E U N C L E C O R N E R F T H R O M S H E C O U D H A R T H Y U D E R T A K E A S E R I N T S P L A C E C O U L S H E (M-AILABS_eng_000211-M-AILABS_eng_000211) +N O S H E R E P L I D E D W I T H I N I S N C A R I O U S I T Y D I D I G I V E T H E M T O Y O U (M-AILABS_eng_000212-M-AILABS_eng_000212) +M A R B R O M I L E S A N T H E A G A C S E N T D W E L I N W E R E H E L D U N D E R L O N G L E A C T S T H E Y M U S T I F P O S I B L E B E R E L E A T (M-AILABS_eng_000213-M-AILABS_eng_000213) +A C A P W A V E O S T O N I S T H E L A D O R (M-AILABS_eng_000214-M-AILABS_eng_000214) +I T B O U N D E D H E A R A N D T H E I R A B O T T H E C I C A N H O U S E A N D A T F I R S T D O R T H C O U L D N O T T E L W H A T I T W A S S W H I L T H E S C R E A C I N G O F T H E C I C O N S N E A R L Y D E F E N D H E R (M-AILABS_eng_000215-M-AILABS_eng_000215) +T H E S O L D E R G A V E A Y A L T H A T A R O U W S E D A S C O R O F H I S C O M R A D S A N D B O G H T T H E M T U M B L I N G I N T O T H E S T R E A T W E N T H E S A W H O T H E B O L R S E P R E S I O U S P R I S N E W A S E S C A P I N G (M-AILABS_eng_000216-M-AILABS_eng_000216) +J I M H A D R E F U S E D T O L E A V E T H E F I E L D O F G R A S S W H E R E H E W A S N G A G E D N B U S I L Y E A T I N G S O T H E W I S U R D G O T O U T O T H E U G A N D J O N E D S E B A N D D O R I T H Y (M-AILABS_eng_000217-M-AILABS_eng_000217) +S E R T N L Y I M A S I N R U T D I T H E C A C E S O U A R B U T I C A N M A K H A D S R T A L S O F I T I R E P L I D (M-AILABS_eng_000218-M-AILABS_eng_000218) +O R A N Y M I C E O R E V E N G R A S H O P E R S (M-AILABS_eng_000219-M-AILABS_eng_000219) +A N D T H E T H A P A S I O D O N T H Y T E L Y O U W A T T O D O O R W H T I N N O T T O D O W E T H E M O N Y T H E Y G I V E Y O U A N J U S T P A M E N T F O Y O U R P A I N S I N T H E R E X T A N G E L I G (M-AILABS_eng_000220-M-AILABS_eng_000220) +W H A T D I S T A T M E A N A S T H E P R I N C E S (M-AILABS_eng_000221-M-AILABS_eng_000221) +H E H A D B E D R O U N D H E W A S F L O T I N G I N A S E O F L I G T A N D N O W N T H E N S H I N I N G L I T L E F I H E S S W E A M I N C Q U I S I T I V E L Y U P T O H I M A N D S T A R E (M-AILABS_eng_000222-M-AILABS_eng_000222) +B U T O L D G U N H A D A T R C K A T O L E F T A N D R E M E M E T H E T A I L I R E D T O Y O U I T H T H O N R O M A B O T H E R T H E F I R S T F T H E R A O N S T O N D T H E W O R L D O F O P L W E R E S O L G E R S S E N T F R O M S O M E B L A S T E D P L A N I T I N O U T R S P A C E T O F I N E A N W H O (M-AILABS_eng_000223-M-AILABS_eng_000223) +P A P A W I L O U S P E K T T H E M E N A N D G E H E O G O A W A Y S H E C A N O T B R E E T H P O R T H I N G W I T T H I S C R O U D O A B O U T H E R (M-AILABS_eng_000224-M-AILABS_eng_000224) +W H E N I T O O K T H I S C A C E H E S A I D I B L E V E D O W N I N D M Y H A R T D I X S O N W A S I N S E N T I S T O B E L E I T B U T M Y F A T H A S B E N R U D T L Y S H A K E (M-AILABS_eng_000225-M-AILABS_eng_000225) +C H A P T R S I C K O F E T H E P I R T O F O R S E A T S (M-AILABS_eng_000226-M-AILABS_eng_000226) +R E M E M B E T H E C A N N O T T U C H U S (M-AILABS_eng_000227-M-AILABS_eng_000227) +I V E M E T I M E A S Y O U R G I V E M E T I M E I F H E R S A N Y T H I N G I H A T I T S A H U R Y I V E N I D A Y O U M A G U S T Y A N D O U N C E T H E S I X T T H E S N U B N O S D P R I N C E S (M-AILABS_eng_000228-M-AILABS_eng_000228) +T O N O F T R A T O C L A R E D T H E S A L E R M A N (M-AILABS_eng_000229-M-AILABS_eng_000229) +A S F O R T H A T S A I D M A R G R I T R E T H E R H O A T A L Y I H O L D I T I S H O N E Y S O I T Q U E E M A L L D E P E N S A Y (M-AILABS_eng_000230-M-AILABS_eng_000230) +W H E N H E H E R D T H E S W O R D S T H E K I N G W H O S H A D W A S F U L O F T H P I N C E S N E V E R S T O P E T O I N Q U I R I F T H E C O U L D B E T R U A N D S M E A R E D H I M S E L F O V E R W I T H F A T A N D S P R A N G I N T T H E O V E N (M-AILABS_eng_000231-M-AILABS_eng_000231) +Y O S H O U L D B E A L E G T P A R T C E F R O M Y O U R W R O M V I O N R E C E V E R I L H A V S O M T O U L S G I V E N O U T H E N H E A T D D E P L O M A S H E H A S T O N D E R S T A N D T H T I N G S H A C N T R O L O F V E N C S (M-AILABS_eng_000232-M-AILABS_eng_000232) +B Y T H E T I M T H E F R O S T H A D S A D I N T H E S H U L B E F A R W A Y F R O M H E L S T O N (M-AILABS_eng_000233-M-AILABS_eng_000233) +W O N T H I N G I W N T T O S A Y B E G A N D C A N I T Y (M-AILABS_eng_000234-M-AILABS_eng_000234) +T H I S M P O R T N T R A F I C W A S C O N F I G E D T O N O O N U T T H E E A L P R O P R I T E R (M-AILABS_eng_000235-M-AILABS_eng_000235) +I N Y E O A R D O B B L A S E D T P O N T B A S O U D O T M Y T H S T I M G G O (cv_eng_000707-cv_eng_000707) +I G H T E A T A E P R I T D S U P S E C T I O N W H I C H D E A L S W I T I S A S P E C T (cv_eng_000708-cv_eng_000708) +O P R A T I O N O F T H E F R U N T L A N G C O N T N E D O N T H E G O U L D A N T T R E S S E L S (cv_eng_000709-cv_eng_000709) +M O N S I O M F L O R I D I S T W E N S P E R E N T O V E R A N E X T R I M L Y W H I H D R A N G O F A V E L I N G S (cv_eng_000710-cv_eng_000710) +F O R J G I N T B E A K I N K S H E A T S S T O R T F R E S H B P A C K E T B U T E A D E S S O N D I L V E D T H E M U N T O R E L E R O L D G A S S (cv_eng_000711-cv_eng_000711) +T H E O T H E F O R T I N C A M P A S A R E T O Y A C A M P S S R E F E R D T O C O L E C T I V E L Y A S T H E Y U N E R S T I C O L A G E (cv_eng_000712-cv_eng_000712) +I T S T O T H E A R D T H O W H E C U I C K L E G O N T O F R G E T M Y N A M E T H (cv_eng_000713-cv_eng_000713) +W O N P O T U R E I N T E G L O R S H O T H H O W D H E A G E N T L Y I N T I R E N T I S T H A T H Y E A L A D G R A R T T O M P O N (cv_eng_000714-cv_eng_000714) +A I M P E R I A L D I Y I A T (cv_eng_000715-cv_eng_000715) +T H E E S U L T I N O M P A N Y E D A S H A R T A E S I C U R I T Y C O T P O R A T I O N (cv_eng_000716-cv_eng_000716) +B E C O I N G M I N I N G C A N B E D O N W I T G O F H I S C A R T S O R W I T E S S P E S I A L I E D H O R D L Y (cv_eng_000717-cv_eng_000717) +T H E Y A L S O L E T H E N A S I A L R A N K I N G (cv_eng_000718-cv_eng_000718) +T R O W S G R A I N S B I S H I P O F N I M E R I C E (cv_eng_000719-cv_eng_000719) +I O N D E D T H A T T H I D O R L H I M U N H E O K M Y P L A S E S (cv_eng_000720-cv_eng_000720) +I T H O U G T I D G I V E T H E C I T S A D R E E T (cv_eng_000721-cv_eng_000721) +A S T H E V I T L D I N I H T O M T H E P I C H E S (cv_eng_000722-cv_eng_000722) +H O W D Y O U R N O S T T O C E T H I S M A Y E F R O M T H E A B L I N G Y O R M O T O R F N T I O N (cv_eng_000723-cv_eng_000723) +A C T H A T S O N D S L A K E T H E A R P R O L O M E M I C (cv_eng_000724-cv_eng_000724) +H I S T R I C A L I G E R W A S N O C L E A R E L Y D E F I N E B O U N G R Y E N T H I S P I T O F T H E A R A B Y E N P N I N S T O L E (cv_eng_000725-cv_eng_000725) +M A R S H I A L S H A V E R O F S L A S H F I L M E G A V E T H E F I L L M E A N A T E O U T O F T E A E N (cv_eng_000726-cv_eng_000726) +A O L P I N D I O T I T H A T (cv_eng_000727-cv_eng_000727) +H I S T T D I L E B E G A N T O R E S E M B L E M I C A L T E M A S S C K E I N O S (cv_eng_000728-cv_eng_000728) +H E I S A L S O L C A P A B L O F F I R N G L I G T I N M B L E W I F I M E N T E D I S R U P T I V E P O W E R (cv_eng_000729-cv_eng_000729) +T H E C L A M E T O W I K E D S C E N I N G L I N P U R M A L Y T I N I N G S A S F O W W I S E A T A N T H U R O N A D E R A S Y L A D L I R E (cv_eng_000730-cv_eng_000730) +S H E E G R U S I L Y T R O W H A T H (cv_eng_000731-cv_eng_000731) +H E M T T H E O R G A N I S E R S O F T H E P R O T E S A N D A G R E D D C R E A T T W O W O R K I N G R O M S (cv_eng_000732-cv_eng_000732) +T H E B O N S T R O C T H O F H O L D W O A R D W I L A B O F T H E R E N O N S T O R D (cv_eng_000733-cv_eng_000733) +I N L Y C A M D O N T O M A S G A R I T A N D G O L D F I L D S O A T I S E E C H I L E B A K C A R W E R U N C O N T E S T E D (cv_eng_000734-cv_eng_000734) +I T I S A C H R D Y S C O L W H O S F E S A R C O U C U L A T E D I N O N I N M E A N S T E S T (cv_eng_000735-cv_eng_000735) +S O M E W E N T A W A Y W H A L I O W A S T H E R A N D O T H E P O P L E C A M (cv_eng_000736-cv_eng_000736) +T H C S A I T H A E D E D U P R E (cv_eng_000737-cv_eng_000737) +T H A T C U R A C O N O T Y W A S L O K C A D E D M A N L Y I T H E H I S T O R I C A L E A N D J E A G R E F I C A L R E A G I O N O F C U R (cv_eng_000738-cv_eng_000738) +U N C H E L O V A T I O N A T H E S I G H T I S A M O F S U L E V B L E (cv_eng_000739-cv_eng_000739) +T O B E A S T R I E D T O N C H E C T C O N O N T E M P T I N T O H I S T O N E (cv_eng_000740-cv_eng_000740) +I H A V E T O W A R L K T H I S S A T O R D Y (cv_eng_000741-cv_eng_000741) +T D E T R A T H E R O N W H S F O U N D T H E S C O L E G E G L E A D W I T H G L A T I N G O N T H E R G N O N E S (cv_eng_000742-cv_eng_000742) +W H E N T H E B I L I N G D O S T H E S S A E L E D F O R B I T T H E B O Y T R M B L E D A T W H A T H E S A W (cv_eng_000743-cv_eng_000743) +D E M A C R A T A M B E R A N B A K E K H E R W O N I T H E O P O N S E E (cv_eng_000744-cv_eng_000744) +W O R T H A V E O R T T E I N T O G E T H E R B A I T S O U O D E N T I N H I E C U A L I E R E U S O U N A T H E S I N P O R O M (cv_eng_000745-cv_eng_000745) +T R I N T E W A S B O R N I N B E L E S S I T E D I N B R I T O S P O N D E R A S (cv_eng_000746-cv_eng_000746) +D O R I T Y F A C E O F L I F M O E S F A S T (cv_eng_000747-cv_eng_000747) +A N O W H H E (cv_eng_000748-cv_eng_000748) +S I V E I N G O R L T D L O L (cv_eng_000749-cv_eng_000749) +A T O N T M B R Y L O U E L I N S T H E Y W A R D F R O M B R A K G B E S T A T I O N I N S O N D I F R E N E R E C I O N S (cv_eng_000750-cv_eng_000750) +C H E C K R E P U P B L I C K E N T E D T W O S H O U T E R S I N T O T H E P A R O L I M P I G C O M P A T I T I O N (cv_eng_000751-cv_eng_000751) +T I T E R W I L I O M S R O E T H E S C G E A N G C L A Y A N D N S S H A R E D S T O R Y R A T I T T H A T T H E P E P I T (cv_eng_000752-cv_eng_000752) +T A I S F A S T O F E A L L D W O R S T O O F R E T E R C H H E R I T Y F I N T H E R A D Y S A D E R O F O Y D T H E R A R T (cv_eng_000753-cv_eng_000753) +O T H E S E N X T R G A R T S W E N E S U R N T T H E R N G O N L E L A L M S W O A G A L F T E R A G W H T T H E A L H A T (cv_eng_000754-cv_eng_000754) +A H U H N D R O N T B A C K T O E S T R L I O A (cv_eng_000755-cv_eng_000755) +P E R M I T M E T O I N T R D U S E Y O U T O H U R R M O G J E S T I E D C Q E A N (cv_eng_000756-cv_eng_000756) +A N O R G I N H E R W O N W A S S U P O S T O T H E N O N A D I C T I V F M O R F E N S U B S T O T (cv_eng_000757-cv_eng_000757) +U S H E I S O F M E K C I C O N D E S S E N T (cv_eng_000758-cv_eng_000758) +C S I M S H O R T E A L E S N O T O N D I S T (cv_eng_000759-cv_eng_000759) +I O W H O U S O N D O N T L O N S A O N T H E S S H R E Y I V G O N T P R A E P E D I T O H L O N O (cv_eng_000760-cv_eng_000760) +I C A L E D A O N S O P E S A R L I N A T I T (cv_eng_000761-cv_eng_000761) +F O R S I M P L I T H Y G U R I N C H E S I S N O R M L Y A R O U N D E D T O H E E R E S H O L N O M B E R (cv_eng_000762-cv_eng_000762) +I F W E A C T I L Y D O O N I S A L E D I T W I L L B E F (cv_eng_000763-cv_eng_000763) +T H E F R O O F T H I C T R Y S A P L S H A P E D (cv_eng_000764-cv_eng_000764) +T H E O U T E X T H A N G E I S N O W O B U Y (cv_eng_000765-cv_eng_000765) +W H A T Y O U E A E T O D A I Y W A L K S A N D T A R K S T O M O R O W (cv_eng_000766-cv_eng_000766) +T H E W A T E D A N F L O S O U T O F T H E S W O M P S A S T H E L O U W O P L A R R I V E R (cv_eng_000767-cv_eng_000767) +A H W H I Y I D I D N D Y O U S E A E S O M E H I N G K (cv_eng_000768-cv_eng_000768) +T H A V O U S E N O M A R (cv_eng_000769-cv_eng_000769) +I C O U L D G O A N F O R D A Y S A B O U T T H E D A D I O U S L O N G S P H E D U S E I N H I S P A R T F T H E W E O R O E D (cv_eng_000770-cv_eng_000770) +T H O S F H E O L A D E O F H E A I N C Q U I R O N N G T I N S I T I C L Y O U T T H E Y E A R (cv_eng_000771-cv_eng_000771) +F A S L E V E I S O F D E C T I S E S S E R G L T O N S L A (cv_eng_000772-cv_eng_000772) +T H E S W E E D S W E R N A B L E T O O U S E R V E I C A L S W H I C H E R S T U C K I N T H E M O D (cv_eng_000773-cv_eng_000773) +T H E A C K D I D N O T B R O R H E B E C T B A Y I N G A R E P R E S E N T I E T O A P E A R I N T H E C O R I C T O (cv_eng_000774-cv_eng_000774) +C H I N G W E R E P L I S T L O P I N G R O R A L A (cv_eng_000775-cv_eng_000775) +H E W A S C O N V I C T E D A N B A N I S D I S I P R S H O R S E V E N Y E A R S W E R P N I S M E N T (cv_eng_000776-cv_eng_000776) +T H E C U P L O F T O C H A L D E N A D A T E R S O F E A U R O S A L E N D A N D T H E S O N O M A T H Y O L B R A V E R Y (cv_eng_000777-cv_eng_000777) +N O F T H H R E R E F P R E N D A M S R E C H H E Q U A R A M O F T H M A G J O R I T Y O F T H O S I N T I T L E D (cv_eng_000778-cv_eng_000778) +I N T I T E R P E N S E C X C E D E D I N D E A R A S T S O M E A R R A S I P C A R A I T I S W H O S A L D O U N E R S E Y T H R A P E A R I E O S T R O N G G R O T H (cv_eng_000779-cv_eng_000779) +H E A R I A M B E P T E N M Y F L O C K A N D M I B U T E R S U R E D T H E B O Y T O S (cv_eng_000780-cv_eng_000780) +T H I S F A L I A H A S T L E T T O S I C X T E A E N P O U L B L E N C S H A D E I N S E A R D A Y S E O F C A L E S T O (cv_eng_000781-cv_eng_000781) +A O O Y S A S D E O (cv_eng_000782-cv_eng_000782) +W H I Y I T H A P L A I N C E P E G O I N O V E R (cv_eng_000783-cv_eng_000783) +A N D N I H A Y A E A E D O N D O S H E F O R W A T F I R T I A L B O C S W I T H O R E S O U L T S (cv_eng_000784-cv_eng_000784) +T H E P L I C A T I O N W A S P U T A P P R O V I T I N F A R B R A V Y (cv_eng_000785-cv_eng_000785) +H E N R Y T O R L E D T O N M N S T I L S W E A R H E H A D A S O U N D I D R A N I N G I N L I T I N G (cv_eng_000786-cv_eng_000786) +I T W A S T I S C O N T I N U E D O T O S C E T H A L I N C O N F L I C S A N V L V E D I N L O S E H I S R E T H I R N T O R E T O R E S T R I A L R E B R A D I O (cv_eng_000787-cv_eng_000787) +A D T T H H E R F A M L Y W A S F R O M E B R E A H O N S A (cv_eng_000788-cv_eng_000788) +A W H A T D I D Y E A E F O R I N O R T H E A P A (cv_eng_000789-cv_eng_000789) +T H A T W A S M Y D R A R T O S I N C E (cv_eng_000790-cv_eng_000790) +H E S C O S E A R I T A M U S T E R E O F S H E A R O S E D C O U O (cv_eng_000791-cv_eng_000791) +T H E H L I N T U R S T O T H E C H U R I S H O A S I N E S T H A T D A L T E R I N D S P E U S E W H E T H E R (cv_eng_000792-cv_eng_000792) +I O U E N O T T H O S W E R E I N H E R A C H L E A D R (cv_eng_000793-cv_eng_000793) +T H E L J O S I E C T E N D F E S T T H E P I Y (cv_eng_000794-cv_eng_000794) +M Y N E S C A N H E L P Y O I T T H A T S (cv_eng_000795-cv_eng_000795) +B U T S A O D H I S T O F E R Y W O N (cv_eng_000796-cv_eng_000796) +H O W F O R T H E B E S T A N D P R P E A E T F U E T H E B O S T (cv_eng_000797-cv_eng_000797) +I N I S H E L Y T H E E P L O U S W A S H R T E N S T R I C K L Y B Y D I T (cv_eng_000798-cv_eng_000798) +A L L W E O N E D B Y T H E E V E R I T M O R E S I N I C I T (cv_eng_000799-cv_eng_000799) +B H A T H T H E N T H E W I L A S R I N G T O M O R O M I L N T H S E O D (cv_eng_000800-cv_eng_000800) +H D O R B I S T E R E C K G M M I N L I P (cv_eng_000801-cv_eng_000801) +O S E I A L P E T R Y T O H E R P A C E A S A C T I N G D I R E C T E R (cv_eng_000802-cv_eng_000802) +T H E B E V E R L Y W L B I F L Y N T E S T H E E S S E N T R P U T O F A T O N S H I P (cv_eng_000803-cv_eng_000803) +T H E T R A C K R E S E R V S T I N G W A S A L S O C O M P L E A T E D (cv_eng_000804-cv_eng_000804) +H I T M A R C H W A S A W E R E O F T H E I M P O R T N E O F E L E C T R M R E C O S C M P I N B Y A E L O U G I C A L R R E S E R C H (cv_eng_000805-cv_eng_000805) +S I N H O W B A S B O R N Y A N T H E H A B A R (cv_eng_000806-cv_eng_000806) +N T H I S W I N C H K E A S E A N O F I A L Y H E R H O D T O A S M A C K R E M P A D R I N T H B Y C O L A S I N F O U S C S E S R A V B E R A I T I N W I L (cv_eng_000807-cv_eng_000807) +I T I S R E S P O N S E I P L F O R W A T E S U P L I Y A N D M A N G E M E N T O F W A T E R R E S O U R S E S A N D M A H A S T R A (cv_eng_000808-cv_eng_000808) +J E S E S T H E F I R S F A I C E O F T H E H O R V E A H E S A Y E D (cv_eng_000809-cv_eng_000809) +T H E G I S I U P L A T O O R G I A N E C R A L P O L E S I N T H E A G I P T I O N V A O L Y O F T H E D E D C O N T A I N G S E V E R A L P E R I M E N D S O F W H I C H T H E G R E A T E P E R M E N T I S T H E L A R T E S S E V E R A L E S M A L T O O N S S E V E A L T E M P L E S A N D T H E G R E A T S P A N K S (fleurs_eng_000413-fleurs_eng_000413) +T W A R D T H E I N D O F T H M I L E A G E S W E S T E R N Y U R U P B E G A N D T O D E V E L T T H E R O N S T I L O N O F T H E B I G E S T V E L M E N S O F T H E T I M E A S A R E S U L T O F T H E C R U C A S P E P U L B E G A N T O U S E B U T E N S T O F A S T N C L O L T I N G R R (fleurs_eng_000414-fleurs_eng_000414) +I F S Y O U O N L Y G O A S H O R E U S I N G S H I P O R I S C U R I O N S Y O U L N O T N E A S E P R T V E S A A S A T W O T H O U S I N D N I N (fleurs_eng_000415-fleurs_eng_000415) +D O U B A L H I S M A R E W I H T O A D L C H E R E N C U D N O T B E W A B I G M P R E S I O N O N M I L E R T O H O M T H E S O R Y W A S R E L A T E D (fleurs_eng_000416-fleurs_eng_000416) +T H E R D I S O P L I N D D E F E N C S B A L H A D L I N G S C I L S A N D E X A L N T E O R K M A D T H E S T A N D O U T A N W A S C L E R H A T H I S W A S T H E E M E T O B E (fleurs_eng_000417-fleurs_eng_000417) +T H E D I S E S I S C A R E D B Y P I G S W H I C H H E N M Y G R E T E S T O H E U M E N S T O R O M S C E T O S (fleurs_eng_000418-fleurs_eng_000418) +F O R T H E S P R I N G B O K S I T D E N D E D A F I V E M A T H L O S I N G S T R E A E K (fleurs_eng_000419-fleurs_eng_000419) +T H E S T H E P I N S A L W I T G O D F R I N D S M A N Y E O P L E W E N I C A M E O U T (fleurs_eng_000420-fleurs_eng_000420) +T H E U S E O F V E O R E C O R I N G H A S L E D T O M P O R N D D I S C O V E R E S I N T H E I N T E R P R I T A T I O N O F M Y K R L E X P R E S T I O N S F A T I A L M O V E M E N S W H I C H L A S A F E U M I L E S S I C K E N S (fleurs_eng_000421-fleurs_eng_000421) +A L S A T T H E N O R T H I S T T H E G R E A T E S A N C U R Y O F O R L A D Y O F A T H E M U S H R I N A P L A C E O F W O L D R I G T E F A M S M E R I A N A V P E R I O N S (fleurs_eng_000422-fleurs_eng_000422) +I F Y O W N T B E C O S E O T H E A C T I O N Y O R E H A E T O O O G E T I N E A L Y T O T A C A M P I N G S I H T C L O S T O T H E M U S I C K (fleurs_eng_000423-fleurs_eng_000423) +M T A G U S C O A R I S B Y F A R E T H E B I G E S T A N D T H E C O N T I N A N T O N I T S O N W H E N I C O M S T O W W I L D L I F (fleurs_eng_000424-fleurs_eng_000424) +W E M E N I T I S R E C O M E N E D T H A T A N Y W O M E N T R O V L O R S S A Y T H E Y A R M A R E D R E G A R L S T O F A C T I U A L M A R I T A L S T A T I S (fleurs_eng_000425-fleurs_eng_000425) +U O M O F I F T Y T H R E B E G A N H I G O V E R M E N T G O V E R S H I P E R I L E R T H I S Y E A R A N D S I N D A B I L E L A S T M O N T H L E G L I S I N G S A I M E S E C X M A R A G E (fleurs_eng_000426-fleurs_eng_000426) +A S L I P L U T I O N N H E R H A D Y W A S N O T T H E C I N D O F P R O B O M T I S T O D A Y T H E R U L Y L O C A T E D I N S I I E S O R A C A M P S E S E A S I E R T O R E A S I O N T H O S B I L A N M O T E N T I M S (fleurs_eng_000427-fleurs_eng_000427) +T H E U L Y H A V E S P E C I A L F O D R I N K A N N R T A M E N O P E R S T O C E G E S A N D A G O D M O D A N C E T H E M A T T H E P R M I S (fleurs_eng_000428-fleurs_eng_000428) +O N T H E O T H E R H A N D I C E S E A N D S N O W Y C O D I O N S A R N O R M A E I N M A N Y C O U N T R E S A N D T R A F I T O E S O N M O S T L Y U N I N T R U P T E D A L L Y E A R R O U N D (fleurs_eng_000429-fleurs_eng_000429) +B E C A R F U L O T T O A L O W F A B R I C T O B E C O M E T O H I Y E W H I C H C A N C A S E S T R A N K A D G E O R I N A S T R E N C A S E S S Q O A R T C H (fleurs_eng_000430-fleurs_eng_000430) +F E I R L C H I L D R N M A H A V E X P E I A N C S O V E R C H I L D H B E S O R T R O M M B E F O R B I N G A B A N D I N R R N G A W A Y (fleurs_eng_000431-fleurs_eng_000431) +P E O P L E M A N O T I N T I C I P A T T H A P A T I O N C S A N D N D R E S T A N G R A L S O N E S E R Y F O R T R O V L E R S R E T R N I N G H O M (fleurs_eng_000432-fleurs_eng_000432) +O N O T E R T H E P R I K O F H U S T I L I T E S B R I T N I N E N T S H E A T E D A N N A V B L E B O C K A D E O F H E R M A N Y (fleurs_eng_000433-fleurs_eng_000433) +T H E O E N R S O F I S S A I D N I N T E N O F T H E I N G U R E D W E P L E A E S O F I S E R S (fleurs_eng_000434-fleurs_eng_000434) +U S I N G S H I P S T O T R E S P O R T S G O O D S I S B Y F A R T H E M O S O F I E N T W A Y T O M O E L A R E M O U T O F P E B L E N G O D A C R O S O T I O N S (fleurs_eng_000435-fleurs_eng_000435) +T H E B R L C R I T I S I S M O F T H E R E C O N S T R C T I N E V E R T N H A S P O K A S O T H E W A R D I N G O F R E C O N S T R C I N G C O N T H A C T T O R I S T E D E W A T I N G A N D I N S I E R S (fleurs_eng_000436-fleurs_eng_000436) +U T W E U C U N S B O W D O B B O D A M O R S E C L T A C X C Y T O G E T A R O U N D G O M A T H E N R M A E W I C K L E P R I C E I S F I V E H U N D R E D C O N D L E S F R O N S F O R T H E M S H O U R T R (fleurs_eng_000437-fleurs_eng_000437) +T H E T H E K I N G D O M S W A S O N E O F T H E B L T B L U D I A S T E R A S A N D A N I E N T C H I N A S H I S T H R E T H O U S O N S O F P E O L E D I E D F I T I N G T O S T I T I N T H H I E A S C E I N T H E G R A N D P A L E S A T S I (fleurs_eng_000438-fleurs_eng_000438) +R T H E S C O U P L E S M A Y C H U S E T O M A K E K A N D A D O U S I N P L A N D F O R T H E R B A V Y (fleurs_eng_000439-fleurs_eng_000439) +N O T H N G A N D B E F E N O T H E R H N T H E C L E A R E B U T I F U L S C A I Y A B O V E A N D T H E M E N Y S U R U N G M O U N S V E R Y L I T O F T H S W A L A N B E S E N O R H U R D F R O M I N S I D T H E C A V E (fleurs_eng_000440-fleurs_eng_000440) +H E W A S S O B S I C U E N T L Y R E L O C A T E D T O A D I N B R O K S H O S P I T L A N C A M B R I A G E (fleurs_eng_000441-fleurs_eng_000441) +T H A I C A N S T I T Y P O P U L A T I O N I S A R O U N D A I N H E U N D R I D T H E I S T H E M A O S T N T E P E N D E C O N T R N T H E W O R A L D A N D T H E P O P U L A T I O N (fleurs_eng_000442-fleurs_eng_000442) +R E G U R A L O U N S M E N T A N T H E P M E T R U A R M A E O L Y I N C A T L O N B U T U N P L A N E D I S T R U P T I O N S A R N O U S E B Y A N O T A M A E D S I S T O M I N A W A D V E R I T Y O F L N W I C G E S N C U T I N G S B A N I S H A N G L I S H F R E N C H E R B E C K A N D H A P O N E E S (fleurs_eng_000443-fleurs_eng_000443) +T H I S O P R E A G O D P R T U N I T I T O S E T H E O W R A B A R I L E S A S T H E S C K G I W I L B E D A R K M O R L E S T R U N T H E C L O C (fleurs_eng_000444-fleurs_eng_000444) +F I R S C K U C R O S O V E N C I A L Y D O U S E T O F I E B Y A L E V E N T H R T Y F I V E P E A M (fleurs_eng_000445-fleurs_eng_000445) +T H I S C A L T O C M I C A L S P E E H E C A N M A K A N I N D I C A T E O U S I N G R E D C A B A G H E J O S (fleurs_eng_000446-fleurs_eng_000446) +I N P R T I C U L R I T I S L A E T H A T O N E C A N D E T E C W E T H E R A P R S O N I S L I N G B Y I N T E R P R I N G M Y G R O L E X S P R E S I O N S C O R E C T L Y (fleurs_eng_000447-fleurs_eng_000447) +T H E S E C H A L F O R I T Y O F T H E C H U C H O D S B E N I N R O M F O R O V E R A T H O U S A N Y E A R S A N D T H I S C O S O N T R A T I O N A F P O W E R A N M O N Y L E D T O M A Y T O C U S T I O N W H E T H E R D I S T E N E T W A S B E N G M E T (fleurs_eng_000448-fleurs_eng_000448) +T H E S U N D A R B O N S A R T H E A R G E S T H E T O R A L M A N G R O E B U L I N T H E W O R L D S T E C H I N G A T Y C L A M I T E R S F I F T Y M I E S I N T O T H E A N G L A D E S H E A N D I N I N D I A N H I N T E R L A N D F R O M T H E C O O S T (fleurs_eng_000449-fleurs_eng_000449) +R E G U L R A N O N S E N C S I N H E E T H O A R M A E O N L Y I N C A T A L E N B U T U N N D I S T R U P T I N S R N O U N E D B Y A N O T M A T E I S S I S T M I N A W A D V E R I T Y O F L I G W I N G E S I N C U D I N G S P A N T I S H I N G L S H R E N C H E R B A C K A N D J H A P O N E S (fleurs_eng_000450-fleurs_eng_000450) +E V E R W N P R T I T B A T I N O U C I T Y A N U S I S T R N S P R T I O N C S I S T O N C S A L M S T E R Y W N C O M P L A I N E O O U T R N S P R T I O N S I S T O M S (fleurs_eng_000451-fleurs_eng_000451) +L A T N H A D A S O R H A N E S O T H E O N S U I R V E T I S N V I R M I N T L B I L D U R N T H E M E N W T T H E P E M A S I N G F R Y T H U R A L A N D C O M P L E T E R E R I D T I N G O F T H E C O N S E R V E T H I S P A R D Y I N I E R M I N A L I L (fleurs_eng_000452-fleurs_eng_000452) +I N Y O N H I S G O N T O T R I E H A H A T L I T A T U D S O R O V E R M U T N P A S S T H R C O N C S I D E T H E P O S I L I T Y O F S N O I C E O R F E S I N G T E M P A T U R S (fleurs_eng_000453-fleurs_eng_000453) +H E S L E I N T R U S T I O N I S H E P R A S T S E S O F H E B O U C A Y W A K N I N B E U R I N G Y O U N O R M A L E S L E E P E R I A D A N D F A L I N G A S L E A S H O U R T T I M E L A T E R C E N T O S I C T E M I N O T S T (fleurs_eng_000454-fleurs_eng_000454) +O U R S W O A R L T H E T O D R I P P O U R S T O G E T H E R A N T H E W I T H C U E N G A W E T H A N S S C U E A E T H E M I N T O A B A L E R E H (fleurs_eng_000455-fleurs_eng_000455) +F O R T H E S P R N G B O C S I T E N D E D A F I E M A C H L O S I N G S T R E K (fleurs_eng_000456-fleurs_eng_000456) +J U S T L I K T H E O N E X P U R T D S A P U L O N T H E E R T H C A S I N T H E I D E S O T A S A M L B Y W A Y E X E R T I F F O R T S O F H E E D I T A R I O U S G A L A C Y (fleurs_eng_000457-fleurs_eng_000457) +T H O R O T H E N I G H T H E T W E N H U D E R D A N F I F T Y A N T O H U D R E D C O P E S W E R E M A D E N O W N O N A S B U N E L A P B R O L D S I D S (fleurs_eng_000458-fleurs_eng_000458) +F I R S T A M O N G I T S E M N D Y A E R E C O M E N D A T I O N S I S T H A T A N N O W D I P L M A I K N I S H I T I V E H U L B E T A K E B E F O R T H E E N D O F T H I S Y E A R T O S E C U R E R R A C X P B O R E R S A G N S H O S T I L I N T R V E N T I O N S A N D T O R E A S T A B L I S E D I P L M A I C R E L A T I O N S W I T H I T S N A B E R S (fleurs_eng_000459-fleurs_eng_000459) +S H A N T E T E R S B R C R U S I S I N C L U D T I M I N T O W N W H O T A S O N G E R S A R X A E N T E D F R O M E S T E R R E Q U I R I M E N T S C H A C K T H E T R M S (fleurs_eng_000460-fleurs_eng_000460) +O C O R D I N T O U P A N S N O G U L R A G E N C S Y R E D Y A L A C T I V E C A S E A M E A N D I A D I N H A S E I D E N I F I E A T T H E P L A N T (fleurs_eng_000461-fleurs_eng_000461) +S A G O G A T I O N A N D R E C O M O N A T I O N S H U F L V E R Y I A T I O N B A C K A N D F O R T H B E T W E N T H E T W O P U L E S W I T H E A C H G E N E R A T I O N (fleurs_eng_000462-fleurs_eng_000462) +E L A M N T L K C H L T H E M N D P A A T I M R C O N C S T E D M E T L S O F P O R S E R A L S O M E T E S L K E S I V E R A N D G O L D (fleurs_eng_000463-fleurs_eng_000463) +T H E C O R L T I O N E T W E E N B R A I P I T H O L A G Y A N D B E H A V Y O U R S U P O R T S I N C S A N D H E R R E S U R C H (fleurs_eng_000464-fleurs_eng_000464) +A N C H A N C H I N A H A D A O U N E K W A Y O F S H O W I N G D I F R E N T T I M E P E R I A D S E A C H S T A C E O F C H I N A E O R E E A C H F A M I L Y T H A T W A S I N M P O W E R W A S T H E D I S T I N T I F D I N I S T Y (fleurs_eng_000465-fleurs_eng_000465) +A S I M P L P O P U L E R D M E R H I S F E C I L Y D R I N T H E T U M E R I S P A A M A L D Y B R E D W I H A L V O I L E T O M A T O N A N Y A V I A B L E C O N T M E N S S U C H S C H E E S E T O N F I S H I T S E T E R (fleurs_eng_000466-fleurs_eng_000466) +T H E N O U N S M E T W A S M A D E A F T E R T R M P A Y F O N G C O M E R S A T I O N W I T T R K I S H P R D I D E N T R E S E P T E E P E R O D O N (fleurs_eng_000467-fleurs_eng_000467) +E R Y S A T E T H T H H E W O L D R E T E N T O T E C X S I S T O U S E S T H E R E S U L T S O F T O N I H T E S C O K I S D I D E R M E N W H T H E R T H E R I S A P A T H F O R D F O R M Y S E L F N T H S R A C E S B U E L E T E R S U T T H A H E W L E R E M A I N I N T H E R A S A N H U B P E N O R I G E N R Y T W E E W O N S O U T A R L I N O P R I M A R Y (fleurs_eng_000468-fleurs_eng_000468) +H E W A T A L S O I N G A G E A I N G R A V I N G B A K N O T S F O R M A N Y C O N T R E S R E S O N I N G S E M P L E S O F H W R K I N C L D T H E M P R I M E M E N T M N I N I S R E A L P O R T R D S O N T H E F I R S T F R O O U T H E F O N T O F T H E N E W C A N A D Y A N F F I V E D O L R I N W O N H N D E R D L D I L (fleurs_eng_000469-fleurs_eng_000469) +E M O R T R A D I N A L C H U R C H E S O F T A N H A L T H E N E S T R R I G U A L N T S A T E D Y N I G H T T U R N T H E E S T R W E K G N W E R T H E C O G R E A T I O N S O T D I M B R E A K I N G I N T O S E L E B R A T I O N A T T H E S R O K O F M I N I G T T O S E L E B R A E C R I C E S R E S E U R E C T I O N (fleurs_eng_000470-fleurs_eng_000470) +F I L E N I S A G R E A E B O T I N G D E S T N A T I O N T H E L A N D O F A T H O U S O N L A E H A S T O U S N D O F I L E N D S T O A N T H E L A K S A N N T H E C O S T A R K P A L O A O S (fleurs_eng_000471-fleurs_eng_000471) +R A N T S H E N A T E R A N D A R G I N C S E N F R S T L A D E C R I S T E N O F O R N D I S A C U R S I N R A N O U L S H E R P E S O N A T H O C A N D I D U S Y O S T R D A Y E V E N G A N L A P L A T H A A S T A D Y F I F T C L O M I T E R S T H E R D Y W N M I L S A W A Y F R O M E N O S I D I S T H (fleurs_eng_000472-fleurs_eng_000472) +S V E R W E T H E R I T E U N A R I C T U N E F O R A N Y D A D E R U S W T H E R F O N A M I N O N W I T T H E P A T N U A L T O C A S D A M A G E S R I O U S S O S I A L D I S T R U P T I O N O R L A S O F H U M N L I F E (fleurs_eng_000473-fleurs_eng_000473) +F O R E X S A M P L E T H E M O S T C O M E A N S T I L I M A G E F I T O C K R I F Y F O R M O U T N H E W O L D I S T H R T Y F I V E M I L O M A T E R W H I C H W A S T H E D O M I N A N T F I L M E S I E S A T T H E C L O S O F T H E A N A L O G F I L M A R A (fleurs_eng_000474-fleurs_eng_000474) +I T I S R E L A T E D T O B U T U L Y N O T I M V L V I N G H L P I N S T I L S C E T O R I N G O R M O T N E R I N G T H E L A T E R O N S D O N I N S P T U R I N G A N D R E C A R I N G M U S H T H I F R S C E S A N D B O T E S (fleurs_eng_000475-fleurs_eng_000475) +I A R N I N G D A M P C L O S C A N H E P E T H E M D R I Y M A N Y H O A T E L S H A E A N I A R N A N D I R N I N G B O R D A V A L A B L E F O R L O N G E V E N I F O N I S N O T P R E S E N I N T H E R O M (fleurs_eng_000476-fleurs_eng_000476) +A E V E D N Y O U N C E R D H O R S L Y S H E J U W H E R C H A R E U N T E R I T E G L O A T O T H E F I E R A N D S C R E D H E R H A N S O U T T O T H E L A S E T H E R E W A S N O O T H E L I G T I N T H E R O N M Y T H I S T I M E T H E W I N W I D O T H O R D E D M I S M A L Y S T I L (mls_eng_000283-mls_eng_000283) +M Y D E A R M O R E A W H I D O Y O U N O T D E S C I S T F O M T H I S C I L Y P E S U T O F A A N M A N D G I N A R Y T L E S E U R W H A T I S T H E V O L Y O U O F M U O N Y W E A R S P A N U R D S N O T S H E R T S L E V E D M E R S I N A R Y P L E A G S O F A M E A I C A E N S (mls_eng_000284-mls_eng_000284) +T H E C R I T I A L T R P A U R I S T A T O F T H E S I N G L A S T H E R M A E L I N W H C H P E S E N S E P O I N T O V N E F L E C T I O N A T H O R S N D T I E N G E N T T H E R I T I A L P R E S E H E R I A L V O L I M E A T H T W O C U L D N E S E O F T H I S P O N T O I N P L E C T I O N (mls_eng_000285-mls_eng_000285) +M U C H L I K A N F O U N U S A N D I F O R M I T Y O N T O T H A T M O N S T E R W H O M T H E H E B A N N I G H T T H E F A T H E R O F T H A T F A T L P R O G A N Y M A D E C I L H E R S E L F F O R V E R Y H A R T S T O S P I H T H A T H E H A D R E D H E R R I L W H I C H N O W W I H T C O U L D E V E R L O S E S W U T S U F E R E D E A D L Y D (mls_eng_000286-mls_eng_000286) +H I S M A S E D M E S E W I H P E I O N P R E S I E S A M O U N T I N G T O T H R E T H U S N A T N S T F I E A N D A L S O T H E R Y S M A L V O L I M S T A N O C U P I E B Y T H E F L O A I D M A S E N D E C O N S I D E R A T I O N T H I S L A S T M E S G E M E N T W H I C H N E S E S I T A T E S N E U M R U S C O R E A C T I N S I S M O S T D E L I C A D P A T T E O P O R A T I O N (mls_eng_000287-mls_eng_000287) +W H I Y S H U L D I T H A E B E N D E D N E C R O M A N C Y T O N D E V E R T O O N B I N T H E S F A T S T O I V O L V E B Y C A R F U L E L M I N A T I O N A N D C H A N G E T O T H E P E R F E C T F O D (mls_eng_000288-mls_eng_000288) +N A Y T H O O F R A S E S B E M Y B E D Y E T I A M R I C H L O V E S A I D B U T U T A R G U D L I V H E T H R I C E F O R N D A R E T H O W T O Y E L T H E S O V E R A N G I F T S O F E R T H T H E V I C T O R S O R D T H E L O R A L D B R O W F O R V I S I O N T H I N G K S O F L I T L W O R T H (mls_eng_000289-mls_eng_000289) +B U C K E M E T O H A V B E N A C E C U L A C T E R A O T H O H A M P E D B Y I L H E L T H A N D A G R E T P I N T O N I S F A V E R I S T H A T I D I S C R I D E D O N L Y T H O S E P L A N S W I T H H A D C O M E U N D E R I S O N P E R S I N A L O B S O V A T I O N (mls_eng_000290-mls_eng_000290) +H A D R A T H E R S R O N G U P A N D H A D N O T C H A I N E D I N T O N E I M P S T H E S H Y F E L T I N T H E T E A M S C O V E R I N T H E M U P A G I N A N D T H E Y U P E A R E D A S P E R F E C T I N S E C T S I N T H E M A Y O F T H E F O L O I N G Y E A R (mls_eng_000291-mls_eng_000291) +N O T H I N G S A Y O O B J E C S A N D T H U T S O F B O U T Y H O U D P R E S E N D T H E M S E S T O T H E U N E S T A N D I N G O F T H E F O R T I O L U T B U R S I N W H O P A R T O K O F I T T H E S E B A T E S W H C Y O A R B R U G T T O M E T R A N S A L A T A R O N S E U R E D W I T T H I S S O U P O E S T I O N (mls_eng_000292-mls_eng_000292) +N O S E M I N S O U P I T I T Y A N D H E D N E R V E H I M S E L F A G A I N S I T H I S F A I S E W A R S O R D O F S V E F F L O U S H D H E W A S T I M I E D E V E N T O R U D N E S (mls_eng_000293-mls_eng_000293) +B E C A M E M O L E L I F E L I K A S T E C H E S F L U S H T H E A S R A R W A R M E F I N O F I N T E M O R N I N T O H E T H A F T D I S P E R I N G S O L T S I D I T L O N G O U R S O F A L L R E D I N G A N T P E R S T E D H E A R T B Y N E V E R S E A S I N G R I M E S Y E T I C O U L N O N D E S T A N D I T (mls_eng_000294-mls_eng_000294) +W O N O F T H E O W I N R I T E R S A I D T H E A O P E H E A V A I S A P O S O N S H A L F I S H T H E S A R B I T E R A N D D E D L Y A N D C A D B E O U S E D I N P U T I N G E N A I M E S T O D E A T H (mls_eng_000295-mls_eng_000295) +T H E B E U T Y U S R O U B S O F H A V E N A S L O N T A D U R I H T E R S N C O L E I T A R H E L O K S I N B O U N L E S M A G H U T Y A B R O A D T O U C H I N G T H G R E N L E A V E S A L L A T R E M B L I T G O L D L I H T (mls_eng_000296-mls_eng_000296) +I C A N D O U N O M O R H A N T H A T I N T L T H I S M A T E R I S A P S A L U T L Y S E T E D T H E A W O R T H O T H A N L I F E I T S E L F T O M E T M S T R B L B U R S E M E D A N O I E D S U R L Y H E P R O T E S T I T W O U N O T G O D O A S T M E T O W A T E T H R E M O N C S U N T I L A T E X A M I N O N E O F T H E S (mls_eng_000297-mls_eng_000297) +R O S E C O N G R E S F O U N D A T I O N R U S H I N A N D T I T E T H A T O R G A N I E D T H E S A I T P E T E R S B U R G I N T E R N A S I N A L E E C A N O M I C F O R O M R O U S N E F T R U S H I O N S T A T O N D O I L E A N D A N A R G Y C O M P A N Y (mls_eng_000298-mls_eng_000298) +H O W G L U T E D N S P A R C K A L E T H E D E L I C A T R O S T W O K Y O U E A T R A C T E D N O D O U T A M A R V E D A T H E D I N T Y T R A I C S O M S B U T F E O U O F A S H A V R E A L Y H A D A N O P O T U N I T Y T O S T A D Y T H E D E T A L O F T H E F R U S T D E S I N S M Y N U T L Y O A V C O N S I D E D H A T T H E W R E O R T I N T H R E Y U R F O R D E S I N S A T M O S T (mls_eng_000299-mls_eng_000299) +T H A T H A T H E O F E N S I N T R I N G T O I N F I C K A W O N T H E M C K I L V E R O F H E N D E R O R W N H I M M O R T H A N T H E I N T E N D A N T O D O A N D T H I S B E C O M S E C C U S F U L A N U T H R D S O T A T H E P R I M I T I V E L A G D S L A T E R S W H E C A E F U L I N O R E C Q U I A R I N T H E R I T A L I T I O N T O B E L I M I T E D T O A N Y F O A N A (mls_eng_000300-mls_eng_000300) +A T S I R E S W O R D T H E J U E S W R E T E R N T H E C O M P N Y T H A T G O G O D S H I C E B E G O N W I T H M R T H A M O N I S H E N D E D B Y T H E F O B U T W O N C E A G A I N T H E W R K E C O S E O N B Y L I S E N S F R O M D U R I O U S E S R A I S S E N T W I T H R O I L E G R U N T A N D G I F T H S F O R I U S P I A S (mls_eng_000301-mls_eng_000301) +A N T P R O D A C K Y E A R I N A N D Y E A R O U T S I V I N H U N D R E F R O A N C E S W H O L I V E D O N I T H A V E N O T S O B A D L Y W R E I U L E X P L A I N E M O R D Y I S O C U P E D T A T H E O R B O H O V S (mls_eng_000302-mls_eng_000302) +T H E A N T H I S I S A L L Y O U R A N T A R T I S T O F A R F O R O N E O F H I S A L I A N C S A N D I W O R E Y O U W T H A T T H I S P L A C E N O M O R S E Y O U A E G X S E I T A N D T E R D F L U R A N S T H E B E S T I S T H E R I S M O R C R O U D T O E T A M A N S A R Y V E N J O N O N S E F O R A C E T H A T S M Y N A M I N D E D (mls_eng_000303-mls_eng_000303) +H E N I R E T U R N D T O T H E H O U S E W H E R E H A D B E A H A P Y C H I L D O N L Y A P I L O F A S H E I S W E A Y I T H A D S T U D I W E P T L O N G A N D T O F O G E T M Y W E P I N G I S A I D O U T O N D E V A T S C A M S S C E O N T H E S E W A T E R S I N A S T A R S A F A Y A N N I G T I P L A D E M Y F L O T T O T H E S U M E R M O N (mls_eng_000304-mls_eng_000304) +D O Y O U N O T S E W H A T L E S U E R I T G I V E S M E W E H A V E G R O N U P T O G E T H E R I N T H I S H O U S E S I N C H E W A S A B O Y I S I M P L Y C A N O R B E A R A S Y O U G A N T H E S I G T O F T H E S M I L L A V I N G H I S F A C E B O U R D E A R H E H A S N O M U S M E N T E X C E P T T H I S P L I N G A T T H S H O P C E P I N G (mls_eng_000305-mls_eng_000305) +I T I S D E V B I U S B O A D Y R E V A L I N G I N Y L I P I C A L O R R G R E E O N G A T I O N L O V E L O V E L O V E W A L N O T B E T H E W O N E D O F C U P I T B U T H E M A N I F I S T A T I O N O F H E E R V E R S A L R E P R D U C T I E I N S T I N C E S (mls_eng_000306-mls_eng_000306) +S H O A R P L Y A S H E S H O O K H A N D W I T H E R G O D B E S Y U M I G T D E A T C H A T H E B I H O P S A I D W H E N S H E C I S E D H I M A N D H I S L I P S M O E D O F T E R W A R D F O R S O M S I C K N T S A S I F H E W E R I N P R A A R H U R M O T H E R F O L O R D H E R O U T O F T H O M A N D T H E N S I L A N D C E T E L (mls_eng_000307-mls_eng_000307) +F O L O E D H I M S T A E L T F U L L Y A N D H E H I W A S I N A S T P I N G P O S T H U R F I L I N G H I S B O C K E T C A M E U P B E H I N E D H I M A N D P L U N C E D A N L O N G N I F E I N T O H I S N E C K (mls_eng_000308-mls_eng_000308) +S A S T H C U R T I E S D O U S N O T J U P I T E R D I S T R E B U T E T O T H E G O D S T H E P R O P O R T I O N A N D D I V I D E N T S P A R I N G L Y A N D S E V E R A L Y A S A G A M A N A N D Y E T O H I S C O M A N D O E R S W H E N H I S G E S T S T R A N G K T O O N E A N O T H E R I F V F O R C O U R I O U S Q U T H C L E E D E M O S A S Y U N E R R A T (mls_eng_000309-mls_eng_000309) +A N D H E R E N O N H A L D A R A S T R A N T T O A M E T A G A I N I N T H O H T S O T H I S N O U S E A N E P I N G B H E R E C H E R U L S P I R I T S T I L N E R D O T H E F A T I S E P I N G F E U T U R G O O D F O R P E S E N I L (mls_eng_000310-mls_eng_000310) +A N D B E C O M E T H E R E C A R D O F W H A T P E P L A V E D O N I T H E R M O R A M U B L E M O E N T S T H E R E C A R D O F H E C O N C Q U E S T S O F P E S E H O W M E N H A V E L I V E D A N D L A V E R D D U G A D B I L T H E U O N A N D C L E R E D G A R D E D A N D R E F O R S T (mls_eng_000311-mls_eng_000311) +T H E L O F L I N G O F T H W L L E S P E T O K I N S R A I N A S W I L A S N Y A N S C A S O N A B L E D A N C S I N G O F M I G E U S I N T H E E V E N I N G S O A C O N S A N D O F E A T A N D R I M T I S I N T H E O N C E S A R D I F U L P R O C U R S S T H E L E A V E S A R A L L A T R M B L E B E F O R T H A T P R O C H E A T T U N D E R (mls_eng_000312-mls_eng_000312) +W A S A S T O R M N G H J G E N E R L D E M P E A R E W A S C K I L E G E N E R A L C O S T I E N W A S B L A I M E D A N I N D E D E S N O W C O M E T O P A R I S T O D E V E X P L O N A T I O N S A G A N S E T A L L W H I C H T H E M O U N T O N A N D A T R O T I O U S M A U A R M U S T E V E N D M A K E H A I L A S T H E C A N (mls_eng_000313-mls_eng_000313) +T H E M O M E N T W A S F E F U L A M I G T Y O F F O H A D N E V E R S W N G T H E B U T L E L A C X O V E R H I M B U T T H E H O B N E R V E D H I S A R M E F O R A D E S P R A T B L O W A N D T H C M S E R F L E P R O S T R A I T D B E F O R H I M (mls_eng_000314-mls_eng_000314) +T H E N T H E W I N E S T O U T T H E C L A O R S T A N D D O A R K A N D N I G H E C A M E O N L I K E E I N K M Y O L D C O T I N C U I L T W A S C A O L D A S I A N M Y S W E E T S O N T O S T I N H I S S C L E E (mls_eng_000315-mls_eng_000315) +Y O U M A Y D O A S Y U P L E A S E T O W O R K O F O R I R I T A T I O N T O K E P Y O U R F A N A T I C S I S M Y O U R E W E L A F F Y O U N E D N O T M I N D T H E C O S T T H E P O R E D O N O T W A N T T S T A N D I N Y O U R W A Y B U T Y U I N S I S T O N T H E S U B M I T I N G T Y O R C O M P U L S I O N (mls_eng_000316-mls_eng_000316) +H E W A S R E D B Y A R E V E R N T E R Y A Y S N I G H T B E I N G B Y O T H M A N E S I X F O A R T O T H I D V I C K W L W A S B O N E A N M A C H A T E N S E V E N T Y N I N A N D H E W A S T H E O N L Y S O V I V E R O F L I T E R O F F I F T E N I T W A S O N T H I S A C O U N T T H A T H W A S A U E D S A F E A N D C O L O R A N D M A K I N G S (mls_eng_000317-mls_eng_000317) +A N D W H A T H A S T I T A K E S O F O L I N T O T H E S E C K A N T T H E R B Y T H I S T I M E D I A F H A N T E O S N E A S E R S E C G I O M O S T A D M R A B L E S E C R E I T O N T H E C O N T R A R Y I T S T U R S M E N O T A W I T W I C H M O S T C O N S U R N E S I T (mls_eng_000318-mls_eng_000318) +T H U R D L Y T H A L S A I D W H E R T H E C I T I S E N S A R E N E A T H E R T O R E A C H N O R T O P O R F O R T H L Y A N A C A U S I S S A I D W H E R E T H O G I N A L L O T H E R E S P E C T S T H E Y A R I A C U L I E T V E R T O U O S M E N A R A D V A N S E D A N D V E S I O U S P E R S O N T H E G R A D E D (mls_eng_000319-mls_eng_000319) +T H E C I N D L Y F R A N K I S S I M P O T H A T I C K A E V E R Y D A Y H E P A S N O T E S B E T W E E N U S A N D I T R I Y T O I N C U R A G E R U S S A L H E W I L I M P R O V E I A S U R H I M H I S T I M E I S H O R T A N D F R E S H A R A N D L I O R T Y W L S O N R E S T O R H I M (mls_eng_000320-mls_eng_000320) +T H I S C U E S T O N S I T I S N O W E V I D E N T M A Y F R E C U E N T L Y B E A N S E R E D W A S E A Q U L L P R O P R I T Y I N O P S I T W A S E S A N D I F T H E R E B E A N Y A C A S I O N S O N W H I C H T H E Y C A N B E A N S E R D O N L Y I N O N W A Y T H E A U N S E R W I L D E P E N D A P O N T H E N A T U R O F T H E A C A S I O N (mls_eng_000321-mls_eng_000321) +I N H I S N O T B O R T H E M I N S T R I L S Y S E C K N A D I O N A T Y N O W A T S C O T S E S T H E B L E D W A S T A K E N D O W N F R O M A O L D W O M E N S R E I T A T I O N A T T H E H E L S A O N M O R L E D M I N E S B Y T H E A G E N T T H E R A N D S E N T B Y H I M T O S U R T E E (mls_eng_000322-mls_eng_000322) +C R I S T O N T H E O L I G O N S (nchlt_eng_001588-nchlt_eng_001588) +O P T A I N E E A G L F H I T H E R S (nchlt_eng_001589-nchlt_eng_001589) +E L A M E N T R Y E S P C I A L E F O N G T I O N S (nchlt_eng_001590-nchlt_eng_001590) +J O R D E W A S I N G S A N N U N V O R S T I T Y (nchlt_eng_001591-nchlt_eng_001591) +S I N S F I C T I O N N O T V E S P R O V E A N (nchlt_eng_001592-nchlt_eng_001592) +C O S T D H I P O P (nchlt_eng_001593-nchlt_eng_001593) +I N V E R S L E P B L A C E T R O N S F O R M E (nchlt_eng_001594-nchlt_eng_001594) +F R I N G H P R O T I S T A N T S (nchlt_eng_001595-nchlt_eng_001595) +A F G U N A E F O R S E S T (nchlt_eng_001596-nchlt_eng_001596) +H E A R O S I N M I S O L A G Y A N D L E A G E N D (nchlt_eng_001597-nchlt_eng_001597) +B U S N S C L A R S S E E T T T E R (nchlt_eng_001598-nchlt_eng_001598) +C L U D B P L A Y C H A R T E T (nchlt_eng_001599-nchlt_eng_001599) +P O S Y T R O N S W E R E R A P O R T E D (nchlt_eng_001600-nchlt_eng_001600) +A L L D V I C K T H E A T A R (nchlt_eng_001601-nchlt_eng_001601) +O R T H E D O C K E S M O N O U C K S (nchlt_eng_001602-nchlt_eng_001602) +N A T I O N S M B E R S T A T E S (nchlt_eng_001603-nchlt_eng_001603) +S H E A F H O W I L D C O U P (nchlt_eng_001604-nchlt_eng_001604) +C R O S R I S C K U W F E A T S (nchlt_eng_001605-nchlt_eng_001605) +A C T H A L F O M E M O A C K R S C O P I T E (nchlt_eng_001606-nchlt_eng_001606) +M E U S I C A L G R O P S R E A S T A B L I S H E D (nchlt_eng_001607-nchlt_eng_001607) +P R O M S C S I N S E R P A E S E (nchlt_eng_001608-nchlt_eng_001608) +F O L N E S I K N E K S (nchlt_eng_001609-nchlt_eng_001609) +T L V I O N S E R Y E S B A C E T (nchlt_eng_001610-nchlt_eng_001610) +N E W P O L I T I C A R P A T Y (nchlt_eng_001611-nchlt_eng_001611) +A N H A N T E A G J I P A C H E V E D (nchlt_eng_001612-nchlt_eng_001612) +F L A T M U S I G N A T R A L (nchlt_eng_001613-nchlt_eng_001613) +A M E R I C A N T I C N O L A D T I G N L E D Y R A T E R S (nchlt_eng_001614-nchlt_eng_001614) +D O A T E S O F B A R I N S (nchlt_eng_001615-nchlt_eng_001615) +P O P U L E T S W E R I S A T R A C T I O N S (nchlt_eng_001616-nchlt_eng_001616) +D O U C H W A S T I N D I A (nchlt_eng_001617-nchlt_eng_001617) +G O L D M I T A E R S I P I E N C E S (nchlt_eng_001618-nchlt_eng_001618) +R E S H I O N S O C H A L D E M A C R E T I C K (nchlt_eng_001619-nchlt_eng_001619) +A M E R I K C A N F O M E P R O D U S E S (nchlt_eng_001620-nchlt_eng_001620) +F R E S O F T E R Y A F O U N D A T I O N (nchlt_eng_001621-nchlt_eng_001621) +R O I L E D R M A T I C T H E A T (nchlt_eng_001622-nchlt_eng_001622) +I T A B L E M O L A S C K S (nchlt_eng_001623-nchlt_eng_001623) +F E A T H E R S I N C L U D B E A C H E R S (nchlt_eng_001624-nchlt_eng_001624) +O C S F O R D I T I O N R Y C H A N G E D (nchlt_eng_001625-nchlt_eng_001625) +S A L C O O P U R S I N G R A Y H O U N D (nchlt_eng_001626-nchlt_eng_001626) +P R I N M N I S T E R C I V E N (nchlt_eng_001627-nchlt_eng_001627) +L A N G A G E S O F Y O U R O C K (nchlt_eng_001628-nchlt_eng_001628) +S O U T H E A S T I N G L A N D (nchlt_eng_001629-nchlt_eng_001629) +N E W L I N E S E N A M A R (nchlt_eng_001630-nchlt_eng_001630) +E A C U L C R A D T S O P A T U O N A T Y (nchlt_eng_001631-nchlt_eng_001631) +S O U T H E S T I N G L A N D (nchlt_eng_001632-nchlt_eng_001632) +M A Y H (nchlt_eng_001633-nchlt_eng_001633) +R E C O L R D H A T E A T S E M I S C R I V E S (nchlt_eng_001634-nchlt_eng_001634) +M U S I C A L G R E P E S F R O M C A L F O R N I E A (nchlt_eng_001635-nchlt_eng_001635) +M A I N B U T L E T I N C K S (nchlt_eng_001636-nchlt_eng_001636) +P O D L I S H M U S I C A L I N S T R A M E N T E S (nchlt_eng_001637-nchlt_eng_001637) +L A N W U G E S O F S A D I E A R A V I A (nchlt_eng_001638-nchlt_eng_001638) +C A L D W A R T I N T I O N S (nchlt_eng_001639-nchlt_eng_001639) +D O A B E W P H (nchlt_eng_001640-nchlt_eng_001640) +A N D Y P O P E C L A M I N T (nchlt_eng_001641-nchlt_eng_001641) +G I T S T H E C N P R I V E A T (nchlt_eng_001642-nchlt_eng_001642) +C I N G F O D A N E A N D (nchlt_eng_001643-nchlt_eng_001643) +I L E C T R N I C M U S I C A L I N S T R M E N C S (nchlt_eng_001644-nchlt_eng_001644) +A G E N O U T W A T E R (nchlt_eng_001645-nchlt_eng_001645) +L O R E N C E L I V E M O R N A S I N A L E (nchlt_eng_001646-nchlt_eng_001646) +L E A G B A C S P A L E P L A Y E R S (nchlt_eng_001647-nchlt_eng_001647) +B U T I S O M A N T H E A N C H A N T M E D A T R A N I O N (nchlt_eng_001648-nchlt_eng_001648) +O U N I T E D S T A T S R E C O C O N I E D (nchlt_eng_001649-nchlt_eng_001649) +P R O P A S I O N A L F E L A C E S (nchlt_eng_001650-nchlt_eng_001650) +S P I C H A L E C N O M I E G S O W N S (nchlt_eng_001651-nchlt_eng_001651) +M A N S T R E A M E W I S T D (nchlt_eng_001652-nchlt_eng_001652) +E V E N G R U S H O W S (nchlt_eng_001653-nchlt_eng_001653) +B Y T H E D I O N S T O K (nchlt_eng_001654-nchlt_eng_001654) +N D T S A R T I C K E H A S N O (nchlt_eng_001655-nchlt_eng_001655) +W E A S T I N M U S I C L E S (nchlt_eng_001656-nchlt_eng_001656) +C O N S E V I T O F J U A T A Y S O M R E G A R T S (nchlt_eng_001657-nchlt_eng_001657) +O P I C K M B E R S T A T S (nchlt_eng_001658-nchlt_eng_001658) +P R I M I N E S S A I D J O N (nchlt_eng_001659-nchlt_eng_001659) +R A C K S F O A R M I N G M O U N T (nchlt_eng_001660-nchlt_eng_001660) +M A G E R L E A K T M S (nchlt_eng_001661-nchlt_eng_001661) +P O L O N A T I O N M A N I G E N T (nchlt_eng_001662-nchlt_eng_001662) +F R E N C H F I S I S T (nchlt_eng_001663-nchlt_eng_001663) +H I Y A R C O M P R E T S I O N R A T S I O (nchlt_eng_001664-nchlt_eng_001664) +R E C O R D N G I N D O U S T R Y A S O C H A T I O N (nchlt_eng_001665-nchlt_eng_001665) +T H E P E A G E O N L I N M A G A S E A N (nchlt_eng_001666-nchlt_eng_001666) +H I P O P E R R E C Q U A D P R E G U O S S E O N S (nchlt_eng_001667-nchlt_eng_001667) +F I N I G H E S T A T E M S H E N S (nchlt_eng_001668-nchlt_eng_001668) +W H I D L Y O U S E D L O C A L E (nchlt_eng_001669-nchlt_eng_001669) +N O R T H E M E R Y C A N C O N T I N A N T (nchlt_eng_001670-nchlt_eng_001670) +A F R C A N A M E R I C A N R E P A S (nchlt_eng_001671-nchlt_eng_001671) +T H R I T O N M E L I D R Y A C T I O N S (nchlt_eng_001672-nchlt_eng_001672) +A T H E W O R D M N N 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(swc_eng_001775-swc_eng_001775) +E R N O T I O N O F Y U J E N I C A N H A N S M E N T T I C N A L A G E S M I G H U N I N T E N T I N A L Y I N C O U R A G E (swc_eng_001776-swc_eng_001776) +A T T H E A T E N I N O F R E S E R T H R S A N B E F O C K E S E D N M P A R T I A L S O L U T I O N S O R S O L T I O N S (swc_eng_001777-swc_eng_001777) +N O W N O F F O R H N D R E D S O F Y E A R S (swc_eng_001778-swc_eng_001778) +N L Y M A U B I A L S H A V E S O V I V E D T T (swc_eng_001779-swc_eng_001779) +T O W H C H A L T H E E D A B L E S P A C E S O F C R U S T A T I O N B E L O N G (swc_eng_001780-swc_eng_001780) +O G E R T H E M R E S U R C H (swc_eng_001781-swc_eng_001781) +N I N T E I N S I C X T Y T O F I L I P S I N V E N T E D H E C O M P A C T O D O C A S E T M E D E O A M F O R O D I O U S T O R G E (swc_eng_001782-swc_eng_001782) +U T R C I N F T H E L O W (swc_eng_001783-swc_eng_001783) +N T F H B I A N S A D R P (swc_eng_001784-swc_eng_001784) +O M E N S W O A L D C H E S 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(swc_eng_001807-swc_eng_001807) +O L O C A T E T H A N U R I S O M (swc_eng_001808-swc_eng_001808) +O R H L O U G I C A L F R E D M (swc_eng_001809-swc_eng_001809) +D H E R J E T I C A T A K I N G S T O W (swc_eng_001810-swc_eng_001810) +A C T L Y F O R T Y A R S A F R T H E C A R N I S H D O N W A S L A T E (swc_eng_001811-swc_eng_001811) +A S E O T H E R E C A G N I T I O N T H (swc_eng_001812-swc_eng_001812) +O R L E C T R N I C K B U T N S O R D I S P L A Y (swc_eng_001813-swc_eng_001813) +I S U N N O N W H T H E R P E A C U L S E M P Y (swc_eng_001814-swc_eng_001814) +H I C H C O M S F R O M T H E V E R B A (swc_eng_001815-swc_eng_001815) +D I S P E P O R T I N T L Y A V A L A B L T T H O E W I H G E A T E R I N A N H A L R E S O R E S (swc_eng_001816-swc_eng_001816) +Y U M N T H R E T S T O T H S V I V L O F M A N Y S P A C E S (swc_eng_001817-swc_eng_001817) +E V E N M O R D I F C A L T (swc_eng_001818-swc_eng_001818) +A N D T W E T Y W N S P E C E S O F I C E A N I G D O L F O N (swc_eng_001819-swc_eng_001819) +A C H E V I N G P R M O T I O N (swc_eng_001820-swc_eng_001820) +E R A N T E W M I N U S E A S U M T I O N (swc_eng_001821-swc_eng_001821) +O N T H E F I R S T B E L L I T (swc_eng_001822-swc_eng_001822) +S T O R Y N D I C A T I V E O F T H E R I S E I N G L O B L S I G N I N G C S O F S H U P O L I S H I S T O L D B Y J E A N (swc_eng_001823-swc_eng_001823) +W H C H S P A R E H I S E R L Y E N T R U S T I N P O L I T I C K (swc_eng_001824-swc_eng_001824) +W A S C A L E D D O L B E A C H E C X P R O W I N F U L A N D P A T N T E (swc_eng_001825-swc_eng_001825) +O U L D S A V E A N D F I N E F I L S B (swc_eng_001826-swc_eng_001826) +A S T R L I A N S N A K E B E L O N G T O S V E N F A M L Y E S (swc_eng_001827-swc_eng_001827) +D V E L P I N P I Y A R S (swc_eng_001828-swc_eng_001828) +L I N D S H A R P L Y S E N C E S P E A K A N (swc_eng_001829-swc_eng_001829) +W A S R E C O R D E D I N T H I R L Y O N A F O R T R A C K C O S E T (swc_eng_001830-swc_eng_001830) +N O R M A S I M P R O V E M E N T I N (swc_eng_001831-swc_eng_001831) +R B N A N D W R O R L L E G U S T A C (swc_eng_001832-swc_eng_001832) +A C H P L Y E R B G I N S (swc_eng_001833-swc_eng_001833) +J E S T A S I N S P I R E D M A N Y C O M E N A T O R I L E P U S L E S (swc_eng_001834-swc_eng_001834) +O R H E O M A N I M A D G E (swc_eng_001835-swc_eng_001835) +E L A S P I R T E D T A P S (swc_eng_001836-swc_eng_001836) +A N T A N D I E S E N S N T O T H O T I O (swc_eng_001837-swc_eng_001837) +P R E S I N P R O T E M P O R O F T H E S T A T S E N I T (swc_eng_001838-swc_eng_001838) +I S H O P C A N M O V E A N Y N U M B E R O F S Q U A R E S (swc_eng_001839-swc_eng_001839) +P E H E I N S I D T H E S C O L (swc_eng_001840-swc_eng_001840) +I N C O N A C T S H E R N E S W I T (swc_eng_001841-swc_eng_001841) +O N T R E S O F T H E E S T E N P A L I A R K T I C F L Y W A Y (swc_eng_001842-swc_eng_001842) +A I N A L S D T I S T I K S E S T M A T E T H E P O P U L A T I O N I N (swc_eng_001843-swc_eng_001843) +O N D I S P U T E D W O R L D C H E S T C H A M P I A N (swc_eng_001844-swc_eng_001844) +Y R O U L L C A P E A B I N C E A (swc_eng_001845-swc_eng_001845) +E R I N A C T E D B Y T H E G E N R A L A S E M B L Y W A S A M E S U R R A T I A L Y S E V G R G A T I N G T H E S T A T E S R E L R O T C A R S (swc_eng_001846-swc_eng_001846) +W H C H R A P S A L M O S T (swc_eng_001847-swc_eng_001847) +S E C T P R T E C S A L N A T I V E F O R N A A N D P R O V I D S (swc_eng_001848-swc_eng_001848) +H E R E A S T H E F E M A L E S P E C U L M I S D A R K B R O N B O R E D W I T H W H I G H T (swc_eng_001849-swc_eng_001849) +O T E R Y E C N T U L S O (swc_eng_001850-swc_eng_001850) +N I N T E A N T W E L E I N R O S F E A R N (swc_eng_001851-swc_eng_001851) +D I G N O C I S E I S G E N R L Y M A D E W I H A S E T E S C A N O F T H E H E A D (swc_eng_001852-swc_eng_001852) +T H E F I R S T G E N R A L Y R E C O N I S E D W O R L D C H E S S C 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(swc_eng_001865-swc_eng_001865) +E F O R H U N D R N D T H E R D Y T H E E F E (swc_eng_001866-swc_eng_001866) +I N G S W H I C H R E S U L T I N A S P E S I F I C K T H I H E A P O N (swc_eng_001867-swc_eng_001867) +F O U R N I N T E N H A N T Y S E V E N (swc_eng_001868-swc_eng_001868) +M I C A T I O N S A N D H E L F (swc_eng_001869-swc_eng_001869) +E A Y A T C H E I N A P E U R S O N N O (swc_eng_001870-swc_eng_001870) +S O M B R E A N D S P I S I F I D T H A T T H E Y M A Y A L S O B E U S E D O N O T H E R N O N P O R O S S M I P T E R I A L S (swc_eng_001871-swc_eng_001871) +H E P O S E I B L Y C O N D P E S I F I G (swc_eng_001872-swc_eng_001872) +M A T O R S I N L A C X (swc_eng_001873-swc_eng_001873) +I T H O U T F I F T Y M O R E T R I N G B R O A (swc_eng_001874-swc_eng_001874) +H I S I C H A C S I N P O U L E S R E S P E C T I E L Y T O K A S L O F A K I (swc_eng_001875-swc_eng_001875) +A V S H O N M L E T E D I S F I R (swc_eng_001876-swc_eng_001876) +E B L E A D I N G R I S T R E M A I N S O F R O U N D F O L T Y (swc_eng_001877-swc_eng_001877) +I L E R I T C H C K M A E (swc_eng_001878-swc_eng_001878) +S O M E S E C U L R H U M I N S C O N C I V E D R A N D S H E U M I N I S M A S A N A L S P R I N G O F T H E E U M N U S T F R E T H O U H T M O V E M E N T A N A R G U Y T H T R A S H U M I N I S T D I F E R F R O M T H E E U M I N I U S T P A I N S T R M B Y H A V I N (swc_eng_001879-swc_eng_001879) +P I N T A L E N E S T A N D C H I C K A R E V O N E R B L E T O P R O D A T I O N B Y M A M L E (swc_eng_001880-swc_eng_001880) +N O R T H E R N P I N T A L I S O N O F T H E S P E C S H E S T O W H I C H T H E A G R M E N T O N T H E C O N S C E R V A T I O N O F A F R A C K A N U R A G I O N M O G R I T O R Y W A T E R B U R D (swc_eng_001881-swc_eng_001881) +A N D I S N E O E F O U N D O N L Y I N T A S M A I (swc_eng_001882-swc_eng_001882) +R P E C T I V E T H E I D A O F M I N D U P L O A T I N G I S A S R T E D T O R E P R O S E N T 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R Y G E M N E D E S T O B E B E T E R D A L O R D T T O T H I G I D A L I N V I R N M E N T T O I S H U R E F A R M I N E R A T I O N T O G R E A T E R S A E N T O O N F O M E T O O N S U M E R E X P E C T A T I O N S (voxpopuli_eng_000503-voxpopuli_eng_000503) +A T C A S E B Y T H E C M I O N A N D M E M B E R S T A T T O N H A N E T H E R S U P O R T T O R E C O N C I L I A T I O N T O S E C U R P E S E A N D T I B I L I T Y A N D A R L A N D I W O L T H E R E F O R E A R D Y U C A L I E S T O P L E A S E S U P O R T I S A M E N M E N (voxpopuli_eng_000504-voxpopuli_eng_000504) +T R A T A G I C K C H O I C E S A B O U T W H E T O E W E S T M U T B E M A D E N O W T A K E N I N E C O U N A N E T O F A S O U T F O R S I L F U L S U P S I T E S B U T T A K T H E G A S A S I O R S O F Y U I T C A N B E A H E L T F U L E B R I G I N G T R U N S I S H O N A R Y M E D I O M T O B E U S E I N M E M I N M E N Y M B E R S T A T I B E O N T O E A C H I V E O V E R A M B I S H I O S C L I M I T A R G I T S (voxpopuli_eng_000505-voxpopuli_eng_000505) +W E A E P O S E I L Y F R A O L E W E C A N C U T H T O P R A S C U E T H E S A M E M P O L I C E S I N T H S A M E M A N E R N O W I N G T H A T W E L E D E T O T H I S A M P R S O S T H E R I S A U L S T H A W E N O D E D E A (voxpopuli_eng_000506-voxpopuli_eng_000506) +U T H E R S A N O P T I O N B (voxpopuli_eng_000507-voxpopuli_eng_000507) +W R E A L L S O N E D A C H A I N G E I N O R I D O L I T I E (voxpopuli_eng_000508-voxpopuli_eng_000508) +A L A D E H B A T O F T H E R E A S O N O F C O U R S E I S I L I G A L F I S C I N G K A N D T H E R E O F O M P T D O N O F E N B Y Y A R R V E S E S W H I C H A R E R E A G I S T E R D T O C O U N T R E S W H I C H L U C K E T H E W I L O F T H E R E S U R C E S T O N F O R S T I N T H E N E S I N A L A G R E M E N S N O M O U N T O F T R E S A B I I T Y M E S E R S O R E E X T R P A P R E W A R E W I L A D E S E T H E P R O B L O U M E O F R E D U S I N G (voxpopuli_eng_000509-voxpopuli_eng_000509) +T H E C O M P R M I S E A L S O I N C L D E D K L A R E R U D S T O T H E F I N E W H I C H M B E R S T A T E A S H E R S T I C T I O N A N D T H E O P R A T I O N I T H I M B E R S T A T S C O N E R D F O R C R U S B R T H E C A C E S A S I L A T H E N E D T O E I N V L L F Y O U R J U S T T H A N Y O F O R W O R K A N D P L A E O U S E U P O R T T O M O H I S E R E C T I V (voxpopuli_eng_000510-voxpopuli_eng_000510) +N O T H E R E N S W O U L D H A V A S B E L E T H A T H E A R B A D B E S C R I M I N A L B E S D E L I B E A T L Y C O N T A M I N A T I N G H U D Y W I T H A D A N E U S N G R E D I E N T B U T I T F A C T I N F A C H E D I N G W H T H U N Y B E S A R A L H V E A L W A S D O N W I H T O C A R Y P O L O N B A C T O T H E R H I V E S T O D T O F E D T H E R O U N (voxpopuli_eng_000511-voxpopuli_eng_000511) +U T I T W A S T H E C O N T R Y I T S E L F B E N G M O R C A P A B L (voxpopuli_eng_000512-voxpopuli_eng_000512) +R I N T O T H E P R T F O L I O O F T H E N E U W C O M I O N A R D E L I N G W I T H F U N D E M E N T E R R I T E S (voxpopuli_eng_000513-voxpopuli_eng_000513) +T H E M E S I Y G I T A T T H E O U D O D T N A T H A V E A N N O U R S O L U I O N S (voxpopuli_eng_000514-voxpopuli_eng_000514) +A R Y O U W I L I N G T O A C T I N E R E F A V E R F O R T H E S O S I A L D E M E N T I O N T O B E I N C L O U D E D I N T H E E U C O M P A T E N C S E S A S P R O P O S E (voxpopuli_eng_000515-voxpopuli_eng_000515) +A N E X T H A T O N P E S P E C T R U P O L I E S T A K I N W I T H E E F O R M O F O U E R T E L I C O N T H F R A M W O R (voxpopuli_eng_000516-voxpopuli_eng_000516) +I B E L E V E H I S R E M A R K S W E R A E X P L I C I T L Y R A C E I S T A N D T H E N A F O B I C K A N D P R M O T E D R A C I A L I N T O L E R A N C E I N A W A Y T H A I S N O T C X C E P T I B L E O R A L O W D I N T E C O N T I T U T I O N O F T H I S H O U S (voxpopuli_eng_000517-voxpopuli_eng_000517) +R E A L I F E G A M P L S H O T H A T S O L V I N G I T I E S R E L A T E T O A D U C A T I O N F E U L E D S T R O N G C O M I N I T D E V E L O P M E N T (voxpopuli_eng_000518-voxpopuli_eng_000518) +S I H O P E T H A T I S I L H V P E O R U S H A A S W E L N D T H A T R U H A C A N A L T S A N D V I S A I G N D E X T R E M E S U C E S S T O R Y A F T E R T H S E G T I S I G N I F I C A N D A T I N O R G S T T H I S Y E A R B (voxpopuli_eng_000519-voxpopuli_eng_000519) +S H E E C X E P T E D T H E F A C T T H A T S I T I S O N S H I P I S A Y N A S I N A L P A R T O F T H E O S I N O G U D I S D I C T I O N B U T H Y O U R L S O S A I D T H A T A C O U R D I N G T O T H E M A S T R I C K T R E A T Y A N D S H E A S R I G H T T H E H A S T O B E A D I Y R E C L I N (voxpopuli_eng_000520-voxpopuli_eng_000520) +T D E Y W O U F A L D E S P E C I A L E A N T H E M S T R A T I N G A U N I F I E D A N D T A F F I S H E N T A P P R O R C H T O L I E M I T C A N G H E T R E A T M E N T A S W E L A S I N S T R A N T H A N I N G K I T S L E D I N G P O L I T I C A L C O S I O N I N D I S A G E N D E R I C O N S C I T H E R T H E R F O R T A K I N G T H I S R E S O L U T I O N A N A C T O F U T M O S T I M P O R T A N S (voxpopuli_eng_000521-voxpopuli_eng_000521) +T H E U N I G T E D S T A T E O F Y U R U O W I L B E A F A C T W I T H S W E D O N A S A P R O V I D E N C (voxpopuli_eng_000522-voxpopuli_eng_000522) +I T M U S B T H E C A P I T A L E O F B O T T H E A T E S A N D W E M U S R E C O N I S E P O L S T I N I S T H A T A S P R O V I D E D F O R I N T H E O V E L O G R E M E N C S (voxpopuli_eng_000523-voxpopuli_eng_000523) +Y O U C R A I N Y S F A C E T W I T H W O N E O F C R U S A L C H A L I N G E S I N I T S H I S T O R Y I T W O U L D B E F U T E M E N T A R L Y R O N G K T O P R E T H E N A T I O N N O W W I T A L T H I P E S O F R E S T R I C T I O N S P O P E L A D E R L C A L E O S T E R I T E P O L I (voxpopuli_eng_000524-voxpopuli_eng_000524) +M O R E R U L S A N D R E G U L A T I O N W I L L N O T I M P R O V E T H I S C I T U A T I O (voxpopuli_eng_000525-voxpopuli_eng_000525) +A T L E A S T W E W O L D L I K E T O N O W T H E S O U R S E O F T H E M O N Y A N D T H E P O S I P L M O R T I E (voxpopuli_eng_000526-voxpopuli_eng_000526) +T O W E R O F T H O S E Y U R U P I N W A L E L A N G I A S H I N T O T H E S G L U B E L I C E D W E A R L D I S I N T T O T H E Y S G O B E L I S D E C O N O M I N D H I S G O B E V I L A C H W H I C H I S G O R S T I A L Y C O N O M I C K S O S I A L E L N P L I T I C O I T S A R M O S T V E L A B L E E S T H E R T F R O M T H E I N T I R E E Y O U T H A T W E M U S T T H A K F O L A C O U N S A N D T (voxpopuli_eng_000527-voxpopuli_eng_000527) +W E A V E T O R E P E T E T H A T A L T H E A Y A N O T B E U S E T O F I N A N S S I U R I T E X P A N C E S B A R T H E R S C O N T R O L O R M L I T R Y S O P O R N T (voxpopuli_eng_000528-voxpopuli_eng_000528) +T H N T H E S I N T I F I R E P O R T S B E C O E M R E M O R E U R G E N T O R A L A R M I N G A N D M O R S H O C K I N G (voxpopuli_eng_000529-voxpopuli_eng_000529) +F I N A L Y M W H E N W E A T T H I N K I N G A B O U N T H E R I N O V A T I V E F I N S I O N I N S T O U M E N T S W H E N O U T H E B O L T H F O R O U R S E L S T O R S U P O A R T O W E R A C O N O M E S B U T A L S S O T O O E S U P O R T T H O S H O E R E I N E A E T (voxpopuli_eng_000530-voxpopuli_eng_000530) +T H T I V E A S O Y U N I E K D O L L I N P E M A K I N G (voxpopuli_eng_000531-voxpopuli_eng_000531) +P A P E R A V E R Y D W E E K P R O P O S L (voxpopuli_eng_000532-voxpopuli_eng_000532) +S R U S H A S A L W A S B E A V E R Y P R O U D N A T I O N W I T H R I C H C O L T C U E R W I T H I N V E N T I O N S W I T H A N A S P L (voxpopuli_eng_000533-voxpopuli_eng_000533) +A R T A C X A T I N E V E N A M O D I C A L O F T A C X A T I O N I N S O M E C A C E S M I G H J U S T H E L P U S E M T O D O W H A T I V E A R E D Y S U E G E S T E D A N W H O N O S E M A K E T H E C A C E F O R T H E R E T R E S P E C T O F B A N K R E C A P I D L I Z A T I O N T H A T W E N E V E R S O (voxpopuli_eng_000534-voxpopuli_eng_000534) +T H E R O P E A N A S I L O M S U P O R T O F H I S M O R O V E R A S A M O N G I T S T H A S T S T O P R M O U T D F E S I L Y T A T A N D C O U R D I N A T E X T C A N G E S O F I N F O R M A T I O N A N D O T H E R A C T I V E I T E S R E L A T E D O E L O C A T I N W T H I N H E U N I O N (voxpopuli_eng_000535-voxpopuli_eng_000535) +H E O N U S O O F T H E F R A M E B O R K A G E M E N T P R O V I D E S A L I G L Y B I N D I N G I N S T R M E N T T O O B G R A T A N D S T R A N T N E U O S T R A L I A B Y L I T H R R A T I O N S A N D T O I N C R E S C O P E R A T I O N (voxpopuli_eng_000536-voxpopuli_eng_000536) +T H E R E F O R W E A E A S T I N T H E C O U S A L A S G M I O N T O R E S E N T H A H A S B A L E T H A O U L D B E T H E S E S T M E N T O F T H E E B A C T O F T H E R I C I S (voxpopuli_eng_000537-voxpopuli_eng_000537) +I N O T H E W O R D S T H E O B J E C T I O N I S N O T W H E T H E R M O N E Y I S P A D O R N O T T H E O B J E C T I O N I S W E T H E R T E R I S A D I D E C T L I N K O R N O (voxpopuli_eng_000538-voxpopuli_eng_000538) +T O T H S T I N G U I S H E S T H E T O M A N O E A R Y O U M E R R I G T A B B U S E B Y T H E C A D A N T G O R M E N T A N D T H E D L A N I A N N U C L A P R O V G D M (voxpopuli_eng_000539-voxpopuli_eng_000539) +Y E S S M A T H M D R U O T H A N K A T H R S E C T I A L H E R A S D M E N T I S A F O R M O F V I L A N C S A N D I T I S T H E M O S T E X T R E A M E F O R M O F G N T E R B A E T H D I S C U M I N A T I (voxpopuli_eng_000540-voxpopuli_eng_000540) +W E C A N L O K T O S O M E U R A N L I N O U M E M B E R S F O R O U O D G X A M P L E S A S R E G A R D E D T H G N O L I G E (voxpopuli_eng_000541-voxpopuli_eng_000541) +Y I M N V A L L V E D F O R T H E R P O S I T E V E A N D C O S T R A C T E I V E A B R O T C H (voxpopuli_eng_000542-voxpopuli_eng_000542) +O I H O P T H A T I S W I L B E C O M P L E A T E D E A R I N H E F A C I V I L F U T U A R T H A T M A N E S M A B E T O A F R E M O N S (voxpopuli_eng_000543-voxpopuli_eng_000543) +O R F O R D E R N D C O U R D S H T H E Y O U H A N D E F O R T S T O B R I N G A M O N G K P E S I N O F G N I S T A N A N T O O V E R C O M E T H E F F R A S I L E S I C U I T Y A N V I R M E N T I N T H E C O N T R Y (voxpopuli_eng_000544-voxpopuli_eng_000544) +B E A N D E R S T A N T T H A T S O M E P E O P E L A R A N G R Y (voxpopuli_eng_000545-voxpopuli_eng_000545) +O N T O B E M R E S T P O N C I V E L (voxpopuli_eng_000546-voxpopuli_eng_000546) +W E M U S T E D A C T I F I E T H T H I S S U T I A T I O N A N D V E A S K T H E O M I O N T O C O N C S I D E R T H E M O S T E D I C U I T C O M B I N S A T I O N M E S E S F O W P A S N G E S (voxpopuli_eng_000547-voxpopuli_eng_000547) +T H E O M I T I O N I N B V I H E D T H E Y U R O P I A N T P U L A M E N T I N T H E U P C O M I N C R E V I S I O N T O O P E N H I S P O S I T I O N O N T H I S M A T E R W H I C H R E L Y C O N S E D A X E S T O S J U S T I S I N Y U R O P A N D T H E E N F O R T M E N T O F R I E S G R A N T E D B Y H E Y U R O P I A N E R Y U N A N L O (voxpopuli_eng_000548-voxpopuli_eng_000548) +I L O M V E R Y M U C H T H R I S O U N T I O N O F T O K E B E T W E N T H E O S R A L I S A N P L E S T I N I O N S A N D S N C E I R L Y H O P T H A T H E W I L S U C E D (voxpopuli_eng_000549-voxpopuli_eng_000549) +W E H A V E A C U M I L A T I O N O F P R O B L E N C S R E S U L T I N G F R O M E T H E A R T I F I H A L A N D D E B A G E A T I N G K A N D V E R P R E V I U S Y U S (voxpopuli_eng_000550-voxpopuli_eng_000550) +L E T U S T N O T B E T H E M A N O F Y E S T E R D Y L N T U N B E P O L D A Y S I N S T H I T U T I O (voxpopuli_eng_000551-voxpopuli_eng_000551) +T I G O U L D E R L S U M T O B E C O M E A M B A S S E T H E S O T H E Y E A R M A K N G I T S A D E A R S A N D A C T I V I T H I S W O W I D L Y N O W N A M O N C S H T O Y U R U P E A T I T I E N S A N D P U T P I I P A T I N G N E V E N S B E T A T Y O U R O P I A N N A S I O N A L L F O R L O K A L E V L (voxpopuli_eng_000552-voxpopuli_eng_000552) +S E R T N L Y S U C H I M P A C E S E S T M E N T C O U L D P R E M T S E R T A N P R O B L O M S S U C H A S T H O S P O S E D B Y T H E E L E C T R O N I K I D E N T I F I C A T I O N O F S H E P A N D S C O T L A N D (voxpopuli_eng_000553-voxpopuli_eng_000553) +T H E C O R T I S C O N T E N T T O S E E T H T I T S W O R K H A S I N F O R M E T H D I S C H A G H R O S A N D H A S C O N T E B U T E D T O R O P O S A L S F O R I M P R O V I N G T H E F I N A N C A L M A N A G H M E N T O F V E Y O U S P E N D I N G A N D B E T H E T A R K A T I N G O F Y O U F U N E (voxpopuli_eng_000554-voxpopuli_eng_000554) +R E G O U T H E R E C L A R I E T H E A N D S E R T A N T Y I S N E D E D F O R T H E O B L I C K S E C T O U R A N D F O R T H E I N D U S T R Y (voxpopuli_eng_000555-voxpopuli_eng_000555) +I S I T R E A L I N O T P O S I B L E T O U S A A T H E R H O U S I N G F A S C I L I D E S W I T H U P R O P R E H R E S E P T I N C O N D I O N S I N T H E M E N T I M E (voxpopuli_eng_000556-voxpopuli_eng_000556) +W H E L Y O U T A K E A C I O N A T L A S T I F N O T T H E N W H E N D (voxpopuli_eng_000557-voxpopuli_eng_000557) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..14522cd1c63d67fcbe0fbb003753962155889b3a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/ref.trn @@ -0,0 +1,1092 @@ +H E R E M A I N E D W O R L D C H A M P I O N U N T I L N I N E T E E N S I X T Y F I V E A Y E A R I N W H I C H H E S U F F E R E D A T E R R I B L E A C C I D E N T (LAD_eng_000254-LAD_eng_000254) +A L I B E R A L C O N S E R V A T I V E H E W A S D E F E A T E D I N E I G H T E E N E I G H T Y T W O (LAD_eng_000255-LAD_eng_000255) +O N E R O A D L A Y E R C A N D R A W T W O R O A D S A T O N C E (LAD_eng_000256-LAD_eng_000256) +S O M E O F T H E C O U N T R I E S H A V E S U R V E Y S F O R M U L T I P L E Y E A R S (LAD_eng_000257-LAD_eng_000257) +B O T H O F T H E V E R S I O N S F E A T U R E T H E S O N G H A P P Y H O L I D A Y (LAD_eng_000258-LAD_eng_000258) +S H A K E S P E A R E M A N Y R E F E R E N C E S A R E M A D E T O S C E N E S I N T E R A C T I O N S O R C H A R A C T E R S F R O M V A R I O U S P L A Y S (LAD_eng_000259-LAD_eng_000259) +I F O N L Y T H E P R O G R A M C O U L D B R E A K O U T J U S T A L I T T L E F R O M I T S T O O F A M I L I A R A P P R O A C H (LAD_eng_000260-LAD_eng_000260) +T H E A L B U M W A S R E L E A S E D I N A U S T R A L I A O N N I N E T E E N T H A U G U S T T W O T H O U S A N D A N D E L E V E N (LAD_eng_000261-LAD_eng_000261) +H E N O W P L A Y S F O R A U S T R A L I A N C L U B P E R T H G L O R Y (LAD_eng_000262-LAD_eng_000262) +I T I S N O T K N O W N H O W M U C H I F A N Y O F H E R C L A I M S A R E T R U E (LAD_eng_000263-LAD_eng_000263) +A S M A L L B U S I N E S S O W N E R B R O A D O P E R A T E D H I S W H E A T A N D S H E E P F A R M F O R S I X T E E N Y E A R S F R O M T H E A G E O F T W E N T Y T W O (LAD_eng_000264-LAD_eng_000264) +I N T H E N I N T H C E N T U R Y H E W A S A N I R I S H P O E T (LAD_eng_000265-LAD_eng_000265) +T H E Y A R E M A R K E D B Y S T R O N G (LAD_eng_000266-LAD_eng_000266) +T H E L A W I S T H E R E F O R E V A L I D (LAD_eng_000267-LAD_eng_000267) +I N T H E E A R L Y S T A G E S C A M E C L O S E T O U S A S L E E P (LAD_eng_000268-LAD_eng_000268) +R U N N I N G E V E R Y T H I R T Y M I N U T E S T H R O U G H O U T S E R V I C E T I M E S (LAD_eng_000269-LAD_eng_000269) +A S A R E S U L T W H E N T H E C O L L E G E R E O P E N E D I T W A S A S A N A L L M A L E C O L L E G E (LAD_eng_000270-LAD_eng_000270) +T H E T I M E B E T W E E N T H E S E P O I N T S I S V A R I A B L E A N D C A N O C C U R A N Y W H E R E F R O M A M I N U T E T O M U C H L O N G E R (LAD_eng_000271-LAD_eng_000271) +W O R K O N T H E E E S S T A R T E D I N M A R C H T W O T H O U S A N D A N D S E V E N A T A C O S T O F F I V E M I L L I O N D O L L A R S (LAD_eng_000272-LAD_eng_000272) +H O W E V E R T H E R E W A S S O M E D I S A G R E E M E N T O V E R T H E E N D I N G T H E M E W H I C H O M O R I A N D Y O S H I M O R I D I S C U S S E D A T L E N G T H O V E R E M A I L (LAD_eng_000273-LAD_eng_000273) +T H E C O U P L E H A D N O C H I L D R E N (LAD_eng_000274-LAD_eng_000274) +T H E O F F I C I A L S I N G L E O F T H A T D E B U T A L B U M P A R I S C A L L I N G H A D A N E L A B O R A T E M U S I C V I D E O (LAD_eng_000275-LAD_eng_000275) +T H E S E R I E S E N D E D O N S I X T H A U G U S T T W O T H O U S A N D A N D F O U R L A S T I N G F O R A T O T A L O F S E V E N T Y O N E D A Y S (LAD_eng_000276-LAD_eng_000276) +H E H A S A L S O C O N T R I B U T E D T O T H E N E W Y O R K R E V I E W O F B O O K S (LAD_eng_000277-LAD_eng_000277) +B Y P L A C I N G S M A L L A R T O B J E C T S T H R O U G H O U T T H E F I L M (LAD_eng_000278-LAD_eng_000278) +I T I S F O U N D I N B R A Z I L (LAD_eng_000279-LAD_eng_000279) +I T W A S T H E S I D E O F T H E F A M I L Y I I D E N T I F I E D M O R E W I T H (LAD_eng_000280-LAD_eng_000280) +C A N D I D A T E S I T E S M U S T A L S O S U B M I T A W O R K P L A N (LAD_eng_000281-LAD_eng_000281) +D U N D E E W O N T H E M A T C H T H R E E T W O (LAD_eng_000282-LAD_eng_000282) +H O W E V E R T H E V I L L A G E R E M A I N E D I S O L A T E D U N T I L T H E A R R I V A L O F T H E F I R S T N E W S P A P E R S E C O N D R E P U B L I C (LAD_eng_000283-LAD_eng_000283) +T H E F I R S T S E R V I C E I N T H E N E W C H U R C H W A S H E L D I N N I N E T E E N F I F T Y O N E A L T H O U G H T H E B U I L D I N G W A S N O T F U L L Y F I N I S H E D (LAD_eng_000284-LAD_eng_000284) +T H E A V E R A G E H O U S E H O L D S I Z E W A S T W O P O I N T T W O S E V E N A N D T H E A V E R A G E F A M I L Y S I Z E W A S T H R E E P O I N T Z E R O Z E R O (LAD_eng_000285-LAD_eng_000285) +I T W A S F I R S T B R O A D C A S T O N T H I R D J A N U A R Y T W O T H O U S A N D A N D T E N (LAD_eng_000286-LAD_eng_000286) +T H E W I N G S W E R E N O W M A D E I N A S I N G L E P R E S S I N G (LAD_eng_000287-LAD_eng_000287) +D O C T O R O F P H I L O S O P H Y I N E N G I N E E R I N G M A N A G E M E N T (LAD_eng_000288-LAD_eng_000288) +T H I S T O O K A W A Y T H E M A I N A R G U M E N T O F S A F E T Y R I S K S (LAD_eng_000289-LAD_eng_000289) +H E W A S A L S O M A D E A L I F E M E M B E R O F S C U N T H O R P E U N I T E D (LAD_eng_000290-LAD_eng_000290) +S H E F E A R S T H E Y W I L L G E T A D I V O R C E B U T T H I S N E V E R H A P P E N S (LAD_eng_000291-LAD_eng_000291) +F O O T D R O P S U N A B L E T O H O L D T H E F O O T S T R A I G H T A C R O S S (LAD_eng_000292-LAD_eng_000292) +W H E T H E R T H E A I R F L O W I S F R E E O R F O R C E D C A N A F F E C T T H E E N E R G Y E F F I C I E N C Y O F T H E W I N D O W (LAD_eng_000293-LAD_eng_000293) +A F T E R G E T T I N G T H E R I G H T M E A S U R E M E N T S T H E Y M A D E T H E N E W D O O R S (LAD_eng_000294-LAD_eng_000294) +F R A G M E N T S O N E A C H F A C E A R E M A R K E D W I T H L E T T E R S A B C (LAD_eng_000295-LAD_eng_000295) +F R O M T H E F I R S T M I N U T E S B O T H T E A M S S H O W E D T H E I R D E S I R E T O C O M P E T E W I T H A G G R E S S I V E A P P R O A C H E S (LAD_eng_000296-LAD_eng_000296) +P H Y S I C A L T H E R A P Y E X E R C I S E S M A Y H E L P P A T I E N T S T O M A I N T A I N M U S C L E S T R E N G T H (LAD_eng_000297-LAD_eng_000297) +H O W E V E R T H E T O W N S H E L I V E S I N N O O N E W A N T S T O H E A R A B O U T H E R (LAD_eng_000298-LAD_eng_000298) +D E S C R I B E S A P P O I N T M E N T S O F A N A C T I N G C H I E F J U S T I C E O R J U D G E O F T H E S U P R E M E C O U R T (LAD_eng_000299-LAD_eng_000299) +T H E S O Y B E A N S O U T E R C O V E R I N G I S T H E N R E M O V E D A N D T H E B E A N S A R E P A R T I A L L Y C O O K E D (LAD_eng_000300-LAD_eng_000300) +T H I S N A T I O N A L M O V E M E N T W H I C H H A D B E G U N W I T H S O M U C H H O P E C A M E T O A S A D E N D (LAD_eng_000301-LAD_eng_000301) +H I S A S S O C I A T E S U S U A L L Y C A L L E D H I M T O R T H E G O O D L O O K I N G G U Y (LAD_eng_000302-LAD_eng_000302) +I T S M A I N O F F I C E S W E R E I N L O N D O N W I T H A S E C O N D O F F I C E B E L F A S T (LAD_eng_000303-LAD_eng_000303) +A C T U A L L Y I H A D N E V E R B E E N T O A V I L L A G E B E F O R E T H A T (LAD_eng_000304-LAD_eng_000304) +H E W A S C H A R G E D W I T H P L A N N I N G T O S E T O F F B O M B S I N E U R O P E A N D T H E U N I T E D S T A T E S (LAD_eng_000305-LAD_eng_000305) +M A K I N G M I R R O R S I S T H E T H I R D S T U D I O A L B U M B Y B E L G I A N A U S T R A L I A N A R T I S T G O T Y E (LAD_eng_000306-LAD_eng_000306) +H E T H E N M O V E D T O W A S H I N G T O N D C A N D W A S A P A R T N E R W I T H W A R D B R O W N U N T I L N I N E T E E N T W E N T Y N I N E (LAD_eng_000307-LAD_eng_000307) +J O S E P H H I G H S C H O O L A N D T H E S C H O O L S T H E Y C O M P E T E A G A I N S T I N A L L S P O R T S (LAD_eng_000308-LAD_eng_000308) +T W E L V E P L U S O N E M A T C H B A N P E R C A R D (LAD_eng_000309-LAD_eng_000309) +I T H I N K I M I G H T B E N O T H I N G (LAD_eng_000310-LAD_eng_000310) +T H E H O M E W A S B U I L T A N D L I V E D I N B Y A N D R E W J A C K S O N K E N N E D Y D E P U T Y C O L L E C T O R F O R T H E I N T E R N A L R E V E N U E S E R V I C E (LAD_eng_000311-LAD_eng_000311) +I N N I N E T E E N S I X T Y F O U R H E W E N T B A C K T O O M S K A N D E N T E R E D T H E A C T O R S S C H O O L O F O M S K (LAD_eng_000312-LAD_eng_000312) +T H E B A N K I S J O I N T L Y O W N E D B Y H I M A N D H I S B R O T H E R S A N D R E L A T I V E S (LAD_eng_000313-LAD_eng_000313) +H E S U B S E Q U E N T L Y W E N T T O S C H O O L I N B R I S T O L (LAD_eng_000314-LAD_eng_000314) +O N E T H O U S A N D E I G H T H U N D R E D A N D F O R T Y S I X F O U R T H E D I T I O N (LAD_eng_000315-LAD_eng_000315) +A P A R T O F L I T T L E E N G L A N D B E Y O N D W A L E S I T H A S B E E N E S S E N T I A L L Y E N G L I S H S P E A K I N G F O R N I N E H U N D R E D Y E A R S (LAD_eng_000316-LAD_eng_000316) +H E P L A Y E D W I T H T E N P L A Y E R S F O R H A L F W A S A G A I N S T T H E T R A D I T I O N I N G S P (LAD_eng_000317-LAD_eng_000317) +T H E P R E S I D I N G J U D G E W A S W E B S T E R T H A Y E R W H O W A S A L R E A D Y A S S I G N E D T O T H E C O U R T B E F O R E T H I S C A S E W A S S C H E D U L E D (LAD_eng_000318-LAD_eng_000318) +B I G B R O T H E R F I V E W A S T H E T H I R D O F T H E M A I N S E R I E S T O F E A T U R E A L I V E L A U N C H (LAD_eng_000319-LAD_eng_000319) +I T S M O T T O I S W H O E V E R Y O U A R E A N D W H E R E V E R Y O U A R E O N T H E J O U R N E Y O F F A I T H Y O U A R E W E L C O M E H E R E (LAD_eng_000320-LAD_eng_000320) +R O B E R T E M I L L E R A S C O A C H W I L S O N (LAD_eng_000321-LAD_eng_000321) +A F T E R A O N E Y E A R B R E A K Z E R O D E G R E E W A S H E R F O L L O W I N G V E N T U R E (LAD_eng_000322-LAD_eng_000322) +A M T M A N U F A C T U R E D A M O D E L K I T O F T H E Z Z R D R A G S T E R (LAD_eng_000323-LAD_eng_000323) +T H E S S A A I M E D T O B U I L D A L E F T W I N G A L T E R N A T I V E T O N E W L A B O U R A N D T H E S N P (LAD_eng_000324-LAD_eng_000324) +H E L I V E S L I K E H E I S A Y O U N G P E R S O N (LAD_eng_000325-LAD_eng_000325) +M A S T E R O F S C I E N C E I N E N G I N E E R I N G M A N A G E M E N T (LAD_eng_000326-LAD_eng_000326) +S H E F A I L E D T O M A K E T H E T O P T H R E E A T T H E K E N Y A N J U N I O R T R A C K T R I A L S T H A T J U N E (LAD_eng_000327-LAD_eng_000327) +A T O U R F O L L O W E D I N S U P P O R T (LAD_eng_000328-LAD_eng_000328) +T H E Y W E R E E S T A B L I S H E D I N E I G H T E E N S E V E N T Y O N E A N D A R E O N E O F T H E O L D E S T C L U B S I N T H E S O U T H O F E N G L A N D (LAD_eng_000329-LAD_eng_000329) +H E W A S A M E M B E R O F T H E Y E S S C O T L A N D A D V I S O R Y B O A R D (LAD_eng_000330-LAD_eng_000330) +T W O T H O U S A N D A N D F I V E G E N T L E M A N (LAD_eng_000331-LAD_eng_000331) +O U R F I L M H A D A S T R O N G R E C E P T I O N I N E U R O P E A N D A C H I E V E D D I S T R I B U T I O N B U T T H A T W A S N O T T H E C A S E H E R E (LAD_eng_000332-LAD_eng_000332) +O R T H O S I S S T R E T C H E S P O S T E R I O R A N K L E S T R U C T U R E S (LAD_eng_000333-LAD_eng_000333) +H E W A S A L S O A T H R E E T I M E F R E N C H N A T I O N A L C H A M P I O N N I N E T E E N N I N E T Y N I N E T E E N N I N E T Y F O U R T W O T H O U S A N D A N D O N E (LAD_eng_000334-LAD_eng_000334) +T H E V I L L A G E S T R U C T U R E S H O W N I N H I S M A P I S T O A G R E A T E X T E N T U N C H A N G E D T O D A Y (LAD_eng_000335-LAD_eng_000335) +R U S S I A I S R E C O G N I Z E D F O R I T S N U C L E A R D I S A S T E R E X P E R T I S E A N D F O R T H E S A F E T Y O F I T S T E C H N O L O G Y (LAD_eng_000336-LAD_eng_000336) +A S O F T W O T H O U S A N D A N D F O U R T E E N M T V I S A V A I L A B L E W I T H I N T H E U N I T E D K I N G D O M O N V I R G I N M E D I A A N D S K Y (LAD_eng_000337-LAD_eng_000337) +N E W Y O R K P E N G U I N R A N D O M H O U S E (LAD_eng_000338-LAD_eng_000338) +T H E D U C H Y W A S S E C U R E D I N T H E O U T C O M E O F T H E G O T H I C W A R (LAD_eng_000339-LAD_eng_000339) +W I T H G O O D P A C E S T A R T E D T H E M A T C H W I T H B O T H T E A M S A L T E R N A T I N G S U P R E M A C Y (LAD_eng_000340-LAD_eng_000340) +T H I S V E R S I O N I S N O T E D F O R B E I N G V E R Y F A I T H F U L T O T H E O R I G I N A L N O V E L (LAD_eng_000341-LAD_eng_000341) +T H I S P R E S U M P T I O N I S N O T F U L F I L L E D O N E H A S T O K N O W A T L E A S T T W O C O N J U G A T E D I A M E T E R S (LAD_eng_000342-LAD_eng_000342) +N O T A B L E T I T L E S I N C L U D E D G O L D E N A X E T H E R E V E N G E O F D E A T H A D D E R R A D M O B I L E O U T R U N N E R S A N D S E G A S O N I C T H E H E D G E H O G (LAD_eng_000343-LAD_eng_000343) +T H E N I N E T E E N N I N E T Y N I N E J U D G M E N T N O T E D T H A T T H E I N F L U E N C E O F T H E F A T H E R O F T H E A C C U S E D H A S B E E N T H E R E (LAD_eng_000344-LAD_eng_000344) +M A C D U F F S W E A R S R E V E N G E A N D J O I N S F O R C E S W I T H M A L C O L M T O O V E R T H R O W M A C B E T H (LAD_eng_000345-LAD_eng_000345) +T H E M E D I A E V A L V I L L A G E C O U R T W A S A L W A Y S A N X I O U S T O K E E P T H E F E N C E A R O U N D T H E V I L L A G E G A P L E S S (LAD_eng_000346-LAD_eng_000346) +T H E R E W A S A N I N E R A N K S Y S T E M E A C H R A N K H A V I N G M O R E P O W E R T H A N T H E L O W E R R A N K (LAD_eng_000347-LAD_eng_000347) +T H E Y E S T A B L I S H E D D I P L O M A T I C R E L A T I O N S O N S E P T E M B E R N I N E T E E N T H N I N E T E E N S E V E N T Y T W O (LAD_eng_000348-LAD_eng_000348) +T H I S W A S F U R T H E R E X T E N D E D T O I N C L U D E M O R E U K D A T E S I N D E C E M B E R T W O T H O U S A N D A N D F O U R T E E N (LAD_eng_000349-LAD_eng_000349) +T H E D U T C H G O V E R N M E N T I S C U R R E N T L Y E X A M I N I N G T H E L E G A L C O N S E Q U E N C E S O F T H E R U L I N G (LAD_eng_000350-LAD_eng_000350) +F R O M N I N E T E E N T H I R T Y T H R E E T O N I N E T E E N F O R T Y N I N E T H E A M E R I C A N L E A G U E W O N T W E L V E O U T O F T H E F I R S T S I X T E E N (LAD_eng_000351-LAD_eng_000351) +T H E R E H E F E L L S I C K W I T H T Y P H U S H I M S E L F (LAD_eng_000352-LAD_eng_000352) +S I X T E A M S H A V E B E E N D I V I D E D I N T W O G R O U P S O F T H R E E T E A M S E A C H (LAD_eng_000353-LAD_eng_000353) +T H E F I R S T S E A S O N P R E M I E R E D O N T W E L F T H J U N E T W O T H O U S A N D A N D F I F T E E N (LAD_eng_000354-LAD_eng_000354) +I T S U C C E E D E D T H E Y B O A R D A N D S Y S T E M T W E N T Y F O U R C O M B I N I N G F E A T U R E S F R O M B O T H (LAD_eng_000355-LAD_eng_000355) +V O L U M E T W O N U M B E R S O N E T W O A N D T H R E E (LAD_eng_000356-LAD_eng_000356) +T H E L O W E R P A R T O F M E N S D R E S S E S W E R E M U C H S H O R T E R I N L E N G T H T H A N T H O S E F O R W O M E N (LAD_eng_000357-LAD_eng_000357) +T H E V I S I G O T H S I N T U R N W E R E S U C C E E D E D B Y T H E M O O R S (LAD_eng_000358-LAD_eng_000358) +J O S E P H H I G H S C H O O L E V E R Y W E E K O F T H E S C H O O L Y E A R (LAD_eng_000359-LAD_eng_000359) +A S A R E S U L T O F A L L T H E A R G U M E N T S G E T T I N G T O H E R (LAD_eng_000360-LAD_eng_000360) +I T S H E A D Q U A R T E R S A R E I N S H E F F I E L D U N I T E D K I N G D O M (LAD_eng_000361-LAD_eng_000361) +L A Y A L S O O F F I C I A L L Y S I G N E D T H E C O N T R A C T O N S T A G E W I T H T H E D I R E C T O R A N D P R O D U C E R S O F T H E G O L D E N E Y E S (LAD_eng_000362-LAD_eng_000362) +P H Y S I C A L T H E R A P Y C A N H E L P P A T I E N T S T O L E A R N H O W T O W A L K W I T H A F O O T D R O P (LAD_eng_000363-LAD_eng_000363) +I T W E N T O N T O S E L L T H R E E H U N D R E D T H O U S A N D U N I T S A C H I E V E F I V E N O (LAD_eng_000364-LAD_eng_000364) +T H E N A M E S T U C K A F T E R T H A T (LAD_eng_000365-LAD_eng_000365) +T H E A L B U M L A T E R B R O K E T H E D I A M O N D R E C O R D O N Q Q M U S I C (LAD_eng_000366-LAD_eng_000366) +I T S E D I T O R I A L W E S U B M I T E A R N E D I T S A U T H O R A P U L I T Z E R P R I Z E (LAD_eng_000367-LAD_eng_000367) +J O S E P H P L A Y S A R E F E A T U R E D E A C H W E E K O N T H E S H O W (LAD_eng_000368-LAD_eng_000368) +T H E Y W A I T F O R A T I M E B U I L D I N G U P T H E I R F O R C E S B E G I N N I N G T O W O N D E R I F T H I S E V I L R E A L L Y E X I S T S (LAD_eng_000369-LAD_eng_000369) +B R I E F M E N T I O N O F T H E C O N V I C T I O N A P P E A R E D O N P A G E T H R E E O F T H E N E W Y O R K T I M E S (LAD_eng_000370-LAD_eng_000370) +O R D E R E D B Y P O S I T I O N O N P I T C H F R O M B A C K R I G H T T O F R O N T L E F T (LAD_eng_000371-LAD_eng_000371) +H E I S M E M B E R O F T H E C O U R T O F T H E R O Y A L C O L L E G E O F A R T L O N D O N U K (LAD_eng_000372-LAD_eng_000372) +D U R I N G T H E C O U R S E O F T H E C A M P A I G N F E R G U S O N V I S I T E D A L L T H I R T Y N I N E W A S H I N G T O N S T A T E C O U N T I E S (LAD_eng_000373-LAD_eng_000373) +A S T R I P O F P A P E R O F L E N G T H (LAD_eng_000374-LAD_eng_000374) +S A T O U H A D F R E Q U E N T L Y W O R K E D T O G E T H E R W I T H Y O K O Y A M A O N P R E V I O U S P R O J E C T S (LAD_eng_000375-LAD_eng_000375) +S H E W A S B O R N O N S C R E E N D U R I N G T H E E P I S O D E B R O A D C A S T O N F O U R T H N O V E M B E R N I N E T E E N N I N E T Y F O U R (LAD_eng_000376-LAD_eng_000376) +H E T U R N E D R O U N D S H E H A D C O M E I N S O G E N T L Y T H A T H E H A D N E V E R H E A R D H E R (M-AILABS_eng_000159-M-AILABS_eng_000159) +A H T O B E S U R E W E M U S T K E E P O U R D O O R S S H U T W E M U S T L E T N O O N E I N (M-AILABS_eng_000160-M-AILABS_eng_000160) +K I N S M E N H E B E G A N M O C K I N G L Y Y O U M A Y H A V E W O N D E R E D W H Y I C A L L E D A T R U C E W H E N I C O U L D J U S T A S W E L L H A V E D E S T R O Y E D Y O U T H A T I D O U B T A T O A N S W E R E D H I M (M-AILABS_eng_000161-M-AILABS_eng_000161) +T H E P E A S A N T T H R E W H I M S E L F U P O N H I M A N D B O U N D H I S F O U R L E G S T I G H T L Y S O T H A T H E C O U L D N O T M O V E (M-AILABS_eng_000162-M-AILABS_eng_000162) +N O R M U S T T H O U S O L I M I T T H E H O L Y O N E O F I S R A E L A S T O T H I N K H E H A T H B U T O N E W A Y I N W H I C H H E C A N G L O R I F Y H I M S E L F B Y T H E E (M-AILABS_eng_000163-M-AILABS_eng_000163) +T H E O L D C O M P A R I S O N B E T W E E N T H E I M P U L S I V E E X E C U T I V E A N D T H E L I B E R A L A R T S M A N W H O H A S L E A R N E D T H A T T H E R E A R E O N L Y O N E O R T W O P O S I T I V E D E C I S I O N S A V A I L A B L E I N A L L T H E W O R L D O F T H I N K I N G (M-AILABS_eng_000164-M-AILABS_eng_000164) +A F T E R T H I S E X P E R I E N C E T H E I N V A D E R S W E R E C A R E F U L T O K E E P A S A F E D I S T A N C E F R O M T H E W A L L (M-AILABS_eng_000165-M-AILABS_eng_000165) +C A N Y O U B E A R S O M E T H I N G F U R T H E R I T H I N K Y O U O U G H T T O K N O W I T I H A V E H E R E A M O S T M Y S T E R I O U S T E L E P A G R A M Y E S W H A T I S I T I S S H E D E A D N O I T I S N O T A B O U T H E R (M-AILABS_eng_000166-M-AILABS_eng_000166) +N O M I S T E R T H O R N T O N S A I D G I V E T H E B A S K E T T O M E I L L T A K E I T (M-AILABS_eng_000167-M-AILABS_eng_000167) +A N A R A B I A N N I G H T E X C L A I M E D T R O T W H Y T H A T W A S A M A G I C N I G H T W A S N T I T T H E R E S D I F F E R E N T S O R T S O F N I G H T S M A T E S A I D T H E S A I L O R A N D T H E K N I G H T B U T T O N B R I G H T M E A N S A I N T T H E S A M E N I G H T Y O U M E A N (M-AILABS_eng_000168-M-AILABS_eng_000168) +I V E T U R N E D O F F U P W A R D S O F A H U N D R E D O F M Y B E S T H A N D S F O R N O O T H E R F A U L T T H A N F O L L O W I N G Y O U A N D S U C H A S Y O U A N D D Y E T H I N K I L L T A K E Y O U O N (M-AILABS_eng_000169-M-AILABS_eng_000169) +B U T W H E N S H O U L D S H E S E E H I M H E R H E A R T L E A P E D U P I N A P P R E H E N S I O N A T E V E R Y R I N G O F T H E D O O R B E L L (M-AILABS_eng_000170-M-AILABS_eng_000170) +T H E S E B O O K S D I X O N I W I L L K E E P A L L T H E R E S T W I L L Y O U S E N D T O M I S T E R B E L L T H E Y A R E O F A K I N D T H A T H E W I L L V A L U E F O R T H E M S E L V E S A S W E L L A S F O R P A P A S S A K E (M-AILABS_eng_000171-M-AILABS_eng_000171) +B U T I N G A W A S N O T A T A L L S U R E T H E Y C O U L D N O T G E T I N T H E G A T E S O P E N E D I N W A R D A N D T H R E E H E A V Y B A R S W E R E H E L D I N P L A C E B Y M E A N S O F S T O U T S T A P L E S R I V E T E D T O T H E S H E E T S O F S T E E L (M-AILABS_eng_000172-M-AILABS_eng_000172) +I W A N T T H A L S A I D H O D D A N C O L D L Y I W A N T A D O Z E N H O R S E S I W A N T M E N T O R I D E T H E M W I T H M E H E P U S H E D H I S W A Y F O R W A R D W H I C H W A Y T O T H E S T A B L E S (M-AILABS_eng_000173-M-AILABS_eng_000173) +T H E R E I S A L I M I T T O W H A T Y O U C A N D O T H E F I R S T T I M E Y O U E N T E R A M A N S H O U S E A N D B E S I D E S T H A T W A S N O T I M E T O A R O U S E S U S P I C I O N I N T H E M I N D O F A N Y O N E (M-AILABS_eng_000174-M-AILABS_eng_000174) +D O Y O U N O T R E M E M B E R T H A T H E S A Y S T H Y D E M O N T H A T S T H Y S P I R I T W H I C H K E E P S T H E E I S N O B L E C O U R A G E O U S H I G H U N M A T C H A B L E (M-AILABS_eng_000175-M-AILABS_eng_000175) +M I S T E R B E L L W H A T C A N H E K N O W O F J O H N H E L I V I N G A L A Z Y L I F E I N A D R O W S Y C O L L E G E (M-AILABS_eng_000176-M-AILABS_eng_000176) +A N D T H E K I T T E N F O L L O W E D D E M U R E L Y A T T H E I R H E E L S (M-AILABS_eng_000177-M-AILABS_eng_000177) +T H E F I R S T T O U C H W O U L D C A U S E A N E X P L O S I O N I N W H I C H A M O N G S U C H H U N D R E D S O F I N F U R I A T E D M E N A N D R E C K L E S S B O Y S (M-AILABS_eng_000178-M-AILABS_eng_000178) +O N E O F T H E G R E A T P L E A S U R E S O F M A R G A R E T S L I F E A T T H I S T I M E W A S I N E D I T H S B O Y (M-AILABS_eng_000179-M-AILABS_eng_000179) +T H E T H I N G H A S G O N E O N L O N G E N O U G H I F T H E R E I S O N E M O R E B I G A C C I D E N T W E S H A L L H A V E T O C O M P R O M I S E W I T H T H E I N T E R R I V E R A N D C A R R Y O N T H E W O R K J O I N T L Y (M-AILABS_eng_000180-M-AILABS_eng_000180) +Y O U A R E L A T E S A I D S H E W E L L S H E H E L D H E R B R E A T H F O R T H E A N S W E R (M-AILABS_eng_000181-M-AILABS_eng_000181) +T R O T T O L D T H E G I R L S T H A T T H E Y M U S T G O W I T H T H E I R F A T H E R T O L I V E I N G H I P G H I S I Z Z L E S L I T T L E O L D C A B I N A N D W H E N T H E Y H E A R D T H I S D R E A D F U L D E C R E E (M-AILABS_eng_000182-M-AILABS_eng_000182) +M A R G A R E T S A T D O W N O N T H E R U G P A R T L Y T O W A R M H E R S E L F F O R T H E D A M P N E S S O F T H E E V E N I N G H U N G A B O U T H E R D R E S S A N D O V E R F A T I G U E H A D M A D E H E R C H I L L Y (M-AILABS_eng_000183-M-AILABS_eng_000183) +O H N O Y O U A R E M I S T A K E N A B O U T T H A T R E P L I E D T H E K I N G T H E Y A R E N O T M Y P R I S O N E R S B U T M Y S L A V E S W H O M I P U R C H A S E D F R O M T H E K I N G O F E V (M-AILABS_eng_000184-M-AILABS_eng_000184) +H E R F A T H E R T O O K U P T H E C O N V E R S A T I O N (M-AILABS_eng_000185-M-AILABS_eng_000185) +I N A C O R N E R W A S A S O R T O F D R E S S I N G T A B L E O N W H I C H L A Y A C O M B A N D B R U S H K E N N E D Y S E E M E D M U C H I N T E R E S T E D I N T H E T A B L E A N D W A S E X A M I N I N G I T W H E N T H E G U R U R E T U R N E D (M-AILABS_eng_000186-M-AILABS_eng_000186) +I H A V E S O M E T I M E S T H O U G H T T H A T M Y S E L F S H E A G R E E D B U T O F C O U R S E I D O N T K N O W S T I L L I H A V E T O B E P R E T T Y C A R E F U L S O M E O N E I S A L W A Y S O V E R H E R E B Y M Y D E S K O R L O O K I N G O V E R H E R E (M-AILABS_eng_000187-M-AILABS_eng_000187) +I S H A L L S T A Y R E P L I E D T H E Y O U N G M A N F O R I M E A N T O S E T Y O U F R E E (M-AILABS_eng_000188-M-AILABS_eng_000188) +W H A T D O Y O U D O A S K E D T H E S O R C E R E R (M-AILABS_eng_000189-M-AILABS_eng_000189) +W H Y T H E Y R E O U R E N E M I E S Y O U R S H O R T H I G H N E S S N O T A N Y M O R E R E P L I E D T R O T I M Q U E E N O F T H E P I N K I E S A N D I M A L S O Q U E E N O F T H E B L U E S S O I W O N T H A V E M Y P E O P L E Q U A R R E L I N G (M-AILABS_eng_000190-M-AILABS_eng_000190) +T Y P E W R I T E R S W E R E C L I C K I N G C L I P P I N G S W E R E B E I N G S N I P P E D O U T O F A H U G E S T A C K O F N E W S P A P E R S A N D P A S T E D I N T O L A R G E S C R A P B O O K S C I R C U L A R S W E R E B E I N G F O L D E D A N D M A D E R E A D Y T O M A I L F O R T H E F I N A L A P P E A L (M-AILABS_eng_000191-M-AILABS_eng_000191) +I T W A S F O U R D A Y S A F T E R T H E S U R P R I S E O F A D L E R S H O R S T W H E N T H E S T R A N G E R S L E F T T H E E S T A T E T O T H E C A R E O F R U G G E D O L D F O R S T E R H E R M A N N (M-AILABS_eng_000192-M-AILABS_eng_000192) +P O O R T E M P L E T O N H E S A I D I U S E D T O K N O W H I M Y E A R S A G O W H E N W E W E R E B O Y S W E N T T O S C H O O L W I T H H I M A N D A L L T H A T S O R T O F T H I N G Y O U K N O W B U T U N T I L I R A N A C R O S S H I M (M-AILABS_eng_000193-M-AILABS_eng_000193) +I F O U N D H E R I N T H E F O R E S T A N D B R O U G H T H E R H E R E A P R I S O N E R R E P L I E D T H E C A P T A I N (M-AILABS_eng_000194-M-AILABS_eng_000194) +W H O M A Y B E C O M P E T E N T E I T H E R F R O M P E R S O N A L E X P E R I E N C E O R T H E E X P E R I E N C E O F O T H E R S T O A N S W E R I T W I T H M O R E O R L E S S C O R R E C T N E S S O R A T L E A S T A N A T T E M P T (M-AILABS_eng_000195-M-AILABS_eng_000195) +O N E H U N D R E D N I N E T Y T W O L A Y T E S T R E E T S A I D H O G A N B I T I N G O F F H I S C I G A R (M-AILABS_eng_000196-M-AILABS_eng_000196) +T R O T W A S S U R P R I S E D T O F I N D S H E C O U L D S E E S O P L A I N L Y T H R O U G H T H E H I G H W A L L O F W A T E R A B O V E H E R B U T T H E S U N W A S A B L E T O S H O O T I T S B E A M S S T R A I G H T D O W N T H R O U G H T H E T R A N S P A R E N T S E A (M-AILABS_eng_000197-M-AILABS_eng_000197) +T H E S P O T W H E R E I T H A D S P R U N G U P (M-AILABS_eng_000198-M-AILABS_eng_000198) +C A L M D E N I A L W H I C H S H E G A V E T O S U C H A S U P P O S I T I O N (M-AILABS_eng_000199-M-AILABS_eng_000199) +Y O U S E E U N T I L T H E S E S C H O O L P I L L S W E R E I N V E N T E D W E W A S T E D A L O T O F T I M E I N S T U D Y T H A T M A Y N O W B E B E T T E R E M P L O Y E D I N P R A C T I C I N G A T H L E T I C S (M-AILABS_eng_000200-M-AILABS_eng_000200) +Y O U V E D O N E I T N O W D E C L A R E D D O R O T H Y T H E S E T E N T S A R E J U S T W O N D E R F U L (M-AILABS_eng_000201-M-AILABS_eng_000201) +F O R T W E N T Y T E N F I V E T H R E E T W O T H E L I N E R W A S B A R E L Y T W E N T Y M I L E S A W A Y W H E N H O D D A N F I R E D H I S R O C K E T S T H E Y M A D E A C O L O S S A L C L O U D O F V A P O R I N E M P T I N E S S (M-AILABS_eng_000202-M-AILABS_eng_000202) +T H E Y P A I D N O A T T E N T I O N T O T H E F A C T T H A T G H I P G H I S I Z Z L E D I D N O T W A N T T O M A R R Y A N Y O F T H E M F O R T H E Y H A D D E T E R M I N E D T H A T W H E N I T W A S A G R E E D W H O S H O U L D H A V E H I M (M-AILABS_eng_000203-M-AILABS_eng_000203) +W H A T D O Y O U T H I N K O F T H A T H E C R I E D O P E N I N G A C O P Y O F T H E R E C O R D A N D L A Y I N G I T F L A T O N T H E L I B R A R Y T A B L E (M-AILABS_eng_000204-M-AILABS_eng_000204) +I T W I L L R E Q U I R E B U T A S H O R T T I M E (M-AILABS_eng_000205-M-AILABS_eng_000205) +A N D L A S T T H E C R O W D O F V E G E T A B L E P E O P L E W H O H A D N O H E A R T S A N D C O U L D N E I T H E R S M I L E N O R F R O W N (M-AILABS_eng_000206-M-AILABS_eng_000206) +T H E N Y O U L L C A T C H I T S A I D T H E W I T C H (M-AILABS_eng_000207-M-AILABS_eng_000207) +W H A T I S I T I Q U E R I E D N O T F E E L I N G C E R T A I N B U T T H A T I T W A S A V E I L E D A T T E M P T T O S E C U R E A L I T T L E F R E E A D V E R T I S I N G F O R T H E V A N D E R V E E R (M-AILABS_eng_000208-M-AILABS_eng_000208) +S O H E G A V E T H E C L E R K T H E T H I R D H U N D R E D D O L L A R S F O R B O O K S A N D A C A S K O F G O O D O L D A L E F O R P E T E R T H E C L E R K D R A N K T H E A L E H I M S E L F A N D G A V E T H E C A L F M I L K (M-AILABS_eng_000209-M-AILABS_eng_000209) +L I K E T H A T I N A L I C E I N W O N D E R L A N D W I T H M E R E L Y A G R I N T H A T F A D E D A W A Y C H A N G I N G I N T O A L Y N X W H I C H I N T U R N D I S A P P E A R E D F O L L O W E D B Y A N U N K N O W N C R E A T U R E W I T H S H O R T N O S E A N D P O I N T E D E A R S (M-AILABS_eng_000210-M-AILABS_eng_000210) +S H E C O U L D N O T D O M A R G A R E T G L A N C E D U N C O N S C I O U S L Y A T T H E U N C L E A N E D C O R N E R S O F T H E R O O M S H E C O U L D H A R D L Y U N D E R T A K E A S E R V A N T S P L A C E C O U L D S H E (M-AILABS_eng_000211-M-AILABS_eng_000211) +N O S H E R E P L I E D W I T H I N N O C E N T C U R I O S I T Y D I D I G I V E T H E M T O Y O U (M-AILABS_eng_000212-M-AILABS_eng_000212) +M A R L B O R O U G H M I L L S A N D T H E A D J A C E N T D W E L L I N G W E R E H E L D U N D E R A L O N G L E A S E T H E Y M U S T I F P O S S I B L E B E R E L E T (M-AILABS_eng_000213-M-AILABS_eng_000213) +A C O P W A V E D A S T U N P I S T O L A T H I M (M-AILABS_eng_000214-M-AILABS_eng_000214) +I T B O U N D E D H E R E A N D T H E R E A B O U T T H E C H I C K E N H O U S E A N D A T F I R S T D O R O T H Y C O U L D N O T T E L L W H A T I T W A S W H I L E T H E S C R E E C H I N G O F T H E C H I C K E N S N E A R L Y D E A F E N E D H E R (M-AILABS_eng_000215-M-AILABS_eng_000215) +T H E S O L D I E R G A V E A Y E L L T H A T A R O U S E D A S C O R E O F H I S C O M R A D E S A N D B R O U G H T T H E M T U M B L I N G I N T O T H E S T R E E T W H E N T H E Y S A W H O W T H E B O O L O O R O O S P R E C I O U S P R I S O N E R W A S E S C A P I N G (M-AILABS_eng_000216-M-AILABS_eng_000216) +J I M H A D R E F U S E D T O L E A V E T H E F I E L D O F G R A S S W H E R E H E W A S E N G A G E D I N B U S I L Y E A T I N G S O T H E W I Z A R D G O T O U T O F T H E B U G G Y A N D J O I N E D Z E B A N D D O R O T H Y (M-AILABS_eng_000217-M-AILABS_eng_000217) +C E R T A I N L Y I A M A S I N T E R E S T E D I N T H E C A S E A S Y O U A R E B U T I C A N T M A K E H E A D S O R T A I L S O F I T I R E P L I E D (M-AILABS_eng_000218-M-AILABS_eng_000218) +O R A N Y M I C E O R E V E N G R A S S H O P P E R S (M-AILABS_eng_000219-M-AILABS_eng_000219) +A N D T H E M T H A T P A Y S Y O D U N T H E Y T E L L Y O W H A T T E N T O D O O R W H A T T E N N O T T O D O W I T H E M O N E Y T H E Y G I V E S Y O U I N J U S T P A Y M E N T F O R Y O U R P A I N S I N F A I R E X C H A N G E L I K E (M-AILABS_eng_000220-M-AILABS_eng_000220) +W H A T D O E S T H A T M E A N A S K E D T H E P R I N C E S S (M-AILABS_eng_000221-M-AILABS_eng_000221) +H E H A D B E E N D R O W N E D H E W A S F L O A T I N G I N A S E A O F L I G H T A N D N O W A N D T H E N S H I N I N G L I T T L E F I S H E S S W A M I N Q U I S I T I V E L Y U P T O H I M A N D S T A R E D (M-AILABS_eng_000222-M-AILABS_eng_000222) +B U T O L D G U N N A R H A D A T R I C K O R T W O L E F T R E M E M B E R T H E T A L E T H A T I R E A D T O Y O U I N T H E T H R O N E R O O M O F B A L D A R T H E F I R S T O F T H E B R O N S T O E N T E R T H E W O R L D O F O P A L W E R E S O L D I E R S S E N T F R O M S O M E B L A S T E D P L A N E T I N O U T E R S P A C E T O F I N D A N E W H O M E (M-AILABS_eng_000223-M-AILABS_eng_000223) +P A P A W I L L Y O U S P E A K T O T H E M E N A N D G E T T H E M T O G O A W A Y S H E C A N N O T B R E A T H E P O O R T H I N G W I T H T H I S C R O W D A B O U T H E R (M-AILABS_eng_000224-M-AILABS_eng_000224) +W H E N I T O O K T H I S C A S E H E S A I D I B E L I E V E D D O W N I N M Y H E A R T T H A T D I X O N W A S I N N O C E N T I S T I L L B E L I E V E I T B U T M Y F A I T H H A S B E E N R U D E L Y S H A K E N (M-AILABS_eng_000225-M-AILABS_eng_000225) +C H A P T E R S I X O F T H E P I R A T E S O F E R S A T Z (M-AILABS_eng_000226-M-AILABS_eng_000226) +R E M E M B E R T H E Y C A N N O T T O U C H U S (M-AILABS_eng_000227-M-AILABS_eng_000227) +G I V E M E T I M E A Z U R E G I V E M E T I M E I F T H E R E S A N Y T H I N G I H A T E I T S A H U R R Y I V E A N I D E A Y O U R M A J E S T Y A N N O U N C E D T H E S I X T H S N U B N O S E D P R I N C E S S (M-AILABS_eng_000228-M-AILABS_eng_000228) +T R U E E N O U G H T R O T D E C L A R E D T H E S A I L O R M A N (M-AILABS_eng_000229-M-AILABS_eng_000229) +A S F O R T H A T S A I D M A R G A R E T R A T H E R H A U G H T I L Y I H O L D I T I S H O N I S O I T Q U I M A L Y P E N S E (M-AILABS_eng_000230-M-AILABS_eng_000230) +W H E N H E H E A R D T H E S E W O R D S T H E K I N G W H O S E H E A D W A S F U L L O F T H E P R I N C E S S N E V E R S T O P P E D T O I N Q U I R E I F T H E Y C O U L D B E T R U E A N D S M E A R E D H I M S E L F O V E R W I T H F A T A N D S P R A N G I N T O T H E O V E N (M-AILABS_eng_000231-M-AILABS_eng_000231) +Y O U S H O U L D B E A B L E T O G E T P A R T S F R O M Y O U R R O O M V I S I O N R E C E I V E R I L L H A V E S O M E T O O L S G I V E N Y O U T H E N H E A D D E D D I P L O M A C Y H A S T O U N D E R S T A N D T H E T H I N G S T H A T C O N T R O L E V E N T S (M-AILABS_eng_000232-M-AILABS_eng_000232) +B Y T H E T I M E T H E F R O S T H A D S E T I N T H E Y S H O U L D B E F A R A W A Y F R O M H E L S T O N E (M-AILABS_eng_000233-M-AILABS_eng_000233) +O N E T H I N G I W A N T T O S A Y B E G A N K E N N E D Y (M-AILABS_eng_000234-M-AILABS_eng_000234) +T H I S I M P O R T A N T T R A F F I C W A S C O N F I D E D T O N O O N E B U T T H E R E A L P R O P R I E T O R (M-AILABS_eng_000235-M-AILABS_eng_000235) +H E W A S R E P L A C E D O N B A S S G U I T A R B Y J U S T I N K L U G (cv_eng_000707-cv_eng_000707) +I D A D D A S E P A R A T E S U B S E C T I O N W H I C H D E A L S W I T H T H I S A S P E C T (cv_eng_000708-cv_eng_000708) +O P E R A T I O N O F T H E T R U N K L I N E C O N T I N U E D O N W O O D E N T R E S T L E S (cv_eng_000709-cv_eng_000709) +M A G N E S I U M F L U O R I D E I S T R A N S P A R E N T O V E R A N E X T R E M E L Y W I D E R A N G E O F W A V E L E N G T H S (cv_eng_000710-cv_eng_000710) +F O U R G I A N T P A C K I N G S H E D S S T O R E D F R E S H P A C K E D P O T A T O E S A N D D E L I V E R E D T H E M O N T O R A I L R O A D C A R S (cv_eng_000711-cv_eng_000711) +T H E O T H E R F O U R T E E N C A M P U S E S A R E T W O Y E A R C A M P U S E S R E F E R R E D T O C O L L E C T I V E L Y A S T H E U N I V E R S I T Y C O L L E G E (cv_eng_000712-cv_eng_000712) +I T S T O O B A D T H A T H E S Q U I C K L Y G O I N G T O F O R G E T M Y N A M E H E T H O U G H T (cv_eng_000713-cv_eng_000713) +O N E P I C T U R E I N T H E G A L L E R Y S H O W S H O W D I L I G E N T S L A V E S E R E C T T H E S T A T U E O F A D M I R A L T H O M P S O N (cv_eng_000714-cv_eng_000714) +I M P E R I A L D I E T (cv_eng_000715-cv_eng_000715) +T H E R E S U L T I N G C O M P A N Y I S S T R A T T E C S E C U R I T Y C O R P O R A T I O N (cv_eng_000716-cv_eng_000716) +B I T C O I N M I N I N G C A N B E D O N E W I T H G R A P H I C S C A R D S O R W I T H S P E C I A L I Z E D H A R D W A R E (cv_eng_000717-cv_eng_000717) +T H E Y A L S O L E A D T H E N A T I O N A L R A N K I N G (cv_eng_000718-cv_eng_000718) +C H A R L E S G R A V E S B I S H O P O F L I M E R I C K (cv_eng_000719-cv_eng_000719) +A N D A T T H A T I T O L D H I M A N D H E T O O K M Y P L A C E (cv_eng_000720-cv_eng_000720) +I T H O U G H T I D G I V E T H E K I D S A T R E A T (cv_eng_000721-cv_eng_000721) +A C E V E D O D E N I E D S H O W I N G T H E P I C T U R E S (cv_eng_000722-cv_eng_000722) +H O L D Y O U R N O S E T O K E E P T H E S M E L L F R O M D I S A B L I N G Y O U R M O T O R F U N C T I O N S (cv_eng_000723-cv_eng_000723) +T H A T S O U N D S L I K E T H E I R P R O B L E M (cv_eng_000724-cv_eng_000724) +H I S T O R I C A L L Y T H E R E W A S N O C L E A R L Y D E F I N E D B O U N D A R Y I N T H I S P A R T O F T H E A R A B I A N P E N I N S U L A (cv_eng_000725-cv_eng_000725) +M A R S H A L L S H A F F E R O F S L A S H F I L M G A V E T H E F I L M A N E I G H T O U T O F T E N (cv_eng_000726-cv_eng_000726) +H O W C A N Y O U S A Y T H A T (cv_eng_000727-cv_eng_000727) +H I S S T Y L E B E G A N T O R E S E M B L E M I C H A E L D A M A S K I N O S (cv_eng_000728-cv_eng_000728) +H E I S A L S O C A P A B L E O F F I R I N G L I G H T N I N G B O L T S W I T H I M M E N S E D E S T R U C T I V E P O W E R (cv_eng_000729-cv_eng_000729) +H E C L A I M E D T W O W I C K E T S I N E N G L A N D S O N L Y I N N I N G S A S B O R D E R W E R E B E A T E N C O M P R E H E N S I V E L Y (cv_eng_000730-cv_eng_000730) +S H E D I D M U C H L I T E R A R Y W O R K (cv_eng_000731-cv_eng_000731) +H E M E T T H E O R G A N I Z E R S O F T H E P R O T E S T S A N D A G R E E D T O C R E A T E T W O W O R K I N G G R O U P S (cv_eng_000732-cv_eng_000732) +T H E B A L L S T R U C K T H E F O U L P O L E W E L L A B O V E T H E G R E E N M O N S T E R (cv_eng_000733-cv_eng_000733) +O N L Y C A M D E N T H O M A S G A R R E T T A N D G O L D F I E L D S S O U T H E Z E K I E L B A K E R W E R E U N C O N T E S T E D (cv_eng_000734-cv_eng_000734) +I T I S A C H A R I T Y S C H O O L W H O S E F E E S A R E C A L C U L A T E D O N A M E A N S T E S T (cv_eng_000735-cv_eng_000735) +S O M E W E N T A W A Y W H I L E I W A S T H E R E A N D O T H E R P E O P L E C A M E (cv_eng_000736-cv_eng_000736) +S E V E N (cv_eng_000737-cv_eng_000737) +T H E K U R A K H A N A T E W A S L O C A T E D M A I N L Y I N T H E H I S T O R I C A L A N D G E O G R A P H I C A L R E G I O N O F K U R A (cv_eng_000738-cv_eng_000738) +T H E E L E V A T I O N A T T H E S I T E I S A B O V E S E A L E V E L (cv_eng_000739-cv_eng_000739) +T O B I A S T R I E D T O I N J E C T C O N T E M P T I N T O H I S T O N E (cv_eng_000740-cv_eng_000740) +I H A V E T O W O R K T H I S S A T U R D A Y (cv_eng_000741-cv_eng_000741) +T H E G R E A T R U L E R S F O U N D T H E S Q U E A K Y G R A T E W A S G R A T I N G O N T H E I R N E R V E S (cv_eng_000742-cv_eng_000742) +W H E N T H E B L I N D I N G D U S T H A D S E T T L E D A B I T T H E B O Y T R E M B L E D A T W H A T H E S A W (cv_eng_000743-cv_eng_000743) +D E M O C R A T A M B E R B A K E R W O N T H E O P E N S E A T (cv_eng_000744-cv_eng_000744) +B O T H A R E P U T T O G E T H E R B Y S T U D E N T S I N T H E C O L L E G E S J O U R N A L I S M P R O G R A M (cv_eng_000745-cv_eng_000745) +T R E N C H W A S B O R N I N B E L I Z E C I T Y I N B R I T I S H H O N D U R A S (cv_eng_000746-cv_eng_000746) +T H E E A R L Y P H A S E O F L I F E M O V E S F A S T (cv_eng_000747-cv_eng_000747) +N O (cv_eng_000748-cv_eng_000748) +S E V E N (cv_eng_000749-cv_eng_000749) +A T O N E T I M E R A I L W A Y L I N E S D I V E R G E D F R O M R U G B Y S T A T I O N I N S E V E N D I F F E R E N T D I R E C T I O N S (cv_eng_000750-cv_eng_000750) +C Z E C H R E P U B L I C E N T E R E D T W O S H O O T E R S I N T O T H E P A R A L Y M P I C C O M P E T I T I O N (cv_eng_000751-cv_eng_000751) +T Y G E R W I L L I A M S W R O T E T H E S C R E E N P L A Y A N D S H A R E D S T O R Y C R E D I T W I T H T H E B R O T H E R S (cv_eng_000752-cv_eng_000752) +T H I S F E S T I V A L W A S T O B E A C H A R I T Y F U N D R A I S E R F O R T H E A R E A (cv_eng_000753-cv_eng_000753) +T H E S E E X T R A C A R D S W E R E I N S E R T E D R A N D O M L Y I N T O P A C K S (cv_eng_000754-cv_eng_000754) +H E N R Y W E N T B A C K T O A U S T R A L I A (cv_eng_000755-cv_eng_000755) +P E R M I T M E T O I N T R O D U C E T O Y O U H E R M A J E S T Y T H E Q U E E N (cv_eng_000756-cv_eng_000756) +I N O R I G I N H E R O I N W A S S U P P O S E D T O B E T H E “ N O N A D D I C T I V E M O R P H I N E S U B S T I T U T E ” (cv_eng_000757-cv_eng_000757) +S H E I S O F M E X I C A N D E S C E N T (cv_eng_000758-cv_eng_000758) +I A M S U R E T H E R E I S N O T O N H I S (cv_eng_000759-cv_eng_000759) +T H O S E W H O D O N T L E A R N F R O M H I S T O R Y A R E D O O M E D T O R E P E A T I T (cv_eng_000760-cv_eng_000760) +I C O U L D N ’ T S T O P S T A R I N G A T I T (cv_eng_000761-cv_eng_000761) +F O R S I M P L I C I T Y G E A R I N C H E S I S N O R M A L L Y R O U N D E D T O T H E N E A R E S T W H O L E N U M B E R (cv_eng_000762-cv_eng_000762) +I F W E A C T U A L L Y D O W A N T I T S O L V E D I T W I L L B E (cv_eng_000763-cv_eng_000763) +T H E F R U I T O F A F I G T R E E I S A P P L E S H A P E D (cv_eng_000764-cv_eng_000764) +F A I R E X C H A N G E I S N O R O B B E R Y (cv_eng_000765-cv_eng_000765) +W H A T Y O U E A T T O D A Y W A L K S A N D T A L K S T O M O R R O W (cv_eng_000766-cv_eng_000766) +T H E W A T E R T H E N F L O W S O U T O F T H E S W A M P S A S T H E L U A P U L A R I V E R (cv_eng_000767-cv_eng_000767) +W H Y D I D N T Y O U S A Y S O M E T H I N G (cv_eng_000768-cv_eng_000768) +H A V E Y O U S E E N O M A R (cv_eng_000769-cv_eng_000769) +I C O U L D G O O N F O R D A Y S A B O U T T H E D E L I C I O U S W I N E S P R O D U C E D I N T H I S P A R T O F T H E W O R L D (cv_eng_000770-cv_eng_000770) +T H E P H I L A D E L P H I A I N Q U I R E R N A M E D H I M C I T Y P L A Y E R O F T H E Y E A R (cv_eng_000771-cv_eng_000771) +B O T S M A Y B E S U B J E C T T O S P E C I A L R U L E S (cv_eng_000772-cv_eng_000772) +T H E S W E D E S W E R E U N A B L E T O U S E T H E I R V E H I C L E S W H I C H W E R E S T U C K I N T H E M U D (cv_eng_000773-cv_eng_000773) +T H E A C T D I D N O T P R O H I B I T P A Y I N G A R E P R E S E N T A T I V E T O A P P E A R I N C O U R T (cv_eng_000774-cv_eng_000774) +C A N W E P L E A S E L E A V E N O W (cv_eng_000775-cv_eng_000775) +H E W A S C O N V I C T E D A N D B A N I S H E D T O C Y P R U S F O R S E V E N Y E A R S F O R P U N I S H M E N T (cv_eng_000776-cv_eng_000776) +T H E C O U P L E H A V E T W O C H I L D R E N A D A U G H T E R S O P H I A R O S A L I N D A A N D A S O N M A T E O B R A V E R Y (cv_eng_000777-cv_eng_000777) +N O N E O F T H E T H R E E R E F E R E N D U M S R E A C H E D T H E Q U O R U M O F T H E M A J O R I T Y O F T H O S E E N T I T L E D (cv_eng_000778-cv_eng_000778) +T U R P I N S U C C E E D E D I N D I R A S A M A R A S E K E R A W H O S A W T H E U N I V E R S I T Y T H R O U G H A P E R I O D O F S T R O N G G R O W T H (cv_eng_000779-cv_eng_000779) +H E R E I A M B E T W E E N M Y F L O C K A N D M Y T R E A S U R E T H E B O Y T H O U G H T (cv_eng_000780-cv_eng_000780) +T H I S F A I L U R E H A S L E D T O S I X T E E N P O W E R P L A N T S H A V I N G Z E R O D A Y S O F C O A L S T O C K (cv_eng_000781-cv_eng_000781) +Y E S (cv_eng_000782-cv_eng_000782) +W H Y D O E S T H A T P L A N E K E E P G O I N G O V E R (cv_eng_000783-cv_eng_000783) +I V E D O N E T H I S B E F O R E W I T H V I R T U A L B O X W I T H G O O D R E S U L T S (cv_eng_000784-cv_eng_000784) +T H E A P P L I C A T I O N W A S A P P R O V E D I N F E B R U A R Y (cv_eng_000785-cv_eng_000785) +H E N R Y T A R L T O N S T I L E S W H E R E H E H A D A S O U N D T R A I N I N G I N L A T I N (cv_eng_000786-cv_eng_000786) +I T W A S D I S C O N T I N U E D D U E T O S C H E D U L I N G C O N F L I C T S I N V O L V E D I N L E W I S S R E T U R N T O T E R R E S T R I A L R A D I O (cv_eng_000787-cv_eng_000787) +H E R F A M I L Y W A S F R O M B R I A N Z A (cv_eng_000788-cv_eng_000788) +W H A T D I D Y O U E A T F O R D I N N E R (cv_eng_000789-cv_eng_000789) +T H A T W A S M Y D R A W T O S C I E N C E (cv_eng_000790-cv_eng_000790) +H E I S C O N S I D E R E D A M A S T E R O F C H I A R O S C U R O (cv_eng_000791-cv_eng_000791) +I T T H E N R E T U R N S T O T H E C H U R C H A S C E N D S A T T H E A L T A R A N D D I S A P P E A R S (cv_eng_000792-cv_eng_000792) +Y O U C A N N O T L O S E W H A T Y O U N E V E R H A D (cv_eng_000793-cv_eng_000793) +T H E J A W S E X T E N D P A S T T H E E Y E (cv_eng_000794-cv_eng_000794) +M Y N I E C E C A N H E L P Y O U W I T H T H A T (cv_eng_000795-cv_eng_000795) +T H A T S T H E K I N D O F S T U F F T H E Y W A N T (cv_eng_000796-cv_eng_000796) +H O P E F O R T H E B E S T A N D P R E P A R E F O R T H E W O R S T (cv_eng_000797-cv_eng_000797) +I N I T I A L L Y T H E W E I G H T L O S S W A S A T T A I N E D S T R I C T L Y B Y D I E T (cv_eng_000798-cv_eng_000798) +A L L W E R E O W N E D B Y T H E E V E R E T T M O O R E S Y N D I C A T E (cv_eng_000799-cv_eng_000799) +W I L L I T R A I N T O M O R R O W (cv_eng_000800-cv_eng_000800) +D U B I S T E W I G M E I N E L I E B E (cv_eng_000801-cv_eng_000801) +L U C I L E P E T R Y T O O K H E R P L A C E A S A C T I N G D I R E C T O R (cv_eng_000802-cv_eng_000802) +T H E B E A V E R R I V E R B R I E F L Y E N T E R S T H E E A S T C E N T R A L P A R T O F T H E T O W N S H I P (cv_eng_000803-cv_eng_000803) +T H E T R A C K R E S U R F A C I N G W A S A L S O C O M P L E T E D (cv_eng_000804-cv_eng_000804) +H I N D M A R S H W A S A W A R E O F T H E I M P O R T A N C E O F E L E C T R O N M I C R O S C O P Y I N B I O L O G I C A L R E S E A R C H (cv_eng_000805-cv_eng_000805) +S I N H A W A S B O R N I N A L L A H A B A D (cv_eng_000806-cv_eng_000806) +T H I S B R I D G E I S U N O F F I C I A L L Y R E F E R R E D T O A S B L A C K W A T E R B R I D G E B Y C O A L I T I O N F O R C E S O P E R A T I N G T H E R E (cv_eng_000807-cv_eng_000807) +I T I S R E S P O N S I B L E F O R W A T E R S U P P L Y A N D M A N A G E M E N T O F W A T E R R E S O U R C E S I N M A H A R A S H T R A (cv_eng_000808-cv_eng_000808) +T H I S I S T H E F I R S T P H A S E O F T H E J O B H E S A I D (cv_eng_000809-cv_eng_000809) +T H E G I Z A P L A T E A U O R G I Z A N E C R O P O L I S I N T H E E G Y P T I A N V A L L E Y O F T H E D E A D C O N T A I N S S E V E R A L P Y R A M I D S O F W H I C H T H E G R E A T P Y R A M I D I S T H E L A R G E S T S E V E R A L S M A L L T O M B S S E V E R A L T E M P L E S A N D T H E G R E A T S P H I N X (fleurs_eng_000413-fleurs_eng_000413) +T O W A R D S T H E E N D O F T H E M I D D L E A G E S W E S T E R N E U R O P E B E G A N T O D E V E L O P T H E I R O W N S T Y L E O N E O F T H E B I G G E S T D E V E L O P M E N T S O F T H E T I M E A S A R E S U L T O F T H E C R U S A D E S P E O P L E B E G A N T O U S E B U T T O N S T O F A S T E N C L O T H I N G (fleurs_eng_000414-fleurs_eng_000414) +I F Y O U O N L Y G O A S H O R E U S I N G S H I P B O A R D E X C U R S I O N S Y O U W I L L N O T N E E D A S E P A R A T E V I S A A S O F 2 0 0 9 (fleurs_eng_000415-fleurs_eng_000415) +D U V A L L W H O I S M A R R I E D W I T H T W O A D U L T C H I L D R E N D I D N O T L E A V E A B I G I M P R E S S I O N O N M I L L E R T O W H O M T H E S T O R Y W A S R E L A T E D (fleurs_eng_000416-fleurs_eng_000416) +T H E I R D I S C I P L I N E D D E F E N C E B A L L H A N D L I N G S K I L L S A N D E X C E L L E N T T E A M W O R K M A D E T H E M S T A N D O U T A N D I T W A S C L E A R T H A T T H I S W A S T H E T E A M T O B E A T (fleurs_eng_000417-fleurs_eng_000417) +T H E D I S E A S E I S C A R R I E D B Y P I G S W H I C H T H E N M I G R A T E S T O H U M A N S T H R O U G H M O S Q U I T O S (fleurs_eng_000418-fleurs_eng_000418) +F O R T H E S P R I N G B O K S I T E N D E D A F I V E M A T C H L O S I N G S T R E A K (fleurs_eng_000419-fleurs_eng_000419) +T H U S T H E P E N C I L W A S A G O O D F R I E N D T O M A N Y P E O P L E W H E N I T C A M E O U T (fleurs_eng_000420-fleurs_eng_000420) +T H E U S E O F V I D E O R E C O R D I N G H A S L E D T O I M P O R T A N T D I S C O V E R I E S I N T H E I N T E R P R E T A T I O N O F M I C R O E X P R E S S I O N S F A C I A L M O V E M E N T S W H I C H L A S T A F E W M I L L I S E C O N D S (fleurs_eng_000421-fleurs_eng_000421) +A L S O T O T H E N O R T H V I S I T T H E G R E A T S A N C T U A R Y O F O U R L A D Y O F F A T I M A S H R I N E A P L A C E O F W O R L D W I D E F A M O U S M A R I A N A P P A R I T I O N S (fleurs_eng_000422-fleurs_eng_000422) +I F Y O U W A N T T O B E C L O S E T O T H E A C T I O N Y O U R E G O I N G T O H A V E T O G E T I N E A R L Y T O G E T A C A M P I N G S I T E C L O S E T O T H E M U S I C (fleurs_eng_000423-fleurs_eng_000423) +M A D A G A S C A R I S B Y F A R T H E B I G G E S T A N D A C O N T I N E N T O N I T S O W N W H E N I T C O M E S T O W I L D L I F E (fleurs_eng_000424-fleurs_eng_000424) +W O M E N I T I S R E C O M M E N D E D T H A T A N Y W O M E N T R A V E L L E R S S A Y T H A T T H E Y A R E M A R R I E D R E G A R D L E S S O F A C T U A L M A R I T A L S T A T U S (fleurs_eng_000425-fleurs_eng_000425) +C U O M O 5 3 B E G A N H I S G O V E R N O R S H I P E A R L I E R T H I S Y E A R A N D S I G N E D A B I L L L A S T M O N T H L E G A L I Z I N G S A M E S E X M A R R I A G E (fleurs_eng_000426-fleurs_eng_000426) +A S L I G H T P O L L U T I O N I N T H E I R H E Y D A Y W A S N O T T H E K I N D O F P R O B L E M I T I S T O D A Y T H E Y A R E U S U A L L Y L O C A T E D I N C I T I E S O R A T C A M P U S E S E A S I E R T O R E A C H T H A N T H O S E B U I L T I N M O D E R N T I M E S (fleurs_eng_000427-fleurs_eng_000427) +T H E Y U S U A L L Y H A V E S P E C I A L F O O D D R I N K A N D E N T E R T A I N M E N T O F F E R S T O K E E P G U E S T S I N A G O O D M O O D A N D K E E P T H E M A T T H E P R E M I S E (fleurs_eng_000428-fleurs_eng_000428) +O N T H E O T H E R H A N D I C Y A N D S N O W Y C O N D I T I O N S A R E N O R M A L I N M A N Y C O U N T R I E S A N D T R A F F I C G O E S O N M O S T L Y U N I N T E R R U P T E D A L L Y E A R R O U N D (fleurs_eng_000429-fleurs_eng_000429) +B E C A R E F U L N O T T O A L L O W F A B R I C T O B E C O M E T O O H O T W H I C H C A N C A U S E S H R I N K A G E O R I N E X T R E M E C A S E S S C O R C H (fleurs_eng_000430-fleurs_eng_000430) +F E R A L C H I L D R E N M A Y H A V E E X P E R I E N C E D S E V E R E C H I L D A B U S E O R T R A U M A B E F O R E B E I N G A B A N D O N E D O R R U N N I N G A W A Y (fleurs_eng_000431-fleurs_eng_000431) +P E O P L E M A Y N O T A N T I C I P A T E T H A T P A T I E N C E A N D U N D E R S T A N D I N G A R E A L S O N E C E S S A R Y F O R T R A V E L L E R S R E T U R N I N G H O M E (fleurs_eng_000432-fleurs_eng_000432) +S O O N A F T E R T H E O U T B R E A K O F H O S T I L I T I E S B R I T A I N I N I T I A T E D A N A V A L B L O C K A D E O F G E R M A N Y (fleurs_eng_000433-fleurs_eng_000433) +T H E G O V E R N O R S O F F I C E S A I D N I N E T E E N O F T H E I N J U R E D W E R E P O L I C E O F F I C E R S (fleurs_eng_000434-fleurs_eng_000434) +U S I N G S H I P S T O T R A N S P O R T G O O D S I S B Y F A R T H E M O S T E F F I C I E N T W A Y T O M O V E L A R G E A M O U N T S O F P E O P L E A N D G O O D S A C R O S S O C E A N S (fleurs_eng_000435-fleurs_eng_000435) +L I B E R A L C R I T I C I S M O F T H E R E C O N S T R U C T I O N E F F O R T H A S F O C U S E D O N T H E A W A R D I N G O F R E C O N S T R U C T I O N C O N T R A C T S T O P E R C E I V E D W A S H I N G T O N I N S I D E R S (fleurs_eng_000436-fleurs_eng_000436) +Y O U C A N U S E B O D A B O D A M O T O R C Y C L E T A X I T O G E T A R O U N D G O M A T H E N O R M A L L O C A L P R I C E I S 5 0 0 C O N G O L E S E F R A N C S F O R T H E S H O R T R I D E (fleurs_eng_000437-fleurs_eng_000437) +T H E T H R E E K I N G D O M S W A S O N E O F T H E B L O O D I E S T E R A S I N A N C I E N T C H I N A S H I S T O R Y T H O U S A N D S O F P E O P L E D I E D F I G H T I N G T O S I T I N T H E H I G H E S T S E A T I N T H E G R A N D P A L A C E A T X I A N (fleurs_eng_000438-fleurs_eng_000438) +T H E S E C O U P L E S M A Y C H O O S E T O M A K E A N A D O P T I O N P L A N F O R T H E I R B A B Y (fleurs_eng_000439-fleurs_eng_000439) +N O T H I N G C A N B E S E E N O T H E R T H A N T H E C L E A R B E A U T I F U L S K Y A B O V E A N D T H E M A N Y S U R R O U N D I N G M O U N T A I N S V E R Y L I T T L E O F T H I S W O R L D C A N B E S E E N O R H E A R D F R O M I N S I D E T H E C A V E (fleurs_eng_000440-fleurs_eng_000440) +H E W A S S U B S E Q U E N T L Y R E L O C A T E D T O A D D E N B R O O K E S H O S P I T A L I N C A M B R I D G E (fleurs_eng_000441-fleurs_eng_000441) +V A T I C A N C I T Y S P O P U L A T I O N I S A R O U N D 8 0 0 I T I S T H E S M A L L E S T I N D E P E N D E N T C O U N T R Y I N T H E W O R L D A N D T H E C O U N T R Y W I T H T H E L O W E S T P O P U L A T I O N (fleurs_eng_000442-fleurs_eng_000442) +R E G U L A R A N N O U N C E M E N T S I N T H E M E T R O A R E M A D E O N L Y I N C A T A L A N B U T U N P L A N N E D D I S R U P T I O N S A R E A N N O U N C E D B Y A N A U T O M A T E D S Y S T E M I N A W I D E V A R I E T Y O F L A N G U A G E S I N C L U D I N G S P A N I S H E N G L I S H F R E N C H A R A B I C A N D J A P A N E S E (fleurs_eng_000443-fleurs_eng_000443) +T H I S O F F E R S A G O O D O P P O R T U N I T Y T O S E E T H E A U R O R A B O R E A L I S A S T H E S K Y W I L L B E D A R K M O R E O R L E S S A R O U N D T H E C L O C K (fleurs_eng_000444-fleurs_eng_000444) +F I R E R E S C U E C R E W S E V E N T U A L L Y D O U S E D T H E F I R E B Y 1 1 3 5 P M (fleurs_eng_000445-fleurs_eng_000445) +T H I S I S C A L L E D A C H E M I C A L S P H Y O U C A N M A K E A N I N D I C A T O R U S I N G R E D C A B B A G E J U I C E (fleurs_eng_000446-fleurs_eng_000446) +I N P A R T I C U L A R I T I S C L A I M E D T H A T O N E C A N D E T E C T W H E T H E R A P E R S O N I S L Y I N G B Y I N T E R P R E T I N G M I C R O E X P R E S S I O N S C O R R E C T L Y (fleurs_eng_000447-fleurs_eng_000447) +T H E C E N T R A L A U T H O R I T Y O F T H E C H U R C H H A D B E E N I N R O M E F O R O V E R A T H O U S A N D Y E A R S A N D T H I S C O N C E N T R A T I O N O F P O W E R A N D M O N E Y L E D M A N Y T O Q U E S T I O N W H E T H E R T H I S T E N E T W A S B E I N G M E T (fleurs_eng_000448-fleurs_eng_000448) +T H E S U N D A R B A N S A R E T H E L A R G E S T L I T T O R A L M A N G R O V E B E L T I N T H E W O R L D S T R E T C H I N G 8 0 K M 5 0 M I I N T O T H E B A N G L A D E S H I A N D I N D I A N H I N T E R L A N D F R O M T H E C O A S T (fleurs_eng_000449-fleurs_eng_000449) +R E G U L A R A N N O U N C E M E N T S I N T H E M E T R O A R E M A D E O N L Y I N C A T A L A N B U T U N P L A N N E D D I S R U P T I O N S A R E A N N O U N C E D B Y A N A U T O M A T E D S Y S T E M I N A W I D E V A R I E T Y O F L A N G U A G E S I N C L U D I N G S P A N I S H E N G L I S H F R E N C H A R A B I C A N D J A P A N E S E (fleurs_eng_000450-fleurs_eng_000450) +E V E R Y O N E P A R T I C I P A T E S I N S O C I E T Y A N D U S E S T R A N S P O R T A T I O N S Y S T E M S A L M O S T E V E R Y O N E C O M P L A I N S A B O U T T R A N S P O R T A T I O N S Y S T E M S (fleurs_eng_000451-fleurs_eng_000451) +L A Y T O N H A D A S K E D F O R C H A N G E S T O T H E C O N S E R V A T I V E S E N V I R O N M E N T A L B I L L D U R I N G T H E M E E T I N G W I T H T H E P M A S K I N G F O R A T H O R O U G H A N D C O M P L E T E R E W R I T I N G O F T H E C O N S E R V A T I V E P A R T Y S E N V I R O N M E N T A L B I L L (fleurs_eng_000452-fleurs_eng_000452) +A N Y O N E W H O S G O I N G T O D R I V E A T H I G H L A T I T U D E S O R O V E R M O U N T A I N P A S S E S S H O U L D C O N S I D E R T H E P O S S I B I L I T Y O F S N O W I C E O R F R E E Z I N G T E M P E R A T U R E S (fleurs_eng_000453-fleurs_eng_000453) +S L E E P I N T E R R U P T I O N I S T H E P R O C E S S O F P U R P O S E F U L L Y A W A K E N I N G D U R I N G Y O U R N O R M A L S L E E P P E R I O D A N D F A L L I N G A S L E E P A S H O R T T I M E L A T E R 1 0 – 6 0 M I N U T E S (fleurs_eng_000454-fleurs_eng_000454) +S W I R L T H E T W O D R Y P O W D E R S T O G E T H E R A N D T H E N W I T H C L E A N W E T H A N D S S Q U E E Z E T H E M I N T O A B A L L (fleurs_eng_000455-fleurs_eng_000455) +F O R T H E S P R I N G B O K S I T E N D E D A F I V E M A T C H L O S I N G S T R E A K (fleurs_eng_000456-fleurs_eng_000456) +J U S T L I K E T H E M O O N E X E R T S A P U L L O N T H E E A R T H C A U S I N G T I D E S S O D O E S T H E M I L K Y W A Y E X E R T A F O R C E O N T H E S A G I T T A R I U S G A L A X Y (fleurs_eng_000457-fleurs_eng_000457) +T H R O U G H T H E N I G H T B E T W E E N 1 5 0 A N D 2 0 0 C O P I E S W E R E M A D E N O W K N O W N A S D U N L A P B R O A D S I D E S (fleurs_eng_000458-fleurs_eng_000458) +F I R S T A M O N G I T S 7 8 R E C O M M E N D A T I O N S I S T H A T A N E W D I P L O M A T I C I N I T I A T I V E S H O U L D B E T A K E N B E F O R E T H E E N D O F T H I S Y E A R T O S E C U R E I R A Q S B O R D E R S A G A I N S T H O S T I L E I N T E R V E N T I O N S A N D T O R E E S T A B L I S H D I P L O M A T I C R E L A T I O N S W I T H I T S N E I G H B O R S (fleurs_eng_000459-fleurs_eng_000459) +S A I N T P E T E R S B U R G C R U I S E S I N C L U D E T I M E I N T O W N C R U I S E P A S S E N G E R S A R E E X E M P T E D F R O M V I S A R E Q U I R E M E N T S C H E C K T H E T E R M S (fleurs_eng_000460-fleurs_eng_000460) +A C C O R D I N G T O J A P A N S N U C L E A R A G E N C Y R A D I O A C T I V E C A E S I U M A N D I O D I N E H A S B E E N I D E N T I F I E D A T T H E P L A N T (fleurs_eng_000461-fleurs_eng_000461) +S E G R E G A T I O N A N D R E C O M B I N A T I O N S H U F F L E V A R I A T I O N B A C K A N D F O R T H B E T W E E N T H E T W O P O O L S W I T H E A C H G E N E R A T I O N (fleurs_eng_000462-fleurs_eng_000462) +E L E M E N T S L I K E C A L C I U M A N D P O T A S S I U M A R E C O N S I D E R E D M E T A L S O F C O U R S E T H E R E A R E A L S O M E T A L S L I K E S I L V E R A N D G O L D (fleurs_eng_000463-fleurs_eng_000463) +T H E C O R R E L A T I O N B E T W E E N B R A I N P A T H O L O G Y A N D B E H A V I O U R S U P P O R T S S C I E N T I S T S I N T H E I R R E S E A R C H (fleurs_eng_000464-fleurs_eng_000464) +A N C I E N T C H I N A H A D A U N I Q U E W A Y O F S H O W I N G D I F F E R E N T T I M E P E R I O D S E A C H S T A G E O F C H I N A O R E A C H F A M I L Y T H A T W A S I N P O W E R W A S A D I S T I N C T I V E D Y N A S T Y (fleurs_eng_000465-fleurs_eng_000465) +A S I M P L E P O P U L A R D I N N E R E S P E C I A L L Y D U R I N G T H E S U M M E R I S T H E P A A M B O L I B R E A D W I T H O L I V E O I L T O M A T O A N D A N Y A V A I L A B L E C O N D I M E N T S S U C H A S C H E E S E T U N A F I S H E T C (fleurs_eng_000466-fleurs_eng_000466) +T H E A N N O U N C E M E N T W A S M A D E A F T E R T R U M P H A D A P H O N E C O N V E R S A T I O N W I T H T U R K I S H P R E S I D E N T R E C E P T A Y Y I P E R D O Ğ A N (fleurs_eng_000467-fleurs_eng_000467) +P E R R Y S T A T E D T H A T H E W O U L D R E T U R N T O T E X A S T O A S S E S S T H E R E S U L T S O F T O N I G H T S C A U C U S D E T E R M I N E W H E T H E R T H E R E I S A P A T H F O R W A R D F O R M Y S E L F I N T H I S R A C E B U T L A T E R S A I D T H A T H E W O U L D R E M A I N I N T H E R A C E A N D C O M P E T E I N T H E J A N U A R Y 2 1 S O U T H C A R O L I N A P R I M A R Y (fleurs_eng_000468-fleurs_eng_000468) +H E W A S A L S O E N G A G E D I N E N G R A V I N G B A N K N O T E S F O R M A N Y C O U N T R I E S R E C E N T E X A M P L E S O F H I S W O R K I N C L U D I N G T H E P R I M E M I N I S T E R I A L P O R T R A I T S O N T H E F R O N T O F T H E N E W C A N A D I A N 5 A N D 1 0 0 B I L L S (fleurs_eng_000469-fleurs_eng_000469) +M O R E T R A D I T I O N A L C H U R C H E S O F T E N H O L D A N E A S T E R V I G I L O N S A T U R D A Y N I G H T D U R I N G T H E E A S T E R W E E K E N D W I T H T H E C O N G R E G A T I O N S O F T E N B R E A K I N G I N T O C E L E B R A T I O N A T T H E S T R O K E O F M I D N I G H T T O C E L E B R A T E C H R I S T S R E S U R R E C T I O N (fleurs_eng_000470-fleurs_eng_000470) +F I N L A N D I S A G R E A T B O A T I N G D E S T I N A T I O N T H E L A N D O F A T H O U S A N D L A K E S H A S T H O U S A N D S O F I S L A N D S T O O I N T H E L A K E S A N D I N T H E C O A S T A L A R C H I P E L A G O S (fleurs_eng_000471-fleurs_eng_000471) +C U R R E N T S E N A T O R A N D A R G E N T I N E F I R S T L A D Y C R I S T I N A F E R N A N D E Z D E K I R C H N E R A N N O U N C E D H E R P R E S I D E N T I A L C A N D I D A C Y Y E S T E R D A Y E V E N I N G I N L A P L A T A A C I T Y 5 0 K I L O M E T E R S 3 1 M I L E S A W A Y F R O M B U E N O S A I R E S (fleurs_eng_000472-fleurs_eng_000472) +S E V E R E W E A T H E R I S T H E G E N E R I C T E R M F O R A N Y D A N G E R O U S W E A T H E R P H E N O M E N O N W I T H T H E P O T E N T I A L T O C A U S E D A M A G E S E R I O U S S O C I A L D I S R U P T I O N O R L O S S O F H U M A N L I F E (fleurs_eng_000473-fleurs_eng_000473) +F O R E X A M P L E T H E M O S T C O M M O N S T I L L I M A G E P H O T O G R A P H Y F O R M A T I N T H E W O R L D I S 3 5 M M W H I C H W A S T H E D O M I N A N T F I L M S I Z E A T T H E C L O S E O F T H E A N A L O G F I L M E R A (fleurs_eng_000474-fleurs_eng_000474) +I T I S R E L A T E D T O B U T U S U A L L Y N O T I N V O L V I N G A L P I N E S T Y L E S K I T O U R I N G O R M O U N T A I N E E R I N G T H E L A T T E R O N E S D O N E I N S T E E P T E R R A I N A N D R E Q U I R I N G M U C H S T I F F E R S K I S A N D B O O T S (fleurs_eng_000475-fleurs_eng_000475) +I R O N I N G D A M P C L O T H E S C A N H E L P T H E M D R Y M A N Y H O T E L S H A V E A N I R O N A N D I R O N I N G B O A R D A V A I L A B L E F O R L O A N E V E N I F O N E I S N O T P R E S E N T I N T H E R O O M (fleurs_eng_000476-fleurs_eng_000476) +E V A D N E A N S W E R E D H O A R S E L Y S H E D R E W H E R C H A I R A L I T T L E C L O S E R T O T H E F I R E A N D S P R E A D H E R H A N D S O U T T O T H E B L A Z E T H E R E W A S N O O T H E R L I G H T I N T H E R O O M B Y T H I S T I M E T H E W I N D W I T H O U T H O W L E D D I S M A L L Y S T I L L (mls_eng_000283-mls_eng_000283) +M Y D E A R M A R I A W H Y D O Y O U N O T D E S I S T F R O M T H I S S I L L Y P U R S U I T O F A N I M A G I N A R Y T R E A S U R E W H A T I S T H E V A L U E O F M O N E Y W E A R E S P A N I A R D S N O T S H I R T S L E E V E D M E R C E N A R Y P I G S O F A M E R I C A N S (mls_eng_000284-mls_eng_000284) +C R I T I C A L T E M P E R A T U R E I S T H A T O F T H E S I N G L E I S O T H E R M A L L I N E W H I C H P R E S E N T S A P O I N T O F I N F L E X I O N A T A H O R I Z O N T A L T A N G E N T T H E C R I T I C A L P R E S S U R E A N D T H E C R I T I C A L V O L U M E A R E T H E T W O C O O R D I N A T E S O F T H I S P O I N T O F I N F L E X I O N (mls_eng_000285-mls_eng_000285) +M U C H L I K E I N F O U L N E S S A N D D E F O R M I T Y U N T O T H A T M O N S T E R W H O M T H E T H E B A N K N I G H T T H E F A T H E R O F T H A T F A T A L P R O G E N Y M A D E K I L L H E R S E L F F O R V E R Y H E A R T S D E S P I T E T H A T H E H A D R E A D H E R R I D D L E W H I C H N O W I G H T C O U L D E V E R L O O S E B U T S U F F E R E D D E A D L Y D U E L (mls_eng_000286-mls_eng_000286) +H E H A S M A N A G E D T O M E A S U R E W I T H P R E C I S I O N P R E S S U R E S A M O U N T I N G T O T H R E E T H O U S A N D A T M O S P H E R E S A N D A L S O T H E V E R Y S M A L L V O L U M E S T H E N O C C U P I E D B Y T H E F L U I D M A S S U N D E R C O N S I D E R A T I O N T H I S L A S T M E A S U R E M E N T W H I C H N E C E S S I T A T E S N U M E R O U S C O R R E C T I O N S I S T H E M O S T D E L I C A T E P A R T O F T H E O P E R A T I O N (mls_eng_000287-mls_eng_000287) +W H Y S H O U L D I T H A V E B E E N D E E M E D N E C R O M A N C Y T O E N D E A V O R T O C O M B I N E T H E S E P A R T S T O E V O L V E B Y C A R E F U L E L I M I N A T I O N A N D C H A N G E T O T H E P E R F E C T F O O D (mls_eng_000288-mls_eng_000288) +N A Y T H O U G H O F R U S H E S B E M Y B E D Y E T I A M R I C H L O V E S A I D B U T A R G U E D L I F E T H R I C E F O N D A R T T H O U T O Y I E L D T H E S O V E R E I G N G I F T S O F E A R T H T H E V I C T O R S W O R D T H E L A U R E L E D B R O W F O R V I S I O N E D T H I N G S O F L I T T L E W O R T H (mls_eng_000289-mls_eng_000289) +B O C K S E E M S T O H A V E B E E N A K E E N C O L L E C T O R A L T H O U G H H A M P E R E D B Y I L L H E A L T H A N D A G R E A T P O I N T I N H I S F A V O U R I S T H A T H E D E S C R I B E D O N L Y T H O S E P L A N T S W H I C H H A D C O M E U N D E R H I S O W N P E R S O N A L O B S E R V A T I O N (mls_eng_000290-mls_eng_000290) +H A D R A T H E R S H R U N K U P A N D H A D N O T C H A N G E D I N T O N Y M P H S T H E S E I L E F T I N T H E S T E M S C O V E R I N G T H E M U P A G A I N A N D T H E Y A P P E A R E D A S P E R F E C T I N S E C T S I N T H E M A Y O F T H E F O L L O W I N G Y E A R (mls_eng_000291-mls_eng_000291) +N O T H I N G S A V E O B J E C T S A N D T H O U G H T S O F B E A U T Y C O U L D P R E S E N T T H E M S E L V E S T O T H E U N D E R S T A N D I N G O F T H E F O R T U N A T E P E R S O N W H O P A R T O O K O F I T T H E S E P A G E S W H I C H Y O U H A V E B R O U G H T T O M E T O T R A N S L A T E A R E C O N C E R N E D W I T H T H I S S U P E R S T I T I O N (mls_eng_000292-mls_eng_000292) +N O W S E E M E D I N S I P I D I T Y A N D H E D N E R V E H I M S E L F A G A I N S T I T H I S F A C E W O R E A S O R T O F S E V E R E F L U S H H E W A S T I M I D E V E N T O R U D E N E S S (mls_eng_000293-mls_eng_000293) +B E C A M E M O R E L I F E L I K E A S T H E C H E E K S F L U S H T H E R E W A S R A R E W A R M T H I N A W I N T E R M O R N I N G T O C H E E R T H E H A L F D E S P A I R I N G S O U L T I R E D A F T E R L O N G H O U R S O F O I L R E A D I N G A N D P I E R C E D T O T H E H E A R T B Y N E V E R C E A S I N G R H Y M E S Y E T I C O U L D N O T U N D E R S T A N D I T (mls_eng_000294-mls_eng_000294) +O N E O F T H E H A W A I I A N W R I T E R S S A I D T H E O P I H I A W A I S A P O I S O N S H E L L F I S H T H E S E A R E B I T T E R A N D D E A D L Y A N D C A N B E U S E D I N P U T T I N G E N E M I E S T O D E A T H (mls_eng_000295-mls_eng_000295) +T H E B E A U T E O U S R O B E S O F H E A V E N A S L A N T T H E D E W B R I G H T E A R T H A N D C O L O U R E D A I R H E L O O K S I N B O U N D L E S S M A J E S T Y A B R O A D T O U C H I N G T H E G R E E N L E A V E S A L L A T R E M B L E W I T H G O L D L I G H T (mls_eng_000296-mls_eng_000296) +I C A N D O N O M O R E T H A N T H A T U N T I L T H I S M A T T E R I S A B S O L U T E L Y S E T T L E D T H E Y A R E W O R T H M O R E T H A N L I F E I T S E L F T O M E M R C O W P E R S E E M E D A N N O Y E D S U R E L Y H E P R O T E S T E D Y O U A R E N O T G O I N G T O A S K M E T O W A I T T H R E E M O N T H S U N T I L I C A N E X A M I N E O N E O F T H E S E (mls_eng_000297-mls_eng_000297) +R O S C O N G R E S S F O U N D A T I O N R U S S I A N E N T I T Y T H A T O R G A N I Z E D T H E S A I N T P E T E R S B U R G I N T E R N A T I O N A L E C O N O M I C F O R U M R O S N E F T R U S S I A N S T A T E O W N E D O I L A N D E N E R G Y C O M P A N Y (mls_eng_000298-mls_eng_000298) +H O W I T G L I T T E R E D A N D S P A R K L E D T H E D E L I C A T E F R O S T W O R K Y O U W E R E A T T R A C T E D N O D O U B T A N D M A R V E L L E D A T T H E D A I N T Y T R A C I N G S B U T F E W O F U S H A V E R E A L L Y H A D A N O P P O R T U N I T Y T O S T U D Y T H E D E T A I L O F T H E S E F R O S T D E S I G N S M I N U T E L Y O R H A V E C O N S I D E R E D T H A T T H E R E W E R E M O R E T H A N T H R E E O R F O U R D E S I G N S A T M O S T (mls_eng_000299-mls_eng_000299) +O T H E R T H A N T H E O F F E N S E I N T R Y I N G T O I N F L I C T A W O U N D T H E Y M A Y K I L L T H E O F F E N D E R O R W O U N D H I M M O R E T H A N T H E Y I N T E N D E D T O D O A N D T H I S B E C O M E S A C A U S E F O R A N E W F E U D S O T H A T T H E P R I M I T I V E L E G I S L A T O R S W E R E C A R E F U L I N R E Q U I R I N G T H E R E T A L I A T I O N T O B E L I M I T E D T O A N E Y E F O R A N E Y E (mls_eng_000300-mls_eng_000300) +A T C Y R U S W O R D T H E J E W S R E T U R N T H E C O M P A N Y T H A T G O G O D S H O U S E B E G U N W I T H M I R T H A N D M O A N I S H I N D E R E D B Y T H E F O E B U T O N C E A G A I N T H E W O R K G O E S O N B Y L I C E N S E F R O M D E R I U S E Z R A I S S E N T W I T H R O Y A L G R A N T A N D G I F T S F O R U S E S P I O U S (mls_eng_000301-mls_eng_000301) +N E T P R O D U C T Y E A R I N A N D Y E A R O U T S E V E N H U N D R E D F R A N C S H E L I V E D I N I T H O W N O T S O B A D L Y W E W I L L E X P L A I N M A R I U S O C C U P I E D I N T H E G O R B E A U H O U S E (mls_eng_000302-mls_eng_000302) +T H E N T H I S I S A L L Y O U R A N S W E R T I S T O O F A I R F O R O N E O F H I S A L L I A N C E A N D I W A R N Y O U T H A T T H I S P L A C E N O M O R E S E E Y O U E X I T E N T E R D E F L O R E S T H E B E S T I S T H E R E I S M O R E G R O U N D T O M E E T A M A N S R E V E N G E O N H O N E S T D E F L O R E S T H A T S M Y N A M E I N D E E D (mls_eng_000303-mls_eng_000303) +W H E N I R E T U R N E D T O T H E H O U S E W H E R E I H A D B E E N A H A P P Y C H I L D O N L Y A P I L E O F A S H E S W H E R E I T H A D S T O O D I W E P T L O N G A N D T O F O R G E T M Y W E E P I N G I S A I L E D O U T O N T H E V A S T C A L M S E A O N T H E S E W A T E R S I N A S T A R S A P P H I R E N I G H T I P L A Y E D M Y F L U T E T O T H E S U M M E R M O O N (mls_eng_000304-mls_eng_000304) +D O Y O U N O T S E E W H A T P L E A S U R E I T G I V E S H I M W E H A V E G R O W N U P T O G E T H E R I N T H I S H O U S E S I N C E H E W A S A B O Y I S I M P L Y C A N N O T B E A R A S Y O U C A N T H E S I G H T O F T H E S M I L E L E A V I N G H I S F A C E P O O R D E A R H E H A S N O A M U S E M E N T E X C E P T T H I S P L A Y I N G A T T H E S H O P K E E P I N G (mls_eng_000305-mls_eng_000305) +I T I S A N E B U L O U S B O D Y R E V O L V I N G I N A N E L L I P T I C A L O R B I T O F G R E A T E L O N G A T I O N L O V E L O V E L O V E W I L L N O T B E T H E W O U N D O F C U P I D B U T T H E M A N I F E S T A T I O N O F U N I V E R S A L R E P R O D U C T I V E I N S T I N C T S (mls_eng_000306-mls_eng_000306) +S H A R P L Y A S H E S H O O K H A N D S W I T H H E R G O D B L E S S Y O U M Y D E A R C H I L D T H E B I S H O P S A I D W H E N S H E K I S S E D H I M A N D H I S L I P S M O V E D A F T E R W A R D F O R S O M E S E C O N D S A S I F H E W E R E I N P R A Y E R H E R M O T H E R F O L L O W E D H E R O U T O F T H E R O O M A N D T H E N S I L E N C E S E T T L E D (mls_eng_000307-mls_eng_000307) +F O L L O W E D H I M S T E A L T H I L Y A N D W H E N H E W A S I N A S T O O P I N G P O S T U R E F I L L I N G H I S B U C K E T C A M E U P B E H I N D H I M A N D P L U N G E D A L O N G K N I F E I N T O H I S N E C K (mls_eng_000308-mls_eng_000308) +S A I T H C H E R S I A S D O E S N O T J U P I T E R D I S T R I B U T E T O T H E G O D S T H E I R P R O P O R T I O N A N D D I V I D E N D S P A R I N G L Y A N D S E V E R A L L Y A S A G A M E M N O N D I D T O H I S C O M M A N D E R S W H E N H I S G U E S T S D R A N K T O O N E A N O T H E R I F C H E R S I A S Q U O T H C L E O D E M U S A S Y O U N A R R A T E (mls_eng_000309-mls_eng_000309) +A N D W H E R E N O N E S H A L L D A R E R E S T R A I N U S W E C A N M E E T A G A I N I N T H O U G H T S O T H E R E S N O U S E I N W E E P I N G B E A R A C H E E R F U L S P I R I T S T I L L N E V E R D O U B T T H A T F A T E I S K E E P I N G F U T U R E G O O D F O R P R E S E N T I L L (mls_eng_000310-mls_eng_000310) +A N D T O B E C O M E T H E R E C O R D O F W H A T P E O P L E H A V E D O N E I N T H E I R M O R E A M I A B L E M O M E N T S T H E R E C O R D O F T H E C O N Q U E S T S O F P E A C E H O W M E N H A V E L I V E D A N D L A B O R E D D U G A N D B U I L T H E W N A N D C L E A R E D G A R D E N E D A N D R E F O R E S T (mls_eng_000311-mls_eng_000311) +T H E L O W F L Y I N G O F T H E S W A L L O W S B E T O K E N S R A I N A S W E L L A S A N Y U N S E A S O N A B L E D A N C I N G O F M I D G E S I N T H E E V E N I N G S O R E C O R N S O N T H E F E E T A N D R H E U M A T I S M I N T H E J O I N T S A R E D I R E F U L P R E C U R S O R S T H E L E A V E S A R E A L L A T R E M B L E B E F O R E T H E A P P R O A C H O F T H U N D E R (mls_eng_000312-mls_eng_000312) +W A S S T O R M E D G E N E R A L D A M P I E R R E W A S K I L L E D G E N E R A L C U S T I N E W A S B L A M E D A N D I N D E E D I S N O W C O M E T O P A R I S T O G I V E E X P L A N A T I O N S A G A I N S T A L L W H I C H T H E M O U N T A I N A N D A T R O C I O U S M A R A T M U S T E V E N M A K E H E A D A S T H E Y C A N (mls_eng_000313-mls_eng_000313) +T H E M O M E N T W A S F E A R F U L A M I G H T I E R F O E H A D N E V E R S W U N G T H E B A T T L E A X E O V E R H I M B U T H O P E N E R V E D H I S A R M F O R A D E S P E R A T E B L O W A N D T E C U M S E H F E L L P R O S T R A T E B E F O R E H I M (mls_eng_000314-mls_eng_000314) +T H E N T H E W I N D S T O P P E D T H E C L O U D S T U R N E D D A R K A N D N I G H T C A M E O N L I K E I N K M Y O L D C O T T O N Q U I L T W A S C O L D A S I R O N M Y S W E E T S O N T O S S E D I N H I S S L E E P (mls_eng_000315-mls_eng_000315) +Y O U M A Y D O A S Y O U P L E A S E T O W O R K O F F Y O U R I R R I T A T I O N T O K E E P U P Y O U R F A N A T I C I S M Y O U A R E W E L L O F F Y O U N E E D N O T M I N D T H E C O S T T H E P O O R D O N O T W A N T T O S T A N D I N Y O U R W A Y B U T Y O U I N S I S T O N T H E I R S U B M I T T I N G T O Y O U R C O M P U L S I O N (mls_eng_000316-mls_eng_000316) +H E W A S B R E D B Y R E V G A S N E Y D B E I N G B Y O T H M A N E S I X F O U R T W O T W O H E D W I G H E W A S B O R N I N M A R C H E I G H T E E N S E V E N T Y N I N E A N D H E W A S T H E O N L Y S U R V I V O R O F A L I T T E R O F F I F T E E N I T W A S O N T H I S A C C O U N T T H A T H E W A S C A L L E D S A F E I N C O L O R A N D M A R K I N G S (mls_eng_000317-mls_eng_000317) +A N D W H A T H A S T E I T M A K E S T O F A L L I N T O T H E S E C O N D T H E R E B Y T H I S T I M E D I A P H A N T A S N E E Z E S A C H O O M O S T A D M I R A B L E S E C R E T O N T H E C O N T R A R Y I T S T I R S M E N O T A W H I T W H I C H M O S T C O N C E R N S I T H A H A H A (mls_eng_000318-mls_eng_000318) +T H I R D L Y T H A L E S S A I D W H E R E T H E C I T I Z E N S A R E N E I T H E R T O O R I C H N O R T O O P O O R F O U R T H L Y A N A C H A R S I S S A I D W H E R E T H O U G H I N A L L O T H E R R E S P E C T S T H E Y A R E E Q U A L Y E T V I R T U O U S M E N A R E A D V A N C E D A N D V I C I O U S P E R S O N D E G R A D E D (mls_eng_000319-mls_eng_000319) +T H E K I N D L Y F R A N K I S S Y M P A T H E T I C E V E R Y D A Y H E P A S S E S N O T E S B E T W E E N U S A N D I T R Y T O E N C O U R A G E R U S S E L L H E W I L L I M P R O V E I A S S U R E H I M H I S T I M E I S S H O R T A N D F R E S H A I R A N D L I B E R T Y W I L L S O O N R E S T O R E H I M (mls_eng_000320-mls_eng_000320) +T H E S E Q U E S T I O N S I T I S N O W E V I D E N T M A Y F R E Q U E N T L Y B E A N S W E R E D W I T H E Q U A L P R O P R I E T Y I N O P P O S I T E W A Y S A N D I F T H E R E B E A N Y O C C A S I O N S O N W H I C H T H E Y C A N B E A N S W E R E D O N L Y I N O N E W A Y T H E A N S W E R W I L L D E P E N D U P O N T H E N A T U R E O F T H E O C C A S I O N (mls_eng_000321-mls_eng_000321) +I N H I S N O T E B O R E T H E M I N S T R E L S Y S E C O N D E D I T I O N E I G H T E E N O H E I G H T S C O T T S A Y S T H E B A L L A D W A S T A K E N D O W N F R O M A N O L D W O M A N S R E C I T A T I O N A T T H E A L S T O N M O O R L E A D M I N E S B Y T H E A G E N T T H E R E A N D S E N T B Y H I M T O S U R T E E S (mls_eng_000322-mls_eng_000322) +C H R I S T I A N T H E O L O G I A N S (nchlt_eng_001588-nchlt_eng_001588) +O B T A I N E A G L E F E A T H E R S (nchlt_eng_001589-nchlt_eng_001589) +E L E M E N T A R Y S P E C I A L F U N C T I O N S (nchlt_eng_001590-nchlt_eng_001590) +G E O R G E W A S H I N G T O N U N I V E R S I T Y (nchlt_eng_001591-nchlt_eng_001591) +S C I E N C E F I C T I O N N O V E L S (nchlt_eng_001592-nchlt_eng_001592) +C O A S T H I P H O P (nchlt_eng_001593-nchlt_eng_001593) +I N V E R S E L A P L A C E T R A N S F O R M (nchlt_eng_001594-nchlt_eng_001594) +F R E N C H P R O T E S T A N T S (nchlt_eng_001595-nchlt_eng_001595) +A F G H A N A I R F O R C E (nchlt_eng_001596-nchlt_eng_001596) +H E R O E S I N M Y T H O L O G Y A N D L E G E N D (nchlt_eng_001597-nchlt_eng_001597) +B U S I N E S S C L A S S S E A T (nchlt_eng_001598-nchlt_eng_001598) +C L U B P L A Y C H A R T (nchlt_eng_001599-nchlt_eng_001599) +P O S I T R O N S W E R E R E P O R T E D (nchlt_eng_001600-nchlt_eng_001600) +O L D V I C T H E A T R E (nchlt_eng_001601-nchlt_eng_001601) +O R T H O D O X M O N A R C H S (nchlt_eng_001602-nchlt_eng_001602) +N A T I O N S M E M B E R S T A T E S (nchlt_eng_001603-nchlt_eng_001603) +F I F A W O R L D C U P (nchlt_eng_001604-nchlt_eng_001604) +C R E W S R E S C U E E F F O R T S (nchlt_eng_001605-nchlt_eng_001605) +A C T U A L F I L M M I C R O S C O P I C A L L Y (nchlt_eng_001606-nchlt_eng_001606) +M U S I C A L G R O U P S R E E S T A B L I S H E D (nchlt_eng_001607-nchlt_eng_001607) +P R I M U S I N T E R P A R E S (nchlt_eng_001608-nchlt_eng_001608) +F I L M T E C H N I Q U E S (nchlt_eng_001609-nchlt_eng_001609) +T E L E V I S I O N S E R I E S B A S E D (nchlt_eng_001610-nchlt_eng_001610) +N E W P O L I T I C A L P A R T Y (nchlt_eng_001611-nchlt_eng_001611) +A N C I E N T E G Y P T A C H I E V E D (nchlt_eng_001612-nchlt_eng_001612) +F L A T M U S I C N A T U R A L (nchlt_eng_001613-nchlt_eng_001613) +A M E R I C A N S T E C H N O L O G Y W R I T E R S (nchlt_eng_001614-nchlt_eng_001614) +D A U G H T E R S O F B A R O N S (nchlt_eng_001615-nchlt_eng_001615) +P O P U L A R T O U R I S T A T T R A C T I O N S (nchlt_eng_001616-nchlt_eng_001616) +D U T C H W E S T I N D I A (nchlt_eng_001617-nchlt_eng_001617) +G O L D M E D A L R E C I P I E N T S (nchlt_eng_001618-nchlt_eng_001618) +R U S S I A N S O C I A L D E M O C R A T I C (nchlt_eng_001619-nchlt_eng_001619) +A M E R I C A N F I L M P R O D U C E R S (nchlt_eng_001620-nchlt_eng_001620) +F R E E S O F T W A R E F O U N D A T I O N (nchlt_eng_001621-nchlt_eng_001621) +R O Y A L D R A M A T I C T H E A T R E (nchlt_eng_001622-nchlt_eng_001622) +E D I B L E M O L L U S C S (nchlt_eng_001623-nchlt_eng_001623) +F E A T U R E S I N C L U D E B E A C H E S (nchlt_eng_001624-nchlt_eng_001624) +O X F O R D D I C T I O N A R Y C H A N G E D (nchlt_eng_001625-nchlt_eng_001625) +S A L U K I P E R S I A N G R E Y H O U N D (nchlt_eng_001626-nchlt_eng_001626) +P R I M E M I N I S T E R K E V I N (nchlt_eng_001627-nchlt_eng_001627) +L A N G U A G E S O F I R A Q (nchlt_eng_001628-nchlt_eng_001628) +S O U T H E A S T E N G L A N D (nchlt_eng_001629-nchlt_eng_001629) +N E W L I N E C I N E M A (nchlt_eng_001630-nchlt_eng_001630) +E Q U A L C R E D I T O P P O R T U N I T Y (nchlt_eng_001631-nchlt_eng_001631) +S O U T H E A S T E N G L A N D (nchlt_eng_001632-nchlt_eng_001632) +M A Y (nchlt_eng_001633-nchlt_eng_001633) +R E C O R D H E T A T M D E S C R I B E S (nchlt_eng_001634-nchlt_eng_001634) +M U S I C A L G R O U P S F R O M C A L I F O R N I A (nchlt_eng_001635-nchlt_eng_001635) +M A I N B A T T L E T A N K S (nchlt_eng_001636-nchlt_eng_001636) +P O L I S H M U S I C A L I N S T R U M E N T S (nchlt_eng_001637-nchlt_eng_001637) +L A N G U A G E S O F S A U D I A R A B I A (nchlt_eng_001638-nchlt_eng_001638) +C O L D W A R T E N S I O N S (nchlt_eng_001639-nchlt_eng_001639) +D U B B Y (nchlt_eng_001640-nchlt_eng_001640) +A N T I P O P E C L E M E N T (nchlt_eng_001641-nchlt_eng_001641) +G E T S T A K E N P R I V A T E (nchlt_eng_001642-nchlt_eng_001642) +K I N G F E R D I N A N D (nchlt_eng_001643-nchlt_eng_001643) +E L E C T R O N I C M U S I C A L I N S T R U M E N T S (nchlt_eng_001644-nchlt_eng_001644) +A G E M E L T W A T E R (nchlt_eng_001645-nchlt_eng_001645) +L A W R E N C E L I V E R M O R E N A T I O N A L (nchlt_eng_001646-nchlt_eng_001646) +L E A G U E B A S E B A L L P L A Y E R S (nchlt_eng_001647-nchlt_eng_001647) +B U D D H I S M I N T H E A N C I E N T M E D I T E R R A N E A N (nchlt_eng_001648-nchlt_eng_001648) +U N I T E D S T A T E S R E C O G N I Z E D (nchlt_eng_001649-nchlt_eng_001649) +P R O P O S I T I O N A L F A L L A C I E S (nchlt_eng_001650-nchlt_eng_001650) +S P E C I A L E C O N O M I C Z O N E S (nchlt_eng_001651-nchlt_eng_001651) +M A I N S T R E A M W E S T (nchlt_eng_001652-nchlt_eng_001652) +E V E N I N G R U S H H O U R S (nchlt_eng_001653-nchlt_eng_001653) +B O F H E D I T I O N S T O O K (nchlt_eng_001654-nchlt_eng_001654) +A N T A R C T I C A H A S N O (nchlt_eng_001655-nchlt_eng_001655) +W E S T E N D M U S I C A L S (nchlt_eng_001656-nchlt_eng_001656) +C O N S E R V A T I V E J U D A I S M R E G A R D S (nchlt_eng_001657-nchlt_eng_001657) +O P E C M E M B E R S T A T E S (nchlt_eng_001658-nchlt_eng_001658) +P R I M E M I N I S T E R J O H N (nchlt_eng_001659-nchlt_eng_001659) +R O C K S F O R M I N G M O N T (nchlt_eng_001660-nchlt_eng_001660) +M A J O R L E A G U E T E A M S (nchlt_eng_001661-nchlt_eng_001661) +P O L L I N A T I O N M A N A G E M E N T (nchlt_eng_001662-nchlt_eng_001662) +F R E N C H P H Y S I C I S T S (nchlt_eng_001663-nchlt_eng_001663) +H I G H E R C O M P R E S S I O N R A T I O (nchlt_eng_001664-nchlt_eng_001664) +R E C O R D I N G I N D U S T R Y A S S O C I A T I O N (nchlt_eng_001665-nchlt_eng_001665) +D P G S O N L I N E M A G A Z I N E (nchlt_eng_001666-nchlt_eng_001666) +H I P H O P R E C O R D P R O D U C E R S (nchlt_eng_001667-nchlt_eng_001667) +F I N I T E S T A T E M A C H I N E S (nchlt_eng_001668-nchlt_eng_001668) +W I D E L Y U S E D L O C A L (nchlt_eng_001669-nchlt_eng_001669) +N O R T H A M E R I C A N C O N T I N E N T (nchlt_eng_001670-nchlt_eng_001670) +A F R I C A N A M E R I C A N R A P P E R S (nchlt_eng_001671-nchlt_eng_001671) +T H R E A T E N E D M I L I T A R Y A C T I O N S (nchlt_eng_001672-nchlt_eng_001672) +T H E W O R D (nchlt_eng_001673-nchlt_eng_001673) +A T O M I C M O L E C U L A R A N D O P T I C A L P H Y S I C S (nchlt_eng_001674-nchlt_eng_001674) +T O W N (nchlt_eng_001675-nchlt_eng_001675) +M A R C E L (nchlt_eng_001676-nchlt_eng_001676) +C O N S T R U C T N E W R A I L G A U G E (nchlt_eng_001677-nchlt_eng_001677) +P A U L I E X C L U S I O N P R I N C I P L E (nchlt_eng_001678-nchlt_eng_001678) +H U E P O R T R A Y D I F F E R E N T (nchlt_eng_001679-nchlt_eng_001679) +S S O V I E T D I S S I D E N T S (nchlt_eng_001680-nchlt_eng_001680) +S I G N A L T R A N S D U C T I O N P A T H W A Y S (nchlt_eng_001681-nchlt_eng_001681) +N E W B O R N M E S S I A H (nchlt_eng_001682-nchlt_eng_001682) +G E N E R A L L Y A C C E P T E D R A N G E S (nchlt_eng_001683-nchlt_eng_001683) +G U I L D A W A R D W I N N E R S (nchlt_eng_001684-nchlt_eng_001684) +S W E D I S H 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O R P H O L O G I C A L F R E E D O M (swc_eng_001809-swc_eng_001809) +E N E R G E T I C A T T A C K I N G S T Y L E (swc_eng_001810-swc_eng_001810) +E X A C T L Y F O R T Y Y E A R S A F T E R T H E C O R N E R S T O N E W A S L A I D (swc_eng_001811-swc_eng_001811) +B A S E D O N T H E R E C O G N I T I O N (swc_eng_001812-swc_eng_001812) +O R E L E C T R O N I C B U T T O N S O R D I S P L A Y (swc_eng_001813-swc_eng_001813) +I S U N K N O W N W H E T H E R P E Q U A L S N P (swc_eng_001814-swc_eng_001814) +W H I C H C O M E S F R O M T H E V E R B A C U E R E (swc_eng_001815-swc_eng_001815) +D I S P R O P O R T I O N A T E L Y A V A I L A B L E T O T H O S E W I T H G R E A T E R F I N A N C I A L R E S O U R C E S (swc_eng_001816-swc_eng_001816) +T H E I M M I N E N T T H R E A T S T O T H E S U R V I V A L O F M A N Y S P E C I E S (swc_eng_001817-swc_eng_001817) +E V E N M O R E D I F F I C U L T (swc_eng_001818-swc_eng_001818) +A N D 2 1 S P E C I E S O F O C E A N I C D O L P H I N (swc_eng_001819-swc_eng_001819) +A C H I E V I N G P R O M O T I O N (swc_eng_001820-swc_eng_001820) +T R A N S H U M A N I S T A S S U M P T I O N (swc_eng_001821-swc_eng_001821) +O N T H E F I R S T B A L L O T (swc_eng_001822-swc_eng_001822) +S T O R Y I N D I C A T I V E O F T H E R I S E I N G L O B A L S I G N I F I C A N C E O F S H O E P O L I S H I S T O L D B Y J E A N (swc_eng_001823-swc_eng_001823) +W H I C H S P A R K E D H I S E A R L Y I N T E R E S T I N P O L I T I C S (swc_eng_001824-swc_eng_001824) +W A S C A L L E D D O L B Y H X P R O I N F U L L A N D P A T E N T E D (swc_eng_001825-swc_eng_001825) +C O U L D S A V E A N D F I N D F I L E S B Y N U M B E R (swc_eng_001826-swc_eng_001826) +A U S T R A L I A N S N A K E S B E L O N G T O S E V E N F A M I L I E S (swc_eng_001827-swc_eng_001827) +D E V E L O P I N G P L A Y E R S (swc_eng_001828-swc_eng_001828) +D E C L I N E D S H A R P L Y S I N C E I T S P E A K I N T H E L A T E 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(swc_eng_001861-swc_eng_001861) +E Q U I V A L E N T T O T H E Q U E S T I O N O F W H E T H E R X I S A M E M B E R O F C O M P O S I T E (swc_eng_001862-swc_eng_001862) +M O V E S T O I T S L A S T R A N K (swc_eng_001863-swc_eng_001863) +P O S T G E N D E R I S M (swc_eng_001864-swc_eng_001864) +C O M P A C T C A S S E T T E Q U I C K L Y F O U N D U S E (swc_eng_001865-swc_eng_001865) +F O U R H U N D R E D T H I R T Y T H R E E F E E T (swc_eng_001866-swc_eng_001866) +W H I C H R E S U L T I N A S P E C I F I C T Y P E O F P A W N S T R U C T U R E (swc_eng_001867-swc_eng_001867) +B E F O R E N I N E T E E N N I N E T Y S E V E N (swc_eng_001868-swc_eng_001868) +C O M M U N I C A T I O N S A N D H E A L T H C A R E (swc_eng_001869-swc_eng_001869) +S A H I N A P E R S O N K N O W N T O (swc_eng_001870-swc_eng_001870) +S O M E B R A N D S S P E C I F Y T H A T T H E Y M A Y A L S O B E U S E D O N O T H E R N O N P O R O U S M A T E R I A L S (swc_eng_001871-swc_eng_001871) +T H E P 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I S S U E I N T H E E U A N D T H E W H O L E I D E A O F A N E C O L A B E L G I V E S A V E R Y U S E F U L O R I E N T A T I O N F O R C O N S U M E R S O F C O U R S E T H E E C O L A B E L S H O U L D B E G I V E N T O T H E M O S T E N V I R O N M E N T A L L Y F R I E N D L Y P R O D U C T S A N D T H E I N F O R M A T I O N S H O U L D B E C L E A R A N D C O R R E C T (voxpopuli_eng_000502-voxpopuli_eng_000502) +H O W E V E R T H E C U R R E N T R E G I M E N E E D S T O B E B E T T E R T A I L O R E D T O T H E D I G I T A L E N V I R O N M E N T I N O R D E R T O E N S U R E F A I R R E M U N E R A T I O N T O C R E A T O R S A N D T O C O N F O R M T O C O N S U M E R E X P E C T A T I O N S (voxpopuli_eng_000503-voxpopuli_eng_000503) +I T C A L L S U P O N T H E C O M M I S S I O N A N D M E M B E R S T A T E S T O E N H A N C E T H E I R S U P P O R T F O R R E C O N C I L I A T I O N T O S E C U R E P E A C E A N D S T A B I L I T Y A N D I R E L A N D I W O U L D T H E R E F O R E U R G E Y O U C O L L E A G U E S T O P L E A S E S U P P O R T T H I S A M E N D M E N T (voxpopuli_eng_000504-voxpopuli_eng_000504) +S T R A T E G I C C H O I C E S A B O U T W H E R E T O I N V E S T M U S T B E M A D E N O W T A K I N G I N T O A C C O U N T T H E N E E D T O P H A S E O U T F O S S I L F U E L S U B S I D I E S B U T T A K E G A S A S A F O S S I L F U E L I T C A N B E A H E L P F U L B R I D G I N G T R A N S I T I O N A R Y M E D I U M T O B E U S E D I N M A N Y M E M B E R S T A T E S I F W E W A N T T O A C H I E V E O U R A M B I T I O U S C L I M A T E T A R G E T S (voxpopuli_eng_000505-voxpopuli_eng_000505) +M I D D L E E A S T W E A R E P O S S I B L Y A T A T H R E S H O L D W E C A N C H O O S E T O P U R S U E T H E S A M E P O L I C I E S I N T H E S A M E M A N N E R K N O W I N G T H A T T H E Y W I L L L E A D T O T H E S A M E R E S U L T S T H E R E S U L T S T H A T (voxpopuli_eng_000506-voxpopuli_eng_000506) +B U T T H E R E I S A N O P T I O N (voxpopuli_eng_000507-voxpopuli_eng_000507) +T H I S W E A L S O N E E D A C H A N G E I N O U R I D E O L O G Y (voxpopuli_eng_000508-voxpopuli_eng_000508) +A L A R G E P A R T O F T H E R E A S O N I S O F C O U R S E I L L E G A L F I S H I N G M O R E O F T E N T H A N N O T B Y V E S S E L S W H I C H A R E R E G I S T E R E D T O C O U N T R I E S W H I C H L A C K T H E W I L L O R T H E R E S O U R C E S T O E N F O R C E I N T E R N A T I O N A L A G R E E M E N T S N O A M O U N T O F T R A C E A B I L I T Y M E A S U R E S O R E X T R A P A P E R W O R K W I L L A D D R E S S T H E P R O B L E M O F R E D U C I N G (voxpopuli_eng_000509-voxpopuli_eng_000509) +T H E C O M P R O M I S E A L S O I N C L U D E S C L E A R R U L E S T O D E F I N E W H I C H M E M B E R S T A T E H A S J U R I S D I C T I O N A N D T H E C O O P E R A T I O N B E T W E E N M E M B E R S T A T E S C O N C E R N E D I N C R O S S B O R D E R C A S E S A S W E L L A S T H E N E E D T O I N V O L V E E U R O J U S T T H A N K Y O U F O R Y O U R W O R K A N D P L E A S E D O S U P P O R T T H I S D I R E C T I V E (voxpopuli_eng_000510-voxpopuli_eng_000510) +T H E G R E E N S W O U L D H A V E U S B E L I E V E T H A T T H E S E A R E B A D B E E S C R I M I N A L B E E S D E L I B E R A T E L Y C O N T A M I N A T I N G H O N E Y W I T H A D A N G E R O U S I N G R E D I E N T B U T I N F A C T T H E Y A R E D O I N G W H A T H O N E Y B E E S H A V E A L W A Y S D O N E W H I C H I S T O C A R R Y P O L L E N B A C K T O T H E I R H I V E S T O F E E D T H E I R Y O U N G (voxpopuli_eng_000511-voxpopuli_eng_000511) +B U T I T W A S T H E C O U N T R Y I T S E L F B E I N G M O R E C A P A B L E (voxpopuli_eng_000512-voxpopuli_eng_000512) +I N T O T H E P O R T F O L I O O F T H E N E W C O M M I S S I O N E R D E A L I N G W I T H F U N D A M E N T A L R I G H T S (voxpopuli_eng_000513-voxpopuli_eng_000513) +T H E M E S S A G E I S T H A T T H E E U D O E S N O T H A V E A N Y N E W S O L U T I O N S (voxpopuli_eng_000514-voxpopuli_eng_000514) +A R E Y O U W I L L I N G T O A C T I N F A V O U R O F T H E S O C I A L D I M E N S I O N T O B E I N C L U D E D I N T H E E U C O M P E T E N C I E S A S P R O P O S E D (voxpopuli_eng_000515-voxpopuli_eng_000515) +T H E N E X T S T E P O N S P E C T R U M P O L I C Y I S B E I N G T A K E N W I T H T H E R E F O R M O F O U R T E L E C O M F R A M E W O R K (voxpopuli_eng_000516-voxpopuli_eng_000516) +I B E L I E V E H I S R E M A R K S W E R E E X P L I C I T L Y R A C I S T A N D X E N O P H O B I C A N D P R O M O T E D R A C I A L I N T O L E R A N C E I N A W A Y T H A T I S N O T A C C E P T A B L E O R A L L O W E D I N T H E C O N S T I T U T I O N O F T H I S H O U S E (voxpopuli_eng_000517-voxpopuli_eng_000517) +R E A L L I F E E X A M P L E S S H O W T H A T S O L V I N G I S S U E S R E L A T E D T O E D U C A T I O N F U E L S S T R O N G C O M M U N I T Y D E V E L O P M E N T (voxpopuli_eng_000518-voxpopuli_eng_000518) +S O I H O P E T H I S W I L L H A P P E N F O R R U S S I A A S W E L L A N D T H A T R U S S I A C A N A L S O E N V I S A G E A N E X T R E M E S U C C E S S S T O R Y A F T E R T H E S I G N I F I C A N T D A T E I N A U G U S T T H I S Y E A R (voxpopuli_eng_000519-voxpopuli_eng_000519) +S H E A C C E P T E D T H E F A C T T H A T C I T I Z E N S H I P I S S U B J E C T T O N A T I O N A L J U R I S D I C T I O N B U T S H E A L S O S A I D T H A T A C C O R D I N G T O T H E M A A S T R I C H T T R E A T Y A N D S H E I S R I G H T T H E R E H A S T O B E A D I R E C T L I N K (voxpopuli_eng_000520-voxpopuli_eng_000520) +T H E E U F A I L E D E S P E C I A L L Y I N D E M O N S T R A T I N G A U N I F I E D A N D E F F I C I E N T A P P R O A C H T O C L I M A T E C H A N G E T R E A T M E N T A S W E L L A S I N S T R E N G T H E N I N G I T S L E A D I N G P O L I T I C A L P O S I T I O N I N T H I S A G E N D A I C O N S I D E R T H E R E F O R E T A K I N G T H I S R E S O L U T I O N A N A C T O F U T M O S T I M P O R T A N C E (voxpopuli_eng_000521-voxpopuli_eng_000521) +T H E U N I T E D S T A T E S O F E U R O P E W I L L B E A F A C T W I T H S W E D E N A S A P R O V I N C E (voxpopuli_eng_000522-voxpopuli_eng_000522) +I T M U S T B E T H E C A P I T A L O F B O T H S T A T E S A N D W E M U S T R E C O G N I S E P A L E S T I N E A S A S T A T E A S P R O V I D E D F O R I N T H E O S L O A G R E E M E N T S (voxpopuli_eng_000523-voxpopuli_eng_000523) +U K R A I N E I S F A C E D W I T H O N E O F T H E C R U C I A L C H A L L E N G E S I N I T S H I S T O R Y I T W O U L D B E F U N D A M E N T A L L Y W R O N G T O P R E S S T H E N A T I O N N O W W I T H A L L T Y P E S O F R E S T R I C T I O N S P O P U L A R L Y C A L L E D A U S T E R I T Y P O L I C Y (voxpopuli_eng_000524-voxpopuli_eng_000524) +M O R E R U L E S A N D R E G U L A T I O N W I L L N O T I M P R O V E T H E S I T U A T I O N (voxpopuli_eng_000525-voxpopuli_eng_000525) +A T L E A S T W E W O U L D L I K E T O K N O W T H E S O U R C E O F T H E M O N E Y A N D T H E P O S S I B L E M O T I V E S (voxpopuli_eng_000526-voxpopuli_eng_000526) +T O H A V E T H O S E E U R O P E A N W O R L D L A N G U A G E S I N T O D A Y S G L O B A L I S E D W O R L D I N T O D A Y S G L O B A L I S E D E C O N O M Y I N T H I S G L O B A L V I L L A G E W H I C H I S C U L T U R A L E C O N O M I C S O C I A L A N D P O L I T I C A L I S A M O S T V A L U A B L E A S S E T F O R T H E E N T I R E E U W H I C H W E M U S T T A K E F U L L A C C O U N T O F A N D (voxpopuli_eng_000527-voxpopuli_eng_000527) +W E H A V E T O R E P E A T T H A T O D A C A N N O T B E U S E D T O F I N A N C E S E C U R I T Y E X P E N S E S B O R D E R C O N T R O L O R M I L I T A R Y S U P P O R T (voxpopuli_eng_000528-voxpopuli_eng_000528) +I F A N Y T H I N G T H E S C I E N T I F I C R E P O R T S A R E B E C O M I N G M O R E U R G E N T M O R E A L A R M I N G A N D M O R E S H O C K I N G (voxpopuli_eng_000529-voxpopuli_eng_000529) +F I N A L L Y W H E N I T C O M E S T O I N N O V A T I V E F I N A N C I A L I N S T R U M E N T S W E N E E D T H E M B O T H F O R O U R S E L V E S T O S U P P O R T O U R E C O N O M I E S B U T A L S O T O S U P P O R T T H O S E W H O A R E I N N E E D (voxpopuli_eng_000530-voxpopuli_eng_000530) +T H A T G I V E S U S A U N I Q U E T O O L I N P E A C E M A K I N G (voxpopuli_eng_000531-voxpopuli_eng_000531) +P A P E R A V E R Y W E A K P R O P O S A L (voxpopuli_eng_000532-voxpopuli_eng_000532) +R U S S I A H A S A L W A Y S B E E N A V E R Y P R O U D N A T I O N W I T H A R I C H C U L T U R E W I T H I N V E N T I O N S A N D E S P R I T (voxpopuli_eng_000533-voxpopuli_eng_000533) +F A I R T A X A T I O N E V E N A M O D I C U M O F T A X A T I O N I N S O M E C A S E S M I G H T J U S T H E L P U S T O D O W H A T I H A V E A L R E A D Y S U G G E S T E D A N D W H O K N O W S M A K E T H E C A S E F O R T H E R E T R O S P E C T I V E B A N K R E C A P I T A L I S A T I O N T H A T W E N E V E R S A W (voxpopuli_eng_000534-voxpopuli_eng_000534) +T H E E U R O P E A N A S Y L U M S U P P O R T O F F I C E M O R E O V E R H A S A M O N G I T S T A S K S T O P R O M O T E F A C I L I T A T E A N D C O O R D I N A T E E X C H A N G E S O F I N F O R M A T I O N A N D O T H E R A C T I V I T I E S R E L A T E D T O R E L O C A T I O N W I T H I N T H E U N I O N (voxpopuli_eng_000535-voxpopuli_eng_000535) +T H E C O N C L U S I O N O F T H E F R A M E W O R K A G R E E M E N T P R O V I D E S A L E G A L L Y B I N D I N G I N S T R U M E N T T O U P G R A D E A N D S T R E N G T H E N E U A U S T R A L I A B I L A T E R A L R E L A T I O N S A N D T O I N C R E A S E C O O P E R A T I O N (voxpopuli_eng_000536-voxpopuli_eng_000536) +T H E R E F O R E W E A R E A S K I N G T H E C O U N C I L A N D T H E C O M M I S S I O N T O P R E S E N T A T R A N S P A R E N T A N D C O M P L E T E A S S E S S M E N T O F T H E I M P A C T O F T H E C R I S I S (voxpopuli_eng_000537-voxpopuli_eng_000537) +I N O T H E R W O R D S T H E O B J E C T I O N I S N O T W H E T H E R M O N E Y I S P A I D O R N O T T H E O B J E C T I O N I S W H E T H E R T H E R E I S A D I R E C T L I N K O R N O T (voxpopuli_eng_000538-voxpopuli_eng_000538) +I T D I S T I N G U I S H E S T H E T W O M A I N D O S S I E R S H U M A N R I G H T S A B U S E S B Y T H E C U R R E N T G O V E R N M E N T A N D T H E I R A N I A N N U C L E A R P R O G R A M M E (voxpopuli_eng_000539-voxpopuli_eng_000539) +M R P R E S I D E N T S E X U A L H A R A S S M E N T I S A F O R M O F V I O L E N C E A N D I T I S T H E M O S T E X T R E M E F O R M O F G E N D E R — B A S E D D I S C R I M I N A T I O N (voxpopuli_eng_000540-voxpopuli_eng_000540) +W E C A N L O O K T O S O M E N O N E U M E M B E R S F O R G O O D E X A M P L E S A S R E G A R D S T E C H N O L O G I E S (voxpopuli_eng_000541-voxpopuli_eng_000541) +I N V O L V E D F O R T H E I R P O S I T I V E A N D C O N S T R U C T I V E A P P R O A C H (voxpopuli_eng_000542-voxpopuli_eng_000542) +S O I H O P E T H A T T H I S W I L L B E C O M P L E T E D I N T H E F O R E S E E A B L E F U T U R E W H I C H M E A N S M A Y B E T W O O R T H R E E M O N T H S (voxpopuli_eng_000543-voxpopuli_eng_000543) +F U R T H E R E N C O U R A G E T H E U N S E F F O R T S T O B R I N G A B O U T P E A C E I N A F G H A N I S T A N A N D T O O V E R C O M E T H E F R A G I L E S E C U R I T Y E N V I R O N M E N T I N T H E C O U N T R Y (voxpopuli_eng_000544-voxpopuli_eng_000544) +W E U N D E R S T A N D T H A T S O M E P E O P L E A R E A N G R Y (voxpopuli_eng_000545-voxpopuli_eng_000545) +W E W A N T T O B E M O R E R E S P O N S I B L E (voxpopuli_eng_000546-voxpopuli_eng_000546) +W E M U S T R E C T I F Y T H I S S I T U A T I O N A N D W E A S K T H E C O M M I S S I O N T O C O N S I D E R T H E M O S T A D E Q U A T E C O M P E N S A T I O N M E A S U R E S F O R O U R P A S S E N G E R S (voxpopuli_eng_000547-voxpopuli_eng_000547) +T H E C O M M I S S I O N I N V I T E S P A R L I A M E N T I N T H E U P C O M I N G R E V I S I O N T O O P E N I T S P O S I T I O N O N T H I S M A T T E R W H I C H R E A L L Y C O N C E R N S A C C E S S T O J U S T I C E I N E U R O P E A N D T H E E N F O R C E M E N T O F R I G H T S G R A N T E D B Y E U R O P E A N U N I O N L A W (voxpopuli_eng_000548-voxpopuli_eng_000548) +I W E L C O M E V E R Y M U C H T H E R E S U M P T I O N O F T A L K S B E T W E E N T H E I S R A E L I S A N D T H E P A L E S T I N I A N S A N D S I N C E R E L Y H O P E T H A T T H E Y W I L L S U C C E E D (voxpopuli_eng_000549-voxpopuli_eng_000549) +W E H A V E A N A C C U M U L A T I O N O F P R O B L E M S R E S U L T I N G F R O M A R T I F I C I A L U N D E R B U D G E T I N G I N P R E V I O U S Y E A R S (voxpopuli_eng_000550-voxpopuli_eng_000550) +L E T U S N O T B E T H E M A N O F Y E S T E R D A Y L E T U S B E T O D A Y S I N S T I T U T I O N (voxpopuli_eng_000551-voxpopuli_eng_000551) +I W O U L D U R G E Y O U T O B E C O M E A M B A S S A D O R S O F T H E Y E A R B Y M A K I N G I T S I D E A S A N D A C T I V I T I E S W I D E L Y K N O W N A M O N G S T E U R O P E A N C I T I Z E N S A N D P A R T I C I P A T I N G I N E V E N T S B E I T A T E U R O P E A N N A T I O N A L O R L O C A L L E V E L (voxpopuli_eng_000552-voxpopuli_eng_000552) +C E R T A I N L Y S U C H I M P A C T A S S E S S M E N T C O U L D P R E E M P T C E R T A I N P R O B L E M S S U C H A S T H O S E P O S E D B Y T H E E L E C T R O N I C I D E N T I F I C A T I O N O F S H E E P I N S C O T L A N D (voxpopuli_eng_000553-voxpopuli_eng_000553) +T H E C O U R T I S C O N T E N T T O S E E T H A T I T S W O R K H A S I N F O R M E D T H E D I S C H A R G E P R O C E S S A N D H A S C O N T R I B U T E D T O P R O P O S A L S F O R I M P R O V I N G T H E F I N A N C I A L M A N A G E M E N T O F E U S P E N D I N G A N D B E T T E R T A R G E T I N G O F E U F U N D S (voxpopuli_eng_000554-voxpopuli_eng_000554) +R E G U L A T O R Y C L A R I T Y A N D C E R T A I N T Y I S N E E D E D F O R T H E P U B L I C S E C T O R A N D F O R I N D U S T R Y (voxpopuli_eng_000555-voxpopuli_eng_000555) +I S I T R E A L L Y N O T P O S S I B L E T O U S E O T H E R H O U S I N G F A C I L I T I E S W I T H A P P R O P R I A T E R E C E P T I O N C O N D I T I O N S I N T H E M E A N T I M E (voxpopuli_eng_000556-voxpopuli_eng_000556) +W I L L Y O U T A K E A C T I O N A T L A S T I F N O T T H E N W H E N (voxpopuli_eng_000557-voxpopuli_eng_000557) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/result.txt b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..e914977b4a3d698c637dac6bbf72edb7e0a5e923 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/result.txt @@ -0,0 +1,12693 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,-----------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/hyp.trn| +|-----------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000254 | 1 100 | 81.0 6.0 13.0 0.0 19.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000255 | 1 60 | 78.3 6.7 15.0 3.3 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000256 | 1 41 | 82.9 7.3 9.8 4.9 22.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000257 | 1 53 | 88.7 3.8 7.5 0.0 11.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000258 | 1 51 | 90.2 3.9 5.9 0.0 9.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000259 | 1 92 | 87.0 3.3 9.8 2.2 15.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000260 | 1 79 | 87.3 2.5 10.1 1.3 13.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000261 | 1 80 | 86.3 8.8 5.0 2.5 16.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000262 | 1 44 | 88.6 6.8 4.5 2.3 13.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000263 | 1 54 | 88.9 0.0 11.1 1.9 13.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000264 | 1 107 | 85.0 1.9 13.1 1.9 16.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000265 | 1 41 | 97.6 2.4 0.0 0.0 2.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000266 | 1 25 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000267 | 1 26 | 80.8 11.5 7.7 3.8 23.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000268 | 1 43 | 93.0 0.0 7.0 2.3 9.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000269 | 1 53 | 75.5 7.5 17.0 0.0 24.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000270 | 1 66 | 92.4 3.0 4.5 4.5 12.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000271 | 1 93 | 87.1 3.2 9.7 2.2 15.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000272 | 1 91 | 93.4 3.3 3.3 2.2 8.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000273 | 1 114 | 89.5 3.5 7.0 1.8 12.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000274 | 1 26 | 92.3 3.8 3.8 0.0 7.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000275 | 1 82 | 82.9 3.7 13.4 1.2 18.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000276 | 1 94 | 88.3 4.3 7.4 1.1 12.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000277 | 1 55 | 96.4 1.8 1.8 0.0 3.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000278 | 1 48 | 87.5 2.1 10.4 0.0 12.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000279 | 1 21 | 90.5 9.5 0.0 0.0 9.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000280 | 1 52 | 94.2 0.0 5.8 0.0 5.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000281 | 1 44 | 90.9 2.3 6.8 13.6 22.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000282 | 1 30 | 83.3 6.7 10.0 0.0 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000283 | 1 94 | 88.3 7.4 4.3 3.2 14.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000284 | 1 111 | 82.9 2.7 14.4 0.0 17.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000285 | 1 104 | 86.5 4.8 8.7 0.0 13.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000286 | 1 60 | 91.7 3.3 5.0 3.3 11.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000287 | 1 44 | 90.9 0.0 9.1 0.0 9.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000288 | 1 46 | 82.6 6.5 10.9 6.5 23.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000289 | 1 48 | 91.7 0.0 8.3 2.1 10.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000290 | 1 51 | 94.1 2.0 3.9 3.9 9.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000291 | 1 56 | 89.3 5.4 5.4 0.0 10.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000292 | 1 50 | 82.0 8.0 10.0 2.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000293 | 1 85 | 74.1 8.2 17.6 0.0 25.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000294 | 1 60 | 83.3 0.0 16.7 0.0 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000295 | 1 52 | 88.5 1.9 9.6 5.8 17.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000296 | 1 91 | 81.3 4.4 14.3 2.2 20.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000297 | 1 72 | 80.6 11.1 8.3 0.0 19.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000298 | 1 60 | 95.0 0.0 5.0 0.0 5.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000299 | 1 79 | 87.3 5.1 7.6 7.6 20.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000300 | 1 78 | 93.6 0.0 6.4 2.6 9.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000301 | 1 74 | 91.9 1.4 6.8 1.4 9.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000302 | 1 58 | 82.8 1.7 15.5 6.9 24.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000303 | 1 60 | 81.7 8.3 10.0 5.0 23.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000304 | 1 50 | 88.0 2.0 10.0 0.0 12.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000305 | 1 77 | 89.6 0.0 10.4 0.0 10.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000306 | 1 74 | 85.1 4.1 10.8 5.4 20.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000307 | 1 91 | 90.1 2.2 7.7 4.4 14.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000308 | 1 69 | 75.4 7.2 17.4 2.9 27.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000309 | 1 34 | 88.2 2.9 8.8 0.0 11.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000310 | 1 26 | 96.2 0.0 3.8 0.0 3.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000311 | 1 107 | 80.4 8.4 11.2 1.9 21.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000312 | 1 81 | 85.2 1.2 13.6 1.2 16.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000313 | 1 63 | 88.9 4.8 6.3 0.0 11.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000314 | 1 41 | 82.9 7.3 9.8 2.4 19.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000315 | 1 55 | 74.5 1.8 23.6 5.5 30.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000316 | 1 100 | 86.0 7.0 7.0 1.0 15.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000317 | 1 70 | 77.1 8.6 14.3 1.4 24.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000318 | 1 107 | 84.1 5.6 10.3 0.0 15.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000319 | 1 74 | 86.5 6.8 6.8 1.4 14.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000320 | 1 94 | 88.3 1.1 10.6 1.1 12.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000321 | 1 31 | 83.9 6.5 9.7 3.2 19.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000322 | 1 59 | 79.7 6.8 13.6 1.7 22.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000323 | 1 52 | 82.7 9.6 7.7 25.0 42.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000324 | 1 75 | 85.3 10.7 4.0 8.0 22.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000325 | 1 34 | 94.1 2.9 2.9 0.0 5.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000326 | 1 43 | 79.1 4.7 16.3 2.3 23.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000327 | 1 76 | 81.6 6.6 11.8 1.3 19.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000328 | 1 26 | 80.8 3.8 15.4 3.8 23.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000329 | 1 101 | 80.2 3.0 16.8 1.0 20.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000330 | 1 50 | 92.0 4.0 4.0 2.0 10.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000331 | 1 31 | 96.8 3.2 0.0 0.0 3.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000332 | 1 98 | 83.7 6.1 10.2 2.0 18.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000333 | 1 45 | 84.4 6.7 8.9 6.7 22.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000334 | 1 107 | 81.3 2.8 15.9 0.9 19.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000335 | 1 75 | 89.3 1.3 9.3 2.7 13.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000336 | 1 92 | 78.3 8.7 13.0 3.3 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000337 | 1 100 | 90.0 5.0 5.0 6.0 16.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000338 | 1 29 | 93.1 0.0 6.9 3.4 10.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000339 | 1 54 | 90.7 1.9 7.4 11.1 20.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000340 | 1 70 | 87.1 4.3 8.6 1.4 14.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000341 | 1 67 | 83.6 3.0 13.4 1.5 17.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000342 | 1 82 | 85.4 1.2 13.4 2.4 17.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000343 | 1 111 | 90.1 3.6 6.3 3.6 13.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000344 | 1 102 | 87.3 1.0 11.8 0.0 12.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000345 | 1 73 | 84.9 5.5 9.6 6.8 21.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000346 | 1 91 | 83.5 4.4 12.1 2.2 18.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000347 | 1 76 | 85.5 2.6 11.8 0.0 14.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000348 | 1 82 | 86.6 2.4 11.0 0.0 13.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000349 | 1 89 | 87.6 5.6 6.7 1.1 13.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000350 | 1 80 | 81.3 6.3 12.5 1.3 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000351 | 1 105 | 86.7 2.9 10.5 1.0 14.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000352 | 1 38 | 84.2 7.9 7.9 2.6 18.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000353 | 1 61 | 86.9 0.0 13.1 4.9 18.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000354 | 1 67 | 88.1 3.0 9.0 1.5 13.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000355 | 1 76 | 82.9 5.3 11.8 3.9 21.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000356 | 1 36 | 86.1 5.6 8.3 2.8 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000357 | 1 79 | 81.0 2.5 16.5 1.3 20.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000358 | 1 49 | 85.7 4.1 10.2 0.0 14.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000359 | 1 48 | 75.0 6.3 18.8 2.1 27.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000360 | 1 47 | 85.1 4.3 10.6 2.1 17.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000361 | 1 48 | 89.6 4.2 6.3 6.3 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000362 | 1 99 | 83.8 4.0 12.1 1.0 17.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000363 | 1 72 | 73.6 12.5 13.9 0.0 26.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000364 | 1 63 | 90.5 0.0 9.5 1.6 11.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000365 | 1 25 | 96.0 0.0 4.0 4.0 8.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000366 | 1 53 | 81.1 9.4 9.4 7.5 26.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000367 | 1 58 | 75.9 8.6 15.5 1.7 25.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000368 | 1 47 | 83.0 6.4 10.6 4.3 21.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000369 | 1 92 | 85.9 0.0 14.1 2.2 16.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000370 | 1 76 | 90.8 1.3 7.9 3.9 13.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000371 | 1 58 | 87.9 1.7 10.3 0.0 12.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000372 | 1 64 | 85.9 6.3 7.8 4.7 18.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000373 | 1 92 | 83.7 6.5 9.8 4.3 20.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000374 | 1 26 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000375 | 1 71 | 85.9 2.8 11.3 8.5 22.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000376 | 1 90 | 83.3 2.2 14.4 3.3 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| m | 77 8404 | 85.3 3.3 11.4 2.0 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000707 | 1 45 | 55.6 33.3 11.1 24.4 68.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000708 | 1 57 | 80.7 8.8 10.5 5.3 24.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000709 | 1 56 | 76.8 14.3 8.9 12.5 35.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000710 | 1 77 | 75.3 9.1 15.6 2.6 27.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000711 | 1 91 | 71.4 18.7 9.9 7.7 36.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000712 | 1 99 | 78.8 4.0 17.2 2.0 23.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000713 | 1 63 | 71.4 7.9 20.6 4.8 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000714 | 1 89 | 60.7 22.5 16.9 9.0 48.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000715 | 1 13 | 92.3 7.7 0.0 30.8 38.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000716 | 1 54 | 77.8 13.0 9.3 7.4 29.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000717 | 1 75 | 77.3 13.3 9.3 2.7 25.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000718 | 1 35 | 85.7 2.9 11.4 2.9 17.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000719 | 1 33 | 69.7 24.2 6.1 0.0 30.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000720 | 1 43 | 74.4 14.0 11.6 11.6 37.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000721 | 1 34 | 82.4 11.8 5.9 0.0 17.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000722 | 1 35 | 54.3 25.7 20.0 11.4 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000723 | 1 68 | 72.1 14.7 13.2 4.4 32.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000724 | 1 30 | 86.7 6.7 6.7 23.3 36.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000725 | 1 88 | 77.3 10.2 12.5 2.3 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000726 | 1 64 | 89.1 3.1 7.8 10.9 21.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000727 | 1 20 | 55.0 35.0 10.0 5.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000728 | 1 46 | 87.0 6.5 6.5 10.9 23.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000729 | 1 75 | 77.3 9.3 13.3 1.3 24.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000730 | 1 85 | 57.6 28.2 14.1 15.3 57.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000731 | 1 26 | 42.3 34.6 23.1 15.4 73.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000732 | 1 77 | 84.4 3.9 11.7 0.0 15.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000733 | 1 58 | 69.0 17.2 13.8 6.9 37.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000734 | 1 78 | 79.5 7.7 12.8 9.0 29.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000735 | 1 64 | 82.8 6.3 10.9 4.7 21.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000736 | 1 54 | 87.0 3.7 9.3 0.0 13.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000737 | 1 5 | 60.0 40.0 0.0 320.0 360.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000738 | 1 85 | 80.0 14.1 5.9 4.7 24.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000739 | 1 44 | 70.5 18.2 11.4 11.4 40.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000740 | 1 45 | 93.3 4.4 2.2 13.3 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000741 | 1 28 | 89.3 7.1 3.6 3.6 14.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000742 | 1 68 | 64.7 26.5 8.8 13.2 48.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000743 | 1 72 | 86.1 6.9 6.9 5.6 19.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000744 | 1 38 | 89.5 7.9 2.6 18.4 28.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000745 | 1 68 | 67.6 22.1 10.3 27.9 60.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000746 | 1 50 | 76.0 16.0 8.0 4.0 28.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000747 | 1 34 | 64.7 14.7 20.6 2.9 38.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000748 | 1 2 | 100.0 0.0 0.0 400.0 400.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000749 | 1 5 | 80.0 20.0 0.0 220.0 240.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000750 | 1 83 | 66.3 18.1 15.7 6.0 39.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000751 | 1 67 | 86.6 10.4 3.0 4.5 17.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000752 | 1 77 | 72.7 14.3 13.0 15.6 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000753 | 1 57 | 71.9 24.6 3.5 42.1 70.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000754 | 1 51 | 58.8 27.5 13.7 58.8 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000755 | 1 28 | 75.0 10.7 14.3 17.9 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000756 | 1 51 | 74.5 13.7 11.8 9.8 35.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000757 | 1 74 | 71.6 6.8 21.6 1.4 29.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000758 | 1 25 | 88.0 12.0 0.0 12.0 24.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000759 | 1 29 | 69.0 13.8 17.2 20.7 51.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000760 | 1 57 | 52.6 26.3 21.1 29.8 77.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000761 | 1 29 | 72.4 13.8 13.8 13.8 41.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000762 | 1 74 | 81.1 4.1 14.9 1.4 20.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000763 | 1 43 | 79.1 7.0 14.0 4.7 25.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000764 | 1 39 | 66.7 10.3 23.1 2.6 35.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000765 | 1 27 | 66.7 25.9 7.4 7.4 40.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000766 | 1 43 | 93.0 4.7 2.3 7.0 14.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000767 | 1 59 | 86.4 6.8 6.8 5.1 18.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000768 | 1 27 | 88.9 11.1 0.0 29.6 40.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000769 | 1 18 | 72.2 0.0 27.8 16.7 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000770 | 1 83 | 81.9 12.0 6.0 3.6 21.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000771 | 1 59 | 61.0 25.4 13.6 10.2 49.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000772 | 1 36 | 30.6 61.1 8.3 5.6 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000773 | 1 72 | 80.6 2.8 16.7 4.2 23.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000774 | 1 67 | 83.6 7.5 9.0 16.4 32.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000775 | 1 23 | 43.5 52.2 4.3 30.4 87.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000776 | 1 70 | 78.6 10.0 11.4 0.0 21.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000777 | 1 80 | 76.3 10.0 13.8 8.8 32.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000778 | 1 82 | 79.3 6.1 14.6 1.2 22.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000779 | 1 93 | 72.0 12.9 15.1 25.8 53.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000780 | 1 58 | 81.0 5.2 13.8 8.6 27.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000781 | 1 75 | 70.7 14.7 14.7 9.3 38.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000782 | 1 3 | 66.7 33.3 0.0 300.0 333.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000783 | 1 35 | 74.3 5.7 20.0 8.6 34.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000784 | 1 54 | 70.4 14.8 14.8 25.9 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000785 | 1 40 | 80.0 10.0 10.0 17.5 37.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000786 | 1 59 | 86.4 6.8 6.8 13.6 27.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000787 | 1 94 | 83.0 9.6 7.4 11.7 28.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000788 | 1 27 | 88.9 7.4 3.7 33.3 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000789 | 1 27 | 70.4 11.1 18.5 33.3 63.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000790 | 1 27 | 85.2 3.7 11.1 0.0 14.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000791 | 1 40 | 72.5 12.5 15.0 17.5 45.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000792 | 1 65 | 63.1 23.1 13.8 16.9 53.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000793 | 1 34 | 50.0 23.5 26.5 23.5 73.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000794 | 1 28 | 75.0 17.9 7.1 10.7 35.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000795 | 1 31 | 80.6 3.2 16.1 3.2 22.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000796 | 1 33 | 36.4 27.3 36.4 3.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000797 | 1 43 | 79.1 14.0 7.0 2.3 23.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000798 | 1 55 | 65.5 16.4 18.2 0.0 34.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000799 | 1 44 | 77.3 9.1 13.6 0.0 22.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000800 | 1 21 | 61.9 38.1 0.0 114.3 152.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000801 | 1 24 | 66.7 16.7 16.7 25.0 58.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000802 | 1 46 | 82.6 8.7 8.7 2.2 19.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000803 | 1 68 | 64.7 13.2 22.1 1.5 36.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000804 | 1 40 | 90.0 10.0 0.0 5.0 15.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000805 | 1 83 | 79.5 12.0 8.4 6.0 26.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000806 | 1 27 | 66.7 25.9 7.4 11.1 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000807 | 1 96 | 59.4 25.0 15.6 14.6 55.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000808 | 1 83 | 88.0 3.6 8.4 3.6 15.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000809 | 1 42 | 71.4 19.0 9.5 14.3 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000413 | 1 195 | 83.6 11.3 5.1 3.6 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000414 | 1 201 | 84.6 3.5 11.9 3.5 18.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000415 | 1 93 | 77.4 9.7 12.9 14.0 36.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000416 | 1 116 | 77.6 4.3 18.1 2.6 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000417 | 1 138 | 76.8 2.9 20.3 0.7 23.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000418 | 1 78 | 79.5 7.7 12.8 2.6 23.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000419 | 1 53 | 98.1 0.0 1.9 7.5 9.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000420 | 1 65 | 73.8 10.8 15.4 0.0 26.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000421 | 1 148 | 85.1 8.1 6.8 2.0 16.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000422 | 1 119 | 79.8 6.7 13.4 4.2 24.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000423 | 1 114 | 78.9 3.5 17.5 1.8 22.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000424 | 1 85 | 88.2 4.7 7.1 7.1 18.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000425 | 1 111 | 82.0 4.5 13.5 0.9 18.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000426 | 1 106 | 86.8 3.8 9.4 17.9 31.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000427 | 1 173 | 75.1 5.2 19.7 0.6 25.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000428 | 1 120 | 73.3 3.3 23.3 0.8 27.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000429 | 1 127 | 90.6 2.4 7.1 2.4 11.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000430 | 1 101 | 86.1 7.9 5.9 5.0 18.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000431 | 1 103 | 74.8 6.8 18.4 2.9 28.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000432 | 1 106 | 79.2 4.7 16.0 0.9 21.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000433 | 1 84 | 77.4 9.5 13.1 7.1 29.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000434 | 1 70 | 74.3 7.1 18.6 1.4 27.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000435 | 1 119 | 81.5 5.0 13.4 1.7 20.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000436 | 1 135 | 75.6 9.6 14.8 5.9 30.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000437 | 1 121 | 74.4 14.0 11.6 18.2 43.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000438 | 1 163 | 83.4 5.5 11.0 4.9 21.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000439 | 1 64 | 87.5 6.3 6.3 6.3 18.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000440 | 1 163 | 80.4 3.1 16.6 2.5 22.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000441 | 1 67 | 85.1 9.0 6.0 1.5 16.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000442 | 1 133 | 66.9 6.8 26.3 9.0 42.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000443 | 1 206 | 76.7 10.7 12.6 1.9 25.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000444 | 1 111 | 73.0 7.2 19.8 2.7 29.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000445 | 1 55 | 65.5 16.4 18.2 30.9 65.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000446 | 1 79 | 74.7 8.9 16.5 3.8 29.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000447 | 1 116 | 84.5 1.7 13.8 3.4 19.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000448 | 1 174 | 83.3 5.7 10.9 2.3 19.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000449 | 1 147 | 86.4 8.2 5.4 12.9 26.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000450 | 1 206 | 75.7 8.3 16.0 2.9 27.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000451 | 1 119 | 69.7 12.6 17.6 4.2 34.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000452 | 1 190 | 72.6 7.4 20.0 2.6 30.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000453 | 1 137 | 70.8 8.0 21.2 1.5 30.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000454 | 1 143 | 74.8 12.6 12.6 11.2 36.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000455 | 1 89 | 82.0 7.9 10.1 16.9 34.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000456 | 1 53 | 90.6 1.9 7.5 3.8 13.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000457 | 1 121 | 71.9 10.7 17.4 4.1 32.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000458 | 1 85 | 80.0 11.8 8.2 20.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000459 | 1 227 | 83.7 6.2 10.1 3.5 19.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000460 | 1 115 | 75.7 9.6 14.8 4.3 28.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000461 | 1 98 | 77.6 9.2 13.3 6.1 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000462 | 1 105 | 89.5 4.8 5.7 1.9 12.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000463 | 1 110 | 66.4 9.1 24.5 0.9 34.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000464 | 1 91 | 76.9 6.6 16.5 3.3 26.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000465 | 1 143 | 88.8 5.6 5.6 5.6 16.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000466 | 1 155 | 75.5 7.1 17.4 6.5 31.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000467 | 1 106 | 74.5 9.4 16.0 1.9 27.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000468 | 1 250 | 70.8 10.4 18.8 4.4 33.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000469 | 1 180 | 77.8 11.7 10.6 22.8 45.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000470 | 1 211 | 79.6 7.6 12.8 3.8 24.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000471 | 1 141 | 80.1 5.0 14.9 0.7 20.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000472 | 1 191 | 64.4 20.4 15.2 8.4 44.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000473 | 1 154 | 77.9 9.1 13.0 0.6 22.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000474 | 1 148 | 87.2 7.4 5.4 14.2 27.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000475 | 1 165 | 74.5 6.7 18.8 1.8 27.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000476 | 1 139 | 89.2 0.0 10.8 4.3 15.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000283 | 1 198 | 78.8 9.1 12.1 4.0 25.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000284 | 1 173 | 84.4 7.5 8.1 7.5 23.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000285 | 1 215 | 71.2 7.0 21.9 4.2 33.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000286 | 1 236 | 86.9 3.8 9.3 1.3 14.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000287 | 1 279 | 76.3 5.4 18.3 1.1 24.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000288 | 1 140 | 85.7 2.9 11.4 0.7 15.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000289 | 1 200 | 88.0 3.5 8.5 3.0 15.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000290 | 1 193 | 78.2 7.8 14.0 0.0 21.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000291 | 1 171 | 89.5 4.1 6.4 2.3 12.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000292 | 1 222 | 78.4 6.8 14.9 2.3 23.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000293 | 1 121 | 84.3 5.0 10.7 5.0 20.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000294 | 1 232 | 76.3 6.0 17.7 2.2 25.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000295 | 1 139 | 83.5 5.0 11.5 4.3 20.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000296 | 1 166 | 77.7 6.6 15.7 2.4 24.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000297 | 1 236 | 79.7 6.4 14.0 2.1 22.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000298 | 1 152 | 88.2 6.6 5.3 7.2 19.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000299 | 1 289 | 78.2 3.8 18.0 2.4 24.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000300 | 1 272 | 74.6 7.7 17.6 1.5 26.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000301 | 1 219 | 84.9 7.3 7.8 3.7 18.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000302 | 1 138 | 80.4 10.1 9.4 6.5 26.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000303 | 1 230 | 84.3 4.8 10.9 4.8 20.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000304 | 1 248 | 84.3 4.4 11.3 2.4 18.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000305 | 1 241 | 90.0 2.5 7.5 0.8 10.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000306 | 1 178 | 79.2 6.7 14.0 2.8 23.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000307 | 1 233 | 83.3 6.4 10.3 1.3 18.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000308 | 1 138 | 89.1 3.6 7.2 3.6 14.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000309 | 1 223 | 86.1 8.1 5.8 3.6 17.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000310 | 1 184 | 74.5 4.3 21.2 1.1 26.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000311 | 1 200 | 86.0 3.5 10.5 1.5 15.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000312 | 1 239 | 78.7 7.5 13.8 2.9 24.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000313 | 1 203 | 87.2 6.9 5.9 6.9 19.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000314 | 1 160 | 85.6 5.6 8.8 6.3 20.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000315 | 1 141 | 84.4 7.8 7.8 3.5 19.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000316 | 1 219 | 90.0 0.5 9.6 0.9 11.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000317 | 1 233 | 82.0 7.7 10.3 5.6 23.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000318 | 1 183 | 83.6 7.1 9.3 3.3 19.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000319 | 1 205 | 85.4 6.3 8.3 3.9 18.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000320 | 1 194 | 87.1 3.6 9.3 1.5 14.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000321 | 1 229 | 88.2 4.4 7.4 1.3 13.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000322 | 1 207 | 81.2 6.3 12.6 0.5 19.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001588 | 1 21 | 71.4 14.3 14.3 4.8 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001589 | 1 21 | 81.0 14.3 4.8 4.8 23.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001590 | 1 28 | 82.1 10.7 7.1 7.1 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001591 | 1 28 | 71.4 17.9 10.7 10.7 39.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001592 | 1 22 | 77.3 4.5 18.2 40.9 63.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001593 | 1 13 | 76.9 0.0 23.1 7.7 30.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001594 | 1 25 | 88.0 8.0 4.0 12.0 24.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001595 | 1 18 | 83.3 16.7 0.0 0.0 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001596 | 1 16 | 68.8 18.8 12.5 18.8 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001597 | 1 30 | 83.3 10.0 6.7 6.7 23.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001598 | 1 19 | 73.7 10.5 15.8 31.6 57.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001599 | 1 15 | 100.0 0.0 0.0 20.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001600 | 1 23 | 91.3 8.7 0.0 4.3 13.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001601 | 1 15 | 86.7 6.7 6.7 20.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001602 | 1 17 | 70.6 29.4 0.0 23.5 52.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001603 | 1 21 | 90.5 0.0 9.5 0.0 9.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001604 | 1 14 | 64.3 28.6 7.1 28.6 64.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001605 | 1 20 | 60.0 25.0 15.0 5.0 45.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001606 | 1 27 | 63.0 18.5 18.5 14.8 51.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001607 | 1 28 | 92.9 3.6 3.6 7.1 14.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001608 | 1 18 | 77.8 16.7 5.6 16.7 38.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001609 | 1 15 | 40.0 40.0 20.0 13.3 73.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001610 | 1 23 | 69.6 17.4 13.0 0.0 30.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001611 | 1 19 | 89.5 5.3 5.3 0.0 10.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001612 | 1 22 | 72.7 13.6 13.6 9.1 36.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001613 | 1 18 | 88.9 5.6 5.6 0.0 11.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001614 | 1 29 | 75.9 10.3 13.8 27.6 51.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001615 | 1 19 | 73.7 5.3 21.1 5.3 31.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001616 | 1 27 | 74.1 14.8 11.1 7.4 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001617 | 1 16 | 87.5 6.3 6.3 6.3 18.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001618 | 1 21 | 71.4 23.8 4.8 4.8 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001619 | 1 25 | 76.0 24.0 0.0 4.0 28.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001620 | 1 23 | 82.6 8.7 8.7 13.0 30.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001621 | 1 24 | 83.3 8.3 8.3 8.3 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001622 | 1 22 | 77.3 4.5 18.2 4.5 27.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001623 | 1 15 | 66.7 33.3 0.0 13.3 46.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001624 | 1 24 | 87.5 4.2 8.3 8.3 20.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001625 | 1 25 | 84.0 8.0 8.0 4.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001626 | 1 24 | 75.0 20.8 4.2 4.2 29.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001627 | 1 20 | 70.0 20.0 10.0 0.0 30.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001628 | 1 17 | 76.5 17.6 5.9 23.5 47.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001629 | 1 18 | 94.4 5.6 0.0 0.0 5.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001630 | 1 15 | 80.0 20.0 0.0 6.7 26.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001631 | 1 24 | 66.7 20.8 12.5 12.5 45.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001632 | 1 18 | 88.9 5.6 5.6 0.0 11.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001633 | 1 3 | 100.0 0.0 0.0 66.7 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001634 | 1 23 | 82.6 13.0 4.3 26.1 43.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001635 | 1 30 | 90.0 3.3 6.7 6.7 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001636 | 1 17 | 82.4 11.8 5.9 5.9 23.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001637 | 1 26 | 96.2 3.8 0.0 7.7 11.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001638 | 1 25 | 80.0 12.0 8.0 4.0 24.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001639 | 1 17 | 76.5 17.6 5.9 0.0 23.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001640 | 1 5 | 40.0 60.0 0.0 80.0 140.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001641 | 1 16 | 68.8 25.0 6.3 12.5 43.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001642 | 1 18 | 77.8 11.1 11.1 16.7 38.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001643 | 1 14 | 71.4 21.4 7.1 21.4 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001644 | 1 30 | 86.7 6.7 6.7 0.0 13.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001645 | 1 14 | 78.6 21.4 0.0 0.0 21.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001646 | 1 27 | 77.8 11.1 11.1 3.7 25.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001647 | 1 23 | 78.3 8.7 13.0 4.3 26.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001648 | 1 37 | 70.3 21.6 8.1 5.4 35.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001649 | 1 24 | 87.5 4.2 8.3 8.3 20.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001650 | 1 23 | 73.9 8.7 17.4 0.0 26.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001651 | 1 22 | 68.2 22.7 9.1 4.5 36.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001652 | 1 16 | 87.5 6.3 6.3 12.5 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001653 | 1 18 | 72.2 5.6 22.2 0.0 27.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001654 | 1 18 | 66.7 11.1 22.2 11.1 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001655 | 1 17 | 82.4 5.9 11.8 23.5 41.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001656 | 1 17 | 82.4 5.9 11.8 11.8 29.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001657 | 1 28 | 71.4 25.0 3.6 7.1 35.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001658 | 1 18 | 77.8 5.6 16.7 5.6 27.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001659 | 1 19 | 57.9 21.1 21.1 5.3 47.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001660 | 1 18 | 94.4 5.6 0.0 11.1 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001661 | 1 18 | 61.1 16.7 22.2 0.0 38.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001662 | 1 22 | 77.3 9.1 13.6 0.0 22.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001663 | 1 17 | 64.7 11.8 23.5 0.0 35.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001664 | 1 24 | 83.3 12.5 4.2 4.2 20.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001665 | 1 30 | 90.0 6.7 3.3 3.3 13.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001666 | 1 21 | 66.7 19.0 14.3 28.6 61.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001667 | 1 24 | 66.7 25.0 8.3 29.2 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001668 | 1 21 | 76.2 14.3 9.5 9.5 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001669 | 1 17 | 94.1 0.0 5.9 17.6 23.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001670 | 1 24 | 87.5 8.3 4.2 8.3 20.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001671 | 1 24 | 79.2 8.3 12.5 4.2 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001672 | 1 27 | 70.4 14.8 14.8 0.0 29.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001673 | 1 8 | 100.0 0.0 0.0 87.5 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001674 | 1 36 | 66.7 22.2 11.1 11.1 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001675 | 1 4 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001676 | 1 6 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001677 | 1 23 | 91.3 0.0 8.7 8.7 17.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001678 | 1 25 | 72.0 16.0 12.0 4.0 32.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001679 | 1 21 | 90.5 0.0 9.5 19.0 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001680 | 1 19 | 63.2 21.1 15.8 0.0 36.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001681 | 1 28 | 96.4 3.6 0.0 17.9 21.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001682 | 1 16 | 56.3 18.8 25.0 0.0 43.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001683 | 1 25 | 92.0 0.0 8.0 8.0 16.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001684 | 1 19 | 78.9 10.5 10.5 10.5 31.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001685 | 1 22 | 95.5 0.0 4.5 0.0 4.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001686 | 1 23 | 73.9 21.7 4.3 8.7 34.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001687 | 1 12 | 91.7 8.3 0.0 0.0 8.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001688 | 1 21 | 76.2 14.3 9.5 28.6 52.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001689 | 1 28 | 67.9 17.9 14.3 3.6 35.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001690 | 1 19 | 94.7 5.3 0.0 0.0 5.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001691 | 1 4 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001692 | 1 22 | 63.6 22.7 13.6 0.0 36.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001693 | 1 23 | 60.9 17.4 21.7 4.3 43.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001694 | 1 22 | 90.9 4.5 4.5 4.5 13.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001695 | 1 8 | 62.5 25.0 12.5 12.5 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001696 | 1 12 | 91.7 8.3 0.0 25.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001697 | 1 17 | 70.6 23.5 5.9 5.9 35.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001698 | 1 13 | 92.3 7.7 0.0 15.4 23.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001699 | 1 26 | 69.2 19.2 11.5 0.0 30.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001700 | 1 35 | 80.0 11.4 8.6 2.9 22.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001701 | 1 16 | 56.3 25.0 18.8 0.0 43.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001702 | 1 23 | 69.6 26.1 4.3 4.3 34.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001703 | 1 12 | 75.0 16.7 8.3 0.0 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001704 | 1 17 | 47.1 29.4 23.5 11.8 64.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001705 | 1 19 | 94.7 5.3 0.0 5.3 10.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001706 | 1 22 | 72.7 27.3 0.0 13.6 40.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001707 | 1 27 | 92.6 7.4 0.0 7.4 14.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001708 | 1 16 | 62.5 18.8 18.8 6.3 43.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001709 | 1 19 | 73.7 15.8 10.5 21.1 47.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001710 | 1 18 | 94.4 5.6 0.0 0.0 5.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001711 | 1 15 | 93.3 0.0 6.7 20.0 26.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001712 | 1 22 | 81.8 4.5 13.6 0.0 18.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001713 | 1 20 | 75.0 15.0 10.0 0.0 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001714 | 1 17 | 82.4 5.9 11.8 5.9 23.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001715 | 1 21 | 66.7 23.8 9.5 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001716 | 1 19 | 89.5 0.0 10.5 5.3 15.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001717 | 1 20 | 85.0 10.0 5.0 15.0 30.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001718 | 1 20 | 80.0 15.0 5.0 10.0 30.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001719 | 1 24 | 83.3 8.3 8.3 12.5 29.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001720 | 1 22 | 81.8 9.1 9.1 18.2 36.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001721 | 1 24 | 58.3 25.0 16.7 8.3 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001722 | 1 8 | 87.5 12.5 0.0 0.0 12.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001723 | 1 22 | 54.5 22.7 22.7 4.5 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001724 | 1 16 | 68.8 18.8 12.5 6.3 37.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001725 | 1 7 | 85.7 14.3 0.0 14.3 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001726 | 1 22 | 90.9 4.5 4.5 4.5 13.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001727 | 1 17 | 82.4 17.6 0.0 17.6 35.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001728 | 1 21 | 100.0 0.0 0.0 9.5 9.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001729 | 1 19 | 89.5 5.3 5.3 10.5 21.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001730 | 1 22 | 68.2 18.2 13.6 0.0 31.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001731 | 1 15 | 73.3 20.0 6.7 0.0 26.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001732 | 1 23 | 82.6 8.7 8.7 8.7 26.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001733 | 1 27 | 66.7 25.9 7.4 14.8 48.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001734 | 1 18 | 77.8 22.2 0.0 11.1 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001735 | 1 25 | 76.0 16.0 8.0 12.0 36.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001736 | 1 19 | 68.4 21.1 10.5 10.5 42.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001737 | 1 13 | 69.2 23.1 7.7 7.7 38.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001738 | 1 27 | 74.1 11.1 14.8 18.5 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001739 | 1 18 | 77.8 11.1 11.1 0.0 22.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001740 | 1 25 | 80.0 16.0 4.0 0.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001741 | 1 5 | 100.0 0.0 0.0 100.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001742 | 1 31 | 74.2 19.4 6.5 9.7 35.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001743 | 1 25 | 60.0 24.0 16.0 4.0 44.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001744 | 1 21 | 76.2 19.0 4.8 9.5 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001745 | 1 24 | 66.7 16.7 16.7 4.2 37.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001746 | 1 26 | 65.4 19.2 15.4 3.8 38.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001747 | 1 20 | 75.0 20.0 5.0 15.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001748 | 1 24 | 75.0 20.8 4.2 8.3 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001749 | 1 23 | 78.3 13.0 8.7 8.7 30.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001750 | 1 22 | 90.9 9.1 0.0 9.1 18.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001751 | 1 23 | 56.5 17.4 26.1 0.0 43.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001752 | 1 31 | 80.6 9.7 9.7 0.0 19.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001753 | 1 25 | 84.0 8.0 8.0 4.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001754 | 1 21 | 61.9 23.8 14.3 0.0 38.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001755 | 1 22 | 54.5 13.6 31.8 4.5 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001756 | 1 22 | 90.9 0.0 9.1 4.5 13.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001757 | 1 8 | 87.5 0.0 12.5 0.0 12.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001758 | 1 20 | 70.0 15.0 15.0 10.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001759 | 1 33 | 84.8 9.1 6.1 9.1 24.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001760 | 1 20 | 85.0 5.0 10.0 0.0 15.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001761 | 1 17 | 82.4 0.0 17.6 0.0 17.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001762 | 1 9 | 55.6 33.3 11.1 22.2 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001763 | 1 21 | 76.2 14.3 9.5 9.5 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001764 | 1 31 | 83.9 12.9 3.2 6.5 22.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001765 | 1 19 | 94.7 5.3 0.0 5.3 10.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001766 | 1 12 | 83.3 8.3 8.3 25.0 41.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001767 | 1 9 | 55.6 22.2 22.2 0.0 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001768 | 1 39 | 76.9 12.8 10.3 0.0 23.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001769 | 1 21 | 71.4 19.0 9.5 9.5 38.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001770 | 1 22 | 63.6 27.3 9.1 9.1 45.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001771 | 1 26 | 76.9 3.8 19.2 3.8 26.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001772 | 1 21 | 66.7 14.3 19.0 9.5 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001773 | 1 17 | 76.5 11.8 11.8 23.5 47.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001774 | 1 18 | 77.8 11.1 11.1 0.0 22.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001775 | 1 21 | 76.2 14.3 9.5 14.3 38.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001776 | 1 19 | 84.2 10.5 5.3 0.0 15.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001777 | 1 27 | 81.5 14.8 3.7 14.8 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001778 | 1 8 | 75.0 25.0 0.0 0.0 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001779 | 1 21 | 85.7 4.8 9.5 14.3 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001780 | 1 30 | 86.7 10.0 3.3 3.3 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001781 | 1 15 | 80.0 13.3 6.7 0.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001782 | 1 17 | 88.2 0.0 11.8 0.0 11.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001783 | 1 23 | 73.9 21.7 4.3 13.0 39.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001784 | 1 19 | 84.2 5.3 10.5 0.0 15.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001785 | 1 19 | 78.9 5.3 15.8 0.0 21.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001786 | 1 28 | 89.3 0.0 10.7 3.6 14.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001787 | 1 5 | 80.0 20.0 0.0 20.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001788 | 1 26 | 80.8 11.5 7.7 3.8 23.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001789 | 1 20 | 90.0 0.0 10.0 10.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001790 | 1 20 | 75.0 15.0 10.0 5.0 30.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001791 | 1 18 | 66.7 27.8 5.6 5.6 38.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001792 | 1 17 | 64.7 11.8 23.5 11.8 47.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001793 | 1 26 | 61.5 34.6 3.8 7.7 46.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001794 | 1 17 | 76.5 23.5 0.0 0.0 23.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001795 | 1 25 | 72.0 4.0 24.0 4.0 32.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001796 | 1 25 | 72.0 20.0 8.0 16.0 44.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001797 | 1 21 | 52.4 33.3 14.3 9.5 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001798 | 1 22 | 63.6 9.1 27.3 9.1 45.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001799 | 1 20 | 80.0 5.0 15.0 10.0 30.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001800 | 1 18 | 77.8 22.2 0.0 11.1 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001801 | 1 21 | 90.5 4.8 4.8 4.8 14.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001802 | 1 23 | 78.3 8.7 13.0 0.0 21.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001803 | 1 28 | 92.9 3.6 3.6 3.6 10.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001804 | 1 24 | 91.7 4.2 4.2 16.7 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001805 | 1 19 | 73.7 5.3 21.1 10.5 36.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001806 | 1 28 | 64.3 21.4 14.3 14.3 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001807 | 1 26 | 92.3 3.8 3.8 7.7 15.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001808 | 1 16 | 75.0 12.5 12.5 6.3 31.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001809 | 1 21 | 57.1 23.8 19.0 14.3 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001810 | 1 20 | 85.0 10.0 5.0 0.0 15.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001811 | 1 20 | 95.0 5.0 0.0 15.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001812 | 1 14 | 50.0 42.9 7.1 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001813 | 1 23 | 91.3 8.7 0.0 4.3 13.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001814 | 1 16 | 68.8 18.8 12.5 0.0 31.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001815 | 1 25 | 88.0 8.0 4.0 8.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001816 | 1 39 | 76.9 10.3 12.8 5.1 28.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001817 | 1 21 | 90.5 9.5 0.0 23.8 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001818 | 1 22 | 90.9 4.5 4.5 9.1 18.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001819 | 1 25 | 64.0 24.0 12.0 4.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001820 | 1 29 | 82.8 13.8 3.4 0.0 17.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001821 | 1 18 | 94.4 5.6 0.0 0.0 5.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001822 | 1 21 | 76.2 14.3 9.5 0.0 23.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001823 | 1 37 | 78.4 10.8 10.8 5.4 27.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001824 | 1 16 | 87.5 6.3 6.3 6.3 18.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001744 | 1 31 | 87.1 3.2 9.7 3.2 16.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001745 | 1 18 | 50.0 27.8 22.2 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001746 | 1 54 | 87.0 3.7 9.3 0.0 13.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001747 | 1 62 | 75.8 9.7 14.5 0.0 24.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001748 | 1 21 | 71.4 4.8 23.8 14.3 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001749 | 1 15 | 73.3 13.3 13.3 26.7 53.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001750 | 1 35 | 71.4 0.0 28.6 2.9 31.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001751 | 1 22 | 81.8 4.5 13.6 0.0 18.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001752 | 1 49 | 83.7 12.2 4.1 4.1 20.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001753 | 1 40 | 72.5 10.0 17.5 2.5 30.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001754 | 1 30 | 80.0 6.7 13.3 3.3 23.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001755 | 1 22 | 72.7 9.1 18.2 13.6 40.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001756 | 1 26 | 88.5 0.0 11.5 34.6 46.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001757 | 1 72 | 69.4 15.3 15.3 5.6 36.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001758 | 1 17 | 64.7 11.8 23.5 0.0 35.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001759 | 1 43 | 69.8 18.6 11.6 2.3 32.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001760 | 1 17 | 82.4 17.6 0.0 5.9 23.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001761 | 1 50 | 84.0 6.0 10.0 6.0 22.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001762 | 1 27 | 92.6 0.0 7.4 7.4 14.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001763 | 1 96 | 80.2 6.3 13.5 0.0 19.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001764 | 1 42 | 64.3 7.1 28.6 4.8 40.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001765 | 1 47 | 78.7 8.5 12.8 4.3 25.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001766 | 1 21 | 57.1 0.0 42.9 19.0 61.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001767 | 1 55 | 70.9 9.1 20.0 1.8 30.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001768 | 1 36 | 83.3 8.3 8.3 5.6 22.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001769 | 1 42 | 85.7 2.4 11.9 4.8 19.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001770 | 1 28 | 89.3 7.1 3.6 39.3 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001771 | 1 25 | 76.0 8.0 16.0 0.0 24.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001772 | 1 38 | 84.2 7.9 7.9 2.6 18.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001773 | 1 18 | 88.9 5.6 5.6 0.0 11.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001774 | 1 21 | 76.2 9.5 14.3 9.5 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001775 | 1 63 | 73.0 11.1 15.9 0.0 27.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001776 | 1 77 | 76.6 14.3 9.1 5.2 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001777 | 1 77 | 85.7 2.6 11.7 9.1 23.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001778 | 1 30 | 90.0 0.0 10.0 0.0 10.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001779 | 1 44 | 61.4 4.5 34.1 0.0 38.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001780 | 1 52 | 82.7 9.6 7.7 0.0 17.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001781 | 1 18 | 66.7 22.2 11.1 5.6 38.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001782 | 1 87 | 78.2 8.0 13.8 3.4 25.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001783 | 1 23 | 65.2 4.3 30.4 0.0 34.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001784 | 1 23 | 52.2 13.0 34.8 0.0 47.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001785 | 1 31 | 74.2 12.9 12.9 0.0 25.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001786 | 1 120 | 75.0 9.2 15.8 3.3 28.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001787 | 1 31 | 74.2 9.7 16.1 6.5 32.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001788 | 1 38 | 86.8 0.0 13.2 2.6 15.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001789 | 1 24 | 75.0 16.7 8.3 4.2 29.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001790 | 1 73 | 80.8 6.8 12.3 0.0 19.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001791 | 1 39 | 74.4 10.3 15.4 0.0 25.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001792 | 1 18 | 83.3 5.6 11.1 0.0 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001793 | 1 30 | 86.7 6.7 6.7 0.0 13.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001794 | 1 58 | 74.1 8.6 17.2 0.0 25.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001795 | 1 23 | 78.3 4.3 17.4 4.3 26.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001796 | 1 23 | 87.0 4.3 8.7 8.7 21.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001797 | 1 49 | 75.5 2.0 22.4 0.0 24.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001798 | 1 17 | 52.9 11.8 35.3 0.0 47.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001799 | 1 31 | 74.2 6.5 19.4 6.5 32.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001800 | 1 22 | 86.4 4.5 9.1 0.0 13.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001801 | 1 54 | 90.7 1.9 7.4 0.0 9.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001802 | 1 29 | 72.4 3.4 24.1 0.0 27.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001803 | 1 45 | 77.8 4.4 17.8 8.9 31.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001804 | 1 36 | 80.6 2.8 16.7 0.0 19.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001805 | 1 36 | 72.2 5.6 22.2 0.0 27.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001806 | 1 19 | 89.5 5.3 5.3 26.3 36.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001807 | 1 25 | 64.0 8.0 28.0 0.0 36.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001808 | 1 22 | 81.8 4.5 13.6 4.5 22.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001809 | 1 21 | 76.2 4.8 19.0 4.8 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001810 | 1 25 | 68.0 20.0 12.0 8.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001811 | 1 50 | 74.0 10.0 16.0 2.0 28.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001812 | 1 24 | 83.3 4.2 12.5 12.5 29.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001813 | 1 32 | 87.5 0.0 12.5 3.1 15.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001814 | 1 31 | 74.2 12.9 12.9 6.5 32.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001815 | 1 32 | 78.1 0.0 21.9 0.0 21.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001816 | 1 70 | 77.1 2.9 20.0 0.0 22.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001817 | 1 52 | 67.3 5.8 26.9 0.0 32.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001818 | 1 19 | 78.9 5.3 15.8 0.0 21.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001819 | 1 33 | 75.8 18.2 6.1 18.2 42.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001820 | 1 19 | 89.5 0.0 10.5 5.3 15.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001821 | 1 24 | 62.5 29.2 8.3 0.0 37.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001822 | 1 19 | 89.5 10.5 0.0 0.0 10.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001823 | 1 82 | 87.8 2.4 9.8 1.2 13.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001824 | 1 44 | 81.8 6.8 11.4 0.0 18.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001825 | 1 45 | 88.9 2.2 8.9 11.1 22.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001826 | 1 35 | 68.6 2.9 28.6 0.0 31.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001827 | 1 42 | 85.7 2.4 11.9 0.0 14.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001828 | 1 18 | 66.7 11.1 22.2 5.6 38.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001829 | 1 43 | 55.8 4.7 39.5 0.0 44.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001830 | 1 51 | 76.5 3.9 19.6 2.0 25.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001831 | 1 23 | 87.0 4.3 8.7 0.0 13.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001832 | 1 27 | 63.0 14.8 22.2 3.7 40.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001833 | 1 18 | 83.3 0.0 16.7 0.0 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001834 | 1 45 | 80.0 8.9 11.1 4.4 24.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001835 | 1 17 | 76.5 5.9 17.6 11.8 35.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001836 | 1 21 | 76.2 0.0 23.8 0.0 23.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001837 | 1 30 | 60.0 20.0 20.0 10.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001838 | 1 37 | 81.1 2.7 16.2 8.1 27.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001839 | 1 48 | 75.0 0.0 25.0 0.0 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001840 | 1 25 | 64.0 12.0 24.0 0.0 36.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001841 | 1 28 | 71.4 3.6 25.0 0.0 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001842 | 1 42 | 83.3 4.8 11.9 2.4 19.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001843 | 1 47 | 80.9 6.4 12.8 0.0 19.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001844 | 1 31 | 90.3 9.7 0.0 3.2 12.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001845 | 1 20 | 65.0 15.0 20.0 15.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001846 | 1 96 | 85.4 4.2 10.4 1.0 15.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001847 | 1 18 | 88.9 0.0 11.1 0.0 11.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001848 | 1 47 | 76.6 6.4 17.0 0.0 23.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001849 | 1 62 | 87.1 0.0 12.9 4.8 17.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001850 | 1 18 | 66.7 11.1 22.2 5.6 38.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001851 | 1 27 | 81.5 7.4 11.1 3.7 22.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001852 | 1 55 | 83.6 7.3 9.1 1.8 18.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001853 | 1 47 | 89.4 4.3 6.4 8.5 19.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001854 | 1 35 | 68.6 0.0 31.4 0.0 31.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001855 | 1 40 | 87.5 5.0 7.5 2.5 15.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001856 | 1 27 | 85.2 0.0 14.8 0.0 14.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001857 | 1 26 | 84.6 7.7 7.7 11.5 26.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001858 | 1 45 | 91.1 2.2 6.7 13.3 22.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001859 | 1 15 | 73.3 20.0 6.7 13.3 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001860 | 1 38 | 86.8 2.6 10.5 0.0 13.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001861 | 1 9 | 88.9 11.1 0.0 11.1 22.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001862 | 1 64 | 85.9 1.6 12.5 3.1 17.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001863 | 1 22 | 86.4 9.1 4.5 4.5 18.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001864 | 1 13 | 92.3 7.7 0.0 0.0 7.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001865 | 1 34 | 82.4 0.0 17.6 5.9 23.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001866 | 1 30 | 76.7 10.0 13.3 6.7 30.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001867 | 1 49 | 63.3 10.2 26.5 14.3 51.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001868 | 1 28 | 71.4 7.1 21.4 3.6 32.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001869 | 1 30 | 56.7 3.3 40.0 0.0 43.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001870 | 1 24 | 70.8 4.2 25.0 29.2 58.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001871 | 1 76 | 89.5 6.6 3.9 5.3 15.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001872 | 1 24 | 79.2 16.7 4.2 4.2 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001873 | 1 11 | 54.5 27.3 18.2 45.5 90.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001874 | 1 35 | 57.1 20.0 22.9 2.9 45.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001875 | 1 52 | 59.6 21.2 19.2 7.7 48.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001876 | 1 27 | 66.7 3.7 29.6 3.7 37.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001877 | 1 36 | 86.1 11.1 2.8 5.6 19.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001878 | 1 17 | 64.7 5.9 29.4 17.6 52.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001879 | 1 174 | 82.2 6.3 11.5 4.0 21.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001880 | 1 63 | 84.1 4.8 11.1 1.6 17.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001881 | 1 122 | 88.5 5.7 5.7 2.5 13.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001882 | 1 33 | 87.9 3.0 9.1 3.0 15.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001883 | 1 51 | 90.2 5.9 3.9 17.6 27.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001884 | 1 32 | 84.4 3.1 12.5 0.0 15.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001885 | 1 34 | 64.7 8.8 26.5 2.9 38.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001886 | 1 32 | 75.0 12.5 12.5 3.1 28.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001887 | 1 33 | 72.7 3.0 24.2 3.0 30.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001888 | 1 43 | 72.1 7.0 20.9 2.3 30.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001889 | 1 15 | 73.3 6.7 20.0 26.7 53.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001890 | 1 12 | 100.0 0.0 0.0 33.3 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001891 | 1 36 | 77.8 8.3 13.9 0.0 22.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001892 | 1 36 | 66.7 8.3 25.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001893 | 1 23 | 60.9 8.7 30.4 0.0 39.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001894 | 1 40 | 70.0 17.5 12.5 7.5 37.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001895 | 1 37 | 86.5 5.4 8.1 5.4 18.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001896 | 1 30 | 90.0 3.3 6.7 0.0 10.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001897 | 1 12 | 91.7 0.0 8.3 0.0 8.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001898 | 1 40 | 72.5 7.5 20.0 2.5 30.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001899 | 1 49 | 87.8 6.1 6.1 0.0 12.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001900 | 1 31 | 77.4 12.9 9.7 0.0 22.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001901 | 1 20 | 75.0 15.0 10.0 0.0 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001902 | 1 42 | 83.3 2.4 14.3 0.0 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001903 | 1 22 | 72.7 9.1 18.2 0.0 27.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001904 | 1 37 | 73.0 0.0 27.0 16.2 43.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001905 | 1 44 | 72.7 4.5 22.7 2.3 29.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001906 | 1 81 | 85.2 1.2 13.6 0.0 14.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001907 | 1 20 | 70.0 5.0 25.0 5.0 35.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001908 | 1 29 | 72.4 6.9 20.7 3.4 31.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001909 | 1 22 | 77.3 4.5 18.2 0.0 22.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001910 | 1 43 | 88.4 4.7 7.0 0.0 11.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001911 | 1 32 | 78.1 9.4 12.5 0.0 21.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001912 | 1 37 | 86.5 5.4 8.1 2.7 16.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001913 | 1 24 | 79.2 0.0 20.8 0.0 20.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001914 | 1 58 | 75.9 8.6 15.5 3.4 27.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001915 | 1 47 | 53.2 12.8 34.0 0.0 46.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001916 | 1 25 | 72.0 8.0 20.0 0.0 28.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001917 | 1 21 | 66.7 14.3 19.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001918 | 1 61 | 80.3 9.8 9.8 3.3 23.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001919 | 1 58 | 84.5 3.4 12.1 1.7 17.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001920 | 1 45 | 91.1 0.0 8.9 0.0 8.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001921 | 1 18 | 77.8 0.0 22.2 16.7 38.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001922 | 1 34 | 85.3 2.9 11.8 8.8 23.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001923 | 1 129 | 84.5 3.1 12.4 3.9 19.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001924 | 1 76 | 77.6 7.9 14.5 2.6 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001925 | 1 48 | 81.3 8.3 10.4 0.0 18.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001926 | 1 27 | 70.4 3.7 25.9 0.0 29.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001927 | 1 23 | 73.9 13.0 13.0 0.0 26.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001928 | 1 19 | 78.9 5.3 15.8 0.0 21.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001929 | 1 26 | 84.6 0.0 15.4 0.0 15.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001930 | 1 32 | 68.8 15.6 15.6 3.1 34.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001931 | 1 14 | 92.9 0.0 7.1 0.0 7.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001932 | 1 45 | 80.0 13.3 6.7 13.3 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001933 | 1 25 | 80.0 8.0 12.0 0.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001934 | 1 38 | 84.2 5.3 10.5 0.0 15.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001935 | 1 26 | 84.6 7.7 7.7 7.7 23.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001936 | 1 44 | 79.5 4.5 15.9 9.1 29.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001937 | 1 15 | 53.3 40.0 6.7 6.7 53.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001938 | 1 72 | 76.4 8.3 15.3 12.5 36.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001939 | 1 32 | 81.3 6.3 12.5 0.0 18.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001940 | 1 45 | 73.3 6.7 20.0 0.0 26.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001941 | 1 36 | 52.8 13.9 33.3 16.7 63.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001942 | 1 19 | 78.9 5.3 15.8 0.0 21.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001943 | 1 44 | 68.2 2.3 29.5 0.0 31.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001944 | 1 43 | 79.1 11.6 9.3 2.3 23.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001945 | 1 37 | 83.8 2.7 13.5 13.5 29.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001946 | 1 106 | 86.8 2.8 10.4 1.9 15.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001947 | 1 23 | 78.3 0.0 21.7 4.3 26.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001948 | 1 23 | 65.2 17.4 17.4 4.3 39.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001949 | 1 38 | 71.1 13.2 15.8 2.6 31.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001950 | 1 15 | 66.7 26.7 6.7 6.7 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001951 | 1 39 | 87.2 5.1 7.7 2.6 15.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001952 | 1 67 | 80.6 7.5 11.9 1.5 20.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001953 | 1 23 | 60.9 13.0 26.1 4.3 43.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001954 | 1 24 | 79.2 4.2 16.7 8.3 29.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001955 | 1 19 | 78.9 0.0 21.1 5.3 26.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001956 | 1 34 | 82.4 8.8 8.8 2.9 20.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001957 | 1 73 | 71.2 5.5 23.3 0.0 28.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001958 | 1 24 | 70.8 12.5 16.7 8.3 37.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001959 | 1 41 | 85.4 7.3 7.3 2.4 17.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001960 | 1 51 | 70.6 9.8 19.6 0.0 29.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001961 | 1 45 | 80.0 6.7 13.3 0.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001962 | 1 14 | 92.9 7.1 0.0 14.3 21.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001963 | 1 35 | 85.7 11.4 2.9 2.9 17.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001964 | 1 15 | 60.0 33.3 6.7 13.3 53.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001965 | 1 24 | 75.0 12.5 12.5 4.2 29.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001966 | 1 38 | 81.6 7.9 10.5 0.0 18.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001967 | 1 60 | 90.0 1.7 8.3 5.0 15.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001968 | 1 63 | 69.8 7.9 22.2 0.0 30.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001969 | 1 64 | 67.2 14.1 18.8 1.6 34.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001970 | 1 42 | 78.6 9.5 11.9 4.8 26.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001971 | 1 13 | 76.9 7.7 15.4 0.0 23.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001972 | 1 61 | 75.4 1.6 23.0 1.6 26.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001973 | 1 50 | 88.0 0.0 12.0 2.0 14.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001974 | 1 18 | 77.8 5.6 16.7 0.0 22.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001975 | 1 43 | 79.1 11.6 9.3 0.0 20.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001976 | 1 27 | 48.1 25.9 25.9 0.0 51.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001977 | 1 22 | 90.9 4.5 4.5 0.0 9.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001978 | 1 15 | 73.3 0.0 26.7 13.3 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001979 | 1 68 | 69.1 10.3 20.6 0.0 30.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001980 | 1 51 | 62.7 3.9 33.3 0.0 37.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001981 | 1 42 | 81.0 7.1 11.9 4.8 23.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001982 | 1 16 | 81.3 18.8 0.0 56.3 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001983 | 1 77 | 77.9 11.7 10.4 3.9 26.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001984 | 1 31 | 77.4 9.7 12.9 3.2 25.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001985 | 1 33 | 78.8 6.1 15.2 3.0 24.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001986 | 1 77 | 81.8 6.5 11.7 1.3 19.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001987 | 1 15 | 66.7 13.3 20.0 6.7 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001988 | 1 25 | 56.0 12.0 32.0 4.0 48.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001989 | 1 51 | 66.7 2.0 31.4 11.8 45.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001990 | 1 38 | 81.6 5.3 13.2 0.0 18.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001991 | 1 35 | 71.4 14.3 14.3 2.9 31.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001992 | 1 15 | 73.3 20.0 6.7 0.0 26.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001993 | 1 44 | 59.1 20.5 20.5 6.8 47.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001994 | 1 29 | 93.1 3.4 3.4 0.0 6.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001995 | 1 21 | 76.2 4.8 19.0 0.0 23.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001996 | 1 31 | 61.3 9.7 29.0 3.2 41.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001997 | 1 34 | 64.7 5.9 29.4 2.9 38.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001998 | 1 53 | 81.1 11.3 7.5 1.9 20.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001999 | 1 20 | 40.0 20.0 40.0 10.0 70.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002000 | 1 64 | 85.9 4.7 9.4 23.4 37.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002001 | 1 21 | 66.7 4.8 28.6 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002002 | 1 17 | 64.7 17.6 17.6 17.6 52.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002003 | 1 23 | 73.9 13.0 13.0 4.3 30.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002004 | 1 23 | 82.6 0.0 17.4 21.7 39.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002005 | 1 25 | 80.0 4.0 16.0 4.0 24.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000874 | 1 57 | 84.2 7.0 8.8 0.0 15.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000875 | 1 20 | 95.0 0.0 5.0 0.0 5.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000876 | 1 46 | 82.6 6.5 10.9 0.0 17.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000877 | 1 50 | 84.0 8.0 8.0 2.0 18.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000878 | 1 24 | 91.7 8.3 0.0 4.2 12.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000879 | 1 49 | 89.8 8.2 2.0 8.2 18.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000880 | 1 45 | 86.7 4.4 8.9 0.0 13.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000883 | 1 26 | 69.2 23.1 7.7 23.1 53.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000884 | 1 60 | 96.7 1.7 1.7 3.3 6.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000885 | 1 36 | 77.8 5.6 16.7 0.0 22.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000886 | 1 62 | 79.0 12.9 8.1 9.7 30.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000887 | 1 65 | 76.9 4.6 18.5 3.1 26.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000888 | 1 40 | 92.5 0.0 7.5 0.0 7.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000889 | 1 36 | 88.9 2.8 8.3 0.0 11.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000890 | 1 23 | 82.6 0.0 17.4 0.0 17.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000891 | 1 81 | 91.4 1.2 7.4 1.2 9.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000892 | 1 21 | 95.2 4.8 0.0 0.0 4.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000893 | 1 26 | 80.8 11.5 7.7 23.1 42.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000894 | 1 91 | 92.3 1.1 6.6 3.3 11.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000895 | 1 57 | 80.7 0.0 19.3 3.5 22.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000896 | 1 27 | 92.6 7.4 0.0 3.7 11.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000897 | 1 30 | 96.7 0.0 3.3 0.0 3.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000898 | 1 37 | 94.6 0.0 5.4 2.7 8.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000899 | 1 22 | 95.5 0.0 4.5 4.5 9.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000900 | 1 61 | 72.1 6.6 21.3 3.3 31.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000901 | 1 31 | 77.4 9.7 12.9 67.7 90.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000902 | 1 24 | 70.8 29.2 0.0 25.0 54.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000903 | 1 45 | 88.9 6.7 4.4 2.2 13.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000904 | 1 32 | 75.0 12.5 12.5 3.1 28.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000905 | 1 48 | 66.7 0.0 33.3 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000906 | 1 50 | 82.0 2.0 16.0 0.0 18.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000907 | 1 60 | 96.7 1.7 1.7 0.0 3.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000908 | 1 42 | 85.7 7.1 7.1 2.4 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000909 | 1 57 | 80.7 1.8 17.5 1.8 21.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000910 | 1 54 | 88.9 1.9 9.3 3.7 14.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000911 | 1 26 | 92.3 0.0 7.7 0.0 7.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000912 | 1 33 | 100.0 0.0 0.0 6.1 6.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000913 | 1 68 | 85.3 5.9 8.8 1.5 16.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000914 | 1 47 | 83.0 0.0 17.0 0.0 17.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000915 | 1 28 | 89.3 0.0 10.7 3.6 14.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000916 | 1 35 | 100.0 0.0 0.0 2.9 2.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000917 | 1 40 | 95.0 5.0 0.0 2.5 7.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000918 | 1 67 | 83.6 7.5 9.0 0.0 16.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000920 | 1 35 | 85.7 5.7 8.6 5.7 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000921 | 1 49 | 85.7 2.0 12.2 4.1 18.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000922 | 1 57 | 91.2 5.3 3.5 3.5 12.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000923 | 1 46 | 89.1 0.0 10.9 4.3 15.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000924 | 1 16 | 87.5 6.3 6.3 0.0 12.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000925 | 1 65 | 80.0 16.9 3.1 10.8 30.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000927 | 1 49 | 95.9 0.0 4.1 2.0 6.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000928 | 1 48 | 85.4 2.1 12.5 2.1 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000929 | 1 19 | 84.2 0.0 15.8 5.3 21.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000930 | 1 58 | 98.3 0.0 1.7 0.0 1.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000931 | 1 57 | 87.7 7.0 5.3 8.8 21.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000932 | 1 51 | 80.4 7.8 11.8 7.8 27.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000933 | 1 31 | 51.6 25.8 22.6 6.5 54.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000934 | 1 38 | 86.8 2.6 10.5 2.6 15.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000935 | 1 38 | 78.9 10.5 10.5 7.9 28.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000938 | 1 26 | 92.3 3.8 3.8 30.8 38.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000939 | 1 74 | 78.4 10.8 10.8 5.4 27.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000940 | 1 55 | 72.7 3.6 23.6 0.0 27.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000942 | 1 51 | 82.4 7.8 9.8 5.9 23.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000943 | 1 39 | 84.6 5.1 10.3 2.6 17.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000944 | 1 102 | 79.4 5.9 14.7 5.9 26.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000945 | 1 57 | 86.0 8.8 5.3 10.5 24.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000946 | 1 32 | 62.5 15.6 21.9 6.3 43.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000947 | 1 50 | 86.0 2.0 12.0 2.0 16.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000948 | 1 32 | 71.9 12.5 15.6 0.0 28.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000949 | 1 68 | 89.7 8.8 1.5 4.4 14.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000950 | 1 47 | 76.6 0.0 23.4 4.3 27.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000951 | 1 23 | 87.0 4.3 8.7 4.3 17.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000952 | 1 22 | 86.4 4.5 9.1 0.0 13.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000953 | 1 48 | 83.3 6.3 10.4 2.1 18.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000954 | 1 52 | 90.4 5.8 3.8 1.9 11.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000955 | 1 76 | 82.9 6.6 10.5 0.0 17.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000956 | 1 52 | 76.9 17.3 5.8 5.8 28.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000957 | 1 23 | 95.7 0.0 4.3 4.3 8.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000958 | 1 48 | 91.7 4.2 4.2 2.1 10.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000959 | 1 118 | 83.9 9.3 6.8 1.7 17.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000960 | 1 87 | 89.7 4.6 5.7 1.1 11.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000961 | 1 74 | 89.2 4.1 6.8 1.4 12.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000962 | 1 23 | 95.7 4.3 0.0 4.3 8.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000963 | 1 60 | 70.0 1.7 28.3 0.0 30.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000964 | 1 67 | 82.1 1.5 16.4 1.5 19.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000965 | 1 37 | 94.6 0.0 5.4 8.1 13.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000966 | 1 25 | 92.0 8.0 0.0 8.0 16.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000967 | 1 31 | 71.0 22.6 6.5 0.0 29.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000968 | 1 47 | 85.1 12.8 2.1 2.1 17.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000971 | 1 41 | 85.4 2.4 12.2 4.9 19.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000972 | 1 61 | 86.9 4.9 8.2 4.9 18.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000973 | 1 81 | 80.2 4.9 14.8 0.0 19.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000974 | 1 71 | 87.3 5.6 7.0 4.2 16.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000975 | 1 68 | 89.7 4.4 5.9 5.9 16.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000976 | 1 42 | 92.9 2.4 4.8 0.0 7.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000977 | 1 28 | 85.7 7.1 7.1 3.6 17.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000978 | 1 30 | 100.0 0.0 0.0 3.3 3.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000979 | 1 46 | 87.0 4.3 8.7 6.5 19.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000980 | 1 52 | 76.9 5.8 17.3 3.8 26.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000981 | 1 56 | 87.5 3.6 8.9 0.0 12.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000982 | 1 57 | 77.2 8.8 14.0 1.8 24.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000983 | 1 55 | 87.3 9.1 3.6 9.1 21.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000984 | 1 23 | 82.6 0.0 17.4 0.0 17.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000985 | 1 43 | 93.0 2.3 4.7 4.7 11.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000986 | 1 85 | 89.4 1.2 9.4 0.0 10.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000987 | 1 82 | 75.6 15.9 8.5 14.6 39.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000988 | 1 39 | 89.7 2.6 7.7 2.6 12.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000989 | 1 97 | 88.7 1.0 10.3 2.1 13.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000990 | 1 44 | 93.2 2.3 4.5 2.3 9.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000991 | 1 45 | 88.9 4.4 6.7 0.0 11.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000992 | 1 38 | 81.6 5.3 13.2 0.0 18.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000993 | 1 41 | 61.0 14.6 24.4 2.4 41.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000994 | 1 54 | 75.9 5.6 18.5 0.0 24.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000995 | 1 91 | 80.2 1.1 18.7 2.2 22.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000996 | 1 47 | 91.5 2.1 6.4 0.0 8.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000997 | 1 69 | 84.1 1.4 14.5 2.9 18.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000998 | 1 39 | 84.6 7.7 7.7 2.6 17.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000999 | 1 25 | 76.0 12.0 12.0 4.0 28.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001000 | 1 68 | 89.7 5.9 4.4 8.8 19.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001001 | 1 50 | 90.0 4.0 6.0 6.0 16.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001002 | 1 28 | 85.7 7.1 7.1 10.7 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001003 | 1 54 | 90.7 9.3 0.0 1.9 11.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001004 | 1 53 | 86.8 5.7 7.5 0.0 13.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000494 | 1 81 | 84.0 2.5 13.6 16.0 32.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000495 | 1 169 | 78.1 10.1 11.8 3.0 24.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000496 | 1 147 | 82.3 8.2 9.5 8.2 25.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000497 | 1 91 | 83.5 4.4 12.1 7.7 24.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000498 | 1 156 | 79.5 7.7 12.8 5.1 25.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000499 | 1 377 | 81.2 6.6 12.2 5.3 24.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000500 | 1 123 | 87.0 3.3 9.8 4.1 17.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000501 | 1 22 | 68.2 22.7 9.1 4.5 36.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000502 | 1 304 | 79.9 5.3 14.8 4.6 24.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000503 | 1 170 | 77.6 5.9 16.5 3.5 25.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000504 | 1 203 | 77.3 5.4 17.2 0.5 23.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000505 | 1 285 | 77.5 9.5 13.0 5.3 27.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000506 | 1 168 | 67.9 7.7 24.4 8.9 41.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000507 | 1 22 | 72.7 0.0 27.3 9.1 36.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000508 | 1 42 | 73.8 7.1 19.0 11.9 38.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000509 | 1 284 | 80.6 9.2 10.2 8.8 28.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000510 | 1 261 | 73.6 8.4 18.0 5.4 31.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000511 | 1 256 | 78.1 4.7 17.2 4.7 26.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000512 | 1 48 | 89.6 0.0 10.4 0.0 10.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000513 | 1 74 | 83.8 5.4 10.8 6.8 23.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000514 | 1 58 | 74.1 15.5 10.3 0.0 25.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000515 | 1 106 | 89.6 6.6 3.8 4.7 15.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000516 | 1 88 | 68.2 9.1 22.7 6.8 38.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000517 | 1 168 | 91.1 3.6 5.4 3.0 11.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000518 | 1 99 | 82.8 6.1 11.1 2.0 19.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000519 | 1 151 | 80.1 4.6 15.2 9.3 29.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000520 | 1 179 | 81.6 12.3 6.1 8.4 26.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000521 | 1 248 | 84.3 6.5 9.3 6.0 21.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000522 | 1 68 | 89.7 2.9 7.4 5.9 16.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000523 | 1 119 | 83.2 3.4 13.4 5.0 21.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000524 | 1 187 | 82.9 7.0 10.2 5.3 22.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000525 | 1 56 | 92.9 3.6 3.6 1.8 8.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000526 | 1 79 | 87.3 2.5 10.1 1.3 13.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000527 | 1 247 | 72.5 16.6 10.9 10.1 37.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000528 | 1 105 | 80.0 8.6 11.4 9.5 29.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000529 | 1 91 | 76.9 2.2 20.9 2.2 25.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000530 | 1 156 | 78.8 10.3 10.9 16.7 37.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000531 | 1 42 | 69.0 11.9 19.0 7.1 38.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000532 | 1 26 | 92.3 3.8 3.8 3.8 11.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000533 | 1 89 | 80.9 3.4 15.7 9.0 28.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000534 | 1 199 | 84.4 6.0 9.5 3.5 19.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000535 | 1 185 | 84.9 5.9 9.2 3.2 18.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000536 | 1 166 | 78.9 6.0 15.1 0.6 21.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000537 | 1 131 | 67.2 12.2 20.6 4.6 37.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000538 | 1 119 | 92.4 0.8 6.7 0.0 7.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000539 | 1 118 | 72.0 9.3 18.6 5.1 33.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000540 | 1 115 | 78.3 14.8 7.0 13.0 34.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000541 | 1 76 | 85.5 6.6 7.9 10.5 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000542 | 1 53 | 84.9 9.4 5.7 7.5 22.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000543 | 1 101 | 68.3 10.9 20.8 6.9 38.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000544 | 1 133 | 79.7 10.5 9.8 9.0 29.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000545 | 1 40 | 87.5 7.5 5.0 5.0 17.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000546 | 1 30 | 56.7 10.0 33.3 6.7 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000547 | 1 127 | 77.2 8.7 14.2 4.7 27.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000548 | 1 204 | 83.3 6.4 10.3 11.8 28.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000549 | 1 127 | 76.4 5.5 18.1 2.4 26.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000550 | 1 95 | 78.9 10.5 10.5 12.6 33.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000551 | 1 63 | 90.5 4.8 4.8 6.3 15.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000552 | 1 194 | 79.4 9.3 11.3 9.3 29.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000553 | 1 138 | 87.7 5.1 7.2 0.7 13.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000554 | 1 196 | 87.8 4.1 8.2 2.6 14.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000555 | 1 81 | 87.7 6.2 6.2 13.6 25.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000556 | 1 111 | 82.9 9.0 8.1 2.7 19.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000557 | 1 45 | 93.3 4.4 2.2 2.2 8.9 100.0 | +|=================================================================================================================| +| Sum/Avg | 1092 67334 | 81.1 7.0 11.9 4.7 23.6 99.6 | +|=================================================================================================================| +| Mean | 1.1 66.3 | 79.5 9.0 11.5 7.5 28.0 99.6 | +| S.D. | 2.4 267.1 | 10.4 7.9 7.2 21.7 24.6 6.3 | +| Median | 1.0 40.0 | 80.7 6.9 10.5 3.8 24.0 100.0 | +`-----------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,-----------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/hyp.trn| +|-----------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000254 | 1 100 | 81 6 13 0 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000255 | 1 60 | 47 4 9 2 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000256 | 1 41 | 34 3 4 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000257 | 1 53 | 47 2 4 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000258 | 1 51 | 46 2 3 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000259 | 1 92 | 80 3 9 2 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000260 | 1 79 | 69 2 8 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000261 | 1 80 | 69 7 4 2 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000262 | 1 44 | 39 3 2 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000263 | 1 54 | 48 0 6 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000264 | 1 107 | 91 2 14 2 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000265 | 1 41 | 40 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000266 | 1 25 | 25 0 0 0 0 0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000267 | 1 26 | 21 3 2 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000268 | 1 43 | 40 0 3 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000269 | 1 53 | 40 4 9 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000270 | 1 66 | 61 2 3 3 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000271 | 1 93 | 81 3 9 2 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000272 | 1 91 | 85 3 3 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000273 | 1 114 | 102 4 8 2 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000274 | 1 26 | 24 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000275 | 1 82 | 68 3 11 1 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000276 | 1 94 | 83 4 7 1 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000277 | 1 55 | 53 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000278 | 1 48 | 42 1 5 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000279 | 1 21 | 19 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000280 | 1 52 | 49 0 3 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000281 | 1 44 | 40 1 3 6 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000282 | 1 30 | 25 2 3 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000283 | 1 94 | 83 7 4 3 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000284 | 1 111 | 92 3 16 0 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000285 | 1 104 | 90 5 9 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000286 | 1 60 | 55 2 3 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000287 | 1 44 | 40 0 4 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000288 | 1 46 | 38 3 5 3 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000289 | 1 48 | 44 0 4 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000290 | 1 51 | 48 1 2 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000291 | 1 56 | 50 3 3 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000292 | 1 50 | 41 4 5 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000293 | 1 85 | 63 7 15 0 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000294 | 1 60 | 50 0 10 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000295 | 1 52 | 46 1 5 3 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000296 | 1 91 | 74 4 13 2 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000297 | 1 72 | 58 8 6 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000298 | 1 60 | 57 0 3 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000299 | 1 79 | 69 4 6 6 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000300 | 1 78 | 73 0 5 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000301 | 1 74 | 68 1 5 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000302 | 1 58 | 48 1 9 4 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000303 | 1 60 | 49 5 6 3 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000304 | 1 50 | 44 1 5 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000305 | 1 77 | 69 0 8 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000306 | 1 74 | 63 3 8 4 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000307 | 1 91 | 82 2 7 4 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000308 | 1 69 | 52 5 12 2 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000309 | 1 34 | 30 1 3 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000310 | 1 26 | 25 0 1 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000311 | 1 107 | 86 9 12 2 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000312 | 1 81 | 69 1 11 1 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000313 | 1 63 | 56 3 4 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000314 | 1 41 | 34 3 4 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000315 | 1 55 | 41 1 13 3 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000316 | 1 100 | 86 7 7 1 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000317 | 1 70 | 54 6 10 1 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000318 | 1 107 | 90 6 11 0 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000319 | 1 74 | 64 5 5 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000320 | 1 94 | 83 1 10 1 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000321 | 1 31 | 26 2 3 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000322 | 1 59 | 47 4 8 1 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000323 | 1 52 | 43 5 4 13 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000324 | 1 75 | 64 8 3 6 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000325 | 1 34 | 32 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000326 | 1 43 | 34 2 7 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000327 | 1 76 | 62 5 9 1 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000328 | 1 26 | 21 1 4 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000329 | 1 101 | 81 3 17 1 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000330 | 1 50 | 46 2 2 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000331 | 1 31 | 30 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000332 | 1 98 | 82 6 10 2 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000333 | 1 45 | 38 3 4 3 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000334 | 1 107 | 87 3 17 1 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000335 | 1 75 | 67 1 7 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000336 | 1 92 | 72 8 12 3 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000337 | 1 100 | 90 5 5 6 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000338 | 1 29 | 27 0 2 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000339 | 1 54 | 49 1 4 6 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000340 | 1 70 | 61 3 6 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000341 | 1 67 | 56 2 9 1 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000342 | 1 82 | 70 1 11 2 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000343 | 1 111 | 100 4 7 4 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000344 | 1 102 | 89 1 12 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000345 | 1 73 | 62 4 7 5 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000346 | 1 91 | 76 4 11 2 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000347 | 1 76 | 65 2 9 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000348 | 1 82 | 71 2 9 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000349 | 1 89 | 78 5 6 1 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000350 | 1 80 | 65 5 10 1 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000351 | 1 105 | 91 3 11 1 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000352 | 1 38 | 32 3 3 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000353 | 1 61 | 53 0 8 3 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000354 | 1 67 | 59 2 6 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000355 | 1 76 | 63 4 9 3 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000356 | 1 36 | 31 2 3 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000357 | 1 79 | 64 2 13 1 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000358 | 1 49 | 42 2 5 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000359 | 1 48 | 36 3 9 1 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000360 | 1 47 | 40 2 5 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000361 | 1 48 | 43 2 3 3 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000362 | 1 99 | 83 4 12 1 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000363 | 1 72 | 53 9 10 0 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000364 | 1 63 | 57 0 6 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000365 | 1 25 | 24 0 1 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000366 | 1 53 | 43 5 5 4 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000367 | 1 58 | 44 5 9 1 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000368 | 1 47 | 39 3 5 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000369 | 1 92 | 79 0 13 2 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000370 | 1 76 | 69 1 6 3 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000371 | 1 58 | 51 1 6 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000372 | 1 64 | 55 4 5 3 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000373 | 1 92 | 77 6 9 4 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000374 | 1 26 | 26 0 0 0 0 0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000375 | 1 71 | 61 2 8 6 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000376 | 1 90 | 75 2 13 3 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| m | 77 8404 | 7167 280 957 169 1406 77 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000707 | 1 45 | 25 15 5 11 31 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000708 | 1 57 | 46 5 6 3 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000709 | 1 56 | 43 8 5 7 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000710 | 1 77 | 58 7 12 2 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000711 | 1 91 | 65 17 9 7 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000712 | 1 99 | 78 4 17 2 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000713 | 1 63 | 45 5 13 3 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000714 | 1 89 | 54 20 15 8 43 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000715 | 1 13 | 12 1 0 4 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000716 | 1 54 | 42 7 5 4 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000717 | 1 75 | 58 10 7 2 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000718 | 1 35 | 30 1 4 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000719 | 1 33 | 23 8 2 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000720 | 1 43 | 32 6 5 5 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000721 | 1 34 | 28 4 2 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000722 | 1 35 | 19 9 7 4 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000723 | 1 68 | 49 10 9 3 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000724 | 1 30 | 26 2 2 7 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000725 | 1 88 | 68 9 11 2 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000726 | 1 64 | 57 2 5 7 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000727 | 1 20 | 11 7 2 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000728 | 1 46 | 40 3 3 5 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000729 | 1 75 | 58 7 10 1 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000730 | 1 85 | 49 24 12 13 49 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000731 | 1 26 | 11 9 6 4 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000732 | 1 77 | 65 3 9 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000733 | 1 58 | 40 10 8 4 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000734 | 1 78 | 62 6 10 7 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000735 | 1 64 | 53 4 7 3 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000736 | 1 54 | 47 2 5 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000737 | 1 5 | 3 2 0 16 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000738 | 1 85 | 68 12 5 4 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000739 | 1 44 | 31 8 5 5 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000740 | 1 45 | 42 2 1 6 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000741 | 1 28 | 25 2 1 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000742 | 1 68 | 44 18 6 9 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000743 | 1 72 | 62 5 5 4 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000744 | 1 38 | 34 3 1 7 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000745 | 1 68 | 46 15 7 19 41 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000746 | 1 50 | 38 8 4 2 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000747 | 1 34 | 22 5 7 1 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000748 | 1 2 | 2 0 0 8 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000749 | 1 5 | 4 1 0 11 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000750 | 1 83 | 55 15 13 5 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000751 | 1 67 | 58 7 2 3 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000752 | 1 77 | 56 11 10 12 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000753 | 1 57 | 41 14 2 24 40 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000754 | 1 51 | 30 14 7 30 51 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000755 | 1 28 | 21 3 4 5 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000756 | 1 51 | 38 7 6 5 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000757 | 1 74 | 53 5 16 1 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000758 | 1 25 | 22 3 0 3 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000759 | 1 29 | 20 4 5 6 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000760 | 1 57 | 30 15 12 17 44 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000761 | 1 29 | 21 4 4 4 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000762 | 1 74 | 60 3 11 1 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000763 | 1 43 | 34 3 6 2 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000764 | 1 39 | 26 4 9 1 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000765 | 1 27 | 18 7 2 2 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000766 | 1 43 | 40 2 1 3 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000767 | 1 59 | 51 4 4 3 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000768 | 1 27 | 24 3 0 8 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000769 | 1 18 | 13 0 5 3 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000770 | 1 83 | 68 10 5 3 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000771 | 1 59 | 36 15 8 6 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000772 | 1 36 | 11 22 3 2 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000773 | 1 72 | 58 2 12 3 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000774 | 1 67 | 56 5 6 11 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000775 | 1 23 | 10 12 1 7 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000776 | 1 70 | 55 7 8 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000777 | 1 80 | 61 8 11 7 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000778 | 1 82 | 65 5 12 1 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000779 | 1 93 | 67 12 14 24 50 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000780 | 1 58 | 47 3 8 5 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000781 | 1 75 | 53 11 11 7 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000782 | 1 3 | 2 1 0 9 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000783 | 1 35 | 26 2 7 3 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000784 | 1 54 | 38 8 8 14 30 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000785 | 1 40 | 32 4 4 7 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000786 | 1 59 | 51 4 4 8 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000787 | 1 94 | 78 9 7 11 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000788 | 1 27 | 24 2 1 9 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000789 | 1 27 | 19 3 5 9 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000790 | 1 27 | 23 1 3 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000791 | 1 40 | 29 5 6 7 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000792 | 1 65 | 41 15 9 11 35 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000793 | 1 34 | 17 8 9 8 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000794 | 1 28 | 21 5 2 3 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000795 | 1 31 | 25 1 5 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000796 | 1 33 | 12 9 12 1 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000797 | 1 43 | 34 6 3 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000798 | 1 55 | 36 9 10 0 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000799 | 1 44 | 34 4 6 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000800 | 1 21 | 13 8 0 24 32 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000801 | 1 24 | 16 4 4 6 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000802 | 1 46 | 38 4 4 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000803 | 1 68 | 44 9 15 1 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000804 | 1 40 | 36 4 0 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000805 | 1 83 | 66 10 7 5 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000806 | 1 27 | 18 7 2 3 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000807 | 1 96 | 57 24 15 14 53 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000808 | 1 83 | 73 3 7 3 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000809 | 1 42 | 30 8 4 6 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000413 | 1 195 | 163 22 10 7 39 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000414 | 1 201 | 170 7 24 7 38 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000415 | 1 93 | 72 9 12 13 34 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000416 | 1 116 | 90 5 21 3 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000417 | 1 138 | 106 4 28 1 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000418 | 1 78 | 62 6 10 2 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000419 | 1 53 | 52 0 1 4 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000420 | 1 65 | 48 7 10 0 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000421 | 1 148 | 126 12 10 3 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000422 | 1 119 | 95 8 16 5 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000423 | 1 114 | 90 4 20 2 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000424 | 1 85 | 75 4 6 6 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000425 | 1 111 | 91 5 15 1 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000426 | 1 106 | 92 4 10 19 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000427 | 1 173 | 130 9 34 1 44 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000428 | 1 120 | 88 4 28 1 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000429 | 1 127 | 115 3 9 3 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000430 | 1 101 | 87 8 6 5 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000431 | 1 103 | 77 7 19 3 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000432 | 1 106 | 84 5 17 1 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000433 | 1 84 | 65 8 11 6 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000434 | 1 70 | 52 5 13 1 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000435 | 1 119 | 97 6 16 2 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000436 | 1 135 | 102 13 20 8 41 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000437 | 1 121 | 90 17 14 22 53 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000438 | 1 163 | 136 9 18 8 35 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000439 | 1 64 | 56 4 4 4 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000440 | 1 163 | 131 5 27 4 36 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000441 | 1 67 | 57 6 4 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000442 | 1 133 | 89 9 35 12 56 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000443 | 1 206 | 158 22 26 4 52 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000444 | 1 111 | 81 8 22 3 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000445 | 1 55 | 36 9 10 17 36 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000446 | 1 79 | 59 7 13 3 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000447 | 1 116 | 98 2 16 4 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000448 | 1 174 | 145 10 19 4 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000449 | 1 147 | 127 12 8 19 39 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000450 | 1 206 | 156 17 33 6 56 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000451 | 1 119 | 83 15 21 5 41 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000452 | 1 190 | 138 14 38 5 57 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000453 | 1 137 | 97 11 29 2 42 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000454 | 1 143 | 107 18 18 16 52 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000455 | 1 89 | 73 7 9 15 31 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000456 | 1 53 | 48 1 4 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000457 | 1 121 | 87 13 21 5 39 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000458 | 1 85 | 68 10 7 17 34 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000459 | 1 227 | 190 14 23 8 45 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000460 | 1 115 | 87 11 17 5 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000461 | 1 98 | 76 9 13 6 28 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000462 | 1 105 | 94 5 6 2 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000463 | 1 110 | 73 10 27 1 38 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000464 | 1 91 | 70 6 15 3 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000465 | 1 143 | 127 8 8 8 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000466 | 1 155 | 117 11 27 10 48 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000467 | 1 106 | 79 10 17 2 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000468 | 1 250 | 177 26 47 11 84 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000469 | 1 180 | 140 21 19 41 81 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000470 | 1 211 | 168 16 27 8 51 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000471 | 1 141 | 113 7 21 1 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000472 | 1 191 | 123 39 29 16 84 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000473 | 1 154 | 120 14 20 1 35 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000474 | 1 148 | 129 11 8 21 40 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000475 | 1 165 | 123 11 31 3 45 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000476 | 1 139 | 124 0 15 6 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000283 | 1 198 | 156 18 24 8 50 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000284 | 1 173 | 146 13 14 13 40 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000285 | 1 215 | 153 15 47 9 71 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000286 | 1 236 | 205 9 22 3 34 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000287 | 1 279 | 213 15 51 3 69 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000288 | 1 140 | 120 4 16 1 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000289 | 1 200 | 176 7 17 6 30 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000290 | 1 193 | 151 15 27 0 42 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000291 | 1 171 | 153 7 11 4 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000292 | 1 222 | 174 15 33 5 53 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000293 | 1 121 | 102 6 13 6 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000294 | 1 232 | 177 14 41 5 60 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000295 | 1 139 | 116 7 16 6 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000296 | 1 166 | 129 11 26 4 41 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000297 | 1 236 | 188 15 33 5 53 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000298 | 1 152 | 134 10 8 11 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000299 | 1 289 | 226 11 52 7 70 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000300 | 1 272 | 203 21 48 4 73 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000301 | 1 219 | 186 16 17 8 41 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000302 | 1 138 | 111 14 13 9 36 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000303 | 1 230 | 194 11 25 11 47 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000304 | 1 248 | 209 11 28 6 45 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000305 | 1 241 | 217 6 18 2 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000306 | 1 178 | 141 12 25 5 42 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000307 | 1 233 | 194 15 24 3 42 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000308 | 1 138 | 123 5 10 5 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000309 | 1 223 | 192 18 13 8 39 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000310 | 1 184 | 137 8 39 2 49 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000311 | 1 200 | 172 7 21 3 31 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000312 | 1 239 | 188 18 33 7 58 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000313 | 1 203 | 177 14 12 14 40 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000314 | 1 160 | 137 9 14 10 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000315 | 1 141 | 119 11 11 5 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000316 | 1 219 | 197 1 21 2 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000317 | 1 233 | 191 18 24 13 55 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000318 | 1 183 | 153 13 17 6 36 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000319 | 1 205 | 175 13 17 8 38 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000320 | 1 194 | 169 7 18 3 28 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000321 | 1 229 | 202 10 17 3 30 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000322 | 1 207 | 168 13 26 1 40 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001588 | 1 21 | 15 3 3 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001589 | 1 21 | 17 3 1 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001590 | 1 28 | 23 3 2 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001591 | 1 28 | 20 5 3 3 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001592 | 1 22 | 17 1 4 9 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001593 | 1 13 | 10 0 3 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001594 | 1 25 | 22 2 1 3 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001595 | 1 18 | 15 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001596 | 1 16 | 11 3 2 3 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001597 | 1 30 | 25 3 2 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001598 | 1 19 | 14 2 3 6 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001599 | 1 15 | 15 0 0 3 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001600 | 1 23 | 21 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001601 | 1 15 | 13 1 1 3 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001602 | 1 17 | 12 5 0 4 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001603 | 1 21 | 19 0 2 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001604 | 1 14 | 9 4 1 4 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001605 | 1 20 | 12 5 3 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001606 | 1 27 | 17 5 5 4 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001607 | 1 28 | 26 1 1 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001608 | 1 18 | 14 3 1 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001609 | 1 15 | 6 6 3 2 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001610 | 1 23 | 16 4 3 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001611 | 1 19 | 17 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001612 | 1 22 | 16 3 3 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001613 | 1 18 | 16 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001614 | 1 29 | 22 3 4 8 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001615 | 1 19 | 14 1 4 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001616 | 1 27 | 20 4 3 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001617 | 1 16 | 14 1 1 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001618 | 1 21 | 15 5 1 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001619 | 1 25 | 19 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001620 | 1 23 | 19 2 2 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001621 | 1 24 | 20 2 2 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001622 | 1 22 | 17 1 4 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001623 | 1 15 | 10 5 0 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001624 | 1 24 | 21 1 2 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001625 | 1 25 | 21 2 2 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001626 | 1 24 | 18 5 1 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001627 | 1 20 | 14 4 2 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001628 | 1 17 | 13 3 1 4 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001629 | 1 18 | 17 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001630 | 1 15 | 12 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001631 | 1 24 | 16 5 3 3 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001632 | 1 18 | 16 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001633 | 1 3 | 3 0 0 2 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001634 | 1 23 | 19 3 1 6 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001635 | 1 30 | 27 1 2 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001636 | 1 17 | 14 2 1 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001637 | 1 26 | 25 1 0 2 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001638 | 1 25 | 20 3 2 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001639 | 1 17 | 13 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001640 | 1 5 | 2 3 0 4 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001641 | 1 16 | 11 4 1 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001642 | 1 18 | 14 2 2 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001643 | 1 14 | 10 3 1 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001644 | 1 30 | 26 2 2 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001645 | 1 14 | 11 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001646 | 1 27 | 21 3 3 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001647 | 1 23 | 18 2 3 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001648 | 1 37 | 26 8 3 2 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001649 | 1 24 | 21 1 2 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001650 | 1 23 | 17 2 4 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001651 | 1 22 | 15 5 2 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001652 | 1 16 | 14 1 1 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001653 | 1 18 | 13 1 4 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001654 | 1 18 | 12 2 4 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001655 | 1 17 | 14 1 2 4 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001656 | 1 17 | 14 1 2 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001657 | 1 28 | 20 7 1 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001658 | 1 18 | 14 1 3 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001659 | 1 19 | 11 4 4 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001660 | 1 18 | 17 1 0 2 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001661 | 1 18 | 11 3 4 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001662 | 1 22 | 17 2 3 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001663 | 1 17 | 11 2 4 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001664 | 1 24 | 20 3 1 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001665 | 1 30 | 27 2 1 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001666 | 1 21 | 14 4 3 6 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001667 | 1 24 | 16 6 2 7 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001668 | 1 21 | 16 3 2 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001669 | 1 17 | 16 0 1 3 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001670 | 1 24 | 21 2 1 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001671 | 1 24 | 19 2 3 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001672 | 1 27 | 19 4 4 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001673 | 1 8 | 8 0 0 7 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001674 | 1 36 | 24 8 4 4 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001675 | 1 4 | 4 0 0 0 0 0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001676 | 1 6 | 3 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001677 | 1 23 | 21 0 2 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001678 | 1 25 | 18 4 3 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001679 | 1 21 | 19 0 2 4 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001680 | 1 19 | 12 4 3 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001681 | 1 28 | 27 1 0 5 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001682 | 1 16 | 9 3 4 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001683 | 1 25 | 23 0 2 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001684 | 1 19 | 15 2 2 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001685 | 1 22 | 21 0 1 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001686 | 1 23 | 17 5 1 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001687 | 1 12 | 11 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001688 | 1 21 | 16 3 2 6 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001689 | 1 28 | 19 5 4 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001690 | 1 19 | 18 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001691 | 1 4 | 4 0 0 0 0 0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001692 | 1 22 | 14 5 3 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001693 | 1 23 | 14 4 5 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001694 | 1 22 | 20 1 1 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001695 | 1 8 | 5 2 1 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001696 | 1 12 | 11 1 0 3 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001697 | 1 17 | 12 4 1 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001698 | 1 13 | 12 1 0 2 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001699 | 1 26 | 18 5 3 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001700 | 1 35 | 28 4 3 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001701 | 1 16 | 9 4 3 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001702 | 1 23 | 16 6 1 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001703 | 1 12 | 9 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001704 | 1 17 | 8 5 4 2 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001705 | 1 19 | 18 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001706 | 1 22 | 16 6 0 3 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001707 | 1 27 | 25 2 0 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001708 | 1 16 | 10 3 3 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001709 | 1 19 | 14 3 2 4 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001710 | 1 18 | 17 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001711 | 1 15 | 14 0 1 3 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001712 | 1 22 | 18 1 3 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001713 | 1 20 | 15 3 2 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001714 | 1 17 | 14 1 2 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001715 | 1 21 | 14 5 2 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001716 | 1 19 | 17 0 2 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001717 | 1 20 | 17 2 1 3 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001718 | 1 20 | 16 3 1 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001719 | 1 24 | 20 2 2 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001720 | 1 22 | 18 2 2 4 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001721 | 1 24 | 14 6 4 2 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001722 | 1 8 | 7 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001723 | 1 22 | 12 5 5 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001724 | 1 16 | 11 3 2 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001725 | 1 7 | 6 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001726 | 1 22 | 20 1 1 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001727 | 1 17 | 14 3 0 3 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001728 | 1 21 | 21 0 0 2 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001729 | 1 19 | 17 1 1 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001730 | 1 22 | 15 4 3 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001731 | 1 15 | 11 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001732 | 1 23 | 19 2 2 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001733 | 1 27 | 18 7 2 4 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001734 | 1 18 | 14 4 0 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001735 | 1 25 | 19 4 2 3 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001736 | 1 19 | 13 4 2 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001737 | 1 13 | 9 3 1 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001738 | 1 27 | 20 3 4 5 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001739 | 1 18 | 14 2 2 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001740 | 1 25 | 20 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001741 | 1 5 | 5 0 0 5 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001742 | 1 31 | 23 6 2 3 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001743 | 1 25 | 15 6 4 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001744 | 1 21 | 16 4 1 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001745 | 1 24 | 16 4 4 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001746 | 1 26 | 17 5 4 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001747 | 1 20 | 15 4 1 3 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001748 | 1 24 | 18 5 1 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001749 | 1 23 | 18 3 2 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001750 | 1 22 | 20 2 0 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001751 | 1 23 | 13 4 6 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001752 | 1 31 | 25 3 3 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001753 | 1 25 | 21 2 2 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001754 | 1 21 | 13 5 3 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001755 | 1 22 | 12 3 7 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001756 | 1 22 | 20 0 2 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001757 | 1 8 | 7 0 1 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001758 | 1 20 | 14 3 3 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001759 | 1 33 | 28 3 2 3 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001760 | 1 20 | 17 1 2 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001761 | 1 17 | 14 0 3 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001762 | 1 9 | 5 3 1 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001763 | 1 21 | 16 3 2 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001764 | 1 31 | 26 4 1 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001765 | 1 19 | 18 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001766 | 1 12 | 10 1 1 3 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001767 | 1 9 | 5 2 2 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001768 | 1 39 | 30 5 4 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001769 | 1 21 | 15 4 2 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001770 | 1 22 | 14 6 2 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001771 | 1 26 | 20 1 5 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001772 | 1 21 | 14 3 4 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001773 | 1 17 | 13 2 2 4 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001774 | 1 18 | 14 2 2 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001775 | 1 21 | 16 3 2 3 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001776 | 1 19 | 16 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001777 | 1 27 | 22 4 1 4 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001778 | 1 8 | 6 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001779 | 1 21 | 18 1 2 3 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001780 | 1 30 | 26 3 1 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001781 | 1 15 | 12 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001782 | 1 17 | 15 0 2 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001783 | 1 23 | 17 5 1 3 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001784 | 1 19 | 16 1 2 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001785 | 1 19 | 15 1 3 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001786 | 1 28 | 25 0 3 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001787 | 1 5 | 4 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001788 | 1 26 | 21 3 2 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001789 | 1 20 | 18 0 2 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001790 | 1 20 | 15 3 2 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001791 | 1 18 | 12 5 1 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001792 | 1 17 | 11 2 4 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001793 | 1 26 | 16 9 1 2 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001794 | 1 17 | 13 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001795 | 1 25 | 18 1 6 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001796 | 1 25 | 18 5 2 4 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001797 | 1 21 | 11 7 3 2 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001798 | 1 22 | 14 2 6 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001799 | 1 20 | 16 1 3 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001800 | 1 18 | 14 4 0 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001801 | 1 21 | 19 1 1 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001802 | 1 23 | 18 2 3 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001803 | 1 28 | 26 1 1 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001804 | 1 24 | 22 1 1 4 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001805 | 1 19 | 14 1 4 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001806 | 1 28 | 18 6 4 4 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001807 | 1 26 | 24 1 1 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001808 | 1 16 | 12 2 2 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001809 | 1 21 | 12 5 4 3 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001810 | 1 20 | 17 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001811 | 1 20 | 19 1 0 3 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001812 | 1 14 | 7 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001813 | 1 23 | 21 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001814 | 1 16 | 11 3 2 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001815 | 1 25 | 22 2 1 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001816 | 1 39 | 30 4 5 2 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001817 | 1 21 | 19 2 0 5 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001818 | 1 22 | 20 1 1 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001819 | 1 25 | 16 6 3 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001820 | 1 29 | 24 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001821 | 1 18 | 17 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001822 | 1 21 | 16 3 2 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001823 | 1 37 | 29 4 4 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001824 | 1 16 | 14 1 1 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001744 | 1 31 | 27 1 3 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001745 | 1 18 | 9 5 4 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001746 | 1 54 | 47 2 5 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001747 | 1 62 | 47 6 9 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001748 | 1 21 | 15 1 5 3 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001749 | 1 15 | 11 2 2 4 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001750 | 1 35 | 25 0 10 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001751 | 1 22 | 18 1 3 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001752 | 1 49 | 41 6 2 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001753 | 1 40 | 29 4 7 1 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001754 | 1 30 | 24 2 4 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001755 | 1 22 | 16 2 4 3 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001756 | 1 26 | 23 0 3 9 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001757 | 1 72 | 50 11 11 4 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001758 | 1 17 | 11 2 4 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001759 | 1 43 | 30 8 5 1 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001760 | 1 17 | 14 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001761 | 1 50 | 42 3 5 3 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001762 | 1 27 | 25 0 2 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001763 | 1 96 | 77 6 13 0 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001764 | 1 42 | 27 3 12 2 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001765 | 1 47 | 37 4 6 2 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001766 | 1 21 | 12 0 9 4 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001767 | 1 55 | 39 5 11 1 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001768 | 1 36 | 30 3 3 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001769 | 1 42 | 36 1 5 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001770 | 1 28 | 25 2 1 11 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001771 | 1 25 | 19 2 4 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001772 | 1 38 | 32 3 3 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001773 | 1 18 | 16 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001774 | 1 21 | 16 2 3 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001775 | 1 63 | 46 7 10 0 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001776 | 1 77 | 59 11 7 4 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001777 | 1 77 | 66 2 9 7 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001778 | 1 30 | 27 0 3 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001779 | 1 44 | 27 2 15 0 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001780 | 1 52 | 43 5 4 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001781 | 1 18 | 12 4 2 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001782 | 1 87 | 68 7 12 3 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001783 | 1 23 | 15 1 7 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001784 | 1 23 | 12 3 8 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001785 | 1 31 | 23 4 4 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001786 | 1 120 | 90 11 19 4 34 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001787 | 1 31 | 23 3 5 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001788 | 1 38 | 33 0 5 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001789 | 1 24 | 18 4 2 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001790 | 1 73 | 59 5 9 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001791 | 1 39 | 29 4 6 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001792 | 1 18 | 15 1 2 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001793 | 1 30 | 26 2 2 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001794 | 1 58 | 43 5 10 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001795 | 1 23 | 18 1 4 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001796 | 1 23 | 20 1 2 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001797 | 1 49 | 37 1 11 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001798 | 1 17 | 9 2 6 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001799 | 1 31 | 23 2 6 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001800 | 1 22 | 19 1 2 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001801 | 1 54 | 49 1 4 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001802 | 1 29 | 21 1 7 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001803 | 1 45 | 35 2 8 4 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001804 | 1 36 | 29 1 6 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001805 | 1 36 | 26 2 8 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001806 | 1 19 | 17 1 1 5 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001807 | 1 25 | 16 2 7 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001808 | 1 22 | 18 1 3 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001809 | 1 21 | 16 1 4 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001810 | 1 25 | 17 5 3 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001811 | 1 50 | 37 5 8 1 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001812 | 1 24 | 20 1 3 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001813 | 1 32 | 28 0 4 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001814 | 1 31 | 23 4 4 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001815 | 1 32 | 25 0 7 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001816 | 1 70 | 54 2 14 0 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001817 | 1 52 | 35 3 14 0 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001818 | 1 19 | 15 1 3 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001819 | 1 33 | 25 6 2 6 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001820 | 1 19 | 17 0 2 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001821 | 1 24 | 15 7 2 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001822 | 1 19 | 17 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001823 | 1 82 | 72 2 8 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001824 | 1 44 | 36 3 5 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001825 | 1 45 | 40 1 4 5 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001826 | 1 35 | 24 1 10 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001827 | 1 42 | 36 1 5 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001828 | 1 18 | 12 2 4 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001829 | 1 43 | 24 2 17 0 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001830 | 1 51 | 39 2 10 1 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001831 | 1 23 | 20 1 2 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001832 | 1 27 | 17 4 6 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001833 | 1 18 | 15 0 3 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001834 | 1 45 | 36 4 5 2 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001835 | 1 17 | 13 1 3 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001836 | 1 21 | 16 0 5 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001837 | 1 30 | 18 6 6 3 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001838 | 1 37 | 30 1 6 3 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001839 | 1 48 | 36 0 12 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001840 | 1 25 | 16 3 6 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001841 | 1 28 | 20 1 7 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001842 | 1 42 | 35 2 5 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001843 | 1 47 | 38 3 6 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001844 | 1 31 | 28 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001845 | 1 20 | 13 3 4 3 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001846 | 1 96 | 82 4 10 1 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001847 | 1 18 | 16 0 2 0 2 1 | 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+|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001872 | 1 24 | 19 4 1 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001873 | 1 11 | 6 3 2 5 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001874 | 1 35 | 20 7 8 1 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001875 | 1 52 | 31 11 10 4 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001876 | 1 27 | 18 1 8 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001877 | 1 36 | 31 4 1 2 7 1 | 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+|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001884 | 1 32 | 27 1 4 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001885 | 1 34 | 22 3 9 1 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001886 | 1 32 | 24 4 4 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001887 | 1 33 | 24 1 8 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001888 | 1 43 | 31 3 9 1 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001889 | 1 15 | 11 1 3 4 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001890 | 1 12 | 12 0 0 4 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001891 | 1 36 | 28 3 5 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001892 | 1 36 | 24 3 9 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001893 | 1 23 | 14 2 7 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001894 | 1 40 | 28 7 5 3 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001895 | 1 37 | 32 2 3 2 7 1 | 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+|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001902 | 1 42 | 35 1 6 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001903 | 1 22 | 16 2 4 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001904 | 1 37 | 27 0 10 6 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001905 | 1 44 | 32 2 10 1 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001906 | 1 81 | 69 1 11 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001907 | 1 20 | 14 1 5 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001908 | 1 29 | 21 2 6 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001909 | 1 22 | 17 1 4 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001910 | 1 43 | 38 2 3 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001911 | 1 32 | 25 3 4 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001912 | 1 37 | 32 2 3 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001913 | 1 24 | 19 0 5 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001914 | 1 58 | 44 5 9 2 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001915 | 1 47 | 25 6 16 0 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001916 | 1 25 | 18 2 5 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001917 | 1 21 | 14 3 4 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001918 | 1 61 | 49 6 6 2 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001919 | 1 58 | 49 2 7 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001920 | 1 45 | 41 0 4 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001921 | 1 18 | 14 0 4 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001922 | 1 34 | 29 1 4 3 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001923 | 1 129 | 109 4 16 5 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001924 | 1 76 | 59 6 11 2 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001925 | 1 48 | 39 4 5 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001926 | 1 27 | 19 1 7 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001927 | 1 23 | 17 3 3 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001928 | 1 19 | 15 1 3 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001929 | 1 26 | 22 0 4 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001930 | 1 32 | 22 5 5 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001931 | 1 14 | 13 0 1 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001932 | 1 45 | 36 6 3 6 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001933 | 1 25 | 20 2 3 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001934 | 1 38 | 32 2 4 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001935 | 1 26 | 22 2 2 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001936 | 1 44 | 35 2 7 4 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001937 | 1 15 | 8 6 1 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001938 | 1 72 | 55 6 11 9 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001939 | 1 32 | 26 2 4 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001940 | 1 45 | 33 3 9 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001941 | 1 36 | 19 5 12 6 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001942 | 1 19 | 15 1 3 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001943 | 1 44 | 30 1 13 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001944 | 1 43 | 34 5 4 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001945 | 1 37 | 31 1 5 5 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001946 | 1 106 | 92 3 11 2 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001947 | 1 23 | 18 0 5 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001948 | 1 23 | 15 4 4 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001949 | 1 38 | 27 5 6 1 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001950 | 1 15 | 10 4 1 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001951 | 1 39 | 34 2 3 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001952 | 1 67 | 54 5 8 1 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001953 | 1 23 | 14 3 6 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001954 | 1 24 | 19 1 4 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001955 | 1 19 | 15 0 4 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001956 | 1 34 | 28 3 3 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001957 | 1 73 | 52 4 17 0 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001958 | 1 24 | 17 3 4 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001959 | 1 41 | 35 3 3 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001960 | 1 51 | 36 5 10 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001961 | 1 45 | 36 3 6 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001962 | 1 14 | 13 1 0 2 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001963 | 1 35 | 30 4 1 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001964 | 1 15 | 9 5 1 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001965 | 1 24 | 18 3 3 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001966 | 1 38 | 31 3 4 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001967 | 1 60 | 54 1 5 3 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001968 | 1 63 | 44 5 14 0 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001969 | 1 64 | 43 9 12 1 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001970 | 1 42 | 33 4 5 2 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001971 | 1 13 | 10 1 2 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001972 | 1 61 | 46 1 14 1 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001973 | 1 50 | 44 0 6 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001974 | 1 18 | 14 1 3 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001975 | 1 43 | 34 5 4 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001976 | 1 27 | 13 7 7 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001977 | 1 22 | 20 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001978 | 1 15 | 11 0 4 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001979 | 1 68 | 47 7 14 0 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001980 | 1 51 | 32 2 17 0 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001981 | 1 42 | 34 3 5 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001982 | 1 16 | 13 3 0 9 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001983 | 1 77 | 60 9 8 3 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001984 | 1 31 | 24 3 4 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001985 | 1 33 | 26 2 5 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001986 | 1 77 | 63 5 9 1 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001987 | 1 15 | 10 2 3 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001988 | 1 25 | 14 3 8 1 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001989 | 1 51 | 34 1 16 6 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001990 | 1 38 | 31 2 5 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001991 | 1 35 | 25 5 5 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001992 | 1 15 | 11 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001993 | 1 44 | 26 9 9 3 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001994 | 1 29 | 27 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001995 | 1 21 | 16 1 4 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001996 | 1 31 | 19 3 9 1 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001997 | 1 34 | 22 2 10 1 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001998 | 1 53 | 43 6 4 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001999 | 1 20 | 8 4 8 2 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002000 | 1 64 | 55 3 6 15 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002001 | 1 21 | 14 1 6 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002002 | 1 17 | 11 3 3 3 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002003 | 1 23 | 17 3 3 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002004 | 1 23 | 19 0 4 5 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002005 | 1 25 | 20 1 4 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000874 | 1 57 | 48 4 5 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000875 | 1 20 | 19 0 1 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000876 | 1 46 | 38 3 5 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000877 | 1 50 | 42 4 4 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000878 | 1 24 | 22 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000879 | 1 49 | 44 4 1 4 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000880 | 1 45 | 39 2 4 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000883 | 1 26 | 18 6 2 6 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000884 | 1 60 | 58 1 1 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000885 | 1 36 | 28 2 6 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000886 | 1 62 | 49 8 5 6 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000887 | 1 65 | 50 3 12 2 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000888 | 1 40 | 37 0 3 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000889 | 1 36 | 32 1 3 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000890 | 1 23 | 19 0 4 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000891 | 1 81 | 74 1 6 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000892 | 1 21 | 20 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000893 | 1 26 | 21 3 2 6 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000894 | 1 91 | 84 1 6 3 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000895 | 1 57 | 46 0 11 2 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000896 | 1 27 | 25 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000897 | 1 30 | 29 0 1 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000898 | 1 37 | 35 0 2 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000899 | 1 22 | 21 0 1 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000900 | 1 61 | 44 4 13 2 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000901 | 1 31 | 24 3 4 21 28 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000902 | 1 24 | 17 7 0 6 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000903 | 1 45 | 40 3 2 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000904 | 1 32 | 24 4 4 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000905 | 1 48 | 32 0 16 0 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000906 | 1 50 | 41 1 8 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000907 | 1 60 | 58 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000908 | 1 42 | 36 3 3 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000909 | 1 57 | 46 1 10 1 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000910 | 1 54 | 48 1 5 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000911 | 1 26 | 24 0 2 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000912 | 1 33 | 33 0 0 2 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000913 | 1 68 | 58 4 6 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000914 | 1 47 | 39 0 8 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000915 | 1 28 | 25 0 3 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000916 | 1 35 | 35 0 0 1 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000917 | 1 40 | 38 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000918 | 1 67 | 56 5 6 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000920 | 1 35 | 30 2 3 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000921 | 1 49 | 42 1 6 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000922 | 1 57 | 52 3 2 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000923 | 1 46 | 41 0 5 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000924 | 1 16 | 14 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000925 | 1 65 | 52 11 2 7 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000927 | 1 49 | 47 0 2 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000928 | 1 48 | 41 1 6 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000929 | 1 19 | 16 0 3 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000930 | 1 58 | 57 0 1 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000931 | 1 57 | 50 4 3 5 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000932 | 1 51 | 41 4 6 4 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000933 | 1 31 | 16 8 7 2 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000934 | 1 38 | 33 1 4 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000935 | 1 38 | 30 4 4 3 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000938 | 1 26 | 24 1 1 8 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000939 | 1 74 | 58 8 8 4 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000940 | 1 55 | 40 2 13 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000942 | 1 51 | 42 4 5 3 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000943 | 1 39 | 33 2 4 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000944 | 1 102 | 81 6 15 6 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000945 | 1 57 | 49 5 3 6 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000946 | 1 32 | 20 5 7 2 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000947 | 1 50 | 43 1 6 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000948 | 1 32 | 23 4 5 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000949 | 1 68 | 61 6 1 3 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000950 | 1 47 | 36 0 11 2 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000951 | 1 23 | 20 1 2 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000952 | 1 22 | 19 1 2 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000953 | 1 48 | 40 3 5 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000954 | 1 52 | 47 3 2 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000955 | 1 76 | 63 5 8 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000956 | 1 52 | 40 9 3 3 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000957 | 1 23 | 22 0 1 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000958 | 1 48 | 44 2 2 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000959 | 1 118 | 99 11 8 2 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000960 | 1 87 | 78 4 5 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000961 | 1 74 | 66 3 5 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000962 | 1 23 | 22 1 0 1 2 1 | 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+|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000977 | 1 28 | 24 2 2 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000978 | 1 30 | 30 0 0 1 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000979 | 1 46 | 40 2 4 3 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000980 | 1 52 | 40 3 9 2 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000981 | 1 56 | 49 2 5 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000982 | 1 57 | 44 5 8 1 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000983 | 1 55 | 48 5 2 5 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000984 | 1 23 | 19 0 4 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000985 | 1 43 | 40 1 2 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000986 | 1 85 | 76 1 8 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000987 | 1 82 | 62 13 7 12 32 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000988 | 1 39 | 35 1 3 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000989 | 1 97 | 86 1 10 2 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000990 | 1 44 | 41 1 2 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000991 | 1 45 | 40 2 3 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000992 | 1 38 | 31 2 5 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000993 | 1 41 | 25 6 10 1 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000994 | 1 54 | 41 3 10 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000995 | 1 91 | 73 1 17 2 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000996 | 1 47 | 43 1 3 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000997 | 1 69 | 58 1 10 2 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000998 | 1 39 | 33 3 3 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000999 | 1 25 | 19 3 3 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001000 | 1 68 | 61 4 3 6 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001001 | 1 50 | 45 2 3 3 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001002 | 1 28 | 24 2 2 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001003 | 1 54 | 49 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001004 | 1 53 | 46 3 4 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000494 | 1 81 | 68 2 11 13 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000495 | 1 169 | 132 17 20 5 42 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000496 | 1 147 | 121 12 14 12 38 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000497 | 1 91 | 76 4 11 7 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000498 | 1 156 | 124 12 20 8 40 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000499 | 1 377 | 306 25 46 20 91 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000500 | 1 123 | 107 4 12 5 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000501 | 1 22 | 15 5 2 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000502 | 1 304 | 243 16 45 14 75 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000503 | 1 170 | 132 10 28 6 44 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000504 | 1 203 | 157 11 35 1 47 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000505 | 1 285 | 221 27 37 15 79 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000506 | 1 168 | 114 13 41 15 69 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000507 | 1 22 | 16 0 6 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000508 | 1 42 | 31 3 8 5 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000509 | 1 284 | 229 26 29 25 80 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000510 | 1 261 | 192 22 47 14 83 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000511 | 1 256 | 200 12 44 12 68 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000512 | 1 48 | 43 0 5 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000513 | 1 74 | 62 4 8 5 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000514 | 1 58 | 43 9 6 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000515 | 1 106 | 95 7 4 5 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000516 | 1 88 | 60 8 20 6 34 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000517 | 1 168 | 153 6 9 5 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000518 | 1 99 | 82 6 11 2 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000519 | 1 151 | 121 7 23 14 44 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000520 | 1 179 | 146 22 11 15 48 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000521 | 1 248 | 209 16 23 15 54 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000522 | 1 68 | 61 2 5 4 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000523 | 1 119 | 99 4 16 6 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000524 | 1 187 | 155 13 19 10 42 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000525 | 1 56 | 52 2 2 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000526 | 1 79 | 69 2 8 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000527 | 1 247 | 179 41 27 25 93 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000528 | 1 105 | 84 9 12 10 31 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000529 | 1 91 | 70 2 19 2 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000530 | 1 156 | 123 16 17 26 59 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000531 | 1 42 | 29 5 8 3 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000532 | 1 26 | 24 1 1 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000533 | 1 89 | 72 3 14 8 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000534 | 1 199 | 168 12 19 7 38 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000535 | 1 185 | 157 11 17 6 34 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000536 | 1 166 | 131 10 25 1 36 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000537 | 1 131 | 88 16 27 6 49 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000538 | 1 119 | 110 1 8 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000539 | 1 118 | 85 11 22 6 39 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000540 | 1 115 | 90 17 8 15 40 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000541 | 1 76 | 65 5 6 8 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000542 | 1 53 | 45 5 3 4 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000543 | 1 101 | 69 11 21 7 39 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000544 | 1 133 | 106 14 13 12 39 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000545 | 1 40 | 35 3 2 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000546 | 1 30 | 17 3 10 2 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000547 | 1 127 | 98 11 18 6 35 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000548 | 1 204 | 170 13 21 24 58 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000549 | 1 127 | 97 7 23 3 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000550 | 1 95 | 75 10 10 12 32 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000551 | 1 63 | 57 3 3 4 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000552 | 1 194 | 154 18 22 18 58 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000553 | 1 138 | 121 7 10 1 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000554 | 1 196 | 172 8 16 5 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000555 | 1 81 | 71 5 5 11 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000556 | 1 111 | 92 10 9 3 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000557 | 1 45 | 42 2 1 1 4 1 | +|=================================================================================================================| +| Sum | 1092 67334 | 54584 4728 8022 3173 15923 1088 | +|=================================================================================================================| +| Mean | 1.1 66.3 | 53.7 4.7 7.9 3.1 15.7 1.1 | +| S.D. | 2.4 267.1 | 227.5 9.8 30.8 6.7 45.8 2.4 | +| Median | 1.0 40.0 | 32.0 3.0 4.0 2.0 10.0 1.0 | +`-----------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_cer/hyp.trn + +Speakers: + 0: lad_eng_000254 + 1: lad_eng_000255 + 2: lad_eng_000256 + 3: lad_eng_000257 + 4: lad_eng_000258 + 5: lad_eng_000259 + 6: lad_eng_000260 + 7: lad_eng_000261 + 8: lad_eng_000262 + 9: lad_eng_000263 + 10: lad_eng_000264 + 11: lad_eng_000265 + 12: lad_eng_000266 + 13: lad_eng_000267 + 14: lad_eng_000268 + 15: lad_eng_000269 + 16: lad_eng_000270 + 17: lad_eng_000271 + 18: lad_eng_000272 + 19: lad_eng_000273 + 20: lad_eng_000274 + 21: lad_eng_000275 + 22: lad_eng_000276 + 23: lad_eng_000277 + 24: lad_eng_000278 + 25: lad_eng_000279 + 26: lad_eng_000280 + 27: lad_eng_000281 + 28: lad_eng_000282 + 29: lad_eng_000283 + 30: lad_eng_000284 + 31: lad_eng_000285 + 32: lad_eng_000286 + 33: lad_eng_000287 + 34: lad_eng_000288 + 35: lad_eng_000289 + 36: lad_eng_000290 + 37: lad_eng_000291 + 38: lad_eng_000292 + 39: lad_eng_000293 + 40: lad_eng_000294 + 41: lad_eng_000295 + 42: lad_eng_000296 + 43: lad_eng_000297 + 44: lad_eng_000298 + 45: lad_eng_000299 + 46: lad_eng_000300 + 47: lad_eng_000301 + 48: lad_eng_000302 + 49: lad_eng_000303 + 50: lad_eng_000304 + 51: lad_eng_000305 + 52: lad_eng_000306 + 53: lad_eng_000307 + 54: lad_eng_000308 + 55: lad_eng_000309 + 56: lad_eng_000310 + 57: lad_eng_000311 + 58: lad_eng_000312 + 59: lad_eng_000313 + 60: lad_eng_000314 + 61: lad_eng_000315 + 62: lad_eng_000316 + 63: lad_eng_000317 + 64: lad_eng_000318 + 65: lad_eng_000319 + 66: lad_eng_000320 + 67: lad_eng_000321 + 68: lad_eng_000322 + 69: lad_eng_000323 + 70: lad_eng_000324 + 71: lad_eng_000325 + 72: lad_eng_000326 + 73: lad_eng_000327 + 74: lad_eng_000328 + 75: lad_eng_000329 + 76: lad_eng_000330 + 77: lad_eng_000331 + 78: lad_eng_000332 + 79: lad_eng_000333 + 80: lad_eng_000334 + 81: lad_eng_000335 + 82: lad_eng_000336 + 83: lad_eng_000337 + 84: lad_eng_000338 + 85: lad_eng_000339 + 86: lad_eng_000340 + 87: lad_eng_000341 + 88: lad_eng_000342 + 89: lad_eng_000343 + 90: lad_eng_000344 + 91: lad_eng_000345 + 92: lad_eng_000346 + 93: lad_eng_000347 + 94: lad_eng_000348 + 95: lad_eng_000349 + 96: lad_eng_000350 + 97: lad_eng_000351 + 98: lad_eng_000352 + 99: lad_eng_000353 + 100: lad_eng_000354 + 101: lad_eng_000355 + 102: lad_eng_000356 + 103: lad_eng_000357 + 104: lad_eng_000358 + 105: lad_eng_000359 + 106: lad_eng_000360 + 107: lad_eng_000361 + 108: lad_eng_000362 + 109: lad_eng_000363 + 110: lad_eng_000364 + 111: lad_eng_000365 + 112: lad_eng_000366 + 113: lad_eng_000367 + 114: lad_eng_000368 + 115: lad_eng_000369 + 116: lad_eng_000370 + 117: lad_eng_000371 + 118: lad_eng_000372 + 119: lad_eng_000373 + 120: lad_eng_000374 + 121: lad_eng_000375 + 122: lad_eng_000376 + 123: m + 124: cv_eng_000707 + 125: cv_eng_000708 + 126: cv_eng_000709 + 127: cv_eng_000710 + 128: cv_eng_000711 + 129: cv_eng_000712 + 130: cv_eng_000713 + 131: cv_eng_000714 + 132: cv_eng_000715 + 133: cv_eng_000716 + 134: cv_eng_000717 + 135: cv_eng_000718 + 136: cv_eng_000719 + 137: cv_eng_000720 + 138: cv_eng_000721 + 139: cv_eng_000722 + 140: cv_eng_000723 + 141: cv_eng_000724 + 142: cv_eng_000725 + 143: cv_eng_000726 + 144: cv_eng_000727 + 145: cv_eng_000728 + 146: cv_eng_000729 + 147: cv_eng_000730 + 148: cv_eng_000731 + 149: cv_eng_000732 + 150: cv_eng_000733 + 151: cv_eng_000734 + 152: cv_eng_000735 + 153: cv_eng_000736 + 154: cv_eng_000737 + 155: cv_eng_000738 + 156: cv_eng_000739 + 157: cv_eng_000740 + 158: cv_eng_000741 + 159: cv_eng_000742 + 160: cv_eng_000743 + 161: cv_eng_000744 + 162: cv_eng_000745 + 163: cv_eng_000746 + 164: cv_eng_000747 + 165: cv_eng_000748 + 166: cv_eng_000749 + 167: cv_eng_000750 + 168: cv_eng_000751 + 169: cv_eng_000752 + 170: cv_eng_000753 + 171: cv_eng_000754 + 172: cv_eng_000755 + 173: cv_eng_000756 + 174: cv_eng_000757 + 175: cv_eng_000758 + 176: cv_eng_000759 + 177: cv_eng_000760 + 178: cv_eng_000761 + 179: cv_eng_000762 + 180: cv_eng_000763 + 181: cv_eng_000764 + 182: cv_eng_000765 + 183: cv_eng_000766 + 184: cv_eng_000767 + 185: cv_eng_000768 + 186: cv_eng_000769 + 187: cv_eng_000770 + 188: cv_eng_000771 + 189: cv_eng_000772 + 190: cv_eng_000773 + 191: cv_eng_000774 + 192: cv_eng_000775 + 193: cv_eng_000776 + 194: cv_eng_000777 + 195: cv_eng_000778 + 196: cv_eng_000779 + 197: cv_eng_000780 + 198: cv_eng_000781 + 199: cv_eng_000782 + 200: cv_eng_000783 + 201: cv_eng_000784 + 202: cv_eng_000785 + 203: cv_eng_000786 + 204: cv_eng_000787 + 205: cv_eng_000788 + 206: cv_eng_000789 + 207: cv_eng_000790 + 208: cv_eng_000791 + 209: cv_eng_000792 + 210: cv_eng_000793 + 211: cv_eng_000794 + 212: cv_eng_000795 + 213: cv_eng_000796 + 214: cv_eng_000797 + 215: cv_eng_000798 + 216: cv_eng_000799 + 217: cv_eng_000800 + 218: cv_eng_000801 + 219: cv_eng_000802 + 220: cv_eng_000803 + 221: cv_eng_000804 + 222: cv_eng_000805 + 223: cv_eng_000806 + 224: cv_eng_000807 + 225: cv_eng_000808 + 226: cv_eng_000809 + 227: fleurs_eng_000413 + 228: fleurs_eng_000414 + 229: fleurs_eng_000415 + 230: fleurs_eng_000416 + 231: fleurs_eng_000417 + 232: fleurs_eng_000418 + 233: fleurs_eng_000419 + 234: fleurs_eng_000420 + 235: fleurs_eng_000421 + 236: fleurs_eng_000422 + 237: fleurs_eng_000423 + 238: fleurs_eng_000424 + 239: fleurs_eng_000425 + 240: fleurs_eng_000426 + 241: fleurs_eng_000427 + 242: fleurs_eng_000428 + 243: fleurs_eng_000429 + 244: fleurs_eng_000430 + 245: fleurs_eng_000431 + 246: fleurs_eng_000432 + 247: fleurs_eng_000433 + 248: fleurs_eng_000434 + 249: fleurs_eng_000435 + 250: fleurs_eng_000436 + 251: fleurs_eng_000437 + 252: fleurs_eng_000438 + 253: fleurs_eng_000439 + 254: fleurs_eng_000440 + 255: fleurs_eng_000441 + 256: fleurs_eng_000442 + 257: fleurs_eng_000443 + 258: fleurs_eng_000444 + 259: fleurs_eng_000445 + 260: fleurs_eng_000446 + 261: fleurs_eng_000447 + 262: fleurs_eng_000448 + 263: fleurs_eng_000449 + 264: fleurs_eng_000450 + 265: fleurs_eng_000451 + 266: fleurs_eng_000452 + 267: fleurs_eng_000453 + 268: fleurs_eng_000454 + 269: fleurs_eng_000455 + 270: fleurs_eng_000456 + 271: fleurs_eng_000457 + 272: fleurs_eng_000458 + 273: fleurs_eng_000459 + 274: fleurs_eng_000460 + 275: fleurs_eng_000461 + 276: fleurs_eng_000462 + 277: fleurs_eng_000463 + 278: fleurs_eng_000464 + 279: fleurs_eng_000465 + 280: fleurs_eng_000466 + 281: fleurs_eng_000467 + 282: fleurs_eng_000468 + 283: fleurs_eng_000469 + 284: fleurs_eng_000470 + 285: fleurs_eng_000471 + 286: fleurs_eng_000472 + 287: fleurs_eng_000473 + 288: fleurs_eng_000474 + 289: fleurs_eng_000475 + 290: fleurs_eng_000476 + 291: mls_eng_000283 + 292: mls_eng_000284 + 293: mls_eng_000285 + 294: mls_eng_000286 + 295: mls_eng_000287 + 296: mls_eng_000288 + 297: mls_eng_000289 + 298: mls_eng_000290 + 299: mls_eng_000291 + 300: mls_eng_000292 + 301: mls_eng_000293 + 302: mls_eng_000294 + 303: mls_eng_000295 + 304: mls_eng_000296 + 305: mls_eng_000297 + 306: mls_eng_000298 + 307: mls_eng_000299 + 308: mls_eng_000300 + 309: mls_eng_000301 + 310: mls_eng_000302 + 311: mls_eng_000303 + 312: mls_eng_000304 + 313: mls_eng_000305 + 314: mls_eng_000306 + 315: mls_eng_000307 + 316: mls_eng_000308 + 317: mls_eng_000309 + 318: mls_eng_000310 + 319: mls_eng_000311 + 320: mls_eng_000312 + 321: mls_eng_000313 + 322: mls_eng_000314 + 323: mls_eng_000315 + 324: mls_eng_000316 + 325: mls_eng_000317 + 326: mls_eng_000318 + 327: mls_eng_000319 + 328: mls_eng_000320 + 329: mls_eng_000321 + 330: mls_eng_000322 + 331: nchlt_eng_001588 + 332: nchlt_eng_001589 + 333: nchlt_eng_001590 + 334: nchlt_eng_001591 + 335: nchlt_eng_001592 + 336: nchlt_eng_001593 + 337: nchlt_eng_001594 + 338: nchlt_eng_001595 + 339: nchlt_eng_001596 + 340: nchlt_eng_001597 + 341: nchlt_eng_001598 + 342: nchlt_eng_001599 + 343: nchlt_eng_001600 + 344: nchlt_eng_001601 + 345: nchlt_eng_001602 + 346: nchlt_eng_001603 + 347: nchlt_eng_001604 + 348: nchlt_eng_001605 + 349: nchlt_eng_001606 + 350: nchlt_eng_001607 + 351: nchlt_eng_001608 + 352: nchlt_eng_001609 + 353: nchlt_eng_001610 + 354: nchlt_eng_001611 + 355: nchlt_eng_001612 + 356: nchlt_eng_001613 + 357: nchlt_eng_001614 + 358: nchlt_eng_001615 + 359: nchlt_eng_001616 + 360: nchlt_eng_001617 + 361: nchlt_eng_001618 + 362: nchlt_eng_001619 + 363: nchlt_eng_001620 + 364: nchlt_eng_001621 + 365: nchlt_eng_001622 + 366: nchlt_eng_001623 + 367: nchlt_eng_001624 + 368: nchlt_eng_001625 + 369: nchlt_eng_001626 + 370: nchlt_eng_001627 + 371: nchlt_eng_001628 + 372: nchlt_eng_001629 + 373: nchlt_eng_001630 + 374: nchlt_eng_001631 + 375: nchlt_eng_001632 + 376: nchlt_eng_001633 + 377: nchlt_eng_001634 + 378: nchlt_eng_001635 + 379: nchlt_eng_001636 + 380: nchlt_eng_001637 + 381: nchlt_eng_001638 + 382: nchlt_eng_001639 + 383: nchlt_eng_001640 + 384: nchlt_eng_001641 + 385: nchlt_eng_001642 + 386: nchlt_eng_001643 + 387: nchlt_eng_001644 + 388: nchlt_eng_001645 + 389: nchlt_eng_001646 + 390: nchlt_eng_001647 + 391: nchlt_eng_001648 + 392: nchlt_eng_001649 + 393: nchlt_eng_001650 + 394: nchlt_eng_001651 + 395: nchlt_eng_001652 + 396: nchlt_eng_001653 + 397: nchlt_eng_001654 + 398: nchlt_eng_001655 + 399: nchlt_eng_001656 + 400: nchlt_eng_001657 + 401: nchlt_eng_001658 + 402: nchlt_eng_001659 + 403: nchlt_eng_001660 + 404: nchlt_eng_001661 + 405: nchlt_eng_001662 + 406: nchlt_eng_001663 + 407: nchlt_eng_001664 + 408: nchlt_eng_001665 + 409: nchlt_eng_001666 + 410: nchlt_eng_001667 + 411: nchlt_eng_001668 + 412: nchlt_eng_001669 + 413: nchlt_eng_001670 + 414: nchlt_eng_001671 + 415: nchlt_eng_001672 + 416: nchlt_eng_001673 + 417: nchlt_eng_001674 + 418: nchlt_eng_001675 + 419: nchlt_eng_001676 + 420: nchlt_eng_001677 + 421: nchlt_eng_001678 + 422: nchlt_eng_001679 + 423: nchlt_eng_001680 + 424: nchlt_eng_001681 + 425: nchlt_eng_001682 + 426: nchlt_eng_001683 + 427: nchlt_eng_001684 + 428: nchlt_eng_001685 + 429: nchlt_eng_001686 + 430: nchlt_eng_001687 + 431: nchlt_eng_001688 + 432: nchlt_eng_001689 + 433: nchlt_eng_001690 + 434: nchlt_eng_001691 + 435: nchlt_eng_001692 + 436: nchlt_eng_001693 + 437: nchlt_eng_001694 + 438: nchlt_eng_001695 + 439: nchlt_eng_001696 + 440: nchlt_eng_001697 + 441: nchlt_eng_001698 + 442: nchlt_eng_001699 + 443: nchlt_eng_001700 + 444: nchlt_eng_001701 + 445: nchlt_eng_001702 + 446: nchlt_eng_001703 + 447: nchlt_eng_001704 + 448: nchlt_eng_001705 + 449: nchlt_eng_001706 + 450: nchlt_eng_001707 + 451: nchlt_eng_001708 + 452: nchlt_eng_001709 + 453: nchlt_eng_001710 + 454: nchlt_eng_001711 + 455: nchlt_eng_001712 + 456: nchlt_eng_001713 + 457: nchlt_eng_001714 + 458: nchlt_eng_001715 + 459: nchlt_eng_001716 + 460: nchlt_eng_001717 + 461: nchlt_eng_001718 + 462: nchlt_eng_001719 + 463: nchlt_eng_001720 + 464: nchlt_eng_001721 + 465: nchlt_eng_001722 + 466: nchlt_eng_001723 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+Scores: (#C #S #D #I) 47 4 9 2 +REF: a * l i b E r a l ******* c o n s e R v A t i v e h e w a s d e f e a t e d i n E I G H t e E n E I G H t y t W o +HYP: a Y l i b * r a l c o n s e * v I t i v e h e w a s d e f e a t e d i n * * * A t e I n * * * A t y t * o +Eval: I D I D S D D D S S D D D S D + +Speaker sentences 2: lad_eng_000256 #utts: 1 +id: (lad_eng_000256-lad_eng_000256) +Scores: (#C #S #D #I) 34 3 4 2 +REF: o n E r o A d l a Y E r c A n d r a W t w o r o A d s a t * o * n c e +HYP: o n * r o * d l a * A r c O n d r a R t w o r o * d s a t W o A n c e +Eval: D D D S S S D I I + +Speaker sentences 3: lad_eng_000257 #utts: 1 +id: (lad_eng_000257-lad_eng_000257) +Scores: (#C #S #D #I) 47 2 4 0 +REF: s o m e o f t h e c o U n t r I e s h A v e s u r v E y s f o r m U l t i p l e y e a r s +HYP: s o m e o f ******* t h e c o * n t r * e s h * v e s u r v A y s f o r m A l t i p l e y e a r s +Eval: D D D D S S + +Speaker sentences 4: lad_eng_000258 #utts: 1 +id: (lad_eng_000258-lad_eng_000258) +Scores: (#C #S #D #I) 46 2 3 0 +REF: b o t h o f t h e v E r s i o n s f e a T U r E t h e s o n g h a P p y h o l i d a y +HYP: b o t h o f t h e v * r s i o n s f e a C H r * t h e s o n g h a * p y h o l i d a y +Eval: D S S D D + +Speaker sentences 5: lad_eng_000259 #utts: 1 +id: (lad_eng_000259-lad_eng_000259) +Scores: (#C #S #D #I) 80 3 9 2 +REF: s h a k E S p E a r E m a n y r e f E r E n c e s a r e m a d e t o s C e n E s i n t E r ******* a c t i o n s o r c H a r A c t e R s f r o m v a r i o u s p l a y * s +HYP: s h a k * X p I a r * m a n y r e f * r * n c e s a r e m a d e t o s * e n * s i n t * r a c t i o n s o r c * a r I c t e * s f r o m v a r i o u s p l a y E s +Eval: D S S D D D D D D I D S D I + +Speaker sentences 6: lad_eng_000260 #utts: 1 +id: (lad_eng_000260-lad_eng_000260) +Scores: (#C #S #D #I) 69 2 8 1 +REF: i f o n l y t h e P r o g r a m c O u l d b r E a k * o u t j u s t a L i T t l e f r o m i t s t o O f A m i l i a r a P p r o A c h +HYP: i f o n l y t h e * r o g r a m c * u l d ******* b r * a k E o u t j u s t a * i * t l e f r o m i t s t o f O m i l i a r a * p r o * c h +Eval: D D D D I D D S S D D + +Speaker sentences 7: lad_eng_000261 #utts: 1 +id: (lad_eng_000261-lad_eng_000261) +Scores: (#C #S #D #I) 69 7 4 2 +REF: t h e * A l b U m w a s r e l e a s e d i n A U s t r a l i a * o n n i n E t e E n t h A U g U s t t w o t h o u s A n d a N d e l e v e n +HYP: t h e H E l b E m w a s r e l e a s e d i n * O s t r a l i a R o n n i n * t e I n t h O R g I s t t w o t h o u s * n d a * d e l e v e n +Eval: I S S D S I D S S S S D D + +Speaker sentences 8: lad_eng_000262 #utts: 1 +id: (lad_eng_000262-lad_eng_000262) +Scores: (#C #S #D #I) 39 3 2 1 +REF: h e n o w p l a Y S f o r a U s t r a l i A n c l U b * p e r t h g l o r y +HYP: h e n o w p l a C E f o r a * s t r a l i * n c l O b E p e r t h g l o r y +Eval: S S D D S I + +Speaker sentences 9: lad_eng_000263 #utts: 1 +id: (lad_eng_000263-lad_eng_000263) +Scores: (#C #S #D #I) 48 0 6 1 +REF: i t i s n o t K n o W n h o w m u c h i f * a n y o f h e R c l a I m s a r E t r u E +HYP: i t i s n o t * n o * n h o w m u c h i f E a n y o f h e * c l a * m s a r * t r u * +Eval: D D I D D D D + +Speaker sentences 10: lad_eng_000264 #utts: 1 +id: (lad_eng_000264-lad_eng_000264) +Scores: (#C #S #D #I) 91 2 14 2 +REF: a s m a L l b U s i n e s s o W n E r b r o a * d o p E r a t e d h i S w H e a t a N d s h E e p f a R m * f o r s i X t E e n y e a r s f r o M t h e a g e o f T w e n t y t W o +HYP: a s m a * l b I s i n e s s o * n * r b r o a R d o p * r a t e d h i * w * e a t a * d s h * e p ******* f a * m E f o r s i C t * e n y e a r s f r o * t h e a g e o f * w e n t y t * o +Eval: D S D D I D D D D D D D I S D D D D + +Speaker sentences 11: lad_eng_000265 #utts: 1 +id: (lad_eng_000265-lad_eng_000265) +Scores: (#C #S #D #I) 40 1 0 0 +REF: i n t h e n i n t h C e n t u r y h e w a s a n i r i s h p o e t +HYP: i n t h e n i n t h S e n t u r y h e w a s a n i r i s h p o e t +Eval: S + +Speaker sentences 12: lad_eng_000266 #utts: 1 +id: (lad_eng_000266-lad_eng_000266) +Scores: (#C #S #D #I) 25 0 0 0 +REF: t h e y a r e m a r k e d b y s t r o n g +HYP: t h e y a r e m a r k e d b y s t r o n g +Eval: + +Speaker sentences 13: lad_eng_000267 #utts: 1 +id: (lad_eng_000267-lad_eng_000267) +Scores: (#C #S #D #I) 21 3 2 1 +REF: t h e l A w i s t h e R E f o r E v a * l I d +HYP: t h e l O w i s t h e * f o r * v a O l E d +Eval: S D S D I S + +Speaker sentences 14: lad_eng_000268 #utts: 1 +id: (lad_eng_000268-lad_eng_000268) +Scores: (#C #S #D #I) 40 0 3 1 +REF: i n t h e E A r l y s t a g e s c a m e c l o s e t o u s a ******* s l E e p +HYP: i n t h e * * r l y s t a g e s c a m e c l o s e t o u s a s l * e p +Eval: D D I D + +Speaker sentences 15: lad_eng_000269 #utts: 1 +id: (lad_eng_000269-lad_eng_000269) +Scores: (#C #S #D #I) 40 4 9 0 +REF: r U N n i n g e v e r y t h I r t y m i n u t E S t h r o U G H O U t s e r v i C E t i m E s +HYP: r * O n i n g e v e r y t h * r t y m i n u t * * t h r o * * * A t s e r v i * S t i m * s +Eval: D S D D D D D D S S D S D + +Speaker sentences 16: lad_eng_000270 #utts: 1 +id: (lad_eng_000270-lad_eng_000270) +Scores: (#C #S #D #I) 61 2 3 3 +REF: a s a r e s * u l t w h e n t h e c o L l E g e r e ******* o p e n E d i t w a s a s a n a l l ******* m a l e c o L l E g e +HYP: a s a r e s I u l t w h e n t h e c o * l I g e r e o p e n * d i t w a s a s a n a l l m a l e c o * l I g e +Eval: I D S I D I D S + +Speaker sentences 17: lad_eng_000271 #utts: 1 +id: (lad_eng_000271-lad_eng_000271) +Scores: (#C #S #D #I) 81 3 9 2 +REF: t h e t i m e b e t w e e N t h e s E p o i n * t S i s v A r i a b l E a n d c a n O C c u r a n y ******* w h e r E f r o M a m i n U t E t o m u c h l o n g e r +HYP: t h e t i m e b e t w e e * t h e s * p o i n C t * i s v E r i a b l * a n d c a n ******* * A c u r a n y w h e r * f r o * a m i n I t * t o m u c h l o n g e r +Eval: D D I D S D D D S I D D S D + +Speaker sentences 18: lad_eng_000272 #utts: 1 +id: (lad_eng_000272-lad_eng_000272) +Scores: (#C #S #D #I) 85 3 3 2 +REF: w o * r k o n t h e e * e S s t a r t e d i n m a r c h t w o t h o u s A n d a n d s e v e n a t a c o s t o f f i v e m i L l i O n d o L l A r s +HYP: w o A r k o n t h e e A e E s t a r t e d i n m a r c h t w o t h o u s * n d a n d s e v e n a t a c o s t o f f i v e m i * l i A n d o * l E r s +Eval: I I S D D S D S + +Speaker sentences 19: lad_eng_000273 #utts: 1 +id: (lad_eng_000273-lad_eng_000273) +Scores: (#C #S #D #I) 102 4 8 2 +REF: h o w e v e r t h e r E w a s s o m e d i S a g r E e m e n t o v E R t h E e n d i n g t h e m e w h i c h o * ******* m o r I a n d y o S h i m o r I d i s c u S s E d a t l e n g t h o v e r e m a I l +HYP: h o w e v e r t h e r * w a s s o m e d i a g r * e m e n t o v * * t h * e n d i n g t h e m e w h i c h o R m o r Y a n d y o * h i m o r Y d i s c u * s T d a t l e n g t h o v e r e m a * l +Eval: D S D D D D I I S D S D S D + +Speaker sentences 20: lad_eng_000274 #utts: 1 +id: (lad_eng_000274-lad_eng_000274) +Scores: (#C #S #D #I) 24 1 1 0 +REF: t h e c o U p l e h a d n o c h i l d r E n +HYP: t h e c o * p l e h a d n o c h i l d r A n +Eval: D S + +Speaker sentences 21: lad_eng_000275 #utts: 1 +id: (lad_eng_000275-lad_eng_000275) +Scores: (#C #S #D #I) 68 3 11 1 +REF: t h e O F f I C i a l s i n g l E o F t h a t d e b u T a l ******* b U m p a r i s c A L l i n g h a d a n e l a b O r A t E m u s i c v i d E o +HYP: t h e * * f * * i a l s i n g l * o * t h a t d e b u * a l b H m p a r i s c * O l i n g h a d a n e l a b * r * t * m u s i c v i d I o +Eval: D D D D D D D I S D S D D D S + +Speaker sentences 22: lad_eng_000276 #utts: 1 +id: (lad_eng_000276-lad_eng_000276) +Scores: (#C #S #D #I) 83 4 7 1 +REF: t h e s e r i E s e n d e d o n s i x t h A U g U s t t W o t h o u s A n d a n d f o U r l a s t i n g f O r a t o * t A L o f s e v e n t y o n E d a y s +HYP: t h e s e r i * s e n d e d o n s i x t h O R g E s t t * o t h o u s * n d a n d f o * r l a s t i n g f * r a t o U t * E o f s e v e n t y o n * d a y s +Eval: D S S S D D D D I D S D + +Speaker sentences 23: lad_eng_000277 #utts: 1 +id: (lad_eng_000277-lad_eng_000277) +Scores: (#C #S #D #I) 53 1 1 0 +REF: h e h a s a l s o c o n t r i b u t e d t o t h e n e w y o r k r e v i E W o f b o o k s +HYP: h e h a s a l s o c o n t r i b u t e d t o t h e n e w y o r k r e v i * O o f b o o k s +Eval: D S + +Speaker sentences 24: lad_eng_000278 #utts: 1 +id: (lad_eng_000278-lad_eng_000278) +Scores: (#C #S #D #I) 42 1 5 0 +REF: b y p l a c i n g s m a L l a r t o b j e c t S t H r o U G H o u t t h e f i l m +HYP: b y p l a c i n g s m a * l a r t o b j e c t * t * r o * * o u t t h e f i l m +Eval: D D D D D S + +Speaker sentences 25: lad_eng_000279 #utts: 1 +id: (lad_eng_000279-lad_eng_000279) +Scores: (#C #S #D #I) 19 2 0 0 +REF: i t i s f o u n d i n b r A Z i l +HYP: i t i s f o u n d i n b r E S i l +Eval: S S + +Speaker sentences 26: lad_eng_000280 #utts: 1 +id: (lad_eng_000280-lad_eng_000280) +Scores: (#C #S #D #I) 49 0 3 0 +REF: i t w A s t h e s i d E o f t h e f a m I l y i i d e n t i f i e d m o r e w i t h +HYP: i t w * s t h e s i d * o f t h e f a m * l y i i d e n t i f i e d m o r e w i t h +Eval: D D D + +Speaker sentences 27: lad_eng_000281 #utts: 1 +id: (lad_eng_000281-lad_eng_000281) +Scores: (#C #S #D #I) 40 1 3 6 +REF: * ******* c a n d ******* i D A t E s i * * t e s m u s t a l s o * s U b m i t a w o r k p l a n +HYP: H c a n d i * * t * s i G H t e s m u s t a l s o R s O b m i t a w o r k p l a n +Eval: I I I D D D I I I S + +Speaker sentences 28: lad_eng_000282 #utts: 1 +id: (lad_eng_000282-lad_eng_000282) +Scores: (#C #S #D #I) 25 2 3 0 +REF: d u n d e E w O n t h e m a T c h t h r E e t W o +HYP: d u n d e Y w H n t h e m a * c h t h r * e t * o +Eval: S S D D D + +Speaker sentences 29: lad_eng_000283 #utts: 1 +id: (lad_eng_000283-lad_eng_000283) +Scores: (#C #S #D #I) 83 7 4 3 +REF: h o w e v e r t h e v i L l A g e r e m a i n E d i S O l a t e d U n t i l t h e A R r i v A l o f t h e f i r s t n E W s ******* p a p e r s e c o n d r e p * u b l i c * +HYP: h o w e v e r t h e v i * l I g e r e m a i n * d i C A l a t e d A n t i l t h e * * r i v E l o f t h e f i r s t n O U s p a p e r s e c o n d r e p O u b l i c K +Eval: D S D S S S D D S S S I I I + +Speaker sentences 30: lad_eng_000284 #utts: 1 +id: (lad_eng_000284-lad_eng_000284) +Scores: (#C #S #D #I) 92 3 16 0 +REF: t h e f I R s t s e r v i C E i N t h e N e W c h u r c H w a s h e l d i N n i n E t E e N f i f t y o n E a l t h o U G H t h e b U i l d i N g w a s n o t f u L l y f i n i s h e d +HYP: t h e f * A s t s e r v i * S i * t h e * e U c h u r c * w a s h e l d i * n i n * t * e * f i f t y o n * a l t h o * * * t h e b * i l d i * g w a s n o t f u * l y f i n i s h e d +Eval: D S D S D D S D D D D D D D D D D D D + +Speaker sentences 31: lad_eng_000285 #utts: 1 +id: (lad_eng_000285-lad_eng_000285) +Scores: (#C #S #D #I) 90 5 9 0 +REF: t h e a v e r A g e h o u s e h O l d s i Z e w a s t w o p o i n t t W o s e v e n A n d t h e a v e r A g E f a m I l y s i Z e w a s t h r E e p o i n t Z E r o Z E r o +HYP: t h e a v e r I g e h o u s e h * l d s i * e w a s t w o p o i n t t * o s e v e n * n d t h e a v e r I g H f a m * l y s i * e w a s t h r * e p o i n t * I r o * S r o +Eval: S D D D D S S D D D D S D S + +Speaker sentences 32: lad_eng_000286 #utts: 1 +id: (lad_eng_000286-lad_eng_000286) +Scores: (#C #S #D #I) 55 2 3 2 +REF: i t w a s f i r s t b r O a d ******* c a s t o n t h i r d J a n * u A r y t w o t h o u s A n d A n d t e n +HYP: i t w a s f i r s t b r * a d c a s t o n t h i r d G a n I u * r y t w o t h o u s O n d * n d t e n +Eval: D I S I D S D + +Speaker sentences 33: lad_eng_000287 #utts: 1 +id: (lad_eng_000287-lad_eng_000287) +Scores: (#C #S #D #I) 40 0 4 0 +REF: t h e w i n g s w e r E N o w m a d E i n a s i n g l e p r e S s i n g +HYP: t h e w i n g s w e r * * o w m a d * i n a s i n g l e p r e * s i n g +Eval: D D D D + +Speaker sentences 34: lad_eng_000288 #utts: 1 +id: (lad_eng_000288-lad_eng_000288) +Scores: (#C #S #D #I) 38 3 5 3 +REF: * * ******* d o c t O r o F P h I l o s o P H y i n e n g I n e E r i n g m a n a g e m e n t +HYP: H E d o c t * r o * * h * l o s o * F y i n e n g E n e A r i n g m a n a g e m e n t +Eval: I I I D D D D D S S S + +Speaker sentences 35: lad_eng_000289 #utts: 1 +id: (lad_eng_000289-lad_eng_000289) +Scores: (#C #S #D #I) 44 0 4 1 +REF: t h i s t O o k A w a y t h e m a i n a r g u m e n t o f s a f E t y r i s K s * +HYP: t h i s t * o k * w a y t h e m a i n a r g u m e n t o f s a f * t y r i s * s K +Eval: D D D D I + +Speaker sentences 36: lad_eng_000290 #utts: 1 +id: (lad_eng_000290-lad_eng_000290) +Scores: (#C #S #D #I) 48 1 2 2 +REF: h e w a s a l s o m a d E a l i f e m e m b e r o f s C u n ******* t h o r p E * u n i t e d +HYP: h e w a s a l s o m a d * a l i f e m e m b e r o f s G u n t h o r p * P u n i t e d +Eval: D S I D I + +Speaker sentences 37: lad_eng_000291 #utts: 1 +id: (lad_eng_000291-lad_eng_000291) +Scores: (#C #S #D #I) 50 3 3 0 +REF: s h e f E a r s t h e y w i L l g e t a d I v o r C e b u t t h i s n e v e r h a P p e n s +HYP: s h e f I a r s t h e y ******* w i * l g e t a d E v o r S e b u t t h i s n e v e r h a * p e n s +Eval: S D D S S D + +Speaker sentences 38: lad_eng_000292 #utts: 1 +id: (lad_eng_000292-lad_eng_000292) +Scores: (#C #S #D #I) 41 4 5 1 +REF: f o O t d r o p s U n ******* a b l e t o h O L d t h e f O o t s t r a I G H t a c r o s S +HYP: f o U t d r o p s I n a b l e t o h * A d t h e f * o t s t r a * * * t a c r o s E +Eval: S S I D S D D D D S + +Speaker sentences 39: lad_eng_000293 #utts: 1 +id: (lad_eng_000293-lad_eng_000293) +Scores: (#C #S #D #I) 63 7 15 0 +REF: w h e t H e R t h e a I r f l o W i s f r e E o r f o r C E D c A n A F f e c T t h e e n E R g y E F f I C i E n c y o f t h e W I n d o W +HYP: w h e t * e * t h e a * r f l o * i s f r e Y o r f o r * S T c * n * * f e c * t h e e n * A g y * A f * * i A n c y o f t h e * E n d o * +Eval: D D D D S D S S D D D D D S D S D D S D S D + +Speaker sentences 40: lad_eng_000294 #utts: 1 +id: (lad_eng_000294-lad_eng_000294) +Scores: (#C #S #D #I) 50 0 10 0 +REF: a f t e r g e T t i n G T h e r i G h t m E a s u r E m e n t S t h e y m a d E t h e n e w d O o r s +HYP: a f t e r g e * t i n * * h e r i * h t m * a s u r * m e n t * t h e y m a d * t h e ******* n e w d * o r s +Eval: D D D D D D D D D D + +Speaker sentences 41: lad_eng_000295 #utts: 1 +id: (lad_eng_000295-lad_eng_000295) +Scores: (#C #S #D #I) 46 1 5 3 +REF: f r a g m e n t s o n E a c h f a c e a r e m a r K e D w I t h l e T t e r s a * b * * C +HYP: f r a g m e n t s o n * a c h f a c e a r e m a r * e * w * t h l e * t e r s a Y b E S E +Eval: D D D D D I I I S + +Speaker sentences 42: lad_eng_000296 #utts: 1 +id: (lad_eng_000296-lad_eng_000296) +Scores: (#C #S #D #I) 74 4 13 2 +REF: f r o m t h e f i r s t m i n U t E s b o t h t e A m * s s h o w E d t h e I R d E s i r e t o c O m p e T e * w i t H A G g R e S S i v e a P p r o A c h e s +HYP: f r o m t h e f i r s t m i n I t * s b o t h t e * m E s s h o w * d t h e * * d I s i r e t o c * m p e * e T w i t * H E g * e * * i v e a * p r o * c h e s +Eval: S D D I D D D S D D I D S S D D D D D + +Speaker sentences 43: lad_eng_000297 #utts: 1 +id: (lad_eng_000297-lad_eng_000297) +Scores: (#C #S #D #I) 58 8 6 0 +REF: P H Y s i c A l T h e r A p y e x E R C i s e s m a y h e l p p a t i E n t S t o m a i n t a i n m u S C l e s t r E n g t h +HYP: * F I s i c * l * h e r I p y e x C U S i s e s m a y h e l p p a t i O n t * t o m a i n t a i n m u * * l e s t r I n g t h +Eval: D S S D D S S S S S D D D S + +Speaker sentences 44: lad_eng_000298 #utts: 1 +id: (lad_eng_000298-lad_eng_000298) +Scores: (#C #S #D #I) 57 0 3 0 +REF: h o w e v e r t h e t o w n s h e l i v E s i n n o o n E w a n t s t o h e A r a b o u t h e r +HYP: h o w e v e r t h e t o w n s h e l i v * s i n n o o n * w a n t s t o h e * r a b o u t h e r +Eval: D D D + +Speaker sentences 45: lad_eng_000299 #utts: 1 +id: (lad_eng_000299-lad_eng_000299) +Scores: (#C #S #D #I) 69 4 6 6 +REF: * * * ******* d E s c r i B e s a P p o i n t ******* m e N t S o f a n a c t i n g c h I e * F j u s t i C E o r j u d g e o f t h e s u p r e m e c o U r t +HYP: A N D d I s c r i V e s a * p o i n t m e * t * o f a n a c t i n g c h * e V E j u s t i * S o r j u d g e o f t h e s u p r e m e c o * r t +Eval: I I I I S S D I D D D I S D S D + +Speaker sentences 46: lad_eng_000300 #utts: 1 +id: (lad_eng_000300-lad_eng_000300) +Scores: (#C #S #D #I) 73 0 5 2 +REF: t h e s o y ******* b e A n s o u t E R * c o v e r i n g i s t h e n r e m o v e d a n d t h e b e A n s a r e p a r t i a L l y c o o k e d +HYP: t h e s o y b e * n s o u t * * A c o v e r i n g i s t h e n r e m o v e d a n d t h e b e * n s a r e p a r t i a * l y c o o k e d +Eval: I D D D I D D + +Speaker sentences 47: lad_eng_000301 #utts: 1 +id: (lad_eng_000301-lad_eng_000301) +Scores: (#C #S #D #I) 68 1 5 1 +REF: t h i s n a T i O n a l * m o v E m e n t w h i c H h a d b e g u n w i t h s o m u c h h o p E c a m e t o a s a d e n d +HYP: t h i s n a S i * n a l E m o v * m e n t w h i c * ******* h a d b e g u n w i t h s o m u c h h o p * c a m e t o a s a d e n d +Eval: S D I D D D D + +Speaker sentences 48: lad_eng_000302 #utts: 1 +id: (lad_eng_000302-lad_eng_000302) +Scores: (#C #S #D #I) 48 1 9 4 +REF: h i s a S s o * c i a t E S * u s u a L l y c a L l E d h i m t * o r t h e G O o d ******* l O o k i n g g U y +HYP: h i s a * s o S c i a t * * Y u s u a * l y c a * l * d h i m t E o r t h e * * o d l * o k i n g g I y +Eval: D I D D I D D D I D D I D S + +Speaker sentences 49: lad_eng_000303 #utts: 1 +id: (lad_eng_000303-lad_eng_000303) +Scores: (#C #S #D #I) 49 5 6 3 +REF: i t s m a i n o F f i c e s w e r E i n l O n d O n w i T H * A s e c O n d o F f i C E b e * l ******* f a s t +HYP: i t s m a i n o * f i c e s w e r * i n l U n d A n w i * E H E s e c * n d o * f i * S b e L l f a s t +Eval: D D S S D S I S D D D S I I + +Speaker sentences 50: lad_eng_000304 #utts: 1 +id: (lad_eng_000304-lad_eng_000304) +Scores: (#C #S #D #I) 44 1 5 0 +REF: a c t u A L l y i h a d n e v e r b E e n t o a v i L l A g e b e f o r E t h a t +HYP: a c t u * * l y i h a d n e v e r b * e n t o a v i * l I g e b e f o r * t h a t +Eval: D D D D S D + +Speaker sentences 51: lad_eng_000305 #utts: 1 +id: (lad_eng_000305-lad_eng_000305) +Scores: (#C #S #D #I) 69 0 8 0 +REF: h e W a s c h a r g e d W i t h p l a N n i n g t o s e t o F f b o m B s i n E u r o p E a n d t h e u n i t e d s t a t e S +HYP: h e * a s c h a r g e d * i t h p l a * n i n g t o s e t o * f b o m * s i n * u r o p * a n d t h e u n i t e d s t a t e * +Eval: D D D D D D D D + +Speaker sentences 52: lad_eng_000306 #utts: 1 +id: (lad_eng_000306-lad_eng_000306) +Scores: (#C #S #D #I) 63 3 8 4 +REF: m a k i n g m I R R O r s i s t h e T h i r d s t u d I o * A l b u m b y b e l g I A n ******* a U s t r a l i a n a r t i s t g o t * * y E +HYP: m a k i n g m * * * E r s i s t h e * h i r d s t u d * o R H l b u m b y b e l g * E n a * s t r a l i a n a r t i s t g o t I A y * +Eval: D D D S D D I S D S I D I I D + +Speaker sentences 53: lad_eng_000307 #utts: 1 +id: (lad_eng_000307-lad_eng_000307) +Scores: (#C #S #D #I) 82 2 7 4 +REF: h e t h e n m o v e d t o w a s H i n g t o n d * ******* * C a n d w a s a p a r t n E r W i t h w a r d b r o W n U n * t i l n i n E t E e n t w e n t y n i n E +HYP: h e t h e n m o v e d t o w a s * i n g t o n d E S E a n d w a s a p a r t n * r * i t h w a r d b r o * n A n D t i l n i n * t * e n t w e n t y n i n * +Eval: D I I I S D D D S I D D D + +Speaker sentences 54: lad_eng_000308 #utts: 1 +id: (lad_eng_000308-lad_eng_000308) +Scores: (#C #S #D #I) 52 5 12 2 +REF: j o s E P H h i G H s c H O o l * a n d t h e s c H O o l * s t h e Y c O m p E T e A g a i n S T i n a L l s p o r t s +HYP: j o s O F h i * Y s c * * o l E a n d t h e s c * * o l E s t h e * c * m p * * e * g a i n * E i n a * l s p o r t s +Eval: S S S D S D D I D D I D D D D D D S D + +Speaker sentences 55: lad_eng_000309 #utts: 1 +id: (lad_eng_000309-lad_eng_000309) +Scores: (#C #S #D #I) 30 1 3 0 +REF: t w e l V E p l u s o n E m a T c h b a n p e r c a r d +HYP: t w e l * F p l u s o n * m a * c h b a n p e r c a r d +Eval: D S D D + +Speaker sentences 56: lad_eng_000310 #utts: 1 +id: (lad_eng_000310-lad_eng_000310) +Scores: (#C #S #D #I) 25 0 1 0 +REF: i T h i n k i m i g h t b e n o t h i n g +HYP: i * h i n k i m i g h t b e n o t h i n g +Eval: D + +Speaker sentences 57: lad_eng_000311 #utts: 1 +id: (lad_eng_000311-lad_eng_000311) +Scores: (#C #S #D #I) 86 9 12 2 +REF: t h e h o M e w a s b U i l t a n d l i v e d i n b y a n d r E W j a c K S O n * K E N n E d y d e p u t y c O L l e c t O R F o R t h e i n t e r n a l r e v E n * u E s e r v i C E +HYP: t h e h o * e w a s b * i l t a n d l i v e d i n b y a n d r * U j a c X A n D * C A n * d y d e p u t y c * * l e c t * E * o * t h e i n t e r n a l r e v I n O u * s e r v i * S +Eval: D D D S S S S I D S S D D D D S D D S I D D S + +Speaker sentences 58: lad_eng_000312 #utts: 1 +id: (lad_eng_000312-lad_eng_000312) +Scores: (#C #S #D #I) 69 1 11 1 +REF: i n n i n E t E e n s i x t y f o U r h e w e n t b a c K t o o m s k a n d e n t E R e D t h e a c t o R S s c H O o l o f o m * s K +HYP: i n n i n * t * e n s i x t y f o * r h e w e n t b a c * t o o m s k a n d e n t * * e * t h e a c t o * A s c * * o l o f o m P s * +Eval: D D D D D D D D S D D I D + +Speaker sentences 59: lad_eng_000313 #utts: 1 +id: (lad_eng_000313-lad_eng_000313) +Scores: (#C #S #D #I) 56 3 4 0 +REF: t h e b a n k i s j o I n t l y o W n E d b y h i m a n d h i s b r o T H e r S a n d r e l A t i v e s +HYP: t h e b a n k i s j o U n t l y o * n * d b y h i m a n d h i s b r o * V e r * a n d r e l I t i v e s +Eval: S D D D S D S + +Speaker sentences 60: lad_eng_000314 #utts: 1 +id: (lad_eng_000314-lad_eng_000314) +Scores: (#C #S #D #I) 34 3 4 1 +REF: h e s u b * s E Q u E n t l y w e n t t o S c H O o l i n b r i s t O l +HYP: h e s u b P s I C u * n t l y w e n t t o * c * * o l i n b r i s t A l +Eval: I S S D D D D S + +Speaker sentences 61: lad_eng_000315 #utts: 1 +id: (lad_eng_000315-lad_eng_000315) +Scores: (#C #S #D #I) 41 1 13 3 +REF: * o n E t h o u s a n d E I G H t h u n d r E D A N d f o * r t y s i * x f o U r T h e d I T i o n +HYP: W o n * t h o u s a n d * * * A t h u n d r * * ******* * * d f o A r t y s i C x f o * r * h e d * * i o n +Eval: I D D D D S D D D D D I I D D D D + +Speaker sentences 62: lad_eng_000316 #utts: 1 +id: (lad_eng_000316-lad_eng_000316) +Scores: (#C #S #D #I) 86 7 7 1 +REF: a p a r t o f l i T t l E E n g l a n d b e y o n d w a l E s i t h a s b E e N E S S e n T I a L l y E n g l i s h ******* s p e a k i n g f o r n i n E h u n d r e d y e a r s +HYP: a p a r t o f l i * t l * I n g l a n d b e y o n d w a l * s i t h a s b * e * A C e n C H a * l y I n g l i s h s p e a k i n g f o r n i n * h u n d r e d y e a r s +Eval: D D S D D D S S S S S D S I D + +Speaker sentences 63: lad_eng_000317 #utts: 1 +id: (lad_eng_000317-lad_eng_000317) +Scores: (#C #S #D #I) 54 6 10 1 +REF: h e p l a Y E d w I t h t e n p l a y E r s f o r h a L f w a s a g a i n S T T H E t r A d I T i o n i n * G s p +HYP: h e p l a * * d w * t h t e n p l a y A r s f o r h a R f w a s a g a i n * E * * A t r * d * * i o n i n J E E s ******* p +Eval: D D D S S D S D D S D D D I S S D + +Speaker sentences 64: lad_eng_000318 #utts: 1 +id: (lad_eng_000318-lad_eng_000318) +Scores: (#C #S #D #I) 90 6 11 0 +REF: t h e P r e s i d i n g j u d g e w a s w e b s t E R T H a Y E r W h o w a s a l r e a d y a S s i G n E d t o t h e c o U r t b e f o r e t h i s c a S e w a s S C h e d U l E d +HYP: t h e * r e s i d i n g j u d g e w a s w e b s t A * F a * I r * h o w a s a l r e a d y a * s i * n * d t o t h e c o * r t b e f o r e t h i s c a C e w a s * * h e d I l * d +Eval: D S S D S D S D D D D D S D D S D + +Speaker sentences 65: lad_eng_000319 #utts: 1 +id: (lad_eng_000319-lad_eng_000319) +Scores: (#C #S #D #I) 64 5 5 1 +REF: b i g b r O t h e r f i v e w a s t h e T h I r d o f T h e m a i n s E r i E s t o f e a T u R e * a l i v e l A U n c h +HYP: b i g b r A t h e r f i v e w a s t h e * h U r d o f * h e m a i n s A r i * s t o f e a C u * e R a l i v e l * O n c h +Eval: S D S D S D S D I D S + +Speaker sentences 66: lad_eng_000320 #utts: 1 +id: (lad_eng_000320-lad_eng_000320) +Scores: (#C #S #D #I) 83 1 10 1 +REF: i t s m o T t o i s w h o ******* e v e R y o u a r E a n d w h e r e v e R y o u a r e o n t h e j O u R n E y o f f a i T h y o u a R e w e l c o m E h e r E +HYP: i t s m o * t o i s w h o e v e * y o u a r * a n d w h e r e v e * y o u a r e o n t h e j * u * n * y o f f a i F h y o u a * e w e l c o m * h e r * +Eval: D I D D D D D D S D D D + +Speaker sentences 67: lad_eng_000321 #utts: 1 +id: (lad_eng_000321-lad_eng_000321) +Scores: (#C #S #D #I) 26 2 3 1 +REF: r o b E R t e * m i L l E r a s c o A c h w i l s o n +HYP: r o b * A t e Y m i * l O r a s c o * c h w i l s o n +Eval: D S I D S D + +Speaker sentences 68: lad_eng_000322 #utts: 1 +id: (lad_eng_000322-lad_eng_000322) +Scores: (#C #S #D #I) 47 4 8 1 +REF: a f t e r A o n E y e a r b r E a k Z E r o d e g r E e w a s h e R f o L l o W i n g v e n t * U r E +HYP: a f t e r ******* * o n y e a r b r * a k S I r o d e g r * e w a s h e * f o * l o * i n g v e n t H A r * +Eval: D D S D S S D D D D I S D + +Speaker sentences 69: lad_eng_000323 #utts: 1 +id: (lad_eng_000323-lad_eng_000323) +Scores: (#C #S #D #I) 43 5 4 13 +REF: a * * m t * * m a n u f a c t U R e d a m o * d E l K i t o f T h e * * Z * * * Z * r d r a * * G s t E r +HYP: a Y A m t E E m a n u f a c t * * e d a m o R d * l C i t o f * h e S E D S E I D A r d r a C K X s t O r +Eval: I I I I D D I D S D I I S I I I S I I I S S + +Speaker sentences 70: lad_eng_000324 #utts: 1 +id: (lad_eng_000324-lad_eng_000324) +Scores: (#C #S #D #I) 64 8 3 6 +REF: t h e * * s s a * a I m e d t o b U i l d a l e f t ******* w i n g A l t E r n A t i v e t o n E w l a b O U r a n d t h e * s n p * +HYP: t h e E S s E s S a Y a * m e d t o b * i l d a l e f t w i n g O l t U r n I t i v e t o n O w l a b * E r a n d t h e E s A n p E +Eval: I I S S I D D I S S S S D S I S I + +Speaker sentences 71: lad_eng_000325 #utts: 1 +id: (lad_eng_000325-lad_eng_000325) +Scores: (#C #S #D #I) 32 1 1 0 +REF: h e l i v e s l i k e h e I s a y o U n g p e r s o n +HYP: h e l i v e s l i k e h e A s a y o * n g p e r s o n +Eval: S D + +Speaker sentences 72: lad_eng_000326 #utts: 1 +id: (lad_eng_000326-lad_eng_000326) +Scores: (#C #S #D #I) 34 2 7 1 +REF: m a s t e R o f s C i E n C e * i n e n g I n e E r i N g m a n a g E M e n t +HYP: m a s t e * o f s * i * n * e S i n e n g E n e A r i * g m a n a g * * e n t +Eval: D D D D I S S D D D + +Speaker sentences 73: lad_eng_000327 #utts: 1 +id: (lad_eng_000327-lad_eng_000327) +Scores: (#C #S #D #I) 62 5 9 1 +REF: s h e f a i l e d t o m a k E T h e t o p t h r E e a t t h e K E n Y a n j u n i O R t r a c K t r i A l * s t h a t j U n E +HYP: s h e f a i l e d t o m a k * * h e t o p t h r * e a t t h e C A n I a n j u n i * * A t r a c * ******* t r i * l E s t h a t j O n * +Eval: D D D S S S D D S D D D I S D + +Speaker sentences 74: lad_eng_000328 #utts: 1 +id: (lad_eng_000328-lad_eng_000328) +Scores: (#C #S #D #I) 21 1 4 1 +REF: a t o U r * f o L l o W e d i n s U P p o r t +HYP: a t o * r E f o * l o * e d i n s * E p o r t +Eval: D I D D D S + +Speaker sentences 75: lad_eng_000329 #utts: 1 +id: (lad_eng_000329-lad_eng_000329) +Scores: (#C #S #D #I) 81 3 17 1 +REF: t h e y W e r E E s t a b L i s h E D i n E I G H t E e n s e v e n t y o n E a n d a r E O n E o F t h e o l d E s t c l * u b s i n T h e s o u t h o f E n g l a n d +HYP: t h e y ******* * e r * * s t a b * i s h * * i n * * * A t * e n s e v e n t y o n * a n d a r * W n * o * t h e o l d * s t c l O u b s i n * h e s o u t h o f I n g l a n d +Eval: D D D D D D D D D D S D D D S D D D I D S + +Speaker sentences 76: lad_eng_000330 #utts: 1 +id: (lad_eng_000330-lad_eng_000330) +Scores: (#C #S #D #I) 46 2 2 1 +REF: h e W a s a m e m b e r o f t h e Y e s * s c o t l a n d a d v i s O r y b o A r d +HYP: h e * a s a m e m b e r o f t h e G e s T s c o t l a n d a d v i s E r y b o * r d +Eval: D S I S D + +Speaker sentences 77: lad_eng_000331 #utts: 1 +id: (lad_eng_000331-lad_eng_000331) +Scores: (#C #S #D #I) 30 1 0 0 +REF: t w o t h o u s a n d a n d f i v e g e n t l e m A n +HYP: t w o t h o u s a n d a n d f i v e g e n t l e m E n +Eval: S + +Speaker sentences 78: lad_eng_000332 #utts: 1 +id: (lad_eng_000332-lad_eng_000332) +Scores: (#C #S #D #I) 82 6 10 2 +REF: * o U r * f i l M H a d A s t r o n g r e C e p t i o n i n E u r O p E a n d a c h i E v e d d i s t R I b u t i o n b u t t h a t w a s n o t t h e c a S e h e r E +HYP: A o * r E f i l E * a d ******* * s t r o n g r e S e p t i o n i n ******* Y u r U p * ******* a n d a c h i * v e d d i s t * O b u t i o n b u t t h a t w a s n o t t h e c a C e h e r * +Eval: I D I S D D D S D S S D D D D S S D + +Speaker sentences 79: lad_eng_000333 #utts: 1 +id: (lad_eng_000333-lad_eng_000333) +Scores: (#C #S #D #I) 38 3 4 3 +REF: * o R t h o S i s s t R e t c h e s p o s t e r i O r a n * * K l E s t r u c t u R e s +HYP: B o L t h o * i s s t * e t c h e s p o s t e r i A r a n G C A l * s t r u c t u * e s +Eval: I S D D S I I S D D + +Speaker sentences 80: lad_eng_000334 #utts: 1 +id: (lad_eng_000334-lad_eng_000334) +Scores: (#C #S #D #I) 87 3 17 1 +REF: h e W a s a l s o a t h R e e t i m e f r e n c h n a T i O N a l c h a m p i O n n i n E t E e N n i n E t y n i n E t E e N n i N E t y f o U r t w o T h o u s A n d a N d * o n E +HYP: h e * a s a l s o a t h * e e t i m e f r e n c h n a S i * * a l c h a m p i A n n i n * t * e * n i n * t y n i n * t I e * n i * * t y f o * r t w o * h o u s * n d a * d W o n * +Eval: D D S D D S D D D D D S D D D D D D D I D + +Speaker sentences 81: lad_eng_000335 #utts: 1 +id: (lad_eng_000335-lad_eng_000335) +Scores: (#C #S #D #I) 67 1 7 2 +REF: t h e v i L l A g e s t r u c t u r E s h o w N i n h i s m a p i s t O a g r e A T e x t e n t u n ******* c h a n g e d T o ******* d a y +HYP: t h e v i * l I g e s t r u c t u r * s h o w * i n h i s m a p i s t * a g r e * * e x t e n t u n c h a n g e d * o d a y +Eval: D S D D D D D I D I + +Speaker sentences 82: lad_eng_000336 #utts: 1 +id: (lad_eng_000336-lad_eng_000336) +Scores: (#C #S #D #I) 72 8 12 3 +REF: r u S S I a i s r e c o g n i Z e d F O R i t S n u c l E a r d i s a * s t ******* E R e x p E r t I s E a n d f o R t h e s a * f E t y o F i t s t e c H n o l O g y +HYP: r u * * H a i s r e c o g n i S e d ******* * * * i t * n u c l * a r d i s a R s t T O e x p A r t E s * a n d f o * t h e s a V f * t y o * i t s t e c K n o l A g y +Eval: D D S S D D D D D D I I S S S S D D I D D S S + +Speaker sentences 83: lad_eng_000337 #utts: 1 +id: (lad_eng_000337-lad_eng_000337) +Scores: (#C #S #D #I) 90 5 5 6 +REF: a s o f t W o t h o u s A n d A N d f o U r t e e n * m t * v * i s a v a i l a b l e w i t h i n t h e u n i t e d K i n g d O m o n v I r g i n m e d i a * a n d s * k * y +HYP: a s o f t * o t h o u s E n d * O d f o * r t e e n E m ******* t Y v E i s a v a i l a b l e w i t h i n t h e ******* u n i t e d C i n g d U m o n v E r g i n m e d i a R a n d s C k I y +Eval: D S D S D I D I I D S S S I I I + +Speaker sentences 84: lad_eng_000338 #utts: 1 +id: (lad_eng_000338-lad_eng_000338) +Scores: (#C #S #D #I) 27 0 2 1 +REF: n e w y o r k p e * n g u i n r a n d O m h o u s e +HYP: n e w ******* y o r k p e A n g u i n r a n d * m h o u s e +Eval: D I D + +Speaker sentences 85: lad_eng_000339 #utts: 1 +id: (lad_eng_000339-lad_eng_000339) +Scores: (#C #S #D #I) 49 1 4 6 +REF: t h e d u * c h * y w a s s * e c u r e D i n t H e O u t ******* c o m e o f t h e g o T H i c * w a * r +HYP: t h e d u T c h E y w a s s C e c u r e * i n t * e * u t c o m e o f t h e g o * F i c K w a O r +Eval: I I I D D D I D S I I + +Speaker sentences 86: lad_eng_000340 #utts: 1 +id: (lad_eng_000340-lad_eng_000340) +Scores: (#C #S #D #I) 61 3 6 1 +REF: w i T h g O o d p a c e s T a r t e D T h e m a t c h w i t h b o t h t e A m * s A l t e R n a t i n g s u p r e m a C y +HYP: w i * h g * o d p a c e s D a r t e * * h e m a t c h w i t h b o t h t e * m E s O l t e * n a t i n g s u p r e m a S y +Eval: D D S D D D I S D S + +Speaker sentences 87: lad_eng_000341 #utts: 1 +id: (lad_eng_000341-lad_eng_000341) +Scores: (#C #S #D #I) 56 2 9 1 +REF: t h i s v E r S i o n i s n o t e * d F o r b E i N g v e r y f a I T H f u l t o t h e O r i g i n a l n o v E l +HYP: t h i s v * r T i o n i s n o t e A d * o r b * i * g v e r y f a * * * f u l t o t h e ******* A r i g i n a l n o v * l +Eval: D S I D D D D D D D S D + +Speaker sentences 88: lad_eng_000342 #utts: 1 +id: (lad_eng_000342-lad_eng_000342) +Scores: (#C #S #D #I) 70 1 11 2 +REF: t h i s p r e s u m p t i o n i s n o t f U l * ******* f i L l e d o n E h a s t o K n o W a t l e a s t t W o c o n J U g a t E d i a m E t e R s +HYP: t h i s p r e s u m p t i o n i s n o t f * l E f i * l e d o n * h a s t o * n o * a t ******* l e a s t t * o c o n * * g a t * d i a m A t e * s +Eval: D I I D D D D D D D D D S D + +Speaker sentences 89: lad_eng_000343 #utts: 1 +id: (lad_eng_000343-lad_eng_000343) +Scores: (#C #S #D #I) 100 4 7 4 +REF: n o t a b l e t i t l e s i n c l u d e d g o l d E n a * x E t h e r e v e n g E o f d e A t h a D d e r r a d m o b i l E o u t ******* r u n N e R s a n d s * E g a * s o n i c t h e h e D g E h o g +HYP: n o t a b l e t i t l e s i n c l u d e d g o l d A n a C x S t h e r e v e n g * o f d e * t h a * d e r r a d m o b i l * o u t r u n O e * s a n d s A K g a R s o n i c t h e h e * g * h o g +Eval: S I S D D D D I S D I S I D D + +Speaker sentences 90: lad_eng_000344 #utts: 1 +id: (lad_eng_000344-lad_eng_000344) +Scores: (#C #S #D #I) 89 1 12 0 +REF: t h e n i n E t E e n n i n E t y n i n E j u D g m e n t n o t e d t h a t t h e i n f l U E n c E o f t h E f a t h e r o f t h e A C c u s e d h a s b e e N t h e r E +HYP: t h e n i n * t * e n n i n * t y n i n * j u * g m e n t n o t e d t h a t t h e i n f l * O n c * o f t h * f a t h e r o f t h e * * c u s e d h a s b e e * t h e r * +Eval: D D D D D D S D D D D D D + +Speaker sentences 91: lad_eng_000345 #utts: 1 +id: (lad_eng_000345-lad_eng_000345) +Scores: (#C #S #D #I) 62 4 7 5 +REF: m A C d * u F f s w E a r s r e v e n g e * a n d j o i n s f o r C e s W i t h m a l c o L m t o o v e r ******* t H r o W m * A C b e * t h +HYP: m O K d A u * f s w * a r s r e v e n g e H a n d j o i n s f o r * e s * i t h m a l c o * m t o o v e r t * r o * m O K b e A t h +Eval: S S I D D I D D D I D D I S S I + +Speaker sentences 92: lad_eng_000346 #utts: 1 +id: (lad_eng_000346-lad_eng_000346) +Scores: (#C #S #D #I) 76 4 11 2 +REF: t h e m e d I A e v A l v i L l A g e c o U r t w a s a l w a y s a n X i o u s t o K E e p * t h e f e n C e a r o U n d t h e V i L l A g e g * a p l e S s +HYP: t h e m e d * Y e v * l v i * l I g e c o * r t w a s a l w a y s a n * i o u s t o * C e p E t h e f e n * e a r o * n d t h e * i * l I g e g C a p l e * s +Eval: D S D D S D D D S I D D D D S I D + +Speaker sentences 93: lad_eng_000347 #utts: 1 +id: (lad_eng_000347-lad_eng_000347) +Scores: (#C #S #D #I) 65 2 9 0 +REF: t h e r E w a s a n i n E r a n k s Y s t E m e a c h r a n k h a v i N g m o r e p o w e R t H a N t h e l o W e R r a n k +HYP: t h e r * w a s a n i n * r a n k s I s t O m e a c h r a n k h a v i * g m o r e p o w e * t * a * t h e l o * e * ******* r a n k +Eval: D D S S D D D D D D D + +Speaker sentences 94: lad_eng_000348 #utts: 1 +id: (lad_eng_000348-lad_eng_000348) +Scores: (#C #S #D #I) 71 2 9 0 +REF: t h e Y E s t a b l i s h e d d i p l O m a t i C r e l a t i o n s o n s e p t e m b E r n i n E t E e n t h n i n E t E e n s e v e n t y t W o +HYP: t h e * A s t a b l i s h e d d i p l A m a t i * r e l a t i o n s o n s e p t e m b * r ******* n i n * t * e n t h n i n * t * e n s e v e n t y t * o +Eval: D S S D D D D D D D D + +Speaker sentences 95: lad_eng_000349 #utts: 1 +id: (lad_eng_000349-lad_eng_000349) +Scores: (#C #S #D #I) 78 5 6 1 +REF: t h i s w a s f U r t h e r E x t e n d e d t o i n c l * u d E m o r E u K d a t e s i n d E C e m b e r t w o t h o u s a n d A n d f o U r t e e n +HYP: t h i s w a s f I r t h e r * x t e n d e d t o i n c l O u d * m o r * u ******* C A d a t e s i n d I S e m b e r t w o t h o u s a n d * n d f o * r t e e n +Eval: S D I D D D S S S S D D + +Speaker sentences 96: lad_eng_000350 #utts: 1 +id: (lad_eng_000350-lad_eng_000350) +Scores: (#C #S #D #I) 65 5 10 1 +REF: t h e D u T c h g o v e r N m e n t i s c U R r E n t l y e x * a m I N i n g t h e L e G a l c o n S E Q u e n c e s o f t h E r U l i n g +HYP: t h e * u * c h g o v e r * m e n t i s c * A r * n t l y e x S a m * * i n g t h e * e * a l c o n C I C u e n c e s o f t h * r O l i n g +Eval: D D D D S D I D D D D S S S D S + +Speaker sentences 97: lad_eng_000351 #utts: 1 +id: (lad_eng_000351-lad_eng_000351) +Scores: (#C #S #D #I) 91 3 11 1 +REF: f r o m n i n E t E e n t h I r t y t h r e e t o n i n E t e e n f o * r t y n i n E t h e A m E r i c A n l e A G U e w o n t w e l v e o u t o F t h e f i r s t s i x t E e n +HYP: f r o m n i n * t * e n t h U r t y t h r e e t o n i n * t e e n f o A r t y n i n * t h e * m A r i c O n l e * * * e w o n t w e l v e o u t ******* o * t h e f i r s t s i x t * e n +Eval: D D S D I D D S S D D D D D D + +Speaker sentences 98: lad_eng_000352 #utts: 1 +id: (lad_eng_000352-lad_eng_000352) +Scores: (#C #S #D #I) 32 3 3 1 +REF: t h e * r E h e f e L l s i c k w i t h t Y P H U s h i m s e l f +HYP: t h e A r * h e f e * l s i c k w i t h t * I F A s h i m s e l f +Eval: I D D D S S S + +Speaker sentences 99: lad_eng_000353 #utts: 1 +id: (lad_eng_000353-lad_eng_000353) +Scores: (#C #S #D #I) 53 0 8 3 +REF: s i x * t e A m s H a v E b E e N d I v i d e d i n * * t w o g r o u p s o f t h r e e t e A m s e a c h +HYP: s i x T t e * m s * a v * ******* b * e * d * v i d e d i n T O t w o g r o u p s o f t h r e e t e * m s e a c h +Eval: I D D D D D D D I I D + +Speaker sentences 100: lad_eng_000354 #utts: 1 +id: (lad_eng_000354-lad_eng_000354) +Scores: (#C #S #D #I) 59 2 6 1 +REF: t h e f i r s t S e a s o n p r e m i E R e d o n t w e l F t h j u * n E t w o t h o u s A n d a N d f i f t E e n +HYP: t h e f i r s t C e a s o n p r e m i * A e d o n t w e l * t h j u O n * t w o t h o u s * n d a * d f i f t * e n +Eval: S D S D I D D D D + +Speaker sentences 101: lad_eng_000355 #utts: 1 +id: (lad_eng_000355-lad_eng_000355) +Scores: (#C #S #D #I) 63 4 9 3 +REF: i t s U C c E e D e d t h e * * Y b o a r d a n d s Y s t E m * t w e n t y f o U r c o m b I N i n g f e a T u R e s f R o m b o t h +HYP: i t s * * c * e * e d t h e W H I b o a r d a n d s I s t A m E t w e n t y f o * r c o m b * * i n g f e a C u * e s f * o m b o t h +Eval: D D D D I I S S S I D D D S D D + +Speaker sentences 102: lad_eng_000356 #utts: 1 +id: (lad_eng_000356-lad_eng_000356) +Scores: (#C #S #D #I) 31 2 3 1 +REF: v O l * u m e t W o n u m b e r s o n E t W o a n d t h r E e +HYP: v L l I u m e t O o n u m b e r s o n * t * o a n d t h r * e +Eval: S I S D D D + +Speaker sentences 103: lad_eng_000357 #utts: 1 +id: (lad_eng_000357-lad_eng_000357) +Scores: (#C #S #D #I) 64 2 13 1 +REF: t h e l o w e R p a r t o f m e n s d R e S s e s w E R e m u c h s H o * r t E R i n l e n G T H t h A N t h o s E f o r w O m e n +HYP: t h e l o w e * p a r t o f m e n s d * e * s e s w * * e m u c h s * o U r t * * i n l e n * * C t h * O t h o s * f o r w * m e n +Eval: D D D D D D I D D D D S D S D D + +Speaker sentences 104: lad_eng_000358 #utts: 1 +id: (lad_eng_000358-lad_eng_000358) +Scores: (#C #S #D #I) 42 2 5 0 +REF: t h e v i s i g o t h s i n t U r n w E R e s U C c e E d e d b y t h e m O o r s +HYP: t h e v i s i g o t h s i n t E r n w * * e s * * c e A d e d b y t h e m * o r s +Eval: S D D D D S D + +Speaker sentences 105: lad_eng_000359 #utts: 1 +id: (lad_eng_000359-lad_eng_000359) +Scores: (#C #S #D #I) 36 3 9 1 +REF: j o s E P H h i G H s c H O o l * e v e r y w E e K o f t h e S c H O o l y e a r +HYP: j o s O F h i * * s c * * o l E e v e r y w * e * o f t h e * c * * o l y e a r +Eval: S S S D D D D I D D D D D + +Speaker sentences 106: lad_eng_000360 #utts: 1 +id: (lad_eng_000360-lad_eng_000360) +Scores: (#C #S #D #I) 40 2 5 1 +REF: a s * A r E s U l t o f a L l t h e a r g u m e n t S g e T t i n g t o h e r +HYP: a s T H r * s I l t o f ******* a * l t h e a r g u m e n t * g e * t i n g t o h e r +Eval: I S D S D D D D + +Speaker sentences 107: lad_eng_000361 #utts: 1 +id: (lad_eng_000361-lad_eng_000361) +Scores: (#C #S #D #I) 43 2 3 3 +REF: i t S h E a d ******* q u a r t e r s a r e i n s h e F f i E l d * * u n i t e d K i n g d o m +HYP: i t * h * a d q u a r t e r s a r e i n s h e * f i A l d Y O u n i t e d C i n g d o m +Eval: D D I D S I I S + +Speaker sentences 108: lad_eng_000362 #utts: 1 +id: (lad_eng_000362-lad_eng_000362) +Scores: (#C #S #D #I) 83 4 12 1 +REF: l a y a l s o O F f I C i a L l y s i G n e D t h e c o n t r a c t o n s t a g e w i t H T h e d i r e c t O r a N d p r O d u C e R s o f t h e g o * l d E n e y e s +HYP: l a y a l s o * * f * * i a * l y s i * n e * t h e c o n t r a c t o n s t a g e w i t * * h e d i r e c t E r a * d p r E d u S e * s o f ******* t h e g o U l d A n e y e s +Eval: D D D D D D D D D S D S S D D I S + +Speaker sentences 109: lad_eng_000363 #utts: 1 +id: (lad_eng_000363-lad_eng_000363) +Scores: (#C #S #D #I) 53 9 10 0 +REF: P H Y s i c A l T H e r A p y c A n h e l P p a t i E n T S t o l E A r n h o W t o w a L k w i t h A f O o t d r o p +HYP: * F I s i c * l * F e r I p y c * n h e l E p a t i O n * E t o l * U r n h o * t o w a R k w i t h ******* * f * o t d r o p +Eval: D S S D D S S D S S D S D S D S D D D + +Speaker sentences 110: lad_eng_000364 #utts: 1 +id: (lad_eng_000364-lad_eng_000364) +Scores: (#C #S #D #I) 57 0 6 1 +REF: i t W e n t o n t o s e L l t h r E e h u n d r e d t h o u s a n d u n i t s a ******* c h I E V e f i v e n o +HYP: i t * e n t o n t o s e * l t h r * e h u n d r e d t h o u s a n d u n i t s a c h * * * e f i v e n o +Eval: D D D I D D D + +Speaker sentences 111: lad_eng_000365 #utts: 1 +id: (lad_eng_000365-lad_eng_000365) +Scores: (#C #S #D #I) 24 0 1 1 +REF: t h e n a m e s t * u c k a f T e r t h a t +HYP: t h e n a m e s t O u c k a f * e r t h a t +Eval: I D + +Speaker sentences 112: lad_eng_000366 #utts: 1 +id: (lad_eng_000366-lad_eng_000366) +Scores: (#C #S #D #I) 43 5 5 4 +REF: t h e A l b U m l a t e r b r o k E t h E d i A m O N d r e c o r d o n * * * Q Q m u s i c * +HYP: t h e H l b * m l a t e r b r o k * t h * d i * m * A d r e c o r d o n C U C U O M m u s i c K +Eval: S D D D D D S I I I S S S I + +Speaker sentences 113: lad_eng_000367 #utts: 1 +id: (lad_eng_000367-lad_eng_000367) +Scores: (#C #S #D #I) 44 5 9 1 +REF: i t s e d I t o r i a l w e s u b m i t E a R n E d i t s A U t h O r a p U l I t Z E R * p r i Z e +HYP: i t s e d A t o r i a l w e s u b m i t * a * n * d i t s * O t h * r a ******* p O l t * * * O p r i Y e +Eval: S D D D D S D D S S D D D I S + +Speaker sentences 114: lad_eng_000368 #utts: 1 +id: (lad_eng_000368-lad_eng_000368) +Scores: (#C #S #D #I) 39 3 5 2 +REF: j o s E P H p l a y * s * A r E f e a t u r e d e a c h w e e K o n t h e S h o W +HYP: j o s * I F p l a y E s O U r * f e a t u r e d e a c h w e e * o n t h e * h o * +Eval: D S S I I S D D D D + +Speaker sentences 115: lad_eng_000369 #utts: 1 +id: (lad_eng_000369-lad_eng_000369) +Scores: (#C #S #D #I) 79 0 13 2 +REF: t h e y w a I t f o r a t i m e * b U i l d i n g u p t h e I r f o r C e s b e g I N N i n G t o W o n d E r i f t h i s e * v I l r e a L l y e x i s t s +HYP: t h e y w a * t f o r ******* a t i m e M b * i l d i n g u p t h e * r f o r * e s b e g * * * i n * t o * o n d * r i f t h i s e A v * l r e a * l y e x i s t s +Eval: D D I D D D D D D D D D I D D + +Speaker sentences 116: lad_eng_000370 #utts: 1 +id: (lad_eng_000370-lad_eng_000370) +Scores: (#C #S #D #I) 69 1 6 3 +REF: b r I e f * m e n t i o n o f t h E c o n v i c t i o n a p p e A r E d o n p a g e t h r E e o f t h e n e w y o * R k t i m e * s +HYP: b r * e f E m e n t i o n o f t h * c o n v i c t i o n a p p e * r * d o n p a g e t h r * e o f t h e n e w ******* y o U O k t i m e M s +Eval: D I D D D D D I S I + +Speaker sentences 117: lad_eng_000371 #utts: 1 +id: (lad_eng_000371-lad_eng_000371) +Scores: (#C #S #D #I) 51 1 6 0 +REF: o R d E R e d b y p o s I T i o n o n p i T c h f r o m b a c k r i g h t t o f r O n t l e f t +HYP: o * d * * e d b y p o s * * i o n o n p i * c h f r o m b a c k r i g h t t o f r U n t l e f t +Eval: D D D D D D S + +Speaker sentences 118: lad_eng_000372 #utts: 1 +id: (lad_eng_000372-lad_eng_000372) +Scores: (#C #S #D #I) 55 4 5 3 +REF: h e i s m e m b e r o f t h e c o u r t o F t h e r O Y A l c o L l E G e o f a r t l o * n d o n * u * K +HYP: h e i s m e m b e r o f t h e c o u r t o * t h e r * * I l c o * l * A e o f a r t l o U n d o n Y u C A Y +Eval: D D D S D D S I I I S S + +Speaker sentences 119: lad_eng_000373 #utts: 1 +id: (lad_eng_000373-lad_eng_000373) +Scores: (#C #S #D #I) 77 6 9 4 +REF: d u r i N g t h e c o u r s e o f t H e c a m p a i G n f E r g U s ******* O n * v i s i t ******* E D a l l t h I r t y n * i n E w a s H i N g t O n s t a t e c o U n t I e s +HYP: d u r i * g t h e c o u r s e o f t * e c a m p a i * n f I r g * s A n D v i s i t A T a l l t h E r t y n E i n * w a s * i * g t A n s t a t e c o * n t * e s +Eval: D D D S D I S I I S S S I D D D S D D + +Speaker sentences 120: lad_eng_000374 #utts: 1 +id: (lad_eng_000374-lad_eng_000374) +Scores: (#C #S #D #I) 26 0 0 0 +REF: a s t r i p o f p a p e r o f l e n g t h +HYP: a s t r i p o f p a p e r o f l e n g t h +Eval: + +Speaker sentences 121: lad_eng_000375 #utts: 1 +id: (lad_eng_000375-lad_eng_000375) +Scores: (#C #S #D #I) 61 2 8 6 +REF: s a t o U h a d f r e Q u e n t l y w o r K e D t o ******* g e t h E R w I t h y o * * k ******* O y a m a * o n p r e v i o U s p R o * j e c t s +HYP: s a t o * h a d f r e C u e n t l y w o r * e * t o g e t h * * w * t h y o U C k A y a m a R o n p r e v i o * s p * o G j e c t s +Eval: D S D D I D D D I I I S I D D I + +Speaker sentences 122: lad_eng_000376 #utts: 1 +id: (lad_eng_000376-lad_eng_000376) +Scores: (#C #S #D #I) 75 2 13 3 +REF: s h e W a s b o r n o n ******* s c r e E n d u R i n G t h e e p I s o d E b r O a d ******* c a s t o n f o U r T h n ******* o v e m b e r n i n E t E e N n i n E t y f o U r +HYP: s h e * a s b o r n o n s c r e A n d u * i n * t h e e p * s o d * b r * a d c a s t o n f o * r * h A n o v e m b e r n i n * t * e * n i n * t y f o * r +Eval: D I S D D D D D I D D S I D D D D D + +Speaker sentences 123: m #utts: 77 +id: (m-ailabs_eng_000159-m-ailabs_eng_000159) +Scores: (#C #S #D #I) 66 0 3 0 +REF: h e t u r n e d r o u n d s h E h a d c o m E i n s o g e n t l y t h a t h e h a d n e v e r h E a r d h e r +HYP: h e t u r n e d r o u n d s h * h a d c o m * i n s o g e n t l y t h a t h e h a d n e v e r h * a r d h e r +Eval: D D D + +id: (m-ailabs_eng_000160-m-ailabs_eng_000160) +Scores: (#C #S #D #I) 52 4 6 7 +REF: a H t o b e s * * u * r ******* * E w e m u s t K E e P o u r d O o r s s h * U t ******* w e m u s T l E t n o o n E i n +HYP: a * t o b e s H O u O r A N w e m u s t * C e * o u r d * o r s s h O A t w e m u s * l A t n o o n * i n +Eval: D I I I I I S D S D D I S I D S D + +id: (m-ailabs_eng_000161-m-ailabs_eng_000161) +Scores: (#C #S #D #I) 123 6 15 4 +REF: K i N s ******* * m E n h e b e g a n m o C k i n g l y y o u m a Y h A v e W o n d E R e d w h * y i c a L l E d a t r * u C E w h e n i c o u l d j u s T a s w e l l h a v e d E s t r o Y e d y o u t h a t i d o u B t a t o a n s W E R e d h i m +HYP: C i D s P m O n h e b e g a n m o * k i n g l y y o u m a * h * v e * o n d * * e d w h I y i c a * l * d a t r O u * S w h e n i c o u l d j u s * a s w e l l ******* h a v e d I s t r o R e d y o u t h a t i d o u * t a t o a n s * * * e d h i m +Eval: S S I I S D D D D D D I D D I D S D D S S D D D D + +id: (m-ailabs_eng_000162-m-ailabs_eng_000162) +Scores: (#C #S #D #I) 79 3 10 0 +REF: t h e p e A s A n t t h r E w h i m s e l f U p o n h i m a n d b o u n d h i s f o U r l E g s t i G H t l y s o t H a t h E c O u l d n o t m o v e +HYP: t h e p e * s * n t t h r U w ******* h i m s e l f A p o n h i m ******* a n d b o u n d h i s f o * r l A g s t i * * t l y s o t * a t h * c * u l d n o t m o v e +Eval: D D S D S D D S D D D D D + +id: (m-ailabs_eng_000163-m-ailabs_eng_000163) +Scores: (#C #S #D #I) 103 2 12 3 +REF: n o r m u s t t h o u s o l i m I t * t h e h O l y o n E o f i s r * a E l a s t o t h i n k h e h a t h b u t o n E w a y i n w h i c H h E c a n g L o r i f * Y h I m s e l f b y t h E e +HYP: n o r m u s t t h o u s o l i m E t H t h e h * l y o n * ******* o f i s r I a * l a s t o t h i n k h e h a t h b u t o n * w a y i n w h i c * ******* h * c a n ******* g * o r i f I E h * m s e l f b y t h * e +Eval: S I D D D I D D D D D D D I S D D + +id: (m-ailabs_eng_000164-m-ailabs_eng_000164) +Scores: (#C #S #D #I) 150 4 26 2 +REF: t h e o l d c o m p a r I s o n b e t w E e N t h e i m p u l s i V e e x * e c U t i v e a n d t h e l i b E r a l a r t s m a n w h o * h a S l E a r n e d t h a T t h E R e A r e o n l y o n E O r t W o p o s i t I v e d E C i s i o n s A V a I l A b l e i n a L l t h e w O R l D o F T h i n k i n g +HYP: t h e o l d c o m p a r * s o n b e t w * e * t h e i m p u l s i * e e x S e c * t i v e a n d t h e l i b * r a l a r t s m a n w h o W h a D l * a r n e d t h a * ******* t h * * e ******* * r e o n l y o n * * r t * o p o s i t * v e d * * i s i o n s F a * l * b l e i n a * l t h e w * A l * o * * h i n k i n g +Eval: D D D D I D D I S D D D D D D D D D D D D D S S D D D D S D D D + +id: (m-ailabs_eng_000165-m-ailabs_eng_000165) +Scores: (#C #S #D #I) 74 4 7 3 +REF: a f t e r t h i s e x p e r i E n c e t h e I n v a D E r s w e r E c a * r E f u l t o K E e p * a s a * f e d i s t A n c e f r o m t h e W a L l +HYP: a f t e r t h i s e x p e r i A n c e t h e * n v a T O r s w e r * c a I r * f u l t o * C e p E a s a V f e d i s t * n c e f r o m t h e * a * l +Eval: S D S S D I D D S I I D D D + +id: (m-ailabs_eng_000166-m-ailabs_eng_000166) +Scores: (#C #S #D #I) 126 4 20 3 +REF: C a n Y o u b E a r s o m E t H i n g f U r t h e r i t h I n K y o u O U G H T t o K n o W i t i h a v e h e r E a m o s t m Y s t e r i o u s t e l E p a * * g r a m y e s w h a t i s i t i S s h e d E A d n o * i t i s n o t a b o u t h e r +HYP: * a n * o u b * a r s o m * t * i n g f I r t h e r i t h * n * y o u * * * * * A t o * n o * i t i h a v e h e r * a m o s t m * s t e r i o u s t e l A p a R I g r a m ******* y e s w h a t i s i t i * ******* s h e d * I d n o W i t i s n o t a b o u t h e r +Eval: D D D D D S D D D D D D D S D D D D S I I D D D D S I + +id: (m-ailabs_eng_000167-m-ailabs_eng_000167) +Scores: (#C #S #D #I) 47 2 7 6 +REF: n o m I s t E r t H o * r N t O n s a i * ******* * * d g i V e t h e B a s k E t t o m e ******* i L l t a k e i t +HYP: n o m * s t * r t * o U r * t A n s a i D A N d g i * e t h e * a s k * t t o m e i A l t a k e i t +Eval: D D D I D S I I I I D D D I S + +id: (m-ailabs_eng_000168-m-ailabs_eng_000168) +Scores: (#C #S #D #I) 171 2 11 2 +REF: a n * a r a b i a n n i g h t e x c l a I m e d t r o t w h * y t h a t w a s a m a g i c n i g h t w a s n T i t t h e r E s d i F f E r e n t s o r t s o F n i g h T s m a t e s a i d t h e s a I l O r a n d t h e K n i g h t b u T t O n b r i g h t m e a n s a I n t t h e s a m e n i g h t y o u m e a n +HYP: a n D a r a b i a n n i g h t e x c l a * m e d t r o t w h I y t h a t w a s a m a g i c n i g h t w a s n * i t t h e r * s d i * f * r e n t s o r t s o * n i g h E s m a t e s a i d t h e s a * l E r a n d t h e * n i g h t b u * t * n b r i g h t m e a n s a * n t t h e s a m e n i g h t y o u m e a n +Eval: I D I D D D D D S D S D D D D + +id: (m-ailabs_eng_000169-m-ailabs_eng_000169) +Scores: (#C #S #D #I) 122 2 12 2 +REF: i v e t U r n e d o F f u p w a r d s o f a h u n d R e d O f m y b e s t * h a n d s f o r n o o t h e r f a U l t t h A N f o L l o W i n g y o u a n d s u c h a s y o u a n D d Y E t h i n k i l l t a k e y o u * o n +HYP: i v e ******* t * r n e d o * f u p w a r d s o f a h u n d * e d * f m y b e s t D h a n d s f o r n o o t h e r f a * l t t h E M f o * l o * i n g y o u a n d s u c h a s y o u a n * ******* d * * t h i n k i l l t a k e y o u A o n +Eval: D D D D D I D S S D D D D D D I + +id: (m-ailabs_eng_000170-m-ailabs_eng_000170) +Scores: (#C #S #D #I) 73 5 15 1 +REF: b u t w H e N S H O U L d s h e s E e h i m h e r h E a r t l e A p E D u P i n a P p r e h e n S i o n a t e v e r y r i n G o f t h E d O o r ******* b E L l +HYP: b u t w * e * * * * W I d s h e s * e h i m h e r h * a r t l e * p * T u * i n a * p r e h e n T i o n a t e v e r y r i n * o f t h * d * o r b * I l +Eval: D D D D D S S D D D D S D D S D D D I D S + +id: (m-ailabs_eng_000171-m-ailabs_eng_000171) +Scores: (#C #S #D #I) 126 5 19 6 +REF: t h e s e b o o k s d i * x * o n i w I L l k E e p * a L l t h e r e s t w I L L Y o u s e n d t o m I s t E r b e L l t h e y a r E o f a K i n d t h A t h e w I L l v * A l * * u E f o r t h E m s e l v e s a s w e L l a s f o r p A p a s s a K E +HYP: t h e s e b o o k s d i C x S o n i w * * l k * e p E a * l t h e r e s t w * * E * o u ******* s e n d t o m * s t * r b e * l t h e y a r * o f a C i n d t h * t h e w * * l v O U l Y O u * f o r t h * m s e l v e s a s w e * l a s f o r p O p a s s a * Y +Eval: I I D D D I D D D S D D D D D D S D D D I S I I D D D S D S + +id: (m-ailabs_eng_000172-m-ailabs_eng_000172) +Scores: (#C #S #D #I) 156 3 12 5 +REF: B u t i n g a w a s n o t a t a L l s * u r ******* * * * E t h e Y c o u l d n o t g e t i n t h e g a t E s o p E N e d i n w a r d a n d t h r E e h e A v y b a r s w e r e h e l d i n p l a c e b y m e A n s o f s t o u t s t a p l e s r i v E t e d t o t h e s h E e t s o f s t E E L +HYP: * u t i n g a w a s n o t a t a * l s H u r T H A T t h e * c o u l d n o t g e t i n t h e g a t * s o p * * e d i n w a r d a n d t h r * e h e * v y b a r s w e r e h e l d i n p l a c e b y m e * n s o f s t o u t s t a p l e s r i v I t e d t o t h e s h * e t s o f s t * * A +Eval: D D I I I I I S D D D D D D D S D D D S + +id: (m-ailabs_eng_000173-m-ailabs_eng_000173) +Scores: (#C #S #D #I) 120 5 10 2 +REF: i w a n t t h A L s a i d h o D d A n c o l d l y i w a n T a d o Z E n h o r s e s i w a n t m e n t o * r i * d E t h e M w i t h m e h e p u s h E d h i S w a y f o R W A r d w h i c h w a y t o t h e s t a b l e s +HYP: i w a n t t h O W s a i d h o * d O n c o l d l y i w a n * a d o S O n h o r s e s i w a n t m e n t o B r i G d * t h e * w i t h m e h e p u s h * d h i * w a y f o * * * r d w h i c h ******* w a y t o t h e s t a b l e s +Eval: S S D S D S S I I D D D D D D D D + +id: (m-ailabs_eng_000174-m-ailabs_eng_000174) +Scores: (#C #S #D #I) 129 1 14 8 +REF: T H e r e i s a l i m i T t O w h a t y O u c a n * d * ******* * o t h e f i r s t t * i M e y o u E n t e r a m a n s h o u s e a n d b e s i d e S t h a t w a s n o t i m e t o a r o u s E s u s p I C i o n I n t h e m i n d * o f a n y ******* * o n E +HYP: * * e r e i s a l i m i * ******* t * w h a t y * u ******* c a n D d O F o t h e f i r s t t H i * e y o u A n t e r a m a n s h o u s e a n d b e s i d e * t h a t w a s n o t i m e t o a r o u s * s u s p * * i o n * n t h e m i n d S o f a n y W o n * +Eval: D D D D D D D I I I I I D S D D D D D I I I D + +id: (m-ailabs_eng_000175-m-ailabs_eng_000175) +Scores: (#C #S #D #I) 98 5 10 2 +REF: d o Y o u n o t r e m e m B e r t h a t h e s a Y s t h y d e m O n t h a t s t h Y s p i r i t w h i c h K E e p * s t h E e i s n o b l e c o U r a g E o u s h i G H u n ******* m a T c h A b l E +HYP: d o * o u n o t r e m e m * e r t h a t h e s a * s t h y d e m A n t h a t s t h E s p i r i t w h i c h * C e p E s t h * e i s n o b l e c o * r a g * o u s h i * Y u n m a * c h I b l * +Eval: D D D S S D S I D D D D S I D S D + +id: (m-ailabs_eng_000176-m-ailabs_eng_000176) +Scores: (#C #S #D #I) 66 4 8 1 +REF: m I s t E r b e l l w h a T c a n h e K n o W o f j o * H n h e l i v i n g a l a Z y l i f E i n a d r o W s y c o L l E g e +HYP: m * s t * r b e l l w h a * ******* c a n h e * n o * o f j o A O n h e l i v i n g a l a S y l i f * i n a d r o U s y c o * l A g e +Eval: D D D D D D I S S D S D S + +id: (m-ailabs_eng_000177-m-ailabs_eng_000177) +Scores: (#C #S #D #I) 36 3 8 1 +REF: a n d t h e K i T t E n f o L l o W e D d E m u * r E l y a t t h e I r h e E l s +HYP: a n d t h e C i * t * n f o * l o * e * ******* d I m u A r * l y a t t h e * r h e A l s +Eval: S D D D D D D S I D D S + +id: (m-ailabs_eng_000178-m-ailabs_eng_000178) +Scores: (#C #S #D #I) 98 1 6 1 +REF: t h e f i R s t t O u * c h w o U l d c a U s e a n e x p l o s i o n i n w h i c h a m o n g s u c h h u n d r e d s o f i n f U r i a t e d m e n a n d r e c k l e S s b o y s +HYP: t h e f i * s t t * u T c h ******* w o * l d c a * s e a n e x p l o s i o n i n w h i c h a m o n g s u c h h u n d r e d s o f i n f E r i a t e d m e n a n d r e c k l e * s b o y s +Eval: D D I D D D S D + +id: (m-ailabs_eng_000179-m-ailabs_eng_000179) +Scores: (#C #S #D #I) 63 2 10 2 +REF: * o n E O f t h E g R e a t p l e A s u R e * s o f m a r g A r E t s l i f E a t t h i s t i m e w a s i n e D I t H s b o y +HYP: W o n * * f t h * g * e a t p l e * s u * e R s o f m a r g * r A t s l i f * a t t h i s t i m e w a s i n e * A t * s b o y +Eval: I D D D D D D I D S D D S D + +id: (m-ailabs_eng_000180-m-ailabs_eng_000180) +Scores: (#C #S #D #I) 119 4 22 1 +REF: t h E t h I n g H A s g o n E o n l o n g E n o U G H I f t h e r E i s o n e M o r e b i * g a c C i d e n t w e s h a L l h a v e t o c o m p r O m i s e w i t H t h e i n T e R r i v e r A n d c a R r y o n t h e w o r k J O i n t l y +HYP: t h * t h * n g * I s g o n * o n l o n g * n o * * * ******* * f t h e r * i s o n e * o r e b i A g a c X i d e n t w e s h a * l h a v e t o c o m p r * m i s e w i t * t h e ******* i n * e * r i v e r * n d c a * r y ******* o n t h e ******* w o r k C U i n t l y +Eval: D D D S D D D D D D D D D I S D D D D D D D D D D S S + +id: (m-ailabs_eng_000181-m-ailabs_eng_000181) +Scores: (#C #S #D #I) 52 0 9 0 +REF: y o u a r E l a t E s a i d s h e w e L l s h e h e l d h e r b r E a t h F o R t h e a n s W E r +HYP: y o u ******* a r * l a t * s a i d s h e w e * l s h e h e l d h e r b r * a t h * o * t h e a n s * * r +Eval: D D D D D D D D D + +id: (m-ailabs_eng_000182-m-ailabs_eng_000182) +Scores: (#C #S #D #I) 115 8 16 1 +REF: t r O t t o l d t h e g i r l s t h a t t h e y m u s T g o w i t H T h e I r f a t h e r t o l i v E I n * g H i p G H I s i Z Z l E s l i T t L e O l d c a b I n a n d W h e n t h e y h e A r d t h I s D r e A d f u l d e c r E e +HYP: t r A t t o l d t h e g i r l s t h a t t h e y m u s * g o w i t * * h e * r f a t h e r t o l i v * A n D g * i p C U s i S I l * s l i * t * e * l d c a b E n a n d * h e n t h e y h e * r d t h * s * r e * d f u l d e c r * e +Eval: S D D D D D S I D S S S S S D D D D S D D D D D D + +id: (m-ailabs_eng_000183-m-ailabs_eng_000183) +Scores: (#C #S #D #I) 122 3 15 1 +REF: m a r g A R E t s a t d o W n o N t h e r U g p a R t l y t o w a r m h e r s e l f f o r t h e d a m p n e S s o F t h e e v E n i n g h u n g A b o u t h e r d r e S s a n d o v e r ******* f A T i G U e h a d m a d E h e r c h i L l y +HYP: m a r g * * I t s a t d o * n o * t h e r O g p a * t l y t o w a r m h e r s e l f f o r t h e d a m p n e * s o * t h e e v * n i n g h u n g * b o u t h e r d r e * s a n d o v e r f * * i * T e h a d m a d * h e r c h i * l y +Eval: D D S D D S D D D D D D I D D D S D D + +id: (m-ailabs_eng_000184-m-ailabs_eng_000184) +Scores: (#C #S #D #I) 113 4 10 2 +REF: o H n o * y o u a r E m I s t a k E n a b o u t t h a t r e P l i E d t h e k i n g t h e y a r e n o t m y p r i s o n e r s b u t m y s l a v e s w h o m * I p u r c H A s e D f r o m t h e K i n g o f e v +HYP: o * n o W y o u ******* a r * m * s t a k A n a b o u t t h a t r e * l i * d t h e k i n g t h e y ******* a r e ******* n o t m y p r i s o n e r s b u t m y s l a v e s w h o m M Y p u r c * U s e * f r o m t h e C i n g o f e v +Eval: D I D D D S D D D D I S D S D S + +id: (m-ailabs_eng_000185-m-ailabs_eng_000185) +Scores: (#C #S #D #I) 26 2 7 0 +REF: h e r f a t h e R t O o k u P t H e c O N V E r s a t i o n +HYP: h e r f a t h e * t * o k ******* u * t * e c * * M B r s a t i o n +Eval: D D D D D D D S S + +id: (m-ailabs_eng_000186-m-ailabs_eng_000186) +Scores: (#C #S #D #I) 137 6 14 1 +REF: i n a c o r n e r w a s a s o r t o f d r e S s i n g ******* t a b l e o n w h i c h l a y a c o m B a n d b r u s h K E N n E d y s E e M e d m u c h i n t E r E s t e d i n t h e t a b l e a n D w a s e x a m I N i n g i t w h E n t h e g U r u r e t U r n e D +HYP: i n a ******* c o r n e r w a s a s o r t o f d r e * s i n g t a b l e o n w h i c h l a y a c o m * a n d b r u s h * C A n I d y s * e * e d m u c h i n t * r U s t e d i n ******* t h e t a b l e a n * w a s e x a m * * i n g i t ******* w h * n t h e g O r u r e t E r n e * +Eval: D D I D D S S S D D D S D D D D D D S S D + +id: (m-ailabs_eng_000187-m-ailabs_eng_000187) +Scores: (#C #S #D #I) 149 2 17 1 +REF: i h a v e s o m e ******* t i m e S t h O U g H t t h a t m y s e l f s h e a g R e e d b u t o f c o u r s E i d o n t K n o w s t i L l i h a v e t o b e p R E T t y c a r E f u l s o m e o n E i s a l w a y s o v e r h e r E b y m y d e s K o r l O o k i n g o v e r h e r E +HYP: i h a v e s o m e t i m e * t h * * g * t t h a t m y s e l f s h e a g * e e d b u t o f ******* c o u r s * i d o n t * n o w s t i * l i h a v e t o b e p * * I t y c a r * f u l s o m e ******* o n * i s a l w a y s o v e r h e r * b y m y d e s S o r l * o k i n g o v e r h e r * +Eval: I D D D D D D D D D D D S D D D D S D D + +id: (m-ailabs_eng_000188-m-ailabs_eng_000188) +Scores: (#C #S #D #I) 53 1 7 0 +REF: i s h A L l s t a y r e p l i E d t h e y o U n g m a n f o r i m e a n t o s E t y o U f r E e +HYP: i s h * * l s t a y r e p l i * d t h e ******* y o * n g m a n f o r i m e a n t o s I t y o * f r * e +Eval: D D D D D S D D + +id: (m-ailabs_eng_000189-m-ailabs_eng_000189) +Scores: (#C #S #D #I) 29 0 4 0 +REF: w h a t d O y o U d o a s K E d t h e s o r c e r e r +HYP: w h a t d * y o * d o a s * * d t h e s o r c e r e r +Eval: D D D D + +id: (m-ailabs_eng_000190-m-ailabs_eng_000190) +Scores: (#C #S #D #I) 138 3 18 2 +REF: w h * y t h e Y r e O U r e n E m I e s y o u r s h o r t h i G H n e S s n o t a n y m o r e r e p l i e d t r o * t i m q u E e N o f t h e P i n k I e s a n d i m a l s o q u E e N o f t h e B l U E s s o i w o n t h a v e m y p e O p l e q u a R r E l i n g +HYP: w h I y t h e * r e * A r e n A m * e s y o u r s h o r t h i * * n e * s n o t a n y m o r e r e p l i e d t r o A t i m q u * e * o f t h e * i n k * e s a n d i m a l s o q u * e * o f t h e ******* * l * O s s o i w o n t h a v e m y p e * p l e q u a * r * l i n g +Eval: I D D S S D D D D I D D D D D D D D D S D D D + +id: (m-ailabs_eng_000191-m-ailabs_eng_000191) +Scores: (#C #S #D #I) 154 8 31 4 +REF: t Y p E W r i t e r S w E R e c l i c k i n g c l i P p i n g S w e r E b E i n g s n i P p E d o U t o f a H u g e S t a c k o f n * e W S P A p e r s a n d p a s T e d i * ******* * n T O l a r g E s c r a p b O o k s C I r C u l A r S w e r E b e I n g f o l d e d a n D m a d E r e a D y t o m a I l f o R t h e f i n a l a P p e A l +HYP: t I p * * r i t e r * w * * e c l i c k i n g c l i * p i n g * w e r * b * i n g s n i * p * d o * t ******* o f a * u g e * t a c k o f n O e * * * p e r s a n d p a s * e d i N A n N l a r g * s c r a p b * o k s S E r K u l E r * w e r * b e * n g f o l d e d a n * m a d * r e a * y t o m a * l f o * t h e f i n a l a * p e * l +Eval: S D D D D D D D D D D D D D D D I D D D S D I I I S S D D S S S S D D D D D D D D D D + +id: (m-ailabs_eng_000192-m-ailabs_eng_000192) +Scores: (#C #S #D #I) 114 3 11 4 +REF: i t w a s f o U r d a y s a f t e r t h e s u R p r i S e * o f a D l * * e r s h o r s T W h e n t h e s t r a n g e r s l e F t t h e E s t a t E t o t h e c a * r E o f r u G g e d o l d f o r s t e r h E r m A N n +HYP: i t w a s f o * r d a y s a f t e r t h e s u * p r i * e S o f a * l T H e r s h o r s * * h e n t h e s t r a n g e r s l e * t t h e A s t a t * t o t h e c a I r * o f r u * g e d o l d f o r s t e r h A r m * E n +Eval: D D D I D I I D D D S D I D D S D S + +id: (m-ailabs_eng_000193-m-ailabs_eng_000193) +Scores: (#C #S #D #I) 123 4 29 9 +REF: * p O o r t e m p l E t o n h e s a i d i u s E D t o K n o w h i m * * * y ******* e a r s a g o W h e N w e W E R e b o y s W e n T t o s c H o O l w i t H h I m a n d a L l t h a t s o R t o f t h I n g y o U K n o w b u t U n ******* t i l i r a n A c r o S s h I m ******* * * +HYP: B p * o r t e m p l * t o n h e s a i d i u s * * ******* t o ******* * n o w h i m M A N y e a r s a g o * h e * w e * * * e b o y s M e n * ******* t o s c * o U l ******* w i t * ******* h * m a n d a * l t h a t s o U t ******* o f ******* t h * n g y o * ******* * n o w b u t A n t i l i r a n * c r o * s h * m O R +Eval: I D D D D D D D I I I I D D D D D S D D D S D D D D D S D D D D D D S I D D D I I I + +id: (m-ailabs_eng_000194-m-ailabs_eng_000194) +Scores: (#C #S #D #I) 64 3 10 0 +REF: i f o U n d h e r i N t h e f O r E s t a n d b R o U g H t h e r h e r E a p r i s O n e R r e p l i e D t h e c a p t A I n +HYP: i f o * n d h e r i * t h e f A r I s t a n d b * o * g * t h e r h e r * a p r i s * n e * r e p l i e * t h e c a p t * O n +Eval: D D S S D D D D D D D D S + +id: (m-ailabs_eng_000195-m-ailabs_eng_000195) +Scores: (#C #S #D #I) 129 5 12 1 +REF: w h o m a y b e c o m p E t e n t E i t h e R f r o m p e r s O n a l e x p e r i E n c e o r t h e e x p E R i E n C E o f o t h e r s t o a n s W e r i t w i t h m o r E o r l e S s c O R r e c t n e S s o r a t l e a s t a n a t t e m P t * +HYP: w h o m a y b e c o m p I t e n t * i t h e * f r o m p e r s I n a l e x p e r i A n c e o r t h e e x p * * i * n * S o f o t h e r s t o a n s * e r i t w i t h m o r * o r l e * s c * U r e c t n e * s o r a t l e a s t a n a t t e m * t D +Eval: S D D S S D D D D S D D D D S D D I + +id: (m-ailabs_eng_000196-m-ailabs_eng_000196) +Scores: (#C #S #D #I) 48 3 15 3 +REF: o N E H U n D R E D n i n E t y ******* t W o l a Y t e s t r e * e t s A i d h o * g A n b i t i n g o F f h i s C I g a r +HYP: o * * ******* * * n * * * * n i n * t y t * o l a * t e ******* s t r e T e t s * i d h o K g E n b i t i n g o * f h i s S A g a r +Eval: D D D D D D D D D D I D D D I D I S D S S + +id: (m-ailabs_eng_000197-m-ailabs_eng_000197) +Scores: (#C #S #D #I) 143 7 24 0 +REF: t r O t w a S s U r p r i S e D t o f i n D s h e c o u L d S E e s o p l a i n l y t h r O U G H t h e h i G H w a L l o f w a t e r a b o v e h e r b u t t h e s U n w a s a b l E t o s h O O t i t s b e A m S s t r a I G H t d o w N t h R o U G H t h e t r a n s p a r e n t s e A +HYP: t r A t w a * s * r p r i * e * t o f i n E s h e c o u * d * C e s o p l a i n l y t h r * * * * t h e h i * Y w a * l o f w a t e r a b o v e h e r b u t t h e s O n w a s a b l * t o s h * U t i t s b e * m E s t r a * * * t d o w * t h * o * * * t h e t r a n s p a r e n t s e * +Eval: S D D D D S D D S D D D D D S D S D D S D S D D D D D D D D D + +id: (m-ailabs_eng_000198-m-ailabs_eng_000198) +Scores: (#C #S #D #I) 23 1 7 0 +REF: t h e s p O t w H e r E i T H A d s p r U n g u p +HYP: t h e s p A t w * e r * i * ******* * * d s p r * n g u p +Eval: S D D D D D D D + +id: (m-ailabs_eng_000199-m-ailabs_eng_000199) +Scores: (#C #S #D #I) 36 1 11 1 +REF: c A L m * d e n i A l w H i c H s h e g a v e T O s u c h a S u P p o s I T i o n +HYP: c * O m E d e n i * l w * i c * s h e g a v e ******* * * s u c h a * u * p o s * * i o n +Eval: D S I D D D D D D D D D D + +id: (m-ailabs_eng_000200-m-ailabs_eng_000200) +Scores: (#C #S #D #I) 116 11 8 5 +REF: y o u s e e U n * t i l t h E S e s c H O o l p i L l s w e r E i n v e n t e d w e w a s t e d a l o t o f t i m e i n * s t u * d y t h a t M A Y N O W b e b e T t e r E m p l o y e d i n * p r a c t i C i n g * a t h l E t i C S +HYP: y o u s e e A n D t i l t h * * e s c * * o l p i * l s w e r * i n v e n t e d w e w a s t e d a l o t o f t i m e i n D s t u A d y t h a t N O W M A Y b e b e * t e r I m p l o y e d i n M p r a c t i S i n g E a t h l A t i * K +Eval: S I D D D D D D I I S S S S S S D S I S I S D S + +id: (m-ailabs_eng_000201-m-ailabs_eng_000201) +Scores: (#C #S #D #I) 55 2 8 0 +REF: y o U v e d o n E i t n o w d E c l a r e D d O r O t h y t h e s E t e n t s a r E j u s t w o n d e r f U l +HYP: y o * v e ******* d o n * i t n o w d I c l a r e * d A r * t h y t h e s * t e n t s a r * j u s t w o n d e r f * l +Eval: D D D S D S D D D D + +id: (m-ailabs_eng_000202-m-ailabs_eng_000202) +Scores: (#C #S #D #I) 121 8 17 2 +REF: f o r t w e n * T Y t e n f i v e t h r E e t w o ******* t h e L i n E R w a s b a r E l y t w e n T y m I L E s a w a y w h E n h o D d A n f i r e d h i s r o c k E t s t h e Y m A d e A c O l o S s a l c l o u d o F v a p O r i n e m P t i n e S s +HYP: f o r t w e n I N G t e n f i v e t h r * e t w o t h e * i n * * w a s b a r * l y t w e n * y m * O U s a w a y w h * n h o * d O n f i r e d h i s r o c k I t s t h e * m * d e ******* * c A l o * s a l c l o u d o * v a p E r i n e m * t i n e * s +Eval: I S S D I D D D D D D S S D D S S D D D D S D D S D D + +id: (m-ailabs_eng_000203-m-ailabs_eng_000203) +Scores: (#C #S #D #I) 134 5 16 1 +REF: t h e y p a I d n o a T t e n T i o n t o t h e f a c T t h a t g H i p ******* g H I s i Z Z l E d i d n o t w A n t t o m a R r y a n y o f t h e m f o r t h e y h A D d e t e r m I n E d t h a t W h e n i t w a s a g r e e d w h o S h o u L d h a v e h i m +HYP: t h e y p a * d n o a * t e n C i o n t o t h e f a c * ******* t h a t g * i p g U S s i * S l * d i d n o t w * n t t o m a * r y a n y o f t h e m f o r t h e y h * * ******* d e t e r m E n * d t h a t * h e n i t w a s a g r e e d w h o * h o u * d h a v e h i m +Eval: D D S D D D I S S D S D D D D D D S D D D D + +id: (m-ailabs_eng_000204-m-ailabs_eng_000204) +Scores: (#C #S #D #I) 84 1 18 1 +REF: w h a t d O Y o u t H i n K o f t h a t h e c r i E d * o p e N I n g a c o p y o F T h e r e c O r d a n d l a Y i N g I t f l a t o n t h e l i b R A r y t a b l e +HYP: w h a t d * ******* * o u ******* t * i n * o f t h a t h e c r i * d E o p e * * n g a c o p y ******* o * * h e r e c A r d a n d l a * i * g * t f l a t o n ******* t h e l i b * * r y t a b l e +Eval: D D D D D D D I D D D D D S D D D D D D + +id: (m-ailabs_eng_000205-m-ailabs_eng_000205) +Scores: (#C #S #D #I) 26 1 5 2 +REF: i t W I L l r e Q u i R e * b u t a s h o * r t t i m E +HYP: i t * * * l r e C u i * e R b u t a s h o U r t t i m * +Eval: D D D S D I I D + +id: (m-ailabs_eng_000206-m-ailabs_eng_000206) +Scores: (#C #S #D #I) 85 2 3 0 +REF: a n d l a s t t h e c r o W d o F v e g E t a b l e p e o p l e w h o h a d n o h E a r t s a n d c o u l d n E i t h e r s m i l e n o r f r o w n +HYP: a n d l a s t t h e c r o U d o * v e g I t a b l e p e o p l e w h o h a d n o h * a r t s a n d c o u l d n * i t h e r s m i l e n o r f r o w n +Eval: S D S D D + +id: (m-ailabs_eng_000207-m-ailabs_eng_000207) +Scores: (#C #S #D #I) 29 0 5 0 +REF: t h e n y o u L l c a T c h i t s A i D t h e w i T c h +HYP: t h e n y o u * l c a * c h i t s * i * t h e w i * c h +Eval: D D D D D + +id: (m-ailabs_eng_000208-m-ailabs_eng_000208) +Scores: (#C #S #D #I) 105 4 19 0 +REF: w h a t i s i t i q u E r I e d n o t f E e l i n g C e r t A I n b u t t h a T i T w a s a v E I l e d a T t e m p T t o s e c u r e A l i T t l E f r E e a d V E r t i s i n g f o r t h e V a n d e R v E e r +HYP: w h a t i s i t i q u I r * e d n o t f * e l i n g S e r t * * n b u t t h a * i * w a s a v * A l e d a * t e m p * t o s e c u r e ******* * l i * t l * f r * e a d * * r t i s i n g f o r ******* t h e * a n d e O v * e r +Eval: S D D S D D D D D S D D D D D D D D D D D S D + +id: (m-ailabs_eng_000209-m-ailabs_eng_000209) +Scores: (#C #S #D #I) 130 4 14 1 +REF: s o h e g a v e t h e C l E r k t h E t h I r d h u n D r E D d o L l A r s f o r b o o k s a n d a c a s k o f g O o d o l d a l E f o r p e t e r t h e c l E r k d r a n k t h e a * l E h i m s e l f a n d g a v e t h e c a L F m i L K +HYP: s o h e g a v e t h e * l U r k t h A t h * r d h u n * r * * ******* d o * l * r s f o r b o o k s a n d a c a s k o f g * o d o l d a l * f o r p e t e r t h e c l U r k d r a n k t h e a I l * h i m s e l f a n d g a v e t h e c a * H m i * * +Eval: D S S D D D D D D D D D S I D D S D D + +id: (m-ailabs_eng_000210-m-ailabs_eng_000210) +Scores: (#C #S #D #I) 154 6 19 7 +REF: * * ******* l i k e t h a t I n a l I C E i n w O n D e r l a n D w i t h m e r E l y a g r i n T h a t f a D e d a w a y c h a n g i n g i n t o a l Y n * x * w h i c h i n t u r n d i s A P p e A r e d f o L L o W e d b y a n u N K n o W n c r e a t u R e * w i t h s h o r t n o * s E a n d p o I n T e d e a r s +HYP: A T l i k e t h a t A n a l * * S i n w * n * e r l a n T w i t h m e r * l y a g r i n * h a t f a T e d a w a y c h a n g i n g i n t o a l I n K x E w h i c h i n ******* t u r n d i s * O p e * r e d f o * * o * e d b y a n u * * n o * n c r e a t u * e R w i t h s h o r t n o W s * a n d p o * n * e d e a r s +Eval: I I I S D D S D D S D D S S I I D D S D D D D D D D D I I D D D + +id: (m-ailabs_eng_000211-m-ailabs_eng_000211) +Scores: (#C #S #D #I) 113 5 18 5 +REF: s h e c o u L d n o t d o ******* m a r g A r E t G l a n C e d u n ******* c o n S C i o u s l y a t t h e u n ******* c l E A N e D c o r n e r S O f t h E r O o m ******* s h e c o u L d h a r D L y u N d e r ******* t a k e a s e r V A n t s p l a c e c o u l D s h e +HYP: s h e c o u * d n o t d o m a r g * r I t * l a n S e d u n c o n * * i o u s l y a t t h e u n c l * * * e * c o r n e r * * f t h * r * o m s h e c o u * d ******* h a r T H y u * d e r t a k e a s e r * I n t s p l a c e c o u l * s h e +Eval: D I D S D S I D D I D D D D D D D D I D D S S D I D S D + +id: (m-ailabs_eng_000212-m-ailabs_eng_000212) +Scores: (#C #S #D #I) 55 3 3 2 +REF: n o s h e r e p l i * e d w i t h i N n O C E n T c U r i o * s i t y d i d i g i v e t h e m t o y o u +HYP: n o s h e r e p l i D e d w i t h i * n * I S n * c A r i o U s i t y d i d i g i v e t h e m t o y o u +Eval: I D D S S D S I + +id: (m-ailabs_eng_000213-m-ailabs_eng_000213) +Scores: (#C #S #D #I) 87 3 13 4 +REF: m a r L b O r o U G H m i l L s a n D t h e a D J a c * e n t d w e L l i n G w e r e h e l d u n d e r A l o n g l e a * * s E t h e y m u s t i f p o S s i b l e b e r e l e * t +HYP: m a r * b * r o * * * m i l E s a n * t h e ******* a G a c S e n t d w e * l i n * w e r e h e l d u n d e r ******* * l o n g l e a C T s * t h e y m u s t i f p o * s i b l e b e r e l e A t +Eval: D D D D D S D D S S I D D D D I I D D I + +id: (m-ailabs_eng_000214-m-ailabs_eng_000214) +Scores: (#C #S #D #I) 18 9 4 2 +REF: a c O p w a v e D A s t U n P i s ******* t O L * a T H I M +HYP: a c A p w a v e * * O s t O n i s t H E L a * ******* D O R +Eval: S D D S S S I S S I D D S S S + +id: (m-ailabs_eng_000215-m-ailabs_eng_000215) +Scores: (#C #S #D #I) 137 3 14 3 +REF: i t b o u n d e d h e * r E a n d t h e * r E a b o U t t h e c H i c K E n h o u s e a n d a t f i r s t d o r O t h Y c o u l d n o t t e L l w h a t i t w a * s w h i l E t h e s c r e E c H i n g o f t h e c H i c K E n s n e a r l y d e A f e n E d h e r +HYP: i t b o u n d e d h e A r * a n d t h e I r * a b o * t t h e c * i c * A n h o u s e a n d a t f i r s t d o r * t h * c o u l d n o t t e * l w h a t i t w a S s w h i l * t h e s c r e A c * i n g o f t h e c * i c * O n s n e a r l y d e * f e n * d h e r +Eval: I D I D D D D S D D D I D S D D D S D D + +id: (m-ailabs_eng_000216-m-ailabs_eng_000216) +Scores: (#C #S #D #I) 147 3 16 3 +REF: t h e s o l d I e r g a v e a y E L l t h a t a r o u * s e d a s c o r E o f h i s c o m r a d E s a n d b R o U g h t t h e m t u m b l i n g i n t o t h e s t r e E t w H e n t h e Y s a w h o W t h e b O o l O O r O O s * p r e C i o u s p r i s O n e R w a s e ******* s c a p i n g +HYP: t h e s o l d * e r g a v e a y * A l t h a t a r o u W s e d a s c o r * o f h i s c o m r a d * s a n d b * o * g h t t h e m t u m b l i n g i n t o t h e s t r e A t w * e n t h e * s a w h o * t h e b * o l * * r * * s E p r e S i o u s p r i s * n e * w a s e s c a p i n g +Eval: D D S I D D D D S D D D D D D D D I S D D I + +id: (m-ailabs_eng_000217-m-ailabs_eng_000217) +Scores: (#C #S #D #I) 132 4 7 0 +REF: j i m h a d r e f u s e d t o l e a v e t h e f i e l d o f g r a s s w h e r e h e w a s E n g a g e d I n b u s i l y e a t i n g s o t h e w i Z A r d g o t o u t o F t h e B u G g Y a n d j o I n e d Z e b a n d d o r O t h y +HYP: j i m h a d r e f u s e d t o l e a v e t h e f i e l d o f g r a s s w h e r e h e w a s * n g a g e d * n b u s i l y e a t i n g s o t h e w i S U r d g o t o u t o * t h e * u * g * a n d j o * n e d S e b a n d d o r I t h y +Eval: D D S S D D D D D S S + +id: (m-ailabs_eng_000218-m-ailabs_eng_000218) +Scores: (#C #S #D #I) 73 3 22 0 +REF: C e r t A I n l y i A m a s i n T E r E S t E d i N t h e c a S e A s Y o u a r E b u t i c a n T m a k E h E a d s O r t a I l s o f i t i r e p l i E d +HYP: S e r t * * n l y i ******* * m ******* a s i n * * r * U t * d i * t h e c a C e ******* * s * o u a r * b u t i c a n * m a k * h * a d s ******* * r ******* t a * l s o f i t i r e p l i * d +Eval: S D D D D D D D D S D D S D D D D D D D D D D D D + +id: (m-ailabs_eng_000219-m-ailabs_eng_000219) +Scores: (#C #S #D #I) 30 1 1 0 +REF: o r a n y m i c e o r e v e n g r a s S h o P p e r s +HYP: o r a n y m i c e o r e v e n g r a s h o * p e r s +Eval: S D + +id: (m-ailabs_eng_000220-m-ailabs_eng_000220) +Scores: (#C #S #D #I) 124 11 18 2 +REF: a n d t h e M t h a T p a Y s Y o d U n t h E y t e L l y o * w H a T t E N t o d o o r w h A t T E n n o t t o d o w I t h e m o n E y t h e y g i v e S y o u I n j u s t p a Y m e n t f o R y o u r p a i n s ******* i n F A I r e x C H a n g e l i K E +HYP: a n d t h e * t h a * p a * s ******* I o d O n t h * y ******* t e * l y o U w * a * t * * t o d o o r w h * t I n n o t t o d o w E t h e m o n * y t h e y g i v e * y o u A n j u s t p a * m e n t f o * y o u r p a i n s i n T H E r e x * T a n g e l i * G +Eval: D D D D S S D D D I D D D D D S S S D D S D D I S S S D S D S + +id: (m-ailabs_eng_000221-m-ailabs_eng_000221) +Scores: (#C #S #D #I) 31 1 6 0 +REF: w h a t d O E s t H a t m e a n a s K E D t h e p r i n c e S s +HYP: w h a t d * I s t * a t m e a n a s * * * t h e p r i n c e * s +Eval: D S D D D D D + +id: (m-ailabs_eng_000222-m-ailabs_eng_000222) +Scores: (#C #S #D #I) 120 1 11 2 +REF: h e h a d b E e N d r o W n E d h e w a s f l o A t i n g i n a s e A o f l i g H t a n d n o w A n D t h e n s h i n i n g l i T t l e f i S h e s s w * a m i n * q u i s i t i v e l y u p t o h i m a n d s t a r e D +HYP: h e h a d b * e * d r o U n * d h e w a s f l o * t i n g i n a s e * o f l i g * t a n d n o w * n * t h e n s h i n i n g l i * t l e f i * h e s s w E a m i n C q u i s i t i v e l y u p t o h i m a n d s t a r e * +Eval: D D S D D D D D D D D I I D + +id: (m-ailabs_eng_000223-m-ailabs_eng_000223) +Scores: (#C #S #D #I) 185 11 35 5 +REF: b u t o l d g u N n A R h a d a t r I c k O R t W o l e f t * * * r e m e m B e R t h e t a * l E T H A T i r e A d t o y o u i N t h E t h R o n E r O o m O F b A L D A r t h e f i r s t O f t h e B r * o n s t o E n T E R t h e w o r l d o f o p A l w e r e s o l D I e r s s e n t f r o m s o m e b l a s t e d p l a n E t i n o u t E r s p a c e t o f i n D a n E w h o M E +HYP: b u t o l d g u * n * * h a d ******* a t r * c k ******* * A t * o l e f t A N D r e m e m * e * t h e t a I l * ******* * * * * i r e * d t o y o u i * t h * t h * o n r * o m * * A b O T H E r t h e f i r s t * f t h e * r A o n s t o * n * * D t h e w o r l d o f o p * l w e r e s o l * G e r s s e n t f r o m s o m e b l a s t e d p l a n I t i n o u t * r s p a c e t o f i n E a ******* n * w h o * * +Eval: D D D D D D D S D I I I D D I D D D D D D D D D D S D D D S S S S S D D I D D D S D D S S D S D D D D + +id: (m-ailabs_eng_000224-m-ailabs_eng_000224) +Scores: (#C #S #D #I) 94 2 14 1 +REF: p a p a w i L l Y o u s p e A k t O t h e m e n a n d g e T T h e M T o g o a w a y s h e c a N n o t b r e A t h E p O o r t h i n g w i t H t h i s c r o W d * a b o u t h e r +HYP: p a p a w i * l * o u ******* s p e * k ******* t * t h e m e n a n d g e * * h e * * o g o a w a y s h e c a * n o t b r e E t h * p * o r t h i n g w i t * t h i s c r o U d O a b o u t h e r +Eval: D D D D D D D D D D D S D D D S I + +id: (m-ailabs_eng_000225-m-ailabs_eng_000225) +Scores: (#C #S #D #I) 110 4 22 2 +REF: w h e n i t o o k t h i s c a S e h e s a i d i b E l I e v e D d o w n i n * m y h E a r T T H A t d i x * o n w a s i N n O C e n t i s t I L L b e l I E V e i t b u t m y f a I t H h a s b E e n r u d E l y s h a k e N +HYP: w h e n i t o o k t h i s c a C e h e s a i d i b * l * e v e * d o w n i n D m y h * a r * ******* * * * t d i x S o n w a s i * n * S e n t i s t * * O b e l * * * e ******* i t b u t m y f a * t * ******* h a s b * e n r u d T l y s h a k e * +Eval: S D D D I D D D D D D I D D S D D S D D D D D D D D S D + +id: (m-ailabs_eng_000226-m-ailabs_eng_000226) +Scores: (#C #S #D #I) 29 3 4 4 +REF: c h a p t E r s i * X o f * t h e p i r A t E S o f E r ******* s * a t Z +HYP: c h a p t * r s i C K o f E t h e p i r * t * * o f O r s E a t S +Eval: D I S I D D D S I I S + +id: (m-ailabs_eng_000227-m-ailabs_eng_000227) +Scores: (#C #S #D #I) 26 0 3 1 +REF: r e m e m b e R t h e Y c a n ******* n o t t O u c h u s +HYP: r e m e m b e * t h e * c a n n o t t * u c h u s +Eval: D D I D + +id: (m-ailabs_eng_000228-m-ailabs_eng_000228) +Scores: (#C #S #D #I) 116 4 13 8 +REF: G i v e m e t i m e a * * Z u r E g i v e m e t i m e i f T h e r E s a n y t h i n g i h a t E i t s a h u R r y i v e A n i ******* d E a y o u R m a J E s t y a n * N o u n c e D t h e s i x * ******* t h * s n u b ******* n o s E d p r i n c e S s +HYP: * i v e m e t i m e a S Y O u r * g i v e m e t i m e i f * h e r * s a n y t h i n g i h a t * i t s a h u * r y i v e ******* * n i d * a y o u * m a G U s t y a n D o u n c e * t h e s i x T t h E s n u b n o s * d p r i n c e * s +Eval: D I I S D D D D D D D I D D S S I S D I I I I D D + +id: (m-ailabs_eng_000229-m-ailabs_eng_000229) +Scores: (#C #S #D #I) 28 6 6 0 +REF: t R U E E n o U G H t r O t D E c l a r e d t h e s a I l O r m a n +HYP: t * * O * n o * * F t r A t O c l a r e d t h e s a * l E r m a n +Eval: D D S D D D S S S S D S + +id: (m-ailabs_eng_000230-m-ailabs_eng_000230) +Scores: (#C #S #D #I) 68 8 5 6 +REF: a s f o r t h a t s a i d m a r g A r E t r A t h e r h * a U G H t I l y i h o l d i t i s h o n * I s o ******* i t q u * I m a l Y * p e n s * E +HYP: a s f o r t h a t s a i d m a r g * r I t r E t h e r ******* h O a * * * t A l y i h o l d i t i s h o n E Y s o i t q u E E m a l L D E p e n s A Y +Eval: D S S D I D D D S I S I I S S S I I S + +id: (m-ailabs_eng_000231-m-ailabs_eng_000231) +Scores: (#C #S #D #I) 161 0 15 0 +REF: w h e n h e h e A r d t h e s E w o r d s t h e k i n g w h o s E h E a d w a s f u L l o f t h E p R i n c e S s n e v e r s t o P p e D t o i n q u i r E i f t h e Y c o u l d b e t r u E a n d s m e a r e d h i m s e l f o v e r w i t h f a t a n d s p r a n g i n t O t h e o v e n +HYP: w h e n h e ******* h e * r d t h e s * w o r d s t h e k i n g w h o s * h * a d w a s f u * l o f t h * p * i n c e * s n e v e r s t o * p e * t o i n q u i r * i f t h e * c o u l d b e t r u * a n d s m e a r e d h i m s e l f o v e r w i t h f a t a n d s p r a n g i n t * t h e o v e n +Eval: D D D D D D D D D D D D D D D + +id: (m-ailabs_eng_000232-m-ailabs_eng_000232) +Scores: (#C #S #D #I) 129 8 27 6 +REF: y o U s h o u l d b e a B l e T O g E t p a r t * S f r o m y o u r * r O o m v I S i o n ******* r e c e I v e r i L l h a v E s o m E t o O l s g i v e n Y o u t h e n h e a D D E d d I p l o m a * ******* C Y h a s t o U n d e r s t a n d t h E t H i n g s T h a T c O n t r o l * E v e n T s +HYP: y o * ******* s h o u l d ******* b e ******* a * l e ******* * * g * t p a r t C E f r o m y o u r W r * o m v * * i o n r e c e * v e r i * l h a v * s o m * t o U l s g i v e n * o u t h e n ******* h e ******* a * * T d d E p l o m a S H E h a s t o * n d e r s t a n d t h * t * i n g s * h a * c * n t r o l O F v e n C s +Eval: D D D D D D D D D I S I D D D I D D D D S D D D D D S S I I S S D D D D D D I S S + +id: (m-ailabs_eng_000233-m-ailabs_eng_000233) +Scores: (#C #S #D #I) 62 2 6 0 +REF: b y t h e t i m E t h e f r o s t h a d s E T i n t h e Y s h O u l D b e f a r A w a y f r o m h e l s t o n E +HYP: b y t h e t i m * t h e f r o s t h a d s A D i n t h e * s h * u l * b e f a r * w a y f r o m h e l s t o n * +Eval: D S S D D D D D + +id: (m-ailabs_eng_000234-m-ailabs_eng_000234) +Scores: (#C #S #D #I) 30 4 3 2 +REF: * o n E t h i n g i w A n t t o s a y b e g a n * K E N n E D y +HYP: W o n * t h i n g i w * n t t o s a y b e g a n D * C A n I T y +Eval: I D D I D S S S S + +id: (m-ailabs_eng_000235-m-ailabs_eng_000235) +Scores: (#C #S #D #I) 58 2 9 0 +REF: t h i s I m p o r t A n T t r a F f i c w a s c o n f i D e d t o n o o n E B u t t h e R e a l p r o p r i E t O r +HYP: t h i s * m p o r t * n * t r a * f i c w a s ******* c o n f i G e d t o n o o n * * u t t h e * e a l p r o p r i * t E r +Eval: D D D D D S D D D D S + +Speaker sentences 124: cv_eng_000707 #utts: 1 +id: (cv_eng_000707-cv_eng_000707) +Scores: (#C #S #D #I) 25 15 5 11 +REF: * * ******* H e * W a S * * R E P l a C e d * * o n * b a S s G u I T A R B y J U s t i * N K L U g * +HYP: I N Y e O * a R D O B B l a S e d T P o n T b a * s O u D O T M y T H s t i M G * * * g O +Eval: I I I S I D S I I S S S S I I I D S S S S S S S S I S D D D I + +Speaker sentences 125: cv_eng_000708 #utts: 1 +id: (cv_eng_000708-cv_eng_000708) +Scores: (#C #S #D #I) 46 5 6 3 +REF: i * * * D a D D a S e p A r A t E s u B s e c t i o n w h i c h d e a l s w i t H T H i s a s p e c t +HYP: i G H T E a * T a * e p * r I t D s u P s e c t i o n w h i c h d e a l s w i t * * * i s a s p e c t +Eval: I I I S D S D D S S S D D D + +Speaker sentences 126: cv_eng_000709 #utts: 1 +id: (cv_eng_000709-cv_eng_000709) +Scores: (#C #S #D #I) 43 8 5 7 +REF: o p E r a t i o n o f t h e T r u n K l I n E c o n t I n U e d o n ******* * * * W o * O d E n * t r e s T L e * s +HYP: o p * r a t i o n o f t h e F r u n * T l A n G c o n t * n * e d o n T H E G o U L d A n T t r e s * S e L s +Eval: D S D S S S D D I I I I S I S S I D S I + +Speaker sentences 127: cv_eng_000710 #utts: 1 +id: (cv_eng_000710-cv_eng_000710) +Scores: (#C #S #D #I) 58 7 12 2 +REF: m A G n E s i U m f l U o r i d E i s t R A n s p A r e n t o v e r a n e x t r E m E l y w * i * d E r a n g E o f W a v e l E n g T H s +HYP: m * O n * s i O m f l * o r i d * i s t W E n s p E r e n t o v e r ******* a n ******* e x t r I m * l y w H i H d * r a n g * o f * a v e l I n g * * s +Eval: D S D S D D S S S D D S D I I D D D S D D + +Speaker sentences 128: cv_eng_000711 #utts: 1 +id: (cv_eng_000711-cv_eng_000711) +Scores: (#C #S #D #I) 65 17 9 7 +REF: f o U r * g i A n t * P a C k i n G s h e * D s s t o r E D f r e s h * p a c k e D P O t * a T O e s A n D d E l I v E R e d t h e m O n t o r A I l * r ******* o A d C a R s +HYP: f o * r J g i * n t B E a * k i n K s h e A T s s t o r T f r e s h B p a c k e T B U t E a * D e s S O n * d I l * v * * e d t h e m U n t o r * E l E r o L d G a S s +Eval: D I D I S D S I S S S I S S S I D S S S D S D D D S D S I I S S S + +Speaker sentences 129: cv_eng_000712 #utts: 1 +id: (cv_eng_000712-cv_eng_000712) +Scores: (#C #S #D #I) 78 4 17 2 +REF: t h e o t h e R f o U r t E E n c a m p U S E s a r e t W o ******* y E a R c a m p U s E s r e f e R r E d t o c o L l e c t i v e l y a s t h e * u n I V e r s I t Y c o L l E g e +HYP: t h e o t h e * f o * r t * I n c a m p * * A s a r e t * o y * a * c a m p * s * s r e f e * r * d t o c o * l e c t i v e l y a s t h e Y u n * * e r s * t I c o * l A g e +Eval: D D D S D D S D I D D D D D D D I D D D S D S + +Speaker sentences 130: cv_eng_000713 #utts: 1 +id: (cv_eng_000713-cv_eng_000713) +Scores: (#C #S #D #I) 45 5 13 3 +REF: i t s t O o * * B a * d t h A T h e S Q u i c k l Y g o I n G t o f O r g e t m y n a m E H e t H O U G h T +HYP: i t s t * o T H E a R d t h O W h e * C u i c k l E g o * n * t o f * r g e t m y n a m * ******* * e t * * * * h * +Eval: D I I S I S S D S S D D D D D D D D D D D + +Speaker sentences 131: cv_eng_000714 #utts: 1 +id: (cv_eng_000714-cv_eng_000714) +Scores: (#C #S #D #I) 54 20 15 8 +REF: * o n E p I C t u r e i n t H e g A L l E r Y s h o ******* W S h o w * D I L I g e n t S l * A V E S E r e C T t H E s t * a t * U e O F a d M I r ******* a * L t H o m p S o n +HYP: W o n * p * O t u r e i n t * e g * * l O r * s h o T H h o w D H E A g e n t ******* * l Y I N T I r e * * N t * * I s t H a t H Y e A L a d * G r a R T t * o m p * o n +Eval: I D D S D D D S D I S S I S S S S D D I S S S S S D D S D D S I I S S S D S I I S D D + +Speaker sentences 132: cv_eng_000715 #utts: 1 +id: (cv_eng_000715-cv_eng_000715) +Scores: (#C #S #D #I) 12 1 0 4 +REF: * ******* i m p e r i a l d * * i E t +HYP: A i m p e r i a l d I Y i A t +Eval: I I I I S + +Speaker sentences 133: cv_eng_000716 #utts: 1 +id: (cv_eng_000716-cv_eng_000716) +Scores: (#C #S #D #I) 42 7 5 4 +REF: t h e R e s u l t i n G C o m p a n y ******* * * I s S T r A t T e C s E c u r i t y c o * R p o r a t i o n +HYP: t h e * e s u l t i n * * o m p a n y E D A s H A r t A e * ******* s I c u r i t y c o T p o r a t i o n +Eval: D D D I I I S S S S S D D S I S + +Speaker sentences 134: cv_eng_000717 #utts: 1 +id: (cv_eng_000717-cv_eng_000717) +Scores: (#C #S #D #I) 58 10 7 2 +REF: b I T c o i n * m i n i n g c a n b e d o n E w i t H g R A P h i C s c a r D s o r w i t * H s p e C i a l i Z e d h A r d W A R E +HYP: b * E c o i n G m i n i n g c a n b e d o n * w i t * g O F h i * s c a r T s o r w i t E S s p e S i a l i * e d h O r d * * L Y +Eval: D S I D D S S S D S I S S D S D D S S + +Speaker sentences 135: cv_eng_000718 #utts: 1 +id: (cv_eng_000718-cv_eng_000718) +Scores: (#C #S #D #I) 30 1 4 1 +REF: t h e y a l ******* s o l e A D t h e n a T i O N a l r a n k i n g +HYP: t h e y a l s o l e * * t h e n a S i * * a l r a n k i n g +Eval: I D D S D D + +Speaker sentences 136: cv_eng_000719 #utts: 1 +id: (cv_eng_000719-cv_eng_000719) +Scores: (#C #S #D #I) 23 8 2 0 +REF: C H A r L E s g r a V E s b i s h O p o f L i m e r i c K +HYP: * * T r O W s g r a I N s b i s h I p o f N i m e r i c E +Eval: D D S S S S S S S S + +Speaker sentences 137: cv_eng_000720 #utts: 1 +id: (cv_eng_000720-cv_eng_000720) +Scores: (#C #S #D #I) 32 6 5 5 +REF: * A n d A T t h a t * * i T o * l D h i m A n D h e T O o k m y p l a C e * +HYP: I O n d ******* E D t h a t T H i D o R l * h i m U n * h e * * o k m y p l a S e S +Eval: I S D S S I I S I D S D D D S I + +Speaker sentences 138: cv_eng_000721 #utts: 1 +id: (cv_eng_000721-cv_eng_000721) +Scores: (#C #S #D #I) 28 4 2 0 +REF: i t h o u g H t i d g i v e t h e K i D s a T r e A t +HYP: i t h o u g * t i d g i v e t h e C i T s a ******* D r e E t +Eval: D S S D S S + +Speaker sentences 139: cv_eng_000722 #utts: 1 +id: (cv_eng_000722-cv_eng_000722) +Scores: (#C #S #D #I) 19 9 7 4 +REF: a * ******* * C e ******* v E D O d E n i E D S H o W I N G t h e p i c T U R e s +HYP: a S T H e v I T L d I n i * H * T o * * * M t h e p i c * * H e s +Eval: I I I S I S S S S D S D S D D D S D D S + +Speaker sentences 140: cv_eng_000723 #utts: 1 +id: (cv_eng_000723-cv_eng_000723) +Scores: (#C #S #D #I) 49 10 9 3 +REF: h o L d y o u r n o s E t o K E e P t h E s ******* m E L L f r o m * D I S a b l i n g y o U r m o t ******* o r f U n C t i o n S +HYP: h o W d y o u r n o s T t o * C e * t h * I s m A Y E f r o m T H E a b l i n g y o * r ******* m o t o r ******* f * n * t i o n * +Eval: S S D S D D S I S S S I S S S D D I D D D D + +Speaker sentences 141: cv_eng_000724 #utts: 1 +id: (cv_eng_000724-cv_eng_000724) +Scores: (#C #S #D #I) 26 2 2 7 +REF: * * ******* t h a t s o U n d s l I k e t h e I r p r o B l * * e m * * +HYP: A C t h a t s o * n d s l A k e t h e A r p r o * l O M e m I C +Eval: I I I D S S D I I I I + +Speaker sentences 142: cv_eng_000725 #utts: 1 +id: (cv_eng_000725-cv_eng_000725) +Scores: (#C #S #D #I) 68 9 11 2 +REF: h i s t O r i c a L l Y T H e r E w a s n o c l e a r * l y d e f i n e D b o u n D A r y I n t h i s p A R t o f t h e a r a b I A n p E n i n s * U l A +HYP: h i s t * r i c a * l * ******* I G e r * w a s n o c l e a r E l y d e f i n e * b o u n * G r y ******* E n t h i s p * I t o f t h e a r a b Y E n ******* p * n i n s T O l E +Eval: D D D D S S D I D D S D S D S S S D D I S S + +Speaker sentences 143: cv_eng_000726 #utts: 1 +id: (cv_eng_000726-cv_eng_000726) +Scores: (#C #S #D #I) 57 2 5 7 +REF: m a r s h * a L l s h a F F e r o f s l a s h f i l m * g a v e t h e f i * l m * a n E I G H t * o u t o f t * * e n +HYP: m a r s h I a * l s h a * V e r o f s l a s h f i l m E g a v e t h e f i L l m E a n * * * A t E o u t o f t E A e n +Eval: I D D S I I I D D D S I I I + +Speaker sentences 144: cv_eng_000727 #utts: 1 +id: (cv_eng_000727-cv_eng_000727) +Scores: (#C #S #D #I) 11 7 2 1 +REF: H o W C A n * Y o U S A Y t h a t +HYP: A o L P I n D I o * * T I t h a t +Eval: S S S S I S D D S S + +Speaker sentences 145: cv_eng_000728 #utts: 1 +id: (cv_eng_000728-cv_eng_000728) +Scores: (#C #S #D #I) 40 3 3 5 +REF: h i s * S t * Y l e b e g a n t o r e s e m b l e m i c H a E l D A m a * s * k * i n o s +HYP: h i s T * t D I l e b e g a n t o r e s e m b l e m i c * a * l T E m a S s C k E i n o s +Eval: I D I S D D S S I I I + +Speaker sentences 146: cv_eng_000729 #utts: 1 +id: (cv_eng_000729-cv_eng_000729) +Scores: (#C #S #D #I) 58 7 10 1 +REF: h e i s a l s o * c a p a b l E o f f i r I n g l i g H t N i n G b O l T S w i T H i M m e n S e d E s T r u C t i v e p o w e r +HYP: h e i s a l s o L c a p a b l * o f f i r * n g ******* l i g * t i n * M b * l * E w i * F i * m e n T e d I s * r u P t i v e p o w e r +Eval: I D D D D S D S D D S D S D S S D S + +Speaker sentences 147: cv_eng_000730 #utts: 1 +id: (cv_eng_000730-cv_eng_000730) +Scores: (#C #S #D #I) 49 24 12 13 +REF: * h e c l a I m e D t W o w i C k e T s * I n E n g l A n ******* D S O N l y * i N n i n g s a s B o R D E R w E R E B e a t E n * * * C o * M P R e H E N s * ******* I V E l * ******* * Y +HYP: T h e c l a * m e * t * o w i * k e D s C E n ******* I n g l I n P U R M A l y T i * n i n g s a s F o * * * W w * I S * e a t A n T H U R o N A D e * R A s Y L A D l I R E +Eval: I D D D D S I S D S S I S S S S S I D S D D D S D S S D S I I I S I S S S D S S I I S S S I I I S + +Speaker sentences 148: cv_eng_000731 #utts: 1 +id: (cv_eng_000731-cv_eng_000731) +Scores: (#C #S #D #I) 11 9 6 4 +REF: s h e D I D * M u C H l * I t E R A r Y w * * O R K +HYP: s h e * * E G R u * S I l Y t * * * r O w H A T H +Eval: D D S I S D S S I S D D D S I I S S S + +Speaker sentences 149: cv_eng_000732 #utts: 1 +id: (cv_eng_000732-cv_eng_000732) +Scores: (#C #S #D #I) 65 3 9 0 +REF: h e m E t t h e o r g a n i Z e r s o f t h e p r o t e S T s a n d a g r E e d T O c r e a t E t w o w o r k i n G g r o U P s +HYP: h e m * t t h e o r g a n i S e r s o f t h e p r o t e * * s a n d a g r * e d * D c r e a t * t w o w o r k i n * ******* g r o * M s +Eval: D S D D D D S D D D D S + +Speaker sentences 150: cv_eng_000733 #utts: 1 +id: (cv_eng_000733-cv_eng_000733) +Scores: (#C #S #D #I) 40 10 8 4 +REF: t h e b A L L s t r U c K t h E f * o U l * P o * L E w E L l a b o V E t h e G r E e n M o n s t E r * +HYP: t h e b * O N s t r O c * t h O f H o * l D W o A R D w * I l a b o * F t h e * r * e n * o n s t O r D +Eval: D S S S D S I D I S I S S D S D S D D D S I + +Speaker sentences 151: cv_eng_000734 #utts: 1 +id: (cv_eng_000734-cv_eng_000734) +Scores: (#C #S #D #I) 62 6 10 7 +REF: O n l y c a m d E n t H o m a s g a R r E T t a n d g o l d ******* f i E l d S s o U t H * * e Z e * K i E l * b a k * E r w e r E u n ******* c o n t e s t e d +HYP: I n l y c a m d O n t * o m a s ******* g a * r * I t a n d g o l d f i * l d * s o A t * I S e * e C H i * l E b a k C A r w e r * u n c o n t e s t e d +Eval: S S D D D D S I D D S D I I D I S D I I S D I + +Speaker sentences 152: cv_eng_000735 #utts: 1 +id: (cv_eng_000735-cv_eng_000735) +Scores: (#C #S #D #I) 53 4 7 3 +REF: i t i s a c h A r I T y s c H O o l w h o s E f E e s a r E c A L c u l a t e d * * o n * A m e a n s t e s t +HYP: i t i s a c h * r * D y s c * * o l w h o s * f * e s a r * c O U c u l a t e d I N o n I N m e a n s t e s t +Eval: D D S D D D D D S S I I I S + +Speaker sentences 153: cv_eng_000736 #utts: 1 +id: (cv_eng_000736-cv_eng_000736) +Scores: (#C #S #D #I) 47 2 5 0 +REF: s o m e w e n t a w a y w h I l E i w a s t h e r E a n d o t h e R p E o p l e c a m E +HYP: s o m e w e n t a w a y w h A l * i O w a s t h e r * a n d o t h e * p * o p l e c a m * +Eval: S D S D D D D + +Speaker sentences 154: cv_eng_000737 #utts: 1 +id: (cv_eng_000737-cv_eng_000737) +Scores: (#C #S #D #I) 3 2 0 16 +REF: * ******* * ******* * s * * * * * e V e * * * ******* * ******* N +HYP: T H C s A I T H A e D e D U P R E +Eval: I I I I I I I I I I S I I I I I I S + +Speaker sentences 155: cv_eng_000738 #utts: 1 +id: (cv_eng_000738-cv_eng_000738) +Scores: (#C #S #D #I) 68 12 5 4 +REF: t h * E K u r a K H A n A t E w a s l o * c a T e d m a I n l y i N t h e h i s t o r i c a l * a n d G e O g r A P H i c a l r e * g i o n o f K u r A +HYP: t h A T C u r a * C O n O t Y w a s l o K c a D e d m a * n l y i * t h e h i s t o r i c a l E a n d J e A g r * E F i c a l r e A g i o n o f C u r * +Eval: I S S D S S S S I S D D I S S D S S I S D + +Speaker sentences 156: cv_eng_000739 #utts: 1 +id: (cv_eng_000739-cv_eng_000739) +Scores: (#C #S #D #I) 31 8 5 5 +REF: T H E * e l E v a t i o n a T t h e s i * * t E i s a * B o V E s E A l e v E l * +HYP: U N C H e l O v a t i o n a * t h e s i G H t * i s a M o * F s * * U l e v B l E +Eval: S S S I S D I I D I S D S D D S S I + +Speaker sentences 157: cv_eng_000740 #utts: 1 +id: (cv_eng_000740-cv_eng_000740) +Scores: (#C #S #D #I) 42 2 1 6 +REF: t o ******* b I a s t r i e d t o I n * J e c t c * * o n ******* t e m p t i n ******* t o h i s t o n e +HYP: t o b E a s t r i e d t o * n C H e c t c O N o n t e m p t i n t o h i s t o n e +Eval: I S D I S I I I I + +Speaker sentences 158: cv_eng_000741 #utts: 1 +id: (cv_eng_000741-cv_eng_000741) +Scores: (#C #S #D #I) 25 2 1 1 +REF: i h a v e t o w O r * k t h i s s a t U r d A y +HYP: i h a v e t o w A r L k t h i s s a t O r d * y +Eval: S I S D + +Speaker sentences 159: cv_eng_000742 #utts: 1 +id: (cv_eng_000742-cv_eng_000742) +Scores: (#C #S #D #I) 44 18 6 9 +REF: t H e * G r E a ******* t * * r * U L E R s f o u n d t h e * S Q U e A K Y g * R a T E w * A S g R a t i n g o n t h e I r * n E R V e s +HYP: t D e T r * a t H E r O N W H s f o u n d ******* t h e S C O L e * G E g L E a * D w I T H g L a t i n g o n t h e * r G n * O N e s +Eval: S I S D I I I I S S S S D I S S S D S S I S D S I S S S D I D S S + +Speaker sentences 160: cv_eng_000743 #utts: 1 +id: (cv_eng_000743-cv_eng_000743) +Scores: (#C #S #D #I) 62 5 5 4 +REF: w h e n t h e b L i N D i n g d U s t h * A D s * e T T l e d * * A b i t t h e b o y t r E m b l e d a t w h a t h e s a w +HYP: w h e n t h e b * i * L i n g d O s t h E S s A e * * l e d F O R b i t t h e b o y t r * m b l e d a t w h a t h e s a w +Eval: D D S S I S S I D D I I S D + +Speaker sentences 161: cv_eng_000744 #utts: 1 +id: (cv_eng_000744-cv_eng_000744) +Scores: (#C #S #D #I) 34 3 1 7 +REF: d e m O c r a t a m b e r * * b a * * k * e r w o n * t ******* h e o p E n s e A T +HYP: d e m A c r a t a m b e r A N b a K E k H e r w o n I t h e o p O n s e * E +Eval: S I I I I I I I S D S + +Speaker sentences 162: cv_eng_000745 #utts: 1 +id: (cv_eng_000745-cv_eng_000745) +Scores: (#C #S #D #I) 46 15 7 19 +REF: B o * t H * a R e * * P U t * * * t o ******* g e t h e r b * Y s ******* T u * d ******* e n t S i n T h * e c * O l L e * G e * s J o u R n a * L I s M * * p R o G r A m +HYP: W o R t * H a V e O R T t E I N t o g e t h e r b A I T s O u O d e n t * i n * h I e ******* c U A l I e R e U s * o u n a T H E s * I N p * o r O m +Eval: S I D I S I I S S I I I I I S S I S I I D D I D I S S I S I D S I S S D I I D S S + +Speaker sentences 163: cv_eng_000746 #utts: 1 +id: (cv_eng_000746-cv_eng_000746) +Scores: (#C #S #D #I) 38 8 4 2 +REF: t r E n C H w a s b o r n i n b e l I Z e * C i t * Y i n b r i t I s H H o n d U r a s +HYP: t r I n * T E w a s b o r n i n b e l * * e S S i t E D i n b r i t O s * P o n d E r a s +Eval: S D S S D D I S I S S D S S + +Speaker sentences 164: cv_eng_000747 #utts: 1 +id: (cv_eng_000747-cv_eng_000747) +Scores: (#C #S #D #I) 22 5 7 1 +REF: T H E E A r * L y P H a S e o f l i f E m o V e s f a s t +HYP: * * * ******* D O r I T y * F a C e o f l i f * m o * e s f a s t +Eval: D D D D S S I S D S S D D + +Speaker sentences 165: cv_eng_000748 #utts: 1 +id: (cv_eng_000748-cv_eng_000748) +Scores: (#C #S #D #I) 2 0 0 8 +REF: * ******* n o * * ******* * ******* * +HYP: A n o W H H E +Eval: I I I I I I I I + +Speaker sentences 166: cv_eng_000749 #utts: 1 +id: (cv_eng_000749-cv_eng_000749) +Scores: (#C #S #D #I) 4 1 0 11 +REF: s E v e * n ******* * * * * * * * * * +HYP: s I v e I n G O R L T D L O L +Eval: S I I I I I I I I I I I + +Speaker sentences 167: cv_eng_000750 #utts: 1 +id: (cv_eng_000750-cv_eng_000750) +Scores: (#C #S #D #I) 55 15 13 5 +REF: a t o n E t I m E * r A I l W A Y l i n E s D I V e * R G E d f r o m * r * U g ******* b Y s t a t i o n i n s E V E n d i F f E r e n T D I r e c T i o n s +HYP: a t o n * t * m * B r Y l * O U E l i n * s * T H e Y W A R d f r o m B r A K g b * E s t a t i o n i n s * O n d i * f * r e n * * E r e c * i o n s +Eval: D D D I S S D S S S D D S S I S S S I I S I D S D S S D D D D S D + +Speaker sentences 168: cv_eng_000751 #utts: 1 +id: (cv_eng_000751-cv_eng_000751) +Scores: (#C #S #D #I) 58 7 2 3 +REF: c Z e c H r e p u * b l i c * e n t E R e d t w o s h o O t e r s i n t o t h e p a r * A l Y m p i C c o m p E t i t i o n +HYP: c H e c K r e p u P b l i c K e n t * * e d t w o s h o U t e r s i n t o t h e p a r O l I m p i G c o m p A t i t i o n +Eval: S S I I D D S I S S S S + +Speaker sentences 169: cv_eng_000752 #utts: 1 +id: (cv_eng_000752-cv_eng_000752) +Scores: (#C #S #D #I) 56 11 10 12 +REF: t ******* * Y G e r w i L l i A m s W r o T e t h e s c R e E n * ******* P l a y a n d ******* * ******* * s h a r e d s t o r y C r * E D i t W I t h * * t h e B R O T H e * R S +HYP: t I T e r w i * l i O m s * r o * e t h e s c G e A n G C l a y a n d N S s h a r e d s t o r y * r A T i t * * t h A T t h e * * * * P e P I T +Eval: I I S S D S D D S S I I S I I I I D I S S D D I I D D D D S I S S + +Speaker sentences 170: cv_eng_000753 #utts: 1 +id: (cv_eng_000753-cv_eng_000753) +Scores: (#C #S #D #I) 41 14 2 24 +REF: t * H i s f E s t ******* * * I V a * l * w * A s t * ******* o * * * B e * * A C h A r i t y f U n ******* * * D r ******* a * * I s * * e r * f o * R t h e a r E A +HYP: t A i s f A s t O F E a L l D w O R s t O o F R E T e R C H * h E r i t y f I n T H E r a D Y s A D e r O f o Y D t h e R a r * T +Eval: I S S I I I S S I I I S I I I I I S I I S D S S I I I S I I I S I I I I S S D S + +Speaker sentences 171: cv_eng_000754 #utts: 1 +id: (cv_eng_000754-cv_eng_000754) +Scores: (#C #S #D #I) 30 14 7 30 +REF: * ******* t h e s E e * x t r A C a r D s w E R e I n * s E r * t ******* E D r A n D o * * * * * * m * * * ******* * * * l * * * ******* * * Y I N t O * * * P a * ******* C K S +HYP: O t h e s * e N x t r * G a r T s w * * e * n E s U r N t T H E r * n G o N L E L A L m S W O A G A l F T E R A G W H t * T H E a L H A T +Eval: I I D I D S S D D D I S I I S S S D S I I I I I I I I I I I I I I I I I I I S S S D I I I S I I S S S + +Speaker sentences 172: cv_eng_000755 #utts: 1 +id: (cv_eng_000755-cv_eng_000755) +Scores: (#C #S #D #I) 21 3 4 5 +REF: * h * ******* E n * r Y W E n t b a c k t o A U s t r A l i * a +HYP: A h U H n D r * * O n t b a c k t o * E s t r * l i O a +Eval: I I I S I D D S D S D I + +Speaker sentences 173: cv_eng_000756 #utts: 1 +id: (cv_eng_000756-cv_eng_000756) +Scores: (#C #S #D #I) 38 7 6 5 +REF: p e r m i t m e t o i n t r O d u C e T o * Y o U h E r * m * A j e s T Y t H e * * q U e E n +HYP: p e r m i t m e t o i n t r * d u S e Y o U T o * h U r R m O G j e s * * ******* t I e D C q * e A n +Eval: D S S I S D S I I S D D D S I I D S + +Speaker sentences 174: cv_eng_000757 #utts: 1 +id: (cv_eng_000757-cv_eng_000757) +Scores: (#C #S #D #I) 53 5 16 1 +REF: I n o r I g i n h e r * o I n w a s s u P p o s E D t o B E t h e “ n o n a D d i c t i v E m o r P H I n E s u b s T I t U t E ” +HYP: A n o r * g i n h e r W o * n w a s s u * p o s * * t o ******* * * t h e *** n o n a * d i c t i v F m o r * F E n * s u b s * * t O t * *** +Eval: S D I D D D D D D D D D S D S S D D D S D D + +Speaker sentences 175: cv_eng_000758 #utts: 1 +id: (cv_eng_000758-cv_eng_000758) +Scores: (#C #S #D #I) 22 3 0 3 +REF: * ******* s h e i s o f m e * X i c A n d e s C e n t +HYP: U s h e i s o f m e K C i c O n d e s S e n t +Eval: I I I S S S + +Speaker sentences 176: cv_eng_000759 #utts: 1 +id: (cv_eng_000759-cv_eng_000759) +Scores: (#C #S #D #I) 20 4 5 6 +REF: * * ******* i A m s * U r E t H e * R e I s n o t o n H i s * +HYP: C S i * m s H O r * t e A L e ******* * s n o t o n ******* D i s T +Eval: I I I D I S D S I S D D D S I + +Speaker sentences 177: cv_eng_000760 #utts: 1 +id: (cv_eng_000760-cv_eng_000760) +Scores: (#C #S #D #I) 30 15 12 17 +REF: * * * T h o * s E W H o * d o n t l E A R n F R o M * h I s T O R Y * A r e * * D O o M E D T O R e p e A T i t ******* * ******* * * * * * +HYP: I O W h o U s * * * o N d o n t l * * O n ******* S A o N T h E s * * * * S H r e Y I V G o * N T P R A e p e * D i t O H L O N O +Eval: I I I S I D D D I D D S D S S S I S D D D D I S I I S S D S S S S S D S I I I I I I I I + +Speaker sentences 178: cv_eng_000761 #utts: 1 +id: (cv_eng_000761-cv_eng_000761) +Scores: (#C #S #D #I) 21 4 4 4 +REF: i c O U l * d ******* N ’ T s T o p * s T a r * i n G a t i t +HYP: i c * A l E d A O N s * o p E s * a r L i n * a t i t +Eval: D S I I S S S D I D I D + +Speaker sentences 179: cv_eng_000762 #utts: 1 +id: (cv_eng_000762-cv_eng_000762) +Scores: (#C #S #D #I) 60 3 11 1 +REF: f o r s i m p l I C i t * y g E A r i n c h e s i s n o r m A L l y r o u n d e d t o T h e N e A r e s T W h o l E n U m b e r +HYP: f o r s i m p l * * i t H y g * U r i n c h e s i s n o r m * * l y A r o u n d e d t o * h e * e * r e s * * h o l * n O m b e r +Eval: D D I D S D D S D D D D D D S + +Speaker sentences 180: cv_eng_000763 #utts: 1 +id: (cv_eng_000763-cv_eng_000763) +Scores: (#C #S #D #I) 34 3 6 2 +REF: i f w e a c t U A L l y d o W A n T i T s O l V e d i t w i l l b e ******* * +HYP: i f w e a c t * * I l y d o * O n * i * s A l * e d i t w i l l b e F +Eval: D D S D S D D S D I I + +Speaker sentences 181: cv_eng_000764 #utts: 1 +id: (cv_eng_000764-cv_eng_000764) +Scores: (#C #S #D #I) 26 4 9 1 +REF: t h e f r U I T o f * A F i G t r E E I s a P p l E s h a p e d +HYP: t h e f r * * O o f T H * i * C t r * Y * s a * p l * ******* s h a p e d +Eval: D D S I S D D S D S D D D D + +Speaker sentences 182: cv_eng_000765 #utts: 1 +id: (cv_eng_000765-cv_eng_000765) +Scores: (#C #S #D #I) 18 7 2 2 +REF: * * F A I R e x C h a n g e i s n o R o B b E R y +HYP: T H E O U T e x T h a n g e i s n o W o * b * U y +Eval: I I S S S S S S D D S + +Speaker sentences 183: cv_eng_000766 #utts: 1 +id: (cv_eng_000766-cv_eng_000766) +Scores: (#C #S #D #I) 40 2 1 3 +REF: w h a t y o u e a T t o ******* d a * y w a l k s a n d t a L k s t o ******* m o R r o w +HYP: w h a t y o u e a E t o d a I y w a l k s a n d t a R k s t o m o * r o w +Eval: S I I S I D + +Speaker sentences 184: cv_eng_000767 #utts: 1 +id: (cv_eng_000767-cv_eng_000767) +Scores: (#C #S #D #I) 51 4 4 3 +REF: t h e w a t e R T H E n f l o W s o u t o f t h e s w A m p s a s t h e l * u * A p U l a * r i v e r +HYP: t h e w a t e D * * A n f l o * s o u t o f t h e s w O m p s a s t h e l O u W O p * l a R r i v e r +Eval: S D D S D S I I S D I + +Speaker sentences 185: cv_eng_000768 #utts: 1 +id: (cv_eng_000768-cv_eng_000768) +Scores: (#C #S #D #I) 24 3 0 8 +REF: * * ******* w h * y ******* * d i d n T y o u s * a Y s o m e T h i n g * +HYP: A H w h I y I d i d n D y o u s E a E s o m e h i n g K +Eval: I I I I I I S I S S I + +Speaker sentences 186: cv_eng_000769 #utts: 1 +id: (cv_eng_000769-cv_eng_000769) +Scores: (#C #S #D #I) 13 0 5 3 +REF: * ******* h a v E Y o u s E e n o ******* m a r +HYP: T h a v * * o u ******* s * e n ******* o m a r +Eval: I I D D D D D I + +Speaker sentences 187: cv_eng_000770 #utts: 1 +id: (cv_eng_000770-cv_eng_000770) +Scores: (#C #S #D #I) 68 10 5 3 +REF: i c o u l d g o O n f o r d a y s a b o u t t h e d E L I C i o u s W I n E s p * R O d u C e D i n T h i s p a r t O f t h e w * o r * L d +HYP: i c o u l d g o A n f o r d a y s a b o u t t h e d * * A D i o u s L O n G s p H E d u S e * i n * h i s p a r t * f t h e w E o r O E d +Eval: S D D S S S S S I S S S D D D I I S + +Speaker sentences 188: cv_eng_000771 #utts: 1 +id: (cv_eng_000771-cv_eng_000771) +Scores: (#C #S #D #I) 36 15 8 6 +REF: t h * E P h * * I l a d e L P h I a i n * q u i R E r * n A M E D H i M C i t Y P l A y E R o * F t h e y e a r +HYP: t h O S F h E O l a d e O F h E a i n C q u i * * r O n * * N G T i N S i t * I C l * y * * o U T t h e y e a r +Eval: I S S I I S S S S I D D I D D S S S S S S D S S D D D I S + +Speaker sentences 189: cv_eng_000772 #utts: 1 +id: (cv_eng_000772-cv_eng_000772) +Scores: (#C #S #D #I) 11 22 3 2 +REF: B O T s M A Y B E S U B J e c t T O * s P e C I A l * R U l E S +HYP: * F A s L E V E I S O F D e c t ******* I S E s S e R G l T O N S l * A +Eval: D S S S S S S S S S S S S D S S I S S S S I S S S D S + +Speaker sentences 190: cv_eng_000773 #utts: 1 +id: (cv_eng_000773-cv_eng_000773) +Scores: (#C #S #D #I) 58 2 12 3 +REF: t h e s w e D e * s w e r E U n a b l e t o * u s E T H e I r v e H i c * l E s w h i c H W e r E s t u c k i n t h e m U d +HYP: t h e s w e * e D s w e r * * n a b l e t o O u s * ******* * * e * r v e * i c A l * s w h i c * H e r * s t u c k i n t h e m O d +Eval: D I D D I D D D D D D I D D S D S + +Speaker sentences 191: cv_eng_000774 #utts: 1 +id: (cv_eng_000774-cv_eng_000774) +Scores: (#C #S #D #I) 56 5 6 11 +REF: t h e a c T d i d n o t P r o * ******* h * I b * I t P a y i n g a r e p r e s e n T A t i V e t o a P p e a r i n ******* * * * c o U r * * t * +HYP: t h e a c K d i d n o t B r o R h E b E C t B a y i n g a r e p r e s e n * * t i * e t o a * p e a r ******* i n T H E c o * r I C t O +Eval: S S I I I S I S S D D D D D I I I I D I I I + +Speaker sentences 192: cv_eng_000775 #utts: 1 +id: (cv_eng_000775-cv_eng_000775) +Scores: (#C #S #D #I) 10 12 1 7 +REF: c * A n w e * p l E A s ******* E l E A V E N o * * * ******* W +HYP: c H I n G w e R E p l * I s T l O P I N G R o R A L A +Eval: I S S I S D S I S S S S S S S I I I I S + +Speaker sentences 193: cv_eng_000776 #utts: 1 +id: (cv_eng_000776-cv_eng_000776) +Scores: (#C #S #D #I) 55 7 8 0 +REF: h e w a s c o n v i c t e d a n D b a n i s H E D T O C Y p r U s F o r s e v e n y e a r s F O r p U n i s H m e n t +HYP: h e w a s c o n v i c t e d a n * b a n i s * * * * D I S I p r * s H o r s e v e n y e a r s W E r p * n i s * m e n t +Eval: D D D D D S S S S D S S S D D + +Speaker sentences 194: cv_eng_000777 #utts: 1 +id: (cv_eng_000777-cv_eng_000777) +Scores: (#C #S #D #I) 61 8 11 7 +REF: t h e c O u p l E H A V E t W o c h I l d R e n a d a U G H t e r s o P H I a * r o s a l I n d a A n d * * A s o n ******* * m a t * E o * b r a v e r y +HYP: t h e c * u p l * * * O F t * o c h A l d * e n a d a * * * t e r s o * F E a U r o s a l E n d a * n d T H E s o n O m a t H Y o L b r a v e r y +Eval: D D D D S S D S D D D D D S S I S D I I S I I I S I + +Speaker sentences 195: cv_eng_000778 #utts: 1 +id: (cv_eng_000778-cv_eng_000778) +Scores: (#C #S #D #I) 65 5 12 1 +REF: N O n E o f t h E T h r E e r e f E r e n d U m s r e A c h E D T h e q u O r U m o f t h E m a * j o r i t y o f t h o s E E n t i t l e d +HYP: * * n * o f t h * * h r * e r e f P r e n d A m s r e * c h * * * h e q u A r A m o f t h * m a G j o r i t y o f t h o s * I n t i t l e d +Eval: D D D D D D S S D D D D S S D I D S + +Speaker sentences 196: cv_eng_000779 #utts: 1 +id: (cv_eng_000779-cv_eng_000779) +Scores: (#C #S #D #I) 67 12 14 24 +REF: * * * ******* * t U r p I n s U c * c E e d e d i n ******* d * I r a * * s A m * ******* a * ******* r a s * * E K E r a ******* * * ******* * * w h o s a * W T H E u n I V e r s I T y t h r O U G H a p e * r i O D o F s t r o n g g r o W t h +HYP: I N T I t E r p E n s E c X c * e d e d i n d E A r a S T s O m E a R r a s I P C A r a I T I S w h o s a L D * * * O u n * * e r s * E y t h r * * * * a p e A r i * E o * s t r o n g g r o * t h +Eval: I I I I I S S S I D I I S I I S I I I I I I S S S I I I I I I I S D D D S D D D S D D D D I D S D D + +Speaker sentences 197: cv_eng_000780 #utts: 1 +id: (cv_eng_000780-cv_eng_000780) +Scores: (#C #S #D #I) 47 3 8 5 +REF: h e * r E i a m b e * t W E e n m y f l o c k a n d m Y * * t R e A s u r e * t h e b o y t H o U G H T +HYP: h e A r * i a m b e P t * * e n m y f l o c k a n d m I B U t * e R s u r e D t h e b o y t * o * * * S +Eval: I D I D D S I I D S I D D D D S + +Speaker sentences 198: cv_eng_000781 #utts: 1 +id: (cv_eng_000781-cv_eng_000781) +Scores: (#C #S #D #I) 53 11 11 7 +REF: t h i s f a I l U R E h a s * l e D t o s i * x t e * e n p o W E R P l A n T s h a * * V i n G Z e * r O d a y s * o f c O a l s t o C K +HYP: t h i s f a * l * I A h a s T l e T t o s i C x t e A e n p o * * U L B l E n C s h a D E i n * ******* S e A r * ******* d a y s E o f c * a l E s t o * * +Eval: D D S S I S I I D D S S S S S I I S D D S I D D I D S D D + +Speaker sentences 199: cv_eng_000782 #utts: 1 +id: (cv_eng_000782-cv_eng_000782) +Scores: (#C #S #D #I) 2 1 0 9 +REF: * * * ******* y * * * ******* * e S +HYP: A O O y S A S D e O +Eval: I I I I I I I I I S + +Speaker sentences 200: cv_eng_000783 #utts: 1 +id: (cv_eng_000783-cv_eng_000783) +Scores: (#C #S #D #I) 26 2 7 3 +REF: w h * y D O E S t h a T p l a * n E K E e p * g o i n G o v e r +HYP: w h I y * * * I t h a * p l a I n * * C e p E g o i n * o v e r +Eval: I D D D S D I D D S I D + +Speaker sentences 201: cv_eng_000784 #utts: 1 +id: (cv_eng_000784-cv_eng_000784) +Scores: (#C #S #D #I) 38 8 8 14 +REF: * * * * i ******* * * * V e * * d o n E T H I s B e ******* f o r E w I t H V i r t U a l ******* b o * X w i t h G O o D r e s * u l t s +HYP: A N D N i H A Y A e A E d o n * ******* * D O s H e f o r * w A t * F i r t I a l b o C S w i t h * * o * r e s O u l t s +Eval: I I I I I I I I S I I D D D S S S I D S D S S I I S D D D I + +Speaker sentences 202: cv_eng_000785 #utts: 1 +id: (cv_eng_000785-cv_eng_000785) +Scores: (#C #S #D #I) 32 4 4 7 +REF: t h e A P p l i c a t i o n w a s ******* * * * a p ******* p r o v ******* E D i n f * E b r U a R y +HYP: t h e ******* * * p l i c a t i o n w a s P U T a p p r o v I T i n f A R b r * a V y +Eval: D D D I I I I I I S S I S D S + +Speaker sentences 203: cv_eng_000786 #utts: 1 +id: (cv_eng_000786-cv_eng_000786) +Scores: (#C #S #D #I) 51 4 4 8 +REF: h e n r y t A r l * * ******* t o n * s t i l E s w H e * r E h e h a d a s o u n * * d T r a I n i n g i n l A t i n * +HYP: h e n r y t O r l E D t o n M N s t i l s w * e A r * h e h a d a s o u n D I d * r a * n i n g i n l I t i n G +Eval: S I I I I S S D I D I I D D S I + +Speaker sentences 204: cv_eng_000787 #utts: 1 +id: (cv_eng_000787-cv_eng_000787) +Scores: (#C #S #D #I) 78 9 7 11 +REF: i t w a s D i s ******* c o n t i n u e D d U E t o s c H e * D U l i n G c o n f l i c T s I n v O l v e d i n l * * e ******* W i S s r e t * U r n t o * * t E R r e s t r i a l * * * r a d i o +HYP: i t w a s T i s c o n t i n u e * d * O t o s c * e T H A l i n * c o n f l i c * s A n v * l v e d i n l O S e H i * s r e t H I r n t o R E t O r e s t r i a l R E B r a d i o +Eval: S I D D S D I S S D D S D I I I S D I S I I S S I I I + +Speaker sentences 205: cv_eng_000788 #utts: 1 +id: (cv_eng_000788-cv_eng_000788) +Scores: (#C #S #D #I) 24 2 1 9 +REF: * * * * * ******* h e r f a m I l y w a s f r o m * b r I a * * n Z a +HYP: A D T T H h e r f a m * l y w a s f r o m E b r E a H O n S a +Eval: I I I I I I D I S I I S + +Speaker sentences 206: cv_eng_000789 #utts: 1 +id: (cv_eng_000789-cv_eng_000789) +Scores: (#C #S #D #I) 19 3 5 9 +REF: * ******* w h a t d i d y O U e a T f o r D i n * * ******* * N e * ******* * R +HYP: A w h a t d i d ******* y * * ******* e a E f o r * i n O R T H e A P A +Eval: I I D D D D S D I I I I S I I I S + +Speaker sentences 207: cv_eng_000790 #utts: 1 +id: (cv_eng_000790-cv_eng_000790) +Scores: (#C #S #D #I) 23 1 3 0 +REF: t h a t w a s m y d r a W t o s C i E n c e +HYP: t h a t w a s m y d r a * R t o s * i * n c e +Eval: D S D D + +Speaker sentences 208: cv_eng_000791 #utts: 1 +id: (cv_eng_000791-cv_eng_000791) +Scores: (#C #S #D #I) 29 5 6 7 +REF: h e I s c o N s I D e * r E D a m A s t e r * o f C h * I a r o s * * ******* c * u R o +HYP: h e ******* * s c o * s * * e A r I T a m U s t e r E o f S h E a r o s E D c O u * o +Eval: D D D D D I S S S I S I S I I I I D + +Speaker sentences 209: cv_eng_000792 #utts: 1 +id: (cv_eng_000792-cv_eng_000792) +Scores: (#C #S #D #I) 41 15 9 11 +REF: I T t h e N * * R E t u r N s t o t h e c h u r * C h * a ******* s C E n D s * * a t T H E a l t A r A n d * D I s * A P P e * * A r S +HYP: * * ******* t h e * H L I N t u r * s t o t h e c h u r I S h O a s * I n E s T H a t * * D a l t E r I n d S P E U s E W H e T H E r * +Eval: D D D D I I S S D I S I I D S S I I D D S S S I S S S I S S S I I S D + +Speaker sentences 210: cv_eng_000793 #utts: 1 +id: (cv_eng_000793-cv_eng_000793) +Scores: (#C #S #D #I) 17 8 9 8 +REF: Y o u * C A N n o t * L o s * * * e * * W h A T Y O U N E V e R H a d * +HYP: I o u E * * * n o t T H o s W E R e I N * h * E * * R A C H L e * * a d R +Eval: S I D D D I S I I I I I D D S D D S S S S S D D I + +Speaker sentences 211: cv_eng_000794 #utts: 1 +id: (cv_eng_000794-cv_eng_000794) +Scores: (#C #S #D #I) 21 5 2 3 +REF: t h e * j A W s * e X t e n d P A s t t h e * E y E +HYP: t h e L j * O s I e C t e n d F E s t t h e P I y * +Eval: I D S I S S S I S D + +Speaker sentences 212: cv_eng_000795 #utts: 1 +id: (cv_eng_000795-cv_eng_000795) +Scores: (#C #S #D #I) 25 1 5 1 +REF: m y n I e C E c a n h e l p y o U W i t H t h a t * +HYP: m y n * e * S c a n h e l p y o * * i t * t h a t S +Eval: D D S D D D I + +Speaker sentences 213: cv_eng_000796 #utts: 1 +id: (cv_eng_000796-cv_eng_000796) +Scores: (#C #S #D #I) 12 9 12 1 +REF: T H A t S T H E K I N d O F s t U F f T H e * y w A n T +HYP: * B U t * * S A * * O d ******* * H I s t * O f ******* * * e R y w O n * +Eval: D S S D D S S D D S D D S S D S D D D I S D + +Speaker sentences 214: cv_eng_000797 #utts: 1 +id: (cv_eng_000797-cv_eng_000797) +Scores: (#C #S #D #I) 34 6 3 1 +REF: h o P E f o r t h e b e s t a n d p r E p * a R e f O R t h e W o R s t +HYP: h o * W f o r t h e b e s t a n d p r p E a * e T f U E t h e B o * s t +Eval: D S S I D S S S S D + +Speaker sentences 215: cv_eng_000798 #utts: 1 +id: (cv_eng_000798-cv_eng_000798) +Scores: (#C #S #D #I) 36 9 10 0 +REF: i n i T I A L l y t h e W e I G H T l o S s w a s A T t A I N e D s t r i c T l y b y d i E t +HYP: i n i * S H E l y t h e * e * * * P l o U s ******* w a s H R t * * * e N s t r i c K l y b y d i * t +Eval: D S S S D D D D S S D S S D D D S S D + +Speaker sentences 216: cv_eng_000799 #utts: 1 +id: (cv_eng_000799-cv_eng_000799) +Scores: (#C #S #D #I) 34 4 6 0 +REF: a l l w E R e o W n e d b y t h e e v e r E t T m O o r e s Y n D i c A t E +HYP: a l l w * * e o * n e d b y t h e e v e r I t m * o r e s I n * i c I t * +Eval: D D D S S D S D S D + +Speaker sentences 217: cv_eng_000800 #utts: 1 +id: (cv_eng_000800-cv_eng_000800) +Scores: (#C #S #D #I) 13 8 0 24 +REF: * * W I L L * * * I t * * * * R a * ******* * i n * t o ******* m o r * * ******* * * * * * ******* * R o W +HYP: B H A T H T H E N t H E W I L a S R i n G t o m o r O M I L N T H S E o D +Eval: I I S S S S I I I S I I I I S I I I I I I I I I I I I I I I S S + +Speaker sentences 218: cv_eng_000801 #utts: 1 +id: (cv_eng_000801-cv_eng_000801) +Scores: (#C #S #D #I) 16 4 4 6 +REF: * ******* d * U b i s t * * e W I g * m E i n E l i E B E +HYP: H d O R b i s t E R e C K g M m * i n * l i * * P +Eval: I I I S I I S S I D D D D S + +Speaker sentences 219: cv_eng_000802 #utts: 1 +id: (cv_eng_000802-cv_eng_000802) +Scores: (#C #S #D #I) 38 4 4 1 +REF: L U C i * l E p e t r y t O o K h e r p L a c e a s a c t i n g d i r e c t O r +HYP: O S E i A l * p e t r y t * o * h e r p * a c e a s a c t i n g d i r e c t E r +Eval: S S S I D D D D S + +Speaker sentences 220: cv_eng_000803 #utts: 1 +id: (cv_eng_000803-cv_eng_000803) +Scores: (#C #S #D #I) 44 9 15 1 +REF: t h e b e A v e R r I V E R b R i E f l y E n t e R s t h e e A s T C e n t r A L p A R t o f T H E t o W n ******* s h i p +HYP: t h e b e * v e * ******* r L Y W L b * i * f l y * n t e * s t h e e * s S e n t r * * p * U t o f * * * A t o * n s h i p +Eval: D D D S S S S S D D D D D S S D D D S D D D S D I + +Speaker sentences 221: cv_eng_000804 #utts: 1 +id: (cv_eng_000804-cv_eng_000804) +Scores: (#C #S #D #I) 36 4 0 2 +REF: t h e t r a c k r e s ******* U r F A C i n g w a s a l s o c o m p l e * t e d +HYP: t h e t r a c k r e s E r V S T i n g w a s a l s o c o m p l e A t e d +Eval: I S S S S I + +Speaker sentences 222: cv_eng_000805 #utts: 1 +id: (cv_eng_000805-cv_eng_000805) +Scores: (#C #S #D #I) 66 10 7 5 +REF: h i N D m a r S h w a s a w A r e o f t h e i m p o r t A n C e o f e l e c t ******* r O N M I c R o s c O p Y i n b * ******* I O l o * g i c a l * r e s e A r c h +HYP: h i T m a r C h w a s a w E r e o f t h e i m p o r t * n * e o f e l e c t r * M R E c * o s c M p * ******* i n b Y A E l o U g i c a l R r e s e * r c h +Eval: S S S S D D I D S S S D S D D I I S S I I D + +Speaker sentences 223: cv_eng_000806 #utts: 1 +id: (cv_eng_000806-cv_eng_000806) +Scores: (#C #S #D #I) 18 7 2 3 +REF: s i n ******* h A w * a s b o r n * I n A L L A h a b a D +HYP: s i n h * O w B a s b o r n Y A n ******* T H E h a b a R +Eval: I D S I I S D S S S S S + +Speaker sentences 224: cv_eng_000807 #utts: 1 +id: (cv_eng_000807-cv_eng_000807) +Scores: (#C #S #D #I) 57 24 15 14 +REF: * t h i s B R i D G E * * I s * U n ******* o F f I C i a L l y R E F e r ******* R E d t o a s B L a c k W A T e * * * R B r i D G E b y c o A l I T i O n f o * R c * e s * * O P e r a * t i n G T H E R E +HYP: N t h i s * W i N C H K E A s E A n o * f * * i a * l y * * H e r H O d t o a s * M a c k * R e M P A D * r i N T H b y c o * l A S i * n f o U S c S e s R A V B e r a I t i n * * * W I L +Eval: I D S S S S I I S I S I D D D D D D S I S S D S D S S I I I S D S S S D S S D I S I I I S S I D D D S S S + +Speaker sentences 225: cv_eng_000808 #utts: 1 +id: (cv_eng_000808-cv_eng_000808) +Scores: (#C #S #D #I) 73 3 7 3 +REF: i t i s r e s p o n s * i B l E f o r w a t e R s u P p l * y a n d m a n A g e m e n t o f w a t e r r e s o u r C e s I n * m a h A R a s H t r a +HYP: i t i s r e s p o n s E i P l * f o r w a t e * s u * p l I y a n d m a n * g e m e n t o f w a t e r r e s o u r S e s A n D m a h * * a s * t r a +Eval: I S D D D I D S S I D D D + +Speaker sentences 226: cv_eng_000809 #utts: 1 +id: (cv_eng_000809-cv_eng_000809) +Scores: (#C #S #D #I) 30 8 4 6 +REF: T H I s I s t h e f i r s T P H a * S e o f t h e J o * * * ******* B h e s a * I d +HYP: * J E s ******* E s t h e f i r s * * F a I C e o f t h e H o R V E A h e s a Y E d +Eval: D S S D S D D S I S S I I I I S I S + +Speaker sentences 227: fleurs_eng_000413 #utts: 1 +id: (fleurs_eng_000413-fleurs_eng_000413) +Scores: (#C #S #D #I) 163 22 10 7 +REF: t h e g i Z A p l a t E A U o r g i Z a n e c r * * O p o l I s i n t h e E g Y p t i A n v a L l E y o f t h e d e A d c o n t a i n S s e v e r a l p Y r A m * I d s o f w h i c h t h e g r e a t * p Y r A m * I D i s t h e l a r G e s T s e v e r a l * s m a L l t o M B s s e v e R a l t e m p l e s a n d t h e g r e a t s p H I n * X +HYP: t h e g i S I U p l a t * * O o r g i * a n e c r A L p o l E s i n t h e A g I p t i O n v a O l * y o f t h e d e * d c o n t a i n G s e v e r a l p E r I m E N d s o f w h i c h t h e g r e a t E p E r * m E N T i s t h e l a r T e s * s e v e r a l E s m a * l t o O N s s e v e * a l t e m p l e s a n d t h e g r e a t s p * A n K S +Eval: S S S D D S D I I S S S S S S D D S S S I S I S D I S S S D I D S S D D S I S + +Speaker sentences 228: fleurs_eng_000414 #utts: 1 +id: (fleurs_eng_000414-fleurs_eng_000414) +Scores: (#C #S #D #I) 170 7 24 7 +REF: t O w a r d S t h e E n d o f t h E m i D D l e a g e s w e s t e r n E u r O p E b e g a n * t o d e v e l O P t h e I r o W n s t Y l E o n E o f t h e b i G g e s t D E v e l O P m e n T s o f t h e t i m e a s a r e s u l t o f t h e c r u S a D E s p e O p * l E b e g a n t o u s e b u T t O n s t o f a s t E n c l o * t H i n g ******* * ******* * +HYP: t * w a r d * t h e I n d o f t h * m i * * l e a g e s w e s t e r n Y u r U p * b e g a n D t o d e v e l * T t h e * r o * n s t I l * o n * o f t h e b i * g e s t * * v e l * * m e n * s o f t h e t i m e a s a r e s u l t o f t h e c r u C a * * s p e * p U l * b e g a n t o u s e b u * t E n s t o f a s t * n c l o L t * i n g R R +Eval: D D S D D D S S D I D S D D S D D D D D D D D S D D D I D D S D I D I I I I + +Speaker sentences 229: fleurs_eng_000415 #utts: 1 +id: (fleurs_eng_000415-fleurs_eng_000415) +Scores: (#C #S #D #I) 72 9 12 13 +REF: i f * y o u o n l y g o a s h o r e u s i n g s h i p B o A r D E X c u r S i o n s y o u W I L l n o t n E e D a s e p A r A t E v I s a a s ******* * ******* * * * * * o * * ******* * F 2 0 0 9 +HYP: i f S y o u o n l y g o a s h o r e u s i n g s h i p o * r * I S c u r * i o n s y o u ******* * * * l n o t n * e * a s e p * r * t * v E s a a s A T W O T H o U S I N D N I N +Eval: I S D D S S D D D D D D D D D D S I I I I I I I I I I I I S S S S S + +Speaker sentences 230: fleurs_eng_000416 #utts: 1 +id: (fleurs_eng_000416-fleurs_eng_000416) +Scores: (#C #S #D #I) 90 5 21 3 +REF: d * u V a L l W h O i s m a R r I e D w i T h t W o a ******* d U l T c h I L D r e n D I d n o t L E A V e * a b i g I m p r e S s i o n o n m i L l e r t o W h o m t h e s T o r y w a s r e l a t e d +HYP: d O u B a * l * h * i s ******* m a * r * e * w i * h t * o a d * l * c h * * E r e n C U d n o t * * * B e W a b i g * m p r e * s i o n o n m i * l e r t o * h o m t h e s * o r y w a s r e l a t e d +Eval: I S D D D D D D D D D I D D D D S S S D D D S I D D D D D + +Speaker sentences 231: fleurs_eng_000417 #utts: 1 +id: (fleurs_eng_000417-fleurs_eng_000417) +Scores: (#C #S #D #I) 106 4 28 1 +REF: t h e I r d i s C I p l i n E d d e f e n c E b a L l h a N d l i n g s K i L l s a n d e x C E L l E n T t e A M W o r k m a d E t h e M s t a n d o u t a n D I T w a s c l e A r T h a t T h i s w a s t h e T e A m * t o b e A T +HYP: t h e * r d i s * O p l i n * d d e f e n c S b a * l h a * d l i n g s C i * l s ******* a n d e x * * A l * n * ******* t e * * * o r k m a d * t h e * s t a n d o u t a n * ******* * * w a s c l e * r * h a t * h i s w a s t h e * e * m E t o b e * * +Eval: D D S D S D D S D D D D S D D D D D D D D D D D D D D D D D I D D + +Speaker sentences 232: fleurs_eng_000418 #utts: 1 +id: (fleurs_eng_000418-fleurs_eng_000418) +Scores: (#C #S #D #I) 62 6 10 2 +REF: t h e d i s e A s E i s c a R r I e d b y p i g s w h i c h T h e n m * I g r A t e s t o h * u m A n s t H r o U G H m O s Q U I t o s +HYP: t h e d i s e * s * i s c a * r * e d b y p i g s w h i c h * h e n m Y g r E t e s t o h E u m E n s t O r o * * * m * s * C E t o s +Eval: D D D D D I S S I S S D D D D D S S + +Speaker sentences 233: fleurs_eng_000419 #utts: 1 +id: (fleurs_eng_000419-fleurs_eng_000419) +Scores: (#C #S #D #I) 52 0 1 4 +REF: f o r t h e s p r i n g ******* b o k s i t * e n d e d a f i v e ******* m a t C h l o s i n g s t r e a * k +HYP: f o r t h e s p r i n g b o k s i t D e n d e d a f i v e m a t * h l o s i n g s t r e a E k +Eval: I I I D I + +Speaker sentences 234: fleurs_eng_000420 #utts: 1 +id: (fleurs_eng_000420-fleurs_eng_000420) +Scores: (#C #S #D #I) 48 7 10 0 +REF: t h U s t h e p E n C I l w A S A g O o d f r i E n d T O m a n y P e o p l e w H e n i T c a m e o u t +HYP: t h E s t h e p I n S A l w * * I T g * o d f r i * n d ******* * S m a n y * e o p l e w * e n ******* i * c a m e o u t +Eval: S S S S D D S S D D D D S D D D D + +Speaker sentences 235: fleurs_eng_000421 #utts: 1 +id: (fleurs_eng_000421-fleurs_eng_000421) +Scores: (#C #S #D #I) 126 12 10 3 +REF: t h e u s e o f v I D e o r e c o r D i n g h a s l e d t o I m p o r T A n T d i s c o v e r I e s i n t h e i n t e r p r E t a t i o n o f m I C r * O e x p r e s S i o n s f a C i a l m o v e m e n T s w h i c h l a s T a f e W m i l * L I s E c * O n D s +HYP: t h e u s e o f v * * e o r e c o r * i n g h a s l e d t o * m p o r * * n D d i s c o v e r * e s i n t h e i n t e r p r I t a t i o n o f m Y K r L e x p r e s T i o n s f a T i a l m o v e m e n * s w h i c h l a s * a f e U m i l E S s I c K E n * s +Eval: D D D D D D S D S S S I S S S D D S I S S S I S D + +Speaker sentences 236: fleurs_eng_000422 #utts: 1 +id: (fleurs_eng_000422-fleurs_eng_000422) +Scores: (#C #S #D #I) 95 8 16 5 +REF: a l s O t O t h e n o r t h V i s I t t h e g r e a t * s a n c T u A r y o f o U r l a d y o f F a t * * I m A s h r i n E a p l a c e o f w o R l d ******* W i * D e f a m O U s m A r i a n a P p A r I T i o n s +HYP: a l s * A t * t h e n o r t h * i s * t t h e g r e a t E s a n c * u * r y o f o * r l a d y o f * a t H E m * U s h r i n * a p l a c e o f w o * l d R i G T e ******* f a m * * s m E r i a n a V p E r * * i o n s +Eval: D S D D D I D D D D I I S D S D D I S I S D D D S S S D D + +Speaker sentences 237: fleurs_eng_000423 #utts: 1 +id: (fleurs_eng_000423-fleurs_eng_000423) +Scores: (#C #S #D #I) 90 4 20 2 +REF: i f y o U w A n T t O b e c L o s e T o t h e a c t i o n y o U r e G O I N G t o H A V E T o g e t i n e a R l y t o G E t a c a m p i n g s i * t E c l o s E t o t h e m u s i c * +HYP: i f y o * w * n * ******* t * b e c * o s e * o t h e a c t i o n y o * r e ******* * * H A E t o * * * O * o g e t i n e a * l y t o * * t a c a m p i n g s i H t * c l o s * t o t h e m u s i c K +Eval: D D D D D D D D D D D S S S D D D S D D D D I D D I + +Speaker sentences 238: fleurs_eng_000424 #utts: 1 +id: (fleurs_eng_000424-fleurs_eng_000424) +Scores: (#C #S #D #I) 75 4 6 6 +REF: m A D a g A s c * a r i s b y f a r * t h e b i G g e s t a n d * * A c o n t i n E n t o n i t s o W n w h e n i T c o m E s t o * w i l d ******* l i f E +HYP: m * T a g U s c O a r i s b y f a r E t h e b i * g e s t a n d T H E c o n t i n A n t o n i t s o * n w h e n i * c o m * s t o W w i l d l i f * +Eval: D S S I I D I I S S D D D I I D + +Speaker sentences 239: fleurs_eng_000425 #utts: 1 +id: (fleurs_eng_000425-fleurs_eng_000425) +Scores: (#C #S #D #I) 91 5 15 1 +REF: w O m e n i t i s r e c o M m e n D e d t h a t a n y w o m e n t r A v E L l E r s s a y T H A T t h e y a r E m a R r I e d r e g a r D l E s S o f a c t * u a l m a r i t a l s t a t U s +HYP: w E m e n i t i s r e c o * m e n * e d t h a t a n y w o m e n ******* t r O v * * l O r s s a y ******* * * * * t h e y a r * m a * r * e d r e g a r * l * s T o f a c t I u a l m a r i t a l s t a t I s +Eval: S D D D S D D S D D D D D D D D D D S I S + +Speaker sentences 240: fleurs_eng_000426 #utts: 1 +id: (fleurs_eng_000426-fleurs_eng_000426) +Scores: (#C #S #D #I) 92 4 10 19 +REF: C u o m o ******* * * * * * * * 5 3 b e g a n h i S g o v e r * * n * ******* * o * * r s h i p e A r L i * e r t h i s y e a r a n d s i G n E d a b i l L l a s t m o n t h l e g A l i Z i n g s a * m e ******* s e * x m a R r I a g e +HYP: * u o m o F I F T Y T H R E b e g a n h i * g o v e r M E n T G o V E r s h i p e * r * i L e r t h i s y e a r a n d s i * n * d ******* a b i l E l a s t m o n t h l e g * l i S i n g s a I m e s e C x m a * r * a g e +Eval: D I I I I I I I I S S D I I I I I I I D D I D D D S D S I I I D D + +Speaker sentences 241: fleurs_eng_000427 #utts: 1 +id: (fleurs_eng_000427-fleurs_eng_000427) +Scores: (#C #S #D #I) 130 9 34 1 +REF: a s l i G H T p O L l u t i o n I n T h e I r h E Y d A y w a s n o t t h e K i n d o f p r o b L E m I t i s t o ******* d a y t h e Y A r E U S u A L l y l o c a t e d i n C i T i e s o r a T c a m p U s e s e a s i e r t o r e a C H T H A n t h o s E b U i l T I n m o D e R n t i m E s +HYP: a s l i * * * ******* p * * l u t i o n * n * h e * r ******* h * A d * y w a s n o t t h e C i n d o f p r o b * O m ******* * t i s t o d a y t h e * ******* * r * * * u * * l y l o c a t e d i n S i * i e s o r a * c a m p * s e s e a s i e r t o r e a * * ******* S I O n t h o s * b * i l * A n m o T e * n t i m * s +Eval: D D D D D D D D D D D S D S D S D D I D D D D D D D D S D D D D D D S S S D D D S S D D + +Speaker sentences 242: fleurs_eng_000428 #utts: 1 +id: (fleurs_eng_000428-fleurs_eng_000428) +Scores: (#C #S #D #I) 88 4 28 1 +REF: t h e Y U S u A L l y h a v e s p e c i a l f O o d D r i n k a n D E n T E r t a I N m e n T o F F e r s t o K E e P g U e S T s I n * a g O o d m O o d a n D K E e P t h e m a t t h e p r E m i s E +HYP: t h e * * * u * * l y h a v e s p e c i a l f * o d * r i n k ******* a n * * n * * r t a * * m e n * o * P e r s t o * C e * g * e * * s A n D a g * o d m * o d a n * * C e * t h e m a t t h e p r * m i s * +Eval: D D D D D D D D D D D D D D D D S D S D D D D S I D D D D S D D D + +Speaker sentences 243: fleurs_eng_000429 #utts: 1 +id: (fleurs_eng_000429-fleurs_eng_000429) +Scores: (#C #S #D #I) 115 3 9 3 +REF: o n t h e o t h e r h a n d i c * * Y a n d s n o w y c o N d I T i o n s a r E n o r m a L i n m a n y c o u n t r I e s a n d t r a F f i C G o e s o n m o s t l y u n ******* i n t E R r u p t e d a l l y e a r r o u n d +HYP: o n t h e o t h e r h a n d i c E S E a n d s n o w y c o * d * * i o n s a r * n o r m a E i n m a n y c o u n t r * e s a n d t r a * f i T * o e s o n m o s t l y u n i n t * * r u p t e d a l l y e a r r o u n d +Eval: I I S D D D D S D D S D I D D + +Speaker sentences 244: fleurs_eng_000430 #utts: 1 +id: (fleurs_eng_000430-fleurs_eng_000430) +Scores: (#C #S #D #I) 87 8 6 5 +REF: b e c a r E f u l N o t t o a L l o w f a b r i c t o b e c o m e t O o h * O T w h i c h c a n c a U s e s H r I n k a * g e o r i n * E X t r e M E c a s e s s C o * r * c h +HYP: b e c a r * f u l * o t t o a * l o w f a b r i c t o b e c o m e t * o h I Y E w h i c h c a n c a * s e s T r A n k a D g e o r i n A S t r e * N c a s e s s Q o A r T c h +Eval: D D D D I S S D S S I I S S D S S I I + +Speaker sentences 245: fleurs_eng_000431 #utts: 1 +id: (fleurs_eng_000431-fleurs_eng_000431) +Scores: (#C #S #D #I) 77 7 19 3 +REF: f e * r A l c h i l d r E n m a Y h a v E e x p e R i E n c E D s * E v e r E c h i l d * A b U s E o r t r A U m A b e f o r E b E i n g a b a n d O n E D O r r U N N I n g a w a y +HYP: f e I r * l c h i l d r * n m a * h a v * e x p e * i A n c * * s O v e r * c h i l d H b E s * o r t r * O m M b e f o r * b * i n g a b a n d I n * * * r r * * * * n g a w a y +Eval: I D D D D D S D D I S D I S S D D S S D D S D D D D D D D + +Speaker sentences 246: fleurs_eng_000432 #utts: 1 +id: (fleurs_eng_000432-fleurs_eng_000432) +Scores: (#C #S #D #I) 84 5 17 1 +REF: p e o p l e m a Y n o t A n t i c i p a t E t h a T p a t i E n c E a n d U n d E r * s t a N D I n g A r E a l s o n E C e S s A r y f o r t r A v E L l e r s r e t U r n i n g h o m E +HYP: p e o p l e m a * n o t I n t i c i p a t * t h a * p a t i O n c S a n d * n d * r E s t a * * * n g * r * a l s o n * * e * s E r y f o r t r O v * * l e r s r e t * r n i n g h o m * +Eval: D S D D S S D D I D D D D D D D D S S D D D D + +Speaker sentences 247: fleurs_eng_000433 #utts: 1 +id: (fleurs_eng_000433-fleurs_eng_000433) +Scores: (#C #S #D #I) 65 8 11 6 +REF: S O o n A F t e r t h e O U T B r E A k o f h O s t i l i t I e s b r i t A I n i n * I t ******* * * I a t e d a * n a v A l * b L o c k a d e o f G e r m a n y +HYP: * * o n * O t e r t h e * * * P r * I k o f h U s t i l i t * e s b r i t * * n i n E N t S H E a t e d a N n a v B l E b * o c k a d e o f H e r m a n y +Eval: D D D S D D D S D S S D D D I S I I I S I S I D S + +Speaker sentences 248: fleurs_eng_000434 #utts: 1 +id: (fleurs_eng_000434-fleurs_eng_000434) +Scores: (#C #S #D #I) 52 5 13 1 +REF: t h e G o V e R n O r s o F f i C E s a i d n i n E t E e n o f t h e i n J u r e d w E R e p O l I C e * o F f i C e r s +HYP: t h e * o * e * n * r s o * f i * S s a i d n i n * t * e n o f ******* t h e i n G u r e d w * * e p * l E A e S o * f i S e r s +Eval: D D D D D D S D D D S D D D S S I D S + +Speaker sentences 249: fleurs_eng_000435 #utts: 1 +id: (fleurs_eng_000435-fleurs_eng_000435) +Scores: (#C #S #D #I) 97 6 16 2 +REF: u s i n g s h i p s t o t r A N s p o r t * g o o d s i s b y f a r t h e m o s T E F f I C i e n t w a y t o m o V e l a r G e A m o u N t S o f p e O P l e A n D g O o d S a ******* c r o S s o C E A n s +HYP: u s i n g s h i p s t o t r * E s p o r t S g o o d s i s b y f a r t h e m o s * * O f * * i e n t w a y t o m o * e l a r * e * m o u * t * o f p e * B l e * n * g * o d * a c r o * s o T I O n s +Eval: D S I D D S D D D D D D D D S D D D D I D S S S + +Speaker sentences 250: fleurs_eng_000436 #utts: 1 +id: (fleurs_eng_000436-fleurs_eng_000436) +Scores: (#C #S #D #I) 102 13 20 8 +REF: L I B e * r A l c r i t i C i s m o f t h e r e c o n s t r U c t i O n e F F O r t * h a s F o * C U s E D o N t h e A w a r d i n g o f r e c o n s t r U c T i O n * c o n t R a c t S t o P E r C E i * V e ******* d * w a S H i n g T O n * i n s i D e r s +HYP: * T H e B r * l c r i t i S i s m o f t h e r e c o n s t r * c t i * n e * V E r t N h a s ******* P o K A s * * o * t h e * w a r d i n g o f r e c o n s t r * c * i * n G c o n t H a c t * t o * * r * * i S T e d E w a * T i n g A n D i n s i * e r s +Eval: D S S I D S D D D S S I D S I S S D D D D D D D I S D D D D D I S I I D S S S I D + +Speaker sentences 251: fleurs_eng_000437 #utts: 1 +id: (fleurs_eng_000437-fleurs_eng_000437) +Scores: (#C #S #D #I) 90 17 14 22 +REF: Y O u * C A N u * s E b o * d ******* * * A b o d ******* a m O T o r ******* C Y c l E t a * x * I t o g e t a r o u n d g o m a t h e n O r m a L L O c A l * p r i c e i s ******* * * * * * * * * 5 0 0 c o n G O l e s E f r A n C s f o r t h e * s h o * r t r I D E +HYP: * * u T W E U C u N s * b o W d O B b o d a m * * o r S E c l * ******* t a C x C Y t o g e t a r o u n d g o m a t h e n * r m a E W I c K l E p r i c e i s F I V E H U N D R E D c o n * D l e s * f r O n * s f o r t h e M s h o U r t r * * * +Eval: D D I S S S S I D I I I I S I D D I S S D D I I S D S S S S I I I I I I I I I I S S S D S D S D I I D D D + +Speaker sentences 252: fleurs_eng_000438 #utts: 1 +id: (fleurs_eng_000438-fleurs_eng_000438) +Scores: (#C #S #D #I) 136 9 18 8 +REF: t h e t h R E e k i n g d o m s w a s o n e o f t h e b l * ******* * O O d i E s t e r a s I n * a n C i e n t c h i n a s h i s ******* t O r Y t h o u s A n D s o f p e o P l e d i e d f i G H t i n g t o s * i t i n t h E h i G H e * s T S e A T i n t h e g r a n d p a l A C e * a t X i A N +HYP: t h e t h * * e k i n g d o m s w a s o n e o f t h e b l T B L U d i A s t e r a s A n D a n * i e n t c h i n a s h i s t H r E t h o u s O n * s o f p e o * l e d i e d f i * * t i n g t o s T i t i n t h * h i * * e A s * ******* C e * * i n t h e g r a n d p a l * * e S a t S i * * +Eval: D D I I I S S S S I D I S S S D D D D I D D D I D D S D D D D I S D D + +Speaker sentences 253: fleurs_eng_000439 #utts: 1 +id: (fleurs_eng_000439-fleurs_eng_000439) +Scores: (#C #S #D #I) 56 4 4 4 +REF: * t h e s E c o u p l e s m a y c h O O s e t o m a k e * a n * a d o P T i O n p l a n * f o r t h e I r b a B y +HYP: R t h e s * c o u p l e s m a y c h * U s e t o m a k e K a n D a d o U S i * n p l a n D f o r t h e * r b a V y +Eval: I D D S I I S S D I D S + +Speaker sentences 254: fleurs_eng_000440 #utts: 1 +id: (fleurs_eng_000440-fleurs_eng_000440) +Scores: (#C #S #D #I) 131 5 27 4 +REF: n o t h I n g C a n * b e S E e n o t h e r T h A n t h e c l e a r * b E A u t i f u l s * * K y a b o v e a n d t h e m A n y s u R r O u N D I n g m o u N T A I n s v e r y l i T t L E o f t h I s w O R l D C a n b e s E e n o r h E A r d f r o m i n s i d E t h e c a v e +HYP: n o t h * n g * a n D b e * F e n o t h e r * h * n t h e c l e a r E b * * u t i f u l s C A I y a b o v e a n d t h e m E n y s u * r * u * * * n g m o u * * * * n s v e r y l i * t * * o f t h * s ******* w * A l * * a n b e s * e n o r h * U r d f r o m i n s i d * t h e c a v e +Eval: D D I D S D D I D D I I S S D D D D D D D D D D D D D D D S D D D D S D + +Speaker sentences 255: fleurs_eng_000441 #utts: 1 +id: (fleurs_eng_000441-fleurs_eng_000441) +Scores: (#C #S #D #I) 57 6 4 1 +REF: h e w a s s U b s E Q u e n t l y r e l o c a t e d t o a D d E n ******* b r O o k E s h o s p i t A l I n c a m b r i D g e +HYP: h e w a s s O b s I C u e n t l y r e l o c a t e d t o a * d I n b r * o k * s h o s p i t * l A n c a m b r i A g e +Eval: S S S D S I D D D S S + +Speaker sentences 256: fleurs_eng_000442 #utts: 1 +id: (fleurs_eng_000442-fleurs_eng_000442) +Scores: (#C #S #D #I) 89 9 35 12 +REF: * V a T i c a n * C i t y S p o p u l a t i o n i s a r o u n d 8 0 0 * * * * * * i * ******* t * i s t h e S m a L L E s t I n D e p e n d e N T c o U n t r Y I n t h e w o r * l d a n d T H E C O U N T R Y W I T H t h E L O W e S T p o p u l a t i o n +HYP: T H a i c a n S T i t y * p o p u l a t i o n i s ******* a r o u n d A I N H E U N D R i D t H E i s t h e * m a * * O s t * n T e p e n d e * * c o * n t r * * n t h e w o r A l d a n d ******* * * * ******* * * * * * * * ******* * * * * t h * ******* * * * e * * p o p u l a t i o n +Eval: I S S I S D D S S S I I I I I I I I I S D D D S D S D D D D D I D D D D D D D D D D D D D D D D D D D D D D D D + +Speaker sentences 257: fleurs_eng_000443 #utts: 1 +id: (fleurs_eng_000443-fleurs_eng_000443) +Scores: (#C #S #D #I) 158 22 26 4 +REF: r e g u L A r a N N o u n C E m e n t S I n t h e * m e t r O a r E m a D e o N l y i n c a t A l A n b u t u n p l a N n e D d i s * r u p t i o n s a r E A N n o u N C e D b y a n A U t O m a T e d s Y s t E m i n a w I d E v A r i E t y o f l A n G U A g e s I n c L u D i n g s P a n i s h E n g l i s h f r e n c h A r A b I c * a n d J a p A n e S e * +HYP: r e g u * * r a * L o u n * S m e n t * A n t h e P m e t r U a r * ******* m a * e o * l y i n c a t * l O n b u t u n p l a * n e * d i s T r u p t i o n s a r * * * n o u * S e * b y a n * O t A m a * e d s I s t O m i n a w A d * v E r i * t y o f l * n W I C g e s * n c * u T i n g s B a n i s h A n g l i s h f r e n c h E r * b E c K a n d H a p O n e * e S +Eval: D D D S D S D S I S D D D D D S D D I D D D D S D D S S D S S S D S D D S S S D D S S S S D S I S S D I + +Speaker sentences 258: fleurs_eng_000444 #utts: 1 +id: (fleurs_eng_000444-fleurs_eng_000444) +Scores: (#C #S #D #I) 81 8 22 3 +REF: t h i s o F F E r S a g O o d O P p O r t u n i t Y t o s E e t h e A U R o * r a b O r E A l I s a s t h e s * k * Y w i L l b e d a r k m O R E o r l e s S A r O u n D t h e c l o c K +HYP: t h i s o * * P r E a ******* g * o d * * p * r t u n i t I t o s * e t h e * * * o W r a ******* b A r * I l E s a s t h e s C k G I w i * l b e d a r k m * * * ******* o r l e s T * r * u n * t h e c l o c * +Eval: D D S S D D D D D S D D D D I D S D S S I I S D D D D D S D D D D + +Speaker sentences 259: fleurs_eng_000445 #utts: 1 +id: (fleurs_eng_000445-fleurs_eng_000445) +Scores: (#C #S #D #I) 36 9 10 17 +REF: f i R E r E s c * u E c r E W s E v e n T U a L l y d o u s e D t H E f i R e b * ******* * ******* * * * * * ******* * * * * y 1 1 3 5 p * * m +HYP: f i * * ******* r * s c K u * c r * O s O v e n C I a * l y d o u s e * t * O f i * e b Y A L E V E N T H R T y F I V E p E A m +Eval: D D D D I D D S S S S D D D S D I I I I I I I I I I I I I I S S S S I I + +Speaker sentences 260: fleurs_eng_000446 #utts: 1 +id: (fleurs_eng_000446-fleurs_eng_000446) +Scores: (#C #S #D #I) 59 7 13 3 +REF: t h I S i s c a L l E D A c H E m i c a l s p * H Y O U c a n m a k E a n i n d i c a t * o R u s i n g r e d c a B b a g * e j U I C E +HYP: t h * * ******* i s c a * l * T O c * * m i c a l s p E E * H E c a n m a k * a n i n d i c a t E o * u s i n g r e d c a * b a g H e j * * O S +Eval: D D D D D S S D D I S D S S D I D D I D D S S + +Speaker sentences 261: fleurs_eng_000447 #utts: 1 +id: (fleurs_eng_000447-fleurs_eng_000447) +Scores: (#C #S #D #I) 98 2 16 4 +REF: i n p A r t i c u l A r i t i s C l a I M e D t h a t o n e c a n d e t e c T w H e t h e r a p E r s o n i s l Y i n g b y i n t e r p r E T i n g m * I C r o * ******* e x * p r e S s i o n s c o R r e c t l y +HYP: i n p * r t i c u l * r i t i s * l a * * e * t h a t o n e c a n ******* d e t e c * w * e t h e r ******* a p * r s o n i s l * i n g b y i n t e r p r * * i n g m Y G r o L e x S p r e * s i o n s c o * r e c t l y +Eval: D D D D D D D D D D D D D D I S S I I I D D + +Speaker sentences 262: fleurs_eng_000448 #utts: 1 +id: (fleurs_eng_000448-fleurs_eng_000448) +Scores: (#C #S #D #I) 145 10 19 4 +REF: t h e C e N T R a l A U T H o r i t y o f t h e c h u R c H h A d * b E e n i n r o m E f o r o v e r a t h o u s a n D y e a r s a n d t h i s c o N C E n t r a t i o n O f p o w e r a n D m o n E y l e d ******* * * m a N y t o Q u E s t i o n w h e t h e r T H i s t e n e t w a s b e I n g m e t +HYP: t h e S e * C H a l * * * F o r i t y o f t h e c h u * c * ******* h O d S b * e n i n r o m * f o r o v e r a t h o u s a n * y e a r s a n d t h i s ******* c o * S O n t r a t i o n A f p o w e r a n * m o n * y l e d T O m a * y t o C u * s t i o n w h e t h e r * D i s ******* t e n e t w a s b e * n g m e t +Eval: S D S S D D D S D D D S I D D D D D S S S D D I I I D S D D S D D + +Speaker sentences 263: fleurs_eng_000449 #utts: 1 +id: (fleurs_eng_000449-fleurs_eng_000449) +Scores: (#C #S #D #I) 127 12 8 19 +REF: t h e s u n ******* d a r b A n s a r E t h e L a r g e s T * L I T t o r a l m a n ******* g r o V e b E l T i n t h e w o r l d s t R e T c h i n g * 8 0 * * K m * * * * * * * * 5 0 m i * * i n t o t h e B a n g l a d e s h I a n d ******* * * i n d i a n h i n t e r l a n d f r o m t h e c o A s t +HYP: t h e s u n d a r b O n s a r * t h e * a r g e s * T H E t o r a l m a n g r o * e b U l * i n t h e w o r l d s t * e * c h i n g A T Y C L A m I T E R S F I F T Y m i E S i n t o t h e * a n g l a d e s h E a n d I N i n d i a n h i n t e r l a n d f r o m t h e c o O s t +Eval: I S D D D I S S S I D S D D D I S S I I S I I I I I I I I S S I I D S I I I S + +Speaker sentences 264: fleurs_eng_000450 #utts: 1 +id: (fleurs_eng_000450-fleurs_eng_000450) +Scores: (#C #S #D #I) 156 17 33 6 +REF: r e g u l A r a N n o U n C E M e n T s i n T h e M e t R o a r E m a D e o n l y i n c a t a l A n b u t u n P L A N n E D d i s * r u p t i O n s A r E A N n o u n C e d b y a n A U t O m a t e * D s Y s t E m i n a w I d E v A r i E t y o f l A N g * U A g e s i n c L u d i n g s p a n * i s h E n g l I s h F r e n c h A r A b I c * a n d j * a p A n e s E +HYP: r e g u l * r a * n o * n * * S e n C s i n * h e * e t H o a r * m a * e o n l y i n c a t a l E n b u t u n * * * n * * ******* d i s T r u p t i * n s * r * * * n o u n * e d b y a n * O t * m a t e I S s I s t * m i n a w A d * ******* v E r i * t y o f l * I g W I N g e s i n c * u d i n g s p a n T i s h I n g l * s h * r e n c h E r * b A c K a n d j H a p O n e s * +Eval: D D D D D S S D D S D D S D D D S D D D I D D D D D D D S D I S S D S D D S D D S I S S D I S D D S D S I I S D + +Speaker sentences 265: fleurs_eng_000451 #utts: 1 +id: (fleurs_eng_000451-fleurs_eng_000451) +Scores: (#C #S #D #I) 83 15 21 5 +REF: e v e r Y O n E p A r t i C I P a t E S i n S o * c i E t y a n D u s ******* E s t r A n s p O r T A t i o n s Y s t * E M s a l m O s t E V e r y ******* O n E c o m p l a i n S A B o u T t r A n s ******* p O r T A t i o n s Y s t E m s +HYP: e v e r W n * p * r t i * T B a t * * i n * o U c i * t y a n * u s I s t r * n s p * r * * t i o n C s I s t O N C s a l m * s t * * e r y W n * c o m p l a i n E O o u * t r * n s p * r * * t i o n s I s t O m s +Eval: S S D D D S S D D D I D D I S D D D D S S I S S D D D I S D S S S D D I D D D S S + +Speaker sentences 266: fleurs_eng_000452 #utts: 1 +id: (fleurs_eng_000452-fleurs_eng_000452) +Scores: (#C #S #D #I) 138 14 38 5 +REF: l a Y t O n h a d a s K E D F o r C h a n G e s T o t h e C o n s * E r v A t i V E s E n v i r O N m E n t A l b i L l d u r I n G t h e m E e T I n G w I t H t h e p * m a s K i n g f O r A t h O r O U G H a n d c o m p l e t e r e W r i * t i n g o f t h e c o n s e r v A t * i V E p a r T y S E n V i * r O N m E n T a l B i L l +HYP: l a * t * n h a d a s * * * * o r * h a n * e s * o t h e * o n s U I r v E t i * * s * n v i r * * m I n t * l b i * l d u r * n * t h e m * e * * n * w * t * t h e p E m a s * i n g f * r ******* Y t h U r * * A L a n d c o m p l e t e ******* r e r i D t i n g o f t h e c o n s e r v E t H i * S p a r D y * I n i E r * * m I n * a l * i * l +Eval: D D D D D D D D D D I S S D D D D D S D D D D D D D D D D I D D D S S D D S S D S I S I D S S D S S I D D S D D D + +Speaker sentences 267: fleurs_eng_000453 #utts: 1 +id: (fleurs_eng_000453-fleurs_eng_000453) +Scores: (#C #S #D #I) 97 11 29 2 +REF: A n y ******* o n E W h O s g o I n G t o D r i V e a T h I G H l A t I t u d E s o r o v e r m O u N t A I n p a S s E s S h O U L D c o n * s i d e R t h e p o S s I B i l i t y o f s n o W i c e o r f R E e Z i n g t e m p E R a t u r E s +HYP: I n y o n * * h I s ******* g o * n * t o T r i * e H a * h * A T l I t A t u d * s o r o v e r m * u * t * * n p a * s * s T h * * * R c o n C s i d e * t h e p o * s * * i l i t y o f s n o * i c e o r ******* f * * e S i n g t e m p * * a t u r * s +Eval: S I D D S D D D S D S D D S S S S D D D D D D D S D D D S I D D D D D D D D S D D D + +Speaker sentences 268: fleurs_eng_000454 #utts: 1 +id: (fleurs_eng_000454-fleurs_eng_000454) +Scores: (#C #S #D #I) 107 18 18 16 +REF: * * ******* s l E e P i n t E R r u P t i o n i s T h e p r O C E s * s o f P U R P o S E F u ******* L L y A w a k E n i n G * D u r i n g y o u R n o r m a l * s l e e P p e r i O d a n d f a L l i n g a s l E e P a s h o * r t t i m e l a t e r ******* * * * ******* * * 1 0 – 6 0 m i n U t E s * +HYP: H E s l * e * i n t * * r u S t i o n i s * h e p r A S T s E s o f H E B o * * * u C A y * w a k * n i n * B E u r i n g y o u * n o r m a l E s l e e * p e r i A d a n d f a * l i n g a s l * e * a ******* s h o U r t t i m e l a t e r C E N T O S I C T E m i n O t * s T +Eval: I I I D D D D S D S S S I S S S S D D D I S S D D D I S D I D S D D D D I I I I I I I I S S S S S S D I + +Speaker sentences 269: fleurs_eng_000455 #utts: 1 +id: (fleurs_eng_000455-fleurs_eng_000455) +Scores: (#C #S #D #I) 73 7 9 15 +REF: * * * ******* s w * I r l t h e t W o d r * Y p o W D E r s t o g e t h e r a n D t h e N w i t h c L e A n * ******* * w e t h a n D s s Q u E e Z e t h e m i n t o a b a l * ******* * ******* * ******* L +HYP: O U R s w O A r l t h e t * o d r I P p o * * U r s t o g e t h e r ******* a n * t h e * w i t h c U e * n G A w e t h a n * s s C u * e A e t h e m i n t o a b a l E R E H +Eval: I I I I I S D I S D D S D D D S D I I I D S D S I I I I I I S + +Speaker sentences 270: fleurs_eng_000456 #utts: 1 +id: (fleurs_eng_000456-fleurs_eng_000456) +Scores: (#C #S #D #I) 48 1 4 2 +REF: f o r t h e s p r I n g ******* b o K s i t e n d e d a f i V e ******* m a T c h l o s i n g s t r e A k +HYP: f o r t h e s p r * n g b o C s i t e n d e d a f i * e m a * c h l o s i n g s t r e * k +Eval: D I S D I D D + +Speaker sentences 271: fleurs_eng_000457 #utts: 1 +id: (fleurs_eng_000457-fleurs_eng_000457) +Scores: (#C #S #D #I) 87 13 21 5 +REF: j u s t l i k E t h e M O o n e x * E r t * s a p u L l o n t h e e A r t h c a U s i n G t * * i d e S s o D O E s T H E m I l K y w a y e x e r t A f o r C E o N T h e S A G i T t a r i * u s g a l a X y +HYP: j u s t ******* l i k * t h e * * o n ******* e x P U r t D s a p u * l o n ******* t h e e * r t h c a * s i n * t H E i d e * s o ******* * T A s * * A m * l B y ******* w a y e x e r t I F f o r T S o F * h e * E D i * t a r i O u s ******* g a l a C y +Eval: D D D D D I S I D D D D D I I D D D S S D D S D S D S S S S S D D S S D I D S + +Speaker sentences 272: fleurs_eng_000458 #utts: 1 +id: (fleurs_eng_000458-fleurs_eng_000458) +Scores: (#C #S #D #I) 68 10 7 17 +REF: t h * r o U G H t h e n i g h t B e t w e * ******* * * * e * * * n * * 1 5 0 a n ******* * D * * * 2 0 0 c o p I e s w e r e m a d e n o w K n o W n a s D u n * l a p b r o A d s i d E s +HYP: t h O r o * * * t h e n i g h t H e t w e N H U D e R D A n F I F T Y a n T O H U D R E D c o p * e s w e r e m a d e n o w * n o * n a s B u n E l a p b r o L d s i d * s +Eval: I D D D S I I I I I I I I I I S S S I I S I I I S S S D D D S I S D + +Speaker sentences 273: fleurs_eng_000459 #utts: 1 +id: (fleurs_eng_000459-fleurs_eng_000459) +Scores: (#C #S #D #I) 190 14 23 8 +REF: f i r s t a m o n g i t ******* s * * * * 7 8 r e c o M m e n d a t i o n s i s t h a t a * n E w d i p l O m a T i C I n i * T i A t i v e S h O u l D b e t a k e N b e f o r E t h e e n d o f t h i s y e a r t o s e c u r e I r a Q S b o r D e r s a g A I n s T h o s t i l E i n t E r v e n t i o n s a n d t o r e ******* E s t a b l i s H d i p l O m a T i c r e l a t i o n s w i t h i t s n E I G H b O r s +HYP: f i r s t ******* a m o n g i t s E M N D Y A E r e c o * m e n d a t i o n s i s t h a t a N n O w d i p l * m a * i K * n i S H i * t i v e * h * u l * b e t a k e * b e f o r * t h e e n d ******* o f t h i s y e a r t o s e c u r e R r a C X P b o r * e r s a g * * n s * h o s t i l * i n t * r v e n t i o n s a n d t o r e A s t a b l i s E d i p l * m a * i c r e l a t i o n s w i t h i t s n * * * A b E r s +Eval: D I I I I I S S S D I S D D S D I S D D D D D D D S S S S D D D D D D I S S D D D D D S S + +Speaker sentences 274: fleurs_eng_000460 #utts: 1 +id: (fleurs_eng_000460-fleurs_eng_000460) +Scores: (#C #S #D #I) 87 11 17 5 +REF: s * a I n t P e t e r s ******* b U r G c r u I s E s i n c l u d E t i m E i n t o w n C R U I S E P a S s E n g e r s a r E E x * e M P t e d f r o m V I s * * A r e q u i r E m e n t s c h E c k t h e t E r m s +HYP: s H a * n t * e t e r s b * r * ******* c r u * s I s i n c l u d * t i m * i n t o w n * * W H O T * a * s O n g e r s a r * * x A e * N t e d f r o m * E s T E R r e q u i r I m e n t s c h A c k t h e t * r m s +Eval: I D D I D D D D S D D D D S S S S D D S D D I D S D S I I S S S D + +Speaker sentences 275: fleurs_eng_000461 #utts: 1 +id: (fleurs_eng_000461-fleurs_eng_000461) +Scores: (#C #S #D #I) 76 9 13 6 +REF: A C c o r d i n G t o J A p a n s n * * u C l E A r a g e n c * y r A d * I O a c t i v e c a E s I U m a n d i ******* O d i n E h a s B E e N i ******* d e n T i f i e D a t t h e p l a n t +HYP: * O c o r d i n * t o * U p a n s n O G u * l * * r a g e n c S y r E d Y A L a c t i v e c a * s E A m E a n d i A d i n * h a s * * e * i d e n * i f i e * a t t h e p l a n t +Eval: D S D D S I I D D D I S I S S D S S S I S D D D D I D D + +Speaker sentences 276: fleurs_eng_000462 #utts: 1 +id: (fleurs_eng_000462-fleurs_eng_000462) +Scores: (#C #S #D #I) 94 5 6 2 +REF: s E g R E g a t i o n a n d r e c o m B I n a t i o n s h u F f l E v A r * i a t i o n b a c k a n d f o r t h b e t w E e n t h e t w o p O O l * s w i t h e a c h g e n e r a t i o n +HYP: s A g * O g a t i o n a n d r e c o m * O n a t i o n s h u * f l * v E r Y i a t i o n b a c k a n d f o r t h b e t w * e n t h e t w o p * U l E s w i t h e a c h g e n e r a t i o n +Eval: S D S D S D D S I D D S I + +Speaker sentences 277: fleurs_eng_000463 #utts: 1 +id: (fleurs_eng_000463-fleurs_eng_000463) +Scores: (#C #S #D #I) 73 10 27 1 +REF: e l E m E n t S l I k E c A l C I U m A n d p O T a S S i U m A r E c o n * s I D E R e d m e t A l s o f C o U r s E T H E R e A r E a l s o m e t A L s l I k e s i L v e r a n d g o l d +HYP: e l A m * n t * l * k * c H l T H E m * n d p * * a A T i * m * r * c o n C s * * * T e d m e t * l s o f P o * r s * ******* * * * * e ******* * r * a l s o m e t * E s l * k e s i * v e r a n d g o l d +Eval: S D D D D S S S S D D D S S D D D I D D D S D S D D D D D D D D D D D S D D + +Speaker sentences 278: fleurs_eng_000464 #utts: 1 +id: (fleurs_eng_000464-fleurs_eng_000464) +Scores: (#C #S #D #I) 70 6 15 3 +REF: t h e c o R r E l A t i o n B e t w e e n b r a i N p A t h o l O g y a n d b e ******* h a v ******* I o u r s u P p o r t S s C i E n T I S T s I n * T h e I r r e s E A r c h +HYP: t h e c o * r * l * t i o n * e t w e e n b r a i * p I t h o l A g y a n d b e h a v Y o u r s u * p o r t * s * i * n * * * C s A n D * h e * r r e s * U r c h +Eval: D D D D D S S I I S D D D D D D D S S I D D D S + +Speaker sentences 279: fleurs_eng_000465 #utts: 1 +id: (fleurs_eng_000465-fleurs_eng_000465) +Scores: (#C #S #D #I) 127 8 8 8 +REF: a n c I E n T c h i n a h a d a * u n I Q U e * w a y o f s h o w i n g d i F f E r e n t t i m e p e r i O d s e a c h s t a G e o f c h i n a * o r * e a c h f a m i l y t h a t w a s i n * p o w e r w a s * * A d i s t i n C t ******* i V E d Y n A s t y +HYP: a n c H A n * c h i n a h a d a O u n * * * e K w a y o f s h o w i n g d i * f * r e n t t i m e p e r i A d s e a c h s t a C e o f c h i n a E o r E e a c h f a m i l y t h a t w a s i n M p o w e r w a s T H E d i s t i n * t i * F d I n I s t y +Eval: S S D I D D D I D D S S I I I I I S D I D S S S + +Speaker sentences 280: fleurs_eng_000466 #utts: 1 +id: (fleurs_eng_000466-fleurs_eng_000466) +Scores: (#C #S #D #I) 117 11 27 10 +REF: a s i m p l E p o p u l A r d I N N e r * E s ******* P e c i A L l y d U r i n G t h e S u M m e r i s T H E p a a m B O l * I b r e A d w i T h O l I v E o i l * t o ******* m a t o A n D a n y a v A i L a b l e c o n D I m e n T s s u c h A s c h e e s e t U n A f i s h ******* * * * e t * C +HYP: a s i m p l * p o p u l E r d * * M e r H I s F e c i * * l y d * r i n * t h e T u * m e r i s ******* * * * p a ******* a m * A l D Y b r e * d w i * h A l * v * ******* o i l E t o m a t o * n * a n y a v * i * a b l e c o n * T m e n * s s u c h * s c h e e s e t O n * f i s h I T S e t E R +Eval: D S D D S I S I S D D D D S D D D D D D D S I S D D S D D D I I D D D D D S D D S D I I I I I S + +Speaker sentences 281: fleurs_eng_000467 #utts: 1 +id: (fleurs_eng_000467-fleurs_eng_000467) +Scores: (#C #S #D #I) 79 10 17 2 +REF: t h e A N n o u n C E m e N t w a s m a d e a f t e r t r U m p H a D A P H o n E c o N V e r s a t i o n w i t H t U r k i s h p r E S i d e n t r e C e p t A Y Y I p e r * ******* d o Ğ A n +HYP: t h e * * n o u n * S m e * t w a s m a d e a f t e r t r * m p ******* * a * ******* Y * F o n G c o * M e r s a t i o n w i t * t * r k i s h p r * D i d e n t r e S e p t * E E p e r O d o ** * n +Eval: D D D S D D D D D D S D S S D S D D D S S D S S S I I D D + +Speaker sentences 282: fleurs_eng_000468 #utts: 1 +id: (fleurs_eng_000468-fleurs_eng_000468) +Scores: (#C #S #D #I) 177 26 47 11 +REF: P e R r y s T a t e D t h A t * h e w o U l d r e t U R n t o t e * x A s * t o A S s e S s t h e r e s u l t s o f t o n i G h t * s c A U C U s d * E T e r m I N e w h E t h e r t h e r E i s a p a t h f o R W A r d f o r m y s e l f I n t h I s r a c e * b u T l A t e r s A I D t h a T h e w O U l D r e m a i n i n t h e r a C E a n D C O M p e T E * i * * n * t H e J A n U A R Y 2 1 s o u t H C a r O l i n ******* A p r i m a r y +HYP: * e * r y s * a t e * t h * t H h e ******* w o * l d r e t * E n t o t e C x * s I S t o * U s e * s ******* t h e r e s u l t s o f t o n i * h t E s c * O K I s d I D e r m * * e N w h * t h e r ******* t h e r * i s a p a t h ******* f o * * * r d f o r ******* m y s e l f * n t h * s r a c e S b u E l E t e r s * U T t h a * h e w * * l * E r e m a i n ******* i n ******* t h e ******* r a * * S a n * H U B p e N O R i G E n R Y t W e E W O n * * * * ******* * * s o u t * * a r * l i n O p r i m a r y +Eval: D D D D D I D D D S I D I S D S D D D I D S S S I S S D D S D D D D D D D D D D I S S D S S D D D D S D D D D D S D S S S S S I I I I S S S S S D D D D D D D D D D I S + +Speaker sentences 283: fleurs_eng_000469 #utts: 1 +id: (fleurs_eng_000469-fleurs_eng_000469) +Scores: (#C #S #D #I) 140 21 19 41 +REF: h e w a S a l ******* s o E n g a g e ******* D i n E N g r a v i n g b a N k n o t E s f o r m a n y c o U n t r I e s r e C E n T E X A m p l e s o f h I S w O r k i n c l U d I N G t h e ******* * p r i * * m e * * m * i n i s T E r I a l p o r t r A I T s o n t h e ******* * * * * * f r * ******* * * ******* * * * ******* * o n t o f t h e n e w c a n a d * I a n 5 * * A N d * * * ******* * * 1 0 0 * * * * * B I l * * l S +HYP: h e w a T a l s o I n g a g e A i n * * g r a v i n g b a * k n o t * s f o r m a n y c o * n t r * e s r e S O n I N G S E m p l e s o f h * * w * r k ******* i n c l * d * * * t h e M p r i M E m e N T m N i n i s * * r E a l p o r t r * * D s o n t h e F I R S T f r O O U T H E F o n t o f t h e n e w c a n a d Y a n F F I V E d O L R I N W O N H N D E R D l D I l * +Eval: S I S I S D D D D D D S S S S S S S D D D D D D D D I I I I I I I D D S D D S I I I I I I I I I I I I I I I I I S S I I S S I I I I I I S S S I I I I I S S I I D + +Speaker sentences 284: fleurs_eng_000470 #utts: 1 +id: (fleurs_eng_000470-fleurs_eng_000470) +Scores: (#C #S #D #I) 168 16 27 8 +REF: * ******* m o r E t r a d I T i O n a l c h u r c h e s o f t E n h O l D * * A n e A s t E r V i g * I l O n * s a t U R d A y n i g h t D u r I n G t h e e A s t E r w E e k E n D w I T H t h e c o N g r e G a t i o n s o F t * E N b r e a k i n g i n t o C e l e b r a t i o n a t t h e s T r o k E o f m i D n i g H t t o C e l e b r a T e c H r i S T s r e s * u R r e c t i o n +HYP: E m o r * t r a d * * i * n a l c h u r c h e s o f t A n h A l * T H E n e * s t * r R i g U A l * n T s a t * E d * y n i g h t T u r * n * t h e e * s t * r w * e k G n * w * E R t h e c o * g r e * a t i o n s o * t D I M b r e a k i n g i n t o S e l e b r a t i o n a t t h e s * r o k * o f m i * n i g * t t o S e l e b r a * e c * r i C E s r e s E u * r e c t i o n +Eval: I I D D D D S S D I I S D D S I S D I D S D S D D D D D S D D S S D D D I S S S D D D D S D D S S I D + +Speaker sentences 285: fleurs_eng_000471 #utts: 1 +id: (fleurs_eng_000471-fleurs_eng_000471) +Scores: (#C #S #D #I) 113 7 21 1 +REF: f i N l A n D i s a g r e a T b o A t i n g d e s t I n a t i o n t h e l a n d o f a t h o u s A n D l a K e S h a s t H o u s A n d S o f i S l A n d s t O o I n t h e l a k E s a n D I n t h e c o A s t A L a r C H I p E l * a G o s +HYP: f i * l E n * i s a g r e a E b o * t i n g d e s t * n a t i o n t h e l a n d o f a t h o u s O n * l a * e * h a s t * o u s * n d * o f i * l E n d s t * o A n t h e l a k * s a n * * n t h e c o * s t * * a r * * K p A l O a * o s +Eval: D S D S D D S D D D D D D D S D S D D D D D D D D S S I D + +Speaker sentences 286: fleurs_eng_000472 #utts: 1 +id: (fleurs_eng_000472-fleurs_eng_000472) +Scores: (#C #S #D #I) 123 39 29 16 +REF: C U R r E n t s * e n a t O r a n d a r g E n T I N e * f I r s t l a d Y c r i s t I n A f E r N A n d E Z D E K I r C H n E r a N n o u N C E D h e r p R e s I D E n T I a * L c a n d i d A C Y y E s t E r d a y e v e N I n g I n l a p l a t * a a C I T y * * 5 0 K I l o m * * e * * ******* t * e r * S 3 1 m i l E s a w a y f r o m B U e n ******* o s A i R E s * * +HYP: * * * r A n t ******* s H e n a t E r a n d a r g I n * C S e N f * r s t ******* l a d E c r i s t E n * O f O r * n d I S * A C U r S I n * r a * n o u * * L S h e r p * e s * * O n * a T H O c a n d i d * U S y O s t * r d a y e v e * * n g A n ******* l a ******* p l a t H a a S T A D y F I F T * C l o m I T e R S t H e r D Y W N m i l * s a w a y f r o m * * e n o s * i D I s T H +Eval: D D D S D I S S D S S I D D S S D S S D S S S D S S S S S D D D D S S D D D S D S I S S D S S S D D D S D D I S S S S I I S S D S I I I I I I I S S S D D D I D S S I I + +Speaker sentences 287: fleurs_eng_000473 #utts: 1 +id: (fleurs_eng_000473-fleurs_eng_000473) +Scores: (#C #S #D #I) 120 14 20 1 +REF: s E v e r E w e A t h e r i S t H e G E n E r i c t E R M f o r a n y d a N G e r O u s w E A t h e r P H E n O m E n o n w i t H t h e p O t E n T I a l t o c a U s E d a m a g e s E r i o u s s o C i a l d i s * r u p t i o n o r l O S s o f h u m A n l i f e +HYP: s * v e r * w e * t h e r i * t * e * U n A r i c ******* t U N E f o r a n y d a * D e r * u s w * * t h e r * F O n A m I n o n w i t * t h e p A t * n * U a l t o c a * s * d a m a g e s * r i o u s s o S i a l d i s T r u p t i o n o r l * A s o f h u m * n l i f e +Eval: D D D D D D S S D S S S D S D D D D S S S S D S D D S D D D S I D S D + +Speaker sentences 288: fleurs_eng_000474 #utts: 1 +id: (fleurs_eng_000474-fleurs_eng_000474) +Scores: (#C #S #D #I) 129 11 8 21 +REF: f o r e x * a m p l e t h e m o s t c o m M O n s t i L l i m a g e P H O t o * G r A P H y f o r m * A t I n T h e w o R l d i s ******* * * * * * * * * 3 5 m * * * m * * * * w h i c h w a s t h e d o m i n a n t f i l m * s i Z e * a t t h e c l o s E o f t h e a n a l o g f i l m E r a +HYP: f o r e x S a m p l e t h e m o s t c o m E A n s t i * l i m a g e * F I t o C K r * I F y f o r m O U t * n * h e w o * l d i s T H R T Y F I V E m I L O m A T E R w h i c h w a s t h e d o m i n a n t f i l m E s i * e S a t t h e c l o s * o f t h e a n a l o g f i l m A r a +Eval: I S S D D S S I S D S S I S D D D I I I I I I I I I S S I I I I I I I I D I D S + +Speaker sentences 289: fleurs_eng_000475 #utts: 1 +id: (fleurs_eng_000475-fleurs_eng_000475) +Scores: (#C #S #D #I) 123 11 31 3 +REF: i t i s r e l a t e d t o b u t U S u A L l y n o t i N v O l v i n g A l p i n E s t Y l E s K I t o U r i n g o r m o U N t A I n E e r i n g t h e l a T t e r o n E s d o n E i n s T E E p t E R r A i n * a n d r e Q U I r i n g m u C h S t * i F f E r s K I s a n d b O o t * s +HYP: i t i s r e l a t e d t o b u t * * u * * l y n o t i M v * l v i n g H l p i n * ******* s t I l * ******* s * C E t o * r i n g o r m o * * t * * n * e r i n g t h e l a * t e r o n * s d o n * i n s * * * p ******* t * U r * i n G a n d r e * C A r i n g m u S h * t H i * f * r ******* s C E s a n d b * o t E s +Eval: D D D D S D S D D S D D D S S D D D D D D D D D D D D D D S D I D S S S D I D D D S S D I + +Speaker sentences 290: fleurs_eng_000476 #utts: 1 +id: (fleurs_eng_000476-fleurs_eng_000476) +Scores: (#C #S #D #I) 124 0 15 6 +REF: i * r O n i n g d a m p c l o T H E s c a n h e L p * t h e m d r * y m a n y h o * t e l s h a V e a n i * r O n a n d i r O n i n g b o A r d a v a I l a b l e f o r l o A n * e v e n i f o n E i s n o t p r e s e n T i n t h e r O o m +HYP: i A r * n i n g d a m p c l o * * * s c a n h e * p E t h e m d r I y m a n y h o A t e l s h a * e ******* a n i A r * n a n d i r * n i n g b o * r d a v a * l a b l e f o r l o * n G e v e n i f o n * i s n o t p r e s e n * i n t h e r * o m +Eval: I D D D D D I I I D D I D D D D D I D D D + +Speaker sentences 291: mls_eng_000283 #utts: 1 +id: (mls_eng_000283-mls_eng_000283) +Scores: (#C #S #D #I) 156 18 24 8 +REF: * e ******* v A d n E * A n S W e r E d h o A r s E l y s h e D R E w h e r c h a I r A L I t * * * t L e C l o S E R * t o t h e f i R e * a n d s P r e A d h e r h a n D s o u t t o t h e B l a Z e t h e r e w a s n o o t h e R l i g H t i n t h e r O o M B y t h i s t i m e t h e w i n D w i T H o U t h o W L e d D i s m a L l y s t i L l +HYP: A e v E d n Y O U n * C e r * d h o * r s * l y s h e * J U w h e r c h a * r ******* E U N t E R I t * e G l o * * * A t o t h e f i * e R a n d s C r e * d h e r h a n * s o u t t o t h e * l a S e t h e r e w a s n o o t h e * l i g * t i n t h e r * o N M y t h i s t i m e t h e w i n * w i * D o * t h o R D e d M i s m a * l y ******* s t i * l +Eval: I I S S I S D S D D D D S S D D S S S I I I D S D D D I D I S D D D S D D D S S D D S D S S S D D D + +Speaker sentences 292: mls_eng_000284 #utts: 1 +id: (mls_eng_000284-mls_eng_000284) +Scores: (#C #S #D #I) 146 13 14 13 +REF: m y d e a r m A r I a w h Y d o y o u n o t d e s * i s t f R o m t h i S s * i L l y p U R s u I t o f a N * I m a * * g i n a r y t R e A s * u r E w h a t i s t h e v A l * * u E o f m * o n E y w e a r E s p a n I A r d s n o t s h I r t s l E e v e d m e r C E n a r y p * * I g s o f a ******* m e R i c a * n s +HYP: m y d e a r m O r E a w h I d o ******* y o u n o t d e s C i s t f * o m t h i * s C i * l y p * E s u * t o f a * A N m a N D g i n a r y t L e * s E u r * w h a t i s t h e v O l Y O u * o f m U o n * y w e a r * s p a n * U r d s n o t s h E r t s l * e v e d m e r S I n a r y p L E A g s o f a m e A i c a E n s +Eval: S S S D I D D I D D S D D I S I I S D I D S I I D I D D D S S D S S I I S I S I + +Speaker sentences 293: mls_eng_000285 #utts: 1 +id: (mls_eng_000285-mls_eng_000285) +Scores: (#C #S #D #I) 153 15 47 9 +REF: * * * ******* c r i t i C a l t E M p E R a T u r E i s t H a t o f t h e s i n g l E I s O t h e r m a L l i n E w h I c h p R e s e n T s A p o i n t o F I n * f l e * X i o n a t A h o r I Z O n T A L t * A n g e n t t h e C r i t i C a l p r e S s U R e A N D T h e C r I T i C a l v o l U m e a R E t h E t w o c O O R d I n A T e s * o f t h i s p o I n t o F i n F l e * X i o n +HYP: T H E c r i t i * a l t * R p * * a * u r * i s t * a t o f t h e s i n g l * A s t h e r m a E l i n * w h * c h p * e s e n * s ******* E p o i n t ******* o V * n E f l e C T i o n ******* a t ******* * h o r * * S n * * D t I E n g e n t t h e * r i t i * a l p r e * s * * e ******* * * * * h e * r * * i * a l v o l I m e a * * ******* t h * t w o c * U L d * n * * e s E o f t h i s p o * n t ******* o * i n P l e C T i o n +Eval: I I I I D D S D D D D D D S S S D D D D D S D S D I I S D D D D D S D D S I S D D D D D D D D D D D D D D S D D D D D S S D D D I D D D S I S + +Speaker sentences 294: mls_eng_000286 #utts: 1 +id: (mls_eng_000286-mls_eng_000286) +Scores: (#C #S #D #I) 205 9 22 3 +REF: m u c h l i k E I n f o u L n E S s a n d D E f o r m i t y U n t o t h a t m o n s t e r w h o m t h e T h e b a n K n i g h t t h e f a t h e r o f t h a t f a t A l p r o g E n y m a d e K i L l h e r s e l f f o r v e r y h E a r t s D E s p i * t E T h a t h e h a d r e A d h e r r i D D l E w h i c h n o * w i G h t c o u l d e v e r l O o s e * B u t s u F f e r e D d e a d l y d U E L +HYP: m u c h l i k * A n f o u * n * U s a n d * I f o r m i t y O n t o t h a t m o n s t e r w h o m t h e * h e b a n * n i g h t t h e f a t h e r o f t h a t f a t * l p r o g A n y m a d e C i * l h e r s e l f f o r v e r y h * a r t s T O s p i H t * * h a t h e h a d r e * d h e r r i * * l * w h i c h n o W w i * h t c o u l d e v e r l * o s e S W u t s u * f e r e * d e a d l y d * * * +Eval: D S D D S D S S D D D S S D D S S I D D D D D D I D D I S D D D D D + +Speaker sentences 295: mls_eng_000287 #utts: 1 +id: (mls_eng_000287-mls_eng_000287) +Scores: (#C #S #D #I) 213 15 51 3 +REF: H E h A s m A N a G e d T O m e A s U R e w i T h p R e C I S i o n p r e S s U R e s a m o u n t i n g t o t h r E e t h O u s A n D a t M O s P H E R e S a n d a l s o t h e V E r y s m a L l v o l U m E s t H E n o C c u p i e D b y t h e f l * U i d m a s S U n d e R c o n s i d e r a t i o n t h i s l a s t m e A s U R e m e n t w h i c h n e C e S s i t a t e s n * u m E r O u s c o R r e * c t i O n s i s T H E m o s t d e l i c a T E p a R t O F t H e o p E r a t i o n +HYP: * * ******* h I s m * * a S e d ******* * * m e * s * * e w i * h p * e * * * i o n p r e * s * I e s a m o u n t i n g t o t h r * e t h * u s * n * a t * N s * T F I e * a n d a l s o t h e * * r y s m a * l v o l I m * s t * A n o * c u p i e * b y t h e f l O A i d m a s E * n d e * c o n s i d e r a t i o n t h i s l a s t m e * s * G e m e n t w h i c h n e S e * s i t a t e s n E u m * r * u s c o * r e A c t i * n s i s ******* * * * m o s t d e l i c a * D p a * t ******* * * t * e o p O r a t i o n +Eval: D D D S D D S D D D D D D D D D D D D D S D D D D D S D S S S D D D D S D D S D D I S S D D D D S S D I D D D I D D D D D D S D D D D D S + +Speaker sentences 296: mls_eng_000288 #utts: 1 +id: (mls_eng_000288-mls_eng_000288) +Scores: (#C #S #D #I) 120 4 16 1 +REF: w h * y s h O u l d i t h a V e b E e n d E E M e d n e c r o m a n c y t o E n d e A v O r t o C o M b i n E t h e s E P a R t s t o E v o l v e b y c a r E f u l e l I m i n a t i o n a n d c h a n g e t o t h e p e r f e c t f O o d +HYP: w h I y s h * u l d ******* i t h a * e b * e n d * * * e d n e c r o m a n c y t o * n d e * v E r t o * o N b i n * t h e s * F a * t s t o I v o l v e b y c a r * f u l e l * m i n a t i o n a n d c h a n g e t o t h e p e r f e c t f * o d +Eval: I D D D D D D D D D S D S D D S D S D D D + +Speaker sentences 297: mls_eng_000289 #utts: 1 +id: (mls_eng_000289-mls_eng_000289) +Scores: (#C #S #D #I) 176 7 17 6 +REF: n a y t h o U G H o f r U s H e s b e m y b e d y e t i a m r i c h l o v e s a i d b * * u t a r ******* g u E d l i * F e t h r i c e f o * n d a r T t h o U t o y I e l D t h e s o v e r E I G n g i f t s o f e A r t h t h e v i c t o r s W o r d t h e l A U r E l E d b r o w f o r v i s i o n E D t h i n g * s o f l i T t l E w o r t h +HYP: n a y t h o * * * o f r A s * e s b e m y b e d y e t i a m r i c h l o v e s a i d b U T u t a r g u * d l i V H e t h r i c e f o R n d a r E t h o W t o y * e l * t h e s o v e r * * A n g i f t s o f e * r t h t h e v i c t o r s * o r d t h e l * O r A l * d b r o w f o r v i s i o n * * t h i n g K s o f l i * t l * w o r t h +Eval: D D D S D I I I D I S I S S D D D D S D D D S S D D D I D D + +Speaker sentences 298: mls_eng_000290 #utts: 1 +id: (mls_eng_000290-mls_eng_000290) +Scores: (#C #S #D #I) 151 15 27 0 +REF: b O c k S E e m S t o h a v E b E e n a K E e N c O L l E c t O r a L t h o U G H h a m p E R e d b y i L l h e A l t h a n d a g r e A t p O i n t I n H i s f a v O U r i s t h a t H E d E s c r i B e d o n l y t h o s e p l a n T s w H i C h h a d c o m e u n d e r H i s o W n p e r s O n a l o b s E R v a t i o n +HYP: b U c k * * e m E t o h a v * b * e n ******* a * C e * c * U l A c t E r a O t h o * * * h a m p * * e d b y i * l h e * l t h a n d a g r e * t p * i n t ******* O n * i s f a v * E r i s t h a t * I d I s c r i D e d o n l y t h o s e p l a n * s ******* w * i T h h a d c o m e u n d e r * i s o * n p e r s I n a l o b s * O v a t i o n +Eval: S D D S D D D D S D D S S S S D D D D D D D D D D S D D S D S S S D D D S D D S D S + +Speaker sentences 299: mls_eng_000291 #utts: 1 +id: (mls_eng_000291-mls_eng_000291) +Scores: (#C #S #D #I) 153 7 11 4 +REF: h a d r a t h e r s H r U n K u p a n d h a d n o t c h a * n G e d i n t o n * Y m p H s t h e s E * I L e F t i n t h e S t e * m s c o v e r i n G t h e m u p a g A i n a n d t h e y A P p e a r e d a s p e r f e c t i n s e c t s i n t h e m a y o f t h e f o L l o W i n g y e a r +HYP: h a d ******* r a t h e r s * r O n G u p a n d h a d n o t c h a I n * e d i n t o n E I m p * s t h e s * H Y F e L t i n t h e * t e A m s c o v e r i n * t h e m u p a g * i n a n d t h e y * U p e a r e d a s p e r f e c t i n s e c t s i n t h e m a y o f t h e f o * l o * i n g y e a r +Eval: D D S S I D I S D D I S S S D I D D D S D D + +Speaker sentences 300: mls_eng_000292 #utts: 1 +id: (mls_eng_000292-mls_eng_000292) +Scores: (#C #S #D #I) 174 15 33 5 +REF: n o t h i n g s a V E o b j e c T s a n d t h O u G H t s o f b E A u t y C o u L d p r e s e n T t h e m s E L V e s t o t h e u n D e R s t a n d i n g o f t h e f o r t * U N A t E P E r s O n w h o p a r t O o k o f i t t h e s e P a G e s w h I c H y o U H a V E b r O u g H t t o m e T O t r a n s * l a t E a r E C o n C e * r N e d w i t H t h i s s * u p * e R s T I t i o n +HYP: n o t h i n g s a Y O o b j e c * s a n d t h * u * * t s o f b * O u t y H o u * d p r e s e n D t h e m s * * * e s t o t h e u n * e * s t a n d i n g o f t h e f o r t I O L U t * B U r s I n ******* w h o p a r t * o k o f i t t h e s e B a T e s w h * c * y o * * a * * R b r * u g * t t o m e ******* * * t r a n s A l a t * a r * * o n S e U r * e d w i t * t h i s s O u p O e * s * * t i o n +Eval: S S D D D D D S S D S D D D D D I S S S D S S S D D S S D D D D D D S D D D D D I D D D S I D D I I D D D + +Speaker sentences 301: mls_eng_000293 #utts: 1 +id: (mls_eng_000293-mls_eng_000293) +Scores: (#C #S #D #I) 102 6 13 6 +REF: n o W s E e m E D i n s * I p i D i t y a n d h e ******* d n e r v e h i m s e l f a g a i n s T i t h i s f a * C e w O r E A s o r T o f s E v e R E f l * u s h * h e w a s t i m i * d e v e n t o r u d E n e S s +HYP: n o * s * e m * * i n s O U p i T i t y a n d h e d n e r v e h i m s e l f a g a i n s * i t h i s f a I S e w A r * ******* * s o r D o f s * v e * F f l O u s h D h e w a s ******* t i m i E d e v e n t o r u d * n e * s +Eval: D D D D I S S I D I S S D D D S D D S I I D I D D + +Speaker sentences 302: mls_eng_000294 #utts: 1 +id: (mls_eng_000294-mls_eng_000294) +Scores: (#C #S #D #I) 177 14 41 5 +REF: b e c a m e m o R e l i f e ******* l i k E a s t H e c h E e K s f l u s h t h E R e W a s r a r E w a r m T H i n A W i n t e R m o r n i n G t o C h E e R t H E h a L f * d E s ******* p A I r i n g s o U l t * i R E d A F t E R l o n g H o u r s o f O I l r e A d i n g a n D p I e r C E D T O t H e * h e a r t b y n e v e r C e a s i n g r H Y m e s y e t i c o u l D n o T U n d e R s t a n d i t +HYP: b e c a m e m o L e l i f e l i k * a s t * e c h * e * s f l u s h t h * * e * a s r a r * w a r m * E F i n O F * i n t e * m o r n i n * t o * h * e * t * * h a * f T d I s p * E r i n g s o * l t S i * * d * I t * * l o n g * o u r s o f A L l r e * d i n g a n T p * e r * * * ******* * * S t * e D h e a r t b y n e v e r S e a s i n g r * I m e s y e t i ******* c o u l * n o * * n d e * s t a n d i t +Eval: S I D D D D D D D D D S S S S D D D D D D D D D I S I D S D I D D D S D D D S S D S D D D D D D D S D I S D S D D D D D + +Speaker sentences 303: mls_eng_000295 #utts: 1 +id: (mls_eng_000295-mls_eng_000295) +Scores: (#C #S #D #I) 116 7 16 6 +REF: * o n E o f t h e H A w A I i A n W r i t e r S s a i d t h e * o p * I h * I a W a i s a p o I s o n s h E L l f i s h t h e s E a r E b i T t e r a n d d e A d l y a n d c a N b e * u s e d i n p u T t i n g e n E M i * e s t o d e a t h +HYP: W o n * o f t h e * O w * * i * n * r i t e r * s a i d t h e A o p E h E a V a i s a p o * s o n s h * A l f i s h t h e s * ******* a r * b i * t e r a n d d e * d l y a n d c a D b e O u s e d i n p u * t i n g e n * A i M e s t o d e a t h +Eval: I D D S D D D D D I I S I S S D D S D D D D D S I D D S I + +Speaker sentences 304: mls_eng_000296 #utts: 1 +id: (mls_eng_000296-mls_eng_000296) +Scores: (#C #S #D #I) 129 11 26 4 +REF: t h e b e A u t E O u s r o * b E s o f h E a v e n a s ******* l A n T t H E d E W B r i G h t e A r T H A n D c o l O U R e * D a I r h e l O o k s i n b o u n D l e S s m a J E S t y a b r o a d t o u c h i n g t h E g r E e n l e a v e s a l l a ******* t r e m b l E W i t H g o l d l i G h t +HYP: t h e b e * u t Y u s r o U b * s o f h * a v e n a s l O n * t A d * U * r i * h t e * r * S * n * c o l * * * e I T a * r h e ******* l * o k s i n b o u n * l e * s m a G H U t y ******* a b r o a d t o u c h i n g t h * g r * e n l e a v e s a l l a t r e m b l * * i t * g o l d l i * h t +Eval: D S S I D D I S D S S D S D D D D S D D D D D I S D D D D D S S S D D D I D D D D + +Speaker sentences 305: mls_eng_000297 #utts: 1 +id: (mls_eng_000297-mls_eng_000297) +Scores: (#C #S #D #I) 188 15 33 5 +REF: i c a n d o * n o m o r E T h a n t h a t U n t I l t h i s m a T t e r i s a B s O l u t E l y s e T t L e d t h e Y a R E w o r t h M o R E t h a n l i f e i t s e l f t o m e * m * * r C O W P E r s E e m e d a N n o Y e d s u r E l y h e p r o t e s t E D Y o u A R E n o t g o I N G T o a s K m e t o w a I t * t h r E e m o n T H s u n t i l I C a N e x a m i n E o n e o f t h e s E +HYP: i c a n d o U n o m o r * * h a n t h a t I n t * l t h i s m a * t e r i s a P s A l u t * l y s e * t * e d t h e * a * * w o r t h * o * * t h a n l i f e i t s e l f t o m e T m S T r * B L B U r s * e m e d a * n o I e d s u r * l y h e p r o t e s t I T W o u ******* * * * n o t g o * * * ******* D o a s T m e t o w a * t E t h r * e m o n * C s u n t i l ******* * * a T e x a m i n * o n e o f t h e s * +Eval: I D D S D D S S D D D D D D D D D I I I D S S S S D D S D S S S D D D D D D D D S S D I D D S D D D S D D + +Speaker sentences 306: mls_eng_000298 #utts: 1 +id: (mls_eng_000298-mls_eng_000298) +Scores: (#C #S #D #I) 134 10 8 11 +REF: r o s * ******* c o n g r e S s f o u n d a t i o n r u s S i A n E n * ******* t i t Y t h a t o r g a n i Z e d t h e s a i N t p e t e r s ******* b u r g i n ******* t e r ******* n a T i O n a l * e c O n o m i c f o r U m r o * s ******* n e f t r u s S i A n s t a t E o W n E d o i l * a n d E n E r g y c o m p a n y +HYP: r o s E c o n g r e * s f o u n d a t i o n r u s H i * n A n D t i t E t h a t o r g a n i * e d t h e s a i * t p e t e r s b u r g i n t e r n a S i * n a l E e c A n o m i c f o r O m r o U s n e f t r u s H i O n s t a t * o * n * d o i l E a n d A n A r g y c o m p a n y +Eval: I I D S D S I I S D D I I I S D I S S I I S S D D D I S S + +Speaker sentences 307: mls_eng_000299 #utts: 1 +id: (mls_eng_000299-mls_eng_000299) +Scores: (#C #S #D #I) 226 11 52 7 +REF: h o w I T g l I T t E R e d A n D s p a r * k * l e D t h e d e l i c a t E F r o s t ******* w o R k y o u W E R e a T t r a c t e d n o d o u B t a N D m a r v E L L e d a T t h e d A i n t y t r a * c I N G s b u t f e * W o f U s h a v E r e a L l y h a d a n o P p o R t u n i t y t o s t U d y t h e d e t a I l o f t h E S e f r O s t d e s i G n s m * I n u t E l y o R H a v E c o n s i d E R e d T h a t t h E R e w E r e M o r E t H A n t h r E e * O r f o U r d e s i G n s a t m o s t +HYP: h o w ******* * * g l * U t * * e d * n * s p a r C k A l e * t h e d e l i c a t * * r o s t w o * k y o u ******* * * * e a * t r a c t e d n o d o u * t a * * m a r v * * * e d a * t h e d * i n t y t r a I c S O M s b u t f e O U o f A s h a v * r e a * l y h a d a n o * p o * t u n i t y t o s t A d y t h e d e t a * l o f ******* t h * * e f r U s t ******* d e s i * n s m Y n u t * l y o * * a v * c o n s i d * * e d * h a t t h * * e w * r e * o r * t * I n t h r * e Y U r f o * r ******* d e s i * n s a t m o s t +Eval: D D D D S D D D D I I D D D I D D D D D D D D D D D D D D I S S S I S S D D D D S D D D D S D D I S D D D D D D D D D D D D D S D I S D D D + +Speaker sentences 308: mls_eng_000300 #utts: 1 +id: (mls_eng_000300-mls_eng_000300) +Scores: (#C #S #D #I) 203 21 48 4 +REF: O t h E R t h a N t h e o F f e n s E i n t r Y i n g t o i n f L i c T a w o U n D t h e Y m A Y k i L l T H e * o f F e n d e r o r w O U n D h i m m o r E t h a n t h e Y i n t e n d E D t o d o a n d t h i s b e c o m E s A c A u s E f O R a n E W F E U d s o t H a T t h e p r i m i t i v e l E g I s l a t O r s w E R e c a R e f u l i n * r e * q u i * r i n G t h e r E t a l i A t i o n t o b e l i m i t e d t o a n E y E f o R a n E Y E +HYP: * t h * * A t h a * t h e o * f e n s * i n ******* t r * i n g t o i n f * i c * K a ******* w o * n * t h e * m * * C k i * l ******* * V e R o f H e n d e r o r w * * n * h i m m o r * t h a n t h e * i n t e n d A N t o d o a n d t h i s b e c o m * s ******* * E c C u s * ******* f U L a ******* n * * U T H R d s o t * a * ******* t h e p r i m i t i v e ******* l A g D s l a t E r s w * H e c a * e f u l i n O r e C q u i A r i n * t h e ******* r I t a l i * t i o n t o b e ******* l i m i t e d t o a n * y * f o * a n ******* * * A +Eval: D D D S D D D D D D D S D D D D D D S D D D S I S D D D D D S S D D D S S D D S S D D D S S S S D D D D S S S D S D I I I D D S D D D D D D D D S + +Speaker sentences 309: mls_eng_000301 #utts: 1 +id: (mls_eng_000301-mls_eng_000301) +Scores: (#C #S #D #I) 186 16 17 8 +REF: a t C Y r U s w o r d t h e j * e W s * r e t U r n t h e c o m p A n y t h a t g o g o d s h O U S e b e g U n w i t h m I r t h a N D m o A n i s h I n d E R e d b y t h e f o E b u t * o n c e a g a i n t h e w O r k * G o E s * o n b y l i C e n s E f r o m d E r i * u s e Z r a i s s e n t w i t h r o Y A l * g r A n t a n d g i f t * s f o r u S E s p i O U s +HYP: a t S I r E s w o r d t h e j U e * s W r e t E r n t h e c o m p * n y t h a t g o g o d s h * I C e b e g O n w i t h m * r t h a * * m o * n i s h E n d * * e d b y t h e f o * b u t W o n c e a g a i n t h e w * r k E C o * s E o n b y l i S e n s * f r o m d U r i O u s e S r a i s s e n t w i t h r o * I l E g r U n t a n d g i f t H s f o r I u * * s p i * A s +Eval: S S S I D I S D D S S S D D D D S D D D I D I S D I S D S I S D S I S I S D D D S + +Speaker sentences 310: mls_eng_000302 #utts: 1 +id: (mls_eng_000302-mls_eng_000302) +Scores: (#C #S #D #I) 111 14 13 9 +REF: * n E t p r o d U c T y e a r i n a n d y e a r o u t s E v E n h u n d r e D f r * a n c * s * h E l i v e d I n i t h * O W n o t s o b a d l y w * e W i L l e x p l a i n * m A r * * i U s o C c u p I e d I N t h e G o r b E A U h o U s E +HYP: A n * t p r o d A c K y e a r i n a n d y e a r o u t s I v I n h u n d r e * f r O a n c E s W h * O l i v e d O n i t h A V E n o t s o b a d l y w R e ******* * i U l e x p l a i n E m O r D Y i * s o * c u p * e d T A t h e * o r b * * * O h o V s * +Eval: I D S S S S D I I I D S S I S S I D D S I S I I D D D S S D D D D S S D + +Speaker sentences 311: mls_eng_000303 #utts: 1 +id: (mls_eng_000303-mls_eng_000303) +Scores: (#C #S #D #I) 194 11 25 11 +REF: t h e * n t h i s i s a l l y o u r a n S W E r t i s t O o f a I r f o r o n e o f h i s a L l i a n c E a n d i w A r N y o u * t h a t t h i s p l a c e n o m o r E s E e y o u * e * x * * i t E n * t e r d E f l O r * E s t h e b e s t i s t h e r E i s m o r E G r o u N d t o M E e t a m a n s r E v e n G E * o n H o n E s T D e f L o r * * e S t h a t s m y n a m E i n d E e d +HYP: t h e A n t h i s i s a l l y o u r ******* a n * T A r t i s t * o f a * r f o r o n e o f h i s a * l i a n c S a n d i w O r E y o u W t h a t t h i s p l a c e n o m o r * s * e y o u A e G x S E i t A n D t e r d * ******* f l U r A N s t h e b e s t i s t h e r * i s m o r * C r o u * d t o * * e t a m a n s A r Y v e n * * J o n * o n * s * * e ******* f * o r A C e * t h a t s m y n a m * i n d * e d +Eval: I D D S S D D D S S S I D D I I I I S I D D S I S D D S D D D S S D D I D D D D D D I I D D D + +Speaker sentences 312: mls_eng_000304 #utts: 1 +id: (mls_eng_000304-mls_eng_000304) +Scores: (#C #S #D #I) 209 11 28 6 +REF: W h e n i r e t u r n E d t o t h e h o u s e w h e r e I h a d b E e N a h a P p y c h i l d o n l y a p i l E o f a s h e * s w H e R E i t h a d s t O O d i w e p t l o n g a n d t o f o R g e t m y w E e p i n g i s a i L E d o u t o n * T H e v a S t * c a L m * s * e A o n t h e s e w a t e r s i n a s ******* t a r s a P P H I R E n i g H t i p l a Y e D m y f l U t E t o t h e s u M m e r m O o n +HYP: * h e n i r e t u r n * d t o t h e h o u s e w h e r e ******* * h a d b * e * a h a * p y c h i l d o n l y a p i l * o f a s h e I s w * e * A Y i t h a d s t * U d ******* i w e p t l o n g a n d t o f o * g e t m y w * e p i n g i s a i * * d o u t o n D * * e v a * t S c a * m S s C e * o n t h e s e w a t e r s i n a ******* s t a r ******* s a F A Y A N n i g * t i p l a D e * m y f l O t * t o t h e s u * m e r m * o n +Eval: D D D D D D D D I D D S S D S D D D D D I D D D I D I I D D I D S S S S S S D S D S D D D + +Speaker sentences 313: mls_eng_000305 #utts: 1 +id: (mls_eng_000305-mls_eng_000305) +Scores: (#C #S #D #I) 217 6 18 2 +REF: d o y o u n o t s E e w h a t P l e A s u R e * i t g i v e s H I m * w e h a v e g r o W n u p t o g e t h e r i n t h i s h o u s e s i n c E h e w a s a b o y i s i m p l y c a N n o T b e a r a s y o u C a n t h e s i g H t o f t h e s m i l E l E a v i n g h i s f a c e P o O r d e a r h e h a s n o A m u s E m e n t e x c e p t t h i s p l A Y i n g a t t h E s h o p K E e p i n g +HYP: d o y o u n o t s * e w h a t * l e * s u * e R i t g i v e s * * m E w e h a v e g r o * n u p t o g e t h e r i n t h i s h o u s e s i n c * h e w a s a ******* b o y i s i m p l y c a * n o R b e a r a s y o u G a n t h e s i g * t o f t h e s m i l * l * a v i n g h i s f a c e B o U r d e a r h e h a s n o * m u s * m e n t e x c e p t t h i s p l * * i n g a t t h * s h o p C e p i n g +Eval: D D D D I D D I D D D D S S D D D S S D D D D D S S + +Speaker sentences 314: mls_eng_000306 #utts: 1 +id: (mls_eng_000306-mls_eng_000306) +Scores: (#C #S #D #I) 141 12 25 5 +REF: i t i s A N e * b U L O u s b o * d y r e v O l V i n g i N A n E L l i p T i c a l o r B I T O F g r e A T e L o n g a t i o n l o v e l o v e l o v e w I L l n o t b e t h e w o U n * d o f c u p i D b u t T h e m a n i f E s t a t i o n o f * * U N I v e r s a l r e p r O d u c t i V e i n s t i n c T s +HYP: i t i s ******* * D e V b * * I u s b o A d y r e v A l * i n g i * ******* * n * Y l i p * i c a l o r * * * ******* * * R g r e * * e * o n g a t i o n l o v e l o v e l o v e ******* w * A l n o t b e t h e w o * n E d o f c u p i T b u t * h e m a n i f I s t a t i o n o f H E E R v e r s a l r e p r * d u c t i * e i n s t i n c E s +Eval: D D S I D D S I S D D D D D S D D D D D D D S D D D D D S D I S D S I I S S S D D S + +Speaker sentences 315: mls_eng_000307 #utts: 1 +id: (mls_eng_000307-mls_eng_000307) +Scores: (#C #S #D #I) 194 15 24 3 +REF: s h * a r p l y a s h e s h o o k h a n d S w i t H h e r g o d b L e S s y O u m * Y * d e a R c h I L D t h e b i S h o p s a i d w h e n s h e K i S s e d h i m a n d h i s l i p s m o V e d A f t e r w a r d f o r s o m E s E c O n D s a s i f h e w e r E i n p r a Y E r h E r m o t h e r f o L l o W E d h e r o u t o f t h E R O o m a n d t h e n s i l E n C E S e T t L e D +HYP: s h O a r p l y a s h e s h o o k h a n d * w i t * h e r g o d b * e * s y * u ******* m I G T d e a T c h * * A t h e b i * h o p s a i d w h e n s h e C i * s e d h i m a n d h i s l i p s m o * e d O f t e r w a r d f o r s o m * s I c K n T s a s ******* i f h e w e r * i n ******* p r a * A r h U r m o t h e r f o * l o * R d h e r o u t o f t h * * * o m a n d t h e n s i l A n * D C e * t * e L +Eval: I D D D D D D I S I S D D S D S D D S D S S S D D D D S S D D S D D D S D S S D D S + +Speaker sentences 316: mls_eng_000308 #utts: 1 +id: (mls_eng_000308-mls_eng_000308) +Scores: (#C #S #D #I) 123 5 10 5 +REF: f o L l o W e d h i m s t E a * l t * H I l y a n d W h e N h E w a s i n a s t O O p i n g p o s t * u r E f i L l i n g h i s b U c k e t c a m e u p b e h i n * d h i m a n d p l u n G e d a * l o n g K n i f e i n t o h i s n e c k +HYP: f o * l o * e d h i m s t * a E l t F U L l y a n d * h e * h I w a s i n a s t * * p i n g p o s t H u r * f i * l i n g h i s b O c k e t c a m e u p b e h i n E d h i m a n d p l u n C e d a N l o n g * n i f e i n t o h i s n e c k +Eval: D D D I I S S D D S D D I D D S I S I D + +Speaker sentences 317: mls_eng_000309 #utts: 1 +id: (mls_eng_000309-mls_eng_000309) +Scores: (#C #S #D #I) 192 18 13 8 +REF: s a I t h c H E r S i A s d o E s n o t j u p i t e r d i s t r I b u t e t o t h e g o d s t h e I R p r o p o r t i o n a n d d i v i d e n D s p a r i n g l y a n d s e v e r a L l y a s a g a m E M n O n d I D t o h i s c o M m a n d * e r s w h e n h i s g U e s t s D r a n * k t o o n e a n o t h e r i * * f ******* * * c H E r S i * A s q u O t h c l e O d e m U s a s y O u n A r r a t E +HYP: s a S t h c * U r T i E s d o U s n o t j u p i t e r d i s t r E b u t e t o t h e g o d s t h e * * p r o p o r t i o n a n d d i v i d e n T s p a r i n g l y a n d s e v e r a * l y a s a g a m * A n A n ******* d Y E t o h i s c o * m a n d O e r s w h e n h i s g * e s t s T r a n G k t o o n e a n o t h e r i F V f O R c O U r * i O U s q u * t h c l e E d e m O s a s y * u ******* n E r r a t * +Eval: S D S S S S S D D S D D S S D S S D I D S I I I I I I S S D I S D S S D D S D + +Speaker sentences 318: mls_eng_000310 #utts: 1 +id: (mls_eng_000310-mls_eng_000310) +Scores: (#C #S #D #I) 137 8 39 2 +REF: a n d W h e r e n o n E S h a L l d a R E r E s t r a I n U S W E C a N m E e t a g a i n i n t h o U G h t s o t h E R E s n o u s e I n W E e p i n g b * e A r A c h E e r F u l s p i r i t s t i L l n E V e r d o U B t T h A T f a t E i s K E e p i n g f * u t u r E g o o d f o r p R e s e n T i L l +HYP: a n d * h e r e n o n * * h a * l d a * * ******* r A s t r a * n * T T O * a * ******* m * e t a g a i n i n t h o * * h t s o t h * * I s n o u s e ******* A n * * e p i n g b H e * r ******* E c h * e r * u l s p i r i t s t i * l n * * e r d o * * t * h * E f a t * ******* i s * * e p i n g f E u t u r * g o o d f o r p * e s e n * i * l +Eval: D D D D D D D S D D S S S D D D D D D D D S D S D D I D D S D D D D D D D D D S D D D D I D D D D + +Speaker sentences 319: mls_eng_000311 #utts: 1 +id: (mls_eng_000311-mls_eng_000311) +Scores: (#C #S #D #I) 172 7 21 3 +REF: a n d T O b e c o m e t h e r e c O r d o f w h a t p e O p l E H a v e d o n E i N t h e I r m o r E a m I A b l e m o M e n t s t h e r e c O r d o f T h e c o n * q u e s t ******* s o f p e A C e h o w m e n h a v e l i v e d a n d l a B O r E d d u g a N d b U i l t h e * W n a n d c l e A r e d g a r d E N e d a n d r e f o r E s t +HYP: a n d ******* * * b e c o m e t h e r e c A r d o f w h a t p e * p l * * a v e d o n * i * t h e * r m o r * a m * U b l e m o * e n t s t h e r e c A r d o f * h e c o n C q u e s t s o f p e * S e h o w m e n h a v e l i v e d a n d l a V E r * d d u g a * d b * i l t h e U O n a n d c l e * r e d g a r d * * e d a n d r e f o r * s t +Eval: D D D S D D D D D D D D S D S D I I D S S S D D D I S D D D D + +Speaker sentences 320: mls_eng_000312 #utts: 1 +id: (mls_eng_000312-mls_eng_000312) +Scores: (#C #S #D #I) 188 18 33 7 +REF: t h e l o W f l Y i n g o f t h E S w A l l O W s B e t o k E n s r a i n a s w E L l a s A n y U n ******* s E a s o n a b l e d a n c * i n g o f m i D g e * s i n t h e e v e n i n g s o R E c o R n s O n T H E f e E t a n d r H E U m A t i s M i n t h e J o I n * T s a r E d i R E f u l p r E c u r s O R s t h e l e a v e s a r E a l l a ******* t r E m b l e b e ******* f o r E t h E a * P p r o A c h O F t H u n d e r +HYP: t h e l o * ******* f l * i n g o f t h * * w * l l * E s P e t o k I n s r a i n a s w * I l a s * n y A n s C a s o n a b l e d a n c S i n g o f m i * g e U s i n t h e e v e n i n g s o * A c o * n s A n ******* * D O f e A t a n d r * * I m * t i s * ******* i n t h e * o * n C E s a r * d i * * f u l p r O c u r s * * s t h e l e a v e s a r * a l l a t r * m b l e b e f o r * t h * ******* a T p r o * c h E A T t * u n d e r +Eval: D D D D D D D S S S D S D S I S I D I D S D S D D S S S D D S D D D D D I S D D D S D D D I D I D D D I S D S S S D + +Speaker sentences 321: mls_eng_000313 #utts: 1 +id: (mls_eng_000313-mls_eng_000313) +Scores: (#C #S #D #I) 177 14 12 14 +REF: w a s ******* * s ******* t o r m * E D * g e n e r A l d A m p I e R r e w a s * k i L l e D g e n e r a l c U s t i N e * w a s b l a * m e d a n D i n ******* d E e d I s n o w c o m e t o p a r i s t o G I v E e x p l A n a t i o n s a g a I n s * t a l l w h i c h t h e m o u n t A I n a n d a ******* t r o C i o u s m a * R a T m u s t e v e n * m a k e h E a * D a s t h e Y c a n +HYP: w a s A s t o r m N G H J g e n e r * l d E m p * e A r e w a s C k i * l e * g e n e r a l c O s t i * e N w a s b l a I m e d a n * i n d * e d E s n o w c o m e t o p a r i s t o D E v * e x p l O n a t i o n s a g a * n s E t a l l w h i c h t h e m o u n t * O n a n d a t r o T i o u s m a U a R m u s t e v e n D m a k e h * a I L a s t h e * c a n +Eval: I I I I S S I D S D S I D D S D I I D I D S S S D S D I D S I S I S S I D I S D + +Speaker sentences 322: mls_eng_000314 #utts: 1 +id: (mls_eng_000314-mls_eng_000314) +Scores: (#C #S #D #I) 137 9 14 10 +REF: t h e m o m e n t w a s f e A R f u l a m i g H t * I E R f o E h a d n e v e r s w U n g t h e b A T t l e * a * x E o v e r h i m b u * ******* t * * h o P E n e r v e d h i s a r m * f o r a d e s p E r a t E b l o w a n d t * E c U m s e H f E l L p r o s t r a * t E b e f o r E h i m +HYP: t h e m o m e n t w a s f e * * f u l a m i g * t Y O F f o * h a d n e v e r s w * n g t h e b * U t l e L a C x * o v e r h i m b u T t H E h o * B n e r v e d h i s a r m E f o r a d e s p * r a t * b l o w a n d t H c * m s e * R f * l E p r o s t r a I t D b e f o r * h i m +Eval: D D D I S S S D D D S I I D I I I I D S I D D I S D D S D S I S D + +Speaker sentences 323: mls_eng_000315 #utts: 1 +id: (mls_eng_000315-mls_eng_000315) +Scores: (#C #S #D #I) 119 11 11 5 +REF: t h e n t h e w i n D s t o P P E D t h e c l * o U D s t U R n E d d * a r k a n d n i g h T c a m e o n l i k e * i n k m y o l d c o t T O n Q u i l t w a s c * o l d a s i R O n m y s w e e t s o n t o S s E D i n h i s s * l e e P +HYP: t h e n t h e w i n * E s t o * * U T t h e c l A o * R s ******* t * A n * d d O a r k a n d n i g h E c a m e o n l i k e E i n k m y o l d c o t I n C u i l t w a s c A o l d a s i * A n m y s w e e t s o n t o * s * T i n h i s s C l e e * +Eval: D S D D S S I D S D D S D I S I S S S I D S D D S I D + +Speaker sentences 324: mls_eng_000316 #utts: 1 +id: (mls_eng_000316-mls_eng_000316) +Scores: (#C #S #D #I) 197 1 21 2 +REF: y o u m a y d o a s y O u p l e a s e t o w o r k o F f Y o U r i R r i t a t i o n t o k E e P U p y o u r f a n a t i c * i s m y o u A r e w e L l O f f y o u n E e d n o t m i n d t h e c o s t t h e p O o r * d o n o t w a n t t O s t a n d i n y o u r w a y b u t y O u i n s i s t o n t h e I R s u b m i T t i n g t O y o U r c o m p u l s i o n +HYP: y o u m a y d o a s y * u p l e a s e t o w o r k o * f * o * r i * r i t a t i o n t o k * e * ******* * p y o u r f a n a t i c S i s m y o u * r e w e * l A f f y o u n * e d n o t m i n d t h e c o s t t h e p * o r E d o n o t w a n t t * s t a n d i n ******* y o u r w a y b u t y * u i n s i s t o n t h e * * s u b m i * t i n g t * y o * r c o m p u l s i o n +Eval: D D D D D D D D D I D D S D D I D D D D D D D D + +Speaker sentences 325: mls_eng_000317 #utts: 1 +id: (mls_eng_000317-mls_eng_000317) +Scores: (#C #S #D #I) 191 18 24 13 +REF: h e w a s B r e d b y ******* * r e v * * * * * G a s ******* n * E Y D b e i n g b y o t h m a n e s i x f o U r t W o t W O h E d W i * G H E w a s b o R n * I n m a R c h E I G H t E e n s e v e n t y ******* n i n E a n d h e w a s t h e o n l y s U R v i v O r o f A l i T t e r o f f i f t E e n i t w a s o n t h i s a C c o u n t t h a t h E w a s C a L L e d s a f e I n * c o l o r a n d m a R k i n g s +HYP: h e w a s * r e d b y A r e v E R N T E R Y a Y s n I G H T b e i n g b y o t h m a n e ******* s i x f o A r t * o t * * h I d V i C K W L w a s b o * n E A n m a * c h * * * A t * e n s e v e n t y n i n * a n d h e w a s t h e o n l y ******* s * O v i v E r o f ******* * l i * t e r o f f i f t * e n i t w a s ******* o n t h i s a * c o u n t t h a t h * w a s * a * U e d s a f e A n D c o l o r a n d m a * k i n g s +Eval: D I I I I I I I S S S I I S S S D S D D D S S I S S S D I S D D D D S D I D D D S S D D D D D D D D D S S I D + +Speaker sentences 326: mls_eng_000318 #utts: 1 +id: (mls_eng_000318-mls_eng_000318) +Scores: (#C #S #D #I) 153 13 17 6 +REF: a n d w h a t h a s t E i t M a k e s T o f A L l i n t o t h e s e c * O n D t h e r E b y t h i s t i m e d i a P h a n t * A s n e E Z e * s A c H O o m o s t a d m I r a b l e s e c r e * t o n t h e c o n t r a r y i t s t I r s m e n o t a w H i t w H i c h m o s t c o n ******* C E r n * s i t H A H A H A +HYP: a n d w h a t h a s t * i t * a k e s * o f * O l i n t o t h e s e c K A n T t h e r * b y t h i s t i m e d i a F h a n t E O s n e A S e R s E c G I o m o s t a d m * r a b l e s e c r e I t o n t h e c o n t r a r y i t s t U r s m e n o t a w * i t w * i c h m o s t c o n S U r n E s i t ******* * * ******* * * ******* * * +Eval: D D D D S I S S D S I S S S I S S S D I S D D I S S I D D D D D D D D D + +Speaker sentences 327: mls_eng_000319 #utts: 1 +id: (mls_eng_000319-mls_eng_000319) +Scores: (#C #S #D #I) 175 13 17 8 +REF: t h I r d l y t h a l E S s a i d w h e r E t h e c i t i Z e n s a r e n e I t h e r t O o r * I c h n o r t O o p O o r f o U r t h l y a n ******* a ******* c H a R s ******* i s s a i d w h e r e t h o U g H i n a l l o t h e R r e s p e c t s t h e y a r E * E Q u A l Y e t v I r t U o u * s m e n a r E a d v a n C e d a n d v I C i o u s p e r s o n * D e ******* g r a d e d +HYP: t h U r d l y t h a l * * s a i d w h e r * t h e c i t i S e n s a r e n e A t h e r t * o r E A c h n o r t * o p * o r f o * r t h l y a n a c * a U s i s s a i d w h e r e t h o * g * i n a l l o t h e * ******* r e s p e c t s t h e y ******* a r * I A C u * l I e t v E r t * o u O s m e n a r * a d v a n S e d a n d v E S i o u s p e r s o n T H e g r a d e d +Eval: S D D D S S D I S D D D I I D S I D D D D D D I S S D S S D I D S S S I S I + +Speaker sentences 328: mls_eng_000320 #utts: 1 +id: (mls_eng_000320-mls_eng_000320) +Scores: (#C #S #D #I) 169 7 18 3 +REF: t h e K i n d l y f r a n k i s s Y m p A t h E t i c * * e v e r y d a y h e p a S S E s n o t e s b e t w e e n u s a n d i t r * y t o E n c O u r a g e r u s s E L l h e w i L l i m p r o v e i a S s u r E h i m h i s t i m e i S s h o r t a n d f r e s h a I r a n d l i B E r t y w I L l s O o n r e s t o r E h i m +HYP: t h e C i n d l y f r a n k i s s I m p O t h A t i c K A e v e r y d a y h e p a * * * s n o t e s b e t w e e n u s a n d i t r I y t o I n c * u r a g e r u s s * A l h e ******* w i * l i m p r o v e i a * s u r * h i m h i s t i m e i * ******* s h o r t a n d f r e s h a * r ******* a n d l i * O r t y w * * l s * o n r e s t o r * h i m +Eval: S S S S I I D D D I S D D S D D D D D D D D D S D D D D + +Speaker sentences 329: mls_eng_000321 #utts: 1 +id: (mls_eng_000321-mls_eng_000321) +Scores: (#C #S #D #I) 202 10 17 3 +REF: t h E s E Q u e s t I o n s i t i s n o w e v i d e n t m a y f r e Q u e n t l y b e a n s W e r e d w I T H e * q u A l p r o p r i E t y i n o P p O s i t E w a * Y s a n d i f t h e r e b e a n y O C c a s i o n s o n w h i c h t h e y c a n b e a n s W e r E d o n l y i n o n E w a y t h e a * n s W e r w i L l d e p e n d U p o n t h e n a t u r E o f t h e O C c a s i o n +HYP: t h I s * C u e s t * o n s i t i s n o w e v i d e n t m a y f r e C u e n t l y b e a n s * e r e d w * A S e A q u L l p r o p r i * t y i n ******* o * p * s i t * w a S E s a n d i f t h e r e b e a n y * A c a s i o n s o n w h i c h t h e y c a n b e a n s * e r * d o n l y i n o n * w a y t h e a U n s * e r w i * l d e p e n d A p o n t h e n a t u r * o f t h e * A c a s i o n +Eval: S D S D S D D S S I S D D D D D I S D S D D D I D D S D D S + +Speaker sentences 330: mls_eng_000322 #utts: 1 +id: (mls_eng_000322-mls_eng_000322) +Scores: (#C #S #D #I) 168 13 26 1 +REF: i n h i s n o t E b o r E t h e m i n s t r E l s y s e c O n D E d I T i o n E I G H t E E n o H E I G H t s c o T t s A Y s t h e b A L l A d w a s t a k e n d o w n f r o m a N o l d w o m A n s r e C i t a t i o n a t t h e * A l s T o n m O o r l e A d m i n e s b y t h e a g e n t t h e r E a n d s e n t b y h i m t o s u r t e e S +HYP: i n h i s n o t * b o r * t h e m i n s t r I l s y s e c K n * ******* A d * * i o n * * * A t Y n ******* o W * * * A t s c o * t ******* s * E s t h e b * * l E d w a s t a k e n d o w n f r o m ******* a * o l d w o m E n s r e * i t a t i o n a t t h e H E l s A o n m * o r l e * d ******* m i n e s b y t h e a g e n t t h e r * a n d s e n t b y h i m t o s u r t e e * +Eval: D D S S D D S D D D D D S S S D S D D D S D D D S D D S D D S D I S S D D D D D + +Speaker sentences 331: nchlt_eng_001588 #utts: 1 +id: (nchlt_eng_001588-nchlt_eng_001588) +Scores: (#C #S #D #I) 15 3 3 1 +REF: c H r i s t I A n t h e ******* o l O g I A n s +HYP: c * r i s t * O n t h e o l I g * O n s +Eval: D D S I S D S + +Speaker sentences 332: nchlt_eng_001589 #utts: 1 +id: (nchlt_eng_001589-nchlt_eng_001589) +Scores: (#C #S #D #I) 17 3 1 1 +REF: o B t a i n * e a g l E f E A t h e r s +HYP: o P t a i n E e a g l * f H I t h e r s +Eval: S I D S S + +Speaker sentences 333: nchlt_eng_001590 #utts: 1 +id: (nchlt_eng_001590-nchlt_eng_001590) +Scores: (#C #S #D #I) 23 3 2 2 +REF: e l E m e n t A r y * s p E c i a l * f U n C t i o n s +HYP: e l A m e n t * r y E s p * c i a l E f O n G t i o n s +Eval: S D I D I S S + +Speaker sentences 334: nchlt_eng_001591 #utts: 1 +id: (nchlt_eng_001591-nchlt_eng_001591) +Scores: (#C #S #D #I) 20 5 3 3 +REF: G E o r G e w a s H i n g * T O n * u n I v E r s * i t y +HYP: * J o r D e w a s * i n g S A n N u n * v O r s T i t y +Eval: D S S D I S S I D S I + +Speaker sentences 335: nchlt_eng_001592 #utts: 1 +id: (nchlt_eng_001592-nchlt_eng_001592) +Scores: (#C #S #D #I) 17 1 4 9 +REF: s C i E n C E f i c t i o n n o * v e L s ******* * * * * * * * +HYP: s * i * n * S f i c t i o n n o T v e * s P R O V E A N +Eval: D D D S I D I I I I I I I I + +Speaker sentences 336: nchlt_eng_001593 #utts: 1 +id: (nchlt_eng_001593-nchlt_eng_001593) +Scores: (#C #S #D #I) 10 0 3 1 +REF: c o A s t * h i p H o p +HYP: c o * s t D h i p ******* * o p +Eval: D I D D + +Speaker sentences 337: nchlt_eng_001594 #utts: 1 +id: (nchlt_eng_001594-nchlt_eng_001594) +Scores: (#C #S #D #I) 22 2 1 3 +REF: i n v e r s E l A p ******* * l a c e t r A n s f o r m * +HYP: i n v e r s * l E p B l a c e t r O n s f o r m E +Eval: D S I I S I + +Speaker sentences 338: nchlt_eng_001595 #utts: 1 +id: (nchlt_eng_001595-nchlt_eng_001595) +Scores: (#C #S #D #I) 15 3 0 0 +REF: f r E n C h p r o t E s t a n t s +HYP: f r I n G h p r o t I s t a n t s +Eval: S S S + +Speaker sentences 339: nchlt_eng_001596 #utts: 1 +id: (nchlt_eng_001596-nchlt_eng_001596) +Scores: (#C #S #D #I) 11 3 2 3 +REF: a f ******* g H A n a I R f o r C e * * +HYP: a f g * U n a * E f o r S e S T +Eval: I D S D S S I I + +Speaker sentences 340: nchlt_eng_001597 #utts: 1 +id: (nchlt_eng_001597-nchlt_eng_001597) +Scores: (#C #S #D #I) 25 3 2 2 +REF: h e * r o E s i n m Y T H o l O g y a n d l e * g e n d +HYP: h e A r o * s i n m * I S o l A g y a n d l e A g e n d +Eval: I D D S S S I + +Speaker sentences 341: nchlt_eng_001598 #utts: 1 +id: (nchlt_eng_001598-nchlt_eng_001598) +Scores: (#C #S #D #I) 14 2 3 6 +REF: b u s I n E S s c l a S s s * e * ******* * A t * * +HYP: b u s * n * * s c l a R s s E e T T t E R +Eval: D D D S I I I I S I I + +Speaker sentences 342: nchlt_eng_001599 #utts: 1 +id: (nchlt_eng_001599-nchlt_eng_001599) +Scores: (#C #S #D #I) 15 0 0 3 +REF: c l u * b p l a y c h a r * * t +HYP: c l u D b p l a y c h a r T E t +Eval: I I I + +Speaker sentences 343: nchlt_eng_001600 #utts: 1 +id: (nchlt_eng_001600-nchlt_eng_001600) +Scores: (#C #S #D #I) 21 2 0 1 +REF: p o s * I t r o n s w e r e r E p o r t e d +HYP: p o s Y t r o n s w e r e r A p o r t e d +Eval: I S S + +Speaker sentences 344: nchlt_eng_001601 #utts: 1 +id: (nchlt_eng_001601-nchlt_eng_001601) +Scores: (#C #S #D #I) 13 1 1 3 +REF: * O l d v i c * t h e a t * r E +HYP: A L l d v i c K t h e a t A r * +Eval: I S I I D + +Speaker sentences 345: nchlt_eng_001602 #utts: 1 +id: (nchlt_eng_001602-nchlt_eng_001602) +Scores: (#C #S #D #I) 12 5 0 4 +REF: o r ******* t h O d o * * * X m o n A R c H s +HYP: o r t h E d o C K E S m o n O U c K s +Eval: I S I I I S S S S + +Speaker sentences 346: nchlt_eng_001603 #utts: 1 +id: (nchlt_eng_001603-nchlt_eng_001603) +Scores: (#C #S #D #I) 19 0 2 0 +REF: n a t i o n s M E m b e r s t a t e s +HYP: n a t i o n s * * m b e r s t a t e s +Eval: D D + +Speaker sentences 347: nchlt_eng_001604 #utts: 1 +id: (nchlt_eng_001604-nchlt_eng_001604) +Scores: (#C #S #D #I) 9 4 1 4 +REF: * * F I f * A w O R l d c * u p +HYP: S H E A f H O w * I l d c O u p +Eval: I I S S I S D S I + +Speaker sentences 348: nchlt_eng_001605 #utts: 1 +id: (nchlt_eng_001605-nchlt_eng_001605) +Scores: (#C #S #D #I) 12 5 3 1 +REF: c r E W s r E s c * u E E F f O R t s +HYP: c r * O s r I s c K u W * * f E A t s +Eval: D S S I S D D S S + +Speaker sentences 349: nchlt_eng_001606 #utts: 1 +id: (nchlt_eng_001606-nchlt_eng_001606) +Scores: (#C #S #D #I) 17 5 5 4 +REF: a c t U a l f I L m * m * I c * r O s ******* c o p i C A L L Y +HYP: a c t H a l f * O m E m O A c K r * s c o p i * * * T E +Eval: S D S I I S I D I D D D S S + +Speaker sentences 350: nchlt_eng_001607 #utts: 1 +id: (nchlt_eng_001607-nchlt_eng_001607) +Scores: (#C #S #D #I) 26 1 1 2 +REF: m * u s i c a l g r o U p s r e ******* E s t a b l i s h e d +HYP: m E u s i c a l g r o * p s r e A s t a b l i s h e d +Eval: I D I S + +Speaker sentences 351: nchlt_eng_001608 #utts: 1 +id: (nchlt_eng_001608-nchlt_eng_001608) +Scores: (#C #S #D #I) 14 3 1 3 +REF: p r I m * U s i n ******* T e r p a R e s * +HYP: p r O m S C s i n S e r p a * e s E +Eval: S I S I S D I + +Speaker sentences 352: nchlt_eng_001609 #utts: 1 +id: (nchlt_eng_001609-nchlt_eng_001609) +Scores: (#C #S #D #I) 6 6 3 2 +REF: f I l M T e C H N i * Q U e * s +HYP: f O l * ******* N e * S i K N e K s +Eval: S D D S D S S I S S I + +Speaker sentences 353: nchlt_eng_001610 #utts: 1 +id: (nchlt_eng_001610-nchlt_eng_001610) +Scores: (#C #S #D #I) 16 4 3 0 +REF: t E l E v I S i o n s e r I e s b a S e D +HYP: t * l v * * i o n s e r Y e s b a C e T +Eval: D S D D S S S + +Speaker sentences 354: nchlt_eng_001611 #utts: 1 +id: (nchlt_eng_001611-nchlt_eng_001611) +Scores: (#C #S #D #I) 17 1 1 0 +REF: n e w p o l i t i c a L p a R t y +HYP: n e w p o l i t i c a R p a * t y +Eval: S D + +Speaker sentences 355: nchlt_eng_001612 #utts: 1 +id: (nchlt_eng_001612-nchlt_eng_001612) +Scores: (#C #S #D #I) 16 3 3 2 +REF: a n C I E n t e * g * Y p T a c h I e v e d +HYP: a n * H A n t e A g J I p * a c h * e v e d +Eval: D S S I I S D D + +Speaker sentences 356: nchlt_eng_001613 #utts: 1 +id: (nchlt_eng_001613-nchlt_eng_001613) +Scores: (#C #S #D #I) 16 1 1 0 +REF: f l a t m u s i C n a t U r a l +HYP: f l a t m u s i G n a t * r a l +Eval: S D + +Speaker sentences 357: nchlt_eng_001614 #utts: 1 +id: (nchlt_eng_001614-nchlt_eng_001614) +Scores: (#C #S #D #I) 22 3 4 8 +REF: a m e r i c a n S t E c H n o l * * ******* * O g * * * * y W r I t e r s +HYP: a m e r i c a n ******* * t I c * n o l A D T I g N L E D y * r A t e r s +Eval: D D S D I I I I S I I I I D S + +Speaker sentences 358: nchlt_eng_001615 #utts: 1 +id: (nchlt_eng_001615-nchlt_eng_001615) +Scores: (#C #S #D #I) 14 1 4 1 +REF: d * a U G H t e R s o f b a r O n s +HYP: d O a * * * t e * s o f b a r I n s +Eval: I D D D D S + +Speaker sentences 359: nchlt_eng_001616 #utts: 1 +id: (nchlt_eng_001616-nchlt_eng_001616) +Scores: (#C #S #D #I) 20 4 3 2 +REF: p o p u l * A R T O U r ******* i s T a T t r a c t i o n s +HYP: p o p u l E T S * W E r i s * a * t r a c t i o n s +Eval: I S S D S S I D D + +Speaker sentences 360: nchlt_eng_001617 #utts: 1 +id: (nchlt_eng_001617-nchlt_eng_001617) +Scores: (#C #S #D #I) 14 1 1 1 +REF: d * u T c h w E s t i n d i a +HYP: d O u * c h w A s t i n d i a +Eval: I D S + +Speaker sentences 361: nchlt_eng_001618 #utts: 1 +id: (nchlt_eng_001618-nchlt_eng_001618) +Scores: (#C #S #D #I) 15 5 1 1 +REF: g o l d m E D a L r E C i p i e n * T s +HYP: g o l d m I T a E r * S i p i e n C E s +Eval: S S S D S I S + +Speaker sentences 362: nchlt_eng_001619 #utts: 1 +id: (nchlt_eng_001619-nchlt_eng_001619) +Scores: (#C #S #D #I) 19 6 0 1 +REF: r U s S i A n s o c I a l d e m O c r A t i c * +HYP: r E s H i O n s o c H a l d e m A c r E t i c K +Eval: S S S S S S I + +Speaker sentences 363: nchlt_eng_001620 #utts: 1 +id: (nchlt_eng_001620-nchlt_eng_001620) +Scores: (#C #S #D #I) 19 2 2 3 +REF: a ******* m e r i * c a n f I L m * p r o d u C e R s +HYP: a m e r i K c a n f * O m E p r o d u S e * s +Eval: I I D S I S D + +Speaker sentences 364: nchlt_eng_001621 #utts: 1 +id: (nchlt_eng_001621-nchlt_eng_001621) +Scores: (#C #S #D #I) 20 2 2 2 +REF: f r E e s o f t W A r * ******* E f o u n d a t i o n +HYP: f r * e s o f t * E r Y A f o u n d a t i o n +Eval: D D S I I S + +Speaker sentences 365: nchlt_eng_001622 #utts: 1 +id: (nchlt_eng_001622-nchlt_eng_001622) +Scores: (#C #S #D #I) 17 1 4 1 +REF: r o Y A l * d r A m a t i c t h e a t R E +HYP: r o * I l E d r * m a t i c t h e a t * * +Eval: D S I D D D + +Speaker sentences 366: nchlt_eng_001623 #utts: 1 +id: (nchlt_eng_001623-nchlt_eng_001623) +Scores: (#C #S #D #I) 10 5 0 2 +REF: * E D I b l e m o l L U s c * s +HYP: I T A b l e m o l A s c K s +Eval: I S S S S S I + +Speaker sentences 367: nchlt_eng_001624 #utts: 1 +id: (nchlt_eng_001624-nchlt_eng_001624) +Scores: (#C #S #D #I) 21 1 2 2 +REF: f e a t U R e * s i n c l u d E b e a c h e * s +HYP: f e a t * H e R s i n c l u d * b e a c h e R s +Eval: D S I D I + +Speaker sentences 368: nchlt_eng_001625 #utts: 1 +id: (nchlt_eng_001625-nchlt_eng_001625) +Scores: (#C #S #D #I) 21 2 2 1 +REF: o * X f o r D d i C t i o n A r y c h a n g e d +HYP: o C S f o r * d i * t i o n r y c h a n g e d +Eval: I S D D S + +Speaker sentences 369: nchlt_eng_001626 #utts: 1 +id: (nchlt_eng_001626-nchlt_eng_001626) +Scores: (#C #S #D #I) 18 5 1 1 +REF: s a l ******* U K I p E r s i A n g r E y h o u n d +HYP: s a l C O O p U r s i * n g r A y h o u n d +Eval: I S S S S D S + +Speaker sentences 370: nchlt_eng_001627 #utts: 1 +id: (nchlt_eng_001627-nchlt_eng_001627) +Scores: (#C #S #D #I) 14 4 2 0 +REF: p r i M E m I n i s t e r K E v I n +HYP: p r i * N m * n i s t e r C I v E n +Eval: D S D S S S + +Speaker sentences 371: nchlt_eng_001628 #utts: 1 +id: (nchlt_eng_001628-nchlt_eng_001628) +Scores: (#C #S #D #I) 13 3 1 4 +REF: l a n g U a g e s o f * * I r ******* * A Q +HYP: l a n g * a g e s o f Y O U r O C K +Eval: D I I S I I S S + +Speaker sentences 372: nchlt_eng_001629 #utts: 1 +id: (nchlt_eng_001629-nchlt_eng_001629) +Scores: (#C #S #D #I) 17 1 0 0 +REF: s o u t h e a s t E n g l a n d +HYP: s o u t h e a s t I n g l a n d +Eval: S + +Speaker sentences 373: nchlt_eng_001630 #utts: 1 +id: (nchlt_eng_001630-nchlt_eng_001630) +Scores: (#C #S #D #I) 12 3 0 1 +REF: n e w l i n e C I n E m a * +HYP: n e w l i n e S E n A m a R +Eval: S S S I + +Speaker sentences 374: nchlt_eng_001631 #utts: 1 +id: (nchlt_eng_001631-nchlt_eng_001631) +Scores: (#C #S #D #I) 16 5 3 3 +REF: e * Q u A l c r E d I t * o P p O R t u * n I t y +HYP: e A C u * l c r A d t S o * p * A t u O n A t y +Eval: I S D S S I D D S I S + +Speaker sentences 375: nchlt_eng_001632 #utts: 1 +id: (nchlt_eng_001632-nchlt_eng_001632) +Scores: (#C #S #D #I) 16 1 1 0 +REF: s o u t h e A s t E n g l a n d +HYP: s o u t h e * s t I n g l a n d +Eval: D S + +Speaker sentences 376: nchlt_eng_001633 #utts: 1 +id: (nchlt_eng_001633-nchlt_eng_001633) +Scores: (#C #S #D #I) 3 0 0 2 +REF: m a y ******* * +HYP: m a y H +Eval: I I + +Speaker sentences 377: nchlt_eng_001634 #utts: 1 +id: (nchlt_eng_001634-nchlt_eng_001634) +Scores: (#C #S #D #I) 19 3 1 6 +REF: r e c o * r d h * * e T a t * * ******* m D E s c r i B e s +HYP: r e c o L r d h A T e a t S E m * I s c r i V e s +Eval: I I I S I I I D S S + +Speaker sentences 378: nchlt_eng_001635 #utts: 1 +id: (nchlt_eng_001635-nchlt_eng_001635) +Scores: (#C #S #D #I) 27 1 2 2 +REF: m u s i c a l g r O U p * s f r o m c a l I f o r n i * a +HYP: m u s i c a l g r * E p E s f r o m c a l * f o r n i E a +Eval: D S I D I + +Speaker sentences 379: nchlt_eng_001636 #utts: 1 +id: (nchlt_eng_001636-nchlt_eng_001636) +Scores: (#C #S #D #I) 14 2 1 1 +REF: m a i n b A T t l e t A n * k s +HYP: m a i n b * U t l e t I n C k s +Eval: D S S I + +Speaker sentences 380: nchlt_eng_001637 #utts: 1 +id: (nchlt_eng_001637-nchlt_eng_001637) +Scores: (#C #S #D #I) 25 1 0 2 +REF: p o * l i s h m u s i c a l i n s t r U m e n t * s +HYP: p o D l i s h m u s i c a l i n s t r A m e n t E s +Eval: I S I + +Speaker sentences 381: nchlt_eng_001638 #utts: 1 +id: (nchlt_eng_001638-nchlt_eng_001638) +Scores: (#C #S #D #I) 20 3 2 1 +REF: l a n G u A g e s o f s a U d i a ******* r a B i a +HYP: l a n W u * g e s o f s a * d i E a r a V i a +Eval: S D D S I S + +Speaker sentences 382: nchlt_eng_001639 #utts: 1 +id: (nchlt_eng_001639-nchlt_eng_001639) +Scores: (#C #S #D #I) 13 3 1 0 +REF: c O l d w a r t E n S i o n s +HYP: c A l d w a r ******* t I n T i o n s +Eval: S D S S + +Speaker sentences 383: nchlt_eng_001640 #utts: 1 +id: (nchlt_eng_001640-nchlt_eng_001640) +Scores: (#C #S #D #I) 2 3 0 4 +REF: d * U b * ******* * B Y +HYP: d O A b E W P H +Eval: I S I I I S S + +Speaker sentences 384: nchlt_eng_001641 #utts: 1 +id: (nchlt_eng_001641-nchlt_eng_001641) +Scores: (#C #S #D #I) 11 4 1 2 +REF: a n * ******* T I p o p e c l E m E n t +HYP: a n D Y p o p e ******* c l A m I n t +Eval: I I S S D S S + +Speaker sentences 385: nchlt_eng_001642 #utts: 1 +id: (nchlt_eng_001642-nchlt_eng_001642) +Scores: (#C #S #D #I) 14 2 2 3 +REF: g E t s t A K e ******* * n p r i v * a t E +HYP: g I t s t * H e C n p r i v E a t * +Eval: S D S I I I D + +Speaker sentences 386: nchlt_eng_001643 #utts: 1 +id: (nchlt_eng_001643-nchlt_eng_001643) +Scores: (#C #S #D #I) 10 3 1 3 +REF: K i n g f E R d ******* I n ******* * a n d +HYP: C i n g f * O d A n E a n d +Eval: S D S I S I I + +Speaker sentences 387: nchlt_eng_001644 #utts: 1 +id: (nchlt_eng_001644-nchlt_eng_001644) +Scores: (#C #S #D #I) 26 2 2 0 +REF: E l e c t r O n i c m u s i c a l i n s t r U m e n T s +HYP: I l e c t r * n i c m u s i c a l i n s t r * m e n C s +Eval: S D D S + +Speaker sentences 388: nchlt_eng_001645 #utts: 1 +id: (nchlt_eng_001645-nchlt_eng_001645) +Scores: (#C #S #D #I) 11 3 0 0 +REF: a g e M E L t w a t e r +HYP: a g e N O U t w a t e r +Eval: S S S + +Speaker sentences 389: nchlt_eng_001646 #utts: 1 +id: (nchlt_eng_001646-nchlt_eng_001646) +Scores: (#C #S #D #I) 21 3 3 1 +REF: l A W r e n c e l i v e R m o r E n a T i O n a l * +HYP: l * O r e n c e l i v e m o r * n a S i * n a l E +Eval: D S S D S D I + +Speaker sentences 390: nchlt_eng_001647 #utts: 1 +id: (nchlt_eng_001647-nchlt_eng_001647) +Scores: (#C #S #D #I) 18 2 3 1 +REF: l e a g U E b a * s E B a l L p l a y e r s +HYP: l e a g * * b a C s * P a l E p l a y e r s +Eval: D D I D S S + +Speaker sentences 391: nchlt_eng_001648 #utts: 1 +id: (nchlt_eng_001648-nchlt_eng_001648) +Scores: (#C #S #D #I) 26 8 3 2 +REF: b u D D H i s * m I n t h e a n c I E n t m e d ******* I t E R r a n E A n +HYP: b u * T i s O m A n t h e a n c H A n t m e d A t * * r a n I O n +Eval: D S S I S S S I S D D S S + +Speaker sentences 392: nchlt_eng_001649 #utts: 1 +id: (nchlt_eng_001649-nchlt_eng_001649) +Scores: (#C #S #D #I) 21 1 2 2 +REF: * u n i t e d s t a t E s r e c o * G n i Z e d +HYP: O u n i t e d s t a t * s r e c o C O n i * e d +Eval: I D I S D + +Speaker sentences 393: nchlt_eng_001650 #utts: 1 +id: (nchlt_eng_001650-nchlt_eng_001650) +Scores: (#C #S #D #I) 17 2 4 0 +REF: p r o p O s I T i o n a l f A L l a c I e s +HYP: p r o p A s * * i o n a l f * E l a c * e s +Eval: S D D D S D + +Speaker sentences 394: nchlt_eng_001651 #utts: 1 +id: (nchlt_eng_001651-nchlt_eng_001651) +Scores: (#C #S #D #I) 15 5 2 1 +REF: s p E c I a l e c O n o m i C Z o * n E s +HYP: s p I c H a l e c * n o m i E G S o W n * s +Eval: S S D S S S I D + +Speaker sentences 395: nchlt_eng_001652 #utts: 1 +id: (nchlt_eng_001652-nchlt_eng_001652) +Scores: (#C #S #D #I) 14 1 1 2 +REF: m a I n s t r e a m * w E s t * +HYP: m a * n s t r e a m E w I s t D +Eval: D I S I + +Speaker sentences 396: nchlt_eng_001653 #utts: 1 +id: (nchlt_eng_001653-nchlt_eng_001653) +Scores: (#C #S #D #I) 13 1 4 0 +REF: e v e N I n g r u s h H o U R s +HYP: e v e * * n g r u s h * o * W s +Eval: D D D D S + +Speaker sentences 397: nchlt_eng_001654 #utts: 1 +id: (nchlt_eng_001654-nchlt_eng_001654) +Scores: (#C #S #D #I) 12 2 4 2 +REF: b * O F h e ******* d I T i o n s t O o k +HYP: b Y T h ******* e d * * i o n s t * o k +Eval: I S S D I D D D + +Speaker sentences 398: nchlt_eng_001655 #utts: 1 +id: (nchlt_eng_001655-nchlt_eng_001655) +Scores: (#C #S #D #I) 14 1 2 4 +REF: A n * t * ******* a r C t i c * A h a s n o +HYP: * n D t S a r * t i c K E h a s n o +Eval: D I I I D I S + +Speaker sentences 399: nchlt_eng_001656 #utts: 1 +id: (nchlt_eng_001656-nchlt_eng_001656) +Scores: (#C #S #D #I) 14 1 2 2 +REF: w e * s t E n D m u s i c A l * s +HYP: w e A s t I n * m u s i c * l E s +Eval: I S D D I + +Speaker sentences 400: nchlt_eng_001657 #utts: 1 +id: (nchlt_eng_001657-nchlt_eng_001657) +Scores: (#C #S #D #I) 20 7 1 2 +REF: c o n s e R v A t I V E j u * D a I s * m r e g a r D s +HYP: c o n s e * v I t O F j u A T a Y s O m r e g a r T s +Eval: D S S S S I S S I S + +Speaker sentences 401: nchlt_eng_001658 #utts: 1 +id: (nchlt_eng_001658-nchlt_eng_001658) +Scores: (#C #S #D #I) 14 1 3 1 +REF: o p E c * M E m b e r s t a t E s +HYP: o p I c K * * m b e r s t a t * s +Eval: S I D D D + +Speaker sentences 402: nchlt_eng_001659 #utts: 1 +id: (nchlt_eng_001659-nchlt_eng_001659) +Scores: (#C #S #D #I) 11 4 4 1 +REF: p r i M E m i n * I s T E R j o H n +HYP: p r i * * ******* m i n E S s A I D j o * n +Eval: D D D I S S S S D + +Speaker sentences 403: nchlt_eng_001660 #utts: 1 +id: (nchlt_eng_001660-nchlt_eng_001660) +Scores: (#C #S #D #I) 17 1 0 2 +REF: r O c k s f o * r m i n g m o * n t +HYP: r A c k s f o A r m i n g m o U n t +Eval: S I I + +Speaker sentences 404: nchlt_eng_001661 #utts: 1 +id: (nchlt_eng_001661-nchlt_eng_001661) +Scores: (#C #S #D #I) 11 3 4 0 +REF: m a J O r l e a G U E t E A m s +HYP: m a G E r l e a * * K t * * m s +Eval: S S D D S D D + +Speaker sentences 405: nchlt_eng_001662 #utts: 1 +id: (nchlt_eng_001662-nchlt_eng_001662) +Scores: (#C #S #D #I) 17 2 3 0 +REF: p o L l I n a t i o n m a n A g E M e n t +HYP: p o * l O n a t i o n m a n I g * * e n t +Eval: D S S D D + +Speaker sentences 406: nchlt_eng_001663 #utts: 1 +id: (nchlt_eng_001663-nchlt_eng_001663) +Scores: (#C #S #D #I) 11 2 4 0 +REF: f r e n c h P H Y S i C i s t S +HYP: f r e n c h * * * F i S i s t * +Eval: D D D S S D + +Speaker sentences 407: nchlt_eng_001664 #utts: 1 +id: (nchlt_eng_001664-nchlt_eng_001664) +Scores: (#C #S #D #I) 20 3 1 1 +REF: h i G H E r c o m p r e S s i o n r a t * i o +HYP: h i * Y A r c o m p r e T s i o n r a t S i o +Eval: D S S S I + +Speaker sentences 408: nchlt_eng_001665 #utts: 1 +id: (nchlt_eng_001665-nchlt_eng_001665) +Scores: (#C #S #D #I) 27 2 1 1 +REF: r e c o r d I n g i n d * u s t r y a S s o c I a t i o n +HYP: r e c o r d n g i n d O u s t r y a * s o c H a t i o n +Eval: S I D S + +Speaker sentences 409: nchlt_eng_001666 #utts: 1 +id: (nchlt_eng_001666-nchlt_eng_001666) +Scores: (#C #S #D #I) 14 4 3 6 +REF: * * D p * * g S o n ******* l i n E m a g a * Z I n E +HYP: T H E p E A g ******* E o n l i n * m a g a S E A n * +Eval: I I S I I D S I D I S S D + +Speaker sentences 410: nchlt_eng_001667 #utts: 1 +id: (nchlt_eng_001667-nchlt_eng_001667) +Scores: (#C #S #D #I) 16 6 2 7 +REF: h i p H o p * * r e c * O R d p r * O D u * * C e * R s +HYP: h i p ******* * o p E R r e c Q U A d p r E G u O S S e O N s +Eval: D D I I I S S I S S I I S I S + +Speaker sentences 411: nchlt_eng_001668 #utts: 1 +id: (nchlt_eng_001668-nchlt_eng_001668) +Scores: (#C #S #D #I) 16 3 2 2 +REF: f i ******* n i * T e s t a t e m A C h I n E s +HYP: f i n i G H e s t a t e m * S h E n * s +Eval: I I S D S S D + +Speaker sentences 412: nchlt_eng_001669 #utts: 1 +id: (nchlt_eng_001669-nchlt_eng_001669) +Scores: (#C #S #D #I) 16 0 1 3 +REF: w * i d E l y * u s e d l o c a l * +HYP: w H i d * l y O u s e d l o c a l E +Eval: I D I I + +Speaker sentences 413: nchlt_eng_001670 #utts: 1 +id: (nchlt_eng_001670-nchlt_eng_001670) +Scores: (#C #S #D #I) 21 2 1 2 +REF: n o r ******* t h * A m e r I c a n c o n t i n E n t +HYP: n o r t h E * m e r Y c a n c o n t i n A n t +Eval: I I D S S + +Speaker sentences 414: nchlt_eng_001671 #utts: 1 +id: (nchlt_eng_001671-nchlt_eng_001671) +Scores: (#C #S #D #I) 19 2 3 1 +REF: a f r I c a n a ******* m e r i c a n r A P p E R s +HYP: a f r * c a n a m e r i c a n r * E p * A s +Eval: D I D S D S + +Speaker sentences 415: nchlt_eng_001672 #utts: 1 +id: (nchlt_eng_001672-nchlt_eng_001672) +Scores: (#C #S #D #I) 19 4 4 0 +REF: t h r E A t E n E D m I l i T A r y a c t i o n s +HYP: t h r * I t O n * * m E l i * D r y a c t i o n s +Eval: D S S D D S D S + +Speaker sentences 416: nchlt_eng_001673 #utts: 1 +id: (nchlt_eng_001673-nchlt_eng_001673) +Scores: (#C #S #D #I) 8 0 0 7 +REF: * ******* t h e w o r d ******* * ******* * * +HYP: A t h e w o r d M N N +Eval: I I I I I I I + +Speaker sentences 417: nchlt_eng_001674 #utts: 1 +id: (nchlt_eng_001674-nchlt_eng_001674) +Scores: (#C #S #D #I) 24 8 4 4 +REF: * * * A t o m i C m O l E C u l A R a n D o p t i c a l P H Y s i c * s +HYP: T H E t o m i K m E l I K u l * * ******* a n * o p t i c a l F O I s i c K s +Eval: I I I S S S S S D D D D S S S I + +Speaker sentences 418: nchlt_eng_001675 #utts: 1 +id: (nchlt_eng_001675-nchlt_eng_001675) +Scores: (#C #S #D #I) 4 0 0 0 +REF: t o w n +HYP: t o w n +Eval: + +Speaker sentences 419: nchlt_eng_001676 #utts: 1 +id: (nchlt_eng_001676-nchlt_eng_001676) +Scores: (#C #S #D #I) 3 3 0 0 +REF: m A r C E l +HYP: m O r S I l +Eval: S S S + +Speaker sentences 420: nchlt_eng_001677 #utts: 1 +id: (nchlt_eng_001677-nchlt_eng_001677) +Scores: (#C #S #D #I) 21 0 2 2 +REF: c o n s t r u c t n e w r a I l ******* g a U g e * +HYP: c o n s t r u c t n e w r a * l g a * g e H +Eval: D I D I + +Speaker sentences 421: nchlt_eng_001678 #utts: 1 +id: (nchlt_eng_001678-nchlt_eng_001678) +Scores: (#C #S #D #I) 18 4 3 1 +REF: p A U l I e x c l u s i O n P r i n c * i P l E +HYP: p O R l Y e x c l u s i * n * r i n c S i B l * +Eval: S S S D D I S D + +Speaker sentences 422: nchlt_eng_001679 #utts: 1 +id: (nchlt_eng_001679-nchlt_eng_001679) +Scores: (#C #S #D #I) 19 0 2 4 +REF: h U e * * p o * r t r a y d i F f e r e n t * +HYP: h * e W O p o U r t r a y d i * f e r e n t S +Eval: D I I I D I + +Speaker sentences 423: nchlt_eng_001680 #utts: 1 +id: (nchlt_eng_001680-nchlt_eng_001680) +Scores: (#C #S #D #I) 12 4 3 0 +REF: S s o v i E t d i S s i d E n T S +HYP: * ******* s o v i A t d i * s i d A n C E +Eval: D D S D S S S + +Speaker sentences 424: nchlt_eng_001681 #utts: 1 +id: (nchlt_eng_001681-nchlt_eng_001681) +Scores: (#C #S #D #I) 27 1 0 5 +REF: s i g n a l * t r A n s * d u c t i o n p a * t h w a y * s * +HYP: s i g n a l E t r O n s T d u c t i o n p a R t h w a y E s E +Eval: I S I I I I + +Speaker sentences 425: nchlt_eng_001682 #utts: 1 +id: (nchlt_eng_001682-nchlt_eng_001682) +Scores: (#C #S #D #I) 9 3 4 0 +REF: N E W b o r n m E S s i A H +HYP: Y O U b o r n m * * s i * * +Eval: S S S D D D D + +Speaker sentences 426: nchlt_eng_001683 #utts: 1 +id: (nchlt_eng_001683-nchlt_eng_001683) +Scores: (#C #S #D #I) 23 0 2 2 +REF: g e n e r A L l y a c * c e p t e d r a n g e * s +HYP: g e n e r * * l y a c X c e p t e d r a n g e R s +Eval: D D I I + +Speaker sentences 427: nchlt_eng_001684 #utts: 1 +id: (nchlt_eng_001684-nchlt_eng_001684) +Scores: (#C #S #D #I) 15 2 2 2 +REF: g U i l * d a ******* w a r d w i N n E R s +HYP: g * i l E d a w a r d w i * n I s +Eval: D I I D S S + +Speaker sentences 428: nchlt_eng_001685 #utts: 1 +id: (nchlt_eng_001685-nchlt_eng_001685) +Scores: (#C #S #D #I) 21 0 1 0 +REF: s w e d i s h m u s i c a l g r o U p s +HYP: s w e d i s h m u s i c a l g r o * p s +Eval: D + +Speaker sentences 429: nchlt_eng_001686 #utts: 1 +id: (nchlt_eng_001686-nchlt_eng_001686) +Scores: (#C #S #D #I) 17 5 1 2 +REF: c h I L d H O O d * a U t i s * m r a t i n g +HYP: c h O W d * E R d O a R t i s O m r a t i n g +Eval: S S D S S I S I + +Speaker sentences 430: nchlt_eng_001687 #utts: 1 +id: (nchlt_eng_001687-nchlt_eng_001687) +Scores: (#C #S #D #I) 11 1 0 0 +REF: d o s A g e f o r m s +HYP: d o s I g e f o r m s +Eval: S + +Speaker sentences 431: nchlt_eng_001688 #utts: 1 +id: (nchlt_eng_001688-nchlt_eng_001688) +Scores: (#C #S #D #I) 16 3 2 6 +REF: o * ******* h i O * s t a t E u * n ******* I v e r s * i t Y +HYP: o F h i G O s t a t * ******* u O n O v e r s T i t E +Eval: I I S I D D I I S I S + +Speaker sentences 432: nchlt_eng_001689 #utts: 1 +id: (nchlt_eng_001689-nchlt_eng_001689) +Scores: (#C #S #D #I) 19 5 4 1 +REF: f o r ******* m E R s E T t L E m e n t s i n t U r k E Y +HYP: f o r m O S s * A t * O m e n t s i n t O r k * * +Eval: I S S D S D S S D D + +Speaker sentences 433: nchlt_eng_001690 #utts: 1 +id: (nchlt_eng_001690-nchlt_eng_001690) +Scores: (#C #S #D #I) 18 1 0 0 +REF: a m e r I c a n i n v e n t i o n s +HYP: a m e r c a n i n v e n t i o n s +Eval: S + +Speaker sentences 434: nchlt_eng_001691 #utts: 1 +id: (nchlt_eng_001691-nchlt_eng_001691) +Scores: (#C #S #D #I) 4 0 0 0 +REF: a r t s +HYP: a r t s +Eval: + +Speaker sentences 435: nchlt_eng_001692 #utts: 1 +id: (nchlt_eng_001692-nchlt_eng_001692) +Scores: (#C #S #D #I) 14 5 3 0 +REF: m O d E R n E u r o p E a n r U S s I a +HYP: m * d * O n Y u r o p I a n r * A s H a +Eval: D D S S S D S S + +Speaker sentences 436: nchlt_eng_001693 #utts: 1 +id: (nchlt_eng_001693-nchlt_eng_001693) +Scores: (#C #S #D #I) 14 4 5 1 +REF: n A t I O n A L l e a G U e * p E N n a n t +HYP: n S t * * n O R l e a * * e G p * I n a n t +Eval: S D D S S D D I D S + +Speaker sentences 437: nchlt_eng_001694 #utts: 1 +id: (nchlt_eng_001694-nchlt_eng_001694) +Scores: (#C #S #D #I) 20 1 1 1 +REF: b * I g f i n i s h p r O d u c t i o n s +HYP: b E A g f i n i s h p r * d u c t i o n s +Eval: I S D + +Speaker sentences 438: nchlt_eng_001695 #utts: 1 +id: (nchlt_eng_001695-nchlt_eng_001695) +Scores: (#C #S #D #I) 5 2 1 1 +REF: n a T i O n A l * +HYP: n a S i * n O l E +Eval: S D S I + +Speaker sentences 439: nchlt_eng_001696 #utts: 1 +id: (nchlt_eng_001696-nchlt_eng_001696) +Scores: (#C #S #D #I) 11 1 0 3 +REF: t r a * g i c * p o E t * s +HYP: t r a D g i c G p o R t E s +Eval: I I S I + +Speaker sentences 440: nchlt_eng_001697 #utts: 1 +id: (nchlt_eng_001697-nchlt_eng_001697) +Scores: (#C #S #D #I) 12 4 1 1 +REF: t O t A l * g r O S S s t a t e +HYP: t I t * l E g r I C E s t a t e +Eval: S D I S S S + +Speaker sentences 441: nchlt_eng_001698 #utts: 1 +id: (nchlt_eng_001698-nchlt_eng_001698) +Scores: (#C #S #D #I) 12 1 0 2 +REF: a * ******* t h e n a h a d A n +HYP: a S t h e n a h a d E n +Eval: I I S + +Speaker sentences 442: nchlt_eng_001699 #utts: 1 +id: (nchlt_eng_001699-nchlt_eng_001699) +Scores: (#C #S #D #I) 18 5 3 0 +REF: e a s t E R n E u r O p E a n c o U n t r I E s +HYP: e a s t * n Y u r A p I a n c o * n t r * Y s +Eval: D S S S S D D S + +Speaker sentences 443: nchlt_eng_001700 #utts: 1 +id: (nchlt_eng_001700-nchlt_eng_001700) +Scores: (#C #S #D #I) 28 4 3 1 +REF: c o n d e m N e d U n * A U t h O r i Z e d t R a n s l a t i o n s +HYP: c o n d e m * e d A n D O t h * r i V e d t * a n s l a t i o n s +Eval: D S I S S D S D + +Speaker sentences 444: nchlt_eng_001701 #utts: 1 +id: (nchlt_eng_001701-nchlt_eng_001701) +Scores: (#C #S #D #I) 9 4 3 0 +REF: C O l D w a r l e A d E R s +HYP: * A l T H w a r l e * d * I s +Eval: D S S S D D S + +Speaker sentences 445: nchlt_eng_001702 #utts: 1 +id: (nchlt_eng_001702-nchlt_eng_001702) +Scores: (#C #S #D #I) 16 6 1 1 +REF: K E n E s a W m o u n t ******* a I n l A n d I s +HYP: C I n A s a R m o u n t a * n l E n d U s +Eval: S S S S I D S S + +Speaker sentences 446: nchlt_eng_001703 #utts: 1 +id: (nchlt_eng_001703-nchlt_eng_001703) +Scores: (#C #S #D #I) 9 2 1 0 +REF: n o b E l F a m i L y +HYP: n o b * l S a m i T y +Eval: D S S + +Speaker sentences 447: nchlt_eng_001704 #utts: 1 +id: (nchlt_eng_001704-nchlt_eng_001704) +Scores: (#C #S #D #I) 8 5 4 2 +REF: * E d ******* w A R d s A I r f O r C E +HYP: A N d w * O d s * E r ******* f U r * S +Eval: I S I D S D S D S D S + +Speaker sentences 448: nchlt_eng_001705 #utts: 1 +id: (nchlt_eng_001705-nchlt_eng_001705) +Scores: (#C #S #D #I) 18 1 0 1 +REF: m o u n t s a i n t v i n c * E n t +HYP: m o u n t s a i n t v i n c S A n t +Eval: I S + +Speaker sentences 449: nchlt_eng_001706 #utts: 1 +id: (nchlt_eng_001706-nchlt_eng_001706) +Scores: (#C #S #D #I) 16 6 0 3 +REF: C i t y * m e * t r O p o l I t A n * A r E a +HYP: S i t y E m e R t r U p o l A t O n E I r O a +Eval: S I I S S S I S S + +Speaker sentences 450: nchlt_eng_001707 #utts: 1 +id: (nchlt_eng_001707-nchlt_eng_001707) +Scores: (#C #S #D #I) 25 2 0 2 +REF: r * U L e r s w h o d i e d a s c h i l d r e * n +HYP: r O O N e r s w h o d i e d a s c h i l d r e A n +Eval: I S S I + +Speaker sentences 451: nchlt_eng_001708 #utts: 1 +id: (nchlt_eng_001708-nchlt_eng_001708) +Scores: (#C #S #D #I) 10 3 3 1 +REF: c h a n C e L l O R s ******* v I L l e +HYP: c h a n * e S l * A s v * O l e +Eval: D S D S I D S + +Speaker sentences 452: nchlt_eng_001709 #utts: 1 +id: (nchlt_eng_001709-nchlt_eng_001709) +Scores: (#C #S #D #I) 14 3 2 4 +REF: i ******* p * * p A c K E t s E n t i R e * l y +HYP: i p E E p E c * A t s I n t i * e R l y +Eval: I I I S D S S D I + +Speaker sentences 453: nchlt_eng_001710 #utts: 1 +id: (nchlt_eng_001710-nchlt_eng_001710) +Scores: (#C #S #D #I) 17 1 0 0 +REF: K i n g e d w a r d s d e a t h +HYP: C i n g e d w a r d s d e a t h +Eval: S + +Speaker sentences 454: nchlt_eng_001711 #utts: 1 +id: (nchlt_eng_001711-nchlt_eng_001711) +Scores: (#C #S #D #I) 14 0 1 3 +REF: a ******* m e r i c a * a ******* m E r i c a +HYP: a m e r i c a R a m * r i c a +Eval: I I I D + +Speaker sentences 455: nchlt_eng_001712 #utts: 1 +id: (nchlt_eng_001712-nchlt_eng_001712) +Scores: (#C #S #D #I) 18 1 3 0 +REF: c o M m e r C I a l s h i p s a I l e d +HYP: c o * m e r * H a l s h i p s a * l e d +Eval: D D S D + +Speaker sentences 456: nchlt_eng_001713 #utts: 1 +id: (nchlt_eng_001713-nchlt_eng_001713) +Scores: (#C #S #D #I) 15 3 2 0 +REF: p e o p l E f r o m m A n N h E I m +HYP: p e o p l * f r o m m E n h * A m +Eval: D S S D S + +Speaker sentences 457: nchlt_eng_001714 #utts: 1 +id: (nchlt_eng_001714-nchlt_eng_001714) +Scores: (#C #S #D #I) 14 1 2 1 +REF: r a i * l c r a s h K i L l E d +HYP: r a i A l c r a s h C i * l * d +Eval: I S D D + +Speaker sentences 458: nchlt_eng_001715 #utts: 1 +id: (nchlt_eng_001715-nchlt_eng_001715) +Scores: (#C #S #D #I) 14 5 2 0 +REF: m u t U a l d e f E n s E t R E A T y +HYP: m u t H a l d e f A n s * t * O U D y +Eval: S S D D S S S + +Speaker sentences 459: nchlt_eng_001716 #utts: 1 +id: (nchlt_eng_001716-nchlt_eng_001716) +Scores: (#C #S #D #I) 17 0 2 1 +REF: m o d e R n c h i l d r u * l e R s +HYP: m o d e * n c h i l d r u O l e * s +Eval: D I D + +Speaker sentences 460: nchlt_eng_001717 #utts: 1 +id: (nchlt_eng_001717-nchlt_eng_001717) +Scores: (#C #S #D #I) 17 2 1 3 +REF: m o t * * O r r i f * l E d I v i s i o n +HYP: m o t E S E r r i f A l * d E v i s i o n +Eval: I I S I D S + +Speaker sentences 461: nchlt_eng_001718 #utts: 1 +id: (nchlt_eng_001718-nchlt_eng_001718) +Scores: (#C #S #D #I) 16 3 1 2 +REF: A u ******* s t r a l i a n * a I R f o r C e +HYP: O u s t r a l i a n E a * Y f o r S e +Eval: S I I D S S + +Speaker sentences 462: nchlt_eng_001719 #utts: 1 +id: (nchlt_eng_001719-nchlt_eng_001719) +Scores: (#C #S #D #I) 20 2 2 3 +REF: a ******* m e r * I c a n m Y s t E r y W r * i t e r s +HYP: a m e r Y K c a n m I s t * r y * r H i t e r s +Eval: I I S S D D I + +Speaker sentences 463: nchlt_eng_001720 #utts: 1 +id: (nchlt_eng_001720-nchlt_eng_001720) +Scores: (#C #S #D #I) 18 2 2 4 +REF: f i n ******* * * e L y g r o u n d g r A P H i * t e +HYP: f i n T H e * y g r o u n d g r * E F i H t e +Eval: I I I D D S S I + +Speaker sentences 464: nchlt_eng_001721 #utts: 1 +id: (nchlt_eng_001721-nchlt_eng_001721) +Scores: (#C #S #D #I) 14 6 4 2 +REF: w O R l D C H A m p i O n s H I P m a * t c h * +HYP: w * I l * * T E m p i * n s O F m a N t c h E +Eval: D S D D S S D S S S I I + +Speaker sentences 465: nchlt_eng_001722 #utts: 1 +id: (nchlt_eng_001722-nchlt_eng_001722) +Scores: (#C #S #D #I) 7 1 0 0 +REF: c a r O l i n a +HYP: c a r I l i n a +Eval: S + +Speaker sentences 466: nchlt_eng_001723 #utts: 1 +id: (nchlt_eng_001723-nchlt_eng_001723) +Scores: (#C #S #D #I) 12 5 5 1 +REF: m * O b I L E P h O n E o p e r a t O R s +HYP: m Y b * * * A T h I n * o p e r a t * E s +Eval: I S D D D S S S D D S + +Speaker sentences 467: nchlt_eng_001724 #utts: 1 +id: (nchlt_eng_001724-nchlt_eng_001724) +Scores: (#C #S #D #I) 11 3 2 1 +REF: * Q u A r t Z v A r i E t i e s +HYP: C O u * r t S v E r i * t i e s +Eval: I S D S S D + +Speaker sentences 468: nchlt_eng_001725 #utts: 1 +id: (nchlt_eng_001725-nchlt_eng_001725) +Scores: (#C #S #D #I) 6 1 0 1 +REF: m * I d r a n d +HYP: m A d r a n d +Eval: I S + +Speaker sentences 469: nchlt_eng_001726 #utts: 1 +id: (nchlt_eng_001726-nchlt_eng_001726) +Scores: (#C #S #D #I) 20 1 1 1 +REF: c a U s e l E t h a l * r e a c t i o n s +HYP: c a * s e l I t h a l E r e a c t i o n s +Eval: D S I + +Speaker sentences 470: nchlt_eng_001727 #utts: 1 +id: (nchlt_eng_001727-nchlt_eng_001727) +Scores: (#C #S #D #I) 14 3 0 3 +REF: E n g l * i s h p * A c i f * I s t s +HYP: I n g l A i s h p E S c i f O U s t s +Eval: S I I S I S + +Speaker sentences 471: nchlt_eng_001728 #utts: 1 +id: (nchlt_eng_001728-nchlt_eng_001728) +Scores: (#C #S #D #I) 21 0 0 2 +REF: * * u n i t e d s t a t e s f e d e r a l +HYP: Y O u n i t e d s t a t e s f e d e r a l +Eval: I I + +Speaker sentences 472: nchlt_eng_001729 #utts: 1 +id: (nchlt_eng_001729-nchlt_eng_001729) +Scores: (#C #S #D #I) 17 1 1 2 +REF: f E d E r a l * r e s e r * v e a c t +HYP: f A d * r a l D r e s e r O v e a c t +Eval: S D I I + +Speaker sentences 473: nchlt_eng_001730 #utts: 1 +id: (nchlt_eng_001730-nchlt_eng_001730) +Scores: (#C #S #D #I) 15 4 3 0 +REF: w i L l I A m h E n r y h A R r I s o n +HYP: w i * l * Y m h I n r y h * E r A s o n +Eval: D D S S D S S + +Speaker sentences 474: nchlt_eng_001731 #utts: 1 +id: (nchlt_eng_001731-nchlt_eng_001731) +Scores: (#C #S #D #I) 11 3 1 0 +REF: c l U B p l a y c h A R t +HYP: c l A P p l a y c h * O t +Eval: S S D S + +Speaker sentences 475: nchlt_eng_001732 #utts: 1 +id: (nchlt_eng_001732-nchlt_eng_001732) +Scores: (#C #S #D #I) 19 2 2 2 +REF: p a s s E n g e r r a I l * s E R v i c * e s +HYP: p a s s O n g e r r a * l E s * O v i c S e s +Eval: S D I D S I + +Speaker sentences 476: nchlt_eng_001733 #utts: 1 +id: (nchlt_eng_001733-nchlt_eng_001733) +Scores: (#C #S #D #I) 18 7 2 4 +REF: a n c I E n T m * A C E d o * n * * i A n G e n E r a l s +HYP: a n c H A n * m E S S A d o R n T H i O n J e n * r a l s +Eval: S S D I S S S I I I S S D + +Speaker sentences 477: nchlt_eng_001734 #utts: 1 +id: (nchlt_eng_001734-nchlt_eng_001734) +Scores: (#C #S #D #I) 14 4 0 2 +REF: K o n g a c t i o n * C I n * E m a +HYP: C o n g a c t i o n D S E n T A m a +Eval: S I S S I S + +Speaker sentences 478: nchlt_eng_001735 #utts: 1 +id: (nchlt_eng_001735-nchlt_eng_001735) +Scores: (#C #S #D #I) 19 4 2 3 +REF: g u n ******* p o W d e R p r O p E L l A n t * * u s e d +HYP: g u n p o U d e A p r * p * I l E n t Y O u s e d +Eval: I S S D D S S I I + +Speaker sentences 479: nchlt_eng_001736 #utts: 1 +id: (nchlt_eng_001736-nchlt_eng_001736) +Scores: (#C #S #D #I) 13 4 2 2 +REF: l o w E s t E n E R g Y s t a * * t E +HYP: l o w s t I n * A g E s t a I G t * +Eval: S S D S S I I D + +Speaker sentences 480: nchlt_eng_001737 #utts: 1 +id: (nchlt_eng_001737-nchlt_eng_001737) +Scores: (#C #S #D #I) 9 3 1 1 +REF: c a l E n d A r * E r A s +HYP: c a l * n d E r Y U r O s +Eval: D S I S S + +Speaker sentences 481: nchlt_eng_001738 #utts: 1 +id: (nchlt_eng_001738-nchlt_eng_001738) +Scores: (#C #S #D #I) 20 3 4 5 +REF: m a * j O r i n t E R n a T i O n a l * * a I R p o r t * * +HYP: m a G j E r i n t * O n a S i * n a l E E a * * p o r t E S +Eval: I S D S S D I I D D I I + +Speaker sentences 482: nchlt_eng_001739 #utts: 1 +id: (nchlt_eng_001739-nchlt_eng_001739) +Scores: (#C #S #D #I) 14 2 2 0 +REF: t o t A l f o r C e a c t i N G +HYP: t o t * l f o r S e a c t i * M +Eval: D S D S + +Speaker sentences 483: nchlt_eng_001740 #utts: 1 +id: (nchlt_eng_001740-nchlt_eng_001740) +Scores: (#C #S #D #I) 20 4 1 0 +REF: l o s S l e s s d a t A c o m p r E S S i o n +HYP: l o s T l e s s d a t E c o m p r * I T i o n +Eval: S S D S S + +Speaker sentences 484: nchlt_eng_001741 #utts: 1 +id: (nchlt_eng_001741-nchlt_eng_001741) +Scores: (#C #S #D #I) 5 0 0 5 +REF: * ******* g r e * e k * * +HYP: A g r e A e k H A +Eval: I I I I I + +Speaker sentences 485: nchlt_eng_001742 #utts: 1 +id: (nchlt_eng_001742-nchlt_eng_001742) +Scores: (#C #S #D #I) 23 6 2 3 +REF: E n ******* v I r O N m E n t a l p r o t E c t i o n a * g E n c * Y +HYP: I n v O r * m * n t a l p r o t I c t i o n a D g A n c S E +Eval: S I S D S D S I S I S + +Speaker sentences 486: nchlt_eng_001743 #utts: 1 +id: (nchlt_eng_001743-nchlt_eng_001743) +Scores: (#C #S #D #I) 15 6 4 1 +REF: m a n * I t o b A s c H O O l s Q U E S t i o n +HYP: m a n Y t o b * I s c * * A l s * G R I t i o n +Eval: I S D S D D S D S S S + +Speaker sentences 487: nchlt_eng_001744 #utts: 1 +id: (nchlt_eng_001744-nchlt_eng_001744) +Scores: (#C #S #D #I) 16 4 1 2 +REF: a n c * I E n T C i t y p i t h u n d * A +HYP: a n c H O A n * S i t y p i t h u n d E R +Eval: I S S D S I S + +Speaker sentences 488: nchlt_eng_001745 #utts: 1 +id: (nchlt_eng_001745-nchlt_eng_001745) +Scores: (#C #S #D #I) 16 4 4 1 +REF: s m a L l o R t h O d * O X s Y n a g o g U E +HYP: s m a * l o * t h E d A C K s I n a g o g * * +Eval: D D S I S S S D D + +Speaker sentences 489: nchlt_eng_001746 #utts: 1 +id: (nchlt_eng_001746-nchlt_eng_001746) +Scores: (#C #S #D #I) 17 5 4 1 +REF: l A R g e s T m E t r O p O l i T a n A r E a * s +HYP: l O D g e s * m * t r * p I l i * a n E r I a R s +Eval: S S D D D S D S S I + +Speaker sentences 490: nchlt_eng_001747 #utts: 1 +id: (nchlt_eng_001747-nchlt_eng_001747) +Scores: (#C #S #D #I) 15 4 1 3 +REF: t i t * l E r e l i g ******* * I o r O m A n A +HYP: t i t A l * r e l i g Y E o r E m O n O +Eval: I D I I S S S S + +Speaker sentences 491: nchlt_eng_001748 #utts: 1 +id: (nchlt_eng_001748-nchlt_eng_001748) +Scores: (#C #S #D #I) 18 5 1 2 +REF: e X a m p l e s I n * ******* c l u d E h U f F m A n +HYP: e G a m p l e s A n D c l u d * h A f m O n +Eval: S S I I D S S S + +Speaker sentences 492: nchlt_eng_001749 #utts: 1 +id: (nchlt_eng_001749-nchlt_eng_001749) +Scores: (#C #S #D #I) 18 3 2 2 +REF: * u n I t ******* E d s t a t e S m a I n t a i n S +HYP: Y u n O t I d s t a t e * m a * n t a i n E +Eval: I S I S D D S + +Speaker sentences 493: nchlt_eng_001750 #utts: 1 +id: (nchlt_eng_001750-nchlt_eng_001750) +Scores: (#C #S #D #I) 20 2 0 2 +REF: b o l d r e p r e s e n T S m * a x * i m a +HYP: b o l d r e p r e s e n C E m E a x S i m a +Eval: S S I I + +Speaker sentences 494: nchlt_eng_001751 #utts: 1 +id: (nchlt_eng_001751-nchlt_eng_001751) +Scores: (#C #S #D #I) 13 4 6 0 +REF: s C i E n C e f I c T i o n A U t h O R s +HYP: s * i * n * e S f * c * i o n O R t h * E s +Eval: D D D S D D S S D S + +Speaker sentences 495: nchlt_eng_001752 #utts: 1 +id: (nchlt_eng_001752-nchlt_eng_001752) +Scores: (#C #S #D #I) 25 3 3 0 +REF: o r d I n a r y d i F f E r e n T I a l e Q u a t i o n s +HYP: o r d * n a r y d i * f * r e n S H a l e C u a t i o n s +Eval: D D D S S S + +Speaker sentences 496: nchlt_eng_001753 #utts: 1 +id: (nchlt_eng_001753-nchlt_eng_001753) +Scores: (#C #S #D #I) 21 2 2 1 +REF: d i p l O m a t s o f t h e h O L y * s E e +HYP: d i p l * m a t s o f t h e h R D y E s * e +Eval: D S S I D + +Speaker sentences 497: nchlt_eng_001754 #utts: 1 +id: (nchlt_eng_001754-nchlt_eng_001754) +Scores: (#C #S #D #I) 13 5 3 0 +REF: s E r i a l K I L l E R m Y s t E r y +HYP: s I r i a l * * C l O M m I s t * r y +Eval: S D D S S S S D + +Speaker sentences 498: nchlt_eng_001755 #utts: 1 +id: (nchlt_eng_001755-nchlt_eng_001755) +Scores: (#C #S #D #I) 12 3 7 1 +REF: * r O Y A l m I L i t A r y c o L l E G E +HYP: U r * * E l m E i t * r y c o * l * * * +Eval: I D D S S S D D D D D + +Speaker sentences 499: nchlt_eng_001756 #utts: 1 +id: (nchlt_eng_001756-nchlt_eng_001756) +Scores: (#C #S #D #I) 20 0 2 1 +REF: s l o w l y l e A d s s O c i a l i s * m +HYP: s l o w l y l e * d s s * c i a l i s O m +Eval: D D I + +Speaker sentences 500: nchlt_eng_001757 #utts: 1 +id: (nchlt_eng_001757-nchlt_eng_001757) +Scores: (#C #S #D #I) 7 0 1 0 +REF: p r i n t e R s +HYP: p r i n t e * s +Eval: D + +Speaker sentences 501: nchlt_eng_001758 #utts: 1 +id: (nchlt_eng_001758-nchlt_eng_001758) +Scores: (#C #S #D #I) 14 3 3 2 +REF: n e W t E s ******* t A M e ******* n T p e o p l E +HYP: n e U t A s t * H e n * p e o p l * +Eval: S S I D S I D D + +Speaker sentences 502: nchlt_eng_001759 #utts: 1 +id: (nchlt_eng_001759-nchlt_eng_001759) +Scores: (#C #S #D #I) 28 3 2 3 +REF: s m a R t c * a r d b a S e * d E l e c t r o n i c * p U r s E +HYP: s m a * t c O a r d b a C e T d I l e c t r o n i c K p E r s * +Eval: D I S I S I S D + +Speaker sentences 503: nchlt_eng_001760 #utts: 1 +id: (nchlt_eng_001760-nchlt_eng_001760) +Scores: (#C #S #D #I) 17 1 2 0 +REF: s t a t e S a r m y s o l d I e r s +HYP: s t a t e * a r m y ******* s o l d G e r s +Eval: D D S + +Speaker sentences 504: nchlt_eng_001761 #utts: 1 +id: (nchlt_eng_001761-nchlt_eng_001761) +Scores: (#C #S #D #I) 14 0 3 0 +REF: l o r d j e S U s c H r i s t +HYP: l o r d j e * * s c * r i s t +Eval: D D D + +Speaker sentences 505: nchlt_eng_001762 #utts: 1 +id: (nchlt_eng_001762-nchlt_eng_001762) +Scores: (#C #S #D #I) 5 3 1 2 +REF: l Y d ******* E n ******* b U R g +HYP: l A d A n b * I g +Eval: S I S I D S + +Speaker sentences 506: nchlt_eng_001763 #utts: 1 +id: (nchlt_eng_001763-nchlt_eng_001763) +Scores: (#C #S #D #I) 16 3 2 2 +REF: * I t a * l i a n n A T i O n a l t e A m +HYP: B E t a E l i a n n E S i * n a l t e * m +Eval: I S I S S D D + +Speaker sentences 507: nchlt_eng_001764 #utts: 1 +id: (nchlt_eng_001764-nchlt_eng_001764) +Scores: (#C #S #D #I) 26 4 1 2 +REF: a n * t * I g U A r e c r E a t i o n g r o u n d t h u m B +HYP: a n D t E A g E R r e c r I a t i o n g r o u n d t h u m * +Eval: I I S S S S D + +Speaker sentences 508: nchlt_eng_001765 #utts: 1 +id: (nchlt_eng_001765-nchlt_eng_001765) +Scores: (#C #S #D #I) 18 1 0 1 +REF: g r o s * s s t a t e p r o d U c t +HYP: g r o s E s s t a t e p r o d E c t +Eval: I S + +Speaker sentences 509: nchlt_eng_001766 #utts: 1 +id: (nchlt_eng_001766-nchlt_eng_001766) +Scores: (#C #S #D #I) 10 1 1 3 +REF: k i n g K o n g v * * s * +HYP: k i n g C o n g ******* v E R s E +Eval: S D I I I + +Speaker sentences 510: nchlt_eng_001767 #utts: 1 +id: (nchlt_eng_001767-nchlt_eng_001767) +Scores: (#C #S #D #I) 5 2 2 0 +REF: b e L l v I L l E +HYP: b e * l v A E l * +Eval: D S S D + +Speaker sentences 511: nchlt_eng_001768 #utts: 1 +id: (nchlt_eng_001768-nchlt_eng_001768) +Scores: (#C #S #D #I) 30 5 4 0 +REF: f I l M o R g A n i Z a t i o n s I N t h e u n i T e d s t a t e s +HYP: f * l E o L g O n i S a t i o n s ******* * * t h e u n i D e d s t a t e s +Eval: D S S S S D D D S + +Speaker sentences 512: nchlt_eng_001769 #utts: 1 +id: (nchlt_eng_001769-nchlt_eng_001769) +Scores: (#C #S #D #I) 15 4 2 2 +REF: I s r A E l * D e ******* f e n s E f o r C e s +HYP: Y s r * I l T H e f e n s * f o r S e s +Eval: S D S I S I D S + +Speaker sentences 513: nchlt_eng_001770 #utts: 1 +id: (nchlt_eng_001770-nchlt_eng_001770) +Scores: (#C #S #D #I) 14 6 2 2 +REF: * A U T O m A t i c * s E n d r e C e I v e +HYP: O R D R m * t i c K s A n d r e S e * v e +Eval: I S S S S D I S S D + +Speaker sentences 514: nchlt_eng_001771 #utts: 1 +id: (nchlt_eng_001771-nchlt_eng_001771) +Scores: (#C #S #D #I) 20 1 5 1 +REF: b r U n * s w i c k s O U t h E R n r a I l w a y +HYP: b r * n D s w i c k s * E t h * * n r a * l w a y +Eval: D I D S D D D + +Speaker sentences 515: nchlt_eng_001772 #utts: 1 +id: (nchlt_eng_001772-nchlt_eng_001772) +Scores: (#C #S #D #I) 14 3 4 2 +REF: a c t r E S s a c a * d E m Y a ******* w A R d +HYP: a c t r * * s a c a T d I m * I a w * O d +Eval: D D I S D S I D S + +Speaker sentences 516: nchlt_eng_001773 #utts: 1 +id: (nchlt_eng_001773-nchlt_eng_001773) +Scores: (#C #S #D #I) 13 2 2 4 +REF: p e O p l E f r o m * t o * k * * Y O +HYP: p e * p l * f r o m E t o C k I A T D +Eval: D D I I I I S S + +Speaker sentences 517: nchlt_eng_001774 #utts: 1 +id: (nchlt_eng_001774-nchlt_eng_001774) +Scores: (#C #S #D #I) 14 2 2 0 +REF: f o r c h a R l E s s i n g E R +HYP: f o r c h a * l D s s i n g * A +Eval: D S D S + +Speaker sentences 518: nchlt_eng_001775 #utts: 1 +id: (nchlt_eng_001775-nchlt_eng_001775) +Scores: (#C #S #D #I) 16 3 2 3 +REF: v * a r * i a b l E v A l V E t * I m i n g +HYP: v E a r Y i a b l * v * l F H t A R m i n g +Eval: I I D D S S I S + +Speaker sentences 519: nchlt_eng_001776 #utts: 1 +id: (nchlt_eng_001776-nchlt_eng_001776) +Scores: (#C #S #D #I) 16 2 1 0 +REF: s o u t h w a l e s v A l L e Y s +HYP: s o u t h w a l e s v E l Y e * s +Eval: S S D + +Speaker sentences 520: nchlt_eng_001777 #utts: 1 +id: (nchlt_eng_001777-nchlt_eng_001777) +Scores: (#C #S #D #I) 22 4 1 4 +REF: c a l i f o r * N I a s t a t E * u ******* * N I v e r s i t y +HYP: c a l i f o r D Y E a s t a t * Y u T H E v e r s i t y +Eval: I S S D I I I S S + +Speaker sentences 521: nchlt_eng_001778 #utts: 1 +id: (nchlt_eng_001778-nchlt_eng_001778) +Scores: (#C #S #D #I) 6 2 0 0 +REF: e l d O r A d o +HYP: e l d E r O d o +Eval: S S + +Speaker sentences 522: nchlt_eng_001779 #utts: 1 +id: (nchlt_eng_001779-nchlt_eng_001779) +Scores: (#C #S #D #I) 18 1 2 3 +REF: o u t ******* d O o r * o r ******* i E n t e d C i t y +HYP: o u t d * o r E o r i * n t e d S i t y +Eval: I D I I D S + +Speaker sentences 523: nchlt_eng_001780 #utts: 1 +id: (nchlt_eng_001780-nchlt_eng_001780) +Scores: (#C #S #D #I) 26 3 1 1 +REF: c l a I m e d p a r T I a l r e s p o n * s I b i l i t y +HYP: c l a * m e d p a r S H a l r e s p o n C s A b i l i t y +Eval: D S S I S + +Speaker sentences 524: nchlt_eng_001781 #utts: 1 +id: (nchlt_eng_001781-nchlt_eng_001781) +Scores: (#C #S #D #I) 12 2 1 0 +REF: c H r i s t I A n t e r m s +HYP: c * r i s t H O n t e r m s +Eval: D S S + +Speaker sentences 525: nchlt_eng_001782 #utts: 1 +id: (nchlt_eng_001782-nchlt_eng_001782) +Scores: (#C #S #D #I) 15 0 2 0 +REF: e v e n T s t O o k p l a c e +HYP: e v e n * s t * o k p l a c e +Eval: D D + +Speaker sentences 526: nchlt_eng_001783 #utts: 1 +id: (nchlt_eng_001783-nchlt_eng_001783) +Scores: (#C #S #D #I) 17 5 1 3 +REF: c A n * ******* C E r * d E A t h s i n f r A n c e +HYP: c E n S S A r D d * I t h s i n f r O n c e +Eval: S I I S S I D S S + +Speaker sentences 527: nchlt_eng_001784 #utts: 1 +id: (nchlt_eng_001784-nchlt_eng_001784) +Scores: (#C #S #D #I) 16 1 2 0 +REF: h i s t O r y o f m i C h I g a n +HYP: h i s t * r y o f m i * h A g a n +Eval: D D S + +Speaker sentences 528: nchlt_eng_001785 #utts: 1 +id: (nchlt_eng_001785-nchlt_eng_001785) +Scores: (#C #S #D #I) 15 1 3 0 +REF: O r i g i n A L l y t h e n a m E +HYP: A r i g i n * * l y t h e n a m * +Eval: S D D D + +Speaker sentences 529: nchlt_eng_001786 #utts: 1 +id: (nchlt_eng_001786-nchlt_eng_001786) +Scores: (#C #S #D #I) 25 0 3 1 +REF: n A t i o n s f r a * m e w O r K c o n v e n t i o n +HYP: n * t i o n s f r a I m e w * r * c o n v e n t i o n +Eval: D I D D + +Speaker sentences 530: nchlt_eng_001787 #utts: 1 +id: (nchlt_eng_001787-nchlt_eng_001787) +Scores: (#C #S #D #I) 4 1 0 1 +REF: L o c a l * +HYP: N o c a l E +Eval: S I + +Speaker sentences 531: nchlt_eng_001788 #utts: 1 +id: (nchlt_eng_001788-nchlt_eng_001788) +Scores: (#C #S #D #I) 21 3 2 1 +REF: A U s t r i a n s c H O o l * e c O n o m i s t s +HYP: O L s t r i a n s c * * o l E e c A n o m i s t s +Eval: S S D D I S + +Speaker sentences 532: nchlt_eng_001789 #utts: 1 +id: (nchlt_eng_001789-nchlt_eng_001789) +Scores: (#C #S #D #I) 18 0 2 2 +REF: m a I n g r O u * p c o m p o u * n d s +HYP: m a * n g r * u O p c o m p o u W n d s +Eval: D D I I + +Speaker sentences 533: nchlt_eng_001790 #utts: 1 +id: (nchlt_eng_001790-nchlt_eng_001790) +Scores: (#C #S #D #I) 15 3 2 1 +REF: r e ******* C Y c l A b l E m A t e r i a l s +HYP: r e S I c l I b l * m * t e r i a l s +Eval: I S S S D D + +Speaker sentences 534: nchlt_eng_001791 #utts: 1 +id: (nchlt_eng_001791-nchlt_eng_001791) +Scores: (#C #S #D #I) 12 5 1 1 +REF: c o m M O n l a * W s Y s t E m S +HYP: c o m I n l a R E s E s t O m * +Eval: S S I S S S D + +Speaker sentences 535: nchlt_eng_001792 #utts: 1 +id: (nchlt_eng_001792-nchlt_eng_001792) +Scores: (#C #S #D #I) 11 2 4 2 +REF: b r o n * * X h i G H s c H O O l +HYP: b r o n G K S h i * * s c * * U l +Eval: I I S D D D D S + +Speaker sentences 536: nchlt_eng_001793 #utts: 1 +id: (nchlt_eng_001793-nchlt_eng_001793) +Scores: (#C #S #D #I) 16 9 1 2 +REF: a * ******* m e r I c a n P O l i t I C A L W R i t e r s +HYP: a N m e r c a n B E l i t * O G O R I H i t e r s +Eval: I I S S S D S S S S S S + +Speaker sentences 537: nchlt_eng_001794 #utts: 1 +id: (nchlt_eng_001794-nchlt_eng_001794) +Scores: (#C #S #D #I) 13 4 0 0 +REF: c H E m i c a l E l E m e n t s +HYP: c A N m i c a l I l A m e n t s +Eval: S S S S + +Speaker sentences 538: nchlt_eng_001795 #utts: 1 +id: (nchlt_eng_001795-nchlt_eng_001795) +Scores: (#C #S #D #I) 18 1 6 1 +REF: G l o b A l * i n t E R n E t c O M m u n i t y +HYP: * l o b * l E i n t * O n * t c * * m u n i t y +Eval: D D I D S D D D + +Speaker sentences 539: nchlt_eng_001796 #utts: 1 +id: (nchlt_eng_001796-nchlt_eng_001796) +Scores: (#C #S #D #I) 18 5 2 4 +REF: * G E o ******* g r * a P H i c * m a g a Z I n E m a r c h +HYP: T D Y o g r E a * F i c E m a g a S E n * m a r c h +Eval: I S S I I D S I S S D + +Speaker sentences 540: nchlt_eng_001797 #utts: 1 +id: (nchlt_eng_001797-nchlt_eng_001797) +Scores: (#C #S #D #I) 11 7 3 2 +REF: w E B s E R v * i C E p r O v i * d E R s +HYP: w * I P s * O v H i * S p r E v i G d A s +Eval: D S S D S I D S S I S S + +Speaker sentences 541: nchlt_eng_001798 #utts: 1 +id: (nchlt_eng_001798-nchlt_eng_001798) +Scores: (#C #S #D #I) 14 2 6 2 +REF: s C i E n C E f I c t i o n N o * V e l * s +HYP: s * i * n * * S f * c t i o n * o B L e l E s +Eval: D D D D S D D I S I + +Speaker sentences 542: nchlt_eng_001799 #utts: 1 +id: (nchlt_eng_001799-nchlt_eng_001799) +Scores: (#C #S #D #I) 16 1 3 2 +REF: s C i E n C e * f i c t i o n f I l * m +HYP: s * i * n * e S f i c t i o n f U l E m +Eval: D D D I S I + +Speaker sentences 543: nchlt_eng_001800 #utts: 1 +id: (nchlt_eng_001800-nchlt_eng_001800) +Scores: (#C #S #D #I) 14 4 0 2 +REF: s u b ******* S E t s U m * p r o b l E m +HYP: s u b C I t s O m E p r o b l O m +Eval: I S S S I S + +Speaker sentences 544: nchlt_eng_001801 #utts: 1 +id: (nchlt_eng_001801-nchlt_eng_001801) +Scores: (#C #S #D #I) 19 1 1 1 +REF: e a s t E R n n o r t h a m e r i c * a +HYP: e a s t * O n n o r t h a m e r i c K a +Eval: D S I + +Speaker sentences 545: nchlt_eng_001802 #utts: 1 +id: (nchlt_eng_001802-nchlt_eng_001802) +Scores: (#C #S #D #I) 18 2 3 0 +REF: p e p Y s w i t n e S s E D l o O t i n g +HYP: p e p E s w i t n e * s * * l o U t i n g +Eval: S D D D S + +Speaker sentences 546: nchlt_eng_001803 #utts: 1 +id: (nchlt_eng_001803-nchlt_eng_001803) +Scores: (#C #S #D #I) 26 1 1 1 +REF: d i s t i n C t i v e v o c a l i n s t r U m e n t * +HYP: d i s t i n G t i v e v o c a l i n s t r * m e n t S +Eval: S D I + +Speaker sentences 547: nchlt_eng_001804 #utts: 1 +id: (nchlt_eng_001804-nchlt_eng_001804) +Scores: (#C #S #D #I) 22 1 1 4 +REF: * ******* a * f r i c a n a ******* m e r i c a n r a p P e R s +HYP: U a E f r i c a n a m e r i c a n r a p I e * s +Eval: I I I I S D + +Speaker sentences 548: nchlt_eng_001805 #utts: 1 +id: (nchlt_eng_001805-nchlt_eng_001805) +Scores: (#C #S #D #I) 14 1 4 2 +REF: p o r ******* t * U g U e s E g e n E R a l s +HYP: p o r t H O g * e s * g e n * * a l s +Eval: I I S D D D D + +Speaker sentences 549: nchlt_eng_001806 #utts: 1 +id: (nchlt_eng_001806-nchlt_eng_001806) +Scores: (#C #S #D #I) 18 6 4 4 +REF: i n t E R n A T i O n a l * A i R p o r t S i A t * ******* a * +HYP: i n t O n E S i * n a l E * i A p o r t ******* * i D t Y a Y +Eval: S S S S D I D S D D S I I I + +Speaker sentences 550: nchlt_eng_001807 #utts: 1 +id: (nchlt_eng_001807-nchlt_eng_001807) +Scores: (#C #S #D #I) 24 1 1 2 +REF: m o u n t a I n r a n g e s o f b O l i v i * a * +HYP: m o u n t a * n r a n g e s o f b E l i v i E a R +Eval: D S I I + +Speaker sentences 551: nchlt_eng_001808 #utts: 1 +id: (nchlt_eng_001808-nchlt_eng_001808) +Scores: (#C #S #D #I) 12 2 2 1 +REF: f r e n c h a I r f o * r C E +HYP: f r e n c h a * r E f o U r * S +Eval: D S I D S + +Speaker sentences 552: nchlt_eng_001809 #utts: 1 +id: (nchlt_eng_001809-nchlt_eng_001809) +Scores: (#C #S #D #I) 12 5 4 3 +REF: * ******* s * U p E r b O W l a P p e A r A n c E +HYP: S s W O p * r A b * A l a * p e * r E n c S +Eval: I I I S D S D S D D S S + +Speaker sentences 553: nchlt_eng_001810 #utts: 1 +id: (nchlt_eng_001810-nchlt_eng_001810) +Scores: (#C #S #D #I) 17 2 1 0 +REF: l o n g t r A v E l i n g p a i R s +HYP: l o n g t r E v * l i n g p a i E s +Eval: S D S + +Speaker sentences 554: nchlt_eng_001811 #utts: 1 +id: (nchlt_eng_001811-nchlt_eng_001811) +Scores: (#C #S #D #I) 19 1 0 3 +REF: d i s t r i c * t c o U r t j * u d g e * +HYP: d i s t r i c K t c o A r t j O u d g e H +Eval: I S I I + +Speaker sentences 555: nchlt_eng_001812 #utts: 1 +id: (nchlt_eng_001812-nchlt_eng_001812) +Scores: (#C #S #D #I) 7 6 1 0 +REF: D U R r A n I E m p i r E +HYP: Y O r O n Y A m p i r * +Eval: S S S S S S D + +Speaker sentences 556: nchlt_eng_001813 #utts: 1 +id: (nchlt_eng_001813-nchlt_eng_001813) +Scores: (#C #S #D #I) 21 2 0 1 +REF: b r i t i s h * n a T i o n a l i t y A c t +HYP: b r i t i s h N n a S i o n a l i t y E c t +Eval: I S S + +Speaker sentences 557: nchlt_eng_001814 #utts: 1 +id: (nchlt_eng_001814-nchlt_eng_001814) +Scores: (#C #S #D #I) 11 3 2 0 +REF: i S s U E d a t E a p r I l +HYP: i * s H O d a t * a p r A l +Eval: D S S D S + +Speaker sentences 558: nchlt_eng_001815 #utts: 1 +id: (nchlt_eng_001815-nchlt_eng_001815) +Scores: (#C #S #D #I) 22 2 1 2 +REF: p * u b l i * C L y t r a d e d c o m p a n I e s +HYP: p O u b l i S I T y t r a d e d c o m p a n * e s +Eval: I I S S D + +Speaker sentences 559: nchlt_eng_001816 #utts: 1 +id: (nchlt_eng_001816-nchlt_eng_001816) +Scores: (#C #S #D #I) 30 4 5 2 +REF: r u s S i A n v i c t i m s o f s o v I e T S r e p R e * * S S i o n s +HYP: r u s H i * n v i c t i m s o f s o v * e * ******* D r e p * e N T A T i o n s +Eval: S D D D D S D I I S S + +Speaker sentences 560: nchlt_eng_001817 #utts: 1 +id: (nchlt_eng_001817-nchlt_eng_001817) +Scores: (#C #S #D #I) 19 2 0 5 +REF: w e * s t ******* * * s l A v i c * l a n g U a g e s +HYP: w e I s t A N s l E v i c K l a n g W a g e s +Eval: I I I I S I S + +Speaker sentences 561: nchlt_eng_001818 #utts: 1 +id: (nchlt_eng_001818-nchlt_eng_001818) +Scores: (#C #S #D #I) 20 1 1 2 +REF: I t a l i a n r o m a n c a t h O l i c * * +HYP: E t a l i a n r o m a n c a t h * l i c E S +Eval: S D I I + +Speaker sentences 562: nchlt_eng_001819 #utts: 1 +id: (nchlt_eng_001819-nchlt_eng_001819) +Scores: (#C #S #D #I) 16 6 3 1 +REF: f r e n C h R E s ******* i s t A N C E M E m b e r s +HYP: f r e n T h * * s i s t * R F N I m b e r s +Eval: S D D I D S S S S S + +Speaker sentences 563: nchlt_eng_001820 #utts: 1 +id: (nchlt_eng_001820-nchlt_eng_001820) +Scores: (#C #S #D #I) 24 4 1 0 +REF: p r O v i n c I a l s Y m b O l s o f O n t a r i o +HYP: p r E v i n c H a l s I m b * l s o f U n t a r i o +Eval: S S S D S + +Speaker sentences 564: nchlt_eng_001821 #utts: 1 +id: (nchlt_eng_001821-nchlt_eng_001821) +Scores: (#C #S #D #I) 17 1 0 0 +REF: r o c k s f o R m i n g m o n t +HYP: r o c k s f o A m i n g m o n t +Eval: S + +Speaker sentences 565: nchlt_eng_001822 #utts: 1 +id: (nchlt_eng_001822-nchlt_eng_001822) +Scores: (#C #S #D #I) 16 3 2 0 +REF: a S s a s s I n a t e d m o n A R c H s +HYP: a * s a s s O n a t e d m o n * O c K s +Eval: D S D S S + +Speaker sentences 566: nchlt_eng_001823 #utts: 1 +id: (nchlt_eng_001823-nchlt_eng_001823) +Scores: (#C #S #D #I) 29 4 4 2 +REF: i n c l u d E i n t e R n a * T i O n A l n o n ******* g O v e r N m e n t A l +HYP: i n c l u d * i n t e n a S H i * n O l n o n g * v e r * m e n t O l +Eval: D S I S D S I D D S + +Speaker sentences 567: nchlt_eng_001824 #utts: 1 +id: (nchlt_eng_001824-nchlt_eng_001824) +Scores: (#C #S #D #I) 14 1 1 1 +REF: m e t r i c * s p a c e S m +HYP: m e t r i c K s p a c e * I m +Eval: I D S + +Speaker sentences 568: swc_eng_001744 #utts: 1 +id: (swc_eng_001744-swc_eng_001744) +Scores: (#C #S #D #I) 27 1 3 1 +REF: o r r e p a I r * t h e b r E a k I n t h e T a p e +HYP: o r r e p a * r E t h e b r * a k * n t h e C a p e +Eval: D I D D S + +Speaker sentences 569: swc_eng_001745 #utts: 1 +id: (swc_eng_001745-swc_eng_001745) +Scores: (#C #S #D #I) 9 5 4 0 +REF: V A r I O U S s u B S t a n C e s +HYP: * E r * * Y H s u * P t a n T e s +Eval: D S D D S S D S S + +Speaker sentences 570: swc_eng_001746 #utts: 1 +id: (swc_eng_001746-swc_eng_001746) +Scores: (#C #S #D #I) 47 2 5 0 +REF: T h i s m o s t c o M m O N l y O C c u r s w h e n n e I t h e r s i d e i s a b l e t o +HYP: * h i s m o s t c o * m * E l y * A c u r s w h e n n e * t h e r s i d e i s a b l e t o +Eval: D D D S D S D + +Speaker sentences 571: swc_eng_001747 #utts: 1 +id: (swc_eng_001747-swc_eng_001747) +Scores: (#C #S #D #I) 47 6 9 0 +REF: g r E a t b a r R i E r r E E f i s m a n a g e d b y t h e g r e A t b a R r i E R r E E f m A r I n E +HYP: g r * a t b a r Y i A r r * I f i s m a n a g e d b y t h e g r e * t b a * r i * * A r * I f m * r E n * +Eval: D S S D S D D D D S D S D S D + +Speaker sentences 572: swc_eng_001748 #utts: 1 +id: (swc_eng_001748-swc_eng_001748) +Scores: (#C #S #D #I) 15 1 5 3 +REF: * * ******* a t l e a s T t h r E e R o U t E s +HYP: B Y a t ******* l e a s * t h r * e V o * t * s +Eval: I I I D D D S D D + +Speaker sentences 573: swc_eng_001749 #utts: 1 +id: (swc_eng_001749-swc_eng_001749) +Scores: (#C #S #D #I) 11 2 2 4 +REF: d e f i C I E n * ******* c I e * s * i n +HYP: d e f i * H A n T c * e A s E i n +Eval: D S S I I D I I + +Speaker sentences 574: swc_eng_001750 #utts: 1 +id: (swc_eng_001750-swc_eng_001750) +Scores: (#C #S #D #I) 25 0 10 1 +REF: w I L l s h o W e v I d e n c e o f h E m O R r H A g E i n * +HYP: w * * l s h o * e v * d e n c e o f h * m * * r * * g * i n T +Eval: D D D D D D D D D D I + +Speaker sentences 575: swc_eng_001751 #utts: 1 +id: (swc_eng_001751-swc_eng_001751) +Scores: (#C #S #D #I) 18 1 3 0 +REF: F i n d a n a n s W e r Q U i c k l y +HYP: * i n d a n a n s * e r * H i c k l y +Eval: D D D S + +Speaker sentences 576: swc_eng_001752 #utts: 1 +id: (swc_eng_001752-swc_eng_001752) +Scores: (#C #S #D #I) 41 6 2 2 +REF: E n a b l e S d I v i * s i v e a n d u n ******* d e m O c r a t i c s o C I a l p o l I c I e s +HYP: * n a b l e * d E v i C s i v e a n d u n d e m A c r a t i c s o S H a l p o l A c Y e s +Eval: D D S I I S S S S S + +Speaker sentences 577: swc_eng_001753 #utts: 1 +id: (swc_eng_001753-swc_eng_001753) +Scores: (#C #S #D #I) 29 4 7 1 +REF: m a d E r e C e n t t * i t l e s a V A i l A b l e o n c A S S E T T e +HYP: m a d * r e S e n t t H i t l e s a * i l I b l e o n c * * * * * O e +Eval: D S I D S S D D D D D S + +Speaker sentences 578: swc_eng_001754 #utts: 1 +id: (swc_eng_001754-swc_eng_001754) +Scores: (#C #S #D #I) 24 2 4 1 +REF: d i s t r i c t i n E I G H t E e n s i * x t y s i X +HYP: d i s t r i c t i n * * * A t * e n s i C x t y s i C +Eval: D D D S D I S + +Speaker sentences 579: swc_eng_001755 #utts: 1 +id: (swc_eng_001755-swc_eng_001755) +Scores: (#C #S #D #I) 16 2 4 3 +REF: A l i T T l e i n ******* t o f * u T u * r i T y +HYP: * ******* l i * * l e i n t o f E u C u I r i D y +Eval: D D D D I I S I S + +Speaker sentences 580: swc_eng_001756 #utts: 1 +id: (swc_eng_001756-swc_eng_001756) +Scores: (#C #S #D #I) 23 0 3 9 +REF: * * ******* * * * * * ******* g r o i n a n d a d v a n c e d t h r o U G H +HYP: A Y I N T H E g r o i n a n d a d v a n c e d t h r o * * * +Eval: I I I I I I I I I D D D + +Speaker sentences 581: swc_eng_001757 #utts: 1 +id: (swc_eng_001757-swc_eng_001757) +Scores: (#C #S #D #I) 50 11 11 4 +REF: T E c H n O l O g I e s I n * i m p l E m e n t i N g t r a n s ******* h * u m A n I s T G O A l s o f E n ******* h a n C e D p e r f o r m A n C E +HYP: * * c * n A l * g Y e s A n D i m p l D m e n t i * g t r a n s h E u m I n U s * ******* * C U l s o f A n h a n S e * p e r f o r m E n * * +Eval: D D D S D S S I S D I I S S D D D S S S I S D S D D + +Speaker sentences 582: swc_eng_001758 #utts: 1 +id: (swc_eng_001758-swc_eng_001758) +Scores: (#C #S #D #I) 11 2 4 0 +REF: I n c L u d i n g n a p H T H A +HYP: * n c O u d i n g n a p * * * S +Eval: D S D D D S + +Speaker sentences 583: swc_eng_001759 #utts: 1 +id: (swc_eng_001759-swc_eng_001759) +Scores: (#C #S #D #I) 30 8 5 1 +REF: B y s p a n i s h C H U r c h m a n l U I S r A m I r E Z d e l * u c E n a +HYP: * y s p a n i s h * T I r c h m a n l * O E r * m E r A S d e ******* l O u c A n a +Eval: D D S S D S S D S S S D I S + +Speaker sentences 584: swc_eng_001760 #utts: 1 +id: (swc_eng_001760-swc_eng_001760) +Scores: (#C #S #D #I) 14 3 0 1 +REF: d I v i * D e d d e m O c r a t s +HYP: d E v i H T e d d e m I c r a t s +Eval: S I S S + +Speaker sentences 585: swc_eng_001761 #utts: 1 +id: (swc_eng_001761-swc_eng_001761) +Scores: (#C #S #D #I) 42 3 5 3 +REF: T h e w o R l d c h a M p i O n ******* s h i p h a s b E e n c o n t r o L l e D b y F i d * ******* e +HYP: * h e w o * l d c h a N p i A n s h i p h a s b * e n c o n t r o * l e * b y E i d Y e +Eval: D D S S I D D D S I I + +Speaker sentences 586: swc_eng_001762 #utts: 1 +id: (swc_eng_001762-swc_eng_001762) +Scores: (#C #S #D #I) 25 0 2 2 +REF: w h e r e t H e s t a r T i n g p o s i t i o n ******* * +HYP: w h e r e t * e s t a r * i n g p o s i t i o n I +Eval: D D I I + +Speaker sentences 587: swc_eng_001763 #utts: 1 +id: (swc_eng_001763-swc_eng_001763) +Scores: (#C #S #D #I) 77 6 13 0 +REF: B E e N c r E a t e d i n e v e r y s t a t E a n d t e R r i t O r y t o p r O t e c t a n d P r e s e r v E t h e c o U n t r Y s u n I Q U E E c o s Y s t E m s +HYP: * * e * c r * a t e d i n e v e r y s t a t * a n d t e * r i t * r y t o p r * t e c t a n d * r e s e r v * t h e c o * n t r E s u n * A K Y * c o s I s t O m s +Eval: D D D D D D D D D D D S D S S S D S S + +Speaker sentences 588: swc_eng_001764 #utts: 1 +id: (swc_eng_001764-swc_eng_001764) +Scores: (#C #S #D #I) 27 3 12 2 +REF: D e d i c a t i O n * o f t h e n E W Z E A l a n d w a r M e * m o R I A L +HYP: * e d i c a t i * n T o f t h e ******* n * * ******* U S I l a n d w a r ******* * e A m o * * * * +Eval: D D I D D D D S S S D D I D D D D + +Speaker sentences 589: swc_eng_001765 #utts: 1 +id: (swc_eng_001765-swc_eng_001765) +Scores: (#C #S #D #I) 37 4 6 2 +REF: a C C l a I m * f r O m t h e r A I l ******* r o A d c o M p a n I e s f o r v e t o i n G +HYP: a * l a * m E f r * m t h e r * E l r o U d c o U p a n * e s f o r v e t o i n * +Eval: D S D I D D S I S S D D + +Speaker sentences 590: swc_eng_001766 #utts: 1 +id: (swc_eng_001766-swc_eng_001766) +Scores: (#C #S #D #I) 12 0 9 4 +REF: * * * ******* t o w n i s s p L i T B E t W E E N +HYP: T H E t o w n i s s p * i * ******* * * t * * * * +Eval: I I I I D D D D D D D D D + +Speaker sentences 591: swc_eng_001767 #utts: 1 +id: (swc_eng_001767-swc_eng_001767) +Scores: (#C #S #D #I) 39 5 11 1 +REF: m o s * Q U i T O f i s h i s a p A r t i c u L A R l y a G g r e S S i v e s p E c I e s K n o W N +HYP: m o s K E T i Y f i s h i s a p * r t i c u * * * l y a * g r e * * i v e s p A c * e s * n o * * +Eval: I S S S S D D D D D D D S D D D D + +Speaker sentences 592: swc_eng_001768 #utts: 1 +id: (swc_eng_001768-swc_eng_001768) +Scores: (#C #S #D #I) 30 3 3 2 +REF: a n d t h e n a T i o n a l c h e s S C h A m P i O n ******* s h i p * s +HYP: a n d t h e n a * i o n a l c h e s E * h E m i * n s h i p E s +Eval: D S D S S D I I + +Speaker sentences 593: swc_eng_001769 #utts: 1 +id: (swc_eng_001769-swc_eng_001769) +Scores: (#C #S #D #I) 36 1 5 2 +REF: P r o b l E m i s K n o w n t o r u n i n p O l y ******* n o ******* m I a l t i m e +HYP: * r o b l O m i s * n o w n t o r u n i n ******* p * l y n o m * a l t i m e +Eval: D S D D D I I D + +Speaker sentences 594: swc_eng_001770 #utts: 1 +id: (swc_eng_001770-swc_eng_001770) +Scores: (#C #S #D #I) 25 2 1 11 +REF: * * * ******* j * * * * r a n d p a r * k e r w a t ******* K I n s h * a r d I n +HYP: L A Y j O N I E r a n d p a r C k e r w a t C O n s h E a r d * n +Eval: I I I I I I I I I I S S I D + +Speaker sentences 595: swc_eng_001771 #utts: 1 +id: (swc_eng_001771-swc_eng_001771) +Scores: (#C #S #D #I) 19 2 4 0 +REF: i n n i N E t E E n s e v e n t y t h r E E +HYP: i n n i * * t I n s e v e n t y t h r * * +Eval: D D S S D D + +Speaker sentences 596: swc_eng_001772 #utts: 1 +id: (swc_eng_001772-swc_eng_001772) +Scores: (#C #S #D #I) 32 3 3 1 +REF: d e v e l o p i n g a n d * u s i n g s u c h t E c H n O l O g I e S +HYP: d e v e l o p i n g a n d O u s i n g s u c h t A c * n A l D g * e * +Eval: I S D S S D D + +Speaker sentences 597: swc_eng_001773 #utts: 1 +id: (swc_eng_001773-swc_eng_001773) +Scores: (#C #S #D #I) 16 1 1 0 +REF: F o r s o m e q u E s t i o n s +HYP: * o r s o m e q u I s t i o n s +Eval: D S + +Speaker sentences 598: swc_eng_001774 #utts: 1 +id: (swc_eng_001774-swc_eng_001774) +Scores: (#C #S #D #I) 16 2 3 2 +REF: C l a I m * O F p r o o F t h a t p * +HYP: * l a * m E * A p r o o E t h a t p E +Eval: D D I D S S I + +Speaker sentences 599: swc_eng_001775 #utts: 1 +id: (swc_eng_001775-swc_eng_001775) +Scores: (#C #S #D #I) 46 7 10 0 +REF: a b l a D d e R c a t h E t E r i s U S u A L l y i n s e r t e d T o m o n I T o R f l u i d b A L A n C E +HYP: a b l a * d e * c a t h A t O r i s * O u * * l y i n s e r t e d S o m o n * S o * f l u i d b * * O n * S +Eval: D D S S D S D D S D S D D D S D S + +Speaker sentences 600: swc_eng_001776 #utts: 1 +id: (swc_eng_001776-swc_eng_001776) +Scores: (#C #S #D #I) 59 11 7 4 +REF: P r O M o t i o n o f E u ******* G e n i c E n ******* h a n C E m e n t t E c H n O l O g I e s m i g h T u n ******* i n t e n t i O n a L l y E n ******* c o u r a g e +HYP: E r N o t i o n o f Y u J e n i c ******* A n h a n * S m e n t t I c * n A l A g * e s m i g h * u n i n t e n t i * n a * l y I n c o u r a g e +Eval: S S S S I S D S I D S S D S S D D I D D S I + +Speaker sentences 601: swc_eng_001777 #utts: 1 +id: (swc_eng_001777-swc_eng_001777) +Scores: (#C #S #D #I) 66 2 9 7 +REF: * * ******* t h e a T t e n T i O n o f r e ******* s e A r C h E r s C a n b e f o c * U s e d O n * p a r t i a l s o l u t i o n s o ******* r s o l U t i o n s +HYP: A T t h e ******* a * t e n * i * n o f r e s e * r T h * r s * a n b e f o c K E s e d * n M p a r t i a l s o l u t i o n s o r s o l * t i o n s +Eval: I I I D D D D I D S D D I S D I I D + +Speaker sentences 602: swc_eng_001778 #utts: 1 +id: (swc_eng_001778-swc_eng_001778) +Scores: (#C #S #D #I) 27 0 3 0 +REF: K n o w n o f f o r h U n d r e d s o f y e a r s +HYP: * n o w n ******* o f f o r h * n d r e d s o f y e a r s +Eval: D D D + +Speaker sentences 603: swc_eng_001779 #utts: 1 +id: (swc_eng_001779-swc_eng_001779) +Scores: (#C #S #D #I) 27 2 15 0 +REF: O n l y m a R S u P i a l s h a v e s U R v i v e d T O t H E P R E S E N t +HYP: * n l y m a * * u B i a l s h a v e s * O v i v e d ******* * * t * * * * * * * * t +Eval: D D D S D S D D D D D D D D D D D + +Speaker sentences 604: swc_eng_001780 #utts: 1 +id: (swc_eng_001780-swc_eng_001780) +Scores: (#C #S #D #I) 43 5 4 0 +REF: t o w h I c h a L l t h e e d I b l e s p E c I e s o f c r u s t a C E A n b e l o n g +HYP: t o ******* w h * c h a * l t h e e d A b l e s p A c * e s o f c r u s t a T I O n b e l o n g +Eval: D D D S S D S S S + +Speaker sentences 605: swc_eng_001781 #utts: 1 +id: (swc_eng_001781-swc_eng_001781) +Scores: (#C #S #D #I) 12 4 2 1 +REF: A L g O r I t h * m r e s E A r c h +HYP: * O g E r t h E m r e s * U r c h +Eval: D S S S I D S + +Speaker sentences 606: swc_eng_001782 #utts: 1 +id: (swc_eng_001782-swc_eng_001782) +Scores: (#C #S #D #I) 68 7 12 3 +REF: n i n E t e E n s i * x t y t W o P H i l i p s i n v e n t e d T h e c o m p a c t A U d I o c a S s e T t E m e d * I U m f o r A U d i o s ******* t o r A g e +HYP: n i n * t e I n s i C x t y t * o * F i l i p s i n v e n t e d * h e c o m p a c t * O d * o ******* c a * s e * t * m e d E O A m f o r * O d i o U s t o r * g e +Eval: D S I D D S D D S D D D D D I S S D S S I D + +Speaker sentences 607: swc_eng_001783 #utts: 1 +id: (swc_eng_001783-swc_eng_001783) +Scores: (#C #S #D #I) 15 1 7 0 +REF: O B S t r U c T i O n O f t h e F l o w +HYP: * * U t r * c * i * n * f t h e * l o w +Eval: D D S D D D D D + +Speaker sentences 608: swc_eng_001784 #utts: 1 +id: (swc_eng_001784-swc_eng_001784) +Scores: (#C #S #D #I) 12 3 8 0 +REF: A M P h I b i a n s a N d r E p T I L E S +HYP: N T F h * b i a n s a * d r * p * * * * * +Eval: S S S D D D D D D D D + +Speaker sentences 609: swc_eng_001785 #utts: 1 +id: (swc_eng_001785-swc_eng_001785) +Scores: (#C #S #D #I) 23 4 4 0 +REF: W o m e n s w o R l d c h e s S C h a m P i O n S h i P +HYP: * o m e n s w o A l d c h e s T * h a m i * n C h i * +Eval: D S S D S D S D + +Speaker sentences 610: swc_eng_001786 #utts: 1 +id: (swc_eng_001786-swc_eng_001786) +Scores: (#C #S #D #I) 90 11 19 4 +REF: c o n t a I n S d E s c r I p t i o n s a n d c o m m E n t a r i E s o n t h e s t a t E o f * * n ******* b ******* i C s C i E n c e a n d T e c H n O l O g y B Y m a J O r c o n t r I B u t O r s t o t h E S E F I e L D S +HYP: c o n t a * n E d I s c r * p t i o n s a n d c o m m A n t a r i Y s o n t h e s t a t * o f A E n b i * s * i * n c e a n d * e c * n A l A g y A S m a G E r c o n t r * * u t E r s t o t h * * * ******* * * e * * * +Eval: D S S D S S D I I I I D D D D D S S S S S S D D S D D D D D D D D D + +Speaker sentences 611: swc_eng_001787 #utts: 1 +id: (swc_eng_001787-swc_eng_001787) +Scores: (#C #S #D #I) 23 3 5 2 +REF: P u E r ******* I l E f * a n t A S y o r s o c i a l t r e n D +HYP: * u * r H l * ******* f H a n t * I y o r s o c i a l t r e n T +Eval: D D I S D D I D S S + +Speaker sentences 612: swc_eng_001788 #utts: 1 +id: (swc_eng_001788-swc_eng_001788) +Scores: (#C #S #D #I) 33 0 5 1 +REF: m o s t c o m p a c T c a S s e T t E s w e r E s o * l d b l a n k +HYP: m o s t c o m p a c * c a * s e * t * s w e r * s o U l d b l a n k +Eval: D D D D D I + +Speaker sentences 613: swc_eng_001789 #utts: 1 +id: (swc_eng_001789-swc_eng_001789) +Scores: (#C #S #D #I) 18 4 2 1 +REF: i f t h e r E i s a n * A l g O r I t h M +HYP: i f ******* t h e r * i s a n O U l g E r t h E +Eval: D D I S S S S + +Speaker sentences 614: swc_eng_001790 #utts: 1 +id: (swc_eng_001790-swc_eng_001790) +Scores: (#C #S #D #I) 59 5 9 0 +REF: T h e s O u t h e R n A U s t r a l i a n c o A s t a n d i n s u b a n t A R c t i c A u s t r a l i a n t e R r i t O r I E s +HYP: * h e s * u t h e * n * O s t r a l i a n c o * s t a n d i n s u b ******* a n t E I c t i c O u s t r a l i a n t e * r i t * r * Y s +Eval: D D D D S D D S S S D D D S + +Speaker sentences 615: swc_eng_001791 #utts: 1 +id: (swc_eng_001791-swc_eng_001791) +Scores: (#C #S #D #I) 29 4 6 0 +REF: D A T a r a t E s o f t Y p i c A L l y f i v e h u N d r e d t O +HYP: * * * a E r a t * s o f t I p i c * K l y f i v e h u * d r e d t W +Eval: D D D S D S D S D S + +Speaker sentences 616: swc_eng_001792 #utts: 1 +id: (swc_eng_001792-swc_eng_001792) +Scores: (#C #S #D #I) 15 1 2 0 +REF: d e p r i v i n G t h e d U c K +HYP: d e p r i v i n * t h e d O c * +Eval: D S D + +Speaker sentences 617: swc_eng_001793 #utts: 1 +id: (swc_eng_001793-swc_eng_001793) +Scores: (#C #S #D #I) 26 2 2 0 +REF: n i n e p E r C e n t o f T h e t o t A l c a s t +HYP: n i n e p O r S e n t o f * h e t o t * l c a s t +Eval: S S D D + +Speaker sentences 618: swc_eng_001794 #utts: 1 +id: (swc_eng_001794-swc_eng_001794) +Scores: (#C #S #D #I) 43 5 10 0 +REF: a n t e r i O R C E r E b r A L a R t E r y a n d a n t e r i O R c o M m u n I c a t i n G a R t e r y +HYP: a n t e r i A S S O r * b r * * a * t * r y a n d a n t e r i * E c o * m u n * c a t i n * a * t e r y +Eval: S S S S D D D D D D S D D D D + +Speaker sentences 619: swc_eng_001795 #utts: 1 +id: (swc_eng_001795-swc_eng_001795) +Scores: (#C #S #D #I) 18 1 4 1 +REF: I T D I d n o t i m p a r t * s h i n E +HYP: * E * * d n o t i m p a r t H s h i n * +Eval: D S D D I D + +Speaker sentences 620: swc_eng_001796 #utts: 1 +id: (swc_eng_001796-swc_eng_001796) +Scores: (#C #S #D #I) 20 1 2 2 +REF: E n t * i R e * d e m O c r a t i c p a r t y +HYP: * n t H i * e R d e m A c r a t i c p a r t y +Eval: D I D I S + +Speaker sentences 621: swc_eng_001797 #utts: 1 +id: (swc_eng_001797-swc_eng_001797) +Scores: (#C #S #D #I) 37 1 11 0 +REF: n o T C h e s o n t o p o f t h e c A S s e T t E S h E L l i n d i c a t E t h E +HYP: n o * * h e s o n t o p o f t h e c * * s e * t * ******* * h * A l i n d i c a t * t h * +Eval: D D D D D D D D D S D D + +Speaker sentences 622: swc_eng_001798 #utts: 1 +id: (swc_eng_001798-swc_eng_001798) +Scores: (#C #S #D #I) 9 2 6 0 +REF: A L l o w O N E t o s H o W +HYP: * * l o w ******* * A T t o s * o * +Eval: D D D D S S D D + +Speaker sentences 623: swc_eng_001799 #utts: 1 +id: (swc_eng_001799-swc_eng_001799) +Scores: (#C #S #D #I) 23 2 6 2 +REF: i S a N E n d a n g E r e d m A r i n E s p E c I e s ******* * +HYP: i * a * I n d a n g * r e d m * r i n * s p * c H e s T +Eval: D D S D D D D S I I + +Speaker sentences 624: swc_eng_001800 #utts: 1 +id: (swc_eng_001800-swc_eng_001800) +Scores: (#C #S #D #I) 19 1 2 0 +REF: b r o w n d e s i r e D E l e c t i O n +HYP: b r o w n d e s i r e * A l e c t i * n +Eval: D S D + +Speaker sentences 625: swc_eng_001801 #utts: 1 +id: (swc_eng_001801-swc_eng_001801) +Scores: (#C #S #D #I) 49 1 4 0 +REF: T h i s f a c T d o E s n t s a y m u c h a b o u t w h e r e t h e p r o b l E m l i E s +HYP: * h i s f a c * d o * s n t s a y m u c h a b o u t w h e r e t h e p r o b l O m l i * s +Eval: D D D S D + +Speaker sentences 626: swc_eng_001802 #utts: 1 +id: (swc_eng_001802-swc_eng_001802) +Scores: (#C #S #D #I) 21 1 7 0 +REF: E c O N o m i c a l s o C i E t y b E g a n a s A +HYP: * c * * o m i c a l s o S i * t y b * g a n a s ******* * +Eval: D D D S D D D D + +Speaker sentences 627: swc_eng_001803 #utts: 1 +id: (swc_eng_001803-swc_eng_001803) +Scores: (#C #S #D #I) 35 2 8 4 +REF: W i t h t o U r i s t s a R r I v i n g * B Y s t e A m * ******* b o A t * a n d t r A i n +HYP: * i t h t o * r i s t s ******* a * r * v i n g T H E s t e * m E b o * t E a n d t r * i n +Eval: D D D D D I S S D I I D I D + +Speaker sentences 628: swc_eng_001804 #utts: 1 +id: (swc_eng_001804-swc_eng_001804) +Scores: (#C #S #D #I) 29 1 6 0 +REF: f I r s t d i A l o g U E b e t w e e n t r a n S h u m A n i s M +HYP: f * r s t d i * l o g * * b e t w e e n t r a n h u m * n i s * +Eval: D D D D S D D + +Speaker sentences 629: swc_eng_001805 #utts: 1 +id: (swc_eng_001805-swc_eng_001805) +Scores: (#C #S #D #I) 26 2 8 0 +REF: n E V e r b E e n p a r t o f t h e O l Y M p i c g a M E s +HYP: n * * e r b * e n p a r t o f t h e ******* * l * I p i c ******* g a * N s +Eval: D D D D D D S D D S + +Speaker sentences 630: swc_eng_001806 #utts: 1 +id: (swc_eng_001806-swc_eng_001806) +Scores: (#C #S #D #I) 17 1 1 5 +REF: r e * g I s f u r n i t * u r E a n d ******* * * +HYP: r e A g E s f u r n i t C u r * a n d T H +Eval: I S I D I I I + +Speaker sentences 631: swc_eng_001807 #utts: 1 +id: (swc_eng_001807-swc_eng_001807) +Scores: (#C #S #D #I) 16 2 7 0 +REF: i n h i G H l E V E l t O U r n A m e n t s +HYP: i n h i * * ******* l * A B l t * * r n * m e n t s +Eval: D D D D S S D D D + +Speaker sentences 632: swc_eng_001808 #utts: 1 +id: (swc_eng_001808-swc_eng_001808) +Scores: (#C #S #D #I) 18 1 3 1 +REF: T o l o c a t e t h E a n E u r Y s * m +HYP: * o l o c a t e t h * a n * u r I s O m +Eval: D D D S I + +Speaker sentences 633: swc_eng_001809 #utts: 1 +id: (swc_eng_001809-swc_eng_001809) +Scores: (#C #S #D #I) 16 1 4 1 +REF: M o r P h O l o * g i c a l f r E e d O m +HYP: * o r h * l o U g i c a l f r * e d * m +Eval: D S D I D D + +Speaker sentences 634: swc_eng_001810 #utts: 1 +id: (swc_eng_001810-swc_eng_001810) +Scores: (#C #S #D #I) 17 5 3 2 +REF: * E N e r ******* G e t i c a T t a C k i n g s t Y L E +HYP: D H e r J e t i c a * t a * k i n g s t * O W +Eval: I S S I S D D D S S + +Speaker sentences 635: swc_eng_001811 #utts: 1 +id: (swc_eng_001811-swc_eng_001811) +Scores: (#C #S #D #I) 37 5 8 1 +REF: E X a c t l y f o r t y Y E a r s a f T E r t h e c O r n E R s * T o n E w a s l a I D +HYP: * * a c t l y f o r t y * * a r s a f * * r t h e c A r n * I s H D o n * w a s l a T E +Eval: D D D D D D S D S I S D S S + +Speaker sentences 636: swc_eng_001812 #utts: 1 +id: (swc_eng_001812-swc_eng_001812) +Scores: (#C #S #D #I) 20 1 3 3 +REF: B a s e D o N t h e r e c O g n i t i o n ******* * * +HYP: * a s e * o * t h e r e c A g n i t i o n T H +Eval: D D D S I I I + +Speaker sentences 637: swc_eng_001813 #utts: 1 +id: (swc_eng_001813-swc_eng_001813) +Scores: (#C #S #D #I) 28 0 4 1 +REF: o r E l e c t r O n i c * b u T t O n s o r d i s p l a y +HYP: o r * l e c t r * n i c K b u * t * n s o r d i s p l a y +Eval: D D I D D + +Speaker sentences 638: swc_eng_001814 #utts: 1 +id: (swc_eng_001814-swc_eng_001814) +Scores: (#C #S #D #I) 23 4 4 2 +REF: i s u n K n o W n w h E t h e r p e * Q u A l s N p * +HYP: i s u n n o * n w h * t h e r p ******* e A C u * l s E M p Y +Eval: S D D D I S D S S I + +Speaker sentences 639: swc_eng_001815 #utts: 1 +id: (swc_eng_001815-swc_eng_001815) +Scores: (#C #S #D #I) 25 0 7 0 +REF: W h i c h c o m E s f r o m t h e v e r b a C U E R E +HYP: * h i c h c o m * s f r o m t h e v e r b a * * * * * +Eval: D D D D D D D + +Speaker sentences 640: swc_eng_001816 #utts: 1 +id: (swc_eng_001816-swc_eng_001816) +Scores: (#C #S #D #I) 54 2 14 0 +REF: d i s p R O p o r t i O n A t E l y a v a I l a b l E t O t h o S e w i T h g R e a t e r F i n a n C I a l r e s o U r C e s +HYP: d i s p * E p o r t i * n * t * l y a v a * l a b l * t * t h o * e w i * h g * e a t e r * i n a n * H a l r e s o * r * e s +Eval: D S D D D D D D D D D D D S D D + +Speaker sentences 641: swc_eng_001817 #utts: 1 +id: (swc_eng_001817-swc_eng_001817) +Scores: (#C #S #D #I) 35 3 14 0 +REF: T H E I M m I N E n T t h r e A t s t o t h E s U R v i v A l o f m a n y s p E c I e s +HYP: * * * ******* Y U m * * * n * t h r e * t s t o t h * s * * v i v * l o f m a n y s p A c * e s +Eval: D D D D S S D D D D D D D D D S D + +Speaker sentences 642: swc_eng_001818 #utts: 1 +id: (swc_eng_001818-swc_eng_001818) +Scores: (#C #S #D #I) 15 1 3 0 +REF: e v e n m o r E d i F f I c U l t +HYP: e v e n m o r * d i * f * c A l t +Eval: D D D S + +Speaker sentences 643: swc_eng_001819 #utts: 1 +id: (swc_eng_001819-swc_eng_001819) +Scores: (#C #S #D #I) 25 6 2 6 +REF: a n d ******* * * * * * 2 1 s p e c I e s o f O c e a n i C d o l P H I n +HYP: a n d T W E T Y W N s p e c * e s o f I c e a n i G d o l * F O n +Eval: I I I I I I S S D S S D S S + +Speaker sentences 644: swc_eng_001820 #utts: 1 +id: (swc_eng_001820-swc_eng_001820) +Scores: (#C #S #D #I) 17 0 2 1 +REF: a ******* c h I e v i n g p r O m o t i o n +HYP: a c h * e v i n g p r * m o t i o n +Eval: I D D + +Speaker sentences 645: swc_eng_001821 #utts: 1 +id: (swc_eng_001821-swc_eng_001821) +Scores: (#C #S #D #I) 15 7 2 0 +REF: T r a n S H U m A n I s T a S s u m P t i o n +HYP: E r a n T E W m I n U s E a * s u m * t i o n +Eval: S S S S S S S D D + +Speaker sentences 646: swc_eng_001822 #utts: 1 +id: (swc_eng_001822-swc_eng_001822) +Scores: (#C #S #D #I) 17 2 0 0 +REF: o n t h e f i r s t b A l l O t +HYP: o n t h e f i r s t b E l l I t +Eval: S S + +Speaker sentences 647: swc_eng_001823 #utts: 1 +id: (swc_eng_001823-swc_eng_001823) +Scores: (#C #S #D #I) 72 2 8 1 +REF: s t o r y I n d i c a t i v e o f t h e r i s e i n g l o b A l s i g n I F i C A n * c E o f s h O E p o l i s h i s t o l d b y j e a n +HYP: s t o r y * n d i c a t i v e o f t h e r i s e i n ******* g l o b * l s i g n * * i * * n G c S o f s h * U p o l i s h i s t o l d b y j e a n +Eval: D D D D D D D I S D S + +Speaker sentences 648: swc_eng_001824 #utts: 1 +id: (swc_eng_001824-swc_eng_001824) +Scores: (#C #S #D #I) 36 3 5 0 +REF: w h I c h s p a r K e D h i s e A r l y I n t E r E s t i n p o l i t i c S +HYP: w h * c h s p a r * e * h i s e * r l y E n t * r U s t i n p o l i t i c K +Eval: D D D D S D S S + +Speaker sentences 649: swc_eng_001825 #utts: 1 +id: (swc_eng_001825-swc_eng_001825) +Scores: (#C #S #D #I) 40 1 4 5 +REF: w a s c a L l e d d o l b Y * * h * * x p r o * i n f u L l a n d p a t E n t e D +HYP: w a s c a * l e d d o l b E A C h E C x p r o W i n f u * l a n d p a t * n t e * +Eval: D S I I I I I D D D + +Speaker sentences 650: swc_eng_001826 #utts: 1 +id: (swc_eng_001826-swc_eng_001826) +Scores: (#C #S #D #I) 24 1 10 0 +REF: C o u l d s a v e a n d f i n D f i l E s B Y N U M b E R +HYP: * o u l d s a v e a n d f i n E f i l * s ******* * * * * * b * * +Eval: D S D D D D D D D D D + +Speaker sentences 651: swc_eng_001827 #utts: 1 +id: (swc_eng_001827-swc_eng_001827) +Scores: (#C #S #D #I) 36 1 5 0 +REF: a U s t r A l i a n s n a k e S b e l o n g t o s E v e n f a m I l I e s +HYP: a * s t r * l i a n s n a k e * b e l o n g t o s * v e n f a m * l Y e s +Eval: D D D D D S + +Speaker sentences 652: swc_eng_001828 #utts: 1 +id: (swc_eng_001828-swc_eng_001828) +Scores: (#C #S #D #I) 12 2 4 1 +REF: d E v e l O p ******* i n G p L A y E r s +HYP: d * v e l * p i n * p * I y A r s +Eval: D D I D D S S + +Speaker sentences 653: swc_eng_001829 #utts: 1 +id: (swc_eng_001829-swc_eng_001829) +Scores: (#C #S #D #I) 24 2 17 0 +REF: D E C l i n E d s h a r p l y s I n c e I T s p e a k I N T H E L a T E +HYP: * * * l i n * d s h a r p l y s E n c e ******* * * s p e a k ******* * * ******* * * * ******* * a * N +Eval: D D D D S D D D D D D D D D D D D D S + +Speaker sentences 654: swc_eng_001830 #utts: 1 +id: (swc_eng_001830-swc_eng_001830) +Scores: (#C #S #D #I) 39 2 10 1 +REF: w a s r e c o r d e d E n t * i r E l y o n a f o U r t r a c k c A S s E T T e t A P E +HYP: w a s r e c o r d e d I n t H i r * l y o n a f o * r t r a c k c * O s * * * e ******* t * * * +Eval: S I D D D S D D D D D D D + +Speaker sentences 655: swc_eng_001831 #utts: 1 +id: (swc_eng_001831-swc_eng_001831) +Scores: (#C #S #D #I) 20 1 2 0 +REF: E n o r m O U s i m p r o v e m e n t i n +HYP: * n o r m * A s i m p r o v e m e n t i n +Eval: D D S + +Speaker sentences 656: swc_eng_001832 #utts: 1 +id: (swc_eng_001832-swc_eng_001832) +Scores: (#C #S #D #I) 17 4 6 1 +REF: U r b A n a n d * r U r A l l e g I s L A t O R S +HYP: * r b * n a n d W r O r * l l e g U s * * t * A C +Eval: D D I S D S D D D S S + +Speaker sentences 657: swc_eng_001833 #utts: 1 +id: (swc_eng_001833-swc_eng_001833) +Scores: (#C #S #D #I) 15 0 3 0 +REF: E a c h p l A y e r b E g i n s +HYP: * a c h p l * y e r b * g i n s +Eval: D D D + +Speaker sentences 658: swc_eng_001834 #utts: 1 +id: (swc_eng_001834-swc_eng_001834) +Scores: (#C #S #D #I) 36 4 5 2 +REF: C H e s S H a s i n ******* s p i r e d m a n y c o m B I n a t o r i A l * p u Z Z l e s +HYP: * J e s T * a s i n s p i r e d m a n y c o m * E n a t o r i * l E p u * S l e s +Eval: D S S D I D S D I D S + +Speaker sentences 659: swc_eng_001835 #utts: 1 +id: (swc_eng_001835-swc_eng_001835) +Scores: (#C #S #D #I) 13 1 3 2 +REF: M o r E h * U m a n E i m a * g e +HYP: * o r * h E O m a n * i m a D g e +Eval: D D I S D I + +Speaker sentences 660: swc_eng_001836 #utts: 1 +id: (swc_eng_001836-swc_eng_001836) +Scores: (#C #S #D #I) 16 0 5 0 +REF: W e L l a s p i r A t e d t a p E s +HYP: * e * l ******* a s p i r * t e d t a p * s +Eval: D D D D D + +Speaker sentences 661: swc_eng_001837 #utts: 1 +id: (swc_eng_001837-swc_eng_001837) +Scores: (#C #S #D #I) 18 6 6 3 +REF: T I n t ******* I n * D e s C e n D s I n ******* t o t h E o C E A N +HYP: * A n t A n D I e s * e n * s * n t o t h * o * T I O +Eval: D S I S I S D D D I D D S S S + +Speaker sentences 662: swc_eng_001838 #utts: 1 +id: (swc_eng_001838-swc_eng_001838) +Scores: (#C #S #D #I) 30 1 6 3 +REF: p r e s i D E n T p r o t e m * * * o f t h e s t a t E s e n A t E +HYP: p r e s i * * n * p r o ******* t e m P O R o f t h e s t a t * s e n I t * +Eval: D D D D I I I D S D + +Speaker sentences 663: swc_eng_001839 #utts: 1 +id: (swc_eng_001839-swc_eng_001839) +Scores: (#C #S #D #I) 36 0 12 0 +REF: B i s h o p c a n m o v e a n y n u m b e r o f s q u a r e s D I A G O N A L L Y +HYP: * i s h o p c a n m o v e a n y n u m b e r o f s q u a r e s ******* * * * * * * * * * * +Eval: D D D D D D D D D D D D + +Speaker sentences 664: swc_eng_001840 #utts: 1 +id: (swc_eng_001840-swc_eng_001840) +Scores: (#C #S #D #I) 16 3 6 0 +REF: p R e S S U R e i n s i d E t h e s K U L l +HYP: p * e * * * H e i n s i d * t h e s * C O l +Eval: D D D D S D D S S + +Speaker sentences 665: swc_eng_001841 #utts: 1 +id: (swc_eng_001841-swc_eng_001841) +Scores: (#C #S #D #I) 20 1 7 0 +REF: L i n E c o N n E c t S s h E e r n e S s w i t H +HYP: * i n * c o * n A c t * s h * e r n e * s w i t * +Eval: D D D S D D D D + +Speaker sentences 666: swc_eng_001842 #utts: 1 +id: (swc_eng_001842-swc_eng_001842) +Scores: (#C #S #D #I) 35 2 5 1 +REF: C o U n t r I e s o f t h e W e s t e R n p a l E a r C t i c f l y ******* w a y +HYP: * o * n t r * e s o f t h e * e s t e * n p a l I a r K t i c f l y w a y +Eval: D D D D D S S I + +Speaker sentences 667: swc_eng_001843 #utts: 1 +id: (swc_eng_001843-swc_eng_001843) +Scores: (#C #S #D #I) 38 3 6 0 +REF: N a T i O n a l s T A t i s t i C s e s t I m a t e D t h e p o p u l a t i o n i n +HYP: * a * i * n a l ******* s D t i s t i K s e s t * m a t e * t h e p o p u l a t i o n i n +Eval: D D D D S S S D D + +Speaker sentences 668: swc_eng_001844 #utts: 1 +id: (swc_eng_001844-swc_eng_001844) +Scores: (#C #S #D #I) 28 3 0 1 +REF: U n ******* d i s p u t e d w o r l d c h e s S c h a m p i O n +HYP: O n d i s p u t e d w o r l d c h e s T c h a m p i A n +Eval: S I S S + +Speaker sentences 669: swc_eng_001845 #utts: 1 +id: (swc_eng_001845-swc_eng_001845) +Scores: (#C #S #D #I) 13 3 4 3 +REF: J O S E r A u * l c a p * a b L A n c * a +HYP: * * * Y r O u L l c a p E a b * I n c E a +Eval: D D D S S I I D S I + +Speaker sentences 670: swc_eng_001846 #utts: 1 +id: (swc_eng_001846-swc_eng_001846) +Scores: (#C #S #D #I) 82 4 10 1 +REF: W e r E E n a c t e d b y t h e g e n E r a l a S s e m b l y w a s a m e A s u r E r a C i a L l y s e * g r E g a t i n g t h e s t a t e s r A I l r o A D c a r s +HYP: * e r * I n a c t e d b y t h e g e n * r a l a * s e m b l y w a s a m e * s u r * r a T i a * l y s e V g r * g a t i n g t h e s t a t e s r * E l r o * T c a r s +Eval: D D S D D D D S D I D D S D S + +Speaker sentences 671: swc_eng_001847 #utts: 1 +id: (swc_eng_001847-swc_eng_001847) +Scores: (#C #S #D #I) 16 0 2 0 +REF: w h I c h W r a p s a l m o s t +HYP: w h * c h * r a p s a l m o s t +Eval: D D + +Speaker sentences 672: swc_eng_001848 #utts: 1 +id: (swc_eng_001848-swc_eng_001848) +Scores: (#C #S #D #I) 36 3 8 0 +REF: T H I s A c t p r O t e c T s a L l n a t i v e f A U n a a n d p r o v i d E s +HYP: * * * s ******* E c t p r * t e c * s a * l n a t i v e f O R n a a n d p r o v i d * s +Eval: D D D D S D D D S S D + +Speaker sentences 673: swc_eng_001849 #utts: 1 +id: (swc_eng_001849-swc_eng_001849) +Scores: (#C #S #D #I) 54 0 8 3 +REF: W h e r e ******* a s t h e f e m a l e S s p e c u l U m i s d a r k b r o W n b o R D E r e d w i t h w h i * * t E +HYP: * h e r e a s t h e f e m a l e * s p e c u l * m i s d a r k b r o * n b o * * * r e d w i t h w h i G H t * +Eval: D I D D D D D D I I D + +Speaker sentences 674: swc_eng_001850 #utts: 1 +id: (swc_eng_001850-swc_eng_001850) +Scores: (#C #S #D #I) 12 2 4 1 +REF: R o t A r y * c O n t R O l s o R +HYP: * o t E r y E c * n t * U l s o * +Eval: D S I D D S D + +Speaker sentences 675: swc_eng_001851 #utts: 1 +id: (swc_eng_001851-swc_eng_001851) +Scores: (#C #S #D #I) 22 2 3 1 +REF: n i n E t e E n t w e l V e i n r o s T H e * r n +HYP: n i n * t e A n t w e l * e i n r o s * F e A r n +Eval: D S D D S I + +Speaker sentences 676: swc_eng_001852 #utts: 1 +id: (swc_eng_001852-swc_eng_001852) +Scores: (#C #S #D #I) 46 4 5 1 +REF: d i A g n o S i s * i s g e n E r A L l y m a d e w i T h a C t s c a n o f t h e h e a d +HYP: d i * g n o C i s E i s g e n * r * * l y m a d e w i * h a S E t E s c a n o f t h e h e a d +Eval: D S I D D D D S S S + +Speaker sentences 677: swc_eng_001853 #utts: 1 +id: (swc_eng_001853-swc_eng_001853) +Scores: (#C #S #D #I) 42 2 3 4 +REF: * * * ******* f i r s t g e n E r a L l y r e c o G n i Z e d w o r l d c h e s s c h a m p i O n +HYP: T H E f i r s t g e n * r a * l y r e c o * n i S e d w o r l d c h e s s c h a m p i A n +Eval: I I I I D D D S S + +Speaker sentences 678: swc_eng_001854 #utts: 1 +id: (swc_eng_001854-swc_eng_001854) +Scores: (#C #S #D #I) 24 0 11 0 +REF: S h e p p E y a n d S i T t i n g B o U R n E w E R e p a r T +HYP: * h e p p * y a n d * i * t i n g * o * * n * w * * e p a r * +Eval: D D D D D D D D D D D + +Speaker sentences 679: swc_eng_001855 #utts: 1 +id: (swc_eng_001855-swc_eng_001855) +Scores: (#C #S #D #I) 35 2 3 1 +REF: H a d r e l E a S e D t h e i r A l b u m s b o t h t o c d * a n d +HYP: * a d r e l * a C e * t h e i r E l b u m s b o t h t o c d Y a n d +Eval: D D S D S I + +Speaker sentences 680: swc_eng_001856 #utts: 1 +id: (swc_eng_001856-swc_eng_001856) +Scores: (#C #S #D #I) 23 0 4 0 +REF: w a t e R s a r o U n D t h e c o n t i n e n T +HYP: w a t e * s a r o * n * t h e c o n t i n e n * +Eval: D D D D + +Speaker sentences 681: swc_eng_001857 #utts: 1 +id: (swc_eng_001857-swc_eng_001857) +Scores: (#C #S #D #I) 22 2 2 3 +REF: * ******* t h e r a N g e p e r s O n A l s t e * r E o s +HYP: F t h e r a * g e p e r s I n * l s t e A r I o s +Eval: I I D S D I S + +Speaker sentences 682: swc_eng_001858 #utts: 1 +id: (swc_eng_001858-swc_eng_001858) +Scores: (#C #S #D #I) 41 1 3 6 +REF: A n d * * * V u * m e t e r s a n d r e c o * r D i n g l e v E l c o n t r o * l s o n +HYP: * n d F E Y O u W m e t e r s a n d r e c o A r * i n g l e v * l c o n t r o U l s o n +Eval: D I I I S I I D D I + +Speaker sentences 683: swc_eng_001859 #utts: 1 +id: (swc_eng_001859-swc_eng_001859) +Scores: (#C #S #D #I) 11 3 1 2 +REF: p o L Y n ******* * o M I a l t i m e +HYP: p o * I n T o E a l t i m e +Eval: D S I I S S + +Speaker sentences 684: swc_eng_001860 #utts: 1 +id: (swc_eng_001860-swc_eng_001860) +Scores: (#C #S #D #I) 33 1 4 0 +REF: A n d i t o f T e n d e s t r o Y E d t h e p l a Y a b i l i t y +HYP: * n d i t o f * e n d e s t r o * R d t h e p l a * a b i l i t y +Eval: D D D S D + +Speaker sentences 685: swc_eng_001861 #utts: 1 +id: (swc_eng_001861-swc_eng_001861) +Scores: (#C #S #D #I) 8 1 0 1 +REF: c o n ******* f u S i o n +HYP: c o n f u T i o n +Eval: I S + +Speaker sentences 686: swc_eng_001862 #utts: 1 +id: (swc_eng_001862-swc_eng_001862) +Scores: (#C #S #D #I) 55 1 8 2 +REF: e Q u i v A l e n t t o t h e q u E s t i o n o f w h E t h e r * * x i s a m e m b e r o f c O m p o S I T E +HYP: e * u i v E l e n t t o t h e q u * s t i o n o f w h * t h e r E C x i s a m e m b e r o f c * m p o * * * * +Eval: D S D D I I D D D D D + +Speaker sentences 687: swc_eng_001863 #utts: 1 +id: (swc_eng_001863-swc_eng_001863) +Scores: (#C #S #D #I) 19 2 1 1 +REF: m o V e S t o i t S l a s t * r a n k +HYP: m o S e * t o i t H l a s t D r a n k +Eval: S D S I + +Speaker sentences 688: swc_eng_001864 #utts: 1 +id: (swc_eng_001864-swc_eng_001864) +Scores: (#C #S #D #I) 12 1 0 0 +REF: p o s t G e n d e r i s m +HYP: p o s t H e n d e r i s m +Eval: S + +Speaker sentences 689: swc_eng_001865 #utts: 1 +id: (swc_eng_001865-swc_eng_001865) +Scores: (#C #S #D #I) 28 0 6 2 +REF: C o m p a c t c a S s e T t E q u i C k l y f o u n d * * u s E +HYP: * o m p a c t c a * s e * t * q u i * k l y f o u n d Y O u s * +Eval: D D D D D I I D + +Speaker sentences 690: swc_eng_001866 #utts: 1 +id: (swc_eng_001866-swc_eng_001866) +Scores: (#C #S #D #I) 23 3 4 2 +REF: * ******* f o U r h u n d r E d t h I r T y t h R e e f E e T +HYP: E f o * r h u n d r N d t h E r D y t h * e e f * e * +Eval: I I D S S S D D D + +Speaker sentences 691: swc_eng_001867 #utts: 1 +id: (swc_eng_001867-swc_eng_001867) +Scores: (#C #S #D #I) 31 5 13 7 +REF: * * * * ******* w h i c h r e s u l t i n a s p e C i f i c * t * Y P e O F p A W n S T R U C T U R E +HYP: I N G S w h i c h r e s u l t i n a ******* s p e S i f i c K t H I H e * A p * O n ******* * * * * * * * * * +Eval: I I I I I D S I I S S D S D S D D D D D D D D D D + +Speaker sentences 692: swc_eng_001868 #utts: 1 +id: (swc_eng_001868-swc_eng_001868) +Scores: (#C #S #D #I) 20 2 6 1 +REF: B E f o * r E n i n E t E e n N I n E t y s e v e n +HYP: * * f o U r * n i n * t * e n H A n * t y s e v e n +Eval: D D I D D D S S D + +Speaker sentences 693: swc_eng_001869 #utts: 1 +id: (swc_eng_001869-swc_eng_001869) +Scores: (#C #S #D #I) 17 1 12 0 +REF: C O M m U N i c a t i o n s a n d h e A l T H C A R E +HYP: * * * m * * i c a t i o n s a n d h e * l * * ******* * * * F +Eval: D D D D D D D D D D D D S + +Speaker sentences 694: swc_eng_001870 #utts: 1 +id: (swc_eng_001870-swc_eng_001870) +Scores: (#C #S #D #I) 17 1 6 7 +REF: * * * S a * * h * i n a p e * r s o n K N O W n T o +HYP: E A Y a T C h E i n a p e U r s o n * * * * n ******* * o +Eval: I I I S I I I I D D D D D D + +Speaker sentences 695: swc_eng_001871 #utts: 1 +id: (swc_eng_001871-swc_eng_001871) +Scores: (#C #S #D #I) 68 5 3 4 +REF: s o m E b r * a n d S s p E C i f * Y t h a t t h e y m a y a l ******* s o b e u s e d o n o t h e r n o n p o r o U s m * A t e r i a l s +HYP: s o m * ******* b r E a n d * s p I S i f I D t h a t t h e y m a y a l s o b e u s e d o n o t h e r n o n p o r o S s m I P t e r i a l s +Eval: D D I D S S I S I S I S + +Speaker sentences 696: swc_eng_001872 #utts: 1 +id: (swc_eng_001872-swc_eng_001872) +Scores: (#C #S #D #I) 19 4 1 1 +REF: T h e p o s S i b l y c o n * S p e C i f i C +HYP: * h e p o s E i b l y c o n D p e S i f i G +Eval: D S I S S S + +Speaker sentences 697: swc_eng_001873 #utts: 1 +id: (swc_eng_001873-swc_eng_001873) +Scores: (#C #S #D #I) 6 3 2 5 +REF: m * * * * * i n l E N G T H +HYP: m A T O R S i n l * * A C X +Eval: I I I I I D D S S S + +Speaker sentences 698: swc_eng_001874 #utts: 1 +id: (swc_eng_001874-swc_eng_001874) +Scores: (#C #S #D #I) 20 7 8 1 +REF: W i t h o u t f i V E Z E R O m o V e D r A W i n g * r U L E +HYP: * i t h o u t f i * * ******* * F T Y m o R e T r * * i n g B r * O A +Eval: D D D D D S S S S S D D I D S S + +Speaker sentences 699: swc_eng_001875 #utts: 1 +id: (swc_eng_001875-swc_eng_001875) +Scores: (#C #S #D #I) 31 11 10 4 +REF: T h i S s i ******* T U a * T i O n p A R A L l e L s r e s p e c t i V e l y C Z E C H O s ******* l o * V a k i A +HYP: * h i * ******* s i C H a C S i * n p * * O U l e * s r e s p e c t i * e l y * T O K A s l o F a k i * +Eval: D D D I S S I S D D D S S D D D S S S S S I I S D + +Speaker sentences 700: swc_eng_001876 #utts: 1 +id: (swc_eng_001876-swc_eng_001876) +Scores: (#C #S #D #I) 18 1 8 1 +REF: F a v E R s h * A m E l e C t e d i T s f i r S T +HYP: * a v * * s h O N m * l e * t e d i * s f i r * * +Eval: D D D I S D D D D D + +Speaker sentences 701: swc_eng_001877 #utts: 1 +id: (swc_eng_001877-swc_eng_001877) +Scores: (#C #S #D #I) 31 4 1 2 +REF: R e b l e E d i n g r i s K r e m a i n s * * A r o u n d f o R t y +HYP: * e b l e A d i n g r i s T r e m a i n s O F r o u n d f o L t y +Eval: D S S I I S S + +Speaker sentences 702: swc_eng_001878 #utts: 1 +id: (swc_eng_001878-swc_eng_001878) +Scores: (#C #S #D #I) 11 1 5 3 +REF: D E L i V e r * * c h E c k ******* m a T e +HYP: * * * i L e r I T c h * c k m a * e +Eval: D D D S I I D I D + +Speaker sentences 703: swc_eng_001879 #utts: 1 +id: (swc_eng_001879-swc_eng_001879) +Scores: (#C #S #D #I) 143 11 20 7 +REF: s o m e s e c u l A r h u m A N i S T s c o n c E i v e * T r a n * s ******* h * u m A n i s m a s a n O F F s p r i n g o f t h e H u m A n I s t f r E e t h o u G h t m o v e m e n t a n D a r g u E t h A t T r a N s ******* h u m A n i s t S d i F f e r f r o m t h e H u m A n i * s t M a i n ******* s t r E A m b y h a v i n G +HYP: s o m e s e c u l * r h u m * * i * N s c o n c * i v e D * r a n D s h E u m I n i s m a s a n * A L s p r i n g o f t h e E u m * n U s t f r * e t h o u * h t m o v e m e n t a n * a r g u * Y t h * t * r a * s h u m I n i s t * d i * f e r f r o m t h e E u m I n i U s t P a i n s t r * * m b y h a v i n * +Eval: D D D D S D I D I I I S D S S S D S D D D D S D D D I S D D S S I S I D D D + +Speaker sentences 704: swc_eng_001880 #utts: 1 +id: (swc_eng_001880-swc_eng_001880) +Scores: (#C #S #D #I) 53 3 7 1 +REF: p i n t a I l * n e s t S a n d c h i c k S a r e v U L n e r A b l e t o p r E d a t i o n b y m a M m A l S +HYP: p i n t a * l E n e s t * a n d c h i c k * a r e v * O n e r * b l e t o p r O d a t i o n b y m a * m * l E +Eval: D I D D D S D S D D S + +Speaker sentences 705: swc_eng_001881 #utts: 1 +id: (swc_eng_001881-swc_eng_001881) +Scores: (#C #S #D #I) 108 7 7 3 +REF: n o r t h e r n p i n t a I l i s o n E o f t h e s p e c * I e s t o w h i c h t h e a g r E E m e n t o n t h e c o n s * e r v a t i o n o f a f r I c * a n E u r a S i A n m I g r A t o r y w a t e r b I r d S +HYP: n o r t h e r n p i n t a * l i s o n * o f t h e s p e c S H e s t o w h i c h t h e a g r * * m e n t o n t h e c o n s C e r v a t i o n o f a f r A c K a n ******* * u r a G i O n m O g r I t o r y w a t e r b U r d * +Eval: D D I S D D I S I D D S S S S S D + +Speaker sentences 706: swc_eng_001882 #utts: 1 +id: (swc_eng_001882-swc_eng_001882) +Scores: (#C #S #D #I) 29 1 3 1 +REF: a n d i s n * o W f o u n d o n l y i n t a s m a N i A +HYP: a n d i s n E o E f o u n d o n l y i n ******* t a s m a * i * +Eval: I S D D D + +Speaker sentences 707: swc_eng_001883 #utts: 1 +id: (swc_eng_001883-swc_eng_001883) +Scores: (#C #S #D #I) 46 3 2 9 +REF: * * * * * * * * ******* t h e i d E a o f m i n d u p l o a D i n g i s a S s E r t e d t o r e p r E s e n t +HYP: R P E C T I V E t h e i d * a o f m i n d u p l o a T i n g i s a s * r t e d t o r e p r O s e n t +Eval: I I I I I I I I I D S S D S + +Speaker sentences 708: swc_eng_001884 #utts: 1 +id: (swc_eng_001884-swc_eng_001884) +Scores: (#C #S #D #I) 27 1 4 0 +REF: A n a v E r A g e o f t w e n t Y o n E p e r d a y +HYP: * n a v * r I g e o f t w e n t * o n * p e r d a y +Eval: D D S D D + +Speaker sentences 709: swc_eng_001885 #utts: 1 +id: (swc_eng_001885-swc_eng_001885) +Scores: (#C #S #D #I) 22 3 9 1 +REF: T h E n I T w o U l d f O L l o W t h a t p * e Q u A l S +HYP: * h A n ******* * * w o * l d f * A l o * t h a t p E e C u * l * +Eval: D S D D D D D S D I S D D + +Speaker sentences 710: swc_eng_001886 #utts: 1 +id: (swc_eng_001886-swc_eng_001886) +Scores: (#C #S #D #I) 24 4 4 1 +REF: A n d b l E e d i n g i n t o v A r i O U s * T U m O r s +HYP: * n d b l * e d i n g i n t o v E r i * * s C H O m E r s +Eval: D D S D D I S S S + +Speaker sentences 711: swc_eng_001887 #utts: 1 +id: (swc_eng_001887-swc_eng_001887) +Scores: (#C #S #D #I) 24 1 8 1 +REF: a * L l O W t h e M t o g l i d E b e t w E e n t r E E s +HYP: a N D l * * t h e * t o ******* g l i d * b e t w * e n t r * * s +Eval: I S D D D D D D D D + +Speaker sentences 712: swc_eng_001888 #utts: 1 +id: (swc_eng_001888-swc_eng_001888) +Scores: (#C #S #D #I) 31 3 9 1 +REF: I f t h e s E p r o b L E M s w E r E E F f i c i E n t l y s O l v * a b l E +HYP: * f t h e s * p r o b * O N s w * r * * * f i c i * n t l y s A l v E a b l * +Eval: D D D S S D D D D D S I D + +Speaker sentences 713: swc_eng_001889 #utts: 1 +id: (swc_eng_001889-swc_eng_001889) +Scores: (#C #S #D #I) 11 1 3 4 +REF: G E o * l o * g i c a l * t ******* i M E +HYP: * * o A l o D g i c a l E t i * N +Eval: D D I I I I D S + +Speaker sentences 714: swc_eng_001890 #utts: 1 +id: (swc_eng_001890-swc_eng_001890) +Scores: (#C #S #D #I) 12 0 0 4 +REF: * * * ******* w h e n f l u s h e d +HYP: R O K w h e n f l u s h e d +Eval: I I I I + +Speaker sentences 715: swc_eng_001891 #utts: 1 +id: (swc_eng_001891-swc_eng_001891) +Scores: (#C #S #D #I) 28 3 5 0 +REF: i n c l u d i n g G e r m I n A l C h o i c e T E c H n o l O g y +HYP: i n c l u d i n g J e r m * n * l T h o i c e * I c * n o l * g y +Eval: S D D S D S D D + +Speaker sentences 716: swc_eng_001892 #utts: 1 +id: (swc_eng_001892-swc_eng_001892) +Scores: (#C #S #D #I) 24 3 9 0 +REF: A P p e A r A n C e o f l e A t h e R s h O E s o r b O o t S +HYP: * * p e * r I n * e o f l e t h e * ******* s h * U s o r b * o t * +Eval: D D D S D S D D D S D D + +Speaker sentences 717: swc_eng_001893 #utts: 1 +id: (swc_eng_001893-swc_eng_001893) +Scores: (#C #S #D #I) 14 2 7 0 +REF: I n E I G H t E e n s i X t y t h r E E +HYP: * n * * * A t * e n s i C t y t h r * * +Eval: D D D D S D S D D + +Speaker sentences 718: swc_eng_001894 #utts: 1 +id: (swc_eng_001894-swc_eng_001894) +Scores: (#C #S #D #I) 28 7 5 3 +REF: m a n * u f A c t u r E s h o E c a R E p R o d U c * T s A l ******* s O s e L l +HYP: m a n I u f E c t u r S s h o * c a * Y p * o d A c K E s O l s * Y s e * l +Eval: I S S D D S D S I S S I D S D + +Speaker sentences 719: swc_eng_001895 #utts: 1 +id: (swc_eng_001895-swc_eng_001895) +Scores: (#C #S #D #I) 32 2 3 2 +REF: * ******* t h e f i r s t n o n s o V i E T c h a l l E n g e r s i n c E +HYP: O t h e f i r s t n o n s o R i * A c h a l l * n g e r s i n c * +Eval: I I S D S D D + +Speaker sentences 720: swc_eng_001896 #utts: 1 +id: (swc_eng_001896-swc_eng_001896) +Scores: (#C #S #D #I) 27 1 2 0 +REF: O P p o n e n t h a s o n l y t h e K i n g a n d +HYP: * * p o n e n t h a s o n l y t h e C i n g a n d +Eval: D D S + +Speaker sentences 721: swc_eng_001897 #utts: 1 +id: (swc_eng_001897-swc_eng_001897) +Scores: (#C #S #D #I) 11 0 1 0 +REF: m a i n a r t i c l E +HYP: m a i n a r t i c l * +Eval: D + +Speaker sentences 722: swc_eng_001898 #utts: 1 +id: (swc_eng_001898-swc_eng_001898) +Scores: (#C #S #D #I) 29 3 8 1 +REF: F o U n d C e r t a I n l E N G t h S * u s E f u l f o r f i T t i n G +HYP: * o W n d S e r t a * n l * * A t h * S u s * f u l f o r f i * t i n * +Eval: D S S D D D S D I D D D + +Speaker sentences 723: swc_eng_001899 #utts: 1 +id: (swc_eng_001899-swc_eng_001899) +Scores: (#C #S #D #I) 43 3 3 0 +REF: t a p e i n t h e s a m e f o r M f a c t O r A s t h e c o m p a c t A U D i o +HYP: t a p e i n t h e s a m e f o r E f a c t E r * s t h e c o m p a c t * * O i o +Eval: S S D D D S + +Speaker sentences 724: swc_eng_001900 #utts: 1 +id: (swc_eng_001900-swc_eng_001900) +Scores: (#C #S #D #I) 24 4 3 0 +REF: C e n S U r E w a s l a t e r E x p u n g e D f r o M +HYP: S e n T H r * w a s l a t e r C x p u n g e * f r o * +Eval: S S S D S D D + +Speaker sentences 725: swc_eng_001901 #utts: 1 +id: (swc_eng_001901-swc_eng_001901) +Scores: (#C #S #D #I) 15 3 2 0 +REF: O r d e F A c t o E q u a l i t y +HYP: * r d e ******* S E c t o C q u a l i t y +Eval: D D S S S + +Speaker sentences 726: swc_eng_001902 #utts: 1 +id: (swc_eng_001902-swc_eng_001902) +Scores: (#C #S #D #I) 35 1 6 0 +REF: i s f o U r t h o u s A n D s i X h u n d r e d b y s i x t Y f E e t +HYP: i s f o * r t h o u s * n * s i * C h u n d r e d b y s i x t * f * e t +Eval: D D D D S D D + +Speaker sentences 727: swc_eng_001903 #utts: 1 +id: (swc_eng_001903-swc_eng_001903) +Scores: (#C #S #D #I) 16 2 4 0 +REF: n i n E t E E n s e v e n T y t h r E E +HYP: n i n * t * I n s e v e n D y t h r * * +Eval: D D S S D D + +Speaker sentences 728: swc_eng_001904 #utts: 1 +id: (swc_eng_001904-swc_eng_001904) +Scores: (#C #S #D #I) 27 0 10 6 +REF: * * * ******* a p l a y e R m a y a l ******* s o l o * s e b y r u N N I n G O U T +HYP: R A L a p l a y e * m a y ******* a l s o l o U s e b y r u * * * n * ******* * * * +Eval: I I I I D D I I D D D D D D D D + +Speaker sentences 729: swc_eng_001905 #utts: 1 +id: (swc_eng_001905-swc_eng_001905) +Scores: (#C #S #D #I) 32 2 10 1 +REF: P u b l i C h E A l t H p r O f e S s O r g r E g O r y s t o * c k p o i n T S +HYP: * u b l i K h * * l t * p r * f e * s E r g r * g * r y s t o A c k p o i n * * +Eval: D S D D D D D S D D I D D + +Speaker sentences 730: swc_eng_001906 #utts: 1 +id: (swc_eng_001906-swc_eng_001906) +Scores: (#C #S #D #I) 69 1 11 0 +REF: b r o W n w a s E l e c t e d t O t h e h o u s e o F r e p r E s e n T A t i v e s f o r t h r E e n o n c o n s e c U t i V e t E r m s +HYP: b r o * n w a s ******* A l e c t e d t * t h e h o u s e o * r e p r * s e n * * t i v e s f o r t h r * e n o n c o n s e c * t i * e t * r m s +Eval: D D S D D D D D D D D D + +Speaker sentences 731: swc_eng_001907 #utts: 1 +id: (swc_eng_001907-swc_eng_001907) +Scores: (#C #S #D #I) 14 1 5 1 +REF: C o ******* e X i s t h a P p I l y w i T H +HYP: * o e G i s t h a * p * l y w i * * +Eval: D I S D D D D + +Speaker sentences 732: swc_eng_001908 #utts: 1 +id: (swc_eng_001908-swc_eng_001908) +Scores: (#C #S #D #I) 21 2 6 1 +REF: a g r o U p o f M A M m A l * s t h a t r a I S E +HYP: a g r o * p o f * * * m E l E s t h a t r a * * C +Eval: D D D D S I D D S + +Speaker sentences 733: swc_eng_001909 #utts: 1 +id: (swc_eng_001909-swc_eng_001909) +Scores: (#C #S #D #I) 17 1 4 0 +REF: A n D t h e w o R l d s l a R g E s t +HYP: * n * t h e w o * l d s l a * g I s t +Eval: D D D D S + +Speaker sentences 734: swc_eng_001910 #utts: 1 +id: (swc_eng_001910-swc_eng_001910) +Scores: (#C #S #D #I) 38 2 3 0 +REF: b r E e d i n g t a k E s p l a c e b e t w e e n a p r I l a n d j U n E +HYP: b r * e d i n g t a k X s p l a c e b e t w e e n a p r * l a n d j O n * +Eval: D S D S D + +Speaker sentences 735: swc_eng_001911 #utts: 1 +id: (swc_eng_001911-swc_eng_001911) +Scores: (#C #S #D #I) 25 3 4 0 +REF: A U s t r a l I A i s a t t h e s o U t h e R n E n d +HYP: * * s t r a l O R i s a t t h e s o * t h e * n I n d +Eval: D D S S D D S + +Speaker sentences 736: swc_eng_001912 #utts: 1 +id: (swc_eng_001912-swc_eng_001912) +Scores: (#C #S #D #I) 32 2 3 1 +REF: t e c H n O l o * g i c a l s i n g u l a r i t y i s p o S s I b l E +HYP: t e c * n A l o U g i c a l s i n g u l a r i t y i s p o * s E b l * +Eval: D S I D S D + +Speaker sentences 737: swc_eng_001913 #utts: 1 +id: (swc_eng_001913-swc_eng_001913) +Scores: (#C #S #D #I) 19 0 5 0 +REF: I n C l u d i n G t h e S l E e p y c o d +HYP: * n * l u d i n * t h e * l * e p y c o d +Eval: D D D D D + +Speaker sentences 738: swc_eng_001914 #utts: 1 +id: (swc_eng_001914-swc_eng_001914) +Scores: (#C #S #D #I) 44 5 9 2 +REF: s E V e n t y f o U r * h a d a h i G H e R E D U c a t i o n Q u A l I f i c a t i o n c o m p A R e d * +HYP: s * * e n t y f o * r E h a d a h i * Y e * R G I c a t i o n C u * l * f i c a t i o n c o m p * * e d T +Eval: D D D I D S D S S S S D D D D I + +Speaker sentences 739: swc_eng_001915 #utts: 1 +id: (swc_eng_001915-swc_eng_001915) +Scores: (#C #S #D #I) 25 6 16 0 +REF: T H i S O c C U r s w H e n t h E O P p o n e N T s K i n g i s I n C H E C K +HYP: * * i * ******* A c K E r s w * e n t h Y * * p o n e * * s C i n g i s ******* A n ******* * * * * * +Eval: D D D D S S S D S D D D D S D S D D D D D D + +Speaker sentences 740: swc_eng_001916 #utts: 1 +id: (swc_eng_001916-swc_eng_001916) +Scores: (#C #S #D #I) 18 2 5 0 +REF: C o n S e R v a t i o n i N A u s t r a L I a +HYP: * o n C e * v a t i o n i * * u s t r a * Y a +Eval: D S D D D D S + +Speaker sentences 741: swc_eng_001917 #utts: 1 +id: (swc_eng_001917-swc_eng_001917) +Scores: (#C #S #D #I) 14 3 4 0 +REF: i s t h E s A l a m a n d E R f I S H +HYP: i s t h * s E l a m a n d * O f * * F +Eval: D S D S D D S + +Speaker sentences 742: swc_eng_001918 #utts: 1 +id: (swc_eng_001918-swc_eng_001918) +Scores: (#C #S #D #I) 49 6 6 2 +REF: f i r s t s e l f d E s c r i B e D T r a n s ******* h u m A n I s t S M e * t f o r m A L l y i n t h e e a R l Y +HYP: f i r s t s e l f d I s c r i V e * * r a n s h u m I n E s t * B e A t f o r m * I l y i n t h e e a * l * +Eval: S S D D I S S D S I D S D D + +Speaker sentences 743: swc_eng_001919 #utts: 1 +id: (swc_eng_001919-swc_eng_001919) +Scores: (#C #S #D #I) 49 2 7 1 +REF: R e C e n t r e ******* s E A r c h i n d i c a t e s t H a t f a c t O r s o t h e R t h a n p r a c t i C E +HYP: * e * e n t r e s * U r c h i n d i c a t e s t * a t f a c t E r s o t h e * t h a n p r a c t i * * +Eval: D D I D S D S D D D + +Speaker sentences 744: swc_eng_001920 #utts: 1 +id: (swc_eng_001920-swc_eng_001920) +Scores: (#C #S #D #I) 41 0 4 0 +REF: A n d p r E v e n t i o n a n d t r e a t m e n t o f C o m p l I c a t i o n s +HYP: * n d p r * v e n t i o n a n d t r e a t m e n t o f * o m p l * c a t i o n s +Eval: D D D D + +Speaker sentences 745: swc_eng_001921 #utts: 1 +id: (swc_eng_001921-swc_eng_001921) +Scores: (#C #S #D #I) 14 0 4 3 +REF: W i T h A r a p i d o n ******* s * e * t +HYP: * i * h ******* * r a p i d o n s A e I t +Eval: D D D D I I I + +Speaker sentences 746: swc_eng_001922 #utts: 1 +id: (swc_eng_001922-swc_eng_001922) +Scores: (#C #S #D #I) 29 1 4 3 +REF: u ******* t A h * * w a r t H e f o u n d a t i O n w a s b U r i e D +HYP: u t * h O W w a r t * e f o u n d a t i * n w a s b A r i e * +Eval: I D I I D D S D + +Speaker sentences 747: swc_eng_001923 #utts: 1 +id: (swc_eng_001923-swc_eng_001923) +Scores: (#C #S #D #I) 109 4 16 5 +REF: n o w a d a y s H o u r l y r e * g i O n a l E x p r e s s t r a i n s b e t W e e N b E r n a n d s * p * i E Z t o b r i * g a n d f r E I G H t t r a I n * s c o n t i n u e t o R u n o n t h E m o U n t a I n r A i l w a Y +HYP: n o w a d a y s * o u r l y r e A g i * n a l * x p r e s s ******* t r a i n s b e t * e e * b U r n a n d s H p E i T S t o b r i C g a n d f r * * * A t t r a * n E s c o n t i n u e t o * u n o n t h * m o * n t a * n r * i l w a * +Eval: D I D D D D D S I I S S I D D D S D I D D D D D D + +Speaker sentences 748: swc_eng_001924 #utts: 1 +id: (swc_eng_001924-swc_eng_001924) +Scores: (#C #S #D #I) 59 6 11 2 +REF: o t h e R f a m I l I E s w i t h A p O t E n T I a L l y g o n d w a n ******* a n o r i g i * n i n c l u d E t h e r e t r O p I N n I d a E +HYP: o t h e * f a m * l * Y s w i t h ******* * p * t I n * U a * l y g o n d w a n a n o r i g i O n i n c l u d * t h e r e t r * p * O n E d a Y +Eval: D D D S D D D S D S D I I D D D S S S + +Speaker sentences 749: swc_eng_001925 #utts: 1 +id: (swc_eng_001925-swc_eng_001925) +Scores: (#C #S #D #I) 39 4 5 0 +REF: b y a n i t a l i A n d o m i n i c a n m o N k j A c o b U s d e C e S s O l I s +HYP: b y a n i t a l i * n d o m i n i c a n m o R k j * c o b E s d e ******* S e * s * l E s +Eval: D S D S D S D D S + +Speaker sentences 750: swc_eng_001926 #utts: 1 +id: (swc_eng_001926-swc_eng_001926) +Scores: (#C #S #D #I) 19 1 7 0 +REF: C O M M a n d w a s n a M e d a f t e R t H E +HYP: * * * * a n d w a s n a I e d a f t e * t * * +Eval: D D D D S D D D + +Speaker sentences 751: swc_eng_001927 #utts: 1 +id: (swc_eng_001927-swc_eng_001927) +Scores: (#C #S #D #I) 17 3 3 0 +REF: a r t I f i C I a l I n t e L l i g e n c E +HYP: a r t H f i * H a l * n t e * l i g e n c S +Eval: S D S D D S + +Speaker sentences 752: swc_eng_001928 #utts: 1 +id: (swc_eng_001928-swc_eng_001928) +Scores: (#C #S #D #I) 15 1 3 0 +REF: a n d i s t h e r E I G N i n g +HYP: a n d i s t h e r * * * A i n g +Eval: D D D S + +Speaker sentences 753: swc_eng_001929 #utts: 1 +id: (swc_eng_001929-swc_eng_001929) +Scores: (#C #S #D #I) 22 0 4 0 +REF: p E r C e n T o f t h e p o p u l a t i o n +HYP: p * r ******* * e n * o f t h e p o p u l a t i o n +Eval: D D D D + +Speaker sentences 754: swc_eng_001930 #utts: 1 +id: (swc_eng_001930-swc_eng_001930) +Scores: (#C #S #D #I) 22 5 5 1 +REF: c h I e F A r E A s o f s h O E p O l i S h s a * l e s +HYP: c h F e * E r I R s o f s h * * U p * l i * h s a E l e s +Eval: S D S S S D D S D D I + +Speaker sentences 755: swc_eng_001931 #utts: 1 +id: (swc_eng_001931-swc_eng_001931) +Scores: (#C #S #D #I) 13 0 1 0 +REF: i m p o s e d b y l a W +HYP: i m p o s e d b y l a * +Eval: D + +Speaker sentences 756: swc_eng_001932 #utts: 1 +id: (swc_eng_001932-swc_eng_001932) +Scores: (#C #S #D #I) 36 6 3 6 +REF: r E f E r E n c e * * * s t o t h e r U l i n g c o * * a L I T i o n * g o v e r N m e n T +HYP: r I f * r I n c e S I S s t o t h e r O l i n g c o R L a Y S H i o n D g o v e r * m e n * +Eval: S D S I I I S I I S S S I D D + +Speaker sentences 757: swc_eng_001933 #utts: 1 +id: (swc_eng_001933-swc_eng_001933) +Scores: (#C #S #D #I) 20 2 3 0 +REF: S p E c I e s o f g l i d i n g p o S s U m +HYP: * p A c H e s o f g l i d i n g p o * s * m +Eval: D S S D D + +Speaker sentences 758: swc_eng_001934 #utts: 1 +id: (swc_eng_001934-swc_eng_001934) +Scores: (#C #S #D #I) 32 2 4 0 +REF: b a S e D o n t h e p r e v i o U s S t r a T E g y o f p l a y +HYP: b a C e * o n t h e p r e v i o * s * t r a * D g y o f p l a y +Eval: S D D D D S + +Speaker sentences 759: swc_eng_001935 #utts: 1 +id: (swc_eng_001935-swc_eng_001935) +Scores: (#C #S #D #I) 22 2 2 2 +REF: A n D i ******* d e A l i s t i c * a s p I r a t i o n s +HYP: * n * i d e L l i s t i c K a s p E r a t i o n s +Eval: D D I S I S + +Speaker sentences 760: swc_eng_001936 #utts: 1 +id: (swc_eng_001936-swc_eng_001936) +Scores: (#C #S #D #I) 35 2 7 4 +REF: p * r O f e S S i O n A l s a n d * h o m E r e c o * r D i n g E N t h * u s i a s t s +HYP: p E r * f e * C i * n * l s a n d W h o m * r e c o A r * i n g * A t h O u s i a s t s +Eval: I D D S D D I D I D D S I + +Speaker sentences 761: swc_eng_001937 #utts: 1 +id: (swc_eng_001937-swc_eng_001937) +Scores: (#C #S #D #I) 8 6 1 1 +REF: F A m ******* I L y E l A p i d a E +HYP: H E m T H y O l * p i d a Y +Eval: S S I S S S D S + +Speaker sentences 762: swc_eng_001938 #utts: 1 +id: (swc_eng_001938-swc_eng_001938) +Scores: (#C #S #D #I) 55 6 11 9 +REF: T H A n A Q U A r t e R o f p e o p l e w i T h a p r e v i O U s * s * * ******* a * h m a Y d E v e l o * P h * * Y p o p i t * u i t A r i s m +HYP: * * * n ******* O * C O r t e * o f p e o p l e w i * h a p r e v i * A s E s A Y a C h m a * d * v e l o U E h I G p o p i t C u i t * r i s m +Eval: D D D D S D S S D D D S I I I I I D D I S I I S I D + +Speaker sentences 763: swc_eng_001939 #utts: 1 +id: (swc_eng_001939-swc_eng_001939) +Scores: (#C #S #D #I) 26 2 4 0 +REF: d I v i d e d i n t o t h r E e f a m I l I E s t h a T +HYP: d E v i d e d i n t o t h r * e f a m * l * Y s t h a * +Eval: S D D D S D + +Speaker sentences 764: swc_eng_001940 #utts: 1 +id: (swc_eng_001940-swc_eng_001940) +Scores: (#C #S #D #I) 33 3 9 0 +REF: S h o w E d s l i g H t i n t E r e s t i n r e l E a S i n g c A S S e T t E S +HYP: * h o w * d s l i g * t i n t * r e s t i n r e l I a E i n g c * * O e * t * * +Eval: D D D D S S D D S D D D + +Speaker sentences 765: swc_eng_001941 #utts: 1 +id: (swc_eng_001941-swc_eng_001941) +Scores: (#C #S #D #I) 19 5 12 6 +REF: * * * * F a ******* * m I l I A r E n o U G H t o h a v e c O M M o N N A M e S +HYP: T H A T a E m * l * U r * n o * F T t o h a v e c * * * o * ******* * * * e N +Eval: I I I I S I I D D S D D S S D D D D D D D D S + +Speaker sentences 766: swc_eng_001942 #utts: 1 +id: (swc_eng_001942-swc_eng_001942) +Scores: (#C #S #D #I) 15 1 3 0 +REF: I n t w o t h O u s a n D s i X +HYP: * n t w o t h * u s a n * s i K +Eval: D D D S + +Speaker sentences 767: swc_eng_001943 #utts: 1 +id: (swc_eng_001943-swc_eng_001943) +Scores: (#C #S #D #I) 30 1 13 0 +REF: s h O E S h i n E b o y s a r E K n o W n a s b O o t p o l i S H B O Y s +HYP: s h * * h i n * b o y s a r * * n o * n a s b * o t p o l i * * ******* * * * s +Eval: D D S D D D D D D D D D D D + +Speaker sentences 768: swc_eng_001944 #utts: 1 +id: (swc_eng_001944-swc_eng_001944) +Scores: (#C #S #D #I) 34 5 4 1 +REF: t h e c A u s e i s r u P t u r E o f a C e r E b R A l a n E u r Y s * m +HYP: t h e c O u s e i s r u * t u r * o f a S e r I b * * l a n D u r I s O m +Eval: S D D S S D D S S I + +Speaker sentences 769: swc_eng_001945 #utts: 1 +id: (swc_eng_001945-swc_eng_001945) +Scores: (#C #S #D #I) 31 1 5 5 +REF: M o s t o f t h e M a * j O r * * u * * s m u s i c c o m p A n I e s +HYP: * o s t o f ******* t h e * a G j E r Y O u E S s m u s i c c o m p * n * e s +Eval: D D D I S I I I I D D + +Speaker sentences 770: swc_eng_001946 #utts: 1 +id: (swc_eng_001946-swc_eng_001946) +Scores: (#C #S #D #I) 92 3 11 2 +REF: o n E s t e R E o p a I r o r o n E m o n o P H o n i c t r a c k i s p l a Y E d o r r e c o r d e d W h e n t h e t a p E I s m o v i n g i n o n E d i r e c t i o n a n d ******* * +HYP: o n * C s t e * I o p a * r o r o n * m o n o * F o n i c t r a c k i s p l a * * d o r r e c o r d e d * h e n t h e t a p * * s m o v i n g i n o n * d i r e c t i o n a n d T +Eval: D S D S D D D S D D D D D D I I + +Speaker sentences 771: swc_eng_001947 #utts: 1 +id: (swc_eng_001947-swc_eng_001947) +Scores: (#C #S #D #I) 18 0 5 1 +REF: W H E R e i t s e a r l y f o r m * i n +HYP: * * * * e ******* i t s e a r l y f o r m E i n +Eval: D D D D D I + +Speaker sentences 772: swc_eng_001948 #utts: 1 +id: (swc_eng_001948-swc_eng_001948) +Scores: (#C #S #D #I) 15 4 4 1 +REF: A s t r A t e * g I c P H i l o s o P H e r +HYP: * ******* s t r t e A g H c * F i l o s o * V e r +Eval: D D S I S D S D S + +Speaker sentences 773: swc_eng_001949 #utts: 1 +id: (swc_eng_001949-swc_eng_001949) +Scores: (#C #S #D #I) 27 5 6 1 +REF: P o s i t i o N I n g A D v a n T a * G e s D U r I n G t h e g a m e +HYP: * o s i t i o * * n g T v a n * a E H e s T E r * n * t h e g a m e +Eval: D D D S S D I S S S D D + +Speaker sentences 774: swc_eng_001950 #utts: 1 +id: (swc_eng_001950-swc_eng_001950) +Scores: (#C #S #D #I) 10 4 1 1 +REF: n e W s O u t H w * A l E s +HYP: n e O s A u t * w H E l L s +Eval: S S D I S S + +Speaker sentences 775: swc_eng_001951 #utts: 1 +id: (swc_eng_001951-swc_eng_001951) +Scores: (#C #S #D #I) 34 2 3 1 +REF: d i s p o s a l o v e r h i s o W n b I O l o * g i c a l n a t U r E +HYP: d i s p o s a l o v e r h i s o * n b * Y l o U g i c a l n a t E r * +Eval: D D S I S D + +Speaker sentences 776: swc_eng_001952 #utts: 1 +id: (swc_eng_001952-swc_eng_001952) +Scores: (#C #S #D #I) 54 5 8 1 +REF: R e p R o d u c t i v e r i g h t s o r e x e r t u n ******* d U E p r e s S U r E s o n p r O s p e c t i V e p a R E n T S +HYP: * e p * o d u c t i v e r i g h t s o r e x e r t u n d * O p r e s H O r * s o n p r E s p e c t i * e p a * I n * * +Eval: D D I D S S S D S D D S D D + +Speaker sentences 777: swc_eng_001953 #utts: 1 +id: (swc_eng_001953-swc_eng_001953) +Scores: (#C #S #D #I) 14 3 6 1 +REF: S T i L l * a n c I E n t I n o r I g I n +HYP: * * i * l H a n c H A n t * n ******* o r * g O n +Eval: D D D I S S D D D S + +Speaker sentences 778: swc_eng_001954 #utts: 1 +id: (swc_eng_001954-swc_eng_001954) +Scores: (#C #S #D #I) 19 1 4 2 +REF: r a s t a ******* p o p * o U l o S s h i R E d g U n +HYP: r a s t a p o p H o * l o * s h i * * d g O n +Eval: I I D D D D S + +Speaker sentences 779: swc_eng_001955 #utts: 1 +id: (swc_eng_001955-swc_eng_001955) +Scores: (#C #S #D #I) 15 0 4 1 +REF: I n * t W o t h O u s a n d t W o +HYP: * n D t * o t h * u s a n d t * o +Eval: D I D D D + +Speaker sentences 780: swc_eng_001956 #utts: 1 +id: (swc_eng_001956-swc_eng_001956) +Scores: (#C #S #D #I) 28 3 3 1 +REF: f o r E X a m p l E I f t h e p l a y E r h a s o n l ******* Y +HYP: f o r * G a m p l * * f t h e p l a y A r h a s o n l T +Eval: D S D D S I S + +Speaker sentences 781: swc_eng_001957 #utts: 1 +id: (swc_eng_001957-swc_eng_001957) +Scores: (#C #S #D #I) 52 4 17 0 +REF: s U F f E R e d a s u b A r a c H n o I d h E m O R r H A g e h a v e c o G n i T i v E i m p a I r m e n t t h a t A F f e c t S +HYP: s * O f * * e d ******* a s u b * r a c K n o * d ******* h * m * E r * * g e h a v e c o L n i * i v * i m p a * r m e n t t h a t * * f e c t * +Eval: D S D D D D S D D D D S D D S D D D D D D + +Speaker sentences 782: swc_eng_001958 #utts: 1 +id: (swc_eng_001958-swc_eng_001958) +Scores: (#C #S #D #I) 17 3 4 2 +REF: p R O v i d e * D P r o G n O s t i c d a t * A +HYP: p * E v i d e I G * r o * n * s t i c d a t E R +Eval: D S I S D D D I S + +Speaker sentences 783: swc_eng_001959 #utts: 1 +id: (swc_eng_001959-swc_eng_001959) +Scores: (#C #S #D #I) 35 3 3 1 +REF: W h o h a d a n E u r Y s * M s d e t e c t e d b y o t h e r m E A n s +HYP: * h o h a d a n * u r I s O N s d e t e c t e d b y o t h e r m * I n s +Eval: D D S I S D S + +Speaker sentences 784: swc_eng_001960 #utts: 1 +id: (swc_eng_001960-swc_eng_001960) +Scores: (#C #S #D #I) 36 5 10 0 +REF: l i F e s t Y l E s d E s i G n E D t o I m p r o V e h e A l t h a n D L O n G e v i t y +HYP: l i O e s t I l * s d I s i * n * * t o * m p r o * e h e * l t h ******* a n * * U n C e v i t y +Eval: S S D S D D D D D D D D D S S + +Speaker sentences 785: swc_eng_001961 #utts: 1 +id: (swc_eng_001961-swc_eng_001961) +Scores: (#C #S #D #I) 36 3 6 0 +REF: h a d m o r E s O P H i S t i c a t e d E n d o f t a P E p r e d i c t i o N +HYP: h a d m o r * s * U F i * t i c a t e d A n d o f t a * * p r e d i c t i o * +Eval: D D S S D S D D D + +Speaker sentences 786: swc_eng_001962 #utts: 1 +id: (swc_eng_001962-swc_eng_001962) +Scores: (#C #S #D #I) 13 1 0 2 +REF: d e ******* h u m a n ******* i Z a t i o n +HYP: d e h u m a n i S a t i o n +Eval: I I S + +Speaker sentences 787: swc_eng_001963 #utts: 1 +id: (swc_eng_001963-swc_eng_001963) +Scores: (#C #S #D #I) 30 4 1 1 +REF: s p E c I e s i n c l * u d E f r e s h w a t e r l a m p r E y S +HYP: s p A c H e s i n c l O u d * f r e s h w a t e r l a m p r A y E +Eval: S S I D S S + +Speaker sentences 788: swc_eng_001964 #utts: 1 +id: (swc_eng_001964-swc_eng_001964) +Scores: (#C #S #D #I) 9 5 1 2 +REF: f I R s t a n * ******* G I O g r A m +HYP: f * O s t a n D Y U g r I m +Eval: D S I I S S S S + +Speaker sentences 789: swc_eng_001965 #utts: 1 +id: (swc_eng_001965-swc_eng_001965) +Scores: (#C #S #D #I) 18 3 3 1 +REF: T h e f r E e E n ******* C Y c l o p e d i a a t +HYP: * h e f r * e ******* A n S I c l o p e d i a a t +Eval: D D D S I S S + +Speaker sentences 790: swc_eng_001966 #utts: 1 +id: (swc_eng_001966-swc_eng_001966) +Scores: (#C #S #D #I) 31 3 4 0 +REF: t h e r E f o r e m e D i c a l i m a g I n g i s g e n E r A L l Y +HYP: t h e r f o r e m e T i c a l i m a g E n g i s g e n * r * * l * +Eval: S S S D D D D + +Speaker sentences 791: swc_eng_001967 #utts: 1 +id: (swc_eng_001967-swc_eng_001967) +Scores: (#C #S #D #I) 54 1 5 3 +REF: S p e * c I e S i s t t h e e x c l u S i o n o f n o n h u m a n a n d p a r t h * u m a n a n i M A l * s +HYP: * p e A c * e * i s t t h e e x c l u * i o n o f n o n h u m a n a n d p a r t h E u m a n a n i * B l E s +Eval: D I D D D I D S I + +Speaker sentences 792: swc_eng_001968 #utts: 1 +id: (swc_eng_001968-swc_eng_001968) +Scores: (#C #S #D #I) 44 5 14 0 +REF: I n p e o p l E W h O h a d p r e v i O U s l y s u F f E r E d a s u b A r a c H N o I D h e m O R r H A g E +HYP: * n p e o p l * * h * h a d p r e v i * A s l y s u * f * r * d a s u b * r a c M o * H h e m * * r * I g * +Eval: D D D D D S D D D D S S D S D D D S D + +Speaker sentences 793: swc_eng_001969 #utts: 1 +id: (swc_eng_001969-swc_eng_001969) +Scores: (#C #S #D #I) 43 9 12 1 +REF: C l A S s i f i e d a s E i t h e R E n ******* d a n g E R e d o r t h r E A t E n E d U n d E R t h e E p B C A C T +HYP: * l * * s i f i e d a s A i t h e * I n d a n g * * e d o r t h r * * t O n * d A n d T O t h e * p * E * B E +Eval: D D D S D S I D D D D S D S S S D D S D S S + +Speaker sentences 794: swc_eng_001970 #utts: 1 +id: (swc_eng_001970-swc_eng_001970) +Scores: (#C #S #D #I) 33 4 5 2 +REF: * a n D a T t O r n E y G e n e r A l p a r * k e r w a t K I n s h a r d I n +HYP: I a n * a * t E r n * y J e n e r * l p a r C k e r w a t C O n s h a r d * n +Eval: I D D S D S D I S S D + +Speaker sentences 795: swc_eng_001971 #utts: 1 +id: (swc_eng_001971-swc_eng_001971) +Scores: (#C #S #D #I) 10 1 2 0 +REF: b u t t Y p i c A L l y +HYP: b u t t I p i c * * l y +Eval: S D D + +Speaker sentences 796: swc_eng_001972 #utts: 1 +id: (swc_eng_001972-swc_eng_001972) +Scores: (#C #S #D #I) 46 1 14 1 +REF: w h i c h i n t u r n * f e d t h e s i g n A l t o t h e h E a d o f t H e c A S S E T T E D e C K +HYP: w h i c h i n ******* t u r n E f e d t h e s i g n * l t o t h e h * a d o f t * e c * * * * * * * ******* O e * * +Eval: D I D D D D D D D D D D D S D D + +Speaker sentences 797: swc_eng_001973 #utts: 1 +id: (swc_eng_001973-swc_eng_001973) +Scores: (#C #S #D #I) 44 0 6 1 +REF: W I t h i N t h e I r o w n c o n v e n T i o n a L l y e x p e c t e d l i f e ******* t i m e s +HYP: * * t h i * t h e * r o w n c o n v e n * i o n a * l y e x p e c t e d l i f e t i m e s +Eval: D D D D D D I + +Speaker sentences 798: swc_eng_001974 #utts: 1 +id: (swc_eng_001974-swc_eng_001974) +Scores: (#C #S #D #I) 14 1 3 0 +REF: S u b s t a n T I a l s t r A i n +HYP: * u b s t a n * H a l s t r * i n +Eval: D D S D + +Speaker sentences 799: swc_eng_001975 #utts: 1 +id: (swc_eng_001975-swc_eng_001975) +Scores: (#C #S #D #I) 34 5 4 0 +REF: t w e n T i E t h C e n t U r y K E n t u c k y c o n g r E S s m a n j o H n +HYP: t w e n Y i * t h S e n t * r y C A n t u c k y c o n g r * * s m a n j o A n +Eval: S D S D S S D D S + +Speaker sentences 800: swc_eng_001976 #utts: 1 +id: (swc_eng_001976-swc_eng_001976) +Scores: (#C #S #D #I) 13 7 7 0 +REF: N I n E t Y p E R C E n t A r E E n d E m i C +HYP: * * n O t * p * * A S I n t O r * I n d I m i * +Eval: D D S D D D S S S S D S S D + +Speaker sentences 801: swc_eng_001977 #utts: 1 +id: (swc_eng_001977-swc_eng_001977) +Scores: (#C #S #D #I) 20 1 1 0 +REF: h u n t i n g w i t h l e A d s h o T +HYP: h u n t i n g w i t h l e * d s h o A +Eval: D S + +Speaker sentences 802: swc_eng_001978 #utts: 1 +id: (swc_eng_001978-swc_eng_001978) +Scores: (#C #S #D #I) 11 0 4 2 +REF: T w E n T y t h * i r ******* t E e n +HYP: * w * n * y t h E i r t * e n +Eval: D D D I I D + +Speaker sentences 803: swc_eng_001979 #utts: 1 +id: (swc_eng_001979-swc_eng_001979) +Scores: (#C #S #D #I) 47 7 14 0 +REF: A L t h O U G H s e v e n p E r C e n t o f t h e w O R l d s B a t S s p E c I e s l i v E i n A u S t r a l I a +HYP: O t h * * * E s e v e n p * r ******* S e n t o f t h e w * * l d s ******* P a t * s p A c H e s l i v * ******* i n * u * t r a l * a +Eval: S S D D D S D D S D D D S D S S D D D D D + +Speaker sentences 804: swc_eng_001980 #utts: 1 +id: (swc_eng_001980-swc_eng_001980) +Scores: (#C #S #D #I) 32 2 17 0 +REF: K A r p o v s r E I G n f i n A L l y e n D E d i n N I n E t E e n E I G h T y f i v E +HYP: * * r p o v s r * * A n f i n * * l y e n * * d i n * * n * t * e n ******* * * * h A y f i v * +Eval: D D D D S D D D D D D D D D D D D S D + +Speaker sentences 805: swc_eng_001981 #utts: 1 +id: (swc_eng_001981-swc_eng_001981) +Scores: (#C #S #D #I) 34 3 5 2 +REF: w H i l E s o m e t r A n * S h u m A n ******* i S T s t a k e a n a b s t r a c t +HYP: w * i l * s o m e t r E n E h u m O n i * * s t a k e ******* a n a b s t r a c t +Eval: D D S I S S I D D D + +Speaker sentences 806: swc_eng_001982 #utts: 1 +id: (swc_eng_001982-swc_eng_001982) +Scores: (#C #S #D #I) 13 3 0 9 +REF: * * * * * ******* * * * W r i T e p r O t e c t i o n +HYP: P O I N T T H E r i H e p r E t e c t i o n +Eval: I I I I I I I I I S S S + +Speaker sentences 807: swc_eng_001983 #utts: 1 +id: (swc_eng_001983-swc_eng_001983) +Scores: (#C #S #D #I) 60 9 8 3 +REF: G R a * P H I S O m o r P H i s m p r o b l E m i s t h e c o m p U t a t i O n a l p r o b l E m o f * D e ******* t e r m i n i n g w H e t h E R +HYP: * * a I C S * A m o r * F i s m p r o b l O m i s t h e c o m p E t a t i * n a l p r o b l O m o f T H e t e r m i n i n g w * e t h * * +Eval: D D I S S D S S D S S S D S I S I D D D + +Speaker sentences 808: swc_eng_001984 #utts: 1 +id: (swc_eng_001984-swc_eng_001984) +Scores: (#C #S #D #I) 24 3 4 1 +REF: F U r ******* t h e R r e s t r i c t o u r c o n C e p t O F +HYP: * O r t h e * r e s t r i c t o u r c o n S e p t ******* * I +Eval: D S I D S D D S + +Speaker sentences 809: swc_eng_001985 #utts: 1 +id: (swc_eng_001985-swc_eng_001985) +Scores: (#C #S #D #I) 26 2 5 1 +REF: T h o S e W h o s U r ******* v i V e h O s p i t a l i Z a t i o n +HYP: * h o * e * h o s A r v i * e h U s p i t a l i * a t i o n +Eval: D D D S I D S D + +Speaker sentences 810: swc_eng_001986 #utts: 1 +id: (swc_eng_001986-swc_eng_001986) +Scores: (#C #S #D #I) 63 5 9 1 +REF: s o m e p R O t e c t i o n o f u n ******* C e r t a I n S I g n i f i c a n c e i s C o n f E R r e d b y c A u c a s i A n E t h n i C i t y +HYP: s o m e ******* p * E t e c t i o n o f u n S e r t a * n * * g n i f i c a n c e i s * o n f * U r e d b y c * u c a s i * n A t h n i T i t y +Eval: D D S I S D D D D D S D D S S + +Speaker sentences 811: swc_eng_001987 #utts: 1 +id: (swc_eng_001987-swc_eng_001987) +Scores: (#C #S #D #I) 10 2 3 1 +REF: C O a * s t a L L a g O o n s +HYP: * H a L s t a E * a g * o n s +Eval: D S I S D D + +Speaker sentences 812: swc_eng_001988 #utts: 1 +id: (swc_eng_001988-swc_eng_001988) +Scores: (#C #S #D #I) 14 3 8 1 +REF: A N D c o g N I t i v e E n ******* h a n C E m e N T +HYP: * * * ******* c o g * E t i v e I n h a n * S m e * * +Eval: D D D D D S S I D S D D + +Speaker sentences 813: swc_eng_001989 #utts: 1 +id: (swc_eng_001989-swc_eng_001989) +Scores: (#C #S #D #I) 34 1 16 6 +REF: * * * * * ******* t h e E I G H t h r a n k a n d b e p r O m o t e d t o A n a L l o W E D P I E C E +HYP: V A N S T t h e * * * A t h r a n k a n d b e p r * m o t e d t o * n ******* a * l o * * * ******* * * * * * +Eval: I I I I I I D D D S D D D D D D D D D D D D D + +Speaker sentences 814: swc_eng_001990 #utts: 1 +id: (swc_eng_001990-swc_eng_001990) +Scores: (#C #S #D #I) 31 2 5 0 +REF: d r a W b a c k o f c o i l i n g i s t h e p O S S I b I l i t Y +HYP: d r a b a c k o f c o i l i n g i s t h e p * * * E b * l i t * +Eval: S D D D S D D + +Speaker sentences 815: swc_eng_001991 #utts: 1 +id: (swc_eng_001991-swc_eng_001991) +Scores: (#C #S #D #I) 25 5 5 1 +REF: i n d i c a t e S A s u b A r ******* a c H n o I d h E m O R r H A g e +HYP: i n d i c a t e * S s u b E r a c K n o * d h * m * B r * I g e +Eval: D S S I S D D D S D S + +Speaker sentences 816: swc_eng_001992 #utts: 1 +id: (swc_eng_001992-swc_eng_001992) +Scores: (#C #S #D #I) 11 3 1 0 +REF: d a m A G E D p o r t i o n +HYP: d a m * I C H p o r t i o n +Eval: D S S S + +Speaker sentences 817: swc_eng_001993 #utts: 1 +id: (swc_eng_001993-swc_eng_001993) +Scores: (#C #S #D #I) 26 9 9 3 +REF: A d o p t i o N o f * e U G e N i C E n * ******* h a n C E m E n T t e C H n O l O G I e s +HYP: N d o p t i o * o f Y e * J e * i K A n D h a n * S m * n * ******* t e * K n A l * J e s +Eval: S D I D S D S S I I D S D D D D S S D S S + +Speaker sentences 818: swc_eng_001994 #utts: 1 +id: (swc_eng_001994-swc_eng_001994) +Scores: (#C #S #D #I) 27 1 1 0 +REF: p o l i s h o n h i s h o r s e A n d w a g O n +HYP: p o l i s h o n h i s h o r s e * n d w a g A n +Eval: D S + +Speaker sentences 819: swc_eng_001995 #utts: 1 +id: (swc_eng_001995-swc_eng_001995) +Scores: (#C #S #D #I) 16 1 4 0 +REF: A N d t h e N e x t C h a m p i O n +HYP: * * d t h e * e x t * h a m p i A n +Eval: D D D D S + +Speaker sentences 820: swc_eng_001996 #utts: 1 +id: (swc_eng_001996-swc_eng_001996) +Scores: (#C #S #D #I) 19 3 9 1 +REF: B R O t h e R o f a U T H O r a l d O U s h u * x l E y +HYP: * * * t h e * o f a * * F E r a l d * I s ******* h u C x l * y +Eval: D D D D D D S S D S D I D + +Speaker sentences 821: swc_eng_001997 #utts: 1 +id: (swc_eng_001997-swc_eng_001997) +Scores: (#C #S #D #I) 22 2 10 1 +REF: W O R l D c h a M p ******* i O n N i n E t E e n t w e n T y o n E +HYP: * * * l * c h a * p i * n H i n C t * e n ******* t w e n * y o n * +Eval: D D D D D I D S S D D D D + +Speaker sentences 822: swc_eng_001998 #utts: 1 +id: (swc_eng_001998-swc_eng_001998) +Scores: (#C #S #D #I) 43 6 4 1 +REF: s u c h a s q U A n t U m c o M p u t a t i o n a n d r a n d O m I Z e d A l g O r I t h * m s +HYP: s u c h a s q * O n t I m c o * p u t a t i o n a n d r a n d * m Y S e d E l g * r E t h E m s +Eval: D S S D D S S S D S I + +Speaker sentences 823: swc_eng_001999 #utts: 1 +id: (swc_eng_001999-swc_eng_001999) +Scores: (#C #S #D #I) 8 4 8 2 +REF: E I G H t E E N n * I N e T Y n * i n E +HYP: * * * A t * * Y n H A D e * * n O i n * +Eval: D D D S D D S I S S D D I D + +Speaker sentences 824: swc_eng_002000 #utts: 1 +id: (swc_eng_002000-swc_eng_002000) +Scores: (#C #S #D #I) 55 3 6 15 +REF: W a s s h o W n * b y l a d n e r t h a t i f p * ≠ n * * ******* * * * * * * * * * p t h E n t h e r E e x i s t p r o * b l E m s I N +HYP: * a s s h o * n E b y l a d n e r t h a t i f p E S n O T A C U L T O A E N p t h A n t h e r * e x i s t p r o V b l U m s ******* * * +Eval: D D I I S I I I I I I I I I I I I S D I S D D D + +Speaker sentences 825: swc_eng_002001 #utts: 1 +id: (swc_eng_002001-swc_eng_002001) +Scores: (#C #S #D #I) 14 1 6 0 +REF: T h e c o m p a c t d i s C G R E W +HYP: * h e c o m p a c t d i s * ******* * * * O +Eval: D D D D D D S + +Speaker sentences 826: swc_eng_002002 #utts: 1 +id: (swc_eng_002002-swc_eng_002002) +Scores: (#C #S #D #I) 11 3 3 3 +REF: * g r e y g O o s C E n ******* A r i * O +HYP: D g r e y g * o ******* s * I n E r i A L +Eval: I D D D S I S I S + +Speaker sentences 827: swc_eng_002003 #utts: 1 +id: (swc_eng_002003-swc_eng_002003) +Scores: (#C #S #D #I) 17 3 3 1 +REF: w a s R e n d e r e d a s * a J E D R e Z +HYP: w a s W e n d e r e d a s I a * * * G e S +Eval: S I D D D S S + +Speaker sentences 828: swc_eng_002004 #utts: 1 +id: (swc_eng_002004-swc_eng_002004) +Scores: (#C #S #D #I) 19 0 4 5 +REF: s * * ******* a * h o r * t o A n o t h e R c A u s E +HYP: s A Y a C h o r E t o * n o t h e * c * u s * +Eval: I I I I I D D D D + +Speaker sentences 829: swc_eng_002005 #utts: 1 +id: (swc_eng_002005-swc_eng_002005) +Scores: (#C #S #D #I) 20 1 4 1 +REF: C o n S t i t u E n c * y o f f a v e R s h A m +HYP: * o n * t i t u * n c E y o f f a v e * s h O m +Eval: D D D I D S + +Speaker sentences 830: voxforge_eng_000874 #utts: 1 +id: (voxforge_eng_000874-voxforge_eng_000874) +Scores: (#C #S #D #I) 48 4 5 0 +REF: t h e f o U r t h a n d f i F t h d a y s p a S s e D w i T h o u t a n y d e v e l O P m e n T S +HYP: t h e f o * r t h a n d f i * t h d a y s p a * s e * w i * h o u t a n y d e v e l L A m e n C E +Eval: D D D D D S S S S + +Speaker sentences 831: voxforge_eng_000875 #utts: 1 +id: (voxforge_eng_000875-voxforge_eng_000875) +Scores: (#C #S #D #I) 19 0 1 0 +REF: t h e y K n o w t h e r e p o r t +HYP: t h e y * n o w t h e r e p o r t +Eval: D + +Speaker sentences 832: voxforge_eng_000876 #utts: 1 +id: (voxforge_eng_000876-voxforge_eng_000876) +Scores: (#C #S #D #I) 38 3 5 0 +REF: s u c h t h i n g s h a d O C c u R r E d b e f o r E h e t o l d P H i l I p +HYP: s u c h t h i n g s h a d * A c u * r * d b e f o r * h e t o l d * F i l A p +Eval: D S D D D D S S + +Speaker sentences 833: voxforge_eng_000877 #utts: 1 +id: (voxforge_eng_000877-voxforge_eng_000877) +Scores: (#C #S #D #I) 42 4 4 1 +REF: t h e y o n l y h a d a l i T t l e t H I r T y t h o u s A n d d o L l A r f i R e * +HYP: t h e y o n l y h a d a l i * t l e t * E r D y t h o u s E n d d o * l E r f i * e R +Eval: D D S S S D S D I + +Speaker sentences 834: voxforge_eng_000878 #utts: 1 +id: (voxforge_eng_000878-voxforge_eng_000878) +Scores: (#C #S #D #I) 22 2 0 1 +REF: i a m g o * i n g t o g e t i t o U T +HYP: i a m g o W i n g t o g e t i t o W D +Eval: I S S + +Speaker sentences 835: voxforge_eng_000879 #utts: 1 +id: (voxforge_eng_000879-voxforge_eng_000879) +Scores: (#C #S #D #I) 44 4 1 4 +REF: * o u T W A R d l y h e m a i n t a i n e d a c * a L m * a n d s m i l i n g a * s p e c t +HYP: H o u * D U d l y h e m a i n t a i n e d a c O a R m E a n d s m i l i n g a S s p e c t +Eval: I D S S S I S I I + +Speaker sentences 836: voxforge_eng_000880 #utts: 1 +id: (voxforge_eng_000880-voxforge_eng_000880) +Scores: (#C #S #D #I) 39 2 4 0 +REF: j o A n l O o k e D t r i u m p H A n t l y a t s h e l d o n w h o b o w E d +HYP: j o * n l * o k e * t r i u m p F E n t l y a t s h e l d o n w h o b o w * d +Eval: D D D S S D + +Speaker sentences 837: voxforge_eng_000883 #utts: 1 +id: (voxforge_eng_000883-voxforge_eng_000883) +Scores: (#C #S #D #I) 18 6 2 6 +REF: C o m e o * * n ******* * d E l * m a r * C H a L l e n G E D +HYP: * o m e o N E n T d I l E m a r T T a * l e n C S T +Eval: D I I I I S I I S S D S S S + +Speaker sentences 838: voxforge_eng_000884 #utts: 1 +id: (voxforge_eng_000884-voxforge_eng_000884) +Scores: (#C #S #D #I) 58 1 1 2 +REF: * ******* i t w a s b e a t i n g a n d w a I t i n g i n t h e a m b U s h o f t h o s e b l a c k p i t s +HYP: E i t w a s b e a t i n g a n d w a * t i n g i n t h e a m b O s h o f t h o s e b l a c k p i t s +Eval: I I D S + +Speaker sentences 839: voxforge_eng_000885 #utts: 1 +id: (voxforge_eng_000885-voxforge_eng_000885) +Scores: (#C #S #D #I) 28 2 6 0 +REF: L E t t h e M g o o u t a n d e A T w i T H m y b o y s +HYP: * I t t h e * g o o u t a n d e * * ******* w i * G m y b o y s +Eval: D S D D D D D S + +Speaker sentences 840: voxforge_eng_000886 #utts: 1 +id: (voxforge_eng_000886-voxforge_eng_000886) +Scores: (#C #S #D #I) 49 8 5 6 +REF: * h e w E n * T d o w n i n M i * D s ******* T r * * e A m s e A r c h i n g t h e s h a d o W s o f b O T H s h o r E s +HYP: S h e w I n E D d o w n i n W i N E s D r E M e m s e * r c h i n g t h e s h a d o * s o f b * * U L s h o r * s +Eval: I S I S S I S I S I I S D D D D S S D + +Speaker sentences 841: voxforge_eng_000887 #utts: 1 +id: (voxforge_eng_000887-voxforge_eng_000887) +Scores: (#C #S #D #I) 50 3 12 2 +REF: i j u s T D o a P p r e c * I a t e I T w i t h ******* o u t b e I N G a B L e T o e x p r e s S m y f E e l i n g s +HYP: i j u s * T o a * p r e c S H a t e ******* * * w i t h o u t b e * * * a * * e * o e x p r e s E m y f * e l i n g s +Eval: D S D I S D D D I D D D D D D S D + +Speaker sentences 842: voxforge_eng_000888 #utts: 1 +id: (voxforge_eng_000888-voxforge_eng_000888) +Scores: (#C #S #D #I) 37 0 3 0 +REF: s h e d o E s n t K n o w w h a t h e i s t a L k i n g a b o u t +HYP: s h e d o * s n t * n o w w h a t h e i s t a * k i n g a b o u t +Eval: D D D + +Speaker sentences 843: voxforge_eng_000889 #utts: 1 +id: (voxforge_eng_000889-voxforge_eng_000889) +Scores: (#C #S #D #I) 32 1 3 0 +REF: y o u r f a t h e r s f i f t H c o M m a n d h e n o D D e d +HYP: y o u r f a t h e r s f i f t * c o * m a n d h e n o * T e d +Eval: D D D S + +Speaker sentences 844: voxforge_eng_000890 #utts: 1 +id: (voxforge_eng_000890-voxforge_eng_000890) +Scores: (#C #S #D #I) 19 0 4 0 +REF: d o n T Y o u s E e i h a T e y o u +HYP: d o n * * o u s * e i h a * e y o u +Eval: D D D D + +Speaker sentences 845: voxforge_eng_000891 #utts: 1 +id: (voxforge_eng_000891-voxforge_eng_000891) +Scores: (#C #S #D #I) 74 1 6 1 +REF: a l i T t l e w a r m * b u t n o t a t a L l A s t o n i s h e d e A t i n g m e l O n s a n d t h r o w i n g T h e r i n d a b o u t +HYP: a ******* l i * t l e w a r m E b u t n o t a t a * l * s t o n i s h e d e * t i n g m e l E n s a n d t h r o w i n g * h e r i n d a b o u t +Eval: D D I D D D S D + +Speaker sentences 846: voxforge_eng_000892 #utts: 1 +id: (voxforge_eng_000892-voxforge_eng_000892) +Scores: (#C #S #D #I) 20 1 0 0 +REF: t h i s i s a g r e a t p a r T y +HYP: t h i s i s a g r e a t p a r D y +Eval: S + +Speaker sentences 847: voxforge_eng_000893 #utts: 1 +id: (voxforge_eng_000893-voxforge_eng_000893) +Scores: (#C #S #D #I) 21 3 2 6 +REF: t h e b o y g r E W A n D p r o s p e r * e * ******* * * * D +HYP: t h e b o y g r * O I n * p r o s p e r C e T H O M E +Eval: D S S D I I I I I I S + +Speaker sentences 848: voxforge_eng_000894 #utts: 1 +id: (voxforge_eng_000894-voxforge_eng_000894) +Scores: (#C #S #D #I) 84 1 6 3 +REF: U n * ******* l e S s s u c h l e T t e r s b e p a t e n t t h a t t h e y m a y b e r e A d t o t h e m a n d w i t h ******* a L l s e a l E D o r t e s t i f i e d +HYP: A n D l e * s s u c h l e * t e r s b e p a t e n t t h a t t h e y m a y b e r e * d t o t h e m a n d w i t h a * l s e a l * * o r t e s t i f i e d +Eval: S I I D D D I D D D + +Speaker sentences 849: voxforge_eng_000895 #utts: 1 +id: (voxforge_eng_000895-voxforge_eng_000895) +Scores: (#C #S #D #I) 46 0 11 2 +REF: h o W c o u L d a w o m A n d * a r E t o v e n t u R e * w H E R e s o m a n y e x p L o R E r s +HYP: h o * c o u * d a w o m * n d E a r * t o v e n t u * e R w * * * e s o m a n y e x p * o * * r s +Eval: D D D I D D I D D D D D D + +Speaker sentences 850: voxforge_eng_000896 #utts: 1 +id: (voxforge_eng_000896-voxforge_eng_000896) +Scores: (#C #S #D #I) 25 2 0 1 +REF: h e r e a d * h i s f r a g m e n t S a l o U d +HYP: h e r e a d S h i s f r a g m e n t E a l o W d +Eval: I S S + +Speaker sentences 851: voxforge_eng_000897 #utts: 1 +id: (voxforge_eng_000897-voxforge_eng_000897) +Scores: (#C #S #D #I) 29 0 1 0 +REF: b u t h o w a r E y o u g o i n g t o d o i t +HYP: b u t h o w a r * y o u g o i n g t o d o i t +Eval: D + +Speaker sentences 852: voxforge_eng_000898 #utts: 1 +id: (voxforge_eng_000898-voxforge_eng_000898) +Scores: (#C #S #D #I) 35 0 2 1 +REF: h o w d o y o u w a n T t o g e t a ******* w a y w i t h t h i s +HYP: h o w d o ******* y o u w a n * t o g e t a w a y w i t h t h i s +Eval: D D I + +Speaker sentences 853: voxforge_eng_000899 #utts: 1 +id: (voxforge_eng_000899-voxforge_eng_000899) +Scores: (#C #S #D #I) 21 0 1 1 +REF: w i L l w e e v e r f o r ******* g e t i t +HYP: w i * l w e e v e r f o r g e t i t +Eval: D I + +Speaker sentences 854: voxforge_eng_000900 #utts: 1 +id: (voxforge_eng_000900-voxforge_eng_000900) +Scores: (#C #S #D #I) 44 4 13 2 +REF: f R o M m y e A r l * i E s T r e c O L l e c t i o n m y s l E e P w A s A p e r i O D o f t * e R R O r +HYP: f * o R m y e * r l Y i * s * r e c * A l e c t i o n m y s l * e * w * s ******* * p e r i A T o f t H e * * * r +Eval: D S D I D D D S D D D D D S S I D D D + +Speaker sentences 855: voxforge_eng_000901 #utts: 1 +id: (voxforge_eng_000901-voxforge_eng_000901) +Scores: (#C #S #D #I) 24 3 4 21 +REF: * * * ******* * ******* * * ******* * * * * * * * ******* w h * y d o G G o n E y o u * * a l l s h a k E A g * a I N +HYP: T I N M I S M O S I E A R w h I y d o F o n * y o u O W a l l s h a k * * g E a * M +Eval: I I I I I I I I I I I I I I I I I I S S D I I D D I D S + +Speaker sentences 856: voxforge_eng_000902 #utts: 1 +id: (voxforge_eng_000902-voxforge_eng_000902) +Scores: (#C #S #D #I) 17 7 0 6 +REF: * I T I S t h e n e a R E s t r e * f u g * e * ******* * +HYP: E D E V T H t h e n e a D I s t r e A f u g Y e Y I +Eval: I S S S S S S S I I I I I + +Speaker sentences 857: voxforge_eng_000903 #utts: 1 +id: (voxforge_eng_000903-voxforge_eng_000903) +Scores: (#C #S #D #I) 40 3 2 1 +REF: h i s s l i m * h a n d s G r I P p e D t h e E d g e s o f t h e t a b l e +HYP: h i s s l i m E h a n d s C r * E p e * t h e A d g e s o f t h e t a b l e +Eval: I S D S D S + +Speaker sentences 858: voxforge_eng_000904 #utts: 1 +id: (voxforge_eng_000904-voxforge_eng_000904) +Scores: (#C #S #D #I) 24 4 4 1 +REF: w h i T E l * E G h o r n S s A i d m R s m o r t I m e r +HYP: w h i * D l A K h o r n * s * i d m * s m o r t O m e r +Eval: D S I S S D D D S + +Speaker sentences 859: voxforge_eng_000905 #utts: 1 +id: (voxforge_eng_000905-voxforge_eng_000905) +Scores: (#C #S #D #I) 32 0 16 0 +REF: i T t O o k h I m h a L f a N H o u R t o r E A c h T h e e d G E o F i t +HYP: i * ******* t * o k h * m h a * f ******* a * * o u * ******* t o r * * c h * h e e d * * o * i t +Eval: D D D D D D D D D D D D D D D D + +Speaker sentences 860: voxforge_eng_000906 #utts: 1 +id: (voxforge_eng_000906-voxforge_eng_000906) +Scores: (#C #S #D #I) 41 1 8 0 +REF: m a r t h a w h e r E d o W E s t a n d o N t h e C o n t r a c t u A l i s S u E s +HYP: m a r t h a w h e r * d o ******* * * s t a n d o * t h e * o n t r a c t u * l i s O u * s +Eval: D D D D D D D S D + +Speaker sentences 861: voxforge_eng_000907 #utts: 1 +id: (voxforge_eng_000907-voxforge_eng_000907) +Scores: (#C #S #D #I) 58 1 1 0 +REF: a s t o b e u n d i s t i n g u i S h a b l e f r o m t h e v a s t w h i T e p l a i n s a r o u n d +HYP: a s t o b e u n d i s t i n g u i * h a b l e f r o m t h e v a s t w h i H e p l a i n s a r o u n d +Eval: D S + +Speaker sentences 862: voxforge_eng_000908 #utts: 1 +id: (voxforge_eng_000908-voxforge_eng_000908) +Scores: (#C #S #D #I) 36 3 3 1 +REF: h e w o u l d d e s t r o y a L l t h i n g s t h a T A r E f i * x E D +HYP: h e w o u l d d e s t r o y a * l t h i n g s t h a * E r * f i C x S T +Eval: D D S D I S S + +Speaker sentences 863: voxforge_eng_000909 #utts: 1 +id: (voxforge_eng_000909-voxforge_eng_000909) +Scores: (#C #S #D #I) 46 1 10 1 +REF: t h e r u S s i A n M u s i c p l a Y E r t h e c o U n t w a s h E r o * b e d i E n T s l a v e +HYP: t h e r u * s i O n * u s i c p l a * * r t h e c o * n t w a s h * r ******* o A b e d i * n * ******* s l a v e +Eval: D S D D D D D D I D D D + +Speaker sentences 864: voxforge_eng_000910 #utts: 1 +id: (voxforge_eng_000910-voxforge_eng_000910) +Scores: (#C #S #D #I) 48 1 5 2 +REF: t o h i s s U R p r i S e h e r a n S W e R w a s f l a t a n d u n ******* c o m p r o m i * s i n g +HYP: t o h i s s * * p r i * e h e r a n * T e * w a s f l a t a n d u n c o m p r o m i Z s i n g +Eval: D D D D S D I I + +Speaker sentences 865: voxforge_eng_000911 #utts: 1 +id: (voxforge_eng_000911-voxforge_eng_000911) +Scores: (#C #S #D #I) 24 0 2 0 +REF: t h i s s H o u l d b e i n t E r e s t i n g +HYP: t h i s s * o u l d b e i n t * r e s t i n g +Eval: D D + +Speaker sentences 866: voxforge_eng_000912 #utts: 1 +id: (voxforge_eng_000912-voxforge_eng_000912) +Scores: (#C #S #D #I) 33 0 0 2 +REF: i a m a ******* f r a i d * i d o n t h a v e m u c h t i m e +HYP: i a m a f r a i d E i d o n t h a v e m u c h t i m e +Eval: I I + +Speaker sentences 867: voxforge_eng_000913 #utts: 1 +id: (voxforge_eng_000913-voxforge_eng_000913) +Scores: (#C #S #D #I) 58 4 6 1 +REF: c H r i s T m A s i s a n e a s y p r o b l E m * c o m p a r E d w i t H a p o l Y n E s i A n g i v i n g f e A s t +HYP: c * r i s * m * s i s a n e a s y p r o b l O m E c o m p a r * d w i t * a p o l * n A s i O n g i v i n g f e C s t +Eval: D D D S I D D D S S S + +Speaker sentences 868: voxforge_eng_000914 #utts: 1 +id: (voxforge_eng_000914-voxforge_eng_000914) +Scores: (#C #S #D #I) 39 0 8 0 +REF: t h e p l A n t e r s a r E a L r E A d y c o n s i d e r i N g t h E m a T t e r +HYP: t h e p l * n t e r s a r * a * r * * d y c o n s i d e r i * g t h * m a * t e r +Eval: D D D D D D D D + +Speaker sentences 869: voxforge_eng_000915 #utts: 1 +id: (voxforge_eng_000915-voxforge_eng_000915) +Scores: (#C #S #D #I) 25 0 3 1 +REF: j o A n c r i e d w i t h s h I N i n g e * y e s +HYP: j o * n c r i e d w i t h s h * * i n g e I y e s +Eval: D D D I + +Speaker sentences 870: voxforge_eng_000916 #utts: 1 +id: (voxforge_eng_000916-voxforge_eng_000916) +Scores: (#C #S #D #I) 35 0 0 1 +REF: w h o ******* e v e r l i v e d o n t h e r a n c h d i d t h a t +HYP: w h o e v e r l i v e d o n t h e r a n c h d i d t h a t +Eval: I + +Speaker sentences 871: voxforge_eng_000917 #utts: 1 +id: (voxforge_eng_000917-voxforge_eng_000917) +Scores: (#C #S #D #I) 38 2 0 1 +REF: w e l e a v e t h e e * v e n t u a l i t y t o t i m e a n d l A W +HYP: w e l e a v e t h e e F v e n t u a l i t y t o t i m e a n d l O R +Eval: I S S + +Speaker sentences 872: voxforge_eng_000918 #utts: 1 +id: (voxforge_eng_000918-voxforge_eng_000918) +Scores: (#C #S #D #I) 56 5 6 0 +REF: a t t h e s a m e t i M e s p E a r s A n d A R r o W s b e g a n t o f a L l a m o n g T h E i N v a D e r s +HYP: a t t h e s a m e t i N e s p I a r s * n d * E r o * s b e g a n t o f a * l a m o n g * h * i M v a T e r s +Eval: S S D D S D D D D S S + +Speaker sentences 873: voxforge_eng_000920 #utts: 1 +id: (voxforge_eng_000920-voxforge_eng_000920) +Scores: (#C #S #D #I) 30 2 3 2 +REF: i t i s m e * r E l y t h e s i m p l e s * u p e R l A t i V E +HYP: i t i s m e A r * l y t h e s i m p l e s O u p e * l I t i * F +Eval: I D I D S D S + +Speaker sentences 874: voxforge_eng_000921 #utts: 1 +id: (voxforge_eng_000921-voxforge_eng_000921) +Scores: (#C #S #D #I) 42 1 6 2 +REF: i n ******* s t e a * d h e a R r i v e d o n t h e n i G h T o f t H e s e c O n D d a y +HYP: i n s t e a I d h e a * r i v e d o n t h e ******* n i * h * o f t * e s e c A n * d a y +Eval: I I D D D D D S D + +Speaker sentences 875: voxforge_eng_000922 #utts: 1 +id: (voxforge_eng_000922-voxforge_eng_000922) +Scores: (#C #S #D #I) 52 3 2 2 +REF: i n h i s a n * X i E t y a n d s O l i C i t u * d E a n d l o v e t h e y d i d n o t c o u n t +HYP: i n h i s a n G S i * t y a n d s U l i S i t u O d * a n d l o v e t h e y d i d n o t c o u n t +Eval: I S D S S I D + +Speaker sentences 876: voxforge_eng_000923 #utts: 1 +id: (voxforge_eng_000923-voxforge_eng_000923) +Scores: (#C #S #D #I) 41 0 5 2 +REF: g o d b l e s s * * i h o p e I L l G o o n s E E i n g t h e m f o r e v e r +HYP: g o d b l e s s H M i h o p e * * l * o o n s * * i n g t h e m f o r e v e r +Eval: I I D D D D D + +Speaker sentences 877: voxforge_eng_000924 #utts: 1 +id: (voxforge_eng_000924-voxforge_eng_000924) +Scores: (#C #S #D #I) 14 1 1 0 +REF: y o U w e r e E n g a g e d +HYP: y o * w e r e I n g a g e d +Eval: D S + +Speaker sentences 878: voxforge_eng_000925 #utts: 1 +id: (voxforge_eng_000925-voxforge_eng_000925) +Scores: (#C #S #D #I) 52 11 2 7 +REF: t h e * l a c e * w a s o f a d e l i c A T e * * i v O r Y c * O l o r f * a i n * t L Y t i n t E D w i t h Y e L l o W +HYP: t h e R l a c e S w a s o f a d e l i c * K e I T i v E r E c A L l o r f R a i n E t O M P t i n t I N w i t h * e A l o L +Eval: I I D S I I S S I S I I S S S S S D S S + +Speaker sentences 879: voxforge_eng_000927 #utts: 1 +id: (voxforge_eng_000927-voxforge_eng_000927) +Scores: (#C #S #D #I) 47 0 2 1 +REF: i t w a S t h e s a m e w a y w i t h o u r r e v o l v e r s a n d r i f * l E s +HYP: i t w a * t h e s a m e w a y w i t h o u r r e v o l v e r s a n d r i f A l * s +Eval: D I D + +Speaker sentences 880: voxforge_eng_000928 #utts: 1 +id: (voxforge_eng_000928-voxforge_eng_000928) +Scores: (#C #S #D #I) 41 1 6 1 +REF: T h e k i n g h a d p r o m i s E D t o E n * q u i r e i N t o t h e m a T t e r +HYP: * h e k i n g h a d p r o m i s * * ******* t o I n C q u i r e i * t o t h e m a * t e r +Eval: D D D D S I D D + +Speaker sentences 881: voxforge_eng_000929 #utts: 1 +id: (voxforge_eng_000929-voxforge_eng_000929) +Scores: (#C #S #D #I) 16 0 3 1 +REF: d o E s t h a T l O o k g o o d * +HYP: d o * s t h a * l * o k g o o d T +Eval: D D D I + +Speaker sentences 882: voxforge_eng_000930 #utts: 1 +id: (voxforge_eng_000930-voxforge_eng_000930) +Scores: (#C #S #D #I) 57 0 1 0 +REF: f o r t h e f i r s t t i m e i n h i s l i f e h e w a s y e a r n i n g f o r a s c r a p +HYP: f o r t h e f i r s t t i m e i n h i s l i f e h e w a s y e a r n i n g f o r a ******* s c r a p +Eval: D + +Speaker sentences 883: voxforge_eng_000931 #utts: 1 +id: (voxforge_eng_000931-voxforge_eng_000931) +Scores: (#C #S #D #I) 50 4 3 5 +REF: i d e f * Y a n y m a n t o g e t a s o l O m o n i S l A n d * * s o r E i n c a l I f o r n * * I a +HYP: i d e f I E a n y m a n t o g e t a s o l A m o n i * l E n d E S s o r * i n c a l * f o r n T H E a +Eval: I S S D S I I D D I I S + +Speaker sentences 884: voxforge_eng_000932 #utts: 1 +id: (voxforge_eng_000932-voxforge_eng_000932) +Scores: (#C #S #D #I) 41 4 6 4 +REF: h e r E y E S s m * I l * e D * t r U T H a t h i m a s h e c a m e U P t h e b a n * k +HYP: h e r I y * * s m O U l T e * S t r * * * a t h i m a s h e c a m e O F t h e b a n G k +Eval: S D D I S I D I D D D S S I + +Speaker sentences 885: voxforge_eng_000933 #utts: 1 +id: (voxforge_eng_000933-voxforge_eng_000933) +Scores: (#C #S #D #I) 16 8 7 2 +REF: A N y ******* w a * Y n O o n E s a W H E R l i K E t h a T +HYP: E T y w a V E n * ******* o n S s a * ******* * * L l i C G D t h a * +Eval: S S I I S D D S D D D D S S S S D + +Speaker sentences 886: voxforge_eng_000934 #utts: 1 +id: (voxforge_eng_000934-voxforge_eng_000934) +Scores: (#C #S #D #I) 33 1 4 1 +REF: m e n w h o E n d * u r E i t c a L l I t l i v i n g d e a t h +HYP: m e n w h o * n d E u r * ******* i t c a * l A t l i v i n g d e a t h +Eval: D I D D D S + +Speaker sentences 887: voxforge_eng_000935 #utts: 1 +id: (voxforge_eng_000935-voxforge_eng_000935) +Scores: (#C #S #D #I) 30 4 4 3 +REF: m A T t H E W s o n w h o s * * t h i s b O o k K E e p e r r o * g e r s +HYP: m * * t * O s o n w h o s E D t h i s b * o k C e p e r r o D g e r s +Eval: D D D S S I I D S S I + +Speaker sentences 888: voxforge_eng_000938 #utts: 1 +id: (voxforge_eng_000938-voxforge_eng_000938) +Scores: (#C #S #D #I) 24 1 1 8 +REF: i o n l y r e a * * * ******* d ******* * t h e Q U o * * t a t i o n s +HYP: i o n l y r e a D T D d H t h e * F o A R t a t i o n s +Eval: I I I I I I D S I I + +Speaker sentences 889: voxforge_eng_000939 #utts: 1 +id: (voxforge_eng_000939-voxforge_eng_000939) +Scores: (#C #S #D #I) 58 8 8 4 +REF: t h E R e w a s p R o p e r d * I v i s i o n o f L a * b O r i N t h e w o r K t h e Y i n d I v * i D u A L l y p E R F o * r m e d +HYP: t h * * e w a s p * o p e r d E v i s i o n o f N a Y b E r i * t h e w o r E t h e * i n d E v R i G u * * l y p * O P o A r m e d +Eval: D D D I S S I S D S D S I S D D D S S I + +Speaker sentences 890: voxforge_eng_000940 #utts: 1 +id: (voxforge_eng_000940-voxforge_eng_000940) +Scores: (#C #S #D #I) 40 2 13 0 +REF: i L l T e L l y o U t h e l i b r a r i A n s a i d w I t h a B r i G h t E N I N G f a c e +HYP: i A l ******* P e * l y o * t h e l i b r a r i * n s a i d w * t h ******* a * r i * h t * * * * * f a c e +Eval: S D S D D D D D D D D D D D D + +Speaker sentences 891: voxforge_eng_000942 #utts: 1 +id: (voxforge_eng_000942-voxforge_eng_000942) +Scores: (#C #S #D #I) 42 4 5 3 +REF: i s A w m * r p * i K E n o * d h i s h e a d g r i m l y A n D s A r c a s t i c A L l y +HYP: i s * w m T r p U i * A G n o R d h i s h e a d g r i m l y I n * s E r c a s t i c * * l y +Eval: D I I D S S I S D S D D + +Speaker sentences 892: voxforge_eng_000943 #utts: 1 +id: (voxforge_eng_000943-voxforge_eng_000943) +Scores: (#C #S #D #I) 33 2 4 1 +REF: t h e r I N G i n g o f t h e b i g b E l L a r o u s E d h i m * +HYP: t h e r * * * i n g o f t h e b i g b I l E a r o u s * d h i m N +Eval: D D D S S D I + +Speaker sentences 893: voxforge_eng_000944 #utts: 1 +id: (voxforge_eng_000944-voxforge_eng_000944) +Scores: (#C #S #D #I) 81 6 15 6 +REF: * * ******* t h e s c r a T c h o f a p i n o n a m a n s h e a d v a s t r e * g I o n s o f t h e e A r t H s S U r f A C E r e m a i n * G e O l o * g i c A L l y u N K n o W n B U T +HYP: O R t h e s c r a * c h o f a p i n o n a m a n s h e a d v a s t r e A g * o n s o f t h e e * r t * s ******* E I r f * I S r e m a i n E J e A l o U g i c * * l y u * * n o * n ******* * * * +Eval: I I I D I D D D D S S D S S I S S I D D D D D D D D D + +Speaker sentences 894: voxforge_eng_000945 #utts: 1 +id: (voxforge_eng_000945-voxforge_eng_000945) +Scores: (#C #S #D #I) 49 5 3 6 +REF: h e h a d b A r * E l y e n t e r * E d T H i s w h e n h e * s * * A w * t h e g l o W o f a f i r E +HYP: h e h a d b U r D I l y e n t e r D A d * D i s w h e n h e S s O U O w D t h e g l o * o f a f i r * +Eval: S I S I S D S I I I S I D D + +Speaker sentences 895: voxforge_eng_000946 #utts: 1 +id: (voxforge_eng_000946-voxforge_eng_000946) +Scores: (#C #S #D #I) 20 5 7 2 +REF: C h A n G e * c h a I r s * D A Y l i G H t c o M m a n D E d +HYP: T h E n * e S c h a * r s T H E l i * * t c o * m a n * * d +Eval: S S D I D I S S S D D D D D + +Speaker sentences 896: voxforge_eng_000947 #utts: 1 +id: (voxforge_eng_000947-voxforge_eng_000947) +Scores: (#C #S #D #I) 43 1 6 1 +REF: i t w a s j e a N n E s i n G i n g s o f T l y o V e r b e y o n D t h e R o * c k s +HYP: i t w a s j e a * n * s i n * i n g s o f E l y o * e r b e y o n * t h e * o A c k s +Eval: D D D S D D D I + +Speaker sentences 897: voxforge_eng_000948 #utts: 1 +id: (voxforge_eng_000948-voxforge_eng_000948) +Scores: (#C #S #D #I) 23 4 5 0 +REF: A f l Y i n g a R r o w P A S s E d b e t w E e n u s +HYP: * O f l * i n g a * r o w * B U s T d b e t w * e n u s +Eval: D S D D D S S S D + +Speaker sentences 898: voxforge_eng_000949 #utts: 1 +id: (voxforge_eng_000949-voxforge_eng_000949) +Scores: (#C #S #D #I) 61 6 1 3 +REF: h a t r e * D a n d m u r d e r a n d l * u s t f o r r e v e n G E t h e y p o S s e S s E d t o o V e r ******* f l o w i n g +HYP: h a t r e I T a n d m u r d e r a n d l O u s t f o r r e v e n C H t h e y p o E s e * s T d t o o F e r f l o w i n g +Eval: I S I S S S D S S I + +Speaker sentences 899: voxforge_eng_000950 #utts: 1 +id: (voxforge_eng_000950-voxforge_eng_000950) +Scores: (#C #S #D #I) 36 0 11 2 +REF: t h A t y o u c O u L d h e A r * a L l u p a n D d o W n t H e L i m p o p o * +HYP: t h * t y o u ******* c * u * d h e * r E a * l u p ******* a n * d o * n t * e * i m p o p o E +Eval: D D D D D I D D D D D D I + +Speaker sentences 900: voxforge_eng_000951 #utts: 1 +id: (voxforge_eng_000951-voxforge_eng_000951) +Scores: (#C #S #D #I) 20 1 2 1 +REF: i t w a s m y * I d e a t o a t E e +HYP: i t w a s m y A d e a t o a ******* t * e +Eval: I S D D + +Speaker sentences 901: voxforge_eng_000952 #utts: 1 +id: (voxforge_eng_000952-voxforge_eng_000952) +Scores: (#C #S #D #I) 19 1 2 0 +REF: s h e d o E s n t w A n T t o w i n +HYP: s h e d o * s n t w O n * t o w i n +Eval: D S D + +Speaker sentences 902: voxforge_eng_000953 #utts: 1 +id: (voxforge_eng_000953-voxforge_eng_000953) +Scores: (#C #S #D #I) 40 3 5 1 +REF: s h e T h i n k S i t i s b e c a U s E h e w A n T s * s o m E t h i n g e l S e +HYP: s h e * h i n k E i t i s b e c a * s * h e w O n * s E s o m * t h i n g e l T e +Eval: D S D D S D I D S + +Speaker sentences 903: voxforge_eng_000954 #utts: 1 +id: (voxforge_eng_000954-voxforge_eng_000954) +Scores: (#C #S #D #I) 47 3 2 1 +REF: h e p u l l e d a n d t h e l o G c r A s H e D d o w n t o b r E a k * h i s b a c k +HYP: h e p u l l e d a n d t h e l o K c r E s * e T d o w n t o b r * a k E h i s b a c k +Eval: S S D S D I + +Speaker sentences 904: voxforge_eng_000955 #utts: 1 +id: (voxforge_eng_000955-voxforge_eng_000955) +Scores: (#C #S #D #I) 63 5 8 0 +REF: t h a t t h e s o c a L l E d f o r C e s a t w o r k i n l i g h t h e A T E l E c t r i C i t y a n d m a g n E t i s m I N +HYP: t h a t t h e s o ******* c a * l * d f o r S e s a t w o r k i n l i g h t h e * E A l * c t r i S i t y a n d m a g n A t i s m ******* * * +Eval: D D D S D S S D S S D D D + +Speaker sentences 905: voxforge_eng_000956 #utts: 1 +id: (voxforge_eng_000956-voxforge_eng_000956) +Scores: (#C #S #D #I) 40 9 3 3 +REF: h e t U r n E d s h a r p l y * a n d F A c e D g r E g s O n a c R o s S t h e * T A B l e * +HYP: h e t O r n * d s h a r p l y D a n d P I c e * g r A g s I n a c * o s T t h e P I V E l e R +Eval: S D I S S D S S D S I S S S I + +Speaker sentences 906: voxforge_eng_000957 #utts: 1 +id: (voxforge_eng_000957-voxforge_eng_000957) +Scores: (#C #S #D #I) 22 0 1 1 +REF: a l ******* s o i w a n t i n f O r m a t i o n +HYP: a l s o i w a n t i n f * r m a t i o n +Eval: I D + +Speaker sentences 907: voxforge_eng_000958 #utts: 1 +id: (voxforge_eng_000958-voxforge_eng_000958) +Scores: (#C #S #D #I) 44 2 2 1 +REF: t h e s i x t H d a y h e s p e n t i n t h e c a B I n w i T h g r e * g s o n +HYP: t h e s i x t * d a y h e s p e n t i n t h e c a V E n w i * h g r e A g s o n +Eval: D S S D I + +Speaker sentences 908: voxforge_eng_000959 #utts: 1 +id: (voxforge_eng_000959-voxforge_eng_000959) +Scores: (#C #S #D #I) 99 11 8 2 +REF: o n t h i s H Y p o t h E S I s t h e h a M m e r i n g o f t h e U l t r A m u n d * A n E c o r p u s C l e s o n t h e b o b c o n f E r s * i t s K I n E t i C E n e R g y o n t h e o n E h a n d +HYP: o n t h i s * I p o t h I C E s t h e h a * m e r i n g o f t h e * l t r * m u n d Y I n G c o r p u s * l e s o n t h e b o b c o n f I r s E i t s C A n A t i K * n e * g y o n t h e o n * h a n d +Eval: D S S S S D D D I S S D S I S S S S D D D + +Speaker sentences 909: voxforge_eng_000960 #utts: 1 +id: (voxforge_eng_000960-voxforge_eng_000960) +Scores: (#C #S #D #I) 78 4 5 1 +REF: n o w a f E r n y w i L l O w Y s t r e A m * a n d e v e r a n D a n o n y o u E m E r g e f r o m a L l t h e g r o v e s a n d f l o w e r s +HYP: n o w a f I r n y w i * l * w E s t r e * m E a n d e v e r a n * a n o n y o u A m U r g e f r o m a * l t h e g r o v e s a n d f l o w e r s +Eval: S D D S D I D S S D + +Speaker sentences 910: voxforge_eng_000961 #utts: 1 +id: (voxforge_eng_000961-voxforge_eng_000961) +Scores: (#C #S #D #I) 66 3 5 1 +REF: w i t h ******* o u t i t t h e m o s T d e n s e l y p o p u l a t e d r E g I o n s o f m o D e R n E u r O p E a n d a m e r i c a +HYP: w i t h o u t i t t h e m o s * d e n s e l y p o p u l a t e d r A g * o n s o f m o T e * n Y u r * p * a n d a m e r i c a +Eval: I D S D S D S D D + +Speaker sentences 911: voxforge_eng_000962 #utts: 1 +id: (voxforge_eng_000962-voxforge_eng_000962) +Scores: (#C #S #D #I) 22 1 0 1 +REF: t o m s p i n k * h a s a h a r p o o n +HYP: t o m E s p i n k E h a s a h a r p o o n +Eval: S I + +Speaker sentences 912: voxforge_eng_000963 #utts: 1 +id: (voxforge_eng_000963-voxforge_eng_000963) +Scores: (#C #S #D #I) 42 1 17 0 +REF: h e w A n t e D T O g I V e t h e F i n i s h t O t h i s f o E a L r e A D y s o f a R g o n E +HYP: h e w * n t e * ******* * * g * * e t h e * i n i s h t * t h i s f o W a * r e * * y ******* s o ******* f a * ******* g o n * +Eval: D D D D D D D D D S D D D D D D D D + +Speaker sentences 913: voxforge_eng_000964 #utts: 1 +id: (voxforge_eng_000964-voxforge_eng_000964) +Scores: (#C #S #D #I) 55 1 11 1 +REF: l I k e a f l a s H h e l A U n c H e d H i m s e l f i n t O t h e f e A t h E R e d m a S s o f t h e * o w l +HYP: l * k e a f l a s * ******* h e l * O n c * e d * i m s e l f i n t * t h e f e * t h * * e d m a * s o f t h e H o w l +Eval: D D D D S D D D D D D D I + +Speaker sentences 914: voxforge_eng_000965 #utts: 1 +id: (voxforge_eng_000965-voxforge_eng_000965) +Scores: (#C #S #D #I) 35 0 2 3 +REF: i t c o n t a i n * s a t o t A l * o f t ******* w e n t y e n t r I e s +HYP: i t c o n t a i n E s a t o t * l E o f t w e n t y e n t r * e s +Eval: I D I I D + +Speaker sentences 915: voxforge_eng_000966 #utts: 1 +id: (voxforge_eng_000966-voxforge_eng_000966) +Scores: (#C #S #D #I) 23 2 0 2 +REF: i ******* * V e F e l t m o r e c o m f o r t a b l e +HYP: i H A e H e l t m o r e c o m f o r t a b l e +Eval: I I S S + +Speaker sentences 916: voxforge_eng_000967 #utts: 1 +id: (voxforge_eng_000967-voxforge_eng_000967) +Scores: (#C #S #D #I) 22 7 2 0 +REF: D I D I p o s s e S s t O o m U c h v I t A l i t y +HYP: T H A A p o s s e * s t * o m A c h v A t E l i t y +Eval: S S S S D D S S S + +Speaker sentences 917: voxforge_eng_000968 #utts: 1 +id: (voxforge_eng_000968-voxforge_eng_000968) +Scores: (#C #S #D #I) 40 6 1 1 +REF: t h e w O l f d o g * t h r U s T h i s g A U n t m u Z Z l e t o w a r d h i m +HYP: t h e w A l f d o g E t h r E s D h i s g * O n t m u S A l e t o w a r d h i m +Eval: S I S S D S S S + +Speaker sentences 918: voxforge_eng_000971 #utts: 1 +id: (voxforge_eng_000971-voxforge_eng_000971) +Scores: (#C #S #D #I) 35 1 5 2 +REF: t h e g a * b R i E l v o i c e o f T h e s A m U r A i * r a n g o u t +HYP: t h e g a V b * i A l v o i c e o f * h e s * m * r * i Y r a n g o u t +Eval: I D S D D D D I + +Speaker sentences 919: voxforge_eng_000972 #utts: 1 +id: (voxforge_eng_000972-voxforge_eng_000972) +Scores: (#C #S #D #I) 53 3 5 3 +REF: i t w a s o U R r i v e r * * E m E r g i n g l i * k e o U r s e l V e s f r o m t h e G r e a t s w A m p +HYP: i t w a s o * * r i v e r N D m A r g i n g l i A k e o * r s e l * e s f r o m t h e * r e a t s w O m p +Eval: D D I I S S I D D D S + +Speaker sentences 920: voxforge_eng_000973 #utts: 1 +id: (voxforge_eng_000973-voxforge_eng_000973) +Scores: (#C #S #D #I) 65 4 12 0 +REF: s a i d t h e m O l E p u L l i n g h i m s e l f t o g e t h e R W i T h a N e F f O r t y o U m u s T t h i n K m e v e r y r U d E +HYP: s a i d t h e m A l * p u * l i n g h i m s e l f t o g e t h e * * i * h ******* a * e * f A r t y o * m u s * ******* t h i n G m e v e r y r O d * +Eval: S D D D D D D D D S D D D S S D + +Speaker sentences 921: voxforge_eng_000974 #utts: 1 +id: (voxforge_eng_000974-voxforge_eng_000974) +Scores: (#C #S #D #I) 62 4 5 3 +REF: i n w h a t b * u c o l i c * s c H o * o L o f f e n c e h e h a d b E e n t A U G H t w a s b e Y o n d i m a g I n i n g +HYP: i n w h a t b E u c o l i c K s c * o U o * o f f e n c e h e h a d b * e n t * * O R t w a s b e o n d i m a g E n i n g +Eval: I I D I D D D D S S S S + +Speaker sentences 922: voxforge_eng_000975 #utts: 1 +id: (voxforge_eng_000975-voxforge_eng_000975) +Scores: (#C #S #D #I) 61 3 4 4 +REF: h a d n o t E n a b l e d i n ******* v e s t i g a t O r s t o o b ******* t a i n * a T c o m p A r A t i v e l y l i T t l E c o s * t +HYP: h a d n o t I n a b l e d i n v e s t i g a t E r s t o o b t a i n E a * c o m p * r I t i v e l y l i * t l * c o s E t +Eval: S I S I I D D S D D I + +Speaker sentences 923: voxforge_eng_000976 #utts: 1 +id: (voxforge_eng_000976-voxforge_eng_000976) +Scores: (#C #S #D #I) 39 1 2 0 +REF: a t r i c K l E o f f r e s h b l o O d r a n o v e r h i s f a c e +HYP: a t r i c * l * o f f r e s h b l o U d r a n o v e r h i s f a c e +Eval: D D S + +Speaker sentences 924: voxforge_eng_000977 #utts: 1 +id: (voxforge_eng_000977-voxforge_eng_000977) +Scores: (#C #S #D #I) 24 2 2 1 +REF: i t w a s a c u r I O u s c o i n C i * d E n c e +HYP: i t w a s a c u r * * u s c o i n E i T d A n c e +Eval: D D S I S + +Speaker sentences 925: voxforge_eng_000978 #utts: 1 +id: (voxforge_eng_000978-voxforge_eng_000978) +Scores: (#C #S #D #I) 30 0 0 1 +REF: i t i s t h e f i r e p a r t l y s h e s a i d * +HYP: i t i s t h e f i r e p a r t l y s h e s a i d N +Eval: I + +Speaker sentences 926: voxforge_eng_000979 #utts: 1 +id: (voxforge_eng_000979-voxforge_eng_000979) +Scores: (#C #S #D #I) 40 2 4 3 +REF: t h e Y j u s t l a y o F f i n t h e B U s h a n d p l * u G G e d a w a y * * +HYP: t h e * j u s t l a y o * f i n t h e * O s h a n d p l O u * K e d a w a y A N +Eval: D D D S I D S I I + +Speaker sentences 927: voxforge_eng_000980 #utts: 1 +id: (voxforge_eng_000980-voxforge_eng_000980) +Scores: (#C #S #D #I) 40 3 9 2 +REF: i K n o W t h a t Y o u A r E i n c h a r * g e t h e r e a n d J e A N N e K n o W s * +HYP: i * n o * t h a t * o u W E r * i n c h a r D g e t h e r e a n d G e * * * e * n o * s E +Eval: D D D S S D I S D D D D D I + +Speaker sentences 928: voxforge_eng_000981 #utts: 1 +id: (voxforge_eng_000981-voxforge_eng_000981) +Scores: (#C #S #D #I) 49 2 5 0 +REF: f o r A t i M e t h e e x C i t i n g t h r i l L o f h i s a d v e n t u R e w a s g o n E +HYP: f o r ******* * t i * e t h e e x S i t i n g t h r i l E o f h i s a d v e n t u * e w a s g o n * +Eval: D D D S S D D + +Speaker sentences 929: voxforge_eng_000982 #utts: 1 +id: (voxforge_eng_000982-voxforge_eng_000982) +Scores: (#C #S #D #I) 44 5 8 1 +REF: S u D d E n l y h i s f i n g e r s c l o s e D t I G h * T l y o v e R t h e h a n D K E R c h i E f +HYP: F u * d * n l y h i s f i n g e r s c l o s e * t * * h I D l y o v e * t h e h a n * G A O c h i * f +Eval: S D D D D D I S D D S S S D + +Speaker sentences 930: voxforge_eng_000983 #utts: 1 +id: (voxforge_eng_000983-voxforge_eng_000983) +Scores: (#C #S #D #I) 48 5 2 5 +REF: d e a r s i r y o U r s e c * O n D v i c t I m h a s f A l l E n o n s c H e * d * * u * l e t i m e +HYP: d e a r s i r y o * r s e c K A n T v i c t O m h a s f O l l O n o n s c * e A d G J u A l e t i m e +Eval: D I S S S S S D I I I I + +Speaker sentences 931: voxforge_eng_000984 #utts: 1 +id: (voxforge_eng_000984-voxforge_eng_000984) +Scores: (#C #S #D #I) 19 0 4 0 +REF: h e c A n c a R e f O r H i m s e l f +HYP: h e c * n c a * e f * r * i m s e l f +Eval: D D D D + +Speaker sentences 932: voxforge_eng_000985 #utts: 1 +id: (voxforge_eng_000985-voxforge_eng_000985) +Scores: (#C #S #D #I) 40 1 2 2 +REF: e a c h i n s u l t a D d e d t o t h e v A l * u E o f t h e c l a i m * +HYP: e a c h i n s u l t a * d e d t o t h e v O l O u * o f t h e c l a i m E +Eval: D S I D I + +Speaker sentences 933: voxforge_eng_000986 #utts: 1 +id: (voxforge_eng_000986-voxforge_eng_000986) +Scores: (#C #S #D #I) 76 1 8 0 +REF: t h o u G H i t m a y b e t r a n s f o r m e d i n t o a n y O n E o f t h e f o r m s o f w h I c h e n E r g y i s s U s C e p t i b l E +HYP: t h o u * * i t m a y b e t r a n s f o r m e d i n t o a n y * n * o f t h e f o r m s o f w h * c h e n * r g y i s s E s * e p t i b l * +Eval: D D D D D D S D D + +Speaker sentences 934: voxforge_eng_000987 #utts: 1 +id: (voxforge_eng_000987-voxforge_eng_000987) +Scores: (#C #S #D #I) 62 13 7 12 +REF: m e R C E d * e s s c r e a m e d C r i e d l * a U G H E D A N D m a n I f e s t e d t h e C h * * a O t i c * a * * b * A n ******* d * O n ******* m e n t o f h Y s t E R i a * +HYP: m e S I T d O e s s c r e a m e d G r i e d l O a * * * * F * * I m a n Y f e s t e d t h e * h I R a D t i c K a N D b O U n d H E n m e n t o f h I s t A D i a R +Eval: S S S I S I D D D D S D D S S D I I S I I I I S I I S I S S S I + +Speaker sentences 935: voxforge_eng_000988 #utts: 1 +id: (voxforge_eng_000988-voxforge_eng_000988) +Scores: (#C #S #D #I) 35 1 3 1 +REF: i w a n T t o K n o W h o w a l l t h i s i s p o s S i * b l e +HYP: i w a n * t o * n o * h o w a l l t h i s i s p o s E i V b l e +Eval: D D D S I + +Speaker sentences 936: voxforge_eng_000989 #utts: 1 +id: (voxforge_eng_000989-voxforge_eng_000989) +Scores: (#C #S #D #I) 86 1 10 2 +REF: p r E S e n t i n g a s i m p l E a n d i n s t r u c t i v E i L l u s t r a t i o n o f t h e s t r U G g l E f o r l i f e a m O n g t h e r i v A l * s p e * c I e s +HYP: p r * * e n t i n g a s i m p l * a n d i n s t r u c t i v * i * l u s t r a t i o n o f t h e s t r * * g l * f o r l i f e a m * n g t h e r i v E l E s p e A c * e s +Eval: D D D D D D D D D S I I D + +Speaker sentences 937: voxforge_eng_000990 #utts: 1 +id: (voxforge_eng_000990-voxforge_eng_000990) +Scores: (#C #S #D #I) 41 1 2 1 +REF: h E l l n e v e r d o a t a p o f w o r k t h e W h o l E v o y a g e * +HYP: h I l l n e v e r d o a t a p o f w o r k t h e * h o l * v o y a g e H +Eval: S D D I + +Speaker sentences 938: voxforge_eng_000991 #utts: 1 +id: (voxforge_eng_000991-voxforge_eng_000991) +Scores: (#C #S #D #I) 40 2 3 0 +REF: i h a V e h u n t e d a l o n g t h i s r i D G e r e p l i e d P H i l i p +HYP: i h a * e h u n t e d a l o n g t h i s r i * C e r e p l i e d * F i l i p +Eval: D D S D S + +Speaker sentences 939: voxforge_eng_000992 #utts: 1 +id: (voxforge_eng_000992-voxforge_eng_000992) +Scores: (#C #S #D #I) 31 2 5 0 +REF: l o r d b u t i m g L A d t o s E e y o U a g A i n P H i l +HYP: l o r d b u t i m g * E d t o s * e y o * a g * i n * F i l +Eval: D S D D D D S + +Speaker sentences 940: voxforge_eng_000993 #utts: 1 +id: (voxforge_eng_000993-voxforge_eng_000993) +Scores: (#C #S #D #I) 25 6 10 1 +REF: h o W v A l i A n T l y i w e n T * a T I T t h a T f I r s T D a Y +HYP: h o * ******* v E l i * n * l y i w e n * D a * D E D t h a * ******* f r s * T a * +Eval: D D S D D D I D S S S D D S D S D + +Speaker sentences 941: voxforge_eng_000994 #utts: 1 +id: (voxforge_eng_000994-voxforge_eng_000994) +Scores: (#C #S #D #I) 41 3 10 0 +REF: t h e Y a r E N o t r e g u l A R o Y s t e r p i r A t E s n i c H O l A s c o n t I n U e d +HYP: t h e * a r * * o t r e g u l * E o * s t e r p i r E t * s n i c * * l E s c o n t * n * e d +Eval: D D D D S D S D D D S D D + +Speaker sentences 942: voxforge_eng_000995 #utts: 1 +id: (voxforge_eng_000995-voxforge_eng_000995) +Scores: (#C #S #D #I) 73 1 17 2 +REF: t h e Y m U s t b e h U r T i n g f o r b u s I n e S s b u t i t h O u g H T y o u m i g H t w a N t t O t a k e A l O o k A t t h e I r s i * * t E +HYP: t h e * m * s t b e h * r D i n g f o r b u s * n e * s b u t i t h * u g * * y o u m i g * t w a * t t * t a k e ******* * l * o k * t t h e * r s i G H t * +Eval: D D D S D D D D D D D D D D D D D I I D + +Speaker sentences 943: voxforge_eng_000996 #utts: 1 +id: (voxforge_eng_000996-voxforge_eng_000996) +Scores: (#C #S #D #I) 43 1 3 0 +REF: t h e r E w a s n o c H a n c e t o f i r e w i t h o u t h i T T i n g h i m +HYP: t h e r * w a s n o c * a n c e t o f i r e w i t h o u t h i * N i n g h i m +Eval: D D D S + +Speaker sentences 944: voxforge_eng_000997 #utts: 1 +id: (voxforge_eng_000997-voxforge_eng_000997) +Scores: (#C #S #D #I) 58 1 10 2 +REF: a s f o r h i m s e l f w E R E n t t h e s t r e * e T r a I l ******* w a y E a r n i n g s i n c r e A S i n g s T E a d I l y +HYP: a s f o r h i m s e l f w * * O n t t h e s t r e A e * r a * l w a y * a r n i n g s i n c r e * * i n g s * * a d * l y +Eval: D D S I D D I D D D D D D + +Speaker sentences 945: voxforge_eng_000998 #utts: 1 +id: (voxforge_eng_000998-voxforge_eng_000998) +Scores: (#C #S #D #I) 33 3 3 1 +REF: d U n ******* h A m c a n y o u r b o y g o A l o n g w i t H J e s s E +HYP: d O n h I m c a n y o u r b o y g o * l o n g w i t * * e s s Y +Eval: S I S D D D S + +Speaker sentences 946: voxforge_eng_000999 #utts: 1 +id: (voxforge_eng_000999-voxforge_eng_000999) +Scores: (#C #S #D #I) 19 3 3 1 +REF: g o O d ******* b y E p I e R r E h e s h o U t e d +HYP: g o L d b y * p * e A r * h e s h o W t e d +Eval: S I D D S D S + +Speaker sentences 947: voxforge_eng_001000 #utts: 1 +id: (voxforge_eng_001000-voxforge_eng_001000) +Scores: (#C #S #D #I) 61 4 3 6 +REF: b u t s u c h ******* * d I v e r * g * e n C E o f O p i n i o n w o u l d c o n s t i t u t E n o m e n * A c e t o s o * c i E t y +HYP: b u t s u c h A d E v e r D g I e n * S o f A p i n i o n w o u l d c o n s t i t u t * n o m e n E N c e t o s o S c i * t y +Eval: I I S I I D S S D I S I D + +Speaker sentences 948: voxforge_eng_001001 #utts: 1 +id: (voxforge_eng_001001-voxforge_eng_001001) +Scores: (#C #S #D #I) 45 2 3 3 +REF: * ******* t h e r e w a s o n e c h a n c e * a n d o n l y o n E o f s a v i n g j E A N n E +HYP: T t h e r e w a s o n e c h a n c e S a n d o n l y o n * o f s a v i n g j * * O n T +Eval: I I I D D D S S + +Speaker sentences 949: voxforge_eng_001002 #utts: 1 +id: (voxforge_eng_001002-voxforge_eng_001002) +Scores: (#C #S #D #I) 24 2 2 3 +REF: * ******* i c a n N o t f o L l o W y o U s h e s a i * d +HYP: I i c a n o t f o * l o E y o * s h e s a i N d +Eval: I I S D S D I + +Speaker sentences 950: voxforge_eng_001003 #utts: 1 +id: (voxforge_eng_001003-voxforge_eng_001003) +Scores: (#C #S #D #I) 49 5 0 1 +REF: o n t h e f a r c o r n e r o f t h e c o m p o u n d f e n C E a * h a W k b r O O d e d +HYP: o n t h e f a r c o r n e r o f t h e c o m p o u n d f e n T S a W h a O k b r E A d e d +Eval: S S I S S S + +Speaker sentences 951: voxforge_eng_001004 #utts: 1 +id: (voxforge_eng_001004-voxforge_eng_001004) +Scores: (#C #S #D #I) 46 3 4 0 +REF: t h e n a g A i n t U D O r h a d s u c H a N i R r i t a t i n g w a y a b o u t h i m +HYP: t h e n a g * i n t O T E r h a d s u c * a * i * r i t a t i n g w a y a b o u t h i m +Eval: D S S S D D D + +Speaker sentences 952: voxpopuli_eng_000494 #utts: 1 +id: (voxpopuli_eng_000494-voxpopuli_eng_000494) +Scores: (#C #S #D #I) 68 2 11 13 +REF: w e a l l K n o w o m a n a s a s u C c e S s f U l * s t a b l E c o U n t r y a r o l E m o * ******* * D e * * * * ******* * * * L f o r t h e W h o l E r e * g I o n +HYP: w e a l l * n o w o m a n a s a s u * c e * s f * l E s t a b l * c o * n t r y a ******* r o l * m o R T H e R F O R T H A T f o r t h e * h o l * r e A g * o n +Eval: D D D D I D D D D I I I S I I I I I I I I S D D I D + +Speaker sentences 953: voxpopuli_eng_000495 #utts: 1 +id: (voxpopuli_eng_000495-voxpopuli_eng_000495) +Scores: (#C #S #D #I) 132 17 20 5 +REF: t h e r e f o r E i t s h i g h t i m e T H A T Y o u c o m e f o r W A R d W I t h A p r o p o s a l f o r r e v I e W W I T H * a n o p E r a T i o n a l s E p A r a T i o n o f t h e * a U d i t a n d n o n a U d i t s e r v i * C e s u n d e r a d i R e c t e * u s ******* U P e R V i s I o n +HYP: t h e r e f o r * i t s h i g h t i m e ******* * * * * * o u c o m e f o r * B O d E t h ******* E p r o p o s a l f o r r e v * e U * * B E D a n ******* o p * r a * i o n a l s U p E r a C i o n o f t h e O a R d i t a n d n o n a * d i t ******* s e r v i S I e s u n d e r a d i * e c t e A u ******* s O B e I T i s * o n +Eval: D D D D D D D D S S S S D S D S D D S S I D D D S S S I S D D I S D I D I S S S S D + +Speaker sentences 954: voxpopuli_eng_000496 #utts: 1 +id: (voxpopuli_eng_000496-voxpopuli_eng_000496) +Scores: (#C #S #D #I) 121 12 14 12 +REF: i t i s c L e a r * t h a t w e h a v e n o t i m e t o w a s t E t h e n * e W r e s U l t s o f t h * e i ******* p * * * C C r e G a r d I N G T H E s C i e n t i f i c b a * s I s o f C l i m A t E C H a * n G e l e A v e n o r o * o m * f o r h e s i t * a T I o n +HYP: i t i s ******* c K e a r E t h a t w e h a v e n o t i m e t o w a s t * t h e n U e * ******* r e s O l t s o f t h E e i p E E S H E r e C a r d * * * * * N s * i e n t i f i c b a C s E s o f G l i m I t * * J a I n S e l e * v e n o r o U o m E f o r h e s i t D a * S o n +Eval: D S I D I D D S I I I I I S S S D D D D D S D I S S S D D S I S D I I I D S + +Speaker sentences 955: voxpopuli_eng_000497 #utts: 1 +id: (voxpopuli_eng_000497-voxpopuli_eng_000497) +Scores: (#C #S #D #I) 76 4 11 7 +REF: * * * 5 s o i n t h e c o n t a i n e r S w h i C h a R E N e v e r E V e n t O u c h e d c o m e s l a v e s c o u n t e R f e I t g O o d s d r u g s ******* * * * e t C +HYP: S E N T s o i n t h e c o n t a i n e r * w h i * h ******* a * * ******* * e v e r * A e n t * u c h e d c o m e s l a v e s c o u n t e O f e * t g * o d s d r u g s I T S e t R +Eval: I I I S D D D D D D D D S D S D D I I I I S + +Speaker sentences 956: voxpopuli_eng_000498 #utts: 1 +id: (voxpopuli_eng_000498-voxpopuli_eng_000498) +Scores: (#C #S #D #I) 124 12 20 8 +REF: i h o p e t h a T t H E c o M m I S S i o n s m o b i * L i * * * * T Y i n i T i A T i v e s W I L L N o * t c r E a t E t h e n e x t p r o b l E m b u t w i l l b e a N a n s W e r f o r e x i s t i n g c h a L l E n g e s o f t h e r ******* o A D t R a n s p o r * T s e c t o R +HYP: i h o p e t h a * ******* t * * c o * m * * * i o n s m o b i T i N E S H E S i n i S i * F i v e s * * H O * o N t c r * a t * t h e n e x t p r o b l O m b u t w i l l b e a * a n s * e r f o r e x i s t i n g c h a * l I n g e s o f t h e ******* r o U T t * a n s p o r E D s e c t o * +Eval: D D D D D D D D I S I I I I S S S D S D D S S D I D D S D D D S D I S S D I S D + +Speaker sentences 957: voxpopuli_eng_000499 #utts: 1 +id: (voxpopuli_eng_000499-voxpopuli_eng_000499) +Scores: (#C #S #D #I) 306 25 46 20 +REF: i n t h e u S i t w a s a d E C i S i o n t a K E n O N l y b y o n e p E r s o n t h e F o r m e r p r e s i d e n t o F t h e U n i * T e d s t a t e s a g a I n S T t h e a R t i c u l a t e d D E m O c r a t i c * * m a j O r i t y o F t h ******* e * U s c o n g r e S s b y a l l o f i t s r e p u b l i c * A n A n d s o m E O f i t s ******* * * * * * * * * * * ******* * d e m O c r a t m e m b e r s i t w a s a n a g r E e m e n t w i t h o u t a n y b i n d I n g o b l i g a t i o n s a S T h e l e A d e R s o f I r A n v e r y O p E n l y a n D +HYP: i n t h e W u E i t w a s ******* a d * * i C i o n t a * G n ******* A U l y b y o n e p * r s o n t h e * o r m e r p r e s i d e n t o * t h e ******* * n i G D e d s t a t e s a g a * n C E t h e a * t i c u l a t e d * * m * c r a t i c D U m a j U r i t y o * t h e U E s c o n g r e * s b y a l l o f i t s r e p u b l i c K E n * n d s o m * * f ******* i t s D E M E C R A T I C T d e m A c r a t m e m b e r s ******* i t w a s ******* a n a g r * e m e n t w i t h o u t a n y b i n d * n g o b l i g a t i o n s a T * h e l e * d e * s o f E r U n v e r y U p A n l y a n * +Eval: S S D D D S D S D S S D D D D D I S D S S D D D D I I S D I I S D I S D D D D I I I I I I I I I I I I I S D D D D S D D D S S S S D + +>> REF: p r e C I s E L Y m a D E C l E A R O n t h e V e r y d a y t h I S s o c a L l E d d e A l w a s p * u B l i s h e D +>> HYP: ******* p r e * * s I D H m a * * P T l * * Y * n ******* t h e * e r y d a y t h * E s o ******* c a * l * d d e * l w a s p O u * l i s h e * +>> Eval: D D D S S S D D S S D D S D D D D S D D D D I D D + +Speaker sentences 958: voxpopuli_eng_000500 #utts: 1 +id: (voxpopuli_eng_000500-voxpopuli_eng_000500) +Scores: (#C #S #D #I) 107 4 12 5 +REF: f r E e s p e E c h i s E S s e n T i * a L l y a C C e P t i N g t h A t p e o p l E a r e f r e e t o s a y t h i n g s w e d * ******* * o * l i k E n o t m E R e l y f r e e t o s a y t h i n g s w e d o l i k E +HYP: f r * e s p e A c h i s * A s e n * i U a * l y a E X e * t i * g t h * t p e o p l * a r e f r e e t o s a y t h i n g s w e d O N o T l i k * n o t m * * e l y f r e e t o s a y t h i n g s w e d o l i k * +Eval: D S D S D I D S S D D D D I I I I D D D D + +Speaker sentences 959: voxpopuli_eng_000501 #utts: 1 +id: (voxpopuli_eng_000501-voxpopuli_eng_000501) +Scores: (#C #S #D #I) 15 5 2 1 +REF: L E t U s l E A r n * f R o m t h i S +HYP: H A t I s l * U r n E f * o m t h i E +Eval: S S S D S I D S + +Speaker sentences 960: voxpopuli_eng_000502 #utts: 1 +id: (voxpopuli_eng_000502-voxpopuli_eng_000502) +Scores: (#C #S #D #I) 243 16 45 14 +REF: W e T H i n K t h a t t h e E n v i R O N m e n t a l e f f e c t o f p r o d u c T s m u s t b e a v E r y i * m p o r t a n t i S s u e i n T h e * * e * u a n d t h e w H o l E i ******* d e a * o F * A N e c O l a b e L g i v E s a v e r Y * u s E F u l o r i E n t a t i o n f o r * * * C o N s u m e r s o f c o u R s E T h e e c O l a b e L S h o u l d B E g i v e n t o t h e m o s t E n * v i r O N m e n t ******* a L L Y f R I E n d L y p R o d u c t S A N D t h e i n f o r m a t i o n s H o u l d b e c l e a r * a n d c O R R e C T +HYP: B e * S i n * t h a t t h e * n v i * * * m e n t a l e f f e c t o f p r o d u c * s m u s t b e a ******* v * r y i N m p o r t a n t i * s u e ******* i n * h e R E e W u a n d t h e w * o l * i d e a E o * T H E e c U l a b e R g i v * s a v e r * Y u s * O u l ******* o r i A n t a t i o n f o r T H E o U s u m e r s o f c o u * s * * h e e c U l a b e R * h o u l d ******* * * g i v e n t o t h e m o s t A n D v i r * * m e n t a * * F f * * A n d * y p * o d u c t * ******* * * * t h e i n f o r m a t i o n s * o u l d b e ******* c l e a r E a n d c * * U e * * +Eval: S D S D D D D D D D D I D D D I I I D D I I D I S S S S D D I D S D S I I I S S D D D S S D D D D S I D D I D D S D D S D D D D D D D D D I D D S D D + +Speaker sentences 961: voxpopuli_eng_000503 #utts: 1 +id: (voxpopuli_eng_000503-voxpopuli_eng_000503) +Scores: (#C #S #D #I) 132 10 28 6 +REF: h o w e v e r t h e c U R r e n T r E g I m E n E e d * s t o b e b e T t e r * T a I l o r E d * t o t h E D i g i T a l E n v i r O n m e n t I N O R D E R t o E N s * u r e f a I r R E m U n e r a t i o n t o C r e a t O r s a * n D t o C o n f o R m * t o C o n s u m e r e x p e c t a t i o n s +HYP: h o w e v e r t h e c * A r e n * D r Y g E m * n * e d E s t o b e b e * t e r D * a * l o r * d T t o t h * * i g i D a l I n v i r * n m e n t ******* * * ******* * * * * * t o * I s H u r e f a * r * * m I n e r a t i o n t o G r e a t E r s a E n * t o * o n f o * m E t o * o n s u m e r e x p e c t a t i o n s +Eval: D S D S S S D D I D I D D D I D D S S D D D D D D D D D D D S I D D D S S S I D D D I D + +Speaker sentences 962: voxpopuli_eng_000504 #utts: 1 +id: (voxpopuli_eng_000504-voxpopuli_eng_000504) +Scores: (#C #S #D #I) 157 11 35 1 +REF: I t c a L L s * U P O N t h e c O M m I S S i o n a n d m e m b e r s t a t E S t o E n h a n C e t h e I r s u P p o r t F o R r e c o n c i l i a t i o n t o s e c u r E p e A C e a n d S t A b i l i t y a n d I r E l a n d i w o U l D t h e r e f o r e U r G E y O u c O L L E a G U e s t o p l e a s e s u P p o r t T H i s a m e n D m e n T +HYP: A t c a * * s E * * B Y t h e c * * m * * * i o n a n d m e m b e r s t a t * * t o * n h a n * e t h e * r ******* s u * p o r t T o * r e c o n c i l i a t i o n t o s e c u r * p e * S e a n d * t I b i l i t y a n d A r * l a n d i ******* w o * l * t h e r e f o r e A r * D y * u c * * * * a L I e s t o p l e a s e s u * p o r t * * i s a m e n * m e n * +Eval: S D D I D D S S D D D D D D D D D D D D S D D D S D S S D D D D S D S D D D D D S S D D D D D + +Speaker sentences 963: voxpopuli_eng_000505 #utts: 1 +id: (voxpopuli_eng_000505-voxpopuli_eng_000505) +Scores: (#C #S #D #I) 221 27 37 15 +REF: S t r a t E g i c * c h o i c e s a b o u t w h E R e t o I N V e s t m u S t b e m a d e n o w t a k I n G i n T O A C c o u n T T H E n E e D t o P H a s E o u t f o * S s i l f u E l s u B s i D I e s b u t t a k ******* * * e g a s a s A F o S s I L f * u E L i t c a n b e a h e l P f u l * b r i D g i n g t r A n s i T I o n a r y m e d i U m t o b e u s e D i n * * m A n Y m e * * ******* m b e r s t a t E S i F W e W A n T t o * a c h i E v e o * U r a m b i * T i o U s c l i m A T E t a r g E t s +HYP: * t r a t A g i c K c h o i c e s a b o u t w h * * e t o E W e s t m u * t b e m a d e n o w t a k E n * i n * * E c o u n * * * A n * e * t o * F a s * o u t f o R s i l ******* f u * l s u P s i * T e s b u t t a k T H e g a s a s I * o R s * * O f Y u * * i t c a n b e a h e l T f u l E b r i * g i n g t r U n s i S H o n a r y m e d i O m t o b e u s e * i n M E m I n * m e N Y m b e r ******* s t a t * * i * B e * O n * ******* t o E a c h i * v e o V E r a m b i S H i o * s c l i m * * * I t a r g I t s +Eval: D S I D D S S S D S D D D S S D D D S D D D S D I S D D S D S I I I S D S D D S I D D S I D S S S S D I I S D I I I D D D D S D S D D I D I S I S D D D D S S + +Speaker sentences 964: voxpopuli_eng_000506 #utts: 1 +id: (voxpopuli_eng_000506-voxpopuli_eng_000506) +Scores: (#C #S #D #I) 114 13 41 15 +REF: M I D D L E E A S T w e a R e p o s S i B l y A T a T H R E S H o l D w e c a n c H O O S E t o p U r * s * u e t h e s a m e * p o l i c I e s i n t h E s a m e m a N n e r K n o w i n g t h a T t H E Y w I L L l e A d * t o t h E s a m E r E s U L T s t h e r E s * u l T s t h a ******* * * * * ******* * * * * T +HYP: * * * * * * ******* * * * * ******* w e a * e p o s E i * l y F R a * * * * * * o l E w e c a n c * * U T H t o p * r A s C u e t h e s a m e M p o l i c * e s i n t h * s a m e m a * n e r * n o w i n g t h a * ******* t * * * w * * E l e * d E t o t h * I s a m * P r * s * * O s t h e r I s A u l * s t h a W E N O D E D E A +Eval: D D D D D D D D D D D D D S D S S D D D D D D S D D S S S D I I I D D D D D D D D D D D S D I D S D S D D D S S I D I I I I I I I I I I S + +Speaker sentences 965: voxpopuli_eng_000507 #utts: 1 +id: (voxpopuli_eng_000507-voxpopuli_eng_000507) +Scores: (#C #S #D #I) 16 0 6 2 +REF: B u t T h e r E I s a n o p t i o n ******* * +HYP: * u t * h e r * * s ******* a n ******* o p t i o n B +Eval: D D D D D D I I + +Speaker sentences 966: voxpopuli_eng_000508 #utts: 1 +id: (voxpopuli_eng_000508-voxpopuli_eng_000508) +Scores: (#C #S #D #I) 31 3 8 5 +REF: T H I S w * e a * l ******* s o n E e d a c h a * n g e i n o U r i d E o l * O G Y +HYP: * * * * ******* w R e a L l s o n * e d a c h a I n g e i n o * r i d * o l I T I E +Eval: D D D D D I I I D I D D I S S S + +Speaker sentences 967: voxpopuli_eng_000509 #utts: 1 +id: (voxpopuli_eng_000509-voxpopuli_eng_000509) +Scores: (#C #S #D #I) 229 26 29 25 +REF: a l a R G e * P a R t o f t h e r e a s o n I S o f c o u r s e i * ******* L l E g a l f i s H i n g * ******* * * * * M O r e o f * T E N t H A n N o * * T b * ******* y * * * v e S s e L s w h i c h a r e r e * g i s t e r E d t o c o u n t r I e s w h i c h l A c k * t h e w i L l o R t h e r e s O u r c e s t o E n f o r C E i n t * e R n A T i O n a l a g r E e m e n T s n o A m o u n t o f t r A C e * a b i L i t y m e A s U r E s o r * e x t r A p a p E r * w O r K w i L l a D d R e s S t h e p r o b l * E m * o f r e d u C i n g +HYP: a l a * D e H B a * t o f t h e r e a s o n ******* * * o f c o u r s e i S I l I g a l ******* f i s C i n g K A N D T H E r e o f O M P t D O n * o F E N b Y y A R R v e * s e * s w h i c h a r e r e A g i s t e r * d t o c o u n t r * e s w h i c h l U c k E t h e w i * l o F t h e r e s * u r c e s t o * n f o r S T i n t H e n E S i * n a l a g r * e m e n * s n o * m o u n t o f t r * * e S a b i * i t y m e * s E r * s o r E e x t r * ******* p a p * r E w A r E w i * l a * d * e s E t h e p r o b l O U m E o f r e d u S i n g +Eval: D S I S D D D D I I S S D S I I I I I I S S I S S S S S D I I S I I I I I D D I D D S I D S D D S S I S S S D D D D D D I D D S D I D D D I S S D D D S I S I S + +Speaker sentences 968: voxpopuli_eng_000510 #utts: 1 +id: (voxpopuli_eng_000510-voxpopuli_eng_000510) +Scores: (#C #S #D #I) 192 22 47 14 +REF: t h e c o m p r O m i s e a l s o i n c l U d e S C l E a r r u L E s t o * D e ******* f i n e w h i c h M E m b e r s t a t e H a s J U r I s D i c t i o n a n d t h e C O o p E r a t i o n B E t W E E N M E m b e r s t a t E s c o n C e r N E d * I N c r O S s b O r ******* * D e R c a S e s a s W E L l a S t h e n E e d t o * i n v O l V E * E u r O j u s t t h a n K y o U f O R Y o U r w o r k a n d p l E a S e D o * s * u P p o r t t * ******* * * ******* h i s D I r e c t i v E +HYP: t h e c o m p r * m i s e a l s o i n c l * d e D K l * a r E r u * D s t o T H e f i n e w h i c h * * m b e r ******* s t a t e * a s H E r * s T i c t i o n a n d t h e * * o p * r a t i o n * I t * * * * ******* H I m b e r ******* s t a t * s c o n * e r * * d F O R c r * U s ******* b * r T H e * c a C e s a s ******* * * I l ******* a * t h e n * e d t o E i n v * l L F Y O u r j u s t t h a n * y o * ******* f * * * o * r w o r k a n d p l * a * e * o U s E u * p o r t t O M O h i s * E r e c t i v * +Eval: D D S S D S D S I S I D D D D S S D S D D D D S D D D D D S S D D D D D I S S D S D D I I S D S D D D S D D D I D S S I S S D D D D D D D D D D I I D I I I I I D S D + +Speaker sentences 969: voxpopuli_eng_000511 #utts: 1 +id: (voxpopuli_eng_000511-voxpopuli_eng_000511) +Scores: (#C #S #D #I) 200 12 44 12 +REF: * * ******* t h e G r E e n s w o u l d h a v E U s b e l I E V e t h a T t h E S e a r E b a d b E e s c r i m i n a l b E e s d e l i b e R a t E l y c o n t a m i n a t i n g h O N E y w i t h a d a n G e R O u s I n g r e d i e n t b u t i N f a c t * T H E Y A R e d O i n g w h A t h O n E y b E e s * * H a * ******* * v e a l w a Y s d o n E w H i C h I S t o c a R r y p o L l E n b a c K t o t h e I r h i v e s * * * t o f E e d t h e I r Y o u n G +HYP: N O t h e * r * e n s w o u l d h a v * A s b e l * * * e ******* t h a * ******* t h * * e a r * b a d b * e s c r i m i n a l b * e s d e l i b e * a t * l y c o n t a m i n a t i n g h * U D y w i t h ******* a d a n * e * * u s * n g r e d i e n t b u t i T f a c t I N F A C * H e d * i n g ******* w h * t h U n * y b * e s A R a L H v e a l w a * s d o n * w * i * h ******* * * t o c a * r y p o * l O n b a c * t o ******* t h e * r h i v e s T O D t o f * e d t h e * r * o u n * +Eval: I I I D D D S D D D D D D D D D D D D D D S S D D D D D S I S S S S D S D D D S D D I I S I I I D D D D D D D D D S D D D I I I D D D D + +Speaker sentences 970: voxpopuli_eng_000512 #utts: 1 +id: (voxpopuli_eng_000512-voxpopuli_eng_000512) +Scores: (#C #S #D #I) 43 0 5 0 +REF: B u t i t w a s t h e c o U n t r y i t s e l f b e I n g m o r E c a p a b l E +HYP: * u t i t w a s t h e c o * n t r y i t s e l f b e * n g m o r * c a p a b l * +Eval: D D D D D + +Speaker sentences 971: voxpopuli_eng_000513 #utts: 1 +id: (voxpopuli_eng_000513-voxpopuli_eng_000513) +Scores: (#C #S #D #I) 62 4 8 5 +REF: * ******* i n t o t h e p O r t ******* f o l i o o f t h e n e * w c o M m I S S i o n E r d e A l i n g w i t h f u n d A m e n t A L r i G H t * s +HYP: R i n t o t h e p * r t f o l i o o f t h e n e U w c o * m * * * i o n A r d e * l i n g w i t h f u n d E m e n t E R r i * * t E s +Eval: I I D I I D D D D S D S S S D D I + +Speaker sentences 972: voxpopuli_eng_000514 #utts: 1 +id: (voxpopuli_eng_000514-voxpopuli_eng_000514) +Scores: (#C #S #D #I) 43 9 6 0 +REF: t h e m e s S A g E i S t H a t t h e E u d o E S n O t h a v e a n Y n E W s o l u T i o n s +HYP: t h e m e s I Y g * ******* i * t * a t t h e O u d o D T n A t h a v e a n * n O U R s o l u * i o n s +Eval: S S D D D D S S S S D S S S D + +Speaker sentences 973: voxpopuli_eng_000515 #utts: 1 +id: (voxpopuli_eng_000515-voxpopuli_eng_000515) +Scores: (#C #S #D #I) 95 7 4 5 +REF: a r E y o u w i L l i n g t o a c t i n * * * f a v O U r * o F t h e s o C i a l d I m e n S i o n t o b e i n c l * u d e d i n t h e e u c o m p E t e n c I e s a s p r o p o s e D +HYP: a r * y o u w i * l i n g t o a c t i n E R E f a v * E r F o R t h e s o S i a l d E m e n T i o n t o b e i n c l O u d e d i n t h e e u c o m p A t e n c S e s a s p r o p o s e * +Eval: D D I I I D S I S S S S I S S D + +Speaker sentences 974: voxpopuli_eng_000516 #utts: 1 +id: (voxpopuli_eng_000516-voxpopuli_eng_000516) +Scores: (#C #S #D #I) 60 8 20 6 +REF: T H E n e x t S t E P o n * * s p e c t r u M p o l I C Y i S B e I N G t a k E n w i T H t h e R e f o r m o f o u * r t e l E c o * ******* * M f r a m E w o r K +HYP: * * A n e x t H A t * * o n P E s p e c t r u * ******* p o l * * * ******* i * ******* * e * * S t a k I n w i * * ******* t h e * e f o r m o f o u E r t e l I c o N T H f r a m w o r * +Eval: D D S S S D D I I D D D D D D D D D D D S S D D D D I S I I I S S D + +Speaker sentences 975: voxpopuli_eng_000517 #utts: 1 +id: (voxpopuli_eng_000517-voxpopuli_eng_000517) +Scores: (#C #S #D #I) 153 6 9 5 +REF: i b e l I e v e h i s r e m a r k s w e r ******* E e x p l i c i t l y r a c * i s t a n d * X e n O P H o b i c * a n d p r O m o t e d r a c i a l i n t o l e r a n c e i n a w a y t h a T i s n o t A c * c e p t A b l e o r a L l o w E d i n t H e c o n S t i t u t i o n o f t h i s h o u s E +HYP: i b e l * e v e h i s r e m a r k s w e r A e x p l i c i t l y r a c E i s t a n d T H e n A F o b i c K a n d p r * m o t e d r a c i a l i n t o l e r a n c e i n a w a y t h a * i s n o t * c X c e p t I b l e o r a * l o w * d i n t * e c o n * t i t u t i o n o f t h i s h o u s * +Eval: D I S I I S S S S I D D D I S D D D D D + +Speaker sentences 976: voxpopuli_eng_000518 #utts: 1 +id: (voxpopuli_eng_000518-voxpopuli_eng_000518) +Scores: (#C #S #D #I) 82 6 11 2 +REF: r e a l L i f e E X a m p l E S s h o W t h a t s o l v i n g i S S U e s r e l a t e D t o E d u c a t i o n f U e * l * S s t r o n g c o M m U n i t Y d e v e l o p m e n t +HYP: r e a l * i f e * G a m p l * * s h o * t h a t s o l v i n g i * T I e s r e l a t e * t o A d u c a t i o n f * e U l E D s t r o n g ******* c o * m I n i t * d e v e l o p m e n t +Eval: D D S D D D D S S D S D I I S D D S D + +Speaker sentences 977: voxpopuli_eng_000519 #utts: 1 +id: (voxpopuli_eng_000519-voxpopuli_eng_000519) +Scores: (#C #S #D #I) 121 7 23 14 +REF: s O i h o p e t h * ******* * i s W i L l h A P p e N F o R r u S s I a a s w e L l A n d t h a t r u S S I a c a n a l * s O E n * ******* v i s a * g E A n * e x t r e m e s u C c e s s S t o r y a f t e r t h * ******* e * * * s i g n i f i c a n T D a t E i n A U g U s t t h i s y e a r * +HYP: s * ******* i h o p e t h A T i s * i * l ******* h * V p e * * o * ******* r u * s H a a s w e * l * n d t h a t r u * * H a c a n a l T s * A n D v i s a I g * * n D e x t r e m e s u * c e s s * t o r y a f t e r t h S e G T I s i g n i f i c a n D * a t * i n O R g * s t t h i s y e a r B +Eval: D D I I I D D D D S D D D D D S D D D D S I D S I I I D D I D D I I I I I S D D S S D I + +Speaker sentences 978: voxpopuli_eng_000520 #utts: 1 +id: (voxpopuli_eng_000520-voxpopuli_eng_000520) +Scores: (#C #S #D #I) 146 22 11 15 +REF: s h e A c C e p t e d t h e f a c t t h a t C i t i Z E n ******* s h i p i s ******* * * * * s * * * U B J E C t T o * N A T I o * * n A L J u R i s d i c t i o n b u t S h * E A l s o s a i d t h a t a C c o * r d i n g t o t h e m A a s t r i c H T t r e a t y a n d s h e I s r i g h t t h E R e h a s t o b e a d i * r e c T l i n K +HYP: s h e E c X e p t e d t h e f a c t t h a t S i t i S O n s h i p i s A Y N A s I N A L P A R t * o F T H E o S I n * O G u D i s d i c t i o n b u t * h Y O U R l s o s a i d t h a t a * c o U r d i n g t o t h e m * a s t r i c * K t r e a t y a n d s h e A s r i g h t t h * * e h a s t o b e a ******* d i Y r e c * l i n * +Eval: S S S S S I I I I I I I I I S S S S S D I S S S S I I D S S S D I S S S D I D D S S D D D I D D + +Speaker sentences 979: voxpopuli_eng_000521 #utts: 1 +id: (voxpopuli_eng_000521-voxpopuli_eng_000521) +Scores: (#C #S #D #I) 209 16 23 15 +REF: t H e * * E u f a I l E d e s p e c i a L l Y * I n * D e ******* m O N s t ******* r a t i n g a u n i f i e d a n d ******* * E f f i C I e n t a p p r o A c h t o C l i * ******* m A t E c H a n g * e t r e a t m e n t a s w e L l a s i n s t r E n G t h E n i n g * i t s l e A d i n g p o l i t i c a l P o s I T i o n i n T H i s a g e n d * A i c o n s * i * D e r t h e r E f o r E t a k i n g t h i s r e s o l u t i o n a n a c t o f u t m o s t i m p o r t a n C E +HYP: t D e Y W O u f a * l * d e s p e c i a * l * E A n T H e m * * s t r a t i n g a ******* u n i f i e d a n d T A f f i S H e n t a p p r o R c h t o * l i E m I t * ******* c * a n g H e t r e a t m e n t a s ******* w e * l a s i n s t r A n * t h A n i n g K i t s l e * d i n g p o l i t i c a l C o s * * i o n i n * D i s ******* a g e n d E R i c o n s C i T H e r t h e r * f o r * t a k i n g t h i s ******* r e s o l u t i o n a n a c t o f u t m o s t i m p o r t a n * S +Eval: S I I S D D D D I S I S I D D I D I I S S S S D I I S D D D I D D S D S I D S D D D S D I S I I S D D D D S + +Speaker sentences 980: voxpopuli_eng_000522 #utts: 1 +id: (voxpopuli_eng_000522-voxpopuli_eng_000522) +Scores: (#C #S #D #I) 61 2 5 4 +REF: t h e u n i * t e d s t a t e S o f E u r * o P E w i L l b e a f a c t w i t h s w e d E n a s a p r o v i * * n c E +HYP: t h e u n i G t e d s t a t e * o f Y u r U o * * w i * l b e a f a c t w i t h s w e d O n a s a p r o v i D E n c * +Eval: I D S I D D D S I I D + +Speaker sentences 981: voxpopuli_eng_000523 #utts: 1 +id: (voxpopuli_eng_000523-voxpopuli_eng_000523) +Scores: (#C #S #D #I) 99 4 16 6 +REF: i t m u s T b E t h e c a p i t a l * o f b o t H S t * * a t e s a n d w e m u s T r e c o G n i s e p A l E s t i n E A s A S t * a t E a s p r o v i d e d f o r i n t h e o * * S l o A g r E e m e n T s +HYP: i t m u s * b * t h e c a p i t a l E o f b o t * * t H E a t e s a n d w e m u s * r e c o * n i s e p O l * s t i n * ******* I s ******* * * t H a t * a s p r o v i d e d f o r i n t h e o V E l o ******* * g r * e m e n C s +Eval: D D I D D I I D D S D D D S D D D I D I I S D D D S + +Speaker sentences 982: voxpopuli_eng_000524 #utts: 1 +id: (voxpopuli_eng_000524-voxpopuli_eng_000524) +Scores: (#C #S #D #I) 155 13 19 10 +REF: * * u ******* K r a i n E I s f a c e ******* D w i t h * o n e o f T H E c r u C I a l c h a L l E n g e s i n i t s h i s t o r y i t w o u l d b e f u ******* N D A m e n t a L l y W r o n g * t o p r e S S t h e n a t i o n n o w w i t H a L l t * Y p e s o f r e s t r i c t i o n s p o p U l a * * r l Y c a L l e D A U s t e r i t Y p o l i C Y +HYP: Y O u C r a i n * ******* Y s f a c e T w i t h W o n e o f ******* * * * c r u * S a l c h a * l I n g e s i n i t s h i s t o r y i t w o u l d b e f u T E m e n t a R l y * r o n g K t o p r e * * t h e n a t i o n n o w w i t * a * l t H I p e s o f r e s t r i c t i o n s p o p E l a D E r l * c a * l e * * O s t e r i t E p o l i * * +Eval: I I I S D D S I S I D D D D D S D S I S S S S D I D D D D I S S I I D D D D S S D D + +Speaker sentences 983: voxpopuli_eng_000525 #utts: 1 +id: (voxpopuli_eng_000525-voxpopuli_eng_000525) +Scores: (#C #S #D #I) 52 2 2 1 +REF: m o r e r u l E s a n d r e g u l a t i o n w i l l n o t i m p r o v e t h * E S i t u a t i o N +HYP: m o r e r u l * s a n d r e g u l a t i o n w i l l n o t i m p r o v e t h I S C i t u a t i o * +Eval: D I S S D + +Speaker sentences 984: voxpopuli_eng_000526 #utts: 1 +id: (voxpopuli_eng_000526-voxpopuli_eng_000526) +Scores: (#C #S #D #I) 69 2 8 1 +REF: a t l e a s t w e w o U l d l i k e t o K n o w t h e s o u r C e o f t h e m o n E y a n d t h e p o S s i B l E m o * t i V e S +HYP: a t l e a s t w e w o * l d ******* l i k e t o * n o w t h e s o u r S e o f t h e m o n * y a n d t h e p o * s i P l * m o R t i * e * +Eval: D D D S D D S D I D D + +Speaker sentences 985: voxpopuli_eng_000527 #utts: 1 +id: (voxpopuli_eng_000527-voxpopuli_eng_000527) +Scores: (#C #S #D #I) 179 41 27 25 +REF: t o * H A V E t h o s e E u r O p E A n w O R l D l a n g U a G E s * i n t o ******* D A Y s g l O b A l i S e d w * O r l d ******* * * i n * t o ******* * D A y s g L o b A l i s E d e c o n o m Y i n T h i s g L o b A L v i L l a G E w h i c h i s * C U L t U R a l * E c o n o m i c * s o C i a l * A n D p O l i t i c A L i * s a * m o s t v A l U a b l e A s * S e * t f * o R t h e E n t i r e e ******* * * u W h I C H w e m u s t t * a k E f U L l a C c o u n T O F a n d ******* * +HYP: t o W E R O F t h o s e Y u r U p * I n w * A l E l a n g I a * * s H i n t o T H E s g l U b E l i C e d w E A r l d I S i n T t o T H E y s ******* g * o b E l i s * d ******* e c o n o m * i n D h i s g * o b * E v i * l a C H w h i c h i s G O R S t * I a l Y * c o n o m i c K s o S i a l E L n * ******* p * l i t i c * O i T s a R m o s t v E l * a b l e E s T H e R t f R o M t h e ******* I n t i r e e Y O u T h * A T w e m u s t t H a k * f * O l a * c o u n * ******* * S a n d T +Eval: I S S S S S S D S D S S S D D I I S S S S S S I S I I I I I I S S D D S D D D S D D S D S S I S S S D S I D I S I S D D D D S I I S D S I S I I S D S I I I S D S S I D D S D D D D S I I + +Speaker sentences 986: voxpopuli_eng_000528 #utts: 1 +id: (voxpopuli_eng_000528-voxpopuli_eng_000528) +Scores: (#C #S #D #I) 84 9 12 10 +REF: w e H a v e t o r e p e A t * t h a t O D a * * * * C a * ******* N n o t b e u s e D t o f i n a n C E s E C u r i t Y e x p E n S e s b O r * D e r * c o n t r o l o r m I l i t A r y s U P p o r * t +HYP: w e ******* * a v e t o r e p e * t E t h a t * * a L T H E a Y A n o t b e u s e * t o f i n a n * S s * I u r i t * e x p A n C e s b A r T H e r S c o n t r o l o r m * l i t * r y s * O p o r N t +Eval: D D D I D D I I I I S I I S D D S D S D S S S I S I D D D S I + +Speaker sentences 987: voxpopuli_eng_000529 #utts: 1 +id: (voxpopuli_eng_000529-voxpopuli_eng_000529) +Scores: (#C #S #D #I) 70 2 19 2 +REF: I F A N Y t h I n G t h e s C i E n t i f i C r e p o r t s A R E b e c o * ******* m I N G m o r e u r g e n t M o r E a l a r m i n g a n d m o r E s h o c k i n g +HYP: * * ******* * * * t h * n * t h e s * i * n t i f i * r e p o r t s ******* * * * b e c o E m * R E m o r e u r g e n t * o r * a l a r m i n g a n d m o r * s h o c k i n g +Eval: D D D D D D D D D D D D D D D I I D S S D D D + +Speaker sentences 988: voxpopuli_eng_000530 #utts: 1 +id: (voxpopuli_eng_000530-voxpopuli_eng_000530) +Scores: (#C #S #D #I) 123 16 17 26 +REF: f i n a L l y * w h e n ******* * * * * * * * * * i * T * C o M E S t * * O i N n o v a t i v e f i N A n C i A L i n s t R u m e n t s w * e n E E D t h e M b o * t h f o r o u r s e l V E s t o * s u P p o * r t o * U r * E c o n o m I e s b u t a l * ******* s o t * o * s u P p o r t t h o s E W h o A r e i N n e * e D +HYP: f i n a * l y M w h e n W E A T T H I N K i N G A B o * U N t H E R i * n o v a t i v e f i * * n S i O N i n s t O u m e n t s w H e ******* n O U t h e * b o L t h f o r o u r s e l * * s t o R s u * p o A r t o W E r A c o n o m * e s b u t a l S s o t O o E s u * p o r t t h o s * * h o ******* E r e i * ******* n e A e T +Eval: D I I I I I I I I I I I I S I S D S S I I S D D D S S S S I D S S S D I D D I D I I S I S D I I I I D D D D S D D I S + +Speaker sentences 989: voxpopuli_eng_000531 #utts: 1 +id: (voxpopuli_eng_000531-voxpopuli_eng_000531) +Scores: (#C #S #D #I) 29 5 8 3 +REF: t h A t G i v e S * U s * A u n i Q U e * T o O l i n p e A C E m a k i n g +HYP: t h * t * i v e * A s O * Y u n i * * e K D o L l i n p e * * m a k i n g +Eval: D D D I S I D S D D I S S D D S + +Speaker sentences 990: voxpopuli_eng_000532 #utts: 1 +id: (voxpopuli_eng_000532-voxpopuli_eng_000532) +Scores: (#C #S #D #I) 24 1 1 1 +REF: p a p e r a v e r y * w e A k p r o p o s A l +HYP: p a p e r a v e r y D w e E k p r o p o s * l +Eval: I S D + +Speaker sentences 991: voxpopuli_eng_000533 #utts: 1 +id: (voxpopuli_eng_000533-voxpopuli_eng_000533) +Scores: (#C #S #D #I) 72 3 14 8 +REF: * r u S s I A h a s a l w a Y s b E e N a v e r y p r o u d n a t i o n w i t h A r i c h c U l t * u R e * w i t h i n v e n t i o n s * * * * a n D E s ******* p R I T +HYP: S r u * s * * ******* h a s a l w a * s b * e * a v e r y p r o u d ******* n a t i o n w i t h ******* * r i c h c O l t C u * e R w i t h i n v e n t i o n s W I T H a n * A s p * * L +Eval: I D D D D D D D D D D S I D I I I I I D S I D D S + +Speaker sentences 992: voxpopuli_eng_000534 #utts: 1 +id: (voxpopuli_eng_000534-voxpopuli_eng_000534) +Scores: (#C #S #D #I) 168 12 19 7 +REF: F a I r t a * x a t i O n e v e n a m o d i c U M o f t a * x a t i o n i n s o m e c a S e s m i g h T j u s t h e l p u s ******* * * t o d o w h a t i H A v e a L r e A d y s u G g e s t e d a n D w h o K n o W s * m a k e t h e c a S e f o r t h e r e t r O s p e c t I V E b a n k r e ******* c a p i T A l i S a t i o n t h a t w e n e v e r s A W +HYP: * a * r ******* t a C x a t i * n e v e n a m o d i c A L o f t a C x a t i o n i n s o m e c a C e s m i g h * j u s t h e l p ******* u s E M t o d o w h a t i ******* * * v e ******* a * r e * d y s u E g e s t e d a n * w h o * n o * s E m a k e t h e c a C e f o r t h e r e t r E s p e c t O F b a n k ******* r e c a p i * D l i Z a t i o n t h a t w e n e v e r ******* s * O +Eval: D D D I D S S I S D D I I I D D D D D D S D D D I S S S S S D I D S S D D S + +Speaker sentences 993: voxpopuli_eng_000535 #utts: 1 +id: (voxpopuli_eng_000535-voxpopuli_eng_000535) +Scores: (#C #S #D #I) 157 11 17 6 +REF: t h E e U r o p e ******* a n a s Y l U m s u P p o r t o f F i C E m o r E o v e r H a s a m o n g i t s t * a s K s t o p r O m o * t E f A C i l I t a t E a n d c o O r d i n a t E e x * c H a n g e s o f i n f o r m a t i o n a n d o t h e r a c t i v * i t I e s r e l a t e d T o R e l o c a t i O n w I t h ******* i n T h e u n i o n +HYP: t h * ******* e * r o p e a n a s I l O m s u * p o r t ******* o f H i * S m o r o v e r * a s a m o n g i t s t H a s T s t o p r * m o U t D f E S i l Y t a t * a n d c o U r d i n a t * e x T c * a n g e s o f i n f o r m a t i o n a n d o t h e r a c t i v E i t * e s r e l a t e d * o * e l o c a t i * n w * t h i n * h e u n i o n +Eval: D D D I S S D D S D S S D I S D I S S S S D S D I D I D D D D D I D + +Speaker sentences 994: voxpopuli_eng_000536 #utts: 1 +id: (voxpopuli_eng_000536-voxpopuli_eng_000536) +Scores: (#C #S #D #I) 131 10 25 1 +REF: T h e C o n C L u s I o N o f t h e f r a m e W o r k a g R E e m e n t p r o v i d e s a l E g A L l y b i n d i n g i n s t r U m e n t t o U P g r a D E a n d s t r E n G t H E n e u A U s t r a l i a b * I l A t E r A L r E L a t i o n s a n d t o i n c r e A s E c O o p e r a t i o n +HYP: * h e * o n * * u s * o * o f t h e f r a m e B o r k a g * * e m e n t p r o v i d e s a l I g * * l y b i n d i n g i n s t r * m e n t t o O B g r a * T a n d s t r A n * t * * n e u * O s t r a l i a b Y l I t H r * * ******* r * * a t i o n s a n d t o i n c r e * s * ******* c * o p e r a t i o n +Eval: D D D D D D S D D S D D D S S D S S D D D D S I S S S D D D D D D D D D + +Speaker sentences 995: voxpopuli_eng_000537 #utts: 1 +id: (voxpopuli_eng_000537-voxpopuli_eng_000537) +Scores: (#C #S #D #I) 88 16 27 6 +REF: t h e r e f o r E w e a R e a s K i n G t h e c o u N C I l a N D T H E C O M m I S S i o n t o P r e s e n t a T R a N s ******* P a R e N t a N D C o M P l * ******* * e ******* t * e A S s e s S m e n t o f t h e I M P a c t o f t h e C r i S i s +HYP: t h e r e f o r * w e ******* a * e ******* a s T i n * t h e c o u * S A l a * * ******* * * S * * G m * * * i o n t o * r e s e n t H a * H a * s B a L e t H a * * * o * U l D B e t H e * * s e s T m e n t o f t h e * E B a c t o f t h e * r i C i s +Eval: D D D D S D D S S D D D D D S D D S D D D D S D S D I S S S S D D D D S I I I I I D D S D S S D S + +Speaker sentences 996: voxpopuli_eng_000538 #utts: 1 +id: (voxpopuli_eng_000538-voxpopuli_eng_000538) +Scores: (#C #S #D #I) 110 1 8 0 +REF: i n o t h e R w o r d s t h e o b j e c t i o n i s n o t w h e t h e r m o n e y i s p a I d o r n o t t h e o b j e c t i o n i s w H e t h e r t H e r E i s a d i R e c t l i n k o r n o T +HYP: i n o t h e * w o r d s t h e o b j e c t i o n i s n o t w h e t h e r m o n e y i s p a * d o r n o t t h e o b j e c t i o n i s w * e t h e r t * e r * i s a d i D e c t ******* l i n k o r ******* n o * +Eval: D D D D D S D D D + +Speaker sentences 997: voxpopuli_eng_000539 #utts: 1 +id: (voxpopuli_eng_000539-voxpopuli_eng_000539) +Scores: (#C #S #D #I) 85 11 22 6 +REF: I t * D I s t i n g u i s h e s t h e t W o m a I n D o S S I e * r S * H u m A N r i g H t S a * ******* b u s e S b y t h e c U R R E n t g o V E r N m e n t a n d t h e I R a n i a n n u c l E a R p r o * g R A M m E +HYP: * t O T H s t i n g u i s h e s t h e t * o m a * n * o * * * e A r * Y O u m E R r i g * t * a B b u s e * b y t h e c * A D A n t g o * * r * m e n t a n d t h e ******* D L a n i a n n u c l * a * ******* p r o V g * * D m * +Eval: D I S S D D D D D D I D I S S S D D I I D D S S S D D D D S S D D D I D D S D + +Speaker sentences 998: voxpopuli_eng_000540 #utts: 1 +id: (voxpopuli_eng_000540-voxpopuli_eng_000540) +Scores: (#C #S #D #I) 90 17 8 15 +REF: * * * * ******* m * * R P r * E S I D E n * * t * * s e * X U a l h A r a s S m e n t i s a f o r m o f v i O l E n c E a n d i t i s t h e m o s t e x t r e * m e f o r m o f g E n D e r — b a S e * D d i s c R I m i n a t i O N +HYP: Y E S S m A T H M D r U O T H A n K A t H R s e C T I a l h E r a s D m e n t i s a f o r m o f v i * l A n c S a n d i t i s ******* t h e m o s t e x t r e A m e f o r m o f g * n T e r *** b a * e T H d i s c * U m i n a t i * * +Eval: I I I I I I I S S S I S S S S S I I I I I S S S S D S S D I D S D D I S D S D D + +Speaker sentences 999: voxpopuli_eng_000541 #utts: 1 +id: (voxpopuli_eng_000541-voxpopuli_eng_000541) +Scores: (#C #S #D #I) 65 5 6 8 +REF: w e c a n l O o k t o s o m e * * * n ******* * O n E u m e m b e r s f o r G o * o d E x a m p l e s a s r e g a r d * S t E C h * n o l O g I e S +HYP: w e c a n l * o k t o s o m e U R A n L I n O u m e m b e r s f o r * o U o d G x a m p l e s a s r e g a r d E D t * * h G n o l I g * e * +Eval: D I I I I I S S D I S I S D D I S D D + +Speaker sentences1000: voxpopuli_eng_000542 #utts: 1 +id: (voxpopuli_eng_000542-voxpopuli_eng_000542) +Scores: (#C #S #D #I) 45 5 3 4 +REF: * i * n v * O l v e d f o r t h e I r p o s i t I v e a n d c o N s t r U c t * i v e a P P r o A c h +HYP: Y i M n v A L l v e d f o r t h e * r p o s i t E v e a n d c o * s t r A c t E i v e a * B r o T c h +Eval: I I I S D S D S I D S S + +Speaker sentences1001: voxpopuli_eng_000543 #utts: 1 +id: (voxpopuli_eng_000543-voxpopuli_eng_000543) +Scores: (#C #S #D #I) 69 11 21 7 +REF: S o i h o p E t h a t T H i s w i L l b e c o m p l e * t e d ******* * * * i n T h e f O R E S E E A B l E f u t u * r E W h I C H m E a n * s m a Y b e t W O o R T H r E e m o n T H s +HYP: * o i h o p * t h a t * * i s ******* w i * l b e c o m p l e A t e d E A R i n * h e f * * * A C I V I l * f u t u A r * T h * A T m * a n E s m a b e t * * ******* o * A F r * e m o n * * s +Eval: D D D D D D I I I I I D D D D S S S S S D I D S D S S D I S D D D D S S D D D + +Speaker sentences1002: voxpopuli_eng_000544 #utts: 1 +id: (voxpopuli_eng_000544-voxpopuli_eng_000544) +Scores: (#C #S #D #I) 106 14 13 12 +REF: * * ******* f U r T H e r E n * c o u r A G E t h e * * u ******* * * n S e F f o r t s t o b r i n g a B o * U T p e A C E i n A f ******* g H A n i s t a n a n D t o o v e r c o m e t h e * f r a G i l e s E c u R i t y E n v i r O N m e n t i n t h e c o U n t r y +HYP: O R f O r * D e r * n D c o u r * D S H t h e Y O u H A n D e * f o r t s t o b r i n g a M o N G K p e * * S i n O f g * * n i s t a n a n * t o o v e r c o m e t h e F f r a S i l e s I c u * i t y A n v i r * * m e n t i n t h e c o * n t r y +Eval: I I I S D S D I D S S S I I I I I S D S I S S D D S S I D D D I S S D S D D D + +Speaker sentences1003: voxpopuli_eng_000545 #utts: 1 +id: (voxpopuli_eng_000545-voxpopuli_eng_000545) +Scores: (#C #S #D #I) 35 3 2 2 +REF: W e U n d e r ******* s t a n D t h a t s o m e p e o p L e * a r E a n g r y +HYP: B e A n d e r s t a n T t h a t s o m e p e o p * e L a r * a n g r y +Eval: S S I S D I D + +Speaker sentences1004: voxpopuli_eng_000546 #utts: 1 +id: (voxpopuli_eng_000546-voxpopuli_eng_000546) +Scores: (#C #S #D #I) 17 3 10 2 +REF: W E W A n T t o b e m O R E r e s * p o n S i B L e * +HYP: * * ******* * O n * t o b e m * * * ******* r e s T p o n C i * V e L +Eval: D D D D S D D D D D I S D S I + +Speaker sentences1005: voxpopuli_eng_000547 #utts: 1 +id: (voxpopuli_eng_000547-voxpopuli_eng_000547) +Scores: (#C #S #D #I) 98 11 18 6 +REF: w e m u s t R e * * c t i f * * * Y t h i s s I t U a t i o n a n d W e a s k t h e C o M m I S S i o n t o c o n * s i d e r t h e m o s t A d E Q u A t E c o m P E n s a t i o n m e A s U R e s f O R o U R p a S s E n g e R s +HYP: w e m u s t * e D A c t i f I E T H t h i s s U t I a t i o n a n d V e ******* a s k t h e * o * m * * * i o n t o c o n C s i d e r t h e m o s t E d I C u I t * c o m B I n s a t i o n m e * s * * e s f * * ******* o * W p a * s * n g e * s +Eval: D I I I I I S S S S D D D D D D I S S S S D S S D D D D D D D S D D D + +Speaker sentences1006: voxpopuli_eng_000548 #utts: 1 +id: (voxpopuli_eng_000548-voxpopuli_eng_000548) +Scores: (#C #S #D #I) 170 13 21 24 +REF: t h e C o M m i S S i o n i n * v i * * * ******* t * e S * * * * p * a * * ******* * R l I a m e n t i n t h e u p c o m i n G r e v i s i o n t o o p e n * i T s p o s i t i o n o n t h i s m a T t e r w h i c h r e A L l y c o n C e R N S a C C e S s t o ******* * j u s t i C E i n E u r o p E a n d t h e e n f o r C E m e n t o f r i G H T s g r a n t e d b y * e ******* * u r o p E a n * * * u n I O n l A W +HYP: t h e * o * m i * T i o n i n B v i H E D t H e * Y U R O p I a N T P U l * a m e n t i n t h e u p c o m i n * C r e v i s i o n t o o p e n H i * s p o s i t i o n o n t h i s m a * t e r w h i c h r e * * l y c o n S e * * D a * X e * s t o S j u s t i * S i n Y u r o p * a n d t h e e n f o r * T m e n t o f r i * * E s g r a n t e d b y H e Y u r o p I a n E R Y u n * A n l * O +Eval: D D D S I I I I I I D I I I I I I I I I S D D S I D D D D S D D S D S D I I D S S D D S D D S I I I S I I I D S D S + +Speaker sentences1007: voxpopuli_eng_000549 #utts: 1 +id: (voxpopuli_eng_000549-voxpopuli_eng_000549) +Scores: (#C #S #D #I) 97 7 23 3 +REF: i W E l C o m E v e r y m u c h t h E r E s * u M P t i o n o f t A L k S b e t w E e n t h e I s ******* r a E l i s a n D T H E p A l e s t i n i A n s a n d s I n c e * r E l y h o p E t h a t T h e Y w i L l s u C c E e d +HYP: i * * l o m * v e r y m u c h t h * r I s O u * N t i o n o f t * O k E b e t w * e n t h e ******* O s r a * l i s a n * ******* * * * p * l e s t i n i O n s a n d s * n c e I r * l y h o p * t h a t * h e * w i * l s u * c * e d +Eval: D D S D D S I D S D S S D D S I D D D D D D D S D I D D D D D D D + +Speaker sentences1008: voxpopuli_eng_000550 #utts: 1 +id: (voxpopuli_eng_000550-voxpopuli_eng_000550) +Scores: (#C #S #D #I) 75 10 10 12 +REF: w e h a v e A N a C c u m U l a t i o n o f p r o b l e * M s r e s u l t i n g f r o m * ******* * * * a r t i f i C I a l U n d E R b U D g e * t i n g * I n * * * * p r e v i O u s y E A R s +HYP: w e h a v e ******* * * a * c u m I l a t i o n o f p r o b l e N C s r e s u l t i n g f r o m E T H E a r t i f i * H a l A n d D E b * A g e A t i n g K A n D V E R p r e v i * u s ******* y * * U s +Eval: D D D D S I S I I I I I D S S S S S D S I I S I I I I D D D D S + +Speaker sentences1009: voxpopuli_eng_000551 #utts: 1 +id: (voxpopuli_eng_000551-voxpopuli_eng_000551) +Scores: (#C #S #D #I) 57 3 3 4 +REF: l e t u s * n o t b e t h e m a n o f y e s t e r d A y l E t u S b e T o * ******* d a y s i n s t * i t u t i o N +HYP: l e t u s T n o t b e t h e m a n o f y e s t e r d * y l N t u N b e ******* P o L d a y s i n s t H i t u t i o * +Eval: I D S S D S I I I D + +Speaker sentences1010: voxpopuli_eng_000552 #utts: 1 +id: (voxpopuli_eng_000552-voxpopuli_eng_000552) +Scores: (#C #S #D #I) 154 18 22 18 +REF: * ******* i W o u l d U R G e Y O u * t o b e c o m e a m b a s s A D O R s o F t h e y e a r B Y m a k I n g i t s * I d e a * s a n d a c t i v i t * i E s ******* * * w i d E l y K n o w n a ******* m o n G s * ******* t * E u r O p e a N C i t i Z e n s a n d p A R t * i C i p a t i n g I n e v e n T s b e I t a t * E u r o p E a n n a T i o n a * l * o r l o C A L * l e v E l +HYP: T i G o u l d * * * e R L S u M t o b e c o m e a m b a s s E T H E s o * t h e y e a r ******* * * m a k * n g i t s A d e a R s a n d a c t i v i t H i * s W O w i d * l y * n o w n a m o n C s H t O Y u r U p e a T * i t i * e n s a n d p * U t P i * i p a t i n g * n e v e n * s b e * t ******* a t Y O u r o p I a n n a S i o n a L l F o r l o * * K A l e v * l +Eval: I I S D D D S S S I S S S S D D D D D I S I I D I I I D D I S I I I S S S D D D S I D D D D D I S S S I I D D S I D + +Speaker sentences1011: voxpopuli_eng_000553 #utts: 1 +id: (voxpopuli_eng_000553-voxpopuli_eng_000553) +Scores: (#C #S #D #I) 121 7 10 1 +REF: C e r t A I n l y s u c h i m p a c T A S s e s S m e n t c o u l d p r E e m P t C e r t a I n p r o b l E m s s u c h a s t h o s E p o s e d b y t h e e l e c t r o n i C i d e n t i f i c a t i o n o f s h E e p I n * s c o t l a n d +HYP: S e r t * * n l y s u c h i m p a c E * * s e s T m e n t c o u l d p r * ******* e m * t S e r t a * n p r o b l O m s s u c h a s t h o s * p o s e d b y t h e e l e c t r o n i K i d e n t i f i c a t i o n o f s h * e p A n D s c o t l a n d +Eval: S D D S D D S D D D S D S D S D S I + +Speaker sentences1012: voxpopuli_eng_000554 #utts: 1 +id: (voxpopuli_eng_000554-voxpopuli_eng_000554) +Scores: (#C #S #D #I) 172 8 16 5 +REF: t h e c o U r t i s c o n t e n t t o s e e t h A t i t s w o r k h a s i n f o r m e D t h E d i s ******* c h a R g E P r o C E S s a n d h a s c o n t R I b u t e d t o P r o p o s a l s f o r i m p r o v i n g t h e f i n a n c I a l m a n a g E m e n t o f * e * * u s p e n d i n g a n d b e t T e R t a r G E t i n g o f * E u f u n D S +HYP: t h e c o * r t ******* i s c o n t e n t t o s e e t h * t i t s w o r k h a s i n f o r m e * t h * d i s c h a * g H * r o * * * s a n d h a s c o n t * E b u t e d t o * r o p o s a l s f o r i m p r o v i n g t h e f i n a n c * a l m a n a g H m e n t o f V e Y O u ******* s p e n d i n g a n d b e t H e * t a r K A t i n g o f Y O u f u n * E +Eval: D D D D D I D S D D D D D S D D S I I I D S D S S I S D S + +Speaker sentences1013: voxpopuli_eng_000555 #utts: 1 +id: (voxpopuli_eng_000555-voxpopuli_eng_000555) +Scores: (#C #S #D #I) 71 5 5 11 +REF: r e g * u L A t * O r Y c l a r i * ******* t * Y a n d C e r t a I n t y i s n E e d e d f o r t h e P U b l i c * s e c t o * r a n d f o r ******* * * * i n d u s t r y +HYP: r e g O u * * t H E r E c l a r i E t H E a n d S e r t a * n t y i s n * e d e d f o r t h e * O b l i c K s e c t o U r a n d f o r T H E i n d u s t r y +Eval: I D D I S S I I I S S D D D S I I I I I I + +Speaker sentences1014: voxpopuli_eng_000556 #utts: 1 +id: (voxpopuli_eng_000556-voxpopuli_eng_000556) +Scores: (#C #S #D #I) 92 10 9 3 +REF: i s i t r e a L l Y n o t p o S s i b l e t o u s ******* E O t h e r h o u s i n g f a * c i l i T I e s w i t h A P p r o p r I A T E r e C e p t i O n c o n d I T i o n s i n t h e m e A n ******* t i m e +HYP: i s i t r e a * l * I n o t p o * s i b l e t o u s A A t h e r h o u s i n g f a S c i l i * D e s w i t h U p r o p r * E H r e S e p t i * n c o n d * * i o n s i n t h e m e * n t i m e +Eval: D D S D I S S I D S S S D S S S S D D D D I + +Speaker sentences1015: voxpopuli_eng_000557 #utts: 1 +id: (voxpopuli_eng_000557-voxpopuli_eng_000557) +Scores: (#C #S #D #I) 42 2 1 1 +REF: w I L l y o u t a k e a c T i o n a t l a s t i f n o t t h e n w h e n * +HYP: w H E l y o u t a k e a c * i o n a t l a s t i f n o t t h e n w h e n D +Eval: S S D I + + diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..5e56f97772462fdff450a342b36fa5073cfb67cc --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/hyp.trn @@ -0,0 +1,1092 @@ +HE REMAIED WEL CHAMPIAN ANTIL NINTEN SIXTY FIVE A YEAR I WHCH SUF RD A TERABLE ACXIDENT (LAD_eng_000254-LAD_eng_000254) +AY LIBRAL CONSEVITIVE HE WAS DEFEATED IN ATEIN ATY TO (LAD_eng_000255-LAD_eng_000255) +ON ROD LAAR CON DRAR TWO RODS AT WOANCE (LAD_eng_000256-LAD_eng_000256) +SOME OFTHE CONTRES HVE SURVAYS FOR MALTIPLE YEARS (LAD_eng_000257-LAD_eng_000257) +BOTH OF THE VRSIONS FEACHR THE SONG HAPY HOLIDAY (LAD_eng_000258-LAD_eng_000258) +SHAKXPIAR MANY REFRNCES ARE MADE TO SENS INTR ACTIONS OR CARICTES FROM VARIOUS PLAYES (LAD_eng_000259-LAD_eng_000259) +IF ONLY THE ROGRAM CULDBRAKE OUT JUST A ITLE FROM ITS TO FOMILIAR APROCH (LAD_eng_000260-LAD_eng_000260) +THE HELBEM WAS RELEASED IN OSTRALIAR ON NINTEINTH ORGIST TWO THOUSND AD ELEVEN (LAD_eng_000261-LAD_eng_000261) +HE NOW PLACE FOR ASTRALIN CLOBE PERTH GLORY (LAD_eng_000262-LAD_eng_000262) +IT IS NOT NON HOW MUCH IF EANY OF HE CLAMS AR TRU (LAD_eng_000263-LAD_eng_000263) +A SMAL BISINESS ONR BROARD OPRATED HI WEAT AD SHEPFAME FOR SICTEN YEARS FRO THE AGE OF WENTY TO (LAD_eng_000264-LAD_eng_000264) +IN THE NINTH SENTURY HE WAS AN IRISH POET (LAD_eng_000265-LAD_eng_000265) +THEY ARE MARKED BY STRONG (LAD_eng_000266-LAD_eng_000266) +THE LOW IS THE FOR VAOLED (LAD_eng_000267-LAD_eng_000267) +IN THE RLY STAGES CAME CLOSE TO US A SLEP (LAD_eng_000268-LAD_eng_000268) +RONING EVERY THRTY MINUT THRO AT SERVIS TIMS (LAD_eng_000269-LAD_eng_000269) +AS A RESIULT WHEN THE COLIGE RE OPEND IT WAS AS AN ALL MALE COLIGE (LAD_eng_000270-LAD_eng_000270) +THE TIME BETWEE THES POINCT IS VERIABL AND CANACUR ANY WHER FRO A MINIT TO MUCH LONGER (LAD_eng_000271-LAD_eng_000271) +WOARK ON THE EA E E STARTED IN MARCH TWO THOUSND AND SEVEN AT A COST OF FIVE MILIAN DOLERS (LAD_eng_000272-LAD_eng_000272) +HOWEVER THER WAS SOME DI AGREMENT OV TH ENDING THEME WHICH OR MORY AND YOHIMORY DISCUSTD AT LENGTH OVER EMAL (LAD_eng_000273-LAD_eng_000273) +THE COPLE HAD NO CHILDRAN (LAD_eng_000274-LAD_eng_000274) +THE FIAL SINGL O THAT DEBU AL BHM PARIS COLING HAD AN ELABRT MUSIC VIDIO (LAD_eng_000275-LAD_eng_000275) +THE SERIS ENDED ON SIXTH ORGEST TO THOUSND AND FOR LASTING FR A TOUTE OF SEVENTY ON DAYS (LAD_eng_000276-LAD_eng_000276) +HE HAS ALSO CONTRIBUTED TO THE NEW YORK REVIO OF BOOKS (LAD_eng_000277-LAD_eng_000277) +BY PLACING SMAL ART OBJECT TRO OUT THE FILM (LAD_eng_000278-LAD_eng_000278) +IT IS FOUND IN BRESIL (LAD_eng_000279-LAD_eng_000279) +IT WS THE SID OF THE FAMLY I IDENTIFIED MORE WITH (LAD_eng_000280-LAD_eng_000280) +H CAND IT SIGHTES MUST ALSOR SOBMIT A WORK PLAN (LAD_eng_000281-LAD_eng_000281) +DUNDEY WHN THE MACH THRE TO (LAD_eng_000282-LAD_eng_000282) +HOWEVER THE VILIGE REMAIND ICALATED ANTIL THE RIVEL OF THE FIRST NOUS PAPER SECOND REPOUBLICK (LAD_eng_000283-LAD_eng_000283) +THE FAST SERVIS I THE EU CHURC WAS HELD I NINTE FIFTY ON ALTHO THE BILDIG WAS NOT FULY FINISHED (LAD_eng_000284-LAD_eng_000284) +THE AVERIGE HOUSEHLD SIE WAS TWO POINT TO SEVEN ND THE AVERIGH FAMLY SIE WAS THRE POINT IRO SRO (LAD_eng_000285-LAD_eng_000285) +IT WAS FIRST BRAD CAST ON THIRD GANIURY TWO THOUSOND ND TEN (LAD_eng_000286-LAD_eng_000286) +THE WINGS WER OW MAD IN A SINGLE PRESING (LAD_eng_000287-LAD_eng_000287) +HE DOCTR O HLOSOFY IN ENGENEARING MANAGEMENT (LAD_eng_000288-LAD_eng_000288) +THIS TOK WAY THE MAIN ARGUMENT OF SAFTY RISSK (LAD_eng_000289-LAD_eng_000289) +HE WAS ALSO MAD A LIFE MEMBER OF SGUN THORP PUNITED (LAD_eng_000290-LAD_eng_000290) +SHE FIARS THEYWIL GET A DEVORSE BUT THIS NEVER HAPENS (LAD_eng_000291-LAD_eng_000291) +FOUT DROPS IN ABLE TO HAD THE FOT STRAT ACROSE (LAD_eng_000292-LAD_eng_000292) +WHETE THE AR FLO IS FREY OR FORST CN FEC THE ENAGY AFIANCY OF THE ENDO (LAD_eng_000293-LAD_eng_000293) +AFTER GETIN HE RIHT MASURMENT THEY MAD THENEW DORS (LAD_eng_000294-LAD_eng_000294) +FRAGMENTS ON ACH FACE ARE MARE WTH LETERS AY BE SE (LAD_eng_000295-LAD_eng_000295) +FROM THE FIRST MINITS BOTH TEMES SHOWD THE DISIRE TO CMPEET WIT HEGEIVE APROCHES (LAD_eng_000296-LAD_eng_000296) +FISICL HERIPY EXCUSISES MAY HELP PATIONT TO MAINTAIN MULE STRINGTH (LAD_eng_000297-LAD_eng_000297) +HOWEVER THE TOWN SHE LIVS IN NO ON WANTS TO HER ABOUT HER (LAD_eng_000298-LAD_eng_000298) +AND DISCRIVES APOINT MET OF AN ACTING CHEVE JUSTIS OR JUDGE OF THE SUPREME CORT (LAD_eng_000299-LAD_eng_000299) +THE SOY BENS OUT ACOVERING IS THEN REMOVED AND THE BENS ARE PARTIALY COOKED (LAD_eng_000300-LAD_eng_000300) +THIS NASINALE MOVMENT WHICHAD BEGUN WITH SO MUCH HOP CAME TO A SAD END (LAD_eng_000301-LAD_eng_000301) +HIS ASOSCIAT YUSUALY CALD HIM TE OR THE OD LOKING GIY (LAD_eng_000302-LAD_eng_000302) +ITS MAIN OFICES WER IN LUNDAN WIE HE SECND OFIS BELL FAST (LAD_eng_000303-LAD_eng_000303) +ACTULY I HAD NEVER BEN TO A VILIGE BEFOR THAT (LAD_eng_000304-LAD_eng_000304) +HE AS CHARGED ITH PLANING TO SET OF BOMS IN UROP AND THE UNITED STATE (LAD_eng_000305-LAD_eng_000305) +MAKING MERS IS THE HIRD STUDOR HLBUM BY BELGEN ASTRALIAN ARTIST GOTIAY (LAD_eng_000306-LAD_eng_000306) +HE THEN MOVED TO WASINGTON DE SE AND WAS A PARTNR ITH WARD BRON ANDTIL NINTEN TWENTY NIN (LAD_eng_000307-LAD_eng_000307) +JOS OF HIY SCOLE AND THE SCOLES THE CMPE GAINE IN AL SPORTS (LAD_eng_000308-LAD_eng_000308) +TWELF PLUS ON MACH BAN PER CARD (LAD_eng_000309-LAD_eng_000309) +I HINK I MIGHT BE NOTHING (LAD_eng_000310-LAD_eng_000310) +THE HOE WAS BILT AND LIVED IN BY ANDRU JACX AND CANDY DEPUTY CLECTE O THE INTERNAL REVINOU SERVIS (LAD_eng_000311-LAD_eng_000311) +IN NINTEN SIXTY FOR HE WENT BAC TO OMSK AND ENTE THE ACTOA SCOL OF OMPS (LAD_eng_000312-LAD_eng_000312) +THE BANK IS JOUNTLY OND BY HIM AND HIS BROVER AND RELITIVES (LAD_eng_000313-LAD_eng_000313) +HE SUBPSICUNTLY WENT TO COL IN BRISTAL (LAD_eng_000314-LAD_eng_000314) +WON THOUSAND AT HUNDRD FOARTY SICX FORH EDION (LAD_eng_000315-LAD_eng_000315) +A PART OF LITL INGLAND BEYOND WALS IT HAS BE A CENCHALY INGLISH SPEAKING FOR NIN HUNDRED YEARS (LAD_eng_000316-LAD_eng_000316) +HE PLAD WTH TEN PLAYARS FOR HARF WAS AGAINE A TRDION IN JEESP (LAD_eng_000317-LAD_eng_000317) +THE RESIDING JUDGE WAS WEBST A FAIR HO WAS ALREADY ASIND TO THE CORT BEFORE THIS CACE WAS HEDILD (LAD_eng_000318-LAD_eng_000318) +BIG BRATHER FIVE WAS THE HURD OF HE MAIN SARIS TO FEACUER A LIVE LONCH (LAD_eng_000319-LAD_eng_000319) +ITS MOTO IS WHO EVE YOU AR AND WHEREVE YOU ARE ON THE JUNY OF FAIFH YOU AE WELCOM HER (LAD_eng_000320-LAD_eng_000320) +ROBAT EY MILOR AS COCH WILSON (LAD_eng_000321-LAD_eng_000321) +AFTER ON YEAR BRAK SIRO DEGRE WAS HE FOLOING VENTHAR (LAD_eng_000322-LAD_eng_000322) +AY AM TEE MANUFACTED A MORDL CIT OF HE SED SEID AR DRACKXSTOR (LAD_eng_000323-LAD_eng_000323) +THE ESSESSAY AMED TO BILD A LEFT WING OLTURNITIVE TO NOW LABER AND THE ESAN PE (LAD_eng_000324-LAD_eng_000324) +HE LIVES LIKE HE AS A YONG PERSON (LAD_eng_000325-LAD_eng_000325) +MASTE OF SINES IN ENGENEARIG MANAGENT (LAD_eng_000326-LAD_eng_000326) +SHE FAILED TO MAK HE TOP THRE AT THE CANIAN JUNIATRACTRILES THAT JON (LAD_eng_000327-LAD_eng_000327) +A TORE FOLOED IN SEPORT (LAD_eng_000328-LAD_eng_000328) +THEYER STABISH IN ATEN SEVENTY ON AND AR WN O THE OLDST CLOUBS IN HE SOUTH OF INGLAND (LAD_eng_000329-LAD_eng_000329) +HE AS A MEMBER OF THE GEST SCOTLAND ADVISERY BORD (LAD_eng_000330-LAD_eng_000330) +TWO THOUSAND AND FIVE GENTLEMEN (LAD_eng_000331-LAD_eng_000331) +AORE FILE AD STRONG RESEPTION INYURUPAND ACHIVED DISTOBUTION BUT THAT WAS NOT THE CACE HER (LAD_eng_000332-LAD_eng_000332) +BOLTHOIS STETCHES POSTERIAR ANGCAL STRUCTUES (LAD_eng_000333-LAD_eng_000333) +HE AS ALSO A THEE TIME FRENCH NASIAL CHAMPIAN NINTE NINTY NINTIE NITY FOR TWO HOUSND AD WON (LAD_eng_000334-LAD_eng_000334) +THE VILIGE STRUCTUR SHOW IN HIS MAP IS T A GRE EXTENT UN CHANGED O DAY (LAD_eng_000335-LAD_eng_000335) +RUHA IS RECOGNISED IT NUCLAR DISARST TO EXPARTES AND FO THE SAVFTY O ITS TECKNOLAGY (LAD_eng_000336-LAD_eng_000336) +AS OF TO THOUSEND OD FORTEEN EMTY VE IS AVAILABLE WITHIN THEUNITED CINGDUM ON VERGIN MEDIAR AND SCKIY (LAD_eng_000337-LAD_eng_000337) +NEWYORK PEANGUIN RANDM HOUSE (LAD_eng_000338-LAD_eng_000338) +THE DUTCHEY WAS SCECURE IN TE UT COME OF THE GOFICK WAOR (LAD_eng_000339-LAD_eng_000339) +WIH GOD PACE SDARTE HE MATCH WITH BOTH TEMES OLTENATING SUPREMASY (LAD_eng_000340-LAD_eng_000340) +THIS VRTION IS NOTEAD OR BIG VERY FAFUL TO THEARIGINAL NOVL (LAD_eng_000341-LAD_eng_000341) +THIS PRESUMPTION IS NOT FLE FILED ON HAS TO NO ATLEAST TO CONGAT DIAMATES (LAD_eng_000342-LAD_eng_000342) +NOTABLE TITLES INCLUDED GOLDAN ACXS THE REVENG OF DETH ADER RAD MOBIL OUT RUNOES AND SAKGAR SONIC THE HEGHOG (LAD_eng_000343-LAD_eng_000343) +THE NINTEN NINTY NIN JUGMENT NOTED THAT THE INFLONC OF TH FATHER OF THE CUSED HAS BEE THER (LAD_eng_000344-LAD_eng_000344) +MOKDAUF SWARS REVENGEH AND JOINS FORES ITH MALCOM TO OVER TRO MOK BEATH (LAD_eng_000345-LAD_eng_000345) +THE MEDYEVL VILIGE CORT WAS ALWAYS ANIOUS TO CEPE THE FENE AROND THE ILIGE GCAPLES (LAD_eng_000346-LAD_eng_000346) +THER WAS A NIN RANK SISTOM EACH RANK HAVIG MORE POWE TA THE LOERANK (LAD_eng_000347-LAD_eng_000347) +THE ASTABLISHED DIPLAMATI RELATIONS ON SEPTEMBRNINTENTH NINTEN SEVENTY TO (LAD_eng_000348-LAD_eng_000348) +THIS WAS FIRTHER XTENDED TO INCLOUD MOR UCADATES IN DISEMBER TWO THOUSAND ND FORTEEN (LAD_eng_000349-LAD_eng_000349) +THE UCH GOVERMENT IS CARNTLY EXSAMING THE EAL CONCICUENCES OF TH ROLING (LAD_eng_000350-LAD_eng_000350) +FROM NINTEN THURTY THREE TO NINTEEN FOARTY NIN THE MARICON LEE WON TWELVE OUTO THE FIRST SIXTEN (LAD_eng_000351-LAD_eng_000351) +THEAR HE FEL SICK WITH TIFAS HIMSELF (LAD_eng_000352-LAD_eng_000352) +SIXT TEMS AVBE DVIDED INTO TWO GROUPS OF THREE TEMS EACH (LAD_eng_000353-LAD_eng_000353) +THE FIRST CEASON PREMIAED ON TWELTH JUON TWO THOUSND AD FIFTEN (LAD_eng_000354-LAD_eng_000354) +IT SCEED THE WHI BOARD AND SISTAME TWENTY FOR COMBING FEACUES FOM BOTH (LAD_eng_000355-LAD_eng_000355) +VLLIUME TOO NUMBERS ON TO AND THRE (LAD_eng_000356-LAD_eng_000356) +THE LOWE PART OF MENS DESES WE MUCH SOURT IN LENC THO THOS FOR WMEN (LAD_eng_000357-LAD_eng_000357) +THE VISIGOTHS IN TERN WE SCEADED BY THE MORS (LAD_eng_000358-LAD_eng_000358) +JOS OF HI SCOLE EVERY WE OF THE COL YEAR (LAD_eng_000359-LAD_eng_000359) +AS TH RSILT OFAL THE ARGUMENT GETING TO HER (LAD_eng_000360-LAD_eng_000360) +IT HAD QUARTERS ARE IN SHEFIALD YOUNITED CINGDOM (LAD_eng_000361-LAD_eng_000361) +LAY ALSO FIALY SINE THE CONTRACT ON STAGE WIT HE DIRECTER AD PREDUSES OFTHE GOULDAN EYES (LAD_eng_000362-LAD_eng_000362) +FISICL FERIPY CN HELE PATIONE TO LURN HO TO WARK WITH FOT DROP (LAD_eng_000363-LAD_eng_000363) +IT ENT ON TO SEL THRE HUNDRED THOUSAND UNITS A CHE FIVE NO (LAD_eng_000364-LAD_eng_000364) +THE NAME STOUCK AFER THAT (LAD_eng_000365-LAD_eng_000365) +THE HLBM LATER BROK TH DIMAD RECORD ON CUCUOM MUSICK (LAD_eng_000366-LAD_eng_000366) +ITS EDATORIAL WE SUBMIT AND ITS OTHR APOL T OPRIYE (LAD_eng_000367-LAD_eng_000367) +JOSIF PLAYES OUR FEATURED EACH WEE ON THE HO (LAD_eng_000368-LAD_eng_000368) +THEY WAT FORA TIMEM BILDING UP THER FORES BEGIN TO ONDR IF THIS EAVL REALY EXISTS (LAD_eng_000369-LAD_eng_000369) +BREFE MENTION OF TH CONVICTION APPERD ON PAGE THRE OF THE NEWYOUOK TIMEMS (LAD_eng_000370-LAD_eng_000370) +ODED BY POSION ON PICH FROM BACK RIGHT TO FRUNT LEFT (LAD_eng_000371-LAD_eng_000371) +HE IS MEMBER OF THE COURT O THE RIL COLAE OF ART LOUNDON YUCAY (LAD_eng_000372-LAD_eng_000372) +DURIG THE COURSE OF TE CAMPAIN FIRGS AND VISIT AT ALL THERTY NEIN WASIGTAN STATE CONTES (LAD_eng_000373-LAD_eng_000373) +A STRIP OF PAPER OF LENGTH (LAD_eng_000374-LAD_eng_000374) +SATO HAD FRECUENTLY WORE TO GETH WTH YOUCK AYAMAR ON PREVIOS POGJECTS (LAD_eng_000375-LAD_eng_000375) +SHE AS BORN ON SCREAN DUIN THE EPSOD BRAD CAST ON FORHAN OVEMBER NINTE NINTY FOR (LAD_eng_000376-LAD_eng_000376) +HE TURNED ROUND SH HAD COM IN SO GENTLY THAT HE HAD NEVER HARD HER (M-AILABS_eng_000159-M-AILABS_eng_000159) +A TO BE SHOUOR AN WE MUST CE OUR DORS SHOAT WE MUS LAT NO ON IN (M-AILABS_eng_000160-M-AILABS_eng_000160) +CIDS PMON HE BEGAN MOKINGLY YOU MA HVE ONDED WHIY I CALD A TROUS WHEN I COULD JUS AS WELLHAVE DISTRORED YOU THAT I DOUT ATO ANSED HIM (M-AILABS_eng_000161-M-AILABS_eng_000161) +THE PESNT THRUWHIMSELF APON HIMAND BOUND HIS FOR LAGS TITLY SO TAT H CULD NOT MOVE (M-AILABS_eng_000162-M-AILABS_eng_000162) +NOR MUST THOU SO LIMETH THE HLY ONOF ISRIAL AS TO THINK HE HATH BUT ON WAY IN WHICH CANGORIFIE HMSELF BY THE (M-AILABS_eng_000163-M-AILABS_eng_000163) +THE OLD COMPARSON BETWE THE IMPULSIE EXSECTIVE AND THE LIBRAL ARTS MAN WHO WHAD LARNED THATHERE ONLY ON R TO POSITVE DISIONS F ALBLE IN AL THE WAL O HINKING (M-AILABS_eng_000164-M-AILABS_eng_000164) +AFTER THIS EXPERIANCE THE NVATORS WER CAIRFUL TO CEPE A SAVFE DISTNCE FROM THE AL (M-AILABS_eng_000165-M-AILABS_eng_000165) +AN OU BAR SOMTING FIRTHER I THN YOU ATO NO IT I HAVE HER A MOST MSTERIOUS TELAPARIGRAMYES WHAT IS IT ISHE DID NOW IT IS NOT ABOUT HER (M-AILABS_eng_000166-M-AILABS_eng_000166) +NO MSTR TOURTAN SAID AND GIE THE ASKT TO ME IAL TAKE IT (M-AILABS_eng_000167-M-AILABS_eng_000167) +AND ARABIAN NIGHT EXCLAMED TROT WHIY THAT WAS A MAGIC NIGHT WASN IT THERS DIFRENT SORTS O NIGHES MATE SAID THE SALER AND THE NIGHT BUTNBRIGHT MEANS ANT THE SAME NIGHT YOU MEAN (M-AILABS_eng_000168-M-AILABS_eng_000168) +IVETRNED OF UPWARDS OF A HUNDED F MY BESTD HANDS FOR NO OTHER FALT THEM FOLOING YOU AND SUCH AS YOU AND THINK ILL TAKE YOU AON (M-AILABS_eng_000169-M-AILABS_eng_000169) +BUT WE WID SHE SE HIM HER HART LEPT U IN APREHENTION AT EVERY RIN OF TH DOR BIL (M-AILABS_eng_000170-M-AILABS_eng_000170) +THESE BOOKS DICXSON I WL KEPE AL THE REST WE OUSEND TO MSTR BEL THEY AR OF A CIND THT HE WL VOULYOU FOR THMSELVES AS WEL AS FOR POPAS SAY (M-AILABS_eng_000171-M-AILABS_eng_000171) +UT INGA WAS NOT AT AL SHUR THAT THE COULD NOT GET IN THE GATS OPED INWARD AND THRE HEVY BARS WERE HELD IN PLACE BY MENS OF STOUT STAPLES RIVITED TO THE SHETS OF STA (M-AILABS_eng_000172-M-AILABS_eng_000172) +I WANT THOW SAID HODON COLDLY I WAN A DOSON HORSES I WANT MEN TO BRIGD THE WITH ME HE PUSHD HI WAY FORD WHICHWAY TO THE STABLES (M-AILABS_eng_000173-M-AILABS_eng_000173) +ERE IS A LIMIT WHAT YUCAND DO FO THE FIRST THIE YOU ANTER A MANS HOUSE AND BESIDE THAT WAS NO TIME TO AROUS SUSPION N THE MINDS OF ANY WON (M-AILABS_eng_000174-M-AILABS_eng_000174) +DO OU NOT REMEMER THAT HE SAS THY DEMAN THATS THE SPIRIT WHICH CEPES THE IS NOBLE CORAGOUS HIY UN MACHIBL (M-AILABS_eng_000175-M-AILABS_eng_000175) +MSTR BELL WHACAN HE NO OF JOAON HE LIVING A LASY LIF IN A DROUSY COLAGE (M-AILABS_eng_000176-M-AILABS_eng_000176) +AND THE CITN FOLOEDIMUARLY AT THER HEALS (M-AILABS_eng_000177-M-AILABS_eng_000177) +THE FIST TUTCHWOLD CASE AN EXPLOSION IN WHICH AMONG SUCH HUNDREDS OF INFERIATED MEN AND RECKLES BOYS (M-AILABS_eng_000178-M-AILABS_eng_000178) +WON F TH GEAT PLESUERS OF MARGRATS LIF AT THIS TIME WAS IN EATS BOY (M-AILABS_eng_000179-M-AILABS_eng_000179) +TH THNG IS GON ON LONG NOF THER IS ONE ORE BIAG ACXIDENT WE SHAL HAVE TO COMPRMISE WIT THEINERIVER ND CARYON THEWORK CUINTLY (M-AILABS_eng_000180-M-AILABS_eng_000180) +YOUAR LAT SAID SHE WEL SHE HELD HER BRATH O THE ANSR (M-AILABS_eng_000181-M-AILABS_eng_000181) +TRAT TOLD THE GIRLS THAT THEY MUS GO WIT HER FATHER TO LIV AND GIP CUSISILS LITE LD CABEN AND HEN THEY HERD THS REDFUL DECRE (M-AILABS_eng_000182-M-AILABS_eng_000182) +MARGIT SAT DON O THE ROG PATLY TO WARM HERSELF FOR THE DAMPNES O THE EVNING HUNG BOUT HER DRES AND OVER FITE HAD MAD HER CHILY (M-AILABS_eng_000183-M-AILABS_eng_000183) +O NOW YOUAR MSTAKAN ABOUT THAT RELID THE KING THEYARENOT MY PRISONERS BUT MY SLAVES WHOM MY PURCUSE FROM THE CING OF EV (M-AILABS_eng_000184-M-AILABS_eng_000184) +HER FATHE TOKU TE CMBRSATION (M-AILABS_eng_000185-M-AILABS_eng_000185) +IN ACORNER WAS A SORT OF DRESING TABLE ON WHICH LAY A COM AND BRUSH CANIDY SEED MUCH INTRUSTED INTHE TABLE AN WAS EXAMING ITWHN THE GORU RETERNE (M-AILABS_eng_000186-M-AILABS_eng_000186) +I HAVE SOME TIME THGT THAT MYSELF SHE AGEED BUT OFCOURS I DONT NOW STIL I HAVE TO BE PITY CARFUL SOMEON IS ALWAYS OVER HER BY MY DESS OR LOKING OVER HER (M-AILABS_eng_000187-M-AILABS_eng_000187) +I SHL STAY REPLID THEYONG MAN FOR I MEAN TO SIT YO FRE (M-AILABS_eng_000188-M-AILABS_eng_000188) +WHAT D YO DO ASD THE SORCERER (M-AILABS_eng_000189-M-AILABS_eng_000189) +WHIY THERE AR ENAMES YOUR SHORT HINES NOT ANY MORE REPLIED TROAT IM QUE OF THE INKES AND IM ALSO QUE OF THELOS SO I WONT HAVE MY PEPLE QUARLING (M-AILABS_eng_000190-M-AILABS_eng_000190) +TIPRITER WE CLICKING CLIPING WER BING SNIPD OTOF A UGE TACK OF NOE PERS AND PASED IN AN N LARG SCRAPBOKS SERKULER WER BENG FOLDED AN MAD REAY TO MAL FO THE FINAL APEL (M-AILABS_eng_000191-M-AILABS_eng_000191) +IT WAS FOR DAYS AFTER THE SUPRIES OF ALTHERS HORS HEN THE STRANGERS LET THE ASTAT TO THE CAIR OF RUGED OLD FORSTER HARMEN (M-AILABS_eng_000192-M-AILABS_eng_000192) +BPOR TEMPLTON HE SAID I USTONOW HIM MANY EARS AGO HE WE E BOYS MENTO SCOULWITHM AND AL THAT SOUTOFTHNG YONOW BUT AN TIL I RAN CROS HM OR (M-AILABS_eng_000193-M-AILABS_eng_000193) +I FOND HER I THE FARIST AND BOGT HER HER A PRISNE REPLIE THE CAPTON (M-AILABS_eng_000194-M-AILABS_eng_000194) +WHO MAY BE COMPITENT ITHE FROM PERSINAL EXPERIANCE OR THE EXPINS OF OTHERS TO ANSER IT WITH MOR OR LES CURECTNES OR AT LEAST AN ATTEMTD (M-AILABS_eng_000195-M-AILABS_eng_000195) +ON NINTY TO LATESTRETET SID HOKGEN BITING OF HIS SAGAR (M-AILABS_eng_000196-M-AILABS_eng_000196) +TRAT WA SRPRIE TO FINE SHE COUD CE SO PLAINLY THR THE HIY WAL OF WATER ABOVE HER BUT THE SON WAS ABL TO SHUT ITS BEME STRAT DOW THO THE TRANSPARENT SE (M-AILABS_eng_000197-M-AILABS_eng_000197) +THE SPAT WER ID SPRNG UP (M-AILABS_eng_000198-M-AILABS_eng_000198) +COME DENIL WIC SHE GAVE SUCH A UPOSION (M-AILABS_eng_000199-M-AILABS_eng_000199) +YOU SEE ANDTIL THE SCOL PILS WER INVENTED WE WASTED A LOT OF TIME IND STUADY THAT NOW MAY BE BETER IMPLOYED INM PRACTISING EATHLATIK (M-AILABS_eng_000200-M-AILABS_eng_000200) +YOVEDON IT NOW DICLARE DARTHY THES TENTS AR JUST WONDERFL (M-AILABS_eng_000201-M-AILABS_eng_000201) +FOR TWENING TEN FIVE THRE TWO THE IN WAS BARLY TWENY MOUS AWAY WHN HODON FIRED HIS ROCKITS THE MDE CALOSAL CLOUD O VAPER IN EMTINES (M-AILABS_eng_000202-M-AILABS_eng_000202) +THEY PAD NO ATENCION TO THE FACTHAT GIP GUSSISL DID NOT WNT TO MARY ANY OF THEM FOR THEY HDETERMEND THAT HEN IT WAS AGREED WHO HOUD HAVE HIM (M-AILABS_eng_000203-M-AILABS_eng_000203) +WHAT DOUTIN OF THAT HE CRIDE OPENG A COPYO HE RECARD AND LAIG T FLAT ONTHE LIBRY TABLE (M-AILABS_eng_000204-M-AILABS_eng_000204) +IT L RECUIER BUT A SHOURT TIM (M-AILABS_eng_000205-M-AILABS_eng_000205) +AND LAST THE CROUD O VEGITABLE PEOPLE WHO HAD NO HARTS AND COULD NITHER SMILE NOR FROWN (M-AILABS_eng_000206-M-AILABS_eng_000206) +THEN YOUL CACH IT SI THE WICH (M-AILABS_eng_000207-M-AILABS_eng_000207) +WHAT IS IT I QUIRED NOT FELING SERTN BUT THA I WAS A VALED ATEMP TO SECURE LITL FRE ADRTISING FORTHE ANDEOVER (M-AILABS_eng_000208-M-AILABS_eng_000208) +SO HE GAVE THE LURK THA THRD HUNRDOLRS FOR BOOKS AND A CASK OF GOD OLD AL FOR PETER THE CLURK DRANK THE AIL HIMSELF AND GAVE THE CAH MI (M-AILABS_eng_000209-M-AILABS_eng_000209) +AT LIKE THAT AN ALS IN WNERLANT WITH MERLY A GRIN HAT FATED AWAY CHANGING INTO A LINKXE WHICH INTURN DISOPERED FOOED BY AN UNON CREATUER WITH SHORT NOWS AND PONED EARS (M-AILABS_eng_000210-M-AILABS_eng_000210) +SHE COUD NOT DO MARGRIT LANSED UN CONIOUSLY AT THE UN CLE CORNER F TH ROM SHE COUDHARTHY UDER TAKE A SERINTS PLACE COUL SHE (M-AILABS_eng_000211-M-AILABS_eng_000211) +NO SHE REPLIDED WITH INISN CARIOUSITY DID I GIVE THEM TO YOU (M-AILABS_eng_000212-M-AILABS_eng_000212) +MARBRO MILES AN THEA GACSENT DWELIN WERE HELD UNDER LONG LEACTS THEY MUST IF POSIBLE BE RELEAT (M-AILABS_eng_000213-M-AILABS_eng_000213) +A CAP WAVE OSTON IS THE LADOR (M-AILABS_eng_000214-M-AILABS_eng_000214) +IT BOUNDED HEAR AND THEIR ABOT THE CICAN HOUSE AND AT FIRST DORTH COULD NOT TEL WHAT IT WASS WHIL THE SCREACING OF THE CICONS NEARLY DEFEND HER (M-AILABS_eng_000215-M-AILABS_eng_000215) +THE SOLDER GAVE A YAL THAT AROUWSED A SCOR OF HIS COMRADS AND BOGHT THEM TUMBLING INTO THE STREAT WEN THE SAW HO THE BOLRSE PRESIOUS PRISNE WAS E SCAPING (M-AILABS_eng_000216-M-AILABS_eng_000216) +JIM HAD REFUSED TO LEAVE THE FIELD OF GRASS WHERE HE WAS NGAGED N BUSILY EATING SO THE WISURD GOT OUT O THE UG AND JONED SEB AND DORITHY (M-AILABS_eng_000217-M-AILABS_eng_000217) +SERTNLY IMAS INRUTD I THE CACES OU AR BUT I CAN MAK HADSRTALS OF IT I REPLID (M-AILABS_eng_000218-M-AILABS_eng_000218) +OR ANY MICE OR EVEN GRAS HOPERS (M-AILABS_eng_000219-M-AILABS_eng_000219) +AND THE THA PASIO DON THYTEL YOU WAT TO DO OR WHT IN NOT TO DO WE THE MONY THEY GIVE YOU AN JUST PAMENT FO YOUR PAINS IN THER EXTANGE LIG (M-AILABS_eng_000220-M-AILABS_eng_000220) +WHAT DIS TAT MEAN AS THE PRINCES (M-AILABS_eng_000221-M-AILABS_eng_000221) +HE HAD BE DROUND HE WAS FLOTING IN A SE OF LIGT AND NOW N THEN SHINING LITLE FIHES SWEAM INCQUISITIVELY UP TO HIM AND STARE (M-AILABS_eng_000222-M-AILABS_eng_000222) +BUT OLD GUN HADA TRCKA TO LEFT ANDREMEME THE TAIL I RED TO YOU I TH THON ROM ABOTHER THE FIRST F THE RAONS TO ND THE WORLD OF OPL WERE SOLGERS SENT FROM SOME BLASTED PLANIT IN OUTR SPACE TO FINE ANW HO (M-AILABS_eng_000223-M-AILABS_eng_000223) +PAPA WIL OUSPEKT THE MEN AND GE HE O GO AWAY SHE CANOT BREETH POR THING WIT THIS CROUD OABOUT HER (M-AILABS_eng_000224-M-AILABS_eng_000224) +WHEN I TOOK THIS CACE HE SAID I BLEVE DOWN IND MY HART DIXSON WAS INSENT I STO BELEIT BUT MY FATHAS BEN RUDTLY SHAKE (M-AILABS_eng_000225-M-AILABS_eng_000225) +CHAPTR SICK OFE THE PIRT OF OR SEATS (M-AILABS_eng_000226-M-AILABS_eng_000226) +REMEMBE THE CAN NOT TUCH US (M-AILABS_eng_000227-M-AILABS_eng_000227) +IVE ME TIME ASYOUR GIVE ME TIME IF HERS ANYTHING I HAT ITS A HURY IVEN I DA YOU MAGUSTY AND OUNCE THE SIXT THE SNUB NOSD PRINCES (M-AILABS_eng_000228-M-AILABS_eng_000228) +TO NOF TRAT O CLARED THE SALER MAN (M-AILABS_eng_000229-M-AILABS_eng_000229) +AS FOR THAT SAID MARGRIT RETHERHOATALY I HOLD IT IS HONEY SO IT QUEE MALLD EPENSAY (M-AILABS_eng_000230-M-AILABS_eng_000230) +WHEN HEHERD THES WORDS THE KING WHOS HAD WAS FUL OF TH PINCES NEVER STOPE TO INQUIR IF THE COULD BE TRU AND SMEARED HIMSELF OVER WITH FAT AND SPRANG INT THE OVEN (M-AILABS_eng_000231-M-AILABS_eng_000231) +YOSHOULDBEALE GT PARTCE FROM YOUR WROM VION RECEVER IL HAV SOM TOULS GIVEN OU THENHEATD DEPLOMAS HE HAS TO NDERSTAND TH TINGS HA CNTROL OFVENCS (M-AILABS_eng_000232-M-AILABS_eng_000232) +BY THE TIM THE FROST HAD SAD IN THE SHUL BE FAR WAY FROM HELSTON (M-AILABS_eng_000233-M-AILABS_eng_000233) +WON THING I WNT TO SAY BEGAND CANITY (M-AILABS_eng_000234-M-AILABS_eng_000234) +THIS MPORTN TRAFIC WASCONFIGED TO NO ON UT THE EAL PROPRITER (M-AILABS_eng_000235-M-AILABS_eng_000235) +IN YEO AR DOB BLASEDT PONT BAS OU DOT MY THSTIMG GO (cv_eng_000707-cv_eng_000707) +IGHTE AT A EPRITD SUPSECTION WHICH DEALS WIT IS ASPECT (cv_eng_000708-cv_eng_000708) +OPRATION OF THE FRUNTLANG CONTNED ON THE GOULDANT TRESSELS (cv_eng_000709-cv_eng_000709) +MONSIOM FLORID IS TWENSPERENT OVERANEXTRIMLY WHIHD RANG OF AVELINGS (cv_eng_000710-cv_eng_000710) +FOR JGINT BEAKINK SHEATS STOR T FRESH BPACKET BUTEADESSON DILVED THEM UNTO RELER OLD GASS (cv_eng_000711-cv_eng_000711) +THE OTHE FORTIN CAMPAS ARE TO YA CAMPSS REFERD TO COLECTIVELY AS THE YUNERSTI COLAGE (cv_eng_000712-cv_eng_000712) +ITS TO THEARD THOW HE CUICKLE GON TO FRGET MY NAME TH (cv_eng_000713-cv_eng_000713) +WON POTURE IN TE GLOR SHO TH HOWD HE AGENTLY INT IRENTISTHATHYE AL ADGR ART TOMPON (cv_eng_000714-cv_eng_000714) +A IMPERIAL DIYIAT (cv_eng_000715-cv_eng_000715) +THE ESULTIN OMPANY ED AS HAR TAESICURITY COT PORATION (cv_eng_000716-cv_eng_000716) +BECOING MINING CAN BE DON WIT GOF HIS CARTS OR WITES SPESIALIED HORDLY (cv_eng_000717-cv_eng_000717) +THEY AL SO LE THE NASIAL RANKING (cv_eng_000718-cv_eng_000718) +TROWS GRAINS BISHIP OF NIMERICE (cv_eng_000719-cv_eng_000719) +IONDED THAT THI DORL HIM UN HE OK MY PLASES (cv_eng_000720-cv_eng_000720) +I THOUGT ID GIVE THE CITS ADREET (cv_eng_000721-cv_eng_000721) +AS THE VITL DINIH TOM THE PICHES (cv_eng_000722-cv_eng_000722) +HOWD YOUR NOST TO CE THIS MAYE FROM THE ABLING YORMOT ORFNTION (cv_eng_000723-cv_eng_000723) +AC THAT SONDS LAKE THEAR PROLOMEMIC (cv_eng_000724-cv_eng_000724) +HISTRICALIGER WAS NO CLEARELY DEFINE BOUNGRYEN THIS PIT OF THE ARABYENPNINSTOLE (cv_eng_000725-cv_eng_000725) +MARSHIAL SHAVER OF SLASH FILME GAVE THE FILLME AN ATE OUT OF TEAEN (cv_eng_000726-cv_eng_000726) +AOL PIND IO TI THAT (cv_eng_000727-cv_eng_000727) +HIST TDILE BEGAN TO RESEMBLE MICAL TEMASSCKEINOS (cv_eng_000728-cv_eng_000728) +HE IS ALSOL CAPABL OF FIRNGLIGT INMBLE WIF IMENTE DISRUPTIVE POWER (cv_eng_000729-cv_eng_000729) +THE CLAME TO WIKEDS CENINGLIN PURMALYT ININGS AS FOW WIS EATAN THURON ADERASY LADLI RE (cv_eng_000730-cv_eng_000730) +SHE E GRUSILY TRO WHAT H (cv_eng_000731-cv_eng_000731) +HE MT THE ORGANISERS OF THE PROTES AND AGRED D CREAT TWO WORKINGROMS (cv_eng_000732-cv_eng_000732) +THE BON STROC THO FHOLD WOARD WIL ABOF THE REN ONSTORD (cv_eng_000733-cv_eng_000733) +INLY CAMDON TOMASGARIT AND GOLD FILD SOAT ISEECHILE BAKCAR WER UN CONTESTED (cv_eng_000734-cv_eng_000734) +IT IS A CHRDY SCOL WHOS FES AR COUCULATEDIN ON IN MEANS TEST (cv_eng_000735-cv_eng_000735) +SOME WENT AWAY WHAL IOWAS THER AND OTHE POPLE CAM (cv_eng_000736-cv_eng_000736) +T H CSAITHAEDEDUP R E (cv_eng_000737-cv_eng_000737) +THAT CURA CONOTY WAS LOKCADED MANLY I THE HISTORICALE AND JEAGREFICAL REAGION OF CUR (cv_eng_000738-cv_eng_000738) +UNC HELOVATION A THE SIGHT IS AM OF SULEVBLE (cv_eng_000739-cv_eng_000739) +TO BEAS TRIED TO NCHECT CONON TEMPT IN TO HIS TONE (cv_eng_000740-cv_eng_000740) +I HAVE TO WARLK THIS SATORDY (cv_eng_000741-cv_eng_000741) +TDE T RA THE RON WHS FOUNDTHES COLEGE GLEAD WITH GLATING ON THERG NONES (cv_eng_000742-cv_eng_000742) +WHEN THE BILING DOST HE S SAELED FOR BIT THE BOY TRMBLED AT WHAT HE SAW (cv_eng_000743-cv_eng_000743) +DEMACRAT AMBERAN BAKEKHER WON IT HE OPON SEE (cv_eng_000744-cv_eng_000744) +WORT HAVE ORT TEIN TO GETHER BAITS OUOD ENT IN HIECUALIER EUS OU NATHES INPO ROM (cv_eng_000745-cv_eng_000745) +TRINTEWAS BORN IN BELES SITED IN BRITOS PONDERAS (cv_eng_000746-cv_eng_000746) +DORITY FACE OF LIF MOES FAST (cv_eng_000747-cv_eng_000747) +A NOWH H E (cv_eng_000748-cv_eng_000748) +SIVEIN GORLTDLOL (cv_eng_000749-cv_eng_000749) +AT ON TM BRY LOUELINS THEYWARD FROM BRAKG BESTATION IN SO N DIFREN ERECIONS (cv_eng_000750-cv_eng_000750) +CHECK REPUPBLICK ENTED TWO SHOUTERS INTO THE PARO LIMPIG COMPATITION (cv_eng_000751-cv_eng_000751) +T I TER WILIOMS ROE THE SCGEANG CLAY AND N S SHARED STORY RAT IT THAT THE PEPIT (cv_eng_000752-cv_eng_000752) +TA IS FAST OFE ALLD WORS TO OF RETERC H HERITY FIN THER ADY SADER OFOYD THERART (cv_eng_000753-cv_eng_000753) +O THES ENXTR GARTS WE NESURNT THERNGONLELALMSWO AGALFTE RAG WHT THE AL HAT (cv_eng_000754-cv_eng_000754) +AHU HNDR ONT BACK TO ESTRLIOA (cv_eng_000755-cv_eng_000755) +PERMIT ME TO INTRDUSE YOU TO HUR RMOGJESTIED CQEAN (cv_eng_000756-cv_eng_000756) +AN ORGIN HERWON WAS SUPOS TO THE NONADICTIVF MORFEN SUBSTOT (cv_eng_000757-cv_eng_000757) +U SHE IS OF MEKCICON DESSENT (cv_eng_000758-cv_eng_000758) +CS I M SHOR T EALES NOT ONDIST (cv_eng_000759-cv_eng_000759) +IOW HOUS ON DONT LONSAON THES SHREY IVGONT PR AEPED IT O HLONO (cv_eng_000760-cv_eng_000760) +I CALED AON SOPE SARLIN AT IT (cv_eng_000761-cv_eng_000761) +FOR SIMPLITHY GUR INCHES IS NORMLYAROUNDED TO HE ERES HOL NOMBER (cv_eng_000762-cv_eng_000762) +IF WE ACTILY DO ON I SALED IT WILL BE F (cv_eng_000763-cv_eng_000763) +THE FRO OF TH ICTRY S APLSHAPED (cv_eng_000764-cv_eng_000764) +THEOUT EXTHANGE IS NO WOBUY (cv_eng_000765-cv_eng_000765) +WHAT YOU EAE TO DAIY WALKS AND TARKS TO MOROW (cv_eng_000766-cv_eng_000766) +THE WATED AN FLOS OUT OF THE SWOMPS AS THE LOUWOPLAR RIVER (cv_eng_000767-cv_eng_000767) +AH WHIY I DIDND YOU SEAE SOME HINGK (cv_eng_000768-cv_eng_000768) +T HAV OUSENO MAR (cv_eng_000769-cv_eng_000769) +I COULD GO AN FOR DAYS ABOUT THE DADIOUS LONGS PHE DUSE IN HIS PART F THE WEOROED (cv_eng_000770-cv_eng_000770) +THOS FHEO LADEOFHEA INCQUIR ON NGTIN SITICLY OUT THE YEAR (cv_eng_000771-cv_eng_000771) +FAS LEVEIS OF DECTIS ESSER GLTONSLA (cv_eng_000772-cv_eng_000772) +THE SWEEDS WER NABLE TO OUSER VEICALS WHIC HER STUCK IN THE MOD (cv_eng_000773-cv_eng_000773) +THE ACK DID NOT BROR HE BECT BAYING A REPRESENTIE TO APEARIN THE CORICTO (cv_eng_000774-cv_eng_000774) +CHINGWEREPLIS T LOPINGRORAL A (cv_eng_000775-cv_eng_000775) +HE WAS CONVICTED AN BANIS DISIPRS HOR SEVEN YEARS WER PNISMENT (cv_eng_000776-cv_eng_000776) +THE CUPL OF TO CHALDEN A DATER SOFEAU ROSALENDA ND THE SON O MATHYOL BRAVERY (cv_eng_000777-cv_eng_000777) +N OF TH HRE REFPRENDAMS RECH HE QUARAM OF TH MAGJORITY OF THOS INTITLED (cv_eng_000778-cv_eng_000778) +INT ITERPEN SECXCEDED IN DEARAST SOME AR RASIP CARA IT IS WHO SALD OUNERSEY THR A PEARIE O STRONG GROTH (cv_eng_000779-cv_eng_000779) +HEAR I AM BEPTEN MY FLOCK AND MI BUTERSURED THE BOY TOS (cv_eng_000780-cv_eng_000780) +THIS FALIA HAST LET TO SICXTEAEN POULBLENCS HADE INSEARDAYSE OF CALESTO (cv_eng_000781-cv_eng_000781) +AOO YSAS DEO (cv_eng_000782-cv_eng_000782) +WHIY I THA PLAIN CEPE GOIN OVER (cv_eng_000783-cv_eng_000783) +ANDNI HAYAE AEDONDOS HE FOR WAT FIRTIAL BOCS WITH O RESOULTS (cv_eng_000784-cv_eng_000784) +THEPLICATION WAS PUT AP PROV IT IN FARBRAVY (cv_eng_000785-cv_eng_000785) +HENRY TORLED TONMNSTIL S WEAR HE HAD A SOUNDID RANING IN LITING (cv_eng_000786-cv_eng_000786) +IT WAS TIS CONTINUE DO TO SCETHALIN CONFLICS ANVLVED IN LOSE HIS RETHIRN TORE TO RESTRIAL REBRADIO (cv_eng_000787-cv_eng_000787) +ADTTH HER FAMLY WAS FROME BREAHONSA (cv_eng_000788-cv_eng_000788) +A WHAT DIDYEAE FOR INOR THEA PA (cv_eng_000789-cv_eng_000789) +THAT WAS MY DRARTO SINCE (cv_eng_000790-cv_eng_000790) +HES COSEARIT A MUSTERE OF SHE AROSED COUO (cv_eng_000791-cv_eng_000791) 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ANDURISOM (swc_eng_001944-swc_eng_001944) +OST OFTHE AGJER YOU ESS MUSIC COMPNES (swc_eng_001945-swc_eng_001945) +ONCSTEIO PAR OR ON MONOFONIC TRACK IS PLAD OR RECORDED HEN THE TAP S MOVING IN ON DIRECTION AND T (swc_eng_001946-swc_eng_001946) +EITS EARLY FORME IN (swc_eng_001947-swc_eng_001947) +STR TEAGHC FILOSOVER (swc_eng_001948-swc_eng_001948) +OSITIONG T VANAEHES TERN THE GAME (swc_eng_001949-swc_eng_001949) +NEO SAUT WHELLS (swc_eng_001950-swc_eng_001950) +DISPOSAL OVER HIS ON BYLOUGICAL NATER (swc_eng_001951-swc_eng_001951) +EPODUCTIVE RIGHTS OR EXERT UN DO PRESHORS ON PRESPECTIE PAIN (swc_eng_001952-swc_eng_001952) +IL HANCHANT NORGON (swc_eng_001953-swc_eng_001953) +RASTA POPHOLOS HID GON (swc_eng_001954-swc_eng_001954) +ND TO THUSAND TO (swc_eng_001955-swc_eng_001955) +FOR GAMPL F THE PLAYAR HAS ONL T (swc_eng_001956-swc_eng_001956) +SOFEDA SUBRACKNODHMERGE HAVE COLNIIV IMPARMENT THAT FECT (swc_eng_001957-swc_eng_001957) +PEVIDEIG RONSTIC DATER 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THE OACKS (voxforge_eng_000947-voxforge_eng_000947) +OFLING AROW BUSTD BETWEN US (voxforge_eng_000948-voxforge_eng_000948) +HATREIT AND MURDER AND LOUST FOR REVENCH THEY POESESTD TO OFER FLOWING (voxforge_eng_000949-voxforge_eng_000949) +THT YOUCUD HERE AL UPAN DON TE IMPOPOE (voxforge_eng_000950-voxforge_eng_000950) +IT WAS MY A DEA TO ATE (voxforge_eng_000951-voxforge_eng_000951) +SHE DOSNT WON TO WIN (voxforge_eng_000952-voxforge_eng_000952) +SHE HINKE IT IS BECAS HE WONSE SOMTHING ELTE (voxforge_eng_000953-voxforge_eng_000953) +HE PULLED AND THE LOK CRESET DOWN TO BRAKE HIS BACK (voxforge_eng_000954-voxforge_eng_000954) +THAT THE SOCALD FORSES AT WORK IN LIGHT HEE ALCTRISITY AND MAGNATISM (voxforge_eng_000955-voxforge_eng_000955) +HE TORND SHARPLYD AND PICE GRAGSIN ACOST THE PIVELER (voxforge_eng_000956-voxforge_eng_000956) +AL SO I WANT INFRMATION (voxforge_eng_000957-voxforge_eng_000957) +THE SIXT DAY HE SPENT IN THE CAVEN WIH GREAGSON (voxforge_eng_000958-voxforge_eng_000958) 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GED TO SE YO AGIN FIL (voxforge_eng_000992-voxforge_eng_000992) +HOVELINLY I WEN DADED THAF RS TA (voxforge_eng_000993-voxforge_eng_000993) +THE AR OT REGULE OSTER PIRETS NICLES CONTNED (voxforge_eng_000994-voxforge_eng_000994) +THE MST BE HRDING FOR BUSNES BUT I THUG YOU MIGT WAT T TAKE LOK T THER SIGHT (voxforge_eng_000995-voxforge_eng_000995) +THER WAS NO CANCE TO FIRE WITHOUT HINING HIM (voxforge_eng_000996-voxforge_eng_000996) +AS FOR HIMSELF WONT THE STREAE RAL WAY ARNINGS INCREING SADLY (voxforge_eng_000997-voxforge_eng_000997) +DON HIM CAN YOUR BOY GO LONG WIT ESSY (voxforge_eng_000998-voxforge_eng_000998) +GOLD BY PEAR HE SHOWTED (voxforge_eng_000999-voxforge_eng_000999) +BUT SUCH A DEVERDGIENS OF APINION WOULD CONSTITUT NO MENENCE TO SOSCITY (voxforge_eng_001000-voxforge_eng_001000) +T THERE WAS ONE CHANCES AND ONLY ON OF SAVING JONT (voxforge_eng_001001-voxforge_eng_001001) +I I CAN OT FOLOE YO SHE SAIND (voxforge_eng_001002-voxforge_eng_001002) +ON THE FAR CORNER OF THE COMPOUND FENTS A WHAOK BREADED (voxforge_eng_001003-voxforge_eng_001003) +THEN AGIN TOTER HAD SUC A IRITATING WAY ABOUT HIM (voxforge_eng_001004-voxforge_eng_001004) +WE ALL NOW OMAN AS A SUCESFLE STABL CONTRY AROL MOR THERFOR THAT FOR THE HOL REAGON (voxpopuli_eng_000494-voxpopuli_eng_000494) +THEREFOR ITS HIGH TIME OU COME FORBOD E THE PROPOSAL FOR REVEU BE DANOPRAIONAL SUPERACION OF THE OARDIT AND NON ADITSERVISIES UNDER A DIECT EAUS OBEITISON (voxpopuli_eng_000495-voxpopuli_eng_000495) +IT ISCKEARE THAT WE HAVE NO TIME TO WAST THE NUERESOLTS OF THEE I PEESHE RECARD N SIENTIFIC BACSES OF GLIMIT JAINSE LEVE NO ROUOME FOR HESITDASON (voxpopuli_eng_000496-voxpopuli_eng_000496) +SENT SO IN THE CONTAINER WHIHAEVER AEN TUCHED COME SLAVES COUNTEOFET GODS DRUGS IT SETR (voxpopuli_eng_000497-voxpopuli_eng_000497) +I HOPE THAT COMIONS MOBIT INESHES INISIFIVES HO ONT CRAT THE NEXT PROBLOM BUT WILL BE A ANSER FOR EXISTING CHALINGES OF THER OUT TANSPORED SECTO (voxpopuli_eng_000498-voxpopuli_eng_000498) +IN THEWUE IT WASA DICION TAGNAULY BY ONE PRSON THE ORMER PRESIDENT O THENIGDED STATES AGANCE THE ATICULATED MCRATIC DUMAJURITY O TH EU ES CONGRES BY ALL OF ITS REPUBLICKEN ND SOM FITS DEMECRATIC T DEMACRAT MEMBERSIT WASAN AGREMENT WITHOUT ANY BINDNG OBLIGATIONS AT HE LEDES OF ERUN VERY UPANLY ANPRESIDH MAPTLY NTHE ERY DAY THE SOCALD DEL WAS POULISHE (voxpopuli_eng_000499-voxpopuli_eng_000499) +FRE SPEACH IS ASENIUALY AEXETIG THT PEOPL ARE FREE TO SAY THINGS WE DO NOT LIK NOT MELY FREE TO SAY THINGS WE DO LIK (voxpopuli_eng_000500-voxpopuli_eng_000500) +HAT IS LURNE FOM THIE (voxpopuli_eng_000501-voxpopuli_eng_000501) +BE SIN THAT THE NVIMENTAL EFFECT OF PRODUCS MUST BE AVRY INMPORTANT ISUEIN HER EEWU AND THE WOL I DEAE O THE ECULABER GIVS A VER YUSOULORIANTATION FOR THE OUSUMERS OF COUS HE ECULABER HOULD GIVEN TO THE MOST ANDVIRMENT AF FANDY PODUCT THE INFORMATION SOULD BECLEARE AND CUE (voxpopuli_eng_000502-voxpopuli_eng_000502) +HOWEVER THE CARENDRYGEM NEDES TO BE BETERD ALORDT TO TH IGIDAL INVIRNMENT TO ISHURE FAR MINERATION TO GREATERS AEN TO ONFOME TO ONSUMER EXPECTATIONS (voxpopuli_eng_000503-voxpopuli_eng_000503) +AT CASE BY THE CMION AND MEMBER STAT TO NHANE THERSUPORT TO RECONCILIATION TO SECUR PESE AND TIBILITY AND ARLAND IWOL THEREFORE ARD YU CALIES TO PLEASE SUPORT IS AMENMEN (voxpopuli_eng_000504-voxpopuli_eng_000504) +TRATAGICK CHOICES ABOUT WHE TO E WEST MUT BE MADE NOW TAKEN IN E COUN A NE TO FAS OUT FOR SILFUL SUPSITES BUT TAK THE GAS AS I ORSOFYU IT CAN BE A HELTFULE BRIGING TRUNSISHONARY MEDIOM TO BE USE IN MEMIN MENY MBERSTAT I BE ONTO EACHIVE OVER AMBISHIOS CLIMITARGITS (voxpopuli_eng_000505-voxpopuli_eng_000505) +WE AE POSEILY FR A OLE WE CAN CUTH TO PRASCUE THE SAMEM POLICES IN TH SAME MANER NOWING THAT WE LEDE TO THISAMPRSOS THE RISAULS THA WENO DEDEA (voxpopuli_eng_000506-voxpopuli_eng_000506) +UT HER SANOPTION B (voxpopuli_eng_000507-voxpopuli_eng_000507) +WRE ALL SO NED A CHAINGE IN OR IDOLITIE (voxpopuli_eng_000508-voxpopuli_eng_000508) +A LADEH BAT OF THE REASON OF COURSE IS ILIGALFISCINGK AND THERE OFOM P TDON OFEN BY YARR VESES WHICH ARE REAGISTERD TO COUNTRES WHICH LUCKE THE WIL OF THE RESURCES TO NFORST INTHE NESINAL AGREMENS NO MOUNT OF TRESABIITY MESERS ORE EXTRPAPREWARE WIL ADESE THE PROBLOUME OF REDUSING (voxpopuli_eng_000509-voxpopuli_eng_000509) +THE COMPRMISE ALSO INCLDED KLARERUDS TO THE FINE WHICH MBERSTATE AS HERSTICTION AND THE OPRATION ITHIMBERSTATS CONERD FOR CRUSBR THE CACES ASILA THE NED TO EINVLLF YOUR JUST THAN YOF OR WORK AND PLAE OU SEUPORT TO MO HIS ERECTIV (voxpopuli_eng_000510-voxpopuli_eng_000510) +NO THE RENS WOULD HAV AS BELETHATHE AR BAD BES CRIMINAL BES DELIBEATLY CONTAMINATING HUDY WITHA DANEUS NGREDIENT BUT IT FACT INFAC HE DINGWHT HUNY BES AR AL HVE ALWAS DON WIH TO CARY POLON BAC TOTHER HIVESTOD TO FED THER OUN (voxpopuli_eng_000511-voxpopuli_eng_000511) +UT IT WAS THE CONTRY ITSELF BENG MOR CAPABL (voxpopuli_eng_000512-voxpopuli_eng_000512) +R INTO THE PRT FOLIO OF THE NEUW COMIONAR DELING WITH FUNDEMENTER RITES (voxpopuli_eng_000513-voxpopuli_eng_000513) +THE MESIYGI TAT THE OU DODT NAT HAVE AN NOURSOLUIONS (voxpopuli_eng_000514-voxpopuli_eng_000514) +AR YOU WILING TO ACT INERE FAVER FOR THE SOSIAL DEMENTION TO BE INCLOUDED IN THE EU COMPATENCSES AS PROPOSE (voxpopuli_eng_000515-voxpopuli_eng_000515) +A NEXTHAT ON PESPECTRUPOLIES TAKIN WITHE EFORM OF OUER TELICON TH FRAM WOR (voxpopuli_eng_000516-voxpopuli_eng_000516) +I BELEVE HIS REMARKS WER A EXPLICITLY RACEIST AND THEN AFOBICK AND PRMOTED RACIAL INTOLERANCE IN A WAY THA IS NOT CXCEPTIBLE OR ALOWD IN TE CONTITUTION OF THIS HOUS (voxpopuli_eng_000517-voxpopuli_eng_000517) +REAL IFE GAMPL SHO THAT SOLVING ITIES RELATE TO ADUCATION FEULED STRONGCOMINIT DEVELOPMENT (voxpopuli_eng_000518-voxpopuli_eng_000518) +SI HOPE THA TIS ILHVPE ORUSHA AS WEL ND THAT RUHA CAN ALTS AND VISAIG ND EXTREME SUCESS TORY AFTER THS EG TISIGNIFICAND AT IN ORGST THIS YEARB (voxpopuli_eng_000519-voxpopuli_eng_000519) +SHE ECXEPTED THE FACT THAT SITISON SHIP IS AY NASINAL PART OF THE OSINO GUDISDICTION BUT HYOURLSO SAID THAT ACOURDING TO THE MASTRICK TREATY AND SHE AS RIGHT THE HAS TO BE ADIYREC LIN (voxpopuli_eng_000520-voxpopuli_eng_000520) +TDEY WOU FALD ESPECIAL EAN THE MST RATING AUNIFIED AND T AFFISHENT APPRORCH TO LIE MITCANGHE TREATMENT ASWEL AS IN STRANTHANINGK ITS LEDING POLITICAL COSION IN DISAGENDER I CONSCITHER THERFOR TAKING THISRESOLUTION AN ACT OF UTMOST IMPORTANS (voxpopuli_eng_000521-voxpopuli_eng_000521) +THE UNIGTED STATE OF YURUO WIL BE A FACT WITH SWEDON AS A PROVIDENC (voxpopuli_eng_000522-voxpopuli_eng_000522) +IT MUS B THE CAPITALE OF BOT THEATES AND WE MUS RECONISE POLSTINIS THAT AS PROVIDED FOR IN THE OVE LOGREMENCS (voxpopuli_eng_000523-voxpopuli_eng_000523) +YOU CRAINYS FACE T WITH WONE OF CRUSAL CHALINGES IN ITS HISTORY IT WOULD BE FU TE MENTARLY RONGK TO PRE THE NATION NOW WIT AL THIPES OF RESTRICTIONS POPELADERL CALE OSTERITE POLI (voxpopuli_eng_000524-voxpopuli_eng_000524) +MORE RULS AND REGULATION WILL NOT IMPROVE THIS CITUATIO (voxpopuli_eng_000525-voxpopuli_eng_000525) +AT LEAST WE WOLDLIKE TO NOW THE SOURSE OF THE MONY AND THE POSIPL MORTIE (voxpopuli_eng_000526-voxpopuli_eng_000526) +TO WEROF THOSE YURUPIN WALE LANGIASH IN TO THES GLUBELICED WEARLD IS INT TO THEYSGOBELISDECONOM IN DHIS GOBE VILACH WHICH IS GORSTIALY CONOMICK SOSIAL ELNPLITICO ITS AR MOST VELABLE ESTHERT FROM THEINTIRE E YOU THAT WE MUST THAK FOL ACOUNS AND T (voxpopuli_eng_000527-voxpopuli_eng_000527) +WEAVE TO REPETE THAT AL THE AY ANOT BE USE TO FINANS SIURIT EXPANCES BARTHERS CONTROL OR MLITRY SOPORNT (voxpopuli_eng_000528-voxpopuli_eng_000528) +THN THE SINTIFI REPORTS BECOE MRE MORE URGENT OR ALARMING AND MOR SHOCKING (voxpopuli_eng_000529-voxpopuli_eng_000529) +FINALYM WHEN WEAT THINKING ABOUN THER INOVATIVE FINSION INSTOUMENTS WHEN OU THE BOLTH FOR OURSELS TOR SUPOART OWER A CONOMES BUT ALS SO TOOE SUPORT THOS HOERE INEAET (voxpopuli_eng_000530-voxpopuli_eng_000530) +THT IVE A SO YUNIEK DOLL IN PE MAKING (voxpopuli_eng_000531-voxpopuli_eng_000531) +PAPER A VERYD WEEK PROPOSL (voxpopuli_eng_000532-voxpopuli_eng_000532) +SRUSHAS ALWAS BE A VERY PROUDNATION WITH RICH COLTCUER WITH INVENTIONS WITHAN AS PL (voxpopuli_eng_000533-voxpopuli_eng_000533) +ARTACXATIN EVEN A MODICAL OF TACXATION IN SOME CACES MIGH JUST HELPUS EM TO DO WHAT IVEAREDY SUEGESTED AN WHO NOSE MAKE THE CACE FOR THE RETRESPECT OF BANKRE CAPIDLIZATION THAT WE NEVERSO (voxpopuli_eng_000534-voxpopuli_eng_000534) +THEROPE AN ASILOM SUPORTOFHIS MOR OVER AS AMONG ITS THASTS TO PRMOUTD FESILYTAT AND COURDINAT EXTCANGES OF INFORMATION AND OTHER ACTIVEITES RELATED O ELOCATIN WTH IN HE UNION (voxpopuli_eng_000535-voxpopuli_eng_000535) +HE ONUSO OF THE FRAMEBORK AGEMENT PROVIDES A LIGLY BINDING INSTRMENT TO OBGRAT AND STRANTN EU OSTRALIA BY LITHRRATIONS AND TO INCRESCOPERATION (voxpopuli_eng_000536-voxpopuli_eng_000536) +THEREFOR WEAEASTIN THE COUSAL AS GMION TO RESENTHA HAS BALE THA OULD BE THE SESTMENT OF THE EBACT OF THE RICIS (voxpopuli_eng_000537-voxpopuli_eng_000537) +IN OTHE WORDS THE OBJECTION IS NOT WHETHER MONEY IS PAD OR NOT THE OBJECTION IS WETHER TER IS A DIDECTLINK ORNO (voxpopuli_eng_000538-voxpopuli_eng_000538) +TO THSTINGUISHES THE TO MAN OEAR YOUMER RIGT AB BUSE BY THE CADANT GORMENT AND THEDLANIAN NUCLAPROVGDM (voxpopuli_eng_000539-voxpopuli_eng_000539) +YESS MATHMDRUO THANKATHR SECTIAL HERASDMENT IS A FORM OF VILANCS AND IT ISTHE MOST EXTREAME FORM OF GNTERBAETH DISCUMINATI (voxpopuli_eng_000540-voxpopuli_eng_000540) +WE CAN LOK TO SOME URAN LIN OU MEMBERS FOR OUOD GXAMPLES AS REGARDED THGNOLIGE (voxpopuli_eng_000541-voxpopuli_eng_000541) +YIMNVALLVED FOR THER POSITEVE AND COSTRACTEIVE ABROTCH (voxpopuli_eng_000542-voxpopuli_eng_000542) +O I HOP THAT ISWIL BE COMPLEATED EAR IN HE FACIVIL FUTUAR THAT MANES MA BE TO AFRE MONS (voxpopuli_eng_000543-voxpopuli_eng_000543) +OR FORDER NDCOURDSHTHE YOU HAND EFORTS TO BRING AMONGK PES IN OF GNISTAN AN TO OVERCOME THEF FRASILE SICUITY ANVIRMENT IN THE CONTRY (voxpopuli_eng_000544-voxpopuli_eng_000544) +BE ANDER STANT THAT SOME PEOPEL AR ANGRY (voxpopuli_eng_000545-voxpopuli_eng_000545) +ON TO BE MRESTPONCIVEL (voxpopuli_eng_000546-voxpopuli_eng_000546) +WE MUST EDACTIFIETH THIS SUTIATION AND VEASK THE OMION TO CONCSIDER THE MOST EDICUIT COMBINSATION MESES FOW PASNGES (voxpopuli_eng_000547-voxpopuli_eng_000547) +THE OMITION INBVIHED THE YUROPIANT PULAMENT IN THE UPCOMINCREVISION TO OPEN HIS POSITION ON THIS MATER WHICH RELY CONSED AXES TO S JUSTIS IN YUROP AND THE ENFORTMENT OF RIES GRANTED BY HE YUROPIANER YUNAN LO (voxpopuli_eng_000548-voxpopuli_eng_000548) +I L OM VERY MUCH TH RISOUNTION OF TOKE BETWEN THEOS RALIS AN PLESTINIONS AND SNCEIRLY HOP THAT HE WIL SUCED (voxpopuli_eng_000549-voxpopuli_eng_000549) +WE HAVE ACUMILATION OF PROBLENCS RESULTING FROME THE ARTIFIHAL AND DEBAGEATINGK AND VERPREVIUSYUS (voxpopuli_eng_000550-voxpopuli_eng_000550) +LET UST NOT BE THE MAN OF YESTERDY LNT UN BEPOL DAYS INSTHITUTIO (voxpopuli_eng_000551-voxpopuli_eng_000551) +T I GOULD ERLSUM TO BECOME AMBASSETHES O THE YEAR MAKNG ITS A DEARS AND ACTIVITHIS WO WIDLY NOWN A MONCSH TO YURUPEAT ITIENS AND PUTPIIPATING N EVENS BE TAT YOUROPIAN NASIONALL FOR LOK ALEVL (voxpopuli_eng_000552-voxpopuli_eng_000552) +SERTNLY SUCH IMPACE SESTMENT COULD PREMT SERTAN PROBLOMS SUCH AS THOS POSED BY THE ELECTRONIK IDENTIFICATION OF SHEP AND SCOTLAND (voxpopuli_eng_000553-voxpopuli_eng_000553) +THE CORTIS CONTENT TO SEE THT ITS WORK HAS INFORME TH DIS CHAGH ROS AND HAS CONTEBUTED TO ROPOSALS FOR IMPROVING THE FINANCAL MANAGHMENT OF VEYOUSPENDING AND BETHE TARKATING OF YOU FUNE (voxpopuli_eng_000554-voxpopuli_eng_000554) +REGOUTHERE CLARIE THE AND SERTANTY IS NEDED FOR THE OBLICK SECTOUR AND FOR THE INDUSTRY (voxpopuli_eng_000555-voxpopuli_eng_000555) +IS IT REALINOT POSIBLE TO US A ATHER HOUSING FASCILIDES WITH U PROPRE H RESEPTIN CONDIONS IN THE MEN TIME (voxpopuli_eng_000556-voxpopuli_eng_000556) +WHEL YOU TAKE ACION AT LAST IF NOT THEN WHEND (voxpopuli_eng_000557-voxpopuli_eng_000557) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..2aa20283f1faa12feb6d729258a115e50789476a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/ref.trn @@ -0,0 +1,1092 @@ +HE REMAINED WORLD CHAMPION UNTIL NINETEEN SIXTY FIVE A YEAR IN WHICH HE SUFFERED A TERRIBLE ACCIDENT (LAD_eng_000254-LAD_eng_000254) +A LIBERALCONSERVATIVE HE WAS DEFEATED IN EIGHTEEN EIGHTY TWO (LAD_eng_000255-LAD_eng_000255) +ONE ROAD LAYER CAN DRAW TWO ROADS AT ONCE (LAD_eng_000256-LAD_eng_000256) +SOME OF THE COUNTRIES HAVE SURVEYS FOR MULTIPLE YEARS (LAD_eng_000257-LAD_eng_000257) +BOTH OF THE VERSIONS FEATURE THE SONG HAPPY HOLIDAY (LAD_eng_000258-LAD_eng_000258) +SHAKESPEARE MANY REFERENCES ARE MADE TO SCENES INTERACTIONS OR CHARACTERS FROM VARIOUS PLAYS (LAD_eng_000259-LAD_eng_000259) +IF ONLY THE PROGRAM COULD BREAK OUT JUST A LITTLE FROM ITS TOOFAMILIAR APPROACH (LAD_eng_000260-LAD_eng_000260) +THE ALBUM WAS RELEASED IN AUSTRALIA ON NINETEENTH AUGUST TWO THOUSAND AND ELEVEN (LAD_eng_000261-LAD_eng_000261) +HE NOW PLAYS FOR AUSTRALIAN CLUB PERTH GLORY (LAD_eng_000262-LAD_eng_000262) +IT IS NOT KNOWN HOW MUCH IF ANY OF HER CLAIMS ARE TRUE (LAD_eng_000263-LAD_eng_000263) +A SMALL BUSINESS OWNER BROAD OPERATED HIS WHEAT AND SHEEP FARM FOR SIXTEEN YEARS FROM THE AGE OF TWENTY TWO (LAD_eng_000264-LAD_eng_000264) +IN THE NINTH CENTURY HE WAS AN IRISH POET (LAD_eng_000265-LAD_eng_000265) +THEY ARE MARKED BY STRONG (LAD_eng_000266-LAD_eng_000266) +THE LAW IS THEREFORE VALID (LAD_eng_000267-LAD_eng_000267) +IN THE EARLY STAGES CAME CLOSE TO US ASLEEP (LAD_eng_000268-LAD_eng_000268) +RUNNING EVERY THIRTY MINUTES THROUGHOUT SERVICE TIMES (LAD_eng_000269-LAD_eng_000269) +AS A RESULT WHEN THE COLLEGE REOPENED IT WAS AS AN ALLMALE COLLEGE (LAD_eng_000270-LAD_eng_000270) +THE TIME BETWEEN THESE POINTS IS VARIABLE AND CAN OCCUR ANYWHERE FROM A MINUTE TO MUCH LONGER (LAD_eng_000271-LAD_eng_000271) +WORK ON THE E E S STARTED IN MARCH TWO THOUSAND AND SEVEN AT A COST OF FIVE MILLION DOLLARS (LAD_eng_000272-LAD_eng_000272) +HOWEVER THERE WAS SOME DISAGREEMENT OVER THE ENDING THEME WHICH OMORI AND YOSHIMORI DISCUSSED AT LENGTH OVER EMAIL (LAD_eng_000273-LAD_eng_000273) +THE COUPLE HAD NO CHILDREN (LAD_eng_000274-LAD_eng_000274) +THE OFFICIAL SINGLE OF THAT DEBUT ALBUM PARIS CALLING HAD AN ELABORATE MUSIC VIDEO (LAD_eng_000275-LAD_eng_000275) +THE SERIES ENDED ON SIXTH AUGUST TWO THOUSAND AND FOUR LASTING FOR A TOTAL OF SEVENTY ONE DAYS (LAD_eng_000276-LAD_eng_000276) +HE HAS ALSO CONTRIBUTED TO THE NEW YORK REVIEW OF BOOKS (LAD_eng_000277-LAD_eng_000277) +BY PLACING SMALL ART OBJECTS THROUGHOUT THE FILM (LAD_eng_000278-LAD_eng_000278) +IT IS FOUND IN BRAZIL (LAD_eng_000279-LAD_eng_000279) +IT WAS THE SIDE OF THE FAMILY I IDENTIFIED MORE WITH (LAD_eng_000280-LAD_eng_000280) +CANDIDATE SITES MUST ALSO SUBMIT A WORK PLAN (LAD_eng_000281-LAD_eng_000281) +DUNDEE WON THE MATCH THREE TWO (LAD_eng_000282-LAD_eng_000282) +HOWEVER THE VILLAGE REMAINED ISOLATED UNTIL THE ARRIVAL OF THE FIRST NEWSPAPER SECOND REPUBLIC (LAD_eng_000283-LAD_eng_000283) +THE FIRST SERVICE IN THE NEW CHURCH WAS HELD IN NINETEEN FIFTY ONE ALTHOUGH THE BUILDING WAS NOT FULLY FINISHED (LAD_eng_000284-LAD_eng_000284) +THE AVERAGE HOUSEHOLD SIZE WAS TWO POINT TWO SEVEN AND THE AVERAGE FAMILY SIZE WAS THREE POINT ZERO ZERO (LAD_eng_000285-LAD_eng_000285) +IT WAS FIRST BROADCAST ON THIRD JANUARY TWO THOUSAND AND TEN (LAD_eng_000286-LAD_eng_000286) +THE WINGS WERE NOW MADE IN A SINGLE PRESSING (LAD_eng_000287-LAD_eng_000287) +DOCTOR OF PHILOSOPHY IN ENGINEERING MANAGEMENT (LAD_eng_000288-LAD_eng_000288) +THIS TOOK AWAY THE MAIN ARGUMENT OF SAFETY RISKS (LAD_eng_000289-LAD_eng_000289) +HE WAS ALSO MADE A LIFE MEMBER OF SCUNTHORPE UNITED (LAD_eng_000290-LAD_eng_000290) +SHE FEARS THEY WILL GET A DIVORCE BUT THIS NEVER HAPPENS (LAD_eng_000291-LAD_eng_000291) +FOOT DROPS UNABLE TO HOLD THE FOOT STRAIGHT ACROSS (LAD_eng_000292-LAD_eng_000292) +WHETHER THE AIR FLOW IS FREE OR FORCED CAN AFFECT THE ENERGY EFFICIENCY OF THE WINDOW (LAD_eng_000293-LAD_eng_000293) +AFTER GETTING THE RIGHT MEASUREMENTS THEY MADE THE NEW DOORS (LAD_eng_000294-LAD_eng_000294) +FRAGMENTS ON EACH FACE ARE MARKED WITH LETTERS A B C (LAD_eng_000295-LAD_eng_000295) +FROM THE FIRST MINUTES BOTH TEAMS SHOWED THEIR DESIRE TO COMPETE WITH AGGRESSIVE APPROACHES (LAD_eng_000296-LAD_eng_000296) +PHYSICAL THERAPY EXERCISES MAY HELP PATIENTS TO MAINTAIN MUSCLE STRENGTH (LAD_eng_000297-LAD_eng_000297) +HOWEVER THE TOWN SHE LIVES IN NO ONE WANTS TO HEAR ABOUT HER (LAD_eng_000298-LAD_eng_000298) +DESCRIBES APPOINTMENTS OF AN ACTING CHIEF JUSTICE OR JUDGE OF THE SUPREME COURT (LAD_eng_000299-LAD_eng_000299) +THE SOYBEANS OUTER COVERING IS THEN REMOVED AND THE BEANS ARE PARTIALLY COOKED (LAD_eng_000300-LAD_eng_000300) +THIS NATIONAL MOVEMENT WHICH HAD BEGUN WITH SO MUCH HOPE CAME TO A SAD END (LAD_eng_000301-LAD_eng_000301) +HIS ASSOCIATES USUALLY CALLED HIM T OR THE GOODLOOKING GUY (LAD_eng_000302-LAD_eng_000302) +ITS MAIN OFFICES WERE IN LONDON WITH A SECOND OFFICE BELFAST (LAD_eng_000303-LAD_eng_000303) +ACTUALLY I HAD NEVER BEEN TO A VILLAGE BEFORE THAT (LAD_eng_000304-LAD_eng_000304) +HE WAS CHARGED WITH PLANNING TO SET OFF BOMBS IN EUROPE AND THE UNITED STATES (LAD_eng_000305-LAD_eng_000305) +MAKING MIRRORS IS THE THIRD STUDIO ALBUM BY BELGIANAUSTRALIAN ARTIST GOTYE (LAD_eng_000306-LAD_eng_000306) +HE THEN MOVED TO WASHINGTON DC AND WAS A PARTNER WITH WARD BROWN UNTIL NINETEEN TWENTY NINE (LAD_eng_000307-LAD_eng_000307) +JOSEPH HIGH SCHOOL AND THE SCHOOLS THEY COMPETE AGAINST IN ALL SPORTS (LAD_eng_000308-LAD_eng_000308) +TWELVE PLUS ONE MATCH BAN PER CARD (LAD_eng_000309-LAD_eng_000309) +I THINK I MIGHT BE NOTHING (LAD_eng_000310-LAD_eng_000310) +THE HOME WAS BUILT AND LIVED IN BY ANDREW JACKSON KENNEDY DEPUTY COLLECTOR FOR THE INTERNAL REVENUE SERVICE (LAD_eng_000311-LAD_eng_000311) +IN NINETEEN SIXTY FOUR HE WENT BACK TO OMSK AND ENTERED THE ACTORS SCHOOL OF OMSK (LAD_eng_000312-LAD_eng_000312) +THE BANK IS JOINTLY OWNED BY HIM AND HIS BROTHERS AND RELATIVES (LAD_eng_000313-LAD_eng_000313) +HE SUBSEQUENTLY WENT TO SCHOOL IN BRISTOL (LAD_eng_000314-LAD_eng_000314) +ONE THOUSAND EIGHT HUNDRED AND FORTY SIX FOURTH EDITION (LAD_eng_000315-LAD_eng_000315) +A PART OF LITTLE ENGLAND BEYOND WALES IT HAS BEEN ESSENTIALLY ENGLISHSPEAKING FOR NINE HUNDRED YEARS (LAD_eng_000316-LAD_eng_000316) +HE PLAYED WITH TEN PLAYERS FOR HALF WAS AGAINST THE TRADITION IN G S P (LAD_eng_000317-LAD_eng_000317) +THE PRESIDING JUDGE WAS WEBSTER THAYER WHO WAS ALREADY ASSIGNED TO THE COURT BEFORE THIS CASE WAS SCHEDULED (LAD_eng_000318-LAD_eng_000318) +BIG BROTHER FIVE WAS THE THIRD OF THE MAIN SERIES TO FEATURE A LIVE LAUNCH (LAD_eng_000319-LAD_eng_000319) +ITS MOTTO IS WHOEVER YOU ARE AND WHEREVER YOU ARE ON THE JOURNEY OF FAITH YOU ARE WELCOME HERE (LAD_eng_000320-LAD_eng_000320) +ROBERT E MILLER AS COACH WILSON (LAD_eng_000321-LAD_eng_000321) +AFTER A ONEYEAR BREAK ZERO DEGREE WAS HER FOLLOWING VENTURE (LAD_eng_000322-LAD_eng_000322) +A M T MANUFACTURED A MODEL KIT OF THE Z Z R DRAGSTER (LAD_eng_000323-LAD_eng_000323) +THE S S A AIMED TO BUILD A LEFTWING ALTERNATIVE TO NEW LABOUR AND THE S N P (LAD_eng_000324-LAD_eng_000324) +HE LIVES LIKE HE IS A YOUNG PERSON (LAD_eng_000325-LAD_eng_000325) +MASTER OF SCIENCE IN ENGINEERING MANAGEMENT (LAD_eng_000326-LAD_eng_000326) +SHE FAILED TO MAKE THE TOP THREE AT THE KENYAN JUNIOR TRACK TRIALS THAT JUNE (LAD_eng_000327-LAD_eng_000327) +A TOUR FOLLOWED IN SUPPORT (LAD_eng_000328-LAD_eng_000328) +THEY WERE ESTABLISHED IN EIGHTEEN SEVENTY ONE AND ARE ONE OF THE OLDEST CLUBS IN THE SOUTH OF ENGLAND (LAD_eng_000329-LAD_eng_000329) +HE WAS A MEMBER OF THE YES SCOTLAND ADVISORY BOARD (LAD_eng_000330-LAD_eng_000330) +TWO THOUSAND AND FIVE GENTLEMAN (LAD_eng_000331-LAD_eng_000331) +OUR FILM HAD A STRONG RECEPTION IN EUROPE AND ACHIEVED DISTRIBUTION BUT THAT WAS NOT THE CASE HERE (LAD_eng_000332-LAD_eng_000332) +ORTHOSIS STRETCHES POSTERIOR ANKLE STRUCTURES (LAD_eng_000333-LAD_eng_000333) +HE WAS ALSO A THREE TIME FRENCH NATIONAL CHAMPION NINETEEN NINETY NINETEEN NINETY FOUR TWO THOUSAND AND ONE (LAD_eng_000334-LAD_eng_000334) +THE VILLAGE STRUCTURE SHOWN IN HIS MAP IS TO A GREAT EXTENT UNCHANGED TODAY (LAD_eng_000335-LAD_eng_000335) +RUSSIA IS RECOGNIZED FOR ITS NUCLEAR DISASTER EXPERTISE AND FOR THE SAFETY OF ITS TECHNOLOGY (LAD_eng_000336-LAD_eng_000336) +AS OF TWO THOUSAND AND FOURTEEN M T V IS AVAILABLE WITHIN THE UNITED KINGDOM ON VIRGIN MEDIA AND SKY (LAD_eng_000337-LAD_eng_000337) +NEW YORK PENGUIN RANDOM HOUSE (LAD_eng_000338-LAD_eng_000338) +THE DUCHY WAS SECURED IN THE OUTCOME OF THE GOTHIC WAR (LAD_eng_000339-LAD_eng_000339) +WITH GOOD PACE STARTED THE MATCH WITH BOTH TEAMS ALTERNATING SUPREMACY (LAD_eng_000340-LAD_eng_000340) +THIS VERSION IS NOTED FOR BEING VERY FAITHFUL TO THE ORIGINAL NOVEL (LAD_eng_000341-LAD_eng_000341) +THIS PRESUMPTION IS NOT FULFILLED ONE HAS TO KNOW AT LEAST TWO CONJUGATE DIAMETERS (LAD_eng_000342-LAD_eng_000342) +NOTABLE TITLES INCLUDED GOLDEN AXE THE REVENGE OF DEATH ADDER RAD MOBILE OUTRUNNERS AND SEGA SONIC THE HEDGEHOG (LAD_eng_000343-LAD_eng_000343) +THE NINETEEN NINETY NINE JUDGMENT NOTED THAT THE INFLUENCE OF THE FATHER OF THE ACCUSED HAS BEEN THERE (LAD_eng_000344-LAD_eng_000344) +MACDUFF SWEARS REVENGE AND JOINS FORCES WITH MALCOLM TO OVERTHROW MACBETH (LAD_eng_000345-LAD_eng_000345) +THE MEDIAEVAL VILLAGE COURT WAS ALWAYS ANXIOUS TO KEEP THE FENCE AROUND THE VILLAGE GAPLESS (LAD_eng_000346-LAD_eng_000346) +THERE WAS A NINE RANK SYSTEM EACH RANK HAVING MORE POWER THAN THE LOWER RANK (LAD_eng_000347-LAD_eng_000347) +THEY ESTABLISHED DIPLOMATIC RELATIONS ON SEPTEMBER NINETEENTH NINETEEN SEVENTY TWO (LAD_eng_000348-LAD_eng_000348) +THIS WAS FURTHER EXTENDED TO INCLUDE MORE U K DATES IN DECEMBER TWO THOUSAND AND FOURTEEN (LAD_eng_000349-LAD_eng_000349) +THE DUTCH GOVERNMENT IS CURRENTLY EXAMINING THE LEGAL CONSEQUENCES OF THE RULING (LAD_eng_000350-LAD_eng_000350) +FROM NINETEEN THIRTY THREE TO NINETEEN FORTY NINE THE AMERICAN LEAGUE WON TWELVE OUT OF THE FIRST SIXTEEN (LAD_eng_000351-LAD_eng_000351) +THERE HE FELL SICK WITH TYPHUS HIMSELF (LAD_eng_000352-LAD_eng_000352) +SIX TEAMS HAVE BEEN DIVIDED IN TWO GROUPS OF THREE TEAMS EACH (LAD_eng_000353-LAD_eng_000353) +THE FIRST SEASON PREMIERED ON TWELFTH JUNE TWO THOUSAND AND FIFTEEN (LAD_eng_000354-LAD_eng_000354) +IT SUCCEEDED THE Y BOARD AND SYSTEM TWENTY FOUR COMBINING FEATURES FROM BOTH (LAD_eng_000355-LAD_eng_000355) +VOLUME TWO NUMBERS ONE TWO AND THREE (LAD_eng_000356-LAD_eng_000356) +THE LOWER PART OF MENS DRESSES WERE MUCH SHORTER IN LENGTH THAN THOSE FOR WOMEN (LAD_eng_000357-LAD_eng_000357) +THE VISIGOTHS IN TURN WERE SUCCEEDED BY THE MOORS (LAD_eng_000358-LAD_eng_000358) +JOSEPH HIGH SCHOOL EVERY WEEK OF THE SCHOOL YEAR (LAD_eng_000359-LAD_eng_000359) +AS A RESULT OF ALL THE ARGUMENTS GETTING TO HER (LAD_eng_000360-LAD_eng_000360) +ITS HEADQUARTERS ARE IN SHEFFIELD UNITED KINGDOM (LAD_eng_000361-LAD_eng_000361) +LAY ALSO OFFICIALLY SIGNED THE CONTRACT ON STAGE WITH THE DIRECTOR AND PRODUCERS OF THE GOLDEN EYES (LAD_eng_000362-LAD_eng_000362) +PHYSICAL THERAPY CAN HELP PATIENTS TO LEARN HOW TO WALK WITH A FOOT DROP (LAD_eng_000363-LAD_eng_000363) +IT WENT ON TO SELL THREE HUNDRED THOUSAND UNITS ACHIEVE FIVE NO (LAD_eng_000364-LAD_eng_000364) +THE NAME STUCK AFTER THAT (LAD_eng_000365-LAD_eng_000365) +THE ALBUM LATER BROKE THE DIAMOND RECORD ON Q Q MUSIC (LAD_eng_000366-LAD_eng_000366) +ITS EDITORIAL WE SUBMIT EARNED ITS AUTHOR A PULITZER PRIZE (LAD_eng_000367-LAD_eng_000367) +JOSEPH PLAYS ARE FEATURED EACH WEEK ON THE SHOW (LAD_eng_000368-LAD_eng_000368) +THEY WAIT FOR A TIME BUILDING UP THEIR FORCES BEGINNING TO WONDER IF THIS EVIL REALLY EXISTS (LAD_eng_000369-LAD_eng_000369) +BRIEF MENTION OF THE CONVICTION APPEARED ON PAGE THREE OF THE NEW YORK TIMES (LAD_eng_000370-LAD_eng_000370) +ORDERED BY POSITION ON PITCH FROM BACK RIGHT TO FRONT LEFT (LAD_eng_000371-LAD_eng_000371) +HE IS MEMBER OF THE COURT OF THE ROYAL COLLEGE OF ART LONDON U K (LAD_eng_000372-LAD_eng_000372) +DURING THE COURSE OF THE CAMPAIGN FERGUSON VISITED ALL THIRTY NINE WASHINGTON STATE COUNTIES (LAD_eng_000373-LAD_eng_000373) +A STRIP OF PAPER OF LENGTH (LAD_eng_000374-LAD_eng_000374) +SATOU HAD FREQUENTLY WORKED TOGETHER WITH YOKOYAMA ON PREVIOUS PROJECTS (LAD_eng_000375-LAD_eng_000375) +SHE WAS BORN ONSCREEN DURING THE EPISODE BROADCAST ON FOURTH NOVEMBER NINETEEN NINETY FOUR (LAD_eng_000376-LAD_eng_000376) +HE TURNED ROUND SHE HAD COME IN SO GENTLY THAT HE HAD NEVER HEARD HER (M-AILABS_eng_000159-M-AILABS_eng_000159) +AH TO BE SURE WE MUST KEEP OUR DOORS SHUTWE MUST LET NO ONE IN (M-AILABS_eng_000160-M-AILABS_eng_000160) +KINSMEN HE BEGAN MOCKINGLY YOU MAY HAVE WONDERED WHY I CALLED A TRUCE WHEN I COULD JUST AS WELL HAVE DESTROYED YOU THAT I DOUBT ATO ANSWERED HIM (M-AILABS_eng_000161-M-AILABS_eng_000161) +THE PEASANT THREW HIMSELF UPON HIM AND BOUND HIS FOUR LEGS TIGHTLY SO THAT HE COULD NOT MOVE (M-AILABS_eng_000162-M-AILABS_eng_000162) +NOR MUST THOU SO LIMIT THE HOLY ONE OF ISRAEL AS TO THINK HE HATH BUT ONE WAY IN WHICH HE CAN GLORIFY HIMSELF BY THEE (M-AILABS_eng_000163-M-AILABS_eng_000163) +THE OLD COMPARISON BETWEEN THE IMPULSIVE EXECUTIVE AND THE LIBERAL ARTS MAN WHO HAS LEARNED THAT THERE ARE ONLY ONE OR TWO POSITIVE DECISIONS AVAILABLE IN ALL THE WORLD OF THINKING (M-AILABS_eng_000164-M-AILABS_eng_000164) +AFTER THIS EXPERIENCE THE INVADERS WERE CAREFUL TO KEEP A SAFE DISTANCE FROM THE WALL (M-AILABS_eng_000165-M-AILABS_eng_000165) +CAN YOU BEAR SOMETHING FURTHER I THINK YOU OUGHT TO KNOW IT I HAVE HERE A MOST MYSTERIOUS TELEPAGRAM YES WHAT IS IT IS SHE DEAD NO IT IS NOT ABOUT HER (M-AILABS_eng_000166-M-AILABS_eng_000166) +NO MISTER THORNTON SAID GIVE THE BASKET TO MEILL TAKE IT (M-AILABS_eng_000167-M-AILABS_eng_000167) +AN ARABIAN NIGHT EXCLAIMED TROT WHY THAT WAS A MAGIC NIGHT WASNT IT THERES DIFFERENT SORTS OF NIGHTS MATE SAID THE SAILOR AND THE KNIGHT BUTTONBRIGHT MEANS AINT THE SAME NIGHT YOU MEAN (M-AILABS_eng_000168-M-AILABS_eng_000168) +IVE TURNED OFF UPWARDS OF A HUNDRED OF MY BEST HANDS FOR NO OTHER FAULT THAN FOLLOWING YOU AND SUCH AS YOU AND DYE THINK ILL TAKE YOU ON (M-AILABS_eng_000169-M-AILABS_eng_000169) +BUT WHEN SHOULD SHE SEE HIM HER HEART LEAPED UP IN APPREHENSION AT EVERY RING OF THE DOORBELL (M-AILABS_eng_000170-M-AILABS_eng_000170) +THESE BOOKS DIXON I WILL KEEP ALL THE REST WILL YOU SEND TO MISTER BELL THEY ARE OF A KIND THAT HE WILL VALUE FOR THEMSELVES AS WELL AS FOR PAPAS SAKE (M-AILABS_eng_000171-M-AILABS_eng_000171) +BUT INGA WAS NOT AT ALL SURE THEY COULD NOT GET IN THE GATES OPENED INWARD AND THREE HEAVY BARS WERE HELD IN PLACE BY MEANS OF STOUT STAPLES RIVETED TO THE SHEETS OF STEEL (M-AILABS_eng_000172-M-AILABS_eng_000172) +I WANT THAL SAID HODDAN COLDLY I WANT A DOZEN HORSES I WANT MEN TO RIDE THEM WITH ME HE PUSHED HIS WAY FORWARD WHICH WAY TO THE STABLES (M-AILABS_eng_000173-M-AILABS_eng_000173) +THERE IS A LIMIT TO WHAT YOU CAN DO THE FIRST TIME YOU ENTER A MANS HOUSE AND BESIDES THAT WAS NO TIME TO AROUSE SUSPICION IN THE MIND OF ANYONE (M-AILABS_eng_000174-M-AILABS_eng_000174) +DO YOU NOT REMEMBER THAT HE SAYS THY DEMON THATS THY SPIRIT WHICH KEEPS THEE IS NOBLE COURAGEOUS HIGH UNMATCHABLE (M-AILABS_eng_000175-M-AILABS_eng_000175) +MISTER BELL WHAT CAN HE KNOW OF JOHN HE LIVING A LAZY LIFE IN A DROWSY COLLEGE (M-AILABS_eng_000176-M-AILABS_eng_000176) +AND THE KITTEN FOLLOWED DEMURELY AT THEIR HEELS (M-AILABS_eng_000177-M-AILABS_eng_000177) +THE FIRST TOUCH WOULD CAUSE AN EXPLOSION IN WHICH AMONG SUCH HUNDREDS OF INFURIATED MEN AND RECKLESS BOYS (M-AILABS_eng_000178-M-AILABS_eng_000178) +ONE OF THE GREAT PLEASURES OF MARGARETS LIFE AT THIS TIME WAS IN EDITHS BOY (M-AILABS_eng_000179-M-AILABS_eng_000179) +THE THING HAS GONE ON LONG ENOUGH IF THERE IS ONE MORE BIG ACCIDENT WE SHALL HAVE TO COMPROMISE WITH THE INTERRIVER AND CARRY ON THE WORK JOINTLY (M-AILABS_eng_000180-M-AILABS_eng_000180) +YOU ARE LATE SAID SHE WELL SHE HELD HER BREATH FOR THE ANSWER (M-AILABS_eng_000181-M-AILABS_eng_000181) +TROT TOLD THE GIRLS THAT THEY MUST GO WITH THEIR FATHER TO LIVE IN GHIPGHISIZZLES LITTLE OLD CABIN AND WHEN THEY HEARD THIS DREADFUL DECREE (M-AILABS_eng_000182-M-AILABS_eng_000182) +MARGARET SAT DOWN ON THE RUG PARTLY TO WARM HERSELF FOR THE DAMPNESS OF THE EVENING HUNG ABOUT HER DRESS AND OVERFATIGUE HAD MADE HER CHILLY (M-AILABS_eng_000183-M-AILABS_eng_000183) +OH NO YOU ARE MISTAKEN ABOUT THAT REPLIED THE KING THEY ARE NOT MY PRISONERS BUT MY SLAVES WHOM I PURCHASED FROM THE KING OF EV (M-AILABS_eng_000184-M-AILABS_eng_000184) +HER FATHER TOOK UP THE CONVERSATION (M-AILABS_eng_000185-M-AILABS_eng_000185) +IN A CORNER WAS A SORT OF DRESSINGTABLE ON WHICH LAY A COMB AND BRUSH KENNEDY SEEMED MUCH INTERESTED IN THE TABLE AND WAS EXAMINING IT WHEN THE GURU RETURNED (M-AILABS_eng_000186-M-AILABS_eng_000186) +I HAVE SOMETIMES THOUGHT THAT MYSELF SHE AGREED BUT OF COURSE I DONT KNOW STILL I HAVE TO BE PRETTY CAREFUL SOME ONE IS ALWAYS OVER HERE BY MY DESK OR LOOKING OVER HERE (M-AILABS_eng_000187-M-AILABS_eng_000187) +I SHALL STAY REPLIED THE YOUNG MAN FOR I MEAN TO SET YOU FREE (M-AILABS_eng_000188-M-AILABS_eng_000188) +WHAT DO YOU DO ASKED THE SORCERER (M-AILABS_eng_000189-M-AILABS_eng_000189) +WHY THEYRE OUR ENEMIES YOUR SHORT HIGHNESS NOT ANY MORE REPLIED TROT IM QUEEN OF THE PINKIES AND IM ALSO QUEEN OF THE BLUES SO I WONT HAVE MY PEOPLE QUARRELING (M-AILABS_eng_000190-M-AILABS_eng_000190) +TYPEWRITERS WERE CLICKING CLIPPINGS WERE BEING SNIPPED OUT OF A HUGE STACK OF NEWSPAPERS AND PASTED INTO LARGE SCRAPBOOKS CIRCULARS WERE BEING FOLDED AND MADE READY TO MAIL FOR THE FINAL APPEAL (M-AILABS_eng_000191-M-AILABS_eng_000191) +IT WAS FOUR DAYS AFTER THE SURPRISE OF ADLERS HORST WHEN THE STRANGERS LEFT THE ESTATE TO THE CARE OF RUGGED OLD FORSTER HERMANN (M-AILABS_eng_000192-M-AILABS_eng_000192) +POOR TEMPLETON HE SAID I USED TO KNOW HIM YEARS AGO WHEN WE WERE BOYS WENT TO SCHOOL WITH HIM AND ALL THAT SORT OF THING YOU KNOW BUT UNTIL I RAN ACROSS HIM (M-AILABS_eng_000193-M-AILABS_eng_000193) +I FOUND HER IN THE FOREST AND BROUGHT HER HERE A PRISONER REPLIED THE CAPTAIN (M-AILABS_eng_000194-M-AILABS_eng_000194) +WHO MAY BE COMPETENT EITHER FROM PERSONAL EXPERIENCE OR THE EXPERIENCE OF OTHERS TO ANSWER IT WITH MORE OR LESS CORRECTNESS OR AT LEAST AN ATTEMPT (M-AILABS_eng_000195-M-AILABS_eng_000195) +ONE HUNDRED NINETYTWO LAYTE STREET SAID HOGAN BITING OFF HIS CIGAR (M-AILABS_eng_000196-M-AILABS_eng_000196) +TROT WAS SURPRISED TO FIND SHE COULD SEE SO PLAINLY THROUGH THE HIGH WALL OF WATER ABOVE HER BUT THE SUN WAS ABLE TO SHOOT ITS BEAMS STRAIGHT DOWN THROUGH THE TRANSPARENT SEA (M-AILABS_eng_000197-M-AILABS_eng_000197) +THE SPOT WHERE IT HAD SPRUNG UP (M-AILABS_eng_000198-M-AILABS_eng_000198) +CALM DENIAL WHICH SHE GAVE TO SUCH A SUPPOSITION (M-AILABS_eng_000199-M-AILABS_eng_000199) +YOU SEE UNTIL THESE SCHOOL PILLS WERE INVENTED WE WASTED A LOT OF TIME IN STUDY THAT MAY NOW BE BETTER EMPLOYED IN PRACTICING ATHLETICS (M-AILABS_eng_000200-M-AILABS_eng_000200) +YOUVE DONE IT NOW DECLARED DOROTHY THESE TENTS ARE JUST WONDERFUL (M-AILABS_eng_000201-M-AILABS_eng_000201) +FOR TWENTY TEN FIVE THREE TWOTHE LINER WAS BARELY TWENTY MILES AWAY WHEN HODDAN FIRED HIS ROCKETS THEY MADE A COLOSSAL CLOUD OF VAPOR IN EMPTINESS (M-AILABS_eng_000202-M-AILABS_eng_000202) +THEY PAID NO ATTENTION TO THE FACT THAT GHIPGHISIZZLE DID NOT WANT TO MARRY ANY OF THEM FOR THEY HAD DETERMINED THAT WHEN IT WAS AGREED WHO SHOULD HAVE HIM (M-AILABS_eng_000203-M-AILABS_eng_000203) +WHAT DO YOU THINK OF THAT HE CRIED OPENING A COPY OF THE RECORD AND LAYING IT FLAT ON THE LIBRARY TABLE (M-AILABS_eng_000204-M-AILABS_eng_000204) +IT WILL REQUIRE BUT A SHORT TIME (M-AILABS_eng_000205-M-AILABS_eng_000205) +AND LAST THE CROWD OF VEGETABLE PEOPLE WHO HAD NO HEARTS AND COULD NEITHER SMILE NOR FROWN (M-AILABS_eng_000206-M-AILABS_eng_000206) +THEN YOULL CATCH IT SAID THE WITCH (M-AILABS_eng_000207-M-AILABS_eng_000207) +WHAT IS IT I QUERIED NOT FEELING CERTAIN BUT THAT IT WAS A VEILED ATTEMPT TO SECURE A LITTLE FREE ADVERTISING FOR THE VANDERVEER (M-AILABS_eng_000208-M-AILABS_eng_000208) +SO HE GAVE THE CLERK THE THIRD HUNDRED DOLLARS FOR BOOKS AND A CASK OF GOOD OLD ALE FOR PETER THE CLERK DRANK THE ALE HIMSELF AND GAVE THE CALF MILK (M-AILABS_eng_000209-M-AILABS_eng_000209) +LIKE THAT IN ALICE IN WONDERLAND WITH MERELY A GRIN THAT FADED AWAY CHANGING INTO A LYNX WHICH IN TURN DISAPPEARED FOLLOWED BY AN UNKNOWN CREATURE WITH SHORT NOSE AND POINTED EARS (M-AILABS_eng_000210-M-AILABS_eng_000210) +SHE COULD NOT DOMARGARET GLANCED UNCONSCIOUSLY AT THE UNCLEANED CORNERS OF THE ROOMSHE COULD HARDLY UNDERTAKE A SERVANTS PLACE COULD SHE (M-AILABS_eng_000211-M-AILABS_eng_000211) +NO SHE REPLIED WITH INNOCENT CURIOSITY DID I GIVE THEM TO YOU (M-AILABS_eng_000212-M-AILABS_eng_000212) +MARLBOROUGH MILLS AND THE ADJACENT DWELLING WERE HELD UNDER A LONG LEASE THEY MUST IF POSSIBLE BE RELET (M-AILABS_eng_000213-M-AILABS_eng_000213) +A COP WAVED A STUNPISTOL AT HIM (M-AILABS_eng_000214-M-AILABS_eng_000214) +IT BOUNDED HERE AND THERE ABOUT THE CHICKEN HOUSE AND AT FIRST DOROTHY COULD NOT TELL WHAT IT WAS WHILE THE SCREECHING OF THE CHICKENS NEARLY DEAFENED HER (M-AILABS_eng_000215-M-AILABS_eng_000215) +THE SOLDIER GAVE A YELL THAT AROUSED A SCORE OF HIS COMRADES AND BROUGHT THEM TUMBLING INTO THE STREET WHEN THEY SAW HOW THE BOOLOOROOS PRECIOUS PRISONER WAS ESCAPING (M-AILABS_eng_000216-M-AILABS_eng_000216) +JIM HAD REFUSED TO LEAVE THE FIELD OF GRASS WHERE HE WAS ENGAGED IN BUSILY EATING SO THE WIZARD GOT OUT OF THE BUGGY AND JOINED ZEB AND DOROTHY (M-AILABS_eng_000217-M-AILABS_eng_000217) +CERTAINLY I AM AS INTERESTED IN THE CASE AS YOU ARE BUT I CANT MAKE HEADS OR TAILS OF IT I REPLIED (M-AILABS_eng_000218-M-AILABS_eng_000218) +OR ANY MICE OR EVEN GRASSHOPPERS (M-AILABS_eng_000219-M-AILABS_eng_000219) +AND THEM THAT PAYS YO DUN THEY TELL YO WHATTEN TO DO OR WHATTEN NOT TO DO WI THE MONEY THEY GIVES YOU IN JUST PAYMENT FOR YOUR PAINSIN FAIR EXCHANGE LIKE (M-AILABS_eng_000220-M-AILABS_eng_000220) +WHAT DOES THAT MEAN ASKED THE PRINCESS (M-AILABS_eng_000221-M-AILABS_eng_000221) +HE HAD BEEN DROWNED HE WAS FLOATING IN A SEA OF LIGHT AND NOW AND THEN SHINING LITTLE FISHES SWAM INQUISITIVELY UP TO HIM AND STARED (M-AILABS_eng_000222-M-AILABS_eng_000222) +BUT OLD GUNNAR HAD A TRICK OR TWO LEFT REMEMBER THE TALE THAT I READ TO YOU IN THE THRONEROOM OF BALDAR THE FIRST OF THE BRONS TO ENTER THE WORLD OF OPAL WERE SOLDIERS SENT FROM SOME BLASTED PLANET IN OUTER SPACE TO FIND A NEW HOME (M-AILABS_eng_000223-M-AILABS_eng_000223) +PAPA WILL YOU SPEAK TO THE MEN AND GET THEM TO GO AWAY SHE CANNOT BREATHE POOR THING WITH THIS CROWD ABOUT HER (M-AILABS_eng_000224-M-AILABS_eng_000224) +WHEN I TOOK THIS CASE HE SAID I BELIEVED DOWN IN MY HEART THAT DIXON WAS INNOCENT I STILL BELIEVE IT BUT MY FAITH HAS BEEN RUDELY SHAKEN (M-AILABS_eng_000225-M-AILABS_eng_000225) +CHAPTER SIX OF THE PIRATES OF ERSATZ (M-AILABS_eng_000226-M-AILABS_eng_000226) +REMEMBER THEY CANNOT TOUCH US (M-AILABS_eng_000227-M-AILABS_eng_000227) +GIVE ME TIME AZURE GIVE ME TIME IF THERES ANYTHING I HATE ITS A HURRY IVE AN IDEA YOUR MAJESTY ANNOUNCED THE SIXTH SNUBNOSED PRINCESS (M-AILABS_eng_000228-M-AILABS_eng_000228) +TRUE ENOUGH TROT DECLARED THE SAILOR MAN (M-AILABS_eng_000229-M-AILABS_eng_000229) +AS FOR THAT SAID MARGARET RATHER HAUGHTILY I HOLD IT IS HONI SOIT QUI MAL Y PENSE (M-AILABS_eng_000230-M-AILABS_eng_000230) +WHEN HE HEARD THESE WORDS THE KING WHOSE HEAD WAS FULL OF THE PRINCESS NEVER STOPPED TO INQUIRE IF THEY COULD BE TRUE AND SMEARED HIMSELF OVER WITH FAT AND SPRANG INTO THE OVEN (M-AILABS_eng_000231-M-AILABS_eng_000231) +YOU SHOULD BE ABLE TO GET PARTS FROM YOUR ROOM VISIONRECEIVER ILL HAVE SOME TOOLS GIVEN YOU THEN HE ADDED DIPLOMACY HAS TO UNDERSTAND THE THINGS THAT CONTROL EVENTS (M-AILABS_eng_000232-M-AILABS_eng_000232) +BY THE TIME THE FROST HAD SET IN THEY SHOULD BE FAR AWAY FROM HELSTONE (M-AILABS_eng_000233-M-AILABS_eng_000233) +ONE THING I WANT TO SAY BEGAN KENNEDY (M-AILABS_eng_000234-M-AILABS_eng_000234) +THIS IMPORTANT TRAFFIC WAS CONFIDED TO NO ONE BUT THE REAL PROPRIETOR (M-AILABS_eng_000235-M-AILABS_eng_000235) +HE WAS REPLACED ON BASS GUITAR BY JUSTIN KLUG (cv_eng_000707-cv_eng_000707) +ID ADD A SEPARATE SUBSECTION WHICH DEALS WITH THIS ASPECT (cv_eng_000708-cv_eng_000708) +OPERATION OF THE TRUNK LINE CONTINUED ON WOODEN TRESTLES (cv_eng_000709-cv_eng_000709) +MAGNESIUM FLUORIDE IS TRANSPARENT OVER AN EXTREMELY WIDE RANGE OF WAVELENGTHS (cv_eng_000710-cv_eng_000710) +FOUR GIANT PACKING SHEDS STORED FRESH PACKED POTATOES AND DELIVERED THEM ONTO RAILROAD CARS (cv_eng_000711-cv_eng_000711) +THE OTHER FOURTEEN CAMPUSES ARE TWOYEAR CAMPUSES REFERRED TO COLLECTIVELY AS THE UNIVERSITY COLLEGE (cv_eng_000712-cv_eng_000712) +ITS TOO BAD THAT HES QUICKLY GOING TO FORGET MY NAME HE THOUGHT (cv_eng_000713-cv_eng_000713) +ONE PICTURE IN THE GALLERY SHOWS HOW DILIGENT SLAVES ERECT THE STATUE OF ADMIRAL THOMPSON (cv_eng_000714-cv_eng_000714) +IMPERIAL DIET (cv_eng_000715-cv_eng_000715) +THE RESULTING COMPANY IS STRATTEC SECURITY CORPORATION (cv_eng_000716-cv_eng_000716) +BITCOIN MINING CAN BE DONE WITH GRAPHICS CARDS OR WITH SPECIALIZED HARDWARE (cv_eng_000717-cv_eng_000717) +THEY ALSO LEAD THE NATIONAL RANKING (cv_eng_000718-cv_eng_000718) +CHARLES GRAVES BISHOP OF LIMERICK (cv_eng_000719-cv_eng_000719) +AND AT THAT I TOLD HIM AND HE TOOK MY PLACE (cv_eng_000720-cv_eng_000720) +I THOUGHT ID GIVE THE KIDS A TREAT (cv_eng_000721-cv_eng_000721) +ACEVEDO DENIED SHOWING THE PICTURES (cv_eng_000722-cv_eng_000722) +HOLD YOUR NOSE TO KEEP THE SMELL FROM DISABLING YOUR MOTOR FUNCTIONS (cv_eng_000723-cv_eng_000723) +THAT SOUNDS LIKE THEIR PROBLEM (cv_eng_000724-cv_eng_000724) +HISTORICALLY THERE WAS NO CLEARLY DEFINED BOUNDARY IN THIS PART OF THE ARABIAN PENINSULA (cv_eng_000725-cv_eng_000725) +MARSHALL SHAFFER OF SLASH FILM GAVE THE FILM AN EIGHT OUT OF TEN (cv_eng_000726-cv_eng_000726) +HOW CAN YOU SAY THAT (cv_eng_000727-cv_eng_000727) +HIS STYLE BEGAN TO RESEMBLE MICHAEL DAMASKINOS (cv_eng_000728-cv_eng_000728) +HE IS ALSO CAPABLE OF FIRING LIGHTNING BOLTS WITH IMMENSE DESTRUCTIVE POWER (cv_eng_000729-cv_eng_000729) +HE CLAIMED TWO WICKETS IN ENGLANDS ONLY INNINGS AS BORDER WERE BEATEN COMPREHENSIVELY (cv_eng_000730-cv_eng_000730) +SHE DID MUCH LITERARY WORK (cv_eng_000731-cv_eng_000731) +HE MET THE ORGANIZERS OF THE PROTESTS AND AGREED TO CREATE TWO WORKING GROUPS (cv_eng_000732-cv_eng_000732) +THE BALL STRUCK THE FOUL POLE WELL ABOVE THE GREEN MONSTER (cv_eng_000733-cv_eng_000733) +ONLY CAMDEN THOMAS GARRETT AND GOLDFIELDS SOUTH EZEKIEL BAKER WERE UNCONTESTED (cv_eng_000734-cv_eng_000734) +IT IS A CHARITY SCHOOL WHOSE FEES ARE CALCULATED ON A MEANS TEST (cv_eng_000735-cv_eng_000735) +SOME WENT AWAY WHILE I WAS THERE AND OTHER PEOPLE CAME (cv_eng_000736-cv_eng_000736) +SEVEN (cv_eng_000737-cv_eng_000737) +THE KURA KHANATE WAS LOCATED MAINLY IN THE HISTORICAL AND GEOGRAPHICAL REGION OF KURA (cv_eng_000738-cv_eng_000738) +THE ELEVATION AT THE SITE IS ABOVE SEA LEVEL (cv_eng_000739-cv_eng_000739) +TOBIAS TRIED TO INJECT CONTEMPT INTO HIS TONE (cv_eng_000740-cv_eng_000740) +I HAVE TO WORK THIS SATURDAY (cv_eng_000741-cv_eng_000741) +THE GREAT RULERS FOUND THE SQUEAKY GRATE WAS GRATING ON THEIR NERVES (cv_eng_000742-cv_eng_000742) +WHEN THE BLINDING DUST HAD SETTLED A BIT THE BOY TREMBLED AT WHAT HE SAW (cv_eng_000743-cv_eng_000743) +DEMOCRAT AMBER BAKER WON THE OPEN SEAT (cv_eng_000744-cv_eng_000744) +BOTH ARE PUT TOGETHER BY STUDENTS IN THE COLLEGES JOURNALISM PROGRAM (cv_eng_000745-cv_eng_000745) +TRENCH WAS BORN IN BELIZE CITY IN BRITISH HONDURAS (cv_eng_000746-cv_eng_000746) +THE EARLY PHASE OF LIFE MOVES FAST (cv_eng_000747-cv_eng_000747) +NO (cv_eng_000748-cv_eng_000748) +SEVEN (cv_eng_000749-cv_eng_000749) +AT ONE TIME RAILWAY LINES DIVERGED FROM RUGBY STATION IN SEVEN DIFFERENT DIRECTIONS (cv_eng_000750-cv_eng_000750) +CZECH REPUBLIC ENTERED TWO SHOOTERS INTO THE PARALYMPIC COMPETITION (cv_eng_000751-cv_eng_000751) +TYGER WILLIAMS WROTE THE SCREENPLAY AND SHARED STORY CREDIT WITH THE BROTHERS (cv_eng_000752-cv_eng_000752) +THIS FESTIVAL WAS TO BE A CHARITY FUNDRAISER FOR THE AREA (cv_eng_000753-cv_eng_000753) +THESE EXTRA CARDS WERE INSERTED RANDOMLY INTO PACKS (cv_eng_000754-cv_eng_000754) +HENRY WENT BACK TO AUSTRALIA (cv_eng_000755-cv_eng_000755) +PERMIT ME TO INTRODUCE TO YOU HER MAJESTY THE QUEEN (cv_eng_000756-cv_eng_000756) +IN ORIGIN HEROIN WAS SUPPOSED TO BE THE “NONADDICTIVE MORPHINE SUBSTITUTE” (cv_eng_000757-cv_eng_000757) +SHE IS OF MEXICAN DESCENT (cv_eng_000758-cv_eng_000758) +I AM SURE THERE IS NOT ON HIS (cv_eng_000759-cv_eng_000759) +THOSE WHO DONT LEARN FROM HISTORY ARE DOOMED TO REPEAT IT (cv_eng_000760-cv_eng_000760) +I COULDN’T STOP STARING AT IT (cv_eng_000761-cv_eng_000761) +FOR SIMPLICITY GEAR INCHES IS NORMALLY ROUNDED TO THE NEAREST WHOLE NUMBER (cv_eng_000762-cv_eng_000762) +IF WE ACTUALLY DO WANT IT SOLVED IT WILL BE (cv_eng_000763-cv_eng_000763) +THE FRUIT OF A FIG TREE IS APPLE SHAPED (cv_eng_000764-cv_eng_000764) +FAIR EXCHANGE IS NO ROBBERY (cv_eng_000765-cv_eng_000765) +WHAT YOU EAT TODAY WALKS AND TALKS TOMORROW (cv_eng_000766-cv_eng_000766) +THE WATER THEN FLOWS OUT OF THE SWAMPS AS THE LUAPULA RIVER (cv_eng_000767-cv_eng_000767) +WHY DIDNT YOU SAY SOMETHING (cv_eng_000768-cv_eng_000768) +HAVE YOU SEEN OMAR (cv_eng_000769-cv_eng_000769) +I COULD GO ON FOR DAYS ABOUT THE DELICIOUS WINES PRODUCED IN THIS PART OF THE WORLD (cv_eng_000770-cv_eng_000770) +THE PHILADELPHIA INQUIRER NAMED HIM CITY PLAYER OF THE YEAR (cv_eng_000771-cv_eng_000771) +BOTS MAY BE SUBJECT TO SPECIAL RULES (cv_eng_000772-cv_eng_000772) +THE SWEDES WERE UNABLE TO USE THEIR VEHICLES WHICH WERE STUCK IN THE MUD (cv_eng_000773-cv_eng_000773) +THE ACT DID NOT PROHIBIT PAYING A REPRESENTATIVE TO APPEAR IN COURT (cv_eng_000774-cv_eng_000774) +CAN WE PLEASE LEAVE NOW (cv_eng_000775-cv_eng_000775) +HE WAS CONVICTED AND BANISHED TO CYPRUS FOR SEVEN YEARS FOR PUNISHMENT (cv_eng_000776-cv_eng_000776) +THE COUPLE HAVE TWO CHILDREN A DAUGHTER SOPHIA ROSALINDA AND A SON MATEO BRAVERY (cv_eng_000777-cv_eng_000777) +NONE OF THE THREE REFERENDUMS REACHED THE QUORUM OF THE MAJORITY OF THOSE ENTITLED (cv_eng_000778-cv_eng_000778) +TURPIN SUCCEEDED INDIRA SAMARASEKERA WHO SAW THE UNIVERSITY THROUGH A PERIOD OF STRONG GROWTH (cv_eng_000779-cv_eng_000779) +HERE I AM BETWEEN MY FLOCK AND MY TREASURE THE BOY THOUGHT (cv_eng_000780-cv_eng_000780) +THIS FAILURE HAS LED TO SIXTEEN POWER PLANTS HAVING ZERO DAYS OF COAL STOCK (cv_eng_000781-cv_eng_000781) +YES (cv_eng_000782-cv_eng_000782) +WHY DOES THAT PLANE KEEP GOING OVER (cv_eng_000783-cv_eng_000783) +IVE DONE THIS BEFORE WITH VIRTUALBOX WITH GOOD RESULTS (cv_eng_000784-cv_eng_000784) +THE APPLICATION WAS APPROVED IN FEBRUARY (cv_eng_000785-cv_eng_000785) +HENRY TARLTON STILES WHERE HE HAD A SOUND TRAINING IN LATIN (cv_eng_000786-cv_eng_000786) +IT WAS DISCONTINUED DUE TO SCHEDULING CONFLICTS INVOLVED IN LEWISS RETURN TO TERRESTRIAL RADIO (cv_eng_000787-cv_eng_000787) +HER FAMILY WAS FROM BRIANZA (cv_eng_000788-cv_eng_000788) +WHAT DID YOU EAT FOR DINNER (cv_eng_000789-cv_eng_000789) +THAT WAS MY DRAW TO SCIENCE (cv_eng_000790-cv_eng_000790) +HE IS CONSIDERED A MASTER OF CHIAROSCURO (cv_eng_000791-cv_eng_000791) +IT THEN RETURNS TO THE CHURCH ASCENDS AT THE ALTAR AND DISAPPEARS (cv_eng_000792-cv_eng_000792) +YOU CANNOT LOSE WHAT YOU NEVER HAD (cv_eng_000793-cv_eng_000793) +THE JAWS EXTEND PAST THE EYE (cv_eng_000794-cv_eng_000794) +MY NIECE CAN HELP YOU WITH THAT (cv_eng_000795-cv_eng_000795) +THATS THE KIND OF STUFF THEY WANT (cv_eng_000796-cv_eng_000796) +HOPE FOR THE BEST AND PREPARE FOR THE WORST (cv_eng_000797-cv_eng_000797) +INITIALLY THE WEIGHT LOSS WAS ATTAINED STRICTLY BY DIET (cv_eng_000798-cv_eng_000798) +ALL WERE OWNED BY THE EVERETTMOORE SYNDICATE (cv_eng_000799-cv_eng_000799) +WILL IT RAIN TOMORROW (cv_eng_000800-cv_eng_000800) +DU BIST EWIG MEINE LIEBE (cv_eng_000801-cv_eng_000801) +LUCILE PETRY TOOK HER PLACE AS ACTING DIRECTOR (cv_eng_000802-cv_eng_000802) +THE BEAVER RIVER BRIEFLY ENTERS THE EASTCENTRAL PART OF THE TOWNSHIP (cv_eng_000803-cv_eng_000803) +THE TRACK RESURFACING WAS ALSO COMPLETED (cv_eng_000804-cv_eng_000804) +HINDMARSH WAS AWARE OF THE IMPORTANCE OF ELECTRON MICROSCOPY IN BIOLOGICAL RESEARCH (cv_eng_000805-cv_eng_000805) +SINHA WAS BORN IN ALLAHABAD (cv_eng_000806-cv_eng_000806) +THIS BRIDGE IS UNOFFICIALLY REFERRED TO AS BLACKWATER BRIDGE BY COALITION FORCES OPERATING THERE (cv_eng_000807-cv_eng_000807) +IT IS RESPONSIBLE FOR WATER SUPPLY AND MANAGEMENT OF WATER RESOURCES IN MAHARASHTRA (cv_eng_000808-cv_eng_000808) +THIS IS THE FIRST PHASE OF THE JOB HE SAID (cv_eng_000809-cv_eng_000809) +THE GIZA PLATEAU OR GIZA NECROPOLIS IN THE EGYPTIAN VALLEY OF THE DEAD CONTAINS SEVERAL PYRAMIDS OF WHICH THE GREAT PYRAMID IS THE LARGEST SEVERAL SMALL TOMBS SEVERAL TEMPLES AND THE GREAT SPHINX (fleurs_eng_000413-fleurs_eng_000413) +TOWARDS THE END OF THE MIDDLE AGES WESTERN EUROPE BEGAN TO DEVELOP THEIR OWN STYLE ONE OF THE BIGGEST DEVELOPMENTS OF THE TIME AS A RESULT OF THE CRUSADES PEOPLE BEGAN TO USE BUTTONS TO FASTEN CLOTHING (fleurs_eng_000414-fleurs_eng_000414) +IF YOU ONLY GO ASHORE USING SHIPBOARD EXCURSIONS YOU WILL NOT NEED A SEPARATE VISA AS OF 2009 (fleurs_eng_000415-fleurs_eng_000415) +DUVALL WHO IS MARRIED WITH TWO ADULT CHILDREN DID NOT LEAVE A BIG IMPRESSION ON MILLER TO WHOM THE STORY WAS RELATED (fleurs_eng_000416-fleurs_eng_000416) +THEIR DISCIPLINED DEFENCE BALL HANDLING SKILLS AND EXCELLENT TEAM WORK MADE THEM STAND OUT AND IT WAS CLEAR THAT THIS WAS THE TEAM TO BEAT (fleurs_eng_000417-fleurs_eng_000417) +THE DISEASE IS CARRIED BY PIGS WHICH THEN MIGRATES TO HUMANS THROUGH MOSQUITOS (fleurs_eng_000418-fleurs_eng_000418) +FOR THE SPRINGBOKS IT ENDED A FIVEMATCH LOSING STREAK (fleurs_eng_000419-fleurs_eng_000419) +THUS THE PENCIL WAS A GOOD FRIEND TO MANY PEOPLE WHEN IT CAME OUT (fleurs_eng_000420-fleurs_eng_000420) +THE USE OF VIDEO RECORDING HAS LED TO IMPORTANT DISCOVERIES IN THE INTERPRETATION OF MICROEXPRESSIONS FACIAL MOVEMENTS WHICH LAST A FEW MILLISECONDS (fleurs_eng_000421-fleurs_eng_000421) +ALSO TO THE NORTH VISIT THE GREAT SANCTUARY OF OUR LADY OF FATIMA SHRINE A PLACE OF WORLDWIDE FAMOUS MARIAN APPARITIONS (fleurs_eng_000422-fleurs_eng_000422) +IF YOU WANT TO BE CLOSE TO THE ACTION YOURE GOING TO HAVE TO GET IN EARLY TO GET A CAMPING SITE CLOSE TO THE MUSIC (fleurs_eng_000423-fleurs_eng_000423) +MADAGASCAR IS BY FAR THE BIGGEST AND A CONTINENT ON ITS OWN WHEN IT COMES TO WILDLIFE (fleurs_eng_000424-fleurs_eng_000424) +WOMEN IT IS RECOMMENDED THAT ANY WOMEN TRAVELLERS SAY THAT THEY ARE MARRIED REGARDLESS OF ACTUAL MARITAL STATUS (fleurs_eng_000425-fleurs_eng_000425) +CUOMO 53 BEGAN HIS GOVERNORSHIP EARLIER THIS YEAR AND SIGNED A BILL LAST MONTH LEGALIZING SAMESEX MARRIAGE (fleurs_eng_000426-fleurs_eng_000426) +AS LIGHT POLLUTION IN THEIR HEYDAY WAS NOT THE KIND OF PROBLEM IT IS TODAY THEY ARE USUALLY LOCATED IN CITIES OR AT CAMPUSES EASIER TO REACH THAN THOSE BUILT IN MODERN TIMES (fleurs_eng_000427-fleurs_eng_000427) +THEY USUALLY HAVE SPECIAL FOOD DRINK AND ENTERTAINMENT OFFERS TO KEEP GUESTS IN A GOOD MOOD AND KEEP THEM AT THE PREMISE (fleurs_eng_000428-fleurs_eng_000428) +ON THE OTHER HAND ICY AND SNOWY CONDITIONS ARE NORMAL IN MANY COUNTRIES AND TRAFFIC GOES ON MOSTLY UNINTERRUPTED ALL YEAR ROUND (fleurs_eng_000429-fleurs_eng_000429) +BE CAREFUL NOT TO ALLOW FABRIC TO BECOME TOO HOT WHICH CAN CAUSE SHRINKAGE OR IN EXTREME CASES SCORCH (fleurs_eng_000430-fleurs_eng_000430) +FERAL CHILDREN MAY HAVE EXPERIENCED SEVERE CHILD ABUSE OR TRAUMA BEFORE BEING ABANDONED OR RUNNING AWAY (fleurs_eng_000431-fleurs_eng_000431) +PEOPLE MAY NOT ANTICIPATE THAT PATIENCE AND UNDERSTANDING ARE ALSO NECESSARY FOR TRAVELLERS RETURNING HOME (fleurs_eng_000432-fleurs_eng_000432) +SOON AFTER THE OUTBREAK OF HOSTILITIES BRITAIN INITIATED A NAVAL BLOCKADE OF GERMANY (fleurs_eng_000433-fleurs_eng_000433) +THE GOVERNORS OFFICE SAID NINETEEN OF THE INJURED WERE POLICE OFFICERS (fleurs_eng_000434-fleurs_eng_000434) +USING SHIPS TO TRANSPORT GOODS IS BY FAR THE MOST EFFICIENT WAY TO MOVE LARGE AMOUNTS OF PEOPLE AND GOODS ACROSS OCEANS (fleurs_eng_000435-fleurs_eng_000435) +LIBERAL CRITICISM OF THE RECONSTRUCTION EFFORT HAS FOCUSED ON THE AWARDING OF RECONSTRUCTION CONTRACTS TO PERCEIVED WASHINGTON INSIDERS (fleurs_eng_000436-fleurs_eng_000436) +YOU CAN USE BODABODA MOTORCYCLE TAXI TO GET AROUND GOMA THE NORMAL LOCAL PRICE IS 500 CONGOLESE FRANCS FOR THE SHORT RIDE (fleurs_eng_000437-fleurs_eng_000437) +THE THREE KINGDOMS WAS ONE OF THE BLOODIEST ERAS IN ANCIENT CHINAS HISTORY THOUSANDS OF PEOPLE DIED FIGHTING TO SIT IN THE HIGHEST SEAT IN THE GRAND PALACE AT XIAN (fleurs_eng_000438-fleurs_eng_000438) +THESE COUPLES MAY CHOOSE TO MAKE AN ADOPTION PLAN FOR THEIR BABY (fleurs_eng_000439-fleurs_eng_000439) +NOTHING CAN BE SEEN OTHER THAN THE CLEAR BEAUTIFUL SKY ABOVE AND THE MANY SURROUNDING MOUNTAINS VERY LITTLE OF THIS WORLD CAN BE SEEN OR HEARD FROM INSIDE THE CAVE (fleurs_eng_000440-fleurs_eng_000440) +HE WAS SUBSEQUENTLY RELOCATED TO ADDENBROOKES HOSPITAL IN CAMBRIDGE (fleurs_eng_000441-fleurs_eng_000441) +VATICAN CITYS POPULATION IS AROUND 800 IT IS THE SMALLEST INDEPENDENT COUNTRY IN THE WORLD AND THE COUNTRY WITH THE LOWEST POPULATION (fleurs_eng_000442-fleurs_eng_000442) +REGULAR ANNOUNCEMENTS IN THE METRO ARE MADE ONLY IN CATALAN BUT UNPLANNED DISRUPTIONS ARE ANNOUNCED BY AN AUTOMATED SYSTEM IN A WIDE VARIETY OF LANGUAGES INCLUDING SPANISH ENGLISH FRENCH ARABIC AND JAPANESE (fleurs_eng_000443-fleurs_eng_000443) +THIS OFFERS A GOOD OPPORTUNITY TO SEE THE AURORA BOREALIS AS THE SKY WILL BE DARK MORE OR LESS AROUND THE CLOCK (fleurs_eng_000444-fleurs_eng_000444) +FIRE RESCUE CREWS EVENTUALLY DOUSED THE FIRE BY 1135 PM (fleurs_eng_000445-fleurs_eng_000445) +THIS IS CALLED A CHEMICALS PH YOU CAN MAKE AN INDICATOR USING RED CABBAGE JUICE (fleurs_eng_000446-fleurs_eng_000446) +IN PARTICULAR IT IS CLAIMED THAT ONE CAN DETECT WHETHER A PERSON IS LYING BY INTERPRETING MICROEXPRESSIONS CORRECTLY (fleurs_eng_000447-fleurs_eng_000447) +THE CENTRAL AUTHORITY OF THE CHURCH HAD BEEN IN ROME FOR OVER A THOUSAND YEARS AND THIS CONCENTRATION OF POWER AND MONEY LED MANY TO QUESTION WHETHER THIS TENET WAS BEING MET (fleurs_eng_000448-fleurs_eng_000448) +THE SUNDARBANS ARE THE LARGEST LITTORAL MANGROVE BELT IN THE WORLD STRETCHING 80 KM 50 MI INTO THE BANGLADESHI AND INDIAN HINTERLAND FROM THE COAST (fleurs_eng_000449-fleurs_eng_000449) +REGULAR ANNOUNCEMENTS IN THE METRO ARE MADE ONLY IN CATALAN BUT UNPLANNED DISRUPTIONS ARE ANNOUNCED BY AN AUTOMATED SYSTEM IN A WIDE VARIETY OF LANGUAGES INCLUDING SPANISH ENGLISH FRENCH ARABIC AND JAPANESE (fleurs_eng_000450-fleurs_eng_000450) +EVERYONE PARTICIPATES IN SOCIETY AND USES TRANSPORTATION SYSTEMS ALMOST EVERYONE COMPLAINS ABOUT TRANSPORTATION SYSTEMS (fleurs_eng_000451-fleurs_eng_000451) +LAYTON HAD ASKED FOR CHANGES TO THE CONSERVATIVES ENVIRONMENTAL BILL DURING THE MEETING WITH THE PM ASKING FOR A THOROUGH AND COMPLETE REWRITING OF THE CONSERVATIVE PARTYS ENVIRONMENTAL BILL (fleurs_eng_000452-fleurs_eng_000452) +ANYONE WHOS GOING TO DRIVE AT HIGH LATITUDES OR OVER MOUNTAIN PASSES SHOULD CONSIDER THE POSSIBILITY OF SNOW ICE OR FREEZING TEMPERATURES (fleurs_eng_000453-fleurs_eng_000453) +SLEEP INTERRUPTION IS THE PROCESS OF PURPOSEFULLY AWAKENING DURING YOUR NORMAL SLEEP PERIOD AND FALLING ASLEEP A SHORT TIME LATER 10–60 MINUTES (fleurs_eng_000454-fleurs_eng_000454) +SWIRL THE TWO DRY POWDERS TOGETHER AND THEN WITH CLEAN WET HANDS SQUEEZE THEM INTO A BALL (fleurs_eng_000455-fleurs_eng_000455) +FOR THE SPRINGBOKS IT ENDED A FIVEMATCH LOSING STREAK (fleurs_eng_000456-fleurs_eng_000456) +JUST LIKE THE MOON EXERTS A PULL ON THE EARTH CAUSING TIDES SO DOES THE MILKY WAY EXERT A FORCE ON THE SAGITTARIUS GALAXY (fleurs_eng_000457-fleurs_eng_000457) +THROUGH THE NIGHT BETWEEN 150 AND 200 COPIES WERE MADE NOW KNOWN AS DUNLAP BROADSIDES (fleurs_eng_000458-fleurs_eng_000458) +FIRST AMONG ITS 78 RECOMMENDATIONS IS THAT A NEW DIPLOMATIC INITIATIVE SHOULD BE TAKEN BEFORE THE END OF THIS YEAR TO SECURE IRAQS BORDERS AGAINST HOSTILE INTERVENTIONS AND TO REESTABLISH DIPLOMATIC RELATIONS WITH ITS NEIGHBORS (fleurs_eng_000459-fleurs_eng_000459) +SAINT PETERSBURG CRUISES INCLUDE TIME IN TOWN CRUISE PASSENGERS ARE EXEMPTED FROM VISA REQUIREMENTS CHECK THE TERMS (fleurs_eng_000460-fleurs_eng_000460) +ACCORDING TO JAPANS NUCLEAR AGENCY RADIOACTIVE CAESIUM AND IODINE HAS BEEN IDENTIFIED AT THE PLANT (fleurs_eng_000461-fleurs_eng_000461) +SEGREGATION AND RECOMBINATION SHUFFLE VARIATION BACK AND FORTH BETWEEN THE TWO POOLS WITH EACH GENERATION (fleurs_eng_000462-fleurs_eng_000462) +ELEMENTS LIKE CALCIUM AND POTASSIUM ARE CONSIDERED METALS OF COURSE THERE ARE ALSO METALS LIKE SILVER AND GOLD (fleurs_eng_000463-fleurs_eng_000463) +THE CORRELATION BETWEEN BRAIN PATHOLOGY AND BEHAVIOUR SUPPORTS SCIENTISTS IN THEIR RESEARCH (fleurs_eng_000464-fleurs_eng_000464) +ANCIENT CHINA HAD A UNIQUE WAY OF SHOWING DIFFERENT TIME PERIODS EACH STAGE OF CHINA OR EACH FAMILY THAT WAS IN POWER WAS A DISTINCTIVE DYNASTY (fleurs_eng_000465-fleurs_eng_000465) +A SIMPLE POPULAR DINNER ESPECIALLY DURING THE SUMMER IS THE PA AMB OLI BREAD WITH OLIVE OIL TOMATO AND ANY AVAILABLE CONDIMENTS SUCH AS CHEESE TUNAFISH ETC (fleurs_eng_000466-fleurs_eng_000466) +THE ANNOUNCEMENT WAS MADE AFTER TRUMP HAD A PHONE CONVERSATION WITH TURKISH PRESIDENT RECEP TAYYIP ERDOĞAN (fleurs_eng_000467-fleurs_eng_000467) +PERRY STATED THAT HE WOULD RETURN TO TEXAS TO ASSESS THE RESULTS OF TONIGHTS CAUCUS DETERMINE WHETHER THERE IS A PATH FORWARD FOR MYSELF IN THIS RACE BUT LATER SAID THAT HE WOULD REMAIN IN THE RACE AND COMPETE IN THE JANUARY 21 SOUTH CAROLINA PRIMARY (fleurs_eng_000468-fleurs_eng_000468) +HE WAS ALSO ENGAGED IN ENGRAVING BANKNOTES FOR MANY COUNTRIES RECENT EXAMPLES OF HIS WORK INCLUDING THE PRIME MINISTERIAL PORTRAITS ON THE FRONT OF THE NEW CANADIAN 5 AND 100 BILLS (fleurs_eng_000469-fleurs_eng_000469) +MORE TRADITIONAL CHURCHES OFTEN HOLD AN EASTER VIGIL ON SATURDAY NIGHT DURING THE EASTER WEEKEND WITH THE CONGREGATIONS OFTEN BREAKING INTO CELEBRATION AT THE STROKE OF MIDNIGHT TO CELEBRATE CHRISTS RESURRECTION (fleurs_eng_000470-fleurs_eng_000470) +FINLAND IS A GREAT BOATING DESTINATION THE LAND OF A THOUSAND LAKES HAS THOUSANDS OF ISLANDS TOO IN THE LAKES AND IN THE COASTAL ARCHIPELAGOS (fleurs_eng_000471-fleurs_eng_000471) +CURRENT SENATOR AND ARGENTINE FIRST LADY CRISTINA FERNANDEZ DE KIRCHNER ANNOUNCED HER PRESIDENTIAL CANDIDACY YESTERDAY EVENING IN LA PLATA A CITY 50 KILOMETERS 31 MILES AWAY FROM BUENOS AIRES (fleurs_eng_000472-fleurs_eng_000472) +SEVERE WEATHER IS THE GENERIC TERM FOR ANY DANGEROUS WEATHER PHENOMENON WITH THE POTENTIAL TO CAUSE DAMAGE SERIOUS SOCIAL DISRUPTION OR LOSS OF HUMAN LIFE (fleurs_eng_000473-fleurs_eng_000473) +FOR EXAMPLE THE MOST COMMON STILL IMAGE PHOTOGRAPHY FORMAT IN THE WORLD IS 35MM WHICH WAS THE DOMINANT FILM SIZE AT THE CLOSE OF THE ANALOG FILM ERA (fleurs_eng_000474-fleurs_eng_000474) +IT IS RELATED TO BUT USUALLY NOT INVOLVING ALPINE STYLE SKI TOURING OR MOUNTAINEERING THE LATTER ONES DONE IN STEEP TERRAIN AND REQUIRING MUCH STIFFER SKIS AND BOOTS (fleurs_eng_000475-fleurs_eng_000475) +IRONING DAMP CLOTHES CAN HELP THEM DRY MANY HOTELS HAVE AN IRON AND IRONING BOARD AVAILABLE FOR LOAN EVEN IF ONE IS NOT PRESENT IN THE ROOM (fleurs_eng_000476-fleurs_eng_000476) +EVADNE ANSWERED HOARSELY SHE DREW HER CHAIR A LITTLE CLOSER TO THE FIRE AND SPREAD HER HANDS OUT TO THE BLAZE THERE WAS NO OTHER LIGHT IN THE ROOM BY THIS TIME THE WIND WITHOUT HOWLED DISMALLY STILL (mls_eng_000283-mls_eng_000283) +MY DEAR MARIA WHY DO YOU NOT DESIST FROM THIS SILLY PURSUIT OF AN IMAGINARY TREASURE WHAT IS THE VALUE OF MONEY WE ARE SPANIARDS NOT SHIRTSLEEVED MERCENARY PIGS OF AMERICANS (mls_eng_000284-mls_eng_000284) +CRITICAL TEMPERATURE IS THAT OF THE SINGLE ISOTHERMAL LINE WHICH PRESENTS A POINT OF INFLEXION AT A HORIZONTAL TANGENT THE CRITICAL PRESSURE AND THE CRITICAL VOLUME ARE THE TWO COORDINATES OF THIS POINT OF INFLEXION (mls_eng_000285-mls_eng_000285) +MUCH LIKE IN FOULNESS AND DEFORMITY UNTO THAT MONSTER WHOM THE THEBAN KNIGHT THE FATHER OF THAT FATAL PROGENY MADE KILL HERSELF FOR VERY HEARTS DESPITE THAT HE HAD READ HER RIDDLE WHICH NO WIGHT COULD EVER LOOSE BUT SUFFERED DEADLY DUEL (mls_eng_000286-mls_eng_000286) +HE HAS MANAGED TO MEASURE WITH PRECISION PRESSURES AMOUNTING TO THREE THOUSAND ATMOSPHERES AND ALSO THE VERY SMALL VOLUMES THEN OCCUPIED BY THE FLUID MASS UNDER CONSIDERATION THIS LAST MEASUREMENT WHICH NECESSITATES NUMEROUS CORRECTIONS IS THE MOST DELICATE PART OF THE OPERATION (mls_eng_000287-mls_eng_000287) +WHY SHOULD IT HAVE BEEN DEEMED NECROMANCY TO ENDEAVOR TO COMBINE THESE PARTS TO EVOLVE BY CAREFUL ELIMINATION AND CHANGE TO THE PERFECT FOOD (mls_eng_000288-mls_eng_000288) +NAY THOUGH OF RUSHES BE MY BED YET I AM RICH LOVE SAID BUT ARGUED LIFE THRICE FOND ART THOU TO YIELD THE SOVEREIGN GIFTS OF EARTH THE VICTOR SWORD THE LAURELED BROW FOR VISIONED THINGS OF LITTLE WORTH (mls_eng_000289-mls_eng_000289) +BOCK SEEMS TO HAVE BEEN A KEEN COLLECTOR ALTHOUGH HAMPERED BY ILL HEALTH AND A GREAT POINT IN HIS FAVOUR IS THAT HE DESCRIBED ONLY THOSE PLANTS WHICH HAD COME UNDER HIS OWN PERSONAL OBSERVATION (mls_eng_000290-mls_eng_000290) +HAD RATHER SHRUNK UP AND HAD NOT CHANGED INTO NYMPHS THESE I LEFT IN THE STEMS COVERING THEM UP AGAIN AND THEY APPEARED AS PERFECT INSECTS IN THE MAY OF THE FOLLOWING YEAR (mls_eng_000291-mls_eng_000291) +NOTHING SAVE OBJECTS AND THOUGHTS OF BEAUTY COULD PRESENT THEMSELVES TO THE UNDERSTANDING OF THE FORTUNATE PERSON WHO PARTOOK OF IT THESE PAGES WHICH YOU HAVE BROUGHT TO ME TO TRANSLATE ARE CONCERNED WITH THIS SUPERSTITION (mls_eng_000292-mls_eng_000292) +NOW SEEMED INSIPIDITY AND HED NERVE HIMSELF AGAINST IT HIS FACE WORE A SORT OF SEVERE FLUSH HE WAS TIMID EVEN TO RUDENESS (mls_eng_000293-mls_eng_000293) +BECAME MORE LIFELIKE AS THE CHEEKS FLUSH THERE WAS RARE WARMTH IN A WINTER MORNING TO CHEER THE HALFDESPAIRING SOUL TIRED AFTER LONG HOURS OF OIL READING AND PIERCED TO THE HEART BY NEVER CEASING RHYMES YET I COULD NOT UNDERSTAND IT (mls_eng_000294-mls_eng_000294) +ONE OF THE HAWAIIAN WRITERS SAID THE OPIHIAWA IS A POISON SHELLFISH THESE ARE BITTER AND DEADLY AND CAN BE USED IN PUTTING ENEMIES TO DEATH (mls_eng_000295-mls_eng_000295) +THE BEAUTEOUS ROBES OF HEAVEN ASLANT THE DEW BRIGHT EARTH AND COLOURED AIR HE LOOKS IN BOUNDLESS MAJESTY ABROAD TOUCHING THE GREEN LEAVES ALL ATREMBLE WITH GOLD LIGHT (mls_eng_000296-mls_eng_000296) +I CAN DO NO MORE THAN THAT UNTIL THIS MATTER IS ABSOLUTELY SETTLED THEY ARE WORTH MORE THAN LIFE ITSELF TO ME MR COWPER SEEMED ANNOYED SURELY HE PROTESTED YOU ARE NOT GOING TO ASK ME TO WAIT THREE MONTHS UNTIL I CAN EXAMINE ONE OF THESE (mls_eng_000297-mls_eng_000297) +ROSCONGRESS FOUNDATION RUSSIAN ENTITY THAT ORGANIZED THE SAINT PETERSBURG INTERNATIONAL ECONOMIC FORUM ROSNEFT RUSSIAN STATEOWNED OIL AND ENERGY COMPANY (mls_eng_000298-mls_eng_000298) +HOW IT GLITTERED AND SPARKLED THE DELICATE FROSTWORK YOU WERE ATTRACTED NO DOUBT AND MARVELLED AT THE DAINTY TRACINGS BUT FEW OF US HAVE REALLY HAD AN OPPORTUNITY TO STUDY THE DETAIL OF THESE FROST DESIGNS MINUTELY OR HAVE CONSIDERED THAT THERE WERE MORE THAN THREE OR FOUR DESIGNS AT MOST (mls_eng_000299-mls_eng_000299) +OTHER THAN THE OFFENSE IN TRYING TO INFLICT A WOUND THEY MAY KILL THE OFFENDER OR WOUND HIM MORE THAN THEY INTENDED TO DO AND THIS BECOMES A CAUSE FOR A NEW FEUD SO THAT THE PRIMITIVE LEGISLATORS WERE CAREFUL IN REQUIRING THE RETALIATION TO BE LIMITED TO AN EYE FOR AN EYE (mls_eng_000300-mls_eng_000300) +AT CYRUS WORD THE JEWS RETURN THE COMPANY THAT GO GODS HOUSE BEGUN WITH MIRTH AND MOAN IS HINDERED BY THE FOE BUT ONCE AGAIN THE WORK GOES ON BY LICENSE FROM DERIUS EZRA IS SENT WITH ROYAL GRANT AND GIFTS FOR USES PIOUS (mls_eng_000301-mls_eng_000301) +NET PRODUCT YEAR IN AND YEAR OUT SEVEN HUNDRED FRANCS HE LIVED IN IT HOW NOT SO BADLY WE WILL EXPLAIN MARIUS OCCUPIED IN THE GORBEAU HOUSE (mls_eng_000302-mls_eng_000302) +THEN THIS IS ALL YOUR ANSWER TIS TOO FAIR FOR ONE OF HIS ALLIANCE AND I WARN YOU THAT THIS PLACE NO MORE SEE YOU EXIT ENTER DE FLORES THE BEST IS THERE IS MORE GROUND TO MEET A MANS REVENGE ON HONEST DE FLORES THATS MY NAME INDEED (mls_eng_000303-mls_eng_000303) +WHEN I RETURNED TO THE HOUSE WHERE I HAD BEEN A HAPPY CHILD ONLY A PILE OF ASHES WHERE IT HAD STOOD I WEPT LONG AND TO FORGET MY WEEPING I SAILED OUT ON THE VAST CALM SEA ON THESE WATERS IN A STAR SAPPHIRE NIGHT I PLAYED MY FLUTE TO THE SUMMER MOON (mls_eng_000304-mls_eng_000304) +DO YOU NOT SEE WHAT PLEASURE IT GIVES HIM WE HAVE GROWN UP TOGETHER IN THIS HOUSE SINCE HE WAS A BOY I SIMPLY CANNOT BEAR AS YOU CAN THE SIGHT OF THE SMILE LEAVING HIS FACE POOR DEAR HE HAS NO AMUSEMENT EXCEPT THIS PLAYING AT THE SHOPKEEPING (mls_eng_000305-mls_eng_000305) +IT IS A NEBULOUS BODY REVOLVING IN AN ELLIPTICAL ORBIT OF GREAT ELONGATION LOVE LOVE LOVE WILL NOT BE THE WOUND OF CUPID BUT THE MANIFESTATION OF UNIVERSAL REPRODUCTIVE INSTINCTS (mls_eng_000306-mls_eng_000306) +SHARPLY AS HE SHOOK HANDS WITH HER GOD BLESS YOU MY DEAR CHILD THE BISHOP SAID WHEN SHE KISSED HIM AND HIS LIPS MOVED AFTERWARD FOR SOME SECONDS AS IF HE WERE IN PRAYER HER MOTHER FOLLOWED HER OUT OF THE ROOM AND THEN SILENCE SETTLED (mls_eng_000307-mls_eng_000307) +FOLLOWED HIM STEALTHILY AND WHEN HE WAS IN A STOOPING POSTURE FILLING HIS BUCKET CAME UP BEHIND HIM AND PLUNGED A LONG KNIFE INTO HIS NECK (mls_eng_000308-mls_eng_000308) +SAITH CHERSIAS DOES NOT JUPITER DISTRIBUTE TO THE GODS THEIR PROPORTION AND DIVIDEND SPARINGLY AND SEVERALLY AS AGAMEMNON DID TO HIS COMMANDERS WHEN HIS GUESTS DRANK TO ONE ANOTHER IF CHERSIAS QUOTH CLEODEMUS AS YOU NARRATE (mls_eng_000309-mls_eng_000309) +AND WHERE NONE SHALL DARE RESTRAIN US WE CAN MEET AGAIN IN THOUGHT SO THERES NO USE IN WEEPING BEAR A CHEERFUL SPIRIT STILL NEVER DOUBT THAT FATE IS KEEPING FUTURE GOOD FOR PRESENT ILL (mls_eng_000310-mls_eng_000310) +AND TO BECOME THE RECORD OF WHAT PEOPLE HAVE DONE IN THEIR MORE AMIABLE MOMENTS THE RECORD OF THE CONQUESTS OF PEACE HOW MEN HAVE LIVED AND LABORED DUG AND BUILT HEWN AND CLEARED GARDENED AND REFOREST (mls_eng_000311-mls_eng_000311) +THE LOW FLYING OF THE SWALLOWS BETOKENS RAIN AS WELL AS ANY UNSEASONABLE DANCING OF MIDGES IN THE EVENING SORE CORNS ON THE FEET AND RHEUMATISM IN THE JOINTS ARE DIREFUL PRECURSORS THE LEAVES ARE ALL ATREMBLE BEFORE THE APPROACH OF THUNDER (mls_eng_000312-mls_eng_000312) +WAS STORMED GENERAL DAMPIERRE WAS KILLED GENERAL CUSTINE WAS BLAMED AND INDEED IS NOW COME TO PARIS TO GIVE EXPLANATIONS AGAINST ALL WHICH THE MOUNTAIN AND ATROCIOUS MARAT MUST EVEN MAKE HEAD AS THEY CAN (mls_eng_000313-mls_eng_000313) +THE MOMENT WAS FEARFUL A MIGHTIER FOE HAD NEVER SWUNG THE BATTLEAXE OVER HIM BUT HOPE NERVED HIS ARM FOR A DESPERATE BLOW AND TECUMSEH FELL PROSTRATE BEFORE HIM (mls_eng_000314-mls_eng_000314) +THEN THE WIND STOPPED THE CLOUDS TURNED DARK AND NIGHT CAME ON LIKE INK MY OLD COTTON QUILT WAS COLD AS IRON MY SWEET SON TOSSED IN HIS SLEEP (mls_eng_000315-mls_eng_000315) +YOU MAY DO AS YOU PLEASE TO WORK OFF YOUR IRRITATION TO KEEP UP YOUR FANATICISM YOU ARE WELL OFF YOU NEED NOT MIND THE COST THE POOR DO NOT WANT TO STAND IN YOUR WAY BUT YOU INSIST ON THEIR SUBMITTING TO YOUR COMPULSION (mls_eng_000316-mls_eng_000316) +HE WAS BRED BY REV G A SNEYD BEING BY OTHMAN E SIX FOUR TWO TWO HEDWIG HE WAS BORN IN MARCH EIGHTEEN SEVENTYNINE AND HE WAS THE ONLY SURVIVOR OF A LITTER OF FIFTEEN IT WAS ON THIS ACCOUNT THAT HE WAS CALLED SAFE IN COLOR AND MARKINGS (mls_eng_000317-mls_eng_000317) +AND WHAT HASTE IT MAKES TO FALL INTO THE SECOND THERE BY THIS TIME DIAPHANTA SNEEZES ACHOO MOST ADMIRABLE SECRET ON THE CONTRARY IT STIRS ME NOT A WHIT WHICH MOST CONCERNS IT HA HA HA (mls_eng_000318-mls_eng_000318) +THIRDLY THALES SAID WHERE THE CITIZENS ARE NEITHER TOO RICH NOR TOO POOR FOURTHLY ANACHARSIS SAID WHERE THOUGH IN ALL OTHER RESPECTS THEY ARE EQUAL YET VIRTUOUS MEN ARE ADVANCED AND VICIOUS PERSON DEGRADED (mls_eng_000319-mls_eng_000319) +THE KINDLY FRANK IS SYMPATHETIC EVERY DAY HE PASSES NOTES BETWEEN US AND I TRY TO ENCOURAGE RUSSELL HE WILL IMPROVE I ASSURE HIM HIS TIME IS SHORT AND FRESH AIR AND LIBERTY WILL SOON RESTORE HIM (mls_eng_000320-mls_eng_000320) +THESE QUESTIONS IT IS NOW EVIDENT MAY FREQUENTLY BE ANSWERED WITH EQUAL PROPRIETY IN OPPOSITE WAYS AND IF THERE BE ANY OCCASIONS ON WHICH THEY CAN BE ANSWERED ONLY IN ONE WAY THE ANSWER WILL DEPEND UPON THE NATURE OF THE OCCASION (mls_eng_000321-mls_eng_000321) +IN HIS NOTE BORE THE MINSTRELSY SECOND EDITION EIGHTEEN OH EIGHT SCOTT SAYS THE BALLAD WAS TAKEN DOWN FROM AN OLD WOMANS RECITATION AT THE ALSTON MOOR LEAD MINES BY THE AGENT THERE AND SENT BY HIM TO SURTEES 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PRODUCTS ALSO SELL (swc_eng_001894-swc_eng_001894) +THE FIRST NON SOVIET CHALLENGER SINCE (swc_eng_001895-swc_eng_001895) +OPPONENT HAS ONLY THE KING AND (swc_eng_001896-swc_eng_001896) +MAIN ARTICLE (swc_eng_001897-swc_eng_001897) +FOUND CERTAIN LENGTHS USEFUL FOR FITTING (swc_eng_001898-swc_eng_001898) +TAPE IN THE SAME FORM FACTOR AS THE COMPACT AUDIO (swc_eng_001899-swc_eng_001899) +CENSURE WAS LATER EXPUNGED FROM (swc_eng_001900-swc_eng_001900) +OR DE FACTO EQUALITY (swc_eng_001901-swc_eng_001901) +IS FOUR THOUSAND SIX HUNDRED BY SIXTY FEET (swc_eng_001902-swc_eng_001902) +NINETEEN SEVENTY THREE (swc_eng_001903-swc_eng_001903) +A PLAYER MAY ALSO LOSE BY RUNNING OUT (swc_eng_001904-swc_eng_001904) +PUBLIC HEALTH PROFESSOR GREGORY STOCK POINTS (swc_eng_001905-swc_eng_001905) +BROWN WAS ELECTED TO THE HOUSE OF REPRESENTATIVES FOR THREE NON CONSECUTIVE TERMS (swc_eng_001906-swc_eng_001906) +COEXIST HAPPILY WITH (swc_eng_001907-swc_eng_001907) +A GROUP OF MAMMALS THAT RAISE (swc_eng_001908-swc_eng_001908) +AND THE WORLDS LARGEST (swc_eng_001909-swc_eng_001909) +BREEDING TAKES PLACE BETWEEN APRIL AND JUNE (swc_eng_001910-swc_eng_001910) +AUSTRALIA IS AT THE SOUTHERN END (swc_eng_001911-swc_eng_001911) +TECHNOLOGICAL SINGULARITY IS POSSIBLE (swc_eng_001912-swc_eng_001912) +INCLUDING THE SLEEPY COD (swc_eng_001913-swc_eng_001913) +SEVENTY FOUR HAD A HIGHER EDUCATION QUALIFICATION COMPARED (swc_eng_001914-swc_eng_001914) +THIS OCCURS WHEN THE OPPONENTS KING IS IN CHECK (swc_eng_001915-swc_eng_001915) +CONSERVATION IN AUSTRALIA (swc_eng_001916-swc_eng_001916) +IS THE SALAMANDERFISH (swc_eng_001917-swc_eng_001917) +FIRST SELF DESCRIBED TRANSHUMANISTS MET FORMALLY IN THE EARLY (swc_eng_001918-swc_eng_001918) +RECENT RESEARCH INDICATES THAT FACTORS OTHER THAN PRACTICE (swc_eng_001919-swc_eng_001919) +AND PREVENTION AND TREATMENT OF COMPLICATIONS (swc_eng_001920-swc_eng_001920) +WITH A RAPID ONSET (swc_eng_001921-swc_eng_001921) +UTAH WAR THE FOUNDATION WAS BURIED (swc_eng_001922-swc_eng_001922) +NOWADAYS HOURLY REGIONAL EXPRESS TRAINS BETWEEN BERN AND SPIEZ TO BRIG AND FREIGHT TRAINS CONTINUE TO RUN ON THE MOUNTAIN RAILWAY (swc_eng_001923-swc_eng_001923) +OTHER FAMILIES WITH A POTENTIALLY GONDWANAN ORIGIN INCLUDE THE RETROPINNIDAE (swc_eng_001924-swc_eng_001924) +BY AN ITALIAN DOMINICAN MONK JACOBUS DE CESSOLIS (swc_eng_001925-swc_eng_001925) +COMMAND WAS NAMED AFTER THE (swc_eng_001926-swc_eng_001926) +ARTIFICIAL INTELLIGENCE (swc_eng_001927-swc_eng_001927) +AND IS THE REIGNING (swc_eng_001928-swc_eng_001928) +PER CENT OF THE POPULATION (swc_eng_001929-swc_eng_001929) +CHIEF AREAS OF SHOE POLISH SALES (swc_eng_001930-swc_eng_001930) +IMPOSED BY LAW (swc_eng_001931-swc_eng_001931) +REFERENCES TO THE RULING COALITION GOVERNMENT (swc_eng_001932-swc_eng_001932) +SPECIES OF GLIDING POSSUM (swc_eng_001933-swc_eng_001933) +BASED ON THE PREVIOUS STRATEGY OF PLAY (swc_eng_001934-swc_eng_001934) +AND IDEALISTIC ASPIRATIONS (swc_eng_001935-swc_eng_001935) +PROFESSIONALS AND HOME RECORDING ENTHUSIASTS (swc_eng_001936-swc_eng_001936) +FAMILY ELAPIDAE (swc_eng_001937-swc_eng_001937) +THAN A QUARTER OF PEOPLE WITH A PREVIOUS SAH MAY DEVELOP HYPOPITUITARISM (swc_eng_001938-swc_eng_001938) +DIVIDED INTO THREE FAMILIES THAT (swc_eng_001939-swc_eng_001939) +SHOWED SLIGHT INTEREST IN RELEASING CASSETTES (swc_eng_001940-swc_eng_001940) +FAMILIAR ENOUGH TO HAVE COMMON NAMES (swc_eng_001941-swc_eng_001941) +IN TWO THOUSAND SIX (swc_eng_001942-swc_eng_001942) +SHOESHINE BOYS ARE KNOWN AS BOOT POLISH BOYS (swc_eng_001943-swc_eng_001943) +THE CAUSE IS RUPTURE OF A CEREBRAL ANEURYSM (swc_eng_001944-swc_eng_001944) +MOST OF THE MAJOR U S MUSIC COMPANIES (swc_eng_001945-swc_eng_001945) +ONE STEREO PAIR OR ONE MONOPHONIC TRACK IS PLAYED OR RECORDED WHEN THE TAPE IS MOVING IN ONE DIRECTION AND (swc_eng_001946-swc_eng_001946) +WHERE ITS EARLY FORM IN (swc_eng_001947-swc_eng_001947) +A STRATEGIC PHILOSOPHER 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(swc_eng_001962-swc_eng_001962) +SPECIES INCLUDE FRESHWATER LAMPREYS (swc_eng_001963-swc_eng_001963) +FIRST ANGIOGRAM (swc_eng_001964-swc_eng_001964) +THE FREE ENCYCLOPEDIA AT (swc_eng_001965-swc_eng_001965) +THEREFORE MEDICAL IMAGING IS GENERALLY (swc_eng_001966-swc_eng_001966) +SPECIESIST THE EXCLUSION OF NON HUMAN AND PART HUMAN ANIMALS (swc_eng_001967-swc_eng_001967) +IN PEOPLE WHO HAD PREVIOUSLY SUFFERED A SUBARACHNOID HEMORRHAGE (swc_eng_001968-swc_eng_001968) +CLASSIFIED AS EITHER ENDANGERED OR THREATENED UNDER THE EPBC ACT (swc_eng_001969-swc_eng_001969) +AND ATTORNEY GENERAL PARKER WATKINS HARDIN (swc_eng_001970-swc_eng_001970) +BUT TYPICALLY (swc_eng_001971-swc_eng_001971) +WHICH IN TURN FED THE SIGNAL TO THE HEAD OF THE CASSETTE DECK (swc_eng_001972-swc_eng_001972) +WITHIN THEIR OWN CONVENTIONALLY EXPECTED LIFETIMES (swc_eng_001973-swc_eng_001973) +SUBSTANTIAL STRAIN (swc_eng_001974-swc_eng_001974) +TWENTIETH CENTURY KENTUCKY CONGRESSMAN JOHN (swc_eng_001975-swc_eng_001975) +NINETY PER CENT ARE ENDEMIC (swc_eng_001976-swc_eng_001976) +HUNTING WITH LEAD SHOT (swc_eng_001977-swc_eng_001977) +TWENTY THIRTEEN (swc_eng_001978-swc_eng_001978) +ALTHOUGH SEVEN PER CENT OF THE WORLDS BATS SPECIES LIVE IN AUSTRALIA (swc_eng_001979-swc_eng_001979) +KARPOVS REIGN FINALLY ENDED IN NINETEEN EIGHTY FIVE (swc_eng_001980-swc_eng_001980) +WHILE SOME TRANSHUMANISTS TAKE AN ABSTRACT (swc_eng_001981-swc_eng_001981) +WRITE PROTECTION (swc_eng_001982-swc_eng_001982) +GRAPH ISOMORPHISM PROBLEM IS THE COMPUTATIONAL PROBLEM OF DETERMINING WHETHER (swc_eng_001983-swc_eng_001983) +FURTHER RESTRICT OUR CONCEPT OF (swc_eng_001984-swc_eng_001984) +THOSE WHO SURVIVE HOSPITALIZATION (swc_eng_001985-swc_eng_001985) +SOME PROTECTION OF UNCERTAIN SIGNIFICANCE IS CONFERRED BY CAUCASIAN ETHNICITY (swc_eng_001986-swc_eng_001986) +COASTAL LAGOONS (swc_eng_001987-swc_eng_001987) +AND COGNITIVE ENHANCEMENT (swc_eng_001988-swc_eng_001988) +THE EIGHTH RANK AND BE PROMOTED TO AN ALLOWED PIECE (swc_eng_001989-swc_eng_001989) +DRAWBACK OF COILING IS THE POSSIBILITY (swc_eng_001990-swc_eng_001990) +INDICATES A SUBARACHNOID HEMORRHAGE (swc_eng_001991-swc_eng_001991) +DAMAGED PORTION (swc_eng_001992-swc_eng_001992) +ADOPTION OF EUGENIC ENHANCEMENT TECHNOLOGIES (swc_eng_001993-swc_eng_001993) +POLISH ON HIS HORSE AND WAGON (swc_eng_001994-swc_eng_001994) +AND THE NEXT CHAMPION (swc_eng_001995-swc_eng_001995) +BROTHER OF AUTHOR ALDOUS HUXLEY (swc_eng_001996-swc_eng_001996) +WORLD CHAMPION NINETEEN TWENTY ONE (swc_eng_001997-swc_eng_001997) +SUCH AS QUANTUM COMPUTATION AND RANDOMIZED ALGORITHMS (swc_eng_001998-swc_eng_001998) +EIGHTEEN NINETY NINE (swc_eng_001999-swc_eng_001999) +WAS SHOWN BY LADNER THAT IF P ≠ N P THEN THERE EXIST PROBLEMS IN (swc_eng_002000-swc_eng_002000) +THE COMPACT DISC GREW (swc_eng_002001-swc_eng_002001) +GREY GOO SCENARIO (swc_eng_002002-swc_eng_002002) +WAS RENDERED AS AJEDREZ (swc_eng_002003-swc_eng_002003) +SAH OR TO ANOTHER CAUSE (swc_eng_002004-swc_eng_002004) +CONSTITUENCY OF FAVERSHAM (swc_eng_002005-swc_eng_002005) +THE FOURTH AND FIFTH DAYS PASSED WITHOUT ANY DEVELOPMENTS (voxforge_eng_000874-voxforge_eng_000874) +THEY KNOW THE REPORT (voxforge_eng_000875-voxforge_eng_000875) +SUCH THINGS HAD OCCURRED BEFORE HE TOLD PHILIP (voxforge_eng_000876-voxforge_eng_000876) +THEY ONLY HAD A LITTLE THIRTY THOUSAND DOLLAR FIRE (voxforge_eng_000877-voxforge_eng_000877) +I AM GOING TO GET IT OUT (voxforge_eng_000878-voxforge_eng_000878) +OUTWARDLY HE MAINTAINED A CALM AND SMILING ASPECT (voxforge_eng_000879-voxforge_eng_000879) +JOAN LOOKED TRIUMPHANTLY AT SHELDON WHO BOWED (voxforge_eng_000880-voxforge_eng_000880) +COME ON DEL MAR CHALLENGED (voxforge_eng_000883-voxforge_eng_000883) +IT WAS BEATING AND WAITING IN THE AMBUSH OF THOSE BLACK PITS (voxforge_eng_000884-voxforge_eng_000884) +LET THEM GO OUT AND EAT WITH MY BOYS (voxforge_eng_000885-voxforge_eng_000885) +HE WENT DOWN IN MIDSTREAM SEARCHING THE SHADOWS OF BOTH SHORES (voxforge_eng_000886-voxforge_eng_000886) +I JUST DO APPRECIATE IT WITHOUT BEING ABLE TO EXPRESS MY FEELINGS (voxforge_eng_000887-voxforge_eng_000887) +SHE DOESNT KNOW WHAT HE IS TALKING ABOUT (voxforge_eng_000888-voxforge_eng_000888) +YOUR FATHERS FIFTH COMMAND HE NODDED (voxforge_eng_000889-voxforge_eng_000889) +DONT YOU SEE I HATE YOU (voxforge_eng_000890-voxforge_eng_000890) +A LITTLE WARM BUT NOT AT ALL ASTONISHED EATING MELONS AND THROWING THE RIND ABOUT (voxforge_eng_000891-voxforge_eng_000891) +THIS IS A GREAT PARTY (voxforge_eng_000892-voxforge_eng_000892) +THE BOY GREW AND PROSPERED (voxforge_eng_000893-voxforge_eng_000893) +UNLESS SUCH LETTERS BE PATENT THAT THEY MAY BE READ TO THEM AND WITHALL SEALED OR TESTIFIED (voxforge_eng_000894-voxforge_eng_000894) +HOW COULD A WOMAN DARE TO VENTURE WHERE SO MANY EXPLORERS (voxforge_eng_000895-voxforge_eng_000895) +HE READ HIS FRAGMENTS ALOUD (voxforge_eng_000896-voxforge_eng_000896) +BUT HOW ARE YOU GOING TO DO IT (voxforge_eng_000897-voxforge_eng_000897) +HOW DO YOU WANT TO GET AWAY WITH THIS (voxforge_eng_000898-voxforge_eng_000898) +WILL WE EVER FORGET IT (voxforge_eng_000899-voxforge_eng_000899) +FROM MY EARLIEST RECOLLECTION MY SLEEP WAS A PERIOD OF TERROR (voxforge_eng_000900-voxforge_eng_000900) +WHY DOGGONE YOU ALL SHAKE AGAIN (voxforge_eng_000901-voxforge_eng_000901) +IT IS THE NEAREST REFUGE (voxforge_eng_000902-voxforge_eng_000902) +HIS SLIM HANDS GRIPPED THE EDGES OF THE TABLE (voxforge_eng_000903-voxforge_eng_000903) +WHITE LEGHORNS SAID MRS MORTIMER (voxforge_eng_000904-voxforge_eng_000904) +IT TOOK HIM HALF AN HOUR TO REACH THE EDGE OF IT (voxforge_eng_000905-voxforge_eng_000905) +MARTHA WHERE DO WE STAND ON THE CONTRACTUAL ISSUES (voxforge_eng_000906-voxforge_eng_000906) +AS TO BE UNDISTINGUISHABLE FROM THE VAST WHITE PLAINS AROUND (voxforge_eng_000907-voxforge_eng_000907) +HE WOULD DESTROY ALL THINGS THAT ARE FIXED (voxforge_eng_000908-voxforge_eng_000908) +THE RUSSIAN MUSIC PLAYER THE COUNT WAS HER OBEDIENT SLAVE (voxforge_eng_000909-voxforge_eng_000909) +TO HIS SURPRISE HER ANSWER WAS FLAT AND UNCOMPROMISING (voxforge_eng_000910-voxforge_eng_000910) +THIS SHOULD BE INTERESTING (voxforge_eng_000911-voxforge_eng_000911) +I AM AFRAID I DONT HAVE MUCH TIME (voxforge_eng_000912-voxforge_eng_000912) +CHRISTMAS IS AN EASY PROBLEM COMPARED WITH A POLYNESIAN GIVING FEAST (voxforge_eng_000913-voxforge_eng_000913) +THE PLANTERS ARE ALREADY CONSIDERING THE MATTER (voxforge_eng_000914-voxforge_eng_000914) +JOAN CRIED WITH SHINING EYES (voxforge_eng_000915-voxforge_eng_000915) +WHOEVER LIVED ON THE RANCH DID THAT (voxforge_eng_000916-voxforge_eng_000916) +WE LEAVE THE EVENTUALITY TO TIME AND LAW (voxforge_eng_000917-voxforge_eng_000917) +AT THE SAME TIME SPEARS AND ARROWS BEGAN TO FALL AMONG THE INVADERS (voxforge_eng_000918-voxforge_eng_000918) +IT IS MERELY THE SIMPLE SUPERLATIVE (voxforge_eng_000920-voxforge_eng_000920) +INSTEAD HE ARRIVED ON THE NIGHT OF THE SECOND DAY (voxforge_eng_000921-voxforge_eng_000921) +IN HIS ANXIETY AND SOLICITUDE AND LOVE THEY DID NOT COUNT (voxforge_eng_000922-voxforge_eng_000922) +GOD BLESS I HOPE ILL GO ON SEEING THEM FOREVER (voxforge_eng_000923-voxforge_eng_000923) +YOU WERE ENGAGED (voxforge_eng_000924-voxforge_eng_000924) +THE LACE WAS OF A DELICATE IVORY COLOR FAINTLY TINTED WITH YELLOW (voxforge_eng_000925-voxforge_eng_000925) +IT WAS THE SAME WAY WITH OUR REVOLVERS AND RIFLES (voxforge_eng_000927-voxforge_eng_000927) +THE KING HAD PROMISED TO ENQUIRE INTO THE MATTER (voxforge_eng_000928-voxforge_eng_000928) +DOES THAT LOOK GOOD (voxforge_eng_000929-voxforge_eng_000929) +FOR THE FIRST TIME IN HIS LIFE HE WAS YEARNING FOR A SCRAP (voxforge_eng_000930-voxforge_eng_000930) +I DEFY ANY MAN TO GET A SOLOMON ISLAND SORE IN CALIFORNIA (voxforge_eng_000931-voxforge_eng_000931) +HER EYES SMILED TRUTH AT HIM AS HE CAME UP THE BANK (voxforge_eng_000932-voxforge_eng_000932) +ANYWAY NO ONE SAW HER LIKE THAT (voxforge_eng_000933-voxforge_eng_000933) +MEN WHO ENDURE IT CALL IT LIVING DEATH (voxforge_eng_000934-voxforge_eng_000934) +MATTHEWSON WHOS THIS BOOKKEEPER ROGERS (voxforge_eng_000935-voxforge_eng_000935) +I ONLY READ THE QUOTATIONS (voxforge_eng_000938-voxforge_eng_000938) +THERE WAS PROPER DIVISION OF LABOR IN THE WORK THEY INDIVIDUALLY PERFORMED (voxforge_eng_000939-voxforge_eng_000939) +ILL TELL YOU THE LIBRARIAN SAID WITH A BRIGHTENING FACE (voxforge_eng_000940-voxforge_eng_000940) +I SAW MR PIKE NOD HIS HEAD GRIMLY AND SARCASTICALLY (voxforge_eng_000942-voxforge_eng_000942) +THE RINGING OF THE BIG BELL AROUSED HIM (voxforge_eng_000943-voxforge_eng_000943) +THE SCRATCH OF A PIN ON A MANS HEAD VAST REGIONS OF THE EARTHS SURFACE REMAIN GEOLOGICALLY UNKNOWN BUT (voxforge_eng_000944-voxforge_eng_000944) +HE HAD BARELY ENTERED THIS WHEN HE SAW THE GLOW OF A FIRE (voxforge_eng_000945-voxforge_eng_000945) +CHANGE CHAIRS DAYLIGHT COMMANDED (voxforge_eng_000946-voxforge_eng_000946) +IT WAS JEANNE SINGING SOFTLY OVER BEYOND THE ROCKS (voxforge_eng_000947-voxforge_eng_000947) +A FLYING ARROW PASSED BETWEEN US (voxforge_eng_000948-voxforge_eng_000948) +HATRED AND MURDER AND LUST FOR REVENGE THEY POSSESSED TO OVERFLOWING (voxforge_eng_000949-voxforge_eng_000949) +THAT YOU COULD HEAR ALL UP AND DOWN THE LIMPOPO (voxforge_eng_000950-voxforge_eng_000950) +IT WAS MY IDEA TO A TEE (voxforge_eng_000951-voxforge_eng_000951) +SHE DOESNT WANT TO WIN (voxforge_eng_000952-voxforge_eng_000952) +SHE THINKS IT IS BECAUSE HE WANTS SOMETHING ELSE (voxforge_eng_000953-voxforge_eng_000953) +HE PULLED AND THE LOG CRASHED DOWN TO BREAK HIS BACK (voxforge_eng_000954-voxforge_eng_000954) +THAT THE SO CALLED FORCES AT WORK IN LIGHT HEAT ELECTRICITY AND MAGNETISM IN (voxforge_eng_000955-voxforge_eng_000955) +HE TURNED SHARPLY AND FACED GREGSON ACROSS THE TABLE (voxforge_eng_000956-voxforge_eng_000956) +ALSO I WANT INFORMATION (voxforge_eng_000957-voxforge_eng_000957) +THE SIXTH DAY HE SPENT IN THE CABIN WITH GREGSON (voxforge_eng_000958-voxforge_eng_000958) +ON THIS HYPOTHESIS THE HAMMERING OF THE ULTRA MUNDANE CORPUSCLES ON THE BOB CONFERS ITS KINETIC ENERGY ON THE ONE HAND (voxforge_eng_000959-voxforge_eng_000959) +NOW A FERNY WILLOWY STREAM AND EVER AND ANON YOU EMERGE FROM ALL THE GROVES AND FLOWERS (voxforge_eng_000960-voxforge_eng_000960) +WITHOUT IT THE MOST DENSELY POPULATED REGIONS OF MODERN EUROPE AND AMERICA (voxforge_eng_000961-voxforge_eng_000961) +TOM SPINK HAS A HARPOON (voxforge_eng_000962-voxforge_eng_000962) +HE WANTED TO GIVE THE FINISH TO THIS FOE ALREADY SO FAR GONE (voxforge_eng_000963-voxforge_eng_000963) +LIKE A FLASH HE LAUNCHED HIMSELF INTO THE FEATHERED MASS OF THE OWL (voxforge_eng_000964-voxforge_eng_000964) +IT CONTAINS A TOTAL OF TWENTY ENTRIES (voxforge_eng_000965-voxforge_eng_000965) +IVE FELT MORE COMFORTABLE (voxforge_eng_000966-voxforge_eng_000966) +DID I POSSESS TOO MUCH VITALITY (voxforge_eng_000967-voxforge_eng_000967) +THE WOLF DOG THRUST HIS GAUNT MUZZLE TOWARD HIM (voxforge_eng_000968-voxforge_eng_000968) +THE GABRIEL VOICE OF THE SAMURAI RANG OUT (voxforge_eng_000971-voxforge_eng_000971) +IT WAS OUR RIVER EMERGING LIKE OURSELVES FROM THE GREAT SWAMP (voxforge_eng_000972-voxforge_eng_000972) +SAID THE MOLE PULLING HIMSELF TOGETHER WITH AN EFFORT YOU MUST THINK ME VERY RUDE (voxforge_eng_000973-voxforge_eng_000973) +IN WHAT BUCOLIC SCHOOL OF FENCE HE HAD BEEN TAUGHT WAS BEYOND IMAGINING (voxforge_eng_000974-voxforge_eng_000974) +HAD NOT ENABLED INVESTIGATORS TO OBTAIN AT COMPARATIVELY LITTLE COST (voxforge_eng_000975-voxforge_eng_000975) +A TRICKLE OF FRESH BLOOD RAN OVER HIS FACE (voxforge_eng_000976-voxforge_eng_000976) +IT WAS A CURIOUS COINCIDENCE (voxforge_eng_000977-voxforge_eng_000977) +IT IS THE FIRE PARTLY SHE SAID (voxforge_eng_000978-voxforge_eng_000978) +THEY JUST LAY OFF IN THE BUSH AND PLUGGED AWAY (voxforge_eng_000979-voxforge_eng_000979) +I KNOW THAT YOU ARE IN CHARGE THERE AND JEANNE KNOWS (voxforge_eng_000980-voxforge_eng_000980) +FOR A TIME THE EXCITING THRILL OF HIS ADVENTURE WAS GONE (voxforge_eng_000981-voxforge_eng_000981) +SUDDENLY HIS FINGERS CLOSED TIGHTLY OVER THE HANDKERCHIEF (voxforge_eng_000982-voxforge_eng_000982) +DEAR SIR YOUR SECOND VICTIM HAS FALLEN ON SCHEDULE TIME (voxforge_eng_000983-voxforge_eng_000983) +HE CAN CARE FOR HIMSELF (voxforge_eng_000984-voxforge_eng_000984) +EACH INSULT ADDED TO THE VALUE OF THE CLAIM (voxforge_eng_000985-voxforge_eng_000985) +THOUGH IT MAY BE TRANSFORMED INTO ANY ONE OF THE FORMS OF WHICH ENERGY IS SUSCEPTIBLE (voxforge_eng_000986-voxforge_eng_000986) +MERCEDES SCREAMED CRIED LAUGHED AND MANIFESTED THE CHAOTIC ABANDONMENT OF HYSTERIA (voxforge_eng_000987-voxforge_eng_000987) +I WANT TO KNOW HOW ALL THIS IS POSSIBLE (voxforge_eng_000988-voxforge_eng_000988) +PRESENTING A SIMPLE AND INSTRUCTIVE ILLUSTRATION OF THE STRUGGLE FOR LIFE AMONG THE RIVAL SPECIES (voxforge_eng_000989-voxforge_eng_000989) +HELL NEVER DO A TAP OF WORK THE WHOLE VOYAGE (voxforge_eng_000990-voxforge_eng_000990) +I HAVE HUNTED ALONG THIS RIDGE REPLIED PHILIP (voxforge_eng_000991-voxforge_eng_000991) +LORD BUT IM GLAD TO SEE YOU AGAIN PHIL (voxforge_eng_000992-voxforge_eng_000992) +HOW VALIANTLY I WENT AT IT THAT FIRST DAY (voxforge_eng_000993-voxforge_eng_000993) +THEY ARE NOT REGULAR OYSTER PIRATES NICHOLAS CONTINUED (voxforge_eng_000994-voxforge_eng_000994) +THEY MUST BE HURTING FOR BUSINESS BUT I THOUGHT YOU MIGHT WANT TO TAKE A LOOK AT THEIR SITE (voxforge_eng_000995-voxforge_eng_000995) +THERE WAS NO CHANCE TO FIRE WITHOUT HITTING HIM (voxforge_eng_000996-voxforge_eng_000996) +AS FOR HIMSELF WERENT THE STREET RAILWAY EARNINGS INCREASING STEADILY (voxforge_eng_000997-voxforge_eng_000997) +DUNHAM CAN YOUR BOY GO ALONG WITH JESSE (voxforge_eng_000998-voxforge_eng_000998) +GOODBYE PIERRE HE SHOUTED (voxforge_eng_000999-voxforge_eng_000999) +BUT SUCH DIVERGENCE OF OPINION WOULD CONSTITUTE NO MENACE TO SOCIETY (voxforge_eng_001000-voxforge_eng_001000) +THERE WAS ONE CHANCE AND ONLY ONE OF SAVING JEANNE (voxforge_eng_001001-voxforge_eng_001001) +I CANNOT FOLLOW YOU SHE SAID (voxforge_eng_001002-voxforge_eng_001002) +ON THE FAR CORNER OF THE COMPOUND FENCE A HAWK BROODED (voxforge_eng_001003-voxforge_eng_001003) +THEN AGAIN TUDOR HAD SUCH AN IRRITATING WAY ABOUT HIM (voxforge_eng_001004-voxforge_eng_001004) +WE ALL KNOW OMAN AS A SUCCESSFUL STABLE COUNTRY A ROLE MODEL FOR THE WHOLE REGION (voxpopuli_eng_000494-voxpopuli_eng_000494) +THEREFORE ITS HIGH TIME THAT YOU COME FORWARD WITH A PROPOSAL FOR REVIEW WITH AN OPERATIONAL SEPARATION OF THE AUDIT AND NON AUDIT SERVICES UNDER A DIRECT EU SUPERVISION (voxpopuli_eng_000495-voxpopuli_eng_000495) +IT IS CLEAR THAT WE HAVE NO TIME TO WASTE THE NEW RESULTS OF THE IPCC REGARDING THE SCIENTIFIC BASIS OF CLIMATE CHANGE LEAVE NO ROOM FOR HESITATION (voxpopuli_eng_000496-voxpopuli_eng_000496) +5 SO IN THE CONTAINERS WHICH ARE NEVER EVEN TOUCHED COME SLAVES COUNTERFEIT GOODS DRUGS ETC (voxpopuli_eng_000497-voxpopuli_eng_000497) +I HOPE THAT THE COMMISSIONS MOBILITY INITIATIVES WILL NOT CREATE THE NEXT PROBLEM BUT WILL BE AN ANSWER FOR EXISTING CHALLENGES OF THE ROAD TRANSPORT SECTOR (voxpopuli_eng_000498-voxpopuli_eng_000498) +IN THE US IT WAS A DECISION TAKEN ONLY BY ONE PERSON THE FORMER PRESIDENT OF THE UNITED STATES AGAINST THE ARTICULATED DEMOCRATIC MAJORITY OF THE US CONGRESS BY ALL OF ITS REPUBLICAN AND SOME OF ITS DEMOCRAT MEMBERS IT WAS AN AGREEMENT WITHOUT ANY BINDING OBLIGATIONS AS THE LEADERS OF IRAN VERY OPENLY AND PRECISELY MADE CLEAR ON THE VERY DAY THIS SO CALLED DEAL WAS PUBLISHED (voxpopuli_eng_000499-voxpopuli_eng_000499) +FREE SPEECH IS ESSENTIALLY ACCEPTING THAT PEOPLE ARE FREE TO SAY THINGS WE DO LIKE NOT MERELY FREE TO SAY THINGS WE DO LIKE (voxpopuli_eng_000500-voxpopuli_eng_000500) +LET US LEARN FROM THIS (voxpopuli_eng_000501-voxpopuli_eng_000501) +WE THINK THAT THE ENVIRONMENTAL EFFECT OF PRODUCTS MUST BE A VERY IMPORTANT ISSUE IN THE EU AND THE WHOLE IDEA OF AN ECOLABEL GIVES A VERY USEFUL ORIENTATION FOR CONSUMERS OF COURSE THE ECOLABEL SHOULD BE GIVEN TO THE MOST ENVIRONMENTALLY FRIENDLY PRODUCTS AND THE INFORMATION SHOULD BE CLEAR AND CORRECT (voxpopuli_eng_000502-voxpopuli_eng_000502) +HOWEVER THE CURRENT REGIME NEEDS TO BE BETTER TAILORED TO THE DIGITAL ENVIRONMENT IN ORDER TO ENSURE FAIR REMUNERATION TO CREATORS AND TO CONFORM TO CONSUMER EXPECTATIONS (voxpopuli_eng_000503-voxpopuli_eng_000503) +IT CALLS UPON THE COMMISSION AND MEMBER STATES TO ENHANCE THEIR SUPPORT FOR RECONCILIATION TO SECURE PEACE AND STABILITY AND IRELAND I WOULD THEREFORE URGE YOU COLLEAGUES TO PLEASE SUPPORT THIS AMENDMENT (voxpopuli_eng_000504-voxpopuli_eng_000504) +STRATEGIC CHOICES ABOUT WHERE TO INVEST MUST BE MADE NOW TAKING INTO ACCOUNT THE NEED TO PHASE OUT FOSSIL FUEL SUBSIDIES BUT TAKE GAS AS A FOSSIL FUEL IT CAN BE A HELPFUL BRIDGING TRANSITIONARY MEDIUM TO BE USED IN MANY MEMBER STATES IF WE WANT TO ACHIEVE OUR AMBITIOUS CLIMATE TARGETS (voxpopuli_eng_000505-voxpopuli_eng_000505) +MIDDLE EAST WE ARE POSSIBLY AT A THRESHOLD WE CAN CHOOSE TO PURSUE THE SAME POLICIES IN THE SAME MANNER KNOWING THAT THEY WILL LEAD TO THE SAME RESULTS THE RESULTS THAT (voxpopuli_eng_000506-voxpopuli_eng_000506) +BUT THERE IS AN OPTION (voxpopuli_eng_000507-voxpopuli_eng_000507) +THIS WE ALSO NEED A CHANGE IN OUR IDEOLOGY (voxpopuli_eng_000508-voxpopuli_eng_000508) +A LARGE PART OF THE REASON IS OF COURSE ILLEGAL FISHING MORE OFTEN THAN NOT BY VESSELS WHICH ARE REGISTERED TO COUNTRIES WHICH LACK THE WILL OR THE RESOURCES TO ENFORCE INTERNATIONAL AGREEMENTS NO AMOUNT OF TRACEABILITY MEASURES OR EXTRA PAPERWORK WILL ADDRESS THE PROBLEM OF REDUCING (voxpopuli_eng_000509-voxpopuli_eng_000509) +THE COMPROMISE ALSO INCLUDES CLEAR RULES TO DEFINE WHICH MEMBER STATE HAS JURISDICTION AND THE COOPERATION BETWEEN MEMBER STATES CONCERNED IN CROSS BORDER CASES AS WELL AS THE NEED TO INVOLVE EUROJUST THANK YOU FOR YOUR WORK AND PLEASE DO SUPPORT THIS DIRECTIVE (voxpopuli_eng_000510-voxpopuli_eng_000510) +THE GREENS WOULD HAVE US BELIEVE THAT THESE ARE BAD BEES CRIMINAL BEES DELIBERATELY CONTAMINATING HONEY WITH A DANGEROUS INGREDIENT BUT IN FACT THEY ARE DOING WHAT HONEY BEES HAVE ALWAYS DONE WHICH IS TO CARRY POLLEN BACK TO THEIR HIVES TO FEED THEIR YOUNG (voxpopuli_eng_000511-voxpopuli_eng_000511) +BUT IT WAS THE COUNTRY ITSELF BEING MORE CAPABLE (voxpopuli_eng_000512-voxpopuli_eng_000512) +INTO THE PORTFOLIO OF THE NEW COMMISSIONER DEALING WITH FUNDAMENTAL RIGHTS (voxpopuli_eng_000513-voxpopuli_eng_000513) +THE MESSAGE IS THAT THE EU DOES NOT HAVE ANY NEW SOLUTIONS (voxpopuli_eng_000514-voxpopuli_eng_000514) +ARE YOU WILLING TO ACT IN FAVOUR OF THE SOCIAL DIMENSION TO BE INCLUDED IN THE EU COMPETENCIES AS PROPOSED (voxpopuli_eng_000515-voxpopuli_eng_000515) +THE NEXT STEP ON SPECTRUM POLICY IS BEING TAKEN WITH THE REFORM OF OUR TELECOM FRAMEWORK (voxpopuli_eng_000516-voxpopuli_eng_000516) +I BELIEVE HIS REMARKS WERE EXPLICITLY RACIST AND XENOPHOBIC AND PROMOTED RACIAL INTOLERANCE IN A WAY THAT IS NOT ACCEPTABLE OR ALLOWED IN THE CONSTITUTION OF THIS HOUSE (voxpopuli_eng_000517-voxpopuli_eng_000517) +REAL LIFE EXAMPLES SHOW THAT SOLVING ISSUES RELATED TO EDUCATION FUELS STRONG COMMUNITY DEVELOPMENT (voxpopuli_eng_000518-voxpopuli_eng_000518) +SO I HOPE THIS WILL HAPPEN FOR RUSSIA AS WELL AND THAT RUSSIA CAN ALSO ENVISAGE AN EXTREME SUCCESS STORY AFTER THE SIGNIFICANT DATE IN AUGUST THIS YEAR (voxpopuli_eng_000519-voxpopuli_eng_000519) +SHE ACCEPTED THE FACT THAT CITIZENSHIP IS SUBJECT TO NATIONAL JURISDICTION BUT SHE ALSO SAID THAT ACCORDING TO THE MAASTRICHT TREATY AND SHE IS RIGHT THERE HAS TO BE A DIRECT LINK (voxpopuli_eng_000520-voxpopuli_eng_000520) +THE EU FAILED ESPECIALLY IN DEMONSTRATING A UNIFIED AND EFFICIENT APPROACH TO CLIMATE CHANGE TREATMENT AS WELL AS IN STRENGTHENING ITS LEADING POLITICAL POSITION IN THIS AGENDA I CONSIDER THEREFORE TAKING THIS RESOLUTION AN ACT OF UTMOST IMPORTANCE (voxpopuli_eng_000521-voxpopuli_eng_000521) +THE UNITED STATES OF EUROPE WILL BE A FACT WITH SWEDEN AS A PROVINCE (voxpopuli_eng_000522-voxpopuli_eng_000522) +IT MUST BE THE CAPITAL OF BOTH STATES AND WE MUST RECOGNISE PALESTINE AS A STATE AS PROVIDED FOR IN THE OSLO AGREEMENTS (voxpopuli_eng_000523-voxpopuli_eng_000523) +UKRAINE IS FACED WITH ONE OF THE CRUCIAL CHALLENGES IN ITS HISTORY IT WOULD BE FUNDAMENTALLY WRONG TO PRESS THE NATION NOW WITH ALL TYPES OF RESTRICTIONS POPULARLY CALLED AUSTERITY POLICY (voxpopuli_eng_000524-voxpopuli_eng_000524) +MORE RULES AND REGULATION WILL NOT IMPROVE THE SITUATION (voxpopuli_eng_000525-voxpopuli_eng_000525) +AT LEAST WE WOULD LIKE TO KNOW THE SOURCE OF THE MONEY AND THE POSSIBLE MOTIVES (voxpopuli_eng_000526-voxpopuli_eng_000526) +TO HAVE THOSE EUROPEAN WORLD LANGUAGES IN TODAYS GLOBALISED WORLD IN TODAYS GLOBALISED ECONOMY IN THIS GLOBAL VILLAGE WHICH IS CULTURAL ECONOMIC SOCIAL AND POLITICAL IS A MOST VALUABLE ASSET FOR THE ENTIRE EU WHICH WE MUST TAKE FULL ACCOUNT OF AND (voxpopuli_eng_000527-voxpopuli_eng_000527) +WE HAVE TO REPEAT THAT ODA CANNOT BE USED TO FINANCE SECURITY EXPENSES BORDER CONTROL OR MILITARY SUPPORT (voxpopuli_eng_000528-voxpopuli_eng_000528) +IF ANYTHING THE SCIENTIFIC REPORTS ARE BECOMING MORE URGENT MORE ALARMING AND MORE SHOCKING (voxpopuli_eng_000529-voxpopuli_eng_000529) +FINALLY WHEN IT COMES TO INNOVATIVE FINANCIAL INSTRUMENTS WE NEED THEM BOTH FOR OURSELVES TO SUPPORT OUR ECONOMIES BUT ALSO TO SUPPORT THOSE WHO ARE IN NEED (voxpopuli_eng_000530-voxpopuli_eng_000530) +THAT GIVES US A UNIQUE TOOL IN PEACEMAKING (voxpopuli_eng_000531-voxpopuli_eng_000531) +PAPER A VERY WEAK PROPOSAL (voxpopuli_eng_000532-voxpopuli_eng_000532) +RUSSIA HAS ALWAYS BEEN A VERY PROUD NATION WITH A RICH CULTURE WITH INVENTIONS AND ESPRIT (voxpopuli_eng_000533-voxpopuli_eng_000533) +FAIR TAXATION EVEN A MODICUM OF TAXATION IN SOME CASES MIGHT JUST HELP US TO DO WHAT I HAVE ALREADY SUGGESTED AND WHO KNOWS MAKE THE CASE FOR THE RETROSPECTIVE BANK RECAPITALISATION THAT WE NEVER SAW (voxpopuli_eng_000534-voxpopuli_eng_000534) +THE EUROPEAN ASYLUM SUPPORT OFFICE MOREOVER HAS AMONG ITS TASKS TO PROMOTE FACILITATE AND COORDINATE EXCHANGES OF INFORMATION AND OTHER ACTIVITIES RELATED TO RELOCATION WITHIN THE UNION (voxpopuli_eng_000535-voxpopuli_eng_000535) +THE CONCLUSION OF THE FRAMEWORK AGREEMENT PROVIDES A LEGALLY BINDING INSTRUMENT TO UPGRADE AND STRENGTHEN EU AUSTRALIA BILATERAL RELATIONS AND TO INCREASE COOPERATION (voxpopuli_eng_000536-voxpopuli_eng_000536) +THEREFORE WE ARE ASKING THE COUNCIL AND THE COMMISSION TO PRESENT A TRANSPARENT AND COMPLETE ASSESSMENT OF THE IMPACT OF THE CRISIS (voxpopuli_eng_000537-voxpopuli_eng_000537) +IN OTHER WORDS THE OBJECTION IS NOT WHETHER MONEY IS PAID OR NOT THE OBJECTION IS WHETHER THERE IS A DIRECT LINK OR NOT (voxpopuli_eng_000538-voxpopuli_eng_000538) +IT DISTINGUISHES THE TWO MAIN DOSSIERS HUMAN RIGHTS ABUSES BY THE CURRENT GOVERNMENT AND THE IRANIAN NUCLEAR PROGRAMME (voxpopuli_eng_000539-voxpopuli_eng_000539) +MR PRESIDENT SEXUAL HARASSMENT IS A FORM OF VIOLENCE AND IT IS THE MOST EXTREME FORM OF GENDER—BASED DISCRIMINATION (voxpopuli_eng_000540-voxpopuli_eng_000540) +WE CAN LOOK TO SOME NON EU MEMBERS FOR GOOD EXAMPLES AS REGARDS TECHNOLOGIES (voxpopuli_eng_000541-voxpopuli_eng_000541) +INVOLVED FOR THEIR POSITIVE AND CONSTRUCTIVE APPROACH (voxpopuli_eng_000542-voxpopuli_eng_000542) +SO I HOPE THAT THIS WILL BE COMPLETED IN THE FORESEEABLE FUTURE WHICH MEANS MAYBE TWO OR THREE MONTHS (voxpopuli_eng_000543-voxpopuli_eng_000543) +FURTHER ENCOURAGE THE UNS EFFORTS TO BRING ABOUT PEACE IN AFGHANISTAN AND TO OVERCOME THE FRAGILE SECURITY ENVIRONMENT IN THE COUNTRY (voxpopuli_eng_000544-voxpopuli_eng_000544) +WE UNDERSTAND THAT SOME PEOPLE ARE ANGRY (voxpopuli_eng_000545-voxpopuli_eng_000545) +WE WANT TO BE MORE RESPONSIBLE (voxpopuli_eng_000546-voxpopuli_eng_000546) +WE MUST RECTIFY THIS SITUATION AND WE ASK THE COMMISSION TO CONSIDER THE MOST ADEQUATE COMPENSATION MEASURES FOR OUR PASSENGERS (voxpopuli_eng_000547-voxpopuli_eng_000547) +THE COMMISSION INVITES PARLIAMENT IN THE UPCOMING REVISION TO OPEN ITS POSITION ON THIS MATTER WHICH REALLY CONCERNS ACCESS TO JUSTICE IN EUROPE AND THE ENFORCEMENT OF RIGHTS GRANTED BY EUROPEAN UNION LAW (voxpopuli_eng_000548-voxpopuli_eng_000548) +I WELCOME VERY MUCH THE RESUMPTION OF TALKS BETWEEN THE ISRAELIS AND THE PALESTINIANS AND SINCERELY HOPE THAT THEY WILL SUCCEED (voxpopuli_eng_000549-voxpopuli_eng_000549) +WE HAVE AN ACCUMULATION OF PROBLEMS RESULTING FROM ARTIFICIAL UNDER BUDGETING IN PREVIOUS YEARS (voxpopuli_eng_000550-voxpopuli_eng_000550) +LET US NOT BE THE MAN OF YESTERDAY LET US BE TODAYS INSTITUTION (voxpopuli_eng_000551-voxpopuli_eng_000551) +I WOULD URGE YOU TO BECOME AMBASSADORS OF THE YEAR BY MAKING ITS IDEAS AND ACTIVITIES WIDELY KNOWN AMONGST EUROPEAN CITIZENS AND PARTICIPATING IN EVENTS BE IT AT EUROPEAN NATIONAL OR LOCAL LEVEL (voxpopuli_eng_000552-voxpopuli_eng_000552) +CERTAINLY SUCH IMPACT ASSESSMENT COULD PRE EMPT CERTAIN PROBLEMS SUCH AS THOSE POSED BY THE ELECTRONIC IDENTIFICATION OF SHEEP IN SCOTLAND (voxpopuli_eng_000553-voxpopuli_eng_000553) +THE COURT IS CONTENT TO SEE THAT ITS WORK HAS INFORMED THE DISCHARGE PROCESS AND HAS CONTRIBUTED TO PROPOSALS FOR IMPROVING THE FINANCIAL MANAGEMENT OF EU SPENDING AND BETTER TARGETING OF EU FUNDS (voxpopuli_eng_000554-voxpopuli_eng_000554) +REGULATORY CLARITY AND CERTAINTY IS NEEDED FOR THE PUBLIC SECTOR AND FOR INDUSTRY (voxpopuli_eng_000555-voxpopuli_eng_000555) +IS IT REALLY NOT POSSIBLE TO USE OTHER HOUSING FACILITIES WITH APPROPRIATE RECEPTION CONDITIONS IN THE MEANTIME (voxpopuli_eng_000556-voxpopuli_eng_000556) +WILL YOU TAKE ACTION AT LAST IF NOT THEN WHEN (voxpopuli_eng_000557-voxpopuli_eng_000557) diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/result.txt b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..3710b0150af73f75c0a531a1720689d80dab4f18 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/result.txt @@ -0,0 +1,12689 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,-----------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/hyp.trn| +|-----------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000254 | 1 17 | 35.3 64.7 0.0 0.0 64.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000255 | 1 9 | 44.4 55.6 0.0 11.1 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000256 | 1 9 | 22.2 77.8 0.0 0.0 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000257 | 1 9 | 33.3 55.6 11.1 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000258 | 1 9 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000259 | 1 13 | 53.8 46.2 0.0 7.7 53.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000260 | 1 14 | 57.1 35.7 7.1 7.1 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000261 | 1 13 | 53.8 46.2 0.0 0.0 46.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000262 | 1 8 | 62.5 37.5 0.0 0.0 37.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000263 | 1 13 | 53.8 46.2 0.0 0.0 46.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000264 | 1 20 | 30.0 65.0 5.0 0.0 70.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000265 | 1 9 | 88.9 11.1 0.0 0.0 11.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000266 | 1 5 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000267 | 1 5 | 40.0 60.0 0.0 20.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000268 | 1 9 | 77.8 22.2 0.0 11.1 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000269 | 1 7 | 14.3 85.7 0.0 14.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000270 | 1 13 | 61.5 38.5 0.0 15.4 53.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000271 | 1 17 | 47.1 52.9 0.0 0.0 52.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000272 | 1 20 | 75.0 20.0 5.0 5.0 30.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000273 | 1 18 | 55.6 44.4 0.0 11.1 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000274 | 1 5 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000275 | 1 14 | 42.9 57.1 0.0 7.1 64.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000276 | 1 18 | 55.6 44.4 0.0 0.0 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000277 | 1 11 | 90.9 9.1 0.0 0.0 9.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000278 | 1 8 | 62.5 37.5 0.0 12.5 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000279 | 1 5 | 80.0 20.0 0.0 0.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000280 | 1 11 | 72.7 27.3 0.0 0.0 27.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000281 | 1 8 | 50.0 50.0 0.0 25.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000282 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000283 | 1 14 | 50.0 50.0 0.0 7.1 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000284 | 1 20 | 45.0 55.0 0.0 0.0 55.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000285 | 1 19 | 42.1 57.9 0.0 0.0 57.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000286 | 1 11 | 63.6 36.4 0.0 9.1 45.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000287 | 1 9 | 55.6 44.4 0.0 0.0 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000288 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000289 | 1 9 | 55.6 44.4 0.0 0.0 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000290 | 1 10 | 70.0 30.0 0.0 10.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000291 | 1 11 | 54.5 36.4 9.1 0.0 45.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000292 | 1 9 | 33.3 66.7 0.0 11.1 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000293 | 1 16 | 37.5 62.5 0.0 0.0 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000294 | 1 10 | 20.0 70.0 10.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000295 | 1 11 | 36.4 63.6 0.0 0.0 63.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000296 | 1 14 | 35.7 64.3 0.0 0.0 64.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000297 | 1 10 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000298 | 1 13 | 76.9 23.1 0.0 0.0 23.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000299 | 1 13 | 61.5 38.5 0.0 15.4 53.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000300 | 1 13 | 61.5 38.5 0.0 7.7 46.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000301 | 1 15 | 66.7 26.7 6.7 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000302 | 1 10 | 40.0 60.0 0.0 10.0 70.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000303 | 1 11 | 27.3 72.7 0.0 9.1 81.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000304 | 1 10 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000305 | 1 15 | 53.3 46.7 0.0 0.0 46.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000306 | 1 11 | 45.5 54.5 0.0 9.1 63.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000307 | 1 17 | 52.9 47.1 0.0 5.9 52.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000308 | 1 12 | 33.3 66.7 0.0 8.3 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000309 | 1 7 | 57.1 42.9 0.0 0.0 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000310 | 1 6 | 83.3 16.7 0.0 0.0 16.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000311 | 1 18 | 50.0 50.0 0.0 5.6 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000312 | 1 16 | 56.3 43.8 0.0 0.0 43.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000313 | 1 12 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000314 | 1 7 | 57.1 42.9 0.0 0.0 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000315 | 1 9 | 11.1 77.8 11.1 0.0 88.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000316 | 1 16 | 56.3 43.8 0.0 12.5 56.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000317 | 1 15 | 33.3 53.3 13.3 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000318 | 1 18 | 55.6 44.4 0.0 5.6 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000319 | 1 15 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000320 | 1 19 | 52.6 47.4 0.0 5.3 52.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000321 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000322 | 1 10 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000323 | 1 13 | 15.4 84.6 0.0 0.0 84.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000324 | 1 18 | 33.3 50.0 16.7 5.6 72.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000325 | 1 8 | 75.0 25.0 0.0 0.0 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000326 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000327 | 1 15 | 46.7 40.0 13.3 0.0 53.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000328 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000329 | 1 19 | 36.8 57.9 5.3 0.0 63.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000330 | 1 10 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000331 | 1 5 | 80.0 20.0 0.0 0.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000332 | 1 18 | 33.3 50.0 16.7 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000333 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000334 | 1 18 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000335 | 1 14 | 50.0 50.0 0.0 14.3 64.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000336 | 1 15 | 26.7 73.3 0.0 0.0 73.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000337 | 1 20 | 35.0 55.0 10.0 0.0 65.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000338 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000339 | 1 11 | 45.5 54.5 0.0 9.1 63.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000340 | 1 11 | 36.4 63.6 0.0 0.0 63.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000341 | 1 12 | 33.3 58.3 8.3 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000342 | 1 14 | 42.9 50.0 7.1 7.1 64.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000343 | 1 18 | 50.0 50.0 0.0 5.6 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000344 | 1 18 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000345 | 1 11 | 27.3 72.7 0.0 18.2 90.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000346 | 1 15 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000347 | 1 15 | 46.7 46.7 6.7 0.0 53.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000348 | 1 10 | 30.0 60.0 10.0 0.0 70.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000349 | 1 16 | 37.5 50.0 12.5 0.0 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000350 | 1 12 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000351 | 1 18 | 44.4 50.0 5.6 0.0 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000352 | 1 7 | 57.1 42.9 0.0 0.0 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000353 | 1 12 | 41.7 50.0 8.3 0.0 58.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000354 | 1 11 | 36.4 63.6 0.0 0.0 63.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000355 | 1 13 | 46.2 53.8 0.0 0.0 53.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000356 | 1 7 | 28.6 71.4 0.0 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000357 | 1 15 | 46.7 53.3 0.0 0.0 53.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000358 | 1 9 | 55.6 44.4 0.0 0.0 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000359 | 1 9 | 44.4 55.6 0.0 11.1 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000360 | 1 10 | 40.0 50.0 10.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000361 | 1 7 | 28.6 71.4 0.0 14.3 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000362 | 1 17 | 41.2 52.9 5.9 0.0 58.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000363 | 1 14 | 28.6 64.3 7.1 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000364 | 1 12 | 66.7 33.3 0.0 8.3 41.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000365 | 1 5 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000366 | 1 11 | 36.4 54.5 9.1 0.0 63.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000367 | 1 10 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000368 | 1 9 | 44.4 55.6 0.0 0.0 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000369 | 1 17 | 35.3 58.8 5.9 0.0 64.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000370 | 1 14 | 50.0 42.9 7.1 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000371 | 1 11 | 63.6 36.4 0.0 0.0 36.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000372 | 1 15 | 60.0 33.3 6.7 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000373 | 1 14 | 35.7 64.3 0.0 14.3 78.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000374 | 1 6 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000375 | 1 10 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000376 | 1 14 | 28.6 71.4 0.0 14.3 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| m | 77 1634 | 48.8 45.9 5.3 2.4 53.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000707 | 1 9 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000708 | 1 10 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000709 | 1 9 | 33.3 55.6 11.1 11.1 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000710 | 1 11 | 18.2 63.6 18.2 0.0 81.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000711 | 1 14 | 14.3 78.6 7.1 14.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000712 | 1 14 | 35.7 64.3 0.0 7.1 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000713 | 1 13 | 30.8 61.5 7.7 0.0 69.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000714 | 1 15 | 6.7 93.3 0.0 6.7 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000715 | 1 2 | 50.0 50.0 0.0 50.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000716 | 1 7 | 14.3 85.7 0.0 28.6 114.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000717 | 1 12 | 33.3 66.7 0.0 8.3 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000718 | 1 6 | 50.0 50.0 0.0 16.7 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000719 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000720 | 1 11 | 36.4 54.5 9.1 0.0 63.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000721 | 1 8 | 50.0 37.5 12.5 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000722 | 1 5 | 20.0 80.0 0.0 40.0 120.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000723 | 1 12 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000724 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000725 | 1 14 | 35.7 42.9 21.4 0.0 64.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000726 | 1 13 | 53.8 46.2 0.0 0.0 46.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000727 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000728 | 1 7 | 42.9 57.1 0.0 0.0 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000729 | 1 12 | 33.3 58.3 8.3 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000730 | 1 13 | 7.7 84.6 7.7 23.1 115.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000731 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000732 | 1 14 | 42.9 50.0 7.1 0.0 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000733 | 1 11 | 18.2 81.8 0.0 0.0 81.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000734 | 1 11 | 9.1 81.8 9.1 18.2 109.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000735 | 1 13 | 46.2 53.8 0.0 0.0 53.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000736 | 1 11 | 36.4 54.5 9.1 0.0 63.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000737 | 1 1 | 0.0 100.0 0.0 400.0 500.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000738 | 1 14 | 28.6 71.4 0.0 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000739 | 1 9 | 22.2 77.8 0.0 0.0 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000740 | 1 8 | 50.0 50.0 0.0 37.5 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000741 | 1 6 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000742 | 1 12 | 16.7 75.0 8.3 25.0 108.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000743 | 1 15 | 60.0 40.0 0.0 6.7 46.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000744 | 1 7 | 14.3 85.7 0.0 14.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000745 | 1 11 | 9.1 90.9 0.0 45.5 136.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000746 | 1 9 | 33.3 55.6 11.1 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000747 | 1 7 | 28.6 57.1 14.3 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000748 | 1 1 | 0.0 100.0 0.0 300.0 400.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000749 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000750 | 1 13 | 23.1 76.9 0.0 7.7 84.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000751 | 1 9 | 33.3 66.7 0.0 11.1 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000752 | 1 12 | 41.7 58.3 0.0 50.0 108.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000753 | 1 11 | 9.1 90.9 0.0 54.5 145.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000754 | 1 8 | 0.0 100.0 0.0 62.5 162.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000755 | 1 5 | 40.0 60.0 0.0 20.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000756 | 1 10 | 40.0 50.0 10.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000757 | 1 11 | 27.3 63.6 9.1 0.0 72.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000758 | 1 5 | 60.0 40.0 0.0 20.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000759 | 1 8 | 25.0 62.5 12.5 12.5 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000760 | 1 11 | 18.2 72.7 9.1 27.3 109.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000761 | 1 6 | 50.0 50.0 0.0 16.7 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000762 | 1 12 | 33.3 58.3 8.3 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000763 | 1 10 | 60.0 40.0 0.0 10.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000764 | 1 9 | 22.2 55.6 22.2 0.0 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000765 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000766 | 1 8 | 50.0 50.0 0.0 25.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000767 | 1 12 | 58.3 41.7 0.0 0.0 41.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000768 | 1 5 | 20.0 80.0 0.0 60.0 140.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000769 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000770 | 1 17 | 58.8 41.2 0.0 5.9 47.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000771 | 1 10 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000772 | 1 7 | 0.0 85.7 14.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000773 | 1 14 | 35.7 57.1 7.1 0.0 64.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000774 | 1 12 | 41.7 58.3 0.0 16.7 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000775 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000776 | 1 12 | 41.7 50.0 8.3 0.0 58.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000777 | 1 14 | 28.6 71.4 0.0 7.1 78.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000778 | 1 14 | 21.4 78.6 0.0 0.0 78.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000779 | 1 14 | 21.4 71.4 7.1 50.0 128.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000780 | 1 12 | 58.3 41.7 0.0 0.0 41.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000781 | 1 14 | 21.4 57.1 21.4 0.0 78.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000782 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000783 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000784 | 1 9 | 11.1 88.9 0.0 22.2 111.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000785 | 1 6 | 33.3 50.0 16.7 50.0 116.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000786 | 1 11 | 45.5 54.5 0.0 9.1 63.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000787 | 1 14 | 35.7 64.3 0.0 21.4 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000788 | 1 5 | 40.0 60.0 0.0 20.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000789 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000790 | 1 6 | 50.0 33.3 16.7 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000791 | 1 7 | 28.6 57.1 14.3 28.6 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000792 | 1 12 | 16.7 75.0 8.3 8.3 91.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000793 | 1 7 | 0.0 85.7 14.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000794 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000795 | 1 7 | 42.9 57.1 0.0 0.0 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000796 | 1 7 | 0.0 57.1 42.9 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000797 | 1 9 | 55.6 44.4 0.0 0.0 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000798 | 1 9 | 22.2 66.7 11.1 0.0 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000799 | 1 7 | 42.9 57.1 0.0 14.3 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000800 | 1 4 | 0.0 100.0 0.0 125.0 225.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000801 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000802 | 1 8 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000803 | 1 11 | 27.3 63.6 9.1 9.1 81.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000804 | 1 6 | 66.7 33.3 0.0 16.7 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000805 | 1 12 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000806 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000807 | 1 14 | 21.4 78.6 0.0 21.4 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000808 | 1 13 | 46.2 53.8 0.0 0.0 53.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000809 | 1 10 | 40.0 50.0 10.0 10.0 70.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000413 | 1 33 | 48.5 48.5 3.0 3.0 54.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000414 | 1 37 | 51.4 48.6 0.0 5.4 54.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000415 | 1 18 | 44.4 55.6 0.0 16.7 72.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000416 | 1 22 | 31.8 68.2 0.0 0.0 68.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000417 | 1 25 | 24.0 64.0 12.0 0.0 76.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000418 | 1 13 | 46.2 53.8 0.0 7.7 61.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000419 | 1 9 | 55.6 44.4 0.0 22.2 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000420 | 1 14 | 28.6 50.0 21.4 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000421 | 1 22 | 50.0 50.0 0.0 9.1 59.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000422 | 1 21 | 42.9 52.4 4.8 0.0 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000423 | 1 26 | 46.2 46.2 7.7 0.0 53.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000424 | 1 17 | 41.2 58.8 0.0 5.9 64.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000425 | 1 18 | 44.4 44.4 11.1 0.0 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000426 | 1 17 | 35.3 58.8 5.9 17.6 82.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000427 | 1 33 | 33.3 54.5 12.1 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000428 | 1 22 | 31.8 63.6 4.5 0.0 68.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000429 | 1 22 | 63.6 36.4 0.0 4.5 40.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000430 | 1 19 | 52.6 47.4 0.0 5.3 52.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000431 | 1 16 | 18.8 81.3 0.0 12.5 93.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000432 | 1 15 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000433 | 1 13 | 23.1 76.9 0.0 7.7 84.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000434 | 1 11 | 18.2 72.7 9.1 0.0 81.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000435 | 1 22 | 50.0 50.0 0.0 4.5 54.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000436 | 1 18 | 27.8 72.2 0.0 11.1 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000437 | 1 22 | 36.4 63.6 0.0 13.6 77.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000438 | 1 30 | 53.3 43.3 3.3 6.7 53.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000439 | 1 12 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000440 | 1 30 | 43.3 53.3 3.3 0.0 56.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000441 | 1 9 | 44.4 55.6 0.0 11.1 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000442 | 1 22 | 27.3 50.0 22.7 4.5 77.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000443 | 1 32 | 31.3 65.6 3.1 0.0 68.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000444 | 1 22 | 36.4 50.0 13.6 0.0 63.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000445 | 1 10 | 10.0 80.0 10.0 30.0 120.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000446 | 1 15 | 33.3 60.0 6.7 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000447 | 1 18 | 38.9 50.0 11.1 11.1 72.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000448 | 1 32 | 46.9 43.8 9.4 3.1 56.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000449 | 1 25 | 48.0 52.0 0.0 16.0 68.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000450 | 1 32 | 31.3 65.6 3.1 0.0 68.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000451 | 1 14 | 7.1 92.9 0.0 28.6 121.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000452 | 1 29 | 24.1 72.4 3.4 3.4 79.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000453 | 1 22 | 27.3 63.6 9.1 0.0 72.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000454 | 1 22 | 22.7 72.7 4.5 22.7 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000455 | 1 17 | 35.3 58.8 5.9 29.4 94.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000456 | 1 9 | 66.7 33.3 0.0 22.2 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000457 | 1 24 | 12.5 54.2 33.3 4.2 91.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000458 | 1 15 | 40.0 60.0 0.0 13.3 73.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000459 | 1 35 | 37.1 54.3 8.6 2.9 65.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000460 | 1 17 | 23.5 76.5 0.0 0.0 76.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000461 | 1 15 | 33.3 66.7 0.0 6.7 73.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000462 | 1 15 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000463 | 1 18 | 22.2 66.7 11.1 0.0 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000464 | 1 12 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000465 | 1 26 | 57.7 42.3 0.0 3.8 46.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000466 | 1 27 | 22.2 70.4 7.4 7.4 85.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000467 | 1 16 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000468 | 1 46 | 15.2 56.5 28.3 0.0 84.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000469 | 1 31 | 35.5 58.1 6.5 32.3 96.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000470 | 1 31 | 32.3 67.7 0.0 3.2 71.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000471 | 1 25 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000472 | 1 29 | 13.8 72.4 13.8 3.4 89.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000473 | 1 25 | 32.0 64.0 4.0 0.0 68.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000474 | 1 28 | 53.6 46.4 0.0 7.1 53.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000475 | 1 28 | 39.3 42.9 17.9 0.0 60.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000476 | 1 27 | 44.4 51.9 3.7 0.0 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000283 | 1 38 | 42.1 52.6 5.3 2.6 60.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000284 | 1 31 | 35.5 61.3 3.2 3.2 67.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000285 | 1 35 | 20.0 62.9 17.1 2.9 82.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000286 | 1 42 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000287 | 1 42 | 31.0 59.5 9.5 0.0 69.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000288 | 1 24 | 41.7 54.2 4.2 0.0 58.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000289 | 1 39 | 59.0 41.0 0.0 2.6 43.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000290 | 1 35 | 31.4 60.0 8.6 0.0 68.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000291 | 1 33 | 60.6 36.4 3.0 0.0 39.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000292 | 1 36 | 36.1 55.6 8.3 0.0 63.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000293 | 1 23 | 39.1 52.2 8.7 4.3 65.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000294 | 1 42 | 26.2 61.9 11.9 4.8 78.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000295 | 1 26 | 46.2 50.0 3.8 7.7 61.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000296 | 1 28 | 25.0 71.4 3.6 10.7 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000297 | 1 47 | 40.4 53.2 6.4 0.0 59.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000298 | 1 19 | 26.3 73.7 0.0 31.6 105.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000299 | 1 51 | 23.5 70.6 5.9 0.0 76.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000300 | 1 53 | 28.3 39.6 32.1 1.9 73.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000301 | 1 44 | 47.7 50.0 2.3 0.0 52.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000302 | 1 27 | 37.0 51.9 11.1 0.0 63.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000303 | 1 49 | 46.9 44.9 8.2 0.0 53.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000304 | 1 54 | 50.0 44.4 5.6 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000305 | 1 49 | 65.3 32.7 2.0 2.0 36.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000306 | 1 30 | 36.7 46.7 16.7 3.3 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000307 | 1 46 | 45.7 47.8 6.5 0.0 54.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000308 | 1 26 | 57.7 38.5 3.8 3.8 46.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000309 | 1 36 | 50.0 44.4 5.6 2.8 52.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000310 | 1 35 | 22.9 62.9 14.3 0.0 77.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000311 | 1 37 | 43.2 54.1 2.7 2.7 59.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000312 | 1 42 | 33.3 59.5 7.1 4.8 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000313 | 1 35 | 48.6 51.4 0.0 14.3 65.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000314 | 1 29 | 58.6 41.4 0.0 6.9 48.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000315 | 1 29 | 55.2 37.9 6.9 3.4 48.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000316 | 1 45 | 55.6 40.0 4.4 0.0 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000317 | 1 49 | 40.8 51.0 8.2 2.0 61.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000318 | 1 36 | 50.0 41.7 8.3 2.8 52.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000319 | 1 34 | 32.4 61.8 5.9 11.8 79.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000320 | 1 37 | 51.4 40.5 8.1 0.0 48.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000321 | 1 42 | 57.1 40.5 2.4 0.0 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000322 | 1 39 | 46.2 43.6 10.3 0.0 53.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001588 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001589 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001590 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001591 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001592 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001593 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001594 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001595 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001596 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001597 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001598 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001599 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001600 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001601 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001602 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001603 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001604 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001605 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001606 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001607 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001608 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001609 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001610 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001611 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001612 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001613 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001614 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001615 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001616 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001617 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001618 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001619 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001620 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001621 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001622 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001623 | 1 2 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001624 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001625 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001626 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001627 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001628 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001629 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001630 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001631 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001632 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001633 | 1 1 | 100.0 0.0 0.0 100.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001634 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001635 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001636 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001637 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001638 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001639 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001640 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001641 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001642 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001643 | 1 2 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001644 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001645 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001646 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001647 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001648 | 1 5 | 20.0 80.0 0.0 40.0 120.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001649 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001650 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001651 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001652 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001653 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001654 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001655 | 1 3 | 66.7 33.3 0.0 33.3 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001656 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001657 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001658 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001659 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001660 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001661 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001662 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001663 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001664 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001665 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001666 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001667 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001668 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001669 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001670 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001671 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001672 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001673 | 1 2 | 100.0 0.0 0.0 150.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001674 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001675 | 1 1 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001676 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001677 | 1 3 | 66.7 33.3 0.0 33.3 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001678 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001679 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001680 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001681 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001682 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001683 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001684 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001685 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001686 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001687 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001688 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001689 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001690 | 1 2 | 50.0 50.0 0.0 50.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001691 | 1 1 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001692 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001693 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001694 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001695 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001696 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001697 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001698 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001699 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001700 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001701 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001702 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001703 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001704 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001705 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001706 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001707 | 1 5 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001708 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001709 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001710 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001711 | 1 2 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001712 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001713 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001714 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001715 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001716 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001717 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001718 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001719 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001720 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001721 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001722 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001723 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001724 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001725 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001726 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001727 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001728 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001729 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001730 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001731 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001732 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001733 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001734 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001735 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001736 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001737 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001738 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001739 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001740 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001741 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001742 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001743 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001744 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001745 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001746 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001747 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001748 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001749 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001750 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001751 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001752 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001753 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001754 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001755 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001756 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001757 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001758 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001759 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001760 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001761 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001762 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001763 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001764 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001765 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001766 | 1 3 | 33.3 33.3 33.3 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001767 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001768 | 1 6 | 33.3 50.0 16.7 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001769 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001770 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001771 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001772 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001773 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001774 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001775 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001776 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001777 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001778 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001779 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001780 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001781 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001782 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001783 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001784 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001785 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001786 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001787 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001788 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001789 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001790 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001791 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001792 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001793 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001794 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001795 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001796 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001797 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001798 | 1 3 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001799 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001800 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001801 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001802 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001803 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001804 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001805 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001806 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001807 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001808 | 1 3 | 33.3 33.3 33.3 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001809 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001810 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001811 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001812 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001813 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001814 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001815 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001816 | 1 6 | 33.3 50.0 16.7 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001817 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001818 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001819 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001820 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001821 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001822 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001823 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001824 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001744 | 1 7 | 42.9 57.1 0.0 0.0 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001745 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001746 | 1 10 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001747 | 1 11 | 36.4 54.5 9.1 0.0 63.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001748 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001749 | 1 2 | 50.0 50.0 0.0 50.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001750 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001751 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001752 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001753 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001754 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001755 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001756 | 1 4 | 75.0 25.0 0.0 50.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001757 | 1 8 | 12.5 87.5 0.0 12.5 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001758 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001759 | 1 7 | 14.3 71.4 14.3 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001760 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001761 | 1 8 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001762 | 1 4 | 50.0 50.0 0.0 25.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001763 | 1 15 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001764 | 1 7 | 14.3 42.9 42.9 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001765 | 1 7 | 28.6 71.4 0.0 28.6 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001766 | 1 4 | 50.0 25.0 25.0 25.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001767 | 1 7 | 28.6 71.4 0.0 14.3 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001768 | 1 5 | 40.0 60.0 0.0 40.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001769 | 1 8 | 50.0 50.0 0.0 12.5 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001770 | 1 5 | 20.0 80.0 0.0 40.0 120.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001771 | 1 4 | 50.0 50.0 0.0 25.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001772 | 1 5 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001773 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001774 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001775 | 1 10 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001776 | 1 8 | 12.5 87.5 0.0 50.0 137.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001777 | 1 12 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001778 | 1 6 | 50.0 33.3 16.7 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001779 | 1 7 | 14.3 71.4 14.3 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001780 | 1 9 | 33.3 55.6 11.1 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001781 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001782 | 1 13 | 23.1 69.2 7.7 0.0 76.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001783 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001784 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001785 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001786 | 1 18 | 33.3 61.1 5.6 11.1 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001787 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001788 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001789 | 1 5 | 40.0 40.0 20.0 20.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001790 | 1 10 | 20.0 70.0 10.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001791 | 1 7 | 28.6 57.1 14.3 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001792 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001793 | 1 6 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001794 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001795 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001796 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001797 | 1 9 | 44.4 44.4 11.1 0.0 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001798 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001799 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001800 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001801 | 1 10 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001802 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001803 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001804 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001805 | 1 7 | 28.6 42.9 28.6 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001806 | 1 3 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001807 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001808 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001809 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001810 | 1 3 | 0.0 100.0 0.0 66.7 166.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001811 | 1 8 | 37.5 62.5 0.0 0.0 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001812 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001813 | 1 5 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001814 | 1 7 | 14.3 71.4 14.3 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001815 | 1 6 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001816 | 1 8 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001817 | 1 9 | 33.3 55.6 11.1 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001818 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001819 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001820 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001821 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001822 | 1 4 | 75.0 25.0 0.0 0.0 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001823 | 1 15 | 66.7 26.7 6.7 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001824 | 1 7 | 28.6 71.4 0.0 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001825 | 1 10 | 30.0 70.0 0.0 0.0 70.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001826 | 1 7 | 28.6 57.1 14.3 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001827 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001828 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001829 | 1 8 | 12.5 37.5 50.0 0.0 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001830 | 1 9 | 55.6 33.3 11.1 0.0 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001831 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001832 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001833 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001834 | 1 6 | 16.7 83.3 0.0 16.7 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001835 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001836 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001837 | 1 5 | 0.0 100.0 0.0 40.0 140.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001838 | 1 7 | 28.6 57.1 14.3 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001839 | 1 8 | 75.0 12.5 12.5 0.0 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001840 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001841 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001842 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001843 | 1 6 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001844 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001845 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001846 | 1 15 | 46.7 53.3 0.0 0.0 53.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001847 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001848 | 1 8 | 25.0 62.5 12.5 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001849 | 1 10 | 40.0 60.0 0.0 10.0 70.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001850 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001851 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001852 | 1 12 | 50.0 33.3 16.7 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001853 | 1 6 | 50.0 50.0 0.0 16.7 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001854 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001855 | 1 8 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001856 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001857 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001858 | 1 8 | 37.5 62.5 0.0 0.0 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001859 | 1 2 | 50.0 50.0 0.0 100.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001860 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001861 | 1 1 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001862 | 1 12 | 58.3 41.7 0.0 0.0 41.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001863 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001864 | 1 1 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001865 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001866 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001867 | 1 9 | 33.3 44.4 22.2 11.1 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001868 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001869 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001870 | 1 6 | 33.3 50.0 16.7 16.7 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001871 | 1 14 | 57.1 35.7 7.1 7.1 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001872 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001873 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001874 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001875 | 1 5 | 0.0 100.0 0.0 60.0 160.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001876 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001877 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001878 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001879 | 1 24 | 37.5 62.5 0.0 8.3 70.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001880 | 1 10 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001881 | 1 19 | 52.6 42.1 5.3 0.0 47.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001882 | 1 7 | 57.1 28.6 14.3 0.0 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001883 | 1 9 | 55.6 44.4 0.0 22.2 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001884 | 1 7 | 42.9 57.1 0.0 0.0 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001885 | 1 7 | 14.3 71.4 14.3 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001886 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001887 | 1 6 | 0.0 83.3 16.7 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001888 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001889 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001890 | 1 2 | 100.0 0.0 0.0 50.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001891 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001892 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001893 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001894 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001895 | 1 6 | 50.0 50.0 0.0 16.7 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001896 | 1 6 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001897 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001898 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001899 | 1 10 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001900 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001901 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001902 | 1 8 | 25.0 62.5 12.5 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001903 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001904 | 1 8 | 25.0 62.5 12.5 12.5 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001905 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001906 | 1 13 | 30.8 61.5 7.7 0.0 69.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001907 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001908 | 1 6 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001909 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001910 | 1 7 | 42.9 57.1 0.0 0.0 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001911 | 1 6 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001912 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001913 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001914 | 1 8 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001915 | 1 9 | 0.0 66.7 33.3 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001916 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001917 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001918 | 1 9 | 44.4 55.6 0.0 11.1 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001919 | 1 8 | 25.0 75.0 0.0 12.5 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001920 | 1 6 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001921 | 1 4 | 25.0 50.0 25.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001922 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001923 | 1 21 | 33.3 61.9 4.8 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001924 | 1 10 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001925 | 1 8 | 37.5 50.0 12.5 0.0 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001926 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001927 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001928 | 1 4 | 75.0 25.0 0.0 0.0 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001929 | 1 5 | 60.0 20.0 20.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001930 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001931 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001932 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001933 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001934 | 1 7 | 57.1 42.9 0.0 0.0 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001935 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001936 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001937 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001938 | 1 12 | 25.0 66.7 8.3 16.7 91.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001939 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001940 | 1 6 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001941 | 1 6 | 33.3 50.0 16.7 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001942 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001943 | 1 8 | 25.0 62.5 12.5 12.5 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001944 | 1 8 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001945 | 1 8 | 12.5 75.0 12.5 0.0 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001946 | 1 20 | 50.0 45.0 5.0 5.0 55.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001947 | 1 5 | 40.0 40.0 20.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001948 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001949 | 1 5 | 40.0 60.0 0.0 20.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001950 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001951 | 1 6 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001952 | 1 9 | 44.4 55.6 0.0 11.1 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001953 | 1 4 | 0.0 75.0 25.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001954 | 1 3 | 0.0 100.0 0.0 33.3 133.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001955 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001956 | 1 7 | 42.9 57.1 0.0 14.3 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001957 | 1 9 | 22.2 55.6 22.2 0.0 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001958 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001959 | 1 7 | 57.1 42.9 0.0 0.0 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001960 | 1 7 | 14.3 71.4 14.3 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001961 | 1 7 | 28.6 71.4 0.0 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001962 | 1 1 | 0.0 100.0 0.0 200.0 300.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001963 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001964 | 1 2 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001965 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001966 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001967 | 1 10 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001968 | 1 9 | 22.2 77.8 0.0 11.1 88.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001969 | 1 10 | 30.0 70.0 0.0 10.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001970 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001971 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001972 | 1 13 | 46.2 38.5 15.4 0.0 53.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001973 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001974 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001975 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001976 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001977 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001978 | 1 2 | 0.0 100.0 0.0 50.0 150.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001979 | 1 12 | 25.0 50.0 25.0 8.3 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001980 | 1 8 | 12.5 75.0 12.5 0.0 87.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001981 | 1 6 | 33.3 66.7 0.0 16.7 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001982 | 1 2 | 0.0 100.0 0.0 100.0 200.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001983 | 1 10 | 30.0 70.0 0.0 20.0 90.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001984 | 1 5 | 40.0 40.0 20.0 20.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001985 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001986 | 1 10 | 30.0 60.0 10.0 10.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001987 | 1 2 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001988 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001989 | 1 10 | 50.0 30.0 20.0 10.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001990 | 1 6 | 66.7 33.3 0.0 16.7 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001991 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001992 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001993 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001994 | 1 6 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001995 | 1 4 | 25.0 75.0 0.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001996 | 1 5 | 20.0 60.0 20.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001997 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001998 | 1 7 | 42.9 57.1 0.0 0.0 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001999 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002000 | 1 15 | 33.3 60.0 6.7 6.7 73.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002001 | 1 4 | 25.0 50.0 25.0 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002002 | 1 3 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002003 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002004 | 1 5 | 20.0 80.0 0.0 20.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002005 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000874 | 1 9 | 44.4 55.6 0.0 0.0 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000875 | 1 4 | 75.0 25.0 0.0 0.0 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000876 | 1 8 | 62.5 37.5 0.0 0.0 37.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000877 | 1 9 | 44.4 55.6 0.0 0.0 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000878 | 1 7 | 71.4 28.6 0.0 0.0 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000879 | 1 8 | 62.5 37.5 0.0 12.5 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000880 | 1 7 | 42.9 57.1 0.0 0.0 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000883 | 1 5 | 20.0 80.0 0.0 40.0 120.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000884 | 1 12 | 83.3 16.7 0.0 8.3 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000885 | 1 9 | 55.6 33.3 11.1 0.0 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000886 | 1 11 | 36.4 54.5 9.1 18.2 81.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000887 | 1 12 | 16.7 83.3 0.0 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000888 | 1 8 | 62.5 37.5 0.0 0.0 37.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000889 | 1 6 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000890 | 1 6 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000891 | 1 15 | 46.7 46.7 6.7 0.0 53.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000892 | 1 5 | 80.0 20.0 0.0 0.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000893 | 1 5 | 40.0 60.0 0.0 20.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000894 | 1 17 | 70.6 29.4 0.0 11.8 41.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000895 | 1 11 | 36.4 63.6 0.0 0.0 63.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000896 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000897 | 1 8 | 87.5 12.5 0.0 0.0 12.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000898 | 1 9 | 55.6 33.3 11.1 11.1 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000899 | 1 5 | 60.0 40.0 0.0 20.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000900 | 1 11 | 27.3 63.6 9.1 0.0 72.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000901 | 1 6 | 0.0 100.0 0.0 83.3 183.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000902 | 1 5 | 20.0 60.0 20.0 20.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000903 | 1 9 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000904 | 1 5 | 0.0 100.0 0.0 20.0 120.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000905 | 1 12 | 8.3 66.7 25.0 0.0 91.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000906 | 1 9 | 44.4 44.4 11.1 0.0 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000907 | 1 10 | 80.0 20.0 0.0 0.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000908 | 1 8 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000909 | 1 10 | 30.0 50.0 20.0 0.0 70.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000910 | 1 9 | 66.7 33.3 0.0 11.1 44.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000911 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000912 | 1 8 | 87.5 12.5 0.0 12.5 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000913 | 1 11 | 45.5 54.5 0.0 0.0 54.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000914 | 1 7 | 14.3 85.7 0.0 0.0 85.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000915 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000916 | 1 7 | 85.7 14.3 0.0 14.3 28.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000917 | 1 8 | 75.0 25.0 0.0 0.0 25.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000918 | 1 13 | 46.2 53.8 0.0 0.0 53.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000920 | 1 6 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000921 | 1 10 | 40.0 50.0 10.0 10.0 70.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000922 | 1 11 | 81.8 18.2 0.0 0.0 18.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000923 | 1 10 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000924 | 1 3 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000925 | 1 12 | 33.3 58.3 8.3 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000927 | 1 10 | 80.0 20.0 0.0 0.0 20.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000928 | 1 9 | 33.3 55.6 11.1 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000929 | 1 4 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000930 | 1 13 | 84.6 7.7 7.7 0.0 15.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000931 | 1 12 | 58.3 41.7 0.0 0.0 41.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000932 | 1 12 | 58.3 41.7 0.0 0.0 41.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000933 | 1 7 | 0.0 71.4 28.6 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000934 | 1 8 | 50.0 37.5 12.5 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000935 | 1 5 | 20.0 80.0 0.0 40.0 120.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000938 | 1 5 | 60.0 40.0 0.0 40.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000939 | 1 12 | 25.0 75.0 0.0 8.3 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000940 | 1 10 | 30.0 50.0 20.0 0.0 70.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000942 | 1 10 | 40.0 50.0 10.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000943 | 1 8 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000944 | 1 19 | 57.9 31.6 10.5 5.3 47.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000945 | 1 13 | 46.2 53.8 0.0 0.0 53.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000946 | 1 4 | 0.0 100.0 0.0 25.0 125.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000947 | 1 9 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000948 | 1 6 | 16.7 66.7 16.7 0.0 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000949 | 1 11 | 54.5 45.5 0.0 9.1 54.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000950 | 1 10 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000951 | 1 7 | 57.1 28.6 14.3 14.3 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000952 | 1 5 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000953 | 1 9 | 44.4 55.6 0.0 0.0 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000954 | 1 11 | 72.7 27.3 0.0 0.0 27.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000955 | 1 14 | 50.0 35.7 14.3 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000956 | 1 9 | 33.3 66.7 0.0 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000957 | 1 4 | 50.0 50.0 0.0 25.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000958 | 1 10 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000959 | 1 21 | 57.1 42.9 0.0 0.0 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000960 | 1 17 | 64.7 35.3 0.0 0.0 35.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000961 | 1 12 | 58.3 41.7 0.0 8.3 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000962 | 1 5 | 60.0 20.0 20.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000963 | 1 13 | 23.1 46.2 30.8 0.0 76.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000964 | 1 13 | 30.8 61.5 7.7 0.0 69.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000965 | 1 7 | 42.9 57.1 0.0 14.3 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000966 | 1 4 | 50.0 50.0 0.0 25.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000967 | 1 6 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000968 | 1 9 | 44.4 55.6 0.0 0.0 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000971 | 1 8 | 62.5 37.5 0.0 0.0 37.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000972 | 1 11 | 45.5 54.5 0.0 9.1 63.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000973 | 1 15 | 33.3 53.3 13.3 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000974 | 1 13 | 53.8 46.2 0.0 7.7 53.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000975 | 1 10 | 30.0 70.0 0.0 20.0 90.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000976 | 1 9 | 77.8 22.2 0.0 0.0 22.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000977 | 1 5 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000978 | 1 7 | 85.7 14.3 0.0 0.0 14.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000979 | 1 10 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000980 | 1 11 | 45.5 45.5 9.1 0.0 54.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000981 | 1 11 | 45.5 45.5 9.1 0.0 54.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000982 | 1 8 | 37.5 62.5 0.0 0.0 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000983 | 1 10 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000984 | 1 5 | 20.0 80.0 0.0 0.0 80.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000985 | 1 9 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000986 | 1 16 | 68.8 31.3 0.0 0.0 31.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000987 | 1 11 | 27.3 72.7 0.0 18.2 90.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000988 | 1 9 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000989 | 1 15 | 46.7 53.3 0.0 0.0 53.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000990 | 1 10 | 70.0 30.0 0.0 0.0 30.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000991 | 1 8 | 62.5 37.5 0.0 0.0 37.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000992 | 1 9 | 44.4 55.6 0.0 0.0 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000993 | 1 9 | 11.1 66.7 22.2 0.0 88.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000994 | 1 8 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000995 | 1 19 | 31.6 63.2 5.3 0.0 68.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000996 | 1 9 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000997 | 1 10 | 40.0 60.0 0.0 10.0 70.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000998 | 1 8 | 50.0 50.0 0.0 12.5 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000999 | 1 4 | 25.0 75.0 0.0 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001000 | 1 11 | 54.5 45.5 0.0 9.1 54.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001001 | 1 10 | 70.0 30.0 0.0 10.0 40.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001002 | 1 6 | 33.3 66.7 0.0 33.3 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001003 | 1 11 | 72.7 27.3 0.0 0.0 27.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001004 | 1 10 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000494 | 1 16 | 43.8 56.3 0.0 6.3 62.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000495 | 1 29 | 41.4 48.3 10.3 0.0 58.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000496 | 1 28 | 42.9 50.0 7.1 3.6 60.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000497 | 1 16 | 37.5 50.0 12.5 6.3 68.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000498 | 1 26 | 42.3 57.7 0.0 0.0 57.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000499 | 1 68 | 27.9 58.8 13.2 1.5 73.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000500 | 1 24 | 62.5 37.5 0.0 4.2 41.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000501 | 1 5 | 0.0 100.0 0.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000502 | 1 52 | 34.6 55.8 9.6 3.8 69.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000503 | 1 27 | 37.0 51.9 11.1 0.0 63.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000504 | 1 32 | 34.4 59.4 6.3 0.0 65.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000505 | 1 52 | 34.6 59.6 5.8 5.8 71.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000506 | 1 32 | 34.4 53.1 12.5 3.1 68.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000507 | 1 5 | 0.0 80.0 20.0 0.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000508 | 1 9 | 22.2 77.8 0.0 0.0 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000509 | 1 47 | 38.3 57.4 4.3 8.5 70.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000510 | 1 43 | 23.3 62.8 14.0 7.0 83.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000511 | 1 45 | 20.0 71.1 8.9 2.2 82.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000512 | 1 9 | 44.4 55.6 0.0 0.0 55.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000513 | 1 11 | 45.5 54.5 0.0 18.2 72.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000514 | 1 12 | 25.0 58.3 16.7 0.0 75.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000515 | 1 20 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000516 | 1 16 | 6.3 75.0 18.8 0.0 93.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000517 | 1 28 | 60.7 39.3 0.0 7.1 46.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000518 | 1 14 | 35.7 57.1 7.1 0.0 64.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000519 | 1 28 | 28.6 64.3 7.1 7.1 78.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000520 | 1 32 | 53.1 40.6 6.3 12.5 59.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000521 | 1 38 | 36.8 55.3 7.9 5.3 68.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000522 | 1 14 | 57.1 42.9 0.0 0.0 42.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000523 | 1 23 | 43.5 47.8 8.7 0.0 56.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000524 | 1 31 | 45.2 51.6 3.2 9.7 64.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000525 | 1 9 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000526 | 1 16 | 56.3 37.5 6.3 0.0 43.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000527 | 1 42 | 23.8 71.4 4.8 4.8 81.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000528 | 1 18 | 33.3 61.1 5.6 11.1 77.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000529 | 1 14 | 50.0 42.9 7.1 0.0 50.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000530 | 1 27 | 11.1 85.2 3.7 7.4 96.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000531 | 1 8 | 25.0 62.5 12.5 25.0 100.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000532 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000533 | 1 16 | 37.5 43.8 18.8 6.3 68.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000534 | 1 36 | 44.4 44.4 11.1 2.8 58.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000535 | 1 27 | 37.0 63.0 0.0 3.7 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000536 | 1 23 | 43.5 52.2 4.3 0.0 56.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000537 | 1 22 | 27.3 59.1 13.6 9.1 81.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000538 | 1 24 | 66.7 25.0 8.3 0.0 33.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000539 | 1 18 | 22.2 66.7 11.1 5.6 83.3 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000540 | 1 19 | 47.4 47.4 5.3 5.3 57.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000541 | 1 14 | 50.0 50.0 0.0 7.1 57.1 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000542 | 1 7 | 28.6 71.4 0.0 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000543 | 1 19 | 21.1 73.7 5.3 5.3 84.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000544 | 1 21 | 33.3 66.7 0.0 9.5 76.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000545 | 1 7 | 42.9 57.1 0.0 14.3 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000546 | 1 6 | 33.3 33.3 33.3 0.0 66.7 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000547 | 1 20 | 40.0 50.0 10.0 0.0 60.0 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000548 | 1 33 | 48.5 48.5 3.0 12.1 63.6 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000549 | 1 21 | 28.6 66.7 4.8 4.8 76.2 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000550 | 1 14 | 28.6 64.3 7.1 0.0 71.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000551 | 1 13 | 46.2 53.8 0.0 0.0 53.8 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000552 | 1 33 | 27.3 63.6 9.1 15.2 87.9 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000553 | 1 21 | 47.6 47.6 4.8 0.0 52.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000554 | 1 33 | 48.5 45.5 6.1 3.0 54.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000555 | 1 13 | 53.8 46.2 0.0 15.4 61.5 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000556 | 1 17 | 41.2 52.9 5.9 23.5 82.4 100.0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000557 | 1 10 | 70.0 30.0 0.0 0.0 30.0 100.0 | +|=================================================================================================================| +| Sum/Avg | 1092 11772 | 39.1 55.9 5.1 5.6 66.5 99.6 | +|=================================================================================================================| +| Mean | 1.1 11.6 | 31.2 65.2 3.7 10.8 79.6 99.6 | +| S.D. | 2.4 51.8 | 23.0 23.5 7.3 26.1 38.4 6.3 | +| Median | 1.0 7.0 | 33.3 63.6 0.0 0.0 72.2 100.0 | +`-----------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,-----------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/hyp.trn| +|-----------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000254 | 1 17 | 6 11 0 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000255 | 1 9 | 4 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000256 | 1 9 | 2 7 0 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000257 | 1 9 | 3 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000258 | 1 9 | 6 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000259 | 1 13 | 7 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000260 | 1 14 | 8 5 1 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000261 | 1 13 | 7 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000262 | 1 8 | 5 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000263 | 1 13 | 7 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000264 | 1 20 | 6 13 1 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000265 | 1 9 | 8 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000266 | 1 5 | 5 0 0 0 0 0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000267 | 1 5 | 2 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000268 | 1 9 | 7 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000269 | 1 7 | 1 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000270 | 1 13 | 8 5 0 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000271 | 1 17 | 8 9 0 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000272 | 1 20 | 15 4 1 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000273 | 1 18 | 10 8 0 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000274 | 1 5 | 3 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000275 | 1 14 | 6 8 0 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000276 | 1 18 | 10 8 0 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000277 | 1 11 | 10 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000278 | 1 8 | 5 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000279 | 1 5 | 4 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000280 | 1 11 | 8 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000281 | 1 8 | 4 4 0 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000282 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000283 | 1 14 | 7 7 0 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000284 | 1 20 | 9 11 0 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000285 | 1 19 | 8 11 0 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000286 | 1 11 | 7 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000287 | 1 9 | 5 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000288 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000289 | 1 9 | 5 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000290 | 1 10 | 7 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000291 | 1 11 | 6 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000292 | 1 9 | 3 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000293 | 1 16 | 6 10 0 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000294 | 1 10 | 2 7 1 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000295 | 1 11 | 4 7 0 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000296 | 1 14 | 5 9 0 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000297 | 1 10 | 4 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000298 | 1 13 | 10 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000299 | 1 13 | 8 5 0 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000300 | 1 13 | 8 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000301 | 1 15 | 10 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000302 | 1 10 | 4 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000303 | 1 11 | 3 8 0 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000304 | 1 10 | 6 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000305 | 1 15 | 8 7 0 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000306 | 1 11 | 5 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000307 | 1 17 | 9 8 0 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000308 | 1 12 | 4 8 0 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000309 | 1 7 | 4 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000310 | 1 6 | 5 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000311 | 1 18 | 9 9 0 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000312 | 1 16 | 9 7 0 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000313 | 1 12 | 8 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000314 | 1 7 | 4 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000315 | 1 9 | 1 7 1 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000316 | 1 16 | 9 7 0 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000317 | 1 15 | 5 8 2 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000318 | 1 18 | 10 8 0 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000319 | 1 15 | 9 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000320 | 1 19 | 10 9 0 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000321 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000322 | 1 10 | 2 8 0 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000323 | 1 13 | 2 11 0 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000324 | 1 18 | 6 9 3 1 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000325 | 1 8 | 6 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000326 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000327 | 1 15 | 7 6 2 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000328 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000329 | 1 19 | 7 11 1 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000330 | 1 10 | 6 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000331 | 1 5 | 4 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000332 | 1 18 | 6 9 3 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000333 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000334 | 1 18 | 6 12 0 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000335 | 1 14 | 7 7 0 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000336 | 1 15 | 4 11 0 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000337 | 1 20 | 7 11 2 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000338 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000339 | 1 11 | 5 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000340 | 1 11 | 4 7 0 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000341 | 1 12 | 4 7 1 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000342 | 1 14 | 6 7 1 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000343 | 1 18 | 9 9 0 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000344 | 1 18 | 9 9 0 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000345 | 1 11 | 3 8 0 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000346 | 1 15 | 6 9 0 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000347 | 1 15 | 7 7 1 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000348 | 1 10 | 3 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000349 | 1 16 | 6 8 2 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000350 | 1 12 | 4 8 0 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000351 | 1 18 | 8 9 1 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000352 | 1 7 | 4 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000353 | 1 12 | 5 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000354 | 1 11 | 4 7 0 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000355 | 1 13 | 6 7 0 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000356 | 1 7 | 2 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000357 | 1 15 | 7 8 0 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000358 | 1 9 | 5 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000359 | 1 9 | 4 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000360 | 1 10 | 4 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000361 | 1 7 | 2 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000362 | 1 17 | 7 9 1 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000363 | 1 14 | 4 9 1 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000364 | 1 12 | 8 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000365 | 1 5 | 3 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000366 | 1 11 | 4 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000367 | 1 10 | 4 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000368 | 1 9 | 4 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000369 | 1 17 | 6 10 1 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000370 | 1 14 | 7 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000371 | 1 11 | 7 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000372 | 1 15 | 9 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000373 | 1 14 | 5 9 0 2 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000374 | 1 6 | 6 0 0 0 0 0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000375 | 1 10 | 2 8 0 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| lad_eng_000376 | 1 14 | 4 10 0 2 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| m | 77 1634 | 798 750 86 40 876 77 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000707 | 1 9 | 0 9 0 3 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000708 | 1 10 | 4 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000709 | 1 9 | 3 5 1 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000710 | 1 11 | 2 7 2 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000711 | 1 14 | 2 11 1 2 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000712 | 1 14 | 5 9 0 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000713 | 1 13 | 4 8 1 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000714 | 1 15 | 1 14 0 1 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000715 | 1 2 | 1 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000716 | 1 7 | 1 6 0 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000717 | 1 12 | 4 8 0 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000718 | 1 6 | 3 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000719 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000720 | 1 11 | 4 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000721 | 1 8 | 4 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000722 | 1 5 | 1 4 0 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000723 | 1 12 | 3 9 0 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000724 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000725 | 1 14 | 5 6 3 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000726 | 1 13 | 7 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000727 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000728 | 1 7 | 3 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000729 | 1 12 | 4 7 1 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000730 | 1 13 | 1 11 1 3 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000731 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000732 | 1 14 | 6 7 1 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000733 | 1 11 | 2 9 0 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000734 | 1 11 | 1 9 1 2 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000735 | 1 13 | 6 7 0 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000736 | 1 11 | 4 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000737 | 1 1 | 0 1 0 4 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000738 | 1 14 | 4 10 0 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000739 | 1 9 | 2 7 0 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000740 | 1 8 | 4 4 0 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000741 | 1 6 | 4 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000742 | 1 12 | 2 9 1 3 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000743 | 1 15 | 9 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000744 | 1 7 | 1 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000745 | 1 11 | 1 10 0 5 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000746 | 1 9 | 3 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000747 | 1 7 | 2 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000748 | 1 1 | 0 1 0 3 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000749 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000750 | 1 13 | 3 10 0 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000751 | 1 9 | 3 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000752 | 1 12 | 5 7 0 6 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000753 | 1 11 | 1 10 0 6 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000754 | 1 8 | 0 8 0 5 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000755 | 1 5 | 2 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000756 | 1 10 | 4 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000757 | 1 11 | 3 7 1 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000758 | 1 5 | 3 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000759 | 1 8 | 2 5 1 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000760 | 1 11 | 2 8 1 3 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000761 | 1 6 | 3 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000762 | 1 12 | 4 7 1 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000763 | 1 10 | 6 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000764 | 1 9 | 2 5 2 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000765 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000766 | 1 8 | 4 4 0 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000767 | 1 12 | 7 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000768 | 1 5 | 1 4 0 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000769 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000770 | 1 17 | 10 7 0 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000771 | 1 10 | 2 8 0 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000772 | 1 7 | 0 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000773 | 1 14 | 5 8 1 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000774 | 1 12 | 5 7 0 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000775 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000776 | 1 12 | 5 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000777 | 1 14 | 4 10 0 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000778 | 1 14 | 3 11 0 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000779 | 1 14 | 3 10 1 7 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000780 | 1 12 | 7 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000781 | 1 14 | 3 8 3 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000782 | 1 1 | 0 1 0 2 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000783 | 1 7 | 1 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000784 | 1 9 | 1 8 0 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000785 | 1 6 | 2 3 1 3 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000786 | 1 11 | 5 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000787 | 1 14 | 5 9 0 3 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000788 | 1 5 | 2 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000789 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000790 | 1 6 | 3 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000791 | 1 7 | 2 4 1 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000792 | 1 12 | 2 9 1 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000793 | 1 7 | 0 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000794 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000795 | 1 7 | 3 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000796 | 1 7 | 0 4 3 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000797 | 1 9 | 5 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000798 | 1 9 | 2 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000799 | 1 7 | 3 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000800 | 1 4 | 0 4 0 5 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000801 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000802 | 1 8 | 4 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000803 | 1 11 | 3 7 1 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000804 | 1 6 | 4 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000805 | 1 12 | 4 8 0 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000806 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000807 | 1 14 | 3 11 0 3 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000808 | 1 13 | 6 7 0 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| cv_eng_000809 | 1 10 | 4 5 1 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000413 | 1 33 | 16 16 1 1 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000414 | 1 37 | 19 18 0 2 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000415 | 1 18 | 8 10 0 3 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000416 | 1 22 | 7 15 0 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000417 | 1 25 | 6 16 3 0 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000418 | 1 13 | 6 7 0 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000419 | 1 9 | 5 4 0 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000420 | 1 14 | 4 7 3 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000421 | 1 22 | 11 11 0 2 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000422 | 1 21 | 9 11 1 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000423 | 1 26 | 12 12 2 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000424 | 1 17 | 7 10 0 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000425 | 1 18 | 8 8 2 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000426 | 1 17 | 6 10 1 3 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000427 | 1 33 | 11 18 4 0 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000428 | 1 22 | 7 14 1 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000429 | 1 22 | 14 8 0 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000430 | 1 19 | 10 9 0 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000431 | 1 16 | 3 13 0 2 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000432 | 1 15 | 5 10 0 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000433 | 1 13 | 3 10 0 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000434 | 1 11 | 2 8 1 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000435 | 1 22 | 11 11 0 1 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000436 | 1 18 | 5 13 0 2 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000437 | 1 22 | 8 14 0 3 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000438 | 1 30 | 16 13 1 2 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000439 | 1 12 | 4 8 0 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000440 | 1 30 | 13 16 1 0 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000441 | 1 9 | 4 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000442 | 1 22 | 6 11 5 1 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000443 | 1 32 | 10 21 1 0 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000444 | 1 22 | 8 11 3 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000445 | 1 10 | 1 8 1 3 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000446 | 1 15 | 5 9 1 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000447 | 1 18 | 7 9 2 2 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000448 | 1 32 | 15 14 3 1 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000449 | 1 25 | 12 13 0 4 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000450 | 1 32 | 10 21 1 0 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000451 | 1 14 | 1 13 0 4 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000452 | 1 29 | 7 21 1 1 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000453 | 1 22 | 6 14 2 0 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000454 | 1 22 | 5 16 1 5 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000455 | 1 17 | 6 10 1 5 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000456 | 1 9 | 6 3 0 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000457 | 1 24 | 3 13 8 1 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000458 | 1 15 | 6 9 0 2 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000459 | 1 35 | 13 19 3 1 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000460 | 1 17 | 4 13 0 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000461 | 1 15 | 5 10 0 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000462 | 1 15 | 9 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000463 | 1 18 | 4 12 2 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000464 | 1 12 | 2 10 0 2 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000465 | 1 26 | 15 11 0 1 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000466 | 1 27 | 6 19 2 2 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000467 | 1 16 | 4 12 0 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000468 | 1 46 | 7 26 13 0 39 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000469 | 1 31 | 11 18 2 10 30 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000470 | 1 31 | 10 21 0 1 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000471 | 1 25 | 10 15 0 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000472 | 1 29 | 4 21 4 1 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000473 | 1 25 | 8 16 1 0 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000474 | 1 28 | 15 13 0 2 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000475 | 1 28 | 11 12 5 0 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| fleurs_eng_000476 | 1 27 | 12 14 1 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000283 | 1 38 | 16 20 2 1 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000284 | 1 31 | 11 19 1 1 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000285 | 1 35 | 7 22 6 1 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000286 | 1 42 | 21 21 0 0 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000287 | 1 42 | 13 25 4 0 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000288 | 1 24 | 10 13 1 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000289 | 1 39 | 23 16 0 1 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000290 | 1 35 | 11 21 3 0 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000291 | 1 33 | 20 12 1 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000292 | 1 36 | 13 20 3 0 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000293 | 1 23 | 9 12 2 1 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000294 | 1 42 | 11 26 5 2 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000295 | 1 26 | 12 13 1 2 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000296 | 1 28 | 7 20 1 3 24 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000297 | 1 47 | 19 25 3 0 28 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000298 | 1 19 | 5 14 0 6 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000299 | 1 51 | 12 36 3 0 39 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000300 | 1 53 | 15 21 17 1 39 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000301 | 1 44 | 21 22 1 0 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000302 | 1 27 | 10 14 3 0 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000303 | 1 49 | 23 22 4 0 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000304 | 1 54 | 27 24 3 0 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000305 | 1 49 | 32 16 1 1 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000306 | 1 30 | 11 14 5 1 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000307 | 1 46 | 21 22 3 0 25 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000308 | 1 26 | 15 10 1 1 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000309 | 1 36 | 18 16 2 1 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000310 | 1 35 | 8 22 5 0 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000311 | 1 37 | 16 20 1 1 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000312 | 1 42 | 14 25 3 2 30 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000313 | 1 35 | 17 18 0 5 23 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000314 | 1 29 | 17 12 0 2 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000315 | 1 29 | 16 11 2 1 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000316 | 1 45 | 25 18 2 0 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000317 | 1 49 | 20 25 4 1 30 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000318 | 1 36 | 18 15 3 1 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000319 | 1 34 | 11 21 2 4 27 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000320 | 1 37 | 19 15 3 0 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000321 | 1 42 | 24 17 1 0 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| mls_eng_000322 | 1 39 | 18 17 4 0 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001588 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001589 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001590 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001591 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001592 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001593 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001594 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001595 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001596 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001597 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001598 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001599 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001600 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001601 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001602 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001603 | 1 3 | 2 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001604 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001605 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001606 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001607 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001608 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001609 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001610 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001611 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001612 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001613 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001614 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001615 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001616 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001617 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001618 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001619 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001620 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001621 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001622 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001623 | 1 2 | 0 2 0 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001624 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001625 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001626 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001627 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001628 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001629 | 1 3 | 2 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001630 | 1 3 | 2 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001631 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001632 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001633 | 1 1 | 1 0 0 1 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001634 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001635 | 1 4 | 2 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001636 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001637 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001638 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001639 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001640 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001641 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001642 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001643 | 1 2 | 0 2 0 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001644 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001645 | 1 3 | 2 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001646 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001647 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001648 | 1 5 | 1 4 0 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001649 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001650 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001651 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001652 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001653 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001654 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001655 | 1 3 | 2 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001656 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001657 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001658 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001659 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001660 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001661 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001662 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001663 | 1 2 | 1 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001664 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001665 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001666 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001667 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001668 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001669 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001670 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001671 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001672 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001673 | 1 2 | 2 0 0 3 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001674 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001675 | 1 1 | 1 0 0 0 0 0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001676 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001677 | 1 3 | 2 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001678 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001679 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001680 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001681 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001682 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001683 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001684 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001685 | 1 3 | 2 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001686 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001687 | 1 2 | 1 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001688 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001689 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001690 | 1 2 | 1 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001691 | 1 1 | 1 0 0 0 0 0 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001692 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001693 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001694 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001695 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001696 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001697 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001698 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001699 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001700 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001701 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001702 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001703 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001704 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001705 | 1 3 | 2 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001706 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001707 | 1 5 | 3 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001708 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001709 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001710 | 1 3 | 2 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001711 | 1 2 | 0 2 0 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001712 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001713 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001714 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001715 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001716 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001717 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001718 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001719 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001720 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001721 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001722 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001723 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001724 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001725 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001726 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001727 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001728 | 1 3 | 2 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001729 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001730 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001731 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001732 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001733 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001734 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001735 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001736 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001737 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001738 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001739 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001740 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001741 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001742 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001743 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001744 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001745 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001746 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001747 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001748 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001749 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001750 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001751 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001752 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001753 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001754 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001755 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001756 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001757 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001758 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001759 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001760 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001761 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001762 | 1 1 | 0 1 0 2 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001763 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001764 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001765 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001766 | 1 3 | 1 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001767 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001768 | 1 6 | 2 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001769 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001770 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001771 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001772 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001773 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001774 | 1 3 | 1 2 0 0 2 1 | 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+|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001787 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001788 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001789 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001790 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001791 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001792 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001793 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001794 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001795 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001796 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001797 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001798 | 1 3 | 0 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001799 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001800 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001801 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001802 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001803 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001804 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001805 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001806 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001807 | 1 4 | 2 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001808 | 1 3 | 1 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001809 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001810 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001811 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001812 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001813 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001814 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001815 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001816 | 1 6 | 2 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001817 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001818 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001819 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001820 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001821 | 1 3 | 2 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001822 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001823 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| nchlt_eng_001824 | 1 4 | 1 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001744 | 1 7 | 3 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001745 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001746 | 1 10 | 6 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001747 | 1 11 | 4 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001748 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001749 | 1 2 | 1 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001750 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001751 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001752 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001753 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001754 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001755 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001756 | 1 4 | 3 1 0 2 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001757 | 1 8 | 1 7 0 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001758 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001759 | 1 7 | 1 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001760 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001761 | 1 8 | 2 6 0 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001762 | 1 4 | 2 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001763 | 1 15 | 6 9 0 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001764 | 1 7 | 1 3 3 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001765 | 1 7 | 2 5 0 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001766 | 1 4 | 2 1 1 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001767 | 1 7 | 2 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001768 | 1 5 | 2 3 0 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001769 | 1 8 | 4 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001770 | 1 5 | 1 4 0 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001771 | 1 4 | 2 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001772 | 1 5 | 3 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001773 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001774 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001775 | 1 10 | 4 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001776 | 1 8 | 1 7 0 4 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001777 | 1 12 | 4 8 0 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001778 | 1 6 | 3 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001779 | 1 7 | 1 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001780 | 1 9 | 3 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001781 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001782 | 1 13 | 3 9 1 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001783 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001784 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001785 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001786 | 1 18 | 6 11 1 2 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001787 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001788 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001789 | 1 5 | 2 2 1 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001790 | 1 10 | 2 7 1 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001791 | 1 7 | 2 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001792 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001793 | 1 6 | 3 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001794 | 1 7 | 1 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001795 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001796 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001797 | 1 9 | 4 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001798 | 1 4 | 1 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001799 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001800 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001801 | 1 10 | 5 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001802 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001803 | 1 7 | 1 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001804 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001805 | 1 7 | 2 3 2 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001806 | 1 3 | 1 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001807 | 1 4 | 1 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001808 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001809 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001810 | 1 3 | 0 3 0 2 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001811 | 1 8 | 3 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001812 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001813 | 1 5 | 3 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001814 | 1 7 | 1 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001815 | 1 6 | 3 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001816 | 1 8 | 0 8 0 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001817 | 1 9 | 3 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001818 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001819 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001820 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001821 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001822 | 1 4 | 3 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001823 | 1 15 | 10 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001824 | 1 7 | 2 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001825 | 1 10 | 3 7 0 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001826 | 1 7 | 2 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001827 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001828 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001829 | 1 8 | 1 3 4 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001830 | 1 9 | 5 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001831 | 1 3 | 2 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001832 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001833 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001834 | 1 6 | 1 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001835 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001836 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001837 | 1 5 | 0 5 0 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001838 | 1 7 | 2 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001839 | 1 8 | 6 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001840 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001841 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001842 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001843 | 1 6 | 3 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001844 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001845 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001846 | 1 15 | 7 8 0 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001847 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001848 | 1 8 | 2 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001849 | 1 10 | 4 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001850 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001851 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001852 | 1 12 | 6 4 2 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001853 | 1 6 | 3 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001854 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001855 | 1 8 | 4 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001856 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001857 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001858 | 1 8 | 3 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001859 | 1 2 | 1 1 0 2 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001860 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001861 | 1 1 | 0 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001862 | 1 12 | 7 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001863 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001864 | 1 1 | 0 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001865 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001866 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001867 | 1 9 | 3 4 2 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001868 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001869 | 1 4 | 1 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001870 | 1 6 | 2 3 1 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001871 | 1 14 | 8 5 1 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001872 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001873 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001874 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001875 | 1 5 | 0 5 0 3 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001876 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001877 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001878 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001879 | 1 24 | 9 15 0 2 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001880 | 1 10 | 4 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001881 | 1 19 | 10 8 1 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001882 | 1 7 | 4 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001883 | 1 9 | 5 4 0 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001884 | 1 7 | 3 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001885 | 1 7 | 1 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001886 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001887 | 1 6 | 0 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001888 | 1 6 | 0 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001889 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001890 | 1 2 | 2 0 0 1 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001891 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001892 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001893 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001894 | 1 6 | 0 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001895 | 1 6 | 3 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001896 | 1 6 | 4 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001897 | 1 2 | 1 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001898 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001899 | 1 10 | 6 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001900 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001901 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001902 | 1 8 | 2 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001903 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001904 | 1 8 | 2 5 1 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001905 | 1 6 | 0 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001906 | 1 13 | 4 8 1 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001907 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001908 | 1 6 | 3 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001909 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001910 | 1 7 | 3 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001911 | 1 6 | 3 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001912 | 1 4 | 2 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001913 | 1 4 | 2 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001914 | 1 8 | 2 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001915 | 1 9 | 0 6 3 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001916 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001917 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001918 | 1 9 | 4 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001919 | 1 8 | 2 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001920 | 1 6 | 3 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001921 | 1 4 | 1 2 1 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001922 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001923 | 1 21 | 7 13 1 0 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001924 | 1 10 | 2 8 0 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001925 | 1 8 | 3 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001926 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001927 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001928 | 1 4 | 3 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001929 | 1 5 | 3 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001930 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001931 | 1 3 | 2 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001932 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001933 | 1 4 | 2 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001934 | 1 7 | 4 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001935 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001936 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001937 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001938 | 1 12 | 3 8 1 2 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001939 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001940 | 1 6 | 1 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001941 | 1 6 | 2 3 1 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001942 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001943 | 1 8 | 2 5 1 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001944 | 1 8 | 4 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001945 | 1 8 | 1 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001946 | 1 20 | 10 9 1 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001947 | 1 5 | 2 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001948 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001949 | 1 5 | 2 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001950 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001951 | 1 6 | 3 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001952 | 1 9 | 4 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001953 | 1 4 | 0 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001954 | 1 3 | 0 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001955 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001956 | 1 7 | 3 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001957 | 1 9 | 2 5 2 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001958 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001959 | 1 7 | 4 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001960 | 1 7 | 1 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001961 | 1 7 | 2 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001962 | 1 1 | 0 1 0 2 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001963 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001964 | 1 2 | 0 2 0 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001965 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001966 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001967 | 1 10 | 6 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001968 | 1 9 | 2 7 0 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001969 | 1 10 | 3 7 0 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001970 | 1 6 | 0 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001971 | 1 2 | 1 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001972 | 1 13 | 6 5 2 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001973 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001974 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001975 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001976 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001977 | 1 4 | 2 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001978 | 1 2 | 0 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001979 | 1 12 | 3 6 3 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001980 | 1 8 | 1 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001981 | 1 6 | 2 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001982 | 1 2 | 0 2 0 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001983 | 1 10 | 3 7 0 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001984 | 1 5 | 2 2 1 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001985 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001986 | 1 10 | 3 6 1 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001987 | 1 2 | 0 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001988 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001989 | 1 10 | 5 3 2 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001990 | 1 6 | 4 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001991 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001992 | 1 2 | 1 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001993 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001994 | 1 6 | 4 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001995 | 1 4 | 1 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001996 | 1 5 | 1 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001997 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001998 | 1 7 | 3 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_001999 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002000 | 1 15 | 5 9 1 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002001 | 1 4 | 1 2 1 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002002 | 1 3 | 0 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002003 | 1 4 | 2 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002004 | 1 5 | 1 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| swc_eng_002005 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000874 | 1 9 | 4 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000875 | 1 4 | 3 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000876 | 1 8 | 5 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000877 | 1 9 | 4 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000878 | 1 7 | 5 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000879 | 1 8 | 5 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000880 | 1 7 | 3 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000883 | 1 5 | 1 4 0 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000884 | 1 12 | 10 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000885 | 1 9 | 5 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000886 | 1 11 | 4 6 1 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000887 | 1 12 | 2 10 0 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000888 | 1 8 | 5 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000889 | 1 6 | 3 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000890 | 1 6 | 2 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000891 | 1 15 | 7 7 1 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000892 | 1 5 | 4 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000893 | 1 5 | 2 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000894 | 1 17 | 12 5 0 2 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000895 | 1 11 | 4 7 0 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000896 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000897 | 1 8 | 7 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000898 | 1 9 | 5 3 1 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000899 | 1 5 | 3 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000900 | 1 11 | 3 7 1 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000901 | 1 6 | 0 6 0 5 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000902 | 1 5 | 1 3 1 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000903 | 1 9 | 6 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000904 | 1 5 | 0 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000905 | 1 12 | 1 8 3 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000906 | 1 9 | 4 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000907 | 1 10 | 8 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000908 | 1 8 | 4 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000909 | 1 10 | 3 5 2 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000910 | 1 9 | 6 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000911 | 1 4 | 2 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000912 | 1 8 | 7 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000913 | 1 11 | 5 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000914 | 1 7 | 1 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000915 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000916 | 1 7 | 6 1 0 1 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000917 | 1 8 | 6 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000918 | 1 13 | 6 7 0 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000920 | 1 6 | 4 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000921 | 1 10 | 4 5 1 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000922 | 1 11 | 9 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000923 | 1 10 | 6 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000924 | 1 3 | 1 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000925 | 1 12 | 4 7 1 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000927 | 1 10 | 8 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000928 | 1 9 | 3 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000929 | 1 4 | 0 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000930 | 1 13 | 11 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000931 | 1 12 | 7 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000932 | 1 12 | 7 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000933 | 1 7 | 0 5 2 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000934 | 1 8 | 4 3 1 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000935 | 1 5 | 1 4 0 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000938 | 1 5 | 3 2 0 2 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000939 | 1 12 | 3 9 0 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000940 | 1 10 | 3 5 2 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000942 | 1 10 | 4 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000943 | 1 8 | 4 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000944 | 1 19 | 11 6 2 1 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000945 | 1 13 | 6 7 0 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000946 | 1 4 | 0 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000947 | 1 9 | 3 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000948 | 1 6 | 1 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000949 | 1 11 | 6 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000950 | 1 10 | 0 8 2 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000951 | 1 7 | 4 2 1 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000952 | 1 5 | 3 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000953 | 1 9 | 4 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000954 | 1 11 | 8 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000955 | 1 14 | 7 5 2 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000956 | 1 9 | 3 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000957 | 1 4 | 2 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000958 | 1 10 | 6 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000959 | 1 21 | 12 9 0 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000960 | 1 17 | 11 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000961 | 1 12 | 7 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000962 | 1 5 | 3 1 1 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000963 | 1 13 | 3 6 4 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000964 | 1 13 | 4 8 1 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000965 | 1 7 | 3 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000966 | 1 4 | 2 2 0 1 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000967 | 1 6 | 0 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000968 | 1 9 | 4 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000971 | 1 8 | 5 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000972 | 1 11 | 5 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000973 | 1 15 | 5 8 2 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000974 | 1 13 | 7 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000975 | 1 10 | 3 7 0 2 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000976 | 1 9 | 7 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000977 | 1 5 | 3 2 0 0 2 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000978 | 1 7 | 6 1 0 0 1 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000979 | 1 10 | 5 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000980 | 1 11 | 5 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000981 | 1 11 | 5 5 1 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000982 | 1 8 | 3 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000983 | 1 10 | 5 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000984 | 1 5 | 1 4 0 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000985 | 1 9 | 6 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000986 | 1 16 | 11 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000987 | 1 11 | 3 8 0 2 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000988 | 1 9 | 6 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000989 | 1 15 | 7 8 0 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000990 | 1 10 | 7 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000991 | 1 8 | 5 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000992 | 1 9 | 4 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000993 | 1 9 | 1 6 2 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000994 | 1 8 | 0 8 0 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000995 | 1 19 | 6 12 1 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000996 | 1 9 | 6 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000997 | 1 10 | 4 6 0 1 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000998 | 1 8 | 4 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_000999 | 1 4 | 1 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001000 | 1 11 | 6 5 0 1 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001001 | 1 10 | 7 3 0 1 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001002 | 1 6 | 2 4 0 2 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001003 | 1 11 | 8 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxforge_eng_001004 | 1 10 | 5 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000494 | 1 16 | 7 9 0 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000495 | 1 29 | 12 14 3 0 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000496 | 1 28 | 12 14 2 1 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000497 | 1 16 | 6 8 2 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000498 | 1 26 | 11 15 0 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000499 | 1 68 | 19 40 9 1 50 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000500 | 1 24 | 15 9 0 1 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000501 | 1 5 | 0 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000502 | 1 52 | 18 29 5 2 36 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000503 | 1 27 | 10 14 3 0 17 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000504 | 1 32 | 11 19 2 0 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000505 | 1 52 | 18 31 3 3 37 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000506 | 1 32 | 11 17 4 1 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000507 | 1 5 | 0 4 1 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000508 | 1 9 | 2 7 0 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000509 | 1 47 | 18 27 2 4 33 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000510 | 1 43 | 10 27 6 3 36 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000511 | 1 45 | 9 32 4 1 37 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000512 | 1 9 | 4 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000513 | 1 11 | 5 6 0 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000514 | 1 12 | 3 7 2 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000515 | 1 20 | 10 10 0 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000516 | 1 16 | 1 12 3 0 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000517 | 1 28 | 17 11 0 2 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000518 | 1 14 | 5 8 1 0 9 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000519 | 1 28 | 8 18 2 2 22 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000520 | 1 32 | 17 13 2 4 19 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000521 | 1 38 | 14 21 3 2 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000522 | 1 14 | 8 6 0 0 6 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000523 | 1 23 | 10 11 2 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000524 | 1 31 | 14 16 1 3 20 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000525 | 1 9 | 6 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000526 | 1 16 | 9 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000527 | 1 42 | 10 30 2 2 34 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000528 | 1 18 | 6 11 1 2 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000529 | 1 14 | 7 6 1 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000530 | 1 27 | 3 23 1 2 26 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000531 | 1 8 | 2 5 1 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000532 | 1 5 | 2 3 0 0 3 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000533 | 1 16 | 6 7 3 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000534 | 1 36 | 16 16 4 1 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000535 | 1 27 | 10 17 0 1 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000536 | 1 23 | 10 12 1 0 13 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000537 | 1 22 | 6 13 3 2 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000538 | 1 24 | 16 6 2 0 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000539 | 1 18 | 4 12 2 1 15 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000540 | 1 19 | 9 9 1 1 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000541 | 1 14 | 7 7 0 1 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000542 | 1 7 | 2 5 0 0 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000543 | 1 19 | 4 14 1 1 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000544 | 1 21 | 7 14 0 2 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000545 | 1 7 | 3 4 0 1 5 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000546 | 1 6 | 2 2 2 0 4 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000547 | 1 20 | 8 10 2 0 12 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000548 | 1 33 | 16 16 1 4 21 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000549 | 1 21 | 6 14 1 1 16 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000550 | 1 14 | 4 9 1 0 10 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000551 | 1 13 | 6 7 0 0 7 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000552 | 1 33 | 9 21 3 5 29 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000553 | 1 21 | 10 10 1 0 11 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000554 | 1 33 | 16 15 2 1 18 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000555 | 1 13 | 7 6 0 2 8 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000556 | 1 17 | 7 9 1 4 14 1 | +|--------------------------+---------------------+----------------------------------------------------------------| +| voxpopuli_eng_000557 | 1 10 | 7 3 0 0 3 1 | +|=================================================================================================================| +| Sum | 1092 11772 | 4600 6577 595 656 7828 1088 | +|=================================================================================================================| +| Mean | 1.1 11.6 | 4.5 6.5 0.6 0.6 7.7 1.1 | +| S.D. | 2.4 51.8 | 25.3 23.9 2.9 1.6 28.0 2.4 | +| Median | 1.0 7.0 | 2.0 4.0 0.0 0.0 5.0 1.0 | +`-----------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/score_wer/hyp.trn + +Speakers: + 0: lad_eng_000254 + 1: lad_eng_000255 + 2: lad_eng_000256 + 3: lad_eng_000257 + 4: lad_eng_000258 + 5: lad_eng_000259 + 6: lad_eng_000260 + 7: lad_eng_000261 + 8: lad_eng_000262 + 9: lad_eng_000263 + 10: lad_eng_000264 + 11: lad_eng_000265 + 12: lad_eng_000266 + 13: lad_eng_000267 + 14: lad_eng_000268 + 15: lad_eng_000269 + 16: lad_eng_000270 + 17: lad_eng_000271 + 18: lad_eng_000272 + 19: lad_eng_000273 + 20: lad_eng_000274 + 21: lad_eng_000275 + 22: lad_eng_000276 + 23: lad_eng_000277 + 24: lad_eng_000278 + 25: lad_eng_000279 + 26: lad_eng_000280 + 27: lad_eng_000281 + 28: lad_eng_000282 + 29: lad_eng_000283 + 30: lad_eng_000284 + 31: lad_eng_000285 + 32: lad_eng_000286 + 33: lad_eng_000287 + 34: lad_eng_000288 + 35: lad_eng_000289 + 36: lad_eng_000290 + 37: lad_eng_000291 + 38: lad_eng_000292 + 39: lad_eng_000293 + 40: lad_eng_000294 + 41: lad_eng_000295 + 42: lad_eng_000296 + 43: lad_eng_000297 + 44: lad_eng_000298 + 45: lad_eng_000299 + 46: lad_eng_000300 + 47: lad_eng_000301 + 48: lad_eng_000302 + 49: lad_eng_000303 + 50: lad_eng_000304 + 51: lad_eng_000305 + 52: lad_eng_000306 + 53: lad_eng_000307 + 54: lad_eng_000308 + 55: lad_eng_000309 + 56: lad_eng_000310 + 57: lad_eng_000311 + 58: lad_eng_000312 + 59: lad_eng_000313 + 60: lad_eng_000314 + 61: lad_eng_000315 + 62: lad_eng_000316 + 63: lad_eng_000317 + 64: lad_eng_000318 + 65: lad_eng_000319 + 66: lad_eng_000320 + 67: lad_eng_000321 + 68: lad_eng_000322 + 69: lad_eng_000323 + 70: lad_eng_000324 + 71: lad_eng_000325 + 72: lad_eng_000326 + 73: lad_eng_000327 + 74: lad_eng_000328 + 75: lad_eng_000329 + 76: lad_eng_000330 + 77: lad_eng_000331 + 78: lad_eng_000332 + 79: lad_eng_000333 + 80: lad_eng_000334 + 81: lad_eng_000335 + 82: lad_eng_000336 + 83: lad_eng_000337 + 84: lad_eng_000338 + 85: lad_eng_000339 + 86: lad_eng_000340 + 87: lad_eng_000341 + 88: lad_eng_000342 + 89: lad_eng_000343 + 90: lad_eng_000344 + 91: lad_eng_000345 + 92: lad_eng_000346 + 93: lad_eng_000347 + 94: lad_eng_000348 + 95: lad_eng_000349 + 96: lad_eng_000350 + 97: lad_eng_000351 + 98: lad_eng_000352 + 99: lad_eng_000353 + 100: lad_eng_000354 + 101: lad_eng_000355 + 102: lad_eng_000356 + 103: lad_eng_000357 + 104: lad_eng_000358 + 105: lad_eng_000359 + 106: lad_eng_000360 + 107: 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372: nchlt_eng_001629 + 373: nchlt_eng_001630 + 374: nchlt_eng_001631 + 375: nchlt_eng_001632 + 376: nchlt_eng_001633 + 377: nchlt_eng_001634 + 378: nchlt_eng_001635 + 379: nchlt_eng_001636 + 380: nchlt_eng_001637 + 381: nchlt_eng_001638 + 382: nchlt_eng_001639 + 383: nchlt_eng_001640 + 384: nchlt_eng_001641 + 385: nchlt_eng_001642 + 386: nchlt_eng_001643 + 387: nchlt_eng_001644 + 388: nchlt_eng_001645 + 389: nchlt_eng_001646 + 390: nchlt_eng_001647 + 391: nchlt_eng_001648 + 392: nchlt_eng_001649 + 393: nchlt_eng_001650 + 394: nchlt_eng_001651 + 395: nchlt_eng_001652 + 396: nchlt_eng_001653 + 397: nchlt_eng_001654 + 398: nchlt_eng_001655 + 399: nchlt_eng_001656 + 400: nchlt_eng_001657 + 401: nchlt_eng_001658 + 402: nchlt_eng_001659 + 403: nchlt_eng_001660 + 404: nchlt_eng_001661 + 405: nchlt_eng_001662 + 406: nchlt_eng_001663 + 407: nchlt_eng_001664 + 408: nchlt_eng_001665 + 409: nchlt_eng_001666 + 410: nchlt_eng_001667 + 411: nchlt_eng_001668 + 412: nchlt_eng_001669 + 413: nchlt_eng_001670 + 414: nchlt_eng_001671 + 415: nchlt_eng_001672 + 416: nchlt_eng_001673 + 417: nchlt_eng_001674 + 418: nchlt_eng_001675 + 419: nchlt_eng_001676 + 420: nchlt_eng_001677 + 421: nchlt_eng_001678 + 422: nchlt_eng_001679 + 423: nchlt_eng_001680 + 424: nchlt_eng_001681 + 425: nchlt_eng_001682 + 426: nchlt_eng_001683 + 427: nchlt_eng_001684 + 428: nchlt_eng_001685 + 429: nchlt_eng_001686 + 430: nchlt_eng_001687 + 431: nchlt_eng_001688 + 432: nchlt_eng_001689 + 433: nchlt_eng_001690 + 434: nchlt_eng_001691 + 435: nchlt_eng_001692 + 436: nchlt_eng_001693 + 437: nchlt_eng_001694 + 438: nchlt_eng_001695 + 439: nchlt_eng_001696 + 440: nchlt_eng_001697 + 441: nchlt_eng_001698 + 442: nchlt_eng_001699 + 443: nchlt_eng_001700 + 444: nchlt_eng_001701 + 445: nchlt_eng_001702 + 446: nchlt_eng_001703 + 447: nchlt_eng_001704 + 448: nchlt_eng_001705 + 449: nchlt_eng_001706 + 450: nchlt_eng_001707 + 451: nchlt_eng_001708 + 452: nchlt_eng_001709 + 453: nchlt_eng_001710 + 454: nchlt_eng_001711 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nchlt_eng_001753 + 497: nchlt_eng_001754 + 498: nchlt_eng_001755 + 499: nchlt_eng_001756 + 500: nchlt_eng_001757 + 501: nchlt_eng_001758 + 502: nchlt_eng_001759 + 503: nchlt_eng_001760 + 504: nchlt_eng_001761 + 505: nchlt_eng_001762 + 506: nchlt_eng_001763 + 507: nchlt_eng_001764 + 508: nchlt_eng_001765 + 509: nchlt_eng_001766 + 510: nchlt_eng_001767 + 511: nchlt_eng_001768 + 512: nchlt_eng_001769 + 513: nchlt_eng_001770 + 514: nchlt_eng_001771 + 515: nchlt_eng_001772 + 516: nchlt_eng_001773 + 517: nchlt_eng_001774 + 518: nchlt_eng_001775 + 519: nchlt_eng_001776 + 520: nchlt_eng_001777 + 521: nchlt_eng_001778 + 522: nchlt_eng_001779 + 523: nchlt_eng_001780 + 524: nchlt_eng_001781 + 525: nchlt_eng_001782 + 526: nchlt_eng_001783 + 527: nchlt_eng_001784 + 528: nchlt_eng_001785 + 529: nchlt_eng_001786 + 530: nchlt_eng_001787 + 531: nchlt_eng_001788 + 532: nchlt_eng_001789 + 533: nchlt_eng_001790 + 534: nchlt_eng_001791 + 535: nchlt_eng_001792 + 536: nchlt_eng_001793 + 537: nchlt_eng_001794 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swc_eng_001756 + 581: swc_eng_001757 + 582: swc_eng_001758 + 583: swc_eng_001759 + 584: swc_eng_001760 + 585: swc_eng_001761 + 586: swc_eng_001762 + 587: swc_eng_001763 + 588: swc_eng_001764 + 589: swc_eng_001765 + 590: swc_eng_001766 + 591: swc_eng_001767 + 592: swc_eng_001768 + 593: swc_eng_001769 + 594: swc_eng_001770 + 595: swc_eng_001771 + 596: swc_eng_001772 + 597: swc_eng_001773 + 598: swc_eng_001774 + 599: swc_eng_001775 + 600: swc_eng_001776 + 601: swc_eng_001777 + 602: swc_eng_001778 + 603: swc_eng_001779 + 604: swc_eng_001780 + 605: swc_eng_001781 + 606: swc_eng_001782 + 607: swc_eng_001783 + 608: swc_eng_001784 + 609: swc_eng_001785 + 610: swc_eng_001786 + 611: swc_eng_001787 + 612: swc_eng_001788 + 613: swc_eng_001789 + 614: swc_eng_001790 + 615: swc_eng_001791 + 616: swc_eng_001792 + 617: swc_eng_001793 + 618: swc_eng_001794 + 619: swc_eng_001795 + 620: swc_eng_001796 + 621: swc_eng_001797 + 622: swc_eng_001798 + 623: swc_eng_001799 + 624: swc_eng_001800 + 625: swc_eng_001801 + 626: swc_eng_001802 + 627: swc_eng_001803 + 628: swc_eng_001804 + 629: swc_eng_001805 + 630: swc_eng_001806 + 631: swc_eng_001807 + 632: swc_eng_001808 + 633: swc_eng_001809 + 634: swc_eng_001810 + 635: swc_eng_001811 + 636: swc_eng_001812 + 637: swc_eng_001813 + 638: swc_eng_001814 + 639: swc_eng_001815 + 640: swc_eng_001816 + 641: swc_eng_001817 + 642: swc_eng_001818 + 643: swc_eng_001819 + 644: swc_eng_001820 + 645: swc_eng_001821 + 646: swc_eng_001822 + 647: swc_eng_001823 + 648: swc_eng_001824 + 649: swc_eng_001825 + 650: swc_eng_001826 + 651: swc_eng_001827 + 652: swc_eng_001828 + 653: swc_eng_001829 + 654: swc_eng_001830 + 655: swc_eng_001831 + 656: swc_eng_001832 + 657: swc_eng_001833 + 658: swc_eng_001834 + 659: swc_eng_001835 + 660: swc_eng_001836 + 661: swc_eng_001837 + 662: swc_eng_001838 + 663: swc_eng_001839 + 664: swc_eng_001840 + 665: swc_eng_001841 + 666: swc_eng_001842 + 667: swc_eng_001843 + 668: swc_eng_001844 + 669: swc_eng_001845 + 670: swc_eng_001846 + 671: swc_eng_001847 + 672: swc_eng_001848 + 673: swc_eng_001849 + 674: swc_eng_001850 + 675: swc_eng_001851 + 676: swc_eng_001852 + 677: swc_eng_001853 + 678: swc_eng_001854 + 679: swc_eng_001855 + 680: swc_eng_001856 + 681: swc_eng_001857 + 682: swc_eng_001858 + 683: swc_eng_001859 + 684: swc_eng_001860 + 685: swc_eng_001861 + 686: swc_eng_001862 + 687: swc_eng_001863 + 688: swc_eng_001864 + 689: swc_eng_001865 + 690: swc_eng_001866 + 691: swc_eng_001867 + 692: swc_eng_001868 + 693: swc_eng_001869 + 694: swc_eng_001870 + 695: swc_eng_001871 + 696: swc_eng_001872 + 697: swc_eng_001873 + 698: swc_eng_001874 + 699: swc_eng_001875 + 700: swc_eng_001876 + 701: swc_eng_001877 + 702: swc_eng_001878 + 703: swc_eng_001879 + 704: swc_eng_001880 + 705: swc_eng_001881 + 706: swc_eng_001882 + 707: swc_eng_001883 + 708: swc_eng_001884 + 709: swc_eng_001885 + 710: swc_eng_001886 + 711: swc_eng_001887 + 712: swc_eng_001888 + 713: swc_eng_001889 + 714: swc_eng_001890 + 715: 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+Eval: S S S S S S S S S S S + +Speaker sentences 1: lad_eng_000255 #utts: 1 +id: (lad_eng_000255-lad_eng_000255) +Scores: (#C #S #D #I) 4 5 0 1 +REF: ** A LIBERALCONSERVATIVE he was defeated in EIGHTEEN EIGHTY TWO +HYP: AY LIBRAL CONSEVITIVE he was defeated in ATEIN ATY TO +Eval: I S S S S S + +Speaker sentences 2: lad_eng_000256 #utts: 1 +id: (lad_eng_000256-lad_eng_000256) +Scores: (#C #S #D #I) 2 7 0 0 +REF: ONE ROAD LAYER CAN DRAW two ROADS at ONCE +HYP: ON ROD LAAR CON DRAR two RODS at WOANCE +Eval: S S S S S S S + +Speaker sentences 3: lad_eng_000257 #utts: 1 +id: (lad_eng_000257-lad_eng_000257) +Scores: (#C #S #D #I) 3 5 1 0 +REF: some OF THE COUNTRIES HAVE SURVEYS for MULTIPLE years +HYP: some ** OFTHE CONTRES HVE SURVAYS for MALTIPLE years +Eval: D S S S S S + +Speaker sentences 4: lad_eng_000258 #utts: 1 +id: (lad_eng_000258-lad_eng_000258) +Scores: (#C #S #D #I) 6 3 0 0 +REF: both of the VERSIONS FEATURE the song HAPPY holiday +HYP: both of the VRSIONS FEACHR the song HAPY holiday +Eval: S S S + +Speaker sentences 5: lad_eng_000259 #utts: 1 +id: (lad_eng_000259-lad_eng_000259) +Scores: (#C #S #D #I) 7 6 0 1 +REF: SHAKESPEARE many REFERENCES are made to **** SCENES INTERACTIONS or CHARACTERS from various PLAYS +HYP: SHAKXPIAR many REFRNCES are made to SENS INTR ACTIONS or CARICTES from various PLAYES +Eval: S S I S S S S + +Speaker sentences 6: lad_eng_000260 #utts: 1 +id: (lad_eng_000260-lad_eng_000260) +Scores: (#C #S #D #I) 8 5 1 1 +REF: if only the PROGRAM COULD BREAK out just a LITTLE from its ** TOOFAMILIAR APPROACH +HYP: if only the ******* ROGRAM CULDBRAKE out just a ITLE from its TO FOMILIAR APROCH +Eval: D S S S I S S + +Speaker sentences 7: lad_eng_000261 #utts: 1 +id: (lad_eng_000261-lad_eng_000261) +Scores: (#C #S #D #I) 7 6 0 0 +REF: the ALBUM was released in AUSTRALIA on NINETEENTH AUGUST two THOUSAND AND eleven +HYP: the HELBEM was released in OSTRALIAR on NINTEINTH ORGIST two THOUSND AD eleven +Eval: S S S S S S + +Speaker sentences 8: lad_eng_000262 #utts: 1 +id: (lad_eng_000262-lad_eng_000262) +Scores: (#C #S #D #I) 5 3 0 0 +REF: he now PLAYS for AUSTRALIAN CLUB perth glory +HYP: he now PLACE for ASTRALIN CLOBE perth glory +Eval: S S S + +Speaker sentences 9: lad_eng_000263 #utts: 1 +id: (lad_eng_000263-lad_eng_000263) +Scores: (#C #S #D #I) 7 6 0 0 +REF: it is not KNOWN how much if ANY of HER CLAIMS ARE TRUE +HYP: it is not NON how much if EANY of HE CLAMS AR TRU +Eval: S S S S S S + +Speaker sentences 10: lad_eng_000264 #utts: 1 +id: (lad_eng_000264-lad_eng_000264) +Scores: (#C #S #D #I) 6 13 1 0 +REF: a SMALL BUSINESS OWNER BROAD OPERATED HIS WHEAT AND SHEEP FARM for SIXTEEN years FROM the age of TWENTY TWO +HYP: a ***** SMAL BISINESS ONR BROARD OPRATED HI WEAT AD SHEPFAME for SICTEN years FRO the age of WENTY TO +Eval: D S S S S S S S S S S S S S + +Speaker sentences 11: lad_eng_000265 #utts: 1 +id: (lad_eng_000265-lad_eng_000265) +Scores: (#C #S #D #I) 8 1 0 0 +REF: in the ninth CENTURY he was an irish poet +HYP: in the ninth SENTURY he was an irish poet +Eval: S + +Speaker sentences 12: lad_eng_000266 #utts: 1 +id: (lad_eng_000266-lad_eng_000266) +Scores: (#C #S #D #I) 5 0 0 0 +REF: they are marked by strong +HYP: they are marked by strong +Eval: + +Speaker sentences 13: lad_eng_000267 #utts: 1 +id: (lad_eng_000267-lad_eng_000267) +Scores: (#C #S #D #I) 2 3 0 1 +REF: the LAW is *** THEREFORE VALID +HYP: the LOW is THE FOR VAOLED +Eval: S I S S + +Speaker sentences 14: lad_eng_000268 #utts: 1 +id: (lad_eng_000268-lad_eng_000268) +Scores: (#C #S #D #I) 7 2 0 1 +REF: in the EARLY stages came close to us * ASLEEP +HYP: in the RLY stages came close to us A SLEP +Eval: S I S + +Speaker sentences 15: lad_eng_000269 #utts: 1 +id: (lad_eng_000269-lad_eng_000269) +Scores: (#C #S #D #I) 1 6 0 1 +REF: RUNNING every ***** THIRTY MINUTES THROUGHOUT SERVICE TIMES +HYP: RONING every THRTY MINUT THRO AT SERVIS TIMS +Eval: S I S S S S S + +Speaker sentences 16: lad_eng_000270 #utts: 1 +id: (lad_eng_000270-lad_eng_000270) +Scores: (#C #S #D #I) 8 5 0 2 +REF: as a RESULT when the ****** COLLEGE REOPENED it was as an *** ALLMALE COLLEGE +HYP: as a RESIULT when the COLIGE RE OPEND it was as an ALL MALE COLIGE +Eval: S I S S I S S + +Speaker sentences 17: lad_eng_000271 #utts: 1 +id: (lad_eng_000271-lad_eng_000271) +Scores: (#C #S #D #I) 8 9 0 0 +REF: the time BETWEEN THESE POINTS is VARIABLE and CAN OCCUR ANYWHERE FROM a MINUTE to much longer +HYP: the time BETWEE THES POINCT is VERIABL and CANACUR ANY WHER FRO a MINIT to much longer +Eval: S S S S S S S S S + +Speaker sentences 18: lad_eng_000272 #utts: 1 +id: (lad_eng_000272-lad_eng_000272) +Scores: (#C #S #D #I) 15 4 1 1 +REF: WORK on the ** e e S started in march two THOUSAND and seven at a cost of five MILLION DOLLARS +HYP: WOARK on the EA e e * started in march two THOUSND and seven at a cost of five MILIAN DOLERS +Eval: S I D S S S + +Speaker sentences 19: lad_eng_000273 #utts: 1 +id: (lad_eng_000273-lad_eng_000273) +Scores: (#C #S #D #I) 10 8 0 2 +REF: however THERE was some ** DISAGREEMENT OVER THE ending theme which ** OMORI and YOSHIMORI DISCUSSED at length over EMAIL +HYP: however THER was some DI AGREMENT OV TH ending theme which OR MORY and YOHIMORY DISCUSTD at length over EMAL +Eval: S I S S S I S S S S + +Speaker sentences 20: lad_eng_000274 #utts: 1 +id: (lad_eng_000274-lad_eng_000274) +Scores: (#C #S #D #I) 3 2 0 0 +REF: the COUPLE had no CHILDREN +HYP: the COPLE had no CHILDRAN +Eval: S S + +Speaker sentences 21: lad_eng_000275 #utts: 1 +id: (lad_eng_000275-lad_eng_000275) +Scores: (#C #S #D #I) 6 8 0 1 +REF: the OFFICIAL SINGLE OF that **** DEBUT ALBUM paris CALLING had an ELABORATE music VIDEO +HYP: the FIAL SINGL O that DEBU AL BHM paris COLING had an ELABRT music VIDIO +Eval: S S S I S S S S S + +Speaker sentences 22: lad_eng_000276 #utts: 1 +id: (lad_eng_000276-lad_eng_000276) +Scores: (#C #S #D #I) 10 8 0 0 +REF: the SERIES ended on sixth AUGUST TWO THOUSAND and FOUR lasting FOR a TOTAL of seventy ONE days +HYP: the SERIS ended on sixth ORGEST TO THOUSND and FOR lasting FR a TOUTE of seventy ON days +Eval: S S S S S S S S + +Speaker sentences 23: lad_eng_000277 #utts: 1 +id: (lad_eng_000277-lad_eng_000277) +Scores: (#C #S #D #I) 10 1 0 0 +REF: he has also contributed to the new york REVIEW of books +HYP: he has also contributed to the new york REVIO of books +Eval: S + +Speaker sentences 24: lad_eng_000278 #utts: 1 +id: (lad_eng_000278-lad_eng_000278) +Scores: (#C #S #D #I) 5 3 0 1 +REF: by placing SMALL art ****** OBJECTS THROUGHOUT the film +HYP: by placing SMAL art OBJECT TRO OUT the film +Eval: S I S S + +Speaker sentences 25: lad_eng_000279 #utts: 1 +id: (lad_eng_000279-lad_eng_000279) +Scores: (#C #S #D #I) 4 1 0 0 +REF: it is found in BRAZIL +HYP: it is found in BRESIL +Eval: S + +Speaker sentences 26: lad_eng_000280 #utts: 1 +id: (lad_eng_000280-lad_eng_000280) +Scores: (#C #S #D #I) 8 3 0 0 +REF: it WAS the SIDE of the FAMILY i identified more with +HYP: it WS the SID of the FAMLY i identified more with +Eval: S S S + +Speaker sentences 27: lad_eng_000281 #utts: 1 +id: (lad_eng_000281-lad_eng_000281) +Scores: (#C #S #D #I) 4 4 0 2 +REF: * **** CANDIDATE SITES must ALSO SUBMIT a work plan +HYP: H CAND IT SIGHTES must ALSOR SOBMIT a work plan +Eval: I I S S S S + +Speaker sentences 28: lad_eng_000282 #utts: 1 +id: (lad_eng_000282-lad_eng_000282) +Scores: (#C #S #D #I) 1 5 0 0 +REF: DUNDEE WON the MATCH THREE TWO +HYP: DUNDEY WHN the MACH THRE TO +Eval: S S S S S + +Speaker sentences 29: lad_eng_000283 #utts: 1 +id: (lad_eng_000283-lad_eng_000283) +Scores: (#C #S #D #I) 7 7 0 1 +REF: however the VILLAGE REMAINED ISOLATED UNTIL the ARRIVAL of the first **** NEWSPAPER second REPUBLIC +HYP: however the VILIGE REMAIND ICALATED ANTIL the RIVEL of the first NOUS PAPER second REPOUBLICK +Eval: S S S S S I S S + +Speaker sentences 30: lad_eng_000284 #utts: 1 +id: (lad_eng_000284-lad_eng_000284) +Scores: (#C #S #D #I) 9 11 0 0 +REF: the FIRST SERVICE IN the NEW CHURCH was held IN NINETEEN fifty ONE ALTHOUGH the BUILDING was not FULLY finished +HYP: the FAST SERVIS I the EU CHURC was held I NINTE fifty ON ALTHO the BILDIG was not FULY finished +Eval: S S S S S S S S S S S + +Speaker sentences 31: lad_eng_000285 #utts: 1 +id: (lad_eng_000285-lad_eng_000285) +Scores: (#C #S #D #I) 8 11 0 0 +REF: the AVERAGE HOUSEHOLD SIZE was two point TWO seven AND the AVERAGE FAMILY SIZE was THREE point ZERO ZERO +HYP: the AVERIGE HOUSEHLD SIE was two point TO seven ND the AVERIGH FAMLY SIE was THRE point IRO SRO +Eval: S S S S S S S S S S S + +Speaker sentences 32: lad_eng_000286 #utts: 1 +id: (lad_eng_000286-lad_eng_000286) +Scores: (#C #S #D #I) 7 4 0 1 +REF: it was first **** BROADCAST on third JANUARY two THOUSAND AND ten +HYP: it was first BRAD CAST on third GANIURY two THOUSOND ND ten +Eval: I S S S S + +Speaker sentences 33: lad_eng_000287 #utts: 1 +id: (lad_eng_000287-lad_eng_000287) +Scores: (#C #S #D #I) 5 4 0 0 +REF: the wings WERE NOW MADE in a single PRESSING +HYP: the wings WER OW MAD in a single PRESING +Eval: S S S S + +Speaker sentences 34: lad_eng_000288 #utts: 1 +id: (lad_eng_000288-lad_eng_000288) +Scores: (#C #S #D #I) 2 4 0 1 +REF: ** DOCTOR OF PHILOSOPHY in ENGINEERING management +HYP: HE DOCTR O HLOSOFY in ENGENEARING management +Eval: I S S S S + +Speaker sentences 35: lad_eng_000289 #utts: 1 +id: (lad_eng_000289-lad_eng_000289) +Scores: (#C #S #D #I) 5 4 0 0 +REF: this TOOK AWAY the main argument of SAFETY RISKS +HYP: this TOK WAY the main argument of SAFTY RISSK +Eval: S S S S + +Speaker sentences 36: lad_eng_000290 #utts: 1 +id: (lad_eng_000290-lad_eng_000290) +Scores: (#C #S #D #I) 7 3 0 1 +REF: he was also MADE a life member of **** SCUNTHORPE UNITED +HYP: he was also MAD a life member of SGUN THORP PUNITED +Eval: S I S S + +Speaker sentences 37: lad_eng_000291 #utts: 1 +id: (lad_eng_000291-lad_eng_000291) +Scores: (#C #S #D #I) 6 4 1 0 +REF: she FEARS THEY WILL get a DIVORCE but this never HAPPENS +HYP: she ***** FIARS THEYWIL get a DEVORSE but this never HAPENS +Eval: D S S S S + +Speaker sentences 38: lad_eng_000292 #utts: 1 +id: (lad_eng_000292-lad_eng_000292) +Scores: (#C #S #D #I) 3 6 0 1 +REF: FOOT drops ** UNABLE to HOLD the FOOT STRAIGHT ACROSS +HYP: FOUT drops IN ABLE to HAD the FOT STRAT ACROSE +Eval: S I S S S S S + +Speaker sentences 39: lad_eng_000293 #utts: 1 +id: (lad_eng_000293-lad_eng_000293) +Scores: (#C #S #D #I) 6 10 0 0 +REF: WHETHER the AIR FLOW is FREE or FORCED CAN AFFECT the ENERGY EFFICIENCY of the WINDOW +HYP: WHETE the AR FLO is FREY or FORST CN FEC the ENAGY AFIANCY of the ENDO +Eval: S S S S S S S S S S + +Speaker sentences 40: lad_eng_000294 #utts: 1 +id: (lad_eng_000294-lad_eng_000294) +Scores: (#C #S #D #I) 2 7 1 0 +REF: after GETTING THE RIGHT MEASUREMENTS they MADE THE NEW DOORS +HYP: after GETIN HE RIHT MASURMENT they **** MAD THENEW DORS +Eval: S S S S D S S S + +Speaker sentences 41: lad_eng_000295 #utts: 1 +id: (lad_eng_000295-lad_eng_000295) +Scores: (#C #S #D #I) 4 7 0 0 +REF: fragments on EACH face are MARKED WITH LETTERS A B C +HYP: fragments on ACH face are MARE WTH LETERS AY BE SE +Eval: S S S S S S S + +Speaker sentences 42: lad_eng_000296 #utts: 1 +id: (lad_eng_000296-lad_eng_000296) +Scores: (#C #S #D #I) 5 9 0 0 +REF: from the first MINUTES both TEAMS SHOWED THEIR DESIRE to COMPETE WITH AGGRESSIVE APPROACHES +HYP: from the first MINITS both TEMES SHOWD THE DISIRE to CMPEET WIT HEGEIVE APROCHES +Eval: S S S S S S S S S + +Speaker sentences 43: lad_eng_000297 #utts: 1 +id: (lad_eng_000297-lad_eng_000297) +Scores: (#C #S #D #I) 4 6 0 0 +REF: PHYSICAL THERAPY EXERCISES may help PATIENTS to maintain MUSCLE STRENGTH +HYP: FISICL HERIPY EXCUSISES may help PATIONT to maintain MULE STRINGTH +Eval: S S S S S S + +Speaker sentences 44: lad_eng_000298 #utts: 1 +id: (lad_eng_000298-lad_eng_000298) +Scores: (#C #S #D #I) 10 3 0 0 +REF: however the town she LIVES in no ONE wants to HEAR about her +HYP: however the town she LIVS in no ON wants to HER about her +Eval: S S S + +Speaker sentences 45: lad_eng_000299 #utts: 1 +id: (lad_eng_000299-lad_eng_000299) +Scores: (#C #S #D #I) 8 5 0 2 +REF: *** ********* DESCRIBES APPOINTMENTS of an acting CHIEF JUSTICE or judge of the supreme COURT +HYP: AND DISCRIVES APOINT MET of an acting CHEVE JUSTIS or judge of the supreme CORT +Eval: I I S S S S S + +Speaker sentences 46: lad_eng_000300 #utts: 1 +id: (lad_eng_000300-lad_eng_000300) +Scores: (#C #S #D #I) 8 5 0 1 +REF: the *** SOYBEANS OUTER COVERING is then removed and the BEANS are PARTIALLY cooked +HYP: the SOY BENS OUT ACOVERING is then removed and the BENS are PARTIALY cooked +Eval: I S S S S S + +Speaker sentences 47: lad_eng_000301 #utts: 1 +id: (lad_eng_000301-lad_eng_000301) +Scores: (#C #S #D #I) 10 4 1 0 +REF: this NATIONAL MOVEMENT WHICH HAD begun with so much HOPE came to a sad end +HYP: this ******** NASINALE MOVMENT WHICHAD begun with so much HOP came to a sad end +Eval: D S S S S + +Speaker sentences 48: lad_eng_000302 #utts: 1 +id: (lad_eng_000302-lad_eng_000302) +Scores: (#C #S #D #I) 4 6 0 1 +REF: his ASSOCIATES USUALLY CALLED him T or the ** GOODLOOKING GUY +HYP: his ASOSCIAT YUSUALY CALD him TE or the OD LOKING GIY +Eval: S S S S I S S + +Speaker sentences 49: lad_eng_000303 #utts: 1 +id: (lad_eng_000303-lad_eng_000303) +Scores: (#C #S #D #I) 3 8 0 1 +REF: its main OFFICES WERE in ****** LONDON WITH A SECOND OFFICE BELFAST +HYP: its main OFICES WER in LUNDAN WIE HE SECND OFIS BELL FAST +Eval: S S I S S S S S S + +Speaker sentences 50: lad_eng_000304 #utts: 1 +id: (lad_eng_000304-lad_eng_000304) +Scores: (#C #S #D #I) 6 4 0 0 +REF: ACTUALLY i had never BEEN to a VILLAGE BEFORE that +HYP: ACTULY i had never BEN to a VILIGE BEFOR that +Eval: S S S S + +Speaker sentences 51: lad_eng_000305 #utts: 1 +id: (lad_eng_000305-lad_eng_000305) +Scores: (#C #S #D #I) 8 7 0 0 +REF: he WAS charged WITH PLANNING to set OFF BOMBS in EUROPE and the united STATES +HYP: he AS charged ITH PLANING to set OF BOMS in UROP and the united STATE +Eval: S S S S S S S + +Speaker sentences 52: lad_eng_000306 #utts: 1 +id: (lad_eng_000306-lad_eng_000306) +Scores: (#C #S #D #I) 5 6 0 1 +REF: making MIRRORS is the THIRD STUDIO ALBUM by ****** BELGIANAUSTRALIAN artist GOTYE +HYP: making MERS is the HIRD STUDOR HLBUM by BELGEN ASTRALIAN artist GOTIAY +Eval: S S S S I S S + +Speaker sentences 53: lad_eng_000307 #utts: 1 +id: (lad_eng_000307-lad_eng_000307) +Scores: (#C #S #D #I) 9 8 0 1 +REF: he then moved to ********* WASHINGTON DC and was a PARTNER WITH ward BROWN UNTIL NINETEEN twenty NINE +HYP: he then moved to WASINGTON DE SE and was a PARTNR ITH ward BRON ANDTIL NINTEN twenty NIN +Eval: I S S S S S S S S + +Speaker sentences 54: lad_eng_000308 #utts: 1 +id: (lad_eng_000308-lad_eng_000308) +Scores: (#C #S #D #I) 4 8 0 1 +REF: *** JOSEPH HIGH SCHOOL and the SCHOOLS THEY COMPETE AGAINST in ALL sports +HYP: JOS OF HIY SCOLE and the SCOLES THE CMPE GAINE in AL sports +Eval: I S S S S S S S S + +Speaker sentences 55: lad_eng_000309 #utts: 1 +id: (lad_eng_000309-lad_eng_000309) +Scores: (#C #S #D #I) 4 3 0 0 +REF: TWELVE plus ONE MATCH ban per card +HYP: TWELF plus ON MACH ban per card +Eval: S S S + +Speaker sentences 56: lad_eng_000310 #utts: 1 +id: (lad_eng_000310-lad_eng_000310) +Scores: (#C #S #D #I) 5 1 0 0 +REF: i THINK i might be nothing +HYP: i HINK i might be nothing +Eval: S + +Speaker sentences 57: lad_eng_000311 #utts: 1 +id: (lad_eng_000311-lad_eng_000311) +Scores: (#C #S #D #I) 9 9 0 1 +REF: the HOME was BUILT and lived in by ***** ANDREW JACKSON KENNEDY deputy COLLECTOR FOR the internal REVENUE SERVICE +HYP: the HOE was BILT and lived in by ANDRU JACX AND CANDY deputy CLECTE O the internal REVINOU SERVIS +Eval: S S I S S S S S S S + +Speaker sentences 58: lad_eng_000312 #utts: 1 +id: (lad_eng_000312-lad_eng_000312) +Scores: (#C #S #D #I) 9 7 0 0 +REF: in NINETEEN sixty FOUR he went BACK to omsk and ENTERED the ACTORS SCHOOL of OMSK +HYP: in NINTEN sixty FOR he went BAC to omsk and ENTE the ACTOA SCOL of OMPS +Eval: S S S S S S S + +Speaker sentences 59: lad_eng_000313 #utts: 1 +id: (lad_eng_000313-lad_eng_000313) +Scores: (#C #S #D #I) 8 4 0 0 +REF: the bank is JOINTLY OWNED by him and his BROTHERS and RELATIVES +HYP: the bank is JOUNTLY OND by him and his BROVER and RELITIVES +Eval: S S S S + +Speaker sentences 60: lad_eng_000314 #utts: 1 +id: (lad_eng_000314-lad_eng_000314) +Scores: (#C #S #D #I) 4 3 0 0 +REF: he SUBSEQUENTLY went to SCHOOL in BRISTOL +HYP: he SUBPSICUNTLY went to COL in BRISTAL +Eval: S S S + +Speaker sentences 61: lad_eng_000315 #utts: 1 +id: (lad_eng_000315-lad_eng_000315) +Scores: (#C #S #D #I) 1 7 1 0 +REF: ONE thousand EIGHT HUNDRED AND FORTY SIX FOURTH EDITION +HYP: WON thousand ***** AT HUNDRD FOARTY SICX FORH EDION +Eval: S D S S S S S S + +Speaker sentences 62: lad_eng_000316 #utts: 1 +id: (lad_eng_000316-lad_eng_000316) +Scores: (#C #S #D #I) 9 7 0 2 +REF: a part of LITTLE ENGLAND beyond WALES it has ** * BEEN ESSENTIALLY ENGLISHSPEAKING for NINE hundred years +HYP: a part of LITL INGLAND beyond WALS it has BE A CENCHALY INGLISH SPEAKING for NIN hundred years +Eval: S S S I I S S S S + +Speaker sentences 63: lad_eng_000317 #utts: 1 +id: (lad_eng_000317-lad_eng_000317) +Scores: (#C #S #D #I) 5 8 2 0 +REF: he PLAYED WITH ten PLAYERS for HALF was AGAINST THE TRADITION in G S P +HYP: he PLAD WTH ten PLAYARS for HARF was AGAINE A TRDION in * * JEESP +Eval: S S S S S S S D D S + +Speaker sentences 64: lad_eng_000318 #utts: 1 +id: (lad_eng_000318-lad_eng_000318) +Scores: (#C #S #D #I) 10 8 0 1 +REF: the PRESIDING judge was ***** WEBSTER THAYER WHO was already ASSIGNED to the COURT before this CASE was SCHEDULED +HYP: the RESIDING judge was WEBST A FAIR HO was already ASIND to the CORT before this CACE was HEDILD +Eval: S I S S S S S S S + +Speaker sentences 65: lad_eng_000319 #utts: 1 +id: (lad_eng_000319-lad_eng_000319) +Scores: (#C #S #D #I) 9 6 0 0 +REF: big BROTHER five was the THIRD of THE main SERIES to FEATURE a live LAUNCH +HYP: big BRATHER five was the HURD of HE main SARIS to FEACUER a live LONCH +Eval: S S S S S S + +Speaker sentences 66: lad_eng_000320 #utts: 1 +id: (lad_eng_000320-lad_eng_000320) +Scores: (#C #S #D #I) 10 9 0 1 +REF: its MOTTO is *** WHOEVER you ARE and WHEREVER you are on the JOURNEY of FAITH you ARE WELCOME HERE +HYP: its MOTO is WHO EVE you AR and WHEREVE you are on the JUNY of FAIFH you AE WELCOM HER +Eval: S I S S S S S S S S + +Speaker sentences 67: lad_eng_000321 #utts: 1 +id: (lad_eng_000321-lad_eng_000321) +Scores: (#C #S #D #I) 2 4 0 0 +REF: ROBERT E MILLER as COACH wilson +HYP: ROBAT EY MILOR as COCH wilson +Eval: S S S S + +Speaker sentences 68: lad_eng_000322 #utts: 1 +id: (lad_eng_000322-lad_eng_000322) +Scores: (#C #S #D #I) 2 8 0 0 +REF: after A ONEYEAR BREAK ZERO DEGREE was HER FOLLOWING VENTURE +HYP: after ON YEAR BRAK SIRO DEGRE was HE FOLOING VENTHAR +Eval: S S S S S S S S + +Speaker sentences 69: lad_eng_000323 #utts: 1 +id: (lad_eng_000323-lad_eng_000323) +Scores: (#C #S #D #I) 2 11 0 0 +REF: A M T MANUFACTURED a MODEL KIT of THE Z Z R DRAGSTER +HYP: AY AM TEE MANUFACTED a MORDL CIT of HE SED SEID AR DRACKXSTOR +Eval: S S S S S S S S S S S + +Speaker sentences 70: lad_eng_000324 #utts: 1 +id: (lad_eng_000324-lad_eng_000324) +Scores: (#C #S #D #I) 6 9 3 1 +REF: the S S A AIMED to BUILD a **** LEFTWING ALTERNATIVE to NEW LABOUR and the S N P +HYP: the * * ESSESSAY AMED to BILD a LEFT WING OLTURNITIVE to NOW LABER and the * ESAN PE +Eval: D D S S S I S S S S D S S + +Speaker sentences 71: lad_eng_000325 #utts: 1 +id: (lad_eng_000325-lad_eng_000325) +Scores: (#C #S #D #I) 6 2 0 0 +REF: he lives like he IS a YOUNG person +HYP: he lives like he AS a YONG person +Eval: S S + +Speaker sentences 72: lad_eng_000326 #utts: 1 +id: (lad_eng_000326-lad_eng_000326) +Scores: (#C #S #D #I) 2 4 0 0 +REF: MASTER of SCIENCE in ENGINEERING MANAGEMENT +HYP: MASTE of SINES in ENGENEARIG MANAGENT +Eval: S S S S + +Speaker sentences 73: lad_eng_000327 #utts: 1 +id: (lad_eng_000327-lad_eng_000327) +Scores: (#C #S #D #I) 7 6 2 0 +REF: she failed to MAKE THE top THREE at the KENYAN JUNIOR TRACK TRIALS that JUNE +HYP: she failed to MAK HE top THRE at the ****** ****** CANIAN JUNIATRACTRILES that JON +Eval: S S S D D S S S + +Speaker sentences 74: lad_eng_000328 #utts: 1 +id: (lad_eng_000328-lad_eng_000328) +Scores: (#C #S #D #I) 2 3 0 0 +REF: a TOUR FOLLOWED in SUPPORT +HYP: a TORE FOLOED in SEPORT +Eval: S S S + +Speaker sentences 75: lad_eng_000329 #utts: 1 +id: (lad_eng_000329-lad_eng_000329) +Scores: (#C #S #D #I) 7 11 1 0 +REF: THEY WERE ESTABLISHED in EIGHTEEN seventy ONE and ARE ONE OF the OLDEST CLUBS in THE south of ENGLAND +HYP: **** THEYER STABISH in ATEN seventy ON and AR WN O the OLDST CLOUBS in HE south of INGLAND +Eval: D S S S S S S S S S S S + +Speaker sentences 76: lad_eng_000330 #utts: 1 +id: (lad_eng_000330-lad_eng_000330) +Scores: (#C #S #D #I) 6 4 0 0 +REF: he WAS a member of the YES scotland ADVISORY BOARD +HYP: he AS a member of the GEST scotland ADVISERY BORD +Eval: S S S S + +Speaker sentences 77: lad_eng_000331 #utts: 1 +id: (lad_eng_000331-lad_eng_000331) +Scores: (#C #S #D #I) 4 1 0 0 +REF: two thousand and five GENTLEMAN +HYP: two thousand and five GENTLEMEN +Eval: S + +Speaker sentences 78: lad_eng_000332 #utts: 1 +id: (lad_eng_000332-lad_eng_000332) +Scores: (#C #S #D #I) 6 9 3 0 +REF: OUR FILM HAD A strong RECEPTION IN EUROPE AND ACHIEVED DISTRIBUTION but that was not the CASE HERE +HYP: *** AORE FILE AD strong ********* ** RESEPTION INYURUPAND ACHIVED DISTOBUTION but that was not the CACE HER +Eval: D S S S D D S S S S S S + +Speaker sentences 79: lad_eng_000333 #utts: 1 +id: (lad_eng_000333-lad_eng_000333) +Scores: (#C #S #D #I) 0 5 0 0 +REF: ORTHOSIS STRETCHES POSTERIOR ANKLE STRUCTURES +HYP: BOLTHOIS STETCHES POSTERIAR ANGCAL STRUCTUES +Eval: S S S S S + +Speaker sentences 80: lad_eng_000334 #utts: 1 +id: (lad_eng_000334-lad_eng_000334) +Scores: (#C #S #D #I) 6 12 0 0 +REF: he WAS also a THREE time french NATIONAL CHAMPION NINETEEN NINETY NINETEEN NINETY FOUR two THOUSAND AND ONE +HYP: he AS also a THEE time french NASIAL CHAMPIAN NINTE NINTY NINTIE NITY FOR two HOUSND AD WON +Eval: S S S S S S S S S S S S + +Speaker sentences 81: lad_eng_000335 #utts: 1 +id: (lad_eng_000335-lad_eng_000335) +Scores: (#C #S #D #I) 7 7 0 2 +REF: the VILLAGE STRUCTURE SHOWN in his map is TO a GREAT extent ** ******* UNCHANGED TODAY +HYP: the VILIGE STRUCTUR SHOW in his map is T a GRE extent UN CHANGED O DAY +Eval: S S S S S I I S S + +Speaker sentences 82: lad_eng_000336 #utts: 1 +id: (lad_eng_000336-lad_eng_000336) +Scores: (#C #S #D #I) 4 11 0 0 +REF: RUSSIA is RECOGNIZED FOR ITS NUCLEAR DISASTER EXPERTISE and FOR the SAFETY OF its TECHNOLOGY +HYP: RUHA is RECOGNISED IT NUCLAR DISARST TO EXPARTES and FO the SAVFTY O its TECKNOLAGY +Eval: S S S S S S S S S S S + +Speaker sentences 83: lad_eng_000337 #utts: 1 +id: (lad_eng_000337-lad_eng_000337) +Scores: (#C #S #D #I) 7 11 2 0 +REF: as of TWO THOUSAND AND FOURTEEN M T V is available within THE UNITED KINGDOM on VIRGIN MEDIA and SKY +HYP: as of *** TO THOUSEND OD FORTEEN EMTY VE is available within *** THEUNITED CINGDUM on VERGIN MEDIAR and SCKIY +Eval: D S S S S S S D S S S S S + +Speaker sentences 84: lad_eng_000338 #utts: 1 +id: (lad_eng_000338-lad_eng_000338) +Scores: (#C #S #D #I) 1 3 1 0 +REF: NEW YORK PENGUIN RANDOM house +HYP: *** NEWYORK PEANGUIN RANDM house +Eval: D S S S + +Speaker sentences 85: lad_eng_000339 #utts: 1 +id: (lad_eng_000339-lad_eng_000339) +Scores: (#C #S #D #I) 5 6 0 1 +REF: the DUCHY was SECURED in ** THE OUTCOME of the GOTHIC WAR +HYP: the DUTCHEY was SCECURE in TE UT COME of the GOFICK WAOR +Eval: S S I S S S S + +Speaker sentences 86: lad_eng_000340 #utts: 1 +id: (lad_eng_000340-lad_eng_000340) +Scores: (#C #S #D #I) 4 7 0 0 +REF: WITH GOOD pace STARTED THE match with both TEAMS ALTERNATING SUPREMACY +HYP: WIH GOD pace SDARTE HE match with both TEMES OLTENATING SUPREMASY +Eval: S S S S S S S + +Speaker sentences 87: lad_eng_000341 #utts: 1 +id: (lad_eng_000341-lad_eng_000341) +Scores: (#C #S #D #I) 4 7 1 0 +REF: this VERSION is NOTED FOR BEING very FAITHFUL to THE ORIGINAL NOVEL +HYP: this VRTION is NOTEAD OR BIG very FAFUL to *** THEARIGINAL NOVL +Eval: S S S S S D S S + +Speaker sentences 88: lad_eng_000342 #utts: 1 +id: (lad_eng_000342-lad_eng_000342) +Scores: (#C #S #D #I) 6 7 1 1 +REF: this presumption is not *** FULFILLED ONE has to KNOW AT LEAST TWO CONJUGATE DIAMETERS +HYP: this presumption is not FLE FILED ON has to **** NO ATLEAST TO CONGAT DIAMATES +Eval: I S S D S S S S S + +Speaker sentences 89: lad_eng_000343 #utts: 1 +id: (lad_eng_000343-lad_eng_000343) +Scores: (#C #S #D #I) 9 9 0 1 +REF: notable titles included GOLDEN AXE the REVENGE of DEATH ADDER rad ***** MOBILE OUTRUNNERS and SEGA sonic the HEDGEHOG +HYP: notable titles included GOLDAN ACXS the REVENG of DETH ADER rad MOBIL OUT RUNOES and SAKGAR sonic the HEGHOG +Eval: S S S S S I S S S S + +Speaker sentences 90: lad_eng_000344 #utts: 1 +id: (lad_eng_000344-lad_eng_000344) +Scores: (#C #S #D #I) 9 9 0 0 +REF: the NINETEEN NINETY NINE JUDGMENT noted that the INFLUENCE of THE father of the ACCUSED has BEEN THERE +HYP: the NINTEN NINTY NIN JUGMENT noted that the INFLONC of TH father of the CUSED has BEE THER +Eval: S S S S S S S S S + +Speaker sentences 91: lad_eng_000345 #utts: 1 +id: (lad_eng_000345-lad_eng_000345) +Scores: (#C #S #D #I) 3 8 0 2 +REF: MACDUFF SWEARS REVENGE and joins FORCES WITH MALCOLM to **** *** OVERTHROW MACBETH +HYP: MOKDAUF SWARS REVENGEH and joins FORES ITH MALCOM to OVER TRO MOK BEATH +Eval: S S S S S S I I S S + +Speaker sentences 92: lad_eng_000346 #utts: 1 +id: (lad_eng_000346-lad_eng_000346) +Scores: (#C #S #D #I) 6 9 0 0 +REF: the MEDIAEVAL VILLAGE COURT was always ANXIOUS to KEEP the FENCE AROUND the VILLAGE GAPLESS +HYP: the MEDYEVL VILIGE CORT was always ANIOUS to CEPE the FENE AROND the ILIGE GCAPLES +Eval: S S S S S S S S S + +Speaker sentences 93: lad_eng_000347 #utts: 1 +id: (lad_eng_000347-lad_eng_000347) +Scores: (#C #S #D #I) 7 7 1 0 +REF: THERE was a NINE rank SYSTEM each rank HAVING more POWER THAN the LOWER RANK +HYP: THER was a NIN rank SISTOM each rank HAVIG more POWE TA the ***** LOERANK +Eval: S S S S S S D S + +Speaker sentences 94: lad_eng_000348 #utts: 1 +id: (lad_eng_000348-lad_eng_000348) +Scores: (#C #S #D #I) 3 6 1 0 +REF: THEY ESTABLISHED DIPLOMATIC relations on SEPTEMBER NINETEENTH NINETEEN seventy TWO +HYP: THE ASTABLISHED DIPLAMATI relations on ********* SEPTEMBRNINTENTH NINTEN seventy TO +Eval: S S S D S S S + +Speaker sentences 95: lad_eng_000349 #utts: 1 +id: (lad_eng_000349-lad_eng_000349) +Scores: (#C #S #D #I) 6 8 2 0 +REF: this was FURTHER EXTENDED to INCLUDE MORE U K DATES in DECEMBER two thousand AND FOURTEEN +HYP: this was FIRTHER XTENDED to ******* **** INCLOUD MOR UCADATES in DISEMBER two thousand ND FORTEEN +Eval: S S D D S S S S S S + +Speaker sentences 96: lad_eng_000350 #utts: 1 +id: (lad_eng_000350-lad_eng_000350) +Scores: (#C #S #D #I) 4 8 0 0 +REF: the DUTCH GOVERNMENT is CURRENTLY EXAMINING the LEGAL CONSEQUENCES of THE RULING +HYP: the UCH GOVERMENT is CARNTLY EXSAMING the EAL CONCICUENCES of TH ROLING +Eval: S S S S S S S S + +Speaker sentences 97: lad_eng_000351 #utts: 1 +id: (lad_eng_000351-lad_eng_000351) +Scores: (#C #S #D #I) 8 9 1 0 +REF: from NINETEEN THIRTY three to NINETEEN FORTY NINE the AMERICAN LEAGUE won twelve OUT OF the first SIXTEEN +HYP: from NINTEN THURTY three to NINTEEN FOARTY NIN the MARICON LEE won twelve *** OUTO the first SIXTEN +Eval: S S S S S S S D S S + +Speaker sentences 98: lad_eng_000352 #utts: 1 +id: (lad_eng_000352-lad_eng_000352) +Scores: (#C #S #D #I) 4 3 0 0 +REF: THERE he FELL sick with TYPHUS himself +HYP: THEAR he FEL sick with TIFAS himself +Eval: S S S + +Speaker sentences 99: lad_eng_000353 #utts: 1 +id: (lad_eng_000353-lad_eng_000353) +Scores: (#C #S #D #I) 5 6 1 0 +REF: SIX TEAMS HAVE BEEN DIVIDED IN two groups of three TEAMS each +HYP: *** SIXT TEMS AVBE DVIDED INTO two groups of three TEMS each +Eval: D S S S S S S + +Speaker sentences 100: lad_eng_000354 #utts: 1 +id: (lad_eng_000354-lad_eng_000354) +Scores: (#C #S #D #I) 4 7 0 0 +REF: the first SEASON PREMIERED on TWELFTH JUNE two THOUSAND AND FIFTEEN +HYP: the first CEASON PREMIAED on TWELTH JUON two THOUSND AD FIFTEN +Eval: S S S S S S S + +Speaker sentences 101: lad_eng_000355 #utts: 1 +id: (lad_eng_000355-lad_eng_000355) +Scores: (#C #S #D #I) 6 7 0 0 +REF: it SUCCEEDED the Y board and SYSTEM twenty FOUR COMBINING FEATURES FROM both +HYP: it SCEED the WHI board and SISTAME twenty FOR COMBING FEACUES FOM both +Eval: S S S S S S S + +Speaker sentences 102: lad_eng_000356 #utts: 1 +id: (lad_eng_000356-lad_eng_000356) +Scores: (#C #S #D #I) 2 5 0 0 +REF: VOLUME TWO numbers ONE TWO and THREE +HYP: VLLIUME TOO numbers ON TO and THRE +Eval: S S S S S + +Speaker sentences 103: lad_eng_000357 #utts: 1 +id: (lad_eng_000357-lad_eng_000357) +Scores: (#C #S #D #I) 7 8 0 0 +REF: the LOWER part of mens DRESSES WERE much SHORTER in LENGTH THAN THOSE for WOMEN +HYP: the LOWE part of mens DESES WE much SOURT in LENC THO THOS for WMEN +Eval: S S S S S S S S + +Speaker sentences 104: lad_eng_000358 #utts: 1 +id: (lad_eng_000358-lad_eng_000358) +Scores: (#C #S #D #I) 5 4 0 0 +REF: the visigoths in TURN WERE SUCCEEDED by the MOORS +HYP: the visigoths in TERN WE SCEADED by the MORS +Eval: S S S S + +Speaker sentences 105: lad_eng_000359 #utts: 1 +id: (lad_eng_000359-lad_eng_000359) +Scores: (#C #S #D #I) 4 5 0 1 +REF: *** JOSEPH HIGH SCHOOL every WEEK of the SCHOOL year +HYP: JOS OF HI SCOLE every WE of the COL year +Eval: I S S S S S + +Speaker sentences 106: lad_eng_000360 #utts: 1 +id: (lad_eng_000360-lad_eng_000360) +Scores: (#C #S #D #I) 4 5 1 0 +REF: as A RESULT OF ALL the ARGUMENTS GETTING to her +HYP: as * TH RSILT OFAL the ARGUMENT GETING to her +Eval: D S S S S S + +Speaker sentences 107: lad_eng_000361 #utts: 1 +id: (lad_eng_000361-lad_eng_000361) +Scores: (#C #S #D #I) 2 5 0 1 +REF: ** ITS HEADQUARTERS are in SHEFFIELD UNITED KINGDOM +HYP: IT HAD QUARTERS are in SHEFIALD YOUNITED CINGDOM +Eval: I S S S S S + +Speaker sentences 108: lad_eng_000362 #utts: 1 +id: (lad_eng_000362-lad_eng_000362) +Scores: (#C #S #D #I) 7 9 1 0 +REF: lay also OFFICIALLY SIGNED the contract on stage WITH THE DIRECTOR AND PRODUCERS OF THE GOLDEN eyes +HYP: lay also FIALY SINE the contract on stage **** WIT HE DIRECTER AD PREDUSES OFTHE GOULDAN eyes +Eval: S S D S S S S S S S + +Speaker sentences 109: lad_eng_000363 #utts: 1 +id: (lad_eng_000363-lad_eng_000363) +Scores: (#C #S #D #I) 4 9 1 0 +REF: PHYSICAL THERAPY CAN HELP PATIENTS to LEARN HOW to WALK with A FOOT drop +HYP: FISICL FERIPY CN HELE PATIONE to LURN HO to WARK with * FOT drop +Eval: S S S S S S S S D S + +Speaker sentences 110: lad_eng_000364 #utts: 1 +id: (lad_eng_000364-lad_eng_000364) +Scores: (#C #S #D #I) 8 4 0 1 +REF: it WENT on to SELL THREE hundred thousand units * ACHIEVE five no +HYP: it ENT on to SEL THRE hundred thousand units A CHE five no +Eval: S S S I S + +Speaker sentences 111: lad_eng_000365 #utts: 1 +id: (lad_eng_000365-lad_eng_000365) +Scores: (#C #S #D #I) 3 2 0 0 +REF: the name STUCK AFTER that +HYP: the name STOUCK AFER that +Eval: S S + +Speaker sentences 112: lad_eng_000366 #utts: 1 +id: (lad_eng_000366-lad_eng_000366) +Scores: (#C #S #D #I) 4 6 1 0 +REF: the ALBUM later BROKE THE DIAMOND record on Q Q MUSIC +HYP: the HLBM later BROK TH DIMAD record on * CUCUOM MUSICK +Eval: S S S S D S S + +Speaker sentences 113: lad_eng_000367 #utts: 1 +id: (lad_eng_000367-lad_eng_000367) +Scores: (#C #S #D #I) 4 6 0 0 +REF: its EDITORIAL we submit EARNED its AUTHOR A PULITZER PRIZE +HYP: its EDATORIAL we submit AND its OTHR APOL T OPRIYE +Eval: S S S S S S + +Speaker sentences 114: lad_eng_000368 #utts: 1 +id: (lad_eng_000368-lad_eng_000368) +Scores: (#C #S #D #I) 4 5 0 0 +REF: JOSEPH PLAYS ARE featured each WEEK on the SHOW +HYP: JOSIF PLAYES OUR featured each WEE on the HO +Eval: S S S S S + +Speaker sentences 115: lad_eng_000369 #utts: 1 +id: (lad_eng_000369-lad_eng_000369) +Scores: (#C #S #D #I) 6 10 1 0 +REF: they WAIT FOR A TIME BUILDING up THEIR FORCES BEGINNING to WONDER if this EVIL REALLY exists +HYP: they **** WAT FORA TIMEM BILDING up THER FORES BEGIN to ONDR if this EAVL REALY exists +Eval: D S S S S S S S S S S + +Speaker sentences 116: lad_eng_000370 #utts: 1 +id: (lad_eng_000370-lad_eng_000370) +Scores: (#C #S #D #I) 7 6 1 0 +REF: BRIEF mention of THE conviction APPEARED on page THREE of the NEW YORK TIMES +HYP: BREFE mention of TH conviction APPERD on page THRE of the *** NEWYOUOK TIMEMS +Eval: S S S S D S S + +Speaker sentences 117: lad_eng_000371 #utts: 1 +id: (lad_eng_000371-lad_eng_000371) +Scores: (#C #S #D #I) 7 4 0 0 +REF: ORDERED by POSITION on PITCH from back right to FRONT left +HYP: ODED by POSION on PICH from back right to FRUNT left +Eval: S S S S + +Speaker sentences 118: lad_eng_000372 #utts: 1 +id: (lad_eng_000372-lad_eng_000372) +Scores: (#C #S #D #I) 9 5 1 0 +REF: he is member of the court OF the ROYAL COLLEGE of art LONDON U K +HYP: he is member of the court O the RIL COLAE of art ****** LOUNDON YUCAY +Eval: S S S D S S + +Speaker sentences 119: lad_eng_000373 #utts: 1 +id: (lad_eng_000373-lad_eng_000373) +Scores: (#C #S #D #I) 5 9 0 2 +REF: DURING the course of ** ******* THE CAMPAIGN FERGUSON VISITED all THIRTY NINE WASHINGTON state COUNTIES +HYP: DURIG the course of TE CAMPAIN FIRGS AND VISIT AT all THERTY NEIN WASIGTAN state CONTES +Eval: S I I S S S S S S S S + +Speaker sentences 120: lad_eng_000374 #utts: 1 +id: (lad_eng_000374-lad_eng_000374) +Scores: (#C #S #D #I) 6 0 0 0 +REF: a strip of paper of length +HYP: a strip of paper of length +Eval: + +Speaker sentences 121: lad_eng_000375 #utts: 1 +id: (lad_eng_000375-lad_eng_000375) +Scores: (#C #S #D #I) 2 8 0 2 +REF: SATOU had ********** **** FREQUENTLY WORKED TOGETHER WITH YOKOYAMA on PREVIOUS PROJECTS +HYP: SATO had FRECUENTLY WORE TO GETH WTH YOUCK AYAMAR on PREVIOS POGJECTS +Eval: S I I S S S S S S S + +Speaker sentences 122: lad_eng_000376 #utts: 1 +id: (lad_eng_000376-lad_eng_000376) +Scores: (#C #S #D #I) 4 10 0 2 +REF: she WAS born ** ONSCREEN DURING the ***** EPISODE BROADCAST on FOURTH NOVEMBER NINETEEN NINETY FOUR +HYP: she AS born ON SCREAN DUIN the EPSOD BRAD CAST on FORHAN OVEMBER NINTE NINTY FOR +Eval: S I S S I S S S S S S S + +Speaker sentences 123: m #utts: 77 +id: (m-ailabs_eng_000159-m-ailabs_eng_000159) +Scores: (#C #S #D #I) 12 3 0 0 +REF: he turned round SHE had COME in so gently that he had never HEARD her +HYP: he turned round SH had COM in so gently that he had never HARD her +Eval: S S S + +id: (m-ailabs_eng_000160-m-ailabs_eng_000160) +Scores: (#C #S #D #I) 7 8 0 2 +REF: AH to be ****** SURE we must KEEP our **** DOORS SHUTWE MUST LET no ONE in +HYP: A to be SHOUOR AN we must CE our DORS SHOAT WE MUS LAT no ON in +Eval: S I S S I S S S S S + +id: (m-ailabs_eng_000161-m-ailabs_eng_000161) +Scores: (#C #S #D #I) 14 13 1 1 +REF: **** KINSMEN he began MOCKINGLY you MAY HAVE WONDERED WHY i CALLED a TRUCE when i could JUST as WELL HAVE DESTROYED you that i DOUBT ato ANSWERED him +HYP: CIDS PMON he began MOKINGLY you MA HVE ONDED WHIY i CALD a TROUS when i could JUS as **** WELLHAVE DISTRORED you that i DOUT ato ANSED him +Eval: I S S S S S S S S S D S S S S + +id: (m-ailabs_eng_000162-m-ailabs_eng_000162) +Scores: (#C #S #D #I) 6 10 2 0 +REF: the PEASANT THREW HIMSELF UPON HIM AND bound his FOUR LEGS TIGHTLY so THAT HE COULD not move +HYP: the ******* ***** PESNT THRUWHIMSELF APON HIMAND bound his FOR LAGS TITLY so TAT H CULD not move +Eval: D D S S S S S S S S S S + +id: (m-ailabs_eng_000163-m-ailabs_eng_000163) +Scores: (#C #S #D #I) 15 8 3 0 +REF: nor must thou so LIMIT the HOLY ONE OF ISRAEL as to think he hath but ONE way in which HE CAN GLORIFY HIMSELF by THEE +HYP: nor must thou so LIMETH the **** HLY ONOF ISRIAL as to think he hath but ON way in which ** *** CANGORIFIE HMSELF by THE +Eval: S D S S S S D D S S S + +id: (m-ailabs_eng_000164-m-ailabs_eng_000164) +Scores: (#C #S #D #I) 11 18 2 1 +REF: the old COMPARISON BETWEEN the IMPULSIVE EXECUTIVE and the LIBERAL arts man who HAS LEARNED THAT THERE ARE only ** ONE OR TWO POSITIVE DECISIONS AVAILABLE in ALL the WORLD OF THINKING +HYP: the old COMPARSON BETWE the IMPULSIE EXSECTIVE and the LIBRAL arts man who *** ******* WHAD LARNED THATHERE only ON R TO POSITVE DISIONS F ALBLE in AL the WAL O HINKING +Eval: S S S S S D D S S S I S S S S S S S S S S + +id: (m-ailabs_eng_000165-m-ailabs_eng_000165) +Scores: (#C #S #D #I) 7 8 0 0 +REF: after this EXPERIENCE the INVADERS WERE CAREFUL to KEEP a SAFE DISTANCE from the WALL +HYP: after this EXPERIANCE the NVATORS WER CAIRFUL to CEPE a SAVFE DISTNCE from the AL +Eval: S S S S S S S S + +id: (m-ailabs_eng_000166-m-ailabs_eng_000166) +Scores: (#C #S #D #I) 15 14 3 0 +REF: CAN YOU BEAR SOMETHING FURTHER i THINK you OUGHT TO KNOW it i have HERE a most MYSTERIOUS TELEPAGRAM YES what is it IS SHE DEAD NO it is not about her +HYP: AN OU BAR SOMTING FIRTHER i THN you ***** ATO NO it i have HER a most ********** MSTERIOUS TELAPARIGRAMYES what is it ** ISHE DID NOW it is not about her +Eval: S S S S S S D S S S D S S D S S S + +id: (m-ailabs_eng_000167-m-ailabs_eng_000167) +Scores: (#C #S #D #I) 6 5 0 2 +REF: no MISTER THORNTON said *** GIVE the BASKET to ** MEILL take it +HYP: no MSTR TOURTAN said AND GIE the ASKT to ME IAL take it +Eval: S S I S S I S + +id: (m-ailabs_eng_000168-m-ailabs_eng_000168) +Scores: (#C #S #D #I) 21 12 0 0 +REF: AN arabian night EXCLAIMED trot WHY that was a magic night WASNT it THERES DIFFERENT sorts OF NIGHTS mate said the SAILOR and the KNIGHT BUTTONBRIGHT means AINT the same night you mean +HYP: AND arabian night EXCLAMED trot WHIY that was a magic night WASN it THERS DIFRENT sorts O NIGHES mate said the SALER and the NIGHT BUTNBRIGHT means ANT the same night you mean +Eval: S S S S S S S S S S S S + +id: (m-ailabs_eng_000169-m-ailabs_eng_000169) +Scores: (#C #S #D #I) 18 9 2 0 +REF: IVE TURNED OFF upwards of a HUNDRED OF my BEST hands for no other FAULT THAN FOLLOWING you and such as you and DYE think ill take you ON +HYP: *** IVETRNED OF upwards of a HUNDED F my BESTD hands for no other FALT THEM FOLOING you and such as you and *** think ill take you AON +Eval: D S S S S S S S S D S + +id: (m-ailabs_eng_000170-m-ailabs_eng_000170) +Scores: (#C #S #D #I) 8 10 0 1 +REF: but WHEN SHOULD she SEE him her HEART LEAPED UP in APPREHENSION at every RING of ** THE DOORBELL +HYP: but WE WID she SE him her HART LEPT U in APREHENTION at every RIN of TH DOR BIL +Eval: S S S S S S S S I S S + +id: (m-ailabs_eng_000171-m-ailabs_eng_000171) +Scores: (#C #S #D #I) 14 17 1 0 +REF: these books DIXON i WILL KEEP ALL the rest WILL YOU SEND to MISTER BELL they ARE of a KIND THAT he WILL VALUE for THEMSELVES as WELL as for PAPAS SAKE +HYP: these books DICXSON i WL KEPE AL the rest **** WE OUSEND to MSTR BEL they AR of a CIND THT he WL VOULYOU for THMSELVES as WEL as for POPAS SAY +Eval: S S S S D S S S S S S S S S S S S S + +id: (m-ailabs_eng_000172-m-ailabs_eng_000172) +Scores: (#C #S #D #I) 23 12 0 1 +REF: BUT inga was not at ** ALL SURE THEY could not get in the GATES OPENED inward and THREE HEAVY bars were held in place by MEANS of stout staples RIVETED to the SHEETS of STEEL +HYP: UT inga was not at AL SHUR THAT THE could not get in the GATS OPED inward and THRE HEVY bars were held in place by MENS of stout staples RIVITED to the SHETS of STA +Eval: S I S S S S S S S S S S S + +id: (m-ailabs_eng_000173-m-ailabs_eng_000173) +Scores: (#C #S #D #I) 18 10 1 0 +REF: i want THAL said HODDAN coldly i WANT a DOZEN horses i want men to RIDE THEM with me he PUSHED HIS way FORWARD WHICH WAY to the stables +HYP: i want THOW said HODON coldly i WAN a DOSON horses i want men to BRIGD THE with me he PUSHD HI way ******* FORD WHICHWAY to the stables +Eval: S S S S S S S S D S S + +id: (m-ailabs_eng_000174-m-ailabs_eng_000174) +Scores: (#C #S #D #I) 19 10 2 2 +REF: THERE is a limit TO what YOU CAN do ** the first TIME you ENTER a mans house and BESIDES that was no time to AROUSE SUSPICION IN the MIND of *** ANYONE +HYP: ERE is a limit ** what *** YUCAND do FO the first THIE you ANTER a mans house and BESIDE that was no time to AROUS SUSPION N the MINDS of ANY WON +Eval: S D D S I S S S S S S S I S + +id: (m-ailabs_eng_000175-m-ailabs_eng_000175) +Scores: (#C #S #D #I) 10 10 0 1 +REF: do YOU not REMEMBER that he SAYS thy DEMON thats THY spirit which KEEPS THEE is noble ******** COURAGEOUS HIGH UNMATCHABLE +HYP: do OU not REMEMER that he SAS thy DEMAN thats THE spirit which CEPES THE is noble CORAGOUS HIY UN MACHIBL +Eval: S S S S S S S I S S S + +id: (m-ailabs_eng_000176-m-ailabs_eng_000176) +Scores: (#C #S #D #I) 8 8 1 0 +REF: MISTER bell WHAT CAN he KNOW of JOHN he living a LAZY LIFE in a DROWSY COLLEGE +HYP: MSTR bell **** WHACAN he NO of JOAON he living a LASY LIF in a DROUSY COLAGE +Eval: S D S S S S S S S + +id: (m-ailabs_eng_000177-m-ailabs_eng_000177) +Scores: (#C #S #D #I) 3 4 1 0 +REF: and the KITTEN FOLLOWED DEMURELY at THEIR HEELS +HYP: and the ****** CITN FOLOEDIMUARLY at THER HEALS +Eval: D S S S S + +id: (m-ailabs_eng_000178-m-ailabs_eng_000178) +Scores: (#C #S #D #I) 12 5 1 0 +REF: the FIRST TOUCH WOULD CAUSE an explosion in which among such hundreds of INFURIATED men and RECKLESS boys +HYP: the ***** FIST TUTCHWOLD CASE an explosion in which among such hundreds of INFERIATED men and RECKLES boys +Eval: D S S S S S + +id: (m-ailabs_eng_000179-m-ailabs_eng_000179) +Scores: (#C #S #D #I) 7 8 0 0 +REF: ONE OF THE GREAT PLEASURES of MARGARETS LIFE at this time was in EDITHS boy +HYP: WON F TH GEAT PLESUERS of MARGRATS LIF at this time was in EATS boy +Eval: S S S S S S S S + +id: (m-ailabs_eng_000180-m-ailabs_eng_000180) +Scores: (#C #S #D #I) 7 17 4 0 +REF: THE THING HAS GONE on long ENOUGH IF THERE is one MORE BIG ACCIDENT we SHALL have to COMPROMISE WITH THE INTERRIVER AND CARRY ON THE WORK JOINTLY +HYP: TH THNG IS GON on long ****** NOF THER is one ORE BIAG ACXIDENT we SHAL have to ********** **** *** COMPRMISE WIT THEINERIVER ND CARYON THEWORK CUINTLY +Eval: S S S S D S S S S S S D D D S S S S S S S + +id: (m-ailabs_eng_000181-m-ailabs_eng_000181) +Scores: (#C #S #D #I) 6 6 1 0 +REF: YOU ARE LATE said she WELL she held her BREATH FOR the ANSWER +HYP: *** YOUAR LAT said she WEL she held her BRATH O the ANSR +Eval: D S S S S S S + +id: (m-ailabs_eng_000182-m-ailabs_eng_000182) +Scores: (#C #S #D #I) 10 15 0 1 +REF: TROT told the girls that they MUST go WITH THEIR father to *** LIVE IN GHIPGHISIZZLES LITTLE OLD CABIN and WHEN they HEARD THIS DREADFUL DECREE +HYP: TRAT told the girls that they MUS go WIT HER father to LIV AND GIP CUSISILS LITE LD CABEN and HEN they HERD THS REDFUL DECRE +Eval: S S S S I S S S S S S S S S S S + +id: (m-ailabs_eng_000183-m-ailabs_eng_000183) +Scores: (#C #S #D #I) 13 13 0 1 +REF: MARGARET sat DOWN ON the RUG PARTLY to warm herself for the DAMPNESS OF the EVENING hung ABOUT her DRESS and **** OVERFATIGUE had MADE her CHILLY +HYP: MARGIT sat DON O the ROG PATLY to warm herself for the DAMPNES O the EVNING hung BOUT her DRES and OVER FITE had MAD her CHILY +Eval: S S S S S S S S S S I S S S + +id: (m-ailabs_eng_000184-m-ailabs_eng_000184) +Scores: (#C #S #D #I) 14 9 3 0 +REF: OH NO YOU ARE MISTAKEN about that REPLIED the king THEY ARE NOT my prisoners but my slaves whom I PURCHASED from the KING of ev +HYP: ** O NOW YOUAR MSTAKAN about that RELID the king **** *** THEYARENOT my prisoners but my slaves whom MY PURCUSE from the CING of ev +Eval: D S S S S S D D S S S S + +id: (m-ailabs_eng_000185-m-ailabs_eng_000185) +Scores: (#C #S #D #I) 1 4 1 0 +REF: her FATHER TOOK UP THE CONVERSATION +HYP: her ****** FATHE TOKU TE CMBRSATION +Eval: D S S S S + +id: (m-ailabs_eng_000186-m-ailabs_eng_000186) +Scores: (#C #S #D #I) 15 12 3 1 +REF: in A CORNER was a sort of ******* DRESSINGTABLE on which lay a COMB and brush KENNEDY SEEMED much INTERESTED IN THE table AND was EXAMINING IT WHEN the GURU RETURNED +HYP: in * ACORNER was a sort of DRESING TABLE on which lay a COM and brush CANIDY SEED much ********** INTRUSTED INTHE table AN was ********* EXAMING ITWHN the GORU RETERNE +Eval: D S I S S S S D S S S D S S S S + +id: (m-ailabs_eng_000187-m-ailabs_eng_000187) +Scores: (#C #S #D #I) 19 13 2 1 +REF: i have **** SOMETIMES THOUGHT that myself she AGREED but OF COURSE i dont KNOW STILL i have to be PRETTY CAREFUL SOME ONE is always over HERE by my DESK or LOOKING over HERE +HYP: i have SOME TIME THGT that myself she AGEED but ** OFCOURS i dont NOW STIL i have to be ****** PITY CARFUL SOMEON is always over HER by my DESS or LOKING over HER +Eval: I S S S D S S S D S S S S S S S + +id: (m-ailabs_eng_000188-m-ailabs_eng_000188) +Scores: (#C #S #D #I) 7 6 1 0 +REF: i SHALL stay REPLIED THE YOUNG man for i mean to SET YOU FREE +HYP: i SHL stay ******* REPLID THEYONG man for i mean to SIT YO FRE +Eval: S D S S S S S + +id: (m-ailabs_eng_000189-m-ailabs_eng_000189) +Scores: (#C #S #D #I) 4 3 0 0 +REF: what DO YOU do ASKED the sorcerer +HYP: what D YO do ASD the sorcerer +Eval: S S S + +id: (m-ailabs_eng_000190-m-ailabs_eng_000190) +Scores: (#C #S #D #I) 18 12 1 0 +REF: WHY THEYRE OUR ENEMIES your short HIGHNESS not any more replied TROT im QUEEN of the PINKIES and im also QUEEN of THE BLUES so i wont have my PEOPLE QUARRELING +HYP: WHIY THERE AR ENAMES your short HINES not any more replied TROAT im QUE of the INKES and im also QUE of *** THELOS so i wont have my PEPLE QUARLING +Eval: S S S S S S S S S D S S S + +id: (m-ailabs_eng_000191-m-ailabs_eng_000191) +Scores: (#C #S #D #I) 8 23 1 3 +REF: TYPEWRITERS WERE clicking CLIPPINGS WERE BEING SNIPPED OUT OF a HUGE STACK of *** NEWSPAPERS and ***** ** PASTED INTO LARGE SCRAPBOOKS CIRCULARS WERE BEING folded AND MADE READY to MAIL FOR the final APPEAL +HYP: TIPRITER WE clicking ********* CLIPING WER BING SNIPD OTOF a UGE TACK of NOE PERS and PASED IN AN N LARG SCRAPBOKS SERKULER WER BENG folded AN MAD REAY to MAL FO the final APEL +Eval: S S D S S S S S S S I S I I S S S S S S S S S S S S S + +id: (m-ailabs_eng_000192-m-ailabs_eng_000192) +Scores: (#C #S #D #I) 14 10 0 0 +REF: it was FOUR days after the SURPRISE of ADLERS HORST WHEN the strangers LEFT the ESTATE to the CARE of RUGGED old forster HERMANN +HYP: it was FOR days after the SUPRIES of ALTHERS HORS HEN the strangers LET the ASTAT to the CAIR of RUGED old forster HARMEN +Eval: S S S S S S S S S S + +id: (m-ailabs_eng_000193-m-ailabs_eng_000193) +Scores: (#C #S #D #I) 12 14 8 3 +REF: POOR TEMPLETON he said i USED TO KNOW him **** YEARS ago WHEN we WERE boys WENT TO SCHOOL WITH HIM and ALL that SORT OF THING YOU KNOW but ** UNTIL i ran **** ACROSS HIM +HYP: BPOR TEMPLTON he said i **** ** USTONOW him MANY EARS ago HE we E boys **** ** ****** MENTO SCOULWITHM and AL that **** ** ***** SOUTOFTHNG YONOW but AN TIL i ran CROS HM OR +Eval: S S D D S I S S S D D D S S S D D D S S I S I S S + +id: (m-ailabs_eng_000194-m-ailabs_eng_000194) +Scores: (#C #S #D #I) 7 8 0 0 +REF: i FOUND her IN the FOREST and BROUGHT her HERE a PRISONER REPLIED the CAPTAIN +HYP: i FOND her I the FARIST and BOGT her HER a PRISNE REPLIE the CAPTON +Eval: S S S S S S S S + +id: (m-ailabs_eng_000195-m-ailabs_eng_000195) +Scores: (#C #S #D #I) 16 10 0 0 +REF: who may be COMPETENT EITHER from PERSONAL EXPERIENCE or the EXPERIENCE of others to ANSWER it with MORE or LESS CORRECTNESS or at least an ATTEMPT +HYP: who may be COMPITENT ITHE from PERSINAL EXPERIANCE or the EXPINS of others to ANSER it with MOR or LES CURECTNES or at least an ATTEMTD +Eval: S S S S S S S S S S + +id: (m-ailabs_eng_000196-m-ailabs_eng_000196) +Scores: (#C #S #D #I) 2 8 1 0 +REF: ONE HUNDRED NINETYTWO LAYTE STREET SAID HOGAN biting OFF his CIGAR +HYP: *** ON NINTY TO LATESTRETET SID HOKGEN biting OF his SAGAR +Eval: D S S S S S S S S + +id: (m-ailabs_eng_000197-m-ailabs_eng_000197) +Scores: (#C #S #D #I) 16 17 0 0 +REF: TROT WAS SURPRISED to FIND she COULD SEE so plainly THROUGH the HIGH WALL of water above her but the SUN was ABLE to SHOOT its BEAMS STRAIGHT DOWN THROUGH the transparent SEA +HYP: TRAT WA SRPRIE to FINE she COUD CE so plainly THR the HIY WAL of water above her but the SON was ABL to SHUT its BEME STRAT DOW THO the transparent SE +Eval: S S S S S S S S S S S S S S S S S + +id: (m-ailabs_eng_000198-m-ailabs_eng_000198) +Scores: (#C #S #D #I) 2 4 1 0 +REF: the SPOT WHERE IT HAD SPRUNG up +HYP: the **** SPAT WER ID SPRNG up +Eval: D S S S S + +id: (m-ailabs_eng_000199-m-ailabs_eng_000199) +Scores: (#C #S #D #I) 4 4 1 0 +REF: CALM DENIAL WHICH she gave TO such a SUPPOSITION +HYP: COME DENIL WIC she gave ** such a UPOSION +Eval: S S S D S + +id: (m-ailabs_eng_000200-m-ailabs_eng_000200) +Scores: (#C #S #D #I) 12 12 1 1 +REF: you see UNTIL THESE SCHOOL PILLS WERE invented we wasted a lot of time IN STUDY that MAY now *** be BETTER EMPLOYED IN PRACTICING ATHLETICS +HYP: you see ANDTIL THE SCOL PILS WER invented we wasted a lot of time IND STUADY that *** now MAY be BETER IMPLOYED INM PRACTISING EATHLATIK +Eval: S S S S S S S D I S S S S S + +id: (m-ailabs_eng_000201-m-ailabs_eng_000201) +Scores: (#C #S #D #I) 4 6 1 0 +REF: YOUVE DONE it now DECLARED DOROTHY THESE tents ARE just WONDERFUL +HYP: ***** YOVEDON it now DICLARE DARTHY THES tents AR just WONDERFL +Eval: D S S S S S S + +id: (m-ailabs_eng_000202-m-ailabs_eng_000202) +Scores: (#C #S #D #I) 9 16 1 1 +REF: for TWENTY ten five **** THREE TWOTHE LINER was BARELY TWENTY MILES away WHEN HODDAN fired his ROCKETS THEY MADE A COLOSSAL cloud OF VAPOR in EMPTINESS +HYP: for TWENING ten five THRE TWO THE IN was BARLY TWENY MOUS away WHN HODON fired his ******* ROCKITS THE MDE CALOSAL cloud O VAPER in EMTINES +Eval: S I S S S S S S S S D S S S S S S S + +id: (m-ailabs_eng_000203-m-ailabs_eng_000203) +Scores: (#C #S #D #I) 19 10 1 0 +REF: they PAID no ATTENTION to the FACT THAT GHIPGHISIZZLE did not WANT to MARRY any of them for they HAD DETERMINED that WHEN it was agreed who SHOULD have him +HYP: they PAD no ATENCION to the FACTHAT GIP GUSSISL did not WNT to MARY any of them for they *** HDETERMEND that HEN it was agreed who HOUD have him +Eval: S S S S S S S D S S S + +id: (m-ailabs_eng_000204-m-ailabs_eng_000204) +Scores: (#C #S #D #I) 8 10 4 0 +REF: what DO YOU THINK of that he CRIED OPENING a COPY OF THE RECORD and LAYING IT flat ON THE LIBRARY table +HYP: what ** *** DOUTIN of that he CRIDE OPENG a **** COPYO HE RECARD and LAIG T flat ** ONTHE LIBRY table +Eval: D D S S S D S S S S S D S S + +id: (m-ailabs_eng_000205-m-ailabs_eng_000205) +Scores: (#C #S #D #I) 3 4 0 0 +REF: it WILL REQUIRE but a SHORT TIME +HYP: it L RECUIER but a SHOURT TIM +Eval: S S S S + +id: (m-ailabs_eng_000206-m-ailabs_eng_000206) +Scores: (#C #S #D #I) 12 5 0 0 +REF: and last the CROWD OF VEGETABLE people who had no HEARTS and could NEITHER smile nor frown +HYP: and last the CROUD O VEGITABLE people who had no HARTS and could NITHER smile nor frown +Eval: S S S S S + +id: (m-ailabs_eng_000207-m-ailabs_eng_000207) +Scores: (#C #S #D #I) 3 4 0 0 +REF: then YOULL CATCH it SAID the WITCH +HYP: then YOUL CACH it SI the WICH +Eval: S S S S + +id: (m-ailabs_eng_000208-m-ailabs_eng_000208) +Scores: (#C #S #D #I) 10 12 2 0 +REF: what is it i QUERIED not FEELING CERTAIN but THAT IT was a VEILED ATTEMPT to secure A LITTLE FREE ADVERTISING FOR THE VANDERVEER +HYP: what is it i QUIRED not FELING SERTN but THA I was a VALED ATEMP to secure * ****** LITL FRE ADRTISING FORTHE ANDEOVER +Eval: S S S S S S S D D S S S S S + +id: (m-ailabs_eng_000209-m-ailabs_eng_000209) +Scores: (#C #S #D #I) 20 10 1 0 +REF: so he gave the CLERK THE THIRD HUNDRED DOLLARS for books and a cask of GOOD old ALE for peter the CLERK drank the ALE himself and gave the CALF MILK +HYP: so he gave the ***** LURK THA THRD HUNRDOLRS for books and a cask of GOD old AL for peter the CLURK drank the AIL himself and gave the CAH MI +Eval: D S S S S S S S S S S + +id: (m-ailabs_eng_000210-m-ailabs_eng_000210) +Scores: (#C #S #D #I) 17 14 1 1 +REF: ** like that IN ALICE in WONDERLAND with MERELY a grin THAT FADED away changing into a LYNX which IN TURN DISAPPEARED FOLLOWED by an UNKNOWN CREATURE with short NOSE and POINTED ears +HYP: AT like that AN ALS in WNERLANT with MERLY a grin HAT FATED away changing into a LINKXE which ** INTURN DISOPERED FOOED by an UNON CREATUER with short NOWS and PONED ears +Eval: I S S S S S S S D S S S S S S S + +id: (m-ailabs_eng_000211-m-ailabs_eng_000211) +Scores: (#C #S #D #I) 7 14 0 4 +REF: she COULD not ** ******* DOMARGARET GLANCED UNCONSCIOUSLY at the ** *** UNCLEANED CORNERS OF THE ROOMSHE COULD HARDLY UNDERTAKE a SERVANTS place COULD she +HYP: she COUD not DO MARGRIT LANSED UN CONIOUSLY at the UN CLE CORNER F TH ROM SHE COUDHARTHY UDER TAKE a SERINTS place COUL she +Eval: S I I S S S I I S S S S S S S S S S + +id: (m-ailabs_eng_000212-m-ailabs_eng_000212) +Scores: (#C #S #D #I) 9 3 0 0 +REF: no she REPLIED with INNOCENT CURIOSITY did i give them to you +HYP: no she REPLIDED with INISN CARIOUSITY did i give them to you +Eval: S S S + +id: (m-ailabs_eng_000213-m-ailabs_eng_000213) +Scores: (#C #S #D #I) 8 9 1 0 +REF: MARLBOROUGH MILLS AND THE ADJACENT DWELLING were held under A long LEASE they must if POSSIBLE be RELET +HYP: MARBRO MILES AN THEA GACSENT DWELIN were held under * long LEACTS they must if POSIBLE be RELEAT +Eval: S S S S S S D S S S + +id: (m-ailabs_eng_000214-m-ailabs_eng_000214) +Scores: (#C #S #D #I) 1 6 0 0 +REF: a COP WAVED A STUNPISTOL AT HIM +HYP: a CAP WAVE OSTON IS THE LADOR +Eval: S S S S S S + +id: (m-ailabs_eng_000215-m-ailabs_eng_000215) +Scores: (#C #S #D #I) 17 11 0 0 +REF: it bounded HERE and THERE ABOUT the CHICKEN house and at first DOROTHY could not TELL what it WAS WHILE the SCREECHING of the CHICKENS nearly DEAFENED her +HYP: it bounded HEAR and THEIR ABOT the CICAN house and at first DORTH could not TEL what it WASS WHIL the SCREACING of the CICONS nearly DEFEND her +Eval: S S S S S S S S S S S + +id: (m-ailabs_eng_000216-m-ailabs_eng_000216) +Scores: (#C #S #D #I) 15 14 0 1 +REF: the SOLDIER gave a YELL that AROUSED a SCORE of his COMRADES and BROUGHT them tumbling into the STREET WHEN THEY saw HOW the BOOLOOROOS PRECIOUS PRISONER was * ESCAPING +HYP: the SOLDER gave a YAL that AROUWSED a SCOR of his COMRADS and BOGHT them tumbling into the STREAT WEN THE saw HO the BOLRSE PRESIOUS PRISNE was E SCAPING +Eval: S S S S S S S S S S S S S I S + +id: (m-ailabs_eng_000217-m-ailabs_eng_000217) +Scores: (#C #S #D #I) 21 8 0 0 +REF: jim had refused to leave the field of grass where he was ENGAGED IN busily eating so the WIZARD got out OF the BUGGY and JOINED ZEB and DOROTHY +HYP: jim had refused to leave the field of grass where he was NGAGED N busily eating so the WISURD got out O the UG and JONED SEB and DORITHY +Eval: S S S S S S S S + +id: (m-ailabs_eng_000218-m-ailabs_eng_000218) +Scores: (#C #S #D #I) 6 11 5 0 +REF: CERTAINLY I AM AS INTERESTED IN the CASE AS YOU ARE but i CANT MAKE HEADS OR TAILS of it i REPLIED +HYP: ********* * SERTNLY IMAS INRUTD I the **** CACES OU AR but i **** **** CAN MAK HADSRTALS of it i REPLID +Eval: D D S S S S D S S S D D S S S S + +id: (m-ailabs_eng_000219-m-ailabs_eng_000219) +Scores: (#C #S #D #I) 5 1 0 1 +REF: or any mice or even **** GRASSHOPPERS +HYP: or any mice or even GRAS HOPERS +Eval: I S + +id: (m-ailabs_eng_000220-m-ailabs_eng_000220) +Scores: (#C #S #D #I) 12 18 2 2 +REF: and THEM THAT PAYS YO DUN THEY TELL YO WHATTEN to do or *** WHATTEN not to do WI the MONEY they GIVES you IN just PAYMENT FOR your ***** PAINSIN FAIR EXCHANGE LIKE +HYP: and **** **** THE THA PASIO DON THYTEL YOU WAT to do or WHT IN not to do WE the MONY they GIVE you AN just PAMENT FO your PAINS IN THER EXTANGE LIG +Eval: D D S S S S S S S I S S S S S S S I S S S S + +id: (m-ailabs_eng_000221-m-ailabs_eng_000221) +Scores: (#C #S #D #I) 3 4 0 0 +REF: what DOES THAT mean ASKED the PRINCESS +HYP: what DIS TAT mean AS the PRINCES +Eval: S S S S + +id: (m-ailabs_eng_000222-m-ailabs_eng_000222) +Scores: (#C #S #D #I) 15 11 0 0 +REF: he had BEEN DROWNED he was FLOATING in a SEA of LIGHT and now AND then shining LITTLE FISHES SWAM INQUISITIVELY up to him and STARED +HYP: he had BE DROUND he was FLOTING in a SE of LIGT and now N then shining LITLE FIHES SWEAM INCQUISITIVELY up to him and STARE +Eval: S S S S S S S S S S S + +id: (m-ailabs_eng_000223-m-ailabs_eng_000223) +Scores: (#C #S #D #I) 22 22 4 0 +REF: but old GUNNAR HAD A TRICK OR TWO left REMEMBER the TALE THAT i READ to you IN THE THRONEROOM OF BALDAR the first OF the BRONS to ENTER the world of OPAL were SOLDIERS sent from some blasted PLANET in OUTER space to FIND A NEW HOME +HYP: but old ****** *** GUN HADA TRCKA TO left ANDREMEME the **** TAIL i RED to you I TH THON ROM ABOTHER the first F the RAONS to ND the world of OPL were SOLGERS sent from some blasted PLANIT in OUTR space to **** FINE ANW HO +Eval: D D S S S S S D S S S S S S S S S S S S S S D S S S + +id: (m-ailabs_eng_000224-m-ailabs_eng_000224) +Scores: (#C #S #D #I) 10 11 2 0 +REF: papa WILL YOU SPEAK TO the men and GET THEM TO go away she CANNOT BREATHE POOR thing WITH this CROWD ABOUT her +HYP: papa **** *** WIL OUSPEKT the men and GE HE O go away she CANOT BREETH POR thing WIT this CROUD OABOUT her +Eval: D D S S S S S S S S S S S + +id: (m-ailabs_eng_000225-m-ailabs_eng_000225) +Scores: (#C #S #D #I) 13 12 3 0 +REF: when i took this CASE he said i BELIEVED down IN my HEART THAT DIXON was INNOCENT i STILL BELIEVE IT but my FAITH HAS BEEN RUDELY SHAKEN +HYP: when i took this CACE he said i BLEVE down IND my ***** HART DIXSON was INSENT i ***** STO BELEIT but my ***** FATHAS BEN RUDTLY SHAKE +Eval: S S S D S S S D S S D S S S S + +id: (m-ailabs_eng_000226-m-ailabs_eng_000226) +Scores: (#C #S #D #I) 2 5 0 1 +REF: CHAPTER SIX OF the PIRATES of ** ERSATZ +HYP: CHAPTR SICK OFE the PIRT of OR SEATS +Eval: S S S S I S + +id: (m-ailabs_eng_000227-m-ailabs_eng_000227) +Scores: (#C #S #D #I) 1 4 0 1 +REF: ******* REMEMBER THEY CANNOT TOUCH us +HYP: REMEMBE THE CAN NOT TUCH us +Eval: I S S S S + +id: (m-ailabs_eng_000228-m-ailabs_eng_000228) +Scores: (#C #S #D #I) 11 14 0 3 +REF: GIVE me time AZURE give me time if THERES anything i HATE its a **** **** * HURRY IVE AN IDEA YOUR MAJESTY ANNOUNCED the SIXTH SNUBNOSED PRINCESS +HYP: IVE me time ASYOUR give me time if HERS anything i HAT its a HURY IVEN I DA YOU MAGUSTY AND OUNCE THE SIXT the SNUB NOSD PRINCES +Eval: S S S S I I I S S S S S S S S S S + +id: (m-ailabs_eng_000229-m-ailabs_eng_000229) +Scores: (#C #S #D #I) 2 5 0 1 +REF: ** TRUE ENOUGH TROT DECLARED the SAILOR man +HYP: TO NOF TRAT O CLARED the SALER man +Eval: I S S S S S + +id: (m-ailabs_eng_000230-m-ailabs_eng_000230) +Scores: (#C #S #D #I) 8 8 1 0 +REF: as for that said MARGARET RATHER HAUGHTILY i hold it is HONI SOIT QUI MAL Y PENSE +HYP: as for that said ******** MARGRIT RETHERHOATALY i hold it is HONEY SO IT QUEE MALLD EPENSAY +Eval: D S S S S S S S S + +id: (m-ailabs_eng_000231-m-ailabs_eng_000231) +Scores: (#C #S #D #I) 21 12 1 0 +REF: when HE HEARD THESE words the king WHOSE HEAD was FULL of THE PRINCESS never STOPPED to INQUIRE if THEY could be TRUE and smeared himself over with fat and sprang INTO the oven +HYP: when ** HEHERD THES words the king WHOS HAD was FUL of TH PINCES never STOPE to INQUIR if THE could be TRU and smeared himself over with fat and sprang INT the oven +Eval: D S S S S S S S S S S S S + +id: (m-ailabs_eng_000232-m-ailabs_eng_000232) +Scores: (#C #S #D #I) 6 17 6 2 +REF: YOU SHOULD BE ABLE TO GET PARTS from your **** ROOM VISIONRECEIVER ILL HAVE SOME TOOLS given ** YOU THEN he ADDED DIPLOMACY has to UNDERSTAND THE THINGS THAT CONTROL EVENTS +HYP: *** ****** ** **** YOSHOULDBEALE GT PARTCE from your WROM VION RECEVER IL HAV SOM TOULS given OU THENHEATD DEPLOMAS he ***** ********* has to NDERSTAND TH TINGS HA CNTROL OFVENCS +Eval: D D D D S S S I S S S S S S I S S D D S S S S S S + +id: (m-ailabs_eng_000233-m-ailabs_eng_000233) +Scores: (#C #S #D #I) 9 6 0 0 +REF: by the TIME the frost had SET in THEY SHOULD be far AWAY from HELSTONE +HYP: by the TIM the frost had SAD in THE SHUL be far WAY from HELSTON +Eval: S S S S S S + +id: (m-ailabs_eng_000234-m-ailabs_eng_000234) +Scores: (#C #S #D #I) 4 4 0 0 +REF: ONE thing i WANT to say BEGAN KENNEDY +HYP: WON thing i WNT to say BEGAND CANITY +Eval: S S S S + +id: (m-ailabs_eng_000235-m-ailabs_eng_000235) +Scores: (#C #S #D #I) 4 7 1 0 +REF: this IMPORTANT TRAFFIC WAS CONFIDED to no ONE BUT the REAL PROPRIETOR +HYP: this ********* MPORTN TRAFIC WASCONFIGED to no ON UT the EAL PROPRITER +Eval: D S S S S S S S + +Speaker sentences 124: cv_eng_000707 #utts: 1 +id: (cv_eng_000707-cv_eng_000707) +Scores: (#C #S #D #I) 0 9 0 3 +REF: ** *** ** HE WAS REPLACED ON BASS GUITAR BY JUSTIN KLUG +HYP: IN YEO AR DOB BLASEDT PONT BAS OU DOT MY THSTIMG GO +Eval: I I I S S S S S S S S S + +Speaker sentences 125: cv_eng_000708 #utts: 1 +id: (cv_eng_000708-cv_eng_000708) +Scores: (#C #S #D #I) 4 6 0 0 +REF: ID ADD a SEPARATE SUBSECTION which deals WITH THIS aspect +HYP: IGHTE AT a EPRITD SUPSECTION which deals WIT IS aspect +Eval: S S S S S S + +Speaker sentences 126: cv_eng_000709 #utts: 1 +id: (cv_eng_000709-cv_eng_000709) +Scores: (#C #S #D #I) 3 5 1 1 +REF: OPERATION of the TRUNK LINE CONTINUED on *** WOODEN TRESTLES +HYP: OPRATION of the ***** FRUNTLANG CONTNED on THE GOULDANT TRESSELS +Eval: S D S S I S S + +Speaker sentences 127: cv_eng_000710 #utts: 1 +id: (cv_eng_000710-cv_eng_000710) +Scores: (#C #S #D #I) 2 7 2 0 +REF: MAGNESIUM FLUORIDE is TRANSPARENT OVER AN EXTREMELY WIDE RANGE of WAVELENGTHS +HYP: MONSIOM FLORID is *********** **** TWENSPERENT OVERANEXTRIMLY WHIHD RANG of AVELINGS +Eval: S S D D S S S S S + +Speaker sentences 128: cv_eng_000711 #utts: 1 +id: (cv_eng_000711-cv_eng_000711) +Scores: (#C #S #D #I) 2 11 1 2 +REF: *** FOUR GIANT PACKING SHEDS STORED fresh PACKED POTATOES AND DELIVERED them **** ONTO RAILROAD CARS +HYP: FOR JGINT BEAKINK SHEATS STOR T fresh ****** BPACKET BUTEADESSON DILVED them UNTO RELER OLD GASS +Eval: I S S S S S D S S S I S S S + +Speaker sentences 129: cv_eng_000712 #utts: 1 +id: (cv_eng_000712-cv_eng_000712) +Scores: (#C #S #D #I) 5 9 0 1 +REF: the OTHER FOURTEEN CAMPUSES are ** TWOYEAR CAMPUSES REFERRED to COLLECTIVELY as the UNIVERSITY COLLEGE +HYP: the OTHE FORTIN CAMPAS are TO YA CAMPSS REFERD to COLECTIVELY as the YUNERSTI COLAGE +Eval: S S S I S S S S S S + +Speaker sentences 130: cv_eng_000713 #utts: 1 +id: (cv_eng_000713-cv_eng_000713) +Scores: (#C #S #D #I) 4 8 1 0 +REF: its TOO BAD THAT HES QUICKLY GOING to FORGET my name HE THOUGHT +HYP: its TO THEARD THOW HE CUICKLE GON to FRGET my name ** TH +Eval: S S S S S S S D S + +Speaker sentences 131: cv_eng_000714 #utts: 1 +id: (cv_eng_000714-cv_eng_000714) +Scores: (#C #S #D #I) 1 14 0 1 +REF: ONE PICTURE in ** THE GALLERY SHOWS HOW DILIGENT SLAVES ERECT THE STATUE OF ADMIRAL THOMPSON +HYP: WON POTURE in TE GLOR SHO TH HOWD HE AGENTLY INT IRENTISTHATHYE AL ADGR ART TOMPON +Eval: S S I S S S S S S S S S S S S + +Speaker sentences 132: cv_eng_000715 #utts: 1 +id: (cv_eng_000715-cv_eng_000715) +Scores: (#C #S #D #I) 1 1 0 1 +REF: * imperial DIET +HYP: A imperial DIYIAT +Eval: I S + +Speaker sentences 133: cv_eng_000716 #utts: 1 +id: (cv_eng_000716-cv_eng_000716) +Scores: (#C #S #D #I) 1 6 0 2 +REF: the ******* ****** RESULTING COMPANY IS STRATTEC SECURITY CORPORATION +HYP: the ESULTIN OMPANY ED AS HAR TAESICURITY COT PORATION +Eval: I I S S S S S S + +Speaker sentences 134: cv_eng_000717 #utts: 1 +id: (cv_eng_000717-cv_eng_000717) +Scores: (#C #S #D #I) 4 8 0 1 +REF: BITCOIN mining can be *** DONE WITH GRAPHICS CARDS or WITH SPECIALIZED HARDWARE +HYP: BECOING mining can be DON WIT GOF HIS CARTS or WITES SPESIALIED HORDLY +Eval: S I S S S S S S S + +Speaker sentences 135: cv_eng_000718 #utts: 1 +id: (cv_eng_000718-cv_eng_000718) +Scores: (#C #S #D #I) 3 3 0 1 +REF: they ** ALSO LEAD the NATIONAL ranking +HYP: they AL SO LE the NASIAL ranking +Eval: I S S S + +Speaker sentences 136: cv_eng_000719 #utts: 1 +id: (cv_eng_000719-cv_eng_000719) +Scores: (#C #S #D #I) 1 4 0 0 +REF: CHARLES GRAVES BISHOP of LIMERICK +HYP: TROWS GRAINS BISHIP of NIMERICE +Eval: S S S S + +Speaker sentences 137: cv_eng_000720 #utts: 1 +id: (cv_eng_000720-cv_eng_000720) +Scores: (#C #S #D #I) 4 6 1 0 +REF: AND AT that I TOLD him AND he TOOK my PLACE +HYP: *** IONDED that THI DORL him UN he OK my PLASES +Eval: D S S S S S S + +Speaker sentences 138: cv_eng_000721 #utts: 1 +id: (cv_eng_000721-cv_eng_000721) +Scores: (#C #S #D #I) 4 3 1 0 +REF: i THOUGHT id give the KIDS A TREAT +HYP: i THOUGT id give the **** CITS ADREET +Eval: S D S S + +Speaker sentences 139: cv_eng_000722 #utts: 1 +id: (cv_eng_000722-cv_eng_000722) +Scores: (#C #S #D #I) 1 4 0 2 +REF: ** *** ACEVEDO DENIED SHOWING the PICTURES +HYP: AS THE VITL DINIH TOM the PICHES +Eval: I I S S S S + +Speaker sentences 140: cv_eng_000723 #utts: 1 +id: (cv_eng_000723-cv_eng_000723) +Scores: (#C #S #D #I) 3 9 0 0 +REF: HOLD your NOSE to KEEP THE SMELL from DISABLING YOUR MOTOR FUNCTIONS +HYP: HOWD your NOST to CE THIS MAYE from THE ABLING YORMOT ORFNTION +Eval: S S S S S S S S S + +Speaker sentences 141: cv_eng_000724 #utts: 1 +id: (cv_eng_000724-cv_eng_000724) +Scores: (#C #S #D #I) 1 4 0 1 +REF: ** that SOUNDS LIKE THEIR PROBLEM +HYP: AC that SONDS LAKE THEAR PROLOMEMIC +Eval: I S S S S + +Speaker sentences 142: cv_eng_000725 #utts: 1 +id: (cv_eng_000725-cv_eng_000725) +Scores: (#C #S #D #I) 5 6 3 0 +REF: HISTORICALLY THERE was no CLEARLY DEFINED BOUNDARY IN this PART of the ARABIAN PENINSULA +HYP: ************ HISTRICALIGER was no ******* CLEARELY DEFINE BOUNGRYEN this PIT of the ******* ARABYENPNINSTOLE +Eval: D S D S S S S D S + +Speaker sentences 143: cv_eng_000726 #utts: 1 +id: (cv_eng_000726-cv_eng_000726) +Scores: (#C #S #D #I) 7 6 0 0 +REF: MARSHALL SHAFFER of slash FILM gave the FILM an EIGHT out of TEN +HYP: MARSHIAL SHAVER of slash FILME gave the FILLME an ATE out of TEAEN +Eval: S S S S S S + +Speaker sentences 144: cv_eng_000727 #utts: 1 +id: (cv_eng_000727-cv_eng_000727) +Scores: (#C #S #D #I) 1 4 0 0 +REF: HOW CAN YOU SAY that +HYP: AOL PIND IO TI that +Eval: S S S S + +Speaker sentences 145: cv_eng_000728 #utts: 1 +id: (cv_eng_000728-cv_eng_000728) +Scores: (#C #S #D #I) 3 4 0 0 +REF: HIS STYLE began to resemble MICHAEL DAMASKINOS +HYP: HIST TDILE began to resemble MICAL TEMASSCKEINOS +Eval: S S S S + +Speaker sentences 146: cv_eng_000729 #utts: 1 +id: (cv_eng_000729-cv_eng_000729) +Scores: (#C #S #D #I) 4 7 1 0 +REF: he is ALSO CAPABLE of FIRING LIGHTNING BOLTS WITH IMMENSE DESTRUCTIVE power +HYP: he is ALSOL CAPABL of ****** FIRNGLIGT INMBLE WIF IMENTE DISRUPTIVE power +Eval: S S D S S S S S + +Speaker sentences 147: cv_eng_000730 #utts: 1 +id: (cv_eng_000730-cv_eng_000730) +Scores: (#C #S #D #I) 1 11 1 3 +REF: HE CLAIMED TWO WICKETS IN ENGLANDS ONLY INNINGS as *** *** ***** BORDER WERE BEATEN COMPREHENSIVELY +HYP: ** THE CLAME TO WIKEDS CENINGLIN PURMALYT ININGS as FOW WIS EATAN THURON ADERASY LADLI RE +Eval: D S S S S S S S I I I S S S S + +Speaker sentences 148: cv_eng_000731 #utts: 1 +id: (cv_eng_000731-cv_eng_000731) +Scores: (#C #S #D #I) 1 4 0 1 +REF: she * DID MUCH LITERARY WORK +HYP: she E GRUSILY TRO WHAT H +Eval: I S S S S + +Speaker sentences 149: cv_eng_000732 #utts: 1 +id: (cv_eng_000732-cv_eng_000732) +Scores: (#C #S #D #I) 6 7 1 0 +REF: he MET the ORGANIZERS of the PROTESTS and AGREED TO CREATE two WORKING GROUPS +HYP: he MT the ORGANISERS of the PROTES and AGRED D CREAT two ******* WORKINGROMS +Eval: S S S S S S D S + +Speaker sentences 150: cv_eng_000733 #utts: 1 +id: (cv_eng_000733-cv_eng_000733) +Scores: (#C #S #D #I) 2 9 0 0 +REF: the BALL STRUCK THE FOUL POLE WELL ABOVE the GREEN MONSTER +HYP: the BON STROC THO FHOLD WOARD WIL ABOF the REN ONSTORD +Eval: S S S S S S S S S + +Speaker sentences 151: cv_eng_000734 #utts: 1 +id: (cv_eng_000734-cv_eng_000734) +Scores: (#C #S #D #I) 1 9 1 2 +REF: ONLY CAMDEN THOMAS GARRETT and **** **** GOLDFIELDS SOUTH EZEKIEL BAKER WERE UNCONTESTED +HYP: **** INLY CAMDON TOMASGARIT and GOLD FILD SOAT ISEECHILE BAKCAR WER UN CONTESTED +Eval: D S S S I I S S S S S S + +Speaker sentences 152: cv_eng_000735 #utts: 1 +id: (cv_eng_000735-cv_eng_000735) +Scores: (#C #S #D #I) 6 7 0 0 +REF: it is a CHARITY SCHOOL WHOSE FEES ARE CALCULATED on A means test +HYP: it is a CHRDY SCOL WHOS FES AR COUCULATEDIN on IN means test +Eval: S S S S S S S + +Speaker sentences 153: cv_eng_000736 #utts: 1 +id: (cv_eng_000736-cv_eng_000736) +Scores: (#C #S #D #I) 4 6 1 0 +REF: some went away WHILE I WAS THERE and OTHER PEOPLE CAME +HYP: some went away ***** WHAL IOWAS THER and OTHE POPLE CAM +Eval: D S S S S S S + +Speaker sentences 154: cv_eng_000737 #utts: 1 +id: (cv_eng_000737-cv_eng_000737) +Scores: (#C #S #D #I) 0 1 0 4 +REF: * * ************* * SEVEN +HYP: T H CSAITHAEDEDUP R E +Eval: I I I I S + +Speaker sentences 155: cv_eng_000738 #utts: 1 +id: (cv_eng_000738-cv_eng_000738) +Scores: (#C #S #D #I) 4 10 0 0 +REF: THE KURA KHANATE was LOCATED MAINLY IN the HISTORICAL and GEOGRAPHICAL REGION of KURA +HYP: THAT CURA CONOTY was LOKCADED MANLY I the HISTORICALE and JEAGREFICAL REAGION of CUR +Eval: S S S S S S S S S S + +Speaker sentences 156: cv_eng_000739 #utts: 1 +id: (cv_eng_000739-cv_eng_000739) +Scores: (#C #S #D #I) 2 7 0 0 +REF: THE ELEVATION AT the SITE is ABOVE SEA LEVEL +HYP: UNC HELOVATION A the SIGHT is AM OF SULEVBLE +Eval: S S S S S S S + +Speaker sentences 157: cv_eng_000740 #utts: 1 +id: (cv_eng_000740-cv_eng_000740) +Scores: (#C #S #D #I) 4 4 0 3 +REF: ** TOBIAS tried to ****** ***** INJECT CONTEMPT INTO his tone +HYP: TO BEAS tried to NCHECT CONON TEMPT IN TO his tone +Eval: I S I I S S S + +Speaker sentences 158: cv_eng_000741 #utts: 1 +id: (cv_eng_000741-cv_eng_000741) +Scores: (#C #S #D #I) 4 2 0 0 +REF: i have to WORK this SATURDAY +HYP: i have to WARLK this SATORDY +Eval: S S + +Speaker sentences 159: cv_eng_000742 #utts: 1 +id: (cv_eng_000742-cv_eng_000742) +Scores: (#C #S #D #I) 2 9 1 3 +REF: *** * ** the GREAT RULERS FOUND THE SQUEAKY GRATE WAS GRATING on THEIR NERVES +HYP: TDE T RA the ***** RON WHS FOUNDTHES COLEGE GLEAD WITH GLATING on THERG NONES +Eval: I I I D S S S S S S S S S + +Speaker sentences 160: cv_eng_000743 #utts: 1 +id: (cv_eng_000743-cv_eng_000743) +Scores: (#C #S #D #I) 9 6 0 1 +REF: when the ****** BLINDING DUST HAD SETTLED A bit the boy TREMBLED at what he saw +HYP: when the BILING DOST HE S SAELED FOR bit the boy TRMBLED at what he saw +Eval: I S S S S S S + +Speaker sentences 161: cv_eng_000744 #utts: 1 +id: (cv_eng_000744-cv_eng_000744) +Scores: (#C #S #D #I) 1 6 0 1 +REF: DEMOCRAT AMBER BAKER won ** THE OPEN SEAT +HYP: DEMACRAT AMBERAN BAKEKHER won IT HE OPON SEE +Eval: S S S I S S S + +Speaker sentences 162: cv_eng_000745 #utts: 1 +id: (cv_eng_000745-cv_eng_000745) +Scores: (#C #S #D #I) 1 10 0 5 +REF: **** **** *** BOTH ARE PUT TOGETHER BY STUDENTS in ********** *** THE COLLEGES JOURNALISM PROGRAM +HYP: WORT HAVE ORT TEIN TO GETHER BAITS OUOD ENT in HIECUALIER EUS OU NATHES INPO ROM +Eval: I I I S S S S S S I I S S S S + +Speaker sentences 163: cv_eng_000746 #utts: 1 +id: (cv_eng_000746-cv_eng_000746) +Scores: (#C #S #D #I) 3 5 1 0 +REF: TRENCH WAS born in BELIZE CITY in BRITISH HONDURAS +HYP: ****** TRINTEWAS born in BELES SITED in BRITOS PONDERAS +Eval: D S S S S S + +Speaker sentences 164: cv_eng_000747 #utts: 1 +id: (cv_eng_000747-cv_eng_000747) +Scores: (#C #S #D #I) 2 4 1 0 +REF: THE EARLY PHASE of LIFE MOVES fast +HYP: *** DORITY FACE of LIF MOES fast +Eval: D S S S S + +Speaker sentences 165: cv_eng_000748 #utts: 1 +id: (cv_eng_000748-cv_eng_000748) +Scores: (#C #S #D #I) 0 1 0 3 +REF: * **** * NO +HYP: A NOWH H E +Eval: I I I S + +Speaker sentences 166: cv_eng_000749 #utts: 1 +id: (cv_eng_000749-cv_eng_000749) +Scores: (#C #S #D #I) 0 1 0 1 +REF: ****** SEVEN +HYP: SIVEIN GORLTDLOL +Eval: I S + +Speaker sentences 167: cv_eng_000750 #utts: 1 +id: (cv_eng_000750-cv_eng_000750) +Scores: (#C #S #D #I) 3 10 0 1 +REF: at ONE TIME RAILWAY LINES DIVERGED from RUGBY STATION in ** SEVEN DIFFERENT DIRECTIONS +HYP: at ON TM BRY LOUELINS THEYWARD from BRAKG BESTATION in SO N DIFREN ERECIONS +Eval: S S S S S S S I S S S + +Speaker sentences 168: cv_eng_000751 #utts: 1 +id: (cv_eng_000751-cv_eng_000751) +Scores: (#C #S #D #I) 3 6 0 1 +REF: CZECH REPUBLIC ENTERED two SHOOTERS into the **** PARALYMPIC COMPETITION +HYP: CHECK REPUPBLICK ENTED two SHOUTERS into the PARO LIMPIG COMPATITION +Eval: S S S S I S S + +Speaker sentences 169: cv_eng_000752 #utts: 1 +id: (cv_eng_000752-cv_eng_000752) +Scores: (#C #S #D #I) 5 7 0 6 +REF: * * TYGER WILLIAMS WROTE the ******* SCREENPLAY and * * shared story *** CREDIT WITH the BROTHERS +HYP: T I TER WILIOMS ROE the SCGEANG CLAY and N S shared story RAT IT THAT the PEPIT +Eval: I I S S S I S I I I S S S + +Speaker sentences 170: cv_eng_000753 #utts: 1 +id: (cv_eng_000753-cv_eng_000753) +Scores: (#C #S #D #I) 1 10 0 6 +REF: ** ** **** THIS FESTIVAL WAS to ** ****** * BE A CHARITY FUNDRAISER FOR THE AREA +HYP: TA IS FAST OFE ALLD WORS to OF RETERC H HERITY FIN THER ADY SADER OFOYD THERART +Eval: I I I S S S I I I S S S S S S S + +Speaker sentences 171: cv_eng_000754 #utts: 1 +id: (cv_eng_000754-cv_eng_000754) +Scores: (#C #S #D #I) 0 8 0 5 +REF: * **** ***** ***** ** THESE EXTRA CARDS WERE INSERTED RANDOMLY INTO PACKS +HYP: O THES ENXTR GARTS WE NESURNT THERNGONLELALMSWO AGALFTE RAG WHT THE AL HAT +Eval: I I I I I S S S S S S S S + +Speaker sentences 172: cv_eng_000755 #utts: 1 +id: (cv_eng_000755-cv_eng_000755) +Scores: (#C #S #D #I) 2 3 0 1 +REF: *** HENRY WENT back to AUSTRALIA +HYP: AHU HNDR ONT back to ESTRLIOA +Eval: I S S S + +Speaker sentences 173: cv_eng_000756 #utts: 1 +id: (cv_eng_000756-cv_eng_000756) +Scores: (#C #S #D #I) 4 5 1 0 +REF: permit me to INTRODUCE TO you HER MAJESTY THE QUEEN +HYP: permit me to ********* INTRDUSE you TO HUR RMOGJESTIED CQEAN +Eval: D S S S S S + +Speaker sentences 174: cv_eng_000757 #utts: 1 +id: (cv_eng_000757-cv_eng_000757) +Scores: (#C #S #D #I) 3 7 1 0 +REF: IN ORIGIN HEROIN was SUPPOSED to BE the “NONADDICTIVE MORPHINE SUBSTITUTE” +HYP: AN ORGIN HERWON was SUPOS to ** the NONADICTIVF MORFEN SUBSTOT +Eval: S S S S D S S S + +Speaker sentences 175: cv_eng_000758 #utts: 1 +id: (cv_eng_000758-cv_eng_000758) +Scores: (#C #S #D #I) 3 2 0 1 +REF: * she is of MEXICAN DESCENT +HYP: U she is of MEKCICON DESSENT +Eval: I S S + +Speaker sentences 176: cv_eng_000759 #utts: 1 +id: (cv_eng_000759-cv_eng_000759) +Scores: (#C #S #D #I) 2 5 1 1 +REF: ** i AM SURE THERE IS not ON HIS +HYP: CS i M SHOR T EALES not ** ONDIST +Eval: I S S S S D S + +Speaker sentences 177: cv_eng_000760 #utts: 1 +id: (cv_eng_000760-cv_eng_000760) +Scores: (#C #S #D #I) 2 8 1 3 +REF: *** THOSE WHO dont LEARN FROM HISTORY ARE DOOMED TO REPEAT it * ***** +HYP: IOW HOUS ON dont ***** LONSAON THES SHREY IVGONT PR AEPED it O HLONO +Eval: I S S D S S S S S S I I + +Speaker sentences 178: cv_eng_000761 #utts: 1 +id: (cv_eng_000761-cv_eng_000761) +Scores: (#C #S #D #I) 3 3 0 1 +REF: i ***** COULDN’T STOP STARING at it +HYP: i CALED AON SOPE SARLIN at it +Eval: I S S S + +Speaker sentences 179: cv_eng_000762 #utts: 1 +id: (cv_eng_000762-cv_eng_000762) +Scores: (#C #S #D #I) 4 7 1 0 +REF: for SIMPLICITY GEAR inches is NORMALLY ROUNDED to THE NEAREST WHOLE NUMBER +HYP: for SIMPLITHY GUR inches is ******** NORMLYAROUNDED to HE ERES HOL NOMBER +Eval: S S D S S S S S + +Speaker sentences 180: cv_eng_000763 #utts: 1 +id: (cv_eng_000763-cv_eng_000763) +Scores: (#C #S #D #I) 6 4 0 1 +REF: if we ACTUALLY do WANT IT SOLVED it will be * +HYP: if we ACTILY do ON I SALED it will be F +Eval: S S S S I + +Speaker sentences 181: cv_eng_000764 #utts: 1 +id: (cv_eng_000764-cv_eng_000764) +Scores: (#C #S #D #I) 2 5 2 0 +REF: the FRUIT of A FIG TREE IS APPLE SHAPED +HYP: the FRO of * *** TH ICTRY S APLSHAPED +Eval: S D D S S S S + +Speaker sentences 182: cv_eng_000765 #utts: 1 +id: (cv_eng_000765-cv_eng_000765) +Scores: (#C #S #D #I) 2 3 0 0 +REF: FAIR EXCHANGE is no ROBBERY +HYP: THEOUT EXTHANGE is no WOBUY +Eval: S S S + +Speaker sentences 183: cv_eng_000766 #utts: 1 +id: (cv_eng_000766-cv_eng_000766) +Scores: (#C #S #D #I) 4 4 0 2 +REF: what you *** EAT TODAY walks and ***** TALKS TOMORROW +HYP: what you EAE TO DAIY walks and TARKS TO MOROW +Eval: I S S I S S + +Speaker sentences 184: cv_eng_000767 #utts: 1 +id: (cv_eng_000767-cv_eng_000767) +Scores: (#C #S #D #I) 7 5 0 0 +REF: the WATER THEN FLOWS out of the SWAMPS as the LUAPULA river +HYP: the WATED AN FLOS out of the SWOMPS as the LOUWOPLAR river +Eval: S S S S S + +Speaker sentences 185: cv_eng_000768 #utts: 1 +id: (cv_eng_000768-cv_eng_000768) +Scores: (#C #S #D #I) 1 4 0 3 +REF: ** **** WHY DIDNT you **** SAY SOMETHING +HYP: AH WHIY I DIDND you SEAE SOME HINGK +Eval: I I S S I S S + +Speaker sentences 186: cv_eng_000769 #utts: 1 +id: (cv_eng_000769-cv_eng_000769) +Scores: (#C #S #D #I) 0 4 0 0 +REF: HAVE YOU SEEN OMAR +HYP: T HAV OUSENO MAR +Eval: S S S S + +Speaker sentences 187: cv_eng_000770 #utts: 1 +id: (cv_eng_000770-cv_eng_000770) +Scores: (#C #S #D #I) 10 7 0 1 +REF: i could go ON for days about the ******* DELICIOUS WINES PRODUCED in THIS part OF the WORLD +HYP: i could go AN for days about the DADIOUS LONGS PHE DUSE in HIS part F the WEOROED +Eval: S I S S S S S S + +Speaker sentences 188: cv_eng_000771 #utts: 1 +id: (cv_eng_000771-cv_eng_000771) +Scores: (#C #S #D #I) 2 8 0 0 +REF: THE PHILADELPHIA INQUIRER NAMED HIM CITY PLAYER OF the year +HYP: THOS FHEO LADEOFHEA INCQUIR ON NGTIN SITICLY OUT the year +Eval: S S S S S S S S + +Speaker sentences 189: cv_eng_000772 #utts: 1 +id: (cv_eng_000772-cv_eng_000772) +Scores: (#C #S #D #I) 0 6 1 0 +REF: BOTS MAY BE SUBJECT TO SPECIAL RULES +HYP: **** FAS LEVEIS OF DECTIS ESSER GLTONSLA +Eval: D S S S S S S + +Speaker sentences 190: cv_eng_000773 #utts: 1 +id: (cv_eng_000773-cv_eng_000773) +Scores: (#C #S #D #I) 5 8 1 0 +REF: the SWEDES WERE UNABLE to USE THEIR VEHICLES WHICH WERE stuck in the MUD +HYP: the SWEEDS WER NABLE to *** OUSER VEICALS WHIC HER stuck in the MOD +Eval: S S S D S S S S S + +Speaker sentences 191: cv_eng_000774 #utts: 1 +id: (cv_eng_000774-cv_eng_000774) +Scores: (#C #S #D #I) 5 7 0 2 +REF: the ACT did not **** ** PROHIBIT PAYING a REPRESENTATIVE to APPEAR IN COURT +HYP: the ACK did not BROR HE BECT BAYING a REPRESENTIE to APEARIN THE CORICTO +Eval: S I I S S S S S S + +Speaker sentences 192: cv_eng_000775 #utts: 1 +id: (cv_eng_000775-cv_eng_000775) +Scores: (#C #S #D #I) 0 4 1 0 +REF: CAN WE PLEASE LEAVE NOW +HYP: *** CHINGWEREPLIS T LOPINGRORAL A +Eval: D S S S S + +Speaker sentences 193: cv_eng_000776 #utts: 1 +id: (cv_eng_000776-cv_eng_000776) +Scores: (#C #S #D #I) 5 6 1 0 +REF: he was convicted AND BANISHED TO CYPRUS FOR seven years FOR PUNISHMENT +HYP: he was convicted *** AN BANIS DISIPRS HOR seven years WER PNISMENT +Eval: D S S S S S S + +Speaker sentences 194: cv_eng_000777 #utts: 1 +id: (cv_eng_000777-cv_eng_000777) +Scores: (#C #S #D #I) 4 10 0 1 +REF: the COUPLE HAVE TWO CHILDREN a DAUGHTER SOPHIA ROSALINDA AND A son * MATEO bravery +HYP: the CUPL OF TO CHALDEN a DATER SOFEAU ROSALENDA ND THE son O MATHYOL bravery +Eval: S S S S S S S S S I S + +Speaker sentences 195: cv_eng_000778 #utts: 1 +id: (cv_eng_000778-cv_eng_000778) +Scores: (#C #S #D #I) 3 11 0 0 +REF: NONE of THE THREE REFERENDUMS REACHED THE QUORUM of THE MAJORITY of THOSE ENTITLED +HYP: N of TH HRE REFPRENDAMS RECH HE QUARAM of TH MAGJORITY of THOS INTITLED +Eval: S S S S S S S S S S S + +Speaker sentences 196: cv_eng_000779 #utts: 1 +id: (cv_eng_000779-cv_eng_000779) +Scores: (#C #S #D #I) 3 10 1 7 +REF: *** ******* ********* ** ******* **** ** TURPIN SUCCEEDED INDIRA SAMARASEKERA who SAW THE UNIVERSITY THROUGH a PERIOD OF strong GROWTH +HYP: INT ITERPEN SECXCEDED IN DEARAST SOME AR RASIP CARA IT IS who *** SALD OUNERSEY THR a PEARIE O strong GROTH +Eval: I I I I I I I S S S S D S S S S S S + +Speaker sentences 197: cv_eng_000780 #utts: 1 +id: (cv_eng_000780-cv_eng_000780) +Scores: (#C #S #D #I) 7 5 0 0 +REF: HERE i am BETWEEN my flock and MY TREASURE the boy THOUGHT +HYP: HEAR i am BEPTEN my flock and MI BUTERSURED the boy TOS +Eval: S S S S S + +Speaker sentences 198: cv_eng_000781 #utts: 1 +id: (cv_eng_000781-cv_eng_000781) +Scores: (#C #S #D #I) 3 8 3 0 +REF: this FAILURE HAS LED to SIXTEEN POWER PLANTS HAVING ZERO DAYS of COAL STOCK +HYP: this FALIA HAST LET to ******* ***** SICXTEAEN POULBLENCS HADE INSEARDAYSE of **** CALESTO +Eval: S S S D D S S S S D S + +Speaker sentences 199: cv_eng_000782 #utts: 1 +id: (cv_eng_000782-cv_eng_000782) +Scores: (#C #S #D #I) 0 1 0 2 +REF: *** **** YES +HYP: AOO YSAS DEO +Eval: I I S + +Speaker sentences 200: cv_eng_000783 #utts: 1 +id: (cv_eng_000783-cv_eng_000783) +Scores: (#C #S #D #I) 1 6 0 0 +REF: WHY DOES THAT PLANE KEEP GOING over +HYP: WHIY I THA PLAIN CEPE GOIN over +Eval: S S S S S S + +Speaker sentences 201: cv_eng_000784 #utts: 1 +id: (cv_eng_000784-cv_eng_000784) +Scores: (#C #S #D #I) 1 8 0 2 +REF: ***** ***** IVE DONE THIS BEFORE WITH VIRTUALBOX with GOOD RESULTS +HYP: ANDNI HAYAE AEDONDOS HE FOR WAT FIRTIAL BOCS with O RESOULTS +Eval: I I S S S S S S S S + +Speaker sentences 202: cv_eng_000785 #utts: 1 +id: (cv_eng_000785-cv_eng_000785) +Scores: (#C #S #D #I) 2 3 1 3 +REF: THE APPLICATION was *** ** **** APPROVED in FEBRUARY +HYP: *** THEPLICATION was PUT AP PROV IT in FARBRAVY +Eval: D S I I I S S + +Speaker sentences 203: cv_eng_000786 #utts: 1 +id: (cv_eng_000786-cv_eng_000786) +Scores: (#C #S #D #I) 5 6 0 1 +REF: henry ****** TARLTON STILES WHERE he had a SOUND TRAINING in LATIN +HYP: henry TORLED TONMNSTIL S WEAR he had a SOUNDID RANING in LITING +Eval: I S S S S S S + +Speaker sentences 204: cv_eng_000787 #utts: 1 +id: (cv_eng_000787-cv_eng_000787) +Scores: (#C #S #D #I) 5 9 0 3 +REF: it was *** DISCONTINUED DUE to SCHEDULING CONFLICTS INVOLVED in **** *** LEWISS RETURN to TERRESTRIAL RADIO +HYP: it was TIS CONTINUE DO to SCETHALIN CONFLICS ANVLVED in LOSE HIS RETHIRN TORE to RESTRIAL REBRADIO +Eval: I S S S S S I I S S S S + +Speaker sentences 205: cv_eng_000788 #utts: 1 +id: (cv_eng_000788-cv_eng_000788) +Scores: (#C #S #D #I) 2 3 0 1 +REF: ***** her FAMILY was FROM BRIANZA +HYP: ADTTH her FAMLY was FROME BREAHONSA +Eval: I S S S + +Speaker sentences 206: cv_eng_000789 #utts: 1 +id: (cv_eng_000789-cv_eng_000789) +Scores: (#C #S #D #I) 1 5 0 1 +REF: * what DID YOU EAT FOR DINNER +HYP: A what DIDYEAE FOR INOR THEA PA +Eval: I S S S S S + +Speaker sentences 207: cv_eng_000790 #utts: 1 +id: (cv_eng_000790-cv_eng_000790) +Scores: (#C #S #D #I) 3 2 1 0 +REF: that was my DRAW TO SCIENCE +HYP: that was my **** DRARTO SINCE +Eval: D S S + +Speaker sentences 208: cv_eng_000791 #utts: 1 +id: (cv_eng_000791-cv_eng_000791) +Scores: (#C #S #D #I) 2 4 1 2 +REF: HE IS CONSIDERED a MASTER of *** ****** CHIAROSCURO +HYP: ** HES COSEARIT a MUSTERE of SHE AROSED COUO +Eval: D S S S I I S + +Speaker sentences 209: cv_eng_000792 #utts: 1 +id: (cv_eng_000792-cv_eng_000792) +Scores: (#C #S #D #I) 2 9 1 1 +REF: IT THEN RETURNS to the ******* CHURCH ASCENDS AT THE ALTAR AND DISAPPEARS +HYP: ** THE HLINTURS to the CHURISH OA SINES THAT D ALTER INDSPEUSE WHETHER +Eval: D S S I S S S S S S S + +Speaker sentences 210: cv_eng_000793 #utts: 1 +id: (cv_eng_000793-cv_eng_000793) +Scores: (#C #S #D #I) 0 6 1 0 +REF: YOU CANNOT LOSE WHAT YOU NEVER HAD +HYP: *** IOUE NOT THOSWEREIN HE RACHLE ADR +Eval: D S S S S S S + +Speaker sentences 211: cv_eng_000794 #utts: 1 +id: (cv_eng_000794-cv_eng_000794) +Scores: (#C #S #D #I) 1 5 0 0 +REF: THE JAWS EXTEND PAST the EYE +HYP: THEL JOS IECTEND FEST the PIY +Eval: S S S S S + +Speaker sentences 212: cv_eng_000795 #utts: 1 +id: (cv_eng_000795-cv_eng_000795) +Scores: (#C #S #D #I) 3 4 0 0 +REF: my NIECE can help YOU WITH THAT +HYP: my NES can help YO IT THATS +Eval: S S S S + +Speaker sentences 213: cv_eng_000796 #utts: 1 +id: (cv_eng_000796-cv_eng_000796) +Scores: (#C #S #D #I) 0 4 3 0 +REF: THATS THE KIND OF STUFF THEY WANT +HYP: ***** *** **** BUT SA ODHISTOFERY WON +Eval: D D D S S S S + +Speaker sentences 214: cv_eng_000797 #utts: 1 +id: (cv_eng_000797-cv_eng_000797) +Scores: (#C #S #D #I) 5 4 0 0 +REF: HOPE for the best and PREPARE FOR the WORST +HYP: HOW for the best and PR PEAETFUE the BOST +Eval: S S S S + +Speaker sentences 215: cv_eng_000798 #utts: 1 +id: (cv_eng_000798-cv_eng_000798) +Scores: (#C #S #D #I) 2 6 1 0 +REF: INITIALLY the WEIGHT LOSS WAS ATTAINED STRICTLY by DIET +HYP: INISHELY the ****** EP LOUSWAS HRTEN STRICKLY by DIT +Eval: S D S S S S S + +Speaker sentences 216: cv_eng_000799 #utts: 1 +id: (cv_eng_000799-cv_eng_000799) +Scores: (#C #S #D #I) 3 4 0 1 +REF: all WERE OWNED by the ****** EVERETTMOORE SYNDICATE +HYP: all WE ONED by the EVERIT MORE SINICIT +Eval: S S I S S + +Speaker sentences 217: cv_eng_000800 #utts: 1 +id: (cv_eng_000800-cv_eng_000800) +Scores: (#C #S #D #I) 0 4 0 5 +REF: ****** *** *** ***** **** WILL IT RAIN TOMORROW +HYP: BHATHT HEN THE WILAS RING TO MOROM ILNTH SEOD +Eval: I I I I I S S S S + +Speaker sentences 218: cv_eng_000801 #utts: 1 +id: (cv_eng_000801-cv_eng_000801) +Scores: (#C #S #D #I) 1 4 0 1 +REF: * DU bist EWIG MEINE LIEBE +HYP: H DOR bist ERECKGM MIN LIP +Eval: I S S S S + +Speaker sentences 219: cv_eng_000802 #utts: 1 +id: (cv_eng_000802-cv_eng_000802) +Scores: (#C #S #D #I) 4 4 0 0 +REF: LUCILE petry TOOK her PLACE as acting DIRECTOR +HYP: OSEIAL petry TO her PACE as acting DIRECTER +Eval: S S S S + +Speaker sentences 220: cv_eng_000803 #utts: 1 +id: (cv_eng_000803-cv_eng_000803) +Scores: (#C #S #D #I) 3 7 1 1 +REF: the BEAVER RIVER BRIEFLY ENTERS the ** EASTCENTRAL PART of THE TOWNSHIP +HYP: the ****** BEVERLY WLBIFLY NTES the ES SENTR PUT of ATON SHIP +Eval: D S S S I S S S S + +Speaker sentences 221: cv_eng_000804 #utts: 1 +id: (cv_eng_000804-cv_eng_000804) +Scores: (#C #S #D #I) 4 2 0 1 +REF: the track *** RESURFACING was also COMPLETED +HYP: the track RES ERVSTING was also COMPLEATED +Eval: I S S + +Speaker sentences 222: cv_eng_000805 #utts: 1 +id: (cv_eng_000805-cv_eng_000805) +Scores: (#C #S #D #I) 4 8 0 2 +REF: *** HINDMARSH was AWARE of the IMPORTANCE of ***** ELECTRON MICROSCOPY IN BIOLOGICAL RESEARCH +HYP: HIT MARCH was AWERE of the IMPORTNE of ELECT RM RECOSCMPIN BY AELOUGICALR RESERCH +Eval: I S S S I S S S S S + +Speaker sentences 223: cv_eng_000806 #utts: 1 +id: (cv_eng_000806-cv_eng_000806) +Scores: (#C #S #D #I) 0 5 0 0 +REF: SINHA WAS BORN IN ALLAHABAD +HYP: SIN HOWBAS BORNY ANTHE HABAR +Eval: S S S S S + +Speaker sentences 224: cv_eng_000807 #utts: 1 +id: (cv_eng_000807-cv_eng_000807) +Scores: (#C #S #D #I) 3 11 0 3 +REF: ***** ***** THIS BRIDGE IS UNOFFICIALLY REFERRED to as **** BLACKWATER BRIDGE by COALITION FORCES OPERATING THERE +HYP: NTHIS WINCH KEASE AN OFIALY HER HOD to as MACK REMPAD RINTH by COLASIN FOUSCSES RAVBERAITIN WIL +Eval: I I S S S S S I S S S S S S + +Speaker sentences 225: cv_eng_000808 #utts: 1 +id: (cv_eng_000808-cv_eng_000808) +Scores: (#C #S #D #I) 6 7 0 0 +REF: it is RESPONSIBLE for WATER SUPPLY and MANAGEMENT of water RESOURCES IN MAHARASHTRA +HYP: it is RESPONSEIPL for WATE SUPLIY and MANGEMENT of water RESOURSES AND MAHASTRA +Eval: S S S S S S S + +Speaker sentences 226: cv_eng_000809 #utts: 1 +id: (cv_eng_000809-cv_eng_000809) +Scores: (#C #S #D #I) 4 5 1 1 +REF: THIS IS the FIRST PHASE of the ***** JOB he SAID +HYP: **** JESES the FIRS FAICE of the HORVE A he SAYED +Eval: D S S S I S S + +Speaker sentences 227: fleurs_eng_000413 #utts: 1 +id: (fleurs_eng_000413-fleurs_eng_000413) +Scores: (#C #S #D #I) 16 16 1 1 +REF: the GIZA PLATEAU or *** GIZA NECROPOLIS in the EGYPTIAN VALLEY of the DEAD CONTAINS several PYRAMIDS of which the GREAT PYRAMID is the LARGEST SEVERAL SMALL TOMBS SEVERAL temples and the great SPHINX +HYP: the **** GISIUPLATO or GIA NECRAL POLES in the AGIPTION VAOLY of the DED CONTAING several PERIMENDS of which the GREATE PERMENT is the LARTES SEVERALE SMAL TOONS SEVEAL temples and the great SPANKS +Eval: D S I S S S S S S S S S S S S S S S + +Speaker sentences 228: fleurs_eng_000414 #utts: 1 +id: (fleurs_eng_000414-fleurs_eng_000414) +Scores: (#C #S #D #I) 19 18 0 2 +REF: TOWARDS the END of THE MIDDLE ages western EUROPE BEGAN to DEVELOP THEIR OWN STYLE ONE of the BIGGEST DEVELOPMENTS of the time as a result of the CRUSADES PEOPLE began to use BUTTONS to ***** ******** FASTEN CLOTHING +HYP: TWARD the IND of TH MILE ages western YURUP BEGAND to DEVELT THER ON STIL ON of the BIGEST VELMENS of the time as a result of the CRUCAS PEPUL began to use BUTENS to FASTN CLOLTING R R +Eval: S S S S S S S S S S S S S S S S I I S S + +Speaker sentences 229: fleurs_eng_000415 #utts: 1 +id: (fleurs_eng_000415-fleurs_eng_000415) +Scores: (#C #S #D #I) 8 10 0 3 +REF: IF you only go ashore using SHIPBOARD EXCURSIONS YOU WILL not NEED a SEPARATE VISA as * *** ***** OF 2009 +HYP: IFS you only go ashore using SHIP OR ISCURIONS YOUL not NE a SEPRT VESA as A TWO THOUS IN DNIN +Eval: S S S S S S S S I I I S S + +Speaker sentences 230: fleurs_eng_000416 #utts: 1 +id: (fleurs_eng_000416-fleurs_eng_000416) +Scores: (#C #S #D #I) 7 15 0 0 +REF: DUVALL WHO IS MARRIED WITH TWO ADULT CHILDREN DID not LEAVE A big IMPRESSION on MILLER to WHOM the STORY was related +HYP: DOUBAL H ISMARE WIH TO A DL CHEREN CUD not BE WA big MPRESION on MILER to HOM the SORY was related +Eval: S S S S S S S S S S S S S S S + +Speaker sentences 231: fleurs_eng_000417 #utts: 1 +id: (fleurs_eng_000417-fleurs_eng_000417) +Scores: (#C #S #D #I) 6 16 3 0 +REF: THEIR DISCIPLINED DEFENCE BALL HANDLING SKILLS AND EXCELLENT TEAM WORK MADE THEM stand out AND IT was CLEAR THAT THIS was the TEAM to BEAT +HYP: ***** *********** THER DISOPLIND DEFENCS BAL HADLING SCILSAND EXALNTE ORK MAD THE stand out *** AN was CLER HAT HIS was the EME to BE +Eval: D D S S S S S S S S S S D S S S S S S + +Speaker sentences 232: fleurs_eng_000418 #utts: 1 +id: (fleurs_eng_000418-fleurs_eng_000418) +Scores: (#C #S #D #I) 6 7 0 1 +REF: the DISEASE is CARRIED by pigs which *** THEN MIGRATES to HUMANS THROUGH MOSQUITOS +HYP: the DISES is CARED by pigs which HEN MY GRETES to HEUMENS TORO MSCETOS +Eval: S S I S S S S S + +Speaker sentences 233: fleurs_eng_000419 #utts: 1 +id: (fleurs_eng_000419-fleurs_eng_000419) +Scores: (#C #S #D #I) 5 4 0 2 +REF: for the ****** SPRINGBOKS IT ended a **** FIVEMATCH losing STREAK +HYP: for the SPRING BOKS ITD ended a FIVE MATH losing STREAEK +Eval: I S S I S S + +Speaker sentences 234: fleurs_eng_000420 #utts: 1 +id: (fleurs_eng_000420-fleurs_eng_000420) +Scores: (#C #S #D #I) 4 7 3 0 +REF: THUS the PENCIL WAS A GOOD FRIEND TO many PEOPLE WHEN IT came out +HYP: THES the ****** *** PINSAL WIT GOD FRINDS many ****** EOPLE WENI came out +Eval: S D D S S S S D S S + +Speaker sentences 235: fleurs_eng_000421 #utts: 1 +id: (fleurs_eng_000421-fleurs_eng_000421) +Scores: (#C #S #D #I) 11 11 0 2 +REF: the use of VIDEO RECORDING has led to IMPORTANT DISCOVERIES in the INTERPRETATION of ***** MICROEXPRESSIONS FACIAL MOVEMENTS which LAST a *** FEW MILLISECONDS +HYP: the use of VEO RECORING has led to MPORND DISCOVERES in the INTERPRITATION of MYKRL EXPRESTIONS FATIAL MOVEMENS which LAS a FEU MILES SICKENS +Eval: S S S S S I S S S S I S S + +Speaker sentences 236: fleurs_eng_000422 #utts: 1 +id: (fleurs_eng_000422-fleurs_eng_000422) +Scores: (#C #S #D #I) 9 11 1 0 +REF: ALSO TO the north VISIT the GREAT SANCTUARY of OUR lady of FATIMA SHRINE a place of WORLDWIDE FAMOUS MARIAN APPARITIONS +HYP: **** ALSAT the north IST the GREATE SANCURY of OR lady of ATHE MUSHRIN a place of WOLD RIGTEFAMS MERIAN AVPERIONS +Eval: D S S S S S S S S S S S + +Speaker sentences 237: fleurs_eng_000423 #utts: 1 +id: (fleurs_eng_000423-fleurs_eng_000423) +Scores: (#C #S #D #I) 12 12 2 0 +REF: if YOU WANT TO be CLOSE TO the action YOURE GOING to HAVE TO get in EARLY to GET a camping SITE CLOSE to the MUSIC +HYP: if *** YO WNT be COSE O the action ***** YOREHAE to O O get in EALY to T a camping SIHT CLOS to the MUSICK +Eval: D S S S S D S S S S S S S S + +Speaker sentences 238: fleurs_eng_000424 #utts: 1 +id: (fleurs_eng_000424-fleurs_eng_000424) +Scores: (#C #S #D #I) 7 10 0 1 +REF: MADAGASCAR is by FAR the BIGGEST and A CONTINENT on its OWN when * IT COMES TO WILDLIFE +HYP: MTAGUSCOAR is by FARE the BIGEST and THE CONTINANT on its ON when I COMS TOW WILD LIF +Eval: S S S S S S I S S S S + +Speaker sentences 239: fleurs_eng_000425 #utts: 1 +id: (fleurs_eng_000425-fleurs_eng_000425) +Scores: (#C #S #D #I) 8 8 2 0 +REF: WOMEN it is RECOMMENDED that any WOMEN TRAVELLERS say THAT they ARE MARRIED REGARDLESS of ACTUAL marital STATUS +HYP: WEMEN it is RECOMENED that any ***** WOMENTROVLORS say **** they AR MARED REGARLST of ACTIUAL marital STATIS +Eval: S S D S D S S S S S + +Speaker sentences 240: fleurs_eng_000426 #utts: 1 +id: (fleurs_eng_000426-fleurs_eng_000426) +Scores: (#C #S #D #I) 6 10 1 3 +REF: **** CUOMO 53 began ** HIS GOVERNORSHIP EARLIER this year and SIGNED A BILL last month ********* LEGALIZING SAMESEX MARRIAGE +HYP: UOMO FIFTY THRE began HI GOVERMENT GOVERSHIP ERILER this year and ****** SINDA BILE last month LEGLISING SAIME SECX MARAGE +Eval: I S S I S S S D S S I S S S + +Speaker sentences 241: fleurs_eng_000427 #utts: 1 +id: (fleurs_eng_000427-fleurs_eng_000427) +Scores: (#C #S #D #I) 11 18 4 0 +REF: as LIGHT POLLUTION IN THEIR HEYDAY was not the KIND of PROBLEM IT is TODAY THEY ARE USUALLY located in CITIES or AT CAMPUSES easier to REACH THAN THOSE BUILT IN MODERN TIMES +HYP: as ***** ********* LIPLUTION N HERHADY was not the CIND of ******* PROBOMT is TO DAY THER ULY located in SIIES or A CAMPSES easier to ***** REASION THOS BIL AN MOTEN TIMS +Eval: D D S S S S D S S S S S S S S D S S S S S S + +Speaker sentences 242: fleurs_eng_000428 #utts: 1 +id: (fleurs_eng_000428-fleurs_eng_000428) +Scores: (#C #S #D #I) 7 14 1 0 +REF: THEY USUALLY have special FOOD DRINK AND ENTERTAINMENT OFFERS to KEEP GUESTS IN a GOOD MOOD AND KEEP them at the PREMISE +HYP: THE ULY have special **** FOD RINKAN NRTAMEN OPERS to CE GES AND a GOD MOD AN CE them at the PRMIS +Eval: S S D S S S S S S S S S S S S + +Speaker sentences 243: fleurs_eng_000429 #utts: 1 +id: (fleurs_eng_000429-fleurs_eng_000429) +Scores: (#C #S #D #I) 14 8 0 1 +REF: on the other hand ICY and snowy CONDITIONS ARE NORMAL in many COUNTRIES and TRAFFIC GOES on mostly ** UNINTERRUPTED all year round +HYP: on the other hand ICESE and snowy CODIONS AR NORMAE in many COUNTRES and TRAFIT OES on mostly UN INTRUPTED all year round +Eval: S S S S S S S I S + +Speaker sentences 244: fleurs_eng_000430 #utts: 1 +id: (fleurs_eng_000430-fleurs_eng_000430) +Scores: (#C #S #D #I) 10 9 0 1 +REF: be CAREFUL NOT to ALLOW fabric to become TOO HOT which can CAUSE SHRINKAGE or in * EXTREME cases SCORCH +HYP: be CARFUL OT to ALOW fabric to become TO HIYE which can CASE STRANKADGE or in A STREN cases SQOARTCH +Eval: S S S S S S S I S S + +Speaker sentences 245: fleurs_eng_000431 #utts: 1 +id: (fleurs_eng_000431-fleurs_eng_000431) +Scores: (#C #S #D #I) 3 13 0 2 +REF: ***** FERAL CHILDREN MAY HAVE EXPERIENCED SEVERE child * ABUSE or TRAUMA BEFORE BEING ABANDONED OR RUNNING away +HYP: FEIRL CHILDRN MA HAV EXPEIANC SO VER child H BES or TROMM BEFOR BING ABANDIN R RNG away +Eval: I S S S S S S I S S S S S S S + +Speaker sentences 246: fleurs_eng_000432 #utts: 1 +id: (fleurs_eng_000432-fleurs_eng_000432) +Scores: (#C #S #D #I) 5 10 0 0 +REF: people MAY not ANTICIPATE THAT PATIENCE and UNDERSTANDING ARE also NECESSARY for TRAVELLERS RETURNING HOME +HYP: people MA not INTICIPAT THA PATIONCS and NDRESTANG R also NESERY for TROVLERS RETRNING HOM +Eval: S S S S S S S S S S + +Speaker sentences 247: fleurs_eng_000433 #utts: 1 +id: (fleurs_eng_000433-fleurs_eng_000433) +Scores: (#C #S #D #I) 3 10 0 1 +REF: SOON AFTER the OUTBREAK of ********** HOSTILITIES BRITAIN INITIATED A NAVAL BLOCKADE of GERMANY +HYP: ON OTER the PRIK of HUSTILITES BRITN INENT SHEATED AN NAVBLE BOCKADE of HERMANY +Eval: S S S I S S S S S S S + +Speaker sentences 248: fleurs_eng_000434 #utts: 1 +id: (fleurs_eng_000434-fleurs_eng_000434) +Scores: (#C #S #D #I) 2 8 1 0 +REF: the GOVERNORS OFFICE said NINETEEN OF THE INJURED WERE POLICE OFFICERS +HYP: the OENRS OFIS said ******** NINTEN OFTHE INGURED WE PLEAES OFISERS +Eval: S S D S S S S S S + +Speaker sentences 249: fleurs_eng_000435 #utts: 1 +id: (fleurs_eng_000435-fleurs_eng_000435) +Scores: (#C #S #D #I) 11 11 0 1 +REF: using ships to TRANSPORT goods is by far the MOST EFFICIENT way to MOVE LARGE AMOUNTS of ***** PEOPLE AND GOODS ACROSS OCEANS +HYP: using ships to TRESPORTS goods is by far the MOS OFIENT way to MOE LARE MOUT of PEBLE N GOD A CROS OTIONS +Eval: S S S S S S I S S S S S + +Speaker sentences 250: fleurs_eng_000436 #utts: 1 +id: (fleurs_eng_000436-fleurs_eng_000436) +Scores: (#C #S #D #I) 5 13 0 2 +REF: LIBERAL CRITICISM of the RECONSTRUCTION EFFORT HAS FOCUSED ON the AWARDING of RECONSTRUCTION CONTRACTS to ***** ** PERCEIVED WASHINGTON INSIDERS +HYP: THEBRL CRITISISM of the RECONSTRCTIN EVERTN HASPOK AS O the WARDING of RECONSTRCING CONTHACT to RISTE DE WATING AND INSIERS +Eval: S S S S S S S S S S I I S S S + +Speaker sentences 251: fleurs_eng_000437 #utts: 1 +id: (fleurs_eng_000437-fleurs_eng_000437) +Scores: (#C #S #D #I) 8 14 0 3 +REF: ** ******* YOU CAN USE BODABODA MOTORCYCLE TAXI to get around goma the NORMAL LOCAL price is **** 500 CONGOLESE FRANCS for THE SHORT RIDE +HYP: UT WEUCUNS BOWD OB BOD A MOR SECLTACXCY to get around goma the NRMAE WICKLE price is FIVE HUNDRED CONDLES FRONS for THEM SHOURT R +Eval: I I S S S S S S S S I S S S S S S + +Speaker sentences 252: fleurs_eng_000438 #utts: 1 +id: (fleurs_eng_000438-fleurs_eng_000438) +Scores: (#C #S #D #I) 16 13 1 2 +REF: the THREE kingdoms was one of the *** BLOODIEST eras IN ANCIENT chinas *** HISTORY THOUSANDS of PEOPLE died FIGHTING to SIT in THE HIGHEST SEAT in the grand PALACE at XIAN +HYP: the THE kingdoms was one of the BLT BLUDIAST eras AND ANIENT chinas HIS THRE THOUSONS of PEOLE died FITING to STIT in *** TH HIEASCE in the grand PALES at SI +Eval: S I S S S I S S S S S D S S S S + +Speaker sentences 253: fleurs_eng_000439 #utts: 1 +id: (fleurs_eng_000439-fleurs_eng_000439) +Scores: (#C #S #D #I) 4 8 0 0 +REF: THESE couples may CHOOSE to MAKE AN ADOPTION PLAN for THEIR BABY +HYP: RTHES couples may CHUSE to MAKEK AND ADOUSIN PLAND for THER BAVY +Eval: S S S S S S S S + +Speaker sentences 254: fleurs_eng_000440 #utts: 1 +id: (fleurs_eng_000440-fleurs_eng_000440) +Scores: (#C #S #D #I) 13 16 1 0 +REF: NOTHING CAN be SEEN other THAN the CLEAR BEAUTIFUL SKY above and the MANY SURROUNDING MOUNTAINS very LITTLE of THIS WORLD CAN be SEEN or HEARD from INSIDE the cave +HYP: NOTHNG AND be FEN other HN the CLEARE BUTIFUL SCAIY above and the MENY SURUNG MOUNS very LIT of **** THSWAL AN be SEN or HURD from INSID the cave +Eval: S S S S S S S S S S S D S S S S S + +Speaker sentences 255: fleurs_eng_000441 #utts: 1 +id: (fleurs_eng_000441-fleurs_eng_000441) +Scores: (#C #S #D #I) 4 5 0 1 +REF: he was SUBSEQUENTLY relocated to **** ADDENBROOKES HOSPITAL IN CAMBRIDGE +HYP: he was SOBSICUENTLY relocated to ADIN BROKS HOSPITL AN CAMBRIAGE +Eval: S I S S S S + +Speaker sentences 256: fleurs_eng_000442 #utts: 1 +id: (fleurs_eng_000442-fleurs_eng_000442) +Scores: (#C #S #D #I) 6 11 5 1 +REF: *** VATICAN CITYS population IS AROUND 800 IT IS the SMALLEST INDEPENDENT COUNTRY IN the WORLD and THE COUNTRY WITH the LOWEST population +HYP: THA ICAN STITY population ** ISAROUND AIN HEUNDRID THEIS the MAOST NTEPENDE CONTR N the WORALD and *** ******* **** the ****** population +Eval: I S S D S S S S S S S S S D D D D + +Speaker sentences 257: fleurs_eng_000443 #utts: 1 +id: (fleurs_eng_000443-fleurs_eng_000443) +Scores: (#C #S #D #I) 10 21 1 0 +REF: REGULAR ANNOUNCEMENTS IN the METRO ARE MADE ONLY in CATALAN but UNPLANNED DISRUPTIONS ARE ANNOUNCED by an AUTOMATED SYSTEM in a WIDE VARIETY of LANGUAGES INCLUDING SPANISH ENGLISH french ARABIC and JAPANESE +HYP: REGUR ALOUNSMENT AN the ***** PMETRU ARMAE OLY in CATLON but UNPLANE DISTRUPTIONS AR NOUSE by an OTAMAED SISTOM in a WAD VERITY of LNWICGES NCUTING SBANISH ANGLISH french ERBECK and HAPONEES +Eval: S S S D S S S S S S S S S S S S S S S S S S + +Speaker sentences 258: fleurs_eng_000444 #utts: 1 +id: (fleurs_eng_000444-fleurs_eng_000444) +Scores: (#C #S #D #I) 8 11 3 0 +REF: this OFFERS A GOOD OPPORTUNITY to SEE the AURORA BOREALIS as the SKY WILL be dark MORE OR LESS AROUND the CLOCK +HYP: this ****** OPRE AGOD PRTUNITI to SE the ****** OWRABARILES as the SCKGI WIL be dark **** MOR LEST RUN the CLOC +Eval: D S S S S D S S S D S S S S + +Speaker sentences 259: fleurs_eng_000445 #utts: 1 +id: (fleurs_eng_000445-fleurs_eng_000445) +Scores: (#C #S #D #I) 1 8 1 3 +REF: FIRE RESCUE CREWS EVENTUALLY DOUSED THE FIRE by * ***** ***** 1135 PM +HYP: **** FIRSCKU CROS OVENCIALY DOUSE TO FIE by A LEVEN THRTY FIVE PEAM +Eval: D S S S S S S I I I S S + +Speaker sentences 260: fleurs_eng_000446 #utts: 1 +id: (fleurs_eng_000446-fleurs_eng_000446) +Scores: (#C #S #D #I) 5 9 1 0 +REF: this IS CALLED A CHEMICALS PH YOU can MAKE an INDICATOR using red CABBAGE JUICE +HYP: this ** CALT O CMICALS PEE HE can MAK an INDICATEO using red CABAGHE JOS +Eval: D S S S S S S S S S + +Speaker sentences 261: fleurs_eng_000447 #utts: 1 +id: (fleurs_eng_000447-fleurs_eng_000447) +Scores: (#C #S #D #I) 7 9 2 2 +REF: in PARTICULAR it is CLAIMED that one CAN DETECT WHETHER A PERSON is LYING by ********** ** INTERPRETING MICROEXPRESSIONS CORRECTLY +HYP: in PRTICULR it is LAE that one *** ****** CANDETEC WETHERA PRSON is LING by INTERPRING MY GROL EXSPRESIONS CORECTLY +Eval: S S D D S S S S I I S S S + +Speaker sentences 262: fleurs_eng_000448 #utts: 1 +id: (fleurs_eng_000448-fleurs_eng_000448) +Scores: (#C #S #D #I) 15 14 3 1 +REF: the CENTRAL AUTHORITY of the CHURCH HAD BEEN in ROME for over a THOUSAND years and THIS CONCENTRATION OF power AND MONEY led ** MANY to QUESTION whether THIS TENET was BEING met +HYP: the SECHAL FORITY of the ****** CHUCHODS BEN in ROM for over a THOUSAN years and **** THISCOSONTRATION AF power AN MONY led TO MAY to CUSTION whether **** DISTENET was BENG met +Eval: S S D S S S S D S S S S I S S D S S + +Speaker sentences 263: fleurs_eng_000449 #utts: 1 +id: (fleurs_eng_000449-fleurs_eng_000449) +Scores: (#C #S #D #I) 12 13 0 4 +REF: the *** ******* ** SUNDARBANS ARE the LARGEST LITTORAL MANGROVE BELT in the world STRETCHING 80 KM 50 MI into the BANGLADESHI and ** indian hinterland from the COAST +HYP: the SUN DARBONS AR THE ARGES the TORAL MAN GROE BUL in the world STECHING ATY CLAMITERS FIFTY MIES into the ANGLADESHE and IN indian hinterland from the COOST +Eval: I I I S S S S S S S S S S S S I S + +Speaker sentences 264: fleurs_eng_000450 #utts: 1 +id: (fleurs_eng_000450-fleurs_eng_000450) +Scores: (#C #S #D #I) 10 21 1 0 +REF: REGULAR ANNOUNCEMENTS in THE METRO ARE MADE only in CATALAN but UNPLANNED DISRUPTIONS ARE ANNOUNCED by an AUTOMATED SYSTEM in a WIDE VARIETY of LANGUAGES INCLUDING SPANISH ENGLISH FRENCH ARABIC and JAPANESE +HYP: REGULR ANONSENCS in HE ETHO AR MAE only in CATALEN but UN NDISTRUPTINS R NOUNED by an OTMATEIS SISTM in a **** WADVERITY of LIGWINGES INCUDING SPANTISH INGLSH RENCH ERBACK and JHAPONES +Eval: S S S S S S S S S S S S S D S S S S S S S S + +Speaker sentences 265: fleurs_eng_000451 #utts: 1 +id: (fleurs_eng_000451-fleurs_eng_000451) +Scores: (#C #S #D #I) 1 13 0 4 +REF: **** EVERYONE PARTICIPATES in ****** ** ** SOCIETY AND USES TRANSPORTATION SYSTEMS ALMOST EVERYONE COMPLAINS ABOUT TRANSPORTATION SYSTEMS +HYP: EVER WN PRTITBAT in OUCITY AN US IS TRNSPRTIONCSISTONCS ALMST ERY WN COMPLAINE O OU TRNS PRTION SISTOMS +Eval: I S S I I I S S S S S S S S S S S + +Speaker sentences 266: fleurs_eng_000452 #utts: 1 +id: (fleurs_eng_000452-fleurs_eng_000452) +Scores: (#C #S #D #I) 7 21 1 1 +REF: LAYTON had ASKED FOR CHANGES TO the CONSERVATIVES ENVIRONMENTAL BILL DURING the MEETING WITH the PM ASKING FOR A THOROUGH and COMPLETE REWRITING of the ************ CONSERVATIVE PARTYS ENVIRONMENTAL BILL +HYP: LATN had AS OR HANES O the ONSUIRVETIS NVIRMINTL BIL DURN the MEN WT the ** PEM ASING FRY THURAL and COMPLETERE RIDTING of the CONSERVETHIS PARDY IN IERMINAL IL +Eval: S S S S S S S S S S S D S S S S S S I S S S S + +Speaker sentences 267: fleurs_eng_000453 #utts: 1 +id: (fleurs_eng_000453-fleurs_eng_000453) +Scores: (#C #S #D #I) 6 14 2 0 +REF: ANYONE WHOS GOING to DRIVE AT HIGH LATITUDES or over MOUNTAIN PASSES SHOULD CONSIDER the POSSIBILITY of SNOW ice OR FREEZING TEMPERATURES +HYP: INY ON HISGON to ***** TRIEHA HAT LITATUDS or over MUTN PASS THR CONCSIDE the POSILITY of SNO ice ** ORFESING TEMPATURS +Eval: S S S D S S S S S S S S S D S S + +Speaker sentences 268: fleurs_eng_000454 #utts: 1 +id: (fleurs_eng_000454-fleurs_eng_000454) +Scores: (#C #S #D #I) 5 16 1 5 +REF: ** SLEEP INTERRUPTION is THE PROCESS of ** *** PURPOSEFULLY AWAKENING DURING YOUR NORMAL SLEEP PERIOD and FALLING ASLEEP A SHORT time later *** ** 10–60 MINUTES +HYP: HE SLE INTRUSTION is HE PRASTSES of HE BOU CAY WAKNIN BEURING YOU NORMALE SLEE PERIAD and ******* FALING ASLE ASHOURT time later CEN TO SICTE MINOTST +Eval: I S S S S I I S S S S S S S D S S S I I S S + +Speaker sentences 269: fleurs_eng_000455 #utts: 1 +id: (fleurs_eng_000455-fleurs_eng_000455) +Scores: (#C #S #D #I) 6 10 1 5 +REF: *** SWIRL the TWO DRY POWDERS TOGETHER AND THEN with ***** CLEAN wet HANDS SQUEEZE them into a **** * * BALL +HYP: OUR SWOARL the *** TO DRIP POURS TOGETHERAN THE with CUENG A wet HANS SCUEAE them into a BALE R E H +Eval: I S D S S S S S I S S S I I I S + +Speaker sentences 270: fleurs_eng_000456 #utts: 1 +id: (fleurs_eng_000456-fleurs_eng_000456) +Scores: (#C #S #D #I) 6 3 0 2 +REF: for the ***** SPRINGBOKS it ended a *** FIVEMATCH losing STREAK +HYP: for the SPRNG BOCS it ended a FIE MACH losing STREK +Eval: I S I S S + +Speaker sentences 271: fleurs_eng_000457 #utts: 1 +id: (fleurs_eng_000457-fleurs_eng_000457) +Scores: (#C #S #D #I) 3 13 8 1 +REF: JUST LIKE the MOON EXERTS a PULL ON THE EARTH CAUSING TIDES SO DOES THE MILKY WAY EXERT a ******* FORCE ON THE SAGITTARIUS GALAXY +HYP: **** JUSTLIK the **** ONEXPURTDS a **** ** *** ***** ******* ***** PUL ONTHE ERTH CASIN THEIDE SOTAS a MLBYWAY EXERTIF FORTS OF HE EDITARIOUSGALACY +Eval: D S D S D D D D D D S S S S S S I S S S S S + +Speaker sentences 272: fleurs_eng_000458 #utts: 1 +id: (fleurs_eng_000458-fleurs_eng_000458) +Scores: (#C #S #D #I) 6 9 0 2 +REF: THROUGH the night ****** ******** BETWEEN 150 AND 200 COPIES were made now KNOWN as DUNLAP BROADSIDES +HYP: THORO the night HETWEN HUDERDAN FIFTY AN TO HUDRED COPES were made now NON as BUNELAP BROLDSIDS +Eval: S I I S S S S S S S S + +Speaker sentences 273: fleurs_eng_000459 #utts: 1 +id: (fleurs_eng_000459-fleurs_eng_000459) +Scores: (#C #S #D #I) 13 19 3 1 +REF: FIRST AMONG ITS 78 RECOMMENDATIONS is that A NEW DIPLOMATIC INITIATIVE SHOULD be TAKEN BEFORE the END OF this year to secure IRAQS BORDERS AGAINST HOSTILE INTERVENTIONS and to ** REESTABLISH DIPLOMATIC relations with its NEIGHBORS +HYP: ***** FIRSTAMONG IT SEMNDYAE RECOMENDATIONS is that AN NOW DIPLMAIK NISHITIVE HUL be TAKE BEFOR the *** ENDOF this year to secure ***** RRACXPBORERS AGNS HOSTIL INTRVENTIONS and to RE ASTABLISE DIPLMAIC relations with its NABERS +Eval: D S S S S S S S S S S S D S D S S S S I S S S + +Speaker sentences 274: fleurs_eng_000460 #utts: 1 +id: (fleurs_eng_000460-fleurs_eng_000460) +Scores: (#C #S #D #I) 4 13 0 0 +REF: SAINT PETERSBURG CRUISES INCLUDE TIME in town CRUISE PASSENGERS ARE EXEMPTED from VISA REQUIREMENTS CHECK the TERMS +HYP: SHANT ETERS BRCRUSIS INCLUD TIM in town WHOT ASONGERS AR XAENTED from ESTER REQUIRIMENTS CHACK the TRMS +Eval: S S S S S S S S S S S S S + +Speaker sentences 275: fleurs_eng_000461 #utts: 1 +id: (fleurs_eng_000461-fleurs_eng_000461) +Scores: (#C #S #D #I) 5 10 0 1 +REF: ACCORDING to JAPANS NUCLEAR AGENCY RADIOACTIVE CAESIUM AND IODINE has * BEEN IDENTIFIED at the plant +HYP: OCORDIN to UPANS NOGULR AGENCSY REDYALACTIVE CASEAMEAND I ADIN has E I DENIFIE at the plant +Eval: S S S S S S S S I S S + +Speaker sentences 276: fleurs_eng_000462 #utts: 1 +id: (fleurs_eng_000462-fleurs_eng_000462) +Scores: (#C #S #D #I) 9 6 0 0 +REF: SEGREGATION and RECOMBINATION SHUFFLE VARIATION back and forth BETWEEN the two POOLS with each generation +HYP: SAGOGATION and RECOMONATION SHUFL VERYIATION back and forth BETWEN the two PULES with each generation +Eval: S S S S S S + +Speaker sentences 277: fleurs_eng_000463 #utts: 1 +id: (fleurs_eng_000463-fleurs_eng_000463) +Scores: (#C #S #D #I) 4 12 2 0 +REF: ELEMENTS LIKE CALCIUM AND POTASSIUM ARE CONSIDERED METALS of COURSE THERE ARE also METALS LIKE SILVER and gold +HYP: ELAMNT LK CHLTHEM ND PAATIM R CONCSTED METLS of ****** ***** PORSER also METES LKE SIVER and gold +Eval: S S S S S S S S D D S S S S + +Speaker sentences 278: fleurs_eng_000464 #utts: 1 +id: (fleurs_eng_000464-fleurs_eng_000464) +Scores: (#C #S #D #I) 2 10 0 2 +REF: the CORRELATION BETWEEN BRAIN PATHOLOGY and ** *** BEHAVIOUR SUPPORTS SCIENTISTS IN THEIR RESEARCH +HYP: the CORLTION ETWEEN BRAI PITHOLAGY and BE HAV YOUR SUPORT SINCS AND HER RESURCH +Eval: S S S S I I S S S S S S + +Speaker sentences 279: fleurs_eng_000465 #utts: 1 +id: (fleurs_eng_000465-fleurs_eng_000465) +Scores: (#C #S #D #I) 15 11 0 1 +REF: ANCIENT china had a UNIQUE way of showing DIFFERENT time PERIODS each STAGE of CHINA OR each family that was IN power was *** A DISTINCTIVE DYNASTY +HYP: ANCHAN china had a OUNEK way of showing DIFRENT time PERIADS each STACE of CHINAE ORE each family that was INM power was THE DISTINT IF DINISTY +Eval: S S S S S S S S I S S S + +Speaker sentences 280: fleurs_eng_000466 #utts: 1 +id: (fleurs_eng_000466-fleurs_eng_000466) +Scores: (#C #S #D #I) 6 19 2 2 +REF: a ***** SIMPLE POPULAR DINNER ESPECIALLY DURING the SUMMER is THE PA AMB OLI BREAD WITH OLIVE OIL TOMATO AND any AVAILABLE CONDIMENTS such AS cheese ******* TUNAFISH ETC +HYP: a SIMPL POPULER DMER HIS FECILY DRIN the TUMER is *** ** PAAM ALDY BRED WIH ALVOILE TO MATO N any AVIABLE CONTMENS such S cheese TONFISH IT SETER +Eval: I S S S S S S D D S S S S S S S S S S S I S S + +Speaker sentences 281: fleurs_eng_000467 #utts: 1 +id: (fleurs_eng_000467-fleurs_eng_000467) +Scores: (#C #S #D #I) 4 12 0 0 +REF: the ANNOUNCEMENT was made after TRUMP HAD A PHONE CONVERSATION WITH TURKISH PRESIDENT RECEP TAYYIP ERDOĞAN +HYP: the NOUNSMET was made after TRMPAY FONG COMERSATION WIT TRKISH PRDIDENT RESEP TE EP ERO DON +Eval: S S S S S S S S S S S S + +Speaker sentences 282: fleurs_eng_000468 #utts: 1 +id: (fleurs_eng_000468-fleurs_eng_000468) +Scores: (#C #S #D #I) 7 26 13 0 +REF: PERRY STATED THAT HE WOULD RETURN TO TEXAS to ASSESS THE results of TONIGHTS CAUCUS DETERMINE WHETHER THERE is a PATH FORWARD FOR MYSELF IN THIS RACE BUT LATER SAID THAT he WOULD REMAIN IN THE RACE AND COMPETE IN THE JANUARY 21 SOUTH CAROLINA primary +HYP: ***** ****** **** ERY SATE THTH HEWOLD RETEN to TECXSISTO USESTHE results of ******** TONIHTES COKIS DI DERMENWHTHERTHER is a **** ******* PATHFORD FORMYSELF N THS RACES BUE LETER SUT THA he ***** ****** ** *** **** *** ******* WLEREMAININTHERASAN HUBPENO RIGENRYTWEEWON SOUT ARLIN O primary +Eval: D D D S S S S S S S D S S S S D D S S S S S S S S S D D D D D D D S S S S S S + +Speaker sentences 283: fleurs_eng_000469 #utts: 1 +id: (fleurs_eng_000469-fleurs_eng_000469) +Scores: (#C #S #D #I) 11 18 2 10 +REF: he *** ** WAS ALSO ENGAGED in ENGRAVING BANKNOTES for many COUNTRIES RECENT EXAMPLES of HIS WORK INCLUDING the * PRIME MINISTERIAL PORTRAITS on *** ***** *** ** the FRONT of the new ****** ** * CANADIAN 5 AND 100 BILLS +HYP: he WAT AL SO INGAGE A in GRAVING BAKNOTS for many ********* CONTRES RESONINGSEMPLES of *** H WRKINCLD the M PRIMEMENT MNINISREAL PORTRDS on THE FIRST FRO OU the FONT of the new CANADY AN F FIVEDOLR IN WON HNDER DLDIL +Eval: I I S S S S S D S S D S S I S S S I I I I S I I I S S S S S + +Speaker sentences 284: fleurs_eng_000470 #utts: 1 +id: (fleurs_eng_000470-fleurs_eng_000470) +Scores: (#C #S #D #I) 10 21 0 1 +REF: * MORE TRADITIONAL churches OFTEN HOLD AN EASTER VIGIL ON SATURDAY night DURING the EASTER WEEKEND WITH the CONGREGATIONS OFTEN breaking into CELEBRATION at the STROKE of MIDNIGHT to CELEBRATE CHRISTS RESURRECTION +HYP: E MOR TRADINAL churches OFTAN HAL THEN ESTR RIGUAL NT SATEDY night TURN the ESTR WEKGN WER the COGREATIONS OTDIM breaking into SELEBRATION at the SROK of MINIGT to SELEBRAE CRICES RESEURECTION +Eval: I S S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 285: fleurs_eng_000471 #utts: 1 +id: (fleurs_eng_000471-fleurs_eng_000471) +Scores: (#C #S #D #I) 10 15 0 0 +REF: FINLAND is a GREAT BOATING DESTINATION the land of a THOUSAND LAKES has THOUSANDS of ISLANDS TOO IN the LAKES AND IN the COASTAL ARCHIPELAGOS +HYP: FILEN is a GREAE BOTING DESTNATION the land of a THOUSON LAE has TOUSND of ILENDS TO AN the LAKS AN N the COST ARKPALOAOS +Eval: S S S S S S S S S S S S S S S + +Speaker sentences 286: fleurs_eng_000472 #utts: 1 +id: (fleurs_eng_000472-fleurs_eng_000472) +Scores: (#C #S #D #I) 4 21 4 1 +REF: CURRENT SENATOR and ARGENTINE FIRST LADY CRISTINA FERNANDEZ DE KIRCHNER ANNOUNCED her PRESIDENTIAL CANDIDACY YESTERDAY EVENING IN LA PLATA A CITY 50 KILOMETERS 31 MILES away from ** BUENOS AIRES +HYP: ******* RANTSHENATER and ********* ARGINCSEN FRSTLADE CRISTENOFOR NDIS A CURSINR ANOULS her ************ ********* PESON ATHOCANDIDUS YOSTRDAY EVENG ANLAPLATHA ASTADY FIFT CLOMITERS THERDY WN MILS away from EN OS IDISTH +Eval: D S D S S S S S S S D D S S S S S S S S S S S I S S + +Speaker sentences 287: fleurs_eng_000473 #utts: 1 +id: (fleurs_eng_000473-fleurs_eng_000473) +Scores: (#C #S #D #I) 8 16 1 0 +REF: SEVERE WEATHER IS THE GENERIC TERM for any DANGEROUS WEATHER PHENOMENON WITH the POTENTIAL to CAUSE damage SERIOUS SOCIAL DISRUPTION or LOSS of HUMAN life +HYP: ****** SVER WETHER I TE UNARICTUNE for any DADERUS WTHER FONAMINON WIT the PATNUAL to CAS damage SRIOUS SOSIAL DISTRUPTION or LAS of HUMN life +Eval: D S S S S S S S S S S S S S S S S + +Speaker sentences 288: fleurs_eng_000474 #utts: 1 +id: (fleurs_eng_000474-fleurs_eng_000474) +Scores: (#C #S #D #I) 15 13 0 2 +REF: for EXAMPLE the most COMMON STILL image PHOTOGRAPHY FORMAT IN THE WORLD is ***** **** 35MM which was the dominant FILM SIZE at the CLOSE of the analog film ERA +HYP: for EXSAMPLE the most COMEAN STIL image FITOCKRIFY FORMOUT N HE WOLD is THRTY FIVE MILOMATER which was the dominant FILME SIES at the CLOS of the analog film ARA +Eval: S S S S S S S S I I S S S S S + +Speaker sentences 289: fleurs_eng_000475 #utts: 1 +id: (fleurs_eng_000475-fleurs_eng_000475) +Scores: (#C #S #D #I) 11 12 5 0 +REF: it is related to but USUALLY not INVOLVING ALPINE STYLE SKI TOURING or MOUNTAINEERING the LATTER ONES DONE in STEEP TERRAIN and REQUIRING MUCH STIFFER SKIS and BOOTS +HYP: it is related to but ULY not ********* ****** ***** IMVLVING HLPINSTILSCETORING or MOTNERING the LATER ONS DON in ***** SPTURING and ********* RECARING MUSH THIFRSCES and BOTES +Eval: S D D D S S S S S S D S D S S S S + +Speaker sentences 290: fleurs_eng_000476 #utts: 1 +id: (fleurs_eng_000476-fleurs_eng_000476) +Scores: (#C #S #D #I) 12 14 1 0 +REF: IRONING damp CLOTHES can HELP them DRY many HOTELS HAVE AN IRON and IRONING BOARD AVAILABLE for LOAN even if ONE is not PRESENT in the ROOM +HYP: IARNING damp CLOS can HEPE them DRIY many ****** HOATELS HAEAN IARN and IRNING BORD AVALABLE for LONG even if ON is not PRESEN in the ROM +Eval: S S S S D S S S S S S S S S S + +Speaker sentences 291: mls_eng_000283 #utts: 1 +id: (mls_eng_000283-mls_eng_000283) +Scores: (#C #S #D #I) 16 20 2 1 +REF: ** EVADNE ANSWERED HOARSELY she DREW her CHAIR A LITTLE CLOSER TO the FIRE and SPREAD her HANDS out to the BLAZE there was no OTHER LIGHT in the ROOM BY this time the WIND WITHOUT HOWLED DISMALLY STILL +HYP: AE VEDNY OUNCERD HORSLY she JUW her ***** CHARE UNTERITE GLO ATO the FIER and SCRED her HANS out to the LASE there was no OTHE LIGT in the RON MY this time the **** WIN WIDOT HORDED MISMALYSTIL +Eval: I S S S S D S S S S S S S S S S S S D S S S S + +Speaker sentences 292: mls_eng_000284 #utts: 1 +id: (mls_eng_000284-mls_eng_000284) +Scores: (#C #S #D #I) 11 19 1 1 +REF: my dear MARIA WHY DO YOU not DESIST FROM THIS SILLY PURSUIT of AN IMAGINARY TREASURE what is the VALUE of MONEY we ARE SPANIARDS not SHIRTSLEEVED MERCENARY PIGS of * AMERICANS +HYP: my dear ***** MOREA WHI DOYOU not DESCIST FOM THI SCILY PESUT of A ANMANDGINARY TLESEUR what is the VOLYOU of MUONY we AR SPANURDS not SHERTSLEVED MERSINARY PLEAGS of A MEAICAENS +Eval: D S S S S S S S S S S S S S S S S S S I S + +Speaker sentences 293: mls_eng_000285 #utts: 1 +id: (mls_eng_000285-mls_eng_000285) +Scores: (#C #S #D #I) 7 22 6 1 +REF: *** CRITICAL TEMPERATURE is THAT of the SINGLE ISOTHERMAL LINE WHICH PRESENTS A POINT OF INFLEXION AT A HORIZONTAL TANGENT the CRITICAL PRESSURE AND THE CRITICAL VOLUME ARE THE two COORDINATES of this POINT OF INFLEXION +HYP: THE CRITIAL TRPAUR is TAT of the ****** ********** **** SINGL AS THERMAE LIN WHCH PESENSE POINTOV NEFLECTIONAT HORSND TIENGENT the ******** ******** RITIAL PRESE HE RIAL VOLIME ATH two CULDNESE of this ***** PONTO INPLECTION +Eval: I S S S D D D S S S S S S S S S S D D S S S S S S S D S S + +Speaker sentences 294: mls_eng_000286 #utts: 1 +id: (mls_eng_000286-mls_eng_000286) +Scores: (#C #S #D #I) 21 21 0 0 +REF: much LIKE IN FOULNESS and DEFORMITY UNTO that monster whom the THEBAN KNIGHT the father of that FATAL PROGENY made KILL herself for very HEARTS DESPITE THAT he had READ her RIDDLE which NO WIGHT could ever LOOSE BUT SUFFERED deadly DUEL +HYP: much LIK AN FOUNUS and IFORMITY ONTO that monster whom the HEBAN NIGHT the father of that FATL PROGANY made CIL herself for very HARTS TOSPIHT HAT he had RED her RIL which NOW WIHT could ever LOSES WUT SUFERE deadly D +Eval: S S S S S S S S S S S S S S S S S S S S S + +Speaker sentences 295: mls_eng_000287 #utts: 1 +id: (mls_eng_000287-mls_eng_000287) +Scores: (#C #S #D #I) 13 25 4 0 +REF: HE HAS MANAGED TO MEASURE WITH PRECISION PRESSURES amounting to THREE THOUSAND ATMOSPHERES and also the VERY SMALL VOLUMES THEN OCCUPIED by the FLUID MASS UNDER consideration this last MEASUREMENT which NECESSITATES NUMEROUS CORRECTIONS is THE most DELICATE PART OF THE OPERATION +HYP: ** *** HIS MASED MESE WIH PEION PRESIES amounting to THRE THUSN ATNSTFIE and also the RY SMAL VOLIMS TAN OCUPIE by the FLOAID MASE NDE consideration this last MESGEMENT which NESESITATES NEUMRUS COREACTINS is *** most ******** DELICAD PAT TE OPORATION +Eval: D D S S S S S S S S S S S S S S S S S S S S S D D S S S S + +Speaker sentences 296: mls_eng_000288 #utts: 1 +id: (mls_eng_000288-mls_eng_000288) +Scores: (#C #S #D #I) 10 13 1 0 +REF: WHY SHOULD IT HAVE BEEN DEEMED necromancy to ENDEAVOR to COMBINE THESE PARTS to EVOLVE by CAREFUL ELIMINATION and change to the perfect FOOD +HYP: *** WHIY SHULDIT HAE BEN DED necromancy to NDEVER to ONBIN THES FATS to IVOLVE by CARFUL ELMINATION and change to the perfect FOD +Eval: D S S S S S S S S S S S S S + +Speaker sentences 297: mls_eng_000289 #utts: 1 +id: (mls_eng_000289-mls_eng_000289) +Scores: (#C #S #D #I) 23 16 0 1 +REF: nay THOUGH of RUSHES be my bed yet i am rich love said ***** BUT ARGUED LIFE thrice FOND ART THOU to YIELD the SOVEREIGN gifts of EARTH the victor SWORD the LAURELED brow for VISIONED THINGS of LITTLE worth +HYP: nay THO of RASES be my bed yet i am rich love said BUTUT AR GUD LIVHE thrice FORND ARE THOW to YEL the SOVERAN gifts of ERTH the victor SORD the LORALD brow for VISION THINGKS of LITL worth +Eval: S S I S S S S S S S S S S S S S S + +Speaker sentences 298: mls_eng_000290 #utts: 1 +id: (mls_eng_000290-mls_eng_000290) +Scores: (#C #S #D #I) 11 21 3 0 +REF: BOCK SEEMS to HAVE BEEN A KEEN COLLECTOR ALTHOUGH HAMPERED by ILL HEALTH and a GREAT POINT IN HIS FAVOUR is that HE DESCRIBED only those PLANTS WHICH had come under HIS OWN PERSONAL OBSERVATION +HYP: BUCK EME to **** HAV BENA CE CULACTER AOTHO HAMPED by IL HELTH and a ***** GRET PINTON IS FAVER is that I DISCRIDED only those ****** PLANSWITH had come under IS ON PERSINAL OBSOVATION +Eval: S S D S S S S S S S S D S S S S S S D S S S S S + +Speaker sentences 299: mls_eng_000291 #utts: 1 +id: (mls_eng_000291-mls_eng_000291) +Scores: (#C #S #D #I) 20 12 1 0 +REF: HAD RATHER SHRUNK up and had not CHANGED into NYMPHS THESE I LEFT in the STEMS COVERING them up AGAIN and they APPEARED as perfect insects in the may of the FOLLOWING year +HYP: *** HADRATHER SRONG up and had not CHAINED into NEIMPS THES HY FELT in the TEAMS COVERIN them up AGIN and they UPEARED as perfect insects in the may of the FOLOING year +Eval: D S S S S S S S S S S S S + +Speaker sentences 300: mls_eng_000292 #utts: 1 +id: (mls_eng_000292-mls_eng_000292) +Scores: (#C #S #D #I) 13 20 3 0 +REF: nothing SAVE OBJECTS and THOUGHTS of BEAUTY COULD PRESENT THEMSELVES to the UNDERSTANDING of the FORTUNATE PERSON WHO PARTOOK of it these PAGES WHICH YOU HAVE BROUGHT to me TO TRANSLATE ARE CONCERNED WITH this SUPERSTITION +HYP: nothing SAYO OBJECS and THUTS of BOUTY HOUD PRESEND THEMSES to the UNESTANDING of the ********* FORTIOLUT BURSINWHO PARTOK of it these ***** BATES WHC YO ARBRUGT to me ** TRANSALAT AR ONSEURED WIT this SOUPOESTION +Eval: S S S S S S S S D S S S D S S S S D S S S S S + +Speaker sentences 301: mls_eng_000293 #utts: 1 +id: (mls_eng_000293-mls_eng_000293) +Scores: (#C #S #D #I) 9 12 2 1 +REF: NOW SEEMED INSIPIDITY and ** HED nerve himself AGAINST it his FACE WORE A SORT of SEVERE FLUSH he WAS TIMID even to RUDENESS +HYP: NO SEM INSOUPITITY and HE D nerve himself AGAINS it his **** FAISE WAR SORD of SVEF FLOUSHD he *** WASTIMIED even to RUDNES +Eval: S S S I S S D S S S S S D S S + +Speaker sentences 302: mls_eng_000294 #utts: 1 +id: (mls_eng_000294-mls_eng_000294) +Scores: (#C #S #D #I) 11 26 5 2 +REF: became **** MORE LIFELIKE as THE CHEEKS flush THERE WAS RARE WARMTH IN A WINTER MORNING to ** CHEER THE HALFDESPAIRING SOUL TIRED AFTER long HOURS of OIL READING AND PIERCED TO THE heart by never CEASING RHYMES yet I COULD NOT UNDERSTAND it +HYP: became MOLE LIFE LIK as TE CHES flush ***** *** THE AS RAR WARMEFINOF INTE MORNIN to HE T HAFTDIS PERING SOL TSID IT long OURS of *** ******* ALL REDING ANT PERSTED heart by never SEASING RIMES yet * ICOUL NO NDESTAND it +Eval: I S S S S D D S S S S S S I S S S S S S S D D S S S S S S D S S S + +Speaker sentences 303: mls_eng_000295 #utts: 1 +id: (mls_eng_000295-mls_eng_000295) +Scores: (#C #S #D #I) 12 13 1 2 +REF: ONE of the HAWAIIAN WRITERS said the **** ** OPIHIAWA is a POISON SHELLFISH THESE ARE BITTER and DEADLY and CAN be USED in PUTTING ENEMIES to death +HYP: WON of the OWIN RITER said the AOPE HE AVA is a ****** POSON SHALFISH THESAR BITER and DEDLY and CAD be OUSED in PUTING ENAIMES to death +Eval: S S S I I S D S S S S S S S S S + +Speaker sentences 304: mls_eng_000296 #utts: 1 +id: (mls_eng_000296-mls_eng_000296) +Scores: (#C #S #D #I) 7 20 1 3 +REF: the **** BEAUTEOUS ROBES of ***** HEAVEN ASLANT THE DEW BRIGHT EARTH AND COLOURED AIR HE LOOKS in BOUNDLESS MAJESTY ABROAD touching THE GREEN leaves all * ATREMBLE WITH gold LIGHT +HYP: the BEUT YUS ROUBS of HAVEN AS LON T A DU RIHT ERS N COLEIT AR HELOKS in ********* BOUNLES MAGHUTYABROAD touching TH GREN leaves all A TREMBL IT gold LIHT +Eval: I S S I S S S S S S S S S S S D S S S S I S S S + +Speaker sentences 305: mls_eng_000297 #utts: 1 +id: (mls_eng_000297-mls_eng_000297) +Scores: (#C #S #D #I) 19 25 3 0 +REF: i can DO no MORE THAN that UNTIL this MATTER is ABSOLUTELY SETTLED THEY ARE worth MORE than life itself to me MR COWPER SEEMED ANNOYED SURELY he PROTESTED YOU ARE not GOING TO ASK me to WAIT THREE MONTHS until I CAN EXAMINE one of THESE +HYP: i can DOU no MOR HAN that INTL this MATER is APSALUTLY SETED THE A worth O than life itself to me TMSTR BLBUR SEMED ANOIED SURLY he ********* PROTESTIT WOU not ***** GODO AST me to WATE THRE MONCS until * AT EXAMIN one of THES +Eval: S S S S S S S S S S S S S S S D S S D S S S S S D S S S + +Speaker sentences 306: mls_eng_000298 #utts: 1 +id: (mls_eng_000298-mls_eng_000298) +Scores: (#C #S #D #I) 5 14 0 6 +REF: **** ROSCONGRESS foundation ****** RUSSIAN ENTITY that ORGANIZED the **** ****** **** ** SAINT PETERSBURG INTERNATIONAL ECONOMIC FORUM ROSNEFT RUSSIAN STATEOWNED OIL and ENERGY company +HYP: ROSE CONGRES foundation RUSHIN AND TITE that ORGANIED the SAIT PETERS BURG IN TER NASINALE ECANOMIC FOROM ROUS NEFT RUSHION STATOND OILE and ANARGY company +Eval: I S I S S S I I I I S S S S S S S S S S + +Speaker sentences 307: mls_eng_000299 #utts: 1 +id: (mls_eng_000299-mls_eng_000299) +Scores: (#C #S #D #I) 12 36 3 0 +REF: how IT GLITTERED AND SPARKLED the DELICATE FROSTWORK YOU WERE ATTRACTED no DOUBT AND MARVELLED AT the DAINTY TRACINGS but FEW of US HAVE REALLY had an OPPORTUNITY to STUDY the DETAIL OF THESE FROST DESIGNS MINUTELY OR HAVE CONSIDERED THAT THERE WERE MORE THAN THREE OR FOUR DESIGNS at most +HYP: how ** GLUTED N SPARCKALE the DELICAT ROST WOK YOUE ATRACTED no DOUT A MARVED A the DINTY TRAICSOMS but FEOU of AS HAV REALY had an OPOTUNITY to STADY the ****** ** DETAL OFTHE FRUSTDESINS MY NUTLY O AV CONSIDED HAT THE WRE OR TIN THRE YUR FORDESINS at most +Eval: D S S S S S S S S S S S S S S S S S S S S D D S S S S S S S S S S S S S S S S + +Speaker sentences 308: mls_eng_000300 #utts: 1 +id: (mls_eng_000300-mls_eng_000300) +Scores: (#C #S #D #I) 15 21 17 1 +REF: OTHER THAN the OFFENSE IN TRYING to INFLICT A WOUND THEY MAY KILL the ******** OFFENDER or WOUND him MORE than THEY INTENDED to do and this BECOMES A CAUSE FOR A NEW FEUD so THAT THE PRIMITIVE LEGISLATORS WERE CAREFUL in REQUIRING THE RETALIATION to BE LIMITED to an EYE FOR AN EYE +HYP: ***** THATHA the ******* OFENS INTRING to ******* * ***** **** *** INFICKAWON the MCKILVER OFHENDER or WN him MOR than THE INTENDAN to do and this ******* * ***** *** * BECOMSECCUSFUL ANUTHRD so **** *** TATHE PRIMITIVELAGDSLATERS WHE CAEFUL in ********* ORECQUIARIN THERITALITION to ** BELIMITED to an *** Y FO ANA +Eval: D S D S S D D D D D S I S S S S S D D D D D S S D D S S S S D S S D S D S S S + +Speaker sentences 309: mls_eng_000301 #utts: 1 +id: (mls_eng_000301-mls_eng_000301) +Scores: (#C #S #D #I) 21 22 1 0 +REF: at CYRUS word the JEWS RETURN the COMPANY that go gods HOUSE BEGUN with MIRTH AND MOAN is HINDERED by the FOE but ONCE again the WORK GOES on by LICENSE from DERIUS EZRA is sent with ROYAL GRANT and GIFTS FOR USES PIOUS +HYP: at SIRES word the JUES WRETERN the COMPNY that go gods HICE BEGON with MRTH A MON is HENDED by the FO but WONCE again the WRKE COSE on by LISENS from DURIOUS ESRA is sent with ROILE GRUNT and ***** GIFTHS FORIUS PIAS +Eval: S S S S S S S S S S S S S S S S S S S D S S S + +Speaker sentences 310: mls_eng_000302 #utts: 1 +id: (mls_eng_000302-mls_eng_000302) +Scores: (#C #S #D #I) 10 14 3 0 +REF: NET PRODUCT year in and year out SEVEN HUNDRED FRANCS HE LIVED IN it HOW not so badly WE WILL EXPLAIN MARIUS OCCUPIED IN the GORBEAU HOUSE +HYP: ANT PRODACK year in and year out ***** SIVIN HUNDRE FROANCES WHOLIVED ON it HAVE not so badly ** WREIUL EXPLAINE MORDYIS OCUPED TA the ******* ORBOHOVS +Eval: S S D S S S S S S D S S S S S D S + +Speaker sentences 311: mls_eng_000303 #utts: 1 +id: (mls_eng_000303-mls_eng_000303) +Scores: (#C #S #D #I) 23 22 4 0 +REF: THEN this is all YOUR ANSWER tis TOO FAIR for one of his ALLIANCE and i WARN YOU that this place no MORE SEE you EXIT ENTER DE FLORES the best is THERE is MORE GROUND to MEET a MANS REVENGE ON HONEST DE FLORES thats my NAME INDEED +HYP: THEAN this is all **** YOURANTAR tis TO FAR for one of his ALIANCS and i WORE YOUW that this place no MOR SE you **** AEGXSEIT ANDTER DFLURANS the best is THER is MOR CROUD to ET a **** ******* MANSARYVEN JON ONS EFORACE thats my NAM INDED +Eval: S D S S S S S S S S D S S S S S S S D D S S S S S S + +Speaker sentences 312: mls_eng_000304 #utts: 1 +id: (mls_eng_000304-mls_eng_000304) +Scores: (#C #S #D #I) 27 24 3 0 +REF: WHEN i RETURNED to the house where I had BEEN a HAPPY child only a PILE of ASHES WHERE IT had STOOD I wept long and to FORGET my WEEPING i SAILED out ON THE VAST CALM SEA on these waters in A STAR SAPPHIRE NIGHT i PLAYED my FLUTE to the SUMMER MOON +HYP: HEN i RETURND to the house where * had BE a HAPY child only a PIL of ***** ASHEIS WEAYIT had ***** STUDI wept long and to FOGET my WEPING i SAID out OND E VATS CAMS SCE on these waters in AS TARSAFAY AN NIGT i PLADE my FLOT to the SUMER MON +Eval: S S D S S S D S S D S S S S S S S S S S S S S S S S S + +Speaker sentences 313: mls_eng_000305 #utts: 1 +id: (mls_eng_000305-mls_eng_000305) +Scores: (#C #S #D #I) 32 16 1 1 +REF: do you not SEE what PLEASURE it gives HIM we have GROWN up together in this house SINCE he was A BOY i simply CANNOT bear as you CAN the SIGHT of the SMILE LEAVING his face POOR dear he has no AMUSEMENT except this PLAYING at ** THE SHOPKEEPING +HYP: do you not SE what LESUER it gives ME we have GRON up together in this house SINC he was * ABOY i simply CANOR bear as you GAN the SIGT of the SMIL LAVING his face BOUR dear he has no MUSMENT except this PLING at TH SHOP CEPING +Eval: S S S S S D S S S S S S S S S I S S + +Speaker sentences 314: mls_eng_000306 #utts: 1 +id: (mls_eng_000306-mls_eng_000306) +Scores: (#C #S #D #I) 11 14 5 1 +REF: it is A NEBULOUS BODY REVOLVING in AN ELLIPTICAL ORBIT OF GREAT ELONGATION LOVE love love WILL not be the WOUND of CUPID but THE MANIFESTATION of ** UNIVERSAL REPRODUCTIVE INSTINCTS +HYP: it is * DEVBIUS BOADY REVALING in ** ********** ***** ** YLIPICAL ORRGRE EONGATION love love LOVEWAL not be the WONED of CUPIT but HE MANIFISTATION of HE ERVERSAL REPRDUCTIE INSTINCES +Eval: D S S S D D D D S S S S S S S S I S S S + +Speaker sentences 315: mls_eng_000307 #utts: 1 +id: (mls_eng_000307-mls_eng_000307) +Scores: (#C #S #D #I) 21 22 3 0 +REF: SHARPLY as he shook HANDS WITH her god BLESS YOU MY DEAR CHILD the BISHOP said when she KISSED him and his lips MOVED AFTERWARD for SOME SECONDS AS IF he WERE IN PRAYER HER mother FOLLOWED her out of THE ROOM and then SILENCE SETTLED +HYP: SHOARPLY as he shook HAND WIT her god ***** BES YUMIG TDEAT CHA the BIHOP said when she CISED him and his lips MOED OFTERWARD for **** SOM SICKNTS ASIF he **** WER INPRAAR HUR mother FOLORD her out of TH OM and then SILAND CETEL +Eval: S S S D S S S S S S S S D S S S D S S S S S S S S + +Speaker sentences 316: mls_eng_000308 #utts: 1 +id: (mls_eng_000308-mls_eng_000308) +Scores: (#C #S #D #I) 15 10 1 1 +REF: FOLLOWED him STEALTHILY and WHEN he ** was in a STOOPING POSTURE FILLING his BUCKET came up BEHIND him and PLUNGED A long KNIFE into his neck +HYP: FOLOED him STAELTFULLY and **** he HI was in a STPING POSTHUR FILING his BOCKET came up BEHINED him and PLUNCED AN long NIFE into his neck +Eval: S S D I S S S S S S S S + +Speaker sentences 317: mls_eng_000309 #utts: 1 +id: (mls_eng_000309-mls_eng_000309) +Scores: (#C #S #D #I) 18 16 2 1 +REF: SAITH CHERSIAS DOES not jupiter DISTRIBUTE to the gods THEIR proportion and DIVIDEND sparingly and SEVERALLY as AGAMEMNON DID to his COMMANDERS when his GUESTS DRANK to one another **** IF CHERSIAS QUOTH CLEODEMUS as YOU NARRATE +HYP: SASTH CURTIES DOUS not jupiter DISTREBUTE to the gods THE proportion and DIVIDENT sparingly and SEVERALY as ********* AGAMANANDYE to his COMANDOERS when his GESTS TRANGK to one another IFVF OR COURIOUS QUTH CLEEDEMOS as *** YUNERRAT +Eval: S S S S S S S D S S S S I S S S S D S + +Speaker sentences 318: mls_eng_000310 #utts: 1 +id: (mls_eng_000310-mls_eng_000310) +Scores: (#C #S #D #I) 8 22 5 0 +REF: and WHERE NONE SHALL DARE RESTRAIN US WE CAN MEET again in THOUGHT so THERES no USE IN WEEPING BEAR A CHEERFUL spirit STILL NEVER DOUBT THAT FATE IS KEEPING FUTURE good for PRESENT ILL +HYP: and ***** **** HERE NON HAL DARASTRAN T TO AMET again in THOHT so THIS no *** ** USEAN EPING BHERE CHERUL spirit ***** STIL NER DOT HE FATIS EPING FEUTUR good for PESEN IL +Eval: D D S S S S S S S S S D D S S S S D S S S S S S S S S + +Speaker sentences 319: mls_eng_000311 #utts: 1 +id: (mls_eng_000311-mls_eng_000311) +Scores: (#C #S #D #I) 16 20 1 1 +REF: and TO become the RECORD of what PEOPLE HAVE DONE IN THEIR MORE AMIABLE MOMENTS the RECORD of ** THE CONQUESTS of PEACE how men have lived and LABORED dug AND BUILT HEWN and CLEARED GARDENED and REFOREST +HYP: and ** become the RECARD of what PEPL AVE DON I THER MOR AMUBLE MOENTS the RECARD of HE CONCQUEST S of PESE how men have lived and LAVERD dug AD BILT HEUON and CLERED GARDED and REFORST +Eval: D S S S S S S S S S S I S S S S S S S S S S + +Speaker sentences 320: mls_eng_000312 #utts: 1 +id: (mls_eng_000312-mls_eng_000312) +Scores: (#C #S #D #I) 14 25 3 2 +REF: the LOW FLYING of THE SWALLOWS BETOKENS rain as WELL as ** ANY UNSEASONABLE DANCING of MIDGES in the evening SORE CORNS ON THE FEET and RHEUMATISM IN the JOINTS ARE DIREFUL PRECURSORS the leaves ARE all * ATREMBLE BEFORE THE APPROACH OF THUNDER +HYP: the *** LOFLING of TH WLLES PETOKINS rain as WIL as NY AN SCASONABLE DANCSING of MIGEUS in the evening **** SOA CONS ANDO FEAT and ********** RIMTISIN the ONCES AR DIFUL PROCURSS the leaves AR all A TRMBLE BE FOR THAT PROCHEAT TUNDER +Eval: D S S S S S I S S S S D S S S S D S S S S S S I S S S S S S + +Speaker sentences 321: mls_eng_000313 #utts: 1 +id: (mls_eng_000313-mls_eng_000313) +Scores: (#C #S #D #I) 17 18 0 5 +REF: was * * STORMED GENERAL DAMPIERRE was KILLED general CUSTINE was ******* BLAMED AND INDEED IS now come to paris to GIVE EXPLANATIONS AGAINST all which the MOUNTAIN and * ******** ATROCIOUS MARAT must EVEN make HEAD as THEY can +HYP: was A S TORMNGH JGENERL DEMPEARE was CKILE general COSTIEN was BLAIMED AN IN DED ES now come to paris to DEV EXPLONATIONS AGANSET all which the MOUNTON and A TROTIOUS MAU AR must EVEND make HAIL as THE can +Eval: I I S S S S S I S S S S S S S S I I S S S S S + +Speaker sentences 322: mls_eng_000314 #utts: 1 +id: (mls_eng_000314-mls_eng_000314) +Scores: (#C #S #D #I) 17 12 0 2 +REF: the moment was FEARFUL a ***** MIGHTIER FOE had never SWUNG the BATTLEAXE over him but *** HOPE nerved his ARM for a DESPERATE blow and TECUMSEH FELL PROSTRATE BEFORE him +HYP: the moment was FEFUL a MIGTY OF FO had never SWNG the BUTLELACX over him but THE HOB nerved his ARME for a DESPRAT blow and TH CMSERFLE PROSTRAITD BEFOR him +Eval: S I S S S S I S S S S S S S + +Speaker sentences 323: mls_eng_000315 #utts: 1 +id: (mls_eng_000315-mls_eng_000315) +Scores: (#C #S #D #I) 16 11 2 1 +REF: then the WIND STOPPED the CLOUDS TURNED DARK and NIGHT came on like INK my old *** COTTON QUILT was COLD as IRON my sweet son TOSSED in his SLEEP +HYP: then the **** WINESTOUT the ****** CLAORSTAND DOARK and NIGHE came on like EINK my old COT IN CUILT was CAOLD as IAN my sweet son TOST in his SCLEE +Eval: D S D S S S S I S S S S S S + +Speaker sentences 324: mls_eng_000316 #utts: 1 +id: (mls_eng_000316-mls_eng_000316) +Scores: (#C #S #D #I) 25 18 2 0 +REF: you may do as YOU please to work OFF YOUR IRRITATION to KEEP UP your FANATICISM you ARE WELL OFF you NEED not mind the cost the POOR do not want TO stand IN YOUR way but YOU insist on THEIR SUBMITTING TO YOUR compulsion +HYP: you may do as YU please to work OF OR IRITATION to **** KEP your FANATICSISM you RE WEL AFF you NED not mind the cost the PORE do not want T stand ** INYOUR way but YU insist on THE SUBMITING T YOR compulsion +Eval: S S S S D S S S S S S S S D S S S S S S + +Speaker sentences 325: mls_eng_000317 #utts: 1 +id: (mls_eng_000317-mls_eng_000317) +Scores: (#C #S #D #I) 20 25 4 1 +REF: he was BRED by REV G A SNEYD being by othman E SIX FOUR TWO TWO HEDWIG HE was **** BORN IN MARCH EIGHTEEN SEVENTYNINE and he was the ONLY SURVIVOR of A LITTER of FIFTEEN it WAS ON this ACCOUNT that HE was CALLED safe IN color and MARKINGS +HYP: he was RED by A REVERNTERY AYS NIGHT being by othman * ESIX FOAR TO T HIDVICK WL was BONE AN MACH ATEN SEVENTY NIN and he was the **** ONLYSOVIVER of * LITER of FIFTEN it *** WASON this ACOUNT that H was AUED safe AND color and MAKINGS +Eval: S S S S S D S S S S S S I S S S S S D S D S S D S S S S S S + +Speaker sentences 326: mls_eng_000318 #utts: 1 +id: (mls_eng_000318-mls_eng_000318) +Scores: (#C #S #D #I) 18 15 3 1 +REF: and what HASTE it MAKES TO FALL into the SECOND THERE by this time DIAPHANTA SNEEZES ACHOO most ADMIRABLE SECRET on the contrary it STIRS me not a WHIT WHICH most *** CONCERNS it HA HA HA +HYP: and what HAST it AKES O FOL into the SECKANT THER by this time DIAFHANTEO SNEASERS ECGIO most ADMRABLE SECREIT on the contrary it STURS me not a WIT WICH most CON SURNES it ** ** ** +Eval: S S S S S S S S S S S S S S I S D D D + +Speaker sentences 327: mls_eng_000319 #utts: 1 +id: (mls_eng_000319-mls_eng_000319) +Scores: (#C #S #D #I) 11 21 2 4 +REF: THIRDLY THALES said WHERE the CITIZENS are NEITHER TOO RICH nor ** *** ******* TOO POOR FOURTHLY ANACHARSIS said where THOUGH in all OTHER RESPECTS THEY ARE EQUAL YET VIRTUOUS men ARE ADVANCED and VICIOUS person *** DEGRADED +HYP: THURDLY THAL said WHER the CITISENS are NEATHER TO REACH nor TO POR FORTHLY AN A CAUS IS said where THOG in all ***** ******** OTHERESPECTS THEYAR IACUL IET VERTOUOS men AR ADVANSED and VESIOUS person THE GRADED +Eval: S S S S S S S I I I S S S S S D D S S S S S S S S I S + +Speaker sentences 328: mls_eng_000320 #utts: 1 +id: (mls_eng_000320-mls_eng_000320) +Scores: (#C #S #D #I) 19 15 3 0 +REF: the KINDLY frank is SYMPATHETIC EVERY day he PASSES notes between us and i TRY to ENCOURAGE RUSSELL HE WILL improve i ASSURE him his time IS SHORT and fresh AIR AND LIBERTY WILL SOON RESTORE him +HYP: the CINDLY frank is SIMPOTHATICK AEVERY day he PAS notes between us and i TRIY to ********* INCURAGE RUSSAL HEWIL improve i ASUR him his time ** ISHORT and fresh *** ARAND LIORTY WL SON RESTOR him +Eval: S S S S S D S S S S D S D S S S S S + +Speaker sentences 329: mls_eng_000321 #utts: 1 +id: (mls_eng_000321-mls_eng_000321) +Scores: (#C #S #D #I) 24 17 1 0 +REF: THESE QUESTIONS it is now evident may FREQUENTLY be ANSWERED WITH EQUAL PROPRIETY IN OPPOSITE WAYS and if there be any OCCASIONS on which they can be ANSWERED only in ONE way the ANSWER WILL depend UPON the NATURE of the OCCASION +HYP: THIS CUESTONS it is now evident may FRECUENTLY be ******** ANSERED WAS EAQULL PROPRITY INOPSIT WASES and if there be any ACASIONS on which they can be ANSERD only in ON way the AUNSER WIL depend APON the NATUR of the ACASION +Eval: S S S D S S S S S S S S S S S S S S + +Speaker sentences 330: mls_eng_000322 #utts: 1 +id: (mls_eng_000322-mls_eng_000322) +Scores: (#C #S #D #I) 18 17 4 0 +REF: in his NOTE BORE the MINSTRELSY SECOND EDITION EIGHTEEN OH EIGHT SCOTT SAYS the BALLAD was taken down FROM AN old WOMANS RECITATION at the ALSTON MOOR LEAD MINES by the agent THERE and sent by him to SURTEES +HYP: in his NOT BOR the ********** ****** MINSTRILSY SECKNADION ATY NOW AT SCOTSES the BLED was taken down **** FROMA old WOMENS REITATION at the ****** HELSAON MOR LEDMINES by the agent THER and sent by him to SURTEE +Eval: S S D D S S S S S S S D S S S D S S S S S + +Speaker sentences 331: nchlt_eng_001588 #utts: 1 +id: (nchlt_eng_001588-nchlt_eng_001588) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ******* CHRISTIAN THEOLOGIANS +HYP: CRISTON THE OLIGONS +Eval: I S S + +Speaker sentences 332: nchlt_eng_001589 #utts: 1 +id: (nchlt_eng_001589-nchlt_eng_001589) +Scores: (#C #S #D #I) 0 3 0 0 +REF: OBTAIN EAGLE FEATHERS +HYP: OPTAINE EAGL FHITHERS +Eval: S S S + +Speaker sentences 333: nchlt_eng_001590 #utts: 1 +id: (nchlt_eng_001590-nchlt_eng_001590) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ELEMENTARY SPECIAL FUNCTIONS +HYP: ELAMENTRYE SPCIALE FONGTIONS +Eval: S S S + +Speaker sentences 334: nchlt_eng_001591 #utts: 1 +id: (nchlt_eng_001591-nchlt_eng_001591) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ***** GEORGE WASHINGTON UNIVERSITY +HYP: JORDE WASINGS AN NUNVORSTITY +Eval: I S S S + +Speaker sentences 335: nchlt_eng_001592 #utts: 1 +id: (nchlt_eng_001592-nchlt_eng_001592) +Scores: (#C #S #D #I) 1 2 0 1 +REF: SCIENCE fiction ****** NOVELS +HYP: SINS fiction NOTVES PROVEAN +Eval: S I S + +Speaker sentences 336: nchlt_eng_001593 #utts: 1 +id: (nchlt_eng_001593-nchlt_eng_001593) +Scores: (#C #S #D #I) 0 2 1 0 +REF: COAST HIP HOP +HYP: ***** COSTD HIPOP +Eval: D S S + +Speaker sentences 337: nchlt_eng_001594 #utts: 1 +id: (nchlt_eng_001594-nchlt_eng_001594) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ****** INVERSE LAPLACE TRANSFORM +HYP: INVERS LEP BLACE TRONSFORME +Eval: I S S S + +Speaker sentences 338: nchlt_eng_001595 #utts: 1 +id: (nchlt_eng_001595-nchlt_eng_001595) +Scores: (#C #S #D #I) 0 2 0 0 +REF: FRENCH PROTESTANTS +HYP: FRINGH PROTISTANTS +Eval: S S + +Speaker sentences 339: nchlt_eng_001596 #utts: 1 +id: (nchlt_eng_001596-nchlt_eng_001596) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** AFGHAN AIR FORCE +HYP: AF GUN AE FORSEST +Eval: I S S S + +Speaker sentences 340: nchlt_eng_001597 #utts: 1 +id: (nchlt_eng_001597-nchlt_eng_001597) +Scores: (#C #S #D #I) 2 3 0 0 +REF: HEROES in MYTHOLOGY and LEGEND +HYP: HEAROS in MISOLAGY and LEAGEND +Eval: S S S + +Speaker sentences 341: nchlt_eng_001598 #utts: 1 +id: (nchlt_eng_001598-nchlt_eng_001598) +Scores: (#C #S #D #I) 0 3 0 2 +REF: ***** ***** BUSINESS CLASS SEAT +HYP: BUSNS CLARS SEET T TER +Eval: I I S S S + +Speaker sentences 342: nchlt_eng_001599 #utts: 1 +id: (nchlt_eng_001599-nchlt_eng_001599) +Scores: (#C #S #D #I) 1 2 0 0 +REF: CLUB play CHART +HYP: CLUDB play CHARTET +Eval: S S + +Speaker sentences 343: nchlt_eng_001600 #utts: 1 +id: (nchlt_eng_001600-nchlt_eng_001600) +Scores: (#C #S #D #I) 1 2 0 1 +REF: **** POSITRONS were REPORTED +HYP: POSY TRONS were RAPORTED +Eval: I S S + +Speaker sentences 344: nchlt_eng_001601 #utts: 1 +id: (nchlt_eng_001601-nchlt_eng_001601) +Scores: (#C #S #D #I) 0 3 0 0 +REF: OLD VIC THEATRE +HYP: ALLD VICK THEATAR +Eval: S S S + +Speaker sentences 345: nchlt_eng_001602 #utts: 1 +id: (nchlt_eng_001602-nchlt_eng_001602) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ** ORTHODOX MONARCHS +HYP: OR THEDOCKES MONOUCKS +Eval: I S S + +Speaker sentences 346: nchlt_eng_001603 #utts: 1 +id: (nchlt_eng_001603-nchlt_eng_001603) +Scores: (#C #S #D #I) 2 1 0 0 +REF: nations MEMBER states +HYP: nations MBER states +Eval: S + +Speaker sentences 347: nchlt_eng_001604 #utts: 1 +id: (nchlt_eng_001604-nchlt_eng_001604) +Scores: (#C #S #D #I) 0 3 0 0 +REF: FIFA WORLD CUP +HYP: SHEAFHO WILD COUP +Eval: S S S + +Speaker sentences 348: nchlt_eng_001605 #utts: 1 +id: (nchlt_eng_001605-nchlt_eng_001605) +Scores: (#C #S #D #I) 0 3 0 0 +REF: CREWS RESCUE EFFORTS +HYP: CROS RISCKUW FEATS +Eval: S S S + +Speaker sentences 349: nchlt_eng_001606 #utts: 1 +id: (nchlt_eng_001606-nchlt_eng_001606) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ****** ACTUAL FILM MICROSCOPICALLY +HYP: ACTHAL FOME MOACKRS COPITE +Eval: I S S S + +Speaker sentences 350: nchlt_eng_001607 #utts: 1 +id: (nchlt_eng_001607-nchlt_eng_001607) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ******** MUSICAL GROUPS REESTABLISHED +HYP: MEUSICAL GROPS RE ASTABLISHED +Eval: I S S S + +Speaker sentences 351: nchlt_eng_001608 #utts: 1 +id: (nchlt_eng_001608-nchlt_eng_001608) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ******* PRIMUS INTER PARES +HYP: PROMSCS IN SER PAESE +Eval: I S S S + +Speaker sentences 352: nchlt_eng_001609 #utts: 1 +id: (nchlt_eng_001609-nchlt_eng_001609) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ***** FILM TECHNIQUES +HYP: FOLNE SIK NEKS +Eval: I S S + +Speaker sentences 353: nchlt_eng_001610 #utts: 1 +id: (nchlt_eng_001610-nchlt_eng_001610) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** TELEVISION SERIES BASED +HYP: TL VION SERYES BACET +Eval: I S S S + +Speaker sentences 354: nchlt_eng_001611 #utts: 1 +id: (nchlt_eng_001611-nchlt_eng_001611) +Scores: (#C #S #D #I) 1 2 0 0 +REF: new POLITICAL PARTY +HYP: new POLITICAR PATY +Eval: S S + +Speaker sentences 355: nchlt_eng_001612 #utts: 1 +id: (nchlt_eng_001612-nchlt_eng_001612) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ANCIENT EGYPT ACHIEVED +HYP: ANHANT EAGJIP ACHEVED +Eval: S S S + +Speaker sentences 356: nchlt_eng_001613 #utts: 1 +id: (nchlt_eng_001613-nchlt_eng_001613) +Scores: (#C #S #D #I) 1 2 0 0 +REF: flat MUSIC NATURAL +HYP: flat MUSIG NATRAL +Eval: S S + +Speaker sentences 357: nchlt_eng_001614 #utts: 1 +id: (nchlt_eng_001614-nchlt_eng_001614) +Scores: (#C #S #D #I) 1 3 0 0 +REF: american S TECHNOLOGY WRITERS +HYP: american TICNOLAD TIGNLEDY RATERS +Eval: S S S + +Speaker sentences 358: nchlt_eng_001615 #utts: 1 +id: (nchlt_eng_001615-nchlt_eng_001615) +Scores: (#C #S #D #I) 1 2 0 0 +REF: DAUGHTERS of BARONS +HYP: DOATES of BARINS +Eval: S S + +Speaker sentences 359: nchlt_eng_001616 #utts: 1 +id: (nchlt_eng_001616-nchlt_eng_001616) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ******** POPULAR TOURIST ATTRACTIONS +HYP: POPULETS WER IS ATRACTIONS +Eval: I S S S + +Speaker sentences 360: nchlt_eng_001617 #utts: 1 +id: (nchlt_eng_001617-nchlt_eng_001617) +Scores: (#C #S #D #I) 1 2 0 0 +REF: DUTCH WEST india +HYP: DOUCH WAST india +Eval: S S + +Speaker sentences 361: nchlt_eng_001618 #utts: 1 +id: (nchlt_eng_001618-nchlt_eng_001618) +Scores: (#C #S #D #I) 1 2 0 0 +REF: gold MEDAL RECIPIENTS +HYP: gold MITAE RSIPIENCES +Eval: S S + +Speaker sentences 362: nchlt_eng_001619 #utts: 1 +id: (nchlt_eng_001619-nchlt_eng_001619) +Scores: (#C #S #D #I) 0 3 0 0 +REF: RUSSIAN SOCIAL DEMOCRATIC +HYP: RESHION SOCHAL DEMACRETICK +Eval: S S S + +Speaker sentences 363: nchlt_eng_001620 #utts: 1 +id: (nchlt_eng_001620-nchlt_eng_001620) +Scores: (#C #S #D #I) 0 3 0 1 +REF: * AMERICAN FILM PRODUCERS +HYP: A MERIKCAN FOME PRODUSES +Eval: I S S S + +Speaker sentences 364: nchlt_eng_001621 #utts: 1 +id: (nchlt_eng_001621-nchlt_eng_001621) +Scores: (#C #S #D #I) 1 2 0 1 +REF: *** FREE SOFTWARE foundation +HYP: FRE SOFTERY A foundation +Eval: I S S + +Speaker sentences 365: nchlt_eng_001622 #utts: 1 +id: (nchlt_eng_001622-nchlt_eng_001622) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ROYAL DRAMATIC THEATRE +HYP: ROILE DRMATIC THEAT +Eval: S S S + +Speaker sentences 366: nchlt_eng_001623 #utts: 1 +id: (nchlt_eng_001623-nchlt_eng_001623) +Scores: (#C #S #D #I) 0 2 0 2 +REF: ** **** EDIBLE MOLLUSCS +HYP: IT ABLE MOL ASCKS +Eval: I I S S + +Speaker sentences 367: nchlt_eng_001624 #utts: 1 +id: (nchlt_eng_001624-nchlt_eng_001624) +Scores: (#C #S #D #I) 0 3 0 0 +REF: FEATURES INCLUDE BEACHES +HYP: FEATHERS INCLUD BEACHERS +Eval: S S S + +Speaker sentences 368: nchlt_eng_001625 #utts: 1 +id: (nchlt_eng_001625-nchlt_eng_001625) +Scores: (#C #S #D #I) 1 2 0 1 +REF: ****** OXFORD DICTIONARY changed +HYP: OCSFOR DITION RY changed +Eval: I S S + +Speaker sentences 369: nchlt_eng_001626 #utts: 1 +id: (nchlt_eng_001626-nchlt_eng_001626) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** SALUKI PERSIAN GREYHOUND +HYP: SAL COO PURSIN GRAYHOUND +Eval: I S S S + +Speaker sentences 370: nchlt_eng_001627 #utts: 1 +id: (nchlt_eng_001627-nchlt_eng_001627) +Scores: (#C #S #D #I) 0 3 0 0 +REF: PRIME MINISTER KEVIN +HYP: PRIN MNISTER CIVEN +Eval: S S S + +Speaker sentences 371: nchlt_eng_001628 #utts: 1 +id: (nchlt_eng_001628-nchlt_eng_001628) +Scores: (#C #S #D #I) 1 2 0 1 +REF: LANGUAGES of **** IRAQ +HYP: LANGAGES of YOUR OCK +Eval: S I S + +Speaker sentences 372: nchlt_eng_001629 #utts: 1 +id: (nchlt_eng_001629-nchlt_eng_001629) +Scores: (#C #S #D #I) 2 1 0 0 +REF: south east ENGLAND +HYP: south east INGLAND +Eval: S + +Speaker sentences 373: nchlt_eng_001630 #utts: 1 +id: (nchlt_eng_001630-nchlt_eng_001630) +Scores: (#C #S #D #I) 2 1 0 0 +REF: new line CINEMA +HYP: new line SENAMAR +Eval: S + +Speaker sentences 374: nchlt_eng_001631 #utts: 1 +id: (nchlt_eng_001631-nchlt_eng_001631) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ***** EQUAL CREDIT OPPORTUNITY +HYP: EACUL CRAD TS OPATUONATY +Eval: I S S S + +Speaker sentences 375: nchlt_eng_001632 #utts: 1 +id: (nchlt_eng_001632-nchlt_eng_001632) +Scores: (#C #S #D #I) 1 2 0 0 +REF: south EAST ENGLAND +HYP: south EST INGLAND +Eval: S S + +Speaker sentences 376: nchlt_eng_001633 #utts: 1 +id: (nchlt_eng_001633-nchlt_eng_001633) +Scores: (#C #S #D #I) 1 0 0 1 +REF: may * +HYP: may H +Eval: I + +Speaker sentences 377: nchlt_eng_001634 #utts: 1 +id: (nchlt_eng_001634-nchlt_eng_001634) +Scores: (#C #S #D #I) 0 3 0 2 +REF: ******* **** RECORD HETATM DESCRIBES +HYP: RECOLRD HATE ATSE M ISCRIVES +Eval: I I S S S + +Speaker sentences 378: nchlt_eng_001635 #utts: 1 +id: (nchlt_eng_001635-nchlt_eng_001635) +Scores: (#C #S #D #I) 2 2 0 0 +REF: musical GROUPS from CALIFORNIA +HYP: musical GREPES from CALFORNIEA +Eval: S S + +Speaker sentences 379: nchlt_eng_001636 #utts: 1 +id: (nchlt_eng_001636-nchlt_eng_001636) +Scores: (#C #S #D #I) 1 2 0 0 +REF: main BATTLE TANKS +HYP: main BUTLE TINCKS +Eval: S S + +Speaker sentences 380: nchlt_eng_001637 #utts: 1 +id: (nchlt_eng_001637-nchlt_eng_001637) +Scores: (#C #S #D #I) 1 2 0 0 +REF: POLISH musical INSTRUMENTS +HYP: PODLISH musical INSTRAMENTES +Eval: S S + +Speaker sentences 381: nchlt_eng_001638 #utts: 1 +id: (nchlt_eng_001638-nchlt_eng_001638) +Scores: (#C #S #D #I) 1 3 0 0 +REF: LANGUAGES of SAUDI ARABIA +HYP: LANWUGES of SADIEA RAVIA +Eval: S S S + +Speaker sentences 382: nchlt_eng_001639 #utts: 1 +id: (nchlt_eng_001639-nchlt_eng_001639) +Scores: (#C #S #D #I) 0 2 1 0 +REF: COLD WAR TENSIONS +HYP: **** CALD WARTINTIONS +Eval: D S S + +Speaker sentences 383: nchlt_eng_001640 #utts: 1 +id: (nchlt_eng_001640-nchlt_eng_001640) +Scores: (#C #S #D #I) 0 1 0 1 +REF: ***** DUBBY +HYP: DOABE WPH +Eval: I S + +Speaker sentences 384: nchlt_eng_001641 #utts: 1 +id: (nchlt_eng_001641-nchlt_eng_001641) +Scores: (#C #S #D #I) 0 2 0 1 +REF: *** ANTIPOPE CLEMENT +HYP: AND Y POPECLAMINT +Eval: I S S + +Speaker sentences 385: nchlt_eng_001642 #utts: 1 +id: (nchlt_eng_001642-nchlt_eng_001642) +Scores: (#C #S #D #I) 0 3 0 1 +REF: **** GETS TAKEN PRIVATE +HYP: GITS THE CN PRIVEAT +Eval: I S S S + +Speaker sentences 386: nchlt_eng_001643 #utts: 1 +id: (nchlt_eng_001643-nchlt_eng_001643) +Scores: (#C #S #D #I) 0 2 0 2 +REF: **** *** KING FERDINAND +HYP: CING FOD AN EAND +Eval: I I S S + +Speaker sentences 387: nchlt_eng_001644 #utts: 1 +id: (nchlt_eng_001644-nchlt_eng_001644) +Scores: (#C #S #D #I) 1 2 0 0 +REF: ELECTRONIC musical INSTRUMENTS +HYP: ILECTRNIC musical INSTRMENCS +Eval: S S + +Speaker sentences 388: nchlt_eng_001645 #utts: 1 +id: (nchlt_eng_001645-nchlt_eng_001645) +Scores: (#C #S #D #I) 2 1 0 0 +REF: age MELT water +HYP: age NOUT water +Eval: S + +Speaker sentences 389: nchlt_eng_001646 #utts: 1 +id: (nchlt_eng_001646-nchlt_eng_001646) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ******* LAWRENCE LIVERMORE NATIONAL +HYP: LORENCE LIVE MOR NASINALE +Eval: I S S S + +Speaker sentences 390: nchlt_eng_001647 #utts: 1 +id: (nchlt_eng_001647-nchlt_eng_001647) +Scores: (#C #S #D #I) 1 2 0 0 +REF: LEAGUE BASEBALL players +HYP: LEAG BACSPALE players +Eval: S S + +Speaker sentences 391: nchlt_eng_001648 #utts: 1 +id: (nchlt_eng_001648-nchlt_eng_001648) +Scores: (#C #S #D #I) 1 4 0 2 +REF: *** BUDDHISM IN the ******* ANCIENT MEDITERRANEAN +HYP: BUT ISOM AN the ANCHANT MED ATRANION +Eval: I S S I S S + +Speaker sentences 392: nchlt_eng_001649 #utts: 1 +id: (nchlt_eng_001649-nchlt_eng_001649) +Scores: (#C #S #D #I) 0 3 0 0 +REF: UNITED STATES RECOGNIZED +HYP: OUNITED STATS RECOCONIED +Eval: S S S + +Speaker sentences 393: nchlt_eng_001650 #utts: 1 +id: (nchlt_eng_001650-nchlt_eng_001650) +Scores: (#C #S #D #I) 0 2 0 0 +REF: PROPOSITIONAL FALLACIES +HYP: PROPASIONAL FELACES +Eval: S S + +Speaker sentences 394: nchlt_eng_001651 #utts: 1 +id: (nchlt_eng_001651-nchlt_eng_001651) +Scores: (#C #S #D #I) 0 2 1 0 +REF: SPECIAL ECONOMIC ZONES +HYP: ******* SPICHAL ECNOMIEGSOWNS +Eval: D S S + +Speaker sentences 395: nchlt_eng_001652 #utts: 1 +id: (nchlt_eng_001652-nchlt_eng_001652) +Scores: (#C #S #D #I) 0 3 0 0 +REF: MAIN STREAM WEST +HYP: MAN STREAME WISTD +Eval: S S S + +Speaker sentences 396: nchlt_eng_001653 #utts: 1 +id: (nchlt_eng_001653-nchlt_eng_001653) +Scores: (#C #S #D #I) 1 2 0 0 +REF: EVENING rush HOURS +HYP: EVENG rush OWS +Eval: S S + +Speaker sentences 397: nchlt_eng_001654 #utts: 1 +id: (nchlt_eng_001654-nchlt_eng_001654) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** BOFH EDITIONS TOOK +HYP: BY THE DIONS TOK +Eval: I S S S + +Speaker sentences 398: nchlt_eng_001655 #utts: 1 +id: (nchlt_eng_001655-nchlt_eng_001655) +Scores: (#C #S #D #I) 2 1 0 1 +REF: **** ANTARCTICA has no +HYP: NDTS ARTICKE has no +Eval: I S + +Speaker sentences 399: nchlt_eng_001656 #utts: 1 +id: (nchlt_eng_001656-nchlt_eng_001656) +Scores: (#C #S #D #I) 0 3 0 0 +REF: WEST END MUSICALS +HYP: WEAST IN MUSICLES +Eval: S S S + +Speaker sentences 400: nchlt_eng_001657 #utts: 1 +id: (nchlt_eng_001657-nchlt_eng_001657) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ******** CONSERVATIVE JUDAISM REGARDS +HYP: CONSEVIT OF JUATAYSOM REGARTS +Eval: I S S S + +Speaker sentences 401: nchlt_eng_001658 #utts: 1 +id: (nchlt_eng_001658-nchlt_eng_001658) +Scores: (#C #S #D #I) 0 3 0 0 +REF: OPEC MEMBER STATES +HYP: OPICK MBER STATS +Eval: S S S + +Speaker sentences 402: nchlt_eng_001659 #utts: 1 +id: (nchlt_eng_001659-nchlt_eng_001659) +Scores: (#C #S #D #I) 0 2 1 0 +REF: PRIME MINISTER JOHN +HYP: ***** PRIMINESSAID JON +Eval: D S S + +Speaker sentences 403: nchlt_eng_001660 #utts: 1 +id: (nchlt_eng_001660-nchlt_eng_001660) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ROCKS FORMING MONT +HYP: RACKS FOARMING MOUNT +Eval: S S S + +Speaker sentences 404: nchlt_eng_001661 #utts: 1 +id: (nchlt_eng_001661-nchlt_eng_001661) +Scores: (#C #S #D #I) 0 3 0 0 +REF: MAJOR LEAGUE TEAMS +HYP: MAGER LEAK TMS +Eval: S S S + +Speaker sentences 405: nchlt_eng_001662 #utts: 1 +id: (nchlt_eng_001662-nchlt_eng_001662) +Scores: (#C #S #D #I) 0 2 0 0 +REF: POLLINATION MANAGEMENT +HYP: POLONATION MANIGENT +Eval: S S + +Speaker sentences 406: nchlt_eng_001663 #utts: 1 +id: (nchlt_eng_001663-nchlt_eng_001663) +Scores: (#C #S #D #I) 1 1 0 0 +REF: french PHYSICISTS +HYP: french FISIST +Eval: S + +Speaker sentences 407: nchlt_eng_001664 #utts: 1 +id: (nchlt_eng_001664-nchlt_eng_001664) +Scores: (#C #S #D #I) 0 3 0 0 +REF: HIGHER COMPRESSION RATIO +HYP: HIYAR COMPRETSION RATSIO +Eval: S S S + +Speaker sentences 408: nchlt_eng_001665 #utts: 1 +id: (nchlt_eng_001665-nchlt_eng_001665) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ****** RECORDING INDUSTRY ASSOCIATION +HYP: RECORD NG INDOUSTRY ASOCHATION +Eval: I S S S + +Speaker sentences 409: nchlt_eng_001666 #utts: 1 +id: (nchlt_eng_001666-nchlt_eng_001666) +Scores: (#C #S #D #I) 0 4 0 0 +REF: DPG S ONLINE MAGAZINE +HYP: THEPEAGE ON LIN MAGASEAN +Eval: S S S S + +Speaker sentences 410: nchlt_eng_001667 #utts: 1 +id: (nchlt_eng_001667-nchlt_eng_001667) +Scores: (#C #S #D #I) 0 4 0 0 +REF: HIP HOP RECORD PRODUCERS +HYP: HIPOPER RECQUAD PRE GUOSSEONS +Eval: S S S S + +Speaker sentences 411: nchlt_eng_001668 #utts: 1 +id: (nchlt_eng_001668-nchlt_eng_001668) +Scores: (#C #S #D #I) 1 2 0 1 +REF: ** FINITE state MACHINES +HYP: FI NIGHE state MSHENS +Eval: I S S + +Speaker sentences 412: nchlt_eng_001669 #utts: 1 +id: (nchlt_eng_001669-nchlt_eng_001669) +Scores: (#C #S #D #I) 0 3 0 0 +REF: WIDELY USED LOCAL +HYP: WHIDLY OUSED LOCALE +Eval: S S S + +Speaker sentences 413: nchlt_eng_001670 #utts: 1 +id: (nchlt_eng_001670-nchlt_eng_001670) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** NORTH AMERICAN CONTINENT +HYP: NOR THE MERYCAN CONTINANT +Eval: I S S S + +Speaker sentences 414: nchlt_eng_001671 #utts: 1 +id: (nchlt_eng_001671-nchlt_eng_001671) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ****** AFRICAN AMERICAN RAPPERS +HYP: AFRCAN A MERICAN REPAS +Eval: I S S S + +Speaker sentences 415: nchlt_eng_001672 #utts: 1 +id: (nchlt_eng_001672-nchlt_eng_001672) +Scores: (#C #S #D #I) 1 2 0 0 +REF: THREATENED MILITARY actions +HYP: THRITON MELIDRY actions +Eval: S S + +Speaker sentences 416: nchlt_eng_001673 #utts: 1 +id: (nchlt_eng_001673-nchlt_eng_001673) +Scores: (#C #S #D #I) 2 0 0 3 +REF: * the word * ** +HYP: A the word M NN +Eval: I I I + +Speaker sentences 417: nchlt_eng_001674 #utts: 1 +id: (nchlt_eng_001674-nchlt_eng_001674) +Scores: (#C #S #D #I) 1 4 0 0 +REF: ATOMIC MOLECULAR AND optical PHYSICS +HYP: THE TOMIK MELIKULAN optical FOISICKS +Eval: S S S S + +Speaker sentences 418: nchlt_eng_001675 #utts: 1 +id: (nchlt_eng_001675-nchlt_eng_001675) +Scores: (#C #S #D #I) 1 0 0 0 +REF: town +HYP: town +Eval: + +Speaker sentences 419: nchlt_eng_001676 #utts: 1 +id: (nchlt_eng_001676-nchlt_eng_001676) +Scores: (#C #S #D #I) 0 1 0 0 +REF: MARCEL +HYP: MORSIL +Eval: S + +Speaker sentences 420: nchlt_eng_001677 #utts: 1 +id: (nchlt_eng_001677-nchlt_eng_001677) +Scores: (#C #S #D #I) 2 1 0 1 +REF: construct new *** RAILGAUGE +HYP: construct new RAL GAGEH +Eval: I S + +Speaker sentences 421: nchlt_eng_001678 #utts: 1 +id: (nchlt_eng_001678-nchlt_eng_001678) +Scores: (#C #S #D #I) 0 3 0 0 +REF: PAULI EXCLUSION PRINCIPLE +HYP: PORLY EXCLUSIN RINCSIBL +Eval: S S S + +Speaker sentences 422: nchlt_eng_001679 #utts: 1 +id: (nchlt_eng_001679-nchlt_eng_001679) +Scores: (#C #S #D #I) 0 3 0 0 +REF: HUE PORTRAY DIFFERENT +HYP: HEWO POURTRAY DIFERENTS +Eval: S S S + +Speaker sentences 423: nchlt_eng_001680 #utts: 1 +id: (nchlt_eng_001680-nchlt_eng_001680) +Scores: (#C #S #D #I) 0 2 1 0 +REF: S SOVIET DISSIDENTS +HYP: * SOVIAT DISIDANCE +Eval: D S S + +Speaker sentences 424: nchlt_eng_001681 #utts: 1 +id: (nchlt_eng_001681-nchlt_eng_001681) +Scores: (#C #S #D #I) 0 3 0 0 +REF: SIGNAL TRANSDUCTION PATHWAYS +HYP: SIGNALE TRONSTDUCTION PARTHWAYESE +Eval: S S S + +Speaker sentences 425: nchlt_eng_001682 #utts: 1 +id: (nchlt_eng_001682-nchlt_eng_001682) +Scores: (#C #S #D #I) 1 2 0 0 +REF: NEW born MESSIAH +HYP: YOU born MSI +Eval: S S + +Speaker sentences 426: nchlt_eng_001683 #utts: 1 +id: (nchlt_eng_001683-nchlt_eng_001683) +Scores: (#C #S #D #I) 0 3 0 0 +REF: GENERALLY ACCEPTED RANGES +HYP: GENERLY ACXCEPTED RANGERS +Eval: S S S + +Speaker sentences 427: nchlt_eng_001684 #utts: 1 +id: (nchlt_eng_001684-nchlt_eng_001684) +Scores: (#C #S #D #I) 0 3 0 2 +REF: ***** * GUILD AWARD WINNERS +HYP: GILED A WARD WIN IS +Eval: I I S S S + +Speaker sentences 428: nchlt_eng_001685 #utts: 1 +id: (nchlt_eng_001685-nchlt_eng_001685) +Scores: (#C #S #D #I) 2 1 0 0 +REF: swedish musical GROUPS +HYP: swedish musical GROPS +Eval: S + +Speaker sentences 429: nchlt_eng_001686 #utts: 1 +id: (nchlt_eng_001686-nchlt_eng_001686) +Scores: (#C #S #D #I) 1 2 0 0 +REF: CHILDHOOD AUTISM rating +HYP: CHOWDERD OARTISOM rating +Eval: S S + +Speaker sentences 430: nchlt_eng_001687 #utts: 1 +id: (nchlt_eng_001687-nchlt_eng_001687) +Scores: (#C #S #D #I) 1 1 0 0 +REF: DOSAGE forms +HYP: DOSIGE forms +Eval: S + +Speaker sentences 431: nchlt_eng_001688 #utts: 1 +id: (nchlt_eng_001688-nchlt_eng_001688) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** OHIO STATE UNIVERSITY +HYP: OF HIG OSTATUON OVERSTITE +Eval: I S S S + +Speaker sentences 432: nchlt_eng_001689 #utts: 1 +id: (nchlt_eng_001689-nchlt_eng_001689) +Scores: (#C #S #D #I) 1 3 0 1 +REF: *** FORMER SETTLEMENTS in TURKEY +HYP: FOR MOS SATOMENTS in TORK +Eval: I S S S + +Speaker sentences 433: nchlt_eng_001690 #utts: 1 +id: (nchlt_eng_001690-nchlt_eng_001690) +Scores: (#C #S #D #I) 1 1 0 1 +REF: **** AMERICAN inventions +HYP: AMER CAN inventions +Eval: I S + +Speaker sentences 434: nchlt_eng_001691 #utts: 1 +id: (nchlt_eng_001691-nchlt_eng_001691) +Scores: (#C #S #D #I) 1 0 0 0 +REF: arts +HYP: arts +Eval: + +Speaker sentences 435: nchlt_eng_001692 #utts: 1 +id: (nchlt_eng_001692-nchlt_eng_001692) +Scores: (#C #S #D #I) 0 3 0 0 +REF: MODERN EUROPEAN RUSSIA +HYP: MDON YUROPIAN RASHA +Eval: S S S + +Speaker sentences 436: nchlt_eng_001693 #utts: 1 +id: (nchlt_eng_001693-nchlt_eng_001693) +Scores: (#C #S #D #I) 0 3 0 0 +REF: NATIONAL LEAGUE PENNANT +HYP: NSTNOR LEAEG PINANT +Eval: S S S + +Speaker sentences 437: nchlt_eng_001694 #utts: 1 +id: (nchlt_eng_001694-nchlt_eng_001694) +Scores: (#C #S #D #I) 1 2 0 0 +REF: BIG finish PRODUCTIONS +HYP: BEAG finish PRDUCTIONS +Eval: S S + +Speaker sentences 438: nchlt_eng_001695 #utts: 1 +id: (nchlt_eng_001695-nchlt_eng_001695) +Scores: (#C #S #D #I) 0 1 0 0 +REF: NATIONAL +HYP: NASINOLE +Eval: S + +Speaker sentences 439: nchlt_eng_001696 #utts: 1 +id: (nchlt_eng_001696-nchlt_eng_001696) +Scores: (#C #S #D #I) 0 2 0 0 +REF: TRAGIC POETS +HYP: TRADGICG PORTES +Eval: S S + +Speaker sentences 440: nchlt_eng_001697 #utts: 1 +id: (nchlt_eng_001697-nchlt_eng_001697) +Scores: (#C #S #D #I) 1 2 0 0 +REF: TOTAL GROSS state +HYP: TITLE GRICE state +Eval: S S + +Speaker sentences 441: nchlt_eng_001698 #utts: 1 +id: (nchlt_eng_001698-nchlt_eng_001698) +Scores: (#C #S #D #I) 1 2 0 1 +REF: ** ATHENA had AN +HYP: AS THENA had EN +Eval: I S S + +Speaker sentences 442: nchlt_eng_001699 #utts: 1 +id: (nchlt_eng_001699-nchlt_eng_001699) +Scores: (#C #S #D #I) 0 3 0 1 +REF: **** EASTERN EUROPEAN COUNTRIES +HYP: EAST N YURAPIAN CONTRYS +Eval: I S S S + +Speaker sentences 443: nchlt_eng_001700 #utts: 1 +id: (nchlt_eng_001700-nchlt_eng_001700) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ******** CONDEMNED UNAUTHORIZED TRANSLATIONS +HYP: CONDEMED AND OTHRIVED TANSLATIONS +Eval: I S S S + +Speaker sentences 444: nchlt_eng_001701 #utts: 1 +id: (nchlt_eng_001701-nchlt_eng_001701) +Scores: (#C #S #D #I) 0 2 1 0 +REF: COLD WAR LEADERS +HYP: **** ALTHWAR LEDIS +Eval: D S S + +Speaker sentences 445: nchlt_eng_001702 #utts: 1 +id: (nchlt_eng_001702-nchlt_eng_001702) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ******* KENESAW MOUNTAIN LANDIS +HYP: CINASAR MOUNT AN LENDUS +Eval: I S S S + +Speaker sentences 446: nchlt_eng_001703 #utts: 1 +id: (nchlt_eng_001703-nchlt_eng_001703) +Scores: (#C #S #D #I) 0 2 0 0 +REF: NOBEL FAMILY +HYP: NOBL SAMITY +Eval: S S + +Speaker sentences 447: nchlt_eng_001704 #utts: 1 +id: (nchlt_eng_001704-nchlt_eng_001704) +Scores: (#C #S #D #I) 0 3 0 0 +REF: EDWARDS AIR FORCE +HYP: AND WODS ERFURS +Eval: S S S + +Speaker sentences 448: nchlt_eng_001705 #utts: 1 +id: (nchlt_eng_001705-nchlt_eng_001705) +Scores: (#C #S #D #I) 2 1 0 0 +REF: mount saint VINCENT +HYP: mount saint VINCSANT +Eval: S + +Speaker sentences 449: nchlt_eng_001706 #utts: 1 +id: (nchlt_eng_001706-nchlt_eng_001706) +Scores: (#C #S #D #I) 0 3 0 0 +REF: CITY METROPOLITAN AREA +HYP: SITYE MERTRUPOLATON EIROA +Eval: S S S + +Speaker sentences 450: nchlt_eng_001707 #utts: 1 +id: (nchlt_eng_001707-nchlt_eng_001707) +Scores: (#C #S #D #I) 3 2 0 0 +REF: RULERS who died as CHILDREN +HYP: ROONERS who died as CHILDREAN +Eval: S S + +Speaker sentences 451: nchlt_eng_001708 #utts: 1 +id: (nchlt_eng_001708-nchlt_eng_001708) +Scores: (#C #S #D #I) 0 1 0 1 +REF: ********* CHANCELLORSVILLE +HYP: CHANESLAS VOLE +Eval: I S + +Speaker sentences 452: nchlt_eng_001709 #utts: 1 +id: (nchlt_eng_001709-nchlt_eng_001709) +Scores: (#C #S #D #I) 0 3 0 1 +REF: * IP PACKETS ENTIRELY +HYP: I PEE PECATS INTIERLY +Eval: I S S S + +Speaker sentences 453: nchlt_eng_001710 #utts: 1 +id: (nchlt_eng_001710-nchlt_eng_001710) +Scores: (#C #S #D #I) 2 1 0 0 +REF: KING edwards death +HYP: CING edwards death +Eval: S + +Speaker sentences 454: nchlt_eng_001711 #utts: 1 +id: (nchlt_eng_001711-nchlt_eng_001711) +Scores: (#C #S #D #I) 0 2 0 2 +REF: * ******* AMERICA AMERICA +HYP: A MERICAR A MRICA +Eval: I I S S + +Speaker sentences 455: nchlt_eng_001712 #utts: 1 +id: (nchlt_eng_001712-nchlt_eng_001712) +Scores: (#C #S #D #I) 1 2 0 0 +REF: COMMERCIAL ship SAILED +HYP: COMERHAL ship SALED +Eval: S S + +Speaker sentences 456: nchlt_eng_001713 #utts: 1 +id: (nchlt_eng_001713-nchlt_eng_001713) +Scores: (#C #S #D #I) 1 2 0 1 +REF: PEOPLE from *** MANNHEIM +HYP: PEOPL from MEN HAM +Eval: S I S + +Speaker sentences 457: nchlt_eng_001714 #utts: 1 +id: (nchlt_eng_001714-nchlt_eng_001714) +Scores: (#C #S #D #I) 1 2 0 0 +REF: RAIL crash KILLED +HYP: RAIAL crash CILD +Eval: S S + +Speaker sentences 458: nchlt_eng_001715 #utts: 1 +id: (nchlt_eng_001715-nchlt_eng_001715) +Scores: (#C #S #D #I) 0 3 0 0 +REF: MUTUAL DEFENSE TREATY +HYP: MUTHAL DEFANS TOUDY +Eval: S S S + +Speaker sentences 459: nchlt_eng_001716 #utts: 1 +id: (nchlt_eng_001716-nchlt_eng_001716) +Scores: (#C #S #D #I) 1 2 0 0 +REF: MODERN child RULERS +HYP: MODEN child RUOLES +Eval: S S + +Speaker sentences 460: nchlt_eng_001717 #utts: 1 +id: (nchlt_eng_001717-nchlt_eng_001717) +Scores: (#C #S #D #I) 0 3 0 0 +REF: MOTOR RIFLE DIVISION +HYP: MOTESER RIFAL DEVISION +Eval: S S S + +Speaker sentences 461: nchlt_eng_001718 #utts: 1 +id: (nchlt_eng_001718-nchlt_eng_001718) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** AUSTRALIAN AIR FORCE +HYP: OU STRALIAN EAY FORSE +Eval: I S S S + +Speaker sentences 462: nchlt_eng_001719 #utts: 1 +id: (nchlt_eng_001719-nchlt_eng_001719) +Scores: (#C #S #D #I) 0 3 0 1 +REF: * AMERICAN MYSTERY WRITERS +HYP: A MERYKCAN MISTRY RHITERS +Eval: I S S S + +Speaker sentences 463: nchlt_eng_001720 #utts: 1 +id: (nchlt_eng_001720-nchlt_eng_001720) +Scores: (#C #S #D #I) 1 2 0 1 +REF: *** FINELY ground GRAPHITE +HYP: FIN THEY ground GREFIHTE +Eval: I S S + +Speaker sentences 464: nchlt_eng_001721 #utts: 1 +id: (nchlt_eng_001721-nchlt_eng_001721) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** WORLD CHAMPIONSHIP MATCH +HYP: WIL TEMPINS OF MANTCHE +Eval: I S S S + +Speaker sentences 465: nchlt_eng_001722 #utts: 1 +id: (nchlt_eng_001722-nchlt_eng_001722) +Scores: (#C #S #D #I) 0 1 0 0 +REF: CAROLINA +HYP: CARILINA +Eval: S + +Speaker sentences 466: nchlt_eng_001723 #utts: 1 +id: (nchlt_eng_001723-nchlt_eng_001723) +Scores: (#C #S #D #I) 0 3 0 0 +REF: MOBILE PHONE OPERATORS +HYP: MY BATHIN OPERATES +Eval: S S S + +Speaker sentences 467: nchlt_eng_001724 #utts: 1 +id: (nchlt_eng_001724-nchlt_eng_001724) +Scores: (#C #S #D #I) 0 2 0 0 +REF: QUARTZ VARIETIES +HYP: COURTS VERITIES +Eval: S S + +Speaker sentences 468: nchlt_eng_001725 #utts: 1 +id: (nchlt_eng_001725-nchlt_eng_001725) +Scores: (#C #S #D #I) 0 1 0 1 +REF: ** MIDRAND +HYP: MA DRAND +Eval: I S + +Speaker sentences 469: nchlt_eng_001726 #utts: 1 +id: (nchlt_eng_001726-nchlt_eng_001726) +Scores: (#C #S #D #I) 1 2 0 0 +REF: CAUSE LETHAL reactions +HYP: CASE LITHALE reactions +Eval: S S + +Speaker sentences 470: nchlt_eng_001727 #utts: 1 +id: (nchlt_eng_001727-nchlt_eng_001727) +Scores: (#C #S #D #I) 0 2 0 0 +REF: ENGLISH PACIFISTS +HYP: INGLAISH PESCIFOUSTS +Eval: S S + +Speaker sentences 471: nchlt_eng_001728 #utts: 1 +id: (nchlt_eng_001728-nchlt_eng_001728) +Scores: (#C #S #D #I) 2 1 0 0 +REF: UNITED states federal +HYP: YOUNITED states federal +Eval: S + +Speaker sentences 472: nchlt_eng_001729 #utts: 1 +id: (nchlt_eng_001729-nchlt_eng_001729) +Scores: (#C #S #D #I) 1 2 0 0 +REF: FEDERAL RESERVE act +HYP: FADRALD RESEROVE act +Eval: S S + +Speaker sentences 473: nchlt_eng_001730 #utts: 1 +id: (nchlt_eng_001730-nchlt_eng_001730) +Scores: (#C #S #D #I) 0 3 0 0 +REF: WILLIAM HENRY HARRISON +HYP: WILYM HINRY HERASON +Eval: S S S + +Speaker sentences 474: nchlt_eng_001731 #utts: 1 +id: (nchlt_eng_001731-nchlt_eng_001731) +Scores: (#C #S #D #I) 1 2 0 0 +REF: CLUB play CHART +HYP: CLAP play CHOT +Eval: S S + +Speaker sentences 475: nchlt_eng_001732 #utts: 1 +id: (nchlt_eng_001732-nchlt_eng_001732) +Scores: (#C #S #D #I) 0 3 0 0 +REF: PASSENGER RAIL SERVICES +HYP: PASSONGER RALE SOVICSES +Eval: S S S + +Speaker sentences 476: nchlt_eng_001733 #utts: 1 +id: (nchlt_eng_001733-nchlt_eng_001733) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ANCIENT MACEDONIAN GENERALS +HYP: ANCHAN MESSADORNTHION JENRALS +Eval: S S S + +Speaker sentences 477: nchlt_eng_001734 #utts: 1 +id: (nchlt_eng_001734-nchlt_eng_001734) +Scores: (#C #S #D #I) 0 3 0 0 +REF: KONG ACTION CINEMA +HYP: CONG ACTIOND SENTAMA +Eval: S S S + +Speaker sentences 478: nchlt_eng_001735 #utts: 1 +id: (nchlt_eng_001735-nchlt_eng_001735) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** GUNPOWDER PROPELLANT USED +HYP: GUN POUDEA PRPILENT YOUSED +Eval: I S S S + +Speaker sentences 479: nchlt_eng_001736 #utts: 1 +id: (nchlt_eng_001736-nchlt_eng_001736) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** LOWEST ENERGY STATE +HYP: LOW ST INAGE STAIGT +Eval: I S S S + +Speaker sentences 480: nchlt_eng_001737 #utts: 1 +id: (nchlt_eng_001737-nchlt_eng_001737) +Scores: (#C #S #D #I) 0 2 0 0 +REF: CALENDAR ERAS +HYP: CALNDER YUROS +Eval: S S + +Speaker sentences 481: nchlt_eng_001738 #utts: 1 +id: (nchlt_eng_001738-nchlt_eng_001738) +Scores: (#C #S #D #I) 0 3 0 0 +REF: MAJOR INTERNATIONAL AIRPORT +HYP: MAGJER INTONASINALE EAPORTES +Eval: S S S + +Speaker sentences 482: nchlt_eng_001739 #utts: 1 +id: (nchlt_eng_001739-nchlt_eng_001739) +Scores: (#C #S #D #I) 0 3 0 0 +REF: TOTAL FORCE ACTING +HYP: TOTL FORSE ACTIM +Eval: S S S + +Speaker sentences 483: nchlt_eng_001740 #utts: 1 +id: (nchlt_eng_001740-nchlt_eng_001740) +Scores: (#C #S #D #I) 0 3 0 0 +REF: LOSSLESS DATA COMPRESSION +HYP: LOSTLESS DATE COMPRITION +Eval: S S S + +Speaker sentences 484: nchlt_eng_001741 #utts: 1 +id: (nchlt_eng_001741-nchlt_eng_001741) +Scores: (#C #S #D #I) 0 1 0 1 +REF: * GREEK +HYP: A GREAEKHA +Eval: I S + +Speaker sentences 485: nchlt_eng_001742 #utts: 1 +id: (nchlt_eng_001742-nchlt_eng_001742) +Scores: (#C #S #D #I) 0 3 0 2 +REF: ** *** ENVIRONMENTAL PROTECTION AGENCY +HYP: IN VOR MNTAL PROTICTION ADGANCSE +Eval: I I S S S + +Speaker sentences 486: nchlt_eng_001743 #utts: 1 +id: (nchlt_eng_001743-nchlt_eng_001743) +Scores: (#C #S #D #I) 0 3 0 0 +REF: MANITOBA SCHOOLS QUESTION +HYP: MANY TOBISCALS GRITION +Eval: S S S + +Speaker sentences 487: nchlt_eng_001744 #utts: 1 +id: (nchlt_eng_001744-nchlt_eng_001744) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ANCIENT CITY PITHUNDA +HYP: ANCHOAN SITY PITHUNDER +Eval: S S S + +Speaker sentences 488: nchlt_eng_001745 #utts: 1 +id: (nchlt_eng_001745-nchlt_eng_001745) +Scores: (#C #S #D #I) 0 3 0 0 +REF: SMALL ORTHODOX SYNAGOGUE +HYP: SMAL OTHEDACK SINAGOG +Eval: S S S + +Speaker sentences 489: nchlt_eng_001746 #utts: 1 +id: (nchlt_eng_001746-nchlt_eng_001746) +Scores: (#C #S #D #I) 0 3 0 0 +REF: LARGEST METROPOLITAN AREAS +HYP: LODGES MTRPILIAN ERIARS +Eval: S S S + +Speaker sentences 490: nchlt_eng_001747 #utts: 1 +id: (nchlt_eng_001747-nchlt_eng_001747) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ***** TITLE RELIGIO ROMANA +HYP: TITAL RELIG YEO REMONO +Eval: I S S S + +Speaker sentences 491: nchlt_eng_001748 #utts: 1 +id: (nchlt_eng_001748-nchlt_eng_001748) +Scores: (#C #S #D #I) 0 3 0 2 +REF: ******** *** EXAMPLES INCLUDE HUFFMAN +HYP: EGAMPLES AND CLUD HAF MON +Eval: I I S S S + +Speaker sentences 492: nchlt_eng_001749 #utts: 1 +id: (nchlt_eng_001749-nchlt_eng_001749) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ***** UNITED STATES MAINTAINS +HYP: YUNOT ID STATE MANTAINE +Eval: I S S S + +Speaker sentences 493: nchlt_eng_001750 #utts: 1 +id: (nchlt_eng_001750-nchlt_eng_001750) +Scores: (#C #S #D #I) 1 2 0 0 +REF: bold REPRESENTS MAXIMA +HYP: bold REPRESENCE MEAXSIMA +Eval: S S + +Speaker sentences 494: nchlt_eng_001751 #utts: 1 +id: (nchlt_eng_001751-nchlt_eng_001751) +Scores: (#C #S #D #I) 0 2 1 0 +REF: SCIENCE FICTION AUTHORS +HYP: ******* SINESFCION ORTHES +Eval: D S S + +Speaker sentences 495: nchlt_eng_001752 #utts: 1 +id: (nchlt_eng_001752-nchlt_eng_001752) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ORDINARY DIFFERENTIAL EQUATIONS +HYP: ORDNARY DIFRENSHAL ECUATIONS +Eval: S S S + +Speaker sentences 496: nchlt_eng_001753 #utts: 1 +id: (nchlt_eng_001753-nchlt_eng_001753) +Scores: (#C #S #D #I) 2 3 0 0 +REF: DIPLOMATS of the HOLY SEE +HYP: DIPLMATS of the HRDYE SE +Eval: S S S + +Speaker sentences 497: nchlt_eng_001754 #utts: 1 +id: (nchlt_eng_001754-nchlt_eng_001754) +Scores: (#C #S #D #I) 0 3 0 0 +REF: SERIAL KILLER MYSTERY +HYP: SIRIAL CLOM MISTRY +Eval: S S S + +Speaker sentences 498: nchlt_eng_001755 #utts: 1 +id: (nchlt_eng_001755-nchlt_eng_001755) +Scores: (#C #S #D #I) 0 3 0 1 +REF: **** ROYAL MILITARY COLLEGE +HYP: UREL ME ITRY COL +Eval: I S S S + +Speaker sentences 499: nchlt_eng_001756 #utts: 1 +id: (nchlt_eng_001756-nchlt_eng_001756) +Scores: (#C #S #D #I) 1 2 0 0 +REF: slowly LEADS SOCIALISM +HYP: slowly LEDS SCIALISOM +Eval: S S + +Speaker sentences 500: nchlt_eng_001757 #utts: 1 +id: (nchlt_eng_001757-nchlt_eng_001757) +Scores: (#C #S #D #I) 0 1 0 0 +REF: PRINTERS +HYP: PRINTES +Eval: S + +Speaker sentences 501: nchlt_eng_001758 #utts: 1 +id: (nchlt_eng_001758-nchlt_eng_001758) +Scores: (#C #S #D #I) 0 3 0 2 +REF: *** *** NEW TESTAMENT PEOPLE +HYP: NEU TAS THE N PEOPL +Eval: I I S S S + +Speaker sentences 502: nchlt_eng_001759 #utts: 1 +id: (nchlt_eng_001759-nchlt_eng_001759) +Scores: (#C #S #D #I) 0 5 0 0 +REF: SMART CARD BASED ELECTRONIC PURSE +HYP: SMAT COARD BACETD ILECTRONICK PERS +Eval: S S S S S + +Speaker sentences 503: nchlt_eng_001760 #utts: 1 +id: (nchlt_eng_001760-nchlt_eng_001760) +Scores: (#C #S #D #I) 0 2 1 0 +REF: STATES ARMY SOLDIERS +HYP: ****** STATE ARMYSOLDGERS +Eval: D S S + +Speaker sentences 504: nchlt_eng_001761 #utts: 1 +id: (nchlt_eng_001761-nchlt_eng_001761) +Scores: (#C #S #D #I) 1 2 0 0 +REF: lord JESUS CHRIST +HYP: lord JES CRIST +Eval: S S + +Speaker sentences 505: nchlt_eng_001762 #utts: 1 +id: (nchlt_eng_001762-nchlt_eng_001762) +Scores: (#C #S #D #I) 0 1 0 2 +REF: *** ** LYDENBURG +HYP: LAD AN BIG +Eval: I I S + +Speaker sentences 506: nchlt_eng_001763 #utts: 1 +id: (nchlt_eng_001763-nchlt_eng_001763) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ITALIAN NATIONAL TEAM +HYP: BETAELIAN NESINAL TEM +Eval: S S S + +Speaker sentences 507: nchlt_eng_001764 #utts: 1 +id: (nchlt_eng_001764-nchlt_eng_001764) +Scores: (#C #S #D #I) 1 3 0 0 +REF: ANTIGUA RECREATION ground THUMB +HYP: ANDTEAGER RECRIATION ground THUM +Eval: S S S + +Speaker sentences 508: nchlt_eng_001765 #utts: 1 +id: (nchlt_eng_001765-nchlt_eng_001765) +Scores: (#C #S #D #I) 1 2 0 0 +REF: GROSS state PRODUCT +HYP: GROSES state PRODECT +Eval: S S + +Speaker sentences 509: nchlt_eng_001766 #utts: 1 +id: (nchlt_eng_001766-nchlt_eng_001766) +Scores: (#C #S #D #I) 1 1 1 0 +REF: king KONG VS +HYP: king **** CONGVERSE +Eval: D S + +Speaker sentences 510: nchlt_eng_001767 #utts: 1 +id: (nchlt_eng_001767-nchlt_eng_001767) +Scores: (#C #S #D #I) 0 1 0 0 +REF: BELLVILLE +HYP: BELVAEL +Eval: S + +Speaker sentences 511: nchlt_eng_001768 #utts: 1 +id: (nchlt_eng_001768-nchlt_eng_001768) +Scores: (#C #S #D #I) 2 3 1 0 +REF: FILM ORGANIZATIONS IN the UNITED states +HYP: **** FLE OLGONISATIONS the UNIDED states +Eval: D S S S + +Speaker sentences 512: nchlt_eng_001769 #utts: 1 +id: (nchlt_eng_001769-nchlt_eng_001769) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ***** ISRAEL DEFENSE FORCES +HYP: YSRIL THE FENS FORSES +Eval: I S S S + +Speaker sentences 513: nchlt_eng_001770 #utts: 1 +id: (nchlt_eng_001770-nchlt_eng_001770) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** AUTOMATIC SEND RECEIVE +HYP: OR DRMTICK SAND RESEVE +Eval: I S S S + +Speaker sentences 514: nchlt_eng_001771 #utts: 1 +id: (nchlt_eng_001771-nchlt_eng_001771) +Scores: (#C #S #D #I) 0 3 0 0 +REF: BRUNSWICK SOUTHERN RAILWAY +HYP: BRNDSWICK SETHN RALWAY +Eval: S S S + +Speaker sentences 515: nchlt_eng_001772 #utts: 1 +id: (nchlt_eng_001772-nchlt_eng_001772) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ACTRESS ACADEMY AWARD +HYP: ACTRS ACATDIMIA WOD +Eval: S S S + +Speaker sentences 516: nchlt_eng_001773 #utts: 1 +id: (nchlt_eng_001773-nchlt_eng_001773) +Scores: (#C #S #D #I) 0 3 0 0 +REF: PEOPLE FROM TOKYO +HYP: PEPL FROME TOCKIATD +Eval: S S S + +Speaker sentences 517: nchlt_eng_001774 #utts: 1 +id: (nchlt_eng_001774-nchlt_eng_001774) +Scores: (#C #S #D #I) 1 2 0 0 +REF: for CHARLES SINGER +HYP: for CHALDS SINGA +Eval: S S + +Speaker sentences 518: nchlt_eng_001775 #utts: 1 +id: (nchlt_eng_001775-nchlt_eng_001775) +Scores: (#C #S #D #I) 0 3 0 0 +REF: VARIABLE VALVE TIMING +HYP: VEARYIABL VLFH TARMING +Eval: S S S + +Speaker sentences 519: nchlt_eng_001776 #utts: 1 +id: (nchlt_eng_001776-nchlt_eng_001776) +Scores: (#C #S #D #I) 2 1 0 0 +REF: south wales VALLEYS +HYP: south wales VELYES +Eval: S + +Speaker sentences 520: nchlt_eng_001777 #utts: 1 +id: (nchlt_eng_001777-nchlt_eng_001777) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *********** CALIFORNIA STATE UNIVERSITY +HYP: CALIFORDYEA STAT YU THEVERSITY +Eval: I S S S + +Speaker sentences 521: nchlt_eng_001778 #utts: 1 +id: (nchlt_eng_001778-nchlt_eng_001778) +Scores: (#C #S #D #I) 0 1 0 0 +REF: ELDORADO +HYP: ELDERODO +Eval: S + +Speaker sentences 522: nchlt_eng_001779 #utts: 1 +id: (nchlt_eng_001779-nchlt_eng_001779) +Scores: (#C #S #D #I) 0 3 0 2 +REF: *** **** OUTDOOR ORIENTED CITY +HYP: OUT DORE OR INTED SITY +Eval: I I S S S + +Speaker sentences 523: nchlt_eng_001780 #utts: 1 +id: (nchlt_eng_001780-nchlt_eng_001780) +Scores: (#C #S #D #I) 0 3 0 0 +REF: CLAIMED PARTIAL RESPONSIBILITY +HYP: CLAMED PARSHAL RESPONCSABILITY +Eval: S S S + +Speaker sentences 524: nchlt_eng_001781 #utts: 1 +id: (nchlt_eng_001781-nchlt_eng_001781) +Scores: (#C #S #D #I) 1 1 0 0 +REF: CHRISTIAN terms +HYP: CRISTHON terms +Eval: S + +Speaker sentences 525: nchlt_eng_001782 #utts: 1 +id: (nchlt_eng_001782-nchlt_eng_001782) +Scores: (#C #S #D #I) 1 2 0 0 +REF: EVENTS TOOK place +HYP: EVENS TOK place +Eval: S S + +Speaker sentences 526: nchlt_eng_001783 #utts: 1 +id: (nchlt_eng_001783-nchlt_eng_001783) +Scores: (#C #S #D #I) 1 3 0 1 +REF: **** CANCER DEATHS in FRANCE +HYP: CENS SARD DITHS in FRONCE +Eval: I S S S + +Speaker sentences 527: nchlt_eng_001784 #utts: 1 +id: (nchlt_eng_001784-nchlt_eng_001784) +Scores: (#C #S #D #I) 1 2 0 0 +REF: HISTORY of MICHIGAN +HYP: HISTRY of MIHAGAN +Eval: S S + +Speaker sentences 528: nchlt_eng_001785 #utts: 1 +id: (nchlt_eng_001785-nchlt_eng_001785) +Scores: (#C #S #D #I) 1 2 0 0 +REF: ORIGINALLY the NAME +HYP: ARIGINLY the NAM +Eval: S S + +Speaker sentences 529: nchlt_eng_001786 #utts: 1 +id: (nchlt_eng_001786-nchlt_eng_001786) +Scores: (#C #S #D #I) 1 2 0 0 +REF: NATIONS FRAMEWORK convention +HYP: NTIONS FRAIMEWR convention +Eval: S S + +Speaker sentences 530: nchlt_eng_001787 #utts: 1 +id: (nchlt_eng_001787-nchlt_eng_001787) +Scores: (#C #S #D #I) 0 1 0 0 +REF: LOCAL +HYP: NOCALE +Eval: S + +Speaker sentences 531: nchlt_eng_001788 #utts: 1 +id: (nchlt_eng_001788-nchlt_eng_001788) +Scores: (#C #S #D #I) 0 3 0 0 +REF: AUSTRIAN SCHOOL ECONOMISTS +HYP: OLSTRIAN SCOLE ECANOMISTS +Eval: S S S + +Speaker sentences 532: nchlt_eng_001789 #utts: 1 +id: (nchlt_eng_001789-nchlt_eng_001789) +Scores: (#C #S #D #I) 0 3 0 0 +REF: MAIN GROUP COMPOUNDS +HYP: MAN GRUOP COMPOUWNDS +Eval: S S S + +Speaker sentences 533: nchlt_eng_001790 #utts: 1 +id: (nchlt_eng_001790-nchlt_eng_001790) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ** RECYCLABLE MATERIALS +HYP: RE SICLIBL MTERIALS +Eval: I S S + +Speaker sentences 534: nchlt_eng_001791 #utts: 1 +id: (nchlt_eng_001791-nchlt_eng_001791) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** COMMON LAW SYSTEMS +HYP: COM IN LARE SESTOM +Eval: I S S S + +Speaker sentences 535: nchlt_eng_001792 #utts: 1 +id: (nchlt_eng_001792-nchlt_eng_001792) +Scores: (#C #S #D #I) 0 3 0 0 +REF: BRONX HIGH SCHOOL +HYP: BRONGKS HI SCUL +Eval: S S S + +Speaker sentences 536: nchlt_eng_001793 #utts: 1 +id: (nchlt_eng_001793-nchlt_eng_001793) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** AMERICAN POLITICAL WRITERS +HYP: AN MER CAN BELITOGORIHITERS +Eval: I S S S + +Speaker sentences 537: nchlt_eng_001794 #utts: 1 +id: (nchlt_eng_001794-nchlt_eng_001794) +Scores: (#C #S #D #I) 0 2 0 0 +REF: CHEMICAL ELEMENTS +HYP: CANMICAL ILAMENTS +Eval: S S + +Speaker sentences 538: nchlt_eng_001795 #utts: 1 +id: (nchlt_eng_001795-nchlt_eng_001795) +Scores: (#C #S #D #I) 0 3 0 0 +REF: GLOBAL INTERNET COMMUNITY +HYP: LOBLE INTONT CMUNITY +Eval: S S S + +Speaker sentences 539: nchlt_eng_001796 #utts: 1 +id: (nchlt_eng_001796-nchlt_eng_001796) +Scores: (#C #S #D #I) 1 2 0 1 +REF: **** GEOGRAPHIC MAGAZINE march +HYP: TDYO GREAFICE MAGASEN march +Eval: I S S + +Speaker sentences 540: nchlt_eng_001797 #utts: 1 +id: (nchlt_eng_001797-nchlt_eng_001797) +Scores: (#C #S #D #I) 0 3 0 0 +REF: WEB SERVICE PROVIDERS +HYP: WIPSOVHIS PREVIGD AS +Eval: S S S + +Speaker sentences 541: nchlt_eng_001798 #utts: 1 +id: (nchlt_eng_001798-nchlt_eng_001798) +Scores: (#C #S #D #I) 0 2 1 0 +REF: SCIENCE FICTION NOVELS +HYP: ******* SINSFCTION OBLELES +Eval: D S S + +Speaker sentences 542: nchlt_eng_001799 #utts: 1 +id: (nchlt_eng_001799-nchlt_eng_001799) +Scores: (#C #S #D #I) 1 2 0 0 +REF: SCIENCE fiction FILM +HYP: SINES fiction FULEM +Eval: S S + +Speaker sentences 543: nchlt_eng_001800 #utts: 1 +id: (nchlt_eng_001800-nchlt_eng_001800) +Scores: (#C #S #D #I) 0 3 0 1 +REF: *** SUBSET SUM PROBLEM +HYP: SUB CIT SOME PROBLOM +Eval: I S S S + +Speaker sentences 544: nchlt_eng_001801 #utts: 1 +id: (nchlt_eng_001801-nchlt_eng_001801) +Scores: (#C #S #D #I) 1 2 0 0 +REF: EASTERN north AMERICA +HYP: EASTON north AMERICKA +Eval: S S + +Speaker sentences 545: nchlt_eng_001802 #utts: 1 +id: (nchlt_eng_001802-nchlt_eng_001802) +Scores: (#C #S #D #I) 0 3 0 0 +REF: PEPYS WITNESSED LOOTING +HYP: PEPES WITNES LOUTING +Eval: S S S + +Speaker sentences 546: nchlt_eng_001803 #utts: 1 +id: (nchlt_eng_001803-nchlt_eng_001803) +Scores: (#C #S #D #I) 1 2 0 0 +REF: DISTINCTIVE vocal INSTRUMENT +HYP: DISTINGTIVE vocal INSTRMENTS +Eval: S S + +Speaker sentences 547: nchlt_eng_001804 #utts: 1 +id: (nchlt_eng_001804-nchlt_eng_001804) +Scores: (#C #S #D #I) 0 3 0 2 +REF: * ******** AFRICAN AMERICAN RAPPERS +HYP: U AEFRICAN A MERICAN RAPIES +Eval: I I S S S + +Speaker sentences 548: nchlt_eng_001805 #utts: 1 +id: (nchlt_eng_001805-nchlt_eng_001805) +Scores: (#C #S #D #I) 0 2 0 1 +REF: *** PORTUGUESE GENERALS +HYP: POR THOGES GENALS +Eval: I S S + +Speaker sentences 549: nchlt_eng_001806 #utts: 1 +id: (nchlt_eng_001806-nchlt_eng_001806) +Scores: (#C #S #D #I) 0 4 0 1 +REF: **** INTERNATIONAL AIRPORT S IATA +HYP: INTO NESINALE IAPORT IDTY AY +Eval: I S S S S + +Speaker sentences 550: nchlt_eng_001807 #utts: 1 +id: (nchlt_eng_001807-nchlt_eng_001807) +Scores: (#C #S #D #I) 2 2 0 0 +REF: MOUNTAIN ranges of BOLIVIA +HYP: MOUNTAN ranges of BELIVIEAR +Eval: S S + +Speaker sentences 551: nchlt_eng_001808 #utts: 1 +id: (nchlt_eng_001808-nchlt_eng_001808) +Scores: (#C #S #D #I) 1 1 1 0 +REF: french AIR FORCE +HYP: french *** AREFOURS +Eval: D S + +Speaker sentences 552: nchlt_eng_001809 #utts: 1 +id: (nchlt_eng_001809-nchlt_eng_001809) +Scores: (#C #S #D #I) 0 3 0 0 +REF: SUPER BOWL APPEARANCE +HYP: S SWOPRABAL APERENCS +Eval: S S S + +Speaker sentences 553: nchlt_eng_001810 #utts: 1 +id: (nchlt_eng_001810-nchlt_eng_001810) +Scores: (#C #S #D #I) 1 2 0 0 +REF: long TRAVELING PAIRS +HYP: long TREVLING PAIES +Eval: S S + +Speaker sentences 554: nchlt_eng_001811 #utts: 1 +id: (nchlt_eng_001811-nchlt_eng_001811) +Scores: (#C #S #D #I) 0 3 0 0 +REF: DISTRICT COURT JUDGE +HYP: DISTRICKT COART JOUDGEH +Eval: S S S + +Speaker sentences 555: nchlt_eng_001812 #utts: 1 +id: (nchlt_eng_001812-nchlt_eng_001812) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ** DURRANI EMPIRE +HYP: YO RONY AMPIR +Eval: I S S + +Speaker sentences 556: nchlt_eng_001813 #utts: 1 +id: (nchlt_eng_001813-nchlt_eng_001813) +Scores: (#C #S #D #I) 0 3 0 0 +REF: BRITISH NATIONALITY ACT +HYP: BRITISHN NASIONALITY ECT +Eval: S S S + +Speaker sentences 557: nchlt_eng_001814 #utts: 1 +id: (nchlt_eng_001814-nchlt_eng_001814) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ISSUE DATE APRIL +HYP: ISHO DAT APRAL +Eval: S S S + +Speaker sentences 558: nchlt_eng_001815 #utts: 1 +id: (nchlt_eng_001815-nchlt_eng_001815) +Scores: (#C #S #D #I) 1 2 0 0 +REF: PUBLICLY traded COMPANIES +HYP: POUBLISITY traded COMPANES +Eval: S S + +Speaker sentences 559: nchlt_eng_001816 #utts: 1 +id: (nchlt_eng_001816-nchlt_eng_001816) +Scores: (#C #S #D #I) 2 3 1 0 +REF: RUSSIAN victims of SOVIET S REPRESSIONS +HYP: RUSHIN victims of ****** SOVED REPENTATIONS +Eval: S D S S + +Speaker sentences 560: nchlt_eng_001817 #utts: 1 +id: (nchlt_eng_001817-nchlt_eng_001817) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ***** WEST SLAVIC LANGUAGES +HYP: WEIST AN SLEVICK LANGWAGES +Eval: I S S S + +Speaker sentences 561: nchlt_eng_001818 #utts: 1 +id: (nchlt_eng_001818-nchlt_eng_001818) +Scores: (#C #S #D #I) 1 2 0 0 +REF: ITALIAN roman CATHOLIC +HYP: ETALIAN roman CATHLICES +Eval: S S + +Speaker sentences 562: nchlt_eng_001819 #utts: 1 +id: (nchlt_eng_001819-nchlt_eng_001819) +Scores: (#C #S #D #I) 0 3 0 2 +REF: ****** * FRENCH RESISTANCE MEMBERS +HYP: FRENTH S ISTR F NIMBERS +Eval: I I S S S + +Speaker sentences 563: nchlt_eng_001820 #utts: 1 +id: (nchlt_eng_001820-nchlt_eng_001820) +Scores: (#C #S #D #I) 1 3 0 0 +REF: PROVINCIAL SYMBOLS of ONTARIO +HYP: PREVINCHAL SIMBLS of UNTARIO +Eval: S S S + +Speaker sentences 564: nchlt_eng_001821 #utts: 1 +id: (nchlt_eng_001821-nchlt_eng_001821) +Scores: (#C #S #D #I) 2 1 0 0 +REF: rocks FORMING mont +HYP: rocks FOAMING mont +Eval: S + +Speaker sentences 565: nchlt_eng_001822 #utts: 1 +id: (nchlt_eng_001822-nchlt_eng_001822) +Scores: (#C #S #D #I) 0 2 0 0 +REF: ASSASSINATED MONARCHS +HYP: ASASSONATED MONOCKS +Eval: S S + +Speaker sentences 566: nchlt_eng_001823 #utts: 1 +id: (nchlt_eng_001823-nchlt_eng_001823) +Scores: (#C #S #D #I) 0 3 0 2 +REF: ****** **** INCLUDE INTERNATIONAL NONGOVERNMENTAL +HYP: INCLUD INTE NASHINOL NON GVERMENTOL +Eval: I I S S S + +Speaker sentences 567: nchlt_eng_001824 #utts: 1 +id: (nchlt_eng_001824-nchlt_eng_001824) +Scores: (#C #S #D #I) 1 2 1 0 +REF: METRIC space S M +HYP: METRICK space * IM +Eval: S D S + +Speaker sentences 568: swc_eng_001744 #utts: 1 +id: (swc_eng_001744-swc_eng_001744) +Scores: (#C #S #D #I) 3 4 0 0 +REF: or REPAIR the BREAK IN the TAPE +HYP: or REPARE the BRAK N the CAPE +Eval: S S S S + +Speaker sentences 569: swc_eng_001745 #utts: 1 +id: (swc_eng_001745-swc_eng_001745) +Scores: (#C #S #D #I) 0 2 0 0 +REF: VARIOUS SUBSTANCES +HYP: ERYH SUPTANTES +Eval: S S + +Speaker sentences 570: swc_eng_001746 #utts: 1 +id: (swc_eng_001746-swc_eng_001746) +Scores: (#C #S #D #I) 6 4 0 0 +REF: THIS most COMMONLY OCCURS when NEITHER side is able to +HYP: HIS most COMELY ACURS when NETHER side is able to +Eval: S S S S + +Speaker sentences 571: swc_eng_001747 #utts: 1 +id: (swc_eng_001747-swc_eng_001747) +Scores: (#C #S #D #I) 4 6 1 0 +REF: GREAT BARRIER REEF is managed by the GREAT BARRIER REEF MARINE +HYP: GRAT BARYIAR RIF is managed by the ***** GRET BARIARIF MREN +Eval: S S S D S S S + +Speaker sentences 572: swc_eng_001748 #utts: 1 +id: (swc_eng_001748-swc_eng_001748) +Scores: (#C #S #D #I) 0 4 0 0 +REF: AT LEAST THREE ROUTES +HYP: BY ATLEAS THRE VOTS +Eval: S S S S + +Speaker sentences 573: swc_eng_001749 #utts: 1 +id: (swc_eng_001749-swc_eng_001749) +Scores: (#C #S #D #I) 1 1 0 1 +REF: ******** DEFICIENCIES in +HYP: DEFIHANT CEASE in +Eval: I S + +Speaker sentences 574: swc_eng_001750 #utts: 1 +id: (swc_eng_001750-swc_eng_001750) +Scores: (#C #S #D #I) 1 5 0 0 +REF: WILL SHOW EVIDENCE of HEMORRHAGE IN +HYP: WL SHO EVDENCE of HMRG INT +Eval: S S S S S + +Speaker sentences 575: swc_eng_001751 #utts: 1 +id: (swc_eng_001751-swc_eng_001751) +Scores: (#C #S #D #I) 1 3 0 0 +REF: FIND an ANSWER QUICKLY +HYP: IND an ANSER HICKLY +Eval: S S S + +Speaker sentences 576: swc_eng_001752 #utts: 1 +id: (swc_eng_001752-swc_eng_001752) +Scores: (#C #S #D #I) 1 5 0 1 +REF: ENABLES DIVISIVE and ** UNDEMOCRATIC SOCIAL POLICIES +HYP: NABLE DEVICSIVE and UN DEMACRATIC SOSHAL POLACYES +Eval: S S I S S S + +Speaker sentences 577: swc_eng_001753 #utts: 1 +id: (swc_eng_001753-swc_eng_001753) +Scores: (#C #S #D #I) 1 5 0 1 +REF: *** MADE RECENT TITLES AVAILABLE on CASSETTE +HYP: MAD RESENT THITLES A ILIBLE on COE +Eval: I S S S S S + +Speaker sentences 578: swc_eng_001754 #utts: 1 +id: (swc_eng_001754-swc_eng_001754) +Scores: (#C #S #D #I) 2 3 0 0 +REF: district in EIGHTEEN SIXTY SIX +HYP: district in ATEN SICXTY SIC +Eval: S S S + +Speaker sentences 579: swc_eng_001755 #utts: 1 +id: (swc_eng_001755-swc_eng_001755) +Scores: (#C #S #D #I) 0 4 0 0 +REF: A LITTLE INTO FUTURITY +HYP: LILE IN TO FEUCUIRIDY +Eval: S S S S + +Speaker sentences 580: swc_eng_001756 #utts: 1 +id: (swc_eng_001756-swc_eng_001756) +Scores: (#C #S #D #I) 3 1 0 2 +REF: ** ***** groin and advanced THROUGH +HYP: AY INTHE groin and advanced THRO +Eval: I I S + +Speaker sentences 581: swc_eng_001757 #utts: 1 +id: (swc_eng_001757-swc_eng_001757) +Scores: (#C #S #D #I) 1 7 0 1 +REF: TECHNOLOGIES IN IMPLEMENTING TRANSHUMANIST GOALS of ** ENHANCED PERFORMANCE +HYP: CNALGYES AND IMPLDMENTIG TRANS HEUMINUSCULS of AN HANSE PERFORMEN +Eval: S S S S S I S S + +Speaker sentences 582: swc_eng_001758 #utts: 1 +id: (swc_eng_001758-swc_eng_001758) +Scores: (#C #S #D #I) 0 2 0 0 +REF: INCLUDING NAPHTHA +HYP: NCOUDING NAPS +Eval: S S + +Speaker sentences 583: swc_eng_001759 #utts: 1 +id: (swc_eng_001759-swc_eng_001759) +Scores: (#C #S #D #I) 1 5 1 0 +REF: BY spanish CHURCHMAN LUIS RAMIREZ DE LUCENA +HYP: Y spanish ********* TIRCHMAN LOE RMERAS DELOUCANA +Eval: S D S S S S + +Speaker sentences 584: swc_eng_001760 #utts: 1 +id: (swc_eng_001760-swc_eng_001760) +Scores: (#C #S #D #I) 0 2 0 0 +REF: DIVIDED DEMOCRATS +HYP: DEVIHTED DEMICRATS +Eval: S S + +Speaker sentences 585: swc_eng_001761 #utts: 1 +id: (swc_eng_001761-swc_eng_001761) +Scores: (#C #S #D #I) 2 6 0 2 +REF: ** THE WORLD CHAMPIONSHIP has BEEN CONTROLLED by **** FIDE +HYP: HE WOLD CHANPIAN SHIP has BEN CONTROLE by EIDY E +Eval: I S S S S S I S + +Speaker sentences 586: swc_eng_001762 #utts: 1 +id: (swc_eng_001762-swc_eng_001762) +Scores: (#C #S #D #I) 2 2 0 1 +REF: where THE STARTING position * +HYP: where TE STARING position I +Eval: S S I + +Speaker sentences 587: swc_eng_001763 #utts: 1 +id: (swc_eng_001763-swc_eng_001763) +Scores: (#C #S #D #I) 6 9 0 0 +REF: BEEN CREATED in every STATE and TERRITORY to PROTECT and PRESERVE the COUNTRYS UNIQUE ECOSYSTEMS +HYP: E CRATED in every STAT and TERITRY to PRTECT and RESERV the CONTRES UNAKY COSISTOMS +Eval: S S S S S S S S S + +Speaker sentences 588: swc_eng_001764 #utts: 1 +id: (swc_eng_001764-swc_eng_001764) +Scores: (#C #S #D #I) 1 3 3 0 +REF: DEDICATION of THE NEW ZEALAND WAR MEMORIAL +HYP: EDICATINT of *** *** ******* THENUSILAND WAREAMO +Eval: S D D D S S + +Speaker sentences 589: swc_eng_001765 #utts: 1 +id: (swc_eng_001765-swc_eng_001765) +Scores: (#C #S #D #I) 2 5 0 2 +REF: * ACCLAIM FROM the *** RAILROAD COMPANIES for VETOING +HYP: A LAME FRM the REL ROUD COUPANES for VETOIN +Eval: I S S I S S S + +Speaker sentences 590: swc_eng_001766 #utts: 1 +id: (swc_eng_001766-swc_eng_001766) +Scores: (#C #S #D #I) 2 1 1 1 +REF: *** town is SPLIT BETWEEN +HYP: THE town is ***** SPIT +Eval: I D S + +Speaker sentences 591: swc_eng_001767 #utts: 1 +id: (swc_eng_001767-swc_eng_001767) +Scores: (#C #S #D #I) 2 5 0 1 +REF: ******** MOSQUITOFISH is a PARTICULARLY AGGRESSIVE SPECIES KNOWN +HYP: MOSKETIY FISH is a PRTICULY AGREIVE SPACES NO +Eval: I S S S S S + +Speaker sentences 592: swc_eng_001768 #utts: 1 +id: (swc_eng_001768-swc_eng_001768) +Scores: (#C #S #D #I) 2 3 0 2 +REF: and the ******* ***** NATIONAL CHESS CHAMPIONSHIPS +HYP: and the NAIONAL CHESE HEM IN SHIPES +Eval: I I S S S + +Speaker sentences 593: swc_eng_001769 #utts: 1 +id: (swc_eng_001769-swc_eng_001769) +Scores: (#C #S #D #I) 4 4 0 1 +REF: PROBLEM is KNOWN to run ***** IN POLYNOMIAL time +HYP: ROBLOM is NOWN to run INPLY NO MAL time +Eval: S S I S S + +Speaker sentences 594: swc_eng_001770 #utts: 1 +id: (swc_eng_001770-swc_eng_001770) +Scores: (#C #S #D #I) 1 4 0 2 +REF: *** JR and ******* PARKER WATKINS HARDIN +HYP: LAY JONIER and PARCKER WAT CONS HEARDN +Eval: I S I S S S + +Speaker sentences 595: swc_eng_001771 #utts: 1 +id: (swc_eng_001771-swc_eng_001771) +Scores: (#C #S #D #I) 2 2 0 1 +REF: in *** NINETEEN seventy THREE +HYP: in NIT IN seventy THR +Eval: I S S + +Speaker sentences 596: swc_eng_001772 #utts: 1 +id: (swc_eng_001772-swc_eng_001772) +Scores: (#C #S #D #I) 3 2 0 0 +REF: developing and USING such TECHNOLOGIES +HYP: developing and OUSING such TACNALDGE +Eval: S S + +Speaker sentences 597: swc_eng_001773 #utts: 1 +id: (swc_eng_001773-swc_eng_001773) +Scores: (#C #S #D #I) 1 2 0 0 +REF: FOR some QUESTIONS +HYP: OR some QUISTIONS +Eval: S S + +Speaker sentences 598: swc_eng_001774 #utts: 1 +id: (swc_eng_001774-swc_eng_001774) +Scores: (#C #S #D #I) 1 4 0 0 +REF: CLAIM OF PROOF that P +HYP: LAME A PROOE that PE +Eval: S S S S + +Speaker sentences 599: swc_eng_001775 #utts: 1 +id: (swc_eng_001775-swc_eng_001775) +Scores: (#C #S #D #I) 4 6 0 0 +REF: a BLADDER CATHETER is USUALLY inserted TO MONITOR fluid BALANCE +HYP: a BLADE CATHATOR is OULY inserted SO MONSO fluid BONS +Eval: S S S S S S + +Speaker sentences 600: swc_eng_001776 #utts: 1 +id: (swc_eng_001776-swc_eng_001776) +Scores: (#C #S #D #I) 1 7 0 4 +REF: ** PROMOTION of ** ******* ******** EUGENIC ENHANCEMENT TECHNOLOGIES MIGHT UNINTENTIONALLY ENCOURAGE +HYP: ER NOTION of YU JENICAN HANSMENT TICNALAGES MIGH UN INTENTINALY IN COURAGE +Eval: I S I I I S S S S S S + +Speaker sentences 601: swc_eng_001777 #utts: 1 +id: (swc_eng_001777-swc_eng_001777) +Scores: (#C #S #D #I) 4 8 0 2 +REF: THE ATTENTION of ** RESEARCHERS CAN be FOCUSED ON partial solutions * OR SOLUTIONS +HYP: AT THEATENIN of RE SERTHRS AN be FOCKESED NM partial solutions O R SOLTIONS +Eval: S S I S S S S I S S + +Speaker sentences 602: swc_eng_001778 #utts: 1 +id: (swc_eng_001778-swc_eng_001778) +Scores: (#C #S #D #I) 3 2 1 0 +REF: KNOWN OF for HUNDREDS of years +HYP: ***** NOWNOF for HNDREDS of years +Eval: D S S + +Speaker sentences 603: swc_eng_001779 #utts: 1 +id: (swc_eng_001779-swc_eng_001779) +Scores: (#C #S #D #I) 1 5 1 0 +REF: ONLY MARSUPIALS have SURVIVED TO THE PRESENT +HYP: NLY MAUBIALS have ******** SOVIVED T T +Eval: S S D S S S + +Speaker sentences 604: swc_eng_001780 #utts: 1 +id: (swc_eng_001780-swc_eng_001780) +Scores: (#C #S #D #I) 3 5 1 0 +REF: TO WHICH ALL the EDIBLE SPECIES of CRUSTACEAN belong +HYP: ** TOWHCH AL the EDABLE SPACES of CRUSTATION belong +Eval: D S S S S S + +Speaker sentences 605: swc_eng_001781 #utts: 1 +id: (swc_eng_001781-swc_eng_001781) +Scores: (#C #S #D #I) 0 2 0 1 +REF: **** ALGORITHM RESEARCH +HYP: OGER THEM RESURCH +Eval: I S S + +Speaker sentences 606: swc_eng_001782 #utts: 1 +id: (swc_eng_001782-swc_eng_001782) +Scores: (#C #S #D #I) 3 9 1 0 +REF: NINETEEN SIXTY TWO PHILIPS invented THE compact AUDIO CASSETTE MEDIUM for AUDIO STORAGE +HYP: NINTEIN SICXTY TO FILIPS invented HE compact ***** ODOCASET MEDEOAM for ODIOUS TORGE +Eval: S S S S S D S S S S + +Speaker sentences 607: swc_eng_001783 #utts: 1 +id: (swc_eng_001783-swc_eng_001783) +Scores: (#C #S #D #I) 1 3 0 0 +REF: OBSTRUCTION OF the FLOW +HYP: UTRCIN F the LOW +Eval: S S S + +Speaker sentences 608: swc_eng_001784 #utts: 1 +id: (swc_eng_001784-swc_eng_001784) +Scores: (#C #S #D #I) 0 3 0 0 +REF: AMPHIBIANS AND REPTILES +HYP: NTFHBIANS AD RP +Eval: S S S + +Speaker sentences 609: swc_eng_001785 #utts: 1 +id: (swc_eng_001785-swc_eng_001785) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ***** WOMENS WORLD CHESS CHAMPIONSHIP +HYP: OMENS WOALD CHEST HAM INCHI +Eval: I S S S S + +Speaker sentences 610: swc_eng_001786 #utts: 1 +id: (swc_eng_001786-swc_eng_001786) +Scores: (#C #S #D #I) 6 11 1 2 +REF: CONTAINS DESCRIPTIONS and COMMENTARIES on the STATE of *** * NBIC SCIENCE and TECHNOLOGY BY MAJOR CONTRIBUTORS to THESE FIELDS +HYP: CONTANE DISCRPTIONS and COMMANTARIYS on the STAT of AEN B I SINCE and ECNALAGY AS MAGER CONTRUTERS to ***** THE +Eval: S S S S I I S S S S S S D S + +Speaker sentences 611: swc_eng_001787 #utts: 1 +id: (swc_eng_001787-swc_eng_001787) +Scores: (#C #S #D #I) 2 3 0 0 +REF: PUERILE FANTASY or social TREND +HYP: UR HLFHANTIY or social TRENT +Eval: S S S + +Speaker sentences 612: swc_eng_001788 #utts: 1 +id: (swc_eng_001788-swc_eng_001788) +Scores: (#C #S #D #I) 2 4 0 0 +REF: most COMPACT CASSETTES WERE SOLD blank +HYP: most COMPAC CASETS WER SOULD blank +Eval: S S S S + +Speaker sentences 613: swc_eng_001789 #utts: 1 +id: (swc_eng_001789-swc_eng_001789) +Scores: (#C #S #D #I) 2 2 1 1 +REF: IF THERE is an ****** ALGORITHM +HYP: ** IFTHER is an OULGER THE +Eval: D S I S + +Speaker sentences 614: swc_eng_001790 #utts: 1 +id: (swc_eng_001790-swc_eng_001790) +Scores: (#C #S #D #I) 2 7 1 0 +REF: THE SOUTHERN AUSTRALIAN COAST and in SUB ANTARCTIC AUSTRALIAN TERRITORIES +HYP: HE SUTHEN OSTRALIAN COST and in *** SUBANTEICTIC OUSTRALIAN TERITRYS +Eval: S S S S D S S S + +Speaker sentences 615: swc_eng_001791 #utts: 1 +id: (swc_eng_001791-swc_eng_001791) +Scores: (#C #S #D #I) 2 4 1 0 +REF: DATA RATES of TYPICALLY five HUNDRED TO +HYP: **** AERATS of TIPICKLY five HUDRED TW +Eval: D S S S S + +Speaker sentences 616: swc_eng_001792 #utts: 1 +id: (swc_eng_001792-swc_eng_001792) +Scores: (#C #S #D #I) 1 2 0 0 +REF: DEPRIVING the DUCK +HYP: DEPRIVIN the DOC +Eval: S S + +Speaker sentences 617: swc_eng_001793 #utts: 1 +id: (swc_eng_001793-swc_eng_001793) +Scores: (#C #S #D #I) 3 3 0 0 +REF: nine PERCENT of THE TOTAL cast +HYP: nine PORSENT of HE TOTL cast +Eval: S S S + +Speaker sentences 618: swc_eng_001794 #utts: 1 +id: (swc_eng_001794-swc_eng_001794) +Scores: (#C #S #D #I) 1 6 0 0 +REF: ANTERIOR CEREBRAL ARTERY and ANTERIOR COMMUNICATING ARTERY +HYP: ANTERIAS SORBR ATRY and ANTERIE COMUNCATIN ATERY +Eval: S S S S S S + +Speaker sentences 619: swc_eng_001795 #utts: 1 +id: (swc_eng_001795-swc_eng_001795) +Scores: (#C #S #D #I) 1 4 0 0 +REF: IT DID not IMPART SHINE +HYP: E D not IMPARTH SHIN +Eval: S S S S + +Speaker sentences 620: swc_eng_001796 #utts: 1 +id: (swc_eng_001796-swc_eng_001796) +Scores: (#C #S #D #I) 1 2 0 0 +REF: ENTIRE DEMOCRATIC party +HYP: NTHIER DEMACRATIC party +Eval: S S + +Speaker sentences 621: swc_eng_001797 #utts: 1 +id: (swc_eng_001797-swc_eng_001797) +Scores: (#C #S #D #I) 4 4 1 0 +REF: NOTCHES on top of the CASSETTE SHELL INDICATE THE +HYP: NOHES on top of the ******** CSETHAL INDICAT TH +Eval: S D S S S + +Speaker sentences 622: swc_eng_001798 #utts: 1 +id: (swc_eng_001798-swc_eng_001798) +Scores: (#C #S #D #I) 1 2 1 0 +REF: ALLOW ONE to SHOW +HYP: ***** LOWAT to SO +Eval: D S S + +Speaker sentences 623: swc_eng_001799 #utts: 1 +id: (swc_eng_001799-swc_eng_001799) +Scores: (#C #S #D #I) 0 5 0 1 +REF: * IS AN ENDANGERED MARINE SPECIES +HYP: I A INDANGRED MRIN SPCHES T +Eval: I S S S S S + +Speaker sentences 624: swc_eng_001800 #utts: 1 +id: (swc_eng_001800-swc_eng_001800) +Scores: (#C #S #D #I) 1 2 0 0 +REF: brown DESIRED ELECTION +HYP: brown DESIRE ALECTIN +Eval: S S + +Speaker sentences 625: swc_eng_001801 #utts: 1 +id: (swc_eng_001801-swc_eng_001801) +Scores: (#C #S #D #I) 5 5 0 0 +REF: THIS FACT DOESNT say much about where the PROBLEM LIES +HYP: HIS FAC DOSNT say much about where the PROBLOM LIS +Eval: S S S S S + +Speaker sentences 626: swc_eng_001802 #utts: 1 +id: (swc_eng_001802-swc_eng_001802) +Scores: (#C #S #D #I) 1 3 1 0 +REF: ECONOMICAL SOCIETY BEGAN as A +HYP: COMICAL SOSITY BGAN as * +Eval: S S S D + +Speaker sentences 627: swc_eng_001803 #utts: 1 +id: (swc_eng_001803-swc_eng_001803) +Scores: (#C #S #D #I) 1 6 0 0 +REF: WITH TOURISTS ARRIVING BY STEAMBOAT and TRAIN +HYP: ITH TORISTSARVING THE STEME BOTE and TRIN +Eval: S S S S S S + +Speaker sentences 628: swc_eng_001804 #utts: 1 +id: (swc_eng_001804-swc_eng_001804) +Scores: (#C #S #D #I) 1 3 0 1 +REF: FIRST DIALOGUE between **** TRANSHUMANISM +HYP: FRST DILOG between TRAN HUMNIS +Eval: S S I S + +Speaker sentences 629: swc_eng_001805 #utts: 1 +id: (swc_eng_001805-swc_eng_001805) +Scores: (#C #S #D #I) 2 3 2 0 +REF: NEVER BEEN part of THE OLYMPIC GAMES +HYP: NER BEN part of *** ******* THELIPICGANS +Eval: S S D D S + +Speaker sentences 630: swc_eng_001806 #utts: 1 +id: (swc_eng_001806-swc_eng_001806) +Scores: (#C #S #D #I) 1 2 0 1 +REF: REGIS FURNITURE and ** +HYP: REAGES FURNITCUR and TH +Eval: S S I + +Speaker sentences 631: swc_eng_001807 #utts: 1 +id: (swc_eng_001807-swc_eng_001807) +Scores: (#C #S #D #I) 1 2 1 0 +REF: in HIGH LEVEL TOURNAMENTS +HYP: in **** HILABL TRNMENTS +Eval: D S S + +Speaker sentences 632: swc_eng_001808 #utts: 1 +id: (swc_eng_001808-swc_eng_001808) +Scores: (#C #S #D #I) 1 3 0 0 +REF: TO locate THE ANEURYSM +HYP: O locate TH ANURISOM +Eval: S S S + +Speaker sentences 633: swc_eng_001809 #utts: 1 +id: (swc_eng_001809-swc_eng_001809) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ** MORPHOLOGICAL FREEDOM +HYP: OR HLOUGICAL FREDM +Eval: I S S + +Speaker sentences 634: swc_eng_001810 #utts: 1 +id: (swc_eng_001810-swc_eng_001810) +Scores: (#C #S #D #I) 0 3 0 2 +REF: * *** ENERGETIC ATTACKING STYLE +HYP: D HER JETIC ATAKING STOW +Eval: I I S S S + +Speaker sentences 635: swc_eng_001811 #utts: 1 +id: (swc_eng_001811-swc_eng_001811) +Scores: (#C #S #D #I) 3 5 0 0 +REF: EXACTLY forty YEARS AFTER the CORNERSTONE was LAID +HYP: ACTLY forty ARS AFR the CARNISHDON was LATE +Eval: S S S S S + +Speaker sentences 636: swc_eng_001812 #utts: 1 +id: (swc_eng_001812-swc_eng_001812) +Scores: (#C #S #D #I) 1 3 0 1 +REF: BASED ON the *********** RECOGNITION +HYP: ASE O the RECAGNITION TH +Eval: S S I S + +Speaker sentences 637: swc_eng_001813 #utts: 1 +id: (swc_eng_001813-swc_eng_001813) +Scores: (#C #S #D #I) 3 2 0 0 +REF: or ELECTRONIC BUTTONS or display +HYP: or LECTRNICK BUTNS or display +Eval: S S + +Speaker sentences 638: swc_eng_001814 #utts: 1 +id: (swc_eng_001814-swc_eng_001814) +Scores: (#C #S #D #I) 1 5 1 0 +REF: is UNKNOWN WHETHER P EQUALS N P +HYP: is ******* UN NON WHTHER PEACULS EMPY +Eval: D S S S S S + +Speaker sentences 639: swc_eng_001815 #utts: 1 +id: (swc_eng_001815-swc_eng_001815) +Scores: (#C #S #D #I) 3 3 0 0 +REF: WHICH COMES from the verb ACUERE +HYP: HICH COMS from the verb A +Eval: S S S + +Speaker sentences 640: swc_eng_001816 #utts: 1 +id: (swc_eng_001816-swc_eng_001816) +Scores: (#C #S #D #I) 0 8 0 0 +REF: DISPROPORTIONATELY AVAILABLE TO THOSE WITH GREATER FINANCIAL RESOURCES +HYP: DISPEPORTINTLY AVALABL T THOE WIH GEATER INANHAL RESORES +Eval: S S S S S S S S + +Speaker sentences 641: swc_eng_001817 #utts: 1 +id: (swc_eng_001817-swc_eng_001817) +Scores: (#C #S #D #I) 3 5 1 0 +REF: THE IMMINENT THREATS to THE SURVIVAL of many SPECIES +HYP: *** YUMN THRETS to TH SVIVL of many SPACES +Eval: D S S S S S + +Speaker sentences 642: swc_eng_001818 #utts: 1 +id: (swc_eng_001818-swc_eng_001818) +Scores: (#C #S #D #I) 1 2 0 0 +REF: even MORE DIFFICULT +HYP: even MOR DIFCALT +Eval: S S + +Speaker sentences 643: swc_eng_001819 #utts: 1 +id: (swc_eng_001819-swc_eng_001819) +Scores: (#C #S #D #I) 2 4 0 1 +REF: and ***** 21 SPECIES of OCEANIC DOLPHIN +HYP: and TWETY WN SPECES of ICEANIG DOLFON +Eval: I S S S S + +Speaker sentences 644: swc_eng_001820 #utts: 1 +id: (swc_eng_001820-swc_eng_001820) +Scores: (#C #S #D #I) 0 2 0 1 +REF: * ACHIEVING PROMOTION +HYP: A CHEVING PRMOTION +Eval: I S S + +Speaker sentences 645: swc_eng_001821 #utts: 1 +id: (swc_eng_001821-swc_eng_001821) +Scores: (#C #S #D #I) 0 2 0 0 +REF: TRANSHUMANIST ASSUMPTION +HYP: ERANTEWMINUSE ASUMTION +Eval: S S + +Speaker sentences 646: swc_eng_001822 #utts: 1 +id: (swc_eng_001822-swc_eng_001822) +Scores: (#C #S #D #I) 3 1 0 0 +REF: on the first BALLOT +HYP: on the first BELLIT +Eval: S + +Speaker sentences 647: swc_eng_001823 #utts: 1 +id: (swc_eng_001823-swc_eng_001823) +Scores: (#C #S #D #I) 10 4 1 0 +REF: story INDICATIVE of the rise IN GLOBAL SIGNIFICANCE of SHOE polish is told by jean +HYP: story NDICATIVE of the rise ** INGLOBL SIGNINGCS of SHU polish is told by jean +Eval: S D S S S + +Speaker sentences 648: swc_eng_001824 #utts: 1 +id: (swc_eng_001824-swc_eng_001824) +Scores: (#C #S #D #I) 2 5 0 0 +REF: WHICH SPARKED his EARLY INTEREST in POLITICS +HYP: WHCH SPARE his ERLY ENTRUST in POLITICK +Eval: S S S S S + +Speaker sentences 649: swc_eng_001825 #utts: 1 +id: (swc_eng_001825-swc_eng_001825) +Scores: (#C #S #D #I) 3 7 0 0 +REF: was CALLED DOLBY H X PRO in FULL and PATENTED +HYP: was CALED DOLBE ACH ECX PROW in FUL and PATNTE +Eval: S S S S S S S + +Speaker sentences 650: swc_eng_001826 #utts: 1 +id: (swc_eng_001826-swc_eng_001826) +Scores: (#C #S #D #I) 2 4 1 0 +REF: COULD save and FIND FILES BY NUMBER +HYP: OULD save and **** FINE FILS B +Eval: S D S S S + +Speaker sentences 651: swc_eng_001827 #utts: 1 +id: (swc_eng_001827-swc_eng_001827) +Scores: (#C #S #D #I) 2 4 0 0 +REF: AUSTRALIAN SNAKES belong to SEVEN FAMILIES +HYP: ASTRLIAN SNAKE belong to SVEN FAMLYES +Eval: S S S S + +Speaker sentences 652: swc_eng_001828 #utts: 1 +id: (swc_eng_001828-swc_eng_001828) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ***** DEVELOPING PLAYERS +HYP: DVELP IN PIYARS +Eval: I S S + +Speaker sentences 653: swc_eng_001829 #utts: 1 +id: (swc_eng_001829-swc_eng_001829) +Scores: (#C #S #D #I) 1 3 4 0 +REF: DECLINED sharply SINCE ITS PEAK IN THE LATE +HYP: LIND sharply ***** *** **** ** SENCES PEAKAN +Eval: S D D D D S S + +Speaker sentences 654: swc_eng_001830 #utts: 1 +id: (swc_eng_001830-swc_eng_001830) +Scores: (#C #S #D #I) 5 3 1 0 +REF: was recorded ENTIRELY on a FOUR track CASSETTE TAPE +HYP: was recorded INTHIRLY on a FOR track ******** COSET +Eval: S S D S + +Speaker sentences 655: swc_eng_001831 #utts: 1 +id: (swc_eng_001831-swc_eng_001831) +Scores: (#C #S #D #I) 2 1 0 0 +REF: ENORMOUS improvement in +HYP: NORMAS improvement in +Eval: S + +Speaker sentences 656: swc_eng_001832 #utts: 1 +id: (swc_eng_001832-swc_eng_001832) +Scores: (#C #S #D #I) 1 3 0 0 +REF: URBAN and RURAL LEGISLATORS +HYP: RBN and WRORL LEGUSTAC +Eval: S S S + +Speaker sentences 657: swc_eng_001833 #utts: 1 +id: (swc_eng_001833-swc_eng_001833) +Scores: (#C #S #D #I) 0 3 0 0 +REF: EACH PLAYER BEGINS +HYP: ACH PLYER BGINS +Eval: S S S + +Speaker sentences 658: swc_eng_001834 #utts: 1 +id: (swc_eng_001834-swc_eng_001834) +Scores: (#C #S #D #I) 1 5 0 1 +REF: **** CHESS HAS INSPIRED many COMBINATORIAL PUZZLES +HYP: JEST AS IN SPIRED many COMENATORILE PUSLES +Eval: I S S S S S + +Speaker sentences 659: swc_eng_001835 #utts: 1 +id: (swc_eng_001835-swc_eng_001835) +Scores: (#C #S #D #I) 0 3 0 0 +REF: MORE HUMANE IMAGE +HYP: OR HEOMAN IMADGE +Eval: S S S + +Speaker sentences 660: swc_eng_001836 #utts: 1 +id: (swc_eng_001836-swc_eng_001836) +Scores: (#C #S #D #I) 0 3 1 0 +REF: WELL AS PIRATED TAPES +HYP: **** ELAS PIRTED TAPS +Eval: D S S S + +Speaker sentences 661: swc_eng_001837 #utts: 1 +id: (swc_eng_001837-swc_eng_001837) +Scores: (#C #S #D #I) 0 5 0 2 +REF: *** *** TINTIN DESCENDS INTO THE OCEAN +HYP: ANT AND IESENS N TO TH OTIO +Eval: I I S S S S S + +Speaker sentences 662: swc_eng_001838 #utts: 1 +id: (swc_eng_001838-swc_eng_001838) +Scores: (#C #S #D #I) 2 4 1 0 +REF: PRESIDENT PRO TEM of the STATE SENATE +HYP: ********* PRESIN PROTEMPOR of the STAT SENIT +Eval: D S S S S + +Speaker sentences 663: swc_eng_001839 #utts: 1 +id: (swc_eng_001839-swc_eng_001839) +Scores: (#C #S #D #I) 6 1 1 0 +REF: BISHOP can move any number of squares DIAGONALLY +HYP: ISHOP can move any number of squares ********** +Eval: S D + +Speaker sentences 664: swc_eng_001840 #utts: 1 +id: (swc_eng_001840-swc_eng_001840) +Scores: (#C #S #D #I) 1 3 0 0 +REF: PRESSURE INSIDE the SKULL +HYP: PEHE INSID the SCOL +Eval: S S S + +Speaker sentences 665: swc_eng_001841 #utts: 1 +id: (swc_eng_001841-swc_eng_001841) +Scores: (#C #S #D #I) 0 4 0 0 +REF: LINE CONNECTS SHEERNESS WITH +HYP: IN CONACT SHERNES WIT +Eval: S S S S + +Speaker sentences 666: swc_eng_001842 #utts: 1 +id: (swc_eng_001842-swc_eng_001842) +Scores: (#C #S #D #I) 2 4 0 1 +REF: COUNTRIES of the ***** WESTERN PALEARCTIC FLYWAY +HYP: ONTRES of the ESTEN PALIARKTIC FLY WAY +Eval: S I S S S + +Speaker sentences 667: swc_eng_001843 #utts: 1 +id: (swc_eng_001843-swc_eng_001843) +Scores: (#C #S #D #I) 3 3 0 0 +REF: NATIONAL STATISTICS ESTIMATED the population in +HYP: AINALS DTISTIKS ESTMATE the population in +Eval: S S S + +Speaker sentences 668: swc_eng_001844 #utts: 1 +id: (swc_eng_001844-swc_eng_001844) +Scores: (#C #S #D #I) 1 3 0 1 +REF: ** UNDISPUTED world CHESS CHAMPION +HYP: ON DISPUTED world CHEST CHAMPIAN +Eval: I S S S + +Speaker sentences 669: swc_eng_001845 #utts: 1 +id: (swc_eng_001845-swc_eng_001845) +Scores: (#C #S #D #I) 0 3 0 0 +REF: JOSE RAUL CAPABLANCA +HYP: Y ROULL CAPEABINCEA +Eval: S S S + +Speaker sentences 670: swc_eng_001846 #utts: 1 +id: (swc_eng_001846-swc_eng_001846) +Scores: (#C #S #D #I) 7 8 0 0 +REF: WERE ENACTED by the GENERAL ASSEMBLY was a MEASURE RACIALLY SEGREGATING the states RAILROAD cars +HYP: ER INACTED by the GENRAL ASEMBLY was a MESUR RATIALY SEVGRGATING the states RELROT cars +Eval: S S S S S S S S + +Speaker sentences 671: swc_eng_001847 #utts: 1 +id: (swc_eng_001847-swc_eng_001847) +Scores: (#C #S #D #I) 1 2 0 0 +REF: WHICH WRAPS almost +HYP: WHCH RAPS almost +Eval: S S + +Speaker sentences 672: swc_eng_001848 #utts: 1 +id: (swc_eng_001848-swc_eng_001848) +Scores: (#C #S #D #I) 2 5 1 0 +REF: THIS ACT PROTECTS ALL native FAUNA and PROVIDES +HYP: **** SECT PRTECS AL native FORNA and PROVIDS +Eval: D S S S S S + +Speaker sentences 673: swc_eng_001849 #utts: 1 +id: (swc_eng_001849-swc_eng_001849) +Scores: (#C #S #D #I) 4 6 0 1 +REF: **** WHEREAS the FEMALES SPECULUM is dark BROWN BORDERED with WHITE +HYP: HERE AS the FEMALE SPECULM is dark BRON BORED with WHIGHT +Eval: I S S S S S S + +Speaker sentences 674: swc_eng_001850 #utts: 1 +id: (swc_eng_001850-swc_eng_001850) +Scores: (#C #S #D #I) 0 3 0 0 +REF: ROTARY CONTROLS OR +HYP: OTERYE CNTULS O +Eval: S S S + +Speaker sentences 675: swc_eng_001851 #utts: 1 +id: (swc_eng_001851-swc_eng_001851) +Scores: (#C #S #D #I) 1 3 0 0 +REF: NINETEEN TWELVE in ROSTHERN +HYP: NINTEAN TWELE in ROSFEARN +Eval: S S S + +Speaker sentences 676: swc_eng_001852 #utts: 1 +id: (swc_eng_001852-swc_eng_001852) +Scores: (#C #S #D #I) 6 4 2 0 +REF: DIAGNOSIS is GENERALLY made WITH a C T SCAN of the head +HYP: DIGNOCISE is GENRLY made WIH a * * SETESCAN of the head +Eval: S S S D D S + +Speaker sentences 677: swc_eng_001853 #utts: 1 +id: (swc_eng_001853-swc_eng_001853) +Scores: (#C #S #D #I) 3 3 0 1 +REF: *** first GENERALLY RECOGNIZED world chess CHAMPION +HYP: THE first GENRALY RECONISED world chess CHAMPIAN +Eval: I S S S + +Speaker sentences 678: swc_eng_001854 #utts: 1 +id: (swc_eng_001854-swc_eng_001854) +Scores: (#C #S #D #I) 1 4 0 0 +REF: SHEPPEY and SITTINGBOURNE WERE PART +HYP: HEPPY and ITINGON WE PAR +Eval: S S S S + +Speaker sentences 679: swc_eng_001855 #utts: 1 +id: (swc_eng_001855-swc_eng_001855) +Scores: (#C #S #D #I) 4 4 0 0 +REF: HAD RELEASED their ALBUMS both to CD and +HYP: AD RELACE their ELBUMS both to CDY and +Eval: S S S S + +Speaker sentences 680: swc_eng_001856 #utts: 1 +id: (swc_eng_001856-swc_eng_001856) +Scores: (#C #S #D #I) 1 3 0 0 +REF: WATERS AROUND the CONTINENT +HYP: WATES ARON the CONTINEN +Eval: S S S + +Speaker sentences 681: swc_eng_001857 #utts: 1 +id: (swc_eng_001857-swc_eng_001857) +Scores: (#C #S #D #I) 1 3 0 1 +REF: * the RANGE PERSONAL STEREOS +HYP: F the RAGE PERSINL STEARIOS +Eval: I S S S + +Speaker sentences 682: swc_eng_001858 #utts: 1 +id: (swc_eng_001858-swc_eng_001858) +Scores: (#C #S #D #I) 3 5 0 0 +REF: AND VU meters and RECORDING LEVEL CONTROLS on +HYP: ND FEYOUW meters and RECOARING LEVL CONTROULS on +Eval: S S S S S + +Speaker sentences 683: swc_eng_001859 #utts: 1 +id: (swc_eng_001859-swc_eng_001859) +Scores: (#C #S #D #I) 1 1 0 2 +REF: **** ** POLYNOMIAL time +HYP: POIN TO EAL time +Eval: I I S + +Speaker sentences 684: swc_eng_001860 #utts: 1 +id: (swc_eng_001860-swc_eng_001860) +Scores: (#C #S #D #I) 2 4 0 0 +REF: AND it OFTEN DESTROYED the PLAYABILITY +HYP: ND it OFEN DESTRORD the PLAABILITY +Eval: S S S S + +Speaker sentences 685: swc_eng_001861 #utts: 1 +id: (swc_eng_001861-swc_eng_001861) +Scores: (#C #S #D #I) 0 1 0 1 +REF: *** CONFUSION +HYP: CON FUTION +Eval: I S + +Speaker sentences 686: swc_eng_001862 #utts: 1 +id: (swc_eng_001862-swc_eng_001862) +Scores: (#C #S #D #I) 7 5 0 0 +REF: EQUIVALENT to the QUESTION of WHETHER X is a member of COMPOSITE +HYP: EUIVELENT to the QUSTION of WHTHER ECX is a member of CMPO +Eval: S S S S S + +Speaker sentences 687: swc_eng_001863 #utts: 1 +id: (swc_eng_001863-swc_eng_001863) +Scores: (#C #S #D #I) 2 3 0 0 +REF: MOVES to ITS LAST rank +HYP: MOSE to ITH LASTD rank +Eval: S S S + +Speaker sentences 688: swc_eng_001864 #utts: 1 +id: (swc_eng_001864-swc_eng_001864) +Scores: (#C #S #D #I) 0 1 0 0 +REF: POSTGENDERISM +HYP: POSTHENDERISM +Eval: S + +Speaker sentences 689: swc_eng_001865 #utts: 1 +id: (swc_eng_001865-swc_eng_001865) +Scores: (#C #S #D #I) 1 4 0 0 +REF: COMPACT CASSETTE QUICKLY found USE +HYP: OMPACT CASET QUIKLY found YOUS +Eval: S S S S + +Speaker sentences 690: swc_eng_001866 #utts: 1 +id: (swc_eng_001866-swc_eng_001866) +Scores: (#C #S #D #I) 0 5 0 1 +REF: * FOUR HUNDRED THIRTY THREE FEET +HYP: E FOR HUNDRND THERDY THEE FE +Eval: I S S S S S + +Speaker sentences 691: swc_eng_001867 #utts: 1 +id: (swc_eng_001867-swc_eng_001867) +Scores: (#C #S #D #I) 3 4 2 1 +REF: **** which result in A SPECIFIC TYPE OF PAWN STRUCTURE +HYP: INGS which result in * ******** ASPESIFICK THIHE A PON +Eval: I D D S S S S + +Speaker sentences 692: swc_eng_001868 #utts: 1 +id: (swc_eng_001868-swc_eng_001868) +Scores: (#C #S #D #I) 1 3 0 0 +REF: BEFORE NINETEEN NINETY seven +HYP: FOUR NINTEN HANTY seven +Eval: S S S + +Speaker sentences 693: swc_eng_001869 #utts: 1 +id: (swc_eng_001869-swc_eng_001869) +Scores: (#C #S #D #I) 1 2 1 0 +REF: COMMUNICATIONS and HEALTH CARE +HYP: MICATIONS and ****** HELF +Eval: S D S + +Speaker sentences 694: swc_eng_001870 #utts: 1 +id: (swc_eng_001870-swc_eng_001870) +Scores: (#C #S #D #I) 2 3 1 1 +REF: *** SAH in a PERSON KNOWN TO +HYP: EAY ATCHE in a ****** PEURSON NO +Eval: I S D S S + +Speaker sentences 695: swc_eng_001871 #utts: 1 +id: (swc_eng_001871-swc_eng_001871) +Scores: (#C #S #D #I) 8 5 1 1 +REF: SOME BRANDS SPECIFY that they may ** ALSO be used on other non POROUS MATERIALS +HYP: **** SOMBREAND SPISIFID that they may AL SO be used on other non POROSS MIPTERIALS +Eval: D S S I S S S + +Speaker sentences 696: swc_eng_001872 #utts: 1 +id: (swc_eng_001872-swc_eng_001872) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ** THE POSSIBLY CONSPECIFIC +HYP: HE POSEIBLY COND PESIFIG +Eval: I S S S + +Speaker sentences 697: swc_eng_001873 #utts: 1 +id: (swc_eng_001873-swc_eng_001873) +Scores: (#C #S #D #I) 1 2 0 0 +REF: M in LENGTH +HYP: MATORS in LACX +Eval: S S + +Speaker sentences 698: swc_eng_001874 #utts: 1 +id: (swc_eng_001874-swc_eng_001874) +Scores: (#C #S #D #I) 0 5 1 0 +REF: WITHOUT FIVE ZERO MOVE DRAWING RULE +HYP: ******* ITHOUT FIFTY MORE TRING BROA +Eval: D S S S S S + +Speaker sentences 699: swc_eng_001875 #utts: 1 +id: (swc_eng_001875-swc_eng_001875) +Scores: (#C #S #D #I) 0 5 0 3 +REF: **** ******* ****** THIS SITUATION PARALLELS RESPECTIVELY CZECHOSLOVAKIA +HYP: HISI CHACSIN POULES RESPECTIELY TOK AS LOF AKI +Eval: I I I S S S S S + +Speaker sentences 700: swc_eng_001876 #utts: 1 +id: (swc_eng_001876-swc_eng_001876) +Scores: (#C #S #D #I) 0 4 0 0 +REF: FAVERSHAM ELECTED ITS FIRST +HYP: AVSHONM LETED IS FIR +Eval: S S S S + +Speaker sentences 701: swc_eng_001877 #utts: 1 +id: (swc_eng_001877-swc_eng_001877) +Scores: (#C #S #D #I) 1 4 0 1 +REF: REBLEEDING RISK remains ** AROUND FORTY +HYP: EBLEADING RIST remains OF ROUND FOLTY +Eval: S S I S S + +Speaker sentences 702: swc_eng_001878 #utts: 1 +id: (swc_eng_001878-swc_eng_001878) +Scores: (#C #S #D #I) 0 2 0 1 +REF: ****** DELIVER CHECKMATE +HYP: ILERIT CHCK MAE +Eval: I S S + +Speaker sentences 703: swc_eng_001879 #utts: 1 +id: (swc_eng_001879-swc_eng_001879) +Scores: (#C #S #D #I) 9 15 0 2 +REF: some ****** SECULAR HUMANISTS CONCEIVE TRANSHUMANISM as an OFFSPRING of the HUMANIST FREETHOUGHT movement AND ARGUE THAT TRANSHUMANISTS DIFFER from the ********* HUMANIST MAINSTREAM by HAVING +HYP: some SECULR HUMINS CONCIVED RANDS HEUMINISM as an ALSPRING of the EUMNUST FRETHOUHT movement AN ARGUYTHT RAS HUMINIST DIFER from the EUMINIUST PAIN STRM by HAVIN +Eval: I S S S S S S S S S S S S I S S S + +Speaker sentences 704: swc_eng_001880 #utts: 1 +id: (swc_eng_001880-swc_eng_001880) +Scores: (#C #S #D #I) 4 6 0 0 +REF: PINTAIL NESTS and CHICKS are VULNERABLE to PREDATION by MAMMALS +HYP: PINTALE NEST and CHICK are VONERBLE to PRODATION by MAMLE +Eval: S S S S S S + +Speaker sentences 705: swc_eng_001881 #utts: 1 +id: (swc_eng_001881-swc_eng_001881) +Scores: (#C #S #D #I) 10 8 1 0 +REF: northern PINTAIL is ONE of the SPECIES to which the AGREEMENT on the CONSERVATION of AFRICAN EURASIAN MIGRATORY WATERBIRDS +HYP: northern PINTAL is ON of the SPECSHES to which the AGRMENT on the CONSCERVATION of ******* AFRACKANURAGION MOGRITORY WATERBURD +Eval: S S S S S D S S S + +Speaker sentences 706: swc_eng_001882 #utts: 1 +id: (swc_eng_001882-swc_eng_001882) +Scores: (#C #S #D #I) 4 2 1 0 +REF: and is NOW found only IN TASMANIA +HYP: and is NEOE found only ** INTASMAI +Eval: S D S + +Speaker sentences 707: swc_eng_001883 #utts: 1 +id: (swc_eng_001883-swc_eng_001883) +Scores: (#C #S #D #I) 5 4 0 2 +REF: ******** the IDEA of mind UPLOADING is * ASSERTED to REPRESENT +HYP: RPECTIVE the IDA of mind UPLOATING is A SRTED to REPROSENT +Eval: I S S I S S + +Speaker sentences 708: swc_eng_001884 #utts: 1 +id: (swc_eng_001884-swc_eng_001884) +Scores: (#C #S #D #I) 3 4 0 0 +REF: AN AVERAGE of TWENTY ONE per day +HYP: N AVRIGE of TWENT ON per day +Eval: S S S S + +Speaker sentences 709: swc_eng_001885 #utts: 1 +id: (swc_eng_001885-swc_eng_001885) +Scores: (#C #S #D #I) 1 5 1 0 +REF: THEN IT WOULD FOLLOW that P EQUALS +HYP: **** HAN WOLD FALO that PE ECUL +Eval: D S S S S S + +Speaker sentences 710: swc_eng_001886 #utts: 1 +id: (swc_eng_001886-swc_eng_001886) +Scores: (#C #S #D #I) 1 4 0 0 +REF: AND BLEEDING into VARIOUS TUMORS +HYP: ND BLEDING into VERIS CHOMERS +Eval: S S S S + +Speaker sentences 711: swc_eng_001887 #utts: 1 +id: (swc_eng_001887-swc_eng_001887) +Scores: (#C #S #D #I) 0 5 1 0 +REF: ALLOW THEM TO GLIDE BETWEEN TREES +HYP: ***** ANDL THE TOGLID BETWEN TRS +Eval: D S S S S S + +Speaker sentences 712: swc_eng_001888 #utts: 1 +id: (swc_eng_001888-swc_eng_001888) +Scores: (#C #S #D #I) 0 6 0 0 +REF: IF THESE PROBLEMS WERE EFFICIENTLY SOLVABLE +HYP: F THES PROBONS WR FICINTLY SALVEABL +Eval: S S S S S S + +Speaker sentences 713: swc_eng_001889 #utts: 1 +id: (swc_eng_001889-swc_eng_001889) +Scores: (#C #S #D #I) 0 2 0 1 +REF: *********** GEOLOGICAL TIME +HYP: OALODGICALE T IN +Eval: I S S + +Speaker sentences 714: swc_eng_001890 #utts: 1 +id: (swc_eng_001890-swc_eng_001890) +Scores: (#C #S #D #I) 2 0 0 1 +REF: *** when flushed +HYP: ROK when flushed +Eval: I + +Speaker sentences 715: swc_eng_001891 #utts: 1 +id: (swc_eng_001891-swc_eng_001891) +Scores: (#C #S #D #I) 1 3 0 0 +REF: including GERMINAL CHOICE TECHNOLOGY +HYP: including JERMNL THOICE ICNOLGY +Eval: S S S + +Speaker sentences 716: swc_eng_001892 #utts: 1 +id: (swc_eng_001892-swc_eng_001892) +Scores: (#C #S #D #I) 2 4 0 0 +REF: APPEARANCE of LEATHER SHOES or BOOTS +HYP: PERINE of LE THESHUS or BOT +Eval: S S S S + +Speaker sentences 717: swc_eng_001893 #utts: 1 +id: (swc_eng_001893-swc_eng_001893) +Scores: (#C #S #D #I) 0 4 0 0 +REF: IN EIGHTEEN SIXTY THREE +HYP: N ATEN SICTY THR +Eval: S S S S + +Speaker sentences 718: swc_eng_001894 #utts: 1 +id: (swc_eng_001894-swc_eng_001894) +Scores: (#C #S #D #I) 0 6 0 0 +REF: MANUFACTURE SHOE CARE PRODUCTS ALSO SELL +HYP: MANIUFECTURS SHO CAY PODACKES OL SYSEL +Eval: S S S S S S + +Speaker sentences 719: swc_eng_001895 #utts: 1 +id: (swc_eng_001895-swc_eng_001895) +Scores: (#C #S #D #I) 3 3 0 1 +REF: * the first non SOVIET CHALLENGER SINCE +HYP: O the first non SORIA CHALLNGER SINC +Eval: I S S S + +Speaker sentences 720: swc_eng_001896 #utts: 1 +id: (swc_eng_001896-swc_eng_001896) +Scores: (#C #S #D #I) 4 2 0 0 +REF: OPPONENT has only the KING and +HYP: PONENT has only the CING and +Eval: S S + +Speaker sentences 721: swc_eng_001897 #utts: 1 +id: (swc_eng_001897-swc_eng_001897) +Scores: (#C #S #D #I) 1 1 0 0 +REF: main ARTICLE +HYP: main ARTICL +Eval: S + +Speaker sentences 722: swc_eng_001898 #utts: 1 +id: (swc_eng_001898-swc_eng_001898) +Scores: (#C #S #D #I) 1 5 0 0 +REF: FOUND CERTAIN LENGTHS USEFUL for FITTING +HYP: OWND SERTAN LATH SUSFUL for FITIN +Eval: S S S S S + +Speaker sentences 723: swc_eng_001899 #utts: 1 +id: (swc_eng_001899-swc_eng_001899) +Scores: (#C #S #D #I) 6 4 0 0 +REF: tape in the same FORM FACTOR AS the compact AUDIO +HYP: tape in the same FORE FACTER S the compact OIO +Eval: S S S S + +Speaker sentences 724: swc_eng_001900 #utts: 1 +id: (swc_eng_001900-swc_eng_001900) +Scores: (#C #S #D #I) 2 3 0 0 +REF: CENSURE was later EXPUNGED FROM +HYP: SENTHR was later CXPUNGE FRO +Eval: S S S + +Speaker sentences 725: swc_eng_001901 #utts: 1 +id: (swc_eng_001901-swc_eng_001901) +Scores: (#C #S #D #I) 0 3 1 0 +REF: OR DE FACTO EQUALITY +HYP: ** R DESECTO CQUALITY +Eval: D S S S + +Speaker sentences 726: swc_eng_001902 #utts: 1 +id: (swc_eng_001902-swc_eng_001902) +Scores: (#C #S #D #I) 2 5 1 0 +REF: is FOUR THOUSAND SIX HUNDRED by SIXTY FEET +HYP: is **** FOR THOUSN SICHUNDRED by SIXT FET +Eval: D S S S S S + +Speaker sentences 727: swc_eng_001903 #utts: 1 +id: (swc_eng_001903-swc_eng_001903) +Scores: (#C #S #D #I) 0 3 0 0 +REF: NINETEEN SEVENTY THREE +HYP: NINTIN SEVENDY THR +Eval: S S S + +Speaker sentences 728: swc_eng_001904 #utts: 1 +id: (swc_eng_001904-swc_eng_001904) +Scores: (#C #S #D #I) 2 5 1 1 +REF: *** a PLAYER MAY ALSO LOSE by RUNNING OUT +HYP: RAL a PLAYE MAYAL SO LOUSE by ******* RUN +Eval: I S S S S D S + +Speaker sentences 729: swc_eng_001905 #utts: 1 +id: (swc_eng_001905-swc_eng_001905) +Scores: (#C #S #D #I) 0 6 0 0 +REF: PUBLIC HEALTH PROFESSOR GREGORY STOCK POINTS +HYP: UBLIK HLT PRFESER GRGRY STOACK POIN +Eval: S S S S S S + +Speaker sentences 730: swc_eng_001906 #utts: 1 +id: (swc_eng_001906-swc_eng_001906) +Scores: (#C #S #D #I) 4 8 1 0 +REF: BROWN WAS ELECTED TO the house OF REPRESENTATIVES for THREE non CONSECUTIVE TERMS +HYP: ***** BRON WASALECTED T the house O REPRSENTIVES for THRE non CONSECTIE TRMS +Eval: D S S S S S S S S + +Speaker sentences 731: swc_eng_001907 #utts: 1 +id: (swc_eng_001907-swc_eng_001907) +Scores: (#C #S #D #I) 0 3 0 1 +REF: * COEXIST HAPPILY WITH +HYP: O EGIST HAPLY WI +Eval: I S S S + +Speaker sentences 732: swc_eng_001908 #utts: 1 +id: (swc_eng_001908-swc_eng_001908) +Scores: (#C #S #D #I) 3 3 0 0 +REF: a GROUP of MAMMALS that RAISE +HYP: a GROP of MELES that RAC +Eval: S S S + +Speaker sentences 733: swc_eng_001909 #utts: 1 +id: (swc_eng_001909-swc_eng_001909) +Scores: (#C #S #D #I) 1 3 0 0 +REF: AND the WORLDS LARGEST +HYP: N the WOLDS LAGIST +Eval: S S S + +Speaker sentences 734: swc_eng_001910 #utts: 1 +id: (swc_eng_001910-swc_eng_001910) +Scores: (#C #S #D #I) 3 4 0 0 +REF: BREEDING TAKES place between APRIL and JUNE +HYP: BREDING TAKXS place between APRL and JON +Eval: S S S S + +Speaker sentences 735: swc_eng_001911 #utts: 1 +id: (swc_eng_001911-swc_eng_001911) +Scores: (#C #S #D #I) 3 3 0 0 +REF: AUSTRALIA is at the SOUTHERN END +HYP: STRALOR is at the SOTHEN IND +Eval: S S S + +Speaker sentences 736: swc_eng_001912 #utts: 1 +id: (swc_eng_001912-swc_eng_001912) +Scores: (#C #S #D #I) 2 2 0 0 +REF: TECHNOLOGICAL singularity is POSSIBLE +HYP: TECNALOUGICAL singularity is POSEBL +Eval: S S + +Speaker sentences 737: swc_eng_001913 #utts: 1 +id: (swc_eng_001913-swc_eng_001913) +Scores: (#C #S #D #I) 2 2 0 0 +REF: INCLUDING the SLEEPY cod +HYP: NLUDIN the LEPY cod +Eval: S S + +Speaker sentences 738: swc_eng_001914 #utts: 1 +id: (swc_eng_001914-swc_eng_001914) +Scores: (#C #S #D #I) 2 6 0 0 +REF: SEVENTY FOUR had a HIGHER EDUCATION QUALIFICATION COMPARED +HYP: SENTY FORE had a HIYE RGICATION CULFICATION COMPEDT +Eval: S S S S S S + +Speaker sentences 739: swc_eng_001915 #utts: 1 +id: (swc_eng_001915-swc_eng_001915) +Scores: (#C #S #D #I) 0 6 3 0 +REF: THIS OCCURS WHEN THE OPPONENTS KING IS IN CHECK +HYP: **** ****** **** IACKERS WEN THY PONES CING ISAN +Eval: D D D S S S S S S + +Speaker sentences 740: swc_eng_001916 #utts: 1 +id: (swc_eng_001916-swc_eng_001916) +Scores: (#C #S #D #I) 0 3 0 0 +REF: CONSERVATION IN AUSTRALIA +HYP: ONCEVATION I USTRAYA +Eval: S S S + +Speaker sentences 741: swc_eng_001917 #utts: 1 +id: (swc_eng_001917-swc_eng_001917) +Scores: (#C #S #D #I) 1 2 0 0 +REF: is THE SALAMANDERFISH +HYP: is TH SELAMANDOFF +Eval: S S + +Speaker sentences 742: swc_eng_001918 #utts: 1 +id: (swc_eng_001918-swc_eng_001918) +Scores: (#C #S #D #I) 4 5 0 1 +REF: first self ******** DESCRIBED TRANSHUMANISTS MET FORMALLY in the EARLY +HYP: first self DISCRIVE RANS HUMINEST BEAT FORMILY in the EAL +Eval: I S S S S S + +Speaker sentences 743: swc_eng_001919 #utts: 1 +id: (swc_eng_001919-swc_eng_001919) +Scores: (#C #S #D #I) 2 6 0 1 +REF: **** RECENT RESEARCH indicates THAT FACTORS OTHER than PRACTICE +HYP: EENT RE SURCH indicates TAT FACTERS OTHE than PRACTI +Eval: I S S S S S S + +Speaker sentences 744: swc_eng_001920 #utts: 1 +id: (swc_eng_001920-swc_eng_001920) +Scores: (#C #S #D #I) 3 3 0 0 +REF: AND PREVENTION and treatment of COMPLICATIONS +HYP: ND PRVENTION and treatment of OMPLCATIONS +Eval: S S S + +Speaker sentences 745: swc_eng_001921 #utts: 1 +id: (swc_eng_001921-swc_eng_001921) +Scores: (#C #S #D #I) 1 2 1 1 +REF: WITH A rapid ** ONSET +HYP: **** IH rapid ON SAEIT +Eval: D S I S + +Speaker sentences 746: swc_eng_001922 #utts: 1 +id: (swc_eng_001922-swc_eng_001922) +Scores: (#C #S #D #I) 2 4 0 1 +REF: * UTAH war THE FOUNDATION was BURIED +HYP: U THOW war TE FOUNDATIN was BARIE +Eval: I S S S S + +Speaker sentences 747: swc_eng_001923 #utts: 1 +id: (swc_eng_001923-swc_eng_001923) +Scores: (#C #S #D #I) 7 13 1 0 +REF: nowadays HOURLY REGIONAL EXPRESS TRAINS BETWEEN BERN and SPIEZ to BRIG and FREIGHT TRAINS continue to RUN on THE MOUNTAIN RAILWAY +HYP: nowadays ****** OURLY REAGINAL XPRESSTRAINS BETEE BURN and SHPEITS to BRICG and FRAT TRANES continue to UN on TH MONTAN RILWA +Eval: D S S S S S S S S S S S S S + +Speaker sentences 748: swc_eng_001924 #utts: 1 +id: (swc_eng_001924-swc_eng_001924) +Scores: (#C #S #D #I) 2 8 0 0 +REF: OTHER FAMILIES with A POTENTIALLY GONDWANAN ORIGIN INCLUDE the RETROPINNIDAE +HYP: OTHE FAMLYS with PTINUALY GONDWAN AN ORIGION INCLUD the RETRPONEDAY +Eval: S S S S S S S S + +Speaker sentences 749: swc_eng_001925 #utts: 1 +id: (swc_eng_001925-swc_eng_001925) +Scores: (#C #S #D #I) 3 4 1 0 +REF: by an ITALIAN dominican MONK JACOBUS DE CESSOLIS +HYP: by an ITALIN dominican **** MORK JCOBES DESESLES +Eval: S D S S S + +Speaker sentences 750: swc_eng_001926 #utts: 1 +id: (swc_eng_001926-swc_eng_001926) +Scores: (#C #S #D #I) 1 4 0 0 +REF: COMMAND was NAMED AFTER THE +HYP: AND was NAIED AFTE T +Eval: S S S S + +Speaker sentences 751: swc_eng_001927 #utts: 1 +id: (swc_eng_001927-swc_eng_001927) +Scores: (#C #S #D #I) 0 2 0 0 +REF: ARTIFICIAL INTELLIGENCE +HYP: ARTHFIHAL NTELIGENCS +Eval: S S + +Speaker sentences 752: swc_eng_001928 #utts: 1 +id: (swc_eng_001928-swc_eng_001928) +Scores: (#C #S #D #I) 3 1 0 0 +REF: and is the REIGNING +HYP: and is the RAING +Eval: S + +Speaker sentences 753: swc_eng_001929 #utts: 1 +id: (swc_eng_001929-swc_eng_001929) +Scores: (#C #S #D #I) 3 1 1 0 +REF: PER CENT of the population +HYP: *** PREN of the population +Eval: D S + +Speaker sentences 754: swc_eng_001930 #utts: 1 +id: (swc_eng_001930-swc_eng_001930) +Scores: (#C #S #D #I) 1 4 1 0 +REF: CHIEF AREAS of SHOE POLISH SALES +HYP: CHFE ERIRS of **** SHUPLIH SAELES +Eval: S S D S S + +Speaker sentences 755: swc_eng_001931 #utts: 1 +id: (swc_eng_001931-swc_eng_001931) +Scores: (#C #S #D #I) 2 1 0 0 +REF: imposed by LAW +HYP: imposed by LA +Eval: S + +Speaker sentences 756: swc_eng_001932 #utts: 1 +id: (swc_eng_001932-swc_eng_001932) +Scores: (#C #S #D #I) 2 4 0 0 +REF: REFERENCES to the RULING COALITION GOVERNMENT +HYP: RIFRINCESISS to the ROLING CORLAYSHIOND GOVERMEN +Eval: S S S S + +Speaker sentences 757: swc_eng_001933 #utts: 1 +id: (swc_eng_001933-swc_eng_001933) +Scores: (#C #S #D #I) 2 2 0 0 +REF: SPECIES of gliding POSSUM +HYP: PACHES of gliding POSM +Eval: S S + +Speaker sentences 758: swc_eng_001934 #utts: 1 +id: (swc_eng_001934-swc_eng_001934) +Scores: (#C #S #D #I) 4 3 0 0 +REF: BASED on the PREVIOUS STRATEGY of play +HYP: BACE on the PREVIOS TRADGY of play +Eval: S S S + +Speaker sentences 759: swc_eng_001935 #utts: 1 +id: (swc_eng_001935-swc_eng_001935) +Scores: (#C #S #D #I) 0 3 0 1 +REF: * AND IDEALISTIC ASPIRATIONS +HYP: N I DELLISTICK ASPERATIONS +Eval: I S S S + +Speaker sentences 760: swc_eng_001936 #utts: 1 +id: (swc_eng_001936-swc_eng_001936) +Scores: (#C #S #D #I) 1 4 0 0 +REF: PROFESSIONALS and HOME RECORDING ENTHUSIASTS +HYP: PERFECINLS and WHOM RECOARING ATHOUSIASTS +Eval: S S S S + +Speaker sentences 761: swc_eng_001937 #utts: 1 +id: (swc_eng_001937-swc_eng_001937) +Scores: (#C #S #D #I) 0 2 0 1 +REF: *** FAMILY ELAPIDAE +HYP: HEM THY OLPIDAY +Eval: I S S + +Speaker sentences 762: swc_eng_001938 #utts: 1 +id: (swc_eng_001938-swc_eng_001938) +Scores: (#C #S #D #I) 3 8 1 2 +REF: THAN A QUARTER of people WITH a ******* **** PREVIOUS SAH MAY DEVELOP HYPOPITUITARISM +HYP: **** NO CORTE of people WIH a PREVIAS ESAY ACH MA DVELOUE HIG POPITCUITRISM +Eval: D S S S I I S S S S S + +Speaker sentences 763: swc_eng_001939 #utts: 1 +id: (swc_eng_001939-swc_eng_001939) +Scores: (#C #S #D #I) 1 4 0 0 +REF: DIVIDED into THREE FAMILIES THAT +HYP: DEVIDED into THRE FAMLYS THA +Eval: S S S S + +Speaker sentences 764: swc_eng_001940 #utts: 1 +id: (swc_eng_001940-swc_eng_001940) +Scores: (#C #S #D #I) 1 5 0 0 +REF: SHOWED SLIGHT INTEREST in RELEASING CASSETTES +HYP: HOWD SLIGT INTREST in RELIAEING COET +Eval: S S S S S + +Speaker sentences 765: swc_eng_001941 #utts: 1 +id: (swc_eng_001941-swc_eng_001941) +Scores: (#C #S #D #I) 2 3 1 2 +REF: **** * FAMILIAR ENOUGH to have COMMON NAMES +HYP: THAT A EMLUR NOFT to have ****** COEN +Eval: I I S S D S + +Speaker sentences 766: swc_eng_001942 #utts: 1 +id: (swc_eng_001942-swc_eng_001942) +Scores: (#C #S #D #I) 1 3 0 0 +REF: IN two THOUSAND SIX +HYP: N two THUSAN SIK +Eval: S S S + +Speaker sentences 767: swc_eng_001943 #utts: 1 +id: (swc_eng_001943-swc_eng_001943) +Scores: (#C #S #D #I) 2 5 1 1 +REF: ** SHOESHINE boys ARE KNOWN as BOOT POLISH BOYS +HYP: SH HIN boys AR NON as **** BOT POLIS +Eval: I S S S D S S + +Speaker sentences 768: swc_eng_001944 #utts: 1 +id: (swc_eng_001944-swc_eng_001944) +Scores: (#C #S #D #I) 4 4 0 0 +REF: the CAUSE is RUPTURE of a CEREBRAL ANEURYSM +HYP: the COUSE is RUTUR of a SERIBL ANDURISOM +Eval: S S S S + +Speaker sentences 769: swc_eng_001945 #utts: 1 +id: (swc_eng_001945-swc_eng_001945) +Scores: (#C #S #D #I) 1 6 1 0 +REF: MOST OF THE MAJOR U S music COMPANIES +HYP: **** OST OFTHE AGJER YOU ESS music COMPNES +Eval: D S S S S S S + +Speaker sentences 770: swc_eng_001946 #utts: 1 +id: (swc_eng_001946-swc_eng_001946) +Scores: (#C #S #D #I) 10 9 1 1 +REF: ONE STEREO PAIR or ONE MONOPHONIC track is PLAYED or recorded WHEN the TAPE IS moving in ONE direction and * +HYP: *** ONCSTEIO PAR or ON MONOFONIC track is PLAD or recorded HEN the TAP S moving in ON direction and T +Eval: D S S S S S S S S S I + +Speaker sentences 771: swc_eng_001947 #utts: 1 +id: (swc_eng_001947-swc_eng_001947) +Scores: (#C #S #D #I) 2 2 1 0 +REF: WHERE ITS early FORM in +HYP: ***** EITS early FORME in +Eval: D S S + +Speaker sentences 772: swc_eng_001948 #utts: 1 +id: (swc_eng_001948-swc_eng_001948) +Scores: (#C #S #D #I) 0 3 0 0 +REF: A STRATEGIC PHILOSOPHER +HYP: STR TEAGHC FILOSOVER +Eval: S S S + +Speaker sentences 773: swc_eng_001949 #utts: 1 +id: (swc_eng_001949-swc_eng_001949) +Scores: (#C #S #D #I) 2 3 0 1 +REF: ******** POSITIONING ADVANTAGES DURING the game +HYP: OSITIONG T VANAEHES TERN the game +Eval: I S S S + +Speaker sentences 774: swc_eng_001950 #utts: 1 +id: (swc_eng_001950-swc_eng_001950) +Scores: (#C #S #D #I) 0 3 0 0 +REF: NEW SOUTH WALES +HYP: NEO SAUT WHELLS +Eval: S S S + +Speaker sentences 775: swc_eng_001951 #utts: 1 +id: (swc_eng_001951-swc_eng_001951) +Scores: (#C #S #D #I) 3 3 0 0 +REF: disposal over his OWN BIOLOGICAL NATURE +HYP: disposal over his ON BYLOUGICAL NATER +Eval: S S S + +Speaker sentences 776: swc_eng_001952 #utts: 1 +id: (swc_eng_001952-swc_eng_001952) +Scores: (#C #S #D #I) 4 5 0 1 +REF: REPRODUCTIVE rights or exert ** UNDUE PRESSURES on PROSPECTIVE PARENTS +HYP: EPODUCTIVE rights or exert UN DO PRESHORS on PRESPECTIE PAIN +Eval: S I S S S S + +Speaker sentences 777: swc_eng_001953 #utts: 1 +id: (swc_eng_001953-swc_eng_001953) +Scores: (#C #S #D #I) 0 3 1 0 +REF: STILL ANCIENT IN ORIGIN +HYP: ***** IL HANCHANT NORGON +Eval: D S S S + +Speaker sentences 778: swc_eng_001954 #utts: 1 +id: (swc_eng_001954-swc_eng_001954) +Scores: (#C #S #D #I) 0 3 0 1 +REF: ***** RASTAPOPOULOSS HIRED GUN +HYP: RASTA POPHOLOS HID GON +Eval: I S S S + +Speaker sentences 779: swc_eng_001955 #utts: 1 +id: (swc_eng_001955-swc_eng_001955) +Scores: (#C #S #D #I) 0 4 0 0 +REF: IN TWO THOUSAND TWO +HYP: ND TO THUSAND TO +Eval: S S S S + +Speaker sentences 780: swc_eng_001956 #utts: 1 +id: (swc_eng_001956-swc_eng_001956) +Scores: (#C #S #D #I) 3 4 0 1 +REF: for EXAMPLE IF the PLAYER has *** ONLY +HYP: for GAMPL F the PLAYAR has ONL T +Eval: S S S I S + +Speaker sentences 781: swc_eng_001957 #utts: 1 +id: (swc_eng_001957-swc_eng_001957) +Scores: (#C #S #D #I) 2 5 2 0 +REF: SUFFERED A SUBARACHNOID HEMORRHAGE have COGNITIVE IMPAIRMENT that AFFECTS +HYP: ******** * SOFEDA SUBRACKNODHMERGE have COLNIIV IMPARMENT that FECT +Eval: D D S S S S S + +Speaker sentences 782: swc_eng_001958 #utts: 1 +id: (swc_eng_001958-swc_eng_001958) +Scores: (#C #S #D #I) 0 3 0 0 +REF: PROVIDED PROGNOSTIC DATA +HYP: PEVIDEIG RONSTIC DATER +Eval: S S S + +Speaker sentences 783: swc_eng_001959 #utts: 1 +id: (swc_eng_001959-swc_eng_001959) +Scores: (#C #S #D #I) 4 3 0 0 +REF: WHO had ANEURYSMS detected by other MEANS +HYP: HO had ANURISONS detected by other MINS +Eval: S S S + +Speaker sentences 784: swc_eng_001960 #utts: 1 +id: (swc_eng_001960-swc_eng_001960) +Scores: (#C #S #D #I) 1 5 1 0 +REF: LIFESTYLES DESIGNED to IMPROVE HEALTH AND LONGEVITY +HYP: LIOESTILS DISIN to ******* MPROE HELTHAN UNCEVITY +Eval: S S D S S S + +Speaker sentences 785: swc_eng_001961 #utts: 1 +id: (swc_eng_001961-swc_eng_001961) +Scores: (#C #S #D #I) 2 5 0 0 +REF: had MORE SOPHISTICATED END of TAPE PREDICTION +HYP: had MOR SUFITICATED AND of TA PREDICTIO +Eval: S S S S S + +Speaker sentences 786: swc_eng_001962 #utts: 1 +id: (swc_eng_001962-swc_eng_001962) +Scores: (#C #S #D #I) 0 1 0 2 +REF: ** ***** DEHUMANIZATION +HYP: DE HUMAN ISATION +Eval: I I S + +Speaker sentences 787: swc_eng_001963 #utts: 1 +id: (swc_eng_001963-swc_eng_001963) +Scores: (#C #S #D #I) 1 3 0 0 +REF: SPECIES INCLUDE freshwater LAMPREYS +HYP: SPACHES INCLOUD freshwater LAMPRAYE +Eval: S S S + +Speaker sentences 788: swc_eng_001964 #utts: 1 +id: (swc_eng_001964-swc_eng_001964) +Scores: (#C #S #D #I) 0 2 0 2 +REF: **** *** FIRST ANGIOGRAM +HYP: FOST AND YU GRIM +Eval: I I S S + +Speaker sentences 789: swc_eng_001965 #utts: 1 +id: (swc_eng_001965-swc_eng_001965) +Scores: (#C #S #D #I) 1 3 0 0 +REF: THE FREE ENCYCLOPEDIA at +HYP: HE FREAN SICLOPEDIA at +Eval: S S S + +Speaker sentences 790: swc_eng_001966 #utts: 1 +id: (swc_eng_001966-swc_eng_001966) +Scores: (#C #S #D #I) 1 4 0 1 +REF: **** THEREFORE MEDICAL IMAGING is GENERALLY +HYP: THER FORE METICAL IMAGENG is GENRL +Eval: I S S S S + +Speaker sentences 791: swc_eng_001967 #utts: 1 +id: (swc_eng_001967-swc_eng_001967) +Scores: (#C #S #D #I) 6 4 0 0 +REF: SPECIESIST the EXCLUSION of non human and part HUMAN ANIMALS +HYP: PEACEIST the EXCLUION of non human and part HEUMAN ANIBLES +Eval: S S S S + +Speaker sentences 792: swc_eng_001968 #utts: 1 +id: (swc_eng_001968-swc_eng_001968) +Scores: (#C #S #D #I) 2 7 0 1 +REF: IN PEOPLE WHO had PREVIOUSLY SUFFERED a ****** SUBARACHNOID HEMORRHAGE +HYP: N PEOPL H had PREVIASLY SUFRD a SUBRAC MOH HEMRIG +Eval: S S S S S I S S + +Speaker sentences 793: swc_eng_001969 #utts: 1 +id: (swc_eng_001969-swc_eng_001969) +Scores: (#C #S #D #I) 3 7 0 1 +REF: CLASSIFIED as ***** EITHER ENDANGERED or THREATENED UNDER the EPBC ACT +HYP: LSIFIED as AITHE IN DANGED or THRTOND ANDTO the PE BE +Eval: S I S S S S S S + +Speaker sentences 794: swc_eng_001970 #utts: 1 +id: (swc_eng_001970-swc_eng_001970) +Scores: (#C #S #D #I) 0 6 0 0 +REF: AND ATTORNEY GENERAL PARKER WATKINS HARDIN +HYP: IAN ATERNY JENERL PARCKER WATCONS HARDN +Eval: S S S S S S + +Speaker sentences 795: swc_eng_001971 #utts: 1 +id: (swc_eng_001971-swc_eng_001971) +Scores: (#C #S #D #I) 1 1 0 0 +REF: but TYPICALLY +HYP: but TIPICLY +Eval: S + +Speaker sentences 796: swc_eng_001972 #utts: 1 +id: (swc_eng_001972-swc_eng_001972) +Scores: (#C #S #D #I) 6 5 2 0 +REF: which IN TURN fed the SIGNAL to the HEAD of THE CASSETTE DECK +HYP: which ** INTURNE fed the SIGNL to the HAD of *** TE COE +Eval: D S S S D S S + +Speaker sentences 797: swc_eng_001973 #utts: 1 +id: (swc_eng_001973-swc_eng_001973) +Scores: (#C #S #D #I) 2 4 0 1 +REF: WITHIN THEIR own CONVENTIONALLY expected **** LIFETIMES +HYP: THI THER own CONVENIONALY expected LIFE TIMES +Eval: S S S I S + +Speaker sentences 798: swc_eng_001974 #utts: 1 +id: (swc_eng_001974-swc_eng_001974) +Scores: (#C #S #D #I) 0 2 0 0 +REF: SUBSTANTIAL STRAIN +HYP: UBSTANHAL STRIN +Eval: S S + +Speaker sentences 799: swc_eng_001975 #utts: 1 +id: (swc_eng_001975-swc_eng_001975) +Scores: (#C #S #D #I) 0 5 0 0 +REF: TWENTIETH CENTURY KENTUCKY CONGRESSMAN JOHN +HYP: TWENYITH SENTRY CANTUCKY CONGRSMAN JOAN +Eval: S S S S S + +Speaker sentences 800: swc_eng_001976 #utts: 1 +id: (swc_eng_001976-swc_eng_001976) +Scores: (#C #S #D #I) 0 4 1 0 +REF: NINETY PER CENT ARE ENDEMIC +HYP: ****** NOT PASINT OR INDIMI +Eval: D S S S S + +Speaker sentences 801: swc_eng_001977 #utts: 1 +id: (swc_eng_001977-swc_eng_001977) +Scores: (#C #S #D #I) 2 2 0 0 +REF: hunting with LEAD SHOT +HYP: hunting with LED SHOA +Eval: S S + +Speaker sentences 802: swc_eng_001978 #utts: 1 +id: (swc_eng_001978-swc_eng_001978) +Scores: (#C #S #D #I) 0 2 0 1 +REF: *** TWENTY THIRTEEN +HYP: WNY THEIR TEN +Eval: I S S + +Speaker sentences 803: swc_eng_001979 #utts: 1 +id: (swc_eng_001979-swc_eng_001979) +Scores: (#C #S #D #I) 3 6 3 1 +REF: * ALTHOUGH seven PER CENT of the WORLDS BATS SPECIES LIVE IN AUSTRALIA +HYP: O THE seven *** PRSENT of the ****** **** WLDSPAT SPACHES LIVIN UTRALA +Eval: I S D S D D S S S S + +Speaker sentences 804: swc_eng_001980 #utts: 1 +id: (swc_eng_001980-swc_eng_001980) +Scores: (#C #S #D #I) 1 6 1 0 +REF: KARPOVS REIGN FINALLY ENDED in NINETEEN EIGHTY FIVE +HYP: RPOVS RAN FINLY END in ******** NTENHAY FIV +Eval: S S S S D S S + +Speaker sentences 805: swc_eng_001981 #utts: 1 +id: (swc_eng_001981-swc_eng_001981) +Scores: (#C #S #D #I) 2 4 0 1 +REF: WHILE some ***** TRANSHUMANISTS TAKE AN abstract +HYP: WIL some TRENE HUMON IS TAKEAN abstract +Eval: S I S S S + +Speaker sentences 806: swc_eng_001982 #utts: 1 +id: (swc_eng_001982-swc_eng_001982) +Scores: (#C #S #D #I) 0 2 0 2 +REF: ***** *** WRITE PROTECTION +HYP: POINT THE RIHE PRETECTION +Eval: I I S S + +Speaker sentences 807: swc_eng_001983 #utts: 1 +id: (swc_eng_001983-swc_eng_001983) +Scores: (#C #S #D #I) 3 7 0 2 +REF: **** GRAPH ISOMORPHISM PROBLEM is the COMPUTATIONAL PROBLEM of *** DETERMINING WHETHER +HYP: AICS A MORFISM PROBLOM is the COMPETATINAL PROBLOM of THE TERMINING WETH +Eval: I S S S S S I S S + +Speaker sentences 808: swc_eng_001984 #utts: 1 +id: (swc_eng_001984-swc_eng_001984) +Scores: (#C #S #D #I) 2 2 1 1 +REF: ** FURTHER restrict our CONCEPT OF +HYP: OR THE restrict our ******* CONSEPTI +Eval: I S D S + +Speaker sentences 809: swc_eng_001985 #utts: 1 +id: (swc_eng_001985-swc_eng_001985) +Scores: (#C #S #D #I) 0 4 0 1 +REF: *** THOSE WHO SURVIVE HOSPITALIZATION +HYP: HOE HO SAR VIE HUSPITALIATION +Eval: I S S S S + +Speaker sentences 810: swc_eng_001986 #utts: 1 +id: (swc_eng_001986-swc_eng_001986) +Scores: (#C #S #D #I) 3 6 1 1 +REF: SOME PROTECTION of ** UNCERTAIN SIGNIFICANCE is CONFERRED by CAUCASIAN ETHNICITY +HYP: **** SOMEPETECTION of UN SERTAN GNIFICANCE is ONFURED by CUCASIN ATHNITITY +Eval: D S I S S S S S + +Speaker sentences 811: swc_eng_001987 #utts: 1 +id: (swc_eng_001987-swc_eng_001987) +Scores: (#C #S #D #I) 0 2 0 0 +REF: COASTAL LAGOONS +HYP: HALSTAE AGONS +Eval: S S + +Speaker sentences 812: swc_eng_001988 #utts: 1 +id: (swc_eng_001988-swc_eng_001988) +Scores: (#C #S #D #I) 0 3 0 0 +REF: AND COGNITIVE ENHANCEMENT +HYP: COGETIVE IN HANSME +Eval: S S S + +Speaker sentences 813: swc_eng_001989 #utts: 1 +id: (swc_eng_001989-swc_eng_001989) +Scores: (#C #S #D #I) 5 3 2 1 +REF: ***** the EIGHTH rank and be PROMOTED to AN ALLOWED PIECE +HYP: VANST the ATH rank and be PRMOTED to ** ******* NALO +Eval: I S S D D S + +Speaker sentences 814: swc_eng_001990 #utts: 1 +id: (swc_eng_001990-swc_eng_001990) +Scores: (#C #S #D #I) 4 2 0 1 +REF: *** DRAWBACK of coiling is the POSSIBILITY +HYP: DRA BACK of coiling is the PEBLIT +Eval: I S S + +Speaker sentences 815: swc_eng_001991 #utts: 1 +id: (swc_eng_001991-swc_eng_001991) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ******** INDICATES A SUBARACHNOID HEMORRHAGE +HYP: INDICATE S SUBER ACKNOD HMBRIGE +Eval: I S S S S + +Speaker sentences 816: swc_eng_001992 #utts: 1 +id: (swc_eng_001992-swc_eng_001992) +Scores: (#C #S #D #I) 1 1 0 0 +REF: DAMAGED portion +HYP: DAMICH portion +Eval: S + +Speaker sentences 817: swc_eng_001993 #utts: 1 +id: (swc_eng_001993-swc_eng_001993) +Scores: (#C #S #D #I) 1 4 0 1 +REF: ADOPTION of ****** EUGENIC ENHANCEMENT TECHNOLOGIES +HYP: NDOPTIO of YEJEIK AND HANSMNTEKNAL JES +Eval: S I S S S + +Speaker sentences 818: swc_eng_001994 #utts: 1 +id: (swc_eng_001994-swc_eng_001994) +Scores: (#C #S #D #I) 4 2 0 0 +REF: polish on his horse AND WAGON +HYP: polish on his horse ND WAGAN +Eval: S S + +Speaker sentences 819: swc_eng_001995 #utts: 1 +id: (swc_eng_001995-swc_eng_001995) +Scores: (#C #S #D #I) 1 3 0 0 +REF: AND the NEXT CHAMPION +HYP: D the EXT HAMPIAN +Eval: S S S + +Speaker sentences 820: swc_eng_001996 #utts: 1 +id: (swc_eng_001996-swc_eng_001996) +Scores: (#C #S #D #I) 1 3 1 0 +REF: BROTHER of AUTHOR ALDOUS HUXLEY +HYP: THE of ****** AFER ALDISHUCXLY +Eval: S D S S + +Speaker sentences 821: swc_eng_001997 #utts: 1 +id: (swc_eng_001997-swc_eng_001997) +Scores: (#C #S #D #I) 0 5 0 0 +REF: WORLD CHAMPION NINETEEN TWENTY ONE +HYP: L CHAP IN HINCTENTWENY ON +Eval: S S S S S + +Speaker sentences 822: swc_eng_001998 #utts: 1 +id: (swc_eng_001998-swc_eng_001998) +Scores: (#C #S #D #I) 3 4 0 0 +REF: such as QUANTUM COMPUTATION and RANDOMIZED ALGORITHMS +HYP: such as QONTIM COPUTATION and RANDMYSED ELGRETHEMS +Eval: S S S S + +Speaker sentences 823: swc_eng_001999 #utts: 1 +id: (swc_eng_001999-swc_eng_001999) +Scores: (#C #S #D #I) 0 3 0 0 +REF: EIGHTEEN NINETY NINE +HYP: ATY NHADE NOIN +Eval: S S S + +Speaker sentences 824: swc_eng_002000 #utts: 1 +id: (swc_eng_002000-swc_eng_002000) +Scores: (#C #S #D #I) 5 9 1 1 +REF: WAS SHOWN by ladner that if ** P ≠ N P THEN THERE exist PROBLEMS IN +HYP: AS SHONE by ladner that if PE S NOT ACULTO AENP THAN THER exist ******** PROVBLUMS +Eval: S S I S S S S S S D S + +Speaker sentences 825: swc_eng_002001 #utts: 1 +id: (swc_eng_002001-swc_eng_002001) +Scores: (#C #S #D #I) 1 2 1 0 +REF: THE compact DISC GREW +HYP: HE compact **** DISO +Eval: S D S + +Speaker sentences 826: swc_eng_002002 #utts: 1 +id: (swc_eng_002002-swc_eng_002002) +Scores: (#C #S #D #I) 0 3 0 0 +REF: GREY GOO SCENARIO +HYP: DGREY GOSIN ERIAL +Eval: S S S + +Speaker sentences 827: swc_eng_002003 #utts: 1 +id: (swc_eng_002003-swc_eng_002003) +Scores: (#C #S #D #I) 2 2 0 0 +REF: was RENDERED as AJEDREZ +HYP: was WENDERED as IAGES +Eval: S S + +Speaker sentences 828: swc_eng_002004 #utts: 1 +id: (swc_eng_002004-swc_eng_002004) +Scores: (#C #S #D #I) 1 4 0 1 +REF: *** SAH OR to ANOTHER CAUSE +HYP: SAY ACH ORE to NOTHE CUS +Eval: I S S S S + +Speaker sentences 829: swc_eng_002005 #utts: 1 +id: (swc_eng_002005-swc_eng_002005) +Scores: (#C #S #D #I) 1 2 0 0 +REF: CONSTITUENCY of FAVERSHAM +HYP: ONTITUNCEY of FAVESHOM +Eval: S S + +Speaker sentences 830: voxforge_eng_000874 #utts: 1 +id: (voxforge_eng_000874-voxforge_eng_000874) +Scores: (#C #S #D #I) 4 5 0 0 +REF: the FOURTH and FIFTH days PASSED WITHOUT any DEVELOPMENTS +HYP: the FORTH and FITH days PASE WIHOUT any DEVELLAMENCE +Eval: S S S S S + +Speaker sentences 831: voxforge_eng_000875 #utts: 1 +id: (voxforge_eng_000875-voxforge_eng_000875) +Scores: (#C #S #D #I) 3 1 0 0 +REF: they KNOW the report +HYP: they NOW the report +Eval: S + +Speaker sentences 832: voxforge_eng_000876 #utts: 1 +id: (voxforge_eng_000876-voxforge_eng_000876) +Scores: (#C #S #D #I) 5 3 0 0 +REF: such things had OCCURRED BEFORE he told PHILIP +HYP: such things had ACURD BEFOR he told FILAP +Eval: S S S + +Speaker sentences 833: voxforge_eng_000877 #utts: 1 +id: (voxforge_eng_000877-voxforge_eng_000877) +Scores: (#C #S #D #I) 4 5 0 0 +REF: they only had a LITTLE THIRTY THOUSAND DOLLAR FIRE +HYP: they only had a LITLE TERDY THOUSEND DOLER FIER +Eval: S S S S S + +Speaker sentences 834: voxforge_eng_000878 #utts: 1 +id: (voxforge_eng_000878-voxforge_eng_000878) +Scores: (#C #S #D #I) 5 2 0 0 +REF: i am GOING to get it OUT +HYP: i am GOWING to get it OWD +Eval: S S + +Speaker sentences 835: voxforge_eng_000879 #utts: 1 +id: (voxforge_eng_000879-voxforge_eng_000879) +Scores: (#C #S #D #I) 5 3 0 1 +REF: *** OUTWARDLY he maintained a CALM and smiling ASPECT +HYP: HOU DUDLY he maintained a COARME and smiling ASSPECT +Eval: I S S S + +Speaker sentences 836: voxforge_eng_000880 #utts: 1 +id: (voxforge_eng_000880-voxforge_eng_000880) +Scores: (#C #S #D #I) 3 4 0 0 +REF: JOAN LOOKED TRIUMPHANTLY at sheldon who BOWED +HYP: JON LOKE TRIUMPFENTLY at sheldon who BOWD +Eval: S S S S + +Speaker sentences 837: voxforge_eng_000883 #utts: 1 +id: (voxforge_eng_000883-voxforge_eng_000883) +Scores: (#C #S #D #I) 1 4 0 2 +REF: *** COME ON DEL mar * CHALLENGED +HYP: OME ONEN T DILE mar T TALENCST +Eval: I S S S I S + +Speaker sentences 838: voxforge_eng_000884 #utts: 1 +id: (voxforge_eng_000884-voxforge_eng_000884) +Scores: (#C #S #D #I) 10 2 0 1 +REF: * it was beating and WAITING in the AMBUSH of those black pits +HYP: E it was beating and WATING in the AMBOSH of those black pits +Eval: I S S + +Speaker sentences 839: voxforge_eng_000885 #utts: 1 +id: (voxforge_eng_000885-voxforge_eng_000885) +Scores: (#C #S #D #I) 5 3 1 0 +REF: LET THEM go out and EAT WITH my boys +HYP: IT THE go out and *** EWIG my boys +Eval: S S D S + +Speaker sentences 840: voxforge_eng_000886 #utts: 1 +id: (voxforge_eng_000886-voxforge_eng_000886) +Scores: (#C #S #D #I) 4 6 1 2 +REF: HE WENT down in ***** ***** MIDSTREAM SEARCHING the SHADOWS of BOTH SHORES +HYP: SHE WINED down in WINES DREME M SERCHING the SHADOS of **** BULSHORS +Eval: S S I I S S S D S + +Speaker sentences 841: voxforge_eng_000887 #utts: 1 +id: (voxforge_eng_000887-voxforge_eng_000887) +Scores: (#C #S #D #I) 2 10 0 0 +REF: i JUST DO APPRECIATE IT WITHOUT BEING ABLE TO EXPRESS my FEELINGS +HYP: i JUS TO APRECSHATE WITH OUT BE AE O EXPRESE my FELINGS +Eval: S S S S S S S S S S + +Speaker sentences 842: voxforge_eng_000888 #utts: 1 +id: (voxforge_eng_000888-voxforge_eng_000888) +Scores: (#C #S #D #I) 5 3 0 0 +REF: she DOESNT KNOW what he is TALKING about +HYP: she DOSNT NOW what he is TAKING about +Eval: S S S + +Speaker sentences 843: voxforge_eng_000889 #utts: 1 +id: (voxforge_eng_000889-voxforge_eng_000889) +Scores: (#C #S #D #I) 3 3 0 0 +REF: your fathers FIFTH COMMAND he NODDED +HYP: your fathers FIFT COMAND he NOTED +Eval: S S S + +Speaker sentences 844: voxforge_eng_000890 #utts: 1 +id: (voxforge_eng_000890-voxforge_eng_000890) +Scores: (#C #S #D #I) 2 4 0 0 +REF: DONT YOU SEE i HATE you +HYP: DON OU SE i HAE you +Eval: S S S S + +Speaker sentences 845: voxforge_eng_000891 #utts: 1 +id: (voxforge_eng_000891-voxforge_eng_000891) +Scores: (#C #S #D #I) 7 7 1 0 +REF: A LITTLE WARM but not at ALL ASTONISHED EATING MELONS and throwing THE rind about +HYP: * ALITLE WARME but not at AL STONISHED ETING MELENS and throwing HE rind about +Eval: D S S S S S S S + +Speaker sentences 846: voxforge_eng_000892 #utts: 1 +id: (voxforge_eng_000892-voxforge_eng_000892) +Scores: (#C #S #D #I) 4 1 0 0 +REF: this is a great PARTY +HYP: this is a great PARDY +Eval: S + +Speaker sentences 847: voxforge_eng_000893 #utts: 1 +id: (voxforge_eng_000893-voxforge_eng_000893) +Scores: (#C #S #D #I) 2 3 0 1 +REF: the boy *** GREW AND PROSPERED +HYP: the boy GRO IN PROSPERCET HOME +Eval: I S S S + +Speaker sentences 848: voxforge_eng_000894 #utts: 1 +id: (voxforge_eng_000894-voxforge_eng_000894) +Scores: (#C #S #D #I) 12 5 0 2 +REF: *** UNLESS such LETTERS be patent that they may be READ to them and **** WITHALL SEALED or testified +HYP: AND LES such LETERS be patent that they may be RED to them and WITH AL SEAL or testified +Eval: I S S S I S S + +Speaker sentences 849: voxforge_eng_000895 #utts: 1 +id: (voxforge_eng_000895-voxforge_eng_000895) +Scores: (#C #S #D #I) 4 7 0 0 +REF: HOW COULD a WOMAN DARE to VENTURE WHERE so many EXPLORERS +HYP: HO COUD a WOMN DEAR to VENTUER WE so many EXPORS +Eval: S S S S S S S + +Speaker sentences 850: voxforge_eng_000896 #utts: 1 +id: (voxforge_eng_000896-voxforge_eng_000896) +Scores: (#C #S #D #I) 2 3 0 0 +REF: he READ his FRAGMENTS ALOUD +HYP: he READS his FRAGMENTE ALOWD +Eval: S S S + +Speaker sentences 851: voxforge_eng_000897 #utts: 1 +id: (voxforge_eng_000897-voxforge_eng_000897) +Scores: (#C #S #D #I) 7 1 0 0 +REF: but how ARE you going to do it +HYP: but how AR you going to do it +Eval: S + +Speaker sentences 852: voxforge_eng_000898 #utts: 1 +id: (voxforge_eng_000898-voxforge_eng_000898) +Scores: (#C #S #D #I) 5 3 1 1 +REF: how DO YOU WANT to get * AWAY with this +HYP: how ** DOYOU WAN to get A WAY with this +Eval: D S S I S + +Speaker sentences 853: voxforge_eng_000899 #utts: 1 +id: (voxforge_eng_000899-voxforge_eng_000899) +Scores: (#C #S #D #I) 3 2 0 1 +REF: WILL we ever *** FORGET it +HYP: WIL we ever FOR GET it +Eval: S I S + +Speaker sentences 854: voxforge_eng_000900 #utts: 1 +id: (voxforge_eng_000900-voxforge_eng_000900) +Scores: (#C #S #D #I) 3 7 1 0 +REF: FROM my EARLIEST RECOLLECTION my SLEEP WAS A PERIOD of TERROR +HYP: FOR my ERLYIS RECALECTION my ***** SLE WS PERIAT of THER +Eval: S S S D S S S S + +Speaker sentences 855: voxforge_eng_000901 #utts: 1 +id: (voxforge_eng_000901-voxforge_eng_000901) +Scores: (#C #S #D #I) 0 6 0 5 +REF: *** * ** ******* **** WHY DOGGONE YOU ALL SHAKE AGAIN +HYP: TIN M IS MOSIEAR WHIY DOF ON YOUO WALL SHAK GEAM +Eval: I I I I I S S S S S S + +Speaker sentences 856: voxforge_eng_000902 #utts: 1 +id: (voxforge_eng_000902-voxforge_eng_000902) +Scores: (#C #S #D #I) 1 3 1 1 +REF: IT IS the ******* NEAREST REFUGE +HYP: ** EDEVTH the NEADIST REAFUGYEY I +Eval: D S I S S + +Speaker sentences 857: voxforge_eng_000903 #utts: 1 +id: (voxforge_eng_000903-voxforge_eng_000903) +Scores: (#C #S #D #I) 6 3 0 0 +REF: his SLIM hands GRIPPED the EDGES of the table +HYP: his SLIME hands CREPE the ADGES of the table +Eval: S S S + +Speaker sentences 858: voxforge_eng_000904 #utts: 1 +id: (voxforge_eng_000904-voxforge_eng_000904) +Scores: (#C #S #D #I) 0 5 0 1 +REF: **** WHITE LEGHORNS SAID MRS MORTIMER +HYP: WHID LAK HORN SID MS MORTOMER +Eval: I S S S S S + +Speaker sentences 859: voxforge_eng_000905 #utts: 1 +id: (voxforge_eng_000905-voxforge_eng_000905) +Scores: (#C #S #D #I) 1 8 3 0 +REF: IT TOOK HIM HALF AN HOUR TO REACH THE EDGE OF it +HYP: ** **** *** ITOK HM HAFA OUTO RCH HE ED O it +Eval: D D D S S S S S S S S + +Speaker sentences 860: voxforge_eng_000906 #utts: 1 +id: (voxforge_eng_000906-voxforge_eng_000906) +Scores: (#C #S #D #I) 4 4 1 0 +REF: martha WHERE do WE stand ON the CONTRACTUAL ISSUES +HYP: martha WHER do ** stand O the ONTRACTUL ISOUS +Eval: S D S S S + +Speaker sentences 861: voxforge_eng_000907 #utts: 1 +id: (voxforge_eng_000907-voxforge_eng_000907) +Scores: (#C #S #D #I) 8 2 0 0 +REF: as to be UNDISTINGUISHABLE from the vast WHITE plains around +HYP: as to be UNDISTINGUIHABLE from the vast WHIHE plains around +Eval: S S + +Speaker sentences 862: voxforge_eng_000908 #utts: 1 +id: (voxforge_eng_000908-voxforge_eng_000908) +Scores: (#C #S #D #I) 4 4 0 0 +REF: he would destroy ALL things THAT ARE FIXED +HYP: he would destroy AL things THA ER FICXST +Eval: S S S S + +Speaker sentences 863: voxforge_eng_000909 #utts: 1 +id: (voxforge_eng_000909-voxforge_eng_000909) +Scores: (#C #S #D #I) 3 5 2 0 +REF: the RUSSIAN MUSIC PLAYER the COUNT was HER OBEDIENT SLAVE +HYP: the RUSION USIC PLAR the CONT was *** ******** HROABEDINSLAVE +Eval: S S S S D D S + +Speaker sentences 864: voxforge_eng_000910 #utts: 1 +id: (voxforge_eng_000910-voxforge_eng_000910) +Scores: (#C #S #D #I) 6 3 0 1 +REF: to his SURPRISE her ANSWER was flat and ** UNCOMPROMISING +HYP: to his SPRIE her ANTE was flat and UN COMPROMIZSING +Eval: S S I S + +Speaker sentences 865: voxforge_eng_000911 #utts: 1 +id: (voxforge_eng_000911-voxforge_eng_000911) +Scores: (#C #S #D #I) 2 2 0 0 +REF: this SHOULD be INTERESTING +HYP: this SOULD be INTRESTING +Eval: S S + +Speaker sentences 866: voxforge_eng_000912 #utts: 1 +id: (voxforge_eng_000912-voxforge_eng_000912) +Scores: (#C #S #D #I) 7 1 0 1 +REF: i am * AFRAID i dont have much time +HYP: i am A FRAIDE i dont have much time +Eval: I S + +Speaker sentences 867: voxforge_eng_000913 #utts: 1 +id: (voxforge_eng_000913-voxforge_eng_000913) +Scores: (#C #S #D #I) 5 6 0 0 +REF: CHRISTMAS is an easy PROBLEM COMPARED WITH a POLYNESIAN giving FEAST +HYP: CRISMS is an easy PROBLOME COMPARD WIT a POLNASION giving FECST +Eval: S S S S S S + +Speaker sentences 868: voxforge_eng_000914 #utts: 1 +id: (voxforge_eng_000914-voxforge_eng_000914) +Scores: (#C #S #D #I) 1 6 0 0 +REF: the PLANTERS ARE ALREADY CONSIDERING THE MATTER +HYP: the PLNTERS AR ARDY CONSIDERIG TH MATER +Eval: S S S S S S + +Speaker sentences 869: voxforge_eng_000915 #utts: 1 +id: (voxforge_eng_000915-voxforge_eng_000915) +Scores: (#C #S #D #I) 2 3 0 0 +REF: JOAN cried with SHINING EYES +HYP: JON cried with SHING EIYES +Eval: S S S + +Speaker sentences 870: voxforge_eng_000916 #utts: 1 +id: (voxforge_eng_000916-voxforge_eng_000916) +Scores: (#C #S #D #I) 6 1 0 1 +REF: *** WHOEVER lived on the ranch did that +HYP: WHO EVER lived on the ranch did that +Eval: I S + +Speaker sentences 871: voxforge_eng_000917 #utts: 1 +id: (voxforge_eng_000917-voxforge_eng_000917) +Scores: (#C #S #D #I) 6 2 0 0 +REF: we leave the EVENTUALITY to time and LAW +HYP: we leave the EFVENTUALITY to time and LOR +Eval: S S + +Speaker sentences 872: voxforge_eng_000918 #utts: 1 +id: (voxforge_eng_000918-voxforge_eng_000918) +Scores: (#C #S #D #I) 6 7 0 0 +REF: at the same TIME SPEARS AND ARROWS began to FALL among THE INVADERS +HYP: at the same TINE SPIARS ND EROS began to FAL among H IMVATERS +Eval: S S S S S S S + +Speaker sentences 873: voxforge_eng_000920 #utts: 1 +id: (voxforge_eng_000920-voxforge_eng_000920) +Scores: (#C #S #D #I) 4 2 0 0 +REF: it is MERELY the simple SUPERLATIVE +HYP: it is MEARLY the simple SOUPELITIF +Eval: S S + +Speaker sentences 874: voxforge_eng_000921 #utts: 1 +id: (voxforge_eng_000921-voxforge_eng_000921) +Scores: (#C #S #D #I) 4 5 1 1 +REF: ** INSTEAD he ARRIVED on THE NIGHT of THE SECOND day +HYP: IN STEAID he ARIVED on *** THENIH of TE SECAN day +Eval: I S S D S S S + +Speaker sentences 875: voxforge_eng_000922 #utts: 1 +id: (voxforge_eng_000922-voxforge_eng_000922) +Scores: (#C #S #D #I) 9 2 0 0 +REF: in his ANXIETY and SOLICITUDE and love they did not count +HYP: in his ANGSITY and SULISITUOD and love they did not count +Eval: S S + +Speaker sentences 876: voxforge_eng_000923 #utts: 1 +id: (voxforge_eng_000923-voxforge_eng_000923) +Scores: (#C #S #D #I) 6 4 0 0 +REF: god BLESS i hope ILL GO on SEEING them forever +HYP: god BLESSHM i hope L O on SING them forever +Eval: S S S S + +Speaker sentences 877: voxforge_eng_000924 #utts: 1 +id: (voxforge_eng_000924-voxforge_eng_000924) +Scores: (#C #S #D #I) 1 2 0 0 +REF: YOU were ENGAGED +HYP: YO were INGAGED +Eval: S S + +Speaker sentences 878: voxforge_eng_000925 #utts: 1 +id: (voxforge_eng_000925-voxforge_eng_000925) +Scores: (#C #S #D #I) 4 7 1 0 +REF: THE LACE was of a DELICATE IVORY COLOR FAINTLY TINTED with YELLOW +HYP: THER LACES was of a ******** DELICKEIT IVERE CALLOR FRAINETOMPTINTIN with EALOL +Eval: S S D S S S S S + +Speaker sentences 879: voxforge_eng_000927 #utts: 1 +id: (voxforge_eng_000927-voxforge_eng_000927) +Scores: (#C #S #D #I) 8 2 0 0 +REF: it WAS the same way with our revolvers and RIFLES +HYP: it WA the same way with our revolvers and RIFALS +Eval: S S + +Speaker sentences 880: voxforge_eng_000928 #utts: 1 +id: (voxforge_eng_000928-voxforge_eng_000928) +Scores: (#C #S #D #I) 3 5 1 0 +REF: THE king had PROMISED TO ENQUIRE INTO the MATTER +HYP: HE king had ******** PROMISTO INCQUIRE ITO the MATER +Eval: S D S S S S + +Speaker sentences 881: voxforge_eng_000929 #utts: 1 +id: (voxforge_eng_000929-voxforge_eng_000929) +Scores: (#C #S #D #I) 0 4 0 0 +REF: DOES THAT LOOK GOOD +HYP: DOS THA LOK GOODT +Eval: S S S S + +Speaker sentences 882: voxforge_eng_000930 #utts: 1 +id: (voxforge_eng_000930-voxforge_eng_000930) +Scores: (#C #S #D #I) 11 1 1 0 +REF: for the first time in his life he was yearning for A SCRAP +HYP: for the first time in his life he was yearning for * ASCRAP +Eval: D S + +Speaker sentences 883: voxforge_eng_000931 #utts: 1 +id: (voxforge_eng_000931-voxforge_eng_000931) +Scores: (#C #S #D #I) 7 5 0 0 +REF: i DEFY any man to get a SOLOMON ISLAND SORE in CALIFORNIA +HYP: i DEFIE any man to get a SOLAMON ILENDES SOR in CALFORNTHEA +Eval: S S S S S + +Speaker sentences 884: voxforge_eng_000932 #utts: 1 +id: (voxforge_eng_000932-voxforge_eng_000932) +Scores: (#C #S #D #I) 7 5 0 0 +REF: her EYES SMILED TRUTH at him as he came UP the BANK +HYP: her IY SMOULTE STR at him as he came OF the BANGK +Eval: S S S S S + +Speaker sentences 885: voxforge_eng_000933 #utts: 1 +id: (voxforge_eng_000933-voxforge_eng_000933) +Scores: (#C #S #D #I) 0 5 2 0 +REF: ANYWAY NO ONE SAW HER LIKE THAT +HYP: ****** ** ETY WAVE NONS SAL LICGDTHA +Eval: D D S S S S S + +Speaker sentences 886: voxforge_eng_000934 #utts: 1 +id: (voxforge_eng_000934-voxforge_eng_000934) +Scores: (#C #S #D #I) 4 3 1 0 +REF: men who ENDURE IT CALL IT living death +HYP: men who ****** NDEURIT CAL AT living death +Eval: D S S S + +Speaker sentences 887: voxforge_eng_000935 #utts: 1 +id: (voxforge_eng_000935-voxforge_eng_000935) +Scores: (#C #S #D #I) 1 4 0 2 +REF: *** MATTHEWSON WHOS this *** BOOKKEEPER ROGERS +HYP: MTO SON WHOSED this BOK CEPER RODGERS +Eval: I S S I S S + +Speaker sentences 888: voxforge_eng_000938 #utts: 1 +id: (voxforge_eng_000938-voxforge_eng_000938) +Scores: (#C #S #D #I) 3 2 0 2 +REF: i only ****** * READ the QUOTATIONS +HYP: i only READTD D H the FOARTATIONS +Eval: I I S S + +Speaker sentences 889: voxforge_eng_000939 #utts: 1 +id: (voxforge_eng_000939-voxforge_eng_000939) +Scores: (#C #S #D #I) 3 9 0 1 +REF: THERE was ***** PROPER DIVISION of LABOR IN the WORK THEY INDIVIDUALLY PERFORMED +HYP: THE was POPER DE VISION of NAYBER I the WORE THE INDEVRIGULY POPOARMED +Eval: S I S S S S S S S S + +Speaker sentences 890: voxforge_eng_000940 #utts: 1 +id: (voxforge_eng_000940-voxforge_eng_000940) +Scores: (#C #S #D #I) 3 5 2 0 +REF: ILL TELL YOU the LIBRARIAN said WITH A BRIGHTENING face +HYP: *** IALPEL YO the LIBRARIN said **** WTHA RIHT face +Eval: D S S S D S S + +Speaker sentences 891: voxforge_eng_000942 #utts: 1 +id: (voxforge_eng_000942-voxforge_eng_000942) +Scores: (#C #S #D #I) 4 5 1 0 +REF: i SAW MR PIKE NOD his head grimly AND SARCASTICALLY +HYP: i *** SW MTR PUIAGNORD his head grimly IN SERCASTICLY +Eval: D S S S S S + +Speaker sentences 892: voxforge_eng_000943 #utts: 1 +id: (voxforge_eng_000943-voxforge_eng_000943) +Scores: (#C #S #D #I) 4 4 0 0 +REF: the RINGING of the big BELL AROUSED HIM +HYP: the RING of the big BILE AROUSD HIMN +Eval: S S S S + +Speaker sentences 893: voxforge_eng_000944 #utts: 1 +id: (voxforge_eng_000944-voxforge_eng_000944) +Scores: (#C #S #D #I) 11 6 2 1 +REF: ** the SCRATCH of a pin on a mans head vast REGIONS of the EARTHS SURFACE REMAIN GEOLOGICALLY UNKNOWN BUT +HYP: OR the SCRACH of a pin on a mans head vast REAGONS of the ****** ******* ERTSEIRFIS REMAINE JEALOUGICLY UNON +Eval: I S S D D S S S S + +Speaker sentences 894: voxforge_eng_000945 #utts: 1 +id: (voxforge_eng_000945-voxforge_eng_000945) +Scores: (#C #S #D #I) 6 7 0 0 +REF: he had BARELY ENTERED THIS when HE SAW the GLOW of a FIRE +HYP: he had BURDILY ENTERDAD DIS when HES SOUOWD the GLO of a FIR +Eval: S S S S S S S + +Speaker sentences 895: voxforge_eng_000946 #utts: 1 +id: (voxforge_eng_000946-voxforge_eng_000946) +Scores: (#C #S #D #I) 0 4 0 1 +REF: ****** CHANGE CHAIRS DAYLIGHT COMMANDED +HYP: THENES CHARS THE LIT COMAND +Eval: I S S S S + +Speaker sentences 896: voxforge_eng_000947 #utts: 1 +id: (voxforge_eng_000947-voxforge_eng_000947) +Scores: (#C #S #D #I) 3 6 0 0 +REF: it was JEANNE SINGING SOFTLY OVER BEYOND the ROCKS +HYP: it was JEAN SINING SOFELY OER BEYON the OACKS +Eval: S S S S S S + +Speaker sentences 897: voxforge_eng_000948 #utts: 1 +id: (voxforge_eng_000948-voxforge_eng_000948) +Scores: (#C #S #D #I) 1 4 1 0 +REF: A FLYING ARROW PASSED BETWEEN us +HYP: * OFLING AROW BUSTD BETWEN us +Eval: D S S S S + +Speaker sentences 898: voxforge_eng_000949 #utts: 1 +id: (voxforge_eng_000949-voxforge_eng_000949) +Scores: (#C #S #D #I) 6 5 0 1 +REF: HATRED and murder and LUST for REVENGE they POSSESSED to **** OVERFLOWING +HYP: HATREIT and murder and LOUST for REVENCH they POESESTD to OFER FLOWING +Eval: S S S S I S + +Speaker sentences 899: voxforge_eng_000950 #utts: 1 +id: (voxforge_eng_000950-voxforge_eng_000950) +Scores: (#C #S #D #I) 0 8 2 0 +REF: THAT YOU COULD HEAR ALL UP AND DOWN THE LIMPOPO +HYP: **** *** THT YOUCUD HERE AL UPAN DON TE IMPOPOE +Eval: D D S S S S S S S S + +Speaker sentences 900: voxforge_eng_000951 #utts: 1 +id: (voxforge_eng_000951-voxforge_eng_000951) +Scores: (#C #S #D #I) 4 2 1 1 +REF: it was my * IDEA to A TEE +HYP: it was my A DEA to * ATE +Eval: I S D S + +Speaker sentences 901: voxforge_eng_000952 #utts: 1 +id: (voxforge_eng_000952-voxforge_eng_000952) +Scores: (#C #S #D #I) 3 2 0 0 +REF: she DOESNT WANT to win +HYP: she DOSNT WON to win +Eval: S S + +Speaker sentences 902: voxforge_eng_000953 #utts: 1 +id: (voxforge_eng_000953-voxforge_eng_000953) +Scores: (#C #S #D #I) 4 5 0 0 +REF: she THINKS it is BECAUSE he WANTS SOMETHING ELSE +HYP: she HINKE it is BECAS he WONSE SOMTHING ELTE +Eval: S S S S S + +Speaker sentences 903: voxforge_eng_000954 #utts: 1 +id: (voxforge_eng_000954-voxforge_eng_000954) +Scores: (#C #S #D #I) 8 3 0 0 +REF: he pulled and the LOG CRASHED down to BREAK his back +HYP: he pulled and the LOK CRESET down to BRAKE his back +Eval: S S S + +Speaker sentences 904: voxforge_eng_000955 #utts: 1 +id: (voxforge_eng_000955-voxforge_eng_000955) +Scores: (#C #S #D #I) 7 5 2 0 +REF: that the SO CALLED FORCES at work in light HEAT ELECTRICITY and MAGNETISM IN +HYP: that the ** SOCALD FORSES at work in light HEE ALCTRISITY and ********* MAGNATISM +Eval: D S S S S D S + +Speaker sentences 905: voxforge_eng_000956 #utts: 1 +id: (voxforge_eng_000956-voxforge_eng_000956) +Scores: (#C #S #D #I) 3 6 0 0 +REF: he TURNED SHARPLY and FACED GREGSON ACROSS the TABLE +HYP: he TORND SHARPLYD and PICE GRAGSIN ACOST the PIVELER +Eval: S S S S S S + +Speaker sentences 906: voxforge_eng_000957 #utts: 1 +id: (voxforge_eng_000957-voxforge_eng_000957) +Scores: (#C #S #D #I) 2 2 0 1 +REF: ** ALSO i want INFORMATION +HYP: AL SO i want INFRMATION +Eval: I S S + +Speaker sentences 907: voxforge_eng_000958 #utts: 1 +id: (voxforge_eng_000958-voxforge_eng_000958) +Scores: (#C #S #D #I) 6 4 0 0 +REF: the SIXTH day he spent in the CABIN WITH GREGSON +HYP: the SIXT day he spent in the CAVEN WIH GREAGSON +Eval: S S S S + +Speaker sentences 908: voxforge_eng_000959 #utts: 1 +id: (voxforge_eng_000959-voxforge_eng_000959) +Scores: (#C #S #D #I) 12 9 0 0 +REF: on this HYPOTHESIS the HAMMERING of the ULTRA MUNDANE CORPUSCLES on the bob CONFERS its KINETIC ENERGY on the ONE hand +HYP: on this IPOTHICES the HAMERING of the LTR MUNDYING CORPUSLES on the bob CONFIRSE its CANATIK NEGY on the ON hand +Eval: S S S S S S S S S + +Speaker sentences 909: voxforge_eng_000960 #utts: 1 +id: (voxforge_eng_000960-voxforge_eng_000960) +Scores: (#C #S #D #I) 11 6 0 0 +REF: now a FERNY WILLOWY STREAM and ever AND anon you EMERGE from ALL the groves and flowers +HYP: now a FIRNY WILWE STREME and ever AN anon you AMURGE from AL the groves and flowers +Eval: S S S S S S + +Speaker sentences 910: voxforge_eng_000961 #utts: 1 +id: (voxforge_eng_000961-voxforge_eng_000961) +Scores: (#C #S #D #I) 7 5 0 1 +REF: **** WITHOUT it the MOST densely populated REGIONS of MODERN EUROPE and america +HYP: WITH OUT it the MOS densely populated RAGONS of MOTEN YURP and america +Eval: I S S S S S + +Speaker sentences 911: voxforge_eng_000962 #utts: 1 +id: (voxforge_eng_000962-voxforge_eng_000962) +Scores: (#C #S #D #I) 3 1 1 0 +REF: TOM SPINK has a harpoon +HYP: *** TOMESPINKE has a harpoon +Eval: D S + +Speaker sentences 912: voxforge_eng_000963 #utts: 1 +id: (voxforge_eng_000963-voxforge_eng_000963) +Scores: (#C #S #D #I) 3 6 4 0 +REF: he WANTED TO GIVE the FINISH TO this FOE ALREADY SO FAR GONE +HYP: he ****** WNTE GE the INISH T this *** ******* ** FOW AREYSOFAGON +Eval: D S S S S D D D S S + +Speaker sentences 913: voxforge_eng_000964 #utts: 1 +id: (voxforge_eng_000964-voxforge_eng_000964) +Scores: (#C #S #D #I) 4 8 1 0 +REF: LIKE a FLASH HE LAUNCHED HIMSELF INTO the FEATHERED MASS of the OWL +HYP: LKE a ***** FLASHE LONCED IMSELF INT the FETHED MAS of the HOWL +Eval: S D S S S S S S S + +Speaker sentences 914: voxforge_eng_000965 #utts: 1 +id: (voxforge_eng_000965-voxforge_eng_000965) +Scores: (#C #S #D #I) 3 4 0 1 +REF: it CONTAINS a TOTAL of * TWENTY ENTRIES +HYP: it CONTAINES a TOTLE of T WENTY ENTRES +Eval: S S I S S + +Speaker sentences 915: voxforge_eng_000966 #utts: 1 +id: (voxforge_eng_000966-voxforge_eng_000966) +Scores: (#C #S #D #I) 2 2 0 1 +REF: * IVE FELT more comfortable +HYP: I HAE HELT more comfortable +Eval: I S S + +Speaker sentences 916: voxforge_eng_000967 #utts: 1 +id: (voxforge_eng_000967-voxforge_eng_000967) +Scores: (#C #S #D #I) 0 6 0 0 +REF: DID I POSSESS TOO MUCH VITALITY +HYP: THA A POSSES TO MACH VATELITY +Eval: S S S S S S + +Speaker sentences 917: voxforge_eng_000968 #utts: 1 +id: (voxforge_eng_000968-voxforge_eng_000968) +Scores: (#C #S #D #I) 4 5 0 0 +REF: the WOLF DOG THRUST his GAUNT MUZZLE toward him +HYP: the WALF DOGE THRESD his GONT MUSALE toward him +Eval: S S S S S + +Speaker sentences 918: voxforge_eng_000971 #utts: 1 +id: (voxforge_eng_000971-voxforge_eng_000971) +Scores: (#C #S #D #I) 5 3 0 0 +REF: the GABRIEL voice of THE SAMURAI rang out +HYP: the GAVBIAL voice of HE SMRIY rang out +Eval: S S S + +Speaker sentences 919: voxforge_eng_000972 #utts: 1 +id: (voxforge_eng_000972-voxforge_eng_000972) +Scores: (#C #S #D #I) 5 6 0 1 +REF: it was OUR river ** EMERGING LIKE OURSELVES from the GREAT SWAMP +HYP: it was O river ND MARGING LIAKE ORSELES from the REAT SWOMP +Eval: S I S S S S S + +Speaker sentences 920: voxforge_eng_000973 #utts: 1 +id: (voxforge_eng_000973-voxforge_eng_000973) +Scores: (#C #S #D #I) 5 8 2 0 +REF: said the MOLE PULLING himself TOGETHER WITH AN EFFORT YOU MUST THINK me very RUDE +HYP: said the MAL PULING himself ******** **** TOGETHE IHA EFART YO MUSTHING me very ROD +Eval: S S D D S S S S S S + +Speaker sentences 921: voxforge_eng_000974 #utts: 1 +id: (voxforge_eng_000974-voxforge_eng_000974) +Scores: (#C #S #D #I) 7 6 0 1 +REF: in what BUCOLIC SCHOOL of fence he had BEEN TAUGHT was ** BEYOND IMAGINING +HYP: in what BEUCOLICK SCOUO of fence he had BEN TORT was BE OND IMAGENING +Eval: S S S S I S S + +Speaker sentences 922: voxforge_eng_000975 #utts: 1 +id: (voxforge_eng_000975-voxforge_eng_000975) +Scores: (#C #S #D #I) 3 7 0 2 +REF: had not ******* ENABLED INVESTIGATORS to ** OBTAIN AT COMPARATIVELY LITTLE COST +HYP: had not INABLED IN VESTIGATERS to OB TAINE A COMPRITIVELY LITL COSET +Eval: I S S I S S S S S + +Speaker sentences 923: voxforge_eng_000976 #utts: 1 +id: (voxforge_eng_000976-voxforge_eng_000976) +Scores: (#C #S #D #I) 7 2 0 0 +REF: a TRICKLE of fresh BLOOD ran over his face +HYP: a TRICL of fresh BLOUD ran over his face +Eval: S S + +Speaker sentences 924: voxforge_eng_000977 #utts: 1 +id: (voxforge_eng_000977-voxforge_eng_000977) +Scores: (#C #S #D #I) 3 2 0 0 +REF: it was a CURIOUS COINCIDENCE +HYP: it was a CURUS COINEITDANCE +Eval: S S + +Speaker sentences 925: voxforge_eng_000978 #utts: 1 +id: (voxforge_eng_000978-voxforge_eng_000978) +Scores: (#C #S #D #I) 6 1 0 0 +REF: it is the fire partly she SAID +HYP: it is the fire partly she SAIDN +Eval: S + +Speaker sentences 926: voxforge_eng_000979 #utts: 1 +id: (voxforge_eng_000979-voxforge_eng_000979) +Scores: (#C #S #D #I) 5 5 0 0 +REF: THEY just lay OFF in the BUSH and PLUGGED AWAY +HYP: THE just lay OF in the OSH and PLOUKED AWAYAN +Eval: S S S S S + +Speaker sentences 927: voxforge_eng_000980 #utts: 1 +id: (voxforge_eng_000980-voxforge_eng_000980) +Scores: (#C #S #D #I) 5 5 1 0 +REF: i KNOW that YOU ARE in CHARGE there and JEANNE KNOWS +HYP: i NO that *** OUWER in CHARDGE there and GEE NOSE +Eval: S D S S S S + +Speaker sentences 928: voxforge_eng_000981 #utts: 1 +id: (voxforge_eng_000981-voxforge_eng_000981) +Scores: (#C #S #D #I) 5 5 1 0 +REF: for A TIME the EXCITING THRILL of his ADVENTURE was GONE +HYP: for * TIE the EXSITING THRILE of his ADVENTUE was GON +Eval: D S S S S S + +Speaker sentences 929: voxforge_eng_000982 #utts: 1 +id: (voxforge_eng_000982-voxforge_eng_000982) +Scores: (#C #S #D #I) 3 5 0 0 +REF: SUDDENLY his fingers CLOSED TIGHTLY OVER the HANDKERCHIEF +HYP: FUDNLY his fingers CLOSE THIDLY OVE the HANGAOCHIF +Eval: S S S S S + +Speaker sentences 930: voxforge_eng_000983 #utts: 1 +id: (voxforge_eng_000983-voxforge_eng_000983) +Scores: (#C #S #D #I) 5 5 0 0 +REF: dear sir YOUR SECOND VICTIM has FALLEN on SCHEDULE time +HYP: dear sir YOR SECKANT VICTOM has FOLLON on SCEADGJUALE time +Eval: S S S S S + +Speaker sentences 931: voxforge_eng_000984 #utts: 1 +id: (voxforge_eng_000984-voxforge_eng_000984) +Scores: (#C #S #D #I) 1 4 0 0 +REF: he CAN CARE FOR HIMSELF +HYP: he CN CAE FR IMSELF +Eval: S S S S + +Speaker sentences 932: voxforge_eng_000985 #utts: 1 +id: (voxforge_eng_000985-voxforge_eng_000985) +Scores: (#C #S #D #I) 6 3 0 0 +REF: each insult ADDED to the VALUE of the CLAIM +HYP: each insult ADED to the VOLOU of the CLAIME +Eval: S S S + +Speaker sentences 933: voxforge_eng_000986 #utts: 1 +id: (voxforge_eng_000986-voxforge_eng_000986) +Scores: (#C #S #D #I) 11 5 0 0 +REF: THOUGH it may be transformed into any ONE of the forms of WHICH ENERGY is SUSCEPTIBLE +HYP: THOU it may be transformed into any N of the forms of WHCH ENRGY is SESEPTIBL +Eval: S S S S S + +Speaker sentences 934: voxforge_eng_000987 #utts: 1 +id: (voxforge_eng_000987-voxforge_eng_000987) +Scores: (#C #S #D #I) 3 8 0 2 +REF: MERCEDES screamed CRIED LAUGHED AND MANIFESTED the ********* ******* CHAOTIC ABANDONMENT of HYSTERIA +HYP: MESITDOES screamed GRIED LOAF I MANYFESTED the HIRADTICK ANDBOUN DHEN MENT of HISTADIAR +Eval: S S S S S I I S S S + +Speaker sentences 935: voxforge_eng_000988 #utts: 1 +id: (voxforge_eng_000988-voxforge_eng_000988) +Scores: (#C #S #D #I) 6 3 0 0 +REF: i WANT to KNOW how all this is POSSIBLE +HYP: i WAN to NO how all this is POSEIVBLE +Eval: S S S + +Speaker sentences 936: voxforge_eng_000989 #utts: 1 +id: (voxforge_eng_000989-voxforge_eng_000989) +Scores: (#C #S #D #I) 7 8 0 0 +REF: PRESENTING a SIMPLE and INSTRUCTIVE ILLUSTRATION of the STRUGGLE for life AMONG the RIVAL SPECIES +HYP: PRENTING a SIMPL and INSTRUCTIV ILUSTRATION of the STRGL for life AMNG the RIVELE SPEACES +Eval: S S S S S S S S + +Speaker sentences 937: voxforge_eng_000990 #utts: 1 +id: (voxforge_eng_000990-voxforge_eng_000990) +Scores: (#C #S #D #I) 7 3 0 0 +REF: HELL never do a tap of work the WHOLE VOYAGE +HYP: HILL never do a tap of work the HOL VOYAGEH +Eval: S S S + +Speaker sentences 938: voxforge_eng_000991 #utts: 1 +id: (voxforge_eng_000991-voxforge_eng_000991) +Scores: (#C #S #D #I) 5 3 0 0 +REF: i HAVE hunted along this RIDGE replied PHILIP +HYP: i HAE hunted along this RICE replied FILIP +Eval: S S S + +Speaker sentences 939: voxforge_eng_000992 #utts: 1 +id: (voxforge_eng_000992-voxforge_eng_000992) +Scores: (#C #S #D #I) 4 5 0 0 +REF: lord but im GLAD to SEE YOU AGAIN PHIL +HYP: lord but im GED to SE YO AGIN FIL +Eval: S S S S S + +Speaker sentences 940: voxforge_eng_000993 #utts: 1 +id: (voxforge_eng_000993-voxforge_eng_000993) +Scores: (#C #S #D #I) 1 6 2 0 +REF: HOW VALIANTLY i WENT AT IT THAT FIRST DAY +HYP: *** HOVELINLY i **** WEN DADED THAF RS TA +Eval: D S D S S S S S + +Speaker sentences 941: voxforge_eng_000994 #utts: 1 +id: (voxforge_eng_000994-voxforge_eng_000994) +Scores: (#C #S #D #I) 0 8 0 0 +REF: THEY ARE NOT REGULAR OYSTER PIRATES NICHOLAS CONTINUED +HYP: THE AR OT REGULE OSTER PIRETS NICLES CONTNED +Eval: S S S S S S S S + +Speaker sentences 942: voxforge_eng_000995 #utts: 1 +id: (voxforge_eng_000995-voxforge_eng_000995) +Scores: (#C #S #D #I) 6 12 1 0 +REF: THEY MUST be HURTING for BUSINESS but i THOUGHT you MIGHT WANT TO take A LOOK AT THEIR SITE +HYP: THE MST be HRDING for BUSNES but i THUG you MIGT WAT T take * LOK T THER SIGHT +Eval: S S S S S S S S D S S S S + +Speaker sentences 943: voxforge_eng_000996 #utts: 1 +id: (voxforge_eng_000996-voxforge_eng_000996) +Scores: (#C #S #D #I) 6 3 0 0 +REF: THERE was no CHANCE to fire without HITTING him +HYP: THER was no CANCE to fire without HINING him +Eval: S S S + +Speaker sentences 944: voxforge_eng_000997 #utts: 1 +id: (voxforge_eng_000997-voxforge_eng_000997) +Scores: (#C #S #D #I) 4 6 0 1 +REF: as for himself WERENT the ****** STREET RAILWAY EARNINGS INCREASING STEADILY +HYP: as for himself WONT the STREAE RAL WAY ARNINGS INCREING SADLY +Eval: S I S S S S S + +Speaker sentences 945: voxforge_eng_000998 #utts: 1 +id: (voxforge_eng_000998-voxforge_eng_000998) +Scores: (#C #S #D #I) 4 4 0 1 +REF: *** DUNHAM can your boy go ALONG WITH JESSE +HYP: DON HIM can your boy go LONG WIT ESSY +Eval: I S S S S + +Speaker sentences 946: voxforge_eng_000999 #utts: 1 +id: (voxforge_eng_000999-voxforge_eng_000999) +Scores: (#C #S #D #I) 1 3 0 1 +REF: **** GOODBYE PIERRE he SHOUTED +HYP: GOLD BY PEAR he SHOWTED +Eval: I S S S + +Speaker sentences 947: voxforge_eng_001000 #utts: 1 +id: (voxforge_eng_001000-voxforge_eng_001000) +Scores: (#C #S #D #I) 6 5 0 1 +REF: but such * DIVERGENCE of OPINION would CONSTITUTE no MENACE to SOCIETY +HYP: but such A DEVERDGIENS of APINION would CONSTITUT no MENENCE to SOSCITY +Eval: I S S S S S + +Speaker sentences 948: voxforge_eng_001001 #utts: 1 +id: (voxforge_eng_001001-voxforge_eng_001001) +Scores: (#C #S #D #I) 7 3 0 1 +REF: * there was one CHANCE and only ONE of saving JEANNE +HYP: T there was one CHANCES and only ON of saving JONT +Eval: I S S S + +Speaker sentences 949: voxforge_eng_001002 #utts: 1 +id: (voxforge_eng_001002-voxforge_eng_001002) +Scores: (#C #S #D #I) 2 4 0 2 +REF: * i *** CANNOT FOLLOW YOU she SAID +HYP: I i CAN OT FOLOE YO she SAIND +Eval: I I S S S S + +Speaker sentences 950: voxforge_eng_001003 #utts: 1 +id: (voxforge_eng_001003-voxforge_eng_001003) +Scores: (#C #S #D #I) 8 3 0 0 +REF: on the far corner of the compound FENCE a HAWK BROODED +HYP: on the far corner of the compound FENTS a WHAOK BREADED +Eval: S S S + +Speaker sentences 951: voxforge_eng_001004 #utts: 1 +id: (voxforge_eng_001004-voxforge_eng_001004) +Scores: (#C #S #D #I) 5 5 0 0 +REF: then AGAIN TUDOR had SUCH AN IRRITATING way about him +HYP: then AGIN TOTER had SUC A IRITATING way about him +Eval: S S S S S + +Speaker sentences 952: voxpopuli_eng_000494 #utts: 1 +id: (voxpopuli_eng_000494-voxpopuli_eng_000494) +Scores: (#C #S #D #I) 7 9 0 1 +REF: we all KNOW oman as a ******** SUCCESSFUL STABLE COUNTRY A ROLE MODEL for the WHOLE REGION +HYP: we all NOW oman as a SUCESFLE STABL CONTRY AROL MOR THERFOR THAT for the HOL REAGON +Eval: S I S S S S S S S S + +Speaker sentences 953: voxpopuli_eng_000495 #utts: 1 +id: (voxpopuli_eng_000495-voxpopuli_eng_000495) +Scores: (#C #S #D #I) 12 14 3 0 +REF: THEREFORE its high time THAT YOU come FORWARD WITH A proposal for REVIEW WITH AN OPERATIONAL SEPARATION of the AUDIT and non AUDIT SERVICES under a DIRECT EU SUPERVISION +HYP: THEREFOR its high time **** OU come FORBOD E THE proposal for ****** REVEU BE DANOPRAIONAL SUPERACION of the OARDIT and non ***** ADITSERVISIES under a DIECT EAUS OBEITISON +Eval: S D S S S S D S S S S S D S S S S + +Speaker sentences 954: voxpopuli_eng_000496 #utts: 1 +id: (voxpopuli_eng_000496-voxpopuli_eng_000496) +Scores: (#C #S #D #I) 12 14 2 1 +REF: it IS CLEAR that we have no time to WASTE the NEW RESULTS of **** THE IPCC REGARDING THE SCIENTIFIC BASIS of CLIMATE CHANGE LEAVE no ROOM for HESITATION +HYP: it ** ISCKEARE that we have no time to WAST the *** NUERESOLTS of THEE I PEESHE RECARD N SIENTIFIC BACSES of GLIMIT JAINSE LEVE no ROUOME for HESITDASON +Eval: D S S D S I S S S S S S S S S S S + +Speaker sentences 955: voxpopuli_eng_000497 #utts: 1 +id: (voxpopuli_eng_000497-voxpopuli_eng_000497) +Scores: (#C #S #D #I) 6 8 2 1 +REF: 5 so in the CONTAINERS WHICH ARE NEVER EVEN TOUCHED come slaves COUNTERFEIT GOODS drugs ** ETC +HYP: SENT so in the ********** ***** CONTAINER WHIHAEVER AEN TUCHED come slaves COUNTEOFET GODS drugs IT SETR +Eval: S D D S S S S S S I S + +Speaker sentences 956: voxpopuli_eng_000498 #utts: 1 +id: (voxpopuli_eng_000498-voxpopuli_eng_000498) +Scores: (#C #S #D #I) 11 15 0 0 +REF: i hope that THE COMMISSIONS MOBILITY INITIATIVES WILL NOT CREATE the next PROBLEM but will be AN ANSWER for existing CHALLENGES of THE ROAD TRANSPORT SECTOR +HYP: i hope that COMIONS MOBIT INESHES INISIFIVES HO ONT CRAT the next PROBLOM but will be A ANSER for existing CHALINGES of THER OUT TANSPORED SECTO +Eval: S S S S S S S S S S S S S S S + +Speaker sentences 957: voxpopuli_eng_000499 #utts: 1 +id: (voxpopuli_eng_000499-voxpopuli_eng_000499) +Scores: (#C #S #D #I) 19 40 9 1 +REF: in THE US it WAS A DECISION TAKEN ONLY by one PERSON the FORMER president OF THE UNITED states AGAINST the ********** ARTICULATED DEMOCRATIC MAJORITY OF THE US CONGRESS by all of its REPUBLICAN AND SOME OF ITS DEMOCRAT MEMBERS IT WAS AN AGREEMENT without any BINDING obligations AS THE LEADERS of IRAN very OPENLY AND PRECISELY MADE CLEAR ON THE VERY day THIS SO CALLED DEAL was PUBLISHED +HYP: in *** THEWUE it *** * WASA DICION TAGNAULY by one PRSON the ORMER president ** O THENIGDED states AGANCE the ATICULATED MCRATIC DUMAJURITY O TH EU ES CONGRES by all of its ********** REPUBLICKEN ND SOM FITS DEMECRATIC T DEMACRAT MEMBERSIT WASAN AGREMENT without any BINDNG obligations AT HE LEDES of ERUN very ****** *** ********* UPANLY ANPRESIDH MAPTLY NTHE ERY day **** THE SOCALD DEL was POULISHE +Eval: D S D D S S S S S D S S S I S S S S S S S D S S S S S S S S S S S S S S S D D D S S S S S D S S S S + +Speaker sentences 958: voxpopuli_eng_000500 #utts: 1 +id: (voxpopuli_eng_000500-voxpopuli_eng_000500) +Scores: (#C #S #D #I) 15 9 0 1 +REF: FREE SPEECH is ESSENTIALLY ACCEPTING THAT PEOPLE are free to say things we do *** LIKE not MERELY free to say things we do LIKE +HYP: FRE SPEACH is ASENIUALY AEXETIG THT PEOPL are free to say things we do NOT LIK not MELY free to say things we do LIK +Eval: S S S S S S I S S S + +Speaker sentences 959: voxpopuli_eng_000501 #utts: 1 +id: (voxpopuli_eng_000501-voxpopuli_eng_000501) +Scores: (#C #S #D #I) 0 5 0 0 +REF: LET US LEARN FROM THIS +HYP: HAT IS LURNE FOM THIE +Eval: S S S S S + +Speaker sentences 960: voxpopuli_eng_000502 #utts: 1 +id: (voxpopuli_eng_000502-voxpopuli_eng_000502) +Scores: (#C #S #D #I) 18 29 5 2 +REF: WE THINK that the ENVIRONMENTAL effect of PRODUCTS must be A VERY IMPORTANT ISSUE IN THE EU and the *** WHOLE IDEA OF AN ECOLABEL GIVES a VERY USEFUL ORIENTATION for *** CONSUMERS of COURSE THE ECOLABEL SHOULD BE given to the most ENVIRONMENTALLY FRIENDLY PRODUCTS AND the information SHOULD BE CLEAR and CORRECT +HYP: BE SIN that the NVIMENTAL effect of PRODUCS must be * **** AVRY INMPORTANT ISUEIN HER EEWU and the WOL I DEAE O THE ECULABER GIVS a **** VER YUSOULORIANTATION for THE OUSUMERS of ****** COUS HE ECULABER HOULD given to the most ANDVIRMENT AF FANDY PODUCT the information ****** SOULD BECLEARE and CUE +Eval: S S S S D D S S S S S I S S S S S S D S S I S D S S S S S S S S D S S S + +Speaker sentences 961: voxpopuli_eng_000503 #utts: 1 +id: (voxpopuli_eng_000503-voxpopuli_eng_000503) +Scores: (#C #S #D #I) 10 14 3 0 +REF: however the CURRENT REGIME NEEDS to be BETTER TAILORED to THE DIGITAL ENVIRONMENT IN ORDER to ENSURE FAIR REMUNERATION to CREATORS AND to CONFORM to CONSUMER expectations +HYP: however the ******* CARENDRYGEM NEDES to be BETERD ALORDT to *** ******* TH IGIDAL INVIRNMENT to ISHURE FAR MINERATION to GREATERS AEN to ONFOME to ONSUMER expectations +Eval: D S S S S D D S S S S S S S S S S + +Speaker sentences 962: voxpopuli_eng_000504 #utts: 1 +id: (voxpopuli_eng_000504-voxpopuli_eng_000504) +Scores: (#C #S #D #I) 11 19 2 0 +REF: IT CALLS UPON the COMMISSION and member STATES to ENHANCE THEIR SUPPORT FOR reconciliation to SECURE PEACE and STABILITY and IRELAND I WOULD therefore URGE YOU COLLEAGUES to please SUPPORT THIS AMENDMENT +HYP: AT CASE BY the CMION and member STAT to ******* NHANE THERSUPORT TO reconciliation to SECUR PESE and TIBILITY and ******* ARLAND IWOL therefore ARD YU CALIES to please SUPORT IS AMENMEN +Eval: S S S S S D S S S S S S D S S S S S S S S + +Speaker sentences 963: voxpopuli_eng_000505 #utts: 1 +id: (voxpopuli_eng_000505-voxpopuli_eng_000505) +Scores: (#C #S #D #I) 18 31 3 3 +REF: STRATEGIC choices about WHERE to * INVEST MUST be made now ***** TAKING INTO ACCOUNT THE NEED to PHASE out FOSSIL FUEL SUBSIDIES but *** TAKE gas as A FOSSIL FUEL it can be a HELPFUL BRIDGING TRANSITIONARY MEDIUM to be USED in MANY MEMBER STATES IF WE WANT TO ACHIEVE OUR AMBITIOUS CLIMATE TARGETS +HYP: TRATAGICK choices about WHE to E WEST MUT be made now TAKEN IN E COUN A NE to FAS out FOR SILFUL SUPSITES but TAK THE gas as * I ORSOFYU it can be a HELTFULE BRIGING TRUNSISHONARY MEDIOM to be USE in **** ****** MEMIN MENY MBERSTAT I BE ONTO EACHIVE OVER AMBISHIOS CLIMITARGITS +Eval: S S I S S I S S S S S S S S S I S D S S S S S S S D D S S S S S S S S S S + +Speaker sentences 964: voxpopuli_eng_000506 #utts: 1 +id: (voxpopuli_eng_000506-voxpopuli_eng_000506) +Scores: (#C #S #D #I) 11 17 4 1 +REF: MIDDLE EAST we ARE POSSIBLY AT a THRESHOLD we can CHOOSE to PURSUE the SAME POLICIES in THE same MANNER KNOWING that THEY WILL LEAD to *********** the SAME RESULTS THE RESULTS THAT +HYP: ****** **** we AE POSEILY FR a OLE we can CUTH to PRASCUE the SAMEM POLICES in TH same MANER NOWING that **** WE LEDE to THISAMPRSOS the **** RISAULS THA WENO DEDEA +Eval: D D S S S S S S S S S S S D S S I D S S S S + +Speaker sentences 965: voxpopuli_eng_000507 #utts: 1 +id: (voxpopuli_eng_000507-voxpopuli_eng_000507) +Scores: (#C #S #D #I) 0 4 1 0 +REF: BUT THERE IS AN OPTION +HYP: *** UT HER SANOPTION B +Eval: D S S S S + +Speaker sentences 966: voxpopuli_eng_000508 #utts: 1 +id: (voxpopuli_eng_000508-voxpopuli_eng_000508) +Scores: (#C #S #D #I) 2 7 0 0 +REF: THIS WE ALSO NEED a CHANGE in OUR IDEOLOGY +HYP: WRE ALL SO NED a CHAINGE in OR IDOLITIE +Eval: S S S S S S S + +Speaker sentences 967: voxpopuli_eng_000509 #utts: 1 +id: (voxpopuli_eng_000509-voxpopuli_eng_000509) +Scores: (#C #S #D #I) 18 27 2 4 +REF: a LARGE PART of the reason IS of course ** ************** ILLEGAL FISHING MORE OFTEN THAN NOT by **** VESSELS which are REGISTERED to COUNTRIES which LACK the WILL OR the RESOURCES to ****** ENFORCE INTERNATIONAL AGREEMENTS no AMOUNT of TRACEABILITY MEASURES OR EXTRA PAPERWORK WILL ADDRESS the PROBLEM of REDUCING +HYP: a LADEH BAT of the reason ** of course IS ILIGALFISCINGK AND THERE OFOM P TDON OFEN by YARR VESES which are REAGISTERD to COUNTRES which LUCKE the WIL OF the RESURCES to NFORST INTHE NESINAL AGREMENS no MOUNT of ************ TRESABIITY MESERS ORE EXTRPAPREWARE WIL ADESE the PROBLOUME of REDUSING +Eval: S S D I I S S S S S S I S S S S S S S I S S S S D S S S S S S S S + +Speaker sentences 968: voxpopuli_eng_000510 #utts: 1 +id: (voxpopuli_eng_000510-voxpopuli_eng_000510) +Scores: (#C #S #D #I) 10 27 6 3 +REF: the COMPROMISE also INCLUDES CLEAR RULES to *** DEFINE which MEMBER STATE HAS JURISDICTION and the COOPERATION BETWEEN MEMBER STATES CONCERNED IN CROSS BORDER CASES AS WELL AS the NEED to INVOLVE EUROJUST THANK YOU FOR YOUR work and **** ** PLEASE DO SUPPORT THIS DIRECTIVE +HYP: the COMPRMISE also ******** INCLDED KLARERUDS to THE FINE which ****** MBERSTATE AS HERSTICTION and the *********** ******* ****** ****** OPRATION ITHIMBERSTATS CONERD FOR CRUSBR THE CACES ASILA the NED to EINVLLF YOUR JUST THAN YOF OR work and PLAE OU SEUPORT TO MO HIS ERECTIV +Eval: S D S S I S D S S S D D D D S S S S S S S S S S S S S S S I I S S S S S + +Speaker sentences 969: voxpopuli_eng_000511 #utts: 1 +id: (voxpopuli_eng_000511-voxpopuli_eng_000511) +Scores: (#C #S #D #I) 9 32 4 1 +REF: ** the GREENS would HAVE US BELIEVE THAT THESE ARE bad BEES criminal BEES DELIBERATELY contaminating HONEY WITH A DANGEROUS INGREDIENT but IN fact THEY ARE DOING WHAT HONEY BEES HAVE ALWAYS DONE WHICH IS to CARRY POLLEN BACK TO THEIR HIVES to FEED THEIR YOUNG +HYP: NO the RENS would **** ** HAV AS BELETHATHE AR bad BES criminal BES DELIBEATLY contaminating ***** HUDY WITHA DANEUS NGREDIENT but IT fact INFAC HE DINGWHT HUNY BES AR AL HVE ALWAS DON WIH to ***** CARY POLON BAC TOTHER HIVESTOD to FED THER OUN +Eval: I S D D S S S S S S S D S S S S S S S S S S S S S S S S D S S S S S S S S + +Speaker sentences 970: voxpopuli_eng_000512 #utts: 1 +id: (voxpopuli_eng_000512-voxpopuli_eng_000512) +Scores: (#C #S #D #I) 4 5 0 0 +REF: BUT it was the COUNTRY itself BEING MORE CAPABLE +HYP: UT it was the CONTRY itself BENG MOR CAPABL +Eval: S S S S S + +Speaker sentences 971: voxpopuli_eng_000513 #utts: 1 +id: (voxpopuli_eng_000513-voxpopuli_eng_000513) +Scores: (#C #S #D #I) 5 6 0 2 +REF: * into the *** PORTFOLIO of the NEW COMMISSIONER DEALING with FUNDAMENTAL RIGHTS +HYP: R into the PRT FOLIO of the NEUW COMIONAR DELING with FUNDEMENTER RITES +Eval: I I S S S S S S + +Speaker sentences 972: voxpopuli_eng_000514 #utts: 1 +id: (voxpopuli_eng_000514-voxpopuli_eng_000514) +Scores: (#C #S #D #I) 3 7 2 0 +REF: the MESSAGE IS THAT the EU DOES NOT have ANY NEW SOLUTIONS +HYP: the ******* MESIYGI TAT the OU DODT NAT have *** AN NOURSOLUIONS +Eval: D S S S S S D S S + +Speaker sentences 973: voxpopuli_eng_000515 #utts: 1 +id: (voxpopuli_eng_000515-voxpopuli_eng_000515) +Scores: (#C #S #D #I) 10 10 0 0 +REF: ARE you WILLING to act IN FAVOUR OF the SOCIAL DIMENSION to be INCLUDED in the eu COMPETENCIES as PROPOSED +HYP: AR you WILING to act INERE FAVER FOR the SOSIAL DEMENTION to be INCLOUDED in the eu COMPATENCSES as PROPOSE +Eval: S S S S S S S S S S + +Speaker sentences 974: voxpopuli_eng_000516 #utts: 1 +id: (voxpopuli_eng_000516-voxpopuli_eng_000516) +Scores: (#C #S #D #I) 1 12 3 0 +REF: THE NEXT STEP on SPECTRUM POLICY IS BEING TAKEN WITH THE REFORM OF OUR TELECOM FRAMEWORK +HYP: *** A NEXTHAT on ******** ****** PESPECTRUPOLIES TAKIN WITHE EFORM OF OUER TELICON TH FRAM WOR +Eval: D S S D D S S S S S S S S S S + +Speaker sentences 975: voxpopuli_eng_000517 #utts: 1 +id: (voxpopuli_eng_000517-voxpopuli_eng_000517) +Scores: (#C #S #D #I) 17 11 0 2 +REF: i BELIEVE his remarks *** WERE explicitly RACIST and **** XENOPHOBIC and PROMOTED racial intolerance in a way THAT is not ACCEPTABLE or ALLOWED in THE CONSTITUTION of this HOUSE +HYP: i BELEVE his remarks WER A explicitly RACEIST and THEN AFOBICK and PRMOTED racial intolerance in a way THA is not CXCEPTIBLE or ALOWD in TE CONTITUTION of this HOUS +Eval: S I S S I S S S S S S S S + +Speaker sentences 976: voxpopuli_eng_000518 #utts: 1 +id: (voxpopuli_eng_000518-voxpopuli_eng_000518) +Scores: (#C #S #D #I) 5 8 1 0 +REF: real LIFE EXAMPLES SHOW that solving ISSUES RELATED to EDUCATION FUELS STRONG COMMUNITY development +HYP: real IFE GAMPL SHO that solving ITIES RELATE to ********* ADUCATION FEULED STRONGCOMINIT development +Eval: S S S S S D S S S + +Speaker sentences 977: voxpopuli_eng_000519 #utts: 1 +id: (voxpopuli_eng_000519-voxpopuli_eng_000519) +Scores: (#C #S #D #I) 8 18 2 2 +REF: SO I hope THIS WILL HAPPEN FOR RUSSIA as WELL AND that RUSSIA can **** ALSO ENVISAGE AN extreme SUCCESS STORY after *** THE SIGNIFICANT DATE in AUGUST this YEAR +HYP: ** SI hope **** THA TIS ILHVPE ORUSHA as WEL ND that RUHA can ALTS AND VISAIG ND extreme SUCESS TORY after THS EG TISIGNIFICAND AT in ORGST this YEARB +Eval: D S D S S S S S S S I S S S S S I S S S S S + +Speaker sentences 978: voxpopuli_eng_000520 #utts: 1 +id: (voxpopuli_eng_000520-voxpopuli_eng_000520) +Scores: (#C #S #D #I) 17 13 2 4 +REF: she ACCEPTED the fact that ******* CITIZENSHIP is ** ******* **** SUBJECT TO NATIONAL JURISDICTION but SHE ALSO said that ACCORDING to the MAASTRICHT treaty and she IS right THERE has to be A DIRECT LINK +HYP: she ECXEPTED the fact that SITISON SHIP is AY NASINAL PART OF THE OSINO GUDISDICTION but *** HYOURLSO said that ACOURDING to the MASTRICK treaty and she AS right THE has to be * ADIYREC LIN +Eval: S I S I I I S S S S D S S S S S D S S + +Speaker sentences 979: voxpopuli_eng_000521 #utts: 1 +id: (voxpopuli_eng_000521-voxpopuli_eng_000521) +Scores: (#C #S #D #I) 14 21 3 2 +REF: **** THE EU FAILED ESPECIALLY IN DEMONSTRATING A UNIFIED and * EFFICIENT APPROACH to CLIMATE CHANGE treatment AS WELL as in STRENGTHENING its LEADING political POSITION in THIS AGENDA i CONSIDER THEREFORE taking THIS RESOLUTION an act of utmost IMPORTANCE +HYP: TDEY WOU FALD ESPECIAL EAN THE MST RATING AUNIFIED and T AFFISHENT APPRORCH to LIE MITCANGHE treatment ** ASWEL as in STRANTHANINGK its LEDING political COSION in **** DISAGENDER i CONSCITHER THERFOR taking **** THISRESOLUTION an act of utmost IMPORTANS +Eval: I S S S S S S S S I S S S S D S S S S D S S S D S S + +Speaker sentences 980: voxpopuli_eng_000522 #utts: 1 +id: (voxpopuli_eng_000522-voxpopuli_eng_000522) +Scores: (#C #S #D #I) 8 6 0 0 +REF: the UNITED STATES of EUROPE WILL be a fact with SWEDEN as a PROVINCE +HYP: the UNIGTED STATE of YURUO WIL be a fact with SWEDON as a PROVIDENC +Eval: S S S S S S + +Speaker sentences 981: voxpopuli_eng_000523 #utts: 1 +id: (voxpopuli_eng_000523-voxpopuli_eng_000523) +Scores: (#C #S #D #I) 10 11 2 0 +REF: it MUST BE the CAPITAL of BOTH STATES and we MUST RECOGNISE PALESTINE AS A STATE as provided for in the OSLO AGREEMENTS +HYP: it MUS B the CAPITALE of BOT THEATES and we **** ********* MUS RECONISE POLSTINIS THAT as provided for in the OVE LOGREMENCS +Eval: S S S S S D D S S S S S S + +Speaker sentences 982: voxpopuli_eng_000524 #utts: 1 +id: (voxpopuli_eng_000524-voxpopuli_eng_000524) +Scores: (#C #S #D #I) 14 16 1 3 +REF: *** UKRAINE IS FACED with ONE of THE CRUCIAL CHALLENGES in its history it would be ** ** FUNDAMENTALLY WRONG to PRESS the nation now WITH ALL TYPES of restrictions POPULARLY CALLED AUSTERITY POLICY +HYP: YOU CRAINYS FACE T with WONE of *** CRUSAL CHALINGES in its history it would be FU TE MENTARLY RONGK to PRE the nation now WIT AL THIPES of restrictions POPELADERL CALE OSTERITE POLI +Eval: I S S S S D S S I I S S S S S S S S S S + +Speaker sentences 983: voxpopuli_eng_000525 #utts: 1 +id: (voxpopuli_eng_000525-voxpopuli_eng_000525) +Scores: (#C #S #D #I) 6 3 0 0 +REF: more RULES and regulation will not improve THE SITUATION +HYP: more RULS and regulation will not improve THIS CITUATIO +Eval: S S S + +Speaker sentences 984: voxpopuli_eng_000526 #utts: 1 +id: (voxpopuli_eng_000526-voxpopuli_eng_000526) +Scores: (#C #S #D #I) 9 6 1 0 +REF: at least we WOULD LIKE to KNOW the SOURCE of the MONEY and the POSSIBLE MOTIVES +HYP: at least we ***** WOLDLIKE to NOW the SOURSE of the MONY and the POSIPL MORTIE +Eval: D S S S S S S + +Speaker sentences 985: voxpopuli_eng_000527 #utts: 1 +id: (voxpopuli_eng_000527-voxpopuli_eng_000527) +Scores: (#C #S #D #I) 10 30 2 2 +REF: to HAVE those EUROPEAN WORLD LANGUAGES in ** TODAYS GLOBALISED WORLD IN TODAYS GLOBALISED ECONOMY in THIS GLOBAL VILLAGE which is CULTURAL ECONOMIC SOCIAL AND POLITICAL IS A most VALUABLE ASSET FOR THE ENTIRE EU WHICH we must TAKE FULL ACCOUNT OF and * +HYP: to WEROF those YURUPIN WALE LANGIASH in TO THES GLUBELICED WEARLD IS INT TO THEYSGOBELISDECONOM in DHIS GOBE VILACH which is ******** GORSTIALY CONOMICK SOSIAL ELNPLITICO ITS AR most VELABLE ESTHERT FROM THEINTIRE E YOU THAT we must **** THAK FOL ACOUNS and T +Eval: S S S S I S S S S S S S S S S D S S S S S S S S S S S S S D S S S I + +Speaker sentences 986: voxpopuli_eng_000528 #utts: 1 +id: (voxpopuli_eng_000528-voxpopuli_eng_000528) +Scores: (#C #S #D #I) 6 11 1 2 +REF: WE HAVE to REPEAT that ** *** ODA CANNOT be USED to FINANCE SECURITY EXPENSES BORDER control or MILITARY SUPPORT +HYP: ** WEAVE to REPETE that AL THE AY ANOT be USE to FINANS SIURIT EXPANCES BARTHERS control or MLITRY SOPORNT +Eval: D S S I I S S S S S S S S S + +Speaker sentences 987: voxpopuli_eng_000529 #utts: 1 +id: (voxpopuli_eng_000529-voxpopuli_eng_000529) +Scores: (#C #S #D #I) 7 6 1 0 +REF: IF ANYTHING the SCIENTIFIC reports ARE BECOMING more urgent MORE alarming and MORE shocking +HYP: ** THN the SINTIFI reports BECOE MRE more urgent OR alarming and MOR shocking +Eval: D S S S S S S + +Speaker sentences 988: voxpopuli_eng_000530 #utts: 1 +id: (voxpopuli_eng_000530-voxpopuli_eng_000530) +Scores: (#C #S #D #I) 3 23 1 2 +REF: FINALLY when **** IT COMES TO INNOVATIVE FINANCIAL INSTRUMENTS WE NEED THEM BOTH for ******* OURSELVES TO SUPPORT OUR ECONOMIES but ALSO TO SUPPORT THOSE WHO ARE IN NEED +HYP: FINALYM when WEAT THINKING ABOUN THER INOVATIVE FINSION INSTOUMENTS WHEN OU THE BOLTH for OURSELS TOR SUPOART OWER A CONOMES but **** ALS SO TOOE SUPORT THOS HOERE INEAET +Eval: S I S S S S S S S S S S I S S S S S D S S S S S S S + +Speaker sentences 989: voxpopuli_eng_000531 #utts: 1 +id: (voxpopuli_eng_000531-voxpopuli_eng_000531) +Scores: (#C #S #D #I) 2 5 1 2 +REF: THAT GIVES US a ** UNIQUE TOOL in ** PEACEMAKING +HYP: **** THT IVE a SO YUNIEK DOLL in PE MAKING +Eval: D S S I S S I S + +Speaker sentences 990: voxpopuli_eng_000532 #utts: 1 +id: (voxpopuli_eng_000532-voxpopuli_eng_000532) +Scores: (#C #S #D #I) 2 3 0 0 +REF: paper a VERY WEAK PROPOSAL +HYP: paper a VERYD WEEK PROPOSL +Eval: S S S + +Speaker sentences 991: voxpopuli_eng_000533 #utts: 1 +id: (voxpopuli_eng_000533-voxpopuli_eng_000533) +Scores: (#C #S #D #I) 6 7 3 1 +REF: RUSSIA HAS ALWAYS BEEN a very PROUD NATION with A rich CULTURE with inventions ****** AND ESPRIT +HYP: ****** SRUSHAS ALWAS BE a very ***** PROUDNATION with * rich COLTCUER with inventions WITHAN AS PL +Eval: D S S S D S D S I S S + +Speaker sentences 992: voxpopuli_eng_000534 #utts: 1 +id: (voxpopuli_eng_000534-voxpopuli_eng_000534) +Scores: (#C #S #D #I) 16 16 4 1 +REF: FAIR TAXATION even a MODICUM of TAXATION in some CASES MIGHT just HELP US to do what I HAVE ALREADY SUGGESTED AND who KNOWS make the CASE for the ********** RETROSPECTIVE BANK RECAPITALISATION that we NEVER SAW +HYP: **** ARTACXATIN even a MODICAL of TACXATION in some CACES MIGH just HELPUS EM to do what * **** IVEAREDY SUEGESTED AN who NOSE make the CACE for the RETRESPECT OF BANKRE CAPIDLIZATION that we ***** NEVERSO +Eval: D S S S S S S S D D S S S S S I S S S D S + +Speaker sentences 993: voxpopuli_eng_000535 #utts: 1 +id: (voxpopuli_eng_000535-voxpopuli_eng_000535) +Scores: (#C #S #D #I) 10 17 0 1 +REF: THE EUROPEAN ASYLUM SUPPORT OFFICE MOREOVER HAS among its TASKS to PROMOTE FACILITATE and COORDINATE EXCHANGES of information and other ACTIVITIES related * TO RELOCATION WITHIN THE union +HYP: THEROPE AN ASILOM SUPORTOFHIS MOR OVER AS among its THASTS to PRMOUTD FESILYTAT and COURDINAT EXTCANGES of information and other ACTIVEITES related O ELOCATIN WTH IN HE union +Eval: S S S S S S S S S S S S S I S S S S + +Speaker sentences 994: voxpopuli_eng_000536 #utts: 1 +id: (voxpopuli_eng_000536-voxpopuli_eng_000536) +Scores: (#C #S #D #I) 10 12 1 0 +REF: THE CONCLUSION of the FRAMEWORK AGREEMENT provides a LEGALLY binding INSTRUMENT to UPGRADE and STRENGTHEN eu AUSTRALIA BILATERAL RELATIONS and to INCREASE COOPERATION +HYP: HE ONUSO of the FRAMEBORK AGEMENT provides a LIGLY binding INSTRMENT to OBGRAT and STRANTN eu OSTRALIA BY LITHRRATIONS and to ******** INCRESCOPERATION +Eval: S S S S S S S S S S S D S + +Speaker sentences 995: voxpopuli_eng_000537 #utts: 1 +id: (voxpopuli_eng_000537-voxpopuli_eng_000537) +Scores: (#C #S #D #I) 6 13 3 2 +REF: THEREFORE WE ARE ASKING the COUNCIL AND THE COMMISSION to ******** *** PRESENT A TRANSPARENT AND COMPLETE ASSESSMENT of the IMPACT of the CRISIS +HYP: ********* ** THEREFOR WEAEASTIN the ******* COUSAL AS GMION to RESENTHA HAS BALE THA OULD BE THE SESTMENT of the EBACT of the RICIS +Eval: D D S S D S S S I I S S S S S S S S + +Speaker sentences 996: voxpopuli_eng_000538 #utts: 1 +id: (voxpopuli_eng_000538-voxpopuli_eng_000538) +Scores: (#C #S #D #I) 16 6 2 0 +REF: in OTHER words the objection is not whether money is PAID or not the objection is WHETHER THERE is a DIRECT LINK OR NOT +HYP: in OTHE words the objection is not whether money is PAD or not the objection is WETHER TER is a ****** **** DIDECTLINK ORNO +Eval: S S S S D D S S + +Speaker sentences 997: voxpopuli_eng_000539 #utts: 1 +id: (voxpopuli_eng_000539-voxpopuli_eng_000539) +Scores: (#C #S #D #I) 4 12 2 1 +REF: IT DISTINGUISHES the ** TWO MAIN DOSSIERS HUMAN RIGHTS ABUSES by the CURRENT GOVERNMENT and THE IRANIAN NUCLEAR PROGRAMME +HYP: TO THSTINGUISHES the TO MAN OEAR YOUMER RIGT AB BUSE by the CADANT GORMENT and *** ******* THEDLANIAN NUCLAPROVGDM +Eval: S S I S S S S S S S S D D S S + +Speaker sentences 998: voxpopuli_eng_000540 #utts: 1 +id: (voxpopuli_eng_000540-voxpopuli_eng_000540) +Scores: (#C #S #D #I) 9 9 1 1 +REF: **** MR PRESIDENT SEXUAL HARASSMENT is a form of VIOLENCE and it IS THE most EXTREME form of GENDER—BASED DISCRIMINATION +HYP: YESS MATHMDRUO THANKATHR SECTIAL HERASDMENT is a form of VILANCS and it ** ISTHE most EXTREAME form of GNTERBAETH DISCUMINATI +Eval: I S S S S S D S S S S + +Speaker sentences 999: voxpopuli_eng_000541 #utts: 1 +id: (voxpopuli_eng_000541-voxpopuli_eng_000541) +Scores: (#C #S #D #I) 7 7 0 1 +REF: we can LOOK to some **** NON EU members for GOOD EXAMPLES as REGARDS TECHNOLOGIES +HYP: we can LOK to some URAN LIN OU members for OUOD GXAMPLES as REGARDED THGNOLIGE +Eval: S I S S S S S S + +Speaker sentences1000: voxpopuli_eng_000542 #utts: 1 +id: (voxpopuli_eng_000542-voxpopuli_eng_000542) +Scores: (#C #S #D #I) 2 5 0 0 +REF: INVOLVED for THEIR POSITIVE and CONSTRUCTIVE APPROACH +HYP: YIMNVALLVED for THER POSITEVE and COSTRACTEIVE ABROTCH +Eval: S S S S S + +Speaker sentences1001: voxpopuli_eng_000543 #utts: 1 +id: (voxpopuli_eng_000543-voxpopuli_eng_000543) +Scores: (#C #S #D #I) 4 14 1 1 +REF: SO i HOPE that THIS WILL be ********** COMPLETED in THE FORESEEABLE FUTURE WHICH MEANS MAYBE TWO OR THREE MONTHS +HYP: O i HOP that **** ISWIL be COMPLEATED EAR in HE FACIVIL FUTUAR THAT MANES MA BE TO AFRE MONS +Eval: S S D S I S S S S S S S S S S S + +Speaker sentences1002: voxpopuli_eng_000544 #utts: 1 +id: (voxpopuli_eng_000544-voxpopuli_eng_000544) +Scores: (#C #S #D #I) 7 14 0 2 +REF: ** FURTHER ENCOURAGE THE UNS EFFORTS to bring ABOUT PEACE in ** AFGHANISTAN AND to overcome THE FRAGILE SECURITY ENVIRONMENT in the COUNTRY +HYP: OR FORDER NDCOURDSHTHE YOU HAND EFORTS to bring AMONGK PES in OF GNISTAN AN to overcome THEF FRASILE SICUITY ANVIRMENT in the CONTRY +Eval: I S S S S S S S I S S S S S S S + +Speaker sentences1003: voxpopuli_eng_000545 #utts: 1 +id: (voxpopuli_eng_000545-voxpopuli_eng_000545) +Scores: (#C #S #D #I) 3 4 0 1 +REF: ** WE UNDERSTAND that some PEOPLE ARE angry +HYP: BE ANDER STANT that some PEOPEL AR angry +Eval: I S S S S + +Speaker sentences1004: voxpopuli_eng_000546 #utts: 1 +id: (voxpopuli_eng_000546-voxpopuli_eng_000546) +Scores: (#C #S #D #I) 2 2 2 0 +REF: WE WANT to be MORE RESPONSIBLE +HYP: ** ON to be **** MRESTPONCIVEL +Eval: D S D S + +Speaker sentences1005: voxpopuli_eng_000547 #utts: 1 +id: (voxpopuli_eng_000547-voxpopuli_eng_000547) +Scores: (#C #S #D #I) 8 10 2 0 +REF: we must RECTIFY this SITUATION and WE ASK the COMMISSION to CONSIDER the most ADEQUATE COMPENSATION MEASURES FOR OUR PASSENGERS +HYP: we must EDACTIFIETH this SUTIATION and ** VEASK the OMION to CONCSIDER the most ******** EDICUIT COMBINSATION MESES FOW PASNGES +Eval: S S D S S S D S S S S S + +Speaker sentences1006: voxpopuli_eng_000548 #utts: 1 +id: (voxpopuli_eng_000548-voxpopuli_eng_000548) +Scores: (#C #S #D #I) 16 16 1 4 +REF: the ******* ******** COMMISSION INVITES PARLIAMENT in the UPCOMING REVISION to open ITS position on this MATTER which REALLY CONCERNS ACCESS to * JUSTICE in EUROPE and the ENFORCEMENT of RIGHTS granted by ** EUROPEAN UNION LAW +HYP: the OMITION INBVIHED THE YUROPIANT PULAMENT in the ******** UPCOMINCREVISION to open HIS position on this MATER which RELY CONSED AXES to S JUSTIS in YUROP and the ENFORTMENT of RIES granted by HE YUROPIANER YUNAN LO +Eval: I I S S S D S S S S S S I S S S S I S S S + +Speaker sentences1007: voxpopuli_eng_000549 #utts: 1 +id: (voxpopuli_eng_000549-voxpopuli_eng_000549) +Scores: (#C #S #D #I) 6 14 1 1 +REF: i * WELCOME very much THE RESUMPTION of TALKS BETWEEN THE ISRAELIS AND THE PALESTINIANS and SINCERELY HOPE that THEY WILL SUCCEED +HYP: i L OM very much TH RISOUNTION of ***** TOKE BETWEN THEOS RALIS AN PLESTINIONS and SNCEIRLY HOP that HE WIL SUCED +Eval: I S S S D S S S S S S S S S S S + +Speaker sentences1008: voxpopuli_eng_000550 #utts: 1 +id: (voxpopuli_eng_000550-voxpopuli_eng_000550) +Scores: (#C #S #D #I) 4 9 1 0 +REF: we have AN ACCUMULATION of PROBLEMS resulting FROM ARTIFICIAL UNDER BUDGETING IN PREVIOUS YEARS +HYP: we have ** ACUMILATION of PROBLENCS resulting FROME THE ARTIFIHAL AND DEBAGEATINGK AND VERPREVIUSYUS +Eval: D S S S S S S S S S + +Speaker sentences1009: voxpopuli_eng_000551 #utts: 1 +id: (voxpopuli_eng_000551-voxpopuli_eng_000551) +Scores: (#C #S #D #I) 6 7 0 0 +REF: let US not be the man of YESTERDAY LET US BE TODAYS INSTITUTION +HYP: let UST not be the man of YESTERDY LNT UN BEPOL DAYS INSTHITUTIO +Eval: S S S S S S S + +Speaker sentences1010: voxpopuli_eng_000552 #utts: 1 +id: (voxpopuli_eng_000552-voxpopuli_eng_000552) +Scores: (#C #S #D #I) 9 21 3 5 +REF: * i WOULD URGE YOU to become AMBASSADORS OF the year BY MAKING its * IDEAS and ********** ** ***** ACTIVITIES WIDELY KNOWN AMONGST EUROPEAN CITIZENS and PARTICIPATING IN EVENTS be IT AT EUROPEAN NATIONAL OR LOCAL LEVEL +HYP: T i ***** GOULD ERLSUM to become AMBASSETHES O the year ** MAKNG its A DEARS and ACTIVITHIS WO WIDLY NOWN A MONCSH TO YURUPEAT ITIENS and PUTPIIPATING N EVENS be ** TAT YOUROPIAN NASIONALL FOR LOK ALEVL +Eval: I D S S S S D S I S I I I S S S S S S S S S D S S S S S S + +Speaker sentences1011: voxpopuli_eng_000553 #utts: 1 +id: (voxpopuli_eng_000553-voxpopuli_eng_000553) +Scores: (#C #S #D #I) 10 10 1 0 +REF: CERTAINLY such IMPACT ASSESSMENT could PRE EMPT CERTAIN PROBLEMS such as THOSE posed by the ELECTRONIC identification of SHEEP IN scotland +HYP: SERTNLY such IMPACE SESTMENT could *** PREMT SERTAN PROBLOMS such as THOS posed by the ELECTRONIK identification of SHEP AND scotland +Eval: S S S D S S S S S S S + +Speaker sentences1012: voxpopuli_eng_000554 #utts: 1 +id: (voxpopuli_eng_000554-voxpopuli_eng_000554) +Scores: (#C #S #D #I) 16 15 2 1 +REF: the COURT IS content to see THAT its work has ******* INFORMED THE DISCHARGE PROCESS and has CONTRIBUTED to PROPOSALS for improving the FINANCIAL MANAGEMENT of EU SPENDING and BETTER TARGETING of EU FUNDS +HYP: the ***** CORTIS content to see THT its work has INFORME TH DIS CHAGH ROS and has CONTEBUTED to ROPOSALS for improving the FINANCAL MANAGHMENT of ** VEYOUSPENDING and BETHE TARKATING of YOU FUNE +Eval: D S S I S S S S S S S S D S S S S S + +Speaker sentences1013: voxpopuli_eng_000555 #utts: 1 +id: (voxpopuli_eng_000555-voxpopuli_eng_000555) +Scores: (#C #S #D #I) 7 6 0 2 +REF: ********** REGULATORY CLARITY and CERTAINTY is NEEDED for the PUBLIC SECTOR and for *** industry +HYP: REGOUTHERE CLARIE THE and SERTANTY is NEDED for the OBLICK SECTOUR and for THE industry +Eval: I S S S S S S I + +Speaker sentences1014: voxpopuli_eng_000556 #utts: 1 +id: (voxpopuli_eng_000556-voxpopuli_eng_000556) +Scores: (#C #S #D #I) 7 9 1 4 +REF: is it REALLY NOT POSSIBLE to ** USE OTHER housing FACILITIES with * ****** APPROPRIATE RECEPTION CONDITIONS in the *** MEANTIME +HYP: is it ****** REALINOT POSIBLE to US A ATHER housing FASCILIDES with U PROPRE H RESEPTIN CONDIONS in the MEN TIME +Eval: D S S I S S S I I S S S I S + +Speaker sentences1015: voxpopuli_eng_000557 #utts: 1 +id: (voxpopuli_eng_000557-voxpopuli_eng_000557) +Scores: (#C #S #D #I) 7 3 0 0 +REF: WILL you take ACTION at last if not then WHEN +HYP: WHEL you take ACION at last if not then WHEND +Eval: S S S + + diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/text b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/text new file mode 100644 index 0000000000000000000000000000000000000000..725293f8401e13d3f1d60ad7ca8232cf28c1e95e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/text @@ -0,0 +1,1092 @@ +LAD_eng_000254 HE REMAIED WEL CHAMPIAN ANTIL NINTEN SIXTY FIVE A YEAR I WHCH SUF RD A TERABLE ACXIDENT +LAD_eng_000255 AY LIBRAL CONSEVITIVE HE WAS DEFEATED IN ATEIN ATY TO +LAD_eng_000256 ON ROD LAAR CON DRAR TWO RODS AT WOANCE +LAD_eng_000257 SOME OFTHE CONTRES HVE SURVAYS FOR MALTIPLE YEARS +LAD_eng_000258 BOTH OF THE VRSIONS FEACHR THE SONG HAPY HOLIDAY +LAD_eng_000259 SHAKXPIAR MANY REFRNCES ARE MADE TO SENS INTR ACTIONS OR CARICTES FROM VARIOUS PLAYES +LAD_eng_000260 IF ONLY THE ROGRAM CULDBRAKE OUT JUST A ITLE FROM ITS TO FOMILIAR APROCH +LAD_eng_000261 THE HELBEM WAS RELEASED IN OSTRALIAR ON NINTEINTH ORGIST TWO THOUSND AD ELEVEN +LAD_eng_000262 HE NOW PLACE FOR ASTRALIN CLOBE PERTH GLORY +LAD_eng_000263 IT IS NOT NON HOW MUCH IF EANY OF HE CLAMS AR TRU +LAD_eng_000264 A SMAL BISINESS ONR BROARD OPRATED HI WEAT AD SHEPFAME FOR SICTEN YEARS FRO THE AGE OF WENTY TO +LAD_eng_000265 IN THE NINTH SENTURY HE WAS AN IRISH POET +LAD_eng_000266 THEY ARE MARKED BY STRONG +LAD_eng_000267 THE LOW IS THE FOR VAOLED +LAD_eng_000268 IN THE RLY STAGES CAME CLOSE TO US A SLEP +LAD_eng_000269 RONING EVERY THRTY MINUT THRO AT SERVIS TIMS +LAD_eng_000270 AS A RESIULT WHEN THE COLIGE RE OPEND IT WAS AS AN ALL MALE COLIGE +LAD_eng_000271 THE TIME BETWEE THES POINCT IS VERIABL AND CANACUR ANY WHER FRO A MINIT TO MUCH LONGER +LAD_eng_000272 WOARK ON THE EA E E STARTED IN MARCH TWO THOUSND AND SEVEN AT A COST OF FIVE MILIAN DOLERS +LAD_eng_000273 HOWEVER THER WAS SOME DI AGREMENT OV TH ENDING THEME WHICH OR MORY AND YOHIMORY DISCUSTD AT LENGTH OVER EMAL +LAD_eng_000274 THE COPLE HAD NO CHILDRAN +LAD_eng_000275 THE FIAL SINGL O THAT DEBU AL BHM PARIS COLING HAD AN ELABRT MUSIC VIDIO +LAD_eng_000276 THE SERIS ENDED ON SIXTH ORGEST TO THOUSND AND FOR LASTING FR A TOUTE OF SEVENTY ON DAYS +LAD_eng_000277 HE HAS ALSO CONTRIBUTED TO THE NEW YORK REVIO OF BOOKS +LAD_eng_000278 BY PLACING SMAL ART OBJECT TRO OUT THE FILM +LAD_eng_000279 IT IS FOUND IN BRESIL +LAD_eng_000280 IT WS THE SID OF THE FAMLY I IDENTIFIED MORE WITH +LAD_eng_000281 H CAND IT SIGHTES MUST ALSOR SOBMIT A WORK PLAN +LAD_eng_000282 DUNDEY WHN THE MACH THRE TO +LAD_eng_000283 HOWEVER THE VILIGE REMAIND ICALATED ANTIL THE RIVEL OF THE FIRST NOUS PAPER SECOND REPOUBLICK +LAD_eng_000284 THE FAST SERVIS I THE EU CHURC WAS HELD I NINTE FIFTY ON ALTHO THE BILDIG WAS NOT FULY FINISHED +LAD_eng_000285 THE AVERIGE HOUSEHLD SIE WAS TWO POINT TO SEVEN ND THE AVERIGH FAMLY SIE WAS THRE POINT IRO SRO +LAD_eng_000286 IT WAS FIRST BRAD CAST ON THIRD GANIURY TWO THOUSOND ND TEN +LAD_eng_000287 THE WINGS WER OW MAD IN A SINGLE PRESING +LAD_eng_000288 HE DOCTR O HLOSOFY IN ENGENEARING MANAGEMENT +LAD_eng_000289 THIS TOK WAY THE MAIN ARGUMENT OF SAFTY RISSK +LAD_eng_000290 HE WAS ALSO MAD A LIFE MEMBER OF SGUN THORP PUNITED +LAD_eng_000291 SHE FIARS THEYWIL GET A DEVORSE BUT THIS NEVER HAPENS +LAD_eng_000292 FOUT DROPS IN ABLE TO HAD THE FOT STRAT ACROSE +LAD_eng_000293 WHETE THE AR FLO IS FREY OR FORST CN FEC THE ENAGY AFIANCY OF THE ENDO +LAD_eng_000294 AFTER GETIN HE RIHT MASURMENT THEY MAD THENEW DORS +LAD_eng_000295 FRAGMENTS ON ACH FACE ARE MARE WTH LETERS AY BE SE +LAD_eng_000296 FROM THE FIRST MINITS BOTH TEMES SHOWD THE DISIRE TO CMPEET WIT HEGEIVE APROCHES +LAD_eng_000297 FISICL HERIPY EXCUSISES MAY HELP PATIONT TO MAINTAIN MULE STRINGTH +LAD_eng_000298 HOWEVER THE TOWN SHE LIVS IN NO ON WANTS TO HER ABOUT HER +LAD_eng_000299 AND DISCRIVES APOINT MET OF AN ACTING CHEVE JUSTIS OR JUDGE OF THE SUPREME CORT +LAD_eng_000300 THE SOY BENS OUT ACOVERING IS THEN REMOVED AND THE BENS ARE PARTIALY COOKED +LAD_eng_000301 THIS NASINALE MOVMENT WHICHAD BEGUN WITH SO MUCH HOP CAME TO A SAD END +LAD_eng_000302 HIS ASOSCIAT YUSUALY CALD HIM TE OR THE OD LOKING GIY +LAD_eng_000303 ITS MAIN OFICES WER IN LUNDAN WIE HE SECND OFIS BELL FAST +LAD_eng_000304 ACTULY I HAD NEVER BEN TO A VILIGE BEFOR THAT +LAD_eng_000305 HE AS CHARGED ITH PLANING TO SET OF BOMS IN UROP AND THE UNITED STATE +LAD_eng_000306 MAKING MERS IS THE HIRD STUDOR HLBUM BY BELGEN ASTRALIAN ARTIST GOTIAY +LAD_eng_000307 HE THEN MOVED TO WASINGTON DE SE AND WAS A PARTNR ITH WARD BRON ANDTIL NINTEN TWENTY NIN +LAD_eng_000308 JOS OF HIY SCOLE AND THE SCOLES THE CMPE GAINE IN AL SPORTS +LAD_eng_000309 TWELF PLUS ON MACH BAN PER CARD +LAD_eng_000310 I HINK I MIGHT BE NOTHING +LAD_eng_000311 THE HOE WAS BILT AND LIVED IN BY ANDRU JACX AND CANDY DEPUTY CLECTE O THE INTERNAL REVINOU SERVIS +LAD_eng_000312 IN NINTEN SIXTY FOR HE WENT BAC TO OMSK AND ENTE THE ACTOA SCOL OF OMPS +LAD_eng_000313 THE BANK IS JOUNTLY OND BY HIM AND HIS BROVER AND RELITIVES +LAD_eng_000314 HE SUBPSICUNTLY WENT TO COL IN BRISTAL +LAD_eng_000315 WON THOUSAND AT HUNDRD FOARTY SICX FORH EDION +LAD_eng_000316 A PART OF LITL INGLAND BEYOND WALS IT HAS BE A CENCHALY INGLISH SPEAKING FOR NIN HUNDRED YEARS +LAD_eng_000317 HE PLAD WTH TEN PLAYARS FOR HARF WAS AGAINE A TRDION IN JEESP +LAD_eng_000318 THE RESIDING JUDGE WAS WEBST A FAIR HO WAS ALREADY ASIND TO THE CORT BEFORE THIS CACE WAS HEDILD +LAD_eng_000319 BIG BRATHER FIVE WAS THE HURD OF HE MAIN SARIS TO FEACUER A LIVE LONCH +LAD_eng_000320 ITS MOTO IS WHO EVE YOU AR AND WHEREVE YOU ARE ON THE JUNY OF FAIFH YOU AE WELCOM HER +LAD_eng_000321 ROBAT EY MILOR AS COCH WILSON +LAD_eng_000322 AFTER ON YEAR BRAK SIRO DEGRE WAS HE FOLOING VENTHAR +LAD_eng_000323 AY AM TEE MANUFACTED A MORDL CIT OF HE SED SEID AR DRACKXSTOR +LAD_eng_000324 THE ESSESSAY AMED TO BILD A LEFT WING OLTURNITIVE TO NOW LABER AND THE ESAN PE +LAD_eng_000325 HE LIVES LIKE HE AS A YONG PERSON +LAD_eng_000326 MASTE OF SINES IN ENGENEARIG MANAGENT +LAD_eng_000327 SHE FAILED TO MAK HE TOP THRE AT THE CANIAN JUNIATRACTRILES THAT JON +LAD_eng_000328 A TORE FOLOED IN SEPORT +LAD_eng_000329 THEYER STABISH IN ATEN SEVENTY ON AND AR WN O THE OLDST CLOUBS IN HE SOUTH OF INGLAND +LAD_eng_000330 HE AS A MEMBER OF THE GEST SCOTLAND ADVISERY BORD +LAD_eng_000331 TWO THOUSAND AND FIVE GENTLEMEN +LAD_eng_000332 AORE FILE AD STRONG RESEPTION INYURUPAND ACHIVED DISTOBUTION BUT THAT WAS NOT THE CACE HER +LAD_eng_000333 BOLTHOIS STETCHES POSTERIAR ANGCAL STRUCTUES +LAD_eng_000334 HE AS ALSO A THEE TIME FRENCH NASIAL CHAMPIAN NINTE NINTY NINTIE NITY FOR TWO HOUSND AD WON +LAD_eng_000335 THE VILIGE STRUCTUR SHOW IN HIS MAP IS T A GRE EXTENT UN CHANGED O DAY +LAD_eng_000336 RUHA IS RECOGNISED IT NUCLAR DISARST TO EXPARTES AND FO THE SAVFTY O ITS TECKNOLAGY +LAD_eng_000337 AS OF TO THOUSEND OD FORTEEN EMTY VE IS AVAILABLE WITHIN THEUNITED CINGDUM ON VERGIN MEDIAR AND SCKIY +LAD_eng_000338 NEWYORK PEANGUIN RANDM HOUSE +LAD_eng_000339 THE DUTCHEY WAS SCECURE IN TE UT COME OF THE GOFICK WAOR +LAD_eng_000340 WIH GOD PACE SDARTE HE MATCH WITH BOTH TEMES OLTENATING SUPREMASY +LAD_eng_000341 THIS VRTION IS NOTEAD OR BIG VERY FAFUL TO THEARIGINAL NOVL +LAD_eng_000342 THIS PRESUMPTION IS NOT FLE FILED ON HAS TO NO ATLEAST TO CONGAT DIAMATES +LAD_eng_000343 NOTABLE TITLES INCLUDED GOLDAN ACXS THE REVENG OF DETH ADER RAD MOBIL OUT RUNOES AND SAKGAR SONIC THE HEGHOG +LAD_eng_000344 THE NINTEN NINTY NIN JUGMENT NOTED THAT THE INFLONC OF TH FATHER OF THE CUSED HAS BEE THER +LAD_eng_000345 MOKDAUF SWARS REVENGEH AND JOINS FORES ITH MALCOM TO OVER TRO MOK BEATH +LAD_eng_000346 THE MEDYEVL VILIGE CORT WAS ALWAYS ANIOUS TO CEPE THE FENE AROND THE ILIGE GCAPLES +LAD_eng_000347 THER WAS A NIN RANK SISTOM EACH RANK HAVIG MORE POWE TA THE LOERANK +LAD_eng_000348 THE ASTABLISHED DIPLAMATI RELATIONS ON SEPTEMBRNINTENTH NINTEN SEVENTY TO +LAD_eng_000349 THIS WAS FIRTHER XTENDED TO INCLOUD MOR UCADATES IN DISEMBER TWO THOUSAND ND FORTEEN +LAD_eng_000350 THE UCH GOVERMENT IS CARNTLY EXSAMING THE EAL CONCICUENCES OF TH ROLING +LAD_eng_000351 FROM NINTEN THURTY THREE TO NINTEEN FOARTY NIN THE MARICON LEE WON TWELVE OUTO THE FIRST SIXTEN +LAD_eng_000352 THEAR HE FEL SICK WITH TIFAS HIMSELF +LAD_eng_000353 SIXT TEMS AVBE DVIDED INTO TWO GROUPS OF THREE TEMS EACH +LAD_eng_000354 THE FIRST CEASON PREMIAED ON TWELTH JUON TWO THOUSND AD FIFTEN +LAD_eng_000355 IT SCEED THE WHI BOARD AND SISTAME TWENTY FOR COMBING FEACUES FOM BOTH +LAD_eng_000356 VLLIUME TOO NUMBERS ON TO AND THRE +LAD_eng_000357 THE LOWE PART OF MENS DESES WE MUCH SOURT IN LENC THO THOS FOR WMEN +LAD_eng_000358 THE VISIGOTHS IN TERN WE SCEADED BY THE MORS +LAD_eng_000359 JOS OF HI SCOLE EVERY WE OF THE COL YEAR +LAD_eng_000360 AS TH RSILT OFAL THE ARGUMENT GETING TO HER +LAD_eng_000361 IT HAD QUARTERS ARE IN SHEFIALD YOUNITED CINGDOM +LAD_eng_000362 LAY ALSO FIALY SINE THE CONTRACT ON STAGE WIT HE DIRECTER AD PREDUSES OFTHE GOULDAN EYES +LAD_eng_000363 FISICL FERIPY CN HELE PATIONE TO LURN HO TO WARK WITH FOT DROP +LAD_eng_000364 IT ENT ON TO SEL THRE HUNDRED THOUSAND UNITS A CHE FIVE NO +LAD_eng_000365 THE NAME STOUCK AFER THAT +LAD_eng_000366 THE HLBM LATER BROK TH DIMAD RECORD ON CUCUOM MUSICK +LAD_eng_000367 ITS EDATORIAL WE SUBMIT AND ITS OTHR APOL T OPRIYE +LAD_eng_000368 JOSIF PLAYES OUR FEATURED EACH WEE ON THE HO +LAD_eng_000369 THEY WAT FORA TIMEM BILDING UP THER FORES BEGIN TO ONDR IF THIS EAVL REALY EXISTS +LAD_eng_000370 BREFE MENTION OF TH CONVICTION APPERD ON PAGE THRE OF THE NEWYOUOK TIMEMS +LAD_eng_000371 ODED BY POSION ON PICH FROM BACK RIGHT TO FRUNT LEFT +LAD_eng_000372 HE IS MEMBER OF THE COURT O THE RIL COLAE OF ART LOUNDON YUCAY +LAD_eng_000373 DURIG THE COURSE OF TE CAMPAIN FIRGS AND VISIT AT ALL THERTY NEIN WASIGTAN STATE CONTES +LAD_eng_000374 A STRIP OF PAPER OF LENGTH +LAD_eng_000375 SATO HAD FRECUENTLY WORE TO GETH WTH YOUCK AYAMAR ON PREVIOS POGJECTS +LAD_eng_000376 SHE AS BORN ON SCREAN DUIN THE EPSOD BRAD CAST ON FORHAN OVEMBER NINTE NINTY FOR +M-AILABS_eng_000159 HE TURNED ROUND SH HAD COM IN SO GENTLY THAT HE HAD NEVER HARD HER +M-AILABS_eng_000160 A TO BE SHOUOR AN WE MUST CE OUR DORS SHOAT WE MUS LAT NO ON IN +M-AILABS_eng_000161 CIDS PMON HE BEGAN MOKINGLY YOU MA HVE ONDED WHIY I CALD A TROUS WHEN I COULD JUS AS WELLHAVE DISTRORED YOU THAT I DOUT ATO ANSED HIM +M-AILABS_eng_000162 THE PESNT THRUWHIMSELF APON HIMAND BOUND HIS FOR LAGS TITLY SO TAT H CULD NOT MOVE +M-AILABS_eng_000163 NOR MUST THOU SO LIMETH THE HLY ONOF ISRIAL AS TO THINK HE HATH BUT ON WAY IN WHICH CANGORIFIE HMSELF BY THE +M-AILABS_eng_000164 THE OLD COMPARSON BETWE THE IMPULSIE EXSECTIVE AND THE LIBRAL ARTS MAN WHO WHAD LARNED THATHERE ONLY ON R TO POSITVE DISIONS F ALBLE IN AL THE WAL O HINKING +M-AILABS_eng_000165 AFTER THIS EXPERIANCE THE NVATORS WER CAIRFUL TO CEPE A SAVFE DISTNCE FROM THE AL +M-AILABS_eng_000166 AN OU BAR SOMTING FIRTHER I THN YOU ATO NO IT I HAVE HER A MOST MSTERIOUS TELAPARIGRAMYES WHAT IS IT ISHE DID NOW IT IS NOT ABOUT HER +M-AILABS_eng_000167 NO MSTR TOURTAN SAID AND GIE THE ASKT TO ME IAL TAKE IT +M-AILABS_eng_000168 AND ARABIAN NIGHT EXCLAMED TROT WHIY THAT WAS A MAGIC NIGHT WASN IT THERS DIFRENT SORTS O NIGHES MATE SAID THE SALER AND THE NIGHT BUTNBRIGHT MEANS ANT THE SAME NIGHT YOU MEAN +M-AILABS_eng_000169 IVETRNED OF UPWARDS OF A HUNDED F MY BESTD HANDS FOR NO OTHER FALT THEM FOLOING YOU AND SUCH AS YOU AND THINK ILL TAKE YOU AON +M-AILABS_eng_000170 BUT WE WID SHE SE HIM HER HART LEPT U IN APREHENTION AT EVERY RIN OF TH DOR BIL +M-AILABS_eng_000171 THESE BOOKS DICXSON I WL KEPE AL THE REST WE OUSEND TO MSTR BEL THEY AR OF A CIND THT HE WL VOULYOU FOR THMSELVES AS WEL AS FOR POPAS SAY +M-AILABS_eng_000172 UT INGA WAS NOT AT AL SHUR THAT THE COULD NOT GET IN THE GATS OPED INWARD AND THRE HEVY BARS WERE HELD IN PLACE BY MENS OF STOUT STAPLES RIVITED TO THE SHETS OF STA +M-AILABS_eng_000173 I WANT THOW SAID HODON COLDLY I WAN A DOSON HORSES I WANT MEN TO BRIGD THE WITH ME HE PUSHD HI WAY FORD WHICHWAY TO THE STABLES +M-AILABS_eng_000174 ERE IS A LIMIT WHAT YUCAND DO FO THE FIRST THIE YOU ANTER A MANS HOUSE AND BESIDE THAT WAS NO TIME TO AROUS SUSPION N THE MINDS OF ANY WON +M-AILABS_eng_000175 DO OU NOT REMEMER THAT HE SAS THY DEMAN THATS THE SPIRIT WHICH CEPES THE IS NOBLE CORAGOUS HIY UN MACHIBL +M-AILABS_eng_000176 MSTR BELL WHACAN HE NO OF JOAON HE LIVING A LASY LIF IN A DROUSY COLAGE +M-AILABS_eng_000177 AND THE CITN FOLOEDIMUARLY AT THER HEALS +M-AILABS_eng_000178 THE FIST TUTCHWOLD CASE AN EXPLOSION IN WHICH AMONG SUCH HUNDREDS OF INFERIATED MEN AND RECKLES BOYS +M-AILABS_eng_000179 WON F TH GEAT PLESUERS OF MARGRATS LIF AT THIS TIME WAS IN EATS BOY +M-AILABS_eng_000180 TH THNG IS GON ON LONG NOF THER IS ONE ORE BIAG ACXIDENT WE SHAL HAVE TO COMPRMISE WIT THEINERIVER ND CARYON THEWORK CUINTLY +M-AILABS_eng_000181 YOUAR LAT SAID SHE WEL SHE HELD HER BRATH O THE ANSR +M-AILABS_eng_000182 TRAT TOLD THE GIRLS THAT THEY MUS GO WIT HER FATHER TO LIV AND GIP CUSISILS LITE LD CABEN AND HEN THEY HERD THS REDFUL DECRE +M-AILABS_eng_000183 MARGIT SAT DON O THE ROG PATLY TO WARM HERSELF FOR THE DAMPNES O THE EVNING HUNG BOUT HER DRES AND OVER FITE HAD MAD HER CHILY +M-AILABS_eng_000184 O NOW YOUAR MSTAKAN ABOUT THAT RELID THE KING THEYARENOT MY PRISONERS BUT MY SLAVES WHOM MY PURCUSE FROM THE CING OF EV +M-AILABS_eng_000185 HER FATHE TOKU TE CMBRSATION +M-AILABS_eng_000186 IN ACORNER WAS A SORT OF DRESING TABLE ON WHICH LAY A COM AND BRUSH CANIDY SEED MUCH INTRUSTED INTHE TABLE AN WAS EXAMING ITWHN THE GORU RETERNE +M-AILABS_eng_000187 I HAVE SOME TIME THGT THAT MYSELF SHE AGEED BUT OFCOURS I DONT NOW STIL I HAVE TO BE PITY CARFUL SOMEON IS ALWAYS OVER HER BY MY DESS OR LOKING OVER HER +M-AILABS_eng_000188 I SHL STAY REPLID THEYONG MAN FOR I MEAN TO SIT YO FRE +M-AILABS_eng_000189 WHAT D YO DO ASD THE SORCERER +M-AILABS_eng_000190 WHIY THERE AR ENAMES YOUR SHORT HINES NOT ANY MORE REPLIED TROAT IM QUE OF THE INKES AND IM ALSO QUE OF THELOS SO I WONT HAVE MY PEPLE QUARLING +M-AILABS_eng_000191 TIPRITER WE CLICKING CLIPING WER BING SNIPD OTOF A UGE TACK OF NOE PERS AND PASED IN AN N LARG SCRAPBOKS SERKULER WER BENG FOLDED AN MAD REAY TO MAL FO THE FINAL APEL +M-AILABS_eng_000192 IT WAS FOR DAYS AFTER THE SUPRIES OF ALTHERS HORS HEN THE STRANGERS LET THE ASTAT TO THE CAIR OF RUGED OLD FORSTER HARMEN +M-AILABS_eng_000193 BPOR TEMPLTON HE SAID I USTONOW HIM MANY EARS AGO HE WE E BOYS MENTO SCOULWITHM AND AL THAT SOUTOFTHNG YONOW BUT AN TIL I RAN CROS HM OR +M-AILABS_eng_000194 I FOND HER I THE FARIST AND BOGT HER HER A PRISNE REPLIE THE CAPTON +M-AILABS_eng_000195 WHO MAY BE COMPITENT ITHE FROM PERSINAL EXPERIANCE OR THE EXPINS OF OTHERS TO ANSER IT WITH MOR OR LES CURECTNES OR AT LEAST AN ATTEMTD +M-AILABS_eng_000196 ON NINTY TO LATESTRETET SID HOKGEN BITING OF HIS SAGAR +M-AILABS_eng_000197 TRAT WA SRPRIE TO FINE SHE COUD CE SO PLAINLY THR THE HIY WAL OF WATER ABOVE HER BUT THE SON WAS ABL TO SHUT ITS BEME STRAT DOW THO THE TRANSPARENT SE +M-AILABS_eng_000198 THE SPAT WER ID SPRNG UP +M-AILABS_eng_000199 COME DENIL WIC SHE GAVE SUCH A UPOSION +M-AILABS_eng_000200 YOU SEE ANDTIL THE SCOL PILS WER INVENTED WE WASTED A LOT OF TIME IND STUADY THAT NOW MAY BE BETER IMPLOYED INM PRACTISING 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IACKERS WEN THY PONES CING ISAN +swc_eng_001916 ONCEVATION I USTRAYA +swc_eng_001917 IS TH SELAMANDOFF +swc_eng_001918 FIRST SELF DISCRIVE RANS HUMINEST BEAT FORMILY IN THE EAL +swc_eng_001919 EENT RE SURCH INDICATES TAT FACTERS OTHE THAN PRACTI +swc_eng_001920 ND PRVENTION AND TREATMENT OF OMPLCATIONS +swc_eng_001921 IH RAPID ON SAEIT +swc_eng_001922 U THOW WAR TE FOUNDATIN WAS BARIE +swc_eng_001923 NOWADAYS OURLY REAGINAL XPRESSTRAINS BETEE BURN AND SHPEITS TO BRICG AND FRAT TRANES CONTINUE TO UN ON TH MONTAN RILWA +swc_eng_001924 OTHE FAMLYS WITH PTINUALY GONDWAN AN ORIGION INCLUD THE RETRPONEDAY +swc_eng_001925 BY AN ITALIN DOMINICAN MORK JCOBES DESESLES +swc_eng_001926 AND WAS NAIED AFTE T +swc_eng_001927 ARTHFIHAL NTELIGENCS +swc_eng_001928 AND IS THE RAING +swc_eng_001929 PREN OF THE POPULATION +swc_eng_001930 CHFE ERIRS OF SHUPLIH SAELES +swc_eng_001931 IMPOSED BY LA +swc_eng_001932 RIFRINCESISS TO THE ROLING CORLAYSHIOND GOVERMEN +swc_eng_001933 PACHES OF GLIDING POSM +swc_eng_001934 BACE ON THE PREVIOS TRADGY OF PLAY +swc_eng_001935 N I DELLISTICK ASPERATIONS +swc_eng_001936 PERFECINLS AND WHOM RECOARING ATHOUSIASTS +swc_eng_001937 HEM THY OLPIDAY +swc_eng_001938 NO CORTE OF PEOPLE WIH A PREVIAS ESAY ACH MA DVELOUE HIG POPITCUITRISM +swc_eng_001939 DEVIDED INTO THRE FAMLYS THA +swc_eng_001940 HOWD SLIGT INTREST IN RELIAEING COET +swc_eng_001941 THAT A EMLUR NOFT TO HAVE COEN +swc_eng_001942 N TWO THUSAN SIK +swc_eng_001943 SH HIN BOYS AR NON AS BOT POLIS +swc_eng_001944 THE COUSE IS RUTUR OF A SERIBL ANDURISOM +swc_eng_001945 OST OFTHE AGJER YOU ESS MUSIC COMPNES +swc_eng_001946 ONCSTEIO PAR OR ON MONOFONIC TRACK IS PLAD OR RECORDED HEN THE TAP S MOVING IN ON DIRECTION AND T +swc_eng_001947 EITS EARLY FORME IN +swc_eng_001948 STR TEAGHC FILOSOVER +swc_eng_001949 OSITIONG T VANAEHES TERN THE GAME +swc_eng_001950 NEO SAUT WHELLS +swc_eng_001951 DISPOSAL OVER HIS ON BYLOUGICAL NATER +swc_eng_001952 EPODUCTIVE RIGHTS OR EXERT UN DO PRESHORS ON PRESPECTIE PAIN +swc_eng_001953 IL HANCHANT NORGON +swc_eng_001954 RASTA POPHOLOS HID GON +swc_eng_001955 ND TO THUSAND TO +swc_eng_001956 FOR GAMPL F THE PLAYAR HAS ONL T +swc_eng_001957 SOFEDA SUBRACKNODHMERGE HAVE COLNIIV IMPARMENT THAT FECT +swc_eng_001958 PEVIDEIG RONSTIC DATER +swc_eng_001959 HO HAD ANURISONS DETECTED BY OTHER MINS +swc_eng_001960 LIOESTILS DISIN TO MPROE HELTHAN UNCEVITY +swc_eng_001961 HAD MOR SUFITICATED AND OF TA PREDICTIO +swc_eng_001962 DE HUMAN ISATION +swc_eng_001963 SPACHES INCLOUD FRESHWATER LAMPRAYE +swc_eng_001964 FOST AND YU GRIM +swc_eng_001965 HE FREAN SICLOPEDIA AT +swc_eng_001966 THER FORE METICAL IMAGENG IS GENRL +swc_eng_001967 PEACEIST THE EXCLUION OF NON HUMAN AND PART HEUMAN ANIBLES +swc_eng_001968 N PEOPL H HAD PREVIASLY SUFRD A SUBRAC MOH HEMRIG +swc_eng_001969 LSIFIED AS AITHE IN DANGED OR THRTOND ANDTO THE PE BE +swc_eng_001970 IAN ATERNY JENERL PARCKER WATCONS HARDN +swc_eng_001971 BUT TIPICLY +swc_eng_001972 WHICH INTURNE FED THE SIGNL TO THE HAD OF TE COE +swc_eng_001973 THI THER OWN CONVENIONALY EXPECTED LIFE TIMES +swc_eng_001974 UBSTANHAL STRIN +swc_eng_001975 TWENYITH SENTRY CANTUCKY CONGRSMAN JOAN +swc_eng_001976 NOT PASINT OR INDIMI +swc_eng_001977 HUNTING WITH LED SHOA +swc_eng_001978 WNY THEIR TEN +swc_eng_001979 O THE SEVEN PRSENT OF THE WLDSPAT SPACHES LIVIN UTRALA +swc_eng_001980 RPOVS RAN FINLY END IN NTENHAY FIV +swc_eng_001981 WIL SOME TRENE HUMON IS TAKEAN ABSTRACT +swc_eng_001982 POINT THE RIHE PRETECTION +swc_eng_001983 AICS A MORFISM PROBLOM IS THE COMPETATINAL PROBLOM OF THE TERMINING WETH +swc_eng_001984 OR THE RESTRICT OUR CONSEPTI +swc_eng_001985 HOE HO SAR VIE HUSPITALIATION +swc_eng_001986 SOMEPETECTION OF UN SERTAN GNIFICANCE IS ONFURED BY CUCASIN ATHNITITY +swc_eng_001987 HALSTAE AGONS +swc_eng_001988 COGETIVE IN HANSME +swc_eng_001989 VANST THE ATH RANK AND BE PRMOTED TO NALO +swc_eng_001990 DRA BACK OF COILING IS THE PEBLIT +swc_eng_001991 INDICATE S SUBER ACKNOD HMBRIGE +swc_eng_001992 DAMICH PORTION +swc_eng_001993 NDOPTIO OF YEJEIK AND HANSMNTEKNAL JES +swc_eng_001994 POLISH ON HIS HORSE ND WAGAN +swc_eng_001995 D THE EXT HAMPIAN +swc_eng_001996 THE OF AFER ALDISHUCXLY +swc_eng_001997 L CHAP IN HINCTENTWENY ON +swc_eng_001998 SUCH AS QONTIM COPUTATION AND RANDMYSED ELGRETHEMS +swc_eng_001999 ATY NHADE NOIN +swc_eng_002000 AS SHONE BY LADNER THAT IF PE S NOT ACULTO AENP THAN THER EXIST PROVBLUMS +swc_eng_002001 HE COMPACT DISO +swc_eng_002002 DGREY GOSIN ERIAL +swc_eng_002003 WAS WENDERED AS IAGES +swc_eng_002004 SAY ACH ORE TO NOTHE CUS +swc_eng_002005 ONTITUNCEY OF FAVESHOM +voxforge_eng_000874 THE FORTH AND FITH DAYS PASE WIHOUT ANY DEVELLAMENCE +voxforge_eng_000875 THEY NOW THE REPORT +voxforge_eng_000876 SUCH THINGS HAD ACURD BEFOR HE TOLD FILAP +voxforge_eng_000877 THEY ONLY HAD A LITLE TERDY THOUSEND DOLER FIER +voxforge_eng_000878 I AM GOWING TO GET IT OWD +voxforge_eng_000879 HOU DUDLY HE MAINTAINED A COARME AND SMILING ASSPECT +voxforge_eng_000880 JON LOKE TRIUMPFENTLY AT SHELDON WHO BOWD +voxforge_eng_000883 OME ONEN T DILE MAR T TALENCST +voxforge_eng_000884 E IT WAS BEATING AND WATING IN THE AMBOSH OF THOSE BLACK PITS +voxforge_eng_000885 IT THE GO OUT AND EWIG MY BOYS +voxforge_eng_000886 SHE WINED DOWN IN WINES DREME M SERCHING THE SHADOS OF BULSHORS +voxforge_eng_000887 I JUS TO APRECSHATE WITH OUT BE AE O EXPRESE MY FELINGS +voxforge_eng_000888 SHE DOSNT NOW WHAT HE IS TAKING ABOUT +voxforge_eng_000889 YOUR FATHERS FIFT COMAND HE NOTED +voxforge_eng_000890 DON OU SE I HAE YOU +voxforge_eng_000891 ALITLE WARME BUT NOT AT AL STONISHED ETING MELENS AND THROWING HE RIND ABOUT +voxforge_eng_000892 THIS IS A GREAT PARDY +voxforge_eng_000893 THE BOY GRO IN PROSPERCET HOME +voxforge_eng_000894 AND LES SUCH LETERS BE PATENT THAT THEY MAY BE RED TO THEM AND WITH AL SEAL OR TESTIFIED +voxforge_eng_000895 HO COUD A WOMN DEAR TO VENTUER WE SO MANY EXPORS +voxforge_eng_000896 HE READS HIS FRAGMENTE ALOWD +voxforge_eng_000897 BUT HOW AR YOU GOING TO DO IT +voxforge_eng_000898 HOW DOYOU WAN TO GET A WAY WITH THIS +voxforge_eng_000899 WIL WE EVER FOR GET IT +voxforge_eng_000900 FOR MY ERLYIS RECALECTION MY SLE WS PERIAT OF THER +voxforge_eng_000901 TIN M IS MOSIEAR WHIY DOF ON YOUO WALL SHAK GEAM +voxforge_eng_000902 EDEVTH THE NEADIST REAFUGYEY I +voxforge_eng_000903 HIS SLIME HANDS CREPE THE ADGES OF THE TABLE +voxforge_eng_000904 WHID LAK HORN SID MS MORTOMER +voxforge_eng_000905 ITOK HM HAFA OUTO RCH HE ED O IT +voxforge_eng_000906 MARTHA WHER DO STAND O THE ONTRACTUL ISOUS +voxforge_eng_000907 AS TO BE UNDISTINGUIHABLE FROM THE VAST WHIHE PLAINS AROUND +voxforge_eng_000908 HE WOULD DESTROY AL THINGS THA ER FICXST +voxforge_eng_000909 THE RUSION USIC PLAR THE CONT WAS HROABEDINSLAVE +voxforge_eng_000910 TO HIS SPRIE HER ANTE WAS FLAT AND UN COMPROMIZSING +voxforge_eng_000911 THIS SOULD BE INTRESTING +voxforge_eng_000912 I AM A FRAIDE I DONT HAVE MUCH TIME +voxforge_eng_000913 CRISMS IS AN EASY PROBLOME COMPARD WIT A POLNASION GIVING FECST +voxforge_eng_000914 THE PLNTERS AR ARDY CONSIDERIG TH MATER +voxforge_eng_000915 JON CRIED WITH SHING EIYES +voxforge_eng_000916 WHO EVER LIVED ON THE RANCH DID THAT +voxforge_eng_000917 WE LEAVE THE EFVENTUALITY TO TIME AND LOR +voxforge_eng_000918 AT THE SAME TINE SPIARS ND EROS BEGAN TO FAL AMONG H IMVATERS +voxforge_eng_000920 IT IS MEARLY THE SIMPLE SOUPELITIF +voxforge_eng_000921 IN STEAID HE ARIVED ON THENIH OF TE SECAN DAY +voxforge_eng_000922 IN HIS ANGSITY AND SULISITUOD AND LOVE THEY DID NOT COUNT +voxforge_eng_000923 GOD BLESSHM I HOPE L O ON SING THEM FOREVER +voxforge_eng_000924 YO WERE INGAGED +voxforge_eng_000925 THER LACES WAS OF A DELICKEIT IVERE CALLOR FRAINETOMPTINTIN WITH EALOL +voxforge_eng_000927 IT WA THE SAME WAY WITH OUR REVOLVERS AND RIFALS +voxforge_eng_000928 HE KING HAD PROMISTO INCQUIRE ITO THE MATER +voxforge_eng_000929 DOS THA LOK GOODT +voxforge_eng_000930 FOR THE FIRST TIME IN HIS LIFE HE WAS YEARNING FOR ASCRAP +voxforge_eng_000931 I DEFIE ANY MAN TO GET A SOLAMON ILENDES SOR IN CALFORNTHEA +voxforge_eng_000932 HER IY SMOULTE STR AT HIM AS HE CAME OF THE BANGK +voxforge_eng_000933 ETY WAVE NONS SAL LICGDTHA +voxforge_eng_000934 MEN WHO NDEURIT CAL AT LIVING DEATH +voxforge_eng_000935 MTO SON WHOSED THIS BOK CEPER RODGERS +voxforge_eng_000938 I ONLY READTD D H THE FOARTATIONS +voxforge_eng_000939 THE WAS POPER DE VISION OF NAYBER I THE WORE THE INDEVRIGULY POPOARMED +voxforge_eng_000940 IALPEL YO THE LIBRARIN SAID WTHA RIHT FACE +voxforge_eng_000942 I SW MTR PUIAGNORD HIS HEAD GRIMLY IN SERCASTICLY +voxforge_eng_000943 THE RING OF THE BIG BILE AROUSD HIMN +voxforge_eng_000944 OR THE SCRACH OF A PIN ON A MANS HEAD VAST REAGONS OF THE ERTSEIRFIS REMAINE JEALOUGICLY UNON +voxforge_eng_000945 HE HAD BURDILY ENTERDAD DIS WHEN HES SOUOWD THE GLO OF A FIR +voxforge_eng_000946 THENES CHARS THE LIT COMAND +voxforge_eng_000947 IT WAS JEAN SINING SOFELY OER BEYON THE OACKS +voxforge_eng_000948 OFLING AROW BUSTD BETWEN US +voxforge_eng_000949 HATREIT AND MURDER AND LOUST FOR REVENCH THEY POESESTD TO OFER FLOWING +voxforge_eng_000950 THT YOUCUD HERE AL UPAN DON TE IMPOPOE +voxforge_eng_000951 IT WAS MY A DEA TO ATE +voxforge_eng_000952 SHE DOSNT WON TO WIN +voxforge_eng_000953 SHE HINKE IT IS BECAS HE WONSE SOMTHING ELTE +voxforge_eng_000954 HE PULLED AND THE LOK CRESET DOWN TO BRAKE HIS BACK +voxforge_eng_000955 THAT THE SOCALD FORSES AT WORK IN LIGHT HEE ALCTRISITY AND MAGNATISM +voxforge_eng_000956 HE TORND SHARPLYD AND PICE GRAGSIN ACOST THE PIVELER +voxforge_eng_000957 AL SO I WANT INFRMATION +voxforge_eng_000958 THE SIXT DAY HE SPENT IN THE CAVEN WIH GREAGSON +voxforge_eng_000959 ON THIS IPOTHICES THE HAMERING OF THE LTR MUNDYING CORPUSLES ON THE BOB CONFIRSE ITS CANATIK NEGY ON THE ON HAND +voxforge_eng_000960 NOW A FIRNY WILWE STREME AND EVER AN ANON YOU AMURGE FROM AL THE GROVES AND FLOWERS +voxforge_eng_000961 WITH OUT IT THE MOS DENSELY POPULATED RAGONS OF MOTEN YURP AND AMERICA +voxforge_eng_000962 TOMESPINKE HAS A HARPOON +voxforge_eng_000963 HE WNTE GE THE INISH T THIS FOW AREYSOFAGON +voxforge_eng_000964 LKE A FLASHE LONCED IMSELF INT THE FETHED MAS OF THE HOWL +voxforge_eng_000965 IT CONTAINES A TOTLE OF T WENTY ENTRES +voxforge_eng_000966 I HAE HELT MORE COMFORTABLE +voxforge_eng_000967 THA A POSSES TO MACH VATELITY +voxforge_eng_000968 THE WALF DOGE THRESD HIS GONT MUSALE TOWARD HIM +voxforge_eng_000971 THE GAVBIAL VOICE OF HE SMRIY RANG OUT +voxforge_eng_000972 IT WAS O RIVER ND MARGING LIAKE ORSELES FROM THE REAT SWOMP +voxforge_eng_000973 SAID THE MAL PULING HIMSELF TOGETHE IHA EFART YO MUSTHING ME VERY ROD +voxforge_eng_000974 IN WHAT BEUCOLICK SCOUO OF FENCE HE HAD BEN TORT WAS BE OND IMAGENING +voxforge_eng_000975 HAD NOT INABLED IN VESTIGATERS TO OB TAINE A COMPRITIVELY LITL COSET +voxforge_eng_000976 A TRICL OF FRESH BLOUD RAN OVER HIS FACE +voxforge_eng_000977 IT WAS A CURUS COINEITDANCE +voxforge_eng_000978 IT IS THE FIRE PARTLY SHE SAIDN +voxforge_eng_000979 THE JUST LAY OF IN THE OSH AND PLOUKED AWAYAN +voxforge_eng_000980 I NO THAT OUWER IN CHARDGE THERE AND GEE NOSE +voxforge_eng_000981 FOR TIE THE EXSITING THRILE OF HIS ADVENTUE WAS GON +voxforge_eng_000982 FUDNLY HIS FINGERS CLOSE THIDLY OVE THE HANGAOCHIF +voxforge_eng_000983 DEAR SIR YOR SECKANT VICTOM HAS FOLLON ON SCEADGJUALE TIME +voxforge_eng_000984 HE CN CAE FR IMSELF +voxforge_eng_000985 EACH INSULT ADED TO THE VOLOU OF THE CLAIME +voxforge_eng_000986 THOU IT MAY BE TRANSFORMED INTO ANY N OF THE FORMS OF WHCH ENRGY IS SESEPTIBL +voxforge_eng_000987 MESITDOES SCREAMED GRIED LOAF I MANYFESTED THE HIRADTICK ANDBOUN DHEN MENT OF HISTADIAR +voxforge_eng_000988 I WAN TO NO HOW ALL THIS IS POSEIVBLE +voxforge_eng_000989 PRENTING A SIMPL AND INSTRUCTIV ILUSTRATION OF THE STRGL FOR LIFE AMNG THE RIVELE SPEACES +voxforge_eng_000990 HILL NEVER DO A TAP OF WORK THE HOL VOYAGEH +voxforge_eng_000991 I HAE HUNTED ALONG THIS RICE REPLIED FILIP +voxforge_eng_000992 LORD BUT IM GED TO SE YO AGIN FIL +voxforge_eng_000993 HOVELINLY I WEN DADED THAF RS TA +voxforge_eng_000994 THE AR OT REGULE OSTER PIRETS NICLES CONTNED +voxforge_eng_000995 THE MST BE HRDING FOR BUSNES BUT I THUG YOU MIGT WAT T TAKE LOK T THER SIGHT +voxforge_eng_000996 THER WAS NO CANCE TO FIRE WITHOUT HINING HIM +voxforge_eng_000997 AS FOR HIMSELF WONT THE STREAE RAL WAY ARNINGS INCREING SADLY +voxforge_eng_000998 DON HIM CAN YOUR BOY GO LONG WIT ESSY +voxforge_eng_000999 GOLD BY PEAR HE SHOWTED +voxforge_eng_001000 BUT SUCH A DEVERDGIENS OF APINION WOULD CONSTITUT NO MENENCE TO SOSCITY +voxforge_eng_001001 T THERE WAS ONE CHANCES AND ONLY ON OF SAVING JONT +voxforge_eng_001002 I I CAN OT FOLOE YO SHE SAIND +voxforge_eng_001003 ON THE FAR CORNER OF THE COMPOUND FENTS A WHAOK BREADED +voxforge_eng_001004 THEN AGIN TOTER HAD SUC A IRITATING WAY ABOUT HIM +voxpopuli_eng_000494 WE ALL NOW OMAN AS A SUCESFLE STABL CONTRY AROL MOR THERFOR THAT FOR THE HOL REAGON +voxpopuli_eng_000495 THEREFOR ITS HIGH TIME OU COME FORBOD E THE PROPOSAL FOR REVEU BE DANOPRAIONAL SUPERACION OF THE OARDIT AND NON ADITSERVISIES UNDER A DIECT EAUS OBEITISON +voxpopuli_eng_000496 IT ISCKEARE THAT WE HAVE NO TIME TO WAST THE NUERESOLTS OF THEE I PEESHE RECARD N SIENTIFIC BACSES OF GLIMIT JAINSE LEVE NO ROUOME FOR HESITDASON +voxpopuli_eng_000497 SENT SO IN THE CONTAINER WHIHAEVER AEN TUCHED COME SLAVES COUNTEOFET GODS DRUGS IT SETR +voxpopuli_eng_000498 I HOPE THAT COMIONS MOBIT INESHES INISIFIVES HO ONT CRAT THE NEXT PROBLOM BUT WILL BE A ANSER FOR EXISTING CHALINGES OF THER OUT TANSPORED SECTO +voxpopuli_eng_000499 IN THEWUE IT WASA DICION TAGNAULY BY ONE PRSON THE ORMER PRESIDENT O THENIGDED STATES AGANCE THE ATICULATED MCRATIC DUMAJURITY O TH EU ES CONGRES BY ALL OF ITS REPUBLICKEN ND SOM FITS DEMECRATIC T DEMACRAT MEMBERSIT WASAN AGREMENT WITHOUT ANY BINDNG OBLIGATIONS AT HE LEDES OF ERUN VERY UPANLY ANPRESIDH MAPTLY NTHE ERY DAY THE SOCALD DEL WAS POULISHE +voxpopuli_eng_000500 FRE SPEACH IS ASENIUALY AEXETIG THT PEOPL ARE FREE TO SAY THINGS WE DO NOT LIK NOT MELY FREE TO SAY THINGS WE DO LIK +voxpopuli_eng_000501 HAT IS LURNE FOM THIE +voxpopuli_eng_000502 BE SIN THAT THE NVIMENTAL EFFECT OF PRODUCS MUST BE AVRY INMPORTANT ISUEIN HER EEWU AND THE WOL I DEAE O THE ECULABER GIVS A VER YUSOULORIANTATION FOR THE OUSUMERS OF COUS HE ECULABER HOULD GIVEN TO THE MOST ANDVIRMENT AF FANDY PODUCT THE INFORMATION SOULD BECLEARE AND CUE +voxpopuli_eng_000503 HOWEVER THE CARENDRYGEM NEDES TO BE BETERD ALORDT TO TH IGIDAL INVIRNMENT TO ISHURE FAR MINERATION TO GREATERS AEN TO ONFOME TO ONSUMER EXPECTATIONS +voxpopuli_eng_000504 AT CASE BY THE CMION AND MEMBER STAT TO NHANE THERSUPORT TO RECONCILIATION TO SECUR PESE AND TIBILITY AND ARLAND IWOL THEREFORE ARD YU CALIES TO PLEASE SUPORT IS AMENMEN +voxpopuli_eng_000505 TRATAGICK CHOICES ABOUT WHE TO E WEST MUT BE MADE NOW TAKEN IN E COUN A NE TO FAS OUT FOR SILFUL SUPSITES BUT TAK THE GAS AS I ORSOFYU IT CAN BE A HELTFULE BRIGING TRUNSISHONARY MEDIOM TO BE USE IN MEMIN MENY MBERSTAT I BE ONTO EACHIVE OVER AMBISHIOS CLIMITARGITS +voxpopuli_eng_000506 WE AE POSEILY FR A OLE WE CAN CUTH TO PRASCUE THE SAMEM POLICES IN TH SAME MANER NOWING THAT WE LEDE TO THISAMPRSOS THE RISAULS THA WENO DEDEA +voxpopuli_eng_000507 UT HER SANOPTION B +voxpopuli_eng_000508 WRE ALL SO NED A CHAINGE IN OR IDOLITIE +voxpopuli_eng_000509 A LADEH BAT OF THE REASON OF COURSE IS ILIGALFISCINGK AND THERE OFOM P TDON OFEN BY YARR VESES WHICH ARE REAGISTERD TO COUNTRES WHICH LUCKE THE WIL OF THE RESURCES TO NFORST INTHE NESINAL AGREMENS NO MOUNT OF TRESABIITY MESERS ORE EXTRPAPREWARE WIL ADESE THE PROBLOUME OF REDUSING +voxpopuli_eng_000510 THE COMPRMISE ALSO INCLDED KLARERUDS TO THE FINE WHICH MBERSTATE AS HERSTICTION AND THE OPRATION ITHIMBERSTATS CONERD FOR CRUSBR THE CACES ASILA THE NED TO EINVLLF YOUR JUST THAN YOF OR WORK AND PLAE OU SEUPORT TO MO HIS ERECTIV +voxpopuli_eng_000511 NO THE RENS WOULD HAV AS BELETHATHE AR BAD BES CRIMINAL BES DELIBEATLY CONTAMINATING HUDY WITHA DANEUS NGREDIENT BUT IT FACT INFAC HE DINGWHT HUNY BES AR AL HVE ALWAS DON WIH TO CARY POLON BAC TOTHER HIVESTOD TO FED THER OUN +voxpopuli_eng_000512 UT IT WAS THE CONTRY ITSELF BENG MOR CAPABL +voxpopuli_eng_000513 R INTO THE PRT FOLIO OF THE NEUW COMIONAR DELING WITH FUNDEMENTER RITES +voxpopuli_eng_000514 THE MESIYGI TAT THE OU DODT NAT HAVE AN NOURSOLUIONS +voxpopuli_eng_000515 AR YOU WILING TO ACT INERE FAVER FOR THE SOSIAL DEMENTION TO BE INCLOUDED IN THE EU COMPATENCSES AS PROPOSE +voxpopuli_eng_000516 A NEXTHAT ON PESPECTRUPOLIES TAKIN WITHE EFORM OF OUER TELICON TH FRAM WOR +voxpopuli_eng_000517 I BELEVE HIS REMARKS WER A EXPLICITLY RACEIST AND THEN AFOBICK AND PRMOTED RACIAL INTOLERANCE IN A WAY THA IS NOT CXCEPTIBLE OR ALOWD IN TE CONTITUTION OF THIS HOUS +voxpopuli_eng_000518 REAL IFE GAMPL SHO THAT SOLVING ITIES RELATE TO ADUCATION FEULED STRONGCOMINIT DEVELOPMENT +voxpopuli_eng_000519 SI HOPE THA TIS ILHVPE ORUSHA AS WEL ND THAT RUHA CAN ALTS AND VISAIG ND EXTREME SUCESS TORY AFTER THS EG TISIGNIFICAND AT IN ORGST THIS YEARB +voxpopuli_eng_000520 SHE ECXEPTED THE FACT THAT SITISON SHIP IS AY NASINAL PART OF THE OSINO GUDISDICTION BUT HYOURLSO SAID THAT ACOURDING TO THE MASTRICK TREATY AND SHE AS RIGHT THE HAS TO BE ADIYREC LIN +voxpopuli_eng_000521 TDEY WOU FALD ESPECIAL EAN THE MST RATING AUNIFIED AND T AFFISHENT APPRORCH TO LIE MITCANGHE TREATMENT ASWEL AS IN STRANTHANINGK ITS LEDING POLITICAL COSION IN DISAGENDER I CONSCITHER THERFOR TAKING THISRESOLUTION AN ACT OF UTMOST IMPORTANS +voxpopuli_eng_000522 THE UNIGTED STATE OF YURUO WIL BE A FACT WITH SWEDON AS A PROVIDENC +voxpopuli_eng_000523 IT MUS B THE CAPITALE OF BOT THEATES AND WE MUS RECONISE POLSTINIS THAT AS PROVIDED FOR IN THE OVE LOGREMENCS +voxpopuli_eng_000524 YOU CRAINYS FACE T WITH WONE OF CRUSAL CHALINGES IN ITS HISTORY IT WOULD BE FU TE MENTARLY RONGK TO PRE THE NATION NOW WIT AL THIPES OF RESTRICTIONS POPELADERL CALE OSTERITE POLI +voxpopuli_eng_000525 MORE RULS AND REGULATION WILL NOT IMPROVE THIS CITUATIO +voxpopuli_eng_000526 AT LEAST WE WOLDLIKE TO NOW THE SOURSE OF THE MONY AND THE POSIPL MORTIE +voxpopuli_eng_000527 TO WEROF THOSE YURUPIN WALE LANGIASH IN TO THES GLUBELICED WEARLD IS INT TO THEYSGOBELISDECONOM IN DHIS GOBE VILACH WHICH IS GORSTIALY CONOMICK SOSIAL ELNPLITICO ITS AR MOST VELABLE ESTHERT FROM THEINTIRE E YOU THAT WE MUST THAK FOL ACOUNS AND T +voxpopuli_eng_000528 WEAVE TO REPETE THAT AL THE AY ANOT BE USE TO FINANS SIURIT EXPANCES BARTHERS CONTROL OR MLITRY SOPORNT +voxpopuli_eng_000529 THN THE SINTIFI REPORTS BECOE MRE MORE URGENT OR ALARMING AND MOR SHOCKING +voxpopuli_eng_000530 FINALYM WHEN WEAT THINKING ABOUN THER INOVATIVE FINSION INSTOUMENTS WHEN OU THE BOLTH FOR OURSELS TOR SUPOART OWER A CONOMES BUT ALS SO TOOE SUPORT THOS HOERE INEAET +voxpopuli_eng_000531 THT IVE A SO YUNIEK DOLL IN PE MAKING +voxpopuli_eng_000532 PAPER A VERYD WEEK PROPOSL +voxpopuli_eng_000533 SRUSHAS ALWAS BE A VERY PROUDNATION WITH RICH COLTCUER WITH INVENTIONS WITHAN AS PL +voxpopuli_eng_000534 ARTACXATIN EVEN A MODICAL OF TACXATION IN SOME CACES MIGH JUST HELPUS EM TO DO WHAT IVEAREDY SUEGESTED AN WHO NOSE MAKE THE CACE FOR THE RETRESPECT OF BANKRE CAPIDLIZATION THAT WE NEVERSO +voxpopuli_eng_000535 THEROPE AN ASILOM SUPORTOFHIS MOR OVER AS AMONG ITS THASTS TO PRMOUTD FESILYTAT AND COURDINAT EXTCANGES OF INFORMATION AND OTHER ACTIVEITES RELATED O ELOCATIN WTH IN HE UNION +voxpopuli_eng_000536 HE ONUSO OF THE FRAMEBORK AGEMENT PROVIDES A LIGLY BINDING INSTRMENT TO OBGRAT AND STRANTN EU OSTRALIA BY LITHRRATIONS AND TO INCRESCOPERATION +voxpopuli_eng_000537 THEREFOR WEAEASTIN THE COUSAL AS GMION TO RESENTHA HAS BALE THA OULD BE THE SESTMENT OF THE EBACT OF THE RICIS +voxpopuli_eng_000538 IN OTHE WORDS THE OBJECTION IS NOT WHETHER MONEY IS PAD OR NOT THE OBJECTION IS WETHER TER IS A DIDECTLINK ORNO +voxpopuli_eng_000539 TO THSTINGUISHES THE TO MAN OEAR YOUMER RIGT AB BUSE BY THE CADANT GORMENT AND THEDLANIAN NUCLAPROVGDM +voxpopuli_eng_000540 YESS MATHMDRUO THANKATHR SECTIAL HERASDMENT IS A FORM OF VILANCS AND IT ISTHE MOST EXTREAME FORM OF GNTERBAETH DISCUMINATI +voxpopuli_eng_000541 WE CAN LOK TO SOME URAN LIN OU MEMBERS FOR OUOD GXAMPLES AS REGARDED THGNOLIGE +voxpopuli_eng_000542 YIMNVALLVED FOR THER POSITEVE AND COSTRACTEIVE ABROTCH +voxpopuli_eng_000543 O I HOP THAT ISWIL BE COMPLEATED EAR IN HE FACIVIL FUTUAR THAT MANES MA BE TO AFRE MONS +voxpopuli_eng_000544 OR FORDER NDCOURDSHTHE YOU HAND EFORTS TO BRING AMONGK PES IN OF GNISTAN AN TO OVERCOME THEF FRASILE SICUITY ANVIRMENT IN THE CONTRY +voxpopuli_eng_000545 BE ANDER STANT THAT SOME PEOPEL AR ANGRY +voxpopuli_eng_000546 ON TO BE MRESTPONCIVEL +voxpopuli_eng_000547 WE MUST EDACTIFIETH THIS SUTIATION AND VEASK THE OMION TO CONCSIDER THE MOST EDICUIT COMBINSATION MESES FOW PASNGES +voxpopuli_eng_000548 THE OMITION INBVIHED THE YUROPIANT PULAMENT IN THE UPCOMINCREVISION TO OPEN HIS POSITION ON THIS MATER WHICH RELY CONSED AXES TO S JUSTIS IN YUROP AND THE ENFORTMENT OF RIES GRANTED BY HE YUROPIANER YUNAN LO +voxpopuli_eng_000549 I L OM VERY MUCH TH RISOUNTION OF TOKE BETWEN THEOS RALIS AN PLESTINIONS AND SNCEIRLY HOP THAT HE WIL SUCED +voxpopuli_eng_000550 WE HAVE ACUMILATION OF PROBLENCS RESULTING FROME THE ARTIFIHAL AND DEBAGEATINGK AND VERPREVIUSYUS +voxpopuli_eng_000551 LET UST NOT BE THE MAN OF YESTERDY LNT UN BEPOL DAYS INSTHITUTIO +voxpopuli_eng_000552 T I GOULD ERLSUM TO BECOME AMBASSETHES O THE YEAR MAKNG ITS A DEARS AND ACTIVITHIS WO WIDLY NOWN A MONCSH TO YURUPEAT ITIENS AND PUTPIIPATING N EVENS BE TAT YOUROPIAN NASIONALL FOR LOK ALEVL +voxpopuli_eng_000553 SERTNLY SUCH IMPACE SESTMENT COULD PREMT SERTAN PROBLOMS SUCH AS THOS POSED BY THE ELECTRONIK IDENTIFICATION OF SHEP AND SCOTLAND +voxpopuli_eng_000554 THE CORTIS CONTENT TO SEE THT ITS WORK HAS INFORME TH DIS CHAGH ROS AND HAS CONTEBUTED TO ROPOSALS FOR IMPROVING THE FINANCAL MANAGHMENT OF VEYOUSPENDING AND BETHE TARKATING OF YOU FUNE +voxpopuli_eng_000555 REGOUTHERE CLARIE THE AND SERTANTY IS NEDED FOR THE OBLICK SECTOUR AND FOR THE INDUSTRY +voxpopuli_eng_000556 IS IT REALINOT POSIBLE TO US A ATHER HOUSING FASCILIDES WITH U PROPRE H RESEPTIN CONDIONS IN THE MEN TIME +voxpopuli_eng_000557 WHEL YOU TAKE ACION AT LAST IF NOT THEN WHEND diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/token b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/token new file mode 100644 index 0000000000000000000000000000000000000000..b91341a30c09df12293efb702aad90098e9ea00e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/token @@ -0,0 +1,1092 @@ +LAD_eng_000254 H E R E M A I E D W E L C H A M P I A N A N T I L N I N T E N S I X T Y F I V E A Y E A R I W H C H S U F R D A T E R A B L E A C X I D E N T +LAD_eng_000255 A Y L I B R A L C O N S E V I T I V E H E W A S D E F E A T E D I N A T E I N A T Y T O +LAD_eng_000256 O N R O D L A A R C O N D R A R T W O R O D S A T W O A N C E +LAD_eng_000257 S O M E O F T H E C O N T R E S H V E S U R V A Y S F O R M A L T I P L E Y E A R S +LAD_eng_000258 B O T H O F T H E V R S I O N S F E A C H R T H E S O N G H A P Y H O L I D A Y +LAD_eng_000259 S H A K X P I A R M A N Y R E F R N C E S A R E M A D E T O S E N S I N T R A C T I O N S O R C A R I C T E S F R O M V A R I O U S P L A Y E S +LAD_eng_000260 I F O N L Y T H E R O G R A M C U L D B R A K E O U T J U S T A I T L E F R O M I T S T O F O M I L I A R A P R O C H +LAD_eng_000261 T H E H E L B E M W A S R E L E A S E D I N O S T R A L I A R O N N I N T E I N T H O R G I S T T W O T H O U S N D A D E L E V E N +LAD_eng_000262 H E N O W P L A C E F O R A S T R A L I N C L O B E P E R T H G L O R Y +LAD_eng_000263 I T I S N O T N O N H O W M U C H I F E A N Y O F H E C L A M S A R T R U +LAD_eng_000264 A S M A L B I S I N E S S O N R B R O A R D O P R A T E D H I W E A T A D S H E P F A M E F O R S I C T E N Y E A R S F R O T H E A G E O F W E N T Y T O +LAD_eng_000265 I N T H E N I N T H S E N T U R Y H E W A S A N I R I S H P O E T +LAD_eng_000266 T H E Y A R E M A R K E D B Y S T R O N G +LAD_eng_000267 T H E L O W I S T H E F O R V A O L E D +LAD_eng_000268 I N T H E R L Y S T A G E S C A M E C L O S E T O U S A S L E P +LAD_eng_000269 R O N I N G E V E R Y T H R T Y M I N U T T H R O A T S E R V I S T I M S +LAD_eng_000270 A S A R E S I U L T W H E N T H E C O L I G E R E O P E N D I T W A S A S A N A L L M A L E C O L I G E +LAD_eng_000271 T H E T I M E B E T W E E T H E S P O I N C T I S V E R I A B L A N D C A N A C U R A N Y W H E R F R O A M I N I T T O M U C H L O N G E R +LAD_eng_000272 W O A R K O N T H E E A E E S T A R T E D I N M A R C H T W O T H O U S N D A N D S E V E N A T A C O S T O F F I V E M I L I A N D O L E R S +LAD_eng_000273 H O W E V E R T H E R W A S S O M E D I A G R E M E N T O V T H E N D I N G T H E M E W H I C H O R M O R Y A N D Y O H I M O R Y D I S C U S T D A T L E N G T H O V E R E M A L +LAD_eng_000274 T H E C O P L E H A D N O C H I L D R A N +LAD_eng_000275 T H E F I A L S I N G L O T H A T D E B U A L B H M P A R I S C O L I N G H A D A N E L A B R T M U S I C V I D I O +LAD_eng_000276 T H E S E R I S E N D E D O N S I X T H O R G E S T T O T H O U S N D A N D F O R L A S T I N G F R A T O U T E O F S E V E N T Y O N D A Y S +LAD_eng_000277 H E H A S A L S O C O N T R I B U T E D T O T H E N E W Y O R K R E V I O O F B O O K S +LAD_eng_000278 B Y P L A C I N G S M A L A R T O B J E C T T R O O U T T H E F I L M +LAD_eng_000279 I T I S F O U N D I N B R E S I L +LAD_eng_000280 I T W S T H E S I D O F T H E F A M L Y I I D E N T I F I E D M O R E W I T H +LAD_eng_000281 H C A N D I T S I G H T E S M U S T A L S O R S O B M I T A W O R K P L A N +LAD_eng_000282 D U N D E Y W H N T H E M A C H T H R E T O +LAD_eng_000283 H O W E V E R T H E V I L I G E R E M A I N D I C A L A T E D A N T I L T H E R I V E L O F T H E F I R S T N O U S P A P E R S E C O N D R E P O U B L I C K +LAD_eng_000284 T H E F A S T S E R V I S I T H E E U C H U R C W A S H E L D I N I N T E F I F T Y O N A L T H O T H E B I L D I G W A S N O T F U L Y F I N I S H E D +LAD_eng_000285 T H E A V E R I G E H O U S E H L D S I E W A S T W O P O I N T T O S E V E N N D T H E A V E R I G H F A M L Y S I E W A S T H R E P O I N T I R O S R O +LAD_eng_000286 I T W A S F I R S T B R A D C A S T O N T H I R D G A N I U R Y T W O T H O U S O N D N D T E N +LAD_eng_000287 T H E W I N G S W E R O W M A D I N A S I N G L E P R E S I N G +LAD_eng_000288 H E D O C T R O H L O S O F Y I N E N G E N E A R I N G M A N A G E M E N T +LAD_eng_000289 T H I S T O K W A Y T H E M A I N A R G U M E N T O F S A F T Y R I S S K +LAD_eng_000290 H E W A S A L S O M A D A L I F E M E M B E R O F S G U N T H O R P P U N I T E D +LAD_eng_000291 S H E F I A R S T H E Y W I L G E T A D E V O R S E B U T T H I S N E V E R H A P E N S +LAD_eng_000292 F O U T D R O P S I N A B L E T O H A D T H E F O T S T R A T A C R O S E +LAD_eng_000293 W H E T E T H E A R F L O I S F R E Y O R F O R S T C N F E C T H E E N A G Y A F I A N C Y O F T H E E N D O +LAD_eng_000294 A F T E R G E T I N H E R I H T M A S U R M E N T T H E Y M A D T H E N E W D O R S +LAD_eng_000295 F R A G M E N T S O N A C H F A C E A R E M A R E W T H L E T E R S A Y B E S E +LAD_eng_000296 F R O M T H E F I R S T M I N I T S B O T H T E M E S S H O W D T H E D I S I R E T O C M P E E T W I T H E G E I V E A P R O C H E S +LAD_eng_000297 F I S I C L H E R I P Y E X C U S I S E S M A Y H E L P P A T I O N T T O M A I N T A I N M U L E S T R I N G T H +LAD_eng_000298 H O W E V E R T H E T O W N S H E L I V S I N N O O N W A N T S T O H E R A B O U T H E R +LAD_eng_000299 A N D D I S C R I V E S A P O I N T M E T O F A N A C T I N G C H E V E J U S T I S O R J U D G E O F T H E S U P R E M E C O R T +LAD_eng_000300 T H E S O Y B E N S O U T A C O V E R I N G I S T H E N R E M O V E D A N D T H E B E N S A R E P A R T I A L Y C O O K E D +LAD_eng_000301 T H I S N A S I N A L E M O V M E N T W H I C H A D B E G U N W I T H S O M U C H H O P C A M E T O A S A D E N D +LAD_eng_000302 H I S A S O S C I A T Y U S U A L Y C A L D H I M T E O R T H E O D L O K I N G G I Y +LAD_eng_000303 I T S M A I N O F I C E S W E R I N L U N D A N W I E H E S E C N D O F I S B E L L F A S T +LAD_eng_000304 A C T U L Y I H A D N E V E R B E N T O A V I L I G E B E F O R T H A T +LAD_eng_000305 H E A S C H A R G E D I T H P L A N I N G T O S E T O F B O M S I N U R O P A N D T H E U N I T E D S T A T E +LAD_eng_000306 M A K I N G M E R S I S T H E H I R D S T U D O R H L B U M B Y B E L G E N A S T R A L I A N A R T I S T G O T I A Y +LAD_eng_000307 H E T H E N M O V E D T O W A S I N G T O N D E S E A N D W A S A P A R T N R I T H W A R D B R O N A N D T I L N I N T E N T W E N T Y N I N +LAD_eng_000308 J O S O F H I Y S C O L E A N D T H E S C O L E S T H E C M P E G A I N E I N A L S P O R T S +LAD_eng_000309 T W E L F P L U S O N M A C H B A N P E R C A R D +LAD_eng_000310 I H I N K I M I G H T B E N O T H I N G +LAD_eng_000311 T H E H O E W A S B I L T A N D L I V E D I N B Y A N D R U J A C X A N D C A N D Y D E P U T Y C L E C T E O T H E I N T E R N A L R E V I N O U S E R V I S +LAD_eng_000312 I N N I N T E N S I X T Y F O R H E W E N T B A C T O O M S K A N D E N T E T H E A C T O A S C O L O F O M P S +LAD_eng_000313 T H E B A N K I S J O U N T L Y O N D B Y H I M A N D H I S B R O V E R A N D R E L I T I V E S +LAD_eng_000314 H E S U B P S I C U N T L Y W E N T T O C O L I N B R I S T A L +LAD_eng_000315 W O N T H O U S A N D A T H U N D R D F O A R T Y S I C X F O R H E D I O N +LAD_eng_000316 A P A R T O F L I T L I N G L A N D B E Y O N D W A L S I T H A S B E A C E N C H A L Y I N G L I S H S P E A K I N G F O R N I N H U N D R E D Y E A R S +LAD_eng_000317 H E P L A D W T H T E N P L A Y A R S F O R H A R F W A S A G A I N E A T R D I O N I N J E E S P +LAD_eng_000318 T H E R E S I D I N G J U D G E W A S W E B S T A F A I R H O W A S A L R E A D Y A S I N D T O T H E C O R T B E F O R E T H I S C A C E W A S H E D I L D +LAD_eng_000319 B I G B R A T H E R F I V E W A S T H E H U R D O F H E M A I N S A R I S T O F E A C U E R A L I V E L O N C H +LAD_eng_000320 I T S M O T O I S W H O E V E Y O U A R A N D W H E R E V E Y O U A R E O N T H E J U N Y O F F A I F H Y O U A E W E L C O M H E R +LAD_eng_000321 R O B A T E Y M I L O R A S C O C H W I L S O N +LAD_eng_000322 A F T E R O N Y E A R B R A K S I R O D E G R E W A S H E F O L O I N G V E N T H A R +LAD_eng_000323 A Y A M T E E M A N U F A C T E D A M O R D L C I T O F H E S E D S E I D A R D R A C K X S T O R +LAD_eng_000324 T H E E S S E S S A Y A M E D T O B I L D A L E F T W I N G O L T U R N I T I V E T O N O W L A B E R A N D T H E E S A N P E +LAD_eng_000325 H E L I V E S L I K E H E A S A Y O N G P E R S O N +LAD_eng_000326 M A S T E O F S I N E S I N E N G E N E A R I G M A N A G E N T +LAD_eng_000327 S H E F A I L E D T O M A K H E T O P T H R E A T T H E C A N I A N J U N I A T R A C T R I L E S T H A T J O N +LAD_eng_000328 A T O R E F O L O E D I N S E P O R T +LAD_eng_000329 T H E Y E R S T A B I S H I N A T E N S E V E N T Y O N A N D A R W N O T H E O L D S T C L O U B S I N H E S O U T H O F I N G L A N D +LAD_eng_000330 H E A S A M E M B E R O F T H E G E S T S C O T L A N D A D V I S E R Y B O R D +LAD_eng_000331 T W O T H O U S A N D A N D F I V E G E N T L E M E N +LAD_eng_000332 A O R E F I L E A D S T R O N G R E S E P T I O N I N Y U R U P A N D A C H I V E D D I S T O B U T I O N B U T T H A T W A S N O T T H E C A C E H E R +LAD_eng_000333 B O L T H O I S S T E T C H E S P O S T E R I A R A N G C A L S T R U C T U E S +LAD_eng_000334 H E A S A L S O A T H E E T I M E F R E N C H N A S I A L C H A M P I A N N I N T E N I N T Y N I N T I E N I T Y F O R T W O H O U S N D A D W O N +LAD_eng_000335 T H E V I L I G E S T R U C T U R S H O W I N H I S M A P I S T A G R E E X T E N T U N C H A N G E D O D A Y +LAD_eng_000336 R U H A I S R E C O G N I S E D I T N U C L A R D I S A R S T T O E X P A R T E S A N D F O T H E S A V F T Y O I T S T E C K N O L A G Y +LAD_eng_000337 A S O F T O T H O U S E N D O D F O R T E E N E M T Y V E I S A V A I L A B L E W I T H I N T H E U N I T E D C I N G D U M O N V E R G I N M E D I A R A N D S C K I Y +LAD_eng_000338 N E W Y O R K P E A N G U I N R A N D M H O U S E +LAD_eng_000339 T H E D U T C H E Y W A S S C E C U R E I N T E U T C O M E O F T H E G O F I C K W A O R +LAD_eng_000340 W I H G O D P A C E S D A R T E H E M A T C H W I T H B O T H T E M E S O L T E N A T I N G S U P R E M A S Y +LAD_eng_000341 T H I S V R T I O N I S N O T E A D O R B I G V E R Y F A F U L T O T H E A R I G I N A L N O V L +LAD_eng_000342 T H I S P R E S U M P T I O N I S N O T F L E F I L E D O N H A S T O N O A T L E A S T T O C O N G A T D I A M A T E S +LAD_eng_000343 N O T A B L E T I T L E S I N C L U D E D G O L D A N A C X S T H E R E V E N G O F D E T H A D E R R A D M O B I L O U T R U N O E S A N D S A K G A R S O N I C T H E H E G H O G +LAD_eng_000344 T H E N I N T E N N I N T Y N I N J U G M E N T N O T E D T H A T T H E I N F L O N C O F T H F A T H E R O F T H E C U S E D H A S B E E T H E R +LAD_eng_000345 M O K D A U F S W A R S R E V E N G E H A N D J O I N S F O R E S I T H M A L C O M T O O V E R T R O M O K B E A T H +LAD_eng_000346 T H E M E D Y E V L V I L I G E C O R T W A S A L W A Y S A N I O U S T O C E P E T H E F E N E A R O N D T H E I L I G E G C A P L E S +LAD_eng_000347 T H E R W A S A N I N R A N K S I S T O M E A C H R A N K H A V I G M O R E P O W E T A T H E L O E R A N K +LAD_eng_000348 T H E A S T A B L I S H E D D I P L A M A T I R E L A T I O N S O N S E P T E M B R N I N T E N T H N I N T E N S E V E N T Y T O +LAD_eng_000349 T H I S W A S F I R T H E R X T E N D E D T O I N C L O U D M O R U C A D A T E S I N D I S E M B E R T W O T H O U S A N D N D F O R T E E N +LAD_eng_000350 T H E U C H G O V E R M E N T I S C A R N T L Y E X S A M I N G T H E E A L C O N C I C U E N C E S O F T H R O L I N G +LAD_eng_000351 F R O M N I N T E N T H U R T Y T H R E E T O N I N T E E N F O A R T Y N I N T H E M A R I C O N L E E W O N T W E L V E O U T O T H E F I R S T S I X T E N +LAD_eng_000352 T H E A R H E F E L S I C K W I T H T I F A S H I M S E L F +LAD_eng_000353 S I X T T E M S A V B E D V I D E D I N T O T W O G R O U P S O F T H R E E T E M S E A C H +LAD_eng_000354 T H E F I R S T C E A S O N P R E M I A E D O N T W E L T H J U O N T W O T H O U S N D A D F I F T E N +LAD_eng_000355 I T S C E E D T H E W H I B O A R D A N D S I S T A M E T W E N T Y F O R C O M B I N G F E A C U E S F O M B O T H +LAD_eng_000356 V L L I U M E T O O N U M B E R S O N T O A N D T H R E +LAD_eng_000357 T H E L O W E P A R T O F M E N S D E S E S W E M U C H S O U R T I N L E N C T H O T H O S F O R W M E N +LAD_eng_000358 T H E V I S I G O T H S I N T E R N W E S C E A D E D B Y T H E M O R S +LAD_eng_000359 J O S O F H I S C O L E E V E R Y W E O F T H E C O L Y E A R +LAD_eng_000360 A S T H R S I L T O F A L T H E A R G U M E N T G E T I N G T O H E R +LAD_eng_000361 I T H A D Q U A R T E R S A R E I N S H E F I A L D Y O U N I T E D C I N G D O M +LAD_eng_000362 L A Y A L S O F I A L Y S I N E T H E C O N T R A C T O N S T A G E W I T H E D I R E C T E R A D P R E D U S E S O F T H E G O U L D A N E Y E S +LAD_eng_000363 F I S I C L F E R I P Y C N H E L E P A T I O N E T O L U R N H O T O W A R K W I T H F O T D R O P +LAD_eng_000364 I T E N T O N T O S E L T H R E H U N D R E D T H O U S A N D U N I T S A C H E F I V E N O +LAD_eng_000365 T H E N A M E S T O U C K A F E R T H A T +LAD_eng_000366 T H E H L B M L A T E R B R O K T H D I M A D R E C O R D O N C U C U O M M U S I C K +LAD_eng_000367 I T S E D A T O R I A L W E S U B M I T A N D I T S O T H R A P O L T O P R I Y E +LAD_eng_000368 J O S I F P L A Y E S O U R F E A T U R E D E A C H W E E O N T H E H O +LAD_eng_000369 T H E Y W A T F O R A T I M E M B I L D I N G U P T H E R F O R E S B E G I N T O O N D R I F T H I S E A V L R E A L Y E X I S T S +LAD_eng_000370 B R E F E M E N T I O N O F T H C O N V I C T I O N A P P E R D O N P A G E T H R E O F T H E N E W Y O U O K T I M E M S +LAD_eng_000371 O D E D B Y P O S I O N O N P I C H F R O M B A C K R I G H T T O F R U N T L E F T +LAD_eng_000372 H E I S M E M B E R O F T H E C O U R T O T H E R I L C O L A E O F A R T L O U N D O N Y U C A Y +LAD_eng_000373 D U R I G T H E C O U R S E O F T E C A M P A I N F I R G S A N D V I S I T A T A L L T H E R T Y N E I N W A S I G T A N S T A T E C O N T E S +LAD_eng_000374 A S T R I P O F P A P E R O F L E N G T H +LAD_eng_000375 S A T O H A D F R E C U E N T L Y W O R E T O G E T H W T H Y O U C K A Y A M A R O N P R E V I O S P O G J E C T S +LAD_eng_000376 S H E A S B O R N O N S C R E A N D U I N T H E E P S O D B R A D C A S T O N F O R H A N O V E M B E R N I N T E N I N T Y F O R +M-AILABS_eng_000159 H E T U R N E D R O U N D S H H A D C O M I N S O G E N T L Y T H A T H E H A D N E V E R H A R D H E R +M-AILABS_eng_000160 A T O B E S H O U O R A N W E M U S T C E O U R D O R S S H O A T W E M U S L A T N O O N I N +M-AILABS_eng_000161 C I D S P M O N H E B E G A N M O K I N G L Y Y O U M A H V E O N D E D W H I Y I C A L D A T R O U S W H E N I C O U L D J U S A S W E L L H A V E D I S T R O R E D Y O U T H A T I D O U T A T O A N S E D H I M +M-AILABS_eng_000162 T H E P E S N T T H R U W H I M S E L F A P O N H I M A N D B O U N D H I S F O R L A G S T I T L Y S O T A T H C U L D N O T M O V E +M-AILABS_eng_000163 N O R M U S T T H O U S O L I M E T H T H E H L Y O N O F I S R I A L A S T O T H I N K H E H A T H B U T O N W A Y I N W H I C H C A N G O R I F I E H M S E L F B Y T H E +M-AILABS_eng_000164 T H E O L D C O M P A R S O N B E T W E T H E I M P U L S I E E X S E C T I V E A N D T H E L I B R A L A R T S M A N W H O W H A D L A R N E D T H A T H E R E O N L Y O N R T O P O S I T V E D I S I O N S F A L B L E I N A L T H E W A L O H I N K I N G +M-AILABS_eng_000165 A F T E R T H I S E X P E R I A N C E T H E N V A T O R S W E R C A I R F U L T O C E P E A S A V F E D I S T N C E F R O M T H E A L +M-AILABS_eng_000166 A N O U B A R S O M T I N G F I R T H E R I T H N Y O U A T O N O I T I H A V E H E R A M O S T M S T E R I O U S T E L A P A R I G R A M Y E S W H A T I S I T I S H E D I D N O W I T I S N O T A B O U T H E R +M-AILABS_eng_000167 N O M S T R T O U R T A N S A I D A N D G I E T H E A S K T T O M E I A L T A K E I T +M-AILABS_eng_000168 A N D A R A B I A N N I G H T E X C L A M E D T R O T W H I Y T H A T W A S A M A G I C N I G H T W A S N I T T H E R S D I F R E N T S O R T S O N I G H E S M A T E S A I D T H E S A L E R A N D T H E N I G H T B U T N B R I G H T M E A N S A N T T H E S A M E N I G H T Y O U M E A N +M-AILABS_eng_000169 I V E T R N E D O F U P W A R D S O F A H U N D E D F M Y B E S T D H A N D S F O R N O O T H E R F A L T T H E M F O L O I N G Y O U A N D S U C H A S Y O U A N D T H I N K I L L T A K E Y O U A O N +M-AILABS_eng_000170 B U T W E W I D S H E S E H I M H E R H A R T L E P T U I N A P R E H E N T I O N A T E V E R Y R I N O F T H D O R B I L +M-AILABS_eng_000171 T H E S E B O O K S D I C X S O N I W L K E P E A L T H E R E S T W E O U S E N D T O M S T R B E L T H E Y A R O F A C I N D T H T H E W L V O U L Y O U F O R T H M S E L V E S A S W E L A S F O R P O P A S S A Y +M-AILABS_eng_000172 U T I N G A W A S N O T A T A L S H U R T H A T T H E C O U L D N O T G E T I N T H E G A T S O P E D I N W A R D A N D T H R E H E V Y B A R S W E R E H E L D I N P L A C E B Y M E N S O F S T O U T S T A P L E S R I V I T E D T O T H E S H E T S O F S T A +M-AILABS_eng_000173 I W A N T T H O W S A I D H O D O N C O L D L Y I W A N A D O S O N H O R S E S I W A N T M E N T O B R I G D T H E W I T H M E H E P U S H D H I W A Y F O R D W H I C H W A Y T O T H E S T A B L E S +M-AILABS_eng_000174 E R E I S A L I M I T W H A T Y U C A N D D O F O T H E F I R S T T H I E Y O U A N T E R A M A N S H O U S E A N D B E S I D E T H A T W A S N O T I M E T O A R O U S S U S P I O N N T H E M I N D S O F A N Y W O N +M-AILABS_eng_000175 D O O U N O T R E M E M E R T H A T H E S A S T H Y D E M A N T H A T S T H E S P I R I T W H I C H C E P E S T H E I S N O B L E C O R A G O U S H I Y U N M A C H I B L +M-AILABS_eng_000176 M S T R B E L L W H A C A N H E N O O F J O A O N H E L I V I N G A L A S Y L I F I N A D R O U S Y C O L A G E +M-AILABS_eng_000177 A N D T H E C I T N F O L O E D I M U A R L Y A T T H E R H E A L S +M-AILABS_eng_000178 T H E F I S T T U T C H W O L D C A S E A N E X P L O S I O N I N W H I C H A M O N G S U C H H U N D R E D S O F I N F E R I A T E D M E N A N D R E C K L E S B O Y S +M-AILABS_eng_000179 W O N F T H G E A T P L E S U E R S O F M A R G R A T S L I F A T T H I S T I M E W A S I N E A T S B O Y +M-AILABS_eng_000180 T H T H N G I S G O N O N L O N G N O F T H E R I S O N E O R E B I A G A C X I D E N T W E S H A L H A V E T O C O M P R M I S E W I T T H E I N E R I V E R N D C A R Y O N T H E W O R K C U I N T L Y +M-AILABS_eng_000181 Y O U A R L A T S A I D S H E W E L S H E H E L D H E R B R A T H O T H E A N S R +M-AILABS_eng_000182 T R A T T O L D T H E G I R L S T H A T T H E Y M U S G O W I T H E R F A T H E R T O L I V A N D G I P C U S I S I L S L I T E L D C A B E N A N D H E N T H E Y H E R D T H S R E D F U L D E C R E +M-AILABS_eng_000183 M A R G I T S A T D O N O T H E R O G P A T L Y T O W A R M H E R S E L F F O R T H E D A M P N E S O T H E E V N I N G H U N G B O U T H E R D R E S A N D O V E R F I T E H A D M A D H E R C H I L Y +M-AILABS_eng_000184 O N O W Y O U A R M S T A K A N A B O U T T H A T R E L I D T H E K I N G T H E Y A R E N O T M Y P R I S O N E R S B U T M Y S L A V E S W H O M M Y P U R C U S E F R O M T H E C I N G O F E V +M-AILABS_eng_000185 H E R F A T H E T O K U T E C M B R S A T I O N +M-AILABS_eng_000186 I N A C O R N E R W A S A S O R T O F D R E S I N G T A B L E O N W H I C H L A Y A C O M A N D B R U S H C A N I D Y S E E D M U C H I N T R U S T E D I N T H E T A B L E A N W A S E X A M I N G I T W H N T H E G O R U R E T E R N E +M-AILABS_eng_000187 I H A V E S O M E T I M E T H G T T H A T M Y S E L F S H E A G E E D B U T O F C O U R S I D O N T N O W S T I L I H A V E T O B E P I T Y C A R F U L S O M E O N I S A L W A Y S O V E R H E R B Y M Y D E S S O R L O K I N G O V E R H E R +M-AILABS_eng_000188 I S H L S T A Y R E P L I D T H E Y O N G M A N F O R I M E A N T O S I T Y O F R E +M-AILABS_eng_000189 W H A T D Y O D O A S D T H E S O R C E R E R +M-AILABS_eng_000190 W H I Y T H E R E A R E N A M E S Y O U R S H O R T H I N E S N O T A N Y M O R E R E P L I E D T R O A T I M Q U E O F T H E I N K E S A N D I M A L S O Q U E O F T H E L O S S O I W O N T H A V E M Y P E P L E Q U A R L I N G +M-AILABS_eng_000191 T I P R I T E R W E C L I C K I N G C L I P I N G W E R B I N G S N I P D O T O F A U G E T A C K O F N O E P E R S A N D P A S E D I N A N N L A R G S C R A P B O K S S E R K U L E R W E R B E N G F O L D E D A N M A D R E A Y T O M A L F O T H E F I N A L A P E L +M-AILABS_eng_000192 I T W A S F O R D A Y S A F T E R T H E S U P R I E S O F A L T H E R S H O R S H E N T H E S T R A N G E R S L E T T H E A S T A T T O T H E C A I R O F R U G E D O L D F O R S T E R H A R M E N +M-AILABS_eng_000193 B P O R T E M P L T O N H E S A I D I U S T O N O W H I M M A N Y E A R S A G O H E W E E B O Y S M E N T O S C O U L W I T H M A N D A L T H A T S O U T O F T H N G Y O N O W B U T A N T I L I R A N C R O S H M O R +M-AILABS_eng_000194 I F O N D H E R I T H E F A R I S T A N D B O G T H E R H E R A P R I S N E R E P L I E T H E C A P T O N +M-AILABS_eng_000195 W H O M A Y B E C O M P I T E N T I T H E F R O M P E R S I N A L E X P E R I A N C E O R T H E E X P I N S O F O T H E R S T O A N S E R I T W I T H M O R O R L E S C U R E C T N E S O R A T L E A S T A N A T T E M T D +M-AILABS_eng_000196 O N N I N T Y T O L A T E S T R E T E T S I D H O K G E N B I T I N G O F H I S S A G A R +M-AILABS_eng_000197 T R A T W A S R P R I E T O F I N E S H E C O U D C E S O P L A I N L Y T H R T H E H I Y W A L O F W A T E R A B O V E H E R B U T T H E S O N W A S A B L T O S H U T I T S B E M E S T R A T D O W T H O T H E T R A N S P A R E N T S E +M-AILABS_eng_000198 T H E S P A T W E R I D S P R N G U P +M-AILABS_eng_000199 C O M E D E N I L W I C S H E G A V E S U C H A U P O S I O N +M-AILABS_eng_000200 Y O U S E E A N D T I L T H E S C O L P I L S W E R I N V E N T E D W E W A S T E D A L O T O F T I M E I N D S T U A D Y T H A T N O W M A Y B E B E T E R I M P L O Y E D I N M P R A C T I S I N G E A T H L A T I K +M-AILABS_eng_000201 Y O V E D O N I T N O W D I C L A R E D A R T H Y T H E S T E N T S A R J U S T W O N D E R F L +M-AILABS_eng_000202 F O R T W E N I N G T E N F I V E T H R E T W O T H E I N W A S B A R L Y T W E N Y M O U S A W A Y W H N H O D O N F I R E D H I S R O C K I T S T H E M D E C A L O S A L C L O U D O V A P E R I N E M T I N E S +M-AILABS_eng_000203 T H E Y P A D N O A T E N C I O N T O T H E F A C T H A T G I P G U S S I S L D I D N O T W N T T O M A R Y A N Y O F T H E M F O R T H E Y H D E T E R M E N D T H A T H E N I T W A S A G R E E D W H O H O U D H A V E H I M +M-AILABS_eng_000204 W H A T D O U T I N O F T H A T H E C R I D E O P E N G A C O P Y O H E R E C A R D A N D L A I G T F L A T O N T H E L I B R Y T A B L E +M-AILABS_eng_000205 I T L R E C U I E R B U T A S H O U R T T I M +M-AILABS_eng_000206 A N D L A S T T H E C R O U D O V E G I T A B L E P E O P L E W H O H A D N O H A R T S A N D C O U L D N I T H E R S M I L E N O R F R O W N +M-AILABS_eng_000207 T H E N Y O U L C A C H I T S I T H E W I C H +M-AILABS_eng_000208 W H A T I S I T I Q U I R E D N O T F E L I N G S E R T N B U T T H A I W A S A V A L E D A T E M P T O S E C U R E L I T L F R E A D R T I S I N G F O R T H E A N D E O V E R +M-AILABS_eng_000209 S O H E G A V E T H E L U R K T H A T H R D H U N R D O L R S F O R B O O K S A N D A C A S K O F G O D O L D A L F O R P E T E R T H E C L U R K D R A N K T H E A I L H I M S E L F A N D G A V E T H E C A H M I +M-AILABS_eng_000210 A T L I K E T H A T A N A L S I N W N E R L A N T W I T H M E R L Y A G R I N H A T F A T E D A W A Y C H A N G I N G I N T O A L I N K X E W H I C H I N T U R N D I S O P E R E D F O O E D B Y A N U N O N C R E A T U E R W I T H S H O R T N O W S A N D P O N E D E A R S +M-AILABS_eng_000211 S H E C O U D N O T D O M A R G R I T L A N S E D U N C O N I O U S L Y A T T H E U N C L E C O R N E R F T H R O M S H E C O U D H A R T H Y U D E R T A K E A S E R I N T S P L A C E C O U L S H E +M-AILABS_eng_000212 N O S H E R E P L I D E D W I T H I N I S N C A R I O U S I T Y D I D I G I V E T H E M T O Y O U +M-AILABS_eng_000213 M A R B R O M I L E S A N T H E A G A C S E N T D W E L I N W E R E H E L D U N D E R L O N G L E A C T S T H E Y M U S T I F P O S I B L E B E R E L E A T +M-AILABS_eng_000214 A C A P W A V E O S T O N I S T H E L A D O R +M-AILABS_eng_000215 I T B O U N D E D H E A R A N D T H E I R A B O T T H E C I C A N H O U S E A N D A T F I R S T D O R T H C O U L D N O T T E L W H A T I T W A S S W H I L T H E S C R E A C I N G O F T H E C I C O N S N E A R L Y D E F E N D H E R +M-AILABS_eng_000216 T H E S O L D E R G A V E A Y A L T H A T A R O U W S E D A S C O R O F H I S C O M R A D S A N D B O G H T T H E M T U M B L I N G I N T O T H E S T R E A T W E N T H E S A W H O T H E B O L R S E P R E S I O U S P R I S N E W A S E S C A P I N G +M-AILABS_eng_000217 J I M H A D R E F U S E D T O L E A V E T H E F I E L D O F G R A S S W H E R E H E W A S N G A G E D N B U S I L Y E A T I N G S O T H E W I S U R D G O T O U T O T H E U G A N D J O N E D S E B A N D D O R I T H Y +M-AILABS_eng_000218 S E R T N L Y I M A S I N R U T D I T H E C A C E S O U A R B U T I C A N M A K H A D S R T A L S O F I T I R E P L I D +M-AILABS_eng_000219 O R A N Y M I C E O R E V E N G R A S H O P E R S +M-AILABS_eng_000220 A N D T H E T H A P A S I O D O N T H Y T E L Y O U W A T T O D O O R W H T I N N O T T O D O W E T H E M O N Y T H E Y G I V E Y O U A N J U S T P A M E N T F O Y O U R P A I N S I N T H E R E X T A N G E L I G +M-AILABS_eng_000221 W H A T D I S T A T M E A N A S T H E P R I N C E S +M-AILABS_eng_000222 H E H A D B E D R O U N D H E W A S F L O T I N G I N A S E O F L I G T A N D N O W N T H E N S H I N I N G L I T L E F I H E S S W E A M I N C Q U I S I T I V E L Y U P T O H I M A N D S T A R E +M-AILABS_eng_000223 B U T O L D G U N H A D A T R C K A T O L E F T A N D R E M E M E T H E T A I L I R E D T O Y O U I T H T H O N R O M A B O T H E R T H E F I R S T F T H E R A O N S T O N D T H E W O R L D O F O P L W E R E S O L G E R S S E N T F R O M S O M E B L A S T E D P L A N I T I N O U T R S P A C E T O F I N E A N W H O +M-AILABS_eng_000224 P A P A W I L O U S P E K T T H E M E N A N D G E H E O G O A W A Y S H E C A N O T B R E E T H P O R T H I N G W I T T H I S C R O U D O A B O U T H E R +M-AILABS_eng_000225 W H E N I T O O K T H I S C A C E H E S A I D I B L E V E D O W N I N D M Y H A R T D I X S O N W A S I N S E N T I S T O B E L E I T B U T M Y F A T H A S B E N R U D T L Y S H A K E +M-AILABS_eng_000226 C H A P T R S I C K O F E T H E P I R T O F O R S E A T S +M-AILABS_eng_000227 R E M E M B E T H E C A N N O T T U C H U S +M-AILABS_eng_000228 I V E M E T I M E A S Y O U R G I V E M E T I M E I F H E R S A N Y T H I N G I H A T I T S A H U R Y I V E N I D A Y O U M A G U S T Y A N D O U N C E T H E S I X T T H E S N U B N O S D P R I N C E S +M-AILABS_eng_000229 T O N O F T R A T O C L A R E D T H E S A L E R M A N +M-AILABS_eng_000230 A S F O R T H A T S A I D M A R G R I T R E T H E R H O A T A L Y I H O L D I T I S H O N E Y S O I T Q U E E M A L L D E P E N S A Y +M-AILABS_eng_000231 W H E N H E H E R D T H E S W O R D S T H E K I N G W H O S H A D W A S F U L O F T H P I N C E S N E V E R S T O P E T O I N Q U I R I F T H E C O U L D B E T R U A N D S M E A R E D H I M S E L F O V E R W I T H F A T A N D S P R A N G I N T T H E O V E N +M-AILABS_eng_000232 Y O S H O U L D B E A L E G T P A R T C E F R O M Y O U R W R O M V I O N R E C E V E R I L H A V S O M T O U L S G I V E N O U T H E N H E A T D D E P L O M A S H E H A S T O N D E R S T A N D T H T I N G S H A C N T R O L O F V E N C S +M-AILABS_eng_000233 B Y T H E T I M T H E F R O S T H A D S A D I N T H E S H U L B E F A R W A Y F R O M H E L S T O N +M-AILABS_eng_000234 W O N T H I N G I W N T T O S A Y B E G A N D C A N I T Y +M-AILABS_eng_000235 T H I S M P O R T N T R A F I C W A S C O N F I G E D T O N O O N U T T H E E A L P R O P R I T E R +cv_eng_000707 I N Y E O A R D O B B L A S E D T P O N T B A S O U D O T M Y T H S T I M G G O +cv_eng_000708 I G H T E A T A E P R I T D S U P S E C T I O N W H I C H D E A L S W I T I S A S P E C T +cv_eng_000709 O P R A T I O N O F T H E F R U N T L A N G C O N T N E D O N T H E G O U L D A N T T R E S S E L S +cv_eng_000710 M O N S I O M F L O R I D I S T W E N S P E R E N T O V E R A N E X T R I M L Y W H I H D R A N G O F A V E L I N G S +cv_eng_000711 F O R J G I N T B E A K I N K S H E A T S S T O R T F R E S H B P A C K E T B U T E A D E S S O N D I L V E D T H E M U N T O R E L E R O L D G A S S +cv_eng_000712 T H E O T H E F O R T I N C A M P A S A R E T O Y A C A M P S S R E F E R D T O C O L E C T I V E L Y A S T H E Y U N E R S T I C O L A G E +cv_eng_000713 I T S T O T H E A R D T H O W H E C U I C K L E G O N T O F R G E T M Y N A M E T H +cv_eng_000714 W O N P O T U R E I N T E G L O R S H O T H H O W D H E A G E N T L Y I N T I R E N T I S T H A T H Y E A L A D G R A R T T O M P O N +cv_eng_000715 A I M P E R I A L D I Y I A T +cv_eng_000716 T H E E S U L T I N O M P A N Y E D A S H A R T A E S I C U R I T Y C O T P O R A T I O N +cv_eng_000717 B E C O I N G M I N I N G C A N B E D O N W I T G O F H I S C A R T S O R W I T E S S P E S I A L I E D H O R D L Y +cv_eng_000718 T H E Y A L S O L E T H E N A S I A L R A N K I N G +cv_eng_000719 T R O W S G R A I N S B I S H I P O F N I M E R I C E +cv_eng_000720 I O N D E D T H A T T H I D O R L H I M U N H E O K M Y P L A S E S +cv_eng_000721 I T H O U G T I D G I V E T H E C I T S A D R E E T +cv_eng_000722 A S T H E V I T L D I N I H T O M T H E P I C H E S +cv_eng_000723 H O W D Y O U R N O S T T O C E T H I S M A Y E F R O M T H E A B L I N G Y O R M O T O R F N T I O N +cv_eng_000724 A C T H A T S O N D S L A K E T H E A R P R O L O M E M I C +cv_eng_000725 H I S T R I C A L I G E R W A S N O C L E A R E L Y D E F I N E B O U N G R Y E N T H I S P I T O F T H E A R A B Y E N P N I N S T O L E +cv_eng_000726 M A R S H I A L S H A V E R O F S L A S H F I L M E G A V E T H E F I L L M E A N A T E O U T O F T E A E N +cv_eng_000727 A O L P I N D I O T I T H A T +cv_eng_000728 H I S T T D I L E B E G A N T O R E S E M B L E M I C A L T E M A S S C K E I N O S +cv_eng_000729 H E I S A L S O L C A P A B L O F F I R N G L I G T I N M B L E W I F I M E N T E D I S R U P T I V E P O W E R +cv_eng_000730 T H E C L A M E T O W I K E D S C E N I N G L I N P U R M A L Y T I N I N G S A S F O W W I S E A T A N T H U R O N A D E R A S Y L A D L I R E +cv_eng_000731 S H E E G R U S I L Y T R O W H A T H +cv_eng_000732 H E M T T H E O R G A N I S E R S O F T H E P R O T E S A N D A G R E D D C R E A T T W O W O R K I N G R O M S +cv_eng_000733 T H E B O N S T R O C T H O F H O L D W O A R D W I L A B O F T H E R E N O N S T O R D +cv_eng_000734 I N L Y C A M D O N T O M A S G A R I T A N D G O L D F I L D S O A T I S E E C H I L E B A K C A R W E R U N C O N T E S T E D +cv_eng_000735 I T I S A C H R D Y S C O L W H O S F E S A R C O U C U L A T E D I N O N I N M E A N S T E S T +cv_eng_000736 S O M E W E N T A W A Y W H A L I O W A S T H E R A N D O T H E P O P L E C A M +cv_eng_000737 T H C S A I T H A E D E D U P R E +cv_eng_000738 T H A T C U R A C O N O T Y W A S L O K C A D E D M A N L Y I T H E H I S T O R I C A L E A N D J E A G R E F I C A L R E A G I O N O F C U R +cv_eng_000739 U N C H E L O V A T I O N A T H E S I G H T I S A M O F S U L E V B L E +cv_eng_000740 T O B E A S T R I E D T O N C H E C T C O N O N T E M P T I N T O H I S T O N E +cv_eng_000741 I H A V E T O W A R L K T H I S S A T O R D Y +cv_eng_000742 T D E T R A T H E R O N W H S F O U N D T H E S C O L E G E G L E A D W I T H G L A T I N G O N T H E R G N O N E S +cv_eng_000743 W H E N T H E B I L I N G D O S T H E S S A E L E D F O R B I T T H E B O Y T R M B L E D A T W H A T H E S A W +cv_eng_000744 D E M A C R A T A M B E R A N B A K E K H E R W O N I T H E O P O N S E E +cv_eng_000745 W O R T H A V E O R T T E I N T O G E T H E R B A I T S O U O D E N T I N H I E C U A L I E R E U S O U N A T H E S I N P O R O M +cv_eng_000746 T R I N T E W A S B O R N I N B E L E S S I T E D I N B R I T O S P O N D E R A S +cv_eng_000747 D O R I T Y F A C E O F L I F M O E S F A S T +cv_eng_000748 A N O W H H E +cv_eng_000749 S I V E I N G O R L T D L O L +cv_eng_000750 A T O N T M B R Y L O U E L I N S T H E Y W A R D F R O M B R A K G B E S T A T I O N I N S O N D I F R E N E R E C I O N S +cv_eng_000751 C H E C K R E P U P B L I C K E N T E D T W O S H O U T E R S I N T O T H E P A R O L I M P I G C O M P A T I T I O N +cv_eng_000752 T I T E R W I L I O M S R O E T H E S C G E A N G C L A Y A N D N S S H A R E D S T O R Y R A T I T T H A T T H E P E P I T +cv_eng_000753 T A I S F A S T O F E A L L D W O R S T O O F R E T E R C H H E R I T Y F I N T H E R A D Y S A D E R O F O Y D T H E R A R T +cv_eng_000754 O T H E S E N X T R G A R T S W E N E S U R N T T H E R N G O N L E L A L M S W O A G A L F T E R A G W H T T H E A L H A T +cv_eng_000755 A H U H N D R O N T B A C K T O E S T R L I O A +cv_eng_000756 P E R M I T M E T O I N T R D U S E Y O U T O H U R R M O G J E S T I E D C Q E A N +cv_eng_000757 A N O R G I N H E R W O N W A S S U P O S T O T H E N O N A D I C T I V F M O R F E N S U B S T O T +cv_eng_000758 U S H E I S O F M E K C I C O N D E S S E N T +cv_eng_000759 C S I M S H O R T E A L E S N O T O N D I S T +cv_eng_000760 I O W H O U S O N D O N T L O N S A O N T H E S S H R E Y I V G O N T P R A E P E D I T O H L O N O +cv_eng_000761 I C A L E D A O N S O P E S A R L I N A T I T +cv_eng_000762 F O R S I M P L I T H Y G U R I N C H E S I S N O R M L Y A R O U N D E D T O H E E R E S H O L N O M B E R +cv_eng_000763 I F W E A C T I L Y D O O N I S A L E D I T W I L L B E F +cv_eng_000764 T H E F R O O F T H I C T R Y S A P L S H A P E D +cv_eng_000765 T H E O U T E X T H A N G E I S N O W O B U Y +cv_eng_000766 W H A T Y O U E A E T O D A I Y W A L K S A N D T A R K S T O M O R O W +cv_eng_000767 T H E W A T E D A N F L O S O U T O F T H E S W O M P S A S T H E L O U W O P L A R R I V E R +cv_eng_000768 A H W H I Y I D I D N D Y O U S E A E S O M E H I N G K +cv_eng_000769 T H A V O U S E N O M A R +cv_eng_000770 I C O U L D G O A N F O R D A Y S A B O U T T H E D A D I O U S L O N G S P H E D U S E I N H I S P A R T F T H E W E O R O E D +cv_eng_000771 T H O S F H E O L A D E O F H E A I N C Q U I R O N N G T I N S I T I C L Y O U T T H E Y E A R +cv_eng_000772 F A S L E V E I S O F D E C T I S E S S E R G L T O N S L A +cv_eng_000773 T H E S W E E D S W E R N A B L E T O O U S E R V E I C A L S W H I C H E R S T U C K I N T H E M O D +cv_eng_000774 T H E A C K D I D N O T B R O R H E B E C T B A Y I N G A R E P R E S E N T I E T O A P E A R I N T H E C O R I C T O +cv_eng_000775 C H I N G W E R E P L I S T L O P I N G R O R A L A +cv_eng_000776 H E W A S C O N V I C T E D A N B A N I S D I S I P R S H O R S E V E N Y E A R S W E R P N I S M E N T +cv_eng_000777 T H E C U P L O F T O C H A L D E N A D A T E R S O F E A U R O S A L E N D A N D T H E S O N O M A T H Y O L B R A V E R Y +cv_eng_000778 N O F T H H R E R E F P R E N D A M S R E C H H E Q U A R A M O F T H M A G J O R I T Y O F T H O S I N T I T L E D +cv_eng_000779 I N T I T E R P E N S E C X C E D E D I N D E A R A S T S O M E A R R A S I P C A R A I T I S W H O S A L D O U N E R S E Y T H R A P E A R I E O S T R O N G G R O T H +cv_eng_000780 H E A R I A M B E P T E N M Y F L O C K A N D M I B U T E R S U R E D T H E B O Y T O S +cv_eng_000781 T H I S F A L I A H A S T L E T T O S I C X T E A E N P O U L B L E N C S H A D E I N S E A R D A Y S E O F C A L E S T O +cv_eng_000782 A O O Y S A S D E O +cv_eng_000783 W H I Y I T H A P L A I N C E P E G O I N O V E R +cv_eng_000784 A N D N I H A Y A E A E D O N D O S H E F O R W A T F I R T I A L B O C S W I T H O R E S O U L T S +cv_eng_000785 T H E P L I C A T I O N W A S P U T A P P R O V I T I N F A R B R A V Y +cv_eng_000786 H E N R Y T O R L E D T O N M N S T I L S W E A R H E H A D A S O U N D I D R A N I N G I N L I T I N G +cv_eng_000787 I T W A S T I S C O N T I N U E D O T O S C E T H A L I N C O N F L I C S A N V L V E D I N L O S E H I S R E T H I R N T O R E T O R E S T R I A L R E B R A D I O +cv_eng_000788 A D T T H H E R F A M L Y W A S F R O M E B R E A H O N S A +cv_eng_000789 A W H A T D I D Y E A E F O R I N O R T H E A P A +cv_eng_000790 T H A T W A S M Y D R A R T O S I N C E +cv_eng_000791 H E S C O S E A R I T A M U S T E R E O F S H E A R O S E D C O U O +cv_eng_000792 T H E H L I N T U R S T O T H E C H U R I S H O A S I N E S T H A T D A L T E R I N D S P E U S E W H E T H E R +cv_eng_000793 I O U E N O T T H O S W E R E I N H E R A C H L E A D R +cv_eng_000794 T H E L J O S I E C T E N D F E S T T H E P I Y +cv_eng_000795 M Y N E S C A N H E L P Y O I T T H A T S +cv_eng_000796 B U T S A O D H I S T O F E R Y W O N +cv_eng_000797 H O W F O R T H E B E S T A N D P R P E A E T F U E T H E B O S T +cv_eng_000798 I N I S H E L Y T H E E P L O U S W A S H R T E N S T R I C K L Y B Y D I T +cv_eng_000799 A L L W E O N E D B Y T H E E V E R I T M O R E S I N I C I T +cv_eng_000800 B H A T H T H E N T H E W I L A S R I N G T O M O R O M I L N T H S E O D +cv_eng_000801 H D O R B I S T E R E C K G M M I N L I P +cv_eng_000802 O S E I A L P E T R Y T O H E R P A C E A S A C T I N G D I R E C T E R +cv_eng_000803 T H E B E V E R L Y W L B I F L Y N T E S T H E E S S E N T R P U T O F A T O N S H I P +cv_eng_000804 T H E T R A C K R E S E R V S T I N G W A S A L S O C O M P L E A T E D +cv_eng_000805 H I T M A R C H W A S A W E R E O F T H E I M P O R T N E O F E L E C T R M R E C O S C M P I N B Y A E L O U G I C A L R R E S E R C H +cv_eng_000806 S I N H O W B A S B O R N Y A N T H E H A B A R +cv_eng_000807 N T H I S W I N C H K E A S E A N O F I A L Y H E R H O D T O A S M A C K R E M P A D R I N T H B Y C O L A S I N F O U S C S E S R A V B E R A I T I N W I L +cv_eng_000808 I T I S R E S P O N S E I P L F O R W A T E S U P L I Y A N D M A N G E M E N T O F W A T E R R E S O U R S E S A N D M A H A S T R A +cv_eng_000809 J E S E S T H E F I R S F A I C E O F T H E H O R V E A H E S A Y E D +fleurs_eng_000413 T H E G I S I U P L A T O O R G I A N E C R A L P O L E S I N T H E A G I P T I O N V A O L Y O F T H E D E D C O N T A I N G S E V E R A L P E R I M E N D S O F W H I C H T H E G R E A T E P E R M E N T I S T H E L A R T E S S E V E R A L E S M A L T O O N S S E V E A L T E M P L E S A N D T H E G R E A T S P A N K S +fleurs_eng_000414 T W A R D T H E I N D O F T H M I L E A G E S W E S T E R N Y U R U P B E G A N D T O D E V E L T T H E R O N S T I L O N O F T H E B I G E S T V E L M E N S O F T H E T I M E A S A R E S U L T O F T H E C R U C A S P E P U L B E G A N T O U S E B U T E N S T O F A S T N C L O L T I N G R R +fleurs_eng_000415 I F S Y O U O N L Y G O A S H O R E U S I N G S H I P O R I S C U R I O N S Y O U L N O T N E A S E P R T V E S A A S A T W O T H O U S I N D N I N +fleurs_eng_000416 D O U B A L H I S M A R E W I H T O A D L C H E R E N C U D N O T B E W A B I G M P R E S I O N O N M I L E R T O H O M T H E S O R Y W A S R E L A T E D +fleurs_eng_000417 T H E R D I S O P L I N D D E F E N C S B A L H A D L I N G S C I L S A N D E X A L N T E O R K M A D T H E S T A N D O U T A N W A S C L E R H A T H I S W A S T H E E M E T O B E +fleurs_eng_000418 T H E D I S E S I S C A R E D B Y P I G S W H I C H H E N M Y G R E T E S T O H E U M E N S T O R O M S C E T O S +fleurs_eng_000419 F O R T H E S P R I N G B O K S I T D E N D E D A F I V E M A T H L O S I N G S T R E A E K +fleurs_eng_000420 T H E S T H E P I N S A L W I T G O D F R I N D S M A N Y E O P L E W E N I C A M E O U T +fleurs_eng_000421 T H E U S E O F V E O R E C O R I N G H A S L E D T O M P O R N D D I S C O V E R E S I N T H E I N T E R P R I T A T I O N O F M Y K R L E X P R E S T I O N S F A T I A L M O V E M E N S W H I C H L A S A F E U M I L E S S I C K E N S +fleurs_eng_000422 A L S A T T H E N O R T H I S T T H E G R E A T E S A N C U R Y O F O R L A D Y O F A T H E M U S H R I N A P L A C E O F W O L D R I G T E F A M S M E R I A N A V P E R I O N S +fleurs_eng_000423 I F Y O W N T B E C O S E O T H E A C T I O N Y O R E H A E T O O O G E T I N E A L Y T O T A C A M P I N G S I H T C L O S T O T H E M U S I C K +fleurs_eng_000424 M T A G U S C O A R I S B Y F A R E T H E B I G E S T A N D T H E C O N T I N A N T O N I T S O N W H E N I C O M S T O W W I L D L I F +fleurs_eng_000425 W E M E N I T I S R E C O M E N E D T H A T A N Y W O M E N T R O V L O R S S A Y T H E Y A R M A R E D R E G A R L S T O F A C T I U A L M A R I T A L S T A T I S +fleurs_eng_000426 U O M O F I F T Y T H R E B E G A N H I G O V E R M E N T G O V E R S H I P E R I L E R T H I S Y E A R A N D S I N D A B I L E L A S T M O N T H L E G L I S I N G S A I M E S E C X M A R A G E +fleurs_eng_000427 A S L I P L U T I O N N H E R H A D Y W A S N O T T H E C I N D O F P R O B O M T I S T O D A Y T H E R U L Y L O C A T E D I N S I I E S O R A C A M P S E S E A S I E R T O R E A S I O N T H O S B I L A N M O T E N T I M S +fleurs_eng_000428 T H E U L Y H A V E S P E C I A L F O D R I N K A N N R T A M E N O P E R S T O C E G E S A N D A G O D M O D A N C E T H E M A T T H E P R M I S +fleurs_eng_000429 O N T H E O T H E R H A N D I C E S E A N D S N O W Y C O D I O N S A R N O R M A E I N M A N Y C O U N T R E S A N D T R A F I T O E S O N M O S T L Y U N I N T R U P T E D A L L Y E A R R O U N D +fleurs_eng_000430 B E C A R F U L O T T O A L O W F A B R I C T O B E C O M E T O H I Y E W H I C H C A N C A S E S T R A N K A D G E O R I N A S T R E N C A S E S S Q O A R T C H +fleurs_eng_000431 F E I R L C H I L D R N M A H A V E X P E I A N C S O V E R C H I L D H B E S O R T R O M M B E F O R B I N G A B A N D I N R R N G A W A Y +fleurs_eng_000432 P E O P L E M A N O T I N T I C I P A T T H A P A T I O N C S A N D N D R E S T A N G R A L S O N E S E R Y F O R T R O V L E R S R E T R N I N G H O M +fleurs_eng_000433 O N O T E R T H E P R I K O F H U S T I L I T E S B R I T N I N E N T S H E A T E D A N N A V B L E B O C K A D E O F H E R M A N Y +fleurs_eng_000434 T H E O E N R S O F I S S A I D N I N T E N O F T H E I N G U R E D W E P L E A E S O F I S E R S +fleurs_eng_000435 U S I N G S H I P S T O T R E S P O R T S G O O D S I S B Y F A R T H E M O S O F I E N T W A Y T O M O E L A R E M O U T O F P E B L E N G O D A C R O S O T I O N S +fleurs_eng_000436 T H E B R L C R I T I S I S M O F T H E R E C O N S T R C T I N E V E R T N H A S P O K A S O T H E W A R D I N G O F R E C O N S T R C I N G C O N T H A C T T O R I S T E D E W A T I N G A N D I N S I E R S +fleurs_eng_000437 U T W E U C U N S B O W D O B B O D A M O R S E C L T A C X C Y T O G E T A R O U N D G O M A T H E N R M A E W I C K L E P R I C E I S F I V E H U N D R E D C O N D L E S F R O N S F O R T H E M S H O U R T R +fleurs_eng_000438 T H E T H E K I N G D O M S W A S O N E O F T H E B L T B L U D I A S T E R A S A N D A N I E N T C H I N A S H I S T H R E T H O U S O N S O F P E O L E D I E D F I T I N G T O S T I T I N T H H I E A S C E I N T H E G R A N D P A L E S A T S I +fleurs_eng_000439 R T H E S C O U P L E S M A Y C H U S E T O M A K E K A N D A D O U S I N P L A N D F O R T H E R B A V Y +fleurs_eng_000440 N O T H N G A N D B E F E N O T H E R H N T H E C L E A R E B U T I F U L S C A I Y A B O V E A N D T H E M E N Y S U R U N G M O U N S V E R Y L I T O F T H S W A L A N B E S E N O R H U R D F R O M I N S I D T H E C A V E +fleurs_eng_000441 H E W A S S O B S I C U E N T L Y R E L O C A T E D T O A D I N B R O K S H O S P I T L A N C A M B R I A G E +fleurs_eng_000442 T H A I C A N S T I T Y P O P U L A T I O N I S A R O U N D A I N H E U N D R I D T H E I S T H E M A O S T N T E P E N D E C O N T R N T H E W O R A L D A N D T H E P O P U L A T I O N +fleurs_eng_000443 R E G U R A L O U N S M E N T A N T H E P M E T R U A R M A E O L Y I N C A T L O N B U T U N P L A N E D I S T R U P T I O N S A R N O U S E B Y A N O T A M A E D S I S T O M I N A W A D V E R I T Y O F L N W I C G E S N C U T I N G S B A N I S H A N G L I S H F R E N C H E R B E C K A N D H A P O N E E S +fleurs_eng_000444 T H I S O P R E A G O D P R T U N I T I T O S E T H E O W R A B A R I L E S A S T H E S C K G I W I L B E D A R K M O R L E S T R U N T H E C L O C +fleurs_eng_000445 F I R S C K U C R O S O V E N C I A L Y D O U S E T O F I E B Y A L E V E N T H R T Y F I V E P E A M +fleurs_eng_000446 T H I S C A L T O C M I C A L S P E E H E C A N M A K A N I N D I C A T E O U S I N G R E D C A B A G H E J O S +fleurs_eng_000447 I N P R T I C U L R I T I S L A E T H A T O N E C A N D E T E C W E T H E R A P R S O N I S L I N G B Y I N T E R P R I N G M Y G R O L E X S P R E S I O N S C O R E C T L Y +fleurs_eng_000448 T H E S E C H A L F O R I T Y O F T H E C H U C H O D S B E N I N R O M F O R O V E R A T H O U S A N Y E A R S A N D T H I S C O S O N T R A T I O N A F P O W E R A N M O N Y L E D T O M A Y T O C U S T I O N W H E T H E R D I S T E N E T W A S B E N G M E T +fleurs_eng_000449 T H E S U N D A R B O N S A R T H E A R G E S T H E T O R A L M A N G R O E B U L I N T H E W O R L D S T E C H I N G A T Y C L A M I T E R S F I F T Y M I E S I N T O T H E A N G L A D E S H E A N D I N I N D I A N H I N T E R L A N D F R O M T H E C O O S T +fleurs_eng_000450 R E G U L R A N O N S E N C S I N H E E T H O A R M A E O N L Y I N C A T A L E N B U T U N N D I S T R U P T I N S R N O U N E D B Y A N O T M A T E I S S I S T M I N A W A D V E R I T Y O F L I G W I N G E S I N C U D I N G S P A N T I S H I N G L S H R E N C H E R B A C K A N D J H A P O N E S +fleurs_eng_000451 E V E R W N P R T I T B A T I N O U C I T Y A N U S I S T R N S P R T I O N C S I S T O N C S A L M S T E R Y W N C O M P L A I N E O O U T R N S P R T I O N S I S T O M S +fleurs_eng_000452 L A T N H A D A S O R H A N E S O T H E O N S U I R V E T I S N V I R M I N T L B I L D U R N T H E M E N W T T H E P E M A S I N G F R Y T H U R A L A N D C O M P L E T E R E R I D T I N G O F T H E C O N S E R V E T H I S P A R D Y I N I E R M I N A L I L +fleurs_eng_000453 I N Y O N H I S G O N T O T R I E H A H A T L I T A T U D S O R O V E R M U T N P A S S T H R C O N C S I D E T H E P O S I L I T Y O F S N O I C E O R F E S I N G T E M P A T U R S +fleurs_eng_000454 H E S L E I N T R U S T I O N I S H E P R A S T S E S O F H E B O U C A Y W A K N I N B E U R I N G Y O U N O R M A L E S L E E P E R I A D A N D F A L I N G A S L E A S H O U R T T I M E L A T E R C E N T O S I C T E M I N O T S T +fleurs_eng_000455 O U R S W O A R L T H E T O D R I P P O U R S T O G E T H E R A N T H E W I T H C U E N G A W E T H A N S S C U E A E T H E M I N T O A B A L E R E H +fleurs_eng_000456 F O R T H E S P R N G B O C S I T E N D E D A F I E M A C H L O S I N G S T R E K +fleurs_eng_000457 J U S T L I K T H E O N E X P U R T D S A P U L O N T H E E R T H C A S I N T H E I D E S O T A S A M L B Y W A Y E X E R T I F F O R T S O F H E E D I T A R I O U S G A L A C Y +fleurs_eng_000458 T H O R O T H E N I G H T H E T W E N H U D E R D A N F I F T Y A N T O H U D R E D C O P E S W E R E M A D E N O W N O N A S B U N E L A P B R O L D S I D S +fleurs_eng_000459 F I R S T A M O N G I T S E M N D Y A E R E C O M E N D A T I O N S I S T H A T A N N O W D I P L M A I K N I S H I T I V E H U L B E T A K E B E F O R T H E E N D O F T H I S Y E A R T O S E C U R E R R A C X P B O R E R S A G N S H O S T I L I N T R V E N T I O N S A N D T O R E A S T A B L I S E D I P L M A I C R E L A T I O N S W I T H I T S N A B E R S +fleurs_eng_000460 S H A N T E T E R S B R C R U S I S I N C L U D T I M I N T O W N W H O T A S O N G E R S A R X A E N T E D F R O M E S T E R R E Q U I R I M E N T S C H A C K T H E T R M S +fleurs_eng_000461 O C O R D I N T O U P A N S N O G U L R A G E N C S Y R E D Y A L A C T I V E C A S E A M E A N D I A D I N H A S E I D E N I F I E A T T H E P L A N T +fleurs_eng_000462 S A G O G A T I O N A N D R E C O M O N A T I O N S H U F L V E R Y I A T I O N B A C K A N D F O R T H B E T W E N T H E T W O P U L E S W I T H E A C H G E N E R A T I O N +fleurs_eng_000463 E L A M N T L K C H L T H E M N D P A A T I M R C O N C S T E D M E T L S O F P O R S E R A L S O M E T E S L K E S I V E R A N D G O L D +fleurs_eng_000464 T H E C O R L T I O N E T W E E N B R A I P I T H O L A G Y A N D B E H A V Y O U R S U P O R T S I N C S A N D H E R R E S U R C H +fleurs_eng_000465 A N C H A N C H I N A H A D A O U N E K W A Y O F S H O W I N G D I F R E N T T I M E P E R I A D S E A C H S T A C E O F C H I N A E O R E E A C H F A M I L Y T H A T W A S I N M P O W E R W A S T H E D I S T I N T I F D I N I S T Y +fleurs_eng_000466 A S I M P L P O P U L E R D M E R H I S F E C I L Y D R I N T H E T U M E R I S P A A M A L D Y B R E D W I H A L V O I L E T O M A T O N A N Y A V I A B L E C O N T M E N S S U C H S C H E E S E T O N F I S H I T S E T E R +fleurs_eng_000467 T H E N O U N S M E T W A S M A D E A F T E R T R M P A Y F O N G C O M E R S A T I O N W I T T R K I S H P R D I D E N T R E S E P T E E P E R O D O N +fleurs_eng_000468 E R Y S A T E T H T H H E W O L D R E T E N T O T E C X S I S T O U S E S T H E R E S U L T S O F T O N I H T E S C O K I S D I D E R M E N W H T H E R T H E R I S A P A T H F O R D F O R M Y S E L F N T H S R A C E S B U E L E T E R S U T T H A H E W L E R E M A I N I N T H E R A S A N H U B P E N O R I G E N R Y T W E E W O N S O U T A R L I N O P R I M A R Y +fleurs_eng_000469 H E W A T A L S O I N G A G E A I N G R A V I N G B A K N O T S F O R M A N Y C O N T R E S R E S O N I N G S E M P L E S O F H W R K I N C L D T H E M P R I M E M E N T M N I N I S R E A L P O R T R D S O N T H E F I R S T F R O O U T H E F O N T O F T H E N E W C A N A D Y A N F F I V E D O L R I N W O N H N D E R D L D I L +fleurs_eng_000470 E M O R T R A D I N A L C H U R C H E S O F T A N H A L T H E N E S T R R I G U A L N T S A T E D Y N I G H T T U R N T H E E S T R W E K G N W E R T H E C O G R E A T I O N S O T D I M B R E A K I N G I N T O S E L E B R A T I O N A T T H E S R O K O F M I N I G T T O S E L E B R A E C R I C E S R E S E U R E C T I O N +fleurs_eng_000471 F I L E N I S A G R E A E B O T I N G D E S T N A T I O N T H E L A N D O F A T H O U S O N L A E H A S T O U S N D O F I L E N D S T O A N T H E L A K S A N N T H E C O S T A R K P A L O A O S +fleurs_eng_000472 R A N T S H E N A T E R A N D A R G I N C S E N F R S T L A D E C R I S T E N O F O R N D I S A C U R S I N R A N O U L S H E R P E S O N A T H O C A N D I D U S Y O S T R D A Y E V E N G A N L A P L A T H A A S T A D Y F I F T C L O M I T E R S T H E R D Y W N M I L S A W A Y F R O M E N O S I D I S T H +fleurs_eng_000473 S V E R W E T H E R I T E U N A R I C T U N E F O R A N Y D A D E R U S W T H E R F O N A M I N O N W I T T H E P A T N U A L T O C A S D A M A G E S R I O U S S O S I A L D I S T R U P T I O N O R L A S O F H U M N L I F E +fleurs_eng_000474 F O R E X S A M P L E T H E M O S T C O M E A N S T I L I M A G E F I T O C K R I F Y F O R M O U T N H E W O L D I S T H R T Y F I V E M I L O M A T E R W H I C H W A S T H E D O M I N A N T F I L M E S I E S A T T H E C L O S O F T H E A N A L O G F I L M A R A +fleurs_eng_000475 I T I S R E L A T E D T O B U T U L Y N O T I M V L V I N G H L P I N S T I L S C E T O R I N G O R M O T N E R I N G T H E L A T E R O N S D O N I N S P T U R I N G A N D R E C A R I N G M U S H T H I F R S C E S A N D B O T E S +fleurs_eng_000476 I A R N I N G D A M P C L O S C A N H E P E T H E M D R I Y M A N Y H O A T E L S H A E A N I A R N A N D I R N I N G B O R D A V A L A B L E F O R L O N G E V E N I F O N I S N O T P R E S E N I N T H E R O M +mls_eng_000283 A E V E D N Y O U N C E R D H O R S L Y S H E J U W H E R C H A R E U N T E R I T E G L O A T O T H E F I E R A N D S C R E D H E R H A N S O U T T O T H E L A S E T H E R E W A S N O O T H E L I G T I N T H E R O N M Y T H I S T I M E T H E W I N W I D O T H O R D E D M I S M A L Y S T I L +mls_eng_000284 M Y D E A R M O R E A W H I D O Y O U N O T D E S C I S T F O M T H I S C I L Y P E S U T O F A A N M A N D G I N A R Y T L E S E U R W H A T I S T H E V O L Y O U O F M U O N Y W E A R S P A N U R D S N O T S H E R T S L E V E D M E R S I N A R Y P L E A G S O F A M E A I C A E N S +mls_eng_000285 T H E C R I T I A L T R P A U R I S T A T O F T H E S I N G L A S T H E R M A E L I N W H C H P E S E N S E P O I N T O V N E F L E C T I O N A T H O R S N D T I E N G E N T T H E R I T I A L P R E S E H E R I A L V O L I M E A T H T W O C U L D N E S E O F T H I S P O N T O I N P L E C T I O N +mls_eng_000286 M U C H L I K A N F O U N U S A N D I F O R M I T Y O N T O T H A T M O N S T E R W H O M T H E H E B A N N I G H T T H E F A T H E R O F T H A T F A T L P R O G A N Y M A D E C I L H E R S E L F F O R V E R Y H A R T S T O S P I H T H A T H E H A D R E D H E R R I L W H I C H N O W W I H T C O U L D E V E R L O S E S W U T S U F E R E D E A D L Y D +mls_eng_000287 H I S M A S E D M E S E W I H P E I O N P R E S I E S A M O U N T I N G T O T H R E T H U S N A T N S T F I E A N D A L S O T H E R Y S M A L V O L I M S T A N O C U P I E B Y T H E F L O A I D M A S E N D E C O N S I D E R A T I O N T H I S L A S T M E S G E M E N T W H I C H N E S E S I T A T E S N E U M R U S C O R E A C T I N S I S M O S T D E L I C A D P A T T E O P O R A T I O N +mls_eng_000288 W H I Y S H U L D I T H A E B E N D E D N E C R O M A N C Y T O N D E V E R T O O N B I N T H E S F A T S T O I V O L V E B Y C A R F U L E L M I N A T I O N A N D C H A N G E T O T H E P E R F E C T F O D +mls_eng_000289 N A Y T H O O F R A S E S B E M Y B E D Y E T I A M R I C H L O V E S A I D B U T U T A R G U D L I V H E T H R I C E F O R N D A R E T H O W T O Y E L T H E S O V E R A N G I F T S O F E R T H T H E V I C T O R S O R D T H E L O R A L D B R O W F O R V I S I O N T H I N G K S O F L I T L W O R T H +mls_eng_000290 B U C K E M E T O H A V B E N A C E C U L A C T E R A O T H O H A M P E D B Y I L H E L T H A N D A G R E T P I N T O N I S F A V E R I S T H A T I D I S C R I D E D O N L Y T H O S E P L A N S W I T H H A D C O M E U N D E R I S O N P E R S I N A L O B S O V A T I O N +mls_eng_000291 H A D R A T H E R S R O N G U P A N D H A D N O T C H A I N E D I N T O N E I M P S T H E S H Y F E L T I N T H E T E A M S C O V E R I N T H E M U P A G I N A N D T H E Y U P E A R E D A S P E R F E C T I N S E C T S I N T H E M A Y O F T H E F O L O I N G Y E A R +mls_eng_000292 N O T H I N G S A Y O O B J E C S A N D T H U T S O F B O U T Y H O U D P R E S E N D T H E M S E S T O T H E U N E S T A N D I N G O F T H E F O R T I O L U T B U R S I N W H O P A R T O K O F I T T H E S E B A T E S W H C Y O A R B R U G T T O M E T R A N S A L A T A R O N S E U R E D W I T T H I S S O U P O E S T I O N +mls_eng_000293 N O S E M I N S O U P I T I T Y A N D H E D N E R V E H I M S E L F A G A I N S I T H I S F A I S E W A R S O R D O F S V E F F L O U S H D H E W A S T I M I E D E V E N T O R U D N E S +mls_eng_000294 B E C A M E M O L E L I F E L I K A S T E C H E S F L U S H T H E A S R A R W A R M E F I N O F I N T E M O R N I N T O H E T H A F T D I S P E R I N G S O L T S I D I T L O N G O U R S O F A L L R E D I N G A N T P E R S T E D H E A R T B Y N E V E R S E A S I N G R I M E S Y E T I C O U L N O N D E S T A N D I T +mls_eng_000295 W O N O F T H E O W I N R I T E R S A I D T H E A O P E H E A V A I S A P O S O N S H A L F I S H T H E S A R B I T E R A N D D E D L Y A N D C A D B E O U S E D I N P U T I N G E N A I M E S T O D E A T H +mls_eng_000296 T H E B E U T Y U S R O U B S O F H A V E N A S L O N T A D U R I H T E R S N C O L E I T A R H E L O K S I N B O U N L E S M A G H U T Y A B R O A D T O U C H I N G T H G R E N L E A V E S A L L A T R E M B L I T G O L D L I H T +mls_eng_000297 I C A N D O U N O M O R H A N T H A T I N T L T H I S M A T E R I S A P S A L U T L Y S E T E D T H E A W O R T H O T H A N L I F E I T S E L F T O M E T M S T R B L B U R S E M E D A N O I E D S U R L Y H E P R O T E S T I T W O U N O T G O D O A S T M E T O W A T E T H R E M O N C S U N T I L A T E X A M I N O N E O F T H E S +mls_eng_000298 R O S E C O N G R E S F O U N D A T I O N R U S H I N A N D T I T E T H A T O R G A N I E D T H E S A I T P E T E R S B U R G I N T E R N A S I N A L E E C A N O M I C F O R O M R O U S N E F T R U S H I O N S T A T O N D O I L E A N D A N A R G Y C O M P A N Y +mls_eng_000299 H O W G L U T E D N S P A R C K A L E T H E D E L I C A T R O S T W O K Y O U E A T R A C T E D N O D O U T A M A R V E D A T H E D I N T Y T R A I C S O M S B U T F E O U O F A S H A V R E A L Y H A D A N O P O T U N I T Y T O S T A D Y T H E D E T A L O F T H E F R U S T D E S I N S M Y N U T L Y O A V C O N S I D E D H A T T H E W R E O R T I N T H R E Y U R F O R D E S I N S A T M O S T +mls_eng_000300 T H A T H A T H E O F E N S I N T R I N G T O I N F I C K A W O N T H E M C K I L V E R O F H E N D E R O R W N H I M M O R T H A N T H E I N T E N D A N T O D O A N D T H I S B E C O M S E C C U S F U L A N U T H R D S O T A T H E P R I M I T I V E L A G D S L A T E R S W H E C A E F U L I N O R E C Q U I A R I N T H E R I T A L I T I O N T O B E L I M I T E D T O A N Y F O A N A +mls_eng_000301 A T S I R E S W O R D T H E J U E S W R E T E R N T H E C O M P N Y T H A T G O G O D S H I C E B E G O N W I T H M R T H A M O N I S H E N D E D B Y T H E F O B U T W O N C E A G A I N T H E W R K E C O S E O N B Y L I S E N S F R O M D U R I O U S E S R A I S S E N T W I T H R O I L E G R U N T A N D G I F T H S F O R I U S P I A S +mls_eng_000302 A N T P R O D A C K Y E A R I N A N D Y E A R O U T S I V I N H U N D R E F R O A N C E S W H O L I V E D O N I T H A V E N O T S O B A D L Y W R E I U L E X P L A I N E M O R D Y I S O C U P E D T A T H E O R B O H O V S +mls_eng_000303 T H E A N T H I S I S A L L Y O U R A N T A R T I S T O F A R F O R O N E O F H I S A L I A N C S A N D I W O R E Y O U W T H A T T H I S P L A C E N O M O R S E Y O U A E G X S E I T A N D T E R D F L U R A N S T H E B E S T I S T H E R I S M O R C R O U D T O E T A M A N S A R Y V E N J O N O N S E F O R A C E T H A T S M Y N A M I N D E D +mls_eng_000304 H E N I R E T U R N D T O T H E H O U S E W H E R E H A D B E A H A P Y C H I L D O N L Y A P I L O F A S H E I S W E A Y I T H A D S T U D I W E P T L O N G A N D T O F O G E T M Y W E P I N G I S A I D O U T O N D E V A T S C A M S S C E O N T H E S E W A T E R S I N A S T A R S A F A Y A N N I G T I P L A D E M Y F L O T T O T H E S U M E R M O N +mls_eng_000305 D O Y O U N O T S E W H A T L E S U E R I T G I V E S M E W E H A V E G R O N U P T O G E T H E R I N T H I S H O U S E S I N C H E W A S A B O Y I S I M P L Y C A N O R B E A R A S Y O U G A N T H E S I G T O F T H E S M I L L A V I N G H I S F A C E B O U R D E A R H E H A S N O M U S M E N T E X C E P T T H I S P L I N G A T T H S H O P C E P I N G +mls_eng_000306 I T I S D E V B I U S B O A D Y R E V A L I N G I N Y L I P I C A L O R R G R E E O N G A T I O N L O V E L O V E L O V E W A L N O T B E T H E W O N E D O F C U P I T B U T H E M A N I F I S T A T I O N O F H E E R V E R S A L R E P R D U C T I E I N S T I N C E S +mls_eng_000307 S H O A R P L Y A S H E S H O O K H A N D W I T H E R G O D B E S Y U M I G T D E A T C H A T H E B I H O P S A I D W H E N S H E C I S E D H I M A N D H I S L I P S M O E D O F T E R W A R D F O R S O M S I C K N T S A S I F H E W E R I N P R A A R H U R M O T H E R F O L O R D H E R O U T O F T H O M A N D T H E N S I L A N D C E T E L +mls_eng_000308 F O L O E D H I M S T A E L T F U L L Y A N D H E H I W A S I N A S T P I N G P O S T H U R F I L I N G H I S B O C K E T C A M E U P B E H I N E D H I M A N D P L U N C E D A N L O N G N I F E I N T O H I S N E C K +mls_eng_000309 S A S T H C U R T I E S D O U S N O T J U P I T E R D I S T R E B U T E T O T H E G O D S T H E P R O P O R T I O N A N D D I V I D E N T S P A R I N G L Y A N D S E V E R A L Y A S A G A M A N A N D Y E T O H I S C O M A N D O E R S W H E N H I S G E S T S T R A N G K T O O N E A N O T H E R I F V F O R C O U R I O U S Q U T H C L E E D E M O S A S Y U N E R R A T +mls_eng_000310 A N D H E R E N O N H A L D A R A S T R A N T T O A M E T A G A I N I N T H O H T S O T H I S N O U S E A N E P I N G B H E R E C H E R U L S P I R I T S T I L N E R D O T H E F A T I S E P I N G F E U T U R G O O D F O R P E S E N I L +mls_eng_000311 A N D B E C O M E T H E R E C A R D O F W H A T P E P L A V E D O N I T H E R M O R A M U B L E M O E N T S T H E R E C A R D O F H E C O N C Q U E S T S O F P E S E H O W M E N H A V E L I V E D A N D L A V E R D D U G A D B I L T H E U O N A N D C L E R E D G A R D E D A N D R E F O R S T +mls_eng_000312 T H E L O F L I N G O F T H W L L E S P E T O K I N S R A I N A S W I L A S N Y A N S C A S O N A B L E D A N C S I N G O F M I G E U S I N T H E E V E N I N G S O A C O N S A N D O F E A T A N D R I M T I S I N T H E O N C E S A R D I F U L P R O C U R S S T H E L E A V E S A R A L L A T R M B L E B E F O R T H A T P R O C H E A T T U N D E R +mls_eng_000313 W A S A S T O R M N G H J G E N E R L D E M P E A R E W A S C K I L E G E N E R A L C O S T I E N W A S B L A I M E D A N I N D E D E S N O W C O M E T O P A R I S T O D E V E X P L O N A T I O N S A G A N S E T A L L W H I C H T H E M O U N T O N A N D A T R O T I O U S M A U A R M U S T E V E N D M A K E H A I L A S T H E C A N +mls_eng_000314 T H E M O M E N T W A S F E F U L A M I G T Y O F F O H A D N E V E R S W N G T H E B U T L E L A C X O V E R H I M B U T T H E H O B N E R V E D H I S A R M E F O R A D E S P R A T B L O W A N D T H C M S E R F L E P R O S T R A I T D B E F O R H I M +mls_eng_000315 T H E N T H E W I N E S T O U T T H E C L A O R S T A N D D O A R K A N D N I G H E C A M E O N L I K E E I N K M Y O L D C O T I N C U I L T W A S C A O L D A S I A N M Y S W E E T S O N T O S T I N H I S S C L E E +mls_eng_000316 Y O U M A Y D O A S Y U P L E A S E T O W O R K O F O R I R I T A T I O N T O K E P Y O U R F A N A T I C S I S M Y O U R E W E L A F F Y O U N E D N O T M I N D T H E C O S T T H E P O R E D O N O T W A N T T S T A N D I N Y O U R W A Y B U T Y U I N S I S T O N T H E S U B M I T I N G T Y O R C O M P U L S I O N +mls_eng_000317 H E W A S R E D B Y A R E V E R N T E R Y A Y S N I G H T B E I N G B Y O T H M A N E S I X F O A R T O T H I D V I C K W L W A S B O N E A N M A C H A T E N S E V E N T Y N I N A N D H E W A S T H E O N L Y S O V I V E R O F L I T E R O F F I F T E N I T W A S O N T H I S A C O U N T T H A T H W A S A U E D S A F E A N D C O L O R A N D M A K I N G S +mls_eng_000318 A N D W H A T H A S T I T A K E S O F O L I N T O T H E S E C K A N T T H E R B Y T H I S T I M E D I A F H A N T E O S N E A S E R S E C G I O M O S T A D M R A B L E S E C R E I T O N T H E C O N T R A R Y I T S T U R S M E N O T A W I T W I C H M O S T C O N S U R N E S I T +mls_eng_000319 T H U R D L Y T H A L S A I D W H E R T H E C I T I S E N S A R E N E A T H E R T O R E A C H N O R T O P O R F O R T H L Y A N A C A U S I S S A I D W H E R E T H O G I N A L L O T H E R E S P E C T S T H E Y A R I A C U L I E T V E R T O U O S M E N A R A D V A N S E D A N D V E S I O U S P E R S O N T H E G R A D E D +mls_eng_000320 T H E C I N D L Y F R A N K I S S I M P O T H A T I C K A E V E R Y D A Y H E P A S N O T E S B E T W E E N U S A N D I T R I Y T O I N C U R A G E R U S S A L H E W I L I M P R O V E I A S U R H I M H I S T I M E I S H O R T A N D F R E S H A R A N D L I O R T Y W L S O N R E S T O R H I M +mls_eng_000321 T H I S C U E S T O N S I T I S N O W E V I D E N T M A Y F R E C U E N T L Y B E A N S E R E D W A S E A Q U L L P R O P R I T Y I N O P S I T W A S E S A N D I F T H E R E B E A N Y A C A S I O N S O N W H I C H T H E Y C A N B E A N S E R D O N L Y I N O N W A Y T H E A U N S E R W I L D E P E N D A P O N T H E N A T U R O F T H E A C A S I O N +mls_eng_000322 I N H I S N O T B O R T H E M I N S T R I L S Y S E C K N A D I O N A T Y N O W A T S C O T S E S T H E B L E D W A S T A K E N D O W N F R O M A O L D W O M E N S R E I T A T I O N A T T H E H E L S A O N M O R L E D M I N E S B Y T H E A G E N T T H E R A N D S E N T B Y H I M T O S U R T E E +nchlt_eng_001588 C R I S T O N T H E O L I G O N S +nchlt_eng_001589 O P T A I N E E A G L F H I T H E R S +nchlt_eng_001590 E L A M E N T R Y E S P C I A L E F O N G T I O N S +nchlt_eng_001591 J O R D E W A S I N G S A N N U N V O R S T I T Y +nchlt_eng_001592 S I N S F I C T I O N N O T V E S P R O V E A N +nchlt_eng_001593 C O S T D H I P O P +nchlt_eng_001594 I N V E R S L E P B L A C E T R O N S F O R M E +nchlt_eng_001595 F R I N G H P R O T I S T A N T S +nchlt_eng_001596 A F G U N A E F O R S E S T +nchlt_eng_001597 H E A R O S I N M I S O L A G Y A N D L E A G E N D +nchlt_eng_001598 B U S N S C L A R S S E E T T T E R +nchlt_eng_001599 C L U D B P L A Y C H A R T E T +nchlt_eng_001600 P O S Y T R O N S W E R E R A P O R T E D +nchlt_eng_001601 A L L D V I C K T H E A T A R +nchlt_eng_001602 O R T H E D O C K E S M O N O U C K S +nchlt_eng_001603 N A T I O N S M B E R S T A T E S +nchlt_eng_001604 S H E A F H O W I L D C O U P +nchlt_eng_001605 C R O S R I S C K U W F E A T S +nchlt_eng_001606 A C T H A L F O M E M O A C K R S C O P I T E +nchlt_eng_001607 M E U S I C A L G R O P S R E A S T A B L I S H E D +nchlt_eng_001608 P R O M S C S I N S E R P A E S E +nchlt_eng_001609 F O L N E S I K N E K S +nchlt_eng_001610 T L V I O N S E R Y E S B A C E T +nchlt_eng_001611 N E W P O L I T I C A R P A T Y +nchlt_eng_001612 A N H A N T E A G J I P A C H E V E D +nchlt_eng_001613 F L A T M U S I G N A T R A L +nchlt_eng_001614 A M E R I C A N T I C N O L A D T I G N L E D Y R A T E R S +nchlt_eng_001615 D O A T E S O F B A R I N S +nchlt_eng_001616 P O P U L E T S W E R I S A T R A C T I O N S +nchlt_eng_001617 D O U C H W A S T I N D I A +nchlt_eng_001618 G O L D M I T A E R S I P I E N C E S +nchlt_eng_001619 R E S H I O N S O C H A L D E M A C R E T I C K +nchlt_eng_001620 A M E R I K C A N F O M E P R O D U S E S +nchlt_eng_001621 F R E S O F T E R Y A F O U N D A T I O N +nchlt_eng_001622 R O I L E D R M A T I C T H E A T +nchlt_eng_001623 I T A B L E M O L A S C K S +nchlt_eng_001624 F E A T H E R S I N C L U D B E A C H E R S +nchlt_eng_001625 O C S F O R D I T I O N R Y C H A N G E D +nchlt_eng_001626 S A L C O O P U R S I N G R A Y H O U N D +nchlt_eng_001627 P R I N M N I S T E R C I V E N +nchlt_eng_001628 L A N G A G E S O F Y O U R O C K +nchlt_eng_001629 S O U T H E A S T I N G L A N D +nchlt_eng_001630 N E W L I N E S E N A M A R +nchlt_eng_001631 E A C U L C R A D T S O P A T U O N A T Y +nchlt_eng_001632 S O U T H E S T I N G L A N D +nchlt_eng_001633 M A Y H +nchlt_eng_001634 R E C O L R D H A T E A T S E M I S C R I V E S +nchlt_eng_001635 M U S I C A L G R E P E S F R O M C A L F O R N I E A +nchlt_eng_001636 M A I N B U T L E T I N C K S +nchlt_eng_001637 P O D L I S H M U S I C A L I N S T R A M E N T E S +nchlt_eng_001638 L A N W U G E S O F S A D I E A R A V I A +nchlt_eng_001639 C A L D W A R T I N T I O N S +nchlt_eng_001640 D O A B E W P H +nchlt_eng_001641 A N D Y P O P E C L A M I N T +nchlt_eng_001642 G I T S T H E C N P R I V E A T +nchlt_eng_001643 C I N G F O D A N E A N D +nchlt_eng_001644 I L E C T R N I C M U S I C A L I N S T R M E N C S +nchlt_eng_001645 A G E N O U T W A T E R +nchlt_eng_001646 L O R E N C E L I V E M O R N A S I N A L E +nchlt_eng_001647 L E A G B A C S P A L E P L A Y E R S +nchlt_eng_001648 B U T I S O M A N T H E A N C H A N T M E D A T R A N I O N +nchlt_eng_001649 O U N I T E D S T A T S R E C O C O N I E D +nchlt_eng_001650 P R O P A S I O N A L F E L A C E S +nchlt_eng_001651 S P I C H A L E C N O M I E G S O W N S +nchlt_eng_001652 M A N S T R E A M E W I S T D +nchlt_eng_001653 E V E N G R U S H O W S +nchlt_eng_001654 B Y T H E D I O N S T O K +nchlt_eng_001655 N D T S A R T I C K E H A S N O +nchlt_eng_001656 W E A S T I N M U S I C L E S +nchlt_eng_001657 C O N S E V I T O F J U A T A Y S O M R E G A R T S +nchlt_eng_001658 O P I C K M B E R S T A T S +nchlt_eng_001659 P R I M I N E S S A I D J O N +nchlt_eng_001660 R A C K S F O A R M I N G M O U N T +nchlt_eng_001661 M A G E R L E A K T M S +nchlt_eng_001662 P O L O N A T I O N M A N I G E N T +nchlt_eng_001663 F R E N C H F I S I S T +nchlt_eng_001664 H I Y A R C O M P R E T S I O N R A T S I O +nchlt_eng_001665 R E C O R D N G I N D O U S T R Y A S O C H A T I O N +nchlt_eng_001666 T H E P E A G E O N L I N M A G A S E A N +nchlt_eng_001667 H I P O P E R R E C Q U A D P R E G U O S S E O N S +nchlt_eng_001668 F I N I G H E S T A T E M S H E N S +nchlt_eng_001669 W H I D L Y O U S E D L O C A L E +nchlt_eng_001670 N O R T H E M E R Y C A N C O N T I N A N T +nchlt_eng_001671 A F R C A N A M E R I C A N R E P A S +nchlt_eng_001672 T H R I T O N M E L I D R Y A C T I O N S +nchlt_eng_001673 A T H E W O R D M N N +nchlt_eng_001674 T H E T O M I K M E L I K U L A N O P T I C A L F O I S I C K S +nchlt_eng_001675 T O W N +nchlt_eng_001676 M O R S I L +nchlt_eng_001677 C O N S T R U C T N E W R A L G A G E H +nchlt_eng_001678 P O R L Y E X C L U S I N R I N C S I B L +nchlt_eng_001679 H E W O P O U R T R A Y D I F E R E N T S +nchlt_eng_001680 S O V I A T D I S I D A N C E +nchlt_eng_001681 S I G N A L E T R O N S T D U C T I O N P A R T H W A Y E S E +nchlt_eng_001682 Y O U B O R N M S I +nchlt_eng_001683 G E N E R L Y A C X C E P T E D R A N G E R S +nchlt_eng_001684 G I L E D A W A R D W I N I S +nchlt_eng_001685 S W E D I S H M U S I C A L G R O P S +nchlt_eng_001686 C H O W D E R D O A R T I S O M R A T I N G +nchlt_eng_001687 D O S I G E F O R M S +nchlt_eng_001688 O F H I G O S T A T U O N O V E R S T I T E +nchlt_eng_001689 F O R M O S S A T O M E N T S I N T O R K +nchlt_eng_001690 A M E R C A N I N V E N T I O N S +nchlt_eng_001691 A R T S +nchlt_eng_001692 M D O N Y U R O P I A N R A S H A +nchlt_eng_001693 N S T N O R L E A E G P I N A N T +nchlt_eng_001694 B E A G F I N I S H P R D U C T I O N S +nchlt_eng_001695 N A S I N O L E +nchlt_eng_001696 T R A D G I C G P O R T E S +nchlt_eng_001697 T I T L E G R I C E S T A T E +nchlt_eng_001698 A S T H E N A H A D E N +nchlt_eng_001699 E A S T N Y U R A P I A N C O N T R Y S +nchlt_eng_001700 C O N D E M E D A N D O T H R I V E D T A N S L A T I O N S +nchlt_eng_001701 A L T H W A R L E D I S +nchlt_eng_001702 C I N A S A R M O U N T A N L E N D U S +nchlt_eng_001703 N O B L S A M I T Y +nchlt_eng_001704 A N D W O D S E R F U R S +nchlt_eng_001705 M O U N T S A I N T V I N C S A N T +nchlt_eng_001706 S I T Y E M E R T R U P O L A T O N E I R O A +nchlt_eng_001707 R O O N E R S W H O D I E D A S C H I L D R E A N +nchlt_eng_001708 C H A N E S L A S V O L E +nchlt_eng_001709 I P E E P E C A T S I N T I E R L Y +nchlt_eng_001710 C I N G E D W A R D S D E A T H +nchlt_eng_001711 A M E R I C A R A M R I C A +nchlt_eng_001712 C O M E R H A L S H I P S A L E D +nchlt_eng_001713 P E O P L F R O M M E N H A M +nchlt_eng_001714 R A I A L C R A S H C I L D +nchlt_eng_001715 M U T H A L D E F A N S T O U D Y +nchlt_eng_001716 M O D E N C H I L D R U O L E S +nchlt_eng_001717 M O T E S E R R I F A L D E V I S I O N +nchlt_eng_001718 O U S T R A L I A N E A Y F O R S E +nchlt_eng_001719 A M E R Y K C A N M I S T R Y R H I T E R S +nchlt_eng_001720 F I N T H E Y G R O U N D G R E F I H T E +nchlt_eng_001721 W I L T E M P I N S O F M A N T C H E +nchlt_eng_001722 C A R I L I N A +nchlt_eng_001723 M Y B A T H I N O P E R A T E S +nchlt_eng_001724 C O U R T S V E R I T I E S +nchlt_eng_001725 M A D R A N D +nchlt_eng_001726 C A S E L I T H A L E R E A C T I O N S +nchlt_eng_001727 I N G L A I S H P E S C I F O U S T S +nchlt_eng_001728 Y O U N I T E D S T A T E S F E D E R A L +nchlt_eng_001729 F A D R A L D R E S E R O V E A C T +nchlt_eng_001730 W I L Y M H I N R Y H E R A S O N +nchlt_eng_001731 C L A P P L A Y C H O T +nchlt_eng_001732 P A S S O N G E R R A L E S O V I C S E S +nchlt_eng_001733 A N C H A N M E S S A D O R N T H I O N J E N R A L S +nchlt_eng_001734 C O N G A C T I O N D S E N T A M A +nchlt_eng_001735 G U N P O U D E A P R P I L E N T Y O U S E D +nchlt_eng_001736 L O W S T I N A G E S T A I G T +nchlt_eng_001737 C A L N D E R Y U R O S +nchlt_eng_001738 M A G J E R I N T O N A S I N A L E E A P O R T E S +nchlt_eng_001739 T O T L F O R S E A C T I M +nchlt_eng_001740 L O S T L E S S D A T E C O M P R I T I O N +nchlt_eng_001741 A G R E A E K H A +nchlt_eng_001742 I N V O R M N T A L P R O T I C T I O N A D G A N C S E +nchlt_eng_001743 M A N Y T O B I S C A L S G R I T I O N +nchlt_eng_001744 A N C H O A N S I T Y P I T H U N D E R +nchlt_eng_001745 S M A L O T H E D A C K S I N A G O G +nchlt_eng_001746 L O D G E S M T R P I L I A N E R I A R S +nchlt_eng_001747 T I T A L R E L I G Y E O R E M O N O +nchlt_eng_001748 E G A M P L E S A N D C L U D H A F M O N +nchlt_eng_001749 Y U N O T I D S T A T E M A N T A I N E +nchlt_eng_001750 B O L D R E P R E S E N C E M E A X S I M A +nchlt_eng_001751 S I N E S F C I O N O R T H E S +nchlt_eng_001752 O R D N A R Y D I F R E N S H A L E C U A T I O N S +nchlt_eng_001753 D I P L M A T S O F T H E H R D Y E S E +nchlt_eng_001754 S I R I A L C L O M M I S T R Y +nchlt_eng_001755 U R E L M E I T R Y C O L +nchlt_eng_001756 S L O W L Y L E D S S C I A L I S O M +nchlt_eng_001757 P R I N T E S +nchlt_eng_001758 N E U T A S T H E N P E O P L +nchlt_eng_001759 S M A T C O A R D B A C E T D I L E C T R O N I C K P E R S +nchlt_eng_001760 S T A T E A R M Y S O L D G E R S +nchlt_eng_001761 L O R D J E S C R I S T +nchlt_eng_001762 L A D A N B I G +nchlt_eng_001763 B E T A E L I A N N E S I N A L T E M +nchlt_eng_001764 A N D T E A G E R R E C R I A T I O N G R O U N D T H U M +nchlt_eng_001765 G R O S E S S T A T E P R O D E C T +nchlt_eng_001766 K I N G C O N G V E R S E +nchlt_eng_001767 B E L V A E L +nchlt_eng_001768 F L E O L G O N I S A T I O N S T H E U N I D E D S T A T E S +nchlt_eng_001769 Y S R I L T H E F E N S F O R S E S +nchlt_eng_001770 O R D R M T I C K S A N D R E S E V E +nchlt_eng_001771 B R N D S W I C K S E T H N R A L W A Y +nchlt_eng_001772 A C T R S A C A T D I M I A W O D +nchlt_eng_001773 P E P L F R O M E T O C K I A T D +nchlt_eng_001774 F O R C H A L D S S I N G A +nchlt_eng_001775 V E A R Y I A B L V L F H T A R M I N G +nchlt_eng_001776 S O U T H W A L E S V E L Y E S +nchlt_eng_001777 C A L I F O R D Y E A S T A T Y U T H E V E R S I T Y +nchlt_eng_001778 E L D E R O D O +nchlt_eng_001779 O U T D O R E O R I N T E D S I T Y +nchlt_eng_001780 C L A M E D P A R S H A L R E S P O N C S A B I L I T Y +nchlt_eng_001781 C R I S T H O N T E R M S +nchlt_eng_001782 E V E N S T O K P L A C E +nchlt_eng_001783 C E N S S A R D D I T H S I N F R O N C E +nchlt_eng_001784 H I S T R Y O F M I H A G A N +nchlt_eng_001785 A R I G I N L Y T H E N A M +nchlt_eng_001786 N T I O N S F R A I M E W R C O N V E N T I O N +nchlt_eng_001787 N O C A L E +nchlt_eng_001788 O L S T R I A N S C O L E E C A N O M I S T S +nchlt_eng_001789 M A N G R U O P C O M P O U W N D S +nchlt_eng_001790 R E S I C L I B L M T E R I A L S +nchlt_eng_001791 C O M I N L A R E S E S T O M +nchlt_eng_001792 B R O N G K S H I S C U L +nchlt_eng_001793 A N M E R C A N B E L I T O G O R I H I T E R S +nchlt_eng_001794 C A N M I C A L I L A M E N T S +nchlt_eng_001795 L O B L E I N T O N T C M U N I T Y +nchlt_eng_001796 T D Y O G R E A F I C E M A G A S E N M A R C H +nchlt_eng_001797 W I P S O V H I S P R E V I G D A S +nchlt_eng_001798 S I N S F C T I O N O B L E L E S +nchlt_eng_001799 S I N E S F I C T I O N F U L E M +nchlt_eng_001800 S U B C I T S O M E P R O B L O M +nchlt_eng_001801 E A S T O N N O R T H A M E R I C K A +nchlt_eng_001802 P E P E S W I T N E S L O U T I N G +nchlt_eng_001803 D I S T I N G T I V E V O C A L I N S T R M E N T S +nchlt_eng_001804 U A E F R I C A N A M E R I C A N R A P I E S +nchlt_eng_001805 P O R T H O G E S G E N A L S +nchlt_eng_001806 I N T O N E S I N A L E I A P O R T I D T Y A Y +nchlt_eng_001807 M O U N T A N R A N G E S O F B E L I V I E A R +nchlt_eng_001808 F R E N C H A R E F O U R S +nchlt_eng_001809 S S W O P R A B A L A P E R E N C S +nchlt_eng_001810 L O N G T R E V L I N G P A I E S +nchlt_eng_001811 D I S T R I C K T C O A R T J O U D G E H +nchlt_eng_001812 Y O R O N Y A M P I R +nchlt_eng_001813 B R I T I S H N N A S I O N A L I T Y E C T +nchlt_eng_001814 I S H O D A T A P R A L +nchlt_eng_001815 P O U B L I S I T Y T R A D E D C O M P A N E S +nchlt_eng_001816 R U S H I N V I C T I M S O F S O V E D R E P E N T A T I O N S +nchlt_eng_001817 W E I S T A N S L E V I C K L A N G W A G E S +nchlt_eng_001818 E T A L I A N R O M A N C A T H L I C E S +nchlt_eng_001819 F R E N T H S I S T R F N I M B E R S +nchlt_eng_001820 P R E V I N C H A L S I M B L S O F U N T A R I O +nchlt_eng_001821 R O C K S F O A M I N G M O N T +nchlt_eng_001822 A S A S S O N A T E D M O N O C K S +nchlt_eng_001823 I N C L U D I N T E N A S H I N O L N O N G V E R M E N T O L +nchlt_eng_001824 M E T R I C K S P A C E I M +swc_eng_001744 O R R E P A R E T H E B R A K N T H E C A P E +swc_eng_001745 E R Y H S U P T A N T E S +swc_eng_001746 H I S M O S T C O M E L Y A C U R S W H E N N E T H E R S I D E I S A B L E T O +swc_eng_001747 G R A T B A R Y I A R R I F I S M A N A G E D B Y T H E G R E T B A R I A R I F M R E N +swc_eng_001748 B Y A T L E A S T H R E V O T S +swc_eng_001749 D E F I H A N T C E A S E I N +swc_eng_001750 W L S H O E V D E N C E O F H M R G I N T +swc_eng_001751 I N D A N A N S E R H I C K L Y +swc_eng_001752 N A B L E D E V I C S I V E A N D U N D E M A C R A T I C S O S H A L P O L A C Y E S +swc_eng_001753 M A D R E S E N T T H I T L E S A I L I B L E O N C O E +swc_eng_001754 D I S T R I C T I N A T E N S I C X T Y S I C +swc_eng_001755 L I L E I N T O F E U C U I R I D Y +swc_eng_001756 A Y I N T H E G R O I N A N D A D V A N C E D T H R O +swc_eng_001757 C N A L G Y E S A N D I M P L D M E N T I G T R A N S H E U M I N U S C U L S O F A N H A N S E P E R F O R M E N +swc_eng_001758 N C O U D I N G N A P S +swc_eng_001759 Y S P A N I S H T I R C H M A N L O E R M E R A S D E L O U C A N A +swc_eng_001760 D E V I H T E D D E M I C R A T S +swc_eng_001761 H E W O L D C H A N P I A N S H I P H A S B E N C O N T R O L E B Y E I D Y E +swc_eng_001762 W H E R E T E S T A R I N G P O S I T I O N I +swc_eng_001763 E C R A T E D I N E V E R Y S T A T A N D T E R I T R Y T O P R T E C T A N D R E S E R V T H E C O N T R E S U N A K Y C O S I S T O M S +swc_eng_001764 E D I C A T I N T O F T H E N U S I L A N D W A R E A M O +swc_eng_001765 A L A M E F R M T H E R E L R O U D C O U P A N E S F O R V E T O I N +swc_eng_001766 T H E T O W N I S S P I T +swc_eng_001767 M O S K E T I Y F I S H I S A P R T I C U L Y A G R E I V E S P A C E S N O +swc_eng_001768 A N D T H E N A I O N A L C H E S E H E M I N S H I P E S +swc_eng_001769 R O B L O M I S N O W N T O R U N I N P L Y N O M A L T I M E +swc_eng_001770 L A Y J O N I E R A N D P A R C K E R W A T C O N S H E A R D N +swc_eng_001771 I N N I T I N S E V E N T Y T H R +swc_eng_001772 D E V E L O P I N G A N D O U S I N G S U C H T A C N A L D G E +swc_eng_001773 O R S O M E Q U I S T I O N S +swc_eng_001774 L A M E A P R O O E T H A T P E +swc_eng_001775 A B L A D E C A T H A T O R I S O U L Y I N S E R T E D S O M O N S O F L U I D B O N S +swc_eng_001776 E R N O T I O N O F Y U J E N I C A N H A N S M E N T T I C N A L A G E S M I G H U N I N T E N T I N A L Y I N C O U R A G E +swc_eng_001777 A T T H E A T E N I N O F R E S E R T H R S A N B E F O C K E S E D N M P A R T I A L S O L U T I O N S O R S O L T I O N S +swc_eng_001778 N O W N O F F O R H N D R E D S O F Y E A R S +swc_eng_001779 N L Y M A U B I A L S H A V E S O V I V E D T T +swc_eng_001780 T O W H C H A L T H E E D A B L E S P A C E S O F C R U S T A T I O N B E L O N G +swc_eng_001781 O G E R T H E M R E S U R C H +swc_eng_001782 N I N T E I N S I C X T Y T O F I L I P S I N V E N T E D H E C O M P A C T O D O C A S E T M E D E O A M F O R O D I O U S T O R G E +swc_eng_001783 U T R C I N F T H E L O W +swc_eng_001784 N T F H B I A N S A D R P +swc_eng_001785 O M E N S W O A L D C H E S T H A M I N C H I +swc_eng_001786 C O N T A N E D I S C R P T I O N S A N D C O M M A N T A R I Y S O N T H E S T A T O F A E N B I S I N C E A N D E C N A L A G Y A S M A G E R C O N T R U T E R S T O T H E +swc_eng_001787 U R H L F H A N T I Y O R S O C I A L T R E N T +swc_eng_001788 M O S T C O M P A C C A S E T S W E R S O U L D B L A N K +swc_eng_001789 I F T H E R I S A N O U L G E R T H E +swc_eng_001790 H E S U T H E N O S T R A L I A N C O S T A N D I N S U B A N T E I C T I C O U S T R A L I A N T E R I T R Y S +swc_eng_001791 A E R A T S O F T I P I C K L Y F I V E H U D R E D T W +swc_eng_001792 D E P R I V I N T H E D O C +swc_eng_001793 N I N E P O R S E N T O F H E T O T L C A S T +swc_eng_001794 A N T E R I A S S O R B R A T R Y A N D A N T E R I E C O M U N C A T I N A T E R Y +swc_eng_001795 E D N O T I M P A R T H S H I N +swc_eng_001796 N T H I E R D E M A C R A T I C P A R T Y +swc_eng_001797 N O H E S O N T O P O F T H E C S E T H A L I N D I C A T T H +swc_eng_001798 L O W A T T O S O +swc_eng_001799 I A I N D A N G R E D M R I N S P C H E S T +swc_eng_001800 B R O W N D E S I R E A L E C T I N +swc_eng_001801 H I S F A C D O S N T S A Y M U C H A B O U T W H E R E T H E P R O B L O M L I S +swc_eng_001802 C O M I C A L S O S I T Y B G A N A S +swc_eng_001803 I T H T O R I S T S A R V I N G T H E S T E M E B O T E A N D T R I N +swc_eng_001804 F R S T D I L O G B E T W E E N T R A N H U M N I S +swc_eng_001805 N E R B E N P A R T O F T H E L I P I C G A N S +swc_eng_001806 R E A G E S F U R N I T C U R A N D T H +swc_eng_001807 I N H I L A B L T R N M E N T S +swc_eng_001808 O L O C A T E T H A N U R I S O M +swc_eng_001809 O R H L O U G I C A L F R E D M +swc_eng_001810 D H E R J E T I C A T A K I N G S T O W +swc_eng_001811 A C T L Y F O R T Y A R S A F R T H E C A R N I S H D O N W A S L A T E +swc_eng_001812 A S E O T H E R E C A G N I T I O N T H +swc_eng_001813 O R L E C T R N I C K B U T N S O R D I S P L A Y +swc_eng_001814 I S U N N O N W H T H E R P E A C U L S E M P Y +swc_eng_001815 H I C H C O M S F R O M T H E V E R B A +swc_eng_001816 D I S P E P O R T I N T L Y A V A L A B L T T H O E W I H G E A T E R I N A N H A L R E S O R E S +swc_eng_001817 Y U M N T H R E T S T O T H S V I V L O F M A N Y S P A C E S +swc_eng_001818 E V E N M O R D I F C A L T +swc_eng_001819 A N D T W E T Y W N S P E C E S O F I C E A N I G D O L F O N +swc_eng_001820 A C H E V I N G P R M O T I O N +swc_eng_001821 E R A N T E W M I N U S E A S U M T I O N +swc_eng_001822 O N T H E F I R S T B E L L I T +swc_eng_001823 S T O R Y N D I C A T I V E O F T H E R I S E I N G L O B L S I G N I N G C S O F S H U P O L I S H I S T O L D B Y J E A N +swc_eng_001824 W H C H S P A R E H I S E R L Y E N T R U S T I N P O L I T I C K +swc_eng_001825 W A S C A L E D D O L B E A C H E C X P R O W I N F U L A N D P A T N T E +swc_eng_001826 O U L D S A V E A N D F I N E F I L S B +swc_eng_001827 A S T R L I A N S N A K E B E L O N G T O S V E N F A M L Y E S +swc_eng_001828 D V E L P I N P I Y A R S +swc_eng_001829 L I N D S H A R P L Y S E N C E S P E A K A N +swc_eng_001830 W A S R E C O R D E D I N T H I R L Y O N A F O R T R A C K C O S E T +swc_eng_001831 N O R M A S I M P R O V E M E N T I N +swc_eng_001832 R B N A N D W R O R L L E G U S T A C +swc_eng_001833 A C H P L Y E R B G I N S +swc_eng_001834 J E S T A S I N S P I R E D M A N Y C O M E N A T O R I L E P U S L E S +swc_eng_001835 O R H E O M A N I M A D G E +swc_eng_001836 E L A S P I R T E D T A P S +swc_eng_001837 A N T A N D I E S E N S N T O T H O T I O +swc_eng_001838 P R E S I N P R O T E M P O R O F T H E S T A T S E N I T +swc_eng_001839 I S H O P C A N M O V E A N Y N U M B E R O F S Q U A R E S +swc_eng_001840 P E H E I N S I D T H E S C O L +swc_eng_001841 I N C O N A C T S H E R N E S W I T +swc_eng_001842 O N T R E S O F T H E E S T E N P A L I A R K T I C F L Y W A Y +swc_eng_001843 A I N A L S D T I S T I K S E S T M A T E T H E P O P U L A T I O N I N +swc_eng_001844 O N D I S P U T E D W O R L D C H E S T C H A M P I A N +swc_eng_001845 Y R O U L L C A P E A B I N C E A +swc_eng_001846 E R I N A C T E D B Y T H E G E N R A L A S E M B L Y W A S A M E S U R R A T I A L Y S E V G R G A T I N G T H E S T A T E S R E L R O T C A R S +swc_eng_001847 W H C H R A P S A L M O S T +swc_eng_001848 S E C T P R T E C S A L N A T I V E F O R N A A N D P R O V I D S +swc_eng_001849 H E R E A S T H E F E M A L E S P E C U L M I S D A R K B R O N B O R E D W I T H W H I G H T +swc_eng_001850 O T E R Y E C N T U L S O +swc_eng_001851 N I N T E A N T W E L E I N R O S F E A R N +swc_eng_001852 D I G N O C I S E I S G E N R L Y M A D E W I H A S E T E S C A N O F T H E H E A D +swc_eng_001853 T H E F I R S T G E N R A L Y R E C O N I S E D W O R L D C H E S S C H A M P I A N +swc_eng_001854 H E P P Y A N D I T I N G O N W E P A R +swc_eng_001855 A D R E L A C E T H E I R E L B U M S B O T H T O C D Y A N D +swc_eng_001856 W A T E S A R O N T H E C O N T I N E N +swc_eng_001857 F T H E R A G E P E R S I N L S T E A R I O S +swc_eng_001858 N D F E Y O U W M E T E R S A N D R E C O A R I N G L E V L C O N T R O U L S O N +swc_eng_001859 P O I N T O E A L T I M E +swc_eng_001860 N D I T O F E N D E S T R O R D T H E P L A A B I L I T Y +swc_eng_001861 C O N F U T I O N +swc_eng_001862 E U I V E L E N T T O T H E Q U S T I O N O F W H T H E R E C X I S A M E M B E R O F C M P O +swc_eng_001863 M O S E T O I T H L A S T D R A N K +swc_eng_001864 P O S T H E N D E R I S M +swc_eng_001865 O M P A C T C A S E T Q U I K L Y F O U N D Y O U S +swc_eng_001866 E F O R H U N D R N D T H E R D Y T H E E F E +swc_eng_001867 I N G S W H I C H R E S U L T I N A S P E S I F I C K T H I H E A P O N +swc_eng_001868 F O U R N I N T E N H A N T Y S E V E N +swc_eng_001869 M I C A T I O N S A N D H E L F +swc_eng_001870 E A Y A T C H E I N A P E U R S O N N O +swc_eng_001871 S O M B R E A N D S P I S I F I D T H A T T H E Y M A Y A L S O B E U S E D O N O T H E R N O N P O R O S S M I P T E R I A L S +swc_eng_001872 H E P O S E I B L Y C O N D P E S I F I G +swc_eng_001873 M A T O R S I N L A C X +swc_eng_001874 I T H O U T F I F T Y M O R E T R I N G B R O A +swc_eng_001875 H I S I C H A C S I N P O U L E S R E S P E C T I E L Y T O K A S L O F A K I +swc_eng_001876 A V S H O N M L E T E D I S F I R +swc_eng_001877 E B L E A D I N G R I S T R E M A I N S O F R O U N D F O L T Y +swc_eng_001878 I L E R I T C H C K M A E +swc_eng_001879 S O M E S E C U L R H U M I N S C O N C I V E D R A N D S H E U M I N I S M A S A N A L S P R I N G O F T H E E U M N U S T F R E T H O U H T M O V E M E N T A N A R G U Y T H T R A S H U M I N I S T D I F E R F R O M T H E E U M I N I U S T P A I N S T R M B Y H A V I N +swc_eng_001880 P I N T A L E N E S T A N D C H I C K A R E V O N E R B L E T O P R O D A T I O N B Y M A M L E +swc_eng_001881 N O R T H E R N P I N T A L I S O N O F T H E S P E C S H E S T O W H I C H T H E A G R M E N T O N T H E C O N S C E R V A T I O N O F A F R A C K A N U R A G I O N M O G R I T O R Y W A T E R B U R D +swc_eng_001882 A N D I S N E O E F O U N D O N L Y I N T A S M A I +swc_eng_001883 R P E C T I V E T H E I D A O F M I N D U P L O A T I N G I S A S R T E D T O R E P R O S E N T +swc_eng_001884 N A V R I G E O F T W E N T O N P E R D A Y +swc_eng_001885 H A N W O L D F A L O T H A T P E E C U L +swc_eng_001886 N D B L E D I N G I N T O V E R I S C H O M E R S +swc_eng_001887 A N D L T H E T O G L I D B E 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T H R +swc_eng_001904 R A L A P L A Y E M A Y A L S O L O U S E B Y R U N +swc_eng_001905 U B L I K H L T P R F E S E R G R G R Y S T O A C K P O I N +swc_eng_001906 B R O N W A S A L E C T E D T T H E H O U S E O R E P R S E N T I V E S F O R T H R E N O N C O N S E C T I E T R M S +swc_eng_001907 O E G I S T H A P L Y W I +swc_eng_001908 A G R O P O F M E L E S T H A T R A C +swc_eng_001909 N T H E W O L D S L A G I S T +swc_eng_001910 B R E D I N G T A K X S P L A C E B E T W E E N A P R L A N D J O N +swc_eng_001911 S T R A L O R I S A T T H E S O T H E N I N D +swc_eng_001912 T E C N A L O U G I C A L S I N G U L A R I T Y I S P O S E B L +swc_eng_001913 N L U D I N T H E L E P Y C O D +swc_eng_001914 S E N T Y F O R E H A D A H I Y E R G I C A T I O N C U L F I C A T I O N C O M P E D T +swc_eng_001915 I A C K E R S W E N T H Y P O N E S C I N G I S A N +swc_eng_001916 O N C E V A T I O N I U S T R A Y A +swc_eng_001917 I S T H S E L A M A N D O F F +swc_eng_001918 F I R S T S E L F D I S C R I V E R A N S H U M I N E S T B E A T F O R M I L Y I N T H E E A L +swc_eng_001919 E E N T R E S U R C H I N D I C A T E S T A T F A C T E R S O T H E T H A N P R A C T I +swc_eng_001920 N D P R V E N T I O N A N D T R E A T M E N T O F O M P L C A T I O N S +swc_eng_001921 I H R A P I D O N S A E I T +swc_eng_001922 U T H O W W A R T E F O U N D A T I N W A S B A R I E +swc_eng_001923 N O W A D A Y S O U R L Y R E A G I N A L X P R E S S T R A I N S B E T E E B U R N A N D S H P E I T S T O B R I C G A N D F R A T T R A N E S C O N T I N U E T O U N O N T H M O N T A N R I L W A +swc_eng_001924 O T H E F A M L Y S W I T H P T I N U A L Y G O N D W A N A N O R I G I O N I N C L U D T H E R E T R P O N E D A Y +swc_eng_001925 B Y A N I T A L I N D O M I N I C A N M O R K J C O B E S D E S E S L E S +swc_eng_001926 A N D W A S N A I E D A F T E T +swc_eng_001927 A R T H F I H A L N T E L I G E N C S +swc_eng_001928 A N D I S T H E R A I N G +swc_eng_001929 P R E N O F T H E P O P U L A T I O N +swc_eng_001930 C H F E E R I R S O F S H U P L I H S A E L E S +swc_eng_001931 I M P O S E D B Y L A +swc_eng_001932 R I F R I N C E S I S S T O T H E R O L I N G C O R L A Y S H I O N D G O V E R M E N +swc_eng_001933 P A C H E S O F G L I D I N G P O S M +swc_eng_001934 B A C E O N T H E P R E V I O S T R A D G Y O F P L A Y +swc_eng_001935 N I D E L L I S T I C K A S P E R A T I O N S +swc_eng_001936 P E R F E C I N L S A N D W H O M R E C O A R I N G A T H O U S I A S T S +swc_eng_001937 H E M T H Y O L P I D A Y +swc_eng_001938 N O C O R T E O F P E O P L E W I H A P R E V I A S E S A Y A C H M A D V E L O U E H I G P O P I T C U I T R I S M +swc_eng_001939 D E V I D E D I N T O T H R E F A M L Y S T H A +swc_eng_001940 H O W D S L I G T I N T R E S T I N R E L I A E I N G C O E T +swc_eng_001941 T H A T A E M L U R N O F T T O H A V E C O E N +swc_eng_001942 N T W O T H U S A N S I K +swc_eng_001943 S H H I N B O Y S A R N O N A S B O T P O L I S +swc_eng_001944 T H E C O U S E I S R U T U R O F A S E R I B L A N D U R I S O M +swc_eng_001945 O S T O F T H E A G J E R Y O U E S S M U S I C C O M P N E S +swc_eng_001946 O N C S T E I O P A R O R O N M O N O F O N I C T R A C K I S P L A D O R R E C O R D E D H E N T H E T A P S M O V I N G I N O N D I R E C T I O N A N D T +swc_eng_001947 E I T S E A R L Y F O R M E I N +swc_eng_001948 S T R T E A G H C F I L O S O V E R +swc_eng_001949 O S I T I O N G T V A N A E H E S T E R N T H E G A M E +swc_eng_001950 N E O S A U T W H E L L S +swc_eng_001951 D I S P O S A L O V E R H I S O N B Y L O U G I C A L N A T E R +swc_eng_001952 E P O D U C T I V E R I G H T S O R E X E R T U N D O P R E S H O R S O N P R E S P E C T I E P A I N +swc_eng_001953 I L H A N C H A N T N O R G O N +swc_eng_001954 R A S T A P O P H O L O S H I D G O N +swc_eng_001955 N D T O T H U S A N D T O +swc_eng_001956 F O R G A M P L F T H E P L A Y A R H A S O N L T +swc_eng_001957 S O F E D A S U B R A C K N O D H M E R G E H A V E C O L N I I V I M P A R M E N T T H A T F E C T +swc_eng_001958 P E V I D E I G R O N S T I C D A T E R +swc_eng_001959 H O H A D A N U R I S O N S D E T E C T E D B Y O T H E R M I N S +swc_eng_001960 L I O E S T I L S D I S I N T O M P R O E H E L T H A N U N C E V I T Y +swc_eng_001961 H A D M O R S U F I T I C A T E D A N D O F T A P R E D I C T I O +swc_eng_001962 D E H U M A N I S A T I O N +swc_eng_001963 S P A C H E S I N C L O U D F R E S H W A T E R L A M P R A Y E +swc_eng_001964 F O S T A N D Y U G R I M +swc_eng_001965 H E F R E A N S I C L O P E D I A A T +swc_eng_001966 T H E R F O R E M E T I C A L I M A G E N G I S G E N R L +swc_eng_001967 P E A C E I S T T H E E X C L U I O N O F N O N H U M A N A N D P A R T H E U M A N A N I B L E S +swc_eng_001968 N P E O P L H H A D P R E V I A S L Y S U F R D A S U B R A C M O H H E M R I G +swc_eng_001969 L S I F I E D A S A I T H E I N D A N G E D O R T H R T O N D A N D T O T H E P E B E +swc_eng_001970 I A N A T E R N Y J E N E R L P A R C K E R W A T C O N S H A R D N +swc_eng_001971 B U T T I P I C L Y +swc_eng_001972 W H I C H I N T U R N E F E D T H E S I G N L T O T H E H A D O F T E C O E +swc_eng_001973 T H I T H E R O W N C O N V E N I O N A L Y E X P E C T E D L I F E T I M E S +swc_eng_001974 U B S T A N H A L S T R I N +swc_eng_001975 T W E N Y I T H S E N T R Y C A N T U C K Y C O N G R S M A N J O A N +swc_eng_001976 N O T P A S I N T O R I N D I M I +swc_eng_001977 H U N T I N G W I T H L E D S H O A +swc_eng_001978 W N Y T H E I R T E N +swc_eng_001979 O T H E S E V E N P R S E N T O F T H E W L D S P A T S P A C H E S L I V I N U T R A L A +swc_eng_001980 R P O V S R A N F I N L Y E N D I N N T E N H A Y F I V +swc_eng_001981 W I L S O M E T R E N E H U M O N I S T A K E A N A B S T R A C T +swc_eng_001982 P O I N T T H E R I H E P R E T E C T I O N +swc_eng_001983 A I C S A M O R F I S M P R O B L O M I S T H E C O M P E T A T I N A L P R O B L O M O F T H E T E R M I N I N G W E T H +swc_eng_001984 O R T H E R E S T R I C T O U R C O N S E P T I +swc_eng_001985 H O E H O S A R V I E H U S P I T A L I A T I O N +swc_eng_001986 S O M E P E T E C T I O N O F U N S E R T A N G N I F I C A N C E I S O N F U R E D B Y C U C A S I N A T H N I T I T Y +swc_eng_001987 H A L S T A E A G O N S +swc_eng_001988 C O G E T I V E I N H A N S M E +swc_eng_001989 V A N S T T H E A T H R A N K A N D B E P R M O T E D T O N A L O +swc_eng_001990 D R A B A C K O F C O I L I N G I S T H E P E B L I T +swc_eng_001991 I N D I C A T E S S U B E R A C K N O D H M B R I G E +swc_eng_001992 D A M I C H P O R T I O N +swc_eng_001993 N D O P T I O O F Y E J E I K A N D H A N S M N T E K N A L J E S +swc_eng_001994 P O L I S H O N H I S H O R S E N D W A G A N +swc_eng_001995 D T H E E X T H A M P I A N +swc_eng_001996 T H E O F A F E R A L D I S H U C X L Y +swc_eng_001997 L C H A P I N H I N C T E N T W E N Y O N +swc_eng_001998 S U C H A S Q O N T I M C O P U T A T I O N A N D R A N D M Y S E D E L G R E T H E M S +swc_eng_001999 A T Y N H A D E N O I N +swc_eng_002000 A S S H O N E B Y L A D N E R T H A T I F P E S N O T A C U L T O A E N P T H A N T H E R E X I S T P R O V B L U M S +swc_eng_002001 H E C O M P A C T D I S O +swc_eng_002002 D G R E Y G O S I N E R I A L +swc_eng_002003 W A S W E N D E R E D A S I A G E S +swc_eng_002004 S A Y A C H O R E T O N O T H E C U S +swc_eng_002005 O N T I T U N C E Y O F F A V E S H O M +voxforge_eng_000874 T H E F O R T H A N D F I T H D A Y S P A S E W I H O U T A N Y D E V E L L A M E N C E +voxforge_eng_000875 T H E Y N O W T H E R E P O R T +voxforge_eng_000876 S U C H T H I N G S H A D A C U R D B E F O R H E T O L D F I L A P +voxforge_eng_000877 T H E Y O N L Y H A D A L I T L E T E R D Y T H O U S E N D D O L E R F I E R +voxforge_eng_000878 I A M G O W I N G T O G E T I T O W D +voxforge_eng_000879 H O U D U D L Y H E M A I N T A I N E D A C O A R M E A N D S M I L I N G A S S P E C T 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R N S I D M S M O R T O M E R +voxforge_eng_000905 I T O K H M H A F A O U T O R C H H E E D O I T +voxforge_eng_000906 M A R T H A W H E R D O S T A N D O T H E O N T R A C T U L I S O U S +voxforge_eng_000907 A S T O B E U N D I S T I N G U I H A B L E F R O M T H E V A S T W H I H E P L A I N S A R O U N D +voxforge_eng_000908 H E W O U L D D E S T R O Y A L T H I N G S T H A E R F I C X S T +voxforge_eng_000909 T H E R U S I O N U S I C P L A R T H E C O N T W A S H R O A B E D I N S L A V E +voxforge_eng_000910 T O H I S S P R I E H E R A N T E W A S F L A T A N D U N C O M P R O M I Z S I N G +voxforge_eng_000911 T H I S S O U L D B E I N T R E S T I N G +voxforge_eng_000912 I A M A F R A I D E I D O N T H A V E M U C H T I M E +voxforge_eng_000913 C R I S M S I S A N E A S Y P R O B L O M E C O M P A R D W I T A P O L N A S I O N G I V I N G F E C S T +voxforge_eng_000914 T H E P L N T E R S A R A R D Y C O N S I D E R I G T H M A T E R +voxforge_eng_000915 J O N C R I E D W I T H S H I N G E I Y E S +voxforge_eng_000916 W H O E V E R L I V E D O N T H E R A N C H D I D T H A T +voxforge_eng_000917 W E L E A V E T H E E F V E N T U A L I T Y T O T I M E A N D L O R +voxforge_eng_000918 A T T H E S A M E T I N E S P I A R S N D E R O S B E G A N T O F A L A M O N G H I M V A T E R S +voxforge_eng_000920 I T I S M E A R L Y T H E S I M P L E S O U P E L I T I F +voxforge_eng_000921 I N S T E A I D H E A R I V E D O N T H E N I H O F T E S E C A N D A Y +voxforge_eng_000922 I N H I S A N G S I T Y A N D S U L I S I T U O D A N D L O V E T H E Y D I D N O T C O U N T +voxforge_eng_000923 G O D B L E S S H M I H O P E L O O N S I N G T H E M F O R E V E R +voxforge_eng_000924 Y O W E R E I N G A G E D +voxforge_eng_000925 T H E R L A C E S W A S O F A D E L I C K E I T I V E R E C A L L O R F R A I N E T O M P T I N T I N W I T H E A L O L +voxforge_eng_000927 I T W A T H E S A M E W A Y W I T H O U R R E V O L V E R S A N D R I F A L S +voxforge_eng_000928 H E K I N G H A D P R O M I S T O I N C Q U I R E I T O T H E M A T E R +voxforge_eng_000929 D O S T H A L O K G O O D T +voxforge_eng_000930 F O R T H E F I R S T T I M E I N H I S L I F E H E W A S Y E A R N I N G F O R A S C R A P +voxforge_eng_000931 I D E F I E A N Y M A N T O G E T A S O L A M O N I L E N D E S S O R I N C A L F O R N T H E A +voxforge_eng_000932 H E R I Y S M O U L T E S T R A T H I M A S H E C A M E O F T H E B A N G K +voxforge_eng_000933 E T Y W A V E N O N S S A L L I C G D T H A +voxforge_eng_000934 M E N W H O N D E U R I T C A L A T L I V I N G D E A T H +voxforge_eng_000935 M T O S O N W H O S E D T H I S B O K C E P E R R O D G E R S +voxforge_eng_000938 I O N L Y R E A D T D D H T H E F O A R T A T I O N S +voxforge_eng_000939 T H E W A S P O P E R D E V I S I O N O F N A Y B E R I T H E W O R E T H E I N D E V R I G U L Y P O P O A R M E D +voxforge_eng_000940 I A L P E L Y O T H E L I B R A R I N S A I D W T H A R I H T F A C E +voxforge_eng_000942 I S W M T R P U I A G N O R D H I S H E A D G R I M L Y I N S E R C A S T I C L Y +voxforge_eng_000943 T H E R I N G O F T H E B I G B I L E A R O U S D H I M N +voxforge_eng_000944 O R T H E S C R A C H O F A P I N O N A M A N S H E A D V A S T R E A G O N S O F T H E E R T S E I R F I S R E M A I N E J E A L O U G I C L Y U N O N +voxforge_eng_000945 H E H A D B U R D I L Y E N T E R D A D D I S W H E N H E S S O U O W D T H E G L O O F A F I R +voxforge_eng_000946 T H E N E S C H A R S T H E L I T C O M A N D +voxforge_eng_000947 I T W A S J E A N S I N I N G S O F E L Y O E R B E Y O N T H E O A C K S +voxforge_eng_000948 O F L I N G A R O W B U S T D B E T W E N U S +voxforge_eng_000949 H A T R E I T A N D M U R D E R A N D L O U S T F O R R E V E N C H T H E Y P O E S E S T D T O O F E R F L O W I N G +voxforge_eng_000950 T H T Y O U C U D H E R E A L U P A N D O N T E I M P O P O E +voxforge_eng_000951 I T W A S M Y A D E A T O A T E +voxforge_eng_000952 S H E D O S N T W O N T O W I N +voxforge_eng_000953 S H E H I N K E I T I S B E C A S H E W O N S E S O M T H I N G E L T E +voxforge_eng_000954 H E P U L L E D A N D T H E L O K C R E S E T D O W N T O B R A K E H I S B A C K +voxforge_eng_000955 T H A T T H E S O C A L D F O R S E S A T W O R K I N L I G H T H E E A L C T R I S I T Y A N D M A G N A T I S M +voxforge_eng_000956 H E T O R N D S H A R P L Y D A N D P I C E G R A G S I N A C O S T T H E P I V E L E R +voxforge_eng_000957 A L S O I W A N T I N F R M A T I O N +voxforge_eng_000958 T H E S I X T D A Y H E S P E N T I N T H E C A V E N W I H G R E A G S O N +voxforge_eng_000959 O N T H I S I P O T H I C E S T H E H A M E R I N G O F T H E L T R M U N D Y I N G C O R P U S L E S O N T H E B O B C O N F I R S E I T S C A N A T I K N E G Y O N T H E O N H A N D +voxforge_eng_000960 N O W A F I R N Y W I L W E S T R E M E A N D E V E R A N A N O N Y O U A M U R G E F R O M A L T H E G R O V E S A N D F L O W E R S +voxforge_eng_000961 W I T H O U T I T T H E M O S D E N S E L Y P O P U L A T E D R A G O N S O F M O T E N Y U R P A N D A M E R I C A +voxforge_eng_000962 T O M E S P I N K E H A S A H A R P O O N +voxforge_eng_000963 H E W N T E G E T H E I N I S H T T H I S F O W A R E Y S O F A G O N +voxforge_eng_000964 L K E A F L A S H E L O N C E D I M S E L F I N T T H E F E T H E D M A S O F T H E H O W L +voxforge_eng_000965 I T C O N T A I N E S A T O T L E O F T W E N T Y E N T R E S +voxforge_eng_000966 I H A E H E L T M O R E C O M F O R T A B L E +voxforge_eng_000967 T H A A P O S S E S T O M A C H V A T E L I T Y +voxforge_eng_000968 T H E W A L F D O G E T H R E S D H I S G O N T M U S A L E T O W A R D H I M +voxforge_eng_000971 T H E G A V B I A L V O I C E O F H E S M R I Y R A N G O U T +voxforge_eng_000972 I T W A S O R I V E R N D M A R G I N G L I A K E O R S E L E S F R O M T H E R E A T S W O M P +voxforge_eng_000973 S A I D T H E M A L P U L I N G H I M S E L F T O G E T H E I H A E F A R T Y O M U S T H I N G M E V E R Y R O D +voxforge_eng_000974 I N W H A T B E U C O L I C K S C O U O O F F E N C E H E H A D B E N T O R T W A S B E O N D I M A G E N I N G +voxforge_eng_000975 H A D N O T I N A B L E D I N V E S T I G A T E R S T O O B T A I N E A C O M P R I T I V E L Y L I T L C O S E T +voxforge_eng_000976 A T R I C L O F F R E S H B L O U D R A N O V E R H I S F A C E +voxforge_eng_000977 I T W A S A C U R U S C O I N E I T D A N C E +voxforge_eng_000978 I T I S T H E F I R E P A R T L Y S H E S A I D N +voxforge_eng_000979 T H E J U S T L A Y O F I N T H E O S H A N D P L O U K E D A W A Y A N +voxforge_eng_000980 I N O T H A T O U W E R I N C H A R D G E T H E R E A N D G E E N O S E +voxforge_eng_000981 F O R T I E T H E E X S I T I N G T H R I L E O F H I S A D V E N T U E W A S G O N +voxforge_eng_000982 F U D N L Y H I S F I N G E R S C L O S E T H I D L Y O V E T H E H A N G A O C H I F +voxforge_eng_000983 D E A R S I R Y O R S E C K A N T V I C T O M H A S F O L L O N O N S C E A D G J U A L E T I M E +voxforge_eng_000984 H E C N C A E F R I M S E L F +voxforge_eng_000985 E A C H I N S U L T A D E D T O T H E V O L O U O F T H E C L A I M E +voxforge_eng_000986 T H O U I T M A Y B E T R A N S F O R M E D I N T O A N Y N O F T H E F O R M S O F W H C H E N R G Y I S S E S E P T I B L +voxforge_eng_000987 M E S I T D O E S S C R E A M E D G R I E D L O A F I M A N Y F E S T E D T H E H I R A D T I C K A N D B O U N D H E N M E N T O F H I S T A D I A R +voxforge_eng_000988 I W A N T O N O H O W A L L T H I S I S P O S E I V B L E +voxforge_eng_000989 P R E N T I N G A S I M P L A N D I N S T R U C T I V I L U S T R A T I O N O F T H E S T R G L F O R L I F E A M N G T H E R I V E L E S P E A C E S +voxforge_eng_000990 H I L L N E V E R D O A T A P O F W O R K T H E H O L V O Y A G E H +voxforge_eng_000991 I H A E H U N T E D A L O N G T H I S R I C E R E P L I E D F I L I P +voxforge_eng_000992 L O R D B U T I M G E D T O S E Y O A G I N F I L +voxforge_eng_000993 H O V E L I N L Y I W E N D A D E D T H A F R S T A +voxforge_eng_000994 T H E A R O T R E G U L E O S T E R P I R E T S N I C L E S C O N T N E D +voxforge_eng_000995 T H E M S T B E H R D I N G F O R B U S N E S B U T I T H U G Y O U M I G T W A T T T A K E L O K T T H E R S I G H T +voxforge_eng_000996 T H E R W A S N O C A N C E T O F I R E W I T H O U T H I N I N G H I M +voxforge_eng_000997 A S F O R H I M S E L F W O N T T H E S T R E A E R A L W A Y A R N I N G S I N C R E I N G S A D L Y +voxforge_eng_000998 D O N H I M C A N Y O U R B O Y G O L O N G W I T E S S Y +voxforge_eng_000999 G O L D B Y P E A R H E S H O W T E D +voxforge_eng_001000 B U T S U C H A D E V E R D G I E N S O F A P I N I O N W O U L D C O N S T I T U T N O M E N E N C E T O S O S C I T Y +voxforge_eng_001001 T T H E R E W A S O N E C H A N C E S A N D O N L Y O N O F S A V I N G J O N T +voxforge_eng_001002 I I C A N O T F O L O E Y O S H E S A I N D +voxforge_eng_001003 O N T H E F A R C O R N E R O F T H E C O M P O U N D F E N T S A W H A O K B R E A D E D +voxforge_eng_001004 T H E N A G I N T O T E R H A D S U C A I R I T A T I N G W A Y A B O U T H I M +voxpopuli_eng_000494 W E A L L N O W O M A N A S A S U C E S F L E S T A B L C O N T R Y A R O L M O R T H E R F O R T H A T F O R T H E H O L R E A G O N +voxpopuli_eng_000495 T H E R E F O R I T S H I G H T I M E O U C O M E F O R B O D E T H E P R O P O S A L F O R R E V E U B E D A N O P R A I O N A L S U P E R A C I O N O F T H E O A R D I T A N D N O N A D I T S E R V I S I E S U N D E R A D I E C T E A U S O B E I T I S O N +voxpopuli_eng_000496 I T I S C K E A R E T H A T W E H A V E N O T I M E T O W A S T T H E N U E R E S O L T S O F T H E E I P E E S H E R E C A R D N S I E N T I F I C B A C S E S O F G L I M I T J A I N S E L E V E N O R O U O M E F O R H E S I T D A S O N +voxpopuli_eng_000497 S E N T S O I N T H E C O N T A I N E R W H I H A E V E R A E N T U C H E D C O M E S L A V E S C O U N T E O F E T G O D S D R U G S I T S E T R +voxpopuli_eng_000498 I H O P E T H A T C O M I O N S M O B I T I N E S H E S I N I S I F I V E S H O O N T C R A T T H E N E X T P R O B L O M B U T W I L L B E A A N S E R F O R E X I S T I N G C H A L I N G E S O F T H E R O U T T A N S P O R E D S E C T O +voxpopuli_eng_000499 I N T H E W U E I T W A S A D I C I O N T A G N A U L Y B Y O N E P R S O N T H E O R M E R P R E S I D E N T O T H E N I G D E D S T A T E S A G A N C E T H E A T I C U L A T E D M C R A T I C D U M A J U R I T Y O T H E U E S C O N G R E S B Y A L L O F I T S R E P U B L I C K E N N D S O M F I T S D E M E C R A T I C T D E M A C R A T M E M B E R S I T W A S A N A G R E M E N T W I T H O U T A N Y B I N D N G O B L I G A T I O N S A T H E L E D E S O F E R U N V E R Y U P A N L Y A N P R E S I D H M A P T L Y N T H E E R Y D A Y T H E S O C A L D D E L W A S P O U L I S H E +voxpopuli_eng_000500 F R E S P E A C H I S A S E N I U A L Y A E X E T I G T H T P E O P L A R E F R E E T O S A Y T H I N G S W E D O N O T L I K N O T M E L Y F R E E T O S A Y T H I N G S W E D O L I K +voxpopuli_eng_000501 H A T I S L U R N E F O M T H I E +voxpopuli_eng_000502 B E S I N T H A T T H E N V I M E N T A L E F F E C T O F P R O D U C S M U S T B E A V R Y I N M P O R T A N T I S U E I N H E R E E W U A N D T H E W O L I D E A E O T H E E C U L A B E R G I V S A V E R Y U S O U L O R I A N T A T I O N F O R T H E O U S U M E R S O F C O U S H E E C U L A B E R H O U L D G I V E N T O T H E M O S T A N D V I R M E N T A F F A N D Y P O D U C T T H E I N F O R M A T I O N S O U L D B E C L E A R E A N D C U E +voxpopuli_eng_000503 H O W E V E R T H E C A R E N D R Y G E M N E D E S T O B E B E T E R D A L O R D T T O T H I G I D A L I N V I R N M E N T T O I S H U R E F A R M I N E R A T I O N T O G R E A T E R S A E N T O O N F O M E T O O N S U M E R E X P E C T A T I O N S +voxpopuli_eng_000504 A T C A S E B Y T H E C M I O N A N D M E M B E R S T A T T O N H A N E T H E R S U P O R T T O R E C O N C I L I A T I O N T O S E C U R P E S E A N D T I B I L I T Y A N D A R L A N D I W O L T H E R E F O R E A R D Y U C A L I E S T O P L E A S E S U P O R T I S A M E N M E N +voxpopuli_eng_000505 T R A T A G I C K C H O I C E S A B O U T W H E T O E W E S T M U T B E M A D E N O W T A K E N I N E C O U N A N E T O F A S O U T F O R S I L F U L S U P S I T E S B U T T A K T H E G A S A S I O R S O F Y U I T C A N B E A H E L T F U L E B R I G I N G T R U N S I S H O N A R Y M E D I O M T O B E U S E I N M E M I N M E N Y M B E R S T A T I B E O N T O E A C H I V E O V E R A M B I S H I O S C L I M I T A R G I T S +voxpopuli_eng_000506 W E A E P O S E I L Y F R A O L E W E C A N C U T H T O P R A S C U E T H E S A M E M P O L I C E S I N T H S A M E M A N E R N O W I N G T H A T W E L E D E T O T H I S A M P R S O S T H E R I S A U L S T H A W E N O D E D E A +voxpopuli_eng_000507 U T H E R S A N O P T I O N B +voxpopuli_eng_000508 W R E A L L S O N E D A C H A I N G E I N O R I D O L I T I E +voxpopuli_eng_000509 A L A D E H B A T O F T H E R E A S O N O F C O U R S E I S I L I G A L F I S C I N G K A N D T H E R E O F O M P T D O N O F E N B Y Y A R R V E S E S W H I C H A R E R E A G I S T E R D T O C O U N T R E S W H I C H L U C K E T H E W I L O F T H E R E S U R C E S T O N F O R S T I N T H E N E S I N A L A G R E M E N S N O M O U N T O F T R E S A B I I T Y M E S E R S O R E E X T R P A P R E W A R E W I L A D E S E T H E P R O B L O U M E O F R E D U S I N G +voxpopuli_eng_000510 T H E C O M P R M I S E A L S O I N C L D E D K L A R E R U D S T O T H E F I N E W H I C H M B E R S T A T E A S H E R S T I C T I O N A N D T H E O P R A T I O N I T H I M B E R S T A T S C O N E R D F O R C R U S B R T H E C A C E S A S I L A T H E N E D T O E I N V L L F Y O U R J U S T T H A N Y O F O R W O R K A N D P L A E O U S E U P O R T T O M O H I S E R E C T I V +voxpopuli_eng_000511 N O T H E R E N S W O U L D H A V A S B E L E T H A T H E A R B A D B E S C R I M I N A L B E S D E L I B E A T L Y C O N T A M I N A T I N G H U D Y W I T H A D A N E U S N G R E D I E N T B U T I T F A C T I N F A C H E D I N G W H T H U N Y B E S A R A L H V E A L W A S D O N W I H T O C A R Y P O L O N B A C T O T H E R H I V E S T O D T O F E D T H E R O U N +voxpopuli_eng_000512 U T I T W A S T H E C O N T R Y I T S E L F B E N G M O R C A P A B L +voxpopuli_eng_000513 R I N T O T H E P R T F O L I O O F T H E N E U W C O M I O N A R D E L I N G W I T H F U N D E M E N T E R R I T E S +voxpopuli_eng_000514 T H E M E S I Y G I T A T T H E O U D O D T N A T H A V E A N N O U R S O L U I O N S +voxpopuli_eng_000515 A R Y O U W I L I N G T O A C T I N E R E F A V E R F O R T H E S O S I A L D E M E N T I O N T O B E I N C L O U D E D I N T H E E U C O M P A T E N C S E S A S P R O P O S E +voxpopuli_eng_000516 A N E X T H A T O N P E S P E C T R U P O L I E S T A K I N W I T H E E F O R M O F O U E R T E L I C O N T H F R A M W O R +voxpopuli_eng_000517 I B E L E V E H I S R E M A R K S W E R A E X P L I C I T L Y R A C E I S T A N D T H E N A F O B I C K A N D P R M O T E D R A C I A L I N T O L E R A N C E I N A W A Y T H A I S N O T C X C E P T I B L E O R A L O W D I N T E C O N T I T U T I O N O F T H I S H O U S +voxpopuli_eng_000518 R E A L I F E G A M P L S H O T H A T S O L V I N G I T I E S R E L A T E T O A D U C A T I O N F E U L E D S T R O N G C O M I N I T D E V E L O P M E N T +voxpopuli_eng_000519 S I H O P E T H A T I S I L H V P E O R U S H A A S W E L N D T H A T R U H A C A N A L T S A N D V I S A I G N D E X T R E M E S U C E S S T O R Y A F T E R T H S E G T I S I G N I F I C A N D A T I N O R G S T T H I S Y E A R B +voxpopuli_eng_000520 S H E E C X E P T E D T H E F A C T T H A T S I T I S O N S H I P I S A Y N A S I N A L P A R T O F T H E O S I N O G U D I S D I C T I O N B U T H Y O U R L S O S A I D T H A T A C O U R D I N G T O T H E M A S T R I C K T R E A T Y A N D S H E A S R I G H T T H E H A S T O B E A D I Y R E C L I N +voxpopuli_eng_000521 T D E Y W O U F A L D E S P E C I A L E A N T H E M S T R A T I N G A U N I F I E D A N D T A F F I S H E N T A P P R O R C H T O L I E M I T C A N G H E T R E A T M E N T A S W E L A S I N S T R A N T H A N I N G K I T S L E D I N G P O L I T I C A L C O S I O N I N D I S A G E N D E R I C O N S C I T H E R T H E R F O R T A K I N G T H I S R E S O L U T I O N A N A C T O F U T M O S T I M P O R T A N S +voxpopuli_eng_000522 T H E U N I G T E D S T A T E O F Y U R U O W I L B E A F A C T W I T H S W E D O N A S A P R O V I D E N C +voxpopuli_eng_000523 I T M U S B T H E C A P I T A L E O F B O T T H E A T E S A N D W E M U S R E C O N I S E P O L S T I N I S T H A T A S P R O V I D E D F O R I N T H E O V E L O G R E M E N C S +voxpopuli_eng_000524 Y O U C R A I N Y S F A C E T W I T H W O N E O F C R U S A L C H A L I N G E S I N I T S H I S T O R Y I T W O U L D B E F U T E M E N T A R L Y R O N G K T O P R E T H E N A T I O N N O W W I T A L T H I P E S O F R E S T R I C T I O N S P O P E L A D E R L C A L E O S T E R I T E P O L I +voxpopuli_eng_000525 M O R E R U L S A N D R E G U L A T I O N W I L L N O T I M P R O V E T H I S C I T U A T I O +voxpopuli_eng_000526 A T L E A S T W E W O L D L I K E T O N O W T H E S O U R S E O F T H E M O N Y A N D T H E P O S I P L M O R T I E +voxpopuli_eng_000527 T O W E R O F T H O S E Y U R U P I N W A L E L A N G I A S H I N T O T H E S G L U B E L I C E D W E A R L D I S I N T T O T H E Y S G O B E L I S D E C O N O M I N D H I S G O B E V I L A C H W H I C H I S G O R S T I A L Y C O N O M I C K S O S I A L E L N P L I T I C O I T S A R M O S T V E L A B L E E S T H E R T F R O M T H E I N T I R E E Y O U T H A T W E M U S T T H A K F O L A C O U N S A N D T +voxpopuli_eng_000528 W E A V E T O R E P E T E T H A T A L T H E A Y A N O T B E U S E T O F I N A N S S I U R I T E X P A N C E S B A R T H E R S C O N T R O L O R M L I T R Y S O P O R N T +voxpopuli_eng_000529 T H N T H E S I N T I F I R E P O R T S B E C O E M R E M O R E U R G E N T O R A L A R M I N G A N D M O R S H O C K I N G +voxpopuli_eng_000530 F I N A L Y M W H E N W E A T T H I N K I N G A B O U N T H E R I N O V A T I V E F I N S I O N I N S T O U M E N T S W H E N O U T H E B O L T H F O R O U R S E L S T O R S U P O A R T O W E R A C O N O M E S B U T A L S S O T O O E S U P O R T T H O S H O E R E I N E A E T +voxpopuli_eng_000531 T H T I V E A S O Y U N I E K D O L L I N P E M A K I N G +voxpopuli_eng_000532 P A P E R A V E R Y D W E E K P R O P O S L +voxpopuli_eng_000533 S R U S H A S A L W A S B E A V E R Y P R O U D N A T I O N W I T H R I C H C O L T C U E R W I T H I N V E N T I O N S W I T H A N A S P L +voxpopuli_eng_000534 A R T A C X A T I N E V E N A M O D I C A L O F T A C X A T I O N I N S O M E C A C E S M I G H J U S T H E L P U S E M T O D O W H A T I V E A R E D Y S U E G E S T E D A N W H O N O S E M A K E T H E C A C E F O R T H E R E T R E S P E C T O F B A N K R E C A P I D L I Z A T I O N T H A T W E N E V E R S O +voxpopuli_eng_000535 T H E R O P E A N A S I L O M S U P O R T O F H I S M O R O V E R A S A M O N G I T S T H A S T S T O P R M O U T D F E S I L Y T A T A N D C O U R D I N A T E X T C A N G E S O F I N F O R M A T I O N A N D O T H E R A C T I V E I T E S R E L A T E D O E L O C A T I N W T H I N H E U N I O N +voxpopuli_eng_000536 H E O N U S O O F T H E F R A M E B O R K A G E M E N T P R O V I D E S A L I G L Y B I N D I N G I N S T R M E N T T O O B G R A T A N D S T R A N T N E U O S T R A L I A B Y L I T H R R A T I O N S A N D T O I N C R E S C O P E R A T I O N +voxpopuli_eng_000537 T H E R E F O R W E A E A S T I N T H E C O U S A L A S G M I O N T O R E S E N T H A H A S B A L E T H A O U L D B E T H E S E S T M E N T O F T H E E B A C T O F T H E R I C I S +voxpopuli_eng_000538 I N O T H E W O R D S T H E O B J E C T I O N I S N O T W H E T H E R M O N E Y I S P A D O R N O T T H E O B J E C T I O N I S W E T H E R T E R I S A D I D E C T L I N K O R N O +voxpopuli_eng_000539 T O T H S T I N G U I S H E S T H E T O M A N O E A R Y O U M E R R I G T A B B U S E B Y T H E C A D A N T G O R M E N T A N D T H E D L A N I A N N U C L A P R O V G D M +voxpopuli_eng_000540 Y E S S M A T H M D R U O T H A N K A T H R S E C T I A L H E R A S D M E N T I S A F O R M O F V I L A N C S A N D I T I S T H E M O S T E X T R E A M E F O R M O F G N T E R B A E T H D I S C U M I N A T I +voxpopuli_eng_000541 W E C A N L O K T O S O M E U R A N L I N O U M E M B E R S F O R O U O D G X A M P L E S A S R E G A R D E D T H G N O L I G E +voxpopuli_eng_000542 Y I M N V A L L V E D F O R T H E R P O S I T E V E A N D C O S T R A C T E I V E A B R O T C H +voxpopuli_eng_000543 O I H O P T H A T I S W I L B E C O M P L E A T E D E A R I N H E F A C I V I L F U T U A R T H A T M A N E S M A B E T O A F R E M O N S +voxpopuli_eng_000544 O R F O R D E R N D C O U R D S H T H E Y O U H A N D E F O R T S T O B R I N G A M O N G K P E S I N O F G N I S T A N A N T O O V E R C O M E T H E F F R A S I L E S I C U I T Y A N V I R M E N T I N T H E C O N T R Y +voxpopuli_eng_000545 B E A N D E R S T A N T T H A T S O M E P E O P E L A R A N G R Y +voxpopuli_eng_000546 O N T O B E M R E S T P O N C I V E L +voxpopuli_eng_000547 W E M U S T E D A C T I F I E T H T H I S S U T I A T I O N A N D V E A S K T H E O M I O N T O C O N C S I D E R T H E M O S T E D I C U I T C O M B I N S A T I O N M E S E S F O W P A S N G E S +voxpopuli_eng_000548 T H E O M I T I O N I N B V I H E D T H E Y U R O P I A N T P U L A M E N T I N T H E U P C O M I N C R E V I S I O N T O O P E N H I S P O S I T I O N O N T H I S M A T E R W H I C H R E L Y C O N S E D A X E S T O S J U S T I S I N Y U R O P A N D T H E E N F O R T M E N T O F R I E S G R A N T E D B Y H E Y U R O P I A N E R Y U N A N L O +voxpopuli_eng_000549 I L O M V E R Y M U C H T H R I S O U N T I O N O F T O K E B E T W E N T H E O S R A L I S A N P L E S T I N I O N S A N D S N C E I R L Y H O P T H A T H E W I L S U C E D +voxpopuli_eng_000550 W E H A V E A C U M I L A T I O N O F P R O B L E N C S R E S U L T I N G F R O M E T H E A R T I F I H A L A N D D E B A G E A T I N G K A N D V E R P R E V I U S Y U S +voxpopuli_eng_000551 L E T U S T N O T B E T H E M A N O F Y E S T E R D Y L N T U N B E P O L D A Y S I N S T H I T U T I O +voxpopuli_eng_000552 T I G O U L D E R L S U M T O B E C O M E A M B A S S E T H E S O T H E Y E A R M A K N G I T S A D E A R S A N D A C T I V I T H I S W O W I D L Y N O W N A M O N C S H T O Y U R U P E A T I T I E N S A N D P U T P I I P A T I N G N E V E N S B E T A T Y O U R O P I A N N A S I O N A L L F O R L O K A L E V L +voxpopuli_eng_000553 S E R T N L Y S U C H I M P A C E S E S T M E N T C O U L D P R E M T S E R T A N P R O B L O M S S U C H A S T H O S P O S E D B Y T H E E L E C T R O N I K I D E N T I F I C A T I O N O F S H E P A N D S C O T L A N D +voxpopuli_eng_000554 T H E C O R T I S C O N T E N T T O S E E T H T I T S W O R K H A S I N F O R M E T H D I S C H A G H R O S A N D H A S C O N T E B U T E D T O R O P O S A L S F O R I M P R O V I N G T H E F I N A N C A L M A N A G H M E N T O F V E Y O U S P E N D I N G A N D B E T H E T A R K A T I N G O F Y O U F U N E +voxpopuli_eng_000555 R E G O U T H E R E C L A R I E T H E A N D S E R T A N T Y I S N E D E D F O R T H E O B L I C K S E C T O U R A N D F O R T H E I N D U S T R Y +voxpopuli_eng_000556 I S I T R E A L I N O T P O S I B L E T O U S A A T H E R H O U S I N G F A S C I L I D E S W I T H U P R O P R E H R E S E P T I N C O N D I O N S I N T H E M E N T I M E +voxpopuli_eng_000557 W H E L Y O U T A K E A C I O N A T L A S T I F N O T T H E N W H E N D diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/token_int b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/token_int new file mode 100644 index 0000000000000000000000000000000000000000..e0e6b5ed0e8d84e028e3152fb631b1d74e3b82de --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/decode_asr_asr_model_valid.loss.ave/test_10min_eng1/token_int @@ -0,0 +1,1092 @@ +LAD_eng_000254 2 10 3 2 11 3 15 5 8 3 12 2 17 3 13 2 16 10 5 15 21 8 5 7 2 5 7 4 8 13 2 7 8 7 4 3 7 2 9 8 25 4 19 2 18 8 23 3 2 5 2 19 3 5 11 2 8 2 17 10 16 10 2 9 14 18 2 11 12 2 5 2 4 3 11 5 22 13 3 2 5 16 25 8 12 3 7 4 +LAD_eng_000255 5 19 2 13 8 22 11 5 13 2 16 6 7 9 3 23 8 4 8 23 3 2 10 3 2 17 5 9 2 12 3 18 3 5 4 3 12 2 8 7 2 5 4 3 8 7 2 5 4 19 2 4 6 +LAD_eng_000256 2 6 7 2 11 6 12 2 13 5 5 11 2 16 6 7 2 12 11 5 11 2 4 17 6 2 11 6 12 9 2 5 4 2 17 6 5 7 16 3 +LAD_eng_000257 2 9 6 15 3 2 6 18 4 10 3 2 16 6 7 4 11 3 9 2 10 23 3 2 9 14 11 23 5 19 9 2 18 6 11 2 15 5 13 4 8 21 13 3 2 19 3 5 11 9 +LAD_eng_000258 22 6 4 10 2 6 18 2 4 10 3 2 23 11 9 8 6 7 9 2 18 3 5 16 10 11 2 4 10 3 2 9 6 7 20 2 10 5 21 19 2 10 6 13 8 12 5 19 +LAD_eng_000259 2 9 10 5 24 25 21 8 5 11 2 15 5 7 19 2 11 3 18 11 7 16 3 9 2 5 11 3 2 15 5 12 3 2 4 6 2 9 3 7 9 2 8 7 4 11 2 5 16 4 8 6 7 9 2 6 11 2 16 5 11 8 16 4 3 9 2 18 11 6 15 2 23 5 11 8 6 14 9 2 21 13 5 19 3 9 +LAD_eng_000260 8 18 2 6 7 13 19 2 4 10 3 2 11 6 20 11 5 15 2 16 14 13 12 22 11 5 24 3 2 6 14 4 2 26 14 9 4 2 5 2 8 4 13 3 2 18 11 6 15 2 8 4 9 2 4 6 2 18 6 15 8 13 8 5 11 2 5 21 11 6 16 10 +LAD_eng_000261 4 10 3 2 10 3 13 22 3 15 2 17 5 9 2 11 3 13 3 5 9 3 12 2 8 7 2 6 9 4 11 5 13 8 5 11 2 6 7 2 7 8 7 4 3 8 7 4 10 2 6 11 20 8 9 4 2 4 17 6 2 4 10 6 14 9 7 12 2 5 12 2 3 13 3 23 3 7 +LAD_eng_000262 2 10 3 2 7 6 17 2 21 13 5 16 3 2 18 6 11 2 5 9 4 11 5 13 8 7 2 16 13 6 22 3 2 21 3 11 4 10 2 20 13 6 11 19 +LAD_eng_000263 8 4 2 8 9 2 7 6 4 2 7 6 7 2 10 6 17 2 15 14 16 10 2 8 18 2 3 5 7 19 2 6 18 2 10 3 2 16 13 5 15 9 2 5 11 2 4 11 14 +LAD_eng_000264 5 2 9 15 5 13 2 22 8 9 8 7 3 9 9 2 6 7 11 2 22 11 6 5 11 12 2 6 21 11 5 4 3 12 2 10 8 2 17 3 5 4 2 5 12 2 9 10 3 21 18 5 15 3 2 18 6 11 2 9 8 16 4 3 7 2 19 3 5 11 9 2 18 11 6 2 4 10 3 2 5 20 3 2 6 18 2 17 3 7 4 19 2 4 6 +LAD_eng_000265 8 7 2 4 10 3 2 7 8 7 4 10 2 9 3 7 4 14 11 19 2 10 3 2 17 5 9 2 5 7 2 8 11 8 9 10 2 21 6 3 4 +LAD_eng_000266 2 4 10 3 19 2 5 11 3 2 15 5 11 24 3 12 2 22 19 2 9 4 11 6 7 20 +LAD_eng_000267 2 4 10 3 2 13 6 17 2 8 9 2 4 10 3 2 18 6 11 2 23 5 6 13 3 12 +LAD_eng_000268 8 7 2 4 10 3 2 11 13 19 2 9 4 5 20 3 9 2 16 5 15 3 2 16 13 6 9 3 2 4 6 2 14 9 2 5 2 9 13 3 21 +LAD_eng_000269 2 11 6 7 8 7 20 2 3 23 3 11 19 2 4 10 11 4 19 2 15 8 7 14 4 2 4 10 11 6 2 5 4 2 9 3 11 23 8 9 2 4 8 15 9 +LAD_eng_000270 5 9 2 5 2 11 3 9 8 14 13 4 2 17 10 3 7 2 4 10 3 2 16 6 13 8 20 3 2 11 3 2 6 21 3 7 12 2 8 4 2 17 5 9 2 5 9 2 5 7 2 5 13 13 2 15 5 13 3 2 16 6 13 8 20 3 +LAD_eng_000271 4 10 3 2 4 8 15 3 2 22 3 4 17 3 3 2 4 10 3 9 2 21 6 8 7 16 4 2 8 9 2 23 3 11 8 5 22 13 2 5 7 12 2 16 5 7 5 16 14 11 2 5 7 19 2 17 10 3 11 2 18 11 6 2 5 2 15 8 7 8 4 2 4 6 2 15 14 16 10 2 13 6 7 20 3 11 +LAD_eng_000272 17 6 5 11 24 2 6 7 2 4 10 3 2 3 5 2 3 2 3 2 9 4 5 11 4 3 12 2 8 7 2 15 5 11 16 10 2 4 17 6 2 4 10 6 14 9 7 12 2 5 7 12 2 9 3 23 3 7 2 5 4 2 5 2 16 6 9 4 2 6 18 2 18 8 23 3 2 15 8 13 8 5 7 2 12 6 13 3 11 9 +LAD_eng_000273 2 10 6 17 3 23 3 11 2 4 10 3 11 2 17 5 9 2 9 6 15 3 2 12 8 2 5 20 11 3 15 3 7 4 2 6 23 2 4 10 2 3 7 12 8 7 20 2 4 10 3 15 3 2 17 10 8 16 10 2 6 11 2 15 6 11 19 2 5 7 12 2 19 6 10 8 15 6 11 19 2 12 8 9 16 14 9 4 12 2 5 4 2 13 3 7 20 4 10 2 6 23 3 11 2 3 15 5 13 +LAD_eng_000274 4 10 3 2 16 6 21 13 3 2 10 5 12 2 7 6 2 16 10 8 13 12 11 5 7 +LAD_eng_000275 4 10 3 2 18 8 5 13 2 9 8 7 20 13 2 6 2 4 10 5 4 2 12 3 22 14 2 5 13 2 22 10 15 2 21 5 11 8 9 2 16 6 13 8 7 20 2 10 5 12 2 5 7 2 3 13 5 22 11 4 2 15 14 9 8 16 2 23 8 12 8 6 +LAD_eng_000276 4 10 3 2 9 3 11 8 9 2 3 7 12 3 12 2 6 7 2 9 8 25 4 10 2 6 11 20 3 9 4 2 4 6 2 4 10 6 14 9 7 12 2 5 7 12 2 18 6 11 2 13 5 9 4 8 7 20 2 18 11 2 5 2 4 6 14 4 3 2 6 18 2 9 3 23 3 7 4 19 2 6 7 2 12 5 19 9 +LAD_eng_000277 10 3 2 10 5 9 2 5 13 9 6 2 16 6 7 4 11 8 22 14 4 3 12 2 4 6 2 4 10 3 2 7 3 17 2 19 6 11 24 2 11 3 23 8 6 2 6 18 2 22 6 6 24 9 +LAD_eng_000278 22 19 2 21 13 5 16 8 7 20 2 9 15 5 13 2 5 11 4 2 6 22 26 3 16 4 2 4 11 6 2 6 14 4 2 4 10 3 2 18 8 13 15 +LAD_eng_000279 2 8 4 2 8 9 2 18 6 14 7 12 2 8 7 2 22 11 3 9 8 13 +LAD_eng_000280 8 4 2 17 9 2 4 10 3 2 9 8 12 2 6 18 2 4 10 3 2 18 5 15 13 19 2 8 2 8 12 3 7 4 8 18 8 3 12 2 15 6 11 3 2 17 8 4 10 +LAD_eng_000281 10 2 16 5 7 12 2 8 4 2 9 8 20 10 4 3 9 2 15 14 9 4 2 5 13 9 6 11 2 9 6 22 15 8 4 2 5 2 17 6 11 24 2 21 13 5 7 +LAD_eng_000282 2 12 14 7 12 3 19 2 17 10 7 2 4 10 3 2 15 5 16 10 2 4 10 11 3 2 4 6 +LAD_eng_000283 2 10 6 17 3 23 3 11 2 4 10 3 2 23 8 13 8 20 3 2 11 3 15 5 8 7 12 2 8 16 5 13 5 4 3 12 2 5 7 4 8 13 2 4 10 3 2 11 8 23 3 13 2 6 18 2 4 10 3 2 18 8 11 9 4 2 7 6 14 9 2 21 5 21 3 11 2 9 3 16 6 7 12 2 11 3 21 6 14 22 13 8 16 24 +LAD_eng_000284 4 10 3 2 18 5 9 4 2 9 3 11 23 8 9 2 8 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0000000000000000000000000000000000000000..2d4d7a305e96df16d0c88b31dd97f2e2b45f11cb Binary files /dev/null and b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/images/wer.png differ diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/latest.pth b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/latest.pth new file mode 120000 index 0000000000000000000000000000000000000000..d52af6357e1c6c465ad7c4b26911c5298cdbde12 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/latest.pth @@ -0,0 +1 @@ +30epoch.pth \ No newline at end of file diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/run.sh b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/run.sh new file mode 100755 index 0000000000000000000000000000000000000000..dce063fde13c88303ee40a584f18c8b682ff4d92 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/run.sh @@ -0,0 +1 @@ +./asr.sh --ngpu 1 --stage 11 --stop_stage 13 --nj 32 --inference_nj 4 --gpu_inference true --lang eng1 --inference_asr_model valid.loss.ave.pth --local_data_opts '--duration 1h --lid false --multilingual false --single_lang eng1' --use_lm false --token_type char --feats_type raw --feats_normalize utterance_mvn --asr_config conf/tuning/train_asr_s3prl_houlsby.yaml --inference_config conf/decode_asr.yaml --train_set train_1h_eng1 --valid_set dev_10min_eng1 --test_sets 'dev_10min_eng1 test_10min_eng1' --asr_tag train_asr_s3prl_houlsby_eng1_1h --expdir test_pr --asr_stats_dir test_pr/asr_stats_eng1_1h --local_score_opts 'false false monolingual' --stage 11 "$@"; exit $? diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/tensorboard/train/events.out.tfevents.1705238293.stan.269130.0 b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/tensorboard/train/events.out.tfevents.1705238293.stan.269130.0 new file mode 100644 index 0000000000000000000000000000000000000000..9e7b2c17426bd712d9498a883bd9cb80fd379aa4 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/tensorboard/train/events.out.tfevents.1705238293.stan.269130.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f27bc344ebb0ae713b4510e56d001d979912b0222c960ee6c8f166a964793b5a +size 62774393 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/tensorboard/train/events.out.tfevents.1705417877.stan.860322.0 b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/tensorboard/train/events.out.tfevents.1705417877.stan.860322.0 new file mode 100644 index 0000000000000000000000000000000000000000..0a3a758ae43b11e7a3771045ac388c4fbba87b8e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/tensorboard/train/events.out.tfevents.1705417877.stan.860322.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b9e66391f8b9763fac5cbd0eaba1da83bc18e7afb60cee9122b22aae87a09e96 +size 63284534 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/tensorboard/valid/events.out.tfevents.1705238293.stan.269130.1 b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/tensorboard/valid/events.out.tfevents.1705238293.stan.269130.1 new file mode 100644 index 0000000000000000000000000000000000000000..6df92006c1c41322ef9f24fe7993531a8591ae2f --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/tensorboard/valid/events.out.tfevents.1705238293.stan.269130.1 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e693c9dd4d7f60101467a45525fd0a68f37a1b2a66d2b305953d58b6d1255dfe +size 11190 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/tensorboard/valid/events.out.tfevents.1705417877.stan.860322.1 b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/tensorboard/valid/events.out.tfevents.1705417877.stan.860322.1 new file mode 100644 index 0000000000000000000000000000000000000000..087eea8e8b5bd925e0299c3bd32a03ee8c0e9ebb --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/tensorboard/valid/events.out.tfevents.1705417877.stan.860322.1 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:faa2bd8225d296f81ff149634285b8f6b1692c17e26ea77e844a93d6c55d0f3b +size 11190 diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/train.1.log b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/train.1.log new file mode 100644 index 0000000000000000000000000000000000000000..b8b02b36a5d04cf4b5048fc2771dc00a1a520e1b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/train.1.log @@ -0,0 +1,1406 @@ +# python3 -m espnet2.bin.asr_train --use_preprocessor true --bpemodel none --token_type char --token_list data/eng1_token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_eng1/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_eng1_1h/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_eng1/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_eng1_1h/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_1h_eng1/text,text,text --train_shape_file test_pr/asr_stats_eng1_1h/train/text_shape.char --valid_data_path_and_name_and_type dump/raw/dev_10min_eng1/text,text,text --valid_shape_file test_pr/asr_stats_eng1_1h/valid/text_shape.char --ngpu 1 --multiprocessing_distributed True +# Started at Sun Jan 14 21:18:09 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_train.py --use_preprocessor true --bpemodel none --token_type char --token_list data/eng1_token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_eng1/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_eng1_1h/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_eng1/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_eng1_1h/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_1h_eng1/text,text,text --train_shape_file test_pr/asr_stats_eng1_1h/train/text_shape.char --valid_data_path_and_name_and_type dump/raw/dev_10min_eng1/text,text,text --valid_shape_file test_pr/asr_stats_eng1_1h/valid/text_shape.char --ngpu 1 --multiprocessing_distributed True +[stan] 2024-01-14 21:18:10,840 (asr:523) INFO: Vocabulary size: 30 +[stan] 2024-01-14 21:18:10,902 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +[stan] 2024-01-14 21:18:10,902 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +[stan] 2024-01-14 21:18:11,013 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +[stan] 2024-01-14 21:18:12,312 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +[stan] 2024-01-14 21:18:13,143 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.mask_emb.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.label_embs_concat.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.0.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.1.0.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.2.0.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.3.0.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.4.0.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.5.0.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.6.0.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_g.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_v.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,144 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,145 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.weight.requires_grad = False +[stan] 2024-01-14 21:18:13,146 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.bias.requires_grad = False +[stan] 2024-01-14 21:18:13,549 (abs_task:1275) INFO: pytorch.version=1.12.0+cu113, cuda.available=True, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True +[stan] 2024-01-14 21:18:13,551 (abs_task:1276) INFO: Model structure: +ESPnetASRModel( + (frontend): S3prlFrontend( + (upstream): S3PRLUpstream( + (upstream): UpstreamExpert( + (model): HubertModel( + (feature_extractor): ConvFeatureExtractionModel( + (conv_layers): ModuleList( + (0): Sequential( + (0): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): Fp32GroupNorm(512, 512, eps=1e-05, affine=True) + (3): GELU(approximate=none) + ) + (1): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (2): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (3): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (4): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (5): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (6): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + ) + ) + (post_extract_proj): Linear(in_features=512, out_features=768, bias=True) + (dropout_input): Dropout(p=0.1, inplace=False) + (dropout_features): Dropout(p=0.1, inplace=False) + (encoder): TransformerEncoder( + (pos_conv): Sequential( + (0): Conv1d(768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16) + (1): SamePad() + (2): GELU(approximate=none) + ) + (layers): ModuleList( + (0): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (1): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (2): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (3): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (4): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (5): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (6): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (7): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (8): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (9): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (10): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (11): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + ) + (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (final_proj): Linear(in_features=768, out_features=256, bias=True) + ) + ) + ) + (featurizer): Featurizer() + ) + (specaug): SpecAug( + (time_warp): TimeWarp(window=5, mode=bicubic) + (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq) + (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time) + ) + (normalize): UtteranceMVN(norm_means=True, norm_vars=False) + (preencoder): LinearProjection( + (linear_out): Linear(in_features=768, out_features=80, bias=True) + (dropout): Dropout(p=0.0, inplace=False) + ) + (encoder): TransformerEncoder( + (embed): Conv2dSubsampling2( + (conv): Sequential( + (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU() + (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) + (3): ReLU() + ) + (out): Sequential( + (0): Linear(in_features=9472, out_features=256, bias=True) + (1): PositionalEncoding( + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + ) + (encoders): MultiSequential( + (0): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (1): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + ) + (criterion_att): LabelSmoothingLoss( + (criterion): KLDivLoss() + ) + (ctc): CTC( + (ctc_lo): Linear(in_features=256, out_features=30, bias=True) + (ctc_loss): CTCLoss() + ) +) + +Model summary: + Class Name: ESPnetASRModel + Total Number of model parameters: 99.96 M + Number of trainable parameters: 5.27 M (5.3%) + Size: 21.07 MB + Type: torch.float32 +[stan] 2024-01-14 21:18:13,551 (abs_task:1279) INFO: Optimizer: +Adam ( +Parameter Group 0 + amsgrad: False + betas: (0.9, 0.999) + capturable: False + eps: 1e-08 + foreach: None + lr: 0.0001 + maximize: False + weight_decay: 1e-06 +) +[stan] 2024-01-14 21:18:13,551 (abs_task:1280) INFO: Scheduler: None +[stan] 2024-01-14 21:18:13,551 (abs_task:1289) INFO: Saving the configuration in test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml +[stan] 2024-01-14 21:18:13,706 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-14 21:18:13,749 (abs_task:1665) INFO: [train] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/train_1h_eng1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/train_1h_eng1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-14 21:18:13,749 (abs_task:1666) INFO: [train] Batch sampler: SortedBatchSampler(N-batch=30, batch_size=8, shape_file=test_pr/asr_stats_eng1_1h/train/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-14 21:18:13,749 (abs_task:1667) INFO: [train] mini-batch sizes summary: N-batch=30, mean=8.1, min=8, max=9 +[stan] 2024-01-14 21:18:13,760 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-14 21:18:13,760 (abs_task:1665) INFO: [valid] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_eng1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_eng1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-14 21:18:13,760 (abs_task:1666) INFO: [valid] Batch sampler: SortedBatchSampler(N-batch=5, batch_size=8, shape_file=test_pr/asr_stats_eng1_1h/valid/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-14 21:18:13,760 (abs_task:1667) INFO: [valid] mini-batch sizes summary: N-batch=5, mean=8.0, min=8, max=8 +[stan] 2024-01-14 21:18:13,761 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-14 21:18:13,771 (abs_task:1665) INFO: [plot_att] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_eng1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_eng1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-14 21:18:13,772 (abs_task:1666) INFO: [plot_att] Batch sampler: UnsortedBatchSampler(N-batch=40, batch_size=1, key_file=test_pr/asr_stats_eng1_1h/valid/speech_shape, +[stan] 2024-01-14 21:18:13,772 (abs_task:1667) INFO: [plot_att] mini-batch sizes summary: N-batch=3, mean=1.0, min=1, max=1 +[stan] 2024-01-14 21:18:13,804 (trainer:300) INFO: 1/30epoch started +[stan] 2024-01-14 21:18:27,381 (trainer:763) INFO: 1epoch:train:1-40batch: iter_time=0.003, forward_time=0.182, loss_ctc=160.053, loss=160.053, backward_time=0.026, grad_norm=1.357e+03, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.354 +[stan] 2024-01-14 21:18:39,895 (trainer:763) INFO: 1epoch:train:41-80batch: iter_time=5.175e-05, forward_time=0.156, loss_ctc=155.216, loss=155.216, backward_time=0.024, grad_norm=364.778, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.253 +[stan] 2024-01-14 21:18:51,766 (trainer:763) INFO: 1epoch:train:81-120batch: iter_time=4.920e-05, forward_time=0.149, loss_ctc=146.953, loss=146.953, backward_time=0.023, grad_norm=457.155, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.187 +[stan] 2024-01-14 21:19:03,651 (trainer:763) INFO: 1epoch:train:121-160batch: iter_time=5.158e-05, forward_time=0.149, loss_ctc=145.869, loss=145.869, backward_time=0.023, grad_norm=162.702, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.188 +[stan] 2024-01-14 21:19:16,016 (trainer:763) INFO: 1epoch:train:161-200batch: iter_time=4.912e-05, forward_time=0.155, loss_ctc=150.668, loss=150.668, backward_time=0.024, grad_norm=251.374, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.236 +[stan] 2024-01-14 21:19:28,204 (trainer:763) INFO: 1epoch:train:201-240batch: iter_time=4.848e-05, forward_time=0.153, loss_ctc=149.216, loss=149.216, backward_time=0.024, grad_norm=319.644, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 21:19:40,634 (trainer:763) INFO: 1epoch:train:241-280batch: iter_time=5.185e-05, forward_time=0.155, loss_ctc=149.868, loss=149.868, backward_time=0.024, grad_norm=455.968, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.243 +[stan] 2024-01-14 21:19:52,570 (trainer:763) INFO: 1epoch:train:281-320batch: iter_time=4.945e-05, forward_time=0.150, loss_ctc=135.936, loss=135.936, backward_time=0.023, grad_norm=347.276, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.194 +[stan] 2024-01-14 21:20:04,713 (trainer:763) INFO: 1epoch:train:321-360batch: iter_time=5.106e-05, forward_time=0.152, loss_ctc=126.247, loss=126.247, backward_time=0.023, grad_norm=268.191, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-14 21:20:16,695 (trainer:763) INFO: 1epoch:train:361-400batch: iter_time=5.053e-05, forward_time=0.150, loss_ctc=112.375, loss=112.375, backward_time=0.023, grad_norm=268.353, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.198 +[stan] 2024-01-14 21:20:29,080 (trainer:763) INFO: 1epoch:train:401-440batch: iter_time=4.894e-05, forward_time=0.155, loss_ctc=106.143, loss=106.143, backward_time=0.024, grad_norm=277.579, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.238 +[stan] 2024-01-14 21:20:41,247 (trainer:763) INFO: 1epoch:train:441-480batch: iter_time=4.933e-05, forward_time=0.152, loss_ctc=96.765, loss=96.765, backward_time=0.024, grad_norm=275.733, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217 +[stan] 2024-01-14 21:20:53,206 (trainer:763) INFO: 1epoch:train:481-520batch: iter_time=4.964e-05, forward_time=0.150, loss_ctc=90.714, loss=90.714, backward_time=0.023, grad_norm=257.343, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.196 +[stan] 2024-01-14 21:21:05,530 (trainer:763) INFO: 1epoch:train:521-560batch: iter_time=4.918e-05, forward_time=0.154, loss_ctc=91.783, loss=91.783, backward_time=0.024, grad_norm=234.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-14 21:21:17,734 (trainer:763) INFO: 1epoch:train:561-600batch: iter_time=5.212e-05, forward_time=0.153, loss_ctc=86.216, loss=86.216, backward_time=0.024, grad_norm=206.869, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-14 21:21:29,731 (trainer:763) INFO: 1epoch:train:601-640batch: iter_time=4.971e-05, forward_time=0.150, loss_ctc=82.455, loss=82.455, backward_time=0.023, grad_norm=220.259, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-14 21:21:41,932 (trainer:763) INFO: 1epoch:train:641-680batch: iter_time=4.923e-05, forward_time=0.153, loss_ctc=81.574, loss=81.574, backward_time=0.024, grad_norm=217.190, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-14 21:21:54,174 (trainer:763) INFO: 1epoch:train:681-720batch: iter_time=4.947e-05, forward_time=0.153, loss_ctc=77.907, loss=77.907, backward_time=0.024, grad_norm=206.885, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.224 +[stan] 2024-01-14 21:22:06,492 (trainer:763) INFO: 1epoch:train:721-760batch: iter_time=5.173e-05, forward_time=0.154, loss_ctc=76.786, loss=76.786, backward_time=0.024, grad_norm=221.793, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-14 21:22:18,159 (trainer:763) INFO: 1epoch:train:761-800batch: iter_time=4.785e-05, forward_time=0.146, loss_ctc=72.835, loss=72.835, backward_time=0.023, grad_norm=215.915, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.167 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-14 21:22:23,250 (trainer:354) INFO: 1epoch results: [train] iter_time=2.176e-04, forward_time=0.154, loss_ctc=114.774, loss=114.774, backward_time=0.024, grad_norm=329.322, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222, time=4 minutes and 4.4 seconds, total_count=800, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=227.347, cer_ctc=0.292, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=227.347, time=1.15 seconds, total_count=5, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.89 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 21:22:24,266 (trainer:417) INFO: The best model has been updated: valid.loss +[stan] 2024-01-14 21:22:24,266 (trainer:288) INFO: 2/30epoch started. Estimated time to finish: 2 hours, 1 minute and 3.39 seconds +[stan] 2024-01-14 21:22:36,953 (trainer:763) INFO: 2epoch:train:1-40batch: iter_time=0.004, forward_time=0.155, loss_ctc=75.179, loss=75.179, backward_time=0.024, grad_norm=242.195, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.268 +[stan] 2024-01-14 21:22:48,946 (trainer:763) INFO: 2epoch:train:41-80batch: iter_time=5.418e-05, forward_time=0.150, loss_ctc=71.268, loss=71.268, backward_time=0.024, grad_norm=223.183, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-14 21:23:01,412 (trainer:763) INFO: 2epoch:train:81-120batch: iter_time=5.133e-05, forward_time=0.156, loss_ctc=71.673, loss=71.673, backward_time=0.024, grad_norm=217.948, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.246 +[stan] 2024-01-14 21:23:13,411 (trainer:763) INFO: 2epoch:train:121-160batch: iter_time=5.005e-05, forward_time=0.150, loss_ctc=68.857, loss=68.857, backward_time=0.024, grad_norm=233.858, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-14 21:23:25,566 (trainer:763) INFO: 2epoch:train:161-200batch: iter_time=4.944e-05, forward_time=0.152, loss_ctc=67.810, loss=67.810, backward_time=0.024, grad_norm=214.648, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-14 21:23:37,508 (trainer:763) INFO: 2epoch:train:201-240batch: iter_time=4.923e-05, forward_time=0.150, loss_ctc=65.545, loss=65.545, backward_time=0.023, grad_norm=226.696, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.194 +[stan] 2024-01-14 21:23:49,932 (trainer:763) INFO: 2epoch:train:241-280batch: iter_time=5.130e-05, forward_time=0.155, loss_ctc=67.364, loss=67.364, backward_time=0.024, grad_norm=225.834, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.242 +[stan] 2024-01-14 21:24:02,430 (trainer:763) INFO: 2epoch:train:281-320batch: iter_time=4.977e-05, forward_time=0.156, loss_ctc=65.239, loss=65.239, backward_time=0.024, grad_norm=276.257, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.250 +[stan] 2024-01-14 21:24:14,187 (trainer:763) INFO: 2epoch:train:321-360batch: iter_time=5.379e-05, forward_time=0.148, loss_ctc=61.607, loss=61.607, backward_time=0.023, grad_norm=212.429, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.176 +[stan] 2024-01-14 21:24:26,462 (trainer:763) INFO: 2epoch:train:361-400batch: iter_time=5.022e-05, forward_time=0.154, loss_ctc=63.471, loss=63.471, backward_time=0.024, grad_norm=270.442, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.227 +[stan] 2024-01-14 21:24:38,516 (trainer:763) INFO: 2epoch:train:401-440batch: iter_time=4.990e-05, forward_time=0.151, loss_ctc=61.453, loss=61.453, backward_time=0.024, grad_norm=254.078, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-14 21:24:50,975 (trainer:763) INFO: 2epoch:train:441-480batch: iter_time=4.936e-05, forward_time=0.156, loss_ctc=63.494, loss=63.494, backward_time=0.024, grad_norm=229.907, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.246 +[stan] 2024-01-14 21:25:02,974 (trainer:763) INFO: 2epoch:train:481-520batch: iter_time=4.904e-05, forward_time=0.151, loss_ctc=58.946, loss=58.946, backward_time=0.024, grad_norm=236.377, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-14 21:25:15,006 (trainer:763) INFO: 2epoch:train:521-560batch: iter_time=5.083e-05, forward_time=0.151, loss_ctc=58.023, loss=58.023, backward_time=0.023, grad_norm=234.817, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-14 21:25:27,348 (trainer:763) INFO: 2epoch:train:561-600batch: iter_time=5.001e-05, forward_time=0.154, loss_ctc=60.880, loss=60.880, backward_time=0.024, grad_norm=243.797, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.234 +[stan] 2024-01-14 21:25:39,476 (trainer:763) INFO: 2epoch:train:601-640batch: iter_time=5.124e-05, forward_time=0.152, loss_ctc=57.621, loss=57.621, backward_time=0.024, grad_norm=250.334, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213 +[stan] 2024-01-14 21:25:51,435 (trainer:763) INFO: 2epoch:train:641-680batch: iter_time=4.956e-05, forward_time=0.150, loss_ctc=56.323, loss=56.323, backward_time=0.023, grad_norm=234.725, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.196 +[stan] 2024-01-14 21:26:03,519 (trainer:763) INFO: 2epoch:train:681-720batch: iter_time=4.942e-05, forward_time=0.151, loss_ctc=56.505, loss=56.505, backward_time=0.024, grad_norm=263.204, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-14 21:26:15,959 (trainer:763) INFO: 2epoch:train:721-760batch: iter_time=5.020e-05, forward_time=0.156, loss_ctc=57.215, loss=57.215, backward_time=0.024, grad_norm=281.888, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.244 +[stan] 2024-01-14 21:26:28,343 (trainer:763) INFO: 2epoch:train:761-800batch: iter_time=4.801e-05, forward_time=0.155, loss_ctc=57.171, loss=57.171, backward_time=0.024, grad_norm=265.123, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.238 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-14 21:26:33,282 (trainer:354) INFO: 2epoch results: [train] iter_time=2.234e-04, forward_time=0.153, loss_ctc=63.279, loss=63.279, backward_time=0.024, grad_norm=241.887, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220, time=4 minutes and 4.16 seconds, total_count=1600, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=184.962, cer_ctc=0.230, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=184.962, time=1.15 seconds, total_count=10, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.71 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 21:26:34,144 (trainer:417) INFO: The best model has been updated: valid.loss +[stan] 2024-01-14 21:26:34,144 (trainer:288) INFO: 3/30epoch started. Estimated time to finish: 1 hour, 56 minutes and 44.76 seconds +[stan] 2024-01-14 21:26:46,040 (trainer:763) INFO: 3epoch:train:1-40batch: iter_time=0.004, forward_time=0.146, loss_ctc=50.387, loss=50.387, backward_time=0.023, grad_norm=241.281, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.189 +[stan] 2024-01-14 21:26:58,481 (trainer:763) INFO: 3epoch:train:41-80batch: iter_time=5.198e-05, forward_time=0.156, loss_ctc=56.488, loss=56.488, backward_time=0.024, grad_norm=259.542, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.244 +[stan] 2024-01-14 21:27:10,737 (trainer:763) INFO: 3epoch:train:81-120batch: iter_time=5.066e-05, forward_time=0.153, loss_ctc=53.021, loss=53.021, backward_time=0.024, grad_norm=254.087, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-14 21:27:22,690 (trainer:763) INFO: 3epoch:train:121-160batch: iter_time=5.171e-05, forward_time=0.150, loss_ctc=52.725, loss=52.725, backward_time=0.023, grad_norm=216.274, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.195 +[stan] 2024-01-14 21:27:34,960 (trainer:763) INFO: 3epoch:train:161-200batch: iter_time=4.865e-05, forward_time=0.154, loss_ctc=53.999, loss=53.999, backward_time=0.024, grad_norm=218.555, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.227 +[stan] 2024-01-14 21:27:47,184 (trainer:763) INFO: 3epoch:train:201-240batch: iter_time=4.883e-05, forward_time=0.153, loss_ctc=51.256, loss=51.256, backward_time=0.024, grad_norm=230.764, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-14 21:27:59,447 (trainer:763) INFO: 3epoch:train:241-280batch: iter_time=5.077e-05, forward_time=0.154, loss_ctc=52.538, loss=52.538, backward_time=0.024, grad_norm=246.444, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-14 21:28:11,475 (trainer:763) INFO: 3epoch:train:281-320batch: iter_time=5.027e-05, forward_time=0.151, loss_ctc=49.789, loss=49.789, backward_time=0.023, grad_norm=219.571, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-14 21:28:23,967 (trainer:763) INFO: 3epoch:train:321-360batch: iter_time=4.993e-05, forward_time=0.156, loss_ctc=51.976, loss=51.976, backward_time=0.024, grad_norm=225.650, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.249 +[stan] 2024-01-14 21:28:36,271 (trainer:763) INFO: 3epoch:train:361-400batch: iter_time=4.950e-05, forward_time=0.154, loss_ctc=50.289, loss=50.289, backward_time=0.024, grad_norm=255.088, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.230 +[stan] 2024-01-14 21:28:47,997 (trainer:763) INFO: 3epoch:train:401-440batch: iter_time=4.938e-05, forward_time=0.147, loss_ctc=46.966, loss=46.966, backward_time=0.023, grad_norm=234.788, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.172 +[stan] 2024-01-14 21:29:00,140 (trainer:763) INFO: 3epoch:train:441-480batch: iter_time=5.143e-05, forward_time=0.152, loss_ctc=48.804, loss=48.804, backward_time=0.023, grad_norm=246.704, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-14 21:29:12,675 (trainer:763) INFO: 3epoch:train:481-520batch: iter_time=5.225e-05, forward_time=0.157, loss_ctc=49.842, loss=49.842, backward_time=0.024, grad_norm=254.091, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.253 +[stan] 2024-01-14 21:29:24,510 (trainer:763) INFO: 3epoch:train:521-560batch: iter_time=4.975e-05, forward_time=0.149, loss_ctc=44.944, loss=44.944, backward_time=0.023, grad_norm=232.813, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.183 +[stan] 2024-01-14 21:29:36,514 (trainer:763) INFO: 3epoch:train:561-600batch: iter_time=5.267e-05, forward_time=0.150, loss_ctc=45.982, loss=45.982, backward_time=0.023, grad_norm=242.661, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-14 21:29:48,881 (trainer:763) INFO: 3epoch:train:601-640batch: iter_time=5.017e-05, forward_time=0.155, loss_ctc=48.312, loss=48.312, backward_time=0.024, grad_norm=249.450, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-14 21:30:01,006 (trainer:763) INFO: 3epoch:train:641-680batch: iter_time=5.143e-05, forward_time=0.152, loss_ctc=46.088, loss=46.088, backward_time=0.024, grad_norm=263.563, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-14 21:30:13,152 (trainer:763) INFO: 3epoch:train:681-720batch: iter_time=4.934e-05, forward_time=0.152, loss_ctc=45.643, loss=45.643, backward_time=0.023, grad_norm=254.547, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-14 21:30:25,552 (trainer:763) INFO: 3epoch:train:721-760batch: iter_time=5.373e-05, forward_time=0.155, loss_ctc=46.610, loss=46.610, backward_time=0.024, grad_norm=249.694, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.240 +[stan] 2024-01-14 21:30:37,479 (trainer:763) INFO: 3epoch:train:761-800batch: iter_time=4.608e-05, forward_time=0.150, loss_ctc=44.133, loss=44.133, backward_time=0.023, grad_norm=256.943, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.193 +[stan] 2024-01-14 21:30:42,480 (trainer:354) INFO: 3epoch results: [train] iter_time=2.285e-04, forward_time=0.152, loss_ctc=49.490, loss=49.490, backward_time=0.024, grad_norm=242.625, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217, time=4 minutes and 3.41 seconds, total_count=2400, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=186.972, cer_ctc=0.215, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=186.972, time=1.17 seconds, total_count=15, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.75 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 21:30:43,448 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:30:43,448 (trainer:288) INFO: 4/30epoch started. Estimated time to finish: 1 hour, 52 minutes and 26.8 seconds +[stan] 2024-01-14 21:30:56,038 (trainer:763) INFO: 4epoch:train:1-40batch: iter_time=0.004, forward_time=0.154, loss_ctc=45.091, loss=45.091, backward_time=0.024, grad_norm=269.056, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.259 +[stan] 2024-01-14 21:31:08,101 (trainer:763) INFO: 4epoch:train:41-80batch: iter_time=4.918e-05, forward_time=0.151, loss_ctc=44.620, loss=44.620, backward_time=0.023, grad_norm=268.107, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.206 +[stan] 2024-01-14 21:31:20,180 (trainer:763) INFO: 4epoch:train:81-120batch: iter_time=5.097e-05, forward_time=0.151, loss_ctc=43.893, loss=43.893, backward_time=0.024, grad_norm=271.433, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-14 21:31:32,176 (trainer:763) INFO: 4epoch:train:121-160batch: iter_time=5.205e-05, forward_time=0.150, loss_ctc=42.739, loss=42.739, backward_time=0.023, grad_norm=330.356, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-14 21:31:44,436 (trainer:763) INFO: 4epoch:train:161-200batch: iter_time=5.151e-05, forward_time=0.153, loss_ctc=44.123, loss=44.123, backward_time=0.024, grad_norm=295.114, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-14 21:31:56,699 (trainer:763) INFO: 4epoch:train:201-240batch: iter_time=5.034e-05, forward_time=0.153, loss_ctc=43.914, loss=43.914, backward_time=0.024, grad_norm=274.057, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-14 21:32:08,551 (trainer:763) INFO: 4epoch:train:241-280batch: iter_time=4.930e-05, forward_time=0.149, loss_ctc=40.881, loss=40.881, backward_time=0.023, grad_norm=254.630, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.185 +[stan] 2024-01-14 21:32:20,817 (trainer:763) INFO: 4epoch:train:281-320batch: iter_time=5.261e-05, forward_time=0.154, loss_ctc=44.075, loss=44.075, backward_time=0.024, grad_norm=252.492, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-14 21:32:33,232 (trainer:763) INFO: 4epoch:train:321-360batch: iter_time=5.323e-05, forward_time=0.155, loss_ctc=42.744, loss=42.744, backward_time=0.024, grad_norm=280.432, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.241 +[stan] 2024-01-14 21:32:45,389 (trainer:763) INFO: 4epoch:train:361-400batch: iter_time=5.222e-05, forward_time=0.152, loss_ctc=41.538, loss=41.538, backward_time=0.024, grad_norm=282.443, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-14 21:32:57,576 (trainer:763) INFO: 4epoch:train:401-440batch: iter_time=5.172e-05, forward_time=0.153, loss_ctc=42.282, loss=42.282, backward_time=0.023, grad_norm=283.099, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 21:33:09,759 (trainer:763) INFO: 4epoch:train:441-480batch: iter_time=4.949e-05, forward_time=0.153, loss_ctc=41.337, loss=41.337, backward_time=0.024, grad_norm=241.567, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-14 21:33:21,922 (trainer:763) INFO: 4epoch:train:481-520batch: iter_time=4.944e-05, forward_time=0.152, loss_ctc=41.410, loss=41.410, backward_time=0.024, grad_norm=252.558, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-14 21:33:33,855 (trainer:763) INFO: 4epoch:train:521-560batch: iter_time=5.228e-05, forward_time=0.150, loss_ctc=38.898, loss=38.898, backward_time=0.023, grad_norm=242.774, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.193 +[stan] 2024-01-14 21:33:46,261 (trainer:763) INFO: 4epoch:train:561-600batch: iter_time=5.040e-05, forward_time=0.155, loss_ctc=40.783, loss=40.783, backward_time=0.024, grad_norm=267.754, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.240 +[stan] 2024-01-14 21:33:58,042 (trainer:763) INFO: 4epoch:train:601-640batch: iter_time=5.293e-05, forward_time=0.148, loss_ctc=39.171, loss=39.171, backward_time=0.023, grad_norm=271.643, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.178 +[stan] 2024-01-14 21:34:10,414 (trainer:763) INFO: 4epoch:train:641-680batch: iter_time=5.244e-05, forward_time=0.155, loss_ctc=40.844, loss=40.844, backward_time=0.024, grad_norm=259.925, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-14 21:34:22,757 (trainer:763) INFO: 4epoch:train:681-720batch: iter_time=5.251e-05, forward_time=0.155, loss_ctc=39.317, loss=39.317, backward_time=0.024, grad_norm=253.966, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.234 +[stan] 2024-01-14 21:34:35,064 (trainer:763) INFO: 4epoch:train:721-760batch: iter_time=5.085e-05, forward_time=0.154, loss_ctc=39.956, loss=39.956, backward_time=0.024, grad_norm=261.132, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-14 21:34:47,291 (trainer:763) INFO: 4epoch:train:761-800batch: iter_time=4.690e-05, forward_time=0.153, loss_ctc=39.302, loss=39.302, backward_time=0.024, grad_norm=276.037, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-14 21:34:52,177 (trainer:354) INFO: 4epoch results: [train] iter_time=2.263e-04, forward_time=0.153, loss_ctc=41.845, loss=41.845, backward_time=0.024, grad_norm=269.429, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219, time=4 minutes and 3.92 seconds, total_count=3200, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=201.710, cer_ctc=0.205, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=201.710, time=1.15 seconds, total_count=20, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.66 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 21:34:53,048 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:34:53,048 (trainer:288) INFO: 5/30epoch started. Estimated time to finish: 1 hour, 48 minutes and 15.08 seconds +[stan] 2024-01-14 21:35:05,292 (trainer:763) INFO: 5epoch:train:1-40batch: iter_time=0.004, forward_time=0.150, loss_ctc=37.325, loss=37.325, backward_time=0.023, grad_norm=245.193, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.224 +[stan] 2024-01-14 21:35:17,586 (trainer:763) INFO: 5epoch:train:41-80batch: iter_time=5.014e-05, forward_time=0.154, loss_ctc=39.332, loss=39.332, backward_time=0.024, grad_norm=251.192, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.229 +[stan] 2024-01-14 21:35:29,581 (trainer:763) INFO: 5epoch:train:81-120batch: iter_time=4.979e-05, forward_time=0.150, loss_ctc=37.618, loss=37.618, backward_time=0.024, grad_norm=243.566, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-14 21:35:41,763 (trainer:763) INFO: 5epoch:train:121-160batch: iter_time=5.023e-05, forward_time=0.153, loss_ctc=39.085, loss=39.085, backward_time=0.024, grad_norm=258.040, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-14 21:35:53,901 (trainer:763) INFO: 5epoch:train:161-200batch: iter_time=5.052e-05, forward_time=0.152, loss_ctc=37.349, loss=37.349, backward_time=0.024, grad_norm=264.048, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-14 21:36:05,888 (trainer:763) INFO: 5epoch:train:201-240batch: iter_time=5.099e-05, forward_time=0.150, loss_ctc=37.274, loss=37.274, backward_time=0.023, grad_norm=250.372, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-14 21:36:18,244 (trainer:763) INFO: 5epoch:train:241-280batch: iter_time=5.088e-05, forward_time=0.155, loss_ctc=37.874, loss=37.874, backward_time=0.024, grad_norm=280.447, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.236 +[stan] 2024-01-14 21:36:30,597 (trainer:763) INFO: 5epoch:train:281-320batch: iter_time=5.141e-05, forward_time=0.155, loss_ctc=37.018, loss=37.018, backward_time=0.024, grad_norm=262.259, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.235 +[stan] 2024-01-14 21:36:42,389 (trainer:763) INFO: 5epoch:train:321-360batch: iter_time=5.064e-05, forward_time=0.148, loss_ctc=37.645, loss=37.645, backward_time=0.023, grad_norm=258.758, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.179 +[stan] 2024-01-14 21:36:54,713 (trainer:763) INFO: 5epoch:train:361-400batch: iter_time=5.371e-05, forward_time=0.154, loss_ctc=37.259, loss=37.259, backward_time=0.023, grad_norm=273.271, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-14 21:37:06,707 (trainer:763) INFO: 5epoch:train:401-440batch: iter_time=5.017e-05, forward_time=0.152, loss_ctc=35.992, loss=35.992, backward_time=0.024, grad_norm=272.096, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-14 21:37:19,427 (trainer:763) INFO: 5epoch:train:441-480batch: iter_time=5.105e-05, forward_time=0.159, loss_ctc=38.952, loss=38.952, backward_time=0.024, grad_norm=270.113, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.272 +[stan] 2024-01-14 21:37:31,282 (trainer:763) INFO: 5epoch:train:481-520batch: iter_time=5.093e-05, forward_time=0.149, loss_ctc=35.592, loss=35.592, backward_time=0.023, grad_norm=243.977, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.185 +[stan] 2024-01-14 21:37:43,411 (trainer:763) INFO: 5epoch:train:521-560batch: iter_time=5.114e-05, forward_time=0.152, loss_ctc=35.533, loss=35.533, backward_time=0.024, grad_norm=251.620, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213 +[stan] 2024-01-14 21:37:55,223 (trainer:763) INFO: 5epoch:train:561-600batch: iter_time=5.258e-05, forward_time=0.148, loss_ctc=34.284, loss=34.284, backward_time=0.023, grad_norm=260.473, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.181 +[stan] 2024-01-14 21:38:07,729 (trainer:763) INFO: 5epoch:train:601-640batch: iter_time=4.936e-05, forward_time=0.156, loss_ctc=37.346, loss=37.346, backward_time=0.024, grad_norm=262.263, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.251 +[stan] 2024-01-14 21:38:19,916 (trainer:763) INFO: 5epoch:train:641-680batch: iter_time=5.177e-05, forward_time=0.153, loss_ctc=36.283, loss=36.283, backward_time=0.024, grad_norm=240.544, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 21:38:32,175 (trainer:763) INFO: 5epoch:train:681-720batch: iter_time=5.084e-05, forward_time=0.153, loss_ctc=35.940, loss=35.940, backward_time=0.023, grad_norm=255.254, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-14 21:38:44,176 (trainer:763) INFO: 5epoch:train:721-760batch: iter_time=5.205e-05, forward_time=0.150, loss_ctc=35.355, loss=35.355, backward_time=0.024, grad_norm=251.500, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-14 21:38:56,427 (trainer:763) INFO: 5epoch:train:761-800batch: iter_time=4.767e-05, forward_time=0.153, loss_ctc=36.194, loss=36.194, backward_time=0.024, grad_norm=285.271, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-14 21:39:01,306 (trainer:354) INFO: 5epoch results: [train] iter_time=2.597e-04, forward_time=0.152, loss_ctc=36.962, loss=36.962, backward_time=0.024, grad_norm=259.013, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217, time=4 minutes and 3.46 seconds, total_count=4000, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=222.449, cer_ctc=0.212, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=222.449, time=1.16 seconds, total_count=25, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.64 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 21:39:02,190 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:39:02,190 (trainer:288) INFO: 6/30epoch started. Estimated time to finish: 1 hour, 44 minutes and 1.93 seconds +[stan] 2024-01-14 21:39:14,721 (trainer:763) INFO: 6epoch:train:1-40batch: iter_time=0.004, forward_time=0.153, loss_ctc=35.092, loss=35.092, backward_time=0.023, grad_norm=289.960, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.253 +[stan] 2024-01-14 21:39:26,693 (trainer:763) INFO: 6epoch:train:41-80batch: iter_time=4.978e-05, forward_time=0.150, loss_ctc=35.077, loss=35.077, backward_time=0.023, grad_norm=279.788, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.197 +[stan] 2024-01-14 21:39:39,033 (trainer:763) INFO: 6epoch:train:81-120batch: iter_time=5.240e-05, forward_time=0.154, loss_ctc=35.185, loss=35.185, backward_time=0.024, grad_norm=247.583, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.234 +[stan] 2024-01-14 21:39:50,952 (trainer:763) INFO: 6epoch:train:121-160batch: iter_time=4.972e-05, forward_time=0.149, loss_ctc=33.396, loss=33.396, backward_time=0.023, grad_norm=258.559, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.192 +[stan] 2024-01-14 21:40:03,200 (trainer:763) INFO: 6epoch:train:161-200batch: iter_time=4.877e-05, forward_time=0.153, loss_ctc=34.979, loss=34.979, backward_time=0.024, grad_norm=261.253, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-14 21:40:15,093 (trainer:763) INFO: 6epoch:train:201-240batch: iter_time=5.328e-05, forward_time=0.149, loss_ctc=33.704, loss=33.704, backward_time=0.023, grad_norm=234.063, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.189 +[stan] 2024-01-14 21:40:27,698 (trainer:763) INFO: 6epoch:train:241-280batch: iter_time=5.444e-05, forward_time=0.157, loss_ctc=36.190, loss=36.190, backward_time=0.024, grad_norm=237.726, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.260 +[stan] 2024-01-14 21:40:39,693 (trainer:763) INFO: 6epoch:train:281-320batch: iter_time=5.406e-05, forward_time=0.150, loss_ctc=33.759, loss=33.759, backward_time=0.024, grad_norm=266.872, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-14 21:40:52,240 (trainer:763) INFO: 6epoch:train:321-360batch: iter_time=5.226e-05, forward_time=0.157, loss_ctc=35.183, loss=35.183, backward_time=0.024, grad_norm=253.997, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.255 +[stan] 2024-01-14 21:41:03,708 (trainer:763) INFO: 6epoch:train:361-400batch: iter_time=5.050e-05, forward_time=0.144, loss_ctc=31.884, loss=31.884, backward_time=0.023, grad_norm=233.319, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.147 +[stan] 2024-01-14 21:41:16,170 (trainer:763) INFO: 6epoch:train:401-440batch: iter_time=5.013e-05, forward_time=0.156, loss_ctc=35.006, loss=35.006, backward_time=0.024, grad_norm=248.278, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.246 +[stan] 2024-01-14 21:41:28,335 (trainer:763) INFO: 6epoch:train:441-480batch: iter_time=5.232e-05, forward_time=0.152, loss_ctc=33.633, loss=33.633, backward_time=0.024, grad_norm=264.042, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-14 21:41:40,667 (trainer:763) INFO: 6epoch:train:481-520batch: iter_time=5.046e-05, forward_time=0.154, loss_ctc=34.937, loss=34.937, backward_time=0.024, grad_norm=249.211, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.233 +[stan] 2024-01-14 21:41:52,626 (trainer:763) INFO: 6epoch:train:521-560batch: iter_time=4.956e-05, forward_time=0.150, loss_ctc=32.467, loss=32.467, backward_time=0.023, grad_norm=260.389, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.196 +[stan] 2024-01-14 21:42:04,672 (trainer:763) INFO: 6epoch:train:561-600batch: iter_time=5.507e-05, forward_time=0.151, loss_ctc=32.191, loss=32.191, backward_time=0.023, grad_norm=238.816, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-14 21:42:17,030 (trainer:763) INFO: 6epoch:train:601-640batch: iter_time=5.267e-05, forward_time=0.155, loss_ctc=33.860, loss=33.860, backward_time=0.024, grad_norm=232.131, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.236 +[stan] 2024-01-14 21:42:29,069 (trainer:763) INFO: 6epoch:train:641-680batch: iter_time=4.919e-05, forward_time=0.151, loss_ctc=31.775, loss=31.775, backward_time=0.023, grad_norm=243.009, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-14 21:42:41,012 (trainer:763) INFO: 6epoch:train:681-720batch: iter_time=5.140e-05, forward_time=0.150, loss_ctc=32.865, loss=32.865, backward_time=0.023, grad_norm=251.054, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.194 +[stan] 2024-01-14 21:42:53,621 (trainer:763) INFO: 6epoch:train:721-760batch: iter_time=5.257e-05, forward_time=0.158, loss_ctc=34.789, loss=34.789, backward_time=0.024, grad_norm=244.220, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.261 +[stan] 2024-01-14 21:43:05,503 (trainer:763) INFO: 6epoch:train:761-800batch: iter_time=4.676e-05, forward_time=0.149, loss_ctc=31.997, loss=31.997, backward_time=0.023, grad_norm=237.104, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.188 +[stan] 2024-01-14 21:43:10,388 (trainer:354) INFO: 6epoch results: [train] iter_time=2.555e-04, forward_time=0.152, loss_ctc=33.897, loss=33.897, backward_time=0.024, grad_norm=251.569, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216, time=4 minutes and 3.39 seconds, total_count=4800, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=231.437, cer_ctc=0.208, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=231.437, time=1.17 seconds, total_count=30, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.63 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 21:43:11,388 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:43:11,388 (trainer:288) INFO: 7/30epoch started. Estimated time to finish: 1 hour, 39 minutes and 50.34 seconds +[stan] 2024-01-14 21:43:23,762 (trainer:763) INFO: 7epoch:train:1-40batch: iter_time=0.003, forward_time=0.152, loss_ctc=32.689, loss=32.689, backward_time=0.024, grad_norm=264.160, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-14 21:43:36,411 (trainer:763) INFO: 7epoch:train:41-80batch: iter_time=5.279e-05, forward_time=0.158, loss_ctc=33.889, loss=33.889, backward_time=0.024, grad_norm=269.869, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.265 +[stan] 2024-01-14 21:43:48,115 (trainer:763) INFO: 7epoch:train:81-120batch: iter_time=4.971e-05, forward_time=0.147, loss_ctc=31.288, loss=31.288, backward_time=0.023, grad_norm=233.189, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.170 +[stan] 2024-01-14 21:44:00,130 (trainer:763) INFO: 7epoch:train:121-160batch: iter_time=5.019e-05, forward_time=0.151, loss_ctc=32.980, loss=32.980, backward_time=0.024, grad_norm=272.130, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-14 21:44:12,352 (trainer:763) INFO: 7epoch:train:161-200batch: iter_time=5.239e-05, forward_time=0.153, loss_ctc=32.771, loss=32.771, backward_time=0.024, grad_norm=233.806, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-14 21:44:24,624 (trainer:763) INFO: 7epoch:train:201-240batch: iter_time=5.228e-05, forward_time=0.154, loss_ctc=32.686, loss=32.686, backward_time=0.024, grad_norm=242.916, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.227 +[stan] 2024-01-14 21:44:36,996 (trainer:763) INFO: 7epoch:train:241-280batch: iter_time=5.311e-05, forward_time=0.155, loss_ctc=32.770, loss=32.770, backward_time=0.024, grad_norm=221.820, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-14 21:44:48,952 (trainer:763) INFO: 7epoch:train:281-320batch: iter_time=5.234e-05, forward_time=0.150, loss_ctc=31.830, loss=31.830, backward_time=0.023, grad_norm=226.193, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.196 +[stan] 2024-01-14 21:45:01,105 (trainer:763) INFO: 7epoch:train:321-360batch: iter_time=5.312e-05, forward_time=0.152, loss_ctc=32.367, loss=32.367, backward_time=0.024, grad_norm=244.710, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-14 21:45:13,190 (trainer:763) INFO: 7epoch:train:361-400batch: iter_time=4.997e-05, forward_time=0.151, loss_ctc=31.691, loss=31.691, backward_time=0.024, grad_norm=229.021, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-14 21:45:25,473 (trainer:763) INFO: 7epoch:train:401-440batch: iter_time=5.364e-05, forward_time=0.154, loss_ctc=32.186, loss=32.186, backward_time=0.024, grad_norm=264.900, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.228 +[stan] 2024-01-14 21:45:37,586 (trainer:763) INFO: 7epoch:train:441-480batch: iter_time=5.274e-05, forward_time=0.152, loss_ctc=32.339, loss=32.339, backward_time=0.024, grad_norm=263.844, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211 +[stan] 2024-01-14 21:45:49,960 (trainer:763) INFO: 7epoch:train:481-520batch: iter_time=5.010e-05, forward_time=0.155, loss_ctc=31.977, loss=31.977, backward_time=0.024, grad_norm=229.373, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-14 21:46:01,813 (trainer:763) INFO: 7epoch:train:521-560batch: iter_time=5.194e-05, forward_time=0.149, loss_ctc=30.750, loss=30.750, backward_time=0.023, grad_norm=231.578, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.185 +[stan] 2024-01-14 21:46:14,055 (trainer:763) INFO: 7epoch:train:561-600batch: iter_time=5.320e-05, forward_time=0.153, loss_ctc=32.368, loss=32.368, backward_time=0.024, grad_norm=232.090, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.224 +[stan] 2024-01-14 21:46:26,359 (trainer:763) INFO: 7epoch:train:601-640batch: iter_time=4.996e-05, forward_time=0.154, loss_ctc=31.981, loss=31.981, backward_time=0.024, grad_norm=238.268, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.230 +[stan] 2024-01-14 21:46:38,530 (trainer:763) INFO: 7epoch:train:641-680batch: iter_time=5.039e-05, forward_time=0.152, loss_ctc=31.653, loss=31.653, backward_time=0.023, grad_norm=236.166, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217 +[stan] 2024-01-14 21:46:50,490 (trainer:763) INFO: 7epoch:train:681-720batch: iter_time=5.178e-05, forward_time=0.150, loss_ctc=31.030, loss=31.030, backward_time=0.023, grad_norm=244.733, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.196 +[stan] 2024-01-14 21:47:02,509 (trainer:763) INFO: 7epoch:train:721-760batch: iter_time=5.356e-05, forward_time=0.151, loss_ctc=31.060, loss=31.060, backward_time=0.023, grad_norm=237.057, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.202 +[stan] 2024-01-14 21:47:14,857 (trainer:763) INFO: 7epoch:train:761-800batch: iter_time=4.885e-05, forward_time=0.154, loss_ctc=32.140, loss=32.140, backward_time=0.024, grad_norm=227.702, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.235 +[stan] 2024-01-14 21:47:19,697 (trainer:354) INFO: 7epoch results: [train] iter_time=2.011e-04, forward_time=0.152, loss_ctc=32.122, loss=32.122, backward_time=0.024, grad_norm=242.176, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217, time=4 minutes and 3.55 seconds, total_count=5600, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=241.863, cer_ctc=0.206, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=241.863, time=1.16 seconds, total_count=35, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.6 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 21:47:20,622 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:47:20,622 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/6epoch.pth +[stan] 2024-01-14 21:47:20,622 (trainer:288) INFO: 8/30epoch started. Estimated time to finish: 1 hour, 35 minutes and 39.55 seconds +[stan] 2024-01-14 21:47:32,985 (trainer:763) INFO: 8epoch:train:1-40batch: iter_time=0.004, forward_time=0.151, loss_ctc=31.382, loss=31.382, backward_time=0.023, grad_norm=257.237, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.236 +[stan] 2024-01-14 21:47:44,938 (trainer:763) INFO: 8epoch:train:41-80batch: iter_time=5.100e-05, forward_time=0.150, loss_ctc=31.185, loss=31.185, backward_time=0.023, grad_norm=252.835, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.195 +[stan] 2024-01-14 21:47:57,192 (trainer:763) INFO: 8epoch:train:81-120batch: iter_time=5.038e-05, forward_time=0.153, loss_ctc=32.362, loss=32.362, backward_time=0.024, grad_norm=269.691, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-14 21:48:09,479 (trainer:763) INFO: 8epoch:train:121-160batch: iter_time=5.227e-05, forward_time=0.154, loss_ctc=31.264, loss=31.264, backward_time=0.024, grad_norm=228.624, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.229 +[stan] 2024-01-14 21:48:21,597 (trainer:763) INFO: 8epoch:train:161-200batch: iter_time=5.149e-05, forward_time=0.152, loss_ctc=31.795, loss=31.795, backward_time=0.024, grad_norm=234.717, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-14 21:48:33,727 (trainer:763) INFO: 8epoch:train:201-240batch: iter_time=5.393e-05, forward_time=0.152, loss_ctc=30.513, loss=30.513, backward_time=0.023, grad_norm=229.846, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213 +[stan] 2024-01-14 21:48:46,000 (trainer:763) INFO: 8epoch:train:241-280batch: iter_time=5.022e-05, forward_time=0.154, loss_ctc=31.512, loss=31.512, backward_time=0.024, grad_norm=269.632, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.227 +[stan] 2024-01-14 21:48:58,276 (trainer:763) INFO: 8epoch:train:281-320batch: iter_time=5.033e-05, forward_time=0.154, loss_ctc=31.470, loss=31.470, backward_time=0.024, grad_norm=281.877, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.227 +[stan] 2024-01-14 21:49:10,532 (trainer:763) INFO: 8epoch:train:321-360batch: iter_time=5.108e-05, forward_time=0.153, loss_ctc=30.771, loss=30.771, backward_time=0.024, grad_norm=232.235, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-14 21:49:22,501 (trainer:763) INFO: 8epoch:train:361-400batch: iter_time=5.061e-05, forward_time=0.150, loss_ctc=30.232, loss=30.232, backward_time=0.023, grad_norm=234.092, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.197 +[stan] 2024-01-14 21:49:34,655 (trainer:763) INFO: 8epoch:train:401-440batch: iter_time=5.085e-05, forward_time=0.152, loss_ctc=31.134, loss=31.134, backward_time=0.024, grad_norm=273.780, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-14 21:49:46,767 (trainer:763) INFO: 8epoch:train:441-480batch: iter_time=5.086e-05, forward_time=0.152, loss_ctc=30.989, loss=30.989, backward_time=0.024, grad_norm=239.363, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211 +[stan] 2024-01-14 21:49:58,996 (trainer:763) INFO: 8epoch:train:481-520batch: iter_time=4.992e-05, forward_time=0.153, loss_ctc=31.159, loss=31.159, backward_time=0.023, grad_norm=241.670, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-14 21:50:11,198 (trainer:763) INFO: 8epoch:train:521-560batch: iter_time=5.021e-05, forward_time=0.153, loss_ctc=30.106, loss=30.106, backward_time=0.024, grad_norm=217.510, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-14 21:50:23,206 (trainer:763) INFO: 8epoch:train:561-600batch: iter_time=5.071e-05, forward_time=0.151, loss_ctc=29.980, loss=29.980, backward_time=0.024, grad_norm=257.140, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-14 21:50:35,463 (trainer:763) INFO: 8epoch:train:601-640batch: iter_time=5.037e-05, forward_time=0.153, loss_ctc=31.365, loss=31.365, backward_time=0.024, grad_norm=252.471, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-14 21:50:47,247 (trainer:763) INFO: 8epoch:train:641-680batch: iter_time=5.032e-05, forward_time=0.148, loss_ctc=29.623, loss=29.623, backward_time=0.023, grad_norm=221.959, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.178 +[stan] 2024-01-14 21:50:59,631 (trainer:763) INFO: 8epoch:train:681-720batch: iter_time=5.057e-05, forward_time=0.155, loss_ctc=31.246, loss=31.246, backward_time=0.024, grad_norm=236.572, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.238 +[stan] 2024-01-14 21:51:11,929 (trainer:763) INFO: 8epoch:train:721-760batch: iter_time=5.071e-05, forward_time=0.154, loss_ctc=30.718, loss=30.718, backward_time=0.024, grad_norm=218.530, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.230 +[stan] 2024-01-14 21:51:23,968 (trainer:763) INFO: 8epoch:train:761-800batch: iter_time=4.898e-05, forward_time=0.151, loss_ctc=29.492, loss=29.492, backward_time=0.024, grad_norm=241.566, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-14 21:51:28,814 (trainer:354) INFO: 8epoch results: [train] iter_time=2.476e-04, forward_time=0.152, loss_ctc=30.914, loss=30.914, backward_time=0.024, grad_norm=244.567, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217, time=4 minutes and 3.42 seconds, total_count=6400, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=248.964, cer_ctc=0.204, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=248.964, time=1.16 seconds, total_count=40, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.61 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 21:51:29,756 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:51:29,756 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/7epoch.pth +[stan] 2024-01-14 21:51:29,756 (trainer:288) INFO: 9/30epoch started. Estimated time to finish: 1 hour, 31 minutes and 28.87 seconds +[stan] 2024-01-14 21:51:42,458 (trainer:763) INFO: 9epoch:train:1-40batch: iter_time=0.004, forward_time=0.155, loss_ctc=31.245, loss=31.245, backward_time=0.024, grad_norm=221.614, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.270 +[stan] 2024-01-14 21:51:54,453 (trainer:763) INFO: 9epoch:train:41-80batch: iter_time=5.215e-05, forward_time=0.150, loss_ctc=29.768, loss=29.768, backward_time=0.023, grad_norm=222.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-14 21:52:06,778 (trainer:763) INFO: 9epoch:train:81-120batch: iter_time=5.348e-05, forward_time=0.154, loss_ctc=31.626, loss=31.626, backward_time=0.024, grad_norm=224.503, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-14 21:52:18,559 (trainer:763) INFO: 9epoch:train:121-160batch: iter_time=5.288e-05, forward_time=0.148, loss_ctc=29.359, loss=29.359, backward_time=0.023, grad_norm=223.570, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.178 +[stan] 2024-01-14 21:52:30,960 (trainer:763) INFO: 9epoch:train:161-200batch: iter_time=5.024e-05, forward_time=0.155, loss_ctc=30.978, loss=30.978, backward_time=0.024, grad_norm=213.278, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.240 +[stan] 2024-01-14 21:52:43,113 (trainer:763) INFO: 9epoch:train:201-240batch: iter_time=5.325e-05, forward_time=0.152, loss_ctc=30.631, loss=30.631, backward_time=0.024, grad_norm=220.206, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-14 21:52:55,177 (trainer:763) INFO: 9epoch:train:241-280batch: iter_time=5.220e-05, forward_time=0.151, loss_ctc=29.985, loss=29.985, backward_time=0.023, grad_norm=225.765, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.206 +[stan] 2024-01-14 21:53:07,434 (trainer:763) INFO: 9epoch:train:281-320batch: iter_time=5.120e-05, forward_time=0.153, loss_ctc=30.596, loss=30.596, backward_time=0.024, grad_norm=256.025, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-14 21:53:19,398 (trainer:763) INFO: 9epoch:train:321-360batch: iter_time=5.301e-05, forward_time=0.150, loss_ctc=29.873, loss=29.873, backward_time=0.023, grad_norm=219.924, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.196 +[stan] 2024-01-14 21:53:31,794 (trainer:763) INFO: 9epoch:train:361-400batch: iter_time=5.459e-05, forward_time=0.155, loss_ctc=30.976, loss=30.976, backward_time=0.024, grad_norm=228.116, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.239 +[stan] 2024-01-14 21:53:43,911 (trainer:763) INFO: 9epoch:train:401-440batch: iter_time=5.367e-05, forward_time=0.152, loss_ctc=29.073, loss=29.073, backward_time=0.024, grad_norm=221.452, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-14 21:53:55,962 (trainer:763) INFO: 9epoch:train:441-480batch: iter_time=5.043e-05, forward_time=0.151, loss_ctc=29.565, loss=29.565, backward_time=0.024, grad_norm=218.330, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-14 21:54:08,332 (trainer:763) INFO: 9epoch:train:481-520batch: iter_time=5.389e-05, forward_time=0.155, loss_ctc=31.888, loss=31.888, backward_time=0.024, grad_norm=220.289, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-14 21:54:20,373 (trainer:763) INFO: 9epoch:train:521-560batch: iter_time=5.453e-05, forward_time=0.151, loss_ctc=29.038, loss=29.038, backward_time=0.023, grad_norm=231.277, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-14 21:54:32,420 (trainer:763) INFO: 9epoch:train:561-600batch: iter_time=5.277e-05, forward_time=0.151, loss_ctc=29.335, loss=29.335, backward_time=0.023, grad_norm=221.711, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-14 21:54:44,572 (trainer:763) INFO: 9epoch:train:601-640batch: iter_time=5.189e-05, forward_time=0.152, loss_ctc=30.275, loss=30.275, backward_time=0.024, grad_norm=287.460, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-14 21:54:56,803 (trainer:763) INFO: 9epoch:train:641-680batch: iter_time=5.227e-05, forward_time=0.153, loss_ctc=30.102, loss=30.102, backward_time=0.023, grad_norm=323.087, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-14 21:55:08,731 (trainer:763) INFO: 9epoch:train:681-720batch: iter_time=5.133e-05, forward_time=0.149, loss_ctc=28.536, loss=28.536, backward_time=0.023, grad_norm=248.580, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.193 +[stan] 2024-01-14 21:55:20,746 (trainer:763) INFO: 9epoch:train:721-760batch: iter_time=5.139e-05, forward_time=0.151, loss_ctc=29.165, loss=29.165, backward_time=0.023, grad_norm=218.652, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-14 21:55:33,231 (trainer:763) INFO: 9epoch:train:761-800batch: iter_time=4.780e-05, forward_time=0.156, loss_ctc=30.578, loss=30.578, backward_time=0.024, grad_norm=232.236, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.248 +[stan] 2024-01-14 21:55:38,111 (trainer:354) INFO: 9epoch results: [train] iter_time=2.351e-04, forward_time=0.152, loss_ctc=30.130, loss=30.130, backward_time=0.024, grad_norm=233.910, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217, time=4 minutes and 3.55 seconds, total_count=7200, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=253.822, cer_ctc=0.205, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=253.822, time=1.17 seconds, total_count=45, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.63 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 21:55:39,170 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:55:39,170 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/8epoch.pth +[stan] 2024-01-14 21:55:39,170 (trainer:288) INFO: 10/30epoch started. Estimated time to finish: 1 hour, 27 minutes and 19.19 seconds +[stan] 2024-01-14 21:55:51,536 (trainer:763) INFO: 10epoch:train:1-40batch: iter_time=0.004, forward_time=0.151, loss_ctc=29.250, loss=29.250, backward_time=0.024, grad_norm=218.230, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.236 +[stan] 2024-01-14 21:56:03,444 (trainer:763) INFO: 10epoch:train:41-80batch: iter_time=5.119e-05, forward_time=0.149, loss_ctc=29.017, loss=29.017, backward_time=0.023, grad_norm=224.359, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.191 +[stan] 2024-01-14 21:56:15,920 (trainer:763) INFO: 10epoch:train:81-120batch: iter_time=4.994e-05, forward_time=0.156, loss_ctc=30.244, loss=30.244, backward_time=0.024, grad_norm=223.559, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.247 +[stan] 2024-01-14 21:56:28,099 (trainer:763) INFO: 10epoch:train:121-160batch: iter_time=5.208e-05, forward_time=0.152, loss_ctc=29.301, loss=29.301, backward_time=0.024, grad_norm=211.441, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-14 21:56:40,014 (trainer:763) INFO: 10epoch:train:161-200batch: iter_time=5.014e-05, forward_time=0.149, loss_ctc=28.158, loss=28.158, backward_time=0.024, grad_norm=206.186, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.191 +[stan] 2024-01-14 21:56:52,418 (trainer:763) INFO: 10epoch:train:201-240batch: iter_time=5.176e-05, forward_time=0.155, loss_ctc=29.321, loss=29.321, backward_time=0.024, grad_norm=228.569, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.240 +[stan] 2024-01-14 21:57:04,363 (trainer:763) INFO: 10epoch:train:241-280batch: iter_time=5.149e-05, forward_time=0.150, loss_ctc=28.008, loss=28.008, backward_time=0.023, grad_norm=223.508, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.194 +[stan] 2024-01-14 21:57:16,892 (trainer:763) INFO: 10epoch:train:281-320batch: iter_time=4.990e-05, forward_time=0.157, loss_ctc=30.114, loss=30.114, backward_time=0.024, grad_norm=220.268, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.253 +[stan] 2024-01-14 21:57:28,887 (trainer:763) INFO: 10epoch:train:321-360batch: iter_time=4.911e-05, forward_time=0.150, loss_ctc=28.789, loss=28.789, backward_time=0.023, grad_norm=207.917, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-14 21:57:41,132 (trainer:763) INFO: 10epoch:train:361-400batch: iter_time=5.042e-05, forward_time=0.153, loss_ctc=29.486, loss=29.486, backward_time=0.023, grad_norm=223.628, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.224 +[stan] 2024-01-14 21:57:53,173 (trainer:763) INFO: 10epoch:train:401-440batch: iter_time=5.139e-05, forward_time=0.151, loss_ctc=29.788, loss=29.788, backward_time=0.024, grad_norm=228.060, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-14 21:58:05,354 (trainer:763) INFO: 10epoch:train:441-480batch: iter_time=5.283e-05, forward_time=0.152, loss_ctc=29.133, loss=29.133, backward_time=0.024, grad_norm=212.822, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-14 21:58:17,473 (trainer:763) INFO: 10epoch:train:481-520batch: iter_time=5.031e-05, forward_time=0.152, loss_ctc=29.183, loss=29.183, backward_time=0.024, grad_norm=215.585, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-14 21:58:29,361 (trainer:763) INFO: 10epoch:train:521-560batch: iter_time=5.251e-05, forward_time=0.149, loss_ctc=28.328, loss=28.328, backward_time=0.023, grad_norm=229.683, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.189 +[stan] 2024-01-14 21:58:41,799 (trainer:763) INFO: 10epoch:train:561-600batch: iter_time=5.349e-05, forward_time=0.155, loss_ctc=30.077, loss=30.077, backward_time=0.024, grad_norm=242.313, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.244 +[stan] 2024-01-14 21:58:53,743 (trainer:763) INFO: 10epoch:train:601-640batch: iter_time=4.913e-05, forward_time=0.150, loss_ctc=27.754, loss=27.754, backward_time=0.023, grad_norm=225.695, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.194 +[stan] 2024-01-14 21:59:06,144 (trainer:763) INFO: 10epoch:train:641-680batch: iter_time=5.190e-05, forward_time=0.155, loss_ctc=30.218, loss=30.218, backward_time=0.024, grad_norm=218.608, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.240 +[stan] 2024-01-14 21:59:18,236 (trainer:763) INFO: 10epoch:train:681-720batch: iter_time=4.938e-05, forward_time=0.151, loss_ctc=29.033, loss=29.033, backward_time=0.024, grad_norm=217.683, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.209 +[stan] 2024-01-14 21:59:30,170 (trainer:763) INFO: 10epoch:train:721-760batch: iter_time=5.210e-05, forward_time=0.150, loss_ctc=28.488, loss=28.488, backward_time=0.023, grad_norm=208.698, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.193 +[stan] 2024-01-14 21:59:42,964 (trainer:763) INFO: 10epoch:train:761-800batch: iter_time=4.960e-05, forward_time=0.160, loss_ctc=30.776, loss=30.776, backward_time=0.025, grad_norm=252.740, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.279 +[stan] 2024-01-14 21:59:47,755 (trainer:354) INFO: 10epoch results: [train] iter_time=2.358e-04, forward_time=0.152, loss_ctc=29.222, loss=29.222, backward_time=0.024, grad_norm=221.978, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219, time=4 minutes and 3.87 seconds, total_count=8000, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=256.616, cer_ctc=0.200, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=256.616, time=1.16 seconds, total_count=50, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.55 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 21:59:48,815 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:59:48,815 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/9epoch.pth +[stan] 2024-01-14 21:59:48,815 (trainer:288) INFO: 11/30epoch started. Estimated time to finish: 1 hour, 23 minutes and 10.02 seconds +[stan] 2024-01-14 22:00:00,772 (trainer:763) INFO: 11epoch:train:1-40batch: iter_time=0.003, forward_time=0.147, loss_ctc=26.913, loss=26.913, backward_time=0.023, grad_norm=227.577, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.195 +[stan] 2024-01-14 22:00:12,996 (trainer:763) INFO: 11epoch:train:41-80batch: iter_time=5.190e-05, forward_time=0.153, loss_ctc=28.863, loss=28.863, backward_time=0.024, grad_norm=221.927, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-14 22:00:24,893 (trainer:763) INFO: 11epoch:train:81-120batch: iter_time=5.212e-05, forward_time=0.149, loss_ctc=28.739, loss=28.739, backward_time=0.024, grad_norm=218.912, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.190 +[stan] 2024-01-14 22:00:37,262 (trainer:763) INFO: 11epoch:train:121-160batch: iter_time=5.243e-05, forward_time=0.155, loss_ctc=28.803, loss=28.803, backward_time=0.024, grad_norm=216.341, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-14 22:00:49,594 (trainer:763) INFO: 11epoch:train:161-200batch: iter_time=4.989e-05, forward_time=0.154, loss_ctc=28.612, loss=28.612, backward_time=0.024, grad_norm=220.655, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.233 +[stan] 2024-01-14 22:01:01,880 (trainer:763) INFO: 11epoch:train:201-240batch: iter_time=4.964e-05, forward_time=0.154, loss_ctc=29.097, loss=29.097, backward_time=0.024, grad_norm=209.828, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.228 +[stan] 2024-01-14 22:01:13,747 (trainer:763) INFO: 11epoch:train:241-280batch: iter_time=5.307e-05, forward_time=0.149, loss_ctc=27.690, loss=27.690, backward_time=0.023, grad_norm=214.501, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.187 +[stan] 2024-01-14 22:01:26,135 (trainer:763) INFO: 11epoch:train:281-320batch: iter_time=5.123e-05, forward_time=0.155, loss_ctc=28.756, loss=28.756, backward_time=0.024, grad_norm=213.430, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.239 +[stan] 2024-01-14 22:01:37,887 (trainer:763) INFO: 11epoch:train:321-360batch: iter_time=5.284e-05, forward_time=0.147, loss_ctc=27.151, loss=27.151, backward_time=0.023, grad_norm=220.541, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.175 +[stan] 2024-01-14 22:01:50,228 (trainer:763) INFO: 11epoch:train:361-400batch: iter_time=5.015e-05, forward_time=0.154, loss_ctc=28.166, loss=28.166, backward_time=0.024, grad_norm=216.094, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.234 +[stan] 2024-01-14 22:02:02,493 (trainer:763) INFO: 11epoch:train:401-440batch: iter_time=4.933e-05, forward_time=0.154, loss_ctc=28.989, loss=28.989, backward_time=0.024, grad_norm=218.799, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-14 22:02:14,546 (trainer:763) INFO: 11epoch:train:441-480batch: iter_time=5.246e-05, forward_time=0.151, loss_ctc=27.647, loss=27.647, backward_time=0.024, grad_norm=200.417, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-14 22:02:26,698 (trainer:763) INFO: 11epoch:train:481-520batch: iter_time=5.018e-05, forward_time=0.152, loss_ctc=28.341, loss=28.341, backward_time=0.023, grad_norm=214.799, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-14 22:02:38,925 (trainer:763) INFO: 11epoch:train:521-560batch: iter_time=5.265e-05, forward_time=0.153, loss_ctc=28.283, loss=28.283, backward_time=0.024, grad_norm=210.512, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-14 22:02:51,049 (trainer:763) INFO: 11epoch:train:561-600batch: iter_time=5.178e-05, forward_time=0.152, loss_ctc=28.677, loss=28.677, backward_time=0.023, grad_norm=220.930, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-14 22:03:03,139 (trainer:763) INFO: 11epoch:train:601-640batch: iter_time=5.137e-05, forward_time=0.151, loss_ctc=28.479, loss=28.479, backward_time=0.024, grad_norm=205.882, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.209 +[stan] 2024-01-14 22:03:15,266 (trainer:763) INFO: 11epoch:train:641-680batch: iter_time=4.999e-05, forward_time=0.152, loss_ctc=27.629, loss=27.629, backward_time=0.024, grad_norm=204.179, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213 +[stan] 2024-01-14 22:03:27,093 (trainer:763) INFO: 11epoch:train:681-720batch: iter_time=5.174e-05, forward_time=0.148, loss_ctc=27.288, loss=27.288, backward_time=0.023, grad_norm=226.935, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.183 +[stan] 2024-01-14 22:03:39,572 (trainer:763) INFO: 11epoch:train:721-760batch: iter_time=5.237e-05, forward_time=0.156, loss_ctc=29.162, loss=29.162, backward_time=0.024, grad_norm=234.050, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.248 +[stan] 2024-01-14 22:03:51,551 (trainer:763) INFO: 11epoch:train:761-800batch: iter_time=4.999e-05, forward_time=0.150, loss_ctc=28.036, loss=28.036, backward_time=0.024, grad_norm=205.008, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.198 +[stan] 2024-01-14 22:03:56,365 (trainer:354) INFO: 11epoch results: [train] iter_time=2.037e-04, forward_time=0.152, loss_ctc=28.266, loss=28.266, backward_time=0.024, grad_norm=216.066, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214, time=4 minutes and 2.82 seconds, total_count=8800, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=262.865, cer_ctc=0.200, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=262.865, time=1.17 seconds, total_count=55, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.57 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 22:03:57,264 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 22:03:57,265 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/10epoch.pth +[stan] 2024-01-14 22:03:57,265 (trainer:288) INFO: 12/30epoch started. Estimated time to finish: 1 hour, 18 minutes and 58.7 seconds +[stan] 2024-01-14 22:04:09,940 (trainer:763) INFO: 12epoch:train:1-40batch: iter_time=0.004, forward_time=0.155, loss_ctc=29.106, loss=29.106, backward_time=0.024, grad_norm=227.480, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.267 +[stan] 2024-01-14 22:04:22,021 (trainer:763) INFO: 12epoch:train:41-80batch: iter_time=4.985e-05, forward_time=0.151, loss_ctc=28.227, loss=28.227, backward_time=0.023, grad_norm=220.780, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-14 22:04:34,284 (trainer:763) INFO: 12epoch:train:81-120batch: iter_time=5.012e-05, forward_time=0.153, loss_ctc=28.368, loss=28.368, backward_time=0.024, grad_norm=226.581, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-14 22:04:46,643 (trainer:763) INFO: 12epoch:train:121-160batch: iter_time=5.361e-05, forward_time=0.155, loss_ctc=29.137, loss=29.137, backward_time=0.024, grad_norm=232.263, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.236 +[stan] 2024-01-14 22:04:58,519 (trainer:763) INFO: 12epoch:train:161-200batch: iter_time=5.226e-05, forward_time=0.149, loss_ctc=27.076, loss=27.076, backward_time=0.023, grad_norm=214.183, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.188 +[stan] 2024-01-14 22:05:10,501 (trainer:763) INFO: 12epoch:train:201-240batch: iter_time=5.054e-05, forward_time=0.150, loss_ctc=27.815, loss=27.815, backward_time=0.023, grad_norm=239.232, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.198 +[stan] 2024-01-14 22:05:22,684 (trainer:763) INFO: 12epoch:train:241-280batch: iter_time=5.138e-05, forward_time=0.153, loss_ctc=28.005, loss=28.005, backward_time=0.024, grad_norm=218.591, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-14 22:05:34,997 (trainer:763) INFO: 12epoch:train:281-320batch: iter_time=5.150e-05, forward_time=0.154, loss_ctc=29.320, loss=29.320, backward_time=0.024, grad_norm=219.866, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-14 22:05:47,435 (trainer:763) INFO: 12epoch:train:321-360batch: iter_time=5.387e-05, forward_time=0.156, loss_ctc=28.301, loss=28.301, backward_time=0.024, grad_norm=223.481, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.244 +[stan] 2024-01-14 22:05:59,262 (trainer:763) INFO: 12epoch:train:361-400batch: iter_time=5.294e-05, forward_time=0.148, loss_ctc=27.159, loss=27.159, backward_time=0.024, grad_norm=219.894, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.183 +[stan] 2024-01-14 22:06:11,460 (trainer:763) INFO: 12epoch:train:401-440batch: iter_time=5.154e-05, forward_time=0.153, loss_ctc=29.417, loss=29.417, backward_time=0.023, grad_norm=220.795, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-14 22:06:23,747 (trainer:763) INFO: 12epoch:train:441-480batch: iter_time=5.121e-05, forward_time=0.154, loss_ctc=28.064, loss=28.064, backward_time=0.024, grad_norm=227.965, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.229 +[stan] 2024-01-14 22:06:35,613 (trainer:763) INFO: 12epoch:train:481-520batch: iter_time=4.993e-05, forward_time=0.149, loss_ctc=26.573, loss=26.573, backward_time=0.023, grad_norm=198.576, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.187 +[stan] 2024-01-14 22:06:47,913 (trainer:763) INFO: 12epoch:train:521-560batch: iter_time=5.032e-05, forward_time=0.154, loss_ctc=27.720, loss=27.720, backward_time=0.024, grad_norm=204.563, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.230 +[stan] 2024-01-14 22:07:00,142 (trainer:763) INFO: 12epoch:train:561-600batch: iter_time=5.178e-05, forward_time=0.153, loss_ctc=28.244, loss=28.244, backward_time=0.024, grad_norm=201.564, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-14 22:07:12,027 (trainer:763) INFO: 12epoch:train:601-640batch: iter_time=5.109e-05, forward_time=0.149, loss_ctc=27.746, loss=27.746, backward_time=0.023, grad_norm=246.484, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.188 +[stan] 2024-01-14 22:07:24,343 (trainer:763) INFO: 12epoch:train:641-680batch: iter_time=5.294e-05, forward_time=0.154, loss_ctc=27.510, loss=27.510, backward_time=0.024, grad_norm=209.797, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-14 22:07:36,218 (trainer:763) INFO: 12epoch:train:681-720batch: iter_time=4.965e-05, forward_time=0.149, loss_ctc=27.519, loss=27.519, backward_time=0.023, grad_norm=204.289, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.187 +[stan] 2024-01-14 22:07:48,368 (trainer:763) INFO: 12epoch:train:721-760batch: iter_time=5.341e-05, forward_time=0.152, loss_ctc=26.964, loss=26.964, backward_time=0.023, grad_norm=199.935, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-14 22:08:00,758 (trainer:763) INFO: 12epoch:train:761-800batch: iter_time=4.917e-05, forward_time=0.155, loss_ctc=28.307, loss=28.307, backward_time=0.024, grad_norm=205.099, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.239 +[stan] 2024-01-14 22:08:05,570 (trainer:354) INFO: 12epoch results: [train] iter_time=2.411e-04, forward_time=0.152, loss_ctc=28.028, loss=28.028, backward_time=0.024, grad_norm=218.071, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217, time=4 minutes and 3.57 seconds, total_count=9600, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=264.888, cer_ctc=0.201, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=264.888, time=1.18 seconds, total_count=60, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.56 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 22:08:06,569 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 22:08:06,569 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/11epoch.pth +[stan] 2024-01-14 22:08:06,569 (trainer:288) INFO: 13/30epoch started. Estimated time to finish: 1 hour, 14 minutes and 49.15 seconds +[stan] 2024-01-14 22:08:18,907 (trainer:763) INFO: 13epoch:train:1-40batch: iter_time=0.004, forward_time=0.151, loss_ctc=27.636, loss=27.636, backward_time=0.023, grad_norm=200.888, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.233 +[stan] 2024-01-14 22:08:31,127 (trainer:763) INFO: 13epoch:train:41-80batch: iter_time=4.919e-05, forward_time=0.153, loss_ctc=27.704, loss=27.704, backward_time=0.024, grad_norm=219.014, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-14 22:08:43,310 (trainer:763) INFO: 13epoch:train:81-120batch: iter_time=5.390e-05, forward_time=0.153, loss_ctc=26.773, loss=26.773, backward_time=0.024, grad_norm=193.020, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-14 22:08:55,567 (trainer:763) INFO: 13epoch:train:121-160batch: iter_time=5.207e-05, forward_time=0.153, loss_ctc=28.030, loss=28.030, backward_time=0.024, grad_norm=200.569, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-14 22:09:07,934 (trainer:763) INFO: 13epoch:train:161-200batch: iter_time=5.295e-05, forward_time=0.155, loss_ctc=27.626, loss=27.626, backward_time=0.024, grad_norm=197.337, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-14 22:09:19,820 (trainer:763) INFO: 13epoch:train:201-240batch: iter_time=5.045e-05, forward_time=0.149, loss_ctc=26.502, loss=26.502, backward_time=0.023, grad_norm=205.006, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.189 +[stan] 2024-01-14 22:09:31,749 (trainer:763) INFO: 13epoch:train:241-280batch: iter_time=5.140e-05, forward_time=0.150, loss_ctc=27.417, loss=27.417, backward_time=0.023, grad_norm=215.728, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.193 +[stan] 2024-01-14 22:09:44,084 (trainer:763) INFO: 13epoch:train:281-320batch: iter_time=5.125e-05, forward_time=0.154, loss_ctc=26.827, loss=26.827, backward_time=0.024, grad_norm=203.439, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.233 +[stan] 2024-01-14 22:09:56,294 (trainer:763) INFO: 13epoch:train:321-360batch: iter_time=5.268e-05, forward_time=0.153, loss_ctc=27.601, loss=27.601, backward_time=0.024, grad_norm=202.934, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.221 +[stan] 2024-01-14 22:10:08,842 (trainer:763) INFO: 13epoch:train:361-400batch: iter_time=5.061e-05, forward_time=0.157, loss_ctc=28.883, loss=28.883, backward_time=0.024, grad_norm=211.181, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.255 +[stan] 2024-01-14 22:10:20,579 (trainer:763) INFO: 13epoch:train:401-440batch: iter_time=5.072e-05, forward_time=0.147, loss_ctc=25.480, loss=25.480, backward_time=0.023, grad_norm=216.411, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.174 +[stan] 2024-01-14 22:10:32,756 (trainer:763) INFO: 13epoch:train:441-480batch: iter_time=5.146e-05, forward_time=0.152, loss_ctc=27.009, loss=27.009, backward_time=0.024, grad_norm=205.006, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-14 22:10:44,804 (trainer:763) INFO: 13epoch:train:481-520batch: iter_time=5.210e-05, forward_time=0.151, loss_ctc=26.145, loss=26.145, backward_time=0.023, grad_norm=194.565, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-14 22:10:57,300 (trainer:763) INFO: 13epoch:train:521-560batch: iter_time=5.452e-05, forward_time=0.156, loss_ctc=27.660, loss=27.660, backward_time=0.024, grad_norm=196.950, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.249 +[stan] 2024-01-14 22:11:09,200 (trainer:763) INFO: 13epoch:train:561-600batch: iter_time=5.312e-05, forward_time=0.149, loss_ctc=26.189, loss=26.189, backward_time=0.023, grad_norm=196.363, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.190 +[stan] 2024-01-14 22:11:21,387 (trainer:763) INFO: 13epoch:train:601-640batch: iter_time=5.271e-05, forward_time=0.153, loss_ctc=27.597, loss=27.597, backward_time=0.023, grad_norm=207.078, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 22:11:33,872 (trainer:763) INFO: 13epoch:train:641-680batch: iter_time=5.103e-05, forward_time=0.156, loss_ctc=27.992, loss=27.992, backward_time=0.024, grad_norm=195.821, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.248 +[stan] 2024-01-14 22:11:45,636 (trainer:763) INFO: 13epoch:train:681-720batch: iter_time=5.259e-05, forward_time=0.148, loss_ctc=25.789, loss=25.789, backward_time=0.023, grad_norm=194.656, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.176 +[stan] 2024-01-14 22:11:57,654 (trainer:763) INFO: 13epoch:train:721-760batch: iter_time=5.160e-05, forward_time=0.151, loss_ctc=27.590, loss=27.590, backward_time=0.024, grad_norm=200.785, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.202 +[stan] 2024-01-14 22:12:10,137 (trainer:763) INFO: 13epoch:train:761-800batch: iter_time=5.010e-05, forward_time=0.156, loss_ctc=27.433, loss=27.433, backward_time=0.024, grad_norm=197.127, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.248 +[stan] 2024-01-14 22:12:14,926 (trainer:354) INFO: 13epoch results: [train] iter_time=2.269e-04, forward_time=0.152, loss_ctc=27.194, loss=27.194, backward_time=0.024, grad_norm=202.694, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218, time=4 minutes and 3.64 seconds, total_count=10400, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=269.106, cer_ctc=0.197, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=269.106, time=1.16 seconds, total_count=65, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.56 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 22:12:15,836 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 22:12:15,836 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/12epoch.pth +[stan] 2024-01-14 22:12:15,836 (trainer:288) INFO: 14/30epoch started. Estimated time to finish: 1 hour, 10 minutes and 39.58 seconds +[stan] 2024-01-14 22:12:28,076 (trainer:763) INFO: 14epoch:train:1-40batch: iter_time=0.004, forward_time=0.150, loss_ctc=26.604, loss=26.604, backward_time=0.023, grad_norm=203.170, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-14 22:12:40,179 (trainer:763) INFO: 14epoch:train:41-80batch: iter_time=5.209e-05, forward_time=0.152, loss_ctc=27.225, loss=27.225, backward_time=0.024, grad_norm=198.738, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.210 +[stan] 2024-01-14 22:12:52,184 (trainer:763) INFO: 14epoch:train:81-120batch: iter_time=5.174e-05, forward_time=0.150, loss_ctc=27.536, loss=27.536, backward_time=0.023, grad_norm=206.576, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-14 22:13:04,563 (trainer:763) INFO: 14epoch:train:121-160batch: iter_time=5.028e-05, forward_time=0.155, loss_ctc=28.021, loss=28.021, backward_time=0.024, grad_norm=197.858, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.238 +[stan] 2024-01-14 22:13:16,823 (trainer:763) INFO: 14epoch:train:161-200batch: iter_time=5.046e-05, forward_time=0.154, loss_ctc=27.323, loss=27.323, backward_time=0.024, grad_norm=198.766, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-14 22:13:28,811 (trainer:763) INFO: 14epoch:train:201-240batch: iter_time=4.936e-05, forward_time=0.150, loss_ctc=26.284, loss=26.284, backward_time=0.023, grad_norm=196.381, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-14 22:13:41,050 (trainer:763) INFO: 14epoch:train:241-280batch: iter_time=5.559e-05, forward_time=0.153, loss_ctc=26.974, loss=26.974, backward_time=0.024, grad_norm=202.475, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.224 +[stan] 2024-01-14 22:13:52,826 (trainer:763) INFO: 14epoch:train:281-320batch: iter_time=5.050e-05, forward_time=0.148, loss_ctc=25.172, loss=25.172, backward_time=0.023, grad_norm=200.341, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.177 +[stan] 2024-01-14 22:14:05,684 (trainer:763) INFO: 14epoch:train:321-360batch: iter_time=5.241e-05, forward_time=0.160, loss_ctc=27.617, loss=27.617, backward_time=0.024, grad_norm=207.968, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.286 +[stan] 2024-01-14 22:14:17,526 (trainer:763) INFO: 14epoch:train:361-400batch: iter_time=5.091e-05, forward_time=0.149, loss_ctc=25.343, loss=25.343, backward_time=0.023, grad_norm=201.233, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.184 +[stan] 2024-01-14 22:14:29,616 (trainer:763) INFO: 14epoch:train:401-440batch: iter_time=5.039e-05, forward_time=0.151, loss_ctc=26.322, loss=26.322, backward_time=0.024, grad_norm=207.371, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.209 +[stan] 2024-01-14 22:14:41,888 (trainer:763) INFO: 14epoch:train:441-480batch: iter_time=5.180e-05, forward_time=0.154, loss_ctc=25.994, loss=25.994, backward_time=0.024, grad_norm=196.309, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.227 +[stan] 2024-01-14 22:14:53,994 (trainer:763) INFO: 14epoch:train:481-520batch: iter_time=5.419e-05, forward_time=0.152, loss_ctc=26.957, loss=26.957, backward_time=0.023, grad_norm=195.048, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.210 +[stan] 2024-01-14 22:15:05,951 (trainer:763) INFO: 14epoch:train:521-560batch: iter_time=5.254e-05, forward_time=0.150, loss_ctc=26.195, loss=26.195, backward_time=0.023, grad_norm=195.294, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.196 +[stan] 2024-01-14 22:15:18,179 (trainer:763) INFO: 14epoch:train:561-600batch: iter_time=5.153e-05, forward_time=0.153, loss_ctc=27.433, loss=27.433, backward_time=0.024, grad_norm=202.649, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-14 22:15:30,438 (trainer:763) INFO: 14epoch:train:601-640batch: iter_time=5.269e-05, forward_time=0.153, loss_ctc=27.030, loss=27.030, backward_time=0.023, grad_norm=203.644, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-14 22:15:42,647 (trainer:763) INFO: 14epoch:train:641-680batch: iter_time=5.036e-05, forward_time=0.153, loss_ctc=26.287, loss=26.287, backward_time=0.024, grad_norm=205.049, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.221 +[stan] 2024-01-14 22:15:54,893 (trainer:763) INFO: 14epoch:train:681-720batch: iter_time=5.264e-05, forward_time=0.153, loss_ctc=26.172, loss=26.172, backward_time=0.024, grad_norm=210.254, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-14 22:16:06,870 (trainer:763) INFO: 14epoch:train:721-760batch: iter_time=5.217e-05, forward_time=0.150, loss_ctc=25.476, loss=25.476, backward_time=0.023, grad_norm=208.245, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.198 +[stan] 2024-01-14 22:16:19,274 (trainer:763) INFO: 14epoch:train:761-800batch: iter_time=4.877e-05, forward_time=0.155, loss_ctc=26.699, loss=26.699, backward_time=0.024, grad_norm=201.132, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.240 +[stan] 2024-01-14 22:16:24,049 (trainer:354) INFO: 14epoch results: [train] iter_time=2.625e-04, forward_time=0.152, loss_ctc=26.633, loss=26.633, backward_time=0.024, grad_norm=201.925, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217, time=4 minutes and 3.51 seconds, total_count=11200, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=269.626, cer_ctc=0.198, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=269.626, time=1.15 seconds, total_count=70, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.55 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 22:16:24,983 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 22:16:24,984 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/13epoch.pth +[stan] 2024-01-14 22:16:24,984 (trainer:288) INFO: 15/30epoch started. Estimated time to finish: 1 hour, 6 minutes and 29.92 seconds +[stan] 2024-01-14 22:16:37,038 (trainer:763) INFO: 15epoch:train:1-40batch: iter_time=0.003, forward_time=0.148, loss_ctc=25.591, loss=25.591, backward_time=0.024, grad_norm=196.876, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-14 22:16:49,278 (trainer:763) INFO: 15epoch:train:41-80batch: iter_time=5.268e-05, forward_time=0.153, loss_ctc=25.811, loss=25.811, backward_time=0.023, grad_norm=201.995, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.224 +[stan] 2024-01-14 22:17:01,311 (trainer:763) INFO: 15epoch:train:81-120batch: iter_time=5.288e-05, forward_time=0.151, loss_ctc=25.992, loss=25.992, backward_time=0.023, grad_norm=198.954, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-14 22:17:13,820 (trainer:763) INFO: 15epoch:train:121-160batch: iter_time=4.994e-05, forward_time=0.156, loss_ctc=26.657, loss=26.657, backward_time=0.024, grad_norm=194.913, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.251 +[stan] 2024-01-14 22:17:25,736 (trainer:763) INFO: 15epoch:train:161-200batch: iter_time=5.074e-05, forward_time=0.149, loss_ctc=25.472, loss=25.472, backward_time=0.023, grad_norm=202.055, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.191 +[stan] 2024-01-14 22:17:38,043 (trainer:763) INFO: 15epoch:train:201-240batch: iter_time=5.006e-05, forward_time=0.154, loss_ctc=27.015, loss=27.015, backward_time=0.024, grad_norm=209.741, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-14 22:17:50,104 (trainer:763) INFO: 15epoch:train:241-280batch: iter_time=5.289e-05, forward_time=0.151, loss_ctc=25.997, loss=25.997, backward_time=0.024, grad_norm=209.637, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.206 +[stan] 2024-01-14 22:18:02,193 (trainer:763) INFO: 15epoch:train:281-320batch: iter_time=5.346e-05, forward_time=0.151, loss_ctc=26.773, loss=26.773, backward_time=0.024, grad_norm=210.845, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.209 +[stan] 2024-01-14 22:18:14,376 (trainer:763) INFO: 15epoch:train:321-360batch: iter_time=5.283e-05, forward_time=0.152, loss_ctc=26.654, loss=26.654, backward_time=0.024, grad_norm=196.735, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-14 22:18:26,427 (trainer:763) INFO: 15epoch:train:361-400batch: iter_time=5.281e-05, forward_time=0.151, loss_ctc=26.412, loss=26.412, backward_time=0.023, grad_norm=196.809, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-14 22:18:38,640 (trainer:763) INFO: 15epoch:train:401-440batch: iter_time=4.945e-05, forward_time=0.153, loss_ctc=26.252, loss=26.252, backward_time=0.024, grad_norm=205.846, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.221 +[stan] 2024-01-14 22:18:50,783 (trainer:763) INFO: 15epoch:train:441-480batch: iter_time=5.102e-05, forward_time=0.152, loss_ctc=26.654, loss=26.654, backward_time=0.023, grad_norm=202.807, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-14 22:19:02,942 (trainer:763) INFO: 15epoch:train:481-520batch: iter_time=5.089e-05, forward_time=0.152, loss_ctc=25.375, loss=25.375, backward_time=0.024, grad_norm=190.721, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-14 22:19:15,065 (trainer:763) INFO: 15epoch:train:521-560batch: iter_time=5.490e-05, forward_time=0.152, loss_ctc=26.084, loss=26.084, backward_time=0.024, grad_norm=204.239, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-14 22:19:27,191 (trainer:763) INFO: 15epoch:train:561-600batch: iter_time=5.023e-05, forward_time=0.152, loss_ctc=25.492, loss=25.492, backward_time=0.023, grad_norm=195.833, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-14 22:19:39,325 (trainer:763) INFO: 15epoch:train:601-640batch: iter_time=5.094e-05, forward_time=0.152, loss_ctc=25.739, loss=25.739, backward_time=0.024, grad_norm=196.850, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213 +[stan] 2024-01-14 22:19:51,492 (trainer:763) INFO: 15epoch:train:641-680batch: iter_time=5.075e-05, forward_time=0.152, loss_ctc=25.993, loss=25.993, backward_time=0.024, grad_norm=204.265, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217 +[stan] 2024-01-14 22:20:03,648 (trainer:763) INFO: 15epoch:train:681-720batch: iter_time=4.984e-05, forward_time=0.152, loss_ctc=26.778, loss=26.778, backward_time=0.023, grad_norm=207.797, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-14 22:20:16,017 (trainer:763) INFO: 15epoch:train:721-760batch: iter_time=5.297e-05, forward_time=0.155, loss_ctc=27.095, loss=27.095, backward_time=0.024, grad_norm=198.527, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-14 22:20:27,921 (trainer:763) INFO: 15epoch:train:761-800batch: iter_time=4.778e-05, forward_time=0.149, loss_ctc=25.199, loss=25.199, backward_time=0.023, grad_norm=188.891, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.190 +[stan] 2024-01-14 22:20:32,722 (trainer:354) INFO: 15epoch results: [train] iter_time=2.190e-04, forward_time=0.152, loss_ctc=26.152, loss=26.152, backward_time=0.024, grad_norm=200.717, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215, time=4 minutes and 3.02 seconds, total_count=12000, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=273.865, cer_ctc=0.197, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=273.865, time=1.17 seconds, total_count=75, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.55 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 22:20:33,754 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 22:20:33,754 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/14epoch.pth +[stan] 2024-01-14 22:20:33,754 (trainer:288) INFO: 16/30epoch started. Estimated time to finish: 1 hour, 2 minutes and 19.95 seconds +[stan] 2024-01-14 22:20:46,175 (trainer:763) INFO: 16epoch:train:1-40batch: iter_time=0.003, forward_time=0.152, loss_ctc=25.557, loss=25.557, backward_time=0.024, grad_norm=200.332, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.242 +[stan] 2024-01-14 22:20:58,297 (trainer:763) INFO: 16epoch:train:41-80batch: iter_time=5.216e-05, forward_time=0.152, loss_ctc=26.281, loss=26.281, backward_time=0.023, grad_norm=199.744, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-14 22:21:10,491 (trainer:763) INFO: 16epoch:train:81-120batch: iter_time=5.242e-05, forward_time=0.153, loss_ctc=26.313, loss=26.313, backward_time=0.024, grad_norm=208.994, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 22:21:22,830 (trainer:763) INFO: 16epoch:train:121-160batch: iter_time=5.030e-05, forward_time=0.154, loss_ctc=26.583, loss=26.583, backward_time=0.024, grad_norm=199.358, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.234 +[stan] 2024-01-14 22:21:34,514 (trainer:763) INFO: 16epoch:train:161-200batch: iter_time=4.971e-05, forward_time=0.147, loss_ctc=24.824, loss=24.824, backward_time=0.023, grad_norm=195.975, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.168 +[stan] 2024-01-14 22:21:46,951 (trainer:763) INFO: 16epoch:train:201-240batch: iter_time=5.177e-05, forward_time=0.155, loss_ctc=26.547, loss=26.547, backward_time=0.024, grad_norm=206.435, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.244 +[stan] 2024-01-14 22:21:59,145 (trainer:763) INFO: 16epoch:train:241-280batch: iter_time=5.261e-05, forward_time=0.153, loss_ctc=25.605, loss=25.605, backward_time=0.024, grad_norm=216.781, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 22:22:11,039 (trainer:763) INFO: 16epoch:train:281-320batch: iter_time=5.220e-05, forward_time=0.149, loss_ctc=25.681, loss=25.681, backward_time=0.023, grad_norm=197.510, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.189 +[stan] 2024-01-14 22:22:23,407 (trainer:763) INFO: 16epoch:train:321-360batch: iter_time=5.307e-05, forward_time=0.155, loss_ctc=26.382, loss=26.382, backward_time=0.024, grad_norm=198.349, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-14 22:22:35,852 (trainer:763) INFO: 16epoch:train:361-400batch: iter_time=5.063e-05, forward_time=0.156, loss_ctc=26.400, loss=26.400, backward_time=0.024, grad_norm=197.360, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.244 +[stan] 2024-01-14 22:22:47,536 (trainer:763) INFO: 16epoch:train:401-440batch: iter_time=5.094e-05, forward_time=0.147, loss_ctc=24.197, loss=24.197, backward_time=0.023, grad_norm=189.023, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.168 +[stan] 2024-01-14 22:22:59,862 (trainer:763) INFO: 16epoch:train:441-480batch: iter_time=5.311e-05, forward_time=0.154, loss_ctc=27.062, loss=27.062, backward_time=0.024, grad_norm=193.123, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.233 +[stan] 2024-01-14 22:23:12,024 (trainer:763) INFO: 16epoch:train:481-520batch: iter_time=5.331e-05, forward_time=0.152, loss_ctc=26.331, loss=26.331, backward_time=0.024, grad_norm=199.398, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-14 22:23:24,089 (trainer:763) INFO: 16epoch:train:521-560batch: iter_time=5.093e-05, forward_time=0.151, loss_ctc=26.023, loss=26.023, backward_time=0.023, grad_norm=202.722, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.206 +[stan] 2024-01-14 22:23:36,288 (trainer:763) INFO: 16epoch:train:561-600batch: iter_time=5.054e-05, forward_time=0.153, loss_ctc=25.774, loss=25.774, backward_time=0.023, grad_norm=204.615, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-14 22:23:48,685 (trainer:763) INFO: 16epoch:train:601-640batch: iter_time=5.383e-05, forward_time=0.155, loss_ctc=26.424, loss=26.424, backward_time=0.024, grad_norm=208.587, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.240 +[stan] 2024-01-14 22:24:00,851 (trainer:763) INFO: 16epoch:train:641-680batch: iter_time=5.365e-05, forward_time=0.152, loss_ctc=25.888, loss=25.888, backward_time=0.024, grad_norm=199.491, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217 +[stan] 2024-01-14 22:24:12,695 (trainer:763) INFO: 16epoch:train:681-720batch: iter_time=5.210e-05, forward_time=0.149, loss_ctc=24.721, loss=24.721, backward_time=0.023, grad_norm=188.434, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.184 +[stan] 2024-01-14 22:24:25,187 (trainer:763) INFO: 16epoch:train:721-760batch: iter_time=5.320e-05, forward_time=0.156, loss_ctc=26.573, loss=26.573, backward_time=0.024, grad_norm=193.390, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.249 +[stan] 2024-01-14 22:24:36,993 (trainer:763) INFO: 16epoch:train:761-800batch: iter_time=4.654e-05, forward_time=0.148, loss_ctc=24.659, loss=24.659, backward_time=0.023, grad_norm=188.073, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.180 +[stan] 2024-01-14 22:24:41,761 (trainer:354) INFO: 16epoch results: [train] iter_time=2.228e-04, forward_time=0.152, loss_ctc=25.891, loss=25.891, backward_time=0.024, grad_norm=199.385, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216, time=4 minutes and 3.32 seconds, total_count=12800, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=276.608, cer_ctc=0.198, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=276.608, time=1.16 seconds, total_count=80, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.53 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 22:24:42,707 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 22:24:42,707 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/15epoch.pth +[stan] 2024-01-14 22:24:42,707 (trainer:288) INFO: 17/30epoch started. Estimated time to finish: 58 minutes and 10.29 seconds +[stan] 2024-01-14 22:24:55,116 (trainer:763) INFO: 17epoch:train:1-40batch: iter_time=0.004, forward_time=0.152, loss_ctc=25.700, loss=25.700, backward_time=0.024, grad_norm=202.922, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.240 +[stan] 2024-01-14 22:25:07,040 (trainer:763) INFO: 17epoch:train:41-80batch: iter_time=5.277e-05, forward_time=0.150, loss_ctc=24.830, loss=24.830, backward_time=0.023, grad_norm=189.356, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.192 +[stan] 2024-01-14 22:25:19,562 (trainer:763) INFO: 17epoch:train:81-120batch: iter_time=5.143e-05, forward_time=0.157, loss_ctc=26.010, loss=26.010, backward_time=0.024, grad_norm=196.945, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.252 +[stan] 2024-01-14 22:25:31,597 (trainer:763) INFO: 17epoch:train:121-160batch: iter_time=4.970e-05, forward_time=0.151, loss_ctc=25.431, loss=25.431, backward_time=0.023, grad_norm=210.665, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-14 22:25:43,783 (trainer:763) INFO: 17epoch:train:161-200batch: iter_time=5.107e-05, forward_time=0.153, loss_ctc=24.875, loss=24.875, backward_time=0.024, grad_norm=193.456, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-14 22:25:55,812 (trainer:763) INFO: 17epoch:train:201-240batch: iter_time=5.318e-05, forward_time=0.151, loss_ctc=24.557, loss=24.557, backward_time=0.024, grad_norm=191.372, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-14 22:26:08,058 (trainer:763) INFO: 17epoch:train:241-280batch: iter_time=5.314e-05, forward_time=0.153, loss_ctc=25.761, loss=25.761, backward_time=0.024, grad_norm=210.759, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.224 +[stan] 2024-01-14 22:26:20,367 (trainer:763) INFO: 17epoch:train:281-320batch: iter_time=5.184e-05, forward_time=0.154, loss_ctc=25.673, loss=25.673, backward_time=0.024, grad_norm=199.089, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-14 22:26:32,065 (trainer:763) INFO: 17epoch:train:321-360batch: iter_time=5.030e-05, forward_time=0.147, loss_ctc=24.617, loss=24.617, backward_time=0.023, grad_norm=197.726, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.170 +[stan] 2024-01-14 22:26:44,523 (trainer:763) INFO: 17epoch:train:361-400batch: iter_time=5.241e-05, forward_time=0.156, loss_ctc=26.033, loss=26.033, backward_time=0.024, grad_norm=207.533, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.246 +[stan] 2024-01-14 22:26:56,506 (trainer:763) INFO: 17epoch:train:401-440batch: iter_time=4.957e-05, forward_time=0.150, loss_ctc=25.028, loss=25.028, backward_time=0.024, grad_norm=200.590, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.198 +[stan] 2024-01-14 22:27:08,760 (trainer:763) INFO: 17epoch:train:441-480batch: iter_time=5.070e-05, forward_time=0.153, loss_ctc=25.444, loss=25.444, backward_time=0.023, grad_norm=204.773, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-14 22:27:20,956 (trainer:763) INFO: 17epoch:train:481-520batch: iter_time=5.215e-05, forward_time=0.153, loss_ctc=25.498, loss=25.498, backward_time=0.024, grad_norm=194.747, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 22:27:32,796 (trainer:763) INFO: 17epoch:train:521-560batch: iter_time=5.034e-05, forward_time=0.148, loss_ctc=24.568, loss=24.568, backward_time=0.023, grad_norm=210.007, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.184 +[stan] 2024-01-14 22:27:44,907 (trainer:763) INFO: 17epoch:train:561-600batch: iter_time=5.042e-05, forward_time=0.152, loss_ctc=25.059, loss=25.059, backward_time=0.023, grad_norm=197.656, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211 +[stan] 2024-01-14 22:27:57,391 (trainer:763) INFO: 17epoch:train:601-640batch: iter_time=5.056e-05, forward_time=0.156, loss_ctc=26.037, loss=26.037, backward_time=0.024, grad_norm=203.786, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.248 +[stan] 2024-01-14 22:28:09,327 (trainer:763) INFO: 17epoch:train:641-680batch: iter_time=5.064e-05, forward_time=0.150, loss_ctc=24.620, loss=24.620, backward_time=0.023, grad_norm=196.228, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.194 +[stan] 2024-01-14 22:28:21,633 (trainer:763) INFO: 17epoch:train:681-720batch: iter_time=4.941e-05, forward_time=0.154, loss_ctc=25.404, loss=25.404, backward_time=0.023, grad_norm=195.718, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-14 22:28:33,813 (trainer:763) INFO: 17epoch:train:721-760batch: iter_time=4.997e-05, forward_time=0.152, loss_ctc=25.261, loss=25.261, backward_time=0.024, grad_norm=197.398, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-14 22:28:45,647 (trainer:763) INFO: 17epoch:train:761-800batch: iter_time=4.931e-05, forward_time=0.148, loss_ctc=24.699, loss=24.699, backward_time=0.023, grad_norm=204.314, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.183 +[stan] 2024-01-14 22:28:50,409 (trainer:354) INFO: 17epoch results: [train] iter_time=2.474e-04, forward_time=0.152, loss_ctc=25.255, loss=25.255, backward_time=0.024, grad_norm=200.252, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215, time=4 minutes and 3.02 seconds, total_count=13600, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=277.016, cer_ctc=0.191, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=277.016, time=1.16 seconds, total_count=85, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.53 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 22:28:51,357 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 22:28:51,357 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/16epoch.pth +[stan] 2024-01-14 22:28:51,357 (trainer:288) INFO: 18/30epoch started. Estimated time to finish: 54 minutes and 0.48 seconds +[stan] 2024-01-14 22:29:04,042 (trainer:763) INFO: 18epoch:train:1-40batch: iter_time=0.004, forward_time=0.155, loss_ctc=25.293, loss=25.293, backward_time=0.024, grad_norm=199.796, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.268 +[stan] 2024-01-14 22:29:16,236 (trainer:763) INFO: 18epoch:train:41-80batch: iter_time=5.102e-05, forward_time=0.153, loss_ctc=25.367, loss=25.367, backward_time=0.023, grad_norm=206.875, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 22:29:28,288 (trainer:763) INFO: 18epoch:train:81-120batch: iter_time=5.302e-05, forward_time=0.151, loss_ctc=24.196, loss=24.196, backward_time=0.024, grad_norm=196.228, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-14 22:29:40,497 (trainer:763) INFO: 18epoch:train:121-160batch: iter_time=5.343e-05, forward_time=0.153, loss_ctc=24.845, loss=24.845, backward_time=0.023, grad_norm=192.144, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.221 +[stan] 2024-01-14 22:29:52,683 (trainer:763) INFO: 18epoch:train:161-200batch: iter_time=5.286e-05, forward_time=0.153, loss_ctc=24.595, loss=24.595, backward_time=0.024, grad_norm=206.628, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-14 22:30:04,761 (trainer:763) INFO: 18epoch:train:201-240batch: iter_time=5.098e-05, forward_time=0.151, loss_ctc=24.424, loss=24.424, backward_time=0.023, grad_norm=205.546, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-14 22:30:16,779 (trainer:763) INFO: 18epoch:train:241-280batch: iter_time=5.326e-05, forward_time=0.151, loss_ctc=24.248, loss=24.248, backward_time=0.024, grad_norm=206.073, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.202 +[stan] 2024-01-14 22:30:29,143 (trainer:763) INFO: 18epoch:train:281-320batch: iter_time=5.077e-05, forward_time=0.155, loss_ctc=24.631, loss=24.631, backward_time=0.024, grad_norm=195.917, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.236 +[stan] 2024-01-14 22:30:41,067 (trainer:763) INFO: 18epoch:train:321-360batch: iter_time=5.081e-05, forward_time=0.149, loss_ctc=24.504, loss=24.504, backward_time=0.023, grad_norm=202.263, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.192 +[stan] 2024-01-14 22:30:53,615 (trainer:763) INFO: 18epoch:train:361-400batch: iter_time=5.070e-05, forward_time=0.157, loss_ctc=25.568, loss=25.568, backward_time=0.024, grad_norm=204.158, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.255 +[stan] 2024-01-14 22:31:05,582 (trainer:763) INFO: 18epoch:train:401-440batch: iter_time=5.347e-05, forward_time=0.150, loss_ctc=24.503, loss=24.503, backward_time=0.024, grad_norm=210.805, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.197 +[stan] 2024-01-14 22:31:17,770 (trainer:763) INFO: 18epoch:train:441-480batch: iter_time=5.048e-05, forward_time=0.153, loss_ctc=25.160, loss=25.160, backward_time=0.024, grad_norm=194.434, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 22:31:29,845 (trainer:763) INFO: 18epoch:train:481-520batch: iter_time=5.339e-05, forward_time=0.151, loss_ctc=24.262, loss=24.262, backward_time=0.023, grad_norm=194.412, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.207 +[stan] 2024-01-14 22:31:41,996 (trainer:763) INFO: 18epoch:train:521-560batch: iter_time=5.270e-05, forward_time=0.152, loss_ctc=25.360, loss=25.360, backward_time=0.024, grad_norm=193.432, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-14 22:31:54,216 (trainer:763) INFO: 18epoch:train:561-600batch: iter_time=5.180e-05, forward_time=0.153, loss_ctc=25.647, loss=25.647, backward_time=0.024, grad_norm=201.912, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-14 22:32:06,356 (trainer:763) INFO: 18epoch:train:601-640batch: iter_time=5.289e-05, forward_time=0.152, loss_ctc=24.330, loss=24.330, backward_time=0.024, grad_norm=199.218, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-14 22:32:18,409 (trainer:763) INFO: 18epoch:train:641-680batch: iter_time=5.307e-05, forward_time=0.151, loss_ctc=23.977, loss=23.977, backward_time=0.023, grad_norm=189.466, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-14 22:32:30,337 (trainer:763) INFO: 18epoch:train:681-720batch: iter_time=5.308e-05, forward_time=0.149, loss_ctc=23.673, loss=23.673, backward_time=0.023, grad_norm=197.067, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.193 +[stan] 2024-01-14 22:32:42,541 (trainer:763) INFO: 18epoch:train:721-760batch: iter_time=5.055e-05, forward_time=0.153, loss_ctc=25.207, loss=25.207, backward_time=0.024, grad_norm=194.545, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-14 22:32:54,802 (trainer:763) INFO: 18epoch:train:761-800batch: iter_time=5.108e-05, forward_time=0.153, loss_ctc=24.682, loss=24.682, backward_time=0.024, grad_norm=195.126, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-14 22:32:59,591 (trainer:354) INFO: 18epoch results: [train] iter_time=2.373e-04, forward_time=0.152, loss_ctc=24.723, loss=24.723, backward_time=0.024, grad_norm=199.302, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217, time=4 minutes and 3.52 seconds, total_count=14400, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=280.333, cer_ctc=0.193, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=280.333, time=1.18 seconds, total_count=90, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.53 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 22:33:00,650 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 22:33:00,651 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/17epoch.pth +[stan] 2024-01-14 22:33:00,651 (trainer:288) INFO: 19/30epoch started. Estimated time to finish: 49 minutes and 51.23 seconds +[stan] 2024-01-14 22:33:13,299 (trainer:763) INFO: 19epoch:train:1-40batch: iter_time=0.004, forward_time=0.155, loss_ctc=25.474, loss=25.474, backward_time=0.024, grad_norm=191.416, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.264 +[stan] 2024-01-14 22:33:25,340 (trainer:763) INFO: 19epoch:train:41-80batch: iter_time=5.044e-05, forward_time=0.151, loss_ctc=24.058, loss=24.058, backward_time=0.024, grad_norm=188.838, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-14 22:33:37,374 (trainer:763) INFO: 19epoch:train:81-120batch: iter_time=5.012e-05, forward_time=0.151, loss_ctc=24.555, loss=24.555, backward_time=0.024, grad_norm=196.073, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-14 22:33:49,322 (trainer:763) INFO: 19epoch:train:121-160batch: iter_time=4.967e-05, forward_time=0.150, loss_ctc=23.593, loss=23.593, backward_time=0.024, grad_norm=201.050, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.195 +[stan] 2024-01-14 22:34:01,674 (trainer:763) INFO: 19epoch:train:161-200batch: iter_time=5.220e-05, forward_time=0.155, loss_ctc=25.910, loss=25.910, backward_time=0.024, grad_norm=199.044, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.235 +[stan] 2024-01-14 22:34:13,826 (trainer:763) INFO: 19epoch:train:201-240batch: iter_time=5.429e-05, forward_time=0.152, loss_ctc=24.533, loss=24.533, backward_time=0.024, grad_norm=198.575, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-14 22:34:26,080 (trainer:763) INFO: 19epoch:train:241-280batch: iter_time=5.043e-05, forward_time=0.153, loss_ctc=25.135, loss=25.135, backward_time=0.023, grad_norm=204.398, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-14 22:34:37,835 (trainer:763) INFO: 19epoch:train:281-320batch: iter_time=5.053e-05, forward_time=0.147, loss_ctc=23.310, loss=23.310, backward_time=0.023, grad_norm=204.727, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.175 +[stan] 2024-01-14 22:34:50,265 (trainer:763) INFO: 19epoch:train:321-360batch: iter_time=5.064e-05, forward_time=0.155, loss_ctc=24.605, loss=24.605, backward_time=0.024, grad_norm=199.351, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.243 +[stan] 2024-01-14 22:35:02,572 (trainer:763) INFO: 19epoch:train:361-400batch: iter_time=5.421e-05, forward_time=0.154, loss_ctc=24.161, loss=24.161, backward_time=0.024, grad_norm=196.957, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-14 22:35:14,635 (trainer:763) INFO: 19epoch:train:401-440batch: iter_time=4.995e-05, forward_time=0.151, loss_ctc=23.592, loss=23.592, backward_time=0.023, grad_norm=202.588, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.206 +[stan] 2024-01-14 22:35:26,703 (trainer:763) INFO: 19epoch:train:441-480batch: iter_time=5.585e-05, forward_time=0.151, loss_ctc=23.609, loss=23.609, backward_time=0.023, grad_norm=193.658, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.207 +[stan] 2024-01-14 22:35:38,501 (trainer:763) INFO: 19epoch:train:481-520batch: iter_time=5.075e-05, forward_time=0.148, loss_ctc=23.379, loss=23.379, backward_time=0.023, grad_norm=190.922, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.180 +[stan] 2024-01-14 22:35:50,905 (trainer:763) INFO: 19epoch:train:521-560batch: iter_time=5.284e-05, forward_time=0.155, loss_ctc=25.524, loss=25.524, backward_time=0.024, grad_norm=197.966, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.240 +[stan] 2024-01-14 22:36:03,131 (trainer:763) INFO: 19epoch:train:561-600batch: iter_time=5.235e-05, forward_time=0.153, loss_ctc=24.045, loss=24.045, backward_time=0.024, grad_norm=197.095, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-14 22:36:15,349 (trainer:763) INFO: 19epoch:train:601-640batch: iter_time=4.997e-05, forward_time=0.153, loss_ctc=23.740, loss=23.740, backward_time=0.024, grad_norm=190.109, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-14 22:36:27,366 (trainer:763) INFO: 19epoch:train:641-680batch: iter_time=5.468e-05, forward_time=0.151, loss_ctc=24.046, loss=24.046, backward_time=0.023, grad_norm=203.169, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.202 +[stan] 2024-01-14 22:36:39,540 (trainer:763) INFO: 19epoch:train:681-720batch: iter_time=5.379e-05, forward_time=0.152, loss_ctc=24.274, loss=24.274, backward_time=0.024, grad_norm=201.228, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217 +[stan] 2024-01-14 22:36:52,070 (trainer:763) INFO: 19epoch:train:721-760batch: iter_time=5.264e-05, forward_time=0.157, loss_ctc=25.620, loss=25.620, backward_time=0.024, grad_norm=195.558, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.253 +[stan] 2024-01-14 22:37:03,978 (trainer:763) INFO: 19epoch:train:761-800batch: iter_time=4.704e-05, forward_time=0.149, loss_ctc=24.457, loss=24.457, backward_time=0.023, grad_norm=191.863, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.191 +[stan] 2024-01-14 22:37:08,739 (trainer:354) INFO: 19epoch results: [train] iter_time=2.306e-04, forward_time=0.152, loss_ctc=24.381, loss=24.381, backward_time=0.024, grad_norm=197.229, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217, time=4 minutes and 3.4 seconds, total_count=15200, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=283.590, cer_ctc=0.200, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=283.590, time=1.16 seconds, total_count=95, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.52 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 22:37:09,709 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 22:37:09,709 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/18epoch.pth +[stan] 2024-01-14 22:37:09,709 (trainer:288) INFO: 20/30epoch started. Estimated time to finish: 45 minutes and 41.84 seconds +[stan] 2024-01-14 22:37:21,980 (trainer:763) INFO: 20epoch:train:1-40batch: iter_time=0.004, forward_time=0.150, loss_ctc=23.671, loss=23.671, backward_time=0.023, grad_norm=196.279, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.227 +[stan] 2024-01-14 22:37:34,172 (trainer:763) INFO: 20epoch:train:41-80batch: iter_time=5.076e-05, forward_time=0.153, loss_ctc=24.041, loss=24.041, backward_time=0.024, grad_norm=205.338, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 22:37:46,369 (trainer:763) INFO: 20epoch:train:81-120batch: iter_time=5.293e-05, forward_time=0.153, loss_ctc=24.000, loss=24.000, backward_time=0.024, grad_norm=206.180, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-14 22:37:58,454 (trainer:763) INFO: 20epoch:train:121-160batch: iter_time=5.208e-05, forward_time=0.151, loss_ctc=23.064, loss=23.064, backward_time=0.023, grad_norm=191.266, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-14 22:38:10,520 (trainer:763) INFO: 20epoch:train:161-200batch: iter_time=5.255e-05, forward_time=0.151, loss_ctc=24.145, loss=24.145, backward_time=0.024, grad_norm=195.105, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.207 +[stan] 2024-01-14 22:38:22,985 (trainer:763) INFO: 20epoch:train:201-240batch: iter_time=5.350e-05, forward_time=0.156, loss_ctc=25.035, loss=25.035, backward_time=0.024, grad_norm=203.460, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.246 +[stan] 2024-01-14 22:38:34,915 (trainer:763) INFO: 20epoch:train:241-280batch: iter_time=5.373e-05, forward_time=0.150, loss_ctc=23.248, loss=23.248, backward_time=0.023, grad_norm=193.250, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.193 +[stan] 2024-01-14 22:38:47,183 (trainer:763) INFO: 20epoch:train:281-320batch: iter_time=5.236e-05, forward_time=0.154, loss_ctc=24.293, loss=24.293, backward_time=0.024, grad_norm=198.707, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.227 +[stan] 2024-01-14 22:38:59,211 (trainer:763) INFO: 20epoch:train:321-360batch: iter_time=5.208e-05, forward_time=0.151, loss_ctc=23.395, loss=23.395, backward_time=0.023, grad_norm=193.948, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-14 22:39:11,385 (trainer:763) INFO: 20epoch:train:361-400batch: iter_time=5.121e-05, forward_time=0.152, loss_ctc=22.843, loss=22.843, backward_time=0.024, grad_norm=189.446, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217 +[stan] 2024-01-14 22:39:23,841 (trainer:763) INFO: 20epoch:train:401-440batch: iter_time=5.091e-05, forward_time=0.156, loss_ctc=25.139, loss=25.139, backward_time=0.024, grad_norm=202.405, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.245 +[stan] 2024-01-14 22:39:35,691 (trainer:763) INFO: 20epoch:train:441-480batch: iter_time=5.326e-05, forward_time=0.149, loss_ctc=23.066, loss=23.066, backward_time=0.024, grad_norm=193.364, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.185 +[stan] 2024-01-14 22:39:47,831 (trainer:763) INFO: 20epoch:train:481-520batch: iter_time=5.048e-05, forward_time=0.152, loss_ctc=23.419, loss=23.419, backward_time=0.023, grad_norm=188.563, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-14 22:39:59,829 (trainer:763) INFO: 20epoch:train:521-560batch: iter_time=5.136e-05, forward_time=0.150, loss_ctc=22.904, loss=22.904, backward_time=0.023, grad_norm=203.188, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-14 22:40:11,721 (trainer:763) INFO: 20epoch:train:561-600batch: iter_time=5.450e-05, forward_time=0.149, loss_ctc=23.532, loss=23.532, backward_time=0.023, grad_norm=198.316, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.189 +[stan] 2024-01-14 22:40:24,262 (trainer:763) INFO: 20epoch:train:601-640batch: iter_time=5.113e-05, forward_time=0.157, loss_ctc=24.817, loss=24.817, backward_time=0.024, grad_norm=200.578, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.254 +[stan] 2024-01-14 22:40:35,938 (trainer:763) INFO: 20epoch:train:641-680batch: iter_time=5.280e-05, forward_time=0.146, loss_ctc=22.262, loss=22.262, backward_time=0.023, grad_norm=191.434, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.168 +[stan] 2024-01-14 22:40:48,230 (trainer:763) INFO: 20epoch:train:681-720batch: iter_time=5.262e-05, forward_time=0.154, loss_ctc=24.714, loss=24.714, backward_time=0.024, grad_norm=199.146, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.229 +[stan] 2024-01-14 22:41:00,659 (trainer:763) INFO: 20epoch:train:721-760batch: iter_time=5.096e-05, forward_time=0.155, loss_ctc=23.963, loss=23.963, backward_time=0.024, grad_norm=189.948, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.243 +[stan] 2024-01-14 22:41:12,681 (trainer:763) INFO: 20epoch:train:761-800batch: iter_time=5.015e-05, forward_time=0.151, loss_ctc=23.525, loss=23.525, backward_time=0.024, grad_norm=211.817, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.202 +[stan] 2024-01-14 22:41:17,422 (trainer:354) INFO: 20epoch results: [train] iter_time=2.649e-04, forward_time=0.152, loss_ctc=23.754, loss=23.754, backward_time=0.024, grad_norm=197.587, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215, time=4 minutes and 3.05 seconds, total_count=16000, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=279.698, cer_ctc=0.193, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=279.698, time=1.15 seconds, total_count=100, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.51 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 22:41:18,344 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 22:41:18,344 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/19epoch.pth +[stan] 2024-01-14 22:41:18,344 (trainer:288) INFO: 21/30epoch started. Estimated time to finish: 41 minutes and 32.27 seconds +[stan] 2024-01-14 22:41:30,644 (trainer:763) INFO: 21epoch:train:1-40batch: iter_time=0.004, forward_time=0.150, loss_ctc=23.474, loss=23.474, backward_time=0.023, grad_norm=197.553, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.229 +[stan] 2024-01-14 22:41:43,050 (trainer:763) INFO: 21epoch:train:41-80batch: iter_time=5.256e-05, forward_time=0.155, loss_ctc=24.239, loss=24.239, backward_time=0.024, grad_norm=194.916, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.240 +[stan] 2024-01-14 22:41:54,964 (trainer:763) INFO: 21epoch:train:81-120batch: iter_time=5.171e-05, forward_time=0.149, loss_ctc=22.711, loss=22.711, backward_time=0.023, grad_norm=195.859, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.191 +[stan] 2024-01-14 22:42:07,519 (trainer:763) INFO: 21epoch:train:121-160batch: iter_time=5.222e-05, forward_time=0.157, loss_ctc=24.754, loss=24.754, backward_time=0.024, grad_norm=202.237, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.255 +[stan] 2024-01-14 22:42:19,509 (trainer:763) INFO: 21epoch:train:161-200batch: iter_time=4.908e-05, forward_time=0.150, loss_ctc=23.564, loss=23.564, backward_time=0.023, grad_norm=199.855, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-14 22:42:31,808 (trainer:763) INFO: 21epoch:train:201-240batch: iter_time=5.212e-05, forward_time=0.154, loss_ctc=24.552, loss=24.552, backward_time=0.024, grad_norm=212.869, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.230 +[stan] 2024-01-14 22:42:43,888 (trainer:763) INFO: 21epoch:train:241-280batch: iter_time=5.029e-05, forward_time=0.151, loss_ctc=23.063, loss=23.063, backward_time=0.023, grad_norm=203.519, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-14 22:42:56,000 (trainer:763) INFO: 21epoch:train:281-320batch: iter_time=5.081e-05, forward_time=0.152, loss_ctc=23.962, loss=23.962, backward_time=0.024, grad_norm=197.927, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211 +[stan] 2024-01-14 22:43:07,762 (trainer:763) INFO: 21epoch:train:321-360batch: iter_time=5.068e-05, forward_time=0.148, loss_ctc=22.014, loss=22.014, backward_time=0.023, grad_norm=194.296, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.176 +[stan] 2024-01-14 22:43:20,300 (trainer:763) INFO: 21epoch:train:361-400batch: iter_time=5.154e-05, forward_time=0.157, loss_ctc=24.053, loss=24.053, backward_time=0.024, grad_norm=197.719, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.254 +[stan] 2024-01-14 22:43:32,485 (trainer:763) INFO: 21epoch:train:401-440batch: iter_time=4.932e-05, forward_time=0.153, loss_ctc=23.241, loss=23.241, backward_time=0.024, grad_norm=198.596, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-14 22:43:44,456 (trainer:763) INFO: 21epoch:train:441-480batch: iter_time=5.118e-05, forward_time=0.150, loss_ctc=22.933, loss=22.933, backward_time=0.023, grad_norm=200.687, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.197 +[stan] 2024-01-14 22:43:56,743 (trainer:763) INFO: 21epoch:train:481-520batch: iter_time=5.180e-05, forward_time=0.154, loss_ctc=24.092, loss=24.092, backward_time=0.024, grad_norm=203.936, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.229 +[stan] 2024-01-14 22:44:08,964 (trainer:763) INFO: 21epoch:train:521-560batch: iter_time=5.285e-05, forward_time=0.153, loss_ctc=23.067, loss=23.067, backward_time=0.024, grad_norm=193.856, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-14 22:44:21,181 (trainer:763) INFO: 21epoch:train:561-600batch: iter_time=5.256e-05, forward_time=0.153, loss_ctc=23.399, loss=23.399, backward_time=0.024, grad_norm=207.243, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-14 22:44:32,825 (trainer:763) INFO: 21epoch:train:601-640batch: iter_time=5.208e-05, forward_time=0.146, loss_ctc=22.273, loss=22.273, backward_time=0.023, grad_norm=191.275, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.164 +[stan] 2024-01-14 22:44:45,427 (trainer:763) INFO: 21epoch:train:641-680batch: iter_time=5.245e-05, forward_time=0.157, loss_ctc=23.354, loss=23.354, backward_time=0.024, grad_norm=193.990, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.260 +[stan] 2024-01-14 22:44:57,641 (trainer:763) INFO: 21epoch:train:681-720batch: iter_time=5.202e-05, forward_time=0.153, loss_ctc=23.258, loss=23.258, backward_time=0.024, grad_norm=199.525, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.221 +[stan] 2024-01-14 22:45:09,959 (trainer:763) INFO: 21epoch:train:721-760batch: iter_time=5.274e-05, forward_time=0.154, loss_ctc=22.976, loss=22.976, backward_time=0.023, grad_norm=189.598, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-14 22:45:21,864 (trainer:763) INFO: 21epoch:train:761-800batch: iter_time=4.675e-05, forward_time=0.149, loss_ctc=23.341, loss=23.341, backward_time=0.023, grad_norm=204.173, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.190 +[stan] 2024-01-14 22:45:26,644 (trainer:354) INFO: 21epoch results: [train] iter_time=2.650e-04, forward_time=0.152, loss_ctc=23.416, loss=23.416, backward_time=0.024, grad_norm=198.981, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217, time=4 minutes and 3.59 seconds, total_count=16800, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=279.345, cer_ctc=0.191, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=279.345, time=1.17 seconds, total_count=105, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.53 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 22:45:27,686 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 22:45:27,686 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/20epoch.pth +[stan] 2024-01-14 22:45:27,686 (trainer:288) INFO: 22/30epoch started. Estimated time to finish: 37 minutes and 23.09 seconds +[stan] 2024-01-14 22:45:40,221 (trainer:763) INFO: 22epoch:train:1-40batch: iter_time=0.003, forward_time=0.154, loss_ctc=23.098, loss=23.098, backward_time=0.024, grad_norm=199.608, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.253 +[stan] 2024-01-14 22:45:52,165 (trainer:763) INFO: 22epoch:train:41-80batch: iter_time=4.971e-05, forward_time=0.150, loss_ctc=23.616, loss=23.616, backward_time=0.023, grad_norm=195.919, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.194 +[stan] 2024-01-14 22:46:04,377 (trainer:763) INFO: 22epoch:train:81-120batch: iter_time=5.108e-05, forward_time=0.153, loss_ctc=23.321, loss=23.321, backward_time=0.023, grad_norm=208.844, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.221 +[stan] 2024-01-14 22:46:16,533 (trainer:763) INFO: 22epoch:train:121-160batch: iter_time=5.211e-05, forward_time=0.152, loss_ctc=23.198, loss=23.198, backward_time=0.023, grad_norm=202.266, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-14 22:46:28,579 (trainer:763) INFO: 22epoch:train:161-200batch: iter_time=4.967e-05, forward_time=0.151, loss_ctc=22.975, loss=22.975, backward_time=0.023, grad_norm=199.435, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-14 22:46:40,857 (trainer:763) INFO: 22epoch:train:201-240batch: iter_time=4.953e-05, forward_time=0.154, loss_ctc=23.116, loss=23.116, backward_time=0.024, grad_norm=207.581, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.228 +[stan] 2024-01-14 22:46:52,918 (trainer:763) INFO: 22epoch:train:241-280batch: iter_time=5.281e-05, forward_time=0.151, loss_ctc=23.084, loss=23.084, backward_time=0.023, grad_norm=203.555, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.206 +[stan] 2024-01-14 22:47:05,347 (trainer:763) INFO: 22epoch:train:281-320batch: iter_time=5.120e-05, forward_time=0.155, loss_ctc=23.226, loss=23.226, backward_time=0.024, grad_norm=197.114, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.243 +[stan] 2024-01-14 22:47:17,346 (trainer:763) INFO: 22epoch:train:321-360batch: iter_time=5.189e-05, forward_time=0.150, loss_ctc=22.926, loss=22.926, backward_time=0.023, grad_norm=200.203, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-14 22:47:29,416 (trainer:763) INFO: 22epoch:train:361-400batch: iter_time=5.087e-05, forward_time=0.151, loss_ctc=23.101, loss=23.101, backward_time=0.024, grad_norm=202.784, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.207 +[stan] 2024-01-14 22:47:41,990 (trainer:763) INFO: 22epoch:train:401-440batch: iter_time=5.099e-05, forward_time=0.157, loss_ctc=24.051, loss=24.051, backward_time=0.024, grad_norm=214.175, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.257 +[stan] 2024-01-14 22:47:53,842 (trainer:763) INFO: 22epoch:train:441-480batch: iter_time=5.369e-05, forward_time=0.149, loss_ctc=22.132, loss=22.132, backward_time=0.023, grad_norm=201.150, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.185 +[stan] 2024-01-14 22:48:05,893 (trainer:763) INFO: 22epoch:train:481-520batch: iter_time=5.311e-05, forward_time=0.151, loss_ctc=22.809, loss=22.809, backward_time=0.023, grad_norm=195.955, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-14 22:48:18,232 (trainer:763) INFO: 22epoch:train:521-560batch: iter_time=5.394e-05, forward_time=0.154, loss_ctc=23.146, loss=23.146, backward_time=0.024, grad_norm=189.173, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.234 +[stan] 2024-01-14 22:48:30,314 (trainer:763) INFO: 22epoch:train:561-600batch: iter_time=4.980e-05, forward_time=0.151, loss_ctc=22.971, loss=22.971, backward_time=0.024, grad_norm=193.539, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-14 22:48:42,614 (trainer:763) INFO: 22epoch:train:601-640batch: iter_time=5.169e-05, forward_time=0.154, loss_ctc=23.835, loss=23.835, backward_time=0.024, grad_norm=201.823, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.230 +[stan] 2024-01-14 22:48:54,485 (trainer:763) INFO: 22epoch:train:641-680batch: iter_time=5.027e-05, forward_time=0.149, loss_ctc=22.365, loss=22.365, backward_time=0.023, grad_norm=195.347, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.187 +[stan] 2024-01-14 22:49:06,781 (trainer:763) INFO: 22epoch:train:681-720batch: iter_time=5.132e-05, forward_time=0.154, loss_ctc=22.907, loss=22.907, backward_time=0.024, grad_norm=196.938, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.229 +[stan] 2024-01-14 22:49:19,188 (trainer:763) INFO: 22epoch:train:721-760batch: iter_time=5.296e-05, forward_time=0.155, loss_ctc=23.780, loss=23.780, backward_time=0.024, grad_norm=206.426, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.241 +[stan] 2024-01-14 22:49:31,153 (trainer:763) INFO: 22epoch:train:761-800batch: iter_time=4.909e-05, forward_time=0.150, loss_ctc=22.315, loss=22.315, backward_time=0.023, grad_norm=194.026, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.196 +[stan] 2024-01-14 22:49:35,912 (trainer:354) INFO: 22epoch results: [train] iter_time=2.055e-04, forward_time=0.152, loss_ctc=23.099, loss=23.099, backward_time=0.024, grad_norm=200.293, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217, time=4 minutes and 3.54 seconds, total_count=17600, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=284.090, cer_ctc=0.190, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=284.090, time=1.16 seconds, total_count=110, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.52 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 22:49:36,870 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 22:49:36,871 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/21epoch.pth +[stan] 2024-01-14 22:49:36,871 (trainer:288) INFO: 23/30epoch started. Estimated time to finish: 33 minutes and 13.84 seconds +[stan] 2024-01-14 22:49:49,209 (trainer:763) INFO: 23epoch:train:1-40batch: iter_time=0.004, forward_time=0.151, loss_ctc=22.393, loss=22.393, backward_time=0.024, grad_norm=202.167, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.233 +[stan] 2024-01-14 22:50:01,573 (trainer:763) INFO: 23epoch:train:41-80batch: iter_time=5.380e-05, forward_time=0.155, loss_ctc=23.006, loss=23.006, backward_time=0.024, grad_norm=195.157, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.236 +[stan] 2024-01-14 22:50:13,305 (trainer:763) INFO: 23epoch:train:81-120batch: iter_time=5.205e-05, forward_time=0.147, loss_ctc=21.407, loss=21.407, backward_time=0.023, grad_norm=188.477, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.173 +[stan] 2024-01-14 22:50:25,640 (trainer:763) INFO: 23epoch:train:121-160batch: iter_time=5.370e-05, forward_time=0.154, loss_ctc=23.032, loss=23.032, backward_time=0.024, grad_norm=205.649, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.233 +[stan] 2024-01-14 22:50:37,897 (trainer:763) INFO: 23epoch:train:161-200batch: iter_time=5.246e-05, forward_time=0.153, loss_ctc=23.072, loss=23.072, backward_time=0.024, grad_norm=202.336, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-14 22:50:49,921 (trainer:763) INFO: 23epoch:train:201-240batch: iter_time=5.322e-05, forward_time=0.151, loss_ctc=22.274, loss=22.274, backward_time=0.023, grad_norm=199.235, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.202 +[stan] 2024-01-14 22:51:02,127 (trainer:763) INFO: 23epoch:train:241-280batch: iter_time=5.182e-05, forward_time=0.153, loss_ctc=22.301, loss=22.301, backward_time=0.024, grad_norm=201.775, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-14 22:51:14,338 (trainer:763) INFO: 23epoch:train:281-320batch: iter_time=5.111e-05, forward_time=0.153, loss_ctc=22.651, loss=22.651, backward_time=0.024, grad_norm=198.436, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.221 +[stan] 2024-01-14 22:51:26,197 (trainer:763) INFO: 23epoch:train:321-360batch: iter_time=5.359e-05, forward_time=0.149, loss_ctc=22.446, loss=22.446, backward_time=0.023, grad_norm=204.028, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.186 +[stan] 2024-01-14 22:51:38,625 (trainer:763) INFO: 23epoch:train:361-400batch: iter_time=5.219e-05, forward_time=0.156, loss_ctc=22.320, loss=22.320, backward_time=0.024, grad_norm=201.270, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.243 +[stan] 2024-01-14 22:51:50,710 (trainer:763) INFO: 23epoch:train:401-440batch: iter_time=5.342e-05, forward_time=0.151, loss_ctc=22.195, loss=22.195, backward_time=0.023, grad_norm=194.641, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-14 22:52:03,255 (trainer:763) INFO: 23epoch:train:441-480batch: iter_time=5.158e-05, forward_time=0.157, loss_ctc=23.661, loss=23.661, backward_time=0.024, grad_norm=208.255, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.254 +[stan] 2024-01-14 22:52:15,123 (trainer:763) INFO: 23epoch:train:481-520batch: iter_time=5.270e-05, forward_time=0.149, loss_ctc=21.183, loss=21.183, backward_time=0.023, grad_norm=197.958, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.187 +[stan] 2024-01-14 22:52:27,193 (trainer:763) INFO: 23epoch:train:521-560batch: iter_time=5.038e-05, forward_time=0.151, loss_ctc=22.404, loss=22.404, backward_time=0.023, grad_norm=197.902, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.207 +[stan] 2024-01-14 22:52:39,293 (trainer:763) INFO: 23epoch:train:561-600batch: iter_time=5.367e-05, forward_time=0.152, loss_ctc=22.984, loss=22.984, backward_time=0.024, grad_norm=196.468, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.210 +[stan] 2024-01-14 22:52:51,605 (trainer:763) INFO: 23epoch:train:601-640batch: iter_time=5.358e-05, forward_time=0.154, loss_ctc=23.003, loss=23.003, backward_time=0.024, grad_norm=196.951, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-14 22:53:03,500 (trainer:763) INFO: 23epoch:train:641-680batch: iter_time=5.105e-05, forward_time=0.149, loss_ctc=22.101, loss=22.101, backward_time=0.023, grad_norm=198.496, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.189 +[stan] 2024-01-14 22:53:15,990 (trainer:763) INFO: 23epoch:train:681-720batch: iter_time=5.382e-05, forward_time=0.156, loss_ctc=23.514, loss=23.514, backward_time=0.024, grad_norm=199.399, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.249 +[stan] 2024-01-14 22:53:28,086 (trainer:763) INFO: 23epoch:train:721-760batch: iter_time=5.112e-05, forward_time=0.152, loss_ctc=22.401, loss=22.401, backward_time=0.024, grad_norm=198.792, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.209 +[stan] 2024-01-14 22:53:40,604 (trainer:763) INFO: 23epoch:train:761-800batch: iter_time=4.899e-05, forward_time=0.157, loss_ctc=22.622, loss=22.622, backward_time=0.024, grad_norm=197.702, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.252 +[stan] 2024-01-14 22:53:45,329 (trainer:354) INFO: 23epoch results: [train] iter_time=2.261e-04, forward_time=0.152, loss_ctc=22.548, loss=22.548, backward_time=0.024, grad_norm=199.255, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219, time=4 minutes and 3.81 seconds, total_count=18400, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=280.841, cer_ctc=0.190, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=280.841, time=1.16 seconds, total_count=115, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.49 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 22:53:46,292 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 22:53:46,293 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/22epoch.pth +[stan] 2024-01-14 22:53:46,293 (trainer:288) INFO: 24/30epoch started. Estimated time to finish: 29 minutes and 4.67 seconds +[stan] 2024-01-14 22:53:58,451 (trainer:763) INFO: 24epoch:train:1-40batch: iter_time=0.004, forward_time=0.149, loss_ctc=21.984, loss=21.984, backward_time=0.023, grad_norm=198.324, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-14 22:54:10,497 (trainer:763) INFO: 24epoch:train:41-80batch: iter_time=5.295e-05, forward_time=0.151, loss_ctc=22.121, loss=22.121, backward_time=0.024, grad_norm=195.693, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-14 22:54:22,684 (trainer:763) INFO: 24epoch:train:81-120batch: iter_time=5.102e-05, forward_time=0.153, loss_ctc=22.763, loss=22.763, backward_time=0.023, grad_norm=205.072, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 22:54:34,939 (trainer:763) INFO: 24epoch:train:121-160batch: iter_time=4.906e-05, forward_time=0.153, loss_ctc=22.075, loss=22.075, backward_time=0.024, grad_norm=190.453, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-14 22:54:46,961 (trainer:763) INFO: 24epoch:train:161-200batch: iter_time=5.084e-05, forward_time=0.151, loss_ctc=21.274, loss=21.274, backward_time=0.023, grad_norm=199.303, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.202 +[stan] 2024-01-14 22:54:59,286 (trainer:763) INFO: 24epoch:train:201-240batch: iter_time=5.179e-05, forward_time=0.154, loss_ctc=22.300, loss=22.300, backward_time=0.024, grad_norm=203.454, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-14 22:55:11,295 (trainer:763) INFO: 24epoch:train:241-280batch: iter_time=5.146e-05, forward_time=0.151, loss_ctc=21.617, loss=21.617, backward_time=0.024, grad_norm=198.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-14 22:55:23,446 (trainer:763) INFO: 24epoch:train:281-320batch: iter_time=5.326e-05, forward_time=0.152, loss_ctc=22.206, loss=22.206, backward_time=0.024, grad_norm=189.598, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-14 22:55:35,768 (trainer:763) INFO: 24epoch:train:321-360batch: iter_time=5.128e-05, forward_time=0.154, loss_ctc=22.695, loss=22.695, backward_time=0.024, grad_norm=202.201, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-14 22:55:47,677 (trainer:763) INFO: 24epoch:train:361-400batch: iter_time=5.167e-05, forward_time=0.149, loss_ctc=21.374, loss=21.374, backward_time=0.023, grad_norm=203.241, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.191 +[stan] 2024-01-14 22:55:59,926 (trainer:763) INFO: 24epoch:train:401-440batch: iter_time=5.255e-05, forward_time=0.153, loss_ctc=22.543, loss=22.543, backward_time=0.024, grad_norm=197.012, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-14 22:56:11,905 (trainer:763) INFO: 24epoch:train:441-480batch: iter_time=5.256e-05, forward_time=0.150, loss_ctc=21.667, loss=21.667, backward_time=0.023, grad_norm=198.322, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.198 +[stan] 2024-01-14 22:56:24,273 (trainer:763) INFO: 24epoch:train:481-520batch: iter_time=5.338e-05, forward_time=0.155, loss_ctc=22.199, loss=22.199, backward_time=0.024, grad_norm=197.209, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-14 22:56:36,414 (trainer:763) INFO: 24epoch:train:521-560batch: iter_time=5.285e-05, forward_time=0.152, loss_ctc=21.490, loss=21.490, backward_time=0.023, grad_norm=200.517, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-14 22:56:48,397 (trainer:763) INFO: 24epoch:train:561-600batch: iter_time=5.152e-05, forward_time=0.150, loss_ctc=21.981, loss=21.981, backward_time=0.024, grad_norm=202.997, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.198 +[stan] 2024-01-14 22:57:01,073 (trainer:763) INFO: 24epoch:train:601-640batch: iter_time=5.315e-05, forward_time=0.158, loss_ctc=23.476, loss=23.476, backward_time=0.024, grad_norm=207.531, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.267 +[stan] 2024-01-14 22:57:12,875 (trainer:763) INFO: 24epoch:train:641-680batch: iter_time=5.294e-05, forward_time=0.148, loss_ctc=21.102, loss=21.102, backward_time=0.023, grad_norm=200.627, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.180 +[stan] 2024-01-14 22:57:25,283 (trainer:763) INFO: 24epoch:train:681-720batch: iter_time=5.191e-05, forward_time=0.155, loss_ctc=23.042, loss=23.042, backward_time=0.024, grad_norm=204.094, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.241 +[stan] 2024-01-14 22:57:36,820 (trainer:763) INFO: 24epoch:train:721-760batch: iter_time=5.260e-05, forward_time=0.145, loss_ctc=21.313, loss=21.313, backward_time=0.023, grad_norm=197.335, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.154 +[stan] 2024-01-14 22:57:49,313 (trainer:763) INFO: 24epoch:train:761-800batch: iter_time=5.126e-05, forward_time=0.156, loss_ctc=22.697, loss=22.697, backward_time=0.024, grad_norm=209.946, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.249 +[stan] 2024-01-14 22:57:54,070 (trainer:354) INFO: 24epoch results: [train] iter_time=2.492e-04, forward_time=0.152, loss_ctc=22.095, loss=22.095, backward_time=0.024, grad_norm=200.053, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215, time=4 minutes and 3.1 seconds, total_count=19200, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=286.538, cer_ctc=0.194, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=286.538, time=1.17 seconds, total_count=120, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.51 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 22:57:55,126 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 22:57:55,127 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/23epoch.pth +[stan] 2024-01-14 22:57:55,127 (trainer:288) INFO: 25/30epoch started. Estimated time to finish: 24 minutes and 55.33 seconds +[stan] 2024-01-14 22:58:07,770 (trainer:763) INFO: 25epoch:train:1-40batch: iter_time=0.004, forward_time=0.155, loss_ctc=23.002, loss=23.002, backward_time=0.024, grad_norm=207.386, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.264 +[stan] 2024-01-14 22:58:19,951 (trainer:763) INFO: 25epoch:train:41-80batch: iter_time=5.110e-05, forward_time=0.153, loss_ctc=21.009, loss=21.009, backward_time=0.024, grad_norm=195.765, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-14 22:58:31,835 (trainer:763) INFO: 25epoch:train:81-120batch: iter_time=5.018e-05, forward_time=0.149, loss_ctc=20.610, loss=20.610, backward_time=0.023, grad_norm=199.756, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.188 +[stan] 2024-01-14 22:58:43,966 (trainer:763) INFO: 25epoch:train:121-160batch: iter_time=5.252e-05, forward_time=0.152, loss_ctc=21.489, loss=21.489, backward_time=0.023, grad_norm=196.161, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213 +[stan] 2024-01-14 22:58:56,045 (trainer:763) INFO: 25epoch:train:161-200batch: iter_time=5.087e-05, forward_time=0.151, loss_ctc=20.838, loss=20.838, backward_time=0.024, grad_norm=205.029, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-14 22:59:08,320 (trainer:763) INFO: 25epoch:train:201-240batch: iter_time=5.226e-05, forward_time=0.154, loss_ctc=22.414, loss=22.414, backward_time=0.023, grad_norm=196.731, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.227 +[stan] 2024-01-14 22:59:20,689 (trainer:763) INFO: 25epoch:train:241-280batch: iter_time=5.277e-05, forward_time=0.155, loss_ctc=21.846, loss=21.846, backward_time=0.024, grad_norm=199.853, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-14 22:59:32,642 (trainer:763) INFO: 25epoch:train:281-320batch: iter_time=5.254e-05, forward_time=0.150, loss_ctc=21.696, loss=21.696, backward_time=0.023, grad_norm=202.494, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.195 +[stan] 2024-01-14 22:59:44,794 (trainer:763) INFO: 25epoch:train:321-360batch: iter_time=5.070e-05, forward_time=0.152, loss_ctc=21.679, loss=21.679, backward_time=0.024, grad_norm=200.174, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-14 22:59:57,024 (trainer:763) INFO: 25epoch:train:361-400batch: iter_time=5.060e-05, forward_time=0.153, loss_ctc=22.064, loss=22.064, backward_time=0.023, grad_norm=201.450, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-14 23:00:09,064 (trainer:763) INFO: 25epoch:train:401-440batch: iter_time=5.338e-05, forward_time=0.151, loss_ctc=21.365, loss=21.365, backward_time=0.024, grad_norm=204.771, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-14 23:00:21,274 (trainer:763) INFO: 25epoch:train:441-480batch: iter_time=5.008e-05, forward_time=0.153, loss_ctc=21.895, loss=21.895, backward_time=0.024, grad_norm=196.782, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.221 +[stan] 2024-01-14 23:00:33,500 (trainer:763) INFO: 25epoch:train:481-520batch: iter_time=5.219e-05, forward_time=0.153, loss_ctc=21.168, loss=21.168, backward_time=0.024, grad_norm=201.094, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-14 23:00:45,484 (trainer:763) INFO: 25epoch:train:521-560batch: iter_time=5.502e-05, forward_time=0.150, loss_ctc=20.345, loss=20.345, backward_time=0.024, grad_norm=197.957, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.198 +[stan] 2024-01-14 23:00:57,740 (trainer:763) INFO: 25epoch:train:561-600batch: iter_time=5.242e-05, forward_time=0.153, loss_ctc=22.220, loss=22.220, backward_time=0.024, grad_norm=199.105, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-14 23:01:09,854 (trainer:763) INFO: 25epoch:train:601-640batch: iter_time=5.431e-05, forward_time=0.152, loss_ctc=21.667, loss=21.667, backward_time=0.023, grad_norm=209.273, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211 +[stan] 2024-01-14 23:01:22,426 (trainer:763) INFO: 25epoch:train:641-680batch: iter_time=5.065e-05, forward_time=0.157, loss_ctc=22.931, loss=22.931, backward_time=0.024, grad_norm=204.466, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.257 +[stan] 2024-01-14 23:01:34,191 (trainer:763) INFO: 25epoch:train:681-720batch: iter_time=4.973e-05, forward_time=0.148, loss_ctc=21.158, loss=21.158, backward_time=0.023, grad_norm=199.537, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.176 +[stan] 2024-01-14 23:01:46,291 (trainer:763) INFO: 25epoch:train:721-760batch: iter_time=5.176e-05, forward_time=0.152, loss_ctc=21.780, loss=21.780, backward_time=0.024, grad_norm=203.283, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.210 +[stan] 2024-01-14 23:01:58,107 (trainer:763) INFO: 25epoch:train:761-800batch: iter_time=4.670e-05, forward_time=0.148, loss_ctc=21.149, loss=21.149, backward_time=0.023, grad_norm=208.747, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.182 +[stan] 2024-01-14 23:02:02,833 (trainer:354) INFO: 25epoch results: [train] iter_time=2.267e-04, forward_time=0.152, loss_ctc=21.615, loss=21.615, backward_time=0.024, grad_norm=201.491, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215, time=4 minutes and 3.06 seconds, total_count=20000, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=285.542, cer_ctc=0.190, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=285.542, time=1.16 seconds, total_count=125, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.49 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 23:02:03,894 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:02:03,895 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/24epoch.pth +[stan] 2024-01-14 23:02:03,895 (trainer:288) INFO: 26/30epoch started. Estimated time to finish: 20 minutes and 46.02 seconds +[stan] 2024-01-14 23:02:16,670 (trainer:763) INFO: 26epoch:train:1-40batch: iter_time=0.003, forward_time=0.156, loss_ctc=21.506, loss=21.506, backward_time=0.024, grad_norm=204.789, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.277 +[stan] 2024-01-14 23:02:28,859 (trainer:763) INFO: 26epoch:train:41-80batch: iter_time=5.256e-05, forward_time=0.153, loss_ctc=22.175, loss=22.175, backward_time=0.024, grad_norm=200.831, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 23:02:40,860 (trainer:763) INFO: 26epoch:train:81-120batch: iter_time=4.984e-05, forward_time=0.151, loss_ctc=21.455, loss=21.455, backward_time=0.023, grad_norm=203.390, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-14 23:02:53,105 (trainer:763) INFO: 26epoch:train:121-160batch: iter_time=5.215e-05, forward_time=0.153, loss_ctc=21.705, loss=21.705, backward_time=0.024, grad_norm=204.000, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.224 +[stan] 2024-01-14 23:03:05,252 (trainer:763) INFO: 26epoch:train:161-200batch: iter_time=5.237e-05, forward_time=0.152, loss_ctc=22.112, loss=22.112, backward_time=0.024, grad_norm=209.528, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-14 23:03:17,627 (trainer:763) INFO: 26epoch:train:201-240batch: iter_time=4.999e-05, forward_time=0.155, loss_ctc=21.901, loss=21.901, backward_time=0.024, grad_norm=206.950, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-14 23:03:29,601 (trainer:763) INFO: 26epoch:train:241-280batch: iter_time=5.053e-05, forward_time=0.150, loss_ctc=21.028, loss=21.028, backward_time=0.024, grad_norm=207.193, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.197 +[stan] 2024-01-14 23:03:41,477 (trainer:763) INFO: 26epoch:train:281-320batch: iter_time=5.072e-05, forward_time=0.149, loss_ctc=20.524, loss=20.524, backward_time=0.023, grad_norm=209.664, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.188 +[stan] 2024-01-14 23:03:54,020 (trainer:763) INFO: 26epoch:train:321-360batch: iter_time=5.261e-05, forward_time=0.157, loss_ctc=22.059, loss=22.059, backward_time=0.024, grad_norm=198.903, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.254 +[stan] 2024-01-14 23:04:06,087 (trainer:763) INFO: 26epoch:train:361-400batch: iter_time=5.145e-05, forward_time=0.151, loss_ctc=21.693, loss=21.693, backward_time=0.024, grad_norm=205.430, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.207 +[stan] 2024-01-14 23:04:17,990 (trainer:763) INFO: 26epoch:train:401-440batch: iter_time=5.091e-05, forward_time=0.149, loss_ctc=21.067, loss=21.067, backward_time=0.023, grad_norm=200.649, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.190 +[stan] 2024-01-14 23:04:30,365 (trainer:763) INFO: 26epoch:train:441-480batch: iter_time=5.364e-05, forward_time=0.155, loss_ctc=21.072, loss=21.072, backward_time=0.024, grad_norm=204.405, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-14 23:04:42,554 (trainer:763) INFO: 26epoch:train:481-520batch: iter_time=5.446e-05, forward_time=0.153, loss_ctc=21.916, loss=21.916, backward_time=0.024, grad_norm=202.288, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 23:04:54,741 (trainer:763) INFO: 26epoch:train:521-560batch: iter_time=5.255e-05, forward_time=0.153, loss_ctc=21.708, loss=21.708, backward_time=0.024, grad_norm=198.992, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 23:05:06,751 (trainer:763) INFO: 26epoch:train:561-600batch: iter_time=5.086e-05, forward_time=0.151, loss_ctc=20.457, loss=20.457, backward_time=0.023, grad_norm=190.655, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-14 23:05:19,031 (trainer:763) INFO: 26epoch:train:601-640batch: iter_time=5.100e-05, forward_time=0.154, loss_ctc=21.456, loss=21.456, backward_time=0.024, grad_norm=203.521, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.228 +[stan] 2024-01-14 23:05:31,288 (trainer:763) INFO: 26epoch:train:641-680batch: iter_time=5.222e-05, forward_time=0.153, loss_ctc=21.420, loss=21.420, backward_time=0.024, grad_norm=201.075, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-14 23:05:43,366 (trainer:763) INFO: 26epoch:train:681-720batch: iter_time=5.118e-05, forward_time=0.151, loss_ctc=20.749, loss=20.749, backward_time=0.023, grad_norm=196.630, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-14 23:05:55,485 (trainer:763) INFO: 26epoch:train:721-760batch: iter_time=5.373e-05, forward_time=0.152, loss_ctc=20.798, loss=20.798, backward_time=0.024, grad_norm=205.454, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-14 23:06:07,593 (trainer:763) INFO: 26epoch:train:761-800batch: iter_time=4.800e-05, forward_time=0.152, loss_ctc=21.011, loss=21.011, backward_time=0.024, grad_norm=206.400, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211 +[stan] 2024-01-14 23:06:12,344 (trainer:354) INFO: 26epoch results: [train] iter_time=2.153e-04, forward_time=0.152, loss_ctc=21.390, loss=21.390, backward_time=0.024, grad_norm=203.037, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218, time=4 minutes and 3.78 seconds, total_count=20800, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=290.777, cer_ctc=0.189, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=290.777, time=1.16 seconds, total_count=130, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.51 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 23:06:13,327 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:06:13,328 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/25epoch.pth +[stan] 2024-01-14 23:06:13,328 (trainer:288) INFO: 27/30epoch started. Estimated time to finish: 16 minutes and 36.85 seconds +[stan] 2024-01-14 23:06:25,908 (trainer:763) INFO: 27epoch:train:1-40batch: iter_time=0.004, forward_time=0.154, loss_ctc=21.220, loss=21.220, backward_time=0.024, grad_norm=205.494, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.258 +[stan] 2024-01-14 23:06:37,945 (trainer:763) INFO: 27epoch:train:41-80batch: iter_time=5.439e-05, forward_time=0.151, loss_ctc=20.967, loss=20.967, backward_time=0.023, grad_norm=196.567, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-14 23:06:50,132 (trainer:763) INFO: 27epoch:train:81-120batch: iter_time=5.310e-05, forward_time=0.153, loss_ctc=21.284, loss=21.284, backward_time=0.023, grad_norm=207.578, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 23:07:02,491 (trainer:763) INFO: 27epoch:train:121-160batch: iter_time=5.067e-05, forward_time=0.155, loss_ctc=21.816, loss=21.816, backward_time=0.024, grad_norm=213.243, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.236 +[stan] 2024-01-14 23:07:14,399 (trainer:763) INFO: 27epoch:train:161-200batch: iter_time=5.100e-05, forward_time=0.149, loss_ctc=20.313, loss=20.313, backward_time=0.024, grad_norm=211.270, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.191 +[stan] 2024-01-14 23:07:26,723 (trainer:763) INFO: 27epoch:train:201-240batch: iter_time=5.122e-05, forward_time=0.154, loss_ctc=21.336, loss=21.336, backward_time=0.024, grad_norm=206.476, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-14 23:07:38,735 (trainer:763) INFO: 27epoch:train:241-280batch: iter_time=5.542e-05, forward_time=0.151, loss_ctc=20.593, loss=20.593, backward_time=0.023, grad_norm=200.218, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-14 23:07:50,845 (trainer:763) INFO: 27epoch:train:281-320batch: iter_time=5.007e-05, forward_time=0.152, loss_ctc=20.914, loss=20.914, backward_time=0.024, grad_norm=201.895, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211 +[stan] 2024-01-14 23:08:02,804 (trainer:763) INFO: 27epoch:train:321-360batch: iter_time=5.470e-05, forward_time=0.150, loss_ctc=20.725, loss=20.725, backward_time=0.023, grad_norm=197.351, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.196 +[stan] 2024-01-14 23:08:15,057 (trainer:763) INFO: 27epoch:train:361-400batch: iter_time=5.412e-05, forward_time=0.153, loss_ctc=20.723, loss=20.723, backward_time=0.024, grad_norm=201.669, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-14 23:08:27,269 (trainer:763) INFO: 27epoch:train:401-440batch: iter_time=5.250e-05, forward_time=0.153, loss_ctc=20.914, loss=20.914, backward_time=0.024, grad_norm=200.402, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.221 +[stan] 2024-01-14 23:08:39,645 (trainer:763) INFO: 27epoch:train:441-480batch: iter_time=5.494e-05, forward_time=0.155, loss_ctc=21.285, loss=21.285, backward_time=0.024, grad_norm=196.934, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-14 23:08:51,383 (trainer:763) INFO: 27epoch:train:481-520batch: iter_time=5.096e-05, forward_time=0.147, loss_ctc=19.574, loss=19.574, backward_time=0.023, grad_norm=200.153, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.174 +[stan] 2024-01-14 23:09:03,679 (trainer:763) INFO: 27epoch:train:521-560batch: iter_time=5.366e-05, forward_time=0.154, loss_ctc=21.239, loss=21.239, backward_time=0.024, grad_norm=204.846, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.229 +[stan] 2024-01-14 23:09:15,505 (trainer:763) INFO: 27epoch:train:561-600batch: iter_time=5.463e-05, forward_time=0.148, loss_ctc=20.561, loss=20.561, backward_time=0.023, grad_norm=202.800, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.183 +[stan] 2024-01-14 23:09:27,997 (trainer:763) INFO: 27epoch:train:601-640batch: iter_time=5.315e-05, forward_time=0.156, loss_ctc=21.894, loss=21.894, backward_time=0.024, grad_norm=204.520, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.249 +[stan] 2024-01-14 23:09:40,077 (trainer:763) INFO: 27epoch:train:641-680batch: iter_time=5.346e-05, forward_time=0.151, loss_ctc=20.793, loss=20.793, backward_time=0.024, grad_norm=201.873, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-14 23:09:52,501 (trainer:763) INFO: 27epoch:train:681-720batch: iter_time=5.203e-05, forward_time=0.155, loss_ctc=21.494, loss=21.494, backward_time=0.024, grad_norm=208.142, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.242 +[stan] 2024-01-14 23:10:04,248 (trainer:763) INFO: 27epoch:train:721-760batch: iter_time=5.119e-05, forward_time=0.147, loss_ctc=19.850, loss=19.850, backward_time=0.023, grad_norm=189.509, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.175 +[stan] 2024-01-14 23:10:16,474 (trainer:763) INFO: 27epoch:train:761-800batch: iter_time=4.841e-05, forward_time=0.153, loss_ctc=20.868, loss=20.868, backward_time=0.024, grad_norm=194.770, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-14 23:10:21,217 (trainer:354) INFO: 27epoch results: [train] iter_time=2.579e-04, forward_time=0.152, loss_ctc=20.918, loss=20.918, backward_time=0.024, grad_norm=202.286, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216, time=4 minutes and 3.22 seconds, total_count=21600, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=288.362, cer_ctc=0.191, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=288.362, time=1.18 seconds, total_count=135, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.49 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 23:10:22,305 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:10:22,306 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/26epoch.pth +[stan] 2024-01-14 23:10:22,306 (trainer:288) INFO: 28/30epoch started. Estimated time to finish: 12 minutes and 27.61 seconds +[stan] 2024-01-14 23:10:34,778 (trainer:763) INFO: 28epoch:train:1-40batch: iter_time=0.004, forward_time=0.153, loss_ctc=20.331, loss=20.331, backward_time=0.024, grad_norm=204.258, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.247 +[stan] 2024-01-14 23:10:46,917 (trainer:763) INFO: 28epoch:train:41-80batch: iter_time=5.126e-05, forward_time=0.152, loss_ctc=20.367, loss=20.367, backward_time=0.023, grad_norm=202.330, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-14 23:10:59,029 (trainer:763) INFO: 28epoch:train:81-120batch: iter_time=5.201e-05, forward_time=0.152, loss_ctc=21.148, loss=21.148, backward_time=0.023, grad_norm=200.282, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211 +[stan] 2024-01-14 23:11:11,168 (trainer:763) INFO: 28epoch:train:121-160batch: iter_time=5.065e-05, forward_time=0.152, loss_ctc=20.345, loss=20.345, backward_time=0.024, grad_norm=195.281, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-14 23:11:23,325 (trainer:763) INFO: 28epoch:train:161-200batch: iter_time=5.171e-05, forward_time=0.152, loss_ctc=20.306, loss=20.306, backward_time=0.024, grad_norm=198.179, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-14 23:11:35,516 (trainer:763) INFO: 28epoch:train:201-240batch: iter_time=4.938e-05, forward_time=0.153, loss_ctc=20.091, loss=20.091, backward_time=0.024, grad_norm=201.197, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 23:11:47,735 (trainer:763) INFO: 28epoch:train:241-280batch: iter_time=5.036e-05, forward_time=0.153, loss_ctc=21.954, loss=21.954, backward_time=0.024, grad_norm=209.437, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-14 23:11:59,967 (trainer:763) INFO: 28epoch:train:281-320batch: iter_time=5.199e-05, forward_time=0.153, loss_ctc=21.239, loss=21.239, backward_time=0.024, grad_norm=200.526, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-14 23:12:12,011 (trainer:763) INFO: 28epoch:train:321-360batch: iter_time=5.227e-05, forward_time=0.151, loss_ctc=20.701, loss=20.701, backward_time=0.024, grad_norm=200.650, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-14 23:12:24,142 (trainer:763) INFO: 28epoch:train:361-400batch: iter_time=4.952e-05, forward_time=0.152, loss_ctc=21.215, loss=21.215, backward_time=0.024, grad_norm=199.335, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213 +[stan] 2024-01-14 23:12:36,184 (trainer:763) INFO: 28epoch:train:401-440batch: iter_time=5.265e-05, forward_time=0.151, loss_ctc=20.066, loss=20.066, backward_time=0.024, grad_norm=195.871, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-14 23:12:48,494 (trainer:763) INFO: 28epoch:train:441-480batch: iter_time=5.324e-05, forward_time=0.154, loss_ctc=20.857, loss=20.857, backward_time=0.024, grad_norm=202.999, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-14 23:13:00,689 (trainer:763) INFO: 28epoch:train:481-520batch: iter_time=5.275e-05, forward_time=0.153, loss_ctc=21.214, loss=21.214, backward_time=0.023, grad_norm=200.924, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 23:13:12,775 (trainer:763) INFO: 28epoch:train:521-560batch: iter_time=5.279e-05, forward_time=0.151, loss_ctc=20.185, loss=20.185, backward_time=0.023, grad_norm=199.264, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-14 23:13:24,973 (trainer:763) INFO: 28epoch:train:561-600batch: iter_time=5.329e-05, forward_time=0.153, loss_ctc=20.404, loss=20.404, backward_time=0.024, grad_norm=203.267, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-14 23:13:37,089 (trainer:763) INFO: 28epoch:train:601-640batch: iter_time=5.186e-05, forward_time=0.152, loss_ctc=20.538, loss=20.538, backward_time=0.024, grad_norm=208.770, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211 +[stan] 2024-01-14 23:13:49,547 (trainer:763) INFO: 28epoch:train:641-680batch: iter_time=5.284e-05, forward_time=0.156, loss_ctc=20.387, loss=20.387, backward_time=0.024, grad_norm=199.189, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.246 +[stan] 2024-01-14 23:14:01,440 (trainer:763) INFO: 28epoch:train:681-720batch: iter_time=5.140e-05, forward_time=0.149, loss_ctc=20.064, loss=20.064, backward_time=0.023, grad_norm=195.057, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.189 +[stan] 2024-01-14 23:14:13,765 (trainer:763) INFO: 28epoch:train:721-760batch: iter_time=4.905e-05, forward_time=0.154, loss_ctc=20.877, loss=20.877, backward_time=0.024, grad_norm=202.389, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-14 23:14:25,765 (trainer:763) INFO: 28epoch:train:761-800batch: iter_time=4.814e-05, forward_time=0.150, loss_ctc=20.435, loss=20.435, backward_time=0.023, grad_norm=199.669, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-14 23:14:30,482 (trainer:354) INFO: 28epoch results: [train] iter_time=2.259e-04, forward_time=0.152, loss_ctc=20.636, loss=20.636, backward_time=0.024, grad_norm=200.944, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217, time=4 minutes and 3.54 seconds, total_count=22400, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=291.551, cer_ctc=0.187, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=291.551, time=1.16 seconds, total_count=140, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.47 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 23:14:31,561 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:14:31,562 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/27epoch.pth +[stan] 2024-01-14 23:14:31,562 (trainer:288) INFO: 29/30epoch started. Estimated time to finish: 8 minutes and 18.41 seconds +[stan] 2024-01-14 23:14:43,960 (trainer:763) INFO: 29epoch:train:1-40batch: iter_time=0.004, forward_time=0.152, loss_ctc=20.520, loss=20.520, backward_time=0.024, grad_norm=195.688, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.239 +[stan] 2024-01-14 23:14:55,884 (trainer:763) INFO: 29epoch:train:41-80batch: iter_time=5.237e-05, forward_time=0.149, loss_ctc=20.022, loss=20.022, backward_time=0.023, grad_norm=199.595, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.192 +[stan] 2024-01-14 23:15:08,117 (trainer:763) INFO: 29epoch:train:81-120batch: iter_time=5.234e-05, forward_time=0.153, loss_ctc=20.705, loss=20.705, backward_time=0.024, grad_norm=190.616, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-14 23:15:20,425 (trainer:763) INFO: 29epoch:train:121-160batch: iter_time=5.192e-05, forward_time=0.154, loss_ctc=20.791, loss=20.791, backward_time=0.024, grad_norm=201.376, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-14 23:15:32,253 (trainer:763) INFO: 29epoch:train:161-200batch: iter_time=5.215e-05, forward_time=0.148, loss_ctc=19.337, loss=19.337, backward_time=0.023, grad_norm=199.825, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.183 +[stan] 2024-01-14 23:15:44,477 (trainer:763) INFO: 29epoch:train:201-240batch: iter_time=5.268e-05, forward_time=0.153, loss_ctc=20.729, loss=20.729, backward_time=0.024, grad_norm=207.189, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-14 23:15:56,927 (trainer:763) INFO: 29epoch:train:241-280batch: iter_time=5.371e-05, forward_time=0.156, loss_ctc=21.071, loss=21.071, backward_time=0.024, grad_norm=202.350, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.245 +[stan] 2024-01-14 23:16:09,325 (trainer:763) INFO: 29epoch:train:281-320batch: iter_time=5.084e-05, forward_time=0.155, loss_ctc=21.315, loss=21.315, backward_time=0.024, grad_norm=207.290, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.240 +[stan] 2024-01-14 23:16:21,255 (trainer:763) INFO: 29epoch:train:321-360batch: iter_time=5.293e-05, forward_time=0.150, loss_ctc=19.243, loss=19.243, backward_time=0.023, grad_norm=203.412, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.193 +[stan] 2024-01-14 23:16:33,426 (trainer:763) INFO: 29epoch:train:361-400batch: iter_time=5.323e-05, forward_time=0.152, loss_ctc=20.436, loss=20.436, backward_time=0.024, grad_norm=198.972, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217 +[stan] 2024-01-14 23:16:45,623 (trainer:763) INFO: 29epoch:train:401-440batch: iter_time=5.020e-05, forward_time=0.153, loss_ctc=20.230, loss=20.230, backward_time=0.023, grad_norm=191.257, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-14 23:16:57,749 (trainer:763) INFO: 29epoch:train:441-480batch: iter_time=5.208e-05, forward_time=0.152, loss_ctc=20.164, loss=20.164, backward_time=0.024, grad_norm=201.988, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213 +[stan] 2024-01-14 23:17:09,910 (trainer:763) INFO: 29epoch:train:481-520batch: iter_time=5.353e-05, forward_time=0.152, loss_ctc=19.041, loss=19.041, backward_time=0.023, grad_norm=191.919, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-14 23:17:22,274 (trainer:763) INFO: 29epoch:train:521-560batch: iter_time=5.099e-05, forward_time=0.155, loss_ctc=20.397, loss=20.397, backward_time=0.024, grad_norm=200.167, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.236 +[stan] 2024-01-14 23:17:33,964 (trainer:763) INFO: 29epoch:train:561-600batch: iter_time=5.200e-05, forward_time=0.147, loss_ctc=18.753, loss=18.753, backward_time=0.023, grad_norm=189.258, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.169 +[stan] 2024-01-14 23:17:46,405 (trainer:763) INFO: 29epoch:train:601-640batch: iter_time=5.080e-05, forward_time=0.156, loss_ctc=20.064, loss=20.064, backward_time=0.024, grad_norm=208.331, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.244 +[stan] 2024-01-14 23:17:58,460 (trainer:763) INFO: 29epoch:train:641-680batch: iter_time=5.115e-05, forward_time=0.151, loss_ctc=20.143, loss=20.143, backward_time=0.023, grad_norm=213.532, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-14 23:18:10,439 (trainer:763) INFO: 29epoch:train:681-720batch: iter_time=5.086e-05, forward_time=0.150, loss_ctc=19.437, loss=19.437, backward_time=0.024, grad_norm=198.977, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.198 +[stan] 2024-01-14 23:18:22,874 (trainer:763) INFO: 29epoch:train:721-760batch: iter_time=5.347e-05, forward_time=0.156, loss_ctc=20.358, loss=20.358, backward_time=0.024, grad_norm=205.434, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.243 +[stan] 2024-01-14 23:18:35,258 (trainer:763) INFO: 29epoch:train:761-800batch: iter_time=4.985e-05, forward_time=0.155, loss_ctc=20.237, loss=20.237, backward_time=0.024, grad_norm=205.137, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.238 +[stan] 2024-01-14 23:18:39,956 (trainer:354) INFO: 29epoch results: [train] iter_time=2.248e-04, forward_time=0.152, loss_ctc=20.149, loss=20.149, backward_time=0.024, grad_norm=200.616, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218, time=4 minutes and 3.77 seconds, total_count=23200, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=290.169, cer_ctc=0.187, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=290.169, time=1.16 seconds, total_count=145, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.46 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 23:18:40,909 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:18:40,909 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/28epoch.pth +[stan] 2024-01-14 23:18:40,909 (trainer:288) INFO: 30/30epoch started. Estimated time to finish: 4 minutes and 9.21 seconds +[stan] 2024-01-14 23:18:52,962 (trainer:763) INFO: 30epoch:train:1-40batch: iter_time=0.004, forward_time=0.147, loss_ctc=19.564, loss=19.564, backward_time=0.023, grad_norm=200.291, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-14 23:19:05,237 (trainer:763) INFO: 30epoch:train:41-80batch: iter_time=5.250e-05, forward_time=0.154, loss_ctc=20.619, loss=20.619, backward_time=0.024, grad_norm=206.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.227 +[stan] 2024-01-14 23:19:17,279 (trainer:763) INFO: 30epoch:train:81-120batch: iter_time=5.236e-05, forward_time=0.151, loss_ctc=19.444, loss=19.444, backward_time=0.023, grad_norm=193.958, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-14 23:19:29,390 (trainer:763) INFO: 30epoch:train:121-160batch: iter_time=5.066e-05, forward_time=0.152, loss_ctc=19.986, loss=19.986, backward_time=0.024, grad_norm=209.139, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211 +[stan] 2024-01-14 23:19:41,702 (trainer:763) INFO: 30epoch:train:161-200batch: iter_time=5.056e-05, forward_time=0.154, loss_ctc=19.847, loss=19.847, backward_time=0.024, grad_norm=201.696, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-14 23:19:53,890 (trainer:763) INFO: 30epoch:train:201-240batch: iter_time=5.118e-05, forward_time=0.153, loss_ctc=20.611, loss=20.611, backward_time=0.023, grad_norm=202.605, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-14 23:20:06,346 (trainer:763) INFO: 30epoch:train:241-280batch: iter_time=5.280e-05, forward_time=0.156, loss_ctc=20.149, loss=20.149, backward_time=0.024, grad_norm=207.425, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.245 +[stan] 2024-01-14 23:20:18,208 (trainer:763) INFO: 30epoch:train:281-320batch: iter_time=5.071e-05, forward_time=0.149, loss_ctc=19.776, loss=19.776, backward_time=0.023, grad_norm=194.875, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.186 +[stan] 2024-01-14 23:20:30,749 (trainer:763) INFO: 30epoch:train:321-360batch: iter_time=5.149e-05, forward_time=0.157, loss_ctc=20.831, loss=20.831, backward_time=0.024, grad_norm=204.959, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.254 +[stan] 2024-01-14 23:20:42,621 (trainer:763) INFO: 30epoch:train:361-400batch: iter_time=5.209e-05, forward_time=0.149, loss_ctc=19.101, loss=19.101, backward_time=0.023, grad_norm=203.635, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.187 +[stan] 2024-01-14 23:20:54,707 (trainer:763) INFO: 30epoch:train:401-440batch: iter_time=5.074e-05, forward_time=0.151, loss_ctc=19.252, loss=19.252, backward_time=0.023, grad_norm=200.210, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.209 +[stan] 2024-01-14 23:21:07,264 (trainer:763) INFO: 30epoch:train:441-480batch: iter_time=5.157e-05, forward_time=0.157, loss_ctc=20.652, loss=20.652, backward_time=0.024, grad_norm=199.910, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.256 +[stan] 2024-01-14 23:21:19,140 (trainer:763) INFO: 30epoch:train:481-520batch: iter_time=5.062e-05, forward_time=0.149, loss_ctc=19.558, loss=19.558, backward_time=0.023, grad_norm=201.224, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.188 +[stan] 2024-01-14 23:21:31,195 (trainer:763) INFO: 30epoch:train:521-560batch: iter_time=5.498e-05, forward_time=0.151, loss_ctc=19.987, loss=19.987, backward_time=0.024, grad_norm=206.109, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-14 23:21:43,220 (trainer:763) INFO: 30epoch:train:561-600batch: iter_time=5.179e-05, forward_time=0.151, loss_ctc=19.297, loss=19.297, backward_time=0.023, grad_norm=198.021, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.202 +[stan] 2024-01-14 23:21:55,590 (trainer:763) INFO: 30epoch:train:601-640batch: iter_time=4.978e-05, forward_time=0.155, loss_ctc=20.428, loss=20.428, backward_time=0.024, grad_norm=217.819, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-14 23:22:07,660 (trainer:763) INFO: 30epoch:train:641-680batch: iter_time=5.068e-05, forward_time=0.151, loss_ctc=19.526, loss=19.526, backward_time=0.023, grad_norm=206.353, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.207 +[stan] 2024-01-14 23:22:20,088 (trainer:763) INFO: 30epoch:train:681-720batch: iter_time=5.206e-05, forward_time=0.156, loss_ctc=20.113, loss=20.113, backward_time=0.024, grad_norm=208.912, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.243 +[stan] 2024-01-14 23:22:31,860 (trainer:763) INFO: 30epoch:train:721-760batch: iter_time=5.093e-05, forward_time=0.148, loss_ctc=19.404, loss=19.404, backward_time=0.023, grad_norm=206.404, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.177 +[stan] 2024-01-14 23:22:44,119 (trainer:763) INFO: 30epoch:train:761-800batch: iter_time=4.686e-05, forward_time=0.153, loss_ctc=20.863, loss=20.863, backward_time=0.024, grad_norm=206.604, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-14 23:22:48,863 (trainer:354) INFO: 30epoch results: [train] iter_time=2.377e-04, forward_time=0.152, loss_ctc=19.951, loss=19.951, backward_time=0.024, grad_norm=203.814, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216, time=4 minutes and 3.29 seconds, total_count=24000, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=292.484, cer_ctc=0.192, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=292.484, time=1.17 seconds, total_count=150, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.49 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-14 23:22:49,908 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 23:22:49,909 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/29epoch.pth +[stan] 2024-01-14 23:22:49,909 (trainer:489) INFO: The training was finished at 30 epochs +[stan] 2024-01-14 23:22:49,924 (average_nbest_models:69) INFO: Averaging 5best models: criterion="valid.loss": test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave_5best.pth +# Accounting: time=7481 threads=1 +# Ended (code 0) at Sun Jan 14 23:22:50 CST 2024, elapsed time 7481 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_eng1_1h/train.log b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/train.log new file mode 100644 index 0000000000000000000000000000000000000000..d18ae0850370658a635f2741c348c8d631779ac2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_eng1_1h/train.log @@ -0,0 +1,1406 @@ +# python3 -m espnet2.bin.asr_train --use_preprocessor true --bpemodel none --token_type char --token_list data/eng1_token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_eng1/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_eng1_1h/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_eng1/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_eng1_1h/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_1h_eng1/text,text,text --train_shape_file test_pr/asr_stats_eng1_1h/train/text_shape.char --valid_data_path_and_name_and_type dump/raw/dev_10min_eng1/text,text,text --valid_shape_file test_pr/asr_stats_eng1_1h/valid/text_shape.char --ngpu 1 --multiprocessing_distributed True +# Started at Tue Jan 16 23:11:13 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_train.py --use_preprocessor true --bpemodel none --token_type char --token_list data/eng1_token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_eng1/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_eng1_1h/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_eng1_1h --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_eng1/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_eng1_1h/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_1h_eng1/text,text,text --train_shape_file test_pr/asr_stats_eng1_1h/train/text_shape.char --valid_data_path_and_name_and_type dump/raw/dev_10min_eng1/text,text,text --valid_shape_file test_pr/asr_stats_eng1_1h/valid/text_shape.char --ngpu 1 --multiprocessing_distributed True +[stan] 2024-01-16 23:11:14,899 (asr:523) INFO: Vocabulary size: 30 +[stan] 2024-01-16 23:11:14,962 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +[stan] 2024-01-16 23:11:14,962 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +[stan] 2024-01-16 23:11:15,075 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +[stan] 2024-01-16 23:11:16,370 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +[stan] 2024-01-16 23:11:17,193 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.mask_emb.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.label_embs_concat.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.0.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.1.0.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.2.0.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.3.0.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.4.0.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.5.0.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.6.0.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_g.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_v.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,194 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,195 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.weight.requires_grad = False +[stan] 2024-01-16 23:11:17,196 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.bias.requires_grad = False +[stan] 2024-01-16 23:11:17,598 (abs_task:1275) INFO: pytorch.version=1.12.0+cu113, cuda.available=True, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True +[stan] 2024-01-16 23:11:17,600 (abs_task:1276) INFO: Model structure: +ESPnetASRModel( + (frontend): S3prlFrontend( + (upstream): S3PRLUpstream( + (upstream): UpstreamExpert( + (model): HubertModel( + (feature_extractor): ConvFeatureExtractionModel( + (conv_layers): ModuleList( + (0): Sequential( + (0): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): Fp32GroupNorm(512, 512, eps=1e-05, affine=True) + (3): GELU(approximate=none) + ) + (1): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (2): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (3): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (4): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (5): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (6): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + ) + ) + (post_extract_proj): Linear(in_features=512, out_features=768, bias=True) + (dropout_input): Dropout(p=0.1, inplace=False) + (dropout_features): Dropout(p=0.1, inplace=False) + (encoder): TransformerEncoder( + (pos_conv): Sequential( + (0): Conv1d(768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16) + (1): SamePad() + (2): GELU(approximate=none) + ) + (layers): ModuleList( + (0): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (1): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (2): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (3): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (4): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (5): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (6): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (7): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (8): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (9): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (10): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (11): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + ) + (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (final_proj): Linear(in_features=768, out_features=256, bias=True) + ) + ) + ) + (featurizer): Featurizer() + ) + (specaug): SpecAug( + (time_warp): TimeWarp(window=5, mode=bicubic) + (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq) + (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time) + ) + (normalize): UtteranceMVN(norm_means=True, norm_vars=False) + (preencoder): LinearProjection( + (linear_out): Linear(in_features=768, out_features=80, bias=True) + (dropout): Dropout(p=0.0, inplace=False) + ) + (encoder): TransformerEncoder( + (embed): Conv2dSubsampling2( + (conv): Sequential( + (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU() + (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) + (3): ReLU() + ) + (out): Sequential( + (0): Linear(in_features=9472, out_features=256, bias=True) + (1): PositionalEncoding( + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + ) + (encoders): MultiSequential( + (0): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (1): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + ) + (criterion_att): LabelSmoothingLoss( + (criterion): KLDivLoss() + ) + (ctc): CTC( + (ctc_lo): Linear(in_features=256, out_features=30, bias=True) + (ctc_loss): CTCLoss() + ) +) + +Model summary: + Class Name: ESPnetASRModel + Total Number of model parameters: 99.96 M + Number of trainable parameters: 5.27 M (5.3%) + Size: 21.07 MB + Type: torch.float32 +[stan] 2024-01-16 23:11:17,600 (abs_task:1279) INFO: Optimizer: +Adam ( +Parameter Group 0 + amsgrad: False + betas: (0.9, 0.999) + capturable: False + eps: 1e-08 + foreach: None + lr: 0.0001 + maximize: False + weight_decay: 1e-06 +) +[stan] 2024-01-16 23:11:17,600 (abs_task:1280) INFO: Scheduler: None +[stan] 2024-01-16 23:11:17,600 (abs_task:1289) INFO: Saving the configuration in test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/config.yaml +[stan] 2024-01-16 23:11:17,752 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-16 23:11:17,794 (abs_task:1665) INFO: [train] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/train_1h_eng1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/train_1h_eng1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-16 23:11:17,794 (abs_task:1666) INFO: [train] Batch sampler: SortedBatchSampler(N-batch=30, batch_size=8, shape_file=test_pr/asr_stats_eng1_1h/train/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-16 23:11:17,794 (abs_task:1667) INFO: [train] mini-batch sizes summary: N-batch=30, mean=8.1, min=8, max=9 +[stan] 2024-01-16 23:11:17,806 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-16 23:11:17,806 (abs_task:1665) INFO: [valid] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_eng1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_eng1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-16 23:11:17,806 (abs_task:1666) INFO: [valid] Batch sampler: SortedBatchSampler(N-batch=5, batch_size=8, shape_file=test_pr/asr_stats_eng1_1h/valid/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-16 23:11:17,806 (abs_task:1667) INFO: [valid] mini-batch sizes summary: N-batch=5, mean=8.0, min=8, max=8 +[stan] 2024-01-16 23:11:17,807 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-16 23:11:17,817 (abs_task:1665) INFO: [plot_att] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_eng1/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_eng1/text", "type": "text"} + preprocess: ) +[stan] 2024-01-16 23:11:17,817 (abs_task:1666) INFO: [plot_att] Batch sampler: UnsortedBatchSampler(N-batch=40, batch_size=1, key_file=test_pr/asr_stats_eng1_1h/valid/speech_shape, +[stan] 2024-01-16 23:11:17,817 (abs_task:1667) INFO: [plot_att] mini-batch sizes summary: N-batch=3, mean=1.0, min=1, max=1 +[stan] 2024-01-16 23:11:17,849 (trainer:303) INFO: 1/30epoch started +[stan] 2024-01-16 23:11:31,437 (trainer:762) INFO: 1epoch:train:1-40batch: iter_time=0.004, forward_time=0.182, loss_ctc=160.053, loss=160.053, backward_time=0.026, grad_norm=1.357e+03, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.355 +[stan] 2024-01-16 23:11:43,974 (trainer:762) INFO: 1epoch:train:41-80batch: iter_time=5.349e-05, forward_time=0.157, loss_ctc=155.215, loss=155.215, backward_time=0.024, grad_norm=364.376, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.255 +[stan] 2024-01-16 23:11:55,840 (trainer:762) INFO: 1epoch:train:81-120batch: iter_time=5.480e-05, forward_time=0.149, loss_ctc=146.954, loss=146.954, backward_time=0.023, grad_norm=457.367, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.186 +[stan] 2024-01-16 23:12:07,716 (trainer:762) INFO: 1epoch:train:121-160batch: iter_time=5.004e-05, forward_time=0.149, loss_ctc=145.869, loss=145.869, backward_time=0.023, grad_norm=162.709, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.188 +[stan] 2024-01-16 23:12:20,053 (trainer:762) INFO: 1epoch:train:161-200batch: iter_time=5.394e-05, forward_time=0.154, loss_ctc=150.669, loss=150.669, backward_time=0.023, grad_norm=252.834, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.234 +[stan] 2024-01-16 23:12:32,235 (trainer:762) INFO: 1epoch:train:201-240batch: iter_time=5.274e-05, forward_time=0.153, loss_ctc=149.222, loss=149.222, backward_time=0.024, grad_norm=320.973, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-16 23:12:44,657 (trainer:762) INFO: 1epoch:train:241-280batch: iter_time=5.072e-05, forward_time=0.155, loss_ctc=149.947, loss=149.947, backward_time=0.024, grad_norm=467.733, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.242 +[stan] 2024-01-16 23:12:56,601 (trainer:762) INFO: 1epoch:train:281-320batch: iter_time=5.070e-05, forward_time=0.150, loss_ctc=136.345, loss=136.345, backward_time=0.023, grad_norm=370.297, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.194 +[stan] 2024-01-16 23:13:08,749 (trainer:762) INFO: 1epoch:train:321-360batch: iter_time=5.497e-05, forward_time=0.152, loss_ctc=126.811, loss=126.811, backward_time=0.023, grad_norm=284.958, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-16 23:13:20,736 (trainer:762) INFO: 1epoch:train:361-400batch: iter_time=5.004e-05, forward_time=0.150, loss_ctc=113.128, loss=113.128, backward_time=0.023, grad_norm=291.044, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-16 23:13:33,137 (trainer:762) INFO: 1epoch:train:401-440batch: iter_time=5.137e-05, forward_time=0.155, loss_ctc=106.775, loss=106.775, backward_time=0.024, grad_norm=271.132, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.240 +[stan] 2024-01-16 23:13:45,307 (trainer:762) INFO: 1epoch:train:441-480batch: iter_time=5.398e-05, forward_time=0.152, loss_ctc=97.032, loss=97.032, backward_time=0.024, grad_norm=256.058, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217 +[stan] 2024-01-16 23:13:57,260 (trainer:762) INFO: 1epoch:train:481-520batch: iter_time=5.080e-05, forward_time=0.150, loss_ctc=90.705, loss=90.705, backward_time=0.023, grad_norm=265.208, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.195 +[stan] 2024-01-16 23:14:09,593 (trainer:762) INFO: 1epoch:train:521-560batch: iter_time=5.118e-05, forward_time=0.154, loss_ctc=91.711, loss=91.711, backward_time=0.024, grad_norm=228.055, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.233 +[stan] 2024-01-16 23:14:21,811 (trainer:762) INFO: 1epoch:train:561-600batch: iter_time=5.104e-05, forward_time=0.153, loss_ctc=86.203, loss=86.203, backward_time=0.024, grad_norm=205.740, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-16 23:14:33,820 (trainer:762) INFO: 1epoch:train:601-640batch: iter_time=5.289e-05, forward_time=0.150, loss_ctc=82.501, loss=82.501, backward_time=0.023, grad_norm=225.732, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-16 23:14:46,025 (trainer:762) INFO: 1epoch:train:641-680batch: iter_time=5.492e-05, forward_time=0.153, loss_ctc=81.634, loss=81.634, backward_time=0.024, grad_norm=209.237, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-16 23:14:58,262 (trainer:762) INFO: 1epoch:train:681-720batch: iter_time=4.983e-05, forward_time=0.153, loss_ctc=77.953, loss=77.953, backward_time=0.024, grad_norm=199.764, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.224 +[stan] 2024-01-16 23:15:10,583 (trainer:762) INFO: 1epoch:train:721-760batch: iter_time=5.099e-05, forward_time=0.154, loss_ctc=76.799, loss=76.799, backward_time=0.024, grad_norm=214.306, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-16 23:15:22,242 (trainer:762) INFO: 1epoch:train:761-800batch: iter_time=4.703e-05, forward_time=0.146, loss_ctc=72.852, loss=72.852, backward_time=0.023, grad_norm=211.453, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.166 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-16 23:15:27,317 (trainer:357) INFO: 1epoch results: [train] iter_time=2.276e-04, forward_time=0.154, loss_ctc=114.914, loss=114.914, backward_time=0.024, grad_norm=330.807, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222, time=4 minutes and 4.43 seconds, total_count=800, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=227.631, cer_ctc=0.293, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=227.631, time=1.14 seconds, total_count=5, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.88 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-16 23:15:28,368 (trainer:416) INFO: The best model has been updated: valid.loss +[stan] 2024-01-16 23:15:28,368 (trainer:291) INFO: 2/30epoch started. Estimated time to finish: 2 hours, 1 minute and 5.04 seconds +[stan] 2024-01-16 23:15:41,066 (trainer:762) INFO: 2epoch:train:1-40batch: iter_time=0.004, forward_time=0.155, loss_ctc=75.167, loss=75.167, backward_time=0.024, grad_norm=240.444, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 23:15:53,035 (trainer:762) INFO: 2epoch:train:41-80batch: iter_time=5.055e-05, forward_time=0.150, loss_ctc=71.253, loss=71.253, backward_time=0.023, grad_norm=220.677, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.197 +[stan] 2024-01-16 23:16:05,484 (trainer:762) INFO: 2epoch:train:81-120batch: iter_time=5.511e-05, forward_time=0.156, loss_ctc=71.696, loss=71.696, backward_time=0.024, grad_norm=223.233, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.245 +[stan] 2024-01-16 23:16:17,471 (trainer:762) INFO: 2epoch:train:121-160batch: iter_time=5.858e-05, forward_time=0.150, loss_ctc=68.844, loss=68.844, backward_time=0.024, grad_norm=228.028, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-16 23:16:29,601 (trainer:762) INFO: 2epoch:train:161-200batch: iter_time=5.131e-05, forward_time=0.152, loss_ctc=67.779, loss=67.779, backward_time=0.024, grad_norm=210.868, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213 +[stan] 2024-01-16 23:16:41,518 (trainer:762) INFO: 2epoch:train:201-240batch: iter_time=5.488e-05, forward_time=0.149, loss_ctc=65.525, loss=65.525, backward_time=0.023, grad_norm=224.260, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.192 +[stan] 2024-01-16 23:16:53,917 (trainer:762) INFO: 2epoch:train:241-280batch: iter_time=5.457e-05, forward_time=0.155, loss_ctc=67.360, loss=67.360, backward_time=0.024, grad_norm=222.702, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.240 +[stan] 2024-01-16 23:17:06,404 (trainer:762) INFO: 2epoch:train:281-320batch: iter_time=5.707e-05, forward_time=0.156, loss_ctc=65.283, loss=65.283, backward_time=0.024, grad_norm=297.676, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.249 +[stan] 2024-01-16 23:17:18,141 (trainer:762) INFO: 2epoch:train:321-360batch: iter_time=5.305e-05, forward_time=0.147, loss_ctc=61.624, loss=61.624, backward_time=0.023, grad_norm=218.047, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.174 +[stan] 2024-01-16 23:17:30,392 (trainer:762) INFO: 2epoch:train:361-400batch: iter_time=5.475e-05, forward_time=0.153, loss_ctc=63.506, loss=63.506, backward_time=0.024, grad_norm=275.214, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-16 23:17:42,419 (trainer:762) INFO: 2epoch:train:401-440batch: iter_time=5.235e-05, forward_time=0.151, loss_ctc=61.461, loss=61.461, backward_time=0.023, grad_norm=246.733, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-16 23:17:54,854 (trainer:762) INFO: 2epoch:train:441-480batch: iter_time=5.223e-05, forward_time=0.155, loss_ctc=63.528, loss=63.528, backward_time=0.024, grad_norm=220.057, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.243 +[stan] 2024-01-16 23:18:06,828 (trainer:762) INFO: 2epoch:train:481-520batch: iter_time=5.450e-05, forward_time=0.150, loss_ctc=58.959, loss=58.959, backward_time=0.024, grad_norm=237.360, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.197 +[stan] 2024-01-16 23:18:18,835 (trainer:762) INFO: 2epoch:train:521-560batch: iter_time=5.265e-05, forward_time=0.150, loss_ctc=58.017, loss=58.017, backward_time=0.023, grad_norm=227.155, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-16 23:18:31,148 (trainer:762) INFO: 2epoch:train:561-600batch: iter_time=5.287e-05, forward_time=0.154, loss_ctc=60.920, loss=60.920, backward_time=0.024, grad_norm=244.382, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-16 23:18:43,246 (trainer:762) INFO: 2epoch:train:601-640batch: iter_time=5.418e-05, forward_time=0.152, loss_ctc=57.628, loss=57.628, backward_time=0.024, grad_norm=247.913, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.210 +[stan] 2024-01-16 23:18:55,180 (trainer:762) INFO: 2epoch:train:641-680batch: iter_time=5.424e-05, forward_time=0.150, loss_ctc=56.349, loss=56.349, backward_time=0.023, grad_norm=244.013, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.193 +[stan] 2024-01-16 23:19:07,237 (trainer:762) INFO: 2epoch:train:681-720batch: iter_time=5.278e-05, forward_time=0.151, loss_ctc=56.555, loss=56.555, backward_time=0.024, grad_norm=266.584, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.206 +[stan] 2024-01-16 23:19:19,652 (trainer:762) INFO: 2epoch:train:721-760batch: iter_time=5.569e-05, forward_time=0.155, loss_ctc=57.225, loss=57.225, backward_time=0.024, grad_norm=262.093, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.241 +[stan] 2024-01-16 23:19:32,016 (trainer:762) INFO: 2epoch:train:761-800batch: iter_time=5.211e-05, forward_time=0.155, loss_ctc=57.224, loss=57.224, backward_time=0.024, grad_norm=263.880, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.236 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-16 23:19:36,935 (trainer:357) INFO: 2epoch results: [train] iter_time=2.470e-04, forward_time=0.152, loss_ctc=63.292, loss=63.292, backward_time=0.024, grad_norm=241.066, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218, time=4 minutes and 3.74 seconds, total_count=1600, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=185.347, cer_ctc=0.229, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=185.347, time=1.14 seconds, total_count=10, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.69 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-16 23:19:37,838 (trainer:416) INFO: The best model has been updated: valid.loss +[stan] 2024-01-16 23:19:37,838 (trainer:291) INFO: 3/30epoch started. Estimated time to finish: 1 hour, 56 minutes and 39.84 seconds +[stan] 2024-01-16 23:19:49,728 (trainer:762) INFO: 3epoch:train:1-40batch: iter_time=0.004, forward_time=0.146, loss_ctc=50.405, loss=50.405, backward_time=0.023, grad_norm=245.315, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.189 +[stan] 2024-01-16 23:20:02,147 (trainer:762) INFO: 3epoch:train:41-80batch: iter_time=5.370e-05, forward_time=0.155, loss_ctc=56.483, loss=56.483, backward_time=0.024, grad_norm=252.860, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.242 +[stan] 2024-01-16 23:20:14,379 (trainer:762) INFO: 3epoch:train:81-120batch: iter_time=5.025e-05, forward_time=0.153, loss_ctc=52.966, loss=52.966, backward_time=0.024, grad_norm=237.234, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-16 23:20:26,306 (trainer:762) INFO: 3epoch:train:121-160batch: iter_time=5.291e-05, forward_time=0.149, loss_ctc=52.740, loss=52.740, backward_time=0.023, grad_norm=217.711, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.193 +[stan] 2024-01-16 23:20:38,552 (trainer:762) INFO: 3epoch:train:161-200batch: iter_time=5.537e-05, forward_time=0.153, loss_ctc=53.970, loss=53.970, backward_time=0.024, grad_norm=212.261, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-16 23:20:50,745 (trainer:762) INFO: 3epoch:train:201-240batch: iter_time=5.388e-05, forward_time=0.153, loss_ctc=51.255, loss=51.255, backward_time=0.024, grad_norm=233.529, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-16 23:21:02,981 (trainer:762) INFO: 3epoch:train:241-280batch: iter_time=5.357e-05, forward_time=0.153, loss_ctc=52.469, loss=52.469, backward_time=0.024, grad_norm=241.304, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-16 23:21:14,981 (trainer:762) INFO: 3epoch:train:281-320batch: iter_time=5.503e-05, forward_time=0.150, loss_ctc=49.779, loss=49.779, backward_time=0.023, grad_norm=220.771, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-16 23:21:27,444 (trainer:762) INFO: 3epoch:train:321-360batch: iter_time=5.149e-05, forward_time=0.156, loss_ctc=51.956, loss=51.956, backward_time=0.024, grad_norm=228.332, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.246 +[stan] 2024-01-16 23:21:39,715 (trainer:762) INFO: 3epoch:train:361-400batch: iter_time=5.387e-05, forward_time=0.154, loss_ctc=50.316, loss=50.316, backward_time=0.023, grad_norm=247.252, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.227 +[stan] 2024-01-16 23:21:51,417 (trainer:762) INFO: 3epoch:train:401-440batch: iter_time=5.213e-05, forward_time=0.147, loss_ctc=46.959, loss=46.959, backward_time=0.023, grad_norm=228.513, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.170 +[stan] 2024-01-16 23:22:03,531 (trainer:762) INFO: 3epoch:train:441-480batch: iter_time=5.450e-05, forward_time=0.152, loss_ctc=48.830, loss=48.830, backward_time=0.023, grad_norm=240.734, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211 +[stan] 2024-01-16 23:22:16,029 (trainer:762) INFO: 3epoch:train:481-520batch: iter_time=5.237e-05, forward_time=0.156, loss_ctc=49.893, loss=49.893, backward_time=0.024, grad_norm=251.539, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.250 +[stan] 2024-01-16 23:22:27,839 (trainer:762) INFO: 3epoch:train:521-560batch: iter_time=5.105e-05, forward_time=0.148, loss_ctc=44.942, loss=44.942, backward_time=0.023, grad_norm=225.227, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.181 +[stan] 2024-01-16 23:22:39,811 (trainer:762) INFO: 3epoch:train:561-600batch: iter_time=5.177e-05, forward_time=0.150, loss_ctc=46.009, loss=46.009, backward_time=0.023, grad_norm=250.919, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.197 +[stan] 2024-01-16 23:22:52,137 (trainer:762) INFO: 3epoch:train:601-640batch: iter_time=5.055e-05, forward_time=0.154, loss_ctc=48.337, loss=48.337, backward_time=0.024, grad_norm=250.942, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-16 23:23:04,232 (trainer:762) INFO: 3epoch:train:641-680batch: iter_time=5.433e-05, forward_time=0.151, loss_ctc=46.107, loss=46.107, backward_time=0.024, grad_norm=255.006, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.209 +[stan] 2024-01-16 23:23:16,354 (trainer:762) INFO: 3epoch:train:681-720batch: iter_time=5.225e-05, forward_time=0.152, loss_ctc=45.688, loss=45.688, backward_time=0.023, grad_norm=252.478, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-16 23:23:28,726 (trainer:762) INFO: 3epoch:train:721-760batch: iter_time=5.328e-05, forward_time=0.155, loss_ctc=46.654, loss=46.654, backward_time=0.024, grad_norm=245.719, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-16 23:23:40,631 (trainer:762) INFO: 3epoch:train:761-800batch: iter_time=4.896e-05, forward_time=0.149, loss_ctc=44.168, loss=44.168, backward_time=0.023, grad_norm=259.703, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.190 +[stan] 2024-01-16 23:23:45,577 (trainer:357) INFO: 3epoch results: [train] iter_time=2.303e-04, forward_time=0.152, loss_ctc=49.496, loss=49.496, backward_time=0.024, grad_norm=239.867, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214, time=4 minutes and 2.87 seconds, total_count=2400, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=186.826, cer_ctc=0.213, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=186.826, time=1.17 seconds, total_count=15, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.69 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-16 23:23:46,580 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 23:23:46,580 (trainer:291) INFO: 4/30epoch started. Estimated time to finish: 1 hour, 52 minutes and 18.58 seconds +[stan] 2024-01-16 23:23:59,148 (trainer:762) INFO: 4epoch:train:1-40batch: iter_time=0.003, forward_time=0.154, loss_ctc=45.126, loss=45.126, backward_time=0.024, grad_norm=268.847, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.256 +[stan] 2024-01-16 23:24:11,185 (trainer:762) INFO: 4epoch:train:41-80batch: iter_time=5.295e-05, forward_time=0.151, loss_ctc=44.679, loss=44.679, backward_time=0.023, grad_norm=258.346, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-16 23:24:23,243 (trainer:762) INFO: 4epoch:train:81-120batch: iter_time=5.652e-05, forward_time=0.151, loss_ctc=43.957, loss=43.957, backward_time=0.024, grad_norm=269.713, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.206 +[stan] 2024-01-16 23:24:35,207 (trainer:762) INFO: 4epoch:train:121-160batch: iter_time=5.516e-05, forward_time=0.150, loss_ctc=42.831, loss=42.831, backward_time=0.023, grad_norm=330.737, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.196 +[stan] 2024-01-16 23:24:47,441 (trainer:762) INFO: 4epoch:train:161-200batch: iter_time=5.402e-05, forward_time=0.153, loss_ctc=44.173, loss=44.173, backward_time=0.024, grad_norm=292.845, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-16 23:24:59,671 (trainer:762) INFO: 4epoch:train:201-240batch: iter_time=5.579e-05, forward_time=0.153, loss_ctc=43.941, loss=43.941, backward_time=0.024, grad_norm=274.732, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-16 23:25:11,492 (trainer:762) INFO: 4epoch:train:241-280batch: iter_time=5.540e-05, forward_time=0.148, loss_ctc=40.927, loss=40.927, backward_time=0.023, grad_norm=247.316, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.182 +[stan] 2024-01-16 23:25:23,725 (trainer:762) INFO: 4epoch:train:281-320batch: iter_time=5.482e-05, forward_time=0.153, loss_ctc=44.109, loss=44.109, backward_time=0.024, grad_norm=248.849, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-16 23:25:36,104 (trainer:762) INFO: 4epoch:train:321-360batch: iter_time=5.401e-05, forward_time=0.155, loss_ctc=42.794, loss=42.794, backward_time=0.024, grad_norm=289.911, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.238 +[stan] 2024-01-16 23:25:48,222 (trainer:762) INFO: 4epoch:train:361-400batch: iter_time=5.548e-05, forward_time=0.152, loss_ctc=41.672, loss=41.672, backward_time=0.023, grad_norm=317.876, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-16 23:26:00,378 (trainer:762) INFO: 4epoch:train:401-440batch: iter_time=5.522e-05, forward_time=0.152, loss_ctc=42.316, loss=42.316, backward_time=0.023, grad_norm=280.708, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-16 23:26:12,530 (trainer:762) INFO: 4epoch:train:441-480batch: iter_time=5.323e-05, forward_time=0.152, loss_ctc=41.363, loss=41.363, backward_time=0.024, grad_norm=252.047, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-16 23:26:24,673 (trainer:762) INFO: 4epoch:train:481-520batch: iter_time=5.501e-05, forward_time=0.152, loss_ctc=41.421, loss=41.421, backward_time=0.024, grad_norm=233.295, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-16 23:26:36,577 (trainer:762) INFO: 4epoch:train:521-560batch: iter_time=5.449e-05, forward_time=0.149, loss_ctc=38.968, loss=38.968, backward_time=0.023, grad_norm=239.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.190 +[stan] 2024-01-16 23:26:48,948 (trainer:762) INFO: 4epoch:train:561-600batch: iter_time=5.476e-05, forward_time=0.155, loss_ctc=40.861, loss=40.861, backward_time=0.024, grad_norm=269.122, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-16 23:27:00,699 (trainer:762) INFO: 4epoch:train:601-640batch: iter_time=5.350e-05, forward_time=0.147, loss_ctc=39.258, loss=39.258, backward_time=0.023, grad_norm=257.009, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.175 +[stan] 2024-01-16 23:27:13,037 (trainer:762) INFO: 4epoch:train:641-680batch: iter_time=5.302e-05, forward_time=0.154, loss_ctc=40.958, loss=40.958, backward_time=0.024, grad_norm=265.185, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.234 +[stan] 2024-01-16 23:27:25,351 (trainer:762) INFO: 4epoch:train:681-720batch: iter_time=5.194e-05, forward_time=0.154, loss_ctc=39.451, loss=39.451, backward_time=0.024, grad_norm=249.607, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-16 23:27:37,632 (trainer:762) INFO: 4epoch:train:721-760batch: iter_time=5.181e-05, forward_time=0.154, loss_ctc=40.102, loss=40.102, backward_time=0.024, grad_norm=278.296, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.228 +[stan] 2024-01-16 23:27:49,828 (trainer:762) INFO: 4epoch:train:761-800batch: iter_time=4.903e-05, forward_time=0.153, loss_ctc=39.351, loss=39.351, backward_time=0.024, grad_norm=261.659, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-16 23:27:54,692 (trainer:357) INFO: 4epoch results: [train] iter_time=2.207e-04, forward_time=0.152, loss_ctc=41.912, loss=41.912, backward_time=0.024, grad_norm=269.260, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216, time=4 minutes and 3.32 seconds, total_count=3200, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=200.666, cer_ctc=0.209, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=200.666, time=1.15 seconds, total_count=20, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.64 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-16 23:27:55,605 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 23:27:55,606 (trainer:291) INFO: 5/30epoch started. Estimated time to finish: 1 hour, 48 minutes and 5.42 seconds +[stan] 2024-01-16 23:28:07,843 (trainer:762) INFO: 5epoch:train:1-40batch: iter_time=0.004, forward_time=0.149, loss_ctc=37.365, loss=37.365, backward_time=0.023, grad_norm=239.268, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-16 23:28:20,109 (trainer:762) INFO: 5epoch:train:41-80batch: iter_time=5.150e-05, forward_time=0.153, loss_ctc=39.397, loss=39.397, backward_time=0.024, grad_norm=252.976, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-16 23:28:32,071 (trainer:762) INFO: 5epoch:train:81-120batch: iter_time=5.268e-05, forward_time=0.150, loss_ctc=37.684, loss=37.684, backward_time=0.023, grad_norm=235.001, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.196 +[stan] 2024-01-16 23:28:44,213 (trainer:762) INFO: 5epoch:train:121-160batch: iter_time=5.354e-05, forward_time=0.152, loss_ctc=39.124, loss=39.124, backward_time=0.024, grad_norm=253.925, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-16 23:28:56,318 (trainer:762) INFO: 5epoch:train:161-200batch: iter_time=5.239e-05, forward_time=0.152, loss_ctc=37.444, loss=37.444, backward_time=0.024, grad_norm=260.896, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.210 +[stan] 2024-01-16 23:29:08,291 (trainer:762) INFO: 5epoch:train:201-240batch: iter_time=5.347e-05, forward_time=0.150, loss_ctc=37.358, loss=37.358, backward_time=0.023, grad_norm=246.779, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.197 +[stan] 2024-01-16 23:29:20,632 (trainer:762) INFO: 5epoch:train:241-280batch: iter_time=5.334e-05, forward_time=0.154, loss_ctc=37.906, loss=37.906, backward_time=0.024, grad_norm=274.737, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.234 +[stan] 2024-01-16 23:29:32,968 (trainer:762) INFO: 5epoch:train:281-320batch: iter_time=5.298e-05, forward_time=0.154, loss_ctc=37.096, loss=37.096, backward_time=0.024, grad_norm=262.490, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.233 +[stan] 2024-01-16 23:29:44,744 (trainer:762) INFO: 5epoch:train:321-360batch: iter_time=5.325e-05, forward_time=0.148, loss_ctc=37.728, loss=37.728, backward_time=0.023, grad_norm=260.955, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.178 +[stan] 2024-01-16 23:29:57,053 (trainer:762) INFO: 5epoch:train:361-400batch: iter_time=5.462e-05, forward_time=0.154, loss_ctc=37.206, loss=37.206, backward_time=0.023, grad_norm=265.933, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-16 23:30:08,922 (trainer:762) INFO: 5epoch:train:401-440batch: iter_time=5.566e-05, forward_time=0.149, loss_ctc=35.995, loss=35.995, backward_time=0.024, grad_norm=258.859, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.187 +[stan] 2024-01-16 23:30:21,615 (trainer:762) INFO: 5epoch:train:441-480batch: iter_time=5.109e-05, forward_time=0.159, loss_ctc=39.045, loss=39.045, backward_time=0.024, grad_norm=263.767, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.269 +[stan] 2024-01-16 23:30:33,454 (trainer:762) INFO: 5epoch:train:481-520batch: iter_time=5.120e-05, forward_time=0.148, loss_ctc=35.623, loss=35.623, backward_time=0.023, grad_norm=241.806, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.184 +[stan] 2024-01-16 23:30:45,572 (trainer:762) INFO: 5epoch:train:521-560batch: iter_time=5.128e-05, forward_time=0.152, loss_ctc=35.512, loss=35.512, backward_time=0.024, grad_norm=251.677, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-16 23:30:57,376 (trainer:762) INFO: 5epoch:train:561-600batch: iter_time=5.086e-05, forward_time=0.148, loss_ctc=34.342, loss=34.342, backward_time=0.023, grad_norm=257.193, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.180 +[stan] 2024-01-16 23:31:09,862 (trainer:762) INFO: 5epoch:train:601-640batch: iter_time=5.294e-05, forward_time=0.156, loss_ctc=37.351, loss=37.351, backward_time=0.024, grad_norm=256.019, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.249 +[stan] 2024-01-16 23:31:22,024 (trainer:762) INFO: 5epoch:train:641-680batch: iter_time=5.140e-05, forward_time=0.152, loss_ctc=36.304, loss=36.304, backward_time=0.024, grad_norm=246.705, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-16 23:31:34,270 (trainer:762) INFO: 5epoch:train:681-720batch: iter_time=5.416e-05, forward_time=0.153, loss_ctc=35.910, loss=35.910, backward_time=0.023, grad_norm=257.634, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.224 +[stan] 2024-01-16 23:31:46,258 (trainer:762) INFO: 5epoch:train:721-760batch: iter_time=5.392e-05, forward_time=0.150, loss_ctc=35.295, loss=35.295, backward_time=0.024, grad_norm=250.899, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-16 23:31:58,497 (trainer:762) INFO: 5epoch:train:761-800batch: iter_time=5.052e-05, forward_time=0.153, loss_ctc=36.215, loss=36.215, backward_time=0.024, grad_norm=274.989, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.224 +[stan] 2024-01-16 23:32:03,368 (trainer:357) INFO: 5epoch results: [train] iter_time=2.714e-04, forward_time=0.152, loss_ctc=36.995, loss=36.995, backward_time=0.024, grad_norm=255.625, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214, time=4 minutes and 2.97 seconds, total_count=4000, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=220.296, cer_ctc=0.211, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=220.296, time=1.15 seconds, total_count=25, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.64 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-16 23:32:04,320 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 23:32:04,321 (trainer:291) INFO: 6/30epoch started. Estimated time to finish: 1 hour, 43 minutes and 52.36 seconds +[stan] 2024-01-16 23:32:16,829 (trainer:762) INFO: 6epoch:train:1-40batch: iter_time=0.004, forward_time=0.153, loss_ctc=35.120, loss=35.120, backward_time=0.023, grad_norm=284.642, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.250 +[stan] 2024-01-16 23:32:28,761 (trainer:762) INFO: 6epoch:train:41-80batch: iter_time=5.175e-05, forward_time=0.150, loss_ctc=35.089, loss=35.089, backward_time=0.023, grad_norm=265.393, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.193 +[stan] 2024-01-16 23:32:41,072 (trainer:762) INFO: 6epoch:train:81-120batch: iter_time=5.288e-05, forward_time=0.154, loss_ctc=35.142, loss=35.142, backward_time=0.024, grad_norm=243.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-16 23:32:52,961 (trainer:762) INFO: 6epoch:train:121-160batch: iter_time=5.297e-05, forward_time=0.149, loss_ctc=33.410, loss=33.410, backward_time=0.023, grad_norm=250.325, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.189 +[stan] 2024-01-16 23:33:05,175 (trainer:762) INFO: 6epoch:train:161-200batch: iter_time=5.484e-05, forward_time=0.153, loss_ctc=34.972, loss=34.972, backward_time=0.024, grad_norm=258.967, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.221 +[stan] 2024-01-16 23:33:17,044 (trainer:762) INFO: 6epoch:train:201-240batch: iter_time=5.226e-05, forward_time=0.149, loss_ctc=33.648, loss=33.648, backward_time=0.023, grad_norm=232.836, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.187 +[stan] 2024-01-16 23:33:29,634 (trainer:762) INFO: 6epoch:train:241-280batch: iter_time=5.316e-05, forward_time=0.157, loss_ctc=36.223, loss=36.223, backward_time=0.024, grad_norm=240.962, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.259 +[stan] 2024-01-16 23:33:41,617 (trainer:762) INFO: 6epoch:train:281-320batch: iter_time=5.180e-05, forward_time=0.150, loss_ctc=33.770, loss=33.770, backward_time=0.023, grad_norm=264.071, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.198 +[stan] 2024-01-16 23:33:54,153 (trainer:762) INFO: 6epoch:train:321-360batch: iter_time=5.189e-05, forward_time=0.157, loss_ctc=35.148, loss=35.148, backward_time=0.024, grad_norm=249.936, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.254 +[stan] 2024-01-16 23:34:05,606 (trainer:762) INFO: 6epoch:train:361-400batch: iter_time=5.490e-05, forward_time=0.144, loss_ctc=31.885, loss=31.885, backward_time=0.023, grad_norm=224.976, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.145 +[stan] 2024-01-16 23:34:18,051 (trainer:762) INFO: 6epoch:train:401-440batch: iter_time=5.272e-05, forward_time=0.156, loss_ctc=35.019, loss=35.019, backward_time=0.024, grad_norm=238.662, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.244 +[stan] 2024-01-16 23:34:30,194 (trainer:762) INFO: 6epoch:train:441-480batch: iter_time=5.116e-05, forward_time=0.152, loss_ctc=33.565, loss=33.565, backward_time=0.024, grad_norm=260.653, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-16 23:34:42,507 (trainer:762) INFO: 6epoch:train:481-520batch: iter_time=5.160e-05, forward_time=0.154, loss_ctc=34.892, loss=34.892, backward_time=0.024, grad_norm=255.887, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-16 23:34:54,454 (trainer:762) INFO: 6epoch:train:521-560batch: iter_time=5.372e-05, forward_time=0.150, loss_ctc=32.482, loss=32.482, backward_time=0.023, grad_norm=260.097, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.195 +[stan] 2024-01-16 23:35:06,489 (trainer:762) INFO: 6epoch:train:561-600batch: iter_time=5.142e-05, forward_time=0.151, loss_ctc=32.134, loss=32.134, backward_time=0.023, grad_norm=239.061, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-16 23:35:18,818 (trainer:762) INFO: 6epoch:train:601-640batch: iter_time=5.362e-05, forward_time=0.154, loss_ctc=33.904, loss=33.904, backward_time=0.023, grad_norm=235.351, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.233 +[stan] 2024-01-16 23:35:30,842 (trainer:762) INFO: 6epoch:train:641-680batch: iter_time=5.333e-05, forward_time=0.151, loss_ctc=31.749, loss=31.749, backward_time=0.023, grad_norm=239.821, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.202 +[stan] 2024-01-16 23:35:42,766 (trainer:762) INFO: 6epoch:train:681-720batch: iter_time=5.316e-05, forward_time=0.149, loss_ctc=32.767, loss=32.767, backward_time=0.023, grad_norm=244.106, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.192 +[stan] 2024-01-16 23:35:55,363 (trainer:762) INFO: 6epoch:train:721-760batch: iter_time=5.151e-05, forward_time=0.157, loss_ctc=34.674, loss=34.674, backward_time=0.024, grad_norm=241.798, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.260 +[stan] 2024-01-16 23:36:07,219 (trainer:762) INFO: 6epoch:train:761-800batch: iter_time=4.809e-05, forward_time=0.149, loss_ctc=31.890, loss=31.890, backward_time=0.023, grad_norm=233.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.186 +[stan] 2024-01-16 23:36:12,117 (trainer:357) INFO: 6epoch results: [train] iter_time=2.511e-04, forward_time=0.152, loss_ctc=33.872, loss=33.872, backward_time=0.024, grad_norm=248.196, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214, time=4 minutes and 2.97 seconds, total_count=4800, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=231.590, cer_ctc=0.210, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=231.590, time=1.16 seconds, total_count=30, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.66 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-16 23:36:13,154 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 23:36:13,154 (trainer:291) INFO: 7/30epoch started. Estimated time to finish: 1 hour, 39 minutes and 41.22 seconds +[stan] 2024-01-16 23:36:25,512 (trainer:762) INFO: 7epoch:train:1-40batch: iter_time=0.003, forward_time=0.152, loss_ctc=32.748, loss=32.748, backward_time=0.024, grad_norm=265.588, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.235 +[stan] 2024-01-16 23:36:38,121 (trainer:762) INFO: 7epoch:train:41-80batch: iter_time=5.288e-05, forward_time=0.157, loss_ctc=33.880, loss=33.880, backward_time=0.024, grad_norm=269.349, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.261 +[stan] 2024-01-16 23:36:49,799 (trainer:762) INFO: 7epoch:train:81-120batch: iter_time=5.322e-05, forward_time=0.147, loss_ctc=31.406, loss=31.406, backward_time=0.023, grad_norm=245.302, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.168 +[stan] 2024-01-16 23:37:01,782 (trainer:762) INFO: 7epoch:train:121-160batch: iter_time=5.362e-05, forward_time=0.150, loss_ctc=32.956, loss=32.956, backward_time=0.023, grad_norm=289.652, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.198 +[stan] 2024-01-16 23:37:13,979 (trainer:762) INFO: 7epoch:train:161-200batch: iter_time=5.348e-05, forward_time=0.153, loss_ctc=32.759, loss=32.759, backward_time=0.024, grad_norm=249.827, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-16 23:37:26,246 (trainer:762) INFO: 7epoch:train:201-240batch: iter_time=5.276e-05, forward_time=0.153, loss_ctc=32.665, loss=32.665, backward_time=0.024, grad_norm=239.552, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.227 +[stan] 2024-01-16 23:37:38,615 (trainer:762) INFO: 7epoch:train:241-280batch: iter_time=5.283e-05, forward_time=0.155, loss_ctc=32.802, loss=32.802, backward_time=0.024, grad_norm=225.751, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-16 23:37:50,567 (trainer:762) INFO: 7epoch:train:281-320batch: iter_time=5.206e-05, forward_time=0.150, loss_ctc=31.878, loss=31.878, backward_time=0.023, grad_norm=226.658, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.195 +[stan] 2024-01-16 23:38:02,705 (trainer:762) INFO: 7epoch:train:321-360batch: iter_time=5.330e-05, forward_time=0.152, loss_ctc=32.258, loss=32.258, backward_time=0.024, grad_norm=237.146, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-16 23:38:14,788 (trainer:762) INFO: 7epoch:train:361-400batch: iter_time=5.490e-05, forward_time=0.151, loss_ctc=31.741, loss=31.741, backward_time=0.024, grad_norm=225.596, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-16 23:38:27,043 (trainer:762) INFO: 7epoch:train:401-440batch: iter_time=5.484e-05, forward_time=0.153, loss_ctc=32.169, loss=32.169, backward_time=0.024, grad_norm=262.982, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-16 23:38:39,147 (trainer:762) INFO: 7epoch:train:441-480batch: iter_time=5.390e-05, forward_time=0.152, loss_ctc=32.260, loss=32.260, backward_time=0.024, grad_norm=254.342, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.210 +[stan] 2024-01-16 23:38:51,495 (trainer:762) INFO: 7epoch:train:481-520batch: iter_time=5.056e-05, forward_time=0.154, loss_ctc=32.057, loss=32.057, backward_time=0.024, grad_norm=226.786, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.235 +[stan] 2024-01-16 23:39:03,337 (trainer:762) INFO: 7epoch:train:521-560batch: iter_time=5.250e-05, forward_time=0.148, loss_ctc=30.737, loss=30.737, backward_time=0.023, grad_norm=231.221, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.184 +[stan] 2024-01-16 23:39:15,556 (trainer:762) INFO: 7epoch:train:561-600batch: iter_time=5.399e-05, forward_time=0.153, loss_ctc=32.323, loss=32.323, backward_time=0.024, grad_norm=235.214, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-16 23:39:27,839 (trainer:762) INFO: 7epoch:train:601-640batch: iter_time=5.375e-05, forward_time=0.154, loss_ctc=31.897, loss=31.897, backward_time=0.024, grad_norm=239.568, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.228 +[stan] 2024-01-16 23:39:40,001 (trainer:762) INFO: 7epoch:train:641-680batch: iter_time=5.424e-05, forward_time=0.152, loss_ctc=31.641, loss=31.641, backward_time=0.023, grad_norm=239.772, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-16 23:39:51,951 (trainer:762) INFO: 7epoch:train:681-720batch: iter_time=5.300e-05, forward_time=0.150, loss_ctc=31.009, loss=31.009, backward_time=0.023, grad_norm=235.228, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.195 +[stan] 2024-01-16 23:40:03,959 (trainer:762) INFO: 7epoch:train:721-760batch: iter_time=5.052e-05, forward_time=0.150, loss_ctc=31.037, loss=31.037, backward_time=0.023, grad_norm=220.636, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-16 23:40:16,293 (trainer:762) INFO: 7epoch:train:761-800batch: iter_time=4.936e-05, forward_time=0.154, loss_ctc=32.210, loss=32.210, backward_time=0.024, grad_norm=235.767, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.233 +[stan] 2024-01-16 23:40:21,112 (trainer:357) INFO: 7epoch results: [train] iter_time=1.978e-04, forward_time=0.152, loss_ctc=32.121, loss=32.121, backward_time=0.024, grad_norm=242.797, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216, time=4 minutes and 3.22 seconds, total_count=5600, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=241.626, cer_ctc=0.201, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=241.626, time=1.14 seconds, total_count=35, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.6 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-16 23:40:22,174 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 23:40:22,176 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/6epoch.pth +[stan] 2024-01-16 23:40:22,176 (trainer:291) INFO: 8/30epoch started. Estimated time to finish: 1 hour, 35 minutes and 31.36 seconds +[stan] 2024-01-16 23:40:34,485 (trainer:762) INFO: 8epoch:train:1-40batch: iter_time=0.003, forward_time=0.151, loss_ctc=31.276, loss=31.276, backward_time=0.023, grad_norm=223.016, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.230 +[stan] 2024-01-16 23:40:46,404 (trainer:762) INFO: 8epoch:train:41-80batch: iter_time=5.227e-05, forward_time=0.149, loss_ctc=31.174, loss=31.174, backward_time=0.023, grad_norm=216.317, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.192 +[stan] 2024-01-16 23:40:58,622 (trainer:762) INFO: 8epoch:train:81-120batch: iter_time=4.954e-05, forward_time=0.153, loss_ctc=32.383, loss=32.383, backward_time=0.024, grad_norm=248.653, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-16 23:41:10,867 (trainer:762) INFO: 8epoch:train:121-160batch: iter_time=5.384e-05, forward_time=0.153, loss_ctc=31.242, loss=31.242, backward_time=0.024, grad_norm=219.367, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.224 +[stan] 2024-01-16 23:41:22,946 (trainer:762) INFO: 8epoch:train:161-200batch: iter_time=5.152e-05, forward_time=0.151, loss_ctc=31.877, loss=31.877, backward_time=0.024, grad_norm=241.485, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-16 23:41:35,033 (trainer:762) INFO: 8epoch:train:201-240batch: iter_time=5.188e-05, forward_time=0.151, loss_ctc=30.554, loss=30.554, backward_time=0.023, grad_norm=233.589, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.209 +[stan] 2024-01-16 23:41:47,289 (trainer:762) INFO: 8epoch:train:241-280batch: iter_time=5.227e-05, forward_time=0.153, loss_ctc=31.510, loss=31.510, backward_time=0.024, grad_norm=273.727, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-16 23:41:59,544 (trainer:762) INFO: 8epoch:train:281-320batch: iter_time=5.353e-05, forward_time=0.153, loss_ctc=31.420, loss=31.420, backward_time=0.024, grad_norm=294.807, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-16 23:42:11,775 (trainer:762) INFO: 8epoch:train:321-360batch: iter_time=5.300e-05, forward_time=0.153, loss_ctc=30.853, loss=30.853, backward_time=0.024, grad_norm=254.779, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-16 23:42:23,724 (trainer:762) INFO: 8epoch:train:361-400batch: iter_time=5.448e-05, forward_time=0.150, loss_ctc=30.210, loss=30.210, backward_time=0.023, grad_norm=229.579, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.195 +[stan] 2024-01-16 23:42:35,847 (trainer:762) INFO: 8epoch:train:401-440batch: iter_time=5.112e-05, forward_time=0.152, loss_ctc=31.010, loss=31.010, backward_time=0.024, grad_norm=248.536, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-16 23:42:47,932 (trainer:762) INFO: 8epoch:train:441-480batch: iter_time=5.056e-05, forward_time=0.151, loss_ctc=30.918, loss=30.918, backward_time=0.023, grad_norm=220.186, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-16 23:43:00,137 (trainer:762) INFO: 8epoch:train:481-520batch: iter_time=5.056e-05, forward_time=0.153, loss_ctc=31.215, loss=31.215, backward_time=0.023, grad_norm=241.420, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-16 23:43:12,323 (trainer:762) INFO: 8epoch:train:521-560batch: iter_time=5.176e-05, forward_time=0.153, loss_ctc=30.161, loss=30.161, backward_time=0.024, grad_norm=212.349, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-16 23:43:24,321 (trainer:762) INFO: 8epoch:train:561-600batch: iter_time=5.134e-05, forward_time=0.150, loss_ctc=30.021, loss=30.021, backward_time=0.024, grad_norm=251.032, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-16 23:43:36,556 (trainer:762) INFO: 8epoch:train:601-640batch: iter_time=5.260e-05, forward_time=0.153, loss_ctc=31.457, loss=31.457, backward_time=0.024, grad_norm=256.722, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-16 23:43:48,316 (trainer:762) INFO: 8epoch:train:641-680batch: iter_time=5.036e-05, forward_time=0.148, loss_ctc=29.739, loss=29.739, backward_time=0.023, grad_norm=224.973, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.176 +[stan] 2024-01-16 23:44:00,679 (trainer:762) INFO: 8epoch:train:681-720batch: iter_time=5.088e-05, forward_time=0.155, loss_ctc=31.427, loss=31.427, backward_time=0.024, grad_norm=255.670, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.236 +[stan] 2024-01-16 23:44:12,958 (trainer:762) INFO: 8epoch:train:721-760batch: iter_time=5.183e-05, forward_time=0.154, loss_ctc=30.682, loss=30.682, backward_time=0.024, grad_norm=227.125, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.228 +[stan] 2024-01-16 23:44:24,980 (trainer:762) INFO: 8epoch:train:761-800batch: iter_time=4.980e-05, forward_time=0.151, loss_ctc=29.526, loss=29.526, backward_time=0.024, grad_norm=236.755, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.202 +[stan] 2024-01-16 23:44:29,808 (trainer:357) INFO: 8epoch results: [train] iter_time=2.229e-04, forward_time=0.152, loss_ctc=30.932, loss=30.932, backward_time=0.024, grad_norm=240.504, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214, time=4 minutes and 2.88 seconds, total_count=6400, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=249.058, cer_ctc=0.203, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=249.058, time=1.15 seconds, total_count=40, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.6 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-16 23:44:30,791 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 23:44:30,792 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/7epoch.pth +[stan] 2024-01-16 23:44:30,792 (trainer:291) INFO: 9/30epoch started. Estimated time to finish: 1 hour, 31 minutes and 20.59 seconds +[stan] 2024-01-16 23:44:43,457 (trainer:762) INFO: 9epoch:train:1-40batch: iter_time=0.004, forward_time=0.155, loss_ctc=31.314, loss=31.314, backward_time=0.024, grad_norm=217.523, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.266 +[stan] 2024-01-16 23:44:55,407 (trainer:762) INFO: 9epoch:train:41-80batch: iter_time=4.999e-05, forward_time=0.150, loss_ctc=29.846, loss=29.846, backward_time=0.023, grad_norm=228.500, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.195 +[stan] 2024-01-16 23:45:07,702 (trainer:762) INFO: 9epoch:train:81-120batch: iter_time=5.231e-05, forward_time=0.154, loss_ctc=31.746, loss=31.746, backward_time=0.024, grad_norm=224.955, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.229 +[stan] 2024-01-16 23:45:19,453 (trainer:762) INFO: 9epoch:train:121-160batch: iter_time=5.241e-05, forward_time=0.147, loss_ctc=29.408, loss=29.408, backward_time=0.023, grad_norm=227.165, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.175 +[stan] 2024-01-16 23:45:31,812 (trainer:762) INFO: 9epoch:train:161-200batch: iter_time=5.351e-05, forward_time=0.155, loss_ctc=30.999, loss=30.999, backward_time=0.024, grad_norm=216.583, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.236 +[stan] 2024-01-16 23:45:43,941 (trainer:762) INFO: 9epoch:train:201-240batch: iter_time=5.162e-05, forward_time=0.152, loss_ctc=30.600, loss=30.600, backward_time=0.024, grad_norm=227.401, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213 +[stan] 2024-01-16 23:45:55,995 (trainer:762) INFO: 9epoch:train:241-280batch: iter_time=5.284e-05, forward_time=0.151, loss_ctc=29.928, loss=29.928, backward_time=0.023, grad_norm=222.881, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-16 23:46:08,241 (trainer:762) INFO: 9epoch:train:281-320batch: iter_time=5.218e-05, forward_time=0.153, loss_ctc=30.530, loss=30.530, backward_time=0.024, grad_norm=240.263, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-16 23:46:20,193 (trainer:762) INFO: 9epoch:train:321-360batch: iter_time=5.401e-05, forward_time=0.150, loss_ctc=29.892, loss=29.892, backward_time=0.023, grad_norm=233.026, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.195 +[stan] 2024-01-16 23:46:32,564 (trainer:762) INFO: 9epoch:train:361-400batch: iter_time=5.292e-05, forward_time=0.155, loss_ctc=30.959, loss=30.959, backward_time=0.024, grad_norm=234.768, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-16 23:46:44,666 (trainer:762) INFO: 9epoch:train:401-440batch: iter_time=5.428e-05, forward_time=0.152, loss_ctc=28.988, loss=28.988, backward_time=0.024, grad_norm=218.036, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.210 +[stan] 2024-01-16 23:46:56,700 (trainer:762) INFO: 9epoch:train:441-480batch: iter_time=5.329e-05, forward_time=0.151, loss_ctc=29.614, loss=29.614, backward_time=0.024, grad_norm=222.165, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-16 23:47:09,055 (trainer:762) INFO: 9epoch:train:481-520batch: iter_time=5.285e-05, forward_time=0.155, loss_ctc=31.905, loss=31.905, backward_time=0.024, grad_norm=217.578, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.235 +[stan] 2024-01-16 23:47:21,083 (trainer:762) INFO: 9epoch:train:521-560batch: iter_time=5.169e-05, forward_time=0.151, loss_ctc=29.091, loss=29.091, backward_time=0.023, grad_norm=215.363, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-16 23:47:33,111 (trainer:762) INFO: 9epoch:train:561-600batch: iter_time=5.341e-05, forward_time=0.151, loss_ctc=29.397, loss=29.397, backward_time=0.023, grad_norm=224.663, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-16 23:47:45,247 (trainer:762) INFO: 9epoch:train:601-640batch: iter_time=5.431e-05, forward_time=0.152, loss_ctc=30.234, loss=30.234, backward_time=0.024, grad_norm=268.142, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-16 23:47:57,461 (trainer:762) INFO: 9epoch:train:641-680batch: iter_time=4.946e-05, forward_time=0.153, loss_ctc=30.168, loss=30.168, backward_time=0.023, grad_norm=303.949, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.221 +[stan] 2024-01-16 23:48:09,372 (trainer:762) INFO: 9epoch:train:681-720batch: iter_time=5.430e-05, forward_time=0.149, loss_ctc=28.448, loss=28.448, backward_time=0.023, grad_norm=214.497, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.191 +[stan] 2024-01-16 23:48:21,370 (trainer:762) INFO: 9epoch:train:721-760batch: iter_time=5.378e-05, forward_time=0.150, loss_ctc=29.272, loss=29.272, backward_time=0.023, grad_norm=224.793, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-16 23:48:33,845 (trainer:762) INFO: 9epoch:train:761-800batch: iter_time=5.011e-05, forward_time=0.156, loss_ctc=30.653, loss=30.653, backward_time=0.024, grad_norm=241.842, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.247 +[stan] 2024-01-16 23:48:38,743 (trainer:357) INFO: 9epoch results: [train] iter_time=2.369e-04, forward_time=0.152, loss_ctc=30.150, loss=30.150, backward_time=0.024, grad_norm=231.205, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215, time=4 minutes and 3.13 seconds, total_count=7200, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=252.185, cer_ctc=0.206, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=252.185, time=1.18 seconds, total_count=45, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.64 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-16 23:48:39,843 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 23:48:39,844 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/8epoch.pth +[stan] 2024-01-16 23:48:39,844 (trainer:291) INFO: 10/30epoch started. Estimated time to finish: 1 hour, 27 minutes and 11.32 seconds +[stan] 2024-01-16 23:48:52,190 (trainer:762) INFO: 10epoch:train:1-40batch: iter_time=0.004, forward_time=0.151, loss_ctc=29.248, loss=29.248, backward_time=0.023, grad_norm=223.864, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.234 +[stan] 2024-01-16 23:49:04,062 (trainer:762) INFO: 10epoch:train:41-80batch: iter_time=5.278e-05, forward_time=0.149, loss_ctc=29.101, loss=29.101, backward_time=0.023, grad_norm=230.751, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.187 +[stan] 2024-01-16 23:49:16,499 (trainer:762) INFO: 10epoch:train:81-120batch: iter_time=5.395e-05, forward_time=0.156, loss_ctc=30.171, loss=30.171, backward_time=0.024, grad_norm=223.753, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.244 +[stan] 2024-01-16 23:49:28,641 (trainer:762) INFO: 10epoch:train:121-160batch: iter_time=5.107e-05, forward_time=0.152, loss_ctc=29.351, loss=29.351, backward_time=0.024, grad_norm=219.624, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-16 23:49:40,527 (trainer:762) INFO: 10epoch:train:161-200batch: iter_time=5.447e-05, forward_time=0.149, loss_ctc=28.129, loss=28.129, backward_time=0.024, grad_norm=221.818, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.189 +[stan] 2024-01-16 23:49:52,911 (trainer:762) INFO: 10epoch:train:201-240batch: iter_time=5.082e-05, forward_time=0.155, loss_ctc=29.284, loss=29.284, backward_time=0.024, grad_norm=226.899, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.238 +[stan] 2024-01-16 23:50:04,837 (trainer:762) INFO: 10epoch:train:241-280batch: iter_time=5.321e-05, forward_time=0.149, loss_ctc=27.999, loss=27.999, backward_time=0.023, grad_norm=228.706, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.193 +[stan] 2024-01-16 23:50:17,353 (trainer:762) INFO: 10epoch:train:281-320batch: iter_time=5.544e-05, forward_time=0.156, loss_ctc=30.110, loss=30.110, backward_time=0.024, grad_norm=216.139, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.252 +[stan] 2024-01-16 23:50:29,329 (trainer:762) INFO: 10epoch:train:321-360batch: iter_time=5.384e-05, forward_time=0.150, loss_ctc=28.776, loss=28.776, backward_time=0.023, grad_norm=208.601, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.198 +[stan] 2024-01-16 23:50:41,547 (trainer:762) INFO: 10epoch:train:361-400batch: iter_time=5.377e-05, forward_time=0.153, loss_ctc=29.502, loss=29.502, backward_time=0.023, grad_norm=222.610, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-16 23:50:53,565 (trainer:762) INFO: 10epoch:train:401-440batch: iter_time=5.108e-05, forward_time=0.151, loss_ctc=29.809, loss=29.809, backward_time=0.024, grad_norm=232.029, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.202 +[stan] 2024-01-16 23:51:05,715 (trainer:762) INFO: 10epoch:train:441-480batch: iter_time=5.161e-05, forward_time=0.152, loss_ctc=29.209, loss=29.209, backward_time=0.024, grad_norm=211.333, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-16 23:51:17,809 (trainer:762) INFO: 10epoch:train:481-520batch: iter_time=5.367e-05, forward_time=0.151, loss_ctc=29.211, loss=29.211, backward_time=0.024, grad_norm=216.091, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.209 +[stan] 2024-01-16 23:51:29,675 (trainer:762) INFO: 10epoch:train:521-560batch: iter_time=5.093e-05, forward_time=0.149, loss_ctc=28.274, loss=28.274, backward_time=0.023, grad_norm=232.266, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.187 +[stan] 2024-01-16 23:51:42,097 (trainer:762) INFO: 10epoch:train:561-600batch: iter_time=5.112e-05, forward_time=0.155, loss_ctc=30.029, loss=30.029, backward_time=0.024, grad_norm=237.129, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.242 +[stan] 2024-01-16 23:51:54,027 (trainer:762) INFO: 10epoch:train:601-640batch: iter_time=5.386e-05, forward_time=0.150, loss_ctc=27.627, loss=27.627, backward_time=0.023, grad_norm=220.245, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.193 +[stan] 2024-01-16 23:52:06,411 (trainer:762) INFO: 10epoch:train:641-680batch: iter_time=5.264e-05, forward_time=0.155, loss_ctc=30.317, loss=30.317, backward_time=0.024, grad_norm=226.163, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.238 +[stan] 2024-01-16 23:52:18,476 (trainer:762) INFO: 10epoch:train:681-720batch: iter_time=5.378e-05, forward_time=0.151, loss_ctc=29.031, loss=29.031, backward_time=0.023, grad_norm=218.759, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.206 +[stan] 2024-01-16 23:52:30,385 (trainer:762) INFO: 10epoch:train:721-760batch: iter_time=5.321e-05, forward_time=0.149, loss_ctc=28.435, loss=28.435, backward_time=0.023, grad_norm=206.758, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.191 +[stan] 2024-01-16 23:52:43,152 (trainer:762) INFO: 10epoch:train:761-800batch: iter_time=5.090e-05, forward_time=0.159, loss_ctc=30.844, loss=30.844, backward_time=0.025, grad_norm=253.601, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.277 +[stan] 2024-01-16 23:52:47,934 (trainer:357) INFO: 10epoch results: [train] iter_time=2.418e-04, forward_time=0.152, loss_ctc=29.222, loss=29.222, backward_time=0.024, grad_norm=223.857, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216, time=4 minutes and 3.38 seconds, total_count=8000, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=255.911, cer_ctc=0.199, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=255.911, time=1.16 seconds, total_count=50, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.55 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-16 23:52:49,050 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 23:52:49,052 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/9epoch.pth +[stan] 2024-01-16 23:52:49,052 (trainer:291) INFO: 11/30epoch started. Estimated time to finish: 1 hour, 23 minutes and 2.41 seconds +[stan] 2024-01-16 23:53:00,989 (trainer:762) INFO: 11epoch:train:1-40batch: iter_time=0.003, forward_time=0.147, loss_ctc=26.879, loss=26.879, backward_time=0.023, grad_norm=223.109, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.193 +[stan] 2024-01-16 23:53:13,179 (trainer:762) INFO: 11epoch:train:41-80batch: iter_time=5.423e-05, forward_time=0.153, loss_ctc=28.844, loss=28.844, backward_time=0.023, grad_norm=218.717, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-16 23:53:25,029 (trainer:762) INFO: 11epoch:train:81-120batch: iter_time=5.039e-05, forward_time=0.149, loss_ctc=28.820, loss=28.820, backward_time=0.023, grad_norm=210.778, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.185 +[stan] 2024-01-16 23:53:37,352 (trainer:762) INFO: 11epoch:train:121-160batch: iter_time=5.397e-05, forward_time=0.154, loss_ctc=28.853, loss=28.853, backward_time=0.024, grad_norm=217.938, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-16 23:53:49,643 (trainer:762) INFO: 11epoch:train:161-200batch: iter_time=5.081e-05, forward_time=0.154, loss_ctc=28.542, loss=28.542, backward_time=0.024, grad_norm=216.987, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.229 +[stan] 2024-01-16 23:54:01,904 (trainer:762) INFO: 11epoch:train:201-240batch: iter_time=4.980e-05, forward_time=0.153, loss_ctc=29.146, loss=29.146, backward_time=0.024, grad_norm=223.830, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-16 23:54:13,745 (trainer:762) INFO: 11epoch:train:241-280batch: iter_time=5.323e-05, forward_time=0.148, loss_ctc=27.714, loss=27.714, backward_time=0.023, grad_norm=208.182, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.184 +[stan] 2024-01-16 23:54:26,114 (trainer:762) INFO: 11epoch:train:281-320batch: iter_time=5.472e-05, forward_time=0.155, loss_ctc=28.776, loss=28.776, backward_time=0.024, grad_norm=212.196, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-16 23:54:37,834 (trainer:762) INFO: 11epoch:train:321-360batch: iter_time=5.320e-05, forward_time=0.147, loss_ctc=27.179, loss=27.179, backward_time=0.023, grad_norm=212.774, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.172 +[stan] 2024-01-16 23:54:50,142 (trainer:762) INFO: 11epoch:train:361-400batch: iter_time=5.220e-05, forward_time=0.154, loss_ctc=28.118, loss=28.118, backward_time=0.024, grad_norm=220.094, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-16 23:55:02,378 (trainer:762) INFO: 11epoch:train:401-440batch: iter_time=5.406e-05, forward_time=0.153, loss_ctc=29.029, loss=29.029, backward_time=0.024, grad_norm=224.774, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-16 23:55:14,398 (trainer:762) INFO: 11epoch:train:441-480batch: iter_time=5.396e-05, forward_time=0.151, loss_ctc=27.623, loss=27.623, backward_time=0.024, grad_norm=205.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.202 +[stan] 2024-01-16 23:55:26,530 (trainer:762) INFO: 11epoch:train:481-520batch: iter_time=5.423e-05, forward_time=0.152, loss_ctc=28.357, loss=28.357, backward_time=0.023, grad_norm=230.502, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213 +[stan] 2024-01-16 23:55:38,732 (trainer:762) INFO: 11epoch:train:521-560batch: iter_time=5.399e-05, forward_time=0.153, loss_ctc=28.267, loss=28.267, backward_time=0.024, grad_norm=215.505, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-16 23:55:50,843 (trainer:762) INFO: 11epoch:train:561-600batch: iter_time=5.512e-05, forward_time=0.152, loss_ctc=28.743, loss=28.743, backward_time=0.023, grad_norm=215.397, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211 +[stan] 2024-01-16 23:56:02,911 (trainer:762) INFO: 11epoch:train:601-640batch: iter_time=5.300e-05, forward_time=0.151, loss_ctc=28.442, loss=28.442, backward_time=0.024, grad_norm=208.838, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.207 +[stan] 2024-01-16 23:56:15,014 (trainer:762) INFO: 11epoch:train:641-680batch: iter_time=5.169e-05, forward_time=0.152, loss_ctc=27.714, loss=27.714, backward_time=0.024, grad_norm=199.101, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.210 +[stan] 2024-01-16 23:56:26,819 (trainer:762) INFO: 11epoch:train:681-720batch: iter_time=5.233e-05, forward_time=0.148, loss_ctc=27.224, loss=27.224, backward_time=0.023, grad_norm=212.531, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.180 +[stan] 2024-01-16 23:56:39,275 (trainer:762) INFO: 11epoch:train:721-760batch: iter_time=5.103e-05, forward_time=0.156, loss_ctc=29.059, loss=29.059, backward_time=0.024, grad_norm=218.917, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.246 +[stan] 2024-01-16 23:56:51,229 (trainer:762) INFO: 11epoch:train:761-800batch: iter_time=4.813e-05, forward_time=0.150, loss_ctc=28.076, loss=28.076, backward_time=0.023, grad_norm=206.174, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.195 +[stan] 2024-01-16 23:56:56,024 (trainer:357) INFO: 11epoch results: [train] iter_time=2.026e-04, forward_time=0.151, loss_ctc=28.270, loss=28.270, backward_time=0.024, grad_norm=215.073, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211, time=4 minutes and 2.25 seconds, total_count=8800, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=260.180, cer_ctc=0.202, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=260.180, time=1.15 seconds, total_count=55, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.57 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-16 23:56:56,979 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 23:56:56,980 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/10epoch.pth +[stan] 2024-01-16 23:56:56,980 (trainer:291) INFO: 12/30epoch started. Estimated time to finish: 1 hour, 18 minutes and 51.23 seconds +[stan] 2024-01-16 23:57:09,622 (trainer:762) INFO: 12epoch:train:1-40batch: iter_time=0.004, forward_time=0.154, loss_ctc=29.119, loss=29.119, backward_time=0.024, grad_norm=236.504, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.264 +[stan] 2024-01-16 23:57:21,666 (trainer:762) INFO: 12epoch:train:41-80batch: iter_time=5.289e-05, forward_time=0.151, loss_ctc=28.241, loss=28.241, backward_time=0.023, grad_norm=223.470, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-16 23:57:33,885 (trainer:762) INFO: 12epoch:train:81-120batch: iter_time=5.078e-05, forward_time=0.153, loss_ctc=28.394, loss=28.394, backward_time=0.024, grad_norm=217.681, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-16 23:57:46,201 (trainer:762) INFO: 12epoch:train:121-160batch: iter_time=5.323e-05, forward_time=0.154, loss_ctc=29.058, loss=29.058, backward_time=0.024, grad_norm=225.619, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-16 23:57:58,047 (trainer:762) INFO: 12epoch:train:161-200batch: iter_time=5.247e-05, forward_time=0.149, loss_ctc=27.080, loss=27.080, backward_time=0.023, grad_norm=213.854, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.185 +[stan] 2024-01-16 23:58:10,007 (trainer:762) INFO: 12epoch:train:201-240batch: iter_time=5.384e-05, forward_time=0.150, loss_ctc=27.801, loss=27.801, backward_time=0.023, grad_norm=224.046, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.196 +[stan] 2024-01-16 23:58:22,163 (trainer:762) INFO: 12epoch:train:241-280batch: iter_time=5.455e-05, forward_time=0.152, loss_ctc=28.033, loss=28.033, backward_time=0.024, grad_norm=217.696, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-16 23:58:34,457 (trainer:762) INFO: 12epoch:train:281-320batch: iter_time=5.134e-05, forward_time=0.154, loss_ctc=29.359, loss=29.359, backward_time=0.024, grad_norm=221.924, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.229 +[stan] 2024-01-16 23:58:46,876 (trainer:762) INFO: 12epoch:train:321-360batch: iter_time=5.009e-05, forward_time=0.155, loss_ctc=28.216, loss=28.216, backward_time=0.024, grad_norm=219.185, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.242 +[stan] 2024-01-16 23:58:58,690 (trainer:762) INFO: 12epoch:train:361-400batch: iter_time=5.089e-05, forward_time=0.148, loss_ctc=27.138, loss=27.138, backward_time=0.024, grad_norm=208.099, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.181 +[stan] 2024-01-16 23:59:10,871 (trainer:762) INFO: 12epoch:train:401-440batch: iter_time=5.134e-05, forward_time=0.153, loss_ctc=29.361, loss=29.361, backward_time=0.023, grad_norm=209.454, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-16 23:59:23,136 (trainer:762) INFO: 12epoch:train:441-480batch: iter_time=5.126e-05, forward_time=0.154, loss_ctc=28.089, loss=28.089, backward_time=0.024, grad_norm=209.152, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-16 23:59:34,974 (trainer:762) INFO: 12epoch:train:481-520batch: iter_time=5.490e-05, forward_time=0.148, loss_ctc=26.539, loss=26.539, backward_time=0.023, grad_norm=195.130, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.184 +[stan] 2024-01-16 23:59:47,243 (trainer:762) INFO: 12epoch:train:521-560batch: iter_time=5.596e-05, forward_time=0.153, loss_ctc=27.778, loss=27.778, backward_time=0.024, grad_norm=203.272, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.227 +[stan] 2024-01-16 23:59:59,452 (trainer:762) INFO: 12epoch:train:561-600batch: iter_time=5.476e-05, forward_time=0.153, loss_ctc=28.321, loss=28.321, backward_time=0.024, grad_norm=216.953, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.221 +[stan] 2024-01-17 00:00:11,319 (trainer:762) INFO: 12epoch:train:601-640batch: iter_time=5.138e-05, forward_time=0.149, loss_ctc=27.781, loss=27.781, backward_time=0.023, grad_norm=243.777, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.187 +[stan] 2024-01-17 00:00:23,613 (trainer:762) INFO: 12epoch:train:641-680batch: iter_time=5.094e-05, forward_time=0.154, loss_ctc=27.524, loss=27.524, backward_time=0.024, grad_norm=215.690, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.229 +[stan] 2024-01-17 00:00:35,466 (trainer:762) INFO: 12epoch:train:681-720batch: iter_time=5.150e-05, forward_time=0.149, loss_ctc=27.559, loss=27.559, backward_time=0.023, grad_norm=198.944, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.185 +[stan] 2024-01-17 00:00:47,588 (trainer:762) INFO: 12epoch:train:721-760batch: iter_time=5.405e-05, forward_time=0.152, loss_ctc=26.950, loss=26.950, backward_time=0.023, grad_norm=198.463, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-17 00:00:59,961 (trainer:762) INFO: 12epoch:train:761-800batch: iter_time=4.789e-05, forward_time=0.155, loss_ctc=28.237, loss=28.237, backward_time=0.024, grad_norm=208.504, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-17 00:01:04,838 (trainer:357) INFO: 12epoch results: [train] iter_time=2.395e-04, forward_time=0.152, loss_ctc=28.028, loss=28.028, backward_time=0.024, grad_norm=215.371, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215, time=4 minutes and 3.06 seconds, total_count=9600, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=263.530, cer_ctc=0.197, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=263.530, time=1.17 seconds, total_count=60, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.63 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 00:01:05,883 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 00:01:05,884 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/11epoch.pth +[stan] 2024-01-17 00:01:05,884 (trainer:291) INFO: 13/30epoch started. Estimated time to finish: 1 hour, 14 minutes and 42.05 seconds +[stan] 2024-01-17 00:01:18,193 (trainer:762) INFO: 13epoch:train:1-40batch: iter_time=0.003, forward_time=0.151, loss_ctc=27.806, loss=27.806, backward_time=0.023, grad_norm=216.435, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.230 +[stan] 2024-01-17 00:01:30,374 (trainer:762) INFO: 13epoch:train:41-80batch: iter_time=5.672e-05, forward_time=0.152, loss_ctc=27.650, loss=27.650, backward_time=0.024, grad_norm=212.338, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-17 00:01:42,513 (trainer:762) INFO: 13epoch:train:81-120batch: iter_time=5.540e-05, forward_time=0.152, loss_ctc=26.811, loss=26.811, backward_time=0.024, grad_norm=196.834, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-17 00:01:54,736 (trainer:762) INFO: 13epoch:train:121-160batch: iter_time=5.316e-05, forward_time=0.153, loss_ctc=28.065, loss=28.065, backward_time=0.024, grad_norm=200.053, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-17 00:02:07,061 (trainer:762) INFO: 13epoch:train:161-200batch: iter_time=5.382e-05, forward_time=0.154, loss_ctc=27.566, loss=27.566, backward_time=0.024, grad_norm=200.317, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-17 00:02:18,922 (trainer:762) INFO: 13epoch:train:201-240batch: iter_time=5.619e-05, forward_time=0.149, loss_ctc=26.365, loss=26.365, backward_time=0.023, grad_norm=192.850, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.186 +[stan] 2024-01-17 00:02:30,831 (trainer:762) INFO: 13epoch:train:241-280batch: iter_time=5.490e-05, forward_time=0.149, loss_ctc=27.406, loss=27.406, backward_time=0.023, grad_norm=204.107, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.191 +[stan] 2024-01-17 00:02:43,153 (trainer:762) INFO: 13epoch:train:281-320batch: iter_time=5.352e-05, forward_time=0.154, loss_ctc=26.941, loss=26.941, backward_time=0.024, grad_norm=211.298, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-17 00:02:55,345 (trainer:762) INFO: 13epoch:train:321-360batch: iter_time=5.259e-05, forward_time=0.153, loss_ctc=27.621, loss=27.621, backward_time=0.024, grad_norm=201.497, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-17 00:03:07,872 (trainer:762) INFO: 13epoch:train:361-400batch: iter_time=5.446e-05, forward_time=0.157, loss_ctc=28.762, loss=28.762, backward_time=0.024, grad_norm=210.659, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.253 +[stan] 2024-01-17 00:03:19,587 (trainer:762) INFO: 13epoch:train:401-440batch: iter_time=5.698e-05, forward_time=0.147, loss_ctc=25.445, loss=25.445, backward_time=0.023, grad_norm=209.072, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.171 +[stan] 2024-01-17 00:03:31,739 (trainer:762) INFO: 13epoch:train:441-480batch: iter_time=5.155e-05, forward_time=0.152, loss_ctc=26.982, loss=26.982, backward_time=0.024, grad_norm=195.324, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-17 00:03:43,772 (trainer:762) INFO: 13epoch:train:481-520batch: iter_time=5.482e-05, forward_time=0.151, loss_ctc=26.141, loss=26.141, backward_time=0.023, grad_norm=192.980, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-17 00:03:56,235 (trainer:762) INFO: 13epoch:train:521-560batch: iter_time=5.178e-05, forward_time=0.156, loss_ctc=27.653, loss=27.653, backward_time=0.024, grad_norm=199.317, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.246 +[stan] 2024-01-17 00:04:08,109 (trainer:762) INFO: 13epoch:train:561-600batch: iter_time=5.182e-05, forward_time=0.149, loss_ctc=26.158, loss=26.158, backward_time=0.023, grad_norm=194.381, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.187 +[stan] 2024-01-17 00:04:20,265 (trainer:762) INFO: 13epoch:train:601-640batch: iter_time=5.603e-05, forward_time=0.152, loss_ctc=27.655, loss=27.655, backward_time=0.023, grad_norm=200.989, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-17 00:04:32,721 (trainer:762) INFO: 13epoch:train:641-680batch: iter_time=5.240e-05, forward_time=0.156, loss_ctc=28.086, loss=28.086, backward_time=0.024, grad_norm=195.855, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.246 +[stan] 2024-01-17 00:04:44,463 (trainer:762) INFO: 13epoch:train:681-720batch: iter_time=5.337e-05, forward_time=0.147, loss_ctc=25.843, loss=25.843, backward_time=0.023, grad_norm=203.884, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.174 +[stan] 2024-01-17 00:04:56,457 (trainer:762) INFO: 13epoch:train:721-760batch: iter_time=5.231e-05, forward_time=0.150, loss_ctc=27.595, loss=27.595, backward_time=0.023, grad_norm=200.325, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-17 00:05:08,911 (trainer:762) INFO: 13epoch:train:761-800batch: iter_time=4.923e-05, forward_time=0.156, loss_ctc=27.499, loss=27.499, backward_time=0.024, grad_norm=199.840, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.245 +[stan] 2024-01-17 00:05:13,732 (trainer:357) INFO: 13epoch results: [train] iter_time=2.154e-04, forward_time=0.152, loss_ctc=27.202, loss=27.202, backward_time=0.024, grad_norm=201.918, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215, time=4 minutes and 3.1 seconds, total_count=10400, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=265.837, cer_ctc=0.192, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=265.837, time=1.15 seconds, total_count=65, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.6 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 00:05:14,693 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 00:05:14,695 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/12epoch.pth +[stan] 2024-01-17 00:05:14,695 (trainer:291) INFO: 14/30epoch started. Estimated time to finish: 1 hour, 10 minutes and 32.8 seconds +[stan] 2024-01-17 00:05:27,020 (trainer:762) INFO: 14epoch:train:1-40batch: iter_time=0.004, forward_time=0.152, loss_ctc=26.625, loss=26.625, backward_time=0.023, grad_norm=204.615, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-17 00:05:39,089 (trainer:762) INFO: 14epoch:train:41-80batch: iter_time=5.050e-05, forward_time=0.151, loss_ctc=27.275, loss=27.275, backward_time=0.023, grad_norm=200.187, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.207 +[stan] 2024-01-17 00:05:51,047 (trainer:762) INFO: 14epoch:train:81-120batch: iter_time=5.114e-05, forward_time=0.150, loss_ctc=27.538, loss=27.538, backward_time=0.023, grad_norm=206.193, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.196 +[stan] 2024-01-17 00:06:03,378 (trainer:762) INFO: 14epoch:train:121-160batch: iter_time=5.353e-05, forward_time=0.154, loss_ctc=28.030, loss=28.030, backward_time=0.024, grad_norm=196.792, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.233 +[stan] 2024-01-17 00:06:15,613 (trainer:762) INFO: 14epoch:train:161-200batch: iter_time=5.301e-05, forward_time=0.153, loss_ctc=27.283, loss=27.283, backward_time=0.024, grad_norm=198.663, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-17 00:06:27,575 (trainer:762) INFO: 14epoch:train:201-240batch: iter_time=5.375e-05, forward_time=0.150, loss_ctc=26.243, loss=26.243, backward_time=0.023, grad_norm=194.956, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.196 +[stan] 2024-01-17 00:06:39,781 (trainer:762) INFO: 14epoch:train:241-280batch: iter_time=5.123e-05, forward_time=0.153, loss_ctc=26.999, loss=26.999, backward_time=0.024, grad_norm=204.279, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.221 +[stan] 2024-01-17 00:06:51,538 (trainer:762) INFO: 14epoch:train:281-320batch: iter_time=5.369e-05, forward_time=0.147, loss_ctc=25.167, loss=25.167, backward_time=0.023, grad_norm=195.272, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.176 +[stan] 2024-01-17 00:07:04,371 (trainer:762) INFO: 14epoch:train:321-360batch: iter_time=5.271e-05, forward_time=0.160, loss_ctc=27.657, loss=27.657, backward_time=0.024, grad_norm=205.842, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.283 +[stan] 2024-01-17 00:07:16,186 (trainer:762) INFO: 14epoch:train:361-400batch: iter_time=5.414e-05, forward_time=0.148, loss_ctc=25.361, loss=25.361, backward_time=0.023, grad_norm=193.882, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.181 +[stan] 2024-01-17 00:07:28,250 (trainer:762) INFO: 14epoch:train:401-440batch: iter_time=5.203e-05, forward_time=0.151, loss_ctc=26.280, loss=26.280, backward_time=0.023, grad_norm=196.526, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.206 +[stan] 2024-01-17 00:07:40,489 (trainer:762) INFO: 14epoch:train:441-480batch: iter_time=5.416e-05, forward_time=0.153, loss_ctc=26.008, loss=26.008, backward_time=0.024, grad_norm=199.107, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.224 +[stan] 2024-01-17 00:07:52,567 (trainer:762) INFO: 14epoch:train:481-520batch: iter_time=5.380e-05, forward_time=0.151, loss_ctc=27.016, loss=27.016, backward_time=0.023, grad_norm=201.489, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-17 00:08:04,496 (trainer:762) INFO: 14epoch:train:521-560batch: iter_time=5.293e-05, forward_time=0.150, loss_ctc=26.213, loss=26.213, backward_time=0.023, grad_norm=197.719, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.193 +[stan] 2024-01-17 00:08:16,698 (trainer:762) INFO: 14epoch:train:561-600batch: iter_time=5.211e-05, forward_time=0.153, loss_ctc=27.382, loss=27.382, backward_time=0.024, grad_norm=204.553, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-17 00:08:28,943 (trainer:762) INFO: 14epoch:train:601-640batch: iter_time=5.182e-05, forward_time=0.153, loss_ctc=26.942, loss=26.942, backward_time=0.023, grad_norm=204.884, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.224 +[stan] 2024-01-17 00:08:41,128 (trainer:762) INFO: 14epoch:train:641-680batch: iter_time=5.300e-05, forward_time=0.152, loss_ctc=26.241, loss=26.241, backward_time=0.023, grad_norm=209.889, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-17 00:08:53,347 (trainer:762) INFO: 14epoch:train:681-720batch: iter_time=5.305e-05, forward_time=0.153, loss_ctc=26.165, loss=26.165, backward_time=0.024, grad_norm=211.195, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-17 00:09:05,301 (trainer:762) INFO: 14epoch:train:721-760batch: iter_time=5.135e-05, forward_time=0.150, loss_ctc=25.568, loss=25.568, backward_time=0.023, grad_norm=205.057, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.195 +[stan] 2024-01-17 00:09:17,674 (trainer:762) INFO: 14epoch:train:761-800batch: iter_time=4.848e-05, forward_time=0.155, loss_ctc=26.760, loss=26.760, backward_time=0.024, grad_norm=202.880, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-17 00:09:22,498 (trainer:357) INFO: 14epoch results: [train] iter_time=2.621e-04, forward_time=0.152, loss_ctc=26.637, loss=26.637, backward_time=0.023, grad_norm=201.699, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215, time=4 minutes and 3.05 seconds, total_count=11200, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=265.955, cer_ctc=0.193, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=265.955, time=1.15 seconds, total_count=70, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.6 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 00:09:23,473 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 00:09:23,474 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/13epoch.pth +[stan] 2024-01-17 00:09:23,474 (trainer:291) INFO: 15/30epoch started. Estimated time to finish: 1 hour, 6 minutes and 23.57 seconds +[stan] 2024-01-17 00:09:35,511 (trainer:762) INFO: 15epoch:train:1-40batch: iter_time=0.003, forward_time=0.148, loss_ctc=25.604, loss=25.604, backward_time=0.024, grad_norm=201.967, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-17 00:09:47,725 (trainer:762) INFO: 15epoch:train:41-80batch: iter_time=5.268e-05, forward_time=0.153, loss_ctc=25.861, loss=25.861, backward_time=0.023, grad_norm=206.417, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.221 +[stan] 2024-01-17 00:09:59,710 (trainer:762) INFO: 15epoch:train:81-120batch: iter_time=5.189e-05, forward_time=0.150, loss_ctc=26.064, loss=26.064, backward_time=0.023, grad_norm=196.984, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.198 +[stan] 2024-01-17 00:10:12,188 (trainer:762) INFO: 15epoch:train:121-160batch: iter_time=5.003e-05, forward_time=0.156, loss_ctc=26.665, loss=26.665, backward_time=0.024, grad_norm=195.567, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.248 +[stan] 2024-01-17 00:10:24,085 (trainer:762) INFO: 15epoch:train:161-200batch: iter_time=5.356e-05, forward_time=0.149, loss_ctc=25.634, loss=25.634, backward_time=0.023, grad_norm=200.099, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.190 +[stan] 2024-01-17 00:10:36,381 (trainer:762) INFO: 15epoch:train:201-240batch: iter_time=5.041e-05, forward_time=0.154, loss_ctc=27.090, loss=27.090, backward_time=0.023, grad_norm=207.435, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.230 +[stan] 2024-01-17 00:10:48,431 (trainer:762) INFO: 15epoch:train:241-280batch: iter_time=5.358e-05, forward_time=0.151, loss_ctc=26.105, loss=26.105, backward_time=0.024, grad_norm=208.793, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-17 00:11:00,509 (trainer:762) INFO: 15epoch:train:281-320batch: iter_time=5.369e-05, forward_time=0.151, loss_ctc=26.800, loss=26.800, backward_time=0.024, grad_norm=217.076, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-17 00:11:12,673 (trainer:762) INFO: 15epoch:train:321-360batch: iter_time=5.381e-05, forward_time=0.152, loss_ctc=26.683, loss=26.683, backward_time=0.024, grad_norm=217.439, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-17 00:11:24,702 (trainer:762) INFO: 15epoch:train:361-400batch: iter_time=5.469e-05, forward_time=0.151, loss_ctc=26.383, loss=26.383, backward_time=0.023, grad_norm=204.548, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-17 00:11:36,904 (trainer:762) INFO: 15epoch:train:401-440batch: iter_time=5.119e-05, forward_time=0.153, loss_ctc=26.133, loss=26.133, backward_time=0.024, grad_norm=204.327, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-17 00:11:49,036 (trainer:762) INFO: 15epoch:train:441-480batch: iter_time=5.547e-05, forward_time=0.152, loss_ctc=26.525, loss=26.525, backward_time=0.023, grad_norm=192.813, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213 +[stan] 2024-01-17 00:12:01,173 (trainer:762) INFO: 15epoch:train:481-520batch: iter_time=5.193e-05, forward_time=0.152, loss_ctc=25.423, loss=25.423, backward_time=0.023, grad_norm=195.881, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-17 00:12:13,277 (trainer:762) INFO: 15epoch:train:521-560batch: iter_time=5.370e-05, forward_time=0.152, loss_ctc=26.083, loss=26.083, backward_time=0.024, grad_norm=197.035, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.210 +[stan] 2024-01-17 00:12:25,385 (trainer:762) INFO: 15epoch:train:561-600batch: iter_time=5.141e-05, forward_time=0.152, loss_ctc=25.413, loss=25.413, backward_time=0.023, grad_norm=189.088, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211 +[stan] 2024-01-17 00:12:37,496 (trainer:762) INFO: 15epoch:train:601-640batch: iter_time=5.344e-05, forward_time=0.152, loss_ctc=25.921, loss=25.921, backward_time=0.023, grad_norm=194.687, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211 +[stan] 2024-01-17 00:12:49,642 (trainer:762) INFO: 15epoch:train:641-680batch: iter_time=5.209e-05, forward_time=0.152, loss_ctc=26.058, loss=26.058, backward_time=0.024, grad_norm=200.986, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-17 00:13:01,770 (trainer:762) INFO: 15epoch:train:681-720batch: iter_time=5.454e-05, forward_time=0.152, loss_ctc=26.869, loss=26.869, backward_time=0.023, grad_norm=205.882, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213 +[stan] 2024-01-17 00:13:14,104 (trainer:762) INFO: 15epoch:train:721-760batch: iter_time=5.342e-05, forward_time=0.154, loss_ctc=27.076, loss=27.076, backward_time=0.024, grad_norm=194.585, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.233 +[stan] 2024-01-17 00:13:25,988 (trainer:762) INFO: 15epoch:train:761-800batch: iter_time=5.133e-05, forward_time=0.149, loss_ctc=25.216, loss=25.216, backward_time=0.023, grad_norm=189.857, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.188 +[stan] 2024-01-17 00:13:30,824 (trainer:357) INFO: 15epoch results: [train] iter_time=2.209e-04, forward_time=0.152, loss_ctc=26.180, loss=26.180, backward_time=0.023, grad_norm=201.073, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212, time=4 minutes and 2.59 seconds, total_count=12000, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=270.072, cer_ctc=0.197, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=270.072, time=1.16 seconds, total_count=75, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.6 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 00:13:31,876 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 00:13:31,878 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/14epoch.pth +[stan] 2024-01-17 00:13:31,878 (trainer:291) INFO: 16/30epoch started. Estimated time to finish: 1 hour, 2 minutes and 14.03 seconds +[stan] 2024-01-17 00:13:44,275 (trainer:762) INFO: 16epoch:train:1-40batch: iter_time=0.004, forward_time=0.152, loss_ctc=25.509, loss=25.509, backward_time=0.024, grad_norm=198.411, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.239 +[stan] 2024-01-17 00:13:56,369 (trainer:762) INFO: 16epoch:train:41-80batch: iter_time=5.178e-05, forward_time=0.152, loss_ctc=26.211, loss=26.211, backward_time=0.023, grad_norm=196.545, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.209 +[stan] 2024-01-17 00:14:08,519 (trainer:762) INFO: 16epoch:train:81-120batch: iter_time=5.451e-05, forward_time=0.152, loss_ctc=26.226, loss=26.226, backward_time=0.024, grad_norm=201.487, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-17 00:14:20,826 (trainer:762) INFO: 16epoch:train:121-160batch: iter_time=5.096e-05, forward_time=0.154, loss_ctc=26.534, loss=26.534, backward_time=0.024, grad_norm=193.622, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-17 00:14:32,493 (trainer:762) INFO: 16epoch:train:161-200batch: iter_time=5.178e-05, forward_time=0.146, loss_ctc=24.815, loss=24.815, backward_time=0.023, grad_norm=193.708, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.167 +[stan] 2024-01-17 00:14:44,915 (trainer:762) INFO: 16epoch:train:201-240batch: iter_time=5.366e-05, forward_time=0.155, loss_ctc=26.558, loss=26.558, backward_time=0.024, grad_norm=207.280, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.242 +[stan] 2024-01-17 00:14:57,091 (trainer:762) INFO: 16epoch:train:241-280batch: iter_time=5.190e-05, forward_time=0.152, loss_ctc=25.584, loss=25.584, backward_time=0.024, grad_norm=214.887, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217 +[stan] 2024-01-17 00:15:08,969 (trainer:762) INFO: 16epoch:train:281-320batch: iter_time=5.186e-05, forward_time=0.149, loss_ctc=25.660, loss=25.660, backward_time=0.023, grad_norm=201.961, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.188 +[stan] 2024-01-17 00:15:21,316 (trainer:762) INFO: 16epoch:train:321-360batch: iter_time=5.131e-05, forward_time=0.155, loss_ctc=26.304, loss=26.304, backward_time=0.024, grad_norm=196.332, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.235 +[stan] 2024-01-17 00:15:33,742 (trainer:762) INFO: 16epoch:train:361-400batch: iter_time=5.621e-05, forward_time=0.155, loss_ctc=26.436, loss=26.436, backward_time=0.024, grad_norm=192.012, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.242 +[stan] 2024-01-17 00:15:45,400 (trainer:762) INFO: 16epoch:train:401-440batch: iter_time=5.212e-05, forward_time=0.146, loss_ctc=24.128, loss=24.128, backward_time=0.023, grad_norm=184.939, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.166 +[stan] 2024-01-17 00:15:57,699 (trainer:762) INFO: 16epoch:train:441-480batch: iter_time=5.470e-05, forward_time=0.154, loss_ctc=27.089, loss=27.089, backward_time=0.024, grad_norm=194.496, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.230 +[stan] 2024-01-17 00:16:09,841 (trainer:762) INFO: 16epoch:train:481-520batch: iter_time=5.283e-05, forward_time=0.152, loss_ctc=26.294, loss=26.294, backward_time=0.023, grad_norm=199.487, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-17 00:16:21,887 (trainer:762) INFO: 16epoch:train:521-560batch: iter_time=5.195e-05, forward_time=0.151, loss_ctc=25.908, loss=25.908, backward_time=0.023, grad_norm=198.360, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-17 00:16:34,069 (trainer:762) INFO: 16epoch:train:561-600batch: iter_time=5.238e-05, forward_time=0.153, loss_ctc=25.843, loss=25.843, backward_time=0.023, grad_norm=206.560, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-17 00:16:46,445 (trainer:762) INFO: 16epoch:train:601-640batch: iter_time=5.501e-05, forward_time=0.155, loss_ctc=26.410, loss=26.410, backward_time=0.024, grad_norm=210.974, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.238 +[stan] 2024-01-17 00:16:58,596 (trainer:762) INFO: 16epoch:train:641-680batch: iter_time=5.218e-05, forward_time=0.152, loss_ctc=25.973, loss=25.973, backward_time=0.024, grad_norm=196.714, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-17 00:17:10,432 (trainer:762) INFO: 16epoch:train:681-720batch: iter_time=5.219e-05, forward_time=0.148, loss_ctc=24.821, loss=24.821, backward_time=0.023, grad_norm=197.381, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.183 +[stan] 2024-01-17 00:17:22,901 (trainer:762) INFO: 16epoch:train:721-760batch: iter_time=5.416e-05, forward_time=0.156, loss_ctc=26.652, loss=26.652, backward_time=0.024, grad_norm=192.950, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.247 +[stan] 2024-01-17 00:17:34,688 (trainer:762) INFO: 16epoch:train:761-800batch: iter_time=5.275e-05, forward_time=0.148, loss_ctc=24.530, loss=24.530, backward_time=0.023, grad_norm=183.156, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.179 +[stan] 2024-01-17 00:17:39,504 (trainer:357) INFO: 16epoch results: [train] iter_time=2.276e-04, forward_time=0.152, loss_ctc=25.874, loss=25.874, backward_time=0.024, grad_norm=198.063, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214, time=4 minutes and 2.88 seconds, total_count=12800, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=273.769, cer_ctc=0.195, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=273.769, time=1.16 seconds, total_count=80, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.59 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 00:17:40,487 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 00:17:40,489 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/15epoch.pth +[stan] 2024-01-17 00:17:40,489 (trainer:291) INFO: 17/30epoch started. Estimated time to finish: 58 minutes and 4.81 seconds +[stan] 2024-01-17 00:17:52,879 (trainer:762) INFO: 17epoch:train:1-40batch: iter_time=0.004, forward_time=0.152, loss_ctc=25.587, loss=25.587, backward_time=0.024, grad_norm=199.472, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.239 +[stan] 2024-01-17 00:18:04,766 (trainer:762) INFO: 17epoch:train:41-80batch: iter_time=5.466e-05, forward_time=0.149, loss_ctc=24.756, loss=24.756, backward_time=0.023, grad_norm=186.736, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.189 +[stan] 2024-01-17 00:18:17,234 (trainer:762) INFO: 17epoch:train:81-120batch: iter_time=5.142e-05, forward_time=0.156, loss_ctc=26.020, loss=26.020, backward_time=0.024, grad_norm=197.358, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.247 +[stan] 2024-01-17 00:18:29,230 (trainer:762) INFO: 17epoch:train:121-160batch: iter_time=5.409e-05, forward_time=0.150, loss_ctc=25.286, loss=25.286, backward_time=0.023, grad_norm=216.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-17 00:18:41,388 (trainer:762) INFO: 17epoch:train:161-200batch: iter_time=5.297e-05, forward_time=0.152, loss_ctc=24.823, loss=24.823, backward_time=0.024, grad_norm=188.732, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-17 00:18:53,402 (trainer:762) INFO: 17epoch:train:201-240batch: iter_time=5.225e-05, forward_time=0.151, loss_ctc=24.585, loss=24.585, backward_time=0.024, grad_norm=201.160, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-17 00:19:05,635 (trainer:762) INFO: 17epoch:train:241-280batch: iter_time=5.348e-05, forward_time=0.153, loss_ctc=25.784, loss=25.784, backward_time=0.024, grad_norm=212.805, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-17 00:19:17,926 (trainer:762) INFO: 17epoch:train:281-320batch: iter_time=5.129e-05, forward_time=0.154, loss_ctc=25.762, loss=25.762, backward_time=0.024, grad_norm=217.223, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.229 +[stan] 2024-01-17 00:19:29,603 (trainer:762) INFO: 17epoch:train:321-360batch: iter_time=5.386e-05, forward_time=0.147, loss_ctc=24.549, loss=24.549, backward_time=0.023, grad_norm=187.486, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.168 +[stan] 2024-01-17 00:19:42,043 (trainer:762) INFO: 17epoch:train:361-400batch: iter_time=5.071e-05, forward_time=0.156, loss_ctc=26.100, loss=26.100, backward_time=0.024, grad_norm=208.959, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.244 +[stan] 2024-01-17 00:19:54,011 (trainer:762) INFO: 17epoch:train:401-440batch: iter_time=5.066e-05, forward_time=0.150, loss_ctc=24.997, loss=24.997, backward_time=0.024, grad_norm=200.184, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.197 +[stan] 2024-01-17 00:20:06,247 (trainer:762) INFO: 17epoch:train:441-480batch: iter_time=5.285e-05, forward_time=0.153, loss_ctc=25.508, loss=25.508, backward_time=0.023, grad_norm=199.081, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-17 00:20:18,425 (trainer:762) INFO: 17epoch:train:481-520batch: iter_time=5.357e-05, forward_time=0.153, loss_ctc=25.563, loss=25.563, backward_time=0.024, grad_norm=193.738, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-17 00:20:30,245 (trainer:762) INFO: 17epoch:train:521-560batch: iter_time=5.066e-05, forward_time=0.148, loss_ctc=24.552, loss=24.552, backward_time=0.023, grad_norm=203.312, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.182 +[stan] 2024-01-17 00:20:42,328 (trainer:762) INFO: 17epoch:train:561-600batch: iter_time=5.366e-05, forward_time=0.151, loss_ctc=25.109, loss=25.109, backward_time=0.023, grad_norm=196.214, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-17 00:20:54,796 (trainer:762) INFO: 17epoch:train:601-640batch: iter_time=5.355e-05, forward_time=0.156, loss_ctc=26.045, loss=26.045, backward_time=0.024, grad_norm=208.817, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.247 +[stan] 2024-01-17 00:21:06,710 (trainer:762) INFO: 17epoch:train:641-680batch: iter_time=5.183e-05, forward_time=0.149, loss_ctc=24.584, loss=24.584, backward_time=0.023, grad_norm=188.809, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.191 +[stan] 2024-01-17 00:21:18,994 (trainer:762) INFO: 17epoch:train:681-720batch: iter_time=5.343e-05, forward_time=0.154, loss_ctc=25.291, loss=25.291, backward_time=0.023, grad_norm=191.886, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.228 +[stan] 2024-01-17 00:21:31,151 (trainer:762) INFO: 17epoch:train:721-760batch: iter_time=5.395e-05, forward_time=0.152, loss_ctc=25.281, loss=25.281, backward_time=0.024, grad_norm=190.923, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-17 00:21:42,960 (trainer:762) INFO: 17epoch:train:761-800batch: iter_time=4.782e-05, forward_time=0.148, loss_ctc=24.806, loss=24.806, backward_time=0.023, grad_norm=202.008, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.181 +[stan] 2024-01-17 00:21:47,719 (trainer:357) INFO: 17epoch results: [train] iter_time=2.423e-04, forward_time=0.152, loss_ctc=25.249, loss=25.249, backward_time=0.024, grad_norm=199.558, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212, time=4 minutes and 2.55 seconds, total_count=13600, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=272.976, cer_ctc=0.190, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=272.976, time=1.15 seconds, total_count=85, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.53 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 00:21:48,695 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 00:21:48,696 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/16epoch.pth +[stan] 2024-01-17 00:21:48,696 (trainer:291) INFO: 18/30epoch started. Estimated time to finish: 53 minutes and 55.35 seconds +[stan] 2024-01-17 00:22:01,351 (trainer:762) INFO: 18epoch:train:1-40batch: iter_time=0.004, forward_time=0.155, loss_ctc=25.357, loss=25.357, backward_time=0.024, grad_norm=206.209, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.265 +[stan] 2024-01-17 00:22:13,498 (trainer:762) INFO: 18epoch:train:41-80batch: iter_time=5.107e-05, forward_time=0.152, loss_ctc=25.320, loss=25.320, backward_time=0.023, grad_norm=197.182, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-17 00:22:25,517 (trainer:762) INFO: 18epoch:train:81-120batch: iter_time=5.371e-05, forward_time=0.151, loss_ctc=24.186, loss=24.186, backward_time=0.024, grad_norm=196.226, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.202 +[stan] 2024-01-17 00:22:37,709 (trainer:762) INFO: 18epoch:train:121-160batch: iter_time=5.324e-05, forward_time=0.153, loss_ctc=24.814, loss=24.814, backward_time=0.023, grad_norm=192.431, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-17 00:22:49,877 (trainer:762) INFO: 18epoch:train:161-200batch: iter_time=5.232e-05, forward_time=0.152, loss_ctc=24.643, loss=24.643, backward_time=0.024, grad_norm=200.890, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217 +[stan] 2024-01-17 00:23:01,934 (trainer:762) INFO: 18epoch:train:201-240batch: iter_time=5.316e-05, forward_time=0.151, loss_ctc=24.351, loss=24.351, backward_time=0.023, grad_norm=209.650, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.206 +[stan] 2024-01-17 00:23:13,933 (trainer:762) INFO: 18epoch:train:241-280batch: iter_time=5.258e-05, forward_time=0.150, loss_ctc=24.185, loss=24.185, backward_time=0.024, grad_norm=199.780, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-17 00:23:26,276 (trainer:762) INFO: 18epoch:train:281-320batch: iter_time=5.142e-05, forward_time=0.154, loss_ctc=24.540, loss=24.540, backward_time=0.024, grad_norm=191.659, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.234 +[stan] 2024-01-17 00:23:38,181 (trainer:762) INFO: 18epoch:train:321-360batch: iter_time=5.362e-05, forward_time=0.149, loss_ctc=24.516, loss=24.516, backward_time=0.023, grad_norm=204.115, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.190 +[stan] 2024-01-17 00:23:50,710 (trainer:762) INFO: 18epoch:train:361-400batch: iter_time=5.144e-05, forward_time=0.157, loss_ctc=25.442, loss=25.442, backward_time=0.024, grad_norm=203.837, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.253 +[stan] 2024-01-17 00:24:02,661 (trainer:762) INFO: 18epoch:train:401-440batch: iter_time=5.446e-05, forward_time=0.150, loss_ctc=24.498, loss=24.498, backward_time=0.023, grad_norm=219.910, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.195 +[stan] 2024-01-17 00:24:14,844 (trainer:762) INFO: 18epoch:train:441-480batch: iter_time=5.383e-05, forward_time=0.153, loss_ctc=25.274, loss=25.274, backward_time=0.024, grad_norm=201.362, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-17 00:24:26,899 (trainer:762) INFO: 18epoch:train:481-520batch: iter_time=5.261e-05, forward_time=0.151, loss_ctc=24.265, loss=24.265, backward_time=0.023, grad_norm=196.971, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-17 00:24:39,040 (trainer:762) INFO: 18epoch:train:521-560batch: iter_time=5.125e-05, forward_time=0.152, loss_ctc=25.268, loss=25.268, backward_time=0.024, grad_norm=193.604, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-17 00:24:51,240 (trainer:762) INFO: 18epoch:train:561-600batch: iter_time=5.350e-05, forward_time=0.153, loss_ctc=25.607, loss=25.607, backward_time=0.024, grad_norm=203.489, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-17 00:25:03,357 (trainer:762) INFO: 18epoch:train:601-640batch: iter_time=5.131e-05, forward_time=0.152, loss_ctc=24.339, loss=24.339, backward_time=0.024, grad_norm=201.460, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-17 00:25:15,394 (trainer:762) INFO: 18epoch:train:641-680batch: iter_time=5.410e-05, forward_time=0.151, loss_ctc=23.963, loss=23.963, backward_time=0.023, grad_norm=188.898, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-17 00:25:27,318 (trainer:762) INFO: 18epoch:train:681-720batch: iter_time=5.594e-05, forward_time=0.149, loss_ctc=23.662, loss=23.662, backward_time=0.023, grad_norm=194.844, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.192 +[stan] 2024-01-17 00:25:39,501 (trainer:762) INFO: 18epoch:train:721-760batch: iter_time=5.133e-05, forward_time=0.152, loss_ctc=25.189, loss=25.189, backward_time=0.024, grad_norm=195.917, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-17 00:25:51,738 (trainer:762) INFO: 18epoch:train:761-800batch: iter_time=4.768e-05, forward_time=0.153, loss_ctc=24.688, loss=24.688, backward_time=0.024, grad_norm=198.254, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.224 +[stan] 2024-01-17 00:25:56,527 (trainer:357) INFO: 18epoch results: [train] iter_time=2.374e-04, forward_time=0.152, loss_ctc=24.705, loss=24.705, backward_time=0.024, grad_norm=199.834, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215, time=4 minutes and 3.12 seconds, total_count=14400, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=274.568, cer_ctc=0.189, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=274.568, time=1.16 seconds, total_count=90, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.55 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 00:25:57,662 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 00:25:57,663 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/17epoch.pth +[stan] 2024-01-17 00:25:57,663 (trainer:291) INFO: 19/30epoch started. Estimated time to finish: 49 minutes and 46.54 seconds +[stan] 2024-01-17 00:26:10,298 (trainer:762) INFO: 19epoch:train:1-40batch: iter_time=0.004, forward_time=0.155, loss_ctc=25.453, loss=25.453, backward_time=0.024, grad_norm=194.847, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.263 +[stan] 2024-01-17 00:26:22,309 (trainer:762) INFO: 19epoch:train:41-80batch: iter_time=5.136e-05, forward_time=0.151, loss_ctc=24.172, loss=24.172, backward_time=0.024, grad_norm=192.788, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-17 00:26:34,320 (trainer:762) INFO: 19epoch:train:81-120batch: iter_time=5.095e-05, forward_time=0.150, loss_ctc=24.452, loss=24.452, backward_time=0.024, grad_norm=192.795, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-17 00:26:46,251 (trainer:762) INFO: 19epoch:train:121-160batch: iter_time=5.237e-05, forward_time=0.150, loss_ctc=23.513, loss=23.513, backward_time=0.024, grad_norm=192.005, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.193 +[stan] 2024-01-17 00:26:58,595 (trainer:762) INFO: 19epoch:train:161-200batch: iter_time=5.068e-05, forward_time=0.154, loss_ctc=25.927, loss=25.927, backward_time=0.024, grad_norm=210.496, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.234 +[stan] 2024-01-17 00:27:10,742 (trainer:762) INFO: 19epoch:train:201-240batch: iter_time=5.428e-05, forward_time=0.152, loss_ctc=24.522, loss=24.522, backward_time=0.023, grad_norm=196.130, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-17 00:27:22,990 (trainer:762) INFO: 19epoch:train:241-280batch: iter_time=5.375e-05, forward_time=0.153, loss_ctc=25.104, loss=25.104, backward_time=0.024, grad_norm=208.730, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-17 00:27:34,741 (trainer:762) INFO: 19epoch:train:281-320batch: iter_time=5.329e-05, forward_time=0.147, loss_ctc=23.321, loss=23.321, backward_time=0.023, grad_norm=205.100, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.175 +[stan] 2024-01-17 00:27:47,163 (trainer:762) INFO: 19epoch:train:321-360batch: iter_time=5.372e-05, forward_time=0.155, loss_ctc=24.554, loss=24.554, backward_time=0.024, grad_norm=196.474, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.242 +[stan] 2024-01-17 00:27:59,462 (trainer:762) INFO: 19epoch:train:361-400batch: iter_time=5.187e-05, forward_time=0.154, loss_ctc=24.243, loss=24.243, backward_time=0.024, grad_norm=194.810, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.230 +[stan] 2024-01-17 00:28:11,510 (trainer:762) INFO: 19epoch:train:401-440batch: iter_time=5.130e-05, forward_time=0.151, loss_ctc=23.625, loss=23.625, backward_time=0.023, grad_norm=200.859, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-17 00:28:23,570 (trainer:762) INFO: 19epoch:train:441-480batch: iter_time=5.075e-05, forward_time=0.151, loss_ctc=23.590, loss=23.590, backward_time=0.023, grad_norm=192.774, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.206 +[stan] 2024-01-17 00:28:35,352 (trainer:762) INFO: 19epoch:train:481-520batch: iter_time=5.303e-05, forward_time=0.148, loss_ctc=23.317, loss=23.317, backward_time=0.023, grad_norm=188.498, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.178 +[stan] 2024-01-17 00:28:47,741 (trainer:762) INFO: 19epoch:train:521-560batch: iter_time=5.349e-05, forward_time=0.155, loss_ctc=25.527, loss=25.527, backward_time=0.024, grad_norm=196.182, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.239 +[stan] 2024-01-17 00:28:59,949 (trainer:762) INFO: 19epoch:train:561-600batch: iter_time=5.219e-05, forward_time=0.153, loss_ctc=24.031, loss=24.031, backward_time=0.024, grad_norm=192.746, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.221 +[stan] 2024-01-17 00:29:12,150 (trainer:762) INFO: 19epoch:train:601-640batch: iter_time=5.232e-05, forward_time=0.153, loss_ctc=23.689, loss=23.689, backward_time=0.024, grad_norm=187.783, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-17 00:29:24,151 (trainer:762) INFO: 19epoch:train:641-680batch: iter_time=5.235e-05, forward_time=0.150, loss_ctc=24.096, loss=24.096, backward_time=0.023, grad_norm=202.660, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-17 00:29:36,306 (trainer:762) INFO: 19epoch:train:681-720batch: iter_time=5.155e-05, forward_time=0.152, loss_ctc=24.288, loss=24.288, backward_time=0.024, grad_norm=198.653, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-17 00:29:48,816 (trainer:762) INFO: 19epoch:train:721-760batch: iter_time=5.294e-05, forward_time=0.156, loss_ctc=25.612, loss=25.612, backward_time=0.024, grad_norm=200.938, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.251 +[stan] 2024-01-17 00:30:00,708 (trainer:762) INFO: 19epoch:train:761-800batch: iter_time=4.809e-05, forward_time=0.149, loss_ctc=24.328, loss=24.328, backward_time=0.023, grad_norm=192.408, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.189 +[stan] 2024-01-17 00:30:05,467 (trainer:357) INFO: 19epoch results: [train] iter_time=2.375e-04, forward_time=0.152, loss_ctc=24.368, loss=24.368, backward_time=0.024, grad_norm=196.884, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215, time=4 minutes and 3.12 seconds, total_count=15200, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=283.758, cer_ctc=0.197, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=283.758, time=1.15 seconds, total_count=95, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.53 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 00:30:06,457 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 00:30:06,459 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/18epoch.pth +[stan] 2024-01-17 00:30:06,459 (trainer:291) INFO: 20/30epoch started. Estimated time to finish: 45 minutes and 37.62 seconds +[stan] 2024-01-17 00:30:18,709 (trainer:762) INFO: 20epoch:train:1-40batch: iter_time=0.004, forward_time=0.150, loss_ctc=23.668, loss=23.668, backward_time=0.023, grad_norm=195.405, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-17 00:30:30,872 (trainer:762) INFO: 20epoch:train:41-80batch: iter_time=5.021e-05, forward_time=0.152, loss_ctc=24.115, loss=24.115, backward_time=0.024, grad_norm=194.937, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-17 00:30:43,021 (trainer:762) INFO: 20epoch:train:81-120batch: iter_time=5.229e-05, forward_time=0.152, loss_ctc=24.063, loss=24.063, backward_time=0.024, grad_norm=202.314, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-17 00:30:55,061 (trainer:762) INFO: 20epoch:train:121-160batch: iter_time=5.072e-05, forward_time=0.151, loss_ctc=23.137, loss=23.137, backward_time=0.023, grad_norm=188.164, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-17 00:31:07,099 (trainer:762) INFO: 20epoch:train:161-200batch: iter_time=5.073e-05, forward_time=0.151, loss_ctc=23.991, loss=23.991, backward_time=0.023, grad_norm=196.598, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-17 00:31:19,550 (trainer:762) INFO: 20epoch:train:201-240batch: iter_time=5.062e-05, forward_time=0.156, loss_ctc=25.013, loss=25.013, backward_time=0.024, grad_norm=200.194, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.245 +[stan] 2024-01-17 00:31:31,454 (trainer:762) INFO: 20epoch:train:241-280batch: iter_time=5.157e-05, forward_time=0.149, loss_ctc=23.188, loss=23.188, backward_time=0.023, grad_norm=188.184, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.190 +[stan] 2024-01-17 00:31:43,689 (trainer:762) INFO: 20epoch:train:281-320batch: iter_time=5.066e-05, forward_time=0.153, loss_ctc=24.203, loss=24.203, backward_time=0.024, grad_norm=195.770, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-17 00:31:55,685 (trainer:762) INFO: 20epoch:train:321-360batch: iter_time=5.054e-05, forward_time=0.150, loss_ctc=23.441, loss=23.441, backward_time=0.023, grad_norm=196.444, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-17 00:32:07,869 (trainer:762) INFO: 20epoch:train:361-400batch: iter_time=5.254e-05, forward_time=0.153, loss_ctc=22.871, loss=22.871, backward_time=0.024, grad_norm=195.112, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-17 00:32:20,298 (trainer:762) INFO: 20epoch:train:401-440batch: iter_time=5.101e-05, forward_time=0.155, loss_ctc=25.064, loss=25.064, backward_time=0.024, grad_norm=201.849, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.243 +[stan] 2024-01-17 00:32:32,135 (trainer:762) INFO: 20epoch:train:441-480batch: iter_time=5.374e-05, forward_time=0.148, loss_ctc=23.023, loss=23.023, backward_time=0.023, grad_norm=190.166, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.184 +[stan] 2024-01-17 00:32:44,256 (trainer:762) INFO: 20epoch:train:481-520batch: iter_time=5.167e-05, forward_time=0.152, loss_ctc=23.331, loss=23.331, backward_time=0.023, grad_norm=193.423, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-17 00:32:56,230 (trainer:762) INFO: 20epoch:train:521-560batch: iter_time=5.263e-05, forward_time=0.150, loss_ctc=22.827, loss=22.827, backward_time=0.023, grad_norm=198.066, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.197 +[stan] 2024-01-17 00:33:08,107 (trainer:762) INFO: 20epoch:train:561-600batch: iter_time=5.129e-05, forward_time=0.149, loss_ctc=23.619, loss=23.619, backward_time=0.023, grad_norm=201.435, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.188 +[stan] 2024-01-17 00:33:20,626 (trainer:762) INFO: 20epoch:train:601-640batch: iter_time=5.117e-05, forward_time=0.156, loss_ctc=24.905, loss=24.905, backward_time=0.024, grad_norm=206.802, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.252 +[stan] 2024-01-17 00:33:32,288 (trainer:762) INFO: 20epoch:train:641-680batch: iter_time=5.110e-05, forward_time=0.146, loss_ctc=22.200, loss=22.200, backward_time=0.023, grad_norm=191.050, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.166 +[stan] 2024-01-17 00:33:44,570 (trainer:762) INFO: 20epoch:train:681-720batch: iter_time=5.045e-05, forward_time=0.154, loss_ctc=24.683, loss=24.683, backward_time=0.024, grad_norm=202.958, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.228 +[stan] 2024-01-17 00:33:56,983 (trainer:762) INFO: 20epoch:train:721-760batch: iter_time=5.043e-05, forward_time=0.155, loss_ctc=23.915, loss=23.915, backward_time=0.024, grad_norm=188.755, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.241 +[stan] 2024-01-17 00:34:08,983 (trainer:762) INFO: 20epoch:train:761-800batch: iter_time=4.818e-05, forward_time=0.150, loss_ctc=23.534, loss=23.534, backward_time=0.023, grad_norm=207.476, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-17 00:34:13,756 (trainer:357) INFO: 20epoch results: [train] iter_time=2.491e-04, forward_time=0.152, loss_ctc=23.739, loss=23.739, backward_time=0.024, grad_norm=196.755, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213, time=4 minutes and 2.6 seconds, total_count=16000, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=281.931, cer_ctc=0.188, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=281.931, time=1.15 seconds, total_count=100, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.54 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 00:34:14,704 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 00:34:14,706 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/19epoch.pth +[stan] 2024-01-17 00:34:14,706 (trainer:291) INFO: 21/30epoch started. Estimated time to finish: 41 minutes and 28.43 seconds +[stan] 2024-01-17 00:34:26,965 (trainer:762) INFO: 21epoch:train:1-40batch: iter_time=0.004, forward_time=0.150, loss_ctc=23.453, loss=23.453, backward_time=0.023, grad_norm=203.881, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-17 00:34:39,348 (trainer:762) INFO: 21epoch:train:41-80batch: iter_time=5.345e-05, forward_time=0.155, loss_ctc=24.201, loss=24.201, backward_time=0.024, grad_norm=199.337, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.238 +[stan] 2024-01-17 00:34:51,237 (trainer:762) INFO: 21epoch:train:81-120batch: iter_time=5.357e-05, forward_time=0.149, loss_ctc=22.709, loss=22.709, backward_time=0.023, grad_norm=200.666, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.189 +[stan] 2024-01-17 00:35:03,757 (trainer:762) INFO: 21epoch:train:121-160batch: iter_time=5.171e-05, forward_time=0.156, loss_ctc=24.787, loss=24.787, backward_time=0.024, grad_norm=200.075, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.252 +[stan] 2024-01-17 00:35:15,715 (trainer:762) INFO: 21epoch:train:161-200batch: iter_time=5.278e-05, forward_time=0.150, loss_ctc=23.595, loss=23.595, backward_time=0.023, grad_norm=201.239, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.196 +[stan] 2024-01-17 00:35:27,980 (trainer:762) INFO: 21epoch:train:201-240batch: iter_time=5.061e-05, forward_time=0.153, loss_ctc=24.600, loss=24.600, backward_time=0.024, grad_norm=209.428, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-17 00:35:40,032 (trainer:762) INFO: 21epoch:train:241-280batch: iter_time=5.295e-05, forward_time=0.151, loss_ctc=23.019, loss=23.019, backward_time=0.023, grad_norm=197.264, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-17 00:35:52,106 (trainer:762) INFO: 21epoch:train:281-320batch: iter_time=5.388e-05, forward_time=0.151, loss_ctc=23.921, loss=23.921, backward_time=0.023, grad_norm=197.372, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.207 +[stan] 2024-01-17 00:36:03,830 (trainer:762) INFO: 21epoch:train:321-360batch: iter_time=5.210e-05, forward_time=0.147, loss_ctc=21.967, loss=21.967, backward_time=0.023, grad_norm=203.722, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.172 +[stan] 2024-01-17 00:36:16,337 (trainer:762) INFO: 21epoch:train:361-400batch: iter_time=5.420e-05, forward_time=0.156, loss_ctc=24.073, loss=24.073, backward_time=0.024, grad_norm=200.323, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.251 +[stan] 2024-01-17 00:36:28,489 (trainer:762) INFO: 21epoch:train:401-440batch: iter_time=5.450e-05, forward_time=0.152, loss_ctc=23.269, loss=23.269, backward_time=0.024, grad_norm=196.393, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-17 00:36:40,425 (trainer:762) INFO: 21epoch:train:441-480batch: iter_time=5.236e-05, forward_time=0.150, loss_ctc=22.874, loss=22.874, backward_time=0.023, grad_norm=199.800, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.194 +[stan] 2024-01-17 00:36:52,672 (trainer:762) INFO: 21epoch:train:481-520batch: iter_time=5.642e-05, forward_time=0.153, loss_ctc=24.059, loss=24.059, backward_time=0.024, grad_norm=202.106, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-17 00:37:04,848 (trainer:762) INFO: 21epoch:train:521-560batch: iter_time=5.422e-05, forward_time=0.152, loss_ctc=23.124, loss=23.124, backward_time=0.024, grad_norm=201.105, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-17 00:37:17,024 (trainer:762) INFO: 21epoch:train:561-600batch: iter_time=5.211e-05, forward_time=0.152, loss_ctc=23.448, loss=23.448, backward_time=0.024, grad_norm=205.518, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-17 00:37:28,636 (trainer:762) INFO: 21epoch:train:601-640batch: iter_time=5.362e-05, forward_time=0.146, loss_ctc=22.305, loss=22.305, backward_time=0.023, grad_norm=192.648, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.161 +[stan] 2024-01-17 00:37:41,204 (trainer:762) INFO: 21epoch:train:641-680batch: iter_time=5.475e-05, forward_time=0.157, loss_ctc=23.297, loss=23.297, backward_time=0.024, grad_norm=186.451, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.257 +[stan] 2024-01-17 00:37:53,390 (trainer:762) INFO: 21epoch:train:681-720batch: iter_time=5.542e-05, forward_time=0.153, loss_ctc=23.242, loss=23.242, backward_time=0.024, grad_norm=199.177, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-17 00:38:05,675 (trainer:762) INFO: 21epoch:train:721-760batch: iter_time=5.503e-05, forward_time=0.154, loss_ctc=23.034, loss=23.034, backward_time=0.023, grad_norm=191.917, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.228 +[stan] 2024-01-17 00:38:17,546 (trainer:762) INFO: 21epoch:train:761-800batch: iter_time=4.801e-05, forward_time=0.149, loss_ctc=23.414, loss=23.414, backward_time=0.023, grad_norm=205.784, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.187 +[stan] 2024-01-17 00:38:22,311 (trainer:357) INFO: 21epoch results: [train] iter_time=2.402e-04, forward_time=0.152, loss_ctc=23.419, loss=23.419, backward_time=0.023, grad_norm=199.710, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214, time=4 minutes and 2.91 seconds, total_count=16800, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=280.659, cer_ctc=0.193, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=280.659, time=1.16 seconds, total_count=105, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.53 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 00:38:23,373 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 00:38:23,374 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/20epoch.pth +[stan] 2024-01-17 00:38:23,374 (trainer:291) INFO: 22/30epoch started. Estimated time to finish: 37 minutes and 19.51 seconds +[stan] 2024-01-17 00:38:35,890 (trainer:762) INFO: 22epoch:train:1-40batch: iter_time=0.003, forward_time=0.153, loss_ctc=23.128, loss=23.128, backward_time=0.024, grad_norm=203.727, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.251 +[stan] 2024-01-17 00:38:47,810 (trainer:762) INFO: 22epoch:train:41-80batch: iter_time=5.400e-05, forward_time=0.149, loss_ctc=23.665, loss=23.665, backward_time=0.023, grad_norm=201.866, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.192 +[stan] 2024-01-17 00:38:59,987 (trainer:762) INFO: 22epoch:train:81-120batch: iter_time=5.316e-05, forward_time=0.152, loss_ctc=23.278, loss=23.278, backward_time=0.023, grad_norm=196.855, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-17 00:39:12,112 (trainer:762) INFO: 22epoch:train:121-160batch: iter_time=5.493e-05, forward_time=0.152, loss_ctc=23.055, loss=23.055, backward_time=0.023, grad_norm=199.203, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-17 00:39:24,131 (trainer:762) INFO: 22epoch:train:161-200batch: iter_time=5.340e-05, forward_time=0.151, loss_ctc=22.945, loss=22.945, backward_time=0.023, grad_norm=189.075, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.202 +[stan] 2024-01-17 00:39:36,390 (trainer:762) INFO: 22epoch:train:201-240batch: iter_time=5.307e-05, forward_time=0.153, loss_ctc=23.168, loss=23.168, backward_time=0.024, grad_norm=202.464, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-17 00:39:48,419 (trainer:762) INFO: 22epoch:train:241-280batch: iter_time=5.438e-05, forward_time=0.151, loss_ctc=22.980, loss=22.980, backward_time=0.023, grad_norm=193.646, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-17 00:40:00,815 (trainer:762) INFO: 22epoch:train:281-320batch: iter_time=5.569e-05, forward_time=0.155, loss_ctc=23.143, loss=23.143, backward_time=0.024, grad_norm=196.053, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.239 +[stan] 2024-01-17 00:40:12,798 (trainer:762) INFO: 22epoch:train:321-360batch: iter_time=5.297e-05, forward_time=0.150, loss_ctc=22.784, loss=22.784, backward_time=0.023, grad_norm=197.449, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.198 +[stan] 2024-01-17 00:40:24,828 (trainer:762) INFO: 22epoch:train:361-400batch: iter_time=5.432e-05, forward_time=0.151, loss_ctc=23.073, loss=23.073, backward_time=0.023, grad_norm=201.706, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-17 00:40:37,362 (trainer:762) INFO: 22epoch:train:401-440batch: iter_time=5.095e-05, forward_time=0.157, loss_ctc=24.090, loss=24.090, backward_time=0.024, grad_norm=215.564, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.253 +[stan] 2024-01-17 00:40:49,174 (trainer:762) INFO: 22epoch:train:441-480batch: iter_time=5.348e-05, forward_time=0.148, loss_ctc=22.264, loss=22.264, backward_time=0.023, grad_norm=205.500, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.181 +[stan] 2024-01-17 00:41:01,183 (trainer:762) INFO: 22epoch:train:481-520batch: iter_time=5.295e-05, forward_time=0.150, loss_ctc=22.836, loss=22.836, backward_time=0.023, grad_norm=193.198, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-17 00:41:13,483 (trainer:762) INFO: 22epoch:train:521-560batch: iter_time=5.205e-05, forward_time=0.154, loss_ctc=23.208, loss=23.208, backward_time=0.023, grad_norm=193.851, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.230 +[stan] 2024-01-17 00:41:25,524 (trainer:762) INFO: 22epoch:train:561-600batch: iter_time=5.182e-05, forward_time=0.151, loss_ctc=23.084, loss=23.084, backward_time=0.024, grad_norm=196.472, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-17 00:41:37,781 (trainer:762) INFO: 22epoch:train:601-640batch: iter_time=5.154e-05, forward_time=0.153, loss_ctc=23.876, loss=23.876, backward_time=0.024, grad_norm=201.891, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.226 +[stan] 2024-01-17 00:41:49,605 (trainer:762) INFO: 22epoch:train:641-680batch: iter_time=5.143e-05, forward_time=0.148, loss_ctc=22.317, loss=22.317, backward_time=0.023, grad_norm=199.546, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.182 +[stan] 2024-01-17 00:42:01,859 (trainer:762) INFO: 22epoch:train:681-720batch: iter_time=5.422e-05, forward_time=0.153, loss_ctc=22.814, loss=22.814, backward_time=0.024, grad_norm=198.127, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-17 00:42:14,234 (trainer:762) INFO: 22epoch:train:721-760batch: iter_time=5.149e-05, forward_time=0.155, loss_ctc=23.700, loss=23.700, backward_time=0.024, grad_norm=200.678, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-17 00:42:26,157 (trainer:762) INFO: 22epoch:train:761-800batch: iter_time=4.812e-05, forward_time=0.149, loss_ctc=22.279, loss=22.279, backward_time=0.023, grad_norm=195.530, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.192 +[stan] 2024-01-17 00:42:30,909 (trainer:357) INFO: 22epoch results: [train] iter_time=2.140e-04, forward_time=0.152, loss_ctc=23.085, loss=23.085, backward_time=0.023, grad_norm=199.120, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214, time=4 minutes and 2.86 seconds, total_count=17600, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=287.380, cer_ctc=0.191, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=287.380, time=1.15 seconds, total_count=110, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.52 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 00:42:31,890 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 00:42:31,891 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/21epoch.pth +[stan] 2024-01-17 00:42:31,891 (trainer:291) INFO: 23/30epoch started. Estimated time to finish: 33 minutes and 10.56 seconds +[stan] 2024-01-17 00:42:44,215 (trainer:762) INFO: 23epoch:train:1-40batch: iter_time=0.004, forward_time=0.151, loss_ctc=22.456, loss=22.456, backward_time=0.024, grad_norm=203.967, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-17 00:42:56,557 (trainer:762) INFO: 23epoch:train:41-80batch: iter_time=5.391e-05, forward_time=0.154, loss_ctc=23.024, loss=23.024, backward_time=0.024, grad_norm=191.867, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.234 +[stan] 2024-01-17 00:43:08,257 (trainer:762) INFO: 23epoch:train:81-120batch: iter_time=5.443e-05, forward_time=0.147, loss_ctc=21.471, loss=21.471, backward_time=0.023, grad_norm=190.461, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.170 +[stan] 2024-01-17 00:43:20,559 (trainer:762) INFO: 23epoch:train:121-160batch: iter_time=5.347e-05, forward_time=0.154, loss_ctc=23.036, loss=23.036, backward_time=0.024, grad_norm=204.272, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.230 +[stan] 2024-01-17 00:43:32,785 (trainer:762) INFO: 23epoch:train:161-200batch: iter_time=5.106e-05, forward_time=0.153, loss_ctc=23.110, loss=23.110, backward_time=0.024, grad_norm=199.129, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-17 00:43:44,772 (trainer:762) INFO: 23epoch:train:201-240batch: iter_time=5.351e-05, forward_time=0.150, loss_ctc=22.269, loss=22.269, backward_time=0.023, grad_norm=198.172, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-17 00:43:56,943 (trainer:762) INFO: 23epoch:train:241-280batch: iter_time=5.396e-05, forward_time=0.152, loss_ctc=22.363, loss=22.363, backward_time=0.024, grad_norm=197.417, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217 +[stan] 2024-01-17 00:44:09,121 (trainer:762) INFO: 23epoch:train:281-320batch: iter_time=5.569e-05, forward_time=0.152, loss_ctc=22.695, loss=22.695, backward_time=0.023, grad_norm=204.131, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-17 00:44:20,937 (trainer:762) INFO: 23epoch:train:321-360batch: iter_time=5.379e-05, forward_time=0.148, loss_ctc=22.450, loss=22.450, backward_time=0.023, grad_norm=202.799, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.181 +[stan] 2024-01-17 00:44:33,325 (trainer:762) INFO: 23epoch:train:361-400batch: iter_time=5.081e-05, forward_time=0.155, loss_ctc=22.341, loss=22.341, backward_time=0.024, grad_norm=203.157, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.239 +[stan] 2024-01-17 00:44:45,379 (trainer:762) INFO: 23epoch:train:401-440batch: iter_time=5.480e-05, forward_time=0.151, loss_ctc=22.134, loss=22.134, backward_time=0.023, grad_norm=194.614, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-17 00:44:57,878 (trainer:762) INFO: 23epoch:train:441-480batch: iter_time=5.155e-05, forward_time=0.156, loss_ctc=23.468, loss=23.468, backward_time=0.024, grad_norm=201.954, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.250 +[stan] 2024-01-17 00:45:09,705 (trainer:762) INFO: 23epoch:train:481-520batch: iter_time=5.442e-05, forward_time=0.148, loss_ctc=21.104, loss=21.104, backward_time=0.023, grad_norm=194.398, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.183 +[stan] 2024-01-17 00:45:21,737 (trainer:762) INFO: 23epoch:train:521-560batch: iter_time=5.387e-05, forward_time=0.151, loss_ctc=22.420, loss=22.420, backward_time=0.023, grad_norm=192.299, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.203 +[stan] 2024-01-17 00:45:33,795 (trainer:762) INFO: 23epoch:train:561-600batch: iter_time=5.584e-05, forward_time=0.151, loss_ctc=22.943, loss=22.943, backward_time=0.024, grad_norm=200.878, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.206 +[stan] 2024-01-17 00:45:46,072 (trainer:762) INFO: 23epoch:train:601-640batch: iter_time=5.153e-05, forward_time=0.154, loss_ctc=23.106, loss=23.106, backward_time=0.024, grad_norm=203.041, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.228 +[stan] 2024-01-17 00:45:57,929 (trainer:762) INFO: 23epoch:train:641-680batch: iter_time=5.095e-05, forward_time=0.149, loss_ctc=22.066, loss=22.066, backward_time=0.023, grad_norm=202.954, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.186 +[stan] 2024-01-17 00:46:10,370 (trainer:762) INFO: 23epoch:train:681-720batch: iter_time=5.301e-05, forward_time=0.156, loss_ctc=23.458, loss=23.458, backward_time=0.024, grad_norm=198.602, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.244 +[stan] 2024-01-17 00:46:22,424 (trainer:762) INFO: 23epoch:train:721-760batch: iter_time=5.319e-05, forward_time=0.151, loss_ctc=22.250, loss=22.250, backward_time=0.024, grad_norm=192.119, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-17 00:46:34,902 (trainer:762) INFO: 23epoch:train:761-800batch: iter_time=4.779e-05, forward_time=0.156, loss_ctc=22.558, loss=22.558, backward_time=0.024, grad_norm=195.019, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.248 +[stan] 2024-01-17 00:46:39,687 (trainer:357) INFO: 23epoch results: [train] iter_time=2.326e-04, forward_time=0.152, loss_ctc=22.536, loss=22.536, backward_time=0.024, grad_norm=198.562, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215, time=4 minutes and 3.09 seconds, total_count=18400, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=282.819, cer_ctc=0.185, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=282.819, time=1.14 seconds, total_count=115, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.57 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 00:46:40,687 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 00:46:40,688 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/22epoch.pth +[stan] 2024-01-17 00:46:40,688 (trainer:291) INFO: 24/30epoch started. Estimated time to finish: 29 minutes and 1.73 seconds +[stan] 2024-01-17 00:46:52,817 (trainer:762) INFO: 24epoch:train:1-40batch: iter_time=0.004, forward_time=0.149, loss_ctc=22.078, loss=22.078, backward_time=0.023, grad_norm=198.789, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-17 00:47:04,828 (trainer:762) INFO: 24epoch:train:41-80batch: iter_time=5.225e-05, forward_time=0.151, loss_ctc=22.108, loss=22.108, backward_time=0.023, grad_norm=197.346, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-17 00:47:16,985 (trainer:762) INFO: 24epoch:train:81-120batch: iter_time=5.116e-05, forward_time=0.152, loss_ctc=22.709, loss=22.709, backward_time=0.023, grad_norm=203.554, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-17 00:47:29,204 (trainer:762) INFO: 24epoch:train:121-160batch: iter_time=5.345e-05, forward_time=0.153, loss_ctc=22.075, loss=22.075, backward_time=0.024, grad_norm=191.574, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-17 00:47:41,196 (trainer:762) INFO: 24epoch:train:161-200batch: iter_time=5.029e-05, forward_time=0.150, loss_ctc=21.340, loss=21.340, backward_time=0.023, grad_norm=203.925, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-17 00:47:53,493 (trainer:762) INFO: 24epoch:train:201-240batch: iter_time=5.037e-05, forward_time=0.154, loss_ctc=22.359, loss=22.359, backward_time=0.024, grad_norm=201.129, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.230 +[stan] 2024-01-17 00:48:05,477 (trainer:762) INFO: 24epoch:train:241-280batch: iter_time=5.195e-05, forward_time=0.150, loss_ctc=21.538, loss=21.538, backward_time=0.023, grad_norm=188.027, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.198 +[stan] 2024-01-17 00:48:17,607 (trainer:762) INFO: 24epoch:train:281-320batch: iter_time=5.204e-05, forward_time=0.152, loss_ctc=22.182, loss=22.182, backward_time=0.024, grad_norm=186.547, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213 +[stan] 2024-01-17 00:48:29,908 (trainer:762) INFO: 24epoch:train:321-360batch: iter_time=5.127e-05, forward_time=0.154, loss_ctc=22.747, loss=22.747, backward_time=0.024, grad_norm=204.946, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.230 +[stan] 2024-01-17 00:48:41,786 (trainer:762) INFO: 24epoch:train:361-400batch: iter_time=5.357e-05, forward_time=0.149, loss_ctc=21.388, loss=21.388, backward_time=0.023, grad_norm=202.694, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.188 +[stan] 2024-01-17 00:48:54,010 (trainer:762) INFO: 24epoch:train:401-440batch: iter_time=5.113e-05, forward_time=0.153, loss_ctc=22.505, loss=22.505, backward_time=0.024, grad_norm=197.863, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-17 00:49:05,955 (trainer:762) INFO: 24epoch:train:441-480batch: iter_time=5.406e-05, forward_time=0.150, loss_ctc=21.733, loss=21.733, backward_time=0.023, grad_norm=201.065, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.194 +[stan] 2024-01-17 00:49:18,287 (trainer:762) INFO: 24epoch:train:481-520batch: iter_time=5.437e-05, forward_time=0.154, loss_ctc=22.250, loss=22.250, backward_time=0.024, grad_norm=196.122, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.233 +[stan] 2024-01-17 00:49:30,385 (trainer:762) INFO: 24epoch:train:521-560batch: iter_time=5.152e-05, forward_time=0.151, loss_ctc=21.540, loss=21.540, backward_time=0.023, grad_norm=202.942, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.210 +[stan] 2024-01-17 00:49:42,340 (trainer:762) INFO: 24epoch:train:561-600batch: iter_time=5.334e-05, forward_time=0.150, loss_ctc=21.905, loss=21.905, backward_time=0.024, grad_norm=205.828, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.195 +[stan] 2024-01-17 00:49:54,981 (trainer:762) INFO: 24epoch:train:601-640batch: iter_time=5.086e-05, forward_time=0.158, loss_ctc=23.430, loss=23.430, backward_time=0.024, grad_norm=202.850, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.264 +[stan] 2024-01-17 00:50:06,750 (trainer:762) INFO: 24epoch:train:641-680batch: iter_time=5.228e-05, forward_time=0.148, loss_ctc=21.110, loss=21.110, backward_time=0.023, grad_norm=201.150, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.177 +[stan] 2024-01-17 00:50:19,129 (trainer:762) INFO: 24epoch:train:681-720batch: iter_time=5.142e-05, forward_time=0.155, loss_ctc=23.036, loss=23.036, backward_time=0.024, grad_norm=206.167, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.238 +[stan] 2024-01-17 00:50:30,635 (trainer:762) INFO: 24epoch:train:721-760batch: iter_time=5.300e-05, forward_time=0.145, loss_ctc=21.236, loss=21.236, backward_time=0.023, grad_norm=193.649, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.151 +[stan] 2024-01-17 00:50:43,087 (trainer:762) INFO: 24epoch:train:761-800batch: iter_time=4.829e-05, forward_time=0.156, loss_ctc=22.574, loss=22.574, backward_time=0.024, grad_norm=205.246, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.245 +[stan] 2024-01-17 00:50:47,843 (trainer:357) INFO: 24epoch results: [train] iter_time=2.339e-04, forward_time=0.152, loss_ctc=22.091, loss=22.091, backward_time=0.024, grad_norm=199.571, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212, time=4 minutes and 2.47 seconds, total_count=19200, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=286.704, cer_ctc=0.191, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=286.704, time=1.16 seconds, total_count=120, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.53 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 00:50:48,947 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 00:50:48,949 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/23epoch.pth +[stan] 2024-01-17 00:50:48,949 (trainer:291) INFO: 25/30epoch started. Estimated time to finish: 24 minutes and 52.77 seconds +[stan] 2024-01-17 00:51:01,585 (trainer:762) INFO: 25epoch:train:1-40batch: iter_time=0.004, forward_time=0.155, loss_ctc=23.034, loss=23.034, backward_time=0.024, grad_norm=207.402, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.263 +[stan] 2024-01-17 00:51:13,733 (trainer:762) INFO: 25epoch:train:41-80batch: iter_time=5.362e-05, forward_time=0.152, loss_ctc=21.038, loss=21.038, backward_time=0.024, grad_norm=199.588, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-17 00:51:25,583 (trainer:762) INFO: 25epoch:train:81-120batch: iter_time=5.086e-05, forward_time=0.149, loss_ctc=20.582, loss=20.582, backward_time=0.023, grad_norm=202.689, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.185 +[stan] 2024-01-17 00:51:37,674 (trainer:762) INFO: 25epoch:train:121-160batch: iter_time=5.107e-05, forward_time=0.151, loss_ctc=21.520, loss=21.520, backward_time=0.023, grad_norm=195.684, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.209 +[stan] 2024-01-17 00:51:49,712 (trainer:762) INFO: 25epoch:train:161-200batch: iter_time=5.315e-05, forward_time=0.151, loss_ctc=20.797, loss=20.797, backward_time=0.024, grad_norm=199.006, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-17 00:52:01,948 (trainer:762) INFO: 25epoch:train:201-240batch: iter_time=5.125e-05, forward_time=0.153, loss_ctc=22.339, loss=22.339, backward_time=0.023, grad_norm=195.026, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.224 +[stan] 2024-01-17 00:52:14,279 (trainer:762) INFO: 25epoch:train:241-280batch: iter_time=5.385e-05, forward_time=0.154, loss_ctc=21.856, loss=21.856, backward_time=0.024, grad_norm=191.026, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.233 +[stan] 2024-01-17 00:52:26,200 (trainer:762) INFO: 25epoch:train:281-320batch: iter_time=5.393e-05, forward_time=0.149, loss_ctc=21.614, loss=21.614, backward_time=0.023, grad_norm=203.389, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.192 +[stan] 2024-01-17 00:52:38,319 (trainer:762) INFO: 25epoch:train:321-360batch: iter_time=5.402e-05, forward_time=0.152, loss_ctc=21.593, loss=21.593, backward_time=0.023, grad_norm=202.000, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-17 00:52:50,511 (trainer:762) INFO: 25epoch:train:361-400batch: iter_time=5.382e-05, forward_time=0.153, loss_ctc=22.037, loss=22.037, backward_time=0.023, grad_norm=199.633, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-17 00:53:02,517 (trainer:762) INFO: 25epoch:train:401-440batch: iter_time=5.366e-05, forward_time=0.150, loss_ctc=21.317, loss=21.317, backward_time=0.024, grad_norm=201.543, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-17 00:53:14,696 (trainer:762) INFO: 25epoch:train:441-480batch: iter_time=5.786e-05, forward_time=0.153, loss_ctc=21.877, loss=21.877, backward_time=0.024, grad_norm=197.795, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-17 00:53:26,883 (trainer:762) INFO: 25epoch:train:481-520batch: iter_time=5.213e-05, forward_time=0.153, loss_ctc=21.207, loss=21.207, backward_time=0.024, grad_norm=197.869, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-17 00:53:38,829 (trainer:762) INFO: 25epoch:train:521-560batch: iter_time=5.222e-05, forward_time=0.150, loss_ctc=20.329, loss=20.329, backward_time=0.024, grad_norm=193.048, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.194 +[stan] 2024-01-17 00:53:51,049 (trainer:762) INFO: 25epoch:train:561-600batch: iter_time=5.494e-05, forward_time=0.153, loss_ctc=22.166, loss=22.166, backward_time=0.023, grad_norm=199.443, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-17 00:54:03,128 (trainer:762) INFO: 25epoch:train:601-640batch: iter_time=5.380e-05, forward_time=0.151, loss_ctc=21.635, loss=21.635, backward_time=0.023, grad_norm=209.498, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-17 00:54:15,664 (trainer:762) INFO: 25epoch:train:641-680batch: iter_time=5.602e-05, forward_time=0.157, loss_ctc=22.949, loss=22.949, backward_time=0.024, grad_norm=201.111, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.253 +[stan] 2024-01-17 00:54:27,395 (trainer:762) INFO: 25epoch:train:681-720batch: iter_time=5.148e-05, forward_time=0.147, loss_ctc=21.085, loss=21.085, backward_time=0.023, grad_norm=196.258, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.173 +[stan] 2024-01-17 00:54:39,454 (trainer:762) INFO: 25epoch:train:721-760batch: iter_time=5.047e-05, forward_time=0.151, loss_ctc=21.855, loss=21.855, backward_time=0.024, grad_norm=193.679, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.206 +[stan] 2024-01-17 00:54:51,237 (trainer:762) INFO: 25epoch:train:761-800batch: iter_time=5.075e-05, forward_time=0.148, loss_ctc=21.050, loss=21.050, backward_time=0.023, grad_norm=205.277, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.178 +[stan] 2024-01-17 00:54:55,976 (trainer:357) INFO: 25epoch results: [train] iter_time=2.374e-04, forward_time=0.152, loss_ctc=21.593, loss=21.593, backward_time=0.023, grad_norm=199.548, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211, time=4 minutes and 2.36 seconds, total_count=20000, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=287.322, cer_ctc=0.186, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=287.322, time=1.16 seconds, total_count=125, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.51 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 00:54:56,990 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 00:54:56,992 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/24epoch.pth +[stan] 2024-01-17 00:54:56,992 (trainer:291) INFO: 26/30epoch started. Estimated time to finish: 20 minutes and 43.83 seconds +[stan] 2024-01-17 00:55:09,779 (trainer:762) INFO: 26epoch:train:1-40batch: iter_time=0.004, forward_time=0.156, loss_ctc=21.530, loss=21.530, backward_time=0.024, grad_norm=202.507, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.278 +[stan] 2024-01-17 00:55:22,056 (trainer:762) INFO: 26epoch:train:41-80batch: iter_time=5.019e-05, forward_time=0.155, loss_ctc=22.224, loss=22.224, backward_time=0.024, grad_norm=194.169, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.228 +[stan] 2024-01-17 00:55:34,029 (trainer:762) INFO: 26epoch:train:81-120batch: iter_time=5.292e-05, forward_time=0.150, loss_ctc=21.445, loss=21.445, backward_time=0.023, grad_norm=197.236, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.197 +[stan] 2024-01-17 00:55:46,245 (trainer:762) INFO: 26epoch:train:121-160batch: iter_time=5.438e-05, forward_time=0.153, loss_ctc=21.678, loss=21.678, backward_time=0.024, grad_norm=194.753, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.221 +[stan] 2024-01-17 00:55:58,359 (trainer:762) INFO: 26epoch:train:161-200batch: iter_time=5.095e-05, forward_time=0.152, loss_ctc=21.999, loss=21.999, backward_time=0.024, grad_norm=206.066, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211 +[stan] 2024-01-17 00:56:10,702 (trainer:762) INFO: 26epoch:train:201-240batch: iter_time=5.286e-05, forward_time=0.154, loss_ctc=21.827, loss=21.827, backward_time=0.024, grad_norm=207.745, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.234 +[stan] 2024-01-17 00:56:22,646 (trainer:762) INFO: 26epoch:train:241-280batch: iter_time=5.068e-05, forward_time=0.150, loss_ctc=21.024, loss=21.024, backward_time=0.023, grad_norm=204.161, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.194 +[stan] 2024-01-17 00:56:34,492 (trainer:762) INFO: 26epoch:train:281-320batch: iter_time=5.033e-05, forward_time=0.149, loss_ctc=20.533, loss=20.533, backward_time=0.023, grad_norm=209.111, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.184 +[stan] 2024-01-17 00:56:47,010 (trainer:762) INFO: 26epoch:train:321-360batch: iter_time=5.674e-05, forward_time=0.157, loss_ctc=22.130, loss=22.130, backward_time=0.024, grad_norm=203.367, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.252 +[stan] 2024-01-17 00:56:59,049 (trainer:762) INFO: 26epoch:train:361-400batch: iter_time=5.401e-05, forward_time=0.151, loss_ctc=21.788, loss=21.788, backward_time=0.024, grad_norm=205.735, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-17 00:57:10,927 (trainer:762) INFO: 26epoch:train:401-440batch: iter_time=5.378e-05, forward_time=0.149, loss_ctc=21.114, loss=21.114, backward_time=0.023, grad_norm=204.394, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.188 +[stan] 2024-01-17 00:57:23,277 (trainer:762) INFO: 26epoch:train:441-480batch: iter_time=5.225e-05, forward_time=0.155, loss_ctc=21.031, loss=21.031, backward_time=0.024, grad_norm=198.118, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.235 +[stan] 2024-01-17 00:57:35,434 (trainer:762) INFO: 26epoch:train:481-520batch: iter_time=5.352e-05, forward_time=0.152, loss_ctc=21.884, loss=21.884, backward_time=0.023, grad_norm=209.898, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-17 00:57:47,585 (trainer:762) INFO: 26epoch:train:521-560batch: iter_time=5.101e-05, forward_time=0.152, loss_ctc=21.653, loss=21.653, backward_time=0.023, grad_norm=194.412, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215 +[stan] 2024-01-17 00:57:59,556 (trainer:762) INFO: 26epoch:train:561-600batch: iter_time=5.304e-05, forward_time=0.150, loss_ctc=20.408, loss=20.408, backward_time=0.023, grad_norm=193.934, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.197 +[stan] 2024-01-17 00:58:11,795 (trainer:762) INFO: 26epoch:train:601-640batch: iter_time=5.193e-05, forward_time=0.153, loss_ctc=21.493, loss=21.493, backward_time=0.024, grad_norm=208.460, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.224 +[stan] 2024-01-17 00:58:24,015 (trainer:762) INFO: 26epoch:train:641-680batch: iter_time=5.059e-05, forward_time=0.153, loss_ctc=21.438, loss=21.438, backward_time=0.024, grad_norm=205.274, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-17 00:58:36,057 (trainer:762) INFO: 26epoch:train:681-720batch: iter_time=5.165e-05, forward_time=0.151, loss_ctc=20.734, loss=20.734, backward_time=0.023, grad_norm=195.104, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-17 00:58:48,138 (trainer:762) INFO: 26epoch:train:721-760batch: iter_time=5.159e-05, forward_time=0.151, loss_ctc=20.744, loss=20.744, backward_time=0.024, grad_norm=202.188, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-17 00:59:00,203 (trainer:762) INFO: 26epoch:train:761-800batch: iter_time=5.003e-05, forward_time=0.151, loss_ctc=21.006, loss=21.006, backward_time=0.024, grad_norm=206.465, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.206 +[stan] 2024-01-17 00:59:04,952 (trainer:357) INFO: 26epoch results: [train] iter_time=2.425e-04, forward_time=0.152, loss_ctc=21.383, loss=21.383, backward_time=0.024, grad_norm=202.155, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216, time=4 minutes and 3.29 seconds, total_count=20800, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=294.809, cer_ctc=0.191, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=294.809, time=1.14 seconds, total_count=130, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.53 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 00:59:05,976 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 00:59:05,977 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/25epoch.pth +[stan] 2024-01-17 00:59:05,977 (trainer:291) INFO: 27/30epoch started. Estimated time to finish: 16 minutes and 35.1 seconds +[stan] 2024-01-17 00:59:18,535 (trainer:762) INFO: 27epoch:train:1-40batch: iter_time=0.004, forward_time=0.153, loss_ctc=21.366, loss=21.366, backward_time=0.024, grad_norm=208.027, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.255 +[stan] 2024-01-17 00:59:30,540 (trainer:762) INFO: 27epoch:train:41-80batch: iter_time=5.215e-05, forward_time=0.150, loss_ctc=20.986, loss=20.986, backward_time=0.023, grad_norm=193.672, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.200 +[stan] 2024-01-17 00:59:42,683 (trainer:762) INFO: 27epoch:train:81-120batch: iter_time=5.515e-05, forward_time=0.152, loss_ctc=21.314, loss=21.314, backward_time=0.023, grad_norm=200.411, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-17 00:59:55,008 (trainer:762) INFO: 27epoch:train:121-160batch: iter_time=5.297e-05, forward_time=0.154, loss_ctc=21.824, loss=21.824, backward_time=0.024, grad_norm=216.054, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.232 +[stan] 2024-01-17 01:00:06,898 (trainer:762) INFO: 27epoch:train:161-200batch: iter_time=5.113e-05, forward_time=0.149, loss_ctc=20.375, loss=20.375, backward_time=0.024, grad_norm=215.282, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.189 +[stan] 2024-01-17 01:00:19,208 (trainer:762) INFO: 27epoch:train:201-240batch: iter_time=5.393e-05, forward_time=0.154, loss_ctc=21.278, loss=21.278, backward_time=0.024, grad_norm=204.428, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.231 +[stan] 2024-01-17 01:00:31,203 (trainer:762) INFO: 27epoch:train:241-280batch: iter_time=5.402e-05, forward_time=0.150, loss_ctc=20.483, loss=20.483, backward_time=0.023, grad_norm=196.115, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-17 01:00:43,296 (trainer:762) INFO: 27epoch:train:281-320batch: iter_time=5.412e-05, forward_time=0.151, loss_ctc=20.868, loss=20.868, backward_time=0.024, grad_norm=191.691, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.209 +[stan] 2024-01-17 01:00:55,240 (trainer:762) INFO: 27epoch:train:321-360batch: iter_time=5.546e-05, forward_time=0.150, loss_ctc=20.630, loss=20.630, backward_time=0.023, grad_norm=201.118, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.194 +[stan] 2024-01-17 01:01:07,475 (trainer:762) INFO: 27epoch:train:361-400batch: iter_time=5.359e-05, forward_time=0.153, loss_ctc=20.771, loss=20.771, backward_time=0.024, grad_norm=202.723, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.223 +[stan] 2024-01-17 01:01:19,669 (trainer:762) INFO: 27epoch:train:401-440batch: iter_time=5.372e-05, forward_time=0.153, loss_ctc=20.960, loss=20.960, backward_time=0.024, grad_norm=199.114, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-17 01:01:32,026 (trainer:762) INFO: 27epoch:train:441-480batch: iter_time=5.311e-05, forward_time=0.155, loss_ctc=21.292, loss=21.292, backward_time=0.024, grad_norm=200.127, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.236 +[stan] 2024-01-17 01:01:43,754 (trainer:762) INFO: 27epoch:train:481-520batch: iter_time=5.087e-05, forward_time=0.147, loss_ctc=19.662, loss=19.662, backward_time=0.023, grad_norm=204.689, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.173 +[stan] 2024-01-17 01:01:56,042 (trainer:762) INFO: 27epoch:train:521-560batch: iter_time=5.415e-05, forward_time=0.154, loss_ctc=21.222, loss=21.222, backward_time=0.024, grad_norm=210.122, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.229 +[stan] 2024-01-17 01:02:07,846 (trainer:762) INFO: 27epoch:train:561-600batch: iter_time=5.479e-05, forward_time=0.148, loss_ctc=20.405, loss=20.405, backward_time=0.023, grad_norm=195.240, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.180 +[stan] 2024-01-17 01:02:20,323 (trainer:762) INFO: 27epoch:train:601-640batch: iter_time=5.090e-05, forward_time=0.156, loss_ctc=21.793, loss=21.793, backward_time=0.024, grad_norm=204.183, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.248 +[stan] 2024-01-17 01:02:32,385 (trainer:762) INFO: 27epoch:train:641-680batch: iter_time=5.010e-05, forward_time=0.151, loss_ctc=20.854, loss=20.854, backward_time=0.024, grad_norm=203.955, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.206 +[stan] 2024-01-17 01:02:44,785 (trainer:762) INFO: 27epoch:train:681-720batch: iter_time=5.645e-05, forward_time=0.155, loss_ctc=21.497, loss=21.497, backward_time=0.024, grad_norm=207.386, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.240 +[stan] 2024-01-17 01:02:56,508 (trainer:762) INFO: 27epoch:train:721-760batch: iter_time=5.091e-05, forward_time=0.147, loss_ctc=19.814, loss=19.814, backward_time=0.023, grad_norm=192.208, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.172 +[stan] 2024-01-17 01:03:08,726 (trainer:762) INFO: 27epoch:train:761-800batch: iter_time=4.861e-05, forward_time=0.153, loss_ctc=20.905, loss=20.905, backward_time=0.024, grad_norm=199.188, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-17 01:03:13,507 (trainer:357) INFO: 27epoch results: [train] iter_time=2.560e-04, forward_time=0.152, loss_ctc=20.915, loss=20.915, backward_time=0.024, grad_norm=202.287, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214, time=4 minutes and 2.82 seconds, total_count=21600, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=290.915, cer_ctc=0.188, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=290.915, time=1.17 seconds, total_count=135, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.54 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 01:03:14,634 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:03:14,635 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/26epoch.pth +[stan] 2024-01-17 01:03:14,635 (trainer:291) INFO: 28/30epoch started. Estimated time to finish: 12 minutes and 26.31 seconds +[stan] 2024-01-17 01:03:27,079 (trainer:762) INFO: 28epoch:train:1-40batch: iter_time=0.004, forward_time=0.152, loss_ctc=20.247, loss=20.247, backward_time=0.024, grad_norm=201.523, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.244 +[stan] 2024-01-17 01:03:39,186 (trainer:762) INFO: 28epoch:train:41-80batch: iter_time=5.159e-05, forward_time=0.152, loss_ctc=20.410, loss=20.410, backward_time=0.023, grad_norm=199.497, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211 +[stan] 2024-01-17 01:03:51,276 (trainer:762) INFO: 28epoch:train:81-120batch: iter_time=5.156e-05, forward_time=0.151, loss_ctc=21.058, loss=21.058, backward_time=0.023, grad_norm=202.075, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.209 +[stan] 2024-01-17 01:04:03,394 (trainer:762) INFO: 28epoch:train:121-160batch: iter_time=5.457e-05, forward_time=0.152, loss_ctc=20.203, loss=20.203, backward_time=0.024, grad_norm=201.035, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.212 +[stan] 2024-01-17 01:04:15,523 (trainer:762) INFO: 28epoch:train:161-200batch: iter_time=5.377e-05, forward_time=0.152, loss_ctc=20.401, loss=20.401, backward_time=0.024, grad_norm=204.124, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213 +[stan] 2024-01-17 01:04:27,686 (trainer:762) INFO: 28epoch:train:201-240batch: iter_time=5.150e-05, forward_time=0.152, loss_ctc=20.157, loss=20.157, backward_time=0.024, grad_norm=204.753, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-17 01:04:39,871 (trainer:762) INFO: 28epoch:train:241-280batch: iter_time=5.145e-05, forward_time=0.153, loss_ctc=21.868, loss=21.868, backward_time=0.023, grad_norm=204.961, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.218 +[stan] 2024-01-17 01:04:52,067 (trainer:762) INFO: 28epoch:train:281-320batch: iter_time=5.115e-05, forward_time=0.153, loss_ctc=21.240, loss=21.240, backward_time=0.023, grad_norm=198.826, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-17 01:05:04,090 (trainer:762) INFO: 28epoch:train:321-360batch: iter_time=5.103e-05, forward_time=0.151, loss_ctc=20.715, loss=20.715, backward_time=0.024, grad_norm=201.748, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.202 +[stan] 2024-01-17 01:05:16,203 (trainer:762) INFO: 28epoch:train:361-400batch: iter_time=5.310e-05, forward_time=0.152, loss_ctc=21.352, loss=21.352, backward_time=0.024, grad_norm=200.593, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.211 +[stan] 2024-01-17 01:05:28,219 (trainer:762) INFO: 28epoch:train:401-440batch: iter_time=5.407e-05, forward_time=0.151, loss_ctc=20.124, loss=20.124, backward_time=0.024, grad_norm=204.250, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-17 01:05:40,495 (trainer:762) INFO: 28epoch:train:441-480batch: iter_time=5.155e-05, forward_time=0.154, loss_ctc=20.806, loss=20.806, backward_time=0.024, grad_norm=202.382, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.228 +[stan] 2024-01-17 01:05:52,655 (trainer:762) INFO: 28epoch:train:481-520batch: iter_time=5.106e-05, forward_time=0.152, loss_ctc=21.269, loss=21.269, backward_time=0.023, grad_norm=204.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-17 01:06:04,712 (trainer:762) INFO: 28epoch:train:521-560batch: iter_time=5.181e-05, forward_time=0.151, loss_ctc=20.081, loss=20.081, backward_time=0.023, grad_norm=196.018, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.206 +[stan] 2024-01-17 01:06:16,880 (trainer:762) INFO: 28epoch:train:561-600batch: iter_time=5.350e-05, forward_time=0.152, loss_ctc=20.379, loss=20.379, backward_time=0.024, grad_norm=196.165, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.217 +[stan] 2024-01-17 01:06:28,968 (trainer:762) INFO: 28epoch:train:601-640batch: iter_time=5.445e-05, forward_time=0.151, loss_ctc=20.426, loss=20.426, backward_time=0.024, grad_norm=202.154, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.209 +[stan] 2024-01-17 01:06:41,394 (trainer:762) INFO: 28epoch:train:641-680batch: iter_time=5.342e-05, forward_time=0.156, loss_ctc=20.491, loss=20.491, backward_time=0.024, grad_norm=200.271, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.242 +[stan] 2024-01-17 01:06:53,252 (trainer:762) INFO: 28epoch:train:681-720batch: iter_time=5.343e-05, forward_time=0.149, loss_ctc=20.147, loss=20.147, backward_time=0.023, grad_norm=196.695, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.186 +[stan] 2024-01-17 01:07:05,543 (trainer:762) INFO: 28epoch:train:721-760batch: iter_time=5.046e-05, forward_time=0.154, loss_ctc=20.849, loss=20.849, backward_time=0.024, grad_norm=200.006, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.229 +[stan] 2024-01-17 01:07:17,511 (trainer:762) INFO: 28epoch:train:761-800batch: iter_time=4.828e-05, forward_time=0.150, loss_ctc=20.365, loss=20.365, backward_time=0.023, grad_norm=196.870, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.197 +[stan] 2024-01-17 01:07:22,260 (trainer:357) INFO: 28epoch results: [train] iter_time=2.283e-04, forward_time=0.152, loss_ctc=20.629, loss=20.629, backward_time=0.024, grad_norm=200.903, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214, time=4 minutes and 2.95 seconds, total_count=22400, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=290.991, cer_ctc=0.187, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=290.991, time=1.15 seconds, total_count=140, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.53 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 01:07:23,373 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:07:23,374 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/27epoch.pth +[stan] 2024-01-17 01:07:23,375 (trainer:291) INFO: 29/30epoch started. Estimated time to finish: 8 minutes and 17.54 seconds +[stan] 2024-01-17 01:07:35,757 (trainer:762) INFO: 29epoch:train:1-40batch: iter_time=0.004, forward_time=0.152, loss_ctc=20.513, loss=20.513, backward_time=0.023, grad_norm=197.296, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.238 +[stan] 2024-01-17 01:07:47,650 (trainer:762) INFO: 29epoch:train:41-80batch: iter_time=5.138e-05, forward_time=0.149, loss_ctc=19.998, loss=19.998, backward_time=0.023, grad_norm=201.900, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.189 +[stan] 2024-01-17 01:07:59,851 (trainer:762) INFO: 29epoch:train:81-120batch: iter_time=5.353e-05, forward_time=0.153, loss_ctc=20.753, loss=20.753, backward_time=0.023, grad_norm=198.017, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.220 +[stan] 2024-01-17 01:08:12,129 (trainer:762) INFO: 29epoch:train:121-160batch: iter_time=5.176e-05, forward_time=0.154, loss_ctc=20.851, loss=20.851, backward_time=0.024, grad_norm=199.683, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.228 +[stan] 2024-01-17 01:08:23,930 (trainer:762) INFO: 29epoch:train:161-200batch: iter_time=5.204e-05, forward_time=0.148, loss_ctc=19.313, loss=19.313, backward_time=0.023, grad_norm=199.390, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.180 +[stan] 2024-01-17 01:08:36,124 (trainer:762) INFO: 29epoch:train:201-240batch: iter_time=5.065e-05, forward_time=0.153, loss_ctc=20.705, loss=20.705, backward_time=0.024, grad_norm=203.150, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.219 +[stan] 2024-01-17 01:08:48,533 (trainer:762) INFO: 29epoch:train:241-280batch: iter_time=5.306e-05, forward_time=0.155, loss_ctc=21.007, loss=21.007, backward_time=0.024, grad_norm=200.230, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.241 +[stan] 2024-01-17 01:09:00,900 (trainer:762) INFO: 29epoch:train:281-320batch: iter_time=5.063e-05, forward_time=0.155, loss_ctc=21.254, loss=21.254, backward_time=0.024, grad_norm=207.262, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.237 +[stan] 2024-01-17 01:09:12,797 (trainer:762) INFO: 29epoch:train:321-360batch: iter_time=5.270e-05, forward_time=0.149, loss_ctc=19.345, loss=19.345, backward_time=0.023, grad_norm=202.260, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.190 +[stan] 2024-01-17 01:09:24,937 (trainer:762) INFO: 29epoch:train:361-400batch: iter_time=5.593e-05, forward_time=0.152, loss_ctc=20.484, loss=20.484, backward_time=0.024, grad_norm=205.161, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.214 +[stan] 2024-01-17 01:09:37,103 (trainer:762) INFO: 29epoch:train:401-440batch: iter_time=5.603e-05, forward_time=0.152, loss_ctc=20.195, loss=20.195, backward_time=0.023, grad_norm=206.180, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-17 01:09:49,197 (trainer:762) INFO: 29epoch:train:441-480batch: iter_time=5.447e-05, forward_time=0.151, loss_ctc=20.161, loss=20.161, backward_time=0.024, grad_norm=205.578, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.209 +[stan] 2024-01-17 01:10:01,330 (trainer:762) INFO: 29epoch:train:481-520batch: iter_time=5.371e-05, forward_time=0.152, loss_ctc=18.976, loss=18.976, backward_time=0.023, grad_norm=193.852, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213 +[stan] 2024-01-17 01:10:13,658 (trainer:762) INFO: 29epoch:train:521-560batch: iter_time=5.372e-05, forward_time=0.154, loss_ctc=20.339, loss=20.339, backward_time=0.024, grad_norm=198.789, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.233 +[stan] 2024-01-17 01:10:25,311 (trainer:762) INFO: 29epoch:train:561-600batch: iter_time=5.220e-05, forward_time=0.146, loss_ctc=18.720, loss=18.720, backward_time=0.023, grad_norm=190.028, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.165 +[stan] 2024-01-17 01:10:37,710 (trainer:762) INFO: 29epoch:train:601-640batch: iter_time=5.160e-05, forward_time=0.155, loss_ctc=19.991, loss=19.991, backward_time=0.024, grad_norm=205.538, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.240 +[stan] 2024-01-17 01:10:49,732 (trainer:762) INFO: 29epoch:train:641-680batch: iter_time=5.318e-05, forward_time=0.151, loss_ctc=20.001, loss=20.001, backward_time=0.023, grad_norm=213.375, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.202 +[stan] 2024-01-17 01:11:01,678 (trainer:762) INFO: 29epoch:train:681-720batch: iter_time=5.450e-05, forward_time=0.150, loss_ctc=19.367, loss=19.367, backward_time=0.023, grad_norm=196.196, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.194 +[stan] 2024-01-17 01:11:14,076 (trainer:762) INFO: 29epoch:train:721-760batch: iter_time=5.202e-05, forward_time=0.155, loss_ctc=20.441, loss=20.441, backward_time=0.024, grad_norm=207.887, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.240 +[stan] 2024-01-17 01:11:26,429 (trainer:762) INFO: 29epoch:train:761-800batch: iter_time=4.888e-05, forward_time=0.154, loss_ctc=20.277, loss=20.277, backward_time=0.024, grad_norm=203.769, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.235 +[stan] 2024-01-17 01:11:31,134 (trainer:357) INFO: 29epoch results: [train] iter_time=2.273e-04, forward_time=0.152, loss_ctc=20.134, loss=20.134, backward_time=0.024, grad_norm=201.777, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.215, time=4 minutes and 3.13 seconds, total_count=23200, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=291.234, cer_ctc=0.189, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=291.234, time=1.15 seconds, total_count=145, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.47 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 01:11:32,154 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:11:32,156 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/28epoch.pth +[stan] 2024-01-17 01:11:32,156 (trainer:291) INFO: 30/30epoch started. Estimated time to finish: 4 minutes and 8.77 seconds +[stan] 2024-01-17 01:11:44,199 (trainer:762) INFO: 30epoch:train:1-40batch: iter_time=0.004, forward_time=0.147, loss_ctc=19.496, loss=19.496, backward_time=0.023, grad_norm=196.267, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-17 01:11:56,446 (trainer:762) INFO: 30epoch:train:41-80batch: iter_time=5.108e-05, forward_time=0.153, loss_ctc=20.685, loss=20.685, backward_time=0.024, grad_norm=209.042, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.225 +[stan] 2024-01-17 01:12:08,454 (trainer:762) INFO: 30epoch:train:81-120batch: iter_time=5.493e-05, forward_time=0.150, loss_ctc=19.481, loss=19.481, backward_time=0.023, grad_norm=202.120, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-17 01:12:20,536 (trainer:762) INFO: 30epoch:train:121-160batch: iter_time=5.377e-05, forward_time=0.151, loss_ctc=19.982, loss=19.982, backward_time=0.024, grad_norm=210.350, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.208 +[stan] 2024-01-17 01:12:32,820 (trainer:762) INFO: 30epoch:train:161-200batch: iter_time=5.102e-05, forward_time=0.154, loss_ctc=19.853, loss=19.853, backward_time=0.024, grad_norm=201.814, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.228 +[stan] 2024-01-17 01:12:44,980 (trainer:762) INFO: 30epoch:train:201-240batch: iter_time=4.981e-05, forward_time=0.152, loss_ctc=20.562, loss=20.562, backward_time=0.023, grad_norm=201.940, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.216 +[stan] 2024-01-17 01:12:57,396 (trainer:762) INFO: 30epoch:train:241-280batch: iter_time=5.187e-05, forward_time=0.155, loss_ctc=20.177, loss=20.177, backward_time=0.024, grad_norm=217.318, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.242 +[stan] 2024-01-17 01:13:09,226 (trainer:762) INFO: 30epoch:train:281-320batch: iter_time=5.240e-05, forward_time=0.148, loss_ctc=19.819, loss=19.819, backward_time=0.023, grad_norm=204.538, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.183 +[stan] 2024-01-17 01:13:21,736 (trainer:762) INFO: 30epoch:train:321-360batch: iter_time=5.475e-05, forward_time=0.156, loss_ctc=20.771, loss=20.771, backward_time=0.024, grad_norm=203.846, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.251 +[stan] 2024-01-17 01:13:33,568 (trainer:762) INFO: 30epoch:train:361-400batch: iter_time=5.115e-05, forward_time=0.148, loss_ctc=19.173, loss=19.173, backward_time=0.023, grad_norm=200.353, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.183 +[stan] 2024-01-17 01:13:45,620 (trainer:762) INFO: 30epoch:train:401-440batch: iter_time=5.627e-05, forward_time=0.151, loss_ctc=19.284, loss=19.284, backward_time=0.023, grad_norm=193.524, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.205 +[stan] 2024-01-17 01:13:58,141 (trainer:762) INFO: 30epoch:train:441-480batch: iter_time=5.481e-05, forward_time=0.157, loss_ctc=20.787, loss=20.787, backward_time=0.024, grad_norm=199.998, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.252 +[stan] 2024-01-17 01:14:09,991 (trainer:762) INFO: 30epoch:train:481-520batch: iter_time=5.147e-05, forward_time=0.149, loss_ctc=19.602, loss=19.602, backward_time=0.023, grad_norm=200.489, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.185 +[stan] 2024-01-17 01:14:22,004 (trainer:762) INFO: 30epoch:train:521-560batch: iter_time=5.229e-05, forward_time=0.150, loss_ctc=19.958, loss=19.958, backward_time=0.024, grad_norm=209.523, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.201 +[stan] 2024-01-17 01:14:33,992 (trainer:762) INFO: 30epoch:train:561-600batch: iter_time=5.625e-05, forward_time=0.150, loss_ctc=19.343, loss=19.343, backward_time=0.023, grad_norm=197.340, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.199 +[stan] 2024-01-17 01:14:46,327 (trainer:762) INFO: 30epoch:train:601-640batch: iter_time=5.275e-05, forward_time=0.154, loss_ctc=20.428, loss=20.428, backward_time=0.024, grad_norm=211.805, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.233 +[stan] 2024-01-17 01:14:58,367 (trainer:762) INFO: 30epoch:train:641-680batch: iter_time=5.127e-05, forward_time=0.151, loss_ctc=19.535, loss=19.535, backward_time=0.023, grad_norm=203.709, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.204 +[stan] 2024-01-17 01:15:10,763 (trainer:762) INFO: 30epoch:train:681-720batch: iter_time=5.547e-05, forward_time=0.155, loss_ctc=20.163, loss=20.163, backward_time=0.024, grad_norm=204.154, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.239 +[stan] 2024-01-17 01:15:22,497 (trainer:762) INFO: 30epoch:train:721-760batch: iter_time=5.115e-05, forward_time=0.147, loss_ctc=19.363, loss=19.363, backward_time=0.023, grad_norm=205.035, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.173 +[stan] 2024-01-17 01:15:34,717 (trainer:762) INFO: 30epoch:train:761-800batch: iter_time=4.783e-05, forward_time=0.153, loss_ctc=20.721, loss=20.721, backward_time=0.024, grad_norm=209.757, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.222 +[stan] 2024-01-17 01:15:39,501 (trainer:357) INFO: 30epoch results: [train] iter_time=2.444e-04, forward_time=0.152, loss_ctc=19.959, loss=19.959, backward_time=0.024, grad_norm=204.146, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=1.213, time=4 minutes and 2.63 seconds, total_count=24000, gpu_max_cached_mem_GB=21.809, [valid] loss_ctc=293.151, cer_ctc=0.188, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=293.151, time=1.17 seconds, total_count=150, gpu_max_cached_mem_GB=21.809, [att_plot] time=3.54 seconds, total_count=0, gpu_max_cached_mem_GB=21.809 +[stan] 2024-01-17 01:15:40,591 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 01:15:40,592 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/29epoch.pth +[stan] 2024-01-17 01:15:40,592 (trainer:488) INFO: The training was finished at 30 epochs +[stan] 2024-01-17 01:15:40,607 (average_nbest_models:69) INFO: Averaging 5best models: criterion="valid.loss": test_pr/asr_train_asr_s3prl_houlsby_eng1_1h/valid.loss.ave_5best.pth +# Accounting: time=7468 threads=1 +# Ended (code 0) at Wed Jan 17 01:15:41 CST 2024, elapsed time 7468 seconds diff --git 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sha256:5f417906141288bb270df6b6d25c348bd186a02a3e3e1ed047ec499b2382d8dd +size 21135182 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/RESULTS.md b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/RESULTS.md new file mode 100644 index 0000000000000000000000000000000000000000..d2348f0ddacdca4bb846f2f49ce796e5f7e313d2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/RESULTS.md @@ -0,0 +1,45 @@ +[INFO] /home/stan/Desktop/espnet/egs2/ml_superb/asr1/../../../tools/activate_python.sh is not present + +# RESULTS +## Environments +- date: `Tue Jan 16 23:11:12 CST 2024` +- python version: `3.8.10 (default, Jun 4 2021, 15:09:15) [GCC 7.5.0]` +- espnet version: `espnet 202310` +- pytorch version: `pytorch 1.12.0+cu113` +- Git hash: `aa855dffb81937a097ee03089926a0d5256426e2` + - Commit date: `Tue Jan 16 19:36:29 2024 +0800` + +## test_pr/asr_train_asr_s3prl_houlsby_jpn_10min +### WER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|decode_asr_asr_model_valid.loss.ave/test_10min_jpn|160|9700|84.1|9.0|6.9|3.1|19.0|98.1| + +### CER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|decode_asr_asr_model_valid.loss.ave/test_10min_jpn|160|20087|87.6|4.9|7.5|2.9|15.3|98.1| + +### TER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +## test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave +### WER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|org/dev_10min_jpn|126|4430|85.7|8.2|6.0|4.6|18.9|96.8| + +### CER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|org/dev_10min_jpn|126|9077|88.9|4.7|6.3|4.3|15.4|96.8| + +### TER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/att_ws/cv_jpn_000674/encoder.encoders.0.self_attn.10ep.png 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0000000000000000000000000000000000000000..abcf4df5aacb87dac5b435bd7412c06d00759adc --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/checkpoint.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f8c42a61c6a505189e0d64655b18ace90678c2f4b5693b295dd7337854216ed +size 63397593 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..13c13048d316ba41113c37016d268cd78ff6ba5c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml @@ -0,0 +1,248 @@ +config: conf/tuning/train_asr_s3prl_houlsby.yaml +print_config: false +log_level: INFO +drop_last_iter: false +dry_run: false +iterator_type: sequence +valid_iterator_type: null +output_dir: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min +ngpu: 1 +seed: 0 +num_workers: 4 +num_att_plot: 3 +dist_backend: nccl +dist_init_method: env:// +dist_world_size: null +dist_rank: null +local_rank: 0 +dist_master_addr: null +dist_master_port: null +dist_launcher: null +multiprocessing_distributed: false +unused_parameters: true +sharded_ddp: false +cudnn_enabled: true +cudnn_benchmark: false +cudnn_deterministic: true +collect_stats: false +write_collected_feats: false +max_epoch: 30 +patience: null +val_scheduler_criterion: +- valid +- loss +early_stopping_criterion: +- valid +- loss +- min +best_model_criterion: +- - valid + - loss + - min +keep_nbest_models: 5 +nbest_averaging_interval: 0 +grad_clip: 5.0 +grad_clip_type: 2.0 +grad_noise: false +accum_grad: 4 +no_forward_run: false +resume: true +train_dtype: float32 +use_amp: false +log_interval: null +use_matplotlib: true +use_tensorboard: true +create_graph_in_tensorboard: false +use_wandb: false +wandb_project: null +wandb_id: null +wandb_entity: null +wandb_name: null +wandb_model_log_interval: -1 +detect_anomaly: false +use_adapter: true +adapter: houlsby +save_adapter_only: true +adapter_conf: + bottleneck: 32 +pretrain_path: null +init_param: [] +ignore_init_mismatch: false +freeze_param: +- frontend.upstream +num_iters_per_epoch: 800 +batch_size: 8 +valid_batch_size: null +batch_bins: 1000000 +valid_batch_bins: null +train_shape_file: +- test_pr/asr_stats_jpn_10min/train/speech_shape +- test_pr/asr_stats_jpn_10min/train/text_shape.word +valid_shape_file: +- test_pr/asr_stats_jpn_10min/valid/speech_shape +- test_pr/asr_stats_jpn_10min/valid/text_shape.word +batch_type: sorted +valid_batch_type: null +fold_length: +- 80000 +- 150 +sort_in_batch: descending +shuffle_within_batch: false +sort_batch: descending +multiple_iterator: false +chunk_length: 500 +chunk_shift_ratio: 0.5 +num_cache_chunks: 1024 +chunk_excluded_key_prefixes: [] +chunk_default_fs: null +train_data_path_and_name_and_type: +- - dump/raw/train_10min_jpn/wav.scp + - speech + - sound +- - dump/raw/train_10min_jpn/text + - text + - text +valid_data_path_and_name_and_type: +- - dump/raw/dev_10min_jpn/wav.scp + - speech + - sound +- - dump/raw/dev_10min_jpn/text + - text + - text +allow_variable_data_keys: false +max_cache_size: 0.0 +max_cache_fd: 32 +allow_multi_rates: false +valid_max_cache_size: null +exclude_weight_decay: false +exclude_weight_decay_conf: {} +optim: adam +optim_conf: + lr: 0.0001 + weight_decay: 1.0e-06 +scheduler: null +scheduler_conf: {} +token_list: +- +- +- a +- o +- i +- e +- k +- u +- t +- n +- r +- m +- s +- N +- d +- sh +- g +- w +- U +- I +- pau +- cl +- j +- y +- h +- b +- ts +- ch +- z +- ky +- p +- f +- ry +- hy +- ny +- gy +- py +- my +- by +- v +- +init: null +input_size: null +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: null + zero_infinity: true + brctc_risk_strategy: exp + brctc_group_strategy: end + brctc_risk_factor: 0.0 +joint_net_conf: null +use_preprocessor: true +use_lang_prompt: false +use_nlp_prompt: false +token_type: word +bpemodel: null +non_linguistic_symbols: null +cleaner: null +g2p: null +speech_volume_normalize: null +rir_scp: null +rir_apply_prob: 1.0 +noise_scp: null +noise_apply_prob: 1.0 +noise_db_range: '13_15' +short_noise_thres: 0.5 +aux_ctc_tasks: [] +frontend: s3prl +frontend_conf: + frontend_conf: + upstream: hubert_base + download_dir: ./hub + multilayer_feature: true + fs: 16k +specaug: specaug +specaug_conf: + apply_time_warp: true + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 27 + num_freq_mask: 2 + apply_time_mask: true + time_mask_width_ratio_range: + - 0.0 + - 0.05 + num_time_mask: 10 +normalize: utterance_mvn +normalize_conf: {} +model: espnet +model_conf: + ctc_weight: 1.0 + extract_feats_in_collect_stats: false +preencoder: linear +preencoder_conf: + input_size: 768 + output_size: 80 +encoder: transformer +encoder_conf: + output_size: 256 + attention_heads: 8 + linear_units: 1024 + num_blocks: 2 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: conv2d2 + normalize_before: true +postencoder: null +postencoder_conf: {} +decoder: null +decoder_conf: {} +preprocessor: default +preprocessor_conf: {} +required: +- output_dir +- token_list +version: '202310' +distributed: false diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.1.log b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.1.log new file mode 100644 index 0000000000000000000000000000000000000000..4e0705428c837f3c8eec76036b5e2590326b213d --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.1.log @@ -0,0 +1,371 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1 --config conf/decode_asr.yaml +# Started at Tue Jan 16 23:08:42 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1 --config conf/decode_asr.yaml +2024-01-16 23:08:43,372 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml +2024-01-16 23:08:43,390 (asr:523) INFO: Vocabulary size: 41 +2024-01-16 23:08:43,453 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 23:08:43,453 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 23:08:43,565 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 23:08:44,870 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 23:08:46,133 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 23:08:46,133 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 23:08:46,133 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 23:08:46,166 (asr_inference:446) INFO: Text tokenizer: WordTokenizer(delimiter="None") +2024-01-16 23:08:46,241 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 23:08:46,354 (asr_inference:494) INFO: speech length: 143424 +2024-01-16 23:08:47,559 (beam_search:428) INFO: decoder input length: 222 +2024-01-16 23:08:47,559 (beam_search:429) INFO: max output length: 222 +2024-01-16 23:08:47,559 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:48,084 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:48,084 (beam_search:476) INFO: -11.02 * 1.0 = -11.02 for ctc +2024-01-16 23:08:48,084 (beam_search:479) INFO: total log probability: -11.02 +2024-01-16 23:08:48,085 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:08:48,085 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:48,085 (beam_search:483) INFO: best hypo: bokunoyiegacltakaidnaNnonamaeNnikuraberutopokuniwanajiminonaimaebaebakaridagkedo + +2024-01-16 23:08:48,110 (asr_inference:494) INFO: speech length: 114048 +2024-01-16 23:08:48,123 (beam_search:428) INFO: decoder input length: 176 +2024-01-16 23:08:48,123 (beam_search:429) INFO: max output length: 176 +2024-01-16 23:08:48,123 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:48,339 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:48,339 (beam_search:476) INFO: -9.99 * 1.0 = -9.99 for ctc +2024-01-16 23:08:48,339 (beam_search:479) INFO: total log probability: -9.99 +2024-01-16 23:08:48,339 (beam_search:480) INFO: normalized log probability: -0.26 +2024-01-16 23:08:48,339 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:48,340 (beam_search:483) INFO: best hypo: maryoosuanomonoerudikuinikigaruketeru + +2024-01-16 23:08:48,341 (asr_inference:494) INFO: speech length: 103680 +2024-01-16 23:08:48,353 (beam_search:428) INFO: decoder input length: 159 +2024-01-16 23:08:48,353 (beam_search:429) INFO: max output length: 159 +2024-01-16 23:08:48,353 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:48,547 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:48,547 (beam_search:476) INFO: -6.18 * 1.0 = -6.18 for ctc +2024-01-16 23:08:48,547 (beam_search:479) INFO: total log probability: -6.18 +2024-01-16 23:08:48,547 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 23:08:48,547 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:48,547 (beam_search:483) INFO: best hypo: bokunoshIteeumonuotowasUkoshichigaclteita + +2024-01-16 23:08:48,548 (asr_inference:494) INFO: speech length: 155520 +2024-01-16 23:08:48,564 (beam_search:428) INFO: decoder input length: 240 +2024-01-16 23:08:48,564 (beam_search:429) INFO: max output length: 240 +2024-01-16 23:08:48,564 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:49,242 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:49,242 (beam_search:476) INFO: -11.20 * 1.0 = -11.20 for ctc +2024-01-16 23:08:49,242 (beam_search:479) INFO: total log probability: -11.20 +2024-01-16 23:08:49,242 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:08:49,242 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:49,243 (beam_search:483) INFO: best hypo: kakaorutachimanioitepausekgaregaishIkimeNtekideariwaruwarenojikogajshIkisayootekidearutokaNeerarerutochI + +2024-01-16 23:08:49,244 (asr_inference:494) INFO: speech length: 108288 +2024-01-16 23:08:49,256 (beam_search:428) INFO: decoder input length: 167 +2024-01-16 23:08:49,256 (beam_search:429) INFO: max output length: 167 +2024-01-16 23:08:49,256 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:49,578 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:49,578 (beam_search:476) INFO: -5.45 * 1.0 = -5.45 for ctc +2024-01-16 23:08:49,578 (beam_search:479) INFO: total log probability: -5.45 +2024-01-16 23:08:49,578 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:08:49,578 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:49,579 (beam_search:483) INFO: best hypo: ienikItaneNgaajiwasaNhakomaihorodepaupauchoodashItabuNtoonajiguraida + +2024-01-16 23:08:49,580 (asr_inference:494) INFO: speech length: 124416 +2024-01-16 23:08:49,593 (beam_search:428) INFO: decoder input length: 192 +2024-01-16 23:08:49,593 (beam_search:429) INFO: max output length: 192 +2024-01-16 23:08:49,593 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:49,951 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:49,951 (beam_search:476) INFO: -11.11 * 1.0 = -11.11 for ctc +2024-01-16 23:08:49,951 (beam_search:479) INFO: total log probability: -11.11 +2024-01-16 23:08:49,951 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 23:08:49,951 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:49,951 (beam_search:483) INFO: best hypo: hawapitamaretsubaagunoseeshiNbyyooiNninuNhIteyirutokinipeshioomu + +2024-01-16 23:08:49,953 (asr_inference:494) INFO: speech length: 133056 +2024-01-16 23:08:49,966 (beam_search:428) INFO: decoder input length: 205 +2024-01-16 23:08:49,966 (beam_search:429) INFO: max output length: 205 +2024-01-16 23:08:49,966 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:50,539 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:50,539 (beam_search:476) INFO: -10.75 * 1.0 = -10.75 for ctc +2024-01-16 23:08:50,539 (beam_search:479) INFO: total log probability: -10.75 +2024-01-16 23:08:50,539 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:08:50,539 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:50,540 (beam_search:483) INFO: best hypo: tadaNdearhaNtanohirogerabaachikochitsugihagiyaarikatagochuinidekitahokorobinaNkakyooneNnomaomoninaclteiru + +2024-01-16 23:08:50,541 (asr_inference:494) INFO: speech length: 141120 +2024-01-16 23:08:50,556 (beam_search:428) INFO: decoder input length: 218 +2024-01-16 23:08:50,556 (beam_search:429) INFO: max output length: 218 +2024-01-16 23:08:50,556 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:51,057 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:51,057 (beam_search:476) INFO: -8.01 * 1.0 = -8.01 for ctc +2024-01-16 23:08:51,057 (beam_search:479) INFO: total log probability: -8.01 +2024-01-16 23:08:51,057 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:08:51,057 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:51,058 (beam_search:483) INFO: best hypo: karagakanodepauberunakanihoomoNkiboonobushooyobichoozakiboonobugomaikaratooridesU + +2024-01-16 23:08:51,059 (asr_inference:494) INFO: speech length: 52416 +2024-01-16 23:08:51,068 (beam_search:428) INFO: decoder input length: 79 +2024-01-16 23:08:51,068 (beam_search:429) INFO: max output length: 79 +2024-01-16 23:08:51,068 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:51,128 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:51,128 (beam_search:476) INFO: -0.91 * 1.0 = -0.91 for ctc +2024-01-16 23:08:51,128 (beam_search:479) INFO: total log probability: -0.91 +2024-01-16 23:08:51,128 (beam_search:480) INFO: normalized log probability: -0.04 +2024-01-16 23:08:51,128 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:51,129 (beam_search:483) INFO: best hypo: koNbawatotemosamidesU + +2024-01-16 23:08:51,130 (asr_inference:494) INFO: speech length: 89856 +2024-01-16 23:08:51,140 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 23:08:51,141 (beam_search:429) INFO: max output length: 138 +2024-01-16 23:08:51,141 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:51,322 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:51,322 (beam_search:476) INFO: -4.57 * 1.0 = -4.57 for ctc +2024-01-16 23:08:51,322 (beam_search:479) INFO: total log probability: -4.57 +2024-01-16 23:08:51,322 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:08:51,322 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:51,322 (beam_search:483) INFO: best hypo: kiNchooshItakotsUkidepauclpacltawadasekinihairu + +2024-01-16 23:08:51,324 (asr_inference:494) INFO: speech length: 99072 +2024-01-16 23:08:51,335 (beam_search:428) INFO: decoder input length: 152 +2024-01-16 23:08:51,335 (beam_search:429) INFO: max output length: 152 +2024-01-16 23:08:51,335 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:51,518 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:51,519 (beam_search:476) INFO: -4.23 * 1.0 = -4.23 for ctc +2024-01-16 23:08:51,519 (beam_search:479) INFO: total log probability: -4.23 +2024-01-16 23:08:51,519 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:08:51,519 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:51,519 (beam_search:483) INFO: best hypo: masaNkyuureNtoicltafukakarokeNchikowutsU + +2024-01-16 23:08:51,520 (asr_inference:494) INFO: speech length: 95616 +2024-01-16 23:08:51,531 (beam_search:428) INFO: decoder input length: 147 +2024-01-16 23:08:51,531 (beam_search:429) INFO: max output length: 147 +2024-01-16 23:08:51,531 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:51,790 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:51,790 (beam_search:476) INFO: -7.72 * 1.0 = -7.72 for ctc +2024-01-16 23:08:51,790 (beam_search:479) INFO: total log probability: -7.72 +2024-01-16 23:08:51,790 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:08:51,790 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:51,790 (beam_search:483) INFO: best hypo: goriyoshidesusumetetssugogawarukunacltaraihIclkomeruyarekUchi + +2024-01-16 23:08:51,791 (asr_inference:494) INFO: speech length: 123264 +2024-01-16 23:08:51,804 (beam_search:428) INFO: decoder input length: 190 +2024-01-16 23:08:51,804 (beam_search:429) INFO: max output length: 190 +2024-01-16 23:08:51,804 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:52,134 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:52,134 (beam_search:476) INFO: -7.55 * 1.0 = -7.55 for ctc +2024-01-16 23:08:52,134 (beam_search:479) INFO: total log probability: -7.55 +2024-01-16 23:08:52,134 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:08:52,134 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:52,134 (beam_search:483) INFO: best hypo: mojurNtekijikootoitsUtekinijikoojishiNokeeseesurushakaiwa + +2024-01-16 23:08:52,136 (asr_inference:494) INFO: speech length: 71424 +2024-01-16 23:08:52,146 (beam_search:428) INFO: decoder input length: 109 +2024-01-16 23:08:52,146 (beam_search:429) INFO: max output length: 109 +2024-01-16 23:08:52,146 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:52,236 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:52,236 (beam_search:476) INFO: -0.76 * 1.0 = -0.76 for ctc +2024-01-16 23:08:52,236 (beam_search:479) INFO: total log probability: -0.76 +2024-01-16 23:08:52,236 (beam_search:480) INFO: normalized log probability: -0.03 +2024-01-16 23:08:52,236 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:52,236 (beam_search:483) INFO: best hypo: haNnoikeNninarasareruna + +2024-01-16 23:08:52,237 (asr_inference:494) INFO: speech length: 99648 +2024-01-16 23:08:52,249 (beam_search:428) INFO: decoder input length: 153 +2024-01-16 23:08:52,249 (beam_search:429) INFO: max output length: 153 +2024-01-16 23:08:52,249 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:52,437 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:52,437 (beam_search:476) INFO: -3.02 * 1.0 = -3.02 for ctc +2024-01-16 23:08:52,437 (beam_search:479) INFO: total log probability: -3.02 +2024-01-16 23:08:52,438 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:08:52,438 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:52,438 (beam_search:483) INFO: best hypo: hinogasobitayorazeNkaidekoclchomiteiru + +2024-01-16 23:08:52,439 (asr_inference:494) INFO: speech length: 80640 +2024-01-16 23:08:52,449 (beam_search:428) INFO: decoder input length: 123 +2024-01-16 23:08:52,449 (beam_search:429) INFO: max output length: 123 +2024-01-16 23:08:52,449 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:52,580 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:52,580 (beam_search:476) INFO: -3.05 * 1.0 = -3.05 for ctc +2024-01-16 23:08:52,580 (beam_search:479) INFO: total log probability: -3.05 +2024-01-16 23:08:52,580 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:08:52,580 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:52,580 (beam_search:483) INFO: best hypo: ijchidowakoNpotajikaNonoNdemitai + +2024-01-16 23:08:52,581 (asr_inference:494) INFO: speech length: 86976 +2024-01-16 23:08:52,592 (beam_search:428) INFO: decoder input length: 133 +2024-01-16 23:08:52,592 (beam_search:429) INFO: max output length: 133 +2024-01-16 23:08:52,592 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:52,779 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:52,780 (beam_search:476) INFO: -3.03 * 1.0 = -3.03 for ctc +2024-01-16 23:08:52,780 (beam_search:479) INFO: total log probability: -3.03 +2024-01-16 23:08:52,780 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 23:08:52,780 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:52,780 (beam_search:483) INFO: best hypo: kooinokoshItepauotoosaNtokasaNwaNdeteikimashIta + +2024-01-16 23:08:52,781 (asr_inference:494) INFO: speech length: 138240 +2024-01-16 23:08:52,795 (beam_search:428) INFO: decoder input length: 213 +2024-01-16 23:08:52,795 (beam_search:429) INFO: max output length: 213 +2024-01-16 23:08:52,795 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:53,434 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:53,434 (beam_search:476) INFO: -7.12 * 1.0 = -7.12 for ctc +2024-01-16 23:08:53,434 (beam_search:479) INFO: total log probability: -7.12 +2024-01-16 23:08:53,434 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 23:08:53,434 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:53,434 (beam_search:483) INFO: best hypo: shikashItesoregatsUkuraretamonokaratsUkurumoroetoshItepaudokomademorawarunisemarutoyutokiarewarinichoclkaNtekidearu + +2024-01-16 23:08:53,436 (asr_inference:494) INFO: speech length: 95040 +2024-01-16 23:08:53,447 (beam_search:428) INFO: decoder input length: 146 +2024-01-16 23:08:53,447 (beam_search:429) INFO: max output length: 146 +2024-01-16 23:08:53,447 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:53,674 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:53,674 (beam_search:476) INFO: -1.89 * 1.0 = -1.89 for ctc +2024-01-16 23:08:53,674 (beam_search:479) INFO: total log probability: -1.89 +2024-01-16 23:08:53,674 (beam_search:480) INFO: normalized log probability: -0.04 +2024-01-16 23:08:53,674 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:53,674 (beam_search:483) INFO: best hypo: hainitamatakemuriohakidashikuraikooenishIseNomokeru + +2024-01-16 23:08:53,675 (asr_inference:494) INFO: speech length: 92160 +2024-01-16 23:08:53,686 (beam_search:428) INFO: decoder input length: 141 +2024-01-16 23:08:53,686 (beam_search:429) INFO: max output length: 141 +2024-01-16 23:08:53,686 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:53,837 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:53,837 (beam_search:476) INFO: -3.58 * 1.0 = -3.58 for ctc +2024-01-16 23:08:53,837 (beam_search:479) INFO: total log probability: -3.58 +2024-01-16 23:08:53,837 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:08:53,837 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:53,837 (beam_search:483) INFO: best hypo: wadawadashInabusokudechiuseNnariso + +2024-01-16 23:08:53,839 (asr_inference:494) INFO: speech length: 146880 +2024-01-16 23:08:53,854 (beam_search:428) INFO: decoder input length: 227 +2024-01-16 23:08:53,854 (beam_search:429) INFO: max output length: 227 +2024-01-16 23:08:53,854 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:54,419 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:54,419 (beam_search:476) INFO: -9.96 * 1.0 = -9.96 for ctc +2024-01-16 23:08:54,419 (beam_search:479) INFO: total log probability: -9.96 +2024-01-16 23:08:54,419 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:08:54,419 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:54,420 (beam_search:483) INFO: best hypo: tseNsekainomosUkatachiitashunoyoayiruseesaiyoshIkItosaiuotowahashItekaNgairukotowadekinai + +2024-01-16 23:08:54,421 (asr_inference:494) INFO: speech length: 73728 +2024-01-16 23:08:54,431 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 23:08:54,431 (beam_search:429) INFO: max output length: 113 +2024-01-16 23:08:54,431 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:54,547 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:54,548 (beam_search:476) INFO: -6.34 * 1.0 = -6.34 for ctc +2024-01-16 23:08:54,548 (beam_search:479) INFO: total log probability: -6.34 +2024-01-16 23:08:54,548 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 23:08:54,548 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:54,548 (beam_search:483) INFO: best hypo: sakenomaegnonipiroubaratoyoeta + +2024-01-16 23:08:54,549 (asr_inference:494) INFO: speech length: 119808 +2024-01-16 23:08:54,562 (beam_search:428) INFO: decoder input length: 185 +2024-01-16 23:08:54,562 (beam_search:429) INFO: max output length: 185 +2024-01-16 23:08:54,562 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:54,905 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:54,905 (beam_search:476) INFO: -6.34 * 1.0 = -6.34 for ctc +2024-01-16 23:08:54,905 (beam_search:479) INFO: total log probability: -6.34 +2024-01-16 23:08:54,905 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:08:54,905 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:54,905 (beam_search:483) INFO: best hypo: soreootadakohokunozeNdaNkaishikuiteedonokaakUtomomimirukotowa + +2024-01-16 23:08:54,907 (asr_inference:494) INFO: speech length: 78336 +2024-01-16 23:08:54,917 (beam_search:428) INFO: decoder input length: 120 +2024-01-16 23:08:54,917 (beam_search:429) INFO: max output length: 120 +2024-01-16 23:08:54,917 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:55,067 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:55,067 (beam_search:476) INFO: -6.76 * 1.0 = -6.76 for ctc +2024-01-16 23:08:55,067 (beam_search:479) INFO: total log probability: -6.76 +2024-01-16 23:08:55,067 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 23:08:55,067 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:55,067 (beam_search:483) INFO: best hypo: okkyomemokfurazunipausumodekeeNmoyaclteita + +2024-01-16 23:08:55,068 (asr_inference:494) INFO: speech length: 85248 +2024-01-16 23:08:55,079 (beam_search:428) INFO: decoder input length: 131 +2024-01-16 23:08:55,079 (beam_search:429) INFO: max output length: 131 +2024-01-16 23:08:55,079 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:55,257 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:55,257 (beam_search:476) INFO: -6.06 * 1.0 = -6.06 for ctc +2024-01-16 23:08:55,257 (beam_search:479) INFO: total log probability: -6.06 +2024-01-16 23:08:55,257 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:08:55,257 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:55,257 (beam_search:483) INFO: best hypo: adaiwaanaiNchshiumatakobayashisaNtoasobeimasU + +2024-01-16 23:08:55,258 (asr_inference:494) INFO: speech length: 105408 +2024-01-16 23:08:55,270 (beam_search:428) INFO: decoder input length: 162 +2024-01-16 23:08:55,270 (beam_search:429) INFO: max output length: 162 +2024-01-16 23:08:55,270 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:55,495 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:55,496 (beam_search:476) INFO: -7.00 * 1.0 = -7.00 for ctc +2024-01-16 23:08:55,496 (beam_search:479) INFO: total log probability: -7.00 +2024-01-16 23:08:55,496 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 23:08:55,496 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:55,496 (beam_search:483) INFO: best hypo: asookoniihiitoogapauimasunepauarohitowataretesho + +2024-01-16 23:08:55,497 (asr_inference:494) INFO: speech length: 72576 +2024-01-16 23:08:55,507 (beam_search:428) INFO: decoder input length: 111 +2024-01-16 23:08:55,507 (beam_search:429) INFO: max output length: 111 +2024-01-16 23:08:55,507 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:55,636 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:55,636 (beam_search:476) INFO: -4.60 * 1.0 = -4.60 for ctc +2024-01-16 23:08:55,636 (beam_search:479) INFO: total log probability: -4.60 +2024-01-16 23:08:55,636 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:08:55,636 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:55,637 (beam_search:483) INFO: best hypo: watajiwakkyenookanapaunoodokaeitaidesU + +2024-01-16 23:08:55,638 (asr_inference:494) INFO: speech length: 62208 +2024-01-16 23:08:55,647 (beam_search:428) INFO: decoder input length: 95 +2024-01-16 23:08:55,647 (beam_search:429) INFO: max output length: 95 +2024-01-16 23:08:55,647 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:55,731 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:55,731 (beam_search:476) INFO: -1.46 * 1.0 = -1.46 for ctc +2024-01-16 23:08:55,731 (beam_search:479) INFO: total log probability: -1.46 +2024-01-16 23:08:55,731 (beam_search:480) INFO: normalized log probability: -0.05 +2024-01-16 23:08:55,731 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:55,731 (beam_search:483) INFO: best hypo: kyooreNkanapeNkyuoshIteimasU + +2024-01-16 23:08:55,732 (asr_inference:494) INFO: speech length: 42048 +2024-01-16 23:08:55,741 (beam_search:428) INFO: decoder input length: 63 +2024-01-16 23:08:55,741 (beam_search:429) INFO: max output length: 63 +2024-01-16 23:08:55,741 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:55,778 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:55,778 (beam_search:476) INFO: -2.98 * 1.0 = -2.98 for ctc +2024-01-16 23:08:55,778 (beam_search:479) INFO: total log probability: -2.98 +2024-01-16 23:08:55,778 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 23:08:55,778 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:55,778 (beam_search:483) INFO: best hypo: aocltosumimrasepau + +2024-01-16 23:08:55,779 (asr_inference:494) INFO: speech length: 115776 +2024-01-16 23:08:55,792 (beam_search:428) INFO: decoder input length: 178 +2024-01-16 23:08:55,792 (beam_search:429) INFO: max output length: 178 +2024-01-16 23:08:55,792 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:56,082 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:56,082 (beam_search:476) INFO: -7.32 * 1.0 = -7.32 for ctc +2024-01-16 23:08:56,082 (beam_search:479) INFO: total log probability: -7.32 +2024-01-16 23:08:56,082 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:08:56,082 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:56,082 (beam_search:483) INFO: best hypo: chikasUpaubonooeNburiewapauyakekiminikaecldehagesUkenoclta + +2024-01-16 23:08:56,084 (asr_inference:494) INFO: speech length: 92736 +2024-01-16 23:08:56,095 (beam_search:428) INFO: decoder input length: 142 +2024-01-16 23:08:56,095 (beam_search:429) INFO: max output length: 142 +2024-01-16 23:08:56,095 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:56,326 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:56,326 (beam_search:476) INFO: -6.24 * 1.0 = -6.24 for ctc +2024-01-16 23:08:56,326 (beam_search:479) INFO: total log probability: -6.24 +2024-01-16 23:08:56,326 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:08:56,326 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:56,327 (beam_search:483) INFO: best hypo: itszumokonoiNpIitsootsUkaclteitanodemijikahaunarimashIta + +2024-01-16 23:08:56,328 (asr_inference:494) INFO: speech length: 53568 +2024-01-16 23:08:56,337 (beam_search:428) INFO: decoder input length: 81 +2024-01-16 23:08:56,337 (beam_search:429) INFO: max output length: 81 +2024-01-16 23:08:56,337 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:08:56,414 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:08:56,415 (beam_search:476) INFO: -3.01 * 1.0 = -3.01 for ctc +2024-01-16 23:08:56,415 (beam_search:479) INFO: total log probability: -3.01 +2024-01-16 23:08:56,415 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:08:56,415 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:08:56,415 (beam_search:483) INFO: best hypo: watashiwapaueigogahanashItemasU + +# Accounting: time=15 threads=1 +# Ended (code 0) at Tue Jan 16 23:08:57 CST 2024, elapsed time 15 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.2.log b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.2.log new file mode 100644 index 0000000000000000000000000000000000000000..c8d0d7e398c9462634b59d8bc5dfed535592c3c1 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.2.log @@ -0,0 +1,371 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2 --config conf/decode_asr.yaml +# Started at Tue Jan 16 23:08:57 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2 --config conf/decode_asr.yaml +2024-01-16 23:08:58,241 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml +2024-01-16 23:08:58,259 (asr:523) INFO: Vocabulary size: 41 +2024-01-16 23:08:58,321 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 23:08:58,321 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 23:08:58,432 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 23:08:59,714 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 23:09:00,936 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 23:09:00,937 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 23:09:00,937 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 23:09:00,969 (asr_inference:446) INFO: Text tokenizer: WordTokenizer(delimiter="None") +2024-01-16 23:09:01,044 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 23:09:01,157 (asr_inference:494) INFO: speech length: 59328 +2024-01-16 23:09:02,370 (beam_search:428) INFO: decoder input length: 90 +2024-01-16 23:09:02,370 (beam_search:429) INFO: max output length: 90 +2024-01-16 23:09:02,370 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:02,456 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:02,456 (beam_search:476) INFO: -4.60 * 1.0 = -4.60 for ctc +2024-01-16 23:09:02,456 (beam_search:479) INFO: total log probability: -4.60 +2024-01-16 23:09:02,456 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 23:09:02,456 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:02,456 (beam_search:483) INFO: best hypo: oseikeeryajibuaNdetocltekina + +2024-01-16 23:09:02,480 (asr_inference:494) INFO: speech length: 105408 +2024-01-16 23:09:02,494 (beam_search:428) INFO: decoder input length: 162 +2024-01-16 23:09:02,494 (beam_search:429) INFO: max output length: 162 +2024-01-16 23:09:02,494 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:02,717 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:02,717 (beam_search:476) INFO: -8.07 * 1.0 = -8.07 for ctc +2024-01-16 23:09:02,717 (beam_search:479) INFO: total log probability: -8.07 +2024-01-16 23:09:02,717 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 23:09:02,717 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:02,717 (beam_search:483) INFO: best hypo: aishuuekaranishuukaNhaigaieruyookooonikimasU + +2024-01-16 23:09:02,718 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 23:09:02,726 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 23:09:02,726 (beam_search:429) INFO: max output length: 51 +2024-01-16 23:09:02,726 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:02,757 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:02,757 (beam_search:476) INFO: -2.01 * 1.0 = -2.01 for ctc +2024-01-16 23:09:02,757 (beam_search:479) INFO: total log probability: -2.01 +2024-01-16 23:09:02,757 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:09:02,757 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:02,757 (beam_search:483) INFO: best hypo: oremokininaruna + +2024-01-16 23:09:02,758 (asr_inference:494) INFO: speech length: 46656 +2024-01-16 23:09:02,767 (beam_search:428) INFO: decoder input length: 70 +2024-01-16 23:09:02,767 (beam_search:429) INFO: max output length: 70 +2024-01-16 23:09:02,767 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:02,841 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:02,841 (beam_search:476) INFO: -2.32 * 1.0 = -2.32 for ctc +2024-01-16 23:09:02,841 (beam_search:479) INFO: total log probability: -2.32 +2024-01-16 23:09:02,841 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 23:09:02,841 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:02,841 (beam_search:483) INFO: best hypo: dareigatsUkaclterunokaahwokarunai + +2024-01-16 23:09:02,842 (asr_inference:494) INFO: speech length: 65664 +2024-01-16 23:09:02,852 (beam_search:428) INFO: decoder input length: 100 +2024-01-16 23:09:02,852 (beam_search:429) INFO: max output length: 100 +2024-01-16 23:09:02,852 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:02,982 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:02,982 (beam_search:476) INFO: -7.56 * 1.0 = -7.56 for ctc +2024-01-16 23:09:02,982 (beam_search:479) INFO: total log probability: -7.56 +2024-01-16 23:09:02,982 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 23:09:02,982 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:02,982 (beam_search:483) INFO: best hypo: puraozanobajoNgapauagarutoosUkoshIiuureshii + +2024-01-16 23:09:02,983 (asr_inference:494) INFO: speech length: 46080 +2024-01-16 23:09:02,992 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 23:09:02,992 (beam_search:429) INFO: max output length: 69 +2024-01-16 23:09:02,992 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:03,060 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:03,060 (beam_search:476) INFO: -1.51 * 1.0 = -1.51 for ctc +2024-01-16 23:09:03,060 (beam_search:479) INFO: total log probability: -1.51 +2024-01-16 23:09:03,060 (beam_search:480) INFO: normalized log probability: -0.05 +2024-01-16 23:09:03,060 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:03,060 (beam_search:483) INFO: best hypo: mataatarashiiaidorugadetekita + +2024-01-16 23:09:03,061 (asr_inference:494) INFO: speech length: 29952 +2024-01-16 23:09:03,068 (beam_search:428) INFO: decoder input length: 44 +2024-01-16 23:09:03,069 (beam_search:429) INFO: max output length: 44 +2024-01-16 23:09:03,069 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:03,096 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:03,096 (beam_search:476) INFO: -0.26 * 1.0 = -0.26 for ctc +2024-01-16 23:09:03,096 (beam_search:479) INFO: total log probability: -0.26 +2024-01-16 23:09:03,096 (beam_search:480) INFO: normalized log probability: -0.02 +2024-01-16 23:09:03,096 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:03,097 (beam_search:483) INFO: best hypo: majideyacltonoka + +2024-01-16 23:09:03,098 (asr_inference:494) INFO: speech length: 85248 +2024-01-16 23:09:03,108 (beam_search:428) INFO: decoder input length: 131 +2024-01-16 23:09:03,108 (beam_search:429) INFO: max output length: 131 +2024-01-16 23:09:03,108 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:03,250 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:03,250 (beam_search:476) INFO: -3.83 * 1.0 = -3.83 for ctc +2024-01-16 23:09:03,250 (beam_search:479) INFO: total log probability: -3.83 +2024-01-16 23:09:03,250 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:09:03,250 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:03,250 (beam_search:483) INFO: best hypo: choodosamotUkinipaukyoojigahaitekita + +2024-01-16 23:09:03,251 (asr_inference:494) INFO: speech length: 58176 +2024-01-16 23:09:03,260 (beam_search:428) INFO: decoder input length: 88 +2024-01-16 23:09:03,260 (beam_search:429) INFO: max output length: 88 +2024-01-16 23:09:03,260 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:03,312 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:03,312 (beam_search:476) INFO: -3.00 * 1.0 = -3.00 for ctc +2024-01-16 23:09:03,312 (beam_search:479) INFO: total log probability: -3.00 +2024-01-16 23:09:03,312 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 23:09:03,313 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:03,313 (beam_search:483) INFO: best hypo: iclsonikchieNshIteo + +2024-01-16 23:09:03,314 (asr_inference:494) INFO: speech length: 83520 +2024-01-16 23:09:03,324 (beam_search:428) INFO: decoder input length: 128 +2024-01-16 23:09:03,324 (beam_search:429) INFO: max output length: 128 +2024-01-16 23:09:03,324 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:03,412 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:03,412 (beam_search:476) INFO: -3.48 * 1.0 = -3.48 for ctc +2024-01-16 23:09:03,412 (beam_search:479) INFO: total log probability: -3.48 +2024-01-16 23:09:03,412 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 23:09:03,412 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:03,412 (beam_search:483) INFO: best hypo: sorekasatenotoresuN + +2024-01-16 23:09:03,413 (asr_inference:494) INFO: speech length: 76032 +2024-01-16 23:09:03,423 (beam_search:428) INFO: decoder input length: 116 +2024-01-16 23:09:03,423 (beam_search:429) INFO: max output length: 116 +2024-01-16 23:09:03,423 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:03,533 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:03,534 (beam_search:476) INFO: -3.75 * 1.0 = -3.75 for ctc +2024-01-16 23:09:03,534 (beam_search:479) INFO: total log probability: -3.75 +2024-01-16 23:09:03,534 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:09:03,534 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:03,534 (beam_search:483) INFO: best hypo: fUtariowarejieikisueesaNshIta + +2024-01-16 23:09:03,535 (asr_inference:494) INFO: speech length: 84672 +2024-01-16 23:09:03,545 (beam_search:428) INFO: decoder input length: 130 +2024-01-16 23:09:03,545 (beam_search:429) INFO: max output length: 130 +2024-01-16 23:09:03,545 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:03,701 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:03,701 (beam_search:476) INFO: -1.83 * 1.0 = -1.83 for ctc +2024-01-16 23:09:03,701 (beam_search:479) INFO: total log probability: -1.83 +2024-01-16 23:09:03,701 (beam_search:480) INFO: normalized log probability: -0.05 +2024-01-16 23:09:03,701 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:03,701 (beam_search:483) INFO: best hypo: tomatokanaNganoakaisosugakakaclteriuo + +2024-01-16 23:09:03,702 (asr_inference:494) INFO: speech length: 73728 +2024-01-16 23:09:03,712 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 23:09:03,712 (beam_search:429) INFO: max output length: 113 +2024-01-16 23:09:03,712 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:03,824 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:03,824 (beam_search:476) INFO: -3.23 * 1.0 = -3.23 for ctc +2024-01-16 23:09:03,824 (beam_search:479) INFO: total log probability: -3.23 +2024-01-16 23:09:03,824 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:09:03,824 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:03,825 (beam_search:483) INFO: best hypo: kogokaratatenaNasunowakibisui + +2024-01-16 23:09:03,826 (asr_inference:494) INFO: speech length: 80064 +2024-01-16 23:09:03,836 (beam_search:428) INFO: decoder input length: 123 +2024-01-16 23:09:03,836 (beam_search:429) INFO: max output length: 123 +2024-01-16 23:09:03,836 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:04,006 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:04,006 (beam_search:476) INFO: -6.51 * 1.0 = -6.51 for ctc +2024-01-16 23:09:04,006 (beam_search:479) INFO: total log probability: -6.51 +2024-01-16 23:09:04,006 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 23:09:04,006 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:04,007 (beam_search:483) INFO: best hypo: nizugeowosUkarishibocltepauajiganajimiyorisuru + +2024-01-16 23:09:04,008 (asr_inference:494) INFO: speech length: 67968 +2024-01-16 23:09:04,017 (beam_search:428) INFO: decoder input length: 104 +2024-01-16 23:09:04,017 (beam_search:429) INFO: max output length: 104 +2024-01-16 23:09:04,017 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:04,128 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:04,128 (beam_search:476) INFO: -2.01 * 1.0 = -2.01 for ctc +2024-01-16 23:09:04,128 (beam_search:479) INFO: total log probability: -2.01 +2024-01-16 23:09:04,128 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-16 23:09:04,128 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:04,128 (beam_search:483) INFO: best hypo: netokinihamacltarakanegagamacltaU + +2024-01-16 23:09:04,129 (asr_inference:494) INFO: speech length: 60480 +2024-01-16 23:09:04,139 (beam_search:428) INFO: decoder input length: 92 +2024-01-16 23:09:04,139 (beam_search:429) INFO: max output length: 92 +2024-01-16 23:09:04,139 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:04,209 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:04,209 (beam_search:476) INFO: -1.88 * 1.0 = -1.88 for ctc +2024-01-16 23:09:04,209 (beam_search:479) INFO: total log probability: -1.88 +2024-01-16 23:09:04,209 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:09:04,209 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:04,209 (beam_search:483) INFO: best hypo: sitakairiyooninaruNda + +2024-01-16 23:09:04,210 (asr_inference:494) INFO: speech length: 73728 +2024-01-16 23:09:04,220 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 23:09:04,220 (beam_search:429) INFO: max output length: 113 +2024-01-16 23:09:04,220 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:04,352 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:04,352 (beam_search:476) INFO: -3.80 * 1.0 = -3.80 for ctc +2024-01-16 23:09:04,352 (beam_search:479) INFO: total log probability: -3.80 +2024-01-16 23:09:04,352 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:09:04,352 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:04,352 (beam_search:483) INFO: best hypo: kosehahaiyutoyuyoriakugatsuyoaekaNji + +2024-01-16 23:09:04,353 (asr_inference:494) INFO: speech length: 95616 +2024-01-16 23:09:04,364 (beam_search:428) INFO: decoder input length: 147 +2024-01-16 23:09:04,364 (beam_search:429) INFO: max output length: 147 +2024-01-16 23:09:04,364 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:04,551 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:04,551 (beam_search:476) INFO: -5.14 * 1.0 = -5.14 for ctc +2024-01-16 23:09:04,551 (beam_search:479) INFO: total log probability: -5.14 +2024-01-16 23:09:04,551 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:09:04,551 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:04,551 (beam_search:483) INFO: best hypo: feijikarunosaomazamadatomisetsUkerareta + +2024-01-16 23:09:04,552 (asr_inference:494) INFO: speech length: 89856 +2024-01-16 23:09:04,563 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 23:09:04,563 (beam_search:429) INFO: max output length: 138 +2024-01-16 23:09:04,563 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:04,742 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:04,742 (beam_search:476) INFO: -5.33 * 1.0 = -5.33 for ctc +2024-01-16 23:09:04,742 (beam_search:479) INFO: total log probability: -5.33 +2024-01-16 23:09:04,743 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:09:04,743 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:04,743 (beam_search:483) INFO: best hypo: kosUpayokedebapausokokonomoNdaeagamaNsure + +2024-01-16 23:09:04,744 (asr_inference:494) INFO: speech length: 67968 +2024-01-16 23:09:04,753 (beam_search:428) INFO: decoder input length: 104 +2024-01-16 23:09:04,753 (beam_search:429) INFO: max output length: 104 +2024-01-16 23:09:04,753 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:04,867 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:04,867 (beam_search:476) INFO: -1.71 * 1.0 = -1.71 for ctc +2024-01-16 23:09:04,868 (beam_search:479) INFO: total log probability: -1.71 +2024-01-16 23:09:04,868 (beam_search:480) INFO: normalized log probability: -0.05 +2024-01-16 23:09:04,868 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:04,868 (beam_search:483) INFO: best hypo: iNnayacltemasUkarataijoobudesuiuo + +2024-01-16 23:09:04,869 (asr_inference:494) INFO: speech length: 79488 +2024-01-16 23:09:04,879 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 23:09:04,879 (beam_search:429) INFO: max output length: 122 +2024-01-16 23:09:04,879 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:05,006 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:05,006 (beam_search:476) INFO: -2.15 * 1.0 = -2.15 for ctc +2024-01-16 23:09:05,006 (beam_search:479) INFO: total log probability: -2.15 +2024-01-16 23:09:05,006 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 23:09:05,006 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:05,006 (beam_search:483) INFO: best hypo: konotojokaNhaicltashuukaNkiniclta + +2024-01-16 23:09:05,007 (asr_inference:494) INFO: speech length: 69696 +2024-01-16 23:09:05,017 (beam_search:428) INFO: decoder input length: 106 +2024-01-16 23:09:05,017 (beam_search:429) INFO: max output length: 106 +2024-01-16 23:09:05,017 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:05,101 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:05,102 (beam_search:476) INFO: -2.58 * 1.0 = -2.58 for ctc +2024-01-16 23:09:05,102 (beam_search:479) INFO: total log probability: -2.58 +2024-01-16 23:09:05,102 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:09:05,102 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:05,102 (beam_search:483) INFO: best hypo: konodeNchifuukirechiaclta + +2024-01-16 23:09:05,103 (asr_inference:494) INFO: speech length: 66816 +2024-01-16 23:09:05,112 (beam_search:428) INFO: decoder input length: 102 +2024-01-16 23:09:05,112 (beam_search:429) INFO: max output length: 102 +2024-01-16 23:09:05,112 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:05,233 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:05,233 (beam_search:476) INFO: -2.00 * 1.0 = -2.00 for ctc +2024-01-16 23:09:05,233 (beam_search:479) INFO: total log probability: -2.00 +2024-01-16 23:09:05,233 (beam_search:480) INFO: normalized log probability: -0.05 +2024-01-16 23:09:05,233 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:05,233 (beam_search:483) INFO: best hypo: amayeudorisurutokoroganakUtekomaclta + +2024-01-16 23:09:05,235 (asr_inference:494) INFO: speech length: 66240 +2024-01-16 23:09:05,244 (beam_search:428) INFO: decoder input length: 101 +2024-01-16 23:09:05,244 (beam_search:429) INFO: max output length: 101 +2024-01-16 23:09:05,244 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:05,346 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:05,346 (beam_search:476) INFO: -3.62 * 1.0 = -3.62 for ctc +2024-01-16 23:09:05,347 (beam_search:479) INFO: total log probability: -3.62 +2024-01-16 23:09:05,347 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:09:05,347 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:05,347 (beam_search:483) INFO: best hypo: yasUkasurueorishIttsuwagetohoshe + +2024-01-16 23:09:05,348 (asr_inference:494) INFO: speech length: 60480 +2024-01-16 23:09:05,356 (beam_search:428) INFO: decoder input length: 92 +2024-01-16 23:09:05,356 (beam_search:429) INFO: max output length: 92 +2024-01-16 23:09:05,356 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:05,458 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:05,458 (beam_search:476) INFO: -1.95 * 1.0 = -1.95 for ctc +2024-01-16 23:09:05,458 (beam_search:479) INFO: total log probability: -1.95 +2024-01-16 23:09:05,458 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-16 23:09:05,458 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:05,458 (beam_search:483) INFO: best hypo: masegokonakoteinarutowamonakaclta + +2024-01-16 23:09:05,459 (asr_inference:494) INFO: speech length: 54720 +2024-01-16 23:09:05,468 (beam_search:428) INFO: decoder input length: 83 +2024-01-16 23:09:05,468 (beam_search:429) INFO: max output length: 83 +2024-01-16 23:09:05,468 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:05,555 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:05,555 (beam_search:476) INFO: -1.79 * 1.0 = -1.79 for ctc +2024-01-16 23:09:05,555 (beam_search:479) INFO: total log probability: -1.79 +2024-01-16 23:09:05,555 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-16 23:09:05,555 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:05,555 (beam_search:483) INFO: best hypo: saigoniwarayotorinikurasUtairu + +2024-01-16 23:09:05,556 (asr_inference:494) INFO: speech length: 57600 +2024-01-16 23:09:05,565 (beam_search:428) INFO: decoder input length: 87 +2024-01-16 23:09:05,565 (beam_search:429) INFO: max output length: 87 +2024-01-16 23:09:05,565 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:05,632 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:05,632 (beam_search:476) INFO: -2.74 * 1.0 = -2.74 for ctc +2024-01-16 23:09:05,632 (beam_search:479) INFO: total log probability: -2.74 +2024-01-16 23:09:05,632 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:09:05,632 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:05,632 (beam_search:483) INFO: best hypo: koraenaNnaeimigarudapau + +2024-01-16 23:09:05,633 (asr_inference:494) INFO: speech length: 34944 +2024-01-16 23:09:05,641 (beam_search:428) INFO: decoder input length: 52 +2024-01-16 23:09:05,641 (beam_search:429) INFO: max output length: 52 +2024-01-16 23:09:05,641 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:05,654 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:05,654 (beam_search:476) INFO: -4.20 * 1.0 = -4.20 for ctc +2024-01-16 23:09:05,654 (beam_search:479) INFO: total log probability: -4.20 +2024-01-16 23:09:05,654 (beam_search:480) INFO: normalized log probability: -0.60 +2024-01-16 23:09:05,654 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:05,654 (beam_search:483) INFO: best hypo: otiia + +2024-01-16 23:09:05,655 (asr_inference:494) INFO: speech length: 33024 +2024-01-16 23:09:05,663 (beam_search:428) INFO: decoder input length: 49 +2024-01-16 23:09:05,663 (beam_search:429) INFO: max output length: 49 +2024-01-16 23:09:05,663 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:05,675 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:05,675 (beam_search:476) INFO: -3.94 * 1.0 = -3.94 for ctc +2024-01-16 23:09:05,675 (beam_search:479) INFO: total log probability: -3.94 +2024-01-16 23:09:05,675 (beam_search:480) INFO: normalized log probability: -0.56 +2024-01-16 23:09:05,675 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:05,675 (beam_search:483) INFO: best hypo: shashei + +2024-01-16 23:09:05,676 (asr_inference:494) INFO: speech length: 23424 +2024-01-16 23:09:05,683 (beam_search:428) INFO: decoder input length: 34 +2024-01-16 23:09:05,683 (beam_search:429) INFO: max output length: 34 +2024-01-16 23:09:05,683 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:05,688 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:05,688 (beam_search:476) INFO: -1.27 * 1.0 = -1.27 for ctc +2024-01-16 23:09:05,688 (beam_search:479) INFO: total log probability: -1.27 +2024-01-16 23:09:05,689 (beam_search:480) INFO: normalized log probability: -0.32 +2024-01-16 23:09:05,689 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:05,689 (beam_search:483) INFO: best hypo: ni + +2024-01-16 23:09:05,690 (asr_inference:494) INFO: speech length: 28416 +2024-01-16 23:09:05,697 (beam_search:428) INFO: decoder input length: 42 +2024-01-16 23:09:05,697 (beam_search:429) INFO: max output length: 42 +2024-01-16 23:09:05,697 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:05,706 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:05,706 (beam_search:476) INFO: -0.91 * 1.0 = -0.91 for ctc +2024-01-16 23:09:05,706 (beam_search:479) INFO: total log probability: -0.91 +2024-01-16 23:09:05,706 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 23:09:05,706 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:05,707 (beam_search:483) INFO: best hypo: hachi + +2024-01-16 23:09:05,707 (asr_inference:494) INFO: speech length: 31104 +2024-01-16 23:09:05,715 (beam_search:428) INFO: decoder input length: 46 +2024-01-16 23:09:05,715 (beam_search:429) INFO: max output length: 46 +2024-01-16 23:09:05,715 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:05,723 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:05,723 (beam_search:476) INFO: -0.53 * 1.0 = -0.53 for ctc +2024-01-16 23:09:05,723 (beam_search:479) INFO: total log probability: -0.53 +2024-01-16 23:09:05,723 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:09:05,723 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:05,723 (beam_search:483) INFO: best hypo: hai + +# Accounting: time=9 threads=1 +# Ended (code 0) at Tue Jan 16 23:09:06 CST 2024, elapsed time 9 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.3.log b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.3.log new file mode 100644 index 0000000000000000000000000000000000000000..0376e3190dfafbd39275febe1b5c18443247bcb1 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.3.log @@ -0,0 +1,360 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3 --config conf/decode_asr.yaml +# Started at Tue Jan 16 23:09:06 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3 --config conf/decode_asr.yaml +2024-01-16 23:09:07,551 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml +2024-01-16 23:09:07,569 (asr:523) INFO: Vocabulary size: 41 +2024-01-16 23:09:07,631 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 23:09:07,631 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 23:09:07,742 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 23:09:09,047 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 23:09:10,281 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 23:09:10,281 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 23:09:10,281 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 23:09:10,314 (asr_inference:446) INFO: Text tokenizer: WordTokenizer(delimiter="None") +2024-01-16 23:09:10,390 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 23:09:10,502 (asr_inference:494) INFO: speech length: 62208 +2024-01-16 23:09:11,713 (beam_search:428) INFO: decoder input length: 95 +2024-01-16 23:09:11,714 (beam_search:429) INFO: max output length: 95 +2024-01-16 23:09:11,714 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:11,801 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:11,801 (beam_search:476) INFO: -4.46 * 1.0 = -4.46 for ctc +2024-01-16 23:09:11,801 (beam_search:479) INFO: total log probability: -4.46 +2024-01-16 23:09:11,801 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 23:09:11,801 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:11,801 (beam_search:483) INFO: best hypo: teburunooyenikabiNgarimasU + +2024-01-16 23:09:11,825 (asr_inference:494) INFO: speech length: 61056 +2024-01-16 23:09:11,835 (beam_search:428) INFO: decoder input length: 93 +2024-01-16 23:09:11,835 (beam_search:429) INFO: max output length: 93 +2024-01-16 23:09:11,835 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:11,923 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:11,923 (beam_search:476) INFO: -2.92 * 1.0 = -2.92 for ctc +2024-01-16 23:09:11,923 (beam_search:479) INFO: total log probability: -2.92 +2024-01-16 23:09:11,923 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:09:11,923 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:11,923 (beam_search:483) INFO: best hypo: wotashiwaomaiyasasaNboshimasU + +2024-01-16 23:09:11,924 (asr_inference:494) INFO: speech length: 58176 +2024-01-16 23:09:11,933 (beam_search:428) INFO: decoder input length: 88 +2024-01-16 23:09:11,933 (beam_search:429) INFO: max output length: 88 +2024-01-16 23:09:11,934 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:12,022 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:12,022 (beam_search:476) INFO: -2.37 * 1.0 = -2.37 for ctc +2024-01-16 23:09:12,022 (beam_search:479) INFO: total log probability: -2.37 +2024-01-16 23:09:12,022 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:09:12,022 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:12,022 (beam_search:483) INFO: best hypo: atarashiikutsuoohaitedekakemasU + +2024-01-16 23:09:12,024 (asr_inference:494) INFO: speech length: 102528 +2024-01-16 23:09:12,036 (beam_search:428) INFO: decoder input length: 158 +2024-01-16 23:09:12,036 (beam_search:429) INFO: max output length: 158 +2024-01-16 23:09:12,036 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:12,321 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:12,321 (beam_search:476) INFO: -4.94 * 1.0 = -4.94 for ctc +2024-01-16 23:09:12,321 (beam_search:479) INFO: total log probability: -4.94 +2024-01-16 23:09:12,321 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:09:12,321 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:12,322 (beam_search:483) INFO: best hypo: kotoshinonatseyasamiowauminimoikimashItashiiyamanimonoribashIta + +2024-01-16 23:09:12,323 (asr_inference:494) INFO: speech length: 91008 +2024-01-16 23:09:12,334 (beam_search:428) INFO: decoder input length: 140 +2024-01-16 23:09:12,334 (beam_search:429) INFO: max output length: 140 +2024-01-16 23:09:12,334 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:12,567 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:12,567 (beam_search:476) INFO: -3.68 * 1.0 = -3.68 for ctc +2024-01-16 23:09:12,567 (beam_search:479) INFO: total log probability: -3.68 +2024-01-16 23:09:12,567 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 23:09:12,567 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:12,567 (beam_search:483) INFO: best hypo: watashiwairoyironobeNgoochibuNnomunedekoshiraetemimashIta + +2024-01-16 23:09:12,569 (asr_inference:494) INFO: speech length: 60480 +2024-01-16 23:09:12,577 (beam_search:428) INFO: decoder input length: 92 +2024-01-16 23:09:12,578 (beam_search:429) INFO: max output length: 92 +2024-01-16 23:09:12,578 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:12,667 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:12,667 (beam_search:476) INFO: -3.98 * 1.0 = -3.98 for ctc +2024-01-16 23:09:12,667 (beam_search:479) INFO: total log probability: -3.98 +2024-01-16 23:09:12,667 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:09:12,667 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:12,667 (beam_search:483) INFO: best hypo: naNdekoomosuoobaihedanaNdaro + +2024-01-16 23:09:12,668 (asr_inference:494) INFO: speech length: 97920 +2024-01-16 23:09:12,679 (beam_search:428) INFO: decoder input length: 150 +2024-01-16 23:09:12,679 (beam_search:429) INFO: max output length: 150 +2024-01-16 23:09:12,679 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:12,884 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:12,884 (beam_search:476) INFO: -9.43 * 1.0 = -9.43 for ctc +2024-01-16 23:09:12,884 (beam_search:479) INFO: total log probability: -9.43 +2024-01-16 23:09:12,884 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 23:09:12,884 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:12,885 (beam_search:483) INFO: best hypo: tareNtokarakyokuananikaeiecltuekeeshIsakIugea + +2024-01-16 23:09:12,886 (asr_inference:494) INFO: speech length: 55296 +2024-01-16 23:09:12,895 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 23:09:12,895 (beam_search:429) INFO: max output length: 84 +2024-01-16 23:09:12,895 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:12,966 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:12,966 (beam_search:476) INFO: -3.70 * 1.0 = -3.70 for ctc +2024-01-16 23:09:12,966 (beam_search:479) INFO: total log probability: -3.70 +2024-01-16 23:09:12,966 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:09:12,966 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:12,966 (beam_search:483) INFO: best hypo: dogezaserebaicltemoNshanai + +2024-01-16 23:09:12,967 (asr_inference:494) INFO: speech length: 101952 +2024-01-16 23:09:12,979 (beam_search:428) INFO: decoder input length: 157 +2024-01-16 23:09:12,979 (beam_search:429) INFO: max output length: 157 +2024-01-16 23:09:12,979 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:13,216 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:13,216 (beam_search:476) INFO: -5.57 * 1.0 = -5.57 for ctc +2024-01-16 23:09:13,216 (beam_search:479) INFO: total log probability: -5.57 +2024-01-16 23:09:13,216 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:09:13,216 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:13,216 (beam_search:483) INFO: best hypo: detanoaidakanojiwajibuNtoiclteenokkyuoriyotanoclta + +2024-01-16 23:09:13,217 (asr_inference:494) INFO: speech length: 64512 +2024-01-16 23:09:13,227 (beam_search:428) INFO: decoder input length: 98 +2024-01-16 23:09:13,227 (beam_search:429) INFO: max output length: 98 +2024-01-16 23:09:13,227 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:13,335 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:13,335 (beam_search:476) INFO: -5.50 * 1.0 = -5.50 for ctc +2024-01-16 23:09:13,335 (beam_search:479) INFO: total log probability: -5.50 +2024-01-16 23:09:13,335 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 23:09:13,335 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:13,335 (beam_search:483) INFO: best hypo: konoreeniNnaNkawahshsashiburininita + +2024-01-16 23:09:13,336 (asr_inference:494) INFO: speech length: 66816 +2024-01-16 23:09:13,346 (beam_search:428) INFO: decoder input length: 102 +2024-01-16 23:09:13,346 (beam_search:429) INFO: max output length: 102 +2024-01-16 23:09:13,346 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:13,436 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:13,436 (beam_search:476) INFO: -5.60 * 1.0 = -5.60 for ctc +2024-01-16 23:09:13,436 (beam_search:479) INFO: total log probability: -5.60 +2024-01-16 23:09:13,436 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 23:09:13,436 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:13,436 (beam_search:483) INFO: best hypo: ookIktasaidaochieNjieosuru + +2024-01-16 23:09:13,437 (asr_inference:494) INFO: speech length: 65088 +2024-01-16 23:09:13,447 (beam_search:428) INFO: decoder input length: 99 +2024-01-16 23:09:13,447 (beam_search:429) INFO: max output length: 99 +2024-01-16 23:09:13,447 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:13,525 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:13,525 (beam_search:476) INFO: -1.64 * 1.0 = -1.64 for ctc +2024-01-16 23:09:13,525 (beam_search:479) INFO: total log probability: -1.64 +2024-01-16 23:09:13,525 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 23:09:13,525 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:13,525 (beam_search:483) INFO: best hypo: karewatamaokakimushiclta + +2024-01-16 23:09:13,526 (asr_inference:494) INFO: speech length: 86976 +2024-01-16 23:09:13,536 (beam_search:428) INFO: decoder input length: 133 +2024-01-16 23:09:13,536 (beam_search:429) INFO: max output length: 133 +2024-01-16 23:09:13,536 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:13,619 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:13,619 (beam_search:476) INFO: -2.56 * 1.0 = -2.56 for ctc +2024-01-16 23:09:13,619 (beam_search:479) INFO: total log probability: -2.56 +2024-01-16 23:09:13,619 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:09:13,619 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:13,619 (beam_search:483) INFO: best hypo: komateshItearimasU + +2024-01-16 23:09:13,620 (asr_inference:494) INFO: speech length: 72000 +2024-01-16 23:09:13,630 (beam_search:428) INFO: decoder input length: 110 +2024-01-16 23:09:13,630 (beam_search:429) INFO: max output length: 110 +2024-01-16 23:09:13,630 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:13,723 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:13,723 (beam_search:476) INFO: -3.78 * 1.0 = -3.78 for ctc +2024-01-16 23:09:13,723 (beam_search:479) INFO: total log probability: -3.78 +2024-01-16 23:09:13,723 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 23:09:13,723 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:13,723 (beam_search:483) INFO: best hypo: onodokUseNkaijowakIiteru + +2024-01-16 23:09:13,724 (asr_inference:494) INFO: speech length: 99648 +2024-01-16 23:09:13,735 (beam_search:428) INFO: decoder input length: 153 +2024-01-16 23:09:13,736 (beam_search:429) INFO: max output length: 153 +2024-01-16 23:09:13,736 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:13,943 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:13,943 (beam_search:476) INFO: -7.33 * 1.0 = -7.33 for ctc +2024-01-16 23:09:13,943 (beam_search:479) INFO: total log probability: -7.33 +2024-01-16 23:09:13,943 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 23:09:13,943 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:13,944 (beam_search:483) INFO: best hypo: reejookoaketacltoUtaNnanigahichuyokawasureta + +2024-01-16 23:09:13,945 (asr_inference:494) INFO: speech length: 33024 +2024-01-16 23:09:13,952 (beam_search:428) INFO: decoder input length: 49 +2024-01-16 23:09:13,952 (beam_search:429) INFO: max output length: 49 +2024-01-16 23:09:13,952 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:13,965 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:13,965 (beam_search:476) INFO: -1.43 * 1.0 = -1.43 for ctc +2024-01-16 23:09:13,965 (beam_search:479) INFO: total log probability: -1.43 +2024-01-16 23:09:13,965 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 23:09:13,965 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:13,965 (beam_search:483) INFO: best hypo: iitchi + +2024-01-16 23:09:13,966 (asr_inference:494) INFO: speech length: 26112 +2024-01-16 23:09:13,973 (beam_search:428) INFO: decoder input length: 38 +2024-01-16 23:09:13,973 (beam_search:429) INFO: max output length: 38 +2024-01-16 23:09:13,973 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:13,982 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:13,982 (beam_search:476) INFO: -0.40 * 1.0 = -0.40 for ctc +2024-01-16 23:09:13,982 (beam_search:479) INFO: total log probability: -0.40 +2024-01-16 23:09:13,982 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 23:09:13,982 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:13,982 (beam_search:483) INFO: best hypo: hachi + +2024-01-16 23:09:13,983 (asr_inference:494) INFO: speech length: 25728 +2024-01-16 23:09:13,990 (beam_search:428) INFO: decoder input length: 38 +2024-01-16 23:09:13,990 (beam_search:429) INFO: max output length: 38 +2024-01-16 23:09:13,990 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:14,001 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:14,001 (beam_search:476) INFO: -1.49 * 1.0 = -1.49 for ctc +2024-01-16 23:09:14,001 (beam_search:479) INFO: total log probability: -1.49 +2024-01-16 23:09:14,001 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 23:09:14,001 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:14,002 (beam_search:483) INFO: best hypo: kiiwea + +2024-01-16 23:09:14,002 (asr_inference:494) INFO: speech length: 22272 +2024-01-16 23:09:14,009 (beam_search:428) INFO: decoder input length: 32 +2024-01-16 23:09:14,009 (beam_search:429) INFO: max output length: 32 +2024-01-16 23:09:14,009 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:14,016 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:14,016 (beam_search:476) INFO: -1.08 * 1.0 = -1.08 for ctc +2024-01-16 23:09:14,016 (beam_search:479) INFO: total log probability: -1.08 +2024-01-16 23:09:14,016 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 23:09:14,016 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:14,016 (beam_search:483) INFO: best hypo: dei + +2024-01-16 23:09:14,017 (asr_inference:494) INFO: speech length: 27648 +2024-01-16 23:09:14,024 (beam_search:428) INFO: decoder input length: 41 +2024-01-16 23:09:14,024 (beam_search:429) INFO: max output length: 41 +2024-01-16 23:09:14,024 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:14,035 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:14,035 (beam_search:476) INFO: -1.11 * 1.0 = -1.11 for ctc +2024-01-16 23:09:14,035 (beam_search:479) INFO: total log probability: -1.11 +2024-01-16 23:09:14,035 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 23:09:14,035 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:14,035 (beam_search:483) INFO: best hypo: ashichi + +2024-01-16 23:09:14,036 (asr_inference:494) INFO: speech length: 64512 +2024-01-16 23:09:14,045 (beam_search:428) INFO: decoder input length: 98 +2024-01-16 23:09:14,045 (beam_search:429) INFO: max output length: 98 +2024-01-16 23:09:14,045 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:14,139 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:14,139 (beam_search:476) INFO: -2.56 * 1.0 = -2.56 for ctc +2024-01-16 23:09:14,139 (beam_search:479) INFO: total log probability: -2.56 +2024-01-16 23:09:14,139 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:09:14,139 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:14,139 (beam_search:483) INFO: best hypo: yooboodasunoNkaushItoasUkunai + +2024-01-16 23:09:14,140 (asr_inference:494) INFO: speech length: 69120 +2024-01-16 23:09:14,150 (beam_search:428) INFO: decoder input length: 105 +2024-01-16 23:09:14,150 (beam_search:429) INFO: max output length: 105 +2024-01-16 23:09:14,150 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:14,276 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:14,276 (beam_search:476) INFO: -5.40 * 1.0 = -5.40 for ctc +2024-01-16 23:09:14,276 (beam_search:479) INFO: total log probability: -5.40 +2024-01-16 23:09:14,276 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:09:14,276 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:14,276 (beam_search:483) INFO: best hypo: nookaretokuyunoikkyuemakasenokomasharu + +2024-01-16 23:09:14,278 (asr_inference:494) INFO: speech length: 69120 +2024-01-16 23:09:14,288 (beam_search:428) INFO: decoder input length: 105 +2024-01-16 23:09:14,288 (beam_search:429) INFO: max output length: 105 +2024-01-16 23:09:14,288 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:14,408 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:14,408 (beam_search:476) INFO: -2.31 * 1.0 = -2.31 for ctc +2024-01-16 23:09:14,408 (beam_search:479) INFO: total log probability: -2.31 +2024-01-16 23:09:14,408 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-16 23:09:14,408 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:14,408 (beam_search:483) INFO: best hypo: konodaikuwaaNkogaookUteyokUkaimasU + +2024-01-16 23:09:14,409 (asr_inference:494) INFO: speech length: 60480 +2024-01-16 23:09:14,418 (beam_search:428) INFO: decoder input length: 92 +2024-01-16 23:09:14,418 (beam_search:429) INFO: max output length: 92 +2024-01-16 23:09:14,418 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:14,512 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:14,512 (beam_search:476) INFO: -2.42 * 1.0 = -2.42 for ctc +2024-01-16 23:09:14,512 (beam_search:479) INFO: total log probability: -2.42 +2024-01-16 23:09:14,512 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:09:14,512 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:14,512 (beam_search:483) INFO: best hypo: deshookinagarashoosuetsooyomimasU + +2024-01-16 23:09:14,513 (asr_inference:494) INFO: speech length: 89856 +2024-01-16 23:09:14,524 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 23:09:14,524 (beam_search:429) INFO: max output length: 138 +2024-01-16 23:09:14,524 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:14,700 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:14,700 (beam_search:476) INFO: -4.52 * 1.0 = -4.52 for ctc +2024-01-16 23:09:14,700 (beam_search:479) INFO: total log probability: -4.52 +2024-01-16 23:09:14,700 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:09:14,700 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:14,700 (beam_search:483) INFO: best hypo: koNnookinaNgogurotsukenaitikenaeiNdesUka + +2024-01-16 23:09:14,702 (asr_inference:494) INFO: speech length: 52416 +2024-01-16 23:09:14,710 (beam_search:428) INFO: decoder input length: 79 +2024-01-16 23:09:14,710 (beam_search:429) INFO: max output length: 79 +2024-01-16 23:09:14,710 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:14,778 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:14,778 (beam_search:476) INFO: -2.39 * 1.0 = -2.39 for ctc +2024-01-16 23:09:14,778 (beam_search:479) INFO: total log probability: -2.39 +2024-01-16 23:09:14,778 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:09:14,778 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:14,778 (beam_search:483) INFO: best hypo: karaenobooikowatomaranai + +2024-01-16 23:09:14,779 (asr_inference:494) INFO: speech length: 37440 +2024-01-16 23:09:14,787 (beam_search:428) INFO: decoder input length: 56 +2024-01-16 23:09:14,787 (beam_search:429) INFO: max output length: 56 +2024-01-16 23:09:14,787 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:14,817 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:14,817 (beam_search:476) INFO: -1.72 * 1.0 = -1.72 for ctc +2024-01-16 23:09:14,817 (beam_search:479) INFO: total log probability: -1.72 +2024-01-16 23:09:14,817 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:09:14,817 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:14,817 (beam_search:483) INFO: best hypo: ikiteikaNmane + +2024-01-16 23:09:14,818 (asr_inference:494) INFO: speech length: 57600 +2024-01-16 23:09:14,827 (beam_search:428) INFO: decoder input length: 87 +2024-01-16 23:09:14,827 (beam_search:429) INFO: max output length: 87 +2024-01-16 23:09:14,827 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:14,926 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:14,926 (beam_search:476) INFO: -11.31 * 1.0 = -11.31 for ctc +2024-01-16 23:09:14,926 (beam_search:479) INFO: total log probability: -11.31 +2024-01-16 23:09:14,926 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 23:09:14,926 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:14,926 (beam_search:483) INFO: best hypo: toojudoshsesakaktekinahatsumedacltane + +2024-01-16 23:09:14,927 (asr_inference:494) INFO: speech length: 40896 +2024-01-16 23:09:14,935 (beam_search:428) INFO: decoder input length: 61 +2024-01-16 23:09:14,935 (beam_search:429) INFO: max output length: 61 +2024-01-16 23:09:14,935 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:14,987 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:14,987 (beam_search:476) INFO: -6.02 * 1.0 = -6.02 for ctc +2024-01-16 23:09:14,987 (beam_search:479) INFO: total log probability: -6.02 +2024-01-16 23:09:14,987 (beam_search:480) INFO: normalized log probability: -0.24 +2024-01-16 23:09:14,987 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:14,987 (beam_search:483) INFO: best hypo: hoNtekiotasoachIkaclUtkaN + +2024-01-16 23:09:14,988 (asr_inference:494) INFO: speech length: 36288 +2024-01-16 23:09:14,996 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 23:09:14,996 (beam_search:429) INFO: max output length: 54 +2024-01-16 23:09:14,996 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:15,031 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:15,031 (beam_search:476) INFO: -2.26 * 1.0 = -2.26 for ctc +2024-01-16 23:09:15,031 (beam_search:479) INFO: total log probability: -2.26 +2024-01-16 23:09:15,031 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:09:15,031 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:15,031 (beam_search:483) INFO: best hypo: kaawahiewaclteida + +2024-01-16 23:09:15,032 (asr_inference:494) INFO: speech length: 46464 +2024-01-16 23:09:15,040 (beam_search:428) INFO: decoder input length: 70 +2024-01-16 23:09:15,040 (beam_search:429) INFO: max output length: 70 +2024-01-16 23:09:15,040 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:15,057 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:15,057 (beam_search:476) INFO: -2.62 * 1.0 = -2.62 for ctc +2024-01-16 23:09:15,057 (beam_search:479) INFO: total log probability: -2.62 +2024-01-16 23:09:15,057 (beam_search:480) INFO: normalized log probability: -0.37 +2024-01-16 23:09:15,057 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:15,057 (beam_search:483) INFO: best hypo: checlke + +# Accounting: time=9 threads=1 +# Ended (code 0) at Tue Jan 16 23:09:15 CST 2024, elapsed time 9 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.4.log b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.4.log new file mode 100644 index 0000000000000000000000000000000000000000..7ee5f633e4867520b0c4f826878173f3f6e83efb --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.4.log @@ -0,0 +1,360 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4 --config conf/decode_asr.yaml +# Started at Tue Jan 16 23:09:15 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4 --config conf/decode_asr.yaml +2024-01-16 23:09:16,865 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml +2024-01-16 23:09:16,883 (asr:523) INFO: Vocabulary size: 41 +2024-01-16 23:09:16,945 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 23:09:16,945 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 23:09:17,055 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 23:09:18,344 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 23:09:19,571 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 23:09:19,571 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 23:09:19,571 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 23:09:19,603 (asr_inference:446) INFO: Text tokenizer: WordTokenizer(delimiter="None") +2024-01-16 23:09:19,678 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 23:09:19,791 (asr_inference:494) INFO: speech length: 45696 +2024-01-16 23:09:21,008 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 23:09:21,008 (beam_search:429) INFO: max output length: 69 +2024-01-16 23:09:21,008 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:21,014 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:21,014 (beam_search:476) INFO: -0.51 * 1.0 = -0.51 for ctc +2024-01-16 23:09:21,014 (beam_search:479) INFO: total log probability: -0.51 +2024-01-16 23:09:21,014 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 23:09:21,014 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:21,015 (beam_search:483) INFO: best hypo: o + +2024-01-16 23:09:21,039 (asr_inference:494) INFO: speech length: 34560 +2024-01-16 23:09:21,048 (beam_search:428) INFO: decoder input length: 51 +2024-01-16 23:09:21,048 (beam_search:429) INFO: max output length: 51 +2024-01-16 23:09:21,048 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:21,065 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:21,065 (beam_search:476) INFO: -1.28 * 1.0 = -1.28 for ctc +2024-01-16 23:09:21,065 (beam_search:479) INFO: total log probability: -1.28 +2024-01-16 23:09:21,065 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:09:21,065 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:21,065 (beam_search:483) INFO: best hypo: ashItchii + +2024-01-16 23:09:21,066 (asr_inference:494) INFO: speech length: 46464 +2024-01-16 23:09:21,075 (beam_search:428) INFO: decoder input length: 70 +2024-01-16 23:09:21,075 (beam_search:429) INFO: max output length: 70 +2024-01-16 23:09:21,075 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:21,084 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:21,084 (beam_search:476) INFO: -0.57 * 1.0 = -0.57 for ctc +2024-01-16 23:09:21,084 (beam_search:479) INFO: total log probability: -0.57 +2024-01-16 23:09:21,084 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:09:21,084 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:21,084 (beam_search:483) INFO: best hypo: ko + +2024-01-16 23:09:21,085 (asr_inference:494) INFO: speech length: 43392 +2024-01-16 23:09:21,094 (beam_search:428) INFO: decoder input length: 65 +2024-01-16 23:09:21,094 (beam_search:429) INFO: max output length: 65 +2024-01-16 23:09:21,094 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:21,108 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:21,108 (beam_search:476) INFO: -0.82 * 1.0 = -0.82 for ctc +2024-01-16 23:09:21,108 (beam_search:479) INFO: total log probability: -0.82 +2024-01-16 23:09:21,108 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:09:21,108 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:21,108 (beam_search:483) INFO: best hypo: iiea + +2024-01-16 23:09:21,109 (asr_inference:494) INFO: speech length: 120960 +2024-01-16 23:09:21,123 (beam_search:428) INFO: decoder input length: 186 +2024-01-16 23:09:21,123 (beam_search:429) INFO: max output length: 186 +2024-01-16 23:09:21,123 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:21,273 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:21,273 (beam_search:476) INFO: -2.19 * 1.0 = -2.19 for ctc +2024-01-16 23:09:21,273 (beam_search:479) INFO: total log probability: -2.19 +2024-01-16 23:09:21,273 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:09:21,273 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:21,273 (beam_search:483) INFO: best hypo: namaikarashetektoosugiru + +2024-01-16 23:09:21,274 (asr_inference:494) INFO: speech length: 119808 +2024-01-16 23:09:21,287 (beam_search:428) INFO: decoder input length: 185 +2024-01-16 23:09:21,287 (beam_search:429) INFO: max output length: 185 +2024-01-16 23:09:21,287 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:21,648 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:21,648 (beam_search:476) INFO: -4.99 * 1.0 = -4.99 for ctc +2024-01-16 23:09:21,648 (beam_search:479) INFO: total log probability: -4.99 +2024-01-16 23:09:21,649 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:09:21,649 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:21,649 (beam_search:483) INFO: best hypo: jikonosotonyarutoyunowataNnijikoneshikNsotssoniarutoyukotodenaku + +2024-01-16 23:09:21,650 (asr_inference:494) INFO: speech length: 55296 +2024-01-16 23:09:21,659 (beam_search:428) INFO: decoder input length: 84 +2024-01-16 23:09:21,659 (beam_search:429) INFO: max output length: 84 +2024-01-16 23:09:21,659 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:21,722 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:21,722 (beam_search:476) INFO: -1.47 * 1.0 = -1.47 for ctc +2024-01-16 23:09:21,723 (beam_search:479) INFO: total log probability: -1.47 +2024-01-16 23:09:21,723 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 23:09:21,723 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:21,723 (beam_search:483) INFO: best hypo: sorwashiranakUteidesU + +2024-01-16 23:09:21,724 (asr_inference:494) INFO: speech length: 89280 +2024-01-16 23:09:21,735 (beam_search:428) INFO: decoder input length: 137 +2024-01-16 23:09:21,735 (beam_search:429) INFO: max output length: 137 +2024-01-16 23:09:21,735 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:21,933 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:21,933 (beam_search:476) INFO: -3.62 * 1.0 = -3.62 for ctc +2024-01-16 23:09:21,933 (beam_search:479) INFO: total log probability: -3.62 +2024-01-16 23:09:21,933 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:09:21,933 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:21,934 (beam_search:483) INFO: best hypo: kimitokonokyootsunoshigewadareitoruipaumiataranai + +2024-01-16 23:09:21,935 (asr_inference:494) INFO: speech length: 75456 +2024-01-16 23:09:21,945 (beam_search:428) INFO: decoder input length: 115 +2024-01-16 23:09:21,945 (beam_search:429) INFO: max output length: 115 +2024-01-16 23:09:21,945 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:22,059 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:22,059 (beam_search:476) INFO: -4.56 * 1.0 = -4.56 for ctc +2024-01-16 23:09:22,059 (beam_search:479) INFO: total log probability: -4.56 +2024-01-16 23:09:22,059 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 23:09:22,059 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:22,059 (beam_search:483) INFO: best hypo: suegeeootoiNnacltekiterunonacl + +2024-01-16 23:09:22,060 (asr_inference:494) INFO: speech length: 54144 +2024-01-16 23:09:22,069 (beam_search:428) INFO: decoder input length: 82 +2024-01-16 23:09:22,069 (beam_search:429) INFO: max output length: 82 +2024-01-16 23:09:22,069 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:22,137 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:22,137 (beam_search:476) INFO: -2.23 * 1.0 = -2.23 for ctc +2024-01-16 23:09:22,137 (beam_search:479) INFO: total log probability: -2.23 +2024-01-16 23:09:22,137 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:09:22,137 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:22,138 (beam_search:483) INFO: best hypo: konoheNdesUkoshiasuimasho + +2024-01-16 23:09:22,139 (asr_inference:494) INFO: speech length: 84864 +2024-01-16 23:09:22,149 (beam_search:428) INFO: decoder input length: 130 +2024-01-16 23:09:22,149 (beam_search:429) INFO: max output length: 130 +2024-01-16 23:09:22,149 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:22,303 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:22,303 (beam_search:476) INFO: -6.36 * 1.0 = -6.36 for ctc +2024-01-16 23:09:22,303 (beam_search:479) INFO: total log probability: -6.36 +2024-01-16 23:09:22,303 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 23:09:22,303 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:22,303 (beam_search:483) INFO: best hypo: deNsharinorutokikicltokaimasUdaUtaUtU + +2024-01-16 23:09:22,304 (asr_inference:494) INFO: speech length: 51456 +2024-01-16 23:09:22,313 (beam_search:428) INFO: decoder input length: 78 +2024-01-16 23:09:22,313 (beam_search:429) INFO: max output length: 78 +2024-01-16 23:09:22,313 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:22,388 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:22,388 (beam_search:476) INFO: -2.77 * 1.0 = -2.77 for ctc +2024-01-16 23:09:22,388 (beam_search:479) INFO: total log probability: -2.77 +2024-01-16 23:09:22,388 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:09:22,388 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:22,388 (beam_search:483) INFO: best hypo: tamaogwaikokojiuraNburaiesU + +2024-01-16 23:09:22,390 (asr_inference:494) INFO: speech length: 91776 +2024-01-16 23:09:22,401 (beam_search:428) INFO: decoder input length: 141 +2024-01-16 23:09:22,401 (beam_search:429) INFO: max output length: 141 +2024-01-16 23:09:22,401 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:22,585 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:22,585 (beam_search:476) INFO: -5.80 * 1.0 = -5.80 for ctc +2024-01-16 23:09:22,585 (beam_search:479) INFO: total log probability: -5.80 +2024-01-16 23:09:22,585 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:09:22,585 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:22,585 (beam_search:483) INFO: best hypo: geesUpiwamakiotsujitepauinestoshiriyacltatUU + +2024-01-16 23:09:22,586 (asr_inference:494) INFO: speech length: 124416 +2024-01-16 23:09:22,599 (beam_search:428) INFO: decoder input length: 192 +2024-01-16 23:09:22,600 (beam_search:429) INFO: max output length: 192 +2024-01-16 23:09:22,600 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:23,025 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:23,025 (beam_search:476) INFO: -13.05 * 1.0 = -13.05 for ctc +2024-01-16 23:09:23,025 (beam_search:479) INFO: total log probability: -13.05 +2024-01-16 23:09:23,025 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 23:09:23,025 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:23,025 (beam_search:483) INFO: best hypo: noogooyanesaruoenaishItoparikaNeNkiyomokomofkIkeoonihIkizurareterutomoosUtsU + +2024-01-16 23:09:23,026 (asr_inference:494) INFO: speech length: 101376 +2024-01-16 23:09:23,038 (beam_search:428) INFO: decoder input length: 156 +2024-01-16 23:09:23,038 (beam_search:429) INFO: max output length: 156 +2024-01-16 23:09:23,038 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:23,244 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:23,244 (beam_search:476) INFO: -4.35 * 1.0 = -4.35 for ctc +2024-01-16 23:09:23,244 (beam_search:479) INFO: total log probability: -4.35 +2024-01-16 23:09:23,244 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:09:23,244 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:23,244 (beam_search:483) INFO: best hypo: aNdekonorocltoshotaimenamuoinareyareseNda + +2024-01-16 23:09:23,246 (asr_inference:494) INFO: speech length: 69696 +2024-01-16 23:09:23,256 (beam_search:428) INFO: decoder input length: 106 +2024-01-16 23:09:23,256 (beam_search:429) INFO: max output length: 106 +2024-01-16 23:09:23,256 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:23,355 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:23,355 (beam_search:476) INFO: -3.50 * 1.0 = -3.50 for ctc +2024-01-16 23:09:23,355 (beam_search:479) INFO: total log probability: -3.50 +2024-01-16 23:09:23,355 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:09:23,355 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:23,355 (beam_search:483) INFO: best hypo: fUtsuudearukotamodiclpanakose + +2024-01-16 23:09:23,356 (asr_inference:494) INFO: speech length: 75456 +2024-01-16 23:09:23,366 (beam_search:428) INFO: decoder input length: 115 +2024-01-16 23:09:23,366 (beam_search:429) INFO: max output length: 115 +2024-01-16 23:09:23,366 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:23,488 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:23,488 (beam_search:476) INFO: -2.36 * 1.0 = -2.36 for ctc +2024-01-16 23:09:23,488 (beam_search:479) INFO: total log probability: -2.36 +2024-01-16 23:09:23,488 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 23:09:23,488 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:23,489 (beam_search:483) INFO: best hypo: tsuyobidetaNjikaNdegokainitameru + +2024-01-16 23:09:23,490 (asr_inference:494) INFO: speech length: 102528 +2024-01-16 23:09:23,502 (beam_search:428) INFO: decoder input length: 158 +2024-01-16 23:09:23,502 (beam_search:429) INFO: max output length: 158 +2024-01-16 23:09:23,502 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:23,745 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:23,745 (beam_search:476) INFO: -3.07 * 1.0 = -3.07 for ctc +2024-01-16 23:09:23,745 (beam_search:479) INFO: total log probability: -3.07 +2024-01-16 23:09:23,745 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-16 23:09:23,745 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:23,746 (beam_search:483) INFO: best hypo: pakumatsunodekigokowapauimanitsujirukyookuNnoyamadesU + +2024-01-16 23:09:23,747 (asr_inference:494) INFO: speech length: 73728 +2024-01-16 23:09:23,757 (beam_search:428) INFO: decoder input length: 113 +2024-01-16 23:09:23,757 (beam_search:429) INFO: max output length: 113 +2024-01-16 23:09:23,757 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:23,877 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:23,877 (beam_search:476) INFO: -2.44 * 1.0 = -2.44 for ctc +2024-01-16 23:09:23,877 (beam_search:479) INFO: total log probability: -2.44 +2024-01-16 23:09:23,877 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 23:09:23,877 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:23,877 (beam_search:483) INFO: best hypo: Nkookaramachinowakarigamietekita + +2024-01-16 23:09:23,878 (asr_inference:494) INFO: speech length: 115200 +2024-01-16 23:09:23,891 (beam_search:428) INFO: decoder input length: 177 +2024-01-16 23:09:23,891 (beam_search:429) INFO: max output length: 177 +2024-01-16 23:09:23,891 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:24,228 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:24,228 (beam_search:476) INFO: -13.96 * 1.0 = -13.96 for ctc +2024-01-16 23:09:24,228 (beam_search:479) INFO: total log probability: -13.96 +2024-01-16 23:09:24,228 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-16 23:09:24,228 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:24,228 (beam_search:483) INFO: best hypo: meniouyuubekIchiawakaranaUtaraimiyubikIkutogaomoyukabanagkacltU + +2024-01-16 23:09:24,230 (asr_inference:494) INFO: speech length: 49536 +2024-01-16 23:09:24,238 (beam_search:428) INFO: decoder input length: 75 +2024-01-16 23:09:24,238 (beam_search:429) INFO: max output length: 75 +2024-01-16 23:09:24,238 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:24,301 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:24,301 (beam_search:476) INFO: -4.71 * 1.0 = -4.71 for ctc +2024-01-16 23:09:24,301 (beam_search:479) INFO: total log probability: -4.71 +2024-01-16 23:09:24,301 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 23:09:24,301 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:24,301 (beam_search:483) INFO: best hypo: tamesinikaenakiyacltemir + +2024-01-16 23:09:24,302 (asr_inference:494) INFO: speech length: 74880 +2024-01-16 23:09:24,312 (beam_search:428) INFO: decoder input length: 114 +2024-01-16 23:09:24,312 (beam_search:429) INFO: max output length: 114 +2024-01-16 23:09:24,312 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:24,469 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:24,469 (beam_search:476) INFO: -6.73 * 1.0 = -6.73 for ctc +2024-01-16 23:09:24,469 (beam_search:479) INFO: total log probability: -6.73 +2024-01-16 23:09:24,469 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 23:09:24,469 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:24,469 (beam_search:483) INFO: best hypo: mokUshIkgaineigimimainaikorewaooteNtschieaika + +2024-01-16 23:09:24,471 (asr_inference:494) INFO: speech length: 102528 +2024-01-16 23:09:24,482 (beam_search:428) INFO: decoder input length: 158 +2024-01-16 23:09:24,482 (beam_search:429) INFO: max output length: 158 +2024-01-16 23:09:24,482 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:24,739 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:24,739 (beam_search:476) INFO: -14.74 * 1.0 = -14.74 for ctc +2024-01-16 23:09:24,739 (beam_search:479) INFO: total log probability: -14.74 +2024-01-16 23:09:24,739 (beam_search:480) INFO: normalized log probability: -0.28 +2024-01-16 23:09:24,739 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:24,739 (beam_search:483) INFO: best hypo: tschkeijokaraenijugotoomaNuNopaumichimapaumukasokaotrineka + +2024-01-16 23:09:24,741 (asr_inference:494) INFO: speech length: 71424 +2024-01-16 23:09:24,750 (beam_search:428) INFO: decoder input length: 109 +2024-01-16 23:09:24,750 (beam_search:429) INFO: max output length: 109 +2024-01-16 23:09:24,750 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:24,897 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:24,897 (beam_search:476) INFO: -8.79 * 1.0 = -8.79 for ctc +2024-01-16 23:09:24,897 (beam_search:479) INFO: total log probability: -8.79 +2024-01-16 23:09:24,897 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 23:09:24,897 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:24,897 (beam_search:483) INFO: best hypo: kaNgujoteehNwakoNclpuoNteinagaichikuusunaclta + +2024-01-16 23:09:24,899 (asr_inference:494) INFO: speech length: 79488 +2024-01-16 23:09:24,909 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 23:09:24,909 (beam_search:429) INFO: max output length: 122 +2024-01-16 23:09:24,909 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:25,064 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:25,064 (beam_search:476) INFO: -3.18 * 1.0 = -3.18 for ctc +2024-01-16 23:09:25,064 (beam_search:479) INFO: total log probability: -3.18 +2024-01-16 23:09:25,064 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:09:25,064 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:25,064 (beam_search:483) INFO: best hypo: korumonokorewagohaharepauotunainartopahaa + +2024-01-16 23:09:25,065 (asr_inference:494) INFO: speech length: 133056 +2024-01-16 23:09:25,079 (beam_search:428) INFO: decoder input length: 205 +2024-01-16 23:09:25,079 (beam_search:429) INFO: max output length: 205 +2024-01-16 23:09:25,079 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:25,537 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:25,537 (beam_search:476) INFO: -6.33 * 1.0 = -6.33 for ctc +2024-01-16 23:09:25,537 (beam_search:479) INFO: total log probability: -6.33 +2024-01-16 23:09:25,537 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:09:25,537 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:25,538 (beam_search:483) INFO: best hypo: kooitekItokaNtekinipaupoipauashisutekinipauwarewaremojikowamasumasupaumetonarunodaru + +2024-01-16 23:09:25,539 (asr_inference:494) INFO: speech length: 128448 +2024-01-16 23:09:25,552 (beam_search:428) INFO: decoder input length: 198 +2024-01-16 23:09:25,553 (beam_search:429) INFO: max output length: 198 +2024-01-16 23:09:25,553 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:26,022 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:26,023 (beam_search:476) INFO: -10.37 * 1.0 = -10.37 for ctc +2024-01-16 23:09:26,023 (beam_search:479) INFO: total log probability: -10.37 +2024-01-16 23:09:26,023 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:09:26,023 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:26,023 (beam_search:483) INFO: best hypo: hijooshIkidarokotowayuchjooimisurunomidenakupaushakaietekinipauakUtomokaNmaeraruuoudaru + +2024-01-16 23:09:26,024 (asr_inference:494) INFO: speech length: 84096 +2024-01-16 23:09:26,035 (beam_search:428) INFO: decoder input length: 129 +2024-01-16 23:09:26,035 (beam_search:429) INFO: max output length: 129 +2024-01-16 23:09:26,035 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:26,236 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:26,236 (beam_search:476) INFO: -6.33 * 1.0 = -6.33 for ctc +2024-01-16 23:09:26,236 (beam_search:479) INFO: total log probability: -6.33 +2024-01-16 23:09:26,236 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:09:26,236 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:26,236 (beam_search:483) INFO: best hypo: jooshukiganaotokushItekinachIshIkidearunihasekarakuwa + +2024-01-16 23:09:26,238 (asr_inference:494) INFO: speech length: 61056 +2024-01-16 23:09:26,247 (beam_search:428) INFO: decoder input length: 93 +2024-01-16 23:09:26,247 (beam_search:429) INFO: max output length: 93 +2024-01-16 23:09:26,247 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:26,343 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:26,343 (beam_search:476) INFO: -2.50 * 1.0 = -2.50 for ctc +2024-01-16 23:09:26,343 (beam_search:479) INFO: total log probability: -2.50 +2024-01-16 23:09:26,343 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:09:26,343 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:26,343 (beam_search:483) INFO: best hypo: koNnakotodeoguraretenasakenhai + +2024-01-16 23:09:26,345 (asr_inference:494) INFO: speech length: 127296 +2024-01-16 23:09:26,358 (beam_search:428) INFO: decoder input length: 196 +2024-01-16 23:09:26,358 (beam_search:429) INFO: max output length: 196 +2024-01-16 23:09:26,358 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:26,876 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:26,876 (beam_search:476) INFO: -13.13 * 1.0 = -13.13 for ctc +2024-01-16 23:09:26,876 (beam_search:479) INFO: total log probability: -13.13 +2024-01-16 23:09:26,876 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:09:26,876 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:26,877 (beam_search:483) INFO: best hypo: kakotomiraietowajikomujuNtekinipaudeNzainochitairikUuutoiuniwapaueNdnayakatachomotamaikeremanaranai + +2024-01-16 23:09:26,878 (asr_inference:494) INFO: speech length: 59328 +2024-01-16 23:09:26,887 (beam_search:428) INFO: decoder input length: 90 +2024-01-16 23:09:26,887 (beam_search:429) INFO: max output length: 90 +2024-01-16 23:09:26,887 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:26,963 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:26,963 (beam_search:476) INFO: -3.49 * 1.0 = -3.49 for ctc +2024-01-16 23:09:26,963 (beam_search:479) INFO: total log probability: -3.49 +2024-01-16 23:09:26,963 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:09:26,963 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:26,963 (beam_search:483) INFO: best hypo: ashokIionotakasegadouNnar + +# Accounting: time=12 threads=1 +# Ended (code 0) at Tue Jan 16 23:09:27 CST 2024, elapsed time 12 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/calculate_rtf.log b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/calculate_rtf.log new file mode 100644 index 0000000000000000000000000000000000000000..efd4c1ea5889730540d2fe436bd8fcb565559877 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/calculate_rtf.log @@ -0,0 +1,9 @@ +# pyscripts/utils/calculate_rtf.py --log-dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir --log-name asr_inference --input-shift 0.0625 --start-times-marker "speech length" --end-times-marker "best hypo" --inf-num 1 +# Started at Tue Jan 16 23:09:27 CST 2024 +# +Total audio duration: 602.388 [sec] +Total decoding time: 26.121 [sec] +RTF: 0.043 +Latency: 207.310 [ms/sentence] +# Accounting: time=0 threads=1 +# Ended (code 0) at Tue Jan 16 23:09:27 CST 2024, elapsed time 0 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.1.scp b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.1.scp new file mode 100644 index 0000000000000000000000000000000000000000..8b21271e228153b3adfda66c7b47438ec6eed5bf --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.1.scp @@ -0,0 +1,32 @@ +cv_jpn_000674 dump/raw/org/dev_10min_jpn/data/format.1/cv_jpn_000674.flac +cv_jpn_000675 dump/raw/org/dev_10min_jpn/data/format.1/cv_jpn_000675.flac +cv_jpn_000676 dump/raw/org/dev_10min_jpn/data/format.1/cv_jpn_000676.flac +cv_jpn_000677 dump/raw/org/dev_10min_jpn/data/format.1/cv_jpn_000677.flac +cv_jpn_000678 dump/raw/org/dev_10min_jpn/data/format.2/cv_jpn_000678.flac +cv_jpn_000679 dump/raw/org/dev_10min_jpn/data/format.2/cv_jpn_000679.flac +cv_jpn_000680 dump/raw/org/dev_10min_jpn/data/format.2/cv_jpn_000680.flac +cv_jpn_000681 dump/raw/org/dev_10min_jpn/data/format.2/cv_jpn_000681.flac +cv_jpn_000682 dump/raw/org/dev_10min_jpn/data/format.3/cv_jpn_000682.flac +cv_jpn_000683 dump/raw/org/dev_10min_jpn/data/format.3/cv_jpn_000683.flac +cv_jpn_000684 dump/raw/org/dev_10min_jpn/data/format.3/cv_jpn_000684.flac +cv_jpn_000685 dump/raw/org/dev_10min_jpn/data/format.3/cv_jpn_000685.flac +cv_jpn_000686 dump/raw/org/dev_10min_jpn/data/format.4/cv_jpn_000686.flac +cv_jpn_000687 dump/raw/org/dev_10min_jpn/data/format.4/cv_jpn_000687.flac +cv_jpn_000688 dump/raw/org/dev_10min_jpn/data/format.4/cv_jpn_000688.flac +cv_jpn_000689 dump/raw/org/dev_10min_jpn/data/format.4/cv_jpn_000689.flac +cv_jpn_000690 dump/raw/org/dev_10min_jpn/data/format.5/cv_jpn_000690.flac +cv_jpn_000691 dump/raw/org/dev_10min_jpn/data/format.5/cv_jpn_000691.flac +cv_jpn_000692 dump/raw/org/dev_10min_jpn/data/format.5/cv_jpn_000692.flac +cv_jpn_000693 dump/raw/org/dev_10min_jpn/data/format.5/cv_jpn_000693.flac +cv_jpn_000694 dump/raw/org/dev_10min_jpn/data/format.6/cv_jpn_000694.flac +cv_jpn_000695 dump/raw/org/dev_10min_jpn/data/format.6/cv_jpn_000695.flac +cv_jpn_000696 dump/raw/org/dev_10min_jpn/data/format.6/cv_jpn_000696.flac +cv_jpn_000697 dump/raw/org/dev_10min_jpn/data/format.6/cv_jpn_000697.flac +cv_jpn_000698 dump/raw/org/dev_10min_jpn/data/format.7/cv_jpn_000698.flac +cv_jpn_000699 dump/raw/org/dev_10min_jpn/data/format.7/cv_jpn_000699.flac +cv_jpn_000700 dump/raw/org/dev_10min_jpn/data/format.7/cv_jpn_000700.flac +cv_jpn_000701 dump/raw/org/dev_10min_jpn/data/format.7/cv_jpn_000701.flac +cv_jpn_000702 dump/raw/org/dev_10min_jpn/data/format.8/cv_jpn_000702.flac +cv_jpn_000703 dump/raw/org/dev_10min_jpn/data/format.8/cv_jpn_000703.flac +cv_jpn_000704 dump/raw/org/dev_10min_jpn/data/format.8/cv_jpn_000704.flac +cv_jpn_000705 dump/raw/org/dev_10min_jpn/data/format.8/cv_jpn_000705.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.2.scp b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.2.scp new file mode 100644 index 0000000000000000000000000000000000000000..3b244aeaebe6bcc7b2da681e4c4f9e77e427b2ac --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.2.scp @@ -0,0 +1,32 @@ +cv_jpn_000706 dump/raw/org/dev_10min_jpn/data/format.9/cv_jpn_000706.flac +cv_jpn_000707 dump/raw/org/dev_10min_jpn/data/format.9/cv_jpn_000707.flac +cv_jpn_000708 dump/raw/org/dev_10min_jpn/data/format.9/cv_jpn_000708.flac +cv_jpn_000709 dump/raw/org/dev_10min_jpn/data/format.9/cv_jpn_000709.flac +cv_jpn_000710 dump/raw/org/dev_10min_jpn/data/format.10/cv_jpn_000710.flac +cv_jpn_000711 dump/raw/org/dev_10min_jpn/data/format.10/cv_jpn_000711.flac +cv_jpn_000712 dump/raw/org/dev_10min_jpn/data/format.10/cv_jpn_000712.flac +cv_jpn_000713 dump/raw/org/dev_10min_jpn/data/format.10/cv_jpn_000713.flac +cv_jpn_000714 dump/raw/org/dev_10min_jpn/data/format.11/cv_jpn_000714.flac +cv_jpn_000715 dump/raw/org/dev_10min_jpn/data/format.11/cv_jpn_000715.flac +cv_jpn_000716 dump/raw/org/dev_10min_jpn/data/format.11/cv_jpn_000716.flac +cv_jpn_000717 dump/raw/org/dev_10min_jpn/data/format.11/cv_jpn_000717.flac +cv_jpn_000718 dump/raw/org/dev_10min_jpn/data/format.12/cv_jpn_000718.flac +cv_jpn_000719 dump/raw/org/dev_10min_jpn/data/format.12/cv_jpn_000719.flac +cv_jpn_000720 dump/raw/org/dev_10min_jpn/data/format.12/cv_jpn_000720.flac +cv_jpn_000721 dump/raw/org/dev_10min_jpn/data/format.12/cv_jpn_000721.flac +cv_jpn_000722 dump/raw/org/dev_10min_jpn/data/format.13/cv_jpn_000722.flac +cv_jpn_000723 dump/raw/org/dev_10min_jpn/data/format.13/cv_jpn_000723.flac +cv_jpn_000724 dump/raw/org/dev_10min_jpn/data/format.13/cv_jpn_000724.flac +cv_jpn_000725 dump/raw/org/dev_10min_jpn/data/format.13/cv_jpn_000725.flac +cv_jpn_000726 dump/raw/org/dev_10min_jpn/data/format.14/cv_jpn_000726.flac +cv_jpn_000727 dump/raw/org/dev_10min_jpn/data/format.14/cv_jpn_000727.flac +cv_jpn_000728 dump/raw/org/dev_10min_jpn/data/format.14/cv_jpn_000728.flac +cv_jpn_000729 dump/raw/org/dev_10min_jpn/data/format.14/cv_jpn_000729.flac +cv_jpn_000730 dump/raw/org/dev_10min_jpn/data/format.15/cv_jpn_000730.flac +cv_jpn_000731 dump/raw/org/dev_10min_jpn/data/format.15/cv_jpn_000731.flac +cv_jpn_000732 dump/raw/org/dev_10min_jpn/data/format.15/cv_jpn_000732.flac +cv_jpn_000733 dump/raw/org/dev_10min_jpn/data/format.15/cv_jpn_000733.flac +cv_jpn_000734 dump/raw/org/dev_10min_jpn/data/format.16/cv_jpn_000734.flac +cv_jpn_000735 dump/raw/org/dev_10min_jpn/data/format.16/cv_jpn_000735.flac +cv_jpn_000736 dump/raw/org/dev_10min_jpn/data/format.16/cv_jpn_000736.flac +cv_jpn_000737 dump/raw/org/dev_10min_jpn/data/format.16/cv_jpn_000737.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.3.scp b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.3.scp new file mode 100644 index 0000000000000000000000000000000000000000..4b5a288344f2fa10049dd1d3ac7252ba5d518893 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.3.scp @@ -0,0 +1,31 @@ +cv_jpn_000738 dump/raw/org/dev_10min_jpn/data/format.17/cv_jpn_000738.flac +cv_jpn_000739 dump/raw/org/dev_10min_jpn/data/format.17/cv_jpn_000739.flac +cv_jpn_000740 dump/raw/org/dev_10min_jpn/data/format.17/cv_jpn_000740.flac +cv_jpn_000741 dump/raw/org/dev_10min_jpn/data/format.17/cv_jpn_000741.flac +cv_jpn_000742 dump/raw/org/dev_10min_jpn/data/format.18/cv_jpn_000742.flac +cv_jpn_000743 dump/raw/org/dev_10min_jpn/data/format.18/cv_jpn_000743.flac +cv_jpn_000744 dump/raw/org/dev_10min_jpn/data/format.18/cv_jpn_000744.flac +cv_jpn_000745 dump/raw/org/dev_10min_jpn/data/format.18/cv_jpn_000745.flac +cv_jpn_000746 dump/raw/org/dev_10min_jpn/data/format.19/cv_jpn_000746.flac +cv_jpn_000747 dump/raw/org/dev_10min_jpn/data/format.19/cv_jpn_000747.flac +cv_jpn_000748 dump/raw/org/dev_10min_jpn/data/format.19/cv_jpn_000748.flac +cv_jpn_000749 dump/raw/org/dev_10min_jpn/data/format.19/cv_jpn_000749.flac +cv_jpn_000750 dump/raw/org/dev_10min_jpn/data/format.20/cv_jpn_000750.flac +cv_jpn_000751 dump/raw/org/dev_10min_jpn/data/format.20/cv_jpn_000751.flac +cv_jpn_000752 dump/raw/org/dev_10min_jpn/data/format.20/cv_jpn_000752.flac +cv_jpn_000753 dump/raw/org/dev_10min_jpn/data/format.20/cv_jpn_000753.flac +cv_jpn_000754 dump/raw/org/dev_10min_jpn/data/format.21/cv_jpn_000754.flac +cv_jpn_000755 dump/raw/org/dev_10min_jpn/data/format.21/cv_jpn_000755.flac +cv_jpn_000756 dump/raw/org/dev_10min_jpn/data/format.21/cv_jpn_000756.flac +cv_jpn_000757 dump/raw/org/dev_10min_jpn/data/format.21/cv_jpn_000757.flac +cv_jpn_000758 dump/raw/org/dev_10min_jpn/data/format.22/cv_jpn_000758.flac +cv_jpn_000759 dump/raw/org/dev_10min_jpn/data/format.22/cv_jpn_000759.flac +cv_jpn_000760 dump/raw/org/dev_10min_jpn/data/format.22/cv_jpn_000760.flac +cv_jpn_000761 dump/raw/org/dev_10min_jpn/data/format.22/cv_jpn_000761.flac +cv_jpn_000762 dump/raw/org/dev_10min_jpn/data/format.23/cv_jpn_000762.flac +cv_jpn_000763 dump/raw/org/dev_10min_jpn/data/format.23/cv_jpn_000763.flac +cv_jpn_000764 dump/raw/org/dev_10min_jpn/data/format.23/cv_jpn_000764.flac +cv_jpn_000765 dump/raw/org/dev_10min_jpn/data/format.23/cv_jpn_000765.flac +cv_jpn_000766 dump/raw/org/dev_10min_jpn/data/format.24/cv_jpn_000766.flac +cv_jpn_000767 dump/raw/org/dev_10min_jpn/data/format.24/cv_jpn_000767.flac +cv_jpn_000768 dump/raw/org/dev_10min_jpn/data/format.24/cv_jpn_000768.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.4.scp b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.4.scp new file mode 100644 index 0000000000000000000000000000000000000000..3d2314d1cb9474f3789fdc5b4f47b06391aef7bb --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.4.scp @@ -0,0 +1,31 @@ +cv_jpn_000769 dump/raw/org/dev_10min_jpn/data/format.24/cv_jpn_000769.flac +cv_jpn_000770 dump/raw/org/dev_10min_jpn/data/format.25/cv_jpn_000770.flac +cv_jpn_000771 dump/raw/org/dev_10min_jpn/data/format.25/cv_jpn_000771.flac +cv_jpn_000772 dump/raw/org/dev_10min_jpn/data/format.25/cv_jpn_000772.flac +cv_jpn_000773 dump/raw/org/dev_10min_jpn/data/format.25/cv_jpn_000773.flac +cv_jpn_000774 dump/raw/org/dev_10min_jpn/data/format.26/cv_jpn_000774.flac +cv_jpn_000775 dump/raw/org/dev_10min_jpn/data/format.26/cv_jpn_000775.flac +cv_jpn_000776 dump/raw/org/dev_10min_jpn/data/format.26/cv_jpn_000776.flac +cv_jpn_000777 dump/raw/org/dev_10min_jpn/data/format.26/cv_jpn_000777.flac +cv_jpn_000778 dump/raw/org/dev_10min_jpn/data/format.27/cv_jpn_000778.flac +cv_jpn_000779 dump/raw/org/dev_10min_jpn/data/format.27/cv_jpn_000779.flac +cv_jpn_000780 dump/raw/org/dev_10min_jpn/data/format.27/cv_jpn_000780.flac +cv_jpn_000781 dump/raw/org/dev_10min_jpn/data/format.27/cv_jpn_000781.flac +cv_jpn_000782 dump/raw/org/dev_10min_jpn/data/format.28/cv_jpn_000782.flac +cv_jpn_000783 dump/raw/org/dev_10min_jpn/data/format.28/cv_jpn_000783.flac +cv_jpn_000784 dump/raw/org/dev_10min_jpn/data/format.28/cv_jpn_000784.flac +cv_jpn_000785 dump/raw/org/dev_10min_jpn/data/format.28/cv_jpn_000785.flac +cv_jpn_000786 dump/raw/org/dev_10min_jpn/data/format.29/cv_jpn_000786.flac +cv_jpn_000787 dump/raw/org/dev_10min_jpn/data/format.29/cv_jpn_000787.flac +cv_jpn_000788 dump/raw/org/dev_10min_jpn/data/format.29/cv_jpn_000788.flac +cv_jpn_000789 dump/raw/org/dev_10min_jpn/data/format.29/cv_jpn_000789.flac +cv_jpn_000790 dump/raw/org/dev_10min_jpn/data/format.30/cv_jpn_000790.flac +cv_jpn_000791 dump/raw/org/dev_10min_jpn/data/format.30/cv_jpn_000791.flac +cv_jpn_000792 dump/raw/org/dev_10min_jpn/data/format.30/cv_jpn_000792.flac +cv_jpn_000793 dump/raw/org/dev_10min_jpn/data/format.30/cv_jpn_000793.flac +cv_jpn_000794 dump/raw/org/dev_10min_jpn/data/format.31/cv_jpn_000794.flac +cv_jpn_000795 dump/raw/org/dev_10min_jpn/data/format.31/cv_jpn_000795.flac +cv_jpn_000796 dump/raw/org/dev_10min_jpn/data/format.31/cv_jpn_000796.flac +cv_jpn_000797 dump/raw/org/dev_10min_jpn/data/format.32/cv_jpn_000797.flac +cv_jpn_000798 dump/raw/org/dev_10min_jpn/data/format.32/cv_jpn_000798.flac +cv_jpn_000799 dump/raw/org/dev_10min_jpn/data/format.32/cv_jpn_000799.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..43362003299d00e9d1619aed0c4b57f0ede691c0 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/score @@ -0,0 +1,32 @@ +cv_jpn_000674 tensor(-11.0185) +cv_jpn_000675 tensor(-9.9876) +cv_jpn_000676 tensor(-6.1756) +cv_jpn_000677 tensor(-11.2046) +cv_jpn_000678 tensor(-5.4497) +cv_jpn_000679 tensor(-11.1056) +cv_jpn_000680 tensor(-10.7534) +cv_jpn_000681 tensor(-8.0064) +cv_jpn_000682 tensor(-0.9139) +cv_jpn_000683 tensor(-4.5666) +cv_jpn_000684 tensor(-4.2275) +cv_jpn_000685 tensor(-7.7207) +cv_jpn_000686 tensor(-7.5515) +cv_jpn_000687 tensor(-0.7582) +cv_jpn_000688 tensor(-3.0247) +cv_jpn_000689 tensor(-3.0464) +cv_jpn_000690 tensor(-3.0319) +cv_jpn_000691 tensor(-7.1164) +cv_jpn_000692 tensor(-1.8942) +cv_jpn_000693 tensor(-3.5789) +cv_jpn_000694 tensor(-9.9579) +cv_jpn_000695 tensor(-6.3388) +cv_jpn_000696 tensor(-6.3364) +cv_jpn_000697 tensor(-6.7559) +cv_jpn_000698 tensor(-6.0599) +cv_jpn_000699 tensor(-6.9964) +cv_jpn_000700 tensor(-4.6046) +cv_jpn_000701 tensor(-1.4595) +cv_jpn_000702 tensor(-2.9812) +cv_jpn_000703 tensor(-7.3210) +cv_jpn_000704 tensor(-6.2380) +cv_jpn_000705 tensor(-3.0066) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..6b9628c40cf9fea7c51677d78b26cf8d90c27b1e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/text @@ -0,0 +1,32 @@ +cv_jpn_000674 b o k u n o y i e g a cl t a k a i d n a N n o n a m a e N n i k u r a b e r u t o p o k u n i w a n a j i m i n o n a i m a e b a e b a k a r i d a g k e d o +cv_jpn_000675 m a r y o o s u a n o m o n o e r u d i k u i n i k i g a r u k e t e r u +cv_jpn_000676 b o k u n o sh I t e e u m o n u o t o w a s U k o sh i ch i g a cl t e i t a +cv_jpn_000677 k a k a o r u t a ch i m a n i o i t e pau s e k g a r e g a i sh I k i m e N t e k i d e a r i w a r u w a r e n o j i k o g a j sh I k i s a y o o t e k i d e a r u t o k a N e e r a r e r u t o ch I +cv_jpn_000678 i e n i k I t a n e N g a a j i w a s a N h a k o m a i h o r o d e pau pau ch o o d a sh I t a b u N t o o n a j i g u r a i d a +cv_jpn_000679 h a w a p i t a m a r e ts u b a a g u n o s e e sh i N by y o o i N n i n u N h I t e y i r u t o k i n i p e sh i o o m u +cv_jpn_000680 t a d a N d e a r h a N t a n o h i r o g e r a b a a ch i k o ch i ts u g i h a g i y a a r i k a t a g o ch u i n i d e k i t a h o k o r o b i n a N k a ky o o n e N n o m a o m o n i n a cl t e i r u +cv_jpn_000681 k a r a g a k a n o d e pau b e r u n a k a n i h o o m o N k i b o o n o b u sh o o y o b i ch o o z a k i b o o n o b u g o m a i k a r a t o o r i d e s U +cv_jpn_000682 k o N b a w a t o t e m o s a m i d e s U +cv_jpn_000683 k i N ch o o sh I t a k o ts U k i d e pau cl p a cl t a w a d a s e k i n i h a i r u +cv_jpn_000684 m a s a N ky u u r e N t o i cl t a f u k a k a r o k e N ch i k o w u ts U +cv_jpn_000685 g o r i y o sh i d e s u s u m e t e ts s u g o g a w a r u k u n a cl t a r a i h I cl k o m e r u y a r e k U ch i +cv_jpn_000686 m o j u r N t e k i j i k o o t o i ts U t e k i n i j i k o o j i sh i N o k e e s e e s u r u sh a k a i w a +cv_jpn_000687 h a N n o i k e N n i n a r a s a r e r u n a +cv_jpn_000688 h i n o g a s o b i t a y o r a z e N k a i d e k o cl ch o m i t e i r u +cv_jpn_000689 i j ch i d o w a k o N p o t a j i k a N o n o N d e m i t a i +cv_jpn_000690 k o o i n o k o sh I t e pau o t o o s a N t o k a s a N w a N d e t e i k i m a sh I t a +cv_jpn_000691 sh i k a sh I t e s o r e g a ts U k u r a r e t a m o n o k a r a ts U k u r u m o r o e t o sh I t e pau d o k o m a d e m o r a w a r u n i s e m a r u t o y u t o k i a r e w a r i n i ch o cl k a N t e k i d e a r u +cv_jpn_000692 h a i n i t a m a t a k e m u r i o h a k i d a sh i k u r a i k o o e n i sh I s e N o m o k e r u +cv_jpn_000693 w a d a w a d a sh I n a b u s o k u d e ch i u s e N n a r i s o +cv_jpn_000694 ts e N s e k a i n o m o s U k a t a ch i i t a sh u n o y o a y i r u s e e s a i y o sh I k I t o s a i u o t o w a h a sh I t e k a N g a i r u k o t o w a d e k i n a i +cv_jpn_000695 s a k e n o m a e g n o n i p i r o u b a r a t o y o e t a +cv_jpn_000696 s o r e o o t a d a k o h o k u n o z e N d a N k a i sh i k u i t e e d o n o k a a k U t o m o m i m i r u k o t o w a +cv_jpn_000697 o k ky o m e m o k f u r a z u n i pau s u m o d e k e e N m o y a cl t e i t a +cv_jpn_000698 a d a i w a a n a i N ch sh i u m a t a k o b a y a sh i s a N t o a s o b e i m a s U +cv_jpn_000699 a s o o k o n i i h i i t o o g a pau i m a s u n e pau a r o h i t o w a t a r e t e sh o +cv_jpn_000700 w a t a j i w a k ky e n o o k a n a pau n o o d o k a e i t a i d e s U +cv_jpn_000701 ky o o r e N k a n a p e N ky u o sh I t e i m a s U +cv_jpn_000702 a o cl t o s u m i m r a s e pau +cv_jpn_000703 ch i k a s U pau b o n o o e N b u r i e w a pau y a k e k i m i n i k a e cl d e h a g e s U k e n o cl t a +cv_jpn_000704 i ts z u m o k o n o i N p I i ts o o ts U k a cl t e i t a n o d e m i j i k a h a u n a r i m a sh I t a +cv_jpn_000705 w a t a sh i w a pau e i g o g a h a n a sh I t e m a s U diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..6b9628c40cf9fea7c51677d78b26cf8d90c27b1e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/token @@ -0,0 +1,32 @@ +cv_jpn_000674 b o k u n o y i e g a cl t a k a i d n a N n o n a m a e N n i k u r a b e r u t o p o k u n i w a n a j i m i n o n a i m a e b a e b a k a r i d a g k e d o +cv_jpn_000675 m a r y o o s u a n o m o n o e r u d i k u i n i k i g a r u k e t e r u +cv_jpn_000676 b o k u n o sh I t e e u m o n u o t o w a s U k o sh i ch i g a cl t e i t a +cv_jpn_000677 k a k a o r u t a ch i m a n i o i t e pau s e k g a r e g a i sh I k i m e N t e k i d e a r i w a r u w a r e n o j i k o g a j sh I k i s a y o o t e k i d e a r u t o k a N e e r a r e r u t o ch I +cv_jpn_000678 i e n i k I t a n e N g a a j i w a s a N h a k o m a i h o r o d e pau pau ch o o d a sh I t a b u N t o o n a j i g u r a i d a +cv_jpn_000679 h a w a p i t a m a r e ts u b a a g u n o s e e sh i N by y o o i N n i n u N h I t e y i r u t o k i n i p e sh i o o m u +cv_jpn_000680 t a d a N d e a r h a N t a n o h i r o g e r a b a a ch i k o ch i ts u g i h a g i y a a r i k a t a g o ch u i n i d e k i t a h o k o r o b i n a N k a ky o o n e N n o m a o m o n i n a cl t e i r u +cv_jpn_000681 k a r a g a k a n o d e pau b e r u n a k a n i h o o m o N k i b o o n o b u sh o o y o b i ch o o z a k i b o o n o b u g o m a i k a r a t o o r i d e s U +cv_jpn_000682 k o N b a w a t o t e m o s a m i d e s U +cv_jpn_000683 k i N ch o o sh I t a k o ts U k i d e pau cl p a cl t a w a d a s e k i n i h a i r u +cv_jpn_000684 m a s a N ky u u r e N t o i cl t a f u k a k a r o k e N ch i k o w u ts U +cv_jpn_000685 g o r i y o sh i d e s u s u m e t e ts s u g o g a w a r u k u n a cl t a r a i h I cl k o m e r u y a r e k U ch i +cv_jpn_000686 m o j u r N t e k i j i k o o t o i ts U t e k i n i j i k o o j i sh i N o k e e s e e s u r u sh a k a i w a +cv_jpn_000687 h a N n o i k e N n i n a r a s a r e r u n a +cv_jpn_000688 h i n o g a s o b i t a y o r a z e N k a i d e k o cl ch o m i t e i r u +cv_jpn_000689 i j ch i d o w a k o N p o t a j i k a N o n o N d e m i t a i +cv_jpn_000690 k o o i n o k o sh I t e pau o t o o s a N t o k a s a N w a N d e t e i k i m a sh I t a +cv_jpn_000691 sh i k a sh I t e s o r e g a ts U k u r a r e t a m o n o k a r a ts U k u r u m o r o e t o sh I t e pau d o k o m a d e m o r a w a r u n i s e m a r u t o y u t o k i a r e w a r i n i ch o cl k a N t e k i d e a r u +cv_jpn_000692 h a i n i t a m a t a k e m u r i o h a k i d a sh i k u r a i k o o e n i sh I s e N o m o k e r u +cv_jpn_000693 w a d a w a d a sh I n a b u s o k u d e ch i u s e N n a r i s o +cv_jpn_000694 ts e N s e k a i n o m o s U k a t a ch i i t a sh u n o y o a y i r u s e e s a i y o sh I k I t o s a i u o t o w a h a sh I t e k a N g a i r u k o t o w a d e k i n a i +cv_jpn_000695 s a k e n o m a e g n o n i p i r o u b a r a t o y o e t a +cv_jpn_000696 s o r e o o t a d a k o h o k u n o z e N d a N k a i sh i k u i t e e d o n o k a a k U t o m o m i m i r u k o t o w a +cv_jpn_000697 o k ky o m e m o k f u r a z u n i pau s u m o d e k e e N m o y a cl t e i t a +cv_jpn_000698 a d a i w a a n a i N ch sh i u m a t a k o b a y a sh i s a N t o a s o b e i m a s U +cv_jpn_000699 a s o o k o n i i h i i t o o g a pau i m a s u n e pau a r o h i t o w a t a r e t e sh o +cv_jpn_000700 w a t a j i w a k ky e n o o k a n a pau n o o d o k a e i t a i d e s U +cv_jpn_000701 ky o o r e N k a n a p e N ky u o sh I t e i m a s U +cv_jpn_000702 a o cl t o s u m i m r a s e pau +cv_jpn_000703 ch i k a s U pau b o n o o e N b u r i e w a pau y a k e k i m i n i k a e cl d e h a g e s U k e n o cl t a +cv_jpn_000704 i ts z u m o k o n o i N p I i ts o o ts U k a cl t e i t a n o d e m i j i k a h a u n a r i m a sh I t a +cv_jpn_000705 w a t a sh i w a pau e i g o g a h a n a sh I t e m a s U diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..a178809be916ea17bfeb8a4658133bbc529232f4 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/token_int @@ -0,0 +1,32 @@ +cv_jpn_000674 25 3 6 7 9 3 23 4 5 16 2 21 8 2 6 2 4 14 9 2 13 9 3 9 2 11 2 5 13 9 4 6 7 10 2 25 5 10 7 8 3 30 3 6 7 9 4 17 2 9 2 22 4 11 4 9 3 9 2 4 11 2 5 25 2 5 25 2 6 2 10 4 14 2 16 6 5 14 3 +cv_jpn_000675 11 2 10 23 3 3 12 7 2 9 3 11 3 9 3 5 10 7 14 4 6 7 4 9 4 6 4 16 2 10 7 6 5 8 5 10 7 +cv_jpn_000676 25 3 6 7 9 3 15 19 8 5 5 7 11 3 9 7 3 8 3 17 2 12 18 6 3 15 4 27 4 16 2 21 8 5 4 8 2 +cv_jpn_000677 6 2 6 2 3 10 7 8 2 27 4 11 2 9 4 3 4 8 5 20 12 5 6 16 2 10 5 16 2 4 15 19 6 4 11 5 13 8 5 6 4 14 5 2 10 4 17 2 10 7 17 2 10 5 9 3 22 4 6 3 16 2 22 15 19 6 4 12 2 23 3 3 8 5 6 4 14 5 2 10 7 8 3 6 2 13 5 5 10 2 10 5 10 7 8 3 27 19 +cv_jpn_000678 4 5 9 4 6 19 8 2 9 5 13 16 2 2 22 4 17 2 12 2 13 24 2 6 3 11 2 4 24 3 10 3 14 5 20 20 27 3 3 14 2 15 19 8 2 25 7 13 8 3 3 9 2 22 4 16 7 10 2 4 14 2 +cv_jpn_000679 24 2 17 2 30 4 8 2 11 2 10 5 26 7 25 2 2 16 7 9 3 12 5 5 15 4 13 38 23 3 3 4 13 9 4 9 7 13 24 19 8 5 23 4 10 7 8 3 6 4 9 4 30 5 15 4 3 3 11 7 +cv_jpn_000680 8 2 14 2 13 14 5 2 10 24 2 13 8 2 9 3 24 4 10 3 16 5 10 2 25 2 2 27 4 6 3 27 4 26 7 16 4 24 2 16 4 23 2 2 10 4 6 2 8 2 16 3 27 7 4 9 4 14 5 6 4 8 2 24 3 6 3 10 3 25 4 9 2 13 6 2 29 3 3 9 5 13 9 3 11 2 3 11 3 9 4 9 2 21 8 5 4 10 7 +cv_jpn_000681 6 2 10 2 16 2 6 2 9 3 14 5 20 25 5 10 7 9 2 6 2 9 4 24 3 3 11 3 13 6 4 25 3 3 9 3 25 7 15 3 3 23 3 25 4 27 3 3 28 2 6 4 25 3 3 9 3 25 7 16 3 11 2 4 6 2 10 2 8 3 3 10 4 14 5 12 18 +cv_jpn_000682 6 3 13 25 2 17 2 8 3 8 5 11 3 12 2 11 4 14 5 12 18 +cv_jpn_000683 6 4 13 27 3 3 15 19 8 2 6 3 26 18 6 4 14 5 20 21 30 2 21 8 2 17 2 14 2 12 5 6 4 9 4 24 2 4 10 7 +cv_jpn_000684 11 2 12 2 13 29 7 7 10 5 13 8 3 4 21 8 2 31 7 6 2 6 2 10 3 6 5 13 27 4 6 3 17 7 26 18 +cv_jpn_000685 16 3 10 4 23 3 15 4 14 5 12 7 12 7 11 5 8 5 26 12 7 16 3 16 2 17 2 10 7 6 7 9 2 21 8 2 10 2 4 24 19 21 6 3 11 5 10 7 23 2 10 5 6 18 27 4 +cv_jpn_000686 11 3 22 7 10 13 8 5 6 4 22 4 6 3 3 8 3 4 26 18 8 5 6 4 9 4 22 4 6 3 3 22 4 15 4 13 3 6 5 5 12 5 5 12 7 10 7 15 2 6 2 4 17 2 +cv_jpn_000687 24 2 13 9 3 4 6 5 13 9 4 9 2 10 2 12 2 10 5 10 7 9 2 +cv_jpn_000688 24 4 9 3 16 2 12 3 25 4 8 2 23 3 10 2 28 5 13 6 2 4 14 5 6 3 21 27 3 11 4 8 5 4 10 7 +cv_jpn_000689 4 22 27 4 14 3 17 2 6 3 13 30 3 8 2 22 4 6 2 13 3 9 3 13 14 5 11 4 8 2 4 +cv_jpn_000690 6 3 3 4 9 3 6 3 15 19 8 5 20 3 8 3 3 12 2 13 8 3 6 2 12 2 13 17 2 13 14 5 8 5 4 6 4 11 2 15 19 8 2 +cv_jpn_000691 15 4 6 2 15 19 8 5 12 3 10 5 16 2 26 18 6 7 10 2 10 5 8 2 11 3 9 3 6 2 10 2 26 18 6 7 10 7 11 3 10 3 5 8 3 15 19 8 5 20 14 3 6 3 11 2 14 5 11 3 10 2 17 2 10 7 9 4 12 5 11 2 10 7 8 3 23 7 8 3 6 4 2 10 5 17 2 10 4 9 4 27 3 21 6 2 13 8 5 6 4 14 5 2 10 7 +cv_jpn_000692 24 2 4 9 4 8 2 11 2 8 2 6 5 11 7 10 4 3 24 2 6 4 14 2 15 4 6 7 10 2 4 6 3 3 5 9 4 15 19 12 5 13 3 11 3 6 5 10 7 +cv_jpn_000693 17 2 14 2 17 2 14 2 15 19 9 2 25 7 12 3 6 7 14 5 27 4 7 12 5 13 9 2 10 4 12 3 +cv_jpn_000694 26 5 13 12 5 6 2 4 9 3 11 3 12 18 6 2 8 2 27 4 4 8 2 15 7 9 3 23 3 2 23 4 10 7 12 5 5 12 2 4 23 3 15 19 6 19 8 3 12 2 4 7 3 8 3 17 2 24 2 15 19 8 5 6 2 13 16 2 4 10 7 6 3 8 3 17 2 14 5 6 4 9 2 4 +cv_jpn_000695 12 2 6 5 9 3 11 2 5 16 9 3 9 4 30 4 10 3 7 25 2 10 2 8 3 23 3 5 8 2 +cv_jpn_000696 12 3 10 5 3 3 8 2 14 2 6 3 24 3 6 7 9 3 28 5 13 14 2 13 6 2 4 15 4 6 7 4 8 5 5 14 3 9 3 6 2 2 6 18 8 3 11 3 11 4 11 4 10 7 6 3 8 3 17 2 +cv_jpn_000697 3 6 29 3 11 5 11 3 6 31 7 10 2 28 7 9 4 20 12 7 11 3 14 5 6 5 5 13 11 3 23 2 21 8 5 4 8 2 +cv_jpn_000698 2 14 2 4 17 2 2 9 2 4 13 27 15 4 7 11 2 8 2 6 3 25 2 23 2 15 4 12 2 13 8 3 2 12 3 25 5 4 11 2 12 18 +cv_jpn_000699 2 12 3 3 6 3 9 4 4 24 4 4 8 3 3 16 2 20 4 11 2 12 7 9 5 20 2 10 3 24 4 8 3 17 2 8 2 10 5 8 5 15 3 +cv_jpn_000700 17 2 8 2 22 4 17 2 6 29 5 9 3 3 6 2 9 2 20 9 3 3 14 3 6 2 5 4 8 2 4 14 5 12 18 +cv_jpn_000701 29 3 3 10 5 13 6 2 9 2 30 5 13 29 7 3 15 19 8 5 4 11 2 12 18 +cv_jpn_000702 2 3 21 8 3 12 7 11 4 11 10 2 12 5 20 +cv_jpn_000703 27 4 6 2 12 18 20 25 3 9 3 3 5 13 25 7 10 4 5 17 2 20 23 2 6 5 6 4 11 4 9 4 6 2 5 21 14 5 24 2 16 5 12 18 6 5 9 3 21 8 2 +cv_jpn_000704 4 26 28 7 11 3 6 3 9 3 4 13 30 19 4 26 3 3 26 18 6 2 21 8 5 4 8 2 9 3 14 5 11 4 22 4 6 2 24 2 7 9 2 10 4 11 2 15 19 8 2 +cv_jpn_000705 17 2 8 2 15 4 17 2 20 5 4 16 3 16 2 24 2 9 2 15 19 8 5 11 2 12 18 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..a072ef78a5673a0be038bb37934acc86a1df3bc7 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/score @@ -0,0 +1,32 @@ +cv_jpn_000706 tensor(-4.6019) +cv_jpn_000707 tensor(-8.0722) +cv_jpn_000708 tensor(-2.0117) +cv_jpn_000709 tensor(-2.3185) +cv_jpn_000710 tensor(-7.5624) +cv_jpn_000711 tensor(-1.5115) +cv_jpn_000712 tensor(-0.2637) +cv_jpn_000713 tensor(-3.8286) +cv_jpn_000714 tensor(-3.0048) +cv_jpn_000715 tensor(-3.4797) +cv_jpn_000716 tensor(-3.7475) +cv_jpn_000717 tensor(-1.8340) +cv_jpn_000718 tensor(-3.2251) +cv_jpn_000719 tensor(-6.5080) +cv_jpn_000720 tensor(-2.0130) +cv_jpn_000721 tensor(-1.8834) +cv_jpn_000722 tensor(-3.7977) +cv_jpn_000723 tensor(-5.1384) +cv_jpn_000724 tensor(-5.3286) +cv_jpn_000725 tensor(-1.7117) +cv_jpn_000726 tensor(-2.1469) +cv_jpn_000727 tensor(-2.5774) +cv_jpn_000728 tensor(-1.9983) +cv_jpn_000729 tensor(-3.6229) +cv_jpn_000730 tensor(-1.9490) +cv_jpn_000731 tensor(-1.7870) +cv_jpn_000732 tensor(-2.7400) +cv_jpn_000733 tensor(-4.1998) +cv_jpn_000734 tensor(-3.9360) +cv_jpn_000735 tensor(-1.2662) +cv_jpn_000736 tensor(-0.9053) +cv_jpn_000737 tensor(-0.5281) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..515547eb2c2a6a0c16a17116b3049d6121fd8258 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/text @@ -0,0 +1,32 @@ +cv_jpn_000706 o s e i k e e r y a j i b u a N d e t o cl t e k i n a +cv_jpn_000707 a i sh u u e k a r a n i sh u u k a N h a i g a i e r u y o o k o o o n i k i m a s U +cv_jpn_000708 o r e m o k i n i n a r u n a +cv_jpn_000709 d a r e i g a ts U k a cl t e r u n o k a a h w o k a r u n a i +cv_jpn_000710 p u r a o z a n o b a j o N g a pau a g a r u t o o s U k o sh I i u u r e sh i i +cv_jpn_000711 m a t a a t a r a sh i i a i d o r u g a d e t e k i t a +cv_jpn_000712 m a j i d e y a cl t o n o k a +cv_jpn_000713 ch o o d o s a m o t U k i n i pau ky o o j i g a h a i t e k i t a +cv_jpn_000714 i cl s o n i k ch i e N sh I t e o +cv_jpn_000715 s o r e k a s a t e n o t o r e s u N +cv_jpn_000716 f U t a r i o w a r e j i e i k i s u e e s a N sh I t a +cv_jpn_000717 t o m a t o k a n a N g a n o a k a i s o s u g a k a k a cl t e r i u o +cv_jpn_000718 k o g o k a r a t a t e n a N a s u n o w a k i b i s u i +cv_jpn_000719 n i z u g e o w o s U k a r i sh i b o cl t e pau a j i g a n a j i m i y o r i s u r u +cv_jpn_000720 n e t o k i n i h a m a cl t a r a k a n e g a g a m a cl t a U +cv_jpn_000721 s i t a k a i r i y o o n i n a r u N d a +cv_jpn_000722 k o s e h a h a i y u t o y u y o r i a k u g a ts u y o a e k a N j i +cv_jpn_000723 f e i j i k a r u n o s a o m a z a m a d a t o m i s e ts U k e r a r e t a +cv_jpn_000724 k o s U p a y o k e d e b a pau s o k o k o n o m o N d a e a g a m a N s u r e +cv_jpn_000725 i N n a y a cl t e m a s U k a r a t a i j o o b u d e s u i u o +cv_jpn_000726 k o n o t o j o k a N h a i cl t a sh u u k a N k i n i cl t a +cv_jpn_000727 k o n o d e N ch i f u u k i r e ch i a cl t a +cv_jpn_000728 a m a y e u d o r i s u r u t o k o r o g a n a k U t e k o m a cl t a +cv_jpn_000729 y a s U k a s u r u e o r i sh I t ts u w a g e t o h o sh e +cv_jpn_000730 m a s e g o k o n a k o t e i n a r u t o w a m o n a k a cl t a +cv_jpn_000731 s a i g o n i w a r a y o t o r i n i k u r a s U t a i r u +cv_jpn_000732 k o r a e n a N n a e i m i g a r u d a pau +cv_jpn_000733 o t i i a +cv_jpn_000734 sh a sh e i +cv_jpn_000735 n i +cv_jpn_000736 h a ch i +cv_jpn_000737 h a i diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..515547eb2c2a6a0c16a17116b3049d6121fd8258 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/token @@ -0,0 +1,32 @@ +cv_jpn_000706 o s e i k e e r y a j i b u a N d e t o cl t e k i n a +cv_jpn_000707 a i sh u u e k a r a n i sh u u k a N h a i g a i e r u y o o k o o o n i k i m a s U +cv_jpn_000708 o r e m o k i n i n a r u n a +cv_jpn_000709 d a r e i g a ts U k a cl t e r u n o k a a h w o k a r u n a i +cv_jpn_000710 p u r a o z a n o b a j o N g a pau a g a r u t o o s U k o sh I i u u r e sh i i +cv_jpn_000711 m a t a a t a r a sh i i a i d o r u g a d e t e k i t a +cv_jpn_000712 m a j i d e y a cl t o n o k a +cv_jpn_000713 ch o o d o s a m o t U k i n i pau ky o o j i g a h a i t e k i t a +cv_jpn_000714 i cl s o n i k ch i e N sh I t e o +cv_jpn_000715 s o r e k a s a t e n o t o r e s u N +cv_jpn_000716 f U t a r i o w a r e j i e i k i s u e e s a N sh I t a +cv_jpn_000717 t o m a t o k a n a N g a n o a k a i s o s u g a k a k a cl t e r i u o +cv_jpn_000718 k o g o k a r a t a t e n a N a s u n o w a k i b i s u i +cv_jpn_000719 n i z u g e o w o s U k a r i sh i b o cl t e pau a j i g a n a j i m i y o r i s u r u +cv_jpn_000720 n e t o k i n i h a m a cl t a r a k a n e g a g a m a cl t a U +cv_jpn_000721 s i t a k a i r i y o o n i n a r u N d a +cv_jpn_000722 k o s e h a h a i y u t o y u y o r i a k u g a ts u y o a e k a N j i +cv_jpn_000723 f e i j i k a r u n o s a o m a z a m a d a t o m i s e ts U k e r a r e t a +cv_jpn_000724 k o s U p a y o k e d e b a pau s o k o k o n o m o N d a e a g a m a N s u r e +cv_jpn_000725 i N n a y a cl t e m a s U k a r a t a i j o o b u d e s u i u o +cv_jpn_000726 k o n o t o j o k a N h a i cl t a sh u u k a N k i n i cl t a +cv_jpn_000727 k o n o d e N ch i f u u k i r e ch i a cl t a +cv_jpn_000728 a m a y e u d o r i s u r u t o k o r o g a n a k U t e k o m a cl t a +cv_jpn_000729 y a s U k a s u r u e o r i sh I t ts u w a g e t o h o sh e +cv_jpn_000730 m a s e g o k o n a k o t e i n a r u t o w a m o n a k a cl t a +cv_jpn_000731 s a i g o n i w a r a y o t o r i n i k u r a s U t a i r u +cv_jpn_000732 k o r a e n a N n a e i m i g a r u d a pau +cv_jpn_000733 o t i i a +cv_jpn_000734 sh a sh e i +cv_jpn_000735 n i +cv_jpn_000736 h a ch i +cv_jpn_000737 h a i diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..bc77db7a95b87fbb4a0e16eb39e0458e985a4177 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/token_int @@ -0,0 +1,32 @@ +cv_jpn_000706 3 12 5 4 6 5 5 10 23 2 22 4 25 7 2 13 14 5 8 3 21 8 5 6 4 9 2 +cv_jpn_000707 2 4 15 7 7 5 6 2 10 2 9 4 15 7 7 6 2 13 24 2 4 16 2 4 5 10 7 23 3 3 6 3 3 3 9 4 6 4 11 2 12 18 +cv_jpn_000708 3 10 5 11 3 6 4 9 4 9 2 10 7 9 2 +cv_jpn_000709 14 2 10 5 4 16 2 26 18 6 2 21 8 5 10 7 9 3 6 2 2 24 17 3 6 2 10 7 9 2 4 +cv_jpn_000710 30 7 10 2 3 28 2 9 3 25 2 22 3 13 16 2 20 2 16 2 10 7 8 3 3 12 18 6 3 15 19 4 7 7 10 5 15 4 4 +cv_jpn_000711 11 2 8 2 2 8 2 10 2 15 4 4 2 4 14 3 10 7 16 2 14 5 8 5 6 4 8 2 +cv_jpn_000712 11 2 22 4 14 5 23 2 21 8 3 9 3 6 2 +cv_jpn_000713 27 3 3 14 3 12 2 11 3 8 18 6 4 9 4 20 29 3 3 22 4 16 2 24 2 4 8 5 6 4 8 2 +cv_jpn_000714 4 21 12 3 9 4 6 27 4 5 13 15 19 8 5 3 +cv_jpn_000715 12 3 10 5 6 2 12 2 8 5 9 3 8 3 10 5 12 7 13 +cv_jpn_000716 31 18 8 2 10 4 3 17 2 10 5 22 4 5 4 6 4 12 7 5 5 12 2 13 15 19 8 2 +cv_jpn_000717 8 3 11 2 8 3 6 2 9 2 13 16 2 9 3 2 6 2 4 12 3 12 7 16 2 6 2 6 2 21 8 5 10 4 7 3 +cv_jpn_000718 6 3 16 3 6 2 10 2 8 2 8 5 9 2 13 2 12 7 9 3 17 2 6 4 25 4 12 7 4 +cv_jpn_000719 9 4 28 7 16 5 3 17 3 12 18 6 2 10 4 15 4 25 3 21 8 5 20 2 22 4 16 2 9 2 22 4 11 4 23 3 10 4 12 7 10 7 +cv_jpn_000720 9 5 8 3 6 4 9 4 24 2 11 2 21 8 2 10 2 6 2 9 5 16 2 16 2 11 2 21 8 2 18 +cv_jpn_000721 12 4 8 2 6 2 4 10 4 23 3 3 9 4 9 2 10 7 13 14 2 +cv_jpn_000722 6 3 12 5 24 2 24 2 4 23 7 8 3 23 7 23 3 10 4 2 6 7 16 2 26 7 23 3 2 5 6 2 13 22 4 +cv_jpn_000723 31 5 4 22 4 6 2 10 7 9 3 12 2 3 11 2 28 2 11 2 14 2 8 3 11 4 12 5 26 18 6 5 10 2 10 5 8 2 +cv_jpn_000724 6 3 12 18 30 2 23 3 6 5 14 5 25 2 20 12 3 6 3 6 3 9 3 11 3 13 14 2 5 2 16 2 11 2 13 12 7 10 5 +cv_jpn_000725 4 13 9 2 23 2 21 8 5 11 2 12 18 6 2 10 2 8 2 4 22 3 3 25 7 14 5 12 7 4 7 3 +cv_jpn_000726 6 3 9 3 8 3 22 3 6 2 13 24 2 4 21 8 2 15 7 7 6 2 13 6 4 9 4 21 8 2 +cv_jpn_000727 6 3 9 3 14 5 13 27 4 31 7 7 6 4 10 5 27 4 2 21 8 2 +cv_jpn_000728 2 11 2 23 5 7 14 3 10 4 12 7 10 7 8 3 6 3 10 3 16 2 9 2 6 18 8 5 6 3 11 2 21 8 2 +cv_jpn_000729 23 2 12 18 6 2 12 7 10 7 5 3 10 4 15 19 8 26 7 17 2 16 5 8 3 24 3 15 5 +cv_jpn_000730 11 2 12 5 16 3 6 3 9 2 6 3 8 5 4 9 2 10 7 8 3 17 2 11 3 9 2 6 2 21 8 2 +cv_jpn_000731 12 2 4 16 3 9 4 17 2 10 2 23 3 8 3 10 4 9 4 6 7 10 2 12 18 8 2 4 10 7 +cv_jpn_000732 6 3 10 2 5 9 2 13 9 2 5 4 11 4 16 2 10 7 14 2 20 +cv_jpn_000733 3 8 4 4 2 +cv_jpn_000734 15 2 15 5 4 +cv_jpn_000735 9 4 +cv_jpn_000736 24 2 27 4 +cv_jpn_000737 24 2 4 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..2701702c59c229dcfc8cbffcb684b81ae0322679 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/score @@ -0,0 +1,31 @@ +cv_jpn_000738 tensor(-4.4565) +cv_jpn_000739 tensor(-2.9190) +cv_jpn_000740 tensor(-2.3658) +cv_jpn_000741 tensor(-4.9426) +cv_jpn_000742 tensor(-3.6796) +cv_jpn_000743 tensor(-3.9769) +cv_jpn_000744 tensor(-9.4279) +cv_jpn_000745 tensor(-3.7001) +cv_jpn_000746 tensor(-5.5705) +cv_jpn_000747 tensor(-5.4982) +cv_jpn_000748 tensor(-5.6039) +cv_jpn_000749 tensor(-1.6378) +cv_jpn_000750 tensor(-2.5597) +cv_jpn_000751 tensor(-3.7843) +cv_jpn_000752 tensor(-7.3307) +cv_jpn_000753 tensor(-1.4260) +cv_jpn_000754 tensor(-0.4020) +cv_jpn_000755 tensor(-1.4855) +cv_jpn_000756 tensor(-1.0794) +cv_jpn_000757 tensor(-1.1086) +cv_jpn_000758 tensor(-2.5595) +cv_jpn_000759 tensor(-5.3968) +cv_jpn_000760 tensor(-2.3119) +cv_jpn_000761 tensor(-2.4207) +cv_jpn_000762 tensor(-4.5164) +cv_jpn_000763 tensor(-2.3885) +cv_jpn_000764 tensor(-1.7193) +cv_jpn_000765 tensor(-11.3094) +cv_jpn_000766 tensor(-6.0213) +cv_jpn_000767 tensor(-2.2565) +cv_jpn_000768 tensor(-2.6178) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..22c338ed5216af65ce00b0e69654923db451e068 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/text @@ -0,0 +1,31 @@ +cv_jpn_000738 t e b u r u n o o y e n i k a b i N g a r i m a s U +cv_jpn_000739 w o t a sh i w a o m a i y a s a s a N b o sh i m a s U +cv_jpn_000740 a t a r a sh i i k u ts u o o h a i t e d e k a k e m a s U +cv_jpn_000741 k o t o sh i n o n a ts e y a s a m i o w a u m i n i m o i k i m a sh I t a sh i i y a m a n i m o n o r i b a sh I t a +cv_jpn_000742 w a t a sh i w a i r o y i r o n o b e N g o o ch i b u N n o m u n e d e k o sh i r a e t e m i m a sh I t a +cv_jpn_000743 n a N d e k o o m o s u o o b a i h e d a n a N d a r o +cv_jpn_000744 t a r e N t o k a r a ky o k u a n a n i k a e i e cl t u e k e e sh I s a k I u g e a +cv_jpn_000745 d o g e z a s e r e b a i cl t e m o N sh a n a i +cv_jpn_000746 d e t a n o a i d a k a n o j i w a j i b u N t o i cl t e e n o k ky u o r i y o t a n o cl t a +cv_jpn_000747 k o n o r e e n i N n a N k a w a h sh s a sh i b u r i n i n i t a +cv_jpn_000748 o o k I k t a s a i d a o ch i e N j i e o s u r u +cv_jpn_000749 k a r e w a t a m a o k a k i m u sh i cl t a +cv_jpn_000750 k o m a t e sh I t e a r i m a s U +cv_jpn_000751 o n o d o k U s e N k a i j o w a k I i t e r u +cv_jpn_000752 r e e j o o k o a k e t a cl t o U t a N n a n i g a h i ch u y o k a w a s u r e t a +cv_jpn_000753 i i t ch i +cv_jpn_000754 h a ch i +cv_jpn_000755 k i i w e a +cv_jpn_000756 d e i +cv_jpn_000757 a sh i ch i +cv_jpn_000758 y o o b o o d a s u n o N k a u sh I t o a s U k u n a i +cv_jpn_000759 n o o k a r e t o k u y u n o i k ky u e m a k a s e n o k o m a sh a r u +cv_jpn_000760 k o n o d a i k u w a a N k o g a o o k U t e y o k U k a i m a s U +cv_jpn_000761 d e sh o o k i n a g a r a sh o o s u e ts o o y o m i m a s U +cv_jpn_000762 k o N n o o k i n a N g o g u r o ts u k e n a i t i k e n a e i N d e s U k a +cv_jpn_000763 k a r a e n o b o o i k o w a t o m a r a n a i +cv_jpn_000764 i k i t e i k a N m a n e +cv_jpn_000765 t o o j u d o sh s e s a k a k t e k i n a h a ts u m e d a cl t a n e +cv_jpn_000766 h o N t e k i o t a s o a ch I k a cl U t k a N +cv_jpn_000767 k a a w a h i e w a cl t e i d a +cv_jpn_000768 ch e cl k e diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..22c338ed5216af65ce00b0e69654923db451e068 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/token @@ -0,0 +1,31 @@ +cv_jpn_000738 t e b u r u n o o y e n i k a b i N g a r i m a s U +cv_jpn_000739 w o t a sh i w a o m a i y a s a s a N b o sh i m a s U +cv_jpn_000740 a t a r a sh i i k u ts u o o h a i t e d e k a k e m a s U +cv_jpn_000741 k o t o sh i n o n a ts e y a s a m i o w a u m i n i m o i k i m a sh I t a sh i i y a m a n i m o n o r i b a sh I t a +cv_jpn_000742 w a t a sh i w a i r o y i r o n o b e N g o o ch i b u N n o m u n e d e k o sh i r a e t e m i m a sh I t a +cv_jpn_000743 n a N d e k o o m o s u o o b a i h e d a n a N d a r o +cv_jpn_000744 t a r e N t o k a r a ky o k u a n a n i k a e i e cl t u e k e e sh I s a k I u g e a +cv_jpn_000745 d o g e z a s e r e b a i cl t e m o N sh a n a i +cv_jpn_000746 d e t a n o a i d a k a n o j i w a j i b u N t o i cl t e e n o k ky u o r i y o t a n o cl t a +cv_jpn_000747 k o n o r e e n i N n a N k a w a h sh s a sh i b u r i n i n i t a +cv_jpn_000748 o o k I k t a s a i d a o ch i e N j i e o s u r u +cv_jpn_000749 k a r e w a t a m a o k a k i m u sh i cl t a +cv_jpn_000750 k o m a t e sh I t e a r i m a s U +cv_jpn_000751 o n o d o k U s e N k a i j o w a k I i t e r u +cv_jpn_000752 r e e j o o k o a k e t a cl t o U t a N n a n i g a h i ch u y o k a w a s u r e t a +cv_jpn_000753 i i t ch i +cv_jpn_000754 h a ch i +cv_jpn_000755 k i i w e a +cv_jpn_000756 d e i +cv_jpn_000757 a sh i ch i +cv_jpn_000758 y o o b o o d a s u n o N k a u sh I t o a s U k u n a i +cv_jpn_000759 n o o k a r e t o k u y u n o i k ky u e m a k a s e n o k o m a sh a r u +cv_jpn_000760 k o n o d a i k u w a a N k o g a o o k U t e y o k U k a i m a s U +cv_jpn_000761 d e sh o o k i n a g a r a sh o o s u e ts o o y o m i m a s U +cv_jpn_000762 k o N n o o k i n a N g o g u r o ts u k e n a i t i k e n a e i N d e s U k a +cv_jpn_000763 k a r a e n o b o o i k o w a t o m a r a n a i +cv_jpn_000764 i k i t e i k a N m a n e +cv_jpn_000765 t o o j u d o sh s e s a k a k t e k i n a h a ts u m e d a cl t a n e +cv_jpn_000766 h o N t e k i o t a s o a ch I k a cl U t k a N +cv_jpn_000767 k a a w a h i e w a cl t e i d a +cv_jpn_000768 ch e cl k e diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..1ab6c0463c7c4ed52dc660a0fc5a6f0b33bcbf1e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/token_int @@ -0,0 +1,31 @@ +cv_jpn_000738 8 5 25 7 10 7 9 3 3 23 5 9 4 6 2 25 4 13 16 2 10 4 11 2 12 18 +cv_jpn_000739 17 3 8 2 15 4 17 2 3 11 2 4 23 2 12 2 12 2 13 25 3 15 4 11 2 12 18 +cv_jpn_000740 2 8 2 10 2 15 4 4 6 7 26 7 3 3 24 2 4 8 5 14 5 6 2 6 5 11 2 12 18 +cv_jpn_000741 6 3 8 3 15 4 9 3 9 2 26 5 23 2 12 2 11 4 3 17 2 7 11 4 9 4 11 3 4 6 4 11 2 15 19 8 2 15 4 4 23 2 11 2 9 4 11 3 9 3 10 4 25 2 15 19 8 2 +cv_jpn_000742 17 2 8 2 15 4 17 2 4 10 3 23 4 10 3 9 3 25 5 13 16 3 3 27 4 25 7 13 9 3 11 7 9 5 14 5 6 3 15 4 10 2 5 8 5 11 4 11 2 15 19 8 2 +cv_jpn_000743 9 2 13 14 5 6 3 3 11 3 12 7 3 3 25 2 4 24 5 14 2 9 2 13 14 2 10 3 +cv_jpn_000744 8 2 10 5 13 8 3 6 2 10 2 29 3 6 7 2 9 2 9 4 6 2 5 4 5 21 8 7 5 6 5 5 15 19 12 2 6 19 7 16 5 2 +cv_jpn_000745 14 3 16 5 28 2 12 5 10 5 25 2 4 21 8 5 11 3 13 15 2 9 2 4 +cv_jpn_000746 14 5 8 2 9 3 2 4 14 2 6 2 9 3 22 4 17 2 22 4 25 7 13 8 3 4 21 8 5 5 9 3 6 29 7 3 10 4 23 3 8 2 9 3 21 8 2 +cv_jpn_000747 6 3 9 3 10 5 5 9 4 13 9 2 13 6 2 17 2 24 15 12 2 15 4 25 7 10 4 9 4 9 4 8 2 +cv_jpn_000748 3 3 6 19 6 8 2 12 2 4 14 2 3 27 4 5 13 22 4 5 3 12 7 10 7 +cv_jpn_000749 6 2 10 5 17 2 8 2 11 2 3 6 2 6 4 11 7 15 4 21 8 2 +cv_jpn_000750 6 3 11 2 8 5 15 19 8 5 2 10 4 11 2 12 18 +cv_jpn_000751 3 9 3 14 3 6 18 12 5 13 6 2 4 22 3 17 2 6 19 4 8 5 10 7 +cv_jpn_000752 10 5 5 22 3 3 6 3 2 6 5 8 2 21 8 3 18 8 2 13 9 2 9 4 16 2 24 4 27 7 23 3 6 2 17 2 12 7 10 5 8 2 +cv_jpn_000753 4 4 8 27 4 +cv_jpn_000754 24 2 27 4 +cv_jpn_000755 6 4 4 17 5 2 +cv_jpn_000756 14 5 4 +cv_jpn_000757 2 15 4 27 4 +cv_jpn_000758 23 3 3 25 3 3 14 2 12 7 9 3 13 6 2 7 15 19 8 3 2 12 18 6 7 9 2 4 +cv_jpn_000759 9 3 3 6 2 10 5 8 3 6 7 23 7 9 3 4 6 29 7 5 11 2 6 2 12 5 9 3 6 3 11 2 15 2 10 7 +cv_jpn_000760 6 3 9 3 14 2 4 6 7 17 2 2 13 6 3 16 2 3 3 6 18 8 5 23 3 6 18 6 2 4 11 2 12 18 +cv_jpn_000761 14 5 15 3 3 6 4 9 2 16 2 10 2 15 3 3 12 7 5 26 3 3 23 3 11 4 11 2 12 18 +cv_jpn_000762 6 3 13 9 3 3 6 4 9 2 13 16 3 16 7 10 3 26 7 6 5 9 2 4 8 4 6 5 9 2 5 4 13 14 5 12 18 6 2 +cv_jpn_000763 6 2 10 2 5 9 3 25 3 3 4 6 3 17 2 8 3 11 2 10 2 9 2 4 +cv_jpn_000764 4 6 4 8 5 4 6 2 13 11 2 9 5 +cv_jpn_000765 8 3 3 22 7 14 3 15 12 5 12 2 6 2 6 8 5 6 4 9 2 24 2 26 7 11 5 14 2 21 8 2 9 5 +cv_jpn_000766 24 3 13 8 5 6 4 3 8 2 12 3 2 27 19 6 2 21 18 8 6 2 13 +cv_jpn_000767 6 2 2 17 2 24 4 5 17 2 21 8 5 4 14 2 +cv_jpn_000768 27 5 21 6 5 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..a49e672416b280760fcc7849ca5b1eb1de3c0f7e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/score @@ -0,0 +1,31 @@ +cv_jpn_000769 tensor(-0.5145) +cv_jpn_000770 tensor(-1.2804) +cv_jpn_000771 tensor(-0.5676) +cv_jpn_000772 tensor(-0.8226) +cv_jpn_000773 tensor(-2.1896) +cv_jpn_000774 tensor(-4.9881) +cv_jpn_000775 tensor(-1.4679) +cv_jpn_000776 tensor(-3.6186) +cv_jpn_000777 tensor(-4.5644) +cv_jpn_000778 tensor(-2.2261) +cv_jpn_000779 tensor(-6.3640) +cv_jpn_000780 tensor(-2.7660) +cv_jpn_000781 tensor(-5.7984) +cv_jpn_000782 tensor(-13.0501) +cv_jpn_000783 tensor(-4.3536) +cv_jpn_000784 tensor(-3.5022) +cv_jpn_000785 tensor(-2.3586) +cv_jpn_000786 tensor(-3.0717) +cv_jpn_000787 tensor(-2.4364) +cv_jpn_000788 tensor(-13.9564) +cv_jpn_000789 tensor(-4.7083) +cv_jpn_000790 tensor(-6.7299) +cv_jpn_000791 tensor(-14.7352) +cv_jpn_000792 tensor(-8.7931) +cv_jpn_000793 tensor(-3.1764) +cv_jpn_000794 tensor(-6.3337) +cv_jpn_000795 tensor(-10.3703) +cv_jpn_000796 tensor(-6.3337) +cv_jpn_000797 tensor(-2.4986) +cv_jpn_000798 tensor(-13.1302) +cv_jpn_000799 tensor(-3.4926) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..30f8e0eed07ffcc540c9eaa38ddc52bd362e586c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/text @@ -0,0 +1,31 @@ +cv_jpn_000769 o +cv_jpn_000770 a sh I t ch i i +cv_jpn_000771 k o +cv_jpn_000772 i i e a +cv_jpn_000773 n a m a i k a r a sh e t e k t o o s u g i r u +cv_jpn_000774 j i k o n o s o t o n y a r u t o y u n o w a t a N n i j i k o n e sh i k N s o ts s o n i a r u t o y u k o t o d e n a k u +cv_jpn_000775 s o r w a sh i r a n a k U t e i d e s U +cv_jpn_000776 k i m i t o k o n o ky o o ts u n o sh i g e w a d a r e i t o r u i pau m i a t a r a n a i +cv_jpn_000777 s u e g e e o o t o i N n a cl t e k i t e r u n o n a cl +cv_jpn_000778 k o n o h e N d e s U k o sh i a s u i m a sh o +cv_jpn_000779 d e N sh a r i n o r u t o k i k i cl t o k a i m a s U d a U t a U t U +cv_jpn_000780 t a m a o g w a i k o k o j i u r a N b u r a i e s U +cv_jpn_000781 g e e s U p i w a m a k i o ts u j i t e pau i n e s t o sh i r i y a cl t a t U U +cv_jpn_000782 n o o g o o y a n e s a r u o e n a i sh I t o p a r i k a N e N k i y o m o k o m o f k I k e o o n i h I k i z u r a r e t e r u t o m o o s U ts U +cv_jpn_000783 a N d e k o n o r o cl t o sh o t a i m e n a m u o i n a r e y a r e s e N d a +cv_jpn_000784 f U ts u u d e a r u k o t a m o d i cl p a n a k o s e +cv_jpn_000785 ts u y o b i d e t a N j i k a N d e g o k a i n i t a m e r u +cv_jpn_000786 p a k u m a ts u n o d e k i g o k o w a pau i m a n i ts u j i r u ky o o k u N n o y a m a d e s U +cv_jpn_000787 N k o o k a r a m a ch i n o w a k a r i g a m i e t e k i t a +cv_jpn_000788 m e n i o u y u u b e k I ch i a w a k a r a n a U t a r a i m i y u b i k I k u t o g a o m o y u k a b a n a g k a cl t U +cv_jpn_000789 t a m e s i n i k a e n a k i y a cl t e m i r +cv_jpn_000790 m o k U sh I k g a i n e i g i m i m a i n a i k o r e w a o o t e N ts ch i e a i k a +cv_jpn_000791 ts ch k e i j o k a r a e n i j u g o t o o m a N u N o pau m i ch i m a pau m u k a s o k a o t r i n e k a +cv_jpn_000792 k a N g u j o t e e h N w a k o N cl p u o N t e i n a g a i ch i k u u s u n a cl t a +cv_jpn_000793 k o r u m o n o k o r e w a g o h a h a r e pau o t u n a i n a r t o p a h a a +cv_jpn_000794 k o o i t e k I t o k a N t e k i n i pau p o i pau a sh i s u t e k i n i pau w a r e w a r e m o j i k o w a m a s u m a s u pau m e t o n a r u n o d a r u +cv_jpn_000795 h i j o o sh I k i d a r o k o t o w a y u ch j o o i m i s u r u n o m i d e n a k u pau sh a k a i e t e k i n i pau a k U t o m o k a N m a e r a r u u o u d a r u +cv_jpn_000796 j o o sh u k i g a n a o t o k u sh I t e k i n a ch I sh I k i d e a r u n i h a s e k a r a k u w a +cv_jpn_000797 k o N n a k o t o d e o g u r a r e t e n a s a k e n h a i +cv_jpn_000798 k a k o t o m i r a i e t o w a j i k o m u j u N t e k i n i pau d e N z a i n o ch i t a i r i k U u u t o i u n i w a pau e N d n a y a k a t a ch o m o t a m a i k e r e m a n a r a n a i +cv_jpn_000799 a sh o k I i o n o t a k a s e g a d o u N n a r diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..30f8e0eed07ffcc540c9eaa38ddc52bd362e586c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/token @@ -0,0 +1,31 @@ +cv_jpn_000769 o +cv_jpn_000770 a sh I t ch i i +cv_jpn_000771 k o +cv_jpn_000772 i i e a +cv_jpn_000773 n a m a i k a r a sh e t e k t o o s u g i r u +cv_jpn_000774 j i k o n o s o t o n y a r u t o y u n o w a t a N n i j i k o n e sh i k N s o ts s o n i a r u t o y u k o t o d e n a k u +cv_jpn_000775 s o r w a sh i r a n a k U t e i d e s U +cv_jpn_000776 k i m i t o k o n o ky o o ts u n o sh i g e w a d a r e i t o r u i pau m i a t a r a n a i +cv_jpn_000777 s u e g e e o o t o i N n a cl t e k i t e r u n o n a cl +cv_jpn_000778 k o n o h e N d e s U k o sh i a s u i m a sh o +cv_jpn_000779 d e N sh a r i n o r u t o k i k i cl t o k a i m a s U d a U t a U t U +cv_jpn_000780 t a m a o g w a i k o k o j i u r a N b u r a i e s U +cv_jpn_000781 g e e s U p i w a m a k i o ts u j i t e pau i n e s t o sh i r i y a cl t a t U U +cv_jpn_000782 n o o g o o y a n e s a r u o e n a i sh I t o p a r i k a N e N k i y o m o k o m o f k I k e o o n i h I k i z u r a r e t e r u t o m o o s U ts U +cv_jpn_000783 a N d e k o n o r o cl t o sh o t a i m e n a m u o i n a r e y a r e s e N d a +cv_jpn_000784 f U ts u u d e a r u k o t a m o d i cl p a n a k o s e +cv_jpn_000785 ts u y o b i d e t a N j i k a N d e g o k a i n i t a m e r u +cv_jpn_000786 p a k u m a ts u n o d e k i g o k o w a pau i m a n i ts u j i r u ky o o k u N n o y a m a d e s U +cv_jpn_000787 N k o o k a r a m a ch i n o w a k a r i g a m i e t e k i t a +cv_jpn_000788 m e n i o u y u u b e k I ch i a w a k a r a n a U t a r a i m i y u b i k I k u t o g a o m o y u k a b a n a g k a cl t U +cv_jpn_000789 t a m e s i n i k a e n a k i y a cl t e m i r +cv_jpn_000790 m o k U sh I k g a i n e i g i m i m a i n a i k o r e w a o o t e N ts ch i e a i k a +cv_jpn_000791 ts ch k e i j o k a r a e n i j u g o t o o m a N u N o pau m i ch i m a pau m u k a s o k a o t r i n e k a +cv_jpn_000792 k a N g u j o t e e h N w a k o N cl p u o N t e i n a g a i ch i k u u s u n a cl t a +cv_jpn_000793 k o r u m o n o k o r e w a g o h a h a r e pau o t u n a i n a r t o p a h a a +cv_jpn_000794 k o o i t e k I t o k a N t e k i n i pau p o i pau a sh i s u t e k i n i pau w a r e w a r e m o j i k o w a m a s u m a s u pau m e t o n a r u n o d a r u +cv_jpn_000795 h i j o o sh I k i d a r o k o t o w a y u ch j o o i m i s u r u n o m i d e n a k u pau sh a k a i e t e k i n i pau a k U t o m o k a N m a e r a r u u o u d a r u +cv_jpn_000796 j o o sh u k i g a n a o t o k u sh I t e k i n a ch I sh I k i d e a r u n i h a s e k a r a k u w a +cv_jpn_000797 k o N n a k o t o d e o g u r a r e t e n a s a k e n h a i +cv_jpn_000798 k a k o t o m i r a i e t o w a j i k o m u j u N t e k i n i pau d e N z a i n o ch i t a i r i k U u u t o i u n i w a pau e N d n a y a k a t a ch o m o t a m a i k e r e m a n a r a n a i +cv_jpn_000799 a sh o k I i o n o t a k a s e g a d o u N n a r diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..cff0e9f89e0bc8af32f37641c004a83aa22ed947 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/token_int @@ -0,0 +1,31 @@ +cv_jpn_000769 3 +cv_jpn_000770 2 15 19 8 27 4 4 +cv_jpn_000771 6 3 +cv_jpn_000772 4 4 5 2 +cv_jpn_000773 9 2 11 2 4 6 2 10 2 15 5 8 5 6 8 3 3 12 7 16 4 10 7 +cv_jpn_000774 22 4 6 3 9 3 12 3 8 3 9 23 2 10 7 8 3 23 7 9 3 17 2 8 2 13 9 4 22 4 6 3 9 5 15 4 6 13 12 3 26 12 3 9 4 2 10 7 8 3 23 7 6 3 8 3 14 5 9 2 6 7 +cv_jpn_000775 12 3 10 17 2 15 4 10 2 9 2 6 18 8 5 4 14 5 12 18 +cv_jpn_000776 6 4 11 4 8 3 6 3 9 3 29 3 3 26 7 9 3 15 4 16 5 17 2 14 2 10 5 4 8 3 10 7 4 20 11 4 2 8 2 10 2 9 2 4 +cv_jpn_000777 12 7 5 16 5 5 3 3 8 3 4 13 9 2 21 8 5 6 4 8 5 10 7 9 3 9 2 21 +cv_jpn_000778 6 3 9 3 24 5 13 14 5 12 18 6 3 15 4 2 12 7 4 11 2 15 3 +cv_jpn_000779 14 5 13 15 2 10 4 9 3 10 7 8 3 6 4 6 4 21 8 3 6 2 4 11 2 12 18 14 2 18 8 2 18 8 18 +cv_jpn_000780 8 2 11 2 3 16 17 2 4 6 3 6 3 22 4 7 10 2 13 25 7 10 2 4 5 12 18 +cv_jpn_000781 16 5 5 12 18 30 4 17 2 11 2 6 4 3 26 7 22 4 8 5 20 4 9 5 12 8 3 15 4 10 4 23 2 21 8 2 8 18 18 +cv_jpn_000782 9 3 3 16 3 3 23 2 9 5 12 2 10 7 3 5 9 2 4 15 19 8 3 30 2 10 4 6 2 13 5 13 6 4 23 3 11 3 6 3 11 3 31 6 19 6 5 3 3 9 4 24 19 6 4 28 7 10 2 10 5 8 5 10 7 8 3 11 3 3 12 18 26 18 +cv_jpn_000783 2 13 14 5 6 3 9 3 10 3 21 8 3 15 3 8 2 4 11 5 9 2 11 7 3 4 9 2 10 5 23 2 10 5 12 5 13 14 2 +cv_jpn_000784 31 18 26 7 7 14 5 2 10 7 6 3 8 2 11 3 14 4 21 30 2 9 2 6 3 12 5 +cv_jpn_000785 26 7 23 3 25 4 14 5 8 2 13 22 4 6 2 13 14 5 16 3 6 2 4 9 4 8 2 11 5 10 7 +cv_jpn_000786 30 2 6 7 11 2 26 7 9 3 14 5 6 4 16 3 6 3 17 2 20 4 11 2 9 4 26 7 22 4 10 7 29 3 3 6 7 13 9 3 23 2 11 2 14 5 12 18 +cv_jpn_000787 13 6 3 3 6 2 10 2 11 2 27 4 9 3 17 2 6 2 10 4 16 2 11 4 5 8 5 6 4 8 2 +cv_jpn_000788 11 5 9 4 3 7 23 7 7 25 5 6 19 27 4 2 17 2 6 2 10 2 9 2 18 8 2 10 2 4 11 4 23 7 25 4 6 19 6 7 8 3 16 2 3 11 3 23 7 6 2 25 2 9 2 16 6 2 21 8 18 +cv_jpn_000789 8 2 11 5 12 4 9 4 6 2 5 9 2 6 4 23 2 21 8 5 11 4 10 +cv_jpn_000790 11 3 6 18 15 19 6 16 2 4 9 5 4 16 4 11 4 11 2 4 9 2 4 6 3 10 5 17 2 3 3 8 5 13 26 27 4 5 2 4 6 2 +cv_jpn_000791 26 27 6 5 4 22 3 6 2 10 2 5 9 4 22 7 16 3 8 3 3 11 2 13 7 13 3 20 11 4 27 4 11 2 20 11 7 6 2 12 3 6 2 3 8 10 4 9 5 6 2 +cv_jpn_000792 6 2 13 16 7 22 3 8 5 5 24 13 17 2 6 3 13 21 30 7 3 13 8 5 4 9 2 16 2 4 27 4 6 7 7 12 7 9 2 21 8 2 +cv_jpn_000793 6 3 10 7 11 3 9 3 6 3 10 5 17 2 16 3 24 2 24 2 10 5 20 3 8 7 9 2 4 9 2 10 8 3 30 2 24 2 2 +cv_jpn_000794 6 3 3 4 8 5 6 19 8 3 6 2 13 8 5 6 4 9 4 20 30 3 4 20 2 15 4 12 7 8 5 6 4 9 4 20 17 2 10 5 17 2 10 5 11 3 22 4 6 3 17 2 11 2 12 7 11 2 12 7 20 11 5 8 3 9 2 10 7 9 3 14 2 10 7 +cv_jpn_000795 24 4 22 3 3 15 19 6 4 14 2 10 3 6 3 8 3 17 2 23 7 27 22 3 3 4 11 4 12 7 10 7 9 3 11 4 14 5 9 2 6 7 20 15 2 6 2 4 5 8 5 6 4 9 4 20 2 6 18 8 3 11 3 6 2 13 11 2 5 10 2 10 7 7 3 7 14 2 10 7 +cv_jpn_000796 22 3 3 15 7 6 4 16 2 9 2 3 8 3 6 7 15 19 8 5 6 4 9 2 27 19 15 19 6 4 14 5 2 10 7 9 4 24 2 12 5 6 2 10 2 6 7 17 2 +cv_jpn_000797 6 3 13 9 2 6 3 8 3 14 5 3 16 7 10 2 10 5 8 5 9 2 12 2 6 5 9 24 2 4 +cv_jpn_000798 6 2 6 3 8 3 11 4 10 2 4 5 8 3 17 2 22 4 6 3 11 7 22 7 13 8 5 6 4 9 4 20 14 5 13 28 2 4 9 3 27 4 8 2 4 10 4 6 18 7 7 8 3 4 7 9 4 17 2 20 5 13 14 9 2 23 2 6 2 8 2 27 3 11 3 8 2 11 2 4 6 5 10 5 11 2 9 2 10 2 9 2 4 +cv_jpn_000799 2 15 3 6 19 4 3 9 3 8 2 6 2 12 5 16 2 14 3 7 13 9 2 10 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score new file mode 100644 index 0000000000000000000000000000000000000000..bf350bd1fc3f84899840e3636675bdc01f050171 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score @@ -0,0 +1,126 @@ +cv_jpn_000674 tensor(-11.0185) +cv_jpn_000675 tensor(-9.9876) +cv_jpn_000676 tensor(-6.1756) +cv_jpn_000677 tensor(-11.2046) +cv_jpn_000678 tensor(-5.4497) +cv_jpn_000679 tensor(-11.1056) +cv_jpn_000680 tensor(-10.7534) +cv_jpn_000681 tensor(-8.0064) +cv_jpn_000682 tensor(-0.9139) +cv_jpn_000683 tensor(-4.5666) +cv_jpn_000684 tensor(-4.2275) +cv_jpn_000685 tensor(-7.7207) +cv_jpn_000686 tensor(-7.5515) +cv_jpn_000687 tensor(-0.7582) +cv_jpn_000688 tensor(-3.0247) +cv_jpn_000689 tensor(-3.0464) +cv_jpn_000690 tensor(-3.0319) +cv_jpn_000691 tensor(-7.1164) +cv_jpn_000692 tensor(-1.8942) +cv_jpn_000693 tensor(-3.5789) +cv_jpn_000694 tensor(-9.9579) +cv_jpn_000695 tensor(-6.3388) +cv_jpn_000696 tensor(-6.3364) +cv_jpn_000697 tensor(-6.7559) +cv_jpn_000698 tensor(-6.0599) +cv_jpn_000699 tensor(-6.9964) +cv_jpn_000700 tensor(-4.6046) +cv_jpn_000701 tensor(-1.4595) +cv_jpn_000702 tensor(-2.9812) +cv_jpn_000703 tensor(-7.3210) +cv_jpn_000704 tensor(-6.2380) +cv_jpn_000705 tensor(-3.0066) +cv_jpn_000706 tensor(-4.6019) +cv_jpn_000707 tensor(-8.0722) +cv_jpn_000708 tensor(-2.0117) +cv_jpn_000709 tensor(-2.3185) +cv_jpn_000710 tensor(-7.5624) +cv_jpn_000711 tensor(-1.5115) +cv_jpn_000712 tensor(-0.2637) +cv_jpn_000713 tensor(-3.8286) +cv_jpn_000714 tensor(-3.0048) +cv_jpn_000715 tensor(-3.4797) +cv_jpn_000716 tensor(-3.7475) +cv_jpn_000717 tensor(-1.8340) +cv_jpn_000718 tensor(-3.2251) +cv_jpn_000719 tensor(-6.5080) +cv_jpn_000720 tensor(-2.0130) +cv_jpn_000721 tensor(-1.8834) +cv_jpn_000722 tensor(-3.7977) +cv_jpn_000723 tensor(-5.1384) +cv_jpn_000724 tensor(-5.3286) +cv_jpn_000725 tensor(-1.7117) +cv_jpn_000726 tensor(-2.1469) +cv_jpn_000727 tensor(-2.5774) +cv_jpn_000728 tensor(-1.9983) +cv_jpn_000729 tensor(-3.6229) +cv_jpn_000730 tensor(-1.9490) +cv_jpn_000731 tensor(-1.7870) +cv_jpn_000732 tensor(-2.7400) +cv_jpn_000733 tensor(-4.1998) +cv_jpn_000734 tensor(-3.9360) +cv_jpn_000735 tensor(-1.2662) +cv_jpn_000736 tensor(-0.9053) +cv_jpn_000737 tensor(-0.5281) +cv_jpn_000738 tensor(-4.4565) +cv_jpn_000739 tensor(-2.9190) +cv_jpn_000740 tensor(-2.3658) +cv_jpn_000741 tensor(-4.9426) +cv_jpn_000742 tensor(-3.6796) +cv_jpn_000743 tensor(-3.9769) +cv_jpn_000744 tensor(-9.4279) +cv_jpn_000745 tensor(-3.7001) +cv_jpn_000746 tensor(-5.5705) +cv_jpn_000747 tensor(-5.4982) +cv_jpn_000748 tensor(-5.6039) +cv_jpn_000749 tensor(-1.6378) +cv_jpn_000750 tensor(-2.5597) +cv_jpn_000751 tensor(-3.7843) +cv_jpn_000752 tensor(-7.3307) +cv_jpn_000753 tensor(-1.4260) +cv_jpn_000754 tensor(-0.4020) +cv_jpn_000755 tensor(-1.4855) +cv_jpn_000756 tensor(-1.0794) +cv_jpn_000757 tensor(-1.1086) +cv_jpn_000758 tensor(-2.5595) +cv_jpn_000759 tensor(-5.3968) +cv_jpn_000760 tensor(-2.3119) +cv_jpn_000761 tensor(-2.4207) +cv_jpn_000762 tensor(-4.5164) +cv_jpn_000763 tensor(-2.3885) +cv_jpn_000764 tensor(-1.7193) +cv_jpn_000765 tensor(-11.3094) +cv_jpn_000766 tensor(-6.0213) +cv_jpn_000767 tensor(-2.2565) +cv_jpn_000768 tensor(-2.6178) +cv_jpn_000769 tensor(-0.5145) +cv_jpn_000770 tensor(-1.2804) +cv_jpn_000771 tensor(-0.5676) +cv_jpn_000772 tensor(-0.8226) +cv_jpn_000773 tensor(-2.1896) +cv_jpn_000774 tensor(-4.9881) +cv_jpn_000775 tensor(-1.4679) +cv_jpn_000776 tensor(-3.6186) +cv_jpn_000777 tensor(-4.5644) +cv_jpn_000778 tensor(-2.2261) +cv_jpn_000779 tensor(-6.3640) +cv_jpn_000780 tensor(-2.7660) +cv_jpn_000781 tensor(-5.7984) +cv_jpn_000782 tensor(-13.0501) +cv_jpn_000783 tensor(-4.3536) +cv_jpn_000784 tensor(-3.5022) +cv_jpn_000785 tensor(-2.3586) +cv_jpn_000786 tensor(-3.0717) +cv_jpn_000787 tensor(-2.4364) +cv_jpn_000788 tensor(-13.9564) +cv_jpn_000789 tensor(-4.7083) +cv_jpn_000790 tensor(-6.7299) +cv_jpn_000791 tensor(-14.7352) +cv_jpn_000792 tensor(-8.7931) +cv_jpn_000793 tensor(-3.1764) +cv_jpn_000794 tensor(-6.3337) +cv_jpn_000795 tensor(-10.3703) +cv_jpn_000796 tensor(-6.3337) +cv_jpn_000797 tensor(-2.4986) +cv_jpn_000798 tensor(-13.1302) +cv_jpn_000799 tensor(-3.4926) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..5f0297581a1db3b8f88e6f9f865885b4390fdc5f --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/hyp.trn @@ -0,0 +1,126 @@ +b o k u n o y i e g a c l t a k a i d n a N n o n a m a e N n i k u r a b e r u t o p o k u n i w a n a j i m i n o n a i m a e b a e b a k a r i d a g k e d o (cv_jpn_000674-cv_jpn_000674) +m a r y o o s u a n o m o n o e r u d i k u i n i k i g a r u k e t e r u (cv_jpn_000675-cv_jpn_000675) +b o k u n o s h I t e e u m o n u o t o w a s U k o s h i c h i g a c l t e i t a (cv_jpn_000676-cv_jpn_000676) +k a k a o r u t a c h i m a n i o i t e p a u s e k g a r e g a i s h I k i m e N t e k i d e a r i w a r u w a r e n o j i k o g a j s h I k i s a y o o t e k i d e a r u t o k a N e e r a r e r u t o c h I (cv_jpn_000677-cv_jpn_000677) +i e n i k I t a n e N g a a j i w a s a N h a k o m a i h o r o d e p a u p a u c h o o d a s h I t a b u N t o o n a j i g u r a i d a (cv_jpn_000678-cv_jpn_000678) +h a w a p i t a m a r e t s u b a a g u n o s e e s h i N b y y o o i N n i n u N h I t e y i r u t o k i n i p e s h i o o m u (cv_jpn_000679-cv_jpn_000679) +t a d a N d e a r h a N t a n o h i r o g e r a b a a c h i k o c h i t s u g i h a g i y a a r i k a t a g o c h u i n i d e k i t a h o k o r o b i n a N k a k y o o n e N n o m a o m o n i n a c l t e i r u (cv_jpn_000680-cv_jpn_000680) +k a r a g a k a n o d e p a u b e r u n a k a n i h o o m o N k i b o o n o b u s h o o y o b i c h o o z a k i b o o n o b u g o m a i k a r a t o o r i d e s U (cv_jpn_000681-cv_jpn_000681) +k o N b a w a t o t e m o s a m i d e s U (cv_jpn_000682-cv_jpn_000682) +k i N c h o o s h I t a k o t s U k i d e p a u c l p a c l t a w a d a s e k i n i h a i r u (cv_jpn_000683-cv_jpn_000683) +m a s a N k y u u r e N t o i c l t a f u k a k a r o k e N c h i k o w u t s U (cv_jpn_000684-cv_jpn_000684) +g o r i y o s h i d e s u s u m e t e t s s u g o g a w a r u k u n a c l t a r a i h I c l k o m e r u y a r e k U c h i (cv_jpn_000685-cv_jpn_000685) +m o j u r N t e k i j i k o o t o i t s U t e k i n i j i k o o j i s h i N o k e e s e e s u r u s h a k a i w a (cv_jpn_000686-cv_jpn_000686) +h a N n o i k e N n i n a r a s a r e r u n a (cv_jpn_000687-cv_jpn_000687) +h i n o g a s o b i t a y o r a z e N k a i d e k o c l c h o m i t e i r u (cv_jpn_000688-cv_jpn_000688) +i j c h i d o w a k o N p o t a j i k a N o n o N d e m i t a i (cv_jpn_000689-cv_jpn_000689) +k o o i n o k o s h I t e p a u o t o o s a N t o k a s a N w a N d e t e i k i m a s h I t a (cv_jpn_000690-cv_jpn_000690) +s h i k a s h I t e s o r e g a t s U k u r a r e t a m o n o k a r a t s U k u r u m o r o e t o s h I t e p a u d o k o m a d e m o r a w a r u n i s e m a r u t o y u t o k i a r e w a r i n i c h o c l k a N t e k i d e a r u (cv_jpn_000691-cv_jpn_000691) +h a i n i t a m a t a k e m u r i o h a k i d a s h i k u r a i k o o e n i s h I s e N o m o k e r u (cv_jpn_000692-cv_jpn_000692) +w a d a w a d a s h I n a b u s o k u d e c h i u s e N n a r i s o (cv_jpn_000693-cv_jpn_000693) +t s e N s e k a i n o m o s U k a t a c h i i t a s h u n o y o a y i r u s e e s a i y o s h I k I t o s a i u o t o w a h a s h I t e k a N g a i r u k o t o w a d e k i n a i (cv_jpn_000694-cv_jpn_000694) +s a k e n o m a e g n o n i p i r o u b a r a t o y o e t a (cv_jpn_000695-cv_jpn_000695) +s o r e o o t a d a k o h o k u n o z e N d a N k a i s h i k u i t e e d o n o k a a k U t o m o m i m i r u k o t o w a (cv_jpn_000696-cv_jpn_000696) +o k k y o m e m o k f u r a z u n i p a u s u m o d e k e e N m o y a c l t e i t a (cv_jpn_000697-cv_jpn_000697) +a d a i w a a n a i N c h s h i u m a t a k o b a y a s h i s a N t o a s o b e i m a s U (cv_jpn_000698-cv_jpn_000698) +a s o o k o n i i h i i t o o g a p a u i m a s u n e p a u a r o h i t o w a t a r e t e s h o (cv_jpn_000699-cv_jpn_000699) +w a t a j i w a k k y e n o o k a n a p a u n o o d o k a e i t a i d e s U (cv_jpn_000700-cv_jpn_000700) +k y o o r e N k a n a p e N k y u o s h I t e i m a s U (cv_jpn_000701-cv_jpn_000701) +a o c l t o s u m i m r a s e p a u (cv_jpn_000702-cv_jpn_000702) +c h i k a s U p a u b o n o o e N b u r i e w a p a u y a k e k i m i n i k a e c l d e h a g e s U k e n o c l t a (cv_jpn_000703-cv_jpn_000703) +i t s z u m o k o n o i N p I i t s o o t s U k a c l t e i t a n o d e m i j i k a h a u n a r i m a s h I t a (cv_jpn_000704-cv_jpn_000704) +w a t a s h i w a p a u e i g o g a h a n a s h I t e m a s U (cv_jpn_000705-cv_jpn_000705) +o s e i k e e r y a j i b u a N d e t o c l t e k i n a (cv_jpn_000706-cv_jpn_000706) +a i s h u u e k a r a n i s h u u k a N h a i g a i e r u y o o k o o o n i k i m a s U (cv_jpn_000707-cv_jpn_000707) +o r e m o k i n i n a r u n a (cv_jpn_000708-cv_jpn_000708) +d a r e i g a t s U k a c l t e r u n o k a a h w o k a r u n a i (cv_jpn_000709-cv_jpn_000709) +p u r a o z a n o b a j o N g a p a u a g a r u t o o s U k o s h I i u u r e s h i i (cv_jpn_000710-cv_jpn_000710) +m a t a a t a r a s h i i a i d o r u g a d e t e k i t a (cv_jpn_000711-cv_jpn_000711) +m a j i d e y a c l t o n o k a (cv_jpn_000712-cv_jpn_000712) +c h o o d o s a m o t U k i n i p a u k y o o j i g a h a i t e k i t a (cv_jpn_000713-cv_jpn_000713) +i c l s o n i k c h i e N s h I t e o (cv_jpn_000714-cv_jpn_000714) +s o r e k a s a t e n o t o r e s u N (cv_jpn_000715-cv_jpn_000715) +f U t a r i o w a r e j i e i k i s u e e s a N s h I t a (cv_jpn_000716-cv_jpn_000716) +t o m a t o k a n a N g a n o a k a i s o s u g a k a k a c l t e r i u o (cv_jpn_000717-cv_jpn_000717) +k o g o k a r a t a t e n a N a s u n o w a k i b i s u i (cv_jpn_000718-cv_jpn_000718) +n i z u g e o w o s U k a r i s h i b o c l t e p a u a j i g a n a j i m i y o r i s u r u (cv_jpn_000719-cv_jpn_000719) +n e t o k i n i h a m a c l t a r a k a n e g a g a m a c l t a U (cv_jpn_000720-cv_jpn_000720) +s i t a k a i r i y o o n i n a r u N d a (cv_jpn_000721-cv_jpn_000721) +k o s e h a h a i y u t o y u y o r i a k u g a t s u y o a e k a N j i (cv_jpn_000722-cv_jpn_000722) +f e i j i k a r u n o s a o m a z a m a d a t o m i s e t s U k e r a r e t a (cv_jpn_000723-cv_jpn_000723) +k o s U p a y o k e d e b a p a u s o k o k o n o m o N d a e a g a m a N s u r e (cv_jpn_000724-cv_jpn_000724) +i N n a y a c l t e m a s U k a r a t a i j o o b u d e s u i u o (cv_jpn_000725-cv_jpn_000725) +k o n o t o j o k a N h a i c l t a s h u u k a N k i n i c l t a (cv_jpn_000726-cv_jpn_000726) +k o n o d e N c h i f u u k i r e c h i a c l t a (cv_jpn_000727-cv_jpn_000727) +a m a y e u d o r i s u r u t o k o r o g a n a k U t e k o m a c l t a (cv_jpn_000728-cv_jpn_000728) +y a s U k a s u r u e o r i s h I t t s u w a g e t o h o s h e (cv_jpn_000729-cv_jpn_000729) +m a s e g o k o n a k o t e i n a r u t o w a m o n a k a c l t a (cv_jpn_000730-cv_jpn_000730) +s a i g o n i w a r a y o t o r i n i k u r a s U t a i r u (cv_jpn_000731-cv_jpn_000731) +k o r a e n a N n a e i m i g a r u d a p a u (cv_jpn_000732-cv_jpn_000732) +o t i i a (cv_jpn_000733-cv_jpn_000733) +s h a s h e i (cv_jpn_000734-cv_jpn_000734) +n i (cv_jpn_000735-cv_jpn_000735) +h a c h i (cv_jpn_000736-cv_jpn_000736) +h a i (cv_jpn_000737-cv_jpn_000737) +t e b u r u n o o y e n i k a b i N g a r i m a s U (cv_jpn_000738-cv_jpn_000738) +w o t a s h i w a o m a i y a s a s a N b o s h i m a s U (cv_jpn_000739-cv_jpn_000739) +a t a r a s h i i k u t s u o o h a i t e d e k a k e m a s U (cv_jpn_000740-cv_jpn_000740) +k o t o s h i n o n a t s e y a s a m i o w a u m i n i m o i k i m a s h I t a s h i i y a m a n i m o n o r i b a s h I t a (cv_jpn_000741-cv_jpn_000741) +w a t a s h i w a i r o y i r o n o b e N g o o c h i b u N n o m u n e d e k o s h i r a e t e m i m a s h I t a (cv_jpn_000742-cv_jpn_000742) +n a N d e k o o m o s u o o b a i h e d a n a N d a r o (cv_jpn_000743-cv_jpn_000743) +t a r e N t o k a r a k y o k u a n a n i k a e i e c l t u e k e e s h I s a k I u g e a (cv_jpn_000744-cv_jpn_000744) +d o g e z a s e r e b a i c l t e m o N s h a n a i (cv_jpn_000745-cv_jpn_000745) +d e t a n o a i d a k a n o j i w a j i b u N t o i c l t e e n o k k y u o r i y o t a n o c l t a (cv_jpn_000746-cv_jpn_000746) +k o n o r e e n i N n a N k a w a h s h s a s h i b u r i n i n i t a (cv_jpn_000747-cv_jpn_000747) +o o k I k t a s a i d a o c h i e N j i e o s u r u (cv_jpn_000748-cv_jpn_000748) +k a r e w a t a m a o k a k i m u s h i c l t a (cv_jpn_000749-cv_jpn_000749) +k o m a t e s h I t e a r i m a s U (cv_jpn_000750-cv_jpn_000750) +o n o d o k U s e N k a i j o w a k I i t e r u (cv_jpn_000751-cv_jpn_000751) +r e e j o o k o a k e t a c l t o U t a N n a n i g a h i c h u y o k a w a s u r e t a (cv_jpn_000752-cv_jpn_000752) +i i t c h i (cv_jpn_000753-cv_jpn_000753) +h a c h i (cv_jpn_000754-cv_jpn_000754) +k i i w e a (cv_jpn_000755-cv_jpn_000755) +d e i (cv_jpn_000756-cv_jpn_000756) +a s h i c h i (cv_jpn_000757-cv_jpn_000757) +y o o b o o d a s u n o N k a u s h I t o a s U k u n a i (cv_jpn_000758-cv_jpn_000758) +n o o k a r e t o k u y u n o i k k y u e m a k a s e n o k o m a s h a r u (cv_jpn_000759-cv_jpn_000759) +k o n o d a i k u w a a N k o g a o o k U t e y o k U k a i m a s U (cv_jpn_000760-cv_jpn_000760) +d e s h o o k i n a g a r a s h o o s u e t s o o y o m i m a s U (cv_jpn_000761-cv_jpn_000761) +k o N n o o k i n a N g o g u r o t s u k e n a i t i k e n a e i N d e s U k a (cv_jpn_000762-cv_jpn_000762) +k a r a e n o b o o i k o w a t o m a r a n a i (cv_jpn_000763-cv_jpn_000763) +i k i t e i k a N m a n e (cv_jpn_000764-cv_jpn_000764) +t o o j u d o s h s e s a k a k t e k i n a h a t s u m e d a c l t a n e (cv_jpn_000765-cv_jpn_000765) +h o N t e k i o t a s o a c h I k a c l U t k a N (cv_jpn_000766-cv_jpn_000766) +k a a w a h i e w a c l t e i d a (cv_jpn_000767-cv_jpn_000767) +c h e c l k e (cv_jpn_000768-cv_jpn_000768) +o (cv_jpn_000769-cv_jpn_000769) +a s h I t c h i i (cv_jpn_000770-cv_jpn_000770) +k o (cv_jpn_000771-cv_jpn_000771) +i i e a (cv_jpn_000772-cv_jpn_000772) +n a m a i k a r a s h e t e k t o o s u g i r u (cv_jpn_000773-cv_jpn_000773) +j i k o n o s o t o n y a r u t o y u n o w a t a N n i j i k o n e s h i k N s o t s s o n i a r u t o y u k o t o d e n a k u (cv_jpn_000774-cv_jpn_000774) +s o r w a s h i r a n a k U t e i d e s U (cv_jpn_000775-cv_jpn_000775) +k i m i t o k o n o k y o o t s u n o s h i g e w a d a r e i t o r u i p a u m i a t a r a n a i (cv_jpn_000776-cv_jpn_000776) +s u e g e e o o t o i N n a c l t e k i t e r u n o n a c l (cv_jpn_000777-cv_jpn_000777) +k o n o h e N d e s U k o s h i a s u i m a s h o (cv_jpn_000778-cv_jpn_000778) +d e N s h a r i n o r u t o k i k i c l t o k a i m a s U d a U t a U t U (cv_jpn_000779-cv_jpn_000779) +t a m a o g w a i k o k o j i u r a N b u r a i e s U (cv_jpn_000780-cv_jpn_000780) +g e e s U p i w a m a k i o t s u j i t e p a u i n e s t o s h i r i y a c l t a t U U (cv_jpn_000781-cv_jpn_000781) +n o o g o o y a n e s a r u o e n a i s h I t o p a r i k a N e N k i y o m o k o m o f k I k e o o n i h I k i z u r a r e t e r u t o m o o s U t s U (cv_jpn_000782-cv_jpn_000782) +a N d e k o n o r o c l t o s h o t a i m e n a m u o i n a r e y a r e s e N d a (cv_jpn_000783-cv_jpn_000783) +f U t s u u d e a r u k o t a m o d i c l p a n a k o s e (cv_jpn_000784-cv_jpn_000784) +t s u y o b i d e t a N j i k a N d e g o k a i n i t a m e r u (cv_jpn_000785-cv_jpn_000785) +p a k u m a t s u n o d e k i g o k o w a p a u i m a n i t s u j i r u k y o o k u N n o y a m a d e s U (cv_jpn_000786-cv_jpn_000786) +N k o o k a r a m a c h i n o w a k a r i g a m i e t e k i t a (cv_jpn_000787-cv_jpn_000787) +m e n i o u y u u b e k I c h i a w a k a r a n a U t a r a i m i y u b i k I k u t o g a o m o y u k a b a n a g k a c l t U (cv_jpn_000788-cv_jpn_000788) +t a m e s i n i k a e n a k i y a c l t e m i r (cv_jpn_000789-cv_jpn_000789) +m o k U s h I k g a i n e i g i m i m a i n a i k o r e w a o o t e N t s c h i e a i k a (cv_jpn_000790-cv_jpn_000790) +t s c h k e i j o k a r a e n i j u g o t o o m a N u N o p a u m i c h i m a p a u m u k a s o k a o t r i n e k a (cv_jpn_000791-cv_jpn_000791) +k a N g u j o t e e h N w a k o N c l p u o N t e i n a g a i c h i k u u s u n a c l t a (cv_jpn_000792-cv_jpn_000792) +k o r u m o n o k o r e w a g o h a h a r e p a u o t u n a i n a r t o p a h a a (cv_jpn_000793-cv_jpn_000793) +k o o i t e k I t o k a N t e k i n i p a u p o i p a u a s h i s u t e k i n i p a u w a r e w a r e m o j i k o w a m a s u m a s u p a u m e t o n a r u n o d a r u (cv_jpn_000794-cv_jpn_000794) +h i j o o s h I k i d a r o k o t o w a y u c h j o o i m i s u r u n o m i d e n a k u p a u s h a k a i e t e k i n i p a u a k U t o m o k a N m a e r a r u u o u d a r u (cv_jpn_000795-cv_jpn_000795) +j o o s h u k i g a n a o t o k u s h I t e k i n a c h I s h I k i d e a r u n i h a s e k a r a k u w a (cv_jpn_000796-cv_jpn_000796) +k o N n a k o t o d e o g u r a r e t e n a s a k e n h a i (cv_jpn_000797-cv_jpn_000797) +k a k o t o m i r a i e t o w a j i k o m u j u N t e k i n i p a u d e N z a i n o c h i t a i r i k U u u t o i u n i w a p a u e N d n a y a k a t a c h o m o t a m a i k e r e m a n a r a n a i (cv_jpn_000798-cv_jpn_000798) +a s h o k I i o n o t a k a s e g a d o u N n a r (cv_jpn_000799-cv_jpn_000799) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..0220102720ac47cd40c5be2f2be1c1e2b826b48b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/ref.trn @@ -0,0 +1,126 @@ +b o k u n o i e g a a c l t a k a i d a N n o n a m a e n i k u r a b e r u t o b o k u n i w a n a j i m i n o n a i n a m a e b a k a r i d a k e d o (cv_jpn_000674-cv_jpn_000674) +n a i y o o s o n o m o n o y o r i f u N i k i g a u k e t e r u (cv_jpn_000675-cv_jpn_000675) +b o k u n o s h i c l t e i r u m o n o t o w a s U k o s h I c h i g a c l t e i t a (cv_jpn_000676-cv_jpn_000676) +k a k a r u t a c h i b a n i o i t e p a u s e k a i g a i s h I k i m e N t e k i d e a r i p a u w a r e w a r e n o j i k o g a i s h I k i s a y o o t e k i d e a r u t o k a N g a e r a r e r u t o k i (cv_jpn_000677-cv_jpn_000677) +i e n i k i t a n e N g a j o o w a p a u s a N b y a k u m a i h o d o d e p a u c h o o d o p a u d a s h I t a b u N t o o n a j i g u r a i d a (cv_jpn_000678-cv_jpn_000678) +h a h a w a p i i t a a m a r i c l t s u b a a g u n o s e e s h i N b y o o i N n i n y u u i N s h I t e i r u t o k i n i b e c l s h i i o u m u (cv_jpn_000679-cv_jpn_000679) +t a t a N d e a r u h a N t e N o h i r o g e r e b a p a u a c h i k o c h i n i t s u g i h a g i g a a r i p a u k a t a g u c h i n i d e k i t a h o k o r o b i n a N k a p a u k y o n e N n o m a m a n i n a c l t e i r u (cv_jpn_000680-cv_jpn_000680) +k a r e g a p a u k o n o s h u c l c h o o c h u u n i p a u h o o m o N k i b o o n o b u s h o p a u o y o b i p a u c h o o s a k i b o o n o b u m o N w a p a u i k a n o t o o r i d e s U (cv_jpn_000681-cv_jpn_000681) +k o N b a N w a t o t e m o s a m u i d e s U (cv_jpn_000682-cv_jpn_000682) +k i N c h o o s h I t a k a o t s U k i d e b a c l t a a w a d a s e k i n i h a i r u (cv_jpn_000683-cv_jpn_000683) +m a s a n i k y u u d e N t o i c l t a f u u k a k u a r u k e N c h I k u b u t s u (cv_jpn_000684-cv_jpn_000684) +g o r i o s h i d e s u s u m e t e t s u g o o g a w a r u k u n a c l t a r a h i c l k o m e r u y a r i k U c h i (cv_jpn_000685-cv_jpn_000685) +m u j u N t e k i j i k o d o o i t s u t e k i n i j i k o j i s h i N o k e e s e e s u r u s h a k a i w a (cv_jpn_000686-cv_jpn_000686) +f a N n o i k e N n i n a g a s a r e r u n a (cv_jpn_000687-cv_jpn_000687) +i n u g a a s o b i t a i o o r a z e N k a i d e k o c l c h i o m i t e r u (cv_jpn_000688-cv_jpn_000688) +i c h i d o w a k o o N p o t a a j u k a N o n o N d e m i t a i (cv_jpn_000689-cv_jpn_000689) +k o o i i n o k o s h I t e p a u o t o o s a N t o o k a a s a N w a d e t e i k i m a s h I t a (cv_jpn_000690-cv_jpn_000690) +s h I k a s h I t e s o r e g a t s U k u r a r e t a m o n o k a r a t s U k u r u m o n o e t o s h I t e p a u d o k o m a d e m o w a r e w a r e n i s e m a r u t o i u t o k i p a u w a r e w a r e n i c h o c l k a N t e k i d e a r u (cv_jpn_000691-cv_jpn_000691) +h a i n i t a m a c l t a k e m u r i o h a k i d a s h i p a u k u r a i k o o e N n i s h I s e N o m u k e r u (cv_jpn_000692-cv_jpn_000692) +m a d a m a d a s h i n a f u s o k u d e c h u u s e N n i n a r i s o o (cv_jpn_000693-cv_jpn_000693) +z e N s e k a i n o m o t s U k a t a c h i p a u w a t a s h i n o i w a y u r u s e e s a N y o o s h I k i t o s a y o o t o w a h a n a s h I t e k a N g a e r u k o t o w a d e k i n a i (cv_jpn_000694-cv_jpn_000694) +s a k e n o m a n a i n o n i b i i r u h a r a t o i w a r e t a (cv_jpn_000695-cv_jpn_000695) +s o r e o t a d a k a g a k u n o z e N d a N k a i p a u h I k u i t e e d o n o k a g a k U t o n o m i m i r u k o t o w a (cv_jpn_000696-cv_jpn_000696) +w a k i m e m o f u r a z u n i s u m a h o d e g e e m u o y a c l t e i t a (cv_jpn_000697-cv_jpn_000697) +w a t a s h i w a r a i s h u u m a t a k o b a y a s h I s a N t o a s o b i m a s U (cv_jpn_000698-cv_jpn_000698) +a s o k o n i h I t o g a i m a s U n e a n o h I t o w a d a r e d e s h o o (cv_jpn_000699-cv_jpn_000699) +w a t a s h i w a k i n o o k a r a n o d o g a i t a i d e s U (cv_jpn_000700-cv_jpn_000700) +k y o n e N k a r a b e N k y o o s h I t e i m a s U (cv_jpn_000701-cv_jpn_000701) +c h o c l t o s u m i m a s e N (cv_jpn_000702-cv_jpn_000702) +s h I k a s h i p a u s o n o o o e N b u r i w a p a u y a k e g i m i n i p a u k a e c l t e h a g e s h i k u n a c l t a (cv_jpn_000703-cv_jpn_000703) +i t s u m o k o n o e N p I t s u o t s U k a c l t e i t a n o d e p a u m i j i k a k u n a r i m a s h I t a (cv_jpn_000704-cv_jpn_000704) +w a t a s h i w a e e g o g a h a n a s e m a s U (cv_jpn_000705-cv_jpn_000705) +h o s h i k e r y a j i b u N d e t o c l t e k i n a (cv_jpn_000706-cv_jpn_000706) +r a i s h u u k a r a n i s h u u k a N p a u k a i g a i e r y o k o o n i i k i m a s U (cv_jpn_000707-cv_jpn_000707) +o r e m o k i n i n a r u n a (cv_jpn_000708-cv_jpn_000708) +d a r e g a t s U k a c l t e r u n o k a w a k a r a n a i (cv_jpn_000709-cv_jpn_000709) +b u r a u z a n o b a a j o N g a a g a r u t o s U k o s h i u r e s h i i (cv_jpn_000710-cv_jpn_000710) +m a t a a t a r a s h i i a i d o r u g a d e t e k i t a (cv_jpn_000711-cv_jpn_000711) +m a j i d e y a c l t a n o k a (cv_jpn_000712-cv_jpn_000712) +c h o o d o s o n o t o k i n i p a u k y o o j u g a h a i c l t e k i t a (cv_jpn_000713-cv_jpn_000713) +i c l s h o n i c h i N s h I t e y o (cv_jpn_000714-cv_jpn_000714) +s o r e g a s a t e N n o d o r e s u (cv_jpn_000715-cv_jpn_000715) +f U t a r i w a r e j i e i k I s e e s a N s h I t a (cv_jpn_000716-cv_jpn_000716) +t o m a t o k a n a N k a n o a k a i s o o s u g a k a k a c l t e r u y o (cv_jpn_000717-cv_jpn_000717) +k o k o k a r a t a t e n a o s u n o w a k i b i s h i i (cv_jpn_000718-cv_jpn_000718) +m i z u k e o s h i c l k a r i s h i b o c l t e a j i g a n a j i m u y o o n i s u r u (cv_jpn_000719-cv_jpn_000719) +n e t o g e n i h a m a c l t a r a k i N g a t a m a c l t a (cv_jpn_000720-cv_jpn_000720) +i t s U k a e r u y o o n i n a r u N d a (cv_jpn_000721-cv_jpn_000721) +k o s e e h a h a i y u u t o i u y o r i a k u g a t s u y o i k a N j i (cv_jpn_000722-cv_jpn_000722) +f i j i k a r u n o s a o m a z a m a z a t o m i s e t s U k e r a r e t a (cv_jpn_000723-cv_jpn_000723) +k o s U p a y o k e r e b a s o k o s o k o n o m o N d a i w a g a m a N s u r u (cv_jpn_000724-cv_jpn_000724) +m i N n a y a c l t e m a s U k a r a d a i j o o b u d e s U y o (cv_jpn_000725-cv_jpn_000725) +k o n o t o s h o k a N p a u h a i c l t a s h u N k a N k i n i i c l t a (cv_jpn_000726-cv_jpn_000726) +k o n o d e N c h i p a u s u g u k i r e c h i c l t a (cv_jpn_000727-cv_jpn_000727) +a m a y a d o r i s u r u t o k o r o g a n a k U t e k o m a c l t a (cv_jpn_000728-cv_jpn_000728) +y a s u k U s u r u y o r i s h I t s u o a g e t e h o s h i i (cv_jpn_000729-cv_jpn_000729) +m a s a k a k o N n a k o t o n i n a r o o t o w a o m o w a n a k a c l t a (cv_jpn_000730-cv_jpn_000730) +s a i g o n i w a r a i o t o r i n i k u r u s U t a i r u (cv_jpn_000731-cv_jpn_000731) +k o r e p a u n a n i n o i m i g a a r u N d a (cv_jpn_000732-cv_jpn_000732) +i i e (cv_jpn_000733-cv_jpn_000733) +s h i (cv_jpn_000734-cv_jpn_000734) +n i (cv_jpn_000735-cv_jpn_000735) +w a c h i (cv_jpn_000736-cv_jpn_000736) +h a i (cv_jpn_000737-cv_jpn_000737) +t e e b u r u n o u e n i k a b i N g a a r i m a s U (cv_jpn_000738-cv_jpn_000738) +w a t a s h i w a m a i a s a s a N p o s h i m a s U (cv_jpn_000739-cv_jpn_000739) +a t a r a s h i i k U t s u o h a i t e d e k a k e m a s U (cv_jpn_000740-cv_jpn_000740) +k o t o s h i n o n a t s u y a s u m i w a p a u u m i n i m o i k i m a s h I t a s h i p a u y a m a n i m o n o b o r i m a s h I t a (cv_jpn_000741-cv_jpn_000741) +w a t a s h i w a p a u i r o i r o n o b e N g o o p a u j i b u N n o m u n e d e k o s h i r a e t e m i m a s h I t a (cv_jpn_000742-cv_jpn_000742) +n a N d e k o o m o s h o o b a i h e t a n a N d a r o (cv_jpn_000743-cv_jpn_000743) +t a r e N t o k a r a k y o k u a n a n i k a e t e k e e h I s a k u g e N (cv_jpn_000744-cv_jpn_000744) +d o g e z a s u r e b a i i c l t e m o N j a n a i (cv_jpn_000745-cv_jpn_000745) +d e e t o n o a i d a p a u k a n o j o w a j i b u N t o i c l t e e n o k y o r i o t a m o c l t a (cv_jpn_000746-cv_jpn_000746) +k o n o g e e n i N n a N k a h I s a s h i b u r i n i m i t a (cv_jpn_000747-cv_jpn_000747) +o o k I k u s a i d o c h e N j i o s u r u (cv_jpn_000748-cv_jpn_000748) +k a r e w a a t a m a o k a k i m u s h i c l t a (cv_jpn_000749-cv_jpn_000749) +o m a c h i s h I t e o r i m a s U (cv_jpn_000750-cv_jpn_000750) +k o n o k y o k u p a u s e N k a i i j o o w a k i i t e r u (cv_jpn_000751-cv_jpn_000751) +r e e z o o k o o a k e t a t o t a N p a u n a n i g a h I t s u y o o k a w a s u r e t a (cv_jpn_000752-cv_jpn_000752) +i c h i (cv_jpn_000753-cv_jpn_000753) +w a c h i (cv_jpn_000754-cv_jpn_000754) +i i e (cv_jpn_000755-cv_jpn_000755) +r e i (cv_jpn_000756-cv_jpn_000756) +s h I c h i (cv_jpn_000757-cv_jpn_000757) +y o o b o o w a d a s u n o n i k a u h I t o w a s U k u n a i (cv_jpn_000758-cv_jpn_000758) +r o o k a r u t o k u y u u n o i k i o i m a k a s e n o k o m a a s h a r u (cv_jpn_000759-cv_jpn_000759) +k o n o d a i f U k u w a a N k o g a o o k U t e y o k U k a i m a s U (cv_jpn_000760-cv_jpn_000760) +j i s h o b i k i n a g a r a s h o o s e t s u o y o m i m a s U (cv_jpn_000761-cv_jpn_000761) +k o N n a o o k i n a g o o g u r u o t s U k e n a i t o i k e n a i N d e s U k a (cv_jpn_000762-cv_jpn_000762) +k a r e e n o b o o r y o k u w a t o m a r a n a i (cv_jpn_000763-cv_jpn_000763) +i k i t e t a N d a n e (cv_jpn_000764-cv_jpn_000764) +t o o j i t o s h I c h a k a c l k I t e k i n a h a t s u m e e d a c l t a n e (cv_jpn_000765-cv_jpn_000765) +s o n o t o k i p a u w a t a s h i w a c h I k a r a t s U k i t a (cv_jpn_000766-cv_jpn_000766) +k a w a g a h i a g a c l t e i t a (cv_jpn_000767-cv_jpn_000767) +i c h i (cv_jpn_000768-cv_jpn_000768) +n i (cv_jpn_000769-cv_jpn_000769) +s h I c h i (cv_jpn_000770-cv_jpn_000770) +g o (cv_jpn_000771-cv_jpn_000771) +i i e (cv_jpn_000772-cv_jpn_000772) +n a m a e k a r a s h I t e t e k I t o o s u g i r u (cv_jpn_000773-cv_jpn_000773) +j i k o n o s o t o n i a r u t o i u n o w a t a N n i j i k o n o i s h I k i n o s o t o n i a r u t o i u k o t o d e n a k u (cv_jpn_000774-cv_jpn_000774) +s o r e w a s h i r a n a k U t e i i d e s U (cv_jpn_000775-cv_jpn_000775) +k i m i t o b o k u n o k y o o t s u u n o s h i r i a i w a d a r e h I t o r i p a u m i a t a r a n a i (cv_jpn_000776-cv_jpn_000776) +s u g e e d a i j i n i n a c l t e k I t e r u n o n a (cv_jpn_000777-cv_jpn_000777) +k o n o a t a r i d e s U k o s h i y a s u m i m a s h o o (cv_jpn_000778-cv_jpn_000778) +d e N s h a n i n o r u t o k i p a u k i c l p u o k a i m a s U (cv_jpn_000779-cv_jpn_000779) +t a m a g o w a i c l k o g o j u u g u r a m u g u r a i d e s U (cv_jpn_000780-cv_jpn_000780) +g i r e s U p i i w a m a c l g i i o t s u u j i t e i n e s U t o s h i r i a c l t a (cv_jpn_000781-cv_jpn_000781) +n o o g y o o o y a m e z a r u o e n a i h I t o g a a r i p a u k a N r e N k i g y o o m o p a u k o n o f U k y o o n i h I k i z u r a r e t e i r u t o i u (cv_jpn_000782-cv_jpn_000782) +n a N d e k o n o r o b o c l t o p a u s h o t a i m e N n a n o n i n a r e n a r e s h i i N d a (cv_jpn_000783-cv_jpn_000783) +f U t s u u d e a r u k o t o m o r i c l p a n a k o s e e (cv_jpn_000784-cv_jpn_000784) +t s u y o b i d e t a N j i k a N d e g o o k a i n i i t a m e r u (cv_jpn_000785-cv_jpn_000785) +b a k u m a t s u n o d e k i g o t o w a i m a n i t s u u j i r u k y o o k u N n o y a m a d e s U (cv_jpn_000786-cv_jpn_000786) +m u k o o k a r a m a c h i n o t o m o r i g a m i e t e k i t a (cv_jpn_000787-cv_jpn_000787) +n a n i o i u b e k I k a w a k a r a n a k a c l t a n a n i m o i u b e k I k o t o g a o m o i u k a b a n a k a c l t a (cv_jpn_000788-cv_jpn_000788) +t a m e s h i n i i c l k a i d a k e y a c l t e m i r u (cv_jpn_000789-cv_jpn_000789) +b o k U s h i k a i n a i k i m i w a i n a i k o r e w a p a u o o k i n a c h i g a i k a (cv_jpn_000790-cv_jpn_000790) +s h u u k a i s h o k a r a n i j u u g o o t o o m a d e n o m i c h i w a m u k a s h I t o k a w a c l t e i n a k a c l t a (cv_jpn_000791-cv_jpn_000791) +k a n o j o n o t e e a N w a k o N p o N t e k i n a k a i k e t s u n i t s u n a g a c l t a (cv_jpn_000792-cv_jpn_000792) +k o d o m o n o k o r o w a g o h a N h a d e p a u o t o n a n i n a r u t o p a N h a (cv_jpn_000793-cv_jpn_000793) +k o o i t e k I c h o c l k a N t e k i n i p a u p o i e s h I s u t e k i n i p a u w a r e w a r e n o j i k o w a m a s u m a s u a k a r i t o n a r u n o d e a r u (cv_jpn_000794-cv_jpn_000794) +h i j o o s h I k i d e a r u k o t o w a p a u m u c h i o i m i s u r u n o m i d e n a k u p a u s h a k a i t e k i n i a k U t o m o k a N g a e r a r e r u n o d e a r u (cv_jpn_000795-cv_jpn_000795) +j o o s h I k i g a n a o t o k U s h u t e k i n a c h I s h i k i d e a r u n i h a N s h i p a u k a g a k u w a (cv_jpn_000796-cv_jpn_000796) +k o N n a k o t o d e o k o r a r e t e n a s a k e n a i (cv_jpn_000797-cv_jpn_000797) +k a k o t o m i r a i t o g a j i k o m u j u N t e k i n i g e N z a i n i o i t e t a i r i t s U s u r u t o i u n i w a p a u g e N z a i g a k a t a c h i o m o t a n a k e r e b a n a r a n a i (cv_jpn_000798-cv_jpn_000798) +s h o k I h i y o o n o t a k a s a g a h a a d o r u n i n a r u (cv_jpn_000799-cv_jpn_000799) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/result.txt b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..807fd993ecb3f9bfc49402b15c8dd590fd54ed76 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/result.txt @@ -0,0 +1,1565 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,---------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/hyp.trn| +|---------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000674 | 1 150 | 96.7 2.0 1.3 6.7 10.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000675 | 1 65 | 89.2 10.8 0.0 12.3 23.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000676 | 1 80 | 92.5 1.3 6.3 2.5 10.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000677 | 1 200 | 94.0 3.0 3.0 3.5 9.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000678 | 1 138 | 84.8 8.7 6.5 0.0 15.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000679 | 1 141 | 82.3 2.1 15.6 2.8 20.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000680 | 1 215 | 88.4 4.2 7.4 1.9 13.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000681 | 1 177 | 76.3 11.3 12.4 1.1 24.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000682 | 1 45 | 91.1 0.0 8.9 0.0 8.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000683 | 1 83 | 94.0 2.4 3.6 7.2 13.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000684 | 1 81 | 87.7 4.9 7.4 0.0 12.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000685 | 1 112 | 96.4 3.6 0.0 3.6 7.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000686 | 1 106 | 97.2 2.8 0.0 3.8 6.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000687 | 1 45 | 95.6 4.4 0.0 0.0 4.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000688 | 1 75 | 89.3 2.7 8.0 5.3 16.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000689 | 1 64 | 92.2 1.6 6.3 3.1 10.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000690 | 1 93 | 93.5 0.0 6.5 2.2 8.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000691 | 1 230 | 93.5 2.2 4.3 0.0 6.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000692 | 1 108 | 90.7 0.9 8.3 0.0 9.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000693 | 1 71 | 85.9 5.6 8.5 0.0 14.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000694 | 1 184 | 87.5 5.4 7.1 0.5 13.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000695 | 1 65 | 78.5 12.3 9.2 0.0 21.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000696 | 1 123 | 91.9 4.1 4.1 1.6 9.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000697 | 1 76 | 85.5 14.5 0.0 3.9 18.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000698 | 1 82 | 89.0 7.3 3.7 8.5 19.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000699 | 1 76 | 93.4 3.9 2.6 21.1 27.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000700 | 1 62 | 91.9 6.5 1.6 17.7 25.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000701 | 1 50 | 92.0 8.0 0.0 4.0 12.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000702 | 1 29 | 89.7 6.9 3.4 13.8 24.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000703 | 1 114 | 86.0 7.0 7.0 1.8 15.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000704 | 1 104 | 93.3 2.9 3.8 5.8 12.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000705 | 1 48 | 97.9 2.1 0.0 18.8 20.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000706 | 1 50 | 94.0 2.0 4.0 12.0 18.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000707 | 1 84 | 89.3 3.6 7.1 8.3 19.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000708 | 1 29 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000709 | 1 57 | 96.5 3.5 0.0 10.5 14.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000710 | 1 73 | 94.5 2.7 2.7 13.7 19.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000711 | 1 56 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000712 | 1 30 | 96.7 3.3 0.0 0.0 3.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000713 | 1 70 | 90.0 5.7 4.3 0.0 10.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000714 | 1 33 | 90.9 0.0 9.1 12.1 21.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000715 | 1 37 | 89.2 5.4 5.4 5.4 16.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000716 | 1 52 | 100.0 0.0 0.0 7.7 7.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000717 | 1 74 | 93.2 4.1 2.7 0.0 6.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000718 | 1 56 | 92.9 5.4 1.8 3.6 10.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000719 | 1 85 | 87.1 9.4 3.5 5.9 18.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000720 | 1 59 | 93.2 6.8 0.0 6.8 13.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000721 | 1 40 | 90.0 7.5 2.5 5.0 15.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000722 | 1 72 | 91.7 2.8 5.6 2.8 11.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000723 | 1 74 | 98.6 1.4 0.0 2.7 4.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000724 | 1 81 | 87.7 9.9 2.5 0.0 12.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000725 | 1 64 | 93.8 3.1 3.1 3.1 9.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000726 | 1 69 | 87.0 2.9 10.1 0.0 13.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000727 | 1 50 | 86.0 2.0 12.0 4.0 18.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000728 | 1 68 | 98.5 1.5 0.0 2.9 4.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000729 | 1 60 | 88.3 8.3 3.3 3.3 15.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000730 | 1 76 | 77.6 6.6 15.8 0.0 22.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000731 | 1 59 | 96.6 3.4 0.0 0.0 3.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000732 | 1 45 | 75.6 15.6 8.9 4.4 28.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000733 | 1 5 | 80.0 20.0 0.0 80.0 100.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000734 | 1 4 | 100.0 0.0 0.0 175.0 175.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000735 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000736 | 1 8 | 87.5 12.5 0.0 0.0 12.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000737 | 1 5 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000738 | 1 53 | 90.6 1.9 7.5 3.8 13.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000739 | 1 51 | 96.1 3.9 0.0 7.8 11.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000740 | 1 57 | 100.0 0.0 0.0 3.5 3.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000741 | 1 128 | 89.1 4.7 6.3 0.0 10.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000742 | 1 114 | 92.1 1.8 6.1 1.8 9.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000743 | 1 54 | 96.3 3.7 0.0 1.9 5.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000744 | 1 74 | 98.6 1.4 0.0 16.2 17.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000745 | 1 50 | 92.0 4.0 4.0 2.0 10.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000746 | 1 96 | 90.6 3.1 6.3 6.3 15.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000747 | 1 62 | 95.2 4.8 0.0 8.1 12.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000748 | 1 42 | 97.6 2.4 0.0 19.0 21.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000749 | 1 47 | 95.7 0.0 4.3 0.0 4.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000750 | 1 33 | 87.9 9.1 3.0 6.1 18.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000751 | 1 58 | 79.3 1.7 19.0 0.0 20.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000752 | 1 88 | 87.5 3.4 9.1 5.7 18.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000753 | 1 6 | 100.0 0.0 0.0 66.7 66.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000754 | 1 8 | 87.5 12.5 0.0 0.0 12.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000755 | 1 5 | 100.0 0.0 0.0 120.0 120.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000756 | 1 5 | 80.0 20.0 0.0 0.0 20.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000757 | 1 9 | 100.0 0.0 0.0 22.2 22.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000758 | 1 63 | 87.3 0.0 12.7 1.6 14.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000759 | 1 76 | 88.2 6.6 5.3 1.3 13.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000760 | 1 71 | 94.4 0.0 5.6 0.0 5.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000761 | 1 62 | 90.3 6.5 3.2 3.2 12.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000762 | 1 82 | 90.2 2.4 7.3 2.4 12.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000763 | 1 50 | 88.0 6.0 6.0 0.0 12.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000764 | 1 23 | 91.3 8.7 0.0 8.7 17.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000765 | 1 76 | 84.2 6.6 9.2 1.3 17.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000766 | 1 62 | 61.3 12.9 25.8 1.6 40.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000767 | 1 34 | 82.4 11.8 5.9 0.0 17.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000768 | 1 6 | 50.0 50.0 0.0 83.3 133.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000769 | 1 3 | 0.0 33.3 66.7 0.0 100.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000770 | 1 9 | 100.0 0.0 0.0 66.7 66.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000771 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000772 | 1 5 | 100.0 0.0 0.0 40.0 40.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000773 | 1 52 | 86.5 1.9 11.5 0.0 13.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000774 | 1 128 | 91.4 5.5 3.1 0.8 9.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000775 | 1 44 | 90.9 0.0 9.1 0.0 9.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000776 | 1 102 | 85.3 2.9 11.8 2.0 16.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000777 | 1 54 | 88.9 7.4 3.7 9.3 20.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000778 | 1 57 | 77.2 5.3 17.5 0.0 22.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000779 | 1 61 | 86.9 3.3 9.8 26.2 39.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000780 | 1 64 | 73.4 9.4 17.2 0.0 26.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000781 | 1 83 | 81.9 2.4 15.7 14.5 32.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000782 | 1 154 | 82.5 4.5 13.0 9.7 27.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000783 | 1 94 | 78.7 5.3 16.0 0.0 21.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000784 | 1 57 | 93.0 3.5 3.5 0.0 7.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000785 | 1 66 | 93.9 0.0 6.1 0.0 6.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000786 | 1 98 | 95.9 2.0 2.0 4.1 8.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000787 | 1 64 | 89.1 7.8 3.1 0.0 10.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000788 | 1 121 | 84.3 9.9 5.8 7.4 23.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000789 | 1 54 | 79.6 5.6 14.8 0.0 20.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000790 | 1 87 | 86.2 9.2 4.6 3.4 17.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000791 | 1 121 | 71.9 8.3 19.8 9.1 37.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000792 | 1 92 | 77.2 9.8 13.0 6.5 29.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000793 | 1 85 | 84.7 8.2 7.1 0.0 15.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000794 | 1 162 | 90.7 3.7 5.6 3.1 12.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000795 | 1 168 | 89.9 4.2 6.0 4.8 14.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000796 | 1 108 | 89.8 3.7 6.5 0.0 10.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000797 | 1 57 | 96.5 3.5 0.0 3.5 7.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000798 | 1 195 | 87.7 6.2 6.2 4.1 16.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000799 | 1 64 | 70.3 1.6 28.1 3.1 32.8 100.0 | +|=====================================================================================================================| +| Sum/Avg | 126 9077 | 88.9 4.7 6.3 4.3 15.4 96.8 | +|=====================================================================================================================| +| Mean | 1.0 72.0 | 88.6 5.5 5.8 9.0 20.4 96.8 | +| S.D. | 0.0 45.9 | 11.4 6.7 7.8 22.7 24.5 17.6 | +| Median | 1.0 64.0 | 90.3 3.7 4.0 3.1 14.2 100.0 | +`---------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,---------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/hyp.trn| +|---------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000674 | 1 150 | 145 3 2 10 15 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000675 | 1 65 | 58 7 0 8 15 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000676 | 1 80 | 74 1 5 2 8 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000677 | 1 200 | 188 6 6 7 19 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000678 | 1 138 | 117 12 9 0 21 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000679 | 1 141 | 116 3 22 4 29 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000680 | 1 215 | 190 9 16 4 29 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000681 | 1 177 | 135 20 22 2 44 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000682 | 1 45 | 41 0 4 0 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000683 | 1 83 | 78 2 3 6 11 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000684 | 1 81 | 71 4 6 0 10 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000685 | 1 112 | 108 4 0 4 8 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000686 | 1 106 | 103 3 0 4 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000687 | 1 45 | 43 2 0 0 2 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000688 | 1 75 | 67 2 6 4 12 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000689 | 1 64 | 59 1 4 2 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000690 | 1 93 | 87 0 6 2 8 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000691 | 1 230 | 215 5 10 0 15 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000692 | 1 108 | 98 1 9 0 10 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000693 | 1 71 | 61 4 6 0 10 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000694 | 1 184 | 161 10 13 1 24 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000695 | 1 65 | 51 8 6 0 14 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000696 | 1 123 | 113 5 5 2 12 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000697 | 1 76 | 65 11 0 3 14 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000698 | 1 82 | 73 6 3 7 16 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000699 | 1 76 | 71 3 2 16 21 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000700 | 1 62 | 57 4 1 11 16 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000701 | 1 50 | 46 4 0 2 6 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000702 | 1 29 | 26 2 1 4 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000703 | 1 114 | 98 8 8 2 18 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000704 | 1 104 | 97 3 4 6 13 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000705 | 1 48 | 47 1 0 9 10 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000706 | 1 50 | 47 1 2 6 9 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000707 | 1 84 | 75 3 6 7 16 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000708 | 1 29 | 29 0 0 0 0 0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000709 | 1 57 | 55 2 0 6 8 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000710 | 1 73 | 69 2 2 10 14 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000711 | 1 56 | 56 0 0 0 0 0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000712 | 1 30 | 29 1 0 0 1 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000713 | 1 70 | 63 4 3 0 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000714 | 1 33 | 30 0 3 4 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000715 | 1 37 | 33 2 2 2 6 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000716 | 1 52 | 52 0 0 4 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000717 | 1 74 | 69 3 2 0 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000718 | 1 56 | 52 3 1 2 6 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000719 | 1 85 | 74 8 3 5 16 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000720 | 1 59 | 55 4 0 4 8 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000721 | 1 40 | 36 3 1 2 6 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000722 | 1 72 | 66 2 4 2 8 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000723 | 1 74 | 73 1 0 2 3 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000724 | 1 81 | 71 8 2 0 10 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000725 | 1 64 | 60 2 2 2 6 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000726 | 1 69 | 60 2 7 0 9 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000727 | 1 50 | 43 1 6 2 9 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000728 | 1 68 | 67 1 0 2 3 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000729 | 1 60 | 53 5 2 2 9 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000730 | 1 76 | 59 5 12 0 17 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000731 | 1 59 | 57 2 0 0 2 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000732 | 1 45 | 34 7 4 2 13 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000733 | 1 5 | 4 1 0 4 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000734 | 1 4 | 4 0 0 7 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000735 | 1 3 | 3 0 0 0 0 0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000736 | 1 8 | 7 1 0 0 1 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000737 | 1 5 | 5 0 0 0 0 0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000738 | 1 53 | 48 1 4 2 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000739 | 1 51 | 49 2 0 4 6 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000740 | 1 57 | 57 0 0 2 2 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000741 | 1 128 | 114 6 8 0 14 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000742 | 1 114 | 105 2 7 2 11 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000743 | 1 54 | 52 2 0 1 3 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000744 | 1 74 | 73 1 0 12 13 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000745 | 1 50 | 46 2 2 1 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000746 | 1 96 | 87 3 6 6 15 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000747 | 1 62 | 59 3 0 5 8 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000748 | 1 42 | 41 1 0 8 9 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000749 | 1 47 | 45 0 2 0 2 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000750 | 1 33 | 29 3 1 2 6 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000751 | 1 58 | 46 1 11 0 12 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000752 | 1 88 | 77 3 8 5 16 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000753 | 1 6 | 6 0 0 4 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000754 | 1 8 | 7 1 0 0 1 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000755 | 1 5 | 5 0 0 6 6 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000756 | 1 5 | 4 1 0 0 1 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000757 | 1 9 | 9 0 0 2 2 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000758 | 1 63 | 55 0 8 1 9 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000759 | 1 76 | 67 5 4 1 10 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000760 | 1 71 | 67 0 4 0 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000761 | 1 62 | 56 4 2 2 8 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000762 | 1 82 | 74 2 6 2 10 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000763 | 1 50 | 44 3 3 0 6 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000764 | 1 23 | 21 2 0 2 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000765 | 1 76 | 64 5 7 1 13 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000766 | 1 62 | 38 8 16 1 25 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000767 | 1 34 | 28 4 2 0 6 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000768 | 1 6 | 3 3 0 5 8 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000769 | 1 3 | 0 1 2 0 3 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000770 | 1 9 | 9 0 0 6 6 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000771 | 1 3 | 2 1 0 0 1 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000772 | 1 5 | 5 0 0 2 2 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000773 | 1 52 | 45 1 6 0 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000774 | 1 128 | 117 7 4 1 12 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000775 | 1 44 | 40 0 4 0 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000776 | 1 102 | 87 3 12 2 17 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000777 | 1 54 | 48 4 2 5 11 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000778 | 1 57 | 44 3 10 0 13 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000779 | 1 61 | 53 2 6 16 24 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000780 | 1 64 | 47 6 11 0 17 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000781 | 1 83 | 68 2 13 12 27 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000782 | 1 154 | 127 7 20 15 42 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000783 | 1 94 | 74 5 15 0 20 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000784 | 1 57 | 53 2 2 0 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000785 | 1 66 | 62 0 4 0 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000786 | 1 98 | 94 2 2 4 8 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000787 | 1 64 | 57 5 2 0 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000788 | 1 121 | 102 12 7 9 28 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000789 | 1 54 | 43 3 8 0 11 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000790 | 1 87 | 75 8 4 3 15 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000791 | 1 121 | 87 10 24 11 45 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000792 | 1 92 | 71 9 12 6 27 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000793 | 1 85 | 72 7 6 0 13 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000794 | 1 162 | 147 6 9 5 20 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000795 | 1 168 | 151 7 10 8 25 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000796 | 1 108 | 97 4 7 0 11 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000797 | 1 57 | 55 2 0 2 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000798 | 1 195 | 171 12 12 8 32 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000799 | 1 64 | 45 1 18 2 21 1 | +|=====================================================================================================================| +| Sum | 126 9077 | 8073 428 576 393 1397 122 | +|=====================================================================================================================| +| Mean | 1.0 72.0 | 64.1 3.4 4.6 3.1 11.1 1.0 | +| S.D. | 0.0 45.9 | 40.8 3.3 5.3 3.6 8.8 0.2 | +| Median | 1.0 64.0 | 57.5 2.5 3.0 2.0 9.0 1.0 | +`---------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/hyp.trn + +Speakers: + 0: cv_jpn_000674 + 1: cv_jpn_000675 + 2: cv_jpn_000676 + 3: cv_jpn_000677 + 4: cv_jpn_000678 + 5: cv_jpn_000679 + 6: cv_jpn_000680 + 7: cv_jpn_000681 + 8: cv_jpn_000682 + 9: cv_jpn_000683 + 10: cv_jpn_000684 + 11: cv_jpn_000685 + 12: cv_jpn_000686 + 13: cv_jpn_000687 + 14: cv_jpn_000688 + 15: cv_jpn_000689 + 16: cv_jpn_000690 + 17: cv_jpn_000691 + 18: cv_jpn_000692 + 19: cv_jpn_000693 + 20: cv_jpn_000694 + 21: cv_jpn_000695 + 22: cv_jpn_000696 + 23: cv_jpn_000697 + 24: cv_jpn_000698 + 25: cv_jpn_000699 + 26: cv_jpn_000700 + 27: cv_jpn_000701 + 28: cv_jpn_000702 + 29: cv_jpn_000703 + 30: cv_jpn_000704 + 31: cv_jpn_000705 + 32: cv_jpn_000706 + 33: cv_jpn_000707 + 34: cv_jpn_000708 + 35: cv_jpn_000709 + 36: cv_jpn_000710 + 37: cv_jpn_000711 + 38: cv_jpn_000712 + 39: cv_jpn_000713 + 40: cv_jpn_000714 + 41: cv_jpn_000715 + 42: cv_jpn_000716 + 43: cv_jpn_000717 + 44: cv_jpn_000718 + 45: cv_jpn_000719 + 46: cv_jpn_000720 + 47: cv_jpn_000721 + 48: cv_jpn_000722 + 49: cv_jpn_000723 + 50: cv_jpn_000724 + 51: cv_jpn_000725 + 52: cv_jpn_000726 + 53: cv_jpn_000727 + 54: cv_jpn_000728 + 55: cv_jpn_000729 + 56: cv_jpn_000730 + 57: cv_jpn_000731 + 58: cv_jpn_000732 + 59: cv_jpn_000733 + 60: cv_jpn_000734 + 61: cv_jpn_000735 + 62: cv_jpn_000736 + 63: cv_jpn_000737 + 64: cv_jpn_000738 + 65: cv_jpn_000739 + 66: cv_jpn_000740 + 67: cv_jpn_000741 + 68: cv_jpn_000742 + 69: cv_jpn_000743 + 70: cv_jpn_000744 + 71: cv_jpn_000745 + 72: cv_jpn_000746 + 73: cv_jpn_000747 + 74: cv_jpn_000748 + 75: cv_jpn_000749 + 76: cv_jpn_000750 + 77: cv_jpn_000751 + 78: cv_jpn_000752 + 79: cv_jpn_000753 + 80: cv_jpn_000754 + 81: cv_jpn_000755 + 82: cv_jpn_000756 + 83: cv_jpn_000757 + 84: cv_jpn_000758 + 85: cv_jpn_000759 + 86: cv_jpn_000760 + 87: cv_jpn_000761 + 88: cv_jpn_000762 + 89: cv_jpn_000763 + 90: cv_jpn_000764 + 91: cv_jpn_000765 + 92: cv_jpn_000766 + 93: cv_jpn_000767 + 94: cv_jpn_000768 + 95: cv_jpn_000769 + 96: cv_jpn_000770 + 97: cv_jpn_000771 + 98: cv_jpn_000772 + 99: cv_jpn_000773 + 100: cv_jpn_000774 + 101: cv_jpn_000775 + 102: cv_jpn_000776 + 103: cv_jpn_000777 + 104: cv_jpn_000778 + 105: cv_jpn_000779 + 106: cv_jpn_000780 + 107: cv_jpn_000781 + 108: cv_jpn_000782 + 109: cv_jpn_000783 + 110: cv_jpn_000784 + 111: cv_jpn_000785 + 112: cv_jpn_000786 + 113: cv_jpn_000787 + 114: cv_jpn_000788 + 115: cv_jpn_000789 + 116: cv_jpn_000790 + 117: cv_jpn_000791 + 118: cv_jpn_000792 + 119: cv_jpn_000793 + 120: cv_jpn_000794 + 121: cv_jpn_000795 + 122: cv_jpn_000796 + 123: cv_jpn_000797 + 124: cv_jpn_000798 + 125: cv_jpn_000799 + +Speaker sentences 0: cv_jpn_000674 #utts: 1 +id: (cv_jpn_000674-cv_jpn_000674) +Scores: (#C #S #D #I) 145 3 2 10 +REF: b o k u n o ******* * i e g A a c l t a k a i d ******* * a n n o n a m a e ******* * n i k u r a b e r u t o B o k u n i w a n a j i m i n o n a i N a ******* * M a e b a k a r i d a ******* * k e d o +HYP: b o k u n o Y i e g ******* * a c l t a k a i d N a n n o n a m a e N n i k u r a b e r u t o P o k u n i w a n a j i m i n o n a i M a E B a e b a k a r i d a G k e d o +Eval: I I D D I I I I S S I I S I I + +Speaker sentences 1: cv_jpn_000675 #utts: 1 +id: (cv_jpn_000675-cv_jpn_000675) +Scores: (#C #S #D #I) 58 7 0 8 +REF: N a I y o o s ******* * O n o m o n o ******* * Y O R i F u ******* * n i k i g a ******* * u k e t e r u +HYP: M a R y o o s U A n o m o n o E R U D i K u I n i k i g a R u k e t e r u +Eval: S S I I S I I S S S S I I I I + +Speaker sentences 2: cv_jpn_000676 #utts: 1 +id: (cv_jpn_000676-cv_jpn_000676) +Scores: (#C #S #D #I) 74 1 5 2 +REF: b o k u n o s h i C L t e I R u m o n ******* * o t o w a s u k o s h i c h i g a c l t e i t a +HYP: b o k u n o s h i ******* * * t e ******* * E u m o n U o t o w a s u k o s h i c h i g a c l t e i t a +Eval: D D D D D S I I + +Speaker sentences 3: cv_jpn_000677 #utts: 1 +id: (cv_jpn_000677-cv_jpn_000677) +Scores: (#C #S #D #I) 188 6 6 7 +REF: k a k a ******* * r u t a c h i B a n i o i t e p a u s e k ******* * a ******* * I g a i s h i k i m e n t e k i d e a r i P A U w a r E w a r e n o j i k o g a I s h i k i s a y o o t e k i d e a r u t o k a n G A e r a r e r u t o +HYP: k a k a O r u t a c h i M a n i o i t e p a u s e k G a R E g a i s h i k i m e n t e k i d e a r i ******* * * * w a r U w a r e n o j i k o g a J s h i k i s a y o o t e k i d e a r u t o k a n ******* * E e r a r e r u t o +Eval: I I S I I I I S D D D D S S D D S + +>> REF: * K i +>> HYP: C H i +>> Eval: I S + +Speaker sentences 4: cv_jpn_000678 #utts: 1 +id: (cv_jpn_000678-cv_jpn_000678) +Scores: (#C #S #D #I) 117 12 9 0 +REF: i e n i k i t a n e n g a J O O w A P a U s a n B Y a k U m a i h o D o d e p a u C H O O D o P A U d a s h i t a b u n t o o n a j i g u r a i d a +HYP: i e n i k i t a n e n g a A J I w ******* * * a * s a n * H a k O m a i h o R o d e p a u * P A U * C H o * * O d a s h i t a b u n t o o n a j i g u r a i d a +Eval: S S S D D D D D S S S D S S S D S S D D S + +Speaker sentences 5: cv_jpn_000679 #utts: 1 +id: (cv_jpn_000679-cv_jpn_000679) +Scores: (#C #S #D #I) 116 3 22 4 +REF: H A h a w a p I i t A a m a r I C L t s u b a a g u n o s e e s h i n b * ******* y o o i n n i n Y U u I n S h i t e ******* * i r u t o k i n i B e C L s h I i o U m u +HYP: * ******* * ******* h a w a p ******* * i t ******* * a m a r ******* * * E t s u b a a g u n o s e e s h i n b Y y o o i n n i n * ******* * u ******* * n * h i t e Y i r u t o k i n i P e ******* * * s h ******* * i o O m u +Eval: D D D D D D D D D D D S I I D D D D D D I I S D D D D D S + +Speaker sentences 6: cv_jpn_000680 #utts: 1 +id: (cv_jpn_000680-cv_jpn_000680) +Scores: (#C #S #D #I) 190 9 16 4 +REF: t a T a n d e a r U h a n t E n o h i r o g e r E b A P a U a c h i k o c h I N i t s u g i h a g i G a a r i P A U k a t a g U c h ******* * i n i d e k i t a h o k o r o b i n a n k a P A U K Y o n e n n o m a ******* * m A n i n +HYP: t a D a n d e a r ******* * h a n t A n o h i r o g e r A b ******* * * a * a c h i k o c h ******* * ******* * i t s u g i h a g i Y a a r i ******* * * * k a t a g O c h U i n i d e k i t a h o k o r o b i n a n k a * K Y * O o n e n n o m a O m O n i n +Eval: S D D S S D D D D D D D D S D D D D S I I D S S D S I I S + +>> REF: a c l t e i r u +>> HYP: a c l t e i r u +>> Eval: + +Speaker sentences 7: cv_jpn_000681 #utts: 1 +id: (cv_jpn_000681-cv_jpn_000681) +Scores: (#C #S #D #I) 135 20 22 2 +REF: k a r E g a * P a U K o N O S H u C L C H O O * C H U U n i P A U h o o m o n k i b o o n o b u s h o P A U o y o b i P A U c h o o S a k i b o o n o b u M o N W A P a U i k a N O t o o r i d e s u +HYP: k a r A g a K a * N o D E * P A u * B * E R U N A K A n i ******* * * * h o o m o n k i b o o n o b u s h o ******* * * * o y o b i ******* * * * c h o o Z a k i b o o n o b u G o ******* * ******* * M * a * i k a R A t o o r i d e s u +Eval: S I S D S S S D S S D S D S S S I S S S S D D D D D D D D D D D D S S D D D D S D D S S + +Speaker sentences 8: cv_jpn_000682 #utts: 1 +id: (cv_jpn_000682-cv_jpn_000682) +Scores: (#C #S #D #I) 41 0 4 0 +REF: k o n b a N w a t o t e m o s a m U i d e s u +HYP: k o n b a ******* * w a t o t e m o s a m ******* * i d e s u +Eval: D D D D + +Speaker sentences 9: cv_jpn_000683 #utts: 1 +id: (cv_jpn_000683-cv_jpn_000683) +Scores: (#C #S #D #I) 78 2 3 6 +REF: k i n c h o o s h i t a k A o t s u k i d e B a * c l T a ******* * * ******* * a w a d a s e k i n i h a i r u +HYP: k i n c h o o s h i t a k ******* * o t s u k i d e * P a U c l P a C L T a w a d a s e k i n i h a i r u +Eval: D D D S I S I I I I I + +Speaker sentences 10: cv_jpn_000684 #utts: 1 +id: (cv_jpn_000684-cv_jpn_000684) +Scores: (#C #S #D #I) 71 4 6 0 +REF: m a s a n I k y u u D e n t o i c l t a f U u k a k U a r U k e n c h i k U B u t s u +HYP: m a s a n ******* * k y u u R e n t o i c l t a f ******* * u k a k ******* * a r O k e n c h i k O W u t s u +Eval: D D S D D D D S S S + +Speaker sentences 11: cv_jpn_000685 #utts: 1 +id: (cv_jpn_000685-cv_jpn_000685) +Scores: (#C #S #D #I) 108 4 0 4 +REF: g o r i ******* * o s h i d e s u s u m e t e t s U G O o g a w a r u k u n a c l t a r a ******* * h i c l k o m e r u y a r I k u c h i +HYP: g o r i Y o s h i d e s u s u m e t e t s S U G o g a w a r u k u n a c l t a r a I h i c l k o m e r u y a r E k u c h i +Eval: I I S S S I I S + +Speaker sentences 12: cv_jpn_000686 #utts: 1 +id: (cv_jpn_000686-cv_jpn_000686) +Scores: (#C #S #D #I) 103 3 0 4 +REF: m U j u ******* * n t e k i j i k o D O o i t s u t e k i n i j i k ******* * o j i s h i n o k e e s e e s u r u s h a k a i w a +HYP: m O j u R n t e k i j i k o O T o i t s u t e k i n i j i k O o j i s h i n o k e e s e e s u r u s h a k a i w a +Eval: S I I S S I I + +Speaker sentences 13: cv_jpn_000687 #utts: 1 +id: (cv_jpn_000687-cv_jpn_000687) +Scores: (#C #S #D #I) 43 2 0 0 +REF: F a n n o i k e n n i n a G a s a r e r u n a +HYP: H a n n o i k e n n i n a R a s a r e r u n a +Eval: S S + +Speaker sentences 14: cv_jpn_000688 #utts: 1 +id: (cv_jpn_000688-cv_jpn_000688) +Scores: (#C #S #D #I) 67 2 6 4 +REF: * ******* i n U g A a s o b i t a I O o r a z e n k a i d e k o c l c h I o m i t e ******* * r u +HYP: H i n O g ******* * a s o b i t a ******* * Y o r a z e n k a i d e k o c l c h ******* * o m i t e I r u +Eval: I I S D D D D S D D I I + +Speaker sentences 15: cv_jpn_000689 #utts: 1 +id: (cv_jpn_000689-cv_jpn_000689) +Scores: (#C #S #D #I) 59 1 4 2 +REF: i ******* * c h i d o w a k O o n p o t A a j U k a n o n o n d e m i t a i +HYP: i J c h i d o w a k ******* * o n p o t ******* * a j I k a n o n o n d e m i t a i +Eval: I I D D D D S + +Speaker sentences 16: cv_jpn_000690 #utts: 1 +id: (cv_jpn_000690-cv_jpn_000690) +Scores: (#C #S #D #I) 87 0 6 2 +REF: k o o I i n o k o s h i t e p a u o t o o s a n t O o k A a s a n w a ******* * d e t e i k i m a s h i t a +HYP: k o o ******* * i n o k o s h i t e p a u o t o o s a n t ******* * o k ******* * a s a n w a N d e t e i k i m a s h i t a +Eval: D D D D D D I I + +Speaker sentences 17: cv_jpn_000691 #utts: 1 +id: (cv_jpn_000691-cv_jpn_000691) +Scores: (#C #S #D #I) 215 5 10 0 +REF: s h i k a s h i t e s o r e g a t s u k u r a r e t a m o n o k a r a t s u k u r u m o N o e t o s h i t e p a u d o k o m a d e m o W A r E w a r E n i s e m a r u t o I u t o k i P A U W a r e w a r E n i c h o c +HYP: s h i k a s h i t e s o r e g a t s u k u r a r e t a m o n o k a r a t s u k u r u m o R o e t o s h i t e p a u d o k o m a d e m o ******* * ******* * r A w a r U n i s e m a r u t o Y u t o k i ******* * * * ******* * a r e w a r I n i c h o c +Eval: S D D D D S S S D D D D D D S + +>> REF: l k a n t e k i d e a r u +>> HYP: l k a n t e k i d e a r u +>> Eval: + +Speaker sentences 18: cv_jpn_000692 #utts: 1 +id: (cv_jpn_000692-cv_jpn_000692) +Scores: (#C #S #D #I) 98 1 9 0 +REF: h a i n i t a m a C L t a k e m u r i o h a k i d a s h i P A U k u r a i k o o e N n i s h i s e n o m U k e r u +HYP: h a i n i t a m a ******* * * t a k e m u r i o h a k i d a s h i ******* * * * k u r a i k o o e ******* * n i s h i s e n o m O k e r u +Eval: D D D D D D D D D S + +Speaker sentences 19: cv_jpn_000693 #utts: 1 +id: (cv_jpn_000693-cv_jpn_000693) +Scores: (#C #S #D #I) 61 4 6 0 +REF: M a d a M a d a s h i n a F u s o k u d e c h U u s e N n I n a r i s O o +HYP: W a d a W a d a s h i n a B u s o k u d e c h I u s e ******* * n ******* * n a r i s ******* * o +Eval: S S S S D D D D D D + +Speaker sentences 20: cv_jpn_000694 #utts: 1 +id: (cv_jpn_000694-cv_jpn_000694) +Scores: (#C #S #D #I) 161 10 13 1 +REF: * Z e n s e k a i n o m o T s u k a t a c h i P A U W A t a s h I n o I W a y U r u s e e s a N y O o s h i k i t o s a Y O o t o w a h A N a s h i t e k a n g a E r u k o t o w a d e k i n a i +HYP: T S e n s e k a i n o m o * s u k a t a c h i ******* * * * ******* * I t a s h U n o Y O a y I r u s e e s a I y ******* * o s h i k i t o s a I U o t o w a h ******* * ******* * a s h i t e k a n g a I r u k o t o w a d e k i n a i +Eval: I S D D D D D D D S S S S S S D D S S D D D D S + +Speaker sentences 21: cv_jpn_000695 #utts: 1 +id: (cv_jpn_000695-cv_jpn_000695) +Scores: (#C #S #D #I) 51 8 6 0 +REF: s a k e n o m a N A I n o n i B i I R u H a r a t o I W A R e t a +HYP: s a k e n o m a ******* * E G n o n i P i R O u B a r a t o ******* * ******* * Y O e t a +Eval: D D S S S S S S D D D D S S + +Speaker sentences 22: cv_jpn_000696 #utts: 1 +id: (cv_jpn_000696-cv_jpn_000696) +Scores: (#C #S #D #I) 113 5 5 2 +REF: s o r e ******* * o t a d a k A G A k u n o z e n d a n k a i P A U h i k u i t e e d o n o k a G a k u t o N o m i m i r u k o t o w a +HYP: s o r e O o t a d a k O H O k u n o z e n d a n k a i * * * S h i k u i t e e d o n o k a ******* * a k u t o M o m i m i r u k o t o w a +Eval: I I S S S D D D S D D S + +Speaker sentences 23: cv_jpn_000697 #utts: 1 +id: (cv_jpn_000697-cv_jpn_000697) +Scores: (#C #S #D #I) 65 11 0 3 +REF: W A k * I m e m o ******* * f u r a z u n i S u M A H o d e G e e M U o y a c l t e i t a +HYP: O K k Y O m e m o K f u r a z u n i P A u S U M o d e K e e N M o y a c l t e i t a +Eval: S S I S I I S S S S S S S S + +Speaker sentences 24: cv_jpn_000698 #utts: 1 +id: (cv_jpn_000698-cv_jpn_000698) +Scores: (#C #S #D #I) 73 6 3 7 +REF: W a T a S H I W a R a i ******* * ******* * * s h U u m a t a k o b a y a s h i s a n t o a s o b ******* * i m a s u +HYP: * ******* a D a * I W A a N a i N C H s h I u m a t a k o b a y a s h i s a n t o a s o b E i m a s u +Eval: D D S D S S S S I I I I I S I I + +Speaker sentences 25: cv_jpn_000699 #utts: 1 +id: (cv_jpn_000699-cv_jpn_000699) +Scores: (#C #S #D #I) 71 3 2 16 +REF: a s ******* * o k o n ******* * i h ******* * i t ******* * o g ******* * * a * i m a s u n e ******* * * * a N o h i t o w a D a r e D e s h O o +HYP: a s O o k o n I i h I i t O o g A P a U i m a s u n e P A U a R o h i t o w a T a r e T e s h ******* * o +Eval: I I I I I I I I I I I I I I I I S S S D D + +Speaker sentences 26: cv_jpn_000700 #utts: 1 +id: (cv_jpn_000700-cv_jpn_000700) +Scores: (#C #S #D #I) 57 4 1 11 +REF: w a t a S H i w a ******* * k * I n o o k a ******* * R * a * n ******* * o d o G a ******* * i t a i d e s u +HYP: w a t a * J i w a K k Y E n o o k a N A P a U n O o d o K a E i t a i d e s u +Eval: D S I I I S I I S I I I I S I I + +Speaker sentences 27: cv_jpn_000701 #utts: 1 +id: (cv_jpn_000701-cv_jpn_000701) +Scores: (#C #S #D #I) 46 4 0 2 +REF: k y ******* * o N e n k a R a B e n k y O o s h i t e i m a s u +HYP: k y O o R e n k a N a P e n k y U o s h i t e i m a s u +Eval: I I S S S S + +Speaker sentences 28: cv_jpn_000702 #utts: 1 +id: (cv_jpn_000702-cv_jpn_000702) +Scores: (#C #S #D #I) 26 2 1 4 +REF: C H o c l t o s u m i m ******* * a s e * * N +HYP: * A o c l t o s u m i m R a s e P A U +Eval: D S I I I I S + +Speaker sentences 29: cv_jpn_000703 #utts: 1 +id: (cv_jpn_000703-cv_jpn_000703) +Scores: (#C #S #D #I) 98 8 8 2 +REF: S h i k a s H I p a u S o n O o o e n b u r i ******* * w a p a u y a k e G i m i n i P A U k a e c l T e h a g e s H I k U n A c l t a +HYP: C h i k a s * U p a u B o n ******* * o o e n b u r i E w a p a u y a k e K i m i n i ******* * * * k a e c l D e h a g e s * U k E n O c l t a +Eval: S D S S D D I I S D D D D S D S S S + +Speaker sentences 30: cv_jpn_000704 #utts: 1 +id: (cv_jpn_000704-cv_jpn_000704) +Scores: (#C #S #D #I) 97 3 4 6 +REF: i t s ******* * u m o k o n o E n p ******* * i t s U o t s u k a c l t e i t a n o d e P A U m i j i k a ******* * K u n a r i m a s h i t a +HYP: i t s Z u m o k o n o I n p I i t s O o t s u k a c l t e i t a n o d e ******* * * * m i j i k a H A u n a r i m a s h i t a +Eval: I I S I I S D D D D I I S + +Speaker sentences 31: cv_jpn_000705 #utts: 1 +id: (cv_jpn_000705-cv_jpn_000705) +Scores: (#C #S #D #I) 47 1 0 9 +REF: w a t a s h i w ******* * * a * e E g o g a h a n a s * ******* * ******* * e m a s u +HYP: w a t a s h i w A P a U e I g o g a h a n a s H I T e m a s u +Eval: I I I I S I I I I I + +Speaker sentences 32: cv_jpn_000706 #utts: 1 +id: (cv_jpn_000706-cv_jpn_000706) +Scores: (#C #S #D #I) 47 1 2 6 +REF: H o s ******* H i k ******* * e r ******* y a j i b u ******* * n d e t o c l t e k i n a +HYP: * ******* o s E i k E e r y a j i b u A n d e t o c l t e k i n a +Eval: D D I S I I I I I + +Speaker sentences 33: cv_jpn_000707 #utts: 1 +id: (cv_jpn_000707-cv_jpn_000707) +Scores: (#C #S #D #I) 75 3 6 7 +REF: R a i s h u u ******* * k a r a n i s h u u k a n P A U K a i g a i e r ******* * ******* y ******* * o k o o N I i k i m a s u +HYP: * ******* a i s h u u E k a r a n i s h u u k a n ******* * * * H a i g a i e r U y O o k o o O N i k i m a s u +Eval: D D I I D D D D S I I I I I S S + +Speaker sentences 34: cv_jpn_000708 #utts: 1 +id: (cv_jpn_000708-cv_jpn_000708) +Scores: (#C #S #D #I) 29 0 0 0 +REF: o r e m o k i n i n a r u n a +HYP: o r e m o k i n i n a r u n a +Eval: + +Speaker sentences 35: cv_jpn_000709 #utts: 1 +id: (cv_jpn_000709-cv_jpn_000709) +Scores: (#C #S #D #I) 55 2 0 6 +REF: d a r e ******* * g a t s u k a c l t e r u n o k ******* * a ******* * w A k a r A n a i +HYP: d a r e I g a t s u k a c l t e r u n o k A a H w O k a r U n a i +Eval: I I I I I I S S + +Speaker sentences 36: cv_jpn_000710 #utts: 1 +id: (cv_jpn_000710-cv_jpn_000710) +Scores: (#C #S #D #I) 69 2 2 10 +REF: B u r a U z a n o b A a j o n g ******* * * a * a g a r u t ******* * o s u k o s h ******* * i ******* * u r e s h i i +HYP: P u r a O z a n o b ******* * a j o n g A P a U a g a r u t O o s u k o s h I i U u r e s h i i +Eval: S S D D I I I I I I I I I I + +Speaker sentences 37: cv_jpn_000711 #utts: 1 +id: (cv_jpn_000711-cv_jpn_000711) +Scores: (#C #S #D #I) 56 0 0 0 +REF: m a t a a t a r a s h i i a i d o r u g a d e t e k i t a +HYP: m a t a a t a r a s h i i a i d o r u g a d e t e k i t a +Eval: + +Speaker sentences 38: cv_jpn_000712 #utts: 1 +id: (cv_jpn_000712-cv_jpn_000712) +Scores: (#C #S #D #I) 29 1 0 0 +REF: m a j i d e y a c l t A n o k a +HYP: m a j i d e y a c l t O n o k a +Eval: S + +Speaker sentences 39: cv_jpn_000713 #utts: 1 +id: (cv_jpn_000713-cv_jpn_000713) +Scores: (#C #S #D #I) 63 4 3 0 +REF: c h o o d o s O N o t O k i n i p a u k y o o j U g a h a i C L t e k i t a +HYP: c h o o d o s A M o t U k i n i p a u k y o o j I g a h a i ******* * * t e k i t a +Eval: S S S S D D D + +Speaker sentences 40: cv_jpn_000714 #utts: 1 +id: (cv_jpn_000714-cv_jpn_000714) +Scores: (#C #S #D #I) 30 0 3 4 +REF: i c l s H o n i ******* * c h i ******* * n s h i t e Y o +HYP: i c l s * o n i K c h i E n s h i t e ******* * o +Eval: D I I I I D D + +Speaker sentences 41: cv_jpn_000715 #utts: 1 +id: (cv_jpn_000715-cv_jpn_000715) +Scores: (#C #S #D #I) 33 2 2 2 +REF: s o r e G a s a t e N n o D o r e s u ******* * +HYP: s o r e K a s a t e ******* * n o T o r e s u N +Eval: S D D S I I + +Speaker sentences 42: cv_jpn_000716 #utts: 1 +id: (cv_jpn_000716-cv_jpn_000716) +Scores: (#C #S #D #I) 52 0 0 4 +REF: f u t a r i ******* * w a r e j i e i k i s ******* * e e s a n s h i t a +HYP: f u t a r i O w a r e j i e i k i s U e e s a n s h i t a +Eval: I I I I + +Speaker sentences 43: cv_jpn_000717 #utts: 1 +id: (cv_jpn_000717-cv_jpn_000717) +Scores: (#C #S #D #I) 69 3 2 0 +REF: t o m a t o k a n a n K a n o a k a i s O o s u g a k a k a c l t e r U Y o +HYP: t o m a t o k a n a n G a n o a k a i s ******* * o s u g a k a k a c l t e r I U o +Eval: S D D S S + +Speaker sentences 44: cv_jpn_000718 #utts: 1 +id: (cv_jpn_000718-cv_jpn_000718) +Scores: (#C #S #D #I) 52 3 1 2 +REF: k o K o k a r a t a t e n a ******* * O s u n o w a k i b i s H I i +HYP: k o G o k a r a t a t e n a N A s u n o w a k i b i s * U i +Eval: S I I S D S + +Speaker sentences 45: cv_jpn_000719 #utts: 1 +id: (cv_jpn_000719-cv_jpn_000719) +Scores: (#C #S #D #I) 74 8 3 5 +REF: M i z u K e o S H I * C L k a r i s h i b o c l t e ******* * * * a j i g a n a j i m U y O o N i s u r u +HYP: N i z u G e o * W O S U k a r i s h i b o c l t e P A U a j i g a n a j i m I y ******* * o R i s u r u +Eval: S S D S S I S S I I I I S D D S + +Speaker sentences 46: cv_jpn_000720 #utts: 1 +id: (cv_jpn_000720-cv_jpn_000720) +Scores: (#C #S #D #I) 55 4 0 4 +REF: n e t o G E n i h a m a c l t a r a k I n ******* * g a T a m a c l t a ******* * +HYP: n e t o K I n i h a m a c l t a r a k A n E g a G a m a c l t a U +Eval: S S S I I S I I + +Speaker sentences 47: cv_jpn_000721 #utts: 1 +id: (cv_jpn_000721-cv_jpn_000721) +Scores: (#C #S #D #I) 36 3 1 2 +REF: * ******* i t S U k a E r U y o o n i n a r u n d a +HYP: S i t * A k a I r I y o o n i n a r u n d a +Eval: I I D S S S + +Speaker sentences 48: cv_jpn_000722 #utts: 1 +id: (cv_jpn_000722-cv_jpn_000722) +Scores: (#C #S #D #I) 66 2 4 2 +REF: k o s E e h a h a i y U u t o I u y o r i a k u g a t s u y o ******* * I k a n j i +HYP: k o s ******* * e h a h a i y ******* * u t o Y u y o r i a k u g a t s u y o A E k a n j i +Eval: D D D D S I I S + +Speaker sentences 49: cv_jpn_000723 #utts: 1 +id: (cv_jpn_000723-cv_jpn_000723) +Scores: (#C #S #D #I) 73 1 0 2 +REF: f ******* * i j i k a r u n o s a o m a z a m a Z a t o m i s e t s u k e r a r e t a +HYP: f E i j i k a r u n o s a o m a z a m a D a t o m i s e t s u k e r a r e t a +Eval: I I S + +Speaker sentences 50: cv_jpn_000724 #utts: 1 +id: (cv_jpn_000724-cv_jpn_000724) +Scores: (#C #S #D #I) 71 8 2 0 +REF: k o s u p a y o k e R e b a S O K o S o k o n o m o n d a I W a g a m a n s u r U +HYP: k o s u p a y o k e D e b a P A U S o K o k o n o m o n d a ******* * E a g a m a n s u r E +Eval: S S S S S S D D S S + +Speaker sentences 51: cv_jpn_000725 #utts: 1 +id: (cv_jpn_000725-cv_jpn_000725) +Scores: (#C #S #D #I) 60 2 2 2 +REF: M i n n a y a c l t e m a s u k a r a D a i j o o b u d e s u ******* * Y o +HYP: * ******* i n n a y a c l t e m a s u k a r a T a i j o o b u d e s u I U o +Eval: D D S I I S + +Speaker sentences 52: cv_jpn_000726 #utts: 1 +id: (cv_jpn_000726-cv_jpn_000726) +Scores: (#C #S #D #I) 60 2 7 0 +REF: k o n o t o S H o k a n P A U h a i c l t a s h u N k a n k i n I i c l t a +HYP: k o n o t o * J o k a n ******* * * * h a i c l t a s h u U k a n k i n ******* * i c l t a +Eval: D S D D D D S D D + +Speaker sentences 53: cv_jpn_000727 #utts: 1 +id: (cv_jpn_000727-cv_jpn_000727) +Scores: (#C #S #D #I) 43 1 6 2 +REF: k o n o d e n c h i P A U S u G u k i r e c h i ******* * c l t a +HYP: k o n o d e n c h i ******* * * * F u ******* * u k i r e c h i A c l t a +Eval: D D D D S D D I I + +Speaker sentences 54: cv_jpn_000728 #utts: 1 +id: (cv_jpn_000728-cv_jpn_000728) +Scores: (#C #S #D #I) 67 1 0 2 +REF: a m a y ******* * A d o r i s u r u t o k o r o g a n a k u t e k o m a c l t a +HYP: a m a y E U d o r i s u r u t o k o r o g a n a k u t e k o m a c l t a +Eval: I I S + +Speaker sentences 55: cv_jpn_000729 #utts: 1 +id: (cv_jpn_000729-cv_jpn_000729) +Scores: (#C #S #D #I) 53 5 2 2 +REF: y a s u k U s u r u Y o r i s h i ******* * t s u O a g e t E h o s h I I +HYP: y a s u k A s u r u E o r i s h i T t s u W a g e t O h o s h ******* * E +Eval: S S I I S S D D S + +Speaker sentences 56: cv_jpn_000730 #utts: 1 +id: (cv_jpn_000730-cv_jpn_000730) +Scores: (#C #S #D #I) 59 5 12 0 +REF: m a s A K A k o N n a k o t O N i n a r O O t o w a O m o W A n a k a c l t a +HYP: m a s E G O k o ******* * n a k o t ******* * E i n a r ******* * U t o w a ******* * m o ******* * ******* * n a k a c l t a +Eval: S S S D D D D S D D S D D D D D D + +Speaker sentences 57: cv_jpn_000731 #utts: 1 +id: (cv_jpn_000731-cv_jpn_000731) +Scores: (#C #S #D #I) 57 2 0 0 +REF: s a i g o n i w a r a I o t o r i n i k u r U s u t a i r u +HYP: s a i g o n i w a r a Y o t o r i n i k u r A s u t a i r u +Eval: S S + +Speaker sentences 58: cv_jpn_000732 #utts: 1 +id: (cv_jpn_000732-cv_jpn_000732) +Scores: (#C #S #D #I) 34 7 4 2 +REF: k o r E P A U n a n I N O i m i g A a r u N D * a * +HYP: k o r A * * E n a n N A E i m i g ******* * a r u D A P a U +Eval: S D D S S S S D D S S I I + +Speaker sentences 59: cv_jpn_000733 #utts: 1 +id: (cv_jpn_000733-cv_jpn_000733) +Scores: (#C #S #D #I) 4 1 0 4 +REF: * ******* * ******* i i E +HYP: O T i i A +Eval: I I I I S + +Speaker sentences 60: cv_jpn_000734 #utts: 1 +id: (cv_jpn_000734-cv_jpn_000734) +Scores: (#C #S #D #I) 4 0 0 7 +REF: * * ******* * ******* s h ******* * i +HYP: S H A s h E i +Eval: I I I I I I I + +Speaker sentences 61: cv_jpn_000735 #utts: 1 +id: (cv_jpn_000735-cv_jpn_000735) +Scores: (#C #S #D #I) 3 0 0 0 +REF: n i +HYP: n i +Eval: + +Speaker sentences 62: cv_jpn_000736 #utts: 1 +id: (cv_jpn_000736-cv_jpn_000736) +Scores: (#C #S #D #I) 7 1 0 0 +REF: W a c h i +HYP: H a c h i +Eval: S + +Speaker sentences 63: cv_jpn_000737 #utts: 1 +id: (cv_jpn_000737-cv_jpn_000737) +Scores: (#C #S #D #I) 5 0 0 0 +REF: h a i +HYP: h a i +Eval: + +Speaker sentences 64: cv_jpn_000738 #utts: 1 +id: (cv_jpn_000738-cv_jpn_000738) +Scores: (#C #S #D #I) 48 1 4 2 +REF: t E e b u r u n ******* * o U e n i k a b i n g A a r i m a s u +HYP: t ******* * e b u r u n O o Y e n i k a b i n g ******* * a r i m a s u +Eval: D D I I S D D + +Speaker sentences 65: cv_jpn_000739 #utts: 1 +id: (cv_jpn_000739-cv_jpn_000739) +Scores: (#C #S #D #I) 49 2 0 4 +REF: w A t a s h i w a ******* * m a i ******* * a s a s a n P o s h i m a s u +HYP: w O t a s h i w a O m a i Y a s a s a n B o s h i m a s u +Eval: S I I I I S + +Speaker sentences 66: cv_jpn_000740 #utts: 1 +id: (cv_jpn_000740-cv_jpn_000740) +Scores: (#C #S #D #I) 57 0 0 2 +REF: a t a r a s h i i k u t s u ******* * o h a i t e d e k a k e m a s u +HYP: a t a r a s h i i k u t s u O o h a i t e d e k a k e m a s u +Eval: I I + +Speaker sentences 67: cv_jpn_000741 #utts: 1 +id: (cv_jpn_000741-cv_jpn_000741) +Scores: (#C #S #D #I) 114 6 8 0 +REF: k o t o s h i n o n a t s U y a s U m i W A P a U u m i n i m o i k i m a s h i t a s h i P A U y a m a n i m o n O B o r i M a s h i t a +HYP: k o t o s h i n o n a t s E y a s A m i O W * a * u m i n i m o i k i m a s h i t a s h i * * I y a m a n i m o n ******* * ******* * o r i B a s h i t a +Eval: S S S S D D D D S D D D D S + +Speaker sentences 68: cv_jpn_000742 #utts: 1 +id: (cv_jpn_000742-cv_jpn_000742) +Scores: (#C #S #D #I) 105 2 7 2 +REF: w a t a s h i w A P a U i r o ******* * i r o n o b e n g o o P A U J i b u n n o m u n e d e k o s h i r a e t e m i m a s h i t a +HYP: w a t a s h i w ******* * * a * i r o Y i r o n o b e n g o o * * * C H i b u n n o m u n e d e k o s h i r a e t e m i m a s h i t a +Eval: D D D D I I D D D S S + +Speaker sentences 69: cv_jpn_000743 #utts: 1 +id: (cv_jpn_000743-cv_jpn_000743) +Scores: (#C #S #D #I) 52 2 0 1 +REF: n a n d e k o o m o s ******* H o o b a i h e T a n a n d a r o +HYP: n a n d e k o o m o s U o o b a i h e D a n a n d a r o +Eval: I S S + +Speaker sentences 70: cv_jpn_000744 #utts: 1 +id: (cv_jpn_000744-cv_jpn_000744) +Scores: (#C #S #D #I) 73 1 0 12 +REF: t a r e n t o k a r a k y o k u a n a n i k a ******* * ******* * e ******* * * t ******* * e k e e * h i s a k ******* * u g e N +HYP: t a r e n t o k a r a k y o k u a n a n i k a E I e C L t U e k e e S h i s a k I u g e A +Eval: I I I I I I I I I I I I S + +Speaker sentences 71: cv_jpn_000745 #utts: 1 +id: (cv_jpn_000745-cv_jpn_000745) +Scores: (#C #S #D #I) 46 2 2 1 +REF: d o g e z a s U r e b a I i c l t e m o n * J a n a i +HYP: d o g e z a s E r e b a ******* * i c l t e m o n S H a n a i +Eval: S D D I S + +Speaker sentences 72: cv_jpn_000746 #utts: 1 +id: (cv_jpn_000746-cv_jpn_000746) +Scores: (#C #S #D #I) 87 3 6 6 +REF: d E e t O n o a i d A P a U k a n o j O w a j i b u n t o i c l t e e n o ******* * k y ******* * o r i ******* * o t a M o c l t a +HYP: d ******* * e t A n o a i d ******* * * a * k a n o j I w a j i b u n t o i c l t e e n o K k y U o r i Y o t a N o c l t a +Eval: D D S D D D D S I I I I I I S + +Speaker sentences 73: cv_jpn_000747 #utts: 1 +id: (cv_jpn_000747-cv_jpn_000747) +Scores: (#C #S #D #I) 59 3 0 5 +REF: k o n o G e e n i n n a n k ******* * ******* * a h * I s a s h i b u r i n i M i t a +HYP: k o n o R e e n i n n a n k A W a h S H s a s h i b u r i n i N i t a +Eval: S I I I I I S S + +Speaker sentences 74: cv_jpn_000748 #utts: 1 +id: (cv_jpn_000748-cv_jpn_000748) +Scores: (#C #S #D #I) 41 1 0 8 +REF: o o k i k ******* * U s a i d ******* * o c h ******* * e n j i ******* * o s u r u +HYP: o o k i k T A s a i d A o c h I e n j i E o s u r u +Eval: I I S I I I I I I + +Speaker sentences 75: cv_jpn_000749 #utts: 1 +id: (cv_jpn_000749-cv_jpn_000749) +Scores: (#C #S #D #I) 45 0 2 0 +REF: k a r e w A a t a m a o k a k i m u s h i c l t a +HYP: k a r e w ******* * a t a m a o k a k i m u s h i c l t a +Eval: D D + +Speaker sentences 76: cv_jpn_000750 #utts: 1 +id: (cv_jpn_000750-cv_jpn_000750) +Scores: (#C #S #D #I) 29 3 1 2 +REF: * ******* o m a C H I s h i t e O r i m a s u +HYP: K o m a * T E s h i t e A r i m a s u +Eval: I I D S S S + +Speaker sentences 77: cv_jpn_000751 #utts: 1 +id: (cv_jpn_000751-cv_jpn_000751) +Scores: (#C #S #D #I) 46 1 11 0 +REF: K o n o K Y o k U P A u s e n k a I i j O o w a k i i t e r u +HYP: * ******* o n o * D o k ******* * * * u s e n k a ******* * i j ******* * o w a k i i t e r u +Eval: D D D S D D D D D D D D + +Speaker sentences 78: cv_jpn_000752 #utts: 1 +id: (cv_jpn_000752-cv_jpn_000752) +Scores: (#C #S #D #I) 77 3 8 5 +REF: r e e Z o o k O o a k e t a ******* * * t o ******* * t a n P A U n a n i g a h i T S u y O o k a w a s u r e t a +HYP: r e e J o o k ******* * o a k e t a C L t o U t a n ******* * * * n a n i g a h i C H u y ******* * o k a w a s u r e t a +Eval: S D D I I I I I D D D D S S D D + +Speaker sentences 79: cv_jpn_000753 #utts: 1 +id: (cv_jpn_000753-cv_jpn_000753) +Scores: (#C #S #D #I) 6 0 0 4 +REF: * ******* i ******* * c h i +HYP: I i T c h i +Eval: I I I I + +Speaker sentences 80: cv_jpn_000754 #utts: 1 +id: (cv_jpn_000754-cv_jpn_000754) +Scores: (#C #S #D #I) 7 1 0 0 +REF: W a c h i +HYP: H a c h i +Eval: S + +Speaker sentences 81: cv_jpn_000755 #utts: 1 +id: (cv_jpn_000755-cv_jpn_000755) +Scores: (#C #S #D #I) 5 0 0 6 +REF: * ******* i i ******* * e ******* * +HYP: K i i W e A +Eval: I I I I I I + +Speaker sentences 82: cv_jpn_000756 #utts: 1 +id: (cv_jpn_000756-cv_jpn_000756) +Scores: (#C #S #D #I) 4 1 0 0 +REF: R e i +HYP: D e i +Eval: S + +Speaker sentences 83: cv_jpn_000757 #utts: 1 +id: (cv_jpn_000757-cv_jpn_000757) +Scores: (#C #S #D #I) 9 0 0 2 +REF: * ******* s h i c h i +HYP: A s h i c h i +Eval: I I + +Speaker sentences 84: cv_jpn_000758 #utts: 1 +id: (cv_jpn_000758-cv_jpn_000758) +Scores: (#C #S #D #I) 55 0 8 1 +REF: y o o b o o W A d a s u n o n I k a u * h i t o W a s u k u n a i +HYP: y o o b o o ******* * ******* * d a s u n o n ******* * k a u S h i t o ******* * a s u k u n a i +Eval: D D D D D D I D D + +Speaker sentences 85: cv_jpn_000759 #utts: 1 +id: (cv_jpn_000759-cv_jpn_000759) +Scores: (#C #S #D #I) 67 5 4 1 +REF: R o o k a r U t o k u y U u n o i k * I O I m a k a s e n o k o m A a s h a r u +HYP: N o o k a r E t o k u y ******* * u n o i k K Y U E m a k a s e n o k o m ******* * a s h a r u +Eval: S S D D I S S S D D + +Speaker sentences 86: cv_jpn_000760 #utts: 1 +id: (cv_jpn_000760-cv_jpn_000760) +Scores: (#C #S #D #I) 67 0 4 0 +REF: k o n o d a i F U k u w a a n k o g a o o k u t e y o k u k a i m a s u +HYP: k o n o d a i ******* * ******* * k u w a a n k o g a o o k u t e y o k u k a i m a s u +Eval: D D D D + +Speaker sentences 87: cv_jpn_000761 #utts: 1 +id: (cv_jpn_000761-cv_jpn_000761) +Scores: (#C #S #D #I) 56 4 2 2 +REF: J I s h o B I k i n a g a r a s h o o s ******* * e t s U o y o m i m a s u +HYP: D E s h o ******* * O k i n a g a r a s h o o s U e t s O o y o m i m a s u +Eval: S S D D S I I S + +Speaker sentences 88: cv_jpn_000762 #utts: 1 +id: (cv_jpn_000762-cv_jpn_000762) +Scores: (#C #S #D #I) 74 2 6 2 +REF: k o n n A o o k i n a G O o g u r U o t s u k e n a i t O i k e n a ******* * i n d e s u k a +HYP: k o n n ******* * o o k i n a N G o g u r ******* * o t s u k e n a i t ******* * i k e n a E i n d e s u k a +Eval: D D S S D D D D I I + +Speaker sentences 89: cv_jpn_000763 #utts: 1 +id: (cv_jpn_000763-cv_jpn_000763) +Scores: (#C #S #D #I) 44 3 3 0 +REF: k a r E e n o b o o R Y O k U w a t o m a r a n a i +HYP: k a r A e n o b o o ******* * * I k O w a t o m a r a n a i +Eval: S D D D S S + +Speaker sentences 90: cv_jpn_000764 #utts: 1 +id: (cv_jpn_000764-cv_jpn_000764) +Scores: (#C #S #D #I) 21 2 0 2 +REF: i k i t e ******* * T a n D a n e +HYP: i k i t e I K a n M a n e +Eval: I I S S + +Speaker sentences 91: cv_jpn_000765 #utts: 1 +id: (cv_jpn_000765-cv_jpn_000765) +Scores: (#C #S #D #I) 64 5 7 1 +REF: t o o j I T o s h I * C H a k a C L k I t e k i n a h a t s u m E e d a c l t a n e +HYP: t o o j U D o s h S E S a k a ******* * * k ******* * t e k i n a h a t s u m ******* * e d a c l t a n e +Eval: S S S I S S D D D D D D D + +Speaker sentences 92: cv_jpn_000766 #utts: 1 +id: (cv_jpn_000766-cv_jpn_000766) +Scores: (#C #S #D #I) 38 8 16 1 +REF: S o n O t O k i P A U W A t a s H I W a c h i k a * R A t S U k I T A +HYP: H o n ******* * t E k i ******* * * * ******* * O t a s * ******* * O a c h i k a C L U t * ******* * k ******* * A N +Eval: S D D S D D D D D D S D D D S I S S D D D D D S S + +Speaker sentences 93: cv_jpn_000767 #utts: 1 +id: (cv_jpn_000767-cv_jpn_000767) +Scores: (#C #S #D #I) 28 4 2 0 +REF: k a W a G a h i A G a c l t e i T a +HYP: k a ******* * a W a h i E W a c l t e i D a +Eval: D D S S S S + +Speaker sentences 94: cv_jpn_000768 #utts: 1 +id: (cv_jpn_000768-cv_jpn_000768) +Scores: (#C #S #D #I) 3 3 0 5 +REF: * * ******* I c * ******* H I +HYP: C H E c L K E +Eval: I I I S I I S S + +Speaker sentences 95: cv_jpn_000769 #utts: 1 +id: (cv_jpn_000769-cv_jpn_000769) +Scores: (#C #S #D #I) 0 1 2 0 +REF: N I +HYP: * ******* O +Eval: D D S + +Speaker sentences 96: cv_jpn_000770 #utts: 1 +id: (cv_jpn_000770-cv_jpn_000770) +Scores: (#C #S #D #I) 9 0 0 6 +REF: * ******* s h i ******* * c h ******* * i +HYP: A s h i T c h I i +Eval: I I I I I I + +Speaker sentences 97: cv_jpn_000771 #utts: 1 +id: (cv_jpn_000771-cv_jpn_000771) +Scores: (#C #S #D #I) 2 1 0 0 +REF: G o +HYP: K o +Eval: S + +Speaker sentences 98: cv_jpn_000772 #utts: 1 +id: (cv_jpn_000772-cv_jpn_000772) +Scores: (#C #S #D #I) 5 0 0 2 +REF: i i e ******* * +HYP: i i e A +Eval: I I + +Speaker sentences 99: cv_jpn_000773 #utts: 1 +id: (cv_jpn_000773-cv_jpn_000773) +Scores: (#C #S #D #I) 45 1 6 0 +REF: n a m a E k a r a s h I T e t e k I t o o s u g i r u +HYP: n a m a I k a r a s h ******* * ******* * e t e k ******* * t o o s u g i r u +Eval: S D D D D D D + +Speaker sentences 100: cv_jpn_000774 #utts: 1 +id: (cv_jpn_000774-cv_jpn_000774) +Scores: (#C #S #D #I) 117 7 4 1 +REF: j i k o n o s o t o n I a r u t o I u n o w a t a n n i j i k o n O I s h i k I N o * s O T o n i a r u t o I u k o t o d e n a k u +HYP: j i k o n o s o t o n Y a r u t o Y u n o w a t a n n i j i k o n ******* * E s h i k N S o T s ******* * S o n i a r u t o Y u k o t o d e n a k u +Eval: S S D D S S S I D D S S + +Speaker sentences 101: cv_jpn_000775 #utts: 1 +id: (cv_jpn_000775-cv_jpn_000775) +Scores: (#C #S #D #I) 40 0 4 0 +REF: s o r E w a s h i r a n a k u t e I i d e s u +HYP: s o r ******* * w a s h i r a n a k u t e ******* * i d e s u +Eval: D D D D + +Speaker sentences 102: cv_jpn_000776 #utts: 1 +id: (cv_jpn_000776-cv_jpn_000776) +Scores: (#C #S #D #I) 87 3 12 2 +REF: k i m i t O B o k U n o k y o o t s U u n o s h I R i A I w a d a r e H i t o r ******* * i p a u m i a t a r a n a i +HYP: k i m i t ******* * ******* * o k O n o k y o o t s ******* * u n o s h ******* * ******* * i G E w a d a r e ******* * i t o r U i p a u m i a t a r a n a i +Eval: D D D D S D D D D D D S S D D I I + +Speaker sentences 103: cv_jpn_000777 #utts: 1 +id: (cv_jpn_000777-cv_jpn_000777) +Scores: (#C #S #D #I) 48 4 2 5 +REF: s u ******* * g e e D A I J i n I n a c l t e k i t e r u n o n a ******* * * +HYP: s u E g e e O O T O i n ******* * n a c l t e k i t e r u n o n a C L +Eval: I I S S S S D D I I I + +Speaker sentences 104: cv_jpn_000778 #utts: 1 +id: (cv_jpn_000778-cv_jpn_000778) +Scores: (#C #S #D #I) 44 3 10 0 +REF: k o n o A T A R I d e s u k o s h i Y a s u M i m a s h O o +HYP: k o n o ******* * ******* * H E N d e s u k o s h i ******* * a s u ******* * i m a s h ******* * o +Eval: D D D D S S S D D D D D D + +Speaker sentences 105: cv_jpn_000779 #utts: 1 +id: (cv_jpn_000779-cv_jpn_000779) +Scores: (#C #S #D #I) 53 2 6 16 +REF: d e n s h a N i n o r u t o k i P A U k i c l P U o k a i m a s ******* * ******* * ******* * ******* * ******* * ******* * ******* * ******* * u +HYP: d e n s h a R i n o r u t o k i ******* * * * k i c l ******* * T o k a i m a s U D A U T A U T u +Eval: S D D D D D D S I I I I I I I I I I I I I I I I + +Speaker sentences 106: cv_jpn_000780 #utts: 1 +id: (cv_jpn_000780-cv_jpn_000780) +Scores: (#C #S #D #I) 47 6 11 0 +REF: t a m a G O w a i C L k o G o j U U G u r a M U G u r a i D e s u +HYP: t a m a O G w a i ******* * * k o K o j ******* * ******* * I u r a ******* * N B u r a i ******* * e s u +Eval: S S D D D S D D D D S D D S S D D + +Speaker sentences 107: cv_jpn_000781 #utts: 1 +id: (cv_jpn_000781-cv_jpn_000781) +Scores: (#C #S #D #I) 68 2 13 12 +REF: g I R e s u p I i w a m a C L G I i o t s U u j i t e ******* * * * i n e s U t o s h i r i ******* * a c l t a ******* * ******* * ******* * +HYP: g ******* * E e s u p ******* * i w a m a ******* * * ******* * K i o t s ******* * u j i t e P A U i n e s ******* * t o s h i r i Y a c l t a T U U +Eval: D D S D D D D D D D S D D I I I I D D I I I I I I I I + +Speaker sentences 108: cv_jpn_000782 #utts: 1 +id: (cv_jpn_000782-cv_jpn_000782) +Scores: (#C #S #D #I) 127 7 20 15 +REF: n o o g Y O o o y a M e Z a r u o e n a i * h i t o G A a r i P A U k a n R e n k i G y O o m o P A U k o N o f ******* * U k ******* Y o o n i h i k i z u r a r e t e I r u t ******* * ******* * ******* * o ******* * ******* * * I u +HYP: n o o g * ******* * o o y a N e S a r u o e n a i S h i t o ******* * P a r i ******* * * * k a n ******* * e n k i * y ******* * o m o ******* * * * k o M o f K I k E o o n i h i k i z u r a r e t e ******* * r u t O M O o S U T S u +Eval: D D D S S I D D S D D D D D D D D D D D D D S I I S I S D D I I I I I I I I I I I S + +Speaker sentences 109: cv_jpn_000783 #utts: 1 +id: (cv_jpn_000783-cv_jpn_000783) +Scores: (#C #S #D #I) 74 5 15 0 +REF: N a n d e k o n o r O B o c l t o P A U s h o t a i m e N n a N O N i n a r e N a r e s H I I n d a +HYP: * ******* a n d e k o n o r ******* * ******* * o c l t o ******* * * * s h o t a i m e ******* * n a M U O i n a r e Y a r e s * ******* * E n d a +Eval: D D D D D D D D D D D D S S S S D D D S + +Speaker sentences 110: cv_jpn_000784 #utts: 1 +id: (cv_jpn_000784-cv_jpn_000784) +Scores: (#C #S #D #I) 53 2 2 0 +REF: f u t s u u d e a r u k o t O m o R i c l p a n a k o s E e +HYP: f u t s u u d e a r u k o t A m o D i c l p a n a k o s ******* * e +Eval: S S D D + +Speaker sentences 111: cv_jpn_000785 #utts: 1 +id: (cv_jpn_000785-cv_jpn_000785) +Scores: (#C #S #D #I) 62 0 4 0 +REF: t s u y o b i d e t a n j i k a n d e g O o k a i n I i t a m e r u +HYP: t s u y o b i d e t a n j i k a n d e g ******* * o k a i n ******* * i t a m e r u +Eval: D D D D + +Speaker sentences 112: cv_jpn_000786 #utts: 1 +id: (cv_jpn_000786-cv_jpn_000786) +Scores: (#C #S #D #I) 94 2 2 4 +REF: B a k u m a t s u n o d e k i g o T o w ******* * * a * i m a n i t s U u j i r u k y o o k u n n o y a m a d e s u +HYP: P a k u m a t s u n o d e k i g o K o w A P a U i m a n i t s ******* * u j i r u k y o o k u n n o y a m a d e s u +Eval: S S I I I I D D + +Speaker sentences 113: cv_jpn_000787 #utts: 1 +id: (cv_jpn_000787-cv_jpn_000787) +Scores: (#C #S #D #I) 57 5 2 0 +REF: M U k o o k a r a m a c h i n o T O M O r i g a m i e t e k i t a +HYP: * ******* N k o o k a r a m a c h i n o W A K A r i g a m i e t e k i t a +Eval: D D S S S S S + +Speaker sentences 114: cv_jpn_000788 #utts: 1 +id: (cv_jpn_000788-cv_jpn_000788) +Scores: (#C #S #D #I) 102 12 7 9 +REF: N A n i o ******* * ******* * I u b e k i ******* * * K a w a k a r a n A K a C L t a N a N i m O I u b E k i k O t o g a o m o I u k a b a n a ******* * k a c l t A +HYP: M E n i o U Y U u b e k i C H I a w a k a r a n ******* * ******* * a * U t a R a ******* * i m I Y u b I k i k U t o g a o m o Y u k a b a n a G k a c l t U +Eval: S S I I I I S I I I S D D D D D S S D D S S S S S I I S + +Speaker sentences 115: cv_jpn_000789 #utts: 1 +id: (cv_jpn_000789-cv_jpn_000789) +Scores: (#C #S #D #I) 43 3 8 0 +REF: t a m e s H i n I i C L k a I D a k E y a c l t e m i r U +HYP: t a m e s * i n ******* * i ******* * * k a E N a k I y a c l t e m i r ******* * +Eval: D D D D D D S S S D D + +Speaker sentences 116: cv_jpn_000790 #utts: 1 +id: (cv_jpn_000790-cv_jpn_000790) +Scores: (#C #S #D #I) 75 8 4 3 +REF: B o k u s h i k ******* * a i n A i K i m i W a i n a i k o r e w A P a U o o K I n * A c h i G a i k a +HYP: M o k u s h i k G a i n E i G i m i M a i n a i k o r e w ******* * * a * o o T E n T S c h i E a i k a +Eval: S I I S S S D D D D S S I S S + +Speaker sentences 117: cv_jpn_000791 #utts: 1 +id: (cv_jpn_000791-cv_jpn_000791) +Scores: (#C #S #D #I) 87 10 24 11 +REF: * s H U U k A i S H o k a r a ******* * n i j U u g O o t o o m a D E n o ******* * * * m i c h i ******* * W * a * m u k a s H I T o k A W a C L t E i n A k A C L T a +HYP: T s * * C H k E i * J o k a r a E n i j ******* * u g ******* * o t o o m a N U n o P A U m i c h i M A P a U m u k a s * ******* * ******* * o k ******* * ******* * a * O t R i n E k ******* * ******* * * ******* * a +Eval: I D D S S S D S I I D D D D S S I I I I I I S I I D D D D D D D D D D S S S D D D D D D D + +Speaker sentences 118: cv_jpn_000792 #utts: 1 +id: (cv_jpn_000792-cv_jpn_000792) +Scores: (#C #S #D #I) 71 9 12 6 +REF: k a n O J O N o t e e A n w a k o n ******* * * p ******* * o n t e K i n a K a i * K E T S u N I T s u n A G a c l t a +HYP: k a n ******* * G U J o t e e H n w a k o n C L p U o n t e ******* * i n a G a i C H I * K u ******* * U * s u n ******* * ******* * a c l t a +Eval: D D S S S S I I I I I D D S I S S D S D D S D D D D D + +Speaker sentences 119: cv_jpn_000793 #utts: 1 +id: (cv_jpn_000793-cv_jpn_000793) +Scores: (#C #S #D #I) 72 7 6 0 +REF: k o D O m o n o k o r O w a g o h a N h a D e p a u o t O n a N i n a r U t o p a N H a +HYP: k o R U m o n o k o r E w a g o h a ******* * h a R e p a u o t U n a ******* * i n a r ******* * t o p a H A a +Eval: S S S D D S S D D D D S S + +Speaker sentences 120: cv_jpn_000794 #utts: 1 +id: (cv_jpn_000794-cv_jpn_000794) +Scores: (#C #S #D #I) 147 6 9 5 +REF: k o o i t e k i C H o C L k a n t e k i n i p a u p o i ******* * * * E s h i s u t e k i n i p a u w a r e w a r e N o j i k o w a m a s u m a s u * a K A R I t o n a r u n o d E a r u +HYP: k o o i t e k i * T o ******* * * k a n t e k i n i p a u p o i P A U A s h i s u t e k i n i p a u w a r e w a r e M o j i k o w a m a s u m a s u P a ******* * ******* U M E t o n a r u n o d ******* * a r u +Eval: D S D D D I I I I S S I D D D S S S D D + +Speaker sentences 121: cv_jpn_000795 #utts: 1 +id: (cv_jpn_000795-cv_jpn_000795) +Scores: (#C #S #D #I) 151 7 10 8 +REF: h i j o o s h i k i d E a r U k o t o w A P a U M u c h ******* * I o i m i s u r u n o m i d e n a k u p a u s h a k a i ******* * t e k i n i ******* * * * a k u t o m o k a n G a e r a R E r u N o D E a r u +HYP: h i j o o s h i k i d ******* * a r O k o t o w ******* * * a * Y u c h J O o i m i s u r u n o m i d e n a k u p a u s h a k a i E t e k i n i P A U a k u t o m o k a n M a e r a ******* * ******* * r u U o U D a r u +Eval: D D S D D D D S I I S I I I I I I S D D D D S S S + +Speaker sentences 122: cv_jpn_000796 #utts: 1 +id: (cv_jpn_000796-cv_jpn_000796) +Scores: (#C #S #D #I) 97 4 7 0 +REF: j o o s h I k i g a n a o t o k u s h U t e k i n a c h i s h i k i d e a r u n i h a N s H I P A U k a G a k u w a +HYP: j o o s h U k i g a n a o t o k u s h I t e k i n a c h i s h i k i d e a r u n i h a ******* * s * ******* * * * E k a R a k u w a +Eval: S S D D D D D D D S S + +Speaker sentences 123: cv_jpn_000797 #utts: 1 +id: (cv_jpn_000797-cv_jpn_000797) +Scores: (#C #S #D #I) 55 2 0 2 +REF: k o n n a k o t o d e o K O r a r e t e n a s a k e n ******* * a i +HYP: k o n n a k o t o d e o G U r a r e t e n a s a k e n H a i +Eval: S S I I + +Speaker sentences 124: cv_jpn_000798 #utts: 1 +id: (cv_jpn_000798-cv_jpn_000798) +Scores: (#C #S #D #I) 171 12 12 8 +REF: k a k o t o m i r a i ******* * t o G a j i k o m u j u n t e k i n i ******* * * * G e n z a i n I o I T E t a i r i T S u S u R u t o i u n i w a p a u G e n Z A I G a k a t a c h I o m o t a N a ******* * k e r e B a n a r a n a i +HYP: k a k o t o m i r a i E t o W a j i k o m u j u n t e k i n i P A U D e n z a i n ******* * o * C H I t a i r i * K u ******* * u ******* * u t o i u n i w a p a u ******* * e n D N A Y a k a t a c h ******* * o m o t a M a I k e r e M a n a r a n a i +Eval: I I S I I I I S D D D S S S D S D D D D D D S S S S D D S I I S + +Speaker sentences 125: cv_jpn_000799 #utts: 1 +id: (cv_jpn_000799-cv_jpn_000799) +Scores: (#C #S #D #I) 45 1 18 2 +REF: * ******* s h o k i H i Y O o n o t a k a s A g A H A a d o R u n I n a r U +HYP: A s h o k i ******* * i ******* * ******* * o n o t a k a s E g ******* * ******* * ******* * a d o ******* * u n ******* * n a r ******* * +Eval: I I D D D D D D S D D D D D D D D D D D D + + diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..f73f922e7b4656fc1ee860a39e73a712eb849da1 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/hyp.trn @@ -0,0 +1,126 @@ +b o k u n o y i e g a cl t a k a i d n a N n o n a m a e N n i k u r a b e r u t o p o k u n i w a n a j i m i n o n a i m a e b a e b a k a r i d a g k e d o (cv_jpn_000674-cv_jpn_000674) +m a r y o o s u a n o m o n o e r u d i k u i n i k i g a r u k e t e r u (cv_jpn_000675-cv_jpn_000675) +b o k u n o sh I t e e u m o n u o t o w a s U k o sh i ch i g a cl t e i t a (cv_jpn_000676-cv_jpn_000676) +k a k a o r u t a ch i m a n i o i t e pau s e k g a r e g a i sh I k i m e N t e k i d e a r i w a r u w a r e n o j i k o g a j sh I k i s a y o o t e k i d e a r u t o k a N e e r a r e r u t o ch I (cv_jpn_000677-cv_jpn_000677) +i e n i k I t a n e N g a a j i w a s a N h a k o m a i h o r o d e pau pau ch o o d a sh I t a b u N t o o n a j i g u r a i d a (cv_jpn_000678-cv_jpn_000678) +h a w a p i t a m a r e ts u b a a g u n o s e e sh i N by y o o i N n i n u N h I t e y i r u t o k i n i p e sh i o o m u (cv_jpn_000679-cv_jpn_000679) +t a d a N d e a r h a N t a n o h i r o g e r a b a a ch i k o ch i ts u g i h a g i y a a r i k a t a g o ch u i n i d e k i t a h o k o r o b i n a N k a ky o o n e N n o m a o m o n i n a cl t e i r u (cv_jpn_000680-cv_jpn_000680) +k a r a g a k a n o d e pau b e r u n a k a n i h o o m o N k i b o o n o b u sh o o y o b i ch o o z a k i b o o n o b u g o m a i k a r a t o o r i d e s U (cv_jpn_000681-cv_jpn_000681) +k o N b a w a t o t e m o s a m i d e s U (cv_jpn_000682-cv_jpn_000682) +k i N ch o o sh I t a k o ts U k i d e pau cl p a cl t a w a d a s e k i n i h a i r u (cv_jpn_000683-cv_jpn_000683) +m a s a N ky u u r e N t o i cl t a f u k a k a r o k e N ch i k o w u ts U (cv_jpn_000684-cv_jpn_000684) +g o r i y o sh i d e s u s u m e t e ts s u g o g a w a r u k u n a cl t a r a i h I cl k o m e r u y a r e k U ch i (cv_jpn_000685-cv_jpn_000685) +m o j u r N t e k i j i k o o t o i ts U t e k i n i j i k o o j i sh i N o k e e s e e s u r u sh a k a i w a (cv_jpn_000686-cv_jpn_000686) +h a N n o i k e N n i n a r a s a r e r u n a (cv_jpn_000687-cv_jpn_000687) +h i n o g a s o b i t a y o r a z e N k a i d e k o cl ch o m i t e i r u (cv_jpn_000688-cv_jpn_000688) +i j ch i d o w a k o N p o t a j i k a N o n o N d e m i t a i (cv_jpn_000689-cv_jpn_000689) +k o o i n o k o sh I t e pau o t o o s a N t o k a s a N w a N d e t e i k i m a sh I t a (cv_jpn_000690-cv_jpn_000690) +sh i k a sh I t e s o r e g a ts U k u r a r e t a m o n o k a r a ts U k u r u m o r o e t o sh I t e pau d o k o m a d e m o r a w a r u n i s e m a r u t o y u t o k i a r e w a r i n i ch o cl k a N t e k i d e a r u (cv_jpn_000691-cv_jpn_000691) +h a i n i t a m a t a k e m u r i o h a k i d a sh i k u r a i k o o e n i sh I s e N o m o k e r u (cv_jpn_000692-cv_jpn_000692) +w a d a w a d a sh I n a b u s o k u d e ch i u s e N n a r i s o (cv_jpn_000693-cv_jpn_000693) +ts e N s e k a i n o m o s U k a t a ch i i t a sh u n o y o a y i r u s e e s a i y o sh I k I t o s a i u o t o w a h a sh I t e k a N g a i r u k o t o w a d e k i n a i (cv_jpn_000694-cv_jpn_000694) +s a k e n o m a e g n o n i p i r o u b a r a t o y o e t a (cv_jpn_000695-cv_jpn_000695) +s o r e o o t a d a k o h o k u n o z e N d a N k a i sh i k u i t e e d o n o k a a k U t o m o m i m i r u k o t o w a (cv_jpn_000696-cv_jpn_000696) +o k ky o m e m o k f u r a z u n i pau s u m o d e k e e N m o y a cl t e i t a (cv_jpn_000697-cv_jpn_000697) +a d a i w a a n a i N ch sh i u m a t a k o b a y a sh i s a N t o a s o b e i m a s U (cv_jpn_000698-cv_jpn_000698) +a s o o k o n i i h i i t o o g a pau i m a s u n e pau a r o h i t o w a t a r e t e sh o (cv_jpn_000699-cv_jpn_000699) +w a t a j i w a k ky e n o o k a n a pau n o o d o k a e i t a i d e s U (cv_jpn_000700-cv_jpn_000700) +ky o o r e N k a n a p e N ky u o sh I t e i m a s U (cv_jpn_000701-cv_jpn_000701) +a o cl t o s u m i m r a s e pau (cv_jpn_000702-cv_jpn_000702) +ch i k a s U pau b o n o o e N b u r i e w a pau y a k e k i m i n i k a e cl d e h a g e s U k e n o cl t a (cv_jpn_000703-cv_jpn_000703) +i ts z u m o k o n o i N p I i ts o o ts U k a cl t e i t a n o d e m i j i k a h a u n a r i m a sh I t a (cv_jpn_000704-cv_jpn_000704) +w a t a sh i w a pau e i g o g a h a n a sh I t e m a s U (cv_jpn_000705-cv_jpn_000705) +o s e i k e e r y a j i b u a N d e t o cl t e k i n a (cv_jpn_000706-cv_jpn_000706) +a i sh u u e k a r a n i sh u u k a N h a i g a i e r u y o o k o o o n i k i m a s U (cv_jpn_000707-cv_jpn_000707) +o r e m o k i n i n a r u n a (cv_jpn_000708-cv_jpn_000708) +d a r e i g a ts U k a cl t e r u n o k a a h w o k a r u n a i (cv_jpn_000709-cv_jpn_000709) +p u r a o z a n o b a j o N g a pau a g a r u t o o s U k o sh I i u u r e sh i i (cv_jpn_000710-cv_jpn_000710) +m a t a a t a r a sh i i a i d o r u g a d e t e k i t a (cv_jpn_000711-cv_jpn_000711) +m a j i d e y a cl t o n o k a (cv_jpn_000712-cv_jpn_000712) +ch o o d o s a m o t U k i n i pau ky o o j i g a h a i t e k i t a (cv_jpn_000713-cv_jpn_000713) +i cl s o n i k ch i e N sh I t e o (cv_jpn_000714-cv_jpn_000714) +s o r e k a s a t e n o t o r e s u N (cv_jpn_000715-cv_jpn_000715) +f U t a r i o w a r e j i e i k i s u e e s a N sh I t a (cv_jpn_000716-cv_jpn_000716) +t o m a t o k a n a N g a n o a k a i s o s u g a k a k a cl t e r i u o (cv_jpn_000717-cv_jpn_000717) +k o g o k a r a t a t e n a N a s u n o w a k i b i s u i (cv_jpn_000718-cv_jpn_000718) +n i z u g e o w o s U k a r i sh i b o cl t e pau a j i g a n a j i m i y o r i s u r u (cv_jpn_000719-cv_jpn_000719) +n e t o k i n i h a m a cl t a r a k a n e g a g a m a cl t a U (cv_jpn_000720-cv_jpn_000720) +s i t a k a i r i y o o n i n a r u N d a (cv_jpn_000721-cv_jpn_000721) +k o s e h a h a i y u t o y u y o r i a k u g a ts u y o a e k a N j i (cv_jpn_000722-cv_jpn_000722) +f e i j i k a r u n o s a o m a z a m a d a t o m i s e ts U k e r a r e t a (cv_jpn_000723-cv_jpn_000723) +k o s U p a y o k e d e b a pau s o k o k o n o m o N d a e a g a m a N s u r e (cv_jpn_000724-cv_jpn_000724) +i N n a y a cl t e m a s U k a r a t a i j o o b u d e s u i u o (cv_jpn_000725-cv_jpn_000725) +k o n o t o j o k a N h a i cl t a sh u u k a N k i n i cl t a (cv_jpn_000726-cv_jpn_000726) +k o n o d e N ch i f u u k i r e ch i a cl t a (cv_jpn_000727-cv_jpn_000727) +a m a y e u d o r i s u r u t o k o r o g a n a k U t e k o m a cl t a (cv_jpn_000728-cv_jpn_000728) +y a s U k a s u r u e o r i sh I t ts u w a g e t o h o sh e (cv_jpn_000729-cv_jpn_000729) +m a s e g o k o n a k o t e i n a r u t o w a m o n a k a cl t a (cv_jpn_000730-cv_jpn_000730) +s a i g o n i w a r a y o t o r i n i k u r a s U t a i r u (cv_jpn_000731-cv_jpn_000731) +k o r a e n a N n a e i m i g a r u d a pau (cv_jpn_000732-cv_jpn_000732) +o t i i a (cv_jpn_000733-cv_jpn_000733) +sh a sh e i (cv_jpn_000734-cv_jpn_000734) +n i (cv_jpn_000735-cv_jpn_000735) +h a ch i (cv_jpn_000736-cv_jpn_000736) +h a i (cv_jpn_000737-cv_jpn_000737) +t e b u r u n o o y e n i k a b i N g a r i m a s U (cv_jpn_000738-cv_jpn_000738) +w o t a sh i w a o m a i y a s a s a N b o sh i m a s U (cv_jpn_000739-cv_jpn_000739) +a t a r a sh i i k u ts u o o h a i t e d e k a k e m a s U (cv_jpn_000740-cv_jpn_000740) +k o t o sh i n o n a ts e y a s a m i o w a u m i n i m o i k i m a sh I t a sh i i y a m a n i m o n o r i b a sh I t a (cv_jpn_000741-cv_jpn_000741) +w a t a sh i w a i r o y i r o n o b e N g o o ch i b u N n o m u n e d e k o sh i r a e t e m i m a sh I t a (cv_jpn_000742-cv_jpn_000742) +n a N d e k o o m o s u o o b a i h e d a n a N d a r o (cv_jpn_000743-cv_jpn_000743) +t a r e N t o k a r a ky o k u a n a n i k a e i e cl t u e k e e sh I s a k I u g e a (cv_jpn_000744-cv_jpn_000744) +d o g e z a s e r e b a i cl t e m o N sh a n a i (cv_jpn_000745-cv_jpn_000745) +d e t a n o a i d a k a n o j i w a j i b u N t o i cl t e e n o k ky u o r i y o t a n o cl t a (cv_jpn_000746-cv_jpn_000746) +k o n o r e e n i N n a N k a w a h sh s a sh i b u r i n i n i t a (cv_jpn_000747-cv_jpn_000747) +o o k I k t a s a i d a o ch i e N j i e o s u r u (cv_jpn_000748-cv_jpn_000748) +k a r e w a t a m a o k a k i m u sh i cl t a (cv_jpn_000749-cv_jpn_000749) +k o m a t e sh I t e a r i m a s U (cv_jpn_000750-cv_jpn_000750) +o n o d o k U s e N k a i j o w a k I i t e r u (cv_jpn_000751-cv_jpn_000751) +r e e j o o k o a k e t a cl t o U t a N n a n i g a h i ch u y o k a w a s u r e t a (cv_jpn_000752-cv_jpn_000752) +i i t ch i (cv_jpn_000753-cv_jpn_000753) +h a ch i (cv_jpn_000754-cv_jpn_000754) +k i i w e a (cv_jpn_000755-cv_jpn_000755) +d e i (cv_jpn_000756-cv_jpn_000756) +a sh i ch i (cv_jpn_000757-cv_jpn_000757) +y o o b o o d a s u n o N k a u sh I t o a s U k u n a i (cv_jpn_000758-cv_jpn_000758) +n o o k a r e t o k u y u n o i k ky u e m a k a s e n o k o m a sh a r u (cv_jpn_000759-cv_jpn_000759) +k o n o d a i k u w a a N k o g a o o k U t e y o k U k a i m a s U (cv_jpn_000760-cv_jpn_000760) +d e sh o o k i n a g a r a sh o o s u e ts o o y o m i m a s U (cv_jpn_000761-cv_jpn_000761) +k o N n o o k i n a N g o g u r o ts u k e n a i t i k e n a e i N d e s U k a (cv_jpn_000762-cv_jpn_000762) +k a r a e n o b o o i k o w a t o m a r a n a i (cv_jpn_000763-cv_jpn_000763) +i k i t e i k a N m a n e (cv_jpn_000764-cv_jpn_000764) +t o o j u d o sh s e s a k a k t e k i n a h a ts u m e d a cl t a n e (cv_jpn_000765-cv_jpn_000765) +h o N t e k i o t a s o a ch I k a cl U t k a N (cv_jpn_000766-cv_jpn_000766) +k a a w a h i e w a cl t e i d a (cv_jpn_000767-cv_jpn_000767) +ch e cl k e (cv_jpn_000768-cv_jpn_000768) +o (cv_jpn_000769-cv_jpn_000769) +a sh I t ch i i (cv_jpn_000770-cv_jpn_000770) +k o (cv_jpn_000771-cv_jpn_000771) +i i e a (cv_jpn_000772-cv_jpn_000772) +n a m a i k a r a sh e t e k t o o s u g i r u (cv_jpn_000773-cv_jpn_000773) +j i k o n o s o t o n y a r u t o y u n o w a t a N n i j i k o n e sh i k N s o ts s o n i a r u t o y u k o t o d e n a k u (cv_jpn_000774-cv_jpn_000774) +s o r w a sh i r a n a k U t e i d e s U (cv_jpn_000775-cv_jpn_000775) +k i m i t o k o n o ky o o ts u n o sh i g e w a d a r e i t o r u i pau m i a t a r a n a i (cv_jpn_000776-cv_jpn_000776) +s u e g e e o o t o i N n a cl t e k i t e r u n o n a cl (cv_jpn_000777-cv_jpn_000777) +k o n o h e N d e s U k o sh i a s u i m a sh o (cv_jpn_000778-cv_jpn_000778) +d e N sh a r i n o r u t o k i k i cl t o k a i m a s U d a U t a U t U (cv_jpn_000779-cv_jpn_000779) +t a m a o g w a i k o k o j i u r a N b u r a i e s U (cv_jpn_000780-cv_jpn_000780) +g e e s U p i w a m a k i o ts u j i t e pau i n e s t o sh i r i y a cl t a t U U (cv_jpn_000781-cv_jpn_000781) +n o o g o o y a n e s a r u o e n a i sh I t o p a r i k a N e N k i y o m o k o m o f k I k e o o n i h I k i z u r a r e t e r u t o m o o s U ts U (cv_jpn_000782-cv_jpn_000782) +a N d e k o n o r o cl t o sh o t a i m e n a m u o i n a r e y a r e s e N d a (cv_jpn_000783-cv_jpn_000783) +f U ts u u d e a r u k o t a m o d i cl p a n a k o s e (cv_jpn_000784-cv_jpn_000784) +ts u y o b i d e t a N j i k a N d e g o k a i n i t a m e r u (cv_jpn_000785-cv_jpn_000785) +p a k u m a ts u n o d e k i g o k o w a pau i m a n i ts u j i r u ky o o k u N n o y a m a d e s U (cv_jpn_000786-cv_jpn_000786) +N k o o k a r a m a ch i n o w a k a r i g a m i e t e k i t a (cv_jpn_000787-cv_jpn_000787) +m e n i o u y u u b e k I ch i a w a k a r a n a U t a r a i m i y u b i k I k u t o g a o m o y u k a b a n a g k a cl t U (cv_jpn_000788-cv_jpn_000788) +t a m e s i n i k a e n a k i y a cl t e m i r (cv_jpn_000789-cv_jpn_000789) +m o k U sh I k g a i n e i g i m i m a i n a i k o r e w a o o t e N ts ch i e a i k a (cv_jpn_000790-cv_jpn_000790) +ts ch k e i j o k a r a e n i j u g o t o o m a N u N o pau m i ch i m a pau m u k a s o k a o t r i n e k a (cv_jpn_000791-cv_jpn_000791) +k a N g u j o t e e h N w a k o N cl p u o N t e i n a g a i ch i k u u s u n a cl t a (cv_jpn_000792-cv_jpn_000792) +k o r u m o n o k o r e w a g o h a h a r e pau o t u n a i n a r t o p a h a a (cv_jpn_000793-cv_jpn_000793) +k o o i t e k I t o k a N t e k i n i pau p o i pau a sh i s u t e k i n i pau w a r e w a r e m o j i k o w a m a s u m a s u pau m e t o n a r u n o d a r u (cv_jpn_000794-cv_jpn_000794) +h i j o o sh I k i d a r o k o t o w a y u ch j o o i m i s u r u n o m i d e n a k u pau sh a k a i e t e k i n i pau a k U t o m o k a N m a e r a r u u o u d a r u (cv_jpn_000795-cv_jpn_000795) +j o o sh u k i g a n a o t o k u sh I t e k i n a ch I sh I k i d e a r u n i h a s e k a r a k u w a (cv_jpn_000796-cv_jpn_000796) +k o N n a k o t o d e o g u r a r e t e n a s a k e n h a i (cv_jpn_000797-cv_jpn_000797) +k a k o t o m i r a i e t o w a j i k o m u j u N t e k i n i pau d e N z a i n o ch i t a i r i k U u u t o i u n i w a pau e N d n a y a k a t a ch o m o t a m a i k e r e m a n a r a n a i (cv_jpn_000798-cv_jpn_000798) +a sh o k I i o n o t a k a s e g a d o u N n a r (cv_jpn_000799-cv_jpn_000799) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..682886c235d5a1f6d054262a3e893267b1c465ce --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/ref.trn @@ -0,0 +1,126 @@ +b o k u n o i e g a a cl t a k a i d a N n o n a m a e n i k u r a b e r u t o b o k u n i w a n a j i m i n o n a i n a m a e b a k a r i d a k e d o (cv_jpn_000674-cv_jpn_000674) +n a i y o o s o n o m o n o y o r i f u N i k i g a u k e t e r u (cv_jpn_000675-cv_jpn_000675) +b o k u n o sh i cl t e i r u m o n o t o w a s U k o sh I ch i g a cl t e i t a (cv_jpn_000676-cv_jpn_000676) +k a k a r u t a ch i b a n i o i t e pau s e k a i g a i sh I k i m e N t e k i d e a r i pau w a r e w a r e n o j i k o g a i sh I k i s a y o o t e k i d e a r u t o k a N g a e r a r e r u t o k i (cv_jpn_000677-cv_jpn_000677) +i e n i k i t a n e N g a j o o w a pau s a N by a k u m a i h o d o d e pau ch o o d o pau d a sh I t a b u N t o o n a j i g u r a i d a (cv_jpn_000678-cv_jpn_000678) +h a h a w a p i i t a a m a r i cl ts u b a a g u n o s e e sh i N by o o i N n i ny u u i N sh I t e i r u t o k i n i b e cl sh i i o u m u (cv_jpn_000679-cv_jpn_000679) +t a t a N d e a r u h a N t e N o h i r o g e r e b a pau a ch i k o ch i n i ts u g i h a g i g a a r i pau k a t a g u ch i n i d e k i t a h o k o r o b i n a N k a pau ky o n e N n o m a m a n i n a cl t e i r u (cv_jpn_000680-cv_jpn_000680) +k a r e g a pau k o n o sh u cl ch o o ch u u n i pau h o o m o N k i b o o n o b u sh o pau o y o b i pau ch o o s a k i b o o n o b u m o N w a pau i k a n o t o o r i d e s U (cv_jpn_000681-cv_jpn_000681) +k o N b a N w a t o t e m o s a m u i d e s U (cv_jpn_000682-cv_jpn_000682) +k i N ch o o sh I t a k a o ts U k i d e b a cl t a a w a d a s e k i n i h a i r u (cv_jpn_000683-cv_jpn_000683) +m a s a n i ky u u d e N t o i cl t a f u u k a k u a r u k e N ch I k u b u ts u (cv_jpn_000684-cv_jpn_000684) +g o r i o sh i d e s u s u m e t e ts u g o o g a w a r u k u n a cl t a r a h i cl k o m e r u y a r i k U ch i (cv_jpn_000685-cv_jpn_000685) +m u j u N t e k i j i k o d o o i ts u t e k i n i j i k o j i sh i N o k e e s e e s u r u sh a k a i w a (cv_jpn_000686-cv_jpn_000686) +f a N n o i k e N n i n a g a s a r e r u n a (cv_jpn_000687-cv_jpn_000687) +i n u g a a s o b i t a i o o r a z e N k a i d e k o cl ch i o m i t e r u (cv_jpn_000688-cv_jpn_000688) +i ch i d o w a k o o N p o t a a j u k a N o n o N d e m i t a i (cv_jpn_000689-cv_jpn_000689) +k o o i i n o k o sh I t e pau o t o o s a N t o o k a a s a N w a d e t e i k i m a sh I t a (cv_jpn_000690-cv_jpn_000690) +sh I k a sh I t e s o r e g a ts U k u r a r e t a m o n o k a r a ts U k u r u m o n o e t o sh I t e pau d o k o m a d e m o w a r e w a r e n i s e m a r u t o i u t o k i pau w a r e w a r e n i ch o cl k a N t e k i d e a r u (cv_jpn_000691-cv_jpn_000691) +h a i n i t a m a cl t a k e m u r i o h a k i d a sh i pau k u r a i k o o e N n i sh I s e N o m u k e r u (cv_jpn_000692-cv_jpn_000692) +m a d a m a d a sh i n a f u s o k u d e ch u u s e N n i n a r i s o o (cv_jpn_000693-cv_jpn_000693) +z e N s e k a i n o m o ts U k a t a ch i pau w a t a sh i n o i w a y u r u s e e s a N y o o sh I k i t o s a y o o t o w a h a n a sh I t e k a N g a e r u k o t o w a d e k i n a i (cv_jpn_000694-cv_jpn_000694) +s a k e n o m a n a i n o n i b i i r u h a r a t o i w a r e t a (cv_jpn_000695-cv_jpn_000695) +s o r e o t a d a k a g a k u n o z e N d a N k a i pau h I k u i t e e d o n o k a g a k U t o n o m i m i r u k o t o w a (cv_jpn_000696-cv_jpn_000696) +w a k i m e m o f u r a z u n i s u m a h o d e g e e m u o y a cl t e i t a (cv_jpn_000697-cv_jpn_000697) +w a t a sh i w a r a i sh u u m a t a k o b a y a sh I s a N t o a s o b i m a s U (cv_jpn_000698-cv_jpn_000698) +a s o k o n i h I t o g a i m a s U n e a n o h I t o w a d a r e d e sh o o (cv_jpn_000699-cv_jpn_000699) +w a t a sh i w a k i n o o k a r a n o d o g a i t a i d e s U (cv_jpn_000700-cv_jpn_000700) +ky o n e N k a r a b e N ky o o sh I t e i m a s U (cv_jpn_000701-cv_jpn_000701) +ch o cl t o s u m i m a s e N (cv_jpn_000702-cv_jpn_000702) +sh I k a sh i pau s o n o o o e N b u r i w a pau y a k e g i m i n i pau k a e cl t e h a g e sh i k u n a cl t a (cv_jpn_000703-cv_jpn_000703) +i ts u m o k o n o e N p I ts u o ts U k a cl t e i t a n o d e pau m i j i k a k u n a r i m a sh I t a (cv_jpn_000704-cv_jpn_000704) +w a t a sh i w a e e g o g a h a n a s e m a s U (cv_jpn_000705-cv_jpn_000705) +h o sh i k e ry a j i b u N d e t o cl t e k i n a (cv_jpn_000706-cv_jpn_000706) +r a i sh u u k a r a n i sh u u k a N pau k a i g a i e ry o k o o n i i k i m a s U (cv_jpn_000707-cv_jpn_000707) +o r e m o k i n i n a r u n a (cv_jpn_000708-cv_jpn_000708) +d a r e g a ts U k a cl t e r u n o k a w a k a r a n a i (cv_jpn_000709-cv_jpn_000709) +b u r a u z a n o b a a j o N g a a g a r u t o s U k o sh i u r e sh i i (cv_jpn_000710-cv_jpn_000710) +m a t a a t a r a sh i i a i d o r u g a d e t e k i t a (cv_jpn_000711-cv_jpn_000711) +m a j i d e y a cl t a n o k a (cv_jpn_000712-cv_jpn_000712) +ch o o d o s o n o t o k i n i pau ky o o j u g a h a i cl t e k i t a (cv_jpn_000713-cv_jpn_000713) +i cl sh o n i ch i N sh I t e y o (cv_jpn_000714-cv_jpn_000714) +s o r e g a s a t e N n o d o r e s u (cv_jpn_000715-cv_jpn_000715) +f U t a r i w a r e j i e i k I s e e s a N sh I t a (cv_jpn_000716-cv_jpn_000716) +t o m a t o k a n a N k a n o a k a i s o o s u g a k a k a cl t e r u y o (cv_jpn_000717-cv_jpn_000717) +k o k o k a r a t a t e n a o s u n o w a k i b i sh i i (cv_jpn_000718-cv_jpn_000718) +m i z u k e o sh i cl k a r i sh i b o cl t e a j i g a n a j i m u y o o n i s u r u (cv_jpn_000719-cv_jpn_000719) +n e t o g e n i h a m a cl t a r a k i N g a t a m a cl t a (cv_jpn_000720-cv_jpn_000720) +i ts U k a e r u y o o n i n a r u N d a (cv_jpn_000721-cv_jpn_000721) +k o s e e h a h a i y u u t o i u y o r i a k u g a ts u y o i k a N j i (cv_jpn_000722-cv_jpn_000722) +f i j i k a r u n o s a o m a z a m a z a t o m i s e ts U k e r a r e t a (cv_jpn_000723-cv_jpn_000723) +k o s U p a y o k e r e b a s o k o s o k o n o m o N d a i w a g a m a N s u r u (cv_jpn_000724-cv_jpn_000724) +m i N n a y a cl t e m a s U k a r a d a i j o o b u d e s U y o (cv_jpn_000725-cv_jpn_000725) +k o n o t o sh o k a N pau h a i cl t a sh u N k a N k i n i i cl t a (cv_jpn_000726-cv_jpn_000726) +k o n o d e N ch i pau s u g u k i r e ch i cl t a (cv_jpn_000727-cv_jpn_000727) +a m a y a d o r i s u r u t o k o r o g a n a k U t e k o m a cl t a (cv_jpn_000728-cv_jpn_000728) +y a s u k U s u r u y o r i sh I ts u o a g e t e h o sh i i (cv_jpn_000729-cv_jpn_000729) +m a s a k a k o N n a k o t o n i n a r o o t o w a o m o w a n a k a cl t a (cv_jpn_000730-cv_jpn_000730) +s a i g o n i w a r a i o t o r i n i k u r u s U t a i r u (cv_jpn_000731-cv_jpn_000731) +k o r e pau n a n i n o i m i g a a r u N d a (cv_jpn_000732-cv_jpn_000732) +i i e (cv_jpn_000733-cv_jpn_000733) +sh i (cv_jpn_000734-cv_jpn_000734) +n i (cv_jpn_000735-cv_jpn_000735) +w a ch i (cv_jpn_000736-cv_jpn_000736) +h a i (cv_jpn_000737-cv_jpn_000737) +t e e b u r u n o u e n i k a b i N g a a r i m a s U (cv_jpn_000738-cv_jpn_000738) +w a t a sh i w a m a i a s a s a N p o sh i m a s U (cv_jpn_000739-cv_jpn_000739) +a t a r a sh i i k U ts u o h a i t e d e k a k e m a s U (cv_jpn_000740-cv_jpn_000740) +k o t o sh i n o n a ts u y a s u m i w a pau u m i n i m o i k i m a sh I t a sh i pau y a m a n i m o n o b o r i m a sh I t a (cv_jpn_000741-cv_jpn_000741) +w a t a sh i w a pau i r o i r o n o b e N g o o pau j i b u N n o m u n e d e k o sh i r a e t e m i m a sh I t a (cv_jpn_000742-cv_jpn_000742) +n a N d e k o o m o sh o o b a i h e t a n a N d a r o (cv_jpn_000743-cv_jpn_000743) +t a r e N t o k a r a ky o k u a n a n i k a e t e k e e h I s a k u g e N (cv_jpn_000744-cv_jpn_000744) +d o g e z a s u r e b a i i cl t e m o N j a n a i (cv_jpn_000745-cv_jpn_000745) +d e e t o n o a i d a pau k a n o j o w a j i b u N t o i cl t e e n o ky o r i o t a m o cl t a (cv_jpn_000746-cv_jpn_000746) +k o n o g e e n i N n a N k a h I s a sh i b u r i n i m i t a (cv_jpn_000747-cv_jpn_000747) +o o k I k u s a i d o ch e N j i o s u r u (cv_jpn_000748-cv_jpn_000748) +k a r e w a a t a m a o k a k i m u sh i cl t a (cv_jpn_000749-cv_jpn_000749) +o m a ch i sh I t e o r i m a s U (cv_jpn_000750-cv_jpn_000750) +k o n o ky o k u pau s e N k a i i j o o w a k i i t e r u (cv_jpn_000751-cv_jpn_000751) +r e e z o o k o o a k e t a t o t a N pau n a n i g a h I ts u y o o k a w a s u r e t a (cv_jpn_000752-cv_jpn_000752) +i ch i (cv_jpn_000753-cv_jpn_000753) +w a ch i (cv_jpn_000754-cv_jpn_000754) +i i e (cv_jpn_000755-cv_jpn_000755) +r e i (cv_jpn_000756-cv_jpn_000756) +sh I ch i (cv_jpn_000757-cv_jpn_000757) +y o o b o o w a d a s u n o n i k a u h I t o w a s U k u n a i (cv_jpn_000758-cv_jpn_000758) +r o o k a r u t o k u y u u n o i k i o i m a k a s e n o k o m a a sh a r u (cv_jpn_000759-cv_jpn_000759) +k o n o d a i f U k u w a a N k o g a o o k U t e y o k U k a i m a s U (cv_jpn_000760-cv_jpn_000760) +j i sh o b i k i n a g a r a sh o o s e ts u o y o m i m a s U (cv_jpn_000761-cv_jpn_000761) +k o N n a o o k i n a g o o g u r u o ts U k e n a i t o i k e n a i N d e s U k a (cv_jpn_000762-cv_jpn_000762) +k a r e e n o b o o ry o k u w a t o m a r a n a i (cv_jpn_000763-cv_jpn_000763) +i k i t e t a N d a n e (cv_jpn_000764-cv_jpn_000764) +t o o j i t o sh I ch a k a cl k I t e k i n a h a ts u m e e d a cl t a n e (cv_jpn_000765-cv_jpn_000765) +s o n o t o k i pau w a t a sh i w a ch I k a r a ts U k i t a (cv_jpn_000766-cv_jpn_000766) +k a w a g a h i a g a cl t e i t a (cv_jpn_000767-cv_jpn_000767) +i ch i (cv_jpn_000768-cv_jpn_000768) +n i (cv_jpn_000769-cv_jpn_000769) +sh I ch i (cv_jpn_000770-cv_jpn_000770) +g o (cv_jpn_000771-cv_jpn_000771) +i i e (cv_jpn_000772-cv_jpn_000772) +n a m a e k a r a sh I t e t e k I t o o s u g i r u (cv_jpn_000773-cv_jpn_000773) +j i k o n o s o t o n i a r u t o i u n o w a t a N n i j i k o n o i sh I k i n o s o t o n i a r u t o i u k o t o d e n a k u (cv_jpn_000774-cv_jpn_000774) +s o r e w a sh i r a n a k U t e i i d e s U (cv_jpn_000775-cv_jpn_000775) +k i m i t o b o k u n o ky o o ts u u n o sh i r i a i w a d a r e h I t o r i pau m i a t a r a n a i (cv_jpn_000776-cv_jpn_000776) +s u g e e d a i j i n i n a cl t e k I t e r u n o n a (cv_jpn_000777-cv_jpn_000777) +k o n o a t a r i d e s U k o sh i y a s u m i m a sh o o (cv_jpn_000778-cv_jpn_000778) +d e N sh a n i n o r u t o k i pau k i cl p u o k a i m a s U (cv_jpn_000779-cv_jpn_000779) +t a m a g o w a i cl k o g o j u u g u r a m u g u r a i d e s U (cv_jpn_000780-cv_jpn_000780) +g i r e s U p i i w a m a cl g i i o ts u u j i t e i n e s U t o sh i r i a cl t a (cv_jpn_000781-cv_jpn_000781) +n o o gy o o o y a m e z a r u o e n a i h I t o g a a r i pau k a N r e N k i gy o o m o pau k o n o f U ky o o n i h I k i z u r a r e t e i r u t o i u (cv_jpn_000782-cv_jpn_000782) +n a N d e k o n o r o b o cl t o pau sh o t a i m e N n a n o n i n a r e n a r e sh i i N d a (cv_jpn_000783-cv_jpn_000783) +f U ts u u d e a r u k o t o m o r i cl p a n a k o s e e (cv_jpn_000784-cv_jpn_000784) +ts u y o b i d e t a N j i k a N d e g o o k a i n i i t a m e r u (cv_jpn_000785-cv_jpn_000785) +b a k u m a ts u n o d e k i g o t o w a i m a n i ts u u j i r u ky o o k u N n o y a m a d e s U (cv_jpn_000786-cv_jpn_000786) +m u k o o k a r a m a ch i n o t o m o r i g a m i e t e k i t a (cv_jpn_000787-cv_jpn_000787) +n a n i o i u b e k I k a w a k a r a n a k a cl t a n a n i m o i u b e k I k o t o g a o m o i u k a b a n a k a cl t a (cv_jpn_000788-cv_jpn_000788) +t a m e sh i n i i cl k a i d a k e y a cl t e m i r u (cv_jpn_000789-cv_jpn_000789) +b o k U sh i k a i n a i k i m i w a i n a i k o r e w a pau o o k i n a ch i g a i k a (cv_jpn_000790-cv_jpn_000790) +sh u u k a i sh o k a r a n i j u u g o o t o o m a d e n o m i ch i w a m u k a sh I t o k a w a cl t e i n a k a cl t a (cv_jpn_000791-cv_jpn_000791) +k a n o j o n o t e e a N w a k o N p o N t e k i n a k a i k e ts u n i ts u n a g a cl t a (cv_jpn_000792-cv_jpn_000792) +k o d o m o n o k o r o w a g o h a N h a d e pau o t o n a n i n a r u t o p a N h a (cv_jpn_000793-cv_jpn_000793) +k o o i t e k I ch o cl k a N t e k i n i pau p o i e sh I s u t e k i n i pau w a r e w a r e n o j i k o w a m a s u m a s u a k a r i t o n a r u n o d e a r u (cv_jpn_000794-cv_jpn_000794) +h i j o o sh I k i d e a r u k o t o w a pau m u ch i o i m i s u r u n o m i d e n a k u pau sh a k a i t e k i n i a k U t o m o k a N g a e r a r e r u n o d e a r u (cv_jpn_000795-cv_jpn_000795) +j o o sh I k i g a n a o t o k U sh u t e k i n a ch I sh i k i d e a r u n i h a N sh i pau k a g a k u w a (cv_jpn_000796-cv_jpn_000796) +k o N n a k o t o d e o k o r a r e t e n a s a k e n a i (cv_jpn_000797-cv_jpn_000797) +k a k o t o m i r a i t o g a j i k o m u j u N t e k i n i g e N z a i n i o i t e t a i r i ts U s u r u t o i u n i w a pau g e N z a i g a k a t a ch i o m o t a n a k e r e b a n a r a n a i (cv_jpn_000798-cv_jpn_000798) +sh o k I h i y o o n o t a k a s a g a h a a d o r u n i n a r u (cv_jpn_000799-cv_jpn_000799) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/result.txt b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..bc3f84bcf1acaac3680e95d7813d0920866041b4 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/result.txt @@ -0,0 +1,1553 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,---------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/hyp.trn| +|---------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000674 | 1 75 | 94.7 4.0 1.3 6.7 12.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000675 | 1 33 | 81.8 15.2 3.0 15.2 33.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000676 | 1 38 | 92.1 2.6 5.3 2.6 10.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000677 | 1 97 | 91.8 6.2 2.1 3.1 11.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000678 | 1 65 | 86.2 6.2 7.7 3.1 16.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000679 | 1 67 | 79.1 7.5 13.4 3.0 23.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000680 | 1 102 | 88.2 5.9 5.9 2.9 14.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000681 | 1 81 | 74.1 17.3 8.6 3.7 29.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000682 | 1 23 | 91.3 0.0 8.7 0.0 8.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000683 | 1 40 | 92.5 2.5 5.0 5.0 12.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000684 | 1 39 | 82.1 10.3 7.7 0.0 17.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000685 | 1 54 | 96.3 1.9 1.9 5.6 9.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000686 | 1 52 | 96.2 1.9 1.9 5.8 9.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000687 | 1 23 | 91.3 8.7 0.0 0.0 8.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000688 | 1 37 | 86.5 5.4 8.1 5.4 18.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000689 | 1 32 | 90.6 3.1 6.3 3.1 12.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000690 | 1 45 | 93.3 0.0 6.7 2.2 8.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000691 | 1 110 | 91.8 4.5 3.6 0.0 8.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000692 | 1 52 | 92.3 1.9 5.8 0.0 7.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000693 | 1 35 | 80.0 11.4 8.6 0.0 20.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000694 | 1 89 | 82.0 12.4 5.6 0.0 18.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000695 | 1 33 | 69.7 18.2 12.1 3.0 33.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000696 | 1 61 | 88.5 8.2 3.3 1.6 13.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000697 | 1 38 | 81.6 7.9 10.5 10.5 28.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000698 | 1 40 | 87.5 7.5 5.0 10.0 22.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000699 | 1 38 | 89.5 7.9 2.6 15.8 26.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000700 | 1 31 | 87.1 12.9 0.0 12.9 25.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000701 | 1 24 | 83.3 16.7 0.0 4.2 20.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000702 | 1 14 | 85.7 14.3 0.0 7.1 21.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000703 | 1 52 | 76.9 19.2 3.8 1.9 25.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000704 | 1 49 | 91.8 6.1 2.0 6.1 14.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000705 | 1 24 | 91.7 8.3 0.0 12.5 20.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000706 | 1 24 | 87.5 8.3 4.2 16.7 29.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000707 | 1 40 | 87.5 5.0 7.5 12.5 25.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000708 | 1 15 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000709 | 1 28 | 92.9 7.1 0.0 10.7 17.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000710 | 1 36 | 91.7 5.6 2.8 11.1 19.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000711 | 1 28 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000712 | 1 15 | 93.3 6.7 0.0 0.0 6.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000713 | 1 33 | 84.8 12.1 3.0 0.0 15.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000714 | 1 15 | 86.7 6.7 6.7 13.3 26.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000715 | 1 19 | 84.2 10.5 5.3 5.3 21.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000716 | 1 26 | 100.0 0.0 0.0 7.7 7.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000717 | 1 37 | 91.9 2.7 5.4 2.7 10.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000718 | 1 28 | 85.7 14.3 0.0 3.6 17.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000719 | 1 41 | 80.5 17.1 2.4 4.9 24.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000720 | 1 29 | 86.2 13.8 0.0 6.9 20.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000721 | 1 20 | 80.0 20.0 0.0 5.0 25.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000722 | 1 36 | 88.9 5.6 5.6 2.8 13.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000723 | 1 37 | 97.3 2.7 0.0 2.7 5.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000724 | 1 41 | 85.4 7.3 7.3 2.4 17.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000725 | 1 32 | 90.6 6.3 3.1 3.1 12.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000726 | 1 32 | 87.5 6.3 6.3 0.0 12.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000727 | 1 23 | 87.0 4.3 8.7 4.3 17.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000728 | 1 34 | 97.1 2.9 0.0 2.9 5.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000729 | 1 29 | 79.3 17.2 3.4 3.4 24.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000730 | 1 38 | 71.1 13.2 15.8 0.0 28.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000731 | 1 30 | 93.3 6.7 0.0 0.0 6.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000732 | 1 22 | 77.3 4.5 18.2 13.6 36.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000733 | 1 3 | 66.7 33.3 0.0 66.7 100.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000734 | 1 2 | 100.0 0.0 0.0 150.0 150.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000735 | 1 2 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000736 | 1 4 | 75.0 25.0 0.0 0.0 25.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000737 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000738 | 1 27 | 88.9 3.7 7.4 3.7 14.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000739 | 1 25 | 92.0 8.0 0.0 8.0 16.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000740 | 1 28 | 100.0 0.0 0.0 3.6 3.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000741 | 1 60 | 88.3 6.7 5.0 1.7 13.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000742 | 1 54 | 94.4 1.9 3.7 1.9 7.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000743 | 1 27 | 92.6 7.4 0.0 3.7 11.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000744 | 1 37 | 94.6 5.4 0.0 13.5 18.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000745 | 1 25 | 88.0 8.0 4.0 0.0 12.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000746 | 1 46 | 89.1 6.5 4.3 6.5 17.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000747 | 1 31 | 90.3 9.7 0.0 6.5 16.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000748 | 1 21 | 95.2 4.8 0.0 19.0 23.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000749 | 1 23 | 95.7 0.0 4.3 0.0 4.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000750 | 1 16 | 81.3 18.8 0.0 6.3 25.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000751 | 1 28 | 82.1 3.6 14.3 0.0 17.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000752 | 1 43 | 88.4 4.7 7.0 4.7 16.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000753 | 1 3 | 100.0 0.0 0.0 66.7 66.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000754 | 1 4 | 75.0 25.0 0.0 0.0 25.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000755 | 1 3 | 100.0 0.0 0.0 100.0 100.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000756 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000757 | 1 4 | 100.0 0.0 0.0 25.0 25.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000758 | 1 32 | 84.4 3.1 12.5 0.0 15.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000759 | 1 38 | 81.6 13.2 5.3 0.0 18.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000760 | 1 36 | 94.4 0.0 5.6 0.0 5.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000761 | 1 30 | 83.3 13.3 3.3 3.3 20.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000762 | 1 41 | 90.2 0.0 9.8 4.9 14.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000763 | 1 25 | 84.0 12.0 4.0 0.0 16.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000764 | 1 12 | 83.3 16.7 0.0 8.3 25.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000765 | 1 36 | 80.6 11.1 8.3 2.8 22.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000766 | 1 29 | 51.7 20.7 27.6 6.9 55.2 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000767 | 1 17 | 70.6 23.5 5.9 0.0 29.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000768 | 1 3 | 33.3 33.3 33.3 100.0 166.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000769 | 1 2 | 0.0 50.0 50.0 0.0 100.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000770 | 1 4 | 100.0 0.0 0.0 75.0 75.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000771 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000772 | 1 3 | 100.0 0.0 0.0 33.3 33.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000773 | 1 26 | 84.6 3.8 11.5 0.0 15.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000774 | 1 64 | 87.5 7.8 4.7 1.6 14.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000775 | 1 22 | 90.9 0.0 9.1 0.0 9.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000776 | 1 49 | 81.6 6.1 12.2 2.0 20.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000777 | 1 27 | 81.5 14.8 3.7 7.4 25.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000778 | 1 28 | 71.4 10.7 17.9 0.0 28.6 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000779 | 1 29 | 86.2 6.9 6.9 27.6 41.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000780 | 1 32 | 68.8 12.5 18.8 3.1 34.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000781 | 1 40 | 80.0 5.0 15.0 12.5 32.5 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000782 | 1 74 | 77.0 13.5 9.5 9.5 32.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000783 | 1 45 | 75.6 8.9 15.6 2.2 26.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000784 | 1 28 | 89.3 7.1 3.6 0.0 10.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000785 | 1 33 | 93.9 0.0 6.1 0.0 6.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000786 | 1 48 | 93.8 4.2 2.1 2.1 8.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000787 | 1 32 | 81.3 15.6 3.1 0.0 18.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000788 | 1 60 | 76.7 16.7 6.7 8.3 31.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000789 | 1 26 | 73.1 15.4 11.5 0.0 26.9 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000790 | 1 42 | 78.6 19.0 2.4 2.4 23.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000791 | 1 58 | 63.8 19.0 17.2 5.2 41.4 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000792 | 1 45 | 68.9 17.8 13.3 6.7 37.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000793 | 1 42 | 78.6 11.9 9.5 2.4 23.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000794 | 1 78 | 87.2 7.7 5.1 1.3 14.1 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000795 | 1 81 | 87.7 6.2 6.2 4.9 17.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000796 | 1 51 | 86.3 9.8 3.9 0.0 13.7 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000797 | 1 29 | 93.1 6.9 0.0 3.4 10.3 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000798 | 1 96 | 84.4 7.3 8.3 5.2 20.8 100.0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000799 | 1 32 | 68.8 3.1 28.1 3.1 34.4 100.0 | +|=====================================================================================================================| +| Sum/Avg | 126 4430 | 85.7 8.2 6.0 4.6 18.9 96.8 | +|=====================================================================================================================| +| Mean | 1.0 35.2 | 84.9 9.3 5.8 8.9 24.0 96.8 | +| S.D. | 0.0 21.9 | 13.1 8.9 7.3 20.7 24.2 17.6 | +| Median | 1.0 32.0 | 87.3 7.1 4.1 3.1 18.6 100.0 | +`---------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,---------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/hyp.trn| +|---------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000674 | 1 75 | 71 3 1 5 9 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000675 | 1 33 | 27 5 1 5 11 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000676 | 1 38 | 35 1 2 1 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000677 | 1 97 | 89 6 2 3 11 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000678 | 1 65 | 56 4 5 2 11 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000679 | 1 67 | 53 5 9 2 16 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000680 | 1 102 | 90 6 6 3 15 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000681 | 1 81 | 60 14 7 3 24 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000682 | 1 23 | 21 0 2 0 2 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000683 | 1 40 | 37 1 2 2 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000684 | 1 39 | 32 4 3 0 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000685 | 1 54 | 52 1 1 3 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000686 | 1 52 | 50 1 1 3 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000687 | 1 23 | 21 2 0 0 2 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000688 | 1 37 | 32 2 3 2 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000689 | 1 32 | 29 1 2 1 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000690 | 1 45 | 42 0 3 1 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000691 | 1 110 | 101 5 4 0 9 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000692 | 1 52 | 48 1 3 0 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000693 | 1 35 | 28 4 3 0 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000694 | 1 89 | 73 11 5 0 16 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000695 | 1 33 | 23 6 4 1 11 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000696 | 1 61 | 54 5 2 1 8 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000697 | 1 38 | 31 3 4 4 11 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000698 | 1 40 | 35 3 2 4 9 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000699 | 1 38 | 34 3 1 6 10 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000700 | 1 31 | 27 4 0 4 8 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000701 | 1 24 | 20 4 0 1 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000702 | 1 14 | 12 2 0 1 3 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000703 | 1 52 | 40 10 2 1 13 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000704 | 1 49 | 45 3 1 3 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000705 | 1 24 | 22 2 0 3 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000706 | 1 24 | 21 2 1 4 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000707 | 1 40 | 35 2 3 5 10 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000708 | 1 15 | 15 0 0 0 0 0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000709 | 1 28 | 26 2 0 3 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000710 | 1 36 | 33 2 1 4 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000711 | 1 28 | 28 0 0 0 0 0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000712 | 1 15 | 14 1 0 0 1 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000713 | 1 33 | 28 4 1 0 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000714 | 1 15 | 13 1 1 2 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000715 | 1 19 | 16 2 1 1 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000716 | 1 26 | 26 0 0 2 2 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000717 | 1 37 | 34 1 2 1 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000718 | 1 28 | 24 4 0 1 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000719 | 1 41 | 33 7 1 2 10 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000720 | 1 29 | 25 4 0 2 6 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000721 | 1 20 | 16 4 0 1 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000722 | 1 36 | 32 2 2 1 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000723 | 1 37 | 36 1 0 1 2 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000724 | 1 41 | 35 3 3 1 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000725 | 1 32 | 29 2 1 1 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000726 | 1 32 | 28 2 2 0 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000727 | 1 23 | 20 1 2 1 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000728 | 1 34 | 33 1 0 1 2 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000729 | 1 29 | 23 5 1 1 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000730 | 1 38 | 27 5 6 0 11 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000731 | 1 30 | 28 2 0 0 2 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000732 | 1 22 | 17 1 4 3 8 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000733 | 1 3 | 2 1 0 2 3 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000734 | 1 2 | 2 0 0 3 3 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000735 | 1 2 | 2 0 0 0 0 0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000736 | 1 4 | 3 1 0 0 1 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000737 | 1 3 | 3 0 0 0 0 0 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000738 | 1 27 | 24 1 2 1 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000739 | 1 25 | 23 2 0 2 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000740 | 1 28 | 28 0 0 1 1 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000741 | 1 60 | 53 4 3 1 8 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000742 | 1 54 | 51 1 2 1 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000743 | 1 27 | 25 2 0 1 3 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000744 | 1 37 | 35 2 0 5 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000745 | 1 25 | 22 2 1 0 3 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000746 | 1 46 | 41 3 2 3 8 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000747 | 1 31 | 28 3 0 2 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000748 | 1 21 | 20 1 0 4 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000749 | 1 23 | 22 0 1 0 1 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000750 | 1 16 | 13 3 0 1 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000751 | 1 28 | 23 1 4 0 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000752 | 1 43 | 38 2 3 2 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000753 | 1 3 | 3 0 0 2 2 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000754 | 1 4 | 3 1 0 0 1 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000755 | 1 3 | 3 0 0 3 3 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000756 | 1 3 | 2 1 0 0 1 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000757 | 1 4 | 4 0 0 1 1 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000758 | 1 32 | 27 1 4 0 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000759 | 1 38 | 31 5 2 0 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000760 | 1 36 | 34 0 2 0 2 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000761 | 1 30 | 25 4 1 1 6 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000762 | 1 41 | 37 0 4 2 6 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000763 | 1 25 | 21 3 1 0 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000764 | 1 12 | 10 2 0 1 3 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000765 | 1 36 | 29 4 3 1 8 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000766 | 1 29 | 15 6 8 2 16 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000767 | 1 17 | 12 4 1 0 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000768 | 1 3 | 1 1 1 3 5 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000769 | 1 2 | 0 1 1 0 2 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000770 | 1 4 | 4 0 0 3 3 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000771 | 1 2 | 1 1 0 0 1 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000772 | 1 3 | 3 0 0 1 1 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000773 | 1 26 | 22 1 3 0 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000774 | 1 64 | 56 5 3 1 9 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000775 | 1 22 | 20 0 2 0 2 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000776 | 1 49 | 40 3 6 1 10 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000777 | 1 27 | 22 4 1 2 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000778 | 1 28 | 20 3 5 0 8 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000779 | 1 29 | 25 2 2 8 12 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000780 | 1 32 | 22 4 6 1 11 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000781 | 1 40 | 32 2 6 5 13 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000782 | 1 74 | 57 10 7 7 24 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000783 | 1 45 | 34 4 7 1 12 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000784 | 1 28 | 25 2 1 0 3 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000785 | 1 33 | 31 0 2 0 2 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000786 | 1 48 | 45 2 1 1 4 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000787 | 1 32 | 26 5 1 0 6 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000788 | 1 60 | 46 10 4 5 19 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000789 | 1 26 | 19 4 3 0 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000790 | 1 42 | 33 8 1 1 10 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000791 | 1 58 | 37 11 10 3 24 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000792 | 1 45 | 31 8 6 3 17 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000793 | 1 42 | 33 5 4 1 10 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000794 | 1 78 | 68 6 4 1 11 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000795 | 1 81 | 71 5 5 4 14 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000796 | 1 51 | 44 5 2 0 7 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000797 | 1 29 | 27 2 0 1 3 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000798 | 1 96 | 81 7 8 5 20 1 | +|--------------------+-----------------------+------------------------------------------------------------------------| +| cv_jpn_000799 | 1 32 | 22 1 9 1 11 1 | +|=====================================================================================================================| +| Sum | 126 4430 | 3797 365 268 204 837 122 | +|=====================================================================================================================| +| Mean | 1.0 35.2 | 30.1 2.9 2.1 1.6 6.6 1.0 | +| S.D. | 0.0 21.9 | 19.1 2.7 2.3 1.7 5.0 0.2 | +| Median | 1.0 32.0 | 28.0 2.0 1.0 1.0 5.0 1.0 | +`---------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/hyp.trn + +Speakers: + 0: cv_jpn_000674 + 1: cv_jpn_000675 + 2: cv_jpn_000676 + 3: cv_jpn_000677 + 4: cv_jpn_000678 + 5: cv_jpn_000679 + 6: cv_jpn_000680 + 7: cv_jpn_000681 + 8: cv_jpn_000682 + 9: cv_jpn_000683 + 10: cv_jpn_000684 + 11: cv_jpn_000685 + 12: cv_jpn_000686 + 13: cv_jpn_000687 + 14: cv_jpn_000688 + 15: cv_jpn_000689 + 16: cv_jpn_000690 + 17: cv_jpn_000691 + 18: cv_jpn_000692 + 19: cv_jpn_000693 + 20: cv_jpn_000694 + 21: cv_jpn_000695 + 22: cv_jpn_000696 + 23: cv_jpn_000697 + 24: cv_jpn_000698 + 25: cv_jpn_000699 + 26: cv_jpn_000700 + 27: cv_jpn_000701 + 28: cv_jpn_000702 + 29: cv_jpn_000703 + 30: cv_jpn_000704 + 31: cv_jpn_000705 + 32: cv_jpn_000706 + 33: cv_jpn_000707 + 34: cv_jpn_000708 + 35: cv_jpn_000709 + 36: cv_jpn_000710 + 37: cv_jpn_000711 + 38: cv_jpn_000712 + 39: cv_jpn_000713 + 40: cv_jpn_000714 + 41: cv_jpn_000715 + 42: cv_jpn_000716 + 43: cv_jpn_000717 + 44: cv_jpn_000718 + 45: cv_jpn_000719 + 46: cv_jpn_000720 + 47: cv_jpn_000721 + 48: cv_jpn_000722 + 49: cv_jpn_000723 + 50: cv_jpn_000724 + 51: cv_jpn_000725 + 52: cv_jpn_000726 + 53: cv_jpn_000727 + 54: cv_jpn_000728 + 55: cv_jpn_000729 + 56: cv_jpn_000730 + 57: cv_jpn_000731 + 58: cv_jpn_000732 + 59: cv_jpn_000733 + 60: cv_jpn_000734 + 61: cv_jpn_000735 + 62: cv_jpn_000736 + 63: cv_jpn_000737 + 64: cv_jpn_000738 + 65: cv_jpn_000739 + 66: cv_jpn_000740 + 67: cv_jpn_000741 + 68: cv_jpn_000742 + 69: cv_jpn_000743 + 70: cv_jpn_000744 + 71: cv_jpn_000745 + 72: cv_jpn_000746 + 73: cv_jpn_000747 + 74: cv_jpn_000748 + 75: cv_jpn_000749 + 76: cv_jpn_000750 + 77: cv_jpn_000751 + 78: cv_jpn_000752 + 79: cv_jpn_000753 + 80: cv_jpn_000754 + 81: cv_jpn_000755 + 82: cv_jpn_000756 + 83: cv_jpn_000757 + 84: cv_jpn_000758 + 85: cv_jpn_000759 + 86: cv_jpn_000760 + 87: cv_jpn_000761 + 88: cv_jpn_000762 + 89: cv_jpn_000763 + 90: cv_jpn_000764 + 91: cv_jpn_000765 + 92: cv_jpn_000766 + 93: cv_jpn_000767 + 94: cv_jpn_000768 + 95: cv_jpn_000769 + 96: cv_jpn_000770 + 97: cv_jpn_000771 + 98: cv_jpn_000772 + 99: cv_jpn_000773 + 100: cv_jpn_000774 + 101: cv_jpn_000775 + 102: cv_jpn_000776 + 103: cv_jpn_000777 + 104: cv_jpn_000778 + 105: cv_jpn_000779 + 106: cv_jpn_000780 + 107: cv_jpn_000781 + 108: cv_jpn_000782 + 109: cv_jpn_000783 + 110: cv_jpn_000784 + 111: cv_jpn_000785 + 112: cv_jpn_000786 + 113: cv_jpn_000787 + 114: cv_jpn_000788 + 115: cv_jpn_000789 + 116: cv_jpn_000790 + 117: cv_jpn_000791 + 118: cv_jpn_000792 + 119: cv_jpn_000793 + 120: cv_jpn_000794 + 121: cv_jpn_000795 + 122: cv_jpn_000796 + 123: cv_jpn_000797 + 124: cv_jpn_000798 + 125: cv_jpn_000799 + +Speaker sentences 0: cv_jpn_000674 #utts: 1 +id: (cv_jpn_000674-cv_jpn_000674) +Scores: (#C #S #D #I) 71 3 1 5 +REF: b o k u n o * i e g A a cl t a k a i d * a n n o n a m a e * n i k u r a b e r u t o B o k u n i w a n a j i m i n o n a i N a * M a e b a k a r i d a * k e d o +HYP: b o k u n o Y i e g * a cl t a k a i d N a n n o n a m a e N n i k u r a b e r u t o P o k u n i w a n a j i m i n o n a i M a E B a e b a k a r i d a G k e d o +Eval: I D I I S S I S I + +Speaker sentences 1: cv_jpn_000675 #utts: 1 +id: (cv_jpn_000675-cv_jpn_000675) +Scores: (#C #S #D #I) 27 5 1 5 +REF: N a I y o o s * O n o m o n o Y O r * * i F u * n i k i g a * u k e t e r u +HYP: M a R y o o s U A n o m o n o * E r U D i K u I n i k i g a R u k e t e r u +Eval: S S I S D S I I S I I + +Speaker sentences 2: cv_jpn_000676 #utts: 1 +id: (cv_jpn_000676-cv_jpn_000676) +Scores: (#C #S #D #I) 35 1 2 1 +REF: b o k u n o sh i CL t e I R u m o n * o t o w a s u k o sh i ch i g a cl t e i t a +HYP: b o k u n o sh i ** t e * E u m o n U o t o w a s u k o sh i ch i g a cl t e i t a +Eval: D D S I + +Speaker sentences 3: cv_jpn_000677 #utts: 1 +id: (cv_jpn_000677-cv_jpn_000677) +Scores: (#C #S #D #I) 89 6 2 3 +REF: k a k a * r u t a ch i B a n i o i t e pau s e k * a * I g a i sh i k i m e n t e k i d e a r i PAU w a r E w a r e n o j i k o g a I sh i k i s a y o o t e k i d e a r u t o k a n G A e r a r e r u t o K i +HYP: k a k a O r u t a ch i M a n i o i t e pau s e k G a R E g a i sh i k i m e n t e k i d e a r i *** w a r U w a r e n o j i k o g a J sh i k i s a y o o t e k i d e a r u t o k a n * E e r a r e r u t o CH i +Eval: I S I I S D S S D S S + +Speaker sentences 4: cv_jpn_000678 #utts: 1 +id: (cv_jpn_000678-cv_jpn_000678) +Scores: (#C #S #D #I) 56 4 5 2 +REF: i e n i k i t a n e n g * a j O O w a PAU s a n BY a k U m a i h o D o d e *** pau ch O o D o PAU d a sh i t a b u n t o o n a j i g u r a i d a +HYP: i e n i k i t a n e n g A a j * I w a *** s a n H a k O m a i h o R o d e PAU pau ch * o * o *** d a sh i t a b u n t o o n a j i g u r a i d a +Eval: I D S D S S S I D D D + +Speaker sentences 5: cv_jpn_000679 #utts: 1 +id: (cv_jpn_000679-cv_jpn_000679) +Scores: (#C #S #D #I) 53 5 9 2 +REF: H A h a w a p I i t A a m a r I CL ts u b a a g u n o s e e sh i n by * o o i n n i NY U u I n SH i t e * i r u t o k i n i B e CL sh I i o U m u +HYP: * * h a w a p * i t * a m a r * E ts u b a a g u n o s e e sh i n by Y o o i n n i ** N u * n H i t e Y i r u t o k i n i P e ** sh * i o O m u +Eval: D D D D D S I D S D S I S D D S + +Speaker sentences 6: cv_jpn_000680 #utts: 1 +id: (cv_jpn_000680-cv_jpn_000680) +Scores: (#C #S #D #I) 90 6 6 3 +REF: t a T a n d e a r U h a n t E n o h i r o g e r E b a PAU a ch i k o ch I N i ts u g i h a g i G a a r i PAU k a t a g U ch * i n i d e k i t a h o k o r o b i n a n k a PAU ky * o n e n n o m a * m A n i n a cl t e i r u +HYP: t a D a n d e a r * h a n t A n o h i r o g e r A b a *** a ch i k o ch * * i ts u g i h a g i Y a a r i *** k a t a g O ch U i n i d e k i t a h o k o r o b i n a n k a *** ky O o n e n n o m a O m O n i n a cl t e i r u +Eval: S D S S D D D S D S I D I I S + +Speaker sentences 7: cv_jpn_000681 #utts: 1 +id: (cv_jpn_000681-cv_jpn_000681) +Scores: (#C #S #D #I) 60 14 7 3 +REF: k a r E g a PAU k O n o SH U CL CH O O CH u * * * U n i PAU h o o m o n k i b o o n o b u sh o PAU o y o b i PAU ch o o S a k i b o o n o b u M o N W a PAU i k a N O t o o r i d e s u +HYP: k a r A g a *** k A n o ** D E PAU B E R u N A K A n i *** h o o m o n k i b o o n o b u sh o *** o y o b i *** ch o o Z a k i b o o n o b u G o * M a *** i k a R A t o o r i d e s u +Eval: S D S D S S S S S S I I I S D D D S S D S D S S + +Speaker sentences 8: cv_jpn_000682 #utts: 1 +id: (cv_jpn_000682-cv_jpn_000682) +Scores: (#C #S #D #I) 21 0 2 0 +REF: k o n b a N w a t o t e m o s a m U i d e s u +HYP: k o n b a * w a t o t e m o s a m * i d e s u +Eval: D D + +Speaker sentences 9: cv_jpn_000683 #utts: 1 +id: (cv_jpn_000683-cv_jpn_000683) +Scores: (#C #S #D #I) 37 1 2 2 +REF: k i n ch o o sh i t a k A o ts u k i d e *** ** B a cl t A a w a d a s e k i n i h a i r u +HYP: k i n ch o o sh i t a k * o ts u k i d e PAU CL P a cl t * a w a d a s e k i n i h a i r u +Eval: D I I S D + +Speaker sentences 10: cv_jpn_000684 #utts: 1 +id: (cv_jpn_000684-cv_jpn_000684) +Scores: (#C #S #D #I) 32 4 3 0 +REF: m a s a n I ky u u D e n t o i cl t a f U u k a k U a r U k e n ch i k U B u ts u +HYP: m a s a n * ky u u R e n t o i cl t a f * u k a k * a r O k e n ch i k O W u ts u +Eval: D S D D S S S + +Speaker sentences 11: cv_jpn_000685 #utts: 1 +id: (cv_jpn_000685-cv_jpn_000685) +Scores: (#C #S #D #I) 52 1 1 3 +REF: g o r i * o sh i d e s u s u m e t e ts * u g O o g a w a r u k u n a cl t a r a * h i cl k o m e r u y a r I k u ch i +HYP: g o r i Y o sh i d e s u s u m e t e ts S u g * o g a w a r u k u n a cl t a r a I h i cl k o m e r u y a r E k u ch i +Eval: I I D I S + +Speaker sentences 12: cv_jpn_000686 #utts: 1 +id: (cv_jpn_000686-cv_jpn_000686) +Scores: (#C #S #D #I) 50 1 1 3 +REF: m U j u * n t e k i j i k o D o * o i ts u t e k i n i j i k * o j i sh i n o k e e s e e s u r u sh a k a i w a +HYP: m O j u R n t e k i j i k o * o T o i ts u t e k i n i j i k O o j i sh i n o k e e s e e s u r u sh a k a i w a +Eval: S I D I I + +Speaker sentences 13: cv_jpn_000687 #utts: 1 +id: (cv_jpn_000687-cv_jpn_000687) +Scores: (#C #S #D #I) 21 2 0 0 +REF: F a n n o i k e n n i n a G a s a r e r u n a +HYP: H a n n o i k e n n i n a R a s a r e r u n a +Eval: S S + +Speaker sentences 14: cv_jpn_000688 #utts: 1 +id: (cv_jpn_000688-cv_jpn_000688) +Scores: (#C #S #D #I) 32 2 3 2 +REF: * i n U g A a s o b i t a I O o r a z e n k a i d e k o cl ch I o m i t e * r u +HYP: H i n O g * a s o b i t a * Y o r a z e n k a i d e k o cl ch * o m i t e I r u +Eval: I S D D S D I + +Speaker sentences 15: cv_jpn_000689 #utts: 1 +id: (cv_jpn_000689-cv_jpn_000689) +Scores: (#C #S #D #I) 29 1 2 1 +REF: i * ch i d o w a k O o n p o t A a j U k a n o n o n d e m i t a i +HYP: i J ch i d o w a k * o n p o t * a j I k a n o n o n d e m i t a i +Eval: I D D S + +Speaker sentences 16: cv_jpn_000690 #utts: 1 +id: (cv_jpn_000690-cv_jpn_000690) +Scores: (#C #S #D #I) 42 0 3 1 +REF: k o o I i n o k o sh i t e pau o t o o s a n t O o k A a s a n w a * d e t e i k i m a sh i t a +HYP: k o o * i n o k o sh i t e pau o t o o s a n t * o k * a s a n w a N d e t e i k i m a sh i t a +Eval: D D D I + +Speaker sentences 17: cv_jpn_000691 #utts: 1 +id: (cv_jpn_000691-cv_jpn_000691) +Scores: (#C #S #D #I) 101 5 4 0 +REF: sh i k a sh i t e s o r e g a ts u k u r a r e t a m o n o k a r a ts u k u r u m o N o e t o sh i t e pau d o k o m a d e m o W A r E w a r E n i s e m a r u t o I u t o k i PAU W a r e w a r E n i ch o cl k a n t e k i d e a r u +HYP: sh i k a sh i t e s o r e g a ts u k u r a r e t a m o n o k a r a ts u k u r u m o R o e t o sh i t e pau d o k o m a d e m o * * r A w a r U n i s e m a r u t o Y u t o k i *** * a r e w a r I n i ch o cl k a n t e k i d e a r u +Eval: S D D S S S D D S + +Speaker sentences 18: cv_jpn_000692 #utts: 1 +id: (cv_jpn_000692-cv_jpn_000692) +Scores: (#C #S #D #I) 48 1 3 0 +REF: h a i n i t a m a CL t a k e m u r i o h a k i d a sh i PAU k u r a i k o o e N n i sh i s e n o m U k e r u +HYP: h a i n i t a m a ** t a k e m u r i o h a k i d a sh i *** k u r a i k o o e * n i sh i s e n o m O k e r u +Eval: D D D S + +Speaker sentences 19: cv_jpn_000693 #utts: 1 +id: (cv_jpn_000693-cv_jpn_000693) +Scores: (#C #S #D #I) 28 4 3 0 +REF: M a d a M a d a sh i n a F u s o k u d e ch U u s e N n I n a r i s O o +HYP: W a d a W a d a sh i n a B u s o k u d e ch I u s e * n * n a r i s * o +Eval: S S S S D D D + +Speaker sentences 20: cv_jpn_000694 #utts: 1 +id: (cv_jpn_000694-cv_jpn_000694) +Scores: (#C #S #D #I) 73 11 5 0 +REF: Z e n s e k a i n o m o TS u k a t a ch i PAU W A t a sh I n o I W a y U r u s e e s a N y O o sh i k i t o s a Y O o t o w a h A N a sh i t e k a n g a E r u k o t o w a d e k i n a i +HYP: TS e n s e k a i n o m o S u k a t a ch i *** * I t a sh U n o Y O a y I r u s e e s a I y * o sh i k i t o s a I U o t o w a h * * a sh i t e k a n g a I r u k o t o w a d e k i n a i +Eval: S S D D S S S S S S D S S D D S + +Speaker sentences 21: cv_jpn_000695 #utts: 1 +id: (cv_jpn_000695-cv_jpn_000695) +Scores: (#C #S #D #I) 23 6 4 1 +REF: s a k e n o m a N A I n o n i B I i r * u H a r a t o I W A R e t a +HYP: s a k e n o m a * E G n o n i * P i r O u B a r a t o * * Y O e t a +Eval: D S S D S I S D D S S + +Speaker sentences 22: cv_jpn_000696 #utts: 1 +id: (cv_jpn_000696-cv_jpn_000696) +Scores: (#C #S #D #I) 54 5 2 1 +REF: s o r e * o t a d a k A G A k u n o z e n d a n k a i PAU H i k u i t e e d o n o k a G a k u t o N o m i m i r u k o t o w a +HYP: s o r e O o t a d a k O H O k u n o z e n d a n k a i *** SH i k u i t e e d o n o k a * a k u t o M o m i m i r u k o t o w a +Eval: I S S S D S D S + +Speaker sentences 23: cv_jpn_000697 #utts: 1 +id: (cv_jpn_000697-cv_jpn_000697) +Scores: (#C #S #D #I) 31 3 4 4 +REF: W A k ** I m e m o * f u r a z u n i *** s u m A H o d e G e e * m U o y a cl t e i t a +HYP: * O k KY O m e m o K f u r a z u n i PAU s u m * * o d e K e e N m * o y a cl t e i t a +Eval: D S I S I I D D S I D + +Speaker sentences 24: cv_jpn_000698 #utts: 1 +id: (cv_jpn_000698-cv_jpn_000698) +Scores: (#C #S #D #I) 35 3 2 4 +REF: W a T a SH i w * a R a i * ** sh U u m a t a k o b a y a sh i s a n t o a s o b * i m a s u +HYP: * a D a ** i w A a N a i N CH sh I u m a t a k o b a y a sh i s a n t o a s o b E i m a s u +Eval: D S D I S I I S I + +Speaker sentences 25: cv_jpn_000699 #utts: 1 +id: (cv_jpn_000699-cv_jpn_000699) +Scores: (#C #S #D #I) 34 3 1 6 +REF: a s * o k o n * i h * i t * o g a *** i m a s u n e *** a N o h i t o w a D a r e D e sh O o +HYP: a s O o k o n I i h I i t O o g a PAU i m a s u n e PAU a R o h i t o w a T a r e T e sh * o +Eval: I I I I I I S S S D + +Speaker sentences 26: cv_jpn_000700 #utts: 1 +id: (cv_jpn_000700-cv_jpn_000700) +Scores: (#C #S #D #I) 27 4 0 4 +REF: w a t a SH i w a k ** I n o o k a R a *** n * o d o G a * i t a i d e s u +HYP: w a t a J i w a k KY E n o o k a N a PAU n O o d o K a E i t a i d e s u +Eval: S I S S I I S I + +Speaker sentences 27: cv_jpn_000701 #utts: 1 +id: (cv_jpn_000701-cv_jpn_000701) +Scores: (#C #S #D #I) 20 4 0 1 +REF: ky * o N e n k a R a B e n ky O o sh i t e i m a s u +HYP: ky O o R e n k a N a P e n ky U o sh i t e i m a s u +Eval: I S S S S + +Speaker sentences 28: cv_jpn_000702 #utts: 1 +id: (cv_jpn_000702-cv_jpn_000702) +Scores: (#C #S #D #I) 12 2 0 1 +REF: CH o cl t o s u m i m * a s e N +HYP: A o cl t o s u m i m R a s e PAU +Eval: S I S + +Speaker sentences 29: cv_jpn_000703 #utts: 1 +id: (cv_jpn_000703-cv_jpn_000703) +Scores: (#C #S #D #I) 40 10 2 1 +REF: SH i k a SH I pau S o n O o o e n b u r i * w a pau y a k e G i m i n i PAU k a e cl T e h a g e SH I k U n A cl t a +HYP: CH i k a S U pau B o n * o o e n b u r i E w a pau y a k e K i m i n i *** k a e cl D e h a g e S U k E n O cl t a +Eval: S S S S D I S D S S S S S + +Speaker sentences 30: cv_jpn_000704 #utts: 1 +id: (cv_jpn_000704-cv_jpn_000704) +Scores: (#C #S #D #I) 45 3 1 3 +REF: i ts * u m o k o n o E n p * i ts U o ts u k a cl t e i t a n o d e PAU m i j i k a * K u n a r i m a sh i t a +HYP: i ts Z u m o k o n o I n p I i ts O o ts u k a cl t e i t a n o d e *** m i j i k a H A u n a r i m a sh i t a +Eval: I S I S D I S + +Speaker sentences 31: cv_jpn_000705 #utts: 1 +id: (cv_jpn_000705-cv_jpn_000705) +Scores: (#C #S #D #I) 22 2 0 3 +REF: w a t a sh i w a *** e E g o g a h a n a ** * S e m a s u +HYP: w a t a sh i w a PAU e I g o g a h a n a SH I T e m a s u +Eval: I S I I S + +Speaker sentences 32: cv_jpn_000706 #utts: 1 +id: (cv_jpn_000706-cv_jpn_000706) +Scores: (#C #S #D #I) 21 2 1 4 +REF: H o * SH i k * e * RY a j i b u * n d e t o cl t e k i n a +HYP: * o S E i k E e R Y a j i b u A n d e t o cl t e k i n a +Eval: D I S I I S I + +Speaker sentences 33: cv_jpn_000707 #utts: 1 +id: (cv_jpn_000707-cv_jpn_000707) +Scores: (#C #S #D #I) 35 2 3 5 +REF: R a i sh u u * k a r a n i sh u u k a n PAU K a i g a i e * * * RY o k * o o n I i k i m a s u +HYP: * a i sh u u E k a r a n i sh u u k a n *** H a i g a i e R U Y O o k O o o n * i k i m a s u +Eval: D I D S I I I S I D + +Speaker sentences 34: cv_jpn_000708 #utts: 1 +id: (cv_jpn_000708-cv_jpn_000708) +Scores: (#C #S #D #I) 15 0 0 0 +REF: o r e m o k i n i n a r u n a +HYP: o r e m o k i n i n a r u n a +Eval: + +Speaker sentences 35: cv_jpn_000709 #utts: 1 +id: (cv_jpn_000709-cv_jpn_000709) +Scores: (#C #S #D #I) 26 2 0 3 +REF: d a r e * g a ts u k a cl t e r u n o k * a * w A k a r A n a i +HYP: d a r e I g a ts u k a cl t e r u n o k A a H w O k a r U n a i +Eval: I I I S S + +Speaker sentences 36: cv_jpn_000710 #utts: 1 +id: (cv_jpn_000710-cv_jpn_000710) +Scores: (#C #S #D #I) 33 2 1 4 +REF: B u r a U z a n o b A a j o n g a *** a g a r u t * o s u k o sh * i * u r e sh i i +HYP: P u r a O z a n o b * a j o n g a PAU a g a r u t O o s u k o sh I i U u r e sh i i +Eval: S S D I I I I + +Speaker sentences 37: cv_jpn_000711 #utts: 1 +id: (cv_jpn_000711-cv_jpn_000711) +Scores: (#C #S #D #I) 28 0 0 0 +REF: m a t a a t a r a sh i i a i d o r u g a d e t e k i t a +HYP: m a t a a t a r a sh i i a i d o r u g a d e t e k i t a +Eval: + +Speaker sentences 38: cv_jpn_000712 #utts: 1 +id: (cv_jpn_000712-cv_jpn_000712) +Scores: (#C #S #D #I) 14 1 0 0 +REF: m a j i d e y a cl t A n o k a +HYP: m a j i d e y a cl t O n o k a +Eval: S + +Speaker sentences 39: cv_jpn_000713 #utts: 1 +id: (cv_jpn_000713-cv_jpn_000713) +Scores: (#C #S #D #I) 28 4 1 0 +REF: ch o o d o s O N o t O k i n i pau ky o o j U g a h a i CL t e k i t a +HYP: ch o o d o s A M o t U k i n i pau ky o o j I g a h a i ** t e k i t a +Eval: S S S S D + +Speaker sentences 40: cv_jpn_000714 #utts: 1 +id: (cv_jpn_000714-cv_jpn_000714) +Scores: (#C #S #D #I) 13 1 1 2 +REF: i cl SH o n i * ch i * n sh i t e Y o +HYP: i cl S o n i K ch i E n sh i t e * o +Eval: S I I D + +Speaker sentences 41: cv_jpn_000715 #utts: 1 +id: (cv_jpn_000715-cv_jpn_000715) +Scores: (#C #S #D #I) 16 2 1 1 +REF: s o r e G a s a t e N n o D o r e s u * +HYP: s o r e K a s a t e * n o T o r e s u N +Eval: S D S I + +Speaker sentences 42: cv_jpn_000716 #utts: 1 +id: (cv_jpn_000716-cv_jpn_000716) +Scores: (#C #S #D #I) 26 0 0 2 +REF: f u t a r i * w a r e j i e i k i s * e e s a n sh i t a +HYP: f u t a r i O w a r e j i e i k i s U e e s a n sh i t a +Eval: I I + +Speaker sentences 43: cv_jpn_000717 #utts: 1 +id: (cv_jpn_000717-cv_jpn_000717) +Scores: (#C #S #D #I) 34 1 2 1 +REF: t o m a t o k a n a n K a n o a k a i s O o s u g a k a k a cl t e r * u Y o +HYP: t o m a t o k a n a n G a n o a k a i s * o s u g a k a k a cl t e r I u * o +Eval: S D I D + +Speaker sentences 44: cv_jpn_000718 #utts: 1 +id: (cv_jpn_000718-cv_jpn_000718) +Scores: (#C #S #D #I) 24 4 0 1 +REF: k o K o k a r a t a t e n a * O s u n o w a k i b i SH I i +HYP: k o G o k a r a t a t e n a N A s u n o w a k i b i S U i +Eval: S I S S S + +Speaker sentences 45: cv_jpn_000719 #utts: 1 +id: (cv_jpn_000719-cv_jpn_000719) +Scores: (#C #S #D #I) 33 7 1 2 +REF: M i z u K e o * SH I CL k a r i sh i b o cl t e *** a j i g a n a j i m U y O o N i s u r u +HYP: N i z u G e o W O S U k a r i sh i b o cl t e PAU a j i g a n a j i m I y * o R i s u r u +Eval: S S I S S S I S D S + +Speaker sentences 46: cv_jpn_000720 #utts: 1 +id: (cv_jpn_000720-cv_jpn_000720) +Scores: (#C #S #D #I) 25 4 0 2 +REF: n e t o G E n i h a m a cl t a r a k I n * g a T a m a cl t a * +HYP: n e t o K I n i h a m a cl t a r a k A n E g a G a m a cl t a U +Eval: S S S I S I + +Speaker sentences 47: cv_jpn_000721 #utts: 1 +id: (cv_jpn_000721-cv_jpn_000721) +Scores: (#C #S #D #I) 16 4 0 1 +REF: * i TS U k a E r U y o o n i n a r u n d a +HYP: S i T A k a I r I y o o n i n a r u n d a +Eval: I S S S S + +Speaker sentences 48: cv_jpn_000722 #utts: 1 +id: (cv_jpn_000722-cv_jpn_000722) +Scores: (#C #S #D #I) 32 2 2 1 +REF: k o s E e h a h a i y U u t o I u y o r i a k u g a ts u y o * I k a n j i +HYP: k o s * e h a h a i y * u t o Y u y o r i a k u g a ts u y o A E k a n j i +Eval: D D S I S + +Speaker sentences 49: cv_jpn_000723 #utts: 1 +id: (cv_jpn_000723-cv_jpn_000723) +Scores: (#C #S #D #I) 36 1 0 1 +REF: f * i j i k a r u n o s a o m a z a m a Z a t o m i s e ts u k e r a r e t a +HYP: f E i j i k a r u n o s a o m a z a m a D a t o m i s e ts u k e r a r e t a +Eval: I S + +Speaker sentences 50: cv_jpn_000724 #utts: 1 +id: (cv_jpn_000724-cv_jpn_000724) +Scores: (#C #S #D #I) 35 3 3 1 +REF: k o s u p a y o k e R e b a *** s o k O S o k o n o m o n d a I W a g a m a n s u r U +HYP: k o s u p a y o k e D e b a PAU s o k * * o k o n o m o n d a * E a g a m a n s u r E +Eval: S I D D D S S + +Speaker sentences 51: cv_jpn_000725 #utts: 1 +id: (cv_jpn_000725-cv_jpn_000725) +Scores: (#C #S #D #I) 29 2 1 1 +REF: M i n n a y a cl t e m a s u k a r a D a i j o o b u d e s u * Y o +HYP: * i n n a y a cl t e m a s u k a r a T a i j o o b u d e s u I U o +Eval: D S I S + +Speaker sentences 52: cv_jpn_000726 #utts: 1 +id: (cv_jpn_000726-cv_jpn_000726) +Scores: (#C #S #D #I) 28 2 2 0 +REF: k o n o t o SH o k a n PAU h a i cl t a sh u N k a n k i n I i cl t a +HYP: k o n o t o J o k a n *** h a i cl t a sh u U k a n k i n * i cl t a +Eval: S D S D + +Speaker sentences 53: cv_jpn_000727 #utts: 1 +id: (cv_jpn_000727-cv_jpn_000727) +Scores: (#C #S #D #I) 20 1 2 1 +REF: k o n o d e n ch i PAU S u G u k i r e ch i * cl t a +HYP: k o n o d e n ch i *** F u * u k i r e ch i A cl t a +Eval: D S D I + +Speaker sentences 54: cv_jpn_000728 #utts: 1 +id: (cv_jpn_000728-cv_jpn_000728) +Scores: (#C #S #D #I) 33 1 0 1 +REF: a m a y * A d o r i s u r u t o k o r o g a n a k u t e k o m a cl t a +HYP: a m a y E U d o r i s u r u t o k o r o g a n a k u t e k o m a cl t a +Eval: I S + +Speaker sentences 55: cv_jpn_000729 #utts: 1 +id: (cv_jpn_000729-cv_jpn_000729) +Scores: (#C #S #D #I) 23 5 1 1 +REF: y a s u k U s u r u Y o r i sh i * ts u O a g e t E h o sh I I +HYP: y a s u k A s u r u E o r i sh i T ts u W a g e t O h o sh * E +Eval: S S I S S D S + +Speaker sentences 56: cv_jpn_000730 #utts: 1 +id: (cv_jpn_000730-cv_jpn_000730) +Scores: (#C #S #D #I) 27 5 6 0 +REF: m a s A K A k o N n a k o t O N i n a r O O t o w a O m o W A n a k a cl t a +HYP: m a s E G O k o * n a k o t * E i n a r * U t o w a * m o * * n a k a cl t a +Eval: S S S D D S D S D D D + +Speaker sentences 57: cv_jpn_000731 #utts: 1 +id: (cv_jpn_000731-cv_jpn_000731) +Scores: (#C #S #D #I) 28 2 0 0 +REF: s a i g o n i w a r a I o t o r i n i k u r U s u t a i r u +HYP: s a i g o n i w a r a Y o t o r i n i k u r A s u t a i r u +Eval: S S + +Speaker sentences 58: cv_jpn_000732 #utts: 1 +id: (cv_jpn_000732-cv_jpn_000732) +Scores: (#C #S #D #I) 17 1 4 3 +REF: k o r * e PAU n a n I n * O i m i g A a r u N d a *** +HYP: k o r A e *** n a n * n A E i m i g * a r u * d a PAU +Eval: I D D I S D D I + +Speaker sentences 59: cv_jpn_000733 #utts: 1 +id: (cv_jpn_000733-cv_jpn_000733) +Scores: (#C #S #D #I) 2 1 0 2 +REF: * * i i E +HYP: O T i i A +Eval: I I S + +Speaker sentences 60: cv_jpn_000734 #utts: 1 +id: (cv_jpn_000734-cv_jpn_000734) +Scores: (#C #S #D #I) 2 0 0 3 +REF: ** * sh * i +HYP: SH A sh E i +Eval: I I I + +Speaker sentences 61: cv_jpn_000735 #utts: 1 +id: (cv_jpn_000735-cv_jpn_000735) +Scores: (#C #S #D #I) 2 0 0 0 +REF: n i +HYP: n i +Eval: + +Speaker sentences 62: cv_jpn_000736 #utts: 1 +id: (cv_jpn_000736-cv_jpn_000736) +Scores: (#C #S #D #I) 3 1 0 0 +REF: W a ch i +HYP: H a ch i +Eval: S + +Speaker sentences 63: cv_jpn_000737 #utts: 1 +id: (cv_jpn_000737-cv_jpn_000737) +Scores: (#C #S #D #I) 3 0 0 0 +REF: h a i +HYP: h a i +Eval: + +Speaker sentences 64: cv_jpn_000738 #utts: 1 +id: (cv_jpn_000738-cv_jpn_000738) +Scores: (#C #S #D #I) 24 1 2 1 +REF: t E e b u r u n * o U e n i k a b i n g A a r i m a s u +HYP: t * e b u r u n O o Y e n i k a b i n g * a r i m a s u +Eval: D I S D + +Speaker sentences 65: cv_jpn_000739 #utts: 1 +id: (cv_jpn_000739-cv_jpn_000739) +Scores: (#C #S #D #I) 23 2 0 2 +REF: w A t a sh i w a * m a i * a s a s a n P o sh i m a s u +HYP: w O t a sh i w a O m a i Y a s a s a n B o sh i m a s u +Eval: S I I S + +Speaker sentences 66: cv_jpn_000740 #utts: 1 +id: (cv_jpn_000740-cv_jpn_000740) +Scores: (#C #S #D #I) 28 0 0 1 +REF: a t a r a sh i i k u ts u * o h a i t e d e k a k e m a s u +HYP: a t a r a sh i i k u ts u O o h a i t e d e k a k e m a s u +Eval: I + +Speaker sentences 67: cv_jpn_000741 #utts: 1 +id: (cv_jpn_000741-cv_jpn_000741) +Scores: (#C #S #D #I) 53 4 3 1 +REF: k o t o sh i n o n a ts U y a s U m i * w a PAU u m i n i m o i k i m a sh i t a sh i PAU y a m a n i m o n O B o r i M a sh i t a +HYP: k o t o sh i n o n a ts E y a s A m i O w a *** u m i n i m o i k i m a sh i t a sh i I y a m a n i m o n * * o r i B a sh i t a +Eval: S S I D S D D S + +Speaker sentences 68: cv_jpn_000742 #utts: 1 +id: (cv_jpn_000742-cv_jpn_000742) +Scores: (#C #S #D #I) 51 1 2 1 +REF: w a t a sh i w a PAU i r o * i r o n o b e n g o o PAU J i b u n n o m u n e d e k o sh i r a e t e m i m a sh i t a +HYP: w a t a sh i w a *** i r o Y i r o n o b e n g o o *** CH i b u n n o m u n e d e k o sh i r a e t e m i m a sh i t a +Eval: D I D S + +Speaker sentences 69: cv_jpn_000743 #utts: 1 +id: (cv_jpn_000743-cv_jpn_000743) +Scores: (#C #S #D #I) 25 2 0 1 +REF: n a n d e k o o m o * SH o o b a i h e T a n a n d a r o +HYP: n a n d e k o o m o S U o o b a i h e D a n a n d a r o +Eval: I S S + +Speaker sentences 70: cv_jpn_000744 #utts: 1 +id: (cv_jpn_000744-cv_jpn_000744) +Scores: (#C #S #D #I) 35 2 0 5 +REF: t a r e n t o k a r a ky o k u a n a n i k a * * e ** t * e k e e H i s a k * u g e N +HYP: t a r e n t o k a r a ky o k u a n a n i k a E I e CL t U e k e e SH i s a k I u g e A +Eval: I I I I S I S + +Speaker sentences 71: cv_jpn_000745 #utts: 1 +id: (cv_jpn_000745-cv_jpn_000745) +Scores: (#C #S #D #I) 22 2 1 0 +REF: d o g e z a s U r e b a I i cl t e m o n J a n a i +HYP: d o g e z a s E r e b a * i cl t e m o n SH a n a i +Eval: S D S + +Speaker sentences 72: cv_jpn_000746 #utts: 1 +id: (cv_jpn_000746-cv_jpn_000746) +Scores: (#C #S #D #I) 41 3 2 3 +REF: d E e t O n o a i d a PAU k a n o j O w a j i b u n t o i cl t e e n o * ky * o r i * o t a M o cl t a +HYP: d * e t A n o a i d a *** k a n o j I w a j i b u n t o i cl t e e n o K ky U o r i Y o t a N o cl t a +Eval: D S D S I I I S + +Speaker sentences 73: cv_jpn_000747 #utts: 1 +id: (cv_jpn_000747-cv_jpn_000747) +Scores: (#C #S #D #I) 28 3 0 2 +REF: k o n o G e e n i n n a n k * * a h I s a sh i b u r i n i M i t a +HYP: k o n o R e e n i n n a n k A W a h SH s a sh i b u r i n i N i t a +Eval: S I I S S + +Speaker sentences 74: cv_jpn_000748 #utts: 1 +id: (cv_jpn_000748-cv_jpn_000748) +Scores: (#C #S #D #I) 20 1 0 4 +REF: o o k i k * U s a i d * o ch * e n j i * o s u r u +HYP: o o k i k T A s a i d A o ch I e n j i E o s u r u +Eval: I S I I I + +Speaker sentences 75: cv_jpn_000749 #utts: 1 +id: (cv_jpn_000749-cv_jpn_000749) +Scores: (#C #S #D #I) 22 0 1 0 +REF: k a r e w A a t a m a o k a k i m u sh i cl t a +HYP: k a r e w * a t a m a o k a k i m u sh i cl t a +Eval: D + +Speaker sentences 76: cv_jpn_000750 #utts: 1 +id: (cv_jpn_000750-cv_jpn_000750) +Scores: (#C #S #D #I) 13 3 0 1 +REF: * o m a CH I sh i t e O r i m a s u +HYP: K o m a T E sh i t e A r i m a s u +Eval: I S S S + +Speaker sentences 77: cv_jpn_000751 #utts: 1 +id: (cv_jpn_000751-cv_jpn_000751) +Scores: (#C #S #D #I) 23 1 4 0 +REF: K o n o KY o k u PAU s e n k a I i j O o w a k i i t e r u +HYP: * o n o D o k u *** s e n k a * i j * o w a k i i t e r u +Eval: D S D D D + +Speaker sentences 78: cv_jpn_000752 #utts: 1 +id: (cv_jpn_000752-cv_jpn_000752) +Scores: (#C #S #D #I) 38 2 3 2 +REF: r e e Z o o k O o a k e t a ** t o * t a n PAU n a n i g a h i TS u y O o k a w a s u r e t a +HYP: r e e J o o k * o a k e t a CL t o U t a n *** n a n i g a h i CH u y * o k a w a s u r e t a +Eval: S D I I D S D + +Speaker sentences 79: cv_jpn_000753 #utts: 1 +id: (cv_jpn_000753-cv_jpn_000753) +Scores: (#C #S #D #I) 3 0 0 2 +REF: * i * ch i +HYP: I i T ch i +Eval: I I + +Speaker sentences 80: cv_jpn_000754 #utts: 1 +id: (cv_jpn_000754-cv_jpn_000754) +Scores: (#C #S #D #I) 3 1 0 0 +REF: W a ch i +HYP: H a ch i +Eval: S + +Speaker sentences 81: cv_jpn_000755 #utts: 1 +id: (cv_jpn_000755-cv_jpn_000755) +Scores: (#C #S #D #I) 3 0 0 3 +REF: * i i * e * +HYP: K i i W e A +Eval: I I I + +Speaker sentences 82: cv_jpn_000756 #utts: 1 +id: (cv_jpn_000756-cv_jpn_000756) +Scores: (#C #S #D #I) 2 1 0 0 +REF: R e i +HYP: D e i +Eval: S + +Speaker sentences 83: cv_jpn_000757 #utts: 1 +id: (cv_jpn_000757-cv_jpn_000757) +Scores: (#C #S #D #I) 4 0 0 1 +REF: * sh i ch i +HYP: A sh i ch i +Eval: I + +Speaker sentences 84: cv_jpn_000758 #utts: 1 +id: (cv_jpn_000758-cv_jpn_000758) +Scores: (#C #S #D #I) 27 1 4 0 +REF: y o o b o o W A d a s u n o n I k a u H i t o W a s u k u n a i +HYP: y o o b o o * * d a s u n o n * k a u SH i t o * a s u k u n a i +Eval: D D D S D + +Speaker sentences 85: cv_jpn_000759 #utts: 1 +id: (cv_jpn_000759-cv_jpn_000759) +Scores: (#C #S #D #I) 31 5 2 0 +REF: R o o k a r U t o k u y U u n o i k I O I m a k a s e n o k o m A a sh a r u +HYP: N o o k a r E t o k u y * u n o i k KY U E m a k a s e n o k o m * a sh a r u +Eval: S S D S S S D + +Speaker sentences 86: cv_jpn_000760 #utts: 1 +id: (cv_jpn_000760-cv_jpn_000760) +Scores: (#C #S #D #I) 34 0 2 0 +REF: k o n o d a i F U k u w a a n k o g a o o k u t e y o k u k a i m a s u +HYP: k o n o d a i * * k u w a a n k o g a o o k u t e y o k u k a i m a s u +Eval: D D + +Speaker sentences 87: cv_jpn_000761 #utts: 1 +id: (cv_jpn_000761-cv_jpn_000761) +Scores: (#C #S #D #I) 25 4 1 1 +REF: J I sh o B I k i n a g a r a sh o o s * e ts U o y o m i m a s u +HYP: D E sh o * O k i n a g a r a sh o o s U e ts O o y o m i m a s u +Eval: S S D S I S + +Speaker sentences 88: cv_jpn_000762 #utts: 1 +id: (cv_jpn_000762-cv_jpn_000762) +Scores: (#C #S #D #I) 37 0 4 2 +REF: k o n n A o o k i n a * g O o g u r U o ts u k e n a i t O i k e n a * i n d e s u k a +HYP: k o n n * o o k i n a N g * o g u r * o ts u k e n a i t * i k e n a E i n d e s u k a +Eval: D I D D D I + +Speaker sentences 89: cv_jpn_000763 #utts: 1 +id: (cv_jpn_000763-cv_jpn_000763) +Scores: (#C #S #D #I) 21 3 1 0 +REF: k a r E e n o b o o RY O k U w a t o m a r a n a i +HYP: k a r A e n o b o o ** I k O w a t o m a r a n a i +Eval: S D S S + +Speaker sentences 90: cv_jpn_000764 #utts: 1 +id: (cv_jpn_000764-cv_jpn_000764) +Scores: (#C #S #D #I) 10 2 0 1 +REF: i k i t e * T a n D a n e +HYP: i k i t e I K a n M a n e +Eval: I S S + +Speaker sentences 91: cv_jpn_000765 #utts: 1 +id: (cv_jpn_000765-cv_jpn_000765) +Scores: (#C #S #D #I) 29 4 3 1 +REF: t o o j I T o sh * I CH a k a CL k I t e k i n a h a ts u m E e d a cl t a n e +HYP: t o o j U D o sh S E S a k a ** k * t e k i n a h a ts u m * e d a cl t a n e +Eval: S S I S S D D D + +Speaker sentences 92: cv_jpn_000766 #utts: 1 +id: (cv_jpn_000766-cv_jpn_000766) +Scores: (#C #S #D #I) 15 6 8 2 +REF: S o n O t O k i PAU W A t a SH I W a ch i k A R a TS u K I t * a * +HYP: H o n * t E k i *** * O t a ** S O a ch i k * * a CL u * * t K a N +Eval: S D S D D S D S S D D S D D I I + +Speaker sentences 93: cv_jpn_000767 #utts: 1 +id: (cv_jpn_000767-cv_jpn_000767) +Scores: (#C #S #D #I) 12 4 1 0 +REF: k a W a G a h i A G a cl t e i T a +HYP: k a * a W a h i E W a cl t e i D a +Eval: D S S S S + +Speaker sentences 94: cv_jpn_000768 #utts: 1 +id: (cv_jpn_000768-cv_jpn_000768) +Scores: (#C #S #D #I) 1 1 1 3 +REF: I ch * ** * I +HYP: * ch E CL K E +Eval: D I I I S + +Speaker sentences 95: cv_jpn_000769 #utts: 1 +id: (cv_jpn_000769-cv_jpn_000769) +Scores: (#C #S #D #I) 0 1 1 0 +REF: N I +HYP: * O +Eval: D S + +Speaker sentences 96: cv_jpn_000770 #utts: 1 +id: (cv_jpn_000770-cv_jpn_000770) +Scores: (#C #S #D #I) 4 0 0 3 +REF: * sh i * ch * i +HYP: A sh i T ch I i +Eval: I I I + +Speaker sentences 97: cv_jpn_000771 #utts: 1 +id: (cv_jpn_000771-cv_jpn_000771) +Scores: (#C #S #D #I) 1 1 0 0 +REF: G o +HYP: K o +Eval: S + +Speaker sentences 98: cv_jpn_000772 #utts: 1 +id: (cv_jpn_000772-cv_jpn_000772) +Scores: (#C #S #D #I) 3 0 0 1 +REF: i i e * +HYP: i i e A +Eval: I + +Speaker sentences 99: cv_jpn_000773 #utts: 1 +id: (cv_jpn_000773-cv_jpn_000773) +Scores: (#C #S #D #I) 22 1 3 0 +REF: n a m a E k a r a sh I T e t e k I t o o s u g i r u +HYP: n a m a I k a r a sh * * e t e k * t o o s u g i r u +Eval: S D D D + +Speaker sentences 100: cv_jpn_000774 #utts: 1 +id: (cv_jpn_000774-cv_jpn_000774) +Scores: (#C #S #D #I) 56 5 3 1 +REF: j i k o n o s o t o n I a r u t o I u n o w a t a n n i j i k o n O I sh i k I n O s o ** T o n i a r u t o I u k o t o d e n a k u +HYP: j i k o n o s o t o n Y a r u t o Y u n o w a t a n n i j i k o n * E sh i k * n * s o TS S o n i a r u t o Y u k o t o d e n a k u +Eval: S S D S D D I S S + +Speaker sentences 101: cv_jpn_000775 #utts: 1 +id: (cv_jpn_000775-cv_jpn_000775) +Scores: (#C #S #D #I) 20 0 2 0 +REF: s o r E w a sh i r a n a k u t e I i d e s u +HYP: s o r * w a sh i r a n a k u t e * i d e s u +Eval: D D + +Speaker sentences 102: cv_jpn_000776 #utts: 1 +id: (cv_jpn_000776-cv_jpn_000776) +Scores: (#C #S #D #I) 40 3 6 1 +REF: k i m i t O B o k U n o ky o o ts U u n o sh I R i A I w a d a r e H i t o r * i pau m i a t a r a n a i +HYP: k i m i t * * o k O n o ky o o ts * u n o sh * * i G E w a d a r e * i t o r U i pau m i a t a r a n a i +Eval: D D S D D D S S D I + +Speaker sentences 103: cv_jpn_000777 #utts: 1 +id: (cv_jpn_000777-cv_jpn_000777) +Scores: (#C #S #D #I) 22 4 1 2 +REF: s u * g e e D A I J i n I n a cl t e k i t e r u n o n a ** +HYP: s u E g e e O O T O i n * n a cl t e k i t e r u n o n a CL +Eval: I S S S S D I + +Speaker sentences 104: cv_jpn_000778 #utts: 1 +id: (cv_jpn_000778-cv_jpn_000778) +Scores: (#C #S #D #I) 20 3 5 0 +REF: k o n o A T A R I d e s u k o sh i Y a s u M i m a sh O o +HYP: k o n o * * H E N d e s u k o sh i * a s u * i m a sh * o +Eval: D D S S S D D D + +Speaker sentences 105: cv_jpn_000779 #utts: 1 +id: (cv_jpn_000779-cv_jpn_000779) +Scores: (#C #S #D #I) 25 2 2 8 +REF: d e n sh a N i n o r u t o k i PAU k i cl P U o k a i m a s * * * * * * * * u +HYP: d e n sh a R i n o r u t o k i *** k i cl * T o k a i m a s U D A U T A U T u +Eval: S D D S I I I I I I I I + +Speaker sentences 106: cv_jpn_000780 #utts: 1 +id: (cv_jpn_000780-cv_jpn_000780) +Scores: (#C #S #D #I) 22 4 6 1 +REF: t a m a G o * w a i CL k o G o j U U G u r a M U G u r a i D e s u +HYP: t a m a * o G w a i ** k o K o j * * I u r a * N B u r a i * e s u +Eval: D I D S D D S D S S D + +Speaker sentences 107: cv_jpn_000781 #utts: 1 +id: (cv_jpn_000781-cv_jpn_000781) +Scores: (#C #S #D #I) 32 2 6 5 +REF: g I R e s u p I i w a m a CL G I i o ts U u j i t e *** i n e s U t o sh i r i * a cl t a * * * +HYP: g * E e s u p * i w a m a ** * K i o ts * u j i t e PAU i n e s * t o sh i r i Y a cl t a T U U +Eval: D S D D D S D I D I I I I + +Speaker sentences 108: cv_jpn_000782 #utts: 1 +id: (cv_jpn_000782-cv_jpn_000782) +Scores: (#C #S #D #I) 57 10 7 7 +REF: n o o GY O o o y a M e Z a r u o e n a i H i t o G A a r i PAU k a n R e n k i GY O o m o PAU k o N o f * * U KY o o n i h i k i z u r a r e t e I r u t * * * o * * I u +HYP: n o o ** G o o y a N e S a r u o e n a i SH i t o * P a r i *** k a n * e n k i ** Y o m o *** k o M o f K I K E o o n i h i k i z u r a r e t e * r u t O M O o S U TS u +Eval: D S S S S D S D D D S D S I I S S D I I I I I S + +Speaker sentences 109: cv_jpn_000783 #utts: 1 +id: (cv_jpn_000783-cv_jpn_000783) +Scores: (#C #S #D #I) 34 4 7 1 +REF: N a n d e k o n o r O B o cl t o PAU sh o t a i m e N n a * N o N i n a r e N a r e SH I I n d a +HYP: * a n d e k o n o r * * o cl t o *** sh o t a i m e * n a M U o * i n a r e Y a r e ** S E n d a +Eval: D D D D D I S D S D S S + +Speaker sentences 110: cv_jpn_000784 #utts: 1 +id: (cv_jpn_000784-cv_jpn_000784) +Scores: (#C #S #D #I) 25 2 1 0 +REF: f u ts u u d e a r u k o t O m o R i cl p a n a k o s E e +HYP: f u ts u u d e a r u k o t A m o D i cl p a n a k o s * e +Eval: S S D + +Speaker sentences 111: cv_jpn_000785 #utts: 1 +id: (cv_jpn_000785-cv_jpn_000785) +Scores: (#C #S #D #I) 31 0 2 0 +REF: ts u y o b i d e t a n j i k a n d e g O o k a i n I i t a m e r u +HYP: ts u y o b i d e t a n j i k a n d e g * o k a i n * i t a m e r u +Eval: D D + +Speaker sentences 112: cv_jpn_000786 #utts: 1 +id: (cv_jpn_000786-cv_jpn_000786) +Scores: (#C #S #D #I) 45 2 1 1 +REF: B a k u m a ts u n o d e k i g o T o w a *** i m a n i ts U u j i r u ky o o k u n n o y a m a d e s u +HYP: P a k u m a ts u n o d e k i g o K o w a PAU i m a n i ts * u j i r u ky o o k u n n o y a m a d e s u +Eval: S S I D + +Speaker sentences 113: cv_jpn_000787 #utts: 1 +id: (cv_jpn_000787-cv_jpn_000787) +Scores: (#C #S #D #I) 26 5 1 0 +REF: M U k o o k a r a m a ch i n o T O M O r i g a m i e t e k i t a +HYP: * N k o o k a r a m a ch i n o W A K A r i g a m i e t e k i t a +Eval: D S S S S S + +Speaker sentences 114: cv_jpn_000788 #utts: 1 +id: (cv_jpn_000788-cv_jpn_000788) +Scores: (#C #S #D #I) 46 10 4 5 +REF: N A n i o * * I u b e k i ** K a w a k a r a n A K a CL t a N a N i m O i * u b E k i k O t o g a o m o I u k a b a n a * k a cl t A +HYP: M E n i o U Y U u b e k i CH I a w a k a r a n * * a U t a R a * i m * i Y u b I k i k U t o g a o m o Y u k a b a n a G k a cl t U +Eval: S S I I S I S D D S S D D I S S S I S + +Speaker sentences 115: cv_jpn_000789 #utts: 1 +id: (cv_jpn_000789-cv_jpn_000789) +Scores: (#C #S #D #I) 19 4 3 0 +REF: t a m e SH i n I i CL k a I D a k E y a cl t e m i r U +HYP: t a m e S i n * i ** k a E N a k I y a cl t e m i r * +Eval: S D D S S S D + +Speaker sentences 116: cv_jpn_000790 #utts: 1 +id: (cv_jpn_000790-cv_jpn_000790) +Scores: (#C #S #D #I) 33 8 1 1 +REF: B o k u sh i k * a i n A i K i m i W a i n a i k o r e w a PAU o o K I n A ch i G a i k a +HYP: M o k u sh i k G a i n E i G i m i M a i n a i k o r e w a *** o o T E n TS ch i E a i k a +Eval: S I S S S D S S S S + +Speaker sentences 117: cv_jpn_000791 #utts: 1 +id: (cv_jpn_000791-cv_jpn_000791) +Scores: (#C #S #D #I) 37 11 10 3 +REF: SH U U k A i SH o k a r a * n i j U u g O o t o o m a D E n o *** m i ch i W a *** m u k a SH I T o k A W a CL t E i n A k A CL T a +HYP: ** TS CH k E i J o k a r a E n i j * u g * o t o o m a N U n o PAU m i ch i M a PAU m u k a ** * S o k * * a O t R i n E k * ** * a +Eval: D S S S S I D D S S I S I D D S D D S S S D D D + +Speaker sentences 118: cv_jpn_000792 #utts: 1 +id: (cv_jpn_000792-cv_jpn_000792) +Scores: (#C #S #D #I) 31 8 6 3 +REF: k a n * O j O N o t e e A n w a k o n ** p * o n t e K i n a K a i K E TS u N I TS u n A G a cl t a +HYP: k a n G U j * * o t e e H n w a k o n CL p U o n t e * i n a G a i CH I K u * U S u n * * a cl t a +Eval: I S D D S I I D S S S S D S S D D + +Speaker sentences 119: cv_jpn_000793 #utts: 1 +id: (cv_jpn_000793-cv_jpn_000793) +Scores: (#C #S #D #I) 33 5 4 1 +REF: k o D O m o n o k o r O w a g o h a N h a D e pau o t O n a N i n a r U t o p a N h * a +HYP: k o R U m o n o k o r E w a g o h a * h a R e pau o t U n a * i n a r * t o p a * h A a +Eval: S S S D S S D D D I + +Speaker sentences 120: cv_jpn_000794 #utts: 1 +id: (cv_jpn_000794-cv_jpn_000794) +Scores: (#C #S #D #I) 68 6 4 1 +REF: k o o i t e k i CH o CL k a n t e k i n i pau p o i *** E sh i s u t e k i n i pau w a r e w a r e N o j i k o w a m a s u m a s u A K A R I t o n a r u n o d E a r u +HYP: k o o i t e k i T o ** k a n t e k i n i pau p o i PAU A sh i s u t e k i n i pau w a r e w a r e M o j i k o w a m a s u m a s u * * PAU M E t o n a r u n o d * a r u +Eval: S D I S S D D S S S D + +Speaker sentences 121: cv_jpn_000795 #utts: 1 +id: (cv_jpn_000795-cv_jpn_000795) +Scores: (#C #S #D #I) 71 5 5 4 +REF: h i j o o sh i k i d E a r U k o t o w a PAU M u ch * I o i m i s u r u n o m i d e n a k u pau sh a k a i * t e k i n i *** a k u t o m o k a n G a e r a R E r u N o * d E a r u +HYP: h i j o o sh i k i d * a r O k o t o w a *** Y u ch J O o i m i s u r u n o m i d e n a k u pau sh a k a i E t e k i n i PAU a k u t o m o k a n M a e r a * * r u U o U d * a r u +Eval: D S D S I S I I S D D S I D + +Speaker sentences 122: cv_jpn_000796 #utts: 1 +id: (cv_jpn_000796-cv_jpn_000796) +Scores: (#C #S #D #I) 44 5 2 0 +REF: j o o sh I k i g a n a o t o k u sh U t e k i n a ch i sh i k i d e a r u n i h a N SH I PAU k a G a k u w a +HYP: j o o sh U k i g a n a o t o k u sh I t e k i n a ch i sh i k i d e a r u n i h a * ** S E k a R a k u w a +Eval: S S D D S S S + +Speaker sentences 123: cv_jpn_000797 #utts: 1 +id: (cv_jpn_000797-cv_jpn_000797) +Scores: (#C #S #D #I) 27 2 0 1 +REF: k o n n a k o t o d e o K O r a r e t e n a s a k e n * a i +HYP: k o n n a k o t o d e o G U r a r e t e n a s a k e n H a i +Eval: S S I + +Speaker sentences 124: cv_jpn_000798 #utts: 1 +id: (cv_jpn_000798-cv_jpn_000798) +Scores: (#C #S #D #I) 81 7 8 5 +REF: k a k o t o m i r a i * t o G a j i k o m u j u n t e k i n i *** G e n z a i n I o ** i T E t a i r i TS u S u R u t o i u n i w a pau G e n * Z a I G a k a t a ch I o m o t a N a * k e r e B a n a r a n a i +HYP: k a k o t o m i r a i E t o W a j i k o m u j u n t e k i n i PAU D e n z a i n * o CH i * * t a i r i K u * u * u t o i u n i w a pau * e n D N a * Y a k a t a ch * o m o t a M a I k e r e M a n a r a n a i +Eval: I S I S D I D D S D D D I S D S D S I S + +Speaker sentences 125: cv_jpn_000799 #utts: 1 +id: (cv_jpn_000799-cv_jpn_000799) +Scores: (#C #S #D #I) 22 1 9 1 +REF: * sh o k i H i Y O o n o t a k a s A g A H A a d o R u n I n a r U +HYP: A sh o k i * i * * o n o t a k a s E g * * * a d o * u n * n a r * +Eval: I D D D S D D D D D D + + diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/text b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/text new file mode 100644 index 0000000000000000000000000000000000000000..1e5f2e0ac392d9e80838c13af2e3a369025f244a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/text @@ -0,0 +1,126 @@ +cv_jpn_000674 b o k u n o y i e g a cl t a k a i d n a N n o n a m a e N n i k u r a b e r u t o p o k u n i w a n a j i m i n o n a i m a e b a e b a k a r i d a g k e d o +cv_jpn_000675 m a r y o o s u a n o m o n o e r u d i k u i n i k i g a r u k e t e r u +cv_jpn_000676 b o k u n o sh I t e e u m o n u o t o w a s U k o sh i ch i g a cl t e i t a +cv_jpn_000677 k a k a o r u t a ch i m a n i o i t e pau s e k g a r e g a i sh I k i m e N t e k i d e a r i w a r u w a r e n o j i k o g a j sh I k i s a y o o t e k i d e a r u t o k a N e e r a r e r u t o ch I +cv_jpn_000678 i e n i k I t a n e N g a a j i w a s a N h a k o m a i h o r o d e pau pau ch o o d a sh I t a b u N t o o n a j i g u r a i d a +cv_jpn_000679 h a w a p i t a m a r e ts u b a a g u n o s e e sh i N by y o o i N n i n u N h I t e y i r u t o k i n i p e sh i o o m u +cv_jpn_000680 t a d a N d e a r h a N t a n o h i r o g e r a b a a ch i k o ch i ts u g i h a g i y a a r i k a t a g o ch u i n i d e k i t a h o k o r o b i n a N k a ky o o n e N n o m a o m o n i n a cl t e i r u +cv_jpn_000681 k a r a g a k a n o d e pau b e r u n a k a n i h o o m o N k i b o o n o b u sh o o y o b i ch o o z a k i b o o n o b u g o m a i k a r a t o o r i d e s U +cv_jpn_000682 k o N b a w a t o t e m o s a m i d e s U +cv_jpn_000683 k i N ch o o sh I t a k o ts U k i d e pau cl p a cl t a w a d a s e k i n i h a i r u +cv_jpn_000684 m a s a N ky u u r e N t o i cl t a f u k a k a r o k e N ch i k o w u ts U +cv_jpn_000685 g o r i y o sh i d e s u s u m e t e ts s u g o g a w a r u k u n a cl t a r a i h I cl k o m e r u y a r e k U ch i +cv_jpn_000686 m o j u r N t e k i j i k o o t o i ts U t e k i n i j i k o o j i sh i N o k e e s e e s u r u sh a k a i w a +cv_jpn_000687 h a N n o i k e N n i n a r a s a r e r u n a +cv_jpn_000688 h i n o g a s o b i t a y o r a z e N k a i d e k o cl ch o m i t e i r u +cv_jpn_000689 i j ch i d o w a k o N p o t a j i k a N o n o N d e m i t a i +cv_jpn_000690 k o o i n o k o sh I t e pau o t o o s a N t o k a s a N w a N d e t e i k i m a sh I t a +cv_jpn_000691 sh i k a sh I t e s o r e g a ts U k u r a r e t a m o n o k a r a ts U k u r u m o r o e t o sh I t e pau d o k o m a d e m o r a w a r u n i s e m a r u t o y u t o k i a r e w a r i n i ch o cl k a N t e k i d e a r u +cv_jpn_000692 h a i n i t a m a t a k e m u r i o h a k i d a sh i k u r a i k o o e n i sh I s e N o m o k e r u +cv_jpn_000693 w a d a w a d a sh I n a b u s o k u d e ch i u s e N n a r i s o +cv_jpn_000694 ts e N s e k a i n o m o s U k a t a ch i i t a sh u n o y o a y i r u s e e s a i y o sh I k I t o s a i u o t o w a h a sh I t e k a N g a i r u k o t o w a d e k i n a i +cv_jpn_000695 s a k e n o m a e g n o n i p i r o u b a r a t o y o e t a +cv_jpn_000696 s o r e o o t a d a k o h o k u n o z e N d a N k a i sh i k u i t e e d o n o k a a k U t o m o m i m i r u k o t o w a +cv_jpn_000697 o k ky o m e m o k f u r a z u n i pau s u m o d e k e e N m o y a cl t e i t a +cv_jpn_000698 a d a i w a a n a i N ch sh i u m a t a k o b a y a sh i s a N t o a s o b e i m a s U +cv_jpn_000699 a s o o k o n i i h i i t o o g a pau i m a s u n e pau a r o h i t o w a t a r e t e sh o +cv_jpn_000700 w a t a j i w a k ky e n o o k a n a pau n o o d o k a e i t a i d e s U +cv_jpn_000701 ky o o r e N k a n a p e N ky u o sh I t e i m a s U +cv_jpn_000702 a o cl t o s u m i m r a s e pau +cv_jpn_000703 ch i k a s U pau b o n o o e N b u r i e w a pau y a k e k i m i n i k a e cl d e h a g e s U k e n o cl t a +cv_jpn_000704 i ts z u m o k o n o i N p I i ts o o ts U k a cl t e i t a n o d e m i j i k a h a u n a r i m a sh I t a +cv_jpn_000705 w a t a sh i w a pau e i g o g a h a n a sh I t e m a s U +cv_jpn_000706 o s e i k e e r y a j i b u a N d e t o cl t e k i n a +cv_jpn_000707 a i sh u u e k a r a n i sh u u k a N h a i g a i e r u y o o k o o o n i k i m a s U +cv_jpn_000708 o r e m o k i n i n a r u n a +cv_jpn_000709 d a r e i g a ts U k a cl t e r u n o k a a h w o k a r u n a i +cv_jpn_000710 p u r a o z a n o b a j o N g a pau a g a r u t o o s U k o sh I i u u r e sh i i +cv_jpn_000711 m a t a a t a r a sh i i a i d o r u g a d e t e k i t a +cv_jpn_000712 m a j i d e y a cl t o n o k a +cv_jpn_000713 ch o o d o s a m o t U k i n i pau ky o o j i g a h a i t e k i t a +cv_jpn_000714 i cl s o n i k ch i e N sh I t e o +cv_jpn_000715 s o r e k a s a t e n o t o r e s u N +cv_jpn_000716 f U t a r i o w a r e j i e i k i s u e e s a N sh I t a +cv_jpn_000717 t o m a t o k a n a N g a n o a k a i s o s u g a k a k a cl t e r i u o +cv_jpn_000718 k o g o k a r a t a t e n a N a s u n o w a k i b i s u i +cv_jpn_000719 n i z u g e o w o s U k a r i sh i b o cl t e pau a j i g a n a j i m i y o r i s u r u +cv_jpn_000720 n e t o k i n i h a m a cl t a r a k a n e g a g a m a cl t a U +cv_jpn_000721 s i t a k a i r i y o o n i n a r u N d a +cv_jpn_000722 k o s e h a h a i y u t o y u y o r i a k u g a ts u y o a e k a N j i +cv_jpn_000723 f e i j i k a r u n o s a o m a z a m a d a t o m i s e ts U k e r a r e t a +cv_jpn_000724 k o s U p a y o k e d e b a pau s o k o k o n o m o N d a e a g a m a N s u r e +cv_jpn_000725 i N n a y a cl t e m a s U k a r a t a i j o o b u d e s u i u o +cv_jpn_000726 k o n o t o j o k a N h a i cl t a sh u u k a N k i n i cl t a +cv_jpn_000727 k o n o d e N ch i f u u k i r e ch i a cl t a +cv_jpn_000728 a m a y e u d o r i s u r u t o k o r o g a n a k U t e k o m a cl t a +cv_jpn_000729 y a s U k a s u r u e o r i sh I t ts u w a g e t o h o sh e +cv_jpn_000730 m a s e g o k o n a k o t e i n a r u t o w a m o n a k a cl t a +cv_jpn_000731 s a i g o n i w a r a y o t o r i n i k u r a s U t a i r u +cv_jpn_000732 k o r a e n a N n a e i m i g a r u d a pau +cv_jpn_000733 o t i i a +cv_jpn_000734 sh a sh e i +cv_jpn_000735 n i +cv_jpn_000736 h a ch i +cv_jpn_000737 h a i +cv_jpn_000738 t e b u r u n o o y e n i k a b i N g a r i m a s U +cv_jpn_000739 w o t a sh i w a o m a i y a s a s a N b o sh i m a s U +cv_jpn_000740 a t a r a sh i i k u ts u o o h a i t e d e k a k e m a s U +cv_jpn_000741 k o t o sh i n o n a ts e y a s a m i o w a u m i n i m o i k i m a sh I t a sh i i y a m a n i m o n o r i b a sh I t a +cv_jpn_000742 w a t a sh i w a i r o y i r o n o b e N g o o ch i b u N n o m u n e d e k o sh i r a e t e m i m a sh I t a +cv_jpn_000743 n a N d e k o o m o s u o o b a i h e d a n a N d a r o +cv_jpn_000744 t a r e N t o k a r a ky o k u a n a n i k a e i e cl t u e k e e sh I s a k I u g e a +cv_jpn_000745 d o g e z a s e r e b a i cl t e m o N sh a n a i +cv_jpn_000746 d e t a n o a i d a k a n o j i w a j i b u N t o i cl t e e n o k ky u o r i y o t a n o cl t a +cv_jpn_000747 k o n o r e e n i N n a N k a w a h sh s a sh i b u r i n i n i t a +cv_jpn_000748 o o k I k t a s a i d a o ch i e N j i e o s u r u +cv_jpn_000749 k a r e w a t a m a o k a k i m u sh i cl t a +cv_jpn_000750 k o m a t e sh I t e a r i m a s U +cv_jpn_000751 o n o d o k U s e N k a i j o w a k I i t e r u +cv_jpn_000752 r e e j o o k o a k e t a cl t o U t a N n a n i g a h i ch u y o k a w a s u r e t a +cv_jpn_000753 i i t ch i +cv_jpn_000754 h a ch i +cv_jpn_000755 k i i w e a +cv_jpn_000756 d e i +cv_jpn_000757 a sh i ch i +cv_jpn_000758 y o o b o o d a s u n o N k a u sh I t o a s U k u n a i +cv_jpn_000759 n o o k a r e t o k u y u n o i k ky u e m a k a s e n o k o m a sh a r u +cv_jpn_000760 k o n o d a i k u w a a N k o g a o o k U t e y o k U k a i m a s U +cv_jpn_000761 d e sh o o k i n a g a r a sh o o s u e ts o o y o m i m a s U +cv_jpn_000762 k o N n o o k i n a N g o g u r o ts u k e n a i t i k e n a e i N d e s U k a +cv_jpn_000763 k a r a e n o b o o i k o w a t o m a r a n a i +cv_jpn_000764 i k i t e i k a N m a n e +cv_jpn_000765 t o o j u d o sh s e s a k a k t e k i n a h a ts u m e d a cl t a n e +cv_jpn_000766 h o N t e k i o t a s o a ch I k a cl U t k a N +cv_jpn_000767 k a a w a h i e w a cl t e i d a +cv_jpn_000768 ch e cl k e +cv_jpn_000769 o +cv_jpn_000770 a sh I t ch i i +cv_jpn_000771 k o +cv_jpn_000772 i i e a +cv_jpn_000773 n a m a i k a r a sh e t e k t o o s u g i r u +cv_jpn_000774 j i k o n o s o t o n y a r u t o y u n o w a t a N n i j i k o n e sh i k N s o ts s o n i a r u t o y u k o t o d e n a k u +cv_jpn_000775 s o r w a sh i r a n a k U t e i d e s U +cv_jpn_000776 k i m i t o k o n o ky o o ts u n o sh i g e w a d a r e i t o r u i pau m i a t a r a n a i +cv_jpn_000777 s u e g e e o o t o i N n a cl t e k i t e r u n o n a cl +cv_jpn_000778 k o n o h e N d e s U k o sh i a s u i m a sh o +cv_jpn_000779 d e N sh a r i n o r u t o k i k i cl t o k a i m a s U d a U t a U t U +cv_jpn_000780 t a m a o g w a i k o k o j i u r a N b u r a i e s U +cv_jpn_000781 g e e s U p i w a m a k i o ts u j i t e pau i n e s t o sh i r i y a cl t a t U U +cv_jpn_000782 n o o g o o y a n e s a r u o e n a i sh I t o p a r i k a N e N k i y o m o k o m o f k I k e o o n i h I k i z u r a r e t e r u t o m o o s U ts U +cv_jpn_000783 a N d e k o n o r o cl t o sh o t a i m e n a m u o i n a r e y a r e s e N d a +cv_jpn_000784 f U ts u u d e a r u k o t a m o d i cl p a n a k o s e +cv_jpn_000785 ts u y o b i d e t a N j i k a N d e g o k a i n i t a m e r u +cv_jpn_000786 p a k u m a ts u n o d e k i g o k o w a pau i m a n i ts u j i r u ky o o k u N n o y a m a d e s U +cv_jpn_000787 N k o o k a r a m a ch i n o w a k a r i g a m i e t e k i t a +cv_jpn_000788 m e n i o u y u u b e k I ch i a w a k a r a n a U t a r a i m i y u b i k I k u t o g a o m o y u k a b a n a g k a cl t U +cv_jpn_000789 t a m e s i n i k a e n a k i y a cl t e m i r +cv_jpn_000790 m o k U sh I k g a i n e i g i m i m a i n a i k o r e w a o o t e N ts ch i e a i k a +cv_jpn_000791 ts ch k e i j o k a r a e n i j u g o t o o m a N u N o pau m i ch i m a pau m u k a s o k a o t r i n e k a +cv_jpn_000792 k a N g u j o t e e h N w a k o N cl p u o N t e i n a g a i ch i k u u s u n a cl t a +cv_jpn_000793 k o r u m o n o k o r e w a g o h a h a r e pau o t u n a i n a r t o p a h a a +cv_jpn_000794 k o o i t e k I t o k a N t e k i n i pau p o i pau a sh i s u t e k i n i pau w a r e w a r e m o j i k o w a m a s u m a s u pau m e t o n a r u n o d a r u +cv_jpn_000795 h i j o o sh I k i d a r o k o t o w a y u ch j o o i m i s u r u n o m i d e n a k u pau sh a k a i e t e k i n i pau a k U t o m o k a N m a e r a r u u o u d a r u +cv_jpn_000796 j o o sh u k i g a n a o t o k u sh I t e k i n a ch I sh I k i d e a r u n i h a s e k a r a k u w a +cv_jpn_000797 k o N n a k o t o d e o g u r a r e t e n a s a k e n h a i +cv_jpn_000798 k a k o t o m i r a i e t o w a j i k o m u j u N t e k i n i pau d e N z a i n o ch i t a i r i k U u u t o i u n i w a pau e N d n a y a k a t a ch o m o t a m a i k e r e m a n a r a n a i +cv_jpn_000799 a sh o k I i o n o t a k a s e g a d o u N n a r diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/token b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/token new file mode 100644 index 0000000000000000000000000000000000000000..1e5f2e0ac392d9e80838c13af2e3a369025f244a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/token @@ -0,0 +1,126 @@ +cv_jpn_000674 b o k u n o y i e g a cl t a k a i d n a N n o n a m a e N n i k u r a b e r u t o p o k u n i w a n a j i m i n o n a i m a e b a e b a k a r i d a g k e d o +cv_jpn_000675 m a r y o o s u a n o m o n o e r u d i k u i n i k i g a r u k e t e r u +cv_jpn_000676 b o k u n o sh I t e e u m o n u o t o w a s U k o sh i ch i g a cl t e i t a +cv_jpn_000677 k a k a o r u t a ch i m a n i o i t e pau s e k g a r e g a i sh I k i m e N t e k i d e a r i w a r u w a r e n o j i k o g a j sh I k i s a y o o t e k i d e a r u t o k a N e e r a r e r u t o ch I +cv_jpn_000678 i e n i k I t a n e N g a a j i w a s a N h a k o m a i h o r o d e pau pau ch o o d a sh I t a b u N t o o n a j i g u r a i d a +cv_jpn_000679 h a w a p i t a m a r e ts u b a a g u n o s e e sh i N by y o o i N n i n u N h I t e y i r u t o k i n i p e sh i o o m u +cv_jpn_000680 t a d a N d e a r h a N t a n o h i r o g e r a b a a ch i k o ch i ts u g i h a g i y a a r i k a t a g o ch u i n i d e k i t a h o k o r o b i n a N k a ky o o n e N n o m a o m o n i n a cl t e i r u +cv_jpn_000681 k a r a g a k a n o d e pau b e r u n a k a n i h o o m o N k i b o o n o b u sh o o y o b i ch o o z a k i b o o n o b u g o m a i k a r a t o o r i d e s U +cv_jpn_000682 k o N b a w a t o t e m o s a m i d e s U +cv_jpn_000683 k i N ch o o sh I t a k o ts U k i d e pau cl p a cl t a w a d a s e k i n i h a i r u +cv_jpn_000684 m a s a N ky u u r e N t o i cl t a f u k a k a r o k e N ch i k o w u ts U +cv_jpn_000685 g o r i y o sh i d e s u s u m e t e ts s u g o g a w a r u k u n a cl t a r a i h I cl k o m e r u y a r e k U ch i +cv_jpn_000686 m o j u r N t e k i j i k o o t o i ts U t e k i n i j i k o o j i sh i N o k e e s e e s u r u sh a k a i w a +cv_jpn_000687 h a N n o i k e N n i n a r a s a r e r u n a +cv_jpn_000688 h i n o g a s o b i t a y o r a z e N k a i d e k o cl ch o m i t e i r u +cv_jpn_000689 i j ch i d o w a k o N p o t a j i k a N o n o N d e m i t a i +cv_jpn_000690 k o o i n o k o sh I t e pau o t o o s a N t o k a s a N w a N d e t e i k i m a sh I t a +cv_jpn_000691 sh i k a sh I t e s o r e g a ts U k u r a r e t a m o n o k a r a ts U k u r u m o r o e t o sh I t e pau d o k o m a d e m o r a w a r u n i s e m a r u t o y u t o k i a r e w a r i n i ch o cl k a N t e k i d e a r u +cv_jpn_000692 h a i n i t a m a t a k e m u r i o h a k i d a sh i k u r a i k o o e n i sh I s e N o m o k e r u +cv_jpn_000693 w a d a w a d a sh I n a b u s o k u d e ch i u s e N n a r i s o +cv_jpn_000694 ts e N s e k a i n o m o s U k a t a ch i i t a sh u n o y o a y i r u s e e s a i y o sh I k I t o s a i u o t o w a h a sh I t e k a N g a i r u k o t o w a d e k i n a i +cv_jpn_000695 s a k e n o m a e g n o n i p i r o u b a r a t o y o e t a +cv_jpn_000696 s o r e o o t a d a k o h o k u n o z e N d a N k a i sh i k u i t e e d o n o k a a k U t o m o m i m i r u k o t o w a +cv_jpn_000697 o k ky o m e m o k f u r a z u n i pau s u m o d e k e e N m o y a cl t e i t a +cv_jpn_000698 a d a i w a a n a i N ch sh i u m a t a k o b a y a sh i s a N t o a s o b e i m a s U +cv_jpn_000699 a s o o k o n i i h i i t o o g a pau i m a s u n e pau a r o h i t o w a t a r e t e sh o +cv_jpn_000700 w a t a j i w a k ky e n o o k a n a pau n o o d o k a e i t a i d e s U +cv_jpn_000701 ky o o r e N k a n a p e N ky u o sh I t e i m a s U +cv_jpn_000702 a o cl t o s u m i m r a s e pau +cv_jpn_000703 ch i k a s U pau b o n o o e N b u r i e w a pau y a k e k i m i n i k a e cl d e h a g e s U k e n o cl t a +cv_jpn_000704 i ts z u m o k o n o i N p I i ts o o ts U k a cl t e i t a n o d e m i j i k a h a u n a r i m a sh I t a +cv_jpn_000705 w a t a sh i w a pau e i g o g a h a n a sh I t e m a s U +cv_jpn_000706 o s e i k e e r y a j i b u a N d e t o cl t e k i n a +cv_jpn_000707 a i sh u u e k a r a n i sh u u k a N h a i g a i e r u y o o k o o o n i k i m a s U +cv_jpn_000708 o r e m o k i n i n a r u n a +cv_jpn_000709 d a r e i g a ts U k a cl t e r u n o k a a h w o k a r u n a i +cv_jpn_000710 p u r a o z a n o b a j o N g a pau a g a r u t o o s U k o sh I i u u r e sh i i +cv_jpn_000711 m a t a a t a r a sh i i a i d o r u g a d e t e k i t a +cv_jpn_000712 m a j i d e y a cl t o n o k a +cv_jpn_000713 ch o o d o s a m o t U k i n i pau ky o o j i g a h a i t e k i t a +cv_jpn_000714 i cl s o n i k ch i e N sh I t e o +cv_jpn_000715 s o r e k a s a t e n o t o r e s u N +cv_jpn_000716 f U t a r i o w a r e j i e i k i s u e e s a N sh I t a +cv_jpn_000717 t o m a t o k a n a N g a n o a k a i s o s u g a k a k a cl t e r i u o +cv_jpn_000718 k o g o k a r a t a t e n a N a s u n o w a k i b i s u i +cv_jpn_000719 n i z u g e o w o s U k a r i sh i b o cl t e pau a j i g a n a j i m i y o r i s u r u +cv_jpn_000720 n e t o k i n i h a m a cl t a r a k a n e g a g a m a cl t a U +cv_jpn_000721 s i t a k a i r i y o o n i n a r u N d a +cv_jpn_000722 k o s e h a h a i y u t o y u y o r i a k u g a ts u y o a e k a N j i +cv_jpn_000723 f e i j i k a r u n o s a o m a z a m a d a t o m i s e ts U k e r a r e t a +cv_jpn_000724 k o s U p a y o k e d e b a pau s o k o k o n o m o N d a e a g a m a N s u r e +cv_jpn_000725 i N n a y a cl t e m a s U k a r a t a i j o o b u d e s u i u o +cv_jpn_000726 k o n o t o j o k a N h a i cl t a sh u u k a N k i n i cl t a +cv_jpn_000727 k o n o d e N ch i f u u k i r e ch i a cl t a +cv_jpn_000728 a m a y e u d o r i s u r u t o k o r o g a n a k U t e k o m a cl t a +cv_jpn_000729 y a s U k a s u r u e o r i sh I t ts u w a g e t o h o sh e +cv_jpn_000730 m a s e g o k o n a k o t e i n a r u t o w a m o n a k a cl t a +cv_jpn_000731 s a i g o n i w a r a y o t o r i n i k u r a s U t a i r u +cv_jpn_000732 k o r a e n a N n a e i m i g a r u d a pau +cv_jpn_000733 o t i i a +cv_jpn_000734 sh a sh e i +cv_jpn_000735 n i +cv_jpn_000736 h a ch i +cv_jpn_000737 h a i +cv_jpn_000738 t e b u r u n o o y e n i k a b i N g a r i m a s U +cv_jpn_000739 w o t a sh i w a o m a i y a s a s a N b o sh i m a s U +cv_jpn_000740 a t a r a sh i i k u ts u o o h a i t e d e k a k e m a s U +cv_jpn_000741 k o t o sh i n o n a ts e y a s a m i o w a u m i n i m o i k i m a sh I t a sh i i y a m a n i m o n o r i b a sh I t a +cv_jpn_000742 w a t a sh i w a i r o y i r o n o b e N g o o ch i b u N n o m u n e d e k o sh i r a e t e m i m a sh I t a +cv_jpn_000743 n a N d e k o o m o s u o o b a i h e d a n a N d a r o +cv_jpn_000744 t a r e N t o k a r a ky o k u a n a n i k a e i e cl t u e k e e sh I s a k I u g e a +cv_jpn_000745 d o g e z a s e r e b a i cl t e m o N sh a n a i +cv_jpn_000746 d e t a n o a i d a k a n o j i w a j i b u N t o i cl t e e n o k ky u o r i y o t a n o cl t a +cv_jpn_000747 k o n o r e e n i N n a N k a w a h sh s a sh i b u r i n i n i t a +cv_jpn_000748 o o k I k t a s a i d a o ch i e N j i e o s u r u +cv_jpn_000749 k a r e w a t a m a o k a k i m u sh i cl t a +cv_jpn_000750 k o m a t e sh I t e a r i m a s U +cv_jpn_000751 o n o d o k U s e N k a i j o w a k I i t e r u +cv_jpn_000752 r e e j o o k o a k e t a cl t o U t a N n a n i g a h i ch u y o k a w a s u r e t a +cv_jpn_000753 i i t ch i +cv_jpn_000754 h a ch i +cv_jpn_000755 k i i w e a +cv_jpn_000756 d e i +cv_jpn_000757 a sh i ch i +cv_jpn_000758 y o o b o o d a s u n o N k a u sh I t o a s U k u n a i +cv_jpn_000759 n o o k a r e t o k u y u n o i k ky u e m a k a s e n o k o m a sh a r u +cv_jpn_000760 k o n o d a i k u w a a N k o g a o o k U t e y o k U k a i m a s U +cv_jpn_000761 d e sh o o k i n a g a r a sh o o s u e ts o o y o m i m a s U +cv_jpn_000762 k o N n o o k i n a N g o g u r o ts u k e n a i t i k e n a e i N d e s U k a +cv_jpn_000763 k a r a e n o b o o i k o w a t o m a r a n a i +cv_jpn_000764 i k i t e i k a N m a n e +cv_jpn_000765 t o o j u d o sh s e s a k a k t e k i n a h a ts u m e d a cl t a n e +cv_jpn_000766 h o N t e k i o t a s o a ch I k a cl U t k a N +cv_jpn_000767 k a a w a h i e w a cl t e i d a +cv_jpn_000768 ch e cl k e +cv_jpn_000769 o +cv_jpn_000770 a sh I t ch i i +cv_jpn_000771 k o +cv_jpn_000772 i i e a +cv_jpn_000773 n a m a i k a r a sh e t e k t o o s u g i r u +cv_jpn_000774 j i k o n o s o t o n y a r u t o y u n o w a t a N n i j i k o n e sh i k N s o ts s o n i a r u t o y u k o t o d e n a k u +cv_jpn_000775 s o r w a sh i r a n a k U t e i d e s U +cv_jpn_000776 k i m i t o k o n o ky o o ts u n o sh i g e w a d a r e i t o r u i pau m i a t a r a n a i +cv_jpn_000777 s u e g e e o o t o i N n a cl t e k i t e r u n o n a cl +cv_jpn_000778 k o n o h e N d e s U k o sh i a s u i m a sh o +cv_jpn_000779 d e N sh a r i n o r u t o k i k i cl t o k a i m a s U d a U t a U t U +cv_jpn_000780 t a m a o g w a i k o k o j i u r a N b u r a i e s U +cv_jpn_000781 g e e s U p i w a m a k i o ts u j i t e pau i n e s t o sh i r i y a cl t a t U U +cv_jpn_000782 n o o g o o y a n e s a r u o e n a i sh I t o p a r i k a N e N k i y o m o k o m o f k I k e o o n i h I k i z u r a r e t e r u t o m o o s U ts U +cv_jpn_000783 a N d e k o n o r o cl t o sh o t a i m e n a m u o i n a r e y a r e s e N d a +cv_jpn_000784 f U ts u u d e a r u k o t a m o d i cl p a n a k o s e +cv_jpn_000785 ts u y o b i d e t a N j i k a N d e g o k a i n i t a m e r u +cv_jpn_000786 p a k u m a ts u n o d e k i g o k o w a pau i m a n i ts u j i r u ky o o k u N n o y a m a d e s U +cv_jpn_000787 N k o o k a r a m a ch i n o w a k a r i g a m i e t e k i t a +cv_jpn_000788 m e n i o u y u u b e k I ch i a w a k a r a n a U t a r a i m i y u b i k I k u t o g a o m o y u k a b a n a g k a cl t U +cv_jpn_000789 t a m e s i n i k a e n a k i y a cl t e m i r +cv_jpn_000790 m o k U sh I k g a i n e i g i m i m a i n a i k o r e w a o o t e N ts ch i e a i k a +cv_jpn_000791 ts ch k e i j o k a r a e n i j u g o t o o m a N u N o pau m i ch i m a pau m u k a s o k a o t r i n e k a +cv_jpn_000792 k a N g u j o t e e h N w a k o N cl p u o N t e i n a g a i ch i k u u s u n a cl t a +cv_jpn_000793 k o r u m o n o k o r e w a g o h a h a r e pau o t u n a i n a r t o p a h a a +cv_jpn_000794 k o o i t e k I t o k a N t e k i n i pau p o i pau a sh i s u t e k i n i pau w a r e w a r e m o j i k o w a m a s u m a s u pau m e t o n a r u n o d a r u +cv_jpn_000795 h i j o o sh I k i d a r o k o t o w a y u ch j o o i m i s u r u n o m i d e n a k u pau sh a k a i e t e k i n i pau a k U t o m o k a N m a e r a r u u o u d a r u +cv_jpn_000796 j o o sh u k i g a n a o t o k u sh I t e k i n a ch I sh I k i d e a r u n i h a s e k a r a k u w a +cv_jpn_000797 k o N n a k o t o d e o g u r a r e t e n a s a k e n h a i +cv_jpn_000798 k a k o t o m i r a i e t o w a j i k o m u j u N t e k i n i pau d e N z a i n o ch i t a i r i k U u u t o i u n i w a pau e N d n a y a k a t a ch o m o t a m a i k e r e m a n a r a n a i +cv_jpn_000799 a sh o k I i o n o t a k a s e g a d o u N n a r diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/token_int new file mode 100644 index 0000000000000000000000000000000000000000..f28b316c74222fdfaca0e1059c90eebdee491ac9 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/token_int @@ -0,0 +1,126 @@ +cv_jpn_000674 25 3 6 7 9 3 23 4 5 16 2 21 8 2 6 2 4 14 9 2 13 9 3 9 2 11 2 5 13 9 4 6 7 10 2 25 5 10 7 8 3 30 3 6 7 9 4 17 2 9 2 22 4 11 4 9 3 9 2 4 11 2 5 25 2 5 25 2 6 2 10 4 14 2 16 6 5 14 3 +cv_jpn_000675 11 2 10 23 3 3 12 7 2 9 3 11 3 9 3 5 10 7 14 4 6 7 4 9 4 6 4 16 2 10 7 6 5 8 5 10 7 +cv_jpn_000676 25 3 6 7 9 3 15 19 8 5 5 7 11 3 9 7 3 8 3 17 2 12 18 6 3 15 4 27 4 16 2 21 8 5 4 8 2 +cv_jpn_000677 6 2 6 2 3 10 7 8 2 27 4 11 2 9 4 3 4 8 5 20 12 5 6 16 2 10 5 16 2 4 15 19 6 4 11 5 13 8 5 6 4 14 5 2 10 4 17 2 10 7 17 2 10 5 9 3 22 4 6 3 16 2 22 15 19 6 4 12 2 23 3 3 8 5 6 4 14 5 2 10 7 8 3 6 2 13 5 5 10 2 10 5 10 7 8 3 27 19 +cv_jpn_000678 4 5 9 4 6 19 8 2 9 5 13 16 2 2 22 4 17 2 12 2 13 24 2 6 3 11 2 4 24 3 10 3 14 5 20 20 27 3 3 14 2 15 19 8 2 25 7 13 8 3 3 9 2 22 4 16 7 10 2 4 14 2 +cv_jpn_000679 24 2 17 2 30 4 8 2 11 2 10 5 26 7 25 2 2 16 7 9 3 12 5 5 15 4 13 38 23 3 3 4 13 9 4 9 7 13 24 19 8 5 23 4 10 7 8 3 6 4 9 4 30 5 15 4 3 3 11 7 +cv_jpn_000680 8 2 14 2 13 14 5 2 10 24 2 13 8 2 9 3 24 4 10 3 16 5 10 2 25 2 2 27 4 6 3 27 4 26 7 16 4 24 2 16 4 23 2 2 10 4 6 2 8 2 16 3 27 7 4 9 4 14 5 6 4 8 2 24 3 6 3 10 3 25 4 9 2 13 6 2 29 3 3 9 5 13 9 3 11 2 3 11 3 9 4 9 2 21 8 5 4 10 7 +cv_jpn_000681 6 2 10 2 16 2 6 2 9 3 14 5 20 25 5 10 7 9 2 6 2 9 4 24 3 3 11 3 13 6 4 25 3 3 9 3 25 7 15 3 3 23 3 25 4 27 3 3 28 2 6 4 25 3 3 9 3 25 7 16 3 11 2 4 6 2 10 2 8 3 3 10 4 14 5 12 18 +cv_jpn_000682 6 3 13 25 2 17 2 8 3 8 5 11 3 12 2 11 4 14 5 12 18 +cv_jpn_000683 6 4 13 27 3 3 15 19 8 2 6 3 26 18 6 4 14 5 20 21 30 2 21 8 2 17 2 14 2 12 5 6 4 9 4 24 2 4 10 7 +cv_jpn_000684 11 2 12 2 13 29 7 7 10 5 13 8 3 4 21 8 2 31 7 6 2 6 2 10 3 6 5 13 27 4 6 3 17 7 26 18 +cv_jpn_000685 16 3 10 4 23 3 15 4 14 5 12 7 12 7 11 5 8 5 26 12 7 16 3 16 2 17 2 10 7 6 7 9 2 21 8 2 10 2 4 24 19 21 6 3 11 5 10 7 23 2 10 5 6 18 27 4 +cv_jpn_000686 11 3 22 7 10 13 8 5 6 4 22 4 6 3 3 8 3 4 26 18 8 5 6 4 9 4 22 4 6 3 3 22 4 15 4 13 3 6 5 5 12 5 5 12 7 10 7 15 2 6 2 4 17 2 +cv_jpn_000687 24 2 13 9 3 4 6 5 13 9 4 9 2 10 2 12 2 10 5 10 7 9 2 +cv_jpn_000688 24 4 9 3 16 2 12 3 25 4 8 2 23 3 10 2 28 5 13 6 2 4 14 5 6 3 21 27 3 11 4 8 5 4 10 7 +cv_jpn_000689 4 22 27 4 14 3 17 2 6 3 13 30 3 8 2 22 4 6 2 13 3 9 3 13 14 5 11 4 8 2 4 +cv_jpn_000690 6 3 3 4 9 3 6 3 15 19 8 5 20 3 8 3 3 12 2 13 8 3 6 2 12 2 13 17 2 13 14 5 8 5 4 6 4 11 2 15 19 8 2 +cv_jpn_000691 15 4 6 2 15 19 8 5 12 3 10 5 16 2 26 18 6 7 10 2 10 5 8 2 11 3 9 3 6 2 10 2 26 18 6 7 10 7 11 3 10 3 5 8 3 15 19 8 5 20 14 3 6 3 11 2 14 5 11 3 10 2 17 2 10 7 9 4 12 5 11 2 10 7 8 3 23 7 8 3 6 4 2 10 5 17 2 10 4 9 4 27 3 21 6 2 13 8 5 6 4 14 5 2 10 7 +cv_jpn_000692 24 2 4 9 4 8 2 11 2 8 2 6 5 11 7 10 4 3 24 2 6 4 14 2 15 4 6 7 10 2 4 6 3 3 5 9 4 15 19 12 5 13 3 11 3 6 5 10 7 +cv_jpn_000693 17 2 14 2 17 2 14 2 15 19 9 2 25 7 12 3 6 7 14 5 27 4 7 12 5 13 9 2 10 4 12 3 +cv_jpn_000694 26 5 13 12 5 6 2 4 9 3 11 3 12 18 6 2 8 2 27 4 4 8 2 15 7 9 3 23 3 2 23 4 10 7 12 5 5 12 2 4 23 3 15 19 6 19 8 3 12 2 4 7 3 8 3 17 2 24 2 15 19 8 5 6 2 13 16 2 4 10 7 6 3 8 3 17 2 14 5 6 4 9 2 4 +cv_jpn_000695 12 2 6 5 9 3 11 2 5 16 9 3 9 4 30 4 10 3 7 25 2 10 2 8 3 23 3 5 8 2 +cv_jpn_000696 12 3 10 5 3 3 8 2 14 2 6 3 24 3 6 7 9 3 28 5 13 14 2 13 6 2 4 15 4 6 7 4 8 5 5 14 3 9 3 6 2 2 6 18 8 3 11 3 11 4 11 4 10 7 6 3 8 3 17 2 +cv_jpn_000697 3 6 29 3 11 5 11 3 6 31 7 10 2 28 7 9 4 20 12 7 11 3 14 5 6 5 5 13 11 3 23 2 21 8 5 4 8 2 +cv_jpn_000698 2 14 2 4 17 2 2 9 2 4 13 27 15 4 7 11 2 8 2 6 3 25 2 23 2 15 4 12 2 13 8 3 2 12 3 25 5 4 11 2 12 18 +cv_jpn_000699 2 12 3 3 6 3 9 4 4 24 4 4 8 3 3 16 2 20 4 11 2 12 7 9 5 20 2 10 3 24 4 8 3 17 2 8 2 10 5 8 5 15 3 +cv_jpn_000700 17 2 8 2 22 4 17 2 6 29 5 9 3 3 6 2 9 2 20 9 3 3 14 3 6 2 5 4 8 2 4 14 5 12 18 +cv_jpn_000701 29 3 3 10 5 13 6 2 9 2 30 5 13 29 7 3 15 19 8 5 4 11 2 12 18 +cv_jpn_000702 2 3 21 8 3 12 7 11 4 11 10 2 12 5 20 +cv_jpn_000703 27 4 6 2 12 18 20 25 3 9 3 3 5 13 25 7 10 4 5 17 2 20 23 2 6 5 6 4 11 4 9 4 6 2 5 21 14 5 24 2 16 5 12 18 6 5 9 3 21 8 2 +cv_jpn_000704 4 26 28 7 11 3 6 3 9 3 4 13 30 19 4 26 3 3 26 18 6 2 21 8 5 4 8 2 9 3 14 5 11 4 22 4 6 2 24 2 7 9 2 10 4 11 2 15 19 8 2 +cv_jpn_000705 17 2 8 2 15 4 17 2 20 5 4 16 3 16 2 24 2 9 2 15 19 8 5 11 2 12 18 +cv_jpn_000706 3 12 5 4 6 5 5 10 23 2 22 4 25 7 2 13 14 5 8 3 21 8 5 6 4 9 2 +cv_jpn_000707 2 4 15 7 7 5 6 2 10 2 9 4 15 7 7 6 2 13 24 2 4 16 2 4 5 10 7 23 3 3 6 3 3 3 9 4 6 4 11 2 12 18 +cv_jpn_000708 3 10 5 11 3 6 4 9 4 9 2 10 7 9 2 +cv_jpn_000709 14 2 10 5 4 16 2 26 18 6 2 21 8 5 10 7 9 3 6 2 2 24 17 3 6 2 10 7 9 2 4 +cv_jpn_000710 30 7 10 2 3 28 2 9 3 25 2 22 3 13 16 2 20 2 16 2 10 7 8 3 3 12 18 6 3 15 19 4 7 7 10 5 15 4 4 +cv_jpn_000711 11 2 8 2 2 8 2 10 2 15 4 4 2 4 14 3 10 7 16 2 14 5 8 5 6 4 8 2 +cv_jpn_000712 11 2 22 4 14 5 23 2 21 8 3 9 3 6 2 +cv_jpn_000713 27 3 3 14 3 12 2 11 3 8 18 6 4 9 4 20 29 3 3 22 4 16 2 24 2 4 8 5 6 4 8 2 +cv_jpn_000714 4 21 12 3 9 4 6 27 4 5 13 15 19 8 5 3 +cv_jpn_000715 12 3 10 5 6 2 12 2 8 5 9 3 8 3 10 5 12 7 13 +cv_jpn_000716 31 18 8 2 10 4 3 17 2 10 5 22 4 5 4 6 4 12 7 5 5 12 2 13 15 19 8 2 +cv_jpn_000717 8 3 11 2 8 3 6 2 9 2 13 16 2 9 3 2 6 2 4 12 3 12 7 16 2 6 2 6 2 21 8 5 10 4 7 3 +cv_jpn_000718 6 3 16 3 6 2 10 2 8 2 8 5 9 2 13 2 12 7 9 3 17 2 6 4 25 4 12 7 4 +cv_jpn_000719 9 4 28 7 16 5 3 17 3 12 18 6 2 10 4 15 4 25 3 21 8 5 20 2 22 4 16 2 9 2 22 4 11 4 23 3 10 4 12 7 10 7 +cv_jpn_000720 9 5 8 3 6 4 9 4 24 2 11 2 21 8 2 10 2 6 2 9 5 16 2 16 2 11 2 21 8 2 18 +cv_jpn_000721 12 4 8 2 6 2 4 10 4 23 3 3 9 4 9 2 10 7 13 14 2 +cv_jpn_000722 6 3 12 5 24 2 24 2 4 23 7 8 3 23 7 23 3 10 4 2 6 7 16 2 26 7 23 3 2 5 6 2 13 22 4 +cv_jpn_000723 31 5 4 22 4 6 2 10 7 9 3 12 2 3 11 2 28 2 11 2 14 2 8 3 11 4 12 5 26 18 6 5 10 2 10 5 8 2 +cv_jpn_000724 6 3 12 18 30 2 23 3 6 5 14 5 25 2 20 12 3 6 3 6 3 9 3 11 3 13 14 2 5 2 16 2 11 2 13 12 7 10 5 +cv_jpn_000725 4 13 9 2 23 2 21 8 5 11 2 12 18 6 2 10 2 8 2 4 22 3 3 25 7 14 5 12 7 4 7 3 +cv_jpn_000726 6 3 9 3 8 3 22 3 6 2 13 24 2 4 21 8 2 15 7 7 6 2 13 6 4 9 4 21 8 2 +cv_jpn_000727 6 3 9 3 14 5 13 27 4 31 7 7 6 4 10 5 27 4 2 21 8 2 +cv_jpn_000728 2 11 2 23 5 7 14 3 10 4 12 7 10 7 8 3 6 3 10 3 16 2 9 2 6 18 8 5 6 3 11 2 21 8 2 +cv_jpn_000729 23 2 12 18 6 2 12 7 10 7 5 3 10 4 15 19 8 26 7 17 2 16 5 8 3 24 3 15 5 +cv_jpn_000730 11 2 12 5 16 3 6 3 9 2 6 3 8 5 4 9 2 10 7 8 3 17 2 11 3 9 2 6 2 21 8 2 +cv_jpn_000731 12 2 4 16 3 9 4 17 2 10 2 23 3 8 3 10 4 9 4 6 7 10 2 12 18 8 2 4 10 7 +cv_jpn_000732 6 3 10 2 5 9 2 13 9 2 5 4 11 4 16 2 10 7 14 2 20 +cv_jpn_000733 3 8 4 4 2 +cv_jpn_000734 15 2 15 5 4 +cv_jpn_000735 9 4 +cv_jpn_000736 24 2 27 4 +cv_jpn_000737 24 2 4 +cv_jpn_000738 8 5 25 7 10 7 9 3 3 23 5 9 4 6 2 25 4 13 16 2 10 4 11 2 12 18 +cv_jpn_000739 17 3 8 2 15 4 17 2 3 11 2 4 23 2 12 2 12 2 13 25 3 15 4 11 2 12 18 +cv_jpn_000740 2 8 2 10 2 15 4 4 6 7 26 7 3 3 24 2 4 8 5 14 5 6 2 6 5 11 2 12 18 +cv_jpn_000741 6 3 8 3 15 4 9 3 9 2 26 5 23 2 12 2 11 4 3 17 2 7 11 4 9 4 11 3 4 6 4 11 2 15 19 8 2 15 4 4 23 2 11 2 9 4 11 3 9 3 10 4 25 2 15 19 8 2 +cv_jpn_000742 17 2 8 2 15 4 17 2 4 10 3 23 4 10 3 9 3 25 5 13 16 3 3 27 4 25 7 13 9 3 11 7 9 5 14 5 6 3 15 4 10 2 5 8 5 11 4 11 2 15 19 8 2 +cv_jpn_000743 9 2 13 14 5 6 3 3 11 3 12 7 3 3 25 2 4 24 5 14 2 9 2 13 14 2 10 3 +cv_jpn_000744 8 2 10 5 13 8 3 6 2 10 2 29 3 6 7 2 9 2 9 4 6 2 5 4 5 21 8 7 5 6 5 5 15 19 12 2 6 19 7 16 5 2 +cv_jpn_000745 14 3 16 5 28 2 12 5 10 5 25 2 4 21 8 5 11 3 13 15 2 9 2 4 +cv_jpn_000746 14 5 8 2 9 3 2 4 14 2 6 2 9 3 22 4 17 2 22 4 25 7 13 8 3 4 21 8 5 5 9 3 6 29 7 3 10 4 23 3 8 2 9 3 21 8 2 +cv_jpn_000747 6 3 9 3 10 5 5 9 4 13 9 2 13 6 2 17 2 24 15 12 2 15 4 25 7 10 4 9 4 9 4 8 2 +cv_jpn_000748 3 3 6 19 6 8 2 12 2 4 14 2 3 27 4 5 13 22 4 5 3 12 7 10 7 +cv_jpn_000749 6 2 10 5 17 2 8 2 11 2 3 6 2 6 4 11 7 15 4 21 8 2 +cv_jpn_000750 6 3 11 2 8 5 15 19 8 5 2 10 4 11 2 12 18 +cv_jpn_000751 3 9 3 14 3 6 18 12 5 13 6 2 4 22 3 17 2 6 19 4 8 5 10 7 +cv_jpn_000752 10 5 5 22 3 3 6 3 2 6 5 8 2 21 8 3 18 8 2 13 9 2 9 4 16 2 24 4 27 7 23 3 6 2 17 2 12 7 10 5 8 2 +cv_jpn_000753 4 4 8 27 4 +cv_jpn_000754 24 2 27 4 +cv_jpn_000755 6 4 4 17 5 2 +cv_jpn_000756 14 5 4 +cv_jpn_000757 2 15 4 27 4 +cv_jpn_000758 23 3 3 25 3 3 14 2 12 7 9 3 13 6 2 7 15 19 8 3 2 12 18 6 7 9 2 4 +cv_jpn_000759 9 3 3 6 2 10 5 8 3 6 7 23 7 9 3 4 6 29 7 5 11 2 6 2 12 5 9 3 6 3 11 2 15 2 10 7 +cv_jpn_000760 6 3 9 3 14 2 4 6 7 17 2 2 13 6 3 16 2 3 3 6 18 8 5 23 3 6 18 6 2 4 11 2 12 18 +cv_jpn_000761 14 5 15 3 3 6 4 9 2 16 2 10 2 15 3 3 12 7 5 26 3 3 23 3 11 4 11 2 12 18 +cv_jpn_000762 6 3 13 9 3 3 6 4 9 2 13 16 3 16 7 10 3 26 7 6 5 9 2 4 8 4 6 5 9 2 5 4 13 14 5 12 18 6 2 +cv_jpn_000763 6 2 10 2 5 9 3 25 3 3 4 6 3 17 2 8 3 11 2 10 2 9 2 4 +cv_jpn_000764 4 6 4 8 5 4 6 2 13 11 2 9 5 +cv_jpn_000765 8 3 3 22 7 14 3 15 12 5 12 2 6 2 6 8 5 6 4 9 2 24 2 26 7 11 5 14 2 21 8 2 9 5 +cv_jpn_000766 24 3 13 8 5 6 4 3 8 2 12 3 2 27 19 6 2 21 18 8 6 2 13 +cv_jpn_000767 6 2 2 17 2 24 4 5 17 2 21 8 5 4 14 2 +cv_jpn_000768 27 5 21 6 5 +cv_jpn_000769 3 +cv_jpn_000770 2 15 19 8 27 4 4 +cv_jpn_000771 6 3 +cv_jpn_000772 4 4 5 2 +cv_jpn_000773 9 2 11 2 4 6 2 10 2 15 5 8 5 6 8 3 3 12 7 16 4 10 7 +cv_jpn_000774 22 4 6 3 9 3 12 3 8 3 9 23 2 10 7 8 3 23 7 9 3 17 2 8 2 13 9 4 22 4 6 3 9 5 15 4 6 13 12 3 26 12 3 9 4 2 10 7 8 3 23 7 6 3 8 3 14 5 9 2 6 7 +cv_jpn_000775 12 3 10 17 2 15 4 10 2 9 2 6 18 8 5 4 14 5 12 18 +cv_jpn_000776 6 4 11 4 8 3 6 3 9 3 29 3 3 26 7 9 3 15 4 16 5 17 2 14 2 10 5 4 8 3 10 7 4 20 11 4 2 8 2 10 2 9 2 4 +cv_jpn_000777 12 7 5 16 5 5 3 3 8 3 4 13 9 2 21 8 5 6 4 8 5 10 7 9 3 9 2 21 +cv_jpn_000778 6 3 9 3 24 5 13 14 5 12 18 6 3 15 4 2 12 7 4 11 2 15 3 +cv_jpn_000779 14 5 13 15 2 10 4 9 3 10 7 8 3 6 4 6 4 21 8 3 6 2 4 11 2 12 18 14 2 18 8 2 18 8 18 +cv_jpn_000780 8 2 11 2 3 16 17 2 4 6 3 6 3 22 4 7 10 2 13 25 7 10 2 4 5 12 18 +cv_jpn_000781 16 5 5 12 18 30 4 17 2 11 2 6 4 3 26 7 22 4 8 5 20 4 9 5 12 8 3 15 4 10 4 23 2 21 8 2 8 18 18 +cv_jpn_000782 9 3 3 16 3 3 23 2 9 5 12 2 10 7 3 5 9 2 4 15 19 8 3 30 2 10 4 6 2 13 5 13 6 4 23 3 11 3 6 3 11 3 31 6 19 6 5 3 3 9 4 24 19 6 4 28 7 10 2 10 5 8 5 10 7 8 3 11 3 3 12 18 26 18 +cv_jpn_000783 2 13 14 5 6 3 9 3 10 3 21 8 3 15 3 8 2 4 11 5 9 2 11 7 3 4 9 2 10 5 23 2 10 5 12 5 13 14 2 +cv_jpn_000784 31 18 26 7 7 14 5 2 10 7 6 3 8 2 11 3 14 4 21 30 2 9 2 6 3 12 5 +cv_jpn_000785 26 7 23 3 25 4 14 5 8 2 13 22 4 6 2 13 14 5 16 3 6 2 4 9 4 8 2 11 5 10 7 +cv_jpn_000786 30 2 6 7 11 2 26 7 9 3 14 5 6 4 16 3 6 3 17 2 20 4 11 2 9 4 26 7 22 4 10 7 29 3 3 6 7 13 9 3 23 2 11 2 14 5 12 18 +cv_jpn_000787 13 6 3 3 6 2 10 2 11 2 27 4 9 3 17 2 6 2 10 4 16 2 11 4 5 8 5 6 4 8 2 +cv_jpn_000788 11 5 9 4 3 7 23 7 7 25 5 6 19 27 4 2 17 2 6 2 10 2 9 2 18 8 2 10 2 4 11 4 23 7 25 4 6 19 6 7 8 3 16 2 3 11 3 23 7 6 2 25 2 9 2 16 6 2 21 8 18 +cv_jpn_000789 8 2 11 5 12 4 9 4 6 2 5 9 2 6 4 23 2 21 8 5 11 4 10 +cv_jpn_000790 11 3 6 18 15 19 6 16 2 4 9 5 4 16 4 11 4 11 2 4 9 2 4 6 3 10 5 17 2 3 3 8 5 13 26 27 4 5 2 4 6 2 +cv_jpn_000791 26 27 6 5 4 22 3 6 2 10 2 5 9 4 22 7 16 3 8 3 3 11 2 13 7 13 3 20 11 4 27 4 11 2 20 11 7 6 2 12 3 6 2 3 8 10 4 9 5 6 2 +cv_jpn_000792 6 2 13 16 7 22 3 8 5 5 24 13 17 2 6 3 13 21 30 7 3 13 8 5 4 9 2 16 2 4 27 4 6 7 7 12 7 9 2 21 8 2 +cv_jpn_000793 6 3 10 7 11 3 9 3 6 3 10 5 17 2 16 3 24 2 24 2 10 5 20 3 8 7 9 2 4 9 2 10 8 3 30 2 24 2 2 +cv_jpn_000794 6 3 3 4 8 5 6 19 8 3 6 2 13 8 5 6 4 9 4 20 30 3 4 20 2 15 4 12 7 8 5 6 4 9 4 20 17 2 10 5 17 2 10 5 11 3 22 4 6 3 17 2 11 2 12 7 11 2 12 7 20 11 5 8 3 9 2 10 7 9 3 14 2 10 7 +cv_jpn_000795 24 4 22 3 3 15 19 6 4 14 2 10 3 6 3 8 3 17 2 23 7 27 22 3 3 4 11 4 12 7 10 7 9 3 11 4 14 5 9 2 6 7 20 15 2 6 2 4 5 8 5 6 4 9 4 20 2 6 18 8 3 11 3 6 2 13 11 2 5 10 2 10 7 7 3 7 14 2 10 7 +cv_jpn_000796 22 3 3 15 7 6 4 16 2 9 2 3 8 3 6 7 15 19 8 5 6 4 9 2 27 19 15 19 6 4 14 5 2 10 7 9 4 24 2 12 5 6 2 10 2 6 7 17 2 +cv_jpn_000797 6 3 13 9 2 6 3 8 3 14 5 3 16 7 10 2 10 5 8 5 9 2 12 2 6 5 9 24 2 4 +cv_jpn_000798 6 2 6 3 8 3 11 4 10 2 4 5 8 3 17 2 22 4 6 3 11 7 22 7 13 8 5 6 4 9 4 20 14 5 13 28 2 4 9 3 27 4 8 2 4 10 4 6 18 7 7 8 3 4 7 9 4 17 2 20 5 13 14 9 2 23 2 6 2 8 2 27 3 11 3 8 2 11 2 4 6 5 10 5 11 2 9 2 10 2 9 2 4 +cv_jpn_000799 2 15 3 6 19 4 3 9 3 8 2 6 2 12 5 16 2 14 3 7 13 9 2 10 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/run.sh b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/run.sh new file mode 100755 index 0000000000000000000000000000000000000000..eaff9f970ff027107eedd03ff2c4225ce47404d8 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/run.sh @@ -0,0 +1 @@ +./asr.sh --ngpu 1 --stage 11 --stop_stage 13 --nj 32 --inference_nj 4 --gpu_inference true --lang jpn --inference_asr_model valid.loss.ave.pth --local_data_opts '--duration 10min --lid false --multilingual false --single_lang jpn' --use_lm false --token_type word --feats_type raw --feats_normalize utterance_mvn --asr_config conf/tuning/train_asr_s3prl_houlsby.yaml --inference_config conf/decode_asr.yaml --train_set train_10min_jpn --valid_set dev_10min_jpn --test_sets 'dev_10min_jpn test_10min_jpn' --asr_tag train_asr_s3prl_houlsby_jpn_10min --expdir test_pr --asr_stats_dir test_pr/asr_stats_jpn_10min --local_score_opts 'false false monolingual' --stage 12 "$@"; exit $? diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.1.log b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.1.log new file mode 100644 index 0000000000000000000000000000000000000000..e9b60f85e013b5194a74edf0f84a7a09d9935cd2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.1.log @@ -0,0 +1,459 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1 --config conf/decode_asr.yaml +# Started at Tue Jan 16 23:09:27 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1 --config conf/decode_asr.yaml +2024-01-16 23:09:28,947 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml +2024-01-16 23:09:28,965 (asr:523) INFO: Vocabulary size: 41 +2024-01-16 23:09:29,027 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 23:09:29,027 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 23:09:29,138 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 23:09:30,436 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 23:09:31,658 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 23:09:31,658 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 23:09:31,658 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 23:09:31,691 (asr_inference:446) INFO: Text tokenizer: WordTokenizer(delimiter="None") +2024-01-16 23:09:31,766 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 23:09:31,879 (asr_inference:494) INFO: speech length: 92736 +2024-01-16 23:09:33,090 (beam_search:428) INFO: decoder input length: 142 +2024-01-16 23:09:33,090 (beam_search:429) INFO: max output length: 142 +2024-01-16 23:09:33,090 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:33,351 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:33,351 (beam_search:476) INFO: -9.38 * 1.0 = -9.38 for ctc +2024-01-16 23:09:33,351 (beam_search:479) INFO: total log probability: -9.38 +2024-01-16 23:09:33,351 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 23:09:33,351 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:33,351 (beam_search:483) INFO: best hypo: kakotomiraitodoujuNtekijikodooitsunarugaiwenipaushIkitekinaNodear + +2024-01-16 23:09:33,375 (asr_inference:494) INFO: speech length: 131328 +2024-01-16 23:09:33,390 (beam_search:428) INFO: decoder input length: 203 +2024-01-16 23:09:33,390 (beam_search:429) INFO: max output length: 203 +2024-01-16 23:09:33,390 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:33,943 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:33,943 (beam_search:476) INFO: -13.59 * 1.0 = -13.59 for ctc +2024-01-16 23:09:33,943 (beam_search:479) INFO: total log probability: -13.59 +2024-01-16 23:09:33,943 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:09:33,943 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:33,944 (beam_search:483) INFO: best hypo: sekayokeeseesurutotomunipaujigkojishiyokeeseserutssoodotekisekainsoozootekiyotoshItepaukomutsugakoutsudearu + +2024-01-16 23:09:33,945 (asr_inference:494) INFO: speech length: 57600 +2024-01-16 23:09:33,954 (beam_search:428) INFO: decoder input length: 87 +2024-01-16 23:09:33,954 (beam_search:429) INFO: max output length: 87 +2024-01-16 23:09:33,954 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:34,047 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:34,047 (beam_search:476) INFO: -8.45 * 1.0 = -8.45 for ctc +2024-01-16 23:09:34,048 (beam_search:479) INFO: total log probability: -8.45 +2024-01-16 23:09:34,048 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-16 23:09:34,048 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:34,048 (beam_search:483) INFO: best hypo: pasokoNnegeemiaruItonahfuetekite + +2024-01-16 23:09:34,049 (asr_inference:494) INFO: speech length: 141696 +2024-01-16 23:09:34,064 (beam_search:428) INFO: decoder input length: 219 +2024-01-16 23:09:34,064 (beam_search:429) INFO: max output length: 219 +2024-01-16 23:09:34,064 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:34,614 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:34,614 (beam_search:476) INFO: -9.16 * 1.0 = -9.16 for ctc +2024-01-16 23:09:34,614 (beam_search:479) INFO: total log probability: -9.16 +2024-01-16 23:09:34,614 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:09:34,614 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:34,615 (beam_search:483) INFO: best hypo: kaakunoshimesatarashiijijitsuatarasikaNneNkaNkyoshihainatarashikanoseomocltepaunaniohajimerukawa + +2024-01-16 23:09:34,616 (asr_inference:494) INFO: speech length: 76032 +2024-01-16 23:09:34,626 (beam_search:428) INFO: decoder input length: 116 +2024-01-16 23:09:34,626 (beam_search:429) INFO: max output length: 116 +2024-01-16 23:09:34,626 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:34,757 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:34,758 (beam_search:476) INFO: -3.82 * 1.0 = -3.82 for ctc +2024-01-16 23:09:34,758 (beam_search:479) INFO: total log probability: -3.82 +2024-01-16 23:09:34,758 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:09:34,758 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:34,758 (beam_search:483) INFO: best hypo: omoshiroenonipauroodonakasugitepaudarui + +2024-01-16 23:09:34,759 (asr_inference:494) INFO: speech length: 58752 +2024-01-16 23:09:34,768 (beam_search:428) INFO: decoder input length: 89 +2024-01-16 23:09:34,768 (beam_search:429) INFO: max output length: 89 +2024-01-16 23:09:34,768 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:34,826 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:34,826 (beam_search:476) INFO: -2.70 * 1.0 = -2.70 for ctc +2024-01-16 23:09:34,826 (beam_search:479) INFO: total log probability: -2.70 +2024-01-16 23:09:34,826 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:09:34,826 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:34,827 (beam_search:483) INFO: best hypo: korejooshuuhaNtoina + +2024-01-16 23:09:34,827 (asr_inference:494) INFO: speech length: 97920 +2024-01-16 23:09:34,839 (beam_search:428) INFO: decoder input length: 150 +2024-01-16 23:09:34,839 (beam_search:429) INFO: max output length: 150 +2024-01-16 23:09:34,839 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:35,102 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:35,102 (beam_search:476) INFO: -6.15 * 1.0 = -6.15 for ctc +2024-01-16 23:09:35,102 (beam_search:479) INFO: total log probability: -6.15 +2024-01-16 23:09:35,102 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:09:35,102 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:35,103 (beam_search:483) INFO: best hypo: kagakUshamosekayohookatssekinipautoochItekinisatsumeshuotoshIteiru + +2024-01-16 23:09:35,104 (asr_inference:494) INFO: speech length: 39744 +2024-01-16 23:09:35,112 (beam_search:428) INFO: decoder input length: 60 +2024-01-16 23:09:35,112 (beam_search:429) INFO: max output length: 60 +2024-01-16 23:09:35,112 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:35,147 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:35,147 (beam_search:476) INFO: -5.34 * 1.0 = -5.34 for ctc +2024-01-16 23:09:35,147 (beam_search:479) INFO: total log probability: -5.34 +2024-01-16 23:09:35,147 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-16 23:09:35,147 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:35,147 (beam_search:483) INFO: best hypo: haItsunitsUemaraN + +2024-01-16 23:09:35,149 (asr_inference:494) INFO: speech length: 44352 +2024-01-16 23:09:35,157 (beam_search:428) INFO: decoder input length: 67 +2024-01-16 23:09:35,157 (beam_search:429) INFO: max output length: 67 +2024-01-16 23:09:35,157 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:35,198 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:35,198 (beam_search:476) INFO: -1.56 * 1.0 = -1.56 for ctc +2024-01-16 23:09:35,198 (beam_search:479) INFO: total log probability: -1.56 +2024-01-16 23:09:35,198 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:09:35,198 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:35,198 (beam_search:483) INFO: best hypo: shiclkarshtekudasai + +2024-01-16 23:09:35,199 (asr_inference:494) INFO: speech length: 158400 +2024-01-16 23:09:35,215 (beam_search:428) INFO: decoder input length: 245 +2024-01-16 23:09:35,215 (beam_search:429) INFO: max output length: 245 +2024-01-16 23:09:35,215 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:35,859 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:35,859 (beam_search:476) INFO: -9.59 * 1.0 = -9.59 for ctc +2024-01-16 23:09:35,859 (beam_search:479) INFO: total log probability: -9.59 +2024-01-16 23:09:35,859 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:09:35,859 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:35,859 (beam_search:483) INFO: best hypo: watashiwaamiginonotokipaudekishItekiseimeenojikakutoyugotokimonomeNshoohootekipauroNbetayuunodearu + +2024-01-16 23:09:35,861 (asr_inference:494) INFO: speech length: 127296 +2024-01-16 23:09:35,874 (beam_search:428) INFO: decoder input length: 196 +2024-01-16 23:09:35,874 (beam_search:429) INFO: max output length: 196 +2024-01-16 23:09:35,874 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:36,298 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:36,298 (beam_search:476) INFO: -5.31 * 1.0 = -5.31 for ctc +2024-01-16 23:09:36,298 (beam_search:479) INFO: total log probability: -5.31 +2024-01-16 23:09:36,298 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 23:09:36,298 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:36,299 (beam_search:483) INFO: best hypo: watashiwapaushakaikeeseenokoNteNniwadeyoonusosutekinamonogapauhataraiteirutomo + +2024-01-16 23:09:36,300 (asr_inference:494) INFO: speech length: 56448 +2024-01-16 23:09:36,309 (beam_search:428) INFO: decoder input length: 86 +2024-01-16 23:09:36,309 (beam_search:429) INFO: max output length: 86 +2024-01-16 23:09:36,309 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:36,381 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:36,381 (beam_search:476) INFO: -1.64 * 1.0 = -1.64 for ctc +2024-01-16 23:09:36,381 (beam_search:479) INFO: total log probability: -1.64 +2024-01-16 23:09:36,381 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-16 23:09:36,381 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:36,382 (beam_search:483) INFO: best hypo: naniosurutsumoridacltanoka + +2024-01-16 23:09:36,383 (asr_inference:494) INFO: speech length: 165888 +2024-01-16 23:09:36,399 (beam_search:428) INFO: decoder input length: 257 +2024-01-16 23:09:36,399 (beam_search:429) INFO: max output length: 257 +2024-01-16 23:09:36,399 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:37,096 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:37,097 (beam_search:476) INFO: -6.93 * 1.0 = -6.93 for ctc +2024-01-16 23:09:37,097 (beam_search:479) INFO: total log probability: -6.93 +2024-01-16 23:09:37,097 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 23:09:37,097 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:37,097 (beam_search:483) INFO: best hypo: kobutsutekItagajikohIteetekinitaNnipauteNshuugootekinikaNgararurutokisoregabutsuritekitekaidearu + +2024-01-16 23:09:37,099 (asr_inference:494) INFO: speech length: 127296 +2024-01-16 23:09:37,112 (beam_search:428) INFO: decoder input length: 196 +2024-01-16 23:09:37,112 (beam_search:429) INFO: max output length: 196 +2024-01-16 23:09:37,112 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:37,375 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:37,375 (beam_search:476) INFO: -4.71 * 1.0 = -4.71 for ctc +2024-01-16 23:09:37,375 (beam_search:479) INFO: total log probability: -4.71 +2024-01-16 23:09:37,375 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:09:37,375 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:37,375 (beam_search:483) INFO: best hypo: anegazucltanodeyaclkinoshiraigarimaseNdeshIta + +2024-01-16 23:09:37,376 (asr_inference:494) INFO: speech length: 86976 +2024-01-16 23:09:37,387 (beam_search:428) INFO: decoder input length: 133 +2024-01-16 23:09:37,387 (beam_search:429) INFO: max output length: 133 +2024-01-16 23:09:37,387 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:37,535 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:37,535 (beam_search:476) INFO: -4.01 * 1.0 = -4.01 for ctc +2024-01-16 23:09:37,535 (beam_search:479) INFO: total log probability: -4.01 +2024-01-16 23:09:37,535 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:09:37,535 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:37,535 (beam_search:483) INFO: best hypo: korewaNrihoNdeuteinaitabemunodesU + +2024-01-16 23:09:37,536 (asr_inference:494) INFO: speech length: 134208 +2024-01-16 23:09:37,550 (beam_search:428) INFO: decoder input length: 207 +2024-01-16 23:09:37,551 (beam_search:429) INFO: max output length: 207 +2024-01-16 23:09:37,551 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:37,835 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:37,835 (beam_search:476) INFO: -5.27 * 1.0 = -5.27 for ctc +2024-01-16 23:09:37,835 (beam_search:479) INFO: total log probability: -5.27 +2024-01-16 23:09:37,835 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:09:37,835 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:37,836 (beam_search:483) INFO: best hypo: wotashiwaheNshuueinoyooneNkuraewayacltacltoomo + +2024-01-16 23:09:37,837 (asr_inference:494) INFO: speech length: 115200 +2024-01-16 23:09:37,849 (beam_search:428) INFO: decoder input length: 177 +2024-01-16 23:09:37,849 (beam_search:429) INFO: max output length: 177 +2024-01-16 23:09:37,849 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:38,067 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:38,067 (beam_search:476) INFO: -6.32 * 1.0 = -6.32 for ctc +2024-01-16 23:09:38,067 (beam_search:479) INFO: total log probability: -6.32 +2024-01-16 23:09:38,067 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 23:09:38,067 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:38,067 (beam_search:483) INFO: best hypo: eisaNniikonokotobpanorimioooshiamashIta + +2024-01-16 23:09:38,068 (asr_inference:494) INFO: speech length: 98496 +2024-01-16 23:09:38,079 (beam_search:428) INFO: decoder input length: 151 +2024-01-16 23:09:38,079 (beam_search:429) INFO: max output length: 151 +2024-01-16 23:09:38,079 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:38,257 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:38,257 (beam_search:476) INFO: -2.92 * 1.0 = -2.92 for ctc +2024-01-16 23:09:38,257 (beam_search:479) INFO: total log probability: -2.92 +2024-01-16 23:09:38,257 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:09:38,257 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:38,257 (beam_search:483) INFO: best hypo: kasegatsUsuyoihiuwateNnisugadekimaseN + +2024-01-16 23:09:38,258 (asr_inference:494) INFO: speech length: 65280 +2024-01-16 23:09:38,268 (beam_search:428) INFO: decoder input length: 99 +2024-01-16 23:09:38,268 (beam_search:429) INFO: max output length: 99 +2024-01-16 23:09:38,268 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:38,286 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:38,286 (beam_search:476) INFO: -1.14 * 1.0 = -1.14 for ctc +2024-01-16 23:09:38,286 (beam_search:479) INFO: total log probability: -1.14 +2024-01-16 23:09:38,286 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 23:09:38,286 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:38,286 (beam_search:483) INFO: best hypo: ichii + +2024-01-16 23:09:38,287 (asr_inference:494) INFO: speech length: 43392 +2024-01-16 23:09:38,295 (beam_search:428) INFO: decoder input length: 65 +2024-01-16 23:09:38,295 (beam_search:429) INFO: max output length: 65 +2024-01-16 23:09:38,295 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:38,306 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:38,306 (beam_search:476) INFO: -0.29 * 1.0 = -0.29 for ctc +2024-01-16 23:09:38,306 (beam_search:479) INFO: total log probability: -0.29 +2024-01-16 23:09:38,306 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-16 23:09:38,306 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:38,306 (beam_search:483) INFO: best hypo: hai + +2024-01-16 23:09:38,307 (asr_inference:494) INFO: speech length: 33408 +2024-01-16 23:09:38,315 (beam_search:428) INFO: decoder input length: 50 +2024-01-16 23:09:38,315 (beam_search:429) INFO: max output length: 50 +2024-01-16 23:09:38,315 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:38,325 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:38,325 (beam_search:476) INFO: -1.29 * 1.0 = -1.29 for ctc +2024-01-16 23:09:38,325 (beam_search:479) INFO: total log probability: -1.29 +2024-01-16 23:09:38,325 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 23:09:38,325 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:38,325 (beam_search:483) INFO: best hypo: onoe + +2024-01-16 23:09:38,326 (asr_inference:494) INFO: speech length: 32640 +2024-01-16 23:09:38,334 (beam_search:428) INFO: decoder input length: 48 +2024-01-16 23:09:38,334 (beam_search:429) INFO: max output length: 48 +2024-01-16 23:09:38,334 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:38,342 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:38,342 (beam_search:476) INFO: -0.97 * 1.0 = -0.97 for ctc +2024-01-16 23:09:38,342 (beam_search:479) INFO: total log probability: -0.97 +2024-01-16 23:09:38,342 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 23:09:38,342 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:38,342 (beam_search:483) INFO: best hypo: dei + +2024-01-16 23:09:38,343 (asr_inference:494) INFO: speech length: 46464 +2024-01-16 23:09:38,351 (beam_search:428) INFO: decoder input length: 70 +2024-01-16 23:09:38,351 (beam_search:429) INFO: max output length: 70 +2024-01-16 23:09:38,351 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:38,368 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:38,368 (beam_search:476) INFO: -0.90 * 1.0 = -0.90 for ctc +2024-01-16 23:09:38,368 (beam_search:479) INFO: total log probability: -0.90 +2024-01-16 23:09:38,368 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:09:38,368 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:38,368 (beam_search:483) INFO: best hypo: atoki + +2024-01-16 23:09:38,369 (asr_inference:494) INFO: speech length: 126720 +2024-01-16 23:09:38,382 (beam_search:428) INFO: decoder input length: 195 +2024-01-16 23:09:38,382 (beam_search:429) INFO: max output length: 195 +2024-01-16 23:09:38,382 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:38,741 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:38,741 (beam_search:476) INFO: -6.33 * 1.0 = -6.33 for ctc +2024-01-16 23:09:38,741 (beam_search:479) INFO: total log probability: -6.33 +2024-01-16 23:09:38,741 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:09:38,741 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:38,741 (beam_search:483) INFO: best hypo: mirutoyukotatopauhatarakUtoiukotogashkabuNditekinakerebanaranai + +2024-01-16 23:09:38,743 (asr_inference:494) INFO: speech length: 85248 +2024-01-16 23:09:38,754 (beam_search:428) INFO: decoder input length: 131 +2024-01-16 23:09:38,754 (beam_search:429) INFO: max output length: 131 +2024-01-16 23:09:38,754 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:38,966 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:38,966 (beam_search:476) INFO: -4.93 * 1.0 = -4.93 for ctc +2024-01-16 23:09:38,966 (beam_search:479) INFO: total log probability: -4.93 +2024-01-16 23:09:38,966 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:09:38,966 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:38,966 (beam_search:483) INFO: best hypo: warewareotamashiinozuokaraugukasumonodenakerebanaranai + +2024-01-16 23:09:38,967 (asr_inference:494) INFO: speech length: 113472 +2024-01-16 23:09:38,980 (beam_search:428) INFO: decoder input length: 175 +2024-01-16 23:09:38,980 (beam_search:429) INFO: max output length: 175 +2024-01-16 23:09:38,980 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:39,349 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:39,349 (beam_search:476) INFO: -7.85 * 1.0 = -7.85 for ctc +2024-01-16 23:09:39,349 (beam_search:479) INFO: total log probability: -7.85 +2024-01-16 23:09:39,349 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:09:39,349 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:39,350 (beam_search:483) INFO: best hypo: setaibeNshoohoodekinarugaiueniideatekIchoclkaNtekikeekiygaclUkumarerunodearu + +2024-01-16 23:09:39,351 (asr_inference:494) INFO: speech length: 111168 +2024-01-16 23:09:39,363 (beam_search:428) INFO: decoder input length: 171 +2024-01-16 23:09:39,363 (beam_search:429) INFO: max output length: 171 +2024-01-16 23:09:39,363 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:39,728 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:39,728 (beam_search:476) INFO: -7.63 * 1.0 = -7.63 for ctc +2024-01-16 23:09:39,728 (beam_search:479) INFO: total log probability: -7.63 +2024-01-16 23:09:39,728 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:09:39,728 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:39,728 (beam_search:483) INFO: best hypo: tokomademotatoichitonasoogoshIeetekinazecltaimujuNtekijikooitsunosekainishIte + +2024-01-16 23:09:39,729 (asr_inference:494) INFO: speech length: 85824 +2024-01-16 23:09:39,740 (beam_search:428) INFO: decoder input length: 132 +2024-01-16 23:09:39,740 (beam_search:429) INFO: max output length: 132 +2024-01-16 23:09:39,740 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:39,953 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:39,953 (beam_search:476) INFO: -2.70 * 1.0 = -2.70 for ctc +2024-01-16 23:09:39,953 (beam_search:479) INFO: total log probability: -2.70 +2024-01-16 23:09:39,953 (beam_search:480) INFO: normalized log probability: -0.05 +2024-01-16 23:09:39,953 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:39,954 (beam_search:483) INFO: best hypo: shikaruniniNgeNtokaNkyootorokaNkeewamotokoonokaNkedeari + +2024-01-16 23:09:39,955 (asr_inference:494) INFO: speech length: 74880 +2024-01-16 23:09:39,965 (beam_search:428) INFO: decoder input length: 114 +2024-01-16 23:09:39,965 (beam_search:429) INFO: max output length: 114 +2024-01-16 23:09:39,965 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:40,095 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:40,096 (beam_search:476) INFO: -3.15 * 1.0 = -3.15 for ctc +2024-01-16 23:09:40,096 (beam_search:479) INFO: total log probability: -3.15 +2024-01-16 23:09:40,096 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:09:40,096 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:40,096 (beam_search:483) INFO: best hypo: iisaNnikonokotobanoimiyooshiyamashIta + +2024-01-16 23:09:40,097 (asr_inference:494) INFO: speech length: 63936 +2024-01-16 23:09:40,107 (beam_search:428) INFO: decoder input length: 97 +2024-01-16 23:09:40,107 (beam_search:429) INFO: max output length: 97 +2024-01-16 23:09:40,107 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:40,176 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:40,176 (beam_search:476) INFO: -1.57 * 1.0 = -1.57 for ctc +2024-01-16 23:09:40,176 (beam_search:479) INFO: total log probability: -1.57 +2024-01-16 23:09:40,176 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 23:09:40,176 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:40,176 (beam_search:483) INFO: best hypo: keekigananatssarimasU + +2024-01-16 23:09:40,177 (asr_inference:494) INFO: speech length: 69696 +2024-01-16 23:09:40,187 (beam_search:428) INFO: decoder input length: 106 +2024-01-16 23:09:40,187 (beam_search:429) INFO: max output length: 106 +2024-01-16 23:09:40,187 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:40,273 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:40,273 (beam_search:476) INFO: -0.71 * 1.0 = -0.71 for ctc +2024-01-16 23:09:40,273 (beam_search:479) INFO: total log probability: -0.71 +2024-01-16 23:09:40,273 (beam_search:480) INFO: normalized log probability: -0.03 +2024-01-16 23:09:40,273 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:40,273 (beam_search:483) INFO: best hypo: kochirabakobiyashisaNdesU + +2024-01-16 23:09:40,274 (asr_inference:494) INFO: speech length: 36288 +2024-01-16 23:09:40,282 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 23:09:40,282 (beam_search:429) INFO: max output length: 54 +2024-01-16 23:09:40,282 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:40,301 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:40,301 (beam_search:476) INFO: -0.91 * 1.0 = -0.91 for ctc +2024-01-16 23:09:40,301 (beam_search:479) INFO: total log probability: -0.91 +2024-01-16 23:09:40,301 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:09:40,301 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:40,301 (beam_search:483) INFO: best hypo: moshimashi + +2024-01-16 23:09:40,302 (asr_inference:494) INFO: speech length: 75456 +2024-01-16 23:09:40,312 (beam_search:428) INFO: decoder input length: 115 +2024-01-16 23:09:40,312 (beam_search:429) INFO: max output length: 115 +2024-01-16 23:09:40,312 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:40,432 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:40,432 (beam_search:476) INFO: -3.58 * 1.0 = -3.58 for ctc +2024-01-16 23:09:40,432 (beam_search:479) INFO: total log probability: -3.58 +2024-01-16 23:09:40,432 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:09:40,432 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:40,432 (beam_search:483) INFO: best hypo: kokowaokIkUteniguyakanamachidesU + +2024-01-16 23:09:40,433 (asr_inference:494) INFO: speech length: 65088 +2024-01-16 23:09:40,443 (beam_search:428) INFO: decoder input length: 99 +2024-01-16 23:09:40,443 (beam_search:429) INFO: max output length: 99 +2024-01-16 23:09:40,443 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:40,540 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:40,540 (beam_search:476) INFO: -1.45 * 1.0 = -1.45 for ctc +2024-01-16 23:09:40,540 (beam_search:479) INFO: total log probability: -1.45 +2024-01-16 23:09:40,541 (beam_search:480) INFO: normalized log probability: -0.05 +2024-01-16 23:09:40,541 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:40,541 (beam_search:483) INFO: best hypo: sonochikaiyakUsarerukaraisogepau + +2024-01-16 23:09:40,542 (asr_inference:494) INFO: speech length: 72000 +2024-01-16 23:09:40,552 (beam_search:428) INFO: decoder input length: 110 +2024-01-16 23:09:40,552 (beam_search:429) INFO: max output length: 110 +2024-01-16 23:09:40,552 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:40,658 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:40,658 (beam_search:476) INFO: -3.43 * 1.0 = -3.43 for ctc +2024-01-16 23:09:40,658 (beam_search:479) INFO: total log probability: -3.43 +2024-01-16 23:09:40,658 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:09:40,658 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:40,658 (beam_search:483) INFO: best hypo: amasagabkusaeiraretetechoodoi + +2024-01-16 23:09:40,659 (asr_inference:494) INFO: speech length: 62784 +2024-01-16 23:09:40,669 (beam_search:428) INFO: decoder input length: 96 +2024-01-16 23:09:40,669 (beam_search:429) INFO: max output length: 96 +2024-01-16 23:09:40,669 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:40,741 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:40,741 (beam_search:476) INFO: -3.80 * 1.0 = -3.80 for ctc +2024-01-16 23:09:40,741 (beam_search:479) INFO: total log probability: -3.80 +2024-01-16 23:09:40,741 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 23:09:40,741 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:40,741 (beam_search:483) INFO: best hypo: hokeNshItsuenodooaaketa + +2024-01-16 23:09:40,742 (asr_inference:494) INFO: speech length: 55872 +2024-01-16 23:09:40,751 (beam_search:428) INFO: decoder input length: 85 +2024-01-16 23:09:40,751 (beam_search:429) INFO: max output length: 85 +2024-01-16 23:09:40,751 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:40,821 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:40,821 (beam_search:476) INFO: -1.15 * 1.0 = -1.15 for ctc +2024-01-16 23:09:40,821 (beam_search:479) INFO: total log probability: -1.15 +2024-01-16 23:09:40,821 (beam_search:480) INFO: normalized log probability: -0.05 +2024-01-16 23:09:40,821 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:40,821 (beam_search:483) INFO: best hypo: modaniowacltemokinishinai + +2024-01-16 23:09:40,822 (asr_inference:494) INFO: speech length: 44352 +2024-01-16 23:09:40,830 (beam_search:428) INFO: decoder input length: 67 +2024-01-16 23:09:40,830 (beam_search:429) INFO: max output length: 67 +2024-01-16 23:09:40,830 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:40,859 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:40,859 (beam_search:476) INFO: -1.23 * 1.0 = -1.23 for ctc +2024-01-16 23:09:40,859 (beam_search:479) INFO: total log probability: -1.23 +2024-01-16 23:09:40,859 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:09:40,859 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:40,859 (beam_search:483) INFO: best hypo: arigacltaya + +2024-01-16 23:09:40,860 (asr_inference:494) INFO: speech length: 69696 +2024-01-16 23:09:40,869 (beam_search:428) INFO: decoder input length: 106 +2024-01-16 23:09:40,869 (beam_search:429) INFO: max output length: 106 +2024-01-16 23:09:40,869 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:41,001 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:41,002 (beam_search:476) INFO: -3.05 * 1.0 = -3.05 for ctc +2024-01-16 23:09:41,002 (beam_search:479) INFO: total log probability: -3.05 +2024-01-16 23:09:41,002 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:09:41,002 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:41,002 (beam_search:483) INFO: best hypo: itoogarokudatojikaNuwosuretetanoshimeru + +2024-01-16 23:09:41,003 (asr_inference:494) INFO: speech length: 83520 +2024-01-16 23:09:41,013 (beam_search:428) INFO: decoder input length: 128 +2024-01-16 23:09:41,013 (beam_search:429) INFO: max output length: 128 +2024-01-16 23:09:41,013 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:41,224 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:41,224 (beam_search:476) INFO: -3.10 * 1.0 = -3.10 for ctc +2024-01-16 23:09:41,224 (beam_search:479) INFO: total log probability: -3.10 +2024-01-16 23:09:41,224 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-16 23:09:41,224 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:41,225 (beam_search:483) INFO: best hypo: kakakuwagizutsUkasareruniojitejooshIkinouchinihaiclteyuku + +# Accounting: time=14 threads=1 +# Ended (code 0) at Tue Jan 16 23:09:41 CST 2024, elapsed time 14 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.2.log b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.2.log new file mode 100644 index 0000000000000000000000000000000000000000..d2cf337775fc9ca7917fe3c8231e3683921692ed --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.2.log @@ -0,0 +1,459 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2 --config conf/decode_asr.yaml +# Started at Tue Jan 16 23:09:41 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2 --config conf/decode_asr.yaml +2024-01-16 23:09:43,035 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml +2024-01-16 23:09:43,054 (asr:523) INFO: Vocabulary size: 41 +2024-01-16 23:09:43,116 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 23:09:43,116 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 23:09:43,226 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 23:09:44,527 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 23:09:45,770 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 23:09:45,770 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 23:09:45,770 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 23:09:45,803 (asr_inference:446) INFO: Text tokenizer: WordTokenizer(delimiter="None") +2024-01-16 23:09:45,877 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 23:09:45,991 (asr_inference:494) INFO: speech length: 133056 +2024-01-16 23:09:47,216 (beam_search:428) INFO: decoder input length: 205 +2024-01-16 23:09:47,216 (beam_search:429) INFO: max output length: 205 +2024-01-16 23:09:47,216 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:47,742 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:47,743 (beam_search:476) INFO: -9.30 * 1.0 = -9.30 for ctc +2024-01-16 23:09:47,743 (beam_search:479) INFO: total log probability: -9.30 +2024-01-16 23:09:47,743 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:09:47,743 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:47,743 (beam_search:483) INFO: best hypo: shikashItokigakabonihairukotosonokotogamirayoomukotodeariaratanaruiclUtaigadetekurukotodearu + +2024-01-16 23:09:47,767 (asr_inference:494) INFO: speech length: 70848 +2024-01-16 23:09:47,777 (beam_search:428) INFO: decoder input length: 108 +2024-01-16 23:09:47,777 (beam_search:429) INFO: max output length: 108 +2024-01-16 23:09:47,777 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:47,915 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:47,915 (beam_search:476) INFO: -5.18 * 1.0 = -5.18 for ctc +2024-01-16 23:09:47,916 (beam_search:479) INFO: total log probability: -5.18 +2024-01-16 23:09:47,916 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:09:47,916 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:47,916 (beam_search:483) INFO: best hypo: terebiokaikaitepauterebiomirujikaNnafueta + +2024-01-16 23:09:47,917 (asr_inference:494) INFO: speech length: 65088 +2024-01-16 23:09:47,927 (beam_search:428) INFO: decoder input length: 99 +2024-01-16 23:09:47,927 (beam_search:429) INFO: max output length: 99 +2024-01-16 23:09:47,927 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:48,048 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:48,048 (beam_search:476) INFO: -2.58 * 1.0 = -2.58 for ctc +2024-01-16 23:09:48,048 (beam_search:479) INFO: total log probability: -2.58 +2024-01-16 23:09:48,048 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 23:09:48,048 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:48,048 (beam_search:483) INFO: best hypo: kakareushItainomiitsumademoikerunodearu + +2024-01-16 23:09:48,049 (asr_inference:494) INFO: speech length: 80640 +2024-01-16 23:09:48,059 (beam_search:428) INFO: decoder input length: 123 +2024-01-16 23:09:48,059 (beam_search:429) INFO: max output length: 123 +2024-01-16 23:09:48,059 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:48,225 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:48,226 (beam_search:476) INFO: -4.32 * 1.0 = -4.32 for ctc +2024-01-16 23:09:48,226 (beam_search:479) INFO: total log probability: -4.32 +2024-01-16 23:09:48,226 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:09:48,226 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:48,226 (beam_search:483) INFO: best hypo: niNkidaameNiyaninaraNdaranijikaNmaochidaclta + +2024-01-16 23:09:48,227 (asr_inference:494) INFO: speech length: 146880 +2024-01-16 23:09:48,242 (beam_search:428) INFO: decoder input length: 227 +2024-01-16 23:09:48,242 (beam_search:429) INFO: max output length: 227 +2024-01-16 23:09:48,242 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:48,801 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:48,802 (beam_search:476) INFO: -11.66 * 1.0 = -11.66 for ctc +2024-01-16 23:09:48,802 (beam_search:479) INFO: total log probability: -11.66 +2024-01-16 23:09:48,802 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:09:48,802 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:48,802 (beam_search:483) INFO: best hypo: soreoomochiiruniNgeNnoyokunitoNseisoshItekorewakarenomoclteirukachenosakudoNnpauidoNsuru + +2024-01-16 23:09:48,804 (asr_inference:494) INFO: speech length: 72000 +2024-01-16 23:09:48,814 (beam_search:428) INFO: decoder input length: 110 +2024-01-16 23:09:48,814 (beam_search:429) INFO: max output length: 110 +2024-01-16 23:09:48,814 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:48,935 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:48,935 (beam_search:476) INFO: -2.74 * 1.0 = -2.74 for ctc +2024-01-16 23:09:48,935 (beam_search:479) INFO: total log probability: -2.74 +2024-01-16 23:09:48,935 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:09:48,935 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:48,935 (beam_search:483) INFO: best hypo: mawaryoamiNnakaNgarukotoyameteita + +2024-01-16 23:09:48,936 (asr_inference:494) INFO: speech length: 168768 +2024-01-16 23:09:48,953 (beam_search:428) INFO: decoder input length: 261 +2024-01-16 23:09:48,953 (beam_search:429) INFO: max output length: 261 +2024-01-16 23:09:48,953 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:49,704 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:49,704 (beam_search:476) INFO: -9.38 * 1.0 = -9.38 for ctc +2024-01-16 23:09:49,704 (beam_search:479) INFO: total log probability: -9.38 +2024-01-16 23:09:49,704 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:09:49,704 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:49,705 (beam_search:483) INFO: best hypo: kooitekijokUkaNtekinisekayomirutoyukotowajakunikooitekichaokclkaNtekinisekayokeeseesurukotokumunodearu + +2024-01-16 23:09:49,706 (asr_inference:494) INFO: speech length: 96768 +2024-01-16 23:09:49,718 (beam_search:428) INFO: decoder input length: 149 +2024-01-16 23:09:49,718 (beam_search:429) INFO: max output length: 149 +2024-01-16 23:09:49,718 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:49,988 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:49,988 (beam_search:476) INFO: -5.91 * 1.0 = -5.91 for ctc +2024-01-16 23:09:49,988 (beam_search:479) INFO: total log probability: -5.91 +2024-01-16 23:09:49,988 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:09:49,988 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:49,989 (beam_search:483) INFO: best hypo: sjeNpaitakesasemaitosurukizukaiygayokeeniseNclpaisaseteshimau + +2024-01-16 23:09:49,990 (asr_inference:494) INFO: speech length: 104256 +2024-01-16 23:09:50,001 (beam_search:428) INFO: decoder input length: 160 +2024-01-16 23:09:50,001 (beam_search:429) INFO: max output length: 160 +2024-01-16 23:09:50,001 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:50,180 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:50,180 (beam_search:476) INFO: -3.06 * 1.0 = -3.06 for ctc +2024-01-16 23:09:50,180 (beam_search:479) INFO: total log probability: -3.06 +2024-01-16 23:09:50,180 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:09:50,180 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:50,180 (beam_search:483) INFO: best hypo: konomichiwatotemosemainodamnaidesU + +2024-01-16 23:09:50,182 (asr_inference:494) INFO: speech length: 61632 +2024-01-16 23:09:50,191 (beam_search:428) INFO: decoder input length: 94 +2024-01-16 23:09:50,191 (beam_search:429) INFO: max output length: 94 +2024-01-16 23:09:50,191 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:50,228 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:50,228 (beam_search:476) INFO: -2.37 * 1.0 = -2.37 for ctc +2024-01-16 23:09:50,228 (beam_search:479) INFO: total log probability: -2.37 +2024-01-16 23:09:50,228 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 23:09:50,228 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:50,228 (beam_search:483) INFO: best hypo: woegariniU + +2024-01-16 23:09:50,230 (asr_inference:494) INFO: speech length: 69696 +2024-01-16 23:09:50,239 (beam_search:428) INFO: decoder input length: 106 +2024-01-16 23:09:50,239 (beam_search:429) INFO: max output length: 106 +2024-01-16 23:09:50,239 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:50,348 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:50,348 (beam_search:476) INFO: -2.77 * 1.0 = -2.77 for ctc +2024-01-16 23:09:50,349 (beam_search:479) INFO: total log probability: -2.77 +2024-01-16 23:09:50,349 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:09:50,349 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:50,349 (beam_search:483) INFO: best hypo: toiyowarokamaitarigaaniarimasU + +2024-01-16 23:09:50,350 (asr_inference:494) INFO: speech length: 71424 +2024-01-16 23:09:50,359 (beam_search:428) INFO: decoder input length: 109 +2024-01-16 23:09:50,360 (beam_search:429) INFO: max output length: 109 +2024-01-16 23:09:50,360 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:50,485 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:50,485 (beam_search:476) INFO: -3.73 * 1.0 = -3.73 for ctc +2024-01-16 23:09:50,485 (beam_search:479) INFO: total log probability: -3.73 +2024-01-16 23:09:50,485 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:09:50,485 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:50,485 (beam_search:483) INFO: best hypo: tadakasaNohidarinikimerasaNgaimasU + +2024-01-16 23:09:50,486 (asr_inference:494) INFO: speech length: 67392 +2024-01-16 23:09:50,496 (beam_search:428) INFO: decoder input length: 103 +2024-01-16 23:09:50,496 (beam_search:429) INFO: max output length: 103 +2024-01-16 23:09:50,496 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:50,580 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:50,580 (beam_search:476) INFO: -4.26 * 1.0 = -4.26 for ctc +2024-01-16 23:09:50,580 (beam_search:479) INFO: total log probability: -4.26 +2024-01-16 23:09:50,580 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 23:09:50,580 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:50,580 (beam_search:483) INFO: best hypo: maclkurunatamabotesugoie + +2024-01-16 23:09:50,581 (asr_inference:494) INFO: speech length: 85248 +2024-01-16 23:09:50,592 (beam_search:428) INFO: decoder input length: 131 +2024-01-16 23:09:50,592 (beam_search:429) INFO: max output length: 131 +2024-01-16 23:09:50,592 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:50,739 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:50,739 (beam_search:476) INFO: -4.34 * 1.0 = -4.34 for ctc +2024-01-16 23:09:50,739 (beam_search:479) INFO: total log probability: -4.34 +2024-01-16 23:09:50,739 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:09:50,739 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:50,739 (beam_search:483) INFO: best hypo: sharshoomitainadokushukasoobumokaita + +2024-01-16 23:09:50,740 (asr_inference:494) INFO: speech length: 128448 +2024-01-16 23:09:50,754 (beam_search:428) INFO: decoder input length: 198 +2024-01-16 23:09:50,754 (beam_search:429) INFO: max output length: 198 +2024-01-16 23:09:50,754 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:51,204 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:51,204 (beam_search:476) INFO: -15.99 * 1.0 = -15.99 for ctc +2024-01-16 23:09:51,204 (beam_search:479) INFO: total log probability: -15.99 +2024-01-16 23:09:51,204 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 23:09:51,204 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:51,204 (beam_search:483) INFO: best hypo: geNjitemosekaiwapautamooichitoshItekecltesuraidekatachioacltosUkaiunakeremonaranai + +2024-01-16 23:09:51,205 (asr_inference:494) INFO: speech length: 95040 +2024-01-16 23:09:51,217 (beam_search:428) INFO: decoder input length: 146 +2024-01-16 23:09:51,217 (beam_search:429) INFO: max output length: 146 +2024-01-16 23:09:51,217 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:51,413 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:51,413 (beam_search:476) INFO: -5.92 * 1.0 = -5.92 for ctc +2024-01-16 23:09:51,413 (beam_search:479) INFO: total log probability: -5.92 +2024-01-16 23:09:51,413 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:09:51,413 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:51,413 (beam_search:483) INFO: best hypo: shoohiNkeNsakukgaokaryasuitoopaukaokinarumai + +2024-01-16 23:09:51,415 (asr_inference:494) INFO: speech length: 80640 +2024-01-16 23:09:51,425 (beam_search:428) INFO: decoder input length: 123 +2024-01-16 23:09:51,425 (beam_search:429) INFO: max output length: 123 +2024-01-16 23:09:51,425 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:51,583 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:51,584 (beam_search:476) INFO: -4.62 * 1.0 = -4.62 for ctc +2024-01-16 23:09:51,584 (beam_search:479) INFO: total log probability: -4.62 +2024-01-16 23:09:51,584 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:09:51,584 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:51,584 (beam_search:483) INFO: best hypo: tseshiIkiwanekIshIteclkateedenakerumonaranai + +2024-01-16 23:09:51,585 (asr_inference:494) INFO: speech length: 110016 +2024-01-16 23:09:51,597 (beam_search:428) INFO: decoder input length: 169 +2024-01-16 23:09:51,597 (beam_search:429) INFO: max output length: 169 +2024-01-16 23:09:51,597 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:51,835 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:51,835 (beam_search:476) INFO: -9.42 * 1.0 = -9.42 for ctc +2024-01-16 23:09:51,835 (beam_search:479) INFO: total log probability: -9.42 +2024-01-16 23:09:51,835 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 23:09:51,835 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:51,835 (beam_search:483) INFO: best hypo: monogotanoNjiNpaNkaerudakedepauumakUikUkotomar + +2024-01-16 23:09:51,837 (asr_inference:494) INFO: speech length: 67968 +2024-01-16 23:09:51,846 (beam_search:428) INFO: decoder input length: 104 +2024-01-16 23:09:51,846 (beam_search:429) INFO: max output length: 104 +2024-01-16 23:09:51,846 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:51,963 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:51,963 (beam_search:476) INFO: -3.35 * 1.0 = -3.35 for ctc +2024-01-16 23:09:51,963 (beam_search:479) INFO: total log probability: -3.35 +2024-01-16 23:09:51,963 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:09:51,963 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:51,963 (beam_search:483) INFO: best hypo: konokIeetsuwakatsuonosashimigazeclpeiN + +2024-01-16 23:09:51,964 (asr_inference:494) INFO: speech length: 84096 +2024-01-16 23:09:51,975 (beam_search:428) INFO: decoder input length: 129 +2024-01-16 23:09:51,975 (beam_search:429) INFO: max output length: 129 +2024-01-16 23:09:51,975 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:52,167 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:52,167 (beam_search:476) INFO: -6.99 * 1.0 = -6.99 for ctc +2024-01-16 23:09:52,167 (beam_search:479) INFO: total log probability: -6.99 +2024-01-16 23:09:52,167 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:09:52,167 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:52,167 (beam_search:483) INFO: best hypo: kakeNnishiclpaishItemapaumochIttsuitesamashItsuoukeideru + +2024-01-16 23:09:52,168 (asr_inference:494) INFO: speech length: 88128 +2024-01-16 23:09:52,179 (beam_search:428) INFO: decoder input length: 135 +2024-01-16 23:09:52,179 (beam_search:429) INFO: max output length: 135 +2024-01-16 23:09:52,179 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:52,379 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:52,379 (beam_search:476) INFO: -5.85 * 1.0 = -5.85 for ctc +2024-01-16 23:09:52,379 (beam_search:479) INFO: total log probability: -5.85 +2024-01-16 23:09:52,379 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:09:52,379 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:52,379 (beam_search:483) INFO: best hypo: soreyuenipautetsugakugazeNtainogakodeyarutosurewa + +2024-01-16 23:09:52,380 (asr_inference:494) INFO: speech length: 79488 +2024-01-16 23:09:52,391 (beam_search:428) INFO: decoder input length: 122 +2024-01-16 23:09:52,391 (beam_search:429) INFO: max output length: 122 +2024-01-16 23:09:52,391 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:52,537 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:52,537 (beam_search:476) INFO: -2.98 * 1.0 = -2.98 for ctc +2024-01-16 23:09:52,538 (beam_search:479) INFO: total log probability: -2.98 +2024-01-16 23:09:52,538 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:09:52,538 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:52,538 (beam_search:483) INFO: best hypo: kiisanaiyaoyadagayasUkUtehaNjoshIteru + +2024-01-16 23:09:52,539 (asr_inference:494) INFO: speech length: 103680 +2024-01-16 23:09:52,551 (beam_search:428) INFO: decoder input length: 159 +2024-01-16 23:09:52,551 (beam_search:429) INFO: max output length: 159 +2024-01-16 23:09:52,551 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:52,846 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:52,847 (beam_search:476) INFO: -9.56 * 1.0 = -9.56 for ctc +2024-01-16 23:09:52,847 (beam_search:479) INFO: total log probability: -9.56 +2024-01-16 23:09:52,847 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 23:09:52,847 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:52,847 (beam_search:483) INFO: best hypo: inifuragakinohuzeNniochjicltepaukokugaiadashItsusurushItamodetekita + +2024-01-16 23:09:52,848 (asr_inference:494) INFO: speech length: 126720 +2024-01-16 23:09:52,862 (beam_search:428) INFO: decoder input length: 195 +2024-01-16 23:09:52,862 (beam_search:429) INFO: max output length: 195 +2024-01-16 23:09:52,862 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:53,283 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:53,283 (beam_search:476) INFO: -12.03 * 1.0 = -12.03 for ctc +2024-01-16 23:09:53,283 (beam_search:479) INFO: total log probability: -12.03 +2024-01-16 23:09:53,283 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 23:09:53,283 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:53,283 (beam_search:483) INFO: best hypo: tssuginikawakuwasoNzayojujunodoikinokacltesurezuraNdeokiNzeititeNikiUsuru + +2024-01-16 23:09:53,285 (asr_inference:494) INFO: speech length: 114048 +2024-01-16 23:09:53,297 (beam_search:428) INFO: decoder input length: 176 +2024-01-16 23:09:53,297 (beam_search:429) INFO: max output length: 176 +2024-01-16 23:09:53,297 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:53,660 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:53,660 (beam_search:476) INFO: -10.45 * 1.0 = -10.45 for ctc +2024-01-16 23:09:53,660 (beam_search:479) INFO: total log probability: -10.45 +2024-01-16 23:09:53,660 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 23:09:53,660 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:53,660 (beam_search:483) INFO: best hypo: soredewatoktoyimaranaseritsUshiowanakupaushirNkaNtoimonomonakunarunodari + +2024-01-16 23:09:53,662 (asr_inference:494) INFO: speech length: 100800 +2024-01-16 23:09:53,673 (beam_search:428) INFO: decoder input length: 155 +2024-01-16 23:09:53,673 (beam_search:429) INFO: max output length: 155 +2024-01-16 23:09:53,673 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:53,903 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:53,903 (beam_search:476) INFO: -3.69 * 1.0 = -3.69 for ctc +2024-01-16 23:09:53,903 (beam_search:479) INFO: total log probability: -3.69 +2024-01-16 23:09:53,903 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:09:53,903 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:53,903 (beam_search:483) INFO: best hypo: hakaiburaNkokoNkuritosenosuberidaikawaitasunaba + +2024-01-16 23:09:53,904 (asr_inference:494) INFO: speech length: 115776 +2024-01-16 23:09:53,917 (beam_search:428) INFO: decoder input length: 178 +2024-01-16 23:09:53,917 (beam_search:429) INFO: max output length: 178 +2024-01-16 23:09:53,917 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:54,308 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:54,308 (beam_search:476) INFO: -8.81 * 1.0 = -8.81 for ctc +2024-01-16 23:09:54,308 (beam_search:479) INFO: total log probability: -8.81 +2024-01-16 23:09:54,308 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:09:54,308 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:54,308 (beam_search:483) INFO: best hypo: sshikashisorowadokomademNkokokaradetepauokoekairikuruseeItomotomodeakewanaranai + +2024-01-16 23:09:54,309 (asr_inference:494) INFO: speech length: 98496 +2024-01-16 23:09:54,321 (beam_search:428) INFO: decoder input length: 151 +2024-01-16 23:09:54,321 (beam_search:429) INFO: max output length: 151 +2024-01-16 23:09:54,321 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:54,565 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:54,565 (beam_search:476) INFO: -5.30 * 1.0 = -5.30 for ctc +2024-01-16 23:09:54,565 (beam_search:479) INFO: total log probability: -5.30 +2024-01-16 23:09:54,565 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:09:54,565 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:54,565 (beam_search:483) INFO: best hypo: aritoarairudemoomakichirashitemiNneakaraouramiokaclteru + +2024-01-16 23:09:54,566 (asr_inference:494) INFO: speech length: 89856 +2024-01-16 23:09:54,577 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 23:09:54,577 (beam_search:429) INFO: max output length: 138 +2024-01-16 23:09:54,577 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:54,743 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:54,743 (beam_search:476) INFO: -0.87 * 1.0 = -0.87 for ctc +2024-01-16 23:09:54,743 (beam_search:479) INFO: total log probability: -0.87 +2024-01-16 23:09:54,743 (beam_search:480) INFO: normalized log probability: -0.02 +2024-01-16 23:09:54,743 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:54,744 (beam_search:483) INFO: best hypo: konoteedosawagininarukotomonainodaro + +2024-01-16 23:09:54,745 (asr_inference:494) INFO: speech length: 81216 +2024-01-16 23:09:54,755 (beam_search:428) INFO: decoder input length: 124 +2024-01-16 23:09:54,755 (beam_search:429) INFO: max output length: 124 +2024-01-16 23:09:54,755 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:54,851 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:54,851 (beam_search:476) INFO: -6.91 * 1.0 = -6.91 for ctc +2024-01-16 23:09:54,851 (beam_search:479) INFO: total log probability: -6.91 +2024-01-16 23:09:54,851 (beam_search:480) INFO: normalized log probability: -0.29 +2024-01-16 23:09:54,851 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:54,851 (beam_search:483) INFO: best hypo: kononeraNdepauuritaukaaa + +2024-01-16 23:09:54,853 (asr_inference:494) INFO: speech length: 82368 +2024-01-16 23:09:54,863 (beam_search:428) INFO: decoder input length: 126 +2024-01-16 23:09:54,863 (beam_search:429) INFO: max output length: 126 +2024-01-16 23:09:54,863 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:55,001 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:55,001 (beam_search:476) INFO: -2.61 * 1.0 = -2.61 for ctc +2024-01-16 23:09:55,001 (beam_search:479) INFO: total log probability: -2.61 +2024-01-16 23:09:55,001 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 23:09:55,001 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:55,002 (beam_search:483) INFO: best hypo: hinokagaeNnichuishinaitopausugukogeru + +2024-01-16 23:09:55,003 (asr_inference:494) INFO: speech length: 141696 +2024-01-16 23:09:55,017 (beam_search:428) INFO: decoder input length: 219 +2024-01-16 23:09:55,017 (beam_search:429) INFO: max output length: 219 +2024-01-16 23:09:55,017 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:55,512 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:55,512 (beam_search:476) INFO: -8.32 * 1.0 = -8.32 for ctc +2024-01-16 23:09:55,512 (beam_search:479) INFO: total log probability: -8.32 +2024-01-16 23:09:55,512 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:09:55,512 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:55,513 (beam_search:483) INFO: best hypo: eNmaNnouweNnipotsuritotsuisananagaitasaishiowatsumayoojiteedonotsiisanapauanadacltaa + +2024-01-16 23:09:55,514 (asr_inference:494) INFO: speech length: 133056 +2024-01-16 23:09:55,527 (beam_search:428) INFO: decoder input length: 205 +2024-01-16 23:09:55,527 (beam_search:429) INFO: max output length: 205 +2024-01-16 23:09:55,527 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:56,031 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:56,031 (beam_search:476) INFO: -10.44 * 1.0 = -10.44 for ctc +2024-01-16 23:09:56,031 (beam_search:479) INFO: total log probability: -10.44 +2024-01-16 23:09:56,031 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:09:56,031 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:56,031 (beam_search:483) INFO: best hypo: sorewamarewareoikashinagarapauwarewareotoreekasurunodearupauwarewarenotamasshiyokorosunodea + +2024-01-16 23:09:56,033 (asr_inference:494) INFO: speech length: 145728 +2024-01-16 23:09:56,047 (beam_search:428) INFO: decoder input length: 225 +2024-01-16 23:09:56,048 (beam_search:429) INFO: max output length: 225 +2024-01-16 23:09:56,048 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:56,685 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:56,685 (beam_search:476) INFO: -13.37 * 1.0 = -13.37 for ctc +2024-01-16 23:09:56,685 (beam_search:479) INFO: total log probability: -13.37 +2024-01-16 23:09:56,685 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:09:56,685 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:56,685 (beam_search:483) INFO: best hypo: rekUshItekiniatarartamonowadecltemujuNtekijigotooitsItekigieNzdainooitesUkaishIekiniataerartamoNutoshIte + +2024-01-16 23:09:56,687 (asr_inference:494) INFO: speech length: 88128 +2024-01-16 23:09:56,697 (beam_search:428) INFO: decoder input length: 135 +2024-01-16 23:09:56,697 (beam_search:429) INFO: max output length: 135 +2024-01-16 23:09:56,697 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:56,932 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:56,932 (beam_search:476) INFO: -6.81 * 1.0 = -6.81 for ctc +2024-01-16 23:09:56,932 (beam_search:479) INFO: total log probability: -6.81 +2024-01-16 23:09:56,932 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:09:56,932 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:56,932 (beam_search:483) INFO: best hypo: muojuNtekIejigodooichItoshItepauitsumokonosekainichooeshItekidea + +2024-01-16 23:09:56,934 (asr_inference:494) INFO: speech length: 112896 +2024-01-16 23:09:56,946 (beam_search:428) INFO: decoder input length: 174 +2024-01-16 23:09:56,946 (beam_search:429) INFO: max output length: 174 +2024-01-16 23:09:56,946 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:57,342 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:57,342 (beam_search:476) INFO: -12.05 * 1.0 = -12.05 for ctc +2024-01-16 23:09:57,342 (beam_search:479) INFO: total log probability: -12.05 +2024-01-16 23:09:57,342 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 23:09:57,342 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:57,342 (beam_search:483) INFO: best hypo: yunideclpeemujuNtekIjigodooishutoshItegeNaekarageNzaeetuwokikusUekaenogeNzainoite + +2024-01-16 23:09:57,344 (asr_inference:494) INFO: speech length: 60480 +2024-01-16 23:09:57,353 (beam_search:428) INFO: decoder input length: 92 +2024-01-16 23:09:57,353 (beam_search:429) INFO: max output length: 92 +2024-01-16 23:09:57,353 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:57,436 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:57,437 (beam_search:476) INFO: -2.90 * 1.0 = -2.90 for ctc +2024-01-16 23:09:57,437 (beam_search:479) INFO: total log probability: -2.90 +2024-01-16 23:09:57,437 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:09:57,437 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:57,437 (beam_search:483) INFO: best hypo: harewotaNoshItoNdashudekinai + +2024-01-16 23:09:57,438 (asr_inference:494) INFO: speech length: 78336 +2024-01-16 23:09:57,448 (beam_search:428) INFO: decoder input length: 120 +2024-01-16 23:09:57,448 (beam_search:429) INFO: max output length: 120 +2024-01-16 23:09:57,448 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:57,627 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:57,627 (beam_search:476) INFO: -3.46 * 1.0 = -3.46 for ctc +2024-01-16 23:09:57,627 (beam_search:479) INFO: total log probability: -3.46 +2024-01-16 23:09:57,627 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-16 23:09:57,627 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:57,628 (beam_search:483) INFO: best hypo: shikashiwatashasokonisekainojikodooitsuokunodewanai + +2024-01-16 23:09:57,629 (asr_inference:494) INFO: speech length: 56448 +2024-01-16 23:09:57,638 (beam_search:428) INFO: decoder input length: 86 +2024-01-16 23:09:57,638 (beam_search:429) INFO: max output length: 86 +2024-01-16 23:09:57,638 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:57,720 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:57,720 (beam_search:476) INFO: -2.81 * 1.0 = -2.81 for ctc +2024-01-16 23:09:57,720 (beam_search:479) INFO: total log probability: -2.81 +2024-01-16 23:09:57,720 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:09:57,720 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:57,720 (beam_search:483) INFO: best hypo: nebukakunarunogapauhayakumaclta + +2024-01-16 23:09:57,722 (asr_inference:494) INFO: speech length: 139968 +2024-01-16 23:09:57,736 (beam_search:428) INFO: decoder input length: 216 +2024-01-16 23:09:57,736 (beam_search:429) INFO: max output length: 216 +2024-01-16 23:09:57,736 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:09:58,299 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:09:58,300 (beam_search:476) INFO: -11.06 * 1.0 = -11.06 for ctc +2024-01-16 23:09:58,300 (beam_search:479) INFO: total log probability: -11.06 +2024-01-16 23:09:58,300 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:09:58,300 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:09:58,300 (beam_search:483) INFO: best hypo: watashiwaiNgeNnoodekIshItekIkeeseenotachibakarapaugejutsuomirunodeaclteooshakaraeNshaomirunodeanai + +# Accounting: time=17 threads=1 +# Ended (code 0) at Tue Jan 16 23:09:58 CST 2024, elapsed time 17 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.3.log b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.3.log new file mode 100644 index 0000000000000000000000000000000000000000..f93c29b0db2c3e4e2b992cb41b49fde5879492bd --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.3.log @@ -0,0 +1,459 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3 --config conf/decode_asr.yaml +# Started at Tue Jan 16 23:09:58 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3 --config conf/decode_asr.yaml +2024-01-16 23:10:00,118 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml +2024-01-16 23:10:00,137 (asr:523) INFO: Vocabulary size: 41 +2024-01-16 23:10:00,200 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 23:10:00,200 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 23:10:00,312 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 23:10:01,615 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 23:10:02,831 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 23:10:02,831 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 23:10:02,831 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 23:10:02,864 (asr_inference:446) INFO: Text tokenizer: WordTokenizer(delimiter="None") +2024-01-16 23:10:02,939 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 23:10:03,055 (asr_inference:494) INFO: speech length: 62208 +2024-01-16 23:10:04,269 (beam_search:428) INFO: decoder input length: 95 +2024-01-16 23:10:04,269 (beam_search:429) INFO: max output length: 95 +2024-01-16 23:10:04,269 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:04,370 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:04,371 (beam_search:476) INFO: -1.82 * 1.0 = -1.82 for ctc +2024-01-16 23:10:04,371 (beam_search:479) INFO: total log probability: -1.82 +2024-01-16 23:10:04,371 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-16 23:10:04,371 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:04,371 (beam_search:483) INFO: best hypo: aoitomatoshikanakUtepaukaukabaiyou + +2024-01-16 23:10:04,395 (asr_inference:494) INFO: speech length: 69696 +2024-01-16 23:10:04,406 (beam_search:428) INFO: decoder input length: 106 +2024-01-16 23:10:04,406 (beam_search:429) INFO: max output length: 106 +2024-01-16 23:10:04,406 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:04,532 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:04,532 (beam_search:476) INFO: -6.01 * 1.0 = -6.01 for ctc +2024-01-16 23:10:04,532 (beam_search:479) INFO: total log probability: -6.01 +2024-01-16 23:10:04,532 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 23:10:04,532 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:04,532 (beam_search:483) INFO: best hypo: seNkezuegyooniookinakitayoyasUteeru + +2024-01-16 23:10:04,533 (asr_inference:494) INFO: speech length: 72576 +2024-01-16 23:10:04,543 (beam_search:428) INFO: decoder input length: 111 +2024-01-16 23:10:04,543 (beam_search:429) INFO: max output length: 111 +2024-01-16 23:10:04,543 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:04,695 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:04,695 (beam_search:476) INFO: -4.91 * 1.0 = -4.91 for ctc +2024-01-16 23:10:04,695 (beam_search:479) INFO: total log probability: -4.91 +2024-01-16 23:10:04,695 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:10:04,695 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:04,696 (beam_search:483) INFO: best hypo: nanikashiranoiNseNtebuwanaitokibuisuenodewa + +2024-01-16 23:10:04,697 (asr_inference:494) INFO: speech length: 118656 +2024-01-16 23:10:04,709 (beam_search:428) INFO: decoder input length: 183 +2024-01-16 23:10:04,709 (beam_search:429) INFO: max output length: 183 +2024-01-16 23:10:04,709 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:04,930 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:04,930 (beam_search:476) INFO: -5.84 * 1.0 = -5.84 for ctc +2024-01-16 23:10:04,930 (beam_search:479) INFO: total log probability: -5.84 +2024-01-16 23:10:04,930 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 23:10:04,930 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:04,931 (beam_search:483) INFO: best hypo: jikoNoshekgeNnoibeNtodesutorushItamari + +2024-01-16 23:10:04,932 (asr_inference:494) INFO: speech length: 66240 +2024-01-16 23:10:04,941 (beam_search:428) INFO: decoder input length: 101 +2024-01-16 23:10:04,941 (beam_search:429) INFO: max output length: 101 +2024-01-16 23:10:04,942 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:05,038 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:05,038 (beam_search:476) INFO: -1.60 * 1.0 = -1.60 for ctc +2024-01-16 23:10:05,038 (beam_search:479) INFO: total log probability: -1.60 +2024-01-16 23:10:05,038 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-16 23:10:05,038 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:05,039 (beam_search:483) INFO: best hypo: marinoshItowaboozeNtoshIteita + +2024-01-16 23:10:05,040 (asr_inference:494) INFO: speech length: 72576 +2024-01-16 23:10:05,050 (beam_search:428) INFO: decoder input length: 111 +2024-01-16 23:10:05,050 (beam_search:429) INFO: max output length: 111 +2024-01-16 23:10:05,050 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:05,181 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:05,181 (beam_search:476) INFO: -3.50 * 1.0 = -3.50 for ctc +2024-01-16 23:10:05,181 (beam_search:479) INFO: total log probability: -3.50 +2024-01-16 23:10:05,181 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:10:05,181 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:05,182 (beam_search:483) INFO: best hypo: soNnanaiyoonomeruwanaNkeNmokUIteita + +2024-01-16 23:10:05,183 (asr_inference:494) INFO: speech length: 56448 +2024-01-16 23:10:05,192 (beam_search:428) INFO: decoder input length: 86 +2024-01-16 23:10:05,192 (beam_search:429) INFO: max output length: 86 +2024-01-16 23:10:05,192 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:05,260 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:05,261 (beam_search:476) INFO: -2.83 * 1.0 = -2.83 for ctc +2024-01-16 23:10:05,261 (beam_search:479) INFO: total log probability: -2.83 +2024-01-16 23:10:05,261 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:10:05,261 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:05,261 (beam_search:483) INFO: best hypo: ijigaidedeesfuishIteita + +2024-01-16 23:10:05,262 (asr_inference:494) INFO: speech length: 129600 +2024-01-16 23:10:05,275 (beam_search:428) INFO: decoder input length: 200 +2024-01-16 23:10:05,275 (beam_search:429) INFO: max output length: 200 +2024-01-16 23:10:05,276 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:05,759 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:05,759 (beam_search:476) INFO: -11.76 * 1.0 = -11.76 for ctc +2024-01-16 23:10:05,759 (beam_search:479) INFO: total log probability: -11.76 +2024-01-16 23:10:05,760 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:10:05,760 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:05,760 (beam_search:483) INFO: best hypo: tokidokichiuuNnokorowawakaranakunorutochigaarudakarabokuakaaneokinotonikajiajemeru + +2024-01-16 23:10:05,761 (asr_inference:494) INFO: speech length: 58176 +2024-01-16 23:10:05,770 (beam_search:428) INFO: decoder input length: 88 +2024-01-16 23:10:05,770 (beam_search:429) INFO: max output length: 88 +2024-01-16 23:10:05,770 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:05,827 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:05,827 (beam_search:476) INFO: -2.60 * 1.0 = -2.60 for ctc +2024-01-16 23:10:05,827 (beam_search:479) INFO: total log probability: -2.60 +2024-01-16 23:10:05,827 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:10:05,827 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:05,827 (beam_search:483) INFO: best hypo: moonigetechatameda + +2024-01-16 23:10:05,828 (asr_inference:494) INFO: speech length: 69120 +2024-01-16 23:10:05,838 (beam_search:428) INFO: decoder input length: 105 +2024-01-16 23:10:05,838 (beam_search:429) INFO: max output length: 105 +2024-01-16 23:10:05,838 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:05,934 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:05,934 (beam_search:476) INFO: -2.15 * 1.0 = -2.15 for ctc +2024-01-16 23:10:05,934 (beam_search:479) INFO: total log probability: -2.15 +2024-01-16 23:10:05,935 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:10:05,935 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:05,935 (beam_search:483) INFO: best hypo: karewapoototajitsUkushIteita + +2024-01-16 23:10:05,936 (asr_inference:494) INFO: speech length: 62208 +2024-01-16 23:10:05,945 (beam_search:428) INFO: decoder input length: 95 +2024-01-16 23:10:05,945 (beam_search:429) INFO: max output length: 95 +2024-01-16 23:10:05,945 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:06,039 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:06,039 (beam_search:476) INFO: -5.22 * 1.0 = -5.22 for ctc +2024-01-16 23:10:06,039 (beam_search:479) INFO: total log probability: -5.22 +2024-01-16 23:10:06,039 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 23:10:06,039 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:06,039 (beam_search:483) INFO: best hypo: daruNimuneiwakowakaketakanai + +2024-01-16 23:10:06,040 (asr_inference:494) INFO: speech length: 77760 +2024-01-16 23:10:06,050 (beam_search:428) INFO: decoder input length: 119 +2024-01-16 23:10:06,050 (beam_search:429) INFO: max output length: 119 +2024-01-16 23:10:06,050 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:06,184 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:06,184 (beam_search:476) INFO: -6.56 * 1.0 = -6.56 for ctc +2024-01-16 23:10:06,185 (beam_search:479) INFO: total log probability: -6.56 +2024-01-16 23:10:06,185 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-16 23:10:06,185 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:06,185 (beam_search:483) INFO: best hypo: pasakatoomoteudoanootoclteonigeclta + +2024-01-16 23:10:06,186 (asr_inference:494) INFO: speech length: 43200 +2024-01-16 23:10:06,194 (beam_search:428) INFO: decoder input length: 65 +2024-01-16 23:10:06,194 (beam_search:429) INFO: max output length: 65 +2024-01-16 23:10:06,194 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:06,220 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:06,220 (beam_search:476) INFO: -1.61 * 1.0 = -1.61 for ctc +2024-01-16 23:10:06,220 (beam_search:479) INFO: total log probability: -1.61 +2024-01-16 23:10:06,220 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 23:10:06,220 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:06,220 (beam_search:483) INFO: best hypo: shteimaseN + +2024-01-16 23:10:06,221 (asr_inference:494) INFO: speech length: 104256 +2024-01-16 23:10:06,232 (beam_search:428) INFO: decoder input length: 160 +2024-01-16 23:10:06,232 (beam_search:429) INFO: max output length: 160 +2024-01-16 23:10:06,233 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:06,554 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:06,554 (beam_search:476) INFO: -2.61 * 1.0 = -2.61 for ctc +2024-01-16 23:10:06,554 (beam_search:479) INFO: total log probability: -2.61 +2024-01-16 23:10:06,554 (beam_search:480) INFO: normalized log probability: -0.04 +2024-01-16 23:10:06,554 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:06,554 (beam_search:483) INFO: best hypo: kayoonishIteshIterutotomonishiclteinaitokorokarataNkyuwahajimarunodearu + +2024-01-16 23:10:06,556 (asr_inference:494) INFO: speech length: 54720 +2024-01-16 23:10:06,564 (beam_search:428) INFO: decoder input length: 83 +2024-01-16 23:10:06,564 (beam_search:429) INFO: max output length: 83 +2024-01-16 23:10:06,564 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:06,617 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:06,617 (beam_search:476) INFO: -2.44 * 1.0 = -2.44 for ctc +2024-01-16 23:10:06,617 (beam_search:479) INFO: total log probability: -2.44 +2024-01-16 23:10:06,617 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:10:06,617 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:06,617 (beam_search:483) INFO: best hypo: aisatsuwadaijidaio + +2024-01-16 23:10:06,619 (asr_inference:494) INFO: speech length: 89856 +2024-01-16 23:10:06,629 (beam_search:428) INFO: decoder input length: 138 +2024-01-16 23:10:06,629 (beam_search:429) INFO: max output length: 138 +2024-01-16 23:10:06,629 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:06,867 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:06,867 (beam_search:476) INFO: -3.04 * 1.0 = -3.04 for ctc +2024-01-16 23:10:06,867 (beam_search:479) INFO: total log probability: -3.04 +2024-01-16 23:10:06,867 (beam_search:480) INFO: normalized log probability: -0.05 +2024-01-16 23:10:06,867 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:06,867 (beam_search:483) INFO: best hypo: tojitamonoikanihirogetemohiraitamunonianararotoiclteiruga + +2024-01-16 23:10:06,869 (asr_inference:494) INFO: speech length: 63360 +2024-01-16 23:10:06,878 (beam_search:428) INFO: decoder input length: 96 +2024-01-16 23:10:06,878 (beam_search:429) INFO: max output length: 96 +2024-01-16 23:10:06,878 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:06,965 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:06,965 (beam_search:476) INFO: -2.61 * 1.0 = -2.61 for ctc +2024-01-16 23:10:06,965 (beam_search:479) INFO: total log probability: -2.61 +2024-01-16 23:10:06,965 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:10:06,965 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:06,965 (beam_search:483) INFO: best hypo: tamushinikutsUkatsUkucltemiowa + +2024-01-16 23:10:06,966 (asr_inference:494) INFO: speech length: 69120 +2024-01-16 23:10:06,976 (beam_search:428) INFO: decoder input length: 105 +2024-01-16 23:10:06,976 (beam_search:429) INFO: max output length: 105 +2024-01-16 23:10:06,976 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:07,076 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:07,076 (beam_search:476) INFO: -2.84 * 1.0 = -2.84 for ctc +2024-01-16 23:10:07,076 (beam_search:479) INFO: total log probability: -2.84 +2024-01-16 23:10:07,076 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:10:07,076 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:07,076 (beam_search:483) INFO: best hypo: UtszeibuNakokinashoobaidaiona + +2024-01-16 23:10:07,077 (asr_inference:494) INFO: speech length: 40704 +2024-01-16 23:10:07,085 (beam_search:428) INFO: decoder input length: 61 +2024-01-16 23:10:07,085 (beam_search:429) INFO: max output length: 61 +2024-01-16 23:10:07,086 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:07,105 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:07,105 (beam_search:476) INFO: -2.09 * 1.0 = -2.09 for ctc +2024-01-16 23:10:07,105 (beam_search:479) INFO: total log probability: -2.09 +2024-01-16 23:10:07,105 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 23:10:07,105 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:07,105 (beam_search:483) INFO: best hypo: whacltei + +2024-01-16 23:10:07,106 (asr_inference:494) INFO: speech length: 65664 +2024-01-16 23:10:07,116 (beam_search:428) INFO: decoder input length: 100 +2024-01-16 23:10:07,116 (beam_search:429) INFO: max output length: 100 +2024-01-16 23:10:07,116 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:07,138 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:07,138 (beam_search:476) INFO: -2.50 * 1.0 = -2.50 for ctc +2024-01-16 23:10:07,138 (beam_search:479) INFO: total log probability: -2.50 +2024-01-16 23:10:07,138 (beam_search:480) INFO: normalized log probability: -0.36 +2024-01-16 23:10:07,138 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:07,138 (beam_search:483) INFO: best hypo: kitei + +2024-01-16 23:10:07,139 (asr_inference:494) INFO: speech length: 47616 +2024-01-16 23:10:07,148 (beam_search:428) INFO: decoder input length: 72 +2024-01-16 23:10:07,148 (beam_search:429) INFO: max output length: 72 +2024-01-16 23:10:07,148 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:07,156 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:07,156 (beam_search:476) INFO: -0.66 * 1.0 = -0.66 for ctc +2024-01-16 23:10:07,156 (beam_search:479) INFO: total log probability: -0.66 +2024-01-16 23:10:07,156 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 23:10:07,156 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:07,156 (beam_search:483) INFO: best hypo: go + +2024-01-16 23:10:07,157 (asr_inference:494) INFO: speech length: 61824 +2024-01-16 23:10:07,166 (beam_search:428) INFO: decoder input length: 94 +2024-01-16 23:10:07,166 (beam_search:429) INFO: max output length: 94 +2024-01-16 23:10:07,166 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:07,194 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:07,194 (beam_search:476) INFO: -2.67 * 1.0 = -2.67 for ctc +2024-01-16 23:10:07,194 (beam_search:479) INFO: total log probability: -2.67 +2024-01-16 23:10:07,194 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 23:10:07,194 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:07,194 (beam_search:483) INFO: best hypo: hashiiti + +2024-01-16 23:10:07,195 (asr_inference:494) INFO: speech length: 45696 +2024-01-16 23:10:07,203 (beam_search:428) INFO: decoder input length: 69 +2024-01-16 23:10:07,204 (beam_search:429) INFO: max output length: 69 +2024-01-16 23:10:07,204 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:07,215 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:07,215 (beam_search:476) INFO: -1.49 * 1.0 = -1.49 for ctc +2024-01-16 23:10:07,215 (beam_search:479) INFO: total log probability: -1.49 +2024-01-16 23:10:07,215 (beam_search:480) INFO: normalized log probability: -0.30 +2024-01-16 23:10:07,215 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:07,215 (beam_search:483) INFO: best hypo: iea + +2024-01-16 23:10:07,216 (asr_inference:494) INFO: speech length: 38784 +2024-01-16 23:10:07,223 (beam_search:428) INFO: decoder input length: 58 +2024-01-16 23:10:07,223 (beam_search:429) INFO: max output length: 58 +2024-01-16 23:10:07,223 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:07,238 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:07,238 (beam_search:476) INFO: -1.61 * 1.0 = -1.61 for ctc +2024-01-16 23:10:07,238 (beam_search:479) INFO: total log probability: -1.61 +2024-01-16 23:10:07,238 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 23:10:07,238 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:07,238 (beam_search:483) INFO: best hypo: haclchi + +2024-01-16 23:10:07,239 (asr_inference:494) INFO: speech length: 36096 +2024-01-16 23:10:07,246 (beam_search:428) INFO: decoder input length: 54 +2024-01-16 23:10:07,246 (beam_search:429) INFO: max output length: 54 +2024-01-16 23:10:07,246 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:07,255 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:07,255 (beam_search:476) INFO: -1.04 * 1.0 = -1.04 for ctc +2024-01-16 23:10:07,255 (beam_search:479) INFO: total log probability: -1.04 +2024-01-16 23:10:07,255 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 23:10:07,256 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:07,256 (beam_search:483) INFO: best hypo: hne + +2024-01-16 23:10:07,256 (asr_inference:494) INFO: speech length: 36864 +2024-01-16 23:10:07,264 (beam_search:428) INFO: decoder input length: 55 +2024-01-16 23:10:07,264 (beam_search:429) INFO: max output length: 55 +2024-01-16 23:10:07,264 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:07,278 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:07,278 (beam_search:476) INFO: -1.45 * 1.0 = -1.45 for ctc +2024-01-16 23:10:07,278 (beam_search:479) INFO: total log probability: -1.45 +2024-01-16 23:10:07,278 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 23:10:07,278 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:07,278 (beam_search:483) INFO: best hypo: ashiii + +2024-01-16 23:10:07,279 (asr_inference:494) INFO: speech length: 36864 +2024-01-16 23:10:07,287 (beam_search:428) INFO: decoder input length: 55 +2024-01-16 23:10:07,287 (beam_search:429) INFO: max output length: 55 +2024-01-16 23:10:07,287 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:07,296 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:07,296 (beam_search:476) INFO: -0.89 * 1.0 = -0.89 for ctc +2024-01-16 23:10:07,296 (beam_search:479) INFO: total log probability: -0.89 +2024-01-16 23:10:07,296 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 23:10:07,296 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:07,296 (beam_search:483) INFO: best hypo: kuo + +2024-01-16 23:10:07,297 (asr_inference:494) INFO: speech length: 40704 +2024-01-16 23:10:07,304 (beam_search:428) INFO: decoder input length: 61 +2024-01-16 23:10:07,304 (beam_search:429) INFO: max output length: 61 +2024-01-16 23:10:07,304 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:07,317 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:07,317 (beam_search:476) INFO: -2.45 * 1.0 = -2.45 for ctc +2024-01-16 23:10:07,317 (beam_search:479) INFO: total log probability: -2.45 +2024-01-16 23:10:07,317 (beam_search:480) INFO: normalized log probability: -0.41 +2024-01-16 23:10:07,317 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:07,317 (beam_search:483) INFO: best hypo: kechi + +2024-01-16 23:10:07,318 (asr_inference:494) INFO: speech length: 114048 +2024-01-16 23:10:07,330 (beam_search:428) INFO: decoder input length: 176 +2024-01-16 23:10:07,330 (beam_search:429) INFO: max output length: 176 +2024-01-16 23:10:07,330 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:07,655 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:07,655 (beam_search:476) INFO: -5.97 * 1.0 = -5.97 for ctc +2024-01-16 23:10:07,655 (beam_search:479) INFO: total log probability: -5.97 +2024-01-16 23:10:07,656 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:10:07,656 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:07,656 (beam_search:483) INFO: best hypo: kakakugapauakirakanisurupaukyyaclkaNtekIshiNdrinishItagaukotoniyoclte + +2024-01-16 23:10:07,657 (asr_inference:494) INFO: speech length: 154368 +2024-01-16 23:10:07,672 (beam_search:428) INFO: decoder input length: 239 +2024-01-16 23:10:07,672 (beam_search:429) INFO: max output length: 239 +2024-01-16 23:10:07,672 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:08,216 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:08,216 (beam_search:476) INFO: -6.93 * 1.0 = -6.93 for ctc +2024-01-16 23:10:08,216 (beam_search:479) INFO: total log probability: -6.93 +2024-01-16 23:10:08,216 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:10:08,216 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:08,217 (beam_search:483) INFO: best hypo: kakotomiraitonopaumujuNtekijikodooitsutoshItenogeNzaigakapachiomoshItoiukotodearu + +2024-01-16 23:10:08,218 (asr_inference:494) INFO: speech length: 150336 +2024-01-16 23:10:08,233 (beam_search:428) INFO: decoder input length: 232 +2024-01-16 23:10:08,233 (beam_search:429) INFO: max output length: 232 +2024-01-16 23:10:08,233 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:08,794 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:08,794 (beam_search:476) INFO: -7.46 * 1.0 = -7.46 for ctc +2024-01-16 23:10:08,794 (beam_search:479) INFO: total log probability: -7.46 +2024-01-16 23:10:08,794 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:10:08,794 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:08,795 (beam_search:483) INFO: best hypo: butsuritekisekaiuwasuugakutekiIkigooniocltearawasarerupausuukakutekIikatachinosekaiaru + +2024-01-16 23:10:08,796 (asr_inference:494) INFO: speech length: 56448 +2024-01-16 23:10:08,805 (beam_search:428) INFO: decoder input length: 86 +2024-01-16 23:10:08,805 (beam_search:429) INFO: max output length: 86 +2024-01-16 23:10:08,805 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:08,886 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:08,886 (beam_search:476) INFO: -3.04 * 1.0 = -3.04 for ctc +2024-01-16 23:10:08,886 (beam_search:479) INFO: total log probability: -3.04 +2024-01-16 23:10:08,886 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:10:08,886 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:08,886 (beam_search:483) INFO: best hypo: wonachigeNishoodesaNkooginaru + +2024-01-16 23:10:08,887 (asr_inference:494) INFO: speech length: 65088 +2024-01-16 23:10:08,897 (beam_search:428) INFO: decoder input length: 99 +2024-01-16 23:10:08,897 (beam_search:429) INFO: max output length: 99 +2024-01-16 23:10:08,897 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:09,006 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:09,006 (beam_search:476) INFO: -2.80 * 1.0 = -2.80 for ctc +2024-01-16 23:10:09,006 (beam_search:479) INFO: total log probability: -2.80 +2024-01-16 23:10:09,006 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:10:09,006 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:09,006 (beam_search:483) INFO: best hypo: gaikokukarakitamonodatoshitebiclkui + +2024-01-16 23:10:09,008 (asr_inference:494) INFO: speech length: 88128 +2024-01-16 23:10:09,018 (beam_search:428) INFO: decoder input length: 135 +2024-01-16 23:10:09,018 (beam_search:429) INFO: max output length: 135 +2024-01-16 23:10:09,018 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:09,210 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:09,210 (beam_search:476) INFO: -4.91 * 1.0 = -4.91 for ctc +2024-01-16 23:10:09,211 (beam_search:479) INFO: total log probability: -4.91 +2024-01-16 23:10:09,211 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:10:09,211 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:09,211 (beam_search:483) INFO: best hypo: iwayorujiIseNyocltekakUtoUkUshIitacltamonodearu + +2024-01-16 23:10:09,212 (asr_inference:494) INFO: speech length: 133440 +2024-01-16 23:10:09,226 (beam_search:428) INFO: decoder input length: 206 +2024-01-16 23:10:09,226 (beam_search:429) INFO: max output length: 206 +2024-01-16 23:10:09,226 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:09,601 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:09,601 (beam_search:476) INFO: -8.42 * 1.0 = -8.42 for ctc +2024-01-16 23:10:09,601 (beam_search:479) INFO: total log probability: -8.42 +2024-01-16 23:10:09,601 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:10:09,601 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:09,601 (beam_search:483) INFO: best hypo: onajiyonidaNsueuwahijzaoouzubohakotogagimuuzukerareteimasU + +2024-01-16 23:10:09,603 (asr_inference:494) INFO: speech length: 197760 +2024-01-16 23:10:09,620 (beam_search:428) INFO: decoder input length: 306 +2024-01-16 23:10:09,620 (beam_search:429) INFO: max output length: 306 +2024-01-16 23:10:09,621 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:10,612 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:10,612 (beam_search:476) INFO: -14.30 * 1.0 = -14.30 for ctc +2024-01-16 23:10:10,613 (beam_search:479) INFO: total log probability: -14.30 +2024-01-16 23:10:10,613 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:10:10,613 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:10,613 (beam_search:483) INFO: best hypo: konosabisakorakseeyahajimetosuruseNptakuiyaeNkakUchidepaudetayaoNseohIsuotosurutaNkeNtairihiNclpanidioosareteimasU + +2024-01-16 23:10:10,615 (asr_inference:494) INFO: speech length: 205440 +2024-01-16 23:10:10,634 (beam_search:428) INFO: decoder input length: 318 +2024-01-16 23:10:10,634 (beam_search:429) INFO: max output length: 318 +2024-01-16 23:10:10,634 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:11,662 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:11,662 (beam_search:476) INFO: -18.17 * 1.0 = -18.17 for ctc +2024-01-16 23:10:11,662 (beam_search:479) INFO: total log probability: -18.17 +2024-01-16 23:10:11,662 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 23:10:11,662 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:11,663 (beam_search:483) INFO: best hypo: kyoofukyokadonokoosuiroobiamakashibaraiupautasumakipaumizukipauoyusaikuroNnaNonokibishiikshoketayasonoekyooniyorumorodesU + +2024-01-16 23:10:11,665 (asr_inference:494) INFO: speech length: 155520 +2024-01-16 23:10:11,680 (beam_search:428) INFO: decoder input length: 240 +2024-01-16 23:10:11,680 (beam_search:429) INFO: max output length: 240 +2024-01-16 23:10:11,680 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:12,235 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:12,236 (beam_search:476) INFO: -8.63 * 1.0 = -8.63 for ctc +2024-01-16 23:10:12,236 (beam_search:479) INFO: total log probability: -8.63 +2024-01-16 23:10:12,236 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:10:12,236 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:12,236 (beam_search:483) INFO: best hypo: iNtanetowamasUkomirukeesheNtotaijiNkomirukeeshoonopaudoyosookaesumaidakaNkyoordesU + +2024-01-16 23:10:12,237 (asr_inference:494) INFO: speech length: 229440 +2024-01-16 23:10:12,258 (beam_search:428) INFO: decoder input length: 356 +2024-01-16 23:10:12,258 (beam_search:429) INFO: max output length: 356 +2024-01-16 23:10:12,258 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:13,395 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:13,395 (beam_search:476) INFO: -10.72 * 1.0 = -10.72 for ctc +2024-01-16 23:10:13,395 (beam_search:479) INFO: total log probability: -10.72 +2024-01-16 23:10:13,395 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-16 23:10:13,395 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:13,395 (beam_search:483) INFO: best hypo: kajinorewatsuujootokubesuraishIkoyapauieNtaateimeNtoyoshIteimasUkyesuwakibuyokushIsesunairetoramaruyooresurutamdesU + +2024-01-16 23:10:13,397 (asr_inference:494) INFO: speech length: 190080 +2024-01-16 23:10:13,415 (beam_search:428) INFO: decoder input length: 294 +2024-01-16 23:10:13,415 (beam_search:429) INFO: max output length: 294 +2024-01-16 23:10:13,415 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:14,210 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:14,210 (beam_search:476) INFO: -11.70 * 1.0 = -11.70 for ctc +2024-01-16 23:10:14,210 (beam_search:479) INFO: total log probability: -11.70 +2024-01-16 23:10:14,210 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:10:14,210 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:14,210 (beam_search:483) INFO: best hypo: shikashikakUteNnobikecltooshinacltatoiNdowapaunanazunobikecltooshinaisaNjuroburajshikadegimaseNdeshIta + +# Accounting: time=16 threads=1 +# Ended (code 0) at Tue Jan 16 23:10:14 CST 2024, elapsed time 16 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.4.log b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.4.log new file mode 100644 index 0000000000000000000000000000000000000000..9f3709ff9de9e4337cec1db594effb8b9a3ccaf6 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.4.log @@ -0,0 +1,459 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4 --config conf/decode_asr.yaml +# Started at Tue Jan 16 23:10:14 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4 --config conf/decode_asr.yaml +2024-01-16 23:10:16,027 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml +2024-01-16 23:10:16,045 (asr:523) INFO: Vocabulary size: 41 +2024-01-16 23:10:16,107 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-16 23:10:16,107 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-16 23:10:16,217 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-16 23:10:17,516 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-16 23:10:18,769 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-16 23:10:18,769 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-16 23:10:18,769 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-16 23:10:18,802 (asr_inference:446) INFO: Text tokenizer: WordTokenizer(delimiter="None") +2024-01-16 23:10:18,877 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-16 23:10:18,991 (asr_inference:494) INFO: speech length: 208320 +2024-01-16 23:10:20,210 (beam_search:428) INFO: decoder input length: 323 +2024-01-16 23:10:20,210 (beam_search:429) INFO: max output length: 323 +2024-01-16 23:10:20,210 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:21,226 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:21,226 (beam_search:476) INFO: -15.23 * 1.0 = -15.23 for ctc +2024-01-16 23:10:21,226 (beam_search:479) INFO: total log probability: -15.23 +2024-01-16 23:10:21,226 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:10:21,226 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:21,227 (beam_search:483) INFO: best hypo: hookuraNdonokooshIketsukawapauhokuraNdashotooboNdoefkepidepauichipoNnogaichieeboNdojiipibiitotookanikotesaredeimasU + +2024-01-16 23:10:21,251 (asr_inference:494) INFO: speech length: 193920 +2024-01-16 23:10:21,269 (beam_search:428) INFO: decoder input length: 300 +2024-01-16 23:10:21,269 (beam_search:429) INFO: max output length: 300 +2024-01-16 23:10:21,269 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:22,205 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:22,206 (beam_search:476) INFO: -18.70 * 1.0 = -18.70 for ctc +2024-01-16 23:10:22,206 (beam_search:479) INFO: total log probability: -18.70 +2024-01-16 23:10:22,206 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 23:10:22,206 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:22,206 (beam_search:483) INFO: best hypo: hajushitanojoookokaowochuugometoodesUiseNjuichineuhachigatsumisekooshiiseNjununeNseaNgatsumunekaitsueshimaseNgeshIta + +2024-01-16 23:10:22,208 (asr_inference:494) INFO: speech length: 168000 +2024-01-16 23:10:22,224 (beam_search:428) INFO: decoder input length: 260 +2024-01-16 23:10:22,224 (beam_search:429) INFO: max output length: 260 +2024-01-16 23:10:22,224 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:22,829 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:22,829 (beam_search:476) INFO: -11.67 * 1.0 = -11.67 for ctc +2024-01-16 23:10:22,829 (beam_search:479) INFO: total log probability: -11.67 +2024-01-16 23:10:22,829 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 23:10:22,829 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:22,829 (beam_search:483) INFO: best hypo: iclpuNkaNdehfucltoosubuchiikimarebahucltosurumareminaNclpaumokakaruchjiikimoarimasU + +2024-01-16 23:10:22,831 (asr_inference:494) INFO: speech length: 148800 +2024-01-16 23:10:22,846 (beam_search:428) INFO: decoder input length: 230 +2024-01-16 23:10:22,846 (beam_search:429) INFO: max output length: 230 +2024-01-16 23:10:22,846 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:23,474 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:23,474 (beam_search:476) INFO: -12.82 * 1.0 = -12.82 for ctc +2024-01-16 23:10:23,474 (beam_search:479) INFO: total log probability: -12.82 +2024-01-16 23:10:23,474 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:10:23,474 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:23,474 (beam_search:483) INFO: best hypo: peramicltanoototoashIkainoshoowapaukonokaNkooshuretokunikoromatajikatanashimeremoyooshinoshitosuresU + +2024-01-16 23:10:23,476 (asr_inference:494) INFO: speech length: 114240 +2024-01-16 23:10:23,488 (beam_search:428) INFO: decoder input length: 176 +2024-01-16 23:10:23,488 (beam_search:429) INFO: max output length: 176 +2024-01-16 23:10:23,488 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:23,790 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:23,790 (beam_search:476) INFO: -6.05 * 1.0 = -6.05 for ctc +2024-01-16 23:10:23,790 (beam_search:479) INFO: total log probability: -6.05 +2024-01-16 23:10:23,790 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:10:23,790 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:23,790 (beam_search:483) INFO: best hypo: sunotanepautainipaudaderutoshItehiookigatsuekasaregachidesU + +2024-01-16 23:10:23,792 (asr_inference:494) INFO: speech length: 262080 +2024-01-16 23:10:23,816 (beam_search:428) INFO: decoder input length: 407 +2024-01-16 23:10:23,816 (beam_search:429) INFO: max output length: 407 +2024-01-16 23:10:23,816 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:25,463 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:25,463 (beam_search:476) INFO: -27.57 * 1.0 = -27.57 for ctc +2024-01-16 23:10:25,463 (beam_search:479) INFO: total log probability: -27.57 +2024-01-16 23:10:25,463 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 23:10:25,463 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:25,464 (beam_search:483) INFO: best hypo: geNzNsurukokogashItareteiranijiogoomaenadaNdaktukurootUsaideowakieNzoNsuraoogaibuNkenosaikorotsashidesUtegakiNyorugeNpoowakeNzooshIteimuaseN + +2024-01-16 23:10:25,465 (asr_inference:494) INFO: speech length: 218880 +2024-01-16 23:10:25,486 (beam_search:428) INFO: decoder input length: 339 +2024-01-16 23:10:25,486 (beam_search:429) INFO: max output length: 339 +2024-01-16 23:10:25,486 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:26,780 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:26,780 (beam_search:476) INFO: -22.24 * 1.0 = -22.24 for ctc +2024-01-16 23:10:26,780 (beam_search:479) INFO: total log probability: -22.24 +2024-01-16 23:10:26,780 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 23:10:26,780 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:26,780 (beam_search:483) INFO: best hypo: kremosesootadashitaomitomerushItomimashItegaookugushItosuonokekodetaiyookedeataiyotosunohokanoshigachikyunoumareidooshserudoashiNchiteimashIta + +2024-01-16 23:10:26,782 (asr_inference:494) INFO: speech length: 252480 +2024-01-16 23:10:26,805 (beam_search:428) INFO: decoder input length: 392 +2024-01-16 23:10:26,805 (beam_search:429) INFO: max output length: 392 +2024-01-16 23:10:26,805 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:28,364 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:28,365 (beam_search:476) INFO: -20.23 * 1.0 = -20.23 for ctc +2024-01-16 23:10:28,365 (beam_search:479) INFO: total log probability: -20.23 +2024-01-16 23:10:28,365 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 23:10:28,365 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:28,365 (beam_search:483) INFO: best hypo: chibecltomeesoochushiNwashiiseeogadesUsaNmazananakamigamioshIkakUkasurukododeenerugiichanerugashiokasarechakrabakasekasaresataruneichIkegoogaremasU + +2024-01-16 23:10:28,367 (asr_inference:494) INFO: speech length: 170880 +2024-01-16 23:10:28,383 (beam_search:428) INFO: decoder input length: 264 +2024-01-16 23:10:28,383 (beam_search:429) INFO: max output length: 264 +2024-01-16 23:10:28,383 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:29,129 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:29,129 (beam_search:476) INFO: -19.69 * 1.0 = -19.69 for ctc +2024-01-16 23:10:29,129 (beam_search:479) INFO: total log probability: -19.69 +2024-01-16 23:10:29,129 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-16 23:10:29,129 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:29,129 (beam_search:483) INFO: best hypo: binaniyahurekaniarusubetenokokuresUkooedadooyonikunokooeniapaumanejiehokoshItoniueNguogaokakarimasU + +2024-01-16 23:10:29,131 (asr_inference:494) INFO: speech length: 126720 +2024-01-16 23:10:29,144 (beam_search:428) INFO: decoder input length: 195 +2024-01-16 23:10:29,144 (beam_search:429) INFO: max output length: 195 +2024-01-16 23:10:29,144 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:29,510 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:29,510 (beam_search:476) INFO: -13.96 * 1.0 = -13.96 for ctc +2024-01-16 23:10:29,510 (beam_search:479) INFO: total log probability: -13.96 +2024-01-16 23:10:29,510 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-16 23:10:29,510 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:29,511 (beam_search:483) INFO: best hypo: desakurumasuNnohokanookunukootsoshiudaNunatssukokareumaremashIta + +2024-01-16 23:10:29,512 (asr_inference:494) INFO: speech length: 208320 +2024-01-16 23:10:29,532 (beam_search:428) INFO: decoder input length: 323 +2024-01-16 23:10:29,532 (beam_search:429) INFO: max output length: 323 +2024-01-16 23:10:29,532 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:30,327 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:30,327 (beam_search:476) INFO: -9.26 * 1.0 = -9.26 for ctc +2024-01-16 23:10:30,327 (beam_search:479) INFO: total log probability: -9.26 +2024-01-16 23:10:30,327 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:10:30,327 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:30,327 (beam_search:483) INFO: best hypo: iNtanecltowamasUkominikeeshoNtotaijiNkominukeeshoNnoyoyoosookanesonaitakaNkyooresU + +2024-01-16 23:10:30,329 (asr_inference:494) INFO: speech length: 211200 +2024-01-16 23:10:30,349 (beam_search:428) INFO: decoder input length: 327 +2024-01-16 23:10:30,349 (beam_search:429) INFO: max output length: 327 +2024-01-16 23:10:30,349 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:31,398 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:31,398 (beam_search:476) INFO: -23.39 * 1.0 = -23.39 for ctc +2024-01-16 23:10:31,398 (beam_search:479) INFO: total log probability: -23.39 +2024-01-16 23:10:31,398 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 23:10:31,398 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:31,398 (beam_search:483) INFO: best hypo: kyooiNdewakaoNseNkaNreitejiushjuNishIsaraeikanienukaNseNgokanoosesegudaminikaNjakakursurundamuisoojoototeimashIUs + +2024-01-16 23:10:31,400 (asr_inference:494) INFO: speech length: 294720 +2024-01-16 23:10:31,427 (beam_search:428) INFO: decoder input length: 458 +2024-01-16 23:10:31,427 (beam_search:429) INFO: max output length: 458 +2024-01-16 23:10:31,427 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:33,448 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:33,448 (beam_search:476) INFO: -23.40 * 1.0 = -23.40 for ctc +2024-01-16 23:10:33,448 (beam_search:479) INFO: total log probability: -23.40 +2024-01-16 23:10:33,449 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 23:10:33,449 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:33,449 (beam_search:483) INFO: best hypo: reNpooikaywamiseNgoneNdokarawaisesuebusutoreshimarihoenoshikiNteekyookaijishiehubiyaywaataruzoborunorijuuninosoosaNyotoonushunakerewadararnaitokIteeshimashIta + +2024-01-16 23:10:33,451 (asr_inference:494) INFO: speech length: 159360 +2024-01-16 23:10:33,467 (beam_search:428) INFO: decoder input length: 246 +2024-01-16 23:10:33,467 (beam_search:429) INFO: max output length: 246 +2024-01-16 23:10:33,467 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:34,084 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:34,084 (beam_search:476) INFO: -23.10 * 1.0 = -23.10 for ctc +2024-01-16 23:10:34,085 (beam_search:479) INFO: total log probability: -23.10 +2024-01-16 23:10:34,085 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 23:10:34,085 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:34,085 (beam_search:483) INFO: best hypo: hiechiderrowakeNsashItkakokUbuesteiukoNmarenaseisaiyuopaupauhiichinehochinoroodeshimasaremisU + +2024-01-16 23:10:34,086 (asr_inference:494) INFO: speech length: 173760 +2024-01-16 23:10:34,103 (beam_search:428) INFO: decoder input length: 269 +2024-01-16 23:10:34,103 (beam_search:429) INFO: max output length: 269 +2024-01-16 23:10:34,103 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:34,859 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:34,860 (beam_search:476) INFO: -12.99 * 1.0 = -12.99 for ctc +2024-01-16 23:10:34,860 (beam_search:479) INFO: total log probability: -12.99 +2024-01-16 23:10:34,860 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:10:34,860 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:34,860 (beam_search:483) INFO: best hypo: soredemopautooruukaranarubaisuoukepausubetenohooshIkeomoriaNzeNjoonokeegobonisaishinochuiuoharaimasho + +2024-01-16 23:10:34,862 (asr_inference:494) INFO: speech length: 209280 +2024-01-16 23:10:34,881 (beam_search:428) INFO: decoder input length: 324 +2024-01-16 23:10:34,881 (beam_search:429) INFO: max output length: 324 +2024-01-16 23:10:34,881 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:35,909 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:35,909 (beam_search:476) INFO: -13.64 * 1.0 = -13.64 for ctc +2024-01-16 23:10:35,909 (beam_search:479) INFO: total log probability: -13.64 +2024-01-16 23:10:35,909 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:10:35,909 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:35,909 (beam_search:483) INFO: best hypo: korerawatamanikoNgatsurokaclkatsukumokenobuichitekaigaiapausamazamanateNpoganaraNdeimasUaNzeNyoebukodogadekimasU + +2024-01-16 23:10:35,911 (asr_inference:494) INFO: speech length: 214080 +2024-01-16 23:10:35,931 (beam_search:428) INFO: decoder input length: 332 +2024-01-16 23:10:35,931 (beam_search:429) INFO: max output length: 332 +2024-01-16 23:10:35,931 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:36,997 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:36,997 (beam_search:476) INFO: -29.46 * 1.0 = -29.46 for ctc +2024-01-16 23:10:36,997 (beam_search:479) INFO: total log probability: -29.46 +2024-01-16 23:10:36,997 (beam_search:480) INFO: normalized log probability: -0.27 +2024-01-16 23:10:36,997 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:36,998 (beam_search:483) INFO: best hypo: shiNnomienaichiidasuNpaunaNdopaunafasUtoseNkeukUachijukiupeejiihakUkyuedosuNzaibomadabazerechiebmunodokujuneyosoderaru + +2024-01-16 23:10:36,999 (asr_inference:494) INFO: speech length: 181440 +2024-01-16 23:10:37,016 (beam_search:428) INFO: decoder input length: 281 +2024-01-16 23:10:37,016 (beam_search:429) INFO: max output length: 281 +2024-01-16 23:10:37,016 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:37,896 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:37,896 (beam_search:476) INFO: -9.89 * 1.0 = -9.89 for ctc +2024-01-16 23:10:37,896 (beam_search:479) INFO: total log probability: -9.89 +2024-01-16 23:10:37,896 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:10:37,896 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:37,897 (beam_search:483) INFO: best hypo: konosarisuwagorakUseohajimetosuruseNbtakuyapaueNgakshiredetayoNseyosuotosurutaNkeNtaNiripauhiNpaniriyosareteimasU + +2024-01-16 23:10:37,898 (asr_inference:494) INFO: speech length: 346560 +2024-01-16 23:10:37,929 (beam_search:428) INFO: decoder input length: 539 +2024-01-16 23:10:37,929 (beam_search:429) INFO: max output length: 539 +2024-01-16 23:10:37,929 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:40,549 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:40,549 (beam_search:476) INFO: -26.12 * 1.0 = -26.12 for ctc +2024-01-16 23:10:40,549 (beam_search:479) INFO: total log probability: -26.12 +2024-01-16 23:10:40,549 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 23:10:40,549 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:40,550 (beam_search:483) INFO: best hypo: sakobapaupaubuenosuaidesUkarakojuclkirosaNjuichimairuhanaretaraburatashinaidepaukeNshagojooiNgiNdewarukurisUteinafurunaNdesudepaukerukinazoshigapaunaitoorooseNenoshIuraoseNgeNshimashIta + +2024-01-16 23:10:40,552 (asr_inference:494) INFO: speech length: 180480 +2024-01-16 23:10:40,568 (beam_search:428) INFO: decoder input length: 279 +2024-01-16 23:10:40,568 (beam_search:429) INFO: max output length: 279 +2024-01-16 23:10:40,568 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:41,453 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:41,453 (beam_search:476) INFO: -15.04 * 1.0 = -15.04 for ctc +2024-01-16 23:10:41,453 (beam_search:479) INFO: total log probability: -15.04 +2024-01-16 23:10:41,453 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:10:41,453 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:41,453 (beam_search:483) INFO: best hypo: onayizukuirimashihatonokasooeurebuisunorugaokyuiwakasoorooobaraNshiipaukabenigekitotsushItepaujuunaraniNgashibooshimashIta + +2024-01-16 23:10:41,455 (asr_inference:494) INFO: speech length: 179520 +2024-01-16 23:10:41,472 (beam_search:428) INFO: decoder input length: 278 +2024-01-16 23:10:41,472 (beam_search:429) INFO: max output length: 278 +2024-01-16 23:10:41,472 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:42,330 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:42,330 (beam_search:476) INFO: -12.99 * 1.0 = -12.99 for ctc +2024-01-16 23:10:42,330 (beam_search:479) INFO: total log probability: -12.99 +2024-01-16 23:10:42,330 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:10:42,330 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:42,331 (beam_search:483) INFO: best hypo: ashishIanajoohookkaNwajuugomedodesUmiseNjuuchinehajigatsunishuuNkoshipaumiseNjuunaniesaNgasumarekaitsoshimaseNdeshIta + +2024-01-16 23:10:42,332 (asr_inference:494) INFO: speech length: 416640 +2024-01-16 23:10:42,370 (beam_search:428) INFO: decoder input length: 648 +2024-01-16 23:10:42,370 (beam_search:429) INFO: max output length: 648 +2024-01-16 23:10:42,370 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:45,975 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:45,975 (beam_search:476) INFO: -25.97 * 1.0 = -25.97 for ctc +2024-01-16 23:10:45,975 (beam_search:479) INFO: total log probability: -25.97 +2024-01-16 23:10:45,976 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:10:45,976 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:45,977 (beam_search:483) INFO: best hypo: buuNneetoukotowawashimiomisururateNgonokeoshishibiresUkarakiItaworishimiNiomisururateNgoromeshishuibiesUtoshiyatoshIkokaomishipaunarunakanokatachidepaushakainokiooteegisurushuibitasUtoumeeshinikaNkeeshItemasU + +2024-01-16 23:10:45,978 (asr_inference:494) INFO: speech length: 183360 +2024-01-16 23:10:45,995 (beam_search:428) INFO: decoder input length: 284 +2024-01-16 23:10:45,995 (beam_search:429) INFO: max output length: 284 +2024-01-16 23:10:45,995 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:46,913 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:46,913 (beam_search:476) INFO: -17.03 * 1.0 = -17.03 for ctc +2024-01-16 23:10:46,913 (beam_search:479) INFO: total log probability: -17.03 +2024-01-16 23:10:46,913 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-16 23:10:46,913 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:46,914 (beam_search:483) INFO: best hypo: sujokokoreaisumokaNkootekeyaryooshatajigahasurotokachikoitekimasUwotodoshikarigoinasumunogataruamarudehoNnayoresU + +2024-01-16 23:10:46,915 (asr_inference:494) INFO: speech length: 127680 +2024-01-16 23:10:46,929 (beam_search:428) INFO: decoder input length: 197 +2024-01-16 23:10:46,929 (beam_search:429) INFO: max output length: 197 +2024-01-16 23:10:46,929 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:47,235 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:47,236 (beam_search:476) INFO: -10.35 * 1.0 = -10.35 for ctc +2024-01-16 23:10:47,236 (beam_search:479) INFO: total log probability: -10.35 +2024-01-16 23:10:47,236 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 23:10:47,236 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:47,236 (beam_search:483) INFO: best hypo: terebinoodoonoNtdogeNpatsUkarahakuegagadteimasUes + +2024-01-16 23:10:47,237 (asr_inference:494) INFO: speech length: 189120 +2024-01-16 23:10:47,255 (beam_search:428) INFO: decoder input length: 293 +2024-01-16 23:10:47,255 (beam_search:429) INFO: max output length: 293 +2024-01-16 23:10:47,255 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:47,920 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:47,921 (beam_search:476) INFO: -6.21 * 1.0 = -6.21 for ctc +2024-01-16 23:10:47,921 (beam_search:479) INFO: total log probability: -6.21 +2024-01-16 23:10:47,921 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-16 23:10:47,921 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:47,921 (beam_search:483) INFO: best hypo: noobyooritokoodoonosookaNkaNkeewapaukayakushatashinokeNkyuuourazukeremonodesU + +2024-01-16 23:10:47,922 (asr_inference:494) INFO: speech length: 156480 +2024-01-16 23:10:47,938 (beam_search:428) INFO: decoder input length: 242 +2024-01-16 23:10:47,938 (beam_search:429) INFO: max output length: 242 +2024-01-16 23:10:47,938 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:48,477 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:48,478 (beam_search:476) INFO: -13.15 * 1.0 = -13.15 for ctc +2024-01-16 23:10:48,478 (beam_search:479) INFO: total log probability: -13.15 +2024-01-16 23:10:48,478 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 23:10:48,478 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:48,478 (beam_search:483) INFO: best hypo: seyoobinaibeNtonatokarupanerowapauseNjikeNgehUkazsunokojiNdesisujooshimashIta + +2024-01-16 23:10:48,479 (asr_inference:494) INFO: speech length: 181440 +2024-01-16 23:10:48,496 (beam_search:428) INFO: decoder input length: 281 +2024-01-16 23:10:48,496 (beam_search:429) INFO: max output length: 281 +2024-01-16 23:10:48,496 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:49,365 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:49,365 (beam_search:476) INFO: -13.22 * 1.0 = -13.22 for ctc +2024-01-16 23:10:49,365 (beam_search:479) INFO: total log probability: -13.22 +2024-01-16 23:10:49,365 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:10:49,365 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:49,366 (beam_search:483) INFO: best hypo: seNehapekureNdairaiguNtaygatoojaksurewarepauhaichiakonobyokinikaNkyeesuruoNdanisoougushItakotowarimaseNdeshIta + +2024-01-16 23:10:49,367 (asr_inference:494) INFO: speech length: 172800 +2024-01-16 23:10:49,383 (beam_search:428) INFO: decoder input length: 267 +2024-01-16 23:10:49,384 (beam_search:429) INFO: max output length: 267 +2024-01-16 23:10:49,384 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:50,096 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:50,096 (beam_search:476) INFO: -15.63 * 1.0 = -15.63 for ctc +2024-01-16 23:10:50,096 (beam_search:479) INFO: total log probability: -15.63 +2024-01-16 23:10:50,096 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 23:10:50,096 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:50,096 (beam_search:483) INFO: best hypo: shikashikakuteNnubikecldooshunacltdadohiNdananatsunerikecltooshuneisaNjorokurashItarekimaseNgeshIta + +2024-01-16 23:10:50,098 (asr_inference:494) INFO: speech length: 257280 +2024-01-16 23:10:50,121 (beam_search:428) INFO: decoder input length: 399 +2024-01-16 23:10:50,121 (beam_search:429) INFO: max output length: 399 +2024-01-16 23:10:50,121 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:51,503 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:51,503 (beam_search:476) INFO: -16.25 * 1.0 = -16.25 for ctc +2024-01-16 23:10:51,503 (beam_search:479) INFO: total log probability: -16.25 +2024-01-16 23:10:51,503 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-16 23:10:51,503 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:51,504 (beam_search:483) INFO: best hypo: kajinodewatsuujootokubetsunaiNshakoyapaueNtateimeNtooyooishIteimasUgestougakibuumyakushisezunainitoromaruyonresurutamerdesU + +2024-01-16 23:10:51,505 (asr_inference:494) INFO: speech length: 165120 +2024-01-16 23:10:51,521 (beam_search:428) INFO: decoder input length: 255 +2024-01-16 23:10:51,521 (beam_search:429) INFO: max output length: 255 +2024-01-16 23:10:51,521 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:52,184 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:52,185 (beam_search:476) INFO: -15.34 * 1.0 = -15.34 for ctc +2024-01-16 23:10:52,185 (beam_search:479) INFO: total log probability: -15.34 +2024-01-16 23:10:52,185 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-16 23:10:52,185 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:52,185 (beam_search:483) INFO: best hypo: soredeotookyukaranarabaisokepausutanuhyoashIkeomoripauaNzeNjionokekonisaisheinochuyuoharaimashao + +2024-01-16 23:10:52,187 (asr_inference:494) INFO: speech length: 143040 +2024-01-16 23:10:52,201 (beam_search:428) INFO: decoder input length: 221 +2024-01-16 23:10:52,201 (beam_search:429) INFO: max output length: 221 +2024-01-16 23:10:52,201 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:52,628 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:52,628 (beam_search:476) INFO: -10.46 * 1.0 = -10.46 for ctc +2024-01-16 23:10:52,628 (beam_search:479) INFO: total log probability: -10.46 +2024-01-16 23:10:52,628 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 23:10:52,628 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:52,628 (beam_search:483) INFO: best hypo: pookaregewarimasekorashIttsumushooarideariatarashiishoonomokakedesU + +2024-01-16 23:10:52,630 (asr_inference:494) INFO: speech length: 220800 +2024-01-16 23:10:52,650 (beam_search:428) INFO: decoder input length: 342 +2024-01-16 23:10:52,650 (beam_search:429) INFO: max output length: 342 +2024-01-16 23:10:52,650 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:53,745 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:53,746 (beam_search:476) INFO: -14.02 * 1.0 = -14.02 for ctc +2024-01-16 23:10:53,746 (beam_search:479) INFO: total log probability: -14.02 +2024-01-16 23:10:53,746 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-16 23:10:53,746 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:53,746 (beam_search:483) INFO: best hypo: sawaritowapauaurikanoyaseudoouzupautokonisawaNnaniruyaseudoogusunokaNsasuomokutekitoshItarikurodenoryokoosashimasU + +2024-01-16 23:10:53,748 (asr_inference:494) INFO: speech length: 240960 +2024-01-16 23:10:53,769 (beam_search:428) INFO: decoder input length: 374 +2024-01-16 23:10:53,769 (beam_search:429) INFO: max output length: 374 +2024-01-16 23:10:53,769 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:55,280 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:55,280 (beam_search:476) INFO: -25.27 * 1.0 = -25.27 for ctc +2024-01-16 23:10:55,280 (beam_search:479) INFO: total log probability: -25.27 +2024-01-16 23:10:55,280 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-16 23:10:55,280 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:55,281 (beam_search:483) INFO: best hypo: uunekitabarutokayoumoodaNsurubaiwapauseisumichikaokunishsekudasaikkoorenNnakotsUkIsumNusainimocltomeekyookeruseitsudeaosorujiuodoosooNnanaribikimasU + +2024-01-16 23:10:55,282 (asr_inference:494) INFO: speech length: 196800 +2024-01-16 23:10:55,300 (beam_search:428) INFO: decoder input length: 305 +2024-01-16 23:10:55,300 (beam_search:429) INFO: max output length: 305 +2024-01-16 23:10:55,300 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:56,423 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:56,423 (beam_search:476) INFO: -15.63 * 1.0 = -15.63 for ctc +2024-01-16 23:10:56,423 (beam_search:479) INFO: total log probability: -15.63 +2024-01-16 23:10:56,423 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:10:56,423 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:56,424 (beam_search:483) INFO: best hypo: kokowapauirisunoshokumiNteshiehashaegajiuNtashinodoorodoshIabashunarudepaushakomiNteijireneshookoosegasootosurekatawaookowarhajiberunagayoiusho + +2024-01-16 23:10:56,425 (asr_inference:494) INFO: speech length: 246720 +2024-01-16 23:10:56,448 (beam_search:428) INFO: decoder input length: 383 +2024-01-16 23:10:56,448 (beam_search:429) INFO: max output length: 383 +2024-01-16 23:10:56,448 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:57,712 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:57,712 (beam_search:476) INFO: -13.14 * 1.0 = -13.14 for ctc +2024-01-16 23:10:57,712 (beam_search:479) INFO: total log probability: -13.14 +2024-01-16 23:10:57,713 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-16 23:10:57,713 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:57,713 (beam_search:483) INFO: best hypo: koshiwasakugeNsurusuuchioosarayaseNdeshItakasakugeNwachiewokunokeezaisaNashIuyoimotozuitepaudishisareudarotoonoimashIta + +2024-01-16 23:10:57,715 (asr_inference:494) INFO: speech length: 286080 +2024-01-16 23:10:57,741 (beam_search:428) INFO: decoder input length: 444 +2024-01-16 23:10:57,741 (beam_search:429) INFO: max output length: 444 +2024-01-16 23:10:57,741 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:10:59,280 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:10:59,280 (beam_search:476) INFO: -18.13 * 1.0 = -18.13 for ctc +2024-01-16 23:10:59,280 (beam_search:479) INFO: total log probability: -18.13 +2024-01-16 23:10:59,280 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-16 23:10:59,280 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:10:59,281 (beam_search:483) INFO: best hypo: sainuukokushokuwapaushiNkooNrekoonojukiegasUkunaikarujaashkuyoremohaekoodozurepaunarabikipauyoreshoojoogakasurekotogarimasU + +2024-01-16 23:10:59,282 (asr_inference:494) INFO: speech length: 273600 +2024-01-16 23:10:59,307 (beam_search:428) INFO: decoder input length: 425 +2024-01-16 23:10:59,308 (beam_search:429) INFO: max output length: 425 +2024-01-16 23:10:59,308 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:11:00,928 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:11:00,928 (beam_search:476) INFO: -27.07 * 1.0 = -27.07 for ctc +2024-01-16 23:11:00,928 (beam_search:479) INFO: total log probability: -27.07 +2024-01-16 23:11:00,928 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-16 23:11:00,928 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:11:00,929 (beam_search:483) INFO: best hypo: kyinoonoasapautorukonogajiaNtepkunokeesasuohomNworepaujidooshabakuranobakasuriorikyieekaafUtaregashiboshipaupaurwushiowashawanijuuniyokoaimashIta + +2024-01-16 23:11:00,930 (asr_inference:494) INFO: speech length: 168960 +2024-01-16 23:11:00,946 (beam_search:428) INFO: decoder input length: 261 +2024-01-16 23:11:00,947 (beam_search:429) INFO: max output length: 261 +2024-01-16 23:11:00,947 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:11:01,641 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:11:01,641 (beam_search:476) INFO: -8.14 * 1.0 = -8.14 for ctc +2024-01-16 23:11:01,641 (beam_search:479) INFO: total log probability: -8.14 +2024-01-16 23:11:01,641 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-16 23:11:01,641 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:11:01,641 (beam_search:483) INFO: best hypo: shokubutsuaniNgiNgasuusaNzootsUkuriniNgeNgakaclkoitoshItehakidasurnisaNkacltaNsootorikoNdeimasU + +2024-01-16 23:11:01,643 (asr_inference:494) INFO: speech length: 212160 +2024-01-16 23:11:01,663 (beam_search:428) INFO: decoder input length: 329 +2024-01-16 23:11:01,663 (beam_search:429) INFO: max output length: 329 +2024-01-16 23:11:01,663 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:11:02,630 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:11:02,630 (beam_search:476) INFO: -11.33 * 1.0 = -11.33 for ctc +2024-01-16 23:11:02,630 (beam_search:479) INFO: total log probability: -11.33 +2024-01-16 23:11:02,630 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:11:02,630 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:11:02,630 (beam_search:483) INFO: best hypo: seNpakurebushioyusoosurunowapauumiyokoitepaukitoayabushueotarireoyisoosuruepaumocltomokoorisekinahoohooresU + +2024-01-16 23:11:02,632 (asr_inference:494) INFO: speech length: 295680 +2024-01-16 23:11:02,659 (beam_search:428) INFO: decoder input length: 459 +2024-01-16 23:11:02,659 (beam_search:429) INFO: max output length: 459 +2024-01-16 23:11:02,659 (beam_search:430) INFO: min output length: 0 +2024-01-16 23:11:04,459 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-16 23:11:04,459 (beam_search:476) INFO: -14.71 * 1.0 = -14.71 for ctc +2024-01-16 23:11:04,460 (beam_search:479) INFO: total log probability: -14.71 +2024-01-16 23:11:04,460 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-16 23:11:04,460 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-16 23:11:04,460 (beam_search:483) INFO: best hypo: karihoruniashuunoaanorudooshuwarutsuneclkachishiwabooryokUtekinabideougeemuwomiseeneNshanihaNbayaeNtasudekotookiNisurohoowaNnishomeshimashIta + +# Accounting: time=51 threads=1 +# Ended (code 0) at Tue Jan 16 23:11:05 CST 2024, elapsed time 51 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/calculate_rtf.log b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/calculate_rtf.log new file mode 100644 index 0000000000000000000000000000000000000000..39b062abf167f3c605ec9a3e5bada364270632d0 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/calculate_rtf.log @@ -0,0 +1,9 @@ +# pyscripts/utils/calculate_rtf.py --log-dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir --log-name asr_inference --input-shift 0.0625 --start-times-marker "speech length" --end-times-marker "best hypo" --inf-num 1 +# Started at Tue Jan 16 23:11:05 CST 2024 +# +Total audio duration: 1194.576 [sec] +Total decoding time: 77.987 [sec] +RTF: 0.065 +Latency: 487.419 [ms/sentence] +# Accounting: time=0 threads=1 +# Ended (code 0) at Tue Jan 16 23:11:05 CST 2024, elapsed time 0 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.1.scp b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.1.scp new file mode 100644 index 0000000000000000000000000000000000000000..42293b0b5c7a906c24e810ad6718f3700cf038da --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.1.scp @@ -0,0 +1,40 @@ +cv_jpn_000800 dump/raw/test_10min_jpn/data/format.1/cv_jpn_000800.flac +cv_jpn_000801 dump/raw/test_10min_jpn/data/format.1/cv_jpn_000801.flac +cv_jpn_000802 dump/raw/test_10min_jpn/data/format.1/cv_jpn_000802.flac +cv_jpn_000803 dump/raw/test_10min_jpn/data/format.1/cv_jpn_000803.flac +cv_jpn_000804 dump/raw/test_10min_jpn/data/format.1/cv_jpn_000804.flac +cv_jpn_000805 dump/raw/test_10min_jpn/data/format.2/cv_jpn_000805.flac +cv_jpn_000806 dump/raw/test_10min_jpn/data/format.2/cv_jpn_000806.flac +cv_jpn_000807 dump/raw/test_10min_jpn/data/format.2/cv_jpn_000807.flac +cv_jpn_000808 dump/raw/test_10min_jpn/data/format.2/cv_jpn_000808.flac +cv_jpn_000809 dump/raw/test_10min_jpn/data/format.2/cv_jpn_000809.flac +cv_jpn_000810 dump/raw/test_10min_jpn/data/format.3/cv_jpn_000810.flac +cv_jpn_000811 dump/raw/test_10min_jpn/data/format.3/cv_jpn_000811.flac +cv_jpn_000812 dump/raw/test_10min_jpn/data/format.3/cv_jpn_000812.flac +cv_jpn_000813 dump/raw/test_10min_jpn/data/format.3/cv_jpn_000813.flac +cv_jpn_000814 dump/raw/test_10min_jpn/data/format.3/cv_jpn_000814.flac +cv_jpn_000815 dump/raw/test_10min_jpn/data/format.4/cv_jpn_000815.flac +cv_jpn_000816 dump/raw/test_10min_jpn/data/format.4/cv_jpn_000816.flac +cv_jpn_000817 dump/raw/test_10min_jpn/data/format.4/cv_jpn_000817.flac +cv_jpn_000818 dump/raw/test_10min_jpn/data/format.4/cv_jpn_000818.flac +cv_jpn_000819 dump/raw/test_10min_jpn/data/format.4/cv_jpn_000819.flac +cv_jpn_000820 dump/raw/test_10min_jpn/data/format.5/cv_jpn_000820.flac +cv_jpn_000821 dump/raw/test_10min_jpn/data/format.5/cv_jpn_000821.flac +cv_jpn_000822 dump/raw/test_10min_jpn/data/format.5/cv_jpn_000822.flac +cv_jpn_000823 dump/raw/test_10min_jpn/data/format.5/cv_jpn_000823.flac +cv_jpn_000824 dump/raw/test_10min_jpn/data/format.5/cv_jpn_000824.flac +cv_jpn_000825 dump/raw/test_10min_jpn/data/format.6/cv_jpn_000825.flac +cv_jpn_000826 dump/raw/test_10min_jpn/data/format.6/cv_jpn_000826.flac +cv_jpn_000827 dump/raw/test_10min_jpn/data/format.6/cv_jpn_000827.flac +cv_jpn_000828 dump/raw/test_10min_jpn/data/format.6/cv_jpn_000828.flac +cv_jpn_000829 dump/raw/test_10min_jpn/data/format.6/cv_jpn_000829.flac +cv_jpn_000830 dump/raw/test_10min_jpn/data/format.7/cv_jpn_000830.flac +cv_jpn_000831 dump/raw/test_10min_jpn/data/format.7/cv_jpn_000831.flac +cv_jpn_000832 dump/raw/test_10min_jpn/data/format.7/cv_jpn_000832.flac +cv_jpn_000833 dump/raw/test_10min_jpn/data/format.7/cv_jpn_000833.flac +cv_jpn_000834 dump/raw/test_10min_jpn/data/format.7/cv_jpn_000834.flac +cv_jpn_000835 dump/raw/test_10min_jpn/data/format.8/cv_jpn_000835.flac +cv_jpn_000836 dump/raw/test_10min_jpn/data/format.8/cv_jpn_000836.flac +cv_jpn_000837 dump/raw/test_10min_jpn/data/format.8/cv_jpn_000837.flac +cv_jpn_000838 dump/raw/test_10min_jpn/data/format.8/cv_jpn_000838.flac +cv_jpn_000839 dump/raw/test_10min_jpn/data/format.8/cv_jpn_000839.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.2.scp b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.2.scp new file mode 100644 index 0000000000000000000000000000000000000000..98b2b6b607548b58919c8889421486630cf3de23 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.2.scp @@ -0,0 +1,40 @@ +cv_jpn_000840 dump/raw/test_10min_jpn/data/format.9/cv_jpn_000840.flac +cv_jpn_000841 dump/raw/test_10min_jpn/data/format.9/cv_jpn_000841.flac +cv_jpn_000842 dump/raw/test_10min_jpn/data/format.9/cv_jpn_000842.flac +cv_jpn_000843 dump/raw/test_10min_jpn/data/format.9/cv_jpn_000843.flac +cv_jpn_000844 dump/raw/test_10min_jpn/data/format.9/cv_jpn_000844.flac +cv_jpn_000845 dump/raw/test_10min_jpn/data/format.10/cv_jpn_000845.flac +cv_jpn_000846 dump/raw/test_10min_jpn/data/format.10/cv_jpn_000846.flac +cv_jpn_000847 dump/raw/test_10min_jpn/data/format.10/cv_jpn_000847.flac +cv_jpn_000848 dump/raw/test_10min_jpn/data/format.10/cv_jpn_000848.flac +cv_jpn_000849 dump/raw/test_10min_jpn/data/format.10/cv_jpn_000849.flac +cv_jpn_000850 dump/raw/test_10min_jpn/data/format.11/cv_jpn_000850.flac +cv_jpn_000851 dump/raw/test_10min_jpn/data/format.11/cv_jpn_000851.flac +cv_jpn_000852 dump/raw/test_10min_jpn/data/format.11/cv_jpn_000852.flac +cv_jpn_000853 dump/raw/test_10min_jpn/data/format.11/cv_jpn_000853.flac +cv_jpn_000854 dump/raw/test_10min_jpn/data/format.11/cv_jpn_000854.flac +cv_jpn_000855 dump/raw/test_10min_jpn/data/format.12/cv_jpn_000855.flac +cv_jpn_000856 dump/raw/test_10min_jpn/data/format.12/cv_jpn_000856.flac +cv_jpn_000857 dump/raw/test_10min_jpn/data/format.12/cv_jpn_000857.flac +cv_jpn_000858 dump/raw/test_10min_jpn/data/format.12/cv_jpn_000858.flac +cv_jpn_000859 dump/raw/test_10min_jpn/data/format.12/cv_jpn_000859.flac +cv_jpn_000860 dump/raw/test_10min_jpn/data/format.13/cv_jpn_000860.flac +cv_jpn_000861 dump/raw/test_10min_jpn/data/format.13/cv_jpn_000861.flac +cv_jpn_000862 dump/raw/test_10min_jpn/data/format.13/cv_jpn_000862.flac +cv_jpn_000863 dump/raw/test_10min_jpn/data/format.13/cv_jpn_000863.flac +cv_jpn_000864 dump/raw/test_10min_jpn/data/format.13/cv_jpn_000864.flac +cv_jpn_000865 dump/raw/test_10min_jpn/data/format.14/cv_jpn_000865.flac +cv_jpn_000866 dump/raw/test_10min_jpn/data/format.14/cv_jpn_000866.flac +cv_jpn_000867 dump/raw/test_10min_jpn/data/format.14/cv_jpn_000867.flac +cv_jpn_000868 dump/raw/test_10min_jpn/data/format.14/cv_jpn_000868.flac +cv_jpn_000869 dump/raw/test_10min_jpn/data/format.14/cv_jpn_000869.flac +cv_jpn_000870 dump/raw/test_10min_jpn/data/format.15/cv_jpn_000870.flac +cv_jpn_000871 dump/raw/test_10min_jpn/data/format.15/cv_jpn_000871.flac +cv_jpn_000872 dump/raw/test_10min_jpn/data/format.15/cv_jpn_000872.flac +cv_jpn_000873 dump/raw/test_10min_jpn/data/format.15/cv_jpn_000873.flac +cv_jpn_000874 dump/raw/test_10min_jpn/data/format.15/cv_jpn_000874.flac +cv_jpn_000875 dump/raw/test_10min_jpn/data/format.16/cv_jpn_000875.flac +cv_jpn_000876 dump/raw/test_10min_jpn/data/format.16/cv_jpn_000876.flac +cv_jpn_000877 dump/raw/test_10min_jpn/data/format.16/cv_jpn_000877.flac +cv_jpn_000878 dump/raw/test_10min_jpn/data/format.16/cv_jpn_000878.flac +cv_jpn_000879 dump/raw/test_10min_jpn/data/format.16/cv_jpn_000879.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.3.scp b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.3.scp new file mode 100644 index 0000000000000000000000000000000000000000..87db93c6dc09c505b842ac6c755a8d107c642bf0 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.3.scp @@ -0,0 +1,40 @@ +cv_jpn_000880 dump/raw/test_10min_jpn/data/format.17/cv_jpn_000880.flac +cv_jpn_000881 dump/raw/test_10min_jpn/data/format.17/cv_jpn_000881.flac +cv_jpn_000882 dump/raw/test_10min_jpn/data/format.17/cv_jpn_000882.flac +cv_jpn_000883 dump/raw/test_10min_jpn/data/format.17/cv_jpn_000883.flac +cv_jpn_000884 dump/raw/test_10min_jpn/data/format.17/cv_jpn_000884.flac +cv_jpn_000885 dump/raw/test_10min_jpn/data/format.18/cv_jpn_000885.flac +cv_jpn_000886 dump/raw/test_10min_jpn/data/format.18/cv_jpn_000886.flac +cv_jpn_000887 dump/raw/test_10min_jpn/data/format.18/cv_jpn_000887.flac +cv_jpn_000888 dump/raw/test_10min_jpn/data/format.18/cv_jpn_000888.flac +cv_jpn_000889 dump/raw/test_10min_jpn/data/format.18/cv_jpn_000889.flac +cv_jpn_000890 dump/raw/test_10min_jpn/data/format.19/cv_jpn_000890.flac +cv_jpn_000891 dump/raw/test_10min_jpn/data/format.19/cv_jpn_000891.flac +cv_jpn_000892 dump/raw/test_10min_jpn/data/format.19/cv_jpn_000892.flac +cv_jpn_000893 dump/raw/test_10min_jpn/data/format.19/cv_jpn_000893.flac +cv_jpn_000894 dump/raw/test_10min_jpn/data/format.19/cv_jpn_000894.flac +cv_jpn_000895 dump/raw/test_10min_jpn/data/format.20/cv_jpn_000895.flac +cv_jpn_000896 dump/raw/test_10min_jpn/data/format.20/cv_jpn_000896.flac +cv_jpn_000897 dump/raw/test_10min_jpn/data/format.20/cv_jpn_000897.flac +cv_jpn_000898 dump/raw/test_10min_jpn/data/format.20/cv_jpn_000898.flac +cv_jpn_000899 dump/raw/test_10min_jpn/data/format.20/cv_jpn_000899.flac +cv_jpn_000900 dump/raw/test_10min_jpn/data/format.21/cv_jpn_000900.flac +cv_jpn_000901 dump/raw/test_10min_jpn/data/format.21/cv_jpn_000901.flac +cv_jpn_000902 dump/raw/test_10min_jpn/data/format.21/cv_jpn_000902.flac +cv_jpn_000903 dump/raw/test_10min_jpn/data/format.21/cv_jpn_000903.flac +cv_jpn_000904 dump/raw/test_10min_jpn/data/format.21/cv_jpn_000904.flac +cv_jpn_000905 dump/raw/test_10min_jpn/data/format.22/cv_jpn_000905.flac +cv_jpn_000906 dump/raw/test_10min_jpn/data/format.22/cv_jpn_000906.flac +cv_jpn_000907 dump/raw/test_10min_jpn/data/format.22/cv_jpn_000907.flac +cv_jpn_000908 dump/raw/test_10min_jpn/data/format.22/cv_jpn_000908.flac +cv_jpn_000909 dump/raw/test_10min_jpn/data/format.22/cv_jpn_000909.flac +cv_jpn_000910 dump/raw/test_10min_jpn/data/format.23/cv_jpn_000910.flac +cv_jpn_000911 dump/raw/test_10min_jpn/data/format.23/cv_jpn_000911.flac +cv_jpn_000912 dump/raw/test_10min_jpn/data/format.23/cv_jpn_000912.flac +cv_jpn_000913 dump/raw/test_10min_jpn/data/format.23/cv_jpn_000913.flac +fleurs_jpn_000346 dump/raw/test_10min_jpn/data/format.23/fleurs_jpn_000346.flac +fleurs_jpn_000347 dump/raw/test_10min_jpn/data/format.24/fleurs_jpn_000347.flac +fleurs_jpn_000348 dump/raw/test_10min_jpn/data/format.24/fleurs_jpn_000348.flac +fleurs_jpn_000349 dump/raw/test_10min_jpn/data/format.24/fleurs_jpn_000349.flac +fleurs_jpn_000350 dump/raw/test_10min_jpn/data/format.24/fleurs_jpn_000350.flac +fleurs_jpn_000351 dump/raw/test_10min_jpn/data/format.24/fleurs_jpn_000351.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.4.scp b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.4.scp new file mode 100644 index 0000000000000000000000000000000000000000..39e28bcaf015a7679f4054fc61621670dc74ddc2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.4.scp @@ -0,0 +1,40 @@ +fleurs_jpn_000352 dump/raw/test_10min_jpn/data/format.25/fleurs_jpn_000352.flac +fleurs_jpn_000353 dump/raw/test_10min_jpn/data/format.25/fleurs_jpn_000353.flac +fleurs_jpn_000354 dump/raw/test_10min_jpn/data/format.25/fleurs_jpn_000354.flac +fleurs_jpn_000355 dump/raw/test_10min_jpn/data/format.25/fleurs_jpn_000355.flac +fleurs_jpn_000356 dump/raw/test_10min_jpn/data/format.25/fleurs_jpn_000356.flac +fleurs_jpn_000357 dump/raw/test_10min_jpn/data/format.26/fleurs_jpn_000357.flac +fleurs_jpn_000358 dump/raw/test_10min_jpn/data/format.26/fleurs_jpn_000358.flac +fleurs_jpn_000359 dump/raw/test_10min_jpn/data/format.26/fleurs_jpn_000359.flac +fleurs_jpn_000360 dump/raw/test_10min_jpn/data/format.26/fleurs_jpn_000360.flac +fleurs_jpn_000361 dump/raw/test_10min_jpn/data/format.26/fleurs_jpn_000361.flac +fleurs_jpn_000362 dump/raw/test_10min_jpn/data/format.27/fleurs_jpn_000362.flac +fleurs_jpn_000363 dump/raw/test_10min_jpn/data/format.27/fleurs_jpn_000363.flac +fleurs_jpn_000364 dump/raw/test_10min_jpn/data/format.27/fleurs_jpn_000364.flac +fleurs_jpn_000365 dump/raw/test_10min_jpn/data/format.27/fleurs_jpn_000365.flac +fleurs_jpn_000366 dump/raw/test_10min_jpn/data/format.27/fleurs_jpn_000366.flac +fleurs_jpn_000367 dump/raw/test_10min_jpn/data/format.28/fleurs_jpn_000367.flac +fleurs_jpn_000368 dump/raw/test_10min_jpn/data/format.28/fleurs_jpn_000368.flac +fleurs_jpn_000369 dump/raw/test_10min_jpn/data/format.28/fleurs_jpn_000369.flac +fleurs_jpn_000370 dump/raw/test_10min_jpn/data/format.28/fleurs_jpn_000370.flac +fleurs_jpn_000371 dump/raw/test_10min_jpn/data/format.28/fleurs_jpn_000371.flac +fleurs_jpn_000372 dump/raw/test_10min_jpn/data/format.29/fleurs_jpn_000372.flac +fleurs_jpn_000373 dump/raw/test_10min_jpn/data/format.29/fleurs_jpn_000373.flac +fleurs_jpn_000374 dump/raw/test_10min_jpn/data/format.29/fleurs_jpn_000374.flac +fleurs_jpn_000375 dump/raw/test_10min_jpn/data/format.29/fleurs_jpn_000375.flac +fleurs_jpn_000376 dump/raw/test_10min_jpn/data/format.29/fleurs_jpn_000376.flac +fleurs_jpn_000377 dump/raw/test_10min_jpn/data/format.30/fleurs_jpn_000377.flac +fleurs_jpn_000378 dump/raw/test_10min_jpn/data/format.30/fleurs_jpn_000378.flac +fleurs_jpn_000379 dump/raw/test_10min_jpn/data/format.30/fleurs_jpn_000379.flac +fleurs_jpn_000380 dump/raw/test_10min_jpn/data/format.30/fleurs_jpn_000380.flac +fleurs_jpn_000381 dump/raw/test_10min_jpn/data/format.30/fleurs_jpn_000381.flac +fleurs_jpn_000382 dump/raw/test_10min_jpn/data/format.31/fleurs_jpn_000382.flac +fleurs_jpn_000383 dump/raw/test_10min_jpn/data/format.31/fleurs_jpn_000383.flac +fleurs_jpn_000384 dump/raw/test_10min_jpn/data/format.31/fleurs_jpn_000384.flac +fleurs_jpn_000385 dump/raw/test_10min_jpn/data/format.31/fleurs_jpn_000385.flac +fleurs_jpn_000386 dump/raw/test_10min_jpn/data/format.31/fleurs_jpn_000386.flac +fleurs_jpn_000387 dump/raw/test_10min_jpn/data/format.32/fleurs_jpn_000387.flac +fleurs_jpn_000388 dump/raw/test_10min_jpn/data/format.32/fleurs_jpn_000388.flac +fleurs_jpn_000389 dump/raw/test_10min_jpn/data/format.32/fleurs_jpn_000389.flac +fleurs_jpn_000390 dump/raw/test_10min_jpn/data/format.32/fleurs_jpn_000390.flac +fleurs_jpn_000391 dump/raw/test_10min_jpn/data/format.32/fleurs_jpn_000391.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..a1999ffeed9f43ecb9eebaec94ea40a05a50485e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/score @@ -0,0 +1,40 @@ +cv_jpn_000800 tensor(-9.3796) +cv_jpn_000801 tensor(-13.5940) +cv_jpn_000802 tensor(-8.4504) +cv_jpn_000803 tensor(-9.1606) +cv_jpn_000804 tensor(-3.8179) +cv_jpn_000805 tensor(-2.7015) +cv_jpn_000806 tensor(-6.1459) +cv_jpn_000807 tensor(-5.3374) +cv_jpn_000808 tensor(-1.5552) +cv_jpn_000809 tensor(-9.5867) +cv_jpn_000810 tensor(-5.3105) +cv_jpn_000811 tensor(-1.6416) +cv_jpn_000812 tensor(-6.9316) +cv_jpn_000813 tensor(-4.7079) +cv_jpn_000814 tensor(-4.0117) +cv_jpn_000815 tensor(-5.2686) +cv_jpn_000816 tensor(-6.3233) +cv_jpn_000817 tensor(-2.9187) +cv_jpn_000818 tensor(-1.1429) +cv_jpn_000819 tensor(-0.2917) +cv_jpn_000820 tensor(-1.2863) +cv_jpn_000821 tensor(-0.9688) +cv_jpn_000822 tensor(-0.9036) +cv_jpn_000823 tensor(-6.3309) +cv_jpn_000824 tensor(-4.9260) +cv_jpn_000825 tensor(-7.8469) +cv_jpn_000826 tensor(-7.6292) +cv_jpn_000827 tensor(-2.7020) +cv_jpn_000828 tensor(-3.1539) +cv_jpn_000829 tensor(-1.5737) +cv_jpn_000830 tensor(-0.7087) +cv_jpn_000831 tensor(-0.9074) +cv_jpn_000832 tensor(-3.5826) +cv_jpn_000833 tensor(-1.4494) +cv_jpn_000834 tensor(-3.4315) +cv_jpn_000835 tensor(-3.8001) +cv_jpn_000836 tensor(-1.1456) +cv_jpn_000837 tensor(-1.2321) +cv_jpn_000838 tensor(-3.0537) +cv_jpn_000839 tensor(-3.1044) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..642911e08846bdabab380c60b77e6356b9403cbd --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/text @@ -0,0 +1,40 @@ +cv_jpn_000800 k a k o t o m i r a i t o d o u j u N t e k i j i k o d o o i ts u n a r u g a i w e n i pau sh I k i t e k i n a N o d e a r +cv_jpn_000801 s e k a y o k e e s e e s u r u t o t o m u n i pau j i g k o j i sh i y o k e e s e s e r u ts s o o d o t e k i s e k a i n s o o z o o t e k i y o t o sh I t e pau k o m u ts u g a k o u ts u d e a r u +cv_jpn_000802 p a s o k o N n e g e e m i a r u I t o n a h f u e t e k i t e +cv_jpn_000803 k a a k u n o sh i m e s a t a r a sh i i j i j i ts u a t a r a s i k a N n e N k a N ky o sh i h a i n a t a r a sh i k a n o s e o m o cl t e pau n a n i o h a j i m e r u k a w a +cv_jpn_000804 o m o sh i r o e n o n i pau r o o d o n a k a s u g i t e pau d a r u i +cv_jpn_000805 k o r e j o o sh u u h a N t o i n a +cv_jpn_000806 k a g a k U sh a m o s e k a y o h o o k a ts s e k i n i pau t o o ch I t e k i n i s a ts u m e sh u o t o sh I t e i r u +cv_jpn_000807 h a I ts u n i ts U e m a r a N +cv_jpn_000808 sh i cl k a r sh t e k u d a s a i +cv_jpn_000809 w a t a sh i w a a m i g i n o n o t o k i pau d e k i sh I t e k i s e i m e e n o j i k a k u t o y u g o t o k i m o n o m e N sh o o h o o t e k i pau r o N b e t a y u u n o d e a r u +cv_jpn_000810 w a t a sh i w a pau sh a k a i k e e s e e n o k o N t e N n i w a d e y o o n u s o s u t e k i n a m o n o g a pau h a t a r a i t e i r u t o m o +cv_jpn_000811 n a n i o s u r u ts u m o r i d a cl t a n o k a +cv_jpn_000812 k o b u ts u t e k I t a g a j i k o h I t e e t e k i n i t a N n i pau t e N sh u u g o o t e k i n i k a N g a r a r u r u t o k i s o r e g a b u ts u r i t e k i t e k a i d e a r u +cv_jpn_000813 a n e g a z u cl t a n o d e y a cl k i n o sh i r a i g a r i m a s e N d e sh I t a +cv_jpn_000814 k o r e w a N r i h o N d e u t e i n a i t a b e m u n o d e s U +cv_jpn_000815 w o t a sh i w a h e N sh u u e i n o y o o n e N k u r a e w a y a cl t a cl t o o m o +cv_jpn_000816 e i s a N n i i k o n o k o t o b p a n o r i m i o o o sh i a m a sh I t a +cv_jpn_000817 k a s e g a ts U s u y o i h i u w a t e N n i s u g a d e k i m a s e N +cv_jpn_000818 i ch i i +cv_jpn_000819 h a i +cv_jpn_000820 o n o e +cv_jpn_000821 d e i +cv_jpn_000822 a t o k i +cv_jpn_000823 m i r u t o y u k o t a t o pau h a t a r a k U t o i u k o t o g a sh k a b u N d i t e k i n a k e r e b a n a r a n a i +cv_jpn_000824 w a r e w a r e o t a m a sh i i n o z u o k a r a u g u k a s u m o n o d e n a k e r e b a n a r a n a i +cv_jpn_000825 s e t a i b e N sh o o h o o d e k i n a r u g a i u e n i i d e a t e k I ch o cl k a N t e k i k e e k i y g a cl U k u m a r e r u n o d e a r u +cv_jpn_000826 t o k o m a d e m o t a t o i ch i t o n a s o o g o sh I e e t e k i n a z e cl t a i m u j u N t e k i j i k o o i ts u n o s e k a i n i sh I t e +cv_jpn_000827 sh i k a r u n i n i N g e N t o k a N ky o o t o r o k a N k e e w a m o t o k o o n o k a N k e d e a r i +cv_jpn_000828 i i s a N n i k o n o k o t o b a n o i m i y o o sh i y a m a sh I t a +cv_jpn_000829 k e e k i g a n a n a ts s a r i m a s U +cv_jpn_000830 k o ch i r a b a k o b i y a sh i s a N d e s U +cv_jpn_000831 m o sh i m a sh i +cv_jpn_000832 k o k o w a o k I k U t e n i g u y a k a n a m a ch i d e s U +cv_jpn_000833 s o n o ch i k a i y a k U s a r e r u k a r a i s o g e pau +cv_jpn_000834 a m a s a g a b k u s a e i r a r e t e t e ch o o d o i +cv_jpn_000835 h o k e N sh I ts u e n o d o o a a k e t a +cv_jpn_000836 m o d a n i o w a cl t e m o k i n i sh i n a i +cv_jpn_000837 a r i g a cl t a y a +cv_jpn_000838 i t o o g a r o k u d a t o j i k a N u w o s u r e t e t a n o sh i m e r u +cv_jpn_000839 k a k a k u w a g i z u ts U k a s a r e r u n i o j i t e j o o sh I k i n o u ch i n i h a i cl t e y u k u diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..642911e08846bdabab380c60b77e6356b9403cbd --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/token @@ -0,0 +1,40 @@ +cv_jpn_000800 k a k o t o m i r a i t o d o u j u N t e k i j i k o d o o i ts u n a r u g a i w e n i pau sh I k i t e k i n a N o d e a r +cv_jpn_000801 s e k a y o k e e s e e s u r u t o t o m u n i pau j i g k o j i sh i y o k e e s e s e r u ts s o o d o t e k i s e k a i n s o o z o o t e k i y o t o sh I t e pau k o m u ts u g a k o u ts u d e a r u +cv_jpn_000802 p a s o k o N n e g e e m i a r u I t o n a h f u e t e k i t e +cv_jpn_000803 k a a k u n o sh i m e s a t a r a sh i i j i j i ts u a t a r a s i k a N n e N k a N ky o sh i h a i n a t a r a sh i k a n o s e o m o cl t e pau n a n i o h a j i m e r u k a w a +cv_jpn_000804 o m o sh i r o e n o n i pau r o o d o n a k a s u g i t e pau d a r u i +cv_jpn_000805 k o r e j o o sh u u h a N t o i n a +cv_jpn_000806 k a g a k U sh a m o s e k a y o h o o k a ts s e k i n i pau t o o ch I t e k i n i s a ts u m e sh u o t o sh I t e i r u +cv_jpn_000807 h a I ts u n i ts U e m a r a N +cv_jpn_000808 sh i cl k a r sh t e k u d a s a i +cv_jpn_000809 w a t a sh i w a a m i g i n o n o t o k i pau d e k i sh I t e k i s e i m e e n o j i k a k u t o y u g o t o k i m o n o m e N sh o o h o o t e k i pau r o N b e t a y u u n o d e a r u +cv_jpn_000810 w a t a sh i w a pau sh a k a i k e e s e e n o k o N t e N n i w a d e y o o n u s o s u t e k i n a m o n o g a pau h a t a r a i t e i r u t o m o +cv_jpn_000811 n a n i o s u r u ts u m o r i d a cl t a n o k a +cv_jpn_000812 k o b u ts u t e k I t a g a j i k o h I t e e t e k i n i t a N n i pau t e N sh u u g o o t e k i n i k a N g a r a r u r u t o k i s o r e g a b u ts u r i t e k i t e k a i d e a r u +cv_jpn_000813 a n e g a z u cl t a n o d e y a cl k i n o sh i r a i g a r i m a s e N d e sh I t a +cv_jpn_000814 k o r e w a N r i h o N d e u t e i n a i t a b e m u n o d e s U +cv_jpn_000815 w o t a sh i w a h e N sh u u e i n o y o o n e N k u r a e w a y a cl t a cl t o o m o +cv_jpn_000816 e i s a N n i i k o n o k o t o b p a n o r i m i o o o sh i a m a sh I t a +cv_jpn_000817 k a s e g a ts U s u y o i h i u w a t e N n i s u g a d e k i m a s e N +cv_jpn_000818 i ch i i +cv_jpn_000819 h a i +cv_jpn_000820 o n o e +cv_jpn_000821 d e i +cv_jpn_000822 a t o k i +cv_jpn_000823 m i r u t o y u k o t a t o pau h a t a r a k U t o i u k o t o g a sh k a b u N d i t e k i n a k e r e b a n a r a n a i +cv_jpn_000824 w a r e w a r e o t a m a sh i i n o z u o k a r a u g u k a s u m o n o d e n a k e r e b a n a r a n a i +cv_jpn_000825 s e t a i b e N sh o o h o o d e k i n a r u g a i u e n i i d e a t e k I ch o cl k a N t e k i k e e k i y g a cl U k u m a r e r u n o d e a r u +cv_jpn_000826 t o k o m a d e m o t a t o i ch i t o n a s o o g o sh I e e t e k i n a z e cl t a i m u j u N t e k i j i k o o i ts u n o s e k a i n i sh I t e +cv_jpn_000827 sh i k a r u n i n i N g e N t o k a N ky o o t o r o k a N k e e w a m o t o k o o n o k a N k e d e a r i +cv_jpn_000828 i i s a N n i k o n o k o t o b a n o i m i y o o sh i y a m a sh I t a +cv_jpn_000829 k e e k i g a n a n a ts s a r i m a s U +cv_jpn_000830 k o ch i r a b a k o b i y a sh i s a N d e s U +cv_jpn_000831 m o sh i m a sh i +cv_jpn_000832 k o k o w a o k I k U t e n i g u y a k a n a m a ch i d e s U +cv_jpn_000833 s o n o ch i k a i y a k U s a r e r u k a r a i s o g e pau +cv_jpn_000834 a m a s a g a b k u s a e i r a r e t e t e ch o o d o i +cv_jpn_000835 h o k e N sh I ts u e n o d o o a a k e t a +cv_jpn_000836 m o d a n i o w a cl t e m o k i n i sh i n a i +cv_jpn_000837 a r i g a cl t a y a +cv_jpn_000838 i t o o g a r o k u d a t o j i k a N u w o s u r e t e t a n o sh i m e r u +cv_jpn_000839 k a k a k u w a g i z u ts U k a s a r e r u n i o j i t e j o o sh I k i n o u ch i n i h a i cl t e y u k u diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..1599bda310089914b1d84b32350697718a7f22d5 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/token_int @@ -0,0 +1,40 @@ +cv_jpn_000800 6 2 6 3 8 3 11 4 10 2 4 8 3 14 3 7 22 7 13 8 5 6 4 22 4 6 3 14 3 3 4 26 7 9 2 10 7 16 2 4 17 5 9 4 20 15 19 6 4 8 5 6 4 9 2 13 3 14 5 2 10 +cv_jpn_000801 12 5 6 2 23 3 6 5 5 12 5 5 12 7 10 7 8 3 8 3 11 7 9 4 20 22 4 16 6 3 22 4 15 4 23 3 6 5 5 12 5 12 5 10 7 26 12 3 3 14 3 8 5 6 4 12 5 6 2 4 9 12 3 3 28 3 3 8 5 6 4 23 3 8 3 15 19 8 5 20 6 3 11 7 26 7 16 2 6 3 7 26 7 14 5 2 10 7 +cv_jpn_000802 30 2 12 3 6 3 13 9 5 16 5 5 11 4 2 10 7 19 8 3 9 2 24 31 7 5 8 5 6 4 8 5 +cv_jpn_000803 6 2 2 6 7 9 3 15 4 11 5 12 2 8 2 10 2 15 4 4 22 4 22 4 26 7 2 8 2 10 2 12 4 6 2 13 9 5 13 6 2 13 29 3 15 4 24 2 4 9 2 8 2 10 2 15 4 6 2 9 3 12 5 3 11 3 21 8 5 20 9 2 9 4 3 24 2 22 4 11 5 10 7 6 2 17 2 +cv_jpn_000804 3 11 3 15 4 10 3 5 9 3 9 4 20 10 3 3 14 3 9 2 6 2 12 7 16 4 8 5 20 14 2 10 7 4 +cv_jpn_000805 6 3 10 5 22 3 3 15 7 7 24 2 13 8 3 4 9 2 +cv_jpn_000806 6 2 16 2 6 18 15 2 11 3 12 5 6 2 23 3 24 3 3 6 2 26 12 5 6 4 9 4 20 8 3 3 27 19 8 5 6 4 9 4 12 2 26 7 11 5 15 7 3 8 3 15 19 8 5 4 10 7 +cv_jpn_000807 24 2 19 26 7 9 4 26 18 5 11 2 10 2 13 +cv_jpn_000808 15 4 21 6 2 10 15 8 5 6 7 14 2 12 2 4 +cv_jpn_000809 17 2 8 2 15 4 17 2 2 11 4 16 4 9 3 9 3 8 3 6 4 20 14 5 6 4 15 19 8 5 6 4 12 5 4 11 5 5 9 3 22 4 6 2 6 7 8 3 23 7 16 3 8 3 6 4 11 3 9 3 11 5 13 15 3 3 24 3 3 8 5 6 4 20 10 3 13 25 5 8 2 23 7 7 9 3 14 5 2 10 7 +cv_jpn_000810 17 2 8 2 15 4 17 2 20 15 2 6 2 4 6 5 5 12 5 5 9 3 6 3 13 8 5 13 9 4 17 2 14 5 23 3 3 9 7 12 3 12 7 8 5 6 4 9 2 11 3 9 3 16 2 20 24 2 8 2 10 2 4 8 5 4 10 7 8 3 11 3 +cv_jpn_000811 9 2 9 4 3 12 7 10 7 26 7 11 3 10 4 14 2 21 8 2 9 3 6 2 +cv_jpn_000812 6 3 25 7 26 7 8 5 6 19 8 2 16 2 22 4 6 3 24 19 8 5 5 8 5 6 4 9 4 8 2 13 9 4 20 8 5 13 15 7 7 16 3 3 8 5 6 4 9 4 6 2 13 16 2 10 2 10 7 10 7 8 3 6 4 12 3 10 5 16 2 25 7 26 7 10 4 8 5 6 4 8 5 6 2 4 14 5 2 10 7 +cv_jpn_000813 2 9 5 16 2 28 7 21 8 2 9 3 14 5 23 2 21 6 4 9 3 15 4 10 2 4 16 2 10 4 11 2 12 5 13 14 5 15 19 8 2 +cv_jpn_000814 6 3 10 5 17 2 13 10 4 24 3 13 14 5 7 8 5 4 9 2 4 8 2 25 5 11 7 9 3 14 5 12 18 +cv_jpn_000815 17 3 8 2 15 4 17 2 24 5 13 15 7 7 5 4 9 3 23 3 3 9 5 13 6 7 10 2 5 17 2 23 2 21 8 2 21 8 3 3 11 3 +cv_jpn_000816 5 4 12 2 13 9 4 4 6 3 9 3 6 3 8 3 25 30 2 9 3 10 4 11 4 3 3 3 15 4 2 11 2 15 19 8 2 +cv_jpn_000817 6 2 12 5 16 2 26 18 12 7 23 3 4 24 4 7 17 2 8 5 13 9 4 12 7 16 2 14 5 6 4 11 2 12 5 13 +cv_jpn_000818 4 27 4 4 +cv_jpn_000819 24 2 4 +cv_jpn_000820 3 9 3 5 +cv_jpn_000821 14 5 4 +cv_jpn_000822 2 8 3 6 4 +cv_jpn_000823 11 4 10 7 8 3 23 7 6 3 8 2 8 3 20 24 2 8 2 10 2 6 18 8 3 4 7 6 3 8 3 16 2 15 6 2 25 7 13 14 4 8 5 6 4 9 2 6 5 10 5 25 2 9 2 10 2 9 2 4 +cv_jpn_000824 17 2 10 5 17 2 10 5 3 8 2 11 2 15 4 4 9 3 28 7 3 6 2 10 2 7 16 7 6 2 12 7 11 3 9 3 14 5 9 2 6 5 10 5 25 2 9 2 10 2 9 2 4 +cv_jpn_000825 12 5 8 2 4 25 5 13 15 3 3 24 3 3 14 5 6 4 9 2 10 7 16 2 4 7 5 9 4 4 14 5 2 8 5 6 19 27 3 21 6 2 13 8 5 6 4 6 5 5 6 4 23 16 2 21 18 6 7 11 2 10 5 10 7 9 3 14 5 2 10 7 +cv_jpn_000826 8 3 6 3 11 2 14 5 11 3 8 2 8 3 4 27 4 8 3 9 2 12 3 3 16 3 15 19 5 5 8 5 6 4 9 2 28 5 21 8 2 4 11 7 22 7 13 8 5 6 4 22 4 6 3 3 4 26 7 9 3 12 5 6 2 4 9 4 15 19 8 5 +cv_jpn_000827 15 4 6 2 10 7 9 4 9 4 13 16 5 13 8 3 6 2 13 29 3 3 8 3 10 3 6 2 13 6 5 5 17 2 11 3 8 3 6 3 3 9 3 6 2 13 6 5 14 5 2 10 4 +cv_jpn_000828 4 4 12 2 13 9 4 6 3 9 3 6 3 8 3 25 2 9 3 4 11 4 23 3 3 15 4 23 2 11 2 15 19 8 2 +cv_jpn_000829 6 5 5 6 4 16 2 9 2 9 2 26 12 2 10 4 11 2 12 18 +cv_jpn_000830 6 3 27 4 10 2 25 2 6 3 25 4 23 2 15 4 12 2 13 14 5 12 18 +cv_jpn_000831 11 3 15 4 11 2 15 4 +cv_jpn_000832 6 3 6 3 17 2 3 6 19 6 18 8 5 9 4 16 7 23 2 6 2 9 2 11 2 27 4 14 5 12 18 +cv_jpn_000833 12 3 9 3 27 4 6 2 4 23 2 6 18 12 2 10 5 10 7 6 2 10 2 4 12 3 16 5 20 +cv_jpn_000834 2 11 2 12 2 16 2 25 6 7 12 2 5 4 10 2 10 5 8 5 8 5 27 3 3 14 3 4 +cv_jpn_000835 24 3 6 5 13 15 19 26 7 5 9 3 14 3 3 2 2 6 5 8 2 +cv_jpn_000836 11 3 14 2 9 4 3 17 2 21 8 5 11 3 6 4 9 4 15 4 9 2 4 +cv_jpn_000837 2 10 4 16 2 21 8 2 23 2 +cv_jpn_000838 4 8 3 3 16 2 10 3 6 7 14 2 8 3 22 4 6 2 13 7 17 3 12 7 10 5 8 5 8 2 9 3 15 4 11 5 10 7 +cv_jpn_000839 6 2 6 2 6 7 17 2 16 4 28 7 26 18 6 2 12 2 10 5 10 7 9 4 3 22 4 8 5 22 3 3 15 19 6 4 9 3 7 27 4 9 4 24 2 4 21 8 5 23 7 6 7 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..b47ed3d896bb917b40fea616a926c6e664ae5e1c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/score @@ -0,0 +1,40 @@ +cv_jpn_000840 tensor(-9.2983) +cv_jpn_000841 tensor(-5.1807) +cv_jpn_000842 tensor(-2.5816) +cv_jpn_000843 tensor(-4.3242) +cv_jpn_000844 tensor(-11.6624) +cv_jpn_000845 tensor(-2.7441) +cv_jpn_000846 tensor(-9.3766) +cv_jpn_000847 tensor(-5.9137) +cv_jpn_000848 tensor(-3.0642) +cv_jpn_000849 tensor(-2.3749) +cv_jpn_000850 tensor(-2.7668) +cv_jpn_000851 tensor(-3.7305) +cv_jpn_000852 tensor(-4.2572) +cv_jpn_000853 tensor(-4.3378) +cv_jpn_000854 tensor(-15.9908) +cv_jpn_000855 tensor(-5.9197) +cv_jpn_000856 tensor(-4.6206) +cv_jpn_000857 tensor(-9.4156) +cv_jpn_000858 tensor(-3.3463) +cv_jpn_000859 tensor(-6.9946) +cv_jpn_000860 tensor(-5.8497) +cv_jpn_000861 tensor(-2.9789) +cv_jpn_000862 tensor(-9.5575) +cv_jpn_000863 tensor(-12.0254) +cv_jpn_000864 tensor(-10.4547) +cv_jpn_000865 tensor(-3.6948) +cv_jpn_000866 tensor(-8.8115) +cv_jpn_000867 tensor(-5.2961) +cv_jpn_000868 tensor(-0.8697) +cv_jpn_000869 tensor(-6.9120) +cv_jpn_000870 tensor(-2.6078) +cv_jpn_000871 tensor(-8.3199) +cv_jpn_000872 tensor(-10.4440) +cv_jpn_000873 tensor(-13.3743) +cv_jpn_000874 tensor(-6.8139) +cv_jpn_000875 tensor(-12.0495) +cv_jpn_000876 tensor(-2.8980) +cv_jpn_000877 tensor(-3.4551) +cv_jpn_000878 tensor(-2.8128) +cv_jpn_000879 tensor(-11.0617) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..013ceb3ade50d09d6f91e0cb556a2bab95f3f2d2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/text @@ -0,0 +1,40 @@ +cv_jpn_000840 sh i k a sh I t o k i g a k a b o n i h a i r u k o t o s o n o k o t o g a m i r a y o o m u k o t o d e a r i a r a t a n a r u i cl U t a i g a d e t e k u r u k o t o d e a r u +cv_jpn_000841 t e r e b i o k a i k a i t e pau t e r e b i o m i r u j i k a N n a f u e t a +cv_jpn_000842 k a k a r e u sh I t a i n o m i i ts u m a d e m o i k e r u n o d e a r u +cv_jpn_000843 n i N k i d a a m e N i y a n i n a r a N d a r a n i j i k a N m a o ch i d a cl t a +cv_jpn_000844 s o r e o o m o ch i i r u n i N g e N n o y o k u n i t o N s e i s o sh I t e k o r e w a k a r e n o m o cl t e i r u k a ch e n o s a k u d o N n pau i d o N s u r u +cv_jpn_000845 m a w a r y o a m i N n a k a N g a r u k o t o y a m e t e i t a +cv_jpn_000846 k o o i t e k i j o k U k a N t e k i n i s e k a y o m i r u t o y u k o t o w a j a k u n i k o o i t e k i ch a o k cl k a N t e k i n i s e k a y o k e e s e e s u r u k o t o k u m u n o d e a r u +cv_jpn_000847 s j e N p a i t a k e s a s e m a i t o s u r u k i z u k a i y g a y o k e e n i s e N cl p a i s a s e t e sh i m a u +cv_jpn_000848 k o n o m i ch i w a t o t e m o s e m a i n o d a m n a i d e s U +cv_jpn_000849 w o e g a r i n i U +cv_jpn_000850 t o i y o w a r o k a m a i t a r i g a a n i a r i m a s U +cv_jpn_000851 t a d a k a s a N o h i d a r i n i k i m e r a s a N g a i m a s U +cv_jpn_000852 m a cl k u r u n a t a m a b o t e s u g o i e +cv_jpn_000853 sh a r sh o o m i t a i n a d o k u sh u k a s o o b u m o k a i t a +cv_jpn_000854 g e N j i t e m o s e k a i w a pau t a m o o i ch i t o sh I t e k e cl t e s u r a i d e k a t a ch i o a cl t o s U k a i u n a k e r e m o n a r a n a i +cv_jpn_000855 sh o o h i N k e N s a k u k g a o k a r y a s u i t o o pau k a o k i n a r u m a i +cv_jpn_000856 ts e sh i I k i w a n e k I sh I t e cl k a t e e d e n a k e r u m o n a r a n a i +cv_jpn_000857 m o n o g o t a n o N j i N p a N k a e r u d a k e d e pau u m a k U i k U k o t o m a r +cv_jpn_000858 k o n o k I e e ts u w a k a ts u o n o s a sh i m i g a z e cl p e i N +cv_jpn_000859 k a k e N n i sh i cl p a i sh I t e m a pau m o ch I t ts u i t e s a m a sh I ts u o u k e i d e r u +cv_jpn_000860 s o r e y u e n i pau t e ts u g a k u g a z e N t a i n o g a k o d e y a r u t o s u r e w a +cv_jpn_000861 k i i s a n a i y a o y a d a g a y a s U k U t e h a N j o sh I t e r u +cv_jpn_000862 i n i f u r a g a k i n o h u z e N n i o ch j i cl t e pau k o k u g a i a d a sh I ts u s u r u sh I t a m o d e t e k i t a +cv_jpn_000863 ts s u g i n i k a w a k u w a s o N z a y o j u j u n o d o i k i n o k a cl t e s u r e z u r a N d e o k i N z e i t i t e N i k i U s u r u +cv_jpn_000864 s o r e d e w a t o k t o y i m a r a n a s e r i ts U sh i o w a n a k u pau sh i r N k a N t o i m o n o m o n a k u n a r u n o d a r i +cv_jpn_000865 h a k a i b u r a N k o k o N k u r i t o s e n o s u b e r i d a i k a w a i t a s u n a b a +cv_jpn_000866 s sh i k a sh i s o r o w a d o k o m a d e m N k o k o k a r a d e t e pau o k o e k a i r i k u r u s e e I t o m o t o m o d e a k e w a n a r a n a i +cv_jpn_000867 a r i t o a r a i r u d e m o o m a k i ch i r a sh i t e m i N n e a k a r a o u r a m i o k a cl t e r u +cv_jpn_000868 k o n o t e e d o s a w a g i n i n a r u k o t o m o n a i n o d a r o +cv_jpn_000869 k o n o n e r a N d e pau u r i t a u k a a a +cv_jpn_000870 h i n o k a g a e N n i ch u i sh i n a i t o pau s u g u k o g e r u +cv_jpn_000871 e N m a N n o u w e N n i p o ts u r i t o ts u i s a n a n a g a i t a s a i sh i o w a ts u m a y o o j i t e e d o n o ts i i s a n a pau a n a d a cl t a a +cv_jpn_000872 s o r e w a m a r e w a r e o i k a sh i n a g a r a pau w a r e w a r e o t o r e e k a s u r u n o d e a r u pau w a r e w a r e n o t a m a s sh i y o k o r o s u n o d e a +cv_jpn_000873 r e k U sh I t e k i n i a t a r a r t a m o n o w a d e cl t e m u j u N t e k i j i g o t o o i ts I t e k i g i e N z d a i n o o i t e s U k a i sh I e k i n i a t a e r a r t a m o N u t o sh I t e +cv_jpn_000874 m u o j u N t e k I e j i g o d o o i ch I t o sh I t e pau i ts u m o k o n o s e k a i n i ch o o e sh I t e k i d e a +cv_jpn_000875 y u n i d e cl p e e m u j u N t e k I j i g o d o o i sh u t o sh I t e g e N a e k a r a g e N z a e e t u w o k i k u s U e k a e n o g e N z a i n o i t e +cv_jpn_000876 h a r e w o t a N o sh I t o N d a sh u d e k i n a i +cv_jpn_000877 sh i k a sh i w a t a sh a s o k o n i s e k a i n o j i k o d o o i ts u o k u n o d e w a n a i +cv_jpn_000878 n e b u k a k u n a r u n o g a pau h a y a k u m a cl t a +cv_jpn_000879 w a t a sh i w a i N g e N n o o d e k I sh I t e k I k e e s e e n o t a ch i b a k a r a pau g e j u ts u o m i r u n o d e a cl t e o o sh a k a r a e N sh a o m i r u n o d e a n a i diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..013ceb3ade50d09d6f91e0cb556a2bab95f3f2d2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/token @@ -0,0 +1,40 @@ +cv_jpn_000840 sh i k a sh I t o k i g a k a b o n i h a i r u k o t o s o n o k o t o g a m i r a y o o m u k o t o d e a r i a r a t a n a r u i cl U t a i g a d e t e k u r u k o t o d e a r u +cv_jpn_000841 t e r e b i o k a i k a i t e pau t e r e b i o m i r u j i k a N n a f u e t a +cv_jpn_000842 k a k a r e u sh I t a i n o m i i ts u m a d e m o i k e r u n o d e a r u +cv_jpn_000843 n i N k i d a a m e N i y a n i n a r a N d a r a n i j i k a N m a o ch i d a cl t a +cv_jpn_000844 s o r e o o m o ch i i r u n i N g e N n o y o k u n i t o N s e i s o sh I t e k o r e w a k a r e n o m o cl t e i r u k a ch e n o s a k u d o N n pau i d o N s u r u +cv_jpn_000845 m a w a r y o a m i N n a k a N g a r u k o t o y a m e t e i t a +cv_jpn_000846 k o o i t e k i j o k U k a N t e k i n i s e k a y o m i r u t o y u k o t o w a j a k u n i k o o i t e k i ch a o k cl k a N t e k i n i s e k a y o k e e s e e s u r u k o t o k u m u n o d e a r u +cv_jpn_000847 s j e N p a i t a k e s a s e m a i t o s u r u k i z u k a i y g a y o k e e n i s e N cl p a i s a s e t e sh i m a u +cv_jpn_000848 k o n o m i ch i w a t o t e m o s e m a i n o d a m n a i d e s U +cv_jpn_000849 w o e g a r i n i U +cv_jpn_000850 t o i y o w a r o k a m a i t a r i g a a n i a r i m a s U +cv_jpn_000851 t a d a k a s a N o h i d a r i n i k i m e r a s a N g a i m a s U +cv_jpn_000852 m a cl k u r u n a t a m a b o t e s u g o i e +cv_jpn_000853 sh a r sh o o m i t a i n a d o k u sh u k a s o o b u m o k a i t a +cv_jpn_000854 g e N j i t e m o s e k a i w a pau t a m o o i ch i t o sh I t e k e cl t e s u r a i d e k a t a ch i o a cl t o s U k a i u n a k e r e m o n a r a n a i +cv_jpn_000855 sh o o h i N k e N s a k u k g a o k a r y a s u i t o o pau k a o k i n a r u m a i +cv_jpn_000856 ts e sh i I k i w a n e k I sh I t e cl k a t e e d e n a k e r u m o n a r a n a i +cv_jpn_000857 m o n o g o t a n o N j i N p a N k a e r u d a k e d e pau u m a k U i k U k o t o m a r +cv_jpn_000858 k o n o k I e e ts u w a k a ts u o n o s a sh i m i g a z e cl p e i N +cv_jpn_000859 k a k e N n i sh i cl p a i sh I t e m a pau m o ch I t ts u i t e s a m a sh I ts u o u k e i d e r u +cv_jpn_000860 s o r e y u e n i pau t e ts u g a k u g a z e N t a i n o g a k o d e y a r u t o s u r e w a +cv_jpn_000861 k i i s a n a i y a o y a d a g a y a s U k U t e h a N j o sh I t e r u +cv_jpn_000862 i n i f u r a g a k i n o h u z e N n i o ch j i cl t e pau k o k u g a i a d a sh I ts u s u r u sh I t a m o d e t e k i t a +cv_jpn_000863 ts s u g i n i k a w a k u w a s o N z a y o j u j u n o d o i k i n o k a cl t e s u r e z u r a N d e o k i N z e i t i t e N i k i U s u r u +cv_jpn_000864 s o r e d e w a t o k t o y i m a r a n a s e r i ts U sh i o w a n a k u pau sh i r N k a N t o i m o n o m o n a k u n a r u n o d a r i +cv_jpn_000865 h a k a i b u r a N k o k o N k u r i t o s e n o s u b e r i d a i k a w a i t a s u n a b a +cv_jpn_000866 s sh i k a sh i s o r o w a d o k o m a d e m N k o k o k a r a d e t e pau o k o e k a i r i k u r u s e e I t o m o t o m o d e a k e w a n a r a n a i +cv_jpn_000867 a r i t o a r a i r u d e m o o m a k i ch i r a sh i t e m i N n e a k a r a o u r a m i o k a cl t e r u +cv_jpn_000868 k o n o t e e d o s a w a g i n i n a r u k o t o m o n a i n o d a r o +cv_jpn_000869 k o n o n e r a N d e pau u r i t a u k a a a +cv_jpn_000870 h i n o k a g a e N n i ch u i sh i n a i t o pau s u g u k o g e r u +cv_jpn_000871 e N m a N n o u w e N n i p o ts u r i t o ts u i s a n a n a g a i t a s a i sh i o w a ts u m a y o o j i t e e d o n o ts i i s a n a pau a n a d a cl t a a +cv_jpn_000872 s o r e w a m a r e w a r e o i k a sh i n a g a r a pau w a r e w a r e o t o r e e k a s u r u n o d e a r u pau w a r e w a r e n o t a m a s sh i y o k o r o s u n o d e a +cv_jpn_000873 r e k U sh I t e k i n i a t a r a r t a m o n o w a d e cl t e m u j u N t e k i j i g o t o o i ts I t e k i g i e N z d a i n o o i t e s U k a i sh I e k i n i a t a e r a r t a m o N u t o sh I t e +cv_jpn_000874 m u o j u N t e k I e j i g o d o o i ch I t o sh I t e pau i ts u m o k o n o s e k a i n i ch o o e sh I t e k i d e a +cv_jpn_000875 y u n i d e cl p e e m u j u N t e k I j i g o d o o i sh u t o sh I t e g e N a e k a r a g e N z a e e t u w o k i k u s U e k a e n o g e N z a i n o i t e +cv_jpn_000876 h a r e w o t a N o sh I t o N d a sh u d e k i n a i +cv_jpn_000877 sh i k a sh i w a t a sh a s o k o n i s e k a i n o j i k o d o o i ts u o k u n o d e w a n a i +cv_jpn_000878 n e b u k a k u n a r u n o g a pau h a y a k u m a cl t a +cv_jpn_000879 w a t a sh i w a i N g e N n o o d e k I sh I t e k I k e e s e e n o t a ch i b a k a r a pau g e j u ts u o m i r u n o d e a cl t e o o sh a k a r a e N sh a o m i r u n o d e a n a i diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..f6df4f48bc881a137f4d45de20e8b6e0cb64a049 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/token_int @@ -0,0 +1,40 @@ +cv_jpn_000840 15 4 6 2 15 19 8 3 6 4 16 2 6 2 25 3 9 4 24 2 4 10 7 6 3 8 3 12 3 9 3 6 3 8 3 16 2 11 4 10 2 23 3 3 11 7 6 3 8 3 14 5 2 10 4 2 10 2 8 2 9 2 10 7 4 21 18 8 2 4 16 2 14 5 8 5 6 7 10 7 6 3 8 3 14 5 2 10 7 +cv_jpn_000841 8 5 10 5 25 4 3 6 2 4 6 2 4 8 5 20 8 5 10 5 25 4 3 11 4 10 7 22 4 6 2 13 9 2 31 7 5 8 2 +cv_jpn_000842 6 2 6 2 10 5 7 15 19 8 2 4 9 3 11 4 4 26 7 11 2 14 5 11 3 4 6 5 10 7 9 3 14 5 2 10 7 +cv_jpn_000843 9 4 13 6 4 14 2 2 11 5 13 4 23 2 9 4 9 2 10 2 13 14 2 10 2 9 4 22 4 6 2 13 11 2 3 27 4 14 2 21 8 2 +cv_jpn_000844 12 3 10 5 3 3 11 3 27 4 4 10 7 9 4 13 16 5 13 9 3 23 3 6 7 9 4 8 3 13 12 5 4 12 3 15 19 8 5 6 3 10 5 17 2 6 2 10 5 9 3 11 3 21 8 5 4 10 7 6 2 27 5 9 3 12 2 6 7 14 3 13 9 20 4 14 3 13 12 7 10 7 +cv_jpn_000845 11 2 17 2 10 23 3 2 11 4 13 9 2 6 2 13 16 2 10 7 6 3 8 3 23 2 11 5 8 5 4 8 2 +cv_jpn_000846 6 3 3 4 8 5 6 4 22 3 6 18 6 2 13 8 5 6 4 9 4 12 5 6 2 23 3 11 4 10 7 8 3 23 7 6 3 8 3 17 2 22 2 6 7 9 4 6 3 3 4 8 5 6 4 27 2 3 6 21 6 2 13 8 5 6 4 9 4 12 5 6 2 23 3 6 5 5 12 5 5 12 7 10 7 6 3 8 3 6 7 11 7 9 3 14 5 2 10 7 +cv_jpn_000847 12 22 5 13 30 2 4 8 2 6 5 12 2 12 5 11 2 4 8 3 12 7 10 7 6 4 28 7 6 2 4 23 16 2 23 3 6 5 5 9 4 12 5 13 21 30 2 4 12 2 12 5 8 5 15 4 11 2 7 +cv_jpn_000848 6 3 9 3 11 4 27 4 17 2 8 3 8 5 11 3 12 5 11 2 4 9 3 14 2 11 9 2 4 14 5 12 18 +cv_jpn_000849 17 3 5 16 2 10 4 9 4 18 +cv_jpn_000850 8 3 4 23 3 17 2 10 3 6 2 11 2 4 8 2 10 4 16 2 2 9 4 2 10 4 11 2 12 18 +cv_jpn_000851 8 2 14 2 6 2 12 2 13 3 24 4 14 2 10 4 9 4 6 4 11 5 10 2 12 2 13 16 2 4 11 2 12 18 +cv_jpn_000852 11 2 21 6 7 10 7 9 2 8 2 11 2 25 3 8 5 12 7 16 3 4 5 +cv_jpn_000853 15 2 10 15 3 3 11 4 8 2 4 9 2 14 3 6 7 15 7 6 2 12 3 3 25 7 11 3 6 2 4 8 2 +cv_jpn_000854 16 5 13 22 4 8 5 11 3 12 5 6 2 4 17 2 20 8 2 11 3 3 4 27 4 8 3 15 19 8 5 6 5 21 8 5 12 7 10 2 4 14 5 6 2 8 2 27 4 3 2 21 8 3 12 18 6 2 4 7 9 2 6 5 10 5 11 3 9 2 10 2 9 2 4 +cv_jpn_000855 15 3 3 24 4 13 6 5 13 12 2 6 7 6 16 2 3 6 2 10 23 2 12 7 4 8 3 3 20 6 2 3 6 4 9 2 10 7 11 2 4 +cv_jpn_000856 26 5 15 4 19 6 4 17 2 9 5 6 19 15 19 8 5 21 6 2 8 5 5 14 5 9 2 6 5 10 7 11 3 9 2 10 2 9 2 4 +cv_jpn_000857 11 3 9 3 16 3 8 2 9 3 13 22 4 13 30 2 13 6 2 5 10 7 14 2 6 5 14 5 20 7 11 2 6 18 4 6 18 6 3 8 3 11 2 10 +cv_jpn_000858 6 3 9 3 6 19 5 5 26 7 17 2 6 2 26 7 3 9 3 12 2 15 4 11 4 16 2 28 5 21 30 5 4 13 +cv_jpn_000859 6 2 6 5 13 9 4 15 4 21 30 2 4 15 19 8 5 11 2 20 11 3 27 19 8 26 7 4 8 5 12 2 11 2 15 19 26 7 3 7 6 5 4 14 5 10 7 +cv_jpn_000860 12 3 10 5 23 7 5 9 4 20 8 5 26 7 16 2 6 7 16 2 28 5 13 8 2 4 9 3 16 2 6 3 14 5 23 2 10 7 8 3 12 7 10 5 17 2 +cv_jpn_000861 6 4 4 12 2 9 2 4 23 2 3 23 2 14 2 16 2 23 2 12 18 6 18 8 5 24 2 13 22 3 15 19 8 5 10 7 +cv_jpn_000862 4 9 4 31 7 10 2 16 2 6 4 9 3 24 7 28 5 13 9 4 3 27 22 4 21 8 5 20 6 3 6 7 16 2 4 2 14 2 15 19 26 7 12 7 10 7 15 19 8 2 11 3 14 5 8 5 6 4 8 2 +cv_jpn_000863 26 12 7 16 4 9 4 6 2 17 2 6 7 17 2 12 3 13 28 2 23 3 22 7 22 7 9 3 14 3 4 6 4 9 3 6 2 21 8 5 12 7 10 5 28 7 10 2 13 14 5 3 6 4 13 28 5 4 8 4 8 5 13 4 6 4 18 12 7 10 7 +cv_jpn_000864 12 3 10 5 14 5 17 2 8 3 6 8 3 23 4 11 2 10 2 9 2 12 5 10 4 26 18 15 4 3 17 2 9 2 6 7 20 15 4 10 13 6 2 13 8 3 4 11 3 9 3 11 3 9 2 6 7 9 2 10 7 9 3 14 2 10 4 +cv_jpn_000865 24 2 6 2 4 25 7 10 2 13 6 3 6 3 13 6 7 10 4 8 3 12 5 9 3 12 7 25 5 10 4 14 2 4 6 2 17 2 4 8 2 12 7 9 2 25 2 +cv_jpn_000866 12 15 4 6 2 15 4 12 3 10 3 17 2 14 3 6 3 11 2 14 5 11 13 6 3 6 3 6 2 10 2 14 5 8 5 20 3 6 3 5 6 2 4 10 4 6 7 10 7 12 5 5 19 8 3 11 3 8 3 11 3 14 5 2 6 5 17 2 9 2 10 2 9 2 4 +cv_jpn_000867 2 10 4 8 3 2 10 2 4 10 7 14 5 11 3 3 11 2 6 4 27 4 10 2 15 4 8 5 11 4 13 9 5 2 6 2 10 2 3 7 10 2 11 4 3 6 2 21 8 5 10 7 +cv_jpn_000868 6 3 9 3 8 5 5 14 3 12 2 17 2 16 4 9 4 9 2 10 7 6 3 8 3 11 3 9 2 4 9 3 14 2 10 3 +cv_jpn_000869 6 3 9 3 9 5 10 2 13 14 5 20 7 10 4 8 2 7 6 2 2 2 +cv_jpn_000870 24 4 9 3 6 2 16 2 5 13 9 4 27 7 4 15 4 9 2 4 8 3 20 12 7 16 7 6 3 16 5 10 7 +cv_jpn_000871 5 13 11 2 13 9 3 7 17 5 13 9 4 30 3 26 7 10 4 8 3 26 7 4 12 2 9 2 9 2 16 2 4 8 2 12 2 4 15 4 3 17 2 26 7 11 2 23 3 3 22 4 8 5 5 14 3 9 3 26 4 4 12 2 9 2 20 2 9 2 14 2 21 8 2 2 +cv_jpn_000872 12 3 10 5 17 2 11 2 10 5 17 2 10 5 3 4 6 2 15 4 9 2 16 2 10 2 20 17 2 10 5 17 2 10 5 3 8 3 10 5 5 6 2 12 7 10 7 9 3 14 5 2 10 7 20 17 2 10 5 17 2 10 5 9 3 8 2 11 2 12 15 4 23 3 6 3 10 3 12 7 9 3 14 5 2 +cv_jpn_000873 10 5 6 18 15 19 8 5 6 4 9 4 2 8 2 10 2 10 8 2 11 3 9 3 17 2 14 5 21 8 5 11 7 22 7 13 8 5 6 4 22 4 16 3 8 3 3 4 26 19 8 5 6 4 16 4 5 13 28 14 2 4 9 3 3 4 8 5 12 18 6 2 4 15 19 5 6 4 9 4 2 8 2 5 10 2 10 8 2 11 3 13 7 8 3 15 19 8 5 +cv_jpn_000874 11 7 3 22 7 13 8 5 6 19 5 22 4 16 3 14 3 3 4 27 19 8 3 15 19 8 5 20 4 26 7 11 3 6 3 9 3 12 5 6 2 4 9 4 27 3 3 5 15 19 8 5 6 4 14 5 2 +cv_jpn_000875 23 7 9 4 14 5 21 30 5 5 11 7 22 7 13 8 5 6 19 22 4 16 3 14 3 3 4 15 7 8 3 15 19 8 5 16 5 13 2 5 6 2 10 2 16 5 13 28 2 5 5 8 7 17 3 6 4 6 7 12 18 5 6 2 5 9 3 16 5 13 28 2 4 9 3 4 8 5 +cv_jpn_000876 24 2 10 5 17 3 8 2 13 3 15 19 8 3 13 14 2 15 7 14 5 6 4 9 2 4 +cv_jpn_000877 15 4 6 2 15 4 17 2 8 2 15 2 12 3 6 3 9 4 12 5 6 2 4 9 3 22 4 6 3 14 3 3 4 26 7 3 6 7 9 3 14 5 17 2 9 2 4 +cv_jpn_000878 9 5 25 7 6 2 6 7 9 2 10 7 9 3 16 2 20 24 2 23 2 6 7 11 2 21 8 2 +cv_jpn_000879 17 2 8 2 15 4 17 2 4 13 16 5 13 9 3 3 14 5 6 19 15 19 8 5 6 19 6 5 5 12 5 5 9 3 8 2 27 4 25 2 6 2 10 2 20 16 5 22 7 26 7 3 11 4 10 7 9 3 14 5 2 21 8 5 3 3 15 2 6 2 10 2 5 13 15 2 3 11 4 10 7 9 3 14 5 2 9 2 4 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..a5ca45a03d685faebd1fb4a6e41a436899b7035d --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/score @@ -0,0 +1,40 @@ +cv_jpn_000880 tensor(-1.8229) +cv_jpn_000881 tensor(-6.0109) +cv_jpn_000882 tensor(-4.9126) +cv_jpn_000883 tensor(-5.8385) +cv_jpn_000884 tensor(-1.5993) +cv_jpn_000885 tensor(-3.4974) +cv_jpn_000886 tensor(-2.8257) +cv_jpn_000887 tensor(-11.7613) +cv_jpn_000888 tensor(-2.5982) +cv_jpn_000889 tensor(-2.1539) +cv_jpn_000890 tensor(-5.2186) +cv_jpn_000891 tensor(-6.5643) +cv_jpn_000892 tensor(-1.6122) +cv_jpn_000893 tensor(-2.6140) +cv_jpn_000894 tensor(-2.4411) +cv_jpn_000895 tensor(-3.0360) +cv_jpn_000896 tensor(-2.6069) +cv_jpn_000897 tensor(-2.8417) +cv_jpn_000898 tensor(-2.0908) +cv_jpn_000899 tensor(-2.4985) +cv_jpn_000900 tensor(-0.6560) +cv_jpn_000901 tensor(-2.6687) +cv_jpn_000902 tensor(-1.4906) +cv_jpn_000903 tensor(-1.6052) +cv_jpn_000904 tensor(-1.0377) +cv_jpn_000905 tensor(-1.4494) +cv_jpn_000906 tensor(-0.8865) +cv_jpn_000907 tensor(-2.4450) +cv_jpn_000908 tensor(-5.9653) +cv_jpn_000909 tensor(-6.9296) +cv_jpn_000910 tensor(-7.4590) +cv_jpn_000911 tensor(-3.0419) +cv_jpn_000912 tensor(-2.8038) +cv_jpn_000913 tensor(-4.9149) +fleurs_jpn_000346 tensor(-8.4199) +fleurs_jpn_000347 tensor(-14.3032) +fleurs_jpn_000348 tensor(-18.1699) +fleurs_jpn_000349 tensor(-8.6297) +fleurs_jpn_000350 tensor(-10.7191) +fleurs_jpn_000351 tensor(-11.6995) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..202a7b5375918c71677c36d73f0f04c98f6bb889 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/text @@ -0,0 +1,40 @@ +cv_jpn_000880 a o i t o m a t o sh i k a n a k U t e pau k a u k a b a i y o u +cv_jpn_000881 s e N k e z u e g y o o n i o o k i n a k i t a y o y a s U t e e r u +cv_jpn_000882 n a n i k a sh i r a n o i N s e N t e b u w a n a i t o k i b u i s u e n o d e w a +cv_jpn_000883 j i k o N o sh e k g e N n o i b e N t o d e s u t o r u sh I t a m a r i +cv_jpn_000884 m a r i n o sh I t o w a b o o z e N t o sh I t e i t a +cv_jpn_000885 s o N n a n a i y o o n o m e r u w a n a N k e N m o k U I t e i t a +cv_jpn_000886 i j i g a i d e d e e s f u i sh I t e i t a +cv_jpn_000887 t o k i d o k i ch i u u N n o k o r o w a w a k a r a n a k u n o r u t o ch i g a a r u d a k a r a b o k u a k a a n e o k i n o t o n i k a j i a j e m e r u +cv_jpn_000888 m o o n i g e t e ch a t a m e d a +cv_jpn_000889 k a r e w a p o o t o t a j i ts U k u sh I t e i t a +cv_jpn_000890 d a r u N i m u n e i w a k o w a k a k e t a k a n a i +cv_jpn_000891 p a s a k a t o o m o t e u d o a n o o t o cl t e o n i g e cl t a +cv_jpn_000892 sh t e i m a s e N +cv_jpn_000893 k a y o o n i sh I t e sh I t e r u t o t o m o n i sh i cl t e i n a i t o k o r o k a r a t a N ky u w a h a j i m a r u n o d e a r u +cv_jpn_000894 a i s a ts u w a d a i j i d a i o +cv_jpn_000895 t o j i t a m o n o i k a n i h i r o g e t e m o h i r a i t a m u n o n i a n a r a r o t o i cl t e i r u g a +cv_jpn_000896 t a m u sh i n i k u ts U k a ts U k u cl t e m i o w a +cv_jpn_000897 U ts z e i b u N a k o k i n a sh o o b a i d a i o n a +cv_jpn_000898 w h a cl t e i +cv_jpn_000899 k i t e i +cv_jpn_000900 g o +cv_jpn_000901 h a sh i i t i +cv_jpn_000902 i e a +cv_jpn_000903 h a cl ch i +cv_jpn_000904 h n e +cv_jpn_000905 a sh i i i +cv_jpn_000906 k u o +cv_jpn_000907 k e ch i +cv_jpn_000908 k a k a k u g a pau a k i r a k a n i s u r u pau ky y a cl k a N t e k I sh i N d r i n i sh I t a g a u k o t o n i y o cl t e +cv_jpn_000909 k a k o t o m i r a i t o n o pau m u j u N t e k i j i k o d o o i ts u t o sh I t e n o g e N z a i g a k a p a ch i o m o sh I t o i u k o t o d e a r u +cv_jpn_000910 b u ts u r i t e k i s e k a i u w a s u u g a k u t e k i I k i g o o n i o cl t e a r a w a s a r e r u pau s u u k a k u t e k I i k a t a ch i n o s e k a i a r u +cv_jpn_000911 w o n a ch i g e N i sh o o d e s a N k o o g i n a r u +cv_jpn_000912 g a i k o k u k a r a k i t a m o n o d a t o sh i t e b i cl k u i +cv_jpn_000913 i w a y o r u j i I s e N y o cl t e k a k U t o U k U sh I i t a cl t a m o n o d e a r u +fleurs_jpn_000346 o n a j i y o n i d a N s u e u w a h i j z a o o u z u b o h a k o t o g a g i m u u z u k e r a r e t e i m a s U +fleurs_jpn_000347 k o n o s a b i s a k o r a k s e e y a h a j i m e t o s u r u s e N p t a k u i y a e N k a k U ch i d e pau d e t a y a o N s e o h I s u o t o s u r u t a N k e N t a i r i h i N cl p a n i d i o o s a r e t e i m a s U +fleurs_jpn_000348 ky o o f u ky o k a d o n o k o o s u i r o o b i a m a k a sh i b a r a i u pau t a s u m a k i pau m i z u k i pau o y u s a i k u r o N n a N o n o k i b i sh i i k sh o k e t a y a s o n o e ky o o n i y o r u m o r o d e s U +fleurs_jpn_000349 i N t a n e t o w a m a s U k o m i r u k e e sh e N t o t a i j i N k o m i r u k e e sh o o n o pau d o y o s o o k a e s u m a i d a k a N ky o o r d e s U +fleurs_jpn_000350 k a j i n o r e w a ts u u j o o t o k u b e s u r a i sh I k o y a pau i e N t a a t e i m e N t o y o sh I t e i m a s U ky e s u w a k i b u y o k u sh I s e s u n a i r e t o r a m a r u y o o r e s u r u t a m d e s U +fleurs_jpn_000351 sh i k a sh i k a k U t e N n o b i k e cl t o o sh i n a cl t a t o i N d o w a pau n a n a z u n o b i k e cl t o o sh i n a i s a N j u r o b u r a j sh i k a d e g i m a s e N d e sh I t a diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..202a7b5375918c71677c36d73f0f04c98f6bb889 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/token @@ -0,0 +1,40 @@ +cv_jpn_000880 a o i t o m a t o sh i k a n a k U t e pau k a u k a b a i y o u +cv_jpn_000881 s e N k e z u e g y o o n i o o k i n a k i t a y o y a s U t e e r u +cv_jpn_000882 n a n i k a sh i r a n o i N s e N t e b u w a n a i t o k i b u i s u e n o d e w a +cv_jpn_000883 j i k o N o sh e k g e N n o i b e N t o d e s u t o r u sh I t a m a r i +cv_jpn_000884 m a r i n o sh I t o w a b o o z e N t o sh I t e i t a +cv_jpn_000885 s o N n a n a i y o o n o m e r u w a n a N k e N m o k U I t e i t a +cv_jpn_000886 i j i g a i d e d e e s f u i sh I t e i t a +cv_jpn_000887 t o k i d o k i ch i u u N n o k o r o w a w a k a r a n a k u n o r u t o ch i g a a r u d a k a r a b o k u a k a a n e o k i n o t o n i k a j i a j e m e r u +cv_jpn_000888 m o o n i g e t e ch a t a m e d a +cv_jpn_000889 k a r e w a p o o t o t a j i ts U k u sh I t e i t a +cv_jpn_000890 d a r u N i m u n e i w a k o w a k a k e t a k a n a i +cv_jpn_000891 p a s a k a t o o m o t e u d o a n o o t o cl t e o n i g e cl t a +cv_jpn_000892 sh t e i m a s e N +cv_jpn_000893 k a y o o n i sh I t e sh I t e r u t o t o m o n i sh i cl t e i n a i t o k o r o k a r a t a N ky u w a h a j i m a r u n o d e a r u +cv_jpn_000894 a i s a ts u w a d a i j i d a i o +cv_jpn_000895 t o j i t a m o n o i k a n i h i r o g e t e m o h i r a i t a m u n o n i a n a r a r o t o i cl t e i r u g a +cv_jpn_000896 t a m u sh i n i k u ts U k a ts U k u cl t e m i o w a +cv_jpn_000897 U ts z e i b u N a k o k i n a sh o o b a i d a i o n a +cv_jpn_000898 w h a cl t e i +cv_jpn_000899 k i t e i +cv_jpn_000900 g o +cv_jpn_000901 h a sh i i t i +cv_jpn_000902 i e a +cv_jpn_000903 h a cl ch i +cv_jpn_000904 h n e +cv_jpn_000905 a sh i i i +cv_jpn_000906 k u o +cv_jpn_000907 k e ch i +cv_jpn_000908 k a k a k u g a pau a k i r a k a n i s u r u pau ky y a cl k a N t e k I sh i N d r i n i sh I t a g a u k o t o n i y o cl t e +cv_jpn_000909 k a k o t o m i r a i t o n o pau m u j u N t e k i j i k o d o o i ts u t o sh I t e n o g e N z a i g a k a p a ch i o m o sh I t o i u k o t o d e a r u +cv_jpn_000910 b u ts u r i t e k i s e k a i u w a s u u g a k u t e k i I k i g o o n i o cl t e a r a w a s a r e r u pau s u u k a k u t e k I i k a t a ch i n o s e k a i a r u +cv_jpn_000911 w o n a ch i g e N i sh o o d e s a N k o o g i n a r u +cv_jpn_000912 g a i k o k u k a r a k i t a m o n o d a t o sh i t e b i cl k u i +cv_jpn_000913 i w a y o r u j i I s e N y o cl t e k a k U t o U k U sh I i t a cl t a m o n o d e a r u +fleurs_jpn_000346 o n a j i y o n i d a N s u e u w a h i j z a o o u z u b o h a k o t o g a g i m u u z u k e r a r e t e i m a s U +fleurs_jpn_000347 k o n o s a b i s a k o r a k s e e y a h a j i m e t o s u r u s e N p t a k u i y a e N k a k U ch i d e pau d e t a y a o N s e o h I s u o t o s u r u t a N k e N t a i r i h i N cl p a n i d i o o s a r e t e i m a s U +fleurs_jpn_000348 ky o o f u ky o k a d o n o k o o s u i r o o b i a m a k a sh i b a r a i u pau t a s u m a k i pau m i z u k i pau o y u s a i k u r o N n a N o n o k i b i sh i i k sh o k e t a y a s o n o e ky o o n i y o r u m o r o d e s U +fleurs_jpn_000349 i N t a n e t o w a m a s U k o m i r u k e e sh e N t o t a i j i N k o m i r u k e e sh o o n o pau d o y o s o o k a e s u m a i d a k a N ky o o r d e s U +fleurs_jpn_000350 k a j i n o r e w a ts u u j o o t o k u b e s u r a i sh I k o y a pau i e N t a a t e i m e N t o y o sh I t e i m a s U ky e s u w a k i b u y o k u sh I s e s u n a i r e t o r a m a r u y o o r e s u r u t a m d e s U +fleurs_jpn_000351 sh i k a sh i k a k U t e N n o b i k e cl t o o sh i n a cl t a t o i N d o w a pau n a n a z u n o b i k e cl t o o sh i n a i s a N j u r o b u r a j sh i k a d e g i m a s e N d e sh I t a diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..63a413454a78ad22c2545b3e5347de8da88dbfe1 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/token_int @@ -0,0 +1,40 @@ +cv_jpn_000880 2 3 4 8 3 11 2 8 3 15 4 6 2 9 2 6 18 8 5 20 6 2 7 6 2 25 2 4 23 3 7 +cv_jpn_000881 12 5 13 6 5 28 7 5 16 23 3 3 9 4 3 3 6 4 9 2 6 4 8 2 23 3 23 2 12 18 8 5 5 10 7 +cv_jpn_000882 9 2 9 4 6 2 15 4 10 2 9 3 4 13 12 5 13 8 5 25 7 17 2 9 2 4 8 3 6 4 25 7 4 12 7 5 9 3 14 5 17 2 +cv_jpn_000883 22 4 6 3 13 3 15 5 6 16 5 13 9 3 4 25 5 13 8 3 14 5 12 7 8 3 10 7 15 19 8 2 11 2 10 4 +cv_jpn_000884 11 2 10 4 9 3 15 19 8 3 17 2 25 3 3 28 5 13 8 3 15 19 8 5 4 8 2 +cv_jpn_000885 12 3 13 9 2 9 2 4 23 3 3 9 3 11 5 10 7 17 2 9 2 13 6 5 13 11 3 6 18 19 8 5 4 8 2 +cv_jpn_000886 4 22 4 16 2 4 14 5 14 5 5 12 31 7 4 15 19 8 5 4 8 2 +cv_jpn_000887 8 3 6 4 14 3 6 4 27 4 7 7 13 9 3 6 3 10 3 17 2 17 2 6 2 10 2 9 2 6 7 9 3 10 7 8 3 27 4 16 2 2 10 7 14 2 6 2 10 2 25 3 6 7 2 6 2 2 9 5 3 6 4 9 3 8 3 9 4 6 2 22 4 2 22 5 11 5 10 7 +cv_jpn_000888 11 3 3 9 4 16 5 8 5 27 2 8 2 11 5 14 2 +cv_jpn_000889 6 2 10 5 17 2 30 3 3 8 3 8 2 22 4 26 18 6 7 15 19 8 5 4 8 2 +cv_jpn_000890 14 2 10 7 13 4 11 7 9 5 4 17 2 6 3 17 2 6 2 6 5 8 2 6 2 9 2 4 +cv_jpn_000891 30 2 12 2 6 2 8 3 3 11 3 8 5 7 14 3 2 9 3 3 8 3 21 8 5 3 9 4 16 5 21 8 2 +cv_jpn_000892 15 8 5 4 11 2 12 5 13 +cv_jpn_000893 6 2 23 3 3 9 4 15 19 8 5 15 19 8 5 10 7 8 3 8 3 11 3 9 4 15 4 21 8 5 4 9 2 4 8 3 6 3 10 3 6 2 10 2 8 2 13 29 7 17 2 24 2 22 4 11 2 10 7 9 3 14 5 2 10 7 +cv_jpn_000894 2 4 12 2 26 7 17 2 14 2 4 22 4 14 2 4 3 +cv_jpn_000895 8 3 22 4 8 2 11 3 9 3 4 6 2 9 4 24 4 10 3 16 5 8 5 11 3 24 4 10 2 4 8 2 11 7 9 3 9 4 2 9 2 10 2 10 3 8 3 4 21 8 5 4 10 7 16 2 +cv_jpn_000896 8 2 11 7 15 4 9 4 6 7 26 18 6 2 26 18 6 7 21 8 5 11 4 3 17 2 +cv_jpn_000897 18 26 28 5 4 25 7 13 2 6 3 6 4 9 2 15 3 3 25 2 4 14 2 4 3 9 2 +cv_jpn_000898 17 24 2 21 8 5 4 +cv_jpn_000899 6 4 8 5 4 +cv_jpn_000900 16 3 +cv_jpn_000901 24 2 15 4 4 8 4 +cv_jpn_000902 4 5 2 +cv_jpn_000903 24 2 21 27 4 +cv_jpn_000904 24 9 5 +cv_jpn_000905 2 15 4 4 4 +cv_jpn_000906 6 7 3 +cv_jpn_000907 6 5 27 4 +cv_jpn_000908 6 2 6 2 6 7 16 2 20 2 6 4 10 2 6 2 9 4 12 7 10 7 20 29 23 2 21 6 2 13 8 5 6 19 15 4 13 14 10 4 9 4 15 19 8 2 16 2 7 6 3 8 3 9 4 23 3 21 8 5 +cv_jpn_000909 6 2 6 3 8 3 11 4 10 2 4 8 3 9 3 20 11 7 22 7 13 8 5 6 4 22 4 6 3 14 3 3 4 26 7 8 3 15 19 8 5 9 3 16 5 13 28 2 4 16 2 6 2 30 2 27 4 3 11 3 15 19 8 3 4 7 6 3 8 3 14 5 2 10 7 +cv_jpn_000910 25 7 26 7 10 4 8 5 6 4 12 5 6 2 4 7 17 2 12 7 7 16 2 6 7 8 5 6 4 19 6 4 16 3 3 9 4 3 21 8 5 2 10 2 17 2 12 2 10 5 10 7 20 12 7 7 6 2 6 7 8 5 6 19 4 6 2 8 2 27 4 9 3 12 5 6 2 4 2 10 7 +cv_jpn_000911 17 3 9 2 27 4 16 5 13 4 15 3 3 14 5 12 2 13 6 3 3 16 4 9 2 10 7 +cv_jpn_000912 16 2 4 6 3 6 7 6 2 10 2 6 4 8 2 11 3 9 3 14 2 8 3 15 4 8 5 25 4 21 6 7 4 +cv_jpn_000913 4 17 2 23 3 10 7 22 4 19 12 5 13 23 3 21 8 5 6 2 6 18 8 3 18 6 18 15 19 4 8 2 21 8 2 11 3 9 3 14 5 2 10 7 +fleurs_jpn_000346 3 9 2 22 4 23 3 9 4 14 2 13 12 7 5 7 17 2 24 4 22 28 2 3 3 7 28 7 25 3 24 2 6 3 8 3 16 2 16 4 11 7 7 28 7 6 5 10 2 10 5 8 5 4 11 2 12 18 +fleurs_jpn_000347 6 3 9 3 12 2 25 4 12 2 6 3 10 2 6 12 5 5 23 2 24 2 22 4 11 5 8 3 12 7 10 7 12 5 13 30 8 2 6 7 4 23 2 5 13 6 2 6 18 27 4 14 5 20 14 5 8 2 23 2 3 13 12 5 3 24 19 12 7 3 8 3 12 7 10 7 8 2 13 6 5 13 8 2 4 10 4 24 4 13 21 30 2 9 4 14 4 3 3 12 2 10 5 8 5 4 11 2 12 18 +fleurs_jpn_000348 29 3 3 31 7 29 3 6 2 14 3 9 3 6 3 3 12 7 4 10 3 3 25 4 2 11 2 6 2 15 4 25 2 10 2 4 7 20 8 2 12 7 11 2 6 4 20 11 4 28 7 6 4 20 3 23 7 12 2 4 6 7 10 3 13 9 2 13 3 9 3 6 4 25 4 15 4 4 6 15 3 6 5 8 2 23 2 12 3 9 3 5 29 3 3 9 4 23 3 10 7 11 3 10 3 14 5 12 18 +fleurs_jpn_000349 4 13 8 2 9 5 8 3 17 2 11 2 12 18 6 3 11 4 10 7 6 5 5 15 5 13 8 3 8 2 4 22 4 13 6 3 11 4 10 7 6 5 5 15 3 3 9 3 20 14 3 23 3 12 3 3 6 2 5 12 7 11 2 4 14 2 6 2 13 29 3 3 10 14 5 12 18 +fleurs_jpn_000350 6 2 22 4 9 3 10 5 17 2 26 7 7 22 3 3 8 3 6 7 25 5 12 7 10 2 4 15 19 6 3 23 2 20 4 5 13 8 2 2 8 5 4 11 5 13 8 3 23 3 15 19 8 5 4 11 2 12 18 29 5 12 7 17 2 6 4 25 7 23 3 6 7 15 19 12 5 12 7 9 2 4 10 5 8 3 10 2 11 2 10 7 23 3 3 10 5 12 7 10 7 8 2 11 14 5 12 18 +fleurs_jpn_000351 15 4 6 2 15 4 6 2 6 18 8 5 13 9 3 25 4 6 5 21 8 3 3 15 4 9 2 21 8 2 8 3 4 13 14 3 17 2 20 9 2 9 2 28 7 9 3 25 4 6 5 21 8 3 3 15 4 9 2 4 12 2 13 22 7 10 3 25 7 10 2 22 15 4 6 2 14 5 16 4 11 2 12 5 13 14 5 15 19 8 2 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..bded6be143a545fcca3ad3a125b1315a4315e1db --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/score @@ -0,0 +1,40 @@ +fleurs_jpn_000352 tensor(-15.2333) +fleurs_jpn_000353 tensor(-18.6987) +fleurs_jpn_000354 tensor(-11.6742) +fleurs_jpn_000355 tensor(-12.8193) +fleurs_jpn_000356 tensor(-6.0499) +fleurs_jpn_000357 tensor(-27.5695) +fleurs_jpn_000358 tensor(-22.2380) +fleurs_jpn_000359 tensor(-20.2296) +fleurs_jpn_000360 tensor(-19.6854) +fleurs_jpn_000361 tensor(-13.9631) +fleurs_jpn_000362 tensor(-9.2567) +fleurs_jpn_000363 tensor(-23.3926) +fleurs_jpn_000364 tensor(-23.4018) +fleurs_jpn_000365 tensor(-23.1044) +fleurs_jpn_000366 tensor(-12.9949) +fleurs_jpn_000367 tensor(-13.6426) +fleurs_jpn_000368 tensor(-29.4578) +fleurs_jpn_000369 tensor(-9.8885) +fleurs_jpn_000370 tensor(-26.1247) +fleurs_jpn_000371 tensor(-15.0359) +fleurs_jpn_000372 tensor(-12.9942) +fleurs_jpn_000373 tensor(-25.9730) +fleurs_jpn_000374 tensor(-17.0322) +fleurs_jpn_000375 tensor(-10.3499) +fleurs_jpn_000376 tensor(-6.2099) +fleurs_jpn_000377 tensor(-13.1509) +fleurs_jpn_000378 tensor(-13.2181) +fleurs_jpn_000379 tensor(-15.6332) +fleurs_jpn_000380 tensor(-16.2478) +fleurs_jpn_000381 tensor(-15.3353) +fleurs_jpn_000382 tensor(-10.4642) +fleurs_jpn_000383 tensor(-14.0170) +fleurs_jpn_000384 tensor(-25.2731) +fleurs_jpn_000385 tensor(-15.6348) +fleurs_jpn_000386 tensor(-13.1399) +fleurs_jpn_000387 tensor(-18.1279) +fleurs_jpn_000388 tensor(-27.0657) +fleurs_jpn_000389 tensor(-8.1403) +fleurs_jpn_000390 tensor(-11.3326) +fleurs_jpn_000391 tensor(-14.7062) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..e8423b5d83dc1dbff068ffb80a37ccfe9c354a85 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/text @@ -0,0 +1,40 @@ +fleurs_jpn_000352 h o o k u r a N d o n o k o o sh I k e ts u k a w a pau h o k u r a N d a sh o t o o b o N d o e f k e p i d e pau i ch i p o N n o g a i ch i e e b o N d o j i i p i b i i t o t o o k a n i k o t e s a r e d e i m a s U +fleurs_jpn_000353 h a j u sh i t a n o j o o o k o k a o w o ch u u g o m e t o o d e s U i s e N j u i ch i n e u h a ch i g a ts u m i s e k o o sh i i s e N j u n u n e N s e a N g a ts u m u n e k a i ts u e sh i m a s e N g e sh I t a +fleurs_jpn_000354 i cl p u N k a N d e h f u cl t o o s u b u ch i i k i m a r e b a h u cl t o s u r u m a r e m i n a N cl pau m o k a k a r u ch j i i k i m o a r i m a s U +fleurs_jpn_000355 p e r a m i cl t a n o o t o t o a sh I k a i n o sh o o w a pau k o n o k a N k o o sh u r e t o k u n i k o r o m a t a j i k a t a n a sh i m e r e m o y o o sh i n o sh i t o s u r e s U +fleurs_jpn_000356 s u n o t a n e pau t a i n i pau d a d e r u t o sh I t e h i o o k i g a ts u e k a s a r e g a ch i d e s U +fleurs_jpn_000357 g e N z N s u r u k o k o g a sh I t a r e t e i r a n i j i o g o o m a e n a d a N d a k t u k u r o o t U s a i d e o w a k i e N z o N s u r a o o g a i b u N k e n o s a i k o r o ts a sh i d e s U t e g a k i N y o r u g e N p o o w a k e N z o o sh I t e i m u a s e N +fleurs_jpn_000358 k r e m o s e s o o t a d a sh i t a o m i t o m e r u sh I t o m i m a sh I t e g a o o k u g u sh I t o s u o n o k e k o d e t a i y o o k e d e a t a i y o t o s u n o h o k a n o sh i g a ch i ky u n o u m a r e i d o o sh s e r u d o a sh i N ch i t e i m a sh I t a +fleurs_jpn_000359 ch i b e cl t o m e e s o o ch u sh i N w a sh i i s e e o g a d e s U s a N m a z a n a n a k a m i g a m i o sh I k a k U k a s u r u k o d o d e e n e r u g i i ch a n e r u g a sh i o k a s a r e ch a k r a b a k a s e k a s a r e s a t a r u n e i ch I k e g o o g a r e m a s U +fleurs_jpn_000360 b i n a n i y a h u r e k a n i a r u s u b e t e n o k o k u r e s U k o o e d a d o o y o n i k u n o k o o e n i a pau m a n e j i e h o k o sh I t o n i u e N g u o g a o k a k a r i m a s U +fleurs_jpn_000361 d e s a k u r u m a s u N n o h o k a n o o k u n u k o o ts o sh i u d a N u n a ts s u k o k a r e u m a r e m a sh I t a +fleurs_jpn_000362 i N t a n e cl t o w a m a s U k o m i n i k e e sh o N t o t a i j i N k o m i n u k e e sh o N n o y o y o o s o o k a n e s o n a i t a k a N ky o o r e s U +fleurs_jpn_000363 ky o o i N d e w a k a o N s e N k a N r e i t e j i u sh j u N i sh I s a r a e i k a n i e n u k a N s e N g o k a n o o s e s e g u d a m i n i k a N j a k a k u r s u r u n d a m u i s o o j o o t o t e i m a sh I U s +fleurs_jpn_000364 r e N p o o i k a y w a m i s e N g o n e N d o k a r a w a i s e s u e b u s u t o r e sh i m a r i h o e n o sh i k i N t e e ky o o k a i j i sh i e h u b i y a y w a a t a r u z o b o r u n o r i j u u n i n o s o o s a N y o t o o n u sh u n a k e r e w a d a r a r n a i t o k I t e e sh i m a sh I t a +fleurs_jpn_000365 h i e ch i d e r r o w a k e N s a sh I t k a k o k U b u e s t e i u k o N m a r e n a s e i s a i y u o pau pau h i i ch i n e h o ch i n o r o o d e sh i m a s a r e m i s U +fleurs_jpn_000366 s o r e d e m o pau t o o r u u k a r a n a r u b a i s u o u k e pau s u b e t e n o h o o sh I k e o m o r i a N z e N j o o n o k e e g o b o n i s a i sh i n o ch u i u o h a r a i m a sh o +fleurs_jpn_000367 k o r e r a w a t a m a n i k o N g a ts u r o k a cl k a ts u k u m o k e n o b u i ch i t e k a i g a i a pau s a m a z a m a n a t e N p o g a n a r a N d e i m a s U a N z e N y o e b u k o d o g a d e k i m a s U +fleurs_jpn_000368 sh i N n o m i e n a i ch i i d a s u N pau n a N d o pau n a f a s U t o s e N k e u k U a ch i j u k i u p e e j i i h a k U ky u e d o s u N z a i b o m a d a b a z e r e ch i e b m u n o d o k u j u n e y o s o d e r a r u +fleurs_jpn_000369 k o n o s a r i s u w a g o r a k U s e o h a j i m e t o s u r u s e N b t a k u y a pau e N g a k sh i r e d e t a y o N s e y o s u o t o s u r u t a N k e N t a N i r i pau h i N p a n i r i y o s a r e t e i m a s U +fleurs_jpn_000370 s a k o b a pau pau b u e n o s u a i d e s U k a r a k o j u cl k i r o s a N j u i ch i m a i r u h a n a r e t a r a b u r a t a sh i n a i d e pau k e N sh a g o j o o i N g i N d e w a r u k u r i s U t e i n a f u r u n a N d e s u d e pau k e r u k i n a z o sh i g a pau n a i t o o r o o s e N e n o sh I u r a o s e N g e N sh i m a sh I t a +fleurs_jpn_000371 o n a y i z u k u i r i m a sh i h a t o n o k a s o o e u r e b u i s u n o r u g a o ky u i w a k a s o o r o o o b a r a N sh i i pau k a b e n i g e k i t o ts u sh I t e pau j u u n a r a n i N g a sh i b o o sh i m a sh I t a +fleurs_jpn_000372 a sh i sh I a n a j o o h o o k k a N w a j u u g o m e d o d e s U m i s e N j u u ch i n e h a j i g a ts u n i sh u u N k o sh i pau m i s e N j u u n a n i e s a N g a s u m a r e k a i ts o sh i m a s e N d e sh I t a +fleurs_jpn_000373 b u u N n e e t o u k o t o w a w a sh i m i o m i s u r u r a t e N g o n o k e o sh i sh i b i r e s U k a r a k i I t a w o r i sh i m i N i o m i s u r u r a t e N g o r o m e sh i sh u i b i e s U t o sh i y a t o sh I k o k a o m i sh i pau n a r u n a k a n o k a t a ch i d e pau sh a k a i n o k i o o t e e g i s u r u sh u i b i t a s U t o u m e e sh i n i k a N k e e sh I t e m a s U +fleurs_jpn_000374 s u j o k o k o r e a i s u m o k a N k o o t e k e y a r y o o sh a t a j i g a h a s u r o t o k a ch i k o i t e k i m a s U w o t o d o sh i k a r i g o i n a s u m u n o g a t a r u a m a r u d e h o N n a y o r e s U +fleurs_jpn_000375 t e r e b i n o o d o o n o N t d o g e N p a ts U k a r a h a k u e g a g a d t e i m a s U e s +fleurs_jpn_000376 n o o b y o o r i t o k o o d o o n o s o o k a N k a N k e e w a pau k a y a k u sh a t a sh i n o k e N ky u u o u r a z u k e r e m o n o d e s U +fleurs_jpn_000377 s e y o o b i n a i b e N t o n a t o k a r u p a n e r o w a pau s e N j i k e N g e h U k a z s u n o k o j i N d e s i s u j o o sh i m a sh I t a +fleurs_jpn_000378 s e N e h a p e k u r e N d a i r a i g u N t a y g a t o o j a k s u r e w a r e pau h a i ch i a k o n o b y o k i n i k a N ky e e s u r u o N d a n i s o o u g u sh I t a k o t o w a r i m a s e N d e sh I t a +fleurs_jpn_000379 sh i k a sh i k a k u t e N n u b i k e cl d o o sh u n a cl t d a d o h i N d a n a n a ts u n e r i k e cl t o o sh u n e i s a N j o r o k u r a sh I t a r e k i m a s e N g e sh I t a +fleurs_jpn_000380 k a j i n o d e w a ts u u j o o t o k u b e ts u n a i N sh a k o y a pau e N t a t e i m e N t o o y o o i sh I t e i m a s U g e s t o u g a k i b u u m y a k u sh i s e z u n a i n i t o r o m a r u y o n r e s u r u t a m e r d e s U +fleurs_jpn_000381 s o r e d e o t o o ky u k a r a n a r a b a i s o k e pau s u t a n u h y o a sh I k e o m o r i pau a N z e N j i o n o k e k o n i s a i sh e i n o ch u y u o h a r a i m a sh a o +fleurs_jpn_000382 p o o k a r e g e w a r i m a s e k o r a sh I t ts u m u sh o o a r i d e a r i a t a r a sh i i sh o o n o m o k a k e d e s U +fleurs_jpn_000383 s a w a r i t o w a pau a u r i k a n o y a s e u d o o u z u pau t o k o n i s a w a N n a n i r u y a s e u d o o g u s u n o k a N s a s u o m o k u t e k i t o sh I t a r i k u r o d e n o r y o k o o s a sh i m a s U +fleurs_jpn_000384 u u n e k i t a b a r u t o k a y o u m o o d a N s u r u b a i w a pau s e i s u m i ch i k a o k u n i sh s e k u d a s a i k k o o r e n N n a k o ts U k I s u m N u s a i n i m o cl t o m e e ky o o k e r u s e i ts u d e a o s o r u j i u o d o o s o o N n a n a r i b i k i m a s U +fleurs_jpn_000385 k o k o w a pau i r i s u n o sh o k u m i N t e sh i e h a sh a e g a j i u N t a sh i n o d o o r o d o sh I a b a sh u n a r u d e pau sh a k o m i N t e i j i r e n e sh o o k o o s e g a s o o t o s u r e k a t a w a o o k o w a r h a j i b e r u n a g a y o i u sh o +fleurs_jpn_000386 k o sh i w a s a k u g e N s u r u s u u ch i o o s a r a y a s e N d e sh I t a k a s a k u g e N w a ch i e w o k u n o k e e z a i s a N a sh I u y o i m o t o z u i t e pau d i sh i s a r e u d a r o t o o n o i m a sh I t a +fleurs_jpn_000387 s a i n u u k o k u sh o k u w a pau sh i N k o o N r e k o o n o j u k i e g a s U k u n a i k a r u j a a sh k u y o r e m o h a e k o o d o z u r e pau n a r a b i k i pau y o r e sh o o j o o g a k a s u r e k o t o g a r i m a s U +fleurs_jpn_000388 ky i n o o n o a s a pau t o r u k o n o g a j i a N t e p k u n o k e e s a s u o h o m N w o r e pau j i d o o sh a b a k u r a n o b a k a s u r i o r i ky i e e k a a f U t a r e g a sh i b o sh i pau pau r w u sh i o w a sh a w a n i j u u n i y o k o a i m a sh I t a +fleurs_jpn_000389 sh o k u b u ts u a n i N g i N g a s u u s a N z o o ts U k u r i n i N g e N g a k a cl k o i t o sh I t e h a k i d a s u r n i s a N k a cl t a N s o o t o r i k o N d e i m a s U +fleurs_jpn_000390 s e N p a k u r e b u sh i o y u s o o s u r u n o w a pau u m i y o k o i t e pau k i t o a y a b u sh u e o t a r i r e o y i s o o s u r u e pau m o cl t o m o k o o r i s e k i n a h o o h o o r e s U +fleurs_jpn_000391 k a r i h o r u n i a sh u u n o a a n o r u d o o sh u w a r u ts u n e cl k a ch i sh i w a b o o r y o k U t e k i n a b i d e o u g e e m u w o m i s e e n e N sh a n i h a N b a y a e N t a s u d e k o t o o k i N i s u r o h o o w a N n i sh o m e sh i m a sh I t a diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..e8423b5d83dc1dbff068ffb80a37ccfe9c354a85 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/token @@ -0,0 +1,40 @@ +fleurs_jpn_000352 h o o k u r a N d o n o k o o sh I k e ts u k a w a pau h o k u r a N d a sh o t o o b o N d o e f k e p i d e pau i ch i p o N n o g a i ch i e e b o N d o j i i p i b i i t o t o o k a n i k o t e s a r e d e i m a s U +fleurs_jpn_000353 h a j u sh i t a n o j o o o k o k a o w o ch u u g o m e t o o d e s U i s e N j u i ch i n e u h a ch i g a ts u m i s e k o o sh i i s e N j u n u n e N s e a N g a ts u m u n e k a i ts u e sh i m a s e N g e sh I t a +fleurs_jpn_000354 i cl p u N k a N d e h f u cl t o o s u b u ch i i k i m a r e b a h u cl t o s u r u m a r e m i n a N cl pau m o k a k a r u ch j i i k i m o a r i m a s U +fleurs_jpn_000355 p e r a m i cl t a n o o t o t o a sh I k a i n o sh o o w a pau k o n o k a N k o o sh u r e t o k u n i k o r o m a t a j i k a t a n a sh i m e r e m o y o o sh i n o sh i t o s u r e s U +fleurs_jpn_000356 s u n o t a n e pau t a i n i pau d a d e r u t o sh I t e h i o o k i g a ts u e k a s a r e g a ch i d e s U +fleurs_jpn_000357 g e N z N s u r u k o k o g a sh I t a r e t e i r a n i j i o g o o m a e n a d a N d a k t u k u r o o t U s a i d e o w a k i e N z o N s u r a o o g a i b u N k e n o s a i k o r o ts a sh i d e s U t e g a k i N y o r u g e N p o o w a k e N z o o sh I t e i m u a s e N +fleurs_jpn_000358 k r e m o s e s o o t a d a sh i t a o m i t o m e r u sh I t o m i m a sh I t e g a o o k u g u sh I t o s u o n o k e k o d e t a i y o o k e d e a t a i y o t o s u n o h o k a n o sh i g a ch i ky u n o u m a r e i d o o sh s e r u d o a sh i N ch i t e i m a sh I t a +fleurs_jpn_000359 ch i b e cl t o m e e s o o ch u sh i N w a sh i i s e e o g a d e s U s a N m a z a n a n a k a m i g a m i o sh I k a k U k a s u r u k o d o d e e n e r u g i i ch a n e r u g a sh i o k a s a r e ch a k r a b a k a s e k a s a r e s a t a r u n e i ch I k e g o o g a r e m a s U +fleurs_jpn_000360 b i n a n i y a h u r e k a n i a r u s u b e t e n o k o k u r e s U k o o e d a d o o y o n i k u n o k o o e n i a pau m a n e j i e h o k o sh I t o n i u e N g u o g a o k a k a r i m a s U +fleurs_jpn_000361 d e s a k u r u m a s u N n o h o k a n o o k u n u k o o ts o sh i u d a N u n a ts s u k o k a r e u m a r e m a sh I t a +fleurs_jpn_000362 i N t a n e cl t o w a m a s U k o m i n i k e e sh o N t o t a i j i N k o m i n u k e e sh o N n o y o y o o s o o k a n e s o n a i t a k a N ky o o r e s U +fleurs_jpn_000363 ky o o i N d e w a k a o N s e N k a N r e i t e j i u sh j u N i sh I s a r a e i k a n i e n u k a N s e N g o k a n o o s e s e g u d a m i n i k a N j a k a k u r s u r u n d a m u i s o o j o o t o t e i m a sh I U s +fleurs_jpn_000364 r e N p o o i k a y w a m i s e N g o n e N d o k a r a w a i s e s u e b u s u t o r e sh i m a r i h o e n o sh i k i N t e e ky o o k a i j i sh i e h u b i y a y w a a t a r u z o b o r u n o r i j u u n i n o s o o s a N y o t o o n u sh u n a k e r e w a d a r a r n a i t o k I t e e sh i m a sh I t a +fleurs_jpn_000365 h i e ch i d e r r o w a k e N s a sh I t k a k o k U b u e s t e i u k o N m a r e n a s e i s a i y u o pau pau h i i ch i n e h o ch i n o r o o d e sh i m a s a r e m i s U +fleurs_jpn_000366 s o r e d e m o pau t o o r u u k a r a n a r u b a i s u o u k e pau s u b e t e n o h o o sh I k e o m o r i a N z e N j o o n o k e e g o b o n i s a i sh i n o ch u i u o h a r a i m a sh o +fleurs_jpn_000367 k o r e r a w a t a m a n i k o N g a ts u r o k a cl k a ts u k u m o k e n o b u i ch i t e k a i g a i a pau s a m a z a m a n a t e N p o g a n a r a N d e i m a s U a N z e N y o e b u k o d o g a d e k i m a s U +fleurs_jpn_000368 sh i N n o m i e n a i ch i i d a s u N pau n a N d o pau n a f a s U t o s e N k e u k U a ch i j u k i u p e e j i i h a k U ky u e d o s u N z a i b o m a d a b a z e r e ch i e b m u n o d o k u j u n e y o s o d e r a r u +fleurs_jpn_000369 k o n o s a r i s u w a g o r a k U s e o h a j i m e t o s u r u s e N b t a k u y a pau 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k a r u p a n e r o w a pau s e N j i k e N g e h U k a z s u n o k o j i N d e s i s u j o o sh i m a sh I t a +fleurs_jpn_000378 s e N e h a p e k u r e N d a i r a i g u N t a y g a t o o j a k s u r e w a r e pau h a i ch i a k o n o b y o k i n i k a N ky e e s u r u o N d a n i s o o u g u sh I t a k o t o w a r i m a s e N d e sh I t a +fleurs_jpn_000379 sh i k a sh i k a k u t e N n u b i k e cl d o o sh u n a cl t d a d o h i N d a n a n a ts u n e r i k e cl t o o sh u n e i s a N j o r o k u r a sh I t a r e k i m a s e N g e sh I t a +fleurs_jpn_000380 k a j i n o d e w a ts u u j o o t o k u b e ts u n a i N sh a k o y a pau e N t a t e i m e N t o o y o o i sh I t e i m a s U g e s t o u g a k i b u u m y a k u sh i s e z u n a i n i t o r o m a r u y o n r e s u r u t a m e r d e s U +fleurs_jpn_000381 s o r e d e o t o o ky u k a r a n a r a b a i s o k e pau s u t a n u h y o a sh I k e o m o r i pau a N z e N j i o n o k e k o n i s a i sh e i n o ch u y u o h a r a i m a sh a o +fleurs_jpn_000382 p o o k a r e g e w a r i m a s e k o r a sh I t ts u m u sh o o a r i d e a r i a t a r a sh i i sh o o n o m o k a k e d e s U +fleurs_jpn_000383 s a w a r i t o w a pau a u r i k a n o y a s e u d o o u z u pau t o k o n i s a w a N n a n i r u y a s e u d o o g u s u n o k a N s a s u o m o k u t e k i t o sh I t a r i k u r o d e n o r y o k o o s a sh i m a s U +fleurs_jpn_000384 u u n e k i t a b a r u t o k a y o u m o o d a N s u r u b a i w a pau s e i s u m i ch i k a o k u n i sh s e k u d a s a i k k o o r e n N n a k o ts U k I s u m N u s a i n i m o cl t o m e e ky o o k e r u s e i ts u d e a o s o r u j i u o d o o s o o N n a n a r i b i k i m a s U +fleurs_jpn_000385 k o k o w a pau i r i s u n o sh o k u m i N t e sh i e h a sh a e g a j i u N t a sh i n o d o o r o d o sh I a b a sh u n a r u d e pau sh a k o m i N t e i j i r e n e sh o o k o o s e g a s o o t o s u r e k a t a w a o o k o w a r h a j i b e r u n a g a y o i u sh o +fleurs_jpn_000386 k o sh i w a s a k u g e N s u r u s u u ch i o o s a r a y a s e N d e sh I t a k a s a k u g e N w a ch i e w o k u n o k e e z a i s a N a sh I u y o i m o t o z u i t e pau d i sh i s a r e u d a r o t o o n o i m a sh I t a +fleurs_jpn_000387 s a i n u u k o k u sh o k u w a pau sh i N k o o N r e k o o n o j u k i e g a s U k u n a i k a r u j a a sh k u y o r e m o h a e k o o d o z u r e pau n a r a b i k i pau y o r e sh o o j o o g a k a s u r e k o t o g a r i m a s U +fleurs_jpn_000388 ky i n o o n o a s a pau t o r u k o n o g a j i a N t e p k u n o k e e s a s u o h o m N w o r e pau j i d o o sh a b a k u r a n o b a k a s u r i o r i ky i e e k a a f U t a r e g a sh i b o sh i pau pau r w u sh i o w a sh a w a n i j u u n i y o k o a i m a sh I t a +fleurs_jpn_000389 sh o k u b u ts u a n i N g i N g a s u u s a N z o o ts U k u r i n i N g e N g a k a cl k o i t o sh I t e h a k i d a s u r n i s a N k a cl t a N s o o t o r i k o N d e i m a s U +fleurs_jpn_000390 s e N p a k u r e b u sh i o y u s o o s u r u n o w a pau u m i y o k o i t e pau k i t o a y a b u sh u e o t a r i r e o y i s o o s u r u e pau m o cl t o m o k o o r i s e k i n a h o o h o o r e s U +fleurs_jpn_000391 k a r i h o r u n i a sh u u n o a a n o r u d o o sh u w a r u ts u n e cl k a ch i sh i w a b o o r y o k U t e k i n a b i d e o u g e e m u w o m i s e e n e N sh a n i h a N b a y a e N t a s u d e k o t o o k i N i s u r o h o o w a N n i sh o m e sh i m a sh I t a diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..54cfb621ae7c3044ad190a663b908cdd875a6548 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/token_int @@ -0,0 +1,40 @@ +fleurs_jpn_000352 24 3 3 6 7 10 2 13 14 3 9 3 6 3 3 15 19 6 5 26 7 6 2 17 2 20 24 3 6 7 10 2 13 14 2 15 3 8 3 3 25 3 13 14 3 5 31 6 5 30 4 14 5 20 4 27 4 30 3 13 9 3 16 2 4 27 4 5 5 25 3 13 14 3 22 4 4 30 4 25 4 4 8 3 8 3 3 6 2 9 4 6 3 8 5 12 2 10 5 14 5 4 11 2 12 18 +fleurs_jpn_000353 24 2 22 7 15 4 8 2 9 3 22 3 3 3 6 3 6 2 3 17 3 27 7 7 16 3 11 5 8 3 3 14 5 12 18 4 12 5 13 22 7 4 27 4 9 5 7 24 2 27 4 16 2 26 7 11 4 12 5 6 3 3 15 4 4 12 5 13 22 7 9 7 9 5 13 12 5 2 13 16 2 26 7 11 7 9 5 6 2 4 26 7 5 15 4 11 2 12 5 13 16 5 15 19 8 2 +fleurs_jpn_000354 4 21 30 7 13 6 2 13 14 5 24 31 7 21 8 3 3 12 7 25 7 27 4 4 6 4 11 2 10 5 25 2 24 7 21 8 3 12 7 10 7 11 2 10 5 11 4 9 2 13 21 20 11 3 6 2 6 2 10 7 27 22 4 4 6 4 11 3 2 10 4 11 2 12 18 +fleurs_jpn_000355 30 5 10 2 11 4 21 8 2 9 3 3 8 3 8 3 2 15 19 6 2 4 9 3 15 3 3 17 2 20 6 3 9 3 6 2 13 6 3 3 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12 5 5 3 16 2 14 5 12 18 12 2 13 11 2 28 2 9 2 9 2 6 2 11 4 16 2 11 4 3 15 19 6 2 6 18 6 2 12 7 10 7 6 3 14 3 14 5 5 9 5 10 7 16 4 4 27 2 9 5 10 7 16 2 15 4 3 6 2 12 2 10 5 27 2 6 10 2 25 2 6 2 12 5 6 2 12 2 10 5 12 2 8 2 10 7 9 5 4 27 19 6 5 16 3 3 16 2 10 5 11 2 12 18 +fleurs_jpn_000360 25 4 9 2 9 4 23 2 24 7 10 5 6 2 9 4 2 10 7 12 7 25 5 8 5 9 3 6 3 6 7 10 5 12 18 6 3 3 5 14 2 14 3 3 23 3 9 4 6 7 9 3 6 3 3 5 9 4 2 20 11 2 9 5 22 4 5 24 3 6 3 15 19 8 3 9 4 7 5 13 16 7 3 16 2 3 6 2 6 2 10 4 11 2 12 18 +fleurs_jpn_000361 14 5 12 2 6 7 10 7 11 2 12 7 13 9 3 24 3 6 2 9 3 3 6 7 9 7 6 3 3 26 3 15 4 7 14 2 13 7 9 2 26 12 7 6 3 6 2 10 5 7 11 2 10 5 11 2 15 19 8 2 +fleurs_jpn_000362 4 13 8 2 9 5 21 8 3 17 2 11 2 12 18 6 3 11 4 9 4 6 5 5 15 3 13 8 3 8 2 4 22 4 13 6 3 11 4 9 7 6 5 5 15 3 13 9 3 23 3 23 3 3 12 3 3 6 2 9 5 12 3 9 2 4 8 2 6 2 13 29 3 3 10 5 12 18 +fleurs_jpn_000363 29 3 3 4 13 14 5 17 2 6 2 3 13 12 5 13 6 2 13 10 5 4 8 5 22 4 7 15 22 7 13 4 15 19 12 2 10 2 5 4 6 2 9 4 5 9 7 6 2 13 12 5 13 16 3 6 2 9 3 3 12 5 12 5 16 7 14 2 11 4 9 4 6 2 13 22 2 6 2 6 7 10 12 7 10 7 9 14 2 11 7 4 12 3 3 22 3 3 8 3 8 5 4 11 2 15 19 18 12 +fleurs_jpn_000364 10 5 13 30 3 3 4 6 2 23 17 2 11 4 12 5 13 16 3 9 5 13 14 3 6 2 10 2 17 2 4 12 5 12 7 5 25 7 12 7 8 3 10 5 15 4 11 2 10 4 24 3 5 9 3 15 4 6 4 13 8 5 5 29 3 3 6 2 4 22 4 15 4 5 24 7 25 4 23 2 23 17 2 2 8 2 10 7 28 3 25 3 10 7 9 3 10 4 22 7 7 9 4 9 3 12 3 3 12 2 13 23 3 8 3 3 9 7 15 7 9 2 6 5 10 5 17 2 14 2 10 2 10 9 2 4 8 3 6 19 8 5 5 15 4 11 2 15 19 8 2 +fleurs_jpn_000365 24 4 5 27 4 14 5 10 10 3 17 2 6 5 13 12 2 15 19 8 6 2 6 3 6 18 25 7 5 12 8 5 4 7 6 3 13 11 2 10 5 9 2 12 5 4 12 2 4 23 7 3 20 20 24 4 4 27 4 9 5 24 3 27 4 9 3 10 3 3 14 5 15 4 11 2 12 2 10 5 11 4 12 18 +fleurs_jpn_000366 12 3 10 5 14 5 11 3 20 8 3 3 10 7 7 6 2 10 2 9 2 10 7 25 2 4 12 7 3 7 6 5 20 12 7 25 5 8 5 9 3 24 3 3 15 19 6 5 3 11 3 10 4 2 13 28 5 13 22 3 3 9 3 6 5 5 16 3 25 3 9 4 12 2 4 15 4 9 3 27 7 4 7 3 24 2 10 2 4 11 2 15 3 +fleurs_jpn_000367 6 3 10 5 10 2 17 2 8 2 11 2 9 4 6 3 13 16 2 26 7 10 3 6 2 21 6 2 26 7 6 7 11 3 6 5 9 3 25 7 4 27 4 8 5 6 2 4 16 2 4 2 20 12 2 11 2 28 2 11 2 9 2 8 5 13 30 3 16 2 9 2 10 2 13 14 5 4 11 2 12 18 2 13 28 5 13 23 3 5 25 7 6 3 14 3 16 2 14 5 6 4 11 2 12 18 +fleurs_jpn_000368 15 4 13 9 3 11 4 5 9 2 4 27 4 4 14 2 12 7 13 20 9 2 13 14 3 20 9 2 31 2 12 18 8 3 12 5 13 6 5 7 6 18 2 27 4 22 7 6 4 7 30 5 5 22 4 4 24 2 6 18 29 7 5 14 3 12 7 13 28 2 4 25 3 11 2 14 2 25 2 28 5 10 5 27 4 5 25 11 7 9 3 14 3 6 7 22 7 9 5 23 3 12 3 14 5 10 2 10 7 +fleurs_jpn_000369 6 3 9 3 12 2 10 4 12 7 17 2 16 3 10 2 6 18 12 5 3 24 2 22 4 11 5 8 3 12 7 10 7 12 5 13 25 8 2 6 7 23 2 20 5 13 16 2 6 15 4 10 5 14 5 8 2 23 3 13 12 5 23 3 12 7 3 8 3 12 7 10 7 8 2 13 6 5 13 8 2 13 4 10 4 20 24 4 13 30 2 9 4 10 4 23 3 12 2 10 5 8 5 4 11 2 12 18 +fleurs_jpn_000370 12 2 6 3 25 2 20 20 25 7 5 9 3 12 7 2 4 14 5 12 18 6 2 10 2 6 3 22 7 21 6 4 10 3 12 2 13 22 7 4 27 4 11 2 4 10 7 24 2 9 2 10 5 8 2 10 2 25 7 10 2 8 2 15 4 9 2 4 14 5 20 6 5 13 15 2 16 3 22 3 3 4 13 16 4 13 14 5 17 2 10 7 6 7 10 4 12 18 8 5 4 9 2 31 7 10 7 9 2 13 14 5 12 7 14 5 20 6 5 10 7 6 4 9 2 28 3 15 4 16 2 20 9 2 4 8 3 3 10 3 3 12 5 13 5 9 3 15 19 7 10 2 3 12 5 13 16 5 13 15 4 11 2 15 19 8 2 +fleurs_jpn_000371 3 9 2 23 4 28 7 6 7 4 10 4 11 2 15 4 24 2 8 3 9 3 6 2 12 3 3 5 7 10 5 25 7 4 12 7 9 3 10 7 16 2 3 29 7 4 17 2 6 2 12 3 3 10 3 3 3 25 2 10 2 13 15 4 4 20 6 2 25 5 9 4 16 5 6 4 8 3 26 7 15 19 8 5 20 22 7 7 9 2 10 2 9 4 13 16 2 15 4 25 3 3 15 4 11 2 15 19 8 2 +fleurs_jpn_000372 2 15 4 15 19 2 9 2 22 3 3 24 3 3 6 6 2 13 17 2 22 7 7 16 3 11 5 14 3 14 5 12 18 11 4 12 5 13 22 7 7 27 4 9 5 24 2 22 4 16 2 26 7 9 4 15 7 7 13 6 3 15 4 20 11 4 12 5 13 22 7 7 9 2 9 4 5 12 2 13 16 2 12 7 11 2 10 5 6 2 4 26 3 15 4 11 2 12 5 13 14 5 15 19 8 2 +fleurs_jpn_000373 25 7 7 13 9 5 5 8 3 7 6 3 8 3 17 2 17 2 15 4 11 4 3 11 4 12 7 10 7 10 2 8 5 13 16 3 9 3 6 5 3 15 4 15 4 25 4 10 5 12 18 6 2 10 2 6 4 19 8 2 17 3 10 4 15 4 11 4 13 4 3 11 4 12 7 10 7 10 2 8 5 13 16 3 10 3 11 5 15 4 15 7 4 25 4 5 12 18 8 3 15 4 23 2 8 3 15 19 6 3 6 2 3 11 4 15 4 20 9 2 10 7 9 2 6 2 9 3 6 2 8 2 27 4 14 5 20 15 2 6 2 4 9 3 6 4 3 3 8 5 5 16 4 12 7 10 7 15 7 4 25 4 8 2 12 18 8 3 7 11 5 5 15 4 9 4 6 2 13 6 5 5 15 19 8 5 11 2 12 18 +fleurs_jpn_000374 12 7 22 3 6 3 6 3 10 5 2 4 12 7 11 3 6 2 13 6 3 3 8 5 6 5 23 2 10 23 3 3 15 2 8 2 22 4 16 2 24 2 12 7 10 3 8 3 6 2 27 4 6 3 4 8 5 6 4 11 2 12 18 17 3 8 3 14 3 15 4 6 2 10 4 16 3 4 9 2 12 7 11 7 9 3 16 2 8 2 10 7 2 11 2 10 7 14 5 24 3 13 9 2 23 3 10 5 12 18 +fleurs_jpn_000375 8 5 10 5 25 4 9 3 3 14 3 3 9 3 13 8 14 3 16 5 13 30 2 26 18 6 2 10 2 24 2 6 7 5 16 2 16 2 14 8 5 4 11 2 12 18 5 12 +fleurs_jpn_000376 9 3 3 25 23 3 3 10 4 8 3 6 3 3 14 3 3 9 3 12 3 3 6 2 13 6 2 13 6 5 5 17 2 20 6 2 23 2 6 7 15 2 8 2 15 4 9 3 6 5 13 29 7 7 3 7 10 2 28 7 6 5 10 5 11 3 9 3 14 5 12 18 +fleurs_jpn_000377 12 5 23 3 3 25 4 9 2 4 25 5 13 8 3 9 2 8 3 6 2 10 7 30 2 9 5 10 3 17 2 20 12 5 13 22 4 6 5 13 16 5 24 18 6 2 28 12 7 9 3 6 3 22 4 13 14 5 12 4 12 7 22 3 3 15 4 11 2 15 19 8 2 +fleurs_jpn_000378 12 5 13 5 24 2 30 5 6 7 10 5 13 14 2 4 10 2 4 16 7 13 8 2 23 16 2 8 3 3 22 2 6 12 7 10 5 17 2 10 5 20 24 2 4 27 4 2 6 3 9 3 25 23 3 6 4 9 4 6 2 13 29 5 5 12 7 10 7 3 13 14 2 9 4 12 3 3 7 16 7 15 19 8 2 6 3 8 3 17 2 10 4 11 2 12 5 13 14 5 15 19 8 2 +fleurs_jpn_000379 15 4 6 2 15 4 6 2 6 7 8 5 13 9 7 25 4 6 5 21 14 3 3 15 7 9 2 21 8 14 2 14 3 24 4 13 14 2 9 2 9 2 26 7 9 5 10 4 6 5 21 8 3 3 15 7 9 5 4 12 2 13 22 3 10 3 6 7 10 2 15 19 8 2 10 5 6 4 11 2 12 5 13 16 5 15 19 8 2 +fleurs_jpn_000380 6 2 22 4 9 3 14 5 17 2 26 7 7 22 3 3 8 3 6 7 25 5 26 7 9 2 4 13 15 2 6 3 23 2 20 5 13 8 2 8 5 4 11 5 13 8 3 3 23 3 3 4 15 19 8 5 4 11 2 12 18 16 5 12 8 3 7 16 2 6 4 25 7 7 11 23 2 6 7 15 4 12 5 28 7 9 2 4 9 4 8 3 10 3 11 2 10 7 23 3 9 10 5 12 7 10 7 8 2 11 5 10 14 5 12 18 +fleurs_jpn_000381 12 3 10 5 14 5 3 8 3 3 29 7 6 2 10 2 9 2 10 2 25 2 4 12 3 6 5 20 12 7 8 2 9 7 24 23 3 2 15 19 6 5 3 11 3 10 4 20 2 13 28 5 13 22 4 3 9 3 6 5 6 3 9 4 12 2 4 15 5 4 9 3 27 7 23 7 3 24 2 10 2 4 11 2 15 2 3 +fleurs_jpn_000382 30 3 3 6 2 10 5 16 5 17 2 10 4 11 2 12 5 6 3 10 2 15 19 8 26 7 11 7 15 3 3 2 10 4 14 5 2 10 4 2 8 2 10 2 15 4 4 15 3 3 9 3 11 3 6 2 6 5 14 5 12 18 +fleurs_jpn_000383 12 2 17 2 10 4 8 3 17 2 20 2 7 10 4 6 2 9 3 23 2 12 5 7 14 3 3 7 28 7 20 8 3 6 3 9 4 12 2 17 2 13 9 2 9 4 10 7 23 2 12 5 7 14 3 3 16 7 12 7 9 3 6 2 13 12 2 12 7 3 11 3 6 7 8 5 6 4 8 3 15 19 8 2 10 4 6 7 10 3 14 5 9 3 10 23 3 6 3 3 12 2 15 4 11 2 12 18 +fleurs_jpn_000384 7 7 9 5 6 4 8 2 25 2 10 7 8 3 6 2 23 3 7 11 3 3 14 2 13 12 7 10 7 25 2 4 17 2 20 12 5 4 12 7 11 4 27 4 6 2 3 6 7 9 4 15 12 5 6 7 14 2 12 2 4 6 6 3 3 10 5 9 13 9 2 6 3 26 18 6 19 12 7 11 13 7 12 2 4 9 4 11 3 21 8 3 11 5 5 29 3 3 6 5 10 7 12 5 4 26 7 14 5 2 3 12 3 10 7 22 4 7 3 14 3 3 12 3 3 13 9 2 9 2 10 4 25 4 6 4 11 2 12 18 +fleurs_jpn_000385 6 3 6 3 17 2 20 4 10 4 12 7 9 3 15 3 6 7 11 4 13 8 5 15 4 5 24 2 15 2 5 16 2 22 4 7 13 8 2 15 4 9 3 14 3 3 10 3 14 3 15 19 2 25 2 15 7 9 2 10 7 14 5 20 15 2 6 3 11 4 13 8 5 4 22 4 10 5 9 5 15 3 3 6 3 3 12 5 16 2 12 3 3 8 3 12 7 10 5 6 2 8 2 17 2 3 3 6 3 17 2 10 24 2 22 4 25 5 10 7 9 2 16 2 23 3 4 7 15 3 +fleurs_jpn_000386 6 3 15 4 17 2 12 2 6 7 16 5 13 12 7 10 7 12 7 7 27 4 3 3 12 2 10 2 23 2 12 5 13 14 5 15 19 8 2 6 2 12 2 6 7 16 5 13 17 2 27 4 5 17 3 6 7 9 3 6 5 5 28 2 4 12 2 13 2 15 19 7 23 3 4 11 3 8 3 28 7 4 8 5 20 14 4 15 4 12 2 10 5 7 14 2 10 3 8 3 3 9 3 4 11 2 15 19 8 2 +fleurs_jpn_000387 12 2 4 9 7 7 6 3 6 7 15 3 6 7 17 2 20 15 4 13 6 3 3 13 10 5 6 3 3 9 3 22 7 6 4 5 16 2 12 18 6 7 9 2 4 6 2 10 7 22 2 2 15 6 7 23 3 10 5 11 3 24 2 5 6 3 3 14 3 28 7 10 5 20 9 2 10 2 25 4 6 4 20 23 3 10 5 15 3 3 22 3 3 16 2 6 2 12 7 10 5 6 3 8 3 16 2 10 4 11 2 12 18 +fleurs_jpn_000388 29 4 9 3 3 9 3 2 12 2 20 8 3 10 7 6 3 9 3 16 2 22 4 2 13 8 5 30 6 7 9 3 6 5 5 12 2 12 7 3 24 3 11 13 17 3 10 5 20 22 4 14 3 3 15 2 25 2 6 7 10 2 9 3 25 2 6 2 12 7 10 4 3 10 4 29 4 5 5 6 2 2 31 18 8 2 10 5 16 2 15 4 25 3 15 4 20 20 10 17 7 15 4 3 17 2 15 2 17 2 9 4 22 7 7 9 4 23 3 6 3 2 4 11 2 15 19 8 2 +fleurs_jpn_000389 15 3 6 7 25 7 26 7 2 9 4 13 16 4 13 16 2 12 7 7 12 2 13 28 3 3 26 18 6 7 10 4 9 4 13 16 5 13 16 2 6 2 21 6 3 4 8 3 15 19 8 5 24 2 6 4 14 2 12 7 10 9 4 12 2 13 6 2 21 8 2 13 12 3 3 8 3 10 4 6 3 13 14 5 4 11 2 12 18 +fleurs_jpn_000390 12 5 13 30 2 6 7 10 5 25 7 15 4 3 23 7 12 3 3 12 7 10 7 9 3 17 2 20 7 11 4 23 3 6 3 4 8 5 20 6 4 8 3 2 23 2 25 7 15 7 5 3 8 2 10 4 10 5 3 23 4 12 3 3 12 7 10 7 5 20 11 3 21 8 3 11 3 6 3 3 10 4 12 5 6 4 9 2 24 3 3 24 3 3 10 5 12 18 +fleurs_jpn_000391 6 2 10 4 24 3 10 7 9 4 2 15 7 7 9 3 2 2 9 3 10 7 14 3 3 15 7 17 2 10 7 26 7 9 5 21 6 2 27 4 15 4 17 2 25 3 3 10 23 3 6 18 8 5 6 4 9 2 25 4 14 5 3 7 16 5 5 11 7 17 3 11 4 12 5 5 9 5 13 15 2 9 4 24 2 13 25 2 23 2 5 13 8 2 12 7 14 5 6 3 8 3 3 6 4 13 4 12 7 10 3 24 3 3 17 2 13 9 4 15 3 11 5 15 4 11 2 15 19 8 2 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score new file mode 100644 index 0000000000000000000000000000000000000000..0bac4afa8b42ebfdc135e93b6693370a04b44f9c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score @@ -0,0 +1,160 @@ +cv_jpn_000800 tensor(-9.3796) +cv_jpn_000801 tensor(-13.5940) +cv_jpn_000802 tensor(-8.4504) +cv_jpn_000803 tensor(-9.1606) +cv_jpn_000804 tensor(-3.8179) +cv_jpn_000805 tensor(-2.7015) +cv_jpn_000806 tensor(-6.1459) +cv_jpn_000807 tensor(-5.3374) +cv_jpn_000808 tensor(-1.5552) +cv_jpn_000809 tensor(-9.5867) +cv_jpn_000810 tensor(-5.3105) +cv_jpn_000811 tensor(-1.6416) +cv_jpn_000812 tensor(-6.9316) +cv_jpn_000813 tensor(-4.7079) +cv_jpn_000814 tensor(-4.0117) +cv_jpn_000815 tensor(-5.2686) +cv_jpn_000816 tensor(-6.3233) +cv_jpn_000817 tensor(-2.9187) +cv_jpn_000818 tensor(-1.1429) +cv_jpn_000819 tensor(-0.2917) +cv_jpn_000820 tensor(-1.2863) +cv_jpn_000821 tensor(-0.9688) +cv_jpn_000822 tensor(-0.9036) +cv_jpn_000823 tensor(-6.3309) +cv_jpn_000824 tensor(-4.9260) +cv_jpn_000825 tensor(-7.8469) +cv_jpn_000826 tensor(-7.6292) +cv_jpn_000827 tensor(-2.7020) +cv_jpn_000828 tensor(-3.1539) +cv_jpn_000829 tensor(-1.5737) +cv_jpn_000830 tensor(-0.7087) +cv_jpn_000831 tensor(-0.9074) +cv_jpn_000832 tensor(-3.5826) +cv_jpn_000833 tensor(-1.4494) +cv_jpn_000834 tensor(-3.4315) +cv_jpn_000835 tensor(-3.8001) +cv_jpn_000836 tensor(-1.1456) +cv_jpn_000837 tensor(-1.2321) +cv_jpn_000838 tensor(-3.0537) +cv_jpn_000839 tensor(-3.1044) +cv_jpn_000840 tensor(-9.2983) +cv_jpn_000841 tensor(-5.1807) +cv_jpn_000842 tensor(-2.5816) +cv_jpn_000843 tensor(-4.3242) +cv_jpn_000844 tensor(-11.6624) +cv_jpn_000845 tensor(-2.7441) +cv_jpn_000846 tensor(-9.3766) +cv_jpn_000847 tensor(-5.9137) +cv_jpn_000848 tensor(-3.0642) +cv_jpn_000849 tensor(-2.3749) +cv_jpn_000850 tensor(-2.7668) +cv_jpn_000851 tensor(-3.7305) +cv_jpn_000852 tensor(-4.2572) +cv_jpn_000853 tensor(-4.3378) +cv_jpn_000854 tensor(-15.9908) +cv_jpn_000855 tensor(-5.9197) +cv_jpn_000856 tensor(-4.6206) +cv_jpn_000857 tensor(-9.4156) +cv_jpn_000858 tensor(-3.3463) +cv_jpn_000859 tensor(-6.9946) +cv_jpn_000860 tensor(-5.8497) +cv_jpn_000861 tensor(-2.9789) +cv_jpn_000862 tensor(-9.5575) +cv_jpn_000863 tensor(-12.0254) +cv_jpn_000864 tensor(-10.4547) +cv_jpn_000865 tensor(-3.6948) +cv_jpn_000866 tensor(-8.8115) +cv_jpn_000867 tensor(-5.2961) +cv_jpn_000868 tensor(-0.8697) +cv_jpn_000869 tensor(-6.9120) +cv_jpn_000870 tensor(-2.6078) +cv_jpn_000871 tensor(-8.3199) +cv_jpn_000872 tensor(-10.4440) +cv_jpn_000873 tensor(-13.3743) +cv_jpn_000874 tensor(-6.8139) +cv_jpn_000875 tensor(-12.0495) +cv_jpn_000876 tensor(-2.8980) +cv_jpn_000877 tensor(-3.4551) +cv_jpn_000878 tensor(-2.8128) +cv_jpn_000879 tensor(-11.0617) +cv_jpn_000880 tensor(-1.8229) +cv_jpn_000881 tensor(-6.0109) +cv_jpn_000882 tensor(-4.9126) +cv_jpn_000883 tensor(-5.8385) +cv_jpn_000884 tensor(-1.5993) +cv_jpn_000885 tensor(-3.4974) +cv_jpn_000886 tensor(-2.8257) +cv_jpn_000887 tensor(-11.7613) +cv_jpn_000888 tensor(-2.5982) +cv_jpn_000889 tensor(-2.1539) +cv_jpn_000890 tensor(-5.2186) +cv_jpn_000891 tensor(-6.5643) +cv_jpn_000892 tensor(-1.6122) +cv_jpn_000893 tensor(-2.6140) +cv_jpn_000894 tensor(-2.4411) +cv_jpn_000895 tensor(-3.0360) +cv_jpn_000896 tensor(-2.6069) +cv_jpn_000897 tensor(-2.8417) +cv_jpn_000898 tensor(-2.0908) +cv_jpn_000899 tensor(-2.4985) +cv_jpn_000900 tensor(-0.6560) +cv_jpn_000901 tensor(-2.6687) +cv_jpn_000902 tensor(-1.4906) +cv_jpn_000903 tensor(-1.6052) +cv_jpn_000904 tensor(-1.0377) +cv_jpn_000905 tensor(-1.4494) +cv_jpn_000906 tensor(-0.8865) +cv_jpn_000907 tensor(-2.4450) +cv_jpn_000908 tensor(-5.9653) +cv_jpn_000909 tensor(-6.9296) +cv_jpn_000910 tensor(-7.4590) +cv_jpn_000911 tensor(-3.0419) +cv_jpn_000912 tensor(-2.8038) +cv_jpn_000913 tensor(-4.9149) +fleurs_jpn_000346 tensor(-8.4199) +fleurs_jpn_000347 tensor(-14.3032) +fleurs_jpn_000348 tensor(-18.1699) +fleurs_jpn_000349 tensor(-8.6297) +fleurs_jpn_000350 tensor(-10.7191) +fleurs_jpn_000351 tensor(-11.6995) +fleurs_jpn_000352 tensor(-15.2333) +fleurs_jpn_000353 tensor(-18.6987) +fleurs_jpn_000354 tensor(-11.6742) +fleurs_jpn_000355 tensor(-12.8193) +fleurs_jpn_000356 tensor(-6.0499) +fleurs_jpn_000357 tensor(-27.5695) +fleurs_jpn_000358 tensor(-22.2380) +fleurs_jpn_000359 tensor(-20.2296) +fleurs_jpn_000360 tensor(-19.6854) +fleurs_jpn_000361 tensor(-13.9631) +fleurs_jpn_000362 tensor(-9.2567) +fleurs_jpn_000363 tensor(-23.3926) +fleurs_jpn_000364 tensor(-23.4018) +fleurs_jpn_000365 tensor(-23.1044) +fleurs_jpn_000366 tensor(-12.9949) +fleurs_jpn_000367 tensor(-13.6426) +fleurs_jpn_000368 tensor(-29.4578) +fleurs_jpn_000369 tensor(-9.8885) +fleurs_jpn_000370 tensor(-26.1247) +fleurs_jpn_000371 tensor(-15.0359) +fleurs_jpn_000372 tensor(-12.9942) +fleurs_jpn_000373 tensor(-25.9730) +fleurs_jpn_000374 tensor(-17.0322) +fleurs_jpn_000375 tensor(-10.3499) +fleurs_jpn_000376 tensor(-6.2099) +fleurs_jpn_000377 tensor(-13.1509) +fleurs_jpn_000378 tensor(-13.2181) +fleurs_jpn_000379 tensor(-15.6332) +fleurs_jpn_000380 tensor(-16.2478) +fleurs_jpn_000381 tensor(-15.3353) +fleurs_jpn_000382 tensor(-10.4642) +fleurs_jpn_000383 tensor(-14.0170) +fleurs_jpn_000384 tensor(-25.2731) +fleurs_jpn_000385 tensor(-15.6348) +fleurs_jpn_000386 tensor(-13.1399) +fleurs_jpn_000387 tensor(-18.1279) +fleurs_jpn_000388 tensor(-27.0657) +fleurs_jpn_000389 tensor(-8.1403) +fleurs_jpn_000390 tensor(-11.3326) +fleurs_jpn_000391 tensor(-14.7062) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..ac66ff92e246dc5291737e13ff668fcdca9ac735 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/hyp.trn @@ -0,0 +1,160 @@ +k a k o t o m i r a i t o d o u j u N t e k i j i k o d o o i t s u n a r u g a i w e n i p a u s h I k i t e k i n a N o d e a r (cv_jpn_000800-cv_jpn_000800) +s e k a y o k e e s e e s u r u t o t o m u n i p a u j i g k o j i s h i y o k e e s e s e r u t s s o o d o t e k i s e k a i n s o o z o o t e k i y o t o s h I t e p a u k o m u t s u g a k o u t s u d e a r u (cv_jpn_000801-cv_jpn_000801) +p a s o k o N n e g e e m i a r u I t o n a h f u e t e k i t e (cv_jpn_000802-cv_jpn_000802) +k a a k u n o s h i m e s a t a r a s h i i j i j i t s u a t a r a s i k a N n e N k a N k y o s h i h a i n a t a r a s h i k a n o s e o m o c l t e p a u n a n i o h a j i m e r u k a w a (cv_jpn_000803-cv_jpn_000803) +o m o s h i r o e n o n i p a u r o o d o n a k a s u g i t e p a u d a r u i (cv_jpn_000804-cv_jpn_000804) +k o r e j o o s h u u h a N t o i n a (cv_jpn_000805-cv_jpn_000805) +k a g a k U s h a m o s e k a y o h o o k a t s s e k i n i p a u t o o c h I t e k i n i s a t s u m e s h u o t o s h I t e i r u (cv_jpn_000806-cv_jpn_000806) +h a I t s u n i t s U e m a r a N (cv_jpn_000807-cv_jpn_000807) +s h i c l k a r s h t e k u d a s a i (cv_jpn_000808-cv_jpn_000808) +w a t a s h i w a a m i g i n o n o t o k i p a u d e k i s h I t e k i s e i m e e n o j i k a k u t o y u g o t o k i m o n o m e N s h o o h o o t e k i p a u r o N b e t a y u u n o d e a r u (cv_jpn_000809-cv_jpn_000809) +w a t a s h i w a p a u s h a k a i k e e s e e n o k o N t e N n i w a d e y o o n u s o s u t e k i n a m o n o g a p a u h a t a r a i t e i r u t o m o (cv_jpn_000810-cv_jpn_000810) +n a n i o s u r u t s u m o r i d a c l t a n o k a (cv_jpn_000811-cv_jpn_000811) +k o b u t s u t e k I t a g a j i k o h I t e e t e k i n i t a N n i p a u t e N s h u u g o o t e k i n i k a N g a r a r u r u t o k i s o r e g a b u t s u r i t e k i t e k a i d e a r u (cv_jpn_000812-cv_jpn_000812) +a n e g a z u c l t a n o d e y a c l k i n o s h i r a i g a r i m a s e N d e s h I t a (cv_jpn_000813-cv_jpn_000813) +k o r e w a N r i h o N d e u t e i n a i t a b e m u n o d e s U (cv_jpn_000814-cv_jpn_000814) +w o t a s h i w a h e N s h u u e i n o y o o n e N k u r a e w a y a c l t a c l t o o m o (cv_jpn_000815-cv_jpn_000815) +e i s a N n i i k o n o k o t o b p a n o r i m i o o o s h i a m a s h I t a (cv_jpn_000816-cv_jpn_000816) +k a s e g a t s U s u y o i h i u w a t e N n i s u g a d e k i m a s e N (cv_jpn_000817-cv_jpn_000817) +i c h i i (cv_jpn_000818-cv_jpn_000818) +h a i (cv_jpn_000819-cv_jpn_000819) +o n o e (cv_jpn_000820-cv_jpn_000820) +d e i (cv_jpn_000821-cv_jpn_000821) +a t o k i (cv_jpn_000822-cv_jpn_000822) +m i r u t o y u k o t a t o p a u h a t a r a k U t o i u k o t o g a s h k a b u N d i t e k i n a k e r e b a n a r a n a i (cv_jpn_000823-cv_jpn_000823) +w a r e w a r e o t a m a s h i i n o z u o k a r a u g u k a s u m o n o d e n a k e r e b a n a r a n a i (cv_jpn_000824-cv_jpn_000824) +s e t a i b e N s h o o h o o d e k i n a r u g a i u e n i i d e a t e k I c h o c l k a N t e k i k e e k i y g a c l U k u m a r e r u n o d e a r u (cv_jpn_000825-cv_jpn_000825) +t o k o m a d e m o t a t o i c h i t o n a s o o g o s h I e e t e k i n a z e c l t a i m u j u N t e k i j i k o o i t s u n o s e k a i n i s h I t e (cv_jpn_000826-cv_jpn_000826) +s h i k a r u n i n i N g e N t o k a N k y o o t o r o k a N k e e w a m o t o k o o n o k a N k e d e a r i (cv_jpn_000827-cv_jpn_000827) +i i s a N n i k o n o k o t o b a n o i m i y o o s h i y a m a s h I t a (cv_jpn_000828-cv_jpn_000828) +k e e k i g a n a n a t s s a r i m a s U (cv_jpn_000829-cv_jpn_000829) +k o c h i r a b a k o b i y a s h i s a N d e s U (cv_jpn_000830-cv_jpn_000830) +m o s h i m a s h i (cv_jpn_000831-cv_jpn_000831) +k o k o w a o k I k U t e n i g u y a k a n a m a c h i d e s U (cv_jpn_000832-cv_jpn_000832) +s o n o c h i k a i y a k U s a r e r u k a r a i s o g e p a u (cv_jpn_000833-cv_jpn_000833) +a m a s a g a b k u s a e i r a r e t e t e c h o o d o i (cv_jpn_000834-cv_jpn_000834) +h o k e N s h I t s u e n o d o o a a k e t a (cv_jpn_000835-cv_jpn_000835) +m o d a n i o w a c l t e m o k i n i s h i n a i (cv_jpn_000836-cv_jpn_000836) +a r i g a c l t a y a (cv_jpn_000837-cv_jpn_000837) +i t o o g a r o k u d a t o j i k a N u w o s u r e t e t a n o s h i m e r u (cv_jpn_000838-cv_jpn_000838) +k a k a k u w a g i z u t s U k a s a r e r u n i o j i t e j o o s h I k i n o u c h i n i h a i c l t e y u k u (cv_jpn_000839-cv_jpn_000839) +s h i k a s h I t o k i g a k a b o n i h a i r u k o t o s o n o k o t o g a m i r a y o o m u k o t o d e a r i a r a t a n a r u i c l U t a i g a d e t e k u r u k o t o d e a r u (cv_jpn_000840-cv_jpn_000840) +t e r e b i o k a i k a i t e p a u t e r e b i o m i r u j i k a N n a f u e t a (cv_jpn_000841-cv_jpn_000841) +k a k a r e u s h I t a i n o m i i t s u m a d e m o i k e r u n o d e a r u (cv_jpn_000842-cv_jpn_000842) +n i N k i d a a m e N i y a n i n a r a N d a r a n i j i k a N m a o c h i d a c l t a (cv_jpn_000843-cv_jpn_000843) +s o r e o o m o c h i i r u n i N g e N n o y o k u n i t o N s e i s o s h I t e k o r e w a k a r e n o m o c l t e i r u k a c h e n o s a k u d o N n p a u i d o N s u r u (cv_jpn_000844-cv_jpn_000844) +m a w a r y o a m i N n a k a N g a r u k o t o y a m e t e i t a (cv_jpn_000845-cv_jpn_000845) +k o o i t e k i j o k U k a N t e k i n i s e k a y o m i r u t o y u k o t o w a j a k u n i k o o i t e k i c h a o k c l k a N t e k i n i s e k a y o k e e s e e s u r u k o t o k u m u n o d e a r u (cv_jpn_000846-cv_jpn_000846) +s j e N p a i t a k e s a s e m a i t o s u r u k i z u k a i y g a y o k e e n i s e N c l p a i s a s e t e s h i m a u (cv_jpn_000847-cv_jpn_000847) +k o n o m i c h i w a t o t e m o s e m a i n o d a m n a i d e s U (cv_jpn_000848-cv_jpn_000848) +w o e g a r i n i U (cv_jpn_000849-cv_jpn_000849) +t o i y o w a r o k a m a i t a r i g a a n i a r i m a s U (cv_jpn_000850-cv_jpn_000850) +t a d a k a s a N o h i d a r i n i k i m e r a s a N g a i m a s U (cv_jpn_000851-cv_jpn_000851) +m a c l k u r u n a t a m a b o t e s u g o i e (cv_jpn_000852-cv_jpn_000852) +s h a r s h o o m i t a i n a d o k u s h u k a s o o b u m o k a i t a (cv_jpn_000853-cv_jpn_000853) +g e N j i t e m o s e k a i w a p a u t a m o o i c h i t o s h I t e k e c l t e s u r a i d e k a t a c h i o a c l t o s U k a i u n a k e r e m o n a r a n a i (cv_jpn_000854-cv_jpn_000854) +s h o o h i N k e N s a k u k g a o k a r y a s u i t o o p a u k a o k i n a r u m a i (cv_jpn_000855-cv_jpn_000855) +t s e s h i I k i w a n e k I s h I t e c l k a t e e d e n a k e r u m o n a r a n a i (cv_jpn_000856-cv_jpn_000856) +m o n o g o t a n o N j i N p a N k a e r u d a k e d e p a u u m a k U i k U k o t o m a r (cv_jpn_000857-cv_jpn_000857) +k o n o k I e e t s u w a k a t s u o n o s a s h i m i g a z e c l p e i N (cv_jpn_000858-cv_jpn_000858) +k a k e N n i s h i c l p a i s h I t e m a p a u m o c h I t t s u i t e s a m a s h I t s u o u k e i d e r u (cv_jpn_000859-cv_jpn_000859) +s o r e y u e n i p a u t e t s u g a k u g a z e N t a i n o g a k o d e y a r u t o s u r e w a (cv_jpn_000860-cv_jpn_000860) +k i i s a n a i y a o y a d a g a y a s U k U t e h a N j o s h I t e r u (cv_jpn_000861-cv_jpn_000861) +i n i f u r a g a k i n o h u z e N n i o c h j i c l t e p a u k o k u g a i a d a s h I t s u s u r u s h I t a m o d e t e k i t a (cv_jpn_000862-cv_jpn_000862) +t s s u g i n i k a w a k u w a s o N z a y o j u j u n o d o i k i n o k a c l t e s u r e z u r a N d e o k i N z e i t i t e N i k i U s u r u (cv_jpn_000863-cv_jpn_000863) +s o r e d e w a t o k t o y i m a r a n a s e r i t s U s h i o w a n a k u p a u s h i r N k a N t o i m o n o m o n a k u n a r u n o d a r i (cv_jpn_000864-cv_jpn_000864) +h a k a i b u r a N k o k o N k u r i t o s e n o s u b e r i d a i k a w a i t a s u n a b a (cv_jpn_000865-cv_jpn_000865) +s s h i k a s h i s o r o w a d o k o m a d e m N k o k o k a r a d e t e p a u o k o e k a i r i k u r u s e e I t o m o t o m o d e a k e w a n a r a n a i (cv_jpn_000866-cv_jpn_000866) +a r i t o a r a i r u d e m o o m a k i c h i r a s h i t e m i N n e a k a r a o u r a m i o k a c l t e r u (cv_jpn_000867-cv_jpn_000867) +k o n o t e e d o s a w a g i n i n a r u k o t o m o n a i n o d a r o (cv_jpn_000868-cv_jpn_000868) +k o n o n e r a N d e p a u u r i t a u k a a a (cv_jpn_000869-cv_jpn_000869) +h i n o k a g a e N n i c h u i s h i n a i t o p a u s u g u k o g e r u (cv_jpn_000870-cv_jpn_000870) +e N m a N n o u w e N n i p o t s u r i t o t s u i s a n a n a g a i t a s a i s h i o w a t s u m a y o o j i t e e d o n o t s i i s a n a p a u a n a d a c l t a a (cv_jpn_000871-cv_jpn_000871) +s o r e w a m a r e w a r e o i k a s h i n a g a r a p a u w a r e w a r e o t o r e e k a s u r u n o d e a r u p a u w a r e w a r e n o t a m a s s h i y o k o r o s u n o d e a (cv_jpn_000872-cv_jpn_000872) +r e k U s h I t e k i n i a t a r a r t a m o n o w a d e c l t e m u j u N t e k i j i g o t o o i t s I t e k i g i e N z d a i n o o i t e s U k a i s h I e k i n i a t a e r a r t a m o N u t o s h I t e (cv_jpn_000873-cv_jpn_000873) +m u o j u N t e k I e j i g o d o o i c h I t o s h I t e p a u i t s u m o k o n o s e k a i n i c h o o e s h I t e k i d e a (cv_jpn_000874-cv_jpn_000874) +y u n i d e c l p e e m u j u N t e k I j i g o d o o i s h u t o s h I t e g e N a e k a r a g e N z a e e t u w o k i k u s U e k a e n o g e N z a i n o i t e (cv_jpn_000875-cv_jpn_000875) +h a r e w o t a N o s h I t o N d a s h u d e k i n a i (cv_jpn_000876-cv_jpn_000876) +s h i k a s h i w a t a s h a s o k o n i s e k a i n o j i k o d o o i t s u o k u n o d e w a n a i (cv_jpn_000877-cv_jpn_000877) +n e b u k a k u n a r u n o g a p a u h a y a k u m a c l t a (cv_jpn_000878-cv_jpn_000878) +w a t a s h i w a i N g e N n o o d e k I s h I t e k I k e e s e e n o t a c h i b a k a r a p a u g e j u t s u o m i r u n o d e a c l t e o o s h a k a r a e N s h a o m i r u n o d e a n a i (cv_jpn_000879-cv_jpn_000879) +a o i t o m a t o s h i k a n a k U t e p a u k a u k a b a i y o u (cv_jpn_000880-cv_jpn_000880) +s e N k e z u e g y o o n i o o k i n a k i t a y o y a s U t e e r u (cv_jpn_000881-cv_jpn_000881) +n a n i k a s h i r a n o i N s e N t e b u w a n a i t o k i b u i s u e n o d e w a (cv_jpn_000882-cv_jpn_000882) +j i k o N o s h e k g e N n o i b e N t o d e s u t o r u s h I t a m a r i (cv_jpn_000883-cv_jpn_000883) +m a r i n o s h I t o w a b o o z e N t o s h I t e i t a (cv_jpn_000884-cv_jpn_000884) +s o N n a n a i y o o n o m e r u w a n a N k e N m o k U I t e i t a (cv_jpn_000885-cv_jpn_000885) +i j i g a i d e d e e s f u i s h I t e i t a (cv_jpn_000886-cv_jpn_000886) +t o k i d o k i c h i u u N n o k o r o w a w a k a r a n a k u n o r u t o c h i g a a r u d a k a r a b o k u a k a a n e o k i n o t o n i k a j i a j e m e r u (cv_jpn_000887-cv_jpn_000887) +m o o n i g e t e c h a t a m e d a (cv_jpn_000888-cv_jpn_000888) +k a r e w a p o o t o t a j i t s U k u s h I t e i t a (cv_jpn_000889-cv_jpn_000889) +d a r u N i m u n e i w a k o w a k a k e t a k a n a i (cv_jpn_000890-cv_jpn_000890) +p a s a k a t o o m o t e u d o a n o o t o c l t e o n i g e c l t a (cv_jpn_000891-cv_jpn_000891) +s h t e i m a s e N (cv_jpn_000892-cv_jpn_000892) +k a y o o n i s h I t e s h I t e r u t o t o m o n i s h i c l t e i n a i t o k o r o k a r a t a N k y u w a h a j i m a r u n o d e a r u (cv_jpn_000893-cv_jpn_000893) +a i s a t s u w a d a i j i d a i o (cv_jpn_000894-cv_jpn_000894) +t o j i t a m o n o i k a n i h i r o g e t e m o h i r a i t a m u n o n i a n a r a r o t o i c l t e i r u g a (cv_jpn_000895-cv_jpn_000895) +t a m u s h i n i k u t s U k a t s U k u c l t e m i o w a (cv_jpn_000896-cv_jpn_000896) +U t s z e i b u N a k o k i n a s h o o b a i d a i o n a (cv_jpn_000897-cv_jpn_000897) +w h a c l t e i (cv_jpn_000898-cv_jpn_000898) +k i t e i (cv_jpn_000899-cv_jpn_000899) +g o (cv_jpn_000900-cv_jpn_000900) +h a s h i i t i (cv_jpn_000901-cv_jpn_000901) +i e a (cv_jpn_000902-cv_jpn_000902) +h a c l c h i (cv_jpn_000903-cv_jpn_000903) +h n e (cv_jpn_000904-cv_jpn_000904) +a s h i i i (cv_jpn_000905-cv_jpn_000905) +k u o (cv_jpn_000906-cv_jpn_000906) +k e c h i (cv_jpn_000907-cv_jpn_000907) +k a k a k u g a p a u a k i r a k a n i s u r u p a u k y y a c l k a N t e k I s h i N d r i n i s h I t a g a u k o t o n i y o c l t e (cv_jpn_000908-cv_jpn_000908) +k a k o t o m i r a i t o n o p a u m u j u N t e k i j i k o d o o i t s u t o s h I t e n o g e N z a i g a k a p a c h i o m o s h I t o i u k o t o d e a r u (cv_jpn_000909-cv_jpn_000909) +b u t s u r i t e k i s e k a i u w a s u u g a k u t e k i I k i g o o n i o c l t e a r a w a s a r e r u p a u s u u k a k u t e k I i k a t a c h i n o s e k a i a r u (cv_jpn_000910-cv_jpn_000910) +w o n a c h i g e N i s h o o d e s a N k o o g i n a r u (cv_jpn_000911-cv_jpn_000911) +g a i k o k u k a r a k i t a m o n o d a t o s h i t e b i c l k u i (cv_jpn_000912-cv_jpn_000912) +i w a y o r u j i I s e N y o c l t e k a k U t o U k U s h I i t a c l t a m o n o d e a r u (cv_jpn_000913-cv_jpn_000913) +o n a j i y o n i d a N s u e u w a h i j z a o o u z u b o h a k o t o g a g i m u u z u k e r a r e t e i m a s U (fleurs_jpn_000346-fleurs_jpn_000346) +k o n o s a b i s a k o r a k s e e y a h a j i m e t o s u r u s e N p t a k u i y a e N k a k U c h i d e p a u d e t a y a o N s e o h I s u o t o s u r u t a N k e N t a i r i h i N c l p a n i d i o o s a r e t e i m a s U (fleurs_jpn_000347-fleurs_jpn_000347) +k y o o f u k y o k a d o n o k o o s u i r o o b i a m a k a s h i b a r a i u p a u t a s u m a k i p a u m i z u k i p a u o y u s a i k u r o N n a N o n o k i b i s h i i k s h o k e t a y a s o n o e k y o o n i y o r u m o r o d e s U (fleurs_jpn_000348-fleurs_jpn_000348) +i N t a n e t o w a m a s U k o m i r u k e e s h e N t o t a i j i N k o m i r u k e e s h o o n o p a u d o y o s o o k a e s u m a i d a k a N k y o o r d e s U (fleurs_jpn_000349-fleurs_jpn_000349) +k a j i n o r e w a t s u u j o o t o k u b e s u r a i s h I k o y a p a u i e N t a a t e i m e N t o y o s h I t e i m a s U k y e s u w a k i b u y o k u s h I s e s u n a i r e t o r a m a r u y o o r e s u r u t a m d e s U (fleurs_jpn_000350-fleurs_jpn_000350) +s h i k a s h i k a k U t e N n o b i k e c l t o o s h i n a c l t a t o i N d o w a p a u n a n a z u n o b i k e c l t o o s h i n a i s a N j u r o b u r a j s h i k a d e g i m a s e N d e s h I t a (fleurs_jpn_000351-fleurs_jpn_000351) +h o o k u r a N d o n o k o o s h I k e t s u k a w a p a u h o k u r a N d a s h o t o o b o N d o e f k e p i d e p a u i c h i p o N n o g a i c h i e e b o N d o j i i p i b i i t o t o o k a n i k o t e s a r e d e i m a s U (fleurs_jpn_000352-fleurs_jpn_000352) +h a j u s h i t a n o j o o o k o k a o w o c h u u g o m e t o o d e s U i s e N j u i c h i n e u h a c h i g a t s u m i s e k o o s h i i s e N j u n u n e N s e a N g a t s u m u n e k a i t s u e s h i m a s e N g e s h I t a (fleurs_jpn_000353-fleurs_jpn_000353) +i c l p u N k a N d e h f u c l t o o s u b u c h i i k i m a r e b a h u c l t o s u r u m a r e m i n a N c l p a u m o k a k a r u c h j i i k i m o a r i m a s U (fleurs_jpn_000354-fleurs_jpn_000354) +p e r a m i c l t a n o o t o t o a s h I k a i n o s h o o w a p a u k o n o k a N k o o s h u r e t o k u n i k o r o m a t a j i k a t a n a s h i m e r e m o y o o s h i n o s h i t o s u r e s U (fleurs_jpn_000355-fleurs_jpn_000355) +s u n o t a n e p a u t a i n i p a u d a d e r u t o s h I t e h i o o k i g a t s u e k a s a r e g a c h i d e s U (fleurs_jpn_000356-fleurs_jpn_000356) +g e N z N s u r u k o k o g a s h I t a r e t e i r a n i j i o g o o m a e n a d a N d a k t u k u r o o t U s a i d e o w a k i e N z o N s u r a o o g a i b u N k e n o s a i k o r o t s a s h i d e s U t e g a k i N y o r u g e N p o o w a k e N z o o s h I t e i m u a s e N (fleurs_jpn_000357-fleurs_jpn_000357) +k r e m o s e s o o t a d a s h i t a o m i t o m e r u s h I t o m i m a s h I t e g a o o k u g u s h I t o s u o n o k e k o d e t a i y o o k e d e a t a i y o t o s u n o h o k a n o s h i g a c h i k y u n o u m a r e i d o o s h s e r u d o a s h i N c h i t e i m a s h I t a (fleurs_jpn_000358-fleurs_jpn_000358) +c h i b e c l t o m e e s o o c h u s h i N w a s h i i s e e o g a d e s U s a N m a z a n a n a k a m i g a m i o s h I k a k U k a s u r u k o d o d e e n e r u g i i c h a n e r u g a s h i o k a s a r e c h a k r a b a k a s e k a s a r e s a t a r u n e i c h I k e g o o g a r e m a s U (fleurs_jpn_000359-fleurs_jpn_000359) +b i n a n i y a h u r e k a n i a r u s u b e t e n o k o k u r e s U k o o e d a d o o y o n i k u n o k o o e n i a p a u m a n e j i e h o k o s h I t o n i u e N g u o g a o k a k a r i m a s U (fleurs_jpn_000360-fleurs_jpn_000360) +d e s a k u r u m a s u N n o h o k a n o o k u n u k o o t s o s h i u d a N u n a t s s u k o k a r e u m a r e m a s h I t a (fleurs_jpn_000361-fleurs_jpn_000361) +i N t a n e c l t o w a m a s U k o m i n i k e e s h o N t o t a i j i N k o m i n u k e e s h o N n o y o y o o s o o k a n e s o n a i t a k a N k y o o r e s U (fleurs_jpn_000362-fleurs_jpn_000362) +k y o o i N d e w a k a o N s e N k a N r e i t e j i u s h j u N i s h I s a r a e i k a n i e n u k a N s e N g o k a n o o s e s e g u d a m i n i k a N j a k a k u r s u r u n d a m u i s o o j o o t o t e i m a s h I U s (fleurs_jpn_000363-fleurs_jpn_000363) +r e N p o o i k a y w a m i s e N g o n e N d o k a r a w a i s e s u e b u s u t o r e s h i m a r i h o e n o s h i k i N t e e k y o o k a i j i s h i e h u b i y a y w a a t a r u z o b o r u n o r i j u u n i n o s o o s a N y o t o o n u s h u n a k e r e w a d a r a r n a i t o k I t e e s h i m a s h I t a (fleurs_jpn_000364-fleurs_jpn_000364) +h i e c h i d e r r o w a k e N s a s h I t k a k o k U b u e s t e i u k o N m a r e n a s e i s a i y u o p a u p a u h i i c h i n e h o c h i n o r o o d e s h i m a s a r e m i s U (fleurs_jpn_000365-fleurs_jpn_000365) +s o r e d e m o p a u t o o r u u k a r a n a r u b a i s u o u k e p a u s u b e t e n o h o o s h I k e o m o r i a N z e N j o o n o k e e g o b o n i s a i s h i n o c h u i u o h a r a i m a s h o (fleurs_jpn_000366-fleurs_jpn_000366) +k o r e r a w a t a m a n i k o N g a t s u r o k a c l k a t s u k u m o k e n o b u i c h i t e k a i g a i a p a u s a m a z a m a n a t e N p o g a n a r a N d e i m a s U a N z e N y o e b u k o d o g a d e k i m a s U (fleurs_jpn_000367-fleurs_jpn_000367) +s h i N n o m i e n a i c h i i d a s u N p a u n a N d o p a u n a f a s U t o s e N k e u k U a c h i j u k i u p e e j i i h a k U k y u e d o s u N z a i b o m a d a b a z e r e c h i e b m u n o d o k u j u n e y o s o d e r a r u (fleurs_jpn_000368-fleurs_jpn_000368) +k o n o s a r i s u w a g o r a k U s e o h a j i m e t o s u r u s e N b t a k u y a p a u e N g a k s h i r e d e t a y o N s e y o s u o t o s u r u t a N k e N t a N i r i p a u h i N p a n i r i y o s a r e t e i m a s U (fleurs_jpn_000369-fleurs_jpn_000369) +s a k o b a p a u p a u b u e n o s u a i d e s U k a r a k o j u c l k i r o s a N j u i c h i m a i r u h a n a r e t a r a b u r a t a s h i n a i d e p a u k e N s h a g o j o o i N g i N d e w a r u k u r i s U t e i n a f u r u n a N d e s u d e p a u k e r u k i n a z o s h i g a p a u n a i t o o r o o s e N e n o s h I u r a o s e N g e N s h i m a s h I t a (fleurs_jpn_000370-fleurs_jpn_000370) +o n a y i z u k u i r i m a s h i h a t o n o k a s o o e u r e b u i s u n o r u g a o k y u i w a k a s o o r o o o b a r a N s h i i p a u k a b e n i g e k i t o t s u s h I t e p a u j u u n a r a n i N g a s h i b o o s h i m a s h I t a (fleurs_jpn_000371-fleurs_jpn_000371) +a s h i s h I a n a j o o h o o k k a N w a j u u g o m e d o d e s U m i s e N j u u c h i n e h a j i g a t s u n i s h u u N k o s h i p a u m i s e N j u u n a n i e s a N g a s u m a r e k a i t s o s h i m a s e N d e s h I t a (fleurs_jpn_000372-fleurs_jpn_000372) +b u u N n e e t o u k o t o w a w a s h i m i o m i s u r u r a t e N g o n o k e o s h i s h i b i r e s U k a r a k i I t a w o r i s h i m i N i o m i s u r u r a t e N g o r o m e s h i s h u i b i e s U t o s h i y a t o s h I k o k a o m i s h i p a u n a r u n a k a n o k a t a c h i d e p a u s h a k a i n o k i o o t e e g i s u r u s h u i b i t a s U t o u m e e s h i n i k a N k e e s h I t e m a s U (fleurs_jpn_000373-fleurs_jpn_000373) +s u j o k o k o r e a i s u m o k a N k o o t e k e y a r y o o s h a t a j i g a h a s u r o t o k a c h i k o i t e k i m a s U w o t o d o s h i k a r i g o i n a s u m u n o g a t a r u a m a r u d e h o N n a y o r e s U (fleurs_jpn_000374-fleurs_jpn_000374) +t e r e b i n o o d o o n o N t d o g e N p a t s U k a r a h a k u e g a g a d t e i m a s U e s (fleurs_jpn_000375-fleurs_jpn_000375) +n o o b y o o r i t o k o o d o o n o s o o k a N k a N k e e w a p a u k a y a k u s h a t a s h i n o k e N k y u u o u r a z u k e r e m o n o d e s U (fleurs_jpn_000376-fleurs_jpn_000376) +s e y o o b i n a i b e N t o n a t o k a r u p a n e r o w a p a u s e N j i k e N g e h U k a z s u n o k o j i N d e s i s u j o o s h i m a s h I t a (fleurs_jpn_000377-fleurs_jpn_000377) +s e N e h a p e k u r e N d a i r a i g u N t a y g a t o o j a k s u r e w a r e p a u h a i c h i a k o n o b y o k i n i k a N k y e e s u r u o N d a n i s o o u g u s h I t a k o t o w a r i m a s e N d e s h I t a (fleurs_jpn_000378-fleurs_jpn_000378) +s h i k a s h i k a k u t e N n u b i k e c l d o o s h u n a c l t d a d o h i N d a n a n a t s u n e r i k e c l t o o s h u n e i s a N j o r o k u r a s h I t a r e k i m a s e N g e s h I t a (fleurs_jpn_000379-fleurs_jpn_000379) +k a j i n o d e w a t s u u j o o t o k u b e t s u n a i N s h a k o y a p a u e N t a t e i m e N t o o y o o i s h I t e i m a s U g e s t o u g a k i b u u m y a k u s h i s e z u n a i n i t o r o m a r u y o n r e s u r u t a m e r d e s U (fleurs_jpn_000380-fleurs_jpn_000380) +s o r e d e o t o o k y u k a r a n a r a b a i s o k e p a u s u t a n u h y o a s h I k e o m o r i p a u a N z e N j i o n o k e k o n i s a i s h e i n o c h u y u o h a r a i m a s h a o (fleurs_jpn_000381-fleurs_jpn_000381) +p o o k a r e g e w a r i m a s e k o r a s h I t t s u m u s h o o a r i d e a r i a t a r a s h i i s h o o n o m o k a k e d e s U (fleurs_jpn_000382-fleurs_jpn_000382) +s a w a r i t o w a p a u a u r i k a n o y a s e u d o o u z u p a u t o k o n i s a w a N n a n i r u y a s e u d o o g u s u n o k a N s a s u o m o k u t e k i t o s h I t a r i k u r o d e n o r y o k o o s a s h i m a s U (fleurs_jpn_000383-fleurs_jpn_000383) +u u n e k i t a b a r u t o k a y o u m o o d a N s u r u b a i w a p a u s e i s u m i c h i k a o k u n i s h s e k u d a s a i k k o o r e n N n a k o t s U k I s u m N u s a i n i m o c l t o m e e k y o o k e r u s e i t s u d e a o s o r u j i u o d o o s o o N n a n a r i b i k i m a s U (fleurs_jpn_000384-fleurs_jpn_000384) +k o k o w a p a u i r i s u n o s h o k u m i N t e s h i e h a s h a e g a j i u N t a s h i n o d o o r o d o s h I a b a s h u n a r u d e p a u s h a k o m i N t e i j i r e n e s h o o k o o s e g a s o o t o s u r e k a t a w a o o k o w a r h a j i b e r u n a g a y o i u s h o (fleurs_jpn_000385-fleurs_jpn_000385) +k o s h i w a s a k u g e N s u r u s u u c h i o o s a r a y a s e N d e s h I t a k a s a k u g e N w a c h i e w o k u n o k e e z a i s a N a s h I u y o i m o t o z u i t e p a u d i s h i s a r e u d a r o t o o n o i m a s h I t a (fleurs_jpn_000386-fleurs_jpn_000386) +s a i n u u k o k u s h o k u w a p a u s h i N k o o N r e k o o n o j u k i e g a s U k u n a i k a r u j a a s h k u y o r e m o h a e k o o d o z u r e p a u n a r a b i k i p a u y o r e s h o o j o o g a k a s u r e k o t o g a r i m a s U (fleurs_jpn_000387-fleurs_jpn_000387) +k y i n o o n o a s a p a u t o r u k o n o g a j i a N t e p k u n o k e e s a s u o h o m N w o r e p a u j i d o o s h a b a k u r a n o b a k a s u r i o r i k y i e e k a a f U t a r e g a s h i b o s h i p a u p a u r w u s h i o w a s h a w a n i j u u n i y o k o a i m a s h I t a (fleurs_jpn_000388-fleurs_jpn_000388) +s h o k u b u t s u a n i N g i N g a s u u s a N z o o t s U k u r i n i N g e N g a k a c l k o i t o s h I t e h a k i d a s u r n i s a N k a c l t a N s o o t o r i k o N d e i m a s U (fleurs_jpn_000389-fleurs_jpn_000389) +s e N p a k u r e b u s h i o y u s o o s u r u n o w a p a u u m i y o k o i t e p a u k i t o a y a b u s h u e o t a r i r e o y i s o o s u r u e p a u m o c l t o m o k o o r i s e k i n a h o o h o o r e s U (fleurs_jpn_000390-fleurs_jpn_000390) +k a r i h o r u n i a s h u u n o a a n o r u d o o s h u w a r u t s u n e c l k a c h i s h i w a b o o r y o k U t e k i n a b i d e o u g e e m u w o m i s e e n e N s h a n i h a N b a y a e N t a s u d e k o t o o k i N i s u r o h o o w a N n i s h o m e s h i m a s h I t a (fleurs_jpn_000391-fleurs_jpn_000391) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..84c2ab5a54d6d3251e487310db57103a25dfbaf7 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/ref.trn @@ -0,0 +1,160 @@ +k a k o t o m i r a i t o n o m u j u N t e k i j i k o d o o i t s u n a r u g a y u e n i p a u i s h I k i t e k i n a n o d e a r u (cv_jpn_000800-cv_jpn_000800) +s e k a i o k e e s e e s u r u t o t o m o n i p a u j i k o j i s h i N o k e e s e e s u r u s o o z o o t e k I s e k a i n o s o o z o o t e k i y o o s o t o s h I t e p a u k o b u t s u g a k o b u t s u d e a r u (cv_jpn_000801-cv_jpn_000801) +p a s o k o N d e g e e m u y a r u n i N g a f u e t e k I t e r u (cv_jpn_000802-cv_jpn_000802) +k a g a k u n o s h i m e s u a t a r a s h i i j i j i t s u p a u a t a r a s h i i k a N n e N p a u k a N k y o o s h i h a i n o a t a r a s h i i k a n o o s e e o m o c l t e n a n i o h a j i m e r u k a w a (cv_jpn_000803-cv_jpn_000803) +o m o s h i r o i n o n i r o o d o n a g a s u g i t e d a r u i (cv_jpn_000804-cv_jpn_000804) +k o r e j o o s h u u h a N c l p o i n a a (cv_jpn_000805-cv_jpn_000805) +k a g a k U s h a m o s e k a i o h o o k a t s u t e k i n i t o o i t s u t e k i n i s e t s u m e e s h i y o o t o s h I t e i r u (cv_jpn_000806-cv_jpn_000806) +f U t s u u n i t s u m a r a N (cv_jpn_000807-cv_jpn_000807) +s h i c l k a r i s h I t e k u d a s a i (cv_jpn_000808-cv_jpn_000808) +w a t a s h i w a m i g i n o g o t o k i r e k I s h i t e k I s e e m e e n o j i k a k U t o i u g o t o k i m o n o o b e N s h o o h o o t e k i r o N r i t o i u n o d e a r u (cv_jpn_000809-cv_jpn_000809) +w a t a s h i w a s h a k a i k e e s e e n o k o N t e e n i w a d i o n y u s o s u t e k i n a m o n o g a h a t a r a i t e i r u t o o m o u (cv_jpn_000810-cv_jpn_000810) +n a n i o s u r u t s u m o r i d a c l t a n o k a (cv_jpn_000811-cv_jpn_000811) +k o b u t s u t e k I t a g a j i k o h I t e e t e k i n i t a N n i t e N s h u u g o o t e k i n i k a N g a e r a r e r u t o k i p a u s o r e g a b u t s u r i t e k I s e k a i d e a r u (cv_jpn_000812-cv_jpn_000812) +a m e g a f u c l t a n o d e p a u y a k y u u n o s h i a i g a a r i m a s e N d e s h I t a (cv_jpn_000813-cv_jpn_000813) +k o r e w a n i c l p o N d e u c l t e i n a i t a b e m o n o d e s U (cv_jpn_000814-cv_jpn_000814) +w a t a s h i w a p a u h e N s h u u i N o p a u y o n e N k u r a i h a y a c l t a t o o m o u (cv_jpn_000815-cv_jpn_000815) +i s a N n i k o n o k o t o b a n o i m i o o s h i e m a s h I t a (cv_jpn_000816-cv_jpn_000816) +k a z e g a t s u y o i h i w a t e n i s u g a d e k i m a s e N (cv_jpn_000817-cv_jpn_000817) +i c h i (cv_jpn_000818-cv_jpn_000818) +h a i (cv_jpn_000819-cv_jpn_000819) +n i (cv_jpn_000820-cv_jpn_000820) +r e i (cv_jpn_000821-cv_jpn_000821) +r o k u (cv_jpn_000822-cv_jpn_000822) +m i r u t o i u k o t o t o h a t a r a k U t o i u k o t o t o g a f U k a b u N r i t e k i d e n a k e r e b a n a r a n a i (cv_jpn_000823-cv_jpn_000823) +w a r e w a r e o t a m a s h i i n o s o k o k a r a u g o k a s u m o n o d e n a k e r e b a n a r a n a i (cv_jpn_000824-cv_jpn_000824) +z e c l t a i b e N s h o o h o o t e k i n a r u g a y u e n i i d e y a t e k I c h o c l k a N t e k I k e e k i g a f U k u m a r e r u n o d e a r u (cv_jpn_000825-cv_jpn_000825) +d o k o m a d e m o t a t o i c h i t o n o s o o g o h I t e e t e k i n a z e c l t a i m u j u N t e k i j i k o d o o i t s u n o s e k a i n i s h I t e (cv_jpn_000826-cv_jpn_000826) +s h I k a r u n i n i N g e N t o k a N k y o o t o n o k a N k e e w a m o t o k o o i n o k a N k e e d e a r i (cv_jpn_000827-cv_jpn_000827) +i s a N n i k o n o k o t o b a n o i m i o o s h i e m a s h I t a (cv_jpn_000828-cv_jpn_000828) +k e e k i g a n a n a t s u a r i m a s U (cv_jpn_000829-cv_jpn_000829) +k o c h i r a w a k o b a y a s h I s a N d e s U (cv_jpn_000830-cv_jpn_000830) +m o s h i m o s h i (cv_jpn_000831-cv_jpn_000831) +k o k o w a o o k I k u t e n i g i y a k a n a m a c h i d e s U (cv_jpn_000832-cv_jpn_000832) +s o n o u c h I k a i a k U s a r e r u k a r a i s o g e (cv_jpn_000833-cv_jpn_000833) +a m a s a g a o s a e r a r e t e t e c h o o d o i i (cv_jpn_000834-cv_jpn_000834) +h o k e N s h I t s u n o d o a o a k e t a (cv_jpn_000835-cv_jpn_000835) +m u d a n i o w a c l t e m o k i n i s h i n a i (cv_jpn_000836-cv_jpn_000836) +a r i g a t a y a (cv_jpn_000837-cv_jpn_000837) +i d o o g a r a k u d a t o j i k a N o w a s u r e t e t a n o s h i m e r u (cv_jpn_000838-cv_jpn_000838) +k a g a k u w a g i j u t s U k a s a r e r u n i o o j i t e j o o s h I k i n o u c h i n i h a i c l t e y u k u (cv_jpn_000839-cv_jpn_000839) +s h I k a s h I t o k i g a k a k o n i h a i r u k o t o s o n o k o t o g a p a u m i r a i o u m u k o t o d e a r i p a u a r a t a n a r u s h U t a i g a d e t e k u r u k o t o d e a r u (cv_jpn_000840-cv_jpn_000840) +t e r e b i o k a i k a e t e p a u t e r e b i o m i r u j i k a N g a f u e t a (cv_jpn_000841-cv_jpn_000841) +k a k a r u s h U t a i n o m i p a u i t s u m a d e m o i k i r u n o d e a r u (cv_jpn_000842-cv_jpn_000842) +n i N k i r a a m e N y a n i n a r a N d a r a n i j i k a N m a c h i d a c l t a (cv_jpn_000843-cv_jpn_000843) +s o r e o m o c h i i r u n i N g e N n o i y o k u n i i z o N s h i p a u s o s h I t e k o r e w a k a r e n o m o c l t e i r u k a c h i n o s h a k u d o n i i z o N s u r u (cv_jpn_000844-cv_jpn_000844) +m a w a r i w a m i N n a p a u k a N g a e r u k o t o o y a m e t e i t a (cv_jpn_000845-cv_jpn_000845) +k o o i t e k I c h o c l k a N t e k i n i s e k a i o m i r u t o i u k o t o w a p a u g y a k u n i k o o i t e k I c h o c l k a N t e k i n i s e k a i o k e e s e e s u r u k o t o o f U k u m u n o d e a r u (cv_jpn_000846-cv_jpn_000846) +s h i N p a i k a k e s a s e m a i t o s u r u k i z u k a i g a p a u y o k e i n i s h i N p a i s a s e t e s h i m a u (cv_jpn_000847-cv_jpn_000847) +k o n o m i c h i w a t o t e m o s e m a i n o d e p a u a b u n a i d e s U (cv_jpn_000848-cv_jpn_000848) +o b o e g a w a r u i n e (cv_jpn_000849-cv_jpn_000849) +t o i r e w a r o o k a n o h i d a r i g a w a n i a r i m a s U (cv_jpn_000850-cv_jpn_000850) +t a n a k a s a N n o h i d a r i n i k i m u r a s a N g a i m a s U (cv_jpn_000851-cv_jpn_000851) +m a c l k u r o n a t a m a g o c l t e s u g o i n e (cv_jpn_000852-cv_jpn_000852) +s h o h y o o m i t a i n a d o k U s h o k a N s o o b u N o k a i t a (cv_jpn_000853-cv_jpn_000853) +g e N j i t s u n o s e k a i w a t a n o i c h i t o s h I t e k e c l t e e s e r a r e t a k a t a c h i o m o c l t a s e k a i d e n a k e r e b a n a r a n a i (cv_jpn_000854-cv_jpn_000854) +s h o o h i N k e N s a k u g a w a k a r i y a s u i t o k a u k i n i n a r u n o n i (cv_jpn_000855-cv_jpn_000855) +c h i s h I k i w a r e k I s h i t e k I k a t e e d e n a k e r e b a n a r a n a i (cv_jpn_000856-cv_jpn_000856) +m o n o g o t o n o j u N b a N o k a e r u d a k e d e u m a k u i k U k o t o m o a r u (cv_jpn_000857-cv_jpn_000857) +k o n o k i s e t s u w a k a t s u o n o s a s h i m i g a z e c l p i N (cv_jpn_000858-cv_jpn_000858) +k a k e n i s h i c l p a i s h I t e m o o c h I t s u i t e s o N s h I t s u o u k e i r e r u (cv_jpn_000859-cv_jpn_000859) +s o r e y u e n i t e t s u g a k u g a z e N t a i n o g a k u d e a r u t o s u r e b a (cv_jpn_000860-cv_jpn_000860) +c h i i s a n a y a o y a d a g a y a s u k U t e h a N j o o s h I t e r u (cv_jpn_000861-cv_jpn_000861) +i N f u r a g a k i n o o f u z e N n i o c h i i c l t e p a u k o k u g a i e d a c l s h U t s u s u r u h I t o m o d e t e k i t a (cv_jpn_000862-cv_jpn_000862) +t s u g i n i k a g a k u w a s o N z a i o s h u j u n o r y o o i k i n i w a k a c l t e s o r e z o r e n o r y o o i k i n i t s u i t e k e N k y u u s u r u (cv_jpn_000863-cv_jpn_000863) +s o r e d e w a t o k i t o i u m o n o n o s e e r i t s U s h i y o o w a n a k u p a u s h u N k a N t o i u m o n o m o n a k u n a r u n o d e a r u (cv_jpn_000864-cv_jpn_000864) +a k a i b u r a N k o p a u k o N k u r i i t o s e e n o s u b e r i d a i p a u k a w a i t a s u n a b a (cv_jpn_000865-cv_jpn_000865) +s h I k a s h I s o r e w a d o k o m a d e m o k o k o k a r a d e t e k o k o e k a e r i k u r u s e e s h I t s u o m o c l t a m o n o d e n a k e r e b a n a r a n a i (cv_jpn_000866-cv_jpn_000866) +a r i t o a r a y u r u d e m a o m a k I c h i r a s h I t e m i N n a k a r a u r a m i o k a c l t e r u (cv_jpn_000867-cv_jpn_000867) +k o n o t e e d o p a u s a w a g i n i n a r u k o t o m o n a i n o d a r o o (cv_jpn_000868-cv_jpn_000868) +k o n o n e d a N d e u r e c h a u k a a (cv_jpn_000869-cv_jpn_000869) +h i n o k a g e N n i c h u u i s h i n a i t o s u g u n i k o g e r u (cv_jpn_000870-cv_jpn_000870) +e N b a N n o u e n i p o t s u r i t o c h i i s a n a a n a g a h i r a i t a s a i s h o w a t s u m a y o o j i t e e d o n o c h i i s a n a a n a d a c l t a (cv_jpn_000871-cv_jpn_000871) +s o r e w a w a r e w a r e o i k a s h i n a g a r a w a r e w a r e o d o r e e k a s u r u n o d e a r u p a u w a r e w a r e n o t a m a s h i i o k o r o s u n o d e a r u (cv_jpn_000872-cv_jpn_000872) +r e k I s h i t e k i n i a t a e r a r e t a m o n o w a p a u z e c l t a i m u j u N t e k i j i k o d o o i t s u t e k i g e N z a i n i o i t e s e k a i s h i t e k i n i a t a e r a r e t a m o n o t o s h I t e (cv_jpn_000873-cv_jpn_000873) +m u j u N t e k i j i k o d o o i t s U t o s h I t e p a u i t s u m o k o n o s e k a i n i c h o o e t s u t e k i d e a r u (cv_jpn_000874-cv_jpn_000874) +y u e n i z e c l t a i m u j u N t e k i j i k o d o o i t s U t o s h I t e g e N z a i k a r a g e N z a i e t o u g o k i i k U s e k a i n o g e N z a i n i o i t e (cv_jpn_000875-cv_jpn_000875) +a r e p a u b o t a N o s h I t e m o d a c l s h U t s u d e k i n a i (cv_jpn_000876-cv_jpn_000876) +s h I k a s h i w a t a s h i w a s o k o n i s e k a i n o j i k o d o o i t s u o o k u n o d e w a n a i (cv_jpn_000877-cv_jpn_000877) +n e m u t a k u n a r u n o g a h a y a k u n a c l t a (cv_jpn_000878-cv_jpn_000878) +w a t a s h i w a n i N g e N n o r e k I s h i t e k I k e e s e e n o t a c h i b a k a r a g e e j u t s u o m i r u n o d e a c l t e p a u k o o s h a k a r a z e N s h a o m i r u n o d e w a n a i (cv_jpn_000879-cv_jpn_000879) +a o i t o m a t o s h I k a n a k U t e k a u k a m a y o u (cv_jpn_000880-cv_jpn_000880) +s h i N k i j i g y o o n i o o k i n a k I t a i o y o s e t e i r u (cv_jpn_000881-cv_jpn_000881) +n a n i k a s h i r a n o i N s e N t i b u g a n a i t o k i b i s h i i n o d e w a (cv_jpn_000882-cv_jpn_000882) +j i k a N s e e g e N n o i b e N t o d e s U t o r e s U t a m a r u (cv_jpn_000883-cv_jpn_000883) +m a w a r i n o h I t o w a b o o z e N t o s h I t e i t a (cv_jpn_000884-cv_jpn_000884) +s o N n a n a i y o o n o m e e r u g a p a u n a N k e N m o k i t e i t a (cv_jpn_000885-cv_jpn_000885) +n i j i k a i d e d e e s u i s h I t e i t a (cv_jpn_000886-cv_jpn_000886) +t o k i d o k i p a u j i b u N n o k o k o r o g a w a k a r a n a k u n a r u t o k i g a a r u d a k a r a b o k u w a k a a t e N o h I k i p a u n o o t o n i k a k I h a j i m e r u (cv_jpn_000887-cv_jpn_000887) +m o o n i g e t e c h a d a m e d a (cv_jpn_000888-cv_jpn_000888) +k a r e w a p a u b o o c l t o t a c h I t s u k u s h I t e i t a (cv_jpn_000889-cv_jpn_000889) +d a r e n i m o m e e w a k u w a k a k e t a k u n a i (cv_jpn_000890-cv_jpn_000890) +m a s a k a p a u t o o m o c l t e d o a n o t o c l t e o n i g i c l t a (cv_jpn_000891-cv_jpn_000891) +s u i m a s e N (cv_jpn_000892-cv_jpn_000892) +k a y o u n i s h I t e s h i c l t e i r u t o t o m o n i s h i c l t e i n a i t o k o r o k a r a t a N k y u u w a h a j i m a r u n o d e a r u (cv_jpn_000893-cv_jpn_000893) +a i s a t s u w a d a i j i d a y o (cv_jpn_000894-cv_jpn_000894) +t o j i t a m o n o o i k a n i h i r o g e t e m o h i r a i t a m o n o n i w a n a r a n u t o i c l t e i r u g a (cv_jpn_000895-cv_jpn_000895) +t a m e s h i n i i k U t s u k a t s U k u c l t e m i y o o (cv_jpn_000896-cv_jpn_000896) +z u i b u N a k o g i n a s h o o b a i d a y o n a a (cv_jpn_000897-cv_jpn_000897) +w a c h i (cv_jpn_000898-cv_jpn_000898) +i c h i (cv_jpn_000899-cv_jpn_000899) +g o (cv_jpn_000900-cv_jpn_000900) +s h I c h i (cv_jpn_000901-cv_jpn_000901) +i i e (cv_jpn_000902-cv_jpn_000902) +w a c h i (cv_jpn_000903-cv_jpn_000903) +r e i (cv_jpn_000904-cv_jpn_000904) +s h i (cv_jpn_000905-cv_jpn_000905) +k u (cv_jpn_000906-cv_jpn_000906) +i c h i (cv_jpn_000907-cv_jpn_000907) +k a g a k u g a a k i r a k a n i s u r u k y a c l k a N t e k I s h i N r i n i s h I t a g a u k o t o n i y o c l t e (cv_jpn_000908-cv_jpn_000908) +k a k o t o m i r a i t o n o m u j u N t e k i j i k o d o o i t s U t o s h I t e n o g e N z a i g a k a t a c h i o m o t s U t o i u k o t o d e a r u (cv_jpn_000909-cv_jpn_000909) +b u t s u r i t e k I s e k a i w a s u u g a k u t e k I k i g o o n i y o c l t e a r a w a s a r e r u s u u g a k u t e k I k a t a c h i n o s e k a i d e a r u (cv_jpn_000910-cv_jpn_000910) +o n a j i g e N s h o o d e s a N k o o n i n a r u (cv_jpn_000911-cv_jpn_000911) +g a i k o k U k a r a k i t a m o n o d a t o s h i c l t e b i c l k u r i (cv_jpn_000912-cv_jpn_000912) +i w a y u r u j i c l s e N n i y o c l t e k a k U t o k U s h i r a i c l t a m o n o d e a r u (cv_jpn_000913-cv_jpn_000913) +o n a j i y o o n i p a u d a N s e e w a h i z a o o o u z u b o N o h a k U k o t o g a g i m u z u k e r a r e t e i m a s U (fleurs_jpn_000346-fleurs_jpn_000346) +k o n o s a a b i s u w a p a u g o r a k u s e N o h a j i m e t o s u r u s e N p a k u y a p a u e N k a k u c h i d e d e e t a y a o N s e e o h I t s u y o o t o s u r u t a N k e N t a i n i h i N p a N n i r i y o o s a r e t e i m a s U (fleurs_jpn_000347-fleurs_jpn_000347) +k y o o f u u p a u h y o o p a u k a d o n o k o o s u i r y o u p a u o y o b i y a m a k a j i w a p a u r a i u p a u t a t s u m a k i p a u m i z u f u k i p a u o y o b i s a i k u r o N n a d o n o k i b i s h i i k I s h o o k e e t a i y a s o n o e e k y o o n i y o r u m o n o d e s U (fleurs_jpn_000348-fleurs_jpn_000348) +i N t a a n e c l t o w a p a u m a s U k o m y u n i k e e s h o N t o t a i j i N k o m y u n i k e e s h o N n o r y o o y o o s o o k a n e s o n a e t a k a N k y o o d e s U (fleurs_jpn_000349-fleurs_jpn_000349) +k a j i n o d e w a t s u u j o o p a u t o k u b e t s u n a i N s h o k u y a e N t a a t e i m e N t o o y o o i s h I t e i m a s U p a u g e s U t o g a k i b u N y o k U s h i s e t s u n a i n i t o m a r u y o o n i s u r u t a m e d e s U (fleurs_jpn_000350-fleurs_jpn_000350) +s h I k a s h i p a u k y a p U t e N n o w i k e c l t o o u s h i n a c l t a a t o p a u i N d o w a n a n a t s u n o w i k e c l t o o u s h i n a i p a u s a N j u u r o k u r a N s h I k a d e k i m a s e N d e s h I t a (fleurs_jpn_000351-fleurs_jpn_000351) +f o o k u r a N d o n o k o o s h I k i t s u u k a w a f o o k u r a N d o s h o t o o p o N d o e f u k e e p i i d e i c h I p o N d o g a i c h i i g i r i s U p o N d o j i i b i i p i i t o t o o k a n i k o t e e s a r e t e i m a s U (fleurs_jpn_000352-fleurs_jpn_000352) +h a s h i s h I t a n o k a m i g a t a k u u k a N w a j u u g o m e e t o r u d e s U p a u n i s e N j u u i c h i n e N h a c h i g a t s u n i s h u N k o o s h i p a u n i s e N j u u n a n a n e N s a N g a t s u m a d e k a i t s u u s h i m a s e N d e s h I t a (fleurs_jpn_000353-fleurs_jpn_000353) +i c l p u N k a N d e f u c l t o o s u r u c h i i k i m o a r e b a p a u f u c l t o o s u r u m a d e n i n a N p u N m o k a k a r u c h i i k i m o a r i m a s U (fleurs_jpn_000354-fleurs_jpn_000354) +p i r a m i c l d o n o o t o t o h I k a r i n o s h o o w a p a u k o n o k a N k o o c h i d e t o k u n i k o d o m o t a c h i g a t a n o s h i m e r u m o y o o s h i n o h I t o t s u d e s U (fleurs_jpn_000355-fleurs_jpn_000355) +s o n o t a m e p a u t a N n i r a b e r u t o s h I t e h y o o k i g a t s u i k a s a r e g a c h i d e s U (fleurs_jpn_000356-fleurs_jpn_000356) +g e N s o N s u r u k o t o g a s h i r a r e t e i r u n i j u u g o m a i n o d a N r a c l p u p a u b u r o o d o s a i d o w a p a u g e N s o N s u r u t o o g a i b u N k e N n o s a i k o n o u t s U s h i d e s U p a u t e g a k i n i y o r u g e N p o N w a g e N s o N s h I t e i m a s e N (fleurs_jpn_000357-fleurs_jpn_000357) +k a r e n o s e t s u o t a d a s h i i t o m i t o m e r u h I t o m o i m a s h I t a g a p a u o o k u n o h I t o w a s o n o g y a k u d e p a u t a i y o o k e e d e w a t a i y o o t o s o n o t a n o h o s h i g a c h I k y u u n o m a w a r i o i d o o s h I t e i r u t o s h i N j i t e i m a s h I t a (fleurs_jpn_000358-fleurs_jpn_000358) +c h i b e c l t o m e e s o o n o c h u u s h i N w a s h i N s e e y o g a d e s U p a u s a m a z a m a n a k a m i g a m i o s h I k a k U k a s u r u k o t o d e p a u e n e r u g i i c h a n e r u g a j o o k a s a r e p a u c h a k u r a g a k a c l s e e k a s a r e p a u s a t o r i n o i s h I k i g a u m a r e m a s U (fleurs_jpn_000359-fleurs_jpn_000359) +m i n a m i a f u r i k a n i a r u s u b e t e n o k o k u r i t s U k o o e N t o d o o y o o n i p a u k o n o k o o e N n i w a m a i n i c h I h o g o h I t o n y u u e N r y o o g a k a k a r i m a s U (fleurs_jpn_000360-fleurs_jpn_000360) +r e c l s h a p a u k u r u m a p a u s o n o t a n o o o k u n o k o o t s u u s h u d a N g a s o k o k a r a u m a r e m a s h I t a (fleurs_jpn_000361-fleurs_jpn_000361) +i N t a a n e c l t o w a p a u m a s U k o m y u n i k e e s h o N t o t a i j i N k o m y u n i k e e s h o N n o r y o o y o o s o o k a n e s o n a e t a k a N k y o o d e s U (fleurs_jpn_000362-fleurs_jpn_000362) +b y o o i N d e w a p a u k a N s e N k a N r i t e j u N s h o n i s h I t a g a i p a u t a n i N e n o k a N s e N n o k a n o o s e e o f U s e g u t a m e n i k a N j a o k a k u r i s u r u n a d o n o s o c h i o t o c l t e i m a s U (fleurs_jpn_000363-fleurs_jpn_000363) +r e N p o o g i k a i w a n i s e N g o n e N d o k a r a w a i s e t s u b u t s U t o r i s h i m a r i h o o e n o s h I k i N t e e k y o o o k a i s h I s h i p a u e f u b i i a i w a a d a r u t o p o r u n o n i j u u n i N n o s o o s a i N o t o o n y u u s h i n a k e r e b a n a r a n a i t o k I t e e s h i m a s h I t a (fleurs_jpn_000364-fleurs_jpn_000364) +p i i e i c h i p a u r e b e r u w a p a u k e N s a s h I t a k a g a k u b u c l s h I t s u n i f U k u m a r e r u s u i s o i o N p i i e i c h i n o e i c h i n o r y o o d e s h i m e s a r e m a s U (fleurs_jpn_000365-fleurs_jpn_000365) +s o r e d e m o p a u t o o k y o k U k a r a n o a d o b a i s u o u k e p a u s u b e t e n o h y o o s h I k i o m a m o r i p a u a N z e N j o o n o k e e k o k u n i s a i s h i N n o c h u u i o h a r a i m a s h o o (fleurs_jpn_000366-fleurs_jpn_000366) +k o r e r a w a t a m a n i k o N z a t s U s u r u k a z o k u m u k e n o b i i c h i d e p a u k a i g a N n i w a s a m a z a m a n a t e N p o g a n a r a N d e i m a s U p a u a N z e N n i o y o g u k o t o g a d e k i m a s U (fleurs_jpn_000367-fleurs_jpn_000367) +s h i N n o p a u m i e n a i c h i i m u p a u e r u e e a a r u e s u o o e n u p a u a N d o p a u e r u e e e f u e e e s u t i i o o p a u s e N k y u u h y a k U h a c h i j u u k y u u p a u p i i h y a k u k y u u n o s o N z a i m o m a t a p a u b a a c h a r u c h i i m u n o d o k u j i n o y o o s o d e a r u (fleurs_jpn_000368-fleurs_jpn_000368) +k o n o s a a b i s u w a p a u g o r a k u s e N o h a j i m e t o s u r u s e N p a k u y a p a u e N k a k u c h i d e d e e t a y a o N s e e o h I t s u y o o t o s u r u t a N k e N t a i n i h i N p a N n i r i y o o s a r e t e i m a s U (fleurs_jpn_000369-fleurs_jpn_000369) +s a k u b a N p a u b u e n o s u a i r e s U k a r a g o j u c l k i r o s a N j u u i c h i m a i r u h a n a r e t a r a p u r a t a s h i n a i d e p a u g e N s h o k u j o o i N g i i N d e a r u k u r i s U t i i n a p a u f e r u n a N d e s u p a u d e p a u k i r u h i n a a j o s h i g a d a i t o o r y o o s e N e n o s h U t s u b a o s e N g e N s h i m a s h I t a (fleurs_jpn_000370-fleurs_jpn_000370) +o n a j i t s U k i n i p a u m a s h U h a d o n o k a c l s o o r o d e b e t s u n o r y o k a k u k i g a k a c l s o o r o o o o b a a r a N s h i p a u k a b e n i g e k I t o t s u s h I t e j u u s h I c h i n i N g a s h i b o o s h i m a s h I t a (fleurs_jpn_000371-fleurs_jpn_000371) +h a s h i s h I t a n o k a m i g a t a k u u k a N w a j u u g o m e e t o r u d e s U p a u n i s e N j u u i c h i n e N h a c h i g a t s u n i s h u N k o o s h i p a u n i s e N j u u n a n a n e N s a N g a t s u m a d e k a i t s u u s h i m a s e N d e s h I t a (fleurs_jpn_000372-fleurs_jpn_000372) +b u N m e e t o i u k o t o b a w a p a u s h i m i N o i m i s u r u r a t e N g o n o k e e y o o s h I s h i i a i b u i a i e r u a i e s u k a r a k i t e o r i p a u s h i m i N o i m i s u r u r a t e N g o n o m e e s h I s h i i a i b u i a i e s u p a u t o s h i y a t o s h I k o c l k a o i m i s h i p a u n a N r a k a n o k a t a c h i d e s h a k a i n o k i b o o t e e g i s u r u s h i i a i b u i a i t i i e e e s u t o i u m e e s h i n i k a N k e e s h I t e i m a s U (fleurs_jpn_000373-fleurs_jpn_000373) +t s u u j o o p a u k o k o d e w a i t s u m o k a N k o o k y a k u y a g y o o s h a t a c h i g a h a c l s u r u o t o g a k I k o e t e k i m a s U p a u o t o t o h I k a r i g a o r i n a s u m o n o g a t a r i w a m a r u d e e h o N n o y o o d e s U (fleurs_jpn_000374-fleurs_jpn_000374) +t e r e b i n o h o o d o o n i y o r u t o p a u g e N p a t s U k a r a s h i r o k e m u r i g a a g a c l t e i m a s U (fleurs_jpn_000375-fleurs_jpn_000375) +n o o b y o o r i t o k o o d o o n o s o o k a N k a N k e e w a p a u k a g a k U s h a t a c h i n o k e N k y u u o u r a z u k e r u m o n o d e s U (fleurs_jpn_000376-fleurs_jpn_000376) +s u i y o o b i n o i b e N t o n o a t o p a u k a r u p a n e d o w a s e N s h u k e N d e f U t a t s u n o k o j i N r e e s u n i s h U t s u j o o s h i m a s h I t a (fleurs_jpn_000377-fleurs_jpn_000377) +s e N h a c l p y a k u n e N d a i i r a i p a u g u N t a i g a t o o c h a k U s u r u m a d e h a i c h i w a k o n o b y o o k i n i k a N k e e s u r u m o N d a i n i s o o g u u s h I t a k o t o w a a r i m a s e N d e s h I t a (fleurs_jpn_000378-fleurs_jpn_000378) +s h I k a s h i p a u k y a p U t e N n o w i k e c l t o o u s h i n a c l t a a t o p a u i N d o w a n a n a t s u n o w i k e c l t o o u s h i n a i p a u s a N j u u r o k u r a N s h I k a d e k i m a s e N d e s h I t a (fleurs_jpn_000379-fleurs_jpn_000379) +k a j i n o d e w a t s u u j o o p a u t o k u b e t s u n a i N s h o k u y a e N t a a t e i m e N t o o y o o i s h I t e i m a s U p a u g e s U t o g a k i b u N y o k U s h i s e t s u n a i n i t o m a r u y o o n i s u r u t a m e d e s U (fleurs_jpn_000380-fleurs_jpn_000380) +s o r e d e m o p a u t o o k y o k U k a r a n o a d o b a i s u o u k e p a u s u b e t e n o h y o o s h I k i o m a m o r i p a u a N z e N j o o n o k e e k o k u n i s a i s h i N n o c h u u i o h a r a i m a s h o o (fleurs_jpn_000381-fleurs_jpn_000381) +o w a k a r e d e w a a r i m a s e N k o r e w a h I t o t s u n o s h o o n o o w a r i d e a r i p a u a t a r a s h i i s h o o n o m a k u a k e d e s U (fleurs_jpn_000382-fleurs_jpn_000382) +s a f a r i t o w a p a u a f u r i k a n o y a s e e d o o b u t s u p a u t o k u n i s a b a N n a n i i r u y a s e e d o o b u t s u n o k a N s a t s u o m o k U t e k I t o s h I t a r i k u r o d e n o r y o k o o o s a s h i m a s U (fleurs_jpn_000383-fleurs_jpn_000383) +f u y u n i k I t a b a r u t o k a i o o o d a N s u r u b a a i w a p a u s e N s h I t s u n o i c h i o k a k u n i N s h I t e k u d a s a i p a u k o o r i n o n a k a o t s U k i s u s u m u s a i n i m o c l t o m o e e k y o o o u k e r u s e N s h I t s u d e w a o s o r o s h i i h o d o n o s o o o N g a n a r i h i b i k i m a s U (fleurs_jpn_000384-fleurs_jpn_000384) +k o k o w a i g i r i s u n o s h o k u m i N c h I s h i h a i s h a g a j i b u N t a c h i n o r y o o d o t o s h I t a b a s h o n a n o d e p a u s h o k u m i N c h i j i d a i n o s h o o k o o s a g a s o o t o s u r u h o o w a p a u k o k o k a r a h a j i m e r u n o g a y o i d e s h o o (fleurs_jpn_000385-fleurs_jpn_000385) +e b i s u s h i w a p a u s a k u g e N s u r u s u u c h i o s a d a m e m a s e N d e s h I t a g a p a u s a k u g e N w a c h u u g o k u n o k e e z a i s a N s h U t s u r y o u n i m o t o z u i t e j i c l s h I s a r e r u d a r o o t o n o b e m a s h I t a (fleurs_jpn_000386-fleurs_jpn_000386) +s a i n y u u k o k U s h o c l k u w a s h i N k o N r y o k o o n o j i k i g a s U k u n a i k a r u c h a a s h o c l k u y o r i m o h a y a k u o t o z u r e p a u n a g a b i k i p a u y o r i s h o o j o o g a a c l k a s u r u k o t o g a a r i m a s U (fleurs_jpn_000387-fleurs_jpn_000387) +k i n o o n o a s a p a u t o r u k o n o g a j i a N t e c l p u n o k e e s a t s U h o N b u d e j i d o o s h a b a k u d a N n o b a k U h a t s u n i y o r i p a u k e e k a N f U t a r i g a s h i b o o s h i p a u f U s h o o s h a w a n i j u u n i N o k o e m a s h I t a (fleurs_jpn_000388-fleurs_jpn_000388) +s h o k u b u t s u w a n i N g e N g a s u u s a N s o o t s U k u r i p a u n i N g e N g a i k I t o s h I t e h a k i d a s u n i s a N k a t a N s o o t o r i k o N d e i m a s U (fleurs_jpn_000389-fleurs_jpn_000389) +s e N p a k u d e b u c l s h i o y u s o o s u r u n o w a p a u u m i o k o e t e h I t o y a b u c l s h i o t a i r y o o y u s o o s u r u m o c l t o m o k o o r i t s u t e k i n a h o o h o o d e s U (fleurs_jpn_000390-fleurs_jpn_000390) +k a r i f o r u n i a s h u u n o a a n o r u d o p a u s h u w a r u t s e n e c l g a a c h i j i w a p a u b o o r y o k u t e k i n a b i d e o g e e m u o m i s e e n e N s h a n i h a N b a i y a r e N t a r u s u r u k o t o o k i N s h I s u r u h o o a N n i s h o m e e s h i m a s h I t a (fleurs_jpn_000391-fleurs_jpn_000391) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/result.txt b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..47717d7820be64a9483ea110a8cda6a4c36290ce --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/result.txt @@ -0,0 +1,2109 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/hyp.trn| +|------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000800 | 1 131 | 93.1 2.3 4.6 0.0 6.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000801 | 1 213 | 90.6 3.8 5.6 1.4 10.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000802 | 1 67 | 82.1 9.0 9.0 3.0 20.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000803 | 1 203 | 87.7 0.0 12.3 2.0 14.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000804 | 1 64 | 96.9 3.1 0.0 12.5 15.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000805 | 1 41 | 85.4 2.4 12.2 0.0 14.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000806 | 1 129 | 86.0 7.8 6.2 1.6 15.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000807 | 1 29 | 86.2 6.9 6.9 13.8 27.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000808 | 1 38 | 89.5 0.0 10.5 0.0 10.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000809 | 1 178 | 93.8 5.1 1.1 6.7 12.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000810 | 1 142 | 95.1 1.4 3.5 8.5 13.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000811 | 1 49 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000812 | 1 188 | 95.7 1.1 3.2 2.1 6.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000813 | 1 89 | 86.5 6.7 6.7 2.2 15.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000814 | 1 69 | 88.4 5.8 5.8 0.0 11.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000815 | 1 90 | 85.6 5.6 8.9 5.6 20.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000816 | 1 65 | 98.5 1.5 0.0 15.4 16.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000817 | 1 64 | 98.4 1.6 0.0 12.5 14.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000818 | 1 6 | 100.0 0.0 0.0 33.3 33.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000819 | 1 5 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000820 | 1 3 | 66.7 33.3 0.0 133.3 166.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000821 | 1 5 | 80.0 20.0 0.0 0.0 20.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000822 | 1 7 | 71.4 28.6 0.0 28.6 57.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000823 | 1 127 | 89.0 3.9 7.1 3.1 14.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000824 | 1 108 | 95.4 2.8 1.9 0.0 4.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000825 | 1 149 | 94.0 2.7 3.4 2.0 8.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000826 | 1 153 | 94.8 1.3 3.9 0.7 5.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000827 | 1 111 | 95.5 0.9 3.6 0.0 4.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000828 | 1 65 | 98.5 1.5 0.0 9.2 10.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000829 | 1 40 | 97.5 2.5 0.0 0.0 2.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000830 | 1 47 | 95.7 4.3 0.0 0.0 4.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000831 | 1 17 | 94.1 5.9 0.0 0.0 5.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000832 | 1 64 | 95.3 1.6 3.1 0.0 4.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000833 | 1 56 | 96.4 0.0 3.6 10.7 14.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000834 | 1 52 | 94.2 1.9 3.8 11.5 17.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000835 | 1 41 | 95.1 4.9 0.0 4.9 9.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000836 | 1 47 | 97.9 2.1 0.0 0.0 2.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000837 | 1 17 | 100.0 0.0 0.0 17.6 17.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000838 | 1 76 | 94.7 5.3 0.0 0.0 5.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000839 | 1 111 | 96.4 1.8 1.8 0.0 3.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000840 | 1 186 | 93.0 2.7 4.3 1.1 8.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000841 | 1 79 | 97.5 2.5 0.0 0.0 2.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000842 | 1 77 | 92.2 2.6 5.2 2.6 10.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000843 | 1 81 | 98.8 1.2 0.0 4.9 6.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000844 | 1 171 | 91.8 3.5 4.7 3.5 11.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000845 | 1 73 | 86.3 2.7 11.0 0.0 13.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000846 | 1 208 | 90.9 3.4 5.8 2.4 11.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000847 | 1 118 | 91.5 5.9 2.5 3.4 11.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000848 | 1 74 | 87.8 1.4 10.8 0.0 12.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000849 | 1 25 | 56.0 20.0 24.0 0.0 44.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000850 | 1 65 | 83.1 7.7 9.2 0.0 16.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000851 | 1 69 | 94.2 2.9 2.9 0.0 5.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000852 | 1 51 | 86.3 3.9 9.8 0.0 13.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000853 | 1 68 | 91.2 4.4 4.4 4.4 13.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000854 | 1 159 | 86.2 8.2 5.7 3.8 17.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000855 | 1 86 | 83.7 10.5 5.8 3.5 19.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000856 | 1 82 | 87.8 11.0 1.2 2.4 14.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000857 | 1 89 | 89.9 3.4 6.7 6.7 16.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000858 | 1 69 | 98.6 1.4 0.0 2.9 4.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000859 | 1 90 | 95.6 4.4 0.0 13.3 17.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000860 | 1 88 | 97.7 2.3 0.0 6.8 9.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000861 | 1 73 | 94.5 1.4 4.1 2.7 8.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000862 | 1 128 | 92.2 3.9 3.9 2.3 10.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000863 | 1 156 | 79.5 10.9 9.6 1.3 21.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000864 | 1 148 | 86.5 5.4 8.1 1.4 14.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000865 | 1 103 | 88.3 0.0 11.7 1.9 13.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000866 | 1 168 | 85.1 3.6 11.3 2.4 17.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000867 | 1 104 | 96.2 1.9 1.9 3.8 7.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000868 | 1 77 | 92.2 0.0 7.8 0.0 7.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000869 | 1 40 | 90.0 7.5 2.5 15.0 25.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000870 | 1 69 | 89.9 7.2 2.9 2.9 13.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000871 | 1 157 | 89.8 3.8 6.4 7.6 17.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000872 | 1 173 | 96.0 1.7 2.3 3.5 7.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000873 | 1 212 | 89.2 4.2 6.6 1.9 12.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000874 | 1 120 | 91.7 4.2 4.2 4.2 12.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000875 | 1 166 | 88.0 5.4 6.6 1.8 13.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000876 | 1 65 | 73.8 4.6 21.5 3.1 29.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000877 | 1 103 | 94.2 0.0 5.8 0.0 5.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000878 | 1 54 | 94.4 5.6 0.0 7.4 13.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000879 | 1 194 | 92.3 1.5 6.2 2.1 9.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000880 | 1 58 | 98.3 1.7 0.0 10.3 12.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000881 | 1 67 | 86.6 11.9 1.5 4.5 17.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000882 | 1 83 | 94.0 4.8 1.2 2.4 8.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000883 | 1 69 | 92.8 7.2 0.0 5.8 13.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000884 | 1 58 | 93.1 0.0 6.9 1.7 8.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000885 | 1 73 | 90.4 1.4 8.2 2.7 12.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000886 | 1 44 | 93.2 2.3 4.5 4.5 11.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000887 | 1 183 | 82.5 4.9 12.6 0.5 18.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000888 | 1 34 | 97.1 2.9 0.0 0.0 2.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000889 | 1 61 | 85.2 1.6 13.1 0.0 14.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000890 | 1 55 | 89.1 10.9 0.0 0.0 10.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000891 | 1 70 | 85.7 7.1 7.1 2.9 17.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000892 | 1 15 | 93.3 6.7 0.0 20.0 26.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000893 | 1 143 | 94.4 0.7 4.9 0.0 5.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000894 | 1 34 | 97.1 2.9 0.0 0.0 2.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000895 | 1 116 | 94.0 2.6 3.4 0.0 6.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000896 | 1 57 | 89.5 7.0 3.5 0.0 10.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000897 | 1 52 | 90.4 5.8 3.8 9.6 19.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000898 | 1 8 | 87.5 12.5 0.0 75.0 87.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000899 | 1 6 | 66.7 33.3 0.0 50.0 83.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000900 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000901 | 1 9 | 77.8 22.2 0.0 55.6 77.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000902 | 1 5 | 60.0 40.0 0.0 0.0 40.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000903 | 1 8 | 87.5 12.5 0.0 37.5 50.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000904 | 1 5 | 40.0 60.0 0.0 0.0 60.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000905 | 1 4 | 100.0 0.0 0.0 150.0 150.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000906 | 1 3 | 100.0 0.0 0.0 66.7 66.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000907 | 1 6 | 83.3 16.7 0.0 33.3 50.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000908 | 1 116 | 99.1 0.9 0.0 10.3 11.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000909 | 1 151 | 98.0 1.3 0.7 3.3 5.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000910 | 1 162 | 95.7 0.6 3.7 6.2 10.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000911 | 1 50 | 96.0 4.0 0.0 10.0 14.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000912 | 1 72 | 93.1 0.0 6.9 0.0 6.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000913 | 1 93 | 89.2 5.4 5.4 2.2 12.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000346 | 1 125 | 85.6 3.2 11.2 3.2 17.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000347 | 1 239 | 85.4 5.9 8.8 2.1 16.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000348 | 1 276 | 80.4 2.2 17.4 0.4 19.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000349 | 1 170 | 82.4 8.8 8.8 1.8 19.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000350 | 1 237 | 84.8 4.6 10.5 4.2 19.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000351 | 1 210 | 86.2 3.3 10.5 1.9 15.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000352 | 1 236 | 84.7 6.8 8.5 1.7 16.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000353 | 1 256 | 77.3 8.6 14.1 0.4 23.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000354 | 1 160 | 90.0 5.0 5.0 3.1 13.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000355 | 1 190 | 91.1 6.8 2.1 2.1 11.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000356 | 1 105 | 93.3 6.7 0.0 4.8 11.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000357 | 1 290 | 84.5 8.3 7.2 2.1 17.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000358 | 1 299 | 81.3 8.0 10.7 1.7 20.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000359 | 1 311 | 85.2 5.5 9.3 0.3 15.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000360 | 1 201 | 84.1 9.0 7.0 3.5 19.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000361 | 1 126 | 81.7 8.7 9.5 7.1 25.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000362 | 1 170 | 90.6 2.9 6.5 0.0 9.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000363 | 1 232 | 80.6 10.3 9.1 4.3 23.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000364 | 1 322 | 89.4 5.0 5.6 1.2 11.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000365 | 1 194 | 74.2 13.4 12.4 3.1 28.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000366 | 1 211 | 86.7 4.7 8.5 0.0 13.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000367 | 1 227 | 86.3 6.6 7.0 2.6 16.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000368 | 1 299 | 65.9 7.7 26.4 2.0 36.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000369 | 1 239 | 85.8 2.9 11.3 3.3 17.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000370 | 1 361 | 90.3 4.7 5.0 2.2 11.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000371 | 1 239 | 83.7 9.6 6.7 3.3 19.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000372 | 1 256 | 80.5 6.3 13.3 0.0 19.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000373 | 1 470 | 79.6 2.3 18.1 2.6 23.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000374 | 1 250 | 81.6 6.0 12.4 1.2 19.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000375 | 1 118 | 72.9 5.1 22.0 3.4 30.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000376 | 1 147 | 98.0 2.0 0.0 0.7 2.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000377 | 1 165 | 81.2 6.1 12.7 3.0 21.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000378 | 1 228 | 85.5 5.7 8.8 2.2 16.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000379 | 1 210 | 81.4 7.6 11.0 0.0 18.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000380 | 1 237 | 90.7 4.6 4.6 5.1 14.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000381 | 1 211 | 80.1 5.7 14.2 0.5 20.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000382 | 1 151 | 79.5 4.6 15.9 0.7 21.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000383 | 1 231 | 92.2 3.0 4.8 0.4 8.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000384 | 1 331 | 80.7 4.8 14.5 1.2 20.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000385 | 1 285 | 82.5 11.9 5.6 1.1 18.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000386 | 1 253 | 81.4 7.1 11.5 1.6 20.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000387 | 1 247 | 87.9 4.0 8.1 3.2 15.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000388 | 1 266 | 88.7 6.0 5.3 7.9 19.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000389 | 1 177 | 94.9 1.7 3.4 6.8 11.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000390 | 1 198 | 89.9 5.1 5.1 8.1 18.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000391 | 1 284 | 89.8 2.8 7.4 2.8 13.0 100.0 | +|==================================================================================================================| +| Sum/Avg | 160 20087 | 87.6 4.9 7.5 2.9 15.3 98.1 | +|==================================================================================================================| +| Mean | 1.0 125.5 | 88.6 5.8 5.6 7.2 18.6 98.1 | +| S.D. | 0.0 90.1 | 8.8 7.4 5.3 18.8 21.0 13.6 | +| Median | 1.0 104.5 | 89.9 4.3 4.8 2.4 14.1 100.0 | +`------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/hyp.trn| +|------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000800 | 1 131 | 122 3 6 0 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000801 | 1 213 | 193 8 12 3 23 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000802 | 1 67 | 55 6 6 2 14 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000803 | 1 203 | 178 0 25 4 29 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000804 | 1 64 | 62 2 0 8 10 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000805 | 1 41 | 35 1 5 0 6 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000806 | 1 129 | 111 10 8 2 20 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000807 | 1 29 | 25 2 2 4 8 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000808 | 1 38 | 34 0 4 0 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000809 | 1 178 | 167 9 2 12 23 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000810 | 1 142 | 135 2 5 12 19 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000811 | 1 49 | 49 0 0 0 0 0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000812 | 1 188 | 180 2 6 4 12 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000813 | 1 89 | 77 6 6 2 14 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000814 | 1 69 | 61 4 4 0 8 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000815 | 1 90 | 77 5 8 5 18 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000816 | 1 65 | 64 1 0 10 11 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000817 | 1 64 | 63 1 0 8 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000818 | 1 6 | 6 0 0 2 2 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000819 | 1 5 | 5 0 0 0 0 0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000820 | 1 3 | 2 1 0 4 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000821 | 1 5 | 4 1 0 0 1 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000822 | 1 7 | 5 2 0 2 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000823 | 1 127 | 113 5 9 4 18 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000824 | 1 108 | 103 3 2 0 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000825 | 1 149 | 140 4 5 3 12 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000826 | 1 153 | 145 2 6 1 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000827 | 1 111 | 106 1 4 0 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000828 | 1 65 | 64 1 0 6 7 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000829 | 1 40 | 39 1 0 0 1 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000830 | 1 47 | 45 2 0 0 2 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000831 | 1 17 | 16 1 0 0 1 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000832 | 1 64 | 61 1 2 0 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000833 | 1 56 | 54 0 2 6 8 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000834 | 1 52 | 49 1 2 6 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000835 | 1 41 | 39 2 0 2 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000836 | 1 47 | 46 1 0 0 1 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000837 | 1 17 | 17 0 0 3 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000838 | 1 76 | 72 4 0 0 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000839 | 1 111 | 107 2 2 0 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000840 | 1 186 | 173 5 8 2 15 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000841 | 1 79 | 77 2 0 0 2 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000842 | 1 77 | 71 2 4 2 8 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000843 | 1 81 | 80 1 0 4 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000844 | 1 171 | 157 6 8 6 20 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000845 | 1 73 | 63 2 8 0 10 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000846 | 1 208 | 189 7 12 5 24 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000847 | 1 118 | 108 7 3 4 14 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000848 | 1 74 | 65 1 8 0 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000849 | 1 25 | 14 5 6 0 11 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000850 | 1 65 | 54 5 6 0 11 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000851 | 1 69 | 65 2 2 0 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000852 | 1 51 | 44 2 5 0 7 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000853 | 1 68 | 62 3 3 3 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000854 | 1 159 | 137 13 9 6 28 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000855 | 1 86 | 72 9 5 3 17 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000856 | 1 82 | 72 9 1 2 12 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000857 | 1 89 | 80 3 6 6 15 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000858 | 1 69 | 68 1 0 2 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000859 | 1 90 | 86 4 0 12 16 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000860 | 1 88 | 86 2 0 6 8 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000861 | 1 73 | 69 1 3 2 6 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000862 | 1 128 | 118 5 5 3 13 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000863 | 1 156 | 124 17 15 2 34 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000864 | 1 148 | 128 8 12 2 22 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000865 | 1 103 | 91 0 12 2 14 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000866 | 1 168 | 143 6 19 4 29 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000867 | 1 104 | 100 2 2 4 8 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000868 | 1 77 | 71 0 6 0 6 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000869 | 1 40 | 36 3 1 6 10 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000870 | 1 69 | 62 5 2 2 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000871 | 1 157 | 141 6 10 12 28 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000872 | 1 173 | 166 3 4 6 13 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000873 | 1 212 | 189 9 14 4 27 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000874 | 1 120 | 110 5 5 5 15 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000875 | 1 166 | 146 9 11 3 23 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000876 | 1 65 | 48 3 14 2 19 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000877 | 1 103 | 97 0 6 0 6 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000878 | 1 54 | 51 3 0 4 7 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000879 | 1 194 | 179 3 12 4 19 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000880 | 1 58 | 57 1 0 6 7 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000881 | 1 67 | 58 8 1 3 12 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000882 | 1 83 | 78 4 1 2 7 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000883 | 1 69 | 64 5 0 4 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000884 | 1 58 | 54 0 4 1 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000885 | 1 73 | 66 1 6 2 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000886 | 1 44 | 41 1 2 2 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000887 | 1 183 | 151 9 23 1 33 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000888 | 1 34 | 33 1 0 0 1 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000889 | 1 61 | 52 1 8 0 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000890 | 1 55 | 49 6 0 0 6 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000891 | 1 70 | 60 5 5 2 12 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000892 | 1 15 | 14 1 0 3 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000893 | 1 143 | 135 1 7 0 8 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000894 | 1 34 | 33 1 0 0 1 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000895 | 1 116 | 109 3 4 0 7 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000896 | 1 57 | 51 4 2 0 6 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000897 | 1 52 | 47 3 2 5 10 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000898 | 1 8 | 7 1 0 6 7 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000899 | 1 6 | 4 2 0 3 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000900 | 1 3 | 3 0 0 0 0 0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000901 | 1 9 | 7 2 0 5 7 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000902 | 1 5 | 3 2 0 0 2 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000903 | 1 8 | 7 1 0 3 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000904 | 1 5 | 2 3 0 0 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000905 | 1 4 | 4 0 0 6 6 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000906 | 1 3 | 3 0 0 2 2 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000907 | 1 6 | 5 1 0 2 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000908 | 1 116 | 115 1 0 12 13 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000909 | 1 151 | 148 2 1 5 8 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000910 | 1 162 | 155 1 6 10 17 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000911 | 1 50 | 48 2 0 5 7 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000912 | 1 72 | 67 0 5 0 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000913 | 1 93 | 83 5 5 2 12 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000346 | 1 125 | 107 4 14 4 22 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000347 | 1 239 | 204 14 21 5 40 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000348 | 1 276 | 222 6 48 1 55 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000349 | 1 170 | 140 15 15 3 33 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000350 | 1 237 | 201 11 25 10 46 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000351 | 1 210 | 181 7 22 4 33 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000352 | 1 236 | 200 16 20 4 40 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000353 | 1 256 | 198 22 36 1 59 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000354 | 1 160 | 144 8 8 5 21 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000355 | 1 190 | 173 13 4 4 21 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000356 | 1 105 | 98 7 0 5 12 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000357 | 1 290 | 245 24 21 6 51 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000358 | 1 299 | 243 24 32 5 61 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000359 | 1 311 | 265 17 29 1 47 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000360 | 1 201 | 169 18 14 7 39 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000361 | 1 126 | 103 11 12 9 32 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000362 | 1 170 | 154 5 11 0 16 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000363 | 1 232 | 187 24 21 10 55 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000364 | 1 322 | 288 16 18 4 38 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000365 | 1 194 | 144 26 24 6 56 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000366 | 1 211 | 183 10 18 0 28 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000367 | 1 227 | 196 15 16 6 37 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000368 | 1 299 | 197 23 79 6 108 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000369 | 1 239 | 205 7 27 8 42 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000370 | 1 361 | 326 17 18 8 43 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000371 | 1 239 | 200 23 16 8 47 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000372 | 1 256 | 206 16 34 0 50 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000373 | 1 470 | 374 11 85 12 108 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000374 | 1 250 | 204 15 31 3 49 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000375 | 1 118 | 86 6 26 4 36 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000376 | 1 147 | 144 3 0 1 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000377 | 1 165 | 134 10 21 5 36 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000378 | 1 228 | 195 13 20 5 38 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000379 | 1 210 | 171 16 23 0 39 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000380 | 1 237 | 215 11 11 12 34 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000381 | 1 211 | 169 12 30 1 43 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000382 | 1 151 | 120 7 24 1 32 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000383 | 1 231 | 213 7 11 1 19 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000384 | 1 331 | 267 16 48 4 68 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000385 | 1 285 | 235 34 16 3 53 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000386 | 1 253 | 206 18 29 4 51 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000387 | 1 247 | 217 10 20 8 38 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000388 | 1 266 | 236 16 14 21 51 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000389 | 1 177 | 168 3 6 12 21 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000390 | 1 198 | 178 10 10 16 36 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000391 | 1 284 | 255 8 21 8 37 1 | +|==================================================================================================================| +| Sum | 160 20087 | 17597 979 1511 588 3078 157 | +|==================================================================================================================| +| Mean | 1.0 125.5 | 110.0 6.1 9.4 3.7 19.2 1.0 | +| S.D. | 0.0 90.1 | 75.5 6.5 12.9 3.7 18.9 0.1 | +| Median | 1.0 104.5 | 97.5 4.0 5.0 3.0 12.0 1.0 | +`------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/hyp.trn + +Speakers: + 0: cv_jpn_000800 + 1: cv_jpn_000801 + 2: cv_jpn_000802 + 3: cv_jpn_000803 + 4: cv_jpn_000804 + 5: cv_jpn_000805 + 6: cv_jpn_000806 + 7: cv_jpn_000807 + 8: cv_jpn_000808 + 9: cv_jpn_000809 + 10: cv_jpn_000810 + 11: cv_jpn_000811 + 12: cv_jpn_000812 + 13: cv_jpn_000813 + 14: cv_jpn_000814 + 15: cv_jpn_000815 + 16: cv_jpn_000816 + 17: cv_jpn_000817 + 18: cv_jpn_000818 + 19: cv_jpn_000819 + 20: cv_jpn_000820 + 21: cv_jpn_000821 + 22: cv_jpn_000822 + 23: cv_jpn_000823 + 24: cv_jpn_000824 + 25: cv_jpn_000825 + 26: cv_jpn_000826 + 27: cv_jpn_000827 + 28: cv_jpn_000828 + 29: cv_jpn_000829 + 30: cv_jpn_000830 + 31: cv_jpn_000831 + 32: cv_jpn_000832 + 33: cv_jpn_000833 + 34: cv_jpn_000834 + 35: cv_jpn_000835 + 36: cv_jpn_000836 + 37: cv_jpn_000837 + 38: cv_jpn_000838 + 39: cv_jpn_000839 + 40: cv_jpn_000840 + 41: cv_jpn_000841 + 42: cv_jpn_000842 + 43: cv_jpn_000843 + 44: cv_jpn_000844 + 45: cv_jpn_000845 + 46: cv_jpn_000846 + 47: cv_jpn_000847 + 48: cv_jpn_000848 + 49: cv_jpn_000849 + 50: cv_jpn_000850 + 51: cv_jpn_000851 + 52: cv_jpn_000852 + 53: cv_jpn_000853 + 54: cv_jpn_000854 + 55: cv_jpn_000855 + 56: cv_jpn_000856 + 57: cv_jpn_000857 + 58: cv_jpn_000858 + 59: cv_jpn_000859 + 60: cv_jpn_000860 + 61: cv_jpn_000861 + 62: cv_jpn_000862 + 63: cv_jpn_000863 + 64: cv_jpn_000864 + 65: cv_jpn_000865 + 66: cv_jpn_000866 + 67: cv_jpn_000867 + 68: cv_jpn_000868 + 69: cv_jpn_000869 + 70: cv_jpn_000870 + 71: cv_jpn_000871 + 72: cv_jpn_000872 + 73: cv_jpn_000873 + 74: cv_jpn_000874 + 75: cv_jpn_000875 + 76: cv_jpn_000876 + 77: cv_jpn_000877 + 78: cv_jpn_000878 + 79: cv_jpn_000879 + 80: cv_jpn_000880 + 81: cv_jpn_000881 + 82: cv_jpn_000882 + 83: cv_jpn_000883 + 84: cv_jpn_000884 + 85: cv_jpn_000885 + 86: cv_jpn_000886 + 87: cv_jpn_000887 + 88: cv_jpn_000888 + 89: cv_jpn_000889 + 90: cv_jpn_000890 + 91: cv_jpn_000891 + 92: cv_jpn_000892 + 93: cv_jpn_000893 + 94: cv_jpn_000894 + 95: cv_jpn_000895 + 96: cv_jpn_000896 + 97: cv_jpn_000897 + 98: cv_jpn_000898 + 99: cv_jpn_000899 + 100: cv_jpn_000900 + 101: cv_jpn_000901 + 102: cv_jpn_000902 + 103: cv_jpn_000903 + 104: cv_jpn_000904 + 105: cv_jpn_000905 + 106: cv_jpn_000906 + 107: cv_jpn_000907 + 108: cv_jpn_000908 + 109: cv_jpn_000909 + 110: cv_jpn_000910 + 111: cv_jpn_000911 + 112: cv_jpn_000912 + 113: cv_jpn_000913 + 114: fleurs_jpn_000346 + 115: fleurs_jpn_000347 + 116: fleurs_jpn_000348 + 117: fleurs_jpn_000349 + 118: fleurs_jpn_000350 + 119: fleurs_jpn_000351 + 120: fleurs_jpn_000352 + 121: fleurs_jpn_000353 + 122: fleurs_jpn_000354 + 123: fleurs_jpn_000355 + 124: fleurs_jpn_000356 + 125: fleurs_jpn_000357 + 126: fleurs_jpn_000358 + 127: fleurs_jpn_000359 + 128: fleurs_jpn_000360 + 129: fleurs_jpn_000361 + 130: fleurs_jpn_000362 + 131: fleurs_jpn_000363 + 132: fleurs_jpn_000364 + 133: fleurs_jpn_000365 + 134: fleurs_jpn_000366 + 135: fleurs_jpn_000367 + 136: fleurs_jpn_000368 + 137: fleurs_jpn_000369 + 138: fleurs_jpn_000370 + 139: fleurs_jpn_000371 + 140: fleurs_jpn_000372 + 141: fleurs_jpn_000373 + 142: fleurs_jpn_000374 + 143: fleurs_jpn_000375 + 144: fleurs_jpn_000376 + 145: fleurs_jpn_000377 + 146: fleurs_jpn_000378 + 147: fleurs_jpn_000379 + 148: fleurs_jpn_000380 + 149: fleurs_jpn_000381 + 150: fleurs_jpn_000382 + 151: fleurs_jpn_000383 + 152: fleurs_jpn_000384 + 153: fleurs_jpn_000385 + 154: fleurs_jpn_000386 + 155: fleurs_jpn_000387 + 156: fleurs_jpn_000388 + 157: fleurs_jpn_000389 + 158: fleurs_jpn_000390 + 159: fleurs_jpn_000391 + +Speaker sentences 0: cv_jpn_000800 #utts: 1 +id: (cv_jpn_000800-cv_jpn_000800) +Scores: (#C #S #D #I) 122 3 6 0 +REF: k a k o t o m i r a i t o N o M u j u n t e k i j i k o d o o i t s u n a r u g a Y U e n i p a u I s h i k i t e k i n a n o d e a r U +HYP: k a k o t o m i r a i t o D o ******* * u j u n t e k i j i k o d o o i t s u n a r u g a I W e n i p a u ******* * s h i k i t e k i n a n o d e a r ******* * +Eval: S D D S S D D D D + +Speaker sentences 1: cv_jpn_000801 #utts: 1 +id: (cv_jpn_000801-cv_jpn_000801) +Scores: (#C #S #D #I) 193 8 12 3 +REF: s e k a I o k e e s e e s u r u t o t o m O n i p a u j i ******* * k o j i s h i N o k e e s E e s U r u * s O o Z O o t e k i s e k a i n O s o o z o o t e k i y O O S o t o s h i t e p a u k o B u t s u g a k o B u t s +HYP: s e k a Y o k e e s e e s u r u t o t o m U n i p a u j i G k o j i s h i Y o k e e s ******* * e s E r u T s S o O D o t e k i s e k a i n ******* * s o o z o o t e k i y ******* * ******* * ******* * o t o s h i t e p a u k o M u t s u g a k o ******* * u t s +Eval: S S I I S D D S I S S S D D D D D D D D S D D + +>> REF: u d e a r u +>> HYP: u d e a r u +>> Eval: + +Speaker sentences 2: cv_jpn_000802 #utts: 1 +id: (cv_jpn_000802-cv_jpn_000802) +Scores: (#C #S #D #I) 55 6 6 2 +REF: p a s o k o n D e g e e m U Y a r u ******* * N I n G A f u e t e k i t e R U +HYP: p a s o k o n N e g e e m ******* * I a r u I T O n A H f u e t e k i t e ******* * ******* * +Eval: S D D S I I S S S S D D D D + +Speaker sentences 3: cv_jpn_000803 #utts: 1 +id: (cv_jpn_000803-cv_jpn_000803) +Scores: (#C #S #D #I) 178 0 25 4 +REF: k a G a k u n o s h i m e s U a t a r a s h i i j i j i t s U P A u a t a r a s H I i k a n n e n P A U k a n k y O o s h i h a i n O a t a r a s h I i k a n O o s E e o m o c l t e ******* * * * n a n i o h a j i m e r u k a w a +HYP: k a ******* * a k u n o s h i m e s ******* * a t a r a s h i i j i j i t s ******* * * * u a t a r a s * ******* * i k a n n e n ******* * * * k a n k y ******* * o s h i h a i n ******* * a t a r a s h ******* * i k a n ******* * o s ******* * e o m o c l t e P A U n a n i o h a j i m e r u k a w a +Eval: D D D D D D D D D D D D D D D D D D D D D D D D D I I I I + +Speaker sentences 4: cv_jpn_000804 #utts: 1 +id: (cv_jpn_000804-cv_jpn_000804) +Scores: (#C #S #D #I) 62 2 0 8 +REF: o m o s h i r o I n o n i ******* * * * r o o d o n a G a s u g i t e ******* * * * d a r u i +HYP: o m o s h i r o E n o n i P A U r o o d o n a K a s u g i t e P A U d a r u i +Eval: S I I I I S I I I I + +Speaker sentences 5: cv_jpn_000805 #utts: 1 +id: (cv_jpn_000805-cv_jpn_000805) +Scores: (#C #S #D #I) 35 1 5 0 +REF: k o r e j o o s h u u h a n C L P o i n A a +HYP: k o r e j o o s h u u h a n ******* * * T o i n ******* * a +Eval: D D D S D D + +Speaker sentences 6: cv_jpn_000806 #utts: 1 +id: (cv_jpn_000806-cv_jpn_000806) +Scores: (#C #S #D #I) 111 10 8 2 +REF: k a g a k u s h a m o s e k a I o h o o k a t s U T e k i n i * * T O o I T S U t e k i n i s E t s u m E e s h I Y O o t o s h i t e i r u +HYP: k a g a k u s h a m o s e k a Y o h o o k a t s ******* * S e k i n i P A U T o O C H I t e k i n i s A t s u m ******* * e s h ******* * ******* * U o t o s h i t e i r u +Eval: S D D S I I S S S S S S S D D D D D D S + +Speaker sentences 7: cv_jpn_000807 #utts: 1 +id: (cv_jpn_000807-cv_jpn_000807) +Scores: (#C #S #D #I) 25 2 2 4 +REF: * ******* F U t s U u n i t s u ******* * m a r a n +HYP: H A I t s ******* * u n i t s u E m a r a n +Eval: I I S S D D I I + +Speaker sentences 8: cv_jpn_000808 #utts: 1 +id: (cv_jpn_000808-cv_jpn_000808) +Scores: (#C #S #D #I) 34 0 4 0 +REF: s h i c l k a r I s h I t e k u d a s a i +HYP: s h i c l k a r ******* * s h ******* * t e k u d a s a i +Eval: D D D D + +Speaker sentences 9: cv_jpn_000809 #utts: 1 +id: (cv_jpn_000809-cv_jpn_000809) +Scores: (#C #S #D #I) 167 9 2 12 +REF: w a t a s h i w ******* * a m i g i n o G o t o k i ******* * * * R e k i s h i t e k i s e E m e e n o j i k a k u t o I u g o t o k i m o n O o B e n s h o o h o o t e k i ******* * * * r o n R I t ******* * O I u n o d e a r u +HYP: w a t a s h i w A a m i g i n o N o t o k i P A U D e k i s h i t e k i s e I m e e n o j i k a k u t o Y u g o t o k i m o n ******* * o M e n s h o o h o o t e k i P A U r o n B E t A Y U u n o d e a r u +Eval: I I S I I I I S S S D D S I I I I S S I I S S + +Speaker sentences 10: cv_jpn_000810 #utts: 1 +id: (cv_jpn_000810-cv_jpn_000810) +Scores: (#C #S #D #I) 135 2 5 12 +REF: w a t a s h i w ******* * * a * s h a k a i k e e s e e n o k o n t e E n i w a d ******* * ******* * I o n Y u s o s u t e k i n a m o n o g ******* * * a * h a t a r a i t e i r u t O o m o U +HYP: w a t a s h i w A P a U s h a k a i k e e s e e n o k o n t e N n i w a d E Y O o n * u s o s u t e k i n a m o n o g A P a U h a t a r a i t e i r u t ******* * o m o ******* * +Eval: I I I I S I I I I S D I I I I D D D D + +Speaker sentences 11: cv_jpn_000811 #utts: 1 +id: (cv_jpn_000811-cv_jpn_000811) +Scores: (#C #S #D #I) 49 0 0 0 +REF: n a n i o s u r u t s u m o r i d a c l t a n o k a +HYP: n a n i o s u r u t s u m o r i d a c l t a n o k a +Eval: + +Speaker sentences 12: cv_jpn_000812 #utts: 1 +id: (cv_jpn_000812-cv_jpn_000812) +Scores: (#C #S #D #I) 180 2 6 4 +REF: k o b u t s u t e k i t a g a j i k o h i t e e t e k i n i t a n n i ******* * * * t e n s h u u g o o t e k i n i k a n g a E r a r E r u t o k i P A U s o r e g a b u t s u r i t e k i S e k a i d e a r u +HYP: k o b u t s u t e k i t a g a j i k o h i t e e t e k i n i t a n n i P A U t e n s h u u g o o t e k i n i k a n g a ******* * r a r U r u t o k i ******* * * * s o r e g a b u t s u r i t e k i T e k a i d e a r u +Eval: I I I I D D S D D D D S + +Speaker sentences 13: cv_jpn_000813 #utts: 1 +id: (cv_jpn_000813-cv_jpn_000813) +Scores: (#C #S #D #I) 77 6 6 2 +REF: a M e g a F u c l t a n o d e P A U y a K Y U U n o s h i ******* * a i g A a r i m a s e n d e s h i t a +HYP: a N e g a Z u c l t a n o d e ******* * * * y a C L K I n o s h i R a i g ******* * a r i m a s e n d e s h i t a +Eval: S S D D D D S S S S I I D D + +Speaker sentences 14: cv_jpn_000814 #utts: 1 +id: (cv_jpn_000814-cv_jpn_000814) +Scores: (#C #S #D #I) 61 4 4 0 +REF: k o r e w a n I C L P o n d e u C L t e i n a i t a b e m O n o d e s u +HYP: k o r e w a n R * I H o n d e u ******* * * t e i n a i t a b e m U n o d e s u +Eval: S D S S D D D S + +Speaker sentences 15: cv_jpn_000815 #utts: 1 +id: (cv_jpn_000815-cv_jpn_000815) +Scores: (#C #S #D #I) 77 5 8 5 +REF: w A t a s h i w A P a U h e n s h u u ******* * i n o P A U Y o n e n k u r a I H a y a c l t a ******* * * t o o m o U +HYP: w O t a s h i w ******* * * a * h e n s h u u E i n o * * Y O o n e n k u r a E W a y a c l t a C L t o o m o ******* * +Eval: S D D D D I I D D S S S S I I I D D + +Speaker sentences 16: cv_jpn_000816 #utts: 1 +id: (cv_jpn_000816-cv_jpn_000816) +Scores: (#C #S #D #I) 64 1 0 10 +REF: * ******* i s a n n ******* * i k o n o k o t o b ******* * a n o ******* * i m i ******* * o o s h i E m a s h i t a +HYP: E i s a n n I i k o n o k o t o b P a n o R i m i O o o s h i A m a s h i t a +Eval: I I I I I I I I I I S + +Speaker sentences 17: cv_jpn_000817 #utts: 1 +id: (cv_jpn_000817-cv_jpn_000817) +Scores: (#C #S #D #I) 63 1 0 8 +REF: k a Z e g a t * ******* * ******* s u y o i h i ******* * w a t e ******* * n i s u g a d e k i m a s e n +HYP: k a S e g a t S U s u y o i h i U w a t e N n i s u g a d e k i m a s e n +Eval: S I I I I I I I I + +Speaker sentences 18: cv_jpn_000818 #utts: 1 +id: (cv_jpn_000818-cv_jpn_000818) +Scores: (#C #S #D #I) 6 0 0 2 +REF: i c h ******* * i +HYP: i c h I i +Eval: I I + +Speaker sentences 19: cv_jpn_000819 #utts: 1 +id: (cv_jpn_000819-cv_jpn_000819) +Scores: (#C #S #D #I) 5 0 0 0 +REF: h a i +HYP: h a i +Eval: + +Speaker sentences 20: cv_jpn_000820 #utts: 1 +id: (cv_jpn_000820-cv_jpn_000820) +Scores: (#C #S #D #I) 2 1 0 4 +REF: * ******* n ******* * I +HYP: O n O E +Eval: I I I I S + +Speaker sentences 21: cv_jpn_000821 #utts: 1 +id: (cv_jpn_000821-cv_jpn_000821) +Scores: (#C #S #D #I) 4 1 0 0 +REF: R e i +HYP: D e i +Eval: S + +Speaker sentences 22: cv_jpn_000822 #utts: 1 +id: (cv_jpn_000822-cv_jpn_000822) +Scores: (#C #S #D #I) 5 2 0 2 +REF: * ******* R o k U +HYP: A T o k I +Eval: I I S S + +Speaker sentences 23: cv_jpn_000823 #utts: 1 +id: (cv_jpn_000823-cv_jpn_000823) +Scores: (#C #S #D #I) 113 5 9 4 +REF: m i r u t o I u k o t O t o ******* * * * h a t a r a k u t o i u k O T o t o g a F U k a b u n R i t e k i D E n a k e r e b a n a r a n a i +HYP: m i r u t o Y u k o t A t o P A U h a t a r a k u t o i u k ******* * ******* * o t o g a * S H k a b u n D i t e k i ******* * ******* * n a k e r e b a n a r a n a i +Eval: S S I I I I D D D D D S S S D D D D + +Speaker sentences 24: cv_jpn_000824 #utts: 1 +id: (cv_jpn_000824-cv_jpn_000824) +Scores: (#C #S #D #I) 103 3 2 0 +REF: w a r e w a r e o t a m a s h i i n o S O K o k a r a u g O k a s u m o n o d e n a k e r e b a n a r a n a i +HYP: w a r e w a r e o t a m a s h i i n o ******* * Z U o k a r a u g U k a s u m o n o d e n a k e r e b a n a r a n a i +Eval: D D S S S + +Speaker sentences 25: cv_jpn_000825 #utts: 1 +id: (cv_jpn_000825-cv_jpn_000825) +Scores: (#C #S #D #I) 140 4 5 3 +REF: Z e C L t a i b e n s h o o h o o T e k i n a r u g a Y u e n i i d e Y a t e k i c h o c l k a n t e k i k e e k i ******* * g a * F u k u m a r e r u n o d e a r u +HYP: S e ******* * * t a i b e n s h o o h o o D e k i n a r u g a I u e n i i d e ******* * a t e k i c h o c l k a n t e k i k e e k i Y g a C L u k u m a r e r u n o d e a r u +Eval: S D D D S S D D I I I S + +Speaker sentences 26: cv_jpn_000826 #utts: 1 +id: (cv_jpn_000826-cv_jpn_000826) +Scores: (#C #S #D #I) 145 2 6 1 +REF: D o k o m a d e m o t a t o i c h i t o n O s o o g o * h i T e e t e k i n a z e c l t a i m u j u n t e k i j i k O D o o i t s u n o s e k a i n i s h i t e +HYP: T o k o m a d e m o t a t o i c h i t o n A s o o g o S h i ******* * e e t e k i n a z e c l t a i m u j u n t e k i j i k ******* * ******* * o o i t s u n o s e k a i n i s h i t e +Eval: S S I D D D D D D + +Speaker sentences 27: cv_jpn_000827 #utts: 1 +id: (cv_jpn_000827-cv_jpn_000827) +Scores: (#C #S #D #I) 106 1 4 0 +REF: s h i k a r u n i n i n g e n t o k a n k y o o t o N o k a n k e e w a m o t o k o o I n o k a n k E e d e a r i +HYP: s h i k a r u n i n i n g e n t o k a n k y o o t o R o k a n k e e w a m o t o k o o ******* * n o k a n k ******* * e d e a r i +Eval: S D D D D + +Speaker sentences 28: cv_jpn_000828 #utts: 1 +id: (cv_jpn_000828-cv_jpn_000828) +Scores: (#C #S #D #I) 64 1 0 6 +REF: * ******* i s a n n i k o n o k o t o b a n o i m i ******* * o o s h i ******* * E m a s h i t a +HYP: I i s a n n i k o n o k o t o b a n o i m i Y o o s h i Y A m a s h i t a +Eval: I I I I I I S + +Speaker sentences 29: cv_jpn_000829 #utts: 1 +id: (cv_jpn_000829-cv_jpn_000829) +Scores: (#C #S #D #I) 39 1 0 0 +REF: k e e k i g a n a n a t s U a r i m a s u +HYP: k e e k i g a n a n a t s S a r i m a s u +Eval: S + +Speaker sentences 30: cv_jpn_000830 #utts: 1 +id: (cv_jpn_000830-cv_jpn_000830) +Scores: (#C #S #D #I) 45 2 0 0 +REF: k o c h i r a W a k o b A y a s h i s a n d e s u +HYP: k o c h i r a B a k o b I y a s h i s a n d e s u +Eval: S S + +Speaker sentences 31: cv_jpn_000831 #utts: 1 +id: (cv_jpn_000831-cv_jpn_000831) +Scores: (#C #S #D #I) 16 1 0 0 +REF: m o s h i m O s h i +HYP: m o s h i m A s h i +Eval: S + +Speaker sentences 32: cv_jpn_000832 #utts: 1 +id: (cv_jpn_000832-cv_jpn_000832) +Scores: (#C #S #D #I) 61 1 2 0 +REF: k o k o w a O o k i k u t e n i g I y a k a n a m a c h i d e s u +HYP: k o k o w a ******* * o k i k u t e n i g U y a k a n a m a c h i d e s u +Eval: D D S + +Speaker sentences 33: cv_jpn_000833 #utts: 1 +id: (cv_jpn_000833-cv_jpn_000833) +Scores: (#C #S #D #I) 54 0 2 6 +REF: s o n o U c h i k a i ******* * a k u s a r e r u k a r a i s o g e ******* * * * +HYP: s o n o ******* * c h i k a i Y a k u s a r e r u k a r a i s o g e P A U +Eval: D D I I I I I I + +Speaker sentences 34: cv_jpn_000834 #utts: 1 +id: (cv_jpn_000834-cv_jpn_000834) +Scores: (#C #S #D #I) 49 1 2 6 +REF: a m a s a g a ******* * ******* * O s a e ******* * r a r e t e t e c h o o d o I i +HYP: a m a s a g a B K U s a e I r a r e t e t e c h o o d o ******* * i +Eval: I I I I S I I D D + +Speaker sentences 35: cv_jpn_000835 #utts: 1 +id: (cv_jpn_000835-cv_jpn_000835) +Scores: (#C #S #D #I) 39 2 0 2 +REF: h o k e n s h i t s u ******* * n o d o A O a k e t a +HYP: h o k e n s h i t s u E n o d o O A a k e t a +Eval: I I S S + +Speaker sentences 36: cv_jpn_000836 #utts: 1 +id: (cv_jpn_000836-cv_jpn_000836) +Scores: (#C #S #D #I) 46 1 0 0 +REF: m U d a n i o w a c l t e m o k i n i s h i n a i +HYP: m O d a n i o w a c l t e m o k i n i s h i n a i +Eval: S + +Speaker sentences 37: cv_jpn_000837 #utts: 1 +id: (cv_jpn_000837-cv_jpn_000837) +Scores: (#C #S #D #I) 17 0 0 3 +REF: a r i g a ******* * * t a y a +HYP: a r i g a C L t a y a +Eval: I I I + +Speaker sentences 38: cv_jpn_000838 #utts: 1 +id: (cv_jpn_000838-cv_jpn_000838) +Scores: (#C #S #D #I) 72 4 0 0 +REF: i D o o g a r A k u d a t o j i k a n O w A s u r e t e t a n o s h i m e r u +HYP: i T o o g a r O k u d a t o j i k a n U w O s u r e t e t a n o s h i m e r u +Eval: S S S S + +Speaker sentences 39: cv_jpn_000839 #utts: 1 +id: (cv_jpn_000839-cv_jpn_000839) +Scores: (#C #S #D #I) 107 2 2 0 +REF: k a G a k u w a g i J u t s u k a s a r e r u n i O o j i t e j o o s h i k i n o u c h i n i h a i c l t e y u k u +HYP: k a K a k u w a g i Z u t s u k a s a r e r u n i ******* * o j i t e j o o s h i k i n o u c h i n i h a i c l t e y u k u +Eval: S S D D + +Speaker sentences 40: cv_jpn_000840 #utts: 1 +id: (cv_jpn_000840-cv_jpn_000840) +Scores: (#C #S #D #I) 173 5 8 2 +REF: s h i k a s h i t o k i g a k a K o n i h a i r u k o t o s o n o k o t o g A P a U m i r a I o U m u k o t o d e a r i P A U a r a t a n a r u ******* * S H u t a i g a d e t e k u r u k o t o d e a r u +HYP: s h i k a s h i t o k i g a k a B o n i h a i r u k o t o s o n o k o t o g ******* * * a * m i r a Y o O m u k o t o d e a r i ******* * * * a r a t a n a r u I C L u t a i g a d e t e k u r u k o t o d e a r u +Eval: S D D D D S S D D D D I I S S + +Speaker sentences 41: cv_jpn_000841 #utts: 1 +id: (cv_jpn_000841-cv_jpn_000841) +Scores: (#C #S #D #I) 77 2 0 0 +REF: t e r e b i o k a i k a E t e p a u t e r e b i o m i r u j i k a n G a f u e t a +HYP: t e r e b i o k a i k a I t e p a u t e r e b i o m i r u j i k a n N a f u e t a +Eval: S S + +Speaker sentences 42: cv_jpn_000842 #utts: 1 +id: (cv_jpn_000842-cv_jpn_000842) +Scores: (#C #S #D #I) 71 2 4 2 +REF: k a k a r ******* * u s h U t a i n o m i P A U i t s u m a d e m o i k I r u n o d e a r u +HYP: k a k a r E u s h I t a i n o m i ******* * * * i t s u m a d e m o i k E r u n o d e a r u +Eval: I I S D D D D S + +Speaker sentences 43: cv_jpn_000843 #utts: 1 +id: (cv_jpn_000843-cv_jpn_000843) +Scores: (#C #S #D #I) 80 1 0 4 +REF: n i n k i R a a m e n ******* * y a n i n a r a n d a r a n i j i k a n m a ******* * c h i d a c l t a +HYP: n i n k i D a a m e n I y a n i n a r a n d a r a n i j i k a n m a O c h i d a c l t a +Eval: S I I I I + +Speaker sentences 44: cv_jpn_000844 #utts: 1 +id: (cv_jpn_000844-cv_jpn_000844) +Scores: (#C #S #D #I) 157 6 8 6 +REF: s o r e ******* * o m o c h i i r u n i n g e n n o I y o k u n I i Z o n s H I P A U s o s h i t e k o r e w a k a r e n o m o c l t e i r u k a c h I n o s H a k u d o ******* * n * * I i Z o n s u r u +HYP: s o r e O o m o c h i i r u n i n g e n n o ******* * y o k u n ******* * i T o n s * E * * I s o s h i t e k o r e w a k a r e n o m o c l t e i r u k a c h E n o s * a k u d o N n P A U i D o n s u r u +Eval: I I D D D D S D S D D S S D I I I I S S + +Speaker sentences 45: cv_jpn_000845 #utts: 1 +id: (cv_jpn_000845-cv_jpn_000845) +Scores: (#C #S #D #I) 63 2 8 0 +REF: m a w a r I W a m i n n A P a U k a n g a E r u k o t O o y a m e t e i t a +HYP: m a w a r Y O a m i n n ******* * * a * k a n g a ******* * r u k o t ******* * o y a m e t e i t a +Eval: S S D D D D D D D D + +Speaker sentences 46: cv_jpn_000846 #utts: 1 +id: (cv_jpn_000846-cv_jpn_000846) +Scores: (#C #S #D #I) 189 7 12 5 +REF: k o o i t e k i C H o * C L k a n t e k i n i s e k a I o m i r u t o I u k o t o w A P a U G Y a k u n i k o o i t e k i c h ******* * o ******* * c l k a n t e k i n i s e k a I o k e e s e e s u r u k o t O o F U k u m u n +HYP: k o o i t e k i * J o K U k a n t e k i n i s e k a Y o m i r u t o Y u k o t o w ******* * * a * * J a k u n i k o o i t e k i c h A o K c l k a n t e k i n i s e k a Y o k e e s e e s u r u k o t ******* * o ******* * ******* * k u m u n +Eval: D S I S S S S D D D D D S I I I I S D D D D D D + +>> REF: o d e a r u +>> HYP: o d e a r u +>> Eval: + +Speaker sentences 47: cv_jpn_000847 #utts: 1 +id: (cv_jpn_000847-cv_jpn_000847) +Scores: (#C #S #D #I) 108 7 3 4 +REF: s ******* H I n p a i K a k e s a s e m a i t o s u r u k i z u k a i G A P a U y o k e I n i s H I n ******* * * p a i s a s e t e s h i m a u +HYP: s J E n p a i T a k e s a s e m a i t o s u r u k i z u k a i Y G * a * y o k e E n i s * E n C L p a i s a s e t e s h i m a u +Eval: I S S S S S D D S D S I I I + +Speaker sentences 48: cv_jpn_000848 #utts: 1 +id: (cv_jpn_000848-cv_jpn_000848) +Scores: (#C #S #D #I) 65 1 8 0 +REF: k o n o m i c h i w a t o t e m o s e m a i n o d E P A U a B U n a i d e s u +HYP: k o n o m i c h i w a t o t e m o s e m a i n o d ******* * ******* * * * a ******* * M n a i d e s u +Eval: D D D D D D D D S + +Speaker sentences 49: cv_jpn_000849 #utts: 1 +id: (cv_jpn_000849-cv_jpn_000849) +Scores: (#C #S #D #I) 14 5 6 0 +REF: O B o e g A W a r U I N E +HYP: * ******* W o e g ******* * ******* * a r I N I U +Eval: D D S D D D D S S S S + +Speaker sentences 50: cv_jpn_000850 #utts: 1 +id: (cv_jpn_000850-cv_jpn_000850) +Scores: (#C #S #D #I) 54 5 6 0 +REF: t o i R E w a r O o k a N O H i D a r i g a W a n i a r i m a s u +HYP: t o i Y O w a r ******* * o k a ******* * M A i T a r i g a ******* * a n i a r i m a s u +Eval: S S D D D D S S S D D + +Speaker sentences 51: cv_jpn_000851 #utts: 1 +id: (cv_jpn_000851-cv_jpn_000851) +Scores: (#C #S #D #I) 65 2 2 0 +REF: t a N a k a s a N n o h i d a r i n i k i m U r a s a n g a i m a s u +HYP: t a D a k a s a ******* * n o h i d a r i n i k i m E r a s a n g a i m a s u +Eval: S D D S + +Speaker sentences 52: cv_jpn_000852 #utts: 1 +id: (cv_jpn_000852-cv_jpn_000852) +Scores: (#C #S #D #I) 44 2 5 0 +REF: m a c l k u r O n a t a m a G o C L t e s u g o i N e +HYP: m a c l k u r U n a t a m a B o ******* * * t e s u g o i ******* * e +Eval: S S D D D D D + +Speaker sentences 53: cv_jpn_000853 #utts: 1 +id: (cv_jpn_000853-cv_jpn_000853) +Scores: (#C #S #D #I) 62 3 3 3 +REF: s h ******* * O * h Y o o m i t a i n a d o k u s h O k a N s o o b u N o k a i t a +HYP: s h A R S h * o o m i t a i n a d o k u s h U k a ******* * s o o b u M o k a i t a +Eval: I I S I D S D D S + +Speaker sentences 54: cv_jpn_000854 #utts: 1 +id: (cv_jpn_000854-cv_jpn_000854) +Scores: (#C #S #D #I) 137 13 9 6 +REF: g e n j i t S U N o s e k a i w ******* * * a * t a ******* * N o i c h i t o s h i t e k e c l t E e s E r a R E T A k a t a c h i o M O c l t A s E k a i D E n a k e r e B A n a r a n a i +HYP: g e n j i t * E M o s e k a i w A P a U t a M O o i c h i t o s h i t e k e c l t ******* * e s U r a ******* * I D E k a t a c h i o ******* * A c l t O s U k a i ******* * U n a k e r e M O n a r a n a i +Eval: D S S I I I I I I S D D S D D S S S D D S S S D D S S S + +Speaker sentences 55: cv_jpn_000855 #utts: 1 +id: (cv_jpn_000855-cv_jpn_000855) +Scores: (#C #S #D #I) 72 9 5 3 +REF: s h o o h i n k e n s a k u G A W A k a r I y a s u i t o K * a u k ******* * I N i n a r u N O N i +HYP: s h o o h i n k e n s a k u K G A O k a r ******* * y a s u i t o O P a ******* u k A O K i n a r u ******* * M A i +Eval: S S S S D D S I D I I S S D D S S + +Speaker sentences 56: cv_jpn_000856 #utts: 1 +id: (cv_jpn_000856-cv_jpn_000856) +Scores: (#C #S #D #I) 72 9 1 2 +REF: C H I s h ******* * i k i w a R e k i s h i t e K I k a t e e d e n a k e r E B A n a r a n a i +HYP: T S E s h I i k i w a N e k i s h i t e * C L k a t e e d e n a k e r U M O n a r a n a i +Eval: S S S I I S D S S S S S + +Speaker sentences 57: cv_jpn_000857 #utts: 1 +id: (cv_jpn_000857-cv_jpn_000857) +Scores: (#C #S #D #I) 80 3 6 6 +REF: m o n o g o t O n o ******* * j U n B a n O k a e r u d a k e d e ******* * * * u m a k u i k u k o t o m O a r U +HYP: m o n o g o t A n o N j I n P a n ******* * k a e r u d a k e d e P A U u m a k u i k u k o t o m ******* * a r ******* * +Eval: S I I S S D D I I I I D D D D + +Speaker sentences 58: cv_jpn_000858 #utts: 1 +id: (cv_jpn_000858-cv_jpn_000858) +Scores: (#C #S #D #I) 68 1 0 2 +REF: k o n o k i S e t s u w a k a t s u o n o s a s h i m i g a z e c l p ******* * i n +HYP: k o n o k i E e t s u w a k a t s u o n o s a s h i m i g a z e c l p E i n +Eval: S I I + +Speaker sentences 59: cv_jpn_000859 #utts: 1 +id: (cv_jpn_000859-cv_jpn_000859) +Scores: (#C #S #D #I) 86 4 0 12 +REF: k a k e ******* * n i s h i c l p a i s h i t e m ******* * ******* * * * O o c h i ******* * t s u i t e s ******* * O N s h i t s u o u k e i R e r u +HYP: k a k e N n i s h i c l p a i s h i t e m A P A U M o c h i T t s u i t e s A M A s h i t s u o u k e i D e r u +Eval: I I I I I I I I S I I I I S S S + +Speaker sentences 60: cv_jpn_000860 #utts: 1 +id: (cv_jpn_000860-cv_jpn_000860) +Scores: (#C #S #D #I) 86 2 0 6 +REF: s o r e y u e n i ******* * * * t e t s u g a k u g a z e n t a i n o g a k U d e ******* * a r u t o s u r e B a +HYP: s o r e y u e n i P A U t e t s u g a k u g a z e n t a i n o g a k O d e Y a r u t o s u r e W a +Eval: I I I I S I I S + +Speaker sentences 61: cv_jpn_000861 #utts: 1 +id: (cv_jpn_000861-cv_jpn_000861) +Scores: (#C #S #D #I) 69 1 3 2 +REF: C H i i s a n a ******* * y a o y a d a g a y a s u k u t e h a n j O o s h i t e r u +HYP: * K i i s a n a I y a o y a d a g a y a s u k u t e h a n j ******* * o s h i t e r u +Eval: D S I I D D + +Speaker sentences 62: cv_jpn_000862 #utts: 1 +id: (cv_jpn_000862-cv_jpn_000862) +Scores: (#C #S #D #I) 118 5 5 3 +REF: i n ******* * f u r a g a k i n O o F u z e n n i o c h I i c l t e p a u k o k u g a i E d a C L s h U t s u s u r u * h i t O m o d e t e k i t a +HYP: i n I f u r a g a k i n ******* * o H u z e n n i o c h J i c l t e p a u k o k u g a i A d a ******* * * s h I t s u s u r u S h i t A m o d e t e k i t a +Eval: I I D D S S S D D D S I S + +Speaker sentences 63: cv_jpn_000863 #utts: 1 +id: (cv_jpn_000863-cv_jpn_000863) +Scores: (#C #S #D #I) 124 17 15 2 +REF: t * ******* s u g i n i k a G a k u w a s o n z a I o S H u j u n o R Y O o i k i n I W A k a c l t e s O r e z O r E n O R Y o O i K I N i t S U i t e K E N k Y U u s u r u +HYP: t S s u g i n i k a W a k u w a s o n z a Y o * J u j u n o ******* * * D o i k i n ******* * ******* * O k a c l t e s U r e z U r A n D * E o K i N Z E i t * ******* * i t e ******* * N I k * I u s u r u +Eval: I I S S D S D D D S D D D D S S S S S D S S S S S D D D D D S S D S + +Speaker sentences 64: cv_jpn_000864 #utts: 1 +id: (cv_jpn_000864-cv_jpn_000864) +Scores: (#C #S #D #I) 128 8 12 2 +REF: s o r e d e w a t o k I t o I U m O N O n O s E e r i t s u s h i Y O o w a n a k u p a u s h ******* * U n k a n t o i U m o n o m o n a k u n a r u n o d E a r U +HYP: s o r e d e w a t o k ******* * t o Y I m A R A n A s ******* * e r i t s u s h i ******* * ******* * o w a n a k u p a u s h I R n k a n t o i ******* * m o n o m o n a k u n a r u n o d ******* * a r I +Eval: D D S S S S S S D D D D D D I I S D D D D S + +Speaker sentences 65: cv_jpn_000865 #utts: 1 +id: (cv_jpn_000865-cv_jpn_000865) +Scores: (#C #S #D #I) 91 0 12 2 +REF: * ******* a k a i b u r a n k o P A U k o n k u r I i t o s E e n o s u b e r i d a i P A U k a w a i t a s u n a b a +HYP: H a k a i b u r a n k o ******* * * * k o n k u r ******* * i t o s ******* * e n o s u b e r i d a i ******* * * * k a w a i t a s u n a b a +Eval: I I D D D D D D D D D D D D + +Speaker sentences 66: cv_jpn_000866 #utts: 1 +id: (cv_jpn_000866-cv_jpn_000866) +Scores: (#C #S #D #I) 143 6 19 4 +REF: * ******* s h i k a s h i s o r E w a d o k o m a d e m O k o k o k a r a d e t e * * K o k o e k a E r i k u r u s e e S H i t S U o m o C L t A M o N o d e N a k E R e B a n a r a n a i +HYP: S s h i k a s h i s o r O w a d o k o m a d e m N k o k o k a r a d e t e P A U o k o e k a I r i k u r u s e e ******* * * i t * ******* * o m o ******* * * t ******* * ******* * o M o d e ******* * a k ******* * ******* * e W a n a r a n a i +Eval: I I S S I I S S D D D D D D D D D D D D D S D D D D D D S + +Speaker sentences 67: cv_jpn_000867 #utts: 1 +id: (cv_jpn_000867-cv_jpn_000867) +Scores: (#C #S #D #I) 100 2 2 4 +REF: a r i t o a r a Y U r u d e m A o m a k i c h i r a s h i t e m i n n ******* * a k a r a ******* * u r a m i o k a c l t e r u +HYP: a r i t o a r a ******* * I r u d e m O o m a k i c h i r a s h i t e m i n n E a k a r a O u r a m i o k a c l t e r u +Eval: D D S S I I I I + +Speaker sentences 68: cv_jpn_000868 #utts: 1 +id: (cv_jpn_000868-cv_jpn_000868) +Scores: (#C #S #D #I) 71 0 6 0 +REF: k o n o t e e d o P A U s a w a g i n i n a r u k o t o m o n a i n o d a r O o +HYP: k o n o t e e d o ******* * * * s a w a g i n i n a r u k o t o m o n a i n o d a r ******* * o +Eval: D D D D D D + +Speaker sentences 69: cv_jpn_000869 #utts: 1 +id: (cv_jpn_000869-cv_jpn_000869) +Scores: (#C #S #D #I) 36 3 1 6 +REF: k o n o n e D a n d e ******* * * * u r E C H a u k ******* * a a +HYP: k o n o n e R a n d e P A U u r I * T a u k A a a +Eval: S I I I I S D S I I + +Speaker sentences 70: cv_jpn_000870 #utts: 1 +id: (cv_jpn_000870-cv_jpn_000870) +Scores: (#C #S #D #I) 62 5 2 2 +REF: h i n o k a g ******* * e n n i c h U u i s h i n a i t o S u G u N I k o g e r u +HYP: h i n o k a g A e n n i c h ******* * u i s h i n a i t o P A u S u G U k o g e r u +Eval: I I D D S S S S S + +Speaker sentences 71: cv_jpn_000871 #utts: 1 +id: (cv_jpn_000871-cv_jpn_000871) +Scores: (#C #S #D #I) 141 6 10 12 +REF: e n B a n n o u ******* * e ******* * n i p o t s u r i t o C H I i s a n A a n a g A H I R a i t a s a i s h ******* * o w a t s u m a y o o j i t e e d o n o C H i i s a n ******* * * a * a n a d a c l t ******* * a +HYP: e n M a n n o u W e N n i p o t s u r i t o T S U i s a n ******* * a n a g ******* * ******* * ******* * ******* * a i t a s a i s h I o w a t s u m a y o o j i t e e d o n o T S i i s a n A P a U a n a d a c l t A a +Eval: S I I I I S S S D D D D D D D D D D I I S S I I I I I I + +Speaker sentences 72: cv_jpn_000872 #utts: 1 +id: (cv_jpn_000872-cv_jpn_000872) +Scores: (#C #S #D #I) 166 3 4 6 +REF: s o r e w a W a r e w a r e o i k a s h i n a g a r ******* * * a * w a r e w a r e o D o r e e k a s u r u n o d e a r u p a u w a r e w a r e n o t a m a ******* * s h i I o k o r o s u n o d e a R U +HYP: s o r e w a M a r e w a r e o i k a s h i n a g a r A P a U w a r e w a r e o T o r e e k a s u r u n o d e a r u p a u w a r e w a r e n o t a m a S s h i Y o k o r o s u n o d e a ******* * ******* * +Eval: S I I I I S I I S D D D D + +Speaker sentences 73: cv_jpn_000873 #utts: 1 +id: (cv_jpn_000873-cv_jpn_000873) +Scores: (#C #S #D #I) 189 9 14 4 +REF: r e k I s h i t e k i n i a t a E r a r E t a m o n o w A P a U Z e c l t A I m u j u n t e k i j i K o D o o i t s U t e k i g ******* * e n z ******* * a i n I o i t e s E k a i s h i T e k i n i a t a e r a r E t a m o n +HYP: r e k U s h i t e k i n i a t a ******* * r a r ******* * t a m o n o w ******* * * a * D e c l t ******* * E m u j u n t e k i j i G o T o o i t s I t e k i g I e n z D a i n O o i t e s U k a i s h i ******* * e k i n i a t a e r a r ******* * t a m o n +Eval: S D D D D D D D D S D D S S S S I I I I S S D D D D + +>> REF: O t o s h i t e +>> HYP: U t o s h i t e +>> Eval: S + +Speaker sentences 74: cv_jpn_000874 #utts: 1 +id: (cv_jpn_000874-cv_jpn_000874) +Scores: (#C #S #D #I) 110 5 5 5 +REF: m u ******* * j u n t e k i ******* * j i K o d o o i T S U t o s h i t e p a u i t s u m o k o n o s e k a i n i c h o o e T s * U t e k i d e a R U +HYP: m u O j u n t e k i E j i G o d o o i C H I t o s h i t e p a u i t s u m o k o n o s e k a i n i c h o o e * s H I t e k i d e a ******* * ******* * +Eval: I I I I S S S S D I S D D D D + +Speaker sentences 75: cv_jpn_000875 #utts: 1 +id: (cv_jpn_000875-cv_jpn_000875) +Scores: (#C #S #D #I) 146 9 11 3 +REF: y u E n i Z e c l T A I m u j u n t e k i j i K o d o o i T s * u t o s h i t e g e n Z a I k a r a g e n z a I e t O u G o k I i k u s ******* * e k a I n o g e n z a i n I o i t e +HYP: y u ******* * n i D e c l P E E m u j u n t e k i j i G o d o o i * s H u t o s h i t e g e n ******* * a E k a r a g e n z a E e t ******* * u W o k ******* * i k u s U e k a E n o g e n z a i n ******* * o i t e +Eval: D D S S S S S D I D D S S D D S D D I I S D D + +Speaker sentences 76: cv_jpn_000876 #utts: 1 +id: (cv_jpn_000876-cv_jpn_000876) +Scores: (#C #S #D #I) 48 3 14 2 +REF: * ******* a r e P A U B o t a n o s h i t E M O d a C L s h U T S u d e k i n a i +HYP: H a r e ******* * * * W o t a n o s h i t ******* * O N d a ******* * * s h ******* * ******* * * u d e k i n a i +Eval: I I D D D D S D D S S D D D D D D D D + +Speaker sentences 77: cv_jpn_000877 #utts: 1 +id: (cv_jpn_000877-cv_jpn_000877) +Scores: (#C #S #D #I) 97 0 6 0 +REF: s h i k a s h i w a t a s h I W a s o k o n i s e k a i n o j i k o d o o i t s u O o k u n o d e w a n a i +HYP: s h i k a s h i w a t a s h ******* * ******* * a s o k o n i s e k a i n o j i k o d o o i t s u ******* * o k u n o d e w a n a i +Eval: D D D D D D + +Speaker sentences 78: cv_jpn_000878 #utts: 1 +id: (cv_jpn_000878-cv_jpn_000878) +Scores: (#C #S #D #I) 51 3 0 4 +REF: n e M u T a k u n a r u n o g ******* * * a * h a y a k u N a c l t a +HYP: n e B u K a k u n a r u n o g A P a U h a y a k u M a c l t a +Eval: S S I I I I S + +Speaker sentences 79: cv_jpn_000879 #utts: 1 +id: (cv_jpn_000879-cv_jpn_000879) +Scores: (#C #S #D #I) 179 3 12 4 +REF: w a t a s h i w a N i n g e n n ******* * o R e k i s h i t e k i k e e s e e n o t a c h i b a k a r a * * G E e j u t s u o m i r u n o d e a c l t e P A U K o o s h a k a r a Z e n s h a o m i r u n o d e W a n a i +HYP: w a t a s h i w a ******* * i n g e n n O o D e k i s h i t e k i k e e s e e n o t a c h i b a k a r a P A U G e j u t s u o m i r u n o d e a c l t e ******* * * * ******* * o o s h a k a r a ******* * e n s h a o m i r u n o d e ******* * a n a i +Eval: D D I I S I I S S D D D D D D D D D D + +Speaker sentences 80: cv_jpn_000880 #utts: 1 +id: (cv_jpn_000880-cv_jpn_000880) +Scores: (#C #S #D #I) 57 1 0 6 +REF: a o i t o m a t o s h i k a n a k u t e ******* * * * k a u k a M a ******* * y o u +HYP: a o i t o m a t o s h i k a n a k u t e P A U k a u k a B a I y o u +Eval: I I I I S I I + +Speaker sentences 81: cv_jpn_000881 #utts: 1 +id: (cv_jpn_000881-cv_jpn_000881) +Scores: (#C #S #D #I) 58 8 1 3 +REF: s H I n k ******* * I J I g ******* y o o n i o o k i n a k i t a I o y O s E t e I r u +HYP: s * E n k E Z U E g y o o n i o o k i n a k i t a Y o y A s U t e E r u +Eval: D S I I S S S I S S S S + +Speaker sentences 82: cv_jpn_000882 #utts: 1 +id: (cv_jpn_000882-cv_jpn_000882) +Scores: (#C #S #D #I) 78 4 1 2 +REF: n a n i k a s h i r a n o i n s e n t I b u G a n a i t o k i b ******* * i s H I I n o d e w a +HYP: n a n i k a s h i r a n o i n s e n t E b u W a n a i t o k i b U i s * U E n o d e w a +Eval: S S I I D S S + +Speaker sentences 83: cv_jpn_000883 #utts: 1 +id: (cv_jpn_000883-cv_jpn_000883) +Scores: (#C #S #D #I) 64 5 0 4 +REF: j i k A n ******* * s * e E g e n n o i b e n t o d e s u t o r E s * U t a m a r U +HYP: j i k O n O s H e K g e n n o i b e n t o d e s u t o r U s H I t a m a r I +Eval: S I I I S S I S S + +Speaker sentences 84: cv_jpn_000884 #utts: 1 +id: (cv_jpn_000884-cv_jpn_000884) +Scores: (#C #S #D #I) 54 0 4 1 +REF: m A W a r i n o * h i t o w a b o o z e n t o s h i t e i t a +HYP: m ******* * ******* * a r i n o S h i t o w a b o o z e n t o s h i t e i t a +Eval: D D D D I + +Speaker sentences 85: cv_jpn_000885 #utts: 1 +id: (cv_jpn_000885-cv_jpn_000885) +Scores: (#C #S #D #I) 66 1 6 2 +REF: s o n n a n a i y o o n o m E e r u G A P a U n a n k e n m o k ******* * i t e i t a +HYP: s o n n a n a i y o o n o m ******* * e r u ******* * W * a * n a n k e n m o k U i t e i t a +Eval: D D D D S D D I I + +Speaker sentences 86: cv_jpn_000886 #utts: 1 +id: (cv_jpn_000886-cv_jpn_000886) +Scores: (#C #S #D #I) 41 1 2 2 +REF: N i j i K a i d e d e e s ******* * u i s h i t e i t a +HYP: * ******* i j i G a i d e d e e s F u i s h i t e i t a +Eval: D D S I I + +Speaker sentences 87: cv_jpn_000887 #utts: 1 +id: (cv_jpn_000887-cv_jpn_000887) +Scores: (#C #S #D #I) 151 9 23 1 +REF: t o k i d o k i P A U J i B u n n O K o k o r o G a w a k a r a n a k u n A r u t o * K i g a a r u d a k a r a b o k u W a k a a T e N o H I k i P A U n O o t o n i k a K i H a j I m e r u +HYP: t o k i d o k i * * * C H i U u n n ******* * ******* * o k o r o W a w a k a r a n a k u n O r u t o C H i g a a r u d a k a r a b o k u ******* * a k a a N e ******* * o ******* * ******* * k i ******* * * * n ******* * o t o n i k a J i ******* * a j E m e r u +Eval: D D D S S S D D D D S S I S D D S D D D D D D D D D D D D S D D S + +Speaker sentences 88: cv_jpn_000888 #utts: 1 +id: (cv_jpn_000888-cv_jpn_000888) +Scores: (#C #S #D #I) 33 1 0 0 +REF: m o o n i g e t e c h a D a m e d a +HYP: m o o n i g e t e c h a T a m e d a +Eval: S + +Speaker sentences 89: cv_jpn_000889 #utts: 1 +id: (cv_jpn_000889-cv_jpn_000889) +Scores: (#C #S #D #I) 52 1 8 0 +REF: k a r e w a p A U B o o C L t o t a C H i t s u k u s h i t e i t a +HYP: k a r e w a p * * ******* * o o ******* * * t o t a * J i t s u k u s h i t e i t a +Eval: D D D D D D D D S + +Speaker sentences 90: cv_jpn_000890 #utts: 1 +id: (cv_jpn_000890-cv_jpn_000890) +Scores: (#C #S #D #I) 49 6 0 0 +REF: d a r E n i m O M e E w a k U w a k a k e t a k U n a i +HYP: d a r U n i m U N e I w a k O w a k a k e t a k A n a i +Eval: S S S S S S + +Speaker sentences 91: cv_jpn_000891 #utts: 1 +id: (cv_jpn_000891-cv_jpn_000891) +Scores: (#C #S #D #I) 60 5 5 2 +REF: M a s a k A P a U t o o m o C L T E d o a n ******* * o t o c l t e o n i g I c l t a +HYP: P a s a k ******* * * a * t o o m o * T E U d o a n O o t o c l t e o n i g E c l t a +Eval: S D D D D D S S S I I S + +Speaker sentences 92: cv_jpn_000892 #utts: 1 +id: (cv_jpn_000892-cv_jpn_000892) +Scores: (#C #S #D #I) 14 1 0 3 +REF: s * ******* * U i m a s e n +HYP: s H T E i m a s e n +Eval: I I I S + +Speaker sentences 93: cv_jpn_000893 #utts: 1 +id: (cv_jpn_000893-cv_jpn_000893) +Scores: (#C #S #D #I) 135 1 7 0 +REF: k a y o U n i s h i t e s h i C L t e I r u t o t o m o n i s h i c l t e i n a i t o k o r o k a r a t a n k y U u w a h a j i m a r u n o d e a r u +HYP: k a y o O n i s h i t e s h i ******* * * t e ******* * r u t o t o m o n i s h i c l t e i n a i t o k o r o k a r a t a n k y ******* * u w a h a j i m a r u n o d e a r u +Eval: S D D D D D D D + +Speaker sentences 94: cv_jpn_000894 #utts: 1 +id: (cv_jpn_000894-cv_jpn_000894) +Scores: (#C #S #D #I) 33 1 0 0 +REF: a i s a t s u w a d a i j i d a Y o +HYP: a i s a t s u w a d a i j i d a I o +Eval: S + +Speaker sentences 95: cv_jpn_000895 #utts: 1 +id: (cv_jpn_000895-cv_jpn_000895) +Scores: (#C #S #D #I) 109 3 4 0 +REF: t o j i t a m o n O o i k a n i h i r o g e t e m o h i r a i t a m O n o n i W a n a r a N U t o i c l t e i r u g a +HYP: t o j i t a m o n ******* * o i k a n i h i r o g e t e m o h i r a i t a m U n o n i ******* * a n a r a R O t o i c l t e i r u g a +Eval: D D S D D S S + +Speaker sentences 96: cv_jpn_000896 #utts: 1 +id: (cv_jpn_000896-cv_jpn_000896) +Scores: (#C #S #D #I) 51 4 2 0 +REF: t a m E s h i n I i k u t s u k a t s u k u c l t e m i Y O O +HYP: t a m U s h i n ******* * i k u t s u k a t s u k u c l t e m i O W A +Eval: S D D S S S + +Speaker sentences 97: cv_jpn_000897 #utts: 1 +id: (cv_jpn_000897-cv_jpn_000897) +Scores: (#C #S #D #I) 47 3 2 5 +REF: * ******* * * ******* z U i b u n a k o G i n a s h o o b a i d a Y o n A a +HYP: U T S z E i b u n a k o K i n a s h o o b a i d a I o n ******* * a +Eval: I I I I I S S S D D + +Speaker sentences 98: cv_jpn_000898 #utts: 1 +id: (cv_jpn_000898-cv_jpn_000898) +Scores: (#C #S #D #I) 7 1 0 6 +REF: w ******* * a c * ******* * ******* H i +HYP: w H a c L T E i +Eval: I I I I I I S + +Speaker sentences 99: cv_jpn_000899 #utts: 1 +id: (cv_jpn_000899-cv_jpn_000899) +Scores: (#C #S #D #I) 4 2 0 3 +REF: * ******* i * C H i +HYP: K i T E i +Eval: I I I S S + +Speaker sentences 100: cv_jpn_000900 #utts: 1 +id: (cv_jpn_000900-cv_jpn_000900) +Scores: (#C #S #D #I) 3 0 0 0 +REF: g o +HYP: g o +Eval: + +Speaker sentences 101: cv_jpn_000901 #utts: 1 +id: (cv_jpn_000901-cv_jpn_000901) +Scores: (#C #S #D #I) 7 2 0 5 +REF: * ******* * ******* s h i * C H i +HYP: H A s h i I T i +Eval: I I I I I S S + +Speaker sentences 102: cv_jpn_000902 #utts: 1 +id: (cv_jpn_000902-cv_jpn_000902) +Scores: (#C #S #D #I) 3 2 0 0 +REF: i I E +HYP: i E A +Eval: S S + +Speaker sentences 103: cv_jpn_000903 #utts: 1 +id: (cv_jpn_000903-cv_jpn_000903) +Scores: (#C #S #D #I) 7 1 0 3 +REF: W a ******* * * c h i +HYP: H a C L c h i +Eval: S I I I + +Speaker sentences 104: cv_jpn_000904 #utts: 1 +id: (cv_jpn_000904-cv_jpn_000904) +Scores: (#C #S #D #I) 2 3 0 0 +REF: R E I +HYP: H N E +Eval: S S S + +Speaker sentences 105: cv_jpn_000905 #utts: 1 +id: (cv_jpn_000905-cv_jpn_000905) +Scores: (#C #S #D #I) 4 0 0 6 +REF: * ******* s h ******* * ******* * i +HYP: A s h I I i +Eval: I I I I I I + +Speaker sentences 106: cv_jpn_000906 #utts: 1 +id: (cv_jpn_000906-cv_jpn_000906) +Scores: (#C #S #D #I) 3 0 0 2 +REF: k u ******* * +HYP: k u O +Eval: I I + +Speaker sentences 107: cv_jpn_000907 #utts: 1 +id: (cv_jpn_000907-cv_jpn_000907) +Scores: (#C #S #D #I) 5 1 0 2 +REF: * ******* I c h i +HYP: K E c h i +Eval: I I S + +Speaker sentences 108: cv_jpn_000908 #utts: 1 +id: (cv_jpn_000908-cv_jpn_000908) +Scores: (#C #S #D #I) 115 1 0 12 +REF: k a G a k u g ******* * * a * a k i r a k a n i s u r ******* * * * u k * ******* y a c l k a n t e k i s h i n ******* * r i n i s h i t a g a u k o t o n i y o c l t e +HYP: k a K a k u g A P a U a k i r a k a n i s u r U P A u k Y y a c l k a n t e k i s h i n D r i n i s h i t a g a u k o t o n i y o c l t e +Eval: S I I I I I I I I I I I I + +Speaker sentences 109: cv_jpn_000909 #utts: 1 +id: (cv_jpn_000909-cv_jpn_000909) +Scores: (#C #S #D #I) 148 2 1 5 +REF: k a k o t o m i r a i t o n o ******* * * * m u j u n t e k i j i k o d o o i t s u t o s h i t e n o g e n z a i g a k a T a c h i o m o T s * U t o i u k o t o d e a r u +HYP: k a k o t o m i r a i t o n o P A U m u j u n t e k i j i k o d o o i t s u t o s h i t e n o g e n z a i g a k a P a c h i o m o * s H I t o i u k o t o d e a r u +Eval: I I I I S D I S + +Speaker sentences 110: cv_jpn_000910 #utts: 1 +id: (cv_jpn_000910-cv_jpn_000910) +Scores: (#C #S #D #I) 155 1 6 10 +REF: b u t s u r i t e k i s e k a i ******* * w a s u u g a k u t e k ******* * i k i g o o n i Y o c l t e a r a w a s a r e r ******* * * * u s u u G a k u t e k ******* * i k a t a c h i n o s e k a i D E a r u +HYP: b u t s u r i t e k i s e k a i U w a s u u g a k u t e k I i k i g o o n i ******* * o c l t e a r a w a s a r e r U P A u s u u K a k u t e k I i k a t a c h i n o s e k a i ******* * ******* * a r u +Eval: I I I I D D I I I I S I I D D D D + +Speaker sentences 111: cv_jpn_000911 #utts: 1 +id: (cv_jpn_000911-cv_jpn_000911) +Scores: (#C #S #D #I) 48 2 0 5 +REF: * ******* o n a * J i g e n ******* * s h o o d e s a n k o o N i n a r u +HYP: W o n a C H i g e n I s h o o d e s a n k o o G i n a r u +Eval: I I I S I I S + +Speaker sentences 112: cv_jpn_000912 #utts: 1 +id: (cv_jpn_000912-cv_jpn_000912) +Scores: (#C #S #D #I) 67 0 5 0 +REF: g a i k o k u k a r a k i t a m o n o d a t o s h i C L t e b i c l k u R i +HYP: g a i k o k u k a r a k i t a m o n o d a t o s h i ******* * * t e b i c l k u ******* * i +Eval: D D D D D + +Speaker sentences 113: cv_jpn_000913 #utts: 1 +id: (cv_jpn_000913-cv_jpn_000913) +Scores: (#C #S #D #I) 83 5 5 2 +REF: i w a y U r u j i C L s e N n I y o c l t e k a k u t o ******* * k u s h i R A I c l t a m o n o d e a r u +HYP: i w a y O r u j i * I s e ******* * n ******* * y o c l t e k a k u t o U k u s h i I T A c l t a m o n o d e a r u +Eval: S D S D D D D I I S S S + +Speaker sentences 114: fleurs_jpn_000346 #utts: 1 +id: (fleurs_jpn_000346-fleurs_jpn_000346) +Scores: (#C #S #D #I) 107 4 14 4 +REF: o n a j i y O o n i P A U d a n s ******* * e E w a h i Z A O o o u z u b O N o h a K U k o t o g a g i m ******* * u z u k e r a r e t e i m a s u +HYP: o n a j i y ******* * o n i ******* * * * d a n s U e U w a h i J Z A o o u z u b ******* * ******* * o h a ******* * ******* * k o t o g a g i m U u z u k e r a r e t e i m a s u +Eval: D D D D D D I I S S S S D D D D D D D D I I + +Speaker sentences 115: fleurs_jpn_000347 #utts: 1 +id: (fleurs_jpn_000347-fleurs_jpn_000347) +Scores: (#C #S #D #I) 204 14 21 5 +REF: k o n o s A a b i s U W A P a U G o r a k U S e N O h a j i m e t o s u r u s e n p ******* * a k u Y A P a U e n k a k u c h i d e * * D E e t a y a o n s E e o h i T s u Y O o t o s u r u t a n k e n t a i N i h i n * P +HYP: k o n o s ******* * a b i s ******* * ******* * ******* * * a * K o r a k S E e Y A h a j i m e t o s u r u s e n p T a k u I Y * a * e n k a k u c h i d e P A U D e t a y a o n s ******* * e o h i * s u ******* * ******* * o t o s u r u t a n k e n t a i R i h i n C L +Eval: D D D D D D D D D D S S S S S I I S S D D I I S S D D D D D D D S I S + +>> REF: A N n i R i Y o o s a r e t e i m a s u +>> HYP: P A n i D i ******* * o o s a r e t e i m a s u +>> Eval: S S S D D + +Speaker sentences 116: fleurs_jpn_000348 #utts: 1 +id: (fleurs_jpn_000348-fleurs_jpn_000348) +Scores: (#C #S #D #I) 222 6 48 1 +REF: k y o o f U U P A u H y O o P A U k a d o n o k o o s u i r Y O U P A U o Y o b i Y a m a k a * J i W A P a U r a i u p a u t a T s u m a k i p a u m i z U F u k i p a u o y O B I s a i k u r o n n a D o n o k i b i s h i i k I s h +HYP: k y o o f ******* * ******* * * * u K y ******* * o ******* * * * k a d o n o k o o s u i r * ******* * ******* * ******* * * * o ******* * o b i ******* * a m a k a S H i ******* * B * a * r a i u p a u t a * s u m a k i p a u m i z ******* * ******* * u k i p a u o y ******* * ******* * U s a i k u r o n n a N o n o k i b i s h i i k ******* * s h ******* +Eval: D D D D D D S D D D D D D D D D D D D D D D D D D D I S D D S D D D D D D D D D D D S S D D D + +>> REF: O o k E e t a I y a s o n o E e k y o o n i y o r u m o N o d e s u +>> HYP: * o k ******* * e t a ******* * y a s o n o ******* * e k y o o n i y o r u m o R o d e s u +>> Eval: D D D D D D D S + +Speaker sentences 117: fleurs_jpn_000349 #utts: 1 +id: (fleurs_jpn_000349-fleurs_jpn_000349) +Scores: (#C #S #D #I) 140 15 15 3 +REF: i n t A a n e C L t o w A P a U m a s u k o m Y U N I k e e s h O n t o t a i j i n k o m Y U N I k e e s h o N n o * R Y O o y O o s o o k a N e s O N a E T a k a n k y o o ******* * d e s u +HYP: i n t ******* * a n e ******* * * t o w ******* * * a * m a s u k o m * I R U k e e s h E n t o t a i j i n k o m * I R U k e e s h o O n o P A U D o y ******* * o s o o k a ******* * e s U M a I D a k a n k y o o R d e s u +Eval: D D D D D D D D D D S S S S D S S S S I S S S D D D D S S S S I I + +Speaker sentences 118: fleurs_jpn_000350 #utts: 1 +id: (fleurs_jpn_000350-fleurs_jpn_000350) +Scores: (#C #S #D #I) 201 11 25 10 +REF: k a j i n o D e w a t s u u j o o P A U t o k u b e T s u N a i N s h O k U y ******* * * a * ******* * e n t a a t e i m e n t O o y O o I s h i t e i m a s u P A U G e s u T O G a k i b u N y o k u s h i s e T s u n a i N I t o ******* * ******* * +HYP: k a j i n o R e w a t s u u j o o ******* * * * t o k u b e * s u R a i ******* * s h I k O y A P a U I e n t a a t e i m e n t ******* * o y ******* * o ******* * s h i t e i m a s u * * * K Y e s u ******* * ******* * W a k i b u ******* * y o k u s h i s e * s u n a i R E t o R A +Eval: S D D D D D S D D S S I I I I I I D D D D D D D D D S S D D D D S D D D S S I I I I + +>> REF: m a r u y o o N I s u r u t a m E d e s u +>> HYP: m a r u y o o R E s u r u t a m ******* * d e s u +>> Eval: S S D D + +Speaker sentences 119: fleurs_jpn_000351 #utts: 1 +id: (fleurs_jpn_000351-fleurs_jpn_000351) +Scores: (#C #S #D #I) 181 7 22 4 +REF: s h i k a s h i P A U k Y a P u t e n n o W i k e c l t o o U s h i n a c l t A a t o P A U i n d o w ******* * * a * n a n a T S u n o W i k e c l t o o U s h i n a i P A U s a n j U u r o K u r a N s h i k a d e K i m a s e n d e s h i +HYP: s h i k a s h i ******* * * * k * a K u t e n n o B i k e c l t o o ******* * s h i n a c l t ******* * a t o ******* * * * i n d o w A P a U n a n a * Z u n o B i k e c l t o o ******* * s h i n a i ******* * * * s a n j ******* * u r o B u r a J s h i k a d e G i m a s e n d e s h i +Eval: D D D D D S S D D D D D D D D I I I I D S S D D D D D D D D S S S + +>> REF: t a +>> HYP: t a +>> Eval: + +Speaker sentences 120: fleurs_jpn_000352 #utts: 1 +id: (fleurs_jpn_000352-fleurs_jpn_000352) +Scores: (#C #S #D #I) 200 16 20 4 +REF: F o o k u r a n d o n o k o o s h i k I t s U u k a w a * * F O o k u r a n d O s h o t o o P o n d o e f U k E e p i I D * * E i c h i p o n D o g a i c h I I G I R i S U P o n d o j i i B I i P i i t o t o o k a +HYP: H o o k u r a n d o n o k o o s h i k E t s ******* * u k a w a P A U H o k u r a n d A s h o t o o B o n d o e f ******* * k ******* * e p i D E P A U i c h i p o n N o g a i c h ******* * ******* * ******* * ******* * ******* * i E E B o n d o j i i ******* * P i B i i t o t o o k a +Eval: S S D D I I S S S S D D D D S S I I S S D D D D D D D D D D S S S D D S S + +>> REF: n i k o t E e s a r e T e i m a s u +>> HYP: n i k o t ******* * e s a r e D e i m a s u +>> Eval: D D S + +Speaker sentences 121: fleurs_jpn_000353 #utts: 1 +id: (fleurs_jpn_000353-fleurs_jpn_000353) +Scores: (#C #S #D #I) 198 22 36 1 +REF: h a S H I s h i t a n o K A M I G A T A k U U k a N w A * J u u g o m E e t o R U d e s U P A u N i s e n j U u i c h i n e N h a c h i g a t s u N i s H U N k o o s h i P A U N i s e n j U u n A n A n E N S a n g a t s +HYP: h a * J U s h i t a n o ******* * ******* * ******* * ******* * J O O O k ******* * O k a O w O C H u u g o m ******* * e t o ******* * O d e s ******* * * * u ******* * i s e n j ******* * u i c h i n e U h a c h i g a t s u M i s * ******* * E k o o s h i ******* * * * ******* * i s e n j ******* * u n U n E n ******* * S E a n g a t s +Eval: D S S D D D D D D D D S S S S D D S S S I S D D D D S D D D D D D D D S S D D D S D D D D D D D D S S D D S S + +>> REF: u m A D e k a i t s u U s h i m a s e n D e s h i t a +>> HYP: u m U N e k a i t s u E s h i m a s e n G e s h i t a +>> Eval: S S S S + +Speaker sentences 122: fleurs_jpn_000354 #utts: 1 +id: (fleurs_jpn_000354-fleurs_jpn_000354) +Scores: (#C #S #D #I) 144 8 8 5 +REF: i c l p u n k a n d e ******* * f u c l t o o s u R u c h i i k i m O a r e b A P a U F u c l t O o s u r u m a D e N i n a n * P U N m o k a k a r u c h ******* * i i k i m o a r i m a s u +HYP: i c l p u n k a n d e H f u c l t o o s u B u c h i i k i m ******* * a r e b ******* * * a * H u c l t ******* * o s u r u m a R e M i n a n C L P A U m o k a k a r u c h J i i k i m o a r i m a s u +Eval: I I S D D D D D D S D D S S I S S S S I I + +Speaker sentences 123: fleurs_jpn_000355 #utts: 1 +id: (fleurs_jpn_000355-fleurs_jpn_000355) +Scores: (#C #S #D #I) 173 13 4 4 +REF: p I r a m i c l D O n o o t o t o ******* * * h i k a R i n o s h o o w a p a u k o n o k a n k o o C h I D e t o k u n i k o D o m O t a C H i G a t a n O s h i m e r U m o y o o s h i n o * h i t o T s u D e s u +HYP: p E r a m i c l T A n o o t o t o A S h i k a ******* * i n o s h o o w a p a u k o n o k a n k o o S h U R e t o k u n i k o R o m A t a * J i K a t a n A s h i m e r E m o y o o s h i n o S h i t o * s u R e s u +Eval: S S S I I I D D S S S S S D S S S S I D S + +Speaker sentences 124: fleurs_jpn_000356 #utts: 1 +id: (fleurs_jpn_000356-fleurs_jpn_000356) +Scores: (#C #S #D #I) 98 7 0 5 +REF: s O n o t a M e p a u t a N n i ******* * * * R a B e r u t o s h i t e h ******* Y o o k i g a t s u I k a s a r e g a c h i d e s u +HYP: s U n o t a N e p a u t a I n i P A U D a D e r u t o s h i t e h I o o k i g a t s u E k a s a r e g a c h i d e s u +Eval: S S S I I I I S S I S S + +Speaker sentences 125: fleurs_jpn_000357 #utts: 1 +id: (fleurs_jpn_000357-fleurs_jpn_000357) +Scores: (#C #S #D #I) 245 24 21 6 +REF: g e n S O n s u r u k o T o g a s h i R a r e t e i r U n i j U U g ******* * o m a I n O d a n R a C L P U P A u B u r o o D O s a i d ******* * o w a P A U G e n S o n s u r U T o o g a i b u n k e N n o s a i k o N o U t s +HYP: g e n ******* * Z n s u r u k o K o g a s h i T a r e t e i r A n i j I O g O o m a E n A d a n D a ******* * * K T * * u K u r o o T U s a i d E o w a * * K I e n Z o n s u r ******* * A o o g a i b u n k e ******* * n o s a i k o R o ******* * t s +Eval: D D S S S S S S I I S S S D D D S S D D S S S I I D D S S S D D S D D S D D + +>> REF: U s h i d e s U P A u t e g a k i n I y o r u g e n p o N w a G e n S o N s h i t e i m ******* * a s e n +>> HYP: A s h i d e s ******* * * * u t e g a k i n ******* * y o r u g e n p o O w a K e n Z o O s h i t e i m U a s e n +>> Eval: S D D D D D D S S S S I I + +Speaker sentences 126: fleurs_jpn_000358 #utts: 1 +id: (fleurs_jpn_000358-fleurs_jpn_000358) +Scores: (#C #S #D #I) 243 24 32 5 +REF: k A r e N o s e T s U o t a d a s h i I T o m i t o m e r u * h i t o m O i m a s h i t A g A P a U o o k u N O * h i t o W A S o n o G Y A k U d e P A U t a i y o o k E e d e W a t a i y O o t o s O n o T A N O H o +HYP: k ******* * r e M o s e * s O o t a d a s h i T A o m i t o m e r u S h i t o m ******* * i m a s h i t E g ******* * * a * o o k u G U S h i t o ******* * S U o n o * K E k O d e ******* * * * t a i y o o k ******* * e d e ******* * a t a i y ******* * o t o s U n o H O K A N o +Eval: D D S D S S S I D D S D D D D S S I D D S S D S S S D D D D D D D D D D S S S S S S + +>> REF: s h i g a c h i k y U u n o M A W a r I O i d o o s h I T e I r u T o ******* * s h i n * J i t e i m a s h i t a +>> HYP: s h i g a c h i k y ******* * u n o ******* * U M a r ******* * E i d o o s h ******* * S e ******* * r u D o A s h i n C H i t e i m a s h i t a +>> Eval: D D D D S S D D S D D S D D S I I I S + +Speaker sentences 127: fleurs_jpn_000359 #utts: 1 +id: (fleurs_jpn_000359-fleurs_jpn_000359) +Scores: (#C #S #D #I) 265 17 29 1 +REF: c h i b e c l t o m e e s O o N o c h U u s h i n w a s h i N s e e Y o g a d e s u P A U S A m a z a M a n a k a m i g a m i o s h i k a k u k a s u r u k o T o d e P A U e n e r u g i i c h a n e r u g a * J O o k a +HYP: c h i b e c l t o m e e s ******* * o ******* * o c h ******* * u s h i n w a s h i I s e e ******* * o g a d e s u * * S A N m a z a N a n a k a m i g a m i o s h i k a k u k a s u r u k o D o d e ******* * * * e n e r u g i i c h a n e r u g a S H I o k a +Eval: D D D D D D S D D D D S S S S S D D D D I S S + +>> REF: s a r e P A U c h a k U r a G a k a C L s E e k a s a r e P A U s a t O r I n O i S h i k I g A U M a r e m a s u +>> HYP: s a r e ******* * * * c h a k ******* * r a B a k a ******* * * s ******* * e k a s a r e ******* * * * s a t A r U n E i C h i k E g O O G a r e m a s u +>> Eval: D D D D D D S D D D D D D D D D S S S S S S S S + +Speaker sentences 128: fleurs_jpn_000360 #utts: 1 +id: (fleurs_jpn_000360-fleurs_jpn_000360) +Scores: (#C #S #D #I) 169 18 14 7 +REF: M i n a M i ******* * a F u r I k a n i a r u s u b e t e n o k o k u r I T s u k o o e N T O d o o y O o n i P A U k O n o k o o e N n i W * a * m a I N I C H I h o G o * h i t o n Y U u e n R Y O o g a ******* * k a k a r i m a +HYP: B i n a N i Y a H u r E k a n i a r u s u b e t e n o k o k u r E * s u k o o e ******* * D A d o o y ******* * o n i ******* * * * k U n o k o o e ******* * n i A P a U m a N E J * I E h o K o S h i t o n * I u e n * G U o g a O k a k a r i m a +Eval: S S I I S S S D D D S S D D D D D D S D D S I I S S S D S S S I D S D S S I I + +>> REF: s u +>> HYP: s u +>> Eval: + +Speaker sentences 129: fleurs_jpn_000361 #utts: 1 +id: (fleurs_jpn_000361-fleurs_jpn_000361) +Scores: (#C #S #D #I) 103 11 12 9 +REF: R e C L s H A P a U k u r u m a P A u ******* * S o N o T a n O o o k u n O k o o t s U U s h ******* * u d a n ******* * G a ******* * * s O k o k a r A u m a r e m a s h i t a +HYP: D e ******* * * s * ******* * * a * k u r u m a S u N N o H o K a n ******* * o o k u n U k o o t s ******* * O s h I u d a n U N a T S s U k o k a r E u m a r e m a s h i t a +Eval: S D D D D D D D D S S I I S S S D D S D D S I I I I S I I I S S + +Speaker sentences 130: fleurs_jpn_000362 #utts: 1 +id: (fleurs_jpn_000362-fleurs_jpn_000362) +Scores: (#C #S #D #I) 154 5 11 0 +REF: i n t A a n e c l t o w A P a U m a s u k o m Y U n i k e e s h o n t o t a i j i n k o m Y U n I k e e s h o n n o R y O o y o o s o o k a n e s o n a E t a k a n k y o o D e s u +HYP: i n t ******* * a n e c l t o w ******* * * a * m a s u k o m * I n i k e e s h o n t o t a i j i n k o m * I n U k e e s h o n n o * y ******* * o y o o s o o k a n e s o n a I t a k a n k y o o R e s u +Eval: D D D D D D D S D S S D D D S S + +Speaker sentences 131: fleurs_jpn_000363 #utts: 1 +id: (fleurs_jpn_000363-fleurs_jpn_000363) +Scores: (#C #S #D #I) 187 24 21 10 +REF: B y o o i n d e w a P A U K A n s e n k a n r ******* * i t e j U N s h ******* * O n i s h i T a G a I P A U T a n i N e n O k a n s e n N o k a n o o s E e O F U s e g u T a m E n i k a n j a O k a k u r I s u r u n A D O N +HYP: K y o o i n d e w a * * K A O n s e n k a n r E i t e j I U s h J U n i s h i S a R a E * * I K a n i ******* * e n U k a n s e n G o k a n o o s ******* * e ******* * ******* * ******* * s e g u D a m I n i k a n j a ******* * k a k u r ******* * s u r u n D A M U +Eval: S D D S S S I I S S I I S S S S D D S S D D S S D D D D D D D D S S D D D D S S S S + +>> REF: O s o * C H I o t o C L t e i m a s * ******* * u ******* * +>> HYP: I s o O J O o t o ******* * * t e i m a s H I u S +>> Eval: S I S S S D D D I I I I I + +Speaker sentences 132: fleurs_jpn_000364 #utts: 1 +id: (fleurs_jpn_000364-fleurs_jpn_000364) +Scores: (#C #S #D #I) 288 16 18 4 +REF: r e n p o o G i k a I w a N i s e n g o n e n d o k a r a w a i s e T s u ******* * b u T s u t o r I s h i m a r i h O o e n o s h i k i n t e e k y O o o k a i S H i s h i P A U e F u b i I a I w a a D a r u T o P o r u +HYP: r e n p o o ******* * i k a Y w a M i s e n g o n e n d o k a r a w a i s e * s u E b u * s u t o r E s h i m a r i h ******* * o e n o s h i k i n t e e k y ******* * o o k a i * J i s h i ******* * * * e H u b i Y a Y w a a T a r u Z o B o r u +Eval: D D S S D I I D S D D D D D S D D D D S S S S S S + +>> REF: n o N i j u u n i N n o s o o s a I N o t o o n Y U u s h I n a k e r e B a N a r a ******* * n a i t o k i t e e s h i m a s h i t a +>> HYP: n o R i j u u n i ******* * n o s o o s a N Y o t o o n * ******* * u s h U n a k e r e W a D a r a R n a i t o k i t e e s h i m a s h i t a +>> Eval: S D D S S D D D S S S I I + +Speaker sentences 133: fleurs_jpn_000365 #utts: 1 +id: (fleurs_jpn_000365-fleurs_jpn_000365) +Scores: (#C #S #D #I) 144 26 24 6 +REF: P I i e I c h i P A U R e B E r U w A P a U k e n s a s h i t A k a G A k u b u C L s H I t S U N i F U K U m a r e R U s U i s O i ******* * ******* * o * * N p I I E i c h i n O E I c h i n o r Y o o d e s h i m E s a r e m A s u +HYP: * ******* H i e ******* * c h i ******* * * * D e ******* * R r O w ******* * * a * k e n s a s h i t ******* * k a K O k u b u * E s * ******* * t * ******* * E i U K O N m a r e N A s E i s A i Y U o P A U p A U H I i c h i n E H O c h i n o r * o o d e s h i m A s a r e m I s u +Eval: D D S D D D D D D S D D S S D D D D D D S S D S D D D D D D S S S S S S S S S I I I I I I S S S S S S S S D S S + +Speaker sentences 134: fleurs_jpn_000366 #utts: 1 +id: (fleurs_jpn_000366-fleurs_jpn_000366) +Scores: (#C #S #D #I) 183 10 18 0 +REF: s o r e d e m o p a u t o o K Y O K u k a r a n O a D O b a i s u o u k e p a u s u b e t e n o h Y o o s h i k I o M A m o r i P A U a n z e n j o o n o k e e K o K U n i s a i s h i N n o c h u U I o h a r a i m a +HYP: s o r e d e m o p a u t o o ******* * * R U u k a r a n ******* * a R U b a i s u o u k e p a u s u b e t e n o h * o o s h i k E o ******* * ******* * m o r i ******* * * * a n z e n j o o n o k e e G o B O n i s a i s h i ******* * n o c h u I U o h a r a i m a +Eval: D D D S S D D S S D S D D D D D D D D S S S D D S S + +>> REF: s h O o +>> HYP: s h ******* * o +>> Eval: D D + +Speaker sentences 135: fleurs_jpn_000367 #utts: 1 +id: (fleurs_jpn_000367-fleurs_jpn_000367) +Scores: (#C #S #D #I) 196 15 16 6 +REF: k o r e r a w a t a m a n i k o n Z a t s u ******* * S U R * U k a * Z O k u m U k e n o b I i c h i D e P A U k a i g a N N i W * a * s a m a z a m a n a t e n p o g a n a r a n d e i m a s U P A u a n z e N n I o Y O G u +HYP: k o r e r a w a t a m a n i k o n G a t s u R O K A C L k a T S U k u m O k e n o b U i c h i T e ******* * * * k a i g a ******* * ******* * i A P a U s a m a z a m a n a t e n p o g a n a r a n d e i m a s ******* * * * u a n z e ******* * n Y o ******* * E B u +Eval: S I I S S S I S I S S S S S D D D D D D D D S I I D D D D D D S D D S S + +>> REF: k o T o g a d e k i m a s u +>> HYP: k o D o g a d e k i m a s u +>> Eval: S + +Speaker sentences 136: fleurs_jpn_000368 #utts: 1 +id: (fleurs_jpn_000368-fleurs_jpn_000368) +Scores: (#C #S #D #I) 197 23 79 6 +REF: s h i n n o P A U m i e n a i c h i i M U P A U E R U E E A a R U E s u O O E n U p a u ******* * a n d o p a U E R u E E E f U E E E s u t I I O o P A U s e n k Y U u H Y A k u H a c h i j u U K Y u U P A U P i i h Y a k u k y u U N o s +HYP: s h i n n o ******* * * * m i e n a i c h i i ******* * ******* * ******* * * * ******* * ******* * ******* * ******* * ******* * D a ******* * ******* * ******* * s u ******* * ******* * ******* * n ******* * p a u N a n d o p a * ******* * ******* * ******* u ******* * N A f ******* * ******* * ******* * A s u t ******* * ******* * ******* * o ******* * * * s e n k * E u ******* * * ******* * k u ******* * a c h i j u K * I u P E E J i i h * a k u k y u E D o s +Eval: D D D D D D D D D D D D D D D D D D D D D D S D D D D D D D D D D D D D D I I D D D D D D D D S S D D D D D D S D D D D D D D D D D D S D D D D D D D S D S S S S S S D S S + +>> REF: O n z a i M o m a T A P a U b A a C H A r U c h i ******* * I m u n o d o k u j I n O y O o s o d e ******* * a r u +>> HYP: U n z a i B o m a ******* * D * a * b ******* * a * Z E r E c h i E B m u n o d o k u j U n E y ******* * o s o d e R a r u +>> Eval: S S D D S D D D D D S S S I I S S S D D I I + +Speaker sentences 137: fleurs_jpn_000369 #utts: 1 +id: (fleurs_jpn_000369-fleurs_jpn_000369) +Scores: (#C #S #D #I) 205 7 27 8 +REF: k o n o s A a B i s u w A P a U g o r a k u s e N o h a j i m e t o s u r u s e n ******* * P a k u y a p a u e n K a k U C h i D e d E e t a y A o n s e E o H I T s u Y O o t o s u r u t a n k e n t a ******* * i N i ******* * * * h i +HYP: k o n o s ******* * a R i s u w ******* * * a * g o r a k u s e ******* * o h a j i m e t o s u r u s e n B T a k u y a p a u e n G a k ******* * S h i R e d ******* * e t a y ******* * o n s e Y o ******* * ******* * * s u ******* * ******* * o t o s u r u t a n k e n t a N i R i P A U h i +Eval: D D S D D D D D D I I S S D D S S D D D D S D D D D D D D D D I I S I I I I + +>> REF: n p a N n i r i y O o s a r e t e i m a s u +>> HYP: n p a ******* * n i r i y ******* * o s a r e t e i m a s u +>> Eval: D D D D + +Speaker sentences 138: fleurs_jpn_000370 #utts: 1 +id: (fleurs_jpn_000370-fleurs_jpn_000370) +Scores: (#C #S #D #I) 326 17 18 8 +REF: s a k U b a * * N p a u b u e n o s u a i R e s u k a r a G o j u c l k i r o s a n j U u i c h i m a i r u h a n a r e t a r a P u r a t a s h i n a i d e p a u G e n s h O K U j o o i n g I i n d e ******* * a r u k u r i +HYP: s a k O b a P A U p a u b u e n o s u a i D e s u k a r a K o j u c l k i r o s a n j ******* * u i c h i m a i r u h a n a r e t a r a B u r a t a s h i n a i d e p a u K e n s h A G O j o o i n g ******* * i n d e W a r u k u r i +Eval: S I I S S S D D S S S S S D D I I + +>> REF: s u t I i n A P a U f E r u n a n d e s U P A u d e p a u k I r u H i n A a J o s h i g ******* * * a * D a i t o o r Y o o s e n e n o s h U T S u B a o s e n g e n s h i m a s h i t a +>> HYP: s u t E i n ******* * * a * f U r u n a n d e s ******* * * * u d e p a u k E r u K i n ******* * a Z o s h i g A P a U N a i t o o r * o o s e n e n o s h ******* * * I u R a o s e n g e n s h i m a s h i t a +>> Eval: S D D D D S D D D D S S D D S I I I I S D D D D S S + +Speaker sentences 139: fleurs_jpn_000371 #utts: 1 +id: (fleurs_jpn_000371-fleurs_jpn_000371) +Scores: (#C #S #D #I) 200 23 16 8 +REF: o n a J i T S u k I N i P A U m a s h U h a D o n o k a C L s o o R O D e b E * T s u n o r Y O K a K * U K i G a k a C L s o o r O o o o b A a r a n s h ******* * i p a u k a b e n i g e k i t o t s u s h i t e ******* * * * j u u S H I C H I +HYP: o n a Y i * Z u k ******* * U i R I m a s h I h a T o n o k a ******* * * s o o E U R e b U I s u n o r * U G a O K Y U i W a k a ******* * * s o o r ******* * o o o b ******* * a r a n s h I i p a u k a b e n i g e k i t o t s u s h i t e P A U j u u * N A * R A +Eval: S D S D D S S S S S S D D D S S S S I S D S S S I S S S D D D D D D D I I I I I I D S S D S S + +>> REF: n i n g a s h i b o o s h i m a s h i t a +>> HYP: n i n g a s h i b o o s h i m a s h i t a +>> Eval: + +Speaker sentences 140: fleurs_jpn_000372 #utts: 1 +id: (fleurs_jpn_000372-fleurs_jpn_000372) +Scores: (#C #S #D #I) 206 16 34 0 +REF: H a s h i s h i T a n O K a M I G A T A k U U k a n w a j u u g o m E e T o R U d e s U P A u N i s e n j u u I c h i n e N h a C H i g a t s u n i s h u N K O o s h i p a u N i s e n j u u n a n A N e N s a n g a T s +HYP: * ******* a s h i s h i ******* * a n ******* * ******* * a J O O H O O k ******* * ******* * k a n w a j u u g o m ******* * e D o ******* * ******* * d e s ******* * * * u M i s e n j u u ******* * c h i n e ******* * h a * J i g a t s u n i s h u U N K o s h i p a u M i s e n j u u n a n ******* * I e ******* * s a n g a * s +Eval: D D D D D D D D S S S S S S D D D D D D S D D D D D D D D S D D D D D S S S S S D D S D D D + +>> REF: u m a D e k a i t s U U s h i m a s e n d e s h i t a +>> HYP: u m a R e k a i t s ******* * O s h i m a s e n d e s h i t a +>> Eval: S D D S + +Speaker sentences 141: fleurs_jpn_000373 #utts: 1 +id: (fleurs_jpn_000373-fleurs_jpn_000373) +Scores: (#C #S #D #I) 374 11 85 12 +REF: b ******* * u n M e e t o I u k o t o B a w A P a U s h i m i N o I m i s u r u r a t e n g o n o k E e Y O o s h i s h I I A i b U I A i E r U A I e s u k a r a k ******* * i t ******* * E o r i P A U s h i m i n O I m i s u r u r a t +HYP: b U u n N e e t o ******* * u k o t o W a w ******* * * a * s h i m i ******* * o ******* * m i s u r u r a t e n g o n o k ******* * e ******* * ******* * o s h i s h ******* * ******* * ******* * i b ******* * ******* * ******* * i ******* * r ******* * ******* * ******* * e s u k a r a k I i t A W o r i ******* * * * s h i m i n I O m i s u r u r a t +Eval: I I S D D S D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D I I I I S D D D D S S + +>> REF: e n g o N o m E e s h i s h I I A I B u i A i e s U P A u t o s h i y a t o s h i k o C L k a o I m i s h i p a u n a ******* * N R a k a n o k a t a c h i d e ******* * * * s h a k a i n o k i B o o t e e g i s u r u s h I I A I B u i A +>> HYP: e n g o R o m ******* * e s h i s h ******* * ******* * ******* * ******* * ******* * u i B i e s ******* * * * u t o s h i y a t o s h i k o ******* * * k a o ******* * m i s h i p a u n a R U N a k a n o k a t a c h i d e P A U s h a k a i n o k i ******* * o o t e e g i s u r u s h ******* * ******* * ******* * ******* * ******* * u i B +>> Eval: S D D D D D D D D D D D D S D D D D D D D D D I I S S I I I I D D D D D D D D D D D D S + +>> REF: i t I I E E E s u t o I u m e e s h i n i k a n k e e s h i t e I m a s u +>> HYP: i t ******* * ******* * ******* * ******* * A s u t o ******* * u m e e s h i n i k a n k e e s h i t e ******* * m a s u +>> Eval: D D D D D D D D S D D D D + +Speaker sentences 142: fleurs_jpn_000374 #utts: 1 +id: (fleurs_jpn_000374-fleurs_jpn_000374) +Scores: (#C #S #D #I) 204 15 31 3 +REF: T s U u j O o P A U k o k o D e W a i T s u m o k a n k o o K Y A k U y a * G y o o s h a t a C H i g a h a C L s u r U o t o G a * K i k o E t e k i m a s u P A U o t o T o * h i k a r i g A o R i n a s u m O n o g a t +HYP: * s ******* * u j ******* * o ******* * * * k o k o R e ******* * a i * s u m o k a n k o o * T E k E y a R y o o s h a t a * J i g a h a ******* * * s u r ******* * o t o K a C H i k o I t e k i m a s u * * W o t o D o S h i k a r i g ******* * o ******* * i n a s u m U n o g a t +Eval: D D D D D D D D D S D D D D S S S I S D S D D D D D S I S S D D S S I D D D D S + +>> REF: a r I W a m a r u d E e h o n n O y O o D e s u +>> HYP: a r ******* * U a m a r u d ******* * e h o n n A y ******* * o R e s u +>> Eval: D D S D D S D D S + +Speaker sentences 143: fleurs_jpn_000375 #utts: 1 +id: (fleurs_jpn_000375-fleurs_jpn_000375) +Scores: (#C #S #D #I) 86 6 26 4 +REF: t e r e b i n O H o o d o o n I Y o R U t O P A U g e n p a t s u k a r a S h I R O k E M u R I g A a g a C L t e i m a s u ******* * ******* * +HYP: t e r e b i n ******* * ******* * o o d o o n ******* * ******* * o ******* * N t D * * O g e n p a t s u k a r a * h ******* * ******* * A k ******* * ******* * u ******* * E g ******* * a g a * D t e i m a s u E S +Eval: D D D D D D D D D D S S D D S D D D D D S D D D D D D S D D D S I I I I + +Speaker sentences 144: fleurs_jpn_000376 #utts: 1 +id: (fleurs_jpn_000376-fleurs_jpn_000376) +Scores: (#C #S #D #I) 144 3 0 1 +REF: n o o b ******* y o o r i t o k o o d o o n o s o o k a n k a n k e e w a p a u k a G a k u s h a t a C h i n o k e n k y u u o u r a z u k e r U m o n o d e s u +HYP: n o o b y o o r i t o k o o d o o n o s o o k a n k a n k e e w a p a u k a Y a k u s h a t a S h i n o k e n k y u u o u r a z u k e r E m o n o d e s u +Eval: I S S S + +Speaker sentences 145: fleurs_jpn_000377 #utts: 1 +id: (fleurs_jpn_000377-fleurs_jpn_000377) +Scores: (#C #S #D #I) 134 10 21 5 +REF: s U I y o o b i n O i b e n t o n O a t o P A U k a r u p a n e D o w ******* * * a * s e n S H U k e n D e F u T a * T s u n o k o j i n R E e s U N i S H U T s u j o o s h i m a s h i t a +HYP: s ******* * E y o o b i n A i b e n t o n ******* * a t o ******* * * * k a r u p a n e R o w A P a U s e n * J I k e n G e H u K a Z s u n o k o j i n ******* * D e s ******* * ******* * i ******* * * ******* * * s u j o o s h i m a s h i t a +Eval: D D S S D D D D D D S I I I I D S S S S S I S D D S D D D D D D D D D D + +Speaker sentences 146: fleurs_jpn_000378 #utts: 1 +id: (fleurs_jpn_000378-fleurs_jpn_000378) +Scores: (#C #S #D #I) 195 13 20 5 +REF: s e n H A C L p Y A k u N e n d a I i r a i P A U g u n t a I g a t o o C H a k U s u r U M a D e ******* * * * h a i c h i W a k o n o b Y O o k i n i k a n k * e e s u r u M o n d a I n i s o o G U u s h i t a k o t o w A +HYP: s e n E H * A p * E k u R e n d a ******* * i r a i ******* * * * g u n t a Y g a t o o * J a k ******* * s u r E W a R e P A U h a i c h i ******* * a k o n o b * Y o k i n i k a n k Y e e s u r u ******* * o n d a ******* * n i s o o U G u s h i t a k o t o w ******* * +Eval: S S D S D S S D D D D D D S D S D D S S S I I I I D D D S I D D D D S S D D + +>> REF: a r i m a s e n d e s h i t a +>> HYP: a r i m a s e n d e s h i t a +>> Eval: + +Speaker sentences 147: fleurs_jpn_000379 #utts: 1 +id: (fleurs_jpn_000379-fleurs_jpn_000379) +Scores: (#C #S #D #I) 171 16 23 0 +REF: s h i k a s h i P A U k Y a P u t e n n O W i k e c l T o o U s h I n a c l t A a T o P A U i n d O W a n a n a t s u n O W i k e c l t o o U s h I n A i P A U s a n j U U r o k u r a N s h i K a D e k i m a s e n D e s h i t +HYP: s h i k a s h i ******* * * * k * a K u t e n n U B i k e c l D o o ******* * s h U n a c l t D a D o * * H i n d ******* * ******* * a n a n a t s u n E R i k e c l t o o ******* * s h U n E i ******* * * * s a n j ******* * O r o k u r a ******* * s h i T a R e k i m a s e n G e s h i t +Eval: D D D D D S S S S D D S S S D D S D D D D S S D D S S D D D D D D S D D S S S + +>> REF: a +>> HYP: a +>> Eval: + +Speaker sentences 148: fleurs_jpn_000380 #utts: 1 +id: (fleurs_jpn_000380-fleurs_jpn_000380) +Scores: (#C #S #D #I) 215 11 11 12 +REF: k a j i n o d e w a t s u u j o o P A U t o k u b e t s u n a i n s h O k U y ******* * * a * e n t A a t e i m e n t o o y o o i s h i t e i m a s U P A u g e s U T O g a k i b ******* * u N y O k u s h i s e T S u n a i n i t ******* * ******* * o +HYP: k a j i n o d e w a t s u u j o o ******* * * * t o k u b e t s u n a i n s h A k O y A P a U e n t ******* * a t e i m e n t o o y o o i s h i t e i m a s ******* * * * u g e s T O U g a k i b U u M y A k u s h i s e * Z u n a i n i t O R o +Eval: D D D D S S I I I I D D D D D D S S S I I S S D S I I I I + +>> REF: m a r u y o O N I s u r u t a m e ******* * d e s u +>> HYP: m a r u y o N R E s u r u t a m e R d e s u +>> Eval: S S S I I + +Speaker sentences 149: fleurs_jpn_000381 #utts: 1 +id: (fleurs_jpn_000381-fleurs_jpn_000381) +Scores: (#C #S #D #I) 169 12 30 1 +REF: s o r e d e M o P A U t o o k y O K u k a r a n O a D O b a i s U o U k e p a u s u B E t E n O h ******* y o O s h i k I o M A m o r i p a u a n z e n j O o n o k E e k o K U n i s a i s h I N n o c h u U I o h a r a i m a +HYP: s o r e d e ******* * o ******* * * * t o o k y ******* * ******* * u k a r a n ******* * a R A b a i s ******* * o ******* * k e p a u s u ******* * ******* * t A n U h y o A s h i k E o ******* * ******* * m o r i p a u a n z e n j I o n o k ******* * e k o ******* * ******* * n i s a i s h E I n o c h u Y U o h a r a i m a +Eval: D D D D D D D D D D D D S S D D D D D D D D S S I S S D D D D S D D D D D D S S S S + +>> REF: s h O o +>> HYP: s h A o +>> Eval: S + +Speaker sentences 150: fleurs_jpn_000382 #utts: 1 +id: (fleurs_jpn_000382-fleurs_jpn_000382) +Scores: (#C #S #D #I) 120 7 24 1 +REF: O W A k a r e D e w A a r i m a s e N k o r E W a * h i t O t s u N O s h O O N o o W a r i d e a r i P A U a t a r a s h i i s h o o n o m A k U a k e d e s u +HYP: P O O k a r e G e w ******* * a r i m a s e ******* * k o r ******* * ******* * a S h i t ******* * t s u M U s h ******* * ******* * ******* * o o ******* * a r i d e a r i ******* * * * a t a r a s h i i s h o o n o m O k ******* * a k e d e s u +Eval: S S S S D D D D D D D D I D D S S D D D D D D D D D D D D S D D + +Speaker sentences 151: fleurs_jpn_000383 #utts: 1 +id: (fleurs_jpn_000383-fleurs_jpn_000383) +Scores: (#C #S #D #I) 213 7 11 1 +REF: s a F a r i t o w a p a u a F u r i k a n o y a s e E d o o B u T S u p a u t o k U n i s a B a n n a n I i r u y a s e E d o o B u T s u n o k a n s a T s u o m o k u t e k i t o s h i t a r i k u r o d e n o r +HYP: s a W a r i t o w a p a u a ******* * u r i k a n o y a s e U d o o ******* * u * Z u p a u t o k O n i s a W a n n a n ******* * i r u y a s e U d o o G u * s u n o k a n s a * s u o m o k u t e k i t o s h i t a r i k u r o d e n o r +Eval: S D D S D D D S S S D D S S D D + +>> REF: ******* y o k O o o s a s h i m a s u +>> HYP: y o k ******* * o o s a s h i m a s u +>> Eval: I D D + +Speaker sentences 152: fleurs_jpn_000384 #utts: 1 +id: (fleurs_jpn_000384-fleurs_jpn_000384) +Scores: (#C #S #D #I) 267 16 48 4 +REF: F u Y u n I k i t a b a r u t o k a I o ******* * ******* * o o d a n s u r u b A a i w a p a u s e N S H i T s u N O i c h i O K A k u n i N s h I T e k u d a s a i P A U k o o r I n O n a k A o t s u k i s u S U M u s a i n i m +HYP: * ******* u ******* * u n E k i t a b a r u t o k a Y o U M o o d a n s u r u b ******* * a i w a p a u s e ******* * ******* * * i * s u ******* * M i c h i K A O k u n i ******* * s h ******* * S e k u d a s a i * * K k o o r E n N n a k ******* * o t s u k i s u ******* * M N u s a i n i m +Eval: D D D D S S I I I I D D D D D D D D D D S S S S D D D D S D D S S S D D D D S S + +>> REF: o c l t o m O e e k y O o o U k e r u s e N S H i t s u d e W a o s o r O S H I i H o d o N o s O o o n G a n a r I H i b i k i m a s u +>> HYP: o c l t o m ******* * e e k y ******* * o o ******* * k e r u s e ******* * ******* * * i t s u d e ******* * a o s o r ******* * * U J i U o d o ******* * o s ******* * o o n N a n a r ******* * ******* * i b i k i m a s u +>> Eval: D D D D D D D D D D D D D D D D S S S D D D D S D D D D + +Speaker sentences 153: fleurs_jpn_000385 #utts: 1 +id: (fleurs_jpn_000385-fleurs_jpn_000385) +Scores: (#C #S #D #I) 235 34 16 3 +REF: k o k o w a I G i r i s u n o s h o k u m i n C H I s h i H A I s h a ******* * g a j i B u n t a C h i n o R Y o o D o T o s h i T a b a s h O n a N O d e p a u s h O k U m i n * C H i j i D A I n O s h o o k o o s A g a s o o +HYP: k o k o w a P A U i r i s u n o s h o k u m i n * T E s h i E H A s h a E g a j i ******* * u n t a S h i n o * D o o R o D o s h i ******* * a b a s h U n a R U d e p a u s h A k O m i n T E i j i ******* * R E n E s h o o k o o s E g a s o o +Eval: S S S D S S S S S I I D D S D S S S D D S S S S S I S S D D S S S S + +>> REF: t o s u r U H O O W A P a U K o k o K a r A h a j i M e r u n O g a y o i D E s h O o +>> HYP: t o s u r E K A T A W * a * O o k o W a r ******* * h a j i B e r u n A g a y o i ******* * U s h ******* * o +>> Eval: S S S S S S D D S S D D S S D D S D D + +Speaker sentences 154: fleurs_jpn_000386 #utts: 1 +id: (fleurs_jpn_000386-fleurs_jpn_000386) +Scores: (#C #S #D #I) 206 18 29 4 +REF: E B I S U s h i w A P a U s a k u g e n s u r u s u u c h i o S A D a M E M a s e n d e s h i t a G A P a U s a k u g e n w a c h U U G o k u n o k e e z a i s a n ******* * s h U T S u R y o U N i m o t o z u i t e * * J I C L s h +HYP: * ******* * ******* * ******* K O s h i w ******* * * a * s a k u g e n s u r u s u u c h i o ******* * O S a R A Y a s e n d e s h i t a ******* * K * a * s a k u g e n w a c h I E W o k u n o k e e z a i s a n A s h ******* * * I u * y o ******* * ******* * i m o t o z u i t e P A U D * I s h +Eval: D D D D D D S S D D D D D D S S S S S D D S D D S S S I I D D D S D D D D D I I S S D S + +>> REF: i s a r e R u d a r o O T o n o B E m a s h i t a +>> HYP: i s a r e ******* * u d a r o T O o n o ******* * I m a s h i t a +>> Eval: D D S S D D S + +Speaker sentences 155: fleurs_jpn_000387 #utts: 1 +id: (fleurs_jpn_000387-fleurs_jpn_000387) +Scores: (#C #S #D #I) 217 10 20 8 +REF: s a i n Y u u k o k u s h o C L k u w ******* * * a * s h i n k ******* * o n r Y O k o o n o j I k i ******* * g a s u k u n a i k a r u C H a a s h O C L k u y o r I m o h a Y A k U o T o z u r e p a u n a G a b i k i p a u y o r I s h o o j o o +HYP: s a i n * u u k o k u s h o ******* * * k u w A P a U s h i n k O o n r * E k o o n o j U k i E g a s u k u n a i k a r u * J a a s h ******* * ******* * * k u y o r E m o h a ******* * E k O o D o z u r e p a u n a R a b i k i p a u y o r E s h o o j o o +Eval: D D D D I I I I I I D S S I I D S D D D D D S D D S S S S S + +>> REF: g A a C L k a s u r U k o t o g A a r i m a s u +>> HYP: g ******* * a ******* * * k a s u r E k o t o g ******* * a r i m a s u +>> Eval: D D D D D S D D + +Speaker sentences 156: fleurs_jpn_000388 #utts: 1 +id: (fleurs_jpn_000388-fleurs_jpn_000388) +Scores: (#C #S #D #I) 236 16 14 21 +REF: k * i n o o n o a s a p a u t o r u k o n o g a j i a n t e C L P u n o k e e s a T s u ******* * h o ******* * n B U D e ******* * * * j i d o o s h a b a k u D a N n o b a k U H a T s u N i Y o r i P A U K e e k a N f u t a r I g a s h i b +HYP: k Y i n o o n o a s a p a u t o r u k o n o g a j i a n t e * P K u n o k e e s a * s u O h o M n W O R e P A U j i d o o s h a b a k u R a ******* * n o b a k ******* * ******* * a * s u R i ******* * o r i * K Y I e e k a A f u t a r E g a s h i b +Eval: I D S S D I I I I S S S I I I I S D D D D D D D S D D D S S S S S + +>> REF: O o s h i ******* * * * p a u ******* * F u s h ******* * o ******* * O s h a w a n i j u u n i N o k o ******* * E m a s h i t a +>> HYP: ******* * o s h i P A U p a u R W u s h I o W A s h a w a n i j u u n i Y o k o A I m a s h i t a +>> Eval: D D I I I I I I S I I I I S S I I S + +Speaker sentences 157: fleurs_jpn_000389 #utts: 1 +id: (fleurs_jpn_000389-fleurs_jpn_000389) +Scores: (#C #S #D #I) 168 3 6 12 +REF: s h o k u b u t s u W a n i n g E n g a s u u s a n S o o t s u k u r i P A U n i n g e n g ******* * ******* * a * I k ******* * i t o s h i t e h a k i d a s u ******* * n i s a n k a ******* * * t a n s o o t o r i k o n d e i m a s u +HYP: s h o k u b u t s u ******* * a n i n g I n g a s u u s a n Z o o t s u k u r i ******* * * * n i n g e n g A K a C L k O i t o s h i t e h a k i d a s u R n i s a n k a C L t a n s o o t o r i k o n d e i m a s u +Eval: D D S S D D D D I I I I I S I I I I I I I + +Speaker sentences 158: fleurs_jpn_000390 #utts: 1 +id: (fleurs_jpn_000390-fleurs_jpn_000390) +Scores: (#C #S #D #I) 178 10 10 16 +REF: s e n p a k u D e b u C L s h i o y u s o o s u r u n o w a p a u u m i ******* * o k o E t e ******* * * * H i t o ******* * y a b u C L S H I o t a ******* * i r Y O o y U s o o s u r ******* * ******* * * * u m o c l t o m o k o o r i T s U T e k i n a h o o h o o +HYP: s e n p a k u R e b u ******* * * s h i o y u s o o s u r u n o w a p a u u m i Y o k o I t e P A U K i t o A y a b u S H * U E o t a R i r * E o y I s o o s u r U E P A u m o c l t o m o k o o r i * s ******* * ******* * e k i n a h o o h o o +Eval: S D D D I I S I I I I S I I S S D S S I I D S S I I I I I I D D D D D + +>> REF: D e s u +>> HYP: R e s u +>> Eval: S + +Speaker sentences 159: fleurs_jpn_000391 #utts: 1 +id: (fleurs_jpn_000391-fleurs_jpn_000391) +Scores: (#C #S #D #I) 255 8 21 8 +REF: k a r i F o r u n i a s h u u n o a a n o r u d o P A U s h u w a r u t s E n e c l G A a c h i * J i w A P a U b o o r ******* y o k u t e k i n a b i d e o ******* * g e e m u ******* * o m i s e e n e n s h a n i h a n b a I y a R e n t +HYP: k a r i H o r u n i a s h u u n o a a n o r u d o * * O s h u w a r u t s U n e c l ******* * K a c h i S H i w ******* * * a * b o o r y o k u t e k i n a b i d e o U g e e m u W o m i s e e n e n s h a n i h a n b a ******* * y a ******* * e n t +Eval: S D D S S D D S I S D D D D I I I I I D D D D + +>> REF: a R U s u R U k o t o o k i n S H i s u r U h o o ******* * a n n i s h o m E e s h i m a s h i t a +>> HYP: a ******* * ******* * s u D E k o t o o k i n ******* * * i s u r O h o o W a n n i s h o m ******* * e s h i m a s h i t a +>> Eval: D D D D S S D D D S I I D D + + diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..ed0eb391a8e7a34bc87edf889dac5cc2ac9b64f1 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/hyp.trn @@ -0,0 +1,160 @@ +k a k o t o m i r a i t o d o u j u N t e k i j i k o d o o i ts u n a r u g a i w e n i pau sh I k i t e k i n a N o d e a r (cv_jpn_000800-cv_jpn_000800) +s e k a y o k e e s e e s u r u t o t o m u n i pau j i g k o j i sh i y o k e e s e s e r u ts s o o d o t e k i s e k a i n s o o z o o t e k i y o t o sh I t e pau k o m u ts u g a k o u ts u d e a r u (cv_jpn_000801-cv_jpn_000801) +p a s o k o N n e g e e m i a r u I t o n a h f u e t e k i t e (cv_jpn_000802-cv_jpn_000802) +k a a k u n o sh i m e s a t a r a sh i i j i j i ts u a t a r a s i k a N n e N k a N ky o sh i h a i n a t a r a sh i k a n o s e o m o cl t e pau n a n i o h a j i m e r u k a w a (cv_jpn_000803-cv_jpn_000803) +o m o sh i r o e n o n i pau r o o d o n a k a s u g i t e pau d a r u i (cv_jpn_000804-cv_jpn_000804) +k o r e j o o sh u u h a N t o i n a (cv_jpn_000805-cv_jpn_000805) +k a g a k U sh a m o s e k a y o h o o k a ts s e k i n i pau t o o ch I t e k i n i s a ts u m e sh u o t o sh I t e i r u (cv_jpn_000806-cv_jpn_000806) +h a I ts u n i ts U e m a r a N (cv_jpn_000807-cv_jpn_000807) +sh i cl k a r sh t e k u d a s a i (cv_jpn_000808-cv_jpn_000808) +w a t a sh i w a a m i g i n o n o t o k i pau d e k i sh I t e k i s e i m e e n o j i k a k u t o y u g o t o k i m o n o m e N sh o o h o o t e k i pau r o N b e t a y u u n o d e a r u (cv_jpn_000809-cv_jpn_000809) +w a t a sh i w a pau sh a k a i k e e s e e n o k o N t e N n i w a d e y o o n u s o s u t e k i n a m o n o g a pau h a t a r a i t e i r u t o m o (cv_jpn_000810-cv_jpn_000810) +n a n i o s u r u ts u m o r i d a cl t a n o k a (cv_jpn_000811-cv_jpn_000811) +k o b u ts u t e k I t a g a j i k o h I t e e t e k i n i t a N n i pau t e N sh u u g o o t e k i n i k a N g a r a r u r u t o k i s o r e g a b u ts u r i t e k i t e k a i d e a r u (cv_jpn_000812-cv_jpn_000812) +a n e g a z u cl t a n o d e y a cl k i n o sh i r a i g a r i m a s e N d e sh I t a (cv_jpn_000813-cv_jpn_000813) +k o r e w a N r i h o N d e u t e i n a i t a b e m u n o d e s U (cv_jpn_000814-cv_jpn_000814) +w o t a sh i w a h e N sh u u e i n o y o o n e N k u r a e w a y a cl t a cl t o o m o (cv_jpn_000815-cv_jpn_000815) +e i s a N n i i k o n o k o t o b p a n o r i m i o o o sh i a m a sh I t a (cv_jpn_000816-cv_jpn_000816) +k a s e g a ts U s u y o i h i u w a t e N n i s u g a d e k i m a s e N (cv_jpn_000817-cv_jpn_000817) +i ch i i (cv_jpn_000818-cv_jpn_000818) +h a i (cv_jpn_000819-cv_jpn_000819) +o n o e (cv_jpn_000820-cv_jpn_000820) +d e i (cv_jpn_000821-cv_jpn_000821) +a t o k i (cv_jpn_000822-cv_jpn_000822) +m i r u t o y u k o t a t o pau h a t a r a k U t o i u k o t o g a sh k a b u N d i t e k i n a k e r e b a n a r a n a i (cv_jpn_000823-cv_jpn_000823) +w a r e w a r e o t a m a sh i i n o z u o k a r a u g u k a s u m o n o d e n a k e r e b a n a r a n a i (cv_jpn_000824-cv_jpn_000824) +s e t a i b e N sh o o h o o d e k i n a r u g a i u e n i i d e a t e k I ch o cl k a N t e k i k e e k i y g a cl U k u m a r e r u n o d e a r u (cv_jpn_000825-cv_jpn_000825) +t o k o m a d e m o t a t o i ch i t o n a s o o g o sh I e e t e k i n a z e cl t a i m u j u N t e k i j i k o o i ts u n o s e k a i n i sh I t e (cv_jpn_000826-cv_jpn_000826) +sh i k a r u n i n i N g e N t o k a N ky o o t o r o k a N k e e w a m o t o k o o n o k a N k e d e a r i (cv_jpn_000827-cv_jpn_000827) +i i s a N n i k o n o k o t o b a n o i m i y o o sh i y a m a sh I t a (cv_jpn_000828-cv_jpn_000828) +k e e k i g a n a n a ts s a r i m a s U (cv_jpn_000829-cv_jpn_000829) +k o ch i r a b a k o b i y a sh i s a N d e s U (cv_jpn_000830-cv_jpn_000830) +m o sh i m a sh i (cv_jpn_000831-cv_jpn_000831) +k o k o w a o k I k U t e n i g u y a k a n a m a ch i d e s U (cv_jpn_000832-cv_jpn_000832) +s o n o ch i k a i y a k U s a r e r u k a r a i s o g e pau (cv_jpn_000833-cv_jpn_000833) +a m a s a g a b k u s a e i r a r e t e t e ch o o d o i (cv_jpn_000834-cv_jpn_000834) +h o k e N sh I ts u e n o d o o a a k e t a (cv_jpn_000835-cv_jpn_000835) +m o d a n i o w a cl t e m o k i n i sh i n a i (cv_jpn_000836-cv_jpn_000836) +a r i g a cl t a y a (cv_jpn_000837-cv_jpn_000837) +i t o o g a r o k u d a t o j i k a N u w o s u r e t e t a n o sh i m e r u (cv_jpn_000838-cv_jpn_000838) +k a k a k u w a g i z u ts U k a s a r e r u n i o j i t e j o o sh I k i n o u ch i n i h a i cl t e y u k u (cv_jpn_000839-cv_jpn_000839) +sh i k a sh I t o k i g a k a b o n i h a i r u k o t o s o n o k o t o g a m i r a y o o m u k o t o d e a r i a r a t a n a r u i cl U t a i g a d e t e k u r u k o t o d e a r u (cv_jpn_000840-cv_jpn_000840) +t e r e b i o k a i k a i t e pau t e r e b i o m i r u j i k a N n a f u e t a (cv_jpn_000841-cv_jpn_000841) +k a k a r e u sh I t a i n o m i i ts u m a d e m o i k e r u n o d e a r u (cv_jpn_000842-cv_jpn_000842) +n i N k i d a a m e N i y a n i n a r a N d a r a n i j i k a N m a o ch i d a cl t a (cv_jpn_000843-cv_jpn_000843) +s o r e o o m o ch i i r u n i N g e N n o y o k u n i t o N s e i s o sh I t e k o r e w a k a r e n o m o cl t e i r u k a ch e n o s a k u d o N n pau i d o N s u r u (cv_jpn_000844-cv_jpn_000844) +m a w a r y o a m i N n a k a N g a r u k o t o y a m e t e i t a (cv_jpn_000845-cv_jpn_000845) +k o o i t e k i j o k U k a N t e k i n i s e k a y o m i r u t o y u k o t o w a j a k u n i k o o i t e k i ch a o k cl k a N t e k i n i s e k a y o k e e s e e s u r u k o t o k u m u n o d e a r u (cv_jpn_000846-cv_jpn_000846) +s j e N p a i t a k e s a s e m a i t o s u r u k i z u k a i y g a y o k e e n i s e N cl p a i s a s e t e sh i m a u (cv_jpn_000847-cv_jpn_000847) +k o n o m i ch i w a t o t e m o s e m a i n o d a m n a i d e s U (cv_jpn_000848-cv_jpn_000848) +w o e g a r i n i U (cv_jpn_000849-cv_jpn_000849) +t o i y o w a r o k a m a i t a r i g a a n i a r i m a s U (cv_jpn_000850-cv_jpn_000850) +t a d a k a s a N o h i d a r i n i k i m e r a s a N g a i m a s U (cv_jpn_000851-cv_jpn_000851) +m a cl k u r u n a t a m a b o t e s u g o i e (cv_jpn_000852-cv_jpn_000852) +sh a r sh o o m i t a i n a d o k u sh u k a s o o b u m o k a i t a (cv_jpn_000853-cv_jpn_000853) +g e N j i t e m o s e k a i w a pau t a m o o i ch i t o sh I t e k e cl t e s u r a i d e k a t a ch i o a cl t o s U k a i u n a k e r e m o n a r a n a i (cv_jpn_000854-cv_jpn_000854) +sh o o h i N k e N s a k u k g a o k a r y a s u i t o o pau k a o k i n a r u m a i (cv_jpn_000855-cv_jpn_000855) +ts e sh i I k i w a n e k I sh I t e cl k a t e e d e n a k e r u m o n a r a n a i (cv_jpn_000856-cv_jpn_000856) +m o n o g o t a n o N j i N p a N k a e r u d a k e d e pau u m a k U i k U k o t o m a r (cv_jpn_000857-cv_jpn_000857) +k o n o k I e e ts u w a k a ts u o n o s a sh i m i g a z e cl p e i N (cv_jpn_000858-cv_jpn_000858) +k a k e N n i sh i cl p a i sh I t e m a pau m o ch I t ts u i t e s a m a sh I ts u o u k e i d e r u (cv_jpn_000859-cv_jpn_000859) +s o r e y u e n i pau t e ts u g a k u g a z e N t a i n o g a k o d e y a r u t o s u r e w a (cv_jpn_000860-cv_jpn_000860) +k i i s a n a i y a o y a d a g a y a s U k U t e h a N j o sh I t e r u (cv_jpn_000861-cv_jpn_000861) +i n i f u r a g a k i n o h u z e N n i o ch j i cl t e pau k o k u g a i a d a sh I ts u s u r u sh I t a m o d e t e k i t a (cv_jpn_000862-cv_jpn_000862) +ts s u g i n i k a w a k u w a s o N z a y o j u j u n o d o i k i n o k a cl t e s u r e z u r a N d e o k i N z e i t i t e N i k i U s u r u (cv_jpn_000863-cv_jpn_000863) +s o r e d e w a t o k t o y i m a r a n a s e r i ts U sh i o w a n a k u pau sh i r N k a N t o i m o n o m o n a k u n a r u n o d a r i (cv_jpn_000864-cv_jpn_000864) +h a k a i b u r a N k o k o N k u r i t o s e n o s u b e r i d a i k a w a i t a s u n a b a (cv_jpn_000865-cv_jpn_000865) +s sh i k a sh i s o r o w a d o k o m a d e m N k o k o k a r a d e t e pau o k o e k a i r i k u r u s e e I t o m o t o m o d e a k e w a n a r a n a i (cv_jpn_000866-cv_jpn_000866) +a r i t o a r a i r u d e m o o m a k i ch i r a sh i t e m i N n e a k a r a o u r a m i o k a cl t e r u (cv_jpn_000867-cv_jpn_000867) +k o n o t e e d o s a w a g i n i n a r u k o t o m o n a i n o d a r o (cv_jpn_000868-cv_jpn_000868) +k o n o n e r a N d e pau u r i t a u k a a a (cv_jpn_000869-cv_jpn_000869) +h i n o k a g a e N n i ch u i sh i n a i t o pau s u g u k o g e r u (cv_jpn_000870-cv_jpn_000870) +e N m a N n o u w e N n i p o ts u r i t o ts u i s a n a n a g a i t a s a i sh i o w a ts u m a y o o j i t e e d o n o ts i i s a n a pau a n a d a cl t a a (cv_jpn_000871-cv_jpn_000871) +s o r e w a m a r e w a r e o i k a sh i n a g a r a pau w a r e w a r e o t o r e e k a s u r u n o d e a r u pau w a r e w a r e n o t a m a s sh i y o k o r o s u n o d e a (cv_jpn_000872-cv_jpn_000872) +r e k U sh I t e k i n i a t a r a r t a m o n o w a d e cl t e m u j u N t e k i j i g o t o o i ts I t e k i g i e N z d a i n o o i t e s U k a i sh I e k i n i a t a e r a r t a m o N u t o sh I t e (cv_jpn_000873-cv_jpn_000873) +m u o j u N t e k I e j i g o d o o i ch I t o sh I t e pau i ts u m o k o n o s e k a i n i ch o o e sh I t e k i d e a (cv_jpn_000874-cv_jpn_000874) +y u n i d e cl p e e m u j u N t e k I j i g o d o o i sh u t o sh I t e g e N a e k a r a g e N z a e e t u w o k i k u s U e k a e n o g e N z a i n o i t e (cv_jpn_000875-cv_jpn_000875) +h a r e w o t a N o sh I t o N d a sh u d e k i n a i (cv_jpn_000876-cv_jpn_000876) +sh i k a sh i w a t a sh a s o k o n i s e k a i n o j i k o d o o i ts u o k u n o d e w a n a i (cv_jpn_000877-cv_jpn_000877) +n e b u k a k u n a r u n o g a pau h a y a k u m a cl t a (cv_jpn_000878-cv_jpn_000878) +w a t a sh i w a i N g e N n o o d e k I sh I t e k I k e e s e e n o t a ch i b a k a r a pau g e j u ts u o m i r u n o d e a cl t e o o sh a k a r a e N sh a o m i r u n o d e a n a i (cv_jpn_000879-cv_jpn_000879) +a o i t o m a t o sh i k a n a k U t e pau k a u k a b a i y o u (cv_jpn_000880-cv_jpn_000880) +s e N k e z u e g y o o n i o o k i n a k i t a y o y a s U t e e r u (cv_jpn_000881-cv_jpn_000881) +n a n i k a sh i r a n o i N s e N t e b u w a n a i t o k i b u i s u e n o d e w a (cv_jpn_000882-cv_jpn_000882) +j i k o N o sh e k g e N n o i b e N t o d e s u t o r u sh I t a m a r i (cv_jpn_000883-cv_jpn_000883) +m a r i n o sh I t o w a b o o z e N t o sh I t e i t a (cv_jpn_000884-cv_jpn_000884) +s o N n a n a i y o o n o m e r u w a n a N k e N m o k U I t e i t a (cv_jpn_000885-cv_jpn_000885) +i j i g a i d e d e e s f u i sh I t e i t a (cv_jpn_000886-cv_jpn_000886) +t o k i d o k i ch i u u N n o k o r o w a w a k a r a n a k u n o r u t o ch i g a a r u d a k a r a b o k u a k a a n e o k i n o t o n i k a j i a j e m e r u (cv_jpn_000887-cv_jpn_000887) +m o o n i g e t e ch a t a m e d a (cv_jpn_000888-cv_jpn_000888) +k a r e w a p o o t o t a j i ts U k u sh I t e i t a (cv_jpn_000889-cv_jpn_000889) +d a r u N i m u n e i w a k o w a k a k e t a k a n a i (cv_jpn_000890-cv_jpn_000890) +p a s a k a t o o m o t e u d o a n o o t o cl t e o n i g e cl t a (cv_jpn_000891-cv_jpn_000891) +sh t e i m a s e N (cv_jpn_000892-cv_jpn_000892) +k a y o o n i sh I t e sh I t e r u t o t o m o n i sh i cl t e i n a i t o k o r o k a r a t a N ky u w a h a j i m a r u n o d e a r u (cv_jpn_000893-cv_jpn_000893) +a i s a ts u w a d a i j i d a i o (cv_jpn_000894-cv_jpn_000894) +t o j i t a m o n o i k a n i h i r o g e t e m o h i r a i t a m u n o n i a n a r a r o t o i cl t e i r u g a (cv_jpn_000895-cv_jpn_000895) +t a m u sh i n i k u ts U k a ts U k u cl t e m i o w a (cv_jpn_000896-cv_jpn_000896) +U ts z e i b u N a k o k i n a sh o o b a i d a i o n a (cv_jpn_000897-cv_jpn_000897) +w h a cl t e i (cv_jpn_000898-cv_jpn_000898) +k i t e i (cv_jpn_000899-cv_jpn_000899) +g o (cv_jpn_000900-cv_jpn_000900) +h a sh i i t i (cv_jpn_000901-cv_jpn_000901) +i e a (cv_jpn_000902-cv_jpn_000902) +h a cl ch i (cv_jpn_000903-cv_jpn_000903) +h n e (cv_jpn_000904-cv_jpn_000904) +a sh i i i (cv_jpn_000905-cv_jpn_000905) +k u o (cv_jpn_000906-cv_jpn_000906) +k e ch i (cv_jpn_000907-cv_jpn_000907) +k a k a k u g a pau a k i r a k a n i s u r u pau ky y a cl k a N t e k I sh i N d r i n i sh I t a g a u k o t o n i y o cl t e (cv_jpn_000908-cv_jpn_000908) +k a k o t o m i r a i t o n o pau m u j u N t e k i j i k o d o o i ts u t o sh I t e n o g e N z a i g a k a p a ch i o m o sh I t o i u k o t o d e a r u (cv_jpn_000909-cv_jpn_000909) +b u ts u r i t e k i s e k a i u w a s u u g a k u t e k i I k i g o o n i o cl t e a r a w a s a r e r u pau s u u k a k u t e k I i k a t a ch i n o s e k a i a r u (cv_jpn_000910-cv_jpn_000910) +w o n a ch i g e N i sh o o d e s a N k o o g i n a r u (cv_jpn_000911-cv_jpn_000911) +g a i k o k u k a r a k i t a m o n o d a t o sh i t e b i cl k u i (cv_jpn_000912-cv_jpn_000912) +i w a y o r u j i I s e N y o cl t e k a k U t o U k U sh I i t a cl t a m o n o d e a r u (cv_jpn_000913-cv_jpn_000913) +o n a j i y o n i d a N s u e u w a h i j z a o o u z u b o h a k o t o g a g i m u u z u k e r a r e t e i m a s U (fleurs_jpn_000346-fleurs_jpn_000346) +k o n o s a b i s a k o r a k s e e y a h a j i m e t o s u r u s e N p t a k u i y a e N k a k U ch i d e pau d e t a y a o N s e o h I s u o t o s u r u t a N k e N t a i r i h i N cl p a n i d i o o s a r e t e i m a s U (fleurs_jpn_000347-fleurs_jpn_000347) +ky o o f u ky o k a d o n o k o o s u i r o o b i a m a k a sh i b a r a i u pau t a s u m a k i pau m i z u k i pau o y u s a i k u r o N n a N o n o k i b i sh i i k sh o k e t a y a s o n o e ky o o n i y o r u m o r o d e s U (fleurs_jpn_000348-fleurs_jpn_000348) +i N t a n e t o w a m a s U k o m i r u k e e sh e N t o t a i j i N k o m i r u k e e sh o o n o pau d o y o s o o k a e s u m a i d a k a N ky o o r d e s U (fleurs_jpn_000349-fleurs_jpn_000349) +k a j i n o r e w a ts u u j o o t o k u b e s u r a i sh I k o y a pau i e N t a a t e i m e N t o y o sh I t e i m a s U ky e s u w a k i b u y o k u sh I s e s u n a i r e t o r a m a r u y o o r e s u r u t a m d e s U (fleurs_jpn_000350-fleurs_jpn_000350) +sh i k a sh i k a k U t e N n o b i k e cl t o o sh i n a cl t a t o i N d o w a pau n a n a z u n o b i k e cl t o o sh i n a i s a N j u r o b u r a j sh i k a d e g i m a s e N d e sh I t a (fleurs_jpn_000351-fleurs_jpn_000351) +h o o k u r a N d o n o k o o sh I k e ts u k a w a pau h o k u r a N d a sh o t o o b o N d o e f k e p i d e pau i ch i p o N n o g a i ch i e e b o N d o j i i p i b i i t o t o o k a n i k o t e s a r e d e i m a s U (fleurs_jpn_000352-fleurs_jpn_000352) +h a j u sh i t a n o j o o o k o k a o w o ch u u g o m e t o o d e s U i s e N j u i ch i n e u h a ch i g a ts u m i s e k o o sh i i s e N j u n u n e N s e a N g a ts u m u n e k a i ts u e sh i m a s e N g e sh I t a (fleurs_jpn_000353-fleurs_jpn_000353) +i cl p u N k a N d e h f u cl t o o s u b u ch i i k i m a r e b a h u cl t o s u r u m a r e m i n a N cl pau m o k a k a r u ch j i i k i m o a r i m a s U (fleurs_jpn_000354-fleurs_jpn_000354) +p e r a m i cl t a n o o t o t o a sh I k a i n o sh o o w a pau k o n o k a N k o o sh u r e t o k u n i k o r o m a t a j i k a t a n a sh i m e r e m o y o o sh i n o sh i t o s u r e s U (fleurs_jpn_000355-fleurs_jpn_000355) +s u n o t a n e pau t a i n i pau d a d e r u t o sh I t e h i o o k i g a ts u e k a s a r e g a ch i d e s U (fleurs_jpn_000356-fleurs_jpn_000356) +g e N z N s u r u k o k o g a sh I t a r e t e i r a n i j i o g o o m a e n a d a N d a k t u k u r o o t U s a i d e o w a k i e N z o N s u r a o o g a i b u N k e n o s a i k o r o ts a sh i d e s U t e g a k i N y o r u g e N p o o w a k e N z o o sh I t e i m u a s e N (fleurs_jpn_000357-fleurs_jpn_000357) +k r e m o s e s o o t a d a sh i t a o m i t o m e r u sh I t o m i m a sh I t e g a o o k u g u sh I t o s u o n o k e k o d e t a i y o o k e d e a t a i y o t o s u n o h o k a n o sh i g a ch i ky u n o u m a r e i d o o sh s e r u d o a sh i N ch i t e i m a sh I t a (fleurs_jpn_000358-fleurs_jpn_000358) +ch i b e cl t o m e e s o o ch u sh i N w a sh i i s e e o g a d e s U s a N m a z a n a n a k a m i g a m i o sh I k a k U k a s u r u k o d o d e e n e r u g i i ch a n e r u g a sh i o k a s a r e ch a k r a b a k a s e k a s a r e s a t a r u n e i ch I k e g o o g a r e m a s U (fleurs_jpn_000359-fleurs_jpn_000359) +b i n a n i y a h u r e k a n i a r u s u b e t e n o k o k u r e s U k o o e d a d o o y o n i k u n o k o o e n i a pau m a n e j i e h o k o sh I t o n i u e N g u o g a o k a k a r i m a s U (fleurs_jpn_000360-fleurs_jpn_000360) +d e s a k u r u m a s u N n o h o k a n o o k u n u k o o ts o sh i u d a N u n a ts s u k o k a r e u m a r e m a sh I t a (fleurs_jpn_000361-fleurs_jpn_000361) +i N t a n e cl t o w a m a s U k o m i n i k e e sh o N t o t a i j i N k o m i n u k e e sh o N n o y o y o o s o o k a n e s o n a i t a k a N ky o o r e s U (fleurs_jpn_000362-fleurs_jpn_000362) +ky o o i N d e w a k a o N s e N k a N r e i t e j i u sh j u N i sh I s a r a e i k a n i e n u k a N s e N g o k a n o o s e s e g u d a m i n i k a N j a k a k u r s u r u n d a m u i s o o j o o t o t e i m a sh I U s (fleurs_jpn_000363-fleurs_jpn_000363) +r e N p o o i k a y w a m i s e N g o n e N d o k a r a w a i s e s u e b u s u t o r e sh i m a r i h o e n o sh i k i N t e e ky o o k a i j i sh i e h u b i y a y w a a t a r u z o b o r u n o r i j u u n i n o s o o s a N y o t o o n u sh u n a k e r e w a d a r a r n a i t o k I t e e sh i m a sh I t a (fleurs_jpn_000364-fleurs_jpn_000364) +h i e ch i d e r r o w a k e N s a sh I t k a k o k U b u e s t e i u k o N m a r e n a s e i s a i y u o pau pau h i i ch i n e h o ch i n o r o o d e sh i m a s a r e m i s U (fleurs_jpn_000365-fleurs_jpn_000365) +s o r e d e m o pau t o o r u u k a r a n a r u b a i s u o u k e pau s u b e t e n o h o o sh I k e o m o r i a N z e N j o o n o k e e g o b o n i s a i sh i n o ch u i u o h a r a i m a sh o (fleurs_jpn_000366-fleurs_jpn_000366) +k o r e r a w a t a m a n i k o N g a ts u r o k a cl k a ts u k u m o k e n o b u i ch i t e k a i g a i a pau s a m a z a m a n a t e N p o g a n a r a N d e i m a s U a N z e N y o e b u k o d o g a d e k i m a s U (fleurs_jpn_000367-fleurs_jpn_000367) +sh i N n o m i e n a i ch i i d a s u N pau n a N d o pau n a f a s U t o s e N k e u k U a ch i j u k i u p e e j i i h a k U ky u e d o s u N z a i b o m a d a b a z e r e ch i e b m u n o d o k u j u n e y o s o d e r a r u (fleurs_jpn_000368-fleurs_jpn_000368) +k o n o s a r i s u w a g o r a k U s e o h a j i m e t o s u r u s e N b t a k u y a pau e N g a k sh i r e d e t a y o N s e y o s u o t o s u r u t a N k e N t a N i r i pau h i N p a n i r i y o s a r e t e i m a s U (fleurs_jpn_000369-fleurs_jpn_000369) +s a k o b a pau pau b u e n o s u a i d e s U k a r a k o j u cl k i r o s a N j u i ch i m a i r u h a n a r e t a r a b u r a t a sh i n a i d e pau k e N sh a g o j o o i N g i N d e w a r u k u r i s U t e i n a f u r u n a N d e s u d e pau k e r u k i n a z o sh i g a pau n a i t o o r o o s e N e n o sh I u r a o s e N g e N sh i m a sh I t a (fleurs_jpn_000370-fleurs_jpn_000370) +o n a y i z u k u i r i m a sh i h a t o n o k a s o o e u r e b u i s u n o r u g a o ky u i w a k a s o o r o o o b a r a N sh i i pau k a b e n i g e k i t o ts u sh I t e pau j u u n a r a n i N g a sh i b o o sh i m a sh I t a (fleurs_jpn_000371-fleurs_jpn_000371) +a sh i sh I a n a j o o h o o k k a N w a j u u g o m e d o d e s U m i s e N j u u ch i n e h a j i g a ts u n i sh u u N k o sh i pau m i s e N j u u n a n i e s a N g a s u m a r e k a i ts o sh i m a s e N d e sh I t a (fleurs_jpn_000372-fleurs_jpn_000372) +b u u N n e e t o u k o t o w a w a sh i m i o m i s u r u r a t e N g o n o k e o sh i sh i b i r e s U k a r a k i I t a w o r i sh i m i N i o m i s u r u r a t e N g o r o m e sh i sh u i b i e s U t o sh i y a t o sh I k o k a o m i sh i pau n a r u n a k a n o k a t a ch i d e pau sh a k a i n o k i o o t e e g i s u r u sh u i b i t a s U t o u m e e sh i n i k a N k e e sh I t e m a s U (fleurs_jpn_000373-fleurs_jpn_000373) +s u j o k o k o r e a i s u m o k a N k o o t e k e y a r y o o sh a t a j i g a h a s u r o t o k a ch i k o i t e k i m a s U w o t o d o sh i k a r i g o i n a s u m u n o g a t a r u a m a r u d e h o N n a y o r e s U (fleurs_jpn_000374-fleurs_jpn_000374) +t e r e b i n o o d o o n o N t d o g e N p a ts U k a r a h a k u e g a g a d t e i m a s U e s (fleurs_jpn_000375-fleurs_jpn_000375) +n o o b y o o r i t o k o o d o o n o s o o k a N k a N k e e w a pau k a y a k u sh a t a sh i n o k e N ky u u o u r a z u k e r e m o n o d e s U (fleurs_jpn_000376-fleurs_jpn_000376) +s e y o o b i n a i b e N t o n a t o k a r u p a n e r o w a pau s e N j i k e N g e h U k a z s u n o k o j i N d e s i s u j o o sh i m a sh I t a (fleurs_jpn_000377-fleurs_jpn_000377) +s e N e h a p e k u r e N d a i r a i g u N t a y g a t o o j a k s u r e w a r e pau h a i ch i a k o n o b y o k i n i k a N ky e e s u r u o N d a n i s o o u g u sh I t a k o t o w a r i m a s e N d e sh I t a (fleurs_jpn_000378-fleurs_jpn_000378) +sh i k a sh i k a k u t e N n u b i k e cl d o o sh u n a cl t d a d o h i N d a n a n a ts u n e r i k e cl t o o sh u n e i s a N j o r o k u r a sh I t a r e k i m a s e N g e sh I t a (fleurs_jpn_000379-fleurs_jpn_000379) +k a j i n o d e w a ts u u j o o t o k u b e ts u n a i N sh a k o y a pau e N t a t e i m e N t o o y o o i sh I t e i m a s U g e s t o u g a k i b u u m y a k u sh i s e z u n a i n i t o r o m a r u y o n r e s u r u t a m e r d e s U (fleurs_jpn_000380-fleurs_jpn_000380) +s o r e d e o t o o ky u k a r a n a r a b a i s o k e pau s u t a n u h y o a sh I k e o m o r i pau a N z e N j i o n o k e k o n i s a i sh e i n o ch u y u o h a r a i m a sh a o (fleurs_jpn_000381-fleurs_jpn_000381) +p o o k a r e g e w a r i m a s e k o r a sh I t ts u m u sh o o a r i d e a r i a t a r a sh i i sh o o n o m o k a k e d e s U (fleurs_jpn_000382-fleurs_jpn_000382) +s a w a r i t o w a pau a u r i k a n o y a s e u d o o u z u pau t o k o n i s a w a N n a n i r u y a s e u d o o g u s u n o k a N s a s u o m o k u t e k i t o sh I t a r i k u r o d e n o r y o k o o s a sh i m a s U (fleurs_jpn_000383-fleurs_jpn_000383) +u u n e k i t a b a r u t o k a y o u m o o d a N s u r u b a i w a pau s e i s u m i ch i k a o k u n i sh s e k u d a s a i k k o o r e n N n a k o ts U k I s u m N u s a i n i m o cl t o m e e ky o o k e r u s e i ts u d e a o s o r u j i u o d o o s o o N n a n a r i b i k i m a s U (fleurs_jpn_000384-fleurs_jpn_000384) +k o k o w a pau i r i s u n o sh o k u m i N t e sh i e h a sh a e g a j i u N t a sh i n o d o o r o d o sh I a b a sh u n a r u d e pau sh a k o m i N t e i j i r e n e sh o o k o o s e g a s o o t o s u r e k a t a w a o o k o w a r h a j i b e r u n a g a y o i u sh o (fleurs_jpn_000385-fleurs_jpn_000385) +k o sh i w a s a k u g e N s u r u s u u ch i o o s a r a y a s e N d e sh I t a k a s a k u g e N w a ch i e w o k u n o k e e z a i s a N a sh I u y o i m o t o z u i t e pau d i sh i s a r e u d a r o t o o n o i m a sh I t a (fleurs_jpn_000386-fleurs_jpn_000386) +s a i n u u k o k u sh o k u w a pau sh i N k o o N r e k o o n o j u k i e g a s U k u n a i k a r u j a a sh k u y o r e m o h a e k o o d o z u r e pau n a r a b i k i pau y o r e sh o o j o o g a k a s u r e k o t o g a r i m a s U (fleurs_jpn_000387-fleurs_jpn_000387) +ky i n o o n o a s a pau t o r u k o n o g a j i a N t e p k u n o k e e s a s u o h o m N w o r e pau j i d o o sh a b a k u r a n o b a k a s u r i o r i ky i e e k a a f U t a r e g a sh i b o sh i pau pau r w u sh i o w a sh a w a n i j u u n i y o k o a i m a sh I t a (fleurs_jpn_000388-fleurs_jpn_000388) +sh o k u b u ts u a n i N g i N g a s u u s a N z o o ts U k u r i n i N g e N g a k a cl k o i t o sh I t e h a k i d a s u r n i s a N k a cl t a N s o o t o r i k o N d e i m a s U (fleurs_jpn_000389-fleurs_jpn_000389) +s e N p a k u r e b u sh i o y u s o o s u r u n o w a pau u m i y o k o i t e pau k i t o a y a b u sh u e o t a r i r e o y i s o o s u r u e pau m o cl t o m o k o o r i s e k i n a h o o h o o r e s U (fleurs_jpn_000390-fleurs_jpn_000390) +k a r i h o r u n i a sh u u n o a a n o r u d o o sh u w a r u ts u n e cl k a ch i sh i w a b o o r y o k U t e k i n a b i d e o u g e e m u w o m i s e e n e N sh a n i h a N b a y a e N t a s u d e k o t o o k i N i s u r o h o o w a N n i sh o m e sh i m a sh I t a (fleurs_jpn_000391-fleurs_jpn_000391) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..e029af95c725500cea9d7907f30f5d2f4bfb87d7 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/ref.trn @@ -0,0 +1,160 @@ +k a k o t o m i r a i t o n o m u j u N t e k i j i k o d o o i ts u n a r u g a y u e n i pau i sh I k i t e k i n a n o d e a r u (cv_jpn_000800-cv_jpn_000800) +s e k a i o k e e s e e s u r u t o t o m o n i pau j i k o j i sh i N o k e e s e e s u r u s o o z o o t e k I s e k a i n o s o o z o o t e k i y o o s o t o sh I t e pau k o b u ts u g a k o b u ts u d e a r u (cv_jpn_000801-cv_jpn_000801) +p a s o k o N d e g e e m u y a r u n i N g a f u e t e k I t e r u (cv_jpn_000802-cv_jpn_000802) +k a g a k u n o sh i m e s u a t a r a sh i i j i j i ts u pau a t a r a sh i i k a N n e N pau k a N ky o o sh i h a i n o a t a r a sh i i k a n o o s e e o m o cl t e n a n i o h a j i m e r u k a w a (cv_jpn_000803-cv_jpn_000803) +o m o sh i r o i n o n i r o o d o n a g a s u g i t e d a r u i (cv_jpn_000804-cv_jpn_000804) +k o r e j o o sh u u h a N cl p o i n a a (cv_jpn_000805-cv_jpn_000805) +k a g a k U sh a m o s e k a i o h o o k a ts u t e k i n i t o o i ts u t e k i n i s e ts u m e e sh i y o o t o sh I t e i r u (cv_jpn_000806-cv_jpn_000806) +f U ts u u n i ts u m a r a N (cv_jpn_000807-cv_jpn_000807) +sh i cl k a r i sh I t e k u d a s a i (cv_jpn_000808-cv_jpn_000808) +w a t a sh i w a m i g i n o g o t o k i r e k I sh i t e k I s e e m e e n o j i k a k U t o i u g o t o k i m o n o o b e N sh o o h o o t e k i r o N r i t o i u n o d e a r u (cv_jpn_000809-cv_jpn_000809) +w a t a sh i w a sh a k a i k e e s e e n o k o N t e e n i w a d i o ny u s o s u t e k i n a m o n o g a h a t a r a i t e i r u t o o m o u (cv_jpn_000810-cv_jpn_000810) +n a n i o s u r u ts u m o r i d a cl t a n o k a (cv_jpn_000811-cv_jpn_000811) +k o b u ts u t e k I t a g a j i k o h I t e e t e k i n i t a N n i t e N sh u u g o o t e k i n i k a N g a e r a r e r u t o k i pau s o r e g a b u ts u r i t e k I s e k a i d e a r u (cv_jpn_000812-cv_jpn_000812) +a m e g a f u cl t a n o d e pau y a ky u u n o sh i a i g a a r i m a s e N d e sh I t a (cv_jpn_000813-cv_jpn_000813) +k o r e w a n i cl p o N d e u cl t e i n a i t a b e m o n o d e s U (cv_jpn_000814-cv_jpn_000814) +w a t a sh i w a pau h e N sh u u i N o pau y o n e N k u r a i h a y a cl t a t o o m o u (cv_jpn_000815-cv_jpn_000815) +i s a N n i k o n o k o t o b a n o i m i o o sh i e m a sh I t a (cv_jpn_000816-cv_jpn_000816) +k a z e g a ts u y o i h i w a t e n i s u g a d e k i m a s e N (cv_jpn_000817-cv_jpn_000817) +i ch i (cv_jpn_000818-cv_jpn_000818) +h a i (cv_jpn_000819-cv_jpn_000819) +n i (cv_jpn_000820-cv_jpn_000820) +r e i (cv_jpn_000821-cv_jpn_000821) +r o k u (cv_jpn_000822-cv_jpn_000822) +m i r u t o i u k o t o t o h a t a r a k U t o i u k o t o t o g a f U k a b u N r i t e k i d e n a k e r e b a n a r a n a i (cv_jpn_000823-cv_jpn_000823) +w a r e w a r e o t a m a sh i i n o s o k o k a r a u g o k a s u m o n o d e n a k e r e b a n a r a n a i (cv_jpn_000824-cv_jpn_000824) +z e cl t a i b e N sh o o h o o t e k i n a r u g a y u e n i i d e y a t e k I ch o cl k a N t e k I k e e k i g a f U k u m a r e r u n o d e a r u (cv_jpn_000825-cv_jpn_000825) +d o k o m a d e m o t a t o i ch i t o n o s o o g o h I t e e t e k i n a z e cl t a i m u j u N t e k i j i k o d o o i ts u n o s e k a i n i sh I t e (cv_jpn_000826-cv_jpn_000826) +sh I k a r u n i n i N g e N t o k a N ky o o t o n o k a N k e e w a m o t o k o o i n o k a N k e e d e a r i (cv_jpn_000827-cv_jpn_000827) +i s a N n i k o n o k o t o b a n o i m i o o sh i e m a sh I t a (cv_jpn_000828-cv_jpn_000828) +k e e k i g a n a n a ts u a r i m a s U (cv_jpn_000829-cv_jpn_000829) +k o ch i r a w a k o b a y a sh I s a N d e s U (cv_jpn_000830-cv_jpn_000830) +m o sh i m o sh i (cv_jpn_000831-cv_jpn_000831) +k o k o w a o o k I k u t e n i g i y a k a n a m a ch i d e s U (cv_jpn_000832-cv_jpn_000832) +s o n o u ch I k a i a k U s a r e r u k a r a i s o g e (cv_jpn_000833-cv_jpn_000833) +a m a s a g a o s a e r a r e t e t e ch o o d o i i (cv_jpn_000834-cv_jpn_000834) +h o k e N sh I ts u n o d o a o a k e t a (cv_jpn_000835-cv_jpn_000835) +m u d a n i o w a cl t e m o k i n i sh i n a i (cv_jpn_000836-cv_jpn_000836) +a r i g a t a y a (cv_jpn_000837-cv_jpn_000837) +i d o o g a r a k u d a t o j i k a N o w a s u r e t e t a n o sh i m e r u (cv_jpn_000838-cv_jpn_000838) +k a g a k u w a g i j u ts U k a s a r e r u n i o o j i t e j o o sh I k i n o u ch i n i h a i cl t e y u k u (cv_jpn_000839-cv_jpn_000839) +sh I k a sh I t o k i g a k a k o n i h a i r u k o t o s o n o k o t o g a pau m i r a i o u m u k o t o d e a r i pau a r a t a n a r u sh U t a i g a d e t e k u r u k o t o d e a r u (cv_jpn_000840-cv_jpn_000840) +t e r e b i o k a i k a e t e pau t e r e b i o m i r u j i k a N g a f u e t a (cv_jpn_000841-cv_jpn_000841) +k a k a r u sh U t a i n o m i pau i ts u m a d e m o i k i r u n o d e a r u (cv_jpn_000842-cv_jpn_000842) +n i N k i r a a m e N y a n i n a r a N d a r a n i j i k a N m a ch i d a cl t a (cv_jpn_000843-cv_jpn_000843) +s o r e o m o ch i i r u n i N g e N n o i y o k u n i i z o N sh i pau s o sh I t e k o r e w a k a r e n o m o cl t e i r u k a ch i n o sh a k u d o n i i z o N s u r u (cv_jpn_000844-cv_jpn_000844) +m a w a r i w a m i N n a pau k a N g a e r u k o t o o y a m e t e i t a (cv_jpn_000845-cv_jpn_000845) +k o o i t e k I ch o cl k a N t e k i n i s e k a i o m i r u t o i u k o t o w a pau gy a k u n i k o o i t e k I ch o cl k a N t e k i n i s e k a i o k e e s e e s u r u k o t o o f U k u m u n o d e a r u (cv_jpn_000846-cv_jpn_000846) +sh i N p a i k a k e s a s e m a i t o s u r u k i z u k a i g a pau y o k e i n i sh i N p a i s a s e t e sh i m a u (cv_jpn_000847-cv_jpn_000847) +k o n o m i ch i w a t o t e m o s e m a i n o d e pau a b u n a i d e s U (cv_jpn_000848-cv_jpn_000848) +o b o e g a w a r u i n e (cv_jpn_000849-cv_jpn_000849) +t o i r e w a r o o k a n o h i d a r i g a w a n i a r i m a s U (cv_jpn_000850-cv_jpn_000850) +t a n a k a s a N n o h i d a r i n i k i m u r a s a N g a i m a s U (cv_jpn_000851-cv_jpn_000851) +m a cl k u r o n a t a m a g o cl t e s u g o i n e (cv_jpn_000852-cv_jpn_000852) +sh o hy o o m i t a i n a d o k U sh o k a N s o o b u N o k a i t a (cv_jpn_000853-cv_jpn_000853) +g e N j i ts u n o s e k a i w a t a n o i ch i t o sh I t e k e cl t e e s e r a r e t a k a t a ch i o m o cl t a s e k a i d e n a k e r e b a n a r a n a i (cv_jpn_000854-cv_jpn_000854) +sh o o h i N k e N s a k u g a w a k a r i y a s u i t o k a u k i n i n a r u n o n i (cv_jpn_000855-cv_jpn_000855) +ch i sh I k i w a r e k I sh i t e k I k a t e e d e n a k e r e b a n a r a n a i (cv_jpn_000856-cv_jpn_000856) +m o n o g o t o n o j u N b a N o k a e r u d a k e d e u m a k u i k U k o t o m o a r u (cv_jpn_000857-cv_jpn_000857) +k o n o k i s e ts u w a k a ts u o n o s a sh i m i g a z e cl p i N (cv_jpn_000858-cv_jpn_000858) +k a k e n i sh i cl p a i sh I t e m o o ch I ts u i t e s o N sh I ts u o u k e i r e r u (cv_jpn_000859-cv_jpn_000859) +s o r e y u e n i t e ts u g a k u g a z e N t a i n o g a k u d e a r u t o s u r e b a (cv_jpn_000860-cv_jpn_000860) +ch i i s a n a y a o y a d a g a y a s u k U t e h a N j o o sh I t e r u (cv_jpn_000861-cv_jpn_000861) +i N f u r a g a k i n o o f u z e N n i o ch i i cl t e pau k o k u g a i e d a cl sh U ts u s u r u h I t o m o d e t e k i t a (cv_jpn_000862-cv_jpn_000862) +ts u g i n i k a g a k u w a s o N z a i o sh u j u n o ry o o i k i n i w a k a cl t e s o r e z o r e n o ry o o i k i n i ts u i t e k e N ky u u s u r u (cv_jpn_000863-cv_jpn_000863) +s o r e d e w a t o k i t o i u m o n o n o s e e r i ts U sh i y o o w a n a k u pau sh u N k a N t o i u m o n o m o n a k u n a r u n o d e a r u (cv_jpn_000864-cv_jpn_000864) +a k a i b u r a N k o pau k o N k u r i i t o s e e n o s u b e r i d a i pau k a w a i t a s u n a b a (cv_jpn_000865-cv_jpn_000865) +sh I k a sh I s o r e w a d o k o m a d e m o k o k o k a r a d e t e k o k o e k a e r i k u r u s e e sh I ts u o m o cl t a m o n o d e n a k e r e b a n a r a n a i (cv_jpn_000866-cv_jpn_000866) +a r i t o a r a y u r u d e m a o m a k I ch i r a sh I t e m i N n a k a r a u r a m i o k a cl t e r u (cv_jpn_000867-cv_jpn_000867) +k o n o t e e d o pau s a w a g i n i n a r u k o t o m o n a i n o d a r o o (cv_jpn_000868-cv_jpn_000868) +k o n o n e d a N d e u r e ch a u k a a (cv_jpn_000869-cv_jpn_000869) +h i n o k a g e N n i ch u u i sh i n a i t o s u g u n i k o g e r u (cv_jpn_000870-cv_jpn_000870) +e N b a N n o u e n i p o ts u r i t o ch i i s a n a a n a g a h i r a i t a s a i sh o w a ts u m a y o o j i t e e d o n o ch i i s a n a a n a d a cl t a (cv_jpn_000871-cv_jpn_000871) +s o r e w a w a r e w a r e o i k a sh i n a g a r a w a r e w a r e o d o r e e k a s u r u n o d e a r u pau w a r e w a r e n o t a m a sh i i o k o r o s u n o d e a r u (cv_jpn_000872-cv_jpn_000872) +r e k I sh i t e k i n i a t a e r a r e t a m o n o w a pau z e cl t a i m u j u N t e k i j i k o d o o i ts u t e k i g e N z a i n i o i t e s e k a i sh i t e k i n i a t a e r a r e t a m o n o t o sh I t e (cv_jpn_000873-cv_jpn_000873) +m u j u N t e k i j i k o d o o i ts U t o sh I t e pau i ts u m o k o n o s e k a i n i ch o o e ts u t e k i d e a r u (cv_jpn_000874-cv_jpn_000874) +y u e n i z e cl t a i m u j u N t e k i j i k o d o o i ts U t o sh I t e g e N z a i k a r a g e N z a i e t o u g o k i i k U s e k a i n o g e N z a i n i o i t e (cv_jpn_000875-cv_jpn_000875) +a r e pau b o t a N o sh I t e m o d a cl sh U ts u d e k i n a i (cv_jpn_000876-cv_jpn_000876) +sh I k a sh i w a t a sh i w a s o k o n i s e k a i n o j i k o d o o i ts u o o k u n o d e w a n a i (cv_jpn_000877-cv_jpn_000877) +n e m u t a k u n a r u n o g a h a y a k u n a cl t a (cv_jpn_000878-cv_jpn_000878) +w a t a sh i w a n i N g e N n o r e k I sh i t e k I k e e s e e n o t a ch i b a k a r a g e e j u ts u o m i r u n o d e a cl t e pau k o o sh a k a r a z e N sh a o m i r u n o d e w a n a i (cv_jpn_000879-cv_jpn_000879) +a o i t o m a t o sh I k a n a k U t e k a u k a m a y o u (cv_jpn_000880-cv_jpn_000880) +sh i N k i j i gy o o n i o o k i n a k I t a i o y o s e t e i r u (cv_jpn_000881-cv_jpn_000881) +n a n i k a sh i r a n o i N s e N t i b u g a n a i t o k i b i sh i i n o d e w a (cv_jpn_000882-cv_jpn_000882) +j i k a N s e e g e N n o i b e N t o d e s U t o r e s U t a m a r u (cv_jpn_000883-cv_jpn_000883) +m a w a r i n o h I t o w a b o o z e N t o sh I t e i t a (cv_jpn_000884-cv_jpn_000884) +s o N n a n a i y o o n o m e e r u g a pau n a N k e N m o k i t e i t a (cv_jpn_000885-cv_jpn_000885) +n i j i k a i d e d e e s u i sh I t e i t a (cv_jpn_000886-cv_jpn_000886) +t o k i d o k i pau j i b u N n o k o k o r o g a w a k a r a n a k u n a r u t o k i g a a r u d a k a r a b o k u w a k a a t e N o h I k i pau n o o t o n i k a k I h a j i m e r u (cv_jpn_000887-cv_jpn_000887) +m o o n i g e t e ch a d a m e d a (cv_jpn_000888-cv_jpn_000888) +k a r e w a pau b o o cl t o t a ch I ts u k u sh I t e i t a (cv_jpn_000889-cv_jpn_000889) +d a r e n i m o m e e w a k u w a k a k e t a k u n a i (cv_jpn_000890-cv_jpn_000890) +m a s a k a pau t o o m o cl t e d o a n o t o cl t e o n i g i cl t a (cv_jpn_000891-cv_jpn_000891) +s u i m a s e N (cv_jpn_000892-cv_jpn_000892) +k a y o u n i sh I t e sh i cl t e i r u t o t o m o n i sh i cl t e i n a i t o k o r o k a r a t a N ky u u w a h a j i m a r u n o d e a r u (cv_jpn_000893-cv_jpn_000893) +a i s a ts u w a d a i j i d a y o (cv_jpn_000894-cv_jpn_000894) +t o j i t a m o n o o i k a n i h i r o g e t e m o h i r a i t a m o n o n i w a n a r a n u t o i cl t e i r u g a (cv_jpn_000895-cv_jpn_000895) +t a m e sh i n i i k U ts u k a ts U k u cl t e m i y o o (cv_jpn_000896-cv_jpn_000896) +z u i b u N a k o g i n a sh o o b a i d a y o n a a (cv_jpn_000897-cv_jpn_000897) +w a ch i (cv_jpn_000898-cv_jpn_000898) +i ch i (cv_jpn_000899-cv_jpn_000899) +g o (cv_jpn_000900-cv_jpn_000900) +sh I ch i (cv_jpn_000901-cv_jpn_000901) +i i e (cv_jpn_000902-cv_jpn_000902) +w a ch i (cv_jpn_000903-cv_jpn_000903) +r e i (cv_jpn_000904-cv_jpn_000904) +sh i (cv_jpn_000905-cv_jpn_000905) +k u (cv_jpn_000906-cv_jpn_000906) +i ch i (cv_jpn_000907-cv_jpn_000907) +k a g a k u g a a k i r a k a n i s u r u ky a cl k a N t e k I sh i N r i n i sh I t a g a u k o t o n i y o cl t e (cv_jpn_000908-cv_jpn_000908) +k a k o t o m i r a i t o n o m u j u N t e k i j i k o d o o i ts U t o sh I t e n o g e N z a i g a k a t a ch i o m o ts U t o i u k o t o d e a r u (cv_jpn_000909-cv_jpn_000909) +b u ts u r i t e k I s e k a i w a s u u g a k u t e k I k i g o o n i y o cl t e a r a w a s a r e r u s u u g a k u t e k I k a t a ch i n o s e k a i d e a r u (cv_jpn_000910-cv_jpn_000910) +o n a j i g e N sh o o d e s a N k o o n i n a r u (cv_jpn_000911-cv_jpn_000911) +g a i k o k U k a r a k i t a m o n o d a t o sh i cl t e b i cl k u r i (cv_jpn_000912-cv_jpn_000912) +i w a y u r u j i cl s e N n i y o cl t e k a k U t o k U sh i r a i cl t a m o n o d e a r u (cv_jpn_000913-cv_jpn_000913) +o n a j i y o o n i pau d a N s e e w a h i z a o o o u z u b o N o h a k U k o t o g a g i m u z u k e r a r e t e i m a s U (fleurs_jpn_000346-fleurs_jpn_000346) +k o n o s a a b i s u w a pau g o r a k u s e N o h a j i m e t o s u r u s e N p a k u y a pau e N k a k u ch i d e d e e t a y a o N s e e o h I ts u y o o t o s u r u t a N k e N t a i n i h i N p a N n i r i y o o s a r e t e i m a s U (fleurs_jpn_000347-fleurs_jpn_000347) +ky o o f u u pau hy o o pau k a d o n o k o o s u i ry o u pau o y o b i y a m a k a j i w a pau r a i u pau t a ts u m a k i pau m i z u f u k i pau o y o b i s a i k u r o N n a d o n o k i b i sh i i k I sh o o k e e t a i y a s o n o e e ky o o n i y o r u m o n o d e s U (fleurs_jpn_000348-fleurs_jpn_000348) +i N t a a n e cl t o w a pau m a s U k o my u n i k e e sh o N t o t a i j i N k o my u n i k e e sh o N n o ry o o y o o s o o k a n e s o n a e t a k a N ky o o d e s U (fleurs_jpn_000349-fleurs_jpn_000349) +k a j i n o d e w a ts u u j o o pau t o k u b e ts u n a i N sh o k u y a e N t a a t e i m e N t o o y o o i sh I t e i m a s U pau g e s U t o g a k i b u N y o k U sh i s e ts u n a i n i t o m a r u y o o n i s u r u t a m e d e s U (fleurs_jpn_000350-fleurs_jpn_000350) +sh I k a sh i pau ky a p U t e N n o w i k e cl t o o u sh i n a cl t a a t o pau i N d o w a n a n a ts u n o w i k e cl t o o u sh i n a i pau s a N j u u r o k u r a N sh I k a d e k i m a s e N d e sh I t a (fleurs_jpn_000351-fleurs_jpn_000351) +f o o k u r a N d o n o k o o sh I k i ts u u k a w a f o o k u r a N d o sh o t o o p o N d o e f u k e e p i i d e i ch I p o N d o g a i ch i i g i r i s U p o N d o j i i b i i p i i t o t o o k a n i k o t e e s a r e t e i m a s U (fleurs_jpn_000352-fleurs_jpn_000352) +h a sh i sh I t a n o k a m i g a t a k u u k a N w a j u u g o m e e t o r u d e s U pau n i s e N j u u i ch i n e N h a ch i g a ts u n i sh u N k o o sh i pau n i s e N j u u n a n a n e N s a N g a ts u m a d e k a i ts u u sh i m a s e N d e sh I t a (fleurs_jpn_000353-fleurs_jpn_000353) +i cl p u N k a N d e f u cl t o o s u r u ch i i k i m o a r e b a pau f u cl t o o s u r u m a d e n i n a N p u N m o k a k a r u ch i i k i m o a r i m a s U (fleurs_jpn_000354-fleurs_jpn_000354) +p i r a m i cl d o n o o t o t o h I k a r i n o sh o o w a pau k o n o k a N k o o ch i d e t o k u n i k o d o m o t a ch i g a t a n o sh i m e r u m o y o o sh i n o h I t o ts u d e s U (fleurs_jpn_000355-fleurs_jpn_000355) +s o n o t a m e pau t a N n i r a b e r u t o sh I t e hy o o k i g a ts u i k a s a r e g a ch i d e s U (fleurs_jpn_000356-fleurs_jpn_000356) +g e N s o N s u r u k o t o g a sh i r a r e t e i r u n i j u u g o m a i n o d a N r a cl p u pau b u r o o d o s a i d o w a pau g e N s o N s u r u t o o g a i b u N k e N n o s a i k o n o u ts U sh i d e s U pau t e g a k i n i y o r u g e N p o N w a g e N s o N sh I t e i m a s e N (fleurs_jpn_000357-fleurs_jpn_000357) +k a r e n o s e ts u o t a d a sh i i t o m i t o m e r u h I t o m o i m a sh I t a g a pau o o k u n o h I t o w a s o n o gy a k u d e pau t a i y o o k e e d e w a t a i y o o t o s o n o t a n o h o sh i g a ch I ky u u n o m a w a r i o i d o o sh I t e i r u t o sh i N j i t e i m a sh I t a (fleurs_jpn_000358-fleurs_jpn_000358) +ch i b e cl t o m e e s o o n o ch u u sh i N w a sh i N s e e y o g a d e s U pau s a m a z a m a n a k a m i g a m i o sh I k a k U k a s u r u k o t o d e pau e n e r u g i i ch a n e r u g a j o o k a s a r e pau ch a k u r a g a k a cl s e e k a s a r e pau s a t o r i n o i sh I k i g a u m a r e m a s U (fleurs_jpn_000359-fleurs_jpn_000359) +m i n a m i a f u r i k a n i a r u s u b e t e n o k o k u r i ts U k o o e N t o d o o y o o n i pau k o n o k o o e N n i w a m a i n i ch I h o g o h I t o ny u u e N ry o o g a k a k a r i m a s U (fleurs_jpn_000360-fleurs_jpn_000360) +r e cl sh a pau k u r u m a pau s o n o t a n o o o k u n o k o o ts u u sh u d a N g a s o k o k a r a u m a r e m a sh I t a (fleurs_jpn_000361-fleurs_jpn_000361) +i N t a a n e cl t o w a pau m a s U k o my u n i k e e sh o N t o t a i j i N k o my u n i k e e sh o N n o ry o o y o o s o o k a n e s o n a e t a k a N ky o o d e s U (fleurs_jpn_000362-fleurs_jpn_000362) +by o o i N d e w a pau k a N s e N k a N r i t e j u N sh o n i sh I t a g a i pau t a n i N e n o k a N s e N n o k a n o o s e e o f U s e g u t a m e n i k a N j a o k a k u r i s u r u n a d o n o s o ch i o t o cl t e i m a s U (fleurs_jpn_000363-fleurs_jpn_000363) +r e N p o o g i k a i w a n i s e N g o n e N d o k a r a w a i s e ts u b u ts U t o r i sh i m a r i h o o e n o sh I k i N t e e ky o o o k a i sh I sh i pau e f u b i i a i w a a d a r u t o p o r u n o n i j u u n i N n o s o o s a i N o t o o ny u u sh i n a k e r e b a n a r a n a i t o k I t e e sh i m a sh I t a (fleurs_jpn_000364-fleurs_jpn_000364) +p i i e i ch i pau r e b e r u w a pau k e N s a sh I t a k a g a k u b u cl sh I ts u n i f U k u m a r e r u s u i s o i o N p i i e i ch i n o e i ch i n o ry o o d e sh i m e s a r e m a s U (fleurs_jpn_000365-fleurs_jpn_000365) +s o r e d e m o pau t o o ky o k U k a r a n o a d o b a i s u o u k e pau s u b e t e n o hy o o sh I k i o m a m o r i pau a N z e N j o o n o k e e k o k u n i s a i sh i N n o ch u u i o h a r a i m a sh o o (fleurs_jpn_000366-fleurs_jpn_000366) +k o r e r a w a t a m a n i k o N z a ts U s u r u k a z o k u m u k e n o b i i ch i d e pau k a i g a N n i w a s a m a z a m a n a t e N p o g a n a r a N d e i m a s U pau a N z e N n i o y o g u k o t o g a d e k i m a s U (fleurs_jpn_000367-fleurs_jpn_000367) +sh i N n o pau m i e n a i ch i i m u pau e r u e e a a r u e s u o o e n u pau a N d o pau e r u e e e f u e e e s u t i i o o pau s e N ky u u hy a k U h a ch i j u u ky u u pau p i i hy a k u ky u u n o s o N z a i m o m a t a pau b a a ch a r u ch i i m u n o d o k u j i n o y o o s o d e a r u (fleurs_jpn_000368-fleurs_jpn_000368) +k o n o s a a b i s u w a pau g o r a k u s e N o h a j i m e t o s u r u s e N p a k u y a pau e N k a k u ch i d e d e e t a y a o N s e e o h I ts u y o o t o s u r u t a N k e N t a i n i h i N p a N n i r i y o o s a r e t e i m a s U (fleurs_jpn_000369-fleurs_jpn_000369) +s a k u b a N pau b u e n o s u a i r e s U k a r a g o j u cl k i r o s a N j u u i ch i m a i r u h a n a r e t a r a p u r a t a sh i n a i d e pau g e N sh o k u j o o i N g i i N d e a r u k u r i s U t i i n a pau f e r u n a N d e s u pau d e pau k i r u h i n a a j o sh i g a d a i t o o ry o o s e N e n o sh U ts u b a o s e N g e N sh i m a sh I t a (fleurs_jpn_000370-fleurs_jpn_000370) +o n a j i ts U k i n i pau m a sh U h a d o n o k a cl s o o r o d e b e ts u n o ry o k a k u k i g a k a cl s o o r o o o o b a a r a N sh i pau k a b e n i g e k I t o ts u sh I t e j u u sh I ch i n i N g a sh i b o o sh i m a sh I t a (fleurs_jpn_000371-fleurs_jpn_000371) +h a sh i sh I t a n o k a m i g a t a k u u k a N w a j u u g o m e e t o r u d e s U pau n i s e N j u u i ch i n e N h a ch i g a ts u n i sh u N k o o sh i pau n i s e N j u u n a n a n e N s a N g a ts u m a d e k a i ts u u sh i m a s e N d e sh I t a (fleurs_jpn_000372-fleurs_jpn_000372) +b u N m e e t o i u k o t o b a w a pau sh i m i N o i m i s u r u r a t e N g o n o k e e y o o sh I sh i i a i b u i a i e r u a i e s u k a r a k i t e o r i pau sh i m i N o i m i s u r u r a t e N g o n o m e e sh I sh i i a i b u i a i e s u pau t o sh i y a t o sh I k o cl k a o i m i sh i pau n a N r a k a n o k a t a ch i d e sh a k a i n o k i b o o t e e g i s u r u sh i i a i b u i a i t i i e e e s u t o i u m e e sh i n i k a N k e e sh I t e i m a s U (fleurs_jpn_000373-fleurs_jpn_000373) +ts u u j o o pau k o k o d e w a i ts u m o k a N k o o ky a k u y a gy o o sh a t a ch i g a h a cl s u r u o t o g a k I k o e t e k i m a s U pau o t o t o h I k a r i g a o r i n a s u m o n o g a t a r i w a m a r u d e e h o N n o y o o d e s U (fleurs_jpn_000374-fleurs_jpn_000374) +t e r e b i n o h o o d o o n i y o r u t o pau g e N p a ts U k a r a sh i r o k e m u r i g a a g a cl t e i m a s U (fleurs_jpn_000375-fleurs_jpn_000375) +n o o by o o r i t o k o o d o o n o s o o k a N k a N k e e w a pau k a g a k U sh a t a ch i n o k e N ky u u o u r a z u k e r u m o n o d e s U (fleurs_jpn_000376-fleurs_jpn_000376) +s u i y o o b i n o i b e N t o n o a t o pau k a r u p a n e d o w a s e N sh u k e N d e f U t a ts u n o k o j i N r e e s u n i sh U ts u j o o sh i m a sh I t a (fleurs_jpn_000377-fleurs_jpn_000377) +s e N h a cl py a k u n e N d a i i r a i pau g u N t a i g a t o o ch a k U s u r u m a d e h a i ch i w a k o n o by o o k i n i k a N k e e s u r u m o N d a i n i s o o g u u sh I t a k o t o w a a r i m a s e N d e sh I t a (fleurs_jpn_000378-fleurs_jpn_000378) +sh I k a sh i pau ky a p U t e N n o w i k e cl t o o u sh i n a cl t a a t o pau i N d o w a n a n a ts u n o w i k e cl t o o u sh i n a i pau s a N j u u r o k u r a N sh I k a d e k i m a s e N d e sh I t a (fleurs_jpn_000379-fleurs_jpn_000379) +k a j i n o d e w a ts u u j o o pau t o k u b e ts u n a i N sh o k u y a e N t a a t e i m e N t o o y o o i sh I t e i m a s U pau g e s U t o g a k i b u N y o k U sh i s e ts u n a i n i t o m a r u y o o n i s u r u t a m e d e s U (fleurs_jpn_000380-fleurs_jpn_000380) +s o r e d e m o pau t o o ky o k U k a r a n o a d o b a i s u o u k e pau s u b e t e n o hy o o sh I k i o m a m o r i pau a N z e N j o o n o k e e k o k u n i s a i sh i N n o ch u u i o h a r a i m a sh o o (fleurs_jpn_000381-fleurs_jpn_000381) +o w a k a r e d e w a a r i m a s e N k o r e w a h I t o ts u n o sh o o n o o w a r i d e a r i pau a t a r a sh i i sh o o n o m a k u a k e d e s U (fleurs_jpn_000382-fleurs_jpn_000382) +s a f a r i t o w a pau a f u r i k a n o y a s e e d o o b u ts u pau t o k u n i s a b a N n a n i i r u y a s e e d o o b u ts u n o k a N s a ts u o m o k U t e k I t o sh I t a r i k u r o d e n o ry o k o o o s a sh i m a s U (fleurs_jpn_000383-fleurs_jpn_000383) +f u y u n i k I t a b a r u t o k a i o o o d a N s u r u b a a i w a pau s e N sh I ts u n o i ch i o k a k u n i N sh I t e k u d a s a i pau k o o r i n o n a k a o ts U k i s u s u m u s a i n i m o cl t o m o e e ky o o o u k e r u s e N sh I ts u d e w a o s o r o sh i i h o d o n o s o o o N g a n a r i h i b i k i m a s U (fleurs_jpn_000384-fleurs_jpn_000384) +k o k o w a i g i r i s u n o sh o k u m i N ch I sh i h a i sh a g a j i b u N t a ch i n o ry o o d o t o sh I t a b a sh o n a n o d e pau sh o k u m i N ch i j i d a i n o sh o o k o o s a g a s o o t o s u r u h o o w a pau k o k o k a r a h a j i m e r u n o g a y o i d e sh o o (fleurs_jpn_000385-fleurs_jpn_000385) +e b i s u sh i w a pau s a k u g e N s u r u s u u ch i o s a d a m e m a s e N d e sh I t a g a pau s a k u g e N w a ch u u g o k u n o k e e z a i s a N sh U ts u ry o u n i m o t o z u i t e j i cl sh I s a r e r u d a r o o t o n o b e m a sh I t a (fleurs_jpn_000386-fleurs_jpn_000386) +s a i ny u u k o k U sh o cl k u w a sh i N k o N ry o k o o n o j i k i g a s U k u n a i k a r u ch a a sh o cl k u y o r i m o h a y a k u o t o z u r e pau n a g a b i k i pau y o r i sh o o j o o g a a cl k a s u r u k o t o g a a r i m a s U (fleurs_jpn_000387-fleurs_jpn_000387) +k i n o o n o a s a pau t o r u k o n o g a j i a N t e cl p u n o k e e s a ts U h o N b u d e j i d o o sh a b a k u d a N n o b a k U h a ts u n i y o r i pau k e e k a N f U t a r i g a sh i b o o sh i pau f U sh o o sh a w a n i j u u n i N o k o e m a sh I t a (fleurs_jpn_000388-fleurs_jpn_000388) +sh o k u b u ts u w a n i N g e N g a s u u s a N s o o ts U k u r i pau n i N g e N g a i k I t o sh I t e h a k i d a s u n i s a N k a t a N s o o t o r i k o N d e i m a s U (fleurs_jpn_000389-fleurs_jpn_000389) +s e N p a k u d e b u cl sh i o y u s o o s u r u n o w a pau u m i o k o e t e h I t o y a b u cl sh i o t a i ry o o y u s o o s u r u m o cl t o m o k o o r i ts u t e k i n a h o o h o o d e s U (fleurs_jpn_000390-fleurs_jpn_000390) +k a r i f o r u n i a sh u u n o a a n o r u d o pau sh u w a r u ts e n e cl g a a ch i j i w a pau b o o ry o k u t e k i n a b i d e o g e e m u o m i s e e n e N sh a n i h a N b a i y a r e N t a r u s u r u k o t o o k i N sh I s u r u h o o a N n i sh o m e e sh i m a sh I t a (fleurs_jpn_000391-fleurs_jpn_000391) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/result.txt b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..edbe23224434a0ad0c705f40d5e690dd2a3643c2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/result.txt @@ -0,0 +1,1961 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/hyp.trn| +|------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000800 | 1 64 | 90.6 4.7 4.7 0.0 9.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000801 | 1 103 | 87.4 5.8 6.8 1.9 14.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000802 | 1 34 | 79.4 5.9 14.7 8.8 29.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000803 | 1 96 | 88.5 1.0 10.4 1.0 12.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000804 | 1 32 | 93.8 6.3 0.0 6.3 12.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000805 | 1 20 | 85.0 5.0 10.0 0.0 15.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000806 | 1 62 | 83.9 6.5 9.7 3.2 19.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000807 | 1 14 | 78.6 14.3 7.1 14.3 35.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000808 | 1 18 | 88.9 0.0 11.1 0.0 11.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000809 | 1 88 | 88.6 10.2 1.1 4.5 15.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000810 | 1 70 | 92.9 4.3 2.9 5.7 12.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000811 | 1 24 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000812 | 1 92 | 95.7 2.2 2.2 1.1 5.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000813 | 1 42 | 83.3 11.9 4.8 2.4 19.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000814 | 1 34 | 88.2 5.9 5.9 2.9 14.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000815 | 1 42 | 85.7 7.1 7.1 7.1 21.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000816 | 1 32 | 96.9 3.1 0.0 15.6 18.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000817 | 1 32 | 96.9 3.1 0.0 12.5 15.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000818 | 1 3 | 100.0 0.0 0.0 33.3 33.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000819 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000820 | 1 2 | 50.0 50.0 0.0 100.0 150.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000821 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000822 | 1 4 | 50.0 50.0 0.0 25.0 75.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000823 | 1 64 | 85.9 6.3 7.8 1.6 15.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000824 | 1 54 | 92.6 5.6 1.9 0.0 7.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000825 | 1 73 | 91.8 5.5 2.7 1.4 9.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000826 | 1 75 | 92.0 4.0 4.0 0.0 8.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000827 | 1 55 | 94.5 1.8 3.6 0.0 5.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000828 | 1 32 | 96.9 3.1 0.0 9.4 12.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000829 | 1 20 | 95.0 5.0 0.0 0.0 5.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000830 | 1 23 | 91.3 8.7 0.0 0.0 8.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000831 | 1 8 | 87.5 12.5 0.0 0.0 12.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000832 | 1 32 | 93.8 3.1 3.1 0.0 6.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000833 | 1 28 | 96.4 0.0 3.6 7.1 10.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000834 | 1 26 | 92.3 3.8 3.8 11.5 19.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000835 | 1 20 | 95.0 0.0 5.0 10.0 15.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000836 | 1 23 | 95.7 4.3 0.0 0.0 4.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000837 | 1 9 | 100.0 0.0 0.0 11.1 11.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000838 | 1 38 | 89.5 10.5 0.0 0.0 10.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000839 | 1 54 | 94.4 3.7 1.9 0.0 5.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000840 | 1 90 | 93.3 4.4 2.2 1.1 7.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000841 | 1 39 | 94.9 5.1 0.0 0.0 5.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000842 | 1 37 | 91.9 5.4 2.7 2.7 10.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000843 | 1 40 | 97.5 2.5 0.0 5.0 7.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000844 | 1 82 | 89.0 7.3 3.7 3.7 14.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000845 | 1 36 | 86.1 5.6 8.3 0.0 13.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000846 | 1 101 | 90.1 5.9 4.0 3.0 12.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000847 | 1 57 | 87.7 10.5 1.8 5.3 17.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000848 | 1 36 | 88.9 2.8 8.3 0.0 11.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000849 | 1 13 | 53.8 15.4 30.8 7.7 53.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000850 | 1 33 | 75.8 15.2 9.1 0.0 24.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000851 | 1 35 | 91.4 5.7 2.9 0.0 8.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000852 | 1 25 | 84.0 8.0 8.0 0.0 16.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000853 | 1 33 | 84.8 12.1 3.0 3.0 18.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000854 | 1 77 | 77.9 15.6 6.5 3.9 26.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000855 | 1 43 | 79.1 9.3 11.6 7.0 27.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000856 | 1 40 | 80.0 17.5 2.5 2.5 22.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000857 | 1 45 | 86.7 6.7 6.7 4.4 17.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000858 | 1 33 | 97.0 3.0 0.0 3.0 6.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000859 | 1 42 | 90.5 9.5 0.0 11.9 21.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000860 | 1 44 | 95.5 4.5 0.0 4.5 9.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000861 | 1 36 | 94.4 2.8 2.8 2.8 8.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000862 | 1 61 | 86.9 9.8 3.3 1.6 14.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000863 | 1 75 | 70.7 21.3 8.0 2.7 32.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000864 | 1 72 | 81.9 8.3 9.7 2.8 20.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000865 | 1 50 | 92.0 0.0 8.0 2.0 10.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000866 | 1 82 | 81.7 8.5 9.8 1.2 19.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000867 | 1 51 | 94.1 3.9 2.0 3.9 9.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000868 | 1 38 | 94.7 0.0 5.3 0.0 5.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000869 | 1 20 | 85.0 15.0 0.0 10.0 25.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000870 | 1 34 | 91.2 0.0 8.8 5.9 14.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000871 | 1 76 | 88.2 5.3 6.6 6.6 18.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000872 | 1 85 | 94.1 3.5 2.4 2.4 8.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000873 | 1 103 | 85.4 8.7 5.8 1.9 16.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000874 | 1 57 | 87.7 8.8 3.5 3.5 15.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000875 | 1 82 | 81.7 12.2 6.1 1.2 19.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000876 | 1 30 | 76.7 3.3 20.0 6.7 30.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000877 | 1 50 | 94.0 0.0 6.0 0.0 6.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000878 | 1 27 | 88.9 11.1 0.0 3.7 14.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000879 | 1 93 | 92.5 1.1 6.5 2.2 9.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000880 | 1 29 | 96.6 3.4 0.0 6.9 10.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000881 | 1 33 | 69.7 30.3 0.0 6.1 36.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000882 | 1 41 | 87.8 12.2 0.0 2.4 14.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000883 | 1 35 | 80.0 20.0 0.0 2.9 22.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000884 | 1 29 | 89.7 3.4 6.9 0.0 10.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000885 | 1 36 | 91.7 2.8 5.6 2.8 11.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000886 | 1 22 | 90.9 4.5 4.5 4.5 13.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000887 | 1 90 | 80.0 8.9 11.1 0.0 20.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000888 | 1 17 | 94.1 5.9 0.0 0.0 5.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000889 | 1 28 | 85.7 7.1 7.1 0.0 14.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000890 | 1 28 | 78.6 21.4 0.0 0.0 21.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000891 | 1 33 | 87.9 6.1 6.1 6.1 18.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000892 | 1 8 | 75.0 25.0 0.0 12.5 37.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000893 | 1 69 | 94.2 1.4 4.3 0.0 5.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000894 | 1 17 | 94.1 5.9 0.0 0.0 5.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000895 | 1 58 | 91.4 5.2 3.4 0.0 8.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000896 | 1 27 | 85.2 7.4 7.4 3.7 18.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000897 | 1 26 | 84.6 11.5 3.8 7.7 23.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000898 | 1 4 | 75.0 25.0 0.0 75.0 100.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000899 | 1 3 | 66.7 33.3 0.0 66.7 100.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000900 | 1 2 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000901 | 1 4 | 75.0 25.0 0.0 75.0 100.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000902 | 1 3 | 66.7 0.0 33.3 33.3 66.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000903 | 1 4 | 75.0 25.0 0.0 25.0 50.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000904 | 1 3 | 33.3 33.3 33.3 33.3 100.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000905 | 1 2 | 100.0 0.0 0.0 150.0 150.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000906 | 1 2 | 100.0 0.0 0.0 50.0 50.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000907 | 1 3 | 66.7 33.3 0.0 33.3 66.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000908 | 1 56 | 98.2 1.8 0.0 7.1 8.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000909 | 1 74 | 95.9 4.1 0.0 1.4 5.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000910 | 1 80 | 95.0 1.3 3.8 5.0 10.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000911 | 1 25 | 92.0 8.0 0.0 8.0 16.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000912 | 1 35 | 94.3 0.0 5.7 0.0 5.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000913 | 1 45 | 86.7 6.7 6.7 4.4 17.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000346 | 1 62 | 87.1 1.6 11.3 4.8 17.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000347 | 1 117 | 84.6 5.1 10.3 4.3 19.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000348 | 1 128 | 78.9 6.3 14.8 0.0 21.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000349 | 1 81 | 74.1 19.8 6.2 1.2 27.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000350 | 1 114 | 80.7 10.5 8.8 3.5 22.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000351 | 1 97 | 84.5 8.2 7.2 1.0 16.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000352 | 1 116 | 79.3 11.2 9.5 0.9 21.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000353 | 1 121 | 70.2 16.5 13.2 0.8 30.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000354 | 1 77 | 87.0 7.8 5.2 2.6 15.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000355 | 1 91 | 81.3 17.6 1.1 1.1 19.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000356 | 1 50 | 86.0 14.0 0.0 4.0 18.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000357 | 1 140 | 77.9 17.1 5.0 2.1 24.3 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000358 | 1 143 | 75.5 11.9 12.6 4.2 28.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000359 | 1 147 | 83.0 9.5 7.5 0.7 17.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000360 | 1 98 | 76.5 17.3 6.1 4.1 27.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000361 | 1 59 | 76.3 15.3 8.5 10.2 33.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000362 | 1 81 | 86.4 9.9 3.7 0.0 13.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000363 | 1 112 | 75.9 13.4 10.7 8.0 32.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000364 | 1 155 | 84.5 11.0 4.5 1.9 17.4 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000365 | 1 91 | 63.7 24.2 12.1 4.4 40.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000366 | 1 100 | 83.0 9.0 8.0 1.0 18.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000367 | 1 111 | 82.0 10.8 7.2 2.7 20.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000368 | 1 138 | 59.4 16.7 23.9 2.9 43.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000369 | 1 117 | 82.9 6.8 10.3 2.6 19.7 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000370 | 1 171 | 86.0 10.5 3.5 1.2 15.2 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000371 | 1 111 | 77.5 17.1 5.4 4.5 27.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000372 | 1 121 | 75.2 11.6 13.2 0.8 25.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000373 | 1 224 | 78.6 3.1 18.3 3.1 24.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000374 | 1 120 | 75.8 15.0 9.2 0.8 25.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000375 | 1 57 | 70.2 8.8 21.1 5.3 35.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000376 | 1 71 | 94.4 5.6 0.0 1.4 7.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000377 | 1 79 | 75.9 13.9 10.1 2.5 26.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000378 | 1 110 | 81.8 10.0 8.2 2.7 20.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000379 | 1 97 | 74.2 17.5 8.2 0.0 25.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000380 | 1 114 | 90.4 5.3 4.4 6.1 15.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000381 | 1 100 | 75.0 9.0 16.0 3.0 28.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000382 | 1 73 | 75.3 8.2 16.4 1.4 26.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000383 | 1 111 | 87.4 9.0 3.6 0.9 13.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000384 | 1 159 | 78.0 7.5 14.5 2.5 24.5 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000385 | 1 135 | 74.1 19.3 6.7 3.0 28.9 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000386 | 1 120 | 78.3 10.0 11.7 3.3 25.0 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000387 | 1 117 | 83.8 10.3 6.0 2.6 18.8 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000388 | 1 126 | 82.5 12.7 4.8 7.1 24.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000389 | 1 86 | 94.2 3.5 2.3 5.8 11.6 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000390 | 1 95 | 86.3 9.5 4.2 7.4 21.1 100.0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000391 | 1 135 | 87.4 6.7 5.9 3.0 15.6 100.0 | +|==================================================================================================================| +| Sum/Avg | 160 9700 | 84.1 9.0 6.9 3.1 19.0 98.1 | +|==================================================================================================================| +| Mean | 1.0 60.6 | 85.0 9.4 5.7 7.4 22.5 98.1 | +| S.D. | 0.0 42.8 | 10.6 8.6 6.1 17.9 22.6 13.6 | +| Median | 1.0 50.5 | 86.7 7.2 4.5 2.8 17.6 100.0 | +`------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/hyp.trn| +|------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000800 | 1 64 | 58 3 3 0 6 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000801 | 1 103 | 90 6 7 2 15 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000802 | 1 34 | 27 2 5 3 10 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000803 | 1 96 | 85 1 10 1 12 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000804 | 1 32 | 30 2 0 2 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000805 | 1 20 | 17 1 2 0 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000806 | 1 62 | 52 4 6 2 12 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000807 | 1 14 | 11 2 1 2 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000808 | 1 18 | 16 0 2 0 2 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000809 | 1 88 | 78 9 1 4 14 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000810 | 1 70 | 65 3 2 4 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000811 | 1 24 | 24 0 0 0 0 0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000812 | 1 92 | 88 2 2 1 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000813 | 1 42 | 35 5 2 1 8 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000814 | 1 34 | 30 2 2 1 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000815 | 1 42 | 36 3 3 3 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000816 | 1 32 | 31 1 0 5 6 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000817 | 1 32 | 31 1 0 4 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000818 | 1 3 | 3 0 0 1 1 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000819 | 1 3 | 3 0 0 0 0 0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000820 | 1 2 | 1 1 0 2 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000821 | 1 3 | 2 1 0 0 1 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000822 | 1 4 | 2 2 0 1 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000823 | 1 64 | 55 4 5 1 10 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000824 | 1 54 | 50 3 1 0 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000825 | 1 73 | 67 4 2 1 7 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000826 | 1 75 | 69 3 3 0 6 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000827 | 1 55 | 52 1 2 0 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000828 | 1 32 | 31 1 0 3 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000829 | 1 20 | 19 1 0 0 1 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000830 | 1 23 | 21 2 0 0 2 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000831 | 1 8 | 7 1 0 0 1 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000832 | 1 32 | 30 1 1 0 2 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000833 | 1 28 | 27 0 1 2 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000834 | 1 26 | 24 1 1 3 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000835 | 1 20 | 19 0 1 2 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000836 | 1 23 | 22 1 0 0 1 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000837 | 1 9 | 9 0 0 1 1 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000838 | 1 38 | 34 4 0 0 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000839 | 1 54 | 51 2 1 0 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000840 | 1 90 | 84 4 2 1 7 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000841 | 1 39 | 37 2 0 0 2 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000842 | 1 37 | 34 2 1 1 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000843 | 1 40 | 39 1 0 2 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000844 | 1 82 | 73 6 3 3 12 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000845 | 1 36 | 31 2 3 0 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000846 | 1 101 | 91 6 4 3 13 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000847 | 1 57 | 50 6 1 3 10 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000848 | 1 36 | 32 1 3 0 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000849 | 1 13 | 7 2 4 1 7 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000850 | 1 33 | 25 5 3 0 8 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000851 | 1 35 | 32 2 1 0 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000852 | 1 25 | 21 2 2 0 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000853 | 1 33 | 28 4 1 1 6 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000854 | 1 77 | 60 12 5 3 20 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000855 | 1 43 | 34 4 5 3 12 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000856 | 1 40 | 32 7 1 1 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000857 | 1 45 | 39 3 3 2 8 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000858 | 1 33 | 32 1 0 1 2 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000859 | 1 42 | 38 4 0 5 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000860 | 1 44 | 42 2 0 2 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000861 | 1 36 | 34 1 1 1 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000862 | 1 61 | 53 6 2 1 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000863 | 1 75 | 53 16 6 2 24 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000864 | 1 72 | 59 6 7 2 15 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000865 | 1 50 | 46 0 4 1 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000866 | 1 82 | 67 7 8 1 16 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000867 | 1 51 | 48 2 1 2 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000868 | 1 38 | 36 0 2 0 2 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000869 | 1 20 | 17 3 0 2 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000870 | 1 34 | 31 0 3 2 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000871 | 1 76 | 67 4 5 5 14 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000872 | 1 85 | 80 3 2 2 7 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000873 | 1 103 | 88 9 6 2 17 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000874 | 1 57 | 50 5 2 2 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000875 | 1 82 | 67 10 5 1 16 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000876 | 1 30 | 23 1 6 2 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000877 | 1 50 | 47 0 3 0 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000878 | 1 27 | 24 3 0 1 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000879 | 1 93 | 86 1 6 2 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000880 | 1 29 | 28 1 0 2 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000881 | 1 33 | 23 10 0 2 12 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000882 | 1 41 | 36 5 0 1 6 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000883 | 1 35 | 28 7 0 1 8 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000884 | 1 29 | 26 1 2 0 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000885 | 1 36 | 33 1 2 1 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000886 | 1 22 | 20 1 1 1 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000887 | 1 90 | 72 8 10 0 18 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000888 | 1 17 | 16 1 0 0 1 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000889 | 1 28 | 24 2 2 0 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000890 | 1 28 | 22 6 0 0 6 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000891 | 1 33 | 29 2 2 2 6 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000892 | 1 8 | 6 2 0 1 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000893 | 1 69 | 65 1 3 0 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000894 | 1 17 | 16 1 0 0 1 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000895 | 1 58 | 53 3 2 0 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000896 | 1 27 | 23 2 2 1 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000897 | 1 26 | 22 3 1 2 6 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000898 | 1 4 | 3 1 0 3 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000899 | 1 3 | 2 1 0 2 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000900 | 1 2 | 2 0 0 0 0 0 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000901 | 1 4 | 3 1 0 3 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000902 | 1 3 | 2 0 1 1 2 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000903 | 1 4 | 3 1 0 1 2 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000904 | 1 3 | 1 1 1 1 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000905 | 1 2 | 2 0 0 3 3 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000906 | 1 2 | 2 0 0 1 1 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000907 | 1 3 | 2 1 0 1 2 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000908 | 1 56 | 55 1 0 4 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000909 | 1 74 | 71 3 0 1 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000910 | 1 80 | 76 1 3 4 8 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000911 | 1 25 | 23 2 0 2 4 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000912 | 1 35 | 33 0 2 0 2 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| cv_jpn_000913 | 1 45 | 39 3 3 2 8 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000346 | 1 62 | 54 1 7 3 11 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000347 | 1 117 | 99 6 12 5 23 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000348 | 1 128 | 101 8 19 0 27 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000349 | 1 81 | 60 16 5 1 22 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000350 | 1 114 | 92 12 10 4 26 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000351 | 1 97 | 82 8 7 1 16 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000352 | 1 116 | 92 13 11 1 25 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000353 | 1 121 | 85 20 16 1 37 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000354 | 1 77 | 67 6 4 2 12 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000355 | 1 91 | 74 16 1 1 18 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000356 | 1 50 | 43 7 0 2 9 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000357 | 1 140 | 109 24 7 3 34 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000358 | 1 143 | 108 17 18 6 41 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000359 | 1 147 | 122 14 11 1 26 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000360 | 1 98 | 75 17 6 4 27 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000361 | 1 59 | 45 9 5 6 20 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000362 | 1 81 | 70 8 3 0 11 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000363 | 1 112 | 85 15 12 9 36 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000364 | 1 155 | 131 17 7 3 27 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000365 | 1 91 | 58 22 11 4 37 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000366 | 1 100 | 83 9 8 1 18 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000367 | 1 111 | 91 12 8 3 23 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000368 | 1 138 | 82 23 33 4 60 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000369 | 1 117 | 97 8 12 3 23 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000370 | 1 171 | 147 18 6 2 26 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000371 | 1 111 | 86 19 6 5 30 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000372 | 1 121 | 91 14 16 1 31 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000373 | 1 224 | 176 7 41 7 55 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000374 | 1 120 | 91 18 11 1 30 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000375 | 1 57 | 40 5 12 3 20 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000376 | 1 71 | 67 4 0 1 5 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000377 | 1 79 | 60 11 8 2 21 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000378 | 1 110 | 90 11 9 3 23 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000379 | 1 97 | 72 17 8 0 25 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000380 | 1 114 | 103 6 5 7 18 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000381 | 1 100 | 75 9 16 3 28 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000382 | 1 73 | 55 6 12 1 19 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000383 | 1 111 | 97 10 4 1 15 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000384 | 1 159 | 124 12 23 4 39 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000385 | 1 135 | 100 26 9 4 39 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000386 | 1 120 | 94 12 14 4 30 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000387 | 1 117 | 98 12 7 3 22 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000388 | 1 126 | 104 16 6 9 31 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000389 | 1 86 | 81 3 2 5 10 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000390 | 1 95 | 82 9 4 7 20 1 | +|-----------------------+----------------------+-------------------------------------------------------------------| +| fleurs_jpn_000391 | 1 135 | 118 9 8 4 21 1 | +|==================================================================================================================| +| Sum | 160 9700 | 8156 870 674 303 1847 157 | +|==================================================================================================================| +| Mean | 1.0 60.6 | 51.0 5.4 4.2 1.9 11.5 1.0 | +| S.D. | 0.0 42.8 | 34.3 5.8 5.8 1.8 11.2 0.1 | +| Median | 1.0 50.5 | 45.5 3.0 2.0 1.0 7.0 1.0 | +`------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/hyp.trn + +Speakers: + 0: cv_jpn_000800 + 1: cv_jpn_000801 + 2: cv_jpn_000802 + 3: cv_jpn_000803 + 4: cv_jpn_000804 + 5: cv_jpn_000805 + 6: cv_jpn_000806 + 7: cv_jpn_000807 + 8: cv_jpn_000808 + 9: cv_jpn_000809 + 10: cv_jpn_000810 + 11: cv_jpn_000811 + 12: cv_jpn_000812 + 13: cv_jpn_000813 + 14: cv_jpn_000814 + 15: cv_jpn_000815 + 16: cv_jpn_000816 + 17: cv_jpn_000817 + 18: cv_jpn_000818 + 19: cv_jpn_000819 + 20: cv_jpn_000820 + 21: cv_jpn_000821 + 22: cv_jpn_000822 + 23: cv_jpn_000823 + 24: cv_jpn_000824 + 25: cv_jpn_000825 + 26: cv_jpn_000826 + 27: cv_jpn_000827 + 28: cv_jpn_000828 + 29: cv_jpn_000829 + 30: cv_jpn_000830 + 31: cv_jpn_000831 + 32: cv_jpn_000832 + 33: cv_jpn_000833 + 34: cv_jpn_000834 + 35: cv_jpn_000835 + 36: cv_jpn_000836 + 37: cv_jpn_000837 + 38: cv_jpn_000838 + 39: cv_jpn_000839 + 40: cv_jpn_000840 + 41: cv_jpn_000841 + 42: cv_jpn_000842 + 43: cv_jpn_000843 + 44: cv_jpn_000844 + 45: cv_jpn_000845 + 46: cv_jpn_000846 + 47: cv_jpn_000847 + 48: cv_jpn_000848 + 49: cv_jpn_000849 + 50: cv_jpn_000850 + 51: cv_jpn_000851 + 52: cv_jpn_000852 + 53: cv_jpn_000853 + 54: cv_jpn_000854 + 55: cv_jpn_000855 + 56: cv_jpn_000856 + 57: cv_jpn_000857 + 58: cv_jpn_000858 + 59: cv_jpn_000859 + 60: cv_jpn_000860 + 61: cv_jpn_000861 + 62: cv_jpn_000862 + 63: cv_jpn_000863 + 64: cv_jpn_000864 + 65: cv_jpn_000865 + 66: cv_jpn_000866 + 67: cv_jpn_000867 + 68: cv_jpn_000868 + 69: cv_jpn_000869 + 70: cv_jpn_000870 + 71: cv_jpn_000871 + 72: cv_jpn_000872 + 73: cv_jpn_000873 + 74: cv_jpn_000874 + 75: cv_jpn_000875 + 76: cv_jpn_000876 + 77: cv_jpn_000877 + 78: cv_jpn_000878 + 79: cv_jpn_000879 + 80: cv_jpn_000880 + 81: cv_jpn_000881 + 82: cv_jpn_000882 + 83: cv_jpn_000883 + 84: cv_jpn_000884 + 85: cv_jpn_000885 + 86: cv_jpn_000886 + 87: cv_jpn_000887 + 88: cv_jpn_000888 + 89: cv_jpn_000889 + 90: cv_jpn_000890 + 91: cv_jpn_000891 + 92: cv_jpn_000892 + 93: cv_jpn_000893 + 94: cv_jpn_000894 + 95: cv_jpn_000895 + 96: cv_jpn_000896 + 97: cv_jpn_000897 + 98: cv_jpn_000898 + 99: cv_jpn_000899 + 100: cv_jpn_000900 + 101: cv_jpn_000901 + 102: cv_jpn_000902 + 103: cv_jpn_000903 + 104: cv_jpn_000904 + 105: cv_jpn_000905 + 106: cv_jpn_000906 + 107: cv_jpn_000907 + 108: cv_jpn_000908 + 109: cv_jpn_000909 + 110: cv_jpn_000910 + 111: cv_jpn_000911 + 112: cv_jpn_000912 + 113: cv_jpn_000913 + 114: fleurs_jpn_000346 + 115: fleurs_jpn_000347 + 116: fleurs_jpn_000348 + 117: fleurs_jpn_000349 + 118: fleurs_jpn_000350 + 119: fleurs_jpn_000351 + 120: fleurs_jpn_000352 + 121: fleurs_jpn_000353 + 122: fleurs_jpn_000354 + 123: fleurs_jpn_000355 + 124: fleurs_jpn_000356 + 125: fleurs_jpn_000357 + 126: fleurs_jpn_000358 + 127: fleurs_jpn_000359 + 128: fleurs_jpn_000360 + 129: fleurs_jpn_000361 + 130: fleurs_jpn_000362 + 131: fleurs_jpn_000363 + 132: fleurs_jpn_000364 + 133: fleurs_jpn_000365 + 134: fleurs_jpn_000366 + 135: fleurs_jpn_000367 + 136: fleurs_jpn_000368 + 137: fleurs_jpn_000369 + 138: fleurs_jpn_000370 + 139: fleurs_jpn_000371 + 140: fleurs_jpn_000372 + 141: fleurs_jpn_000373 + 142: fleurs_jpn_000374 + 143: fleurs_jpn_000375 + 144: fleurs_jpn_000376 + 145: fleurs_jpn_000377 + 146: fleurs_jpn_000378 + 147: fleurs_jpn_000379 + 148: fleurs_jpn_000380 + 149: fleurs_jpn_000381 + 150: fleurs_jpn_000382 + 151: fleurs_jpn_000383 + 152: fleurs_jpn_000384 + 153: fleurs_jpn_000385 + 154: fleurs_jpn_000386 + 155: fleurs_jpn_000387 + 156: fleurs_jpn_000388 + 157: fleurs_jpn_000389 + 158: fleurs_jpn_000390 + 159: fleurs_jpn_000391 + +Speaker sentences 0: cv_jpn_000800 #utts: 1 +id: (cv_jpn_000800-cv_jpn_000800) +Scores: (#C #S #D #I) 58 3 3 0 +REF: k a k o t o m i r a i t o N o M u j u n t e k i j i k o d o o i ts u n a r u g a Y U e n i pau I sh i k i t e k i n a n o d e a r U +HYP: k a k o t o m i r a i t o D o * u j u n t e k i j i k o d o o i ts u n a r u g a I W e n i pau * sh i k i t e k i n a n o d e a r * +Eval: S D S S D D + +Speaker sentences 1: cv_jpn_000801 #utts: 1 +id: (cv_jpn_000801-cv_jpn_000801) +Scores: (#C #S #D #I) 90 6 7 2 +REF: s e k a I o k e e s e e s u r u t o t o m O n i pau j i * k o j i sh i N o k e e s E e s U r u ** s o o Z O o t e k i s e k a i n O s o o z o o t e k i y O O S o t o sh i t e pau k o B u ts u g a k o B u ts u d e a r u +HYP: s e k a Y o k e e s e e s u r u t o t o m U n i pau j i G k o j i sh i Y o k e e s * e s E r u TS s o o * D o t e k i s e k a i n * s o o z o o t e k i y * * * o t o sh i t e pau k o M u ts u g a k o * u ts u d e a r u +Eval: S S I S D S I D S D D D D S D + +Speaker sentences 2: cv_jpn_000802 #utts: 1 +id: (cv_jpn_000802-cv_jpn_000802) +Scores: (#C #S #D #I) 27 2 5 3 +REF: p a s o k o n D e g e e m U Y a r u N i * * n G a * f u e t e k i t e R U +HYP: p a s o k o n N e g e e m * I a r u * i T O n * a H f u e t e k i t e * * +Eval: S D S D I I D I D D + +Speaker sentences 3: cv_jpn_000803 #utts: 1 +id: (cv_jpn_000803-cv_jpn_000803) +Scores: (#C #S #D #I) 85 1 10 1 +REF: k a G a k u n o sh i m e s U a t a r a sh i i j i j i ts u PAU a t a r a SH I i k a n n e n PAU k a n ky O o sh i h a i n O a t a r a sh I i k a n O o s E e o m o cl t e *** n a n i o h a j i m e r u k a w a +HYP: k a * a k u n o sh i m e s * a t a r a sh i i j i j i ts u *** a t a r a ** S i k a n n e n *** k a n ky * o sh i h a i n * a t a r a sh * i k a n * o s * e o m o cl t e PAU n a n i o h a j i m e r u k a w a +Eval: D D D D S D D D D D D I + +Speaker sentences 4: cv_jpn_000804 #utts: 1 +id: (cv_jpn_000804-cv_jpn_000804) +Scores: (#C #S #D #I) 30 2 0 2 +REF: o m o sh i r o I n o n i *** r o o d o n a G a s u g i t e *** d a r u i +HYP: o m o sh i r o E n o n i PAU r o o d o n a K a s u g i t e PAU d a r u i +Eval: S I S I + +Speaker sentences 5: cv_jpn_000805 #utts: 1 +id: (cv_jpn_000805-cv_jpn_000805) +Scores: (#C #S #D #I) 17 1 2 0 +REF: k o r e j o o sh u u h a n CL P o i n A a +HYP: k o r e j o o sh u u h a n ** T o i n * a +Eval: D S D + +Speaker sentences 6: cv_jpn_000806 #utts: 1 +id: (cv_jpn_000806-cv_jpn_000806) +Scores: (#C #S #D #I) 52 4 6 2 +REF: k a g a k u sh a m o s e k a I o h o o k a ts U T e k i n i *** t o o ** i TS U t e k i n i s E ts u m E e sh I Y O o t o sh i t e i r u +HYP: k a g a k u sh a m o s e k a Y o h o o k a ts * S e k i n i PAU t o o CH i ** * t e k i n i s A ts u m * e sh * * U o t o sh i t e i r u +Eval: S D S I I D D S D D D S + +Speaker sentences 7: cv_jpn_000807 #utts: 1 +id: (cv_jpn_000807-cv_jpn_000807) +Scores: (#C #S #D #I) 11 2 1 2 +REF: * F U ts U u n i ts u * m a r a n +HYP: H A I ts * u n i ts u E m a r a n +Eval: I S S D I + +Speaker sentences 8: cv_jpn_000808 #utts: 1 +id: (cv_jpn_000808-cv_jpn_000808) +Scores: (#C #S #D #I) 16 0 2 0 +REF: sh i cl k a r I sh I t e k u d a s a i +HYP: sh i cl k a r * sh * t e k u d a s a i +Eval: D D + +Speaker sentences 9: cv_jpn_000809 #utts: 1 +id: (cv_jpn_000809-cv_jpn_000809) +Scores: (#C #S #D #I) 78 9 1 4 +REF: w a t a sh i w * a m i g i n o G o t o k i *** R e k i sh i t e k i s e E m e e n o j i k a k u t o I u g o t o k i m o n O o B e n sh o o h o o t e k i *** r o n R I t * O I u n o d e a r u +HYP: w a t a sh i w A a m i g i n o N o t o k i PAU D e k i sh i t e k i s e I m e e n o j i k a k u t o Y u g o t o k i m o n * o M e n sh o o h o o t e k i PAU r o n B E t A Y U u n o d e a r u +Eval: I S I S S S D S I S S I S S + +Speaker sentences 10: cv_jpn_000810 #utts: 1 +id: (cv_jpn_000810-cv_jpn_000810) +Scores: (#C #S #D #I) 65 3 2 4 +REF: w a t a sh i w a *** sh a k a i k e e s e e n o k o n t e E n i w a d * * I o NY u s o s u t e k i n a m o n o g a *** h a t a r a i t e i r u t O o m o U +HYP: w a t a sh i w a PAU sh a k a i k e e s e e n o k o n t e N n i w a d E Y O o N u s o s u t e k i n a m o n o g a PAU h a t a r a i t e i r u t * o m o * +Eval: I S I I S S I D D + +Speaker sentences 11: cv_jpn_000811 #utts: 1 +id: (cv_jpn_000811-cv_jpn_000811) +Scores: (#C #S #D #I) 24 0 0 0 +REF: n a n i o s u r u ts u m o r i d a cl t a n o k a +HYP: n a n i o s u r u ts u m o r i d a cl t a n o k a +Eval: + +Speaker sentences 12: cv_jpn_000812 #utts: 1 +id: (cv_jpn_000812-cv_jpn_000812) +Scores: (#C #S #D #I) 88 2 2 1 +REF: k o b u ts u t e k i t a g a j i k o h i t e e t e k i n i t a n n i *** t e n sh u u g o o t e k i n i k a n g a E r a r E r u t o k i PAU s o r e g a b u ts u r i t e k i S e k a i d e a r u +HYP: k o b u ts u t e k i t a g a j i k o h i t e e t e k i n i t a n n i PAU t e n sh u u g o o t e k i n i k a n g a * r a r U r u t o k i *** s o r e g a b u ts u r i t e k i T e k a i d e a r u +Eval: I D S D S + +Speaker sentences 13: cv_jpn_000813 #utts: 1 +id: (cv_jpn_000813-cv_jpn_000813) +Scores: (#C #S #D #I) 35 5 2 1 +REF: a M e g a F u cl t a n o d e PAU y a KY U U n o sh i * a i g A a r i m a s e n d e sh i t a +HYP: a N e g a Z u cl t a n o d e *** y a CL K I n o sh i R a i g * a r i m a s e n d e sh i t a +Eval: S S D S S S I D + +Speaker sentences 14: cv_jpn_000814 #utts: 1 +id: (cv_jpn_000814-cv_jpn_000814) +Scores: (#C #S #D #I) 30 2 2 1 +REF: k o r e w a n * i CL P o n d e u CL t e i n a i t a b e m O n o d e s u +HYP: k o r e w a n R i ** H o n d e u ** t e i n a i t a b e m U n o d e s u +Eval: I D S D S + +Speaker sentences 15: cv_jpn_000815 #utts: 1 +id: (cv_jpn_000815-cv_jpn_000815) +Scores: (#C #S #D #I) 36 3 3 3 +REF: w A t a sh i w a PAU h e n sh u u * i n o PAU y * o n e n k u r a I H a y a cl t a ** t o o m o U +HYP: w O t a sh i w a *** h e n sh u u E i n o *** y O o n e n k u r a E W a y a cl t a CL t o o m o * +Eval: S D I D I S S I D + +Speaker sentences 16: cv_jpn_000816 #utts: 1 +id: (cv_jpn_000816-cv_jpn_000816) +Scores: (#C #S #D #I) 31 1 0 5 +REF: * i s a n n * i k o n o k o t o b * a n o * i m i * o o sh i E m a sh i t a +HYP: E i s a n n I i k o n o k o t o b P a n o R i m i O o o sh i A m a sh i t a +Eval: I I I I I S + +Speaker sentences 17: cv_jpn_000817 #utts: 1 +id: (cv_jpn_000817-cv_jpn_000817) +Scores: (#C #S #D #I) 31 1 0 4 +REF: k a Z e g a ts * * u y o i h i * w a t e * n i s u g a d e k i m a s e n +HYP: k a S e g a ts U S u y o i h i U w a t e N n i s u g a d e k i m a s e n +Eval: S I I I I + +Speaker sentences 18: cv_jpn_000818 #utts: 1 +id: (cv_jpn_000818-cv_jpn_000818) +Scores: (#C #S #D #I) 3 0 0 1 +REF: i ch * i +HYP: i ch I i +Eval: I + +Speaker sentences 19: cv_jpn_000819 #utts: 1 +id: (cv_jpn_000819-cv_jpn_000819) +Scores: (#C #S #D #I) 3 0 0 0 +REF: h a i +HYP: h a i +Eval: + +Speaker sentences 20: cv_jpn_000820 #utts: 1 +id: (cv_jpn_000820-cv_jpn_000820) +Scores: (#C #S #D #I) 1 1 0 2 +REF: * n * I +HYP: O n O E +Eval: I I S + +Speaker sentences 21: cv_jpn_000821 #utts: 1 +id: (cv_jpn_000821-cv_jpn_000821) +Scores: (#C #S #D #I) 2 1 0 0 +REF: R e i +HYP: D e i +Eval: S + +Speaker sentences 22: cv_jpn_000822 #utts: 1 +id: (cv_jpn_000822-cv_jpn_000822) +Scores: (#C #S #D #I) 2 2 0 1 +REF: * R o k U +HYP: A T o k I +Eval: I S S + +Speaker sentences 23: cv_jpn_000823 #utts: 1 +id: (cv_jpn_000823-cv_jpn_000823) +Scores: (#C #S #D #I) 55 4 5 1 +REF: m i r u t o I u k o t O t o *** h a t a r a k u t o i u k O T o t o g a F U k a b u n R i t e k i D E n a k e r e b a n a r a n a i +HYP: m i r u t o Y u k o t A t o PAU h a t a r a k u t o i u k * * o t o g a * SH k a b u n D i t e k i * * n a k e r e b a n a r a n a i +Eval: S S I D D D S S D D + +Speaker sentences 24: cv_jpn_000824 #utts: 1 +id: (cv_jpn_000824-cv_jpn_000824) +Scores: (#C #S #D #I) 50 3 1 0 +REF: w a r e w a r e o t a m a sh i i n o S O K o k a r a u g O k a s u m o n o d e n a k e r e b a n a r a n a i +HYP: w a r e w a r e o t a m a sh i i n o * Z U o k a r a u g U k a s u m o n o d e n a k e r e b a n a r a n a i +Eval: D S S S + +Speaker sentences 25: cv_jpn_000825 #utts: 1 +id: (cv_jpn_000825-cv_jpn_000825) +Scores: (#C #S #D #I) 67 4 2 1 +REF: Z e CL t a i b e n sh o o h o o T e k i n a r u g a Y u e n i i d e Y a t e k i ch o cl k a n t e k i k e e k i * g a F u k u m a r e r u n o d e a r u +HYP: S e ** t a i b e n sh o o h o o D e k i n a r u g a I u e n i i d e * a t e k i ch o cl k a n t e k i k e e k i Y g a CL u k u m a r e r u n o d e a r u +Eval: S D S S D I S + +Speaker sentences 26: cv_jpn_000826 #utts: 1 +id: (cv_jpn_000826-cv_jpn_000826) +Scores: (#C #S #D #I) 69 3 3 0 +REF: D o k o m a d e m o t a t o i ch i t o n O s o o g o H i T e e t e k i n a z e cl t a i m u j u n t e k i j i k O D o o i ts u n o s e k a i n i sh i t e +HYP: T o k o m a d e m o t a t o i ch i t o n A s o o g o SH i * e e t e k i n a z e cl t a i m u j u n t e k i j i k * * o o i ts u n o s e k a i n i sh i t e +Eval: S S S D D D + +Speaker sentences 27: cv_jpn_000827 #utts: 1 +id: (cv_jpn_000827-cv_jpn_000827) +Scores: (#C #S #D #I) 52 1 2 0 +REF: sh i k a r u n i n i n g e n t o k a n ky o o t o N o k a n k e e w a m o t o k o o I n o k a n k E e d e a r i +HYP: sh i k a r u n i n i n g e n t o k a n ky o o t o R o k a n k e e w a m o t o k o o * n o k a n k * e d e a r i +Eval: S D D + +Speaker sentences 28: cv_jpn_000828 #utts: 1 +id: (cv_jpn_000828-cv_jpn_000828) +Scores: (#C #S #D #I) 31 1 0 3 +REF: * i s a n n i k o n o k o t o b a n o i m i * o o sh i * E m a sh i t a +HYP: I i s a n n i k o n o k o t o b a n o i m i Y o o sh i Y A m a sh i t a +Eval: I I I S + +Speaker sentences 29: cv_jpn_000829 #utts: 1 +id: (cv_jpn_000829-cv_jpn_000829) +Scores: (#C #S #D #I) 19 1 0 0 +REF: k e e k i g a n a n a ts U a r i m a s u +HYP: k e e k i g a n a n a ts S a r i m a s u +Eval: S + +Speaker sentences 30: cv_jpn_000830 #utts: 1 +id: (cv_jpn_000830-cv_jpn_000830) +Scores: (#C #S #D #I) 21 2 0 0 +REF: k o ch i r a W a k o b A y a sh i s a n d e s u +HYP: k o ch i r a B a k o b I y a sh i s a n d e s u +Eval: S S + +Speaker sentences 31: cv_jpn_000831 #utts: 1 +id: (cv_jpn_000831-cv_jpn_000831) +Scores: (#C #S #D #I) 7 1 0 0 +REF: m o sh i m O sh i +HYP: m o sh i m A sh i +Eval: S + +Speaker sentences 32: cv_jpn_000832 #utts: 1 +id: (cv_jpn_000832-cv_jpn_000832) +Scores: (#C #S #D #I) 30 1 1 0 +REF: k o k o w a O o k i k u t e n i g I y a k a n a m a ch i d e s u +HYP: k o k o w a * o k i k u t e n i g U y a k a n a m a ch i d e s u +Eval: D S + +Speaker sentences 33: cv_jpn_000833 #utts: 1 +id: (cv_jpn_000833-cv_jpn_000833) +Scores: (#C #S #D #I) 27 0 1 2 +REF: s o n o U ch i k a i * a k u s a r e r u k a r a i s o g e *** +HYP: s o n o * ch i k a i Y a k u s a r e r u k a r a i s o g e PAU +Eval: D I I + +Speaker sentences 34: cv_jpn_000834 #utts: 1 +id: (cv_jpn_000834-cv_jpn_000834) +Scores: (#C #S #D #I) 24 1 1 3 +REF: a m a s a g a * * O s a e * r a r e t e t e ch o o d o I i +HYP: a m a s a g a B K U s a e I r a r e t e t e ch o o d o * i +Eval: I I S I D + +Speaker sentences 35: cv_jpn_000835 #utts: 1 +id: (cv_jpn_000835-cv_jpn_000835) +Scores: (#C #S #D #I) 19 0 1 2 +REF: h o k e n sh i ts u * n o d o A o * a k e t a +HYP: h o k e n sh i ts u E n o d o * o A a k e t a +Eval: I D I + +Speaker sentences 36: cv_jpn_000836 #utts: 1 +id: (cv_jpn_000836-cv_jpn_000836) +Scores: (#C #S #D #I) 22 1 0 0 +REF: m U d a n i o w a cl t e m o k i n i sh i n a i +HYP: m O d a n i o w a cl t e m o k i n i sh i n a i +Eval: S + +Speaker sentences 37: cv_jpn_000837 #utts: 1 +id: (cv_jpn_000837-cv_jpn_000837) +Scores: (#C #S #D #I) 9 0 0 1 +REF: a r i g a ** t a y a +HYP: a r i g a CL t a y a +Eval: I + +Speaker sentences 38: cv_jpn_000838 #utts: 1 +id: (cv_jpn_000838-cv_jpn_000838) +Scores: (#C #S #D #I) 34 4 0 0 +REF: i D o o g a r A k u d a t o j i k a n O w A s u r e t e t a n o sh i m e r u +HYP: i T o o g a r O k u d a t o j i k a n U w O s u r e t e t a n o sh i m e r u +Eval: S S S S + +Speaker sentences 39: cv_jpn_000839 #utts: 1 +id: (cv_jpn_000839-cv_jpn_000839) +Scores: (#C #S #D #I) 51 2 1 0 +REF: k a G a k u w a g i J u ts u k a s a r e r u n i O o j i t e j o o sh i k i n o u ch i n i h a i cl t e y u k u +HYP: k a K a k u w a g i Z u ts u k a s a r e r u n i * o j i t e j o o sh i k i n o u ch i n i h a i cl t e y u k u +Eval: S S D + +Speaker sentences 40: cv_jpn_000840 #utts: 1 +id: (cv_jpn_000840-cv_jpn_000840) +Scores: (#C #S #D #I) 84 4 2 1 +REF: sh i k a sh i t o k i g a k a K o n i h a i r u k o t o s o n o k o t o g a PAU m i r a I o U m u k o t o d e a r i PAU a r a t a n a r u * SH u t a i g a d e t e k u r u k o t o d e a r u +HYP: sh i k a sh i t o k i g a k a B o n i h a i r u k o t o s o n o k o t o g a *** m i r a Y o O m u k o t o d e a r i *** a r a t a n a r u I CL u t a i g a d e t e k u r u k o t o d e a r u +Eval: S D S S D I S + +Speaker sentences 41: cv_jpn_000841 #utts: 1 +id: (cv_jpn_000841-cv_jpn_000841) +Scores: (#C #S #D #I) 37 2 0 0 +REF: t e r e b i o k a i k a E t e pau t e r e b i o m i r u j i k a n G a f u e t a +HYP: t e r e b i o k a i k a I t e pau t e r e b i o m i r u j i k a n N a f u e t a +Eval: S S + +Speaker sentences 42: cv_jpn_000842 #utts: 1 +id: (cv_jpn_000842-cv_jpn_000842) +Scores: (#C #S #D #I) 34 2 1 1 +REF: k a k a r * u sh U t a i n o m i PAU i ts u m a d e m o i k I r u n o d e a r u +HYP: k a k a r E u sh I t a i n o m i *** i ts u m a d e m o i k E r u n o d e a r u +Eval: I S D S + +Speaker sentences 43: cv_jpn_000843 #utts: 1 +id: (cv_jpn_000843-cv_jpn_000843) +Scores: (#C #S #D #I) 39 1 0 2 +REF: n i n k i R a a m e n * y a n i n a r a n d a r a n i j i k a n m a * ch i d a cl t a +HYP: n i n k i D a a m e n I y a n i n a r a n d a r a n i j i k a n m a O ch i d a cl t a +Eval: S I I + +Speaker sentences 44: cv_jpn_000844 #utts: 1 +id: (cv_jpn_000844-cv_jpn_000844) +Scores: (#C #S #D #I) 73 6 3 3 +REF: s o r e * o m o ch i i r u n i n g e n n o I y o k u n I i Z o n * SH i PAU s o sh i t e k o r e w a k a r e n o m o cl t e i r u k a ch I n o SH a k u d o * n I i Z o n s u r u +HYP: s o r e O o m o ch i i r u n i n g e n n o * y o k u n * i T o n S E i *** s o sh i t e k o r e w a k a r e n o m o cl t e i r u k a ch E n o S a k u d o N n PAU i D o n s u r u +Eval: I D D S I S D S S I S S + +Speaker sentences 45: cv_jpn_000845 #utts: 1 +id: (cv_jpn_000845-cv_jpn_000845) +Scores: (#C #S #D #I) 31 2 3 0 +REF: m a w a r I W a m i n n a PAU k a n g a E r u k o t O o y a m e t e i t a +HYP: m a w a r Y O a m i n n a *** k a n g a * r u k o t * o y a m e t e i t a +Eval: S S D D D + +Speaker sentences 46: cv_jpn_000846 #utts: 1 +id: (cv_jpn_000846-cv_jpn_000846) +Scores: (#C #S #D #I) 91 6 4 3 +REF: k o o i t e k i CH o * CL k a n t e k i n i s e k a I o m i r u t o I u k o t o w a PAU GY a k u n i k o o i t e k i ch * o * cl k a n t e k i n i s e k a I o k e e s e e s u r u k o t O o F U k u m u n o d e a r u +HYP: k o o i t e k i J o K U k a n t e k i n i s e k a Y o m i r u t o Y u k o t o w a *** J a k u n i k o o i t e k i ch A o K cl k a n t e k i n i s e k a Y o k e e s e e s u r u k o t * o * * k u m u n o d e a r u +Eval: S I S S S D S I I S D D D + +Speaker sentences 47: cv_jpn_000847 #utts: 1 +id: (cv_jpn_000847-cv_jpn_000847) +Scores: (#C #S #D #I) 50 6 1 3 +REF: * SH I n p a i K a k e s a s e m a i t o s u r u k i z u k a i * g a PAU y o k e I n i SH I n ** p a i s a s e t e sh i m a u +HYP: S J E n p a i T a k e s a s e m a i t o s u r u k i z u k a i Y g a *** y o k e E n i S E n CL p a i s a s e t e sh i m a u +Eval: I S S S I D S S S I + +Speaker sentences 48: cv_jpn_000848 #utts: 1 +id: (cv_jpn_000848-cv_jpn_000848) +Scores: (#C #S #D #I) 32 1 3 0 +REF: k o n o m i ch i w a t o t e m o s e m a i n o d E PAU a B U n a i d e s u +HYP: k o n o m i ch i w a t o t e m o s e m a i n o d * *** a * M n a i d e s u +Eval: D D D S + +Speaker sentences 49: cv_jpn_000849 #utts: 1 +id: (cv_jpn_000849-cv_jpn_000849) +Scores: (#C #S #D #I) 7 2 4 1 +REF: O B o e g A W a r U i n * E +HYP: * W o e g * * a r * i n I U +Eval: D S D D D I S + +Speaker sentences 50: cv_jpn_000850 #utts: 1 +id: (cv_jpn_000850-cv_jpn_000850) +Scores: (#C #S #D #I) 25 5 3 0 +REF: t o i R E w a r O o k a N O H i D a r i g a W a n i a r i m a s u +HYP: t o i Y O w a r * o k a * M A i T a r i g a * a n i a r i m a s u +Eval: S S D D S S S D + +Speaker sentences 51: cv_jpn_000851 #utts: 1 +id: (cv_jpn_000851-cv_jpn_000851) +Scores: (#C #S #D #I) 32 2 1 0 +REF: t a N a k a s a N n o h i d a r i n i k i m U r a s a n g a i m a s u +HYP: t a D a k a s a * n o h i d a r i n i k i m E r a s a n g a i m a s u +Eval: S D S + +Speaker sentences 52: cv_jpn_000852 #utts: 1 +id: (cv_jpn_000852-cv_jpn_000852) +Scores: (#C #S #D #I) 21 2 2 0 +REF: m a cl k u r O n a t a m a G o CL t e s u g o i N e +HYP: m a cl k u r U n a t a m a B o ** t e s u g o i * e +Eval: S S D D + +Speaker sentences 53: cv_jpn_000853 #utts: 1 +id: (cv_jpn_000853-cv_jpn_000853) +Scores: (#C #S #D #I) 28 4 1 1 +REF: sh * O HY o o m i t a i n a d o k u sh O k a N s o o b u N o k a i t a +HYP: sh A R SH o o m i t a i n a d o k u sh U k a * s o o b u M o k a i t a +Eval: I S S S D S + +Speaker sentences 54: cv_jpn_000854 #utts: 1 +id: (cv_jpn_000854-cv_jpn_000854) +Scores: (#C #S #D #I) 60 12 5 3 +REF: g e n j i TS U N o s e k a i w a *** t a * N o i ch i t o sh i t e k e cl t E e s E r a * R e T A k a t a ch i o M O cl t A s E k a i D E n a k e r e B A n a r a n a i +HYP: g e n j i T E M o s e k a i w a PAU t a M O o i ch i t o sh i t e k e cl t * e s U r a I D e * * k a t a ch i o * A cl t O s U k a i * U n a k e r e M O n a r a n a i +Eval: S S S I I S D S I S D D D S S S D S S S + +Speaker sentences 55: cv_jpn_000855 #utts: 1 +id: (cv_jpn_000855-cv_jpn_000855) +Scores: (#C #S #D #I) 34 4 5 3 +REF: sh o o h i n k e n s a k u * g a W A k a r I y a s u i t * o *** k a U k I N i n a r u N O N i +HYP: sh o o h i n k e n s a k u K g a * O k a r * y a s u i t O o PAU k a O k * * i n a r u * M A i +Eval: I D S D I I S D D D S S + +Speaker sentences 56: cv_jpn_000856 #utts: 1 +id: (cv_jpn_000856-cv_jpn_000856) +Scores: (#C #S #D #I) 32 7 1 1 +REF: CH I sh * i k i w a R e k i sh i t e K I k a t e e d e n a k e r E B A n a r a n a i +HYP: TS E sh I i k i w a N e k i sh i t e * CL k a t e e d e n a k e r U M O n a r a n a i +Eval: S S I S D S S S S + +Speaker sentences 57: cv_jpn_000857 #utts: 1 +id: (cv_jpn_000857-cv_jpn_000857) +Scores: (#C #S #D #I) 39 3 3 2 +REF: m o n o g o t O n o * j U n B a n O k a e r u d a k e d e *** u m a k u i k u k o t o m O a r U +HYP: m o n o g o t A n o N j I n P a n * k a e r u d a k e d e PAU u m a k u i k u k o t o m * a r * +Eval: S I S S D I D D + +Speaker sentences 58: cv_jpn_000858 #utts: 1 +id: (cv_jpn_000858-cv_jpn_000858) +Scores: (#C #S #D #I) 32 1 0 1 +REF: k o n o k i S e ts u w a k a ts u o n o s a sh i m i g a z e cl p * i n +HYP: k o n o k i E e ts u w a k a ts u o n o s a sh i m i g a z e cl p E i n +Eval: S I + +Speaker sentences 59: cv_jpn_000859 #utts: 1 +id: (cv_jpn_000859-cv_jpn_000859) +Scores: (#C #S #D #I) 38 4 0 5 +REF: k a k e * n i sh i cl p a i sh i t e m * *** O o ch i * ts u i t e s * O N sh i ts u o u k e i R e r u +HYP: k a k e N n i sh i cl p a i sh i t e m A PAU M o ch i T ts u i t e s A M A sh i ts u o u k e i D e r u +Eval: I I I S I I S S S + +Speaker sentences 60: cv_jpn_000860 #utts: 1 +id: (cv_jpn_000860-cv_jpn_000860) +Scores: (#C #S #D #I) 42 2 0 2 +REF: s o r e y u e n i *** t e ts u g a k u g a z e n t a i n o g a k U d e * a r u t o s u r e B a +HYP: s o r e y u e n i PAU t e ts u g a k u g a z e n t a i n o g a k O d e Y a r u t o s u r e W a +Eval: I S I S + +Speaker sentences 61: cv_jpn_000861 #utts: 1 +id: (cv_jpn_000861-cv_jpn_000861) +Scores: (#C #S #D #I) 34 1 1 1 +REF: CH i i s a n a * y a o y a d a g a y a s u k u t e h a n j O o sh i t e r u +HYP: K i i s a n a I y a o y a d a g a y a s u k u t e h a n j * o sh i t e r u +Eval: S I D + +Speaker sentences 62: cv_jpn_000862 #utts: 1 +id: (cv_jpn_000862-cv_jpn_000862) +Scores: (#C #S #D #I) 53 6 2 1 +REF: i n * f u r a g a k i n O o F u z e n n i o ch I i cl t e pau k o k u g a i E d a CL sh U ts u s u r u H i t O m o d e t e k i t a +HYP: i n I f u r a g a k i n * o H u z e n n i o ch J i cl t e pau k o k u g a i A d a ** sh I ts u s u r u SH i t A m o d e t e k i t a +Eval: I D S S S D S S S + +Speaker sentences 63: cv_jpn_000863 #utts: 1 +id: (cv_jpn_000863-cv_jpn_000863) +Scores: (#C #S #D #I) 53 16 6 2 +REF: ts * u g i n i k a G a k u w a s o n z a I o SH u j u n o RY O o i k i n I W A k a cl t e s O r e z O r E n O RY O o I k i n I TS U i t E K e n * KY U u s u r u +HYP: ts S u g i n i k a W a k u w a s o n z a Y o J u j u n o ** D o i k i n * * O k a cl t e s U r e z U r A n * D E o * k i n * Z E i t I T e n I K I u s u r u +Eval: I S S S D S D D S S S S D S S D D S S S S I S S + +Speaker sentences 64: cv_jpn_000864 #utts: 1 +id: (cv_jpn_000864-cv_jpn_000864) +Scores: (#C #S #D #I) 59 6 7 2 +REF: s o r e d e w a t o k I t o * i U m O N O n O s E e r i ts u sh i Y O o w a n a k u pau sh * U n k a n t o i U m o n o m o n a k u n a r u n o d E a r U +HYP: s o r e d e w a t o k * t o Y i * m A R A n A s * e r i ts u sh i * * o w a n a k u pau sh I R n k a n t o i * m o n o m o n a k u n a r u n o d * a r I +Eval: D I D S S S S D D D I S D D S + +Speaker sentences 65: cv_jpn_000865 #utts: 1 +id: (cv_jpn_000865-cv_jpn_000865) +Scores: (#C #S #D #I) 46 0 4 1 +REF: * a k a i b u r a n k o PAU k o n k u r I i t o s E e n o s u b e r i d a i PAU k a w a i t a s u n a b a +HYP: H a k a i b u r a n k o *** k o n k u r * i t o s * e n o s u b e r i d a i *** k a w a i t a s u n a b a +Eval: I D D D D + +Speaker sentences 66: cv_jpn_000866 #utts: 1 +id: (cv_jpn_000866-cv_jpn_000866) +Scores: (#C #S #D #I) 67 7 8 1 +REF: * sh i k a sh i s o r E w a d o k o m a d e m O k o k o k a r a d e t e K o k o e k a E r i k u r u s e e SH i TS U o m o CL t A M o N o d e N a k E R e B a n a r a n a i +HYP: S sh i k a sh i s o r O w a d o k o m a d e m N k o k o k a r a d e t e PAU o k o e k a I r i k u r u s e e ** i ** T o m o ** t * * o M o d e * a k * * e W a n a r a n a i +Eval: I S S S S D D S D D D S D D D S + +Speaker sentences 67: cv_jpn_000867 #utts: 1 +id: (cv_jpn_000867-cv_jpn_000867) +Scores: (#C #S #D #I) 48 2 1 2 +REF: a r i t o a r a Y U r u d e m A o m a k i ch i r a sh i t e m i n n * a k a r a * u r a m i o k a cl t e r u +HYP: a r i t o a r a * I r u d e m O o m a k i ch i r a sh i t e m i n n E a k a r a O u r a m i o k a cl t e r u +Eval: D S S I I + +Speaker sentences 68: cv_jpn_000868 #utts: 1 +id: (cv_jpn_000868-cv_jpn_000868) +Scores: (#C #S #D #I) 36 0 2 0 +REF: k o n o t e e d o PAU s a w a g i n i n a r u k o t o m o n a i n o d a r O o +HYP: k o n o t e e d o *** s a w a g i n i n a r u k o t o m o n a i n o d a r * o +Eval: D D + +Speaker sentences 69: cv_jpn_000869 #utts: 1 +id: (cv_jpn_000869-cv_jpn_000869) +Scores: (#C #S #D #I) 17 3 0 2 +REF: k o n o n e D a n d e *** u r E CH a u k * a a +HYP: k o n o n e R a n d e PAU u r I T a u k A a a +Eval: S I S S I + +Speaker sentences 70: cv_jpn_000870 #utts: 1 +id: (cv_jpn_000870-cv_jpn_000870) +Scores: (#C #S #D #I) 31 0 3 2 +REF: h i n o k a g * e n n i ch U u i sh i n a i t o *** s u g u N I k o g e r u +HYP: h i n o k a g A e n n i ch * u i sh i n a i t o PAU s u g u * * k o g e r u +Eval: I D I D D + +Speaker sentences 71: cv_jpn_000871 #utts: 1 +id: (cv_jpn_000871-cv_jpn_000871) +Scores: (#C #S #D #I) 67 4 5 5 +REF: e n B a n n o u * e * n i p o ts u r i t o CH I i s a n A a n a g A H I R a i t a s a i sh * o w a ts u m a y o o j i t e e d o n o CH i i s a n a *** a n a d a cl t * a +HYP: e n M a n n o u W e N n i p o ts u r i t o TS U i s a n * a n a g * * * * a i t a s a i sh I o w a ts u m a y o o j i t e e d o n o TS i i s a n a PAU a n a d a cl t A a +Eval: S I I S S D D D D D I S I I + +Speaker sentences 72: cv_jpn_000872 #utts: 1 +id: (cv_jpn_000872-cv_jpn_000872) +Scores: (#C #S #D #I) 80 3 2 2 +REF: s o r e w a W a r e w a r e o i k a sh i n a g a r a *** w a r e w a r e o D o r e e k a s u r u n o d e a r u pau w a r e w a r e n o t a m a * sh i I o k o r o s u n o d e a R U +HYP: s o r e w a M a r e w a r e o i k a sh i n a g a r a PAU w a r e w a r e o T o r e e k a s u r u n o d e a r u pau w a r e w a r e n o t a m a S sh i Y o k o r o s u n o d e a * * +Eval: S I S I S D D + +Speaker sentences 73: cv_jpn_000873 #utts: 1 +id: (cv_jpn_000873-cv_jpn_000873) +Scores: (#C #S #D #I) 88 9 6 2 +REF: r e k I sh i t e k i n i a t a E r a r E t a m o n o w a PAU Z e cl t A I m u j u n t e k i j i K o D o o i ts U t e k i g * e n z * a i n I o i t e s E k a i sh i T e k i n i a t a e r a r E t a m o n O t o sh i t e +HYP: r e k U sh i t e k i n i a t a * r a r * t a m o n o w a *** D e cl t * E m u j u n t e k i j i G o T o o i ts I t e k i g I e n z D a i n O o i t e s U k a i sh i * e k i n i a t a e r a r * t a m o n U t o sh i t e +Eval: S D D D S D S S S S I I S S D D S + +Speaker sentences 74: cv_jpn_000874 #utts: 1 +id: (cv_jpn_000874-cv_jpn_000874) +Scores: (#C #S #D #I) 50 5 2 2 +REF: m u * j u n t e k i * j i K o d o o i TS U t o sh i t e pau i ts u m o k o n o s e k a i n i ch o o e TS U t e k i d e a R U +HYP: m u O j u n t e k i E j i G o d o o i CH I t o sh i t e pau i ts u m o k o n o s e k a i n i ch o o e SH I t e k i d e a * * +Eval: I I S S S S S D D + +Speaker sentences 75: cv_jpn_000875 #utts: 1 +id: (cv_jpn_000875-cv_jpn_000875) +Scores: (#C #S #D #I) 67 10 5 1 +REF: y u E n i Z e cl T A I m u j u n t e k i j i K o d o o i TS u t o sh i t e g e n Z a I k a r a g e n z a I e t O u G o k I i k u s * e k a I n o g e n z a i n I o i t e +HYP: y u * n i D e cl P E E m u j u n t e k i j i G o d o o i SH u t o sh i t e g e n * a E k a r a g e n z a E e t * u W o k * i k u s U e k a E n o g e n z a i n * o i t e +Eval: D S S S S S S D S S D S D I S D + +Speaker sentences 76: cv_jpn_000876 #utts: 1 +id: (cv_jpn_000876-cv_jpn_000876) +Scores: (#C #S #D #I) 23 1 6 2 +REF: * a r e PAU B o t a n o sh i t E M o * d a CL sh U TS u d e k i n a i +HYP: H a r e *** W o t a n o sh i t * * o N d a ** sh * ** u d e k i n a i +Eval: I D S D D I D D D + +Speaker sentences 77: cv_jpn_000877 #utts: 1 +id: (cv_jpn_000877-cv_jpn_000877) +Scores: (#C #S #D #I) 47 0 3 0 +REF: sh i k a sh i w a t a sh I W a s o k o n i s e k a i n o j i k o d o o i ts u O o k u n o d e w a n a i +HYP: sh i k a sh i w a t a sh * * a s o k o n i s e k a i n o j i k o d o o i ts u * o k u n o d e w a n a i +Eval: D D D + +Speaker sentences 78: cv_jpn_000878 #utts: 1 +id: (cv_jpn_000878-cv_jpn_000878) +Scores: (#C #S #D #I) 24 3 0 1 +REF: n e M u T a k u n a r u n o g a *** h a y a k u N a cl t a +HYP: n e B u K a k u n a r u n o g a PAU h a y a k u M a cl t a +Eval: S S I S + +Speaker sentences 79: cv_jpn_000879 #utts: 1 +id: (cv_jpn_000879-cv_jpn_000879) +Scores: (#C #S #D #I) 86 1 6 2 +REF: w a t a sh i w a N i n g e n n * o R e k i sh i t e k i k e e s e e n o t a ch i b a k a r a *** g E e j u ts u o m i r u n o d e a cl t e PAU K o o sh a k a r a Z e n sh a o m i r u n o d e W a n a i +HYP: w a t a sh i w a * i n g e n n O o D e k i sh i t e k i k e e s e e n o t a ch i b a k a r a PAU g * e j u ts u o m i r u n o d e a cl t e *** * o o sh a k a r a * e n sh a o m i r u n o d e * a n a i +Eval: D I S I D D D D D + +Speaker sentences 80: cv_jpn_000880 #utts: 1 +id: (cv_jpn_000880-cv_jpn_000880) +Scores: (#C #S #D #I) 28 1 0 2 +REF: a o i t o m a t o sh i k a n a k u t e *** k a u k a M a * y o u +HYP: a o i t o m a t o sh i k a n a k u t e PAU k a u k a B a I y o u +Eval: I S I + +Speaker sentences 81: cv_jpn_000881 #utts: 1 +id: (cv_jpn_000881-cv_jpn_000881) +Scores: (#C #S #D #I) 23 10 0 2 +REF: SH I n k * * I J I GY o o n i o o k i n a k i t a I o y O s E t e I r u +HYP: S E n k E Z U E G Y o o n i o o k i n a k i t a Y o y A s U t e E r u +Eval: S S I I S S S S S S S S + +Speaker sentences 82: cv_jpn_000882 #utts: 1 +id: (cv_jpn_000882-cv_jpn_000882) +Scores: (#C #S #D #I) 36 5 0 1 +REF: n a n i k a sh i r a n o i n s e n t I b u G a n a i t o k i b * i SH I I n o d e w a +HYP: n a n i k a sh i r a n o i n s e n t E b u W a n a i t o k i b U i S U E n o d e w a +Eval: S S I S S S + +Speaker sentences 83: cv_jpn_000883 #utts: 1 +id: (cv_jpn_000883-cv_jpn_000883) +Scores: (#C #S #D #I) 28 7 0 1 +REF: j i k A n * S e E g e n n o i b e n t o d e s u t o r E S U t a m a r U +HYP: j i k O n O SH e K g e n n o i b e n t o d e s u t o r U SH I t a m a r I +Eval: S I S S S S S S + +Speaker sentences 84: cv_jpn_000884 #utts: 1 +id: (cv_jpn_000884-cv_jpn_000884) +Scores: (#C #S #D #I) 26 1 2 0 +REF: m A W a r i n o H i t o w a b o o z e n t o sh i t e i t a +HYP: m * * a r i n o SH i t o w a b o o z e n t o sh i t e i t a +Eval: D D S + +Speaker sentences 85: cv_jpn_000885 #utts: 1 +id: (cv_jpn_000885-cv_jpn_000885) +Scores: (#C #S #D #I) 33 1 2 1 +REF: s o n n a n a i y o o n o m E e r u G a PAU n a n k e n m o k * i t e i t a +HYP: s o n n a n a i y o o n o m * e r u W a *** n a n k e n m o k U i t e i t a +Eval: D S D I + +Speaker sentences 86: cv_jpn_000886 #utts: 1 +id: (cv_jpn_000886-cv_jpn_000886) +Scores: (#C #S #D #I) 20 1 1 1 +REF: N i j i K a i d e d e e s * u i sh i t e i t a +HYP: * i j i G a i d e d e e s F u i sh i t e i t a +Eval: D S I + +Speaker sentences 87: cv_jpn_000887 #utts: 1 +id: (cv_jpn_000887-cv_jpn_000887) +Scores: (#C #S #D #I) 72 8 10 0 +REF: t o k i d o k i PAU J i B u n n O K o k o r o G a w a k a r a n a k u n A r u t o K i g a a r u d a k a r a b o k u W a k a a T e N o H I k i PAU n O o t o n i k a K i H a j I m e r u +HYP: t o k i d o k i *** CH i U u n n * * o k o r o W a w a k a r a n a k u n O r u t o CH i g a a r u d a k a r a b o k u * a k a a N e * o * * k i *** n * o t o n i k a J i * a j E m e r u +Eval: D S S D D S S S D S D D D D D S D S + +Speaker sentences 88: cv_jpn_000888 #utts: 1 +id: (cv_jpn_000888-cv_jpn_000888) +Scores: (#C #S #D #I) 16 1 0 0 +REF: m o o n i g e t e ch a D a m e d a +HYP: m o o n i g e t e ch a T a m e d a +Eval: S + +Speaker sentences 89: cv_jpn_000889 #utts: 1 +id: (cv_jpn_000889-cv_jpn_000889) +Scores: (#C #S #D #I) 24 2 2 0 +REF: k a r e w a PAU B o o CL t o t a CH i ts u k u sh i t e i t a +HYP: k a r e w a *** P o o ** t o t a J i ts u k u sh i t e i t a +Eval: D S D S + +Speaker sentences 90: cv_jpn_000890 #utts: 1 +id: (cv_jpn_000890-cv_jpn_000890) +Scores: (#C #S #D #I) 22 6 0 0 +REF: d a r E n i m O M e E w a k U w a k a k e t a k U n a i +HYP: d a r U n i m U N e I w a k O w a k a k e t a k A n a i +Eval: S S S S S S + +Speaker sentences 91: cv_jpn_000891 #utts: 1 +id: (cv_jpn_000891-cv_jpn_000891) +Scores: (#C #S #D #I) 29 2 2 2 +REF: M a s a k a PAU t o o m o CL t e * d o a n * o t o cl t e o n i g I cl t a +HYP: P a s a k a *** t o o m o ** t e U d o a n O o t o cl t e o n i g E cl t a +Eval: S D D I I S + +Speaker sentences 92: cv_jpn_000892 #utts: 1 +id: (cv_jpn_000892-cv_jpn_000892) +Scores: (#C #S #D #I) 6 2 0 1 +REF: ** S U i m a s e n +HYP: SH T E i m a s e n +Eval: I S S + +Speaker sentences 93: cv_jpn_000893 #utts: 1 +id: (cv_jpn_000893-cv_jpn_000893) +Scores: (#C #S #D #I) 65 1 3 0 +REF: k a y o U n i sh i t e sh i CL t e I r u t o t o m o n i sh i cl t e i n a i t o k o r o k a r a t a n ky U u w a h a j i m a r u n o d e a r u +HYP: k a y o O n i sh i t e sh i ** t e * r u t o t o m o n i sh i cl t e i n a i t o k o r o k a r a t a n ky * u w a h a j i m a r u n o d e a r u +Eval: S D D D + +Speaker sentences 94: cv_jpn_000894 #utts: 1 +id: (cv_jpn_000894-cv_jpn_000894) +Scores: (#C #S #D #I) 16 1 0 0 +REF: a i s a ts u w a d a i j i d a Y o +HYP: a i s a ts u w a d a i j i d a I o +Eval: S + +Speaker sentences 95: cv_jpn_000895 #utts: 1 +id: (cv_jpn_000895-cv_jpn_000895) +Scores: (#C #S #D #I) 53 3 2 0 +REF: t o j i t a m o n O o i k a n i h i r o g e t e m o h i r a i t a m O n o n i W a n a r a N U t o i cl t e i r u g a +HYP: t o j i t a m o n * o i k a n i h i r o g e t e m o h i r a i t a m U n o n i * a n a r a R O t o i cl t e i r u g a +Eval: D S D S S + +Speaker sentences 96: cv_jpn_000896 #utts: 1 +id: (cv_jpn_000896-cv_jpn_000896) +Scores: (#C #S #D #I) 23 2 2 1 +REF: t a m E sh i n I i k u ts u k a ts u k u cl t e m i Y o * O +HYP: t a m U sh i n * i k u ts u k a ts u k u cl t e m i * o W A +Eval: S D D I S + +Speaker sentences 97: cv_jpn_000897 #utts: 1 +id: (cv_jpn_000897-cv_jpn_000897) +Scores: (#C #S #D #I) 22 3 1 2 +REF: * ** z U i b u n a k o G i n a sh o o b a i d a Y o n A a +HYP: U TS z E i b u n a k o K i n a sh o o b a i d a I o n * a +Eval: I I S S S D + +Speaker sentences 98: cv_jpn_000898 #utts: 1 +id: (cv_jpn_000898-cv_jpn_000898) +Scores: (#C #S #D #I) 3 1 0 3 +REF: w * a ** * CH i +HYP: w H a CL T E i +Eval: I I I S + +Speaker sentences 99: cv_jpn_000899 #utts: 1 +id: (cv_jpn_000899-cv_jpn_000899) +Scores: (#C #S #D #I) 2 1 0 2 +REF: * i * CH i +HYP: K i T E i +Eval: I I S + +Speaker sentences 100: cv_jpn_000900 #utts: 1 +id: (cv_jpn_000900-cv_jpn_000900) +Scores: (#C #S #D #I) 2 0 0 0 +REF: g o +HYP: g o +Eval: + +Speaker sentences 101: cv_jpn_000901 #utts: 1 +id: (cv_jpn_000901-cv_jpn_000901) +Scores: (#C #S #D #I) 3 1 0 3 +REF: * * sh * i CH i +HYP: H A sh I i T i +Eval: I I I S + +Speaker sentences 102: cv_jpn_000902 #utts: 1 +id: (cv_jpn_000902-cv_jpn_000902) +Scores: (#C #S #D #I) 2 0 1 1 +REF: I i e * +HYP: * i e A +Eval: D I + +Speaker sentences 103: cv_jpn_000903 #utts: 1 +id: (cv_jpn_000903-cv_jpn_000903) +Scores: (#C #S #D #I) 3 1 0 1 +REF: W a ** ch i +HYP: H a CL ch i +Eval: S I + +Speaker sentences 104: cv_jpn_000904 #utts: 1 +id: (cv_jpn_000904-cv_jpn_000904) +Scores: (#C #S #D #I) 1 1 1 1 +REF: * R e I +HYP: H N e * +Eval: I S D + +Speaker sentences 105: cv_jpn_000905 #utts: 1 +id: (cv_jpn_000905-cv_jpn_000905) +Scores: (#C #S #D #I) 2 0 0 3 +REF: * sh * * i +HYP: A sh I I i +Eval: I I I + +Speaker sentences 106: cv_jpn_000906 #utts: 1 +id: (cv_jpn_000906-cv_jpn_000906) +Scores: (#C #S #D #I) 2 0 0 1 +REF: k u * +HYP: k u O +Eval: I + +Speaker sentences 107: cv_jpn_000907 #utts: 1 +id: (cv_jpn_000907-cv_jpn_000907) +Scores: (#C #S #D #I) 2 1 0 1 +REF: * I ch i +HYP: K E ch i +Eval: I S + +Speaker sentences 108: cv_jpn_000908 #utts: 1 +id: (cv_jpn_000908-cv_jpn_000908) +Scores: (#C #S #D #I) 55 1 0 4 +REF: k a G a k u g a *** a k i r a k a n i s u r u *** ky * a cl k a n t e k i sh i n * r i n i sh i t a g a u k o t o n i y o cl t e +HYP: k a K a k u g a PAU a k i r a k a n i s u r u PAU ky Y a cl k a n t e k i sh i n D r i n i sh i t a g a u k o t o n i y o cl t e +Eval: S I I I I + +Speaker sentences 109: cv_jpn_000909 #utts: 1 +id: (cv_jpn_000909-cv_jpn_000909) +Scores: (#C #S #D #I) 71 3 0 1 +REF: k a k o t o m i r a i t o n o *** m u j u n t e k i j i k o d o o i ts u t o sh i t e n o g e n z a i g a k a T a ch i o m o TS U t o i u k o t o d e a r u +HYP: k a k o t o m i r a i t o n o PAU m u j u n t e k i j i k o d o o i ts u t o sh i t e n o g e n z a i g a k a P a ch i o m o SH I t o i u k o t o d e a r u +Eval: I S S S + +Speaker sentences 110: cv_jpn_000910 #utts: 1 +id: (cv_jpn_000910-cv_jpn_000910) +Scores: (#C #S #D #I) 76 1 3 4 +REF: b u ts u r i t e k i s e k a i * w a s u u g a k u t e k * i k i g o o n i Y o cl t e a r a w a s a r e r u *** s u u G a k u t e k * i k a t a ch i n o s e k a i D E a r u +HYP: b u ts u r i t e k i s e k a i U w a s u u g a k u t e k I i k i g o o n i * o cl t e a r a w a s a r e r u PAU s u u K a k u t e k I i k a t a ch i n o s e k a i * * a r u +Eval: I I D I S I D D + +Speaker sentences 111: cv_jpn_000911 #utts: 1 +id: (cv_jpn_000911-cv_jpn_000911) +Scores: (#C #S #D #I) 23 2 0 2 +REF: * o n a J i g e n * sh o o d e s a n k o o N i n a r u +HYP: W o n a CH i g e n I sh o o d e s a n k o o G i n a r u +Eval: I S I S + +Speaker sentences 112: cv_jpn_000912 #utts: 1 +id: (cv_jpn_000912-cv_jpn_000912) +Scores: (#C #S #D #I) 33 0 2 0 +REF: g a i k o k u k a r a k i t a m o n o d a t o sh i CL t e b i cl k u R i +HYP: g a i k o k u k a r a k i t a m o n o d a t o sh i ** t e b i cl k u * i +Eval: D D + +Speaker sentences 113: cv_jpn_000913 #utts: 1 +id: (cv_jpn_000913-cv_jpn_000913) +Scores: (#C #S #D #I) 39 3 3 2 +REF: i w a y U r u j i CL s e N n I y o cl t e k a k u t o * k u sh * i R a I cl t a m o n o d e a r u +HYP: i w a y O r u j i I s e * n * y o cl t e k a k u t o U k u sh I i T a * cl t a m o n o d e a r u +Eval: S S D D I I S D + +Speaker sentences 114: fleurs_jpn_000346 #utts: 1 +id: (fleurs_jpn_000346-fleurs_jpn_000346) +Scores: (#C #S #D #I) 54 1 7 3 +REF: o n a j i y O o n i PAU d a n s * e E w a h i * z a O o o u z u b O N o h a K U k o t o g a g i m * u z u k e r a r e t e i m a s u +HYP: o n a j i y * o n i *** d a n s U e U w a h i J z a * o o u z u b * * o h a * * k o t o g a g i m U u z u k e r a r e t e i m a s u +Eval: D D I S I D D D D D I + +Speaker sentences 115: fleurs_jpn_000347 #utts: 1 +id: (fleurs_jpn_000347-fleurs_jpn_000347) +Scores: (#C #S #D #I) 99 6 12 5 +REF: k o n o s A a b i s U W a PAU G o r a k U s * e N O h a j i m e t o s u r u s e n p * a k u * y a PAU e n k a k u ch i d e *** d E e t a y a o n s E e o h i TS u Y O o t o s u r u t a n k e n t a i N i h i n ** p a N n i R i Y o o s a r e t e i m a s u +HYP: k o n o s * a b i s * * a *** K o r a k * s E e Y A h a j i m e t o s u r u s e n p T a k u I y a *** e n k a k u ch i d e PAU d * e t a y a o n s * e o h i S u * * o t o s u r u t a n k e n t a i R i h i n CL p a * n i D i * o o s a r e t e i m a s u +Eval: D D D D S D I S S I I D I D D S D D S I D S D + +Speaker sentences 116: fleurs_jpn_000348 #utts: 1 +id: (fleurs_jpn_000348-fleurs_jpn_000348) +Scores: (#C #S #D #I) 101 8 19 0 +REF: ky o o f U u PAU HY O o PAU k a d o n o k o o s u i RY O U PAU o Y o b i Y a m a k a J i W a PAU r a i u pau t a TS u m a k i pau m i z U F u k i pau o y O B I s a i k u r o n n a D o n o k i b i sh i i k I sh O o k E e t a I y a s o n o E e ky o o n i y o r u m o N o d e s u +HYP: ky o o f * u *** ** KY o *** k a d o n o k o o s u i ** * * R o * o b i * a m a k a SH i B a *** r a i u pau t a S u m a k i pau m i z * * u k i pau o y * * U s a i k u r o n n a N o n o k i b i sh i i k * sh * o k * e t a * y a s o n o * e ky o o n i y o r u m o R o d e s u +Eval: D D D S D D D D S D D S S D S D D D D S S D D D D D S + +Speaker sentences 117: fleurs_jpn_000349 #utts: 1 +id: (fleurs_jpn_000349-fleurs_jpn_000349) +Scores: (#C #S #D #I) 60 16 5 1 +REF: i n t A a n e CL t o w a PAU m a s u k o MY U N I k e e sh O n t o t a i j i n k o MY U N I k e e sh o N n o RY O o y O o s o o k a N e s O N a E T a k a n ky o o * d e s u +HYP: i n t * a n e ** t o w a *** m a s u k o M I R U k e e sh E n t o t a i j i n k o M I R U k e e sh o O n o PAU D o y * o s o o k a * e s U M a I D a k a n ky o o R d e s u +Eval: D D D S S S S S S S S S S S S D D S S S S I + +Speaker sentences 118: fleurs_jpn_000350 #utts: 1 +id: (fleurs_jpn_000350-fleurs_jpn_000350) +Scores: (#C #S #D #I) 92 12 10 4 +REF: k a j i n o D e w a ts u u j o o PAU t o k u b e TS u N a i N sh O k U y a *** * e n t a a t e i m e n t O o y O o I sh i t e i m a s u PAU G e s u T O G a k i b u N y o k u sh i s e TS u n a i N I t o * * m a r u y o o N I s u r u t a m E d e s u +HYP: k a j i n o R e w a ts u u j o o *** t o k u b e S u R a i * sh I k O y a PAU I e n t a a t e i m e n t * o y * o * sh i t e i m a s u *** KY e s u * * W a k i b u * y o k u sh i s e S u n a i R E t o R A m a r u y o o R E s u r u t a m * d e s u +Eval: S D S S D S S I I D D D D S D D S D S S S I I S S D + +Speaker sentences 119: fleurs_jpn_000351 #utts: 1 +id: (fleurs_jpn_000351-fleurs_jpn_000351) +Scores: (#C #S #D #I) 82 8 7 1 +REF: sh i k a sh i PAU KY a P u t e n n o W i k e cl t o o U sh i n a cl t A a t o PAU i n d o w a *** n a n a TS u n o W i k e cl t o o U sh i n a i PAU s a n j U u r o K u r a N sh i k a d e K i m a s e n d e sh i t a +HYP: sh i k a sh i *** K a K u t e n n o B i k e cl t o o * sh i n a cl t * a t o *** i n d o w a PAU n a n a Z u n o B i k e cl t o o * sh i n a i *** s a n j * u r o B u r a J sh i k a d e G i m a s e n d e sh i t a +Eval: D S S S D D D I S S D D D S S S + +Speaker sentences 120: fleurs_jpn_000352 #utts: 1 +id: (fleurs_jpn_000352-fleurs_jpn_000352) +Scores: (#C #S #D #I) 92 13 11 1 +REF: F o o k u r a n d o n o k o o sh i k I ts U u k a w a F O o k u r a n d O sh o t o o P o n d o e f U k E e p I i d e *** i ch i p o n D o g a i ch I I G I R i S U P o n d o j i i B I i P i i t o t o o k a n i k o t E e s a r e T e i m a s u +HYP: H o o k u r a n d o n o k o o sh i k E ts * u k a w a PAU H o k u r a n d A sh o t o o B o n d o e f * k * e p * i d e PAU i ch i p o n N o g a i ch * * * * * i E E B o n d o j i i * P i B i i t o t o o k a n i k o t * e s a r e D e i m a s u +Eval: S S D S S S S D D D I S D D D D D S S S D S S D S + +Speaker sentences 121: fleurs_jpn_000353 #utts: 1 +id: (fleurs_jpn_000353-fleurs_jpn_000353) +Scores: (#C #S #D #I) 85 20 16 1 +REF: h a SH I sh i t a n o K A M I G A T A k U U k a N w A J u u g o m E e t o R U d e s u PAU N i s e n j U u i ch i n e N h a ch i g a ts u N i SH U N k o o sh i PAU N i s e n j U u N A n A n e n s * a n g a ts u m A D e k a i ts u U sh i m a s e n D e sh i t a +HYP: h a J U sh i t a n o * * * * J O O O k * O k a O w O CH u u g o m * e t o * O d e s u *** * i s e n j * u i ch i n e U h a ch i g a ts u M i ** S E k o o sh i *** * i s e n j * u * * n U n e n s E a n g a ts u m U N e k a i ts u E sh i m a s e n G e sh i t a +Eval: S S D D D D S S S S D S S S S D D S D D D S S D S S D D D D D S I S S S S + +Speaker sentences 122: fleurs_jpn_000354 #utts: 1 +id: (fleurs_jpn_000354-fleurs_jpn_000354) +Scores: (#C #S #D #I) 67 6 4 2 +REF: i cl p u n k a n d e * f u cl t o o s u R u ch i i k i m O a r e b a PAU F u cl t O o s u r u m a D e N i n a n P U N m o k a k a r u ch * i i k i m o a r i m a s u +HYP: i cl p u n k a n d e H f u cl t o o s u B u ch i i k i m * a r e b a *** H u cl t * o s u r u m a R e M i n a n * CL PAU m o k a k a r u ch J i i k i m o a r i m a s u +Eval: I S D D S D S S D S S I + +Speaker sentences 123: fleurs_jpn_000355 #utts: 1 +id: (fleurs_jpn_000355-fleurs_jpn_000355) +Scores: (#C #S #D #I) 74 16 1 1 +REF: p I r a m i cl D O n o o t o t o * H i k a R i n o sh o o w a pau k o n o k a n k o o CH I D e t o k u n i k o D o m O t a CH i G a t a n O sh i m e r U m o y o o sh i n o H i t o TS u D e s u +HYP: p E r a m i cl T A n o o t o t o A SH i k a * i n o sh o o w a pau k o n o k a n k o o SH U R e t o k u n i k o R o m A t a J i K a t a n A sh i m e r E m o y o o sh i n o SH i t o S u R e s u +Eval: S S S I S D S S S S S S S S S S S S + +Speaker sentences 124: fleurs_jpn_000356 #utts: 1 +id: (fleurs_jpn_000356-fleurs_jpn_000356) +Scores: (#C #S #D #I) 43 7 0 2 +REF: s O n o t a M e pau t a N n i *** R a B e r u t o sh i t e * HY o o k i g a ts u I k a s a r e g a ch i d e s u +HYP: s U n o t a N e pau t a I n i PAU D a D e r u t o sh i t e H I o o k i g a ts u E k a s a r e g a ch i d e s u +Eval: S S S I S S I S S + +Speaker sentences 125: fleurs_jpn_000357 #utts: 1 +id: (fleurs_jpn_000357-fleurs_jpn_000357) +Scores: (#C #S #D #I) 109 24 7 3 +REF: g e n S O n s u r u k o T o g a sh i R a r e t e i r U n i j U U g * o m a I n O d a n R a CL P u PAU B u r o o D O s a i d * o w a PAU G e n S o n s u r U T o o g a i b u n k e N n o s a i k o N o U ts U sh i d e s u PAU t e g a k i n I y o r u g e n p o N w a G e n S o N sh i t e i m * a s e n +HYP: g e n * Z n s u r u k o K o g a sh i T a r e t e i r A n i j I O g O o m a E n A d a n D a K T u *** K u r o o T U s a i d E o w a K I e n Z o n s u r * A o o g a i b u n k e * n o s a i k o R o * ts A sh i d e s u *** t e g a k i n * y o r u g e n p o O w a K e n Z o O sh i t e i m U a s e n +Eval: D S S S S S S I S S S S S D S S S I S S S D S D S D S D D S S S S I + +Speaker sentences 126: fleurs_jpn_000358 #utts: 1 +id: (fleurs_jpn_000358-fleurs_jpn_000358) +Scores: (#C #S #D #I) 108 17 18 6 +REF: k A r e N o s e TS U o t a d a sh I i t * o m i t o m e r u H i t o m O i m a sh i t A g a PAU o o k u N O H i t o W A s * o n o GY A k U d e PAU t a i y o o k E e d e W a t a i y O o t o s * * o N o T a n O H o sh i g a ch i ky U u n o * m A W a r I O i d o o sh I T e I r u T o * sh i n J i t e i m a sh i t a +HYP: k * r e M o s e S O o t a d a sh * i t A o m i t o m e r u SH i t o m * i m a sh i t E g a *** o o k u G U SH i t o * * s U o n o K E k O d e *** t a i y o o k * e d e * a t a i y * o t o s U N o H o K a n * * o sh i g a ch i ky * u n o U m * * a r * E i d o o sh * S e * r u D o A sh i n CH i t e i m a sh i t a +Eval: D S S S D I S D S D S S S D D I S S S D D D D I I S S D D D I D D D S D S D S I S + +Speaker sentences 127: fleurs_jpn_000359 #utts: 1 +id: (fleurs_jpn_000359-fleurs_jpn_000359) +Scores: (#C #S #D #I) 122 14 11 1 +REF: ch i b e cl t o m e e s O o N o ch U u sh i n w a sh i N s e e Y o g a d e s u PAU s a * m a z a M a n a k a m i g a m i o sh i k a k u k a s u r u k o T o d e PAU e n e r u g i i ch a n e r u g a J O o k a s a r e PAU ch a k U r a G a k a CL s E e k a s a r e PAU s a t O r I n O i SH i k I g A U M a r e m a s u +HYP: ch i b e cl t o m e e s * o * o ch * u sh i n w a sh i I s e e * o g a d e s u *** s a N m a z a N a n a k a m i g a m i o sh i k a k u k a s u r u k o D o d e *** e n e r u g i i ch a n e r u g a SH I o k a s a r e *** ch a k * r a B a k a ** s * e k a s a r e *** s a t A r U n E i CH i k E g O O G a r e m a s u +Eval: D D D S D D I S S D S S D D S D D D S S S S S S S S + +Speaker sentences 128: fleurs_jpn_000360 #utts: 1 +id: (fleurs_jpn_000360-fleurs_jpn_000360) +Scores: (#C #S #D #I) 75 17 6 4 +REF: M i n a M i * a F u r I k a n i a r u s u b e t e n o k o k u r I TS u k o o e N T O d o o y O o n i PAU k O n o k o o e N n i W a *** m a I n I CH i * h o G o H i t o NY U u e n RY O o g a * k a k a r i m a s u +HYP: B i n a N i Y a H u r E k a n i a r u s u b e t e n o k o k u r E S u k o o e * D A d o o y * o n i *** k U n o k o o e * n i * a PAU m a * n E J i E h o K o SH i t o N I u e n G U o g a O k a k a r i m a s u +Eval: S S I S S S S D S S D D S D D I D S S I S S S S S S I + +Speaker sentences 129: fleurs_jpn_000361 #utts: 1 +id: (fleurs_jpn_000361-fleurs_jpn_000361) +Scores: (#C #S #D #I) 45 9 5 6 +REF: R e CL SH a PAU k u r u m a PAU s * * * o N o T a n O o o k u n O k o o ts U U sh * u d a n * G a ** s O k o k a r A u m a r e m a sh i t a +HYP: D e ** S a *** k u r u m a *** s U N N o H o K a n * o o k u n U k o o ts * O sh I u d a n U N a TS s U k o k a r E u m a r e m a sh i t a +Eval: S D S D D I I I S S D S D S I I S I S S + +Speaker sentences 130: fleurs_jpn_000362 #utts: 1 +id: (fleurs_jpn_000362-fleurs_jpn_000362) +Scores: (#C #S #D #I) 70 8 3 0 +REF: i n t A a n e cl t o w a PAU m a s u k o MY U n i k e e sh o n t o t a i j i n k o MY U n I k e e sh o n n o RY O o y o o s o o k a n e s o n a E t a k a n ky o o D e s u +HYP: i n t * a n e cl t o w a *** m a s u k o M I n i k e e sh o n t o t a i j i n k o M I n U k e e sh o n n o ** Y o y o o s o o k a n e s o n a I t a k a n ky o o R e s u +Eval: D D S S S S S D S S S + +Speaker sentences 131: fleurs_jpn_000363 #utts: 1 +id: (fleurs_jpn_000363-fleurs_jpn_000363) +Scores: (#C #S #D #I) 85 15 12 9 +REF: BY o o i n d e w a PAU k a * n s e n k a n r * i t e j * u N sh * O n i sh i T a G a * i PAU T a n i N e n O k a n s e n N o k a n o o s E e O F U s e g u T a m E n i k a n j a O k a k u r I s u r u n A d * O N O s * o CH I o t o CL t e i m a ** S u * +HYP: KY o o i n d e w a *** k a O n s e n k a n r E i t e j I u * sh J U n i sh i S a R a E i *** K a n i * e n U k a n s e n G o k a n o o s * e * * * s e g u D a m I n i k a n j a * k a k u r * s u r u n * d A M U I s O o J O o t o ** t e i m a SH I u S +Eval: S D I I I D I S S S I D S D S S D D D D S S D D D I S S S I S S D I S I + +Speaker sentences 132: fleurs_jpn_000364 #utts: 1 +id: (fleurs_jpn_000364-fleurs_jpn_000364) +Scores: (#C #S #D #I) 131 17 7 3 +REF: r e n p o o G i k a I w a N i s e n g o n e n d o k a r a w a i s e TS u * b u TS u t o r I sh i m a r i h O o e n o sh i k i n t e e ky O o o k a i SH i sh i PAU e F u b i I a I w a a D a r u T o P o r u n o N i j u u n i N n o s o o s a I n * o t o o NY U u sh I n a k e r e B a N a r a * n a i t o k i t e e sh i m a sh i t a +HYP: r e n p o o * i k a Y w a M i s e n g o n e n d o k a r a w a i s e S u E b u S u t o r E sh i m a r i h * o e n o sh i k i n t e e ky * o o k a i J i sh i *** e H u b i Y a Y w a a T a r u Z o B o r u n o R i j u u n i * n o s o o s a * n Y o t o o ** N u sh U n a k e r e W a D a r a R n a i t o k i t e e sh i m a sh i t a +Eval: D S S S I S S D D S D S S S S S S S D D I D S S S S I + +Speaker sentences 133: fleurs_jpn_000365 #utts: 1 +id: (fleurs_jpn_000365-fleurs_jpn_000365) +Scores: (#C #S #D #I) 58 22 11 4 +REF: P I i e I ch i PAU R e B E r U w a PAU k e n s a sh i t A k a G A k u b u CL SH I TS U N i F u k * U m a r e R U s U i s O i * * o N P I i E i ch i n O e * I ch i n o RY o o d e sh i m E s a r e m A s u +HYP: * H i e * ch i *** D e * R r O w a *** k e n s a sh i t * k a K O k u b u ** ** E S T E i * u k O N m a r e N A s E i s A i Y U o PAU PAU H i * i ch i n * e H O ch i n o R o o d e sh i m A s a r e m I s u +Eval: D S D D S D S S D D S S D D S S S S D I S S S S S I I S S S D D I S S S S + +Speaker sentences 134: fleurs_jpn_000366 #utts: 1 +id: (fleurs_jpn_000366-fleurs_jpn_000366) +Scores: (#C #S #D #I) 83 9 8 1 +REF: s o r e d e m o pau t o o KY O K u k a r a n O a D O b a i s u o u k e pau s u b e t e n o HY o o sh i k I o M A m o r i PAU a n z e n j o o n o k e e K o K U n i s a i sh i N n o ch U u i * o h a r a i m a sh O o +HYP: s o r e d e m o pau t o o ** R U u k a r a n * a R U b a i s u o u k e pau s u b e t e n o H o o sh i k E o * * m o r i *** a n z e n j o o n o k e e G o B O n i s a i sh i * n o ch * u i U o h a r a i m a sh * o +Eval: D S S D S S S S D D D S S S D D I D + +Speaker sentences 135: fleurs_jpn_000367 #utts: 1 +id: (fleurs_jpn_000367-fleurs_jpn_000367) +Scores: (#C #S #D #I) 91 12 8 3 +REF: k o r e r a w a t a m a n i k o n Z a ts u * S U R U k a Z O k u m U k e n o b I i ch i D e PAU k a i g a N N i W a *** s a m a z a m a n a t e n p o g a n a r a n d e i m a s u PAU a n z e N n I O y o * G u k o T o g a d e k i m a s u +HYP: k o r e r a w a t a m a n i k o n G a ts u R O K A CL k a TS U k u m O k e n o b U i ch i T e *** k a i g a * * i * a PAU s a m a z a m a n a t e n p o g a n a r a n d e i m a s u *** a n z e * n * * y o E B u k o D o g a d e k i m a s u +Eval: S I S S S S S S S S S D D D D I D D D D I S S + +Speaker sentences 136: fleurs_jpn_000368 #utts: 1 +id: (fleurs_jpn_000368-fleurs_jpn_000368) +Scores: (#C #S #D #I) 82 23 33 4 +REF: sh i n n o PAU m i e n a i ch i i M U PAU E R U E E A a R U E s u O O E n U pau * a n d o pau E R U E E E f U E E E s u t I I O o PAU s e n KY U u HY A k u H a ch i j u U KY u * U PAU P i i HY a k u ky u U N o s O n z a i M o m a T a PAU b A a CH A r U ch i * I m u n o d o k u j I n O y O o s o d e * a r u +HYP: sh i n n o *** m i e n a i ch i i * * *** * * * * * D a * * * s u * * * n * pau N a n d o pau * * * * N A f * * * A s u t * * * o *** s e n K E u ** * k u * a ch i j u K I u P E E J i i H a k u ky u E D o s U n z a i B o m a D a *** b * a Z E r E ch i E B m u n o d o k u j U n E y * o s o d e R a r u +Eval: D D D D D D D D D S D D D D D D D I D D D D S S D D D S D D D D S S D D D S S I S S S S S S S S S D D S S S I S S S D I + +Speaker sentences 137: fleurs_jpn_000369 #utts: 1 +id: (fleurs_jpn_000369-fleurs_jpn_000369) +Scores: (#C #S #D #I) 97 8 12 3 +REF: k o n o s A a B i s u w a PAU g o r a k u s e N o h a j i m e t o s u r u s e n * P a k u y a pau e n K a k U CH i D e d E e t a y A o n s e E o H I TS u Y O o t o s u r u t a n k e n t a * i N i *** h i n p a N n i r i y O o s a r e t e i m a s u +HYP: k o n o s * a R i s u w a *** g o r a k u s e * o h a j i m e t o s u r u s e n B T a k u y a pau e n G a k * SH i R e d * e t a y * o n s e Y o * * S u * * o t o s u r u t a n k e n t a N i R i PAU h i n p a * n i r i y * o s a r e t e i m a s u +Eval: D S D D I S S D S S D D S D D S D D I S I D D + +Speaker sentences 138: fleurs_jpn_000370 #utts: 1 +id: (fleurs_jpn_000370-fleurs_jpn_000370) +Scores: (#C #S #D #I) 147 18 6 2 +REF: s a k U b a N pau b u e n o s u a i R e s u k a r a G o j u cl k i r o s a n j U u i ch i m a i r u h a n a r e t a r a P u r a t a sh i n a i d e pau G e n sh O K U j o o i n g I i n d e * a r u k u r i s u t I i n a PAU f E r u n a n d e s u PAU d e pau k I r u H i n A a J o sh i g a *** D a i t o o RY o o s e n e n o sh U TS u B a o s e n g e n sh i m a sh i t a +HYP: s a k O b a PAU pau b u e n o s u a i D e s u k a r a K o j u cl k i r o s a n j * u i ch i m a i r u h a n a r e t a r a B u r a t a sh i n a i d e pau K e n sh A G O j o o i n g * i n d e W a r u k u r i s u t E i n a *** f U r u n a n d e s u *** d e pau k E r u K i n * a Z o sh i g a PAU N a i t o o R o o s e n e n o sh * I u R a o s e n g e n sh i m a sh i t a +Eval: S S S S D S S S S S D I S D S D S S D S I S S D S S + +Speaker sentences 139: fleurs_jpn_000371 #utts: 1 +id: (fleurs_jpn_000371-fleurs_jpn_000371) +Scores: (#C #S #D #I) 86 19 6 5 +REF: o n a J i TS u k * i N i PAU m a sh U h a D o n o k a CL s o o R O D e b * E TS u n o RY O K a * K u K i G a k a CL s o o r O o o o b A a r a n sh * i pau k a b e n i g e k i t o ts u sh i t e *** j u u SH I CH I n i n g a sh i b o o sh i m a sh i t a +HYP: o n a Y i Z u k U i R i *** m a sh I h a T o n o k a ** s o o E U R e b U I S u n o R U G a O KY u * i W a k a ** s o o r * o o o b * a r a n sh I i pau k a b e n i g e k i t o ts u sh i t e PAU j u u N A R A n i n g a sh i b o o sh i m a sh i t a +Eval: S S I S D S S D S S S I S S S S S I S D S D D D I I S S S S + +Speaker sentences 140: fleurs_jpn_000372 #utts: 1 +id: (fleurs_jpn_000372-fleurs_jpn_000372) +Scores: (#C #S #D #I) 91 14 16 1 +REF: H a sh i sh i T a n O K a M I G A T A k U U k a n w a j u u g o m E e T o R U d e s u PAU N i s e n j u u I ch i n e N h a CH i g a ts u n i sh * u n k O o sh i pau N i s e n j u u n a n A N e N s a n g a TS u m a D e k a i ts U U sh i m a s e n d e sh i t a +HYP: * a sh i sh i * a n * * a J O O H O O k * * k a n w a j u u g o m * e D o * * d e s u *** M i s e n j u u * ch i n e * h a J i g a ts u n i sh U u n k * o sh i pau M i s e n j u u n a n * I e * s a n g a S u m a R e k a i ts * O sh i m a s e n d e sh i t a +Eval: D D D D S S S S S S D D D S D D D S D D S I D S D S D S S D S + +Speaker sentences 141: fleurs_jpn_000373 #utts: 1 +id: (fleurs_jpn_000373-fleurs_jpn_000373) +Scores: (#C #S #D #I) 176 7 41 7 +REF: b * u n M e e t o I u k o t o B a w a PAU sh i m i N o I m i s u r u r a t e n g o n o k E e Y O o sh i sh I I A i b U I A i E r U A I e s u k a r a k * i t * E o r i PAU sh i m i n O i * m i s u r u r a t e n g o N o m E e sh i sh I I A I B u i A i e s u PAU t o sh i y a t o sh i k o CL k a o I m i sh i pau n a N r * * a k a n o k a t a ch i d e *** sh a k a i n o k i B o o t e e g i s u r u sh I I A I B u i A i t I I E E E s u t o I u m e e sh i n i k a n k e e sh i t e I m a s u +HYP: b U u n N e e t o * u k o t o W a w a *** sh i m i * o * m i s u r u r a t e n g o n o k * e * * o sh i sh * * * i b * * * i * r * * * e s u k a r a k I i t A W o r i *** sh i m i n * i O m i s u r u r a t e n g o R o m * e sh i sh * * * * * u i B i e s u *** t o sh i y a t o sh i k o ** k a o * m i sh i pau n a * r U N a k a n o k a t a ch i d e PAU sh a k a i n o k i * o o t e e g i s u r u sh * * * * * u i B i t * * * * A s u t o * u m e e sh i n i k a n k e e sh i t e * m a s u +Eval: I S D S D D D D D D D D D D D D D D D D I I S D D I S D D D D D D S D D D D I I I D D D D D D S D D D D S D D + +Speaker sentences 142: fleurs_jpn_000374 #utts: 1 +id: (fleurs_jpn_000374-fleurs_jpn_000374) +Scores: (#C #S #D #I) 91 18 11 1 +REF: TS U u j O o PAU k o k o D e W a i TS u m o k a n k o o KY A k U y a * GY o o sh a t a CH i g a h a CL s u r U o t o G a K i k o E t e k i m a s u PAU o t o T o H i k a r i g A o R i n a s u m O n o g a t a r I W a m a r u d E e h o n n O y O o D e s u +HYP: ** S u j * o *** k o k o R e * a i S u m o k a n k o o T E k E y a R Y o o sh a t a J i g a h a ** s u r * o t o K a CH i k o I t e k i m a s u W o t o D o SH i k a r i g * o * i n a s u m U n o g a t a r * U a m a r u d * e h o n n A y * o R e s u +Eval: D S D D S D S S S S I S S D D S S S S S S D D S D S D S D S + +Speaker sentences 143: fleurs_jpn_000375 #utts: 1 +id: (fleurs_jpn_000375-fleurs_jpn_000375) +Scores: (#C #S #D #I) 40 5 12 3 +REF: t e r e b i n O H o o d o o n I Y o R U t * o PAU g e n p a ts u k a r a SH I R O k E M u R I g A a g a CL t e i m a s u * * +HYP: t e r e b i n * * o o d o o n * * o * N t D o *** g e n p a ts u k a r a ** * H A k * * u * E g * a g a D t e i m a s u E S +Eval: D D D D D S I D D D S S D D D S D S I I + +Speaker sentences 144: fleurs_jpn_000376 #utts: 1 +id: (fleurs_jpn_000376-fleurs_jpn_000376) +Scores: (#C #S #D #I) 67 4 0 1 +REF: n o o * BY o o r i t o k o o d o o n o s o o k a n k a n k e e w a pau k a G a k u sh a t a CH i n o k e n ky u u o u r a z u k e r U m o n o d e s u +HYP: n o o B Y o o r i t o k o o d o o n o s o o k a n k a n k e e w a pau k a Y a k u sh a t a SH i n o k e n ky u u o u r a z u k e r E m o n o d e s u +Eval: I S S S S + +Speaker sentences 145: fleurs_jpn_000377 #utts: 1 +id: (fleurs_jpn_000377-fleurs_jpn_000377) +Scores: (#C #S #D #I) 60 11 8 2 +REF: s U I y o o b i n O i b e n t o n O a t o PAU k a r u p a n e D o w a *** s e n SH U k e n D e F u T a * TS u n o k o j i n R E e s U N i SH U TS u j o o sh i m a sh i t a +HYP: s * E y o o b i n A i b e n t o n * a t o *** k a r u p a n e R o w a PAU s e n J I k e n G e H u K a Z S u n o k o j i n * D e s * * i ** * S u j o o sh i m a sh i t a +Eval: D S S D D S I S S S S S I S D S D D D D S + +Speaker sentences 146: fleurs_jpn_000378 #utts: 1 +id: (fleurs_jpn_000378-fleurs_jpn_000378) +Scores: (#C #S #D #I) 90 11 9 3 +REF: s e n * h a CL PY A k u N e n d a I i r a i PAU g u n t a I g a t o o CH a k U s u r U M a D e *** h a i ch i W a k o n o BY O o k i n i k a n K e e s u r u M o n d a I n i s o o G u * u sh i t a k o t o w A a r i m a s e n d e sh i t a +HYP: s e n E h a ** P E k u R e n d a * i r a i *** g u n t a Y g a t o o J a k * s u r E W a R e PAU h a i ch i * a k o n o B Y o k i n i k a n KY e e s u r u * o n d a * n i s o o * u G u sh i t a k o t o w * a r i m a s e n d e sh i t a +Eval: I D S S S D D S S D S S S I D S S S D D D I D + +Speaker sentences 147: fleurs_jpn_000379 #utts: 1 +id: (fleurs_jpn_000379-fleurs_jpn_000379) +Scores: (#C #S #D #I) 72 17 8 0 +REF: sh i k a sh i PAU KY a P u t e n n O W i k e cl T o o U sh I n a cl t A a T o PAU i n d O W a n a n a ts u n O W i k e cl t o o U sh I n A i PAU s a n j U U r o k u r a N sh i K a D e k i m a s e n D e sh i t a +HYP: sh i k a sh i *** K a K u t e n n U B i k e cl D o o * sh U n a cl t D a D o H i n d * * a n a n a ts u n E R i k e cl t o o * sh U n E i *** s a n j * O r o k u r a * sh i T a R e k i m a s e n G e sh i t a +Eval: D S S S S S D S S S S D D S S D S S D D S D S S S + +Speaker sentences 148: fleurs_jpn_000380 #utts: 1 +id: (fleurs_jpn_000380-fleurs_jpn_000380) +Scores: (#C #S #D #I) 103 6 5 7 +REF: k a j i n o d e w a ts u u j o o PAU t o k u b e ts u n a i n sh O k U y a *** e n t A a t e i m e n t o o y o o i sh i t e i m a s u PAU g e s U t o * g a k i b * u N y O k u sh i s e TS u n a i n i t * * o m a r u y O o n * I s u r u t a m e * d e s u +HYP: k a j i n o d e w a ts u u j o o *** t o k u b e ts u n a i n sh A k O y a PAU e n t * a t e i m e n t o o y o o i sh i t e i m a s u *** g e s * t o U g a k i b U u M y A k u sh i s e Z u n a i n i t O R o m a r u y * o n R E s u r u t a m e R d e s u +Eval: D S S I D D D I I S S S I I D I S I + +Speaker sentences 149: fleurs_jpn_000381 #utts: 1 +id: (fleurs_jpn_000381-fleurs_jpn_000381) +Scores: (#C #S #D #I) 75 9 16 3 +REF: s o r e d e M o PAU t o o ky O K u k a r a n O a D O b a i s U o U k e pau s u B E t E n * O HY o O sh i k I o M A m o r i pau a n z e n j O o n o k E e k o K U n i s a i sh * i N n o ch u * u I o h a r a i m a sh O o +HYP: s o r e d e * o *** t o o ky * * u k a r a n * a R A b a i s * o * k e pau s u * * t A n U H Y o A sh i k E o * * m o r i pau a n z e n j I o n o k * e k o * * n i s a i sh E i * n o ch u Y u * o h a r a i m a sh A o +Eval: D D D D D S S D D D D S I S S S S D D S D D D I D I D S + +Speaker sentences 150: fleurs_jpn_000382 #utts: 1 +id: (fleurs_jpn_000382-fleurs_jpn_000382) +Scores: (#C #S #D #I) 55 6 12 1 +REF: * o W A k a r e D e w A a r i m a s e N k o r E W a H i t O ts u N O sh O O N o o W a r i d e a r i PAU a t a r a sh i i sh o o n o m A k U a k e d e s u +HYP: P o * O k a r e G e w * a r i m a s e * k o r * * a SH i t * ts u M U sh * * * o o * a r i d e a r i *** a t a r a sh i i sh o o n o m O k * a k e d e s u +Eval: I D S S D D D D S D S S D D D D D S D + +Speaker sentences 151: fleurs_jpn_000383 #utts: 1 +id: (fleurs_jpn_000383-fleurs_jpn_000383) +Scores: (#C #S #D #I) 97 10 4 1 +REF: s a F a r i t o w a pau a F u r i k a n o y a s e E d o o B u TS u pau t o k U n i s a B a n n a n I i r u y a s e E d o o B u TS u n o k a n s a TS u o m o k u t e k i t o sh i t a r i k u r o d e n o * RY o k O o o s a sh i m a s u +HYP: s a W a r i t o w a pau a * u r i k a n o y a s e U d o o * u Z u pau t o k O n i s a W a n n a n * i r u y a s e U d o o G u S u n o k a n s a S u o m o k u t e k i t o sh i t a r i k u r o d e n o R Y o k * o o s a sh i m a s u +Eval: S D S D S S S D S S S S I S D + +Speaker sentences 152: fleurs_jpn_000384 #utts: 1 +id: (fleurs_jpn_000384-fleurs_jpn_000384) +Scores: (#C #S #D #I) 124 12 23 4 +REF: F u Y u n I k i t a b a r u t o k a I o * * o o d a n s u r u b A a i w a pau s e N SH i TS u N O i ch i O k a * k u n i N sh I T e k u d a s a i PAU k o o r I n O n a k A o ts u k i S U s u m * u s a i n i m o cl t o m O e e ky O o o U k e r u s e N SH i ts u d e W a o s o r O SH I i H o d o N o s O o o n G a n a r I H i b i k i m a s u +HYP: * u * u n E k i t a b a r u t o k a Y o U M o o d a n s u r u b * a i w a pau s e * ** i S u * M i ch i * k a O k u n i * sh * S e k u d a s a i K k o o r E n N n a k * o ts u k i * * s u m N u s a i n i m o cl t o m * e e ky * o o * k e r u s e * ** i ts u d e * a o s o r * U J i U o d o * o s * o o n N a n a r * * i b i k i m a s u +Eval: D D S S I I D D D S D S D I D D S S S S D D D I D D D D D D D S S S D D S D D + +Speaker sentences 153: fleurs_jpn_000385 #utts: 1 +id: (fleurs_jpn_000385-fleurs_jpn_000385) +Scores: (#C #S #D #I) 100 26 9 4 +REF: k o k o w a I G i r i s u n o sh o k u m i n CH I sh i * h a I sh a * g a j i B u n t a CH i n o RY o o D o T o sh i T a b a sh O n a N O d e pau sh O k U m i n * CH i j i D A I n O sh o o k o o s A g a s o o t o s u r * U H O O w a PAU K o k o K a r A h a j i M e r u n O g a y o i D E sh O o +HYP: k o k o w a * PAU i r i s u n o sh o k u m i n T E sh i E h a * sh a E g a j i * u n t a SH i n o D o o R o D o sh i * a b a sh U n a R U d e pau sh A k O m i n T E i j i * R E n E sh o o k o o s E g a s o o t o s u r E K A T A w a *** O o k o W a r * h a j i B e r u n A g a y o i * U sh * o +Eval: D S S S I D I D S S S S D S S S S S I S D S S S S I S S S S D S S D S S D S D + +Speaker sentences 154: fleurs_jpn_000386 #utts: 1 +id: (fleurs_jpn_000386-fleurs_jpn_000386) +Scores: (#C #S #D #I) 94 12 14 4 +REF: E B I S U sh i w a PAU s a k u g e n s u r u s u u ch i * o s a D a M E M a s e n d e sh i t a G a PAU s a k u g e n w a ch U U G o k u n o k e e z a i s a n * sh U TS u RY o U N i m o t o z u i t e *** J i CL sh i s a r e R u d a r O o t * o n o B E m a sh i t a +HYP: * * * K O sh i w a *** s a k u g e n s u r u s u u ch i O o s a R a * * Y a s e n d e sh i t a K a *** s a k u g e n w a ch I E W o k u n o k e e z a i s a n A sh * I u Y o * * i m o t o z u i t e PAU D i ** sh i s a r e * u d a r * o t O o n o * I m a sh i t a +Eval: D D D S S D I S D D S S D S S S I D S S D D I S D D D I D S + +Speaker sentences 155: fleurs_jpn_000387 #utts: 1 +id: (fleurs_jpn_000387-fleurs_jpn_000387) +Scores: (#C #S #D #I) 98 12 7 3 +REF: s a i NY u u k o k u sh o CL k u w a *** sh i n k * o n RY O k o o n o j I k i * g a s u k u n a i k a r u CH a a sh O CL k u y o r I m o h a Y A k U o T o z u r e pau n a G a b i k i pau y o r I sh o o j o o g A a CL k a s u r U k o t o g A a r i m a s u +HYP: s a i N u u k o k u sh o ** k u w a PAU sh i n k O o n R E k o o n o j U k i E g a s u k u n a i k a r u J a a sh * ** k u y o r E m o h a * E k O o D o z u r e pau n a R a b i k i pau y o r E sh o o j o o g * a ** k a s u r E k o t o g * a r i m a s u +Eval: S D I I S S S I S D D S D S S S S S D D S D + +Speaker sentences 156: fleurs_jpn_000388 #utts: 1 +id: (fleurs_jpn_000388-fleurs_jpn_000388) +Scores: (#C #S #D #I) 104 16 6 9 +REF: K i n o o n o a s a pau t o r u k o n o g a j i a n t e CL p * u n o k e e s a TS u * h o * n B U D e *** j i d o o sh a b a k u D a N n o b a k U H a TS u N i Y o r i PAU K e e k a N f u t a r I g a sh i b O o sh i *** pau * F u sh * o * O sh a w a n i j u u n i N o k o * E m a sh i t a +HYP: KY i n o o n o a s a pau t o r u k o n o g a j i a n t e ** p K u n o k e e s a S u O h o M n W O R e PAU j i d o o sh a b a k u R a * n o b a k * * a S u R i * o r i KY I e e k a A f u t a r E g a sh i b * o sh i PAU pau R W u sh I o W A sh a w a n i j u u n i Y o k o A I m a sh i t a +Eval: S D I S I I S S S I S D D D S S D S S S S D I I S I I S S I S + +Speaker sentences 157: fleurs_jpn_000389 #utts: 1 +id: (fleurs_jpn_000389-fleurs_jpn_000389) +Scores: (#C #S #D #I) 81 3 2 5 +REF: sh o k u b u ts u W a n i n g E n g a s u u s a n S o o ts u k u r i PAU n i n g e n g * * a I k * i t o sh i t e h a k i d a s u * n i s a n k a ** t a n s o o t o r i k o n d e i m a s u +HYP: sh o k u b u ts u * a n i n g I n g a s u u s a n Z o o ts u k u r i *** n i n g e n g A K a CL k O i t o sh i t e h a k i d a s u R n i s a n k a CL t a n s o o t o r i k o n d e i m a s u +Eval: D S S D I I S I I I + +Speaker sentences 158: fleurs_jpn_000390 #utts: 1 +id: (fleurs_jpn_000390-fleurs_jpn_000390) +Scores: (#C #S #D #I) 82 9 4 7 +REF: s e n p a k u D e b u CL sh i o y u s o o s u r u n o w a pau u m i * o k o E t e *** H i t o * y a b u CL sh * I o t a * i RY O o y U s o o s u r u * *** m o cl t o m o k o o r i TS U T e k i n a h o o h o o D e s u +HYP: s e n p a k u R e b u ** sh i o y u s o o s u r u n o w a pau u m i Y o k o I t e PAU K i t o A y a b u ** sh U E o t a R i R E o y I s o o s u r u E PAU m o cl t o m o k o o r i ** * S e k i n a h o o h o o R e s u +Eval: S D I S I S I D I S I S S S I I D D S S + +Speaker sentences 159: fleurs_jpn_000391 #utts: 1 +id: (fleurs_jpn_000391-fleurs_jpn_000391) +Scores: (#C #S #D #I) 118 9 8 4 +REF: k a r i F o r u n i a sh u u n o a a n o r u d o PAU sh u w a r u ts E n e cl G A a ch i J i w a PAU b o o * RY o k u t e k i n a b i d e o * g e e m u * o m i s e e n e n sh a n i h a n b a I y a R e n t a R U s u R U k o t o o k i n SH i s u r U h o o * a n n i sh o m E e sh i m a sh i t a +HYP: k a r i H o r u n i a sh u u n o a a n o r u d o O sh u w a r u ts U n e cl * K a ch i SH i w a *** b o o R Y o k u t e k i n a b i d e o U g e e m u W o m i s e e n e n sh a n i h a n b a * y a * e n t a * * s u D E k o t o o k i n ** i s u r O h o o W a n n i sh o m * e sh i m a sh i t a +Eval: S S S D S S D I S I I D D D D S S D S I D + + diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/text b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/text new file mode 100644 index 0000000000000000000000000000000000000000..cfdad6aec4cefc4dd639c4a509277f747d2c228a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/text @@ -0,0 +1,160 @@ +cv_jpn_000800 k a k o t o m i r a i t o d o u j u N t e k i j i k o d o o i ts u n a r u g a i w e n i pau sh I k i t e k i n a N o d e a r +cv_jpn_000801 s e k a y o k e e s e e s u r u t o t o m u n i pau j i g k o j i sh i y o k e e s e s e r u ts s o o d o t e k i s e k a i n s o o z o o t e k i y o t o sh I t e pau k o m u ts u g a k o u ts u d e a r u +cv_jpn_000802 p a s o k o N n e g e e m i a r u I t o n a h f u e t e k i t e +cv_jpn_000803 k a a k u n o sh i m e s a t a r a sh i i j i j i ts u a t a r a s i k a N n e N k a N ky o sh i h a i n a t a r a sh i k a n o s e o m o cl t e pau n a n i o h a j i m e r u k a w a +cv_jpn_000804 o m o sh i r o e n o n i pau r o o d o n a k a s u g i t e pau d a r u i +cv_jpn_000805 k o r e j o o sh u u h a N t o i n a +cv_jpn_000806 k a g a k U sh a m o s e k a y o h o o k a ts s e k i n i pau t o o ch I t e k i n i s a ts u m e sh u o t o sh I t e i r u +cv_jpn_000807 h a I ts u n i ts U e m a r a N +cv_jpn_000808 sh i cl k a r sh t e k u d a s a i +cv_jpn_000809 w a t a sh i w a a m i g i n o n o t o k i pau d e k i sh I t e k i s e i m e e n o j i k a k u t o y u g o t o k i m o n o m e N sh o o h o o t e k i pau r o N b e t a y u u n o d e a r u +cv_jpn_000810 w a t a sh i w a pau sh a k a i k e e s e e n o k o N t e N n i w a d e y o o n u s o s u t e k i n a m o n o g a pau h a t a r a i t e i r u t o m o +cv_jpn_000811 n a n i o s u r u ts u m o r i d a cl t a n o k a +cv_jpn_000812 k o b u ts u t e k I t a g a j i k o h I t e e t e k i n i t a N n i pau t e N sh u u g o o t e k i n i k a N g a r a r u r u t o k i s o r e g a b u ts u r i t e k i t e k a i d e a r u +cv_jpn_000813 a n e g a z u cl t a n o d e y a cl k i n o sh i r a i g a r i m a s e N d e sh I t a +cv_jpn_000814 k o r e w a N r i h o N d e u t e i n a i t a b e m u n o d e s U +cv_jpn_000815 w o t a sh i w a h e N sh u u e i n o y o o n e N k u r a e w a y a cl t a cl t o o m o +cv_jpn_000816 e i s a N n i i k o n o k o t o b p a n o r i m i o o o sh i a m a sh I t a +cv_jpn_000817 k a s e g a ts U s u y o i h i u w a t e N n i s u g a d e k i m a s e N +cv_jpn_000818 i ch i i +cv_jpn_000819 h a i +cv_jpn_000820 o n o e +cv_jpn_000821 d e i +cv_jpn_000822 a t o k i +cv_jpn_000823 m i r u t o y u k o t a t o pau h a t a r a k U t o i u k o t o g a sh k a b u N d i t e k i n a k e r e b a n a r a n a i +cv_jpn_000824 w a r e w a r e o t a m a sh i i n o z u o k a r a u g u k a s u m o n o d e n a k e r e b a n a r a n a i +cv_jpn_000825 s e t a i b e N sh o o h o o d e k i n a r u g a i u e n i i d e a t e k I ch o cl k a N t e k i k e e k i y g a cl U k u m a r e r u n o d e a r u +cv_jpn_000826 t o k o m a d e m o t a t o i ch i t o n a s o o g o sh I e e t e k i n a z e cl t a i m u j u N t e k i j i k o o i ts u n o s e k a i n i sh I t e +cv_jpn_000827 sh i k a r u n i n i N g e N t o k a N ky o o t o r o k a N k e e w a m o t o k o o n o k a N k e d e a r i +cv_jpn_000828 i i s a N n i k o n o k o t o b a n o i m i y o o sh i y a m a sh I t a +cv_jpn_000829 k e e k i g a n a n a ts s a r i m a s U +cv_jpn_000830 k o ch i r a b a k o b i y a sh i s a N d e s U +cv_jpn_000831 m o sh i m a sh i +cv_jpn_000832 k o k o w a o k I k U t e n i g u y a k a n a m a ch i d e s U +cv_jpn_000833 s o n o ch i k a i y a k U s a r e r u k a r a i s o g e pau +cv_jpn_000834 a m a s a g a b k u s a e i r a r e t e t e ch o o d o i +cv_jpn_000835 h o k e N sh I ts u e n o d o o a a k e t a +cv_jpn_000836 m o d a n i o w a cl t e m o k i n i sh i n a i +cv_jpn_000837 a r i g a cl t a y a +cv_jpn_000838 i t o o g a r o k u d a t o j i k a N u w o s u r e t e t a n o sh i m e r u +cv_jpn_000839 k a k a k u w a g i z u ts U k a s a r e r u n i o j i t e j o o sh I k i n o u ch i n i h a i cl t e y u k u +cv_jpn_000840 sh i k a sh I t o k i g a k a b o n i h a i r u k o t o s o n o k o t o g a m i r a y o o m u k o t o d e a r i a r a t a n a r u i cl U t a i g a d e t e k u r u k o t o d e a r u +cv_jpn_000841 t e r e b i o k a i k a i t e pau t e r e b i o m i r u j i k a N n a f u e t a +cv_jpn_000842 k a k a r e u sh I t a i n o m i i ts u m a d e m o i k e r u n o d e a r u +cv_jpn_000843 n i N k i d a a m e N i y a n i n a r a N d a r a n i j i k a N m a o ch i d a cl t a +cv_jpn_000844 s o r e o o m o ch i i r u n i N g e N n o y o k u n i t o N s e i s o sh I t e k o r e w a k a r e n o m o cl t e i r u k a ch e n o s a k u d o N n pau i d o N s u r u +cv_jpn_000845 m a w a r y o a m i N n a k a N g a r u k o t o y a m e t e i t a +cv_jpn_000846 k o o i t e k i j o k U k a N t e k i n i s e k a y o m i r u t o y u k o t o w a j a k u n i k o o i t e k i ch a o k cl k a N t e k i n i s e k a y o k e e s e e s u r u k o t o k u m u n o d e a r u +cv_jpn_000847 s j e N p a i t a k e s a s e m a i t o s u r u k i z u k a i y g a y o k e e n i s e N cl p a i s a s e t e sh i m a u +cv_jpn_000848 k o n o m i ch i w a t o t e m o s e m a i n o d a m n a i d e s U +cv_jpn_000849 w o e g a r i n i U +cv_jpn_000850 t o i y o w a r o k a m a i t a r i g a a n i a r i m a s U +cv_jpn_000851 t a d a k a s a N o h i d a r i n i k i m e r a s a N g a i m a s U +cv_jpn_000852 m a cl k u r u n a t a m a b o t e s u g o i e +cv_jpn_000853 sh a r sh o o m i t a i n a d o k u sh u k a s o o b u m o k a i t a +cv_jpn_000854 g e N j i t e m o s e k a i w a pau t a m o o i ch i t o sh I t e k e cl t e s u r a i d e k a t a ch i o a cl t o s U k a i u n a k e r e m o n a r a n a i +cv_jpn_000855 sh o o h i N k e N s a k u k g a o k a r y a s u i t o o pau k a o k i n a r u m a i +cv_jpn_000856 ts e sh i I k i w a n e k I sh I t e cl k a t e e d e n a k e r u m o n a r a n a i +cv_jpn_000857 m o n o g o t a n o N j i N p a N k a e r u d a k e d e pau u m a k U i k U k o t o m a r +cv_jpn_000858 k o n o k I e e ts u w a k a ts u o n o s a sh i m i g a z e cl p e i N +cv_jpn_000859 k a k e N n i sh i cl p a i sh I t e m a pau m o ch I t ts u i t e s a m a sh I ts u o u k e i d e r u +cv_jpn_000860 s o r e y u e n i pau t e ts u g a k u g a z e N t a i n o g a k o d e y a r u t o s u r e w a +cv_jpn_000861 k i i s a n a i y a o y a d a g a y a s U k U t e h a N j o sh I t e r u +cv_jpn_000862 i n i f u r a g a k i n o h u z e N n i o ch j i cl t e pau k o k u g a i a d a sh I ts u s u r u sh I t a m o d e t e k i t a +cv_jpn_000863 ts s u g i n i k a w a k u w a s o N z a y o j u j u n o d o i k i n o k a cl t e s u r e z u r a N d e o k i N z e i t i t e N i k i U s u r u +cv_jpn_000864 s o r e d e w a t o k t o y i m a r a n a s e r i ts U sh i o w a n a k u pau sh i r N k a N t o i m o n o m o n a k u n a r u n o d a r i +cv_jpn_000865 h a k a i b u r a N k o k o N k u r i t o s e n o s u b e r i d a i k a w a i t a s u n a b a +cv_jpn_000866 s sh i k a sh i s o r o w a d o k o m a d e m N k o k o k a r a d e t e pau o k o e k a i r i k u r u s e e I t o m o t o m o d e a k e w a n a r a n a i +cv_jpn_000867 a r i t o a r a i r u d e m o o m a k i ch i r a sh i t e m i N n e a k a r a o u r a m i o k a cl t e r u +cv_jpn_000868 k o n o t e e d o s a w a g i n i n a r u k o t o m o n a i n o d a r o +cv_jpn_000869 k o n o n e r a N d e pau u r i t a u k a a a +cv_jpn_000870 h i n o k a g a e N n i ch u i sh i n a i t o pau s u g u k o g e r u +cv_jpn_000871 e N m a N n o u w e N n i p o ts u r i t o ts u i s a n a n a g a i t a s a i sh i o w a ts u m a y o o j i t e e d o n o ts i i s a n a pau a n a d a cl t a a +cv_jpn_000872 s o r e w a m a r e w a r e o i k a sh i n a g a r a pau w a r e w a r e o t o r e e k a s u r u n o d e a r u pau w a r e w a r e n o t a m a s sh i y o k o r o s u n o d e a +cv_jpn_000873 r e k U sh I t e k i n i a t a r a r t a m o n o w a d e cl t e m u j u N t e k i j i g o t o o i ts I t e k i g i e N z d a i n o o i t e s U k a i sh I e k i n i a t a e r a r t a m o N u t o sh I t e +cv_jpn_000874 m u o j u N t e k I e j i g o d o o i ch I t o sh I t e pau i ts u m o k o n o s e k a i n i ch o o e sh I t e k i d e a +cv_jpn_000875 y u n i d e cl p e e m u j u N t e k I j i g o d o o i sh u t o sh I t e g e N a e k a r a g e N z a e e t u w o k i k u s U e k a e n o g e N z a i n o i t e +cv_jpn_000876 h a r e w o t a N o sh I t o N d a sh u d e k i n a i +cv_jpn_000877 sh i k a sh i w a t a sh a s o k o n i s e k a i n o j i k o d o o i ts u o k u n o d e w a n a i +cv_jpn_000878 n e b u k a k u n a r u n o g a pau h a y a k u m a cl t a +cv_jpn_000879 w a t a sh i w a i N g e N n o o d e k I sh I t e k I k e e s e e n o t a ch i b a k a r a pau g e j u ts u o m i r u n o d e a cl t e o o sh a k a r a e N sh a o m i r u n o d e a n a i +cv_jpn_000880 a o i t o m a t o sh i k a n a k U t e pau k a u k a b a i y o u +cv_jpn_000881 s e N k e z u e g y o o n i o o k i n a k i t a y o y a s U t e e r u +cv_jpn_000882 n a n i k a sh i r a n o i N s e N t e b u w a n a i t o k i b u i s u e n o d e w a +cv_jpn_000883 j i k o N o sh e k g e N n o i b e N t o d e s u t o r u sh I t a m a r i +cv_jpn_000884 m a r i n o sh I t o w a b o o z e N t o sh I t e i t a +cv_jpn_000885 s o N n a n a i y o o n o m e r u w a n a N k e N m o k U I t e i t a +cv_jpn_000886 i j i g a i d e d e e s f u i sh I t e i t a +cv_jpn_000887 t o k i d o k i ch i u u N n o k o r o w a w a k a r a n a k u n o r u t o ch i g a a r u d a k a r a b o k u a k a a n e o k i n o t o n i k a j i a j e m e r u +cv_jpn_000888 m o o n i g e t e ch a t a m e d a +cv_jpn_000889 k a r e w a p o o t o t a j i ts U k u sh I t e i t a +cv_jpn_000890 d a r u N i m u n e i w a k o w a k a k e t a k a n a i +cv_jpn_000891 p a s a k a t o o m o t e u d o a n o o t o cl t e o n i g e cl t a +cv_jpn_000892 sh t e i m a s e N +cv_jpn_000893 k a y o o n i sh I t e sh I t e r u t o t o m o n i sh i cl t e i n a i t o k o r o k a r a t a N ky u w a h a j i m a r u n o d e a r u +cv_jpn_000894 a i s a ts u w a d a i j i d a i o +cv_jpn_000895 t o j i t a m o n o i k a n i h i r o g e t e m o h i r a i t a m u n o n i a n a r a r o t o i cl t e i r u g a +cv_jpn_000896 t a m u sh i n i k u ts U k a ts U k u cl t e m i o w a +cv_jpn_000897 U ts z e i b u N a k o k i n a sh o o b a i d a i o n a +cv_jpn_000898 w h a cl t e i +cv_jpn_000899 k i t e i +cv_jpn_000900 g o +cv_jpn_000901 h a sh i i t i +cv_jpn_000902 i e a +cv_jpn_000903 h a cl ch i +cv_jpn_000904 h n e +cv_jpn_000905 a sh i i i +cv_jpn_000906 k u o +cv_jpn_000907 k e ch i +cv_jpn_000908 k a k a k u g a pau a k i r a k a n i s u r u pau ky y a cl k a N t e k I sh i N d r i n i sh I t a g a u k o t o n i y o cl t e +cv_jpn_000909 k a k o t o m i r a i t o n o pau m u j u N t e k i j i k o d o o i ts u t o sh I t e n o g e N z a i g a k a p a ch i o m o sh I t o i u k o t o d e a r u +cv_jpn_000910 b u ts u r i t e k i s e k a i u w a s u u g a k u t e k i I k i g o o n i o cl t e a r a w a s a r e r u pau s u u k a k u t e k I i k a t a ch i n o s e k a i a r u +cv_jpn_000911 w o n a ch i g e N i sh o o d e s a N k o o g i n a r u +cv_jpn_000912 g a i k o k u k a r a k i t a m o n o d a t o sh i t e b i cl k u i +cv_jpn_000913 i w a y o r u j i I s e N y o cl t e k a k U t o U k U sh I i t a cl t a m o n o d e a r u +fleurs_jpn_000346 o n a j i y o n i d a N s u e u w a h i j z a o o u z u b o h a k o t o g a g i m u u z u k e r a r e t e i m a s U +fleurs_jpn_000347 k o n o s a b i s a k o r a k s e e y a h a j i m e t o s u r u s e N p t a k u i y a e N k a k U ch i d e pau d e t a y a o N s e o h I s u o t o s u r u t a N k e N t a i r i h i N cl p a n i d i o o s a r e t e i m a s U +fleurs_jpn_000348 ky o o f u ky o k a d o n o k o o s u i r o o b i a m a k a sh i b a r a i u pau t a s u m a k i pau m i z u k i pau o y u s a i k u r o N n a N o n o k i b i sh i i k sh o k e t a y a s o n o e ky o o n i y o r u m o r o d e s U +fleurs_jpn_000349 i N t a n e t o w a m a s U k o m i r u k e e sh e N t o t a i j i N k o m i r u k e e sh o o n o pau d o y o s o o k a e s u m a i d a k a N ky o o r d e s U +fleurs_jpn_000350 k a j i n o r e w a ts u u j o o t o k u b e s u r a i sh I k o y a pau i e N t a a t e i m e N t o y o sh I t e i m a s U ky e s u w a k i b u y o k u sh I s e s u n a i r e t o r a m a r u y o o r e s u r u t a m d e s U +fleurs_jpn_000351 sh i k a sh i k a k U t e N n o b i k e cl t o o sh i n a cl t a t o i N d o w a pau n a n a z u n o b i k e cl t o o sh i n a i s a N j u r o b u r a j sh i k a d e g i m a s e N d e sh I t a +fleurs_jpn_000352 h o o k u r a N d o n o k o o sh I k e ts u k a w a pau h o k u r a N d a sh o t o o b o N d o e f k e p i d e pau i ch i p o N n o g a i ch i e e b o N d o j i i p i b i i t o t o o k a n i k o t e s a r e d e i m a s U +fleurs_jpn_000353 h a j u sh i t a n o j o o o k o k a o w o ch u u g o m e t o o d e s U i s e N j u i ch i n e u h a ch i g a ts u m i s e k o o sh i i s e N j u n u n e N s e a N g a ts u m u n e k a i ts u e sh i m a s e N g e sh I t a +fleurs_jpn_000354 i cl p u N k a N d e h f u cl t o o s u b u ch i i k i m a r e b a h u cl t o s u r u m a r e m i n a N cl pau m o k a k a r u ch j i i k i m o a r i m a s U +fleurs_jpn_000355 p e r a m i cl t a n o o t o t o a sh I k a i n o sh o o w a pau k o n o k a N k o o sh u r e t o k u n i k o r o m a t a j i k a t a n a sh i m e r e m o y o o sh i n o sh i t o s u r e s U +fleurs_jpn_000356 s u n o t a n e pau t a i n i pau d a d e r u t o sh I t e h i o o k i g a ts u e k a s a r e g a ch i d e s U +fleurs_jpn_000357 g e N z N s u r u k o k o g a sh I t a r e t e i r a n i j i o g o o m a e n a d a N d a k t u k u r o o t U s a i d e o w a k i e N z o N s u r a o o g a i b u N k e n o s a i k o r o ts a sh i d e s U t e g a k i N y o r u g e N p o o w a k e N z o o sh I t e i m u a s e N +fleurs_jpn_000358 k r e m o s e s o o t a d a sh i t a o m i t o m e r u sh I t o m i m a sh I t e g a o o k u g u sh I t o s u o n o k e k o d e t a i y o o k e d e a t a i y o t o s u n o h o k a n o sh i g a ch i ky u n o u m a r e i d o o sh s e r u d o a sh i N ch i t e i m a sh I t a +fleurs_jpn_000359 ch i b e cl t o m e e s o o ch u sh i N w a sh i i s e e o g a d e s U s a N m a z a n a n a k a m i g a m i o sh I k a k U k a s u r u k o d o d e e n e r u g i i ch a n e r u g a sh i o k a s a r e ch a k r a b a k a s e k a s a r e s a t a r u n e i ch I k e g o o g a r e m a s U +fleurs_jpn_000360 b i n a n i y a h u r e k a n i a r u s u b e t e n o k o k u r e s U k o o e d a d o o y o n i k u n o k o o e n i a pau m a n e j i e h o k o sh I t o n i u e N g u o g a o k a k a r i m a s U +fleurs_jpn_000361 d e s a k u r u m a s u N n o h o k a n o o k u n u k o o ts o sh i u d a N u n a ts s u k o k a r e u m a r e m a sh I t a +fleurs_jpn_000362 i N t a n e cl t o w a m a s U k o m i n i k e e sh o N t o t a i j i N k o m i n u k e e sh o N n o y o y o o s o o k a n e s o n a i t a k a N ky o o r e s U +fleurs_jpn_000363 ky o o i N d e w a k a o N s e N k a N r e i t e j i u sh j u N i sh I s a r a e i k a n i e n u k a N s e N g o k a n o o s e s e g u d a m i n i k a N j a k a k u r s u r u n d a m u i s o o j o o t o t e i m a sh I U s +fleurs_jpn_000364 r e N p o o i k a y w a m i s e N g o n e N d o k a r a w a i s e s u e b u s u t o r e sh i m a r i h o e n o sh i k i N t e e ky o o k a i j i sh i e h u b i y a y w a a t a r u z o b o r u n o r i j u u n i n o s o o s a N y o t o o n u sh u n a k e r e w a d a r a r n a i t o k I t e e sh i m a sh I t a +fleurs_jpn_000365 h i e ch i d e r r o w a k e N s a sh I t k a k o k U b u e s t e i u k o N m a r e n a s e i s a i y u o pau pau h i i ch i n e h o ch i n o r o o d e sh i m a s a r e m i s U +fleurs_jpn_000366 s o r e d e m o pau t o o r u u k a r a n a r u b a i s u o u k e pau s u b e t e n o h o o sh I k e o m o r i a N z e N j o o n o k e e g o b o n i s a i sh i n o ch u i u o h a r a i m a sh o +fleurs_jpn_000367 k o r e r a w a t a m a n i k o N g a ts u r o k a cl k a ts u k u m o k e n o b u i ch i t e k a i g a i a pau s a m a z a m a n a t e N p o g a n a r a N d e i m a s U a N z e N y o e b u k o d o g a d e k i m a s U +fleurs_jpn_000368 sh i N n o m i e n a i ch i i d a s u N pau n a N d o pau n a f a s U t o s e N k e u k U a ch i j u k i u p e e j i i h a k U ky u e d o s u N z a i b o m a d a b a z e r e ch i e b m u n o d o k u j u n e y o s o d e r a r u +fleurs_jpn_000369 k o n o s a r i s u w a g o r a k U s e o h a j i m e t o s u r u s e N b t a k u y a pau e N g a k sh i r e d e t a y o N s e y o s u o t o s u r u t a N k e N t a N i r i pau h i N p a n i r i y o s a r e t e i m a s U +fleurs_jpn_000370 s a k o b a pau pau b u e n o s u a i d e s U k a r a k o j u cl k i r o s a N j u i ch i m a i r u h a n a r e t a r a b u r a t a sh i n a i d e pau k e N sh a g o j o o i N g i N d e w a r u k u r i s U t e i n a f u r u n a N d e s u d e pau k e r u k i n a z o sh i g a pau n a i t o o r o o s e N e n o sh I u r a o s e N g e N sh i m a sh I t a +fleurs_jpn_000371 o n a y i z u k u i r i m a sh i h a t o n o k a s o o e u r e b u i s u n o r u g a o ky u i w a k a s o o r o o o b a r a N sh i i pau k a b e n i g e k i t o ts u sh I t e pau j u u n a r a n i N g a sh i b o o sh i m a sh I t a +fleurs_jpn_000372 a sh i sh I a n a j o o h o o k k a N w a j u u g o m e d o d e s U m i s e N j u u ch i n e h a j i g a ts u n i sh u u N k o sh i pau m i s e N j u u n a n i e s a N g a s u m a r e k a i ts o sh i m a s e N d e sh I t a +fleurs_jpn_000373 b u u N n e e t o u k o t o w a w a sh i m i o m i s u r u r a t e N g o n o k e o sh i sh i b i r e s U k a r a k i I t a w o r i sh i m i N i o m i s u r u r a t e N g o r o m e sh i sh u i b i e s U t o sh i y a t o sh I k o k a o m i sh i pau n a r u n a k a n o k a t a ch i d e pau sh a k a i n o k i o o t e e g i s u r u sh u i b i t a s U t o u m e e sh i n i k a N k e e sh I t e m a s U +fleurs_jpn_000374 s u j o k o k o r e a i s u m o k a N k o o t e k e y a r y o o sh a t a j i g a h a s u r o t o k a ch i k o i t e k i m a s U w o t o d o sh i k a r i g o i n a s u m u n o g a t a r u a m a r u d e h o N n a y o r e s U +fleurs_jpn_000375 t e r e b i n o o d o o n o N t d o g e N p a ts U k a r a h a k u e g a g a d t e i m a s U e s +fleurs_jpn_000376 n o o b y o o r i t o k o o d o o n o s o o k a N k a N k e e w a pau k a y a k u sh a t a sh i n o k e N ky u u o u r a z u k e r e m o n o d e s U +fleurs_jpn_000377 s e y o o b i n a i b e N t o n a t o k a r u p a n e r o w a pau s e N j i k e N g e h U k a z s u n o k o j i N d e s i s u j o o sh i m a sh I t a +fleurs_jpn_000378 s e N e h a p e k u r e N d a i r a i g u N t a y g a t o o j a k s u r e w a r e pau h a i ch i a k o n o b y o k i n i k a N ky e e s u r u o N d a n i s o o u g u sh I t a k o t o w a r i m a s e N d e sh I t a +fleurs_jpn_000379 sh i k a sh i k a k u t e N n u b i k e cl d o o sh u n a cl t d a d o h i N d a n a n a ts u n e r i k e cl t o o sh u n e i s a N j o r o k u r a sh I t a r e k i m a s e N g e sh I t a +fleurs_jpn_000380 k a j i n o d e w a ts u u j o o t o k u b e ts u n a i N sh a k o y a pau e N t a t e i m e N t o o y o o i sh I t e i m a s U g e s t o u g a k i b u u m y a k u sh i s e z u n a i n i t o r o m a r u y o n r e s u r u t a m e r d e s U +fleurs_jpn_000381 s o r e d e o t o o ky u k a r a n a r a b a i s o k e pau s u t a n u h y o a sh I k e o m o r i pau a N z e N j i o n o k e k o n i s a i sh e i n o ch u y u o h a r a i m a sh a o +fleurs_jpn_000382 p o o k a r e g e w a r i m a s e k o r a sh I t ts u m u sh o o a r i d e a r i a t a r a sh i i sh o o n o m o k a k e d e s U +fleurs_jpn_000383 s a w a r i t o w a pau a u r i k a n o y a s e u d o o u z u pau t o k o n i s a w a N n a n i r u y a s e u d o o g u s u n o k a N s a s u o m o k u t e k i t o sh I t a r i k u r o d e n o r y o k o o s a sh i m a s U +fleurs_jpn_000384 u u n e k i t a b a r u t o k a y o u m o o d a N s u r u b a i w a pau s e i s u m i ch i k a o k u n i sh s e k u d a s a i k k o o r e n N n a k o ts U k I s u m N u s a i n i m o cl t o m e e ky o o k e r u s e i ts u d e a o s o r u j i u o d o o s o o N n a n a r i b i k i m a s U +fleurs_jpn_000385 k o k o w a pau i r i s u n o sh o k u m i N t e sh i e h a sh a e g a j i u N t a sh i n o d o o r o d o sh I a b a sh u n a r u d e pau sh a k o m i N t e i j i r e n e sh o o k o o s e g a s o o t o s u r e k a t a w a o o k o w a r h a j i b e r u n a g a y o i u sh o +fleurs_jpn_000386 k o sh i w a s a k u g e N s u r u s u u ch i o o s a r a y a s e N d e sh I t a k a s a k u g e N w a ch i e w o k u n o k e e z a i s a N a sh I u y o i m o t o z u i t e pau d i sh i s a r e u d a r o t o o n o i m a sh I t a +fleurs_jpn_000387 s a i n u u k o k u sh o k u w a pau sh i N k o o N r e k o o n o j u k i e g a s U k u n a i k a r u j a a sh k u y o r e m o h a e k o o d o z u r e pau n a r a b i k i pau y o r e sh o o j o o g a k a s u r e k o t o g a r i m a s U +fleurs_jpn_000388 ky i n o o n o a s a pau t o r u k o n o g a j i a N t e p k u n o k e e s a s u o h o m N w o r e pau j i d o o sh a b a k u r a n o b a k a s u r i o r i ky i e e k a a f U t a r e g a sh i b o sh i pau pau r w u sh i o w a sh a w a n i j u u n i y o k o a i m a sh I t a +fleurs_jpn_000389 sh o k u b u ts u a n i N g i N g a s u u s a N z o o ts U k u r i n i N g e N g a k a cl k o i t o sh I t e h a k i d a s u r n i s a N k a cl t a N s o o t o r i k o N d e i m a s U +fleurs_jpn_000390 s e N p a k u r e b u sh i o y u s o o s u r u n o w a pau u m i y o k o i t e pau k i t o a y a b u sh u e o t a r i r e o y i s o o s u r u e pau m o cl t o m o k o o r i s e k i n a h o o h o o r e s U +fleurs_jpn_000391 k a r i h o r u n i a sh u u n o a a n o r u d o o sh u w a r u ts u n e cl k a ch i sh i w a b o o r y o k U t e k i n a b i d e o u g e e m u w o m i s e e n e N sh a n i h a N b a y a e N t a s u d e k o t o o k i N i s u r o h o o w a N n i sh o m e sh i m a sh I t a diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/token b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/token new file mode 100644 index 0000000000000000000000000000000000000000..cfdad6aec4cefc4dd639c4a509277f747d2c228a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/token @@ -0,0 +1,160 @@ +cv_jpn_000800 k a k o t o m i r a i t o d o u j u N t e k i j i k o d o o i ts u n a r u g a i w e n i pau sh I k i t e k i n a N o d e a r +cv_jpn_000801 s e k a y o k e e s e e s u r u t o t o m u n i pau j i g k o j i sh i y o k e e s e s e r u ts s o o d o t e k i s e k a i n s o o z o o t e k i y o t o sh I t e pau k o m u ts u g a k o u ts u d e a r u +cv_jpn_000802 p a s o k o N n e g e e m i a r u I t o n a h f u e t e k i t e +cv_jpn_000803 k a a k u n o sh i m e s a t a r a sh i i j i j i ts u a t a r a s i k a N n e N k a N ky o sh i h a i n a t a r a sh i k a n o s e o m o cl t e pau n a n i o h a j i m e r u k a w a +cv_jpn_000804 o m o sh i r o e n o n i pau r o o d o n a k a s u g i t e pau d a r u i +cv_jpn_000805 k o r e j o o sh u u h a N t o i n a +cv_jpn_000806 k a g a k U sh a m o s e k a y o h o o k a ts s e k i n i pau t o o ch I t e k i n i s a ts u m e sh u o t o sh I t e i r u +cv_jpn_000807 h a I ts u n i ts U e m a r a N +cv_jpn_000808 sh i cl k a r sh t e k u d a s a i +cv_jpn_000809 w a t a sh i w a a m i g i n o n o t o k i pau d e k i sh I t e k i s e i m e e n o j i k a k u t o y u g o t o k i m o n o m e N sh o o h o o t e k i pau r o N b e t a y u u n o d e a r u +cv_jpn_000810 w a t a sh i w a pau sh a k a i k e e s e e n o k o N t e N n i w a d e y o o n u s o s u t e k i n a m o n o g a pau h a t a r a i t e i r u t o m o +cv_jpn_000811 n a n i o s u r u ts u m o r i d a cl t a n o k a +cv_jpn_000812 k o b u ts u t e k I t a g a j i k o h I t e e t e k i n i t a N n i pau t e N sh u u g o o t e k i n i k a N g a r a r u r u t o k i s o r e g a b u ts u r i t e k i t e k a i d e a r u +cv_jpn_000813 a n e g a z u cl t a n o d e y a cl k i n o sh i r a i g a r i m a s e N d e sh I t a +cv_jpn_000814 k o r e w a N r i h o N d e u t e i n a i t a b e m u n o d e s U +cv_jpn_000815 w o t a sh i w a h e N sh u u e i n o y o o n e N k u r a e w a y a cl t a cl t o o m o +cv_jpn_000816 e i s a N n i i k o n o k o t o b p a n o r i m i o o o sh i a m a sh I t a +cv_jpn_000817 k a s e g a ts U s u y o i h i u w a t e N n i s u g a d e k i m a s e N +cv_jpn_000818 i ch i i +cv_jpn_000819 h a i +cv_jpn_000820 o n o e +cv_jpn_000821 d e i +cv_jpn_000822 a t o k i +cv_jpn_000823 m i r u t o y u k o t a t o pau h a t a r a k U t o i u k o t o g a sh k a b u N d i t e k i n a k e r e b a n a r a n a i +cv_jpn_000824 w a r e w a r e o t a m a sh i i n o z u o k a r a u g u k a s u m o n o d e n a k e r e b a n a r a n a i +cv_jpn_000825 s e t a i b e N sh o o h o o d e k i n a r u g a i u e n i i d e a t e k I ch o cl k a N t e k i k e e k i y g a cl U k u m a r e r u n o d e a r u +cv_jpn_000826 t o k o m a d e m o t a t o i ch i t o n a s o o g o sh I e e t e k i n a z e cl t a i m u j u N t e k i j i k o o i ts u n o s e k a i n i sh I t e +cv_jpn_000827 sh i k a r u n i n i N g e N t o k a N ky o o t o r o k a N k e e w a m o t o k o o n o k a N k e d e a r i +cv_jpn_000828 i i s a N n i k o n o k o t o b a n o i m i y o o sh i y a m a sh I t a +cv_jpn_000829 k e e k i g a n a n a ts s a r i m a s U +cv_jpn_000830 k o ch i r a b a k o b i y a sh i s a N d e s U +cv_jpn_000831 m o sh i m a sh i +cv_jpn_000832 k o k o w a o k I k U t e n i g u y a k a n a m a ch i d e s U +cv_jpn_000833 s o n o ch i k a i y a k U s a r e r u k a r a i s o g e pau +cv_jpn_000834 a m a s a g a b k u s a e i r a r e t e t e ch o o d o i +cv_jpn_000835 h o k e N sh I ts u e n o d o o a a k e t a +cv_jpn_000836 m o d a n i o w a cl t e m o k i n i sh i n a i +cv_jpn_000837 a r i g a cl t a y a +cv_jpn_000838 i t o o g a r o k u d a t o j i k a N u w o s u r e t e t a n o sh i m e r u +cv_jpn_000839 k a k a k u w a g i z u ts U k a s a r e r u n i o j i t e j o o sh I k i n o u ch i n i h a i cl t e y u k u +cv_jpn_000840 sh i k a sh I t o k i g a k a b o n i h a i r u k o t o s o n o k o t o g a m i r a y o o m u k o t o d e a r i a r a t a n a r u i cl U t a i g a d e t e k u r u k o t o d e a r u +cv_jpn_000841 t e r e b i o k a i k a i t e pau t e r e b i o m i r u j i k a N n a f u e t a +cv_jpn_000842 k a k a r e u sh I t a i n o m i i ts u m a d e m o i k e r u n o d e a r u +cv_jpn_000843 n i N k i d a a m e N i y a n i n a r a N d a r a n i j i k a N m a o ch i d a cl t a +cv_jpn_000844 s o r e o o m o ch i i r u n i N g e N n o y o k u n i t o N s e i s o sh I t e k o r e w a k a r e n o m o cl t e i r u k a ch e n o s a k u d o N n pau i d o N s u r u +cv_jpn_000845 m a w a r y o a m i N n a k a N g a r u k o t o y a m e t e i t a +cv_jpn_000846 k o o i t e k i j o k U k a N t e k i n i s e k a y o m i r u t o y u k o t o w a j a k u n i k o o i t e k i ch a o k cl k a N t e k i n i s e k a y o k e e s e e s u r u k o t o k u m u n o d e a r u +cv_jpn_000847 s j e N p a i t a k e s a s e m a i t o s u r u k i z u k a i y g a y o k e e n i s e N cl p a i s a s e t e sh i m a u +cv_jpn_000848 k o n o m i ch i w a t o t e m o s e m a i n o d a m n a i d e s U +cv_jpn_000849 w o e g a r i n i U +cv_jpn_000850 t o i y o w a r o k a m a i t a r i g a a n i a r i m a s U +cv_jpn_000851 t a d a k a s a N o h i d a r i n i k i m e r a s a N g a i m a s U +cv_jpn_000852 m a cl k u r u n a t a m a b o t e s u g o i e +cv_jpn_000853 sh a r sh o o m i t a i n a d o k u sh u k a s o o b u m o k a i t a +cv_jpn_000854 g e N j i t e m o s e k a i w a pau t a m o o i ch i t o sh I t e k e cl t e s u r a i d e k a t a ch i o a cl t o s U k a i u n a k e r e m o n a r a n a i +cv_jpn_000855 sh o o h i N k e N s a k u k g a o k a r y a s u i t o o pau k a o k i n a r u m a i +cv_jpn_000856 ts e sh i I k i w a n e k I sh I t e cl k a t e e d e n a k e r u m o n a r a n a i +cv_jpn_000857 m o n o g o t a n o N j i N p a N k a e r u d a k e d e pau u m a k U i k U k o t o m a r +cv_jpn_000858 k o n o k I e e ts u w a k a ts u o n o s a sh i m i g a z e cl p e i N +cv_jpn_000859 k a k e N n i sh i cl p a i sh I t e m a pau m o ch I t ts u i t e s a m a sh I ts u o u k e i d e r u +cv_jpn_000860 s o r e y u e n i pau t e ts u g a k u g a z e N t a i n o g a k o d e y a r u t o s u r e w a +cv_jpn_000861 k i i s a n a i y a o y a d a g a y a s U k U t e h a N j o sh I t e r u +cv_jpn_000862 i n i f u r a g a k i n o h u z e N n i o ch j i cl t e pau k o k u g a i a d a sh I ts u s u r u sh I t a m o d e t e k i t a +cv_jpn_000863 ts s u g i n i k a w a k u w a s o N z a y o j u j u n o d o i k i n o k a cl t e s u r e z u r a N d e o k i N z e i t i t e N i k i U s u r u +cv_jpn_000864 s o r e d e w a t o k t o y i m a r a n a s e r i ts U sh i o w a n a k u pau sh i r N k a N t o i m o n o m o n a k u n a r u n o d a r i +cv_jpn_000865 h a k a i b u r a N k o k o N k u r i t o s e n o s u b e r i d a i k a w a i t a s u n a b a +cv_jpn_000866 s sh i k a sh i s o r o w a d o k o m a d e m N k o k o k a r a d e t e pau o k o e k a i r i k u r u s e e I t o m o t o m o d e a k e w a n a r a n a i +cv_jpn_000867 a r i t o a r a i r u d e m o o m a k i ch i r a sh i t e m i N n e a k a r a o u r a m i o k a cl t e r u +cv_jpn_000868 k o n o t e e d o s a w a g i n i n a r u k o t o m o n a i n o d a r o +cv_jpn_000869 k o n o n e r a N d e pau u r i t a u k a a a +cv_jpn_000870 h i n o k a g a e N n i ch u i sh i n a i t o pau s u g u k o g e r u +cv_jpn_000871 e N m a N n o u w e N n i p o ts u r i t o ts u i s a n a n a g a i t a s a i sh i o w a ts u m a y o o j i t e e d o n o ts i i s a n a pau a n a d a cl t a a +cv_jpn_000872 s o r e w a m a r e w a r e o i k a sh i n a g a r a pau w a r e w a r e o t o r e e k a s u r u n o d e a r u pau w a r e w a r e n o t a m a s sh i y o k o r o s u n o d e a +cv_jpn_000873 r e k U sh I t e k i n i a t a r a r t a m o n o w a d e cl t e m u j u N t e k i j i g o t o o i ts I t e k i g i e N z d a i n o o i t e s U k a i sh I e k i n i a t a e r a r t a m o N u t o sh I t e +cv_jpn_000874 m u o j u N t e k I e j i g o d o o i ch I t o sh I t e pau i ts u m o k o n o s e k a i n i ch o o e sh I t e k i d e a +cv_jpn_000875 y u n i d e cl p e e m u j u N t e k I j i g o d o o i sh u t o sh I t e g e N a e k a r a g e N z a e e t u w o k i k u s U e k a e n o g e N z a i n o i t e +cv_jpn_000876 h a r e w o t a N o sh I t o N d a sh u d e k i n a i +cv_jpn_000877 sh i k a sh i w a t a sh a s o k o n i s e k a i n o j i k o d o o i ts u o k u n o d e w a n a i +cv_jpn_000878 n e b u k a k u n a r u n o g a pau h a y a k u m a cl t a +cv_jpn_000879 w a t a sh i w a i N g e N n o o d e k I sh I t e k I k e e s e e n o t a ch i b a k a r a pau g e j u ts u o m i r u n o d e a cl t e o o sh a k a r a e N sh a o m i r u n o d e a n a i +cv_jpn_000880 a o i t o m a t o sh i k a n a k U t e pau k a u k a b a i y o u +cv_jpn_000881 s e N k e z u e g y o o n i o o k i n a k i t a y o y a s U t e e r u +cv_jpn_000882 n a n i k a sh i r a n o i N s e N t e b u w a n a i t o k i b u i s u e n o d e w a +cv_jpn_000883 j i k o N o sh e k g e N n o i b e N t o d e s u t o r u sh I t a m a r i +cv_jpn_000884 m a r i n o sh I t o w a b o o z e N t o sh I t e i t a +cv_jpn_000885 s o N n a n a i y o o n o m e r u w a n a N k e N m o k U I t e i t a +cv_jpn_000886 i j i g a i d e d e e s f u i sh I t e i t a +cv_jpn_000887 t o k i d o k i ch i u u N n o k o r o w a w a k a r a n a k u n o r u t o ch i g a a r u d a k a r a b o k u a k a a n e o k i n o t o n i k a j i a j e m e r u +cv_jpn_000888 m o o n i g e t e ch a t a m e d a +cv_jpn_000889 k a r e w a p o o t o t a j i ts U k u sh I t e i t a +cv_jpn_000890 d a r u N i m u n e i w a k o w a k a k e t a k a n a i +cv_jpn_000891 p a s a k a t o o m o t e u d o a n o o t o cl t e o n i g e cl t a +cv_jpn_000892 sh t e i m a s e N +cv_jpn_000893 k a y o o n i sh I t e sh I t e r u t o t o m o n i sh i cl t e i n a i t o k o r o k a r a t a N ky u w a h a j i m a r u n o d e a r u +cv_jpn_000894 a i s a ts u w a d a i j i d a i o +cv_jpn_000895 t o j i t a m o n o i k a n i h i r o g e t e m o h i r a i t a m u n o n i a n a r a r o t o i cl t e i r u g a +cv_jpn_000896 t a m u sh i n i k u ts U k a ts U k u cl t e m i o w a +cv_jpn_000897 U ts z e i b u N a k o k i n a sh o o b a i d a i o n a +cv_jpn_000898 w h a cl t e i +cv_jpn_000899 k i t e i +cv_jpn_000900 g o +cv_jpn_000901 h a sh i i t i +cv_jpn_000902 i e a +cv_jpn_000903 h a cl ch i +cv_jpn_000904 h n e +cv_jpn_000905 a sh i i i +cv_jpn_000906 k u o +cv_jpn_000907 k e ch i +cv_jpn_000908 k a k a k u g a pau a k i r a k a n i s u r u pau ky y a cl k a N t e k I sh i N d r i n i sh I t a g a u k o t o n i y o cl t e +cv_jpn_000909 k a k o t o m i r a i t o n o pau m u j u N t e k i j i k o d o o i ts u t o sh I t e n o g e N z a i g a k a p a ch i o m o sh I t o i u k o t o d e a r u +cv_jpn_000910 b u ts u r i t e k i s e k a i u w a s u u g a k u t e k i I k i g o o n i o cl t e a r a w a s a r e r u pau s u u k a k u t e k I i k a t a ch i n o s e k a i a r u +cv_jpn_000911 w o n a ch i g e N i sh o o d e s a N k o o g i n a r u +cv_jpn_000912 g a i k o k u k a r a k i t a m o n o d a t o sh i t e b i cl k u i +cv_jpn_000913 i w a y o r u j i I s e N y o cl t e k a k U t o U k U sh I i t a cl t a m o n o d e a r u +fleurs_jpn_000346 o n a j i y o n i d a N s u e u w a h i j z a o o u z u b o h a k o t o g a g i m u u z u k e r a r e t e i m a s U +fleurs_jpn_000347 k o n o s a b i s a k o r a k s e e y a h a j i m e t o s u r u s e N p t a k u i y a e N k a k U ch i d e pau d e t a y a o N s e o h I s u o t o s u r u t a N k e N t a i r i h i N cl p a n i d i o o s a r e t e i m a s U +fleurs_jpn_000348 ky o o f u ky o k a d o n o k o o s u i r o o b i a m a k a sh i b a r a i u pau t a s u m a k i pau m i z u k i pau o y u s a i k u r o N n a N o n o k i b i sh i i k sh o k e t a y a s o n o e ky o o n i y o r u m o r o d e s U +fleurs_jpn_000349 i N t a n e t o w a m a s U k o m i r u k e e sh e N t o t a i j i N k o m i r u k e e sh o o n o pau d o y o s o o k a e s u m a i d a k a N ky o o r d e s U +fleurs_jpn_000350 k a j i n o r e w a ts u u j o o t o k u b e s u r a i sh I k o y a pau i e N t a a t e i m e N t o y o sh I t e i m a s U ky e s u w a k i b u y o k u sh I s e s u n a i r e t o r a m a r u y o o r e s u r u t a m d e s U +fleurs_jpn_000351 sh i k a sh i k a k U t e N n o b i k e cl t o o sh i n a cl t a t o i N d o w a pau n a n a z u n o b i k e cl t o o sh i n a i s a N j u r o b u r a j sh i k a d e g i m a s e N d e sh I t a +fleurs_jpn_000352 h o o k u r a N d o n o k o o sh I k e ts u k a w a pau h o k u r a N d a sh o t o o b o N d o e f k e p i d e pau i ch i p o N n o g a i ch i e e b o N d o j i i p i b i i t o t o o k a n i k o t e s a r e d e i m a s U +fleurs_jpn_000353 h a j u sh i t a n o j o o o k o k a o w o ch u u g o m e t o o d e s U i s e N j u i ch i n e u h a ch i g a ts u m i s e k o o sh i i s e N j u n u n e N s e a N g a ts u m u n e k a i ts u e sh i m a s e N g e sh I t a +fleurs_jpn_000354 i cl p u N k a N d e h f u cl t o o s u b u ch i i k i m a r e b a h u cl t o s u r u m a r e m i n a N cl pau m o k a k a r u ch j i i k i m o a r i m a s U +fleurs_jpn_000355 p e r a m i cl t a n o o t o t o a sh I k a i n o sh o o w a pau k o n o k a N k o o sh u r e t o k u n i k o r o m a t a j i k a t a n a sh i m e r e m o y o o sh i n o sh i t o s u r e s U +fleurs_jpn_000356 s u n o t a n e pau t a i n i pau d a d e r u t o sh I t e h i o o k i g a ts u e k a s a r e g a ch i d e s U +fleurs_jpn_000357 g e N z N s u r u k o k o g a sh I t a r e t e i r a n i j i o g o o m a e n a d a N d a k t u k u r o o t U s a i d e o w a k i e N z o N s u r a o o g a i b u N k e n o s a i k o r o ts a sh i d e s U t e g a k i N y o r u g e N p o o w a k e N z o o sh I t e i m u a s e N +fleurs_jpn_000358 k r e m o s e s o o t a d a sh i t a o m i t o m e r u sh I t o m i m a sh I t e g a o o k u g u sh I t o s u o n o k e k o d e t a i y o o k e d e a t a i y o t o s u n o h o k a n o sh i g a ch i ky u n o u m a r e i d o o sh s e r u d o a sh i N ch i t e i m a sh I t a +fleurs_jpn_000359 ch i b e cl t o m e e s o o ch u sh i N w a sh i i s e e o g a d e s U s a N m a z a n a n a k a m i g a m i o sh I k a k U k a s u r u k o d o d e e n e r u g i i ch a n e r u g a sh i o k a s a r e ch a k r a b a k a s e k a s a r e s a t a r u n e i ch I k e g o o g a r e m a s U +fleurs_jpn_000360 b i n a n i y a h u r e k a n i a r u s u b e t e n o k o k u r e s U k o o e d a d o o y o n i k u n o k o o e n i a pau m a n e j i e h o k o sh I t o n i u e N g u o g a o k a k a r i m a s U +fleurs_jpn_000361 d e s a k u r u m a s u N n o h o k a n o o k u n u k o o ts o sh i u d a N u n a ts s u k o k a r e u m a r e m a sh I t a +fleurs_jpn_000362 i N t a n e cl t o w a m a s U k o m i n i k e e sh o N t o t a i j i N k o m i n u k e e sh o N n o y o y o o s o o k a n e s o n a i t a k a N ky o o r e s U +fleurs_jpn_000363 ky o o i N d e w a k a o N s e N k a N r e i t e j i u sh j u N i sh I s a r a e i k a n i e n u k a N s e N g o k a n o o s e s e g u d a m i n i k a N j a k a k u r s u r u n d a m u i s o o j o o t o t e i m a sh I U s +fleurs_jpn_000364 r e N p o o i k a y w a m i s e N g o n e N d o k a r a w a i s e s u e b u s u t o r e sh i m a r i h o e n o sh i k i N t e e ky o o k a i j i sh i e h u b i y a y w a a t a r u z o b o r u n o r i j u u n i n o s o o s a N y o t o o n u sh u n a k e r e w a d a r a r n a i t o k I t e e sh i m a sh I t a +fleurs_jpn_000365 h i e ch i d e r r o w a k e N s a sh I t k a k o k U b u e s t e i u k o N m a r e n a s e i s a i y u o pau pau h i i ch i n e h o ch i n o r o o d e sh i m a s a r e m i s U +fleurs_jpn_000366 s o r e d e m o pau t o o r u u k a r a n a r u b a i s u o u k e pau s u b e t e n o h o o sh I k e o m o r i a N z e N j o o n o k e e g o b o n i s a i sh i n o ch u i u o h a r a i m a sh o +fleurs_jpn_000367 k o r e r a w a t a m a n i k o N g a ts u r o k a cl k a ts u k u m o k e n o b u i ch i t e k a i g a i a pau s a m a z a m a n a t e N p o g a n a r a N d e i m a s U a N z e N y o e b u k o d o g a d e k i m a s U +fleurs_jpn_000368 sh i N n o m i e n a i ch i i d a s u N pau n a N d o pau n a f a s U t o s e N k e u k U a ch i j u k i u p e e j i i h a k U ky u e d o s u N z a i b o m a d a b a z e r e ch i e b m u n o d o k u j u n e y o s o d e r a r u +fleurs_jpn_000369 k o n o s a r i s u w a g o r a k U s e o h a j i m e t o s u r u s e N b t a k u y a pau e N g a k sh i r e d e t a y o N s e y o s u o t o s u r u t a N k e N t a N i r i pau h i N p a n i r i y o s a r e t e i m a s U +fleurs_jpn_000370 s a k o b a pau pau b u e n o s u a i d e s U k a r a k o j u cl k i r o s a N j u i ch i m a i r u h a n a r e t a r a b u r a t a sh i n a i d e pau k e N sh a g o j o o i N g i N d e w a r u k u r i s U t e i n a f u r u n a N d e s u d e pau k e r u k i n a z o sh i g a pau n a i t o o r o o s e N e n o sh I u r a o s e N g e N sh i m a sh I t a +fleurs_jpn_000371 o n a y i z u k u i r i m a sh i h a t o n o k a s o o e u r e b u i s u n o r u g a o ky u i w a k a s o o r o o o b a r a N sh i i pau k a b e n i g e k i t o ts u sh I t e pau j u u n a r a n i N g a sh i b o o sh i m a sh I t a +fleurs_jpn_000372 a sh i sh I a n a j o o h o o k k a N w a j u u g o m e d o d e s U m i s e N j u u ch i n e h a j i g a ts u n i sh u u N k o sh i pau m i s e N j u u n a n i e s a N g a s u m a r e k a i ts o sh i m a s e N d e sh I t a +fleurs_jpn_000373 b u u N n e e t o u k o t o w a w a sh i m i o m i s u r u r a t e N g o n o k e o sh i sh i b i r e s U k a r a k i I t a w o r i sh i m i N i o m i s u r u r a t e N g o r o m e sh i sh u i b i e s U t o sh i y a t o sh I k o k a o m i sh i pau n a r u n a k a n o k a t a ch i d e pau sh a k a i n o k i o o t e e g i s u r u sh u i b i t a s U t o u m e e sh i n i k a N k e e sh I t e m a s U +fleurs_jpn_000374 s u j o k o k o r e a i s u m o k a N k o o t e k e y a r y o o sh a t a j i g a h a s u r o t o k a ch i k o i t e k i m a s U w o t o d o sh i k a r i g o i n a s u m u n o g a t a r u a m a r u d e h o N n a y o r e s U +fleurs_jpn_000375 t e r e b i n o o d o o n o N t d o g e N p a ts U k a r a h a k u e g a g a d t e i m a s U e s +fleurs_jpn_000376 n o o b y o o r i t o k o o d o o n o s o o k a N k a N k e e w a pau k a y a k u sh a t a sh i n o k e N ky u u o u r a z u k e r e m o n o d e s U +fleurs_jpn_000377 s e y o o b i n a i b e N t o n a t o k a r u p a n e r o w a pau s e N j i k e N g e h U k a z s u n o k o j i N d e s i s u j o o sh i m a sh I t a +fleurs_jpn_000378 s e N e h a p e k u r e N d a i r a i g u N t a y g a t o o j a k s u r e w a r e pau h a i ch i a k o n o b y o k i n i k a N ky e e s u r u o N d a n i s o o u g u sh I t a k o t o w a r i m a s e N d e sh I t a +fleurs_jpn_000379 sh i k a sh i k a k u t e N n u b i k e cl d o o sh u n a cl t d a d o h i N d a n a n a ts u n e r i k e cl t o o sh u n e i s a N j o r o k u r a sh I t a r e k i m a s e N g e sh I t a +fleurs_jpn_000380 k a j i n o d e w a ts u u j o o t o k u b e ts u n a i N sh a k o y a pau e N t a t e i m e N t o o y o o i sh I t e i m a s U g e s t o u g a k i b u u m y a k u sh i s e z u n a i n i t o r o m a r u y o n r e s u r u t a m e r d e s U +fleurs_jpn_000381 s o r e d e o t o o ky u k a r a n a r a b a i s o k e pau s u t a n u h y o a sh I k e o m o r i pau a N z e N j i o n o k e k o n i s a i sh e i n o ch u y u o h a r a i m a sh a o +fleurs_jpn_000382 p o o k a r e g e w a r i m a s e k o r a sh I t ts u m u sh o o a r i d e a r i a t a r a sh i i sh o o n o m o k a k e d e s U +fleurs_jpn_000383 s a w a r i t o w a pau a u r i k a n o y a s e u d o o u z u pau t o k o n i s a w a N n a n i r u y a s e u d o o g u s u n o k a N s a s u o m o k u t e k i t o sh I t a r i k u r o d e n o r y o k o o s a sh i m a s U +fleurs_jpn_000384 u u n e k i t a b a r u t o k a y o u m o o d a N s u r u b a i w a pau s e i s u m i ch i k a o k u n i sh s e k u d a s a i k k o o r e n N n a k o ts U k I s u m N u s a i n i m o cl t o m e e ky o o k e r u s e i ts u d e a o s o r u j i u o d o o s o o N n a n a r i b i k i m a s U +fleurs_jpn_000385 k o k o w a pau i r i s u n o sh o k u m i N t e sh i e h a sh a e g a j i u N t a sh i n o d o o r o d o sh I a b a sh u n a r u d e pau sh a k o m i N t e i j i r e n e sh o o k o o s e g a s o o t o s u r e k a t a w a o o k o w a r h a j i b e r u n a g a y o i u sh o +fleurs_jpn_000386 k o sh i w a s a k u g e N s u r u s u u ch i o o s a r a y a s e N d e sh I t a k a s a k u g e N w a ch i e w o k u n o k e e z a i s a N a sh I u y o i m o t o z u i t e pau d i sh i s a r e u d a r o t o o n o i m a sh I t a 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+./asr.sh --ngpu 1 --stage 11 --stop_stage 13 --nj 32 --inference_nj 4 --gpu_inference true --lang jpn --inference_asr_model valid.loss.ave.pth --local_data_opts '--duration 10min --lid false --multilingual false --single_lang jpn' --use_lm false --token_type word --feats_type raw --feats_normalize utterance_mvn --asr_config conf/tuning/train_asr_s3prl_houlsby.yaml --inference_config conf/decode_asr.yaml --train_set train_10min_jpn --valid_set dev_10min_jpn --test_sets 'dev_10min_jpn test_10min_jpn' --asr_tag train_asr_s3prl_houlsby_jpn_10min --expdir test_pr --asr_stats_dir test_pr/asr_stats_jpn_10min --local_score_opts 'false false monolingual' --stage 11 "$@"; exit $? diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/tensorboard/train/events.out.tfevents.1705235238.stan.257507.0 b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/tensorboard/train/events.out.tfevents.1705235238.stan.257507.0 new file mode 100644 index 0000000000000000000000000000000000000000..167e23d4c9b66d0c1f55dd8d529cdf772364197e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/tensorboard/train/events.out.tfevents.1705235238.stan.257507.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:34eccae8aa034327f470ce175b55304ccb38596bc30e92c3f4043c555bb72b57 +size 48464852 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/tensorboard/train/events.out.tfevents.1705414921.stan.847670.0 b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/tensorboard/train/events.out.tfevents.1705414921.stan.847670.0 new file mode 100644 index 0000000000000000000000000000000000000000..c497a12d4363daae98d22d89b995ff2749aca15f --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/tensorboard/train/events.out.tfevents.1705414921.stan.847670.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9f7edf7cbd07889c58f28fa5f028a1e87a9015dbaa47a5ebfcde0372a9173251 +size 49006025 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/tensorboard/valid/events.out.tfevents.1705235238.stan.257507.1 b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/tensorboard/valid/events.out.tfevents.1705235238.stan.257507.1 new file mode 100644 index 0000000000000000000000000000000000000000..860b48c1431e270cf785bb52e2cf08df95b76f07 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/tensorboard/valid/events.out.tfevents.1705235238.stan.257507.1 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8502e03fe42ebaa7b471918c8b37a293f735cad82eef0c9cfa294c7e75bffaff +size 11190 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/tensorboard/valid/events.out.tfevents.1705414921.stan.847670.1 b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/tensorboard/valid/events.out.tfevents.1705414921.stan.847670.1 new file mode 100644 index 0000000000000000000000000000000000000000..c0c98434ff50497a5127273609a02081d6225a1e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/tensorboard/valid/events.out.tfevents.1705414921.stan.847670.1 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d6d9e630b63c16c58563efd38f627deab877f4384c5dceffe8a58f4091e973db +size 11190 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/train.1.log b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/train.1.log new file mode 100644 index 0000000000000000000000000000000000000000..323ce2327c2faad261bb6fc65d246c14cea853b3 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/train.1.log @@ -0,0 +1,1406 @@ +# python3 -m espnet2.bin.asr_train --use_preprocessor true --bpemodel none --token_type word --token_list data/jpn_token_list/word/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_jpn/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_jpn_10min/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_10min_jpn/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_jpn_10min/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_10min_jpn/text,text,text --train_shape_file test_pr/asr_stats_jpn_10min/train/text_shape.word --valid_data_path_and_name_and_type dump/raw/dev_10min_jpn/text,text,text --valid_shape_file test_pr/asr_stats_jpn_10min/valid/text_shape.word --ngpu 1 --multiprocessing_distributed True +# Started at Sun Jan 14 20:27:13 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_train.py --use_preprocessor true --bpemodel none --token_type word --token_list data/jpn_token_list/word/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_jpn/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_jpn_10min/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_10min_jpn/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_jpn_10min/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_10min_jpn/text,text,text --train_shape_file test_pr/asr_stats_jpn_10min/train/text_shape.word --valid_data_path_and_name_and_type dump/raw/dev_10min_jpn/text,text,text --valid_shape_file test_pr/asr_stats_jpn_10min/valid/text_shape.word --ngpu 1 --multiprocessing_distributed True +[stan] 2024-01-14 20:27:15,195 (asr:523) INFO: Vocabulary size: 41 +[stan] 2024-01-14 20:27:15,257 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +[stan] 2024-01-14 20:27:15,257 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +[stan] 2024-01-14 20:27:15,369 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +[stan] 2024-01-14 20:27:16,660 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +[stan] 2024-01-14 20:27:17,504 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.mask_emb.requires_grad = False +[stan] 2024-01-14 20:27:17,504 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.label_embs_concat.requires_grad = False +[stan] 2024-01-14 20:27:17,504 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.0.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,504 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.1.0.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.2.0.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.3.0.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.4.0.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.5.0.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.6.0.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_g.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_v.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,505 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,506 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,507 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,507 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,507 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,507 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,507 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,507 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,507 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,507 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,507 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,507 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.weight.requires_grad = False +[stan] 2024-01-14 20:27:17,507 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.bias.requires_grad = False +[stan] 2024-01-14 20:27:17,912 (abs_task:1275) INFO: pytorch.version=1.12.0+cu113, cuda.available=True, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True +[stan] 2024-01-14 20:27:17,914 (abs_task:1276) INFO: Model structure: +ESPnetASRModel( + (frontend): S3prlFrontend( + (upstream): S3PRLUpstream( + (upstream): UpstreamExpert( + (model): HubertModel( + (feature_extractor): ConvFeatureExtractionModel( + (conv_layers): ModuleList( + (0): Sequential( + (0): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): Fp32GroupNorm(512, 512, eps=1e-05, affine=True) + (3): GELU(approximate=none) + ) + (1): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (2): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (3): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (4): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (5): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (6): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + ) + ) + (post_extract_proj): Linear(in_features=512, out_features=768, bias=True) + (dropout_input): Dropout(p=0.1, inplace=False) + (dropout_features): Dropout(p=0.1, inplace=False) + (encoder): TransformerEncoder( + (pos_conv): Sequential( + (0): Conv1d(768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16) + (1): SamePad() + (2): GELU(approximate=none) + ) + (layers): ModuleList( + (0): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (1): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (2): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (3): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (4): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (5): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (6): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (7): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (8): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (9): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (10): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (11): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + ) + (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (final_proj): Linear(in_features=768, out_features=256, bias=True) + ) + ) + ) + (featurizer): Featurizer() + ) + (specaug): SpecAug( + (time_warp): TimeWarp(window=5, mode=bicubic) + (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq) + (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time) + ) + (normalize): UtteranceMVN(norm_means=True, norm_vars=False) + (preencoder): LinearProjection( + (linear_out): Linear(in_features=768, out_features=80, bias=True) + (dropout): Dropout(p=0.0, inplace=False) + ) + (encoder): TransformerEncoder( + (embed): Conv2dSubsampling2( + (conv): Sequential( + (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU() + (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) + (3): ReLU() + ) + (out): Sequential( + (0): Linear(in_features=9472, out_features=256, bias=True) + (1): PositionalEncoding( + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + ) + (encoders): MultiSequential( + (0): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (1): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + ) + (criterion_att): LabelSmoothingLoss( + (criterion): KLDivLoss() + ) + (ctc): CTC( + (ctc_lo): Linear(in_features=256, out_features=41, bias=True) + (ctc_loss): CTCLoss() + ) +) + +Model summary: + Class Name: ESPnetASRModel + Total Number of model parameters: 99.97 M + Number of trainable parameters: 5.27 M (5.3%) + Size: 21.08 MB + Type: torch.float32 +[stan] 2024-01-14 20:27:17,914 (abs_task:1279) INFO: Optimizer: +Adam ( +Parameter Group 0 + amsgrad: False + betas: (0.9, 0.999) + capturable: False + eps: 1e-08 + foreach: None + lr: 0.0001 + maximize: False + weight_decay: 1e-06 +) +[stan] 2024-01-14 20:27:17,914 (abs_task:1280) INFO: Scheduler: None +[stan] 2024-01-14 20:27:17,914 (abs_task:1289) INFO: Saving the configuration in test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml +[stan] 2024-01-14 20:27:18,068 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-14 20:27:18,110 (abs_task:1665) INFO: [train] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/train_10min_jpn/wav.scp", "type": "sound"} + text: {"path": "dump/raw/train_10min_jpn/text", "type": "text"} + preprocess: ) +[stan] 2024-01-14 20:27:18,110 (abs_task:1666) INFO: [train] Batch sampler: SortedBatchSampler(N-batch=13, batch_size=8, shape_file=test_pr/asr_stats_jpn_10min/train/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-14 20:27:18,110 (abs_task:1667) INFO: [train] mini-batch sizes summary: N-batch=13, mean=8.2, min=8, max=9 +[stan] 2024-01-14 20:27:18,121 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-14 20:27:18,121 (abs_task:1665) INFO: [valid] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_jpn/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_jpn/text", "type": "text"} + preprocess: ) +[stan] 2024-01-14 20:27:18,121 (abs_task:1666) INFO: [valid] Batch sampler: SortedBatchSampler(N-batch=15, batch_size=8, shape_file=test_pr/asr_stats_jpn_10min/valid/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-14 20:27:18,121 (abs_task:1667) INFO: [valid] mini-batch sizes summary: N-batch=15, mean=8.4, min=8, max=9 +[stan] 2024-01-14 20:27:18,122 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-14 20:27:18,133 (abs_task:1665) INFO: [plot_att] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_jpn/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_jpn/text", "type": "text"} + preprocess: ) +[stan] 2024-01-14 20:27:18,133 (abs_task:1666) INFO: [plot_att] Batch sampler: UnsortedBatchSampler(N-batch=126, batch_size=1, key_file=test_pr/asr_stats_jpn_10min/valid/speech_shape, +[stan] 2024-01-14 20:27:18,133 (abs_task:1667) INFO: [plot_att] mini-batch sizes summary: N-batch=3, mean=1.0, min=1, max=1 +[stan] 2024-01-14 20:27:18,166 (trainer:300) INFO: 1/30epoch started +[stan] 2024-01-14 20:27:24,135 (trainer:763) INFO: 1epoch:train:1-40batch: iter_time=0.002, forward_time=0.087, loss_ctc=42.929, loss=42.929, backward_time=0.011, grad_norm=784.002, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.593 +[stan] 2024-01-14 20:27:28,499 (trainer:763) INFO: 1epoch:train:41-80batch: iter_time=4.304e-05, forward_time=0.057, loss_ctc=33.314, loss=33.314, backward_time=0.009, grad_norm=107.809, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:27:32,813 (trainer:763) INFO: 1epoch:train:81-120batch: iter_time=4.179e-05, forward_time=0.056, loss_ctc=32.920, loss=32.920, backward_time=0.009, grad_norm=115.145, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-14 20:27:37,082 (trainer:763) INFO: 1epoch:train:121-160batch: iter_time=4.480e-05, forward_time=0.056, loss_ctc=32.396, loss=32.396, backward_time=0.009, grad_norm=100.261, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-14 20:27:41,521 (trainer:763) INFO: 1epoch:train:161-200batch: iter_time=4.261e-05, forward_time=0.058, loss_ctc=33.057, loss=33.057, backward_time=0.009, grad_norm=57.045, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-14 20:27:45,845 (trainer:763) INFO: 1epoch:train:201-240batch: iter_time=4.293e-05, forward_time=0.056, loss_ctc=31.856, loss=31.856, backward_time=0.009, grad_norm=77.780, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 20:27:50,329 (trainer:763) INFO: 1epoch:train:241-280batch: iter_time=4.155e-05, forward_time=0.058, loss_ctc=29.818, loss=29.818, backward_time=0.009, grad_norm=115.389, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-14 20:27:54,465 (trainer:763) INFO: 1epoch:train:281-320batch: iter_time=4.587e-05, forward_time=0.054, loss_ctc=23.496, loss=23.496, backward_time=0.008, grad_norm=116.409, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.413 +[stan] 2024-01-14 20:27:59,105 (trainer:763) INFO: 1epoch:train:321-360batch: iter_time=4.424e-05, forward_time=0.060, loss_ctc=22.862, loss=22.862, backward_time=0.009, grad_norm=128.878, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.464 +[stan] 2024-01-14 20:28:03,427 (trainer:763) INFO: 1epoch:train:361-400batch: iter_time=4.496e-05, forward_time=0.056, loss_ctc=18.266, loss=18.266, backward_time=0.009, grad_norm=100.815, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 20:28:07,786 (trainer:763) INFO: 1epoch:train:401-440batch: iter_time=4.462e-05, forward_time=0.057, loss_ctc=16.477, loss=16.477, backward_time=0.009, grad_norm=96.929, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:28:12,153 (trainer:763) INFO: 1epoch:train:441-480batch: iter_time=4.599e-05, forward_time=0.057, loss_ctc=15.155, loss=15.155, backward_time=0.009, grad_norm=131.635, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:28:16,534 (trainer:763) INFO: 1epoch:train:481-520batch: iter_time=4.090e-05, forward_time=0.057, loss_ctc=13.791, loss=13.791, backward_time=0.009, grad_norm=86.105, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:28:20,914 (trainer:763) INFO: 1epoch:train:521-560batch: iter_time=4.137e-05, forward_time=0.057, loss_ctc=12.536, loss=12.536, backward_time=0.009, grad_norm=99.002, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:28:25,358 (trainer:763) INFO: 1epoch:train:561-600batch: iter_time=4.295e-05, forward_time=0.058, loss_ctc=11.921, loss=11.921, backward_time=0.009, grad_norm=93.049, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-14 20:28:29,756 (trainer:763) INFO: 1epoch:train:601-640batch: iter_time=4.444e-05, forward_time=0.057, loss_ctc=10.988, loss=10.988, backward_time=0.009, grad_norm=98.525, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 20:28:34,070 (trainer:763) INFO: 1epoch:train:641-680batch: iter_time=4.257e-05, forward_time=0.056, loss_ctc=10.334, loss=10.334, backward_time=0.008, grad_norm=143.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-14 20:28:38,532 (trainer:763) INFO: 1epoch:train:681-720batch: iter_time=4.401e-05, forward_time=0.058, loss_ctc=10.126, loss=10.126, backward_time=0.009, grad_norm=97.740, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 20:28:42,820 (trainer:763) INFO: 1epoch:train:721-760batch: iter_time=4.347e-05, forward_time=0.056, loss_ctc=9.168, loss=9.168, backward_time=0.009, grad_norm=87.513, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-14 20:28:47,202 (trainer:763) INFO: 1epoch:train:761-800batch: iter_time=4.139e-05, forward_time=0.057, loss_ctc=9.151, loss=9.151, backward_time=0.009, grad_norm=111.398, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-14 20:28:51,687 (trainer:354) INFO: 1epoch results: [train] iter_time=1.509e-04, forward_time=0.059, loss_ctc=21.030, loss=21.030, backward_time=0.009, grad_norm=137.428, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445, time=1 minute and 29.08 seconds, total_count=800, gpu_max_cached_mem_GB=5.986, [valid] loss_ctc=31.642, cer_ctc=0.239, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=31.642, time=1.07 seconds, total_count=15, gpu_max_cached_mem_GB=6.701, [att_plot] time=3.36 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:28:52,711 (trainer:417) INFO: The best model has been updated: valid.loss +[stan] 2024-01-14 20:28:52,711 (trainer:288) INFO: 2/30epoch started. Estimated time to finish: 45 minutes and 41.82 seconds +[stan] 2024-01-14 20:28:57,466 (trainer:763) INFO: 2epoch:train:1-40batch: iter_time=0.002, forward_time=0.059, loss_ctc=8.808, loss=8.808, backward_time=0.009, grad_norm=95.095, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.475 +[stan] 2024-01-14 20:29:01,838 (trainer:763) INFO: 2epoch:train:41-80batch: iter_time=4.292e-05, forward_time=0.057, loss_ctc=8.089, loss=8.089, backward_time=0.009, grad_norm=123.093, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:29:06,158 (trainer:763) INFO: 2epoch:train:81-120batch: iter_time=4.258e-05, forward_time=0.056, loss_ctc=7.891, loss=7.891, backward_time=0.009, grad_norm=105.179, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 20:29:10,531 (trainer:763) INFO: 2epoch:train:121-160batch: iter_time=4.623e-05, forward_time=0.057, loss_ctc=7.319, loss=7.319, backward_time=0.009, grad_norm=132.936, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:29:14,919 (trainer:763) INFO: 2epoch:train:161-200batch: iter_time=4.317e-05, forward_time=0.057, loss_ctc=6.935, loss=6.935, backward_time=0.009, grad_norm=116.481, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:29:19,378 (trainer:763) INFO: 2epoch:train:201-240batch: iter_time=4.223e-05, forward_time=0.058, loss_ctc=7.000, loss=7.000, backward_time=0.009, grad_norm=111.722, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 20:29:23,838 (trainer:763) INFO: 2epoch:train:241-280batch: iter_time=4.365e-05, forward_time=0.058, loss_ctc=6.849, loss=6.849, backward_time=0.009, grad_norm=128.561, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 20:29:28,113 (trainer:763) INFO: 2epoch:train:281-320batch: iter_time=4.585e-05, forward_time=0.056, loss_ctc=6.222, loss=6.222, backward_time=0.009, grad_norm=96.644, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-14 20:29:32,583 (trainer:763) INFO: 2epoch:train:321-360batch: iter_time=4.384e-05, forward_time=0.058, loss_ctc=6.630, loss=6.630, backward_time=0.009, grad_norm=108.876, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 20:29:37,056 (trainer:763) INFO: 2epoch:train:361-400batch: iter_time=4.517e-05, forward_time=0.058, loss_ctc=6.173, loss=6.173, backward_time=0.009, grad_norm=103.929, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 20:29:41,420 (trainer:763) INFO: 2epoch:train:401-440batch: iter_time=4.232e-05, forward_time=0.057, loss_ctc=5.807, loss=5.807, backward_time=0.009, grad_norm=102.967, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:29:45,893 (trainer:763) INFO: 2epoch:train:441-480batch: iter_time=4.519e-05, forward_time=0.058, loss_ctc=5.807, loss=5.807, backward_time=0.009, grad_norm=101.417, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 20:29:50,221 (trainer:763) INFO: 2epoch:train:481-520batch: iter_time=4.473e-05, forward_time=0.057, loss_ctc=5.534, loss=5.534, backward_time=0.009, grad_norm=103.059, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-14 20:29:54,605 (trainer:763) INFO: 2epoch:train:521-560batch: iter_time=4.645e-05, forward_time=0.057, loss_ctc=5.374, loss=5.374, backward_time=0.009, grad_norm=121.299, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:29:58,920 (trainer:763) INFO: 2epoch:train:561-600batch: iter_time=4.543e-05, forward_time=0.056, loss_ctc=4.885, loss=4.885, backward_time=0.009, grad_norm=93.149, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-14 20:30:03,349 (trainer:763) INFO: 2epoch:train:601-640batch: iter_time=4.405e-05, forward_time=0.058, loss_ctc=5.097, loss=5.097, backward_time=0.009, grad_norm=110.953, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 20:30:07,775 (trainer:763) INFO: 2epoch:train:641-680batch: iter_time=4.244e-05, forward_time=0.058, loss_ctc=5.166, loss=5.166, backward_time=0.009, grad_norm=95.075, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 20:30:12,230 (trainer:763) INFO: 2epoch:train:681-720batch: iter_time=4.315e-05, forward_time=0.058, loss_ctc=5.001, loss=5.001, backward_time=0.009, grad_norm=109.183, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:30:16,645 (trainer:763) INFO: 2epoch:train:721-760batch: iter_time=4.668e-05, forward_time=0.058, loss_ctc=4.745, loss=4.745, backward_time=0.009, grad_norm=122.213, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 20:30:21,030 (trainer:763) INFO: 2epoch:train:761-800batch: iter_time=4.000e-05, forward_time=0.057, loss_ctc=4.752, loss=4.752, backward_time=0.009, grad_norm=114.944, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-14 20:30:25,284 (trainer:354) INFO: 2epoch results: [train] iter_time=1.621e-04, forward_time=0.058, loss_ctc=6.204, loss=6.204, backward_time=0.009, grad_norm=109.839, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.4 seconds, total_count=1600, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=34.574, cer_ctc=0.239, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=34.574, time=1.08 seconds, total_count=30, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.1 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:30:26,149 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:30:26,150 (trainer:288) INFO: 3/30epoch started. Estimated time to finish: 43 minutes and 51.78 seconds +[stan] 2024-01-14 20:30:30,780 (trainer:763) INFO: 3epoch:train:1-40batch: iter_time=0.003, forward_time=0.057, loss_ctc=4.470, loss=4.470, backward_time=0.009, grad_norm=91.903, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.462 +[stan] 2024-01-14 20:30:35,234 (trainer:763) INFO: 3epoch:train:41-80batch: iter_time=4.353e-05, forward_time=0.058, loss_ctc=4.554, loss=4.554, backward_time=0.009, grad_norm=93.675, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:30:39,713 (trainer:763) INFO: 3epoch:train:81-120batch: iter_time=4.303e-05, forward_time=0.058, loss_ctc=4.606, loss=4.606, backward_time=0.009, grad_norm=105.335, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-14 20:30:44,126 (trainer:763) INFO: 3epoch:train:121-160batch: iter_time=4.541e-05, forward_time=0.058, loss_ctc=4.379, loss=4.379, backward_time=0.009, grad_norm=107.014, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 20:30:48,507 (trainer:763) INFO: 3epoch:train:161-200batch: iter_time=4.201e-05, forward_time=0.057, loss_ctc=4.217, loss=4.217, backward_time=0.009, grad_norm=93.872, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:30:52,787 (trainer:763) INFO: 3epoch:train:201-240batch: iter_time=4.369e-05, forward_time=0.056, loss_ctc=4.024, loss=4.024, backward_time=0.009, grad_norm=90.872, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-14 20:30:57,380 (trainer:763) INFO: 3epoch:train:241-280batch: iter_time=4.454e-05, forward_time=0.060, loss_ctc=4.659, loss=4.659, backward_time=0.009, grad_norm=108.171, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.459 +[stan] 2024-01-14 20:31:01,749 (trainer:763) INFO: 3epoch:train:281-320batch: iter_time=4.600e-05, forward_time=0.057, loss_ctc=4.293, loss=4.293, backward_time=0.009, grad_norm=107.039, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:31:06,137 (trainer:763) INFO: 3epoch:train:321-360batch: iter_time=4.440e-05, forward_time=0.057, loss_ctc=3.841, loss=3.841, backward_time=0.009, grad_norm=86.859, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:31:10,448 (trainer:763) INFO: 3epoch:train:361-400batch: iter_time=4.367e-05, forward_time=0.056, loss_ctc=3.694, loss=3.694, backward_time=0.009, grad_norm=121.408, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-14 20:31:14,923 (trainer:763) INFO: 3epoch:train:401-440batch: iter_time=4.328e-05, forward_time=0.058, loss_ctc=4.023, loss=4.023, backward_time=0.009, grad_norm=103.298, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 20:31:19,275 (trainer:763) INFO: 3epoch:train:441-480batch: iter_time=4.283e-05, forward_time=0.057, loss_ctc=3.548, loss=3.548, backward_time=0.009, grad_norm=82.395, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:31:23,630 (trainer:763) INFO: 3epoch:train:481-520batch: iter_time=4.692e-05, forward_time=0.057, loss_ctc=3.654, loss=3.654, backward_time=0.009, grad_norm=86.961, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:31:28,046 (trainer:763) INFO: 3epoch:train:521-560batch: iter_time=4.619e-05, forward_time=0.058, loss_ctc=4.090, loss=4.090, backward_time=0.009, grad_norm=85.062, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 20:31:32,388 (trainer:763) INFO: 3epoch:train:561-600batch: iter_time=4.489e-05, forward_time=0.057, loss_ctc=3.557, loss=3.557, backward_time=0.009, grad_norm=83.175, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-14 20:31:36,926 (trainer:763) INFO: 3epoch:train:601-640batch: iter_time=4.454e-05, forward_time=0.059, loss_ctc=4.159, loss=4.159, backward_time=0.009, grad_norm=93.650, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.454 +[stan] 2024-01-14 20:31:41,274 (trainer:763) INFO: 3epoch:train:641-680batch: iter_time=4.297e-05, forward_time=0.057, loss_ctc=3.502, loss=3.502, backward_time=0.009, grad_norm=92.647, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:31:45,659 (trainer:763) INFO: 3epoch:train:681-720batch: iter_time=4.633e-05, forward_time=0.057, loss_ctc=3.599, loss=3.599, backward_time=0.009, grad_norm=89.748, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:31:50,115 (trainer:763) INFO: 3epoch:train:721-760batch: iter_time=4.301e-05, forward_time=0.058, loss_ctc=3.627, loss=3.627, backward_time=0.009, grad_norm=87.459, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 20:31:54,366 (trainer:763) INFO: 3epoch:train:761-800batch: iter_time=4.209e-05, forward_time=0.055, loss_ctc=3.544, loss=3.544, backward_time=0.009, grad_norm=80.095, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-14 20:31:58,606 (trainer:354) INFO: 3epoch results: [train] iter_time=1.879e-04, forward_time=0.057, loss_ctc=4.002, loss=4.002, backward_time=0.009, grad_norm=94.532, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.29 seconds, total_count=2400, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=37.962, cer_ctc=0.244, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=37.962, time=1.09 seconds, total_count=45, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.07 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:31:59,561 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:31:59,562 (trainer:288) INFO: 4/30epoch started. Estimated time to finish: 42 minutes and 12.56 seconds +[stan] 2024-01-14 20:32:04,214 (trainer:763) INFO: 4epoch:train:1-40batch: iter_time=0.002, forward_time=0.058, loss_ctc=3.556, loss=3.556, backward_time=0.009, grad_norm=92.283, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.464 +[stan] 2024-01-14 20:32:08,743 (trainer:763) INFO: 4epoch:train:41-80batch: iter_time=4.543e-05, forward_time=0.059, loss_ctc=3.676, loss=3.676, backward_time=0.009, grad_norm=88.766, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.453 +[stan] 2024-01-14 20:32:13,090 (trainer:763) INFO: 4epoch:train:81-120batch: iter_time=4.280e-05, forward_time=0.057, loss_ctc=3.374, loss=3.374, backward_time=0.009, grad_norm=90.062, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:32:17,500 (trainer:763) INFO: 4epoch:train:121-160batch: iter_time=4.474e-05, forward_time=0.058, loss_ctc=3.113, loss=3.113, backward_time=0.009, grad_norm=74.513, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 20:32:21,857 (trainer:763) INFO: 4epoch:train:161-200batch: iter_time=4.329e-05, forward_time=0.057, loss_ctc=3.242, loss=3.242, backward_time=0.009, grad_norm=93.880, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:32:26,230 (trainer:763) INFO: 4epoch:train:201-240batch: iter_time=4.286e-05, forward_time=0.057, loss_ctc=3.407, loss=3.407, backward_time=0.009, grad_norm=98.445, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:32:30,747 (trainer:763) INFO: 4epoch:train:241-280batch: iter_time=4.410e-05, forward_time=0.059, loss_ctc=3.440, loss=3.440, backward_time=0.009, grad_norm=81.137, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.452 +[stan] 2024-01-14 20:32:35,123 (trainer:763) INFO: 4epoch:train:281-320batch: iter_time=4.413e-05, forward_time=0.057, loss_ctc=3.250, loss=3.250, backward_time=0.009, grad_norm=78.260, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:32:39,374 (trainer:763) INFO: 4epoch:train:321-360batch: iter_time=4.388e-05, forward_time=0.056, loss_ctc=2.977, loss=2.977, backward_time=0.009, grad_norm=82.269, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-14 20:32:43,887 (trainer:763) INFO: 4epoch:train:361-400batch: iter_time=4.555e-05, forward_time=0.059, loss_ctc=3.385, loss=3.385, backward_time=0.009, grad_norm=81.137, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.451 +[stan] 2024-01-14 20:32:48,208 (trainer:763) INFO: 4epoch:train:401-440batch: iter_time=4.352e-05, forward_time=0.056, loss_ctc=3.186, loss=3.186, backward_time=0.009, grad_norm=74.501, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 20:32:52,593 (trainer:763) INFO: 4epoch:train:441-480batch: iter_time=4.369e-05, forward_time=0.057, loss_ctc=3.280, loss=3.280, backward_time=0.009, grad_norm=77.032, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:32:57,097 (trainer:763) INFO: 4epoch:train:481-520batch: iter_time=4.472e-05, forward_time=0.059, loss_ctc=3.040, loss=3.040, backward_time=0.009, grad_norm=73.109, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-14 20:33:01,368 (trainer:763) INFO: 4epoch:train:521-560batch: iter_time=4.337e-05, forward_time=0.056, loss_ctc=3.006, loss=3.006, backward_time=0.009, grad_norm=93.147, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-14 20:33:05,818 (trainer:763) INFO: 4epoch:train:561-600batch: iter_time=4.387e-05, forward_time=0.058, loss_ctc=3.061, loss=3.061, backward_time=0.009, grad_norm=88.043, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:33:10,271 (trainer:763) INFO: 4epoch:train:601-640batch: iter_time=4.601e-05, forward_time=0.058, loss_ctc=3.127, loss=3.127, backward_time=0.009, grad_norm=79.438, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:33:14,535 (trainer:763) INFO: 4epoch:train:641-680batch: iter_time=4.456e-05, forward_time=0.056, loss_ctc=2.941, loss=2.941, backward_time=0.009, grad_norm=87.485, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-14 20:33:18,986 (trainer:763) INFO: 4epoch:train:681-720batch: iter_time=4.568e-05, forward_time=0.058, loss_ctc=3.028, loss=3.028, backward_time=0.009, grad_norm=84.440, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:33:23,336 (trainer:763) INFO: 4epoch:train:721-760batch: iter_time=4.342e-05, forward_time=0.057, loss_ctc=2.978, loss=2.978, backward_time=0.009, grad_norm=76.108, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:33:27,699 (trainer:763) INFO: 4epoch:train:761-800batch: iter_time=4.362e-05, forward_time=0.057, loss_ctc=2.704, loss=2.704, backward_time=0.009, grad_norm=81.795, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:33:31,893 (trainer:354) INFO: 4epoch results: [train] iter_time=1.604e-04, forward_time=0.057, loss_ctc=3.188, loss=3.188, backward_time=0.009, grad_norm=83.793, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.22 seconds, total_count=3200, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=41.338, cer_ctc=0.241, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=41.338, time=1.07 seconds, total_count=60, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:33:32,778 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:33:32,778 (trainer:288) INFO: 5/30epoch started. Estimated time to finish: 40 minutes and 34.98 seconds +[stan] 2024-01-14 20:33:37,397 (trainer:763) INFO: 5epoch:train:1-40batch: iter_time=0.003, forward_time=0.057, loss_ctc=2.973, loss=2.973, backward_time=0.009, grad_norm=72.964, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.461 +[stan] 2024-01-14 20:33:41,908 (trainer:763) INFO: 5epoch:train:41-80batch: iter_time=4.430e-05, forward_time=0.059, loss_ctc=3.044, loss=3.044, backward_time=0.009, grad_norm=79.138, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.451 +[stan] 2024-01-14 20:33:46,333 (trainer:763) INFO: 5epoch:train:81-120batch: iter_time=4.343e-05, forward_time=0.058, loss_ctc=2.954, loss=2.954, backward_time=0.009, grad_norm=79.325, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 20:33:50,721 (trainer:763) INFO: 5epoch:train:121-160batch: iter_time=4.435e-05, forward_time=0.057, loss_ctc=2.896, loss=2.896, backward_time=0.009, grad_norm=76.660, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:33:55,006 (trainer:763) INFO: 5epoch:train:161-200batch: iter_time=4.360e-05, forward_time=0.056, loss_ctc=2.572, loss=2.572, backward_time=0.009, grad_norm=76.486, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-14 20:33:59,577 (trainer:763) INFO: 5epoch:train:201-240batch: iter_time=4.429e-05, forward_time=0.060, loss_ctc=3.261, loss=3.261, backward_time=0.010, grad_norm=89.322, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.457 +[stan] 2024-01-14 20:34:03,932 (trainer:763) INFO: 5epoch:train:241-280batch: iter_time=4.691e-05, forward_time=0.057, loss_ctc=2.829, loss=2.829, backward_time=0.009, grad_norm=75.584, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:34:08,251 (trainer:763) INFO: 5epoch:train:281-320batch: iter_time=4.448e-05, forward_time=0.056, loss_ctc=2.743, loss=2.743, backward_time=0.009, grad_norm=85.005, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 20:34:12,710 (trainer:763) INFO: 5epoch:train:321-360batch: iter_time=4.707e-05, forward_time=0.058, loss_ctc=2.942, loss=2.942, backward_time=0.009, grad_norm=80.638, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 20:34:17,021 (trainer:763) INFO: 5epoch:train:361-400batch: iter_time=4.806e-05, forward_time=0.056, loss_ctc=2.718, loss=2.718, backward_time=0.009, grad_norm=84.649, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-14 20:34:21,455 (trainer:763) INFO: 5epoch:train:401-440batch: iter_time=4.451e-05, forward_time=0.058, loss_ctc=2.890, loss=2.890, backward_time=0.009, grad_norm=79.468, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 20:34:25,846 (trainer:763) INFO: 5epoch:train:441-480batch: iter_time=4.629e-05, forward_time=0.057, loss_ctc=2.871, loss=2.871, backward_time=0.009, grad_norm=67.473, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:34:30,196 (trainer:763) INFO: 5epoch:train:481-520batch: iter_time=4.320e-05, forward_time=0.057, loss_ctc=2.577, loss=2.577, backward_time=0.009, grad_norm=77.838, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:34:34,508 (trainer:763) INFO: 5epoch:train:521-560batch: iter_time=4.465e-05, forward_time=0.056, loss_ctc=2.515, loss=2.515, backward_time=0.009, grad_norm=71.647, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-14 20:34:38,972 (trainer:763) INFO: 5epoch:train:561-600batch: iter_time=4.582e-05, forward_time=0.058, loss_ctc=2.864, loss=2.864, backward_time=0.009, grad_norm=77.971, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 20:34:43,363 (trainer:763) INFO: 5epoch:train:601-640batch: iter_time=4.357e-05, forward_time=0.057, loss_ctc=2.660, loss=2.660, backward_time=0.009, grad_norm=71.425, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:34:47,864 (trainer:763) INFO: 5epoch:train:641-680batch: iter_time=4.545e-05, forward_time=0.059, loss_ctc=2.729, loss=2.729, backward_time=0.009, grad_norm=92.648, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-14 20:34:52,063 (trainer:763) INFO: 5epoch:train:681-720batch: iter_time=4.491e-05, forward_time=0.055, loss_ctc=2.500, loss=2.500, backward_time=0.009, grad_norm=66.545, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.420 +[stan] 2024-01-14 20:34:56,518 (trainer:763) INFO: 5epoch:train:721-760batch: iter_time=4.441e-05, forward_time=0.058, loss_ctc=2.765, loss=2.765, backward_time=0.009, grad_norm=72.371, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:35:00,873 (trainer:763) INFO: 5epoch:train:761-800batch: iter_time=4.245e-05, forward_time=0.057, loss_ctc=2.837, loss=2.837, backward_time=0.009, grad_norm=81.785, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:35:05,173 (trainer:354) INFO: 5epoch results: [train] iter_time=1.842e-04, forward_time=0.057, loss_ctc=2.807, loss=2.807, backward_time=0.009, grad_norm=77.947, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.17 seconds, total_count=4000, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=43.044, cer_ctc=0.237, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=43.044, time=1.09 seconds, total_count=75, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.13 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:35:06,082 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:35:06,082 (trainer:288) INFO: 6/30epoch started. Estimated time to finish: 38 minutes and 59.58 seconds +[stan] 2024-01-14 20:35:10,849 (trainer:763) INFO: 6epoch:train:1-40batch: iter_time=0.003, forward_time=0.059, loss_ctc=2.803, loss=2.803, backward_time=0.009, grad_norm=75.285, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.476 +[stan] 2024-01-14 20:35:15,356 (trainer:763) INFO: 6epoch:train:41-80batch: iter_time=4.832e-05, forward_time=0.059, loss_ctc=2.789, loss=2.789, backward_time=0.009, grad_norm=76.809, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.451 +[stan] 2024-01-14 20:35:19,720 (trainer:763) INFO: 6epoch:train:81-120batch: iter_time=4.641e-05, forward_time=0.057, loss_ctc=2.508, loss=2.508, backward_time=0.009, grad_norm=68.330, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:35:24,098 (trainer:763) INFO: 6epoch:train:121-160batch: iter_time=4.480e-05, forward_time=0.057, loss_ctc=2.521, loss=2.521, backward_time=0.009, grad_norm=68.997, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:35:28,453 (trainer:763) INFO: 6epoch:train:161-200batch: iter_time=4.764e-05, forward_time=0.057, loss_ctc=2.587, loss=2.587, backward_time=0.009, grad_norm=68.663, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:35:32,870 (trainer:763) INFO: 6epoch:train:201-240batch: iter_time=4.421e-05, forward_time=0.058, loss_ctc=2.544, loss=2.544, backward_time=0.009, grad_norm=67.180, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 20:35:37,282 (trainer:763) INFO: 6epoch:train:241-280batch: iter_time=4.736e-05, forward_time=0.058, loss_ctc=2.658, loss=2.658, backward_time=0.009, grad_norm=68.417, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 20:35:41,582 (trainer:763) INFO: 6epoch:train:281-320batch: iter_time=4.396e-05, forward_time=0.056, loss_ctc=2.355, loss=2.355, backward_time=0.009, grad_norm=73.406, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-14 20:35:46,089 (trainer:763) INFO: 6epoch:train:321-360batch: iter_time=4.315e-05, forward_time=0.059, loss_ctc=2.768, loss=2.768, backward_time=0.009, grad_norm=73.175, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.451 +[stan] 2024-01-14 20:35:50,656 (trainer:763) INFO: 6epoch:train:361-400batch: iter_time=4.622e-05, forward_time=0.060, loss_ctc=2.774, loss=2.774, backward_time=0.009, grad_norm=72.473, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.457 +[stan] 2024-01-14 20:35:54,931 (trainer:763) INFO: 6epoch:train:401-440batch: iter_time=4.758e-05, forward_time=0.056, loss_ctc=2.516, loss=2.516, backward_time=0.009, grad_norm=66.302, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-14 20:35:59,323 (trainer:763) INFO: 6epoch:train:441-480batch: iter_time=4.473e-05, forward_time=0.057, loss_ctc=2.497, loss=2.497, backward_time=0.009, grad_norm=68.311, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:36:03,500 (trainer:763) INFO: 6epoch:train:481-520batch: iter_time=4.434e-05, forward_time=0.055, loss_ctc=2.206, loss=2.206, backward_time=0.009, grad_norm=70.577, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.418 +[stan] 2024-01-14 20:36:08,174 (trainer:763) INFO: 6epoch:train:521-560batch: iter_time=4.481e-05, forward_time=0.061, loss_ctc=2.749, loss=2.749, backward_time=0.009, grad_norm=74.907, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.467 +[stan] 2024-01-14 20:36:12,574 (trainer:763) INFO: 6epoch:train:561-600batch: iter_time=4.611e-05, forward_time=0.057, loss_ctc=2.463, loss=2.463, backward_time=0.009, grad_norm=70.034, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 20:36:16,913 (trainer:763) INFO: 6epoch:train:601-640batch: iter_time=4.581e-05, forward_time=0.057, loss_ctc=2.491, loss=2.491, backward_time=0.009, grad_norm=74.644, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-14 20:36:21,289 (trainer:763) INFO: 6epoch:train:641-680batch: iter_time=4.639e-05, forward_time=0.057, loss_ctc=2.519, loss=2.519, backward_time=0.009, grad_norm=76.302, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:36:25,649 (trainer:763) INFO: 6epoch:train:681-720batch: iter_time=4.636e-05, forward_time=0.057, loss_ctc=2.362, loss=2.362, backward_time=0.009, grad_norm=68.372, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:36:30,005 (trainer:763) INFO: 6epoch:train:721-760batch: iter_time=4.595e-05, forward_time=0.057, loss_ctc=2.278, loss=2.278, backward_time=0.009, grad_norm=65.742, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:36:34,506 (trainer:763) INFO: 6epoch:train:761-800batch: iter_time=4.287e-05, forward_time=0.059, loss_ctc=2.611, loss=2.611, backward_time=0.009, grad_norm=71.792, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-14 20:36:38,710 (trainer:354) INFO: 6epoch results: [train] iter_time=2.073e-04, forward_time=0.058, loss_ctc=2.550, loss=2.550, backward_time=0.009, grad_norm=70.986, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442, time=1 minute and 28.51 seconds, total_count=4800, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=45.231, cer_ctc=0.244, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=45.231, time=1.08 seconds, total_count=90, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.04 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:36:39,621 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:36:39,621 (trainer:288) INFO: 7/30epoch started. Estimated time to finish: 37 minutes and 25.82 seconds +[stan] 2024-01-14 20:36:44,253 (trainer:763) INFO: 7epoch:train:1-40batch: iter_time=0.003, forward_time=0.057, loss_ctc=2.522, loss=2.522, backward_time=0.009, grad_norm=83.906, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.462 +[stan] 2024-01-14 20:36:48,643 (trainer:763) INFO: 7epoch:train:41-80batch: iter_time=4.658e-05, forward_time=0.057, loss_ctc=2.406, loss=2.406, backward_time=0.009, grad_norm=75.496, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:36:53,197 (trainer:763) INFO: 7epoch:train:81-120batch: iter_time=4.636e-05, forward_time=0.059, loss_ctc=2.502, loss=2.502, backward_time=0.009, grad_norm=72.662, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.455 +[stan] 2024-01-14 20:36:57,412 (trainer:763) INFO: 7epoch:train:121-160batch: iter_time=4.924e-05, forward_time=0.055, loss_ctc=2.130, loss=2.130, backward_time=0.009, grad_norm=66.336, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.421 +[stan] 2024-01-14 20:37:01,933 (trainer:763) INFO: 7epoch:train:161-200batch: iter_time=4.693e-05, forward_time=0.059, loss_ctc=2.400, loss=2.400, backward_time=0.009, grad_norm=71.356, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.452 +[stan] 2024-01-14 20:37:06,361 (trainer:763) INFO: 7epoch:train:201-240batch: iter_time=4.538e-05, forward_time=0.058, loss_ctc=2.302, loss=2.302, backward_time=0.009, grad_norm=67.916, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 20:37:10,719 (trainer:763) INFO: 7epoch:train:241-280batch: iter_time=4.794e-05, forward_time=0.057, loss_ctc=2.351, loss=2.351, backward_time=0.009, grad_norm=67.383, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:37:15,153 (trainer:763) INFO: 7epoch:train:281-320batch: iter_time=4.417e-05, forward_time=0.058, loss_ctc=2.421, loss=2.421, backward_time=0.009, grad_norm=65.900, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 20:37:19,424 (trainer:763) INFO: 7epoch:train:321-360batch: iter_time=4.598e-05, forward_time=0.056, loss_ctc=2.232, loss=2.232, backward_time=0.009, grad_norm=62.973, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-14 20:37:23,906 (trainer:763) INFO: 7epoch:train:361-400batch: iter_time=4.366e-05, forward_time=0.059, loss_ctc=2.411, loss=2.411, backward_time=0.009, grad_norm=70.562, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-14 20:37:28,300 (trainer:763) INFO: 7epoch:train:401-440batch: iter_time=4.391e-05, forward_time=0.057, loss_ctc=2.298, loss=2.298, backward_time=0.009, grad_norm=65.691, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:37:32,633 (trainer:763) INFO: 7epoch:train:441-480batch: iter_time=4.455e-05, forward_time=0.057, loss_ctc=2.361, loss=2.361, backward_time=0.009, grad_norm=68.037, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-14 20:37:37,067 (trainer:763) INFO: 7epoch:train:481-520batch: iter_time=4.599e-05, forward_time=0.058, loss_ctc=2.396, loss=2.396, backward_time=0.009, grad_norm=62.710, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 20:37:41,510 (trainer:763) INFO: 7epoch:train:521-560batch: iter_time=4.511e-05, forward_time=0.058, loss_ctc=2.598, loss=2.598, backward_time=0.009, grad_norm=74.347, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-14 20:37:45,807 (trainer:763) INFO: 7epoch:train:561-600batch: iter_time=4.914e-05, forward_time=0.056, loss_ctc=2.154, loss=2.154, backward_time=0.009, grad_norm=61.730, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-14 20:37:50,281 (trainer:763) INFO: 7epoch:train:601-640batch: iter_time=4.455e-05, forward_time=0.058, loss_ctc=2.514, loss=2.514, backward_time=0.009, grad_norm=65.482, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 20:37:54,689 (trainer:763) INFO: 7epoch:train:641-680batch: iter_time=4.410e-05, forward_time=0.058, loss_ctc=2.392, loss=2.392, backward_time=0.009, grad_norm=63.093, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 20:37:58,945 (trainer:763) INFO: 7epoch:train:681-720batch: iter_time=4.618e-05, forward_time=0.056, loss_ctc=2.067, loss=2.067, backward_time=0.009, grad_norm=61.438, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-14 20:38:03,410 (trainer:763) INFO: 7epoch:train:721-760batch: iter_time=4.674e-05, forward_time=0.058, loss_ctc=2.307, loss=2.307, backward_time=0.009, grad_norm=72.475, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 20:38:07,862 (trainer:763) INFO: 7epoch:train:761-800batch: iter_time=4.496e-05, forward_time=0.058, loss_ctc=2.343, loss=2.343, backward_time=0.009, grad_norm=65.735, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:38:12,082 (trainer:354) INFO: 7epoch results: [train] iter_time=2.143e-04, forward_time=0.057, loss_ctc=2.355, loss=2.355, backward_time=0.009, grad_norm=68.261, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.32 seconds, total_count=5600, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=45.487, cer_ctc=0.232, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=45.487, time=1.09 seconds, total_count=105, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:38:13,112 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:38:13,112 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/6epoch.pth +[stan] 2024-01-14 20:38:13,112 (trainer:288) INFO: 8/30epoch started. Estimated time to finish: 35 minutes and 51.97 seconds +[stan] 2024-01-14 20:38:17,775 (trainer:763) INFO: 8epoch:train:1-40batch: iter_time=0.002, forward_time=0.058, loss_ctc=2.252, loss=2.252, backward_time=0.009, grad_norm=71.223, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.466 +[stan] 2024-01-14 20:38:22,163 (trainer:763) INFO: 8epoch:train:41-80batch: iter_time=4.401e-05, forward_time=0.057, loss_ctc=2.185, loss=2.185, backward_time=0.009, grad_norm=63.271, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:38:26,529 (trainer:763) INFO: 8epoch:train:81-120batch: iter_time=4.333e-05, forward_time=0.057, loss_ctc=2.198, loss=2.198, backward_time=0.009, grad_norm=66.183, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:38:30,895 (trainer:763) INFO: 8epoch:train:121-160batch: iter_time=4.536e-05, forward_time=0.057, loss_ctc=2.170, loss=2.170, backward_time=0.009, grad_norm=66.902, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:38:35,292 (trainer:763) INFO: 8epoch:train:161-200batch: iter_time=5.069e-05, forward_time=0.057, loss_ctc=2.060, loss=2.060, backward_time=0.009, grad_norm=60.057, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 20:38:39,641 (trainer:763) INFO: 8epoch:train:201-240batch: iter_time=4.484e-05, forward_time=0.057, loss_ctc=2.116, loss=2.116, backward_time=0.009, grad_norm=61.746, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:38:44,178 (trainer:763) INFO: 8epoch:train:241-280batch: iter_time=4.398e-05, forward_time=0.059, loss_ctc=2.284, loss=2.284, backward_time=0.009, grad_norm=69.273, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.454 +[stan] 2024-01-14 20:38:48,493 (trainer:763) INFO: 8epoch:train:281-320batch: iter_time=4.361e-05, forward_time=0.056, loss_ctc=2.186, loss=2.186, backward_time=0.009, grad_norm=66.105, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-14 20:38:52,868 (trainer:763) INFO: 8epoch:train:321-360batch: iter_time=4.413e-05, forward_time=0.057, loss_ctc=2.040, loss=2.040, backward_time=0.009, grad_norm=58.023, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:38:57,325 (trainer:763) INFO: 8epoch:train:361-400batch: iter_time=4.728e-05, forward_time=0.058, loss_ctc=2.226, loss=2.226, backward_time=0.009, grad_norm=69.545, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 20:39:01,757 (trainer:763) INFO: 8epoch:train:401-440batch: iter_time=4.575e-05, forward_time=0.058, loss_ctc=2.156, loss=2.156, backward_time=0.009, grad_norm=63.404, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 20:39:06,150 (trainer:763) INFO: 8epoch:train:441-480batch: iter_time=4.655e-05, forward_time=0.057, loss_ctc=2.196, loss=2.196, backward_time=0.009, grad_norm=67.280, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:39:10,416 (trainer:763) INFO: 8epoch:train:481-520batch: iter_time=4.311e-05, forward_time=0.056, loss_ctc=2.061, loss=2.061, backward_time=0.009, grad_norm=68.860, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-14 20:39:14,872 (trainer:763) INFO: 8epoch:train:521-560batch: iter_time=4.545e-05, forward_time=0.058, loss_ctc=2.104, loss=2.104, backward_time=0.009, grad_norm=63.914, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:39:19,264 (trainer:763) INFO: 8epoch:train:561-600batch: iter_time=4.301e-05, forward_time=0.057, loss_ctc=2.157, loss=2.157, backward_time=0.009, grad_norm=62.394, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:39:23,642 (trainer:763) INFO: 8epoch:train:601-640batch: iter_time=4.661e-05, forward_time=0.057, loss_ctc=1.919, loss=1.919, backward_time=0.009, grad_norm=62.120, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:39:28,006 (trainer:763) INFO: 8epoch:train:641-680batch: iter_time=4.669e-05, forward_time=0.057, loss_ctc=2.074, loss=2.074, backward_time=0.009, grad_norm=61.943, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:39:32,376 (trainer:763) INFO: 8epoch:train:681-720batch: iter_time=4.400e-05, forward_time=0.057, loss_ctc=2.029, loss=2.029, backward_time=0.009, grad_norm=60.666, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:39:37,009 (trainer:763) INFO: 8epoch:train:721-760batch: iter_time=4.465e-05, forward_time=0.060, loss_ctc=2.479, loss=2.479, backward_time=0.010, grad_norm=70.838, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.463 +[stan] 2024-01-14 20:39:41,248 (trainer:763) INFO: 8epoch:train:761-800batch: iter_time=4.183e-05, forward_time=0.055, loss_ctc=1.916, loss=1.916, backward_time=0.009, grad_norm=65.493, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-14 20:39:45,457 (trainer:354) INFO: 8epoch results: [train] iter_time=1.661e-04, forward_time=0.057, loss_ctc=2.140, loss=2.140, backward_time=0.009, grad_norm=64.962, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.21 seconds, total_count=6400, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=46.809, cer_ctc=0.238, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=46.809, time=1.08 seconds, total_count=120, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.06 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:39:46,405 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:39:46,406 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/7epoch.pth +[stan] 2024-01-14 20:39:46,406 (trainer:288) INFO: 9/30epoch started. Estimated time to finish: 34 minutes and 17.66 seconds +[stan] 2024-01-14 20:39:51,080 (trainer:763) INFO: 9epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=2.098, loss=2.098, backward_time=0.009, grad_norm=65.521, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.467 +[stan] 2024-01-14 20:39:55,459 (trainer:763) INFO: 9epoch:train:41-80batch: iter_time=4.842e-05, forward_time=0.057, loss_ctc=1.993, loss=1.993, backward_time=0.009, grad_norm=62.514, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:39:59,817 (trainer:763) INFO: 9epoch:train:81-120batch: iter_time=4.800e-05, forward_time=0.057, loss_ctc=1.922, loss=1.922, backward_time=0.009, grad_norm=54.564, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:40:04,273 (trainer:763) INFO: 9epoch:train:121-160batch: iter_time=4.719e-05, forward_time=0.058, loss_ctc=2.319, loss=2.319, backward_time=0.009, grad_norm=68.771, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 20:40:08,636 (trainer:763) INFO: 9epoch:train:161-200batch: iter_time=4.518e-05, forward_time=0.057, loss_ctc=2.098, loss=2.098, backward_time=0.009, grad_norm=63.237, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:40:13,038 (trainer:763) INFO: 9epoch:train:201-240batch: iter_time=4.627e-05, forward_time=0.057, loss_ctc=2.125, loss=2.125, backward_time=0.009, grad_norm=64.995, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 20:40:17,469 (trainer:763) INFO: 9epoch:train:241-280batch: iter_time=4.604e-05, forward_time=0.058, loss_ctc=2.042, loss=2.042, backward_time=0.009, grad_norm=56.299, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 20:40:21,888 (trainer:763) INFO: 9epoch:train:281-320batch: iter_time=4.530e-05, forward_time=0.058, loss_ctc=1.876, loss=1.876, backward_time=0.009, grad_norm=60.823, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 20:40:26,234 (trainer:763) INFO: 9epoch:train:321-360batch: iter_time=4.370e-05, forward_time=0.057, loss_ctc=1.890, loss=1.890, backward_time=0.009, grad_norm=56.882, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:40:30,596 (trainer:763) INFO: 9epoch:train:361-400batch: iter_time=4.894e-05, forward_time=0.057, loss_ctc=1.988, loss=1.988, backward_time=0.009, grad_norm=61.268, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:40:34,940 (trainer:763) INFO: 9epoch:train:401-440batch: iter_time=4.727e-05, forward_time=0.057, loss_ctc=1.939, loss=1.939, backward_time=0.009, grad_norm=58.680, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-14 20:40:39,292 (trainer:763) INFO: 9epoch:train:441-480batch: iter_time=4.680e-05, forward_time=0.057, loss_ctc=2.037, loss=2.037, backward_time=0.009, grad_norm=60.359, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:40:43,912 (trainer:763) INFO: 9epoch:train:481-520batch: iter_time=4.614e-05, forward_time=0.061, loss_ctc=2.082, loss=2.082, backward_time=0.009, grad_norm=65.474, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.462 +[stan] 2024-01-14 20:40:48,186 (trainer:763) INFO: 9epoch:train:521-560batch: iter_time=4.617e-05, forward_time=0.056, loss_ctc=2.067, loss=2.067, backward_time=0.009, grad_norm=63.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-14 20:40:52,765 (trainer:763) INFO: 9epoch:train:561-600batch: iter_time=4.531e-05, forward_time=0.060, loss_ctc=2.141, loss=2.141, backward_time=0.009, grad_norm=64.620, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.458 +[stan] 2024-01-14 20:40:57,135 (trainer:763) INFO: 9epoch:train:601-640batch: iter_time=4.695e-05, forward_time=0.057, loss_ctc=1.876, loss=1.876, backward_time=0.009, grad_norm=54.383, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:41:01,381 (trainer:763) INFO: 9epoch:train:641-680batch: iter_time=4.766e-05, forward_time=0.056, loss_ctc=1.879, loss=1.879, backward_time=0.009, grad_norm=66.255, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-14 20:41:05,766 (trainer:763) INFO: 9epoch:train:681-720batch: iter_time=4.671e-05, forward_time=0.057, loss_ctc=2.039, loss=2.039, backward_time=0.009, grad_norm=58.515, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:41:10,224 (trainer:763) INFO: 9epoch:train:721-760batch: iter_time=4.501e-05, forward_time=0.058, loss_ctc=1.927, loss=1.927, backward_time=0.009, grad_norm=61.178, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 20:41:14,673 (trainer:763) INFO: 9epoch:train:761-800batch: iter_time=4.317e-05, forward_time=0.058, loss_ctc=2.019, loss=2.019, backward_time=0.009, grad_norm=62.780, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:41:18,879 (trainer:354) INFO: 9epoch results: [train] iter_time=1.843e-04, forward_time=0.058, loss_ctc=2.018, loss=2.018, backward_time=0.009, grad_norm=61.518, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.35 seconds, total_count=7200, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=47.032, cer_ctc=0.234, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=47.032, time=1.08 seconds, total_count=135, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:41:19,836 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:41:19,837 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/8epoch.pth +[stan] 2024-01-14 20:41:19,837 (trainer:288) INFO: 10/30epoch started. Estimated time to finish: 32 minutes and 43.9 seconds +[stan] 2024-01-14 20:41:24,468 (trainer:763) INFO: 10epoch:train:1-40batch: iter_time=0.003, forward_time=0.057, loss_ctc=1.926, loss=1.926, backward_time=0.009, grad_norm=62.784, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.462 +[stan] 2024-01-14 20:41:28,841 (trainer:763) INFO: 10epoch:train:41-80batch: iter_time=4.489e-05, forward_time=0.057, loss_ctc=1.677, loss=1.677, backward_time=0.009, grad_norm=59.322, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:41:33,202 (trainer:763) INFO: 10epoch:train:81-120batch: iter_time=4.523e-05, forward_time=0.057, loss_ctc=1.747, loss=1.747, backward_time=0.009, grad_norm=58.223, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:41:37,582 (trainer:763) INFO: 10epoch:train:121-160batch: iter_time=4.431e-05, forward_time=0.057, loss_ctc=1.899, loss=1.899, backward_time=0.009, grad_norm=60.834, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:41:42,103 (trainer:763) INFO: 10epoch:train:161-200batch: iter_time=4.768e-05, forward_time=0.059, loss_ctc=2.127, loss=2.127, backward_time=0.009, grad_norm=62.881, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.452 +[stan] 2024-01-14 20:41:46,384 (trainer:763) INFO: 10epoch:train:201-240batch: iter_time=4.413e-05, forward_time=0.056, loss_ctc=1.685, loss=1.685, backward_time=0.009, grad_norm=55.468, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-14 20:41:50,824 (trainer:763) INFO: 10epoch:train:241-280batch: iter_time=4.721e-05, forward_time=0.058, loss_ctc=1.846, loss=1.846, backward_time=0.009, grad_norm=53.891, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-14 20:41:55,280 (trainer:763) INFO: 10epoch:train:281-320batch: iter_time=4.355e-05, forward_time=0.058, loss_ctc=1.935, loss=1.935, backward_time=0.009, grad_norm=58.465, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 20:41:59,689 (trainer:763) INFO: 10epoch:train:321-360batch: iter_time=4.748e-05, forward_time=0.058, loss_ctc=1.977, loss=1.977, backward_time=0.009, grad_norm=60.649, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 20:42:04,043 (trainer:763) INFO: 10epoch:train:361-400batch: iter_time=4.391e-05, forward_time=0.057, loss_ctc=1.904, loss=1.904, backward_time=0.009, grad_norm=58.486, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:42:08,324 (trainer:763) INFO: 10epoch:train:401-440batch: iter_time=4.318e-05, forward_time=0.056, loss_ctc=1.727, loss=1.727, backward_time=0.009, grad_norm=55.599, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-14 20:42:12,791 (trainer:763) INFO: 10epoch:train:441-480batch: iter_time=4.492e-05, forward_time=0.058, loss_ctc=1.935, loss=1.935, backward_time=0.009, grad_norm=58.138, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 20:42:17,267 (trainer:763) INFO: 10epoch:train:481-520batch: iter_time=4.271e-05, forward_time=0.058, loss_ctc=1.983, loss=1.983, backward_time=0.009, grad_norm=64.840, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 20:42:21,633 (trainer:763) INFO: 10epoch:train:521-560batch: iter_time=4.607e-05, forward_time=0.057, loss_ctc=1.704, loss=1.704, backward_time=0.009, grad_norm=56.540, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:42:25,974 (trainer:763) INFO: 10epoch:train:561-600batch: iter_time=4.580e-05, forward_time=0.057, loss_ctc=1.847, loss=1.847, backward_time=0.009, grad_norm=60.193, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-14 20:42:30,347 (trainer:763) INFO: 10epoch:train:601-640batch: iter_time=4.521e-05, forward_time=0.057, loss_ctc=1.895, loss=1.895, backward_time=0.009, grad_norm=57.053, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:42:34,695 (trainer:763) INFO: 10epoch:train:641-680batch: iter_time=4.446e-05, forward_time=0.057, loss_ctc=1.791, loss=1.791, backward_time=0.009, grad_norm=62.892, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:42:39,197 (trainer:763) INFO: 10epoch:train:681-720batch: iter_time=4.633e-05, forward_time=0.059, loss_ctc=1.882, loss=1.882, backward_time=0.009, grad_norm=58.073, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-14 20:42:43,544 (trainer:763) INFO: 10epoch:train:721-760batch: iter_time=4.401e-05, forward_time=0.057, loss_ctc=1.923, loss=1.923, backward_time=0.009, grad_norm=59.333, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:42:47,898 (trainer:763) INFO: 10epoch:train:761-800batch: iter_time=4.184e-05, forward_time=0.057, loss_ctc=1.675, loss=1.675, backward_time=0.009, grad_norm=56.402, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:42:52,101 (trainer:354) INFO: 10epoch results: [train] iter_time=1.747e-04, forward_time=0.057, loss_ctc=1.854, loss=1.854, backward_time=0.009, grad_norm=59.003, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.14 seconds, total_count=8000, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=48.864, cer_ctc=0.239, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=48.864, time=1.08 seconds, total_count=150, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:42:53,171 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:42:53,171 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/9epoch.pth +[stan] 2024-01-14 20:42:53,171 (trainer:288) INFO: 11/30epoch started. Estimated time to finish: 31 minutes and 10.01 seconds +[stan] 2024-01-14 20:42:57,778 (trainer:763) INFO: 11epoch:train:1-40batch: iter_time=0.003, forward_time=0.057, loss_ctc=1.715, loss=1.715, backward_time=0.009, grad_norm=52.938, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.460 +[stan] 2024-01-14 20:43:02,144 (trainer:763) INFO: 11epoch:train:41-80batch: iter_time=4.545e-05, forward_time=0.057, loss_ctc=1.700, loss=1.700, backward_time=0.009, grad_norm=53.943, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:43:06,733 (trainer:763) INFO: 11epoch:train:81-120batch: iter_time=4.337e-05, forward_time=0.060, loss_ctc=2.039, loss=2.039, backward_time=0.009, grad_norm=58.704, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.459 +[stan] 2024-01-14 20:43:11,083 (trainer:763) INFO: 11epoch:train:121-160batch: iter_time=4.275e-05, forward_time=0.057, loss_ctc=1.746, loss=1.746, backward_time=0.009, grad_norm=54.622, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:43:15,391 (trainer:763) INFO: 11epoch:train:161-200batch: iter_time=4.462e-05, forward_time=0.056, loss_ctc=1.626, loss=1.626, backward_time=0.009, grad_norm=56.483, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-14 20:43:19,922 (trainer:763) INFO: 11epoch:train:201-240batch: iter_time=4.245e-05, forward_time=0.059, loss_ctc=1.847, loss=1.847, backward_time=0.009, grad_norm=54.258, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.453 +[stan] 2024-01-14 20:43:24,306 (trainer:763) INFO: 11epoch:train:241-280batch: iter_time=4.532e-05, forward_time=0.057, loss_ctc=1.807, loss=1.807, backward_time=0.009, grad_norm=59.271, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:43:28,663 (trainer:763) INFO: 11epoch:train:281-320batch: iter_time=4.500e-05, forward_time=0.057, loss_ctc=1.741, loss=1.741, backward_time=0.009, grad_norm=56.077, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:43:33,034 (trainer:763) INFO: 11epoch:train:321-360batch: iter_time=4.336e-05, forward_time=0.057, loss_ctc=1.794, loss=1.794, backward_time=0.009, grad_norm=56.191, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:43:37,427 (trainer:763) INFO: 11epoch:train:361-400batch: iter_time=4.300e-05, forward_time=0.057, loss_ctc=1.807, loss=1.807, backward_time=0.009, grad_norm=59.315, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:43:41,994 (trainer:763) INFO: 11epoch:train:401-440batch: iter_time=4.299e-05, forward_time=0.060, loss_ctc=1.922, loss=1.922, backward_time=0.009, grad_norm=58.782, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.457 +[stan] 2024-01-14 20:43:46,238 (trainer:763) INFO: 11epoch:train:441-480batch: iter_time=4.240e-05, forward_time=0.056, loss_ctc=1.518, loss=1.518, backward_time=0.009, grad_norm=56.692, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-14 20:43:50,631 (trainer:763) INFO: 11epoch:train:481-520batch: iter_time=4.616e-05, forward_time=0.057, loss_ctc=1.728, loss=1.728, backward_time=0.009, grad_norm=58.690, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:43:54,901 (trainer:763) INFO: 11epoch:train:521-560batch: iter_time=4.783e-05, forward_time=0.056, loss_ctc=1.720, loss=1.720, backward_time=0.009, grad_norm=56.224, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-14 20:43:59,393 (trainer:763) INFO: 11epoch:train:561-600batch: iter_time=4.514e-05, forward_time=0.059, loss_ctc=1.833, loss=1.833, backward_time=0.009, grad_norm=62.553, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-14 20:44:03,777 (trainer:763) INFO: 11epoch:train:601-640batch: iter_time=4.551e-05, forward_time=0.057, loss_ctc=1.672, loss=1.672, backward_time=0.009, grad_norm=52.815, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:44:08,248 (trainer:763) INFO: 11epoch:train:641-680batch: iter_time=4.548e-05, forward_time=0.058, loss_ctc=1.743, loss=1.743, backward_time=0.009, grad_norm=55.257, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 20:44:12,586 (trainer:763) INFO: 11epoch:train:681-720batch: iter_time=4.453e-05, forward_time=0.057, loss_ctc=1.755, loss=1.755, backward_time=0.009, grad_norm=58.103, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-14 20:44:17,066 (trainer:763) INFO: 11epoch:train:721-760batch: iter_time=4.535e-05, forward_time=0.058, loss_ctc=1.858, loss=1.858, backward_time=0.009, grad_norm=55.460, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-14 20:44:21,361 (trainer:763) INFO: 11epoch:train:761-800batch: iter_time=4.430e-05, forward_time=0.056, loss_ctc=1.679, loss=1.679, backward_time=0.009, grad_norm=55.442, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-14 20:44:25,526 (trainer:354) INFO: 11epoch results: [train] iter_time=1.746e-04, forward_time=0.057, loss_ctc=1.762, loss=1.762, backward_time=0.009, grad_norm=56.591, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.27 seconds, total_count=8800, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=48.161, cer_ctc=0.232, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=48.161, time=1.07 seconds, total_count=165, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.01 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:44:26,449 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:44:26,449 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/10epoch.pth +[stan] 2024-01-14 20:44:26,449 (trainer:288) INFO: 12/30epoch started. Estimated time to finish: 29 minutes and 36.13 seconds +[stan] 2024-01-14 20:44:31,127 (trainer:763) INFO: 12epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=1.695, loss=1.695, backward_time=0.009, grad_norm=54.516, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.467 +[stan] 2024-01-14 20:44:35,546 (trainer:763) INFO: 12epoch:train:41-80batch: iter_time=4.591e-05, forward_time=0.058, loss_ctc=1.676, loss=1.676, backward_time=0.009, grad_norm=57.352, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 20:44:39,862 (trainer:763) INFO: 12epoch:train:81-120batch: iter_time=4.899e-05, forward_time=0.056, loss_ctc=1.640, loss=1.640, backward_time=0.009, grad_norm=55.570, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 20:44:44,378 (trainer:763) INFO: 12epoch:train:121-160batch: iter_time=4.875e-05, forward_time=0.059, loss_ctc=1.900, loss=1.900, backward_time=0.009, grad_norm=57.679, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.452 +[stan] 2024-01-14 20:44:48,694 (trainer:763) INFO: 12epoch:train:161-200batch: iter_time=5.232e-05, forward_time=0.056, loss_ctc=1.469, loss=1.469, backward_time=0.009, grad_norm=53.515, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 20:44:53,184 (trainer:763) INFO: 12epoch:train:201-240batch: iter_time=4.518e-05, forward_time=0.059, loss_ctc=1.770, loss=1.770, backward_time=0.009, grad_norm=60.099, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-14 20:44:57,619 (trainer:763) INFO: 12epoch:train:241-280batch: iter_time=4.281e-05, forward_time=0.058, loss_ctc=1.686, loss=1.686, backward_time=0.009, grad_norm=58.880, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 20:45:01,928 (trainer:763) INFO: 12epoch:train:281-320batch: iter_time=4.667e-05, forward_time=0.056, loss_ctc=1.646, loss=1.646, backward_time=0.009, grad_norm=56.682, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-14 20:45:06,283 (trainer:763) INFO: 12epoch:train:321-360batch: iter_time=4.495e-05, forward_time=0.057, loss_ctc=1.614, loss=1.614, backward_time=0.009, grad_norm=55.266, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:45:10,832 (trainer:763) INFO: 12epoch:train:361-400batch: iter_time=4.380e-05, forward_time=0.059, loss_ctc=1.714, loss=1.714, backward_time=0.009, grad_norm=55.870, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.455 +[stan] 2024-01-14 20:45:15,099 (trainer:763) INFO: 12epoch:train:401-440batch: iter_time=4.543e-05, forward_time=0.056, loss_ctc=1.609, loss=1.609, backward_time=0.009, grad_norm=56.132, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-14 20:45:19,450 (trainer:763) INFO: 12epoch:train:441-480batch: iter_time=4.876e-05, forward_time=0.057, loss_ctc=1.574, loss=1.574, backward_time=0.009, grad_norm=52.764, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:45:23,904 (trainer:763) INFO: 12epoch:train:481-520batch: iter_time=4.630e-05, forward_time=0.058, loss_ctc=1.806, loss=1.806, backward_time=0.009, grad_norm=56.949, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:45:28,293 (trainer:763) INFO: 12epoch:train:521-560batch: iter_time=4.720e-05, forward_time=0.057, loss_ctc=1.681, loss=1.681, backward_time=0.009, grad_norm=56.349, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:45:32,655 (trainer:763) INFO: 12epoch:train:561-600batch: iter_time=4.516e-05, forward_time=0.057, loss_ctc=1.623, loss=1.623, backward_time=0.009, grad_norm=56.966, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:45:37,087 (trainer:763) INFO: 12epoch:train:601-640batch: iter_time=4.449e-05, forward_time=0.058, loss_ctc=1.727, loss=1.727, backward_time=0.009, grad_norm=56.865, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 20:45:41,522 (trainer:763) INFO: 12epoch:train:641-680batch: iter_time=4.561e-05, forward_time=0.058, loss_ctc=1.638, loss=1.638, backward_time=0.009, grad_norm=57.655, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 20:45:45,825 (trainer:763) INFO: 12epoch:train:681-720batch: iter_time=4.735e-05, forward_time=0.056, loss_ctc=1.569, loss=1.569, backward_time=0.009, grad_norm=55.014, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-14 20:45:50,227 (trainer:763) INFO: 12epoch:train:721-760batch: iter_time=4.579e-05, forward_time=0.057, loss_ctc=1.617, loss=1.617, backward_time=0.009, grad_norm=57.234, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 20:45:54,752 (trainer:763) INFO: 12epoch:train:761-800batch: iter_time=4.556e-05, forward_time=0.059, loss_ctc=1.642, loss=1.642, backward_time=0.009, grad_norm=52.125, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.452 +[stan] 2024-01-14 20:45:58,968 (trainer:354) INFO: 12epoch results: [train] iter_time=2.051e-04, forward_time=0.057, loss_ctc=1.665, loss=1.665, backward_time=0.009, grad_norm=56.174, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.38 seconds, total_count=9600, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=50.232, cer_ctc=0.242, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=50.232, time=1.08 seconds, total_count=180, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:45:59,994 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:45:59,994 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/11epoch.pth +[stan] 2024-01-14 20:45:59,994 (trainer:288) INFO: 13/30epoch started. Estimated time to finish: 28 minutes and 2.74 seconds +[stan] 2024-01-14 20:46:04,550 (trainer:763) INFO: 13epoch:train:1-40batch: iter_time=0.003, forward_time=0.056, loss_ctc=1.540, loss=1.540, backward_time=0.009, grad_norm=55.009, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.455 +[stan] 2024-01-14 20:46:08,914 (trainer:763) INFO: 13epoch:train:41-80batch: iter_time=4.346e-05, forward_time=0.057, loss_ctc=1.534, loss=1.534, backward_time=0.009, grad_norm=54.580, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:46:13,327 (trainer:763) INFO: 13epoch:train:81-120batch: iter_time=4.449e-05, forward_time=0.058, loss_ctc=1.608, loss=1.608, backward_time=0.009, grad_norm=56.020, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 20:46:17,798 (trainer:763) INFO: 13epoch:train:121-160batch: iter_time=4.404e-05, forward_time=0.058, loss_ctc=1.571, loss=1.571, backward_time=0.009, grad_norm=52.816, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 20:46:22,224 (trainer:763) INFO: 13epoch:train:161-200batch: iter_time=4.627e-05, forward_time=0.058, loss_ctc=1.491, loss=1.491, backward_time=0.009, grad_norm=52.005, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 20:46:26,475 (trainer:763) INFO: 13epoch:train:201-240batch: iter_time=4.388e-05, forward_time=0.056, loss_ctc=1.344, loss=1.344, backward_time=0.009, grad_norm=51.377, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-14 20:46:30,924 (trainer:763) INFO: 13epoch:train:241-280batch: iter_time=4.457e-05, forward_time=0.058, loss_ctc=1.730, loss=1.730, backward_time=0.009, grad_norm=57.758, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:46:35,295 (trainer:763) INFO: 13epoch:train:281-320batch: iter_time=4.801e-05, forward_time=0.057, loss_ctc=1.606, loss=1.606, backward_time=0.009, grad_norm=53.132, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:46:39,619 (trainer:763) INFO: 13epoch:train:321-360batch: iter_time=4.344e-05, forward_time=0.056, loss_ctc=1.514, loss=1.514, backward_time=0.009, grad_norm=57.034, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 20:46:44,075 (trainer:763) INFO: 13epoch:train:361-400batch: iter_time=4.386e-05, forward_time=0.058, loss_ctc=1.575, loss=1.575, backward_time=0.009, grad_norm=54.348, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:46:48,435 (trainer:763) INFO: 13epoch:train:401-440batch: iter_time=4.594e-05, forward_time=0.057, loss_ctc=1.490, loss=1.490, backward_time=0.009, grad_norm=53.297, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:46:52,881 (trainer:763) INFO: 13epoch:train:441-480batch: iter_time=4.456e-05, forward_time=0.058, loss_ctc=1.565, loss=1.565, backward_time=0.009, grad_norm=49.818, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:46:57,320 (trainer:763) INFO: 13epoch:train:481-520batch: iter_time=4.726e-05, forward_time=0.058, loss_ctc=1.589, loss=1.589, backward_time=0.009, grad_norm=55.635, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-14 20:47:01,690 (trainer:763) INFO: 13epoch:train:521-560batch: iter_time=4.543e-05, forward_time=0.057, loss_ctc=1.557, loss=1.557, backward_time=0.009, grad_norm=53.560, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:47:06,107 (trainer:763) INFO: 13epoch:train:561-600batch: iter_time=4.502e-05, forward_time=0.058, loss_ctc=1.591, loss=1.591, backward_time=0.009, grad_norm=53.865, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 20:47:10,505 (trainer:763) INFO: 13epoch:train:601-640batch: iter_time=4.380e-05, forward_time=0.057, loss_ctc=1.505, loss=1.505, backward_time=0.009, grad_norm=56.000, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 20:47:14,859 (trainer:763) INFO: 13epoch:train:641-680batch: iter_time=4.496e-05, forward_time=0.057, loss_ctc=1.500, loss=1.500, backward_time=0.009, grad_norm=53.708, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:47:19,249 (trainer:763) INFO: 13epoch:train:681-720batch: iter_time=4.755e-05, forward_time=0.057, loss_ctc=1.522, loss=1.522, backward_time=0.009, grad_norm=54.391, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:47:23,644 (trainer:763) INFO: 13epoch:train:721-760batch: iter_time=4.411e-05, forward_time=0.057, loss_ctc=1.418, loss=1.418, backward_time=0.009, grad_norm=51.786, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:47:28,036 (trainer:763) INFO: 13epoch:train:761-800batch: iter_time=4.481e-05, forward_time=0.057, loss_ctc=1.465, loss=1.465, backward_time=0.009, grad_norm=53.254, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:47:32,217 (trainer:354) INFO: 13epoch results: [train] iter_time=1.794e-04, forward_time=0.057, loss_ctc=1.536, loss=1.536, backward_time=0.009, grad_norm=53.970, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.12 seconds, total_count=10400, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=50.126, cer_ctc=0.237, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=50.126, time=1.08 seconds, total_count=195, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.03 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:47:33,127 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:47:33,127 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/12epoch.pth +[stan] 2024-01-14 20:47:33,127 (trainer:288) INFO: 14/30epoch started. Estimated time to finish: 26 minutes and 28.8 seconds +[stan] 2024-01-14 20:47:37,792 (trainer:763) INFO: 14epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=1.573, loss=1.573, backward_time=0.009, grad_norm=56.605, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.466 +[stan] 2024-01-14 20:47:42,245 (trainer:763) INFO: 14epoch:train:41-80batch: iter_time=4.525e-05, forward_time=0.058, loss_ctc=1.512, loss=1.512, backward_time=0.009, grad_norm=54.869, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:47:46,552 (trainer:763) INFO: 14epoch:train:81-120batch: iter_time=4.487e-05, forward_time=0.056, loss_ctc=1.450, loss=1.450, backward_time=0.009, grad_norm=48.410, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-14 20:47:50,974 (trainer:763) INFO: 14epoch:train:121-160batch: iter_time=4.453e-05, forward_time=0.058, loss_ctc=1.670, loss=1.670, backward_time=0.009, grad_norm=57.228, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 20:47:55,298 (trainer:763) INFO: 14epoch:train:161-200batch: iter_time=4.664e-05, forward_time=0.056, loss_ctc=1.471, loss=1.471, backward_time=0.009, grad_norm=52.121, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 20:47:59,719 (trainer:763) INFO: 14epoch:train:201-240batch: iter_time=4.893e-05, forward_time=0.058, loss_ctc=1.458, loss=1.458, backward_time=0.009, grad_norm=51.051, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 20:48:04,040 (trainer:763) INFO: 14epoch:train:241-280batch: iter_time=4.605e-05, forward_time=0.056, loss_ctc=1.363, loss=1.363, backward_time=0.009, grad_norm=49.835, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 20:48:08,491 (trainer:763) INFO: 14epoch:train:281-320batch: iter_time=5.041e-05, forward_time=0.058, loss_ctc=1.633, loss=1.633, backward_time=0.009, grad_norm=59.257, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:48:13,003 (trainer:763) INFO: 14epoch:train:321-360batch: iter_time=4.894e-05, forward_time=0.059, loss_ctc=1.623, loss=1.623, backward_time=0.009, grad_norm=56.402, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.451 +[stan] 2024-01-14 20:48:17,337 (trainer:763) INFO: 14epoch:train:361-400batch: iter_time=4.736e-05, forward_time=0.057, loss_ctc=1.374, loss=1.374, backward_time=0.009, grad_norm=50.874, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-14 20:48:21,786 (trainer:763) INFO: 14epoch:train:401-440batch: iter_time=4.590e-05, forward_time=0.058, loss_ctc=1.489, loss=1.489, backward_time=0.009, grad_norm=51.877, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:48:26,238 (trainer:763) INFO: 14epoch:train:441-480batch: iter_time=4.575e-05, forward_time=0.059, loss_ctc=1.426, loss=1.426, backward_time=0.009, grad_norm=50.027, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:48:30,609 (trainer:763) INFO: 14epoch:train:481-520batch: iter_time=4.855e-05, forward_time=0.057, loss_ctc=1.421, loss=1.421, backward_time=0.009, grad_norm=52.560, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:48:35,019 (trainer:763) INFO: 14epoch:train:521-560batch: iter_time=4.903e-05, forward_time=0.058, loss_ctc=1.437, loss=1.437, backward_time=0.009, grad_norm=54.162, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 20:48:39,439 (trainer:763) INFO: 14epoch:train:561-600batch: iter_time=4.403e-05, forward_time=0.058, loss_ctc=1.450, loss=1.450, backward_time=0.009, grad_norm=53.467, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 20:48:43,823 (trainer:763) INFO: 14epoch:train:601-640batch: iter_time=4.684e-05, forward_time=0.057, loss_ctc=1.551, loss=1.551, backward_time=0.009, grad_norm=55.864, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:48:48,233 (trainer:763) INFO: 14epoch:train:641-680batch: iter_time=4.306e-05, forward_time=0.058, loss_ctc=1.510, loss=1.510, backward_time=0.009, grad_norm=52.604, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 20:48:52,651 (trainer:763) INFO: 14epoch:train:681-720batch: iter_time=4.680e-05, forward_time=0.058, loss_ctc=1.528, loss=1.528, backward_time=0.009, grad_norm=53.743, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 20:48:56,905 (trainer:763) INFO: 14epoch:train:721-760batch: iter_time=4.679e-05, forward_time=0.056, loss_ctc=1.334, loss=1.334, backward_time=0.009, grad_norm=53.004, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-14 20:49:01,346 (trainer:763) INFO: 14epoch:train:761-800batch: iter_time=4.487e-05, forward_time=0.058, loss_ctc=1.424, loss=1.424, backward_time=0.009, grad_norm=50.788, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-14 20:49:05,529 (trainer:354) INFO: 14epoch results: [train] iter_time=1.875e-04, forward_time=0.058, loss_ctc=1.485, loss=1.485, backward_time=0.009, grad_norm=53.237, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.3 seconds, total_count=11200, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=51.839, cer_ctc=0.236, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=51.839, time=1.07 seconds, total_count=210, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.03 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:49:06,455 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:49:06,456 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/13epoch.pth +[stan] 2024-01-14 20:49:06,456 (trainer:288) INFO: 15/30epoch started. Estimated time to finish: 24 minutes and 55.19 seconds +[stan] 2024-01-14 20:49:11,235 (trainer:763) INFO: 15epoch:train:1-40batch: iter_time=0.003, forward_time=0.059, loss_ctc=1.459, loss=1.459, backward_time=0.010, grad_norm=51.414, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.477 +[stan] 2024-01-14 20:49:15,504 (trainer:763) INFO: 15epoch:train:41-80batch: iter_time=4.328e-05, forward_time=0.056, loss_ctc=1.322, loss=1.322, backward_time=0.009, grad_norm=51.461, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-14 20:49:19,876 (trainer:763) INFO: 15epoch:train:81-120batch: iter_time=4.329e-05, forward_time=0.057, loss_ctc=1.396, loss=1.396, backward_time=0.009, grad_norm=49.891, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:49:24,319 (trainer:763) INFO: 15epoch:train:121-160batch: iter_time=4.333e-05, forward_time=0.058, loss_ctc=1.505, loss=1.505, backward_time=0.009, grad_norm=53.102, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-14 20:49:28,692 (trainer:763) INFO: 15epoch:train:161-200batch: iter_time=4.542e-05, forward_time=0.057, loss_ctc=1.364, loss=1.364, backward_time=0.009, grad_norm=50.782, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:49:33,140 (trainer:763) INFO: 15epoch:train:201-240batch: iter_time=4.644e-05, forward_time=0.058, loss_ctc=1.441, loss=1.441, backward_time=0.009, grad_norm=52.137, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:49:37,545 (trainer:763) INFO: 15epoch:train:241-280batch: iter_time=4.493e-05, forward_time=0.057, loss_ctc=1.444, loss=1.444, backward_time=0.009, grad_norm=54.516, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 20:49:41,923 (trainer:763) INFO: 15epoch:train:281-320batch: iter_time=4.791e-05, forward_time=0.057, loss_ctc=1.315, loss=1.315, backward_time=0.009, grad_norm=49.592, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:49:46,407 (trainer:763) INFO: 15epoch:train:321-360batch: iter_time=4.431e-05, forward_time=0.059, loss_ctc=1.532, loss=1.532, backward_time=0.009, grad_norm=57.591, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-14 20:49:50,734 (trainer:763) INFO: 15epoch:train:361-400batch: iter_time=4.336e-05, forward_time=0.057, loss_ctc=1.359, loss=1.359, backward_time=0.009, grad_norm=48.053, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-14 20:49:55,018 (trainer:763) INFO: 15epoch:train:401-440batch: iter_time=4.550e-05, forward_time=0.056, loss_ctc=1.231, loss=1.231, backward_time=0.009, grad_norm=48.930, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-14 20:49:59,546 (trainer:763) INFO: 15epoch:train:441-480batch: iter_time=4.310e-05, forward_time=0.059, loss_ctc=1.461, loss=1.461, backward_time=0.009, grad_norm=52.153, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.453 +[stan] 2024-01-14 20:50:03,905 (trainer:763) INFO: 15epoch:train:481-520batch: iter_time=4.850e-05, forward_time=0.057, loss_ctc=1.340, loss=1.340, backward_time=0.009, grad_norm=49.046, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:50:08,394 (trainer:763) INFO: 15epoch:train:521-560batch: iter_time=4.564e-05, forward_time=0.059, loss_ctc=1.403, loss=1.403, backward_time=0.009, grad_norm=55.332, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-14 20:50:12,677 (trainer:763) INFO: 15epoch:train:561-600batch: iter_time=4.352e-05, forward_time=0.056, loss_ctc=1.274, loss=1.274, backward_time=0.009, grad_norm=48.658, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-14 20:50:17,155 (trainer:763) INFO: 15epoch:train:601-640batch: iter_time=4.554e-05, forward_time=0.058, loss_ctc=1.408, loss=1.408, backward_time=0.009, grad_norm=53.807, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-14 20:50:21,484 (trainer:763) INFO: 15epoch:train:641-680batch: iter_time=4.277e-05, forward_time=0.057, loss_ctc=1.298, loss=1.298, backward_time=0.009, grad_norm=52.289, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-14 20:50:25,936 (trainer:763) INFO: 15epoch:train:681-720batch: iter_time=4.565e-05, forward_time=0.058, loss_ctc=1.397, loss=1.397, backward_time=0.009, grad_norm=53.454, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 20:50:30,286 (trainer:763) INFO: 15epoch:train:721-760batch: iter_time=4.412e-05, forward_time=0.057, loss_ctc=1.318, loss=1.318, backward_time=0.009, grad_norm=51.954, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:50:34,639 (trainer:763) INFO: 15epoch:train:761-800batch: iter_time=4.290e-05, forward_time=0.057, loss_ctc=1.237, loss=1.237, backward_time=0.009, grad_norm=47.730, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:50:38,833 (trainer:354) INFO: 15epoch results: [train] iter_time=1.707e-04, forward_time=0.057, loss_ctc=1.375, loss=1.375, backward_time=0.009, grad_norm=51.595, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.26 seconds, total_count=12000, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=51.058, cer_ctc=0.236, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=51.058, time=1.09 seconds, total_count=225, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.03 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:50:39,761 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:50:39,761 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/14epoch.pth +[stan] 2024-01-14 20:50:39,761 (trainer:288) INFO: 16/30epoch started. Estimated time to finish: 23 minutes and 21.6 seconds +[stan] 2024-01-14 20:50:44,407 (trainer:763) INFO: 16epoch:train:1-40batch: iter_time=0.003, forward_time=0.057, loss_ctc=1.470, loss=1.470, backward_time=0.009, grad_norm=54.581, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.464 +[stan] 2024-01-14 20:50:48,902 (trainer:763) INFO: 16epoch:train:41-80batch: iter_time=4.638e-05, forward_time=0.059, loss_ctc=1.387, loss=1.387, backward_time=0.009, grad_norm=51.805, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-14 20:50:53,248 (trainer:763) INFO: 16epoch:train:81-120batch: iter_time=4.412e-05, forward_time=0.057, loss_ctc=1.227, loss=1.227, backward_time=0.009, grad_norm=45.795, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:50:57,620 (trainer:763) INFO: 16epoch:train:121-160batch: iter_time=4.485e-05, forward_time=0.057, loss_ctc=1.265, loss=1.265, backward_time=0.009, grad_norm=48.943, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:51:02,214 (trainer:763) INFO: 16epoch:train:161-200batch: iter_time=4.687e-05, forward_time=0.060, loss_ctc=1.409, loss=1.409, backward_time=0.010, grad_norm=54.020, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.459 +[stan] 2024-01-14 20:51:06,458 (trainer:763) INFO: 16epoch:train:201-240batch: iter_time=4.811e-05, forward_time=0.056, loss_ctc=1.208, loss=1.208, backward_time=0.009, grad_norm=52.382, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-14 20:51:10,863 (trainer:763) INFO: 16epoch:train:241-280batch: iter_time=4.424e-05, forward_time=0.058, loss_ctc=1.340, loss=1.340, backward_time=0.009, grad_norm=50.152, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 20:51:15,379 (trainer:763) INFO: 16epoch:train:281-320batch: iter_time=4.378e-05, forward_time=0.059, loss_ctc=1.344, loss=1.344, backward_time=0.009, grad_norm=49.248, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.451 +[stan] 2024-01-14 20:51:19,772 (trainer:763) INFO: 16epoch:train:321-360batch: iter_time=4.503e-05, forward_time=0.057, loss_ctc=1.386, loss=1.386, backward_time=0.009, grad_norm=52.755, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:51:24,045 (trainer:763) INFO: 16epoch:train:361-400batch: iter_time=4.552e-05, forward_time=0.057, loss_ctc=1.172, loss=1.172, backward_time=0.009, grad_norm=48.054, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-14 20:51:28,559 (trainer:763) INFO: 16epoch:train:401-440batch: iter_time=4.298e-05, forward_time=0.059, loss_ctc=1.357, loss=1.357, backward_time=0.009, grad_norm=49.918, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.451 +[stan] 2024-01-14 20:51:32,925 (trainer:763) INFO: 16epoch:train:441-480batch: iter_time=4.449e-05, forward_time=0.057, loss_ctc=1.325, loss=1.325, backward_time=0.009, grad_norm=53.127, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:51:37,383 (trainer:763) INFO: 16epoch:train:481-520batch: iter_time=4.396e-05, forward_time=0.058, loss_ctc=1.262, loss=1.262, backward_time=0.009, grad_norm=51.170, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 20:51:41,701 (trainer:763) INFO: 16epoch:train:521-560batch: iter_time=4.532e-05, forward_time=0.056, loss_ctc=1.354, loss=1.354, backward_time=0.009, grad_norm=52.097, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 20:51:46,098 (trainer:763) INFO: 16epoch:train:561-600batch: iter_time=4.300e-05, forward_time=0.057, loss_ctc=1.166, loss=1.166, backward_time=0.009, grad_norm=46.576, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 20:51:50,534 (trainer:763) INFO: 16epoch:train:601-640batch: iter_time=4.572e-05, forward_time=0.058, loss_ctc=1.366, loss=1.366, backward_time=0.009, grad_norm=54.525, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 20:51:54,922 (trainer:763) INFO: 16epoch:train:641-680batch: iter_time=4.558e-05, forward_time=0.057, loss_ctc=1.274, loss=1.274, backward_time=0.009, grad_norm=49.511, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:51:59,182 (trainer:763) INFO: 16epoch:train:681-720batch: iter_time=4.334e-05, forward_time=0.056, loss_ctc=1.184, loss=1.184, backward_time=0.009, grad_norm=49.859, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-14 20:52:03,725 (trainer:763) INFO: 16epoch:train:721-760batch: iter_time=4.682e-05, forward_time=0.059, loss_ctc=1.397, loss=1.397, backward_time=0.009, grad_norm=52.248, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.454 +[stan] 2024-01-14 20:52:08,001 (trainer:763) INFO: 16epoch:train:761-800batch: iter_time=4.472e-05, forward_time=0.056, loss_ctc=1.251, loss=1.251, backward_time=0.009, grad_norm=49.427, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-14 20:52:12,170 (trainer:354) INFO: 16epoch results: [train] iter_time=1.878e-04, forward_time=0.057, loss_ctc=1.307, loss=1.307, backward_time=0.009, grad_norm=50.810, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.32 seconds, total_count=12800, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=52.104, cer_ctc=0.233, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=52.104, time=1.07 seconds, total_count=240, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.02 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:52:13,118 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:52:13,118 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/15epoch.pth +[stan] 2024-01-14 20:52:13,118 (trainer:288) INFO: 17/30epoch started. Estimated time to finish: 21 minutes and 48.08 seconds +[stan] 2024-01-14 20:52:17,919 (trainer:763) INFO: 17epoch:train:1-40batch: iter_time=0.003, forward_time=0.059, loss_ctc=1.300, loss=1.300, backward_time=0.009, grad_norm=49.358, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.479 +[stan] 2024-01-14 20:52:22,272 (trainer:763) INFO: 17epoch:train:41-80batch: iter_time=4.675e-05, forward_time=0.057, loss_ctc=1.323, loss=1.323, backward_time=0.009, grad_norm=51.914, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:52:26,583 (trainer:763) INFO: 17epoch:train:81-120batch: iter_time=4.330e-05, forward_time=0.056, loss_ctc=1.212, loss=1.212, backward_time=0.009, grad_norm=49.931, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-14 20:52:31,115 (trainer:763) INFO: 17epoch:train:121-160batch: iter_time=4.580e-05, forward_time=0.059, loss_ctc=1.322, loss=1.322, backward_time=0.009, grad_norm=52.938, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.453 +[stan] 2024-01-14 20:52:35,487 (trainer:763) INFO: 17epoch:train:161-200batch: iter_time=4.351e-05, forward_time=0.057, loss_ctc=1.322, loss=1.322, backward_time=0.009, grad_norm=50.019, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:52:39,877 (trainer:763) INFO: 17epoch:train:201-240batch: iter_time=4.404e-05, forward_time=0.057, loss_ctc=1.190, loss=1.190, backward_time=0.009, grad_norm=49.075, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:52:44,312 (trainer:763) INFO: 17epoch:train:241-280batch: iter_time=4.415e-05, forward_time=0.058, loss_ctc=1.315, loss=1.315, backward_time=0.009, grad_norm=49.495, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 20:52:48,630 (trainer:763) INFO: 17epoch:train:281-320batch: iter_time=4.528e-05, forward_time=0.056, loss_ctc=1.290, loss=1.290, backward_time=0.009, grad_norm=50.891, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 20:52:53,054 (trainer:763) INFO: 17epoch:train:321-360batch: iter_time=4.378e-05, forward_time=0.058, loss_ctc=1.244, loss=1.244, backward_time=0.009, grad_norm=47.518, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 20:52:57,664 (trainer:763) INFO: 17epoch:train:361-400batch: iter_time=4.274e-05, forward_time=0.060, loss_ctc=1.499, loss=1.499, backward_time=0.009, grad_norm=57.210, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.461 +[stan] 2024-01-14 20:53:02,023 (trainer:763) INFO: 17epoch:train:401-440batch: iter_time=4.325e-05, forward_time=0.057, loss_ctc=1.237, loss=1.237, backward_time=0.009, grad_norm=48.643, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:53:06,271 (trainer:763) INFO: 17epoch:train:441-480batch: iter_time=4.459e-05, forward_time=0.056, loss_ctc=1.172, loss=1.172, backward_time=0.009, grad_norm=50.519, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-14 20:53:10,716 (trainer:763) INFO: 17epoch:train:481-520batch: iter_time=4.447e-05, forward_time=0.058, loss_ctc=1.173, loss=1.173, backward_time=0.009, grad_norm=46.671, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-14 20:53:15,085 (trainer:763) INFO: 17epoch:train:521-560batch: iter_time=4.591e-05, forward_time=0.057, loss_ctc=1.336, loss=1.336, backward_time=0.009, grad_norm=50.772, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:53:19,439 (trainer:763) INFO: 17epoch:train:561-600batch: iter_time=4.352e-05, forward_time=0.057, loss_ctc=1.165, loss=1.165, backward_time=0.009, grad_norm=48.362, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:53:23,896 (trainer:763) INFO: 17epoch:train:601-640batch: iter_time=4.360e-05, forward_time=0.058, loss_ctc=1.237, loss=1.237, backward_time=0.009, grad_norm=49.347, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 20:53:28,202 (trainer:763) INFO: 17epoch:train:641-680batch: iter_time=4.455e-05, forward_time=0.056, loss_ctc=1.180, loss=1.180, backward_time=0.009, grad_norm=47.694, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-14 20:53:32,621 (trainer:763) INFO: 17epoch:train:681-720batch: iter_time=4.348e-05, forward_time=0.058, loss_ctc=1.222, loss=1.222, backward_time=0.009, grad_norm=47.752, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 20:53:37,034 (trainer:763) INFO: 17epoch:train:721-760batch: iter_time=4.969e-05, forward_time=0.058, loss_ctc=1.197, loss=1.197, backward_time=0.009, grad_norm=49.819, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 20:53:41,392 (trainer:763) INFO: 17epoch:train:761-800batch: iter_time=4.061e-05, forward_time=0.057, loss_ctc=1.272, loss=1.272, backward_time=0.009, grad_norm=50.268, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:53:45,612 (trainer:354) INFO: 17epoch results: [train] iter_time=1.944e-04, forward_time=0.057, loss_ctc=1.260, loss=1.260, backward_time=0.009, grad_norm=49.910, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.35 seconds, total_count=13600, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=54.490, cer_ctc=0.240, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=54.490, time=1.09 seconds, total_count=255, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:53:46,648 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:53:46,648 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/16epoch.pth +[stan] 2024-01-14 20:53:46,648 (trainer:288) INFO: 18/30epoch started. Estimated time to finish: 20 minutes and 14.72 seconds +[stan] 2024-01-14 20:53:51,295 (trainer:763) INFO: 18epoch:train:1-40batch: iter_time=0.002, forward_time=0.058, loss_ctc=1.184, loss=1.184, backward_time=0.009, grad_norm=48.695, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.464 +[stan] 2024-01-14 20:53:55,754 (trainer:763) INFO: 18epoch:train:41-80batch: iter_time=4.390e-05, forward_time=0.058, loss_ctc=1.265, loss=1.265, backward_time=0.009, grad_norm=51.366, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 20:54:00,223 (trainer:763) INFO: 18epoch:train:81-120batch: iter_time=4.332e-05, forward_time=0.058, loss_ctc=1.224, loss=1.224, backward_time=0.009, grad_norm=48.108, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 20:54:04,605 (trainer:763) INFO: 18epoch:train:121-160batch: iter_time=4.457e-05, forward_time=0.057, loss_ctc=1.335, loss=1.335, backward_time=0.009, grad_norm=49.223, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:54:08,947 (trainer:763) INFO: 18epoch:train:161-200batch: iter_time=4.491e-05, forward_time=0.057, loss_ctc=1.138, loss=1.138, backward_time=0.009, grad_norm=48.720, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-14 20:54:13,392 (trainer:763) INFO: 18epoch:train:201-240batch: iter_time=4.599e-05, forward_time=0.058, loss_ctc=1.292, loss=1.292, backward_time=0.009, grad_norm=51.374, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-14 20:54:17,797 (trainer:763) INFO: 18epoch:train:241-280batch: iter_time=4.589e-05, forward_time=0.058, loss_ctc=1.303, loss=1.303, backward_time=0.009, grad_norm=54.116, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 20:54:22,017 (trainer:763) INFO: 18epoch:train:281-320batch: iter_time=4.706e-05, forward_time=0.055, loss_ctc=1.154, loss=1.154, backward_time=0.009, grad_norm=49.055, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.422 +[stan] 2024-01-14 20:54:26,486 (trainer:763) INFO: 18epoch:train:321-360batch: iter_time=4.770e-05, forward_time=0.058, loss_ctc=1.249, loss=1.249, backward_time=0.009, grad_norm=47.710, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 20:54:30,838 (trainer:763) INFO: 18epoch:train:361-400batch: iter_time=4.332e-05, forward_time=0.057, loss_ctc=1.190, loss=1.190, backward_time=0.009, grad_norm=51.076, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:54:35,273 (trainer:763) INFO: 18epoch:train:401-440batch: iter_time=4.486e-05, forward_time=0.058, loss_ctc=1.232, loss=1.232, backward_time=0.009, grad_norm=51.970, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 20:54:39,660 (trainer:763) INFO: 18epoch:train:441-480batch: iter_time=4.435e-05, forward_time=0.057, loss_ctc=1.177, loss=1.177, backward_time=0.009, grad_norm=48.726, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:54:44,007 (trainer:763) INFO: 18epoch:train:481-520batch: iter_time=4.892e-05, forward_time=0.057, loss_ctc=1.145, loss=1.145, backward_time=0.009, grad_norm=47.559, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:54:48,374 (trainer:763) INFO: 18epoch:train:521-560batch: iter_time=4.419e-05, forward_time=0.057, loss_ctc=1.149, loss=1.149, backward_time=0.009, grad_norm=46.794, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:54:52,855 (trainer:763) INFO: 18epoch:train:561-600batch: iter_time=4.490e-05, forward_time=0.058, loss_ctc=1.212, loss=1.212, backward_time=0.009, grad_norm=48.250, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-14 20:54:57,186 (trainer:763) INFO: 18epoch:train:601-640batch: iter_time=4.241e-05, forward_time=0.057, loss_ctc=1.187, loss=1.187, backward_time=0.009, grad_norm=48.307, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-14 20:55:01,456 (trainer:763) INFO: 18epoch:train:641-680batch: iter_time=4.301e-05, forward_time=0.056, loss_ctc=1.146, loss=1.146, backward_time=0.009, grad_norm=51.285, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-14 20:55:05,928 (trainer:763) INFO: 18epoch:train:681-720batch: iter_time=4.519e-05, forward_time=0.058, loss_ctc=1.307, loss=1.307, backward_time=0.009, grad_norm=50.836, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 20:55:10,402 (trainer:763) INFO: 18epoch:train:721-760batch: iter_time=4.315e-05, forward_time=0.058, loss_ctc=1.238, loss=1.238, backward_time=0.009, grad_norm=49.544, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 20:55:14,797 (trainer:763) INFO: 18epoch:train:761-800batch: iter_time=4.337e-05, forward_time=0.057, loss_ctc=1.224, loss=1.224, backward_time=0.009, grad_norm=48.367, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:55:18,980 (trainer:354) INFO: 18epoch results: [train] iter_time=1.656e-04, forward_time=0.057, loss_ctc=1.217, loss=1.217, backward_time=0.009, grad_norm=49.554, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.22 seconds, total_count=14400, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=54.957, cer_ctc=0.241, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=54.957, time=1.07 seconds, total_count=270, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.03 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:55:19,957 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:55:19,957 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/17epoch.pth +[stan] 2024-01-14 20:55:19,957 (trainer:288) INFO: 19/30epoch started. Estimated time to finish: 18 minutes and 41.19 seconds +[stan] 2024-01-14 20:55:24,530 (trainer:763) INFO: 19epoch:train:1-40batch: iter_time=0.003, forward_time=0.056, loss_ctc=1.107, loss=1.107, backward_time=0.009, grad_norm=47.330, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.457 +[stan] 2024-01-14 20:55:29,051 (trainer:763) INFO: 19epoch:train:41-80batch: iter_time=4.637e-05, forward_time=0.059, loss_ctc=1.259, loss=1.259, backward_time=0.009, grad_norm=50.996, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.452 +[stan] 2024-01-14 20:55:33,384 (trainer:763) INFO: 19epoch:train:81-120batch: iter_time=4.356e-05, forward_time=0.057, loss_ctc=1.051, loss=1.051, backward_time=0.009, grad_norm=44.058, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-14 20:55:37,794 (trainer:763) INFO: 19epoch:train:121-160batch: iter_time=4.372e-05, forward_time=0.058, loss_ctc=1.167, loss=1.167, backward_time=0.009, grad_norm=48.594, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 20:55:42,184 (trainer:763) INFO: 19epoch:train:161-200batch: iter_time=4.605e-05, forward_time=0.057, loss_ctc=1.149, loss=1.149, backward_time=0.009, grad_norm=48.237, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:55:46,597 (trainer:763) INFO: 19epoch:train:201-240batch: iter_time=4.287e-05, forward_time=0.058, loss_ctc=1.122, loss=1.122, backward_time=0.009, grad_norm=47.441, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 20:55:50,974 (trainer:763) INFO: 19epoch:train:241-280batch: iter_time=4.682e-05, forward_time=0.057, loss_ctc=1.162, loss=1.162, backward_time=0.009, grad_norm=49.113, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:55:55,455 (trainer:763) INFO: 19epoch:train:281-320batch: iter_time=4.625e-05, forward_time=0.058, loss_ctc=1.245, loss=1.245, backward_time=0.009, grad_norm=55.989, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-14 20:55:59,894 (trainer:763) INFO: 19epoch:train:321-360batch: iter_time=4.769e-05, forward_time=0.058, loss_ctc=1.275, loss=1.275, backward_time=0.009, grad_norm=54.573, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-14 20:56:04,212 (trainer:763) INFO: 19epoch:train:361-400batch: iter_time=4.620e-05, forward_time=0.056, loss_ctc=1.059, loss=1.059, backward_time=0.009, grad_norm=45.055, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 20:56:08,490 (trainer:763) INFO: 19epoch:train:401-440batch: iter_time=4.570e-05, forward_time=0.056, loss_ctc=1.101, loss=1.101, backward_time=0.009, grad_norm=46.439, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-14 20:56:12,725 (trainer:763) INFO: 19epoch:train:441-480batch: iter_time=4.457e-05, forward_time=0.055, loss_ctc=0.988, loss=0.988, backward_time=0.009, grad_norm=45.822, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.423 +[stan] 2024-01-14 20:56:17,361 (trainer:763) INFO: 19epoch:train:481-520batch: iter_time=4.674e-05, forward_time=0.060, loss_ctc=1.340, loss=1.340, backward_time=0.009, grad_norm=54.563, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.463 +[stan] 2024-01-14 20:56:21,782 (trainer:763) INFO: 19epoch:train:521-560batch: iter_time=4.529e-05, forward_time=0.058, loss_ctc=1.184, loss=1.184, backward_time=0.009, grad_norm=47.865, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 20:56:26,179 (trainer:763) INFO: 19epoch:train:561-600batch: iter_time=4.596e-05, forward_time=0.057, loss_ctc=1.096, loss=1.096, backward_time=0.009, grad_norm=46.058, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 20:56:30,501 (trainer:763) INFO: 19epoch:train:601-640batch: iter_time=4.651e-05, forward_time=0.056, loss_ctc=1.085, loss=1.085, backward_time=0.009, grad_norm=45.740, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 20:56:34,896 (trainer:763) INFO: 19epoch:train:641-680batch: iter_time=4.512e-05, forward_time=0.057, loss_ctc=1.130, loss=1.130, backward_time=0.009, grad_norm=46.556, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:56:39,255 (trainer:763) INFO: 19epoch:train:681-720batch: iter_time=4.784e-05, forward_time=0.057, loss_ctc=1.171, loss=1.171, backward_time=0.009, grad_norm=52.161, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:56:43,659 (trainer:763) INFO: 19epoch:train:721-760batch: iter_time=4.440e-05, forward_time=0.058, loss_ctc=1.157, loss=1.157, backward_time=0.009, grad_norm=48.021, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 20:56:48,130 (trainer:763) INFO: 19epoch:train:761-800batch: iter_time=4.228e-05, forward_time=0.058, loss_ctc=1.134, loss=1.134, backward_time=0.009, grad_norm=48.084, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 20:56:52,340 (trainer:354) INFO: 19epoch results: [train] iter_time=1.831e-04, forward_time=0.057, loss_ctc=1.149, loss=1.149, backward_time=0.009, grad_norm=48.635, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.25 seconds, total_count=15200, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=53.158, cer_ctc=0.234, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=53.158, time=1.09 seconds, total_count=285, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.04 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:56:53,387 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:56:53,388 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/18epoch.pth +[stan] 2024-01-14 20:56:53,388 (trainer:288) INFO: 20/30epoch started. Estimated time to finish: 17 minutes and 7.76 seconds +[stan] 2024-01-14 20:56:58,116 (trainer:763) INFO: 20epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=1.131, loss=1.131, backward_time=0.009, grad_norm=49.431, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.472 +[stan] 2024-01-14 20:57:02,464 (trainer:763) INFO: 20epoch:train:41-80batch: iter_time=4.384e-05, forward_time=0.057, loss_ctc=1.120, loss=1.120, backward_time=0.009, grad_norm=45.426, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:57:06,865 (trainer:763) INFO: 20epoch:train:81-120batch: iter_time=4.404e-05, forward_time=0.057, loss_ctc=1.203, loss=1.203, backward_time=0.009, grad_norm=51.921, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 20:57:11,248 (trainer:763) INFO: 20epoch:train:121-160batch: iter_time=4.417e-05, forward_time=0.057, loss_ctc=1.064, loss=1.064, backward_time=0.009, grad_norm=46.942, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:57:15,484 (trainer:763) INFO: 20epoch:train:161-200batch: iter_time=4.352e-05, forward_time=0.055, loss_ctc=0.958, loss=0.958, backward_time=0.009, grad_norm=43.496, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.423 +[stan] 2024-01-14 20:57:19,929 (trainer:763) INFO: 20epoch:train:201-240batch: iter_time=4.295e-05, forward_time=0.058, loss_ctc=1.107, loss=1.107, backward_time=0.009, grad_norm=46.534, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-14 20:57:24,331 (trainer:763) INFO: 20epoch:train:241-280batch: iter_time=4.553e-05, forward_time=0.057, loss_ctc=1.105, loss=1.105, backward_time=0.009, grad_norm=48.189, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 20:57:28,834 (trainer:763) INFO: 20epoch:train:281-320batch: iter_time=4.576e-05, forward_time=0.059, loss_ctc=1.095, loss=1.095, backward_time=0.009, grad_norm=48.571, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-14 20:57:33,168 (trainer:763) INFO: 20epoch:train:321-360batch: iter_time=4.396e-05, forward_time=0.057, loss_ctc=1.029, loss=1.029, backward_time=0.009, grad_norm=47.759, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-14 20:57:37,587 (trainer:763) INFO: 20epoch:train:361-400batch: iter_time=4.737e-05, forward_time=0.058, loss_ctc=1.082, loss=1.082, backward_time=0.009, grad_norm=48.047, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 20:57:42,027 (trainer:763) INFO: 20epoch:train:401-440batch: iter_time=4.368e-05, forward_time=0.058, loss_ctc=1.088, loss=1.088, backward_time=0.009, grad_norm=47.817, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-14 20:57:46,364 (trainer:763) INFO: 20epoch:train:441-480batch: iter_time=4.393e-05, forward_time=0.057, loss_ctc=1.064, loss=1.064, backward_time=0.009, grad_norm=46.042, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-14 20:57:50,740 (trainer:763) INFO: 20epoch:train:481-520batch: iter_time=4.422e-05, forward_time=0.057, loss_ctc=1.073, loss=1.073, backward_time=0.009, grad_norm=44.358, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:57:55,134 (trainer:763) INFO: 20epoch:train:521-560batch: iter_time=4.276e-05, forward_time=0.057, loss_ctc=1.060, loss=1.060, backward_time=0.009, grad_norm=44.131, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 20:57:59,558 (trainer:763) INFO: 20epoch:train:561-600batch: iter_time=4.597e-05, forward_time=0.058, loss_ctc=1.118, loss=1.118, backward_time=0.009, grad_norm=48.981, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 20:58:04,021 (trainer:763) INFO: 20epoch:train:601-640batch: iter_time=4.383e-05, forward_time=0.058, loss_ctc=1.159, loss=1.159, backward_time=0.009, grad_norm=51.215, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 20:58:08,332 (trainer:763) INFO: 20epoch:train:641-680batch: iter_time=4.702e-05, forward_time=0.056, loss_ctc=0.964, loss=0.964, backward_time=0.009, grad_norm=43.814, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-14 20:58:12,703 (trainer:763) INFO: 20epoch:train:681-720batch: iter_time=4.390e-05, forward_time=0.057, loss_ctc=1.102, loss=1.102, backward_time=0.009, grad_norm=48.444, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:58:17,189 (trainer:763) INFO: 20epoch:train:721-760batch: iter_time=4.471e-05, forward_time=0.059, loss_ctc=1.120, loss=1.120, backward_time=0.009, grad_norm=47.917, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-14 20:58:21,594 (trainer:763) INFO: 20epoch:train:761-800batch: iter_time=4.373e-05, forward_time=0.057, loss_ctc=1.105, loss=1.105, backward_time=0.009, grad_norm=46.397, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 20:58:25,812 (trainer:354) INFO: 20epoch results: [train] iter_time=2.055e-04, forward_time=0.057, loss_ctc=1.087, loss=1.087, backward_time=0.009, grad_norm=47.272, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.29 seconds, total_count=16000, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=54.290, cer_ctc=0.233, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=54.290, time=1.1 seconds, total_count=300, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.04 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 20:58:26,735 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 20:58:26,736 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/19epoch.pth +[stan] 2024-01-14 20:58:26,736 (trainer:288) INFO: 21/30epoch started. Estimated time to finish: 15 minutes and 34.29 seconds +[stan] 2024-01-14 20:58:31,308 (trainer:763) INFO: 21epoch:train:1-40batch: iter_time=0.003, forward_time=0.056, loss_ctc=0.968, loss=0.968, backward_time=0.009, grad_norm=43.822, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.456 +[stan] 2024-01-14 20:58:35,767 (trainer:763) INFO: 21epoch:train:41-80batch: iter_time=4.689e-05, forward_time=0.058, loss_ctc=1.102, loss=1.102, backward_time=0.009, grad_norm=47.190, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 20:58:40,189 (trainer:763) INFO: 21epoch:train:81-120batch: iter_time=4.442e-05, forward_time=0.058, loss_ctc=1.104, loss=1.104, backward_time=0.009, grad_norm=49.682, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 20:58:44,588 (trainer:763) INFO: 21epoch:train:121-160batch: iter_time=4.400e-05, forward_time=0.057, loss_ctc=1.161, loss=1.161, backward_time=0.009, grad_norm=48.875, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 20:58:48,934 (trainer:763) INFO: 21epoch:train:161-200batch: iter_time=4.403e-05, forward_time=0.057, loss_ctc=1.022, loss=1.022, backward_time=0.009, grad_norm=46.612, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:58:53,509 (trainer:763) INFO: 21epoch:train:201-240batch: iter_time=4.659e-05, forward_time=0.060, loss_ctc=1.140, loss=1.140, backward_time=0.010, grad_norm=47.253, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.457 +[stan] 2024-01-14 20:58:57,878 (trainer:763) INFO: 21epoch:train:241-280batch: iter_time=4.938e-05, forward_time=0.057, loss_ctc=1.060, loss=1.060, backward_time=0.009, grad_norm=47.306, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 20:59:02,191 (trainer:763) INFO: 21epoch:train:281-320batch: iter_time=4.453e-05, forward_time=0.056, loss_ctc=0.973, loss=0.973, backward_time=0.009, grad_norm=44.938, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-14 20:59:06,479 (trainer:763) INFO: 21epoch:train:321-360batch: iter_time=4.580e-05, forward_time=0.056, loss_ctc=0.930, loss=0.930, backward_time=0.009, grad_norm=44.090, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-14 20:59:10,970 (trainer:763) INFO: 21epoch:train:361-400batch: iter_time=4.798e-05, forward_time=0.059, loss_ctc=1.101, loss=1.101, backward_time=0.009, grad_norm=48.034, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-14 20:59:15,255 (trainer:763) INFO: 21epoch:train:401-440batch: iter_time=4.440e-05, forward_time=0.056, loss_ctc=0.938, loss=0.938, backward_time=0.009, grad_norm=46.699, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-14 20:59:19,916 (trainer:763) INFO: 21epoch:train:441-480batch: iter_time=4.360e-05, forward_time=0.062, loss_ctc=1.077, loss=1.077, backward_time=0.010, grad_norm=49.382, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.466 +[stan] 2024-01-14 20:59:24,241 (trainer:763) INFO: 21epoch:train:481-520batch: iter_time=4.704e-05, forward_time=0.057, loss_ctc=1.018, loss=1.018, backward_time=0.009, grad_norm=47.210, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 20:59:28,621 (trainer:763) INFO: 21epoch:train:521-560batch: iter_time=4.609e-05, forward_time=0.057, loss_ctc=1.028, loss=1.028, backward_time=0.009, grad_norm=47.450, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 20:59:33,030 (trainer:763) INFO: 21epoch:train:561-600batch: iter_time=4.583e-05, forward_time=0.058, loss_ctc=1.102, loss=1.102, backward_time=0.009, grad_norm=47.509, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 20:59:37,434 (trainer:763) INFO: 21epoch:train:601-640batch: iter_time=4.849e-05, forward_time=0.058, loss_ctc=0.962, loss=0.962, backward_time=0.009, grad_norm=45.160, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 20:59:41,877 (trainer:763) INFO: 21epoch:train:641-680batch: iter_time=4.282e-05, forward_time=0.058, loss_ctc=1.090, loss=1.090, backward_time=0.009, grad_norm=49.781, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-14 20:59:46,242 (trainer:763) INFO: 21epoch:train:681-720batch: iter_time=4.342e-05, forward_time=0.057, loss_ctc=1.073, loss=1.073, backward_time=0.009, grad_norm=48.490, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 20:59:50,699 (trainer:763) INFO: 21epoch:train:721-760batch: iter_time=4.483e-05, forward_time=0.058, loss_ctc=1.051, loss=1.051, backward_time=0.009, grad_norm=45.664, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 20:59:55,051 (trainer:763) INFO: 21epoch:train:761-800batch: iter_time=4.076e-05, forward_time=0.057, loss_ctc=1.062, loss=1.062, backward_time=0.009, grad_norm=50.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 20:59:59,246 (trainer:354) INFO: 21epoch results: [train] iter_time=2.126e-04, forward_time=0.058, loss_ctc=1.048, loss=1.048, backward_time=0.009, grad_norm=47.270, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.4 seconds, total_count=16800, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=54.768, cer_ctc=0.230, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=54.768, time=1.08 seconds, total_count=315, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.03 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 21:00:00,199 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:00:00,199 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/20epoch.pth +[stan] 2024-01-14 21:00:00,200 (trainer:288) INFO: 22/30epoch started. Estimated time to finish: 14 minutes and 0.87 seconds +[stan] 2024-01-14 21:00:04,747 (trainer:763) INFO: 22epoch:train:1-40batch: iter_time=0.003, forward_time=0.056, loss_ctc=0.946, loss=0.946, backward_time=0.009, grad_norm=44.260, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.454 +[stan] 2024-01-14 21:00:09,132 (trainer:763) INFO: 22epoch:train:41-80batch: iter_time=4.479e-05, forward_time=0.057, loss_ctc=1.078, loss=1.078, backward_time=0.009, grad_norm=49.986, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 21:00:13,717 (trainer:763) INFO: 22epoch:train:81-120batch: iter_time=4.511e-05, forward_time=0.060, loss_ctc=1.049, loss=1.049, backward_time=0.009, grad_norm=48.352, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.458 +[stan] 2024-01-14 21:00:18,012 (trainer:763) INFO: 22epoch:train:121-160batch: iter_time=4.551e-05, forward_time=0.056, loss_ctc=0.930, loss=0.930, backward_time=0.009, grad_norm=43.535, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-14 21:00:22,377 (trainer:763) INFO: 22epoch:train:161-200batch: iter_time=4.390e-05, forward_time=0.057, loss_ctc=0.987, loss=0.987, backward_time=0.009, grad_norm=43.591, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 21:00:26,744 (trainer:763) INFO: 22epoch:train:201-240batch: iter_time=4.728e-05, forward_time=0.057, loss_ctc=0.925, loss=0.925, backward_time=0.009, grad_norm=43.904, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 21:00:31,129 (trainer:763) INFO: 22epoch:train:241-280batch: iter_time=4.398e-05, forward_time=0.057, loss_ctc=0.937, loss=0.937, backward_time=0.009, grad_norm=44.019, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 21:00:35,617 (trainer:763) INFO: 22epoch:train:281-320batch: iter_time=4.470e-05, forward_time=0.059, loss_ctc=1.047, loss=1.047, backward_time=0.009, grad_norm=49.938, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-14 21:00:40,011 (trainer:763) INFO: 22epoch:train:321-360batch: iter_time=4.852e-05, forward_time=0.057, loss_ctc=1.017, loss=1.017, backward_time=0.009, grad_norm=45.854, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 21:00:44,416 (trainer:763) INFO: 22epoch:train:361-400batch: iter_time=4.536e-05, forward_time=0.057, loss_ctc=0.957, loss=0.957, backward_time=0.009, grad_norm=43.477, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 21:00:48,721 (trainer:763) INFO: 22epoch:train:401-440batch: iter_time=4.531e-05, forward_time=0.056, loss_ctc=0.906, loss=0.906, backward_time=0.009, grad_norm=43.035, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-14 21:00:53,215 (trainer:763) INFO: 22epoch:train:441-480batch: iter_time=4.518e-05, forward_time=0.059, loss_ctc=1.044, loss=1.044, backward_time=0.009, grad_norm=47.294, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-14 21:00:57,529 (trainer:763) INFO: 22epoch:train:481-520batch: iter_time=4.576e-05, forward_time=0.056, loss_ctc=0.951, loss=0.951, backward_time=0.009, grad_norm=46.447, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-14 21:01:01,979 (trainer:763) INFO: 22epoch:train:521-560batch: iter_time=4.674e-05, forward_time=0.058, loss_ctc=1.084, loss=1.084, backward_time=0.009, grad_norm=45.912, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 21:01:06,340 (trainer:763) INFO: 22epoch:train:561-600batch: iter_time=4.461e-05, forward_time=0.057, loss_ctc=0.955, loss=0.955, backward_time=0.009, grad_norm=44.489, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 21:01:10,831 (trainer:763) INFO: 22epoch:train:601-640batch: iter_time=4.643e-05, forward_time=0.059, loss_ctc=0.965, loss=0.965, backward_time=0.009, grad_norm=43.909, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-14 21:01:15,147 (trainer:763) INFO: 22epoch:train:641-680batch: iter_time=4.499e-05, forward_time=0.056, loss_ctc=0.993, loss=0.993, backward_time=0.009, grad_norm=44.778, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 21:01:19,620 (trainer:763) INFO: 22epoch:train:681-720batch: iter_time=4.546e-05, forward_time=0.058, loss_ctc=1.032, loss=1.032, backward_time=0.009, grad_norm=48.281, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 21:01:23,972 (trainer:763) INFO: 22epoch:train:721-760batch: iter_time=4.656e-05, forward_time=0.057, loss_ctc=0.964, loss=0.964, backward_time=0.009, grad_norm=45.601, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 21:01:28,315 (trainer:763) INFO: 22epoch:train:761-800batch: iter_time=4.234e-05, forward_time=0.057, loss_ctc=1.009, loss=1.009, backward_time=0.009, grad_norm=46.905, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-14 21:01:32,540 (trainer:354) INFO: 22epoch results: [train] iter_time=1.853e-04, forward_time=0.057, loss_ctc=0.989, loss=0.989, backward_time=0.009, grad_norm=45.678, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.19 seconds, total_count=17600, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=56.878, cer_ctc=0.243, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=56.878, time=1.09 seconds, total_count=330, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 21:01:33,577 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:01:33,577 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/21epoch.pth +[stan] 2024-01-14 21:01:33,577 (trainer:288) INFO: 23/30epoch started. Estimated time to finish: 12 minutes and 27.42 seconds +[stan] 2024-01-14 21:01:38,320 (trainer:763) INFO: 23epoch:train:1-40batch: iter_time=0.003, forward_time=0.059, loss_ctc=0.994, loss=0.994, backward_time=0.009, grad_norm=47.170, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.474 +[stan] 2024-01-14 21:01:42,683 (trainer:763) INFO: 23epoch:train:41-80batch: iter_time=4.251e-05, forward_time=0.057, loss_ctc=0.937, loss=0.937, backward_time=0.009, grad_norm=43.597, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 21:01:47,055 (trainer:763) INFO: 23epoch:train:81-120batch: iter_time=4.267e-05, forward_time=0.057, loss_ctc=0.939, loss=0.939, backward_time=0.009, grad_norm=46.235, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 21:01:51,456 (trainer:763) INFO: 23epoch:train:121-160batch: iter_time=4.642e-05, forward_time=0.057, loss_ctc=0.950, loss=0.950, backward_time=0.009, grad_norm=48.058, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 21:01:55,913 (trainer:763) INFO: 23epoch:train:161-200batch: iter_time=4.564e-05, forward_time=0.058, loss_ctc=0.998, loss=0.998, backward_time=0.009, grad_norm=46.916, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 21:02:00,267 (trainer:763) INFO: 23epoch:train:201-240batch: iter_time=4.276e-05, forward_time=0.057, loss_ctc=0.975, loss=0.975, backward_time=0.009, grad_norm=47.314, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 21:02:04,601 (trainer:763) INFO: 23epoch:train:241-280batch: iter_time=4.313e-05, forward_time=0.057, loss_ctc=0.934, loss=0.934, backward_time=0.009, grad_norm=44.375, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-14 21:02:08,993 (trainer:763) INFO: 23epoch:train:281-320batch: iter_time=4.453e-05, forward_time=0.057, loss_ctc=0.890, loss=0.890, backward_time=0.009, grad_norm=43.483, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 21:02:13,494 (trainer:763) INFO: 23epoch:train:321-360batch: iter_time=4.604e-05, forward_time=0.059, loss_ctc=0.970, loss=0.970, backward_time=0.009, grad_norm=43.812, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-14 21:02:18,006 (trainer:763) INFO: 23epoch:train:361-400batch: iter_time=4.286e-05, forward_time=0.059, loss_ctc=0.985, loss=0.985, backward_time=0.009, grad_norm=45.458, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.451 +[stan] 2024-01-14 21:02:22,268 (trainer:763) INFO: 23epoch:train:401-440batch: iter_time=4.369e-05, forward_time=0.056, loss_ctc=0.886, loss=0.886, backward_time=0.009, grad_norm=43.483, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-14 21:02:26,623 (trainer:763) INFO: 23epoch:train:441-480batch: iter_time=4.673e-05, forward_time=0.057, loss_ctc=0.934, loss=0.934, backward_time=0.009, grad_norm=46.706, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 21:02:31,098 (trainer:763) INFO: 23epoch:train:481-520batch: iter_time=4.309e-05, forward_time=0.058, loss_ctc=0.950, loss=0.950, backward_time=0.009, grad_norm=43.646, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 21:02:35,416 (trainer:763) INFO: 23epoch:train:521-560batch: iter_time=4.306e-05, forward_time=0.056, loss_ctc=0.949, loss=0.949, backward_time=0.009, grad_norm=44.024, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 21:02:39,875 (trainer:763) INFO: 23epoch:train:561-600batch: iter_time=4.651e-05, forward_time=0.058, loss_ctc=0.995, loss=0.995, backward_time=0.009, grad_norm=45.048, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 21:02:44,248 (trainer:763) INFO: 23epoch:train:601-640batch: iter_time=4.221e-05, forward_time=0.057, loss_ctc=0.920, loss=0.920, backward_time=0.009, grad_norm=44.832, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 21:02:48,749 (trainer:763) INFO: 23epoch:train:641-680batch: iter_time=4.362e-05, forward_time=0.059, loss_ctc=0.989, loss=0.989, backward_time=0.009, grad_norm=46.849, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-14 21:02:53,151 (trainer:763) INFO: 23epoch:train:681-720batch: iter_time=4.570e-05, forward_time=0.057, loss_ctc=0.946, loss=0.946, backward_time=0.010, grad_norm=44.794, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 21:02:57,457 (trainer:763) INFO: 23epoch:train:721-760batch: iter_time=4.425e-05, forward_time=0.056, loss_ctc=0.837, loss=0.837, backward_time=0.009, grad_norm=43.482, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-14 21:03:01,760 (trainer:763) INFO: 23epoch:train:761-800batch: iter_time=4.076e-05, forward_time=0.056, loss_ctc=0.885, loss=0.885, backward_time=0.009, grad_norm=45.457, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-14 21:03:05,976 (trainer:354) INFO: 23epoch results: [train] iter_time=1.770e-04, forward_time=0.057, loss_ctc=0.943, loss=0.943, backward_time=0.009, grad_norm=45.237, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.26 seconds, total_count=18400, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=56.647, cer_ctc=0.238, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=56.647, time=1.07 seconds, total_count=345, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.06 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 21:03:06,942 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:03:06,942 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/22epoch.pth +[stan] 2024-01-14 21:03:06,942 (trainer:288) INFO: 24/30epoch started. Estimated time to finish: 10 minutes and 53.98 seconds +[stan] 2024-01-14 21:03:11,766 (trainer:763) INFO: 24epoch:train:1-40batch: iter_time=0.003, forward_time=0.060, loss_ctc=0.992, loss=0.992, backward_time=0.009, grad_norm=47.934, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.482 +[stan] 2024-01-14 21:03:16,197 (trainer:763) INFO: 24epoch:train:41-80batch: iter_time=4.938e-05, forward_time=0.058, loss_ctc=0.967, loss=0.967, backward_time=0.009, grad_norm=45.541, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 21:03:20,494 (trainer:763) INFO: 24epoch:train:81-120batch: iter_time=4.875e-05, forward_time=0.056, loss_ctc=0.870, loss=0.870, backward_time=0.009, grad_norm=47.943, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-14 21:03:24,818 (trainer:763) INFO: 24epoch:train:121-160batch: iter_time=4.652e-05, forward_time=0.057, loss_ctc=0.936, loss=0.936, backward_time=0.009, grad_norm=44.318, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 21:03:29,269 (trainer:763) INFO: 24epoch:train:161-200batch: iter_time=4.458e-05, forward_time=0.058, loss_ctc=0.931, loss=0.931, backward_time=0.009, grad_norm=45.171, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 21:03:33,721 (trainer:763) INFO: 24epoch:train:201-240batch: iter_time=4.804e-05, forward_time=0.058, loss_ctc=0.975, loss=0.975, backward_time=0.009, grad_norm=44.564, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 21:03:38,086 (trainer:763) INFO: 24epoch:train:241-280batch: iter_time=4.883e-05, forward_time=0.057, loss_ctc=0.901, loss=0.901, backward_time=0.009, grad_norm=44.432, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 21:03:42,495 (trainer:763) INFO: 24epoch:train:281-320batch: iter_time=4.848e-05, forward_time=0.058, loss_ctc=0.902, loss=0.902, backward_time=0.009, grad_norm=46.032, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 21:03:46,891 (trainer:763) INFO: 24epoch:train:321-360batch: iter_time=4.754e-05, forward_time=0.057, loss_ctc=0.919, loss=0.919, backward_time=0.009, grad_norm=44.353, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 21:03:51,272 (trainer:763) INFO: 24epoch:train:361-400batch: iter_time=4.563e-05, forward_time=0.057, loss_ctc=0.921, loss=0.921, backward_time=0.009, grad_norm=44.722, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 21:03:55,580 (trainer:763) INFO: 24epoch:train:401-440batch: iter_time=4.865e-05, forward_time=0.056, loss_ctc=0.759, loss=0.759, backward_time=0.009, grad_norm=39.853, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-14 21:04:00,136 (trainer:763) INFO: 24epoch:train:441-480batch: iter_time=4.601e-05, forward_time=0.059, loss_ctc=0.921, loss=0.921, backward_time=0.009, grad_norm=44.726, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.455 +[stan] 2024-01-14 21:04:04,685 (trainer:763) INFO: 24epoch:train:481-520batch: iter_time=4.539e-05, forward_time=0.059, loss_ctc=0.863, loss=0.863, backward_time=0.009, grad_norm=43.793, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.455 +[stan] 2024-01-14 21:04:08,936 (trainer:763) INFO: 24epoch:train:521-560batch: iter_time=4.565e-05, forward_time=0.056, loss_ctc=0.855, loss=0.855, backward_time=0.009, grad_norm=43.715, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-14 21:04:13,259 (trainer:763) INFO: 24epoch:train:561-600batch: iter_time=4.960e-05, forward_time=0.056, loss_ctc=0.871, loss=0.871, backward_time=0.009, grad_norm=42.988, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 21:04:17,755 (trainer:763) INFO: 24epoch:train:601-640batch: iter_time=4.654e-05, forward_time=0.059, loss_ctc=0.979, loss=0.979, backward_time=0.009, grad_norm=47.682, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-14 21:04:22,037 (trainer:763) INFO: 24epoch:train:641-680batch: iter_time=4.780e-05, forward_time=0.056, loss_ctc=0.882, loss=0.882, backward_time=0.009, grad_norm=44.279, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-14 21:04:26,383 (trainer:763) INFO: 24epoch:train:681-720batch: iter_time=4.488e-05, forward_time=0.057, loss_ctc=0.905, loss=0.905, backward_time=0.009, grad_norm=50.008, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 21:04:30,823 (trainer:763) INFO: 24epoch:train:721-760batch: iter_time=4.392e-05, forward_time=0.058, loss_ctc=0.988, loss=0.988, backward_time=0.009, grad_norm=48.934, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-14 21:04:35,230 (trainer:763) INFO: 24epoch:train:761-800batch: iter_time=4.532e-05, forward_time=0.058, loss_ctc=0.883, loss=0.883, backward_time=0.009, grad_norm=42.496, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 21:04:39,490 (trainer:354) INFO: 24epoch results: [train] iter_time=1.909e-04, forward_time=0.057, loss_ctc=0.911, loss=0.911, backward_time=0.009, grad_norm=45.174, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.37 seconds, total_count=19200, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=56.587, cer_ctc=0.231, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=56.587, time=1.09 seconds, total_count=360, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.09 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 21:04:40,544 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:04:40,544 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/23epoch.pth +[stan] 2024-01-14 21:04:40,544 (trainer:288) INFO: 25/30epoch started. Estimated time to finish: 9 minutes and 20.59 seconds +[stan] 2024-01-14 21:04:45,218 (trainer:763) INFO: 25epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=0.800, loss=0.800, backward_time=0.009, grad_norm=41.364, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.467 +[stan] 2024-01-14 21:04:49,596 (trainer:763) INFO: 25epoch:train:41-80batch: iter_time=4.477e-05, forward_time=0.057, loss_ctc=0.880, loss=0.880, backward_time=0.009, grad_norm=46.378, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 21:04:53,950 (trainer:763) INFO: 25epoch:train:81-120batch: iter_time=4.548e-05, forward_time=0.057, loss_ctc=0.871, loss=0.871, backward_time=0.009, grad_norm=42.966, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 21:04:58,440 (trainer:763) INFO: 25epoch:train:121-160batch: iter_time=4.524e-05, forward_time=0.059, loss_ctc=0.927, loss=0.927, backward_time=0.009, grad_norm=45.745, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-14 21:05:02,992 (trainer:763) INFO: 25epoch:train:161-200batch: iter_time=4.605e-05, forward_time=0.059, loss_ctc=0.988, loss=0.988, backward_time=0.009, grad_norm=44.455, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.455 +[stan] 2024-01-14 21:05:07,185 (trainer:763) INFO: 25epoch:train:201-240batch: iter_time=4.538e-05, forward_time=0.055, loss_ctc=0.793, loss=0.793, backward_time=0.009, grad_norm=41.448, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.419 +[stan] 2024-01-14 21:05:11,522 (trainer:763) INFO: 25epoch:train:241-280batch: iter_time=4.524e-05, forward_time=0.057, loss_ctc=0.825, loss=0.825, backward_time=0.009, grad_norm=41.333, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-14 21:05:16,068 (trainer:763) INFO: 25epoch:train:281-320batch: iter_time=4.560e-05, forward_time=0.059, loss_ctc=0.960, loss=0.960, backward_time=0.009, grad_norm=48.176, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.455 +[stan] 2024-01-14 21:05:20,254 (trainer:763) INFO: 25epoch:train:321-360batch: iter_time=4.383e-05, forward_time=0.055, loss_ctc=0.808, loss=0.808, backward_time=0.009, grad_norm=40.965, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.418 +[stan] 2024-01-14 21:05:24,891 (trainer:763) INFO: 25epoch:train:361-400batch: iter_time=4.517e-05, forward_time=0.060, loss_ctc=1.005, loss=1.005, backward_time=0.009, grad_norm=47.268, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.464 +[stan] 2024-01-14 21:05:29,193 (trainer:763) INFO: 25epoch:train:401-440batch: iter_time=4.412e-05, forward_time=0.056, loss_ctc=0.829, loss=0.829, backward_time=0.009, grad_norm=44.703, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-14 21:05:33,556 (trainer:763) INFO: 25epoch:train:441-480batch: iter_time=4.651e-05, forward_time=0.057, loss_ctc=0.848, loss=0.848, backward_time=0.009, grad_norm=43.745, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 21:05:37,979 (trainer:763) INFO: 25epoch:train:481-520batch: iter_time=4.700e-05, forward_time=0.058, loss_ctc=0.837, loss=0.837, backward_time=0.009, grad_norm=44.303, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 21:05:42,386 (trainer:763) INFO: 25epoch:train:521-560batch: iter_time=4.690e-05, forward_time=0.058, loss_ctc=0.842, loss=0.842, backward_time=0.009, grad_norm=43.320, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 21:05:46,852 (trainer:763) INFO: 25epoch:train:561-600batch: iter_time=4.704e-05, forward_time=0.058, loss_ctc=0.853, loss=0.853, backward_time=0.009, grad_norm=42.562, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 21:05:51,140 (trainer:763) INFO: 25epoch:train:601-640batch: iter_time=4.283e-05, forward_time=0.056, loss_ctc=0.862, loss=0.862, backward_time=0.009, grad_norm=44.078, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-14 21:05:55,601 (trainer:763) INFO: 25epoch:train:641-680batch: iter_time=4.713e-05, forward_time=0.058, loss_ctc=0.856, loss=0.856, backward_time=0.009, grad_norm=44.479, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-14 21:05:59,923 (trainer:763) INFO: 25epoch:train:681-720batch: iter_time=4.528e-05, forward_time=0.056, loss_ctc=0.826, loss=0.826, backward_time=0.009, grad_norm=43.023, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 21:06:04,303 (trainer:763) INFO: 25epoch:train:721-760batch: iter_time=4.730e-05, forward_time=0.057, loss_ctc=0.777, loss=0.777, backward_time=0.009, grad_norm=41.168, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 21:06:08,712 (trainer:763) INFO: 25epoch:train:761-800batch: iter_time=4.377e-05, forward_time=0.058, loss_ctc=0.890, loss=0.890, backward_time=0.009, grad_norm=45.741, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 21:06:12,933 (trainer:354) INFO: 25epoch results: [train] iter_time=1.700e-04, forward_time=0.057, loss_ctc=0.864, loss=0.864, backward_time=0.009, grad_norm=43.861, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.24 seconds, total_count=20000, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=58.168, cer_ctc=0.238, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=58.168, time=1.08 seconds, total_count=375, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.07 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 21:06:13,935 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:06:13,935 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/24epoch.pth +[stan] 2024-01-14 21:06:13,935 (trainer:288) INFO: 26/30epoch started. Estimated time to finish: 7 minutes and 47.15 seconds +[stan] 2024-01-14 21:06:18,615 (trainer:763) INFO: 26epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=0.880, loss=0.880, backward_time=0.009, grad_norm=44.037, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.467 +[stan] 2024-01-14 21:06:23,044 (trainer:763) INFO: 26epoch:train:41-80batch: iter_time=4.559e-05, forward_time=0.058, loss_ctc=0.839, loss=0.839, backward_time=0.009, grad_norm=43.191, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 21:06:27,304 (trainer:763) INFO: 26epoch:train:81-120batch: iter_time=4.361e-05, forward_time=0.056, loss_ctc=0.809, loss=0.809, backward_time=0.009, grad_norm=42.370, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-14 21:06:31,901 (trainer:763) INFO: 26epoch:train:121-160batch: iter_time=4.595e-05, forward_time=0.060, loss_ctc=0.854, loss=0.854, backward_time=0.010, grad_norm=43.435, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.460 +[stan] 2024-01-14 21:06:36,282 (trainer:763) INFO: 26epoch:train:161-200batch: iter_time=4.421e-05, forward_time=0.057, loss_ctc=0.819, loss=0.819, backward_time=0.009, grad_norm=42.667, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 21:06:40,691 (trainer:763) INFO: 26epoch:train:201-240batch: iter_time=4.346e-05, forward_time=0.058, loss_ctc=0.877, loss=0.877, backward_time=0.009, grad_norm=44.720, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 21:06:45,046 (trainer:763) INFO: 26epoch:train:241-280batch: iter_time=4.635e-05, forward_time=0.057, loss_ctc=0.818, loss=0.818, backward_time=0.009, grad_norm=43.161, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 21:06:49,526 (trainer:763) INFO: 26epoch:train:281-320batch: iter_time=4.572e-05, forward_time=0.058, loss_ctc=0.871, loss=0.871, backward_time=0.009, grad_norm=45.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-14 21:06:53,861 (trainer:763) INFO: 26epoch:train:321-360batch: iter_time=4.668e-05, forward_time=0.057, loss_ctc=0.813, loss=0.813, backward_time=0.009, grad_norm=42.445, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-14 21:06:58,146 (trainer:763) INFO: 26epoch:train:361-400batch: iter_time=4.524e-05, forward_time=0.056, loss_ctc=0.812, loss=0.812, backward_time=0.009, grad_norm=44.914, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-14 21:07:02,695 (trainer:763) INFO: 26epoch:train:401-440batch: iter_time=4.442e-05, forward_time=0.059, loss_ctc=0.882, loss=0.882, backward_time=0.009, grad_norm=48.142, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.455 +[stan] 2024-01-14 21:07:06,957 (trainer:763) INFO: 26epoch:train:441-480batch: iter_time=4.420e-05, forward_time=0.056, loss_ctc=0.756, loss=0.756, backward_time=0.009, grad_norm=39.658, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-14 21:07:11,408 (trainer:763) INFO: 26epoch:train:481-520batch: iter_time=4.791e-05, forward_time=0.058, loss_ctc=0.960, loss=0.960, backward_time=0.010, grad_norm=44.269, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 21:07:15,714 (trainer:763) INFO: 26epoch:train:521-560batch: iter_time=4.691e-05, forward_time=0.056, loss_ctc=0.706, loss=0.706, backward_time=0.009, grad_norm=39.301, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-14 21:07:20,219 (trainer:763) INFO: 26epoch:train:561-600batch: iter_time=4.573e-05, forward_time=0.059, loss_ctc=0.879, loss=0.879, backward_time=0.009, grad_norm=46.054, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-14 21:07:24,621 (trainer:763) INFO: 26epoch:train:601-640batch: iter_time=4.467e-05, forward_time=0.058, loss_ctc=0.803, loss=0.803, backward_time=0.009, grad_norm=42.719, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 21:07:28,958 (trainer:763) INFO: 26epoch:train:641-680batch: iter_time=4.421e-05, forward_time=0.057, loss_ctc=0.817, loss=0.817, backward_time=0.009, grad_norm=43.834, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-14 21:07:33,424 (trainer:763) INFO: 26epoch:train:681-720batch: iter_time=4.279e-05, forward_time=0.058, loss_ctc=0.889, loss=0.889, backward_time=0.009, grad_norm=47.003, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 21:07:37,784 (trainer:763) INFO: 26epoch:train:721-760batch: iter_time=4.401e-05, forward_time=0.057, loss_ctc=0.762, loss=0.762, backward_time=0.009, grad_norm=41.334, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 21:07:42,156 (trainer:763) INFO: 26epoch:train:761-800batch: iter_time=4.400e-05, forward_time=0.057, loss_ctc=0.866, loss=0.866, backward_time=0.009, grad_norm=45.579, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 21:07:46,395 (trainer:354) INFO: 26epoch results: [train] iter_time=2.070e-04, forward_time=0.057, loss_ctc=0.836, loss=0.836, backward_time=0.009, grad_norm=43.704, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.3 seconds, total_count=20800, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=57.635, cer_ctc=0.237, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=57.635, time=1.08 seconds, total_count=390, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.08 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 21:07:47,481 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:07:47,482 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/25epoch.pth +[stan] 2024-01-14 21:07:47,482 (trainer:288) INFO: 27/30epoch started. Estimated time to finish: 6 minutes and 13.74 seconds +[stan] 2024-01-14 21:07:52,152 (trainer:763) INFO: 27epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=0.866, loss=0.866, backward_time=0.009, grad_norm=43.221, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.466 +[stan] 2024-01-14 21:07:56,578 (trainer:763) INFO: 27epoch:train:41-80batch: iter_time=4.574e-05, forward_time=0.058, loss_ctc=0.855, loss=0.855, backward_time=0.009, grad_norm=44.008, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 21:08:00,912 (trainer:763) INFO: 27epoch:train:81-120batch: iter_time=4.314e-05, forward_time=0.057, loss_ctc=0.825, loss=0.825, backward_time=0.009, grad_norm=41.226, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-14 21:08:05,368 (trainer:763) INFO: 27epoch:train:121-160batch: iter_time=4.279e-05, forward_time=0.058, loss_ctc=0.804, loss=0.804, backward_time=0.009, grad_norm=39.275, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 21:08:09,739 (trainer:763) INFO: 27epoch:train:161-200batch: iter_time=4.266e-05, forward_time=0.057, loss_ctc=0.857, loss=0.857, backward_time=0.009, grad_norm=43.880, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 21:08:14,086 (trainer:763) INFO: 27epoch:train:201-240batch: iter_time=4.561e-05, forward_time=0.057, loss_ctc=0.787, loss=0.787, backward_time=0.009, grad_norm=42.771, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 21:08:18,500 (trainer:763) INFO: 27epoch:train:241-280batch: iter_time=4.554e-05, forward_time=0.058, loss_ctc=0.810, loss=0.810, backward_time=0.009, grad_norm=43.218, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 21:08:22,994 (trainer:763) INFO: 27epoch:train:281-320batch: iter_time=4.433e-05, forward_time=0.059, loss_ctc=0.816, loss=0.816, backward_time=0.009, grad_norm=42.571, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-14 21:08:27,409 (trainer:763) INFO: 27epoch:train:321-360batch: iter_time=4.344e-05, forward_time=0.058, loss_ctc=0.834, loss=0.834, backward_time=0.009, grad_norm=42.019, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 21:08:31,786 (trainer:763) INFO: 27epoch:train:361-400batch: iter_time=4.443e-05, forward_time=0.057, loss_ctc=0.915, loss=0.915, backward_time=0.009, grad_norm=49.575, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 21:08:36,055 (trainer:763) INFO: 27epoch:train:401-440batch: iter_time=4.536e-05, forward_time=0.056, loss_ctc=0.790, loss=0.790, backward_time=0.009, grad_norm=42.128, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-14 21:08:40,552 (trainer:763) INFO: 27epoch:train:441-480batch: iter_time=4.448e-05, forward_time=0.059, loss_ctc=0.815, loss=0.815, backward_time=0.009, grad_norm=42.789, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-14 21:08:44,951 (trainer:763) INFO: 27epoch:train:481-520batch: iter_time=4.385e-05, forward_time=0.057, loss_ctc=0.765, loss=0.765, backward_time=0.009, grad_norm=41.586, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 21:08:49,407 (trainer:763) INFO: 27epoch:train:521-560batch: iter_time=4.430e-05, forward_time=0.058, loss_ctc=0.839, loss=0.839, backward_time=0.009, grad_norm=44.466, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 21:08:53,664 (trainer:763) INFO: 27epoch:train:561-600batch: iter_time=4.554e-05, forward_time=0.056, loss_ctc=0.766, loss=0.766, backward_time=0.009, grad_norm=41.845, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-14 21:08:58,055 (trainer:763) INFO: 27epoch:train:601-640batch: iter_time=4.780e-05, forward_time=0.057, loss_ctc=0.784, loss=0.784, backward_time=0.009, grad_norm=40.670, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 21:09:02,644 (trainer:763) INFO: 27epoch:train:641-680batch: iter_time=4.393e-05, forward_time=0.060, loss_ctc=0.817, loss=0.817, backward_time=0.010, grad_norm=42.252, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.459 +[stan] 2024-01-14 21:09:07,016 (trainer:763) INFO: 27epoch:train:681-720batch: iter_time=4.593e-05, forward_time=0.057, loss_ctc=0.852, loss=0.852, backward_time=0.009, grad_norm=46.170, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 21:09:11,280 (trainer:763) INFO: 27epoch:train:721-760batch: iter_time=4.485e-05, forward_time=0.056, loss_ctc=0.759, loss=0.759, backward_time=0.008, grad_norm=41.550, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-14 21:09:15,657 (trainer:763) INFO: 27epoch:train:761-800batch: iter_time=4.175e-05, forward_time=0.057, loss_ctc=0.812, loss=0.812, backward_time=0.009, grad_norm=41.541, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 21:09:19,843 (trainer:354) INFO: 27epoch results: [train] iter_time=1.867e-04, forward_time=0.057, loss_ctc=0.818, loss=0.818, backward_time=0.009, grad_norm=42.838, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.25 seconds, total_count=21600, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=58.310, cer_ctc=0.241, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=58.310, time=1.07 seconds, total_count=405, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.04 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 21:09:20,832 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:09:20,832 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/26epoch.pth +[stan] 2024-01-14 21:09:20,832 (trainer:288) INFO: 28/30epoch started. Estimated time to finish: 4 minutes and 40.3 seconds +[stan] 2024-01-14 21:09:25,516 (trainer:763) INFO: 28epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=0.714, loss=0.714, backward_time=0.009, grad_norm=40.196, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.468 +[stan] 2024-01-14 21:09:29,923 (trainer:763) INFO: 28epoch:train:41-80batch: iter_time=4.636e-05, forward_time=0.058, loss_ctc=0.776, loss=0.776, backward_time=0.009, grad_norm=41.360, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-14 21:09:34,313 (trainer:763) INFO: 28epoch:train:81-120batch: iter_time=4.642e-05, forward_time=0.057, loss_ctc=0.800, loss=0.800, backward_time=0.009, grad_norm=43.424, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-14 21:09:38,681 (trainer:763) INFO: 28epoch:train:121-160batch: iter_time=4.705e-05, forward_time=0.057, loss_ctc=0.758, loss=0.758, backward_time=0.009, grad_norm=42.956, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 21:09:43,163 (trainer:763) INFO: 28epoch:train:161-200batch: iter_time=4.846e-05, forward_time=0.058, loss_ctc=0.803, loss=0.803, backward_time=0.009, grad_norm=44.632, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-14 21:09:47,517 (trainer:763) INFO: 28epoch:train:201-240batch: iter_time=4.581e-05, forward_time=0.057, loss_ctc=0.875, loss=0.875, backward_time=0.009, grad_norm=47.039, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 21:09:51,873 (trainer:763) INFO: 28epoch:train:241-280batch: iter_time=4.716e-05, forward_time=0.057, loss_ctc=0.757, loss=0.757, backward_time=0.009, grad_norm=39.707, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 21:09:56,302 (trainer:763) INFO: 28epoch:train:281-320batch: iter_time=4.767e-05, forward_time=0.058, loss_ctc=0.755, loss=0.755, backward_time=0.009, grad_norm=41.935, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 21:10:00,734 (trainer:763) INFO: 28epoch:train:321-360batch: iter_time=4.912e-05, forward_time=0.058, loss_ctc=0.781, loss=0.781, backward_time=0.009, grad_norm=40.619, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 21:10:04,954 (trainer:763) INFO: 28epoch:train:361-400batch: iter_time=4.673e-05, forward_time=0.055, loss_ctc=0.680, loss=0.680, backward_time=0.009, grad_norm=40.991, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.422 +[stan] 2024-01-14 21:10:09,562 (trainer:763) INFO: 28epoch:train:401-440batch: iter_time=4.716e-05, forward_time=0.060, loss_ctc=0.802, loss=0.802, backward_time=0.010, grad_norm=43.552, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.461 +[stan] 2024-01-14 21:10:13,909 (trainer:763) INFO: 28epoch:train:441-480batch: iter_time=4.699e-05, forward_time=0.057, loss_ctc=0.758, loss=0.758, backward_time=0.009, grad_norm=41.339, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 21:10:18,351 (trainer:763) INFO: 28epoch:train:481-520batch: iter_time=4.553e-05, forward_time=0.058, loss_ctc=0.803, loss=0.803, backward_time=0.009, grad_norm=42.756, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-14 21:10:22,655 (trainer:763) INFO: 28epoch:train:521-560batch: iter_time=4.570e-05, forward_time=0.056, loss_ctc=0.740, loss=0.740, backward_time=0.009, grad_norm=41.613, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-14 21:10:26,984 (trainer:763) INFO: 28epoch:train:561-600batch: iter_time=4.438e-05, forward_time=0.057, loss_ctc=0.687, loss=0.687, backward_time=0.009, grad_norm=38.682, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-14 21:10:31,618 (trainer:763) INFO: 28epoch:train:601-640batch: iter_time=4.479e-05, forward_time=0.061, loss_ctc=0.779, loss=0.779, backward_time=0.009, grad_norm=43.542, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.463 +[stan] 2024-01-14 21:10:35,909 (trainer:763) INFO: 28epoch:train:641-680batch: iter_time=4.642e-05, forward_time=0.056, loss_ctc=0.697, loss=0.697, backward_time=0.009, grad_norm=41.520, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-14 21:10:40,280 (trainer:763) INFO: 28epoch:train:681-720batch: iter_time=4.425e-05, forward_time=0.057, loss_ctc=0.814, loss=0.814, backward_time=0.009, grad_norm=43.172, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 21:10:44,677 (trainer:763) INFO: 28epoch:train:721-760batch: iter_time=4.512e-05, forward_time=0.057, loss_ctc=0.753, loss=0.753, backward_time=0.009, grad_norm=41.651, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 21:10:49,130 (trainer:763) INFO: 28epoch:train:761-800batch: iter_time=4.461e-05, forward_time=0.058, loss_ctc=0.764, loss=0.764, backward_time=0.009, grad_norm=41.034, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-14 21:10:53,326 (trainer:354) INFO: 28epoch results: [train] iter_time=1.971e-04, forward_time=0.058, loss_ctc=0.765, loss=0.765, backward_time=0.009, grad_norm=42.086, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.38 seconds, total_count=22400, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=58.450, cer_ctc=0.239, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=58.450, time=1.07 seconds, total_count=420, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 21:10:54,318 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:10:54,319 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/27epoch.pth +[stan] 2024-01-14 21:10:54,319 (trainer:288) INFO: 29/30epoch started. Estimated time to finish: 3 minutes and 6.87 seconds +[stan] 2024-01-14 21:10:58,960 (trainer:763) INFO: 29epoch:train:1-40batch: iter_time=0.003, forward_time=0.057, loss_ctc=0.783, loss=0.783, backward_time=0.009, grad_norm=45.026, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.463 +[stan] 2024-01-14 21:11:03,472 (trainer:763) INFO: 29epoch:train:41-80batch: iter_time=4.564e-05, forward_time=0.059, loss_ctc=0.811, loss=0.811, backward_time=0.009, grad_norm=42.328, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.451 +[stan] 2024-01-14 21:11:07,850 (trainer:763) INFO: 29epoch:train:81-120batch: iter_time=4.255e-05, forward_time=0.057, loss_ctc=0.761, loss=0.761, backward_time=0.009, grad_norm=41.862, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 21:11:12,048 (trainer:763) INFO: 29epoch:train:121-160batch: iter_time=4.384e-05, forward_time=0.055, loss_ctc=0.698, loss=0.698, backward_time=0.008, grad_norm=38.689, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.420 +[stan] 2024-01-14 21:11:16,625 (trainer:763) INFO: 29epoch:train:161-200batch: iter_time=4.356e-05, forward_time=0.060, loss_ctc=0.752, loss=0.752, backward_time=0.009, grad_norm=41.717, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.458 +[stan] 2024-01-14 21:11:20,917 (trainer:763) INFO: 29epoch:train:201-240batch: iter_time=4.230e-05, forward_time=0.056, loss_ctc=0.675, loss=0.675, backward_time=0.009, grad_norm=39.587, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-14 21:11:25,334 (trainer:763) INFO: 29epoch:train:241-280batch: iter_time=4.598e-05, forward_time=0.058, loss_ctc=0.715, loss=0.715, backward_time=0.009, grad_norm=39.892, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 21:11:29,639 (trainer:763) INFO: 29epoch:train:281-320batch: iter_time=4.570e-05, forward_time=0.056, loss_ctc=0.753, loss=0.753, backward_time=0.009, grad_norm=42.415, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-14 21:11:34,138 (trainer:763) INFO: 29epoch:train:321-360batch: iter_time=4.307e-05, forward_time=0.059, loss_ctc=0.761, loss=0.761, backward_time=0.009, grad_norm=42.163, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-14 21:11:38,564 (trainer:763) INFO: 29epoch:train:361-400batch: iter_time=4.417e-05, forward_time=0.058, loss_ctc=0.795, loss=0.795, backward_time=0.009, grad_norm=43.484, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 21:11:42,898 (trainer:763) INFO: 29epoch:train:401-440batch: iter_time=4.375e-05, forward_time=0.057, loss_ctc=0.701, loss=0.701, backward_time=0.009, grad_norm=39.652, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-14 21:11:47,321 (trainer:763) INFO: 29epoch:train:441-480batch: iter_time=4.534e-05, forward_time=0.058, loss_ctc=0.766, loss=0.766, backward_time=0.009, grad_norm=40.999, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 21:11:51,739 (trainer:763) INFO: 29epoch:train:481-520batch: iter_time=4.370e-05, forward_time=0.058, loss_ctc=0.741, loss=0.741, backward_time=0.009, grad_norm=42.508, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 21:11:56,208 (trainer:763) INFO: 29epoch:train:521-560batch: iter_time=4.389e-05, forward_time=0.058, loss_ctc=0.743, loss=0.743, backward_time=0.009, grad_norm=42.217, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-14 21:12:00,590 (trainer:763) INFO: 29epoch:train:561-600batch: iter_time=4.273e-05, forward_time=0.057, loss_ctc=0.772, loss=0.772, backward_time=0.009, grad_norm=41.619, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 21:12:04,956 (trainer:763) INFO: 29epoch:train:601-640batch: iter_time=4.323e-05, forward_time=0.057, loss_ctc=0.675, loss=0.675, backward_time=0.009, grad_norm=39.225, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 21:12:09,335 (trainer:763) INFO: 29epoch:train:641-680batch: iter_time=4.331e-05, forward_time=0.057, loss_ctc=0.724, loss=0.724, backward_time=0.009, grad_norm=40.991, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 21:12:13,927 (trainer:763) INFO: 29epoch:train:681-720batch: iter_time=4.453e-05, forward_time=0.060, loss_ctc=0.720, loss=0.720, backward_time=0.009, grad_norm=42.445, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.459 +[stan] 2024-01-14 21:12:18,106 (trainer:763) INFO: 29epoch:train:721-760batch: iter_time=4.557e-05, forward_time=0.055, loss_ctc=0.647, loss=0.647, backward_time=0.009, grad_norm=37.256, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.418 +[stan] 2024-01-14 21:12:22,604 (trainer:763) INFO: 29epoch:train:761-800batch: iter_time=4.265e-05, forward_time=0.059, loss_ctc=0.736, loss=0.736, backward_time=0.009, grad_norm=43.228, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-14 21:12:26,831 (trainer:354) INFO: 29epoch results: [train] iter_time=1.762e-04, forward_time=0.057, loss_ctc=0.736, loss=0.736, backward_time=0.009, grad_norm=41.365, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.38 seconds, total_count=23200, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=59.224, cer_ctc=0.258, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=59.224, time=1.09 seconds, total_count=435, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.04 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 21:12:27,883 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:12:27,884 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/28epoch.pth +[stan] 2024-01-14 21:12:27,884 (trainer:288) INFO: 30/30epoch started. Estimated time to finish: 1 minute and 33.44 seconds +[stan] 2024-01-14 21:12:32,507 (trainer:763) INFO: 30epoch:train:1-40batch: iter_time=0.003, forward_time=0.057, loss_ctc=0.677, loss=0.677, backward_time=0.009, grad_norm=39.456, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.462 +[stan] 2024-01-14 21:12:36,862 (trainer:763) INFO: 30epoch:train:41-80batch: iter_time=4.236e-05, forward_time=0.057, loss_ctc=0.741, loss=0.741, backward_time=0.009, grad_norm=44.606, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-14 21:12:41,227 (trainer:763) INFO: 30epoch:train:81-120batch: iter_time=4.334e-05, forward_time=0.057, loss_ctc=0.729, loss=0.729, backward_time=0.009, grad_norm=42.947, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 21:12:45,709 (trainer:763) INFO: 30epoch:train:121-160batch: iter_time=4.313e-05, forward_time=0.059, loss_ctc=0.705, loss=0.705, backward_time=0.009, grad_norm=40.903, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-14 21:12:50,073 (trainer:763) INFO: 30epoch:train:161-200batch: iter_time=4.580e-05, forward_time=0.057, loss_ctc=0.727, loss=0.727, backward_time=0.009, grad_norm=40.682, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 21:12:54,437 (trainer:763) INFO: 30epoch:train:201-240batch: iter_time=4.274e-05, forward_time=0.057, loss_ctc=0.666, loss=0.666, backward_time=0.009, grad_norm=37.872, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-14 21:12:58,816 (trainer:763) INFO: 30epoch:train:241-280batch: iter_time=4.255e-05, forward_time=0.057, loss_ctc=0.748, loss=0.748, backward_time=0.009, grad_norm=41.569, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-14 21:13:03,221 (trainer:763) INFO: 30epoch:train:281-320batch: iter_time=4.669e-05, forward_time=0.058, loss_ctc=0.719, loss=0.719, backward_time=0.009, grad_norm=40.238, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-14 21:13:07,666 (trainer:763) INFO: 30epoch:train:321-360batch: iter_time=4.293e-05, forward_time=0.058, loss_ctc=0.697, loss=0.697, backward_time=0.009, grad_norm=40.756, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-14 21:13:11,949 (trainer:763) INFO: 30epoch:train:361-400batch: iter_time=4.738e-05, forward_time=0.056, loss_ctc=0.602, loss=0.602, backward_time=0.009, grad_norm=36.103, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-14 21:13:16,521 (trainer:763) INFO: 30epoch:train:401-440batch: iter_time=4.450e-05, forward_time=0.060, loss_ctc=0.800, loss=0.800, backward_time=0.009, grad_norm=45.822, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.457 +[stan] 2024-01-14 21:13:20,862 (trainer:763) INFO: 30epoch:train:441-480batch: iter_time=4.551e-05, forward_time=0.057, loss_ctc=0.663, loss=0.663, backward_time=0.009, grad_norm=41.058, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-14 21:13:25,207 (trainer:763) INFO: 30epoch:train:481-520batch: iter_time=4.559e-05, forward_time=0.057, loss_ctc=0.650, loss=0.650, backward_time=0.009, grad_norm=37.930, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-14 21:13:29,633 (trainer:763) INFO: 30epoch:train:521-560batch: iter_time=4.348e-05, forward_time=0.058, loss_ctc=0.691, loss=0.691, backward_time=0.009, grad_norm=40.561, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 21:13:34,051 (trainer:763) INFO: 30epoch:train:561-600batch: iter_time=4.563e-05, forward_time=0.058, loss_ctc=0.726, loss=0.726, backward_time=0.009, grad_norm=41.752, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-14 21:13:38,486 (trainer:763) INFO: 30epoch:train:601-640batch: iter_time=4.472e-05, forward_time=0.058, loss_ctc=0.685, loss=0.685, backward_time=0.009, grad_norm=41.001, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-14 21:13:42,802 (trainer:763) INFO: 30epoch:train:641-680batch: iter_time=4.373e-05, forward_time=0.056, loss_ctc=0.650, loss=0.650, backward_time=0.009, grad_norm=38.339, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-14 21:13:47,245 (trainer:763) INFO: 30epoch:train:681-720batch: iter_time=4.577e-05, forward_time=0.058, loss_ctc=0.693, loss=0.693, backward_time=0.009, grad_norm=41.066, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-14 21:13:51,620 (trainer:763) INFO: 30epoch:train:721-760batch: iter_time=4.294e-05, forward_time=0.057, loss_ctc=0.643, loss=0.643, backward_time=0.009, grad_norm=38.598, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-14 21:13:56,167 (trainer:763) INFO: 30epoch:train:761-800batch: iter_time=4.267e-05, forward_time=0.059, loss_ctc=0.802, loss=0.802, backward_time=0.009, grad_norm=43.374, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.455 +[stan] 2024-01-14 21:14:00,352 (trainer:354) INFO: 30epoch results: [train] iter_time=1.791e-04, forward_time=0.057, loss_ctc=0.701, loss=0.701, backward_time=0.009, grad_norm=40.732, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.36 seconds, total_count=24000, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=60.683, cer_ctc=0.247, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=60.683, time=1.07 seconds, total_count=450, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.03 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-14 21:14:01,307 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-14 21:14:01,307 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/29epoch.pth +[stan] 2024-01-14 21:14:01,307 (trainer:489) INFO: The training was finished at 30 epochs +[stan] 2024-01-14 21:14:01,325 (average_nbest_models:69) INFO: Averaging 5best models: criterion="valid.loss": test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave_5best.pth +# Accounting: time=2809 threads=1 +# Ended (code 0) at Sun Jan 14 21:14:02 CST 2024, elapsed time 2809 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_10min/train.log b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/train.log new file mode 100644 index 0000000000000000000000000000000000000000..1926ee10f6833248f48bd94ae718f7896b85f8da --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_10min/train.log @@ -0,0 +1,1406 @@ +# python3 -m espnet2.bin.asr_train --use_preprocessor true --bpemodel none --token_type word --token_list data/jpn_token_list/word/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_jpn/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_jpn_10min/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_10min_jpn/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_jpn_10min/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_10min_jpn/text,text,text --train_shape_file test_pr/asr_stats_jpn_10min/train/text_shape.word --valid_data_path_and_name_and_type dump/raw/dev_10min_jpn/text,text,text --valid_shape_file test_pr/asr_stats_jpn_10min/valid/text_shape.word --ngpu 1 --multiprocessing_distributed True +# Started at Tue Jan 16 22:21:56 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_train.py --use_preprocessor true --bpemodel none --token_type word --token_list data/jpn_token_list/word/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_jpn/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_jpn_10min/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_10min --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_10min_jpn/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_jpn_10min/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_10min_jpn/text,text,text --train_shape_file test_pr/asr_stats_jpn_10min/train/text_shape.word --valid_data_path_and_name_and_type dump/raw/dev_10min_jpn/text,text,text --valid_shape_file test_pr/asr_stats_jpn_10min/valid/text_shape.word --ngpu 1 --multiprocessing_distributed True +[stan] 2024-01-16 22:21:58,109 (asr:523) INFO: Vocabulary size: 41 +[stan] 2024-01-16 22:21:58,171 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +[stan] 2024-01-16 22:21:58,171 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +[stan] 2024-01-16 22:21:58,281 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +[stan] 2024-01-16 22:21:59,589 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.mask_emb.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.label_embs_concat.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.0.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.1.0.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.2.0.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.3.0.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.4.0.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.5.0.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.6.0.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_g.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_v.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,438 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,439 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.weight.requires_grad = False +[stan] 2024-01-16 22:22:00,440 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.bias.requires_grad = False +[stan] 2024-01-16 22:22:00,836 (abs_task:1275) INFO: pytorch.version=1.12.0+cu113, cuda.available=True, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True +[stan] 2024-01-16 22:22:00,839 (abs_task:1276) INFO: Model structure: +ESPnetASRModel( + (frontend): S3prlFrontend( + (upstream): S3PRLUpstream( + (upstream): UpstreamExpert( + (model): HubertModel( + (feature_extractor): ConvFeatureExtractionModel( + (conv_layers): ModuleList( + (0): Sequential( + (0): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): Fp32GroupNorm(512, 512, eps=1e-05, affine=True) + (3): GELU(approximate=none) + ) + (1): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (2): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (3): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (4): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (5): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (6): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + ) + ) + (post_extract_proj): Linear(in_features=512, out_features=768, bias=True) + (dropout_input): Dropout(p=0.1, inplace=False) + (dropout_features): Dropout(p=0.1, inplace=False) + (encoder): TransformerEncoder( + (pos_conv): Sequential( + (0): Conv1d(768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16) + (1): SamePad() + (2): GELU(approximate=none) + ) + (layers): ModuleList( + (0): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (1): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (2): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (3): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (4): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (5): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (6): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (7): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (8): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (9): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (10): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (11): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + ) + (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (final_proj): Linear(in_features=768, out_features=256, bias=True) + ) + ) + ) + (featurizer): Featurizer() + ) + (specaug): SpecAug( + (time_warp): TimeWarp(window=5, mode=bicubic) + (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq) + (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time) + ) + (normalize): UtteranceMVN(norm_means=True, norm_vars=False) + (preencoder): LinearProjection( + (linear_out): Linear(in_features=768, out_features=80, bias=True) + (dropout): Dropout(p=0.0, inplace=False) + ) + (encoder): TransformerEncoder( + (embed): Conv2dSubsampling2( + (conv): Sequential( + (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU() + (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) + (3): ReLU() + ) + (out): Sequential( + (0): Linear(in_features=9472, out_features=256, bias=True) + (1): PositionalEncoding( + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + ) + (encoders): MultiSequential( + (0): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (1): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + ) + (criterion_att): LabelSmoothingLoss( + (criterion): KLDivLoss() + ) + (ctc): CTC( + (ctc_lo): Linear(in_features=256, out_features=41, bias=True) + (ctc_loss): CTCLoss() + ) +) + +Model summary: + Class Name: ESPnetASRModel + Total Number of model parameters: 99.97 M + Number of trainable parameters: 5.27 M (5.3%) + Size: 21.08 MB + Type: torch.float32 +[stan] 2024-01-16 22:22:00,839 (abs_task:1279) INFO: Optimizer: +Adam ( +Parameter Group 0 + amsgrad: False + betas: (0.9, 0.999) + capturable: False + eps: 1e-08 + foreach: None + lr: 0.0001 + maximize: False + weight_decay: 1e-06 +) +[stan] 2024-01-16 22:22:00,839 (abs_task:1280) INFO: Scheduler: None +[stan] 2024-01-16 22:22:00,839 (abs_task:1289) INFO: Saving the configuration in test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/config.yaml +[stan] 2024-01-16 22:22:00,990 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-16 22:22:01,032 (abs_task:1665) INFO: [train] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/train_10min_jpn/wav.scp", "type": "sound"} + text: {"path": "dump/raw/train_10min_jpn/text", "type": "text"} + preprocess: ) +[stan] 2024-01-16 22:22:01,032 (abs_task:1666) INFO: [train] Batch sampler: SortedBatchSampler(N-batch=13, batch_size=8, shape_file=test_pr/asr_stats_jpn_10min/train/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-16 22:22:01,032 (abs_task:1667) INFO: [train] mini-batch sizes summary: N-batch=13, mean=8.2, min=8, max=9 +[stan] 2024-01-16 22:22:01,043 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-16 22:22:01,043 (abs_task:1665) INFO: [valid] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_jpn/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_jpn/text", "type": "text"} + preprocess: ) +[stan] 2024-01-16 22:22:01,043 (abs_task:1666) INFO: [valid] Batch sampler: SortedBatchSampler(N-batch=15, batch_size=8, shape_file=test_pr/asr_stats_jpn_10min/valid/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-16 22:22:01,043 (abs_task:1667) INFO: [valid] mini-batch sizes summary: N-batch=15, mean=8.4, min=8, max=9 +[stan] 2024-01-16 22:22:01,044 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-16 22:22:01,054 (abs_task:1665) INFO: [plot_att] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_jpn/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_jpn/text", "type": "text"} + preprocess: ) +[stan] 2024-01-16 22:22:01,054 (abs_task:1666) INFO: [plot_att] Batch sampler: UnsortedBatchSampler(N-batch=126, batch_size=1, key_file=test_pr/asr_stats_jpn_10min/valid/speech_shape, +[stan] 2024-01-16 22:22:01,055 (abs_task:1667) INFO: [plot_att] mini-batch sizes summary: N-batch=3, mean=1.0, min=1, max=1 +[stan] 2024-01-16 22:22:01,087 (trainer:303) INFO: 1/30epoch started +[stan] 2024-01-16 22:22:07,022 (trainer:762) INFO: 1epoch:train:1-40batch: iter_time=0.002, forward_time=0.087, loss_ctc=42.929, loss=42.929, backward_time=0.011, grad_norm=784.000, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.590 +[stan] 2024-01-16 22:22:11,385 (trainer:762) INFO: 1epoch:train:41-80batch: iter_time=4.702e-05, forward_time=0.057, loss_ctc=33.314, loss=33.314, backward_time=0.009, grad_norm=107.838, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:22:15,700 (trainer:762) INFO: 1epoch:train:81-120batch: iter_time=4.419e-05, forward_time=0.056, loss_ctc=32.921, loss=32.921, backward_time=0.009, grad_norm=115.323, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 22:22:19,962 (trainer:762) INFO: 1epoch:train:121-160batch: iter_time=4.624e-05, forward_time=0.056, loss_ctc=32.393, loss=32.393, backward_time=0.009, grad_norm=99.925, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-16 22:22:24,401 (trainer:762) INFO: 1epoch:train:161-200batch: iter_time=4.596e-05, forward_time=0.058, loss_ctc=33.053, loss=33.053, backward_time=0.009, grad_norm=55.302, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 22:22:28,718 (trainer:762) INFO: 1epoch:train:201-240batch: iter_time=4.289e-05, forward_time=0.056, loss_ctc=31.824, loss=31.824, backward_time=0.009, grad_norm=74.614, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 22:22:33,200 (trainer:762) INFO: 1epoch:train:241-280batch: iter_time=4.638e-05, forward_time=0.059, loss_ctc=29.622, loss=29.622, backward_time=0.009, grad_norm=103.123, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-16 22:22:37,329 (trainer:762) INFO: 1epoch:train:281-320batch: iter_time=4.622e-05, forward_time=0.054, loss_ctc=23.083, loss=23.083, backward_time=0.008, grad_norm=106.211, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.413 +[stan] 2024-01-16 22:22:41,963 (trainer:762) INFO: 1epoch:train:321-360batch: iter_time=4.368e-05, forward_time=0.060, loss_ctc=22.440, loss=22.440, backward_time=0.009, grad_norm=121.258, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.463 +[stan] 2024-01-16 22:22:46,277 (trainer:762) INFO: 1epoch:train:361-400batch: iter_time=4.444e-05, forward_time=0.056, loss_ctc=18.028, loss=18.028, backward_time=0.009, grad_norm=130.983, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 22:22:50,630 (trainer:762) INFO: 1epoch:train:401-440batch: iter_time=4.582e-05, forward_time=0.057, loss_ctc=16.543, loss=16.543, backward_time=0.009, grad_norm=122.520, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:22:54,985 (trainer:762) INFO: 1epoch:train:441-480batch: iter_time=4.396e-05, forward_time=0.057, loss_ctc=15.158, loss=15.158, backward_time=0.009, grad_norm=92.176, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:22:59,358 (trainer:762) INFO: 1epoch:train:481-520batch: iter_time=4.339e-05, forward_time=0.057, loss_ctc=14.032, loss=14.032, backward_time=0.009, grad_norm=143.340, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:23:03,732 (trainer:762) INFO: 1epoch:train:521-560batch: iter_time=4.898e-05, forward_time=0.057, loss_ctc=12.778, loss=12.778, backward_time=0.009, grad_norm=112.824, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:23:08,165 (trainer:762) INFO: 1epoch:train:561-600batch: iter_time=4.839e-05, forward_time=0.058, loss_ctc=12.233, loss=12.233, backward_time=0.009, grad_norm=106.051, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:23:12,554 (trainer:762) INFO: 1epoch:train:601-640batch: iter_time=4.731e-05, forward_time=0.057, loss_ctc=11.276, loss=11.276, backward_time=0.009, grad_norm=94.043, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:23:16,864 (trainer:762) INFO: 1epoch:train:641-680batch: iter_time=4.608e-05, forward_time=0.056, loss_ctc=10.530, loss=10.530, backward_time=0.008, grad_norm=129.177, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 22:23:21,318 (trainer:762) INFO: 1epoch:train:681-720batch: iter_time=4.306e-05, forward_time=0.058, loss_ctc=10.382, loss=10.382, backward_time=0.009, grad_norm=121.917, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:23:25,604 (trainer:762) INFO: 1epoch:train:721-760batch: iter_time=4.295e-05, forward_time=0.056, loss_ctc=9.378, loss=9.378, backward_time=0.009, grad_norm=87.024, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-16 22:23:29,979 (trainer:762) INFO: 1epoch:train:761-800batch: iter_time=4.032e-05, forward_time=0.057, loss_ctc=9.352, loss=9.352, backward_time=0.009, grad_norm=110.624, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-16 22:23:34,506 (trainer:357) INFO: 1epoch results: [train] iter_time=1.535e-04, forward_time=0.059, loss_ctc=21.066, loss=21.066, backward_time=0.009, grad_norm=140.914, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444, time=1 minute and 28.93 seconds, total_count=800, gpu_max_cached_mem_GB=5.986, [valid] loss_ctc=32.028, cer_ctc=0.240, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=32.028, time=1.07 seconds, total_count=15, gpu_max_cached_mem_GB=6.701, [att_plot] time=3.41 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:23:35,563 (trainer:416) INFO: The best model has been updated: valid.loss +[stan] 2024-01-16 22:23:35,563 (trainer:291) INFO: 2/30epoch started. Estimated time to finish: 45 minutes and 39.81 seconds +[stan] 2024-01-16 22:23:40,295 (trainer:762) INFO: 2epoch:train:1-40batch: iter_time=0.002, forward_time=0.059, loss_ctc=9.011, loss=9.011, backward_time=0.009, grad_norm=95.633, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.473 +[stan] 2024-01-16 22:23:44,659 (trainer:762) INFO: 2epoch:train:41-80batch: iter_time=4.421e-05, forward_time=0.057, loss_ctc=8.204, loss=8.204, backward_time=0.009, grad_norm=107.921, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:23:48,971 (trainer:762) INFO: 2epoch:train:81-120batch: iter_time=4.406e-05, forward_time=0.056, loss_ctc=8.018, loss=8.018, backward_time=0.009, grad_norm=107.916, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 22:23:53,337 (trainer:762) INFO: 2epoch:train:121-160batch: iter_time=4.717e-05, forward_time=0.057, loss_ctc=7.348, loss=7.348, backward_time=0.009, grad_norm=100.766, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:23:57,714 (trainer:762) INFO: 2epoch:train:161-200batch: iter_time=4.416e-05, forward_time=0.057, loss_ctc=6.983, loss=6.983, backward_time=0.009, grad_norm=112.367, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:24:02,170 (trainer:762) INFO: 2epoch:train:201-240batch: iter_time=4.688e-05, forward_time=0.058, loss_ctc=7.063, loss=7.063, backward_time=0.009, grad_norm=117.915, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-16 22:24:06,631 (trainer:762) INFO: 2epoch:train:241-280batch: iter_time=4.810e-05, forward_time=0.058, loss_ctc=6.893, loss=6.893, backward_time=0.009, grad_norm=124.675, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-16 22:24:10,905 (trainer:762) INFO: 2epoch:train:281-320batch: iter_time=4.816e-05, forward_time=0.056, loss_ctc=6.231, loss=6.231, backward_time=0.009, grad_norm=94.590, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-16 22:24:15,374 (trainer:762) INFO: 2epoch:train:321-360batch: iter_time=4.686e-05, forward_time=0.058, loss_ctc=6.671, loss=6.671, backward_time=0.009, grad_norm=115.701, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-16 22:24:19,848 (trainer:762) INFO: 2epoch:train:361-400batch: iter_time=4.633e-05, forward_time=0.058, loss_ctc=6.174, loss=6.174, backward_time=0.009, grad_norm=99.389, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-16 22:24:24,212 (trainer:762) INFO: 2epoch:train:401-440batch: iter_time=4.624e-05, forward_time=0.057, loss_ctc=5.832, loss=5.832, backward_time=0.009, grad_norm=103.871, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:24:28,683 (trainer:762) INFO: 2epoch:train:441-480batch: iter_time=4.553e-05, forward_time=0.058, loss_ctc=5.843, loss=5.843, backward_time=0.009, grad_norm=106.826, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-16 22:24:33,025 (trainer:762) INFO: 2epoch:train:481-520batch: iter_time=4.442e-05, forward_time=0.057, loss_ctc=5.554, loss=5.554, backward_time=0.009, grad_norm=100.203, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:24:37,422 (trainer:762) INFO: 2epoch:train:521-560batch: iter_time=4.478e-05, forward_time=0.057, loss_ctc=5.393, loss=5.393, backward_time=0.009, grad_norm=119.152, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:24:41,739 (trainer:762) INFO: 2epoch:train:561-600batch: iter_time=4.561e-05, forward_time=0.056, loss_ctc=4.917, loss=4.917, backward_time=0.009, grad_norm=93.775, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 22:24:46,163 (trainer:762) INFO: 2epoch:train:601-640batch: iter_time=4.871e-05, forward_time=0.058, loss_ctc=5.127, loss=5.127, backward_time=0.009, grad_norm=108.406, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 22:24:50,584 (trainer:762) INFO: 2epoch:train:641-680batch: iter_time=4.438e-05, forward_time=0.058, loss_ctc=5.232, loss=5.232, backward_time=0.009, grad_norm=105.166, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 22:24:55,033 (trainer:762) INFO: 2epoch:train:681-720batch: iter_time=4.526e-05, forward_time=0.058, loss_ctc=5.011, loss=5.011, backward_time=0.009, grad_norm=115.007, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:24:59,434 (trainer:762) INFO: 2epoch:train:721-760batch: iter_time=4.695e-05, forward_time=0.058, loss_ctc=4.726, loss=4.726, backward_time=0.009, grad_norm=109.439, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:25:03,816 (trainer:762) INFO: 2epoch:train:761-800batch: iter_time=4.269e-05, forward_time=0.057, loss_ctc=4.778, loss=4.778, backward_time=0.009, grad_norm=116.838, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-16 22:25:08,051 (trainer:357) INFO: 2epoch results: [train] iter_time=1.558e-04, forward_time=0.057, loss_ctc=6.250, loss=6.250, backward_time=0.009, grad_norm=107.778, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.32 seconds, total_count=1600, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=34.380, cer_ctc=0.239, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=34.380, time=1.06 seconds, total_count=30, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.11 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:25:08,963 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:25:08,963 (trainer:291) INFO: 3/30epoch started. Estimated time to finish: 43 minutes and 50.27 seconds +[stan] 2024-01-16 22:25:13,566 (trainer:762) INFO: 3epoch:train:1-40batch: iter_time=0.003, forward_time=0.057, loss_ctc=4.493, loss=4.493, backward_time=0.009, grad_norm=86.913, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.460 +[stan] 2024-01-16 22:25:18,003 (trainer:762) INFO: 3epoch:train:41-80batch: iter_time=4.721e-05, forward_time=0.058, loss_ctc=4.569, loss=4.569, backward_time=0.009, grad_norm=87.752, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 22:25:22,465 (trainer:762) INFO: 3epoch:train:81-120batch: iter_time=4.915e-05, forward_time=0.058, loss_ctc=4.604, loss=4.604, backward_time=0.009, grad_norm=109.097, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-16 22:25:26,869 (trainer:762) INFO: 3epoch:train:121-160batch: iter_time=4.966e-05, forward_time=0.058, loss_ctc=4.373, loss=4.373, backward_time=0.009, grad_norm=106.741, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:25:31,237 (trainer:762) INFO: 3epoch:train:161-200batch: iter_time=4.510e-05, forward_time=0.057, loss_ctc=4.220, loss=4.220, backward_time=0.009, grad_norm=93.284, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:25:35,503 (trainer:762) INFO: 3epoch:train:201-240batch: iter_time=4.556e-05, forward_time=0.056, loss_ctc=4.025, loss=4.025, backward_time=0.009, grad_norm=87.297, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-16 22:25:40,078 (trainer:762) INFO: 3epoch:train:241-280batch: iter_time=4.479e-05, forward_time=0.060, loss_ctc=4.653, loss=4.653, backward_time=0.009, grad_norm=103.781, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.457 +[stan] 2024-01-16 22:25:44,427 (trainer:762) INFO: 3epoch:train:281-320batch: iter_time=4.362e-05, forward_time=0.057, loss_ctc=4.309, loss=4.309, backward_time=0.009, grad_norm=111.827, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:25:48,782 (trainer:762) INFO: 3epoch:train:321-360batch: iter_time=4.543e-05, forward_time=0.057, loss_ctc=3.852, loss=3.852, backward_time=0.009, grad_norm=89.045, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:25:53,076 (trainer:762) INFO: 3epoch:train:361-400batch: iter_time=4.470e-05, forward_time=0.056, loss_ctc=3.638, loss=3.638, backward_time=0.009, grad_norm=111.480, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-16 22:25:57,537 (trainer:762) INFO: 3epoch:train:401-440batch: iter_time=4.490e-05, forward_time=0.058, loss_ctc=4.015, loss=4.015, backward_time=0.009, grad_norm=97.355, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-16 22:26:01,877 (trainer:762) INFO: 3epoch:train:441-480batch: iter_time=4.651e-05, forward_time=0.057, loss_ctc=3.538, loss=3.538, backward_time=0.009, grad_norm=80.640, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:26:06,226 (trainer:762) INFO: 3epoch:train:481-520batch: iter_time=4.640e-05, forward_time=0.057, loss_ctc=3.642, loss=3.642, backward_time=0.009, grad_norm=84.853, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:26:10,631 (trainer:762) INFO: 3epoch:train:521-560batch: iter_time=4.765e-05, forward_time=0.058, loss_ctc=4.088, loss=4.088, backward_time=0.009, grad_norm=88.146, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:26:14,969 (trainer:762) INFO: 3epoch:train:561-600batch: iter_time=4.665e-05, forward_time=0.057, loss_ctc=3.540, loss=3.540, backward_time=0.009, grad_norm=78.359, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:26:19,492 (trainer:762) INFO: 3epoch:train:601-640batch: iter_time=4.466e-05, forward_time=0.059, loss_ctc=4.121, loss=4.121, backward_time=0.009, grad_norm=87.412, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.452 +[stan] 2024-01-16 22:26:23,830 (trainer:762) INFO: 3epoch:train:641-680batch: iter_time=4.919e-05, forward_time=0.057, loss_ctc=3.497, loss=3.497, backward_time=0.009, grad_norm=97.154, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:26:28,207 (trainer:762) INFO: 3epoch:train:681-720batch: iter_time=4.642e-05, forward_time=0.057, loss_ctc=3.599, loss=3.599, backward_time=0.009, grad_norm=93.376, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:26:32,658 (trainer:762) INFO: 3epoch:train:721-760batch: iter_time=4.478e-05, forward_time=0.058, loss_ctc=3.605, loss=3.605, backward_time=0.009, grad_norm=86.000, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:26:36,902 (trainer:762) INFO: 3epoch:train:761-800batch: iter_time=4.572e-05, forward_time=0.055, loss_ctc=3.543, loss=3.543, backward_time=0.009, grad_norm=80.650, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-16 22:26:41,133 (trainer:357) INFO: 3epoch results: [train] iter_time=1.774e-04, forward_time=0.057, loss_ctc=3.996, loss=3.996, backward_time=0.009, grad_norm=93.058, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.01 seconds, total_count=2400, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=38.048, cer_ctc=0.241, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=38.048, time=1.08 seconds, total_count=45, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.08 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:26:42,113 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:26:42,113 (trainer:291) INFO: 4/30epoch started. Estimated time to finish: 42 minutes and 9.24 seconds +[stan] 2024-01-16 22:26:46,757 (trainer:762) INFO: 4epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=3.524, loss=3.524, backward_time=0.009, grad_norm=86.510, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.464 +[stan] 2024-01-16 22:26:51,269 (trainer:762) INFO: 4epoch:train:41-80batch: iter_time=4.677e-05, forward_time=0.059, loss_ctc=3.682, loss=3.682, backward_time=0.009, grad_norm=92.212, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.451 +[stan] 2024-01-16 22:26:55,603 (trainer:762) INFO: 4epoch:train:81-120batch: iter_time=4.337e-05, forward_time=0.057, loss_ctc=3.355, loss=3.355, backward_time=0.009, grad_norm=82.497, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-16 22:27:00,000 (trainer:762) INFO: 4epoch:train:121-160batch: iter_time=4.403e-05, forward_time=0.057, loss_ctc=3.120, loss=3.120, backward_time=0.009, grad_norm=74.586, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:27:04,340 (trainer:762) INFO: 4epoch:train:161-200batch: iter_time=4.513e-05, forward_time=0.057, loss_ctc=3.192, loss=3.192, backward_time=0.009, grad_norm=84.947, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:27:08,705 (trainer:762) INFO: 4epoch:train:201-240batch: iter_time=4.599e-05, forward_time=0.057, loss_ctc=3.393, loss=3.393, backward_time=0.009, grad_norm=99.038, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:27:13,219 (trainer:762) INFO: 4epoch:train:241-280batch: iter_time=4.442e-05, forward_time=0.059, loss_ctc=3.434, loss=3.434, backward_time=0.009, grad_norm=82.270, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.451 +[stan] 2024-01-16 22:27:17,583 (trainer:762) INFO: 4epoch:train:281-320batch: iter_time=4.413e-05, forward_time=0.057, loss_ctc=3.245, loss=3.245, backward_time=0.009, grad_norm=80.899, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:27:21,823 (trainer:762) INFO: 4epoch:train:321-360batch: iter_time=4.541e-05, forward_time=0.055, loss_ctc=2.972, loss=2.972, backward_time=0.009, grad_norm=84.580, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-16 22:27:26,329 (trainer:762) INFO: 4epoch:train:361-400batch: iter_time=4.492e-05, forward_time=0.059, loss_ctc=3.371, loss=3.371, backward_time=0.009, grad_norm=81.571, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-16 22:27:30,644 (trainer:762) INFO: 4epoch:train:401-440batch: iter_time=4.617e-05, forward_time=0.056, loss_ctc=3.165, loss=3.165, backward_time=0.009, grad_norm=72.820, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 22:27:35,022 (trainer:762) INFO: 4epoch:train:441-480batch: iter_time=4.515e-05, forward_time=0.057, loss_ctc=3.260, loss=3.260, backward_time=0.009, grad_norm=80.964, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:27:39,516 (trainer:762) INFO: 4epoch:train:481-520batch: iter_time=4.712e-05, forward_time=0.059, loss_ctc=3.053, loss=3.053, backward_time=0.009, grad_norm=78.990, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-16 22:27:43,783 (trainer:762) INFO: 4epoch:train:521-560batch: iter_time=4.377e-05, forward_time=0.056, loss_ctc=2.988, loss=2.988, backward_time=0.009, grad_norm=94.683, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-16 22:27:48,229 (trainer:762) INFO: 4epoch:train:561-600batch: iter_time=4.460e-05, forward_time=0.058, loss_ctc=3.075, loss=3.075, backward_time=0.009, grad_norm=87.884, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:27:52,675 (trainer:762) INFO: 4epoch:train:601-640batch: iter_time=4.451e-05, forward_time=0.058, loss_ctc=3.134, loss=3.134, backward_time=0.009, grad_norm=81.379, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:27:56,933 (trainer:762) INFO: 4epoch:train:641-680batch: iter_time=4.380e-05, forward_time=0.056, loss_ctc=2.880, loss=2.880, backward_time=0.009, grad_norm=78.336, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-16 22:28:01,376 (trainer:762) INFO: 4epoch:train:681-720batch: iter_time=4.458e-05, forward_time=0.058, loss_ctc=2.998, loss=2.998, backward_time=0.009, grad_norm=81.056, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 22:28:05,723 (trainer:762) INFO: 4epoch:train:721-760batch: iter_time=4.675e-05, forward_time=0.057, loss_ctc=2.973, loss=2.973, backward_time=0.009, grad_norm=75.849, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:28:10,072 (trainer:762) INFO: 4epoch:train:761-800batch: iter_time=4.285e-05, forward_time=0.057, loss_ctc=2.722, loss=2.722, backward_time=0.009, grad_norm=84.335, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:28:14,304 (trainer:357) INFO: 4epoch results: [train] iter_time=1.703e-04, forward_time=0.057, loss_ctc=3.177, loss=3.177, backward_time=0.009, grad_norm=83.270, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.03 seconds, total_count=3200, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=41.569, cer_ctc=0.239, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=41.569, time=1.07 seconds, total_count=60, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.09 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:28:15,264 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:28:15,264 (trainer:291) INFO: 5/30epoch started. Estimated time to finish: 40 minutes and 32.15 seconds +[stan] 2024-01-16 22:28:19,879 (trainer:762) INFO: 5epoch:train:1-40batch: iter_time=0.003, forward_time=0.057, loss_ctc=2.941, loss=2.941, backward_time=0.009, grad_norm=72.087, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.461 +[stan] 2024-01-16 22:28:24,375 (trainer:762) INFO: 5epoch:train:41-80batch: iter_time=4.880e-05, forward_time=0.059, loss_ctc=3.056, loss=3.056, backward_time=0.009, grad_norm=80.789, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-16 22:28:28,778 (trainer:762) INFO: 5epoch:train:81-120batch: iter_time=4.865e-05, forward_time=0.057, loss_ctc=2.963, loss=2.963, backward_time=0.009, grad_norm=79.503, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:28:33,153 (trainer:762) INFO: 5epoch:train:121-160batch: iter_time=4.510e-05, forward_time=0.057, loss_ctc=2.884, loss=2.884, backward_time=0.009, grad_norm=75.188, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:28:37,426 (trainer:762) INFO: 5epoch:train:161-200batch: iter_time=4.864e-05, forward_time=0.056, loss_ctc=2.597, loss=2.597, backward_time=0.009, grad_norm=82.614, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-16 22:28:41,979 (trainer:762) INFO: 5epoch:train:201-240batch: iter_time=4.801e-05, forward_time=0.059, loss_ctc=3.265, loss=3.265, backward_time=0.010, grad_norm=85.744, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.455 +[stan] 2024-01-16 22:28:46,316 (trainer:762) INFO: 5epoch:train:241-280batch: iter_time=4.535e-05, forward_time=0.057, loss_ctc=2.825, loss=2.825, backward_time=0.009, grad_norm=79.372, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:28:50,625 (trainer:762) INFO: 5epoch:train:281-320batch: iter_time=4.729e-05, forward_time=0.056, loss_ctc=2.725, loss=2.725, backward_time=0.009, grad_norm=81.899, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 22:28:55,073 (trainer:762) INFO: 5epoch:train:321-360batch: iter_time=4.695e-05, forward_time=0.058, loss_ctc=2.898, loss=2.898, backward_time=0.009, grad_norm=77.200, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:28:59,379 (trainer:762) INFO: 5epoch:train:361-400batch: iter_time=4.565e-05, forward_time=0.056, loss_ctc=2.698, loss=2.698, backward_time=0.009, grad_norm=82.580, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 22:29:03,807 (trainer:762) INFO: 5epoch:train:401-440batch: iter_time=4.704e-05, forward_time=0.058, loss_ctc=2.886, loss=2.886, backward_time=0.009, grad_norm=79.985, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:29:08,192 (trainer:762) INFO: 5epoch:train:441-480batch: iter_time=4.649e-05, forward_time=0.057, loss_ctc=2.862, loss=2.862, backward_time=0.009, grad_norm=68.633, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:29:12,535 (trainer:762) INFO: 5epoch:train:481-520batch: iter_time=4.590e-05, forward_time=0.057, loss_ctc=2.592, loss=2.592, backward_time=0.009, grad_norm=77.558, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:29:16,839 (trainer:762) INFO: 5epoch:train:521-560batch: iter_time=4.641e-05, forward_time=0.056, loss_ctc=2.489, loss=2.489, backward_time=0.009, grad_norm=68.694, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-16 22:29:21,294 (trainer:762) INFO: 5epoch:train:561-600batch: iter_time=4.577e-05, forward_time=0.058, loss_ctc=2.886, loss=2.886, backward_time=0.009, grad_norm=81.246, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:29:25,677 (trainer:762) INFO: 5epoch:train:601-640batch: iter_time=4.632e-05, forward_time=0.057, loss_ctc=2.670, loss=2.670, backward_time=0.009, grad_norm=70.306, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:29:30,180 (trainer:762) INFO: 5epoch:train:641-680batch: iter_time=4.734e-05, forward_time=0.059, loss_ctc=2.750, loss=2.750, backward_time=0.009, grad_norm=99.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-16 22:29:34,378 (trainer:762) INFO: 5epoch:train:681-720batch: iter_time=4.583e-05, forward_time=0.055, loss_ctc=2.492, loss=2.492, backward_time=0.009, grad_norm=67.373, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.420 +[stan] 2024-01-16 22:29:38,824 (trainer:762) INFO: 5epoch:train:721-760batch: iter_time=4.692e-05, forward_time=0.058, loss_ctc=2.759, loss=2.759, backward_time=0.009, grad_norm=72.344, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:29:43,169 (trainer:762) INFO: 5epoch:train:761-800batch: iter_time=4.421e-05, forward_time=0.057, loss_ctc=2.803, loss=2.803, backward_time=0.009, grad_norm=76.896, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:29:47,389 (trainer:357) INFO: 5epoch results: [train] iter_time=1.887e-04, forward_time=0.057, loss_ctc=2.802, loss=2.802, backward_time=0.009, grad_norm=77.956, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439, time=1 minute and 27.97 seconds, total_count=4000, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=43.719, cer_ctc=0.239, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=43.719, time=1.08 seconds, total_count=75, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.07 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:29:48,409 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:29:48,409 (trainer:291) INFO: 6/30epoch started. Estimated time to finish: 38 minutes and 56.61 seconds +[stan] 2024-01-16 22:29:53,145 (trainer:762) INFO: 6epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=2.795, loss=2.795, backward_time=0.009, grad_norm=77.567, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.473 +[stan] 2024-01-16 22:29:57,630 (trainer:762) INFO: 6epoch:train:41-80batch: iter_time=4.623e-05, forward_time=0.059, loss_ctc=2.775, loss=2.775, backward_time=0.009, grad_norm=83.907, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-16 22:30:01,976 (trainer:762) INFO: 6epoch:train:81-120batch: iter_time=4.907e-05, forward_time=0.057, loss_ctc=2.522, loss=2.522, backward_time=0.009, grad_norm=69.511, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:30:06,346 (trainer:762) INFO: 6epoch:train:121-160batch: iter_time=4.726e-05, forward_time=0.057, loss_ctc=2.507, loss=2.507, backward_time=0.009, grad_norm=66.723, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:30:10,696 (trainer:762) INFO: 6epoch:train:161-200batch: iter_time=4.623e-05, forward_time=0.057, loss_ctc=2.591, loss=2.591, backward_time=0.009, grad_norm=71.304, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:30:15,100 (trainer:762) INFO: 6epoch:train:201-240batch: iter_time=4.483e-05, forward_time=0.057, loss_ctc=2.559, loss=2.559, backward_time=0.009, grad_norm=69.588, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:30:19,503 (trainer:762) INFO: 6epoch:train:241-280batch: iter_time=4.384e-05, forward_time=0.057, loss_ctc=2.656, loss=2.656, backward_time=0.009, grad_norm=66.925, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:30:23,794 (trainer:762) INFO: 6epoch:train:281-320batch: iter_time=4.374e-05, forward_time=0.056, loss_ctc=2.358, loss=2.358, backward_time=0.009, grad_norm=69.522, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-16 22:30:28,295 (trainer:762) INFO: 6epoch:train:321-360batch: iter_time=4.624e-05, forward_time=0.059, loss_ctc=2.757, loss=2.757, backward_time=0.009, grad_norm=69.113, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-16 22:30:32,844 (trainer:762) INFO: 6epoch:train:361-400batch: iter_time=4.508e-05, forward_time=0.059, loss_ctc=2.798, loss=2.798, backward_time=0.009, grad_norm=74.941, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.455 +[stan] 2024-01-16 22:30:37,106 (trainer:762) INFO: 6epoch:train:401-440batch: iter_time=4.713e-05, forward_time=0.056, loss_ctc=2.527, loss=2.527, backward_time=0.009, grad_norm=68.673, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-16 22:30:41,486 (trainer:762) INFO: 6epoch:train:441-480batch: iter_time=4.467e-05, forward_time=0.057, loss_ctc=2.473, loss=2.473, backward_time=0.009, grad_norm=64.762, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:30:45,648 (trainer:762) INFO: 6epoch:train:481-520batch: iter_time=4.667e-05, forward_time=0.055, loss_ctc=2.179, loss=2.179, backward_time=0.009, grad_norm=68.166, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-16 22:30:50,310 (trainer:762) INFO: 6epoch:train:521-560batch: iter_time=4.478e-05, forward_time=0.061, loss_ctc=2.757, loss=2.757, backward_time=0.009, grad_norm=74.669, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.466 +[stan] 2024-01-16 22:30:54,694 (trainer:762) INFO: 6epoch:train:561-600batch: iter_time=4.871e-05, forward_time=0.057, loss_ctc=2.460, loss=2.460, backward_time=0.009, grad_norm=69.842, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:30:59,019 (trainer:762) INFO: 6epoch:train:601-640batch: iter_time=4.412e-05, forward_time=0.057, loss_ctc=2.459, loss=2.459, backward_time=0.009, grad_norm=74.242, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 22:31:03,379 (trainer:762) INFO: 6epoch:train:641-680batch: iter_time=4.612e-05, forward_time=0.057, loss_ctc=2.471, loss=2.471, backward_time=0.009, grad_norm=70.125, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:31:07,727 (trainer:762) INFO: 6epoch:train:681-720batch: iter_time=5.038e-05, forward_time=0.057, loss_ctc=2.351, loss=2.351, backward_time=0.009, grad_norm=67.633, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:31:12,070 (trainer:762) INFO: 6epoch:train:721-760batch: iter_time=4.597e-05, forward_time=0.057, loss_ctc=2.270, loss=2.270, backward_time=0.009, grad_norm=67.671, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:31:16,561 (trainer:762) INFO: 6epoch:train:761-800batch: iter_time=4.387e-05, forward_time=0.059, loss_ctc=2.623, loss=2.623, backward_time=0.009, grad_norm=72.247, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-16 22:31:20,735 (trainer:357) INFO: 6epoch results: [train] iter_time=1.909e-04, forward_time=0.057, loss_ctc=2.544, loss=2.544, backward_time=0.009, grad_norm=70.856, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.22 seconds, total_count=4800, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=45.745, cer_ctc=0.245, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=45.745, time=1.08 seconds, total_count=90, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.03 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:31:21,681 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:31:21,681 (trainer:291) INFO: 7/30epoch started. Estimated time to finish: 37 minutes and 22.38 seconds +[stan] 2024-01-16 22:31:26,305 (trainer:762) INFO: 7epoch:train:1-40batch: iter_time=0.003, forward_time=0.057, loss_ctc=2.490, loss=2.490, backward_time=0.009, grad_norm=79.235, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.462 +[stan] 2024-01-16 22:31:30,679 (trainer:762) INFO: 7epoch:train:41-80batch: iter_time=4.666e-05, forward_time=0.057, loss_ctc=2.406, loss=2.406, backward_time=0.009, grad_norm=76.838, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:31:35,210 (trainer:762) INFO: 7epoch:train:81-120batch: iter_time=4.719e-05, forward_time=0.059, loss_ctc=2.525, loss=2.525, backward_time=0.009, grad_norm=71.889, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.453 +[stan] 2024-01-16 22:31:39,407 (trainer:762) INFO: 7epoch:train:121-160batch: iter_time=4.661e-05, forward_time=0.055, loss_ctc=2.128, loss=2.128, backward_time=0.009, grad_norm=66.253, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.420 +[stan] 2024-01-16 22:31:43,901 (trainer:762) INFO: 7epoch:train:161-200batch: iter_time=4.498e-05, forward_time=0.059, loss_ctc=2.371, loss=2.371, backward_time=0.009, grad_norm=69.902, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-16 22:31:48,305 (trainer:762) INFO: 7epoch:train:201-240batch: iter_time=4.557e-05, forward_time=0.058, loss_ctc=2.298, loss=2.298, backward_time=0.009, grad_norm=64.516, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:31:52,649 (trainer:762) INFO: 7epoch:train:241-280batch: iter_time=4.482e-05, forward_time=0.057, loss_ctc=2.343, loss=2.343, backward_time=0.009, grad_norm=63.642, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:31:57,066 (trainer:762) INFO: 7epoch:train:281-320batch: iter_time=4.626e-05, forward_time=0.058, loss_ctc=2.392, loss=2.392, backward_time=0.009, grad_norm=64.430, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 22:32:01,323 (trainer:762) INFO: 7epoch:train:321-360batch: iter_time=4.491e-05, forward_time=0.056, loss_ctc=2.265, loss=2.265, backward_time=0.009, grad_norm=65.233, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-16 22:32:05,787 (trainer:762) INFO: 7epoch:train:361-400batch: iter_time=4.444e-05, forward_time=0.058, loss_ctc=2.464, loss=2.464, backward_time=0.009, grad_norm=75.184, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-16 22:32:10,178 (trainer:762) INFO: 7epoch:train:401-440batch: iter_time=4.751e-05, forward_time=0.057, loss_ctc=2.294, loss=2.294, backward_time=0.009, grad_norm=64.851, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:32:14,505 (trainer:762) INFO: 7epoch:train:441-480batch: iter_time=4.604e-05, forward_time=0.057, loss_ctc=2.335, loss=2.335, backward_time=0.009, grad_norm=68.542, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-16 22:32:18,940 (trainer:762) INFO: 7epoch:train:481-520batch: iter_time=5.136e-05, forward_time=0.058, loss_ctc=2.402, loss=2.402, backward_time=0.009, grad_norm=65.124, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:32:23,426 (trainer:762) INFO: 7epoch:train:521-560batch: iter_time=4.506e-05, forward_time=0.059, loss_ctc=2.574, loss=2.574, backward_time=0.009, grad_norm=73.264, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-16 22:32:27,719 (trainer:762) INFO: 7epoch:train:561-600batch: iter_time=4.736e-05, forward_time=0.056, loss_ctc=2.175, loss=2.175, backward_time=0.009, grad_norm=63.194, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-16 22:32:32,194 (trainer:762) INFO: 7epoch:train:601-640batch: iter_time=4.552e-05, forward_time=0.058, loss_ctc=2.493, loss=2.493, backward_time=0.009, grad_norm=66.216, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-16 22:32:36,605 (trainer:762) INFO: 7epoch:train:641-680batch: iter_time=4.871e-05, forward_time=0.058, loss_ctc=2.382, loss=2.382, backward_time=0.009, grad_norm=63.380, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 22:32:40,856 (trainer:762) INFO: 7epoch:train:681-720batch: iter_time=4.414e-05, forward_time=0.056, loss_ctc=2.064, loss=2.064, backward_time=0.009, grad_norm=61.305, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-16 22:32:45,324 (trainer:762) INFO: 7epoch:train:721-760batch: iter_time=4.312e-05, forward_time=0.058, loss_ctc=2.287, loss=2.287, backward_time=0.009, grad_norm=70.175, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-16 22:32:49,773 (trainer:762) INFO: 7epoch:train:761-800batch: iter_time=4.212e-05, forward_time=0.058, loss_ctc=2.292, loss=2.292, backward_time=0.009, grad_norm=65.702, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:32:53,953 (trainer:357) INFO: 7epoch results: [train] iter_time=2.137e-04, forward_time=0.057, loss_ctc=2.349, loss=2.349, backward_time=0.009, grad_norm=67.944, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.17 seconds, total_count=5600, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=45.431, cer_ctc=0.236, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=45.431, time=1.07 seconds, total_count=105, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.04 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:32:54,951 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:32:54,952 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/6epoch.pth +[stan] 2024-01-16 22:32:54,952 (trainer:291) INFO: 8/30epoch started. Estimated time to finish: 35 minutes and 48.41 seconds +[stan] 2024-01-16 22:32:59,605 (trainer:762) INFO: 8epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=2.260, loss=2.260, backward_time=0.009, grad_norm=73.274, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.465 +[stan] 2024-01-16 22:33:03,982 (trainer:762) INFO: 8epoch:train:41-80batch: iter_time=4.462e-05, forward_time=0.057, loss_ctc=2.167, loss=2.167, backward_time=0.009, grad_norm=65.096, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:33:08,341 (trainer:762) INFO: 8epoch:train:81-120batch: iter_time=4.622e-05, forward_time=0.057, loss_ctc=2.191, loss=2.191, backward_time=0.009, grad_norm=63.376, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:33:12,704 (trainer:762) INFO: 8epoch:train:121-160batch: iter_time=4.569e-05, forward_time=0.057, loss_ctc=2.122, loss=2.122, backward_time=0.009, grad_norm=62.280, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:33:17,090 (trainer:762) INFO: 8epoch:train:161-200batch: iter_time=4.525e-05, forward_time=0.057, loss_ctc=2.081, loss=2.081, backward_time=0.009, grad_norm=62.877, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:33:21,443 (trainer:762) INFO: 8epoch:train:201-240batch: iter_time=4.443e-05, forward_time=0.057, loss_ctc=2.103, loss=2.103, backward_time=0.009, grad_norm=64.081, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:33:25,978 (trainer:762) INFO: 8epoch:train:241-280batch: iter_time=4.599e-05, forward_time=0.059, loss_ctc=2.281, loss=2.281, backward_time=0.009, grad_norm=68.836, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.453 +[stan] 2024-01-16 22:33:30,293 (trainer:762) INFO: 8epoch:train:281-320batch: iter_time=4.848e-05, forward_time=0.056, loss_ctc=2.179, loss=2.179, backward_time=0.009, grad_norm=66.748, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 22:33:34,665 (trainer:762) INFO: 8epoch:train:321-360batch: iter_time=4.742e-05, forward_time=0.057, loss_ctc=2.072, loss=2.072, backward_time=0.009, grad_norm=61.258, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:33:39,122 (trainer:762) INFO: 8epoch:train:361-400batch: iter_time=4.645e-05, forward_time=0.058, loss_ctc=2.218, loss=2.218, backward_time=0.009, grad_norm=66.653, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-16 22:33:43,551 (trainer:762) INFO: 8epoch:train:401-440batch: iter_time=4.425e-05, forward_time=0.058, loss_ctc=2.145, loss=2.145, backward_time=0.009, grad_norm=65.967, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:33:47,945 (trainer:762) INFO: 8epoch:train:441-480batch: iter_time=4.486e-05, forward_time=0.057, loss_ctc=2.192, loss=2.192, backward_time=0.009, grad_norm=68.363, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:33:52,208 (trainer:762) INFO: 8epoch:train:481-520batch: iter_time=4.700e-05, forward_time=0.056, loss_ctc=2.029, loss=2.029, backward_time=0.009, grad_norm=65.479, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-16 22:33:56,663 (trainer:762) INFO: 8epoch:train:521-560batch: iter_time=4.582e-05, forward_time=0.058, loss_ctc=2.149, loss=2.149, backward_time=0.009, grad_norm=68.194, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:34:01,054 (trainer:762) INFO: 8epoch:train:561-600batch: iter_time=4.482e-05, forward_time=0.057, loss_ctc=2.153, loss=2.153, backward_time=0.009, grad_norm=62.163, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:34:05,424 (trainer:762) INFO: 8epoch:train:601-640batch: iter_time=4.708e-05, forward_time=0.057, loss_ctc=1.879, loss=1.879, backward_time=0.009, grad_norm=59.144, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:34:09,776 (trainer:762) INFO: 8epoch:train:641-680batch: iter_time=5.035e-05, forward_time=0.057, loss_ctc=2.052, loss=2.052, backward_time=0.009, grad_norm=59.718, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:34:14,135 (trainer:762) INFO: 8epoch:train:681-720batch: iter_time=4.610e-05, forward_time=0.057, loss_ctc=1.988, loss=1.988, backward_time=0.009, grad_norm=58.873, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:34:18,759 (trainer:762) INFO: 8epoch:train:721-760batch: iter_time=4.415e-05, forward_time=0.060, loss_ctc=2.442, loss=2.442, backward_time=0.010, grad_norm=68.087, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.462 +[stan] 2024-01-16 22:34:22,987 (trainer:762) INFO: 8epoch:train:761-800batch: iter_time=4.248e-05, forward_time=0.055, loss_ctc=1.913, loss=1.913, backward_time=0.009, grad_norm=66.441, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.423 +[stan] 2024-01-16 22:34:27,209 (trainer:357) INFO: 8epoch results: [train] iter_time=1.695e-04, forward_time=0.057, loss_ctc=2.131, loss=2.131, backward_time=0.009, grad_norm=64.845, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.11 seconds, total_count=6400, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=47.015, cer_ctc=0.234, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=47.015, time=1.07 seconds, total_count=120, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.07 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:34:28,274 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:34:28,275 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/7epoch.pth +[stan] 2024-01-16 22:34:28,275 (trainer:291) INFO: 9/30epoch started. Estimated time to finish: 34 minutes and 14.77 seconds +[stan] 2024-01-16 22:34:32,928 (trainer:762) INFO: 9epoch:train:1-40batch: iter_time=0.002, forward_time=0.058, loss_ctc=2.096, loss=2.096, backward_time=0.009, grad_norm=63.698, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.465 +[stan] 2024-01-16 22:34:37,301 (trainer:762) INFO: 9epoch:train:41-80batch: iter_time=4.418e-05, forward_time=0.057, loss_ctc=2.029, loss=2.029, backward_time=0.009, grad_norm=65.103, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:34:41,661 (trainer:762) INFO: 9epoch:train:81-120batch: iter_time=4.789e-05, forward_time=0.057, loss_ctc=1.929, loss=1.929, backward_time=0.009, grad_norm=55.719, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:34:46,104 (trainer:762) INFO: 9epoch:train:121-160batch: iter_time=4.695e-05, forward_time=0.058, loss_ctc=2.296, loss=2.296, backward_time=0.009, grad_norm=63.719, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 22:34:50,454 (trainer:762) INFO: 9epoch:train:161-200batch: iter_time=4.445e-05, forward_time=0.057, loss_ctc=2.084, loss=2.084, backward_time=0.009, grad_norm=63.496, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:34:54,841 (trainer:762) INFO: 9epoch:train:201-240batch: iter_time=4.451e-05, forward_time=0.057, loss_ctc=2.136, loss=2.136, backward_time=0.009, grad_norm=66.509, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:34:59,270 (trainer:762) INFO: 9epoch:train:241-280batch: iter_time=4.442e-05, forward_time=0.058, loss_ctc=2.020, loss=2.020, backward_time=0.009, grad_norm=57.783, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:35:03,694 (trainer:762) INFO: 9epoch:train:281-320batch: iter_time=4.461e-05, forward_time=0.058, loss_ctc=1.890, loss=1.890, backward_time=0.009, grad_norm=64.333, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 22:35:08,042 (trainer:762) INFO: 9epoch:train:321-360batch: iter_time=4.622e-05, forward_time=0.057, loss_ctc=1.862, loss=1.862, backward_time=0.009, grad_norm=56.630, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:35:12,403 (trainer:762) INFO: 9epoch:train:361-400batch: iter_time=4.911e-05, forward_time=0.057, loss_ctc=2.014, loss=2.014, backward_time=0.009, grad_norm=64.695, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:35:16,751 (trainer:762) INFO: 9epoch:train:401-440batch: iter_time=4.557e-05, forward_time=0.057, loss_ctc=1.941, loss=1.941, backward_time=0.009, grad_norm=58.410, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:35:21,107 (trainer:762) INFO: 9epoch:train:441-480batch: iter_time=4.466e-05, forward_time=0.057, loss_ctc=2.036, loss=2.036, backward_time=0.009, grad_norm=61.503, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:35:25,649 (trainer:762) INFO: 9epoch:train:481-520batch: iter_time=4.459e-05, forward_time=0.059, loss_ctc=2.075, loss=2.075, backward_time=0.009, grad_norm=64.952, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.454 +[stan] 2024-01-16 22:35:29,913 (trainer:762) INFO: 9epoch:train:521-560batch: iter_time=4.515e-05, forward_time=0.056, loss_ctc=2.049, loss=2.049, backward_time=0.009, grad_norm=61.197, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-16 22:35:34,480 (trainer:762) INFO: 9epoch:train:561-600batch: iter_time=4.784e-05, forward_time=0.060, loss_ctc=2.129, loss=2.129, backward_time=0.009, grad_norm=64.870, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.457 +[stan] 2024-01-16 22:35:38,845 (trainer:762) INFO: 9epoch:train:601-640batch: iter_time=4.795e-05, forward_time=0.057, loss_ctc=1.886, loss=1.886, backward_time=0.009, grad_norm=55.144, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:35:43,090 (trainer:762) INFO: 9epoch:train:641-680batch: iter_time=4.697e-05, forward_time=0.056, loss_ctc=1.875, loss=1.875, backward_time=0.009, grad_norm=68.318, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-16 22:35:47,469 (trainer:762) INFO: 9epoch:train:681-720batch: iter_time=4.688e-05, forward_time=0.057, loss_ctc=2.086, loss=2.086, backward_time=0.009, grad_norm=63.392, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:35:51,917 (trainer:762) INFO: 9epoch:train:721-760batch: iter_time=4.838e-05, forward_time=0.058, loss_ctc=1.920, loss=1.920, backward_time=0.009, grad_norm=61.858, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:35:56,359 (trainer:762) INFO: 9epoch:train:761-800batch: iter_time=4.237e-05, forward_time=0.058, loss_ctc=2.002, loss=2.002, backward_time=0.009, grad_norm=62.154, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 22:36:00,527 (trainer:357) INFO: 9epoch results: [train] iter_time=1.596e-04, forward_time=0.057, loss_ctc=2.018, loss=2.018, backward_time=0.009, grad_norm=62.174, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.15 seconds, total_count=7200, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=47.945, cer_ctc=0.233, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=47.945, time=1.06 seconds, total_count=135, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.04 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:36:01,521 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:36:01,523 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/8epoch.pth +[stan] 2024-01-16 22:36:01,523 (trainer:291) INFO: 10/30epoch started. Estimated time to finish: 32 minutes and 41.02 seconds +[stan] 2024-01-16 22:36:06,167 (trainer:762) INFO: 10epoch:train:1-40batch: iter_time=0.003, forward_time=0.057, loss_ctc=1.879, loss=1.879, backward_time=0.009, grad_norm=59.932, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.464 +[stan] 2024-01-16 22:36:10,538 (trainer:762) INFO: 10epoch:train:41-80batch: iter_time=5.227e-05, forward_time=0.057, loss_ctc=1.668, loss=1.668, backward_time=0.009, grad_norm=58.359, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:36:14,886 (trainer:762) INFO: 10epoch:train:81-120batch: iter_time=4.831e-05, forward_time=0.057, loss_ctc=1.743, loss=1.743, backward_time=0.009, grad_norm=56.991, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:36:19,249 (trainer:762) INFO: 10epoch:train:121-160batch: iter_time=4.496e-05, forward_time=0.057, loss_ctc=1.871, loss=1.871, backward_time=0.009, grad_norm=61.359, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:36:23,754 (trainer:762) INFO: 10epoch:train:161-200batch: iter_time=4.731e-05, forward_time=0.059, loss_ctc=2.160, loss=2.160, backward_time=0.009, grad_norm=62.523, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-16 22:36:28,026 (trainer:762) INFO: 10epoch:train:201-240batch: iter_time=4.549e-05, forward_time=0.056, loss_ctc=1.690, loss=1.690, backward_time=0.009, grad_norm=56.130, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-16 22:36:32,454 (trainer:762) INFO: 10epoch:train:241-280batch: iter_time=4.555e-05, forward_time=0.058, loss_ctc=1.861, loss=1.861, backward_time=0.009, grad_norm=54.285, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:36:36,891 (trainer:762) INFO: 10epoch:train:281-320batch: iter_time=4.753e-05, forward_time=0.058, loss_ctc=1.914, loss=1.914, backward_time=0.009, grad_norm=56.455, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 22:36:41,295 (trainer:762) INFO: 10epoch:train:321-360batch: iter_time=4.709e-05, forward_time=0.058, loss_ctc=1.966, loss=1.966, backward_time=0.009, grad_norm=59.854, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:36:45,649 (trainer:762) INFO: 10epoch:train:361-400batch: iter_time=4.610e-05, forward_time=0.057, loss_ctc=1.873, loss=1.873, backward_time=0.009, grad_norm=56.671, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:36:49,928 (trainer:762) INFO: 10epoch:train:401-440batch: iter_time=4.511e-05, forward_time=0.056, loss_ctc=1.709, loss=1.709, backward_time=0.009, grad_norm=54.638, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-16 22:36:54,391 (trainer:762) INFO: 10epoch:train:441-480batch: iter_time=4.965e-05, forward_time=0.058, loss_ctc=1.928, loss=1.928, backward_time=0.009, grad_norm=58.232, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-16 22:36:58,862 (trainer:762) INFO: 10epoch:train:481-520batch: iter_time=4.572e-05, forward_time=0.058, loss_ctc=1.984, loss=1.984, backward_time=0.009, grad_norm=64.470, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-16 22:37:03,220 (trainer:762) INFO: 10epoch:train:521-560batch: iter_time=4.616e-05, forward_time=0.057, loss_ctc=1.732, loss=1.732, backward_time=0.009, grad_norm=55.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:37:07,558 (trainer:762) INFO: 10epoch:train:561-600batch: iter_time=4.484e-05, forward_time=0.057, loss_ctc=1.802, loss=1.802, backward_time=0.009, grad_norm=60.355, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:37:11,918 (trainer:762) INFO: 10epoch:train:601-640batch: iter_time=4.800e-05, forward_time=0.057, loss_ctc=1.888, loss=1.888, backward_time=0.009, grad_norm=57.686, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:37:16,262 (trainer:762) INFO: 10epoch:train:641-680batch: iter_time=4.878e-05, forward_time=0.057, loss_ctc=1.718, loss=1.718, backward_time=0.009, grad_norm=56.959, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:37:20,766 (trainer:762) INFO: 10epoch:train:681-720batch: iter_time=4.819e-05, forward_time=0.059, loss_ctc=1.871, loss=1.871, backward_time=0.009, grad_norm=58.936, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-16 22:37:25,109 (trainer:762) INFO: 10epoch:train:721-760batch: iter_time=4.921e-05, forward_time=0.057, loss_ctc=1.862, loss=1.862, backward_time=0.009, grad_norm=57.718, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:37:29,460 (trainer:762) INFO: 10epoch:train:761-800batch: iter_time=4.378e-05, forward_time=0.057, loss_ctc=1.660, loss=1.660, backward_time=0.009, grad_norm=55.737, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:37:33,667 (trainer:357) INFO: 10epoch results: [train] iter_time=2.037e-04, forward_time=0.057, loss_ctc=1.839, loss=1.839, backward_time=0.009, grad_norm=58.120, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.01 seconds, total_count=8000, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=48.789, cer_ctc=0.238, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=48.789, time=1.08 seconds, total_count=150, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:37:34,764 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:37:34,766 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/9epoch.pth +[stan] 2024-01-16 22:37:34,766 (trainer:291) INFO: 11/30epoch started. Estimated time to finish: 31 minutes and 7.36 seconds +[stan] 2024-01-16 22:37:39,349 (trainer:762) INFO: 11epoch:train:1-40batch: iter_time=0.002, forward_time=0.057, loss_ctc=1.739, loss=1.739, backward_time=0.009, grad_norm=57.902, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.458 +[stan] 2024-01-16 22:37:43,693 (trainer:762) INFO: 11epoch:train:41-80batch: iter_time=4.495e-05, forward_time=0.057, loss_ctc=1.682, loss=1.682, backward_time=0.009, grad_norm=53.299, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:37:48,272 (trainer:762) INFO: 11epoch:train:81-120batch: iter_time=4.496e-05, forward_time=0.060, loss_ctc=2.055, loss=2.055, backward_time=0.009, grad_norm=60.075, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.458 +[stan] 2024-01-16 22:37:52,610 (trainer:762) INFO: 11epoch:train:121-160batch: iter_time=4.475e-05, forward_time=0.057, loss_ctc=1.731, loss=1.731, backward_time=0.009, grad_norm=56.938, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:37:56,907 (trainer:762) INFO: 11epoch:train:161-200batch: iter_time=4.367e-05, forward_time=0.056, loss_ctc=1.587, loss=1.587, backward_time=0.009, grad_norm=53.757, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-16 22:38:01,425 (trainer:762) INFO: 11epoch:train:201-240batch: iter_time=4.672e-05, forward_time=0.059, loss_ctc=1.838, loss=1.838, backward_time=0.009, grad_norm=53.724, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.452 +[stan] 2024-01-16 22:38:05,791 (trainer:762) INFO: 11epoch:train:241-280batch: iter_time=4.847e-05, forward_time=0.057, loss_ctc=1.797, loss=1.797, backward_time=0.009, grad_norm=57.361, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:38:10,137 (trainer:762) INFO: 11epoch:train:281-320batch: iter_time=4.918e-05, forward_time=0.057, loss_ctc=1.750, loss=1.750, backward_time=0.009, grad_norm=56.005, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:38:14,498 (trainer:762) INFO: 11epoch:train:321-360batch: iter_time=4.574e-05, forward_time=0.057, loss_ctc=1.828, loss=1.828, backward_time=0.009, grad_norm=61.938, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:38:18,885 (trainer:762) INFO: 11epoch:train:361-400batch: iter_time=4.726e-05, forward_time=0.057, loss_ctc=1.803, loss=1.803, backward_time=0.009, grad_norm=57.298, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:38:23,453 (trainer:762) INFO: 11epoch:train:401-440batch: iter_time=4.442e-05, forward_time=0.060, loss_ctc=1.926, loss=1.926, backward_time=0.009, grad_norm=60.075, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.457 +[stan] 2024-01-16 22:38:27,700 (trainer:762) INFO: 11epoch:train:441-480batch: iter_time=4.385e-05, forward_time=0.056, loss_ctc=1.503, loss=1.503, backward_time=0.009, grad_norm=54.685, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-16 22:38:32,088 (trainer:762) INFO: 11epoch:train:481-520batch: iter_time=4.445e-05, forward_time=0.057, loss_ctc=1.755, loss=1.755, backward_time=0.009, grad_norm=61.943, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:38:36,353 (trainer:762) INFO: 11epoch:train:521-560batch: iter_time=4.952e-05, forward_time=0.056, loss_ctc=1.714, loss=1.714, backward_time=0.009, grad_norm=59.145, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-16 22:38:40,840 (trainer:762) INFO: 11epoch:train:561-600batch: iter_time=4.827e-05, forward_time=0.059, loss_ctc=1.803, loss=1.803, backward_time=0.009, grad_norm=59.010, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-16 22:38:45,220 (trainer:762) INFO: 11epoch:train:601-640batch: iter_time=4.417e-05, forward_time=0.057, loss_ctc=1.677, loss=1.677, backward_time=0.009, grad_norm=53.847, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:38:49,688 (trainer:762) INFO: 11epoch:train:641-680batch: iter_time=4.492e-05, forward_time=0.058, loss_ctc=1.739, loss=1.739, backward_time=0.009, grad_norm=56.640, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-16 22:38:54,029 (trainer:762) INFO: 11epoch:train:681-720batch: iter_time=4.666e-05, forward_time=0.057, loss_ctc=1.724, loss=1.724, backward_time=0.009, grad_norm=55.105, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:38:58,511 (trainer:762) INFO: 11epoch:train:721-760batch: iter_time=4.807e-05, forward_time=0.059, loss_ctc=1.855, loss=1.855, backward_time=0.009, grad_norm=58.153, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-16 22:39:02,809 (trainer:762) INFO: 11epoch:train:761-800batch: iter_time=4.255e-05, forward_time=0.056, loss_ctc=1.645, loss=1.645, backward_time=0.009, grad_norm=52.025, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-16 22:39:07,030 (trainer:357) INFO: 11epoch results: [train] iter_time=1.660e-04, forward_time=0.057, loss_ctc=1.757, loss=1.757, backward_time=0.009, grad_norm=56.946, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.12 seconds, total_count=8800, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=49.192, cer_ctc=0.235, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=49.192, time=1.06 seconds, total_count=165, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.08 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:39:07,957 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:39:07,958 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/10epoch.pth +[stan] 2024-01-16 22:39:07,958 (trainer:291) INFO: 12/30epoch started. Estimated time to finish: 29 minutes and 33.69 seconds +[stan] 2024-01-16 22:39:12,637 (trainer:762) INFO: 12epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=1.692, loss=1.692, backward_time=0.009, grad_norm=57.590, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.467 +[stan] 2024-01-16 22:39:17,044 (trainer:762) INFO: 12epoch:train:41-80batch: iter_time=4.598e-05, forward_time=0.058, loss_ctc=1.686, loss=1.686, backward_time=0.009, grad_norm=58.966, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 22:39:21,348 (trainer:762) INFO: 12epoch:train:81-120batch: iter_time=5.205e-05, forward_time=0.056, loss_ctc=1.666, loss=1.666, backward_time=0.009, grad_norm=55.990, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-16 22:39:25,852 (trainer:762) INFO: 12epoch:train:121-160batch: iter_time=4.534e-05, forward_time=0.059, loss_ctc=1.959, loss=1.959, backward_time=0.009, grad_norm=65.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-16 22:39:30,156 (trainer:762) INFO: 12epoch:train:161-200batch: iter_time=4.446e-05, forward_time=0.056, loss_ctc=1.477, loss=1.477, backward_time=0.009, grad_norm=50.985, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-16 22:39:34,633 (trainer:762) INFO: 12epoch:train:201-240batch: iter_time=4.379e-05, forward_time=0.058, loss_ctc=1.750, loss=1.750, backward_time=0.009, grad_norm=58.834, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-16 22:39:39,051 (trainer:762) INFO: 12epoch:train:241-280batch: iter_time=4.391e-05, forward_time=0.058, loss_ctc=1.670, loss=1.670, backward_time=0.009, grad_norm=55.618, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 22:39:43,339 (trainer:762) INFO: 12epoch:train:281-320batch: iter_time=4.874e-05, forward_time=0.056, loss_ctc=1.624, loss=1.624, backward_time=0.009, grad_norm=58.507, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-16 22:39:47,674 (trainer:762) INFO: 12epoch:train:321-360batch: iter_time=4.591e-05, forward_time=0.057, loss_ctc=1.618, loss=1.618, backward_time=0.009, grad_norm=53.613, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-16 22:39:52,207 (trainer:762) INFO: 12epoch:train:361-400batch: iter_time=4.571e-05, forward_time=0.059, loss_ctc=1.703, loss=1.703, backward_time=0.009, grad_norm=55.460, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.453 +[stan] 2024-01-16 22:39:56,465 (trainer:762) INFO: 12epoch:train:401-440batch: iter_time=4.465e-05, forward_time=0.056, loss_ctc=1.626, loss=1.626, backward_time=0.009, grad_norm=55.824, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-16 22:40:00,811 (trainer:762) INFO: 12epoch:train:441-480batch: iter_time=4.832e-05, forward_time=0.057, loss_ctc=1.554, loss=1.554, backward_time=0.009, grad_norm=52.585, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:40:05,368 (trainer:762) INFO: 12epoch:train:481-520batch: iter_time=4.532e-05, forward_time=0.061, loss_ctc=1.790, loss=1.790, backward_time=0.009, grad_norm=56.153, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.456 +[stan] 2024-01-16 22:40:09,763 (trainer:762) INFO: 12epoch:train:521-560batch: iter_time=4.617e-05, forward_time=0.057, loss_ctc=1.703, loss=1.703, backward_time=0.009, grad_norm=58.164, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:40:14,130 (trainer:762) INFO: 12epoch:train:561-600batch: iter_time=4.525e-05, forward_time=0.057, loss_ctc=1.641, loss=1.641, backward_time=0.009, grad_norm=56.024, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:40:18,565 (trainer:762) INFO: 12epoch:train:601-640batch: iter_time=4.766e-05, forward_time=0.058, loss_ctc=1.719, loss=1.719, backward_time=0.009, grad_norm=57.085, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:40:22,996 (trainer:762) INFO: 12epoch:train:641-680batch: iter_time=4.672e-05, forward_time=0.058, loss_ctc=1.637, loss=1.637, backward_time=0.009, grad_norm=55.705, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:40:27,304 (trainer:762) INFO: 12epoch:train:681-720batch: iter_time=4.712e-05, forward_time=0.056, loss_ctc=1.562, loss=1.562, backward_time=0.009, grad_norm=54.879, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 22:40:31,703 (trainer:762) INFO: 12epoch:train:721-760batch: iter_time=4.354e-05, forward_time=0.057, loss_ctc=1.593, loss=1.593, backward_time=0.009, grad_norm=54.780, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:40:36,226 (trainer:762) INFO: 12epoch:train:761-800batch: iter_time=4.222e-05, forward_time=0.059, loss_ctc=1.640, loss=1.640, backward_time=0.009, grad_norm=54.858, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.452 +[stan] 2024-01-16 22:40:40,421 (trainer:357) INFO: 12epoch results: [train] iter_time=2.003e-04, forward_time=0.058, loss_ctc=1.666, loss=1.666, backward_time=0.009, grad_norm=56.336, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.35 seconds, total_count=9600, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=50.416, cer_ctc=0.235, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=50.416, time=1.06 seconds, total_count=180, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:40:41,368 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:40:41,369 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/11epoch.pth +[stan] 2024-01-16 22:40:41,369 (trainer:291) INFO: 13/30epoch started. Estimated time to finish: 28 minutes and 0.42 seconds +[stan] 2024-01-16 22:40:45,916 (trainer:762) INFO: 13epoch:train:1-40batch: iter_time=0.003, forward_time=0.056, loss_ctc=1.536, loss=1.536, backward_time=0.009, grad_norm=52.481, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.454 +[stan] 2024-01-16 22:40:50,264 (trainer:762) INFO: 13epoch:train:41-80batch: iter_time=4.487e-05, forward_time=0.057, loss_ctc=1.523, loss=1.523, backward_time=0.009, grad_norm=52.001, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:40:54,661 (trainer:762) INFO: 13epoch:train:81-120batch: iter_time=4.609e-05, forward_time=0.057, loss_ctc=1.580, loss=1.580, backward_time=0.009, grad_norm=57.481, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:40:59,117 (trainer:762) INFO: 13epoch:train:121-160batch: iter_time=4.566e-05, forward_time=0.058, loss_ctc=1.582, loss=1.582, backward_time=0.009, grad_norm=53.180, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-16 22:41:03,527 (trainer:762) INFO: 13epoch:train:161-200batch: iter_time=4.552e-05, forward_time=0.058, loss_ctc=1.531, loss=1.531, backward_time=0.009, grad_norm=54.659, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 22:41:07,774 (trainer:762) INFO: 13epoch:train:201-240batch: iter_time=4.357e-05, forward_time=0.055, loss_ctc=1.338, loss=1.338, backward_time=0.009, grad_norm=49.112, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-16 22:41:12,223 (trainer:762) INFO: 13epoch:train:241-280batch: iter_time=4.875e-05, forward_time=0.058, loss_ctc=1.723, loss=1.723, backward_time=0.009, grad_norm=57.562, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:41:16,589 (trainer:762) INFO: 13epoch:train:281-320batch: iter_time=4.387e-05, forward_time=0.057, loss_ctc=1.618, loss=1.618, backward_time=0.009, grad_norm=54.672, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:41:20,912 (trainer:762) INFO: 13epoch:train:321-360batch: iter_time=4.358e-05, forward_time=0.056, loss_ctc=1.512, loss=1.512, backward_time=0.009, grad_norm=55.627, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 22:41:25,362 (trainer:762) INFO: 13epoch:train:361-400batch: iter_time=4.484e-05, forward_time=0.058, loss_ctc=1.585, loss=1.585, backward_time=0.009, grad_norm=54.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:41:29,718 (trainer:762) INFO: 13epoch:train:401-440batch: iter_time=4.445e-05, forward_time=0.057, loss_ctc=1.475, loss=1.475, backward_time=0.009, grad_norm=51.194, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:41:34,164 (trainer:762) INFO: 13epoch:train:441-480batch: iter_time=4.450e-05, forward_time=0.058, loss_ctc=1.562, loss=1.562, backward_time=0.009, grad_norm=48.937, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:41:38,604 (trainer:762) INFO: 13epoch:train:481-520batch: iter_time=4.560e-05, forward_time=0.058, loss_ctc=1.597, loss=1.597, backward_time=0.009, grad_norm=54.872, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 22:41:42,972 (trainer:762) INFO: 13epoch:train:521-560batch: iter_time=4.546e-05, forward_time=0.057, loss_ctc=1.522, loss=1.522, backward_time=0.009, grad_norm=53.195, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:41:47,395 (trainer:762) INFO: 13epoch:train:561-600batch: iter_time=4.407e-05, forward_time=0.058, loss_ctc=1.599, loss=1.599, backward_time=0.009, grad_norm=53.651, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 22:41:51,788 (trainer:762) INFO: 13epoch:train:601-640batch: iter_time=4.493e-05, forward_time=0.057, loss_ctc=1.513, loss=1.513, backward_time=0.009, grad_norm=54.145, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:41:56,137 (trainer:762) INFO: 13epoch:train:641-680batch: iter_time=4.408e-05, forward_time=0.057, loss_ctc=1.467, loss=1.467, backward_time=0.009, grad_norm=52.443, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:42:00,515 (trainer:762) INFO: 13epoch:train:681-720batch: iter_time=4.440e-05, forward_time=0.057, loss_ctc=1.486, loss=1.486, backward_time=0.009, grad_norm=52.833, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:42:04,905 (trainer:762) INFO: 13epoch:train:721-760batch: iter_time=4.579e-05, forward_time=0.057, loss_ctc=1.413, loss=1.413, backward_time=0.009, grad_norm=52.910, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:42:09,287 (trainer:762) INFO: 13epoch:train:761-800batch: iter_time=4.116e-05, forward_time=0.057, loss_ctc=1.436, loss=1.436, backward_time=0.009, grad_norm=52.440, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:42:13,525 (trainer:357) INFO: 13epoch results: [train] iter_time=1.811e-04, forward_time=0.057, loss_ctc=1.530, loss=1.530, backward_time=0.009, grad_norm=53.382, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439, time=1 minute and 27.99 seconds, total_count=10400, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=50.845, cer_ctc=0.244, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=50.845, time=1.07 seconds, total_count=195, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.09 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:42:14,570 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:42:14,571 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/12epoch.pth +[stan] 2024-01-16 22:42:14,571 (trainer:291) INFO: 14/30epoch started. Estimated time to finish: 26 minutes and 26.86 seconds +[stan] 2024-01-16 22:42:19,202 (trainer:762) INFO: 14epoch:train:1-40batch: iter_time=0.003, forward_time=0.057, loss_ctc=1.557, loss=1.557, backward_time=0.009, grad_norm=54.119, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.462 +[stan] 2024-01-16 22:42:23,637 (trainer:762) INFO: 14epoch:train:41-80batch: iter_time=4.309e-05, forward_time=0.058, loss_ctc=1.470, loss=1.470, backward_time=0.009, grad_norm=53.395, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:42:27,934 (trainer:762) INFO: 14epoch:train:81-120batch: iter_time=4.392e-05, forward_time=0.056, loss_ctc=1.438, loss=1.438, backward_time=0.009, grad_norm=49.051, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-16 22:42:32,345 (trainer:762) INFO: 14epoch:train:121-160batch: iter_time=4.539e-05, forward_time=0.058, loss_ctc=1.682, loss=1.682, backward_time=0.009, grad_norm=58.613, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 22:42:36,658 (trainer:762) INFO: 14epoch:train:161-200batch: iter_time=4.369e-05, forward_time=0.056, loss_ctc=1.453, loss=1.453, backward_time=0.009, grad_norm=51.254, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 22:42:41,081 (trainer:762) INFO: 14epoch:train:201-240batch: iter_time=4.440e-05, forward_time=0.058, loss_ctc=1.508, loss=1.508, backward_time=0.009, grad_norm=57.242, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 22:42:45,400 (trainer:762) INFO: 14epoch:train:241-280batch: iter_time=4.550e-05, forward_time=0.057, loss_ctc=1.364, loss=1.364, backward_time=0.009, grad_norm=51.693, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 22:42:49,845 (trainer:762) INFO: 14epoch:train:281-320batch: iter_time=4.495e-05, forward_time=0.058, loss_ctc=1.605, loss=1.605, backward_time=0.009, grad_norm=58.762, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 22:42:54,344 (trainer:762) INFO: 14epoch:train:321-360batch: iter_time=4.463e-05, forward_time=0.059, loss_ctc=1.632, loss=1.632, backward_time=0.009, grad_norm=54.359, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-16 22:42:58,663 (trainer:762) INFO: 14epoch:train:361-400batch: iter_time=4.780e-05, forward_time=0.056, loss_ctc=1.370, loss=1.370, backward_time=0.009, grad_norm=52.835, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 22:43:03,103 (trainer:762) INFO: 14epoch:train:401-440batch: iter_time=4.542e-05, forward_time=0.058, loss_ctc=1.476, loss=1.476, backward_time=0.009, grad_norm=53.449, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 22:43:07,453 (trainer:762) INFO: 14epoch:train:441-480batch: iter_time=4.573e-05, forward_time=0.057, loss_ctc=1.440, loss=1.440, backward_time=0.009, grad_norm=50.050, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:43:11,823 (trainer:762) INFO: 14epoch:train:481-520batch: iter_time=5.284e-05, forward_time=0.057, loss_ctc=1.387, loss=1.387, backward_time=0.009, grad_norm=49.940, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:43:16,234 (trainer:762) INFO: 14epoch:train:521-560batch: iter_time=5.055e-05, forward_time=0.058, loss_ctc=1.408, loss=1.408, backward_time=0.009, grad_norm=55.094, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 22:43:20,651 (trainer:762) INFO: 14epoch:train:561-600batch: iter_time=4.755e-05, forward_time=0.058, loss_ctc=1.434, loss=1.434, backward_time=0.009, grad_norm=54.547, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 22:43:25,033 (trainer:762) INFO: 14epoch:train:601-640batch: iter_time=4.826e-05, forward_time=0.057, loss_ctc=1.567, loss=1.567, backward_time=0.009, grad_norm=54.566, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:43:29,440 (trainer:762) INFO: 14epoch:train:641-680batch: iter_time=4.721e-05, forward_time=0.058, loss_ctc=1.496, loss=1.496, backward_time=0.009, grad_norm=53.008, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 22:43:33,861 (trainer:762) INFO: 14epoch:train:681-720batch: iter_time=4.799e-05, forward_time=0.058, loss_ctc=1.516, loss=1.516, backward_time=0.009, grad_norm=55.207, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 22:43:38,118 (trainer:762) INFO: 14epoch:train:721-760batch: iter_time=4.805e-05, forward_time=0.056, loss_ctc=1.316, loss=1.316, backward_time=0.009, grad_norm=50.679, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-16 22:43:42,550 (trainer:762) INFO: 14epoch:train:761-800batch: iter_time=4.302e-05, forward_time=0.058, loss_ctc=1.419, loss=1.419, backward_time=0.009, grad_norm=50.522, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:43:46,753 (trainer:357) INFO: 14epoch results: [train] iter_time=1.697e-04, forward_time=0.057, loss_ctc=1.477, loss=1.477, backward_time=0.009, grad_norm=53.419, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.05 seconds, total_count=11200, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=52.281, cer_ctc=0.243, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=52.281, time=1.08 seconds, total_count=210, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:43:47,719 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:43:47,720 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/13epoch.pth +[stan] 2024-01-16 22:43:47,720 (trainer:291) INFO: 15/30epoch started. Estimated time to finish: 24 minutes and 53.3 seconds +[stan] 2024-01-16 22:43:52,492 (trainer:762) INFO: 15epoch:train:1-40batch: iter_time=0.003, forward_time=0.059, loss_ctc=1.429, loss=1.429, backward_time=0.009, grad_norm=52.281, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.476 +[stan] 2024-01-16 22:43:56,750 (trainer:762) INFO: 15epoch:train:41-80batch: iter_time=4.601e-05, forward_time=0.056, loss_ctc=1.347, loss=1.347, backward_time=0.009, grad_norm=51.081, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-16 22:44:01,104 (trainer:762) INFO: 15epoch:train:81-120batch: iter_time=4.504e-05, forward_time=0.057, loss_ctc=1.401, loss=1.401, backward_time=0.009, grad_norm=51.137, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:44:05,534 (trainer:762) INFO: 15epoch:train:121-160batch: iter_time=4.513e-05, forward_time=0.058, loss_ctc=1.475, loss=1.475, backward_time=0.009, grad_norm=49.900, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:44:09,894 (trainer:762) INFO: 15epoch:train:161-200batch: iter_time=4.590e-05, forward_time=0.057, loss_ctc=1.354, loss=1.354, backward_time=0.009, grad_norm=48.915, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:44:14,332 (trainer:762) INFO: 15epoch:train:201-240batch: iter_time=4.863e-05, forward_time=0.058, loss_ctc=1.417, loss=1.417, backward_time=0.009, grad_norm=51.303, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 22:44:18,719 (trainer:762) INFO: 15epoch:train:241-280batch: iter_time=5.001e-05, forward_time=0.057, loss_ctc=1.413, loss=1.413, backward_time=0.009, grad_norm=52.951, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:44:23,083 (trainer:762) INFO: 15epoch:train:281-320batch: iter_time=4.701e-05, forward_time=0.057, loss_ctc=1.300, loss=1.300, backward_time=0.009, grad_norm=48.733, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:44:27,561 (trainer:762) INFO: 15epoch:train:321-360batch: iter_time=4.766e-05, forward_time=0.058, loss_ctc=1.543, loss=1.543, backward_time=0.009, grad_norm=56.795, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-16 22:44:31,882 (trainer:762) INFO: 15epoch:train:361-400batch: iter_time=4.858e-05, forward_time=0.056, loss_ctc=1.357, loss=1.357, backward_time=0.009, grad_norm=48.530, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 22:44:36,158 (trainer:762) INFO: 15epoch:train:401-440batch: iter_time=4.849e-05, forward_time=0.056, loss_ctc=1.229, loss=1.229, backward_time=0.009, grad_norm=48.633, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-16 22:44:40,681 (trainer:762) INFO: 15epoch:train:441-480batch: iter_time=4.711e-05, forward_time=0.059, loss_ctc=1.457, loss=1.457, backward_time=0.009, grad_norm=54.005, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.452 +[stan] 2024-01-16 22:44:45,037 (trainer:762) INFO: 15epoch:train:481-520batch: iter_time=4.659e-05, forward_time=0.057, loss_ctc=1.367, loss=1.367, backward_time=0.009, grad_norm=49.675, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:44:49,521 (trainer:762) INFO: 15epoch:train:521-560batch: iter_time=4.824e-05, forward_time=0.059, loss_ctc=1.368, loss=1.368, backward_time=0.009, grad_norm=49.885, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-16 22:44:53,800 (trainer:762) INFO: 15epoch:train:561-600batch: iter_time=4.527e-05, forward_time=0.056, loss_ctc=1.268, loss=1.268, backward_time=0.009, grad_norm=48.902, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-16 22:44:58,277 (trainer:762) INFO: 15epoch:train:601-640batch: iter_time=4.630e-05, forward_time=0.058, loss_ctc=1.365, loss=1.365, backward_time=0.009, grad_norm=50.899, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-16 22:45:02,611 (trainer:762) INFO: 15epoch:train:641-680batch: iter_time=4.860e-05, forward_time=0.057, loss_ctc=1.283, loss=1.283, backward_time=0.009, grad_norm=51.229, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-16 22:45:07,057 (trainer:762) INFO: 15epoch:train:681-720batch: iter_time=4.608e-05, forward_time=0.058, loss_ctc=1.375, loss=1.375, backward_time=0.009, grad_norm=53.059, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:45:11,399 (trainer:762) INFO: 15epoch:train:721-760batch: iter_time=4.793e-05, forward_time=0.057, loss_ctc=1.329, loss=1.329, backward_time=0.009, grad_norm=51.447, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:45:15,738 (trainer:762) INFO: 15epoch:train:761-800batch: iter_time=4.583e-05, forward_time=0.057, loss_ctc=1.249, loss=1.249, backward_time=0.009, grad_norm=48.387, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:45:19,967 (trainer:357) INFO: 15epoch results: [train] iter_time=1.814e-04, forward_time=0.057, loss_ctc=1.366, loss=1.366, backward_time=0.009, grad_norm=50.887, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.09 seconds, total_count=12000, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=51.746, cer_ctc=0.240, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=51.746, time=1.08 seconds, total_count=225, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.08 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:45:21,022 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:45:21,024 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/14epoch.pth +[stan] 2024-01-16 22:45:21,024 (trainer:291) INFO: 16/30epoch started. Estimated time to finish: 23 minutes and 19.94 seconds +[stan] 2024-01-16 22:45:25,643 (trainer:762) INFO: 16epoch:train:1-40batch: iter_time=0.002, forward_time=0.057, loss_ctc=1.445, loss=1.445, backward_time=0.009, grad_norm=53.198, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.461 +[stan] 2024-01-16 22:45:30,130 (trainer:762) INFO: 16epoch:train:41-80batch: iter_time=4.535e-05, forward_time=0.059, loss_ctc=1.438, loss=1.438, backward_time=0.009, grad_norm=53.002, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-16 22:45:34,467 (trainer:762) INFO: 16epoch:train:81-120batch: iter_time=4.976e-05, forward_time=0.057, loss_ctc=1.242, loss=1.242, backward_time=0.009, grad_norm=48.470, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:45:38,826 (trainer:762) INFO: 16epoch:train:121-160batch: iter_time=4.741e-05, forward_time=0.057, loss_ctc=1.304, loss=1.304, backward_time=0.009, grad_norm=49.661, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:45:43,406 (trainer:762) INFO: 16epoch:train:161-200batch: iter_time=4.657e-05, forward_time=0.060, loss_ctc=1.417, loss=1.417, backward_time=0.010, grad_norm=53.301, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.458 +[stan] 2024-01-16 22:45:47,641 (trainer:762) INFO: 16epoch:train:201-240batch: iter_time=4.777e-05, forward_time=0.055, loss_ctc=1.190, loss=1.190, backward_time=0.009, grad_norm=50.862, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.423 +[stan] 2024-01-16 22:45:52,035 (trainer:762) INFO: 16epoch:train:241-280batch: iter_time=4.497e-05, forward_time=0.057, loss_ctc=1.357, loss=1.357, backward_time=0.009, grad_norm=53.717, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:45:56,545 (trainer:762) INFO: 16epoch:train:281-320batch: iter_time=4.647e-05, forward_time=0.059, loss_ctc=1.361, loss=1.361, backward_time=0.009, grad_norm=49.893, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.451 +[stan] 2024-01-16 22:46:00,921 (trainer:762) INFO: 16epoch:train:321-360batch: iter_time=4.428e-05, forward_time=0.057, loss_ctc=1.361, loss=1.361, backward_time=0.009, grad_norm=51.260, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:46:05,125 (trainer:762) INFO: 16epoch:train:361-400batch: iter_time=4.829e-05, forward_time=0.055, loss_ctc=1.159, loss=1.159, backward_time=0.009, grad_norm=46.759, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.420 +[stan] 2024-01-16 22:46:09,621 (trainer:762) INFO: 16epoch:train:401-440batch: iter_time=4.707e-05, forward_time=0.059, loss_ctc=1.355, loss=1.355, backward_time=0.009, grad_norm=50.722, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-16 22:46:13,973 (trainer:762) INFO: 16epoch:train:441-480batch: iter_time=4.966e-05, forward_time=0.057, loss_ctc=1.345, loss=1.345, backward_time=0.009, grad_norm=50.868, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:46:18,415 (trainer:762) INFO: 16epoch:train:481-520batch: iter_time=4.660e-05, forward_time=0.058, loss_ctc=1.281, loss=1.281, backward_time=0.009, grad_norm=51.880, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 22:46:22,725 (trainer:762) INFO: 16epoch:train:521-560batch: iter_time=4.635e-05, forward_time=0.056, loss_ctc=1.347, loss=1.347, backward_time=0.009, grad_norm=50.244, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 22:46:27,103 (trainer:762) INFO: 16epoch:train:561-600batch: iter_time=4.912e-05, forward_time=0.057, loss_ctc=1.139, loss=1.139, backward_time=0.009, grad_norm=45.209, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:46:31,533 (trainer:762) INFO: 16epoch:train:601-640batch: iter_time=4.597e-05, forward_time=0.058, loss_ctc=1.367, loss=1.367, backward_time=0.009, grad_norm=51.967, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:46:35,913 (trainer:762) INFO: 16epoch:train:641-680batch: iter_time=4.939e-05, forward_time=0.057, loss_ctc=1.243, loss=1.243, backward_time=0.009, grad_norm=47.939, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:46:40,167 (trainer:762) INFO: 16epoch:train:681-720batch: iter_time=4.593e-05, forward_time=0.056, loss_ctc=1.208, loss=1.208, backward_time=0.009, grad_norm=52.141, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-16 22:46:44,701 (trainer:762) INFO: 16epoch:train:721-760batch: iter_time=4.573e-05, forward_time=0.059, loss_ctc=1.439, loss=1.439, backward_time=0.009, grad_norm=55.202, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.453 +[stan] 2024-01-16 22:46:48,966 (trainer:762) INFO: 16epoch:train:761-800batch: iter_time=4.554e-05, forward_time=0.056, loss_ctc=1.261, loss=1.261, backward_time=0.009, grad_norm=48.711, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-16 22:46:53,155 (trainer:357) INFO: 16epoch results: [train] iter_time=1.687e-04, forward_time=0.057, loss_ctc=1.313, loss=1.313, backward_time=0.009, grad_norm=50.750, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.02 seconds, total_count=12800, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=53.933, cer_ctc=0.241, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=53.933, time=1.07 seconds, total_count=240, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.04 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:46:54,137 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:46:54,138 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/15epoch.pth +[stan] 2024-01-16 22:46:54,138 (trainer:291) INFO: 17/30epoch started. Estimated time to finish: 21 minutes and 46.42 seconds +[stan] 2024-01-16 22:46:58,929 (trainer:762) INFO: 17epoch:train:1-40batch: iter_time=0.003, forward_time=0.059, loss_ctc=1.280, loss=1.280, backward_time=0.009, grad_norm=50.565, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.478 +[stan] 2024-01-16 22:47:03,271 (trainer:762) INFO: 17epoch:train:41-80batch: iter_time=4.836e-05, forward_time=0.057, loss_ctc=1.350, loss=1.350, backward_time=0.009, grad_norm=50.828, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:47:07,567 (trainer:762) INFO: 17epoch:train:81-120batch: iter_time=4.541e-05, forward_time=0.056, loss_ctc=1.226, loss=1.226, backward_time=0.009, grad_norm=51.003, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-16 22:47:12,089 (trainer:762) INFO: 17epoch:train:121-160batch: iter_time=4.669e-05, forward_time=0.059, loss_ctc=1.312, loss=1.312, backward_time=0.009, grad_norm=50.841, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.452 +[stan] 2024-01-16 22:47:16,444 (trainer:762) INFO: 17epoch:train:161-200batch: iter_time=4.950e-05, forward_time=0.057, loss_ctc=1.329, loss=1.329, backward_time=0.009, grad_norm=51.005, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:47:20,810 (trainer:762) INFO: 17epoch:train:201-240batch: iter_time=4.562e-05, forward_time=0.057, loss_ctc=1.188, loss=1.188, backward_time=0.009, grad_norm=48.562, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:47:25,239 (trainer:762) INFO: 17epoch:train:241-280batch: iter_time=4.878e-05, forward_time=0.058, loss_ctc=1.304, loss=1.304, backward_time=0.009, grad_norm=49.523, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:47:29,558 (trainer:762) INFO: 17epoch:train:281-320batch: iter_time=4.646e-05, forward_time=0.056, loss_ctc=1.275, loss=1.275, backward_time=0.009, grad_norm=50.653, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 22:47:34,051 (trainer:762) INFO: 17epoch:train:321-360batch: iter_time=4.628e-05, forward_time=0.060, loss_ctc=1.266, loss=1.266, backward_time=0.009, grad_norm=49.397, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-16 22:47:38,663 (trainer:762) INFO: 17epoch:train:361-400batch: iter_time=4.939e-05, forward_time=0.060, loss_ctc=1.481, loss=1.481, backward_time=0.009, grad_norm=56.042, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.461 +[stan] 2024-01-16 22:47:43,022 (trainer:762) INFO: 17epoch:train:401-440batch: iter_time=4.567e-05, forward_time=0.057, loss_ctc=1.222, loss=1.222, backward_time=0.009, grad_norm=47.433, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:47:47,268 (trainer:762) INFO: 17epoch:train:441-480batch: iter_time=4.912e-05, forward_time=0.056, loss_ctc=1.143, loss=1.143, backward_time=0.009, grad_norm=49.783, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-16 22:47:51,704 (trainer:762) INFO: 17epoch:train:481-520batch: iter_time=4.545e-05, forward_time=0.058, loss_ctc=1.161, loss=1.161, backward_time=0.009, grad_norm=44.746, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 22:47:56,064 (trainer:762) INFO: 17epoch:train:521-560batch: iter_time=5.049e-05, forward_time=0.057, loss_ctc=1.295, loss=1.295, backward_time=0.009, grad_norm=50.703, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:48:00,410 (trainer:762) INFO: 17epoch:train:561-600batch: iter_time=4.868e-05, forward_time=0.057, loss_ctc=1.153, loss=1.153, backward_time=0.009, grad_norm=48.035, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:48:04,865 (trainer:762) INFO: 17epoch:train:601-640batch: iter_time=4.513e-05, forward_time=0.058, loss_ctc=1.251, loss=1.251, backward_time=0.009, grad_norm=50.393, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:48:09,168 (trainer:762) INFO: 17epoch:train:641-680batch: iter_time=4.790e-05, forward_time=0.056, loss_ctc=1.199, loss=1.199, backward_time=0.009, grad_norm=50.823, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-16 22:48:13,578 (trainer:762) INFO: 17epoch:train:681-720batch: iter_time=4.589e-05, forward_time=0.058, loss_ctc=1.250, loss=1.250, backward_time=0.009, grad_norm=49.241, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 22:48:17,985 (trainer:762) INFO: 17epoch:train:721-760batch: iter_time=4.742e-05, forward_time=0.058, loss_ctc=1.208, loss=1.208, backward_time=0.009, grad_norm=50.521, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 22:48:22,341 (trainer:762) INFO: 17epoch:train:761-800batch: iter_time=4.506e-05, forward_time=0.057, loss_ctc=1.289, loss=1.289, backward_time=0.009, grad_norm=51.014, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:48:26,553 (trainer:357) INFO: 17epoch results: [train] iter_time=2.047e-04, forward_time=0.057, loss_ctc=1.259, loss=1.259, backward_time=0.009, grad_norm=50.056, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.28 seconds, total_count=13600, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=54.350, cer_ctc=0.254, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=54.350, time=1.07 seconds, total_count=255, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.06 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:48:27,549 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:48:27,550 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/16epoch.pth +[stan] 2024-01-16 22:48:27,550 (trainer:291) INFO: 18/30epoch started. Estimated time to finish: 20 minutes and 13.18 seconds +[stan] 2024-01-16 22:48:32,184 (trainer:762) INFO: 18epoch:train:1-40batch: iter_time=0.003, forward_time=0.057, loss_ctc=1.135, loss=1.135, backward_time=0.009, grad_norm=45.367, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.463 +[stan] 2024-01-16 22:48:36,629 (trainer:762) INFO: 18epoch:train:41-80batch: iter_time=4.834e-05, forward_time=0.058, loss_ctc=1.244, loss=1.244, backward_time=0.009, grad_norm=51.462, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 22:48:41,084 (trainer:762) INFO: 18epoch:train:81-120batch: iter_time=4.849e-05, forward_time=0.058, loss_ctc=1.265, loss=1.265, backward_time=0.009, grad_norm=52.644, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:48:45,451 (trainer:762) INFO: 18epoch:train:121-160batch: iter_time=4.930e-05, forward_time=0.057, loss_ctc=1.302, loss=1.302, backward_time=0.009, grad_norm=50.143, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:48:49,765 (trainer:762) INFO: 18epoch:train:161-200batch: iter_time=4.582e-05, forward_time=0.056, loss_ctc=1.135, loss=1.135, backward_time=0.009, grad_norm=46.779, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 22:48:54,192 (trainer:762) INFO: 18epoch:train:201-240batch: iter_time=4.354e-05, forward_time=0.058, loss_ctc=1.278, loss=1.278, backward_time=0.009, grad_norm=50.162, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:48:58,596 (trainer:762) INFO: 18epoch:train:241-280batch: iter_time=4.641e-05, forward_time=0.057, loss_ctc=1.272, loss=1.272, backward_time=0.009, grad_norm=53.241, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:49:02,808 (trainer:762) INFO: 18epoch:train:281-320batch: iter_time=4.559e-05, forward_time=0.055, loss_ctc=1.109, loss=1.109, backward_time=0.009, grad_norm=47.170, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.421 +[stan] 2024-01-16 22:49:07,275 (trainer:762) INFO: 18epoch:train:321-360batch: iter_time=4.534e-05, forward_time=0.058, loss_ctc=1.241, loss=1.241, backward_time=0.009, grad_norm=47.669, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-16 22:49:11,623 (trainer:762) INFO: 18epoch:train:361-400batch: iter_time=4.837e-05, forward_time=0.057, loss_ctc=1.192, loss=1.192, backward_time=0.009, grad_norm=49.950, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:49:16,052 (trainer:762) INFO: 18epoch:train:401-440batch: iter_time=4.670e-05, forward_time=0.058, loss_ctc=1.218, loss=1.218, backward_time=0.009, grad_norm=49.237, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:49:20,438 (trainer:762) INFO: 18epoch:train:441-480batch: iter_time=4.494e-05, forward_time=0.057, loss_ctc=1.154, loss=1.154, backward_time=0.009, grad_norm=46.380, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:49:24,780 (trainer:762) INFO: 18epoch:train:481-520batch: iter_time=4.962e-05, forward_time=0.057, loss_ctc=1.104, loss=1.104, backward_time=0.009, grad_norm=45.008, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:49:29,140 (trainer:762) INFO: 18epoch:train:521-560batch: iter_time=4.549e-05, forward_time=0.057, loss_ctc=1.187, loss=1.187, backward_time=0.009, grad_norm=47.236, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:49:33,614 (trainer:762) INFO: 18epoch:train:561-600batch: iter_time=4.604e-05, forward_time=0.058, loss_ctc=1.203, loss=1.203, backward_time=0.009, grad_norm=47.286, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-16 22:49:37,939 (trainer:762) INFO: 18epoch:train:601-640batch: iter_time=4.424e-05, forward_time=0.057, loss_ctc=1.182, loss=1.182, backward_time=0.009, grad_norm=48.179, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 22:49:42,203 (trainer:762) INFO: 18epoch:train:641-680batch: iter_time=4.449e-05, forward_time=0.056, loss_ctc=1.131, loss=1.131, backward_time=0.009, grad_norm=46.727, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-16 22:49:46,658 (trainer:762) INFO: 18epoch:train:681-720batch: iter_time=4.717e-05, forward_time=0.058, loss_ctc=1.300, loss=1.300, backward_time=0.009, grad_norm=50.401, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:49:51,121 (trainer:762) INFO: 18epoch:train:721-760batch: iter_time=4.490e-05, forward_time=0.058, loss_ctc=1.212, loss=1.212, backward_time=0.009, grad_norm=47.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-16 22:49:55,511 (trainer:762) INFO: 18epoch:train:761-800batch: iter_time=4.252e-05, forward_time=0.057, loss_ctc=1.240, loss=1.240, backward_time=0.009, grad_norm=50.339, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:49:59,749 (trainer:357) INFO: 18epoch results: [train] iter_time=1.751e-04, forward_time=0.057, loss_ctc=1.205, loss=1.205, backward_time=0.009, grad_norm=48.632, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.03 seconds, total_count=14400, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=54.658, cer_ctc=0.238, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=54.658, time=1.08 seconds, total_count=270, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.09 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:50:00,848 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:50:00,849 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/17epoch.pth +[stan] 2024-01-16 22:50:00,849 (trainer:291) INFO: 19/30epoch started. Estimated time to finish: 18 minutes and 39.84 seconds +[stan] 2024-01-16 22:50:05,401 (trainer:762) INFO: 19epoch:train:1-40batch: iter_time=0.002, forward_time=0.056, loss_ctc=1.143, loss=1.143, backward_time=0.009, grad_norm=49.935, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.454 +[stan] 2024-01-16 22:50:09,907 (trainer:762) INFO: 19epoch:train:41-80batch: iter_time=4.616e-05, forward_time=0.059, loss_ctc=1.244, loss=1.244, backward_time=0.009, grad_norm=47.316, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.451 +[stan] 2024-01-16 22:50:14,236 (trainer:762) INFO: 19epoch:train:81-120batch: iter_time=4.855e-05, forward_time=0.057, loss_ctc=1.022, loss=1.022, backward_time=0.009, grad_norm=44.421, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-16 22:50:18,646 (trainer:762) INFO: 19epoch:train:121-160batch: iter_time=4.613e-05, forward_time=0.058, loss_ctc=1.201, loss=1.201, backward_time=0.009, grad_norm=49.089, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 22:50:23,035 (trainer:762) INFO: 19epoch:train:161-200batch: iter_time=4.699e-05, forward_time=0.057, loss_ctc=1.152, loss=1.152, backward_time=0.009, grad_norm=48.111, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:50:27,442 (trainer:762) INFO: 19epoch:train:201-240batch: iter_time=4.563e-05, forward_time=0.058, loss_ctc=1.116, loss=1.116, backward_time=0.009, grad_norm=49.000, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 22:50:31,821 (trainer:762) INFO: 19epoch:train:241-280batch: iter_time=4.827e-05, forward_time=0.057, loss_ctc=1.137, loss=1.137, backward_time=0.009, grad_norm=48.212, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:50:36,299 (trainer:762) INFO: 19epoch:train:281-320batch: iter_time=4.606e-05, forward_time=0.059, loss_ctc=1.225, loss=1.225, backward_time=0.009, grad_norm=51.669, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-16 22:50:40,735 (trainer:762) INFO: 19epoch:train:321-360batch: iter_time=4.586e-05, forward_time=0.058, loss_ctc=1.242, loss=1.242, backward_time=0.009, grad_norm=50.038, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:50:45,051 (trainer:762) INFO: 19epoch:train:361-400batch: iter_time=4.812e-05, forward_time=0.056, loss_ctc=1.053, loss=1.053, backward_time=0.009, grad_norm=44.887, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 22:50:49,328 (trainer:762) INFO: 19epoch:train:401-440batch: iter_time=4.588e-05, forward_time=0.056, loss_ctc=1.076, loss=1.076, backward_time=0.009, grad_norm=47.965, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-16 22:50:53,564 (trainer:762) INFO: 19epoch:train:441-480batch: iter_time=4.723e-05, forward_time=0.055, loss_ctc=1.013, loss=1.013, backward_time=0.009, grad_norm=47.605, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.423 +[stan] 2024-01-16 22:50:58,196 (trainer:762) INFO: 19epoch:train:481-520batch: iter_time=4.900e-05, forward_time=0.060, loss_ctc=1.383, loss=1.383, backward_time=0.009, grad_norm=56.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.463 +[stan] 2024-01-16 22:51:02,619 (trainer:762) INFO: 19epoch:train:521-560batch: iter_time=4.821e-05, forward_time=0.058, loss_ctc=1.185, loss=1.185, backward_time=0.009, grad_norm=47.569, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 22:51:07,017 (trainer:762) INFO: 19epoch:train:561-600batch: iter_time=4.834e-05, forward_time=0.057, loss_ctc=1.109, loss=1.109, backward_time=0.009, grad_norm=45.658, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:51:11,337 (trainer:762) INFO: 19epoch:train:601-640batch: iter_time=4.770e-05, forward_time=0.056, loss_ctc=1.042, loss=1.042, backward_time=0.009, grad_norm=43.779, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 22:51:15,735 (trainer:762) INFO: 19epoch:train:641-680batch: iter_time=4.738e-05, forward_time=0.057, loss_ctc=1.111, loss=1.111, backward_time=0.009, grad_norm=45.106, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:51:20,095 (trainer:762) INFO: 19epoch:train:681-720batch: iter_time=4.774e-05, forward_time=0.057, loss_ctc=1.161, loss=1.161, backward_time=0.009, grad_norm=50.892, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:51:24,498 (trainer:762) INFO: 19epoch:train:721-760batch: iter_time=4.929e-05, forward_time=0.058, loss_ctc=1.216, loss=1.216, backward_time=0.009, grad_norm=54.427, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:51:28,973 (trainer:762) INFO: 19epoch:train:761-800batch: iter_time=4.509e-05, forward_time=0.058, loss_ctc=1.130, loss=1.130, backward_time=0.009, grad_norm=46.211, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-16 22:51:33,202 (trainer:357) INFO: 19epoch results: [train] iter_time=1.681e-04, forward_time=0.057, loss_ctc=1.148, loss=1.148, backward_time=0.009, grad_norm=48.400, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.2 seconds, total_count=15200, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=55.817, cer_ctc=0.247, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=55.817, time=1.08 seconds, total_count=285, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.07 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:51:34,204 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:51:34,206 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/18epoch.pth +[stan] 2024-01-16 22:51:34,206 (trainer:291) INFO: 20/30epoch started. Estimated time to finish: 17 minutes and 6.54 seconds +[stan] 2024-01-16 22:51:38,936 (trainer:762) INFO: 20epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=1.129, loss=1.129, backward_time=0.009, grad_norm=51.179, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.472 +[stan] 2024-01-16 22:51:43,270 (trainer:762) INFO: 20epoch:train:41-80batch: iter_time=4.468e-05, forward_time=0.057, loss_ctc=1.143, loss=1.143, backward_time=0.009, grad_norm=46.442, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-16 22:51:47,659 (trainer:762) INFO: 20epoch:train:81-120batch: iter_time=4.806e-05, forward_time=0.057, loss_ctc=1.185, loss=1.185, backward_time=0.009, grad_norm=52.037, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:51:52,025 (trainer:762) INFO: 20epoch:train:121-160batch: iter_time=4.397e-05, forward_time=0.057, loss_ctc=1.009, loss=1.009, backward_time=0.009, grad_norm=46.791, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:51:56,245 (trainer:762) INFO: 20epoch:train:161-200batch: iter_time=4.612e-05, forward_time=0.055, loss_ctc=0.936, loss=0.936, backward_time=0.009, grad_norm=43.937, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.422 +[stan] 2024-01-16 22:52:00,679 (trainer:762) INFO: 20epoch:train:201-240batch: iter_time=4.814e-05, forward_time=0.058, loss_ctc=1.088, loss=1.088, backward_time=0.009, grad_norm=49.038, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:52:05,068 (trainer:762) INFO: 20epoch:train:241-280batch: iter_time=4.799e-05, forward_time=0.057, loss_ctc=1.095, loss=1.095, backward_time=0.009, grad_norm=48.008, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:52:09,560 (trainer:762) INFO: 20epoch:train:281-320batch: iter_time=4.813e-05, forward_time=0.059, loss_ctc=1.101, loss=1.101, backward_time=0.009, grad_norm=46.614, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-16 22:52:13,885 (trainer:762) INFO: 20epoch:train:321-360batch: iter_time=4.546e-05, forward_time=0.057, loss_ctc=1.029, loss=1.029, backward_time=0.009, grad_norm=44.750, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 22:52:18,294 (trainer:762) INFO: 20epoch:train:361-400batch: iter_time=4.912e-05, forward_time=0.058, loss_ctc=1.095, loss=1.095, backward_time=0.009, grad_norm=47.124, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 22:52:22,727 (trainer:762) INFO: 20epoch:train:401-440batch: iter_time=4.831e-05, forward_time=0.058, loss_ctc=1.096, loss=1.096, backward_time=0.009, grad_norm=46.866, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:52:27,059 (trainer:762) INFO: 20epoch:train:441-480batch: iter_time=4.851e-05, forward_time=0.057, loss_ctc=1.078, loss=1.078, backward_time=0.009, grad_norm=46.240, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-16 22:52:31,424 (trainer:762) INFO: 20epoch:train:481-520batch: iter_time=4.620e-05, forward_time=0.057, loss_ctc=1.086, loss=1.086, backward_time=0.009, grad_norm=45.961, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:52:35,806 (trainer:762) INFO: 20epoch:train:521-560batch: iter_time=4.540e-05, forward_time=0.057, loss_ctc=1.061, loss=1.061, backward_time=0.009, grad_norm=47.006, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:52:40,220 (trainer:762) INFO: 20epoch:train:561-600batch: iter_time=4.793e-05, forward_time=0.058, loss_ctc=1.111, loss=1.111, backward_time=0.009, grad_norm=50.323, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 22:52:44,677 (trainer:762) INFO: 20epoch:train:601-640batch: iter_time=4.802e-05, forward_time=0.058, loss_ctc=1.171, loss=1.171, backward_time=0.009, grad_norm=49.527, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-16 22:52:48,994 (trainer:762) INFO: 20epoch:train:641-680batch: iter_time=4.969e-05, forward_time=0.056, loss_ctc=0.948, loss=0.948, backward_time=0.009, grad_norm=43.724, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 22:52:53,360 (trainer:762) INFO: 20epoch:train:681-720batch: iter_time=4.713e-05, forward_time=0.057, loss_ctc=1.101, loss=1.101, backward_time=0.009, grad_norm=48.855, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:52:57,848 (trainer:762) INFO: 20epoch:train:721-760batch: iter_time=5.121e-05, forward_time=0.059, loss_ctc=1.099, loss=1.099, backward_time=0.009, grad_norm=45.769, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-16 22:53:02,247 (trainer:762) INFO: 20epoch:train:761-800batch: iter_time=4.259e-05, forward_time=0.057, loss_ctc=1.147, loss=1.147, backward_time=0.009, grad_norm=50.416, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:53:06,501 (trainer:357) INFO: 20epoch results: [train] iter_time=2.122e-04, forward_time=0.057, loss_ctc=1.085, loss=1.085, backward_time=0.009, grad_norm=47.530, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.11 seconds, total_count=16000, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=56.451, cer_ctc=0.242, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=56.451, time=1.08 seconds, total_count=300, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.1 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:53:07,545 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:53:07,546 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/19epoch.pth +[stan] 2024-01-16 22:53:07,546 (trainer:291) INFO: 21/30epoch started. Estimated time to finish: 15 minutes and 33.23 seconds +[stan] 2024-01-16 22:53:12,090 (trainer:762) INFO: 21epoch:train:1-40batch: iter_time=0.003, forward_time=0.056, loss_ctc=0.928, loss=0.928, backward_time=0.009, grad_norm=42.417, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.454 +[stan] 2024-01-16 22:53:16,533 (trainer:762) INFO: 21epoch:train:41-80batch: iter_time=4.676e-05, forward_time=0.058, loss_ctc=1.119, loss=1.119, backward_time=0.009, grad_norm=49.431, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 22:53:20,941 (trainer:762) INFO: 21epoch:train:81-120batch: iter_time=4.521e-05, forward_time=0.058, loss_ctc=1.132, loss=1.132, backward_time=0.009, grad_norm=48.864, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 22:53:25,329 (trainer:762) INFO: 21epoch:train:121-160batch: iter_time=4.474e-05, forward_time=0.057, loss_ctc=1.137, loss=1.137, backward_time=0.009, grad_norm=49.175, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:53:29,668 (trainer:762) INFO: 21epoch:train:161-200batch: iter_time=4.489e-05, forward_time=0.057, loss_ctc=1.013, loss=1.013, backward_time=0.009, grad_norm=46.944, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:53:34,233 (trainer:762) INFO: 21epoch:train:201-240batch: iter_time=4.326e-05, forward_time=0.060, loss_ctc=1.127, loss=1.127, backward_time=0.010, grad_norm=46.985, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.456 +[stan] 2024-01-16 22:53:38,590 (trainer:762) INFO: 21epoch:train:241-280batch: iter_time=4.751e-05, forward_time=0.057, loss_ctc=1.030, loss=1.030, backward_time=0.009, grad_norm=44.705, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:53:42,893 (trainer:762) INFO: 21epoch:train:281-320batch: iter_time=4.787e-05, forward_time=0.056, loss_ctc=0.967, loss=0.967, backward_time=0.009, grad_norm=46.019, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-16 22:53:47,179 (trainer:762) INFO: 21epoch:train:321-360batch: iter_time=4.700e-05, forward_time=0.056, loss_ctc=0.966, loss=0.966, backward_time=0.009, grad_norm=45.182, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-16 22:53:51,665 (trainer:762) INFO: 21epoch:train:361-400batch: iter_time=4.859e-05, forward_time=0.059, loss_ctc=1.108, loss=1.108, backward_time=0.009, grad_norm=47.623, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-16 22:53:55,947 (trainer:762) INFO: 21epoch:train:401-440batch: iter_time=4.773e-05, forward_time=0.056, loss_ctc=0.938, loss=0.938, backward_time=0.009, grad_norm=43.488, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-16 22:54:00,526 (trainer:762) INFO: 21epoch:train:441-480batch: iter_time=4.528e-05, forward_time=0.060, loss_ctc=1.094, loss=1.094, backward_time=0.010, grad_norm=48.759, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.458 +[stan] 2024-01-16 22:54:04,846 (trainer:762) INFO: 21epoch:train:481-520batch: iter_time=4.395e-05, forward_time=0.056, loss_ctc=1.021, loss=1.021, backward_time=0.009, grad_norm=46.484, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 22:54:09,218 (trainer:762) INFO: 21epoch:train:521-560batch: iter_time=4.883e-05, forward_time=0.057, loss_ctc=1.026, loss=1.026, backward_time=0.009, grad_norm=47.274, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:54:13,622 (trainer:762) INFO: 21epoch:train:561-600batch: iter_time=4.484e-05, forward_time=0.058, loss_ctc=1.120, loss=1.120, backward_time=0.009, grad_norm=49.727, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:54:18,017 (trainer:762) INFO: 21epoch:train:601-640batch: iter_time=4.560e-05, forward_time=0.057, loss_ctc=0.950, loss=0.950, backward_time=0.009, grad_norm=42.623, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:54:22,449 (trainer:762) INFO: 21epoch:train:641-680batch: iter_time=4.441e-05, forward_time=0.058, loss_ctc=1.070, loss=1.070, backward_time=0.009, grad_norm=47.378, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:54:26,800 (trainer:762) INFO: 21epoch:train:681-720batch: iter_time=4.482e-05, forward_time=0.057, loss_ctc=1.083, loss=1.083, backward_time=0.009, grad_norm=49.908, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:54:31,246 (trainer:762) INFO: 21epoch:train:721-760batch: iter_time=4.679e-05, forward_time=0.058, loss_ctc=1.045, loss=1.045, backward_time=0.009, grad_norm=44.194, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 22:54:35,586 (trainer:762) INFO: 21epoch:train:761-800batch: iter_time=4.157e-05, forward_time=0.057, loss_ctc=1.030, loss=1.030, backward_time=0.009, grad_norm=46.681, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:54:39,797 (trainer:357) INFO: 21epoch results: [train] iter_time=1.779e-04, forward_time=0.057, loss_ctc=1.045, loss=1.045, backward_time=0.009, grad_norm=46.693, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.12 seconds, total_count=16800, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=54.980, cer_ctc=0.243, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=54.980, time=1.07 seconds, total_count=315, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.06 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:54:40,779 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:54:40,780 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/20epoch.pth +[stan] 2024-01-16 22:54:40,780 (trainer:291) INFO: 22/30epoch started. Estimated time to finish: 13 minutes and 59.87 seconds +[stan] 2024-01-16 22:54:45,313 (trainer:762) INFO: 22epoch:train:1-40batch: iter_time=0.003, forward_time=0.056, loss_ctc=0.931, loss=0.931, backward_time=0.009, grad_norm=42.713, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.453 +[stan] 2024-01-16 22:54:49,691 (trainer:762) INFO: 22epoch:train:41-80batch: iter_time=4.920e-05, forward_time=0.057, loss_ctc=1.046, loss=1.046, backward_time=0.009, grad_norm=46.645, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:54:54,269 (trainer:762) INFO: 22epoch:train:81-120batch: iter_time=4.944e-05, forward_time=0.060, loss_ctc=1.041, loss=1.041, backward_time=0.009, grad_norm=46.142, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.458 +[stan] 2024-01-16 22:54:58,557 (trainer:762) INFO: 22epoch:train:121-160batch: iter_time=5.110e-05, forward_time=0.056, loss_ctc=0.919, loss=0.919, backward_time=0.009, grad_norm=42.565, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-16 22:55:02,916 (trainer:762) INFO: 22epoch:train:161-200batch: iter_time=5.230e-05, forward_time=0.057, loss_ctc=0.986, loss=0.986, backward_time=0.009, grad_norm=44.983, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:55:07,279 (trainer:762) INFO: 22epoch:train:201-240batch: iter_time=4.708e-05, forward_time=0.057, loss_ctc=0.914, loss=0.914, backward_time=0.009, grad_norm=44.075, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:55:11,665 (trainer:762) INFO: 22epoch:train:241-280batch: iter_time=4.902e-05, forward_time=0.057, loss_ctc=0.935, loss=0.935, backward_time=0.009, grad_norm=44.255, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:55:16,152 (trainer:762) INFO: 22epoch:train:281-320batch: iter_time=4.607e-05, forward_time=0.059, loss_ctc=1.039, loss=1.039, backward_time=0.009, grad_norm=48.875, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-16 22:55:20,543 (trainer:762) INFO: 22epoch:train:321-360batch: iter_time=4.740e-05, forward_time=0.057, loss_ctc=1.011, loss=1.011, backward_time=0.009, grad_norm=44.481, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:55:24,942 (trainer:762) INFO: 22epoch:train:361-400batch: iter_time=5.006e-05, forward_time=0.057, loss_ctc=0.962, loss=0.962, backward_time=0.009, grad_norm=44.970, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:55:29,248 (trainer:762) INFO: 22epoch:train:401-440batch: iter_time=4.946e-05, forward_time=0.056, loss_ctc=0.914, loss=0.914, backward_time=0.009, grad_norm=43.964, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 22:55:33,749 (trainer:762) INFO: 22epoch:train:441-480batch: iter_time=4.635e-05, forward_time=0.059, loss_ctc=1.049, loss=1.049, backward_time=0.009, grad_norm=46.885, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-16 22:55:38,061 (trainer:762) INFO: 22epoch:train:481-520batch: iter_time=4.658e-05, forward_time=0.056, loss_ctc=0.959, loss=0.959, backward_time=0.009, grad_norm=44.765, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 22:55:42,508 (trainer:762) INFO: 22epoch:train:521-560batch: iter_time=4.658e-05, forward_time=0.058, loss_ctc=1.055, loss=1.055, backward_time=0.009, grad_norm=44.833, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:55:46,865 (trainer:762) INFO: 22epoch:train:561-600batch: iter_time=4.747e-05, forward_time=0.057, loss_ctc=0.950, loss=0.950, backward_time=0.009, grad_norm=44.233, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:55:51,351 (trainer:762) INFO: 22epoch:train:601-640batch: iter_time=5.290e-05, forward_time=0.059, loss_ctc=0.972, loss=0.972, backward_time=0.009, grad_norm=45.060, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-16 22:55:55,662 (trainer:762) INFO: 22epoch:train:641-680batch: iter_time=4.705e-05, forward_time=0.056, loss_ctc=0.974, loss=0.974, backward_time=0.009, grad_norm=43.566, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 22:56:00,124 (trainer:762) INFO: 22epoch:train:681-720batch: iter_time=4.751e-05, forward_time=0.058, loss_ctc=1.013, loss=1.013, backward_time=0.009, grad_norm=47.909, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-16 22:56:04,476 (trainer:762) INFO: 22epoch:train:721-760batch: iter_time=4.870e-05, forward_time=0.057, loss_ctc=0.955, loss=0.955, backward_time=0.009, grad_norm=45.719, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:56:08,868 (trainer:762) INFO: 22epoch:train:761-800batch: iter_time=4.885e-05, forward_time=0.058, loss_ctc=0.991, loss=0.991, backward_time=0.009, grad_norm=46.013, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:56:13,083 (trainer:357) INFO: 22epoch results: [train] iter_time=1.920e-04, forward_time=0.057, loss_ctc=0.981, loss=0.981, backward_time=0.009, grad_norm=45.132, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.16 seconds, total_count=17600, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=56.479, cer_ctc=0.247, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=56.479, time=1.07 seconds, total_count=330, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.07 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:56:14,083 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:56:14,085 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/21epoch.pth +[stan] 2024-01-16 22:56:14,085 (trainer:291) INFO: 23/30epoch started. Estimated time to finish: 12 minutes and 26.54 seconds +[stan] 2024-01-16 22:56:18,807 (trainer:762) INFO: 23epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=0.978, loss=0.978, backward_time=0.009, grad_norm=45.283, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.471 +[stan] 2024-01-16 22:56:23,151 (trainer:762) INFO: 23epoch:train:41-80batch: iter_time=4.611e-05, forward_time=0.057, loss_ctc=0.928, loss=0.928, backward_time=0.009, grad_norm=43.787, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:56:27,504 (trainer:762) INFO: 23epoch:train:81-120batch: iter_time=4.622e-05, forward_time=0.057, loss_ctc=0.918, loss=0.918, backward_time=0.009, grad_norm=44.898, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:56:31,890 (trainer:762) INFO: 23epoch:train:121-160batch: iter_time=4.657e-05, forward_time=0.057, loss_ctc=0.960, loss=0.960, backward_time=0.009, grad_norm=47.419, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:56:36,337 (trainer:762) INFO: 23epoch:train:161-200batch: iter_time=4.808e-05, forward_time=0.058, loss_ctc=0.998, loss=0.998, backward_time=0.009, grad_norm=46.706, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:56:40,683 (trainer:762) INFO: 23epoch:train:201-240batch: iter_time=4.776e-05, forward_time=0.057, loss_ctc=0.943, loss=0.943, backward_time=0.009, grad_norm=44.261, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:56:45,003 (trainer:762) INFO: 23epoch:train:241-280batch: iter_time=4.743e-05, forward_time=0.056, loss_ctc=0.971, loss=0.971, backward_time=0.009, grad_norm=48.310, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 22:56:49,383 (trainer:762) INFO: 23epoch:train:281-320batch: iter_time=4.641e-05, forward_time=0.057, loss_ctc=0.907, loss=0.907, backward_time=0.009, grad_norm=44.476, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:56:53,872 (trainer:762) INFO: 23epoch:train:321-360batch: iter_time=4.624e-05, forward_time=0.059, loss_ctc=0.978, loss=0.978, backward_time=0.009, grad_norm=45.205, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-16 22:56:58,380 (trainer:762) INFO: 23epoch:train:361-400batch: iter_time=4.733e-05, forward_time=0.059, loss_ctc=0.974, loss=0.974, backward_time=0.009, grad_norm=45.934, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.451 +[stan] 2024-01-16 22:57:02,637 (trainer:762) INFO: 23epoch:train:401-440batch: iter_time=4.730e-05, forward_time=0.056, loss_ctc=0.899, loss=0.899, backward_time=0.009, grad_norm=44.017, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-16 22:57:06,982 (trainer:762) INFO: 23epoch:train:441-480batch: iter_time=4.704e-05, forward_time=0.057, loss_ctc=0.919, loss=0.919, backward_time=0.009, grad_norm=43.751, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 22:57:11,448 (trainer:762) INFO: 23epoch:train:481-520batch: iter_time=4.661e-05, forward_time=0.058, loss_ctc=0.955, loss=0.955, backward_time=0.009, grad_norm=43.797, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-16 22:57:15,756 (trainer:762) INFO: 23epoch:train:521-560batch: iter_time=4.651e-05, forward_time=0.056, loss_ctc=0.920, loss=0.920, backward_time=0.009, grad_norm=44.365, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 22:57:20,205 (trainer:762) INFO: 23epoch:train:561-600batch: iter_time=4.621e-05, forward_time=0.058, loss_ctc=0.977, loss=0.977, backward_time=0.009, grad_norm=43.666, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 22:57:24,569 (trainer:762) INFO: 23epoch:train:601-640batch: iter_time=4.738e-05, forward_time=0.057, loss_ctc=0.895, loss=0.895, backward_time=0.009, grad_norm=44.334, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:57:29,061 (trainer:762) INFO: 23epoch:train:641-680batch: iter_time=4.877e-05, forward_time=0.059, loss_ctc=0.957, loss=0.957, backward_time=0.009, grad_norm=45.313, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-16 22:57:33,453 (trainer:762) INFO: 23epoch:train:681-720batch: iter_time=4.861e-05, forward_time=0.057, loss_ctc=0.945, loss=0.945, backward_time=0.009, grad_norm=45.092, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:57:37,757 (trainer:762) INFO: 23epoch:train:721-760batch: iter_time=4.593e-05, forward_time=0.056, loss_ctc=0.832, loss=0.832, backward_time=0.009, grad_norm=42.996, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-16 22:57:42,058 (trainer:762) INFO: 23epoch:train:761-800batch: iter_time=4.243e-05, forward_time=0.056, loss_ctc=0.853, loss=0.853, backward_time=0.009, grad_norm=42.557, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-16 22:57:46,301 (trainer:357) INFO: 23epoch results: [train] iter_time=1.797e-04, forward_time=0.057, loss_ctc=0.935, loss=0.935, backward_time=0.009, grad_norm=44.808, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.05 seconds, total_count=18400, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=56.983, cer_ctc=0.250, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=56.983, time=1.08 seconds, total_count=345, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.09 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:57:47,403 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:57:47,405 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/22epoch.pth +[stan] 2024-01-16 22:57:47,405 (trainer:291) INFO: 24/30epoch started. Estimated time to finish: 10 minutes and 53.23 seconds +[stan] 2024-01-16 22:57:52,208 (trainer:762) INFO: 24epoch:train:1-40batch: iter_time=0.003, forward_time=0.060, loss_ctc=0.985, loss=0.985, backward_time=0.009, grad_norm=49.130, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.480 +[stan] 2024-01-16 22:57:56,632 (trainer:762) INFO: 24epoch:train:41-80batch: iter_time=4.362e-05, forward_time=0.058, loss_ctc=0.927, loss=0.927, backward_time=0.009, grad_norm=42.852, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 22:58:00,921 (trainer:762) INFO: 24epoch:train:81-120batch: iter_time=4.784e-05, forward_time=0.056, loss_ctc=0.888, loss=0.888, backward_time=0.009, grad_norm=45.981, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-16 22:58:05,232 (trainer:762) INFO: 24epoch:train:121-160batch: iter_time=4.876e-05, forward_time=0.056, loss_ctc=0.903, loss=0.903, backward_time=0.009, grad_norm=44.704, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 22:58:09,669 (trainer:762) INFO: 24epoch:train:161-200batch: iter_time=4.530e-05, forward_time=0.058, loss_ctc=0.971, loss=0.971, backward_time=0.009, grad_norm=45.791, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 22:58:14,114 (trainer:762) INFO: 24epoch:train:201-240batch: iter_time=4.830e-05, forward_time=0.058, loss_ctc=0.942, loss=0.942, backward_time=0.009, grad_norm=44.357, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 22:58:18,472 (trainer:762) INFO: 24epoch:train:241-280batch: iter_time=4.558e-05, forward_time=0.057, loss_ctc=0.882, loss=0.882, backward_time=0.009, grad_norm=44.546, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 22:58:22,879 (trainer:762) INFO: 24epoch:train:281-320batch: iter_time=4.482e-05, forward_time=0.058, loss_ctc=0.901, loss=0.901, backward_time=0.009, grad_norm=44.509, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 22:58:27,274 (trainer:762) INFO: 24epoch:train:321-360batch: iter_time=4.614e-05, forward_time=0.057, loss_ctc=0.867, loss=0.867, backward_time=0.009, grad_norm=41.800, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 22:58:31,654 (trainer:762) INFO: 24epoch:train:361-400batch: iter_time=4.883e-05, forward_time=0.057, loss_ctc=0.889, loss=0.889, backward_time=0.009, grad_norm=43.846, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 22:58:35,955 (trainer:762) INFO: 24epoch:train:401-440batch: iter_time=4.471e-05, forward_time=0.056, loss_ctc=0.795, loss=0.795, backward_time=0.009, grad_norm=40.627, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-16 22:58:40,508 (trainer:762) INFO: 24epoch:train:441-480batch: iter_time=4.481e-05, forward_time=0.059, loss_ctc=0.951, loss=0.951, backward_time=0.009, grad_norm=46.697, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.455 +[stan] 2024-01-16 22:58:45,041 (trainer:762) INFO: 24epoch:train:481-520batch: iter_time=4.488e-05, forward_time=0.059, loss_ctc=0.850, loss=0.850, backward_time=0.009, grad_norm=41.865, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.453 +[stan] 2024-01-16 22:58:49,283 (trainer:762) INFO: 24epoch:train:521-560batch: iter_time=4.871e-05, forward_time=0.056, loss_ctc=0.842, loss=0.842, backward_time=0.009, grad_norm=41.588, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-16 22:58:53,607 (trainer:762) INFO: 24epoch:train:561-600batch: iter_time=4.468e-05, forward_time=0.057, loss_ctc=0.850, loss=0.850, backward_time=0.009, grad_norm=43.138, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 22:58:58,096 (trainer:762) INFO: 24epoch:train:601-640batch: iter_time=4.769e-05, forward_time=0.059, loss_ctc=1.002, loss=1.002, backward_time=0.009, grad_norm=47.307, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-16 22:59:02,376 (trainer:762) INFO: 24epoch:train:641-680batch: iter_time=4.876e-05, forward_time=0.056, loss_ctc=0.841, loss=0.841, backward_time=0.009, grad_norm=41.257, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-16 22:59:06,726 (trainer:762) INFO: 24epoch:train:681-720batch: iter_time=4.429e-05, forward_time=0.057, loss_ctc=0.889, loss=0.889, backward_time=0.009, grad_norm=46.668, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:59:11,162 (trainer:762) INFO: 24epoch:train:721-760batch: iter_time=4.733e-05, forward_time=0.058, loss_ctc=0.978, loss=0.978, backward_time=0.009, grad_norm=45.989, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 22:59:15,565 (trainer:762) INFO: 24epoch:train:761-800batch: iter_time=4.326e-05, forward_time=0.058, loss_ctc=0.916, loss=0.916, backward_time=0.009, grad_norm=45.225, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 22:59:19,814 (trainer:357) INFO: 24epoch results: [train] iter_time=1.706e-04, forward_time=0.057, loss_ctc=0.903, loss=0.903, backward_time=0.009, grad_norm=44.394, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.24 seconds, total_count=19200, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=57.034, cer_ctc=0.240, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=57.034, time=1.07 seconds, total_count=360, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.09 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 22:59:20,822 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 22:59:20,823 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/23epoch.pth +[stan] 2024-01-16 22:59:20,823 (trainer:291) INFO: 25/30epoch started. Estimated time to finish: 9 minutes and 19.93 seconds +[stan] 2024-01-16 22:59:25,499 (trainer:762) INFO: 25epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=0.822, loss=0.822, backward_time=0.009, grad_norm=41.500, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.467 +[stan] 2024-01-16 22:59:29,873 (trainer:762) INFO: 25epoch:train:41-80batch: iter_time=4.397e-05, forward_time=0.057, loss_ctc=0.900, loss=0.900, backward_time=0.009, grad_norm=45.481, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 22:59:34,221 (trainer:762) INFO: 25epoch:train:81-120batch: iter_time=4.475e-05, forward_time=0.057, loss_ctc=0.879, loss=0.879, backward_time=0.009, grad_norm=42.819, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 22:59:38,702 (trainer:762) INFO: 25epoch:train:121-160batch: iter_time=4.691e-05, forward_time=0.059, loss_ctc=0.940, loss=0.940, backward_time=0.009, grad_norm=45.886, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-16 22:59:43,240 (trainer:762) INFO: 25epoch:train:161-200batch: iter_time=4.785e-05, forward_time=0.059, loss_ctc=0.960, loss=0.960, backward_time=0.009, grad_norm=44.407, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.454 +[stan] 2024-01-16 22:59:47,426 (trainer:762) INFO: 25epoch:train:201-240batch: iter_time=4.571e-05, forward_time=0.055, loss_ctc=0.786, loss=0.786, backward_time=0.009, grad_norm=40.798, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.419 +[stan] 2024-01-16 22:59:51,753 (trainer:762) INFO: 25epoch:train:241-280batch: iter_time=4.798e-05, forward_time=0.057, loss_ctc=0.828, loss=0.828, backward_time=0.009, grad_norm=41.560, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-16 22:59:56,288 (trainer:762) INFO: 25epoch:train:281-320batch: iter_time=4.575e-05, forward_time=0.059, loss_ctc=0.979, loss=0.979, backward_time=0.009, grad_norm=50.478, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.453 +[stan] 2024-01-16 23:00:00,471 (trainer:762) INFO: 25epoch:train:321-360batch: iter_time=4.638e-05, forward_time=0.055, loss_ctc=0.821, loss=0.821, backward_time=0.009, grad_norm=42.331, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.418 +[stan] 2024-01-16 23:00:05,109 (trainer:762) INFO: 25epoch:train:361-400batch: iter_time=4.708e-05, forward_time=0.060, loss_ctc=0.945, loss=0.945, backward_time=0.010, grad_norm=43.569, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.464 +[stan] 2024-01-16 23:00:09,408 (trainer:762) INFO: 25epoch:train:401-440batch: iter_time=4.630e-05, forward_time=0.056, loss_ctc=0.819, loss=0.819, backward_time=0.009, grad_norm=44.019, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-16 23:00:13,774 (trainer:762) INFO: 25epoch:train:441-480batch: iter_time=4.625e-05, forward_time=0.057, loss_ctc=0.874, loss=0.874, backward_time=0.009, grad_norm=44.349, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 23:00:18,192 (trainer:762) INFO: 25epoch:train:481-520batch: iter_time=4.927e-05, forward_time=0.058, loss_ctc=0.823, loss=0.823, backward_time=0.009, grad_norm=42.444, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 23:00:22,599 (trainer:762) INFO: 25epoch:train:521-560batch: iter_time=4.947e-05, forward_time=0.058, loss_ctc=0.836, loss=0.836, backward_time=0.009, grad_norm=45.147, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 23:00:27,068 (trainer:762) INFO: 25epoch:train:561-600batch: iter_time=4.595e-05, forward_time=0.058, loss_ctc=0.863, loss=0.863, backward_time=0.009, grad_norm=42.786, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-16 23:00:31,354 (trainer:762) INFO: 25epoch:train:601-640batch: iter_time=4.455e-05, forward_time=0.056, loss_ctc=0.828, loss=0.828, backward_time=0.009, grad_norm=42.462, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-16 23:00:35,802 (trainer:762) INFO: 25epoch:train:641-680batch: iter_time=4.800e-05, forward_time=0.058, loss_ctc=0.831, loss=0.831, backward_time=0.009, grad_norm=41.407, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 23:00:40,119 (trainer:762) INFO: 25epoch:train:681-720batch: iter_time=4.855e-05, forward_time=0.056, loss_ctc=0.831, loss=0.831, backward_time=0.009, grad_norm=44.627, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-16 23:00:44,493 (trainer:762) INFO: 25epoch:train:721-760batch: iter_time=4.754e-05, forward_time=0.057, loss_ctc=0.776, loss=0.776, backward_time=0.009, grad_norm=39.128, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 23:00:48,902 (trainer:762) INFO: 25epoch:train:761-800batch: iter_time=4.367e-05, forward_time=0.058, loss_ctc=0.880, loss=0.880, backward_time=0.009, grad_norm=43.066, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 23:00:53,158 (trainer:357) INFO: 25epoch results: [train] iter_time=1.890e-04, forward_time=0.057, loss_ctc=0.861, loss=0.861, backward_time=0.009, grad_norm=43.413, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.16 seconds, total_count=20000, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=58.405, cer_ctc=0.244, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=58.405, time=1.09 seconds, total_count=375, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.09 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 23:00:54,262 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 23:00:54,264 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/24epoch.pth +[stan] 2024-01-16 23:00:54,264 (trainer:291) INFO: 26/30epoch started. Estimated time to finish: 7 minutes and 46.64 seconds +[stan] 2024-01-16 23:00:58,913 (trainer:762) INFO: 26epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=0.865, loss=0.865, backward_time=0.009, grad_norm=43.248, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.464 +[stan] 2024-01-16 23:01:03,332 (trainer:762) INFO: 26epoch:train:41-80batch: iter_time=4.561e-05, forward_time=0.058, loss_ctc=0.823, loss=0.823, backward_time=0.009, grad_norm=44.576, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 23:01:07,578 (trainer:762) INFO: 26epoch:train:81-120batch: iter_time=4.542e-05, forward_time=0.056, loss_ctc=0.799, loss=0.799, backward_time=0.009, grad_norm=41.793, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-16 23:01:12,165 (trainer:762) INFO: 26epoch:train:121-160batch: iter_time=4.529e-05, forward_time=0.060, loss_ctc=0.839, loss=0.839, backward_time=0.010, grad_norm=44.077, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.459 +[stan] 2024-01-16 23:01:16,534 (trainer:762) INFO: 26epoch:train:161-200batch: iter_time=4.760e-05, forward_time=0.057, loss_ctc=0.802, loss=0.802, backward_time=0.009, grad_norm=41.682, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 23:01:20,933 (trainer:762) INFO: 26epoch:train:201-240batch: iter_time=4.787e-05, forward_time=0.057, loss_ctc=0.924, loss=0.924, backward_time=0.009, grad_norm=46.731, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 23:01:25,279 (trainer:762) INFO: 26epoch:train:241-280batch: iter_time=4.671e-05, forward_time=0.057, loss_ctc=0.832, loss=0.832, backward_time=0.009, grad_norm=44.700, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 23:01:29,749 (trainer:762) INFO: 26epoch:train:281-320batch: iter_time=4.514e-05, forward_time=0.058, loss_ctc=0.869, loss=0.869, backward_time=0.009, grad_norm=42.786, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-16 23:01:34,093 (trainer:762) INFO: 26epoch:train:321-360batch: iter_time=5.046e-05, forward_time=0.057, loss_ctc=0.822, loss=0.822, backward_time=0.009, grad_norm=41.240, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 23:01:38,378 (trainer:762) INFO: 26epoch:train:361-400batch: iter_time=5.005e-05, forward_time=0.056, loss_ctc=0.777, loss=0.777, backward_time=0.009, grad_norm=41.723, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-16 23:01:42,920 (trainer:762) INFO: 26epoch:train:401-440batch: iter_time=4.896e-05, forward_time=0.059, loss_ctc=0.862, loss=0.862, backward_time=0.009, grad_norm=45.093, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.454 +[stan] 2024-01-16 23:01:47,182 (trainer:762) INFO: 26epoch:train:441-480batch: iter_time=4.774e-05, forward_time=0.056, loss_ctc=0.747, loss=0.747, backward_time=0.009, grad_norm=39.625, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-16 23:01:51,633 (trainer:762) INFO: 26epoch:train:481-520batch: iter_time=4.588e-05, forward_time=0.058, loss_ctc=0.974, loss=0.974, backward_time=0.010, grad_norm=46.925, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 23:01:55,943 (trainer:762) INFO: 26epoch:train:521-560batch: iter_time=4.755e-05, forward_time=0.056, loss_ctc=0.716, loss=0.716, backward_time=0.009, grad_norm=39.814, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 23:02:00,449 (trainer:762) INFO: 26epoch:train:561-600batch: iter_time=4.735e-05, forward_time=0.059, loss_ctc=0.871, loss=0.871, backward_time=0.009, grad_norm=44.872, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.451 +[stan] 2024-01-16 23:02:04,850 (trainer:762) INFO: 26epoch:train:601-640batch: iter_time=4.816e-05, forward_time=0.058, loss_ctc=0.839, loss=0.839, backward_time=0.009, grad_norm=43.653, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 23:02:09,186 (trainer:762) INFO: 26epoch:train:641-680batch: iter_time=4.785e-05, forward_time=0.057, loss_ctc=0.819, loss=0.819, backward_time=0.009, grad_norm=43.662, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 23:02:13,643 (trainer:762) INFO: 26epoch:train:681-720batch: iter_time=4.554e-05, forward_time=0.058, loss_ctc=0.868, loss=0.868, backward_time=0.009, grad_norm=46.448, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-16 23:02:17,993 (trainer:762) INFO: 26epoch:train:721-760batch: iter_time=4.546e-05, forward_time=0.057, loss_ctc=0.762, loss=0.762, backward_time=0.009, grad_norm=42.198, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 23:02:22,360 (trainer:762) INFO: 26epoch:train:761-800batch: iter_time=4.813e-05, forward_time=0.057, loss_ctc=0.914, loss=0.914, backward_time=0.009, grad_norm=45.613, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 23:02:26,613 (trainer:357) INFO: 26epoch results: [train] iter_time=1.779e-04, forward_time=0.057, loss_ctc=0.836, loss=0.836, backward_time=0.009, grad_norm=43.523, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.17 seconds, total_count=20800, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=58.668, cer_ctc=0.241, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=58.668, time=1.07 seconds, total_count=390, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.11 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 23:02:27,644 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 23:02:27,646 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/25epoch.pth +[stan] 2024-01-16 23:02:27,646 (trainer:291) INFO: 27/30epoch started. Estimated time to finish: 6 minutes and 13.32 seconds +[stan] 2024-01-16 23:02:32,311 (trainer:762) INFO: 27epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=0.865, loss=0.865, backward_time=0.009, grad_norm=43.032, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.466 +[stan] 2024-01-16 23:02:36,728 (trainer:762) INFO: 27epoch:train:41-80batch: iter_time=4.833e-05, forward_time=0.058, loss_ctc=0.854, loss=0.854, backward_time=0.009, grad_norm=44.173, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 23:02:41,055 (trainer:762) INFO: 27epoch:train:81-120batch: iter_time=4.547e-05, forward_time=0.057, loss_ctc=0.831, loss=0.831, backward_time=0.009, grad_norm=43.439, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-16 23:02:45,505 (trainer:762) INFO: 27epoch:train:121-160batch: iter_time=4.770e-05, forward_time=0.058, loss_ctc=0.822, loss=0.822, backward_time=0.009, grad_norm=41.351, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 23:02:49,866 (trainer:762) INFO: 27epoch:train:161-200batch: iter_time=4.751e-05, forward_time=0.057, loss_ctc=0.832, loss=0.832, backward_time=0.009, grad_norm=42.512, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 23:02:54,208 (trainer:762) INFO: 27epoch:train:201-240batch: iter_time=5.215e-05, forward_time=0.057, loss_ctc=0.797, loss=0.797, backward_time=0.009, grad_norm=41.716, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 23:02:58,611 (trainer:762) INFO: 27epoch:train:241-280batch: iter_time=4.550e-05, forward_time=0.057, loss_ctc=0.811, loss=0.811, backward_time=0.009, grad_norm=41.756, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 23:03:03,090 (trainer:762) INFO: 27epoch:train:281-320batch: iter_time=4.761e-05, forward_time=0.059, loss_ctc=0.779, loss=0.779, backward_time=0.009, grad_norm=40.195, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-16 23:03:07,489 (trainer:762) INFO: 27epoch:train:321-360batch: iter_time=5.017e-05, forward_time=0.057, loss_ctc=0.804, loss=0.804, backward_time=0.009, grad_norm=43.632, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 23:03:11,852 (trainer:762) INFO: 27epoch:train:361-400batch: iter_time=4.945e-05, forward_time=0.057, loss_ctc=0.868, loss=0.868, backward_time=0.009, grad_norm=46.045, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 23:03:16,106 (trainer:762) INFO: 27epoch:train:401-440batch: iter_time=4.591e-05, forward_time=0.056, loss_ctc=0.793, loss=0.793, backward_time=0.009, grad_norm=41.899, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-16 23:03:20,591 (trainer:762) INFO: 27epoch:train:441-480batch: iter_time=4.954e-05, forward_time=0.059, loss_ctc=0.791, loss=0.791, backward_time=0.009, grad_norm=41.243, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-16 23:03:24,981 (trainer:762) INFO: 27epoch:train:481-520batch: iter_time=4.784e-05, forward_time=0.057, loss_ctc=0.767, loss=0.767, backward_time=0.009, grad_norm=41.283, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 23:03:29,428 (trainer:762) INFO: 27epoch:train:521-560batch: iter_time=4.949e-05, forward_time=0.058, loss_ctc=0.811, loss=0.811, backward_time=0.009, grad_norm=42.712, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 23:03:33,680 (trainer:762) INFO: 27epoch:train:561-600batch: iter_time=4.930e-05, forward_time=0.056, loss_ctc=0.760, loss=0.760, backward_time=0.009, grad_norm=42.398, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-16 23:03:38,073 (trainer:762) INFO: 27epoch:train:601-640batch: iter_time=4.805e-05, forward_time=0.057, loss_ctc=0.794, loss=0.794, backward_time=0.009, grad_norm=39.694, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 23:03:42,663 (trainer:762) INFO: 27epoch:train:641-680batch: iter_time=4.623e-05, forward_time=0.060, loss_ctc=0.806, loss=0.806, backward_time=0.010, grad_norm=44.060, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.459 +[stan] 2024-01-16 23:03:47,039 (trainer:762) INFO: 27epoch:train:681-720batch: iter_time=4.723e-05, forward_time=0.057, loss_ctc=0.830, loss=0.830, backward_time=0.009, grad_norm=45.357, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 23:03:51,306 (trainer:762) INFO: 27epoch:train:721-760batch: iter_time=4.925e-05, forward_time=0.056, loss_ctc=0.727, loss=0.727, backward_time=0.008, grad_norm=42.312, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-16 23:03:55,688 (trainer:762) INFO: 27epoch:train:761-800batch: iter_time=4.595e-05, forward_time=0.057, loss_ctc=0.768, loss=0.768, backward_time=0.009, grad_norm=42.347, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 23:03:59,929 (trainer:357) INFO: 27epoch results: [train] iter_time=1.958e-04, forward_time=0.057, loss_ctc=0.806, loss=0.806, backward_time=0.009, grad_norm=42.558, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.12 seconds, total_count=21600, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=58.160, cer_ctc=0.241, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=58.160, time=1.09 seconds, total_count=405, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.08 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 23:04:01,046 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 23:04:01,048 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/26epoch.pth +[stan] 2024-01-16 23:04:01,048 (trainer:291) INFO: 28/30epoch started. Estimated time to finish: 4 minutes and 40 seconds +[stan] 2024-01-16 23:04:05,723 (trainer:762) INFO: 28epoch:train:1-40batch: iter_time=0.003, forward_time=0.058, loss_ctc=0.684, loss=0.684, backward_time=0.009, grad_norm=39.837, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.467 +[stan] 2024-01-16 23:04:10,132 (trainer:762) INFO: 28epoch:train:41-80batch: iter_time=4.560e-05, forward_time=0.058, loss_ctc=0.796, loss=0.796, backward_time=0.009, grad_norm=43.868, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 23:04:14,517 (trainer:762) INFO: 28epoch:train:81-120batch: iter_time=4.539e-05, forward_time=0.057, loss_ctc=0.768, loss=0.768, backward_time=0.009, grad_norm=41.817, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-16 23:04:18,884 (trainer:762) INFO: 28epoch:train:121-160batch: iter_time=4.752e-05, forward_time=0.057, loss_ctc=0.722, loss=0.722, backward_time=0.009, grad_norm=39.255, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 23:04:23,362 (trainer:762) INFO: 28epoch:train:161-200batch: iter_time=4.848e-05, forward_time=0.058, loss_ctc=0.789, loss=0.789, backward_time=0.009, grad_norm=43.325, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-16 23:04:27,711 (trainer:762) INFO: 28epoch:train:201-240batch: iter_time=4.762e-05, forward_time=0.057, loss_ctc=0.837, loss=0.837, backward_time=0.009, grad_norm=45.537, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 23:04:32,061 (trainer:762) INFO: 28epoch:train:241-280batch: iter_time=4.586e-05, forward_time=0.057, loss_ctc=0.782, loss=0.782, backward_time=0.009, grad_norm=42.470, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 23:04:36,485 (trainer:762) INFO: 28epoch:train:281-320batch: iter_time=4.534e-05, forward_time=0.058, loss_ctc=0.777, loss=0.777, backward_time=0.009, grad_norm=43.325, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 23:04:40,914 (trainer:762) INFO: 28epoch:train:321-360batch: iter_time=4.508e-05, forward_time=0.058, loss_ctc=0.769, loss=0.769, backward_time=0.009, grad_norm=40.934, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 23:04:45,130 (trainer:762) INFO: 28epoch:train:361-400batch: iter_time=4.516e-05, forward_time=0.055, loss_ctc=0.698, loss=0.698, backward_time=0.009, grad_norm=41.254, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.422 +[stan] 2024-01-16 23:04:49,735 (trainer:762) INFO: 28epoch:train:401-440batch: iter_time=4.677e-05, forward_time=0.060, loss_ctc=0.796, loss=0.796, backward_time=0.010, grad_norm=41.926, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.460 +[stan] 2024-01-16 23:04:54,080 (trainer:762) INFO: 28epoch:train:441-480batch: iter_time=4.858e-05, forward_time=0.057, loss_ctc=0.732, loss=0.732, backward_time=0.009, grad_norm=40.083, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 23:04:58,523 (trainer:762) INFO: 28epoch:train:481-520batch: iter_time=4.554e-05, forward_time=0.058, loss_ctc=0.774, loss=0.774, backward_time=0.009, grad_norm=41.611, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-16 23:05:02,828 (trainer:762) INFO: 28epoch:train:521-560batch: iter_time=4.600e-05, forward_time=0.056, loss_ctc=0.735, loss=0.735, backward_time=0.009, grad_norm=40.669, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-16 23:05:07,159 (trainer:762) INFO: 28epoch:train:561-600batch: iter_time=4.439e-05, forward_time=0.057, loss_ctc=0.668, loss=0.668, backward_time=0.009, grad_norm=37.642, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-16 23:05:11,735 (trainer:762) INFO: 28epoch:train:601-640batch: iter_time=4.598e-05, forward_time=0.060, loss_ctc=0.807, loss=0.807, backward_time=0.009, grad_norm=42.587, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.457 +[stan] 2024-01-16 23:05:16,026 (trainer:762) INFO: 28epoch:train:641-680batch: iter_time=4.618e-05, forward_time=0.056, loss_ctc=0.686, loss=0.686, backward_time=0.009, grad_norm=40.747, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-16 23:05:20,398 (trainer:762) INFO: 28epoch:train:681-720batch: iter_time=4.526e-05, forward_time=0.057, loss_ctc=0.812, loss=0.812, backward_time=0.009, grad_norm=42.876, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 23:05:24,797 (trainer:762) INFO: 28epoch:train:721-760batch: iter_time=4.928e-05, forward_time=0.057, loss_ctc=0.736, loss=0.736, backward_time=0.009, grad_norm=41.490, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-16 23:05:29,249 (trainer:762) INFO: 28epoch:train:761-800batch: iter_time=4.480e-05, forward_time=0.058, loss_ctc=0.790, loss=0.790, backward_time=0.009, grad_norm=43.598, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.445 +[stan] 2024-01-16 23:05:33,457 (trainer:357) INFO: 28epoch results: [train] iter_time=1.860e-04, forward_time=0.057, loss_ctc=0.758, loss=0.758, backward_time=0.009, grad_norm=41.743, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441, time=1 minute and 28.27 seconds, total_count=22400, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=59.976, cer_ctc=0.244, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=59.976, time=1.07 seconds, total_count=420, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.06 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 23:05:34,569 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 23:05:34,571 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/27epoch.pth +[stan] 2024-01-16 23:05:34,571 (trainer:291) INFO: 29/30epoch started. Estimated time to finish: 3 minutes and 6.68 seconds +[stan] 2024-01-16 23:05:39,181 (trainer:762) INFO: 29epoch:train:1-40batch: iter_time=0.003, forward_time=0.057, loss_ctc=0.765, loss=0.765, backward_time=0.009, grad_norm=43.379, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.460 +[stan] 2024-01-16 23:05:43,672 (trainer:762) INFO: 29epoch:train:41-80batch: iter_time=4.709e-05, forward_time=0.059, loss_ctc=0.787, loss=0.787, backward_time=0.009, grad_norm=43.564, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-16 23:05:48,041 (trainer:762) INFO: 29epoch:train:81-120batch: iter_time=4.388e-05, forward_time=0.057, loss_ctc=0.734, loss=0.734, backward_time=0.009, grad_norm=39.455, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 23:05:52,236 (trainer:762) INFO: 29epoch:train:121-160batch: iter_time=4.895e-05, forward_time=0.055, loss_ctc=0.709, loss=0.709, backward_time=0.008, grad_norm=40.101, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.419 +[stan] 2024-01-16 23:05:56,801 (trainer:762) INFO: 29epoch:train:161-200batch: iter_time=4.585e-05, forward_time=0.060, loss_ctc=0.742, loss=0.742, backward_time=0.009, grad_norm=40.878, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.456 +[stan] 2024-01-16 23:06:01,088 (trainer:762) INFO: 29epoch:train:201-240batch: iter_time=4.453e-05, forward_time=0.056, loss_ctc=0.680, loss=0.680, backward_time=0.009, grad_norm=38.720, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-16 23:06:05,503 (trainer:762) INFO: 29epoch:train:241-280batch: iter_time=4.422e-05, forward_time=0.058, loss_ctc=0.718, loss=0.718, backward_time=0.009, grad_norm=40.228, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 23:06:09,804 (trainer:762) INFO: 29epoch:train:281-320batch: iter_time=4.440e-05, forward_time=0.056, loss_ctc=0.760, loss=0.760, backward_time=0.009, grad_norm=42.712, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-16 23:06:14,305 (trainer:762) INFO: 29epoch:train:321-360batch: iter_time=4.614e-05, forward_time=0.059, loss_ctc=0.736, loss=0.736, backward_time=0.009, grad_norm=39.794, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.450 +[stan] 2024-01-16 23:06:18,723 (trainer:762) INFO: 29epoch:train:361-400batch: iter_time=4.525e-05, forward_time=0.058, loss_ctc=0.772, loss=0.772, backward_time=0.009, grad_norm=41.279, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 23:06:23,051 (trainer:762) INFO: 29epoch:train:401-440batch: iter_time=4.622e-05, forward_time=0.057, loss_ctc=0.681, loss=0.681, backward_time=0.009, grad_norm=39.659, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-16 23:06:27,465 (trainer:762) INFO: 29epoch:train:441-480batch: iter_time=4.612e-05, forward_time=0.058, loss_ctc=0.770, loss=0.770, backward_time=0.009, grad_norm=43.322, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 23:06:31,874 (trainer:762) INFO: 29epoch:train:481-520batch: iter_time=4.505e-05, forward_time=0.058, loss_ctc=0.759, loss=0.759, backward_time=0.009, grad_norm=40.627, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 23:06:36,336 (trainer:762) INFO: 29epoch:train:521-560batch: iter_time=4.857e-05, forward_time=0.058, loss_ctc=0.749, loss=0.749, backward_time=0.009, grad_norm=45.265, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-16 23:06:40,709 (trainer:762) INFO: 29epoch:train:561-600batch: iter_time=4.804e-05, forward_time=0.057, loss_ctc=0.785, loss=0.785, backward_time=0.009, grad_norm=44.284, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-16 23:06:45,066 (trainer:762) INFO: 29epoch:train:601-640batch: iter_time=4.805e-05, forward_time=0.057, loss_ctc=0.689, loss=0.689, backward_time=0.009, grad_norm=42.254, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 23:06:49,431 (trainer:762) INFO: 29epoch:train:641-680batch: iter_time=4.786e-05, forward_time=0.057, loss_ctc=0.699, loss=0.699, backward_time=0.009, grad_norm=40.441, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 23:06:54,011 (trainer:762) INFO: 29epoch:train:681-720batch: iter_time=4.544e-05, forward_time=0.060, loss_ctc=0.707, loss=0.707, backward_time=0.009, grad_norm=39.651, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.458 +[stan] 2024-01-16 23:06:58,178 (trainer:762) INFO: 29epoch:train:721-760batch: iter_time=4.425e-05, forward_time=0.055, loss_ctc=0.630, loss=0.630, backward_time=0.009, grad_norm=38.005, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-16 23:07:02,665 (trainer:762) INFO: 29epoch:train:761-800batch: iter_time=4.660e-05, forward_time=0.059, loss_ctc=0.712, loss=0.712, backward_time=0.009, grad_norm=41.656, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-16 23:07:06,898 (trainer:357) INFO: 29epoch results: [train] iter_time=1.694e-04, forward_time=0.057, loss_ctc=0.729, loss=0.729, backward_time=0.009, grad_norm=41.264, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.17 seconds, total_count=23200, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=59.948, cer_ctc=0.241, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=59.948, time=1.07 seconds, total_count=435, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.08 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 23:07:07,887 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 23:07:07,888 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/28epoch.pth +[stan] 2024-01-16 23:07:07,888 (trainer:291) INFO: 30/30epoch started. Estimated time to finish: 1 minute and 33.34 seconds +[stan] 2024-01-16 23:07:12,518 (trainer:762) INFO: 30epoch:train:1-40batch: iter_time=0.003, forward_time=0.057, loss_ctc=0.656, loss=0.656, backward_time=0.009, grad_norm=40.327, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.462 +[stan] 2024-01-16 23:07:16,865 (trainer:762) INFO: 30epoch:train:41-80batch: iter_time=4.582e-05, forward_time=0.057, loss_ctc=0.742, loss=0.742, backward_time=0.009, grad_norm=45.627, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 23:07:21,214 (trainer:762) INFO: 30epoch:train:81-120batch: iter_time=4.477e-05, forward_time=0.057, loss_ctc=0.666, loss=0.666, backward_time=0.009, grad_norm=40.441, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 23:07:25,680 (trainer:762) INFO: 30epoch:train:121-160batch: iter_time=4.594e-05, forward_time=0.058, loss_ctc=0.715, loss=0.715, backward_time=0.009, grad_norm=43.443, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-16 23:07:30,032 (trainer:762) INFO: 30epoch:train:161-200batch: iter_time=4.534e-05, forward_time=0.057, loss_ctc=0.726, loss=0.726, backward_time=0.009, grad_norm=40.581, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 23:07:34,380 (trainer:762) INFO: 30epoch:train:201-240batch: iter_time=4.786e-05, forward_time=0.057, loss_ctc=0.679, loss=0.679, backward_time=0.009, grad_norm=40.491, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-16 23:07:38,742 (trainer:762) INFO: 30epoch:train:241-280batch: iter_time=4.707e-05, forward_time=0.057, loss_ctc=0.764, loss=0.764, backward_time=0.009, grad_norm=41.149, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 23:07:43,132 (trainer:762) INFO: 30epoch:train:281-320batch: iter_time=5.074e-05, forward_time=0.057, loss_ctc=0.752, loss=0.752, backward_time=0.009, grad_norm=42.289, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-16 23:07:47,561 (trainer:762) INFO: 30epoch:train:321-360batch: iter_time=4.531e-05, forward_time=0.058, loss_ctc=0.702, loss=0.702, backward_time=0.009, grad_norm=41.488, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 23:07:51,837 (trainer:762) INFO: 30epoch:train:361-400batch: iter_time=4.807e-05, forward_time=0.056, loss_ctc=0.592, loss=0.592, backward_time=0.009, grad_norm=35.852, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-16 23:07:56,399 (trainer:762) INFO: 30epoch:train:401-440batch: iter_time=4.782e-05, forward_time=0.059, loss_ctc=0.751, loss=0.751, backward_time=0.009, grad_norm=41.641, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.456 +[stan] 2024-01-16 23:08:00,735 (trainer:762) INFO: 30epoch:train:441-480batch: iter_time=4.689e-05, forward_time=0.057, loss_ctc=0.662, loss=0.662, backward_time=0.009, grad_norm=38.863, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-16 23:08:05,080 (trainer:762) INFO: 30epoch:train:481-520batch: iter_time=4.822e-05, forward_time=0.057, loss_ctc=0.650, loss=0.650, backward_time=0.009, grad_norm=39.753, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-16 23:08:09,504 (trainer:762) INFO: 30epoch:train:521-560batch: iter_time=4.986e-05, forward_time=0.058, loss_ctc=0.644, loss=0.644, backward_time=0.009, grad_norm=39.370, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 23:08:13,917 (trainer:762) INFO: 30epoch:train:561-600batch: iter_time=4.767e-05, forward_time=0.058, loss_ctc=0.646, loss=0.646, backward_time=0.009, grad_norm=40.132, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-16 23:08:18,342 (trainer:762) INFO: 30epoch:train:601-640batch: iter_time=4.697e-05, forward_time=0.058, loss_ctc=0.699, loss=0.699, backward_time=0.009, grad_norm=43.553, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-16 23:08:22,650 (trainer:762) INFO: 30epoch:train:641-680batch: iter_time=4.550e-05, forward_time=0.056, loss_ctc=0.682, loss=0.682, backward_time=0.009, grad_norm=41.438, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-16 23:08:27,081 (trainer:762) INFO: 30epoch:train:681-720batch: iter_time=4.724e-05, forward_time=0.058, loss_ctc=0.666, loss=0.666, backward_time=0.009, grad_norm=40.976, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.443 +[stan] 2024-01-16 23:08:31,444 (trainer:762) INFO: 30epoch:train:721-760batch: iter_time=4.797e-05, forward_time=0.057, loss_ctc=0.653, loss=0.653, backward_time=0.009, grad_norm=38.442, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-16 23:08:35,976 (trainer:762) INFO: 30epoch:train:761-800batch: iter_time=4.682e-05, forward_time=0.059, loss_ctc=0.803, loss=0.803, backward_time=0.009, grad_norm=44.807, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.453 +[stan] 2024-01-16 23:08:40,208 (trainer:357) INFO: 30epoch results: [train] iter_time=1.953e-04, forward_time=0.057, loss_ctc=0.692, loss=0.692, backward_time=0.009, grad_norm=41.033, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440, time=1 minute and 28.17 seconds, total_count=24000, gpu_max_cached_mem_GB=6.705, [valid] loss_ctc=60.104, cer_ctc=0.241, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=60.104, time=1.09 seconds, total_count=450, gpu_max_cached_mem_GB=6.705, [att_plot] time=3.07 seconds, total_count=0, gpu_max_cached_mem_GB=6.705 +[stan] 2024-01-16 23:08:41,285 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-16 23:08:41,286 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/29epoch.pth +[stan] 2024-01-16 23:08:41,286 (trainer:488) INFO: The training was finished at 30 epochs +[stan] 2024-01-16 23:08:41,300 (average_nbest_models:69) INFO: Averaging 5best models: criterion="valid.loss": test_pr/asr_train_asr_s3prl_houlsby_jpn_10min/valid.loss.ave_5best.pth +# Accounting: time=2806 threads=1 +# Ended (code 0) at Tue Jan 16 23:08:42 CST 2024, elapsed time 2806 seconds diff --git 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sha256:389e663dec7d715fdc21e2568eaa7a81510aafc50a6c42494c49f45d582d2b5c +size 21135182 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/RESULTS.md b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/RESULTS.md new file mode 100644 index 0000000000000000000000000000000000000000..495043e9ea729667a238c4b51ecd9222d0cd6724 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/RESULTS.md @@ -0,0 +1,45 @@ +[INFO] /home/stan/Desktop/espnet/egs2/ml_superb/asr1/../../../tools/activate_python.sh is not present + +# RESULTS +## Environments +- date: `Wed Jan 17 02:42:28 CST 2024` +- python version: `3.8.10 (default, Jun 4 2021, 15:09:15) [GCC 7.5.0]` +- espnet version: `espnet 202310` +- pytorch version: `pytorch 1.12.0+cu113` +- Git hash: `aa855dffb81937a097ee03089926a0d5256426e2` + - Commit date: `Tue Jan 16 19:36:29 2024 +0800` + +## test_pr/asr_train_asr_s3prl_houlsby_jpn_1h +### WER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|decode_asr_asr_model_valid.loss.ave/test_10min_jpn|160|9700|87.5|6.9|5.5|1.9|14.4|93.1| + +### CER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|decode_asr_asr_model_valid.loss.ave/test_10min_jpn|160|20087|90.0|3.5|6.5|1.9|11.9|93.1| + +### TER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +## test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave +### WER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|org/dev_10min_jpn|126|4430|88.5|6.8|4.7|3.0|14.4|91.3| + +### CER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| +|org/dev_10min_jpn|126|9077|91.1|3.6|5.3|2.8|11.7|91.3| + +### TER + +|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| +|---|---|---|---|---|---|---|---|---| diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/att_ws/cv_jpn_000674/encoder.encoders.0.self_attn.10ep.png 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+local_rank: 0 +dist_master_addr: null +dist_master_port: null +dist_launcher: null +multiprocessing_distributed: false +unused_parameters: true +sharded_ddp: false +cudnn_enabled: true +cudnn_benchmark: false +cudnn_deterministic: true +collect_stats: false +write_collected_feats: false +max_epoch: 30 +patience: null +val_scheduler_criterion: +- valid +- loss +early_stopping_criterion: +- valid +- loss +- min +best_model_criterion: +- - valid + - loss + - min +keep_nbest_models: 5 +nbest_averaging_interval: 0 +grad_clip: 5.0 +grad_clip_type: 2.0 +grad_noise: false +accum_grad: 4 +no_forward_run: false +resume: true +train_dtype: float32 +use_amp: false +log_interval: null +use_matplotlib: true +use_tensorboard: true +create_graph_in_tensorboard: false +use_wandb: false +wandb_project: null +wandb_id: null +wandb_entity: null +wandb_name: null +wandb_model_log_interval: -1 +detect_anomaly: false +use_adapter: true +adapter: houlsby +save_adapter_only: true +adapter_conf: + bottleneck: 32 +pretrain_path: null +init_param: [] +ignore_init_mismatch: false +freeze_param: +- frontend.upstream +num_iters_per_epoch: 800 +batch_size: 8 +valid_batch_size: null +batch_bins: 1000000 +valid_batch_bins: null +train_shape_file: +- test_pr/asr_stats_jpn_1h/train/speech_shape +- test_pr/asr_stats_jpn_1h/train/text_shape.word +valid_shape_file: +- test_pr/asr_stats_jpn_1h/valid/speech_shape +- test_pr/asr_stats_jpn_1h/valid/text_shape.word +batch_type: sorted +valid_batch_type: null +fold_length: +- 80000 +- 150 +sort_in_batch: descending +shuffle_within_batch: false +sort_batch: descending +multiple_iterator: false +chunk_length: 500 +chunk_shift_ratio: 0.5 +num_cache_chunks: 1024 +chunk_excluded_key_prefixes: [] +chunk_default_fs: null +train_data_path_and_name_and_type: +- - dump/raw/train_1h_jpn/wav.scp + - speech + - sound +- - dump/raw/train_1h_jpn/text + - text + - text +valid_data_path_and_name_and_type: +- - dump/raw/dev_10min_jpn/wav.scp + - speech + - sound +- - dump/raw/dev_10min_jpn/text + - text + - text +allow_variable_data_keys: false +max_cache_size: 0.0 +max_cache_fd: 32 +allow_multi_rates: false +valid_max_cache_size: null +exclude_weight_decay: false +exclude_weight_decay_conf: {} +optim: adam +optim_conf: + lr: 0.0001 + weight_decay: 1.0e-06 +scheduler: null +scheduler_conf: {} +token_list: +- +- +- a +- o +- i +- e +- k +- u +- t +- n +- r +- m +- s +- N +- d +- sh +- g +- w +- U +- I +- pau +- cl +- j +- y +- h +- b +- ts +- ch +- z +- ky +- p +- f +- ry +- hy +- ny +- gy +- py +- my +- by +- v +- +init: null +input_size: null +ctc_conf: + dropout_rate: 0.0 + ctc_type: builtin + reduce: true + ignore_nan_grad: null + zero_infinity: true + brctc_risk_strategy: exp + brctc_group_strategy: end + brctc_risk_factor: 0.0 +joint_net_conf: null +use_preprocessor: true +use_lang_prompt: false +use_nlp_prompt: false +token_type: word +bpemodel: null +non_linguistic_symbols: null +cleaner: null +g2p: null +speech_volume_normalize: null +rir_scp: null +rir_apply_prob: 1.0 +noise_scp: null +noise_apply_prob: 1.0 +noise_db_range: '13_15' +short_noise_thres: 0.5 +aux_ctc_tasks: [] +frontend: s3prl +frontend_conf: + frontend_conf: + upstream: hubert_base + download_dir: ./hub + multilayer_feature: true + fs: 16k +specaug: specaug +specaug_conf: + apply_time_warp: true + time_warp_window: 5 + time_warp_mode: bicubic + apply_freq_mask: true + freq_mask_width_range: + - 0 + - 27 + num_freq_mask: 2 + apply_time_mask: true + time_mask_width_ratio_range: + - 0.0 + - 0.05 + num_time_mask: 10 +normalize: utterance_mvn +normalize_conf: {} +model: espnet +model_conf: + ctc_weight: 1.0 + extract_feats_in_collect_stats: false +preencoder: linear +preencoder_conf: + input_size: 768 + output_size: 80 +encoder: transformer +encoder_conf: + output_size: 256 + attention_heads: 8 + linear_units: 1024 + num_blocks: 2 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + input_layer: conv2d2 + normalize_before: true +postencoder: null +postencoder_conf: {} +decoder: null +decoder_conf: {} +preprocessor: default +preprocessor_conf: {} +required: +- output_dir +- token_list +version: '202310' +distributed: false diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.1.log b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.1.log new file mode 100644 index 0000000000000000000000000000000000000000..af57ea7675bea287eeb931f0747c2922fb6d099e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.1.log @@ -0,0 +1,371 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1 --config conf/decode_asr.yaml +# Started at Wed Jan 17 02:39:56 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1 --config conf/decode_asr.yaml +2024-01-17 02:39:57,969 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml +2024-01-17 02:39:57,987 (asr:523) INFO: Vocabulary size: 41 +2024-01-17 02:39:58,049 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 02:39:58,049 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 02:39:58,161 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 02:39:59,458 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 02:40:00,724 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 02:40:00,724 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 02:40:00,724 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 02:40:00,757 (asr_inference:446) INFO: Text tokenizer: WordTokenizer(delimiter="None") +2024-01-17 02:40:00,832 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 02:40:00,978 (asr_inference:494) INFO: speech length: 143424 +2024-01-17 02:40:02,186 (beam_search:428) INFO: decoder input length: 222 +2024-01-17 02:40:02,186 (beam_search:429) INFO: max output length: 222 +2024-01-17 02:40:02,186 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:02,697 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:02,697 (beam_search:476) INFO: -4.79 * 1.0 = -4.79 for ctc +2024-01-17 02:40:02,697 (beam_search:479) INFO: total log probability: -4.79 +2024-01-17 02:40:02,697 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:40:02,697 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:02,698 (beam_search:483) INFO: best hypo: bokunoieegacltakainaNnonamaeNnikuraberutobokuniwanajiminonaipaunabaebakaridakedo + +2024-01-17 02:40:02,722 (asr_inference:494) INFO: speech length: 114048 +2024-01-17 02:40:02,735 (beam_search:428) INFO: decoder input length: 176 +2024-01-17 02:40:02,735 (beam_search:429) INFO: max output length: 176 +2024-01-17 02:40:02,735 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:02,934 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:02,934 (beam_search:476) INFO: -5.27 * 1.0 = -5.27 for ctc +2024-01-17 02:40:02,934 (beam_search:479) INFO: total log probability: -5.27 +2024-01-17 02:40:02,934 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 02:40:02,934 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:02,934 (beam_search:483) INFO: best hypo: naioosonomonoyoripaufuinikigauketeru + +2024-01-17 02:40:02,935 (asr_inference:494) INFO: speech length: 103680 +2024-01-17 02:40:02,947 (beam_search:428) INFO: decoder input length: 159 +2024-01-17 02:40:02,947 (beam_search:429) INFO: max output length: 159 +2024-01-17 02:40:02,947 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:03,140 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:03,140 (beam_search:476) INFO: -2.60 * 1.0 = -2.60 for ctc +2024-01-17 02:40:03,140 (beam_search:479) INFO: total log probability: -2.60 +2024-01-17 02:40:03,140 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:40:03,140 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:03,140 (beam_search:483) INFO: best hypo: bokunoshIclteiumonotowasUkoshichigaclteita + +2024-01-17 02:40:03,141 (asr_inference:494) INFO: speech length: 155520 +2024-01-17 02:40:03,157 (beam_search:428) INFO: decoder input length: 240 +2024-01-17 02:40:03,157 (beam_search:429) INFO: max output length: 240 +2024-01-17 02:40:03,157 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:03,853 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:03,853 (beam_search:476) INFO: -11.52 * 1.0 = -11.52 for ctc +2024-01-17 02:40:03,853 (beam_search:479) INFO: total log probability: -11.52 +2024-01-17 02:40:03,853 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:40:03,853 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:03,853 (beam_search:483) INFO: best hypo: kakaarutachimaniooicltepausekaigaijshIkimeNtekidearipauwarewarenojikogajshIkisayootekidearutokaNgeerarerutokshi + +2024-01-17 02:40:03,855 (asr_inference:494) INFO: speech length: 108288 +2024-01-17 02:40:03,867 (beam_search:428) INFO: decoder input length: 167 +2024-01-17 02:40:03,867 (beam_search:429) INFO: max output length: 167 +2024-01-17 02:40:03,867 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:04,172 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:04,172 (beam_search:476) INFO: -7.62 * 1.0 = -7.62 for ctc +2024-01-17 02:40:04,172 (beam_search:479) INFO: total log probability: -7.62 +2024-01-17 02:40:04,172 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 02:40:04,172 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:04,172 (beam_search:483) INFO: best hypo: ieniktaneNgaajiwasaNhyakumaihorodepauchoodashItabuNtoonajiguraida + +2024-01-17 02:40:04,174 (asr_inference:494) INFO: speech length: 124416 +2024-01-17 02:40:04,187 (beam_search:428) INFO: decoder input length: 192 +2024-01-17 02:40:04,187 (beam_search:429) INFO: max output length: 192 +2024-01-17 02:40:04,187 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:04,540 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:04,540 (beam_search:476) INFO: -10.25 * 1.0 = -10.25 for ctc +2024-01-17 02:40:04,540 (beam_search:479) INFO: total log probability: -10.25 +2024-01-17 02:40:04,540 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 02:40:04,540 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:04,540 (beam_search:483) INFO: best hypo: haawapitamaritsubaagunoseeshiNyooininuuNhIteirutokinibeshioomo + +2024-01-17 02:40:04,542 (asr_inference:494) INFO: speech length: 133056 +2024-01-17 02:40:04,555 (beam_search:428) INFO: decoder input length: 205 +2024-01-17 02:40:04,555 (beam_search:429) INFO: max output length: 205 +2024-01-17 02:40:04,555 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:05,125 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:05,125 (beam_search:476) INFO: -13.66 * 1.0 = -13.66 for ctc +2024-01-17 02:40:05,125 (beam_search:479) INFO: total log probability: -13.66 +2024-01-17 02:40:05,125 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 02:40:05,125 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:05,126 (beam_search:483) INFO: best hypo: tataNdearhaNtenohirogerabaachikochiNitsugihagiaaripaukatakochinidekitahokorobinaNkakyooeNnomamoninaclteiru + +2024-01-17 02:40:05,127 (asr_inference:494) INFO: speech length: 141120 +2024-01-17 02:40:05,142 (beam_search:428) INFO: decoder input length: 218 +2024-01-17 02:40:05,142 (beam_search:429) INFO: max output length: 218 +2024-01-17 02:40:05,142 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:05,647 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:05,647 (beam_search:476) INFO: -7.68 * 1.0 = -7.68 for ctc +2024-01-17 02:40:05,647 (beam_search:479) INFO: total log probability: -7.68 +2024-01-17 02:40:05,647 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:40:05,647 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:05,648 (beam_search:483) INFO: best hypo: karagakanodeberenaakanihoomoNkiboonobushooyobichoozakiboonoobugomaikaratooridesU + +2024-01-17 02:40:05,649 (asr_inference:494) INFO: speech length: 52416 +2024-01-17 02:40:05,658 (beam_search:428) INFO: decoder input length: 79 +2024-01-17 02:40:05,658 (beam_search:429) INFO: max output length: 79 +2024-01-17 02:40:05,658 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:05,721 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:05,721 (beam_search:476) INFO: -1.11 * 1.0 = -1.11 for ctc +2024-01-17 02:40:05,721 (beam_search:479) INFO: total log probability: -1.11 +2024-01-17 02:40:05,721 (beam_search:480) INFO: normalized log probability: -0.05 +2024-01-17 02:40:05,721 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:05,721 (beam_search:483) INFO: best hypo: kaNbawaatotemosamidesU + +2024-01-17 02:40:05,723 (asr_inference:494) INFO: speech length: 89856 +2024-01-17 02:40:05,734 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 02:40:05,734 (beam_search:429) INFO: max output length: 138 +2024-01-17 02:40:05,734 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:05,919 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:05,919 (beam_search:476) INFO: -6.73 * 1.0 = -6.73 for ctc +2024-01-17 02:40:05,919 (beam_search:479) INFO: total log probability: -6.73 +2024-01-17 02:40:05,919 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 02:40:05,919 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:05,919 (beam_search:483) INFO: best hypo: kiNchooshItakahaotsUkidepacltawadasekinihairu + +2024-01-17 02:40:05,920 (asr_inference:494) INFO: speech length: 99072 +2024-01-17 02:40:05,931 (beam_search:428) INFO: decoder input length: 152 +2024-01-17 02:40:05,931 (beam_search:429) INFO: max output length: 152 +2024-01-17 02:40:05,931 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:06,125 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:06,125 (beam_search:476) INFO: -5.87 * 1.0 = -5.87 for ctc +2024-01-17 02:40:06,125 (beam_search:479) INFO: total log probability: -5.87 +2024-01-17 02:40:06,125 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 02:40:06,125 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:06,125 (beam_search:483) INFO: best hypo: masaNkyuureNtoicltafsuukakarokeNjikibutssU + +2024-01-17 02:40:06,126 (asr_inference:494) INFO: speech length: 95616 +2024-01-17 02:40:06,138 (beam_search:428) INFO: decoder input length: 147 +2024-01-17 02:40:06,138 (beam_search:429) INFO: max output length: 147 +2024-01-17 02:40:06,138 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:06,389 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:06,389 (beam_search:476) INFO: -6.03 * 1.0 = -6.03 for ctc +2024-01-17 02:40:06,389 (beam_search:479) INFO: total log probability: -6.03 +2024-01-17 02:40:06,389 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:40:06,389 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:06,389 (beam_search:483) INFO: best hypo: gojiyoshidesusumetetssogoNgawarukunacltarahIclkomeruearekUchi + +2024-01-17 02:40:06,390 (asr_inference:494) INFO: speech length: 123264 +2024-01-17 02:40:06,403 (beam_search:428) INFO: decoder input length: 190 +2024-01-17 02:40:06,403 (beam_search:429) INFO: max output length: 190 +2024-01-17 02:40:06,403 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:06,730 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:06,730 (beam_search:476) INFO: -4.41 * 1.0 = -4.41 for ctc +2024-01-17 02:40:06,730 (beam_search:479) INFO: total log probability: -4.41 +2024-01-17 02:40:06,730 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:40:06,730 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:06,731 (beam_search:483) INFO: best hypo: mujuNtekipaujikotooitsUtekinipaujikoojishiNokeeseesorushakaiwa + +2024-01-17 02:40:06,732 (asr_inference:494) INFO: speech length: 71424 +2024-01-17 02:40:06,742 (beam_search:428) INFO: decoder input length: 109 +2024-01-17 02:40:06,742 (beam_search:429) INFO: max output length: 109 +2024-01-17 02:40:06,742 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:06,831 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:06,831 (beam_search:476) INFO: -2.80 * 1.0 = -2.80 for ctc +2024-01-17 02:40:06,831 (beam_search:479) INFO: total log probability: -2.80 +2024-01-17 02:40:06,831 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:40:06,831 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:06,831 (beam_search:483) INFO: best hypo: faNnoikeNninarasaderuna + +2024-01-17 02:40:06,832 (asr_inference:494) INFO: speech length: 99648 +2024-01-17 02:40:06,843 (beam_search:428) INFO: decoder input length: 153 +2024-01-17 02:40:06,843 (beam_search:429) INFO: max output length: 153 +2024-01-17 02:40:06,843 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:07,035 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:07,035 (beam_search:476) INFO: -4.54 * 1.0 = -4.54 for ctc +2024-01-17 02:40:07,035 (beam_search:479) INFO: total log probability: -4.54 +2024-01-17 02:40:07,035 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:40:07,035 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:07,035 (beam_search:483) INFO: best hypo: hinegaasobitayoorazeNkaidekochoomiteiru + +2024-01-17 02:40:07,036 (asr_inference:494) INFO: speech length: 80640 +2024-01-17 02:40:07,047 (beam_search:428) INFO: decoder input length: 123 +2024-01-17 02:40:07,047 (beam_search:429) INFO: max output length: 123 +2024-01-17 02:40:07,047 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:07,175 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:07,175 (beam_search:476) INFO: -2.43 * 1.0 = -2.43 for ctc +2024-01-17 02:40:07,175 (beam_search:479) INFO: total log probability: -2.43 +2024-01-17 02:40:07,175 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:40:07,175 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:07,175 (beam_search:483) INFO: best hypo: ichidowakoNpotaajikaNonoNdemitai + +2024-01-17 02:40:07,176 (asr_inference:494) INFO: speech length: 86976 +2024-01-17 02:40:07,187 (beam_search:428) INFO: decoder input length: 133 +2024-01-17 02:40:07,187 (beam_search:429) INFO: max output length: 133 +2024-01-17 02:40:07,187 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:07,374 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:07,374 (beam_search:476) INFO: -4.75 * 1.0 = -4.75 for ctc +2024-01-17 02:40:07,374 (beam_search:479) INFO: total log probability: -4.75 +2024-01-17 02:40:07,374 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:40:07,374 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:07,374 (beam_search:483) INFO: best hypo: kooinokoshItepauotoosaNtokaasaNwaNdeteikimashIta + +2024-01-17 02:40:07,375 (asr_inference:494) INFO: speech length: 138240 +2024-01-17 02:40:07,390 (beam_search:428) INFO: decoder input length: 213 +2024-01-17 02:40:07,390 (beam_search:429) INFO: max output length: 213 +2024-01-17 02:40:07,390 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:08,015 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:08,015 (beam_search:476) INFO: -7.04 * 1.0 = -7.04 for ctc +2024-01-17 02:40:08,015 (beam_search:479) INFO: total log probability: -7.04 +2024-01-17 02:40:08,015 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:40:08,015 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:08,016 (beam_search:483) INFO: best hypo: shIkashItesoregatsUkraretamonokaratsUkurumonoetoshItedokumademorawarunisemarutoiutokipauwarewarinichoclkaNtekidearu + +2024-01-17 02:40:08,018 (asr_inference:494) INFO: speech length: 95040 +2024-01-17 02:40:08,029 (beam_search:428) INFO: decoder input length: 146 +2024-01-17 02:40:08,029 (beam_search:429) INFO: max output length: 146 +2024-01-17 02:40:08,029 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:08,256 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:08,257 (beam_search:476) INFO: -3.28 * 1.0 = -3.28 for ctc +2024-01-17 02:40:08,257 (beam_search:479) INFO: total log probability: -3.28 +2024-01-17 02:40:08,257 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:40:08,257 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:08,257 (beam_search:483) INFO: best hypo: hainitamacltakemuriohakidashikuraikooenishIseNomokeru + +2024-01-17 02:40:08,258 (asr_inference:494) INFO: speech length: 92160 +2024-01-17 02:40:08,269 (beam_search:428) INFO: decoder input length: 141 +2024-01-17 02:40:08,269 (beam_search:429) INFO: max output length: 141 +2024-01-17 02:40:08,269 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:08,419 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:08,419 (beam_search:476) INFO: -5.79 * 1.0 = -5.79 for ctc +2024-01-17 02:40:08,419 (beam_search:479) INFO: total log probability: -5.79 +2024-01-17 02:40:08,419 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 02:40:08,419 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:08,419 (beam_search:483) INFO: best hypo: wadawadashnabusokudechiseNiNnariso + +2024-01-17 02:40:08,420 (asr_inference:494) INFO: speech length: 146880 +2024-01-17 02:40:08,435 (beam_search:428) INFO: decoder input length: 227 +2024-01-17 02:40:08,435 (beam_search:429) INFO: max output length: 227 +2024-01-17 02:40:08,435 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:09,009 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:09,009 (beam_search:476) INFO: -13.46 * 1.0 = -13.46 for ctc +2024-01-17 02:40:09,009 (beam_search:479) INFO: total log probability: -13.46 +2024-01-17 02:40:09,010 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 02:40:09,010 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:09,010 (beam_search:483) INFO: best hypo: tseNshekainomotsUkatachshitashinoiyaiuruseesaiyoshIkutosaiotowapauhanashItekaNgaerukotowadekinai + +2024-01-17 02:40:09,011 (asr_inference:494) INFO: speech length: 73728 +2024-01-17 02:40:09,022 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 02:40:09,022 (beam_search:429) INFO: max output length: 113 +2024-01-17 02:40:09,022 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:09,137 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:09,137 (beam_search:476) INFO: -4.87 * 1.0 = -4.87 for ctc +2024-01-17 02:40:09,137 (beam_search:479) INFO: total log probability: -4.87 +2024-01-17 02:40:09,137 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 02:40:09,137 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:09,137 (beam_search:483) INFO: best hypo: sakenomainonipiirubaratoyoreta + +2024-01-17 02:40:09,138 (asr_inference:494) INFO: speech length: 119808 +2024-01-17 02:40:09,151 (beam_search:428) INFO: decoder input length: 185 +2024-01-17 02:40:09,151 (beam_search:429) INFO: max output length: 185 +2024-01-17 02:40:09,151 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:09,480 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:09,481 (beam_search:476) INFO: -6.86 * 1.0 = -6.86 for ctc +2024-01-17 02:40:09,481 (beam_search:479) INFO: total log probability: -6.86 +2024-01-17 02:40:09,481 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:40:09,481 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:09,481 (beam_search:483) INFO: best hypo: soreotatakuhakunozeNdaNkaishIkuiteedonkaakUtonomimirukotowa + +2024-01-17 02:40:09,482 (asr_inference:494) INFO: speech length: 78336 +2024-01-17 02:40:09,492 (beam_search:428) INFO: decoder input length: 120 +2024-01-17 02:40:09,492 (beam_search:429) INFO: max output length: 120 +2024-01-17 02:40:09,492 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:09,634 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:09,634 (beam_search:476) INFO: -5.87 * 1.0 = -5.87 for ctc +2024-01-17 02:40:09,634 (beam_search:479) INFO: total log probability: -5.87 +2024-01-17 02:40:09,634 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 02:40:09,634 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:09,635 (beam_search:483) INFO: best hypo: nokkyoeNofuatsunisumohodegeemoyaclteita + +2024-01-17 02:40:09,636 (asr_inference:494) INFO: speech length: 85248 +2024-01-17 02:40:09,646 (beam_search:428) INFO: decoder input length: 131 +2024-01-17 02:40:09,646 (beam_search:429) INFO: max output length: 131 +2024-01-17 02:40:09,646 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:09,819 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:09,819 (beam_search:476) INFO: -9.52 * 1.0 = -9.52 for ctc +2024-01-17 02:40:09,819 (beam_search:479) INFO: total log probability: -9.52 +2024-01-17 02:40:09,819 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 02:40:09,819 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:09,820 (beam_search:483) INFO: best hypo: wadaiwaanaishumataNkobaeshisaNtooasobeimasU + +2024-01-17 02:40:09,821 (asr_inference:494) INFO: speech length: 105408 +2024-01-17 02:40:09,833 (beam_search:428) INFO: decoder input length: 162 +2024-01-17 02:40:09,833 (beam_search:429) INFO: max output length: 162 +2024-01-17 02:40:09,833 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:10,060 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:10,061 (beam_search:476) INFO: -8.38 * 1.0 = -8.38 for ctc +2024-01-17 02:40:10,061 (beam_search:479) INFO: total log probability: -8.38 +2024-01-17 02:40:10,061 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 02:40:10,061 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:10,061 (beam_search:483) INFO: best hypo: hasookonihetoogaimasunearohItoowataareeteshoo + +2024-01-17 02:40:10,062 (asr_inference:494) INFO: speech length: 72576 +2024-01-17 02:40:10,072 (beam_search:428) INFO: decoder input length: 111 +2024-01-17 02:40:10,072 (beam_search:429) INFO: max output length: 111 +2024-01-17 02:40:10,072 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:10,193 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:10,193 (beam_search:476) INFO: -5.69 * 1.0 = -5.69 for ctc +2024-01-17 02:40:10,193 (beam_search:479) INFO: total log probability: -5.69 +2024-01-17 02:40:10,194 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 02:40:10,194 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:10,194 (beam_search:483) INFO: best hypo: watashiwakinookananoodoogaitaitesU + +2024-01-17 02:40:10,195 (asr_inference:494) INFO: speech length: 62208 +2024-01-17 02:40:10,204 (beam_search:428) INFO: decoder input length: 95 +2024-01-17 02:40:10,204 (beam_search:429) INFO: max output length: 95 +2024-01-17 02:40:10,204 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:10,291 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:10,291 (beam_search:476) INFO: -2.52 * 1.0 = -2.52 for ctc +2024-01-17 02:40:10,291 (beam_search:479) INFO: total log probability: -2.52 +2024-01-17 02:40:10,291 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:40:10,291 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:10,291 (beam_search:483) INFO: best hypo: kyooreNkanapeNkyuooshIteimasU + +2024-01-17 02:40:10,292 (asr_inference:494) INFO: speech length: 42048 +2024-01-17 02:40:10,300 (beam_search:428) INFO: decoder input length: 63 +2024-01-17 02:40:10,301 (beam_search:429) INFO: max output length: 63 +2024-01-17 02:40:10,301 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:10,333 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:10,333 (beam_search:476) INFO: -1.78 * 1.0 = -1.78 for ctc +2024-01-17 02:40:10,333 (beam_search:479) INFO: total log probability: -1.78 +2024-01-17 02:40:10,333 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 02:40:10,333 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:10,333 (beam_search:483) INFO: best hypo: chocltosumimase + +2024-01-17 02:40:10,334 (asr_inference:494) INFO: speech length: 115776 +2024-01-17 02:40:10,347 (beam_search:428) INFO: decoder input length: 178 +2024-01-17 02:40:10,347 (beam_search:429) INFO: max output length: 178 +2024-01-17 02:40:10,347 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:10,634 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:10,634 (beam_search:476) INFO: -8.83 * 1.0 = -8.83 for ctc +2024-01-17 02:40:10,634 (beam_search:479) INFO: total log probability: -8.83 +2024-01-17 02:40:10,634 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 02:40:10,634 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:10,634 (beam_search:483) INFO: best hypo: chIkashtonooooeNburyowayakekiminikaecldehagesUpeunoclta + +2024-01-17 02:40:10,635 (asr_inference:494) INFO: speech length: 92736 +2024-01-17 02:40:10,647 (beam_search:428) INFO: decoder input length: 142 +2024-01-17 02:40:10,647 (beam_search:429) INFO: max output length: 142 +2024-01-17 02:40:10,647 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:10,879 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:10,879 (beam_search:476) INFO: -8.52 * 1.0 = -8.52 for ctc +2024-01-17 02:40:10,879 (beam_search:479) INFO: total log probability: -8.52 +2024-01-17 02:40:10,879 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 02:40:10,879 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:10,879 (beam_search:483) INFO: best hypo: itsumokonoeNpIitssootsshUkaclteitanotemijikahahanarimashIta + +2024-01-17 02:40:10,880 (asr_inference:494) INFO: speech length: 53568 +2024-01-17 02:40:10,889 (beam_search:428) INFO: decoder input length: 81 +2024-01-17 02:40:10,889 (beam_search:429) INFO: max output length: 81 +2024-01-17 02:40:10,889 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:10,960 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:10,960 (beam_search:476) INFO: -3.89 * 1.0 = -3.89 for ctc +2024-01-17 02:40:10,960 (beam_search:479) INFO: total log probability: -3.89 +2024-01-17 02:40:10,960 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 02:40:10,960 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:10,960 (beam_search:483) INFO: best hypo: watashiwaeigogahanastemosU + +# Accounting: time=15 threads=1 +# Ended (code 0) at Wed Jan 17 02:40:11 CST 2024, elapsed time 15 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.2.log b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.2.log new file mode 100644 index 0000000000000000000000000000000000000000..ce8f275852cf95d81cf0705a6363f220af1c369c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.2.log @@ -0,0 +1,371 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2 --config conf/decode_asr.yaml +# Started at Wed Jan 17 02:40:11 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2 --config conf/decode_asr.yaml +2024-01-17 02:40:12,778 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml +2024-01-17 02:40:12,796 (asr:523) INFO: Vocabulary size: 41 +2024-01-17 02:40:12,858 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 02:40:12,858 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 02:40:12,969 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 02:40:14,257 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 02:40:15,493 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 02:40:15,493 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 02:40:15,493 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 02:40:15,526 (asr_inference:446) INFO: Text tokenizer: WordTokenizer(delimiter="None") +2024-01-17 02:40:15,600 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 02:40:15,713 (asr_inference:494) INFO: speech length: 59328 +2024-01-17 02:40:16,916 (beam_search:428) INFO: decoder input length: 90 +2024-01-17 02:40:16,916 (beam_search:429) INFO: max output length: 90 +2024-01-17 02:40:16,916 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:17,000 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:17,000 (beam_search:476) INFO: -2.90 * 1.0 = -2.90 for ctc +2024-01-17 02:40:17,000 (beam_search:479) INFO: total log probability: -2.90 +2024-01-17 02:40:17,000 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:40:17,000 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:17,000 (beam_search:483) INFO: best hypo: oshiikeeryajibaNdetocltaekina + +2024-01-17 02:40:17,025 (asr_inference:494) INFO: speech length: 105408 +2024-01-17 02:40:17,039 (beam_search:428) INFO: decoder input length: 162 +2024-01-17 02:40:17,039 (beam_search:429) INFO: max output length: 162 +2024-01-17 02:40:17,039 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:17,267 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:17,268 (beam_search:476) INFO: -7.55 * 1.0 = -7.55 for ctc +2024-01-17 02:40:17,268 (beam_search:479) INFO: total log probability: -7.55 +2024-01-17 02:40:17,268 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 02:40:17,268 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:17,268 (beam_search:483) INFO: best hypo: aishuukaranishuuukaNhaigaeeeruyookoooniikimasU + +2024-01-17 02:40:17,269 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 02:40:17,277 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 02:40:17,277 (beam_search:429) INFO: max output length: 51 +2024-01-17 02:40:17,277 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:17,310 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:17,310 (beam_search:476) INFO: -1.63 * 1.0 = -1.63 for ctc +2024-01-17 02:40:17,310 (beam_search:479) INFO: total log probability: -1.63 +2024-01-17 02:40:17,310 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:40:17,310 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:17,310 (beam_search:483) INFO: best hypo: ooremokininaruna + +2024-01-17 02:40:17,311 (asr_inference:494) INFO: speech length: 46656 +2024-01-17 02:40:17,319 (beam_search:428) INFO: decoder input length: 70 +2024-01-17 02:40:17,319 (beam_search:429) INFO: max output length: 70 +2024-01-17 02:40:17,319 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:17,391 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:17,391 (beam_search:476) INFO: -4.01 * 1.0 = -4.01 for ctc +2024-01-17 02:40:17,391 (beam_search:479) INFO: total log probability: -4.01 +2024-01-17 02:40:17,391 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 02:40:17,391 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:17,391 (beam_search:483) INFO: best hypo: daregatssUkaclterunokaawakarunai + +2024-01-17 02:40:17,393 (asr_inference:494) INFO: speech length: 65664 +2024-01-17 02:40:17,402 (beam_search:428) INFO: decoder input length: 100 +2024-01-17 02:40:17,402 (beam_search:429) INFO: max output length: 100 +2024-01-17 02:40:17,402 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:17,519 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:17,519 (beam_search:476) INFO: -5.97 * 1.0 = -5.97 for ctc +2024-01-17 02:40:17,519 (beam_search:479) INFO: total log probability: -5.97 +2024-01-17 02:40:17,519 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 02:40:17,519 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:17,519 (beam_search:483) INFO: best hypo: porazanobajoNgapauagarutoosUkoshiureshi + +2024-01-17 02:40:17,520 (asr_inference:494) INFO: speech length: 46080 +2024-01-17 02:40:17,529 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 02:40:17,529 (beam_search:429) INFO: max output length: 69 +2024-01-17 02:40:17,529 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:17,595 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:17,595 (beam_search:476) INFO: -2.28 * 1.0 = -2.28 for ctc +2024-01-17 02:40:17,595 (beam_search:479) INFO: total log probability: -2.28 +2024-01-17 02:40:17,595 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:40:17,595 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:17,595 (beam_search:483) INFO: best hypo: mataatarashiaidorugadetekita + +2024-01-17 02:40:17,596 (asr_inference:494) INFO: speech length: 29952 +2024-01-17 02:40:17,603 (beam_search:428) INFO: decoder input length: 44 +2024-01-17 02:40:17,603 (beam_search:429) INFO: max output length: 44 +2024-01-17 02:40:17,603 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:17,631 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:17,631 (beam_search:476) INFO: -0.60 * 1.0 = -0.60 for ctc +2024-01-17 02:40:17,631 (beam_search:479) INFO: total log probability: -0.60 +2024-01-17 02:40:17,631 (beam_search:480) INFO: normalized log probability: -0.04 +2024-01-17 02:40:17,631 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:17,631 (beam_search:483) INFO: best hypo: majideyacltonoka + +2024-01-17 02:40:17,632 (asr_inference:494) INFO: speech length: 85248 +2024-01-17 02:40:17,643 (beam_search:428) INFO: decoder input length: 131 +2024-01-17 02:40:17,643 (beam_search:429) INFO: max output length: 131 +2024-01-17 02:40:17,643 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:17,783 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:17,783 (beam_search:476) INFO: -5.35 * 1.0 = -5.35 for ctc +2024-01-17 02:40:17,783 (beam_search:479) INFO: total log probability: -5.35 +2024-01-17 02:40:17,783 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 02:40:17,783 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:17,783 (beam_search:483) INFO: best hypo: choodosumotokinikyoojugahaiItekida + +2024-01-17 02:40:17,784 (asr_inference:494) INFO: speech length: 58176 +2024-01-17 02:40:17,793 (beam_search:428) INFO: decoder input length: 88 +2024-01-17 02:40:17,793 (beam_search:429) INFO: max output length: 88 +2024-01-17 02:40:17,793 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:17,840 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:17,840 (beam_search:476) INFO: -1.43 * 1.0 = -1.43 for ctc +2024-01-17 02:40:17,840 (beam_search:479) INFO: total log probability: -1.43 +2024-01-17 02:40:17,840 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:40:17,840 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:17,840 (beam_search:483) INFO: best hypo: iclsonichiNshIteo + +2024-01-17 02:40:17,841 (asr_inference:494) INFO: speech length: 83520 +2024-01-17 02:40:17,852 (beam_search:428) INFO: decoder input length: 128 +2024-01-17 02:40:17,852 (beam_search:429) INFO: max output length: 128 +2024-01-17 02:40:17,852 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:17,945 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:17,946 (beam_search:476) INFO: -3.35 * 1.0 = -3.35 for ctc +2024-01-17 02:40:17,946 (beam_search:479) INFO: total log probability: -3.35 +2024-01-17 02:40:17,946 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 02:40:17,946 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:17,946 (beam_search:483) INFO: best hypo: soorekashatenotooresu + +2024-01-17 02:40:17,947 (asr_inference:494) INFO: speech length: 76032 +2024-01-17 02:40:17,957 (beam_search:428) INFO: decoder input length: 116 +2024-01-17 02:40:17,957 (beam_search:429) INFO: max output length: 116 +2024-01-17 02:40:17,957 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:18,056 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:18,057 (beam_search:476) INFO: -4.26 * 1.0 = -4.26 for ctc +2024-01-17 02:40:18,057 (beam_search:479) INFO: total log probability: -4.26 +2024-01-17 02:40:18,057 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 02:40:18,057 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:18,057 (beam_search:483) INFO: best hypo: fUtariwarejiikiseesaNshIta + +2024-01-17 02:40:18,058 (asr_inference:494) INFO: speech length: 84672 +2024-01-17 02:40:18,068 (beam_search:428) INFO: decoder input length: 130 +2024-01-17 02:40:18,068 (beam_search:429) INFO: max output length: 130 +2024-01-17 02:40:18,068 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:18,229 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:18,229 (beam_search:476) INFO: -3.57 * 1.0 = -3.57 for ctc +2024-01-17 02:40:18,229 (beam_search:479) INFO: total log probability: -3.57 +2024-01-17 02:40:18,229 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:40:18,229 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:18,230 (beam_search:483) INFO: best hypo: tomatokanaNganowakaisousugakakaclteriuo + +2024-01-17 02:40:18,231 (asr_inference:494) INFO: speech length: 73728 +2024-01-17 02:40:18,241 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 02:40:18,241 (beam_search:429) INFO: max output length: 113 +2024-01-17 02:40:18,241 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:18,351 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:18,351 (beam_search:476) INFO: -2.06 * 1.0 = -2.06 for ctc +2024-01-17 02:40:18,351 (beam_search:479) INFO: total log probability: -2.06 +2024-01-17 02:40:18,351 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:40:18,351 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:18,351 (beam_search:483) INFO: best hypo: kokokaratatenaNasunowakibisii + +2024-01-17 02:40:18,353 (asr_inference:494) INFO: speech length: 80064 +2024-01-17 02:40:18,363 (beam_search:428) INFO: decoder input length: 123 +2024-01-17 02:40:18,363 (beam_search:429) INFO: max output length: 123 +2024-01-17 02:40:18,363 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:18,528 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:18,528 (beam_search:476) INFO: -9.20 * 1.0 = -9.20 for ctc +2024-01-17 02:40:18,528 (beam_search:479) INFO: total log probability: -9.20 +2024-01-17 02:40:18,528 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 02:40:18,528 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:18,528 (beam_search:483) INFO: best hypo: nizukeosshIclkarishibocltepauajikanajimuyohisuru + +2024-01-17 02:40:18,529 (asr_inference:494) INFO: speech length: 67968 +2024-01-17 02:40:18,539 (beam_search:428) INFO: decoder input length: 104 +2024-01-17 02:40:18,539 (beam_search:429) INFO: max output length: 104 +2024-01-17 02:40:18,539 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:18,647 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:18,647 (beam_search:476) INFO: -2.21 * 1.0 = -2.21 for ctc +2024-01-17 02:40:18,647 (beam_search:479) INFO: total log probability: -2.21 +2024-01-17 02:40:18,647 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:40:18,647 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:18,647 (beam_search:483) INFO: best hypo: netogenihamacltarakanegagamacltaU + +2024-01-17 02:40:18,648 (asr_inference:494) INFO: speech length: 60480 +2024-01-17 02:40:18,657 (beam_search:428) INFO: decoder input length: 92 +2024-01-17 02:40:18,657 (beam_search:429) INFO: max output length: 92 +2024-01-17 02:40:18,657 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:18,723 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:18,723 (beam_search:476) INFO: -3.28 * 1.0 = -3.28 for ctc +2024-01-17 02:40:18,723 (beam_search:479) INFO: total log probability: -3.28 +2024-01-17 02:40:18,723 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 02:40:18,723 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:18,724 (beam_search:483) INFO: best hypo: itokaeriyooninaruNda + +2024-01-17 02:40:18,725 (asr_inference:494) INFO: speech length: 73728 +2024-01-17 02:40:18,734 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 02:40:18,734 (beam_search:429) INFO: max output length: 113 +2024-01-17 02:40:18,734 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:18,864 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:18,864 (beam_search:476) INFO: -5.89 * 1.0 = -5.89 for ctc +2024-01-17 02:40:18,864 (beam_search:479) INFO: total log probability: -5.89 +2024-01-17 02:40:18,864 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 02:40:18,864 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:18,865 (beam_search:483) INFO: best hypo: koseehahaiyutoiuyoriakugatsuyoekaNji + +2024-01-17 02:40:18,866 (asr_inference:494) INFO: speech length: 95616 +2024-01-17 02:40:18,877 (beam_search:428) INFO: decoder input length: 147 +2024-01-17 02:40:18,877 (beam_search:429) INFO: max output length: 147 +2024-01-17 02:40:18,877 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:19,058 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:19,058 (beam_search:476) INFO: -2.29 * 1.0 = -2.29 for ctc +2024-01-17 02:40:19,058 (beam_search:479) INFO: total log probability: -2.29 +2024-01-17 02:40:19,058 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:40:19,058 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:19,058 (beam_search:483) INFO: best hypo: fijikarunosaopaumazamazatomishetsUkerareta + +2024-01-17 02:40:19,060 (asr_inference:494) INFO: speech length: 89856 +2024-01-17 02:40:19,070 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 02:40:19,070 (beam_search:429) INFO: max output length: 138 +2024-01-17 02:40:19,070 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:19,256 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:19,256 (beam_search:476) INFO: -3.89 * 1.0 = -3.89 for ctc +2024-01-17 02:40:19,256 (beam_search:479) INFO: total log probability: -3.89 +2024-01-17 02:40:19,256 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:40:19,256 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:19,256 (beam_search:483) INFO: best hypo: kosUpayokerebasokosokonomoNdaewagaamaNseru + +2024-01-17 02:40:19,258 (asr_inference:494) INFO: speech length: 67968 +2024-01-17 02:40:19,267 (beam_search:428) INFO: decoder input length: 104 +2024-01-17 02:40:19,267 (beam_search:429) INFO: max output length: 104 +2024-01-17 02:40:19,267 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:19,378 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:19,378 (beam_search:476) INFO: -2.86 * 1.0 = -2.86 for ctc +2024-01-17 02:40:19,378 (beam_search:479) INFO: total log probability: -2.86 +2024-01-17 02:40:19,378 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:40:19,378 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:19,378 (beam_search:483) INFO: best hypo: miNnayacltemasUkarataijoobudesUyo + +2024-01-17 02:40:19,379 (asr_inference:494) INFO: speech length: 79488 +2024-01-17 02:40:19,389 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 02:40:19,389 (beam_search:429) INFO: max output length: 122 +2024-01-17 02:40:19,389 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:19,519 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:19,519 (beam_search:476) INFO: -2.44 * 1.0 = -2.44 for ctc +2024-01-17 02:40:19,519 (beam_search:479) INFO: total log probability: -2.44 +2024-01-17 02:40:19,519 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:40:19,520 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:19,520 (beam_search:483) INFO: best hypo: konotoshokaNpauhaicltashuNkaNkiniiclta + +2024-01-17 02:40:19,521 (asr_inference:494) INFO: speech length: 69696 +2024-01-17 02:40:19,531 (beam_search:428) INFO: decoder input length: 106 +2024-01-17 02:40:19,531 (beam_search:429) INFO: max output length: 106 +2024-01-17 02:40:19,531 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:19,610 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:19,610 (beam_search:476) INFO: -1.57 * 1.0 = -1.57 for ctc +2024-01-17 02:40:19,610 (beam_search:479) INFO: total log probability: -1.57 +2024-01-17 02:40:19,610 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:40:19,610 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:19,610 (beam_search:483) INFO: best hypo: konodeNchisuukirechaclta + +2024-01-17 02:40:19,611 (asr_inference:494) INFO: speech length: 66816 +2024-01-17 02:40:19,620 (beam_search:428) INFO: decoder input length: 102 +2024-01-17 02:40:19,620 (beam_search:429) INFO: max output length: 102 +2024-01-17 02:40:19,621 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:19,735 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:19,736 (beam_search:476) INFO: -2.98 * 1.0 = -2.98 for ctc +2024-01-17 02:40:19,736 (beam_search:479) INFO: total log probability: -2.98 +2024-01-17 02:40:19,736 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:40:19,736 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:19,736 (beam_search:483) INFO: best hypo: amayodorisurutokoroganakUtekomaclta + +2024-01-17 02:40:19,737 (asr_inference:494) INFO: speech length: 66240 +2024-01-17 02:40:19,746 (beam_search:428) INFO: decoder input length: 101 +2024-01-17 02:40:19,747 (beam_search:429) INFO: max output length: 101 +2024-01-17 02:40:19,747 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:19,843 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:19,843 (beam_search:476) INFO: -2.81 * 1.0 = -2.81 for ctc +2024-01-17 02:40:19,843 (beam_search:479) INFO: total log probability: -2.81 +2024-01-17 02:40:19,843 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:40:19,843 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:19,843 (beam_search:483) INFO: best hypo: yasUkusuruyorishItsuwagetahoshi + +2024-01-17 02:40:19,844 (asr_inference:494) INFO: speech length: 60480 +2024-01-17 02:40:19,853 (beam_search:428) INFO: decoder input length: 92 +2024-01-17 02:40:19,853 (beam_search:429) INFO: max output length: 92 +2024-01-17 02:40:19,853 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:19,954 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:19,954 (beam_search:476) INFO: -2.18 * 1.0 = -2.18 for ctc +2024-01-17 02:40:19,954 (beam_search:479) INFO: total log probability: -2.18 +2024-01-17 02:40:19,954 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:40:19,954 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:19,954 (beam_search:483) INFO: best hypo: masegokoNnakotoinarotowamonakaclta + +2024-01-17 02:40:19,955 (asr_inference:494) INFO: speech length: 54720 +2024-01-17 02:40:19,964 (beam_search:428) INFO: decoder input length: 83 +2024-01-17 02:40:19,964 (beam_search:429) INFO: max output length: 83 +2024-01-17 02:40:19,964 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:20,048 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:20,048 (beam_search:476) INFO: -1.34 * 1.0 = -1.34 for ctc +2024-01-17 02:40:20,048 (beam_search:479) INFO: total log probability: -1.34 +2024-01-17 02:40:20,048 (beam_search:480) INFO: normalized log probability: -0.04 +2024-01-17 02:40:20,048 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:20,049 (beam_search:483) INFO: best hypo: saigoniwarayotorinikurusUtairu + +2024-01-17 02:40:20,050 (asr_inference:494) INFO: speech length: 57600 +2024-01-17 02:40:20,059 (beam_search:428) INFO: decoder input length: 87 +2024-01-17 02:40:20,059 (beam_search:429) INFO: max output length: 87 +2024-01-17 02:40:20,059 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:20,120 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:20,120 (beam_search:476) INFO: -4.09 * 1.0 = -4.09 for ctc +2024-01-17 02:40:20,120 (beam_search:479) INFO: total log probability: -4.09 +2024-01-17 02:40:20,120 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 02:40:20,120 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:20,120 (beam_search:483) INFO: best hypo: koraeananaimigaruda + +2024-01-17 02:40:20,121 (asr_inference:494) INFO: speech length: 34944 +2024-01-17 02:40:20,129 (beam_search:428) INFO: decoder input length: 52 +2024-01-17 02:40:20,129 (beam_search:429) INFO: max output length: 52 +2024-01-17 02:40:20,129 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:20,137 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:20,137 (beam_search:476) INFO: -1.27 * 1.0 = -1.27 for ctc +2024-01-17 02:40:20,137 (beam_search:479) INFO: total log probability: -1.27 +2024-01-17 02:40:20,138 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 02:40:20,138 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:20,138 (beam_search:483) INFO: best hypo: iie + +2024-01-17 02:40:20,139 (asr_inference:494) INFO: speech length: 33024 +2024-01-17 02:40:20,146 (beam_search:428) INFO: decoder input length: 49 +2024-01-17 02:40:20,146 (beam_search:429) INFO: max output length: 49 +2024-01-17 02:40:20,146 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:20,152 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:20,153 (beam_search:476) INFO: -0.70 * 1.0 = -0.70 for ctc +2024-01-17 02:40:20,153 (beam_search:479) INFO: total log probability: -0.70 +2024-01-17 02:40:20,153 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 02:40:20,153 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:20,153 (beam_search:483) INFO: best hypo: shi + +2024-01-17 02:40:20,154 (asr_inference:494) INFO: speech length: 23424 +2024-01-17 02:40:20,160 (beam_search:428) INFO: decoder input length: 34 +2024-01-17 02:40:20,160 (beam_search:429) INFO: max output length: 34 +2024-01-17 02:40:20,161 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:20,165 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:20,166 (beam_search:476) INFO: -0.80 * 1.0 = -0.80 for ctc +2024-01-17 02:40:20,166 (beam_search:479) INFO: total log probability: -0.80 +2024-01-17 02:40:20,166 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 02:40:20,166 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:20,166 (beam_search:483) INFO: best hypo: ni + +2024-01-17 02:40:20,166 (asr_inference:494) INFO: speech length: 28416 +2024-01-17 02:40:20,174 (beam_search:428) INFO: decoder input length: 42 +2024-01-17 02:40:20,174 (beam_search:429) INFO: max output length: 42 +2024-01-17 02:40:20,174 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:20,183 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:20,183 (beam_search:476) INFO: -0.77 * 1.0 = -0.77 for ctc +2024-01-17 02:40:20,183 (beam_search:479) INFO: total log probability: -0.77 +2024-01-17 02:40:20,183 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 02:40:20,183 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:20,183 (beam_search:483) INFO: best hypo: hachi + +2024-01-17 02:40:20,184 (asr_inference:494) INFO: speech length: 31104 +2024-01-17 02:40:20,191 (beam_search:428) INFO: decoder input length: 46 +2024-01-17 02:40:20,191 (beam_search:429) INFO: max output length: 46 +2024-01-17 02:40:20,191 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:20,199 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:20,199 (beam_search:476) INFO: -0.15 * 1.0 = -0.15 for ctc +2024-01-17 02:40:20,199 (beam_search:479) INFO: total log probability: -0.15 +2024-01-17 02:40:20,199 (beam_search:480) INFO: normalized log probability: -0.03 +2024-01-17 02:40:20,199 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:20,199 (beam_search:483) INFO: best hypo: hai + +# Accounting: time=9 threads=1 +# Ended (code 0) at Wed Jan 17 02:40:20 CST 2024, elapsed time 9 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.3.log b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.3.log new file mode 100644 index 0000000000000000000000000000000000000000..20bde93027c7ee1d36aa694b1c510758ab499bfa --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.3.log @@ -0,0 +1,360 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3 --config conf/decode_asr.yaml +# Started at Wed Jan 17 02:40:20 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3 --config conf/decode_asr.yaml +2024-01-17 02:40:22,018 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml +2024-01-17 02:40:22,036 (asr:523) INFO: Vocabulary size: 41 +2024-01-17 02:40:22,098 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 02:40:22,098 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 02:40:22,208 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 02:40:23,503 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 02:40:24,741 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 02:40:24,741 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 02:40:24,741 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 02:40:24,774 (asr_inference:446) INFO: Text tokenizer: WordTokenizer(delimiter="None") +2024-01-17 02:40:24,848 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 02:40:24,959 (asr_inference:494) INFO: speech length: 62208 +2024-01-17 02:40:26,163 (beam_search:428) INFO: decoder input length: 95 +2024-01-17 02:40:26,163 (beam_search:429) INFO: max output length: 95 +2024-01-17 02:40:26,163 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:26,253 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:26,253 (beam_search:476) INFO: -2.52 * 1.0 = -2.52 for ctc +2024-01-17 02:40:26,253 (beam_search:479) INFO: total log probability: -2.52 +2024-01-17 02:40:26,253 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:40:26,253 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:26,254 (beam_search:483) INFO: best hypo: teebrunooienikabiNgaarimasU + +2024-01-17 02:40:26,278 (asr_inference:494) INFO: speech length: 61056 +2024-01-17 02:40:26,288 (beam_search:428) INFO: decoder input length: 93 +2024-01-17 02:40:26,288 (beam_search:429) INFO: max output length: 93 +2024-01-17 02:40:26,288 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:26,375 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:26,375 (beam_search:476) INFO: -2.41 * 1.0 = -2.41 for ctc +2024-01-17 02:40:26,375 (beam_search:479) INFO: total log probability: -2.41 +2024-01-17 02:40:26,375 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:40:26,375 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:26,375 (beam_search:483) INFO: best hypo: watashiwaamaiyasasaNposhimasU + +2024-01-17 02:40:26,376 (asr_inference:494) INFO: speech length: 58176 +2024-01-17 02:40:26,385 (beam_search:428) INFO: decoder input length: 88 +2024-01-17 02:40:26,385 (beam_search:429) INFO: max output length: 88 +2024-01-17 02:40:26,385 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:26,473 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:26,473 (beam_search:476) INFO: -1.83 * 1.0 = -1.83 for ctc +2024-01-17 02:40:26,473 (beam_search:479) INFO: total log probability: -1.83 +2024-01-17 02:40:26,473 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:40:26,473 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:26,473 (beam_search:483) INFO: best hypo: atarashiikutsoohaicltedekakemasU + +2024-01-17 02:40:26,474 (asr_inference:494) INFO: speech length: 102528 +2024-01-17 02:40:26,486 (beam_search:428) INFO: decoder input length: 158 +2024-01-17 02:40:26,486 (beam_search:429) INFO: max output length: 158 +2024-01-17 02:40:26,486 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:26,781 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:26,781 (beam_search:476) INFO: -7.12 * 1.0 = -7.12 for ctc +2024-01-17 02:40:26,781 (beam_search:479) INFO: total log probability: -7.12 +2024-01-17 02:40:26,781 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:40:26,781 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:26,781 (beam_search:483) INFO: best hypo: kotoshinonatseyasumiwapaubuminimoikimashItashiiyamanimoonooribashIta + +2024-01-17 02:40:26,783 (asr_inference:494) INFO: speech length: 91008 +2024-01-17 02:40:26,794 (beam_search:428) INFO: decoder input length: 140 +2024-01-17 02:40:26,794 (beam_search:429) INFO: max output length: 140 +2024-01-17 02:40:26,794 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:27,025 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:27,025 (beam_search:476) INFO: -2.98 * 1.0 = -2.98 for ctc +2024-01-17 02:40:27,025 (beam_search:479) INFO: total log probability: -2.98 +2024-01-17 02:40:27,025 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:40:27,025 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:27,025 (beam_search:483) INFO: best hypo: watashiwairoironobeNgoojibuNnomunedekoshiraetemimashIta + +2024-01-17 02:40:27,027 (asr_inference:494) INFO: speech length: 60480 +2024-01-17 02:40:27,035 (beam_search:428) INFO: decoder input length: 92 +2024-01-17 02:40:27,035 (beam_search:429) INFO: max output length: 92 +2024-01-17 02:40:27,035 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:27,122 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:27,122 (beam_search:476) INFO: -4.88 * 1.0 = -4.88 for ctc +2024-01-17 02:40:27,122 (beam_search:479) INFO: total log probability: -4.88 +2024-01-17 02:40:27,122 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 02:40:27,122 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:27,122 (beam_search:483) INFO: best hypo: naNdekumoshooobaihetanaNdaro + +2024-01-17 02:40:27,123 (asr_inference:494) INFO: speech length: 97920 +2024-01-17 02:40:27,135 (beam_search:428) INFO: decoder input length: 150 +2024-01-17 02:40:27,135 (beam_search:429) INFO: max output length: 150 +2024-01-17 02:40:27,135 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:27,316 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:27,316 (beam_search:476) INFO: -7.14 * 1.0 = -7.14 for ctc +2024-01-17 02:40:27,316 (beam_search:479) INFO: total log probability: -7.14 +2024-01-17 02:40:27,316 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 02:40:27,316 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:27,316 (beam_search:483) INFO: best hypo: tareNtokarakyokuananikaetekeehIshakige + +2024-01-17 02:40:27,318 (asr_inference:494) INFO: speech length: 55296 +2024-01-17 02:40:27,326 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 02:40:27,326 (beam_search:429) INFO: max output length: 84 +2024-01-17 02:40:27,327 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:27,398 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:27,398 (beam_search:476) INFO: -3.01 * 1.0 = -3.01 for ctc +2024-01-17 02:40:27,398 (beam_search:479) INFO: total log probability: -3.01 +2024-01-17 02:40:27,398 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 02:40:27,398 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:27,398 (beam_search:483) INFO: best hypo: dogezaserebaicltemoNshanai + +2024-01-17 02:40:27,400 (asr_inference:494) INFO: speech length: 101952 +2024-01-17 02:40:27,411 (beam_search:428) INFO: decoder input length: 157 +2024-01-17 02:40:27,411 (beam_search:429) INFO: max output length: 157 +2024-01-17 02:40:27,411 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:27,656 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:27,656 (beam_search:476) INFO: -4.00 * 1.0 = -4.00 for ctc +2024-01-17 02:40:27,656 (beam_search:479) INFO: total log probability: -4.00 +2024-01-17 02:40:27,656 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:40:27,656 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:27,656 (beam_search:483) INFO: best hypo: deetanowaidakanochiwajibuNtooiclteenokyooriyotanoclta + +2024-01-17 02:40:27,657 (asr_inference:494) INFO: speech length: 64512 +2024-01-17 02:40:27,667 (beam_search:428) INFO: decoder input length: 98 +2024-01-17 02:40:27,667 (beam_search:429) INFO: max output length: 98 +2024-01-17 02:40:27,667 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:27,775 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:27,775 (beam_search:476) INFO: -3.92 * 1.0 = -3.92 for ctc +2024-01-17 02:40:27,775 (beam_search:479) INFO: total log probability: -3.92 +2024-01-17 02:40:27,775 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:40:27,775 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:27,775 (beam_search:483) INFO: best hypo: konogeeniNnaNkawahIsashiburinimita + +2024-01-17 02:40:27,776 (asr_inference:494) INFO: speech length: 66816 +2024-01-17 02:40:27,786 (beam_search:428) INFO: decoder input length: 102 +2024-01-17 02:40:27,786 (beam_search:429) INFO: max output length: 102 +2024-01-17 02:40:27,786 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:27,866 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:27,866 (beam_search:476) INFO: -3.32 * 1.0 = -3.32 for ctc +2024-01-17 02:40:27,866 (beam_search:479) INFO: total log probability: -3.32 +2024-01-17 02:40:27,866 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 02:40:27,866 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:27,866 (beam_search:483) INFO: best hypo: ookikusaidochieNjiosoru + +2024-01-17 02:40:27,867 (asr_inference:494) INFO: speech length: 65088 +2024-01-17 02:40:27,877 (beam_search:428) INFO: decoder input length: 99 +2024-01-17 02:40:27,877 (beam_search:429) INFO: max output length: 99 +2024-01-17 02:40:27,877 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:27,955 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:27,955 (beam_search:476) INFO: -2.36 * 1.0 = -2.36 for ctc +2024-01-17 02:40:27,955 (beam_search:479) INFO: total log probability: -2.36 +2024-01-17 02:40:27,955 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:40:27,955 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:27,955 (beam_search:483) INFO: best hypo: karewaatamakakimushiclta + +2024-01-17 02:40:27,956 (asr_inference:494) INFO: speech length: 86976 +2024-01-17 02:40:27,967 (beam_search:428) INFO: decoder input length: 133 +2024-01-17 02:40:27,967 (beam_search:429) INFO: max output length: 133 +2024-01-17 02:40:27,967 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:28,049 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:28,049 (beam_search:476) INFO: -4.08 * 1.0 = -4.08 for ctc +2024-01-17 02:40:28,049 (beam_search:479) INFO: total log probability: -4.08 +2024-01-17 02:40:28,049 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 02:40:28,049 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:28,050 (beam_search:483) INFO: best hypo: omacheshItewarimasU + +2024-01-17 02:40:28,051 (asr_inference:494) INFO: speech length: 72000 +2024-01-17 02:40:28,060 (beam_search:428) INFO: decoder input length: 110 +2024-01-17 02:40:28,060 (beam_search:429) INFO: max output length: 110 +2024-01-17 02:40:28,060 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:28,153 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:28,153 (beam_search:476) INFO: -4.33 * 1.0 = -4.33 for ctc +2024-01-17 02:40:28,153 (beam_search:479) INFO: total log probability: -4.33 +2024-01-17 02:40:28,153 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 02:40:28,153 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:28,153 (beam_search:483) INFO: best hypo: onokyokUseNkaijowakiiteru + +2024-01-17 02:40:28,154 (asr_inference:494) INFO: speech length: 99648 +2024-01-17 02:40:28,166 (beam_search:428) INFO: decoder input length: 153 +2024-01-17 02:40:28,166 (beam_search:429) INFO: max output length: 153 +2024-01-17 02:40:28,166 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:28,381 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:28,382 (beam_search:476) INFO: -5.97 * 1.0 = -5.97 for ctc +2024-01-17 02:40:28,382 (beam_search:479) INFO: total log probability: -5.97 +2024-01-17 02:40:28,382 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 02:40:28,382 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:28,382 (beam_search:483) INFO: best hypo: reejzookooaketatotaNnanigahichuyokawasureclta + +2024-01-17 02:40:28,383 (asr_inference:494) INFO: speech length: 33024 +2024-01-17 02:40:28,391 (beam_search:428) INFO: decoder input length: 49 +2024-01-17 02:40:28,391 (beam_search:429) INFO: max output length: 49 +2024-01-17 02:40:28,391 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:28,400 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:28,400 (beam_search:476) INFO: -0.35 * 1.0 = -0.35 for ctc +2024-01-17 02:40:28,400 (beam_search:479) INFO: total log probability: -0.35 +2024-01-17 02:40:28,400 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:40:28,400 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:28,400 (beam_search:483) INFO: best hypo: ichi + +2024-01-17 02:40:28,401 (asr_inference:494) INFO: speech length: 26112 +2024-01-17 02:40:28,409 (beam_search:428) INFO: decoder input length: 38 +2024-01-17 02:40:28,409 (beam_search:429) INFO: max output length: 38 +2024-01-17 02:40:28,409 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:28,417 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:28,417 (beam_search:476) INFO: -0.16 * 1.0 = -0.16 for ctc +2024-01-17 02:40:28,417 (beam_search:479) INFO: total log probability: -0.16 +2024-01-17 02:40:28,417 (beam_search:480) INFO: normalized log probability: -0.03 +2024-01-17 02:40:28,417 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:28,417 (beam_search:483) INFO: best hypo: hachi + +2024-01-17 02:40:28,418 (asr_inference:494) INFO: speech length: 25728 +2024-01-17 02:40:28,425 (beam_search:428) INFO: decoder input length: 38 +2024-01-17 02:40:28,425 (beam_search:429) INFO: max output length: 38 +2024-01-17 02:40:28,426 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:28,432 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:28,433 (beam_search:476) INFO: -0.70 * 1.0 = -0.70 for ctc +2024-01-17 02:40:28,433 (beam_search:479) INFO: total log probability: -0.70 +2024-01-17 02:40:28,433 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 02:40:28,433 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:28,433 (beam_search:483) INFO: best hypo: iie + +2024-01-17 02:40:28,434 (asr_inference:494) INFO: speech length: 22272 +2024-01-17 02:40:28,441 (beam_search:428) INFO: decoder input length: 32 +2024-01-17 02:40:28,441 (beam_search:429) INFO: max output length: 32 +2024-01-17 02:40:28,441 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:28,447 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:28,447 (beam_search:476) INFO: -0.95 * 1.0 = -0.95 for ctc +2024-01-17 02:40:28,447 (beam_search:479) INFO: total log probability: -0.95 +2024-01-17 02:40:28,448 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 02:40:28,448 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:28,448 (beam_search:483) INFO: best hypo: dei + +2024-01-17 02:40:28,448 (asr_inference:494) INFO: speech length: 27648 +2024-01-17 02:40:28,456 (beam_search:428) INFO: decoder input length: 41 +2024-01-17 02:40:28,456 (beam_search:429) INFO: max output length: 41 +2024-01-17 02:40:28,456 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:28,465 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:28,465 (beam_search:476) INFO: -0.24 * 1.0 = -0.24 for ctc +2024-01-17 02:40:28,465 (beam_search:479) INFO: total log probability: -0.24 +2024-01-17 02:40:28,465 (beam_search:480) INFO: normalized log probability: -0.04 +2024-01-17 02:40:28,465 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:28,465 (beam_search:483) INFO: best hypo: shichi + +2024-01-17 02:40:28,467 (asr_inference:494) INFO: speech length: 64512 +2024-01-17 02:40:28,476 (beam_search:428) INFO: decoder input length: 98 +2024-01-17 02:40:28,476 (beam_search:429) INFO: max output length: 98 +2024-01-17 02:40:28,476 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:28,576 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:28,576 (beam_search:476) INFO: -1.98 * 1.0 = -1.98 for ctc +2024-01-17 02:40:28,576 (beam_search:479) INFO: total log probability: -1.98 +2024-01-17 02:40:28,576 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:40:28,576 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:28,576 (beam_search:483) INFO: best hypo: yooboodasunoNikauhItowasUkunai + +2024-01-17 02:40:28,577 (asr_inference:494) INFO: speech length: 69120 +2024-01-17 02:40:28,587 (beam_search:428) INFO: decoder input length: 105 +2024-01-17 02:40:28,587 (beam_search:429) INFO: max output length: 105 +2024-01-17 02:40:28,587 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:28,715 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:28,715 (beam_search:476) INFO: -5.19 * 1.0 = -5.19 for ctc +2024-01-17 02:40:28,715 (beam_search:479) INFO: total log probability: -5.19 +2024-01-17 02:40:28,715 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 02:40:28,715 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:28,715 (beam_search:483) INFO: best hypo: rookarutokuyiunoiIkyoemakasenokomasharu + +2024-01-17 02:40:28,717 (asr_inference:494) INFO: speech length: 69120 +2024-01-17 02:40:28,726 (beam_search:428) INFO: decoder input length: 105 +2024-01-17 02:40:28,726 (beam_search:429) INFO: max output length: 105 +2024-01-17 02:40:28,726 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:28,846 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:28,846 (beam_search:476) INFO: -2.28 * 1.0 = -2.28 for ctc +2024-01-17 02:40:28,846 (beam_search:479) INFO: total log probability: -2.28 +2024-01-17 02:40:28,846 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:40:28,846 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:28,846 (beam_search:483) INFO: best hypo: konodaikuwaaNkogaookUteyokUkaimasU + +2024-01-17 02:40:28,847 (asr_inference:494) INFO: speech length: 60480 +2024-01-17 02:40:28,856 (beam_search:428) INFO: decoder input length: 92 +2024-01-17 02:40:28,856 (beam_search:429) INFO: max output length: 92 +2024-01-17 02:40:28,856 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:28,950 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:28,950 (beam_search:476) INFO: -2.47 * 1.0 = -2.47 for ctc +2024-01-17 02:40:28,951 (beam_search:479) INFO: total log probability: -2.47 +2024-01-17 02:40:28,951 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:40:28,951 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:28,951 (beam_search:483) INFO: best hypo: jishoohkinagarashoosetsooyomimasU + +2024-01-17 02:40:28,952 (asr_inference:494) INFO: speech length: 89856 +2024-01-17 02:40:28,963 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 02:40:28,963 (beam_search:429) INFO: max output length: 138 +2024-01-17 02:40:28,963 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:29,132 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:29,132 (beam_search:476) INFO: -3.55 * 1.0 = -3.55 for ctc +2024-01-17 02:40:29,132 (beam_search:479) INFO: total log probability: -3.55 +2024-01-17 02:40:29,132 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:40:29,132 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:29,132 (beam_search:483) INFO: best hypo: koNnaokinagogurotsUkenaitikenaiNdesUka + +2024-01-17 02:40:29,134 (asr_inference:494) INFO: speech length: 52416 +2024-01-17 02:40:29,142 (beam_search:428) INFO: decoder input length: 79 +2024-01-17 02:40:29,142 (beam_search:429) INFO: max output length: 79 +2024-01-17 02:40:29,142 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:29,212 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:29,212 (beam_search:476) INFO: -2.54 * 1.0 = -2.54 for ctc +2024-01-17 02:40:29,212 (beam_search:479) INFO: total log probability: -2.54 +2024-01-17 02:40:29,212 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:40:29,212 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:29,212 (beam_search:483) INFO: best hypo: kareenoboorikowatomaranai + +2024-01-17 02:40:29,213 (asr_inference:494) INFO: speech length: 37440 +2024-01-17 02:40:29,221 (beam_search:428) INFO: decoder input length: 56 +2024-01-17 02:40:29,221 (beam_search:429) INFO: max output length: 56 +2024-01-17 02:40:29,221 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:29,251 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:29,251 (beam_search:476) INFO: -1.43 * 1.0 = -1.43 for ctc +2024-01-17 02:40:29,251 (beam_search:479) INFO: total log probability: -1.43 +2024-01-17 02:40:29,251 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:40:29,251 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:29,251 (beam_search:483) INFO: best hypo: ikiteikaNmane + +2024-01-17 02:40:29,252 (asr_inference:494) INFO: speech length: 57600 +2024-01-17 02:40:29,261 (beam_search:428) INFO: decoder input length: 87 +2024-01-17 02:40:29,261 (beam_search:429) INFO: max output length: 87 +2024-01-17 02:40:29,261 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:29,356 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:29,356 (beam_search:476) INFO: -5.18 * 1.0 = -5.18 for ctc +2024-01-17 02:40:29,356 (beam_search:479) INFO: total log probability: -5.18 +2024-01-17 02:40:29,356 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 02:40:29,356 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:29,356 (beam_search:483) INFO: best hypo: taojitosakakttekinihatsumedaclpane + +2024-01-17 02:40:29,358 (asr_inference:494) INFO: speech length: 40896 +2024-01-17 02:40:29,366 (beam_search:428) INFO: decoder input length: 61 +2024-01-17 02:40:29,366 (beam_search:429) INFO: max output length: 61 +2024-01-17 02:40:29,366 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:29,421 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:29,421 (beam_search:476) INFO: -5.12 * 1.0 = -5.12 for ctc +2024-01-17 02:40:29,421 (beam_search:479) INFO: total log probability: -5.12 +2024-01-17 02:40:29,421 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 02:40:29,421 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:29,421 (beam_search:483) INFO: best hypo: saNtekiotashowachIkaafUteka + +2024-01-17 02:40:29,422 (asr_inference:494) INFO: speech length: 36288 +2024-01-17 02:40:29,430 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 02:40:29,430 (beam_search:429) INFO: max output length: 54 +2024-01-17 02:40:29,430 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:29,466 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:29,466 (beam_search:476) INFO: -1.30 * 1.0 = -1.30 for ctc +2024-01-17 02:40:29,466 (beam_search:479) INFO: total log probability: -1.30 +2024-01-17 02:40:29,466 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:40:29,466 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:29,466 (beam_search:483) INFO: best hypo: kawagahiagaclteida + +2024-01-17 02:40:29,467 (asr_inference:494) INFO: speech length: 46464 +2024-01-17 02:40:29,475 (beam_search:428) INFO: decoder input length: 70 +2024-01-17 02:40:29,476 (beam_search:429) INFO: max output length: 70 +2024-01-17 02:40:29,476 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:29,487 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:29,487 (beam_search:476) INFO: -1.10 * 1.0 = -1.10 for ctc +2024-01-17 02:40:29,487 (beam_search:479) INFO: total log probability: -1.10 +2024-01-17 02:40:29,487 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 02:40:29,487 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:29,487 (beam_search:483) INFO: best hypo: ichu + +# Accounting: time=10 threads=1 +# Ended (code 0) at Wed Jan 17 02:40:30 CST 2024, elapsed time 10 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.4.log b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.4.log new file mode 100644 index 0000000000000000000000000000000000000000..c29bb1518eb020becfc93949061034b82ab448e9 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/asr_inference.4.log @@ -0,0 +1,360 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4 --config conf/decode_asr.yaml +# Started at Wed Jan 17 02:40:30 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/org/dev_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4 --config conf/decode_asr.yaml +2024-01-17 02:40:31,283 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml +2024-01-17 02:40:31,303 (asr:523) INFO: Vocabulary size: 41 +2024-01-17 02:40:31,366 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 02:40:31,366 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 02:40:31,476 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 02:40:32,766 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 02:40:33,982 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 02:40:33,982 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 02:40:33,982 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 02:40:34,015 (asr_inference:446) INFO: Text tokenizer: WordTokenizer(delimiter="None") +2024-01-17 02:40:34,090 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 02:40:34,202 (asr_inference:494) INFO: speech length: 45696 +2024-01-17 02:40:35,409 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 02:40:35,409 (beam_search:429) INFO: max output length: 69 +2024-01-17 02:40:35,409 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:35,418 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:35,418 (beam_search:476) INFO: -0.38 * 1.0 = -0.38 for ctc +2024-01-17 02:40:35,418 (beam_search:479) INFO: total log probability: -0.38 +2024-01-17 02:40:35,418 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:40:35,418 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:35,418 (beam_search:483) INFO: best hypo: no + +2024-01-17 02:40:35,442 (asr_inference:494) INFO: speech length: 34560 +2024-01-17 02:40:35,451 (beam_search:428) INFO: decoder input length: 51 +2024-01-17 02:40:35,451 (beam_search:429) INFO: max output length: 51 +2024-01-17 02:40:35,451 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:35,462 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:35,462 (beam_search:476) INFO: -0.25 * 1.0 = -0.25 for ctc +2024-01-17 02:40:35,462 (beam_search:479) INFO: total log probability: -0.25 +2024-01-17 02:40:35,462 (beam_search:480) INFO: normalized log probability: -0.04 +2024-01-17 02:40:35,462 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:35,462 (beam_search:483) INFO: best hypo: shIchi + +2024-01-17 02:40:35,463 (asr_inference:494) INFO: speech length: 46464 +2024-01-17 02:40:35,473 (beam_search:428) INFO: decoder input length: 70 +2024-01-17 02:40:35,473 (beam_search:429) INFO: max output length: 70 +2024-01-17 02:40:35,473 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:35,481 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:35,481 (beam_search:476) INFO: -0.36 * 1.0 = -0.36 for ctc +2024-01-17 02:40:35,481 (beam_search:479) INFO: total log probability: -0.36 +2024-01-17 02:40:35,481 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:40:35,481 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:35,481 (beam_search:483) INFO: best hypo: ko + +2024-01-17 02:40:35,482 (asr_inference:494) INFO: speech length: 43392 +2024-01-17 02:40:35,491 (beam_search:428) INFO: decoder input length: 65 +2024-01-17 02:40:35,491 (beam_search:429) INFO: max output length: 65 +2024-01-17 02:40:35,491 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:35,502 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:35,502 (beam_search:476) INFO: -0.98 * 1.0 = -0.98 for ctc +2024-01-17 02:40:35,502 (beam_search:479) INFO: total log probability: -0.98 +2024-01-17 02:40:35,502 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 02:40:35,502 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:35,502 (beam_search:483) INFO: best hypo: iie + +2024-01-17 02:40:35,503 (asr_inference:494) INFO: speech length: 120960 +2024-01-17 02:40:35,517 (beam_search:428) INFO: decoder input length: 186 +2024-01-17 02:40:35,517 (beam_search:429) INFO: max output length: 186 +2024-01-17 02:40:35,517 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:35,679 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:35,679 (beam_search:476) INFO: -2.85 * 1.0 = -2.85 for ctc +2024-01-17 02:40:35,679 (beam_search:479) INFO: total log probability: -2.85 +2024-01-17 02:40:35,680 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:40:35,680 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:35,680 (beam_search:483) INFO: best hypo: namaiekarashetekUtoosugiru + +2024-01-17 02:40:35,681 (asr_inference:494) INFO: speech length: 119808 +2024-01-17 02:40:35,695 (beam_search:428) INFO: decoder input length: 185 +2024-01-17 02:40:35,695 (beam_search:429) INFO: max output length: 185 +2024-01-17 02:40:35,695 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:36,052 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:36,052 (beam_search:476) INFO: -4.30 * 1.0 = -4.30 for ctc +2024-01-17 02:40:36,052 (beam_search:479) INFO: total log probability: -4.30 +2024-01-17 02:40:36,052 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:40:36,052 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:36,052 (beam_search:483) INFO: best hypo: jikonosotoniarutoiunowataNnijikonoshIkaNsotssoniarutoiukotodenaku + +2024-01-17 02:40:36,053 (asr_inference:494) INFO: speech length: 55296 +2024-01-17 02:40:36,062 (beam_search:428) INFO: decoder input length: 84 +2024-01-17 02:40:36,062 (beam_search:429) INFO: max output length: 84 +2024-01-17 02:40:36,062 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:36,126 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:36,126 (beam_search:476) INFO: -2.10 * 1.0 = -2.10 for ctc +2024-01-17 02:40:36,126 (beam_search:479) INFO: total log probability: -2.10 +2024-01-17 02:40:36,126 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:40:36,126 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:36,126 (beam_search:483) INFO: best hypo: soruwashiranakUteidesU + +2024-01-17 02:40:36,127 (asr_inference:494) INFO: speech length: 89280 +2024-01-17 02:40:36,138 (beam_search:428) INFO: decoder input length: 137 +2024-01-17 02:40:36,138 (beam_search:429) INFO: max output length: 137 +2024-01-17 02:40:36,138 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:36,341 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:36,341 (beam_search:476) INFO: -4.10 * 1.0 = -4.10 for ctc +2024-01-17 02:40:36,341 (beam_search:479) INFO: total log probability: -4.10 +2024-01-17 02:40:36,341 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:40:36,341 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:36,342 (beam_search:483) INFO: best hypo: kimitobokunokyootsunoshigeewadarehItorimiataranai + +2024-01-17 02:40:36,343 (asr_inference:494) INFO: speech length: 75456 +2024-01-17 02:40:36,353 (beam_search:428) INFO: decoder input length: 115 +2024-01-17 02:40:36,353 (beam_search:429) INFO: max output length: 115 +2024-01-17 02:40:36,353 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:36,452 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:36,452 (beam_search:476) INFO: -2.39 * 1.0 = -2.39 for ctc +2024-01-17 02:40:36,452 (beam_search:479) INFO: total log probability: -2.39 +2024-01-17 02:40:36,452 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:40:36,452 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:36,452 (beam_search:483) INFO: best hypo: sugeeootoNnacltekiterunona + +2024-01-17 02:40:36,453 (asr_inference:494) INFO: speech length: 54144 +2024-01-17 02:40:36,462 (beam_search:428) INFO: decoder input length: 82 +2024-01-17 02:40:36,462 (beam_search:429) INFO: max output length: 82 +2024-01-17 02:40:36,462 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:36,532 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:36,532 (beam_search:476) INFO: -1.97 * 1.0 = -1.97 for ctc +2024-01-17 02:40:36,532 (beam_search:479) INFO: total log probability: -1.97 +2024-01-17 02:40:36,532 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:40:36,532 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:36,532 (beam_search:483) INFO: best hypo: konoheNdesUkoshiyasumashoo + +2024-01-17 02:40:36,534 (asr_inference:494) INFO: speech length: 84864 +2024-01-17 02:40:36,544 (beam_search:428) INFO: decoder input length: 130 +2024-01-17 02:40:36,544 (beam_search:429) INFO: max output length: 130 +2024-01-17 02:40:36,544 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:36,669 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:36,669 (beam_search:476) INFO: -2.79 * 1.0 = -2.79 for ctc +2024-01-17 02:40:36,669 (beam_search:479) INFO: total log probability: -2.79 +2024-01-17 02:40:36,669 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:40:36,669 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:36,669 (beam_search:483) INFO: best hypo: deNsharinorutokikicltookainasU + +2024-01-17 02:40:36,671 (asr_inference:494) INFO: speech length: 51456 +2024-01-17 02:40:36,679 (beam_search:428) INFO: decoder input length: 78 +2024-01-17 02:40:36,679 (beam_search:429) INFO: max output length: 78 +2024-01-17 02:40:36,679 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:36,754 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:36,754 (beam_search:476) INFO: -4.07 * 1.0 = -4.07 for ctc +2024-01-17 02:40:36,754 (beam_search:479) INFO: total log probability: -4.07 +2024-01-17 02:40:36,754 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 02:40:36,754 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:36,754 (beam_search:483) INFO: best hypo: tamagaikokojiuguraNburaiesU + +2024-01-17 02:40:36,756 (asr_inference:494) INFO: speech length: 91776 +2024-01-17 02:40:36,766 (beam_search:428) INFO: decoder input length: 141 +2024-01-17 02:40:36,766 (beam_search:429) INFO: max output length: 141 +2024-01-17 02:40:36,766 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:36,960 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:36,960 (beam_search:476) INFO: -7.30 * 1.0 = -7.30 for ctc +2024-01-17 02:40:36,960 (beam_search:479) INFO: total log probability: -7.30 +2024-01-17 02:40:36,960 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 02:40:36,960 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:36,960 (beam_search:483) INFO: best hypo: geesUpiiwamaclkiiotsujiteinesUtoshidrigacltatu + +2024-01-17 02:40:36,961 (asr_inference:494) INFO: speech length: 124416 +2024-01-17 02:40:36,974 (beam_search:428) INFO: decoder input length: 192 +2024-01-17 02:40:36,974 (beam_search:429) INFO: max output length: 192 +2024-01-17 02:40:36,974 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:37,422 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:37,422 (beam_search:476) INFO: -14.11 * 1.0 = -14.11 for ctc +2024-01-17 02:40:37,422 (beam_search:479) INFO: total log probability: -14.11 +2024-01-17 02:40:37,422 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 02:40:37,422 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:37,423 (beam_search:483) INFO: best hypo: noogooyamesaruoenaikhItokaarikaNneNkiyoomokonofUkIkeoonihIkizurareteirutoomoosU + +2024-01-17 02:40:37,424 (asr_inference:494) INFO: speech length: 101376 +2024-01-17 02:40:37,436 (beam_search:428) INFO: decoder input length: 156 +2024-01-17 02:40:37,436 (beam_search:429) INFO: max output length: 156 +2024-01-17 02:40:37,436 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:37,638 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:37,638 (beam_search:476) INFO: -3.44 * 1.0 = -3.44 for ctc +2024-01-17 02:40:37,638 (beam_search:479) INFO: total log probability: -3.44 +2024-01-17 02:40:37,638 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:40:37,638 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:37,638 (beam_search:483) INFO: best hypo: naNdekonorocltoshotaimenanoninaregareshiNda + +2024-01-17 02:40:37,640 (asr_inference:494) INFO: speech length: 69696 +2024-01-17 02:40:37,649 (beam_search:428) INFO: decoder input length: 106 +2024-01-17 02:40:37,649 (beam_search:429) INFO: max output length: 106 +2024-01-17 02:40:37,649 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:37,750 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:37,750 (beam_search:476) INFO: -2.85 * 1.0 = -2.85 for ctc +2024-01-17 02:40:37,750 (beam_search:479) INFO: total log probability: -2.85 +2024-01-17 02:40:37,750 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:40:37,750 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:37,750 (beam_search:483) INFO: best hypo: fUtsuudearukotamoriclpanakose + +2024-01-17 02:40:37,751 (asr_inference:494) INFO: speech length: 75456 +2024-01-17 02:40:37,761 (beam_search:428) INFO: decoder input length: 115 +2024-01-17 02:40:37,761 (beam_search:429) INFO: max output length: 115 +2024-01-17 02:40:37,761 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:37,888 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:37,888 (beam_search:476) INFO: -2.12 * 1.0 = -2.12 for ctc +2024-01-17 02:40:37,888 (beam_search:479) INFO: total log probability: -2.12 +2024-01-17 02:40:37,888 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:40:37,888 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:37,888 (beam_search:483) INFO: best hypo: tsuyobidetaNjikaNdegookainiitameru + +2024-01-17 02:40:37,889 (asr_inference:494) INFO: speech length: 102528 +2024-01-17 02:40:37,901 (beam_search:428) INFO: decoder input length: 158 +2024-01-17 02:40:37,901 (beam_search:429) INFO: max output length: 158 +2024-01-17 02:40:37,901 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:38,147 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:38,147 (beam_search:476) INFO: -4.07 * 1.0 = -4.07 for ctc +2024-01-17 02:40:38,147 (beam_search:479) INFO: total log probability: -4.07 +2024-01-17 02:40:38,147 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:40:38,147 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:38,147 (beam_search:483) INFO: best hypo: bakumatsunodekigokawaimanitsurujirokyookuNnoyamadesU + +2024-01-17 02:40:38,148 (asr_inference:494) INFO: speech length: 73728 +2024-01-17 02:40:38,158 (beam_search:428) INFO: decoder input length: 113 +2024-01-17 02:40:38,158 (beam_search:429) INFO: max output length: 113 +2024-01-17 02:40:38,158 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:38,280 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:38,280 (beam_search:476) INFO: -2.07 * 1.0 = -2.07 for ctc +2024-01-17 02:40:38,280 (beam_search:479) INFO: total log probability: -2.07 +2024-01-17 02:40:38,280 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:40:38,280 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:38,280 (beam_search:483) INFO: best hypo: mNkookaramachinowakarigamietekita + +2024-01-17 02:40:38,281 (asr_inference:494) INFO: speech length: 115200 +2024-01-17 02:40:38,293 (beam_search:428) INFO: decoder input length: 177 +2024-01-17 02:40:38,293 (beam_search:429) INFO: max output length: 177 +2024-01-17 02:40:38,293 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:38,635 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:38,635 (beam_search:476) INFO: -12.53 * 1.0 = -12.53 for ctc +2024-01-17 02:40:38,635 (beam_search:479) INFO: total log probability: -12.53 +2024-01-17 02:40:38,635 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 02:40:38,635 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:38,636 (beam_search:483) INFO: best hypo: meNniwuiubekIchihwakananakacltanarimiyubikIkotogaomoyukabanakacltu + +2024-01-17 02:40:38,637 (asr_inference:494) INFO: speech length: 49536 +2024-01-17 02:40:38,645 (beam_search:428) INFO: decoder input length: 75 +2024-01-17 02:40:38,645 (beam_search:429) INFO: max output length: 75 +2024-01-17 02:40:38,645 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:38,707 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:38,707 (beam_search:476) INFO: -2.75 * 1.0 = -2.75 for ctc +2024-01-17 02:40:38,707 (beam_search:479) INFO: total log probability: -2.75 +2024-01-17 02:40:38,707 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:40:38,707 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:38,707 (beam_search:483) INFO: best hypo: tamisinikaenakiacltemiru + +2024-01-17 02:40:38,708 (asr_inference:494) INFO: speech length: 74880 +2024-01-17 02:40:38,718 (beam_search:428) INFO: decoder input length: 114 +2024-01-17 02:40:38,718 (beam_search:429) INFO: max output length: 114 +2024-01-17 02:40:38,718 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:38,867 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:38,867 (beam_search:476) INFO: -6.63 * 1.0 = -6.63 for ctc +2024-01-17 02:40:38,867 (beam_search:479) INFO: total log probability: -6.63 +2024-01-17 02:40:38,867 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 02:40:38,867 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:38,868 (beam_search:483) INFO: best hypo: bokUshIkainaigimimainaikorewaooteNsegaika + +2024-01-17 02:40:38,869 (asr_inference:494) INFO: speech length: 102528 +2024-01-17 02:40:38,880 (beam_search:428) INFO: decoder input length: 158 +2024-01-17 02:40:38,880 (beam_search:429) INFO: max output length: 158 +2024-01-17 02:40:38,880 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:39,115 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:39,115 (beam_search:476) INFO: -10.59 * 1.0 = -10.59 for ctc +2024-01-17 02:40:39,115 (beam_search:479) INFO: total log probability: -10.59 +2024-01-17 02:40:39,115 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 02:40:39,115 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:39,116 (beam_search:483) INFO: best hypo: chikeijokanijugootomanunomichiwamukasokaclteinaka + +2024-01-17 02:40:39,117 (asr_inference:494) INFO: speech length: 71424 +2024-01-17 02:40:39,127 (beam_search:428) INFO: decoder input length: 109 +2024-01-17 02:40:39,127 (beam_search:429) INFO: max output length: 109 +2024-01-17 02:40:39,127 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:39,266 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:39,266 (beam_search:476) INFO: -9.60 * 1.0 = -9.60 for ctc +2024-01-17 02:40:39,266 (beam_search:479) INFO: total log probability: -9.60 +2024-01-17 02:40:39,266 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 02:40:39,266 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:39,266 (beam_search:483) INFO: best hypo: kagujoteNwapaukoNpoNteinakaichikuusunaaclta + +2024-01-17 02:40:39,267 (asr_inference:494) INFO: speech length: 79488 +2024-01-17 02:40:39,277 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 02:40:39,277 (beam_search:429) INFO: max output length: 122 +2024-01-17 02:40:39,277 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:39,438 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:39,438 (beam_search:476) INFO: -6.69 * 1.0 = -6.69 for ctc +2024-01-17 02:40:39,438 (beam_search:479) INFO: total log probability: -6.69 +2024-01-17 02:40:39,438 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 02:40:39,438 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:39,438 (beam_search:483) INFO: best hypo: korumonokorewagohaNhareotunaNnartopaNhaa + +2024-01-17 02:40:39,440 (asr_inference:494) INFO: speech length: 133056 +2024-01-17 02:40:39,453 (beam_search:428) INFO: decoder input length: 205 +2024-01-17 02:40:39,453 (beam_search:429) INFO: max output length: 205 +2024-01-17 02:40:39,453 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:39,922 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:39,922 (beam_search:476) INFO: -6.81 * 1.0 = -6.81 for ctc +2024-01-17 02:40:39,922 (beam_search:479) INFO: total log probability: -6.81 +2024-01-17 02:40:39,922 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:40:39,922 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:39,923 (beam_search:483) INFO: best hypo: kooitekItoclkaNtekinipaupooishIsutekiniwarewaremojikowamasumasuzumeetonarunodearu + +2024-01-17 02:40:39,924 (asr_inference:494) INFO: speech length: 128448 +2024-01-17 02:40:39,937 (beam_search:428) INFO: decoder input length: 198 +2024-01-17 02:40:39,937 (beam_search:429) INFO: max output length: 198 +2024-01-17 02:40:39,937 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:40,408 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:40,408 (beam_search:476) INFO: -10.48 * 1.0 = -10.48 for ctc +2024-01-17 02:40:40,408 (beam_search:479) INFO: total log probability: -10.48 +2024-01-17 02:40:40,408 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 02:40:40,408 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:40,408 (beam_search:483) INFO: best hypo: hijooshIkidaarukotowapaubuchooimisurunomidenakurupaushakaietekiniakUtomokaNgaeraruudearu + +2024-01-17 02:40:40,409 (asr_inference:494) INFO: speech length: 84096 +2024-01-17 02:40:40,420 (beam_search:428) INFO: decoder input length: 129 +2024-01-17 02:40:40,420 (beam_search:429) INFO: max output length: 129 +2024-01-17 02:40:40,420 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:40,621 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:40,621 (beam_search:476) INFO: -5.05 * 1.0 = -5.05 for ctc +2024-01-17 02:40:40,621 (beam_search:479) INFO: total log probability: -5.05 +2024-01-17 02:40:40,621 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:40:40,621 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:40,621 (beam_search:483) INFO: best hypo: jooshIkiganaotokUushutekinachishIkidearunihashikarakuwa + +2024-01-17 02:40:40,622 (asr_inference:494) INFO: speech length: 61056 +2024-01-17 02:40:40,631 (beam_search:428) INFO: decoder input length: 93 +2024-01-17 02:40:40,631 (beam_search:429) INFO: max output length: 93 +2024-01-17 02:40:40,631 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:40,729 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:40,729 (beam_search:476) INFO: -4.07 * 1.0 = -4.07 for ctc +2024-01-17 02:40:40,729 (beam_search:479) INFO: total log probability: -4.07 +2024-01-17 02:40:40,729 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 02:40:40,729 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:40,729 (beam_search:483) INFO: best hypo: koNnakotoodeoguraretenasakinhai + +2024-01-17 02:40:40,730 (asr_inference:494) INFO: speech length: 127296 +2024-01-17 02:40:40,743 (beam_search:428) INFO: decoder input length: 196 +2024-01-17 02:40:40,743 (beam_search:429) INFO: max output length: 196 +2024-01-17 02:40:40,743 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:41,265 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:41,265 (beam_search:476) INFO: -9.60 * 1.0 = -9.60 for ctc +2024-01-17 02:40:41,265 (beam_search:479) INFO: total log probability: -9.60 +2024-01-17 02:40:41,265 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:40:41,265 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:41,266 (beam_search:483) INFO: best hypo: kakotomiraetogajikomujuNtekinigeNzainioitetarikIsurutoiuiniwageNdagakatachomotamaekeremanaranai + +2024-01-17 02:40:41,267 (asr_inference:494) INFO: speech length: 59328 +2024-01-17 02:40:41,276 (beam_search:428) INFO: decoder input length: 90 +2024-01-17 02:40:41,276 (beam_search:429) INFO: max output length: 90 +2024-01-17 02:40:41,276 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:41,352 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:41,352 (beam_search:476) INFO: -5.02 * 1.0 = -5.02 for ctc +2024-01-17 02:40:41,352 (beam_search:479) INFO: total log probability: -5.02 +2024-01-17 02:40:41,352 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 02:40:41,352 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:41,352 (beam_search:483) INFO: best hypo: shokionotakasagahadouNnau + +# Accounting: time=11 threads=1 +# Ended (code 0) at Wed Jan 17 02:40:41 CST 2024, elapsed time 11 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/calculate_rtf.log b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/calculate_rtf.log new file mode 100644 index 0000000000000000000000000000000000000000..51681657fe5389a189cbb6e70800cd88d5db42d5 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/calculate_rtf.log @@ -0,0 +1,9 @@ +# pyscripts/utils/calculate_rtf.py --log-dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir --log-name asr_inference --input-shift 0.0625 --start-times-marker "speech length" --end-times-marker "best hypo" --inf-num 1 +# Started at Wed Jan 17 02:40:41 CST 2024 +# +Total audio duration: 602.388 [sec] +Total decoding time: 25.911 [sec] +RTF: 0.043 +Latency: 205.643 [ms/sentence] +# Accounting: time=1 threads=1 +# Ended (code 0) at Wed Jan 17 02:40:42 CST 2024, elapsed time 1 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.1.scp b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.1.scp new file mode 100644 index 0000000000000000000000000000000000000000..8b21271e228153b3adfda66c7b47438ec6eed5bf --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.1.scp @@ -0,0 +1,32 @@ +cv_jpn_000674 dump/raw/org/dev_10min_jpn/data/format.1/cv_jpn_000674.flac +cv_jpn_000675 dump/raw/org/dev_10min_jpn/data/format.1/cv_jpn_000675.flac +cv_jpn_000676 dump/raw/org/dev_10min_jpn/data/format.1/cv_jpn_000676.flac +cv_jpn_000677 dump/raw/org/dev_10min_jpn/data/format.1/cv_jpn_000677.flac +cv_jpn_000678 dump/raw/org/dev_10min_jpn/data/format.2/cv_jpn_000678.flac +cv_jpn_000679 dump/raw/org/dev_10min_jpn/data/format.2/cv_jpn_000679.flac +cv_jpn_000680 dump/raw/org/dev_10min_jpn/data/format.2/cv_jpn_000680.flac +cv_jpn_000681 dump/raw/org/dev_10min_jpn/data/format.2/cv_jpn_000681.flac +cv_jpn_000682 dump/raw/org/dev_10min_jpn/data/format.3/cv_jpn_000682.flac +cv_jpn_000683 dump/raw/org/dev_10min_jpn/data/format.3/cv_jpn_000683.flac +cv_jpn_000684 dump/raw/org/dev_10min_jpn/data/format.3/cv_jpn_000684.flac +cv_jpn_000685 dump/raw/org/dev_10min_jpn/data/format.3/cv_jpn_000685.flac +cv_jpn_000686 dump/raw/org/dev_10min_jpn/data/format.4/cv_jpn_000686.flac +cv_jpn_000687 dump/raw/org/dev_10min_jpn/data/format.4/cv_jpn_000687.flac +cv_jpn_000688 dump/raw/org/dev_10min_jpn/data/format.4/cv_jpn_000688.flac +cv_jpn_000689 dump/raw/org/dev_10min_jpn/data/format.4/cv_jpn_000689.flac +cv_jpn_000690 dump/raw/org/dev_10min_jpn/data/format.5/cv_jpn_000690.flac +cv_jpn_000691 dump/raw/org/dev_10min_jpn/data/format.5/cv_jpn_000691.flac +cv_jpn_000692 dump/raw/org/dev_10min_jpn/data/format.5/cv_jpn_000692.flac +cv_jpn_000693 dump/raw/org/dev_10min_jpn/data/format.5/cv_jpn_000693.flac +cv_jpn_000694 dump/raw/org/dev_10min_jpn/data/format.6/cv_jpn_000694.flac +cv_jpn_000695 dump/raw/org/dev_10min_jpn/data/format.6/cv_jpn_000695.flac +cv_jpn_000696 dump/raw/org/dev_10min_jpn/data/format.6/cv_jpn_000696.flac +cv_jpn_000697 dump/raw/org/dev_10min_jpn/data/format.6/cv_jpn_000697.flac +cv_jpn_000698 dump/raw/org/dev_10min_jpn/data/format.7/cv_jpn_000698.flac +cv_jpn_000699 dump/raw/org/dev_10min_jpn/data/format.7/cv_jpn_000699.flac +cv_jpn_000700 dump/raw/org/dev_10min_jpn/data/format.7/cv_jpn_000700.flac +cv_jpn_000701 dump/raw/org/dev_10min_jpn/data/format.7/cv_jpn_000701.flac +cv_jpn_000702 dump/raw/org/dev_10min_jpn/data/format.8/cv_jpn_000702.flac +cv_jpn_000703 dump/raw/org/dev_10min_jpn/data/format.8/cv_jpn_000703.flac +cv_jpn_000704 dump/raw/org/dev_10min_jpn/data/format.8/cv_jpn_000704.flac +cv_jpn_000705 dump/raw/org/dev_10min_jpn/data/format.8/cv_jpn_000705.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.2.scp b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.2.scp new file mode 100644 index 0000000000000000000000000000000000000000..3b244aeaebe6bcc7b2da681e4c4f9e77e427b2ac --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.2.scp @@ -0,0 +1,32 @@ +cv_jpn_000706 dump/raw/org/dev_10min_jpn/data/format.9/cv_jpn_000706.flac +cv_jpn_000707 dump/raw/org/dev_10min_jpn/data/format.9/cv_jpn_000707.flac +cv_jpn_000708 dump/raw/org/dev_10min_jpn/data/format.9/cv_jpn_000708.flac +cv_jpn_000709 dump/raw/org/dev_10min_jpn/data/format.9/cv_jpn_000709.flac +cv_jpn_000710 dump/raw/org/dev_10min_jpn/data/format.10/cv_jpn_000710.flac +cv_jpn_000711 dump/raw/org/dev_10min_jpn/data/format.10/cv_jpn_000711.flac +cv_jpn_000712 dump/raw/org/dev_10min_jpn/data/format.10/cv_jpn_000712.flac +cv_jpn_000713 dump/raw/org/dev_10min_jpn/data/format.10/cv_jpn_000713.flac +cv_jpn_000714 dump/raw/org/dev_10min_jpn/data/format.11/cv_jpn_000714.flac +cv_jpn_000715 dump/raw/org/dev_10min_jpn/data/format.11/cv_jpn_000715.flac +cv_jpn_000716 dump/raw/org/dev_10min_jpn/data/format.11/cv_jpn_000716.flac +cv_jpn_000717 dump/raw/org/dev_10min_jpn/data/format.11/cv_jpn_000717.flac +cv_jpn_000718 dump/raw/org/dev_10min_jpn/data/format.12/cv_jpn_000718.flac +cv_jpn_000719 dump/raw/org/dev_10min_jpn/data/format.12/cv_jpn_000719.flac +cv_jpn_000720 dump/raw/org/dev_10min_jpn/data/format.12/cv_jpn_000720.flac +cv_jpn_000721 dump/raw/org/dev_10min_jpn/data/format.12/cv_jpn_000721.flac +cv_jpn_000722 dump/raw/org/dev_10min_jpn/data/format.13/cv_jpn_000722.flac +cv_jpn_000723 dump/raw/org/dev_10min_jpn/data/format.13/cv_jpn_000723.flac +cv_jpn_000724 dump/raw/org/dev_10min_jpn/data/format.13/cv_jpn_000724.flac +cv_jpn_000725 dump/raw/org/dev_10min_jpn/data/format.13/cv_jpn_000725.flac +cv_jpn_000726 dump/raw/org/dev_10min_jpn/data/format.14/cv_jpn_000726.flac +cv_jpn_000727 dump/raw/org/dev_10min_jpn/data/format.14/cv_jpn_000727.flac +cv_jpn_000728 dump/raw/org/dev_10min_jpn/data/format.14/cv_jpn_000728.flac +cv_jpn_000729 dump/raw/org/dev_10min_jpn/data/format.14/cv_jpn_000729.flac +cv_jpn_000730 dump/raw/org/dev_10min_jpn/data/format.15/cv_jpn_000730.flac +cv_jpn_000731 dump/raw/org/dev_10min_jpn/data/format.15/cv_jpn_000731.flac +cv_jpn_000732 dump/raw/org/dev_10min_jpn/data/format.15/cv_jpn_000732.flac +cv_jpn_000733 dump/raw/org/dev_10min_jpn/data/format.15/cv_jpn_000733.flac +cv_jpn_000734 dump/raw/org/dev_10min_jpn/data/format.16/cv_jpn_000734.flac +cv_jpn_000735 dump/raw/org/dev_10min_jpn/data/format.16/cv_jpn_000735.flac +cv_jpn_000736 dump/raw/org/dev_10min_jpn/data/format.16/cv_jpn_000736.flac +cv_jpn_000737 dump/raw/org/dev_10min_jpn/data/format.16/cv_jpn_000737.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.3.scp b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.3.scp new file mode 100644 index 0000000000000000000000000000000000000000..4b5a288344f2fa10049dd1d3ac7252ba5d518893 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.3.scp @@ -0,0 +1,31 @@ +cv_jpn_000738 dump/raw/org/dev_10min_jpn/data/format.17/cv_jpn_000738.flac +cv_jpn_000739 dump/raw/org/dev_10min_jpn/data/format.17/cv_jpn_000739.flac +cv_jpn_000740 dump/raw/org/dev_10min_jpn/data/format.17/cv_jpn_000740.flac +cv_jpn_000741 dump/raw/org/dev_10min_jpn/data/format.17/cv_jpn_000741.flac +cv_jpn_000742 dump/raw/org/dev_10min_jpn/data/format.18/cv_jpn_000742.flac +cv_jpn_000743 dump/raw/org/dev_10min_jpn/data/format.18/cv_jpn_000743.flac +cv_jpn_000744 dump/raw/org/dev_10min_jpn/data/format.18/cv_jpn_000744.flac +cv_jpn_000745 dump/raw/org/dev_10min_jpn/data/format.18/cv_jpn_000745.flac +cv_jpn_000746 dump/raw/org/dev_10min_jpn/data/format.19/cv_jpn_000746.flac +cv_jpn_000747 dump/raw/org/dev_10min_jpn/data/format.19/cv_jpn_000747.flac +cv_jpn_000748 dump/raw/org/dev_10min_jpn/data/format.19/cv_jpn_000748.flac +cv_jpn_000749 dump/raw/org/dev_10min_jpn/data/format.19/cv_jpn_000749.flac +cv_jpn_000750 dump/raw/org/dev_10min_jpn/data/format.20/cv_jpn_000750.flac +cv_jpn_000751 dump/raw/org/dev_10min_jpn/data/format.20/cv_jpn_000751.flac +cv_jpn_000752 dump/raw/org/dev_10min_jpn/data/format.20/cv_jpn_000752.flac +cv_jpn_000753 dump/raw/org/dev_10min_jpn/data/format.20/cv_jpn_000753.flac +cv_jpn_000754 dump/raw/org/dev_10min_jpn/data/format.21/cv_jpn_000754.flac +cv_jpn_000755 dump/raw/org/dev_10min_jpn/data/format.21/cv_jpn_000755.flac +cv_jpn_000756 dump/raw/org/dev_10min_jpn/data/format.21/cv_jpn_000756.flac +cv_jpn_000757 dump/raw/org/dev_10min_jpn/data/format.21/cv_jpn_000757.flac +cv_jpn_000758 dump/raw/org/dev_10min_jpn/data/format.22/cv_jpn_000758.flac +cv_jpn_000759 dump/raw/org/dev_10min_jpn/data/format.22/cv_jpn_000759.flac +cv_jpn_000760 dump/raw/org/dev_10min_jpn/data/format.22/cv_jpn_000760.flac +cv_jpn_000761 dump/raw/org/dev_10min_jpn/data/format.22/cv_jpn_000761.flac +cv_jpn_000762 dump/raw/org/dev_10min_jpn/data/format.23/cv_jpn_000762.flac +cv_jpn_000763 dump/raw/org/dev_10min_jpn/data/format.23/cv_jpn_000763.flac +cv_jpn_000764 dump/raw/org/dev_10min_jpn/data/format.23/cv_jpn_000764.flac +cv_jpn_000765 dump/raw/org/dev_10min_jpn/data/format.23/cv_jpn_000765.flac +cv_jpn_000766 dump/raw/org/dev_10min_jpn/data/format.24/cv_jpn_000766.flac +cv_jpn_000767 dump/raw/org/dev_10min_jpn/data/format.24/cv_jpn_000767.flac +cv_jpn_000768 dump/raw/org/dev_10min_jpn/data/format.24/cv_jpn_000768.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.4.scp b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.4.scp new file mode 100644 index 0000000000000000000000000000000000000000..3d2314d1cb9474f3789fdc5b4f47b06391aef7bb --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/keys.4.scp @@ -0,0 +1,31 @@ +cv_jpn_000769 dump/raw/org/dev_10min_jpn/data/format.24/cv_jpn_000769.flac +cv_jpn_000770 dump/raw/org/dev_10min_jpn/data/format.25/cv_jpn_000770.flac +cv_jpn_000771 dump/raw/org/dev_10min_jpn/data/format.25/cv_jpn_000771.flac +cv_jpn_000772 dump/raw/org/dev_10min_jpn/data/format.25/cv_jpn_000772.flac +cv_jpn_000773 dump/raw/org/dev_10min_jpn/data/format.25/cv_jpn_000773.flac +cv_jpn_000774 dump/raw/org/dev_10min_jpn/data/format.26/cv_jpn_000774.flac +cv_jpn_000775 dump/raw/org/dev_10min_jpn/data/format.26/cv_jpn_000775.flac +cv_jpn_000776 dump/raw/org/dev_10min_jpn/data/format.26/cv_jpn_000776.flac +cv_jpn_000777 dump/raw/org/dev_10min_jpn/data/format.26/cv_jpn_000777.flac +cv_jpn_000778 dump/raw/org/dev_10min_jpn/data/format.27/cv_jpn_000778.flac +cv_jpn_000779 dump/raw/org/dev_10min_jpn/data/format.27/cv_jpn_000779.flac +cv_jpn_000780 dump/raw/org/dev_10min_jpn/data/format.27/cv_jpn_000780.flac +cv_jpn_000781 dump/raw/org/dev_10min_jpn/data/format.27/cv_jpn_000781.flac +cv_jpn_000782 dump/raw/org/dev_10min_jpn/data/format.28/cv_jpn_000782.flac +cv_jpn_000783 dump/raw/org/dev_10min_jpn/data/format.28/cv_jpn_000783.flac +cv_jpn_000784 dump/raw/org/dev_10min_jpn/data/format.28/cv_jpn_000784.flac +cv_jpn_000785 dump/raw/org/dev_10min_jpn/data/format.28/cv_jpn_000785.flac +cv_jpn_000786 dump/raw/org/dev_10min_jpn/data/format.29/cv_jpn_000786.flac +cv_jpn_000787 dump/raw/org/dev_10min_jpn/data/format.29/cv_jpn_000787.flac +cv_jpn_000788 dump/raw/org/dev_10min_jpn/data/format.29/cv_jpn_000788.flac +cv_jpn_000789 dump/raw/org/dev_10min_jpn/data/format.29/cv_jpn_000789.flac +cv_jpn_000790 dump/raw/org/dev_10min_jpn/data/format.30/cv_jpn_000790.flac +cv_jpn_000791 dump/raw/org/dev_10min_jpn/data/format.30/cv_jpn_000791.flac +cv_jpn_000792 dump/raw/org/dev_10min_jpn/data/format.30/cv_jpn_000792.flac +cv_jpn_000793 dump/raw/org/dev_10min_jpn/data/format.30/cv_jpn_000793.flac +cv_jpn_000794 dump/raw/org/dev_10min_jpn/data/format.31/cv_jpn_000794.flac +cv_jpn_000795 dump/raw/org/dev_10min_jpn/data/format.31/cv_jpn_000795.flac +cv_jpn_000796 dump/raw/org/dev_10min_jpn/data/format.31/cv_jpn_000796.flac +cv_jpn_000797 dump/raw/org/dev_10min_jpn/data/format.32/cv_jpn_000797.flac +cv_jpn_000798 dump/raw/org/dev_10min_jpn/data/format.32/cv_jpn_000798.flac +cv_jpn_000799 dump/raw/org/dev_10min_jpn/data/format.32/cv_jpn_000799.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..41aac3d31aae1c24dafb931843fcaccbc897e3ca --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/score @@ -0,0 +1,32 @@ +cv_jpn_000674 tensor(-4.7865) +cv_jpn_000675 tensor(-5.2669) +cv_jpn_000676 tensor(-2.5958) +cv_jpn_000677 tensor(-11.5213) +cv_jpn_000678 tensor(-7.6152) +cv_jpn_000679 tensor(-10.2451) +cv_jpn_000680 tensor(-13.6647) +cv_jpn_000681 tensor(-7.6773) +cv_jpn_000682 tensor(-1.1140) +cv_jpn_000683 tensor(-6.7306) +cv_jpn_000684 tensor(-5.8702) +cv_jpn_000685 tensor(-6.0293) +cv_jpn_000686 tensor(-4.4056) +cv_jpn_000687 tensor(-2.7974) +cv_jpn_000688 tensor(-4.5413) +cv_jpn_000689 tensor(-2.4262) +cv_jpn_000690 tensor(-4.7471) +cv_jpn_000691 tensor(-7.0449) +cv_jpn_000692 tensor(-3.2825) +cv_jpn_000693 tensor(-5.7881) +cv_jpn_000694 tensor(-13.4578) +cv_jpn_000695 tensor(-4.8678) +cv_jpn_000696 tensor(-6.8595) +cv_jpn_000697 tensor(-5.8728) +cv_jpn_000698 tensor(-9.5205) +cv_jpn_000699 tensor(-8.3750) +cv_jpn_000700 tensor(-5.6947) +cv_jpn_000701 tensor(-2.5160) +cv_jpn_000702 tensor(-1.7816) +cv_jpn_000703 tensor(-8.8324) +cv_jpn_000704 tensor(-8.5238) +cv_jpn_000705 tensor(-3.8933) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..1be72ccf1bdf4e2f84e36bf2adcc390a190cae9c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/text @@ -0,0 +1,32 @@ +cv_jpn_000674 b o k u n o i e e g a cl t a k a i n a N n o n a m a e N n i k u r a b e r u t o b o k u n i w a n a j i m i n o n a i pau n a b a e b a k a r i d a k e d o +cv_jpn_000675 n a i o o s o n o m o n o y o r i pau f u i n i k i g a u k e t e r u +cv_jpn_000676 b o k u n o sh I cl t e i u m o n o t o w a s U k o sh i ch i g a cl t e i t a +cv_jpn_000677 k a k a a r u t a ch i m a n i o o i cl t e pau s e k a i g a i j sh I k i m e N t e k i d e a r i pau w a r e w a r e n o j i k o g a j sh I k i s a y o o t e k i d e a r u t o k a N g e e r a r e r u t o k sh i +cv_jpn_000678 i e n i k t a n e N g a a j i w a s a N hy a k u m a i h o r o d e pau ch o o d a sh I t a b u N t o o n a j i g u r a i d a +cv_jpn_000679 h a a w a p i t a m a r i ts u b a a g u n o s e e sh i N y o o i n i n u u N h I t e i r u t o k i n i b e sh i o o m o +cv_jpn_000680 t a t a N d e a r h a N t e n o h i r o g e r a b a a ch i k o ch i N i ts u g i h a g i a a r i pau k a t a k o ch i n i d e k i t a h o k o r o b i n a N k a ky o o e N n o m a m o n i n a cl t e i r u +cv_jpn_000681 k a r a g a k a n o d e b e r e n a a k a n i h o o m o N k i b o o n o b u sh o o y o b i ch o o z a k i b o o n o o b u g o m a i k a r a t o o r i d e s U +cv_jpn_000682 k a N b a w a a t o t e m o s a m i d e s U +cv_jpn_000683 k i N ch o o sh I t a k a h a o ts U k i d e p a cl t a w a d a s e k i n i h a i r u +cv_jpn_000684 m a s a N ky u u r e N t o i cl t a f s u u k a k a r o k e N j i k i b u ts s U +cv_jpn_000685 g o j i y o sh i d e s u s u m e t e ts s o g o N g a w a r u k u n a cl t a r a h I cl k o m e r u e a r e k U ch i +cv_jpn_000686 m u j u N t e k i pau j i k o t o o i ts U t e k i n i pau j i k o o j i sh i N o k e e s e e s o r u sh a k a i w a +cv_jpn_000687 f a N n o i k e N n i n a r a s a d e r u n a +cv_jpn_000688 h i n e g a a s o b i t a y o o r a z e N k a i d e k o ch o o m i t e i r u +cv_jpn_000689 i ch i d o w a k o N p o t a a j i k a N o n o N d e m i t a i +cv_jpn_000690 k o o i n o k o sh I t e pau o t o o s a N t o k a a s a N w a N d e t e i k i m a sh I t a +cv_jpn_000691 sh I k a sh I t e s o r e g a ts U k r a r e t a m o n o k a r a ts U k u r u m o n o e t o sh I t e d o k u m a d e m o r a w a r u n i s e m a r u t o i u t o k i pau w a r e w a r i n i ch o cl k a N t e k i d e a r u +cv_jpn_000692 h a i n i t a m a cl t a k e m u r i o h a k i d a sh i k u r a i k o o e n i sh I s e N o m o k e r u +cv_jpn_000693 w a d a w a d a sh n a b u s o k u d e ch i s e N i N n a r i s o +cv_jpn_000694 ts e N sh e k a i n o m o ts U k a t a ch sh i t a sh i n o i y a i u r u s e e s a i y o sh I k u t o s a i o t o w a pau h a n a sh I t e k a N g a e r u k o t o w a d e k i n a i +cv_jpn_000695 s a k e n o m a i n o n i p i i r u b a r a t o y o r e t a +cv_jpn_000696 s o r e o t a t a k u h a k u n o z e N d a N k a i sh I k u i t e e d o n k a a k U t o n o m i m i r u k o t o w a +cv_jpn_000697 n o k ky o e N o f u a ts u n i s u m o h o d e g e e m o y a cl t e i t a +cv_jpn_000698 w a d a i w a a n a i sh u m a t a N k o b a e sh i s a N t o o a s o b e i m a s U +cv_jpn_000699 h a s o o k o n i h e t o o g a i m a s u n e a r o h I t o o w a t a a r e e t e sh o o +cv_jpn_000700 w a t a sh i w a k i n o o k a n a n o o d o o g a i t a i t e s U +cv_jpn_000701 ky o o r e N k a n a p e N ky u o o sh I t e i m a s U +cv_jpn_000702 ch o cl t o s u m i m a s e +cv_jpn_000703 ch I k a sh t o n o o o o e N b u r y o w a y a k e k i m i n i k a e cl d e h a g e s U p e u n o cl t a +cv_jpn_000704 i ts u m o k o n o e N p I i ts s o o ts sh U k a cl t e i t a n o t e m i j i k a h a h a n a r i m a sh I t a +cv_jpn_000705 w a t a sh i w a e i g o g a h a n a s t e m o s U diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..1be72ccf1bdf4e2f84e36bf2adcc390a190cae9c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/token @@ -0,0 +1,32 @@ +cv_jpn_000674 b o k u n o i e e g a cl t a k a i n a N n o n a m a e N n i k u r a b e r u t o b o k u n i w a n a j i m i n o n a i pau n a b a e b a k a r i d a k e d o +cv_jpn_000675 n a i o o s o n o m o n o y o r i pau f u i n i k i g a u k e t e r u +cv_jpn_000676 b o k u n o sh I cl t e i u m o n o t o w a s U k o sh i ch i g a cl t e i t a +cv_jpn_000677 k a k a a r u t a ch i m a n i o o i cl t e pau s e k a i g a i j sh I k i m e N t e k i d e a r i pau w a r e w a r e n o j i k o g a j sh I k i s a y o o t e k i d e a r u t o k a N g e e r a r e r u t o k sh i +cv_jpn_000678 i e n i k t a n e N g a a j i w a s a N hy a k u m a i h o r o d e pau ch o o d a sh I t a b u N t o o n a j i g u r a i d a +cv_jpn_000679 h a a w a p i t a m a r i ts u b a a g u n o s e e sh i N y o o i n i n u u N h I t e i r u t o k i n i b e sh i o o m o +cv_jpn_000680 t a t a N d e a r h a N t e n o h i r o g e r a b a a ch i k o ch i N i ts u g i h a g i a a r i pau k a t a k o ch i n i d e k i t a h o k o r o b i n a N k a ky o o e N n o m a m o n i n a cl t e i r u +cv_jpn_000681 k a r a g a k a n o d e b e r e n a a k a n i h o o m o N k i b o o n o b u sh o o y o b i ch o o z a k i b o o n o o b u g o m a i k a r a t o o r i d e s U +cv_jpn_000682 k a N b a w a a t o t e m o s a m i d e s U +cv_jpn_000683 k i N ch o o sh I t a k a h a o ts U k i d e p a cl t a w a d a s e k i n i h a i r u +cv_jpn_000684 m a s a N ky u u r e N t o i cl t a f s u u k a k a r o k e N j i k i b u ts s U +cv_jpn_000685 g o j i y o sh i d e s u s u m e t e ts s o g o N g a w a r u k u n a cl t a r a h I cl k o m e r u e a r e k U ch i +cv_jpn_000686 m u j u N t e k i pau j i k o t o o i ts U t e k i n i pau j i k o o j i sh i N o k e e s e e s o r u sh a k a i w a +cv_jpn_000687 f a N n o i k e N n i n a r a s a d e r u n a +cv_jpn_000688 h i n e g a a s o b i t a y o o r a z e N k a i d e k o ch o o m i t e i r u +cv_jpn_000689 i ch i d o w a k o N p o t a a j i k a N o n o N d e m i t a i +cv_jpn_000690 k o o i n o k o sh I t e pau o t o o s a N t o k a a s a N w a N d e t e i k i m a sh I t a +cv_jpn_000691 sh I k a sh I t e s o r e g a ts U k r a r e t a m o n o k a r a ts U k u r u m o n o e t o sh I t e d o k u m a d e m o r a w a r u n i s e m a r u t o i u t o k i pau w a r e w a r i n i ch o cl k a N t e k i d e a r u +cv_jpn_000692 h a i n i t a m a cl t a k e m u r i o h a k i d a sh i k u r a i k o o e n i sh I s e N o m o k e r u +cv_jpn_000693 w a d a w a d a sh n a b u s o k u d e ch i s e N i N n a r i s o +cv_jpn_000694 ts e N sh e k a i n o m o ts U k a t a ch sh i t a sh i n o i y a i u r u s e e s a i y o sh I k u t o s a i o t o w a pau h a n a sh I t e k a N g a e r u k o t o w a d e k i n a i +cv_jpn_000695 s a k e n o m a i n o n i p i i r u b a r a t o y o r e t a +cv_jpn_000696 s o r e o t a t a k u h a k u n o z e N d a N k a i sh I k u i t e e d o n k a a k U t o n o m i m i r u k o t o w a +cv_jpn_000697 n o k ky o e N o f u a ts u n i s u m o h o d e g e e m o y a cl t e i t a +cv_jpn_000698 w a d a i w a a n a i sh u m a t a N k o b a e sh i s a N t o o a s o b e i m a s U +cv_jpn_000699 h a s o o k o n i h e t o o g a i m a s u n e a r o h I t o o w a t a a r e e t e sh o o +cv_jpn_000700 w a t a sh i w a k i n o o k a n a n o o d o o g a i t a i t e s U +cv_jpn_000701 ky o o r e N k a n a p e N ky u o o sh I t e i m a s U +cv_jpn_000702 ch o cl t o s u m i m a s e +cv_jpn_000703 ch I k a sh t o n o o o o e N b u r y o w a y a k e k i m i n i k a e cl d e h a g e s U p e u n o cl t a +cv_jpn_000704 i ts u m o k o n o e N p I i ts s o o ts sh U k a cl t e i t a n o t e m i j i k a h a h a n a r i m a sh I t a +cv_jpn_000705 w a t a sh i w a e i g o g a h a n a s t e m o s U diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..76d03929db467e2ecf8c4218dc6558fd089e169c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.1/1best_recog/token_int @@ -0,0 +1,32 @@ +cv_jpn_000674 25 3 6 7 9 3 4 5 5 16 2 21 8 2 6 2 4 9 2 13 9 3 9 2 11 2 5 13 9 4 6 7 10 2 25 5 10 7 8 3 25 3 6 7 9 4 17 2 9 2 22 4 11 4 9 3 9 2 4 20 9 2 25 2 5 25 2 6 2 10 4 14 2 6 5 14 3 +cv_jpn_000675 9 2 4 3 3 12 3 9 3 11 3 9 3 23 3 10 4 20 31 7 4 9 4 6 4 16 2 7 6 5 8 5 10 7 +cv_jpn_000676 25 3 6 7 9 3 15 19 21 8 5 4 7 11 3 9 3 8 3 17 2 12 18 6 3 15 4 27 4 16 2 21 8 5 4 8 2 +cv_jpn_000677 6 2 6 2 2 10 7 8 2 27 4 11 2 9 4 3 3 4 21 8 5 20 12 5 6 2 4 16 2 4 22 15 19 6 4 11 5 13 8 5 6 4 14 5 2 10 4 20 17 2 10 5 17 2 10 5 9 3 22 4 6 3 16 2 22 15 19 6 4 12 2 23 3 3 8 5 6 4 14 5 2 10 7 8 3 6 2 13 16 5 5 10 2 10 5 10 7 8 3 6 15 4 +cv_jpn_000678 4 5 9 4 6 8 2 9 5 13 16 2 2 22 4 17 2 12 2 13 33 2 6 7 11 2 4 24 3 10 3 14 5 20 27 3 3 14 2 15 19 8 2 25 7 13 8 3 3 9 2 22 4 16 7 10 2 4 14 2 +cv_jpn_000679 24 2 2 17 2 30 4 8 2 11 2 10 4 26 7 25 2 2 16 7 9 3 12 5 5 15 4 13 23 3 3 4 9 4 9 7 7 13 24 19 8 5 4 10 7 8 3 6 4 9 4 25 5 15 4 3 3 11 3 +cv_jpn_000680 8 2 8 2 13 14 5 2 10 24 2 13 8 5 9 3 24 4 10 3 16 5 10 2 25 2 2 27 4 6 3 27 4 13 4 26 7 16 4 24 2 16 4 2 2 10 4 20 6 2 8 2 6 3 27 4 9 4 14 5 6 4 8 2 24 3 6 3 10 3 25 4 9 2 13 6 2 29 3 3 5 13 9 3 11 2 11 3 9 4 9 2 21 8 5 4 10 7 +cv_jpn_000681 6 2 10 2 16 2 6 2 9 3 14 5 25 5 10 5 9 2 2 6 2 9 4 24 3 3 11 3 13 6 4 25 3 3 9 3 25 7 15 3 3 23 3 25 4 27 3 3 28 2 6 4 25 3 3 9 3 3 25 7 16 3 11 2 4 6 2 10 2 8 3 3 10 4 14 5 12 18 +cv_jpn_000682 6 2 13 25 2 17 2 2 8 3 8 5 11 3 12 2 11 4 14 5 12 18 +cv_jpn_000683 6 4 13 27 3 3 15 19 8 2 6 2 24 2 3 26 18 6 4 14 5 30 2 21 8 2 17 2 14 2 12 5 6 4 9 4 24 2 4 10 7 +cv_jpn_000684 11 2 12 2 13 29 7 7 10 5 13 8 3 4 21 8 2 31 12 7 7 6 2 6 2 10 3 6 5 13 22 4 6 4 25 7 26 12 18 +cv_jpn_000685 16 3 22 4 23 3 15 4 14 5 12 7 12 7 11 5 8 5 26 12 3 16 3 13 16 2 17 2 10 7 6 7 9 2 21 8 2 10 2 24 19 21 6 3 11 5 10 7 5 2 10 5 6 18 27 4 +cv_jpn_000686 11 7 22 7 13 8 5 6 4 20 22 4 6 3 8 3 3 4 26 18 8 5 6 4 9 4 20 22 4 6 3 3 22 4 15 4 13 3 6 5 5 12 5 5 12 3 10 7 15 2 6 2 4 17 2 +cv_jpn_000687 31 2 13 9 3 4 6 5 13 9 4 9 2 10 2 12 2 14 5 10 7 9 2 +cv_jpn_000688 24 4 9 5 16 2 2 12 3 25 4 8 2 23 3 3 10 2 28 5 13 6 2 4 14 5 6 3 27 3 3 11 4 8 5 4 10 7 +cv_jpn_000689 4 27 4 14 3 17 2 6 3 13 30 3 8 2 2 22 4 6 2 13 3 9 3 13 14 5 11 4 8 2 4 +cv_jpn_000690 6 3 3 4 9 3 6 3 15 19 8 5 20 3 8 3 3 12 2 13 8 3 6 2 2 12 2 13 17 2 13 14 5 8 5 4 6 4 11 2 15 19 8 2 +cv_jpn_000691 15 19 6 2 15 19 8 5 12 3 10 5 16 2 26 18 6 10 2 10 5 8 2 11 3 9 3 6 2 10 2 26 18 6 7 10 7 11 3 9 3 5 8 3 15 19 8 5 14 3 6 7 11 2 14 5 11 3 10 2 17 2 10 7 9 4 12 5 11 2 10 7 8 3 4 7 8 3 6 4 20 17 2 10 5 17 2 10 4 9 4 27 3 21 6 2 13 8 5 6 4 14 5 2 10 7 +cv_jpn_000692 24 2 4 9 4 8 2 11 2 21 8 2 6 5 11 7 10 4 3 24 2 6 4 14 2 15 4 6 7 10 2 4 6 3 3 5 9 4 15 19 12 5 13 3 11 3 6 5 10 7 +cv_jpn_000693 17 2 14 2 17 2 14 2 15 9 2 25 7 12 3 6 7 14 5 27 4 12 5 13 4 13 9 2 10 4 12 3 +cv_jpn_000694 26 5 13 15 5 6 2 4 9 3 11 3 26 18 6 2 8 2 27 15 4 8 2 15 4 9 3 4 23 2 4 7 10 7 12 5 5 12 2 4 23 3 15 19 6 7 8 3 12 2 4 3 8 3 17 2 20 24 2 9 2 15 19 8 5 6 2 13 16 2 5 10 7 6 3 8 3 17 2 14 5 6 4 9 2 4 +cv_jpn_000695 12 2 6 5 9 3 11 2 4 9 3 9 4 30 4 4 10 7 25 2 10 2 8 3 23 3 10 5 8 2 +cv_jpn_000696 12 3 10 5 3 8 2 8 2 6 7 24 2 6 7 9 3 28 5 13 14 2 13 6 2 4 15 19 6 7 4 8 5 5 14 3 9 6 2 2 6 18 8 3 9 3 11 4 11 4 10 7 6 3 8 3 17 2 +cv_jpn_000697 9 3 6 29 3 5 13 3 31 7 2 26 7 9 4 12 7 11 3 24 3 14 5 16 5 5 11 3 23 2 21 8 5 4 8 2 +cv_jpn_000698 17 2 14 2 4 17 2 2 9 2 4 15 7 11 2 8 2 13 6 3 25 2 5 15 4 12 2 13 8 3 3 2 12 3 25 5 4 11 2 12 18 +cv_jpn_000699 24 2 12 3 3 6 3 9 4 24 5 8 3 3 16 2 4 11 2 12 7 9 5 2 10 3 24 19 8 3 3 17 2 8 2 2 10 5 5 8 5 15 3 3 +cv_jpn_000700 17 2 8 2 15 4 17 2 6 4 9 3 3 6 2 9 2 9 3 3 14 3 3 16 2 4 8 2 4 8 5 12 18 +cv_jpn_000701 29 3 3 10 5 13 6 2 9 2 30 5 13 29 7 3 3 15 19 8 5 4 11 2 12 18 +cv_jpn_000702 27 3 21 8 3 12 7 11 4 11 2 12 5 +cv_jpn_000703 27 19 6 2 15 8 3 9 3 3 3 3 5 13 25 7 10 23 3 17 2 23 2 6 5 6 4 11 4 9 4 6 2 5 21 14 5 24 2 16 5 12 18 30 5 7 9 3 21 8 2 +cv_jpn_000704 4 26 7 11 3 6 3 9 3 5 13 30 19 4 26 12 3 3 26 15 18 6 2 21 8 5 4 8 2 9 3 8 5 11 4 22 4 6 2 24 2 24 2 9 2 10 4 11 2 15 19 8 2 +cv_jpn_000705 17 2 8 2 15 4 17 2 5 4 16 3 16 2 24 2 9 2 12 8 5 11 3 12 18 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..83d6cccb81f6e7744abd61b27271b02298a7de6b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/score @@ -0,0 +1,32 @@ +cv_jpn_000706 tensor(-2.8964) +cv_jpn_000707 tensor(-7.5510) +cv_jpn_000708 tensor(-1.6345) +cv_jpn_000709 tensor(-4.0085) +cv_jpn_000710 tensor(-5.9747) +cv_jpn_000711 tensor(-2.2814) +cv_jpn_000712 tensor(-0.5955) +cv_jpn_000713 tensor(-5.3540) +cv_jpn_000714 tensor(-1.4331) +cv_jpn_000715 tensor(-3.3482) +cv_jpn_000716 tensor(-4.2575) +cv_jpn_000717 tensor(-3.5661) +cv_jpn_000718 tensor(-2.0556) +cv_jpn_000719 tensor(-9.1988) +cv_jpn_000720 tensor(-2.2123) +cv_jpn_000721 tensor(-3.2793) +cv_jpn_000722 tensor(-5.8913) +cv_jpn_000723 tensor(-2.2871) +cv_jpn_000724 tensor(-3.8928) +cv_jpn_000725 tensor(-2.8580) +cv_jpn_000726 tensor(-2.4423) +cv_jpn_000727 tensor(-1.5716) +cv_jpn_000728 tensor(-2.9786) +cv_jpn_000729 tensor(-2.8097) +cv_jpn_000730 tensor(-2.1821) +cv_jpn_000731 tensor(-1.3403) +cv_jpn_000732 tensor(-4.0948) +cv_jpn_000733 tensor(-1.2719) +cv_jpn_000734 tensor(-0.7033) +cv_jpn_000735 tensor(-0.8032) +cv_jpn_000736 tensor(-0.7715) +cv_jpn_000737 tensor(-0.1481) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..9dee4198383a44fde082b04ec81055476b22e5d7 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/text @@ -0,0 +1,32 @@ +cv_jpn_000706 o sh i i k e e r y a j i b a N d e t o cl t a e k i n a +cv_jpn_000707 a i sh u u k a r a n i sh u u u k a N h a i g a e e e r u y o o k o o o n i i k i m a s U +cv_jpn_000708 o o r e m o k i n i n a r u n a +cv_jpn_000709 d a r e g a ts s U k a cl t e r u n o k a a w a k a r u n a i +cv_jpn_000710 p o r a z a n o b a j o N g a pau a g a r u t o o s U k o sh i u r e sh i +cv_jpn_000711 m a t a a t a r a sh i a i d o r u g a d e t e k i t a +cv_jpn_000712 m a j i d e y a cl t o n o k a +cv_jpn_000713 ch o o d o s u m o t o k i n i ky o o j u g a h a i I t e k i d a +cv_jpn_000714 i cl s o n i ch i N sh I t e o +cv_jpn_000715 s o o r e k a s h a t e n o t o o r e s u +cv_jpn_000716 f U t a r i w a r e j i i k i s e e s a N sh I t a +cv_jpn_000717 t o m a t o k a n a N g a n o w a k a i s o u s u g a k a k a cl t e r i u o +cv_jpn_000718 k o k o k a r a t a t e n a N a s u n o w a k i b i s i i +cv_jpn_000719 n i z u k e o s sh I cl k a r i sh i b o cl t e pau a j i k a n a j i m u y o h i s u r u +cv_jpn_000720 n e t o g e n i h a m a cl t a r a k a n e g a g a m a cl t a U +cv_jpn_000721 i t o k a e r i y o o n i n a r u N d a +cv_jpn_000722 k o s e e h a h a i y u t o i u y o r i a k u g a ts u y o e k a N j i +cv_jpn_000723 f i j i k a r u n o s a o pau m a z a m a z a t o m i sh e ts U k e r a r e t a +cv_jpn_000724 k o s U p a y o k e r e b a s o k o s o k o n o m o N d a e w a g a a m a N s e r u +cv_jpn_000725 m i N n a y a cl t e m a s U k a r a t a i j o o b u d e s U y o +cv_jpn_000726 k o n o t o sh o k a N pau h a i cl t a sh u N k a N k i n i i cl t a +cv_jpn_000727 k o n o d e N ch i s u u k i r e ch a cl t a +cv_jpn_000728 a m a y o d o r i s u r u t o k o r o g a n a k U t e k o m a cl t a +cv_jpn_000729 y a s U k u s u r u y o r i sh I ts u w a g e t a h o sh i +cv_jpn_000730 m a s e g o k o N n a k o t o i n a r o t o w a m o n a k a cl t a +cv_jpn_000731 s a i g o n i w a r a y o t o r i n i k u r u s U t a i r u +cv_jpn_000732 k o r a e a n a n a i m i g a r u d a +cv_jpn_000733 i i e +cv_jpn_000734 sh i +cv_jpn_000735 n i +cv_jpn_000736 h a ch i +cv_jpn_000737 h a i diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..9dee4198383a44fde082b04ec81055476b22e5d7 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/token @@ -0,0 +1,32 @@ +cv_jpn_000706 o sh i i k e e r y a j i b a N d e t o cl t a e k i n a +cv_jpn_000707 a i sh u u k a r a n i sh u u u k a N h a i g a e e e r u y o o k o o o n i i k i m a s U +cv_jpn_000708 o o r e m o k i n i n a r u n a +cv_jpn_000709 d a r e g a ts s U k a cl t e r u n o k a a w a k a r u n a i +cv_jpn_000710 p o r a z a n o b a j o N g a pau a g a r u t o o s U k o sh i u r e sh i +cv_jpn_000711 m a t a a t a r a sh i a i d o r u g a d e t e k i t a +cv_jpn_000712 m a j i d e y a cl t o n o k a +cv_jpn_000713 ch o o d o s u m o t o k i n i ky o o j u g a h a i I t e k i d a +cv_jpn_000714 i cl s o n i ch i N sh I t e o +cv_jpn_000715 s o o r e k a s h a t e n o t o o r e s u +cv_jpn_000716 f U t a r i w a r e j i i k i s e e s a N sh I t a +cv_jpn_000717 t o m a t o k a n a N g a n o w a k a i s o u s u g a k a k a cl t e r i u o +cv_jpn_000718 k o k o k a r a t a t e n a N a s u n o w a k i b i s i i +cv_jpn_000719 n i z u k e o s sh I cl k a r i sh i b o cl t e pau a j i k a n a j i m u y o h i s u r u +cv_jpn_000720 n e t o g e n i h a m a cl t a r a k a n e g a g a m a cl t a U +cv_jpn_000721 i t o k a e r i y o o n i n a r u N d a +cv_jpn_000722 k o s e e h a h a i y u t o i u y o r i a k u g a ts u y o e k a N j i +cv_jpn_000723 f i j i k a r u n o s a o pau m a z a m a z a t o m i sh e ts U k e r a r e t a +cv_jpn_000724 k o s U p a y o k e r e b a s o k o s o k o n o m o N d a e w a g a a m a N s e r u +cv_jpn_000725 m i N n a y a cl t e m a s U k a r a t a i j o o b u d e s U y o +cv_jpn_000726 k o n o t o sh o k a N pau h a i cl t a sh u N k a N k i n i i cl t a +cv_jpn_000727 k o n o d e N ch i s u u k i r e ch a cl t a +cv_jpn_000728 a m a y o d o r i s u r u t o k o r o g a n a k U t e k o m a cl t a +cv_jpn_000729 y a s U k u s u r u y o r i sh I ts u w a g e t a h o sh i +cv_jpn_000730 m a s e g o k o N n a k o t o i n a r o t o w a m o n a k a cl t a +cv_jpn_000731 s a i g o n i w a r a y o t o r i n i k u r u s U t a i r u +cv_jpn_000732 k o r a e a n a n a i m i g a r u d a +cv_jpn_000733 i i e +cv_jpn_000734 sh i +cv_jpn_000735 n i +cv_jpn_000736 h a ch i +cv_jpn_000737 h a i diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..d1b6458ce3f6c9d4d85ce2006ee4e90d15b8dd9a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.2/1best_recog/token_int @@ -0,0 +1,32 @@ +cv_jpn_000706 3 15 4 4 6 5 5 10 23 2 22 4 25 2 13 14 5 8 3 21 8 2 5 6 4 9 2 +cv_jpn_000707 2 4 15 7 7 6 2 10 2 9 4 15 7 7 7 6 2 13 24 2 4 16 2 5 5 5 10 7 23 3 3 6 3 3 3 9 4 4 6 4 11 2 12 18 +cv_jpn_000708 3 3 10 5 11 3 6 4 9 4 9 2 10 7 9 2 +cv_jpn_000709 14 2 10 5 16 2 26 12 18 6 2 21 8 5 10 7 9 3 6 2 2 17 2 6 2 10 7 9 2 4 +cv_jpn_000710 30 3 10 2 28 2 9 3 25 2 22 3 13 16 2 20 2 16 2 10 7 8 3 3 12 18 6 3 15 4 7 10 5 15 4 +cv_jpn_000711 11 2 8 2 2 8 2 10 2 15 4 2 4 14 3 10 7 16 2 14 5 8 5 6 4 8 2 +cv_jpn_000712 11 2 22 4 14 5 23 2 21 8 3 9 3 6 2 +cv_jpn_000713 27 3 3 14 3 12 7 11 3 8 3 6 4 9 4 29 3 3 22 7 16 2 24 2 4 19 8 5 6 4 14 2 +cv_jpn_000714 4 21 12 3 9 4 27 4 13 15 19 8 5 3 +cv_jpn_000715 12 3 3 10 5 6 2 12 24 2 8 5 9 3 8 3 3 10 5 12 7 +cv_jpn_000716 31 18 8 2 10 4 17 2 10 5 22 4 4 6 4 12 5 5 12 2 13 15 19 8 2 +cv_jpn_000717 8 3 11 2 8 3 6 2 9 2 13 16 2 9 3 17 2 6 2 4 12 3 7 12 7 16 2 6 2 6 2 21 8 5 10 4 7 3 +cv_jpn_000718 6 3 6 3 6 2 10 2 8 2 8 5 9 2 13 2 12 7 9 3 17 2 6 4 25 4 12 4 4 +cv_jpn_000719 9 4 28 7 6 5 3 12 15 19 21 6 2 10 4 15 4 25 3 21 8 5 20 2 22 4 6 2 9 2 22 4 11 7 23 3 24 4 12 7 10 7 +cv_jpn_000720 9 5 8 3 16 5 9 4 24 2 11 2 21 8 2 10 2 6 2 9 5 16 2 16 2 11 2 21 8 2 18 +cv_jpn_000721 4 8 3 6 2 5 10 4 23 3 3 9 4 9 2 10 7 13 14 2 +cv_jpn_000722 6 3 12 5 5 24 2 24 2 4 23 7 8 3 4 7 23 3 10 4 2 6 7 16 2 26 7 23 3 5 6 2 13 22 4 +cv_jpn_000723 31 4 22 4 6 2 10 7 9 3 12 2 3 20 11 2 28 2 11 2 28 2 8 3 11 4 15 5 26 18 6 5 10 2 10 5 8 2 +cv_jpn_000724 6 3 12 18 30 2 23 3 6 5 10 5 25 2 12 3 6 3 12 3 6 3 9 3 11 3 13 14 2 5 17 2 16 2 2 11 2 13 12 5 10 7 +cv_jpn_000725 11 4 13 9 2 23 2 21 8 5 11 2 12 18 6 2 10 2 8 2 4 22 3 3 25 7 14 5 12 18 23 3 +cv_jpn_000726 6 3 9 3 8 3 15 3 6 2 13 20 24 2 4 21 8 2 15 7 13 6 2 13 6 4 9 4 4 21 8 2 +cv_jpn_000727 6 3 9 3 14 5 13 27 4 12 7 7 6 4 10 5 27 2 21 8 2 +cv_jpn_000728 2 11 2 23 3 14 3 10 4 12 7 10 7 8 3 6 3 10 3 16 2 9 2 6 18 8 5 6 3 11 2 21 8 2 +cv_jpn_000729 23 2 12 18 6 7 12 7 10 7 23 3 10 4 15 19 26 7 17 2 16 5 8 2 24 3 15 4 +cv_jpn_000730 11 2 12 5 16 3 6 3 13 9 2 6 3 8 3 4 9 2 10 3 8 3 17 2 11 3 9 2 6 2 21 8 2 +cv_jpn_000731 12 2 4 16 3 9 4 17 2 10 2 23 3 8 3 10 4 9 4 6 7 10 7 12 18 8 2 4 10 7 +cv_jpn_000732 6 3 10 2 5 2 9 2 9 2 4 11 4 16 2 10 7 14 2 +cv_jpn_000733 4 4 5 +cv_jpn_000734 15 4 +cv_jpn_000735 9 4 +cv_jpn_000736 24 2 27 4 +cv_jpn_000737 24 2 4 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..667585f4b2c5e52659e96a96fc8fd8b708255d67 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/score @@ -0,0 +1,31 @@ +cv_jpn_000738 tensor(-2.5240) +cv_jpn_000739 tensor(-2.4127) +cv_jpn_000740 tensor(-1.8337) +cv_jpn_000741 tensor(-7.1202) +cv_jpn_000742 tensor(-2.9845) +cv_jpn_000743 tensor(-4.8759) +cv_jpn_000744 tensor(-7.1440) +cv_jpn_000745 tensor(-3.0080) +cv_jpn_000746 tensor(-3.9989) +cv_jpn_000747 tensor(-3.9217) +cv_jpn_000748 tensor(-3.3189) +cv_jpn_000749 tensor(-2.3591) +cv_jpn_000750 tensor(-4.0770) +cv_jpn_000751 tensor(-4.3251) +cv_jpn_000752 tensor(-5.9719) +cv_jpn_000753 tensor(-0.3511) +cv_jpn_000754 tensor(-0.1613) +cv_jpn_000755 tensor(-0.7038) +cv_jpn_000756 tensor(-0.9497) +cv_jpn_000757 tensor(-0.2403) +cv_jpn_000758 tensor(-1.9798) +cv_jpn_000759 tensor(-5.1895) +cv_jpn_000760 tensor(-2.2785) +cv_jpn_000761 tensor(-2.4720) +cv_jpn_000762 tensor(-3.5529) +cv_jpn_000763 tensor(-2.5379) +cv_jpn_000764 tensor(-1.4283) +cv_jpn_000765 tensor(-5.1784) +cv_jpn_000766 tensor(-5.1200) +cv_jpn_000767 tensor(-1.2981) +cv_jpn_000768 tensor(-1.1048) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..bb27a752d75ebcc9a51f75a5f118b0d0e2e36e57 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/text @@ -0,0 +1,31 @@ +cv_jpn_000738 t e e b r u n o o i e n i k a b i N g a a r i m a s U +cv_jpn_000739 w a t a sh i w a a m a i y a s a s a N p o sh i m a s U +cv_jpn_000740 a t a r a sh i i k u ts o o h a i cl t e d e k a k e m a s U +cv_jpn_000741 k o t o sh i n o n a ts e y a s u m i w a pau b u m i n i m o i k i m a sh I t a sh i i y a m a n i m o o n o o r i b a sh I t a +cv_jpn_000742 w a t a sh i w a i r o i r o n o b e N g o o j i b u N n o m u n e d e k o sh i r a e t e m i m a sh I t a +cv_jpn_000743 n a N d e k u m o sh o o o b a i h e t a n a N d a r o +cv_jpn_000744 t a r e N t o k a r a ky o k u a n a n i k a e t e k e e h I sh a k i g e +cv_jpn_000745 d o g e z a s e r e b a i cl t e m o N sh a n a i +cv_jpn_000746 d e e t a n o w a i d a k a n o ch i w a j i b u N t o o i cl t e e n o ky o o r i y o t a n o cl t a +cv_jpn_000747 k o n o g e e n i N n a N k a w a h I s a sh i b u r i n i m i t a +cv_jpn_000748 o o k i k u s a i d o ch i e N j i o s o r u +cv_jpn_000749 k a r e w a a t a m a k a k i m u sh i cl t a +cv_jpn_000750 o m a ch e sh I t e w a r i m a s U +cv_jpn_000751 o n o ky o k U s e N k a i j o w a k i i t e r u +cv_jpn_000752 r e e j z o o k o o a k e t a t o t a N n a n i g a h i ch u y o k a w a s u r e cl t a +cv_jpn_000753 i ch i +cv_jpn_000754 h a ch i +cv_jpn_000755 i i e +cv_jpn_000756 d e i +cv_jpn_000757 sh i ch i +cv_jpn_000758 y o o b o o d a s u n o N i k a u h I t o w a s U k u n a i +cv_jpn_000759 r o o k a r u t o k u y i u n o i I ky o e m a k a s e n o k o m a sh a r u +cv_jpn_000760 k o n o d a i k u w a a N k o g a o o k U t e y o k U k a i m a s U +cv_jpn_000761 j i sh o o h k i n a g a r a sh o o s e ts o o y o m i m a s U +cv_jpn_000762 k o N n a o k i n a g o g u r o ts U k e n a i t i k e n a i N d e s U k a +cv_jpn_000763 k a r e e n o b o o r i k o w a t o m a r a n a i +cv_jpn_000764 i k i t e i k a N m a n e +cv_jpn_000765 t a o j i t o s a k a k t t e k i n i h a ts u m e d a cl p a n e +cv_jpn_000766 s a N t e k i o t a sh o w a ch I k a a f U t e k a +cv_jpn_000767 k a w a g a h i a g a cl t e i d a +cv_jpn_000768 i ch u diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..bb27a752d75ebcc9a51f75a5f118b0d0e2e36e57 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/token @@ -0,0 +1,31 @@ +cv_jpn_000738 t e e b r u n o o i e n i k a b i N g a a r i m a s U +cv_jpn_000739 w a t a sh i w a a m a i y a s a s a N p o sh i m a s U +cv_jpn_000740 a t a r a sh i i k u ts o o h a i cl t e d e k a k e m a s U +cv_jpn_000741 k o t o sh i n o n a ts e y a s u m i w a pau b u m i n i m o i k i m a sh I t a sh i i y a m a n i m o o n o o r i b a sh I t a +cv_jpn_000742 w a t a sh i w a i r o i r o n o b e N g o o j i b u N n o m u n e d e k o sh i r a e t e m i m a sh I t a +cv_jpn_000743 n a N d e k u m o sh o o o b a i h e t a n a N d a r o +cv_jpn_000744 t a r e N t o k a r a ky o k u a n a n i k a e t e k e e h I sh a k i g e +cv_jpn_000745 d o g e z a s e r e b a i cl t e m o N sh a n a i +cv_jpn_000746 d e e t a n o w a i d a k a n o ch i w a j i b u N t o o i cl t e e n o ky o o r i y o t a n o cl t a +cv_jpn_000747 k o n o g e e n i N n a N k a w a h I s a sh i b u r i n i m i t a +cv_jpn_000748 o o k i k u s a i d o ch i e N j i o s o r u +cv_jpn_000749 k a r e w a a t a m a k a k i m u sh i cl t a +cv_jpn_000750 o m a ch e sh I t e w a r i m a s U +cv_jpn_000751 o n o ky o k U s e N k a i j o w a k i i t e r u +cv_jpn_000752 r e e j z o o k o o a k e t a t o t a N n a n i g a h i ch u y o k a w a s u r e cl t a +cv_jpn_000753 i ch i +cv_jpn_000754 h a ch i +cv_jpn_000755 i i e +cv_jpn_000756 d e i +cv_jpn_000757 sh i ch i +cv_jpn_000758 y o o b o o d a s u n o N i k a u h I t o w a s U k u n a i +cv_jpn_000759 r o o k a r u t o k u y i u n o i I ky o e m a k a s e n o k o m a sh a r u +cv_jpn_000760 k o n o d a i k u w a a N k o g a o o k U t e y o k U k a i m a s U +cv_jpn_000761 j i sh o o h k i n a g a r a sh o o s e ts o o y o m i m a s U +cv_jpn_000762 k o N n a o k i n a g o g u r o ts U k e n a i t i k e n a i N d e s U k a +cv_jpn_000763 k a r e e n o b o o r i k o w a t o m a r a n a i +cv_jpn_000764 i k i t e i k a N m a n e +cv_jpn_000765 t a o j i t o s a k a k t t e k i n i h a ts u m e d a cl p a n e +cv_jpn_000766 s a N t e k i o t a sh o w a ch I k a a f U t e k a +cv_jpn_000767 k a w a g a h i a g a cl t e i d a +cv_jpn_000768 i ch u diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..9b6e895c6f8df439d792ab4cf508d8ece57d45bd --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.3/1best_recog/token_int @@ -0,0 +1,31 @@ +cv_jpn_000738 8 5 5 25 10 7 9 3 3 4 5 9 4 6 2 25 4 13 16 2 2 10 4 11 2 12 18 +cv_jpn_000739 17 2 8 2 15 4 17 2 2 11 2 4 23 2 12 2 12 2 13 30 3 15 4 11 2 12 18 +cv_jpn_000740 2 8 2 10 2 15 4 4 6 7 26 3 3 24 2 4 21 8 5 14 5 6 2 6 5 11 2 12 18 +cv_jpn_000741 6 3 8 3 15 4 9 3 9 2 26 5 23 2 12 7 11 4 17 2 20 25 7 11 4 9 4 11 3 4 6 4 11 2 15 19 8 2 15 4 4 23 2 11 2 9 4 11 3 3 9 3 3 10 4 25 2 15 19 8 2 +cv_jpn_000742 17 2 8 2 15 4 17 2 4 10 3 4 10 3 9 3 25 5 13 16 3 3 22 4 25 7 13 9 3 11 7 9 5 14 5 6 3 15 4 10 2 5 8 5 11 4 11 2 15 19 8 2 +cv_jpn_000743 9 2 13 14 5 6 7 11 3 15 3 3 3 25 2 4 24 5 8 2 9 2 13 14 2 10 3 +cv_jpn_000744 8 2 10 5 13 8 3 6 2 10 2 29 3 6 7 2 9 2 9 4 6 2 5 8 5 6 5 5 24 19 15 2 6 4 16 5 +cv_jpn_000745 14 3 16 5 28 2 12 5 10 5 25 2 4 21 8 5 11 3 13 15 2 9 2 4 +cv_jpn_000746 14 5 5 8 2 9 3 17 2 4 14 2 6 2 9 3 27 4 17 2 22 4 25 7 13 8 3 3 4 21 8 5 5 9 3 29 3 3 10 4 23 3 8 2 9 3 21 8 2 +cv_jpn_000747 6 3 9 3 16 5 5 9 4 13 9 2 13 6 2 17 2 24 19 12 2 15 4 25 7 10 4 9 4 11 4 8 2 +cv_jpn_000748 3 3 6 4 6 7 12 2 4 14 3 27 4 5 13 22 4 3 12 3 10 7 +cv_jpn_000749 6 2 10 5 17 2 2 8 2 11 2 6 2 6 4 11 7 15 4 21 8 2 +cv_jpn_000750 3 11 2 27 5 15 19 8 5 17 2 10 4 11 2 12 18 +cv_jpn_000751 3 9 3 29 3 6 18 12 5 13 6 2 4 22 3 17 2 6 4 4 8 5 10 7 +cv_jpn_000752 10 5 5 22 28 3 3 6 3 3 2 6 5 8 2 8 3 8 2 13 9 2 9 4 16 2 24 4 27 7 23 3 6 2 17 2 12 7 10 5 21 8 2 +cv_jpn_000753 4 27 4 +cv_jpn_000754 24 2 27 4 +cv_jpn_000755 4 4 5 +cv_jpn_000756 14 5 4 +cv_jpn_000757 15 4 27 4 +cv_jpn_000758 23 3 3 25 3 3 14 2 12 7 9 3 13 4 6 2 7 24 19 8 3 17 2 12 18 6 7 9 2 4 +cv_jpn_000759 10 3 3 6 2 10 7 8 3 6 7 23 4 7 9 3 4 19 29 3 5 11 2 6 2 12 5 9 3 6 3 11 2 15 2 10 7 +cv_jpn_000760 6 3 9 3 14 2 4 6 7 17 2 2 13 6 3 16 2 3 3 6 18 8 5 23 3 6 18 6 2 4 11 2 12 18 +cv_jpn_000761 22 4 15 3 3 24 6 4 9 2 16 2 10 2 15 3 3 12 5 26 3 3 23 3 11 4 11 2 12 18 +cv_jpn_000762 6 3 13 9 2 3 6 4 9 2 16 3 16 7 10 3 26 18 6 5 9 2 4 8 4 6 5 9 2 4 13 14 5 12 18 6 2 +cv_jpn_000763 6 2 10 5 5 9 3 25 3 3 10 4 6 3 17 2 8 3 11 2 10 2 9 2 4 +cv_jpn_000764 4 6 4 8 5 4 6 2 13 11 2 9 5 +cv_jpn_000765 8 2 3 22 4 8 3 12 2 6 2 6 8 8 5 6 4 9 4 24 2 26 7 11 5 14 2 21 30 2 9 5 +cv_jpn_000766 12 2 13 8 5 6 4 3 8 2 15 3 17 2 27 19 6 2 2 31 18 8 5 6 2 +cv_jpn_000767 6 2 17 2 16 2 24 4 2 16 2 21 8 5 4 14 2 +cv_jpn_000768 4 27 7 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..163d967e19648e983b9f73628c64292bdb00d770 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/score @@ -0,0 +1,31 @@ +cv_jpn_000769 tensor(-0.3849) +cv_jpn_000770 tensor(-0.2501) +cv_jpn_000771 tensor(-0.3566) +cv_jpn_000772 tensor(-0.9793) +cv_jpn_000773 tensor(-2.8481) +cv_jpn_000774 tensor(-4.2992) +cv_jpn_000775 tensor(-2.1015) +cv_jpn_000776 tensor(-4.1025) +cv_jpn_000777 tensor(-2.3930) +cv_jpn_000778 tensor(-1.9699) +cv_jpn_000779 tensor(-2.7866) +cv_jpn_000780 tensor(-4.0702) +cv_jpn_000781 tensor(-7.3020) +cv_jpn_000782 tensor(-14.1145) +cv_jpn_000783 tensor(-3.4423) +cv_jpn_000784 tensor(-2.8450) +cv_jpn_000785 tensor(-2.1184) +cv_jpn_000786 tensor(-4.0696) +cv_jpn_000787 tensor(-2.0722) +cv_jpn_000788 tensor(-12.5293) +cv_jpn_000789 tensor(-2.7545) +cv_jpn_000790 tensor(-6.6307) +cv_jpn_000791 tensor(-10.5875) +cv_jpn_000792 tensor(-9.6005) +cv_jpn_000793 tensor(-6.6856) +cv_jpn_000794 tensor(-6.8149) +cv_jpn_000795 tensor(-10.4779) +cv_jpn_000796 tensor(-5.0523) +cv_jpn_000797 tensor(-4.0708) +cv_jpn_000798 tensor(-9.5973) +cv_jpn_000799 tensor(-5.0210) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..1c8596299448ed607354affc17878584b7bbefa4 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/text @@ -0,0 +1,31 @@ +cv_jpn_000769 n o +cv_jpn_000770 sh I ch i +cv_jpn_000771 k o +cv_jpn_000772 i i e +cv_jpn_000773 n a m a i e k a r a sh e t e k U t o o s u g i r u +cv_jpn_000774 j i k o n o s o t o n i a r u t o i u n o w a t a N n i j i k o n o sh I k a N s o ts s o n i a r u t o i u k o t o d e n a k u +cv_jpn_000775 s o r u w a sh i r a n a k U t e i d e s U +cv_jpn_000776 k i m i t o b o k u n o ky o o ts u n o sh i g e e w a d a r e h I t o r i m i a t a r a n a i +cv_jpn_000777 s u g e e o o t o N n a cl t e k i t e r u n o n a +cv_jpn_000778 k o n o h e N d e s U k o sh i y a s u m a sh o o +cv_jpn_000779 d e N sh a r i n o r u t o k i k i cl t o o k a i n a s U +cv_jpn_000780 t a m a g a i k o k o j i u g u r a N b u r a i e s U +cv_jpn_000781 g e e s U p i i w a m a cl k i i o ts u j i t e i n e s U t o sh i d r i g a cl t a t u +cv_jpn_000782 n o o g o o y a m e s a r u o e n a i k h I t o k a a r i k a N n e N k i y o o m o k o n o f U k I k e o o n i h I k i z u r a r e t e i r u t o o m o o s U +cv_jpn_000783 n a N d e k o n o r o cl t o sh o t a i m e n a n o n i n a r e g a r e sh i N d a +cv_jpn_000784 f U ts u u d e a r u k o t a m o r i cl p a n a k o s e +cv_jpn_000785 ts u y o b i d e t a N j i k a N d e g o o k a i n i i t a m e r u +cv_jpn_000786 b a k u m a ts u n o d e k i g o k a w a i m a n i ts u r u j i r o ky o o k u N n o y a m a d e s U +cv_jpn_000787 m N k o o k a r a m a ch i n o w a k a r i g a m i e t e k i t a +cv_jpn_000788 m e N n i w u i u b e k I ch i h w a k a n a n a k a cl t a n a r i m i y u b i k I k o t o g a o m o y u k a b a n a k a cl t u +cv_jpn_000789 t a m i s i n i k a e n a k i a cl t e m i r u +cv_jpn_000790 b o k U sh I k a i n a i g i m i m a i n a i k o r e w a o o t e N s e g a i k a +cv_jpn_000791 ch i k e i j o k a n i j u g o o t o m a n u n o m i ch i w a m u k a s o k a cl t e i n a k a +cv_jpn_000792 k a g u j o t e N w a pau k o N p o N t e i n a k a i ch i k u u s u n a a cl t a +cv_jpn_000793 k o r u m o n o k o r e w a g o h a N h a r e o t u n a N n a r t o p a N h a a +cv_jpn_000794 k o o i t e k I t o cl k a N t e k i n i pau p o o i sh I s u t e k i n i w a r e w a r e m o j i k o w a m a s u m a s u z u m e e t o n a r u n o d e a r u +cv_jpn_000795 h i j o o sh I k i d a a r u k o t o w a pau b u ch o o i m i s u r u n o m i d e n a k u r u pau sh a k a i e t e k i n i a k U t o m o k a N g a e r a r u u d e a r u +cv_jpn_000796 j o o sh I k i g a n a o t o k U u sh u t e k i n a ch i sh I k i d e a r u n i h a sh i k a r a k u w a +cv_jpn_000797 k o N n a k o t o o d e o g u r a r e t e n a s a k i n h a i +cv_jpn_000798 k a k o t o m i r a e t o g a j i k o m u j u N t e k i n i g e N z a i n i o i t e t a r i k I s u r u t o i u i n i w a g e N d a g a k a t a ch o m o t a m a e k e r e m a n a r a n a i +cv_jpn_000799 sh o k i o n o t a k a s a g a h a d o u N n a u diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..1c8596299448ed607354affc17878584b7bbefa4 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/token @@ -0,0 +1,31 @@ +cv_jpn_000769 n o +cv_jpn_000770 sh I ch i +cv_jpn_000771 k o +cv_jpn_000772 i i e +cv_jpn_000773 n a m a i e k a r a sh e t e k U t o o s u g i r u +cv_jpn_000774 j i k o n o s o t o n i a r u t o i u n o w a t a N n i j i k o n o sh I k a N s o ts s o n i a r u t o i u k o t o d e n a k u +cv_jpn_000775 s o r u w a sh i r a n a k U t e i d e s U +cv_jpn_000776 k i m i t o b o k u n o ky o o ts u n o sh i g e e w a d a r e h I t o r i m i a t a r a n a i +cv_jpn_000777 s u g e e o o t o N n a cl t e k i t e r u n o n a +cv_jpn_000778 k o n o h e N d e s U k o sh i y a s u m a sh o o +cv_jpn_000779 d e N sh a r i n o r u t o k i k i cl t o o k a i n a s U +cv_jpn_000780 t a m a g a i k o k o j i u g u r a N b u r a i e s U +cv_jpn_000781 g e e s U p i i w a m a cl k i i o ts u j i t e i n e s U t o sh i d r i g a cl t a t u +cv_jpn_000782 n o o g o o y a m e s a r u o e n a i k h I t o k a a r i k a N n e N k i y o o m o k o n o f U k I k e o o n i h I k i z u r a r e t e i r u t o o m o o s U +cv_jpn_000783 n a N d e k o n o r o cl t o sh o t a i m e n a n o n i n a r e g a r e sh i N d a +cv_jpn_000784 f U ts u u d e a r u k o t a m o r i cl p a n a k o s e +cv_jpn_000785 ts u y o b i d e t a N j i k a N d e g o o k a i n i i t a m e r u +cv_jpn_000786 b a k u m a ts u n o d e k i g o k a w a i m a n i ts u r u j i r o ky o o k u N n o y a m a d e s U +cv_jpn_000787 m N k o o k a r a m a ch i n o w a k a r i g a m i e t e k i t a +cv_jpn_000788 m e N n i w u i u b e k I ch i h w a k a n a n a k a cl t a n a r i m i y u b i k I k o t o g a o m o y u k a b a n a k a cl t u +cv_jpn_000789 t a m i s i n i k a e n a k i a cl t e m i r u +cv_jpn_000790 b o k U sh I k a i n a i g i m i m a i n a i k o r e w a o o t e N s e g a i k a +cv_jpn_000791 ch i k e i j o k a n i j u g o o t o m a n u n o m i ch i w a m u k a s o k a cl t e i n a k a +cv_jpn_000792 k a g u j o t e N w a pau k o N p o N t e i n a k a i ch i k u u s u n a a cl t a +cv_jpn_000793 k o r u m o n o k o r e w a g o h a N h a r e o t u n a N n a r t o p a N h a a +cv_jpn_000794 k o o i t e k I t o cl k a N t e k i n i pau p o o i sh I s u t e k i n i w a r e w a r e m o j i k o w a m a s u m a s u z u m e e t o n a r u n o d e a r u +cv_jpn_000795 h i j o o sh I k i d a a r u k o t o w a pau b u ch o o i m i s u r u n o m i d e n a k u r u pau sh a k a i e t e k i n i a k U t o m o k a N g a e r a r u u d e a r u +cv_jpn_000796 j o o sh I k i g a n a o t o k U u sh u t e k i n a ch i sh I k i d e a r u n i h a sh i k a r a k u w a +cv_jpn_000797 k o N n a k o t o o d e o g u r a r e t e n a s a k i n h a i +cv_jpn_000798 k a k o t o m i r a e t o g a j i k o m u j u N t e k i n i g e N z a i n i o i t e t a r i k I s u r u t o i u i n i w a g e N d a g a k a t a ch o m o t a m a e k e r e m a n a r a n a i +cv_jpn_000799 sh o k i o n o t a k a s a g a h a d o u N n a u diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..4c8e9603493cda8d5e0f8b735b5aa0bab1cdd640 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/logdir/output.4/1best_recog/token_int @@ -0,0 +1,31 @@ +cv_jpn_000769 9 3 +cv_jpn_000770 15 19 27 4 +cv_jpn_000771 6 3 +cv_jpn_000772 4 4 5 +cv_jpn_000773 9 2 11 2 4 5 6 2 10 2 15 5 8 5 6 18 8 3 3 12 7 16 4 10 7 +cv_jpn_000774 22 4 6 3 9 3 12 3 8 3 9 4 2 10 7 8 3 4 7 9 3 17 2 8 2 13 9 4 22 4 6 3 9 3 15 19 6 2 13 12 3 26 12 3 9 4 2 10 7 8 3 4 7 6 3 8 3 14 5 9 2 6 7 +cv_jpn_000775 12 3 10 7 17 2 15 4 10 2 9 2 6 18 8 5 4 14 5 12 18 +cv_jpn_000776 6 4 11 4 8 3 25 3 6 7 9 3 29 3 3 26 7 9 3 15 4 16 5 5 17 2 14 2 10 5 24 19 8 3 10 4 11 4 2 8 2 10 2 9 2 4 +cv_jpn_000777 12 7 16 5 5 3 3 8 3 13 9 2 21 8 5 6 4 8 5 10 7 9 3 9 2 +cv_jpn_000778 6 3 9 3 24 5 13 14 5 12 18 6 3 15 4 23 2 12 7 11 2 15 3 3 +cv_jpn_000779 14 5 13 15 2 10 4 9 3 10 7 8 3 6 4 6 4 21 8 3 3 6 2 4 9 2 12 18 +cv_jpn_000780 8 2 11 2 16 2 4 6 3 6 3 22 4 7 16 7 10 2 13 25 7 10 2 4 5 12 18 +cv_jpn_000781 16 5 5 12 18 30 4 4 17 2 11 2 21 6 4 4 3 26 7 22 4 8 5 4 9 5 12 18 8 3 15 4 14 10 4 16 2 21 8 2 8 7 +cv_jpn_000782 9 3 3 16 3 3 23 2 11 5 12 2 10 7 3 5 9 2 4 6 24 19 8 3 6 2 2 10 4 6 2 13 9 5 13 6 4 23 3 3 11 3 6 3 9 3 31 18 6 19 6 5 3 3 9 4 24 19 6 4 28 7 10 2 10 5 8 5 4 10 7 8 3 3 11 3 3 12 18 +cv_jpn_000783 9 2 13 14 5 6 3 9 3 10 3 21 8 3 15 3 8 2 4 11 5 9 2 9 3 9 4 9 2 10 5 16 2 10 5 15 4 13 14 2 +cv_jpn_000784 31 18 26 7 7 14 5 2 10 7 6 3 8 2 11 3 10 4 21 30 2 9 2 6 3 12 5 +cv_jpn_000785 26 7 23 3 25 4 14 5 8 2 13 22 4 6 2 13 14 5 16 3 3 6 2 4 9 4 4 8 2 11 5 10 7 +cv_jpn_000786 25 2 6 7 11 2 26 7 9 3 14 5 6 4 16 3 6 2 17 2 4 11 2 9 4 26 7 10 7 22 4 10 3 29 3 3 6 7 13 9 3 23 2 11 2 14 5 12 18 +cv_jpn_000787 11 13 6 3 3 6 2 10 2 11 2 27 4 9 3 17 2 6 2 10 4 16 2 11 4 5 8 5 6 4 8 2 +cv_jpn_000788 11 5 13 9 4 17 7 4 7 25 5 6 19 27 4 24 17 2 6 2 9 2 9 2 6 2 21 8 2 9 2 10 4 11 4 23 7 25 4 6 19 6 3 8 3 16 2 3 11 3 23 7 6 2 25 2 9 2 6 2 21 8 7 +cv_jpn_000789 8 2 11 4 12 4 9 4 6 2 5 9 2 6 4 2 21 8 5 11 4 10 7 +cv_jpn_000790 25 3 6 18 15 19 6 2 4 9 2 4 16 4 11 4 11 2 4 9 2 4 6 3 10 5 17 2 3 3 8 5 13 12 5 16 2 4 6 2 +cv_jpn_000791 27 4 6 5 4 22 3 6 2 9 4 22 7 16 3 3 8 3 11 2 9 7 9 3 11 4 27 4 17 2 11 7 6 2 12 3 6 2 21 8 5 4 9 2 6 2 +cv_jpn_000792 6 2 16 7 22 3 8 5 13 17 2 20 6 3 13 30 3 13 8 5 4 9 2 6 2 4 27 4 6 7 7 12 7 9 2 2 21 8 2 +cv_jpn_000793 6 3 10 7 11 3 9 3 6 3 10 5 17 2 16 3 24 2 13 24 2 10 5 3 8 7 9 2 13 9 2 10 8 3 30 2 13 24 2 2 +cv_jpn_000794 6 3 3 4 8 5 6 19 8 3 21 6 2 13 8 5 6 4 9 4 20 30 3 3 4 15 19 12 7 8 5 6 4 9 4 17 2 10 5 17 2 10 5 11 3 22 4 6 3 17 2 11 2 12 7 11 2 12 7 28 7 11 5 5 8 3 9 2 10 7 9 3 14 5 2 10 7 +cv_jpn_000795 24 4 22 3 3 15 19 6 4 14 2 2 10 7 6 3 8 3 17 2 20 25 7 27 3 3 4 11 4 12 7 10 7 9 3 11 4 14 5 9 2 6 7 10 7 20 15 2 6 2 4 5 8 5 6 4 9 4 2 6 18 8 3 11 3 6 2 13 16 2 5 10 2 10 7 7 14 5 2 10 7 +cv_jpn_000796 22 3 3 15 19 6 4 16 2 9 2 3 8 3 6 18 7 15 7 8 5 6 4 9 2 27 4 15 19 6 4 14 5 2 10 7 9 4 24 2 15 4 6 2 10 2 6 7 17 2 +cv_jpn_000797 6 3 13 9 2 6 3 8 3 3 14 5 3 16 7 10 2 10 5 8 5 9 2 12 2 6 4 9 24 2 4 +cv_jpn_000798 6 2 6 3 8 3 11 4 10 2 5 8 3 16 2 22 4 6 3 11 7 22 7 13 8 5 6 4 9 4 16 5 13 28 2 4 9 4 3 4 8 5 8 2 10 4 6 19 12 7 10 7 8 3 4 7 4 9 4 17 2 16 5 13 14 2 16 2 6 2 8 2 27 3 11 3 8 2 11 2 5 6 5 10 5 11 2 9 2 10 2 9 2 4 +cv_jpn_000799 15 3 6 4 3 9 3 8 2 6 2 12 2 16 2 24 2 14 3 7 13 9 2 7 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score new file mode 100644 index 0000000000000000000000000000000000000000..90b39d711da773dcb2c87db0889563030318704b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score @@ -0,0 +1,126 @@ +cv_jpn_000674 tensor(-4.7865) +cv_jpn_000675 tensor(-5.2669) +cv_jpn_000676 tensor(-2.5958) +cv_jpn_000677 tensor(-11.5213) +cv_jpn_000678 tensor(-7.6152) +cv_jpn_000679 tensor(-10.2451) +cv_jpn_000680 tensor(-13.6647) +cv_jpn_000681 tensor(-7.6773) +cv_jpn_000682 tensor(-1.1140) +cv_jpn_000683 tensor(-6.7306) +cv_jpn_000684 tensor(-5.8702) +cv_jpn_000685 tensor(-6.0293) +cv_jpn_000686 tensor(-4.4056) +cv_jpn_000687 tensor(-2.7974) +cv_jpn_000688 tensor(-4.5413) +cv_jpn_000689 tensor(-2.4262) +cv_jpn_000690 tensor(-4.7471) +cv_jpn_000691 tensor(-7.0449) +cv_jpn_000692 tensor(-3.2825) +cv_jpn_000693 tensor(-5.7881) +cv_jpn_000694 tensor(-13.4578) +cv_jpn_000695 tensor(-4.8678) +cv_jpn_000696 tensor(-6.8595) +cv_jpn_000697 tensor(-5.8728) +cv_jpn_000698 tensor(-9.5205) +cv_jpn_000699 tensor(-8.3750) +cv_jpn_000700 tensor(-5.6947) +cv_jpn_000701 tensor(-2.5160) +cv_jpn_000702 tensor(-1.7816) +cv_jpn_000703 tensor(-8.8324) +cv_jpn_000704 tensor(-8.5238) +cv_jpn_000705 tensor(-3.8933) +cv_jpn_000706 tensor(-2.8964) +cv_jpn_000707 tensor(-7.5510) +cv_jpn_000708 tensor(-1.6345) +cv_jpn_000709 tensor(-4.0085) +cv_jpn_000710 tensor(-5.9747) +cv_jpn_000711 tensor(-2.2814) +cv_jpn_000712 tensor(-0.5955) +cv_jpn_000713 tensor(-5.3540) +cv_jpn_000714 tensor(-1.4331) +cv_jpn_000715 tensor(-3.3482) +cv_jpn_000716 tensor(-4.2575) +cv_jpn_000717 tensor(-3.5661) +cv_jpn_000718 tensor(-2.0556) +cv_jpn_000719 tensor(-9.1988) +cv_jpn_000720 tensor(-2.2123) +cv_jpn_000721 tensor(-3.2793) +cv_jpn_000722 tensor(-5.8913) +cv_jpn_000723 tensor(-2.2871) +cv_jpn_000724 tensor(-3.8928) +cv_jpn_000725 tensor(-2.8580) +cv_jpn_000726 tensor(-2.4423) +cv_jpn_000727 tensor(-1.5716) +cv_jpn_000728 tensor(-2.9786) +cv_jpn_000729 tensor(-2.8097) +cv_jpn_000730 tensor(-2.1821) +cv_jpn_000731 tensor(-1.3403) +cv_jpn_000732 tensor(-4.0948) +cv_jpn_000733 tensor(-1.2719) +cv_jpn_000734 tensor(-0.7033) +cv_jpn_000735 tensor(-0.8032) +cv_jpn_000736 tensor(-0.7715) +cv_jpn_000737 tensor(-0.1481) +cv_jpn_000738 tensor(-2.5240) +cv_jpn_000739 tensor(-2.4127) +cv_jpn_000740 tensor(-1.8337) +cv_jpn_000741 tensor(-7.1202) +cv_jpn_000742 tensor(-2.9845) +cv_jpn_000743 tensor(-4.8759) +cv_jpn_000744 tensor(-7.1440) +cv_jpn_000745 tensor(-3.0080) +cv_jpn_000746 tensor(-3.9989) +cv_jpn_000747 tensor(-3.9217) +cv_jpn_000748 tensor(-3.3189) +cv_jpn_000749 tensor(-2.3591) +cv_jpn_000750 tensor(-4.0770) +cv_jpn_000751 tensor(-4.3251) +cv_jpn_000752 tensor(-5.9719) +cv_jpn_000753 tensor(-0.3511) +cv_jpn_000754 tensor(-0.1613) +cv_jpn_000755 tensor(-0.7038) +cv_jpn_000756 tensor(-0.9497) +cv_jpn_000757 tensor(-0.2403) +cv_jpn_000758 tensor(-1.9798) +cv_jpn_000759 tensor(-5.1895) +cv_jpn_000760 tensor(-2.2785) +cv_jpn_000761 tensor(-2.4720) +cv_jpn_000762 tensor(-3.5529) +cv_jpn_000763 tensor(-2.5379) +cv_jpn_000764 tensor(-1.4283) +cv_jpn_000765 tensor(-5.1784) +cv_jpn_000766 tensor(-5.1200) +cv_jpn_000767 tensor(-1.2981) +cv_jpn_000768 tensor(-1.1048) +cv_jpn_000769 tensor(-0.3849) +cv_jpn_000770 tensor(-0.2501) +cv_jpn_000771 tensor(-0.3566) +cv_jpn_000772 tensor(-0.9793) +cv_jpn_000773 tensor(-2.8481) +cv_jpn_000774 tensor(-4.2992) +cv_jpn_000775 tensor(-2.1015) +cv_jpn_000776 tensor(-4.1025) +cv_jpn_000777 tensor(-2.3930) +cv_jpn_000778 tensor(-1.9699) +cv_jpn_000779 tensor(-2.7866) +cv_jpn_000780 tensor(-4.0702) +cv_jpn_000781 tensor(-7.3020) +cv_jpn_000782 tensor(-14.1145) +cv_jpn_000783 tensor(-3.4423) +cv_jpn_000784 tensor(-2.8450) +cv_jpn_000785 tensor(-2.1184) +cv_jpn_000786 tensor(-4.0696) +cv_jpn_000787 tensor(-2.0722) +cv_jpn_000788 tensor(-12.5293) +cv_jpn_000789 tensor(-2.7545) +cv_jpn_000790 tensor(-6.6307) +cv_jpn_000791 tensor(-10.5875) +cv_jpn_000792 tensor(-9.6005) +cv_jpn_000793 tensor(-6.6856) +cv_jpn_000794 tensor(-6.8149) +cv_jpn_000795 tensor(-10.4779) +cv_jpn_000796 tensor(-5.0523) +cv_jpn_000797 tensor(-4.0708) +cv_jpn_000798 tensor(-9.5973) +cv_jpn_000799 tensor(-5.0210) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..abd57dc1025b6bb4451d61e4b4a998b5cc2314b2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/hyp.trn @@ -0,0 +1,126 @@ +b o k u n o i e e g a c l t a k a i n a N n o n a m a e N n i k u r a b e r u t o b o k u n i w a n a j i m i n o n a i p a u n a b a e b a k a r i d a k e d o (cv_jpn_000674-cv_jpn_000674) +n a i o o s o n o m o n o y o r i p a u f u i n i k i g a u k e t e r u (cv_jpn_000675-cv_jpn_000675) +b o k u n o s h I c l t e i u m o n o t o w a s U k o s h i c h i g a c l t e i t a (cv_jpn_000676-cv_jpn_000676) +k a k a a r u t a c h i m a n i o o i c l t e p a u s e k a i g a i j s h I k i m e N t e k i d e a r i p a u w a r e w a r e n o j i k o g a j s h I k i s a y o o t e k i d e a r u t o k a N g e e r a r e r u t o k s h i (cv_jpn_000677-cv_jpn_000677) +i e n i k t a n e N g a a j i w a s a N h y a k u m a i h o r o d e p a u c h o o d a s h I t a b u N t o o n a j i g u r a i d a (cv_jpn_000678-cv_jpn_000678) +h a a w a p i t a m a r i t s u b a a g u n o s e e s h i N y o o i n i n u u N h I t e i r u t o k i n i b e s h i o o m o (cv_jpn_000679-cv_jpn_000679) +t a t a N d e a r h a N t e n o h i r o g e r a b a a c h i k o c h i N i t s u g i h a g i a a r i p a u k a t a k o c h i n i d e k i t a h o k o r o b i n a N k a k y o o e N n o m a m o n i n a c l t e i r u (cv_jpn_000680-cv_jpn_000680) +k a r a g a k a n o d e b e r e n a a k a n i h o o m o N k i b o o n o b u s h o o y o b i c h o o z a k i b o o n o o b u g o m a i k a r a t o o r i d e s U (cv_jpn_000681-cv_jpn_000681) +k a N b a w a a t o t e m o s a m i d e s U (cv_jpn_000682-cv_jpn_000682) +k i N c h o o s h I t a k a h a o t s U k i d e p a c l t a w a d a s e k i n i h a i r u (cv_jpn_000683-cv_jpn_000683) +m a s a N k y u u r e N t o i c l t a f s u u k a k a r o k e N j i k i b u t s s U (cv_jpn_000684-cv_jpn_000684) +g o j i y o s h i d e s u s u m e t e t s s o g o N g a w a r u k u n a c l t a r a h I c l k o m e r u e a r e k U c h i (cv_jpn_000685-cv_jpn_000685) +m u j u N t e k i p a u j i k o t o o i t s U t e k i n i p a u j i k o o j i s h i N o k e e s e e s o r u s h a k a i w a (cv_jpn_000686-cv_jpn_000686) +f a N n o i k e N n i n a r a s a d e r u n a (cv_jpn_000687-cv_jpn_000687) +h i n e g a a s o b i t a y o o r a z e N k a i d e k o c h o o m i t e i r u (cv_jpn_000688-cv_jpn_000688) +i c h i d o w a k o N p o t a a j i k a N o n o N d e m i t a i (cv_jpn_000689-cv_jpn_000689) +k o o i n o k o s h I t e p a u o t o o s a N t o k a a s a N w a N d e t e i k i m a s h I t a (cv_jpn_000690-cv_jpn_000690) +s h I k a s h I t e s o r e g a t s U k r a r e t a m o n o k a r a t s U k u r u m o n o e t o s h I t e d o k u m a d e m o r a w a r u n i s e m a r u t o i u t o k i p a u w a r e w a r i n i c h o c l k a N t e k i d e a r u (cv_jpn_000691-cv_jpn_000691) +h a i n i t a m a c l t a k e m u r i o h a k i d a s h i k u r a i k o o e n i s h I s e N o m o k e r u (cv_jpn_000692-cv_jpn_000692) +w a d a w a d a s h n a b u s o k u d e c h i s e N i N n a r i s o (cv_jpn_000693-cv_jpn_000693) +t s e N s h e k a i n o m o t s U k a t a c h s h i t a s h i n o i y a i u r u s e e s a i y o s h I k u t o s a i o t o w a p a u h a n a s h I t e k a N g a e r u k o t o w a d e k i n a i (cv_jpn_000694-cv_jpn_000694) +s a k e n o m a i n o n i p i i r u b a r a t o y o r e t a (cv_jpn_000695-cv_jpn_000695) +s o r e o t a t a k u h a k u n o z e N d a N k a i s h I k u i t e e d o n k a a k U t o n o m i m i r u k o t o w a (cv_jpn_000696-cv_jpn_000696) +n o k k y o e N o f u a t s u n i s u m o h o d e g e e m o y a c l t e i t a (cv_jpn_000697-cv_jpn_000697) +w a d a i w a a n a i s h u m a t a N k o b a e s h i s a N t o o a s o b e i m a s U (cv_jpn_000698-cv_jpn_000698) +h a s o o k o n i h e t o o g a i m a s u n e a r o h I t o o w a t a a r e e t e s h o o (cv_jpn_000699-cv_jpn_000699) +w a t a s h i w a k i n o o k a n a n o o d o o g a i t a i t e s U (cv_jpn_000700-cv_jpn_000700) +k y o o r e N k a n a p e N k y u o o s h I t e i m a s U (cv_jpn_000701-cv_jpn_000701) +c h o c l t o s u m i m a s e (cv_jpn_000702-cv_jpn_000702) +c h I k a s h t o n o o o o e N b u r y o w a y a k e k i m i n i k a e c l d e h a g e s U p e u n o c l t a (cv_jpn_000703-cv_jpn_000703) +i t s u m o k o n o e N p I i t s s o o t s s h U k a c l t e i t a n o t e m i j i k a h a h a n a r i m a s h I t a (cv_jpn_000704-cv_jpn_000704) +w a t a s h i w a e i g o g a h a n a s t e m o s U (cv_jpn_000705-cv_jpn_000705) +o s h i i k e e r y a j i b a N d e t o c l t a e k i n a (cv_jpn_000706-cv_jpn_000706) +a i s h u u k a r a n i s h u u u k a N h a i g a e e e r u y o o k o o o n i i k i m a s U (cv_jpn_000707-cv_jpn_000707) +o o r e m o k i n i n a r u n a (cv_jpn_000708-cv_jpn_000708) +d a r e g a t s s U k a c l t e r u n o k a a w a k a r u n a i (cv_jpn_000709-cv_jpn_000709) +p o r a z a n o b a j o N g a p a u a g a r u t o o s U k o s h i u r e s h i (cv_jpn_000710-cv_jpn_000710) +m a t a a t a r a s h i a i d o r u g a d e t e k i t a (cv_jpn_000711-cv_jpn_000711) +m a j i d e y a c l t o n o k a (cv_jpn_000712-cv_jpn_000712) +c h o o d o s u m o t o k i n i k y o o j u g a h a i I t e k i d a (cv_jpn_000713-cv_jpn_000713) +i c l s o n i c h i N s h I t e o (cv_jpn_000714-cv_jpn_000714) +s o o r e k a s h a t e n o t o o r e s u (cv_jpn_000715-cv_jpn_000715) +f U t a r i w a r e j i i k i s e e s a N s h I t a (cv_jpn_000716-cv_jpn_000716) +t o m a t o k a n a N g a n o w a k a i s o u s u g a k a k a c l t e r i u o (cv_jpn_000717-cv_jpn_000717) +k o k o k a r a t a t e n a N a s u n o w a k i b i s i i (cv_jpn_000718-cv_jpn_000718) +n i z u k e o s s h I c l k a r i s h i b o c l t e p a u a j i k a n a j i m u y o h i s u r u (cv_jpn_000719-cv_jpn_000719) +n e t o g e n i h a m a c l t a r a k a n e g a g a m a c l t a U (cv_jpn_000720-cv_jpn_000720) +i t o k a e r i y o o n i n a r u N d a (cv_jpn_000721-cv_jpn_000721) +k o s e e h a h a i y u t o i u y o r i a k u g a t s u y o e k a N j i (cv_jpn_000722-cv_jpn_000722) +f i j i k a r u n o s a o p a u m a z a m a z a t o m i s h e t s U k e r a r e t a (cv_jpn_000723-cv_jpn_000723) +k o s U p a y o k e r e b a s o k o s o k o n o m o N d a e w a g a a m a N s e r u (cv_jpn_000724-cv_jpn_000724) +m i N n a y a c l t e m a s U k a r a t a i j o o b u d e s U y o (cv_jpn_000725-cv_jpn_000725) +k o n o t o s h o k a N p a u h a i c l t a s h u N k a N k i n i i c l t a (cv_jpn_000726-cv_jpn_000726) +k o n o d e N c h i s u u k i r e c h a c l t a (cv_jpn_000727-cv_jpn_000727) +a m a y o d o r i s u r u t o k o r o g a n a k U t e k o m a c l t a (cv_jpn_000728-cv_jpn_000728) +y a s U k u s u r u y o r i s h I t s u w a g e t a h o s h i (cv_jpn_000729-cv_jpn_000729) +m a s e g o k o N n a k o t o i n a r o t o w a m o n a k a c l t a (cv_jpn_000730-cv_jpn_000730) +s a i g o n i w a r a y o t o r i n i k u r u s U t a i r u (cv_jpn_000731-cv_jpn_000731) +k o r a e a n a n a i m i g a r u d a (cv_jpn_000732-cv_jpn_000732) +i i e (cv_jpn_000733-cv_jpn_000733) +s h i (cv_jpn_000734-cv_jpn_000734) +n i (cv_jpn_000735-cv_jpn_000735) +h a c h i (cv_jpn_000736-cv_jpn_000736) +h a i (cv_jpn_000737-cv_jpn_000737) +t e e b r u n o o i e n i k a b i N g a a r i m a s U (cv_jpn_000738-cv_jpn_000738) +w a t a s h i w a a m a i y a s a s a N p o s h i m a s U (cv_jpn_000739-cv_jpn_000739) +a t a r a s h i i k u t s o o h a i c l t e d e k a k e m a s U (cv_jpn_000740-cv_jpn_000740) +k o t o s h i n o n a t s e y a s u m i w a p a u b u m i n i m o i k i m a s h I t a s h i i y a m a n i m o o n o o r i b a s h I t a (cv_jpn_000741-cv_jpn_000741) +w a t a s h i w a i r o i r o n o b e N g o o j i b u N n o m u n e d e k o s h i r a e t e m i m a s h I t a (cv_jpn_000742-cv_jpn_000742) +n a N d e k u m o s h o o o b a i h e t a n a N d a r o (cv_jpn_000743-cv_jpn_000743) +t a r e N t o k a r a k y o k u a n a n i k a e t e k e e h I s h a k i g e (cv_jpn_000744-cv_jpn_000744) +d o g e z a s e r e b a i c l t e m o N s h a n a i (cv_jpn_000745-cv_jpn_000745) +d e e t a n o w a i d a k a n o c h i w a j i b u N t o o i c l t e e n o k y o o r i y o t a n o c l t a (cv_jpn_000746-cv_jpn_000746) +k o n o g e e n i N n a N k a w a h I s a s h i b u r i n i m i t a (cv_jpn_000747-cv_jpn_000747) +o o k i k u s a i d o c h i e N j i o s o r u (cv_jpn_000748-cv_jpn_000748) +k a r e w a a t a m a k a k i m u s h i c l t a (cv_jpn_000749-cv_jpn_000749) +o m a c h e s h I t e w a r i m a s U (cv_jpn_000750-cv_jpn_000750) +o n o k y o k U s e N k a i j o w a k i i t e r u (cv_jpn_000751-cv_jpn_000751) +r e e j z o o k o o a k e t a t o t a N n a n i g a h i c h u y o k a w a s u r e c l t a (cv_jpn_000752-cv_jpn_000752) +i c h i (cv_jpn_000753-cv_jpn_000753) +h a c h i (cv_jpn_000754-cv_jpn_000754) +i i e (cv_jpn_000755-cv_jpn_000755) +d e i (cv_jpn_000756-cv_jpn_000756) +s h i c h i (cv_jpn_000757-cv_jpn_000757) +y o o b o o d a s u n o N i k a u h I t o w a s U k u n a i (cv_jpn_000758-cv_jpn_000758) +r o o k a r u t o k u y i u n o i I k y o e m a k a s e n o k o m a s h a r u (cv_jpn_000759-cv_jpn_000759) +k o n o d a i k u w a a N k o g a o o k U t e y o k U k a i m a s U (cv_jpn_000760-cv_jpn_000760) +j i s h o o h k i n a g a r a s h o o s e t s o o y o m i m a s U (cv_jpn_000761-cv_jpn_000761) +k o N n a o k i n a g o g u r o t s U k e n a i t i k e n a i N d e s U k a (cv_jpn_000762-cv_jpn_000762) +k a r e e n o b o o r i k o w a t o m a r a n a i (cv_jpn_000763-cv_jpn_000763) +i k i t e i k a N m a n e (cv_jpn_000764-cv_jpn_000764) +t a o j i t o s a k a k t t e k i n i h a t s u m e d a c l p a n e (cv_jpn_000765-cv_jpn_000765) +s a N t e k i o t a s h o w a c h I k a a f U t e k a (cv_jpn_000766-cv_jpn_000766) +k a w a g a h i a g a c l t e i d a (cv_jpn_000767-cv_jpn_000767) +i c h u (cv_jpn_000768-cv_jpn_000768) +n o (cv_jpn_000769-cv_jpn_000769) +s h I c h i (cv_jpn_000770-cv_jpn_000770) +k o (cv_jpn_000771-cv_jpn_000771) +i i e (cv_jpn_000772-cv_jpn_000772) +n a m a i e k a r a s h e t e k U t o o s u g i r u (cv_jpn_000773-cv_jpn_000773) +j i k o n o s o t o n i a r u t o i u n o w a t a N n i j i k o n o s h I k a N s o t s s o n i a r u t o i u k o t o d e n a k u (cv_jpn_000774-cv_jpn_000774) +s o r u w a s h i r a n a k U t e i d e s U (cv_jpn_000775-cv_jpn_000775) +k i m i t o b o k u n o k y o o t s u n o s h i g e e w a d a r e h I t o r i m i a t a r a n a i (cv_jpn_000776-cv_jpn_000776) +s u g e e o o t o N n a c l t e k i t e r u n o n a (cv_jpn_000777-cv_jpn_000777) +k o n o h e N d e s U k o s h i y a s u m a s h o o (cv_jpn_000778-cv_jpn_000778) +d e N s h a r i n o r u t o k i k i c l t o o k a i n a s U (cv_jpn_000779-cv_jpn_000779) +t a m a g a i k o k o j i u g u r a N b u r a i e s U (cv_jpn_000780-cv_jpn_000780) +g e e s U p i i w a m a c l k i i o t s u j i t e i n e s U t o s h i d r i g a c l t a t u (cv_jpn_000781-cv_jpn_000781) +n o o g o o y a m e s a r u o e n a i k h I t o k a a r i k a N n e N k i y o o m o k o n o f U k I k e o o n i h I k i z u r a r e t e i r u t o o m o o s U (cv_jpn_000782-cv_jpn_000782) +n a N d e k o n o r o c l t o s h o t a i m e n a n o n i n a r e g a r e s h i N d a (cv_jpn_000783-cv_jpn_000783) +f U t s u u d e a r u k o t a m o r i c l p a n a k o s e (cv_jpn_000784-cv_jpn_000784) +t s u y o b i d e t a N j i k a N d e g o o k a i n i i t a m e r u (cv_jpn_000785-cv_jpn_000785) +b a k u m a t s u n o d e k i g o k a w a i m a n i t s u r u j i r o k y o o k u N n o y a m a d e s U (cv_jpn_000786-cv_jpn_000786) +m N k o o k a r a m a c h i n o w a k a r i g a m i e t e k i t a (cv_jpn_000787-cv_jpn_000787) +m e N n i w u i u b e k I c h i h w a k a n a n a k a c l t a n a r i m i y u b i k I k o t o g a o m o y u k a b a n a k a c l t u (cv_jpn_000788-cv_jpn_000788) +t a m i s i n i k a e n a k i a c l t e m i r u (cv_jpn_000789-cv_jpn_000789) +b o k U s h I k a i n a i g i m i m a i n a i k o r e w a o o t e N s e g a i k a (cv_jpn_000790-cv_jpn_000790) +c h i k e i j o k a n i j u g o o t o m a n u n o m i c h i w a m u k a s o k a c l t e i n a k a (cv_jpn_000791-cv_jpn_000791) +k a g u j o t e N w a p a u k o N p o N t e i n a k a i c h i k u u s u n a a c l t a (cv_jpn_000792-cv_jpn_000792) +k o r u m o n o k o r e w a g o h a N h a r e o t u n a N n a r t o p a N h a a (cv_jpn_000793-cv_jpn_000793) +k o o i t e k I t o c l k a N t e k i n i p a u p o o i s h I s u t e k i n i w a r e w a r e m o j i k o w a m a s u m a s u z u m e e t o n a r u n o d e a r u (cv_jpn_000794-cv_jpn_000794) +h i j o o s h I k i d a a r u k o t o w a p a u b u c h o o i m i s u r u n o m i d e n a k u r u p a u s h a k a i e t e k i n i a k U t o m o k a N g a e r a r u u d e a r u (cv_jpn_000795-cv_jpn_000795) +j o o s h I k i g a n a o t o k U u s h u t e k i n a c h i s h I k i d e a r u n i h a s h i k a r a k u w a (cv_jpn_000796-cv_jpn_000796) +k o N n a k o t o o d e o g u r a r e t e n a s a k i n h a i (cv_jpn_000797-cv_jpn_000797) +k a k o t o m i r a e t o g a j i k o m u j u N t e k i n i g e N z a i n i o i t e t a r i k I s u r u t o i u i n i w a g e N d a g a k a t a c h o m o t a m a e k e r e m a n a r a n a i (cv_jpn_000798-cv_jpn_000798) +s h o k i o n o t a k a s a g a h a d o u N n a u (cv_jpn_000799-cv_jpn_000799) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..0220102720ac47cd40c5be2f2be1c1e2b826b48b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/ref.trn @@ -0,0 +1,126 @@ +b o k u n o i e g a a c l t a k a i d a N n o n a m a e n i k u r a b e r u t o b o k u n i w a n a j i m i n o n a i n a m a e b a k a r i d a k e d o (cv_jpn_000674-cv_jpn_000674) +n a i y o o s o n o m o n o y o r i f u N i k i g a u k e t e r u (cv_jpn_000675-cv_jpn_000675) +b o k u n o s h i c l t e i r u m o n o t o w a s U k o s h I c h i g a c l t e i t a (cv_jpn_000676-cv_jpn_000676) +k a k a r u t a c h i b a n i o i t e p a u s e k a i g a i s h I k i m e N t e k i d e a r i p a u w a r e w a r e n o j i k o g a i s h I k i s a y o o t e k i d e a r u t o k a N g a e r a r e r u t o k i (cv_jpn_000677-cv_jpn_000677) +i e n i k i t a n e N g a j o o w a p a u s a N b y a k u m a i h o d o d e p a u c h o o d o p a u d a s h I t a b u N t o o n a j i g u r a i d a (cv_jpn_000678-cv_jpn_000678) +h a h a w a p i i t a a m a r i c l t s u b a a g u n o s e e s h i N b y o o i N n i n y u u i N s h I t e i r u t o k i n i b e c l s h i i o u m u (cv_jpn_000679-cv_jpn_000679) +t a t a N d e a r u h a N t e N o h i r o g e r e b a p a u a c h i k o c h i n i t s u g i h a g i g a a r i p a u k a t a g u c h i n i d e k i t a h o k o r o b i n a N k a p a u k y o n e N n o m a m a n i n a c l t e i r u (cv_jpn_000680-cv_jpn_000680) +k a r e g a p a u k o n o s h u c l c h o o c h u u n i p a u h o o m o N k i b o o n o b u s h o p a u o y o b i p a u c h o o s a k i b o o n o b u m o N w a p a u i k a n o t o o r i d e s U (cv_jpn_000681-cv_jpn_000681) +k o N b a N w a t o t e m o s a m u i d e s U (cv_jpn_000682-cv_jpn_000682) +k i N c h o o s h I t a k a o t s U k i d e b a c l t a a w a d a s e k i n i h a i r u (cv_jpn_000683-cv_jpn_000683) +m a s a n i k y u u d e N t o i c l t a f u u k a k u a r u k e N c h I k u b u t s u (cv_jpn_000684-cv_jpn_000684) +g o r i o s h i d e s u s u m e t e t s u g o o g a w a r u k u n a c l t a r a h i c l k o m e r u y a r i k U c h i (cv_jpn_000685-cv_jpn_000685) +m u j u N t e k i j i k o d o o i t s u t e k i n i j i k o j i s h i N o k e e s e e s u r u s h a k a i w a (cv_jpn_000686-cv_jpn_000686) +f a N n o i k e N n i n a g a s a r e r u n a (cv_jpn_000687-cv_jpn_000687) +i n u g a a s o b i t a i o o r a z e N k a i d e k o c l c h i o m i t e r u (cv_jpn_000688-cv_jpn_000688) +i c h i d o w a k o o N p o t a a j u k a N o n o N d e m i t a i (cv_jpn_000689-cv_jpn_000689) +k o o i i n o k o s h I t e p a u o t o o s a N t o o k a a s a N w a d e t e i k i m a s h I t a (cv_jpn_000690-cv_jpn_000690) +s h I k a s h I t e s o r e g a t s U k u r a r e t a m o n o k a r a t s U k u r u m o n o e t o s h I t e p a u d o k o m a d e m o w a r e w a r e n i s e m a r u t o i u t o k i p a u w a r e w a r e n i c h o c l k a N t e k i d e a r u (cv_jpn_000691-cv_jpn_000691) +h a i n i t a m a c l t a k e m u r i o h a k i d a s h i p a u k u r a i k o o e N n i s h I s e N o m u k e r u (cv_jpn_000692-cv_jpn_000692) +m a d a m a d a s h i n a f u s o k u d e c h u u s e N n i n a r i s o o (cv_jpn_000693-cv_jpn_000693) +z e N s e k a i n o m o t s U k a t a c h i p a u w a t a s h i n o i w a y u r u s e e s a N y o o s h I k i t o s a y o o t o w a h a n a s h I t e k a N g a e r u k o t o w a d e k i n a i (cv_jpn_000694-cv_jpn_000694) +s a k e n o m a n a i n o n i b i i r u h a r a t o i w a r e t a (cv_jpn_000695-cv_jpn_000695) +s o r e o t a d a k a g a k u n o z e N d a N k a i p a u h I k u i t e e d o n o k a g a k U t o n o m i m i r u k o t o w a (cv_jpn_000696-cv_jpn_000696) +w a k i m e m o f u r a z u n i s u m a h o d e g e e m u o y a c l t e i t a (cv_jpn_000697-cv_jpn_000697) +w a t a s h i w a r a i s h u u m a t a k o b a y a s h I s a N t o a s o b i m a s U (cv_jpn_000698-cv_jpn_000698) +a s o k o n i h I t o g a i m a s U n e a n o h I t o w a d a r e d e s h o o (cv_jpn_000699-cv_jpn_000699) +w a t a s h i w a k i n o o k a r a n o d o g a i t a i d e s U (cv_jpn_000700-cv_jpn_000700) +k y o n e N k a r a b e N k y o o s h I t e i m a s U (cv_jpn_000701-cv_jpn_000701) +c h o c l t o s u m i m a s e N (cv_jpn_000702-cv_jpn_000702) +s h I k a s h i p a u s o n o o o e N b u r i w a p a u y a k e g i m i n i p a u k a e c l t e h a g e s h i k u n a c l t a (cv_jpn_000703-cv_jpn_000703) +i t s u m o k o n o e N p I t s u o t s U k a c l t e i t a n o d e p a u m i j i k a k u n a r i m a s h I t a (cv_jpn_000704-cv_jpn_000704) +w a t a s h i w a e e g o g a h a n a s e m a s U (cv_jpn_000705-cv_jpn_000705) +h o s h i k e r y a j i b u N d e t o c l t e k i n a (cv_jpn_000706-cv_jpn_000706) +r a i s h u u k a r a n i s h u u k a N p a u k a i g a i e r y o k o o n i i k i m a s U (cv_jpn_000707-cv_jpn_000707) +o r e m o k i n i n a r u n a (cv_jpn_000708-cv_jpn_000708) +d a r e g a t s U k a c l t e r u n o k a w a k a r a n a i (cv_jpn_000709-cv_jpn_000709) +b u r a u z a n o b a a j o N g a a g a r u t o s U k o s h i u r e s h i i (cv_jpn_000710-cv_jpn_000710) +m a t a a t a r a s h i i a i d o r u g a d e t e k i t a (cv_jpn_000711-cv_jpn_000711) +m a j i d e y a c l t a n o k a (cv_jpn_000712-cv_jpn_000712) +c h o o d o s o n o t o k i n i p a u k y o o j u g a h a i c l t e k i t a (cv_jpn_000713-cv_jpn_000713) +i c l s h o n i c h i N s h I t e y o (cv_jpn_000714-cv_jpn_000714) +s o r e g a s a t e N n o d o r e s u (cv_jpn_000715-cv_jpn_000715) +f U t a r i w a r e j i e i k I s e e s a N s h I t a (cv_jpn_000716-cv_jpn_000716) +t o m a t o k a n a N k a n o a k a i s o o s u g a k a k a c l t e r u y o (cv_jpn_000717-cv_jpn_000717) +k o k o k a r a t a t e n a o s u n o w a k i b i s h i i (cv_jpn_000718-cv_jpn_000718) +m i z u k e o s h i c l k a r i s h i b o c l t e a j i g a n a j i m u y o o n i s u r u (cv_jpn_000719-cv_jpn_000719) +n e t o g e n i h a m a c l t a r a k i N g a t a m a c l t a (cv_jpn_000720-cv_jpn_000720) +i t s U k a e r u y o o n i n a r u N d a (cv_jpn_000721-cv_jpn_000721) +k o s e e h a h a i y u u t o i u y o r i a k u g a t s u y o i k a N j i (cv_jpn_000722-cv_jpn_000722) +f i j i k a r u n o s a o m a z a m a z a t o m i s e t s U k e r a r e t a (cv_jpn_000723-cv_jpn_000723) +k o s U p a y o k e r e b a s o k o s o k o n o m o N d a i w a g a m a N s u r u (cv_jpn_000724-cv_jpn_000724) +m i N n a y a c l t e m a s U k a r a d a i j o o b u d e s U y o (cv_jpn_000725-cv_jpn_000725) +k o n o t o s h o k a N p a u h a i c l t a s h u N k a N k i n i i c l t a (cv_jpn_000726-cv_jpn_000726) +k o n o d e N c h i p a u s u g u k i r e c h i c l t a (cv_jpn_000727-cv_jpn_000727) +a m a y a d o r i s u r u t o k o r o g a n a k U t e k o m a c l t a (cv_jpn_000728-cv_jpn_000728) +y a s u k U s u r u y o r i s h I t s u o a g e t e h o s h i i (cv_jpn_000729-cv_jpn_000729) +m a s a k a k o N n a k o t o n i n a r o o t o w a o m o w a n a k a c l t a (cv_jpn_000730-cv_jpn_000730) +s a i g o n i w a r a i o t o r i n i k u r u s U t a i r u (cv_jpn_000731-cv_jpn_000731) +k o r e p a u n a n i n o i m i g a a r u N d a (cv_jpn_000732-cv_jpn_000732) +i i e (cv_jpn_000733-cv_jpn_000733) +s h i (cv_jpn_000734-cv_jpn_000734) +n i (cv_jpn_000735-cv_jpn_000735) +w a c h i (cv_jpn_000736-cv_jpn_000736) +h a i (cv_jpn_000737-cv_jpn_000737) +t e e b u r u n o u e n i k a b i N g a a r i m a s U (cv_jpn_000738-cv_jpn_000738) +w a t a s h i w a m a i a s a s a N p o s h i m a s U (cv_jpn_000739-cv_jpn_000739) +a t a r a s h i i k U t s u o h a i t e d e k a k e m a s U (cv_jpn_000740-cv_jpn_000740) +k o t o s h i n o n a t s u y a s u m i w a p a u u m i n i m o i k i m a s h I t a s h i p a u y a m a n i m o n o b o r i m a s h I t a (cv_jpn_000741-cv_jpn_000741) +w a t a s h i w a p a u i r o i r o n o b e N g o o p a u j i b u N n o m u n e d e k o s h i r a e t e m i m a s h I t a (cv_jpn_000742-cv_jpn_000742) +n a N d e k o o m o s h o o b a i h e t a n a N d a r o (cv_jpn_000743-cv_jpn_000743) +t a r e N t o k a r a k y o k u a n a n i k a e t e k e e h I s a k u g e N (cv_jpn_000744-cv_jpn_000744) +d o g e z a s u r e b a i i c l t e m o N j a n a i (cv_jpn_000745-cv_jpn_000745) +d e e t o n o a i d a p a u k a n o j o w a j i b u N t o i c l t e e n o k y o r i o t a m o c l t a (cv_jpn_000746-cv_jpn_000746) +k o n o g e e n i N n a N k a h I s a s h i b u r i n i m i t a (cv_jpn_000747-cv_jpn_000747) +o o k I k u s a i d o c h e N j i o s u r u (cv_jpn_000748-cv_jpn_000748) +k a r e w a a t a m a o k a k i m u s h i c l t a (cv_jpn_000749-cv_jpn_000749) +o m a c h i s h I t e o r i m a s U (cv_jpn_000750-cv_jpn_000750) +k o n o k y o k u p a u s e N k a i i j o o w a k i i t e r u (cv_jpn_000751-cv_jpn_000751) +r e e z o o k o o a k e t a t o t a N p a u n a n i g a h I t s u y o o k a w a s u r e t a (cv_jpn_000752-cv_jpn_000752) +i c h i (cv_jpn_000753-cv_jpn_000753) +w a c h i (cv_jpn_000754-cv_jpn_000754) +i i e (cv_jpn_000755-cv_jpn_000755) +r e i (cv_jpn_000756-cv_jpn_000756) +s h I c h i (cv_jpn_000757-cv_jpn_000757) +y o o b o o w a d a s u n o n i k a u h I t o w a s U k u n a i (cv_jpn_000758-cv_jpn_000758) +r o o k a r u t o k u y u u n o i k i o i m a k a s e n o k o m a a s h a r u (cv_jpn_000759-cv_jpn_000759) +k o n o d a i f U k u w a a N k o g a o o k U t e y o k U k a i m a s U (cv_jpn_000760-cv_jpn_000760) +j i s h o b i k i n a g a r a s h o o s e t s u o y o m i m a s U (cv_jpn_000761-cv_jpn_000761) +k o N n a o o k i n a g o o g u r u o t s U k e n a i t o i k e n a i N d e s U k a (cv_jpn_000762-cv_jpn_000762) +k a r e e n o b o o r y o k u w a t o m a r a n a i (cv_jpn_000763-cv_jpn_000763) +i k i t e t a N d a n e (cv_jpn_000764-cv_jpn_000764) +t o o j i t o s h I c h a k a c l k I t e k i n a h a t s u m e e d a c l t a n e (cv_jpn_000765-cv_jpn_000765) +s o n o t o k i p a u w a t a s h i w a c h I k a r a t s U k i t a (cv_jpn_000766-cv_jpn_000766) +k a w a g a h i a g a c l t e i t a (cv_jpn_000767-cv_jpn_000767) +i c h i (cv_jpn_000768-cv_jpn_000768) +n i (cv_jpn_000769-cv_jpn_000769) +s h I c h i (cv_jpn_000770-cv_jpn_000770) +g o (cv_jpn_000771-cv_jpn_000771) +i i e (cv_jpn_000772-cv_jpn_000772) +n a m a e k a r a s h I t e t e k I t o o s u g i r u (cv_jpn_000773-cv_jpn_000773) +j i k o n o s o t o n i a r u t o i u n o w a t a N n i j i k o n o i s h I k i n o s o t o n i a r u t o i u k o t o d e n a k u (cv_jpn_000774-cv_jpn_000774) +s o r e w a s h i r a n a k U t e i i d e s U (cv_jpn_000775-cv_jpn_000775) +k i m i t o b o k u n o k y o o t s u u n o s h i r i a i w a d a r e h I t o r i p a u m i a t a r a n a i (cv_jpn_000776-cv_jpn_000776) +s u g e e d a i j i n i n a c l t e k I t e r u n o n a (cv_jpn_000777-cv_jpn_000777) +k o n o a t a r i d e s U k o s h i y a s u m i m a s h o o (cv_jpn_000778-cv_jpn_000778) +d e N s h a n i n o r u t o k i p a u k i c l p u o k a i m a s U (cv_jpn_000779-cv_jpn_000779) +t a m a g o w a i c l k o g o j u u g u r a m u g u r a i d e s U (cv_jpn_000780-cv_jpn_000780) +g i r e s U p i i w a m a c l g i i o t s u u j i t e i n e s U t o s h i r i a c l t a (cv_jpn_000781-cv_jpn_000781) +n o o g y o o o y a m e z a r u o e n a i h I t o g a a r i p a u k a N r e N k i g y o o m o p a u k o n o f U k y o o n i h I k i z u r a r e t e i r u t o i u (cv_jpn_000782-cv_jpn_000782) +n a N d e k o n o r o b o c l t o p a u s h o t a i m e N n a n o n i n a r e n a r e s h i i N d a (cv_jpn_000783-cv_jpn_000783) +f U t s u u d e a r u k o t o m o r i c l p a n a k o s e e (cv_jpn_000784-cv_jpn_000784) +t s u y o b i d e t a N j i k a N d e g o o k a i n i i t a m e r u (cv_jpn_000785-cv_jpn_000785) +b a k u m a t s u n o d e k i g o t o w a i m a n i t s u u j i r u k y o o k u N n o y a m a d e s U (cv_jpn_000786-cv_jpn_000786) +m u k o o k a r a m a c h i n o t o m o r i g a m i e t e k i t a (cv_jpn_000787-cv_jpn_000787) +n a n i o i u b e k I k a w a k a r a n a k a c l t a n a n i m o i u b e k I k o t o g a o m o i u k a b a n a k a c l t a (cv_jpn_000788-cv_jpn_000788) +t a m e s h i n i i c l k a i d a k e y a c l t e m i r u (cv_jpn_000789-cv_jpn_000789) +b o k U s h i k a i n a i k i m i w a i n a i k o r e w a p a u o o k i n a c h i g a i k a (cv_jpn_000790-cv_jpn_000790) +s h u u k a i s h o k a r a n i j u u g o o t o o m a d e n o m i c h i w a m u k a s h I t o k a w a c l t e i n a k a c l t a (cv_jpn_000791-cv_jpn_000791) +k a n o j o n o t e e a N w a k o N p o N t e k i n a k a i k e t s u n i t s u n a g a c l t a (cv_jpn_000792-cv_jpn_000792) +k o d o m o n o k o r o w a g o h a N h a d e p a u o t o n a n i n a r u t o p a N h a (cv_jpn_000793-cv_jpn_000793) +k o o i t e k I c h o c l k a N t e k i n i p a u p o i e s h I s u t e k i n i p a u w a r e w a r e n o j i k o w a m a s u m a s u a k a r i t o n a r u n o d e a r u (cv_jpn_000794-cv_jpn_000794) +h i j o o s h I k i d e a r u k o t o w a p a u m u c h i o i m i s u r u n o m i d e n a k u p a u s h a k a i t e k i n i a k U t o m o k a N g a e r a r e r u n o d e a r u (cv_jpn_000795-cv_jpn_000795) +j o o s h I k i g a n a o t o k U s h u t e k i n a c h I s h i k i d e a r u n i h a N s h i p a u k a g a k u w a (cv_jpn_000796-cv_jpn_000796) +k o N n a k o t o d e o k o r a r e t e n a s a k e n a i (cv_jpn_000797-cv_jpn_000797) +k a k o t o m i r a i t o g a j i k o m u j u N t e k i n i g e N z a i n i o i t e t a i r i t s U s u r u t o i u n i w a p a u g e N z a i g a k a t a c h i o m o t a n a k e r e b a n a r a n a i (cv_jpn_000798-cv_jpn_000798) +s h o k I h i y o o n o t a k a s a g a h a a d o r u n i n a r u (cv_jpn_000799-cv_jpn_000799) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/result.txt b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..83b5da2c5385eb4e8e6d4e7ee07c59dee2028b7d --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/result.txt @@ -0,0 +1,1565 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/hyp.trn| +|------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000674 | 1 150 | 97.3 2.7 0.0 4.0 6.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000675 | 1 65 | 96.9 0.0 3.1 9.2 12.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000676 | 1 80 | 97.5 0.0 2.5 0.0 2.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000677 | 1 200 | 98.5 1.5 0.0 6.0 7.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000678 | 1 138 | 86.2 3.6 10.1 0.0 13.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000679 | 1 141 | 83.7 1.4 14.9 0.0 16.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000680 | 1 215 | 92.1 2.3 5.6 0.0 7.9 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000681 | 1 177 | 76.3 10.7 13.0 1.7 25.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000682 | 1 45 | 88.9 6.7 4.4 0.0 11.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000683 | 1 83 | 96.4 1.2 2.4 4.8 8.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000684 | 1 81 | 88.9 4.9 6.2 4.9 16.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000685 | 1 112 | 95.5 4.5 0.0 3.6 8.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000686 | 1 106 | 98.1 1.9 0.0 9.4 11.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000687 | 1 45 | 95.6 4.4 0.0 0.0 4.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000688 | 1 75 | 92.0 4.0 4.0 5.3 13.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000689 | 1 64 | 95.3 1.6 3.1 0.0 4.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000690 | 1 93 | 95.7 0.0 4.3 2.2 6.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000691 | 1 230 | 93.9 1.7 4.3 0.0 6.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000692 | 1 108 | 93.5 0.9 5.6 0.0 6.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000693 | 1 71 | 83.1 8.5 8.5 0.0 16.9 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000694 | 1 184 | 90.2 4.9 4.9 3.3 13.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000695 | 1 65 | 84.6 6.2 9.2 0.0 15.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000696 | 1 123 | 91.1 3.3 5.7 0.0 8.9 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000697 | 1 76 | 85.5 9.2 5.3 2.6 17.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000698 | 1 82 | 86.6 7.3 6.1 7.3 20.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000699 | 1 76 | 94.7 5.3 0.0 15.8 21.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000700 | 1 62 | 96.8 3.2 0.0 6.5 9.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000701 | 1 50 | 94.0 6.0 0.0 8.0 14.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000702 | 1 29 | 93.1 0.0 6.9 0.0 6.9 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000703 | 1 114 | 79.8 11.4 8.8 0.9 21.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000704 | 1 104 | 92.3 3.8 3.8 10.6 18.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000705 | 1 48 | 95.8 4.2 0.0 4.2 8.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000706 | 1 50 | 94.0 2.0 4.0 14.0 20.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000707 | 1 84 | 90.5 2.4 7.1 13.1 22.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000708 | 1 29 | 100.0 0.0 0.0 6.9 6.9 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000709 | 1 57 | 98.2 1.8 0.0 7.0 8.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000710 | 1 73 | 89.0 2.7 8.2 8.2 19.2 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000711 | 1 56 | 96.4 0.0 3.6 0.0 3.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000712 | 1 30 | 96.7 3.3 0.0 0.0 3.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000713 | 1 70 | 87.1 5.7 7.1 0.0 12.9 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000714 | 1 33 | 90.9 0.0 9.1 0.0 9.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000715 | 1 37 | 89.2 10.8 0.0 10.8 21.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000716 | 1 52 | 96.2 0.0 3.8 0.0 3.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000717 | 1 74 | 94.6 5.4 0.0 2.7 8.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000718 | 1 56 | 96.4 1.8 1.8 3.6 7.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000719 | 1 85 | 94.1 3.5 2.4 7.1 12.9 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000720 | 1 59 | 96.6 3.4 0.0 6.8 10.2 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000721 | 1 40 | 92.5 5.0 2.5 0.0 7.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000722 | 1 72 | 95.8 1.4 2.8 0.0 4.2 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000723 | 1 74 | 100.0 0.0 0.0 6.8 6.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000724 | 1 81 | 97.5 2.5 0.0 2.5 4.9 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000725 | 1 64 | 98.4 1.6 0.0 0.0 1.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000726 | 1 69 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000727 | 1 50 | 86.0 2.0 12.0 0.0 14.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000728 | 1 68 | 98.5 1.5 0.0 0.0 1.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000729 | 1 60 | 93.3 3.3 3.3 0.0 6.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000730 | 1 76 | 82.9 3.9 13.2 0.0 17.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000731 | 1 59 | 98.3 1.7 0.0 0.0 1.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000732 | 1 45 | 75.6 6.7 17.8 0.0 24.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000733 | 1 5 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000734 | 1 4 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000735 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000736 | 1 8 | 87.5 12.5 0.0 0.0 12.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000737 | 1 5 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000738 | 1 53 | 94.3 1.9 3.8 3.8 9.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000739 | 1 51 | 100.0 0.0 0.0 7.8 7.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000740 | 1 57 | 98.2 1.8 0.0 5.3 7.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000741 | 1 128 | 93.8 4.7 1.6 1.6 7.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000742 | 1 114 | 93.0 0.0 7.0 0.0 7.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000743 | 1 54 | 94.4 1.9 3.7 3.7 9.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000744 | 1 74 | 95.9 1.4 2.7 1.4 5.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000745 | 1 50 | 92.0 4.0 4.0 2.0 10.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000746 | 1 96 | 91.7 4.2 4.2 9.4 17.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000747 | 1 62 | 100.0 0.0 0.0 6.5 6.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000748 | 1 42 | 97.6 2.4 0.0 4.8 7.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000749 | 1 47 | 95.7 0.0 4.3 0.0 4.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000750 | 1 33 | 93.9 6.1 0.0 6.1 12.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000751 | 1 58 | 82.8 0.0 17.2 0.0 17.2 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000752 | 1 88 | 90.9 2.3 6.8 5.7 14.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000753 | 1 6 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000754 | 1 8 | 87.5 12.5 0.0 0.0 12.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000755 | 1 5 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000756 | 1 5 | 80.0 20.0 0.0 0.0 20.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000757 | 1 9 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000758 | 1 63 | 93.7 0.0 6.3 0.0 6.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000759 | 1 76 | 92.1 5.3 2.6 1.3 9.2 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000760 | 1 71 | 94.4 0.0 5.6 0.0 5.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000761 | 1 62 | 95.2 4.8 0.0 0.0 4.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000762 | 1 82 | 90.2 0.0 9.8 0.0 9.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000763 | 1 50 | 94.0 4.0 2.0 0.0 6.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000764 | 1 23 | 91.3 8.7 0.0 8.7 17.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000765 | 1 76 | 80.3 5.3 14.5 0.0 19.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000766 | 1 62 | 69.4 12.9 17.7 0.0 30.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000767 | 1 34 | 97.1 2.9 0.0 0.0 2.9 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000768 | 1 6 | 83.3 16.7 0.0 0.0 16.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000769 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000770 | 1 9 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000771 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000772 | 1 5 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000773 | 1 52 | 90.4 1.9 7.7 3.8 13.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000774 | 1 128 | 96.1 0.8 3.1 2.3 6.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000775 | 1 44 | 93.2 2.3 4.5 0.0 6.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000776 | 1 102 | 89.2 2.9 7.8 0.0 10.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000777 | 1 54 | 85.2 7.4 7.4 0.0 14.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000778 | 1 57 | 80.7 5.3 14.0 0.0 19.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000779 | 1 61 | 86.9 6.6 6.6 0.0 13.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000780 | 1 64 | 76.6 6.3 17.2 0.0 23.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000781 | 1 83 | 92.8 2.4 4.8 9.6 16.9 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000782 | 1 154 | 89.0 3.2 7.8 9.7 20.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000783 | 1 94 | 86.2 1.1 12.8 0.0 13.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000784 | 1 57 | 94.7 1.8 3.5 0.0 5.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000785 | 1 66 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000786 | 1 98 | 96.9 3.1 0.0 2.0 5.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000787 | 1 64 | 92.2 7.8 0.0 0.0 7.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000788 | 1 121 | 90.1 9.9 0.0 5.8 15.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000789 | 1 54 | 77.8 7.4 14.8 0.0 22.2 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000790 | 1 87 | 85.1 6.9 8.0 0.0 14.9 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000791 | 1 121 | 72.7 5.0 22.3 0.0 27.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000792 | 1 92 | 75.0 10.9 14.1 2.2 27.2 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000793 | 1 85 | 84.7 5.9 9.4 2.4 17.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000794 | 1 162 | 91.4 5.6 3.1 0.0 8.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000795 | 1 168 | 94.0 2.4 3.6 3.6 9.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000796 | 1 108 | 93.5 0.9 5.6 1.9 8.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000797 | 1 57 | 94.7 5.3 0.0 7.0 12.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000798 | 1 195 | 91.3 3.1 5.6 2.1 10.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000799 | 1 64 | 75.0 0.0 25.0 0.0 25.0 100.0 | +|==================================================================================================================| +| Sum/Avg | 126 9077 | 91.1 3.6 5.3 2.8 11.7 91.3 | +|==================================================================================================================| +| Mean | 1.0 72.0 | 91.4 4.1 4.5 2.5 11.1 91.3 | +| S.D. | 0.0 45.9 | 7.5 5.2 5.3 3.6 7.6 28.3 | +| Median | 1.0 64.0 | 93.5 2.8 3.2 0.0 9.3 100.0 | +`------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/hyp.trn| +|------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000674 | 1 150 | 146 4 0 6 10 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000675 | 1 65 | 63 0 2 6 8 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000676 | 1 80 | 78 0 2 0 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000677 | 1 200 | 197 3 0 12 15 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000678 | 1 138 | 119 5 14 0 19 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000679 | 1 141 | 118 2 21 0 23 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000680 | 1 215 | 198 5 12 0 17 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000681 | 1 177 | 135 19 23 3 45 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000682 | 1 45 | 40 3 2 0 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000683 | 1 83 | 80 1 2 4 7 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000684 | 1 81 | 72 4 5 4 13 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000685 | 1 112 | 107 5 0 4 9 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000686 | 1 106 | 104 2 0 10 12 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000687 | 1 45 | 43 2 0 0 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000688 | 1 75 | 69 3 3 4 10 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000689 | 1 64 | 61 1 2 0 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000690 | 1 93 | 89 0 4 2 6 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000691 | 1 230 | 216 4 10 0 14 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000692 | 1 108 | 101 1 6 0 7 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000693 | 1 71 | 59 6 6 0 12 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000694 | 1 184 | 166 9 9 6 24 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000695 | 1 65 | 55 4 6 0 10 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000696 | 1 123 | 112 4 7 0 11 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000697 | 1 76 | 65 7 4 2 13 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000698 | 1 82 | 71 6 5 6 17 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000699 | 1 76 | 72 4 0 12 16 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000700 | 1 62 | 60 2 0 4 6 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000701 | 1 50 | 47 3 0 4 7 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000702 | 1 29 | 27 0 2 0 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000703 | 1 114 | 91 13 10 1 24 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000704 | 1 104 | 96 4 4 11 19 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000705 | 1 48 | 46 2 0 2 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000706 | 1 50 | 47 1 2 7 10 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000707 | 1 84 | 76 2 6 11 19 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000708 | 1 29 | 29 0 0 2 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000709 | 1 57 | 56 1 0 4 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000710 | 1 73 | 65 2 6 6 14 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000711 | 1 56 | 54 0 2 0 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000712 | 1 30 | 29 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000713 | 1 70 | 61 4 5 0 9 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000714 | 1 33 | 30 0 3 0 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000715 | 1 37 | 33 4 0 4 8 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000716 | 1 52 | 50 0 2 0 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000717 | 1 74 | 70 4 0 2 6 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000718 | 1 56 | 54 1 1 2 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000719 | 1 85 | 80 3 2 6 11 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000720 | 1 59 | 57 2 0 4 6 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000721 | 1 40 | 37 2 1 0 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000722 | 1 72 | 69 1 2 0 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000723 | 1 74 | 74 0 0 5 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000724 | 1 81 | 79 2 0 2 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000725 | 1 64 | 63 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000726 | 1 69 | 69 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000727 | 1 50 | 43 1 6 0 7 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000728 | 1 68 | 67 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000729 | 1 60 | 56 2 2 0 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000730 | 1 76 | 63 3 10 0 13 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000731 | 1 59 | 58 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000732 | 1 45 | 34 3 8 0 11 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000733 | 1 5 | 5 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000734 | 1 4 | 4 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000735 | 1 3 | 3 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000736 | 1 8 | 7 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000737 | 1 5 | 5 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000738 | 1 53 | 50 1 2 2 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000739 | 1 51 | 51 0 0 4 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000740 | 1 57 | 56 1 0 3 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000741 | 1 128 | 120 6 2 2 10 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000742 | 1 114 | 106 0 8 0 8 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000743 | 1 54 | 51 1 2 2 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000744 | 1 74 | 71 1 2 1 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000745 | 1 50 | 46 2 2 1 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000746 | 1 96 | 88 4 4 9 17 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000747 | 1 62 | 62 0 0 4 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000748 | 1 42 | 41 1 0 2 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000749 | 1 47 | 45 0 2 0 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000750 | 1 33 | 31 2 0 2 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000751 | 1 58 | 48 0 10 0 10 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000752 | 1 88 | 80 2 6 5 13 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000753 | 1 6 | 6 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000754 | 1 8 | 7 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000755 | 1 5 | 5 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000756 | 1 5 | 4 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000757 | 1 9 | 9 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000758 | 1 63 | 59 0 4 0 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000759 | 1 76 | 70 4 2 1 7 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000760 | 1 71 | 67 0 4 0 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000761 | 1 62 | 59 3 0 0 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000762 | 1 82 | 74 0 8 0 8 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000763 | 1 50 | 47 2 1 0 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000764 | 1 23 | 21 2 0 2 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000765 | 1 76 | 61 4 11 0 15 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000766 | 1 62 | 43 8 11 0 19 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000767 | 1 34 | 33 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000768 | 1 6 | 5 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000769 | 1 3 | 2 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000770 | 1 9 | 9 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000771 | 1 3 | 2 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000772 | 1 5 | 5 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000773 | 1 52 | 47 1 4 2 7 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000774 | 1 128 | 123 1 4 3 8 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000775 | 1 44 | 41 1 2 0 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000776 | 1 102 | 91 3 8 0 11 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000777 | 1 54 | 46 4 4 0 8 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000778 | 1 57 | 46 3 8 0 11 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000779 | 1 61 | 53 4 4 0 8 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000780 | 1 64 | 49 4 11 0 15 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000781 | 1 83 | 77 2 4 8 14 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000782 | 1 154 | 137 5 12 15 32 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000783 | 1 94 | 81 1 12 0 13 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000784 | 1 57 | 54 1 2 0 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000785 | 1 66 | 66 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000786 | 1 98 | 95 3 0 2 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000787 | 1 64 | 59 5 0 0 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000788 | 1 121 | 109 12 0 7 19 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000789 | 1 54 | 42 4 8 0 12 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000790 | 1 87 | 74 6 7 0 13 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000791 | 1 121 | 88 6 27 0 33 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000792 | 1 92 | 69 10 13 2 25 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000793 | 1 85 | 72 5 8 2 15 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000794 | 1 162 | 148 9 5 0 14 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000795 | 1 168 | 158 4 6 6 16 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000796 | 1 108 | 101 1 6 2 9 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000797 | 1 57 | 54 3 0 4 7 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000798 | 1 195 | 178 6 11 4 21 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000799 | 1 64 | 48 0 16 0 16 1 | +|==================================================================================================================| +| Sum | 126 9077 | 8270 325 482 255 1062 115 | +|==================================================================================================================| +| Mean | 1.0 72.0 | 65.6 2.6 3.8 2.0 8.4 0.9 | +| S.D. | 0.0 45.9 | 41.9 2.9 5.0 3.1 7.7 0.3 | +| Median | 1.0 64.0 | 59.5 2.0 2.0 0.0 7.0 1.0 | +`------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_cer/hyp.trn + +Speakers: + 0: cv_jpn_000674 + 1: cv_jpn_000675 + 2: cv_jpn_000676 + 3: cv_jpn_000677 + 4: cv_jpn_000678 + 5: cv_jpn_000679 + 6: cv_jpn_000680 + 7: cv_jpn_000681 + 8: cv_jpn_000682 + 9: cv_jpn_000683 + 10: cv_jpn_000684 + 11: cv_jpn_000685 + 12: cv_jpn_000686 + 13: cv_jpn_000687 + 14: cv_jpn_000688 + 15: cv_jpn_000689 + 16: cv_jpn_000690 + 17: cv_jpn_000691 + 18: cv_jpn_000692 + 19: cv_jpn_000693 + 20: cv_jpn_000694 + 21: cv_jpn_000695 + 22: cv_jpn_000696 + 23: cv_jpn_000697 + 24: cv_jpn_000698 + 25: cv_jpn_000699 + 26: cv_jpn_000700 + 27: cv_jpn_000701 + 28: cv_jpn_000702 + 29: cv_jpn_000703 + 30: cv_jpn_000704 + 31: cv_jpn_000705 + 32: cv_jpn_000706 + 33: cv_jpn_000707 + 34: cv_jpn_000708 + 35: cv_jpn_000709 + 36: cv_jpn_000710 + 37: cv_jpn_000711 + 38: cv_jpn_000712 + 39: cv_jpn_000713 + 40: cv_jpn_000714 + 41: cv_jpn_000715 + 42: cv_jpn_000716 + 43: cv_jpn_000717 + 44: cv_jpn_000718 + 45: cv_jpn_000719 + 46: cv_jpn_000720 + 47: cv_jpn_000721 + 48: cv_jpn_000722 + 49: cv_jpn_000723 + 50: cv_jpn_000724 + 51: cv_jpn_000725 + 52: cv_jpn_000726 + 53: cv_jpn_000727 + 54: cv_jpn_000728 + 55: cv_jpn_000729 + 56: cv_jpn_000730 + 57: cv_jpn_000731 + 58: cv_jpn_000732 + 59: cv_jpn_000733 + 60: cv_jpn_000734 + 61: cv_jpn_000735 + 62: cv_jpn_000736 + 63: cv_jpn_000737 + 64: cv_jpn_000738 + 65: cv_jpn_000739 + 66: cv_jpn_000740 + 67: cv_jpn_000741 + 68: cv_jpn_000742 + 69: cv_jpn_000743 + 70: cv_jpn_000744 + 71: cv_jpn_000745 + 72: cv_jpn_000746 + 73: cv_jpn_000747 + 74: cv_jpn_000748 + 75: cv_jpn_000749 + 76: cv_jpn_000750 + 77: cv_jpn_000751 + 78: cv_jpn_000752 + 79: cv_jpn_000753 + 80: cv_jpn_000754 + 81: cv_jpn_000755 + 82: cv_jpn_000756 + 83: cv_jpn_000757 + 84: cv_jpn_000758 + 85: cv_jpn_000759 + 86: cv_jpn_000760 + 87: cv_jpn_000761 + 88: cv_jpn_000762 + 89: cv_jpn_000763 + 90: cv_jpn_000764 + 91: cv_jpn_000765 + 92: cv_jpn_000766 + 93: cv_jpn_000767 + 94: cv_jpn_000768 + 95: cv_jpn_000769 + 96: cv_jpn_000770 + 97: cv_jpn_000771 + 98: cv_jpn_000772 + 99: cv_jpn_000773 + 100: cv_jpn_000774 + 101: cv_jpn_000775 + 102: cv_jpn_000776 + 103: cv_jpn_000777 + 104: cv_jpn_000778 + 105: cv_jpn_000779 + 106: cv_jpn_000780 + 107: cv_jpn_000781 + 108: cv_jpn_000782 + 109: cv_jpn_000783 + 110: cv_jpn_000784 + 111: cv_jpn_000785 + 112: cv_jpn_000786 + 113: cv_jpn_000787 + 114: cv_jpn_000788 + 115: cv_jpn_000789 + 116: cv_jpn_000790 + 117: cv_jpn_000791 + 118: cv_jpn_000792 + 119: cv_jpn_000793 + 120: cv_jpn_000794 + 121: cv_jpn_000795 + 122: cv_jpn_000796 + 123: cv_jpn_000797 + 124: cv_jpn_000798 + 125: cv_jpn_000799 + +Speaker sentences 0: cv_jpn_000674 #utts: 1 +id: (cv_jpn_000674-cv_jpn_000674) +Scores: (#C #S #D #I) 146 4 0 6 +REF: b o k u n o i e G A a c l t a k a i D a n n o n a m a e ******* * n i k u r a b e r u t o b o k u n i w a n a j i m i n o n a i ******* * * * n a M a e b a k a r i d a k e d o +HYP: b o k u n o i e E G a c l t a k a i N a n n o n a m a e N n i k u r a b e r u t o b o k u n i w a n a j i m i n o n a i P A U n a B a e b a k a r i d a k e d o +Eval: S S S I I I I I I S + +Speaker sentences 1: cv_jpn_000675 #utts: 1 +id: (cv_jpn_000675-cv_jpn_000675) +Scores: (#C #S #D #I) 63 0 2 6 +REF: n a i Y o o s o n o m o n o y o r i ******* * * * f u ******* * n i k i g a u k e t e r u +HYP: n a i ******* * o o s o n o m o n o y o r i P A U f u I n i k i g a u k e t e r u +Eval: D D I I I I I I + +Speaker sentences 2: cv_jpn_000676 #utts: 1 +id: (cv_jpn_000676-cv_jpn_000676) +Scores: (#C #S #D #I) 78 0 2 0 +REF: b o k u n o s h i c l t e i R u m o n o t o w a s u k o s h i c h i g a c l t e i t a +HYP: b o k u n o s h i c l t e i ******* * u m o n o t o w a s u k o s h i c h i g a c l t e i t a +Eval: D D + +Speaker sentences 3: cv_jpn_000677 #utts: 1 +id: (cv_jpn_000677-cv_jpn_000677) +Scores: (#C #S #D #I) 197 3 0 12 +REF: k a k ******* * a r u t a c h i B a n i ******* * o i ******* * * t e p a u s e k a i g a i ******* * s h i k i m e n t e k i d e a r i p a u w a r e w a r e n o j i k o g a I s h i k i s a y o o t e k i d e a r u t o k a n g A e r a r e r u t +HYP: k a k A a r u t a c h i M a n i O o i C L t e p a u s e k a i g a i J s h i k i m e n t e k i d e a r i p a u w a r e w a r e n o j i k o g a J s h i k i s a y o o t e k i d e a r u t o k a n g E e r a r e r u t +Eval: I I S I I I I I I I S S + +>> REF: o k ******* * * i +>> HYP: o k S H i +>> Eval: I I I + +Speaker sentences 4: cv_jpn_000678 #utts: 1 +id: (cv_jpn_000678-cv_jpn_000678) +Scores: (#C #S #D #I) 119 5 14 0 +REF: i e n i k I t a n e n g a J O O w A P a U s a n B y a k u m a i h o D o d e p a u c h O o D o P A U d a s h i t a b u n t o o n a j i g u r a i d a +HYP: i e n i k ******* * t a n e n g a A J I w ******* * * a * s a n H y a k u m a i h o R o d e p a u c h ******* * o ******* * o ******* * * * d a s h i t a b u n t o o n a j i g u r a i d a +Eval: D D S S S D D D D S S D D D D D D D D + +Speaker sentences 5: cv_jpn_000679 #utts: 1 +id: (cv_jpn_000679-cv_jpn_000679) +Scores: (#C #S #D #I) 118 2 21 0 +REF: h a H a w a p I i t A a m a r i C L t s u b a a g u n o s e e s h i n B y o o i N n i n Y u u I n S h i t e i r u t o k i n i b e C L s h I i o U m U +HYP: h a ******* * a w a p ******* * i t ******* * a m a r i ******* * * t s u b a a g u n o s e e s h i n * y o o i ******* * n i n * u u ******* * n * h i t e i r u t o k i n i b e ******* * * s h ******* * i o O m O +Eval: D D D D D D D D D D D D D D D D D D D D D S S + +Speaker sentences 6: cv_jpn_000680 #utts: 1 +id: (cv_jpn_000680-cv_jpn_000680) +Scores: (#C #S #D #I) 198 5 12 0 +REF: t a t a n d e a r U h a n t e n o h i r o g e r E b A P a U a c h i k o c h i n i t s u g i h a g i G a a r i p a u k a t a G U c h i n i d e k i t a h o k o r o b i n a n k A P a U k y o N e n n o m a m A n i n a c l +HYP: t a t a n d e a r ******* * h a n t e n o h i r o g e r A b ******* * * a * a c h i k o c h i n i t s u g i h a g i ******* * a a r i p a u k a t a K O c h i n i d e k i t a h o k o r o b i n a n k ******* * * a * k y o O e n n o m a m O n i n a c l +Eval: D D S D D D D D D S S D D D D S S + +>> REF: t e i r u +>> HYP: t e i r u +>> Eval: + +Speaker sentences 7: cv_jpn_000681 #utts: 1 +id: (cv_jpn_000681-cv_jpn_000681) +Scores: (#C #S #D #I) 135 19 23 3 +REF: k a r E g a * P a U K o N O S H U C L C H O O C H U U n i P A U h o o m o n k i b o o n o b u s h o P A U o y o b i P A U c h o o S a k i b o o n ******* * o b u M o N W A P a U i k a N O t o o r i d e s u +HYP: k a r A g a K a * N o D E * B E * R * E N A * A K A n i ******* * * * h o o m o n k i b o o n o b u s h o ******* * * * o y o b i ******* * * * c h o o Z a k i b o o n O o b u G o ******* * ******* * M * a * i k a R A t o o r i d e s u +Eval: S I S D S S S D S S D S D S S S D S S S D D D D D D D D D D D D S I I S D D D D S D D S S + +Speaker sentences 8: cv_jpn_000682 #utts: 1 +id: (cv_jpn_000682-cv_jpn_000682) +Scores: (#C #S #D #I) 40 3 2 0 +REF: k O n b a N W a t o t e m o s a m U i d e s u +HYP: k A n b a W A a t o t e m o s a m ******* * i d e s u +Eval: S S S D D + +Speaker sentences 9: cv_jpn_000683 #utts: 1 +id: (cv_jpn_000683-cv_jpn_000683) +Scores: (#C #S #D #I) 80 1 2 4 +REF: k i n c h o o s h i t a k ******* * ******* * a o t s u k i d e B a c l t A a w a d a s e k i n i h a i r u +HYP: k i n c h o o s h i t a k A H a o t s u k i d e P a c l t ******* * a w a d a s e k i n i h a i r u +Eval: I I I I S D D + +Speaker sentences 10: cv_jpn_000684 #utts: 1 +id: (cv_jpn_000684-cv_jpn_000684) +Scores: (#C #S #D #I) 72 4 5 4 +REF: m a s a n I k y u u D e n t o i c l t a f ******* * u u k a k U a r U k e n C H i k U b u t * ******* s u +HYP: m a s a n ******* * k y u u R e n t o i c l t a f S u u k a k ******* * a r O k e n * J i k I b u t S s u +Eval: D D S I I D D S D S S I I + +Speaker sentences 11: cv_jpn_000685 #utts: 1 +id: (cv_jpn_000685-cv_jpn_000685) +Scores: (#C #S #D #I) 107 5 0 4 +REF: g o R i ******* * o s h i d e s u s u m e t e t * ******* s U g o O g a w a r u k u n a c l t a r a h i c l k o m e r u Y a r I k u c h i +HYP: g o J i Y o s h i d e s u s u m e t e t S s O g o N g a w a r u k u n a c l t a r a h i c l k o m e r u E a r E k u c h i +Eval: S I I I I S S S S + +Speaker sentences 12: cv_jpn_000686 #utts: 1 +id: (cv_jpn_000686-cv_jpn_000686) +Scores: (#C #S #D #I) 104 2 0 10 +REF: m u j u n t e k i ******* * * * j i k o D o o i t s u t e k i n i ******* * * * j i k ******* * o j i s h i n o k e e s e e s U r u s h a k a i w a +HYP: m u j u n t e k i P A U j i k o T o o i t s u t e k i n i P A U j i k O o j i s h i n o k e e s e e s O r u s h a k a i w a +Eval: I I I I S I I I I I I S + +Speaker sentences 13: cv_jpn_000687 #utts: 1 +id: (cv_jpn_000687-cv_jpn_000687) +Scores: (#C #S #D #I) 43 2 0 0 +REF: f a n n o i k e n n i n a G a s a R e r u n a +HYP: f a n n o i k e n n i n a R a s a D e r u n a +Eval: S S + +Speaker sentences 14: cv_jpn_000688 #utts: 1 +id: (cv_jpn_000688-cv_jpn_000688) +Scores: (#C #S #D #I) 69 3 3 4 +REF: * ******* i n U g a a s o b i t a I o o r a z e n k a i d e k o C L c h I o m i t e ******* * r u +HYP: H i n E g a a s o b i t a Y o o r a z e n k a i d e k o ******* * * c h O o m i t e I r u +Eval: I I S S D D D S I I + +Speaker sentences 15: cv_jpn_000689 #utts: 1 +id: (cv_jpn_000689-cv_jpn_000689) +Scores: (#C #S #D #I) 61 1 2 0 +REF: i c h i d o w a k O o n p o t a a j U k a n o n o n d e m i t a i +HYP: i c h i d o w a k ******* * o n p o t a a j I k a n o n o n d e m i t a i +Eval: D D S + +Speaker sentences 16: cv_jpn_000690 #utts: 1 +id: (cv_jpn_000690-cv_jpn_000690) +Scores: (#C #S #D #I) 89 0 4 2 +REF: k o o I i n o k o s h i t e p a u o t o o s a n t O o k a a s a n w a ******* * d e t e i k i m a s h i t a +HYP: k o o ******* * i n o k o s h i t e p a u o t o o s a n t ******* * o k a a s a n w a N d e t e i k i m a s h i t a +Eval: D D D D I I + +Speaker sentences 17: cv_jpn_000691 #utts: 1 +id: (cv_jpn_000691-cv_jpn_000691) +Scores: (#C #S #D #I) 216 4 10 0 +REF: s h i k a s h i t e s o r e g a t s u k U r a r e t a m o n o k a r a t s u k u r u m o n o e t o s h i t e P A U d o k O m a d e m o W A r E w a r E n i s e m a r u t o i u t o k i p a u w a r e w a r E n i c h o c +HYP: s h i k a s h i t e s o r e g a t s u k ******* * r a r e t a m o n o k a r a t s u k u r u m o n o e t o s h i t e ******* * * * d o k U m a d e m o ******* * ******* * r A w a r U n i s e m a r u t o i u t o k i p a u w a r e w a r I n i c h o c +Eval: D D D D D D S D D D D S S S + +>> REF: l k a n t e k i d e a r u +>> HYP: l k a n t e k i d e a r u +>> Eval: + +Speaker sentences 18: cv_jpn_000692 #utts: 1 +id: (cv_jpn_000692-cv_jpn_000692) +Scores: (#C #S #D #I) 101 1 6 0 +REF: h a i n i t a m a c l t a k e m u r i o h a k i d a s h i P A U k u r a i k o o e N n i s h i s e n o m U k e r u +HYP: h a i n i t a m a c l t a k e m u r i o h a k i d a s h i ******* * * * k u r a i k o o e ******* * n i s h i s e n o m O k e r u +Eval: D D D D D D S + +Speaker sentences 19: cv_jpn_000693 #utts: 1 +id: (cv_jpn_000693-cv_jpn_000693) +Scores: (#C #S #D #I) 59 6 6 0 +REF: M a d a M a d a s h I n a F u s o k u d e c h U U s e n N I n a r i s O o +HYP: W a d a W a d a s h ******* * n a B u s o k u d e c h ******* * I s e n I N n a r i s ******* * o +Eval: S S D D S D D S S S D D + +Speaker sentences 20: cv_jpn_000694 #utts: 1 +id: (cv_jpn_000694-cv_jpn_000694) +Scores: (#C #S #D #I) 166 9 9 6 +REF: * Z e n s * e k a i n o m o t s u k a t a c h I P A U W A t a s h i n o i W a Y u r u s e e s a N y O o s h i k I t o s a Y O o t o w ******* * * a * h a n a s h i t e k a n g a e r u k o t o w a d e k i n a i +HYP: T S e n s H e k a i n o m o t s u k a t a c h ******* * * * * S H I t a s h i n o i Y a I u r u s e e s a I y ******* * o s h i k U t o s a ******* * I o t o w A P a U h a n a s h i t e k a n g a e r u k o t o w a d e k i n a i +Eval: I S I D D D D D S S S S S S D D S D D S I I I I + +Speaker sentences 21: cv_jpn_000695 #utts: 1 +id: (cv_jpn_000695-cv_jpn_000695) +Scores: (#C #S #D #I) 55 4 6 0 +REF: s a k e n o m A N a i n o n i B i i r u H a r a t o I W A r e t a +HYP: s a k e n o m ******* * ******* * a i n o n i P i i r u B a r a t o ******* * Y O r e t a +Eval: D D D D S S D D S S + +Speaker sentences 22: cv_jpn_000696 #utts: 1 +id: (cv_jpn_000696-cv_jpn_000696) +Scores: (#C #S #D #I) 112 4 7 0 +REF: s o r e o t a D a k A G a k u n o z e n d a n k a i P A U h i k u i t e e d o n O k a G a k u t o n o m i m i r u k o t o w a +HYP: s o r e o t a T a k U H a k u n o z e n d a n k a i * * * S h i k u i t e e d o n ******* * k a ******* * a k u t o n o m i m i r u k o t o w a +Eval: S S S D D D S D D D D + +Speaker sentences 23: cv_jpn_000697 #utts: 1 +id: (cv_jpn_000697-cv_jpn_000697) +Scores: (#C #S #D #I) 65 7 4 2 +REF: W A k * I M e M o f u R a * Z u n i s u m A h o d e g e e m U o y a c l t e i t a +HYP: N O k K Y O e N o f u ******* * a T S u n i s u m O h o d e g e e m ******* * o y a c l t e i t a +Eval: S S I S S S D D I S S D D + +Speaker sentences 24: cv_jpn_000698 #utts: 1 +id: (cv_jpn_000698-cv_jpn_000698) +Scores: (#C #S #D #I) 71 6 5 6 +REF: w a T a S H I W a R a i s h U u m a t a ******* * k o b a Y A s h i s a n t ******* * o a s o b ******* * i m a s u +HYP: w a D a * I W A a N a i s h ******* * u m a t a N k o b a ******* * E s h i s a n t O o a s o b E i m a s u +Eval: S D S S S S D D I I D D S I I I I + +Speaker sentences 25: cv_jpn_000699 #utts: 1 +id: (cv_jpn_000699-cv_jpn_000699) +Scores: (#C #S #D #I) 72 4 0 12 +REF: * ******* a s ******* * o k o n i h I t ******* * o g a i m a s u n e a N o h i t ******* * o w a ******* * D a r ******* * e D e s h o o +HYP: H a s O o k o n i h E t O o g a i m a s u n e a R o h i t O o w a T A a r E e T e s h o o +Eval: I I I I S I I S I I I I S I I S + +Speaker sentences 26: cv_jpn_000700 #utts: 1 +id: (cv_jpn_000700-cv_jpn_000700) +Scores: (#C #S #D #I) 60 2 0 4 +REF: w a t a s h i w a k i n o o k a R a n ******* * o d ******* * o g a i t a i D e s u +HYP: w a t a s h i w a k i n o o k a N a n O o d O o g a i t a i T e s u +Eval: S I I I I S + +Speaker sentences 27: cv_jpn_000701 #utts: 1 +id: (cv_jpn_000701-cv_jpn_000701) +Scores: (#C #S #D #I) 47 3 0 4 +REF: k y ******* * o N e n k a R a B e n k y ******* * o o s h i t e i m a s u +HYP: k y O o R e n k a N a P e n k y U o o s h i t e i m a s u +Eval: I I S S S I I + +Speaker sentences 28: cv_jpn_000702 #utts: 1 +id: (cv_jpn_000702-cv_jpn_000702) +Scores: (#C #S #D #I) 27 0 2 0 +REF: c h o c l t o s u m i m a s e N +HYP: c h o c l t o s u m i m a s e ******* * +Eval: D D + +Speaker sentences 29: cv_jpn_000703 #utts: 1 +id: (cv_jpn_000703-cv_jpn_000703) +Scores: (#C #S #D #I) 91 13 10 1 +REF: S h i k a s h I P A U S o N o o o e n b u r I W A P a U y a k e G i m i n i P A U k a e c l T e h a g e s ******* H I K u n A c l t a +HYP: C h i k a s h T * * O N o ******* * o o o e n b u r Y O W * a * y a k e K i m i n i ******* * * * k a e c l D e h a g e s U P E u n O c l t a +Eval: S S D D S S D D S S S D D S D D D D S I S S S S + +Speaker sentences 30: cv_jpn_000704 #utts: 1 +id: (cv_jpn_000704-cv_jpn_000704) +Scores: (#C #S #D #I) 96 4 4 11 +REF: i t s u m o k o n o e n p ******* * i t * ******* s U o t * ******* s * u k a c l t e i t a n o D e P A U m i j i k ******* * ******* * a K U n a r i m a s h i t a +HYP: i t s u m o k o n o e n p I i t S s O o t S s H u k a c l t e i t a n o T e ******* * * * m i j i k A H a H A n a r i m a s h i t a +Eval: I I I I S I I I S D D D D I I I I S S + +Speaker sentences 31: cv_jpn_000705 #utts: 1 +id: (cv_jpn_000705-cv_jpn_000705) +Scores: (#C #S #D #I) 46 2 0 2 +REF: w a t a s h i w a e E g o g a h a n a s ******* * e m A s u +HYP: w a t a s h i w a e I g o g a h a n a s T e m O s u +Eval: S I I S + +Speaker sentences 32: cv_jpn_000706 #utts: 1 +id: (cv_jpn_000706-cv_jpn_000706) +Scores: (#C #S #D #I) 47 1 2 7 +REF: H o s h ******* * i k ******* * e r ******* y a j i b U n d e t o c l t ******* * e k i n a +HYP: * ******* o s h I i k E e r y a j i b A n d e t o c l t A e k i n a +Eval: D D I I I I I S I I + +Speaker sentences 33: cv_jpn_000707 #utts: 1 +id: (cv_jpn_000707-cv_jpn_000707) +Scores: (#C #S #D #I) 76 2 6 11 +REF: R a i s h u u k a r a n i s h ******* * u u k a n P A U K a i g a ******* * I e r ******* * ******* y ******* * o k ******* * o o n i i k i m a s u +HYP: * ******* a i s h u u k a r a n i s h U u u k a n ******* * * * H a i g a E E e r U y O o k O o o n i i k i m a s u +Eval: D D I I D D D D S I I S I I I I I I I + +Speaker sentences 34: cv_jpn_000708 #utts: 1 +id: (cv_jpn_000708-cv_jpn_000708) +Scores: (#C #S #D #I) 29 0 0 2 +REF: * ******* o r e m o k i n i n a r u n a +HYP: O o r e m o k i n i n a r u n a +Eval: I I + +Speaker sentences 35: cv_jpn_000709 #utts: 1 +id: (cv_jpn_000709-cv_jpn_000709) +Scores: (#C #S #D #I) 56 1 0 4 +REF: d a r e g a t * ******* s u k a c l t e r u n o k ******* * a w a k a r A n a i +HYP: d a r e g a t S s u k a c l t e r u n o k A a w a k a r U n a i +Eval: I I I I S + +Speaker sentences 36: cv_jpn_000710 #utts: 1 +id: (cv_jpn_000710-cv_jpn_000710) +Scores: (#C #S #D #I) 65 2 6 6 +REF: B U r a U z a n o b A a j o n g ******* * * a * a g a r u t ******* * o s u k o s h i u r e s h I i +HYP: P O r a ******* * z a n o b ******* * a j o n g A P a U a g a r u t O o s u k o s h i u r e s h ******* * i +Eval: S S D D D D I I I I I I D D + +Speaker sentences 37: cv_jpn_000711 #utts: 1 +id: (cv_jpn_000711-cv_jpn_000711) +Scores: (#C #S #D #I) 54 0 2 0 +REF: m a t a a t a r a s h I i a i d o r u g a d e t e k i t a +HYP: m a t a a t a r a s h ******* * i a i d o r u g a d e t e k i t a +Eval: D D + +Speaker sentences 38: cv_jpn_000712 #utts: 1 +id: (cv_jpn_000712-cv_jpn_000712) +Scores: (#C #S #D #I) 29 1 0 0 +REF: m a j i d e y a c l t A n o k a +HYP: m a j i d e y a c l t O n o k a +Eval: S + +Speaker sentences 39: cv_jpn_000713 #utts: 1 +id: (cv_jpn_000713-cv_jpn_000713) +Scores: (#C #S #D #I) 61 4 5 0 +REF: c h o o d o s O N o t o k i n i P A U k y o o j u g a h a i C L t e k i T a +HYP: c h o o d o s U M o t o k i n i ******* * * * k y o o j u g a h a i * I t e k i D a +Eval: S S D D D D D S S + +Speaker sentences 40: cv_jpn_000714 #utts: 1 +id: (cv_jpn_000714-cv_jpn_000714) +Scores: (#C #S #D #I) 30 0 3 0 +REF: i c l s H o n i c h i n s h i t e Y o +HYP: i c l s * o n i c h i n s h i t e ******* * o +Eval: D D D + +Speaker sentences 41: cv_jpn_000715 #utts: 1 +id: (cv_jpn_000715-cv_jpn_000715) +Scores: (#C #S #D #I) 33 4 0 4 +REF: s ******* * o r e G a s ******* * a t e n N O D o r e s u +HYP: s O o r e K a s H a t e n O T O o r e s u +Eval: I I S I I S S S + +Speaker sentences 42: cv_jpn_000716 #utts: 1 +id: (cv_jpn_000716-cv_jpn_000716) +Scores: (#C #S #D #I) 50 0 2 0 +REF: f u t a r i w a r e j i E i k i s e e s a n s h i t a +HYP: f u t a r i w a r e j i ******* * i k i s e e s a n s h i t a +Eval: D D + +Speaker sentences 43: cv_jpn_000717 #utts: 1 +id: (cv_jpn_000717-cv_jpn_000717) +Scores: (#C #S #D #I) 70 4 0 2 +REF: t o m a t o k a n a n K a n o ******* * a k a i s o O s u g a k a k a c l t e r U Y o +HYP: t o m a t o k a n a n G a n o W a k a i s o U s u g a k a k a c l t e r I U o +Eval: S I I S S S + +Speaker sentences 44: cv_jpn_000718 #utts: 1 +id: (cv_jpn_000718-cv_jpn_000718) +Scores: (#C #S #D #I) 54 1 1 2 +REF: k o k o k a r a t a t e n a ******* * O s u n o w a k i b i s H i i +HYP: k o k o k a r a t a t e n a N A s u n o w a k i b i s * i i +Eval: I I S D + +Speaker sentences 45: cv_jpn_000719 #utts: 1 +id: (cv_jpn_000719-cv_jpn_000719) +Scores: (#C #S #D #I) 80 3 2 6 +REF: M i z u k e o ******* * s h i c l k a r i s h i b o c l t e ******* * * * a j i G a n a j i m u y O o N i s u r u +HYP: N i z u k e o S s h i c l k a r i s h i b o c l t e P A U a j i K a n a j i m u y ******* * o H i s u r u +Eval: S I I I I I I S D D S + +Speaker sentences 46: cv_jpn_000720 #utts: 1 +id: (cv_jpn_000720-cv_jpn_000720) +Scores: (#C #S #D #I) 57 2 0 4 +REF: n e t o g e n i h a m a c l t a r a k I n ******* * g a T a m a c l t a ******* * +HYP: n e t o g e n i h a m a c l t a r a k A n E g a G a m a c l t a U +Eval: S I I S I I + +Speaker sentences 47: cv_jpn_000721 #utts: 1 +id: (cv_jpn_000721-cv_jpn_000721) +Scores: (#C #S #D #I) 37 2 1 0 +REF: i t S U k a e r U y o o n i n a r u n d a +HYP: i t * O k a e r I y o o n i n a r u n d a +Eval: D S S + +Speaker sentences 48: cv_jpn_000722 #utts: 1 +id: (cv_jpn_000722-cv_jpn_000722) +Scores: (#C #S #D #I) 69 1 2 0 +REF: k o s e e h a h a i y U u t o i u y o r i a k u g a t s u y o I k a n j i +HYP: k o s e e h a h a i y ******* * u t o i u y o r i a k u g a t s u y o E k a n j i +Eval: D D S + +Speaker sentences 49: cv_jpn_000723 #utts: 1 +id: (cv_jpn_000723-cv_jpn_000723) +Scores: (#C #S #D #I) 74 0 0 5 +REF: f i j i k a r u n o s a o ******* * * * m a z a m a z a t o m i s * e t s u k e r a r e t a +HYP: f i j i k a r u n o s a o P A U m a z a m a z a t o m i s H e t s u k e r a r e t a +Eval: I I I I I + +Speaker sentences 50: cv_jpn_000724 #utts: 1 +id: (cv_jpn_000724-cv_jpn_000724) +Scores: (#C #S #D #I) 79 2 0 2 +REF: k o s u p a y o k e r e b a s o k o s o k o n o m o n d a I w a g ******* * a m a n s U r u +HYP: k o s u p a y o k e r e b a s o k o s o k o n o m o n d a E w a g A a m a n s E r u +Eval: S I I S + +Speaker sentences 51: cv_jpn_000725 #utts: 1 +id: (cv_jpn_000725-cv_jpn_000725) +Scores: (#C #S #D #I) 63 1 0 0 +REF: m i n n a y a c l t e m a s u k a r a D a i j o o b u d e s u y o +HYP: m i n n a y a c l t e m a s u k a r a T a i j o o b u d e s u y o +Eval: S + +Speaker sentences 52: cv_jpn_000726 #utts: 1 +id: (cv_jpn_000726-cv_jpn_000726) +Scores: (#C #S #D #I) 69 0 0 0 +REF: k o n o t o s h o k a n p a u h a i c l t a s h u n k a n k i n i i c l t a +HYP: k o n o t o s h o k a n p a u h a i c l t a s h u n k a n k i n i i c l t a +Eval: + +Speaker sentences 53: cv_jpn_000727 #utts: 1 +id: (cv_jpn_000727-cv_jpn_000727) +Scores: (#C #S #D #I) 43 1 6 0 +REF: k o n o d e n c h i P A U s u G u k i r e c h I c l t a +HYP: k o n o d e n c h i ******* * * * s u ******* * u k i r e c h A c l t a +Eval: D D D D D D S + +Speaker sentences 54: cv_jpn_000728 #utts: 1 +id: (cv_jpn_000728-cv_jpn_000728) +Scores: (#C #S #D #I) 67 1 0 0 +REF: a m a y A d o r i s u r u t o k o r o g a n a k u t e k o m a c l t a +HYP: a m a y O d o r i s u r u t o k o r o g a n a k u t e k o m a c l t a +Eval: S + +Speaker sentences 55: cv_jpn_000729 #utts: 1 +id: (cv_jpn_000729-cv_jpn_000729) +Scores: (#C #S #D #I) 56 2 2 0 +REF: y a s u k u s u r u y o r i s h i t s u O a g e t E h o s h I i +HYP: y a s u k u s u r u y o r i s h i t s u W a g e t A h o s h ******* * i +Eval: S S D D + +Speaker sentences 56: cv_jpn_000730 #utts: 1 +id: (cv_jpn_000730-cv_jpn_000730) +Scores: (#C #S #D #I) 63 3 10 0 +REF: m a s A K A k o n n a k o t o N i n a r O o t o w a O m o W A n a k a c l t a +HYP: m a s E G O k o n n a k o t o ******* * i n a r ******* * o t o w a ******* * m o ******* * ******* * n a k a c l t a +Eval: S S S D D D D D D D D D D + +Speaker sentences 57: cv_jpn_000731 #utts: 1 +id: (cv_jpn_000731-cv_jpn_000731) +Scores: (#C #S #D #I) 58 1 0 0 +REF: s a i g o n i w a r a I o t o r i n i k u r u s u t a i r u +HYP: s a i g o n i w a r a Y o t o r i n i k u r u s u t a i r u +Eval: S + +Speaker sentences 58: cv_jpn_000732 #utts: 1 +id: (cv_jpn_000732-cv_jpn_000732) +Scores: (#C #S #D #I) 34 3 8 0 +REF: k o r E P a U N a n I n O i m i g A a r u N d a +HYP: k o r ******* * * a * E a n A n A i m i g ******* * a r u ******* * d a +Eval: D D D D S S S D D D D + +Speaker sentences 59: cv_jpn_000733 #utts: 1 +id: (cv_jpn_000733-cv_jpn_000733) +Scores: (#C #S #D #I) 5 0 0 0 +REF: i i e +HYP: i i e +Eval: + +Speaker sentences 60: cv_jpn_000734 #utts: 1 +id: (cv_jpn_000734-cv_jpn_000734) +Scores: (#C #S #D #I) 4 0 0 0 +REF: s h i +HYP: s h i +Eval: + +Speaker sentences 61: cv_jpn_000735 #utts: 1 +id: (cv_jpn_000735-cv_jpn_000735) +Scores: (#C #S #D #I) 3 0 0 0 +REF: n i +HYP: n i +Eval: + +Speaker sentences 62: cv_jpn_000736 #utts: 1 +id: (cv_jpn_000736-cv_jpn_000736) +Scores: (#C #S #D #I) 7 1 0 0 +REF: W a c h i +HYP: H a c h i +Eval: S + +Speaker sentences 63: cv_jpn_000737 #utts: 1 +id: (cv_jpn_000737-cv_jpn_000737) +Scores: (#C #S #D #I) 5 0 0 0 +REF: h a i +HYP: h a i +Eval: + +Speaker sentences 64: cv_jpn_000738 #utts: 1 +id: (cv_jpn_000738-cv_jpn_000738) +Scores: (#C #S #D #I) 50 1 2 2 +REF: t e e b U r u n ******* * o U e n i k a b i n g a a r i m a s u +HYP: t e e b ******* * r u n O o I e n i k a b i n g a a r i m a s u +Eval: D D I I S + +Speaker sentences 65: cv_jpn_000739 #utts: 1 +id: (cv_jpn_000739-cv_jpn_000739) +Scores: (#C #S #D #I) 51 0 0 4 +REF: w a t a s h i w ******* * a m a i ******* * a s a s a n p o s h i m a s u +HYP: w a t a s h i w A a m a i Y a s a s a n p o s h i m a s u +Eval: I I I I + +Speaker sentences 66: cv_jpn_000740 #utts: 1 +id: (cv_jpn_000740-cv_jpn_000740) +Scores: (#C #S #D #I) 56 1 0 3 +REF: a t a r a s h i i k u t s U o h a i ******* * * t e d e k a k e m a s u +HYP: a t a r a s h i i k u t s O o h a i C L t e d e k a k e m a s u +Eval: S I I I + +Speaker sentences 67: cv_jpn_000741 #utts: 1 +id: (cv_jpn_000741-cv_jpn_000741) +Scores: (#C #S #D #I) 120 6 2 2 +REF: k o t o s h i n o n a t s U y a s u m i w a p a u ******* * u m i n i m o i k i m a s h i t a s h i P A U y a m a n i m o N O B o r i M a s h i t a +HYP: k o t o s h i n o n a t s E y a s u m i w a p a u B u m i n i m o i k i m a s h i t a s h i * * I y a m a n i m o O N O o r i B a s h i t a +Eval: S I I D D S S S S S + +Speaker sentences 68: cv_jpn_000742 #utts: 1 +id: (cv_jpn_000742-cv_jpn_000742) +Scores: (#C #S #D #I) 106 0 8 0 +REF: w a t a s h i w A P a U i r o i r o n o b e n g o o P A U j i b u n n o m u n e d e k o s h i r a e t e m i m a s h i t a +HYP: w a t a s h i w ******* * * a * i r o i r o n o b e n g o o ******* * * * j i b u n n o m u n e d e k o s h i r a e t e m i m a s h i t a +Eval: D D D D D D D D + +Speaker sentences 69: cv_jpn_000743 #utts: 1 +id: (cv_jpn_000743-cv_jpn_000743) +Scores: (#C #S #D #I) 51 1 2 2 +REF: n a n d e k O O m o s h ******* * o o b a i h e t a n a n d a r o +HYP: n a n d e k ******* * U m o s h O o o b a i h e t a n a n d a r o +Eval: D D S I I + +Speaker sentences 70: cv_jpn_000744 #utts: 1 +id: (cv_jpn_000744-cv_jpn_000744) +Scores: (#C #S #D #I) 71 1 2 1 +REF: t a r e n t o k a r a k y o k u a n a n i k a e t e k e e h i s * a k U g e N +HYP: t a r e n t o k a r a k y o k u a n a n i k a e t e k e e h i s H a k I g e ******* * +Eval: I S D D + +Speaker sentences 71: cv_jpn_000745 #utts: 1 +id: (cv_jpn_000745-cv_jpn_000745) +Scores: (#C #S #D #I) 46 2 2 1 +REF: d o g e z a s U r e b a I i c l t e m o n * J a n a i +HYP: d o g e z a s E r e b a ******* * i c l t e m o n S H a n a i +Eval: S D D I S + +Speaker sentences 72: cv_jpn_000746 #utts: 1 +id: (cv_jpn_000746-cv_jpn_000746) +Scores: (#C #S #D #I) 88 4 4 9 +REF: d e e t O n o ******* * a i d A P a U k a n o * J O w a j i b u n t ******* * o i c l t e e n o k y ******* * o r i ******* * o t a M o c l t a +HYP: d e e t A n o W a i d ******* * * a * k a n o C H I w a j i b u n t O o i c l t e e n o k y O o r i Y o t a N o c l t a +Eval: S I I D D D D I S S I I I I I I S + +Speaker sentences 73: cv_jpn_000747 #utts: 1 +id: (cv_jpn_000747-cv_jpn_000747) +Scores: (#C #S #D #I) 62 0 0 4 +REF: k o n o g e e n i n n a n k ******* * ******* * a h i s a s h i b u r i n i m i t a +HYP: k o n o g e e n i n n a n k A W a h i s a s h i b u r i n i m i t a +Eval: I I I I + +Speaker sentences 74: cv_jpn_000748 #utts: 1 +id: (cv_jpn_000748-cv_jpn_000748) +Scores: (#C #S #D #I) 41 1 0 2 +REF: o o k i k u s a i d o c h ******* * e n j i o s U r u +HYP: o o k i k u s a i d o c h I e n j i o s O r u +Eval: I I S + +Speaker sentences 75: cv_jpn_000749 #utts: 1 +id: (cv_jpn_000749-cv_jpn_000749) +Scores: (#C #S #D #I) 45 0 2 0 +REF: k a r e w a a t a m a O k a k i m u s h i c l t a +HYP: k a r e w a a t a m a ******* * k a k i m u s h i c l t a +Eval: D D + +Speaker sentences 76: cv_jpn_000750 #utts: 1 +id: (cv_jpn_000750-cv_jpn_000750) +Scores: (#C #S #D #I) 31 2 0 2 +REF: o m a c h I s h i t e ******* * O r i m a s u +HYP: o m a c h E s h i t e W A r i m a s u +Eval: S I I S + +Speaker sentences 77: cv_jpn_000751 #utts: 1 +id: (cv_jpn_000751-cv_jpn_000751) +Scores: (#C #S #D #I) 48 0 10 0 +REF: K o n o k y o k U P A u s e n k a I i j O o w a k i i t e r u +HYP: * ******* o n o k y o k ******* * * * u s e n k a ******* * i j ******* * o w a k i i t e r u +Eval: D D D D D D D D D D + +Speaker sentences 78: cv_jpn_000752 #utts: 1 +id: (cv_jpn_000752-cv_jpn_000752) +Scores: (#C #S #D #I) 80 2 6 5 +REF: r e e ******* * z o o k o o a k e t a t o t a n P A U n a n i g a h i T S u y O o k a w a s u r e ******* * * t a +HYP: r e e J z o o k o o a k e t a t o t a n ******* * * * n a n i g a h i C H u y ******* * o k a w a s u r e C L t a +Eval: I I D D D D S S D D I I I + +Speaker sentences 79: cv_jpn_000753 #utts: 1 +id: (cv_jpn_000753-cv_jpn_000753) +Scores: (#C #S #D #I) 6 0 0 0 +REF: i c h i +HYP: i c h i +Eval: + +Speaker sentences 80: cv_jpn_000754 #utts: 1 +id: (cv_jpn_000754-cv_jpn_000754) +Scores: (#C #S #D #I) 7 1 0 0 +REF: W a c h i +HYP: H a c h i +Eval: S + +Speaker sentences 81: cv_jpn_000755 #utts: 1 +id: (cv_jpn_000755-cv_jpn_000755) +Scores: (#C #S #D #I) 5 0 0 0 +REF: i i e +HYP: i i e +Eval: + +Speaker sentences 82: cv_jpn_000756 #utts: 1 +id: (cv_jpn_000756-cv_jpn_000756) +Scores: (#C #S #D #I) 4 1 0 0 +REF: R e i +HYP: D e i +Eval: S + +Speaker sentences 83: cv_jpn_000757 #utts: 1 +id: (cv_jpn_000757-cv_jpn_000757) +Scores: (#C #S #D #I) 9 0 0 0 +REF: s h i c h i +HYP: s h i c h i +Eval: + +Speaker sentences 84: cv_jpn_000758 #utts: 1 +id: (cv_jpn_000758-cv_jpn_000758) +Scores: (#C #S #D #I) 59 0 4 0 +REF: y o o b o o W A d a s u n o n i k a u h i t o w a s u k u n a i +HYP: y o o b o o ******* * ******* * d a s u n o n i k a u h i t o w a s u k u n a i +Eval: D D D D + +Speaker sentences 85: cv_jpn_000759 #utts: 1 +id: (cv_jpn_000759-cv_jpn_000759) +Scores: (#C #S #D #I) 70 4 2 1 +REF: r o o k a r u t o k u y U u n o i K * I o I m a k a s e n o k o m A a s h a r u +HYP: r o o k a r u t o k u y I u n o i I K Y o E m a k a s e n o k o m ******* * a s h a r u +Eval: S S I S S D D + +Speaker sentences 86: cv_jpn_000760 #utts: 1 +id: (cv_jpn_000760-cv_jpn_000760) +Scores: (#C #S #D #I) 67 0 4 0 +REF: k o n o d a i F U k u w a a n k o g a o o k u t e y o k u k a i m a s u +HYP: k o n o d a i ******* * ******* * k u w a a n k o g a o o k u t e y o k u k a i m a s u +Eval: D D D D + +Speaker sentences 87: cv_jpn_000761 #utts: 1 +id: (cv_jpn_000761-cv_jpn_000761) +Scores: (#C #S #D #I) 59 3 0 0 +REF: j i s h o B I k i n a g a r a s h o o s e t s U o y o m i m a s u +HYP: j i s h o O H k i n a g a r a s h o o s e t s O o y o m i m a s u +Eval: S S S + +Speaker sentences 88: cv_jpn_000762 #utts: 1 +id: (cv_jpn_000762-cv_jpn_000762) +Scores: (#C #S #D #I) 74 0 8 0 +REF: k o n n a O o k i n a g O o g u r U o t s u k e n a i t O i k e n a i n d e s u k a +HYP: k o n n a ******* * o k i n a g ******* * o g u r ******* * o t s u k e n a i t ******* * i k e n a i n d e s u k a +Eval: D D D D D D D D + +Speaker sentences 89: cv_jpn_000763 #utts: 1 +id: (cv_jpn_000763-cv_jpn_000763) +Scores: (#C #S #D #I) 47 2 1 0 +REF: k a r e e n o b o o r Y O k U w a t o m a r a n a i +HYP: k a r e e n o b o o r * I k O w a t o m a r a n a i +Eval: D S S + +Speaker sentences 90: cv_jpn_000764 #utts: 1 +id: (cv_jpn_000764-cv_jpn_000764) +Scores: (#C #S #D #I) 21 2 0 2 +REF: i k i t e ******* * T a n D a n e +HYP: i k i t e I K a n M a n e +Eval: I I S S + +Speaker sentences 91: cv_jpn_000765 #utts: 1 +id: (cv_jpn_000765-cv_jpn_000765) +Scores: (#C #S #D #I) 61 4 11 0 +REF: t O o j i t o s H I C H a k a C L k I t e k i n A h a t s u m E e d a c l T a n e +HYP: t A o j i t o s * ******* * ******* * * a k a ******* * * k T t e k i n I h a t s u m ******* * e d a c l P a n e +Eval: S D D D D D D D D D S S D D S + +Speaker sentences 92: cv_jpn_000766 #utts: 1 +id: (cv_jpn_000766-cv_jpn_000766) +Scores: (#C #S #D #I) 43 8 11 0 +REF: s O n O t O k i P A U W A t a s h I w a c h i k a R a T S u K I T a +HYP: s A n ******* * t E k i ******* * * * ******* * O t a s h O w a c h i k a ******* * a * F u T E K a +Eval: S D D S D D D D D D S S D D D S S S S + +Speaker sentences 93: cv_jpn_000767 #utts: 1 +id: (cv_jpn_000767-cv_jpn_000767) +Scores: (#C #S #D #I) 33 1 0 0 +REF: k a w a g a h i a g a c l t e i T a +HYP: k a w a g a h i a g a c l t e i D a +Eval: S + +Speaker sentences 94: cv_jpn_000768 #utts: 1 +id: (cv_jpn_000768-cv_jpn_000768) +Scores: (#C #S #D #I) 5 1 0 0 +REF: i c h I +HYP: i c h U +Eval: S + +Speaker sentences 95: cv_jpn_000769 #utts: 1 +id: (cv_jpn_000769-cv_jpn_000769) +Scores: (#C #S #D #I) 2 1 0 0 +REF: n I +HYP: n O +Eval: S + +Speaker sentences 96: cv_jpn_000770 #utts: 1 +id: (cv_jpn_000770-cv_jpn_000770) +Scores: (#C #S #D #I) 9 0 0 0 +REF: s h i c h i +HYP: s h i c h i +Eval: + +Speaker sentences 97: cv_jpn_000771 #utts: 1 +id: (cv_jpn_000771-cv_jpn_000771) +Scores: (#C #S #D #I) 2 1 0 0 +REF: G o +HYP: K o +Eval: S + +Speaker sentences 98: cv_jpn_000772 #utts: 1 +id: (cv_jpn_000772-cv_jpn_000772) +Scores: (#C #S #D #I) 5 0 0 0 +REF: i i e +HYP: i i e +Eval: + +Speaker sentences 99: cv_jpn_000773 #utts: 1 +id: (cv_jpn_000773-cv_jpn_000773) +Scores: (#C #S #D #I) 47 1 4 2 +REF: n a m a ******* * e k a r a s h I T e t e k I t o o s u g i r u +HYP: n a m a I e k a r a s h ******* * ******* * e t e k U t o o s u g i r u +Eval: I I D D D D S + +Speaker sentences 100: cv_jpn_000774 #utts: 1 +id: (cv_jpn_000774-cv_jpn_000774) +Scores: (#C #S #D #I) 123 1 4 3 +REF: j i k o n o s o t o n i a r u t o i u n o w a t a n n i j i k o n o I s h i k I n O s o t * ******* * o n i a r u t o i u k o t o d e n a k u +HYP: j i k o n o s o t o n i a r u t o i u n o w a t a n n i j i k o n o ******* * s h i k A n ******* * s o t S S o n i a r u t o i u k o t o d e n a k u +Eval: D D S D D I I I + +Speaker sentences 101: cv_jpn_000775 #utts: 1 +id: (cv_jpn_000775-cv_jpn_000775) +Scores: (#C #S #D #I) 41 1 2 0 +REF: s o r E w a s h i r a n a k u t e I i d e s u +HYP: s o r U w a s h i r a n a k u t e ******* * i d e s u +Eval: S D D + +Speaker sentences 102: cv_jpn_000776 #utts: 1 +id: (cv_jpn_000776-cv_jpn_000776) +Scores: (#C #S #D #I) 91 3 8 0 +REF: k i m i t o b o k u n o k y o o t s U u n o s h i R I A I w a d a r e h i t o r i P A U m i a t a r a n a i +HYP: k i m i t o b o k u n o k y o o t s ******* * u n o s h i ******* * G E E w a d a r e h i t o r i ******* * * * m i a t a r a n a i +Eval: D D D D S S S D D D D + +Speaker sentences 103: cv_jpn_000777 #utts: 1 +id: (cv_jpn_000777-cv_jpn_000777) +Scores: (#C #S #D #I) 46 4 4 0 +REF: s u g e e D A I J I n I n a c l t e k i t e r u n o n a +HYP: s u g e e ******* * O O T O n ******* * n a c l t e k i t e r u n o n a +Eval: D D S S S S D D + +Speaker sentences 104: cv_jpn_000778 #utts: 1 +id: (cv_jpn_000778-cv_jpn_000778) +Scores: (#C #S #D #I) 46 3 8 0 +REF: k o n o A T A R I d e s u k o s h i y a s u M I m a s h o o +HYP: k o n o ******* * ******* * H E N d e s u k o s h i y a s u ******* * ******* * m a s h o o +Eval: D D D D S S S D D D D + +Speaker sentences 105: cv_jpn_000779 #utts: 1 +id: (cv_jpn_000779-cv_jpn_000779) +Scores: (#C #S #D #I) 53 4 4 0 +REF: d e n s h a N i n o r u t o k i P A U k i c l P U o k a i M a s u +HYP: d e n s h a R i n o r u t o k i ******* * * * k i c l T O o k a i N a s u +Eval: S D D D D S S S + +Speaker sentences 106: cv_jpn_000780 #utts: 1 +id: (cv_jpn_000780-cv_jpn_000780) +Scores: (#C #S #D #I) 49 4 11 0 +REF: t a m a g O W a i C L k o G o j U u g u r a M U G u r a i D e s u +HYP: t a m a g ******* * ******* * a i ******* * * k o K o j I u g u r a ******* * N B u r a i ******* * e s u +Eval: D D D D D D D S S D D S S D D + +Speaker sentences 107: cv_jpn_000781 #utts: 1 +id: (cv_jpn_000781-cv_jpn_000781) +Scores: (#C #S #D #I) 77 2 4 8 +REF: g I R e s u p i i w a m a c l G i i o t s U u j i t e i n e s u t o s h i ******* * r i ******* * a c l t a ******* * ******* * +HYP: g ******* * E e s u p i i w a m a c l K i i o t s ******* * u j i t e i n e s u t o s h i D r i G a c l t a T U +Eval: D D S S D D I I I I I I I I + +Speaker sentences 108: cv_jpn_000782 #utts: 1 +id: (cv_jpn_000782-cv_jpn_000782) +Scores: (#C #S #D #I) 137 5 12 15 +REF: n o o g Y O o o y a m e Z a r u o e n a i ******* * h i t o G a a r i P A U k a n R e n k i G y o o m o P A U k o n o f u ******* * ******* * k ******* Y o o n i h i k i z u r a r e t e i r u t ******* * ******* * ******* * ******* * o I u +HYP: n o o g * ******* * o o y a m e S a r u o e n a i K h i t o K a a r i ******* * * * k a n N e n k i * y o o m o ******* * * * k o n o f u K I k E o o n i h i k i z u r a r e t e i r u t O O M O o S u +Eval: D D D S I I S D D D D S D D D D D I I I I I S I I I I I I I I S + +Speaker sentences 109: cv_jpn_000783 #utts: 1 +id: (cv_jpn_000783-cv_jpn_000783) +Scores: (#C #S #D #I) 81 1 12 0 +REF: n a n d e k o n o r O B o c l t o P A U s h o t a i m e N n a n o n i n a r e N a r e s h I i n d a +HYP: n a n d e k o n o r ******* * ******* * o c l t o ******* * * * s h o t a i m e ******* * n a n o n i n a r e G a r e s h ******* * i n d a +Eval: D D D D D D D D D D S D D + +Speaker sentences 110: cv_jpn_000784 #utts: 1 +id: (cv_jpn_000784-cv_jpn_000784) +Scores: (#C #S #D #I) 54 1 2 0 +REF: f u t s u u d e a r u k o t O m o r i c l p a n a k o s E e +HYP: f u t s u u d e a r u k o t A m o r i c l p a n a k o s ******* * e +Eval: S D D + +Speaker sentences 111: cv_jpn_000785 #utts: 1 +id: (cv_jpn_000785-cv_jpn_000785) +Scores: (#C #S #D #I) 66 0 0 0 +REF: t s u y o b i d e t a n j i k a n d e g o o k a i n i i t a m e r u +HYP: t s u y o b i d e t a n j i k a n d e g o o k a i n i i t a m e r u +Eval: + +Speaker sentences 112: cv_jpn_000786 #utts: 1 +id: (cv_jpn_000786-cv_jpn_000786) +Scores: (#C #S #D #I) 95 3 0 2 +REF: b a k u m a t s u n o d e k i g o T O w a i m a n i t s u ******* * u j i r U k y o o k u n n o y a m a d e s u +HYP: b a k u m a t s u n o d e k i g o K A w a i m a n i t s u R u j i r O k y o o k u n n o y a m a d e s u +Eval: S S I I S + +Speaker sentences 113: cv_jpn_000787 #utts: 1 +id: (cv_jpn_000787-cv_jpn_000787) +Scores: (#C #S #D #I) 59 5 0 0 +REF: m U k o o k a r a m a c h i n o T O M O r i g a m i e t e k i t a +HYP: m N k o o k a r a m a c h i n o W A K A r i g a m i e t e k i t a +Eval: S S S S S + +Speaker sentences 114: cv_jpn_000788 #utts: 1 +id: (cv_jpn_000788-cv_jpn_000788) +Scores: (#C #S #D #I) 109 12 0 7 +REF: * ******* N A n i ******* * O i u b e k i ******* * * K A w a k a R a n a k a c l t a n a N i m O I u b E k i k o t o g a o m o I u k a b a n a k a c l t A +HYP: M E N n i W U i u b e k i C H I H w a k a N a n a k a c l t a n a R i m I Y u b I k i k o t o g a o m o Y u k a b a n a k a c l t U +Eval: I I S S I I S I I I S S S S S S S S S + +Speaker sentences 115: cv_jpn_000789 #utts: 1 +id: (cv_jpn_000789-cv_jpn_000789) +Scores: (#C #S #D #I) 42 4 8 0 +REF: t a m E s H i n I i C L k a I D a k E Y a c l t e m i r u +HYP: t a m I s * i n ******* * i ******* * * k a E N a k ******* * I a c l t e m i r u +Eval: S D D D D D D S S D D S + +Speaker sentences 116: cv_jpn_000790 #utts: 1 +id: (cv_jpn_000790-cv_jpn_000790) +Scores: (#C #S #D #I) 74 6 7 0 +REF: b o k u s h i k a i n a i K i m i W a i n a i k o r e w A P a U o o K I n A C H I g a i k a +HYP: b o k u s h i k a i n a i G i m i M a i n a i k o r e w ******* * * a * o o T E n ******* * * S E g a i k a +Eval: S S D D D D S S D D D S S + +Speaker sentences 117: cv_jpn_000791 #utts: 1 +id: (cv_jpn_000791-cv_jpn_000791) +Scores: (#C #S #D #I) 88 6 27 0 +REF: S h U U k A i S H o k A R a n i j U u g o o t O o m a D E n o m i c h i w a m u k a s H I T o k A W a c l t e i n a k A C L T a +HYP: C h ******* * I k E i * J o k ******* * ******* * a n i j ******* * u g o o t ******* * o m a N U n o m i c h i w a m u k a s * ******* * ******* * o k ******* * ******* * a c l t e i n a k ******* * ******* * * ******* * a +Eval: S D D S S D S D D D D D D D D S S D D D D D D D D D D D D D D D D + +Speaker sentences 118: cv_jpn_000792 #utts: 1 +id: (cv_jpn_000792-cv_jpn_000792) +Scores: (#C #S #D #I) 69 10 13 2 +REF: k a N O j O N o t e E A N W a * k o n p o n t e K i n a k a i * K E T S u N I T s u n a G a c l t a +HYP: k a G U j ******* * ******* * o t e N W A * P a U k o n p o n t e ******* * i n a k a i C H I * K u ******* * U * s u n a ******* * a c l t a +Eval: S S D D D D S S S D S I D D I S S D S D D S D D D + +Speaker sentences 119: cv_jpn_000793 #utts: 1 +id: (cv_jpn_000793-cv_jpn_000793) +Scores: (#C #S #D #I) 72 5 8 2 +REF: k o D O m o n o k o r O w a g o h a n h a D e P A U o t O n a n I n a r U t o p a n h ******* * a +HYP: k o R U m o n o k o r E w a g o h a n h a R e ******* * * * o t U n a n ******* * n a r ******* * t o p a n h A a +Eval: S S S S D D D D S D D D D I I + +Speaker sentences 120: cv_jpn_000794 #utts: 1 +id: (cv_jpn_000794-cv_jpn_000794) +Scores: (#C #S #D #I) 148 9 5 0 +REF: k o o i t e k i C H o c l k a n t e k i n i p a u p o I E s h i s u t e k i n i P A U w a r e w a r e N o j i k o w a m a s u m a s u A K A R I t o n a r u n o d e a r u +HYP: k o o i t e k i * T o c l k a n t e k i n i p a u p o O I s h i s u t e k i n i ******* * * * w a r e w a r e M o j i k o w a m a s u m a s u Z U M E E t o n a r u n o d e a r u +Eval: D S S S D D D D S S S S S S + +Speaker sentences 121: cv_jpn_000795 #utts: 1 +id: (cv_jpn_000795-cv_jpn_000795) +Scores: (#C #S #D #I) 158 4 6 6 +REF: h i j o o s h i k i d E a r u k o t o w a p a u M u c h I o i m i s u r u n o m i d e n a k ******* * ******* * u p a u s h a k a i ******* * t e k i n i a k u t o m o k a n g a e r a R E r u N O d e a r u +HYP: h i j o o s h i k i d A a r u k o t o w a p a u B u c h O o i m i s u r u n o m i d e n a k U R u p a u s h a k a i E t e k i n i a k u t o m o k a n g a e r a ******* * ******* * r u ******* * U d e a r u +Eval: S S S I I I I I I D D D D D D S + +Speaker sentences 122: cv_jpn_000796 #utts: 1 +id: (cv_jpn_000796-cv_jpn_000796) +Scores: (#C #S #D #I) 101 1 6 2 +REF: j o o s h i k i g a n a o t o k ******* * u s h u t e k i n a c h i s h i k i d e a r u n i h a N s h i P A U k a G a k u w a +HYP: j o o s h i k i g a n a o t o k U u s h u t e k i n a c h i s h i k i d e a r u n i h a ******* * s h i ******* * * * k a R a k u w a +Eval: I I D D D D D D S + +Speaker sentences 123: cv_jpn_000797 #utts: 1 +id: (cv_jpn_000797-cv_jpn_000797) +Scores: (#C #S #D #I) 54 3 0 4 +REF: k o n n a k o t ******* * o d e o K O r a r e t e n a s a k E n ******* * a i +HYP: k o n n a k o t O o d e o G U r a r e t e n a s a k I n H a i +Eval: I I S S S I I + +Speaker sentences 124: cv_jpn_000798 #utts: 1 +id: (cv_jpn_000798-cv_jpn_000798) +Scores: (#C #S #D #I) 178 6 11 4 +REF: k a k o t o m i r a I t o g a j i k o m u j u n t e k i n i g e n z a i n i o i t e t a I r i T S U s u r u t o i u ******* * n i w A P a U g e n Z a I g a k a t a c h I o m o t a N a ******* * k e r e B a n a r a n a i +HYP: k a k o t o m i r a E t o g a j i k o m u j u n t e k i n i g e n z a i n i o i t e t a ******* * r i * K I s u r u t o i u I n i w ******* * * a * g e n D a ******* * g a k a t a c h ******* * o m o t a M a E k e r e M a n a r a n a i +Eval: S D D D S S I I D D D D S D D D D S I I S + +Speaker sentences 125: cv_jpn_000799 #utts: 1 +id: (cv_jpn_000799-cv_jpn_000799) +Scores: (#C #S #D #I) 48 0 16 0 +REF: s h o k I H i Y O o n o t a k a s a g a h A a d o R u n I n a R u +HYP: s h o k ******* * ******* * i ******* * ******* * o n o t a k a s a g a h ******* * a d o ******* * u n ******* * n a ******* * u +Eval: D D D D D D D D D D D D D D D D + + diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..b09f4e5ce8f2bd0af9689510b3e10b01949bc6c0 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/hyp.trn @@ -0,0 +1,126 @@ +b o k u n o i e e g a cl t a k a i n a N n o n a m a e N n i k u r a b e r u t o b o k u n i w a n a j i m i n o n a i pau n a b a e b a k a r i d a k e d o (cv_jpn_000674-cv_jpn_000674) +n a i o o s o n o m o n o y o r i pau f u i n i k i g a u k e t e r u (cv_jpn_000675-cv_jpn_000675) +b o k u n o sh I cl t e i u m o n o t o w a s U k o sh i ch i g a cl t e i t a (cv_jpn_000676-cv_jpn_000676) +k a k a a r u t a ch i m a n i o o i cl t e pau s e k a i g a i j sh I k i m e N t e k i d e a r i pau w a r e w a r e n o j i k o g a j sh I k i s a y o o t e k i d e a r u t o k a N g e e r a r e r u t o k sh i (cv_jpn_000677-cv_jpn_000677) +i e n i k t a n e N g a a j i w a s a N hy a k u m a i h o r o d e pau ch o o d a sh I t a b u N t o o n a j i g u r a i d a (cv_jpn_000678-cv_jpn_000678) +h a a w a p i t a m a r i ts u b a a g u n o s e e sh i N y o o i n i n u u N h I t e i r u t o k i n i b e sh i o o m o (cv_jpn_000679-cv_jpn_000679) +t a t a N d e a r h a N t e n o h i r o g e r a b a a ch i k o ch i N i ts u g i h a g i a a r i pau k a t a k o ch i n i d e k i t a h o k o r o b i n a N k a ky o o e N n o m a m o n i n a cl t e i r u (cv_jpn_000680-cv_jpn_000680) +k a r a g a k a n o d e b e r e n a a k a n i h o o m o N k i b o o n o b u sh o o y o b i ch o o z a k i b o o n o o b u g o m a i k a r a t o o r i d e s U (cv_jpn_000681-cv_jpn_000681) +k a N b a w a a t o t e m o s a m i d e s U (cv_jpn_000682-cv_jpn_000682) +k i N ch o o sh I t a k a h a o ts U k i d e p a cl t a w a d a s e k i n i h a i r u (cv_jpn_000683-cv_jpn_000683) +m a s a N ky u u r e N t o i cl t a f s u u k a k a r o k e N j i k i b u ts s U (cv_jpn_000684-cv_jpn_000684) +g o j i y o sh i d e s u s u m e t e ts s o g o N g a w a r u k u n a cl t a r a h I cl k o m e r u e a r e k U ch i (cv_jpn_000685-cv_jpn_000685) +m u j u N t e k i pau j i k o t o o i ts U t e k i n i pau j i k o o j i sh i N o k e e s e e s o r u sh a k a i w a (cv_jpn_000686-cv_jpn_000686) +f a N n o i k e N n i n a r a s a d e r u n a (cv_jpn_000687-cv_jpn_000687) +h i n e g a a s o b i t a y o o r a z e N k a i d e k o ch o o m i t e i r u (cv_jpn_000688-cv_jpn_000688) +i ch i d o w a k o N p o t a a j i k a N o n o N d e m i t a i (cv_jpn_000689-cv_jpn_000689) +k o o i n o k o sh I t e pau o t o o s a N t o k a a s a N w a N d e t e i k i m a sh I t a (cv_jpn_000690-cv_jpn_000690) +sh I k a sh I t e s o r e g a ts U k r a r e t a m o n o k a r a ts U k u r u m o n o e t o sh I t e d o k u m a d e m o r a w a r u n i s e m a r u t o i u t o k i pau w a r e w a r i n i ch o cl k a N t e k i d e a r u (cv_jpn_000691-cv_jpn_000691) +h a i n i t a m a cl t a k e m u r i o h a k i d a sh i k u r a i k o o e n i sh I s e N o m o k e r u (cv_jpn_000692-cv_jpn_000692) +w a d a w a d a sh n a b u s o k u d e ch i s e N i N n a r i s o (cv_jpn_000693-cv_jpn_000693) +ts e N sh e k a i n o m o ts U k a t a ch sh i t a sh i n o i y a i u r u s e e s a i y o sh I k u t o s a i o t o w a pau h a n a sh I t e k a N g a e r u k o t o w a d e k i n a i (cv_jpn_000694-cv_jpn_000694) +s a k e n o m a i n o n i p i i r u b a r a t o y o r e t a (cv_jpn_000695-cv_jpn_000695) +s o r e o t a t a k u h a k u n o z e N d a N k a i sh I k u i t e e d o n k a a k U t o n o m i m i r u k o t o w a (cv_jpn_000696-cv_jpn_000696) +n o k ky o e N o f u a ts u n i s u m o h o d e g e e m o y a cl t e i t a (cv_jpn_000697-cv_jpn_000697) +w a d a i w a a n a i sh u m a t a N k o b a e sh i s a N t o o a s o b e i m a s U (cv_jpn_000698-cv_jpn_000698) +h a s o o k o n i h e t o o g a i m a s u n e a r o h I t o o w a t a a r e e t e sh o o (cv_jpn_000699-cv_jpn_000699) +w a t a sh i w a k i n o o k a n a n o o d o o g a i t a i t e s U (cv_jpn_000700-cv_jpn_000700) +ky o o r e N k a n a p e N ky u o o sh I t e i m a s U (cv_jpn_000701-cv_jpn_000701) +ch o cl t o s u m i m a s e (cv_jpn_000702-cv_jpn_000702) +ch I k a sh t o n o o o o e N b u r y o w a y a k e k i m i n i k a e cl d e h a g e s U p e u n o cl t a (cv_jpn_000703-cv_jpn_000703) +i ts u m o k o n o e N p I i ts s o o ts sh U k a cl t e i t a n o t e m i j i k a h a h a n a r i m a sh I t a (cv_jpn_000704-cv_jpn_000704) +w a t a sh i w a e i g o g a h a n a s t e m o s U (cv_jpn_000705-cv_jpn_000705) +o sh i i k e e r y a j i b a N d e t o cl t a e k i n a (cv_jpn_000706-cv_jpn_000706) +a i sh u u k a r a n i sh u u u k a N h a i g a e e e r u y o o k o o o n i i k i m a s U (cv_jpn_000707-cv_jpn_000707) +o o r e m o k i n i n a r u n a (cv_jpn_000708-cv_jpn_000708) +d a r e g a ts s U k a cl t e r u n o k a a w a k a r u n a i (cv_jpn_000709-cv_jpn_000709) +p o r a z a n o b a j o N g a pau a g a r u t o o s U k o sh i u r e sh i (cv_jpn_000710-cv_jpn_000710) +m a t a a t a r a sh i a i d o r u g a d e t e k i t a (cv_jpn_000711-cv_jpn_000711) +m a j i d e y a cl t o n o k a (cv_jpn_000712-cv_jpn_000712) +ch o o d o s u m o t o k i n i ky o o j u g a h a i I t e k i d a (cv_jpn_000713-cv_jpn_000713) +i cl s o n i ch i N sh I t e o (cv_jpn_000714-cv_jpn_000714) +s o o r e k a s h a t e n o t o o r e s u (cv_jpn_000715-cv_jpn_000715) +f U t a r i w a r e j i i k i s e e s a N sh I t a (cv_jpn_000716-cv_jpn_000716) +t o m a t o k a n a N g a n o w a k a i s o u s u g a k a k a cl t e r i u o (cv_jpn_000717-cv_jpn_000717) +k o k o k a r a t a t e n a N a s u n o w a k i b i s i i (cv_jpn_000718-cv_jpn_000718) +n i z u k e o s sh I cl k a r i sh i b o cl t e pau a j i k a n a j i m u y o h i s u r u (cv_jpn_000719-cv_jpn_000719) +n e t o g e n i h a m a cl t a r a k a n e g a g a m a cl t a U (cv_jpn_000720-cv_jpn_000720) +i t o k a e r i y o o n i n a r u N d a (cv_jpn_000721-cv_jpn_000721) +k o s e e h a h a i y u t o i u y o r i a k u g a ts u y o e k a N j i (cv_jpn_000722-cv_jpn_000722) +f i j i k a r u n o s a o pau m a z a m a z a t o m i sh e ts U k e r a r e t a (cv_jpn_000723-cv_jpn_000723) +k o s U p a y o k e r e b a s o k o s o k o n o m o N d a e w a g a a m a N s e r u (cv_jpn_000724-cv_jpn_000724) +m i N n a y a cl t e m a s U k a r a t a i j o o b u d e s U y o (cv_jpn_000725-cv_jpn_000725) +k o n o t o sh o k a N pau h a i cl t a sh u N k a N k i n i i cl t a (cv_jpn_000726-cv_jpn_000726) +k o n o d e N ch i s u u k i r e ch a cl t a (cv_jpn_000727-cv_jpn_000727) +a m a y o d o r i s u r u t o k o r o g a n a k U t e k o m a cl t a (cv_jpn_000728-cv_jpn_000728) +y a s U k u s u r u y o r i sh I ts u w a g e t a h o sh i (cv_jpn_000729-cv_jpn_000729) +m a s e g o k o N n a k o t o i n a r o t o w a m o n a k a cl t a (cv_jpn_000730-cv_jpn_000730) +s a i g o n i w a r a y o t o r i n i k u r u s U t a i r u (cv_jpn_000731-cv_jpn_000731) +k o r a e a n a n a i m i g a r u d a (cv_jpn_000732-cv_jpn_000732) +i i e (cv_jpn_000733-cv_jpn_000733) +sh i (cv_jpn_000734-cv_jpn_000734) +n i (cv_jpn_000735-cv_jpn_000735) +h a ch i (cv_jpn_000736-cv_jpn_000736) +h a i (cv_jpn_000737-cv_jpn_000737) +t e e b r u n o o i e n i k a b i N g a a r i m a s U (cv_jpn_000738-cv_jpn_000738) +w a t a sh i w a a m a i y a s a s a N p o sh i m a s U (cv_jpn_000739-cv_jpn_000739) +a t a r a sh i i k u ts o o h a i cl t e d e k a k e m a s U (cv_jpn_000740-cv_jpn_000740) +k o t o sh i n o n a ts e y a s u m i w a pau b u m i n i m o i k i m a sh I t a sh i i y a m a n i m o o n o o r i b a sh I t a (cv_jpn_000741-cv_jpn_000741) +w a t a sh i w a i r o i r o n o b e N g o o j i b u N n o m u n e d e k o sh i r a e t e m i m a sh I t a (cv_jpn_000742-cv_jpn_000742) +n a N d e k u m o sh o o o b a i h e t a n a N d a r o (cv_jpn_000743-cv_jpn_000743) +t a r e N t o k a r a ky o k u a n a n i k a e t e k e e h I sh a k i g e (cv_jpn_000744-cv_jpn_000744) +d o g e z a s e r e b a i cl t e m o N sh a n a i (cv_jpn_000745-cv_jpn_000745) +d e e t a n o w a i d a k a n o ch i w a j i b u N t o o i cl t e e n o ky o o r i y o t a n o cl t a (cv_jpn_000746-cv_jpn_000746) +k o n o g e e n i N n a N k a w a h I s a sh i b u r i n i m i t a (cv_jpn_000747-cv_jpn_000747) +o o k i k u s a i d o ch i e N j i o s o r u (cv_jpn_000748-cv_jpn_000748) +k a r e w a a t a m a k a k i m u sh i cl t a (cv_jpn_000749-cv_jpn_000749) +o m a ch e sh I t e w a r i m a s U (cv_jpn_000750-cv_jpn_000750) +o n o ky o k U s e N k a i j o w a k i i t e r u (cv_jpn_000751-cv_jpn_000751) +r e e j z o o k o o a k e t a t o t a N n a n i g a h i ch u y o k a w a s u r e cl t a (cv_jpn_000752-cv_jpn_000752) +i ch i (cv_jpn_000753-cv_jpn_000753) +h a ch i (cv_jpn_000754-cv_jpn_000754) +i i e (cv_jpn_000755-cv_jpn_000755) +d e i (cv_jpn_000756-cv_jpn_000756) +sh i ch i (cv_jpn_000757-cv_jpn_000757) +y o o b o o d a s u n o N i k a u h I t o w a s U k u n a i (cv_jpn_000758-cv_jpn_000758) +r o o k a r u t o k u y i u n o i I ky o e m a k a s e n o k o m a sh a r u (cv_jpn_000759-cv_jpn_000759) +k o n o d a i k u w a a N k o g a o o k U t e y o k U k a i m a s U (cv_jpn_000760-cv_jpn_000760) +j i sh o o h k i n a g a r a sh o o s e ts o o y o m i m a s U (cv_jpn_000761-cv_jpn_000761) +k o N n a o k i n a g o g u r o ts U k e n a i t i k e n a i N d e s U k a (cv_jpn_000762-cv_jpn_000762) +k a r e e n o b o o r i k o w a t o m a r a n a i (cv_jpn_000763-cv_jpn_000763) +i k i t e i k a N m a n e (cv_jpn_000764-cv_jpn_000764) +t a o j i t o s a k a k t t e k i n i h a ts u m e d a cl p a n e (cv_jpn_000765-cv_jpn_000765) +s a N t e k i o t a sh o w a ch I k a a f U t e k a (cv_jpn_000766-cv_jpn_000766) +k a w a g a h i a g a cl t e i d a (cv_jpn_000767-cv_jpn_000767) +i ch u (cv_jpn_000768-cv_jpn_000768) +n o (cv_jpn_000769-cv_jpn_000769) +sh I ch i (cv_jpn_000770-cv_jpn_000770) +k o (cv_jpn_000771-cv_jpn_000771) +i i e (cv_jpn_000772-cv_jpn_000772) +n a m a i e k a r a sh e t e k U t o o s u g i r u (cv_jpn_000773-cv_jpn_000773) +j i k o n o s o t o n i a r u t o i u n o w a t a N n i j i k o n o sh I k a N s o ts s o n i a r u t o i u k o t o d e n a k u (cv_jpn_000774-cv_jpn_000774) +s o r u w a sh i r a n a k U t e i d e s U (cv_jpn_000775-cv_jpn_000775) +k i m i t o b o k u n o ky o o ts u n o sh i g e e w a d a r e h I t o r i m i a t a r a n a i (cv_jpn_000776-cv_jpn_000776) +s u g e e o o t o N n a cl t e k i t e r u n o n a (cv_jpn_000777-cv_jpn_000777) +k o n o h e N d e s U k o sh i y a s u m a sh o o (cv_jpn_000778-cv_jpn_000778) +d e N sh a r i n o r u t o k i k i cl t o o k a i n a s U (cv_jpn_000779-cv_jpn_000779) +t a m a g a i k o k o j i u g u r a N b u r a i e s U (cv_jpn_000780-cv_jpn_000780) +g e e s U p i i w a m a cl k i i o ts u j i t e i n e s U t o sh i d r i g a cl t a t u (cv_jpn_000781-cv_jpn_000781) +n o o g o o y a m e s a r u o e n a i k h I t o k a a r i k a N n e N k i y o o m o k o n o f U k I k e o o n i h I k i z u r a r e t e i r u t o o m o o s U (cv_jpn_000782-cv_jpn_000782) +n a N d e k o n o r o cl t o sh o t a i m e n a n o n i n a r e g a r e sh i N d a (cv_jpn_000783-cv_jpn_000783) +f U ts u u d e a r u k o t a m o r i cl p a n a k o s e (cv_jpn_000784-cv_jpn_000784) +ts u y o b i d e t a N j i k a N d e g o o k a i n i i t a m e r u (cv_jpn_000785-cv_jpn_000785) +b a k u m a ts u n o d e k i g o k a w a i m a n i ts u r u j i r o ky o o k u N n o y a m a d e s U (cv_jpn_000786-cv_jpn_000786) +m N k o o k a r a m a ch i n o w a k a r i g a m i e t e k i t a (cv_jpn_000787-cv_jpn_000787) +m e N n i w u i u b e k I ch i h w a k a n a n a k a cl t a n a r i m i y u b i k I k o t o g a o m o y u k a b a n a k a cl t u (cv_jpn_000788-cv_jpn_000788) +t a m i s i n i k a e n a k i a cl t e m i r u (cv_jpn_000789-cv_jpn_000789) +b o k U sh I k a i n a i g i m i m a i n a i k o r e w a o o t e N s e g a i k a (cv_jpn_000790-cv_jpn_000790) +ch i k e i j o k a n i j u g o o t o m a n u n o m i ch i w a m u k a s o k a cl t e i n a k a (cv_jpn_000791-cv_jpn_000791) +k a g u j o t e N w a pau k o N p o N t e i n a k a i ch i k u u s u n a a cl t a (cv_jpn_000792-cv_jpn_000792) +k o r u m o n o k o r e w a g o h a N h a r e o t u n a N n a r t o p a N h a a (cv_jpn_000793-cv_jpn_000793) +k o o i t e k I t o cl k a N t e k i n i pau p o o i sh I s u t e k i n i w a r e w a r e m o j i k o w a m a s u m a s u z u m e e t o n a r u n o d e a r u (cv_jpn_000794-cv_jpn_000794) +h i j o o sh I k i d a a r u k o t o w a pau b u ch o o i m i s u r u n o m i d e n a k u r u pau sh a k a i e t e k i n i a k U t o m o k a N g a e r a r u u d e a r u (cv_jpn_000795-cv_jpn_000795) +j o o sh I k i g a n a o t o k U u sh u t e k i n a ch i sh I k i d e a r u n i h a sh i k a r a k u w a (cv_jpn_000796-cv_jpn_000796) +k o N n a k o t o o d e o g u r a r e t e n a s a k i n h a i (cv_jpn_000797-cv_jpn_000797) +k a k o t o m i r a e t o g a j i k o m u j u N t e k i n i g e N z a i n i o i t e t a r i k I s u r u t o i u i n i w a g e N d a g a k a t a ch o m o t a m a e k e r e m a n a r a n a i (cv_jpn_000798-cv_jpn_000798) +sh o k i o n o t a k a s a g a h a d o u N n a u (cv_jpn_000799-cv_jpn_000799) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..682886c235d5a1f6d054262a3e893267b1c465ce --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/ref.trn @@ -0,0 +1,126 @@ +b o k u n o i e g a a cl t a k a i d a N n o n a m a e n i k u r a b e r u t o b o k u n i w a n a j i m i n o n a i n a m a e b a k a r i d a k e d o (cv_jpn_000674-cv_jpn_000674) +n a i y o o s o n o m o n o y o r i f u N i k i g a u k e t e r u (cv_jpn_000675-cv_jpn_000675) +b o k u n o sh i cl t e i r u m o n o t o w a s U k o sh I ch i g a cl t e i t a (cv_jpn_000676-cv_jpn_000676) +k a k a r u t a ch i b a n i o i t e pau s e k a i g a i sh I k i m e N t e k i d e a r i pau w a r e w a r e n o j i k o g a i sh I k i s a y o o t e k i d e a r u t o k a N g a e r a r e r u t o k i (cv_jpn_000677-cv_jpn_000677) +i e n i k i t a n e N g a j o o w a pau s a N by a k u m a i h o d o d e pau ch o o d o pau d a sh I t a b u N t o o n a j i g u r a i d a (cv_jpn_000678-cv_jpn_000678) +h a h a w a p i i t a a m a r i cl ts u b a a g u n o s e e sh i N by o o i N n i ny u u i N sh I t e i r u t o k i n i b e cl sh i i o u m u (cv_jpn_000679-cv_jpn_000679) +t a t a N d e a r u h a N t e N o h i r o g e r e b a pau a ch i k o ch i n i ts u g i h a g i g a a r i pau k a t a g u ch i n i d e k i t a h o k o r o b i n a N k a pau ky o n e N n o m a m a n i n a cl t e i r u (cv_jpn_000680-cv_jpn_000680) +k a r e g a pau k o n o sh u cl ch o o ch u u n i pau h o o m o N k i b o o n o b u sh o pau o y o b i pau ch o o s a k i b o o n o b u m o N w a pau i k a n o t o o r i d e s U (cv_jpn_000681-cv_jpn_000681) +k o N b a N w a t o t e m o s a m u i d e s U (cv_jpn_000682-cv_jpn_000682) +k i N ch o o sh I t a k a o ts U k i d e b a cl t a a w a d a s e k i n i h a i r u (cv_jpn_000683-cv_jpn_000683) +m a s a n i ky u u d e N t o i cl t a f u u k a k u a r u k e N ch I k u b u ts u (cv_jpn_000684-cv_jpn_000684) +g o r i o sh i d e s u s u m e t e ts u g o o g a w a r u k u n a cl t a r a h i cl k o m e r u y a r i k U ch i (cv_jpn_000685-cv_jpn_000685) +m u j u N t e k i j i k o d o o i ts u t e k i n i j i k o j i sh i N o k e e s e e s u r u sh a k a i w a (cv_jpn_000686-cv_jpn_000686) +f a N n o i k e N n i n a g a s a r e r u n a (cv_jpn_000687-cv_jpn_000687) +i n u g a a s o b i t a i o o r a z e N k a i d e k o cl ch i o m i t e r u (cv_jpn_000688-cv_jpn_000688) +i ch i d o w a k o o N p o t a a j u k a N o n o N d e m i t a i (cv_jpn_000689-cv_jpn_000689) +k o o i i n o k o sh I t e pau o t o o s a N t o o k a a s a N w a d e t e i k i m a sh I t a (cv_jpn_000690-cv_jpn_000690) +sh I k a sh I t e s o r e g a ts U k u r a r e t a m o n o k a r a ts U k u r u m o n o e t o sh I t e pau d o k o m a d e m o w a r e w a r e n i s e m a r u t o i u t o k i pau w a r e w a r e n i ch o cl k a N t e k i d e a r u (cv_jpn_000691-cv_jpn_000691) +h a i n i t a m a cl t a k e m u r i o h a k i d a sh i pau k u r a i k o o e N n i sh I s e N o m u k e r u (cv_jpn_000692-cv_jpn_000692) +m a d a m a d a sh i n a f u s o k u d e ch u u s e N n i n a r i s o o (cv_jpn_000693-cv_jpn_000693) +z e N s e k a i n o m o ts U k a t a ch i pau w a t a sh i n o i w a y u r u s e e s a N y o o sh I k i t o s a y o o t o w a h a n a sh I t e k a N g a e r u k o t o w a d e k i n a i (cv_jpn_000694-cv_jpn_000694) +s a k e n o m a n a i n o n i b i i r u h a r a t o i w a r e t a (cv_jpn_000695-cv_jpn_000695) +s o r e o t a d a k a g a k u n o z e N d a N k a i pau h I k u i t e e d o n o k a g a k U t o n o m i m i r u k o t o w a (cv_jpn_000696-cv_jpn_000696) +w a k i m e m o f u r a z u n i s u m a h o d e g e e m u o y a cl t e i t a (cv_jpn_000697-cv_jpn_000697) +w a t a sh i w a r a i sh u u m a t a k o b a y a sh I s a N t o a s o b i m a s U (cv_jpn_000698-cv_jpn_000698) +a s o k o n i h I t o g a i m a s U n e a n o h I t o w a d a r e d e sh o o (cv_jpn_000699-cv_jpn_000699) +w a t a sh i w a k i n o o k a r a n o d o g a i t a i d e s U (cv_jpn_000700-cv_jpn_000700) +ky o n e N k a r a b e N ky o o sh I t e i m a s U (cv_jpn_000701-cv_jpn_000701) +ch o cl t o s u m i m a s e N (cv_jpn_000702-cv_jpn_000702) +sh I k a sh i pau s o n o o o e N b u r i w a pau y a k e g i m i n i pau k a e cl t e h a g e sh i k u n a cl t a (cv_jpn_000703-cv_jpn_000703) +i ts u m o k o n o e N p I ts u o ts U k a cl t e i t a n o d e pau m i j i k a k u n a r i m a sh I t a (cv_jpn_000704-cv_jpn_000704) +w a t a sh i w a e e g o g a h a n a s e m a s U (cv_jpn_000705-cv_jpn_000705) +h o sh i k e ry a j i b u N d e t o cl t e k i n a (cv_jpn_000706-cv_jpn_000706) +r a i sh u u k a r a n i sh u u k a N pau k a i g a i e ry o k o o n i i k i m a s U (cv_jpn_000707-cv_jpn_000707) +o r e m o k i n i n a r u n a (cv_jpn_000708-cv_jpn_000708) +d a r e g a ts U k a cl t e r u n o k a w a k a r a n a i (cv_jpn_000709-cv_jpn_000709) +b u r a u z a n o b a a j o N g a a g a r u t o s U k o sh i u r e sh i i (cv_jpn_000710-cv_jpn_000710) +m a t a a t a r a sh i i a i d o r u g a d e t e k i t a (cv_jpn_000711-cv_jpn_000711) +m a j i d e y a cl t a n o k a (cv_jpn_000712-cv_jpn_000712) +ch o o d o s o n o t o k i n i pau ky o o j u g a h a i cl t e k i t a (cv_jpn_000713-cv_jpn_000713) +i cl sh o n i ch i N sh I t e y o (cv_jpn_000714-cv_jpn_000714) +s o r e g a s a t e N n o d o r e s u (cv_jpn_000715-cv_jpn_000715) +f U t a r i w a r e j i e i k I s e e s a N sh I t a (cv_jpn_000716-cv_jpn_000716) +t o m a t o k a n a N k a n o a k a i s o o s u g a k a k a cl t e r u y o (cv_jpn_000717-cv_jpn_000717) +k o k o k a r a t a t e n a o s u n o w a k i b i sh i i (cv_jpn_000718-cv_jpn_000718) +m i z u k e o sh i cl k a r i sh i b o cl t e a j i g a n a j i m u y o o n i s u r u (cv_jpn_000719-cv_jpn_000719) +n e t o g e n i h a m a cl t a r a k i N g a t a m a cl t a (cv_jpn_000720-cv_jpn_000720) +i ts U k a e r u y o o n i n a r u N d a (cv_jpn_000721-cv_jpn_000721) +k o s e e h a h a i y u u t o i u y o r i a k u g a ts u y o i k a N j i (cv_jpn_000722-cv_jpn_000722) +f i j i k a r u n o s a o m a z a m a z a t o m i s e ts U k e r a r e t a (cv_jpn_000723-cv_jpn_000723) +k o s U p a y o k e r e b a s o k o s o k o n o m o N d a i w a g a m a N s u r u (cv_jpn_000724-cv_jpn_000724) +m i N n a y a cl t e m a s U k a r a d a i j o o b u d e s U y o (cv_jpn_000725-cv_jpn_000725) +k o n o t o sh o k a N pau h a i cl t a sh u N k a N k i n i i cl t a (cv_jpn_000726-cv_jpn_000726) +k o n o d e N ch i pau s u g u k i r e ch i cl t a (cv_jpn_000727-cv_jpn_000727) +a m a y a d o r i s u r u t o k o r o g a n a k U t e k o m a cl t a (cv_jpn_000728-cv_jpn_000728) +y a s u k U s u r u y o r i sh I ts u o a g e t e h o sh i i (cv_jpn_000729-cv_jpn_000729) +m a s a k a k o N n a k o t o n i n a r o o t o w a o m o w a n a k a cl t a (cv_jpn_000730-cv_jpn_000730) +s a i g o n i w a r a i o t o r i n i k u r u s U t a i r u (cv_jpn_000731-cv_jpn_000731) +k o r e pau n a n i n o i m i g a a r u N d a (cv_jpn_000732-cv_jpn_000732) +i i e (cv_jpn_000733-cv_jpn_000733) +sh i (cv_jpn_000734-cv_jpn_000734) +n i (cv_jpn_000735-cv_jpn_000735) +w a ch i (cv_jpn_000736-cv_jpn_000736) +h a i (cv_jpn_000737-cv_jpn_000737) +t e e b u r u n o u e n i k a b i N g a a r i m a s U (cv_jpn_000738-cv_jpn_000738) +w a t a sh i w a m a i a s a s a N p o sh i m a s U (cv_jpn_000739-cv_jpn_000739) +a t a r a sh i i k U ts u o h a i t e d e k a k e m a s U (cv_jpn_000740-cv_jpn_000740) +k o t o sh i n o n a ts u y a s u m i w a pau u m i n i m o i k i m a sh I t a sh i pau y a m a n i m o n o b o r i m a sh I t a (cv_jpn_000741-cv_jpn_000741) +w a t a sh i w a pau i r o i r o n o b e N g o o pau j i b u N n o m u n e d e k o sh i r a e t e m i m a sh I t a (cv_jpn_000742-cv_jpn_000742) +n a N d e k o o m o sh o o b a i h e t a n a N d a r o (cv_jpn_000743-cv_jpn_000743) +t a r e N t o k a r a ky o k u a n a n i k a e t e k e e h I s a k u g e N (cv_jpn_000744-cv_jpn_000744) +d o g e z a s u r e b a i i cl t e m o N j a n a i (cv_jpn_000745-cv_jpn_000745) +d e e t o n o a i d a pau k a n o j o w a j i b u N t o i cl t e e n o ky o r i o t a m o cl t a (cv_jpn_000746-cv_jpn_000746) +k o n o g e e n i N n a N k a h I s a sh i b u r i n i m i t a (cv_jpn_000747-cv_jpn_000747) +o o k I k u s a i d o ch e N j i o s u r u (cv_jpn_000748-cv_jpn_000748) +k a r e w a a t a m a o k a k i m u sh i cl t a (cv_jpn_000749-cv_jpn_000749) +o m a ch i sh I t e o r i m a s U (cv_jpn_000750-cv_jpn_000750) +k o n o ky o k u pau s e N k a i i j o o w a k i i t e r u (cv_jpn_000751-cv_jpn_000751) +r e e z o o k o o a k e t a t o t a N pau n a n i g a h I ts u y o o k a w a s u r e t a (cv_jpn_000752-cv_jpn_000752) +i ch i (cv_jpn_000753-cv_jpn_000753) +w a ch i (cv_jpn_000754-cv_jpn_000754) +i i e (cv_jpn_000755-cv_jpn_000755) +r e i (cv_jpn_000756-cv_jpn_000756) +sh I ch i (cv_jpn_000757-cv_jpn_000757) +y o o b o o w a d a s u n o n i k a u h I t o w a s U k u n a i (cv_jpn_000758-cv_jpn_000758) +r o o k a r u t o k u y u u n o i k i o i m a k a s e n o k o m a a sh a r u (cv_jpn_000759-cv_jpn_000759) +k o n o d a i f U k u w a a N k o g a o o k U t e y o k U k a i m a s U (cv_jpn_000760-cv_jpn_000760) +j i sh o b i k i n a g a r a sh o o s e ts u o y o m i m a s U (cv_jpn_000761-cv_jpn_000761) +k o N n a o o k i n a g o o g u r u o ts U k e n a i t o i k e n a i N d e s U k a (cv_jpn_000762-cv_jpn_000762) +k a r e e n o b o o ry o k u w a t o m a r a n a i (cv_jpn_000763-cv_jpn_000763) +i k i t e t a N d a n e (cv_jpn_000764-cv_jpn_000764) +t o o j i t o sh I ch a k a cl k I t e k i n a h a ts u m e e d a cl t a n e (cv_jpn_000765-cv_jpn_000765) +s o n o t o k i pau w a t a sh i w a ch I k a r a ts U k i t a (cv_jpn_000766-cv_jpn_000766) +k a w a g a h i a g a cl t e i t a (cv_jpn_000767-cv_jpn_000767) +i ch i (cv_jpn_000768-cv_jpn_000768) +n i (cv_jpn_000769-cv_jpn_000769) +sh I ch i (cv_jpn_000770-cv_jpn_000770) +g o (cv_jpn_000771-cv_jpn_000771) +i i e (cv_jpn_000772-cv_jpn_000772) +n a m a e k a r a sh I t e t e k I t o o s u g i r u (cv_jpn_000773-cv_jpn_000773) +j i k o n o s o t o n i a r u t o i u n o w a t a N n i j i k o n o i sh I k i n o s o t o n i a r u t o i u k o t o d e n a k u (cv_jpn_000774-cv_jpn_000774) +s o r e w a sh i r a n a k U t e i i d e s U (cv_jpn_000775-cv_jpn_000775) +k i m i t o b o k u n o ky o o ts u u n o sh i r i a i w a d a r e h I t o r i pau m i a t a r a n a i (cv_jpn_000776-cv_jpn_000776) +s u g e e d a i j i n i n a cl t e k I t e r u n o n a (cv_jpn_000777-cv_jpn_000777) +k o n o a t a r i d e s U k o sh i y a s u m i m a sh o o (cv_jpn_000778-cv_jpn_000778) +d e N sh a n i n o r u t o k i pau k i cl p u o k a i m a s U (cv_jpn_000779-cv_jpn_000779) +t a m a g o w a i cl k o g o j u u g u r a m u g u r a i d e s U (cv_jpn_000780-cv_jpn_000780) +g i r e s U p i i w a m a cl g i i o ts u u j i t e i n e s U t o sh i r i a cl t a (cv_jpn_000781-cv_jpn_000781) +n o o gy o o o y a m e z a r u o e n a i h I t o g a a r i pau k a N r e N k i gy o o m o pau k o n o f U ky o o n i h I k i z u r a r e t e i r u t o i u (cv_jpn_000782-cv_jpn_000782) +n a N d e k o n o r o b o cl t o pau sh o t a i m e N n a n o n i n a r e n a r e sh i i N d a (cv_jpn_000783-cv_jpn_000783) +f U ts u u d e a r u k o t o m o r i cl p a n a k o s e e (cv_jpn_000784-cv_jpn_000784) +ts u y o b i d e t a N j i k a N d e g o o k a i n i i t a m e r u (cv_jpn_000785-cv_jpn_000785) +b a k u m a ts u n o d e k i g o t o w a i m a n i ts u u j i r u ky o o k u N n o y a m a d e s U (cv_jpn_000786-cv_jpn_000786) +m u k o o k a r a m a ch i n o t o m o r i g a m i e t e k i t a (cv_jpn_000787-cv_jpn_000787) +n a n i o i u b e k I k a w a k a r a n a k a cl t a n a n i m o i u b e k I k o t o g a o m o i u k a b a n a k a cl t a (cv_jpn_000788-cv_jpn_000788) +t a m e sh i n i i cl k a i d a k e y a cl t e m i r u (cv_jpn_000789-cv_jpn_000789) +b o k U sh i k a i n a i k i m i w a i n a i k o r e w a pau o o k i n a ch i g a i k a (cv_jpn_000790-cv_jpn_000790) +sh u u k a i sh o k a r a n i j u u g o o t o o m a d e n o m i ch i w a m u k a sh I t o k a w a cl t e i n a k a cl t a (cv_jpn_000791-cv_jpn_000791) +k a n o j o n o t e e a N w a k o N p o N t e k i n a k a i k e ts u n i ts u n a g a cl t a (cv_jpn_000792-cv_jpn_000792) +k o d o m o n o k o r o w a g o h a N h a d e pau o t o n a n i n a r u t o p a N h a (cv_jpn_000793-cv_jpn_000793) +k o o i t e k I ch o cl k a N t e k i n i pau p o i e sh I s u t e k i n i pau w a r e w a r e n o j i k o w a m a s u m a s u a k a r i t o n a r u n o d e a r u (cv_jpn_000794-cv_jpn_000794) +h i j o o sh I k i d e a r u k o t o w a pau m u ch i o i m i s u r u n o m i d e n a k u pau sh a k a i t e k i n i a k U t o m o k a N g a e r a r e r u n o d e a r u (cv_jpn_000795-cv_jpn_000795) +j o o sh I k i g a n a o t o k U sh u t e k i n a ch I sh i k i d e a r u n i h a N sh i pau k a g a k u w a (cv_jpn_000796-cv_jpn_000796) +k o N n a k o t o d e o k o r a r e t e n a s a k e n a i (cv_jpn_000797-cv_jpn_000797) +k a k o t o m i r a i t o g a j i k o m u j u N t e k i n i g e N z a i n i o i t e t a i r i ts U s u r u t o i u n i w a pau g e N z a i g a k a t a ch i o m o t a n a k e r e b a n a r a n a i (cv_jpn_000798-cv_jpn_000798) +sh o k I h i y o o n o t a k a s a g a h a a d o r u n i n a r u (cv_jpn_000799-cv_jpn_000799) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/result.txt b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..bb0dc7c19a845002f038a236f3979f5f7ee39823 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/result.txt @@ -0,0 +1,1553 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/hyp.trn| +|------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000674 | 1 75 | 96.0 2.7 1.3 4.0 8.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000675 | 1 33 | 97.0 0.0 3.0 6.1 9.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000676 | 1 38 | 97.4 0.0 2.6 0.0 2.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000677 | 1 97 | 96.9 3.1 0.0 5.2 8.2 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000678 | 1 65 | 86.2 4.6 9.2 1.5 15.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000679 | 1 67 | 80.6 7.5 11.9 0.0 19.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000680 | 1 102 | 91.2 4.9 3.9 0.0 8.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000681 | 1 81 | 72.8 19.8 7.4 3.7 30.9 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000682 | 1 23 | 87.0 4.3 8.7 4.3 17.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000683 | 1 40 | 95.0 2.5 2.5 5.0 10.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000684 | 1 39 | 84.6 10.3 5.1 5.1 20.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000685 | 1 54 | 90.7 9.3 0.0 3.7 13.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000686 | 1 52 | 96.2 3.8 0.0 5.8 9.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000687 | 1 23 | 91.3 8.7 0.0 0.0 8.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000688 | 1 37 | 89.2 8.1 2.7 5.4 16.2 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000689 | 1 32 | 93.8 3.1 3.1 0.0 6.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000690 | 1 45 | 95.6 0.0 4.4 2.2 6.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000691 | 1 110 | 92.7 3.6 3.6 0.0 7.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000692 | 1 52 | 94.2 1.9 3.8 0.0 5.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000693 | 1 35 | 77.1 11.4 11.4 2.9 25.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000694 | 1 89 | 86.5 7.9 5.6 2.2 15.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000695 | 1 33 | 78.8 12.1 9.1 0.0 21.2 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000696 | 1 61 | 88.5 6.6 4.9 0.0 11.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000697 | 1 38 | 76.3 18.4 5.3 0.0 23.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000698 | 1 40 | 85.0 7.5 7.5 10.0 25.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000699 | 1 38 | 89.5 10.5 0.0 15.8 26.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000700 | 1 31 | 93.5 6.5 0.0 6.5 12.9 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000701 | 1 24 | 87.5 12.5 0.0 8.3 20.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000702 | 1 14 | 92.9 0.0 7.1 0.0 7.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000703 | 1 52 | 75.0 17.3 7.7 5.8 30.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000704 | 1 49 | 89.8 8.2 2.0 10.2 20.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000705 | 1 24 | 91.7 8.3 0.0 4.2 12.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000706 | 1 24 | 87.5 8.3 4.2 16.7 29.2 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000707 | 1 40 | 87.5 7.5 5.0 15.0 27.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000708 | 1 15 | 100.0 0.0 0.0 6.7 6.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000709 | 1 28 | 96.4 3.6 0.0 7.1 10.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000710 | 1 36 | 86.1 5.6 8.3 5.6 19.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000711 | 1 28 | 96.4 0.0 3.6 0.0 3.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000712 | 1 15 | 93.3 6.7 0.0 0.0 6.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000713 | 1 33 | 84.8 12.1 3.0 0.0 15.2 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000714 | 1 15 | 86.7 6.7 6.7 0.0 13.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000715 | 1 19 | 84.2 10.5 5.3 15.8 31.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000716 | 1 26 | 96.2 0.0 3.8 0.0 3.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000717 | 1 37 | 91.9 5.4 2.7 5.4 13.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000718 | 1 28 | 92.9 7.1 0.0 3.6 10.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000719 | 1 41 | 90.2 7.3 2.4 4.9 14.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000720 | 1 29 | 93.1 6.9 0.0 6.9 13.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000721 | 1 20 | 85.0 15.0 0.0 0.0 15.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000722 | 1 36 | 94.4 2.8 2.8 0.0 5.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000723 | 1 37 | 97.3 2.7 0.0 2.7 5.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000724 | 1 41 | 95.1 4.9 0.0 2.4 7.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000725 | 1 32 | 96.9 3.1 0.0 0.0 3.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000726 | 1 32 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000727 | 1 23 | 87.0 4.3 8.7 0.0 13.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000728 | 1 34 | 97.1 2.9 0.0 0.0 2.9 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000729 | 1 29 | 89.7 6.9 3.4 0.0 10.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000730 | 1 38 | 78.9 7.9 13.2 0.0 21.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000731 | 1 30 | 96.7 3.3 0.0 0.0 3.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000732 | 1 22 | 72.7 9.1 18.2 4.5 31.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000733 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000734 | 1 2 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000735 | 1 2 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000736 | 1 4 | 75.0 25.0 0.0 0.0 25.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000737 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000738 | 1 27 | 92.6 3.7 3.7 3.7 11.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000739 | 1 25 | 100.0 0.0 0.0 8.0 8.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000740 | 1 28 | 96.4 3.6 0.0 3.6 7.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000741 | 1 60 | 93.3 5.0 1.7 3.3 10.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000742 | 1 54 | 96.3 0.0 3.7 0.0 3.7 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000743 | 1 27 | 92.6 3.7 3.7 3.7 11.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000744 | 1 37 | 91.9 5.4 2.7 0.0 8.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000745 | 1 25 | 88.0 8.0 4.0 0.0 12.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000746 | 1 46 | 89.1 8.7 2.2 8.7 19.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000747 | 1 31 | 100.0 0.0 0.0 6.5 6.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000748 | 1 21 | 95.2 4.8 0.0 4.8 9.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000749 | 1 23 | 95.7 0.0 4.3 0.0 4.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000750 | 1 16 | 87.5 12.5 0.0 6.3 18.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000751 | 1 28 | 85.7 0.0 14.3 0.0 14.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000752 | 1 43 | 93.0 2.3 4.7 4.7 11.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000753 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000754 | 1 4 | 75.0 25.0 0.0 0.0 25.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000755 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000756 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000757 | 1 4 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000758 | 1 32 | 93.8 0.0 6.3 0.0 6.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000759 | 1 38 | 89.5 5.3 5.3 2.6 13.2 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000760 | 1 36 | 94.4 0.0 5.6 0.0 5.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000761 | 1 30 | 90.0 10.0 0.0 0.0 10.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000762 | 1 41 | 90.2 0.0 9.8 0.0 9.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000763 | 1 25 | 88.0 12.0 0.0 0.0 12.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000764 | 1 12 | 83.3 16.7 0.0 8.3 25.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000765 | 1 36 | 75.0 13.9 11.1 0.0 25.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000766 | 1 29 | 58.6 27.6 13.8 0.0 41.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000767 | 1 17 | 94.1 5.9 0.0 0.0 5.9 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000768 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000769 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000770 | 1 4 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000771 | 1 2 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000772 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000773 | 1 26 | 88.5 3.8 7.7 3.8 15.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000774 | 1 64 | 93.8 3.1 3.1 1.6 7.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000775 | 1 22 | 90.9 4.5 4.5 0.0 9.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000776 | 1 49 | 87.8 6.1 6.1 0.0 12.2 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000777 | 1 27 | 77.8 14.8 7.4 0.0 22.2 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000778 | 1 28 | 75.0 10.7 14.3 0.0 25.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000779 | 1 29 | 82.8 13.8 3.4 0.0 17.2 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000780 | 1 32 | 71.9 12.5 15.6 0.0 28.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000781 | 1 40 | 90.0 5.0 5.0 10.0 20.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000782 | 1 74 | 86.5 9.5 4.1 10.8 24.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000783 | 1 45 | 86.7 2.2 11.1 0.0 13.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000784 | 1 28 | 92.9 3.6 3.6 0.0 7.1 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000785 | 1 33 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000786 | 1 48 | 93.8 6.3 0.0 2.1 8.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000787 | 1 32 | 84.4 15.6 0.0 0.0 15.6 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000788 | 1 60 | 83.3 13.3 3.3 8.3 25.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000789 | 1 26 | 69.2 19.2 11.5 0.0 30.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000790 | 1 42 | 81.0 14.3 4.8 0.0 19.0 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000791 | 1 58 | 67.2 12.1 20.7 0.0 32.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000792 | 1 45 | 68.9 15.6 15.6 2.2 33.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000793 | 1 42 | 81.0 11.9 7.1 2.4 21.4 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000794 | 1 78 | 88.5 9.0 2.6 1.3 12.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000795 | 1 81 | 91.4 4.9 3.7 3.7 12.3 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000796 | 1 51 | 94.1 2.0 3.9 2.0 7.8 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000797 | 1 29 | 89.7 10.3 0.0 6.9 17.2 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000798 | 1 96 | 89.6 6.3 4.2 2.1 12.5 100.0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000799 | 1 32 | 75.0 0.0 25.0 0.0 25.0 100.0 | +|==================================================================================================================| +| Sum/Avg | 126 4430 | 88.5 6.8 4.7 3.0 14.4 91.3 | +|==================================================================================================================| +| Mean | 1.0 35.2 | 88.2 7.7 4.1 2.7 14.4 91.3 | +| S.D. | 0.0 21.9 | 10.0 8.6 4.8 3.8 10.4 28.3 | +| Median | 1.0 32.0 | 90.1 5.7 3.1 0.0 12.4 100.0 | +`------------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,------------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/hyp.trn| +|------------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000674 | 1 75 | 72 2 1 3 6 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000675 | 1 33 | 32 0 1 2 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000676 | 1 38 | 37 0 1 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000677 | 1 97 | 94 3 0 5 8 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000678 | 1 65 | 56 3 6 1 10 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000679 | 1 67 | 54 5 8 0 13 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000680 | 1 102 | 93 5 4 0 9 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000681 | 1 81 | 59 16 6 3 25 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000682 | 1 23 | 20 1 2 1 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000683 | 1 40 | 38 1 1 2 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000684 | 1 39 | 33 4 2 2 8 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000685 | 1 54 | 49 5 0 2 7 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000686 | 1 52 | 50 2 0 3 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000687 | 1 23 | 21 2 0 0 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000688 | 1 37 | 33 3 1 2 6 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000689 | 1 32 | 30 1 1 0 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000690 | 1 45 | 43 0 2 1 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000691 | 1 110 | 102 4 4 0 8 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000692 | 1 52 | 49 1 2 0 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000693 | 1 35 | 27 4 4 1 9 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000694 | 1 89 | 77 7 5 2 14 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000695 | 1 33 | 26 4 3 0 7 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000696 | 1 61 | 54 4 3 0 7 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000697 | 1 38 | 29 7 2 0 9 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000698 | 1 40 | 34 3 3 4 10 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000699 | 1 38 | 34 4 0 6 10 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000700 | 1 31 | 29 2 0 2 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000701 | 1 24 | 21 3 0 2 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000702 | 1 14 | 13 0 1 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000703 | 1 52 | 39 9 4 3 16 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000704 | 1 49 | 44 4 1 5 10 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000705 | 1 24 | 22 2 0 1 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000706 | 1 24 | 21 2 1 4 7 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000707 | 1 40 | 35 3 2 6 11 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000708 | 1 15 | 15 0 0 1 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000709 | 1 28 | 27 1 0 2 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000710 | 1 36 | 31 2 3 2 7 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000711 | 1 28 | 27 0 1 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000712 | 1 15 | 14 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000713 | 1 33 | 28 4 1 0 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000714 | 1 15 | 13 1 1 0 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000715 | 1 19 | 16 2 1 3 6 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000716 | 1 26 | 25 0 1 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000717 | 1 37 | 34 2 1 2 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000718 | 1 28 | 26 2 0 1 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000719 | 1 41 | 37 3 1 2 6 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000720 | 1 29 | 27 2 0 2 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000721 | 1 20 | 17 3 0 0 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000722 | 1 36 | 34 1 1 0 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000723 | 1 37 | 36 1 0 1 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000724 | 1 41 | 39 2 0 1 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000725 | 1 32 | 31 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000726 | 1 32 | 32 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000727 | 1 23 | 20 1 2 0 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000728 | 1 34 | 33 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000729 | 1 29 | 26 2 1 0 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000730 | 1 38 | 30 3 5 0 8 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000731 | 1 30 | 29 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000732 | 1 22 | 16 2 4 1 7 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000733 | 1 3 | 3 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000734 | 1 2 | 2 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000735 | 1 2 | 2 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000736 | 1 4 | 3 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000737 | 1 3 | 3 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000738 | 1 27 | 25 1 1 1 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000739 | 1 25 | 25 0 0 2 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000740 | 1 28 | 27 1 0 1 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000741 | 1 60 | 56 3 1 2 6 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000742 | 1 54 | 52 0 2 0 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000743 | 1 27 | 25 1 1 1 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000744 | 1 37 | 34 2 1 0 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000745 | 1 25 | 22 2 1 0 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000746 | 1 46 | 41 4 1 4 9 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000747 | 1 31 | 31 0 0 2 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000748 | 1 21 | 20 1 0 1 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000749 | 1 23 | 22 0 1 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000750 | 1 16 | 14 2 0 1 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000751 | 1 28 | 24 0 4 0 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000752 | 1 43 | 40 1 2 2 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000753 | 1 3 | 3 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000754 | 1 4 | 3 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000755 | 1 3 | 3 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000756 | 1 3 | 2 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000757 | 1 4 | 4 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000758 | 1 32 | 30 0 2 0 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000759 | 1 38 | 34 2 2 1 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000760 | 1 36 | 34 0 2 0 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000761 | 1 30 | 27 3 0 0 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000762 | 1 41 | 37 0 4 0 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000763 | 1 25 | 22 3 0 0 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000764 | 1 12 | 10 2 0 1 3 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000765 | 1 36 | 27 5 4 0 9 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000766 | 1 29 | 17 8 4 0 12 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000767 | 1 17 | 16 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000768 | 1 3 | 2 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000769 | 1 2 | 1 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000770 | 1 4 | 4 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000771 | 1 2 | 1 1 0 0 1 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000772 | 1 3 | 3 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000773 | 1 26 | 23 1 2 1 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000774 | 1 64 | 60 2 2 1 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000775 | 1 22 | 20 1 1 0 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000776 | 1 49 | 43 3 3 0 6 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000777 | 1 27 | 21 4 2 0 6 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000778 | 1 28 | 21 3 4 0 7 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000779 | 1 29 | 24 4 1 0 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000780 | 1 32 | 23 4 5 0 9 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000781 | 1 40 | 36 2 2 4 8 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000782 | 1 74 | 64 7 3 8 18 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000783 | 1 45 | 39 1 5 0 6 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000784 | 1 28 | 26 1 1 0 2 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000785 | 1 33 | 33 0 0 0 0 0 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000786 | 1 48 | 45 3 0 1 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000787 | 1 32 | 27 5 0 0 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000788 | 1 60 | 50 8 2 5 15 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000789 | 1 26 | 18 5 3 0 8 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000790 | 1 42 | 34 6 2 0 8 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000791 | 1 58 | 39 7 12 0 19 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000792 | 1 45 | 31 7 7 1 15 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000793 | 1 42 | 34 5 3 1 9 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000794 | 1 78 | 69 7 2 1 10 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000795 | 1 81 | 74 4 3 3 10 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000796 | 1 51 | 48 1 2 1 4 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000797 | 1 29 | 26 3 0 2 5 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000798 | 1 96 | 86 6 4 2 12 1 | +|--------------------+----------------------+----------------------------------------------------------------------| +| cv_jpn_000799 | 1 32 | 24 0 8 0 8 1 | +|==================================================================================================================| +| Sum | 126 4430 | 3922 302 206 131 639 115 | +|==================================================================================================================| +| Mean | 1.0 35.2 | 31.1 2.4 1.6 1.0 5.1 0.9 | +| S.D. | 0.0 21.9 | 19.8 2.4 2.0 1.5 4.5 0.3 | +| Median | 1.0 32.0 | 28.5 2.0 1.0 0.0 4.0 1.0 | +`------------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/score_wer/hyp.trn + +Speakers: + 0: cv_jpn_000674 + 1: cv_jpn_000675 + 2: cv_jpn_000676 + 3: cv_jpn_000677 + 4: cv_jpn_000678 + 5: cv_jpn_000679 + 6: cv_jpn_000680 + 7: cv_jpn_000681 + 8: cv_jpn_000682 + 9: cv_jpn_000683 + 10: cv_jpn_000684 + 11: cv_jpn_000685 + 12: cv_jpn_000686 + 13: cv_jpn_000687 + 14: cv_jpn_000688 + 15: cv_jpn_000689 + 16: cv_jpn_000690 + 17: cv_jpn_000691 + 18: cv_jpn_000692 + 19: cv_jpn_000693 + 20: cv_jpn_000694 + 21: cv_jpn_000695 + 22: cv_jpn_000696 + 23: cv_jpn_000697 + 24: cv_jpn_000698 + 25: cv_jpn_000699 + 26: cv_jpn_000700 + 27: cv_jpn_000701 + 28: cv_jpn_000702 + 29: cv_jpn_000703 + 30: cv_jpn_000704 + 31: cv_jpn_000705 + 32: cv_jpn_000706 + 33: cv_jpn_000707 + 34: cv_jpn_000708 + 35: cv_jpn_000709 + 36: cv_jpn_000710 + 37: cv_jpn_000711 + 38: cv_jpn_000712 + 39: cv_jpn_000713 + 40: cv_jpn_000714 + 41: cv_jpn_000715 + 42: cv_jpn_000716 + 43: cv_jpn_000717 + 44: cv_jpn_000718 + 45: cv_jpn_000719 + 46: cv_jpn_000720 + 47: cv_jpn_000721 + 48: cv_jpn_000722 + 49: cv_jpn_000723 + 50: cv_jpn_000724 + 51: cv_jpn_000725 + 52: cv_jpn_000726 + 53: cv_jpn_000727 + 54: cv_jpn_000728 + 55: cv_jpn_000729 + 56: cv_jpn_000730 + 57: cv_jpn_000731 + 58: cv_jpn_000732 + 59: cv_jpn_000733 + 60: cv_jpn_000734 + 61: cv_jpn_000735 + 62: cv_jpn_000736 + 63: cv_jpn_000737 + 64: cv_jpn_000738 + 65: cv_jpn_000739 + 66: cv_jpn_000740 + 67: cv_jpn_000741 + 68: cv_jpn_000742 + 69: cv_jpn_000743 + 70: cv_jpn_000744 + 71: cv_jpn_000745 + 72: cv_jpn_000746 + 73: cv_jpn_000747 + 74: cv_jpn_000748 + 75: cv_jpn_000749 + 76: cv_jpn_000750 + 77: cv_jpn_000751 + 78: cv_jpn_000752 + 79: cv_jpn_000753 + 80: cv_jpn_000754 + 81: cv_jpn_000755 + 82: cv_jpn_000756 + 83: cv_jpn_000757 + 84: cv_jpn_000758 + 85: cv_jpn_000759 + 86: cv_jpn_000760 + 87: cv_jpn_000761 + 88: cv_jpn_000762 + 89: cv_jpn_000763 + 90: cv_jpn_000764 + 91: cv_jpn_000765 + 92: cv_jpn_000766 + 93: cv_jpn_000767 + 94: cv_jpn_000768 + 95: cv_jpn_000769 + 96: cv_jpn_000770 + 97: cv_jpn_000771 + 98: cv_jpn_000772 + 99: cv_jpn_000773 + 100: cv_jpn_000774 + 101: cv_jpn_000775 + 102: cv_jpn_000776 + 103: cv_jpn_000777 + 104: cv_jpn_000778 + 105: cv_jpn_000779 + 106: cv_jpn_000780 + 107: cv_jpn_000781 + 108: cv_jpn_000782 + 109: cv_jpn_000783 + 110: cv_jpn_000784 + 111: cv_jpn_000785 + 112: cv_jpn_000786 + 113: cv_jpn_000787 + 114: cv_jpn_000788 + 115: cv_jpn_000789 + 116: cv_jpn_000790 + 117: cv_jpn_000791 + 118: cv_jpn_000792 + 119: cv_jpn_000793 + 120: cv_jpn_000794 + 121: cv_jpn_000795 + 122: cv_jpn_000796 + 123: cv_jpn_000797 + 124: cv_jpn_000798 + 125: cv_jpn_000799 + +Speaker sentences 0: cv_jpn_000674 #utts: 1 +id: (cv_jpn_000674-cv_jpn_000674) +Scores: (#C #S #D #I) 72 2 1 3 +REF: b o k u n o i * e g A a cl t a k a i D a n n o n a m a e * n i k u r a b e r u t o b o k u n i w a n a j i m i n o n a i *** n a M a e b a k a r i d a k e d o +HYP: b o k u n o i E e g * a cl t a k a i N a n n o n a m a e N n i k u r a b e r u t o b o k u n i w a n a j i m i n o n a i PAU n a B a e b a k a r i d a k e d o +Eval: I D S I I S + +Speaker sentences 1: cv_jpn_000675 #utts: 1 +id: (cv_jpn_000675-cv_jpn_000675) +Scores: (#C #S #D #I) 32 0 1 2 +REF: n a i Y o o s o n o m o n o y o r i *** f u * n i k i g a u k e t e r u +HYP: n a i * o o s o n o m o n o y o r i PAU f u I n i k i g a u k e t e r u +Eval: D I I + +Speaker sentences 2: cv_jpn_000676 #utts: 1 +id: (cv_jpn_000676-cv_jpn_000676) +Scores: (#C #S #D #I) 37 0 1 0 +REF: b o k u n o sh i cl t e i R u m o n o t o w a s u k o sh i ch i g a cl t e i t a +HYP: b o k u n o sh i cl t e i * u m o n o t o w a s u k o sh i ch i g a cl t e i t a +Eval: D + +Speaker sentences 3: cv_jpn_000677 #utts: 1 +id: (cv_jpn_000677-cv_jpn_000677) +Scores: (#C #S #D #I) 94 3 0 5 +REF: k a k * a r u t a ch i B a n i * o i ** t e pau s e k a i g a i * sh i k i m e n t e k i d e a r i pau w a r e w a r e n o j i k o g a I sh i k i s a y o o t e k i d e a r u t o k a n g A e r a r e r u t o k ** i +HYP: k a k A a r u t a ch i M a n i O o i CL t e pau s e k a i g a i J sh i k i m e n t e k i d e a r i pau w a r e w a r e n o j i k o g a J sh i k i s a y o o t e k i d e a r u t o k a n g E e r a r e r u t o k SH i +Eval: I S I I I S S I + +Speaker sentences 4: cv_jpn_000678 #utts: 1 +id: (cv_jpn_000678-cv_jpn_000678) +Scores: (#C #S #D #I) 56 3 6 1 +REF: i e n i k I t a n e n g * a j O O w a PAU s a n BY a k u m a i h o D o d e pau ch O o D o PAU d a sh i t a b u n t o o n a j i g u r a i d a +HYP: i e n i k * t a n e n g A a j * I w a *** s a n HY a k u m a i h o R o d e pau ch * o * o *** d a sh i t a b u n t o o n a j i g u r a i d a +Eval: D I D S D S S D D D + +Speaker sentences 5: cv_jpn_000679 #utts: 1 +id: (cv_jpn_000679-cv_jpn_000679) +Scores: (#C #S #D #I) 54 5 8 0 +REF: h a H a w a p I i t A a m a r i CL ts u b a a g u n o s e e sh i n BY o o i N n i NY u u I n SH i t e i r u t o k i n i b e CL sh I i o U m U +HYP: h a * a w a p * i t * a m a r i ** ts u b a a g u n o s e e sh i n Y o o i * n i N u u * n H i t e i r u t o k i n i b e ** sh * i o O m O +Eval: D D D D S D S D S D D S S + +Speaker sentences 6: cv_jpn_000680 #utts: 1 +id: (cv_jpn_000680-cv_jpn_000680) +Scores: (#C #S #D #I) 93 5 4 0 +REF: t a t a n d e a r U h a n t e n o h i r o g e r E b a PAU a ch i k o ch i n i ts u g i h a g i G a a r i pau k a t a G U ch i n i d e k i t a h o k o r o b i n a n k a PAU ky o N e n n o m a m A n i n a cl t e i r u +HYP: t a t a n d e a r * h a n t e n o h i r o g e r A b a *** a ch i k o ch i n i ts u g i h a g i * a a r i pau k a t a K O ch i n i d e k i t a h o k o r o b i n a n k a *** ky o O e n n o m a m O n i n a cl t e i r u +Eval: D S D D S S D S S + +Speaker sentences 7: cv_jpn_000681 #utts: 1 +id: (cv_jpn_000681-cv_jpn_000681) +Scores: (#C #S #D #I) 59 16 6 3 +REF: k a r E g a PAU k O n o * * SH U CL CH O O CH U U n i PAU h o o m o n k i b o o n o b u sh o PAU o y o b i PAU ch o o S a k i b o o n * o b u M o N W a PAU i k a N O t o o r i d e s u +HYP: k a r A g a *** k A n o D E B E R E N A A K A n i *** h o o m o n k i b o o n o b u sh o *** o y o b i *** ch o o Z a k i b o o n O o b u G o * M a *** i k a R A t o o r i d e s u +Eval: S D S I I S S S S S S S S S D D D S I S D S D S S + +Speaker sentences 8: cv_jpn_000682 #utts: 1 +id: (cv_jpn_000682-cv_jpn_000682) +Scores: (#C #S #D #I) 20 1 2 1 +REF: k O n b a N w * a t o t e m o s a m U i d e s u +HYP: k A n b a * w A a t o t e m o s a m * i d e s u +Eval: S D I D + +Speaker sentences 9: cv_jpn_000683 #utts: 1 +id: (cv_jpn_000683-cv_jpn_000683) +Scores: (#C #S #D #I) 38 1 1 2 +REF: k i n ch o o sh i t a k * * a o ts u k i d e B a cl t A a w a d a s e k i n i h a i r u +HYP: k i n ch o o sh i t a k A H a o ts u k i d e P a cl t * a w a d a s e k i n i h a i r u +Eval: I I S D + +Speaker sentences 10: cv_jpn_000684 #utts: 1 +id: (cv_jpn_000684-cv_jpn_000684) +Scores: (#C #S #D #I) 33 4 2 2 +REF: m a s a n I ky u u D e n t o i cl t a f * u u k a k U a r U k e n CH i k U b u ts * u +HYP: m a s a n * ky u u R e n t o i cl t a f S u u k a k * a r O k e n J i k I b u ts S u +Eval: D S I D S S S I + +Speaker sentences 11: cv_jpn_000685 #utts: 1 +id: (cv_jpn_000685-cv_jpn_000685) +Scores: (#C #S #D #I) 49 5 0 2 +REF: g o R i * o sh i d e s u s u m e t e ts * U g o O g a w a r u k u n a cl t a r a h i cl k o m e r u Y a r I k u ch i +HYP: g o J i Y o sh i d e s u s u m e t e ts S O g o N g a w a r u k u n a cl t a r a h i cl k o m e r u E a r E k u ch i +Eval: S I I S S S S + +Speaker sentences 12: cv_jpn_000686 #utts: 1 +id: (cv_jpn_000686-cv_jpn_000686) +Scores: (#C #S #D #I) 50 2 0 3 +REF: m u j u n t e k i *** j i k o D o o i ts u t e k i n i *** j i k * o j i sh i n o k e e s e e s U r u sh a k a i w a +HYP: m u j u n t e k i PAU j i k o T o o i ts u t e k i n i PAU j i k O o j i sh i n o k e e s e e s O r u sh a k a i w a +Eval: I S I I S + +Speaker sentences 13: cv_jpn_000687 #utts: 1 +id: (cv_jpn_000687-cv_jpn_000687) +Scores: (#C #S #D #I) 21 2 0 0 +REF: f a n n o i k e n n i n a G a s a R e r u n a +HYP: f a n n o i k e n n i n a R a s a D e r u n a +Eval: S S + +Speaker sentences 14: cv_jpn_000688 #utts: 1 +id: (cv_jpn_000688-cv_jpn_000688) +Scores: (#C #S #D #I) 33 3 1 2 +REF: * i n U g a a s o b i t a I o o r a z e n k a i d e k o CL ch I o m i t e * r u +HYP: H i n E g a a s o b i t a Y o o r a z e n k a i d e k o ** ch O o m i t e I r u +Eval: I S S D S I + +Speaker sentences 15: cv_jpn_000689 #utts: 1 +id: (cv_jpn_000689-cv_jpn_000689) +Scores: (#C #S #D #I) 30 1 1 0 +REF: i ch i d o w a k O o n p o t a a j U k a n o n o n d e m i t a i +HYP: i ch i d o w a k * o n p o t a a j I k a n o n o n d e m i t a i +Eval: D S + +Speaker sentences 16: cv_jpn_000690 #utts: 1 +id: (cv_jpn_000690-cv_jpn_000690) +Scores: (#C #S #D #I) 43 0 2 1 +REF: k o o I i n o k o sh i t e pau o t o o s a n t O o k a a s a n w a * d e t e i k i m a sh i t a +HYP: k o o * i n o k o sh i t e pau o t o o s a n t * o k a a s a n w a N d e t e i k i m a sh i t a +Eval: D D I + +Speaker sentences 17: cv_jpn_000691 #utts: 1 +id: (cv_jpn_000691-cv_jpn_000691) +Scores: (#C #S #D #I) 102 4 4 0 +REF: sh i k a sh i t e s o r e g a ts u k U r a r e t a m o n o k a r a ts u k u r u m o n o e t o sh i t e PAU d o k O m a d e m o W A r E w a r E n i s e m a r u t o i u t o k i pau w a r e w a r E n i ch o cl k a n t e k i d e a r u +HYP: sh i k a sh i t e s o r e g a ts u k * r a r e t a m o n o k a r a ts u k u r u m o n o e t o sh i t e *** d o k U m a d e m o * * r A w a r U n i s e m a r u t o i u t o k i pau w a r e w a r I n i ch o cl k a n t e k i d e a r u +Eval: D D S D D S S S + +Speaker sentences 18: cv_jpn_000692 #utts: 1 +id: (cv_jpn_000692-cv_jpn_000692) +Scores: (#C #S #D #I) 49 1 2 0 +REF: h a i n i t a m a cl t a k e m u r i o h a k i d a sh i PAU k u r a i k o o e N n i sh i s e n o m U k e r u +HYP: h a i n i t a m a cl t a k e m u r i o h a k i d a sh i *** k u r a i k o o e * n i sh i s e n o m O k e r u +Eval: D D S + +Speaker sentences 19: cv_jpn_000693 #utts: 1 +id: (cv_jpn_000693-cv_jpn_000693) +Scores: (#C #S #D #I) 27 4 4 1 +REF: M a d a M a d a sh I n a F u s o k u d e ch U U s e N n i * n a r i s O o +HYP: W a d a W a d a sh * n a B u s o k u d e ch * I s e * n i N n a r i s * o +Eval: S S D S D S D I D + +Speaker sentences 20: cv_jpn_000694 #utts: 1 +id: (cv_jpn_000694-cv_jpn_000694) +Scores: (#C #S #D #I) 77 7 5 2 +REF: Z e n S e k a i n o m o ts u k a t a ch ** i PAU W A t a sh i n o i W a Y u r u s e e s a N y O o sh i k I t o s a Y O o t o w a *** h a n a sh i t e k a n g a e r u k o t o w a d e k i n a i +HYP: TS e n SH e k a i n o m o ts u k a t a ch SH i *** * * t a sh i n o i Y a I u r u s e e s a I y * o sh i k U t o s a * I o t o w a PAU h a n a sh i t e k a n g a e r u k o t o w a d e k i n a i +Eval: S S I D D D S S S D S D S I + +Speaker sentences 21: cv_jpn_000695 #utts: 1 +id: (cv_jpn_000695-cv_jpn_000695) +Scores: (#C #S #D #I) 26 4 3 0 +REF: s a k e n o m A N a i n o n i B i i r u H a r a t o I W A r e t a +HYP: s a k e n o m * * a i n o n i P i i r u B a r a t o * Y O r e t a +Eval: D D S S D S S + +Speaker sentences 22: cv_jpn_000696 #utts: 1 +id: (cv_jpn_000696-cv_jpn_000696) +Scores: (#C #S #D #I) 54 4 3 0 +REF: s o r e o t a D a k A G a k u n o z e n d a n k a i PAU H i k u i t e e d o n O k a G a k u t o n o m i m i r u k o t o w a +HYP: s o r e o t a T a k U H a k u n o z e n d a n k a i *** SH i k u i t e e d o n * k a * a k u t o n o m i m i r u k o t o w a +Eval: S S S D S D D + +Speaker sentences 23: cv_jpn_000697 #utts: 1 +id: (cv_jpn_000697-cv_jpn_000697) +Scores: (#C #S #D #I) 29 7 2 0 +REF: W A k I M e M o f u R a Z u n i s u m A h o d e g e e m U o y a cl t e i t a +HYP: N O k KY O e N o f u * a TS u n i s u m O h o d e g e e m * o y a cl t e i t a +Eval: S S S S S D S S D + +Speaker sentences 24: cv_jpn_000698 #utts: 1 +id: (cv_jpn_000698-cv_jpn_000698) +Scores: (#C #S #D #I) 34 3 3 4 +REF: w a T a SH i w * a R a i sh U u m a t a * k o b a Y A sh i s a n t * o a s o b * i m a s u +HYP: w a D a ** i w A a N a i sh * u m a t a N k o b a * E sh i s a n t O o a s o b E i m a s u +Eval: S D I S D I D S I I + +Speaker sentences 25: cv_jpn_000699 #utts: 1 +id: (cv_jpn_000699-cv_jpn_000699) +Scores: (#C #S #D #I) 34 4 0 6 +REF: * a s * o k o n i h I t * o g a i m a s u n e a N o h i t * o w a * D a r * e D e sh o o +HYP: H a s O o k o n i h E t O o g a i m a s u n e a R o h i t O o w a T A a r E e T e sh o o +Eval: I I S I S I I S I S + +Speaker sentences 26: cv_jpn_000700 #utts: 1 +id: (cv_jpn_000700-cv_jpn_000700) +Scores: (#C #S #D #I) 29 2 0 2 +REF: w a t a sh i w a k i n o o k a R a n * o d * o g a i t a i D e s u +HYP: w a t a sh i w a k i n o o k a N a n O o d O o g a i t a i T e s u +Eval: S I I S + +Speaker sentences 27: cv_jpn_000701 #utts: 1 +id: (cv_jpn_000701-cv_jpn_000701) +Scores: (#C #S #D #I) 21 3 0 2 +REF: ky * o N e n k a R a B e n ky * o o sh i t e i m a s u +HYP: ky O o R e n k a N a P e n ky U o o sh i t e i m a s u +Eval: I S S S I + +Speaker sentences 28: cv_jpn_000702 #utts: 1 +id: (cv_jpn_000702-cv_jpn_000702) +Scores: (#C #S #D #I) 13 0 1 0 +REF: ch o cl t o s u m i m a s e N +HYP: ch o cl t o s u m i m a s e * +Eval: D + +Speaker sentences 29: cv_jpn_000703 #utts: 1 +id: (cv_jpn_000703-cv_jpn_000703) +Scores: (#C #S #D #I) 39 9 4 3 +REF: SH i k a sh I PAU S o n * o o o e n b u r * I w a PAU y a k e G i m i n i PAU k a e cl T e h a g e * SH I K u n A cl t a +HYP: CH i k a sh * *** T o n O o o o e n b u r Y O w a *** y a k e K i m i n i *** k a e cl D e h a g e S U P E u n O cl t a +Eval: S D D S I I S D S D S I S S S S + +Speaker sentences 30: cv_jpn_000704 #utts: 1 +id: (cv_jpn_000704-cv_jpn_000704) +Scores: (#C #S #D #I) 44 4 1 5 +REF: i ts u m o k o n o e n p * i ts * U o ts ** u k a cl t e i t a n o D e PAU m i j i k * * a K U n a r i m a sh i t a +HYP: i ts u m o k o n o e n p I i ts S O o ts SH u k a cl t e i t a n o T e *** m i j i k A H a H A n a r i m a sh i t a +Eval: I I S I S D I I S S + +Speaker sentences 31: cv_jpn_000705 #utts: 1 +id: (cv_jpn_000705-cv_jpn_000705) +Scores: (#C #S #D #I) 22 2 0 1 +REF: w a t a sh i w a e E g o g a h a n a s * e m A s u +HYP: w a t a sh i w a e I g o g a h a n a s T e m O s u +Eval: S I S + +Speaker sentences 32: cv_jpn_000706 #utts: 1 +id: (cv_jpn_000706-cv_jpn_000706) +Scores: (#C #S #D #I) 21 2 1 4 +REF: H o sh * i k * e * RY a j i b U n d e t o cl t * e k i n a +HYP: * o sh I i k E e R Y a j i b A n d e t o cl t A e k i n a +Eval: D I I I S S I + +Speaker sentences 33: cv_jpn_000707 #utts: 1 +id: (cv_jpn_000707-cv_jpn_000707) +Scores: (#C #S #D #I) 35 3 2 6 +REF: R a i sh u u k a r a n i sh * u u k a n PAU K a i g a * I e * * * RY o k * o o n i i k i m a s u +HYP: * a i sh u u k a r a n i sh U u u k a n *** H a i g a E E e R U Y O o k O o o n i i k i m a s u +Eval: D I D S I S I I I S I + +Speaker sentences 34: cv_jpn_000708 #utts: 1 +id: (cv_jpn_000708-cv_jpn_000708) +Scores: (#C #S #D #I) 15 0 0 1 +REF: * o r e m o k i n i n a r u n a +HYP: O o r e m o k i n i n a r u n a +Eval: I + +Speaker sentences 35: cv_jpn_000709 #utts: 1 +id: (cv_jpn_000709-cv_jpn_000709) +Scores: (#C #S #D #I) 27 1 0 2 +REF: d a r e g a ts * u k a cl t e r u n o k * a w a k a r A n a i +HYP: d a r e g a ts S u k a cl t e r u n o k A a w a k a r U n a i +Eval: I I S + +Speaker sentences 36: cv_jpn_000710 #utts: 1 +id: (cv_jpn_000710-cv_jpn_000710) +Scores: (#C #S #D #I) 31 2 3 2 +REF: B U r a U z a n o b A a j o n g a *** a g a r u t * o s u k o sh i u r e sh I i +HYP: P O r a * z a n o b * a j o n g a PAU a g a r u t O o s u k o sh i u r e sh * i +Eval: S S D D I I D + +Speaker sentences 37: cv_jpn_000711 #utts: 1 +id: (cv_jpn_000711-cv_jpn_000711) +Scores: (#C #S #D #I) 27 0 1 0 +REF: m a t a a t a r a sh I i a i d o r u g a d e t e k i t a +HYP: m a t a a t a r a sh * i a i d o r u g a d e t e k i t a +Eval: D + +Speaker sentences 38: cv_jpn_000712 #utts: 1 +id: (cv_jpn_000712-cv_jpn_000712) +Scores: (#C #S #D #I) 14 1 0 0 +REF: m a j i d e y a cl t A n o k a +HYP: m a j i d e y a cl t O n o k a +Eval: S + +Speaker sentences 39: cv_jpn_000713 #utts: 1 +id: (cv_jpn_000713-cv_jpn_000713) +Scores: (#C #S #D #I) 28 4 1 0 +REF: ch o o d o s O N o t o k i n i PAU ky o o j u g a h a i CL t e k i T a +HYP: ch o o d o s U M o t o k i n i *** ky o o j u g a h a i I t e k i D a +Eval: S S D S S + +Speaker sentences 40: cv_jpn_000714 #utts: 1 +id: (cv_jpn_000714-cv_jpn_000714) +Scores: (#C #S #D #I) 13 1 1 0 +REF: i cl SH o n i ch i n sh i t e Y o +HYP: i cl S o n i ch i n sh i t e * o +Eval: S D + +Speaker sentences 41: cv_jpn_000715 #utts: 1 +id: (cv_jpn_000715-cv_jpn_000715) +Scores: (#C #S #D #I) 16 2 1 3 +REF: s * o r e G a s * a t e N n o * D o r e s u +HYP: s O o r e K a s H a t e * n o T O o r e s u +Eval: I S I D I S + +Speaker sentences 42: cv_jpn_000716 #utts: 1 +id: (cv_jpn_000716-cv_jpn_000716) +Scores: (#C #S #D #I) 25 0 1 0 +REF: f u t a r i w a r e j i E i k i s e e s a n sh i t a +HYP: f u t a r i w a r e j i * i k i s e e s a n sh i t a +Eval: D + +Speaker sentences 43: cv_jpn_000717 #utts: 1 +id: (cv_jpn_000717-cv_jpn_000717) +Scores: (#C #S #D #I) 34 2 1 2 +REF: t o m a t o k a n a n K a n o * a k a i s o O s u g a k a k a cl t e r * u Y o +HYP: t o m a t o k a n a n G a n o W a k a i s o U s u g a k a k a cl t e r I u * o +Eval: S I S I D + +Speaker sentences 44: cv_jpn_000718 #utts: 1 +id: (cv_jpn_000718-cv_jpn_000718) +Scores: (#C #S #D #I) 26 2 0 1 +REF: k o k o k a r a t a t e n a * O s u n o w a k i b i SH i i +HYP: k o k o k a r a t a t e n a N A s u n o w a k i b i S i i +Eval: I S S + +Speaker sentences 45: cv_jpn_000719 #utts: 1 +id: (cv_jpn_000719-cv_jpn_000719) +Scores: (#C #S #D #I) 37 3 1 2 +REF: M i z u k e o * sh i cl k a r i sh i b o cl t e *** a j i G a n a j i m u y O o N i s u r u +HYP: N i z u k e o S sh i cl k a r i sh i b o cl t e PAU a j i K a n a j i m u y * o H i s u r u +Eval: S I I S D S + +Speaker sentences 46: cv_jpn_000720 #utts: 1 +id: (cv_jpn_000720-cv_jpn_000720) +Scores: (#C #S #D #I) 27 2 0 2 +REF: n e t o g e n i h a m a cl t a r a k I n * g a T a m a cl t a * +HYP: n e t o g e n i h a m a cl t a r a k A n E g a G a m a cl t a U +Eval: S I S I + +Speaker sentences 47: cv_jpn_000721 #utts: 1 +id: (cv_jpn_000721-cv_jpn_000721) +Scores: (#C #S #D #I) 17 3 0 0 +REF: i TS U k a e r U y o o n i n a r u n d a +HYP: i T O k a e r I y o o n i n a r u n d a +Eval: S S S + +Speaker sentences 48: cv_jpn_000722 #utts: 1 +id: (cv_jpn_000722-cv_jpn_000722) +Scores: (#C #S #D #I) 34 1 1 0 +REF: k o s e e h a h a i y U u t o i u y o r i a k u g a ts u y o I k a n j i +HYP: k o s e e h a h a i y * u t o i u y o r i a k u g a ts u y o E k a n j i +Eval: D S + +Speaker sentences 49: cv_jpn_000723 #utts: 1 +id: (cv_jpn_000723-cv_jpn_000723) +Scores: (#C #S #D #I) 36 1 0 1 +REF: f i j i k a r u n o s a o *** m a z a m a z a t o m i S e ts u k e r a r e t a +HYP: f i j i k a r u n o s a o PAU m a z a m a z a t o m i SH e ts u k e r a r e t a +Eval: I S + +Speaker sentences 50: cv_jpn_000724 #utts: 1 +id: (cv_jpn_000724-cv_jpn_000724) +Scores: (#C #S #D #I) 39 2 0 1 +REF: k o s u p a y o k e r e b a s o k o s o k o n o m o n d a I w a g * a m a n s U r u +HYP: k o s u p a y o k e r e b a s o k o s o k o n o m o n d a E w a g A a m a n s E r u +Eval: S I S + +Speaker sentences 51: cv_jpn_000725 #utts: 1 +id: (cv_jpn_000725-cv_jpn_000725) +Scores: (#C #S #D #I) 31 1 0 0 +REF: m i n n a y a cl t e m a s u k a r a D a i j o o b u d e s u y o +HYP: m i n n a y a cl t e m a s u k a r a T a i j o o b u d e s u y o +Eval: S + +Speaker sentences 52: cv_jpn_000726 #utts: 1 +id: (cv_jpn_000726-cv_jpn_000726) +Scores: (#C #S #D #I) 32 0 0 0 +REF: k o n o t o sh o k a n pau h a i cl t a sh u n k a n k i n i i cl t a +HYP: k o n o t o sh o k a n pau h a i cl t a sh u n k a n k i n i i cl t a +Eval: + +Speaker sentences 53: cv_jpn_000727 #utts: 1 +id: (cv_jpn_000727-cv_jpn_000727) +Scores: (#C #S #D #I) 20 1 2 0 +REF: k o n o d e n ch i PAU s u G u k i r e ch I cl t a +HYP: k o n o d e n ch i *** s u * u k i r e ch A cl t a +Eval: D D S + +Speaker sentences 54: cv_jpn_000728 #utts: 1 +id: (cv_jpn_000728-cv_jpn_000728) +Scores: (#C #S #D #I) 33 1 0 0 +REF: a m a y A d o r i s u r u t o k o r o g a n a k u t e k o m a cl t a +HYP: a m a y O d o r i s u r u t o k o r o g a n a k u t e k o m a cl t a +Eval: S + +Speaker sentences 55: cv_jpn_000729 #utts: 1 +id: (cv_jpn_000729-cv_jpn_000729) +Scores: (#C #S #D #I) 26 2 1 0 +REF: y a s u k u s u r u y o r i sh i ts u O a g e t E h o sh I i +HYP: y a s u k u s u r u y o r i sh i ts u W a g e t A h o sh * i +Eval: S S D + +Speaker sentences 56: cv_jpn_000730 #utts: 1 +id: (cv_jpn_000730-cv_jpn_000730) +Scores: (#C #S #D #I) 30 3 5 0 +REF: m a s A K A k o n n a k o t o N i n a r O o t o w a O m o W A n a k a cl t a +HYP: m a s E G O k o n n a k o t o * i n a r * o t o w a * m o * * n a k a cl t a +Eval: S S S D D D D D + +Speaker sentences 57: cv_jpn_000731 #utts: 1 +id: (cv_jpn_000731-cv_jpn_000731) +Scores: (#C #S #D #I) 29 1 0 0 +REF: s a i g o n i w a r a I o t o r i n i k u r u s u t a i r u +HYP: s a i g o n i w a r a Y o t o r i n i k u r u s u t a i r u +Eval: S + +Speaker sentences 58: cv_jpn_000732 #utts: 1 +id: (cv_jpn_000732-cv_jpn_000732) +Scores: (#C #S #D #I) 16 2 4 1 +REF: k o r * e PAU N a n I n O i m i g A a r u N d a +HYP: k o r A e *** * a n A n A i m i g * a r u * d a +Eval: I D D S S D D + +Speaker sentences 59: cv_jpn_000733 #utts: 1 +id: (cv_jpn_000733-cv_jpn_000733) +Scores: (#C #S #D #I) 3 0 0 0 +REF: i i e +HYP: i i e +Eval: + +Speaker sentences 60: cv_jpn_000734 #utts: 1 +id: (cv_jpn_000734-cv_jpn_000734) +Scores: (#C #S #D #I) 2 0 0 0 +REF: sh i +HYP: sh i +Eval: + +Speaker sentences 61: cv_jpn_000735 #utts: 1 +id: (cv_jpn_000735-cv_jpn_000735) +Scores: (#C #S #D #I) 2 0 0 0 +REF: n i +HYP: n i +Eval: + +Speaker sentences 62: cv_jpn_000736 #utts: 1 +id: (cv_jpn_000736-cv_jpn_000736) +Scores: (#C #S #D #I) 3 1 0 0 +REF: W a ch i +HYP: H a ch i +Eval: S + +Speaker sentences 63: cv_jpn_000737 #utts: 1 +id: (cv_jpn_000737-cv_jpn_000737) +Scores: (#C #S #D #I) 3 0 0 0 +REF: h a i +HYP: h a i +Eval: + +Speaker sentences 64: cv_jpn_000738 #utts: 1 +id: (cv_jpn_000738-cv_jpn_000738) +Scores: (#C #S #D #I) 25 1 1 1 +REF: t e e b U r u n * o U e n i k a b i n g a a r i m a s u +HYP: t e e b * r u n O o I e n i k a b i n g a a r i m a s u +Eval: D I S + +Speaker sentences 65: cv_jpn_000739 #utts: 1 +id: (cv_jpn_000739-cv_jpn_000739) +Scores: (#C #S #D #I) 25 0 0 2 +REF: w a t a sh i w * a m a i * a s a s a n p o sh i m a s u +HYP: w a t a sh i w A a m a i Y a s a s a n p o sh i m a s u +Eval: I I + +Speaker sentences 66: cv_jpn_000740 #utts: 1 +id: (cv_jpn_000740-cv_jpn_000740) +Scores: (#C #S #D #I) 27 1 0 1 +REF: a t a r a sh i i k u ts U o h a i ** t e d e k a k e m a s u +HYP: a t a r a sh i i k u ts O o h a i CL t e d e k a k e m a s u +Eval: S I + +Speaker sentences 67: cv_jpn_000741 #utts: 1 +id: (cv_jpn_000741-cv_jpn_000741) +Scores: (#C #S #D #I) 56 3 1 2 +REF: k o t o sh i n o n a ts U y a s u m i w a pau * u m i n i m o i k i m a sh i t a sh i PAU y a m a n i m * o n o B o r i M a sh i t a +HYP: k o t o sh i n o n a ts E y a s u m i w a pau B u m i n i m o i k i m a sh i t a sh i I y a m a n i m O o n o * o r i B a sh i t a +Eval: S I S I D S + +Speaker sentences 68: cv_jpn_000742 #utts: 1 +id: (cv_jpn_000742-cv_jpn_000742) +Scores: (#C #S #D #I) 52 0 2 0 +REF: w a t a sh i w a PAU i r o i r o n o b e n g o o PAU j i b u n n o m u n e d e k o sh i r a e t e m i m a sh i t a +HYP: w a t a sh i w a *** i r o i r o n o b e n g o o *** j i b u n n o m u n e d e k o sh i r a e t e m i m a sh i t a +Eval: D D + +Speaker sentences 69: cv_jpn_000743 #utts: 1 +id: (cv_jpn_000743-cv_jpn_000743) +Scores: (#C #S #D #I) 25 1 1 1 +REF: n a n d e k O O m o sh * o o b a i h e t a n a n d a r o +HYP: n a n d e k * U m o sh O o o b a i h e t a n a n d a r o +Eval: D S I + +Speaker sentences 70: cv_jpn_000744 #utts: 1 +id: (cv_jpn_000744-cv_jpn_000744) +Scores: (#C #S #D #I) 34 2 1 0 +REF: t a r e n t o k a r a ky o k u a n a n i k a e t e k e e h i S a k U g e N +HYP: t a r e n t o k a r a ky o k u a n a n i k a e t e k e e h i SH a k I g e * +Eval: S S D + +Speaker sentences 71: cv_jpn_000745 #utts: 1 +id: (cv_jpn_000745-cv_jpn_000745) +Scores: (#C #S #D #I) 22 2 1 0 +REF: d o g e z a s U r e b a I i cl t e m o n J a n a i +HYP: d o g e z a s E r e b a * i cl t e m o n SH a n a i +Eval: S D S + +Speaker sentences 72: cv_jpn_000746 #utts: 1 +id: (cv_jpn_000746-cv_jpn_000746) +Scores: (#C #S #D #I) 41 4 1 4 +REF: d e e t O n o * a i d a PAU k a n o J O w a j i b u n t * o i cl t e e n o ky * o r i * o t a M o cl t a +HYP: d e e t A n o W a i d a *** k a n o CH I w a j i b u n t O o i cl t e e n o ky O o r i Y o t a N o cl t a +Eval: S I D S S I I I S + +Speaker sentences 73: cv_jpn_000747 #utts: 1 +id: (cv_jpn_000747-cv_jpn_000747) +Scores: (#C #S #D #I) 31 0 0 2 +REF: k o n o g e e n i n n a n k * * a h i s a sh i b u r i n i m i t a +HYP: k o n o g e e n i n n a n k A W a h i s a sh i b u r i n i m i t a +Eval: I I + +Speaker sentences 74: cv_jpn_000748 #utts: 1 +id: (cv_jpn_000748-cv_jpn_000748) +Scores: (#C #S #D #I) 20 1 0 1 +REF: o o k i k u s a i d o ch * e n j i o s U r u +HYP: o o k i k u s a i d o ch I e n j i o s O r u +Eval: I S + +Speaker sentences 75: cv_jpn_000749 #utts: 1 +id: (cv_jpn_000749-cv_jpn_000749) +Scores: (#C #S #D #I) 22 0 1 0 +REF: k a r e w a a t a m a O k a k i m u sh i cl t a +HYP: k a r e w a a t a m a * k a k i m u sh i cl t a +Eval: D + +Speaker sentences 76: cv_jpn_000750 #utts: 1 +id: (cv_jpn_000750-cv_jpn_000750) +Scores: (#C #S #D #I) 14 2 0 1 +REF: o m a ch I sh i t e * O r i m a s u +HYP: o m a ch E sh i t e W A r i m a s u +Eval: S I S + +Speaker sentences 77: cv_jpn_000751 #utts: 1 +id: (cv_jpn_000751-cv_jpn_000751) +Scores: (#C #S #D #I) 24 0 4 0 +REF: K o n o ky o k u PAU s e n k a I i j O o w a k i i t e r u +HYP: * o n o ky o k u *** s e n k a * i j * o w a k i i t e r u +Eval: D D D D + +Speaker sentences 78: cv_jpn_000752 #utts: 1 +id: (cv_jpn_000752-cv_jpn_000752) +Scores: (#C #S #D #I) 40 1 2 2 +REF: r e e * z o o k o o a k e t a t o t a n PAU n a n i g a h i TS u y O o k a w a s u r e ** t a +HYP: r e e J z o o k o o a k e t a t o t a n *** n a n i g a h i CH u y * o k a w a s u r e CL t a +Eval: I D S D I + +Speaker sentences 79: cv_jpn_000753 #utts: 1 +id: (cv_jpn_000753-cv_jpn_000753) +Scores: (#C #S #D #I) 3 0 0 0 +REF: i ch i +HYP: i ch i +Eval: + +Speaker sentences 80: cv_jpn_000754 #utts: 1 +id: (cv_jpn_000754-cv_jpn_000754) +Scores: (#C #S #D #I) 3 1 0 0 +REF: W a ch i +HYP: H a ch i +Eval: S + +Speaker sentences 81: cv_jpn_000755 #utts: 1 +id: (cv_jpn_000755-cv_jpn_000755) +Scores: (#C #S #D #I) 3 0 0 0 +REF: i i e +HYP: i i e +Eval: + +Speaker sentences 82: cv_jpn_000756 #utts: 1 +id: (cv_jpn_000756-cv_jpn_000756) +Scores: (#C #S #D #I) 2 1 0 0 +REF: R e i +HYP: D e i +Eval: S + +Speaker sentences 83: cv_jpn_000757 #utts: 1 +id: (cv_jpn_000757-cv_jpn_000757) +Scores: (#C #S #D #I) 4 0 0 0 +REF: sh i ch i +HYP: sh i ch i +Eval: + +Speaker sentences 84: cv_jpn_000758 #utts: 1 +id: (cv_jpn_000758-cv_jpn_000758) +Scores: (#C #S #D #I) 30 0 2 0 +REF: y o o b o o W A d a s u n o n i k a u h i t o w a s u k u n a i +HYP: y o o b o o * * d a s u n o n i k a u h i t o w a s u k u n a i +Eval: D D + +Speaker sentences 85: cv_jpn_000759 #utts: 1 +id: (cv_jpn_000759-cv_jpn_000759) +Scores: (#C #S #D #I) 34 2 2 1 +REF: r o o k a r u t o k u y U u n o i K i ** o I m a k a s e n o k o m A a sh a r u +HYP: r o o k a r u t o k u y I u n o i * i KY o E m a k a s e n o k o m * a sh a r u +Eval: S D I S D + +Speaker sentences 86: cv_jpn_000760 #utts: 1 +id: (cv_jpn_000760-cv_jpn_000760) +Scores: (#C #S #D #I) 34 0 2 0 +REF: k o n o d a i F U k u w a a n k o g a o o k u t e y o k u k a i m a s u +HYP: k o n o d a i * * k u w a a n k o g a o o k u t e y o k u k a i m a s u +Eval: D D + +Speaker sentences 87: cv_jpn_000761 #utts: 1 +id: (cv_jpn_000761-cv_jpn_000761) +Scores: (#C #S #D #I) 27 3 0 0 +REF: j i sh o B I k i n a g a r a sh o o s e ts U o y o m i m a s u +HYP: j i sh o O H k i n a g a r a sh o o s e ts O o y o m i m a s u +Eval: S S S + +Speaker sentences 88: cv_jpn_000762 #utts: 1 +id: (cv_jpn_000762-cv_jpn_000762) +Scores: (#C #S #D #I) 37 0 4 0 +REF: k o n n a O o k i n a g O o g u r U o ts u k e n a i t O i k e n a i n d e s u k a +HYP: k o n n a * o k i n a g * o g u r * o ts u k e n a i t * i k e n a i n d e s u k a +Eval: D D D D + +Speaker sentences 89: cv_jpn_000763 #utts: 1 +id: (cv_jpn_000763-cv_jpn_000763) +Scores: (#C #S #D #I) 22 3 0 0 +REF: k a r e e n o b o o RY O k U w a t o m a r a n a i +HYP: k a r e e n o b o o R I k O w a t o m a r a n a i +Eval: S S S + +Speaker sentences 90: cv_jpn_000764 #utts: 1 +id: (cv_jpn_000764-cv_jpn_000764) +Scores: (#C #S #D #I) 10 2 0 1 +REF: i k i t e * T a n D a n e +HYP: i k i t e I K a n M a n e +Eval: I S S + +Speaker sentences 91: cv_jpn_000765 #utts: 1 +id: (cv_jpn_000765-cv_jpn_000765) +Scores: (#C #S #D #I) 27 5 4 0 +REF: t O o j i t o SH I CH a k a CL k I t e k i n A h a ts u m E e d a cl T a n e +HYP: t A o j i t o ** * S a k a ** k T t e k i n I h a ts u m * e d a cl P a n e +Eval: S D D S D S S D S + +Speaker sentences 92: cv_jpn_000766 #utts: 1 +id: (cv_jpn_000766-cv_jpn_000766) +Scores: (#C #S #D #I) 17 8 4 0 +REF: s O n O t O k i PAU W A t a sh I w a ch i k a R a TS u K I T a +HYP: s A n * t E k i *** * O t a sh O w a ch i k a * a F u T E K a +Eval: S D S D D S S D S S S S + +Speaker sentences 93: cv_jpn_000767 #utts: 1 +id: (cv_jpn_000767-cv_jpn_000767) +Scores: (#C #S #D #I) 16 1 0 0 +REF: k a w a g a h i a g a cl t e i T a +HYP: k a w a g a h i a g a cl t e i D a +Eval: S + +Speaker sentences 94: cv_jpn_000768 #utts: 1 +id: (cv_jpn_000768-cv_jpn_000768) +Scores: (#C #S #D #I) 2 1 0 0 +REF: i ch I +HYP: i ch U +Eval: S + +Speaker sentences 95: cv_jpn_000769 #utts: 1 +id: (cv_jpn_000769-cv_jpn_000769) +Scores: (#C #S #D #I) 1 1 0 0 +REF: n I +HYP: n O +Eval: S + +Speaker sentences 96: cv_jpn_000770 #utts: 1 +id: (cv_jpn_000770-cv_jpn_000770) +Scores: (#C #S #D #I) 4 0 0 0 +REF: sh i ch i +HYP: sh i ch i +Eval: + +Speaker sentences 97: cv_jpn_000771 #utts: 1 +id: (cv_jpn_000771-cv_jpn_000771) +Scores: (#C #S #D #I) 1 1 0 0 +REF: G o +HYP: K o +Eval: S + +Speaker sentences 98: cv_jpn_000772 #utts: 1 +id: (cv_jpn_000772-cv_jpn_000772) +Scores: (#C #S #D #I) 3 0 0 0 +REF: i i e +HYP: i i e +Eval: + +Speaker sentences 99: cv_jpn_000773 #utts: 1 +id: (cv_jpn_000773-cv_jpn_000773) +Scores: (#C #S #D #I) 23 1 2 1 +REF: n a m a * e k a r a sh I T e t e k I t o o s u g i r u +HYP: n a m a I e k a r a sh * * e t e k U t o o s u g i r u +Eval: I D D S + +Speaker sentences 100: cv_jpn_000774 #utts: 1 +id: (cv_jpn_000774-cv_jpn_000774) +Scores: (#C #S #D #I) 60 2 2 1 +REF: j i k o n o s o t o n i a r u t o i u n o w a t a n n i j i k o n o I sh i k I n O s o ** T o n i a r u t o i u k o t o d e n a k u +HYP: j i k o n o s o t o n i a r u t o i u n o w a t a n n i j i k o n o * sh i k A n * s o TS S o n i a r u t o i u k o t o d e n a k u +Eval: D S D I S + +Speaker sentences 101: cv_jpn_000775 #utts: 1 +id: (cv_jpn_000775-cv_jpn_000775) +Scores: (#C #S #D #I) 20 1 1 0 +REF: s o r E w a sh i r a n a k u t e I i d e s u +HYP: s o r U w a sh i r a n a k u t e * i d e s u +Eval: S D + +Speaker sentences 102: cv_jpn_000776 #utts: 1 +id: (cv_jpn_000776-cv_jpn_000776) +Scores: (#C #S #D #I) 43 3 3 0 +REF: k i m i t o b o k u n o ky o o ts U u n o sh i R I A I w a d a r e h i t o r i PAU m i a t a r a n a i +HYP: k i m i t o b o k u n o ky o o ts * u n o sh i * G E E w a d a r e h i t o r i *** m i a t a r a n a i +Eval: D D S S S D + +Speaker sentences 103: cv_jpn_000777 #utts: 1 +id: (cv_jpn_000777-cv_jpn_000777) +Scores: (#C #S #D #I) 21 4 2 0 +REF: s u g e e D A I J I n I n a cl t e k i t e r u n o n a +HYP: s u g e e * O O T O n * n a cl t e k i t e r u n o n a +Eval: D S S S S D + +Speaker sentences 104: cv_jpn_000778 #utts: 1 +id: (cv_jpn_000778-cv_jpn_000778) +Scores: (#C #S #D #I) 21 3 4 0 +REF: k o n o A T A R I d e s u k o sh i y a s u M I m a sh o o +HYP: k o n o * * H E N d e s u k o sh i y a s u * * m a sh o o +Eval: D D S S S D D + +Speaker sentences 105: cv_jpn_000779 #utts: 1 +id: (cv_jpn_000779-cv_jpn_000779) +Scores: (#C #S #D #I) 24 4 1 0 +REF: d e n sh a N i n o r u t o k i PAU k i cl P U o k a i M a s u +HYP: d e n sh a R i n o r u t o k i *** k i cl T O o k a i N a s u +Eval: S D S S S + +Speaker sentences 106: cv_jpn_000780 #utts: 1 +id: (cv_jpn_000780-cv_jpn_000780) +Scores: (#C #S #D #I) 23 4 5 0 +REF: t a m a g O W a i CL k o G o j U u g u r a M U G u r a i D e s u +HYP: t a m a g * * a i ** k o K o j I u g u r a * N B u r a i * e s u +Eval: D D D S S D S S D + +Speaker sentences 107: cv_jpn_000781 #utts: 1 +id: (cv_jpn_000781-cv_jpn_000781) +Scores: (#C #S #D #I) 36 2 2 4 +REF: g I R e s u p i i w a m a cl G i i o ts U u j i t e i n e s u t o sh i * r i * a cl t a * * +HYP: g * E e s u p i i w a m a cl K i i o ts * u j i t e i n e s u t o sh i D r i G a cl t a T U +Eval: D S S D I I I I + +Speaker sentences 108: cv_jpn_000782 #utts: 1 +id: (cv_jpn_000782-cv_jpn_000782) +Scores: (#C #S #D #I) 64 7 3 8 +REF: n o o GY O o o y a m e Z a r u o e n a i * h i t o G a a r i PAU k a n R e n k i GY o o m o PAU k o n o f u * * * KY o o n i h i k i z u r a r e t e i r u t * * * * o I u +HYP: n o o ** G o o y a m e S a r u o e n a i K h i t o K a a r i *** k a n N e n k i Y o o m o *** k o n o f u K I K E o o n i h i k i z u r a r e t e i r u t O O M O o S u +Eval: D S S I S D S S D I I I S I I I I S + +Speaker sentences 109: cv_jpn_000783 #utts: 1 +id: (cv_jpn_000783-cv_jpn_000783) +Scores: (#C #S #D #I) 39 1 5 0 +REF: n a n d e k o n o r O B o cl t o PAU sh o t a i m e N n a n o n i n a r e N a r e sh I i n d a +HYP: n a n d e k o n o r * * o cl t o *** sh o t a i m e * n a n o n i n a r e G a r e sh * i n d a +Eval: D D D D S D + +Speaker sentences 110: cv_jpn_000784 #utts: 1 +id: (cv_jpn_000784-cv_jpn_000784) +Scores: (#C #S #D #I) 26 1 1 0 +REF: f u ts u u d e a r u k o t O m o r i cl p a n a k o s E e +HYP: f u ts u u d e a r u k o t A m o r i cl p a n a k o s * e +Eval: S D + +Speaker sentences 111: cv_jpn_000785 #utts: 1 +id: (cv_jpn_000785-cv_jpn_000785) +Scores: (#C #S #D #I) 33 0 0 0 +REF: ts u y o b i d e t a n j i k a n d e g o o k a i n i i t a m e r u +HYP: ts u y o b i d e t a n j i k a n d e g o o k a i n i i t a m e r u +Eval: + +Speaker sentences 112: cv_jpn_000786 #utts: 1 +id: (cv_jpn_000786-cv_jpn_000786) +Scores: (#C #S #D #I) 45 3 0 1 +REF: b a k u m a ts u n o d e k i g o T O w a i m a n i ts u * u j i r U ky o o k u n n o y a m a d e s u +HYP: b a k u m a ts u n o d e k i g o K A w a i m a n i ts u R u j i r O ky o o k u n n o y a m a d e s u +Eval: S S I S + +Speaker sentences 113: cv_jpn_000787 #utts: 1 +id: (cv_jpn_000787-cv_jpn_000787) +Scores: (#C #S #D #I) 27 5 0 0 +REF: m U k o o k a r a m a ch i n o T O M O r i g a m i e t e k i t a +HYP: m N k o o k a r a m a ch i n o W A K A r i g a m i e t e k i t a +Eval: S S S S S + +Speaker sentences 114: cv_jpn_000788 #utts: 1 +id: (cv_jpn_000788-cv_jpn_000788) +Scores: (#C #S #D #I) 50 8 2 5 +REF: * * n A n i * O i u b e k i ** K A w a k a R a n a k a cl t a n a N i m O i * u b E k i k o t o g a o m o I u k a b a n a k a cl t A +HYP: M E n * n i W U i u b e k i CH I H w a k a N a n a k a cl t a n a R i m * i Y u b I k i k o t o g a o m o Y u k a b a n a k a cl t U +Eval: I I D I S I S S S S D I S S S + +Speaker sentences 115: cv_jpn_000789 #utts: 1 +id: (cv_jpn_000789-cv_jpn_000789) +Scores: (#C #S #D #I) 18 5 3 0 +REF: t a m E SH i n I i CL k a I D a k E Y a cl t e m i r u +HYP: t a m I S i n * i ** k a E N a k * I a cl t e m i r u +Eval: S S D D S S D S + +Speaker sentences 116: cv_jpn_000790 #utts: 1 +id: (cv_jpn_000790-cv_jpn_000790) +Scores: (#C #S #D #I) 34 6 2 0 +REF: b o k u sh i k a i n a i K i m i W a i n a i k o r e w a PAU o o K I n A CH I g a i k a +HYP: b o k u sh i k a i n a i G i m i M a i n a i k o r e w a *** o o T E n * S E g a i k a +Eval: S S D S S D S S + +Speaker sentences 117: cv_jpn_000791 #utts: 1 +id: (cv_jpn_000791-cv_jpn_000791) +Scores: (#C #S #D #I) 39 7 12 0 +REF: SH U U k A i SH o k A R a n i j U u g o o t O o m a D E n o m i ch i w a m u k a SH I T o k A W a cl t e i n a k A CL T a +HYP: ** CH I k E i J o k * * a n i j * u g o o t * o m a N U n o m i ch i w a m u k a ** * S o k * * a cl t e i n a k * ** * a +Eval: D S S S S D D D D S S D D S D D D D D + +Speaker sentences 118: cv_jpn_000792 #utts: 1 +id: (cv_jpn_000792-cv_jpn_000792) +Scores: (#C #S #D #I) 31 7 7 1 +REF: k a N O j O N o t E e A n w a *** k o n p o n t e K i n a k a i K E TS u N I TS u n a G a cl t a +HYP: k a G U j * * o t * e * n w a PAU k o n p o n t e * i n a k a i CH I K u * U S u n a * a cl t a +Eval: S S D D D D I D S S S D S S D + +Speaker sentences 119: cv_jpn_000793 #utts: 1 +id: (cv_jpn_000793-cv_jpn_000793) +Scores: (#C #S #D #I) 34 5 3 1 +REF: k o D O m o n o k o r O w a g o h a n h a D e PAU o t O n a n I n a r U t o p a n h * a +HYP: k o R U m o n o k o r E w a g o h a n h a R e *** o t U n a n * n a r * t o p a n h A a +Eval: S S S S D S D D I + +Speaker sentences 120: cv_jpn_000794 #utts: 1 +id: (cv_jpn_000794-cv_jpn_000794) +Scores: (#C #S #D #I) 69 7 2 1 +REF: k o o i t e k i CH o cl k a n t e k i n i pau p * o i E sh i s u t e k i n i PAU w a r e w a r e N o j i k o w a m a s u m a s u A K A R I t o n a r u n o d e a r u +HYP: k o o i t e k i T o cl k a n t e k i n i pau p O o i * sh i s u t e k i n i *** w a r e w a r e M o j i k o w a m a s u m a s u Z U M E E t o n a r u n o d e a r u +Eval: S I D D S S S S S S + +Speaker sentences 121: cv_jpn_000795 #utts: 1 +id: (cv_jpn_000795-cv_jpn_000795) +Scores: (#C #S #D #I) 74 4 3 3 +REF: h i j o o sh i k i d E a r u k o t o w a pau M u ch I o i m i s u r u n o m i d e n a k * * u pau sh a k a i * t e k i n i a k u t o m o k a n g a e r a R E r u N O d e a r u +HYP: h i j o o sh i k i d A a r u k o t o w a pau B u ch O o i m i s u r u n o m i d e n a k U R u pau sh a k a i E t e k i n i a k u t o m o k a n g a e r a * * r u * U d e a r u +Eval: S S S I I I D D D S + +Speaker sentences 122: cv_jpn_000796 #utts: 1 +id: (cv_jpn_000796-cv_jpn_000796) +Scores: (#C #S #D #I) 48 1 2 1 +REF: j o o sh i k i g a n a o t o k * u sh u t e k i n a ch i sh i k i d e a r u n i h a N sh i PAU k a G a k u w a +HYP: j o o sh i k i g a n a o t o k U u sh u t e k i n a ch i sh i k i d e a r u n i h a * sh i *** k a R a k u w a +Eval: I D D S + +Speaker sentences 123: cv_jpn_000797 #utts: 1 +id: (cv_jpn_000797-cv_jpn_000797) +Scores: (#C #S #D #I) 26 3 0 2 +REF: k o n n a k o t * o d e o K O r a r e t e n a s a k E n * a i +HYP: k o n n a k o t O o d e o G U r a r e t e n a s a k I n H a i +Eval: I S S S I + +Speaker sentences 124: cv_jpn_000798 #utts: 1 +id: (cv_jpn_000798-cv_jpn_000798) +Scores: (#C #S #D #I) 86 6 4 2 +REF: k a k o t o m i r a I t o g a j i k o m u j u n t e k i n i g e n z a i n i o i t e t a I r i TS U s u r u t o i u * n i w a PAU g e n Z a I g a k a t a ch I o m o t a N a * k e r e B a n a r a n a i +HYP: k a k o t o m i r a E t o g a j i k o m u j u n t e k i n i g e n z a i n i o i t e t a * r i K I s u r u t o i u I n i w a *** g e n D a * g a k a t a ch * o m o t a M a E k e r e M a n a r a n a i +Eval: S D S S I D S D D S I S + +Speaker sentences 125: cv_jpn_000799 #utts: 1 +id: (cv_jpn_000799-cv_jpn_000799) +Scores: (#C #S #D #I) 24 0 8 0 +REF: sh o k I H i Y O o n o t a k a s a g a h A a d o R u n I n a R u +HYP: sh o k * * i * * o n o t a k a s a g a h * a d o * u n * n a * u +Eval: D D D D D D D D + + diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/text b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/text new file mode 100644 index 0000000000000000000000000000000000000000..0f76d26884912c6f178e831d8d21f79e2562bd38 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/text @@ -0,0 +1,126 @@ +cv_jpn_000674 b o k u n o i e e g a cl t a k a i n a N n o n a m a e N n i k u r a b e r u t o b o k u n i w a n a j i m i n o n a i pau n a b a e b a k a r i d a k e d o +cv_jpn_000675 n a i o o s o n o m o n o y o r i pau f u i n i k i g a u k e t e r u +cv_jpn_000676 b o k u n o sh I cl t e i u m o n o t o w a s U k o sh i ch i g a cl t e i t a +cv_jpn_000677 k a k a a r u t a ch i m a n i o o i cl t e pau s e k a i g a i j sh I k i m e N t e k i d e a r i pau w a r e w a r e n o j i k o g a j sh I k i s a y o o t e k i d e a r u t o k a N g e e r a r e r u t o k sh i +cv_jpn_000678 i e n i k t a n e N g a a j i w a s a N hy a k u m a i h o r o d e pau ch o o d a sh I t a b u N t o o n a j i g u r a i d a +cv_jpn_000679 h a a w a p i t a m a r i ts u b a a g u n o s e e sh i N y o o i n i n u u N h I t e i r u t o k i n i b e sh i o o m o +cv_jpn_000680 t a t a N d e a r h a N t e n o h i r o g e r a b a a ch i k o ch i N i ts u g i h a g i a a r i pau k a t a k o ch i n i d e k i t a h o k o r o b i n a N k a ky o o e N n o m a m o n i n a cl t e i r u +cv_jpn_000681 k a r a g a k a n o d e b e r e n a a k a n i h o o m o N k i b o o n o b u sh o o y o b i ch o o z a k i b o o n o o b u g o m a i k a r a t o o r i d e s U +cv_jpn_000682 k a N b a w a a t o t e m o s a m i d e s U +cv_jpn_000683 k i N ch o o sh I t a k a h a o ts U k i d e p a cl t a w a d a s e k i n i h a i r u +cv_jpn_000684 m a s a N ky u u r e N t o i cl t a f s u u k a k a r o k e N j i k i b u ts s U +cv_jpn_000685 g o j i y o sh i d e s u s u m e t e ts s o g o N g a w a r u k u n a cl t a r a h I cl k o m e r u e a r e k U ch i +cv_jpn_000686 m u j u N t e k i pau j i k o t o o i ts U t e k i n i pau j i k o o j i sh i N o k e e s e e s o r u sh a k a i w a +cv_jpn_000687 f a N n o i k e N n i n a r a s a d e r u n a +cv_jpn_000688 h i n e g a a s o b i t a y o o r a z e N k a i d e k o ch o o m i t e i r u +cv_jpn_000689 i ch i d o w a k o N p o t a a j i k a N o n o N d e m i t a i +cv_jpn_000690 k o o i n o k o sh I t e pau o t o o s a N t o k a a s a N w a N d e t e i k i m a sh I t a +cv_jpn_000691 sh I k a sh I t e s o r e g a ts U k r a r e t a m o n o k a r a ts U k u r u m o n o e t o sh I t e d o k u m a d e m o r a w a r u n i s e m a r u t o i u t o k i pau w a r e w a r i n i ch o cl k a N t e k i d e a r u +cv_jpn_000692 h a i n i t a m a cl t a k e m u r i o h a k i d a sh i k u r a i k o o e n i sh I s e N o m o k e r u +cv_jpn_000693 w a d a w a d a sh n a b u s o k u d e ch i s e N i N n a r i s o +cv_jpn_000694 ts e N sh e k a i n o m o ts U k a t a ch sh i t a sh i n o i y a i u r u s e e s a i y o sh I k u t o s a i o t o w a pau h a n a sh I t e k a N g a e r u k o t o w a d e k i n a i +cv_jpn_000695 s a k e n o m a i n o n i p i i r u b a r a t o y o r e t a +cv_jpn_000696 s o r e o t a t a k u h a k u n o z e N d a N k a i sh I k u i t e e d o n k a a k U t o n o m i m i r u k o t o w a +cv_jpn_000697 n o k ky o e N o f u a ts u n i s u m o h o d e g e e m o y a cl t e i t a +cv_jpn_000698 w a d a i w a a n a i sh u m a t a N k o b a e sh i s a N t o o a s o b e i m a s U +cv_jpn_000699 h a s o o k o n i h e t o o g a i m a s u n e a r o h I t o o w a t a a r e e t e sh o o +cv_jpn_000700 w a t a sh i w a k i n o o k a n a n o o d o o g a i t a i t e s U +cv_jpn_000701 ky o o r e N k a n a p e N ky u o o sh I t e i m a s U +cv_jpn_000702 ch o cl t o s u m i m a s e +cv_jpn_000703 ch I k a sh t o n o o o o e N b u r y o w a y a k e k i m i n i k a e cl d e h a g e s U p e u n o cl t a +cv_jpn_000704 i ts u m o k o n o e N p I i ts s o o ts sh U k a cl t e i t a n o t e m i j i k a h a h a n a r i m a sh I t a +cv_jpn_000705 w a t a sh i w a e i g o g a h a n a s t e m o s U +cv_jpn_000706 o sh i i k e e r y a j i b a N d e t o cl t a e k i n a +cv_jpn_000707 a i sh u u k a r a n i sh u u u k a N h a i g a e e e r u y o o k o o o n i i k i m a s U +cv_jpn_000708 o o r e m o k i n i n a r u n a +cv_jpn_000709 d a r e g a ts s U k a cl t e r u n o k a a w a k a r u n a i +cv_jpn_000710 p o r a z a n o b a j o N g a pau a g a r u t o o s U k o sh i u r e sh i +cv_jpn_000711 m a t a a t a r a sh i a i d o r u g a d e t e k i t a +cv_jpn_000712 m a j i d e y a cl t o n o k a +cv_jpn_000713 ch o o d o s u m o t o k i n i ky o o j u g a h a i I t e k i d a +cv_jpn_000714 i cl s o n i ch i N sh I t e o +cv_jpn_000715 s o o r e k a s h a t e n o t o o r e s u +cv_jpn_000716 f U t a r i w a r e j i i k i s e e s a N sh I t a +cv_jpn_000717 t o m a t o k a n a N g a n o w a k a i s o u s u g a k a k a cl t e r i u o +cv_jpn_000718 k o k o k a r a t a t e n a N a s u n o w a k i b i s i i +cv_jpn_000719 n i z u k e o s sh I cl k a r i sh i b o cl t e pau a j i k a n a j i m u y o h i s u r u +cv_jpn_000720 n e t o g e n i h a m a cl t a r a k a n e g a g a m a cl t a U +cv_jpn_000721 i t o k a e r i y o o n i n a r u N d a +cv_jpn_000722 k o s e e h a h a i y u t o i u y o r i a k u g a ts u y o e k a N j i +cv_jpn_000723 f i j i k a r u n o s a o pau m a z a m a z a t o m i sh e ts U k e r a r e t a +cv_jpn_000724 k o s U p a y o k e r e b a s o k o s o k o n o m o N d a e w a g a a m a N s e r u +cv_jpn_000725 m i N n a y a cl t e m a s U k a r a t a i j o o b u d e s U y o +cv_jpn_000726 k o n o t o sh o k a N pau h a i cl t a sh u N k a N k i n i i cl t a +cv_jpn_000727 k o n o d e N ch i s u u k i r e ch a cl t a +cv_jpn_000728 a m a y o d o r i s u r u t o k o r o g a n a k U t e k o m a cl t a +cv_jpn_000729 y a s U k u s u r u y o r i sh I ts u w a g e t a h o sh i +cv_jpn_000730 m a s e g o k o N n a k o t o i n a r o t o w a m o n a k a cl t a +cv_jpn_000731 s a i g o n i w a r a y o t o r i n i k u r u s U t a i r u +cv_jpn_000732 k o r a e a n a n a i m i g a r u d a +cv_jpn_000733 i i e +cv_jpn_000734 sh i +cv_jpn_000735 n i +cv_jpn_000736 h a ch i +cv_jpn_000737 h a i +cv_jpn_000738 t e e b r u n o o i e n i k a b i N g a a r i m a s U +cv_jpn_000739 w a t a sh i w a a m a i y a s a s a N p o sh i m a s U +cv_jpn_000740 a t a r a sh i i k u ts o o h a i cl t e d e k a k e m a s U +cv_jpn_000741 k o t o sh i n o n a ts e y a s u m i w a pau b u m i n i m o i k i m a sh I t a sh i i y a m a n i m o o n o o r i b a sh I t a +cv_jpn_000742 w a t a sh i w a i r o i r o n o b e N g o o j i b u N n o m u n e d e k o sh i r a e t e m i m a sh I t a +cv_jpn_000743 n a N d e k u m o sh o o o b a i h e t a n a N d a r o +cv_jpn_000744 t a r e N t o k a r a ky o k u a n a n i k a e t e k e e h I sh a k i g e +cv_jpn_000745 d o g e z a s e r e b a i cl t e m o N sh a n a i +cv_jpn_000746 d e e t a n o w a i d a k a n o ch i w a j i b u N t o o i cl t e e n o ky o o r i y o t a n o cl t a +cv_jpn_000747 k o n o g e e n i N n a N k a w a h I s a sh i b u r i n i m i t a +cv_jpn_000748 o o k i k u s a i d o ch i e N j i o s o r u +cv_jpn_000749 k a r e w a a t a m a k a k i m u sh i cl t a +cv_jpn_000750 o m a ch e sh I t e w a r i m a s U +cv_jpn_000751 o n o ky o k U s e N k a i j o w a k i i t e r u +cv_jpn_000752 r e e j z o o k o o a k e t a t o t a N n a n i g a h i ch u y o k a w a s u r e cl t a +cv_jpn_000753 i ch i +cv_jpn_000754 h a ch i +cv_jpn_000755 i i e +cv_jpn_000756 d e i +cv_jpn_000757 sh i ch i +cv_jpn_000758 y o o b o o d a s u n o N i k a u h I t o w a s U k u n a i +cv_jpn_000759 r o o k a r u t o k u y i u n o i I ky o e m a k a s e n o k o m a sh a r u +cv_jpn_000760 k o n o d a i k u w a a N k o g a o o k U t e y o k U k a i m a s U +cv_jpn_000761 j i sh o o h k i n a g a r a sh o o s e ts o o y o m i m a s U +cv_jpn_000762 k o N n a o k i n a g o g u r o ts U k e n a i t i k e n a i N d e s U k a +cv_jpn_000763 k a r e e n o b o o r i k o w a t o m a r a n a i +cv_jpn_000764 i k i t e i k a N m a n e +cv_jpn_000765 t a o j i t o s a k a k t t e k i n i h a ts u m e d a cl p a n e +cv_jpn_000766 s a N t e k i o t a sh o w a ch I k a a f U t e k a +cv_jpn_000767 k a w a g a h i a g a cl t e i d a +cv_jpn_000768 i ch u +cv_jpn_000769 n o +cv_jpn_000770 sh I ch i +cv_jpn_000771 k o +cv_jpn_000772 i i e +cv_jpn_000773 n a m a i e k a r a sh e t e k U t o o s u g i r u +cv_jpn_000774 j i k o n o s o t o n i a r u t o i u n o w a t a N n i j i k o n o sh I k a N s o ts s o n i a r u t o i u k o t o d e n a k u +cv_jpn_000775 s o r u w a sh i r a n a k U t e i d e s U +cv_jpn_000776 k i m i t o b o k u n o ky o o ts u n o sh i g e e w a d a r e h I t o r i m i a t a r a n a i +cv_jpn_000777 s u g e e o o t o N n a cl t e k i t e r u n o n a +cv_jpn_000778 k o n o h e N d e s U k o sh i y a s u m a sh o o +cv_jpn_000779 d e N sh a r i n o r u t o k i k i cl t o o k a i n a s U +cv_jpn_000780 t a m a g a i k o k o j i u g u r a N b u r a i e s U +cv_jpn_000781 g e e s U p i i w a m a cl k i i o ts u j i t e i n e s U t o sh i d r i g a cl t a t u +cv_jpn_000782 n o o g o o y a m e s a r u o e n a i k h I t o k a a r i k a N n e N k i y o o m o k o n o f U k I k e o o n i h I k i z u r a r e t e i r u t o o m o o s U +cv_jpn_000783 n a N d e k o n o r o cl t o sh o t a i m e n a n o n i n a r e g a r e sh i N d a +cv_jpn_000784 f U ts u u d e a r u k o t a m o r i cl p a n a k o s e +cv_jpn_000785 ts u y o b i d e t a N j i k a N d e g o o k a i n i i t a m e r u +cv_jpn_000786 b a k u m a ts u n o d e k i g o k a w a i m a n i ts u r u j i r o ky o o k u N n o y a m a d e s U +cv_jpn_000787 m N k o o k a r a m a ch i n o w a k a r i g a m i e t e k i t a +cv_jpn_000788 m e N n i w u i u b e k I ch i h w a k a n a n a k a cl t a n a r i m i y u b i k I k o t o g a o m o y u k a b a n a k a cl t u +cv_jpn_000789 t a m i s i n i k a e n a k i a cl t e m i r u +cv_jpn_000790 b o k U sh I k a i n a i g i m i m a i n a i k o r e w a o o t e N s e g a i k a +cv_jpn_000791 ch i k e i j o k a n i j u g o o t o m a n u n o m i ch i w a m u k a s o k a cl t e i n a k a +cv_jpn_000792 k a g u j o t e N w a pau k o N p o N t e i n a k a i ch i k u u s u n a a cl t a +cv_jpn_000793 k o r u m o n o k o r e w a g o h a N h a r e o t u n a N n a r t o p a N h a a +cv_jpn_000794 k o o i t e k I t o cl k a N t e k i n i pau p o o i sh I s u t e k i n i w a r e w a r e m o j i k o w a m a s u m a s u z u m e e t o n a r u n o d e a r u +cv_jpn_000795 h i j o o sh I k i d a a r u k o t o w a pau b u ch o o i m i s u r u n o m i d e n a k u r u pau sh a k a i e t e k i n i a k U t o m o k a N g a e r a r u u d e a r u +cv_jpn_000796 j o o sh I k i g a n a o t o k U u sh u t e k i n a ch i sh I k i d e a r u n i h a sh i k a r a k u w a +cv_jpn_000797 k o N n a k o t o o d e o g u r a r e t e n a s a k i n h a i +cv_jpn_000798 k a k o t o m i r a e t o g a j i k o m u j u N t e k i n i g e N z a i n i o i t e t a r i k I s u r u t o i u i n i w a g e N d a g a k a t a ch o m o t a m a e k e r e m a n a r a n a i +cv_jpn_000799 sh o k i o n o t a k a s a g a h a d o u N n a u diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/token b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/token new file mode 100644 index 0000000000000000000000000000000000000000..0f76d26884912c6f178e831d8d21f79e2562bd38 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/token @@ -0,0 +1,126 @@ +cv_jpn_000674 b o k u n o i e e g a cl t a k a i n a N n o n a m a e N n i k u r a b e r u t o b o k u n i w a n a j i m i n o n a i pau n a b a e b a k a r i d a k e d o +cv_jpn_000675 n a i o o s o n o m o n o y o r i pau f u i n i k i g a u k e t e r u +cv_jpn_000676 b o k u n o sh I cl t e i u m o n o t o w a s U k o sh i ch i g a cl t e i t a +cv_jpn_000677 k a k a a r u t a ch i m a n i o o i cl t e pau s e k a i g a i j sh I k i m e N t e k i d e a r i pau w a r e w a r e n o j i k o g a j sh I k i s a y o o t e k i d e a r u t o k a N g e e r a r e r u t o k sh i +cv_jpn_000678 i e n i k t a n e N g a a j i w a s a N hy a k u m a i h o r o d e pau ch o o d a sh I t a b u N t o o n a j i g u r a i d a +cv_jpn_000679 h a a w a p i t a m a r i ts u b a a g u n o s e e sh i N y o o i n i n u u N h I t e i r u t o k i n i b e sh i o o m o +cv_jpn_000680 t a t a N d e a r h a N t e n o h i r o g e r a b a a ch i k o ch i N i ts u g i h a g i a a r i pau k a t a k o ch i n i d e k i t a h o k o r o b i n a N k a ky o o e N n o m a m o n i n a cl t e i r u +cv_jpn_000681 k a r a g a k a n o d e b e r e n a a k a n i h o o m o N k i b o o n o b u sh o o y o b i ch o o z a k i b o o n o o b u g o m a i k a r a t o o r i d e s U +cv_jpn_000682 k a N b a w a a t o t e m o s a m i d e s U +cv_jpn_000683 k i N ch o o sh I t a k a h a o ts U k i d e p a cl t a w a d a s e k i n i h a i r u +cv_jpn_000684 m a s a N ky u u r e N t o i cl t a f s u u k a k a r o k e N j i k i b u ts s U +cv_jpn_000685 g o j i y o sh i d e s u s u m e t e ts s o g o N g a w a r u k u n a cl t a r a h I cl k o m e r u e a r e k U ch i +cv_jpn_000686 m u j u N t e k i pau j i k o t o o i ts U t e k i n i pau j i k o o j i sh i N o k e e s e e s o r u sh a k a i w a +cv_jpn_000687 f a N n o i k e N n i n a r a s a d e r u n a +cv_jpn_000688 h i n e g a a s o b i t a y o o r a z e N k a i d e k o ch o o m i t e i r u +cv_jpn_000689 i ch i d o w a k o N p o t a a j i k a N o n o N d e m i t a i +cv_jpn_000690 k o o i n o k o sh I t e pau o t o o s a N t o k a a s a N w a N d e t e i k i m a sh I t a +cv_jpn_000691 sh I k a sh I t e s o r e g a ts U k r a r e t a m o n o k a r a ts U k u r u m o n o e t o sh I t e d o k u m a d e m o r a w a r u n i s e m a r u t o i u t o k i pau w a r e w a r i n i ch o cl k a N t e k i d e a r u +cv_jpn_000692 h a i n i t a m a cl t a k e m u r i o h a k i d a sh i k u r a i k o o e n i sh I s e N o m o k e r u +cv_jpn_000693 w a d a w a d a sh n a b u s o k u d e ch i s e N i N n a r i s o +cv_jpn_000694 ts e N sh e k a i n o m o ts U k a t a ch sh i t a sh i n o i y a i u r u s e e s a i y o sh I k u t o s a i o t o w a pau h a n a sh I t e k a N g a e r u k o t o w a d e k i n a i +cv_jpn_000695 s a k e n o m a i n o n i p i i r u b a r a t o y o r e t a +cv_jpn_000696 s o r e o t a t a k u h a k u n o z e N d a N k a i sh I k u i t e e d o n k a a k U t o n o m i m i r u k o t o w a +cv_jpn_000697 n o k ky o e N o f u a ts u n i s u m o h o d e g e e m o y a cl t e i t a +cv_jpn_000698 w a d a i w a a n a i sh u m a t a N k o b a e sh i s a N t o o a s o b e i m a s U +cv_jpn_000699 h a s o o k o n i h e t o o g a i m a s u n e a r o h I t o o w a t a a r e e t e sh o o +cv_jpn_000700 w a t a sh i w a k i n o o k a n a n o o d o o g a i t a i t e s U +cv_jpn_000701 ky o o r e N k a n a p e N ky u o o sh I t e i m a s U +cv_jpn_000702 ch o cl t o s u m i m a s e +cv_jpn_000703 ch I k a sh t o n o o o o e N b u r y o w a y a k e k i m i n i k a e cl d e h a g e s U p e u n o cl t a +cv_jpn_000704 i ts u m o k o n o e N p I i ts s o o ts sh U k a cl t e i t a n o t e m i j i k a h a h a n a r i m a sh I t a +cv_jpn_000705 w a t a sh i w a e i g o g a h a n a s t e m o s U +cv_jpn_000706 o sh i i k e e r y a j i b a N d e t o cl t a e k i n a +cv_jpn_000707 a i sh u u k a r a n i sh u u u k a N h a i g a e e e r u y o o k o o o n i i k i m a s U +cv_jpn_000708 o o r e m o k i n i n a r u n a +cv_jpn_000709 d a r e g a ts s U k a cl t e r u n o k a a w a k a r u n a i +cv_jpn_000710 p o r a z a n o b a j o N g a pau a g a r u t o o s U k o sh i u r e sh i +cv_jpn_000711 m a t a a t a r a sh i a i d o r u g a d e t e k i t a +cv_jpn_000712 m a j i d e y a cl t o n o k a +cv_jpn_000713 ch o o d o s u m o t o k i n i ky o o j u g a h a i I t e k i d a +cv_jpn_000714 i cl s o n i ch i N sh I t e o +cv_jpn_000715 s o o r e k a s h a t e n o t o o r e s u +cv_jpn_000716 f U t a r i w a r e j i i k i s e e s a N sh I t a +cv_jpn_000717 t o m a t o k a n a N g a n o w a k a i s o u s u g a k a k a cl t e r i u o +cv_jpn_000718 k o k o k a r a t a t e n a N a s u n o w a k i b i s i i +cv_jpn_000719 n i z u k e o s sh I cl k a r i sh i b o cl t e pau a j i k a n a j i m u y o h i s u r u +cv_jpn_000720 n e t o g e n i h a m a cl t a r a k a n e g a g a m a cl t a U +cv_jpn_000721 i t o k a e r i y o o n i n a r u N d a +cv_jpn_000722 k o s e e h a h a i y u t o i u y o r i a k u g a ts u y o e k a N j i +cv_jpn_000723 f i j i k a r u n o s a o pau m a z a m a z a t o m i sh e ts U k e r a r e t a +cv_jpn_000724 k o s U p a y o k e r e b a s o k o s o k o n o m o N d a e w a g a a m a N s e r u +cv_jpn_000725 m i N n a y a cl t e m a s U k a r a t a i j o o b u d e s U y o +cv_jpn_000726 k o n o t o sh o k a N pau h a i cl t a sh u N k a N k i n i i cl t a +cv_jpn_000727 k o n o d e N ch i s u u k i r e ch a cl t a +cv_jpn_000728 a m a y o d o r i s u r u t o k o r o g a n a k U t e k o m a cl t a +cv_jpn_000729 y a s U k u s u r u y o r i sh I ts u w a g e t a h o sh i +cv_jpn_000730 m a s e g o k o N n a k o t o i n a r o t o w a m o n a k a cl t a +cv_jpn_000731 s a i g o n i w a r a y o t o r i n i k u r u s U t a i r u +cv_jpn_000732 k o r a e a n a n a i m i g a r u d a +cv_jpn_000733 i i e +cv_jpn_000734 sh i +cv_jpn_000735 n i +cv_jpn_000736 h a ch i +cv_jpn_000737 h a i +cv_jpn_000738 t e e b r u n o o i e n i k a b i N g a a r i m a s U +cv_jpn_000739 w a t a sh i w a a m a i y a s a s a N p o sh i m a s U +cv_jpn_000740 a t a r a sh i i k u ts o o h a i cl t e d e k a k e m a s U +cv_jpn_000741 k o t o sh i n o n a ts e y a s u m i w a pau b u m i n i m o i k i m a sh I t a sh i i y a m a n i m o o n o o r i b a sh I t a +cv_jpn_000742 w a t a sh i w a i r o i r o n o b e N g o o j i b u N n o m u n e d e k o sh i r a e t e m i m a sh I t a +cv_jpn_000743 n a N d e k u m o sh o o o b a i h e t a n a N d a r o +cv_jpn_000744 t a r e N t o k a r a ky o k u a n a n i k a e t e k e e h I sh a k i g e +cv_jpn_000745 d o g e z a s e r e b a i cl t e m o N sh a n a i +cv_jpn_000746 d e e t a n o w a i d a k a n o ch i w a j i b u N t o o i cl t e e n o ky o o r i y o t a n o cl t a +cv_jpn_000747 k o n o g e e n i N n a N k a w a h I s a sh i b u r i n i m i t a +cv_jpn_000748 o o k i k u s a i d o ch i e N j i o s o r u +cv_jpn_000749 k a r e w a a t a m a k a k i m u sh i cl t a +cv_jpn_000750 o m a ch e sh I t e w a r i m a s U +cv_jpn_000751 o n o ky o k U s e N k a i j o w a k i i t e r u +cv_jpn_000752 r e e j z o o k o o a k e t a t o t a N n a n i g a h i ch u y o k a w a s u r e cl t a +cv_jpn_000753 i ch i +cv_jpn_000754 h a ch i +cv_jpn_000755 i i e +cv_jpn_000756 d e i +cv_jpn_000757 sh i ch i +cv_jpn_000758 y o o b o o d a s u n o N i k a u h I t o w a s U k u n a i +cv_jpn_000759 r o o k a r u t o k u y i u n o i I ky o e m a k a s e n o k o m a sh a r u +cv_jpn_000760 k o n o d a i k u w a a N k o g a o o k U t e y o k U k a i m a s U +cv_jpn_000761 j i sh o o h k i n a g a r a sh o o s e ts o o y o m i m a s U +cv_jpn_000762 k o N n a o k i n a g o g u r o ts U k e n a i t i k e n a i N d e s U k a +cv_jpn_000763 k a r e e n o b o o r i k o w a t o m a r a n a i +cv_jpn_000764 i k i t e i k a N m a n e +cv_jpn_000765 t a o j i t o s a k a k t t e k i n i h a ts u m e d a cl p a n e +cv_jpn_000766 s a N t e k i o t a sh o w a ch I k a a f U t e k a +cv_jpn_000767 k a w a g a h i a g a cl t e i d a +cv_jpn_000768 i ch u +cv_jpn_000769 n o +cv_jpn_000770 sh I ch i +cv_jpn_000771 k o +cv_jpn_000772 i i e +cv_jpn_000773 n a m a i e k a r a sh e t e k U t o o s u g i r u +cv_jpn_000774 j i k o n o s o t o n i a r u t o i u n o w a t a N n i j i k o n o sh I k a N s o ts s o n i a r u t o i u k o t o d e n a k u +cv_jpn_000775 s o r u w a sh i r a n a k U t e i d e s U +cv_jpn_000776 k i m i t o b o k u n o ky o o ts u n o sh i g e e w a d a r e h I t o r i m i a t a r a n a i +cv_jpn_000777 s u g e e o o t o N n a cl t e k i t e r u n o n a +cv_jpn_000778 k o n o h e N d e s U k o sh i y a s u m a sh o o +cv_jpn_000779 d e N sh a r i n o r u t o k i k i cl t o o k a i n a s U +cv_jpn_000780 t a m a g a i k o k o j i u g u r a N b u r a i e s U +cv_jpn_000781 g e e s U p i i w a m a cl k i i o ts u j i t e i n e s U t o sh i d r i g a cl t a t u +cv_jpn_000782 n o o g o o y a m e s a r u o e n a i k h I t o k a a r i k a N n e N k i y o o m o k o n o f U k I k e o o n i h I k i z u r a r e t e i r u t o o m o o s U +cv_jpn_000783 n a N d e k o n o r o cl t o sh o t a i m e n a n o n i n a r e g a r e sh i N d a +cv_jpn_000784 f U ts u u d e a r u k o t a m o r i cl p a n a k o s e +cv_jpn_000785 ts u y o b i d e t a N j i k a N d e g o o k a i n i i t a m e r u +cv_jpn_000786 b a k u m a ts u n o d e k i g o k a w a i m a n i ts u r u j i r o ky o o k u N n o y a m a d e s U +cv_jpn_000787 m N k o o k a r a m a ch i n o w a k a r i g a m i e t e k i t a +cv_jpn_000788 m e N n i w u i u b e k I ch i h w a k a n a n a k a cl t a n a r i m i y u b i k I k o t o g a o m o y u k a b a n a k a cl t u +cv_jpn_000789 t a m i s i n i k a e n a k i a cl t e m i r u +cv_jpn_000790 b o k U sh I k a i n a i g i m i m a i n a i k o r e w a o o t e N s e g a i k a +cv_jpn_000791 ch i k e i j o k a n i j u g o o t o m a n u n o m i ch i w a m u k a s o k a cl t e i n a k a +cv_jpn_000792 k a g u j o t e N w a pau k o N p o N t e i n a k a i ch i k u u s u n a a cl t a +cv_jpn_000793 k o r u m o n o k o r e w a g o h a N h a r e o t u n a N n a r t o p a N h a a +cv_jpn_000794 k o o i t e k I t o cl k a N t e k i n i pau p o o i sh I s u t e k i n i w a r e w a r e m o j i k o w a m a s u m a s u z u m e e t o n a r u n o d e a r u +cv_jpn_000795 h i j o o sh I k i d a a r u k o t o w a pau b u ch o o i m i s u r u n o m i d e n a k u r u pau sh a k a i e t e k i n i a k U t o m o k a N g a e r a r u u d e a r u +cv_jpn_000796 j o o sh I k i g a n a o t o k U u sh u t e k i n a ch i sh I k i d e a r u n i h a sh i k a r a k u w a +cv_jpn_000797 k o N n a k o t o o d e o g u r a r e t e n a s a k i n h a i +cv_jpn_000798 k a k o t o m i r a e t o g a j i k o m u j u N t e k i n i g e N z a i n i o i t e t a r i k I s u r u t o i u i n i w a g e N d a g a k a t a ch o m o t a m a e k e r e m a n a r a n a i +cv_jpn_000799 sh o k i o n o t a k a s a g a h a d o u N n a u diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/token_int new file mode 100644 index 0000000000000000000000000000000000000000..09eba78026f16876e7023382eac98cd48637dd6d --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/org/dev_10min_jpn/token_int @@ -0,0 +1,126 @@ +cv_jpn_000674 25 3 6 7 9 3 4 5 5 16 2 21 8 2 6 2 4 9 2 13 9 3 9 2 11 2 5 13 9 4 6 7 10 2 25 5 10 7 8 3 25 3 6 7 9 4 17 2 9 2 22 4 11 4 9 3 9 2 4 20 9 2 25 2 5 25 2 6 2 10 4 14 2 6 5 14 3 +cv_jpn_000675 9 2 4 3 3 12 3 9 3 11 3 9 3 23 3 10 4 20 31 7 4 9 4 6 4 16 2 7 6 5 8 5 10 7 +cv_jpn_000676 25 3 6 7 9 3 15 19 21 8 5 4 7 11 3 9 3 8 3 17 2 12 18 6 3 15 4 27 4 16 2 21 8 5 4 8 2 +cv_jpn_000677 6 2 6 2 2 10 7 8 2 27 4 11 2 9 4 3 3 4 21 8 5 20 12 5 6 2 4 16 2 4 22 15 19 6 4 11 5 13 8 5 6 4 14 5 2 10 4 20 17 2 10 5 17 2 10 5 9 3 22 4 6 3 16 2 22 15 19 6 4 12 2 23 3 3 8 5 6 4 14 5 2 10 7 8 3 6 2 13 16 5 5 10 2 10 5 10 7 8 3 6 15 4 +cv_jpn_000678 4 5 9 4 6 8 2 9 5 13 16 2 2 22 4 17 2 12 2 13 33 2 6 7 11 2 4 24 3 10 3 14 5 20 27 3 3 14 2 15 19 8 2 25 7 13 8 3 3 9 2 22 4 16 7 10 2 4 14 2 +cv_jpn_000679 24 2 2 17 2 30 4 8 2 11 2 10 4 26 7 25 2 2 16 7 9 3 12 5 5 15 4 13 23 3 3 4 9 4 9 7 7 13 24 19 8 5 4 10 7 8 3 6 4 9 4 25 5 15 4 3 3 11 3 +cv_jpn_000680 8 2 8 2 13 14 5 2 10 24 2 13 8 5 9 3 24 4 10 3 16 5 10 2 25 2 2 27 4 6 3 27 4 13 4 26 7 16 4 24 2 16 4 2 2 10 4 20 6 2 8 2 6 3 27 4 9 4 14 5 6 4 8 2 24 3 6 3 10 3 25 4 9 2 13 6 2 29 3 3 5 13 9 3 11 2 11 3 9 4 9 2 21 8 5 4 10 7 +cv_jpn_000681 6 2 10 2 16 2 6 2 9 3 14 5 25 5 10 5 9 2 2 6 2 9 4 24 3 3 11 3 13 6 4 25 3 3 9 3 25 7 15 3 3 23 3 25 4 27 3 3 28 2 6 4 25 3 3 9 3 3 25 7 16 3 11 2 4 6 2 10 2 8 3 3 10 4 14 5 12 18 +cv_jpn_000682 6 2 13 25 2 17 2 2 8 3 8 5 11 3 12 2 11 4 14 5 12 18 +cv_jpn_000683 6 4 13 27 3 3 15 19 8 2 6 2 24 2 3 26 18 6 4 14 5 30 2 21 8 2 17 2 14 2 12 5 6 4 9 4 24 2 4 10 7 +cv_jpn_000684 11 2 12 2 13 29 7 7 10 5 13 8 3 4 21 8 2 31 12 7 7 6 2 6 2 10 3 6 5 13 22 4 6 4 25 7 26 12 18 +cv_jpn_000685 16 3 22 4 23 3 15 4 14 5 12 7 12 7 11 5 8 5 26 12 3 16 3 13 16 2 17 2 10 7 6 7 9 2 21 8 2 10 2 24 19 21 6 3 11 5 10 7 5 2 10 5 6 18 27 4 +cv_jpn_000686 11 7 22 7 13 8 5 6 4 20 22 4 6 3 8 3 3 4 26 18 8 5 6 4 9 4 20 22 4 6 3 3 22 4 15 4 13 3 6 5 5 12 5 5 12 3 10 7 15 2 6 2 4 17 2 +cv_jpn_000687 31 2 13 9 3 4 6 5 13 9 4 9 2 10 2 12 2 14 5 10 7 9 2 +cv_jpn_000688 24 4 9 5 16 2 2 12 3 25 4 8 2 23 3 3 10 2 28 5 13 6 2 4 14 5 6 3 27 3 3 11 4 8 5 4 10 7 +cv_jpn_000689 4 27 4 14 3 17 2 6 3 13 30 3 8 2 2 22 4 6 2 13 3 9 3 13 14 5 11 4 8 2 4 +cv_jpn_000690 6 3 3 4 9 3 6 3 15 19 8 5 20 3 8 3 3 12 2 13 8 3 6 2 2 12 2 13 17 2 13 14 5 8 5 4 6 4 11 2 15 19 8 2 +cv_jpn_000691 15 19 6 2 15 19 8 5 12 3 10 5 16 2 26 18 6 10 2 10 5 8 2 11 3 9 3 6 2 10 2 26 18 6 7 10 7 11 3 9 3 5 8 3 15 19 8 5 14 3 6 7 11 2 14 5 11 3 10 2 17 2 10 7 9 4 12 5 11 2 10 7 8 3 4 7 8 3 6 4 20 17 2 10 5 17 2 10 4 9 4 27 3 21 6 2 13 8 5 6 4 14 5 2 10 7 +cv_jpn_000692 24 2 4 9 4 8 2 11 2 21 8 2 6 5 11 7 10 4 3 24 2 6 4 14 2 15 4 6 7 10 2 4 6 3 3 5 9 4 15 19 12 5 13 3 11 3 6 5 10 7 +cv_jpn_000693 17 2 14 2 17 2 14 2 15 9 2 25 7 12 3 6 7 14 5 27 4 12 5 13 4 13 9 2 10 4 12 3 +cv_jpn_000694 26 5 13 15 5 6 2 4 9 3 11 3 26 18 6 2 8 2 27 15 4 8 2 15 4 9 3 4 23 2 4 7 10 7 12 5 5 12 2 4 23 3 15 19 6 7 8 3 12 2 4 3 8 3 17 2 20 24 2 9 2 15 19 8 5 6 2 13 16 2 5 10 7 6 3 8 3 17 2 14 5 6 4 9 2 4 +cv_jpn_000695 12 2 6 5 9 3 11 2 4 9 3 9 4 30 4 4 10 7 25 2 10 2 8 3 23 3 10 5 8 2 +cv_jpn_000696 12 3 10 5 3 8 2 8 2 6 7 24 2 6 7 9 3 28 5 13 14 2 13 6 2 4 15 19 6 7 4 8 5 5 14 3 9 6 2 2 6 18 8 3 9 3 11 4 11 4 10 7 6 3 8 3 17 2 +cv_jpn_000697 9 3 6 29 3 5 13 3 31 7 2 26 7 9 4 12 7 11 3 24 3 14 5 16 5 5 11 3 23 2 21 8 5 4 8 2 +cv_jpn_000698 17 2 14 2 4 17 2 2 9 2 4 15 7 11 2 8 2 13 6 3 25 2 5 15 4 12 2 13 8 3 3 2 12 3 25 5 4 11 2 12 18 +cv_jpn_000699 24 2 12 3 3 6 3 9 4 24 5 8 3 3 16 2 4 11 2 12 7 9 5 2 10 3 24 19 8 3 3 17 2 8 2 2 10 5 5 8 5 15 3 3 +cv_jpn_000700 17 2 8 2 15 4 17 2 6 4 9 3 3 6 2 9 2 9 3 3 14 3 3 16 2 4 8 2 4 8 5 12 18 +cv_jpn_000701 29 3 3 10 5 13 6 2 9 2 30 5 13 29 7 3 3 15 19 8 5 4 11 2 12 18 +cv_jpn_000702 27 3 21 8 3 12 7 11 4 11 2 12 5 +cv_jpn_000703 27 19 6 2 15 8 3 9 3 3 3 3 5 13 25 7 10 23 3 17 2 23 2 6 5 6 4 11 4 9 4 6 2 5 21 14 5 24 2 16 5 12 18 30 5 7 9 3 21 8 2 +cv_jpn_000704 4 26 7 11 3 6 3 9 3 5 13 30 19 4 26 12 3 3 26 15 18 6 2 21 8 5 4 8 2 9 3 8 5 11 4 22 4 6 2 24 2 24 2 9 2 10 4 11 2 15 19 8 2 +cv_jpn_000705 17 2 8 2 15 4 17 2 5 4 16 3 16 2 24 2 9 2 12 8 5 11 3 12 18 +cv_jpn_000706 3 15 4 4 6 5 5 10 23 2 22 4 25 2 13 14 5 8 3 21 8 2 5 6 4 9 2 +cv_jpn_000707 2 4 15 7 7 6 2 10 2 9 4 15 7 7 7 6 2 13 24 2 4 16 2 5 5 5 10 7 23 3 3 6 3 3 3 9 4 4 6 4 11 2 12 18 +cv_jpn_000708 3 3 10 5 11 3 6 4 9 4 9 2 10 7 9 2 +cv_jpn_000709 14 2 10 5 16 2 26 12 18 6 2 21 8 5 10 7 9 3 6 2 2 17 2 6 2 10 7 9 2 4 +cv_jpn_000710 30 3 10 2 28 2 9 3 25 2 22 3 13 16 2 20 2 16 2 10 7 8 3 3 12 18 6 3 15 4 7 10 5 15 4 +cv_jpn_000711 11 2 8 2 2 8 2 10 2 15 4 2 4 14 3 10 7 16 2 14 5 8 5 6 4 8 2 +cv_jpn_000712 11 2 22 4 14 5 23 2 21 8 3 9 3 6 2 +cv_jpn_000713 27 3 3 14 3 12 7 11 3 8 3 6 4 9 4 29 3 3 22 7 16 2 24 2 4 19 8 5 6 4 14 2 +cv_jpn_000714 4 21 12 3 9 4 27 4 13 15 19 8 5 3 +cv_jpn_000715 12 3 3 10 5 6 2 12 24 2 8 5 9 3 8 3 3 10 5 12 7 +cv_jpn_000716 31 18 8 2 10 4 17 2 10 5 22 4 4 6 4 12 5 5 12 2 13 15 19 8 2 +cv_jpn_000717 8 3 11 2 8 3 6 2 9 2 13 16 2 9 3 17 2 6 2 4 12 3 7 12 7 16 2 6 2 6 2 21 8 5 10 4 7 3 +cv_jpn_000718 6 3 6 3 6 2 10 2 8 2 8 5 9 2 13 2 12 7 9 3 17 2 6 4 25 4 12 4 4 +cv_jpn_000719 9 4 28 7 6 5 3 12 15 19 21 6 2 10 4 15 4 25 3 21 8 5 20 2 22 4 6 2 9 2 22 4 11 7 23 3 24 4 12 7 10 7 +cv_jpn_000720 9 5 8 3 16 5 9 4 24 2 11 2 21 8 2 10 2 6 2 9 5 16 2 16 2 11 2 21 8 2 18 +cv_jpn_000721 4 8 3 6 2 5 10 4 23 3 3 9 4 9 2 10 7 13 14 2 +cv_jpn_000722 6 3 12 5 5 24 2 24 2 4 23 7 8 3 4 7 23 3 10 4 2 6 7 16 2 26 7 23 3 5 6 2 13 22 4 +cv_jpn_000723 31 4 22 4 6 2 10 7 9 3 12 2 3 20 11 2 28 2 11 2 28 2 8 3 11 4 15 5 26 18 6 5 10 2 10 5 8 2 +cv_jpn_000724 6 3 12 18 30 2 23 3 6 5 10 5 25 2 12 3 6 3 12 3 6 3 9 3 11 3 13 14 2 5 17 2 16 2 2 11 2 13 12 5 10 7 +cv_jpn_000725 11 4 13 9 2 23 2 21 8 5 11 2 12 18 6 2 10 2 8 2 4 22 3 3 25 7 14 5 12 18 23 3 +cv_jpn_000726 6 3 9 3 8 3 15 3 6 2 13 20 24 2 4 21 8 2 15 7 13 6 2 13 6 4 9 4 4 21 8 2 +cv_jpn_000727 6 3 9 3 14 5 13 27 4 12 7 7 6 4 10 5 27 2 21 8 2 +cv_jpn_000728 2 11 2 23 3 14 3 10 4 12 7 10 7 8 3 6 3 10 3 16 2 9 2 6 18 8 5 6 3 11 2 21 8 2 +cv_jpn_000729 23 2 12 18 6 7 12 7 10 7 23 3 10 4 15 19 26 7 17 2 16 5 8 2 24 3 15 4 +cv_jpn_000730 11 2 12 5 16 3 6 3 13 9 2 6 3 8 3 4 9 2 10 3 8 3 17 2 11 3 9 2 6 2 21 8 2 +cv_jpn_000731 12 2 4 16 3 9 4 17 2 10 2 23 3 8 3 10 4 9 4 6 7 10 7 12 18 8 2 4 10 7 +cv_jpn_000732 6 3 10 2 5 2 9 2 9 2 4 11 4 16 2 10 7 14 2 +cv_jpn_000733 4 4 5 +cv_jpn_000734 15 4 +cv_jpn_000735 9 4 +cv_jpn_000736 24 2 27 4 +cv_jpn_000737 24 2 4 +cv_jpn_000738 8 5 5 25 10 7 9 3 3 4 5 9 4 6 2 25 4 13 16 2 2 10 4 11 2 12 18 +cv_jpn_000739 17 2 8 2 15 4 17 2 2 11 2 4 23 2 12 2 12 2 13 30 3 15 4 11 2 12 18 +cv_jpn_000740 2 8 2 10 2 15 4 4 6 7 26 3 3 24 2 4 21 8 5 14 5 6 2 6 5 11 2 12 18 +cv_jpn_000741 6 3 8 3 15 4 9 3 9 2 26 5 23 2 12 7 11 4 17 2 20 25 7 11 4 9 4 11 3 4 6 4 11 2 15 19 8 2 15 4 4 23 2 11 2 9 4 11 3 3 9 3 3 10 4 25 2 15 19 8 2 +cv_jpn_000742 17 2 8 2 15 4 17 2 4 10 3 4 10 3 9 3 25 5 13 16 3 3 22 4 25 7 13 9 3 11 7 9 5 14 5 6 3 15 4 10 2 5 8 5 11 4 11 2 15 19 8 2 +cv_jpn_000743 9 2 13 14 5 6 7 11 3 15 3 3 3 25 2 4 24 5 8 2 9 2 13 14 2 10 3 +cv_jpn_000744 8 2 10 5 13 8 3 6 2 10 2 29 3 6 7 2 9 2 9 4 6 2 5 8 5 6 5 5 24 19 15 2 6 4 16 5 +cv_jpn_000745 14 3 16 5 28 2 12 5 10 5 25 2 4 21 8 5 11 3 13 15 2 9 2 4 +cv_jpn_000746 14 5 5 8 2 9 3 17 2 4 14 2 6 2 9 3 27 4 17 2 22 4 25 7 13 8 3 3 4 21 8 5 5 9 3 29 3 3 10 4 23 3 8 2 9 3 21 8 2 +cv_jpn_000747 6 3 9 3 16 5 5 9 4 13 9 2 13 6 2 17 2 24 19 12 2 15 4 25 7 10 4 9 4 11 4 8 2 +cv_jpn_000748 3 3 6 4 6 7 12 2 4 14 3 27 4 5 13 22 4 3 12 3 10 7 +cv_jpn_000749 6 2 10 5 17 2 2 8 2 11 2 6 2 6 4 11 7 15 4 21 8 2 +cv_jpn_000750 3 11 2 27 5 15 19 8 5 17 2 10 4 11 2 12 18 +cv_jpn_000751 3 9 3 29 3 6 18 12 5 13 6 2 4 22 3 17 2 6 4 4 8 5 10 7 +cv_jpn_000752 10 5 5 22 28 3 3 6 3 3 2 6 5 8 2 8 3 8 2 13 9 2 9 4 16 2 24 4 27 7 23 3 6 2 17 2 12 7 10 5 21 8 2 +cv_jpn_000753 4 27 4 +cv_jpn_000754 24 2 27 4 +cv_jpn_000755 4 4 5 +cv_jpn_000756 14 5 4 +cv_jpn_000757 15 4 27 4 +cv_jpn_000758 23 3 3 25 3 3 14 2 12 7 9 3 13 4 6 2 7 24 19 8 3 17 2 12 18 6 7 9 2 4 +cv_jpn_000759 10 3 3 6 2 10 7 8 3 6 7 23 4 7 9 3 4 19 29 3 5 11 2 6 2 12 5 9 3 6 3 11 2 15 2 10 7 +cv_jpn_000760 6 3 9 3 14 2 4 6 7 17 2 2 13 6 3 16 2 3 3 6 18 8 5 23 3 6 18 6 2 4 11 2 12 18 +cv_jpn_000761 22 4 15 3 3 24 6 4 9 2 16 2 10 2 15 3 3 12 5 26 3 3 23 3 11 4 11 2 12 18 +cv_jpn_000762 6 3 13 9 2 3 6 4 9 2 16 3 16 7 10 3 26 18 6 5 9 2 4 8 4 6 5 9 2 4 13 14 5 12 18 6 2 +cv_jpn_000763 6 2 10 5 5 9 3 25 3 3 10 4 6 3 17 2 8 3 11 2 10 2 9 2 4 +cv_jpn_000764 4 6 4 8 5 4 6 2 13 11 2 9 5 +cv_jpn_000765 8 2 3 22 4 8 3 12 2 6 2 6 8 8 5 6 4 9 4 24 2 26 7 11 5 14 2 21 30 2 9 5 +cv_jpn_000766 12 2 13 8 5 6 4 3 8 2 15 3 17 2 27 19 6 2 2 31 18 8 5 6 2 +cv_jpn_000767 6 2 17 2 16 2 24 4 2 16 2 21 8 5 4 14 2 +cv_jpn_000768 4 27 7 +cv_jpn_000769 9 3 +cv_jpn_000770 15 19 27 4 +cv_jpn_000771 6 3 +cv_jpn_000772 4 4 5 +cv_jpn_000773 9 2 11 2 4 5 6 2 10 2 15 5 8 5 6 18 8 3 3 12 7 16 4 10 7 +cv_jpn_000774 22 4 6 3 9 3 12 3 8 3 9 4 2 10 7 8 3 4 7 9 3 17 2 8 2 13 9 4 22 4 6 3 9 3 15 19 6 2 13 12 3 26 12 3 9 4 2 10 7 8 3 4 7 6 3 8 3 14 5 9 2 6 7 +cv_jpn_000775 12 3 10 7 17 2 15 4 10 2 9 2 6 18 8 5 4 14 5 12 18 +cv_jpn_000776 6 4 11 4 8 3 25 3 6 7 9 3 29 3 3 26 7 9 3 15 4 16 5 5 17 2 14 2 10 5 24 19 8 3 10 4 11 4 2 8 2 10 2 9 2 4 +cv_jpn_000777 12 7 16 5 5 3 3 8 3 13 9 2 21 8 5 6 4 8 5 10 7 9 3 9 2 +cv_jpn_000778 6 3 9 3 24 5 13 14 5 12 18 6 3 15 4 23 2 12 7 11 2 15 3 3 +cv_jpn_000779 14 5 13 15 2 10 4 9 3 10 7 8 3 6 4 6 4 21 8 3 3 6 2 4 9 2 12 18 +cv_jpn_000780 8 2 11 2 16 2 4 6 3 6 3 22 4 7 16 7 10 2 13 25 7 10 2 4 5 12 18 +cv_jpn_000781 16 5 5 12 18 30 4 4 17 2 11 2 21 6 4 4 3 26 7 22 4 8 5 4 9 5 12 18 8 3 15 4 14 10 4 16 2 21 8 2 8 7 +cv_jpn_000782 9 3 3 16 3 3 23 2 11 5 12 2 10 7 3 5 9 2 4 6 24 19 8 3 6 2 2 10 4 6 2 13 9 5 13 6 4 23 3 3 11 3 6 3 9 3 31 18 6 19 6 5 3 3 9 4 24 19 6 4 28 7 10 2 10 5 8 5 4 10 7 8 3 3 11 3 3 12 18 +cv_jpn_000783 9 2 13 14 5 6 3 9 3 10 3 21 8 3 15 3 8 2 4 11 5 9 2 9 3 9 4 9 2 10 5 16 2 10 5 15 4 13 14 2 +cv_jpn_000784 31 18 26 7 7 14 5 2 10 7 6 3 8 2 11 3 10 4 21 30 2 9 2 6 3 12 5 +cv_jpn_000785 26 7 23 3 25 4 14 5 8 2 13 22 4 6 2 13 14 5 16 3 3 6 2 4 9 4 4 8 2 11 5 10 7 +cv_jpn_000786 25 2 6 7 11 2 26 7 9 3 14 5 6 4 16 3 6 2 17 2 4 11 2 9 4 26 7 10 7 22 4 10 3 29 3 3 6 7 13 9 3 23 2 11 2 14 5 12 18 +cv_jpn_000787 11 13 6 3 3 6 2 10 2 11 2 27 4 9 3 17 2 6 2 10 4 16 2 11 4 5 8 5 6 4 8 2 +cv_jpn_000788 11 5 13 9 4 17 7 4 7 25 5 6 19 27 4 24 17 2 6 2 9 2 9 2 6 2 21 8 2 9 2 10 4 11 4 23 7 25 4 6 19 6 3 8 3 16 2 3 11 3 23 7 6 2 25 2 9 2 6 2 21 8 7 +cv_jpn_000789 8 2 11 4 12 4 9 4 6 2 5 9 2 6 4 2 21 8 5 11 4 10 7 +cv_jpn_000790 25 3 6 18 15 19 6 2 4 9 2 4 16 4 11 4 11 2 4 9 2 4 6 3 10 5 17 2 3 3 8 5 13 12 5 16 2 4 6 2 +cv_jpn_000791 27 4 6 5 4 22 3 6 2 9 4 22 7 16 3 3 8 3 11 2 9 7 9 3 11 4 27 4 17 2 11 7 6 2 12 3 6 2 21 8 5 4 9 2 6 2 +cv_jpn_000792 6 2 16 7 22 3 8 5 13 17 2 20 6 3 13 30 3 13 8 5 4 9 2 6 2 4 27 4 6 7 7 12 7 9 2 2 21 8 2 +cv_jpn_000793 6 3 10 7 11 3 9 3 6 3 10 5 17 2 16 3 24 2 13 24 2 10 5 3 8 7 9 2 13 9 2 10 8 3 30 2 13 24 2 2 +cv_jpn_000794 6 3 3 4 8 5 6 19 8 3 21 6 2 13 8 5 6 4 9 4 20 30 3 3 4 15 19 12 7 8 5 6 4 9 4 17 2 10 5 17 2 10 5 11 3 22 4 6 3 17 2 11 2 12 7 11 2 12 7 28 7 11 5 5 8 3 9 2 10 7 9 3 14 5 2 10 7 +cv_jpn_000795 24 4 22 3 3 15 19 6 4 14 2 2 10 7 6 3 8 3 17 2 20 25 7 27 3 3 4 11 4 12 7 10 7 9 3 11 4 14 5 9 2 6 7 10 7 20 15 2 6 2 4 5 8 5 6 4 9 4 2 6 18 8 3 11 3 6 2 13 16 2 5 10 2 10 7 7 14 5 2 10 7 +cv_jpn_000796 22 3 3 15 19 6 4 16 2 9 2 3 8 3 6 18 7 15 7 8 5 6 4 9 2 27 4 15 19 6 4 14 5 2 10 7 9 4 24 2 15 4 6 2 10 2 6 7 17 2 +cv_jpn_000797 6 3 13 9 2 6 3 8 3 3 14 5 3 16 7 10 2 10 5 8 5 9 2 12 2 6 4 9 24 2 4 +cv_jpn_000798 6 2 6 3 8 3 11 4 10 2 5 8 3 16 2 22 4 6 3 11 7 22 7 13 8 5 6 4 9 4 16 5 13 28 2 4 9 4 3 4 8 5 8 2 10 4 6 19 12 7 10 7 8 3 4 7 4 9 4 17 2 16 5 13 14 2 16 2 6 2 8 2 27 3 11 3 8 2 11 2 5 6 5 10 5 11 2 9 2 10 2 9 2 4 +cv_jpn_000799 15 3 6 4 3 9 3 8 2 6 2 12 2 16 2 24 2 14 3 7 13 9 2 7 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/run.sh b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/run.sh new file mode 100755 index 0000000000000000000000000000000000000000..486101d73bd8f21099d7b795f3c227e8a9d5217f --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/run.sh @@ -0,0 +1 @@ +./asr.sh --ngpu 1 --stage 11 --stop_stage 13 --nj 32 --inference_nj 4 --gpu_inference true --lang jpn --inference_asr_model valid.loss.ave.pth --local_data_opts '--duration 1h --lid false --multilingual false --single_lang jpn' --use_lm false --token_type word --feats_type raw --feats_normalize utterance_mvn --asr_config conf/tuning/train_asr_s3prl_houlsby.yaml --inference_config conf/decode_asr.yaml --train_set train_1h_jpn --valid_set dev_10min_jpn --test_sets 'dev_10min_jpn test_10min_jpn' --asr_tag train_asr_s3prl_houlsby_jpn_1h --expdir test_pr --asr_stats_dir test_pr/asr_stats_jpn_1h --local_score_opts 'false false monolingual' --stage 12 "$@"; exit $? diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.1.log b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.1.log new file mode 100644 index 0000000000000000000000000000000000000000..7e6530d97ce8529e2bd1c34128470e620bfe4da6 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.1.log @@ -0,0 +1,459 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1 --config conf/decode_asr.yaml +# Started at Wed Jan 17 02:40:42 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.1.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1 --config conf/decode_asr.yaml +2024-01-17 02:40:43,312 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml +2024-01-17 02:40:43,330 (asr:523) INFO: Vocabulary size: 41 +2024-01-17 02:40:43,392 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 02:40:43,392 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 02:40:43,503 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 02:40:44,790 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 02:40:46,021 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 02:40:46,021 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 02:40:46,021 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 02:40:46,054 (asr_inference:446) INFO: Text tokenizer: WordTokenizer(delimiter="None") +2024-01-17 02:40:46,129 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 02:40:46,242 (asr_inference:494) INFO: speech length: 92736 +2024-01-17 02:40:47,444 (beam_search:428) INFO: decoder input length: 142 +2024-01-17 02:40:47,444 (beam_search:429) INFO: max output length: 142 +2024-01-17 02:40:47,444 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:47,712 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:47,713 (beam_search:476) INFO: -4.72 * 1.0 = -4.72 for ctc +2024-01-17 02:40:47,713 (beam_search:479) INFO: total log probability: -4.72 +2024-01-17 02:40:47,713 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:40:47,713 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:47,713 (beam_search:483) INFO: best hypo: kakotomiraetodogujuNtekijikodooitsunarugayoeniishIkitekinanodearu + +2024-01-17 02:40:47,737 (asr_inference:494) INFO: speech length: 131328 +2024-01-17 02:40:47,752 (beam_search:428) INFO: decoder input length: 203 +2024-01-17 02:40:47,752 (beam_search:429) INFO: max output length: 203 +2024-01-17 02:40:47,752 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:48,311 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:48,311 (beam_search:476) INFO: -10.11 * 1.0 = -10.11 for ctc +2024-01-17 02:40:48,311 (beam_search:479) INFO: total log probability: -10.11 +2024-01-17 02:40:48,311 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:40:48,311 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:48,312 (beam_search:483) INFO: best hypo: sekayokeeseesurutotomnijikojishiNyokeeseserusoootekisekainosoozootekiyoosotoshItepaukobutsugakobutsudearu + +2024-01-17 02:40:48,313 (asr_inference:494) INFO: speech length: 57600 +2024-01-17 02:40:48,322 (beam_search:428) INFO: decoder input length: 87 +2024-01-17 02:40:48,322 (beam_search:429) INFO: max output length: 87 +2024-01-17 02:40:48,322 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:48,415 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:48,416 (beam_search:476) INFO: -6.00 * 1.0 = -6.00 for ctc +2024-01-17 02:40:48,416 (beam_search:479) INFO: total log probability: -6.00 +2024-01-17 02:40:48,416 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 02:40:48,416 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:48,416 (beam_search:483) INFO: best hypo: pazokoNdegeemiaruItonohfuitekite + +2024-01-17 02:40:48,417 (asr_inference:494) INFO: speech length: 141696 +2024-01-17 02:40:48,431 (beam_search:428) INFO: decoder input length: 219 +2024-01-17 02:40:48,431 (beam_search:429) INFO: max output length: 219 +2024-01-17 02:40:48,431 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:49,007 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:49,007 (beam_search:476) INFO: -8.58 * 1.0 = -8.58 for ctc +2024-01-17 02:40:49,007 (beam_search:479) INFO: total log probability: -8.58 +2024-01-17 02:40:49,007 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:40:49,007 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:49,008 (beam_search:483) INFO: best hypo: kaNakunoshimesaatarashijijiutsuatarashiikaNneNkaNkyoshihainwatarashiikanooseomocltepaunanihajimerukawa + +2024-01-17 02:40:49,009 (asr_inference:494) INFO: speech length: 76032 +2024-01-17 02:40:49,019 (beam_search:428) INFO: decoder input length: 116 +2024-01-17 02:40:49,019 (beam_search:429) INFO: max output length: 116 +2024-01-17 02:40:49,019 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:49,147 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:49,147 (beam_search:476) INFO: -3.78 * 1.0 = -3.78 for ctc +2024-01-17 02:40:49,147 (beam_search:479) INFO: total log probability: -3.78 +2024-01-17 02:40:49,147 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:40:49,147 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:49,147 (beam_search:483) INFO: best hypo: omoshirounonipaurootonagasugitedarui + +2024-01-17 02:40:49,148 (asr_inference:494) INFO: speech length: 58752 +2024-01-17 02:40:49,157 (beam_search:428) INFO: decoder input length: 89 +2024-01-17 02:40:49,157 (beam_search:429) INFO: max output length: 89 +2024-01-17 02:40:49,157 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:49,216 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:49,216 (beam_search:476) INFO: -1.11 * 1.0 = -1.11 for ctc +2024-01-17 02:40:49,216 (beam_search:479) INFO: total log probability: -1.11 +2024-01-17 02:40:49,216 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:40:49,216 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:49,216 (beam_search:483) INFO: best hypo: korejooshuuhaNpoina + +2024-01-17 02:40:49,217 (asr_inference:494) INFO: speech length: 97920 +2024-01-17 02:40:49,228 (beam_search:428) INFO: decoder input length: 150 +2024-01-17 02:40:49,228 (beam_search:429) INFO: max output length: 150 +2024-01-17 02:40:49,228 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:49,492 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:49,492 (beam_search:476) INFO: -8.55 * 1.0 = -8.55 for ctc +2024-01-17 02:40:49,492 (beam_search:479) INFO: total log probability: -8.55 +2024-01-17 02:40:49,492 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 02:40:49,492 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:49,492 (beam_search:483) INFO: best hypo: kagakUshamosekaiohookatssekinitooichitekinisatsumeshuotoshIteiru + +2024-01-17 02:40:49,493 (asr_inference:494) INFO: speech length: 39744 +2024-01-17 02:40:49,501 (beam_search:428) INFO: decoder input length: 60 +2024-01-17 02:40:49,501 (beam_search:429) INFO: max output length: 60 +2024-01-17 02:40:49,501 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:49,534 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:49,534 (beam_search:476) INFO: -1.30 * 1.0 = -1.30 for ctc +2024-01-17 02:40:49,534 (beam_search:479) INFO: total log probability: -1.30 +2024-01-17 02:40:49,534 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:40:49,534 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:49,535 (beam_search:483) INFO: best hypo: fUtsuunitsumaraN + +2024-01-17 02:40:49,536 (asr_inference:494) INFO: speech length: 44352 +2024-01-17 02:40:49,544 (beam_search:428) INFO: decoder input length: 67 +2024-01-17 02:40:49,544 (beam_search:429) INFO: max output length: 67 +2024-01-17 02:40:49,544 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:49,588 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:49,588 (beam_search:476) INFO: -2.06 * 1.0 = -2.06 for ctc +2024-01-17 02:40:49,588 (beam_search:479) INFO: total log probability: -2.06 +2024-01-17 02:40:49,588 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:40:49,588 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:49,588 (beam_search:483) INFO: best hypo: shIclkariItekudasai + +2024-01-17 02:40:49,589 (asr_inference:494) INFO: speech length: 158400 +2024-01-17 02:40:49,605 (beam_search:428) INFO: decoder input length: 245 +2024-01-17 02:40:49,605 (beam_search:429) INFO: max output length: 245 +2024-01-17 02:40:49,605 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:50,236 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:50,236 (beam_search:476) INFO: -8.82 * 1.0 = -8.82 for ctc +2024-01-17 02:40:50,236 (beam_search:479) INFO: total log probability: -8.82 +2024-01-17 02:40:50,236 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:40:50,236 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:50,236 (beam_search:483) INFO: best hypo: watashiwaamiginonotokidekIshItekIseimeenojikakutoiugotokimonobeNshoohootekiroNbitoyuunodearu + +2024-01-17 02:40:50,238 (asr_inference:494) INFO: speech length: 127296 +2024-01-17 02:40:50,251 (beam_search:428) INFO: decoder input length: 196 +2024-01-17 02:40:50,251 (beam_search:429) INFO: max output length: 196 +2024-01-17 02:40:50,251 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:50,669 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:50,669 (beam_search:476) INFO: -7.96 * 1.0 = -7.96 for ctc +2024-01-17 02:40:50,669 (beam_search:479) INFO: total log probability: -7.96 +2024-01-17 02:40:50,669 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:40:50,669 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:50,669 (beam_search:483) INFO: best hypo: watashiwapaushakaikeeseenokoNteeniwapaudeyoonusosutekinamonogahataraiteirutomo + +2024-01-17 02:40:50,670 (asr_inference:494) INFO: speech length: 56448 +2024-01-17 02:40:50,679 (beam_search:428) INFO: decoder input length: 86 +2024-01-17 02:40:50,679 (beam_search:429) INFO: max output length: 86 +2024-01-17 02:40:50,679 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:50,752 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:50,752 (beam_search:476) INFO: -1.99 * 1.0 = -1.99 for ctc +2024-01-17 02:40:50,752 (beam_search:479) INFO: total log probability: -1.99 +2024-01-17 02:40:50,752 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:40:50,752 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:50,752 (beam_search:483) INFO: best hypo: naniosurutsumoridacltanoka + +2024-01-17 02:40:50,753 (asr_inference:494) INFO: speech length: 165888 +2024-01-17 02:40:50,769 (beam_search:428) INFO: decoder input length: 257 +2024-01-17 02:40:50,769 (beam_search:429) INFO: max output length: 257 +2024-01-17 02:40:50,769 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:51,451 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:51,451 (beam_search:476) INFO: -6.36 * 1.0 = -6.36 for ctc +2024-01-17 02:40:51,451 (beam_search:479) INFO: total log probability: -6.36 +2024-01-17 02:40:51,451 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:40:51,451 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:51,451 (beam_search:483) INFO: best hypo: kobutsutekItagajikohIteetekinitaNniteNshuugootekinikaNgaerarurutokisoregabuzuritekItekaidearu + +2024-01-17 02:40:51,453 (asr_inference:494) INFO: speech length: 127296 +2024-01-17 02:40:51,466 (beam_search:428) INFO: decoder input length: 196 +2024-01-17 02:40:51,466 (beam_search:429) INFO: max output length: 196 +2024-01-17 02:40:51,466 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:51,748 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:51,748 (beam_search:476) INFO: -5.92 * 1.0 = -5.92 for ctc +2024-01-17 02:40:51,748 (beam_search:479) INFO: total log probability: -5.92 +2024-01-17 02:40:51,748 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 02:40:51,748 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:51,749 (beam_search:483) INFO: best hypo: anegazucltanodeapauyaclkinoshiraaigaarimaseNdeshIta + +2024-01-17 02:40:51,750 (asr_inference:494) INFO: speech length: 86976 +2024-01-17 02:40:51,761 (beam_search:428) INFO: decoder input length: 133 +2024-01-17 02:40:51,761 (beam_search:429) INFO: max output length: 133 +2024-01-17 02:40:51,761 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:51,904 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:51,904 (beam_search:476) INFO: -4.48 * 1.0 = -4.48 for ctc +2024-01-17 02:40:51,904 (beam_search:479) INFO: total log probability: -4.48 +2024-01-17 02:40:51,904 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 02:40:51,904 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:51,904 (beam_search:483) INFO: best hypo: korewarihoNdeuteinaitabemonodesU + +2024-01-17 02:40:51,905 (asr_inference:494) INFO: speech length: 134208 +2024-01-17 02:40:51,919 (beam_search:428) INFO: decoder input length: 207 +2024-01-17 02:40:51,919 (beam_search:429) INFO: max output length: 207 +2024-01-17 02:40:51,919 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:52,192 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:52,192 (beam_search:476) INFO: -4.92 * 1.0 = -4.92 for ctc +2024-01-17 02:40:52,192 (beam_search:479) INFO: total log probability: -4.92 +2024-01-17 02:40:52,192 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 02:40:52,192 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:52,192 (beam_search:483) INFO: best hypo: watashiwaheNshuuinoyoneNkuraewayacltacltoomo + +2024-01-17 02:40:52,193 (asr_inference:494) INFO: speech length: 115200 +2024-01-17 02:40:52,205 (beam_search:428) INFO: decoder input length: 177 +2024-01-17 02:40:52,206 (beam_search:429) INFO: max output length: 177 +2024-01-17 02:40:52,206 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:52,413 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:52,413 (beam_search:476) INFO: -5.02 * 1.0 = -5.02 for ctc +2024-01-17 02:40:52,413 (beam_search:479) INFO: total log probability: -5.02 +2024-01-17 02:40:52,413 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 02:40:52,413 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:52,413 (beam_search:483) INFO: best hypo: isaNnikonokotobanoimiyooooshiamashIta + +2024-01-17 02:40:52,414 (asr_inference:494) INFO: speech length: 98496 +2024-01-17 02:40:52,425 (beam_search:428) INFO: decoder input length: 151 +2024-01-17 02:40:52,425 (beam_search:429) INFO: max output length: 151 +2024-01-17 02:40:52,425 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:52,597 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:52,597 (beam_search:476) INFO: -3.18 * 1.0 = -3.18 for ctc +2024-01-17 02:40:52,597 (beam_search:479) INFO: total log probability: -3.18 +2024-01-17 02:40:52,597 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:40:52,597 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:52,597 (beam_search:483) INFO: best hypo: kasegatsUsuyoihiwatenisugadekimaseN + +2024-01-17 02:40:52,598 (asr_inference:494) INFO: speech length: 65280 +2024-01-17 02:40:52,608 (beam_search:428) INFO: decoder input length: 99 +2024-01-17 02:40:52,608 (beam_search:429) INFO: max output length: 99 +2024-01-17 02:40:52,608 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:52,623 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:52,623 (beam_search:476) INFO: -0.33 * 1.0 = -0.33 for ctc +2024-01-17 02:40:52,623 (beam_search:479) INFO: total log probability: -0.33 +2024-01-17 02:40:52,623 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:40:52,623 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:52,623 (beam_search:483) INFO: best hypo: ichi + +2024-01-17 02:40:52,624 (asr_inference:494) INFO: speech length: 43392 +2024-01-17 02:40:52,632 (beam_search:428) INFO: decoder input length: 65 +2024-01-17 02:40:52,632 (beam_search:429) INFO: max output length: 65 +2024-01-17 02:40:52,632 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:52,643 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:52,643 (beam_search:476) INFO: -0.30 * 1.0 = -0.30 for ctc +2024-01-17 02:40:52,643 (beam_search:479) INFO: total log probability: -0.30 +2024-01-17 02:40:52,643 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:40:52,643 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:52,643 (beam_search:483) INFO: best hypo: hai + +2024-01-17 02:40:52,644 (asr_inference:494) INFO: speech length: 33408 +2024-01-17 02:40:52,651 (beam_search:428) INFO: decoder input length: 50 +2024-01-17 02:40:52,651 (beam_search:429) INFO: max output length: 50 +2024-01-17 02:40:52,651 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:52,658 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:52,658 (beam_search:476) INFO: -0.15 * 1.0 = -0.15 for ctc +2024-01-17 02:40:52,658 (beam_search:479) INFO: total log probability: -0.15 +2024-01-17 02:40:52,658 (beam_search:480) INFO: normalized log probability: -0.04 +2024-01-17 02:40:52,658 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:52,658 (beam_search:483) INFO: best hypo: ni + +2024-01-17 02:40:52,659 (asr_inference:494) INFO: speech length: 32640 +2024-01-17 02:40:52,666 (beam_search:428) INFO: decoder input length: 48 +2024-01-17 02:40:52,666 (beam_search:429) INFO: max output length: 48 +2024-01-17 02:40:52,666 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:52,675 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:52,675 (beam_search:476) INFO: -1.08 * 1.0 = -1.08 for ctc +2024-01-17 02:40:52,675 (beam_search:479) INFO: total log probability: -1.08 +2024-01-17 02:40:52,675 (beam_search:480) INFO: normalized log probability: -0.22 +2024-01-17 02:40:52,675 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:52,675 (beam_search:483) INFO: best hypo: dei + +2024-01-17 02:40:52,676 (asr_inference:494) INFO: speech length: 46464 +2024-01-17 02:40:52,684 (beam_search:428) INFO: decoder input length: 70 +2024-01-17 02:40:52,684 (beam_search:429) INFO: max output length: 70 +2024-01-17 02:40:52,684 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:52,698 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:52,698 (beam_search:476) INFO: -0.29 * 1.0 = -0.29 for ctc +2024-01-17 02:40:52,698 (beam_search:479) INFO: total log probability: -0.29 +2024-01-17 02:40:52,698 (beam_search:480) INFO: normalized log probability: -0.05 +2024-01-17 02:40:52,698 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:52,698 (beam_search:483) INFO: best hypo: toki + +2024-01-17 02:40:52,699 (asr_inference:494) INFO: speech length: 126720 +2024-01-17 02:40:52,712 (beam_search:428) INFO: decoder input length: 195 +2024-01-17 02:40:52,712 (beam_search:429) INFO: max output length: 195 +2024-01-17 02:40:52,712 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:53,087 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:53,087 (beam_search:476) INFO: -4.10 * 1.0 = -4.10 for ctc +2024-01-17 02:40:53,087 (beam_search:479) INFO: total log probability: -4.10 +2024-01-17 02:40:53,087 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:40:53,087 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:53,088 (beam_search:483) INFO: best hypo: mirutoiukototopauhatarakUtoiukotogapausUkabuNritekinakerebanaranai + +2024-01-17 02:40:53,089 (asr_inference:494) INFO: speech length: 85248 +2024-01-17 02:40:53,100 (beam_search:428) INFO: decoder input length: 131 +2024-01-17 02:40:53,100 (beam_search:429) INFO: max output length: 131 +2024-01-17 02:40:53,100 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:53,317 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:53,317 (beam_search:476) INFO: -3.83 * 1.0 = -3.83 for ctc +2024-01-17 02:40:53,317 (beam_search:479) INFO: total log probability: -3.83 +2024-01-17 02:40:53,317 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:40:53,317 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:53,318 (beam_search:483) INFO: best hypo: warewareootamashiinozukokaragugasumonodenakerebanaranai + +2024-01-17 02:40:53,319 (asr_inference:494) INFO: speech length: 113472 +2024-01-17 02:40:53,331 (beam_search:428) INFO: decoder input length: 175 +2024-01-17 02:40:53,331 (beam_search:429) INFO: max output length: 175 +2024-01-17 02:40:53,331 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:53,704 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:53,704 (beam_search:476) INFO: -5.60 * 1.0 = -5.60 for ctc +2024-01-17 02:40:53,704 (beam_search:479) INFO: total log probability: -5.60 +2024-01-17 02:40:53,704 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:40:53,704 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:53,704 (beam_search:483) INFO: best hypo: zecltaibeNshoohootekinarugaiueniideatekIchoclkaNtekIkeekigaclUkumarerunodearu + +2024-01-17 02:40:53,705 (asr_inference:494) INFO: speech length: 111168 +2024-01-17 02:40:53,718 (beam_search:428) INFO: decoder input length: 171 +2024-01-17 02:40:53,718 (beam_search:429) INFO: max output length: 171 +2024-01-17 02:40:53,718 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:54,086 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:54,087 (beam_search:476) INFO: -4.74 * 1.0 = -4.74 for ctc +2024-01-17 02:40:54,087 (beam_search:479) INFO: total log probability: -4.74 +2024-01-17 02:40:54,087 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:40:54,087 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:54,087 (beam_search:483) INFO: best hypo: dokomademotatoichitonosoogohIteetekinazecltaimujuNtekijikodoitsunosekainishIte + +2024-01-17 02:40:54,088 (asr_inference:494) INFO: speech length: 85824 +2024-01-17 02:40:54,099 (beam_search:428) INFO: decoder input length: 132 +2024-01-17 02:40:54,099 (beam_search:429) INFO: max output length: 132 +2024-01-17 02:40:54,099 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:54,320 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:54,320 (beam_search:476) INFO: -3.18 * 1.0 = -3.18 for ctc +2024-01-17 02:40:54,320 (beam_search:479) INFO: total log probability: -3.18 +2024-01-17 02:40:54,320 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:40:54,320 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:54,320 (beam_search:483) INFO: best hypo: shIkaruniniNgeNtokaNkyootorokaNkeewamotokooinokaNkyeedeari + +2024-01-17 02:40:54,322 (asr_inference:494) INFO: speech length: 74880 +2024-01-17 02:40:54,331 (beam_search:428) INFO: decoder input length: 114 +2024-01-17 02:40:54,331 (beam_search:429) INFO: max output length: 114 +2024-01-17 02:40:54,331 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:54,460 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:54,460 (beam_search:476) INFO: -4.87 * 1.0 = -4.87 for ctc +2024-01-17 02:40:54,460 (beam_search:479) INFO: total log probability: -4.87 +2024-01-17 02:40:54,460 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 02:40:54,460 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:54,460 (beam_search:483) INFO: best hypo: iisanikoonokotobanoimiyooshiamashIta + +2024-01-17 02:40:54,461 (asr_inference:494) INFO: speech length: 63936 +2024-01-17 02:40:54,471 (beam_search:428) INFO: decoder input length: 97 +2024-01-17 02:40:54,471 (beam_search:429) INFO: max output length: 97 +2024-01-17 02:40:54,471 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:54,537 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:54,537 (beam_search:476) INFO: -3.22 * 1.0 = -3.22 for ctc +2024-01-17 02:40:54,537 (beam_search:479) INFO: total log probability: -3.22 +2024-01-17 02:40:54,537 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 02:40:54,537 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:54,538 (beam_search:483) INFO: best hypo: gekigananatssarimasU + +2024-01-17 02:40:54,539 (asr_inference:494) INFO: speech length: 69696 +2024-01-17 02:40:54,548 (beam_search:428) INFO: decoder input length: 106 +2024-01-17 02:40:54,548 (beam_search:429) INFO: max output length: 106 +2024-01-17 02:40:54,548 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:54,637 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:54,637 (beam_search:476) INFO: -2.50 * 1.0 = -2.50 for ctc +2024-01-17 02:40:54,637 (beam_search:479) INFO: total log probability: -2.50 +2024-01-17 02:40:54,637 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:40:54,637 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:54,637 (beam_search:483) INFO: best hypo: kochirabakobuyashiisaNdesU + +2024-01-17 02:40:54,638 (asr_inference:494) INFO: speech length: 36288 +2024-01-17 02:40:54,646 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 02:40:54,646 (beam_search:429) INFO: max output length: 54 +2024-01-17 02:40:54,646 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:54,668 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:54,668 (beam_search:476) INFO: -1.14 * 1.0 = -1.14 for ctc +2024-01-17 02:40:54,668 (beam_search:479) INFO: total log probability: -1.14 +2024-01-17 02:40:54,668 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:40:54,668 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:54,668 (beam_search:483) INFO: best hypo: moshiimashi + +2024-01-17 02:40:54,669 (asr_inference:494) INFO: speech length: 75456 +2024-01-17 02:40:54,679 (beam_search:428) INFO: decoder input length: 115 +2024-01-17 02:40:54,679 (beam_search:429) INFO: max output length: 115 +2024-01-17 02:40:54,679 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:54,798 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:54,798 (beam_search:476) INFO: -3.48 * 1.0 = -3.48 for ctc +2024-01-17 02:40:54,798 (beam_search:479) INFO: total log probability: -3.48 +2024-01-17 02:40:54,798 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:40:54,798 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:54,798 (beam_search:483) INFO: best hypo: kokowaokikUtenikuyakanamachidesU + +2024-01-17 02:40:54,799 (asr_inference:494) INFO: speech length: 65088 +2024-01-17 02:40:54,808 (beam_search:428) INFO: decoder input length: 99 +2024-01-17 02:40:54,808 (beam_search:429) INFO: max output length: 99 +2024-01-17 02:40:54,808 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:54,906 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:54,906 (beam_search:476) INFO: -0.71 * 1.0 = -0.71 for ctc +2024-01-17 02:40:54,906 (beam_search:479) INFO: total log probability: -0.71 +2024-01-17 02:40:54,906 (beam_search:480) INFO: normalized log probability: -0.02 +2024-01-17 02:40:54,906 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:54,906 (beam_search:483) INFO: best hypo: sonouchikaiyakUsarerukaraisoge + +2024-01-17 02:40:54,907 (asr_inference:494) INFO: speech length: 72000 +2024-01-17 02:40:54,917 (beam_search:428) INFO: decoder input length: 110 +2024-01-17 02:40:54,917 (beam_search:429) INFO: max output length: 110 +2024-01-17 02:40:54,917 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:55,016 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:55,016 (beam_search:476) INFO: -2.84 * 1.0 = -2.84 for ctc +2024-01-17 02:40:55,016 (beam_search:479) INFO: total log probability: -2.84 +2024-01-17 02:40:55,016 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:40:55,016 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:55,016 (beam_search:483) INFO: best hypo: amasagafusairaretetechoodoi + +2024-01-17 02:40:55,017 (asr_inference:494) INFO: speech length: 62784 +2024-01-17 02:40:55,027 (beam_search:428) INFO: decoder input length: 96 +2024-01-17 02:40:55,027 (beam_search:429) INFO: max output length: 96 +2024-01-17 02:40:55,027 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:55,096 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:55,096 (beam_search:476) INFO: -3.72 * 1.0 = -3.72 for ctc +2024-01-17 02:40:55,096 (beam_search:479) INFO: total log probability: -3.72 +2024-01-17 02:40:55,096 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 02:40:55,096 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:55,096 (beam_search:483) INFO: best hypo: hogeNshItsunodooaaketa + +2024-01-17 02:40:55,097 (asr_inference:494) INFO: speech length: 55872 +2024-01-17 02:40:55,106 (beam_search:428) INFO: decoder input length: 85 +2024-01-17 02:40:55,106 (beam_search:429) INFO: max output length: 85 +2024-01-17 02:40:55,106 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:55,181 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:55,181 (beam_search:476) INFO: -1.74 * 1.0 = -1.74 for ctc +2024-01-17 02:40:55,181 (beam_search:479) INFO: total log probability: -1.74 +2024-01-17 02:40:55,181 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:40:55,181 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:55,181 (beam_search:483) INFO: best hypo: modaNnioowacltemokinishinai + +2024-01-17 02:40:55,182 (asr_inference:494) INFO: speech length: 44352 +2024-01-17 02:40:55,190 (beam_search:428) INFO: decoder input length: 67 +2024-01-17 02:40:55,190 (beam_search:429) INFO: max output length: 67 +2024-01-17 02:40:55,190 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:55,217 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:55,218 (beam_search:476) INFO: -1.47 * 1.0 = -1.47 for ctc +2024-01-17 02:40:55,218 (beam_search:479) INFO: total log probability: -1.47 +2024-01-17 02:40:55,218 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 02:40:55,218 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:55,218 (beam_search:483) INFO: best hypo: arigacltaya + +2024-01-17 02:40:55,219 (asr_inference:494) INFO: speech length: 69696 +2024-01-17 02:40:55,228 (beam_search:428) INFO: decoder input length: 106 +2024-01-17 02:40:55,228 (beam_search:429) INFO: max output length: 106 +2024-01-17 02:40:55,228 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:55,359 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:55,359 (beam_search:476) INFO: -3.99 * 1.0 = -3.99 for ctc +2024-01-17 02:40:55,359 (beam_search:479) INFO: total log probability: -3.99 +2024-01-17 02:40:55,359 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:40:55,359 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:55,359 (beam_search:483) INFO: best hypo: itoogarakudatojikaNwaosuretetanoshimeru + +2024-01-17 02:40:55,360 (asr_inference:494) INFO: speech length: 83520 +2024-01-17 02:40:55,371 (beam_search:428) INFO: decoder input length: 128 +2024-01-17 02:40:55,371 (beam_search:429) INFO: max output length: 128 +2024-01-17 02:40:55,371 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:40:55,577 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:40:55,578 (beam_search:476) INFO: -3.54 * 1.0 = -3.54 for ctc +2024-01-17 02:40:55,578 (beam_search:479) INFO: total log probability: -3.54 +2024-01-17 02:40:55,578 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:40:55,578 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:40:55,578 (beam_search:483) INFO: best hypo: kakakuwagizutsUkasareruniojitejooshIkinouchinihaiclteiuku + +# Accounting: time=14 threads=1 +# Ended (code 0) at Wed Jan 17 02:40:56 CST 2024, elapsed time 14 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.2.log b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.2.log new file mode 100644 index 0000000000000000000000000000000000000000..be97bdd63b2499cf74b43a2574654fdec8f7e8d6 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.2.log @@ -0,0 +1,459 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2 --config conf/decode_asr.yaml +# Started at Wed Jan 17 02:40:56 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.2.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2 --config conf/decode_asr.yaml +2024-01-17 02:40:57,388 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml +2024-01-17 02:40:57,406 (asr:523) INFO: Vocabulary size: 41 +2024-01-17 02:40:57,468 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 02:40:57,468 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 02:40:57,577 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 02:40:58,872 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 02:41:00,115 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 02:41:00,115 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 02:41:00,115 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 02:41:00,148 (asr_inference:446) INFO: Text tokenizer: WordTokenizer(delimiter="None") +2024-01-17 02:41:00,223 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 02:41:00,337 (asr_inference:494) INFO: speech length: 133056 +2024-01-17 02:41:01,541 (beam_search:428) INFO: decoder input length: 205 +2024-01-17 02:41:01,542 (beam_search:429) INFO: max output length: 205 +2024-01-17 02:41:01,542 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:02,087 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:02,088 (beam_search:476) INFO: -10.73 * 1.0 = -10.73 for ctc +2024-01-17 02:41:02,088 (beam_search:479) INFO: total log probability: -10.73 +2024-01-17 02:41:02,088 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:41:02,088 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:02,088 (beam_search:483) INFO: best hypo: shIkashItokigakaonihairukotosonokotogapaumiraayoomukotodeariwaratanaruiclsutaigadetekurukotodearu + +2024-01-17 02:41:02,112 (asr_inference:494) INFO: speech length: 70848 +2024-01-17 02:41:02,122 (beam_search:428) INFO: decoder input length: 108 +2024-01-17 02:41:02,122 (beam_search:429) INFO: max output length: 108 +2024-01-17 02:41:02,122 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:02,261 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:02,261 (beam_search:476) INFO: -4.30 * 1.0 = -4.30 for ctc +2024-01-17 02:41:02,261 (beam_search:479) INFO: total log probability: -4.30 +2024-01-17 02:41:02,261 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:41:02,261 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:02,262 (beam_search:483) INFO: best hypo: terebiokaikaitepauterebiomirujikaNgafueta + +2024-01-17 02:41:02,263 (asr_inference:494) INFO: speech length: 65088 +2024-01-17 02:41:02,273 (beam_search:428) INFO: decoder input length: 99 +2024-01-17 02:41:02,273 (beam_search:429) INFO: max output length: 99 +2024-01-17 02:41:02,273 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:02,391 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:02,391 (beam_search:476) INFO: -2.56 * 1.0 = -2.56 for ctc +2024-01-17 02:41:02,391 (beam_search:479) INFO: total log probability: -2.56 +2024-01-17 02:41:02,391 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:41:02,391 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:02,391 (beam_search:483) INFO: best hypo: kakarishItainomiitsumademoikirunodearu + +2024-01-17 02:41:02,392 (asr_inference:494) INFO: speech length: 80640 +2024-01-17 02:41:02,403 (beam_search:428) INFO: decoder input length: 123 +2024-01-17 02:41:02,403 (beam_search:429) INFO: max output length: 123 +2024-01-17 02:41:02,403 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:02,567 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:02,567 (beam_search:476) INFO: -1.94 * 1.0 = -1.94 for ctc +2024-01-17 02:41:02,567 (beam_search:479) INFO: total log probability: -1.94 +2024-01-17 02:41:02,567 (beam_search:480) INFO: normalized log probability: -0.05 +2024-01-17 02:41:02,567 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:02,567 (beam_search:483) INFO: best hypo: niNkidaameNiyanigaraNdaranijikaNmachidaclta + +2024-01-17 02:41:02,568 (asr_inference:494) INFO: speech length: 146880 +2024-01-17 02:41:02,584 (beam_search:428) INFO: decoder input length: 227 +2024-01-17 02:41:02,584 (beam_search:429) INFO: max output length: 227 +2024-01-17 02:41:02,584 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:03,137 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:03,137 (beam_search:476) INFO: -7.11 * 1.0 = -7.11 for ctc +2024-01-17 02:41:03,137 (beam_search:479) INFO: total log probability: -7.11 +2024-01-17 02:41:03,137 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:41:03,137 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:03,138 (beam_search:483) INFO: best hypo: soreomochiiruniNgeNnoiyokuniitoNshisoshItekorewakarenomoclteirukachinoshakudoniitoNsuru + +2024-01-17 02:41:03,139 (asr_inference:494) INFO: speech length: 72000 +2024-01-17 02:41:03,149 (beam_search:428) INFO: decoder input length: 110 +2024-01-17 02:41:03,149 (beam_search:429) INFO: max output length: 110 +2024-01-17 02:41:03,149 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:03,284 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:03,284 (beam_search:476) INFO: -3.79 * 1.0 = -3.79 for ctc +2024-01-17 02:41:03,284 (beam_search:479) INFO: total log probability: -3.79 +2024-01-17 02:41:03,284 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:41:03,284 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:03,284 (beam_search:483) INFO: best hypo: mawaruiowamiNnakaNgaerukotooyameteita + +2024-01-17 02:41:03,285 (asr_inference:494) INFO: speech length: 168768 +2024-01-17 02:41:03,302 (beam_search:428) INFO: decoder input length: 261 +2024-01-17 02:41:03,302 (beam_search:429) INFO: max output length: 261 +2024-01-17 02:41:03,302 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:04,071 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:04,071 (beam_search:476) INFO: -7.74 * 1.0 = -7.74 for ctc +2024-01-17 02:41:04,071 (beam_search:479) INFO: total log probability: -7.74 +2024-01-17 02:41:04,071 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:41:04,071 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:04,071 (beam_search:483) INFO: best hypo: kooitekichokclUkaNtekinisekayomirutoiukotowajakunikooitekichoclkaNtekinisekayokeeseesurukotoohkumunodearu + +2024-01-17 02:41:04,073 (asr_inference:494) INFO: speech length: 96768 +2024-01-17 02:41:04,085 (beam_search:428) INFO: decoder input length: 149 +2024-01-17 02:41:04,085 (beam_search:429) INFO: max output length: 149 +2024-01-17 02:41:04,085 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:04,347 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:04,347 (beam_search:476) INFO: -3.29 * 1.0 = -3.29 for ctc +2024-01-17 02:41:04,347 (beam_search:479) INFO: total log probability: -3.29 +2024-01-17 02:41:04,347 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:41:04,347 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:04,348 (beam_search:483) INFO: best hypo: shiNclpaitakesasemaitosurukizukaigayokeenishiNpaisaseteshimau + +2024-01-17 02:41:04,349 (asr_inference:494) INFO: speech length: 104256 +2024-01-17 02:41:04,361 (beam_search:428) INFO: decoder input length: 160 +2024-01-17 02:41:04,361 (beam_search:429) INFO: max output length: 160 +2024-01-17 02:41:04,361 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:04,542 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:04,542 (beam_search:476) INFO: -4.26 * 1.0 = -4.26 for ctc +2024-01-17 02:41:04,542 (beam_search:479) INFO: total log probability: -4.26 +2024-01-17 02:41:04,542 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 02:41:04,542 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:04,543 (beam_search:483) INFO: best hypo: konomichiwatotemosemainodeabnaidesU + +2024-01-17 02:41:04,544 (asr_inference:494) INFO: speech length: 61632 +2024-01-17 02:41:04,553 (beam_search:428) INFO: decoder input length: 94 +2024-01-17 02:41:04,553 (beam_search:429) INFO: max output length: 94 +2024-01-17 02:41:04,553 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:04,597 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:04,597 (beam_search:476) INFO: -2.80 * 1.0 = -2.80 for ctc +2024-01-17 02:41:04,597 (beam_search:479) INFO: total log probability: -2.80 +2024-01-17 02:41:04,597 (beam_search:480) INFO: normalized log probability: -0.20 +2024-01-17 02:41:04,597 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:04,597 (beam_search:483) INFO: best hypo: wohoegaarini + +2024-01-17 02:41:04,598 (asr_inference:494) INFO: speech length: 69696 +2024-01-17 02:41:04,608 (beam_search:428) INFO: decoder input length: 106 +2024-01-17 02:41:04,608 (beam_search:429) INFO: max output length: 106 +2024-01-17 02:41:04,608 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:04,713 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:04,713 (beam_search:476) INFO: -2.52 * 1.0 = -2.52 for ctc +2024-01-17 02:41:04,713 (beam_search:479) INFO: total log probability: -2.52 +2024-01-17 02:41:04,713 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:41:04,713 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:04,714 (beam_search:483) INFO: best hypo: toiwarookamoitarigaaniarimasU + +2024-01-17 02:41:04,715 (asr_inference:494) INFO: speech length: 71424 +2024-01-17 02:41:04,724 (beam_search:428) INFO: decoder input length: 109 +2024-01-17 02:41:04,724 (beam_search:429) INFO: max output length: 109 +2024-01-17 02:41:04,724 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:04,849 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:04,849 (beam_search:476) INFO: -2.85 * 1.0 = -2.85 for ctc +2024-01-17 02:41:04,849 (beam_search:479) INFO: total log probability: -2.85 +2024-01-17 02:41:04,849 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:41:04,849 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:04,849 (beam_search:483) INFO: best hypo: tanakasaNnohitainikimurasaNgaimasU + +2024-01-17 02:41:04,850 (asr_inference:494) INFO: speech length: 67392 +2024-01-17 02:41:04,860 (beam_search:428) INFO: decoder input length: 103 +2024-01-17 02:41:04,860 (beam_search:429) INFO: max output length: 103 +2024-01-17 02:41:04,860 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:04,950 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:04,951 (beam_search:476) INFO: -3.63 * 1.0 = -3.63 for ctc +2024-01-17 02:41:04,951 (beam_search:479) INFO: total log probability: -3.63 +2024-01-17 02:41:04,951 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 02:41:04,951 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:04,951 (beam_search:483) INFO: best hypo: maclkuranotamabotesugoinie + +2024-01-17 02:41:04,952 (asr_inference:494) INFO: speech length: 85248 +2024-01-17 02:41:04,963 (beam_search:428) INFO: decoder input length: 131 +2024-01-17 02:41:04,963 (beam_search:429) INFO: max output length: 131 +2024-01-17 02:41:04,963 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:05,113 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:05,113 (beam_search:476) INFO: -5.81 * 1.0 = -5.81 for ctc +2024-01-17 02:41:05,113 (beam_search:479) INFO: total log probability: -5.81 +2024-01-17 02:41:05,113 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 02:41:05,113 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:05,113 (beam_search:483) INFO: best hypo: shohoomitainadokUshukaNsoobuNmokaita + +2024-01-17 02:41:05,114 (asr_inference:494) INFO: speech length: 128448 +2024-01-17 02:41:05,128 (beam_search:428) INFO: decoder input length: 198 +2024-01-17 02:41:05,128 (beam_search:429) INFO: max output length: 198 +2024-01-17 02:41:05,128 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:05,571 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:05,571 (beam_search:476) INFO: -10.31 * 1.0 = -10.31 for ctc +2024-01-17 02:41:05,571 (beam_search:479) INFO: total log probability: -10.31 +2024-01-17 02:41:05,571 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 02:41:05,571 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:05,571 (beam_search:483) INFO: best hypo: geNjitsunosekaiwatamooichitoshItekecltesuraidakatashuoacltosekaizunakeremanaranai + +2024-01-17 02:41:05,573 (asr_inference:494) INFO: speech length: 95040 +2024-01-17 02:41:05,584 (beam_search:428) INFO: decoder input length: 146 +2024-01-17 02:41:05,584 (beam_search:429) INFO: max output length: 146 +2024-01-17 02:41:05,584 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:05,775 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:05,775 (beam_search:476) INFO: -3.27 * 1.0 = -3.27 for ctc +2024-01-17 02:41:05,775 (beam_search:479) INFO: total log probability: -3.27 +2024-01-17 02:41:05,775 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:41:05,775 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:05,775 (beam_search:483) INFO: best hypo: shoohiNkeNsakugaokareasuitookaukiinarunai + +2024-01-17 02:41:05,777 (asr_inference:494) INFO: speech length: 80640 +2024-01-17 02:41:05,787 (beam_search:428) INFO: decoder input length: 123 +2024-01-17 02:41:05,787 (beam_search:429) INFO: max output length: 123 +2024-01-17 02:41:05,787 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:05,951 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:05,951 (beam_search:476) INFO: -6.54 * 1.0 = -6.54 for ctc +2024-01-17 02:41:05,951 (beam_search:479) INFO: total log probability: -6.54 +2024-01-17 02:41:05,951 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 02:41:05,951 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:05,951 (beam_search:483) INFO: best hypo: tsishIkiwapaurekIshIteclkateerdenakeremanaranai + +2024-01-17 02:41:05,952 (asr_inference:494) INFO: speech length: 110016 +2024-01-17 02:41:05,965 (beam_search:428) INFO: decoder input length: 169 +2024-01-17 02:41:05,965 (beam_search:429) INFO: max output length: 169 +2024-01-17 02:41:05,965 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:06,210 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:06,210 (beam_search:476) INFO: -4.90 * 1.0 = -4.90 for ctc +2024-01-17 02:41:06,210 (beam_search:479) INFO: total log probability: -4.90 +2024-01-17 02:41:06,210 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:41:06,210 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:06,210 (beam_search:483) INFO: best hypo: monogotonNjiNpaNkaerudakedeumakuikUkotomowaru + +2024-01-17 02:41:06,211 (asr_inference:494) INFO: speech length: 67968 +2024-01-17 02:41:06,221 (beam_search:428) INFO: decoder input length: 104 +2024-01-17 02:41:06,221 (beam_search:429) INFO: max output length: 104 +2024-01-17 02:41:06,221 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:06,339 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:06,339 (beam_search:476) INFO: -3.98 * 1.0 = -3.98 for ctc +2024-01-17 02:41:06,339 (beam_search:479) INFO: total log probability: -3.98 +2024-01-17 02:41:06,339 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:41:06,339 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:06,340 (beam_search:483) INFO: best hypo: konokiseetsuwakatsuonosashimigazeclpiN + +2024-01-17 02:41:06,341 (asr_inference:494) INFO: speech length: 84096 +2024-01-17 02:41:06,351 (beam_search:428) INFO: decoder input length: 129 +2024-01-17 02:41:06,351 (beam_search:429) INFO: max output length: 129 +2024-01-17 02:41:06,351 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:06,530 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:06,531 (beam_search:476) INFO: -7.42 * 1.0 = -7.42 for ctc +2024-01-17 02:41:06,531 (beam_search:479) INFO: total log probability: -7.42 +2024-01-17 02:41:06,531 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 02:41:06,531 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:06,531 (beam_search:483) INFO: best hypo: kakenishiclpaishItemoochtsUtsuitesaNshItsuoukeideru + +2024-01-17 02:41:06,532 (asr_inference:494) INFO: speech length: 88128 +2024-01-17 02:41:06,543 (beam_search:428) INFO: decoder input length: 135 +2024-01-17 02:41:06,543 (beam_search:429) INFO: max output length: 135 +2024-01-17 02:41:06,543 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:06,734 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:06,734 (beam_search:476) INFO: -4.76 * 1.0 = -4.76 for ctc +2024-01-17 02:41:06,734 (beam_search:479) INFO: total log probability: -4.76 +2024-01-17 02:41:06,734 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:41:06,734 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:06,734 (beam_search:483) INFO: best hypo: soreyuenitetsugapugazeNtainogakodearutosurewa + +2024-01-17 02:41:06,735 (asr_inference:494) INFO: speech length: 79488 +2024-01-17 02:41:06,745 (beam_search:428) INFO: decoder input length: 122 +2024-01-17 02:41:06,745 (beam_search:429) INFO: max output length: 122 +2024-01-17 02:41:06,745 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:06,888 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:06,889 (beam_search:476) INFO: -4.55 * 1.0 = -4.55 for ctc +2024-01-17 02:41:06,889 (beam_search:479) INFO: total log probability: -4.55 +2024-01-17 02:41:06,889 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 02:41:06,889 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:06,889 (beam_search:483) INFO: best hypo: kiizanayaoyadagayesUkUtehaNjoshIteru + +2024-01-17 02:41:06,890 (asr_inference:494) INFO: speech length: 103680 +2024-01-17 02:41:06,902 (beam_search:428) INFO: decoder input length: 159 +2024-01-17 02:41:06,902 (beam_search:429) INFO: max output length: 159 +2024-01-17 02:41:06,902 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:07,202 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:07,202 (beam_search:476) INFO: -9.40 * 1.0 = -9.40 for ctc +2024-01-17 02:41:07,202 (beam_search:479) INFO: total log probability: -9.40 +2024-01-17 02:41:07,202 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 02:41:07,202 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:07,202 (beam_search:483) INFO: best hypo: inifuragaakinohuzeNniyochiicltepaukokugaiedashUtsusuruhItomodetekita + +2024-01-17 02:41:07,204 (asr_inference:494) INFO: speech length: 126720 +2024-01-17 02:41:07,217 (beam_search:428) INFO: decoder input length: 195 +2024-01-17 02:41:07,217 (beam_search:429) INFO: max output length: 195 +2024-01-17 02:41:07,217 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:07,644 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:07,644 (beam_search:476) INFO: -13.12 * 1.0 = -13.12 for ctc +2024-01-17 02:41:07,644 (beam_search:479) INFO: total log probability: -13.12 +2024-01-17 02:41:07,644 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 02:41:07,644 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:07,645 (beam_search:483) INFO: best hypo: tsuginikagakuwasoNzayopaushujunodooekiniwakacltesorezuraNryooukiNzitIkeNikIsuru + +2024-01-17 02:41:07,646 (asr_inference:494) INFO: speech length: 114048 +2024-01-17 02:41:07,659 (beam_search:428) INFO: decoder input length: 176 +2024-01-17 02:41:07,659 (beam_search:429) INFO: max output length: 176 +2024-01-17 02:41:07,659 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:08,019 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:08,019 (beam_search:476) INFO: -7.51 * 1.0 = -7.51 for ctc +2024-01-17 02:41:08,019 (beam_search:479) INFO: total log probability: -7.51 +2024-01-17 02:41:08,019 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:41:08,019 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:08,019 (beam_search:483) INFO: best hypo: soredewapautoktoiumononoseritsushiowanakupaushuNkaNtoimonomonakuarunodearu + +2024-01-17 02:41:08,021 (asr_inference:494) INFO: speech length: 100800 +2024-01-17 02:41:08,032 (beam_search:428) INFO: decoder input length: 155 +2024-01-17 02:41:08,032 (beam_search:429) INFO: max output length: 155 +2024-01-17 02:41:08,032 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:08,265 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:08,265 (beam_search:476) INFO: -3.39 * 1.0 = -3.39 for ctc +2024-01-17 02:41:08,265 (beam_search:479) INFO: total log probability: -3.39 +2024-01-17 02:41:08,265 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:41:08,265 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:08,265 (beam_search:483) INFO: best hypo: akaiburaNkohoNkuritoseenosuberidaikawaitasunaba + +2024-01-17 02:41:08,267 (asr_inference:494) INFO: speech length: 115776 +2024-01-17 02:41:08,279 (beam_search:428) INFO: decoder input length: 178 +2024-01-17 02:41:08,279 (beam_search:429) INFO: max output length: 178 +2024-01-17 02:41:08,279 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:08,679 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:08,679 (beam_search:476) INFO: -8.76 * 1.0 = -8.76 for ctc +2024-01-17 02:41:08,679 (beam_search:479) INFO: total log probability: -8.76 +2024-01-17 02:41:08,679 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:41:08,679 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:08,679 (beam_search:483) INFO: best hypo: shIkashisorewadokomademNkUkokararitepaupokoekaerikuruseshItsomotomodenakebanaranai + +2024-01-17 02:41:08,680 (asr_inference:494) INFO: speech length: 98496 +2024-01-17 02:41:08,692 (beam_search:428) INFO: decoder input length: 151 +2024-01-17 02:41:08,692 (beam_search:429) INFO: max output length: 151 +2024-01-17 02:41:08,692 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:08,938 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:08,938 (beam_search:476) INFO: -3.33 * 1.0 = -3.33 for ctc +2024-01-17 02:41:08,938 (beam_search:479) INFO: total log probability: -3.33 +2024-01-17 02:41:08,938 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:41:08,938 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:08,938 (beam_search:483) INFO: best hypo: aritowarairudemoomakIchirashItepaumiNnekarauramiokaclteru + +2024-01-17 02:41:08,940 (asr_inference:494) INFO: speech length: 89856 +2024-01-17 02:41:08,950 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 02:41:08,950 (beam_search:429) INFO: max output length: 138 +2024-01-17 02:41:08,951 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:09,124 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:09,124 (beam_search:476) INFO: -2.24 * 1.0 = -2.24 for ctc +2024-01-17 02:41:09,124 (beam_search:479) INFO: total log probability: -2.24 +2024-01-17 02:41:09,124 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:41:09,124 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:09,124 (beam_search:483) INFO: best hypo: konoclteedosawagininarukotomonainodaroo + +2024-01-17 02:41:09,125 (asr_inference:494) INFO: speech length: 81216 +2024-01-17 02:41:09,136 (beam_search:428) INFO: decoder input length: 124 +2024-01-17 02:41:09,136 (beam_search:429) INFO: max output length: 124 +2024-01-17 02:41:09,136 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:09,233 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:09,233 (beam_search:476) INFO: -4.59 * 1.0 = -4.59 for ctc +2024-01-17 02:41:09,233 (beam_search:479) INFO: total log probability: -4.59 +2024-01-17 02:41:09,233 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 02:41:09,233 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:09,233 (beam_search:483) INFO: best hypo: kononedaNdewurichaNkaaN + +2024-01-17 02:41:09,234 (asr_inference:494) INFO: speech length: 82368 +2024-01-17 02:41:09,245 (beam_search:428) INFO: decoder input length: 126 +2024-01-17 02:41:09,245 (beam_search:429) INFO: max output length: 126 +2024-01-17 02:41:09,245 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:09,378 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:09,379 (beam_search:476) INFO: -2.17 * 1.0 = -2.17 for ctc +2024-01-17 02:41:09,379 (beam_search:479) INFO: total log probability: -2.17 +2024-01-17 02:41:09,379 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:41:09,379 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:09,379 (beam_search:483) INFO: best hypo: hinokageNnichuishinaitosugukogeru + +2024-01-17 02:41:09,380 (asr_inference:494) INFO: speech length: 141696 +2024-01-17 02:41:09,394 (beam_search:428) INFO: decoder input length: 219 +2024-01-17 02:41:09,394 (beam_search:429) INFO: max output length: 219 +2024-01-17 02:41:09,394 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:09,883 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:09,883 (beam_search:476) INFO: -6.00 * 1.0 = -6.00 for ctc +2024-01-17 02:41:09,883 (beam_search:479) INFO: total log probability: -6.00 +2024-01-17 02:41:09,883 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:41:09,883 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:09,883 (beam_search:483) INFO: best hypo: eNmaNdowenipotsuritochiisanaanagaitasaishowatsumayoojiteedonochiisanaanadaclta + +2024-01-17 02:41:09,885 (asr_inference:494) INFO: speech length: 133056 +2024-01-17 02:41:09,898 (beam_search:428) INFO: decoder input length: 205 +2024-01-17 02:41:09,898 (beam_search:429) INFO: max output length: 205 +2024-01-17 02:41:09,898 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:10,413 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:10,413 (beam_search:476) INFO: -7.75 * 1.0 = -7.75 for ctc +2024-01-17 02:41:10,413 (beam_search:479) INFO: total log probability: -7.75 +2024-01-17 02:41:10,413 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:41:10,413 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:10,413 (beam_search:483) INFO: best hypo: sorewapauarewareoikashinagarawarewareotoreekasurunodearupauwarebarenotamashiiokorosunodearu + +2024-01-17 02:41:10,415 (asr_inference:494) INFO: speech length: 145728 +2024-01-17 02:41:10,430 (beam_search:428) INFO: decoder input length: 225 +2024-01-17 02:41:10,430 (beam_search:429) INFO: max output length: 225 +2024-01-17 02:41:10,430 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:11,073 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:11,073 (beam_search:476) INFO: -9.07 * 1.0 = -9.07 for ctc +2024-01-17 02:41:11,073 (beam_search:479) INFO: total log probability: -9.07 +2024-01-17 02:41:11,073 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:41:11,073 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:11,074 (beam_search:483) INFO: best hypo: rekIshItekiniataerartamonowadecltaimujuNtekijigotooitsutekigeNzainoitesUkaihItekiniataeraretamonotoshIti + +2024-01-17 02:41:11,075 (asr_inference:494) INFO: speech length: 88128 +2024-01-17 02:41:11,085 (beam_search:428) INFO: decoder input length: 135 +2024-01-17 02:41:11,085 (beam_search:429) INFO: max output length: 135 +2024-01-17 02:41:11,086 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:11,307 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:11,307 (beam_search:476) INFO: -4.16 * 1.0 = -4.16 for ctc +2024-01-17 02:41:11,307 (beam_search:479) INFO: total log probability: -4.16 +2024-01-17 02:41:11,307 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:41:11,307 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:11,308 (beam_search:483) INFO: best hypo: mujuNtekijigodooitsUtoshItepauitsumokonosekainichooeshtkidea + +2024-01-17 02:41:11,309 (asr_inference:494) INFO: speech length: 112896 +2024-01-17 02:41:11,321 (beam_search:428) INFO: decoder input length: 174 +2024-01-17 02:41:11,321 (beam_search:429) INFO: max output length: 174 +2024-01-17 02:41:11,321 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:11,724 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:11,724 (beam_search:476) INFO: -11.82 * 1.0 = -11.82 for ctc +2024-01-17 02:41:11,724 (beam_search:479) INFO: total log probability: -11.82 +2024-01-17 02:41:11,724 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 02:41:11,724 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:11,724 (beam_search:483) INFO: best hypo: yuunizecltaemujuNtekijigodooitsUtoshItegeNsaikarageNzaetobokiikusekainogeNzainoite + +2024-01-17 02:41:11,725 (asr_inference:494) INFO: speech length: 60480 +2024-01-17 02:41:11,734 (beam_search:428) INFO: decoder input length: 92 +2024-01-17 02:41:11,734 (beam_search:429) INFO: max output length: 92 +2024-01-17 02:41:11,734 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:11,824 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:11,825 (beam_search:476) INFO: -3.56 * 1.0 = -3.56 for ctc +2024-01-17 02:41:11,825 (beam_search:479) INFO: total log probability: -3.56 +2024-01-17 02:41:11,825 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 02:41:11,825 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:11,825 (beam_search:483) INFO: best hypo: harepauwotaNoshItomNdashudekinai + +2024-01-17 02:41:11,826 (asr_inference:494) INFO: speech length: 78336 +2024-01-17 02:41:11,836 (beam_search:428) INFO: decoder input length: 120 +2024-01-17 02:41:11,836 (beam_search:429) INFO: max output length: 120 +2024-01-17 02:41:11,836 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:12,019 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:12,019 (beam_search:476) INFO: -2.07 * 1.0 = -2.07 for ctc +2024-01-17 02:41:12,019 (beam_search:479) INFO: total log probability: -2.07 +2024-01-17 02:41:12,019 (beam_search:480) INFO: normalized log probability: -0.04 +2024-01-17 02:41:12,019 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:12,019 (beam_search:483) INFO: best hypo: shIkashiwatashiasokonisekainojikodooitsuokunodewanai + +2024-01-17 02:41:12,021 (asr_inference:494) INFO: speech length: 56448 +2024-01-17 02:41:12,030 (beam_search:428) INFO: decoder input length: 86 +2024-01-17 02:41:12,030 (beam_search:429) INFO: max output length: 86 +2024-01-17 02:41:12,030 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:12,111 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:12,111 (beam_search:476) INFO: -2.24 * 1.0 = -2.24 for ctc +2024-01-17 02:41:12,111 (beam_search:479) INFO: total log probability: -2.24 +2024-01-17 02:41:12,111 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:41:12,111 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:12,111 (beam_search:483) INFO: best hypo: nebekakunarunogahayakunaclta + +2024-01-17 02:41:12,112 (asr_inference:494) INFO: speech length: 139968 +2024-01-17 02:41:12,127 (beam_search:428) INFO: decoder input length: 216 +2024-01-17 02:41:12,127 (beam_search:429) INFO: max output length: 216 +2024-01-17 02:41:12,127 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:12,691 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:12,691 (beam_search:476) INFO: -6.17 * 1.0 = -6.17 for ctc +2024-01-17 02:41:12,691 (beam_search:479) INFO: total log probability: -6.17 +2024-01-17 02:41:12,691 (beam_search:480) INFO: normalized log probability: -0.07 +2024-01-17 02:41:12,691 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:12,692 (beam_search:483) INFO: best hypo: watashiwaiNgeNnoorekIshItekIkeeseenotachibakaragejutsuomirunodeaclteooshakaradeNshaomirunodewanai + +# Accounting: time=17 threads=1 +# Ended (code 0) at Wed Jan 17 02:41:13 CST 2024, elapsed time 17 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.3.log b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.3.log new file mode 100644 index 0000000000000000000000000000000000000000..70d20458ee8cd4e3db3f4a2c834fe3c6fa2de98e --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.3.log @@ -0,0 +1,459 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3 --config conf/decode_asr.yaml +# Started at Wed Jan 17 02:41:13 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.3.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3 --config conf/decode_asr.yaml +2024-01-17 02:41:14,524 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml +2024-01-17 02:41:14,542 (asr:523) INFO: Vocabulary size: 41 +2024-01-17 02:41:14,604 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 02:41:14,604 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 02:41:14,715 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 02:41:16,007 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 02:41:17,229 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 02:41:17,229 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 02:41:17,229 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 02:41:17,262 (asr_inference:446) INFO: Text tokenizer: WordTokenizer(delimiter="None") +2024-01-17 02:41:17,337 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 02:41:17,450 (asr_inference:494) INFO: speech length: 62208 +2024-01-17 02:41:18,678 (beam_search:428) INFO: decoder input length: 95 +2024-01-17 02:41:18,678 (beam_search:429) INFO: max output length: 95 +2024-01-17 02:41:18,678 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:18,778 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:18,778 (beam_search:476) INFO: -4.30 * 1.0 = -4.30 for ctc +2024-01-17 02:41:18,778 (beam_search:479) INFO: total log probability: -4.30 +2024-01-17 02:41:18,779 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 02:41:18,779 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:18,779 (beam_search:483) INFO: best hypo: aoitomatoshIkanakUtekaukabmaiyo + +2024-01-17 02:41:18,803 (asr_inference:494) INFO: speech length: 69696 +2024-01-17 02:41:18,815 (beam_search:428) INFO: decoder input length: 106 +2024-01-17 02:41:18,815 (beam_search:429) INFO: max output length: 106 +2024-01-17 02:41:18,815 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:18,937 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:18,937 (beam_search:476) INFO: -6.66 * 1.0 = -6.66 for ctc +2024-01-17 02:41:18,937 (beam_search:479) INFO: total log probability: -6.66 +2024-01-17 02:41:18,937 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 02:41:18,937 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:18,937 (beam_search:483) INFO: best hypo: siNkizuigyooniookinakItayoyosUteiru + +2024-01-17 02:41:18,938 (asr_inference:494) INFO: speech length: 72576 +2024-01-17 02:41:18,948 (beam_search:428) INFO: decoder input length: 111 +2024-01-17 02:41:18,948 (beam_search:429) INFO: max output length: 111 +2024-01-17 02:41:18,948 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:19,105 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:19,105 (beam_search:476) INFO: -2.74 * 1.0 = -2.74 for ctc +2024-01-17 02:41:19,105 (beam_search:479) INFO: total log probability: -2.74 +2024-01-17 02:41:19,105 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:41:19,105 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:19,105 (beam_search:483) INFO: best hypo: nanikashiranoiniNseNtibuwanaitokibishiinodewa + +2024-01-17 02:41:19,107 (asr_inference:494) INFO: speech length: 118656 +2024-01-17 02:41:19,119 (beam_search:428) INFO: decoder input length: 183 +2024-01-17 02:41:19,119 (beam_search:429) INFO: max output length: 183 +2024-01-17 02:41:19,119 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:19,346 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:19,346 (beam_search:476) INFO: -4.46 * 1.0 = -4.46 for ctc +2024-01-17 02:41:19,346 (beam_search:479) INFO: total log probability: -4.46 +2024-01-17 02:41:19,346 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:41:19,346 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:19,346 (beam_search:483) INFO: best hypo: jikoNsheegeNnoibeNtodesUtorushItamarubu + +2024-01-17 02:41:19,347 (asr_inference:494) INFO: speech length: 66240 +2024-01-17 02:41:19,357 (beam_search:428) INFO: decoder input length: 101 +2024-01-17 02:41:19,357 (beam_search:429) INFO: max output length: 101 +2024-01-17 02:41:19,357 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:19,455 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:19,455 (beam_search:476) INFO: -3.05 * 1.0 = -3.05 for ctc +2024-01-17 02:41:19,455 (beam_search:479) INFO: total log probability: -3.05 +2024-01-17 02:41:19,455 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:41:19,455 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:19,455 (beam_search:483) INFO: best hypo: maarinohItowaboozeNtoshIteita + +2024-01-17 02:41:19,456 (asr_inference:494) INFO: speech length: 72576 +2024-01-17 02:41:19,466 (beam_search:428) INFO: decoder input length: 111 +2024-01-17 02:41:19,466 (beam_search:429) INFO: max output length: 111 +2024-01-17 02:41:19,466 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:19,600 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:19,600 (beam_search:476) INFO: -3.29 * 1.0 = -3.29 for ctc +2024-01-17 02:41:19,600 (beam_search:479) INFO: total log probability: -3.29 +2024-01-17 02:41:19,600 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:41:19,601 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:19,601 (beam_search:483) INFO: best hypo: soNnanaiyoonomeerewanaNkeNmokUIteita + +2024-01-17 02:41:19,602 (asr_inference:494) INFO: speech length: 56448 +2024-01-17 02:41:19,611 (beam_search:428) INFO: decoder input length: 86 +2024-01-17 02:41:19,611 (beam_search:429) INFO: max output length: 86 +2024-01-17 02:41:19,611 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:19,678 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:19,678 (beam_search:476) INFO: -3.89 * 1.0 = -3.89 for ctc +2024-01-17 02:41:19,678 (beam_search:479) INFO: total log probability: -3.89 +2024-01-17 02:41:19,678 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 02:41:19,678 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:19,678 (beam_search:483) INFO: best hypo: njikaidrdeesuishIteita + +2024-01-17 02:41:19,679 (asr_inference:494) INFO: speech length: 129600 +2024-01-17 02:41:19,692 (beam_search:428) INFO: decoder input length: 200 +2024-01-17 02:41:19,692 (beam_search:429) INFO: max output length: 200 +2024-01-17 02:41:19,693 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:20,177 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:20,178 (beam_search:476) INFO: -10.25 * 1.0 = -10.25 for ctc +2024-01-17 02:41:20,178 (beam_search:479) INFO: total log probability: -10.25 +2024-01-17 02:41:20,178 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 02:41:20,178 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:20,178 (beam_search:483) INFO: best hypo: tokidokichiuNnokorowawakaranakunorutokigaarudakarabokuwakanioclkinootonikachiajimeru + +2024-01-17 02:41:20,179 (asr_inference:494) INFO: speech length: 58176 +2024-01-17 02:41:20,188 (beam_search:428) INFO: decoder input length: 88 +2024-01-17 02:41:20,188 (beam_search:429) INFO: max output length: 88 +2024-01-17 02:41:20,189 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:20,245 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:20,246 (beam_search:476) INFO: -2.65 * 1.0 = -2.65 for ctc +2024-01-17 02:41:20,246 (beam_search:479) INFO: total log probability: -2.65 +2024-01-17 02:41:20,246 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 02:41:20,246 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:20,246 (beam_search:483) INFO: best hypo: moonigetechatameda + +2024-01-17 02:41:20,247 (asr_inference:494) INFO: speech length: 69120 +2024-01-17 02:41:20,257 (beam_search:428) INFO: decoder input length: 105 +2024-01-17 02:41:20,257 (beam_search:429) INFO: max output length: 105 +2024-01-17 02:41:20,257 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:20,353 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:20,353 (beam_search:476) INFO: -2.35 * 1.0 = -2.35 for ctc +2024-01-17 02:41:20,353 (beam_search:479) INFO: total log probability: -2.35 +2024-01-17 02:41:20,353 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:41:20,353 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:20,354 (beam_search:483) INFO: best hypo: karewapoototajitsUkushiteita + +2024-01-17 02:41:20,355 (asr_inference:494) INFO: speech length: 62208 +2024-01-17 02:41:20,364 (beam_search:428) INFO: decoder input length: 95 +2024-01-17 02:41:20,364 (beam_search:429) INFO: max output length: 95 +2024-01-17 02:41:20,364 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:20,460 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:20,461 (beam_search:476) INFO: -4.05 * 1.0 = -4.05 for ctc +2024-01-17 02:41:20,461 (beam_search:479) INFO: total log probability: -4.05 +2024-01-17 02:41:20,461 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 02:41:20,461 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:20,461 (beam_search:483) INFO: best hypo: daraunimomeiwakowakaketakunai + +2024-01-17 02:41:20,462 (asr_inference:494) INFO: speech length: 77760 +2024-01-17 02:41:20,472 (beam_search:428) INFO: decoder input length: 119 +2024-01-17 02:41:20,472 (beam_search:429) INFO: max output length: 119 +2024-01-17 02:41:20,472 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:20,607 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:20,607 (beam_search:476) INFO: -5.47 * 1.0 = -5.47 for ctc +2024-01-17 02:41:20,607 (beam_search:479) INFO: total log probability: -5.47 +2024-01-17 02:41:20,607 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 02:41:20,607 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:20,607 (beam_search:483) INFO: best hypo: wasakatoomotetowanootoclteonigeclta + +2024-01-17 02:41:20,608 (asr_inference:494) INFO: speech length: 43200 +2024-01-17 02:41:20,617 (beam_search:428) INFO: decoder input length: 65 +2024-01-17 02:41:20,617 (beam_search:429) INFO: max output length: 65 +2024-01-17 02:41:20,617 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:20,642 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:20,642 (beam_search:476) INFO: -1.67 * 1.0 = -1.67 for ctc +2024-01-17 02:41:20,642 (beam_search:479) INFO: total log probability: -1.67 +2024-01-17 02:41:20,642 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 02:41:20,642 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:20,642 (beam_search:483) INFO: best hypo: shteimaseN + +2024-01-17 02:41:20,643 (asr_inference:494) INFO: speech length: 104256 +2024-01-17 02:41:20,655 (beam_search:428) INFO: decoder input length: 160 +2024-01-17 02:41:20,655 (beam_search:429) INFO: max output length: 160 +2024-01-17 02:41:20,655 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:20,989 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:20,989 (beam_search:476) INFO: -3.80 * 1.0 = -3.80 for ctc +2024-01-17 02:41:20,989 (beam_search:479) INFO: total log probability: -3.80 +2024-01-17 02:41:20,989 (beam_search:480) INFO: normalized log probability: -0.05 +2024-01-17 02:41:20,989 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:20,989 (beam_search:483) INFO: best hypo: kayoonishIteshIclteirutotomonishiclteinaitokorokarataNkyuuwahajimarunodearu + +2024-01-17 02:41:20,990 (asr_inference:494) INFO: speech length: 54720 +2024-01-17 02:41:20,999 (beam_search:428) INFO: decoder input length: 83 +2024-01-17 02:41:20,999 (beam_search:429) INFO: max output length: 83 +2024-01-17 02:41:20,999 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:21,050 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:21,050 (beam_search:476) INFO: -1.53 * 1.0 = -1.53 for ctc +2024-01-17 02:41:21,050 (beam_search:479) INFO: total log probability: -1.53 +2024-01-17 02:41:21,050 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:41:21,050 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:21,051 (beam_search:483) INFO: best hypo: aisauwadaijidaio + +2024-01-17 02:41:21,052 (asr_inference:494) INFO: speech length: 89856 +2024-01-17 02:41:21,062 (beam_search:428) INFO: decoder input length: 138 +2024-01-17 02:41:21,062 (beam_search:429) INFO: max output length: 138 +2024-01-17 02:41:21,063 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:21,304 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:21,304 (beam_search:476) INFO: -1.97 * 1.0 = -1.97 for ctc +2024-01-17 02:41:21,304 (beam_search:479) INFO: total log probability: -1.97 +2024-01-17 02:41:21,304 (beam_search:480) INFO: normalized log probability: -0.03 +2024-01-17 02:41:21,304 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:21,304 (beam_search:483) INFO: best hypo: tojitamonoikanihirogetemohiraitamononianararotoiclteiruga + +2024-01-17 02:41:21,306 (asr_inference:494) INFO: speech length: 63360 +2024-01-17 02:41:21,315 (beam_search:428) INFO: decoder input length: 96 +2024-01-17 02:41:21,315 (beam_search:429) INFO: max output length: 96 +2024-01-17 02:41:21,315 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:21,407 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:21,407 (beam_search:476) INFO: -2.55 * 1.0 = -2.55 for ctc +2024-01-17 02:41:21,407 (beam_search:479) INFO: total log probability: -2.55 +2024-01-17 02:41:21,407 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:41:21,407 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:21,407 (beam_search:483) INFO: best hypo: tameshiniikutsUkatsUkucltemiyoa + +2024-01-17 02:41:21,408 (asr_inference:494) INFO: speech length: 69120 +2024-01-17 02:41:21,417 (beam_search:428) INFO: decoder input length: 105 +2024-01-17 02:41:21,417 (beam_search:429) INFO: max output length: 105 +2024-01-17 02:41:21,417 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:21,511 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:21,511 (beam_search:476) INFO: -1.58 * 1.0 = -1.58 for ctc +2024-01-17 02:41:21,511 (beam_search:479) INFO: total log probability: -1.58 +2024-01-17 02:41:21,511 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:41:21,511 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:21,511 (beam_search:483) INFO: best hypo: zuibuNakoginashoobaidaiona + +2024-01-17 02:41:21,512 (asr_inference:494) INFO: speech length: 40704 +2024-01-17 02:41:21,520 (beam_search:428) INFO: decoder input length: 61 +2024-01-17 02:41:21,520 (beam_search:429) INFO: max output length: 61 +2024-01-17 02:41:21,520 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:21,535 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:21,535 (beam_search:476) INFO: -2.14 * 1.0 = -2.14 for ctc +2024-01-17 02:41:21,535 (beam_search:479) INFO: total log probability: -2.14 +2024-01-17 02:41:21,535 (beam_search:480) INFO: normalized log probability: -0.31 +2024-01-17 02:41:21,535 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:21,535 (beam_search:483) INFO: best hypo: watei + +2024-01-17 02:41:21,536 (asr_inference:494) INFO: speech length: 65664 +2024-01-17 02:41:21,546 (beam_search:428) INFO: decoder input length: 100 +2024-01-17 02:41:21,546 (beam_search:429) INFO: max output length: 100 +2024-01-17 02:41:21,546 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:21,561 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:21,561 (beam_search:476) INFO: -0.79 * 1.0 = -0.79 for ctc +2024-01-17 02:41:21,561 (beam_search:479) INFO: total log probability: -0.79 +2024-01-17 02:41:21,561 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 02:41:21,561 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:21,561 (beam_search:483) INFO: best hypo: ichi + +2024-01-17 02:41:21,562 (asr_inference:494) INFO: speech length: 47616 +2024-01-17 02:41:21,570 (beam_search:428) INFO: decoder input length: 72 +2024-01-17 02:41:21,571 (beam_search:429) INFO: max output length: 72 +2024-01-17 02:41:21,571 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:21,579 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:21,579 (beam_search:476) INFO: -0.38 * 1.0 = -0.38 for ctc +2024-01-17 02:41:21,579 (beam_search:479) INFO: total log probability: -0.38 +2024-01-17 02:41:21,579 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:41:21,579 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:21,579 (beam_search:483) INFO: best hypo: go + +2024-01-17 02:41:21,580 (asr_inference:494) INFO: speech length: 61824 +2024-01-17 02:41:21,589 (beam_search:428) INFO: decoder input length: 94 +2024-01-17 02:41:21,589 (beam_search:429) INFO: max output length: 94 +2024-01-17 02:41:21,589 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:21,607 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:21,607 (beam_search:476) INFO: -1.35 * 1.0 = -1.35 for ctc +2024-01-17 02:41:21,607 (beam_search:479) INFO: total log probability: -1.35 +2024-01-17 02:41:21,607 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 02:41:21,607 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:21,608 (beam_search:483) INFO: best hypo: shiki + +2024-01-17 02:41:21,609 (asr_inference:494) INFO: speech length: 45696 +2024-01-17 02:41:21,617 (beam_search:428) INFO: decoder input length: 69 +2024-01-17 02:41:21,617 (beam_search:429) INFO: max output length: 69 +2024-01-17 02:41:21,617 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:21,628 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:21,628 (beam_search:476) INFO: -1.05 * 1.0 = -1.05 for ctc +2024-01-17 02:41:21,628 (beam_search:479) INFO: total log probability: -1.05 +2024-01-17 02:41:21,628 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 02:41:21,628 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:21,628 (beam_search:483) INFO: best hypo: iie + +2024-01-17 02:41:21,629 (asr_inference:494) INFO: speech length: 38784 +2024-01-17 02:41:21,637 (beam_search:428) INFO: decoder input length: 58 +2024-01-17 02:41:21,637 (beam_search:429) INFO: max output length: 58 +2024-01-17 02:41:21,637 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:21,649 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:21,649 (beam_search:476) INFO: -1.38 * 1.0 = -1.38 for ctc +2024-01-17 02:41:21,649 (beam_search:479) INFO: total log probability: -1.38 +2024-01-17 02:41:21,649 (beam_search:480) INFO: normalized log probability: -0.23 +2024-01-17 02:41:21,649 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:21,649 (beam_search:483) INFO: best hypo: hachi + +2024-01-17 02:41:21,650 (asr_inference:494) INFO: speech length: 36096 +2024-01-17 02:41:21,658 (beam_search:428) INFO: decoder input length: 54 +2024-01-17 02:41:21,658 (beam_search:429) INFO: max output length: 54 +2024-01-17 02:41:21,658 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:21,667 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:21,667 (beam_search:476) INFO: -0.39 * 1.0 = -0.39 for ctc +2024-01-17 02:41:21,667 (beam_search:479) INFO: total log probability: -0.39 +2024-01-17 02:41:21,667 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:41:21,667 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:21,667 (beam_search:483) INFO: best hypo: nei + +2024-01-17 02:41:21,668 (asr_inference:494) INFO: speech length: 36864 +2024-01-17 02:41:21,676 (beam_search:428) INFO: decoder input length: 55 +2024-01-17 02:41:21,676 (beam_search:429) INFO: max output length: 55 +2024-01-17 02:41:21,676 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:21,685 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:21,685 (beam_search:476) INFO: -0.88 * 1.0 = -0.88 for ctc +2024-01-17 02:41:21,685 (beam_search:479) INFO: total log probability: -0.88 +2024-01-17 02:41:21,685 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 02:41:21,685 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:21,685 (beam_search:483) INFO: best hypo: shii + +2024-01-17 02:41:21,687 (asr_inference:494) INFO: speech length: 36864 +2024-01-17 02:41:21,694 (beam_search:428) INFO: decoder input length: 55 +2024-01-17 02:41:21,694 (beam_search:429) INFO: max output length: 55 +2024-01-17 02:41:21,694 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:21,701 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:21,701 (beam_search:476) INFO: -0.57 * 1.0 = -0.57 for ctc +2024-01-17 02:41:21,702 (beam_search:479) INFO: total log probability: -0.57 +2024-01-17 02:41:21,702 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 02:41:21,702 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:21,702 (beam_search:483) INFO: best hypo: ku + +2024-01-17 02:41:21,703 (asr_inference:494) INFO: speech length: 40704 +2024-01-17 02:41:21,710 (beam_search:428) INFO: decoder input length: 61 +2024-01-17 02:41:21,710 (beam_search:429) INFO: max output length: 61 +2024-01-17 02:41:21,710 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:21,720 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:21,720 (beam_search:476) INFO: -0.21 * 1.0 = -0.21 for ctc +2024-01-17 02:41:21,721 (beam_search:479) INFO: total log probability: -0.21 +2024-01-17 02:41:21,721 (beam_search:480) INFO: normalized log probability: -0.04 +2024-01-17 02:41:21,721 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:21,721 (beam_search:483) INFO: best hypo: ichi + +2024-01-17 02:41:21,721 (asr_inference:494) INFO: speech length: 114048 +2024-01-17 02:41:21,734 (beam_search:428) INFO: decoder input length: 176 +2024-01-17 02:41:21,734 (beam_search:429) INFO: max output length: 176 +2024-01-17 02:41:21,734 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:22,053 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:22,053 (beam_search:476) INFO: -3.27 * 1.0 = -3.27 for ctc +2024-01-17 02:41:22,053 (beam_search:479) INFO: total log probability: -3.27 +2024-01-17 02:41:22,053 (beam_search:480) INFO: normalized log probability: -0.06 +2024-01-17 02:41:22,053 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:22,053 (beam_search:483) INFO: best hypo: kakakugaakirakanisurupaukyaclkaNtekIshiNrinishItagaukotoniyoclte + +2024-01-17 02:41:22,055 (asr_inference:494) INFO: speech length: 154368 +2024-01-17 02:41:22,070 (beam_search:428) INFO: decoder input length: 239 +2024-01-17 02:41:22,070 (beam_search:429) INFO: max output length: 239 +2024-01-17 02:41:22,070 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:22,618 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:22,618 (beam_search:476) INFO: -7.13 * 1.0 = -7.13 for ctc +2024-01-17 02:41:22,618 (beam_search:479) INFO: total log probability: -7.13 +2024-01-17 02:41:22,618 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:41:22,618 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:22,619 (beam_search:483) INFO: best hypo: kakotomiraitonomujuNtekijikodooitsutoshItenogeNzaigapaukatachiomoshUtoiukotodearu + +2024-01-17 02:41:22,620 (asr_inference:494) INFO: speech length: 150336 +2024-01-17 02:41:22,635 (beam_search:428) INFO: decoder input length: 232 +2024-01-17 02:41:22,635 (beam_search:429) INFO: max output length: 232 +2024-01-17 02:41:22,635 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:23,209 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:23,209 (beam_search:476) INFO: -6.39 * 1.0 = -6.39 for ctc +2024-01-17 02:41:23,209 (beam_search:479) INFO: total log probability: -6.39 +2024-01-17 02:41:23,209 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:41:23,209 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:23,210 (beam_search:483) INFO: best hypo: butsuritekIsekaiwapausuugakutekIkigooniyocltearawasarerupausuugakutekIkatachinoshekaidearu + +2024-01-17 02:41:23,211 (asr_inference:494) INFO: speech length: 56448 +2024-01-17 02:41:23,220 (beam_search:428) INFO: decoder input length: 86 +2024-01-17 02:41:23,220 (beam_search:429) INFO: max output length: 86 +2024-01-17 02:41:23,220 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:23,300 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:23,300 (beam_search:476) INFO: -2.97 * 1.0 = -2.97 for ctc +2024-01-17 02:41:23,300 (beam_search:479) INFO: total log probability: -2.97 +2024-01-17 02:41:23,300 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:41:23,300 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:23,300 (beam_search:483) INFO: best hypo: wonajigeNshoodesaNkooginaru + +2024-01-17 02:41:23,302 (asr_inference:494) INFO: speech length: 65088 +2024-01-17 02:41:23,311 (beam_search:428) INFO: decoder input length: 99 +2024-01-17 02:41:23,311 (beam_search:429) INFO: max output length: 99 +2024-01-17 02:41:23,311 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:23,426 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:23,426 (beam_search:476) INFO: -1.77 * 1.0 = -1.77 for ctc +2024-01-17 02:41:23,427 (beam_search:479) INFO: total log probability: -1.77 +2024-01-17 02:41:23,427 (beam_search:480) INFO: normalized log probability: -0.05 +2024-01-17 02:41:23,427 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:23,427 (beam_search:483) INFO: best hypo: gaikokukarakitamonodatoshicltebiclkuri + +2024-01-17 02:41:23,428 (asr_inference:494) INFO: speech length: 88128 +2024-01-17 02:41:23,439 (beam_search:428) INFO: decoder input length: 135 +2024-01-17 02:41:23,439 (beam_search:429) INFO: max output length: 135 +2024-01-17 02:41:23,439 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:23,641 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:23,641 (beam_search:476) INFO: -6.01 * 1.0 = -6.01 for ctc +2024-01-17 02:41:23,641 (beam_search:479) INFO: total log probability: -6.01 +2024-01-17 02:41:23,641 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 02:41:23,641 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:23,641 (beam_search:483) INFO: best hypo: iwayorujiclseNniyocltekakUtoUkUshikitacltamonodearu + +2024-01-17 02:41:23,642 (asr_inference:494) INFO: speech length: 133440 +2024-01-17 02:41:23,656 (beam_search:428) INFO: decoder input length: 206 +2024-01-17 02:41:23,656 (beam_search:429) INFO: max output length: 206 +2024-01-17 02:41:23,656 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:24,040 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:24,040 (beam_search:476) INFO: -8.37 * 1.0 = -8.37 for ctc +2024-01-17 02:41:24,040 (beam_search:479) INFO: total log probability: -8.37 +2024-01-17 02:41:24,040 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 02:41:24,040 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:24,041 (beam_search:483) INFO: best hypo: onajiyonidaNseewahizaoouzuboNohaclkotogapaugimuuzukerareteimasU + +2024-01-17 02:41:24,042 (asr_inference:494) INFO: speech length: 197760 +2024-01-17 02:41:24,060 (beam_search:428) INFO: decoder input length: 306 +2024-01-17 02:41:24,060 (beam_search:429) INFO: max output length: 306 +2024-01-17 02:41:24,060 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:25,061 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:25,061 (beam_search:476) INFO: -15.94 * 1.0 = -15.94 for ctc +2024-01-17 02:41:25,061 (beam_search:479) INFO: total log probability: -15.94 +2024-01-17 02:41:25,061 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 02:41:25,061 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:25,062 (beam_search:483) INFO: best hypo: konosaabisakorakUseewahajimetosuruseNpakuyaeNkakUchidepaudeetayaoNseohIsuotosurutaNgeNtainipauhiNclpaniriyoosareteimasU + +2024-01-17 02:41:25,063 (asr_inference:494) INFO: speech length: 205440 +2024-01-17 02:41:25,082 (beam_search:428) INFO: decoder input length: 318 +2024-01-17 02:41:25,082 (beam_search:429) INFO: max output length: 318 +2024-01-17 02:41:25,082 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:26,085 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:26,085 (beam_search:476) INFO: -19.79 * 1.0 = -19.79 for ctc +2024-01-17 02:41:26,085 (beam_search:479) INFO: total log probability: -19.79 +2024-01-17 02:41:26,085 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 02:41:26,085 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:26,086 (beam_search:483) INFO: best hypo: kyoofuuhyokatonokoosuirooobiamakashibaraiutatsumakiizukiobesaikurnodanokibishiikIshokeetayasonoeekyoniyorumonoresU + +2024-01-17 02:41:26,087 (asr_inference:494) INFO: speech length: 155520 +2024-01-17 02:41:26,103 (beam_search:428) INFO: decoder input length: 240 +2024-01-17 02:41:26,103 (beam_search:429) INFO: max output length: 240 +2024-01-17 02:41:26,103 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:26,679 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:26,679 (beam_search:476) INFO: -7.52 * 1.0 = -7.52 for ctc +2024-01-17 02:41:26,680 (beam_search:479) INFO: total log probability: -7.52 +2024-01-17 02:41:26,680 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:41:26,680 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:26,680 (beam_search:483) INFO: best hypo: iNtaanecltowamasUkomirikeeshuNtotaijiNkomiruikeeshoonoryooyosookanesunaetakaNkyoodesU + +2024-01-17 02:41:26,681 (asr_inference:494) INFO: speech length: 229440 +2024-01-17 02:41:26,702 (beam_search:428) INFO: decoder input length: 356 +2024-01-17 02:41:26,702 (beam_search:429) INFO: max output length: 356 +2024-01-17 02:41:26,702 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:27,863 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:27,863 (beam_search:476) INFO: -11.64 * 1.0 = -11.64 for ctc +2024-01-17 02:41:27,863 (beam_search:479) INFO: total log probability: -11.64 +2024-01-17 02:41:27,863 (beam_search:480) INFO: normalized log probability: -0.10 +2024-01-17 02:41:27,863 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:27,864 (beam_search:483) INFO: best hypo: kajinorewatsuujootokubesuraiNshIkoyaeNtaateimeNtoyooshIteimasUgesuwakibuiyokushIsesunainitoramaruyoorisurutamedesU + +2024-01-17 02:41:27,865 (asr_inference:494) INFO: speech length: 190080 +2024-01-17 02:41:27,883 (beam_search:428) INFO: decoder input length: 294 +2024-01-17 02:41:27,883 (beam_search:429) INFO: max output length: 294 +2024-01-17 02:41:27,883 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:28,716 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:28,716 (beam_search:476) INFO: -11.15 * 1.0 = -11.15 for ctc +2024-01-17 02:41:28,716 (beam_search:479) INFO: total log probability: -11.15 +2024-01-17 02:41:28,716 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 02:41:28,716 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:28,716 (beam_search:483) INFO: best hypo: shIkashipaukakuteNnobikecltooshinacltaadtoiNdowananatsunobikecltooshinaisaNjuurokurashIkadekimaseNdeshita + +# Accounting: time=16 threads=1 +# Ended (code 0) at Wed Jan 17 02:41:29 CST 2024, elapsed time 16 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.4.log b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.4.log new file mode 100644 index 0000000000000000000000000000000000000000..49f4cc42de14ef77fa223c38977c82b5d7719687 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/asr_inference.4.log @@ -0,0 +1,459 @@ +# python3 -m espnet2.bin.asr_inference --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4 --config conf/decode_asr.yaml +# Started at Wed Jan 17 02:41:29 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_inference.py --batch_size 1 --ngpu 1 --data_path_and_name_and_type dump/raw/test_10min_jpn/wav.scp,speech,sound --key_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.4.scp --asr_train_config test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml --asr_model_file test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4 --config conf/decode_asr.yaml +2024-01-17 02:41:30,554 (abs_task:2046) INFO: config file: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml +2024-01-17 02:41:30,572 (asr:523) INFO: Vocabulary size: 41 +2024-01-17 02:41:30,634 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +2024-01-17 02:41:30,634 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +2024-01-17 02:41:30,745 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +2024-01-17 02:41:32,026 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +2024-01-17 02:41:33,249 (asr_inference:353) INFO: BatchBeamSearch implementation is selected. +2024-01-17 02:41:33,249 (asr_inference:364) INFO: Beam_search: BatchBeamSearch( + (nn_dict): ModuleDict() +) +2024-01-17 02:41:33,249 (asr_inference:365) INFO: Decoding device=cuda, dtype=float32 +2024-01-17 02:41:33,282 (asr_inference:446) INFO: Text tokenizer: WordTokenizer(delimiter="None") +2024-01-17 02:41:33,357 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +2024-01-17 02:41:33,473 (asr_inference:494) INFO: speech length: 208320 +2024-01-17 02:41:34,694 (beam_search:428) INFO: decoder input length: 323 +2024-01-17 02:41:34,694 (beam_search:429) INFO: max output length: 323 +2024-01-17 02:41:34,694 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:35,768 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:35,768 (beam_search:476) INFO: -13.93 * 1.0 = -13.93 for ctc +2024-01-17 02:41:35,768 (beam_search:479) INFO: total log probability: -13.93 +2024-01-17 02:41:35,769 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 02:41:35,769 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:35,769 (beam_search:483) INFO: best hypo: hookuraNdonokooshIketsukawahookuraNdoshotookoNdoefUkeepiideichipoNnogaichieeboNdojiipiibiitotookanikoteesareteimasU + +2024-01-17 02:41:35,793 (asr_inference:494) INFO: speech length: 193920 +2024-01-17 02:41:35,812 (beam_search:428) INFO: decoder input length: 300 +2024-01-17 02:41:35,812 (beam_search:429) INFO: max output length: 300 +2024-01-17 02:41:35,812 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:36,840 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:36,840 (beam_search:476) INFO: -19.96 * 1.0 = -19.96 for ctc +2024-01-17 02:41:36,840 (beam_search:479) INFO: total log probability: -19.96 +2024-01-17 02:41:36,840 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 02:41:36,840 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:36,841 (beam_search:483) INFO: best hypo: hashIshItaNnojoohookokaNwochjuugometorodesUniseNjuichinehachigatsunisekooshipauniseNjuunoneNnisaNgatsumanekaitsueshimaseNgdeshIta + +2024-01-17 02:41:36,842 (asr_inference:494) INFO: speech length: 168000 +2024-01-17 02:41:36,859 (beam_search:428) INFO: decoder input length: 260 +2024-01-17 02:41:36,859 (beam_search:429) INFO: max output length: 260 +2024-01-17 02:41:36,859 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:37,446 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:37,446 (beam_search:476) INFO: -10.39 * 1.0 = -10.39 for ctc +2024-01-17 02:41:37,446 (beam_search:479) INFO: total log probability: -10.39 +2024-01-17 02:41:37,446 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 02:41:37,446 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:37,446 (beam_search:483) INFO: best hypo: iclpuNkaNdehfucltosoguchiikimarebafucltoosurumademinaNmokakaruchiikimoarimasU + +2024-01-17 02:41:37,448 (asr_inference:494) INFO: speech length: 148800 +2024-01-17 02:41:37,463 (beam_search:428) INFO: decoder input length: 230 +2024-01-17 02:41:37,463 (beam_search:429) INFO: max output length: 230 +2024-01-17 02:41:37,463 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:38,081 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:38,081 (beam_search:476) INFO: -11.66 * 1.0 = -11.66 for ctc +2024-01-17 02:41:38,081 (beam_search:479) INFO: total log probability: -11.66 +2024-01-17 02:41:38,081 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 02:41:38,081 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:38,081 (beam_search:483) INFO: best hypo: piramicltanoototoshikainoshoowakonokaNkooshidetokunikoromoatashikatanoshimeremoyooshunohItosuresU + +2024-01-17 02:41:38,083 (asr_inference:494) INFO: speech length: 114240 +2024-01-17 02:41:38,095 (beam_search:428) INFO: decoder input length: 176 +2024-01-17 02:41:38,095 (beam_search:429) INFO: max output length: 176 +2024-01-17 02:41:38,095 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:38,376 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:38,376 (beam_search:476) INFO: -5.41 * 1.0 = -5.41 for ctc +2024-01-17 02:41:38,376 (beam_search:479) INFO: total log probability: -5.41 +2024-01-17 02:41:38,376 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:41:38,376 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:38,377 (beam_search:483) INFO: best hypo: sonotanetainidaderutoshItehookigatsuikasaregachidesU + +2024-01-17 02:41:38,378 (asr_inference:494) INFO: speech length: 262080 +2024-01-17 02:41:38,402 (beam_search:428) INFO: decoder input length: 407 +2024-01-17 02:41:38,402 (beam_search:429) INFO: max output length: 407 +2024-01-17 02:41:38,402 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:40,019 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:40,019 (beam_search:476) INFO: -22.74 * 1.0 = -22.74 for ctc +2024-01-17 02:41:40,019 (beam_search:479) INFO: total log probability: -22.74 +2024-01-17 02:41:40,019 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 02:41:40,019 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:40,020 (beam_search:483) INFO: best hypo: geNzuNsurukotogashitareteirunijiogoomainataNdacltupurootusaidowageNzuNsuratoogaebuNkenosaikonoushidesUtegakinyorugeNpoowageNzooshIteimoseN + +2024-01-17 02:41:40,022 (asr_inference:494) INFO: speech length: 218880 +2024-01-17 02:41:40,042 (beam_search:428) INFO: decoder input length: 339 +2024-01-17 02:41:40,042 (beam_search:429) INFO: max output length: 339 +2024-01-17 02:41:40,042 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:41,358 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:41,358 (beam_search:476) INFO: -19.84 * 1.0 = -19.84 for ctc +2024-01-17 02:41:41,358 (beam_search:479) INFO: total log probability: -19.84 +2024-01-17 02:41:41,358 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 02:41:41,358 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:41,358 (beam_search:483) INFO: best hypo: karemosesootadashitomitomerushItomimashItagaookuguhItoosunogekodepautaiyookedeataiotosinohokanohoshigapauchIkuunomareidooshIserutoshiNchiteimashIta + +2024-01-17 02:41:41,360 (asr_inference:494) INFO: speech length: 252480 +2024-01-17 02:41:41,383 (beam_search:428) INFO: decoder input length: 392 +2024-01-17 02:41:41,383 (beam_search:429) INFO: max output length: 392 +2024-01-17 02:41:41,383 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:42,995 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:42,995 (beam_search:476) INFO: -16.44 * 1.0 = -16.44 for ctc +2024-01-17 02:41:42,995 (beam_search:479) INFO: total log probability: -16.44 +2024-01-17 02:41:42,995 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 02:41:42,995 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:42,996 (beam_search:483) INFO: best hypo: chibecltomeesoochushiNwashiiseeiogadesUsamazananakamigamiyoshIkakUkasurukotodeenerugiichanerugashookasarechakuragakaseekasaresatoruneishIkegoomaremasU + +2024-01-17 02:41:42,997 (asr_inference:494) INFO: speech length: 170880 +2024-01-17 02:41:43,014 (beam_search:428) INFO: decoder input length: 264 +2024-01-17 02:41:43,014 (beam_search:429) INFO: max output length: 264 +2024-01-17 02:41:43,014 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:43,783 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:43,783 (beam_search:476) INFO: -17.31 * 1.0 = -17.31 for ctc +2024-01-17 02:41:43,783 (beam_search:479) INFO: total log probability: -17.31 +2024-01-17 02:41:43,783 (beam_search:480) INFO: normalized log probability: -0.17 +2024-01-17 02:41:43,783 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:43,784 (beam_search:483) INFO: best hypo: minamiyahurekaniyarusubetenrukokurisUkooetodooyonikonukooeniamainijiahogoshItoniuueNguogotakarimasU + +2024-01-17 02:41:43,785 (asr_inference:494) INFO: speech length: 126720 +2024-01-17 02:41:43,799 (beam_search:428) INFO: decoder input length: 195 +2024-01-17 02:41:43,799 (beam_search:429) INFO: max output length: 195 +2024-01-17 02:41:43,799 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:44,163 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:44,163 (beam_search:476) INFO: -11.74 * 1.0 = -11.74 for ctc +2024-01-17 02:41:44,163 (beam_search:479) INFO: total log probability: -11.74 +2024-01-17 02:41:44,163 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 02:41:44,163 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:44,164 (beam_search:483) INFO: best hypo: declshsaclkurungasunuhokanookunukootsushudaNgasukokarumaremashIta + +2024-01-17 02:41:44,165 (asr_inference:494) INFO: speech length: 208320 +2024-01-17 02:41:44,184 (beam_search:428) INFO: decoder input length: 323 +2024-01-17 02:41:44,184 (beam_search:429) INFO: max output length: 323 +2024-01-17 02:41:44,184 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:45,010 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:45,010 (beam_search:476) INFO: -9.60 * 1.0 = -9.60 for ctc +2024-01-17 02:41:45,010 (beam_search:479) INFO: total log probability: -9.60 +2024-01-17 02:41:45,010 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:41:45,010 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:45,011 (beam_search:483) INFO: best hypo: iNtaanecltowamasUkominigeeshoNtotaijiNkominigeeshaoNnoryooyoosookanisonaietakaNkyooresU + +2024-01-17 02:41:45,012 (asr_inference:494) INFO: speech length: 211200 +2024-01-17 02:41:45,032 (beam_search:428) INFO: decoder input length: 327 +2024-01-17 02:41:45,032 (beam_search:429) INFO: max output length: 327 +2024-01-17 02:41:45,032 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:46,065 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:46,065 (beam_search:476) INFO: -22.67 * 1.0 = -22.67 for ctc +2024-01-17 02:41:46,065 (beam_search:479) INFO: total log probability: -22.67 +2024-01-17 02:41:46,065 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 02:41:46,065 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:46,066 (beam_search:483) INFO: best hypo: kyooiNdewakaNseNkaNritejuushoNishIkagaipaukaniienukaNseNgokanoosesekutamenikaNjakakursuradamomisochotoclteimushIU + +2024-01-17 02:41:46,067 (asr_inference:494) INFO: speech length: 294720 +2024-01-17 02:41:46,094 (beam_search:428) INFO: decoder input length: 458 +2024-01-17 02:41:46,094 (beam_search:429) INFO: max output length: 458 +2024-01-17 02:41:46,094 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:48,185 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:48,185 (beam_search:476) INFO: -21.23 * 1.0 = -21.23 for ctc +2024-01-17 02:41:48,185 (beam_search:479) INFO: total log probability: -21.23 +2024-01-17 02:41:48,185 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 02:41:48,185 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:48,186 (beam_search:483) INFO: best hypo: reNpoorikaiwaniseNgoneNdokarawaisesubusutoreshumariohoenoshIkiNteekyookaijishipauerubiiaiwaataruzoporunonijuuninosoosaNiNyotoounushunakerewanaranaitokIteshimashIta + +2024-01-17 02:41:48,187 (asr_inference:494) INFO: speech length: 159360 +2024-01-17 02:41:48,203 (beam_search:428) INFO: decoder input length: 246 +2024-01-17 02:41:48,203 (beam_search:429) INFO: max output length: 246 +2024-01-17 02:41:48,203 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:48,839 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:48,839 (beam_search:476) INFO: -17.46 * 1.0 = -17.46 for ctc +2024-01-17 02:41:48,839 (beam_search:479) INFO: total log probability: -17.46 +2024-01-17 02:41:48,839 (beam_search:480) INFO: normalized log probability: -0.21 +2024-01-17 02:41:48,839 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:48,839 (beam_search:483) INFO: best hypo: piiechiderirowakeNsashItkakokubushseiukugaransuisoiyunpiieichineichinorryoodeshimasaremesU + +2024-01-17 02:41:48,841 (asr_inference:494) INFO: speech length: 173760 +2024-01-17 02:41:48,857 (beam_search:428) INFO: decoder input length: 269 +2024-01-17 02:41:48,857 (beam_search:429) INFO: max output length: 269 +2024-01-17 02:41:48,857 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:49,623 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:49,623 (beam_search:476) INFO: -14.60 * 1.0 = -14.60 for ctc +2024-01-17 02:41:49,623 (beam_search:479) INFO: total log probability: -14.60 +2024-01-17 02:41:49,623 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 02:41:49,623 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:49,624 (beam_search:483) INFO: best hypo: soredemotooryiukarunarubaisoukepausubetenoooshIkioomaoriaNzeNjoonokeegogonisaishiinochuioharaimashoo + +2024-01-17 02:41:49,625 (asr_inference:494) INFO: speech length: 209280 +2024-01-17 02:41:49,644 (beam_search:428) INFO: decoder input length: 324 +2024-01-17 02:41:49,644 (beam_search:429) INFO: max output length: 324 +2024-01-17 02:41:49,645 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:50,706 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:50,706 (beam_search:476) INFO: -12.39 * 1.0 = -12.39 for ctc +2024-01-17 02:41:50,706 (beam_search:479) INFO: total log probability: -12.39 +2024-01-17 02:41:50,706 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:41:50,706 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:50,707 (beam_search:483) INFO: best hypo: korerawatamanikoNgatssurokaclkatsokumukenobichitekaigainisamazamanateNpowonaraNdeimasUaNzeNiooebukotogadekimasU + +2024-01-17 02:41:50,708 (asr_inference:494) INFO: speech length: 214080 +2024-01-17 02:41:50,729 (beam_search:428) INFO: decoder input length: 332 +2024-01-17 02:41:50,729 (beam_search:429) INFO: max output length: 332 +2024-01-17 02:41:50,729 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:51,801 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:51,801 (beam_search:476) INFO: -26.84 * 1.0 = -26.84 for ctc +2024-01-17 02:41:51,801 (beam_search:479) INFO: total log probability: -26.84 +2024-01-17 02:41:51,801 (beam_search:480) INFO: normalized log probability: -0.25 +2024-01-17 02:41:51,801 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:51,802 (beam_search:483) INFO: best hypo: shiNdomienaichiudaasoNopaunaNdorasasUtoseNkyeukachijukiupeejikyakUkyuunosuNzaimonodtabazeruchiemunodogushunayosodearu + +2024-01-17 02:41:51,804 (asr_inference:494) INFO: speech length: 181440 +2024-01-17 02:41:51,820 (beam_search:428) INFO: decoder input length: 281 +2024-01-17 02:41:51,820 (beam_search:429) INFO: max output length: 281 +2024-01-17 02:41:51,820 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:52,737 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:52,737 (beam_search:476) INFO: -10.14 * 1.0 = -10.14 for ctc +2024-01-17 02:41:52,738 (beam_search:479) INFO: total log probability: -10.14 +2024-01-17 02:41:52,738 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:41:52,738 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:52,738 (beam_search:483) INFO: best hypo: konosaabisuwagorakUseNohajimetosuruseNpakuyaeNgakUshiredeetayoNseyosuyotosurutaNkeNtairihiNpaaniriyoosareteimasU + +2024-01-17 02:41:52,740 (asr_inference:494) INFO: speech length: 346560 +2024-01-17 02:41:52,770 (beam_search:428) INFO: decoder input length: 539 +2024-01-17 02:41:52,771 (beam_search:429) INFO: max output length: 539 +2024-01-17 02:41:52,771 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:55,466 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:55,466 (beam_search:476) INFO: -23.57 * 1.0 = -23.57 for ctc +2024-01-17 02:41:55,466 (beam_search:479) INFO: total log probability: -23.57 +2024-01-17 02:41:55,466 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 02:41:55,466 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:55,467 (beam_search:483) INFO: best hypo: sakubabueenosuaidesUkarakojuclkirosaNjuichimairuhanaretarapuratashinaidegeNshugojooiNgiNdewarukurisuteinaferuunaNdesudekirukinaazoshiganaitooryooseeNenoshIsudaoseNgeNshimashIta + +2024-01-17 02:41:55,469 (asr_inference:494) INFO: speech length: 180480 +2024-01-17 02:41:55,486 (beam_search:428) INFO: decoder input length: 279 +2024-01-17 02:41:55,486 (beam_search:429) INFO: max output length: 279 +2024-01-17 02:41:55,486 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:56,335 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:56,335 (beam_search:476) INFO: -16.00 * 1.0 = -16.00 for ctc +2024-01-17 02:41:56,335 (beam_search:479) INFO: total log probability: -16.00 +2024-01-17 02:41:56,335 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 02:41:56,335 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:56,336 (beam_search:483) INFO: best hypo: onoizukuinimashIhatonokasoorebusunorugaclkigakasoorooobaaraNshipaukabenigekitottsushItepaujuunaniNgashioshimashIta + +2024-01-17 02:41:56,337 (asr_inference:494) INFO: speech length: 179520 +2024-01-17 02:41:56,354 (beam_search:428) INFO: decoder input length: 278 +2024-01-17 02:41:56,354 (beam_search:429) INFO: max output length: 278 +2024-01-17 02:41:56,354 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:41:57,249 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:41:57,249 (beam_search:476) INFO: -11.63 * 1.0 = -11.63 for ctc +2024-01-17 02:41:57,249 (beam_search:479) INFO: total log probability: -11.63 +2024-01-17 02:41:57,249 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:41:57,249 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:41:57,250 (beam_search:483) INFO: best hypo: hashIshItanojoohokukawajuugometoodesUniseNjuuchinehajigatsunishuNkoshipauniseNjuunaniesaNgasumarekaitsushimaseNdeshIta + +2024-01-17 02:41:57,252 (asr_inference:494) INFO: speech length: 416640 +2024-01-17 02:41:57,289 (beam_search:428) INFO: decoder input length: 648 +2024-01-17 02:41:57,289 (beam_search:429) INFO: max output length: 648 +2024-01-17 02:41:57,289 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:01,060 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:01,060 (beam_search:476) INFO: -24.98 * 1.0 = -24.98 for ctc +2024-01-17 02:42:01,060 (beam_search:479) INFO: total log probability: -24.98 +2024-01-17 02:42:01,060 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 02:42:01,060 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:01,061 (beam_search:483) INFO: best hypo: buNneetoiukotowawashimiiomisuruarateNgonokieooshishibirdisUkarakIteworishimeiNiomisururateNkoromeeshishibisUtoshiyatoshIkoclkaomishinaNnakanokatachireshakainokibooteegisurushuibitasUtoyumeeshinikaNkeeshIteimasU + +2024-01-17 02:42:01,063 (asr_inference:494) INFO: speech length: 183360 +2024-01-17 02:42:01,080 (beam_search:428) INFO: decoder input length: 284 +2024-01-17 02:42:01,080 (beam_search:429) INFO: max output length: 284 +2024-01-17 02:42:01,080 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:02,036 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:02,036 (beam_search:476) INFO: -15.02 * 1.0 = -15.02 for ctc +2024-01-17 02:42:02,036 (beam_search:479) INFO: total log probability: -15.02 +2024-01-17 02:42:02,036 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 02:42:02,036 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:02,036 (beam_search:483) INFO: best hypo: tsuujookokorewaisumakaNkotekuyaryooshatashikahasurotokachikoetekimasUwototoshIikarigaoinasumunogatariuamarudehoNnaoyooresU + +2024-01-17 02:42:02,038 (asr_inference:494) INFO: speech length: 127680 +2024-01-17 02:42:02,052 (beam_search:428) INFO: decoder input length: 197 +2024-01-17 02:42:02,052 (beam_search:429) INFO: max output length: 197 +2024-01-17 02:42:02,052 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:02,376 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:02,377 (beam_search:476) INFO: -9.28 * 1.0 = -9.28 for ctc +2024-01-17 02:42:02,377 (beam_search:479) INFO: total log probability: -9.28 +2024-01-17 02:42:02,377 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 02:42:02,377 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:02,377 (beam_search:483) INFO: best hypo: terebinohoodoonioNdopaugeNpatsUkaraakuegagaclteimasUide + +2024-01-17 02:42:02,378 (asr_inference:494) INFO: speech length: 189120 +2024-01-17 02:42:02,396 (beam_search:428) INFO: decoder input length: 293 +2024-01-17 02:42:02,396 (beam_search:429) INFO: max output length: 293 +2024-01-17 02:42:02,396 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:03,061 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:03,061 (beam_search:476) INFO: -6.28 * 1.0 = -6.28 for ctc +2024-01-17 02:42:03,061 (beam_search:479) INFO: total log probability: -6.28 +2024-01-17 02:42:03,061 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:42:03,061 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:03,062 (beam_search:483) INFO: best hypo: noobooritokoodoonosookaNkaNkeewakayakushatashinokeNkyuuwaourazukeremonodesU + +2024-01-17 02:42:03,063 (asr_inference:494) INFO: speech length: 156480 +2024-01-17 02:42:03,079 (beam_search:428) INFO: decoder input length: 242 +2024-01-17 02:42:03,079 (beam_search:429) INFO: max output length: 242 +2024-01-17 02:42:03,079 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:03,615 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:03,615 (beam_search:476) INFO: -10.00 * 1.0 = -10.00 for ctc +2024-01-17 02:42:03,615 (beam_search:479) INFO: total log probability: -10.00 +2024-01-17 02:42:03,615 (beam_search:480) INFO: normalized log probability: -0.14 +2024-01-17 02:42:03,615 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:03,616 (beam_search:483) INFO: best hypo: suiyoobinibeNtonatokarupanerowaseNshIkeNgoUkasunukojiNdesiIsujooshimashIta + +2024-01-17 02:42:03,617 (asr_inference:494) INFO: speech length: 181440 +2024-01-17 02:42:03,633 (beam_search:428) INFO: decoder input length: 281 +2024-01-17 02:42:03,633 (beam_search:429) INFO: max output length: 281 +2024-01-17 02:42:03,633 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:04,499 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:04,499 (beam_search:476) INFO: -11.44 * 1.0 = -11.44 for ctc +2024-01-17 02:42:04,499 (beam_search:479) INFO: total log probability: -11.44 +2024-01-17 02:42:04,499 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:42:04,499 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:04,500 (beam_search:483) INFO: best hypo: seNhapekuneNdairaiguNtaigatoojaksurewarehaichiwakonobyokinikaNkyeesureboNdainisoogushItakotowarimaseNdeshIta + +2024-01-17 02:42:04,501 (asr_inference:494) INFO: speech length: 172800 +2024-01-17 02:42:04,517 (beam_search:428) INFO: decoder input length: 267 +2024-01-17 02:42:04,517 (beam_search:429) INFO: max output length: 267 +2024-01-17 02:42:04,517 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:05,241 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:05,241 (beam_search:476) INFO: -13.72 * 1.0 = -13.72 for ctc +2024-01-17 02:42:05,241 (beam_search:479) INFO: total log probability: -13.72 +2024-01-17 02:42:05,241 (beam_search:480) INFO: normalized log probability: -0.15 +2024-01-17 02:42:05,241 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:05,242 (beam_search:483) INFO: best hypo: shIkashiclkkyakutenobikecldooshunacldatoiNdoananatsunebikecltooshinaisaNjorokurashIkarekimaseNdeshIta + +2024-01-17 02:42:05,243 (asr_inference:494) INFO: speech length: 257280 +2024-01-17 02:42:05,267 (beam_search:428) INFO: decoder input length: 399 +2024-01-17 02:42:05,267 (beam_search:429) INFO: max output length: 399 +2024-01-17 02:42:05,267 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:06,621 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:06,621 (beam_search:476) INFO: -9.15 * 1.0 = -9.15 for ctc +2024-01-17 02:42:06,621 (beam_search:479) INFO: total log probability: -9.15 +2024-01-17 02:42:06,621 (beam_search:480) INFO: normalized log probability: -0.08 +2024-01-17 02:42:06,621 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:06,622 (beam_search:483) INFO: best hypo: kajinodewatsuujootokubetsunaiNshakoyaeNtaateimeNtooyooishIteimasUgesogakibuyakushisesunainitoromaruyonisurutameresU + +2024-01-17 02:42:06,623 (asr_inference:494) INFO: speech length: 165120 +2024-01-17 02:42:06,640 (beam_search:428) INFO: decoder input length: 255 +2024-01-17 02:42:06,640 (beam_search:429) INFO: max output length: 255 +2024-01-17 02:42:06,640 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:07,325 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:07,325 (beam_search:476) INFO: -14.22 * 1.0 = -14.22 for ctc +2024-01-17 02:42:07,325 (beam_search:479) INFO: total log probability: -14.22 +2024-01-17 02:42:07,325 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 02:42:07,325 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:07,326 (beam_search:483) INFO: best hypo: soredeotookyiukaranaarabaisogepausubetenohyowashIkmaoriaNzeNjonokekonisaishiiNnochuioharaimasho + +2024-01-17 02:42:07,327 (asr_inference:494) INFO: speech length: 143040 +2024-01-17 02:42:07,342 (beam_search:428) INFO: decoder input length: 221 +2024-01-17 02:42:07,342 (beam_search:429) INFO: max output length: 221 +2024-01-17 02:42:07,342 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:07,788 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:07,788 (beam_search:476) INFO: -11.78 * 1.0 = -11.78 for ctc +2024-01-17 02:42:07,788 (beam_search:479) INFO: total log probability: -11.78 +2024-01-17 02:42:07,788 (beam_search:480) INFO: normalized log probability: -0.18 +2024-01-17 02:42:07,788 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:07,788 (beam_search:483) INFO: best hypo: owokarugewarimasepaukorashItotsuoshoowaridearipauatarashishoonomokakedesU + +2024-01-17 02:42:07,790 (asr_inference:494) INFO: speech length: 220800 +2024-01-17 02:42:07,810 (beam_search:428) INFO: decoder input length: 342 +2024-01-17 02:42:07,810 (beam_search:429) INFO: max output length: 342 +2024-01-17 02:42:07,810 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:08,934 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:08,934 (beam_search:476) INFO: -11.93 * 1.0 = -11.93 for ctc +2024-01-17 02:42:08,934 (beam_search:479) INFO: total log probability: -11.93 +2024-01-17 02:42:08,934 (beam_search:480) INFO: normalized log probability: -0.11 +2024-01-17 02:42:08,934 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:08,935 (beam_search:483) INFO: best hypo: sawaritowaafurikanoyaseedooguzupautokonisawaNnaniruyaseredoogutsunokaNsasuamokutekitoshItarikurodenoyokoosashimasU + +2024-01-17 02:42:08,937 (asr_inference:494) INFO: speech length: 240960 +2024-01-17 02:42:08,958 (beam_search:428) INFO: decoder input length: 374 +2024-01-17 02:42:08,958 (beam_search:429) INFO: max output length: 374 +2024-01-17 02:42:08,958 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:10,438 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:10,438 (beam_search:476) INFO: -25.78 * 1.0 = -25.78 for ctc +2024-01-17 02:42:10,438 (beam_search:479) INFO: total log probability: -25.78 +2024-01-17 02:42:10,438 (beam_search:480) INFO: normalized log probability: -0.19 +2024-01-17 02:42:10,438 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:10,439 (beam_search:483) INFO: best hypo: uyunekitabarutokayomodaNsurobaiwaseisuichikakunishsekudasaikIkoorenonakotsIkitssuusainimocltomekyookeruseitsudeosorujuumonosooganarikibikigasU + +2024-01-17 02:42:10,440 (asr_inference:494) INFO: speech length: 196800 +2024-01-17 02:42:10,458 (beam_search:428) INFO: decoder input length: 305 +2024-01-17 02:42:10,458 (beam_search:429) INFO: max output length: 305 +2024-01-17 02:42:10,458 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:11,591 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:11,591 (beam_search:476) INFO: -21.05 * 1.0 = -21.05 for ctc +2024-01-17 02:42:11,591 (beam_search:479) INFO: total log probability: -21.05 +2024-01-17 02:42:11,591 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 02:42:11,591 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:11,591 (beam_search:483) INFO: best hypo: kokowairisunoshokumiNishehaieshaegajibuNtashinoryoorotoshabashunaroudepaushokomiNtijirenishookoosekasootosurekatawakokoarhajiberenagayoiushoo + +2024-01-17 02:42:11,593 (asr_inference:494) INFO: speech length: 246720 +2024-01-17 02:42:11,616 (beam_search:428) INFO: decoder input length: 383 +2024-01-17 02:42:11,616 (beam_search:429) INFO: max output length: 383 +2024-01-17 02:42:11,616 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:12,918 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:12,918 (beam_search:476) INFO: -13.66 * 1.0 = -13.66 for ctc +2024-01-17 02:42:12,918 (beam_search:479) INFO: total log probability: -13.66 +2024-01-17 02:42:12,918 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 02:42:12,918 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:12,919 (beam_search:483) INFO: best hypo: koshiwasakugeNsurusuuchoosarayawaseNdeshItakapausakugeNwachiiwokunokeezaisaNshIsuyonimotozuitejishisarudarotoNnooimashIta + +2024-01-17 02:42:12,920 (asr_inference:494) INFO: speech length: 286080 +2024-01-17 02:42:12,946 (beam_search:428) INFO: decoder input length: 444 +2024-01-17 02:42:12,947 (beam_search:429) INFO: max output length: 444 +2024-01-17 02:42:12,947 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:14,495 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:14,495 (beam_search:476) INFO: -15.28 * 1.0 = -15.28 for ctc +2024-01-17 02:42:14,495 (beam_search:479) INFO: total log probability: -15.28 +2024-01-17 02:42:14,495 (beam_search:480) INFO: normalized log probability: -0.13 +2024-01-17 02:42:14,495 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:14,496 (beam_search:483) INFO: best hypo: sauinuukukushoclkuwashiNkoNryokoonojikigasUkunaipaukaruechaashoclkuyoremohaekootozurenawabikiyorishoojogakasurukotogaarimasU + +2024-01-17 02:42:14,498 (asr_inference:494) INFO: speech length: 273600 +2024-01-17 02:42:14,523 (beam_search:428) INFO: decoder input length: 425 +2024-01-17 02:42:14,523 (beam_search:429) INFO: max output length: 425 +2024-01-17 02:42:14,523 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:16,135 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:16,136 (beam_search:476) INFO: -20.19 * 1.0 = -20.19 for ctc +2024-01-17 02:42:16,136 (beam_search:479) INFO: total log probability: -20.19 +2024-01-17 02:42:16,136 (beam_search:480) INFO: normalized log probability: -0.16 +2024-01-17 02:42:16,136 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:16,136 (beam_search:483) INFO: best hypo: kyinoonoasatorukonogajiaNteclpunokeesasuohoNworejidooshabakuranobakaasoriyoripaukyeekaNfutaregashiboshipauhoshowashawanijuuniyokoaimashIta + +2024-01-17 02:42:16,138 (asr_inference:494) INFO: speech length: 168960 +2024-01-17 02:42:16,154 (beam_search:428) INFO: decoder input length: 261 +2024-01-17 02:42:16,154 (beam_search:429) INFO: max output length: 261 +2024-01-17 02:42:16,154 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:16,866 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:16,866 (beam_search:476) INFO: -8.27 * 1.0 = -8.27 for ctc +2024-01-17 02:42:16,866 (beam_search:479) INFO: total log probability: -8.27 +2024-01-17 02:42:16,866 (beam_search:480) INFO: normalized log probability: -0.09 +2024-01-17 02:42:16,866 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:16,867 (beam_search:483) INFO: best hypo: shokubutsudaniNgiNgasuusaNzootsUkuriniNgeNgakaclkoikitoshItehakidasunisaNkataNsootorikoNdeimasU + +2024-01-17 02:42:16,868 (asr_inference:494) INFO: speech length: 212160 +2024-01-17 02:42:16,888 (beam_search:428) INFO: decoder input length: 329 +2024-01-17 02:42:16,888 (beam_search:429) INFO: max output length: 329 +2024-01-17 02:42:16,888 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:17,824 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:17,824 (beam_search:476) INFO: -11.49 * 1.0 = -11.49 for ctc +2024-01-17 02:42:17,825 (beam_search:479) INFO: total log probability: -11.49 +2024-01-17 02:42:17,825 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 02:42:17,825 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:17,825 (beam_search:483) INFO: best hypo: seNpakureebushioisoosurunowaumiokoetehItoyabushiotariryooyisoosurumocltomokoorisekinahoohooresU + +2024-01-17 02:42:17,827 (asr_inference:494) INFO: speech length: 295680 +2024-01-17 02:42:17,853 (beam_search:428) INFO: decoder input length: 459 +2024-01-17 02:42:17,853 (beam_search:429) INFO: max output length: 459 +2024-01-17 02:42:17,853 (beam_search:430) INFO: min output length: 0 +2024-01-17 02:42:19,732 (beam_search:447) INFO: no hypothesis. Finish decoding. +2024-01-17 02:42:19,732 (beam_search:476) INFO: -16.23 * 1.0 = -16.23 for ctc +2024-01-17 02:42:19,732 (beam_search:479) INFO: total log probability: -16.23 +2024-01-17 02:42:19,732 (beam_search:480) INFO: normalized log probability: -0.12 +2024-01-17 02:42:19,732 (beam_search:481) INFO: total number of ended hypotheses: 1 +2024-01-17 02:42:19,732 (beam_search:483) INFO: best hypo: karihoruniashuunoawanorudoshuwarutsuneclkaachishiwabooryokutekinabideogeemuomiseeneNshanihaNbayareNtaesureukotookiNshisuruhoowanishaomeeshimashIta + +# Accounting: time=51 threads=1 +# Ended (code 0) at Wed Jan 17 02:42:20 CST 2024, elapsed time 51 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/calculate_rtf.log b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/calculate_rtf.log new file mode 100644 index 0000000000000000000000000000000000000000..0d73e38b0ee51847962a9c73038b16f32bd2a494 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/calculate_rtf.log @@ -0,0 +1,9 @@ +# pyscripts/utils/calculate_rtf.py --log-dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir --log-name asr_inference --input-shift 0.0625 --start-times-marker "speech length" --end-times-marker "best hypo" --inf-num 1 +# Started at Wed Jan 17 02:42:20 CST 2024 +# +Total audio duration: 1194.576 [sec] +Total decoding time: 78.934 [sec] +RTF: 0.066 +Latency: 493.338 [ms/sentence] +# Accounting: time=0 threads=1 +# Ended (code 0) at Wed Jan 17 02:42:20 CST 2024, elapsed time 0 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.1.scp b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.1.scp new file mode 100644 index 0000000000000000000000000000000000000000..42293b0b5c7a906c24e810ad6718f3700cf038da --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.1.scp @@ -0,0 +1,40 @@ +cv_jpn_000800 dump/raw/test_10min_jpn/data/format.1/cv_jpn_000800.flac +cv_jpn_000801 dump/raw/test_10min_jpn/data/format.1/cv_jpn_000801.flac +cv_jpn_000802 dump/raw/test_10min_jpn/data/format.1/cv_jpn_000802.flac +cv_jpn_000803 dump/raw/test_10min_jpn/data/format.1/cv_jpn_000803.flac +cv_jpn_000804 dump/raw/test_10min_jpn/data/format.1/cv_jpn_000804.flac +cv_jpn_000805 dump/raw/test_10min_jpn/data/format.2/cv_jpn_000805.flac +cv_jpn_000806 dump/raw/test_10min_jpn/data/format.2/cv_jpn_000806.flac +cv_jpn_000807 dump/raw/test_10min_jpn/data/format.2/cv_jpn_000807.flac +cv_jpn_000808 dump/raw/test_10min_jpn/data/format.2/cv_jpn_000808.flac +cv_jpn_000809 dump/raw/test_10min_jpn/data/format.2/cv_jpn_000809.flac +cv_jpn_000810 dump/raw/test_10min_jpn/data/format.3/cv_jpn_000810.flac +cv_jpn_000811 dump/raw/test_10min_jpn/data/format.3/cv_jpn_000811.flac +cv_jpn_000812 dump/raw/test_10min_jpn/data/format.3/cv_jpn_000812.flac +cv_jpn_000813 dump/raw/test_10min_jpn/data/format.3/cv_jpn_000813.flac +cv_jpn_000814 dump/raw/test_10min_jpn/data/format.3/cv_jpn_000814.flac +cv_jpn_000815 dump/raw/test_10min_jpn/data/format.4/cv_jpn_000815.flac +cv_jpn_000816 dump/raw/test_10min_jpn/data/format.4/cv_jpn_000816.flac +cv_jpn_000817 dump/raw/test_10min_jpn/data/format.4/cv_jpn_000817.flac +cv_jpn_000818 dump/raw/test_10min_jpn/data/format.4/cv_jpn_000818.flac +cv_jpn_000819 dump/raw/test_10min_jpn/data/format.4/cv_jpn_000819.flac +cv_jpn_000820 dump/raw/test_10min_jpn/data/format.5/cv_jpn_000820.flac +cv_jpn_000821 dump/raw/test_10min_jpn/data/format.5/cv_jpn_000821.flac +cv_jpn_000822 dump/raw/test_10min_jpn/data/format.5/cv_jpn_000822.flac +cv_jpn_000823 dump/raw/test_10min_jpn/data/format.5/cv_jpn_000823.flac +cv_jpn_000824 dump/raw/test_10min_jpn/data/format.5/cv_jpn_000824.flac +cv_jpn_000825 dump/raw/test_10min_jpn/data/format.6/cv_jpn_000825.flac +cv_jpn_000826 dump/raw/test_10min_jpn/data/format.6/cv_jpn_000826.flac +cv_jpn_000827 dump/raw/test_10min_jpn/data/format.6/cv_jpn_000827.flac +cv_jpn_000828 dump/raw/test_10min_jpn/data/format.6/cv_jpn_000828.flac +cv_jpn_000829 dump/raw/test_10min_jpn/data/format.6/cv_jpn_000829.flac +cv_jpn_000830 dump/raw/test_10min_jpn/data/format.7/cv_jpn_000830.flac +cv_jpn_000831 dump/raw/test_10min_jpn/data/format.7/cv_jpn_000831.flac +cv_jpn_000832 dump/raw/test_10min_jpn/data/format.7/cv_jpn_000832.flac +cv_jpn_000833 dump/raw/test_10min_jpn/data/format.7/cv_jpn_000833.flac +cv_jpn_000834 dump/raw/test_10min_jpn/data/format.7/cv_jpn_000834.flac +cv_jpn_000835 dump/raw/test_10min_jpn/data/format.8/cv_jpn_000835.flac +cv_jpn_000836 dump/raw/test_10min_jpn/data/format.8/cv_jpn_000836.flac +cv_jpn_000837 dump/raw/test_10min_jpn/data/format.8/cv_jpn_000837.flac +cv_jpn_000838 dump/raw/test_10min_jpn/data/format.8/cv_jpn_000838.flac +cv_jpn_000839 dump/raw/test_10min_jpn/data/format.8/cv_jpn_000839.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.2.scp b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.2.scp new file mode 100644 index 0000000000000000000000000000000000000000..98b2b6b607548b58919c8889421486630cf3de23 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.2.scp @@ -0,0 +1,40 @@ +cv_jpn_000840 dump/raw/test_10min_jpn/data/format.9/cv_jpn_000840.flac +cv_jpn_000841 dump/raw/test_10min_jpn/data/format.9/cv_jpn_000841.flac +cv_jpn_000842 dump/raw/test_10min_jpn/data/format.9/cv_jpn_000842.flac +cv_jpn_000843 dump/raw/test_10min_jpn/data/format.9/cv_jpn_000843.flac +cv_jpn_000844 dump/raw/test_10min_jpn/data/format.9/cv_jpn_000844.flac +cv_jpn_000845 dump/raw/test_10min_jpn/data/format.10/cv_jpn_000845.flac +cv_jpn_000846 dump/raw/test_10min_jpn/data/format.10/cv_jpn_000846.flac +cv_jpn_000847 dump/raw/test_10min_jpn/data/format.10/cv_jpn_000847.flac +cv_jpn_000848 dump/raw/test_10min_jpn/data/format.10/cv_jpn_000848.flac +cv_jpn_000849 dump/raw/test_10min_jpn/data/format.10/cv_jpn_000849.flac +cv_jpn_000850 dump/raw/test_10min_jpn/data/format.11/cv_jpn_000850.flac +cv_jpn_000851 dump/raw/test_10min_jpn/data/format.11/cv_jpn_000851.flac +cv_jpn_000852 dump/raw/test_10min_jpn/data/format.11/cv_jpn_000852.flac +cv_jpn_000853 dump/raw/test_10min_jpn/data/format.11/cv_jpn_000853.flac +cv_jpn_000854 dump/raw/test_10min_jpn/data/format.11/cv_jpn_000854.flac +cv_jpn_000855 dump/raw/test_10min_jpn/data/format.12/cv_jpn_000855.flac +cv_jpn_000856 dump/raw/test_10min_jpn/data/format.12/cv_jpn_000856.flac +cv_jpn_000857 dump/raw/test_10min_jpn/data/format.12/cv_jpn_000857.flac +cv_jpn_000858 dump/raw/test_10min_jpn/data/format.12/cv_jpn_000858.flac +cv_jpn_000859 dump/raw/test_10min_jpn/data/format.12/cv_jpn_000859.flac +cv_jpn_000860 dump/raw/test_10min_jpn/data/format.13/cv_jpn_000860.flac +cv_jpn_000861 dump/raw/test_10min_jpn/data/format.13/cv_jpn_000861.flac +cv_jpn_000862 dump/raw/test_10min_jpn/data/format.13/cv_jpn_000862.flac +cv_jpn_000863 dump/raw/test_10min_jpn/data/format.13/cv_jpn_000863.flac +cv_jpn_000864 dump/raw/test_10min_jpn/data/format.13/cv_jpn_000864.flac +cv_jpn_000865 dump/raw/test_10min_jpn/data/format.14/cv_jpn_000865.flac +cv_jpn_000866 dump/raw/test_10min_jpn/data/format.14/cv_jpn_000866.flac +cv_jpn_000867 dump/raw/test_10min_jpn/data/format.14/cv_jpn_000867.flac +cv_jpn_000868 dump/raw/test_10min_jpn/data/format.14/cv_jpn_000868.flac +cv_jpn_000869 dump/raw/test_10min_jpn/data/format.14/cv_jpn_000869.flac +cv_jpn_000870 dump/raw/test_10min_jpn/data/format.15/cv_jpn_000870.flac +cv_jpn_000871 dump/raw/test_10min_jpn/data/format.15/cv_jpn_000871.flac +cv_jpn_000872 dump/raw/test_10min_jpn/data/format.15/cv_jpn_000872.flac +cv_jpn_000873 dump/raw/test_10min_jpn/data/format.15/cv_jpn_000873.flac +cv_jpn_000874 dump/raw/test_10min_jpn/data/format.15/cv_jpn_000874.flac +cv_jpn_000875 dump/raw/test_10min_jpn/data/format.16/cv_jpn_000875.flac +cv_jpn_000876 dump/raw/test_10min_jpn/data/format.16/cv_jpn_000876.flac +cv_jpn_000877 dump/raw/test_10min_jpn/data/format.16/cv_jpn_000877.flac +cv_jpn_000878 dump/raw/test_10min_jpn/data/format.16/cv_jpn_000878.flac +cv_jpn_000879 dump/raw/test_10min_jpn/data/format.16/cv_jpn_000879.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.3.scp b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.3.scp new file mode 100644 index 0000000000000000000000000000000000000000..87db93c6dc09c505b842ac6c755a8d107c642bf0 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.3.scp @@ -0,0 +1,40 @@ +cv_jpn_000880 dump/raw/test_10min_jpn/data/format.17/cv_jpn_000880.flac +cv_jpn_000881 dump/raw/test_10min_jpn/data/format.17/cv_jpn_000881.flac +cv_jpn_000882 dump/raw/test_10min_jpn/data/format.17/cv_jpn_000882.flac +cv_jpn_000883 dump/raw/test_10min_jpn/data/format.17/cv_jpn_000883.flac +cv_jpn_000884 dump/raw/test_10min_jpn/data/format.17/cv_jpn_000884.flac +cv_jpn_000885 dump/raw/test_10min_jpn/data/format.18/cv_jpn_000885.flac +cv_jpn_000886 dump/raw/test_10min_jpn/data/format.18/cv_jpn_000886.flac +cv_jpn_000887 dump/raw/test_10min_jpn/data/format.18/cv_jpn_000887.flac +cv_jpn_000888 dump/raw/test_10min_jpn/data/format.18/cv_jpn_000888.flac +cv_jpn_000889 dump/raw/test_10min_jpn/data/format.18/cv_jpn_000889.flac +cv_jpn_000890 dump/raw/test_10min_jpn/data/format.19/cv_jpn_000890.flac +cv_jpn_000891 dump/raw/test_10min_jpn/data/format.19/cv_jpn_000891.flac +cv_jpn_000892 dump/raw/test_10min_jpn/data/format.19/cv_jpn_000892.flac +cv_jpn_000893 dump/raw/test_10min_jpn/data/format.19/cv_jpn_000893.flac +cv_jpn_000894 dump/raw/test_10min_jpn/data/format.19/cv_jpn_000894.flac +cv_jpn_000895 dump/raw/test_10min_jpn/data/format.20/cv_jpn_000895.flac +cv_jpn_000896 dump/raw/test_10min_jpn/data/format.20/cv_jpn_000896.flac +cv_jpn_000897 dump/raw/test_10min_jpn/data/format.20/cv_jpn_000897.flac +cv_jpn_000898 dump/raw/test_10min_jpn/data/format.20/cv_jpn_000898.flac +cv_jpn_000899 dump/raw/test_10min_jpn/data/format.20/cv_jpn_000899.flac +cv_jpn_000900 dump/raw/test_10min_jpn/data/format.21/cv_jpn_000900.flac +cv_jpn_000901 dump/raw/test_10min_jpn/data/format.21/cv_jpn_000901.flac +cv_jpn_000902 dump/raw/test_10min_jpn/data/format.21/cv_jpn_000902.flac +cv_jpn_000903 dump/raw/test_10min_jpn/data/format.21/cv_jpn_000903.flac +cv_jpn_000904 dump/raw/test_10min_jpn/data/format.21/cv_jpn_000904.flac +cv_jpn_000905 dump/raw/test_10min_jpn/data/format.22/cv_jpn_000905.flac +cv_jpn_000906 dump/raw/test_10min_jpn/data/format.22/cv_jpn_000906.flac +cv_jpn_000907 dump/raw/test_10min_jpn/data/format.22/cv_jpn_000907.flac +cv_jpn_000908 dump/raw/test_10min_jpn/data/format.22/cv_jpn_000908.flac +cv_jpn_000909 dump/raw/test_10min_jpn/data/format.22/cv_jpn_000909.flac +cv_jpn_000910 dump/raw/test_10min_jpn/data/format.23/cv_jpn_000910.flac +cv_jpn_000911 dump/raw/test_10min_jpn/data/format.23/cv_jpn_000911.flac +cv_jpn_000912 dump/raw/test_10min_jpn/data/format.23/cv_jpn_000912.flac +cv_jpn_000913 dump/raw/test_10min_jpn/data/format.23/cv_jpn_000913.flac +fleurs_jpn_000346 dump/raw/test_10min_jpn/data/format.23/fleurs_jpn_000346.flac +fleurs_jpn_000347 dump/raw/test_10min_jpn/data/format.24/fleurs_jpn_000347.flac +fleurs_jpn_000348 dump/raw/test_10min_jpn/data/format.24/fleurs_jpn_000348.flac +fleurs_jpn_000349 dump/raw/test_10min_jpn/data/format.24/fleurs_jpn_000349.flac +fleurs_jpn_000350 dump/raw/test_10min_jpn/data/format.24/fleurs_jpn_000350.flac +fleurs_jpn_000351 dump/raw/test_10min_jpn/data/format.24/fleurs_jpn_000351.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.4.scp b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.4.scp new file mode 100644 index 0000000000000000000000000000000000000000..39e28bcaf015a7679f4054fc61621670dc74ddc2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/keys.4.scp @@ -0,0 +1,40 @@ +fleurs_jpn_000352 dump/raw/test_10min_jpn/data/format.25/fleurs_jpn_000352.flac +fleurs_jpn_000353 dump/raw/test_10min_jpn/data/format.25/fleurs_jpn_000353.flac +fleurs_jpn_000354 dump/raw/test_10min_jpn/data/format.25/fleurs_jpn_000354.flac +fleurs_jpn_000355 dump/raw/test_10min_jpn/data/format.25/fleurs_jpn_000355.flac +fleurs_jpn_000356 dump/raw/test_10min_jpn/data/format.25/fleurs_jpn_000356.flac +fleurs_jpn_000357 dump/raw/test_10min_jpn/data/format.26/fleurs_jpn_000357.flac +fleurs_jpn_000358 dump/raw/test_10min_jpn/data/format.26/fleurs_jpn_000358.flac +fleurs_jpn_000359 dump/raw/test_10min_jpn/data/format.26/fleurs_jpn_000359.flac +fleurs_jpn_000360 dump/raw/test_10min_jpn/data/format.26/fleurs_jpn_000360.flac +fleurs_jpn_000361 dump/raw/test_10min_jpn/data/format.26/fleurs_jpn_000361.flac +fleurs_jpn_000362 dump/raw/test_10min_jpn/data/format.27/fleurs_jpn_000362.flac +fleurs_jpn_000363 dump/raw/test_10min_jpn/data/format.27/fleurs_jpn_000363.flac +fleurs_jpn_000364 dump/raw/test_10min_jpn/data/format.27/fleurs_jpn_000364.flac +fleurs_jpn_000365 dump/raw/test_10min_jpn/data/format.27/fleurs_jpn_000365.flac +fleurs_jpn_000366 dump/raw/test_10min_jpn/data/format.27/fleurs_jpn_000366.flac +fleurs_jpn_000367 dump/raw/test_10min_jpn/data/format.28/fleurs_jpn_000367.flac +fleurs_jpn_000368 dump/raw/test_10min_jpn/data/format.28/fleurs_jpn_000368.flac +fleurs_jpn_000369 dump/raw/test_10min_jpn/data/format.28/fleurs_jpn_000369.flac +fleurs_jpn_000370 dump/raw/test_10min_jpn/data/format.28/fleurs_jpn_000370.flac +fleurs_jpn_000371 dump/raw/test_10min_jpn/data/format.28/fleurs_jpn_000371.flac +fleurs_jpn_000372 dump/raw/test_10min_jpn/data/format.29/fleurs_jpn_000372.flac +fleurs_jpn_000373 dump/raw/test_10min_jpn/data/format.29/fleurs_jpn_000373.flac +fleurs_jpn_000374 dump/raw/test_10min_jpn/data/format.29/fleurs_jpn_000374.flac +fleurs_jpn_000375 dump/raw/test_10min_jpn/data/format.29/fleurs_jpn_000375.flac +fleurs_jpn_000376 dump/raw/test_10min_jpn/data/format.29/fleurs_jpn_000376.flac +fleurs_jpn_000377 dump/raw/test_10min_jpn/data/format.30/fleurs_jpn_000377.flac +fleurs_jpn_000378 dump/raw/test_10min_jpn/data/format.30/fleurs_jpn_000378.flac +fleurs_jpn_000379 dump/raw/test_10min_jpn/data/format.30/fleurs_jpn_000379.flac +fleurs_jpn_000380 dump/raw/test_10min_jpn/data/format.30/fleurs_jpn_000380.flac +fleurs_jpn_000381 dump/raw/test_10min_jpn/data/format.30/fleurs_jpn_000381.flac +fleurs_jpn_000382 dump/raw/test_10min_jpn/data/format.31/fleurs_jpn_000382.flac +fleurs_jpn_000383 dump/raw/test_10min_jpn/data/format.31/fleurs_jpn_000383.flac +fleurs_jpn_000384 dump/raw/test_10min_jpn/data/format.31/fleurs_jpn_000384.flac +fleurs_jpn_000385 dump/raw/test_10min_jpn/data/format.31/fleurs_jpn_000385.flac +fleurs_jpn_000386 dump/raw/test_10min_jpn/data/format.31/fleurs_jpn_000386.flac +fleurs_jpn_000387 dump/raw/test_10min_jpn/data/format.32/fleurs_jpn_000387.flac +fleurs_jpn_000388 dump/raw/test_10min_jpn/data/format.32/fleurs_jpn_000388.flac +fleurs_jpn_000389 dump/raw/test_10min_jpn/data/format.32/fleurs_jpn_000389.flac +fleurs_jpn_000390 dump/raw/test_10min_jpn/data/format.32/fleurs_jpn_000390.flac +fleurs_jpn_000391 dump/raw/test_10min_jpn/data/format.32/fleurs_jpn_000391.flac diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..f3b3a610df42d0b900fe722c6c862671392718fc --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/score @@ -0,0 +1,40 @@ +cv_jpn_000800 tensor(-4.7181) +cv_jpn_000801 tensor(-10.1120) +cv_jpn_000802 tensor(-6.0003) +cv_jpn_000803 tensor(-8.5784) +cv_jpn_000804 tensor(-3.7765) +cv_jpn_000805 tensor(-1.1088) +cv_jpn_000806 tensor(-8.5532) +cv_jpn_000807 tensor(-1.3019) +cv_jpn_000808 tensor(-2.0603) +cv_jpn_000809 tensor(-8.8196) +cv_jpn_000810 tensor(-7.9576) +cv_jpn_000811 tensor(-1.9876) +cv_jpn_000812 tensor(-6.3624) +cv_jpn_000813 tensor(-5.9219) +cv_jpn_000814 tensor(-4.4759) +cv_jpn_000815 tensor(-4.9182) +cv_jpn_000816 tensor(-5.0222) +cv_jpn_000817 tensor(-3.1824) +cv_jpn_000818 tensor(-0.3335) +cv_jpn_000819 tensor(-0.3036) +cv_jpn_000820 tensor(-0.1470) +cv_jpn_000821 tensor(-1.0813) +cv_jpn_000822 tensor(-0.2898) +cv_jpn_000823 tensor(-4.1020) +cv_jpn_000824 tensor(-3.8301) +cv_jpn_000825 tensor(-5.5983) +cv_jpn_000826 tensor(-4.7382) +cv_jpn_000827 tensor(-3.1788) +cv_jpn_000828 tensor(-4.8686) +cv_jpn_000829 tensor(-3.2158) +cv_jpn_000830 tensor(-2.4958) +cv_jpn_000831 tensor(-1.1376) +cv_jpn_000832 tensor(-3.4827) +cv_jpn_000833 tensor(-0.7081) +cv_jpn_000834 tensor(-2.8385) +cv_jpn_000835 tensor(-3.7153) +cv_jpn_000836 tensor(-1.7373) +cv_jpn_000837 tensor(-1.4652) +cv_jpn_000838 tensor(-3.9856) +cv_jpn_000839 tensor(-3.5415) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..f78a74b1350c5a418dbe38e2f9e09f7b23ff27ba --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/text @@ -0,0 +1,40 @@ +cv_jpn_000800 k a k o t o m i r a e t o d o g u j u N t e k i j i k o d o o i ts u n a r u g a y o e n i i sh I k i t e k i n a n o d e a r u +cv_jpn_000801 s e k a y o k e e s e e s u r u t o t o m n i j i k o j i sh i N y o k e e s e s e r u s o o o t e k i s e k a i n o s o o z o o t e k i y o o s o t o sh I t e pau k o b u ts u g a k o b u ts u d e a r u +cv_jpn_000802 p a z o k o N d e g e e m i a r u I t o n o h f u i t e k i t e +cv_jpn_000803 k a N a k u n o sh i m e s a a t a r a sh i j i j i u ts u a t a r a sh i i k a N n e N k a N ky o sh i h a i n w a t a r a sh i i k a n o o s e o m o cl t e pau n a n i h a j i m e r u k a w a +cv_jpn_000804 o m o sh i r o u n o n i pau r o o t o n a g a s u g i t e d a r u i +cv_jpn_000805 k o r e j o o sh u u h a N p o i n a +cv_jpn_000806 k a g a k U sh a m o s e k a i o h o o k a ts s e k i n i t o o i ch i t e k i n i s a ts u m e sh u o t o sh I t e i r u +cv_jpn_000807 f U ts u u n i ts u m a r a N +cv_jpn_000808 sh I cl k a r i I t e k u d a s a i +cv_jpn_000809 w a t a sh i w a a m i g i n o n o t o k i d e k I sh I t e k I s e i m e e n o j i k a k u t o i u g o t o k i m o n o b e N sh o o h o o t e k i r o N b i t o y u u n o d e a r u +cv_jpn_000810 w a t a sh i w a pau sh a k a i k e e s e e n o k o N t e e n i w a pau d e y o o n u s o s u t e k i n a m o n o g a h a t a r a i t e i r u t o m o +cv_jpn_000811 n a n i o s u r u ts u m o r i d a cl t a n o k a +cv_jpn_000812 k o b u ts u t e k I t a g a j i k o h I t e e t e k i n i t a N n i t e N sh u u g o o t e k i n i k a N g a e r a r u r u t o k i s o r e g a b u z u r i t e k I t e k a i d e a r u +cv_jpn_000813 a n e g a z u cl t a n o d e a pau y a cl k i n o sh i r a a i g a a r i m a s e N d e sh I t a +cv_jpn_000814 k o r e w a r i h o N d e u t e i n a i t a b e m o n o d e s U +cv_jpn_000815 w a t a sh i w a h e N sh u u i n o y o n e N k u r a e w a y a cl t a cl t o o m o +cv_jpn_000816 i s a N n i k o n o k o t o b a n o i m i y o o o o sh i a m a sh I t a +cv_jpn_000817 k a s e g a ts U s u y o i h i w a t e n i s u g a d e k i m a s e N +cv_jpn_000818 i ch i +cv_jpn_000819 h a i +cv_jpn_000820 n i +cv_jpn_000821 d e i +cv_jpn_000822 t o k i +cv_jpn_000823 m i r u t o i u k o t o t o pau h a t a r a k U t o i u k o t o g a pau s U k a b u N r i t e k i n a k e r e b a n a r a n a i +cv_jpn_000824 w a r e w a r e o o t a m a sh i i n o z u k o k a r a g u g a s u m o n o d e n a k e r e b a n a r a n a i +cv_jpn_000825 z e cl t a i b e N sh o o h o o t e k i n a r u g a i u e n i i d e a t e k I ch o cl k a N t e k I k e e k i g a cl U k u m a r e r u n o d e a r u +cv_jpn_000826 d o k o m a d e m o t a t o i ch i t o n o s o o g o h I t e e t e k i n a z e cl t a i m u j u N t e k i j i k o d o i ts u n o s e k a i n i sh I t e +cv_jpn_000827 sh I k a r u n i n i N g e N t o k a N ky o o t o r o k a N k e e w a m o t o k o o i n o k a N ky e e d e a r i +cv_jpn_000828 i i s a n i k o o n o k o t o b a n o i m i y o o sh i a m a sh I t a +cv_jpn_000829 g e k i g a n a n a ts s a r i m a s U +cv_jpn_000830 k o ch i r a b a k o b u y a sh i i s a N d e s U +cv_jpn_000831 m o sh i i m a sh i +cv_jpn_000832 k o k o w a o k i k U t e n i k u y a k a n a m a ch i d e s U +cv_jpn_000833 s o n o u ch i k a i y a k U s a r e r u k a r a i s o g e +cv_jpn_000834 a m a s a g a f u s a i r a r e t e t e ch o o d o i +cv_jpn_000835 h o g e N sh I ts u n o d o o a a k e t a +cv_jpn_000836 m o d a N n i o o w a cl t e m o k i n i sh i n a i +cv_jpn_000837 a r i g a cl t a y a +cv_jpn_000838 i t o o g a r a k u d a t o j i k a N w a o s u r e t e t a n o sh i m e r u +cv_jpn_000839 k a k a k u w a g i z u ts U k a s a r e r u n i o j i t e j o o sh I k i n o u ch i n i h a i cl t e i u k u diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..f78a74b1350c5a418dbe38e2f9e09f7b23ff27ba --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/token @@ -0,0 +1,40 @@ +cv_jpn_000800 k a k o t o m i r a e t o d o g u j u N t e k i j i k o d o o i ts u n a r u g a y o e n i i sh I k i t e k i n a n o d e a r u +cv_jpn_000801 s e k a y o k e e s e e s u r u t o t o m n i j i k o j i sh i N y o k e e s e s e r u s o o o t e k i s e k a i n o s o o z o o t e k i y o o s o t o sh I t e pau k o b u ts u g a k o b u ts u d e a r u +cv_jpn_000802 p a z o k o N d e g e e m i a r u I t o n o h f u i t e k i t e +cv_jpn_000803 k a N a k u n o sh i m e s a a t a r a sh i j i j i u ts u a t a r a sh i i k a N n e N k a N ky o sh i h a i n w a t a r a sh i i k a n o o s e o m o cl t e pau n a n i h a j i m e r u k a w a +cv_jpn_000804 o m o sh i r o u n o n i pau r o o t o n a g a s u g i t e d a r u i +cv_jpn_000805 k o r e j o o sh u u h a N p o i n a +cv_jpn_000806 k a g a k U sh a m o s e k a i o h o o k a ts s e k i n i t o o i ch i t e k i n i s a ts u m e sh u o t o sh I t e i r u +cv_jpn_000807 f U ts u u n i ts u m a r a N +cv_jpn_000808 sh I cl k a r i I t e k u d a s a i +cv_jpn_000809 w a t a sh i w a a m i g i n o n o t o k i d e k I sh I t e k I s e i m e e n o j i k a k u t o i u g o t o k i m o n o b e N sh o o h o o t e k i r o N b i t o y u u n o d e a r u +cv_jpn_000810 w a t a sh i w a pau sh a k a i k e e s e e n o k o N t e e n i w a pau d e y o o n u s o s u t e k i n a m o n o g a h a t a r a i t e i r u t o m o +cv_jpn_000811 n a n i o s u r u ts u m o r i d a cl t a n o k a +cv_jpn_000812 k o b u ts u t e k I t a g a j i k o h I t e e t e k i n i t a N n i t e N sh u u g o o t e k i n i k a N g a e r a r u r u t o k i s o r e g a b u z u r i t e k I t e k a i d e a r u +cv_jpn_000813 a n e g a z u cl t a n o d e a pau y a cl k i n o sh i r a a i g a a r i m a s e N d e sh I t a +cv_jpn_000814 k o r e w a r i h o N d e u t e i n a i t a b e m o n o d e s U +cv_jpn_000815 w a t a sh i w a h e N sh u u i n o y o n e N k u r a e w a y a cl t a cl t o o m o +cv_jpn_000816 i s a N n i k o n o k o t o b a n o i m i y o o o o sh i a m a sh I t a +cv_jpn_000817 k a s e g a ts U s u y o i h i w a t e n i s u g a d e k i m a s e N +cv_jpn_000818 i ch i +cv_jpn_000819 h a i +cv_jpn_000820 n i +cv_jpn_000821 d e i +cv_jpn_000822 t o k i +cv_jpn_000823 m i r u t o i u k o t o t o pau h a t a r a k U t o i u k o t o g a pau s U k a b u N r i t e k i n a k e r e b a n a r a n a i +cv_jpn_000824 w a r e w a r e o o t a m a sh i i n o z u k o k a r a g u g a s u m o n o d e n a k e r e b a n a r a n a i +cv_jpn_000825 z e cl t a i b e N sh o o h o o t e k i n a r u g a i u e n i i d e a t e k I ch o cl k a N t e k I k e e k i g a cl U k u m a r e r u n o d e a r u +cv_jpn_000826 d o k o m a d e m o t a t o i ch i t o n o s o o g o h I t e e t e k i n a z e cl t a i m u j u N t e k i j i k o d o i ts u n o s e k a i n i sh I t e +cv_jpn_000827 sh I k a r u n i n i N g e N t o k a N ky o o t o r o k a N k e e w a m o t o k o o i n o k a N ky e e d e a r i +cv_jpn_000828 i i s a n i k o o n o k o t o b a n o i m i y o o sh i a m a sh I t a +cv_jpn_000829 g e k i g a n a n a ts s a r i m a s U +cv_jpn_000830 k o ch i r a b a k o b u y a sh i i s a N d e s U +cv_jpn_000831 m o sh i i m a sh i +cv_jpn_000832 k o k o w a o k i k U t e n i k u y a k a n a m a ch i d e s U +cv_jpn_000833 s o n o u ch i k a i y a k U s a r e r u k a r a i s o g e +cv_jpn_000834 a m a s a g a f u s a i r a r e t e t e ch o o d o i +cv_jpn_000835 h o g e N sh I ts u n o d o o a a k e t a +cv_jpn_000836 m o d a N n i o o w a cl t e m o k i n i sh i n a i +cv_jpn_000837 a r i g a cl t a y a +cv_jpn_000838 i t o o g a r a k u d a t o j i k a N w a o s u r e t e t a n o sh i m e r u +cv_jpn_000839 k a k a k u w a g i z u ts U k a s a r e r u n i o j i t e j o o sh I k i n o u ch i n i h a i cl t e i u k u diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..127932ade2537b6a704da6a4b0bb8dc99c8f9ab2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.1/1best_recog/token_int @@ -0,0 +1,40 @@ +cv_jpn_000800 6 2 6 3 8 3 11 4 10 2 5 8 3 14 3 16 7 22 7 13 8 5 6 4 22 4 6 3 14 3 3 4 26 7 9 2 10 7 16 2 23 3 5 9 4 4 15 19 6 4 8 5 6 4 9 2 9 3 14 5 2 10 7 +cv_jpn_000801 12 5 6 2 23 3 6 5 5 12 5 5 12 7 10 7 8 3 8 3 11 9 4 22 4 6 3 22 4 15 4 13 23 3 6 5 5 12 5 12 5 10 7 12 3 3 3 8 5 6 4 12 5 6 2 4 9 3 12 3 3 28 3 3 8 5 6 4 23 3 3 12 3 8 3 15 19 8 5 20 6 3 25 7 26 7 16 2 6 3 25 7 26 7 14 5 2 10 7 +cv_jpn_000802 30 2 28 3 6 3 13 14 5 16 5 5 11 4 2 10 7 19 8 3 9 3 24 31 7 4 8 5 6 4 8 5 +cv_jpn_000803 6 2 13 2 6 7 9 3 15 4 11 5 12 2 2 8 2 10 2 15 4 22 4 22 4 7 26 7 2 8 2 10 2 15 4 4 6 2 13 9 5 13 6 2 13 29 3 15 4 24 2 4 9 17 2 8 2 10 2 15 4 4 6 2 9 3 3 12 5 3 11 3 21 8 5 20 9 2 9 4 24 2 22 4 11 5 10 7 6 2 17 2 +cv_jpn_000804 3 11 3 15 4 10 3 7 9 3 9 4 20 10 3 3 8 3 9 2 16 2 12 7 16 4 8 5 14 2 10 7 4 +cv_jpn_000805 6 3 10 5 22 3 3 15 7 7 24 2 13 30 3 4 9 2 +cv_jpn_000806 6 2 16 2 6 18 15 2 11 3 12 5 6 2 4 3 24 3 3 6 2 26 12 5 6 4 9 4 8 3 3 4 27 4 8 5 6 4 9 4 12 2 26 7 11 5 15 7 3 8 3 15 19 8 5 4 10 7 +cv_jpn_000807 31 18 26 7 7 9 4 26 7 11 2 10 2 13 +cv_jpn_000808 15 19 21 6 2 10 4 19 8 5 6 7 14 2 12 2 4 +cv_jpn_000809 17 2 8 2 15 4 17 2 2 11 4 16 4 9 3 9 3 8 3 6 4 14 5 6 19 15 19 8 5 6 19 12 5 4 11 5 5 9 3 22 4 6 2 6 7 8 3 4 7 16 3 8 3 6 4 11 3 9 3 25 5 13 15 3 3 24 3 3 8 5 6 4 10 3 13 25 4 8 3 23 7 7 9 3 14 5 2 10 7 +cv_jpn_000810 17 2 8 2 15 4 17 2 20 15 2 6 2 4 6 5 5 12 5 5 9 3 6 3 13 8 5 5 9 4 17 2 20 14 5 23 3 3 9 7 12 3 12 7 8 5 6 4 9 2 11 3 9 3 16 2 24 2 8 2 10 2 4 8 5 4 10 7 8 3 11 3 +cv_jpn_000811 9 2 9 4 3 12 7 10 7 26 7 11 3 10 4 14 2 21 8 2 9 3 6 2 +cv_jpn_000812 6 3 25 7 26 7 8 5 6 19 8 2 16 2 22 4 6 3 24 19 8 5 5 8 5 6 4 9 4 8 2 13 9 4 8 5 13 15 7 7 16 3 3 8 5 6 4 9 4 6 2 13 16 2 5 10 2 10 7 10 7 8 3 6 4 12 3 10 5 16 2 25 7 28 7 10 4 8 5 6 19 8 5 6 2 4 14 5 2 10 7 +cv_jpn_000813 2 9 5 16 2 28 7 21 8 2 9 3 14 5 2 20 23 2 21 6 4 9 3 15 4 10 2 2 4 16 2 2 10 4 11 2 12 5 13 14 5 15 19 8 2 +cv_jpn_000814 6 3 10 5 17 2 10 4 24 3 13 14 5 7 8 5 4 9 2 4 8 2 25 5 11 3 9 3 14 5 12 18 +cv_jpn_000815 17 2 8 2 15 4 17 2 24 5 13 15 7 7 4 9 3 23 3 9 5 13 6 7 10 2 5 17 2 23 2 21 8 2 21 8 3 3 11 3 +cv_jpn_000816 4 12 2 13 9 4 6 3 9 3 6 3 8 3 25 2 9 3 4 11 4 23 3 3 3 3 15 4 2 11 2 15 19 8 2 +cv_jpn_000817 6 2 12 5 16 2 26 18 12 7 23 3 4 24 4 17 2 8 5 9 4 12 7 16 2 14 5 6 4 11 2 12 5 13 +cv_jpn_000818 4 27 4 +cv_jpn_000819 24 2 4 +cv_jpn_000820 9 4 +cv_jpn_000821 14 5 4 +cv_jpn_000822 8 3 6 4 +cv_jpn_000823 11 4 10 7 8 3 4 7 6 3 8 3 8 3 20 24 2 8 2 10 2 6 18 8 3 4 7 6 3 8 3 16 2 20 12 18 6 2 25 7 13 10 4 8 5 6 4 9 2 6 5 10 5 25 2 9 2 10 2 9 2 4 +cv_jpn_000824 17 2 10 5 17 2 10 5 3 3 8 2 11 2 15 4 4 9 3 28 7 6 3 6 2 10 2 16 7 16 2 12 7 11 3 9 3 14 5 9 2 6 5 10 5 25 2 9 2 10 2 9 2 4 +cv_jpn_000825 28 5 21 8 2 4 25 5 13 15 3 3 24 3 3 8 5 6 4 9 2 10 7 16 2 4 7 5 9 4 4 14 5 2 8 5 6 19 27 3 21 6 2 13 8 5 6 19 6 5 5 6 4 16 2 21 18 6 7 11 2 10 5 10 7 9 3 14 5 2 10 7 +cv_jpn_000826 14 3 6 3 11 2 14 5 11 3 8 2 8 3 4 27 4 8 3 9 3 12 3 3 16 3 24 19 8 5 5 8 5 6 4 9 2 28 5 21 8 2 4 11 7 22 7 13 8 5 6 4 22 4 6 3 14 3 4 26 7 9 3 12 5 6 2 4 9 4 15 19 8 5 +cv_jpn_000827 15 19 6 2 10 7 9 4 9 4 13 16 5 13 8 3 6 2 13 29 3 3 8 3 10 3 6 2 13 6 5 5 17 2 11 3 8 3 6 3 3 4 9 3 6 2 13 29 5 5 14 5 2 10 4 +cv_jpn_000828 4 4 12 2 9 4 6 3 3 9 3 6 3 8 3 25 2 9 3 4 11 4 23 3 3 15 4 2 11 2 15 19 8 2 +cv_jpn_000829 16 5 6 4 16 2 9 2 9 2 26 12 2 10 4 11 2 12 18 +cv_jpn_000830 6 3 27 4 10 2 25 2 6 3 25 7 23 2 15 4 4 12 2 13 14 5 12 18 +cv_jpn_000831 11 3 15 4 4 11 2 15 4 +cv_jpn_000832 6 3 6 3 17 2 3 6 4 6 18 8 5 9 4 6 7 23 2 6 2 9 2 11 2 27 4 14 5 12 18 +cv_jpn_000833 12 3 9 3 7 27 4 6 2 4 23 2 6 18 12 2 10 5 10 7 6 2 10 2 4 12 3 16 5 +cv_jpn_000834 2 11 2 12 2 16 2 31 7 12 2 4 10 2 10 5 8 5 8 5 27 3 3 14 3 4 +cv_jpn_000835 24 3 16 5 13 15 19 26 7 9 3 14 3 3 2 2 6 5 8 2 +cv_jpn_000836 11 3 14 2 13 9 4 3 3 17 2 21 8 5 11 3 6 4 9 4 15 4 9 2 4 +cv_jpn_000837 2 10 4 16 2 21 8 2 23 2 +cv_jpn_000838 4 8 3 3 16 2 10 2 6 7 14 2 8 3 22 4 6 2 13 17 2 3 12 7 10 5 8 5 8 2 9 3 15 4 11 5 10 7 +cv_jpn_000839 6 2 6 2 6 7 17 2 16 4 28 7 26 18 6 2 12 2 10 5 10 7 9 4 3 22 4 8 5 22 3 3 15 19 6 4 9 3 7 27 4 9 4 24 2 4 21 8 5 4 7 6 7 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..27aaff9c007c248f11e0e9472acb12f0e927a96d --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/score @@ -0,0 +1,40 @@ +cv_jpn_000840 tensor(-10.7257) +cv_jpn_000841 tensor(-4.3035) +cv_jpn_000842 tensor(-2.5631) +cv_jpn_000843 tensor(-1.9402) +cv_jpn_000844 tensor(-7.1149) +cv_jpn_000845 tensor(-3.7933) +cv_jpn_000846 tensor(-7.7386) +cv_jpn_000847 tensor(-3.2881) +cv_jpn_000848 tensor(-4.2617) +cv_jpn_000849 tensor(-2.8019) +cv_jpn_000850 tensor(-2.5225) +cv_jpn_000851 tensor(-2.8504) +cv_jpn_000852 tensor(-3.6251) +cv_jpn_000853 tensor(-5.8141) +cv_jpn_000854 tensor(-10.3066) +cv_jpn_000855 tensor(-3.2725) +cv_jpn_000856 tensor(-6.5354) +cv_jpn_000857 tensor(-4.9037) +cv_jpn_000858 tensor(-3.9820) +cv_jpn_000859 tensor(-7.4239) +cv_jpn_000860 tensor(-4.7633) +cv_jpn_000861 tensor(-4.5496) +cv_jpn_000862 tensor(-9.3958) +cv_jpn_000863 tensor(-13.1167) +cv_jpn_000864 tensor(-7.5129) +cv_jpn_000865 tensor(-3.3878) +cv_jpn_000866 tensor(-8.7559) +cv_jpn_000867 tensor(-3.3318) +cv_jpn_000868 tensor(-2.2427) +cv_jpn_000869 tensor(-4.5921) +cv_jpn_000870 tensor(-2.1736) +cv_jpn_000871 tensor(-5.9992) +cv_jpn_000872 tensor(-7.7549) +cv_jpn_000873 tensor(-9.0724) +cv_jpn_000874 tensor(-4.1596) +cv_jpn_000875 tensor(-11.8180) +cv_jpn_000876 tensor(-3.5649) +cv_jpn_000877 tensor(-2.0670) +cv_jpn_000878 tensor(-2.2402) +cv_jpn_000879 tensor(-6.1698) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..9572fcc303cefd3faf13eaa3168a65990d1ec01c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/text @@ -0,0 +1,40 @@ +cv_jpn_000840 sh I k a sh I t o k i g a k a o n i h a i r u k o t o s o n o k o t o g a pau m i r a a y o o m u k o t o d e a r i w a r a t a n a r u i cl s u t a i g a d e t e k u r u k o t o d e a r u +cv_jpn_000841 t e r e b i o k a i k a i t e pau t e r e b i o m i r u j i k a N g a f u e t a +cv_jpn_000842 k a k a r i sh I t a i n o m i i ts u m a d e m o i k i r u n o d e a r u +cv_jpn_000843 n i N k i d a a m e N i y a n i g a r a N d a r a n i j i k a N m a ch i d a cl t a +cv_jpn_000844 s o r e o m o ch i i r u n i N g e N n o i y o k u n i i t o N sh i s o sh I t e k o r e w a k a r e n o m o cl t e i r u k a ch i n o sh a k u d o n i i t o N s u r u +cv_jpn_000845 m a w a r u i o w a m i N n a k a N g a e r u k o t o o y a m e t e i t a +cv_jpn_000846 k o o i t e k i ch o k cl U k a N t e k i n i s e k a y o m i r u t o i u k o t o w a j a k u n i k o o i t e k i ch o cl k a N t e k i n i s e k a y o k e e s e e s u r u k o t o o h k u m u n o d e a r u +cv_jpn_000847 sh i N cl p a i t a k e s a s e m a i t o s u r u k i z u k a i g a y o k e e n i sh i N p a i s a s e t e sh i m a u +cv_jpn_000848 k o n o m i ch i w a t o t e m o s e m a i n o d e a b n a i d e s U +cv_jpn_000849 w o h o e g a a r i n i +cv_jpn_000850 t o i w a r o o k a m o i t a r i g a a n i a r i m a s U +cv_jpn_000851 t a n a k a s a N n o h i t a i n i k i m u r a s a N g a i m a s U +cv_jpn_000852 m a cl k u r a n o t a m a b o t e s u g o i n i e +cv_jpn_000853 sh o h o o m i t a i n a d o k U sh u k a N s o o b u N m o k a i t a +cv_jpn_000854 g e N j i ts u n o s e k a i w a t a m o o i ch i t o sh I t e k e cl t e s u r a i d a k a t a sh u o a cl t o s e k a i z u n a k e r e m a n a r a n a i +cv_jpn_000855 sh o o h i N k e N s a k u g a o k a r e a s u i t o o k a u k i i n a r u n a i +cv_jpn_000856 ts i sh I k i w a pau r e k I sh I t e cl k a t e e r d e n a k e r e m a n a r a n a i +cv_jpn_000857 m o n o g o t o n N j i N p a N k a e r u d a k e d e u m a k u i k U k o t o m o w a r u +cv_jpn_000858 k o n o k i s e e ts u w a k a ts u o n o s a sh i m i g a z e cl p i N +cv_jpn_000859 k a k e n i sh i cl p a i sh I t e m o o ch ts U ts u i t e s a N sh I ts u o u k e i d e r u +cv_jpn_000860 s o r e y u e n i t e ts u g a p u g a z e N t a i n o g a k o d e a r u t o s u r e w a +cv_jpn_000861 k i i z a n a y a o y a d a g a y e s U k U t e h a N j o sh I t e r u +cv_jpn_000862 i n i f u r a g a a k i n o h u z e N n i y o ch i i cl t e pau k o k u g a i e d a sh U ts u s u r u h I t o m o d e t e k i t a +cv_jpn_000863 ts u g i n i k a g a k u w a s o N z a y o pau sh u j u n o d o o e k i n i w a k a cl t e s o r e z u r a N ry o o u k i N z i t I k e N i k I s u r u +cv_jpn_000864 s o r e d e w a pau t o k t o i u m o n o n o s e r i ts u sh i o w a n a k u pau sh u N k a N t o i m o n o m o n a k u a r u n o d e a r u +cv_jpn_000865 a k a i b u r a N k o h o N k u r i t o s e e n o s u b e r i d a i k a w a i t a s u n a b a +cv_jpn_000866 sh I k a sh i s o r e w a d o k o m a d e m N k U k o k a r a r i t e pau p o k o e k a e r i k u r u s e sh I ts o m o t o m o d e n a k e b a n a r a n a i +cv_jpn_000867 a r i t o w a r a i r u d e m o o m a k I ch i r a sh I t e pau m i N n e k a r a u r a m i o k a cl t e r u +cv_jpn_000868 k o n o cl t e e d o s a w a g i n i n a r u k o t o m o n a i n o d a r o o +cv_jpn_000869 k o n o n e d a N d e w u r i ch a N k a a N +cv_jpn_000870 h i n o k a g e N n i ch u i sh i n a i t o s u g u k o g e r u +cv_jpn_000871 e N m a N d o w e n i p o ts u r i t o ch i i s a n a a n a g a i t a s a i sh o w a ts u m a y o o j i t e e d o n o ch i i s a n a a n a d a cl t a +cv_jpn_000872 s o r e w a pau a r e w a r e o i k a sh i n a g a r a w a r e w a r e o t o r e e k a s u r u n o d e a r u pau w a r e b a r e n o t a m a sh i i o k o r o s u n o d e a r u +cv_jpn_000873 r e k I sh I t e k i n i a t a e r a r t a m o n o w a d e cl t a i m u j u N t e k i j i g o t o o i ts u t e k i g e N z a i n o i t e s U k a i h I t e k i n i a t a e r a r e t a m o n o t o sh I t i +cv_jpn_000874 m u j u N t e k i j i g o d o o i ts U t o sh I t e pau i ts u m o k o n o s e k a i n i ch o o e sh t k i d e a +cv_jpn_000875 y u u n i z e cl t a e m u j u N t e k i j i g o d o o i ts U t o sh I t e g e N s a i k a r a g e N z a e t o b o k i i k u s e k a i n o g e N z a i n o i t e +cv_jpn_000876 h a r e pau w o t a N o sh I t o m N d a sh u d e k i n a i +cv_jpn_000877 sh I k a sh i w a t a sh i a s o k o n i s e k a i n o j i k o d o o i ts u o k u n o d e w a n a i +cv_jpn_000878 n e b e k a k u n a r u n o g a h a y a k u n a cl t a +cv_jpn_000879 w a t a sh i w a i N g e N n o o r e k I sh I t e k I k e e s e e n o t a ch i b a k a r a g e j u ts u o m i r u n o d e a cl t e o o sh a k a r a d e N sh a o m i r u n o d e w a n a i diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..9572fcc303cefd3faf13eaa3168a65990d1ec01c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/token @@ -0,0 +1,40 @@ +cv_jpn_000840 sh I k a sh I t o k i g a k a o n i h a i r u k o t o s o n o k o t o g a pau m i r a a y o o m u k o t o d e a r i w a r a t a n a r u i cl s u t a i g a d e t e k u r u k o t o d e a r u +cv_jpn_000841 t e r e b i o k a i k a i t e pau t e r e b i o m i r u j i k a N g a f u e t a +cv_jpn_000842 k a k a r i sh I t a i n o m i i ts u m a d e m o i k i r u n o d e a r u +cv_jpn_000843 n i N k i d a a m e N i y a n i g a r a N d a r a n i j i k a N m a ch i d a cl t a +cv_jpn_000844 s o r e o m o ch i i r u n i N g e N n o i y o k u n i i t o N sh i s o sh I t e k o r e w a k a r e n o m o cl t e i r u k a ch i n o sh a k u d o n i i t o N s u r u +cv_jpn_000845 m a w a r u i o w a m i N n a k a N g a e r u k o t o o y a m e t e i t a +cv_jpn_000846 k o o i t e k i ch o k cl U k a N t e k i n i s e k a y o m i r u t o i u k o t o w a j a k u n i k o o i t e k i ch o cl k a N t e k i n i s e k a y o k e e s e e s u r u k o t o o h k u m u n o d e a r u +cv_jpn_000847 sh i N cl p a i t a k e s a s e m a i t o s u r u k i z u k a i g a y o k e e n i sh i N p a i s a s e t e sh i m a u +cv_jpn_000848 k o n o m i ch i w a t o t e m o s e m a i n o d e a b n a i d e s U +cv_jpn_000849 w o h o e g a a r i n i +cv_jpn_000850 t o i w a r o o k a m o i t a r i g a a n i a r i m a s U +cv_jpn_000851 t a n a k a s a N n o h i t a i n i k i m u r a s a N g a i m a s U +cv_jpn_000852 m a cl k u r a n o t a m a b o t e s u g o i n i e +cv_jpn_000853 sh o h o o m i t a i n a d o k U sh u k a N s o o b u N m o k a i t a +cv_jpn_000854 g e N j i ts u n o s e k a i w a t a m o o i ch i t o sh I t e k e cl t e s u r a i d a k a t a sh u o a cl t o s e k a i z u n a k e r e m a n a r a n a i +cv_jpn_000855 sh o o h i N k e N s a k u g a o k a r e a s u i t o o k a u k i i n a r u n a i +cv_jpn_000856 ts i sh I k i w a pau r e k I sh I t e cl k a t e e r d e n a k e r e m a n a r a n a i +cv_jpn_000857 m o n o g o t o n N j i N p a N k a e r u d a k e d e u m a k u i k U k o t o m o w a r u +cv_jpn_000858 k o n o k i s e e ts u w a k a ts u o n o s a sh i m i g a z e cl p i N +cv_jpn_000859 k a k e n i sh i cl p a i sh I t e m o o ch ts U ts u i t e s a N sh I ts u o u k e i d e r u +cv_jpn_000860 s o r e y u e n i t e ts u g a p u g a z e N t a i n o g a k o d e a r u t o s u r e w a +cv_jpn_000861 k i i z a n a y a o y a d a g a y e s U k U t e h a N j o sh I t e r u +cv_jpn_000862 i n i f u r a g a a k i n o h u z e N n i y o ch i i cl t e pau k o k u g a i e d a sh U ts u s u r u h I t o m o d e t e k i t a +cv_jpn_000863 ts u g i n i k a g a k u w a s o N z a y o pau sh u j u n o d o o e k i n i w a k a cl t e s o r e z u r a N ry o o u k i N z i t I k e N i k I s u r u +cv_jpn_000864 s o r e d e w a pau t o k t o i u m o n o n o s e r i ts u sh i o w a n a k u pau sh u N k a N t o i m o n o m o n a k u a r u n o d e a r u +cv_jpn_000865 a k a i b u r a N k o h o N k u r i t o s e e n o s u b e r i d a i k a w a i t a s u n a b a +cv_jpn_000866 sh I k a sh i s o r e w a d o k o m a d e m N k U k o k a r a r i t e pau p o k o e k a e r i k u r u s e sh I ts o m o t o m o d e n a k e b a n a r a n a i +cv_jpn_000867 a r i t o w a r a i r u d e m o o m a k I ch i r a sh I t e pau m i N n e k a r a u r a m i o k a cl t e r u +cv_jpn_000868 k o n o cl t e e d o s a w a g i n i n a r u k o t o m o n a i n o d a r o o +cv_jpn_000869 k o n o n e d a N d e w u r i ch a N k a a N +cv_jpn_000870 h i n o k a g e N n i ch u i sh i n a i t o s u g u k o g e r u +cv_jpn_000871 e N m a N d o w e n i p o ts u r i t o ch i i s a n a a n a g a i t a s a i sh o w a ts u m a y o o j i t e e d o n o ch i i s a n a a n a d a cl t a +cv_jpn_000872 s o r e w a pau a r e w a r e o i k a sh i n a g a r a w a r e w a r e o t o r e e k a s u r u n o d e a r u pau w a r e b a r e n o t a m a sh i i o k o r o s u n o d e a r u +cv_jpn_000873 r e k I sh I t e k i n i a t a e r a r t a m o n o w a d e cl t a i m u j u N t e k i j i g o t o o i ts u t e k i g e N z a i n o i t e s U k a i h I t e k i n i a t a e r a r e t a m o n o t o sh I t i +cv_jpn_000874 m u j u N t e k i j i g o d o o i ts U t o sh I t e pau i ts u m o k o n o s e k a i n i ch o o e sh t k i d e a +cv_jpn_000875 y u u n i z e cl t a e m u j u N t e k i j i g o d o o i ts U t o sh I t e g e N s a i k a r a g e N z a e t o b o k i i k u s e k a i n o g e N z a i n o i t e +cv_jpn_000876 h a r e pau w o t a N o sh I t o m N d a sh u d e k i n a i +cv_jpn_000877 sh I k a sh i w a t a sh i a s o k o n i s e k a i n o j i k o d o o i ts u o k u n o d e w a n a i +cv_jpn_000878 n e b e k a k u n a r u n o g a h a y a k u n a cl t a +cv_jpn_000879 w a t a sh i w a i N g e N n o o r e k I sh I t e k I k e e s e e n o t a ch i b a k a r a g e j u ts u o m i r u n o d e a cl t e o o sh a k a r a d e N sh a o m i r u n o d e w a n a i diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..ecaea9e4586201e74c321af5fada6030e86d18d9 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.2/1best_recog/token_int @@ -0,0 +1,40 @@ +cv_jpn_000840 15 19 6 2 15 19 8 3 6 4 16 2 6 2 3 9 4 24 2 4 10 7 6 3 8 3 12 3 9 3 6 3 8 3 16 2 20 11 4 10 2 2 23 3 3 11 7 6 3 8 3 14 5 2 10 4 17 2 10 2 8 2 9 2 10 7 4 21 12 7 8 2 4 16 2 14 5 8 5 6 7 10 7 6 3 8 3 14 5 2 10 7 +cv_jpn_000841 8 5 10 5 25 4 3 6 2 4 6 2 4 8 5 20 8 5 10 5 25 4 3 11 4 10 7 22 4 6 2 13 16 2 31 7 5 8 2 +cv_jpn_000842 6 2 6 2 10 4 15 19 8 2 4 9 3 11 4 4 26 7 11 2 14 5 11 3 4 6 4 10 7 9 3 14 5 2 10 7 +cv_jpn_000843 9 4 13 6 4 14 2 2 11 5 13 4 23 2 9 4 16 2 10 2 13 14 2 10 2 9 4 22 4 6 2 13 11 2 27 4 14 2 21 8 2 +cv_jpn_000844 12 3 10 5 3 11 3 27 4 4 10 7 9 4 13 16 5 13 9 3 4 23 3 6 7 9 4 4 8 3 13 15 4 12 3 15 19 8 5 6 3 10 5 17 2 6 2 10 5 9 3 11 3 21 8 5 4 10 7 6 2 27 4 9 3 15 2 6 7 14 3 9 4 4 8 3 13 12 7 10 7 +cv_jpn_000845 11 2 17 2 10 7 4 3 17 2 11 4 13 9 2 6 2 13 16 2 5 10 7 6 3 8 3 3 23 2 11 5 8 5 4 8 2 +cv_jpn_000846 6 3 3 4 8 5 6 4 27 3 6 21 18 6 2 13 8 5 6 4 9 4 12 5 6 2 23 3 11 4 10 7 8 3 4 7 6 3 8 3 17 2 22 2 6 7 9 4 6 3 3 4 8 5 6 4 27 3 21 6 2 13 8 5 6 4 9 4 12 5 6 2 23 3 6 5 5 12 5 5 12 7 10 7 6 3 8 3 3 24 6 7 11 7 9 3 14 5 2 10 7 +cv_jpn_000847 15 4 13 21 30 2 4 8 2 6 5 12 2 12 5 11 2 4 8 3 12 7 10 7 6 4 28 7 6 2 4 16 2 23 3 6 5 5 9 4 15 4 13 30 2 4 12 2 12 5 8 5 15 4 11 2 7 +cv_jpn_000848 6 3 9 3 11 4 27 4 17 2 8 3 8 5 11 3 12 5 11 2 4 9 3 14 5 2 25 9 2 4 14 5 12 18 +cv_jpn_000849 17 3 24 3 5 16 2 2 10 4 9 4 +cv_jpn_000850 8 3 4 17 2 10 3 3 6 2 11 3 4 8 2 10 4 16 2 2 9 4 2 10 4 11 2 12 18 +cv_jpn_000851 8 2 9 2 6 2 12 2 13 9 3 24 4 8 2 4 9 4 6 4 11 7 10 2 12 2 13 16 2 4 11 2 12 18 +cv_jpn_000852 11 2 21 6 7 10 2 9 3 8 2 11 2 25 3 8 5 12 7 16 3 4 9 4 5 +cv_jpn_000853 15 3 24 3 3 11 4 8 2 4 9 2 14 3 6 18 15 7 6 2 13 12 3 3 25 7 13 11 3 6 2 4 8 2 +cv_jpn_000854 16 5 13 22 4 26 7 9 3 12 5 6 2 4 17 2 8 2 11 3 3 4 27 4 8 3 15 19 8 5 6 5 21 8 5 12 7 10 2 4 14 2 6 2 8 2 15 7 3 2 21 8 3 12 5 6 2 4 28 7 9 2 6 5 10 5 11 2 9 2 10 2 9 2 4 +cv_jpn_000855 15 3 3 24 4 13 6 5 13 12 2 6 7 16 2 3 6 2 10 5 2 12 7 4 8 3 3 6 2 7 6 4 4 9 2 10 7 9 2 4 +cv_jpn_000856 26 4 15 19 6 4 17 2 20 10 5 6 19 15 19 8 5 21 6 2 8 5 5 10 14 5 9 2 6 5 10 5 11 2 9 2 10 2 9 2 4 +cv_jpn_000857 11 3 9 3 16 3 8 3 9 13 22 4 13 30 2 13 6 2 5 10 7 14 2 6 5 14 5 7 11 2 6 7 4 6 18 6 3 8 3 11 3 17 2 10 7 +cv_jpn_000858 6 3 9 3 6 4 12 5 5 26 7 17 2 6 2 26 7 3 9 3 12 2 15 4 11 4 16 2 28 5 21 30 4 13 +cv_jpn_000859 6 2 6 5 9 4 15 4 21 30 2 4 15 19 8 5 11 3 3 27 26 18 26 7 4 8 5 12 2 13 15 19 26 7 3 7 6 5 4 14 5 10 7 +cv_jpn_000860 12 3 10 5 23 7 5 9 4 8 5 26 7 16 2 30 7 16 2 28 5 13 8 2 4 9 3 16 2 6 3 14 5 2 10 7 8 3 12 7 10 5 17 2 +cv_jpn_000861 6 4 4 28 2 9 2 23 2 3 23 2 14 2 16 2 23 5 12 18 6 18 8 5 24 2 13 22 3 15 19 8 5 10 7 +cv_jpn_000862 4 9 4 31 7 10 2 16 2 2 6 4 9 3 24 7 28 5 13 9 4 23 3 27 4 4 21 8 5 20 6 3 6 7 16 2 4 5 14 2 15 18 26 7 12 7 10 7 24 19 8 3 11 3 14 5 8 5 6 4 8 2 +cv_jpn_000863 26 7 16 4 9 4 6 2 16 2 6 7 17 2 12 3 13 28 2 23 3 20 15 7 22 7 9 3 14 3 3 5 6 4 9 4 17 2 6 2 21 8 5 12 3 10 5 28 7 10 2 13 32 3 3 7 6 4 13 28 4 8 19 6 5 13 4 6 19 12 7 10 7 +cv_jpn_000864 12 3 10 5 14 5 17 2 20 8 3 6 8 3 4 7 11 3 9 3 9 3 12 5 10 4 26 7 15 4 3 17 2 9 2 6 7 20 15 7 13 6 2 13 8 3 4 11 3 9 3 11 3 9 2 6 7 2 10 7 9 3 14 5 2 10 7 +cv_jpn_000865 2 6 2 4 25 7 10 2 13 6 3 24 3 13 6 7 10 4 8 3 12 5 5 9 3 12 7 25 5 10 4 14 2 4 6 2 17 2 4 8 2 12 7 9 2 25 2 +cv_jpn_000866 15 19 6 2 15 4 12 3 10 5 17 2 14 3 6 3 11 2 14 5 11 13 6 18 6 3 6 2 10 2 10 4 8 5 20 30 3 6 3 5 6 2 5 10 4 6 7 10 7 12 5 15 19 26 3 11 3 8 3 11 3 14 5 9 2 6 5 25 2 9 2 10 2 9 2 4 +cv_jpn_000867 2 10 4 8 3 17 2 10 2 4 10 7 14 5 11 3 3 11 2 6 19 27 4 10 2 15 19 8 5 20 11 4 13 9 5 6 2 10 2 7 10 2 11 4 3 6 2 21 8 5 10 7 +cv_jpn_000868 6 3 9 3 21 8 5 5 14 3 12 2 17 2 16 4 9 4 9 2 10 7 6 3 8 3 11 3 9 2 4 9 3 14 2 10 3 3 +cv_jpn_000869 6 3 9 3 9 5 14 2 13 14 5 17 7 10 4 27 2 13 6 2 2 13 +cv_jpn_000870 24 4 9 3 6 2 16 5 13 9 4 27 7 4 15 4 9 2 4 8 3 12 7 16 7 6 3 16 5 10 7 +cv_jpn_000871 5 13 11 2 13 14 3 17 5 9 4 30 3 26 7 10 4 8 3 27 4 4 12 2 9 2 2 9 2 16 2 4 8 2 12 2 4 15 3 17 2 26 7 11 2 23 3 3 22 4 8 5 5 14 3 9 3 27 4 4 12 2 9 2 2 9 2 14 2 21 8 2 +cv_jpn_000872 12 3 10 5 17 2 20 2 10 5 17 2 10 5 3 4 6 2 15 4 9 2 16 2 10 2 17 2 10 5 17 2 10 5 3 8 3 10 5 5 6 2 12 7 10 7 9 3 14 5 2 10 7 20 17 2 10 5 25 2 10 5 9 3 8 2 11 2 15 4 4 3 6 3 10 3 12 7 9 3 14 5 2 10 7 +cv_jpn_000873 10 5 6 19 15 19 8 5 6 4 9 4 2 8 2 5 10 2 10 8 2 11 3 9 3 17 2 14 5 21 8 2 4 11 7 22 7 13 8 5 6 4 22 4 16 3 8 3 3 4 26 7 8 5 6 4 16 5 13 28 2 4 9 3 4 8 5 12 18 6 2 4 24 19 8 5 6 4 9 4 2 8 2 5 10 2 10 5 8 2 11 3 9 3 8 3 15 19 8 4 +cv_jpn_000874 11 7 22 7 13 8 5 6 4 22 4 16 3 14 3 3 4 26 18 8 3 15 19 8 5 20 4 26 7 11 3 6 3 9 3 12 5 6 2 4 9 4 27 3 3 5 15 8 6 4 14 5 2 +cv_jpn_000875 23 7 7 9 4 28 5 21 8 2 5 11 7 22 7 13 8 5 6 4 22 4 16 3 14 3 3 4 26 18 8 3 15 19 8 5 16 5 13 12 2 4 6 2 10 2 16 5 13 28 2 5 8 3 25 3 6 4 4 6 7 12 5 6 2 4 9 3 16 5 13 28 2 4 9 3 4 8 5 +cv_jpn_000876 24 2 10 5 20 17 3 8 2 13 3 15 19 8 3 11 13 14 2 15 7 14 5 6 4 9 2 4 +cv_jpn_000877 15 19 6 2 15 4 17 2 8 2 15 4 2 12 3 6 3 9 4 12 5 6 2 4 9 3 22 4 6 3 14 3 3 4 26 7 3 6 7 9 3 14 5 17 2 9 2 4 +cv_jpn_000878 9 5 25 5 6 2 6 7 9 2 10 7 9 3 16 2 24 2 23 2 6 7 9 2 21 8 2 +cv_jpn_000879 17 2 8 2 15 4 17 2 4 13 16 5 13 9 3 3 10 5 6 19 15 19 8 5 6 19 6 5 5 12 5 5 9 3 8 2 27 4 25 2 6 2 10 2 16 5 22 7 26 7 3 11 4 10 7 9 3 14 5 2 21 8 5 3 3 15 2 6 2 10 2 14 5 13 15 2 3 11 4 10 7 9 3 14 5 17 2 9 2 4 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..cd565d75f119d753b424fc96279c9db63f99c59a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/score @@ -0,0 +1,40 @@ +cv_jpn_000880 tensor(-4.2964) +cv_jpn_000881 tensor(-6.6556) +cv_jpn_000882 tensor(-2.7405) +cv_jpn_000883 tensor(-4.4585) +cv_jpn_000884 tensor(-3.0480) +cv_jpn_000885 tensor(-3.2873) +cv_jpn_000886 tensor(-3.8877) +cv_jpn_000887 tensor(-10.2507) +cv_jpn_000888 tensor(-2.6494) +cv_jpn_000889 tensor(-2.3477) +cv_jpn_000890 tensor(-4.0468) +cv_jpn_000891 tensor(-5.4691) +cv_jpn_000892 tensor(-1.6660) +cv_jpn_000893 tensor(-3.7962) +cv_jpn_000894 tensor(-1.5321) +cv_jpn_000895 tensor(-1.9720) +cv_jpn_000896 tensor(-2.5488) +cv_jpn_000897 tensor(-1.5819) +cv_jpn_000898 tensor(-2.1431) +cv_jpn_000899 tensor(-0.7946) +cv_jpn_000900 tensor(-0.3835) +cv_jpn_000901 tensor(-1.3530) +cv_jpn_000902 tensor(-1.0515) +cv_jpn_000903 tensor(-1.3782) +cv_jpn_000904 tensor(-0.3892) +cv_jpn_000905 tensor(-0.8825) +cv_jpn_000906 tensor(-0.5737) +cv_jpn_000907 tensor(-0.2051) +cv_jpn_000908 tensor(-3.2743) +cv_jpn_000909 tensor(-7.1312) +cv_jpn_000910 tensor(-6.3916) +cv_jpn_000911 tensor(-2.9724) +cv_jpn_000912 tensor(-1.7722) +cv_jpn_000913 tensor(-6.0145) +fleurs_jpn_000346 tensor(-8.3701) +fleurs_jpn_000347 tensor(-15.9440) +fleurs_jpn_000348 tensor(-19.7870) +fleurs_jpn_000349 tensor(-7.5196) +fleurs_jpn_000350 tensor(-11.6380) +fleurs_jpn_000351 tensor(-11.1524) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..d8bad12bc52bfc75592f28f194f79bf483c12285 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/text @@ -0,0 +1,40 @@ +cv_jpn_000880 a o i t o m a t o sh I k a n a k U t e k a u k a b m a i y o +cv_jpn_000881 s i N k i z u i gy o o n i o o k i n a k I t a y o y o s U t e i r u +cv_jpn_000882 n a n i k a sh i r a n o i n i N s e N t i b u w a n a i t o k i b i sh i i n o d e w a +cv_jpn_000883 j i k o N sh e e g e N n o i b e N t o d e s U t o r u sh I t a m a r u b u +cv_jpn_000884 m a a r i n o h I t o w a b o o z e N t o sh I t e i t a +cv_jpn_000885 s o N n a n a i y o o n o m e e r e w a n a N k e N m o k U I t e i t a +cv_jpn_000886 n j i k a i d r d e e s u i sh I t e i t a +cv_jpn_000887 t o k i d o k i ch i u N n o k o r o w a w a k a r a n a k u n o r u t o k i g a a r u d a k a r a b o k u w a k a n i o cl k i n o o t o n i k a ch i a j i m e r u +cv_jpn_000888 m o o n i g e t e ch a t a m e d a +cv_jpn_000889 k a r e w a p o o t o t a j i ts U k u sh i t e i t a +cv_jpn_000890 d a r a u n i m o m e i w a k o w a k a k e t a k u n a i +cv_jpn_000891 w a s a k a t o o m o t e t o w a n o o t o cl t e o n i g e cl t a +cv_jpn_000892 sh t e i m a s e N +cv_jpn_000893 k a y o o n i sh I t e sh I cl t e i r u t o t o m o n i sh i cl t e i n a i t o k o r o k a r a t a N ky u u w a h a j i m a r u n o d e a r u +cv_jpn_000894 a i s a u w a d a i j i d a i o +cv_jpn_000895 t o j i t a m o n o i k a n i h i r o g e t e m o h i r a i t a m o n o n i a n a r a r o t o i cl t e i r u g a +cv_jpn_000896 t a m e sh i n i i k u ts U k a ts U k u cl t e m i y o a +cv_jpn_000897 z u i b u N a k o g i n a sh o o b a i d a i o n a +cv_jpn_000898 w a t e i +cv_jpn_000899 i ch i +cv_jpn_000900 g o +cv_jpn_000901 sh i k i +cv_jpn_000902 i i e +cv_jpn_000903 h a ch i +cv_jpn_000904 n e i +cv_jpn_000905 sh i i +cv_jpn_000906 k u +cv_jpn_000907 i ch i +cv_jpn_000908 k a k a k u g a a k i r a k a n i s u r u pau ky a cl k a N t e k I sh i N r i n i sh I t a g a u k o t o n i y o cl t e +cv_jpn_000909 k a k o t o m i r a i t o n o m u j u N t e k i j i k o d o o i ts u t o sh I t e n o g e N z a i g a pau k a t a ch i o m o sh U t o i u k o t o d e a r u +cv_jpn_000910 b u ts u r i t e k I s e k a i w a pau s u u g a k u t e k I k i g o o n i y o cl t e a r a w a s a r e r u pau s u u g a k u t e k I k a t a ch i n o sh e k a i d e a r u +cv_jpn_000911 w o n a j i g e N sh o o d e s a N k o o g i n a r u +cv_jpn_000912 g a i k o k u k a r a k i t a m o n o d a t o sh i cl t e b i cl k u r i +cv_jpn_000913 i w a y o r u j i cl s e N n i y o cl t e k a k U t o U k U sh i k i t a cl t a m o n o d e a r u +fleurs_jpn_000346 o n a j i y o n i d a N s e e w a h i z a o o u z u b o N o h a cl k o t o g a pau g i m u u z u k e r a r e t e i m a s U +fleurs_jpn_000347 k o n o s a a b i s a k o r a k U s e e w a h a j i m e t o s u r u s e N p a k u y a e N k a k U ch i d e pau d e e t a y a o N s e o h I s u o t o s u r u t a N g e N t a i n i pau h i N cl p a n i r i y o o s a r e t e i m a s U +fleurs_jpn_000348 ky o o f u u hy o k a t o n o k o o s u i r o o o b i a m a k a sh i b a r a i u t a ts u m a k i i z u k i o b e s a i k u r n o d a n o k i b i sh i i k I sh o k e e t a y a s o n o e e ky o n i y o r u m o n o r e s U +fleurs_jpn_000349 i N t a a n e cl t o w a m a s U k o m i r i k e e sh u N t o t a i j i N k o m i r u i k e e sh o o n o ry o o y o s o o k a n e s u n a e t a k a N ky o o d e s U +fleurs_jpn_000350 k a j i n o r e w a ts u u j o o t o k u b e s u r a i N sh I k o y a e N t a a t e i m e N t o y o o sh I t e i m a s U g e s u w a k i b u i y o k u sh I s e s u n a i n i t o r a m a r u y o o r i s u r u t a m e d e s U +fleurs_jpn_000351 sh I k a sh i pau k a k u t e N n o b i k e cl t o o sh i n a cl t a a d t o i N d o w a n a n a ts u n o b i k e cl t o o sh i n a i s a N j u u r o k u r a sh I k a d e k i m a s e N d e sh i t a diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..d8bad12bc52bfc75592f28f194f79bf483c12285 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/token @@ -0,0 +1,40 @@ +cv_jpn_000880 a o i t o m a t o sh I k a n a k U t e k a u k a b m a i y o +cv_jpn_000881 s i N k i z u i gy o o n i o o k i n a k I t a y o y o s U t e i r u +cv_jpn_000882 n a n i k a sh i r a n o i n i N s e N t i b u w a n a i t o k i b i sh i i n o d e w a +cv_jpn_000883 j i k o N sh e e g e N n o i b e N t o d e s U t o r u sh I t a m a r u b u +cv_jpn_000884 m a a r i n o h I t o w a b o o z e N t o sh I t e i t a +cv_jpn_000885 s o N n a n a i y o o n o m e e r e w a n a N k e N m o k U I t e i t a +cv_jpn_000886 n j i k a i d r d e e s u i sh I t e i t a +cv_jpn_000887 t o k i d o k i ch i u N n o k o r o w a w a k a r a n a k u n o r u t o k i g a a r u d a k a r a b o k u w a k a n i o cl k i n o o t o n i k a ch i a j i m e r u +cv_jpn_000888 m o o n i g e t e ch a t a m e d a +cv_jpn_000889 k a r e w a p o o t o t a j i ts U k u sh i t e i t a +cv_jpn_000890 d a r a u n i m o m e i w a k o w a k a k e t a k u n a i +cv_jpn_000891 w a s a k a t o o m o t e t o w a n o o t o cl t e o n i g e cl t a +cv_jpn_000892 sh t e i m a s e N +cv_jpn_000893 k a y o o n i sh I t e sh I cl t e i r u t o t o m o n i sh i cl t e i n a i t o k o r o k a r a t a N ky u u w a h a j i m a r u n o d e a r u +cv_jpn_000894 a i s a u w a d a i j i d a i o +cv_jpn_000895 t o j i t a m o n o i k a n i h i r o g e t e m o h i r a i t a m o n o n i a n a r a r o t o i cl t e i r u g a +cv_jpn_000896 t a m e sh i n i i k u ts U k a ts U k u cl t e m i y o a +cv_jpn_000897 z u i b u N a k o g i n a sh o o b a i d a i o n a +cv_jpn_000898 w a t e i +cv_jpn_000899 i ch i +cv_jpn_000900 g o +cv_jpn_000901 sh i k i +cv_jpn_000902 i i e +cv_jpn_000903 h a ch i +cv_jpn_000904 n e i +cv_jpn_000905 sh i i +cv_jpn_000906 k u +cv_jpn_000907 i ch i +cv_jpn_000908 k a k a k u g a a k i r a k a n i s u r u pau ky a cl k a N t e k I sh i N r i n i sh I t a g a u k o t o n i y o cl t e +cv_jpn_000909 k a k o t o m i r a i t o n o m u j u N t e k i j i k o d o o i ts u t o sh I t e n o g e N z a i g a pau k a t a ch i o m o sh U t o i u k o t o d e a r u +cv_jpn_000910 b u ts u r i t e k I s e k a i w a pau s u u g a k u t e k I k i g o o n i y o cl t e a r a w a s a r e r u pau s u u g a k u t e k I k a t a ch i n o sh e k a i d e a r u +cv_jpn_000911 w o n a j i g e N sh o o d e s a N k o o g i n a r u +cv_jpn_000912 g a i k o k u k a r a k i t a m o n o d a t o sh i cl t e b i cl k u r i +cv_jpn_000913 i w a y o r u j i cl s e N n i y o cl t e k a k U t o U k U sh i k i t a cl t a m o n o d e a r u +fleurs_jpn_000346 o n a j i y o n i d a N s e e w a h i z a o o u z u b o N o h a cl k o t o g a pau g i m u u z u k e r a r e t e i m a s U +fleurs_jpn_000347 k o n o s a a b i s a k o r a k U s e e w a h a j i m e t o s u r u s e N p a k u y a e N k a k U ch i d e pau d e e t a y a o N s e o h I s u o t o s u r u t a N g e N t a i n i pau h i N cl p a n i r i y o o s a r e t e i m a s U +fleurs_jpn_000348 ky o o f u u hy o k a t o n o k o o s u i r o o o b i a m a k a sh i b a r a i u t a ts u m a k i i z u k i o b e s a i k u r n o d a n o k i b i sh i i k I sh o k e e t a y a s o n o e e ky o n i y o r u m o n o r e s U +fleurs_jpn_000349 i N t a a n e cl t o w a m a s U k o m i r i k e e sh u N t o t a i j i N k o m i r u i k e e sh o o n o ry o o y o s o o k a n e s u n a e t a k a N ky o o d e s U +fleurs_jpn_000350 k a j i n o r e w a ts u u j o o t o k u b e s u r a i N sh I k o y a e N t a a t e i m e N t o y o o sh I t e i m a s U g e s u w a k i b u i y o k u sh I s e s u n a i n i t o r a m a r u y o o r i s u r u t a m e d e s U +fleurs_jpn_000351 sh I k a sh i pau k a k u t e N n o b i k e cl t o o sh i n a cl t a a d t o i N d o w a n a n a ts u n o b i k e cl t o o sh i n a i s a N j u u r o k u r a sh I k a d e k i m a s e N d e sh i t a diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..096e18110c1549b812feab4711b0885869b3dbfa --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.3/1best_recog/token_int @@ -0,0 +1,40 @@ +cv_jpn_000880 2 3 4 8 3 11 2 8 3 15 19 6 2 9 2 6 18 8 5 6 2 7 6 2 25 11 2 4 23 3 +cv_jpn_000881 12 4 13 6 4 28 7 4 35 3 3 9 4 3 3 6 4 9 2 6 19 8 2 23 3 23 3 12 18 8 5 4 10 7 +cv_jpn_000882 9 2 9 4 6 2 15 4 10 2 9 3 4 9 4 13 12 5 13 8 4 25 7 17 2 9 2 4 8 3 6 4 25 4 15 4 4 9 3 14 5 17 2 +cv_jpn_000883 22 4 6 3 13 15 5 5 16 5 13 9 3 4 25 5 13 8 3 14 5 12 18 8 3 10 7 15 19 8 2 11 2 10 7 25 7 +cv_jpn_000884 11 2 2 10 4 9 3 24 19 8 3 17 2 25 3 3 28 5 13 8 3 15 19 8 5 4 8 2 +cv_jpn_000885 12 3 13 9 2 9 2 4 23 3 3 9 3 11 5 5 10 5 17 2 9 2 13 6 5 13 11 3 6 18 19 8 5 4 8 2 +cv_jpn_000886 9 22 4 6 2 4 14 10 14 5 5 12 7 4 15 19 8 5 4 8 2 +cv_jpn_000887 8 3 6 4 14 3 6 4 27 4 7 13 9 3 6 3 10 3 17 2 17 2 6 2 10 2 9 2 6 7 9 3 10 7 8 3 6 4 16 2 2 10 7 14 2 6 2 10 2 25 3 6 7 17 2 6 2 9 4 3 21 6 4 9 3 3 8 3 9 4 6 2 27 4 2 22 4 11 5 10 7 +cv_jpn_000888 11 3 3 9 4 16 5 8 5 27 2 8 2 11 5 14 2 +cv_jpn_000889 6 2 10 5 17 2 30 3 3 8 3 8 2 22 4 26 18 6 7 15 4 8 5 4 8 2 +cv_jpn_000890 14 2 10 2 7 9 4 11 3 11 5 4 17 2 6 3 17 2 6 2 6 5 8 2 6 7 9 2 4 +cv_jpn_000891 17 2 12 2 6 2 8 3 3 11 3 8 5 8 3 17 2 9 3 3 8 3 21 8 5 3 9 4 16 5 21 8 2 +cv_jpn_000892 15 8 5 4 11 2 12 5 13 +cv_jpn_000893 6 2 23 3 3 9 4 15 19 8 5 15 19 21 8 5 4 10 7 8 3 8 3 11 3 9 4 15 4 21 8 5 4 9 2 4 8 3 6 3 10 3 6 2 10 2 8 2 13 29 7 7 17 2 24 2 22 4 11 2 10 7 9 3 14 5 2 10 7 +cv_jpn_000894 2 4 12 2 7 17 2 14 2 4 22 4 14 2 4 3 +cv_jpn_000895 8 3 22 4 8 2 11 3 9 3 4 6 2 9 4 24 4 10 3 16 5 8 5 11 3 24 4 10 2 4 8 2 11 3 9 3 9 4 2 9 2 10 2 10 3 8 3 4 21 8 5 4 10 7 16 2 +cv_jpn_000896 8 2 11 5 15 4 9 4 4 6 7 26 18 6 2 26 18 6 7 21 8 5 11 4 23 3 2 +cv_jpn_000897 28 7 4 25 7 13 2 6 3 16 4 9 2 15 3 3 25 2 4 14 2 4 3 9 2 +cv_jpn_000898 17 2 8 5 4 +cv_jpn_000899 4 27 4 +cv_jpn_000900 16 3 +cv_jpn_000901 15 4 6 4 +cv_jpn_000902 4 4 5 +cv_jpn_000903 24 2 27 4 +cv_jpn_000904 9 5 4 +cv_jpn_000905 15 4 4 +cv_jpn_000906 6 7 +cv_jpn_000907 4 27 4 +cv_jpn_000908 6 2 6 2 6 7 16 2 2 6 4 10 2 6 2 9 4 12 7 10 7 20 29 2 21 6 2 13 8 5 6 19 15 4 13 10 4 9 4 15 19 8 2 16 2 7 6 3 8 3 9 4 23 3 21 8 5 +cv_jpn_000909 6 2 6 3 8 3 11 4 10 2 4 8 3 9 3 11 7 22 7 13 8 5 6 4 22 4 6 3 14 3 3 4 26 7 8 3 15 19 8 5 9 3 16 5 13 28 2 4 16 2 20 6 2 8 2 27 4 3 11 3 15 18 8 3 4 7 6 3 8 3 14 5 2 10 7 +cv_jpn_000910 25 7 26 7 10 4 8 5 6 19 12 5 6 2 4 17 2 20 12 7 7 16 2 6 7 8 5 6 19 6 4 16 3 3 9 4 23 3 21 8 5 2 10 2 17 2 12 2 10 5 10 7 20 12 7 7 16 2 6 7 8 5 6 19 6 2 8 2 27 4 9 3 15 5 6 2 4 14 5 2 10 7 +cv_jpn_000911 17 3 9 2 22 4 16 5 13 15 3 3 14 5 12 2 13 6 3 3 16 4 9 2 10 7 +cv_jpn_000912 16 2 4 6 3 6 7 6 2 10 2 6 4 8 2 11 3 9 3 14 2 8 3 15 4 21 8 5 25 4 21 6 7 10 4 +cv_jpn_000913 4 17 2 23 3 10 7 22 4 21 12 5 13 9 4 23 3 21 8 5 6 2 6 18 8 3 18 6 18 15 4 6 4 8 2 21 8 2 11 3 9 3 14 5 2 10 7 +fleurs_jpn_000346 3 9 2 22 4 23 3 9 4 14 2 13 12 5 5 17 2 24 4 28 2 3 3 7 28 7 25 3 13 3 24 2 21 6 3 8 3 16 2 20 16 4 11 7 7 28 7 6 5 10 2 10 5 8 5 4 11 2 12 18 +fleurs_jpn_000347 6 3 9 3 12 2 2 25 4 12 2 6 3 10 2 6 18 12 5 5 17 2 24 2 22 4 11 5 8 3 12 7 10 7 12 5 13 30 2 6 7 23 2 5 13 6 2 6 18 27 4 14 5 20 14 5 5 8 2 23 2 3 13 12 5 3 24 19 12 7 3 8 3 12 7 10 7 8 2 13 16 5 13 8 2 4 9 4 20 24 4 13 21 30 2 9 4 10 4 23 3 3 12 2 10 5 8 5 4 11 2 12 18 +fleurs_jpn_000348 29 3 3 31 7 7 33 3 6 2 8 3 9 3 6 3 3 12 7 4 10 3 3 3 25 4 2 11 2 6 2 15 4 25 2 10 2 4 7 8 2 26 7 11 2 6 4 4 28 7 6 4 3 25 5 12 2 4 6 7 10 9 3 14 2 9 3 6 4 25 4 15 4 4 6 19 15 3 6 5 5 8 2 23 2 12 3 9 3 5 5 29 3 9 4 23 3 10 7 11 3 9 3 10 5 12 18 +fleurs_jpn_000349 4 13 8 2 2 9 5 21 8 3 17 2 11 2 12 18 6 3 11 4 10 4 6 5 5 15 7 13 8 3 8 2 4 22 4 13 6 3 11 4 10 7 4 6 5 5 15 3 3 9 3 32 3 3 23 3 12 3 3 6 2 9 5 12 7 9 2 5 8 2 6 2 13 29 3 3 14 5 12 18 +fleurs_jpn_000350 6 2 22 4 9 3 10 5 17 2 26 7 7 22 3 3 8 3 6 7 25 5 12 7 10 2 4 13 15 19 6 3 23 2 5 13 8 2 2 8 5 4 11 5 13 8 3 23 3 3 15 19 8 5 4 11 2 12 18 16 5 12 7 17 2 6 4 25 7 4 23 3 6 7 15 19 12 5 12 7 9 2 4 9 4 8 3 10 2 11 2 10 7 23 3 3 10 4 12 7 10 7 8 2 11 5 14 5 12 18 +fleurs_jpn_000351 15 19 6 2 15 4 20 6 2 6 7 8 5 13 9 3 25 4 6 5 21 8 3 3 15 4 9 2 21 8 2 2 14 8 3 4 13 14 3 17 2 9 2 9 2 26 7 9 3 25 4 6 5 21 8 3 3 15 4 9 2 4 12 2 13 22 7 7 10 3 6 7 10 2 15 19 6 2 14 5 6 4 11 2 12 5 13 14 5 15 4 8 2 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/score b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/score new file mode 100644 index 0000000000000000000000000000000000000000..ebacffafb0796dece62c142ad892335adc891b55 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/score @@ -0,0 +1,40 @@ +fleurs_jpn_000352 tensor(-13.9326) +fleurs_jpn_000353 tensor(-19.9641) +fleurs_jpn_000354 tensor(-10.3937) +fleurs_jpn_000355 tensor(-11.6588) +fleurs_jpn_000356 tensor(-5.4101) +fleurs_jpn_000357 tensor(-22.7375) +fleurs_jpn_000358 tensor(-19.8433) +fleurs_jpn_000359 tensor(-16.4442) +fleurs_jpn_000360 tensor(-17.3094) +fleurs_jpn_000361 tensor(-11.7366) +fleurs_jpn_000362 tensor(-9.6006) +fleurs_jpn_000363 tensor(-22.6747) +fleurs_jpn_000364 tensor(-21.2283) +fleurs_jpn_000365 tensor(-17.4570) +fleurs_jpn_000366 tensor(-14.6032) +fleurs_jpn_000367 tensor(-12.3891) +fleurs_jpn_000368 tensor(-26.8359) +fleurs_jpn_000369 tensor(-10.1370) +fleurs_jpn_000370 tensor(-23.5747) +fleurs_jpn_000371 tensor(-15.9976) +fleurs_jpn_000372 tensor(-11.6268) +fleurs_jpn_000373 tensor(-24.9771) +fleurs_jpn_000374 tensor(-15.0155) +fleurs_jpn_000375 tensor(-9.2818) +fleurs_jpn_000376 tensor(-6.2772) +fleurs_jpn_000377 tensor(-9.9986) +fleurs_jpn_000378 tensor(-11.4436) +fleurs_jpn_000379 tensor(-13.7193) +fleurs_jpn_000380 tensor(-9.1459) +fleurs_jpn_000381 tensor(-14.2229) +fleurs_jpn_000382 tensor(-11.7824) +fleurs_jpn_000383 tensor(-11.9337) +fleurs_jpn_000384 tensor(-25.7841) +fleurs_jpn_000385 tensor(-21.0479) +fleurs_jpn_000386 tensor(-13.6644) +fleurs_jpn_000387 tensor(-15.2781) +fleurs_jpn_000388 tensor(-20.1880) +fleurs_jpn_000389 tensor(-8.2744) +fleurs_jpn_000390 tensor(-11.4901) +fleurs_jpn_000391 tensor(-16.2298) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/text b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/text new file mode 100644 index 0000000000000000000000000000000000000000..95b6aa63c44304bad6a611729233e1cc4f092bad --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/text @@ -0,0 +1,40 @@ +fleurs_jpn_000352 h o o k u r a N d o n o k o o sh I k e ts u k a w a h o o k u r a N d o sh o t o o k o N d o e f U k e e p i i d e i ch i p o N n o g a i ch i e e b o N d o j i i p i i b i i t o t o o k a n i k o t e e s a r e t e i m a s U +fleurs_jpn_000353 h a sh I sh I t a N n o j o o h o o k o k a N w o ch j u u g o m e t o r o d e s U n i s e N j u i ch i n e h a ch i g a ts u n i s e k o o sh i pau n i s e N j u u n o n e N n i s a N g a ts u m a n e k a i ts u e sh i m a s e N g d e sh I t a +fleurs_jpn_000354 i cl p u N k a N d e h f u cl t o s o g u ch i i k i m a r e b a f u cl t o o s u r u m a d e m i n a N m o k a k a r u ch i i k i m o a r i m a s U +fleurs_jpn_000355 p i r a m i cl t a n o o t o t o sh i k a i n o sh o o w a k o n o k a N k o o sh i d e t o k u n i k o r o m o a t a sh i k a t a n o sh i m e r e m o y o o sh u n o h I t o s u r e s U +fleurs_jpn_000356 s o n o t a n e t a i n i d a d e r u t o sh I t e h o o k i g a ts u i k a s a r e g a ch i d e s U +fleurs_jpn_000357 g e N z u N s u r u k o t o g a sh i t a r e t e i r u n i j i o g o o m a i n a t a N d a cl t u p u r o o t u s a i d o w a g e N z u N s u r a t o o g a e b u N k e n o s a i k o n o u sh i d e s U t e g a k i n y o r u g e N p o o w a g e N z o o sh I t e i m o s e N +fleurs_jpn_000358 k a r e m o s e s o o t a d a sh i t o m i t o m e r u sh I t o m i m a sh I t a g a o o k u g u h I t o o s u n o g e k o d e pau t a i y o o k e d e a t a i o t o s i n o h o k a n o h o sh i g a pau ch I k u u n o m a r e i d o o sh I s e r u t o sh i N ch i t e i m a sh I t a +fleurs_jpn_000359 ch i b e cl t o m e e s o o ch u sh i N w a sh i i s e e i o g a d e s U s a m a z a n a n a k a m i g a m i y o sh I k a k U k a s u r u k o t o d e e n e r u g i i ch a n e r u g a sh o o k a s a r e ch a k u r a g a k a s e e k a s a r e s a t o r u n e i sh I k e g o o m a r e m a s U +fleurs_jpn_000360 m i n a m i y a h u r e k a n i y a r u s u b e t e n r u k o k u r i s U k o o e t o d o o y o n i k o n u k o o e n i a m a i n i j i a h o g o sh I t o n i u u e N g u o g o t a k a r i m a s U +fleurs_jpn_000361 d e cl sh s a cl k u r u n g a s u n u h o k a n o o k u n u k o o ts u sh u d a N g a s u k o k a r u m a r e m a sh I t a +fleurs_jpn_000362 i N t a a n e cl t o w a m a s U k o m i n i g e e sh o N t o t a i j i N k o m i n i g e e sh a o N n o ry o o y o o s o o k a n i s o n a i e t a k a N ky o o r e s U +fleurs_jpn_000363 ky o o i N d e w a k a N s e N k a N r i t e j u u sh o N i sh I k a g a i pau k a n i i e n u k a N s e N g o k a n o o s e s e k u t a m e n i k a N j a k a k u r s u r a d a m o m i s o ch o t o cl t e i m u sh I U +fleurs_jpn_000364 r e N p o o r i k a i w a n i s e N g o n e N d o k a r a w a i s e s u b u s u t o r e sh u m a r i o h o e n o sh I k i N t e e ky o o k a i j i sh i pau e r u b i i a i w a a t a r u z o p o r u n o n i j u u n i n o s o o s a N i N y o t o o u n u sh u n a k e r e w a n a r a n a i t o k I t e sh i m a sh I t a +fleurs_jpn_000365 p i i e ch i d e r i r o w a k e N s a sh I t k a k o k u b u sh s e i u k u g a r a n s u i s o i y u n p i i e i ch i n e i ch i n o r ry o o d e sh i m a s a r e m e s U +fleurs_jpn_000366 s o r e d e m o t o o ry i u k a r u n a r u b a i s o u k e pau s u b e t e n o o o sh I k i o o m a o r i a N z e N j o o n o k e e g o g o n i s a i sh i i n o ch u i o h a r a i m a sh o o +fleurs_jpn_000367 k o r e r a w a t a m a n i k o N g a ts s u r o k a cl k a ts o k u m u k e n o b i ch i t e k a i g a i n i s a m a z a m a n a t e N p o w o n a r a N d e i m a s U a N z e N i o o e b u k o t o g a d e k i m a s U +fleurs_jpn_000368 sh i N d o m i e n a i ch i u d a a s o N o pau n a N d o r a s a s U t o s e N ky e u k a ch i j u k i u p e e j i ky a k U ky u u n o s u N z a i m o n o d t a b a z e r u ch i e m u n o d o g u sh u n a y o s o d e a r u +fleurs_jpn_000369 k o n o s a a b i s u w a g o r a k U s e N o h a j i m e t o s u r u s e N p a k u y a e N g a k U sh i r e d e e t a y o N s e y o s u y o t o s u r u t a N k e N t a i r i h i N p a a n i r i y o o s a r e t e i m a s U +fleurs_jpn_000370 s a k u b a b u e e n o s u a i d e s U k a r a k o j u cl k i r o s a N j u i ch i m a i r u h a n a r e t a r a p u r a t a sh i n a i d e g e N sh u g o j o o i N g i N d e w a r u k u r i s u t e i n a f e r u u n a N d e s u d e k i r u k i n a a z o sh i g a n a i t o o ry o o s e e N e n o sh I s u d a o s e N g e N sh i m a sh I t a +fleurs_jpn_000371 o n o i z u k u i n i m a sh I h a t o n o k a s o o r e b u s u n o r u g a cl k i g a k a s o o r o o o b a a r a N sh i pau k a b e n i g e k i t o t ts u sh I t e pau j u u n a n i N g a sh i o sh i m a sh I t a +fleurs_jpn_000372 h a sh I sh I t a n o j o o h o k u k a w a j u u g o m e t o o d e s U n i s e N j u u ch i n e h a j i g a ts u n i sh u N k o sh i pau n i s e N j u u n a n i e s a N g a s u m a r e k a i ts u sh i m a s e N d e sh I t a +fleurs_jpn_000373 b u N n e e t o i u k o t o w a w a sh i m i i o m i s u r u a r a t e N g o n o k i e o o sh i sh i b i r d i s U k a r a k I t e w o r i sh i m e i N i o m i s u r u r a t e N k o r o m e e sh i sh i b i s U t o sh i y a t o sh I k o cl k a o m i sh i n a N n a k a n o k a t a ch i r e sh a k a i n o k i b o o t e e g i s u r u sh u i b i t a s U t o y u m e e sh i n i k a N k e e sh I t e i m a s U +fleurs_jpn_000374 ts u u j o o k o k o r e w a i s u m a k a N k o t e k u y a ry o o sh a t a sh i k a h a s u r o t o k a ch i k o e t e k i m a s U w o t o t o sh I i k a r i g a o i n a s u m u n o g a t a r i u a m a r u d e h o N n a o y o o r e s U +fleurs_jpn_000375 t e r e b i n o h o o d o o n i o N d o pau g e N p a ts U k a r a a k u e g a g a cl t e i m a s U i d e +fleurs_jpn_000376 n o o b o o r i t o k o o d o o n o s o o k a N k a N k e e w a k a y a k u sh a t a sh i n o k e N ky u u w a o u r a z u k e r e m o n o d e s U +fleurs_jpn_000377 s u i y o o b i n i b e N t o n a t o k a r u p a n e r o w a s e N sh I k e N g o U k a s u n u k o j i N d e s i I s u j o o sh i m a sh I t a +fleurs_jpn_000378 s e N h a p e k u n e N d a i r a i g u N t a i g a t o o j a k s u r e w a r e h a i ch i w a k o n o b y o k i n i k a N ky e e s u r e b o N d a i n i s o o g u sh I t a k o t o w a r i m a s e N d e sh I t a +fleurs_jpn_000379 sh I k a sh i cl k ky a k u t e n o b i k e cl d o o sh u n a cl d a t o i N d o a n a n a ts u n e b i k e cl t o o sh i n a i s a N j o r o k u r a sh I k a r e k i m a s e N d e sh I t a +fleurs_jpn_000380 k a j i n o d e w a ts u u j o o t o k u b e ts u n a i N sh a k o y a e N t a a t e i m e N t o o y o o i sh I t e i m a s U g e s o g a k i b u y a k u sh i s e s u n a i n i t o r o m a r u y o n i s u r u t a m e r e s U +fleurs_jpn_000381 s o r e d e o t o o ky i u k a r a n a a r a b a i s o g e pau s u b e t e n o hy o w a sh I k m a o r i a N z e N j o n o k e k o n i s a i sh i i N n o ch u i o h a r a i m a sh o +fleurs_jpn_000382 o w o k a r u g e w a r i m a s e pau k o r a sh I t o ts u o sh o o w a r i d e a r i pau a t a r a sh i sh o o n o m o k a k e d e s U +fleurs_jpn_000383 s a w a r i t o w a a f u r i k a n o y a s e e d o o g u z u pau t o k o n i s a w a N n a n i r u y a s e r e d o o g u ts u n o k a N s a s u a m o k u t e k i t o sh I t a r i k u r o d e n o y o k o o s a sh i m a s U +fleurs_jpn_000384 u y u n e k i t a b a r u t o k a y o m o d a N s u r o b a i w a s e i s u i ch i k a k u n i sh s e k u d a s a i k I k o o r e n o n a k o ts I k i ts s u u s a i n i m o cl t o m e ky o o k e r u s e i ts u d e o s o r u j u u m o n o s o o g a n a r i k i b i k i g a s U +fleurs_jpn_000385 k o k o w a i r i s u n o sh o k u m i N i sh e h a i e sh a e g a j i b u N t a sh i n o ry o o r o t o sh a b a sh u n a r o u d e pau sh o k o m i N t i j i r e n i sh o o k o o s e k a s o o t o s u r e k a t a w a k o k o a r h a j i b e r e n a g a y o i u sh o o +fleurs_jpn_000386 k o sh i w a s a k u g e N s u r u s u u ch o o s a r a y a w a s e N d e sh I t a k a pau s a k u g e N w a ch i i w o k u n o k e e z a i s a N sh I s u y o n i m o t o z u i t e j i sh i s a r u d a r o t o N n o o i m a sh I t a +fleurs_jpn_000387 s a u i n u u k u k u sh o cl k u w a sh i N k o N ry o k o o n o j i k i g a s U k u n a i pau k a r u e ch a a sh o cl k u y o r e m o h a e k o o t o z u r e n a w a b i k i y o r i sh o o j o g a k a s u r u k o t o g a a r i m a s U +fleurs_jpn_000388 ky i n o o n o a s a t o r u k o n o g a j i a N t e cl p u n o k e e s a s u o h o N w o r e j i d o o sh a b a k u r a n o b a k a a s o r i y o r i pau ky e e k a N f u t a r e g a sh i b o sh i pau h o sh o w a sh a w a n i j u u n i y o k o a i m a sh I t a +fleurs_jpn_000389 sh o k u b u ts u d a n i N g i N g a s u u s a N z o o ts U k u r i n i N g e N g a k a cl k o i k i t o sh I t e h a k i d a s u n i s a N k a t a N s o o t o r i k o N d e i m a s U +fleurs_jpn_000390 s e N p a k u r e e b u sh i o i s o o s u r u n o w a u m i o k o e t e h I t o y a b u sh i o t a r i ry o o y i s o o s u r u m o cl t o m o k o o r i s e k i n a h o o h o o r e s U +fleurs_jpn_000391 k a r i h o r u n i a sh u u n o a w a n o r u d o sh u w a r u ts u n e cl k a a ch i sh i w a b o o r y o k u t e k i n a b i d e o g e e m u o m i s e e n e N sh a n i h a N b a y a r e N t a e s u r e u k o t o o k i N sh i s u r u h o o w a n i sh a o m e e sh i m a sh I t a diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/token b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/token new file mode 100644 index 0000000000000000000000000000000000000000..95b6aa63c44304bad6a611729233e1cc4f092bad --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/token @@ -0,0 +1,40 @@ +fleurs_jpn_000352 h o o k u r a N d o n o k o o sh I k e ts u k a w a h o o k u r a N d o sh o t o o k o N d o e f U k e e p i i d e i ch i p o N n o g a i ch i e e b o N d o j i i p i i b i i t o t o o k a n i k o t e e s a r e t e i m a s U +fleurs_jpn_000353 h a sh I sh I t a N n o j o o h o o k o k a N w o ch j u u g o m e t o r o d e s U n i s e N j u i ch i n e h a ch i g a ts u n i s e k o o sh i pau n i s e N j u u n o n e N n i s a N g a ts u m a n e k a i ts u e sh i m a s e N g d e sh I t a +fleurs_jpn_000354 i cl p u N k a N d e h f u cl t o s o g u ch i i k i m a r e b a f u cl t o o s u r u m a d e m i n a N m o k a k a r u ch i i k i m o a r i m a s U +fleurs_jpn_000355 p i r a m i cl t a n o o t o t o sh i k a i n o sh o o w a k o n o k a N k o o sh i d e t o k u n i k o r o m o a t a sh i k a t a n o sh i m e r e m o y o o sh u n o h I t o s u r e s U +fleurs_jpn_000356 s o n o t a n e t a i n i d a d e r u t o sh I t e h o o k i g a ts u i k a s a r e g a ch i d e s U +fleurs_jpn_000357 g e N z u N s u r u k o t o g a sh i t a r e t e i r u n i j i o g o o m a i n a t a N d a cl t u p u r o o t u s a i d o w a g e N z u N s u r a t o o g a e b u N k e n o s a i k o n o u sh i d e s U t e g a k i n y o r u g e N p o o w a g e N z o o sh I t e i m o s e N +fleurs_jpn_000358 k a r e m o s e s o o t a d a sh i t o m i t o m e r u sh I t o m i m a sh I t a g a o o k u g u h I t o o s u n o g e k o d e pau t a i y o o k e d e a t a i o t o s i n o h o k a n o h o sh i g a pau ch I k u u n o m a r e i d o o sh I s e r u t o sh i N ch i t e i m a sh I t a +fleurs_jpn_000359 ch i b e cl t o m e e s o o ch u sh i N w a sh i i s e e i o g a d e s U s a m a z a n a n a k a m i g a m i y o sh I k a k U k a s u r u k o t o d e e n e r u g i i ch a n e r u g a sh o o k a s a r e ch a k u r a g a k a s e e k a s a r e s a t o r u n e i sh I k e g o o m a r e m a s U +fleurs_jpn_000360 m i n a m i y a h u r e k a n i y a r u s u b e t e n r u k o k u r i s U k o o e t o d o o y o n i k o n u k o o e n i a m a i n i j i a h o g o sh I t o n i u u e N g u o g o t a k a r i m a s U +fleurs_jpn_000361 d e cl sh s a cl k u r u n g a s u n u h o k a n o o k u n u k o o ts u sh u d a N g a s u k o k a r u m a r e m a sh I t a +fleurs_jpn_000362 i N t a a n e cl t o w a m a s U k o m i n i g e e sh o N t o t a i j i N k o m i n i g e e sh a o N n o ry o o y o o s o o k a n i s o n a i e t a k a N ky o o r e s U +fleurs_jpn_000363 ky o o i N d e w a k a N s e N k a N r i t e j u u sh o N i sh I k a g a i pau k a n i i e n u k a N s e N g o k a n o o s e s e k u t a m e n i k a N j a k a k u r s u r a d a m o m i s o ch o t o cl t e i m u sh I U +fleurs_jpn_000364 r e N p o o r i k a i w a n i s e N g o n e N d o k a r a w a i s e s u b u s u t o r e sh u m a r i o h o e n o sh I k i N t e e ky o o k a i j i sh i pau e r u b i i a i w a a t a r u z o p o r u n o n i j u u n i n o s o o s a N i N y o t o o u n u sh u n a k e r e w a n a r a n a i t o k I t e sh i m a sh I t a +fleurs_jpn_000365 p i i e ch i d e r i r o w a k e N s a sh I t k a k o k u b u sh s e i u k u g a r a n s u i s o i y u n p i i e i ch i n e i ch i n o r ry o o d e sh i m a s a r e m e s U +fleurs_jpn_000366 s o r e d e m o t o o ry i u k a r u n a r u b a i s o u k e pau s u b e t e n o o o sh I k i o o m a o r i a N z e N j o o n o k e e g o g o n i s a i sh i i n o ch u i o h a r a i m a sh o o +fleurs_jpn_000367 k o r e r a w a t a m a n i k o N g a ts s u r o k a cl k a ts o k u m u k e n o b i ch i t e k a i g a i n i s a m a z a m a n a t e N p o w o n a r a N d e i m a s U a N z e N i o o e b u k o t o g a d e k i m a s U +fleurs_jpn_000368 sh i N d o m i e n a i ch i u d a a s o N o pau n a N d o r a s a s U t o s e N ky e u k a ch i j u k i u p e e j i ky a k U ky u u n o s u N z a i m o n o d t a b a z e r u ch i e m u n o d o g u sh u n a y o s o d e a r u +fleurs_jpn_000369 k o n o s a a b i s u w a g o r a k U s e N o h a j i m e t o s u r u s e N p a k u y a e N g a k U sh i r e d e e t a y o N s e y o s u y o t o s u r u t a N k e N t a i r i h i N p a a n i r i y o o s a r e t e i m a s U +fleurs_jpn_000370 s a k u b a b u e e n o s u a i d e s U k a r a k o j u cl k i r o s a N j u i ch i m a i r u h a n a r e t a r a p u r a t a sh i n a i d e g e N sh u g o j o o i N g i N d e w a r u k u r i s u t e i n a f e r u u n a N d e s u d e k i r u k i n a a z o sh i g a n a i t o o ry o o s e e N e n o sh I s u d a o s e N g e N sh i m a sh I t a +fleurs_jpn_000371 o n o i z u k u i n i m a sh I h a t o n o k a s o o r e b u s u n o r u g a cl k i g a k a s o o r o o o b a a r a N sh i pau k a b e n i g e k i t o t ts u sh I t e pau j u u n a n i N g a sh i o sh i m a sh I t a +fleurs_jpn_000372 h a sh I sh I t a n o j o o h o k u k a w a j u u g o m e t o o d e s U n i s e N j u u ch i n e h a j i g a ts u n i sh u N k o sh i pau n i s e N j u u n a n i e s a N g a s u m a r e k a i ts u sh i m a s e N d e sh I t a +fleurs_jpn_000373 b u N n e e t o i u k o t o w a w a sh i m i i o m i s u r u a r a t e N g o n o k i e o o sh i sh i b i r d i s U k a r a k I t e w o r i sh i m e i N i o m i s u r u r a t e N k o r o m e e sh i sh i b i s U t o sh i y a t o sh I k o cl k a o m i sh i n a N n a k a n o k a t a ch i r e sh a k a i n o k i b o o t e e g i s u r u sh u i b i t a s U t o y u m e e sh i n i k a N k e e sh I t e i m a s U +fleurs_jpn_000374 ts u u j o o k o k o r e w a i s u m a k a N k o t e k u y a ry o o sh a t a sh i k a h a s u r o t o k a ch i k o e t e k i m a s U w o t o t o sh I i k a r i g a o i n a s u m u n o g a t a r i u a m a r u d e h o N n a o y o o r e s U +fleurs_jpn_000375 t e r e b i n o h o o d o o n i o N d o pau g e N p a ts U k a r a a k u e g a g a cl t e i m a s U i d e +fleurs_jpn_000376 n o o b o o r i t o k o o d o o n o s o o k a N k a N k e e w a k a y a k u sh a t a sh i n o k e N ky u u w a o u r a z u k e r e m o n o d e s U +fleurs_jpn_000377 s u i y o o b i n i b e N t o n a t o k a r u p a n e r o w a s e N sh I k e N g o U k a s u n u k o j i N d e s i I s u j o o sh i m a sh I t a +fleurs_jpn_000378 s e N h a p e k u n e N d a i r a i g u N t a i g a t o o j a k s u r e w a r e h a i ch i w a k o n o b y o k i n i k a N ky e e s u r e b o N d a i n i s o o g u sh I t a k o t o w a r i m a s e N d e sh I t a +fleurs_jpn_000379 sh I k a sh i cl k ky a k u t e n o b i k e cl d o o sh u n a cl d a t o i N d o a n a n a ts u n e b i k e cl t o o sh i n a i s a N j o r o k u r a sh I k a r e k i m a s e N d e sh I t a +fleurs_jpn_000380 k a j i n o d e w a ts u u j o o t o k u b e ts u n a i N sh a k o y a e N t a a t e i m e N t o o y o o i sh I t e i m a s U g e s o g a k i b u y a k u sh i s e s u n a i n i t o r o m a r u y o n i s u r u t a m e r e s U +fleurs_jpn_000381 s o r e d e o t o o ky i u k a r a n a a r a b a i s o g e pau s u b e t e n o hy o w a sh I k m a o r i a N z e N j o n o k e k o n i s a i sh i i N n o ch u i o h a r a i m a sh o +fleurs_jpn_000382 o w o k a r u g e w a r i m a s e pau k o r a sh I t o ts u o sh o o w a r i d e a r i pau a t a r a sh i sh o o n o m o k a k e d e s U +fleurs_jpn_000383 s a w a r i t o w a a f u r i k a n o y a s e e d o o g u z u pau t o k o n i s a w a N n a n i r u y a s e r e d o o g u ts u n o k a N s a s u a m o k u t e k i t o sh I t a r i k u r o d e n o y o k o o s a sh i m a s U +fleurs_jpn_000384 u y u n e k i t a b a r u t o k a y o m o d a N s u r o b a i w a s e i s u i ch i k a k u n i sh s e k u d a s a i k I k o o r e n o n a k o ts I k i ts s u u s a i n i m o cl t o m e ky o o k e r u s e i ts u d e o s o r u j u u m o n o s o o g a n a r i k i b i k i g a s U +fleurs_jpn_000385 k o k o w a i r i s u n o sh o k u m i N i sh e h a i e sh a e g a j i b u N t a sh i n o ry o o r o t o sh a b a sh u n a r o u d e pau sh o k o m i N t i j i r e n i sh o o k o o s e k a s o o t o s u r e k a t a w a k o k o a r h a j i b e r e n a g a y o i u sh o o +fleurs_jpn_000386 k o sh i w a s a k u g e N s u r u s u u ch o o s a r a y a w a s e N d e sh I t a k a pau s a k u g e N w a ch i i w o k u n o k e e z a i s a N sh I s u y o n i m o t o z u i t e j i sh i s a r u d a r o t o N n o o i m a sh I t a +fleurs_jpn_000387 s a u i n u u k u k u sh o cl k u w a sh i N k o N ry o k o o n o j i k i g a s U k u n a i pau k a r u e ch a a sh o cl k u y o r e m o h a e k o o t o z u r e n a w a b i k i y o r i sh o o j o g a k a s u r u k o t o g a a r i m a s U +fleurs_jpn_000388 ky i n o o n o a s a t o r u k o n o g a j i a N t e cl p u n o k e e s a s u o h o N w o r e j i d o o sh a b a k u r a n o b a k a a s o r i y o r i pau ky e e k a N f u t a r e g a sh i b o sh i pau h o sh o w a sh a w a n i j u u n i y o k o a i m a sh I t a +fleurs_jpn_000389 sh o k u b u ts u d a n i N g i N g a s u u s a N z o o ts U k u r i n i N g e N g a k a cl k o i k i t o sh I t e h a k i d a s u n i s a N k a t a N s o o t o r i k o N d e i m a s U +fleurs_jpn_000390 s e N p a k u r e e b u sh i o i s o o s u r u n o w a u m i o k o e t e h I t o y a b u sh i o t a r i ry o o y i s o o s u r u m o cl t o m o k o o r i s e k i n a h o o h o o r e s U +fleurs_jpn_000391 k a r i h o r u n i a sh u u n o a w a n o r u d o sh u w a r u ts u n e cl k a a ch i sh i w a b o o r y o k u t e k i n a b i d e o g e e m u o m i s e e n e N sh a n i h a N b a y a r e N t a e s u r e u k o t o o k i N sh i s u r u h o o w a n i sh a o m e e sh i m a sh I t a diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/token_int new file mode 100644 index 0000000000000000000000000000000000000000..11bc04bd012607a0c304bbc79118e725ea7e92e7 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/logdir/output.4/1best_recog/token_int @@ -0,0 +1,40 @@ +fleurs_jpn_000352 24 3 3 6 7 10 2 13 14 3 9 3 6 3 3 15 19 6 5 26 7 6 2 17 2 24 3 3 6 7 10 2 13 14 3 15 3 8 3 3 6 3 13 14 3 5 31 18 6 5 5 30 4 4 14 5 4 27 4 30 3 13 9 3 16 2 4 27 4 5 5 25 3 13 14 3 22 4 4 30 4 4 25 4 4 8 3 8 3 3 6 2 9 4 6 3 8 5 5 12 2 10 5 8 5 4 11 2 12 18 +fleurs_jpn_000353 24 2 15 19 15 19 8 2 13 9 3 22 3 3 24 3 3 6 3 6 2 13 17 3 27 22 7 7 16 3 11 5 8 3 10 3 14 5 12 18 9 4 12 5 13 22 7 4 27 4 9 5 24 2 27 4 16 2 26 7 9 4 12 5 6 3 3 15 4 20 9 4 12 5 13 22 7 7 9 3 9 5 13 9 4 12 2 13 16 2 26 7 11 2 9 5 6 2 4 26 7 5 15 4 11 2 12 5 13 16 14 5 15 19 8 2 +fleurs_jpn_000354 4 21 30 7 13 6 2 13 14 5 24 31 7 21 8 3 12 3 16 7 27 4 4 6 4 11 2 10 5 25 2 31 7 21 8 3 3 12 7 10 7 11 2 14 5 11 4 9 2 13 11 3 6 2 6 2 10 7 27 4 4 6 4 11 3 2 10 4 11 2 12 18 +fleurs_jpn_000355 30 4 10 2 11 4 21 8 2 9 3 3 8 3 8 3 15 4 6 2 4 9 3 15 3 3 17 2 6 3 9 3 6 2 13 6 3 3 15 4 14 5 8 3 6 7 9 4 6 3 10 3 11 3 2 8 2 15 4 6 2 8 2 9 3 15 4 11 5 10 5 11 3 23 3 3 15 7 9 3 24 19 8 3 12 7 10 5 12 18 +fleurs_jpn_000356 12 3 9 3 8 2 9 5 8 2 4 9 4 14 2 14 5 10 7 8 3 15 19 8 5 24 3 3 6 4 16 2 26 7 4 6 2 12 2 10 5 16 2 27 4 14 5 12 18 +fleurs_jpn_000357 16 5 13 28 7 13 12 7 10 7 6 3 8 3 16 2 15 4 8 2 10 5 8 5 4 10 7 9 4 22 4 3 16 3 3 11 2 4 9 2 8 2 13 14 2 21 8 7 30 7 10 3 3 8 7 12 2 4 14 3 17 2 16 5 13 28 7 13 12 7 10 2 8 3 3 16 2 5 25 7 13 6 5 9 3 12 2 4 6 3 9 3 7 15 4 14 5 12 18 8 5 16 2 6 4 9 23 3 10 7 16 5 13 30 3 3 17 2 16 5 13 28 3 3 15 19 8 5 4 11 3 12 5 13 +fleurs_jpn_000358 6 2 10 5 11 3 12 5 12 3 3 8 2 14 2 15 4 8 3 11 4 8 3 11 5 10 7 15 19 8 3 11 4 11 2 15 19 8 2 16 2 3 3 6 7 16 7 24 19 8 3 3 12 7 9 3 16 5 6 3 14 5 20 8 2 4 23 3 3 6 5 14 5 2 8 2 4 3 8 3 12 4 9 3 24 3 6 2 9 3 24 3 15 4 16 2 20 27 19 6 7 7 9 3 11 2 10 5 4 14 3 3 15 19 12 5 10 7 8 3 15 4 13 27 4 8 5 4 11 2 15 19 8 2 +fleurs_jpn_000359 27 4 25 5 21 8 3 11 5 5 12 3 3 27 7 15 4 13 17 2 15 4 4 12 5 5 4 3 16 2 14 5 12 18 12 2 11 2 28 2 9 2 9 2 6 2 11 4 16 2 11 4 23 3 15 19 6 2 6 18 6 2 12 7 10 7 6 3 8 3 14 5 5 9 5 10 7 16 4 4 27 2 9 5 10 7 16 2 15 3 3 6 2 12 2 10 5 27 2 6 7 10 2 16 2 6 2 12 5 5 6 2 12 2 10 5 12 2 8 3 10 7 9 5 4 15 19 6 5 16 3 3 11 2 10 5 11 2 12 18 +fleurs_jpn_000360 11 4 9 2 11 4 23 2 24 7 10 5 6 2 9 4 23 2 10 7 12 7 25 5 8 5 9 10 7 6 3 6 7 10 4 12 18 6 3 3 5 8 3 14 3 3 23 3 9 4 6 3 9 7 6 3 3 5 9 4 2 11 2 4 9 4 22 4 2 24 3 16 3 15 19 8 3 9 4 7 7 5 13 16 7 3 16 3 8 2 6 2 10 4 11 2 12 18 +fleurs_jpn_000361 14 5 21 15 12 2 21 6 7 10 7 9 16 2 12 7 9 7 24 3 6 2 9 3 3 6 7 9 7 6 3 3 26 7 15 7 14 2 13 16 2 12 7 6 3 6 2 10 7 11 2 10 5 11 2 15 19 8 2 +fleurs_jpn_000362 4 13 8 2 2 9 5 21 8 3 17 2 11 2 12 18 6 3 11 4 9 4 16 5 5 15 3 13 8 3 8 2 4 22 4 13 6 3 11 4 9 4 16 5 5 15 2 3 13 9 3 32 3 3 23 3 3 12 3 3 6 2 9 4 12 3 9 2 4 5 8 2 6 2 13 29 3 3 10 5 12 18 +fleurs_jpn_000363 29 3 3 4 13 14 5 17 2 6 2 13 12 5 13 6 2 13 10 4 8 5 22 7 7 15 3 13 4 15 19 6 2 16 2 4 20 6 2 9 4 4 5 9 7 6 2 13 12 5 13 16 3 6 2 9 3 3 12 5 12 5 6 7 8 2 11 5 9 4 6 2 13 22 2 6 2 6 7 10 12 7 10 2 14 2 11 3 11 4 12 3 27 3 8 3 21 8 5 4 11 7 15 19 18 +fleurs_jpn_000364 10 5 13 30 3 3 10 4 6 2 4 17 2 9 4 12 5 13 16 3 9 5 13 14 3 6 2 10 2 17 2 4 12 5 12 7 25 7 12 7 8 3 10 5 15 7 11 2 10 4 3 24 3 5 9 3 15 19 6 4 13 8 5 5 29 3 3 6 2 4 22 4 15 4 20 5 10 7 25 4 4 2 4 17 2 2 8 2 10 7 28 3 30 3 10 7 9 3 9 4 22 7 7 9 4 9 3 12 3 3 12 2 13 4 13 23 3 8 3 3 7 9 7 15 7 9 2 6 5 10 5 17 2 9 2 10 2 9 2 4 8 3 6 19 8 5 15 4 11 2 15 19 8 2 +fleurs_jpn_000365 30 4 4 5 27 4 14 5 10 4 10 3 17 2 6 5 13 12 2 15 19 8 6 2 6 3 6 7 25 7 15 12 5 4 7 6 7 16 2 10 2 9 12 7 4 12 3 4 23 7 9 30 4 4 5 4 27 4 9 5 4 27 4 9 3 10 32 3 3 14 5 15 4 11 2 12 2 10 5 11 5 12 18 +fleurs_jpn_000366 12 3 10 5 14 5 11 3 8 3 3 32 4 7 6 2 10 7 9 2 10 7 25 2 4 12 3 7 6 5 20 12 7 25 5 8 5 9 3 3 3 15 19 6 4 3 3 11 2 3 10 4 2 13 28 5 13 22 3 3 9 3 6 5 5 16 3 16 3 9 4 12 2 4 15 4 4 9 3 27 7 4 3 24 2 10 2 4 11 2 15 3 3 +fleurs_jpn_000367 6 3 10 5 10 2 17 2 8 2 11 2 9 4 6 3 13 16 2 26 12 7 10 3 6 2 21 6 2 26 3 6 7 11 7 6 5 9 3 25 4 27 4 8 5 6 2 4 16 2 4 9 4 12 2 11 2 28 2 11 2 9 2 8 5 13 30 3 17 3 9 2 10 2 13 14 5 4 11 2 12 18 2 13 28 5 13 4 3 3 5 25 7 6 3 8 3 16 2 14 5 6 4 11 2 12 18 +fleurs_jpn_000368 15 4 13 14 3 11 4 5 9 2 4 27 4 7 14 2 2 12 3 13 3 20 9 2 13 14 3 10 2 12 2 12 18 8 3 12 5 13 29 5 7 6 2 27 4 22 7 6 4 7 30 5 5 22 4 29 2 6 18 29 7 7 9 3 12 7 13 28 2 4 11 3 9 3 14 8 2 25 2 28 5 10 7 27 4 5 11 7 9 3 14 3 16 7 15 7 9 2 23 3 12 3 14 5 2 10 7 +fleurs_jpn_000369 6 3 9 3 12 2 2 25 4 12 7 17 2 16 3 10 2 6 18 12 5 13 3 24 2 22 4 11 5 8 3 12 7 10 7 12 5 13 30 2 6 7 23 2 5 13 16 2 6 18 15 4 10 5 14 5 5 8 2 23 3 13 12 5 23 3 12 7 23 3 8 3 12 7 10 7 8 2 13 6 5 13 8 2 4 10 4 24 4 13 30 2 2 9 4 10 4 23 3 3 12 2 10 5 8 5 4 11 2 12 18 +fleurs_jpn_000370 12 2 6 7 25 2 25 7 5 5 9 3 12 7 2 4 14 5 12 18 6 2 10 2 6 3 22 7 21 6 4 10 3 12 2 13 22 7 4 27 4 11 2 4 10 7 24 2 9 2 10 5 8 2 10 2 30 7 10 2 8 2 15 4 9 2 4 14 5 16 5 13 15 7 16 3 22 3 3 4 13 16 4 13 14 5 17 2 10 7 6 7 10 4 12 7 8 5 4 9 2 31 5 10 7 7 9 2 13 14 5 12 7 14 5 6 4 10 7 6 4 9 2 2 28 3 15 4 16 2 9 2 4 8 3 3 32 3 3 12 5 5 13 5 9 3 15 19 12 7 14 2 3 12 5 13 16 5 13 15 4 11 2 15 19 8 2 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2 12 18 +fleurs_jpn_000374 26 7 7 22 3 3 6 3 6 3 10 5 17 2 4 12 7 11 2 6 2 13 6 3 8 5 6 7 23 2 32 3 3 15 2 8 2 15 4 6 2 24 2 12 7 10 3 8 3 6 2 27 4 6 3 5 8 5 6 4 11 2 12 18 17 3 8 3 8 3 15 19 4 6 2 10 4 16 2 3 4 9 2 12 7 11 7 9 3 16 2 8 2 10 4 7 2 11 2 10 7 14 5 24 3 13 9 2 3 23 3 3 10 5 12 18 +fleurs_jpn_000375 8 5 10 5 25 4 9 3 24 3 3 14 3 3 9 4 3 13 14 3 20 16 5 13 30 2 26 18 6 2 10 2 2 6 7 5 16 2 16 2 21 8 5 4 11 2 12 18 4 14 5 +fleurs_jpn_000376 9 3 3 25 3 3 10 4 8 3 6 3 3 14 3 3 9 3 12 3 3 6 2 13 6 2 13 6 5 5 17 2 6 2 23 2 6 7 15 2 8 2 15 4 9 3 6 5 13 29 7 7 17 2 3 7 10 2 28 7 6 5 10 5 11 3 9 3 14 5 12 18 +fleurs_jpn_000377 12 7 4 23 3 3 25 4 9 4 25 5 13 8 3 9 2 8 3 6 2 10 7 30 2 9 5 10 3 17 2 12 5 13 15 19 6 5 13 16 3 18 6 2 12 7 9 7 6 3 22 4 13 14 5 12 4 19 12 7 22 3 3 15 4 11 2 15 19 8 2 +fleurs_jpn_000378 12 5 13 24 2 30 5 6 7 9 5 13 14 2 4 10 2 4 16 7 13 8 2 4 16 2 8 3 3 22 2 6 12 7 10 5 17 2 10 5 24 2 4 27 4 17 2 6 3 9 3 25 23 3 6 4 9 4 6 2 13 29 5 5 12 7 10 5 25 3 13 14 2 4 9 4 12 3 3 16 7 15 19 8 2 6 3 8 3 17 2 10 4 11 2 12 5 13 14 5 15 19 8 2 +fleurs_jpn_000379 15 19 6 2 15 4 21 6 29 2 6 7 8 5 9 3 25 4 6 5 21 14 3 3 15 7 9 2 21 14 2 8 3 4 13 14 3 2 9 2 9 2 26 7 9 5 25 4 6 5 21 8 3 3 15 4 9 2 4 12 2 13 22 3 10 3 6 7 10 2 15 19 6 2 10 5 6 4 11 2 12 5 13 14 5 15 19 8 2 +fleurs_jpn_000380 6 2 22 4 9 3 14 5 17 2 26 7 7 22 3 3 8 3 6 7 25 5 26 7 9 2 4 13 15 2 6 3 23 2 5 13 8 2 2 8 5 4 11 5 13 8 3 3 23 3 3 4 15 19 8 5 4 11 2 12 18 16 5 12 3 16 2 6 4 25 7 23 2 6 7 15 4 12 5 12 7 9 2 4 9 4 8 3 10 3 11 2 10 7 23 3 9 4 12 7 10 7 8 2 11 5 10 5 12 18 +fleurs_jpn_000381 12 3 10 5 14 5 3 8 3 3 29 4 7 6 2 10 2 9 2 2 10 2 25 2 4 12 3 16 5 20 12 7 25 5 8 5 9 3 33 3 17 2 15 19 6 11 2 3 10 4 2 13 28 5 13 22 3 9 3 6 5 6 3 9 4 12 2 4 15 4 4 13 9 3 27 7 4 3 24 2 10 2 4 11 2 15 3 +fleurs_jpn_000382 3 17 3 6 2 10 7 16 5 17 2 10 4 11 2 12 5 20 6 3 10 2 15 19 8 3 26 7 3 15 3 3 17 2 10 4 14 5 2 10 4 20 2 8 2 10 2 15 4 15 3 3 9 3 11 3 6 2 6 5 14 5 12 18 +fleurs_jpn_000383 12 2 17 2 10 4 8 3 17 2 2 31 7 10 4 6 2 9 3 23 2 12 5 5 14 3 3 16 7 28 7 20 8 3 6 3 9 4 12 2 17 2 13 9 2 9 4 10 7 23 2 12 5 10 5 14 3 3 16 7 26 7 9 3 6 2 13 12 2 12 7 2 11 3 6 7 8 5 6 4 8 3 15 19 8 2 10 4 6 7 10 3 14 5 9 3 23 3 6 3 3 12 2 15 4 11 2 12 18 +fleurs_jpn_000384 7 23 7 9 5 6 4 8 2 25 2 10 7 8 3 6 2 23 3 11 3 14 2 13 12 7 10 3 25 2 4 17 2 12 5 4 12 7 4 27 4 6 2 6 7 9 4 15 12 5 6 7 14 2 12 2 4 6 19 6 3 3 10 5 9 3 9 2 6 3 26 19 6 4 26 12 7 7 12 2 4 9 4 11 3 21 8 3 11 5 29 3 3 6 5 10 7 12 5 4 26 7 14 5 3 12 3 10 7 22 7 7 11 3 9 3 12 3 3 16 2 9 2 10 4 6 4 25 4 6 4 16 2 12 18 +fleurs_jpn_000385 6 3 6 3 17 2 4 10 4 12 7 9 3 15 3 6 7 11 4 13 4 15 5 24 2 4 5 15 2 5 16 2 22 4 25 7 13 8 2 15 4 9 3 32 3 3 10 3 8 3 15 2 25 2 15 7 9 2 10 3 7 14 5 20 15 3 6 3 11 4 13 8 4 22 4 10 5 9 4 15 3 3 6 3 3 12 5 6 2 12 3 3 8 3 12 7 10 5 6 2 8 2 17 2 6 3 6 3 2 10 24 2 22 4 25 5 10 5 9 2 16 2 23 3 4 7 15 3 3 +fleurs_jpn_000386 6 3 15 4 17 2 12 2 6 7 16 5 13 12 7 10 7 12 7 7 27 3 3 12 2 10 2 23 2 17 2 12 5 13 14 5 15 19 8 2 6 2 20 12 2 6 7 16 5 13 17 2 27 4 4 17 3 6 7 9 3 6 5 5 28 2 4 12 2 13 15 19 12 7 23 3 9 4 11 3 8 3 28 7 4 8 5 22 4 15 4 12 2 10 7 14 2 10 3 8 3 13 9 3 3 4 11 2 15 19 8 2 +fleurs_jpn_000387 12 2 7 4 9 7 7 6 7 6 7 15 3 21 6 7 17 2 15 4 13 6 3 13 32 3 6 3 3 9 3 22 4 6 4 16 2 12 18 6 7 9 2 4 20 6 2 10 7 5 27 2 2 15 3 21 6 7 23 3 10 5 11 3 24 2 5 6 3 3 8 3 28 7 10 5 9 2 17 2 25 4 6 4 23 3 10 4 15 3 3 22 3 16 2 6 2 12 7 10 7 6 3 8 3 16 2 2 10 4 11 2 12 18 +fleurs_jpn_000388 29 4 9 3 3 9 3 2 12 2 8 3 10 7 6 3 9 3 16 2 22 4 2 13 8 5 21 30 7 9 3 6 5 5 12 2 12 7 3 24 3 13 17 3 10 5 22 4 14 3 3 15 2 25 2 6 7 10 2 9 3 25 2 6 2 2 12 3 10 4 23 3 10 4 20 29 5 5 6 2 13 31 7 8 2 10 5 16 2 15 4 25 3 15 4 20 24 3 15 3 17 2 15 2 17 2 9 4 22 7 7 9 4 23 3 6 3 2 4 11 2 15 19 8 2 +fleurs_jpn_000389 15 3 6 7 25 7 26 7 14 2 9 4 13 16 4 13 16 2 12 7 7 12 2 13 28 3 3 26 18 6 7 10 4 9 4 13 16 5 13 16 2 6 2 21 6 3 4 6 4 8 3 15 19 8 5 24 2 6 4 14 2 12 7 9 4 12 2 13 6 2 8 2 13 12 3 3 8 3 10 4 6 3 13 14 5 4 11 2 12 18 +fleurs_jpn_000390 12 5 13 30 2 6 7 10 5 5 25 7 15 4 3 4 12 3 3 12 7 10 7 9 3 17 2 7 11 4 3 6 3 5 8 5 24 19 8 3 23 2 25 7 15 4 3 8 2 10 4 32 3 3 23 4 12 3 3 12 7 10 7 11 3 21 8 3 11 3 6 3 3 10 4 12 5 6 4 9 2 24 3 3 24 3 3 10 5 12 18 +fleurs_jpn_000391 6 2 10 4 24 3 10 7 9 4 2 15 7 7 9 3 2 17 2 9 3 10 7 14 3 15 7 17 2 10 7 26 7 9 5 21 6 2 2 27 4 15 4 17 2 25 3 3 10 23 3 6 7 8 5 6 4 9 2 25 4 14 5 3 16 5 5 11 7 3 11 4 12 5 5 9 5 13 15 2 9 4 24 2 13 25 2 23 2 10 5 13 8 2 5 12 7 10 5 7 6 3 8 3 3 6 4 13 15 4 12 7 10 7 24 3 3 17 2 9 4 15 2 3 11 5 5 15 4 11 2 15 19 8 2 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score new file mode 100644 index 0000000000000000000000000000000000000000..6a8d9fd374d2cda550a4c82e8530e71971c932a4 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score @@ -0,0 +1,160 @@ +cv_jpn_000800 tensor(-4.7181) +cv_jpn_000801 tensor(-10.1120) +cv_jpn_000802 tensor(-6.0003) +cv_jpn_000803 tensor(-8.5784) +cv_jpn_000804 tensor(-3.7765) +cv_jpn_000805 tensor(-1.1088) +cv_jpn_000806 tensor(-8.5532) +cv_jpn_000807 tensor(-1.3019) +cv_jpn_000808 tensor(-2.0603) +cv_jpn_000809 tensor(-8.8196) +cv_jpn_000810 tensor(-7.9576) +cv_jpn_000811 tensor(-1.9876) +cv_jpn_000812 tensor(-6.3624) +cv_jpn_000813 tensor(-5.9219) +cv_jpn_000814 tensor(-4.4759) +cv_jpn_000815 tensor(-4.9182) +cv_jpn_000816 tensor(-5.0222) +cv_jpn_000817 tensor(-3.1824) +cv_jpn_000818 tensor(-0.3335) +cv_jpn_000819 tensor(-0.3036) +cv_jpn_000820 tensor(-0.1470) +cv_jpn_000821 tensor(-1.0813) +cv_jpn_000822 tensor(-0.2898) +cv_jpn_000823 tensor(-4.1020) +cv_jpn_000824 tensor(-3.8301) +cv_jpn_000825 tensor(-5.5983) +cv_jpn_000826 tensor(-4.7382) +cv_jpn_000827 tensor(-3.1788) +cv_jpn_000828 tensor(-4.8686) +cv_jpn_000829 tensor(-3.2158) +cv_jpn_000830 tensor(-2.4958) +cv_jpn_000831 tensor(-1.1376) +cv_jpn_000832 tensor(-3.4827) +cv_jpn_000833 tensor(-0.7081) +cv_jpn_000834 tensor(-2.8385) +cv_jpn_000835 tensor(-3.7153) +cv_jpn_000836 tensor(-1.7373) +cv_jpn_000837 tensor(-1.4652) +cv_jpn_000838 tensor(-3.9856) +cv_jpn_000839 tensor(-3.5415) +cv_jpn_000840 tensor(-10.7257) +cv_jpn_000841 tensor(-4.3035) +cv_jpn_000842 tensor(-2.5631) +cv_jpn_000843 tensor(-1.9402) +cv_jpn_000844 tensor(-7.1149) +cv_jpn_000845 tensor(-3.7933) +cv_jpn_000846 tensor(-7.7386) +cv_jpn_000847 tensor(-3.2881) +cv_jpn_000848 tensor(-4.2617) +cv_jpn_000849 tensor(-2.8019) +cv_jpn_000850 tensor(-2.5225) +cv_jpn_000851 tensor(-2.8504) +cv_jpn_000852 tensor(-3.6251) +cv_jpn_000853 tensor(-5.8141) +cv_jpn_000854 tensor(-10.3066) +cv_jpn_000855 tensor(-3.2725) +cv_jpn_000856 tensor(-6.5354) +cv_jpn_000857 tensor(-4.9037) +cv_jpn_000858 tensor(-3.9820) +cv_jpn_000859 tensor(-7.4239) +cv_jpn_000860 tensor(-4.7633) +cv_jpn_000861 tensor(-4.5496) +cv_jpn_000862 tensor(-9.3958) +cv_jpn_000863 tensor(-13.1167) +cv_jpn_000864 tensor(-7.5129) +cv_jpn_000865 tensor(-3.3878) +cv_jpn_000866 tensor(-8.7559) +cv_jpn_000867 tensor(-3.3318) +cv_jpn_000868 tensor(-2.2427) +cv_jpn_000869 tensor(-4.5921) +cv_jpn_000870 tensor(-2.1736) +cv_jpn_000871 tensor(-5.9992) +cv_jpn_000872 tensor(-7.7549) +cv_jpn_000873 tensor(-9.0724) +cv_jpn_000874 tensor(-4.1596) +cv_jpn_000875 tensor(-11.8180) +cv_jpn_000876 tensor(-3.5649) +cv_jpn_000877 tensor(-2.0670) +cv_jpn_000878 tensor(-2.2402) +cv_jpn_000879 tensor(-6.1698) +cv_jpn_000880 tensor(-4.2964) +cv_jpn_000881 tensor(-6.6556) +cv_jpn_000882 tensor(-2.7405) +cv_jpn_000883 tensor(-4.4585) +cv_jpn_000884 tensor(-3.0480) +cv_jpn_000885 tensor(-3.2873) +cv_jpn_000886 tensor(-3.8877) +cv_jpn_000887 tensor(-10.2507) +cv_jpn_000888 tensor(-2.6494) +cv_jpn_000889 tensor(-2.3477) +cv_jpn_000890 tensor(-4.0468) +cv_jpn_000891 tensor(-5.4691) +cv_jpn_000892 tensor(-1.6660) +cv_jpn_000893 tensor(-3.7962) +cv_jpn_000894 tensor(-1.5321) +cv_jpn_000895 tensor(-1.9720) +cv_jpn_000896 tensor(-2.5488) +cv_jpn_000897 tensor(-1.5819) +cv_jpn_000898 tensor(-2.1431) +cv_jpn_000899 tensor(-0.7946) +cv_jpn_000900 tensor(-0.3835) +cv_jpn_000901 tensor(-1.3530) +cv_jpn_000902 tensor(-1.0515) +cv_jpn_000903 tensor(-1.3782) +cv_jpn_000904 tensor(-0.3892) +cv_jpn_000905 tensor(-0.8825) +cv_jpn_000906 tensor(-0.5737) +cv_jpn_000907 tensor(-0.2051) +cv_jpn_000908 tensor(-3.2743) +cv_jpn_000909 tensor(-7.1312) +cv_jpn_000910 tensor(-6.3916) +cv_jpn_000911 tensor(-2.9724) +cv_jpn_000912 tensor(-1.7722) +cv_jpn_000913 tensor(-6.0145) +fleurs_jpn_000346 tensor(-8.3701) +fleurs_jpn_000347 tensor(-15.9440) +fleurs_jpn_000348 tensor(-19.7870) +fleurs_jpn_000349 tensor(-7.5196) +fleurs_jpn_000350 tensor(-11.6380) +fleurs_jpn_000351 tensor(-11.1524) +fleurs_jpn_000352 tensor(-13.9326) +fleurs_jpn_000353 tensor(-19.9641) +fleurs_jpn_000354 tensor(-10.3937) +fleurs_jpn_000355 tensor(-11.6588) +fleurs_jpn_000356 tensor(-5.4101) +fleurs_jpn_000357 tensor(-22.7375) +fleurs_jpn_000358 tensor(-19.8433) +fleurs_jpn_000359 tensor(-16.4442) +fleurs_jpn_000360 tensor(-17.3094) +fleurs_jpn_000361 tensor(-11.7366) +fleurs_jpn_000362 tensor(-9.6006) +fleurs_jpn_000363 tensor(-22.6747) +fleurs_jpn_000364 tensor(-21.2283) +fleurs_jpn_000365 tensor(-17.4570) +fleurs_jpn_000366 tensor(-14.6032) +fleurs_jpn_000367 tensor(-12.3891) +fleurs_jpn_000368 tensor(-26.8359) +fleurs_jpn_000369 tensor(-10.1370) +fleurs_jpn_000370 tensor(-23.5747) +fleurs_jpn_000371 tensor(-15.9976) +fleurs_jpn_000372 tensor(-11.6268) +fleurs_jpn_000373 tensor(-24.9771) +fleurs_jpn_000374 tensor(-15.0155) +fleurs_jpn_000375 tensor(-9.2818) +fleurs_jpn_000376 tensor(-6.2772) +fleurs_jpn_000377 tensor(-9.9986) +fleurs_jpn_000378 tensor(-11.4436) +fleurs_jpn_000379 tensor(-13.7193) +fleurs_jpn_000380 tensor(-9.1459) +fleurs_jpn_000381 tensor(-14.2229) +fleurs_jpn_000382 tensor(-11.7824) +fleurs_jpn_000383 tensor(-11.9337) +fleurs_jpn_000384 tensor(-25.7841) +fleurs_jpn_000385 tensor(-21.0479) +fleurs_jpn_000386 tensor(-13.6644) +fleurs_jpn_000387 tensor(-15.2781) +fleurs_jpn_000388 tensor(-20.1880) +fleurs_jpn_000389 tensor(-8.2744) +fleurs_jpn_000390 tensor(-11.4901) +fleurs_jpn_000391 tensor(-16.2298) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..3fad9181940da44efe9ddc6223e079ae1521997a --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/hyp.trn @@ -0,0 +1,160 @@ +k a k o t o m i r a e t o d o g u j u N t e k i j i k o d o o i t s u n a r u g a y o e n i i s h I k i t e k i n a n o d e a r u (cv_jpn_000800-cv_jpn_000800) +s e k a y o k e e s e e s u r u t o t o m n i j i k o j i s h i N y o k e e s e s e r u s o o o t e k i s e k a i n o s o o z o o t e k i y o o s o t o s h I t e p a u k o b u t s u g a k o b u t s u d e a r u (cv_jpn_000801-cv_jpn_000801) +p a z o k o N d e g e e m i a r u I t o n o h f u i t e k i t e (cv_jpn_000802-cv_jpn_000802) +k a N a k u n o s h i m e s a a t a r a s h i j i j i u t s u a t a r a s h i i k a N n e N k a N k y o s h i h a i n w a t a r a s h i i k a n o o s e o m o c l t e p a u n a n i h a j i m e r u k a w a (cv_jpn_000803-cv_jpn_000803) +o m o s h i r o u n o n i p a u r o o t o n a g a s u g i t e d a r u i (cv_jpn_000804-cv_jpn_000804) +k o r e j o o s h u u h a N p o i n a (cv_jpn_000805-cv_jpn_000805) +k a g a k U s h a m o s e k a i o h o o k a t s s e k i n i t o o i c h i t e k i n i s a t s u m e s h u o t o s h I t e i r u (cv_jpn_000806-cv_jpn_000806) +f U t s u u n i t s u m a r a N (cv_jpn_000807-cv_jpn_000807) +s h I c l k a r i I t e k u d a s a i (cv_jpn_000808-cv_jpn_000808) +w a t a s h i w a a m i g i n o n o t o k i d e k I s h I t e k I s e i m e e n o j i k a k u t o i u g o t o k i m o n o b e N s h o o h o o t e k i r o N b i t o y u u n o d e a r u (cv_jpn_000809-cv_jpn_000809) +w a t a s h i w a p a u s h a k a i k e e s e e n o k o N t e e n i w a p a u d e y o o n u s o s u t e k i n a m o n o g a h a t a r a i t e i r u t o m o (cv_jpn_000810-cv_jpn_000810) +n a n i o s u r u t s u m o r i d a c l t a n o k a (cv_jpn_000811-cv_jpn_000811) +k o b u t s u t e k I t a g a j i k o h I t e e t e k i n i t a N n i t e N s h u u g o o t e k i n i k a N g a e r a r u r u t o k i s o r e g a b u z u r i t e k I t e k a i d e a r u (cv_jpn_000812-cv_jpn_000812) +a n e g a z u c l t a n o d e a p a u y a c l k i n o s h i r a a i g a a r i m a s e N d e s h I t a (cv_jpn_000813-cv_jpn_000813) +k o r e w a r i h o N d e u t e i n a i t a b e m o n o d e s U (cv_jpn_000814-cv_jpn_000814) +w a t a s h i w a h e N s h u u i n o y o n e N k u r a e w a y a c l t a c l t o o m o (cv_jpn_000815-cv_jpn_000815) +i s a N n i k o n o k o t o b a n o i m i y o o o o s h i a m a s h I t a (cv_jpn_000816-cv_jpn_000816) +k a s e g a t s U s u y o i h i w a t e n i s u g a d e k i m a s e N (cv_jpn_000817-cv_jpn_000817) +i c h i (cv_jpn_000818-cv_jpn_000818) +h a i (cv_jpn_000819-cv_jpn_000819) +n i (cv_jpn_000820-cv_jpn_000820) +d e i (cv_jpn_000821-cv_jpn_000821) +t o k i (cv_jpn_000822-cv_jpn_000822) +m i r u t o i u k o t o t o p a u h a t a r a k U t o i u k o t o g a p a u s U k a b u N r i t e k i n a k e r e b a n a r a n a i (cv_jpn_000823-cv_jpn_000823) +w a r e w a r e o o t a m a s h i i n o z u k o k a r a g u g a s u m o n o d e n a k e r e b a n a r a n a i (cv_jpn_000824-cv_jpn_000824) +z e c l t a i b e N s h o o h o o t e k i n a r u g a i u e n i i d e a t e k I c h o c l k a N t e k I k e e k i g a c l U k u m a r e r u n o d e a r u (cv_jpn_000825-cv_jpn_000825) +d o k o m a d e m o t a t o i c h i t o n o s o o g o h I t e e t e k i n a z e c l t a i m u j u N t e k i j i k o d o i t s u n o s e k a i n i s h I t e (cv_jpn_000826-cv_jpn_000826) +s h I k a r u n i n i N g e N t o k a N k y o o t o r o k a N k e e w a m o t o k o o i n o k a N k y e e d e a r i (cv_jpn_000827-cv_jpn_000827) +i i s a n i k o o n o k o t o b a n o i m i y o o s h i a m a s h I t a (cv_jpn_000828-cv_jpn_000828) +g e k i g a n a n a t s s a r i m a s U (cv_jpn_000829-cv_jpn_000829) +k o c h i r a b a k o b u y a s h i i s a N d e s U (cv_jpn_000830-cv_jpn_000830) +m o s h i i m a s h i (cv_jpn_000831-cv_jpn_000831) +k o k o w a o k i k U t e n i k u y a k a n a m a c h i d e s U (cv_jpn_000832-cv_jpn_000832) +s o n o u c h i k a i y a k U s a r e r u k a r a i s o g e (cv_jpn_000833-cv_jpn_000833) +a m a s a g a f u s a i r a r e t e t e c h o o d o i (cv_jpn_000834-cv_jpn_000834) +h o g e N s h I t s u n o d o o a a k e t a (cv_jpn_000835-cv_jpn_000835) +m o d a N n i o o w a c l t e m o k i n i s h i n a i (cv_jpn_000836-cv_jpn_000836) +a r i g a c l t a y a (cv_jpn_000837-cv_jpn_000837) +i t o o g a r a k u d a t o j i k a N w a o s u r e t e t a n o s h i m e r u (cv_jpn_000838-cv_jpn_000838) +k a k a k u w a g i z u t s U k a s a r e r u n i o j i t e j o o s h I k i n o u c h i n i h a i c l t e i u k u (cv_jpn_000839-cv_jpn_000839) +s h I k a s h I t o k i g a k a o n i h a i r u k o t o s o n o k o t o g a p a u m i r a a y o o m u k o t o d e a r i w a r a t a n a r u i c l s u t a i g a d e t e k u r u k o t o d e a r u (cv_jpn_000840-cv_jpn_000840) +t e r e b i o k a i k a i t e p a u t e r e b i o m i r u j i k a N g a f u e t a (cv_jpn_000841-cv_jpn_000841) +k a k a r i s h I t a i n o m i i t s u m a d e m o i k i r u n o d e a r u (cv_jpn_000842-cv_jpn_000842) +n i N k i d a a m e N i y a n i g a r a N d a r a n i j i k a N m a c h i d a c l t a (cv_jpn_000843-cv_jpn_000843) +s o r e o m o c h i i r u n i N g e N n o i y o k u n i i t o N s h i s o s h I t e k o r e w a k a r e n o m o c l t e i r u k a c h i n o s h a k u d o n i i t o N s u r u (cv_jpn_000844-cv_jpn_000844) +m a w a r u i o w a m i N n a k a N g a e r u k o t o o y a m e t e i t a (cv_jpn_000845-cv_jpn_000845) +k o o i t e k i c h o k c l U k a N t e k i n i s e k a y o m i r u t o i u k o t o w a j a k u n i k o o i t e k i c h o c l k a N t e k i n i s e k a y o k e e s e e s u r u k o t o o h k u m u n o d e a r u (cv_jpn_000846-cv_jpn_000846) +s h i N c l p a i t a k e s a s e m a i t o s u r u k i z u k a i g a y o k e e n i s h i N p a i s a s e t e s h i m a u (cv_jpn_000847-cv_jpn_000847) +k o n o m i c h i w a t o t e m o s e m a i n o d e a b n a i d e s U (cv_jpn_000848-cv_jpn_000848) +w o h o e g a a r i n i (cv_jpn_000849-cv_jpn_000849) +t o i w a r o o k a m o i t a r i g a a n i a r i m a s U (cv_jpn_000850-cv_jpn_000850) +t a n a k a s a N n o h i t a i n i k i m u r a s a N g a i m a s U (cv_jpn_000851-cv_jpn_000851) +m a c l k u r a n o t a m a b o t e s u g o i n i e (cv_jpn_000852-cv_jpn_000852) +s h o h o o m i t a i n a d o k U s h u k a N s o o b u N m o k a i t a (cv_jpn_000853-cv_jpn_000853) +g e N j i t s u n o s e k a i w a t a m o o i c h i t o s h I t e k e c l t e s u r a i d a k a t a s h u o a c l t o s e k a i z u n a k e r e m a n a r a n a i (cv_jpn_000854-cv_jpn_000854) +s h o o h i N k e N s a k u g a o k a r e a s u i t o o k a u k i i n a r u n a i (cv_jpn_000855-cv_jpn_000855) +t s i s h I k i w a p a u r e k I s h I t e c l k a t e e r d e n a k e r e m a n a r a n a i (cv_jpn_000856-cv_jpn_000856) +m o n o g o t o n N j i N p a N k a e r u d a k e d e u m a k u i k U k o t o m o w a r u (cv_jpn_000857-cv_jpn_000857) +k o n o k i s e e t s u w a k a t s u o n o s a s h i m i g a z e c l p i N (cv_jpn_000858-cv_jpn_000858) +k a k e n i s h i c l p a i s h I t e m o o c h t s U t s u i t e s a N s h I t s u o u k e i d e r u (cv_jpn_000859-cv_jpn_000859) +s o r e y u e n i t e t s u g a p u g a z e N t a i n o g a k o d e a r u t o s u r e w a (cv_jpn_000860-cv_jpn_000860) +k i i z a n a y a o y a d a g a y e s U k U t e h a N j o s h I t e r u (cv_jpn_000861-cv_jpn_000861) +i n i f u r a g a a k i n o h u z e N n i y o c h i i c l t e p a u k o k u g a i e d a s h U t s u s u r u h I t o m o d e t e k i t a (cv_jpn_000862-cv_jpn_000862) +t s u g i n i k a g a k u w a s o N z a y o p a u s h u j u n o d o o e k i n i w a k a c l t e s o r e z u r a N r y o o u k i N z i t I k e N i k I s u r u (cv_jpn_000863-cv_jpn_000863) +s o r e d e w a p a u t o k t o i u m o n o n o s e r i t s u s h i o w a n a k u p a u s h u N k a N t o i m o n o m o n a k u a r u n o d e a r u (cv_jpn_000864-cv_jpn_000864) +a k a i b u r a N k o h o N k u r i t o s e e n o s u b e r i d a i k a w a i t a s u n a b a (cv_jpn_000865-cv_jpn_000865) +s h I k a s h i s o r e w a d o k o m a d e m N k U k o k a r a r i t e p a u p o k o e k a e r i k u r u s e s h I t s o m o t o m o d e n a k e b a n a r a n a i (cv_jpn_000866-cv_jpn_000866) +a r i t o w a r a i r u d e m o o m a k I c h i r a s h I t e p a u m i N n e k a r a u r a m i o k a c l t e r u (cv_jpn_000867-cv_jpn_000867) +k o n o c l t e e d o s a w a g i n i n a r u k o t o m o n a i n o d a r o o (cv_jpn_000868-cv_jpn_000868) +k o n o n e d a N d e w u r i c h a N k a a N (cv_jpn_000869-cv_jpn_000869) +h i n o k a g e N n i c h u i s h i n a i t o s u g u k o g e r u (cv_jpn_000870-cv_jpn_000870) +e N m a N d o w e n i p o t s u r i t o c h i i s a n a a n a g a i t a s a i s h o w a t s u m a y o o j i t e e d o n o c h i i s a n a a n a d a c l t a (cv_jpn_000871-cv_jpn_000871) +s o r e w a p a u a r e w a r e o i k a s h i n a g a r a w a r e w a r e o t o r e e k a s u r u n o d e a r u p a u w a r e b a r e n o t a m a s h i i o k o r o s u n o d e a r u (cv_jpn_000872-cv_jpn_000872) +r e k I s h I t e k i n i a t a e r a r t a m o n o w a d e c l t a i m u j u N t e k i j i g o t o o i t s u t e k i g e N z a i n o i t e s U k a i h I t e k i n i a t a e r a r e t a m o n o t o s h I t i (cv_jpn_000873-cv_jpn_000873) +m u j u N t e k i j i g o d o o i t s U t o s h I t e p a u i t s u m o k o n o s e k a i n i c h o o e s h t k i d e a (cv_jpn_000874-cv_jpn_000874) +y u u n i z e c l t a e m u j u N t e k i j i g o d o o i t s U t o s h I t e g e N s a i k a r a g e N z a e t o b o k i i k u s e k a i n o g e N z a i n o i t e (cv_jpn_000875-cv_jpn_000875) +h a r e p a u w o t a N o s h I t o m N d a s h u d e k i n a i (cv_jpn_000876-cv_jpn_000876) +s h I k a s h i w a t a s h i a s o k o n i s e k a i n o j i k o d o o i t s u o k u n o d e w a n a i (cv_jpn_000877-cv_jpn_000877) +n e b e k a k u n a r u n o g a h a y a k u n a c l t a (cv_jpn_000878-cv_jpn_000878) +w a t a s h i w a i N g e N n o o r e k I s h I t e k I k e e s e e n o t a c h i b a k a r a g e j u t s u o m i r u n o d e a c l t e o o s h a k a r a d e N s h a o m i r u n o d e w a n a i (cv_jpn_000879-cv_jpn_000879) +a o i t o m a t o s h I k a n a k U t e k a u k a b m a i y o (cv_jpn_000880-cv_jpn_000880) +s i N k i z u i g y o o n i o o k i n a k I t a y o y o s U t e i r u (cv_jpn_000881-cv_jpn_000881) +n a n i k a s h i r a n o i n i N s e N t i b u w a n a i t o k i b i s h i i n o d e w a (cv_jpn_000882-cv_jpn_000882) +j i k o N s h e e g e N n o i b e N t o d e s U t o r u s h I t a m a r u b u (cv_jpn_000883-cv_jpn_000883) +m a a r i n o h I t o w a b o o z e N t o s h I t e i t a (cv_jpn_000884-cv_jpn_000884) +s o N n a n a i y o o n o m e e r e w a n a N k e N m o k U I t e i t a (cv_jpn_000885-cv_jpn_000885) +n j i k a i d r d e e s u i s h I t e i t a (cv_jpn_000886-cv_jpn_000886) +t o k i d o k i c h i u N n o k o r o w a w a k a r a n a k u n o r u t o k i g a a r u d a k a r a b o k u w a k a n i o c l k i n o o t o n i k a c h i a j i m e r u (cv_jpn_000887-cv_jpn_000887) +m o o n i g e t e c h a t a m e d a (cv_jpn_000888-cv_jpn_000888) +k a r e w a p o o t o t a j i t s U k u s h i t e i t a (cv_jpn_000889-cv_jpn_000889) +d a r a u n i m o m e i w a k o w a k a k e t a k u n a i (cv_jpn_000890-cv_jpn_000890) +w a s a k a t o o m o t e t o w a n o o t o c l t e o n i g e c l t a (cv_jpn_000891-cv_jpn_000891) +s h t e i m a s e N (cv_jpn_000892-cv_jpn_000892) +k a y o o n i s h I t e s h I c l t e i r u t o t o m o n i s h i c l t e i n a i t o k o r o k a r a t a N k y u u w a h a j i m a r u n o d e a r u (cv_jpn_000893-cv_jpn_000893) +a i s a u w a d a i j i d a i o (cv_jpn_000894-cv_jpn_000894) +t o j i t a m o n o i k a n i h i r o g e t e m o h i r a i t a m o n o n i a n a r a r o t o i c l t e i r u g a (cv_jpn_000895-cv_jpn_000895) +t a m e s h i n i i k u t s U k a t s U k u c l t e m i y o a (cv_jpn_000896-cv_jpn_000896) +z u i b u N a k o g i n a s h o o b a i d a i o n a (cv_jpn_000897-cv_jpn_000897) +w a t e i (cv_jpn_000898-cv_jpn_000898) +i c h i (cv_jpn_000899-cv_jpn_000899) +g o (cv_jpn_000900-cv_jpn_000900) +s h i k i (cv_jpn_000901-cv_jpn_000901) +i i e (cv_jpn_000902-cv_jpn_000902) +h a c h i (cv_jpn_000903-cv_jpn_000903) +n e i (cv_jpn_000904-cv_jpn_000904) +s h i i (cv_jpn_000905-cv_jpn_000905) +k u (cv_jpn_000906-cv_jpn_000906) +i c h i (cv_jpn_000907-cv_jpn_000907) +k a k a k u g a a k i r a k a n i s u r u p a u k y a c l k a N t e k I s h i N r i n i s h I t a g a u k o t o n i y o c l t e (cv_jpn_000908-cv_jpn_000908) +k a k o t o m i r a i t o n o m u j u N t e k i j i k o d o o i t s u t o s h I t e n o g e N z a i g a p a u k a t a c h i o m o s h U t o i u k o t o d e a r u (cv_jpn_000909-cv_jpn_000909) +b u t s u r i t e k I s e k a i w a p a u s u u g a k u t e k I k i g o o n i y o c l t e a r a w a s a r e r u p a u s u u g a k u t e k I k a t a c h i n o s h e k a i d e a r u (cv_jpn_000910-cv_jpn_000910) +w o n a j i g e N s h o o d e s a N k o o g i n a r u (cv_jpn_000911-cv_jpn_000911) +g a i k o k u k a r a k i t a m o n o d a t o s h i c l t e b i c l k u r i (cv_jpn_000912-cv_jpn_000912) +i w a y o r u j i c l s e N n i y o c l t e k a k U t o U k U s h i k i t a c l t a m o n o d e a r u (cv_jpn_000913-cv_jpn_000913) +o n a j i y o n i d a N s e e w a h i z a o o u z u b o N o h a c l k o t o g a p a u g i m u u z u k e r a r e t e i m a s U (fleurs_jpn_000346-fleurs_jpn_000346) +k o n o s a a b i s a k o r a k U s e e w a h a j i m e t o s u r u s e N p a k u y a e N k a k U c h i d e p a u d e e t a y a o N s e o h I s u o t o s u r u t a N g e N t a i n i p a u h i N c l p a n i r i y o o s a r e t e i m a s U (fleurs_jpn_000347-fleurs_jpn_000347) +k y o o f u u h y o k a t o n o k o o s u i r o o o b i a m a k a s h i b a r a i u t a t s u m a k i i z u k i o b e s a i k u r n o d a n o k i b i s h i i k I s h o k e e t a y a s o n o e e k y o n i y o r u m o n o r e s U (fleurs_jpn_000348-fleurs_jpn_000348) +i N t a a n e c l t o w a m a s U k o m i r i k e e s h u N t o t a i j i N k o m i r u i k e e s h o o n o r y o o y o s o o k a n e s u n a e t a k a N k y o o d e s U (fleurs_jpn_000349-fleurs_jpn_000349) +k a j i n o r e w a t s u u j o o t o k u b e s u r a i N s h I k o y a e N t a a t e i m e N t o y o o s h I t e i m a s U g e s u w a k i b u i y o k u s h I s e s u n a i n i t o r a m a r u y o o r i s u r u t a m e d e s U (fleurs_jpn_000350-fleurs_jpn_000350) +s h I k a s h i p a u k a k u t e N n o b i k e c l t o o s h i n a c l t a a d t o i N d o w a n a n a t s u n o b i k e c l t o o s h i n a i s a N j u u r o k u r a s h I k a d e k i m a s e N d e s h i t a (fleurs_jpn_000351-fleurs_jpn_000351) +h o o k u r a N d o n o k o o s h I k e t s u k a w a h o o k u r a N d o s h o t o o k o N d o e f U k e e p i i d e i c h i p o N n o g a i c h i e e b o N d o j i i p i i b i i t o t o o k a n i k o t e e s a r e t e i m a s U (fleurs_jpn_000352-fleurs_jpn_000352) +h a s h I s h I t a N n o j o o h o o k o k a N w o c h j u u g o m e t o r o d e s U n i s e N j u i c h i n e h a c h i g a t s u n i s e k o o s h i p a u n i s e N j u u n o n e N n i s a N g a t s u m a n e k a i t s u e s h i m a s e N g d e s h I t a (fleurs_jpn_000353-fleurs_jpn_000353) +i c l p u N k a N d e h f u c l t o s o g u c h i i k i m a r e b a f u c l t o o s u r u m a d e m i n a N m o k a k a r u c h i i k i m o a r i m a s U (fleurs_jpn_000354-fleurs_jpn_000354) +p i r a m i c l t a n o o t o t o s h i k a i n o s h o o w a k o n o k a N k o o s h i d e t o k u n i k o r o m o a t a s h i k a t a n o s h i m e r e m o y o o s h u n o h I t o s u r e s U (fleurs_jpn_000355-fleurs_jpn_000355) +s o n o t a n e t a i n i d a d e r u t o s h I t e h o o k i g a t s u i k a s a r e g a c h i d e s U (fleurs_jpn_000356-fleurs_jpn_000356) +g e N z u N s u r u k o t o g a s h i t a r e t e i r u n i j i o g o o m a i n a t a N d a c l t u p u r o o t u s a i d o w a g e N z u N s u r a t o o g a e b u N k e n o s a i k o n o u s h i d e s U t e g a k i n y o r u g e N p o o w a g e N z o o s h I t e i m o s e N (fleurs_jpn_000357-fleurs_jpn_000357) +k a r e m o s e s o o t a d a s h i t o m i t o m e r u s h I t o m i m a s h I t a g a o o k u g u h I t o o s u n o g e k o d e p a u t a i y o o k e d e a t a i o t o s i n o h o k a n o h o s h i g a p a u c h I k u u n o m a r e i d o o s h I s e r u t o s h i N c h i t e i m a s h I t a (fleurs_jpn_000358-fleurs_jpn_000358) +c h i b e c l t o m e e s o o c h u s h i N w a s h i i s e e i o g a d e s U s a m a z a n a n a k a m i g a m i y o s h I k a k U k a s u r u k o t o d e e n e r u g i i c h a n e r u g a s h o o k a s a r e c h a k u r a g a k a s e e k a s a r e s a t o r u n e i s h I k e g o o m a r e m a s U (fleurs_jpn_000359-fleurs_jpn_000359) +m i n a m i y a h u r e k a n i y a r u s u b e t e n r u k o k u r i s U k o o e t o d o o y o n i k o n u k o o e n i a m a i n i j i a h o g o s h I t o n i u u e N g u o g o t a k a r i m a s U (fleurs_jpn_000360-fleurs_jpn_000360) +d e c l s h s a c l k u r u n g a s u n u h o k a n o o k u n u k o o t s u s h u d a N g a s u k o k a r u m a r e m a s h I t a (fleurs_jpn_000361-fleurs_jpn_000361) +i N t a a n e c l t o w a m a s U k o m i n i g e e s h o N t o t a i j i N k o m i n i g e e s h a o N n o r y o o y o o s o o k a n i s o n a i e t a k a N k y o o r e s U (fleurs_jpn_000362-fleurs_jpn_000362) +k y o o i N d e w a k a N s e N k a N r i t e j u u s h o N i s h I k a g a i p a u k a n i i e n u k a N s e N g o k a n o o s e s e k u t a m e n i k a N j a k a k u r s u r a d a m o m i s o c h o t o c l t e i m u s h I U (fleurs_jpn_000363-fleurs_jpn_000363) +r e N p o o r i k a i w a n i s e N g o n e N d o k a r a w a i s e s u b u s u t o r e s h u m a r i o h o e n o s h I k i N t e e k y o o k a i j i s h i p a u e r u b i i a i w a a t a r u z o p o r u n o n i j u u n i n o s o o s a N i N y o t o o u n u s h u n a k e r e w a n a r a n a i t o k I t e s h i m a s h I t a (fleurs_jpn_000364-fleurs_jpn_000364) +p i i e c h i d e r i r o w a k e N s a s h I t k a k o k u b u s h s e i u k u g a r a n s u i s o i y u n p i i e i c h i n e i c h i n o r r y o o d e s h i m a s a r e m e s U (fleurs_jpn_000365-fleurs_jpn_000365) +s o r e d e m o t o o r y i u k a r u n a r u b a i s o u k e p a u s u b e t e n o o o s h I k i o o m a o r i a N z e N j o o n o k e e g o g o n i s a i s h i i n o c h u i o h a r a i m a s h o o (fleurs_jpn_000366-fleurs_jpn_000366) +k o r e r a w a t a m a n i k o N g a t s s u r o k a c l k a t s o k u m u k e n o b i c h i t e k a i g a i n i s a m a z a m a n a t e N p o w o n a r a N d e i m a s U a N z e N i o o e b u k o t o g a d e k i m a s U (fleurs_jpn_000367-fleurs_jpn_000367) +s h i N d o m i e n a i c h i u d a a s o N o p a u n a N d o r a s a s U t o s e N k y e u k a c h i j u k i u p e e j i k y a k U k y u u n o s u N z a i m o n o d t a b a z e r u c h i e m u n o d o g u s h u n a y o s o d e a r u (fleurs_jpn_000368-fleurs_jpn_000368) +k o n o s a a b i s u w a g o r a k U s e N o h a j i m e t o s u r u s e N p a k u y a e N g a k U s h i r e d e e t a y o N s e y o s u y o t o s u r u t a N k e N t a i r i h i N p a a n i r i y o o s a r e t e i m a s U (fleurs_jpn_000369-fleurs_jpn_000369) +s a k u b a b u e e n o s u a i d e s U k a r a k o j u c l k i r o s a N j u i c h i m a i r u h a n a r e t a r a p u r a t a s h i n a i d e g e N s h u g o j o o i N g i N d e w a r u k u r i s u t e i n a f e r u u n a N d e s u d e k i r u k i n a a z o s h i g a n a i t o o r y o o s e e N e n o s h I s u d a o s e N g e N s h i m a s h I t a (fleurs_jpn_000370-fleurs_jpn_000370) +o n o i z u k u i n i m a s h I h a t o n o k a s o o r e b u s u n o r u g a c l k i g a k a s o o r o o o b a a r a N s h i p a u k a b e n i g e k i t o t t s u s h I t e p a u j u u n a n i N g a s h i o s h i m a s h I t a (fleurs_jpn_000371-fleurs_jpn_000371) +h a s h I s h I t a n o j o o h o k u k a w a j u u g o m e t o o d e s U n i s e N j u u c h i n e h a j i g a t s u n i s h u N k o s h i p a u n i s e N j u u n a n i e s a N g a s u m a r e k a i t s u s h i m a s e N d e s h I t a (fleurs_jpn_000372-fleurs_jpn_000372) +b u N n e e t o i u k o t o w a w a s h i m i i o m i s u r u a r a t e N g o n o k i e o o s h i s h i b i r d i s U k a r a k I t e w o r i s h i m e i N i o m i s u r u r a t e N k o r o m e e s h i s h i b i s U t o s h i y a t o s h I k o c l k a o m i s h i n a N n a k a n o k a t a c h i r e s h a k a i n o k i b o o t e e g i s u r u s h u i b i t a s U t o y u m e e s h i n i k a N k e e s h I t e i m a s U (fleurs_jpn_000373-fleurs_jpn_000373) +t s u u j o o k o k o r e w a i s u m a k a N k o t e k u y a r y o o s h a t a s h i k a h a s u r o t o k a c h i k o e t e k i m a s U w o t o t o s h I i k a r i g a o i n a s u m u n o g a t a r i u a m a r u d e h o N n a o y o o r e s U (fleurs_jpn_000374-fleurs_jpn_000374) +t e r e b i n o h o o d o o n i o N d o p a u g e N p a t s U k a r a a k u e g a g a c l t e i m a s U i d e (fleurs_jpn_000375-fleurs_jpn_000375) +n o o b o o r i t o k o o d o o n o s o o k a N k a N k e e w a k a y a k u s h a t a s h i n o k e N k y u u w a o u r a z u k e r e m o n o d e s U (fleurs_jpn_000376-fleurs_jpn_000376) +s u i y o o b i n i b e N t o n a t o k a r u p a n e r o w a s e N s h I k e N g o U k a s u n u k o j i N d e s i I s u j o o s h i m a s h I t a (fleurs_jpn_000377-fleurs_jpn_000377) +s e N h a p e k u n e N d a i r a i g u N t a i g a t o o j a k s u r e w a r e h a i c h i w a k o n o b y o k i n i k a N k y e e s u r e b o N d a i n i s o o g u s h I t a k o t o w a r i m a s e N d e s h I t a (fleurs_jpn_000378-fleurs_jpn_000378) +s h I k a s h i c l k k y a k u t e n o b i k e c l d o o s h u n a c l d a t o i N d o a n a n a t s u n e b i k e c l t o o s h i n a i s a N j o r o k u r a s h I k a r e k i m a s e N d e s h I t a (fleurs_jpn_000379-fleurs_jpn_000379) +k a j i n o d e w a t s u u j o o t o k u b e t s u n a i N s h a k o y a e N t a a t e i m e N t o o y o o i s h I t e i m a s U g e s o g a k i b u y a k u s h i s e s u n a i n i t o r o m a r u y o n i s u r u t a m e r e s U (fleurs_jpn_000380-fleurs_jpn_000380) +s o r e d e o t o o k y i u k a r a n a a r a b a i s o g e p a u s u b e t e n o h y o w a s h I k m a o r i a N z e N j o n o k e k o n i s a i s h i i N n o c h u i o h a r a i m a s h o (fleurs_jpn_000381-fleurs_jpn_000381) +o w o k a r u g e w a r i m a s e p a u k o r a s h I t o t s u o s h o o w a r i d e a r i p a u a t a r a s h i s h o o n o m o k a k e d e s U (fleurs_jpn_000382-fleurs_jpn_000382) +s a w a r i t o w a a f u r i k a n o y a s e e d o o g u z u p a u t o k o n i s a w a N n a n i r u y a s e r e d o o g u t s u n o k a N s a s u a m o k u t e k i t o s h I t a r i k u r o d e n o y o k o o s a s h i m a s U (fleurs_jpn_000383-fleurs_jpn_000383) +u y u n e k i t a b a r u t o k a y o m o d a N s u r o b a i w a s e i s u i c h i k a k u n i s h s e k u d a s a i k I k o o r e n o n a k o t s I k i t s s u u s a i n i m o c l t o m e k y o o k e r u s e i t s u d e o s o r u j u u m o n o s o o g a n a r i k i b i k i g a s U (fleurs_jpn_000384-fleurs_jpn_000384) +k o k o w a i r i s u n o s h o k u m i N i s h e h a i e s h a e g a j i b u N t a s h i n o r y o o r o t o s h a b a s h u n a r o u d e p a u s h o k o m i N t i j i r e n i s h o o k o o s e k a s o o t o s u r e k a t a w a k o k o a r h a j i b e r e n a g a y o i u s h o o (fleurs_jpn_000385-fleurs_jpn_000385) +k o s h i w a s a k u g e N s u r u s u u c h o o s a r a y a w a s e N d e s h I t a k a p a u s a k u g e N w a c h i i w o k u n o k e e z a i s a N s h I s u y o n i m o t o z u i t e j i s h i s a r u d a r o t o N n o o i m a s h I t a (fleurs_jpn_000386-fleurs_jpn_000386) +s a u i n u u k u k u s h o c l k u w a s h i N k o N r y o k o o n o j i k i g a s U k u n a i p a u k a r u e c h a a s h o c l k u y o r e m o h a e k o o t o z u r e n a w a b i k i y o r i s h o o j o g a k a s u r u k o t o g a a r i m a s U (fleurs_jpn_000387-fleurs_jpn_000387) +k y i n o o n o a s a t o r u k o n o g a j i a N t e c l p u n o k e e s a s u o h o N w o r e j i d o o s h a b a k u r a n o b a k a a s o r i y o r i p a u k y e e k a N f u t a r e g a s h i b o s h i p a u h o s h o w a s h a w a n i j u u n i y o k o a i m a s h I t a (fleurs_jpn_000388-fleurs_jpn_000388) +s h o k u b u t s u d a n i N g i N g a s u u s a N z o o t s U k u r i n i N g e N g a k a c l k o i k i t o s h I t e h a k i d a s u n i s a N k a t a N s o o t o r i k o N d e i m a s U (fleurs_jpn_000389-fleurs_jpn_000389) +s e N p a k u r e e b u s h i o i s o o s u r u n o w a u m i o k o e t e h I t o y a b u s h i o t a r i r y o o y i s o o s u r u m o c l t o m o k o o r i s e k i n a h o o h o o r e s U (fleurs_jpn_000390-fleurs_jpn_000390) +k a r i h o r u n i a s h u u n o a w a n o r u d o s h u w a r u t s u n e c l k a a c h i s h i w a b o o r y o k u t e k i n a b i d e o g e e m u o m i s e e n e N s h a n i h a N b a y a r e N t a e s u r e u k o t o o k i N s h i s u r u h o o w a n i s h a o m e e s h i m a s h I t a (fleurs_jpn_000391-fleurs_jpn_000391) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..84c2ab5a54d6d3251e487310db57103a25dfbaf7 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/ref.trn @@ -0,0 +1,160 @@ +k a k o t o m i r a i t o n o m u j u N t e k i j i k o d o o i t s u n a r u g a y u e n i p a u i s h I k i t e k i n a n o d e a r u (cv_jpn_000800-cv_jpn_000800) +s e k a i o k e e s e e s u r u t o t o m o n i p a u j i k o j i s h i N o k e e s e e s u r u s o o z o o t e k I s e k a i n o s o o z o o t e k i y o o s o t o s h I t e p a u k o b u t s u g a k o b u t s u d e a r u (cv_jpn_000801-cv_jpn_000801) +p a s o k o N d e g e e m u y a r u n i N g a f u e t e k I t e r u (cv_jpn_000802-cv_jpn_000802) +k a g a k u n o s h i m e s u a t a r a s h i i j i j i t s u p a u a t a r a s h i i k a N n e N p a u k a N k y o o s h i h a i n o a t a r a s h i i k a n o o s e e o m o c l t e n a n i o h a j i m e r u k a w a (cv_jpn_000803-cv_jpn_000803) +o m o s h i r o i n o n i r o o d o n a g a s u g i t e d a r u i (cv_jpn_000804-cv_jpn_000804) +k o r e j o o s h u u h a N c l p o i n a a (cv_jpn_000805-cv_jpn_000805) +k a g a k U s h a m o s e k a i o h o o k a t s u t e k i n i t o o i t s u t e k i n i s e t s u m e e s h i y o o t o s h I t e i r u (cv_jpn_000806-cv_jpn_000806) +f U t s u u n i t s u m a r a N (cv_jpn_000807-cv_jpn_000807) +s h i c l k a r i s h I t e k u d a s a i (cv_jpn_000808-cv_jpn_000808) +w a t a s h i w a m i g i n o g o t o k i r e k I s h i t e k I s e e m e e n o j i k a k U t o i u g o t o k i m o n o o b e N s h o o h o o t e k i r o N r i t o i u n o d e a r u (cv_jpn_000809-cv_jpn_000809) +w a t a s h i w a s h a k a i k e e s e e n o k o N t e e n i w a d i o n y u s o s u t e k i n a m o n o g a h a t a r a i t e i r u t o o m o u (cv_jpn_000810-cv_jpn_000810) +n a n i o s u r u t s u m o r i d a c l t a n o k a (cv_jpn_000811-cv_jpn_000811) +k o b u t s u t e k I t a g a j i k o h I t e e t e k i n i t a N n i t e N s h u u g o o t e k i n i k a N g a e r a r e r u t o k i p a u s o r e g a b u t s u r i t e k I s e k a i d e a r u (cv_jpn_000812-cv_jpn_000812) +a m e g a f u c l t a n o d e p a u y a k y u u n o s h i a i g a a r i m a s e N d e s h I t a (cv_jpn_000813-cv_jpn_000813) +k o r e w a n i c l p o N d e u c l t e i n a i t a b e m o n o d e s U (cv_jpn_000814-cv_jpn_000814) +w a t a s h i w a p a u h e N s h u u i N o p a u y o n e N k u r a i h a y a c l t a t o o m o u (cv_jpn_000815-cv_jpn_000815) +i s a N n i k o n o k o t o b a n o i m i o o s h i e m a s h I t a (cv_jpn_000816-cv_jpn_000816) +k a z e g a t s u y o i h i w a t e n i s u g a d e k i m a s e N (cv_jpn_000817-cv_jpn_000817) +i c h i (cv_jpn_000818-cv_jpn_000818) +h a i (cv_jpn_000819-cv_jpn_000819) +n i (cv_jpn_000820-cv_jpn_000820) +r e i (cv_jpn_000821-cv_jpn_000821) +r o k u (cv_jpn_000822-cv_jpn_000822) +m i r u t o i u k o t o t o h a t a r a k U t o i u k o t o t o g a f U k a b u N r i t e k i d e n a k e r e b a n a r a n a i (cv_jpn_000823-cv_jpn_000823) +w a r e w a r e o t a m a s h i i n o s o k o k a r a u g o k a s u m o n o d e n a k e r e b a n a r a n a i (cv_jpn_000824-cv_jpn_000824) +z e c l t a i b e N s h o o h o o t e k i n a r u g a y u e n i i d e y a t e k I c h o c l k a N t e k I k e e k i g a f U k u m a r e r u n o d e a r u (cv_jpn_000825-cv_jpn_000825) +d o k o m a d e m o t a t o i c h i t o n o s o o g o h I t e e t e k i n a z e c l t a i m u j u N t e k i j i k o d o o i t s u n o s e k a i n i s h I t e (cv_jpn_000826-cv_jpn_000826) +s h I k a r u n i n i N g e N t o k a N k y o o t o n o k a N k e e w a m o t o k o o i n o k a N k e e d e a r i (cv_jpn_000827-cv_jpn_000827) +i s a N n i k o n o k o t o b a n o i m i o o s h i e m a s h I t a (cv_jpn_000828-cv_jpn_000828) +k e e k i g a n a n a t s u a r i m a s U (cv_jpn_000829-cv_jpn_000829) +k o c h i r a w a k o b a y a s h I s a N d e s U (cv_jpn_000830-cv_jpn_000830) +m o s h i m o s h i (cv_jpn_000831-cv_jpn_000831) +k o k o w a o o k I k u t e n i g i y a k a n a m a c h i d e s U (cv_jpn_000832-cv_jpn_000832) +s o n o u c h I k a i a k U s a r e r u k a r a i s o g e (cv_jpn_000833-cv_jpn_000833) +a m a s a g a o s a e r a r e t e t e c h o o d o i i (cv_jpn_000834-cv_jpn_000834) +h o k e N s h I t s u n o d o a o a k e t a (cv_jpn_000835-cv_jpn_000835) +m u d a n i o w a c l t e m o k i n i s h i n a i (cv_jpn_000836-cv_jpn_000836) +a r i g a t a y a (cv_jpn_000837-cv_jpn_000837) +i d o o g a r a k u d a t o j i k a N o w a s u r e t e t a n o s h i m e r u (cv_jpn_000838-cv_jpn_000838) +k a g a k u w a g i j u t s U k a s a r e r u n i o o j i t e j o o s h I k i n o u c h i n i h a i c l t e y u k u (cv_jpn_000839-cv_jpn_000839) +s h I k a s h I t o k i g a k a k o n i h a i r u k o t o s o n o k o t o g a p a u m i r a i o u m u k o t o d e a r i p a u a r a t a n a r u s h U t a i g a d e t e k u r u k o t o d e a r u (cv_jpn_000840-cv_jpn_000840) +t e r e b i o k a i k a e t e p a u t e r e b i o m i r u j i k a N g a f u e t a (cv_jpn_000841-cv_jpn_000841) +k a k a r u s h U t a i n o m i p a u i t s u m a d e m o i k i r u n o d e a r u (cv_jpn_000842-cv_jpn_000842) +n i N k i r a a m e N y a n i n a r a N d a r a n i j i k a N m a c h i d a c l t a (cv_jpn_000843-cv_jpn_000843) +s o r e o m o c h i i r u n i N g e N n o i y o k u n i i z o N s h i p a u s o s h I t e k o r e w a k a r e n o m o c l t e i r u k a c h i n o s h a k u d o n i i z o N s u r u (cv_jpn_000844-cv_jpn_000844) +m a w a r i w a m i N n a p a u k a N g a e r u k o t o o y a m e t e i t a (cv_jpn_000845-cv_jpn_000845) +k o o i t e k I c h o c l k a N t e k i n i s e k a i o m i r u t o i u k o t o w a p a u g y a k u n i k o o i t e k I c h o c l k a N t e k i n i s e k a i o k e e s e e s u r u k o t o o f U k u m u n o d e a r u (cv_jpn_000846-cv_jpn_000846) +s h i N p a i k a k e s a s e m a i t o s u r u k i z u k a i g a p a u y o k e i n i s h i N p a i s a s e t e s h i m a u (cv_jpn_000847-cv_jpn_000847) +k o n o m i c h i w a t o t e m o s e m a i n o d e p a u a b u n a i d e s U (cv_jpn_000848-cv_jpn_000848) +o b o e g a w a r u i n e (cv_jpn_000849-cv_jpn_000849) +t o i r e w a r o o k a n o h i d a r i g a w a n i a r i m a s U (cv_jpn_000850-cv_jpn_000850) +t a n a k a s a N n o h i d a r i n i k i m u r a s a N g a i m a s U (cv_jpn_000851-cv_jpn_000851) +m a c l k u r o n a t a m a g o c l t e s u g o i n e (cv_jpn_000852-cv_jpn_000852) +s h o h y o o m i t a i n a d o k U s h o k a N s o o b u N o k a i t a (cv_jpn_000853-cv_jpn_000853) +g e N j i t s u n o s e k a i w a t a n o i c h i t o s h I t e k e c l t e e s e r a r e t a k a t a c h i o m o c l t a s e k a i d e n a k e r e b a n a r a n a i (cv_jpn_000854-cv_jpn_000854) +s h o o h i N k e N s a k u g a w a k a r i y a s u i t o k a u k i n i n a r u n o n i (cv_jpn_000855-cv_jpn_000855) +c h i s h I k i w a r e k I s h i t e k I k a t e e d e n a k e r e b a n a r a n a i (cv_jpn_000856-cv_jpn_000856) +m o n o g o t o n o j u N b a N o k a e r u d a k e d e u m a k u i k U k o t o m o a r u (cv_jpn_000857-cv_jpn_000857) +k o n o k i s e t s u w a k a t s u o n o s a s h i m i g a z e c l p i N (cv_jpn_000858-cv_jpn_000858) +k a k e n i s h i c l p a i s h I t e m o o c h I t s u i t e s o N s h I t s u o u k e i r e r u (cv_jpn_000859-cv_jpn_000859) +s o r e y u e n i t e t s u g a k u g a z e N t a i n o g a k u d e a r u t o s u r e b a (cv_jpn_000860-cv_jpn_000860) +c h i i s a n a y a o y a d a g a y a s u k U t e h a N j o o s h I t e r u (cv_jpn_000861-cv_jpn_000861) +i N f u r a g a k i n o o f u z e N n i o c h i i c l t e p a u k o k u g a i e d a c l s h U t s u s u r u h I t o m o d e t e k i t a (cv_jpn_000862-cv_jpn_000862) +t s u g i n i k a g a k u w a s o N z a i o s h u j u n o r y o o i k i n i w a k a c l t e s o r e z o r e n o r y o o i k i n i t s u i t e k e N k y u u s u r u (cv_jpn_000863-cv_jpn_000863) +s o r e d e w a t o k i t o i u m o n o n o s e e r i t s U s h i y o o w a n a k u p a u s h u N k a N t o i u m o n o m o n a k u n a r u n o d e a r u (cv_jpn_000864-cv_jpn_000864) +a k a i b u r a N k o p a u k o N k u r i i t o s e e n o s u b e r i d a i p a u k a w a i t a s u n a b a (cv_jpn_000865-cv_jpn_000865) +s h I k a s h I s o r e w a d o k o m a d e m o k o k o k a r a d e t e k o k o e k a e r i k u r u s e e s h I t s u o m o c l t a m o n o d e n a k e r e b a n a r a n a i (cv_jpn_000866-cv_jpn_000866) +a r i t o a r a y u r u d e m a o m a k I c h i r a s h I t e m i N n a k a r a u r a m i o k a c l t e r u (cv_jpn_000867-cv_jpn_000867) +k o n o t e e d o p a u s a w a g i n i n a r u k o t o m o n a i n o d a r o o (cv_jpn_000868-cv_jpn_000868) +k o n o n e d a N d e u r e c h a u k a a (cv_jpn_000869-cv_jpn_000869) +h i n o k a g e N n i c h u u i s h i n a i t o s u g u n i k o g e r u (cv_jpn_000870-cv_jpn_000870) +e N b a N n o u e n i p o t s u r i t o c h i i s a n a a n a g a h i r a i t a s a i s h o w a t s u m a y o o j i t e e d o n o c h i i s a n a a n a d a c l t a (cv_jpn_000871-cv_jpn_000871) +s o r e w a w a r e w a r e o i k a s h i n a g a r a w a r e w a r e o d o r e e k a s u r u n o d e a r u p a u w a r e w a r e n o t a m a s h i i o k o r o s u n o d e a r u (cv_jpn_000872-cv_jpn_000872) +r e k I s h i t e k i n i a t a e r a r e t a m o n o w a p a u z e c l t a i m u j u N t e k i j i k o d o o i t s u t e k i g e N z a i n i o i t e s e k a i s h i t e k i n i a t a e r a r e t a m o n o t o s h I t e (cv_jpn_000873-cv_jpn_000873) +m u j u N t e k i j i k o d o o i t s U t o s h I t e p a u i t s u m o k o n o s e k a i n i c h o o e t s u t e k i d e a r u (cv_jpn_000874-cv_jpn_000874) +y u e n i z e c l t a i m u j u N t e k i j i k o d o o i t s U t o s h I t e g e N z a i k a r a g e N z a i e t o u g o k i i k U s e k a i n o g e N z a i n i o i t e (cv_jpn_000875-cv_jpn_000875) +a r e p a u b o t a N o s h I t e m o d a c l s h U t s u d e k i n a i (cv_jpn_000876-cv_jpn_000876) +s h I k a s h i w a t a s h i w a s o k o n i s e k a i n o j i k o d o o i t s u o o k u n o d e w a n a i (cv_jpn_000877-cv_jpn_000877) +n e m u t a k u n a r u n o g a h a y a k u n a c l t a (cv_jpn_000878-cv_jpn_000878) +w a t a s h i w a n i N g e N n o r e k I s h i t e k I k e e s e e n o t a c h i b a k a r a g e e j u t s u o m i r u n o d e a c l t e p a u k o o s h a k a r a z e N s h a o m i r u n o d e w a n a i (cv_jpn_000879-cv_jpn_000879) +a o i t o m a t o s h I k a n a k U t e k a u k a m a y o u (cv_jpn_000880-cv_jpn_000880) +s h i N k i j i g y o o n i o o k i n a k I t a i o y o s e t e i r u (cv_jpn_000881-cv_jpn_000881) +n a n i k a s h i r a n o i N s e N t i b u g a n a i t o k i b i s h i i n o d e w a (cv_jpn_000882-cv_jpn_000882) +j i k a N s e e g e N n o i b e N t o d e s U t o r e s U t a m a r u (cv_jpn_000883-cv_jpn_000883) +m a w a r i n o h I t o w a b o o z e N t o s h I t e i t a (cv_jpn_000884-cv_jpn_000884) +s o N n a n a i y o o n o m e e r u g a p a u n a N k e N m o k i t e i t a (cv_jpn_000885-cv_jpn_000885) +n i j i k a i d e d e e s u i s h I t e i t a (cv_jpn_000886-cv_jpn_000886) +t o k i d o k i p a u j i b u N n o k o k o r o g a w a k a r a n a k u n a r u t o k i g a a r u d a k a r a b o k u w a k a a t e N o h I k i p a u n o o t o n i k a k I h a j i m e r u (cv_jpn_000887-cv_jpn_000887) +m o o n i g e t e c h a d a m e d a (cv_jpn_000888-cv_jpn_000888) +k a r e w a p a u b o o c l t o t a c h I t s u k u s h I t e i t a (cv_jpn_000889-cv_jpn_000889) +d a r e n i m o m e e w a k u w a k a k e t a k u n a i (cv_jpn_000890-cv_jpn_000890) +m a s a k a p a u t o o m o c l t e d o a n o t o c l t e o n i g i c l t a (cv_jpn_000891-cv_jpn_000891) +s u i m a s e N (cv_jpn_000892-cv_jpn_000892) +k a y o u n i s h I t e s h i c l t e i r u t o t o m o n i s h i c l t e i n a i t o k o r o k a r a t a N k y u u w a h a j i m a r u n o d e a r u (cv_jpn_000893-cv_jpn_000893) +a i s a t s u w a d a i j i d a y o (cv_jpn_000894-cv_jpn_000894) +t o j i t a m o n o o i k a n i h i r o g e t e m o h i r a i t a m o n o n i w a n a r a n u t o i c l t e i r u g a (cv_jpn_000895-cv_jpn_000895) +t a m e s h i n i i k U t s u k a t s U k u c l t e m i y o o (cv_jpn_000896-cv_jpn_000896) +z u i b u N a k o g i n a s h o o b a i d a y o n a a (cv_jpn_000897-cv_jpn_000897) +w a c h i (cv_jpn_000898-cv_jpn_000898) +i c h i (cv_jpn_000899-cv_jpn_000899) +g o (cv_jpn_000900-cv_jpn_000900) +s h I c h i (cv_jpn_000901-cv_jpn_000901) +i i e (cv_jpn_000902-cv_jpn_000902) +w a c h i (cv_jpn_000903-cv_jpn_000903) +r e i (cv_jpn_000904-cv_jpn_000904) +s h i (cv_jpn_000905-cv_jpn_000905) +k u (cv_jpn_000906-cv_jpn_000906) +i c h i (cv_jpn_000907-cv_jpn_000907) +k a g a k u g a a k i r a k a n i s u r u k y a c l k a N t e k I s h i N r i n i s h I t a g a u k o t o n i y o c l t e (cv_jpn_000908-cv_jpn_000908) +k a k o t o m i r a i t o n o m u j u N t e k i j i k o d o o i t s U t o s h I t e n o g e N z a i g a k a t a c h i o m o t s U t o i u k o t o d e a r u (cv_jpn_000909-cv_jpn_000909) +b u t s u r i t e k I s e k a i w a s u u g a k u t e k I k i g o o n i y o c l t e a r a w a s a r e r u s u u g a k u t e k I k a t a c h i n o s e k a i d e a r u (cv_jpn_000910-cv_jpn_000910) +o n a j i g e N s h o o d e s a N k o o n i n a r u (cv_jpn_000911-cv_jpn_000911) +g a i k o k U k a r a k i t a m o n o d a t o s h i c l t e b i c l k u r i (cv_jpn_000912-cv_jpn_000912) +i w a y u r u j i c l s e N n i y o c l t e k a k U t o k U s h i r a i c l t a m o n o d e a r u (cv_jpn_000913-cv_jpn_000913) +o n a j i y o o n i p a u d a N s e e w a h i z a o o o u z u b o N o h a k U k o t o g a g i m u z u k e r a r e t e i m a s U (fleurs_jpn_000346-fleurs_jpn_000346) +k o n o s a a b i s u w a p a u g o r a k u s e N o h a j i m e t o s u r u s e N p a k u y a p a u e N k a k u c h i d e d e e t a y a o N s e e o h I t s u y o o t o s u r u t a N k e N t a i n i h i N p a N n i r i y o o s a r e t e i m a s U (fleurs_jpn_000347-fleurs_jpn_000347) +k y o o f u u p a u h y o o p a u k a d o n o k o o s u i r y o u p a u o y o b i y a m a k a j i w a p a u r a i u p a u t a t s u m a k i p a u m i z u f u k i p a u o y o b i s a i k u r o N n a d o n o k i b i s h i i k I s h o o k e e t a i y a s o n o e e k y o o n i y o r u m o n o d e s U (fleurs_jpn_000348-fleurs_jpn_000348) +i N t a a n e c l t o w a p a u m a s U k o m y u n i k e e s h o N t o t a i j i N k o m y u n i k e e s h o N n o r y o o y o o s o o k a n e s o n a e t a k a N k y o o d e s U (fleurs_jpn_000349-fleurs_jpn_000349) +k a j i n o d e w a t s u u j o o p a u t o k u b e t s u n a i N s h o k u y a e N t a a t e i m e N t o o y o o i s h I t e i m a s U p a u g e s U t o g a k i b u N y o k U s h i s e t s u n a i n i t o m a r u y o o n i s u r u t a m e d e s U (fleurs_jpn_000350-fleurs_jpn_000350) +s h I k a s h i p a u k y a p U t e N n o w i k e c l t o o u s h i n a c l t a a t o p a u i N d o w a n a n a t s u n o w i k e c l t o o u s h i n a i p a u s a N j u u r o k u r a N s h I k a d e k i m a s e N d e s h I t a (fleurs_jpn_000351-fleurs_jpn_000351) +f o o k u r a N d o n o k o o s h I k i t s u u k a w a f o o k u r a N d o s h o t o o p o N d o e f u k e e p i i d e i c h I p o N d o g a i c h i i g i r i s U p o N d o j i i b i i p i i t o t o o k a n i k o t e e s a r e t e i m a s U (fleurs_jpn_000352-fleurs_jpn_000352) +h a s h i s h I t a n o k a m i g a t a k u u k a N w a j u u g o m e e t o r u d e s U p a u n i s e N j u u i c h i n e N h a c h i g a t s u n i s h u N k o o s h i p a u n i s e N j u u n a n a n e N s a N g a t s u m a d e k a i t s u u s h i m a s e N d e s h I t a (fleurs_jpn_000353-fleurs_jpn_000353) +i c l p u N k a N d e f u c l t o o s u r u c h i i k i m o a r e b a p a u f u c l t o o s u r u m a d e n i n a N p u N m o k a k a r u c h i i k i m o a r i m a s U (fleurs_jpn_000354-fleurs_jpn_000354) +p i r a m i c l d o n o o t o t o h I k a r i n o s h o o w a p a u k o n o k a N k o o c h i d e t o k u n i k o d o m o t a c h i g a t a n o s h i m e r u m o y o o s h i n o h I t o t s u d e s U (fleurs_jpn_000355-fleurs_jpn_000355) +s o n o t a m e p a u t a N n i r a b e r u t o s h I t e h y o o k i g a t s u i k a s a r e g a c h i d e s U (fleurs_jpn_000356-fleurs_jpn_000356) +g e N s o N s u r u k o t o g a s h i r a r e t e i r u n i j u u g o m a i n o d a N r a c l p u p a u b u r o o d o s a i d o w a p a u g e N s o N s u r u t o o g a i b u N k e N n o s a i k o n o u t s U s h i d e s U p a u t e g a k i n i y o r u g e N p o N w a g e N s o N s h I t e i m a s e N (fleurs_jpn_000357-fleurs_jpn_000357) +k a r e n o s e t s u o t a d a s h i i t o m i t o m e r u h I t o m o i m a s h I t a g a p a u o o k u n o h I t o w a s o n o g y a k u d e p a u t a i y o o k e e d e w a t a i y o o t o s o n o t a n o h o s h i g a c h I k y u u n o m a w a r i o i d o o s h I t e i r u t o s h i N j i t e i m a s h I t a (fleurs_jpn_000358-fleurs_jpn_000358) +c h i b e c l t o m e e s o o n o c h u u s h i N w a s h i N s e e y o g a d e s U p a u s a m a z a m a n a k a m i g a m i o s h I k a k U k a s u r u k o t o d e p a u e n e r u g i i c h a n e r u g a j o o k a s a r e p a u c h a k u r a g a k a c l s e e k a s a r e p a u s a t o r i n o i s h I k i g a u m a r e m a s U (fleurs_jpn_000359-fleurs_jpn_000359) +m i n a m i a f u r i k a n i a r u s u b e t e n o k o k u r i t s U k o o e N t o d o o y o o n i p a u k o n o k o o e N n i w a m a i n i c h I h o g o h I t o n y u u e N r y o o g a k a k a r i m a s U (fleurs_jpn_000360-fleurs_jpn_000360) +r e c l s h a p a u k u r u m a p a u s o n o t a n o o o k u n o k o o t s u u s h u d a N g a s o k o k a r a u m a r e m a s h I t a (fleurs_jpn_000361-fleurs_jpn_000361) +i N t a a n e c l t o w a p a u m a s U k o m y u n i k e e s h o N t o t a i j i N k o m y u n i k e e s h o N n o r y o o y o o s o o k a n e s o n a e t a k a N k y o o d e s U (fleurs_jpn_000362-fleurs_jpn_000362) +b y o o i N d e w a p a u k a N s e N k a N r i t e j u N s h o n i s h I t a g a i p a u t a n i N e n o k a N s e N n o k a n o o s e e o f U s e g u t a m e n i k a N j a o k a k u r i s u r u n a d o n o s o c h i o t o c l t e i m a s U (fleurs_jpn_000363-fleurs_jpn_000363) +r e N p o o g i k a i w a n i s e N g o n e N d o k a r a w a i s e t s u b u t s U t o r i s h i m a r i h o o e n o s h I k i N t e e k y o o o k a i s h I s h i p a u e f u b i i a i w a a d a r u t o p o r u n o n i j u u n i N n o s o o s a i N o t o o n y u u s h i n a k e r e b a n a r a n a i t o k I t e e s h i m a s h I t a (fleurs_jpn_000364-fleurs_jpn_000364) +p i i e i c h i p a u r e b e r u w a p a u k e N s a s h I t a k a g a k u b u c l s h I t s u n i f U k u m a r e r u s u i s o i o N p i i e i c h i n o e i c h i n o r y o o d e s h i m e s a r e m a s U (fleurs_jpn_000365-fleurs_jpn_000365) +s o r e d e m o p a u t o o k y o k U k a r a n o a d o b a i s u o u k e p a u s u b e t e n o h y o o s h I k i o m a m o r i p a u a N z e N j o o n o k e e k o k u n i s a i s h i N n o c h u u i o h a r a i m a s h o o (fleurs_jpn_000366-fleurs_jpn_000366) +k o r e r a w a t a m a n i k o N z a t s U s u r u k a z o k u m u k e n o b i i c h i d e p a u k a i g a N n i w a s a m a z a m a n a t e N p o g a n a r a N d e i m a s U p a u a N z e N n i o y o g u k o t o g a d e k i m a s U (fleurs_jpn_000367-fleurs_jpn_000367) +s h i N n o p a u m i e n a i c h i i m u p a u e r u e e a a r u e s u o o e n u p a u a N d o p a u e r u e e e f u e e e s u t i i o o p a u s e N k y u u h y a k U h a c h i j u u k y u u p a u p i i h y a k u k y u u n o s o N z a i m o m a t a p a u b a a c h a r u c h i i m u n o d o k u j i n o y o o s o d e a r u (fleurs_jpn_000368-fleurs_jpn_000368) +k o n o s a a b i s u w a p a u g o r a k u s e N o h a j i m e t o s u r u s e N p a k u y a p a u e N k a k u c h i d e d e e t a y a o N s e e o h I t s u y o o t o s u r u t a N k e N t a i n i h i N p a N n i r i y o o s a r e t e i m a s U (fleurs_jpn_000369-fleurs_jpn_000369) +s a k u b a N p a u b u e n o s u a i r e s U k a r a g o j u c l k i r o s a N j u u i c h i m a i r u h a n a r e t a r a p u r a t a s h i n a i d e p a u g e N s h o k u j o o i N g i i N d e a r u k u r i s U t i i n a p a u f e r u n a N d e s u p a u d e p a u k i r u h i n a a j o s h i g a d a i t o o r y o o s e N e n o s h U t s u b a o s e N g e N s h i m a s h I t a (fleurs_jpn_000370-fleurs_jpn_000370) +o n a j i t s U k i n i p a u m a s h U h a d o n o k a c l s o o r o d e b e t s u n o r y o k a k u k i g a k a c l s o o r o o o o b a a r a N s h i p a u k a b e n i g e k I t o t s u s h I t e j u u s h I c h i n i N g a s h i b o o s h i m a s h I t a (fleurs_jpn_000371-fleurs_jpn_000371) +h a s h i s h I t a n o k a m i g a t a k u u k a N w a j u u g o m e e t o r u d e s U p a u n i s e N j u u i c h i n e N h a c h i g a t s u n i s h u N k o o s h i p a u n i s e N j u u n a n a n e N s a N g a t s u m a d e k a i t s u u s h i m a s e N d e s h I t a (fleurs_jpn_000372-fleurs_jpn_000372) +b u N m e e t o i u k o t o b a w a p a u s h i m i N o i m i s u r u r a t e N g o n o k e e y o o s h I s h i i a i b u i a i e r u a i e s u k a r a k i t e o r i p a u s h i m i N o i m i s u r u r a t e N g o n o m e e s h I s h i i a i b u i a i e s u p a u t o s h i y a t o s h I k o c l k a o i m i s h i p a u n a N r a k a n o k a t a c h i d e s h a k a i n o k i b o o t e e g i s u r u s h i i a i b u i a i t i i e e e s u t o i u m e e s h i n i k a N k e e s h I t e i m a s U (fleurs_jpn_000373-fleurs_jpn_000373) +t s u u j o o p a u k o k o d e w a i t s u m o k a N k o o k y a k u y a g y o o s h a t a c h i g a h a c l s u r u o t o g a k I k o e t e k i m a s U p a u o t o t o h I k a r i g a o r i n a s u m o n o g a t a r i w a m a r u d e e h o N n o y o o d e s U (fleurs_jpn_000374-fleurs_jpn_000374) +t e r e b i n o h o o d o o n i y o r u t o p a u g e N p a t s U k a r a s h i r o k e m u r i g a a g a c l t e i m a s U (fleurs_jpn_000375-fleurs_jpn_000375) +n o o b y o o r i t o k o o d o o n o s o o k a N k a N k e e w a p a u k a g a k U s h a t a c h i n o k e N k y u u o u r a z u k e r u m o n o d e s U (fleurs_jpn_000376-fleurs_jpn_000376) +s u i y o o b i n o i b e N t o n o a t o p a u k a r u p a n e d o w a s e N s h u k e N d e f U t a t s u n o k o j i N r e e s u n i s h U t s u j o o s h i m a s h I t a (fleurs_jpn_000377-fleurs_jpn_000377) +s e N h a c l p y a k u n e N d a i i r a i p a u g u N t a i g a t o o c h a k U s u r u m a d e h a i c h i w a k o n o b y o o k i n i k a N k e e s u r u m o N d a i n i s o o g u u s h I t a k o t o w a a r i m a s e N d e s h I t a (fleurs_jpn_000378-fleurs_jpn_000378) +s h I k a s h i p a u k y a p U t e N n o w i k e c l t o o u s h i n a c l t a a t o p a u i N d o w a n a n a t s u n o w i k e c l t o o u s h i n a i p a u s a N j u u r o k u r a N s h I k a d e k i m a s e N d e s h I t a (fleurs_jpn_000379-fleurs_jpn_000379) +k a j i n o d e w a t s u u j o o p a u t o k u b e t s u n a i N s h o k u y a e N t a a t e i m e N t o o y o o i s h I t e i m a s U p a u g e s U t o g a k i b u N y o k U s h i s e t s u n a i n i t o m a r u y o o n i s u r u t a m e d e s U (fleurs_jpn_000380-fleurs_jpn_000380) +s o r e d e m o p a u t o o k y o k U k a r a n o a d o b a i s u o u k e p a u s u b e t e n o h y o o s h I k i o m a m o r i p a u a N z e N j o o n o k e e k o k u n i s a i s h i N n o c h u u i o h a r a i m a s h o o (fleurs_jpn_000381-fleurs_jpn_000381) +o w a k a r e d e w a a r i m a s e N k o r e w a h I t o t s u n o s h o o n o o w a r i d e a r i p a u a t a r a s h i i s h o o n o m a k u a k e d e s U (fleurs_jpn_000382-fleurs_jpn_000382) +s a f a r i t o w a p a u a f u r i k a n o y a s e e d o o b u t s u p a u t o k u n i s a b a N n a n i i r u y a s e e d o o b u t s u n o k a N s a t s u o m o k U t e k I t o s h I t a r i k u r o d e n o r y o k o o o s a s h i m a s U (fleurs_jpn_000383-fleurs_jpn_000383) +f u y u n i k I t a b a r u t o k a i o o o d a N s u r u b a a i w a p a u s e N s h I t s u n o i c h i o k a k u n i N s h I t e k u d a s a i p a u k o o r i n o n a k a o t s U k i s u s u m u s a i n i m o c l t o m o e e k y o o o u k e r u s e N s h I t s u d e w a o s o r o s h i i h o d o n o s o o o N g a n a r i h i b i k i m a s U (fleurs_jpn_000384-fleurs_jpn_000384) +k o k o w a i g i r i s u n o s h o k u m i N c h I s h i h a i s h a g a j i b u N t a c h i n o r y o o d o t o s h I t a b a s h o n a n o d e p a u s h o k u m i N c h i j i d a i n o s h o o k o o s a g a s o o t o s u r u h o o w a p a u k o k o k a r a h a j i m e r u n o g a y o i d e s h o o (fleurs_jpn_000385-fleurs_jpn_000385) +e b i s u s h i w a p a u s a k u g e N s u r u s u u c h i o s a d a m e m a s e N d e s h I t a g a p a u s a k u g e N w a c h u u g o k u n o k e e z a i s a N s h U t s u r y o u n i m o t o z u i t e j i c l s h I s a r e r u d a r o o t o n o b e m a s h I t a (fleurs_jpn_000386-fleurs_jpn_000386) +s a i n y u u k o k U s h o c l k u w a s h i N k o N r y o k o o n o j i k i g a s U k u n a i k a r u c h a a s h o c l k u y o r i m o h a y a k u o t o z u r e p a u n a g a b i k i p a u y o r i s h o o j o o g a a c l k a s u r u k o t o g a a r i m a s U (fleurs_jpn_000387-fleurs_jpn_000387) +k i n o o n o a s a p a u t o r u k o n o g a j i a N t e c l p u n o k e e s a t s U h o N b u d e j i d o o s h a b a k u d a N n o b a k U h a t s u n i y o r i p a u k e e k a N f U t a r i g a s h i b o o s h i p a u f U s h o o s h a w a n i j u u n i N o k o e m a s h I t a (fleurs_jpn_000388-fleurs_jpn_000388) +s h o k u b u t s u w a n i N g e N g a s u u s a N s o o t s U k u r i p a u n i N g e N g a i k I t o s h I t e h a k i d a s u n i s a N k a t a N s o o t o r i k o N d e i m a s U (fleurs_jpn_000389-fleurs_jpn_000389) +s e N p a k u d e b u c l s h i o y u s o o s u r u n o w a p a u u m i o k o e t e h I t o y a b u c l s h i o t a i r y o o y u s o o s u r u m o c l t o m o k o o r i t s u t e k i n a h o o h o o d e s U (fleurs_jpn_000390-fleurs_jpn_000390) +k a r i f o r u n i a s h u u n o a a n o r u d o p a u s h u w a r u t s e n e c l g a a c h i j i w a p a u b o o r y o k u t e k i n a b i d e o g e e m u o m i s e e n e N s h a n i h a N b a i y a r e N t a r u s u r u k o t o o k i N s h I s u r u h o o a N n i s h o m e e s h i m a s h I t a (fleurs_jpn_000391-fleurs_jpn_000391) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/result.txt b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..a63a42de25f35f647790cf48e291e5750c847198 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/result.txt @@ -0,0 +1,2109 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,---------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/hyp.trn| +|---------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000800 | 1 131 | 93.9 3.1 3.1 0.0 6.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000801 | 1 213 | 93.4 0.9 5.6 0.9 7.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000802 | 1 67 | 80.6 10.4 9.0 3.0 22.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000803 | 1 203 | 90.6 1.5 7.9 3.0 12.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000804 | 1 64 | 96.9 3.1 0.0 6.3 9.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000805 | 1 41 | 87.8 0.0 12.2 0.0 12.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000806 | 1 129 | 89.1 4.7 6.2 0.0 10.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000807 | 1 29 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000808 | 1 38 | 92.1 0.0 7.9 0.0 7.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000809 | 1 178 | 96.1 2.8 1.1 2.2 6.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000810 | 1 142 | 95.8 0.7 3.5 8.5 12.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000811 | 1 49 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000812 | 1 188 | 95.7 1.6 2.7 0.0 4.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000813 | 1 89 | 93.3 6.7 0.0 6.7 13.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000814 | 1 69 | 88.4 2.9 8.7 0.0 11.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000815 | 1 90 | 86.7 2.2 11.1 3.3 16.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000816 | 1 65 | 98.5 1.5 0.0 9.2 10.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000817 | 1 64 | 98.4 1.6 0.0 6.3 7.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000818 | 1 6 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000819 | 1 5 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000820 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000821 | 1 5 | 80.0 20.0 0.0 0.0 20.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000822 | 1 7 | 71.4 28.6 0.0 0.0 28.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000823 | 1 127 | 92.9 3.1 3.9 3.9 11.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000824 | 1 108 | 94.4 3.7 1.9 1.9 7.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000825 | 1 149 | 97.3 1.3 1.3 0.7 3.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000826 | 1 153 | 98.7 0.0 1.3 0.0 1.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000827 | 1 111 | 99.1 0.9 0.0 0.9 1.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000828 | 1 65 | 93.8 6.2 0.0 6.2 12.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000829 | 1 40 | 90.0 5.0 5.0 0.0 10.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000830 | 1 47 | 95.7 4.3 0.0 4.3 8.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000831 | 1 17 | 94.1 5.9 0.0 11.8 17.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000832 | 1 64 | 93.8 3.1 3.1 0.0 6.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000833 | 1 56 | 100.0 0.0 0.0 3.6 3.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000834 | 1 52 | 92.3 3.8 3.8 3.8 11.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000835 | 1 41 | 92.7 7.3 0.0 0.0 7.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000836 | 1 47 | 97.9 2.1 0.0 8.5 10.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000837 | 1 17 | 100.0 0.0 0.0 17.6 17.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000838 | 1 76 | 94.7 5.3 0.0 0.0 5.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000839 | 1 111 | 95.5 2.7 1.8 0.0 4.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000840 | 1 186 | 95.7 1.6 2.7 3.8 8.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000841 | 1 79 | 98.7 1.3 0.0 0.0 1.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000842 | 1 77 | 92.2 2.6 5.2 0.0 7.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000843 | 1 81 | 97.5 2.5 0.0 2.5 4.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000844 | 1 171 | 96.5 1.2 2.3 0.0 3.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000845 | 1 73 | 94.5 0.0 5.5 5.5 11.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000846 | 1 208 | 94.7 1.9 3.4 1.9 7.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000847 | 1 118 | 94.9 1.7 3.4 2.5 7.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000848 | 1 74 | 91.9 0.0 8.1 0.0 8.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000849 | 1 25 | 76.0 8.0 16.0 8.0 32.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000850 | 1 65 | 84.6 3.1 12.3 0.0 15.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000851 | 1 69 | 95.7 1.4 2.9 0.0 4.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000852 | 1 51 | 88.2 5.9 5.9 3.9 15.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000853 | 1 68 | 97.1 1.5 1.5 2.9 5.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000854 | 1 159 | 89.3 6.9 3.8 1.3 11.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000855 | 1 86 | 87.2 3.5 9.3 2.3 15.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000856 | 1 82 | 92.7 6.1 1.2 7.3 14.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000857 | 1 89 | 94.4 3.4 2.2 2.2 7.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000858 | 1 69 | 100.0 0.0 0.0 2.9 2.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000859 | 1 90 | 96.7 3.3 0.0 3.3 6.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000860 | 1 88 | 96.6 3.4 0.0 0.0 3.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000861 | 1 73 | 91.8 4.1 4.1 0.0 8.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000862 | 1 128 | 95.3 0.8 3.9 4.7 9.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000863 | 1 156 | 87.2 7.1 5.8 2.6 15.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000864 | 1 148 | 91.9 0.0 8.1 2.7 10.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000865 | 1 103 | 89.3 1.0 9.7 0.0 10.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000866 | 1 168 | 87.5 3.6 8.9 2.4 14.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000867 | 1 104 | 95.2 2.9 1.9 5.8 10.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000868 | 1 77 | 94.8 0.0 5.2 3.9 9.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000869 | 1 40 | 95.0 5.0 0.0 10.0 15.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000870 | 1 69 | 91.3 0.0 8.7 0.0 8.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000871 | 1 157 | 93.0 1.9 5.1 0.0 7.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000872 | 1 173 | 98.3 1.7 0.0 1.2 2.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000873 | 1 212 | 93.4 2.4 4.2 0.0 6.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000874 | 1 120 | 91.7 1.7 6.7 0.0 8.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000875 | 1 166 | 93.4 3.0 3.6 0.0 6.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000876 | 1 65 | 83.1 4.6 12.3 3.1 20.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000877 | 1 103 | 96.1 0.0 3.9 0.0 3.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000878 | 1 54 | 94.4 5.6 0.0 0.0 5.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000879 | 1 194 | 94.3 0.5 5.2 1.0 6.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000880 | 1 58 | 94.8 5.2 0.0 3.4 8.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000881 | 1 67 | 94.0 4.5 1.5 3.0 9.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000882 | 1 83 | 98.8 1.2 0.0 4.8 6.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000883 | 1 69 | 95.7 4.3 0.0 8.7 13.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000884 | 1 58 | 96.6 0.0 3.4 0.0 3.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000885 | 1 73 | 91.8 2.7 5.5 2.7 11.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000886 | 1 44 | 93.2 2.3 4.5 0.0 6.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000887 | 1 183 | 84.2 4.9 10.9 0.5 16.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000888 | 1 34 | 97.1 2.9 0.0 0.0 2.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000889 | 1 61 | 85.2 1.6 13.1 0.0 14.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000890 | 1 55 | 94.5 5.5 0.0 3.6 9.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000891 | 1 70 | 85.7 4.3 10.0 5.7 20.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000892 | 1 15 | 93.3 6.7 0.0 20.0 26.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000893 | 1 143 | 99.3 0.7 0.0 0.0 0.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000894 | 1 34 | 88.2 2.9 8.8 0.0 11.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000895 | 1 116 | 94.8 1.7 3.4 0.0 5.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000896 | 1 57 | 98.2 1.8 0.0 0.0 1.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000897 | 1 52 | 94.2 1.9 3.8 0.0 5.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000898 | 1 8 | 75.0 25.0 0.0 12.5 37.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000899 | 1 6 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000900 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000901 | 1 9 | 77.8 11.1 11.1 0.0 22.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000902 | 1 5 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000903 | 1 8 | 87.5 12.5 0.0 0.0 12.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000904 | 1 5 | 80.0 20.0 0.0 0.0 20.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000905 | 1 4 | 100.0 0.0 0.0 50.0 50.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000906 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000907 | 1 6 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000908 | 1 116 | 99.1 0.9 0.0 3.4 4.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000909 | 1 151 | 99.3 0.0 0.7 3.3 4.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000910 | 1 162 | 100.0 0.0 0.0 5.6 5.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000911 | 1 50 | 98.0 2.0 0.0 4.0 6.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000912 | 1 72 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000913 | 1 93 | 95.7 4.3 0.0 4.3 8.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000346 | 1 125 | 91.2 1.6 7.2 4.8 13.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000347 | 1 239 | 89.1 2.9 7.9 4.6 15.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000348 | 1 276 | 76.8 2.5 20.7 0.4 23.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000349 | 1 170 | 91.2 4.7 4.1 0.6 9.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000350 | 1 237 | 89.5 3.0 7.6 1.7 12.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000351 | 1 210 | 91.0 2.9 6.2 0.0 9.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000352 | 1 236 | 90.7 4.2 5.1 0.0 9.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000353 | 1 256 | 86.3 7.0 6.6 2.0 15.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000354 | 1 160 | 89.4 1.9 8.8 1.3 11.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000355 | 1 190 | 91.6 4.7 3.7 1.6 10.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000356 | 1 105 | 91.4 3.8 4.8 0.0 8.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000357 | 1 290 | 86.2 6.6 7.2 0.7 14.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000358 | 1 299 | 85.6 6.0 8.4 2.0 16.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000359 | 1 311 | 89.1 2.9 8.0 1.0 11.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000360 | 1 201 | 87.6 5.0 7.5 5.0 17.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000361 | 1 126 | 84.9 9.5 5.6 3.2 18.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000362 | 1 170 | 92.9 3.5 3.5 2.4 9.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000363 | 1 232 | 86.2 6.0 7.8 1.3 15.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000364 | 1 322 | 92.9 4.0 3.1 1.2 8.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000365 | 1 194 | 79.9 6.7 13.4 2.1 22.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000366 | 1 211 | 85.3 5.7 9.0 0.0 14.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000367 | 1 227 | 87.2 5.7 7.0 2.6 15.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000368 | 1 299 | 65.2 8.4 26.4 1.0 35.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000369 | 1 239 | 90.4 2.5 7.1 0.0 9.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000370 | 1 361 | 89.5 3.6 6.9 1.7 12.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000371 | 1 239 | 81.6 6.7 11.7 1.7 20.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000372 | 1 256 | 84.0 3.5 12.5 0.0 16.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000373 | 1 470 | 81.7 3.0 15.3 1.3 19.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000374 | 1 250 | 87.2 5.2 7.6 2.4 15.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000375 | 1 118 | 80.5 3.4 16.1 5.1 24.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000376 | 1 147 | 94.6 2.0 3.4 2.7 8.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000377 | 1 165 | 82.4 4.8 12.7 0.0 17.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000378 | 1 228 | 88.6 3.5 7.9 0.4 11.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000379 | 1 210 | 83.8 5.7 10.5 0.5 16.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000380 | 1 237 | 91.1 1.7 7.2 1.7 10.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000381 | 1 211 | 81.0 2.8 16.1 1.9 20.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000382 | 1 151 | 84.8 3.3 11.9 2.0 17.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000383 | 1 231 | 92.2 3.0 4.8 0.9 8.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000384 | 1 331 | 77.9 5.1 16.9 0.3 22.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000385 | 1 285 | 84.6 7.7 7.7 2.1 17.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000386 | 1 253 | 85.0 6.7 8.3 0.0 15.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000387 | 1 247 | 90.7 2.0 7.3 3.2 12.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000388 | 1 266 | 90.6 4.9 4.5 3.0 12.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000389 | 1 177 | 96.0 1.7 2.3 6.2 10.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000390 | 1 198 | 88.9 3.5 7.6 1.0 12.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000391 | 1 284 | 93.3 2.5 4.2 2.8 9.5 100.0 | +|===============================================================================================================| +| Sum/Avg | 160 20087 | 90.0 3.5 6.5 1.9 11.9 93.1 | +|===============================================================================================================| +| Mean | 1.0 125.5 | 91.6 3.6 4.8 2.6 11.0 93.1 | +| S.D. | 0.0 90.1 | 6.5 4.1 4.9 4.9 7.6 25.4 | +| Median | 1.0 104.5 | 92.9 2.9 3.8 1.3 9.8 100.0 | +`---------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,---------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/hyp.trn| +|---------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000800 | 1 131 | 123 4 4 0 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000801 | 1 213 | 199 2 12 2 16 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000802 | 1 67 | 54 7 6 2 15 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000803 | 1 203 | 184 3 16 6 25 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000804 | 1 64 | 62 2 0 4 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000805 | 1 41 | 36 0 5 0 5 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000806 | 1 129 | 115 6 8 0 14 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000807 | 1 29 | 29 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000808 | 1 38 | 35 0 3 0 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000809 | 1 178 | 171 5 2 4 11 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000810 | 1 142 | 136 1 5 12 18 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000811 | 1 49 | 49 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000812 | 1 188 | 180 3 5 0 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000813 | 1 89 | 83 6 0 6 12 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000814 | 1 69 | 61 2 6 0 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000815 | 1 90 | 78 2 10 3 15 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000816 | 1 65 | 64 1 0 6 7 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000817 | 1 64 | 63 1 0 4 5 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000818 | 1 6 | 6 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000819 | 1 5 | 5 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000820 | 1 3 | 3 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000821 | 1 5 | 4 1 0 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000822 | 1 7 | 5 2 0 0 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000823 | 1 127 | 118 4 5 5 14 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000824 | 1 108 | 102 4 2 2 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000825 | 1 149 | 145 2 2 1 5 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000826 | 1 153 | 151 0 2 0 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000827 | 1 111 | 110 1 0 1 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000828 | 1 65 | 61 4 0 4 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000829 | 1 40 | 36 2 2 0 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000830 | 1 47 | 45 2 0 2 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000831 | 1 17 | 16 1 0 2 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000832 | 1 64 | 60 2 2 0 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000833 | 1 56 | 56 0 0 2 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000834 | 1 52 | 48 2 2 2 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000835 | 1 41 | 38 3 0 0 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000836 | 1 47 | 46 1 0 4 5 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000837 | 1 17 | 17 0 0 3 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000838 | 1 76 | 72 4 0 0 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000839 | 1 111 | 106 3 2 0 5 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000840 | 1 186 | 178 3 5 7 15 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000841 | 1 79 | 78 1 0 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000842 | 1 77 | 71 2 4 0 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000843 | 1 81 | 79 2 0 2 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000844 | 1 171 | 165 2 4 0 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000845 | 1 73 | 69 0 4 4 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000846 | 1 208 | 197 4 7 4 15 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000847 | 1 118 | 112 2 4 3 9 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000848 | 1 74 | 68 0 6 0 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000849 | 1 25 | 19 2 4 2 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000850 | 1 65 | 55 2 8 0 10 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000851 | 1 69 | 66 1 2 0 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000852 | 1 51 | 45 3 3 2 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000853 | 1 68 | 66 1 1 2 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000854 | 1 159 | 142 11 6 2 19 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000855 | 1 86 | 75 3 8 2 13 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000856 | 1 82 | 76 5 1 6 12 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000857 | 1 89 | 84 3 2 2 7 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000858 | 1 69 | 69 0 0 2 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000859 | 1 90 | 87 3 0 3 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000860 | 1 88 | 85 3 0 0 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000861 | 1 73 | 67 3 3 0 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000862 | 1 128 | 122 1 5 6 12 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000863 | 1 156 | 136 11 9 4 24 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000864 | 1 148 | 136 0 12 4 16 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000865 | 1 103 | 92 1 10 0 11 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000866 | 1 168 | 147 6 15 4 25 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000867 | 1 104 | 99 3 2 6 11 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000868 | 1 77 | 73 0 4 3 7 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000869 | 1 40 | 38 2 0 4 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000870 | 1 69 | 63 0 6 0 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000871 | 1 157 | 146 3 8 0 11 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000872 | 1 173 | 170 3 0 2 5 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000873 | 1 212 | 198 5 9 0 14 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000874 | 1 120 | 110 2 8 0 10 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000875 | 1 166 | 155 5 6 0 11 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000876 | 1 65 | 54 3 8 2 13 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000877 | 1 103 | 99 0 4 0 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000878 | 1 54 | 51 3 0 0 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000879 | 1 194 | 183 1 10 2 13 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000880 | 1 58 | 55 3 0 2 5 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000881 | 1 67 | 63 3 1 2 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000882 | 1 83 | 82 1 0 4 5 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000883 | 1 69 | 66 3 0 6 9 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000884 | 1 58 | 56 0 2 0 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000885 | 1 73 | 67 2 4 2 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000886 | 1 44 | 41 1 2 0 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000887 | 1 183 | 154 9 20 1 30 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000888 | 1 34 | 33 1 0 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000889 | 1 61 | 52 1 8 0 9 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000890 | 1 55 | 52 3 0 2 5 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000891 | 1 70 | 60 3 7 4 14 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000892 | 1 15 | 14 1 0 3 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000893 | 1 143 | 142 1 0 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000894 | 1 34 | 30 1 3 0 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000895 | 1 116 | 110 2 4 0 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000896 | 1 57 | 56 1 0 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000897 | 1 52 | 49 1 2 0 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000898 | 1 8 | 6 2 0 1 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000899 | 1 6 | 6 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000900 | 1 3 | 3 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000901 | 1 9 | 7 1 1 0 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000902 | 1 5 | 5 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000903 | 1 8 | 7 1 0 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000904 | 1 5 | 4 1 0 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000905 | 1 4 | 4 0 0 2 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000906 | 1 3 | 3 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000907 | 1 6 | 6 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000908 | 1 116 | 115 1 0 4 5 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000909 | 1 151 | 150 0 1 5 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000910 | 1 162 | 162 0 0 9 9 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000911 | 1 50 | 49 1 0 2 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000912 | 1 72 | 72 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000913 | 1 93 | 89 4 0 4 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000346 | 1 125 | 114 2 9 6 17 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000347 | 1 239 | 213 7 19 11 37 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000348 | 1 276 | 212 7 57 1 65 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000349 | 1 170 | 155 8 7 1 16 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000350 | 1 237 | 212 7 18 4 29 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000351 | 1 210 | 191 6 13 0 19 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000352 | 1 236 | 214 10 12 0 22 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000353 | 1 256 | 221 18 17 5 40 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000354 | 1 160 | 143 3 14 2 19 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000355 | 1 190 | 174 9 7 3 19 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000356 | 1 105 | 96 4 5 0 9 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000357 | 1 290 | 250 19 21 2 42 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000358 | 1 299 | 256 18 25 6 49 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000359 | 1 311 | 277 9 25 3 37 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000360 | 1 201 | 176 10 15 10 35 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000361 | 1 126 | 107 12 7 4 23 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000362 | 1 170 | 158 6 6 4 16 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000363 | 1 232 | 200 14 18 3 35 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000364 | 1 322 | 299 13 10 4 27 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000365 | 1 194 | 155 13 26 4 43 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000366 | 1 211 | 180 12 19 0 31 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000367 | 1 227 | 198 13 16 6 35 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000368 | 1 299 | 195 25 79 3 107 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000369 | 1 239 | 216 6 17 0 23 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000370 | 1 361 | 323 13 25 6 44 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000371 | 1 239 | 195 16 28 4 48 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000372 | 1 256 | 215 9 32 0 41 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000373 | 1 470 | 384 14 72 6 92 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000374 | 1 250 | 218 13 19 6 38 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000375 | 1 118 | 95 4 19 6 29 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000376 | 1 147 | 139 3 5 4 12 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000377 | 1 165 | 136 8 21 0 29 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000378 | 1 228 | 202 8 18 1 27 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000379 | 1 210 | 176 12 22 1 35 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000380 | 1 237 | 216 4 17 4 25 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000381 | 1 211 | 171 6 34 4 44 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000382 | 1 151 | 128 5 18 3 26 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000383 | 1 231 | 213 7 11 2 20 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000384 | 1 331 | 258 17 56 1 74 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000385 | 1 285 | 241 22 22 6 50 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000386 | 1 253 | 215 17 21 0 38 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000387 | 1 247 | 224 5 18 8 31 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000388 | 1 266 | 241 13 12 8 33 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000389 | 1 177 | 170 3 4 11 18 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000390 | 1 198 | 176 7 15 2 24 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000391 | 1 284 | 265 7 12 8 27 1 | +|===============================================================================================================| +| Sum | 160 20087 | 18080 700 1307 383 2390 149 | +|===============================================================================================================| +| Mean | 1.0 125.5 | 113.0 4.4 8.2 2.4 14.9 0.9 | +| S.D. | 0.0 90.1 | 77.5 5.0 12.2 2.6 17.0 0.3 | +| Median | 1.0 104.5 | 97.5 3.0 4.0 2.0 8.0 1.0 | +`---------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_cer/hyp.trn + +Speakers: + 0: cv_jpn_000800 + 1: cv_jpn_000801 + 2: cv_jpn_000802 + 3: cv_jpn_000803 + 4: cv_jpn_000804 + 5: cv_jpn_000805 + 6: cv_jpn_000806 + 7: cv_jpn_000807 + 8: cv_jpn_000808 + 9: cv_jpn_000809 + 10: cv_jpn_000810 + 11: cv_jpn_000811 + 12: cv_jpn_000812 + 13: cv_jpn_000813 + 14: cv_jpn_000814 + 15: cv_jpn_000815 + 16: cv_jpn_000816 + 17: cv_jpn_000817 + 18: cv_jpn_000818 + 19: cv_jpn_000819 + 20: cv_jpn_000820 + 21: cv_jpn_000821 + 22: cv_jpn_000822 + 23: cv_jpn_000823 + 24: cv_jpn_000824 + 25: cv_jpn_000825 + 26: cv_jpn_000826 + 27: cv_jpn_000827 + 28: cv_jpn_000828 + 29: cv_jpn_000829 + 30: cv_jpn_000830 + 31: cv_jpn_000831 + 32: cv_jpn_000832 + 33: cv_jpn_000833 + 34: cv_jpn_000834 + 35: cv_jpn_000835 + 36: cv_jpn_000836 + 37: cv_jpn_000837 + 38: cv_jpn_000838 + 39: cv_jpn_000839 + 40: cv_jpn_000840 + 41: cv_jpn_000841 + 42: cv_jpn_000842 + 43: cv_jpn_000843 + 44: cv_jpn_000844 + 45: cv_jpn_000845 + 46: cv_jpn_000846 + 47: cv_jpn_000847 + 48: cv_jpn_000848 + 49: cv_jpn_000849 + 50: cv_jpn_000850 + 51: cv_jpn_000851 + 52: cv_jpn_000852 + 53: cv_jpn_000853 + 54: cv_jpn_000854 + 55: cv_jpn_000855 + 56: cv_jpn_000856 + 57: cv_jpn_000857 + 58: cv_jpn_000858 + 59: cv_jpn_000859 + 60: cv_jpn_000860 + 61: cv_jpn_000861 + 62: cv_jpn_000862 + 63: cv_jpn_000863 + 64: cv_jpn_000864 + 65: cv_jpn_000865 + 66: cv_jpn_000866 + 67: cv_jpn_000867 + 68: cv_jpn_000868 + 69: cv_jpn_000869 + 70: cv_jpn_000870 + 71: cv_jpn_000871 + 72: cv_jpn_000872 + 73: cv_jpn_000873 + 74: cv_jpn_000874 + 75: cv_jpn_000875 + 76: cv_jpn_000876 + 77: cv_jpn_000877 + 78: cv_jpn_000878 + 79: cv_jpn_000879 + 80: cv_jpn_000880 + 81: cv_jpn_000881 + 82: cv_jpn_000882 + 83: cv_jpn_000883 + 84: cv_jpn_000884 + 85: cv_jpn_000885 + 86: cv_jpn_000886 + 87: cv_jpn_000887 + 88: cv_jpn_000888 + 89: cv_jpn_000889 + 90: cv_jpn_000890 + 91: cv_jpn_000891 + 92: cv_jpn_000892 + 93: cv_jpn_000893 + 94: cv_jpn_000894 + 95: cv_jpn_000895 + 96: cv_jpn_000896 + 97: cv_jpn_000897 + 98: cv_jpn_000898 + 99: cv_jpn_000899 + 100: cv_jpn_000900 + 101: cv_jpn_000901 + 102: cv_jpn_000902 + 103: cv_jpn_000903 + 104: cv_jpn_000904 + 105: cv_jpn_000905 + 106: cv_jpn_000906 + 107: cv_jpn_000907 + 108: cv_jpn_000908 + 109: cv_jpn_000909 + 110: cv_jpn_000910 + 111: cv_jpn_000911 + 112: cv_jpn_000912 + 113: cv_jpn_000913 + 114: fleurs_jpn_000346 + 115: fleurs_jpn_000347 + 116: fleurs_jpn_000348 + 117: fleurs_jpn_000349 + 118: fleurs_jpn_000350 + 119: fleurs_jpn_000351 + 120: fleurs_jpn_000352 + 121: fleurs_jpn_000353 + 122: fleurs_jpn_000354 + 123: fleurs_jpn_000355 + 124: fleurs_jpn_000356 + 125: fleurs_jpn_000357 + 126: fleurs_jpn_000358 + 127: fleurs_jpn_000359 + 128: fleurs_jpn_000360 + 129: fleurs_jpn_000361 + 130: fleurs_jpn_000362 + 131: fleurs_jpn_000363 + 132: fleurs_jpn_000364 + 133: fleurs_jpn_000365 + 134: fleurs_jpn_000366 + 135: fleurs_jpn_000367 + 136: fleurs_jpn_000368 + 137: fleurs_jpn_000369 + 138: fleurs_jpn_000370 + 139: fleurs_jpn_000371 + 140: fleurs_jpn_000372 + 141: fleurs_jpn_000373 + 142: fleurs_jpn_000374 + 143: fleurs_jpn_000375 + 144: fleurs_jpn_000376 + 145: fleurs_jpn_000377 + 146: fleurs_jpn_000378 + 147: fleurs_jpn_000379 + 148: fleurs_jpn_000380 + 149: fleurs_jpn_000381 + 150: fleurs_jpn_000382 + 151: fleurs_jpn_000383 + 152: fleurs_jpn_000384 + 153: fleurs_jpn_000385 + 154: fleurs_jpn_000386 + 155: fleurs_jpn_000387 + 156: fleurs_jpn_000388 + 157: fleurs_jpn_000389 + 158: fleurs_jpn_000390 + 159: fleurs_jpn_000391 + +Speaker sentences 0: cv_jpn_000800 #utts: 1 +id: (cv_jpn_000800-cv_jpn_000800) +Scores: (#C #S #D #I) 123 4 4 0 +REF: k a k o t o m i r a I t o N o M u j u n t e k i j i k o d o o i t s u n a r u g a y U e n i P A U i s h i k i t e k i n a n o d e a r u +HYP: k a k o t o m i r a E t o D o G u j u n t e k i j i k o d o o i t s u n a r u g a y O e n i ******* * * * i s h i k i t e k i n a n o d e a r u +Eval: S S S S D D D D + +Speaker sentences 1: cv_jpn_000801 #utts: 1 +id: (cv_jpn_000801-cv_jpn_000801) +Scores: (#C #S #D #I) 199 2 12 2 +REF: s e k a I o k e e s e e s u r u t o t o m O n i P A U j i k o j i s h i n ******* * o k e e s E e s U r u s O o Z o o t e k i s e k a i n o s o o z o o t e k i y o o s o t o s h i t e p a u k o b u t s u g a k o b u t s +HYP: s e k a Y o k e e s e e s u r u t o t o m ******* * n i ******* * * * j i k o j i s h i n Y o k e e s ******* * e s E r u s ******* * o ******* * o o t e k i s e k a i n o s o o z o o t e k i y o o s o t o s h i t e p a u k o b u t s u g a k o b u t s +Eval: S D D D D D D I I D D S D D D D + +>> REF: u d e a r u +>> HYP: u d e a r u +>> Eval: + +Speaker sentences 2: cv_jpn_000802 #utts: 1 +id: (cv_jpn_000802-cv_jpn_000802) +Scores: (#C #S #D #I) 54 7 6 2 +REF: p a S o k o n d e g e e m U Y a r u ******* * N I n G A f u E t e k i t e R U +HYP: p a Z o k o n d e g e e m ******* * I a r u I T O n O H f u I t e k i t e ******* * ******* * +Eval: S D D S I I S S S S S D D D D + +Speaker sentences 3: cv_jpn_000803 #utts: 1 +id: (cv_jpn_000803-cv_jpn_000803) +Scores: (#C #S #D #I) 184 3 16 6 +REF: k a G a k u n o s h i m e s U a t a r a s h I i j i j i ******* * t s U P A u a t a r a s h i i k a n n e n P A U k a n k y O o s h i h a i n O a t a r a s h i i k a n o o s E e o m o c l t e ******* * * * n a n i O h a j i m e r u k a w +HYP: k a N a k u n o s h i m e s A a t a r a s h ******* * i j i j i U t s ******* * * * u a t a r a s h i i k a n n e n ******* * * * k a n k y ******* * o s h i h a i n W a t a r a s h i i k a n o o s ******* * e o m o c l t e P A U n a n i ******* * h a j i m e r u k a w +Eval: S S D D I I D D D D D D D D D D S D D I I I I D D + +>> REF: a +>> HYP: a +>> Eval: + +Speaker sentences 4: cv_jpn_000804 #utts: 1 +id: (cv_jpn_000804-cv_jpn_000804) +Scores: (#C #S #D #I) 62 2 0 4 +REF: o m o s h i r o I n o n i ******* * * * r o o D o n a g a s u g i t e d a r u i +HYP: o m o s h i r o U n o n i P A U r o o T o n a g a s u g i t e d a r u i +Eval: S I I I I S + +Speaker sentences 5: cv_jpn_000805 #utts: 1 +id: (cv_jpn_000805-cv_jpn_000805) +Scores: (#C #S #D #I) 36 0 5 0 +REF: k o r e j o o s h u u h a n C L p o i n A a +HYP: k o r e j o o s h u u h a n ******* * * p o i n ******* * a +Eval: D D D D D + +Speaker sentences 6: cv_jpn_000806 #utts: 1 +id: (cv_jpn_000806-cv_jpn_000806) +Scores: (#C #S #D #I) 115 6 8 0 +REF: k a g a k u s h a m o s e k a i o h o o k a t s U T e k i n i t o o i T S U t e k i n i s E t s u m E e s h I Y O o t o s h i t e i r u +HYP: k a g a k u s h a m o s e k a i o h o o k a t s ******* * S e k i n i t o o i C H I t e k i n i s A t s u m ******* * e s h ******* * ******* * U o t o s h i t e i r u +Eval: D D S S S S S D D D D D D S + +Speaker sentences 7: cv_jpn_000807 #utts: 1 +id: (cv_jpn_000807-cv_jpn_000807) +Scores: (#C #S #D #I) 29 0 0 0 +REF: f u t s u u n i t s u m a r a n +HYP: f u t s u u n i t s u m a r a n +Eval: + +Speaker sentences 8: cv_jpn_000808 #utts: 1 +id: (cv_jpn_000808-cv_jpn_000808) +Scores: (#C #S #D #I) 35 0 3 0 +REF: s h i c l k a r i S H i t e k u d a s a i +HYP: s h i c l k a r i ******* * * i t e k u d a s a i +Eval: D D D + +Speaker sentences 9: cv_jpn_000809 #utts: 1 +id: (cv_jpn_000809-cv_jpn_000809) +Scores: (#C #S #D #I) 171 5 2 4 +REF: w a t a s h i w ******* * a m i g i n o G o t o k i R e k i s h i t e k i s e E m e e n o j i k a k u t o i u g o t o k i m o n O o b e n s h o o h o o t e k i r o n R i t o ******* * I u n o d e a r u +HYP: w a t a s h i w A a m i g i n o N o t o k i D e k i s h i t e k i s e I m e e n o j i k a k u t o i u g o t o k i m o n ******* * o b e n s h o o h o o t e k i r o n B i t o Y U u n o d e a r u +Eval: I I S S S D D S I I S + +Speaker sentences 10: cv_jpn_000810 #utts: 1 +id: (cv_jpn_000810-cv_jpn_000810) +Scores: (#C #S #D #I) 136 1 5 12 +REF: w a t a s h i w ******* * * a * s h a k a i k e e s e e n o k o n t e e n i w ******* * * a * d ******* * ******* * I o n Y u s o s u t e k i n a m o n o g a h a t a r a i t e i r u t O o m o U +HYP: w a t a s h i w A P a U s h a k a i k e e s e e n o k o n t e e n i w A P a U d E Y O o n * u s o s u t e k i n a m o n o g a h a t a r a i t e i r u t ******* * o m o ******* * +Eval: I I I I I I I I I I I I S D D D D D + +Speaker sentences 11: cv_jpn_000811 #utts: 1 +id: (cv_jpn_000811-cv_jpn_000811) +Scores: (#C #S #D #I) 49 0 0 0 +REF: n a n i o s u r u t s u m o r i d a c l t a n o k a +HYP: n a n i o s u r u t s u m o r i d a c l t a n o k a +Eval: + +Speaker sentences 12: cv_jpn_000812 #utts: 1 +id: (cv_jpn_000812-cv_jpn_000812) +Scores: (#C #S #D #I) 180 3 5 0 +REF: k o b u t s u t e k i t a g a j i k o h i t e e t e k i n i t a n n i t e n s h u u g o o t e k i n i k a n g a e r a r E r u t o k i P A U s o r e g a b u T S u r i t e k i S e k a i d e a r u +HYP: k o b u t s u t e k i t a g a j i k o h i t e e t e k i n i t a n n i t e n s h u u g o o t e k i n i k a n g a e r a r U r u t o k i ******* * * * s o r e g a b u * Z u r i t e k i T e k a i d e a r u +Eval: S D D D D D S S + +Speaker sentences 13: cv_jpn_000813 #utts: 1 +id: (cv_jpn_000813-cv_jpn_000813) +Scores: (#C #S #D #I) 83 6 0 6 +REF: a M e g a F u c l t a n o d e ******* * p a u y a K Y U U n o s h i ******* * ******* * a i g a a r i m a s e n d e s h i t a +HYP: a N e g a Z u c l t a n o d e A p a u y a C L K I n o s h i R A a i g a a r i m a s e n d e s h i t a +Eval: S S I I S S S S I I I I + +Speaker sentences 14: cv_jpn_000814 #utts: 1 +id: (cv_jpn_000814-cv_jpn_000814) +Scores: (#C #S #D #I) 61 2 6 0 +REF: k o r e w a N i C L P o n d e u C L t e i n a i t a b e m o n o d e s u +HYP: k o r e w a R i ******* * * H o n d e u ******* * * t e i n a i t a b e m o n o d e s u +Eval: S D D D S D D D + +Speaker sentences 15: cv_jpn_000815 #utts: 1 +id: (cv_jpn_000815-cv_jpn_000815) +Scores: (#C #S #D #I) 78 2 10 3 +REF: w a t a s h i w A P a U h e n s h u u i n o P A U y o n e n k u r a I H a y a c l t a ******* * * t o o m o U +HYP: w a t a s h i w ******* * * a * h e n s h u u i n o ******* * * * y o n e n k u r a E W a y a c l t a C L t o o m o ******* * +Eval: D D D D D D D D S S I I I D D + +Speaker sentences 16: cv_jpn_000816 #utts: 1 +id: (cv_jpn_000816-cv_jpn_000816) +Scores: (#C #S #D #I) 64 1 0 6 +REF: i s a n n i k o n o k o t o b a n o i m i ******* * ******* * ******* * o o s h i E m a s h i t a +HYP: i s a n n i k o n o k o t o b a n o i m i Y O O o o s h i A m a s h i t a +Eval: I I I I I I S + +Speaker sentences 17: cv_jpn_000817 #utts: 1 +id: (cv_jpn_000817-cv_jpn_000817) +Scores: (#C #S #D #I) 63 1 0 4 +REF: k a Z e g a t * ******* * ******* s u y o i h i w a t e n i s u g a d e k i m a s e n +HYP: k a S e g a t S U s u y o i h i w a t e n i s u g a d e k i m a s e n +Eval: S I I I I + +Speaker sentences 18: cv_jpn_000818 #utts: 1 +id: (cv_jpn_000818-cv_jpn_000818) +Scores: (#C #S #D #I) 6 0 0 0 +REF: i c h i +HYP: i c h i +Eval: + +Speaker sentences 19: cv_jpn_000819 #utts: 1 +id: (cv_jpn_000819-cv_jpn_000819) +Scores: (#C #S #D #I) 5 0 0 0 +REF: h a i +HYP: h a i +Eval: + +Speaker sentences 20: cv_jpn_000820 #utts: 1 +id: (cv_jpn_000820-cv_jpn_000820) +Scores: (#C #S #D #I) 3 0 0 0 +REF: n i +HYP: n i +Eval: + +Speaker sentences 21: cv_jpn_000821 #utts: 1 +id: (cv_jpn_000821-cv_jpn_000821) +Scores: (#C #S #D #I) 4 1 0 0 +REF: R e i +HYP: D e i +Eval: S + +Speaker sentences 22: cv_jpn_000822 #utts: 1 +id: (cv_jpn_000822-cv_jpn_000822) +Scores: (#C #S #D #I) 5 2 0 0 +REF: R o k U +HYP: T o k I +Eval: S S + +Speaker sentences 23: cv_jpn_000823 #utts: 1 +id: (cv_jpn_000823-cv_jpn_000823) +Scores: (#C #S #D #I) 118 4 5 5 +REF: m i r u t o i u k o t o t o ******* * * * h a t a r a k u t o i u k o t o T O G a * F u k a b u n r i t e k i D E n a k e r e b a n a r a n a i +HYP: m i r u t o i u k o t o t o P A U h a t a r a k u t o i u k o t o G A * P a U S u k a b u n r i t e k i ******* * ******* * n a k e r e b a n a r a n a i +Eval: I I I I S S D S I S D D D D + +Speaker sentences 24: cv_jpn_000824 #utts: 1 +id: (cv_jpn_000824-cv_jpn_000824) +Scores: (#C #S #D #I) 102 4 2 2 +REF: w a r e w a r e ******* * o t a m a s h i i n o S O k o k a r a U g O K a s u m o n o d e n a k e r e b a n a r a n a i +HYP: w a r e w a r e O o t a m a s h i i n o Z U k o k a r a ******* * g U G a s u m o n o d e n a k e r e b a n a r a n a i +Eval: I I S S D D S S + +Speaker sentences 25: cv_jpn_000825 #utts: 1 +id: (cv_jpn_000825-cv_jpn_000825) +Scores: (#C #S #D #I) 145 2 2 1 +REF: z e c l t a i b e n s h o o h o o t e k i n a r u g a Y u e n i i d e Y a t e k i c h o c l k a n t e k i k e e k i g a * F u k u m a r e r u n o d e a r u +HYP: z e c l t a i b e n s h o o h o o t e k i n a r u g a I u e n i i d e ******* * a t e k i c h o c l k a n t e k i k e e k i g a C L u k u m a r e r u n o d e a r u +Eval: S D D I S + +Speaker sentences 26: cv_jpn_000826 #utts: 1 +id: (cv_jpn_000826-cv_jpn_000826) +Scores: (#C #S #D #I) 151 0 2 0 +REF: d o k o m a d e m o t a t o i c h i t o n o s o o g o h i t e e t e k i n a z e c l t a i m u j u n t e k i j i k o d O o i t s u n o s e k a i n i s h i t e +HYP: d o k o m a d e m o t a t o i c h i t o n o s o o g o h i t e e t e k i n a z e c l t a i m u j u n t e k i j i k o d ******* * o i t s u n o s e k a i n i s h i t e +Eval: D D + +Speaker sentences 27: cv_jpn_000827 #utts: 1 +id: (cv_jpn_000827-cv_jpn_000827) +Scores: (#C #S #D #I) 110 1 0 1 +REF: s h i k a r u n i n i n g e n t o k a n k y o o t o N o k a n k e e w a m o t o k o o i n o k a n k * e e d e a r i +HYP: s h i k a r u n i n i n g e n t o k a n k y o o t o R o k a n k e e w a m o t o k o o i n o k a n k Y e e d e a r i +Eval: S I + +Speaker sentences 28: cv_jpn_000828 #utts: 1 +id: (cv_jpn_000828-cv_jpn_000828) +Scores: (#C #S #D #I) 61 4 0 4 +REF: * ******* i s a n N I K o n o k o t o b a n o i m i ******* * o o s h i E m a s h i t a +HYP: I i s a n I K O o n o k o t o b a n o i m i Y o o s h i A m a s h i t a +Eval: I I S S S I I S + +Speaker sentences 29: cv_jpn_000829 #utts: 1 +id: (cv_jpn_000829-cv_jpn_000829) +Scores: (#C #S #D #I) 36 2 2 0 +REF: K E e k i g a n a n a t s U a r i m a s u +HYP: * ******* G e k i g a n a n a t s S a r i m a s u +Eval: D D S S + +Speaker sentences 30: cv_jpn_000830 #utts: 1 +id: (cv_jpn_000830-cv_jpn_000830) +Scores: (#C #S #D #I) 45 2 0 2 +REF: k o c h i r a W a k o b A y a s h ******* * i s a n d e s u +HYP: k o c h i r a B a k o b U y a s h I i s a n d e s u +Eval: S S I I + +Speaker sentences 31: cv_jpn_000831 #utts: 1 +id: (cv_jpn_000831-cv_jpn_000831) +Scores: (#C #S #D #I) 16 1 0 2 +REF: m o s h ******* * i m O s h i +HYP: m o s h I i m A s h i +Eval: I I S + +Speaker sentences 32: cv_jpn_000832 #utts: 1 +id: (cv_jpn_000832-cv_jpn_000832) +Scores: (#C #S #D #I) 60 2 2 0 +REF: k o k o w a O o k i k u t e n i G I y a k a n a m a c h i d e s u +HYP: k o k o w a ******* * o k i k u t e n i K U y a k a n a m a c h i d e s u +Eval: D D S S + +Speaker sentences 33: cv_jpn_000833 #utts: 1 +id: (cv_jpn_000833-cv_jpn_000833) +Scores: (#C #S #D #I) 56 0 0 2 +REF: s o n o u c h i k a i ******* * a k u s a r e r u k a r a i s o g e +HYP: s o n o u c h i k a i Y a k u s a r e r u k a r a i s o g e +Eval: I I + +Speaker sentences 34: cv_jpn_000834 #utts: 1 +id: (cv_jpn_000834-cv_jpn_000834) +Scores: (#C #S #D #I) 48 2 2 2 +REF: a m a s a g a ******* * O s a E r a r e t e t e c h o o d o I i +HYP: a m a s a g a F U s a I r a r e t e t e c h o o d o ******* * i +Eval: I I S S D D + +Speaker sentences 35: cv_jpn_000835 #utts: 1 +id: (cv_jpn_000835-cv_jpn_000835) +Scores: (#C #S #D #I) 38 3 0 0 +REF: h o K e n s h i t s u n o d o A O a k e t a +HYP: h o G e n s h i t s u n o d o O A a k e t a +Eval: S S S + +Speaker sentences 36: cv_jpn_000836 #utts: 1 +id: (cv_jpn_000836-cv_jpn_000836) +Scores: (#C #S #D #I) 46 1 0 4 +REF: m U d a ******* * n i ******* * o w a c l t e m o k i n i s h i n a i +HYP: m O d a N n i O o w a c l t e m o k i n i s h i n a i +Eval: S I I I I + +Speaker sentences 37: cv_jpn_000837 #utts: 1 +id: (cv_jpn_000837-cv_jpn_000837) +Scores: (#C #S #D #I) 17 0 0 3 +REF: a r i g a ******* * * t a y a +HYP: a r i g a C L t a y a +Eval: I I I + +Speaker sentences 38: cv_jpn_000838 #utts: 1 +id: (cv_jpn_000838-cv_jpn_000838) +Scores: (#C #S #D #I) 72 4 0 0 +REF: i D o o g a r a k u d a t o j i k a n O W A s u r e t e t a n o s h i m e r u +HYP: i T o o g a r a k u d a t o j i k a n W A O s u r e t e t a n o s h i m e r u +Eval: S S S S + +Speaker sentences 39: cv_jpn_000839 #utts: 1 +id: (cv_jpn_000839-cv_jpn_000839) +Scores: (#C #S #D #I) 106 3 2 0 +REF: k a G a k u w a g i J u t s u k a s a r e r u n i O o j i t e j o o s h i k i n o u c h i n i h a i c l t e Y u k u +HYP: k a K a k u w a g i Z u t s u k a s a r e r u n i ******* * o j i t e j o o s h i k i n o u c h i n i h a i c l t e I u k u +Eval: S S D D S + +Speaker sentences 40: cv_jpn_000840 #utts: 1 +id: (cv_jpn_000840-cv_jpn_000840) +Scores: (#C #S #D #I) 178 3 5 7 +REF: s h i k a s h i t o k i g a k a K o n i h a i r u k o t o s o n o k o t o g a p a u m i r ******* * a I o U m u k o t o d e a r i P A U a r a t a n a r u ******* * ******* * * s H u t a i g a d e t e k u r u k o t o d e a r u +HYP: s h i k a s h i t o k i g a k a ******* * o n i h a i r u k o t o s o n o k o t o g a p a u m i r A a Y o O m u k o t o d e a r i * * W a r a t a n a r u I C L s * u t a i g a d e t e k u r u k o t o d e a r u +Eval: D D I I S S D D S I I I I I D + +Speaker sentences 41: cv_jpn_000841 #utts: 1 +id: (cv_jpn_000841-cv_jpn_000841) +Scores: (#C #S #D #I) 78 1 0 0 +REF: t e r e b i o k a i k a E t e p a u t e r e b i o m i r u j i k a n g a f u e t a +HYP: t e r e b i o k a i k a I t e p a u t e r e b i o m i r u j i k a n g a f u e t a +Eval: S + +Speaker sentences 42: cv_jpn_000842 #utts: 1 +id: (cv_jpn_000842-cv_jpn_000842) +Scores: (#C #S #D #I) 71 2 4 0 +REF: k a k a r U s h U t a i n o m i P A U i t s u m a d e m o i k i r u n o d e a r u +HYP: k a k a r I s h I t a i n o m i ******* * * * i t s u m a d e m o i k i r u n o d e a r u +Eval: S S D D D D + +Speaker sentences 43: cv_jpn_000843 #utts: 1 +id: (cv_jpn_000843-cv_jpn_000843) +Scores: (#C #S #D #I) 79 2 0 2 +REF: n i n k i R a a m e n ******* * y a n i N a r a n d a r a n i j i k a n m a c h i d a c l t a +HYP: n i n k i D a a m e n I y a n i G a r a n d a r a n i j i k a n m a c h i d a c l t a +Eval: S I I S + +Speaker sentences 44: cv_jpn_000844 #utts: 1 +id: (cv_jpn_000844-cv_jpn_000844) +Scores: (#C #S #D #I) 165 2 4 0 +REF: s o r e o m o c h i i r u n i n g e n n o i y o k u n i i Z o n s h i P A U s o s h i t e k o r e w a k a r e n o m o c l t e i r u k a c h i n o s h a k u d o n i i Z o n s u r u +HYP: s o r e o m o c h i i r u n i n g e n n o i y o k u n i i T o n s h i ******* * * * s o s h i t e k o r e w a k a r e n o m o c l t e i r u k a c h i n o s h a k u d o n i i T o n s u r u +Eval: S D D D D S + +Speaker sentences 45: cv_jpn_000845 #utts: 1 +id: (cv_jpn_000845-cv_jpn_000845) +Scores: (#C #S #D #I) 69 0 4 4 +REF: m a w a r ******* * i ******* * w a m i n n A P a U k a n g a e r u k o t o o y a m e t e i t a +HYP: m a w a r U i O w a m i n n ******* * * a * k a n g a e r u k o t o o y a m e t e i t a +Eval: I I I I D D D D + +Speaker sentences 46: cv_jpn_000846 #utts: 1 +id: (cv_jpn_000846-cv_jpn_000846) +Scores: (#C #S #D #I) 197 4 7 4 +REF: k o o i t e k i c h o ******* * c l ******* * k a n t e k i n i s e k a I o m i r u t o i u k o t o w A P a U G Y a k u n i k o o i t e k i c h o c l k a n t e k i n i s e k a I o k e e s e e s u r u k o t o o F U k u m u n o +HYP: k o o i t e k i c h o K c l U k a n t e k i n i s e k a Y o m i r u t o i u k o t o w ******* * * a * * J a k u n i k o o i t e k i c h o c l k a n t e k i n i s e k a Y o k e e s e e s u r u k o t o o ******* * H k u m u n o +Eval: I I I I S D D D D D S S D D S + +>> REF: d e a r u +>> HYP: d e a r u +>> Eval: + +Speaker sentences 47: cv_jpn_000847 #utts: 1 +id: (cv_jpn_000847-cv_jpn_000847) +Scores: (#C #S #D #I) 112 2 4 3 +REF: s h i n ******* * * p a i K a k e s a s e m a i t o s u r u k i z u k a i g A P a U y o k e I n i s h i n p a i s a s e t e s h i m a u +HYP: s h i n C L p a i T a k e s a s e m a i t o s u r u k i z u k a i g ******* * * a * y o k e E n i s h i n p a i s a s e t e s h i m a u +Eval: I I I S D D D D S + +Speaker sentences 48: cv_jpn_000848 #utts: 1 +id: (cv_jpn_000848-cv_jpn_000848) +Scores: (#C #S #D #I) 68 0 6 0 +REF: k o n o m i c h i w a t o t e m o s e m a i n o d e P A U a b U n a i d e s u +HYP: k o n o m i c h i w a t o t e m o s e m a i n o d e ******* * * * a b ******* * n a i d e s u +Eval: D D D D D D + +Speaker sentences 49: cv_jpn_000849 #utts: 1 +id: (cv_jpn_000849-cv_jpn_000849) +Scores: (#C #S #D #I) 19 2 4 2 +REF: * ******* o B o e g a W a r U i n E +HYP: W o H o e g a ******* * a r ******* * i n I +Eval: I I S D D D D S + +Speaker sentences 50: cv_jpn_000850 #utts: 1 +id: (cv_jpn_000850-cv_jpn_000850) +Scores: (#C #S #D #I) 55 2 8 0 +REF: t o i R E w a r o o k a N o H i D a r i g a W a n i a r i m a s u +HYP: t o i ******* * ******* * w a r o o k a M o ******* * i T a r i g a ******* * a n i a r i m a s u +Eval: D D D D S D D S D D + +Speaker sentences 51: cv_jpn_000851 #utts: 1 +id: (cv_jpn_000851-cv_jpn_000851) +Scores: (#C #S #D #I) 66 1 2 0 +REF: t a n a k a s a n n o h i D a R i n i k i m u r a s a n g a i m a s u +HYP: t a n a k a s a n n o h i T a ******* * i n i k i m u r a s a n g a i m a s u +Eval: S D D + +Speaker sentences 52: cv_jpn_000852 #utts: 1 +id: (cv_jpn_000852-cv_jpn_000852) +Scores: (#C #S #D #I) 45 3 3 2 +REF: m a c l k u r O n A t a m a G o C L t e s u g o i n ******* * e +HYP: m a c l k u r A n O t a m a B o ******* * * t e s u g o i n I e +Eval: S S S D D D I I + +Speaker sentences 53: cv_jpn_000853 #utts: 1 +id: (cv_jpn_000853-cv_jpn_000853) +Scores: (#C #S #D #I) 66 1 1 2 +REF: s h o h Y o o m i t a i n a d o k u s h O k a n s o o b u n ******* * o k a i t a +HYP: s h o h * o o m i t a i n a d o k u s h U k a n s o o b u n M o k a i t a +Eval: D S I I + +Speaker sentences 54: cv_jpn_000854 #utts: 1 +id: (cv_jpn_000854-cv_jpn_000854) +Scores: (#C #S #D #I) 142 11 6 2 +REF: g e n j i t s u n o s e k a i w a t a ******* * N o i c h i t o s h i t e k e c l t E e s E r a R E T a k a t a C h I o M O c l t A s e k a i D E n a k e r e B a n a r a n a i +HYP: g e n j i t s u n o s e k a i w a t a M O o i c h i t o s h i t e k e c l t ******* * e s U r a ******* * I D a k a t a S h U o ******* * A c l t O s e k a i Z U n a k e r e M a n a r a n a i +Eval: I I S D D S D D S S S S D D S S S S S + +Speaker sentences 55: cv_jpn_000855 #utts: 1 +id: (cv_jpn_000855-cv_jpn_000855) +Scores: (#C #S #D #I) 75 3 8 2 +REF: s h o o h i n k e n s a k u g a W A k a r I Y a s u i t ******* * o k a u k i N i n a r u n O N i +HYP: s h o o h i n k e n s a k u g a ******* * O k a r ******* * E a s u i t O o k a u k i ******* * i n a r u n ******* * A i +Eval: D D S D D S I I D D D D S + +Speaker sentences 56: cv_jpn_000856 #utts: 1 +id: (cv_jpn_000856-cv_jpn_000856) +Scores: (#C #S #D #I) 76 5 1 6 +REF: C H i s h i k i w ******* * * a * r e k i s h i t e K I k a t e e ******* * d e n a k e r e B a n a r a n a i +HYP: T S i s h i k i w A P a U r e k i s h i t e * C L k a t e e R d e n a k e r e M a n a r a n a i +Eval: S S I I I I D S S I I S + +Speaker sentences 57: cv_jpn_000857 #utts: 1 +id: (cv_jpn_000857-cv_jpn_000857) +Scores: (#C #S #D #I) 84 3 2 2 +REF: m o n o g o t o n O j U n B a n O k a e r u d a k e d e u m a k u i k u k o t o m o ******* * a r u +HYP: m o n o g o t o n N j I n P a n ******* * k a e r u d a k e d e u m a k u i k u k o t o m o W a r u +Eval: S S S D D I I + +Speaker sentences 58: cv_jpn_000858 #utts: 1 +id: (cv_jpn_000858-cv_jpn_000858) +Scores: (#C #S #D #I) 69 0 0 2 +REF: k o n o k i s ******* * e t s u w a k a t s u o n o s a s h i m i g a z e c l p i n +HYP: k o n o k i s E e t s u w a k a t s u o n o s a s h i m i g a z e c l p i n +Eval: I I + +Speaker sentences 59: cv_jpn_000859 #utts: 1 +id: (cv_jpn_000859-cv_jpn_000859) +Scores: (#C #S #D #I) 87 3 0 3 +REF: k a k e n i s h i c l p a i s h i t e m o o c h ******* * * I t s u i t e s O n s h i t s u o u k e i R e r u +HYP: k a k e n i s h i c l p a i s h i t e m o o c h T S U t s u i t e s A n s h i t s u o u k e i D e r u +Eval: I I I S S S + +Speaker sentences 60: cv_jpn_000860 #utts: 1 +id: (cv_jpn_000860-cv_jpn_000860) +Scores: (#C #S #D #I) 85 3 0 0 +REF: s o r e y u e n i t e t s u g a K u g a z e n t a i n o g a k U d e a r u t o s u r e B a +HYP: s o r e y u e n i t e t s u g a P u g a z e n t a i n o g a k O d e a r u t o s u r e W a +Eval: S S S + +Speaker sentences 61: cv_jpn_000861 #utts: 1 +id: (cv_jpn_000861-cv_jpn_000861) +Scores: (#C #S #D #I) 67 3 3 0 +REF: C H i i S a n a y a o y a d a g a y A s u k u t e h a n j O o s h i t e r u +HYP: * K i i Z a n a y a o y a d a g a y E s u k u t e h a n j ******* * o s h i t e r u +Eval: D S S S D D + +Speaker sentences 62: cv_jpn_000862 #utts: 1 +id: (cv_jpn_000862-cv_jpn_000862) +Scores: (#C #S #D #I) 122 1 5 6 +REF: i n ******* * f u r a g ******* * a k i n O o F u z e n n i ******* * o c h i i c l t e p a u k o k u g a i e d a C L s h u t s u s u r u h i t o m o d e t e k i t a +HYP: i n I f u r a g A a k i n ******* * o H u z e n n i Y o c h i i c l t e p a u k o k u g a i e d a ******* * * s h u t s u s u r u h i t o m o d e t e k i t a +Eval: I I I I D D S I I D D D + +Speaker sentences 63: cv_jpn_000863 #utts: 1 +id: (cv_jpn_000863-cv_jpn_000863) +Scores: (#C #S #D #I) 136 11 9 4 +REF: t s u g i n i k a g a k u w a s o n z a I o ******* * * * s h u j u n o R Y o o I k i n i w a k a c l t e s o r e z O r E n O r y o o I k i n I T S U i t E k e n K Y U U s u r u +HYP: t s u g i n i k a g a k u w a s o n z a Y o P A U s h u j u n o * D o o E k i n i w a k a c l t e s o r e z U r A n ******* * r y o o U k i n ******* * ******* * * Z i t I k e n * I K I s u r u +Eval: S I I I I D S S S S D D S D D D D D S S D S S S + +Speaker sentences 64: cv_jpn_000864 #utts: 1 +id: (cv_jpn_000864-cv_jpn_000864) +Scores: (#C #S #D #I) 136 0 12 4 +REF: s o r e d e w ******* * * a * t o k I t o i u m o n o n o s E e r i t s u s h i Y O o w a n a k u p a u s h u n k a n t o i U m o n o m o n a k u N a r u n o d e a r u +HYP: s o r e d e w A P a U t o k ******* * t o i u m o n o n o s ******* * e r i t s u s h i ******* * ******* * o w a n a k u p a u s h u n k a n t o i ******* * m o n o m o n a k u ******* * a r u n o d e a r u +Eval: I I I I D D D D D D D D D D D D + +Speaker sentences 65: cv_jpn_000865 #utts: 1 +id: (cv_jpn_000865-cv_jpn_000865) +Scores: (#C #S #D #I) 92 1 10 0 +REF: a k a i b u r a n k o P A U K o n k u r I i t o s e e n o s u b e r i d a i P A U k a w a i t a s u n a b a +HYP: a k a i b u r a n k o ******* * * * H o n k u r ******* * i t o s e e n o s u b e r i d a i ******* * * * k a w a i t a s u n a b a +Eval: D D D D S D D D D D D + +Speaker sentences 66: cv_jpn_000866 #utts: 1 +id: (cv_jpn_000866-cv_jpn_000866) +Scores: (#C #S #D #I) 147 6 15 4 +REF: s h i k a s h i s o r e w a d o k o m a d e m O k O k o k a r a D E t e ******* * * * K o k o e k a e r i k u r u s E e s h i t s U o m o C L t A M o N o d e n a k E R e b a n a r a n a i +HYP: s h i k a s h i s o r e w a d o k o m a d e m N k U k o k a r a R I t e P A U P o k o e k a e r i k u r u s ******* * e s h i t s ******* * o m o ******* * * t ******* * ******* * o M o d e n a k ******* * ******* * e b a n a r a n a i +Eval: S S S S I I I I S D D D D D D D D D D D S D D D D + +Speaker sentences 67: cv_jpn_000867 #utts: 1 +id: (cv_jpn_000867-cv_jpn_000867) +Scores: (#C #S #D #I) 99 3 2 6 +REF: a r i t o ******* * a r a Y U r u d e m A o m a k i c h i r a s h i t e ******* * * * m i n n A k a r a u r a m i o k a c l t e r u +HYP: a r i t o W a r a ******* * I r u d e m O o m a k i c h i r a s h i t e P A U m i n n E k a r a u r a m i o k a c l t e r u +Eval: I I D D S S I I I I S + +Speaker sentences 68: cv_jpn_000868 #utts: 1 +id: (cv_jpn_000868-cv_jpn_000868) +Scores: (#C #S #D #I) 73 0 4 3 +REF: k o n o ******* * * t e e d o P A U s a w a g i n i n a r u k o t o m o n a i n o d a r o o +HYP: k o n o C L t e e d o ******* * * * s a w a g i n i n a r u k o t o m o n a i n o d a r o o +Eval: I I I D D D D + +Speaker sentences 69: cv_jpn_000869 #utts: 1 +id: (cv_jpn_000869-cv_jpn_000869) +Scores: (#C #S #D #I) 38 2 0 4 +REF: k o n o n e d a n d e ******* * u r E c h a U k a a ******* * +HYP: k o n o n e d a n d e W u r I c h a N k a a N +Eval: I I S S I I + +Speaker sentences 70: cv_jpn_000870 #utts: 1 +id: (cv_jpn_000870-cv_jpn_000870) +Scores: (#C #S #D #I) 63 0 6 0 +REF: h i n o k a g e n n i c h U u i s h i n a i t o s u g u N I k o g e r u +HYP: h i n o k a g e n n i c h ******* * u i s h i n a i t o s u g u ******* * ******* * k o g e r u +Eval: D D D D D D + +Speaker sentences 71: cv_jpn_000871 #utts: 1 +id: (cv_jpn_000871-cv_jpn_000871) +Scores: (#C #S #D #I) 146 3 8 0 +REF: e n B a n N o U e n i p o t s u r i t o c h i i s a n a a n a g A H I R a i t a s a i s h o w a t s u m a y o o j i t e e d o n o c h i i s a n a a n a d a c l t a +HYP: e n M a n D o W e n i p o t s u r i t o c h i i s a n a a n a g ******* * ******* * ******* * ******* * a i t a s a i s h o w a t s u m a y o o j i t e e d o n o c h i i s a n a a n a d a c l t a +Eval: S S S D D D D D D D D + +Speaker sentences 72: cv_jpn_000872 #utts: 1 +id: (cv_jpn_000872-cv_jpn_000872) +Scores: (#C #S #D #I) 170 3 0 2 +REF: s o r e w a * * W a r e w a r e o i k a s h i n a g a r a w a r e w a r e o D o r e e k a s u r u n o d e a r u p a u w a r e W a r e n o t a m a s h i i o k o r o s u n o d e a r u +HYP: s o r e w a P A U a r e w a r e o i k a s h i n a g a r a w a r e w a r e o T o r e e k a s u r u n o d e a r u p a u w a r e B a r e n o t a m a s h i i o k o r o s u n o d e a r u +Eval: I I S S S + +Speaker sentences 73: cv_jpn_000873 #utts: 1 +id: (cv_jpn_000873-cv_jpn_000873) +Scores: (#C #S #D #I) 198 5 9 0 +REF: r e k i s h i t e k i n i a t a e r a r E t a m o n o w A P a U Z e c l t a i m u j u n t e k i j i K o D o o i t s u t e k i g e n z a i n I o i t e s E k a i S h i t e k i n i a t a e r a r e t a m o n o t +HYP: r e k i s h i t e k i n i a t a e r a r ******* * t a m o n o w ******* * * a * D e c l t a i m u j u n t e k i j i G o T o o i t s u t e k i g e n z a i n ******* * o i t e s U k a i * h i t e k i n i a t a e r a r e t a m o n o t +Eval: D D D D D D S S S D D S D + +>> REF: o s h i t E +>> HYP: o s h i t I +>> Eval: S + +Speaker sentences 74: cv_jpn_000874 #utts: 1 +id: (cv_jpn_000874-cv_jpn_000874) +Scores: (#C #S #D #I) 110 2 8 0 +REF: m u j u n t e k i j i K o d o o i t s u t o s h i t e p a u i t s u m o k o n o s e k a i n i c h o o e T s U t E k i d e a R U +HYP: m u j u n t e k i j i G o d o o i t s u t o s h i t e p a u i t s u m o k o n o s e k a i n i c h o o e * s ******* H t ******* * k i d e a ******* * ******* * +Eval: S D D S D D D D D D + +Speaker sentences 75: cv_jpn_000875 #utts: 1 +id: (cv_jpn_000875-cv_jpn_000875) +Scores: (#C #S #D #I) 155 5 6 0 +REF: y u E n i z e c l t a I m u j u n t e k i j i K o d o o i t s u t o s h i t e g e n Z a i k a r a g e n z a I e t o U G o k i i k u s e k a i n o g e n z a i n I o i t e +HYP: y u U n i z e c l t a E m u j u n t e k i j i G o d o o i t s u t o s h i t e g e n S a i k a r a g e n z a ******* * e t o ******* * B o k i i k u s e k a i n o g e n z a i n ******* * o i t e +Eval: S S S S D D D D S D D + +Speaker sentences 76: cv_jpn_000876 #utts: 1 +id: (cv_jpn_000876-cv_jpn_000876) +Scores: (#C #S #D #I) 54 3 8 2 +REF: * ******* a r e p a u B o t a n o s h i t E m O d a C L s h U T S u d e k i n a i +HYP: H a r e p a u W o t a n o s h i t O m N d a ******* * * s h ******* * ******* * * u d e k i n a i +Eval: I I S S S D D D D D D D D + +Speaker sentences 77: cv_jpn_000877 #utts: 1 +id: (cv_jpn_000877-cv_jpn_000877) +Scores: (#C #S #D #I) 99 0 4 0 +REF: s h i k a s h i w a t a s h i W a s o k o n i s e k a i n o j i k o d o o i t s u O o k u n o d e w a n a i +HYP: s h i k a s h i w a t a s h i ******* * a s o k o n i s e k a i n o j i k o d o o i t s u ******* * o k u n o d e w a n a i +Eval: D D D D + +Speaker sentences 78: cv_jpn_000878 #utts: 1 +id: (cv_jpn_000878-cv_jpn_000878) +Scores: (#C #S #D #I) 51 3 0 0 +REF: n e M U T a k u n a r u n o g a h a y a k u n a c l t a +HYP: n e B E K a k u n a r u n o g a h a y a k u n a c l t a +Eval: S S S + +Speaker sentences 79: cv_jpn_000879 #utts: 1 +id: (cv_jpn_000879-cv_jpn_000879) +Scores: (#C #S #D #I) 183 1 10 2 +REF: w a t a s h i w a N i n g e n n ******* * o r e k i s h i t e k i k e e s e e n o t a c h i b a k a r a g E e j u t s u o m i r u n o d e a c l t e P A U K o o s h a k a r a Z e n s h a o m i r u n o d e w a n a i +HYP: w a t a s h i w a ******* * i n g e n n O o r e k i s h i t e k i k e e s e e n o t a c h i b a k a r a g ******* * e j u t s u o m i r u n o d e a c l t e ******* * * * ******* * o o s h a k a r a D e n s h a o m i r u n o d e w a n a i +Eval: D D I I D D D D D D D D S + +Speaker sentences 80: cv_jpn_000880 #utts: 1 +id: (cv_jpn_000880-cv_jpn_000880) +Scores: (#C #S #D #I) 55 3 0 2 +REF: a o i t o m a t o s h i k a n a k u t e k a u k a ******* * m a Y O U +HYP: a o i t o m a t o s h i k a n a k u t e k a u k a B m a I Y O +Eval: I I S S S + +Speaker sentences 81: cv_jpn_000881 #utts: 1 +id: (cv_jpn_000881-cv_jpn_000881) +Scores: (#C #S #D #I) 63 3 1 2 +REF: s H i n k i ******* * J i g y o o n i o o k i n a k i t a I o y o s E t e i r u +HYP: s * i n k i Z U i g y o o n i o o k i n a k i t a Y o y o s U t e i r u +Eval: D I I S S S + +Speaker sentences 82: cv_jpn_000882 #utts: 1 +id: (cv_jpn_000882-cv_jpn_000882) +Scores: (#C #S #D #I) 82 1 0 4 +REF: n a n i k a s h i r a n o ******* * ******* * i n s e n t i b u G a n a i t o k i b i s h i i n o d e w a +HYP: n a n i k a s h i r a n o I N i n s e n t i b u W a n a i t o k i b i s h i i n o d e w a +Eval: I I I I S + +Speaker sentences 83: cv_jpn_000883 #utts: 1 +id: (cv_jpn_000883-cv_jpn_000883) +Scores: (#C #S #D #I) 66 3 0 6 +REF: j i k A n s * e e g e n n o i b e n t o d e s u t o r E s * U t a m a r ******* * ******* * u +HYP: j i k O n s H e e g e n n o i b e n t o d e s u t o r U s H I t a m a r U B u +Eval: S I S I S I I I I + +Speaker sentences 84: cv_jpn_000884 #utts: 1 +id: (cv_jpn_000884-cv_jpn_000884) +Scores: (#C #S #D #I) 56 0 2 0 +REF: m a W a r i n o h i t o w a b o o z e n t o s h i t e i t a +HYP: m a ******* * a r i n o h i t o w a b o o z e n t o s h i t e i t a +Eval: D D + +Speaker sentences 85: cv_jpn_000885 #utts: 1 +id: (cv_jpn_000885-cv_jpn_000885) +Scores: (#C #S #D #I) 67 2 4 2 +REF: s o n n a n a i y o o n o m e e r U G A P a U n a n k e n m o k ******* * i t e i t a +HYP: s o n n a n a i y o o n o m e e r ******* * E W * a * n a n k e n m o k U i t e i t a +Eval: D D S S D D I I + +Speaker sentences 86: cv_jpn_000886 #utts: 1 +id: (cv_jpn_000886-cv_jpn_000886) +Scores: (#C #S #D #I) 41 1 2 0 +REF: n I j i k a i d E d e e s u i s h i t e i t a +HYP: n ******* * j i k a i d R d e e s u i s h i t e i t a +Eval: D D S + +Speaker sentences 87: cv_jpn_000887 #utts: 1 +id: (cv_jpn_000887-cv_jpn_000887) +Scores: (#C #S #D #I) 154 9 20 1 +REF: t o k i d o k i P A U J i B u n n O K o k o r o G a w a k a r a n a k u n A r u t o k i g a a r u d a k a r a b o k u w a k A a T E N o H I k i P A U n o o t o n i k a * K i H a j i m e r u +HYP: t o k i d o k i * * * C H i ******* * u n n ******* * ******* * o k o r o W a w a k a r a n a k u n O r u t o k i g a a r u d a k a r a b o k u w a k ******* * a ******* * N I o * C L k i ******* * * * n o o t o n i k a C H i ******* * a j i m e r u +Eval: D D D S S D D D D D D S S D D D D S S D S S D D D D I S D D + +Speaker sentences 88: cv_jpn_000888 #utts: 1 +id: (cv_jpn_000888-cv_jpn_000888) +Scores: (#C #S #D #I) 33 1 0 0 +REF: m o o n i g e t e c h a D a m e d a +HYP: m o o n i g e t e c h a T a m e d a +Eval: S + +Speaker sentences 89: cv_jpn_000889 #utts: 1 +id: (cv_jpn_000889-cv_jpn_000889) +Scores: (#C #S #D #I) 52 1 8 0 +REF: k a r e w a p A U B o o C L t o t a C H i t s u k u s h i t e i t a +HYP: k a r e w a p * * ******* * o o ******* * * t o t a * J i t s u k u s h i t e i t a +Eval: D D D D D D D D S + +Speaker sentences 90: cv_jpn_000890 #utts: 1 +id: (cv_jpn_000890-cv_jpn_000890) +Scores: (#C #S #D #I) 52 3 0 2 +REF: d a r ******* * E n i m o m e E w a k U w a k a k e t a k u n a i +HYP: d a r A U n i m o m e I w a k O w a k a k e t a k u n a i +Eval: I I S S S + +Speaker sentences 91: cv_jpn_000891 #utts: 1 +id: (cv_jpn_000891-cv_jpn_000891) +Scores: (#C #S #D #I) 60 3 7 4 +REF: M a s a k A P a U t o o m o C L t e D o ******* * a n ******* * o t o c l t e o n i g I c l t a +HYP: W a s a k ******* * * a * t o o m o ******* * * t e T o W a n O o t o c l t e o n i g E c l t a +Eval: S D D D D D D D S I I I I S + +Speaker sentences 92: cv_jpn_000892 #utts: 1 +id: (cv_jpn_000892-cv_jpn_000892) +Scores: (#C #S #D #I) 14 1 0 3 +REF: s * ******* * U i m a s e n +HYP: s H T E i m a s e n +Eval: I I I S + +Speaker sentences 93: cv_jpn_000893 #utts: 1 +id: (cv_jpn_000893-cv_jpn_000893) +Scores: (#C #S #D #I) 142 1 0 0 +REF: k a y o U n i s h i t e s h i c l t e i r u t o t o m o n i s h i c l t e i n a i t o k o r o k a r a t a n k y u u w a h a j i m a r u n o d e a r u +HYP: k a y o O n i s h i t e s h i c l t e i r u t o t o m o n i s h i c l t e i n a i t o k o r o k a r a t a n k y u u w a h a j i m a r u n o d e a r u +Eval: S + +Speaker sentences 94: cv_jpn_000894 #utts: 1 +id: (cv_jpn_000894-cv_jpn_000894) +Scores: (#C #S #D #I) 30 1 3 0 +REF: a i s a T S u w a d a i j i d a Y o +HYP: a i s a ******* * * u w a d a i j i d a I o +Eval: D D D S + +Speaker sentences 95: cv_jpn_000895 #utts: 1 +id: (cv_jpn_000895-cv_jpn_000895) +Scores: (#C #S #D #I) 110 2 4 0 +REF: t o j i t a m o n O o i k a n i h i r o g e t e m o h i r a i t a m o n o n i W a n a r a N U t o i c l t e i r u g a +HYP: t o j i t a m o n ******* * o i k a n i h i r o g e t e m o h i r a i t a m o n o n i ******* * a n a r a R O t o i c l t e i r u g a +Eval: D D D D S S + +Speaker sentences 96: cv_jpn_000896 #utts: 1 +id: (cv_jpn_000896-cv_jpn_000896) +Scores: (#C #S #D #I) 56 1 0 0 +REF: t a m e s h i n i i k u t s u k a t s u k u c l t e m i y o O +HYP: t a m e s h i n i i k u t s u k a t s u k u c l t e m i y o A +Eval: S + +Speaker sentences 97: cv_jpn_000897 #utts: 1 +id: (cv_jpn_000897-cv_jpn_000897) +Scores: (#C #S #D #I) 49 1 2 0 +REF: z u i b u n a k o g i n a s h o o b a i d a Y o n A a +HYP: z u i b u n a k o g i n a s h o o b a i d a I o n ******* * a +Eval: S D D + +Speaker sentences 98: cv_jpn_000898 #utts: 1 +id: (cv_jpn_000898-cv_jpn_000898) +Scores: (#C #S #D #I) 6 2 0 1 +REF: w a * C H i +HYP: w a T E i +Eval: I S S + +Speaker sentences 99: cv_jpn_000899 #utts: 1 +id: (cv_jpn_000899-cv_jpn_000899) +Scores: (#C #S #D #I) 6 0 0 0 +REF: i c h i +HYP: i c h i +Eval: + +Speaker sentences 100: cv_jpn_000900 #utts: 1 +id: (cv_jpn_000900-cv_jpn_000900) +Scores: (#C #S #D #I) 3 0 0 0 +REF: g o +HYP: g o +Eval: + +Speaker sentences 101: cv_jpn_000901 #utts: 1 +id: (cv_jpn_000901-cv_jpn_000901) +Scores: (#C #S #D #I) 7 1 1 0 +REF: s h i C H i +HYP: s h i * K i +Eval: D S + +Speaker sentences 102: cv_jpn_000902 #utts: 1 +id: (cv_jpn_000902-cv_jpn_000902) +Scores: (#C #S #D #I) 5 0 0 0 +REF: i i e +HYP: i i e +Eval: + +Speaker sentences 103: cv_jpn_000903 #utts: 1 +id: (cv_jpn_000903-cv_jpn_000903) +Scores: (#C #S #D #I) 7 1 0 0 +REF: W a c h i +HYP: H a c h i +Eval: S + +Speaker sentences 104: cv_jpn_000904 #utts: 1 +id: (cv_jpn_000904-cv_jpn_000904) +Scores: (#C #S #D #I) 4 1 0 0 +REF: R e i +HYP: N e i +Eval: S + +Speaker sentences 105: cv_jpn_000905 #utts: 1 +id: (cv_jpn_000905-cv_jpn_000905) +Scores: (#C #S #D #I) 4 0 0 2 +REF: s h ******* * i +HYP: s h I i +Eval: I I + +Speaker sentences 106: cv_jpn_000906 #utts: 1 +id: (cv_jpn_000906-cv_jpn_000906) +Scores: (#C #S #D #I) 3 0 0 0 +REF: k u +HYP: k u +Eval: + +Speaker sentences 107: cv_jpn_000907 #utts: 1 +id: (cv_jpn_000907-cv_jpn_000907) +Scores: (#C #S #D #I) 6 0 0 0 +REF: i c h i +HYP: i c h i +Eval: + +Speaker sentences 108: cv_jpn_000908 #utts: 1 +id: (cv_jpn_000908-cv_jpn_000908) +Scores: (#C #S #D #I) 115 1 0 4 +REF: k a G a k u g a a k i r a k a n i s u r ******* * * * u k y a c l k a n t e k i s h i n r i n i s h i t a g a u k o t o n i y o c l t e +HYP: k a K a k u g a a k i r a k a n i s u r U P A u k y a c l k a n t e k i s h i n r i n i s h i t a g a u k o t o n i y o c l t e +Eval: S I I I I + +Speaker sentences 109: cv_jpn_000909 #utts: 1 +id: (cv_jpn_000909-cv_jpn_000909) +Scores: (#C #S #D #I) 150 0 1 5 +REF: k a k o t o m i r a i t o n o m u j u n t e k i j i k o d o o i t s u t o s h i t e n o g e n z a i g ******* * * a * k a t a c h i o m o T s * u t o i u k o t o d e a r u +HYP: k a k o t o m i r a i t o n o m u j u n t e k i j i k o d o o i t s u t o s h i t e n o g e n z a i g A P a U k a t a c h i o m o * s H u t o i u k o t o d e a r u +Eval: I I I I D I + +Speaker sentences 110: cv_jpn_000910 #utts: 1 +id: (cv_jpn_000910-cv_jpn_000910) +Scores: (#C #S #D #I) 162 0 0 9 +REF: b u t s u r i t e k i s e k a i w ******* * * a * s u u g a k u t e k i k i g o o n i y o c l t e a r a w a s a r e r ******* * * * u s u u g a k u t e k i k a t a c h i n o s * e k a i d e a r u +HYP: b u t s u r i t e k i s e k a i w A P a U s u u g a k u t e k i k i g o o n i y o c l t e a r a w a s a r e r U P A u s u u g a k u t e k i k a t a c h i n o s H e k a i d e a r u +Eval: I I I I I I I I I + +Speaker sentences 111: cv_jpn_000911 #utts: 1 +id: (cv_jpn_000911-cv_jpn_000911) +Scores: (#C #S #D #I) 49 1 0 2 +REF: * ******* o n a j i g e n s h o o d e s a n k o o N i n a r u +HYP: W o n a j i g e n s h o o d e s a n k o o G i n a r u +Eval: I I S + +Speaker sentences 112: cv_jpn_000912 #utts: 1 +id: (cv_jpn_000912-cv_jpn_000912) +Scores: (#C #S #D #I) 72 0 0 0 +REF: g a i k o k u k a r a k i t a m o n o d a t o s h i c l t e b i c l k u r i +HYP: g a i k o k u k a r a k i t a m o n o d a t o s h i c l t e b i c l k u r i +Eval: + +Speaker sentences 113: cv_jpn_000913 #utts: 1 +id: (cv_jpn_000913-cv_jpn_000913) +Scores: (#C #S #D #I) 89 4 0 4 +REF: i w a y U r u j i c l s e n n i y o c l t e k a k u t o ******* * k u s h i ******* * R A I c l t a m o n o d e a r u +HYP: i w a y O r u j i c l s e n n i y o c l t e k a k u t o U k u s h i K I T A c l t a m o n o d e a r u +Eval: S I I I I S S S + +Speaker sentences 114: fleurs_jpn_000346 #utts: 1 +id: (fleurs_jpn_000346-fleurs_jpn_000346) +Scores: (#C #S #D #I) 114 2 9 6 +REF: o n a j i y O o n i P A U d a n s e e w a h i z a O o o u z u b o n o h a K U k o t o g ******* * * a * g i m ******* * u z u k e r a r e t e i m a s u +HYP: o n a j i y ******* * o n i ******* * * * d a n s e e w a h i z a ******* * o o u z u b o n o h a * C L k o t o g A P a U g i m U u z u k e r a r e t e i m a s u +Eval: D D D D D D D D D S S I I I I I I + +Speaker sentences 115: fleurs_jpn_000347 #utts: 1 +id: (fleurs_jpn_000347-fleurs_jpn_000347) +Scores: (#C #S #D #I) 213 7 19 11 +REF: k o n o s a a b i s U W A P a U G o r a k u s ******* * e N O h a j i m e t o s u r u s e n p a k u y A P a U e n k a k u c h i d e ******* * * * d e e t a y a o n s E e o h i T s u Y O o t o s u r u t a n K e n t a i n i ******* * * * h i +HYP: k o n o s a a b i s ******* * ******* * ******* * * a * K o r a k u s E e W A h a j i m e t o s u r u s e n p a k u y ******* * * a * e n k a k u c h i d e P A U d e e t a y a o n s ******* * e o h i * s u ******* * ******* * o t o s u r u t a n G e n t a i n i P A U h i +Eval: D D D D D D D D S I I S S D D D D I I I I D D D D D D D S I I I I + +>> REF: n * P A N n i r i y o o s a r e t e i m a s u +>> HYP: n C L P A n i r i y o o s a r e t e i m a s u +>> Eval: I S S S + +Speaker sentences 116: fleurs_jpn_000348 #utts: 1 +id: (fleurs_jpn_000348-fleurs_jpn_000348) +Scores: (#C #S #D #I) 212 7 57 1 +REF: k y o o f U u P A u h y O o P A U k a D o n o k o o s u i r Y o U P A U o Y o b i Y a m a k a * J i W A P a U r a i U P A u t a t s u m a k i P A U M i z U F u k i P A U O Y o b I s a i k u r O N n A d O n o k i b i s h i i k i s h +HYP: k y o o f ******* * u * * u h y ******* * o ******* * * * k a T o n o k o o s u i r * o ******* * ******* * * * o ******* * o b i ******* * a m a k a S H i ******* * B * a * r a i ******* * * * u t a t s u m a k i ******* * * * ******* * i z ******* * ******* * u k i ******* * * * ******* * ******* * o b E s a i k u r ******* * ******* * n O d A n o k i b i s h i i k i s h ******* +Eval: D D D D D D D D D D S D D D D D D D D D D D I S D D S D D D D D D D D D D D D D D D D D D D D D D D D S D D D D S S D + +>> REF: O o k e e t a I y a s o n o e e k y O o n i y o r u m o n o D e s u +>> HYP: * o k e e t a ******* * y a s o n o e e k y ******* * o n i y o r u m o n o R e s u +>> Eval: D D D D D S + +Speaker sentences 117: fleurs_jpn_000349 #utts: 1 +id: (fleurs_jpn_000349-fleurs_jpn_000349) +Scores: (#C #S #D #I) 155 8 7 1 +REF: i n t a a n e c l t o w A P a U m a s u k o m Y U N i k e e s h O n t o t a i j i n k o m ******* Y U N i k e e s h o N n o r y o o y O o s o o k a n e s O n a e t a k a n k y o o d e s u +HYP: i n t a a n e c l t o w ******* * * a * m a s u k o m * I R i k e e s h U n t o t a i j i n k o m I R U i k e e s h o O n o r y o o y ******* * o s o o k a n e s U n a e t a k a n k y o o d e s u +Eval: D D D D D S S S I S S S S D D S + +Speaker sentences 118: fleurs_jpn_000350 #utts: 1 +id: (fleurs_jpn_000350-fleurs_jpn_000350) +Scores: (#C #S #D #I) 212 7 18 4 +REF: k a j i n o D e w a t s u u j o o P A U t o k u b e T s u N a i n s h O k U y a e n t a a t e i m e n t O o y o o I s h i t e i m a s U P A u g e s u T O G a k i b u N y o k u s h i s e T s u n a i n i t o ******* * ******* * m a r +HYP: k a j i n o R e w a t s u u j o o ******* * * * t o k u b e * s u R a i n s h I k O y a e n t a a t e i m e n t ******* * o y o o ******* * s h i t e i m a s ******* * * * u g e s u ******* * ******* * W a k i b u I y o k u s h i s e * s u n a i n i t o R A m a r +Eval: S D D D D D S S S D D D D D D D D D D D D S S D I I I I + +>> REF: u y o o N i s u r u t a m e d e s u +>> HYP: u y o o R i s u r u t a m e d e s u +>> Eval: S + +Speaker sentences 119: fleurs_jpn_000351 #utts: 1 +id: (fleurs_jpn_000351-fleurs_jpn_000351) +Scores: (#C #S #D #I) 191 6 13 0 +REF: s h i k a s h i p a u k Y a P u t e n n o W i k e c l t o o U s h i n a c l t a a T O P A U i n d o w a n a n a t s u n o W i k e c l t o o U s h i n a i P A U s a n j u u r o k u r a N s h i k a d e k i m a s e n d e s h i t +HYP: s h i k a s h i p a u k * a K u t e n n o B i k e c l t o o ******* * s h i n a c l t a a D T * * O i n d o w a n a n a t s u n o B i k e c l t o o ******* * s h i n a i ******* * * * s a n j u u r o k u r a ******* * s h i k a d e k i m a s e n d e s h i t +Eval: D S S D D S S D D S S D D D D D D D D + +>> REF: a +>> HYP: a +>> Eval: + +Speaker sentences 120: fleurs_jpn_000352 #utts: 1 +id: (fleurs_jpn_000352-fleurs_jpn_000352) +Scores: (#C #S #D #I) 214 10 12 0 +REF: F o o k u r a n d o n o k o o s h i k I t s U u k a w a F o o k u r a n d o s h o t o o P o n d o e f u k e e p i i d e i c h i p o n D o g a i c h I I G I R i S U P o n d o j i i B i i P i i t o t o o k a n +HYP: H o o k u r a n d o n o k o o s h i k E t s ******* * u k a w a H o o k u r a n d o s h o t o o K o n d o e f u k e e p i i d e i c h i p o n N o g a i c h ******* * ******* * ******* * ******* * ******* * i E E B o n d o j i i P i i B i i t o t o o k a n +Eval: S S D D S S S D D D D D D D D D D S S S S S + +>> REF: i k o t e e s a r e t e i m a s u +>> HYP: i k o t e e s a r e t e i m a s u +>> Eval: + +Speaker sentences 121: fleurs_jpn_000353 #utts: 1 +id: (fleurs_jpn_000353-fleurs_jpn_000353) +Scores: (#C #S #D #I) 221 18 17 5 +REF: h a s h i s h i t a n O K A M I G A T A k U U k a n w ******* * * A j u u g o m E e t o r U d e s U P A u n i s e n j U u i c h i n e N h a c h i g a t s u n i s H U N k o o s h i p a u n i s e n j u u n A n A n E N s a n g a +HYP: h a s h i s h i t a n ******* * N O J O O H O O k ******* * O k a n w O C H j u u g o m ******* * e t o r O d e s ******* * * * u n i s e n j ******* * u i c h i n e ******* * h a c h i g a t s u n i s * ******* * E k o o s h i p a u n i s e n j u u n O n E n N I s a n g a +Eval: D D S S S S S S S S D D S I I I S D D S D D D D D D D D D D D S S S S S + +>> REF: t s u m a D e k a i t s u U s h i m a s e n ******* * d e s h i t a +>> HYP: t s u m a N e k a i t s u E s h i m a s e n G d e s h i t a +>> Eval: S S I I + +Speaker sentences 122: fleurs_jpn_000354 #utts: 1 +id: (fleurs_jpn_000354-fleurs_jpn_000354) +Scores: (#C #S #D #I) 143 3 14 2 +REF: i c l p u n k a n d e ******* * f u c l t O o s U R u c h i i k i m O a r e b A P a U f u c l t o o s u r u m a d e N i n a N P U n m o k a k a r u c h i i k i m o a r i m a s u +HYP: i c l p u n k a n d e H f u c l t ******* * o s O G u c h i i k i m ******* * a r e b ******* * * a * f u c l t o o s u r u m a d e M i n a ******* * ******* * ******* * n m o k a k a r u c h i i k i m o a r i m a s u +Eval: I I D D S S D D D D D D S D D D D D D + +Speaker sentences 123: fleurs_jpn_000355 #utts: 1 +id: (fleurs_jpn_000355-fleurs_jpn_000355) +Scores: (#C #S #D #I) 174 9 7 3 +REF: p i r a m i c l D O n o o t o t o * h i k a R i n o s h o o w A P a U k o n o k a n k o o C h i d e t o k u n i k o D o m o ******* * t a C h i G a t a n o s h i m e r U m o y o o s h I n o h i t o T s u D e s u +HYP: p i r a m i c l T A n o o t o t o S h i k a ******* * i n o s h o o w ******* * * a * k o n o k a n k o o S h i d e t o k u n i k o R o m o A t a S h i K a t a n o s h i m e r E m o y o o s h U n o h i t o * s u R e s u +Eval: S S I D D D D D D S S I I S S S S D S + +Speaker sentences 124: fleurs_jpn_000356 #utts: 1 +id: (fleurs_jpn_000356-fleurs_jpn_000356) +Scores: (#C #S #D #I) 96 4 5 0 +REF: s o n o t a M e P A U t a N n i R a B e r u t o s h i t e h Y o o k i g a t s u i k a s a r e g a c h i d e s u +HYP: s o n o t a N e ******* * * * t a I n i D a D e r u t o s h i t e h * o o k i g a t s u i k a s a r e g a c h i d e s u +Eval: S D D D D S S S D + +Speaker sentences 125: fleurs_jpn_000357 #utts: 1 +id: (fleurs_jpn_000357-fleurs_jpn_000357) +Scores: (#C #S #D #I) 250 19 21 2 +REF: g e n S O n s u r u k o t o g a s h i R a r e t e i r u n i j U U g ******* * o m a i n O D a n R a c l P u p A U B u r o o D O s a i d o w A P a U g e n S O n s u r U t o o g a I b u n k e N n o s a i k o n o U T S u +HYP: g e n Z U n s u r u k o t o g a s h i T a r e t e i r u n i j I O g O o m a i n A T a n D a c l T u p * * ******* * u r o o T U s a i d o w ******* * * a * g e n Z U n s u r A t o o g a E b u n k e ******* * n o s a i k o n o ******* * ******* * * u +Eval: S S S S S I I S S S S D D D D S S D D D D S S S S D D D D D D D + +>> REF: s h i d e s U P A u t e g a k i n I y o r u g e n p o N w a g e n S o N s h i t e i m A s e n +>> HYP: s h i d e s ******* * * * u t e g a k i n ******* * y o r u g e n p o O w a g e n Z o O s h i t e i m O s e n +>> Eval: D D D D D D S S S S + +Speaker sentences 126: fleurs_jpn_000358 #utts: 1 +id: (fleurs_jpn_000358-fleurs_jpn_000358) +Scores: (#C #S #D #I) 256 18 25 6 +REF: k a r e N o s e T s U o t a d a s h I i t o m i t o m e r u * h i t o m O i m a s h i t a g A P a U o o k u N O h i t o W A s O n o g Y A k U d e p a u t a i y o o k E e d e W a t a i Y O o T O S o N o T a n o h o +HYP: k a r e M o s e * s O o t a d a s h ******* * i t o m i t o m e r u S h i t o m ******* * i m a s h i t a g ******* * * a * o o k u G U h i t o ******* * O s U n o g * E k O d e p a u t a i y o o k ******* * e d e ******* * a t a i O T o S I N o H o K a n o h o +Eval: S D S D D I D D D D D D S S D D S S D S S D D D D S S S S S S S + +>> REF: s h i g ******* * * a * c h i k Y u u n o m A W a r I O i d o o s h i T e I r u t o s h i n * J i t e i m a s h i t a +>> HYP: s h i g A P a U c h i k * u u n o m ******* * ******* * a r ******* * E i d o o s h i S e ******* * r u t o s h i n C H i t e i m a s h i t a +>> Eval: I I I I D D D D D D D S S D D I S + +Speaker sentences 127: fleurs_jpn_000359 #utts: 1 +id: (fleurs_jpn_000359-fleurs_jpn_000359) +Scores: (#C #S #D #I) 277 9 25 3 +REF: c h i b e c l t o m e e s O o N o c h U u s h i n w a s h i N s e e Y o g a d e s U P A u s a m a z a M a n a k a m i g a m i ******* * o s h i k a k u k a s u r u k o t o d e P A U e n e r u g i i c h a n e r u g a * J o o k +HYP: c h i b e c l t o m e e s ******* * o ******* * o c h ******* * u s h i n w a s h i I s e e I o g a d e s ******* * * * u s a m a z a N a n a k a m i g a m i Y o s h i k a k u k a s u r u k o t o d e ******* * * * e n e r u g i i c h a n e r u g a S H o o k +Eval: D D D D D D S S D D D D S I I D D D D I S + +>> REF: a s a r e P A U c h a k u r a g a k a C L s e e k a s a r e P A U s a t o r I n O i s h i k I g A U m a r e m a s u +>> HYP: a s a r e ******* * * * c h a k u r a g a k a ******* * * s e e k a s a r e ******* * * * s a t o r U n E i s h i k E g O O m a r e m a s u +>> Eval: D D D D D D D D D D D S S S S S + +Speaker sentences 128: fleurs_jpn_000360 #utts: 1 +id: (fleurs_jpn_000360-fleurs_jpn_000360) +Scores: (#C #S #D #I) 176 10 15 10 +REF: m i n a m i ******* * a F u r I k a n i ******* * a r u s u b e t e n ******* * O k o k u r i T s u k o o e N t o d o o y O o n i P A U k o n O k o o e N n i W a m a i n i C H i ******* * h o g o * h i t o n ******* Y u u e n R Y O o g A K a k a r +HYP: m i n a m i Y a H u r E k a n i Y a r u s u b e t e n R U k o k u r i * s u k o o e ******* * t o d o o y ******* * o n i ******* * * * k o n U k o o e ******* * n i ******* * a m a i n i * J i A h o g o S h i t o n I u u e n * G U o g O T a k a r +Eval: I I S S I I I I S D D D D D D D D D S D D D D D S I I I I S D S S S S + +>> REF: i m a s u +>> HYP: i m a s u +>> Eval: + +Speaker sentences 129: fleurs_jpn_000361 #utts: 1 +id: (fleurs_jpn_000361-fleurs_jpn_000361) +Scores: (#C #S #D #I) 107 12 7 4 +REF: R e c l s h ******* * a P A U k u r u ******* * M a P A u S O N o T a n O o o k u n O k o o t s U u s h u d a n g a s O k o k a r A u m a r e m a s h i t a +HYP: D e c l s h S a * C L k u r u N G a S u N U H o K a n ******* * o o k u n U k o o t s ******* * u s h u d a n g a s U k o k a r ******* * u m a r e m a s h i t a +Eval: S I I D S S I I S S S S S S S D D S D D S D D + +Speaker sentences 130: fleurs_jpn_000362 #utts: 1 +id: (fleurs_jpn_000362-fleurs_jpn_000362) +Scores: (#C #S #D #I) 158 6 6 4 +REF: i n t a a n e c l t o w A P a U m a s u k o m Y U n i K e e s h o n t o t a i j i n k o m Y U n i K e e s h ******* * o n n o r y o o y o o s o o k a n E s o n a ******* * e t a k a n k y o o D e s u +HYP: i n t a a n e c l t o w ******* * * a * m a s u k o m * I n i G e e s h o n t o t a i j i n k o m * I n i G e e s h A o n n o r y o o y o o s o o k a n I s o n a I e t a k a n k y o o R e s u +Eval: D D D D D S S D S S I I S I I S + +Speaker sentences 131: fleurs_jpn_000363 #utts: 1 +id: (fleurs_jpn_000363-fleurs_jpn_000363) +Scores: (#C #S #D #I) 200 14 18 3 +REF: B y o o i n d e w A P a U k a n s e n k a n r i t e j u N s h o n i s h i T a g a i p a u T a n i N e n O k a n s e n N o k a n o o s E e O F U s e G u t a m e n i k a n j a O k a k u r I s u r U N a D o N O s +HYP: K y o o i n d e w ******* * * a * k a n s e n k a n r i t e j u U s h o n i s h i K a g a i p a u K a n i I e n U k a n s e n G o k a n o o s ******* * e ******* * ******* * ******* * s e K u t a m e n i k a n j a ******* * k a k u r ******* * s u r A D a M o M I s +Eval: S D D D D S S S S S S D D D D D D D D S D D D D S S S S S + +>> REF: o c h I o t o c l t e i m A s * ******* * u +>> HYP: o c h ******* * o t o c l t e i m U s H I u +>> Eval: D D S I I I + +Speaker sentences 132: fleurs_jpn_000364 #utts: 1 +id: (fleurs_jpn_000364-fleurs_jpn_000364) +Scores: (#C #S #D #I) 299 13 10 4 +REF: r e n p o o G i k a i w a n i s e n g o n e n d o k a r a w a i s e T s u b u T s u t o r I s h I m a r i H O o e n o s h i k i n t e e k y O o o k a i S H i s h i p a u e F u b i i a i w a a D a r u T o p o r u n +HYP: r e n p o o R i k a i w a n i s e n g o n e n d o k a r a w a i s e * s u b u * s u t o r E s h U m a r i O H o e n o s h i k i n t e e k y ******* * o o k a i * J i s h i p a u e R u b i i a i w a a T a r u Z o p o r u n +Eval: S D D S S S S D D D S S S S + +>> REF: o n i j u u n i N n o s o o s a ******* * i n ******* * o t o o N Y U u s h I n a k e r e B a n a r a n a i t o k i t E e s h i m a s h i t a +>> HYP: o n i j u u n i ******* * n o s o o s a N i n Y o t o o * U N u s h U n a k e r e W a n a r a n a i t o k i t ******* * e s h i m a s h i t a +>> Eval: D D I I I I D S S S S D D + +Speaker sentences 133: fleurs_jpn_000365 #utts: 1 +id: (fleurs_jpn_000365-fleurs_jpn_000365) +Scores: (#C #S #D #I) 155 13 26 4 +REF: p i i e I c h i P A U R e B E r U w A P a U k e n s a s h i t A k a G A k u b u C L s h I T s U N i F u k u M a r E R U s u i s o i ******* * O n p i i e i c h i n O e i c h i n o ******* * r y o o d e s h i m E s a r e m A s u +HYP: p i i e ******* * c h i ******* * * * D e R I r O w ******* * * a * k e n s a s h i t ******* * k a K O k u b u ******* * * s h ******* * * s ******* * E i ******* * u k u G a r ******* * A N s u i s o i Y U n p i i e i c h i n ******* * e i c h i n o R r y o o d e s h i m A s a r e m E s u +Eval: D D D D D D S S S S D D D D D D S S D D D D D D D D S D D S D D S S I I S D D I I S S + +Speaker sentences 134: fleurs_jpn_000366 #utts: 1 +id: (fleurs_jpn_000366-fleurs_jpn_000366) +Scores: (#C #S #D #I) 180 12 19 0 +REF: s o r e d e m o P A U t o o K y O K u k a r A n O a D O b a i s U o u k e p a u s u b e t e n o H Y o o s h i k i o M A M o r i P A U a n z e n j o o n o k e e K o K U n i s a i s h i N n o c h U u i o h a r a i m a +HYP: s o r e d e m o ******* * * * t o o R y ******* * I u k a r U n ******* * a R U b a i s ******* * o u k e p a u s u b e t e n o ******* * * o o s h i k i o O M A o r i ******* * * * a n z e n j o o n o k e e G o G O n i s a i s h i I n o c h ******* * u i o h a r a i m a +Eval: D D D D S D D S S D D S S D D D D D S S S D D D D S S S S D D + +>> REF: s h o o +>> HYP: s h o o +>> Eval: + +Speaker sentences 135: fleurs_jpn_000367 #utts: 1 +id: (fleurs_jpn_000367-fleurs_jpn_000367) +Scores: (#C #S #D #I) 198 13 16 6 +REF: k o r e r a w a t a m a n i k o n Z a t * ******* s u ******* * S U R * U k a * Z o k u m u k e n o b I i c h i D e P A U k a i g a N n i W A s a m a z a m a n a t e n p o G A n a r a n d e i m a s U P A u a n z e N n i o Y O G +HYP: k o r e r a w a t a m a n i k o n G a t S s u R O K A C L k a T S o k u m u k e n o b ******* * i c h i T e ******* * * * k a i g a I n i ******* * ******* * s a m a z a m a n a t e n p o W O n a r a n d e i m a s ******* * * * u a n z e ******* * n i o O E B +Eval: S I I I I S S S I S I S D D S D D D D S D D D D S S D D D D D D S S S + +>> REF: u k o t o g a d e k i m a s u +>> HYP: u k o t o g a d e k i m a s u +>> Eval: + +Speaker sentences 136: fleurs_jpn_000368 #utts: 1 +id: (fleurs_jpn_000368-fleurs_jpn_000368) +Scores: (#C #S #D #I) 195 25 79 3 +REF: s h i n N o P A U m i e n a i c h I i M U P A U E R u E E a a R U E s U O o E n U p a u ******* * a n d o P A U E r U E E E F U E E E s u t I I O o P A U s e n k y U u H Y A k U H a c h i j U u k Y U u p A U P I i H y a k u k y u u n o s +HYP: s h i n D o ******* * * * m i e n a i c h ******* * i ******* * ******* * ******* * * * ******* * ******* * u ******* * D a a ******* * ******* * ******* * s ******* * ******* * o ******* * n O p a u N a n d o ******* * * * ******* * r ******* * ******* * ******* * ******* * ******* * ******* * A S A s u t ******* * ******* * ******* * o ******* * * * s e n k y E u ******* * * ******* * k ******* * ******* * a c h i j ******* * u k * I u p E E J i K y a k u k y u u n o s +Eval: S D D D D D D D D D D D D D D D D D D D D S D D D D D D D D D D D D S I I D D D D D D D D D D D D D D D D D D S S S D D D D D D D D D D S D D D D D D D D D D D D S S S S S S + +>> REF: O n z a i m o M A T A P a U b A a C H A r u c h i I m u n o d o K u * J I n O y O o s o d e a r u +>> HYP: U n z a i m o N O D T * a * b ******* * a * Z E r u c h i E m u n o d o G u S H U n A y ******* * o s o d e a r u +>> Eval: S S S S S D D D D D S S S S I S S S D D + +Speaker sentences 137: fleurs_jpn_000369 #utts: 1 +id: (fleurs_jpn_000369-fleurs_jpn_000369) +Scores: (#C #S #D #I) 216 6 17 0 +REF: k o n o s a a b i s u w A P a U g o r a k u s e n o h a j i m e t o s u r u s e n p a k u y A P a U e n K a k u C h i D e d e e t a y A o n s e E o H I T s u y O o t o s u r u t a n k e n t a i N i h i n p a +HYP: k o n o s a a b i s u w ******* * * a * g o r a k u s e n o h a j i m e t o s u r u s e n p a k u y ******* * * a * e n G a k u S h i R e d e e t a y ******* * o n s e Y o ******* * ******* * * s u y ******* * o t o s u r u t a n k e n t a i R i h i n p a +Eval: D D D D D D D D S S S D D S D D D D D D D S + +>> REF: N n i r i y o o s a r e t e i m a s u +>> HYP: A n i r i y o o s a r e t e i m a s u +>> Eval: S + +Speaker sentences 138: fleurs_jpn_000370 #utts: 1 +id: (fleurs_jpn_000370-fleurs_jpn_000370) +Scores: (#C #S #D #I) 323 13 25 6 +REF: s a k u b a N P A u B U e n o s u a i R e s u k a r a G o j u c l k i r o s a n j U u i c h i m a i r u h a n a r e t a r a p u r a t a s h i n a i d e P A U g e n s h O K U j o o i n g I i n d e ******* * a r u k u r i +HYP: s a k u b a B * * u ******* * E e n o s u a i D e s u k a r a K o j u c l k i r o s a n j ******* * u i c h i m a i r u h a n a r e t a r a p u r a t a s h i n a i d e ******* * * * g e n s h U G O j o o i n g ******* * i n d e W a r u k u r i +Eval: S D D D D S S S D D D D D D S S S D D I I + +>> REF: s u t I i n A P a U f e r ******* * u n a n d e s U P A u d e P A U k i r u H i n a a J o s h i g a D a i t o o r y o o s ******* * e n e n o s h U T s u B a o s e n g e n s h i m a s h i t a +>> HYP: s u t E i n ******* * * a * f e r U u n a n d e s ******* * * * u d e ******* * * * k i r u K i n a a Z o s h i g a N a i t o o r y o o s E e n e n o s h I * s u D a o s e n g e n s h i m a s h i t a +>> Eval: S D D D D I I D D D D D D D D S S S I I S D S + +Speaker sentences 139: fleurs_jpn_000371 #utts: 1 +id: (fleurs_jpn_000371-fleurs_jpn_000371) +Scores: (#C #S #D #I) 195 16 28 4 +REF: o n A J i T S u k ******* * i n i P A U m a s h U h a D o n o k a C L s o o r O D e b E T s u n o r Y O K a K U k i g a k a C L s o o r O o o o b a a r a n s h i p a u k a b e n i g e k i t o ******* * t s u s h i t e J u U S H I C H I n i +HYP: o n ******* * O i * Z u k U i n i ******* * * * m a s h I h a T o n o k a ******* * * s o o r ******* * ******* * e b U * s u n o r * U G a * C L k i g a k a ******* * * s o o r ******* * o o o b a a r a n s h i p a u k a b e n i g e k i t o T t s u s h i t e P A u J * U U * N A n i +Eval: D D S D S I I D D D D S S D D D D D D D S D D S S D S S D D D D D I I S S S D S S D S S + +>> REF: n g a s h i B O o s h i m a s h i t a +>> HYP: n g a s h i ******* * ******* * o s h i m a s h i t a +>> Eval: D D D D + +Speaker sentences 140: fleurs_jpn_000372 #utts: 1 +id: (fleurs_jpn_000372-fleurs_jpn_000372) +Scores: (#C #S #D #I) 215 9 32 0 +REF: h a s h i s h i t a n o K A M I G A T A k U u k a N w a j u u g o m E e t o R U d e s U P A u n i s e n j u u I c h i n e N h a C H i g a t s u n i s h u n k O o s h i p a u n i s e n j u u n a n A N e N s a n g a T s +HYP: h a s h i s h i t a n o ******* * ******* * ******* * J O O H O k ******* * u k a ******* * w a j u u g o m ******* * e t o ******* * O d e s ******* * * * u n i s e n j u u ******* * c h i n e ******* * h a * J i g a t s u n i s h u n k ******* * o s h i p a u n i s e n j u u n a n ******* * I e ******* * s a n g a * s +Eval: D D D D D D S S S S S D D D D D D D D S D D D D D D D D D S D D D D S D D D + +>> REF: u m a D e k a i t s U u s h i m a s e n d e s h i t a +>> HYP: u m a R e k a i t s ******* * u s h i m a s e n d e s h i t a +>> Eval: S D D + +Speaker sentences 141: fleurs_jpn_000373 #utts: 1 +id: (fleurs_jpn_000373-fleurs_jpn_000373) +Scores: (#C #S #D #I) 384 14 72 6 +REF: b u n M e e t o i u k o t o B a w A P a U s h i m i N o I m i s u r u ******* * r a t e n g o n o k E e Y o o s h i s h I I A i b U I A i E r U A i E s u k a r a k i t e ******* * o r i P A U s h i m ******* * i n O I m i s u r u r a t +HYP: b u n N e e t o i u k o t o W a w ******* * * a * s h i m i I o ******* * m i s u r u A r a t e n g o n o k I e ******* * o o s h i s h ******* * ******* * ******* * i b ******* * ******* * ******* * i ******* * r ******* * D i ******* * s u k a r a k i t e W o r i ******* * * * s h i m E i n I O m i s u r u r a t +Eval: S S D D D D S D D I I S D D D D D D D D D D D D D D D D D D S D D I I D D D D I I S S + +>> REF: e n G o N o m e e s h i s h I I A i b U I A i E s U P A u t o s h i y a t o s h i k o c l k a o I m i s h i P A U n a n R a k a n o k a t a c h i D e s h a k a i n o k i b o o t e e g i s u r u s h I I A I B u i A i t +>> HYP: e n K o R o m e e s h i s h ******* * ******* * ******* * i b ******* * ******* * ******* * i ******* * s ******* * * * u t o s h i y a t o s h i k o c l k a o ******* * m i s h i ******* * * * n a n N a k a n o k a t a c h i R e s h a k a i n o k i b o o t e e g i s u r u s h ******* * ******* * ******* * ******* * ******* * u i B i t +>> Eval: S S D D D D D D D D D D D D D D D D D D D D D D D D S S D D D D D D D D D D S + +>> REF: I I E E E s u t o I u m e e s h i n i k a n k e e s h i t e i m a s u +>> HYP: ******* * ******* * ******* * ******* * A s u t o Y u m e e s h i n i k a n k e e s h i t e i m a s u +>> Eval: D D D D D D D D S S + +Speaker sentences 142: fleurs_jpn_000374 #utts: 1 +id: (fleurs_jpn_000374-fleurs_jpn_000374) +Scores: (#C #S #D #I) 218 13 19 6 +REF: t s u u j o o P A U k o k o D e w a i T s u m O k a n k O o K Y A k u y a G y o o s h a t a C h i G a h a C L s u r U o t o G a * K i k o e t e k i m a s u P A U o t o t o * h ******* * i k a r i g a o R i n a s u m O n o g a t +HYP: t s u u j o o ******* * * * k o k o R e w a i * s u m A k a n k ******* * o * T E k u y a R y o o s h a t a S h i K a h a ******* * * s u r ******* * o t o K a C H i k o e t e k i m a s u * * W o t o t o S h I i k a r i g a o ******* * i n a s u m U n o g a t +Eval: D D D D S D S D D D S S S S S D D D D D S I S D D S I I I D D S + +>> REF: a r i W a m a r u d E e h o n n ******* * o y o o D e s u +>> HYP: a r i U a m a r u d ******* * e h o n n A o y o o R e s u +>> Eval: S D D I I S + +Speaker sentences 143: fleurs_jpn_000375 #utts: 1 +id: (fleurs_jpn_000375-fleurs_jpn_000375) +Scores: (#C #S #D #I) 95 4 19 6 +REF: t e r e b i n o h o o d o o n i Y o R U T o p a u g e n p a t s u k a r a S H I R O k E M u R I g A a g a c l t e i m a s u ******* * ******* * ******* * +HYP: t e r e b i n o h o o d o o n i ******* * o ******* * N D o p a u g e n p a t s u k a r a ******* * * ******* * ******* * A k ******* * ******* * u ******* * E g ******* * a g a c l t e i m a s u I D E +Eval: D D D D S S D D D D D D D S D D D D D D S D D I I I I I I + +Speaker sentences 144: fleurs_jpn_000376 #utts: 1 +id: (fleurs_jpn_000376-fleurs_jpn_000376) +Scores: (#C #S #D #I) 139 3 5 4 +REF: n o o b Y o o r i t o k o o d o o n o s o o k a n k a n k e e w A P a U k a G a k u s h a t a C h i n o k e n k y u u ******* * ******* * o u r a z u k e r U m o n o d e s u +HYP: n o o b * o o r i t o k o o d o o n o s o o k a n k a n k e e w ******* * * a * k a Y a k u s h a t a S h i n o k e n k y u u W A o u r a z u k e r E m o n o d e s u +Eval: D D D D D S S I I I I S + +Speaker sentences 145: fleurs_jpn_000377 #utts: 1 +id: (fleurs_jpn_000377-fleurs_jpn_000377) +Scores: (#C #S #D #I) 136 8 21 0 +REF: s u i y o o b i n O i b e n t o n O a t o P A U k a r u p a n e D o w a s e n s h U k e n D E F u T a T s u n O k o j i n R E e s U N i S H U T s u j o o s h i m a s h i t a +HYP: s u i y o o b i n ******* * i b e n t o n ******* * a t o ******* * * * k a r u p a n e R o w a s e n s h I k e n ******* * G O u K a * s u n U k o j i n ******* * D e s ******* * ******* * i ******* * * I * s u j o o s h i m a s h i t a +Eval: D D D D D D D D S S D D S S S D S D D S D D D D D D D S D + +Speaker sentences 146: fleurs_jpn_000378 #utts: 1 +id: (fleurs_jpn_000378-fleurs_jpn_000378) +Scores: (#C #S #D #I) 202 8 18 1 +REF: s e n h a C L p Y A k u n e n d a I i r a i P A U g u n t a i g a t o o C H a k U s u r U M a D e h a i c h i w a k o n o b Y O o k i n i k a n k * e e s u r U M o n d a i n i s o o g U u s h i t a k o t o w A a r +HYP: s e n h a ******* * * p * E k u n e n d a ******* * i r a i ******* * * * g u n t a i g a t o o * J a k ******* * s u r E W a R e h a i c h i w a k o n o b * Y o k i n i k a n k Y e e s u r E B o n d a i n i s o o g ******* * u s h i t a k o t o w ******* * a r +Eval: D D D D S D D D D D D D S D D S S S D S I S S D D D D + +>> REF: i m a s e n d e s h i t a +>> HYP: i m a s e n d e s h i t a +>> Eval: + +Speaker sentences 147: fleurs_jpn_000379 #utts: 1 +id: (fleurs_jpn_000379-fleurs_jpn_000379) +Scores: (#C #S #D #I) 176 12 22 1 +REF: s h i k a s h i * P A U k y a P u t e N n o W i k e c l T o o U s h I n a c l T A a t o P A U i n d o W a n a n a t s u n O W i k e c l t o o U s h i n a i P A U s a n j U U r o k u r a N s h i k a D e k i m a s e n d e s h i +HYP: s h i k a s h i C L K k y a K u t e ******* * n o B i k e c l D o o ******* * s h U n a c l ******* * D a t o ******* * * * i n d o ******* * a n a n a t s u n E B i k e c l t o o ******* * s h i n a i ******* * * * s a n j ******* * O r o k u r a ******* * s h i k a R e k i m a s e n d e s h i +Eval: I S S S S D D S S D D S D D S D D D D D D S S D D D D D D D D S D D S + +>> REF: t a +>> HYP: t a +>> Eval: + +Speaker sentences 148: fleurs_jpn_000380 #utts: 1 +id: (fleurs_jpn_000380-fleurs_jpn_000380) +Scores: (#C #S #D #I) 216 4 17 4 +REF: k a j i n o d e w a t s u u j o o P A U t o k u b e t s u n a i n s h O k U y a e n t a a t e i m e n t o o y o o i s h i t e i m a s U P A u g e s U T o g a k i b u N y O k u s h i s e T s u n a i n i t ******* * ******* * o m a r +HYP: k a j i n o d e w a t s u u j o o ******* * * * t o k u b e t s u n a i n s h A k O y a e n t a a t e i m e n t o o y o o i s h i t e i m a s ******* * * * u g e s ******* * ******* * o g a k i b u ******* * y A k u s h i s e * s u n a i n i t O R o m a r +Eval: D D D D S S D D D D D D D D D D S D I I I I + +>> REF: u y O o n i s u r u t a m e D e s u +>> HYP: u y ******* * o n i s u r u t a m e R e s u +>> Eval: D D S + +Speaker sentences 149: fleurs_jpn_000381 #utts: 1 +id: (fleurs_jpn_000381-fleurs_jpn_000381) +Scores: (#C #S #D #I) 171 6 34 4 +REF: s o r e d e M o P A U t o o k y O K u k a r a n O a D O b a i s U o U K e p a u s u b e t e n o h y o ******* * O s h i k I O m a M o r i P A U a n z e n j O o n o k E e k o K U n i s a i s h ******* * i n n o c h U u i o h a r a i +HYP: s o r e d e ******* * o ******* * * * t o o k y ******* * I u k a r a n A a R A b a i s ******* * o ******* * G e p a u s u b e t e n o h y o W A s h i k ******* * ******* * m a ******* * o r i ******* * * * a n z e n j ******* * o n o k ******* * e k o ******* * ******* * n i s a i s h I i n n o c h ******* * u i o h a r a i +Eval: D D D D D D D D S S S S D D D D S I I S D D D D D D D D D D D D D D D D D D I I D D + +>> REF: m a s h O o +>> HYP: m a s h ******* * o +>> Eval: D D + +Speaker sentences 150: fleurs_jpn_000382 #utts: 1 +id: (fleurs_jpn_000382-fleurs_jpn_000382) +Scores: (#C #S #D #I) 128 5 18 3 +REF: o w A k a r E D e w A a r i m a s e * * N k o r E W a * h i t o t s u N o s h O O N o o w a r i d e a r i p a u a t a r a s h I i s h o o n o m A k U a k e d e s u +HYP: o w O k a r U G e w ******* * a r i m a s e P A U k o r ******* * ******* * a S h i t o t s u ******* * o s h ******* * ******* * ******* * o o w a r i d e a r i p a u a t a r a s h ******* * i s h o o n o m O k ******* * a k e d e s u +Eval: S S S D D I I S D D D D I D D D D D D D D D D S D D + +Speaker sentences 151: fleurs_jpn_000383 #utts: 1 +id: (fleurs_jpn_000383-fleurs_jpn_000383) +Scores: (#C #S #D #I) 213 7 11 2 +REF: s a F a r i t o w A P a U a f u r i k a n o y a s e e d o o B u T S u p a u t o k U n i s a B a n n a n I i r u y a s e ******* * e d o o B u t s u n o k a n s a T s u O m o k u t e k i t o s h i t a r i k u r o d e n o +HYP: s a W a r i t o w ******* * * a * a f u r i k a n o y a s e e d o o G u * Z u p a u t o k O n i s a W a n n a n ******* * i r u y a s e R e d o o G u t s u n o k a n s a * s u A m o k u t e k i t o s h i t a r i k u r o d e n o +Eval: S D D D D S D S S S D D I I S D S + +>> REF: R y o k O o o s a s h i m a s u +>> HYP: * y o k ******* * o o s a s h i m a s u +>> Eval: D D D + +Speaker sentences 152: fleurs_jpn_000384 #utts: 1 +id: (fleurs_jpn_000384-fleurs_jpn_000384) +Scores: (#C #S #D #I) 258 17 56 1 +REF: F u y u n I k i t a b a r u t o k a I o O o d a n s u r U b A a i w A P a U s e N S H i T s u N O i c h i O k a k u n i N s h I T e k u d a s a i P A U k o o r I n o n a k A o t s U k i * s U s u M u s a i n i m o +HYP: * ******* u y u n E k i t a b a r u t o k a Y o M o d a n s u r O b ******* * a i w ******* * * a * s e ******* * ******* * * i * s u ******* * ******* * i c h i ******* * k a k u n i ******* * s h ******* * S e k u d a s a i K I k o o r E n o n a k ******* * o t s I k i T s ******* * s u ******* * u s a i n i m o +Eval: D D S S S S D D D D D D D D D D D D D D D D D D D D D D S S S S S D D S I D D D D + +>> REF: c l t o m O E e k y O o o U k e r u s e N S H i t s u d e W A o s o r O S H I I H O D o n o s O o o N g a n a r i H i b i k i M a s u +>> HYP: c l t o m ******* * ******* * e k y ******* * o o ******* * k e r u s e ******* * ******* * * i t s u d e ******* * ******* * o s o r ******* * ******* * * U J U U M o n o s ******* * o o ******* * g a n a r i K i b i k i G a s u +>> Eval: D D D D D D D D D D D D D D D D D D D D D D S S S S S D D D D S S + +Speaker sentences 153: fleurs_jpn_000385 #utts: 1 +id: (fleurs_jpn_000385-fleurs_jpn_000385) +Scores: (#C #S #D #I) 241 22 22 6 +REF: k o k o w a I G i r i s u n o s h o k u m i n C H i s h I h a i ******* * s h a ******* * g a j i b u n t a C h i n o r y o o D o t o s h I T a b a s h O n a N o ******* * d e p a u s h o k U m i n C H i j i D A I n O s h o o k o o s A G a s o +HYP: k o k o w a ******* * ******* * i r i s u n o s h o k u m i n ******* * * i s h E h a i E s h a E g a j i b u n t a S h i n o r y o o R o t o s h ******* * ******* * a b a s h U n a R o U d e p a u s h o k O m i n * T i j i ******* * R E n I s h o o k o o s E K a s o +Eval: D D D D D D D S I I I I S S D D D D S S I I S D S D D S S S S S + +>> REF: o t o s u r U H O O W A P a U k o k o K a r A h a j i M e r U n O g a y o i D E s h o o +>> HYP: o t o s u r E K A T A W * a * k o k o ******* * a r ******* * h a j i B e r E n A g a y o i ******* * U s h o o +>> Eval: S S S S S S D D D D D D S S S D D S + +Speaker sentences 154: fleurs_jpn_000386 #utts: 1 +id: (fleurs_jpn_000386-fleurs_jpn_000386) +Scores: (#C #S #D #I) 215 17 21 0 +REF: E B I S U s h i w A P a U s a k u g e n s u r u s u u c h I o s a D a M E M a s e n d e s h i t a G a p a u s a k u g e n w a c h U U G o k u n o k e e z a i s a n s h U T s u R y o U n i m o t o z u i t e j i C L s h i +HYP: * ******* * ******* * ******* K O s h i w ******* * * a * s a k u g e n s u r u s u u c h O o s a R a Y A W a s e n d e s h i t a K a p a u s a k u g e n w a c h I I W o k u n o k e e z a i s a n s h I * s u * y o ******* * n i m o t o z u i t e j i ******* * * s h i +Eval: D D D D D D S S D D D D S S S S S S S S S S D D D D D D D + +>> REF: s a R E r u d a r o O T O n o B E m a s h i t a +>> HYP: s a ******* * ******* * r u d a r o T O N n o O I m a s h i t a +>> Eval: D D D D S S S S S + +Speaker sentences 155: fleurs_jpn_000387 #utts: 1 +id: (fleurs_jpn_000387-fleurs_jpn_000387) +Scores: (#C #S #D #I) 224 5 18 8 +REF: s a ******* * i n Y u u k O k u s h o c l k u w a s h i n k o n r y o k o o n o j i k i g a s u k u n a i ******* * * * k a r u ******* * c h a a s h o c l k u y o r I m o h a Y A k U o t o z u r e P A U n a G a b i k i P A U y o r i s h o o j O o +HYP: s a U i n * u u k U k u s h o c l k u w a s h i n k o n r y o k o o n o j i k i g a s u k u n a i P A U k a r u E c h a a s h o c l k u y o r E m o h a ******* * E k O o t o z u r e ******* * * * n a W a b i k i ******* * * * y o r i s h o o j ******* * o +Eval: I I D S I I I I I I S D D S S D D D D S D D D D D D + +>> REF: g A a C L k a s u r u k o t o g a a r i m a s u +>> HYP: g ******* * a ******* * * k a s u r u k o t o g a a r i m a s u +>> Eval: D D D D D + +Speaker sentences 156: fleurs_jpn_000388 #utts: 1 +id: (fleurs_jpn_000388-fleurs_jpn_000388) +Scores: (#C #S #D #I) 241 13 12 8 +REF: k * i n o o n o a s A P a U t o r u k o n o g a j i a n t e c l p u n o k e e s a T s u ******* * h o n B U D e j i d o o s h a b a k u D a N n o b a k U H a T s U N i y o r i p a u k * e e k a n f u t a r I g a s h i b O o +HYP: k Y i n o o n o a s ******* * * a * t o r u k o n o g a j i a n t e c l p u n o k e e s a * s u O h o n W O R e j i d o o s h a b a k u R a ******* * n o b a k ******* * A a * s O R i y o r i p a u k Y e e k a n f u t a r E g a s h i b ******* * o +Eval: I D D D D D I I S S S S D D D D S D S S I S D D + +>> REF: s h i p a u F U s h o ******* * O s h a w a n i j u u n i N o k o ******* * E m a s h i t a +>> HYP: s h i p a u H O s h o W A s h a w a n i j u u n i Y o k o A I m a s h i t a +>> Eval: S S I I S S I I S + +Speaker sentences 157: fleurs_jpn_000389 #utts: 1 +id: (fleurs_jpn_000389-fleurs_jpn_000389) +Scores: (#C #S #D #I) 170 3 4 11 +REF: s h o k u b u t s u W a n i n g E n g a s u u s a n S o o t s u k u r i P A U n i n g e n g ******* * ******* * a ******* * * ******* * ******* * i k i t o s h i t e h a k i d a s u n i s a n k a t a n s o o t o r i k o n d e i m a s u +HYP: s h o k u b u t s u D a n i n g I n g a s u u s a n Z o o t s u k u r i ******* * * * n i n g e n g A K a C L K O i k i t o s h i t e h a k i d a s u n i s a n k a t a n s o o t o r i k o n d e i m a s u +Eval: S S S D D D D I I I I I I I I I I I + +Speaker sentences 158: fleurs_jpn_000390 #utts: 1 +id: (fleurs_jpn_000390-fleurs_jpn_000390) +Scores: (#C #S #D #I) 176 7 15 2 +REF: s e n p a k u D e B U C L s h i o Y U s o o s u r u n o w A P a U u m i o k o e t e h i t o y a b u C L s h i o t a ******* * i r y o o y U s o o s u r u m o c l t o m o k o o r i T s U T e k i n a h o o h o o D e s u +HYP: s e n p a k u R e E B * U s h i o ******* * I s o o s u r u n o w ******* * * a * u m i o k o e t e h i t o y a b u ******* * * s h i o t a R i r y o o y I s o o s u r u m o c l t o m o k o o r i * s ******* * ******* * e k i n a h o o h o o R e s u +Eval: S S S D S D D S D D D D D D D I I S D D D D D S + +Speaker sentences 159: fleurs_jpn_000391 #utts: 1 +id: (fleurs_jpn_000391-fleurs_jpn_000391) +Scores: (#C #S #D #I) 265 7 12 8 +REF: k a r i F o r u n i a s h u u n o a ******* * a n o r u d o P A U s h u w a r u t s E n e c l G a a c h i * J i w A P a U b o o r ******* y o k u t e k i n a b i d e o g e e m u o m i s e e n e n s h a n i h a n b a I y a r e n t a +HYP: k a r i H o r u n i a s h u u n o a W a n o r u d o ******* * * * s h u w a r u t s U n e c l K a a c h i S H i w ******* * * a * b o o r y o k u t e k i n a b i d e o g e e m u o m i s e e n e n s h a n i h a n b a ******* * y a r e n t a ******* +Eval: S I I D D D D S S I S D D D D I D D D + +>> REF: R U s u r ******* * u k o t o o k i n s h i s u r u h o o A N n i s h ******* * o m e e s h i m a s h i t a +>> HYP: * E s u r E u k o t o o k i n s h i s u r u h o o W A n i s h A o m e e s h i m a s h i t a +>> Eval: D S I I S S I I + + diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/hyp.trn b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/hyp.trn new file mode 100644 index 0000000000000000000000000000000000000000..dc73236a47cad8158bfe9b2beba2985d508737e4 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/hyp.trn @@ -0,0 +1,160 @@ +k a k o t o m i r a e t o d o g u j u N t e k i j i k o d o o i ts u n a r u g a y o e n i i sh I k i t e k i n a n o d e a r u (cv_jpn_000800-cv_jpn_000800) +s e k a y o k e e s e e s u r u t o t o m n i j i k o j i sh i N y o k e e s e s e r u s o o o t e k i s e k a i n o s o o z o o t e k i y o o s o t o sh I t e pau k o b u ts u g a k o b u ts u d e a r u (cv_jpn_000801-cv_jpn_000801) +p a z o k o N d e g e e m i a r u I t o n o h f u i t e k i t e (cv_jpn_000802-cv_jpn_000802) +k a N a k u n o sh i m e s a a t a r a sh i j i j i u ts u a t a r a sh i i k a N n e N k a N ky o sh i h a i n w a t a r a sh i i k a n o o s e o m o cl t e pau n a n i h a j i m e r u k a w a (cv_jpn_000803-cv_jpn_000803) +o m o sh i r o u n o n i pau r o o t o n a g a s u g i t e d a r u i (cv_jpn_000804-cv_jpn_000804) +k o r e j o o sh u u h a N p o i n a (cv_jpn_000805-cv_jpn_000805) +k a g a k U sh a m o s e k a i o h o o k a ts s e k i n i t o o i ch i t e k i n i s a ts u m e sh u o t o sh I t e i r u (cv_jpn_000806-cv_jpn_000806) +f U ts u u n i ts u m a r a N (cv_jpn_000807-cv_jpn_000807) +sh I cl k a r i I t e k u d a s a i (cv_jpn_000808-cv_jpn_000808) +w a t a sh i w a a m i g i n o n o t o k i d e k I sh I t e k I s e i m e e n o j i k a k u t o i u g o t o k i m o n o b e N sh o o h o o t e k i r o N b i t o y u u n o d e a r u (cv_jpn_000809-cv_jpn_000809) +w a t a sh i w a pau sh a k a i k e e s e e n o k o N t e e n i w a pau d e y o o n u s o s u t e k i n a m o n o g a h a t a r a i t e i r u t o m o (cv_jpn_000810-cv_jpn_000810) +n a n i o s u r u ts u m o r i d a cl t a n o k a (cv_jpn_000811-cv_jpn_000811) +k o b u ts u t e k I t a g a j i k o h I t e e t e k i n i t a N n i t e N sh u u g o o t e k i n i k a N g a e r a r u r u t o k i s o r e g a b u z u r i t e k I t e k a i d e a r u (cv_jpn_000812-cv_jpn_000812) +a n e g a z u cl t a n o d e a pau y a cl k i n o sh i r a a i g a a r i m a s e N d e sh I t a (cv_jpn_000813-cv_jpn_000813) +k o r e w a r i h o N d e u t e i n a i t a b e m o n o d e s U (cv_jpn_000814-cv_jpn_000814) +w a t a sh i w a h e N sh u u i n o y o n e N k u r a e w a y a cl t a cl t o o m o (cv_jpn_000815-cv_jpn_000815) +i s a N n i k o n o k o t o b a n o i m i y o o o o sh i a m a sh I t a (cv_jpn_000816-cv_jpn_000816) +k a s e g a ts U s u y o i h i w a t e n i s u g a d e k i m a s e N (cv_jpn_000817-cv_jpn_000817) +i ch i (cv_jpn_000818-cv_jpn_000818) +h a i (cv_jpn_000819-cv_jpn_000819) +n i (cv_jpn_000820-cv_jpn_000820) +d e i (cv_jpn_000821-cv_jpn_000821) +t o k i (cv_jpn_000822-cv_jpn_000822) +m i r u t o i u k o t o t o pau h a t a r a k U t o i u k o t o g a pau s U k a b u N r i t e k i n a k e r e b a n a r a n a i (cv_jpn_000823-cv_jpn_000823) +w a r e w a r e o o t a m a sh i i n o z u k o k a r a g u g a s u m o n o d e n a k e r e b a n a r a n a i (cv_jpn_000824-cv_jpn_000824) +z e cl t a i b e N sh o o h o o t e k i n a r u g a i u e n i i d e a t e k I ch o cl k a N t e k I k e e k i g a cl U k u m a r e r u n o d e a r u (cv_jpn_000825-cv_jpn_000825) +d o k o m a d e m o t a t o i ch i t o n o s o o g o h I t e e t e k i n a z e cl t a i m u j u N t e k i j i k o d o i ts u n o s e k a i n i sh I t e (cv_jpn_000826-cv_jpn_000826) +sh I k a r u n i n i N g e N t o k a N ky o o t o r o k a N k e e w a m o t o k o o i n o k a N ky e e d e a r i (cv_jpn_000827-cv_jpn_000827) +i i s a n i k o o n o k o t o b a n o i m i y o o sh i a m a sh I t a (cv_jpn_000828-cv_jpn_000828) +g e k i g a n a n a ts s a r i m a s U (cv_jpn_000829-cv_jpn_000829) +k o ch i r a b a k o b u y a sh i i s a N d e s U (cv_jpn_000830-cv_jpn_000830) +m o sh i i m a sh i (cv_jpn_000831-cv_jpn_000831) +k o k o w a o k i k U t e n i k u y a k a n a m a ch i d e s U (cv_jpn_000832-cv_jpn_000832) +s o n o u ch i k a i y a k U s a r e r u k a r a i s o g e (cv_jpn_000833-cv_jpn_000833) +a m a s a g a f u s a i r a r e t e t e ch o o d o i (cv_jpn_000834-cv_jpn_000834) +h o g e N sh I ts u n o d o o a a k e t a (cv_jpn_000835-cv_jpn_000835) +m o d a N n i o o w a cl t e m o k i n i sh i n a i (cv_jpn_000836-cv_jpn_000836) +a r i g a cl t a y a (cv_jpn_000837-cv_jpn_000837) +i t o o g a r a k u d a t o j i k a N w a o s u r e t e t a n o sh i m e r u (cv_jpn_000838-cv_jpn_000838) +k a k a k u w a g i z u ts U k a s a r e r u n i o j i t e j o o sh I k i n o u ch i n i h a i cl t e i u k u (cv_jpn_000839-cv_jpn_000839) +sh I k a sh I t o k i g a k a o n i h a i r u k o t o s o n o k o t o g a pau m i r a a y o o m u k o t o d e a r i w a r a t a n a r u i cl s u t a i g a d e t e k u r u k o t o d e a r u (cv_jpn_000840-cv_jpn_000840) +t e r e b i o k a i k a i t e pau t e r e b i o m i r u j i k a N g a f u e t a (cv_jpn_000841-cv_jpn_000841) +k a k a r i sh I t a i n o m i i ts u m a d e m o i k i r u n o d e a r u (cv_jpn_000842-cv_jpn_000842) +n i N k i d a a m e N i y a n i g a r a N d a r a n i j i k a N m a ch i d a cl t a (cv_jpn_000843-cv_jpn_000843) +s o r e o m o ch i i r u n i N g e N n o i y o k u n i i t o N sh i s o sh I t e k o r e w a k a r e n o m o cl t e i r u k a ch i n o sh a k u d o n i i t o N s u r u (cv_jpn_000844-cv_jpn_000844) +m a w a r u i o w a m i N n a k a N g a e r u k o t o o y a m e t e i t a (cv_jpn_000845-cv_jpn_000845) +k o o i t e k i ch o k cl U k a N t e k i n i s e k a y o m i r u t o i u k o t o w a j a k u n i k o o i t e k i ch o cl k a N t e k i n i s e k a y o k e e s e e s u r u k o t o o h k u m u n o d e a r u (cv_jpn_000846-cv_jpn_000846) +sh i N cl p a i t a k e s a s e m a i t o s u r u k i z u k a i g a y o k e e n i sh i N p a i s a s e t e sh i m a u (cv_jpn_000847-cv_jpn_000847) +k o n o m i ch i w a t o t e m o s e m a i n o d e a b n a i d e s U (cv_jpn_000848-cv_jpn_000848) +w o h o e g a a r i n i (cv_jpn_000849-cv_jpn_000849) +t o i w a r o o k a m o i t a r i g a a n i a r i m a s U (cv_jpn_000850-cv_jpn_000850) +t a n a k a s a N n o h i t a i n i k i m u r a s a N g a i m a s U (cv_jpn_000851-cv_jpn_000851) +m a cl k u r a n o t a m a b o t e s u g o i n i e (cv_jpn_000852-cv_jpn_000852) +sh o h o o m i t a i n a d o k U sh u k a N s o o b u N m o k a i t a (cv_jpn_000853-cv_jpn_000853) +g e N j i ts u n o s e k a i w a t a m o o i ch i t o sh I t e k e cl t e s u r a i d a k a t a sh u o a cl t o s e k a i z u n a k e r e m a n a r a n a i (cv_jpn_000854-cv_jpn_000854) +sh o o h i N k e N s a k u g a o k a r e a s u i t o o k a u k i i n a r u n a i (cv_jpn_000855-cv_jpn_000855) +ts i sh I k i w a pau r e k I sh I t e cl k a t e e r d e n a k e r e m a n a r a n a i (cv_jpn_000856-cv_jpn_000856) +m o n o g o t o n N j i N p a N k a e r u d a k e d e u m a k u i k U k o t o m o w a r u (cv_jpn_000857-cv_jpn_000857) +k o n o k i s e e ts u w a k a ts u o n o s a sh i m i g a z e cl p i N (cv_jpn_000858-cv_jpn_000858) +k a k e n i sh i cl p a i sh I t e m o o ch ts U ts u i t e s a N sh I ts u o u k e i d e r u (cv_jpn_000859-cv_jpn_000859) +s o r e y u e n i t e ts u g a p u g a z e N t a i n o g a k o d e a r u t o s u r e w a (cv_jpn_000860-cv_jpn_000860) +k i i z a n a y a o y a d a g a y e s U k U t e h a N j o sh I t e r u (cv_jpn_000861-cv_jpn_000861) +i n i f u r a g a a k i n o h u z e N n i y o ch i i cl t e pau k o k u g a i e d a sh U ts u s u r u h I t o m o d e t e k i t a (cv_jpn_000862-cv_jpn_000862) +ts u g i n i k a g a k u w a s o N z a y o pau sh u j u n o d o o e k i n i w a k a cl t e s o r e z u r a N ry o o u k i N z i t I k e N i k I s u r u (cv_jpn_000863-cv_jpn_000863) +s o r e d e w a pau t o k t o i u m o n o n o s e r i ts u sh i o w a n a k u pau sh u N k a N t o i m o n o m o n a k u a r u n o d e a r u (cv_jpn_000864-cv_jpn_000864) +a k a i b u r a N k o h o N k u r i t o s e e n o s u b e r i d a i k a w a i t a s u n a b a (cv_jpn_000865-cv_jpn_000865) +sh I k a sh i s o r e w a d o k o m a d e m N k U k o k a r a r i t e pau p o k o e k a e r i k u r u s e sh I ts o m o t o m o d e n a k e b a n a r a n a i (cv_jpn_000866-cv_jpn_000866) +a r i t o w a r a i r u d e m o o m a k I ch i r a sh I t e pau m i N n e k a r a u r a m i o k a cl t e r u (cv_jpn_000867-cv_jpn_000867) +k o n o cl t e e d o s a w a g i n i n a r u k o t o m o n a i n o d a r o o (cv_jpn_000868-cv_jpn_000868) +k o n o n e d a N d e w u r i ch a N k a a N (cv_jpn_000869-cv_jpn_000869) +h i n o k a g e N n i ch u i sh i n a i t o s u g u k o g e r u (cv_jpn_000870-cv_jpn_000870) +e N m a N d o w e n i p o ts u r i t o ch i i s a n a a n a g a i t a s a i sh o w a ts u m a y o o j i t e e d o n o ch i i s a n a a n a d a cl t a (cv_jpn_000871-cv_jpn_000871) +s o r e w a pau a r e w a r e o i k a sh i n a g a r a w a r e w a r e o t o r e e k a s u r u n o d e a r u pau w a r e b a r e n o t a m a sh i i o k o r o s u n o d e a r u (cv_jpn_000872-cv_jpn_000872) +r e k I sh I t e k i n i a t a e r a r t a m o n o w a d e cl t a i m u j u N t e k i j i g o t o o i ts u t e k i g e N z a i n o i t e s U k a i h I t e k i n i a t a e r a r e t a m o n o t o sh I t i (cv_jpn_000873-cv_jpn_000873) +m u j u N t e k i j i g o d o o i ts U t o sh I t e pau i ts u m o k o n o s e k a i n i ch o o e sh t k i d e a (cv_jpn_000874-cv_jpn_000874) +y u u n i z e cl t a e m u j u N t e k i j i g o d o o i ts U t o sh I t e g e N s a i k a r a g e N z a e t o b o k i i k u s e k a i n o g e N z a i n o i t e (cv_jpn_000875-cv_jpn_000875) +h a r e pau w o t a N o sh I t o m N d a sh u d e k i n a i (cv_jpn_000876-cv_jpn_000876) +sh I k a sh i w a t a sh i a s o k o n i s e k a i n o j i k o d o o i ts u o k u n o d e w a n a i (cv_jpn_000877-cv_jpn_000877) +n e b e k a k u n a r u n o g a h a y a k u n a cl t a (cv_jpn_000878-cv_jpn_000878) +w a t a sh i w a i N g e N n o o r e k I sh I t e k I k e e s e e n o t a ch i b a k a r a g e j u ts u o m i r u n o d e a cl t e o o sh a k a r a d e N sh a o m i r u n o d e w a n a i (cv_jpn_000879-cv_jpn_000879) +a o i t o m a t o sh I k a n a k U t e k a u k a b m a i y o (cv_jpn_000880-cv_jpn_000880) +s i N k i z u i gy o o n i o o k i n a k I t a y o y o s U t e i r u (cv_jpn_000881-cv_jpn_000881) +n a n i k a sh i r a n o i n i N s e N t i b u w a n a i t o k i b i sh i i n o d e w a (cv_jpn_000882-cv_jpn_000882) +j i k o N sh e e g e N n o i b e N t o d e s U t o r u sh I t a m a r u b u (cv_jpn_000883-cv_jpn_000883) +m a a r i n o h I t o w a b o o z e N t o sh I t e i t a (cv_jpn_000884-cv_jpn_000884) +s o N n a n a i y o o n o m e e r e w a n a N k e N m o k U I t e i t a (cv_jpn_000885-cv_jpn_000885) +n j i k a i d r d e e s u i sh I t e i t a (cv_jpn_000886-cv_jpn_000886) +t o k i d o k i ch i u N n o k o r o w a w a k a r a n a k u n o r u t o k i g a a r u d a k a r a b o k u w a k a n i o cl k i n o o t o n i k a ch i a j i m e r u (cv_jpn_000887-cv_jpn_000887) +m o o n i g e t e ch a t a m e d a (cv_jpn_000888-cv_jpn_000888) +k a r e w a p o o t o t a j i ts U k u sh i t e i t a (cv_jpn_000889-cv_jpn_000889) +d a r a u n i m o m e i w a k o w a k a k e t a k u n a i (cv_jpn_000890-cv_jpn_000890) +w a s a k a t o o m o t e t o w a n o o t o cl t e o n i g e cl t a (cv_jpn_000891-cv_jpn_000891) +sh t e i m a s e N (cv_jpn_000892-cv_jpn_000892) +k a y o o n i sh I t e sh I cl t e i r u t o t o m o n i sh i cl t e i n a i t o k o r o k a r a t a N ky u u w a h a j i m a r u n o d e a r u (cv_jpn_000893-cv_jpn_000893) +a i s a u w a d a i j i d a i o (cv_jpn_000894-cv_jpn_000894) +t o j i t a m o n o i k a n i h i r o g e t e m o h i r a i t a m o n o n i a n a r a r o t o i cl t e i r u g a (cv_jpn_000895-cv_jpn_000895) +t a m e sh i n i i k u ts U k a ts U k u cl t e m i y o a (cv_jpn_000896-cv_jpn_000896) +z u i b u N a k o g i n a sh o o b a i d a i o n a (cv_jpn_000897-cv_jpn_000897) +w a t e i (cv_jpn_000898-cv_jpn_000898) +i ch i (cv_jpn_000899-cv_jpn_000899) +g o (cv_jpn_000900-cv_jpn_000900) +sh i k i (cv_jpn_000901-cv_jpn_000901) +i i e (cv_jpn_000902-cv_jpn_000902) +h a ch i (cv_jpn_000903-cv_jpn_000903) +n e i (cv_jpn_000904-cv_jpn_000904) +sh i i (cv_jpn_000905-cv_jpn_000905) +k u (cv_jpn_000906-cv_jpn_000906) +i ch i (cv_jpn_000907-cv_jpn_000907) +k a k a k u g a a k i r a k a n i s u r u pau ky a cl k a N t e k I sh i N r i n i sh I t a g a u k o t o n i y o cl t e (cv_jpn_000908-cv_jpn_000908) +k a k o t o m i r a i t o n o m u j u N t e k i j i k o d o o i ts u t o sh I t e n o g e N z a i g a pau k a t a ch i o m o sh U t o i u k o t o d e a r u (cv_jpn_000909-cv_jpn_000909) +b u ts u r i t e k I s e k a i w a pau s u u g a k u t e k I k i g o o n i y o cl t e a r a w a s a r e r u pau s u u g a k u t e k I k a t a ch i n o sh e k a i d e a r u (cv_jpn_000910-cv_jpn_000910) +w o n a j i g e N sh o o d e s a N k o o g i n a r u (cv_jpn_000911-cv_jpn_000911) +g a i k o k u k a r a k i t a m o n o d a t o sh i cl t e b i cl k u r i (cv_jpn_000912-cv_jpn_000912) +i w a y o r u j i cl s e N n i y o cl t e k a k U t o U k U sh i k i t a cl t a m o n o d e a r u (cv_jpn_000913-cv_jpn_000913) +o n a j i y o n i d a N s e e w a h i z a o o u z u b o N o h a cl k o t o g a pau g i m u u z u k e r a r e t e i m a s U (fleurs_jpn_000346-fleurs_jpn_000346) +k o n o s a a b i s a k o r a k U s e e w a h a j i m e t o s u r u s e N p a k u y a e N k a k U ch i d e pau d e e t a y a o N s e o h I s u o t o s u r u t a N g e N t a i n i pau h i N cl p a n i r i y o o s a r e t e i m a s U (fleurs_jpn_000347-fleurs_jpn_000347) +ky o o f u u hy o k a t o n o k o o s u i r o o o b i a m a k a sh i b a r a i u t a ts u m a k i i z u k i o b e s a i k u r n o d a n o k i b i sh i i k I sh o k e e t a y a s o n o e e ky o n i y o r u m o n o r e s U (fleurs_jpn_000348-fleurs_jpn_000348) +i N t a a n e cl t o w a m a s U k o m i r i k e e sh u N t o t a i j i N k o m i r u i k e e sh o o n o ry o o y o s o o k a n e s u n a e t a k a N ky o o d e s U (fleurs_jpn_000349-fleurs_jpn_000349) +k a j i n o r e w a ts u u j o o t o k u b e s u r a i N sh I k o y a e N t a a t e i m e N t o y o o sh I t e i m a s U g e s u w a k i b u i y o k u sh I s e s u n a i n i t o r a m a r u y o o r i s u r u t a m e d e s U (fleurs_jpn_000350-fleurs_jpn_000350) +sh I k a sh i pau k a k u t e N n o b i k e cl t o o sh i n a cl t a a d t o i N d o w a n a n a ts u n o b i k e cl t o o sh i n a i s a N j u u r o k u r a sh I k a d e k i m a s e N d e sh i t a (fleurs_jpn_000351-fleurs_jpn_000351) +h o o k u r a N d o n o k o o sh I k e ts u k a w a h o o k u r a N d o sh o t o o k o N d o e f U k e e p i i d e i ch i p o N n o g a i ch i e e b o N d o j i i p i i b i i t o t o o k a n i k o t e e s a r e t e i m a s U (fleurs_jpn_000352-fleurs_jpn_000352) +h a sh I sh I t a N n o j o o h o o k o k a N w o ch j u u g o m e t o r o d e s U n i s e N j u i ch i n e h a ch i g a ts u n i s e k o o sh i pau n i s e N j u u n o n e N n i s a N g a ts u m a n e k a i ts u e sh i m a s e N g d e sh I t a (fleurs_jpn_000353-fleurs_jpn_000353) +i cl p u N k a N d e h f u cl t o s o g u ch i i k i m a r e b a f u cl t o o s u r u m a d e m i n a N m o k a k a r u ch i i k i m o a r i m a s U (fleurs_jpn_000354-fleurs_jpn_000354) +p i r a m i cl t a n o o t o t o sh i k a i n o sh o o w a k o n o k a N k o o sh i d e t o k u n i k o r o m o a t a sh i k a t a n o sh i m e r e m o y o o sh u n o h I t o s u r e s U (fleurs_jpn_000355-fleurs_jpn_000355) +s o n o t a n e t a i n i d a d e r u t o sh I t e h o o k i g a ts u i k a s a r e g a ch i d e s U (fleurs_jpn_000356-fleurs_jpn_000356) +g e N z u N s u r u k o t o g a sh i t a r e t e i r u n i j i o g o o m a i n a t a N d a cl t u p u r o o t u s a i d o w a g e N z u N s u r a t o o g a e b u N k e n o s a i k o n o u sh i d e s U t e g a k i n y o r u g e N p o o w a g e N z o o sh I t e i m o s e N (fleurs_jpn_000357-fleurs_jpn_000357) +k a r e m o s e s o o t a d a sh i t o m i t o m e r u sh I t o m i m a sh I t a g a o o k u g u h I t o o s u n o g e k o d e pau t a i y o o k e d e a t a i o t o s i n o h o k a n o h o sh i g a pau ch I k u u n o m a r e i d o o sh I s e r u t o sh i N ch i t e i m a sh I t a (fleurs_jpn_000358-fleurs_jpn_000358) +ch i b e cl t o m e e s o o ch u sh i N w a sh i i s e e i o g a d e s U s a m a z a n a n a k a m i g a m i y o sh I k a k U k a s u r u k o t o d e e n e r u g i i ch a n e r u g a sh o o k a s a r e ch a k u r a g a k a s e e k a s a r e s a t o r u n e i sh I k e g o o m a r e m a s U (fleurs_jpn_000359-fleurs_jpn_000359) +m i n a m i y a h u r e k a n i y a r u s u b e t e n r u k o k u r i s U k o o e t o d o o y o n i k o n u k o o e n i a m a i n i j i a h o g o sh I t o n i u u e N g u o g o t a k a r i m a s U (fleurs_jpn_000360-fleurs_jpn_000360) +d e cl sh s a cl k u r u n g a s u n u h o k a n o o k u n u k o o ts u sh u d a N g a s u k o k a r u m a r e m a sh I t a (fleurs_jpn_000361-fleurs_jpn_000361) +i N t a a n e cl t o w a m a s U k o m i n i g e e sh o N t o t a i j i N k o m i n i g e e sh a o N n o ry o o y o o s o o k a n i s o n a i e t a k a N ky o o r e s U (fleurs_jpn_000362-fleurs_jpn_000362) +ky o o i N d e w a k a N s e N k a N r i t e j u u sh o N i sh I k a g a i pau k a n i i e n u k a N s e N g o k a n o o s e s e k u t a m e n i k a N j a k a k u r s u r a d a m o m i s o ch o t o cl t e i m u sh I U (fleurs_jpn_000363-fleurs_jpn_000363) +r e N p o o r i k a i w a n i s e N g o n e N d o k a r a w a i s e s u b u s u t o r e sh u m a r i o h o e n o sh I k i N t e e ky o o k a i j i sh i pau e r u b i i a i w a a t a r u z o p o r u n o n i j u u n i n o s o o s a N i N y o t o o u n u sh u n a k e r e w a n a r a n a i t o k I t e sh i m a sh I t a (fleurs_jpn_000364-fleurs_jpn_000364) +p i i e ch i d e r i r o w a k e N s a sh I t k a k o k u b u sh s e i u k u g a r a n s u i s o i y u n p i i e i ch i n e i ch i n o r ry o o d e sh i m a s a r e m e s U (fleurs_jpn_000365-fleurs_jpn_000365) +s o r e d e m o t o o ry i u k a r u n a r u b a i s o u k e pau s u b e t e n o o o sh I k i o o m a o r i a N z e N j o o n o k e e g o g o n i s a i sh i i n o ch u i o h a r a i m a sh o o (fleurs_jpn_000366-fleurs_jpn_000366) +k o r e r a w a t a m a n i k o N g a ts s u r o k a cl k a ts o k u m u k e n o b i ch i t e k a i g a i n i s a m a z a m a n a t e N p o w o n a r a N d e i m a s U a N z e N i o o e b u k o t o g a d e k i m a s U (fleurs_jpn_000367-fleurs_jpn_000367) +sh i N d o m i e n a i ch i u d a a s o N o pau n a N d o r a s a s U t o s e N ky e u k a ch i j u k i u p e e j i ky a k U ky u u n o s u N z a i m o n o d t a b a z e r u ch i e m u n o d o g u sh u n a y o s o d e a r u (fleurs_jpn_000368-fleurs_jpn_000368) +k o n o s a a b i s u w a g o r a k U s e N o h a j i m e t o s u r u s e N p a k u y a e N g a k U sh i r e d e e t a y o N s e y o s u y o t o s u r u t a N k e N t a i r i h i N p a a n i r i y o o s a r e t e i m a s U (fleurs_jpn_000369-fleurs_jpn_000369) +s a k u b a b u e e n o s u a i d e s U k a r a k o j u cl k i r o s a N j u i ch i m a i r u h a n a r e t a r a p u r a t a sh i n a i d e g e N sh u g o j o o i N g i N d e w a r u k u r i s u t e i n a f e r u u n a N d e s u d e k i r u k i n a a z o sh i g a n a i t o o ry o o s e e N e n o sh I s u d a o s e N g e N sh i m a sh I t a (fleurs_jpn_000370-fleurs_jpn_000370) +o n o i z u k u i n i m a sh I h a t o n o k a s o o r e b u s u n o r u g a cl k i g a k a s o o r o o o b a a r a N sh i pau k a b e n i g e k i t o t ts u sh I t e pau j u u n a n i N g a sh i o sh i m a sh I t a (fleurs_jpn_000371-fleurs_jpn_000371) +h a sh I sh I t a n o j o o h o k u k a w a j u u g o m e t o o d e s U n i s e N j u u ch i n e h a j i g a ts u n i sh u N k o sh i pau n i s e N j u u n a n i e s a N g a s u m a r e k a i ts u sh i m a s e N d e sh I t a (fleurs_jpn_000372-fleurs_jpn_000372) +b u N n e e t o i u k o t o w a w a sh i m i i o m i s u r u a r a t e N g o n o k i e o o sh i sh i b i r d i s U k a r a k I t e w o r i sh i m e i N i o m i s u r u r a t e N k o r o m e e sh i sh i b i s U t o sh i y a t o sh I k o cl k a o m i sh i n a N n a k a n o k a t a ch i r e sh a k a i n o k i b o o t e e g i s u r u sh u i b i t a s U t o y u m e e sh i n i k a N k e e sh I t e i m a s U (fleurs_jpn_000373-fleurs_jpn_000373) +ts u u j o o k o k o r e w a i s u m a k a N k o t e k u y a ry o o sh a t a sh i k a h a s u r o t o k a ch i k o e t e k i m a s U w o t o t o sh I i k a r i g a o i n a s u m u n o g a t a r i u a m a r u d e h o N n a o y o o r e s U (fleurs_jpn_000374-fleurs_jpn_000374) +t e r e b i n o h o o d o o n i o N d o pau g e N p a ts U k a r a a k u e g a g a cl t e i m a s U i d e (fleurs_jpn_000375-fleurs_jpn_000375) +n o o b o o r i t o k o o d o o n o s o o k a N k a N k e e w a k a y a k u sh a t a sh i n o k e N ky u u w a o u r a z u k e r e m o n o d e s U (fleurs_jpn_000376-fleurs_jpn_000376) +s u i y o o b i n i b e N t o n a t o k a r u p a n e r o w a s e N sh I k e N g o U k a s u n u k o j i N d e s i I s u j o o sh i m a sh I t a (fleurs_jpn_000377-fleurs_jpn_000377) +s e N h a p e k u n e N d a i r a i g u N t a i g a t o o j a k s u r e w a r e h a i ch i w a k o n o b y o k i n i k a N ky e e s u r e b o N d a i n i s o o g u sh I t a k o t o w a r i m a s e N d e sh I t a (fleurs_jpn_000378-fleurs_jpn_000378) +sh I k a sh i cl k ky a k u t e n o b i k e cl d o o sh u n a cl d a t o i N d o a n a n a ts u n e b i k e cl t o o sh i n a i s a N j o r o k u r a sh I k a r e k i m a s e N d e sh I t a (fleurs_jpn_000379-fleurs_jpn_000379) +k a j i n o d e w a ts u u j o o t o k u b e ts u n a i N sh a k o y a e N t a a t e i m e N t o o y o o i sh I t e i m a s U g e s o g a k i b u y a k u sh i s e s u n a i n i t o r o m a r u y o n i s u r u t a m e r e s U (fleurs_jpn_000380-fleurs_jpn_000380) +s o r e d e o t o o ky i u k a r a n a a r a b a i s o g e pau s u b e t e n o hy o w a sh I k m a o r i a N z e N j o n o k e k o n i s a i sh i i N n o ch u i o h a r a i m a sh o (fleurs_jpn_000381-fleurs_jpn_000381) +o w o k a r u g e w a r i m a s e pau k o r a sh I t o ts u o sh o o w a r i d e a r i pau a t a r a sh i sh o o n o m o k a k e d e s U (fleurs_jpn_000382-fleurs_jpn_000382) +s a w a r i t o w a a f u r i k a n o y a s e e d o o g u z u pau t o k o n i s a w a N n a n i r u y a s e r e d o o g u ts u n o k a N s a s u a m o k u t e k i t o sh I t a r i k u r o d e n o y o k o o s a sh i m a s U (fleurs_jpn_000383-fleurs_jpn_000383) +u y u n e k i t a b a r u t o k a y o m o d a N s u r o b a i w a s e i s u i ch i k a k u n i sh s e k u d a s a i k I k o o r e n o n a k o ts I k i ts s u u s a i n i m o cl t o m e ky o o k e r u s e i ts u d e o s o r u j u u m o n o s o o g a n a r i k i b i k i g a s U (fleurs_jpn_000384-fleurs_jpn_000384) +k o k o w a i r i s u n o sh o k u m i N i sh e h a i e sh a e g a j i b u N t a sh i n o ry o o r o t o sh a b a sh u n a r o u d e pau sh o k o m i N t i j i r e n i sh o o k o o s e k a s o o t o s u r e k a t a w a k o k o a r h a j i b e r e n a g a y o i u sh o o (fleurs_jpn_000385-fleurs_jpn_000385) +k o sh i w a s a k u g e N s u r u s u u ch o o s a r a y a w a s e N d e sh I t a k a pau s a k u g e N w a ch i i w o k u n o k e e z a i s a N sh I s u y o n i m o t o z u i t e j i sh i s a r u d a r o t o N n o o i m a sh I t a (fleurs_jpn_000386-fleurs_jpn_000386) +s a u i n u u k u k u sh o cl k u w a sh i N k o N ry o k o o n o j i k i g a s U k u n a i pau k a r u e ch a a sh o cl k u y o r e m o h a e k o o t o z u r e n a w a b i k i y o r i sh o o j o g a k a s u r u k o t o g a a r i m a s U (fleurs_jpn_000387-fleurs_jpn_000387) +ky i n o o n o a s a t o r u k o n o g a j i a N t e cl p u n o k e e s a s u o h o N w o r e j i d o o sh a b a k u r a n o b a k a a s o r i y o r i pau ky e e k a N f u t a r e g a sh i b o sh i pau h o sh o w a sh a w a n i j u u n i y o k o a i m a sh I t a (fleurs_jpn_000388-fleurs_jpn_000388) +sh o k u b u ts u d a n i N g i N g a s u u s a N z o o ts U k u r i n i N g e N g a k a cl k o i k i t o sh I t e h a k i d a s u n i s a N k a t a N s o o t o r i k o N d e i m a s U (fleurs_jpn_000389-fleurs_jpn_000389) +s e N p a k u r e e b u sh i o i s o o s u r u n o w a u m i o k o e t e h I t o y a b u sh i o t a r i ry o o y i s o o s u r u m o cl t o m o k o o r i s e k i n a h o o h o o r e s U (fleurs_jpn_000390-fleurs_jpn_000390) +k a r i h o r u n i a sh u u n o a w a n o r u d o sh u w a r u ts u n e cl k a a ch i sh i w a b o o r y o k u t e k i n a b i d e o g e e m u o m i s e e n e N sh a n i h a N b a y a r e N t a e s u r e u k o t o o k i N sh i s u r u h o o w a n i sh a o m e e sh i m a sh I t a (fleurs_jpn_000391-fleurs_jpn_000391) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/ref.trn b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/ref.trn new file mode 100644 index 0000000000000000000000000000000000000000..e029af95c725500cea9d7907f30f5d2f4bfb87d7 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/ref.trn @@ -0,0 +1,160 @@ +k a k o t o m i r a i t o n o m u j u N t e k i j i k o d o o i ts u n a r u g a y u e n i pau i sh I k i t e k i n a n o d e a r u (cv_jpn_000800-cv_jpn_000800) +s e k a i o k e e s e e s u r u t o t o m o n i pau j i k o j i sh i N o k e e s e e s u r u s o o z o o t e k I s e k a i n o s o o z o o t e k i y o o s o t o sh I t e pau k o b u ts u g a k o b u ts u d e a r u (cv_jpn_000801-cv_jpn_000801) +p a s o k o N d e g e e m u y a r u n i N g a f u e t e k I t e r u (cv_jpn_000802-cv_jpn_000802) +k a g a k u n o sh i m e s u a t a r a sh i i j i j i ts u pau a t a r a sh i i k a N n e N pau k a N ky o o sh i h a i n o a t a r a sh i i k a n o o s e e o m o cl t e n a n i o h a j i m e r u k a w a (cv_jpn_000803-cv_jpn_000803) +o m o sh i r o i n o n i r o o d o n a g a s u g i t e d a r u i (cv_jpn_000804-cv_jpn_000804) +k o r e j o o sh u u h a N cl p o i n a a (cv_jpn_000805-cv_jpn_000805) +k a g a k U sh a m o s e k a i o h o o k a ts u t e k i n i t o o i ts u t e k i n i s e ts u m e e sh i y o o t o sh I t e i r u (cv_jpn_000806-cv_jpn_000806) +f U ts u u n i ts u m a r a N (cv_jpn_000807-cv_jpn_000807) +sh i cl k a r i sh I t e k u d a s a i (cv_jpn_000808-cv_jpn_000808) +w a t a sh i w a m i g i n o g o t o k i r e k I sh i t e k I s e e m e e n o j i k a k U t o i u g o t o k i m o n o o b e N sh o o h o o t e k i r o N r i t o i u n o d e a r u (cv_jpn_000809-cv_jpn_000809) +w a t a sh i w a sh a k a i k e e s e e n o k o N t e e n i w a d i o ny u s o s u t e k i n a m o n o g a h a t a r a i t e i r u t o o m o u (cv_jpn_000810-cv_jpn_000810) +n a n i o s u r u ts u m o r i d a cl t a n o k a (cv_jpn_000811-cv_jpn_000811) +k o b u ts u t e k I t a g a j i k o h I t e e t e k i n i t a N n i t e N sh u u g o o t e k i n i k a N g a e r a r e r u t o k i pau s o r e g a b u ts u r i t e k I s e k a i d e a r u (cv_jpn_000812-cv_jpn_000812) +a m e g a f u cl t a n o d e pau y a ky u u n o sh i a i g a a r i m a s e N d e sh I t a (cv_jpn_000813-cv_jpn_000813) +k o r e w a n i cl p o N d e u cl t e i n a i t a b e m o n o d e s U (cv_jpn_000814-cv_jpn_000814) +w a t a sh i w a pau h e N sh u u i N o pau y o n e N k u r a i h a y a cl t a t o o m o u (cv_jpn_000815-cv_jpn_000815) +i s a N n i k o n o k o t o b a n o i m i o o sh i e m a sh I t a (cv_jpn_000816-cv_jpn_000816) +k a z e g a ts u y o i h i w a t e n i s u g a d e k i m a s e N (cv_jpn_000817-cv_jpn_000817) +i ch i (cv_jpn_000818-cv_jpn_000818) +h a i (cv_jpn_000819-cv_jpn_000819) +n i (cv_jpn_000820-cv_jpn_000820) +r e i (cv_jpn_000821-cv_jpn_000821) +r o k u (cv_jpn_000822-cv_jpn_000822) +m i r u t o i u k o t o t o h a t a r a k U t o i u k o t o t o g a f U k a b u N r i t e k i d e n a k e r e b a n a r a n a i (cv_jpn_000823-cv_jpn_000823) +w a r e w a r e o t a m a sh i i n o s o k o k a r a u g o k a s u m o n o d e n a k e r e b a n a r a n a i (cv_jpn_000824-cv_jpn_000824) +z e cl t a i b e N sh o o h o o t e k i n a r u g a y u e n i i d e y a t e k I ch o cl k a N t e k I k e e k i g a f U k u m a r e r u n o d e a r u (cv_jpn_000825-cv_jpn_000825) +d o k o m a d e m o t a t o i ch i t o n o s o o g o h I t e e t e k i n a z e cl t a i m u j u N t e k i j i k o d o o i ts u n o s e k a i n i sh I t e (cv_jpn_000826-cv_jpn_000826) +sh I k a r u n i n i N g e N t o k a N ky o o t o n o k a N k e e w a m o t o k o o i n o k a N k e e d e a r i (cv_jpn_000827-cv_jpn_000827) +i s a N n i k o n o k o t o b a n o i m i o o sh i e m a sh I t a (cv_jpn_000828-cv_jpn_000828) +k e e k i g a n a n a ts u a r i m a s U (cv_jpn_000829-cv_jpn_000829) +k o ch i r a w a k o b a y a sh I s a N d e s U (cv_jpn_000830-cv_jpn_000830) +m o sh i m o sh i (cv_jpn_000831-cv_jpn_000831) +k o k o w a o o k I k u t e n i g i y a k a n a m a ch i d e s U (cv_jpn_000832-cv_jpn_000832) +s o n o u ch I k a i a k U s a r e r u k a r a i s o g e (cv_jpn_000833-cv_jpn_000833) +a m a s a g a o s a e r a r e t e t e ch o o d o i i (cv_jpn_000834-cv_jpn_000834) +h o k e N sh I ts u n o d o a o a k e t a (cv_jpn_000835-cv_jpn_000835) +m u d a n i o w a cl t e m o k i n i sh i n a i (cv_jpn_000836-cv_jpn_000836) +a r i g a t a y a (cv_jpn_000837-cv_jpn_000837) +i d o o g a r a k u d a t o j i k a N o w a s u r e t e t a n o sh i m e r u (cv_jpn_000838-cv_jpn_000838) +k a g a k u w a g i j u ts U k a s a r e r u n i o o j i t e j o o sh I k i n o u ch i n i h a i cl t e y u k u (cv_jpn_000839-cv_jpn_000839) +sh I k a sh I t o k i g a k a k o n i h a i r u k o t o s o n o k o t o g a pau m i r a i o u m u k o t o d e a r i pau a r a t a n a r u sh U t a i g a d e t e k u r u k o t o d e a r u (cv_jpn_000840-cv_jpn_000840) +t e r e b i o k a i k a e t e pau t e r e b i o m i r u j i k a N g a f u e t a (cv_jpn_000841-cv_jpn_000841) +k a k a r u sh U t a i n o m i pau i ts u m a d e m o i k i r u n o d e a r u (cv_jpn_000842-cv_jpn_000842) +n i N k i r a a m e N y a n i n a r a N d a r a n i j i k a N m a ch i d a cl t a (cv_jpn_000843-cv_jpn_000843) +s o r e o m o ch i i r u n i N g e N n o i y o k u n i i z o N sh i pau s o sh I t e k o r e w a k a r e n o m o cl t e i r u k a ch i n o sh a k u d o n i i z o N s u r u (cv_jpn_000844-cv_jpn_000844) +m a w a r i w a m i N n a pau k a N g a e r u k o t o o y a m e t e i t a (cv_jpn_000845-cv_jpn_000845) +k o o i t e k I ch o cl k a N t e k i n i s e k a i o m i r u t o i u k o t o w a pau gy a k u n i k o o i t e k I ch o cl k a N t e k i n i s e k a i o k e e s e e s u r u k o t o o f U k u m u n o d e a r u (cv_jpn_000846-cv_jpn_000846) +sh i N p a i k a k e s a s e m a i t o s u r u k i z u k a i g a pau y o k e i n i sh i N p a i s a s e t e sh i m a u (cv_jpn_000847-cv_jpn_000847) +k o n o m i ch i w a t o t e m o s e m a i n o d e pau a b u n a i d e s U (cv_jpn_000848-cv_jpn_000848) +o b o e g a w a r u i n e (cv_jpn_000849-cv_jpn_000849) +t o i r e w a r o o k a n o h i d a r i g a w a n i a r i m a s U (cv_jpn_000850-cv_jpn_000850) +t a n a k a s a N n o h i d a r i n i k i m u r a s a N g a i m a s U (cv_jpn_000851-cv_jpn_000851) +m a cl k u r o n a t a m a g o cl t e s u g o i n e (cv_jpn_000852-cv_jpn_000852) +sh o hy o o m i t a i n a d o k U sh o k a N s o o b u N o k a i t a (cv_jpn_000853-cv_jpn_000853) +g e N j i ts u n o s e k a i w a t a n o i ch i t o sh I t e k e cl t e e s e r a r e t a k a t a ch i o m o cl t a s e k a i d e n a k e r e b a n a r a n a i (cv_jpn_000854-cv_jpn_000854) +sh o o h i N k e N s a k u g a w a k a r i y a s u i t o k a u k i n i n a r u n o n i (cv_jpn_000855-cv_jpn_000855) +ch i sh I k i w a r e k I sh i t e k I k a t e e d e n a k e r e b a n a r a n a i (cv_jpn_000856-cv_jpn_000856) +m o n o g o t o n o j u N b a N o k a e r u d a k e d e u m a k u i k U k o t o m o a r u (cv_jpn_000857-cv_jpn_000857) +k o n o k i s e ts u w a k a ts u o n o s a sh i m i g a z e cl p i N (cv_jpn_000858-cv_jpn_000858) +k a k e n i sh i cl p a i sh I t e m o o ch I ts u i t e s o N sh I ts u o u k e i r e r u (cv_jpn_000859-cv_jpn_000859) +s o r e y u e n i t e ts u g a k u g a z e N t a i n o g a k u d e a r u t o s u r e b a (cv_jpn_000860-cv_jpn_000860) +ch i i s a n a y a o y a d a g a y a s u k U t e h a N j o o sh I t e r u (cv_jpn_000861-cv_jpn_000861) +i N f u r a g a k i n o o f u z e N n i o ch i i cl t e pau k o k u g a i e d a cl sh U ts u s u r u h I t o m o d e t e k i t a (cv_jpn_000862-cv_jpn_000862) +ts u g i n i k a g a k u w a s o N z a i o sh u j u n o ry o o i k i n i w a k a cl t e s o r e z o r e n o ry o o i k i n i ts u i t e k e N ky u u s u r u (cv_jpn_000863-cv_jpn_000863) +s o r e d e w a t o k i t o i u m o n o n o s e e r i ts U sh i y o o w a n a k u pau sh u N k a N t o i u m o n o m o n a k u n a r u n o d e a r u (cv_jpn_000864-cv_jpn_000864) +a k a i b u r a N k o pau k o N k u r i i t o s e e n o s u b e r i d a i pau k a w a i t a s u n a b a (cv_jpn_000865-cv_jpn_000865) +sh I k a sh I s o r e w a d o k o m a d e m o k o k o k a r a d e t e k o k o e k a e r i k u r u s e e sh I ts u o m o cl t a m o n o d e n a k e r e b a n a r a n a i (cv_jpn_000866-cv_jpn_000866) +a r i t o a r a y u r u d e m a o m a k I ch i r a sh I t e m i N n a k a r a u r a m i o k a cl t e r u (cv_jpn_000867-cv_jpn_000867) +k o n o t e e d o pau s a w a g i n i n a r u k o t o m o n a i n o d a r o o (cv_jpn_000868-cv_jpn_000868) +k o n o n e d a N d e u r e ch a u k a a (cv_jpn_000869-cv_jpn_000869) +h i n o k a g e N n i ch u u i sh i n a i t o s u g u n i k o g e r u (cv_jpn_000870-cv_jpn_000870) +e N b a N n o u e n i p o ts u r i t o ch i i s a n a a n a g a h i r a i t a s a i sh o w a ts u m a y o o j i t e e d o n o ch i i s a n a a n a d a cl t a (cv_jpn_000871-cv_jpn_000871) +s o r e w a w a r e w a r e o i k a sh i n a g a r a w a r e w a r e o d o r e e k a s u r u n o d e a r u pau w a r e w a r e n o t a m a sh i i o k o r o s u n o d e a r u (cv_jpn_000872-cv_jpn_000872) +r e k I sh i t e k i n i a t a e r a r e t a m o n o w a pau z e cl t a i m u j u N t e k i j i k o d o o i ts u t e k i g e N z a i n i o i t e s e k a i sh i t e k i n i a t a e r a r e t a m o n o t o sh I t e (cv_jpn_000873-cv_jpn_000873) +m u j u N t e k i j i k o d o o i ts U t o sh I t e pau i ts u m o k o n o s e k a i n i ch o o e ts u t e k i d e a r u (cv_jpn_000874-cv_jpn_000874) +y u e n i z e cl t a i m u j u N t e k i j i k o d o o i ts U t o sh I t e g e N z a i k a r a g e N z a i e t o u g o k i i k U s e k a i n o g e N z a i n i o i t e (cv_jpn_000875-cv_jpn_000875) +a r e pau b o t a N o sh I t e m o d a cl sh U ts u d e k i n a i (cv_jpn_000876-cv_jpn_000876) +sh I k a sh i w a t a sh i w a s o k o n i s e k a i n o j i k o d o o i ts u o o k u n o d e w a n a i (cv_jpn_000877-cv_jpn_000877) +n e m u t a k u n a r u n o g a h a y a k u n a cl t a (cv_jpn_000878-cv_jpn_000878) +w a t a sh i w a n i N g e N n o r e k I sh i t e k I k e e s e e n o t a ch i b a k a r a g e e j u ts u o m i r u n o d e a cl t e pau k o o sh a k a r a z e N sh a o m i r u n o d e w a n a i (cv_jpn_000879-cv_jpn_000879) +a o i t o m a t o sh I k a n a k U t e k a u k a m a y o u (cv_jpn_000880-cv_jpn_000880) +sh i N k i j i gy o o n i o o k i n a k I t a i o y o s e t e i r u (cv_jpn_000881-cv_jpn_000881) +n a n i k a sh i r a n o i N s e N t i b u g a n a i t o k i b i sh i i n o d e w a (cv_jpn_000882-cv_jpn_000882) +j i k a N s e e g e N n o i b e N t o d e s U t o r e s U t a m a r u (cv_jpn_000883-cv_jpn_000883) +m a w a r i n o h I t o w a b o o z e N t o sh I t e i t a (cv_jpn_000884-cv_jpn_000884) +s o N n a n a i y o o n o m e e r u g a pau n a N k e N m o k i t e i t a (cv_jpn_000885-cv_jpn_000885) +n i j i k a i d e d e e s u i sh I t e i t a (cv_jpn_000886-cv_jpn_000886) +t o k i d o k i pau j i b u N n o k o k o r o g a w a k a r a n a k u n a r u t o k i g a a r u d a k a r a b o k u w a k a a t e N o h I k i pau n o o t o n i k a k I h a j i m e r u (cv_jpn_000887-cv_jpn_000887) +m o o n i g e t e ch a d a m e d a (cv_jpn_000888-cv_jpn_000888) +k a r e w a pau b o o cl t o t a ch I ts u k u sh I t e i t a (cv_jpn_000889-cv_jpn_000889) +d a r e n i m o m e e w a k u w a k a k e t a k u n a i (cv_jpn_000890-cv_jpn_000890) +m a s a k a pau t o o m o cl t e d o a n o t o cl t e o n i g i cl t a (cv_jpn_000891-cv_jpn_000891) +s u i m a s e N (cv_jpn_000892-cv_jpn_000892) +k a y o u n i sh I t e sh i cl t e i r u t o t o m o n i sh i cl t e i n a i t o k o r o k a r a t a N ky u u w a h a j i m a r u n o d e a r u (cv_jpn_000893-cv_jpn_000893) +a i s a ts u w a d a i j i d a y o (cv_jpn_000894-cv_jpn_000894) +t o j i t a m o n o o i k a n i h i r o g e t e m o h i r a i t a m o n o n i w a n a r a n u t o i cl t e i r u g a (cv_jpn_000895-cv_jpn_000895) +t a m e sh i n i i k U ts u k a ts U k u cl t e m i y o o (cv_jpn_000896-cv_jpn_000896) +z u i b u N a k o g i n a sh o o b a i d a y o n a a (cv_jpn_000897-cv_jpn_000897) +w a ch i (cv_jpn_000898-cv_jpn_000898) +i ch i (cv_jpn_000899-cv_jpn_000899) +g o (cv_jpn_000900-cv_jpn_000900) +sh I ch i (cv_jpn_000901-cv_jpn_000901) +i i e (cv_jpn_000902-cv_jpn_000902) +w a ch i (cv_jpn_000903-cv_jpn_000903) +r e i (cv_jpn_000904-cv_jpn_000904) +sh i (cv_jpn_000905-cv_jpn_000905) +k u (cv_jpn_000906-cv_jpn_000906) +i ch i (cv_jpn_000907-cv_jpn_000907) +k a g a k u g a a k i r a k a n i s u r u ky a cl k a N t e k I sh i N r i n i sh I t a g a u k o t o n i y o cl t e (cv_jpn_000908-cv_jpn_000908) +k a k o t o m i r a i t o n o m u j u N t e k i j i k o d o o i ts U t o sh I t e n o g e N z a i g a k a t a ch i o m o ts U t o i u k o t o d e a r u (cv_jpn_000909-cv_jpn_000909) +b u ts u r i t e k I s e k a i w a s u u g a k u t e k I k i g o o n i y o cl t e a r a w a s a r e r u s u u g a k u t e k I k a t a ch i n o s e k a i d e a r u (cv_jpn_000910-cv_jpn_000910) +o n a j i g e N sh o o d e s a N k o o n i n a r u (cv_jpn_000911-cv_jpn_000911) +g a i k o k U k a r a k i t a m o n o d a t o sh i cl t e b i cl k u r i (cv_jpn_000912-cv_jpn_000912) +i w a y u r u j i cl s e N n i y o cl t e k a k U t o k U sh i r a i cl t a m o n o d e a r u (cv_jpn_000913-cv_jpn_000913) +o n a j i y o o n i pau d a N s e e w a h i z a o o o u z u b o N o h a k U k o t o g a g i m u z u k e r a r e t e i m a s U (fleurs_jpn_000346-fleurs_jpn_000346) +k o n o s a a b i s u w a pau g o r a k u s e N o h a j i m e t o s u r u s e N p a k u y a pau e N k a k u ch i d e d e e t a y a o N s e e o h I ts u y o o t o s u r u t a N k e N t a i n i h i N p a N n i r i y o o s a r e t e i m a s U (fleurs_jpn_000347-fleurs_jpn_000347) +ky o o f u u pau hy o o pau k a d o n o k o o s u i ry o u pau o y o b i y a m a k a j i w a pau r a i u pau t a ts u m a k i pau m i z u f u k i pau o y o b i s a i k u r o N n a d o n o k i b i sh i i k I sh o o k e e t a i y a s o n o e e ky o o n i y o r u m o n o d e s U (fleurs_jpn_000348-fleurs_jpn_000348) +i N t a a n e cl t o w a pau m a s U k o my u n i k e e sh o N t o t a i j i N k o my u n i k e e sh o N n o ry o o y o o s o o k a n e s o n a e t a k a N ky o o d e s U (fleurs_jpn_000349-fleurs_jpn_000349) +k a j i n o d e w a ts u u j o o pau t o k u b e ts u n a i N sh o k u y a e N t a a t e i m e N t o o y o o i sh I t e i m a s U pau g e s U t o g a k i b u N y o k U sh i s e ts u n a i n i t o m a r u y o o n i s u r u t a m e d e s U (fleurs_jpn_000350-fleurs_jpn_000350) +sh I k a sh i pau ky a p U t e N n o w i k e cl t o o u sh i n a cl t a a t o pau i N d o w a n a n a ts u n o w i k e cl t o o u sh i n a i pau s a N j u u r o k u r a N sh I k a d e k i m a s e N d e sh I t a (fleurs_jpn_000351-fleurs_jpn_000351) +f o o k u r a N d o n o k o o sh I k i ts u u k a w a f o o k u r a N d o sh o t o o p o N d o e f u k e e p i i d e i ch I p o N d o g a i ch i i g i r i s U p o N d o j i i b i i p i i t o t o o k a n i k o t e e s a r e t e i m a s U (fleurs_jpn_000352-fleurs_jpn_000352) +h a sh i sh I t a n o k a m i g a t a k u u k a N w a j u u g o m e e t o r u d e s U pau n i s e N j u u i ch i n e N h a ch i g a ts u n i sh u N k o o sh i pau n i s e N j u u n a n a n e N s a N g a ts u m a d e k a i ts u u sh i m a s e N d e sh I t a (fleurs_jpn_000353-fleurs_jpn_000353) +i cl p u N k a N d e f u cl t o o s u r u ch i i k i m o a r e b a pau f u cl t o o s u r u m a d e n i n a N p u N m o k a k a r u ch i i k i m o a r i m a s U (fleurs_jpn_000354-fleurs_jpn_000354) +p i r a m i cl d o n o o t o t o h I k a r i n o sh o o w a pau k o n o k a N k o o ch i d e t o k u n i k o d o m o t a ch i g a t a n o sh i m e r u m o y o o sh i n o h I t o ts u d e s U (fleurs_jpn_000355-fleurs_jpn_000355) +s o n o t a m e pau t a N n i r a b e r u t o sh I t e hy o o k i g a ts u i k a s a r e g a ch i d e s U (fleurs_jpn_000356-fleurs_jpn_000356) +g e N s o N s u r u k o t o g a sh i r a r e t e i r u n i j u u g o m a i n o d a N r a cl p u pau b u r o o d o s a i d o w a pau g e N s o N s u r u t o o g a i b u N k e N n o s a i k o n o u ts U sh i d e s U pau t e g a k i n i y o r u g e N p o N w a g e N s o N sh I t e i m a s e N (fleurs_jpn_000357-fleurs_jpn_000357) +k a r e n o s e ts u o t a d a sh i i t o m i t o m e r u h I t o m o i m a sh I t a g a pau o o k u n o h I t o w a s o n o gy a k u d e pau t a i y o o k e e d e w a t a i y o o t o s o n o t a n o h o sh i g a ch I ky u u n o m a w a r i o i d o o sh I t e i r u t o sh i N j i t e i m a sh I t a (fleurs_jpn_000358-fleurs_jpn_000358) +ch i b e cl t o m e e s o o n o ch u u sh i N w a sh i N s e e y o g a d e s U pau s a m a z a m a n a k a m i g a m i o sh I k a k U k a s u r u k o t o d e pau e n e r u g i i ch a n e r u g a j o o k a s a r e pau ch a k u r a g a k a cl s e e k a s a r e pau s a t o r i n o i sh I k i g a u m a r e m a s U (fleurs_jpn_000359-fleurs_jpn_000359) +m i n a m i a f u r i k a n i a r u s u b e t e n o k o k u r i ts U k o o e N t o d o o y o o n i pau k o n o k o o e N n i w a m a i n i ch I h o g o h I t o ny u u e N ry o o g a k a k a r i m a s U (fleurs_jpn_000360-fleurs_jpn_000360) +r e cl sh a pau k u r u m a pau s o n o t a n o o o k u n o k o o ts u u sh u d a N g a s o k o k a r a u m a r e m a sh I t a (fleurs_jpn_000361-fleurs_jpn_000361) +i N t a a n e cl t o w a pau m a s U k o my u n i k e e sh o N t o t a i j i N k o my u n i k e e sh o N n o ry o o y o o s o o k a n e s o n a e t a k a N ky o o d e s U (fleurs_jpn_000362-fleurs_jpn_000362) +by o o i N d e w a pau k a N s e N k a N r i t e j u N sh o n i sh I t a g a i pau t a n i N e n o k a N s e N n o k a n o o s e e o f U s e g u t a m e n i k a N j a o k a k u r i s u r u n a d o n o s o ch i o t o cl t e i m a s U (fleurs_jpn_000363-fleurs_jpn_000363) +r e N p o o g i k a i w a n i s e N g o n e N d o k a r a w a i s e ts u b u ts U t o r i sh i m a r i h o o e n o sh I k i N t e e ky o o o k a i sh I sh i pau e f u b i i a i w a a d a r u t o p o r u n o n i j u u n i N n o s o o s a i N o t o o ny u u sh i n a k e r e b a n a r a n a i t o k I t e e sh i m a sh I t a (fleurs_jpn_000364-fleurs_jpn_000364) +p i i e i ch i pau r e b e r u w a pau k e N s a sh I t a k a g a k u b u cl sh I ts u n i f U k u m a r e r u s u i s o i o N p i i e i ch i n o e i ch i n o ry o o d e sh i m e s a r e m a s U (fleurs_jpn_000365-fleurs_jpn_000365) +s o r e d e m o pau t o o ky o k U k a r a n o a d o b a i s u o u k e pau s u b e t e n o hy o o sh I k i o m a m o r i pau a N z e N j o o n o k e e k o k u n i s a i sh i N n o ch u u i o h a r a i m a sh o o (fleurs_jpn_000366-fleurs_jpn_000366) +k o r e r a w a t a m a n i k o N z a ts U s u r u k a z o k u m u k e n o b i i ch i d e pau k a i g a N n i w a s a m a z a m a n a t e N p o g a n a r a N d e i m a s U pau a N z e N n i o y o g u k o t o g a d e k i m a s U (fleurs_jpn_000367-fleurs_jpn_000367) +sh i N n o pau m i e n a i ch i i m u pau e r u e e a a r u e s u o o e n u pau a N d o pau e r u e e e f u e e e s u t i i o o pau s e N ky u u hy a k U h a ch i j u u ky u u pau p i i hy a k u ky u u n o s o N z a i m o m a t a pau b a a ch a r u ch i i m u n o d o k u j i n o y o o s o d e a r u (fleurs_jpn_000368-fleurs_jpn_000368) +k o n o s a a b i s u w a pau g o r a k u s e N o h a j i m e t o s u r u s e N p a k u y a pau e N k a k u ch i d e d e e t a y a o N s e e o h I ts u y o o t o s u r u t a N k e N t a i n i h i N p a N n i r i y o o s a r e t e i m a s U (fleurs_jpn_000369-fleurs_jpn_000369) +s a k u b a N pau b u e n o s u a i r e s U k a r a g o j u cl k i r o s a N j u u i ch i m a i r u h a n a r e t a r a p u r a t a sh i n a i d e pau g e N sh o k u j o o i N g i i N d e a r u k u r i s U t i i n a pau f e r u n a N d e s u pau d e pau k i r u h i n a a j o sh i g a d a i t o o ry o o s e N e n o sh U ts u b a o s e N g e N sh i m a sh I t a (fleurs_jpn_000370-fleurs_jpn_000370) +o n a j i ts U k i n i pau m a sh U h a d o n o k a cl s o o r o d e b e ts u n o ry o k a k u k i g a k a cl s o o r o o o o b a a r a N sh i pau k a b e n i g e k I t o ts u sh I t e j u u sh I ch i n i N g a sh i b o o sh i m a sh I t a (fleurs_jpn_000371-fleurs_jpn_000371) +h a sh i sh I t a n o k a m i g a t a k u u k a N w a j u u g o m e e t o r u d e s U pau n i s e N j u u i ch i n e N h a ch i g a ts u n i sh u N k o o sh i pau n i s e N j u u n a n a n e N s a N g a ts u m a d e k a i ts u u sh i m a s e N d e sh I t a (fleurs_jpn_000372-fleurs_jpn_000372) +b u N m e e t o i u k o t o b a w a pau sh i m i N o i m i s u r u r a t e N g o n o k e e y o o sh I sh i i a i b u i a i e r u a i e s u k a r a k i t e o r i pau sh i m i N o i m i s u r u r a t e N g o n o m e e sh I sh i i a i b u i a i e s u pau t o sh i y a t o sh I k o cl k a o i m i sh i pau n a N r a k a n o k a t a ch i d e sh a k a i n o k i b o o t e e g i s u r u sh i i a i b u i a i t i i e e e s u t o i u m e e sh i n i k a N k e e sh I t e i m a s U (fleurs_jpn_000373-fleurs_jpn_000373) +ts u u j o o pau k o k o d e w a i ts u m o k a N k o o ky a k u y a gy o o sh a t a ch i g a h a cl s u r u o t o g a k I k o e t e k i m a s U pau o t o t o h I k a r i g a o r i n a s u m o n o g a t a r i w a m a r u d e e h o N n o y o o d e s U (fleurs_jpn_000374-fleurs_jpn_000374) +t e r e b i n o h o o d o o n i y o r u t o pau g e N p a ts U k a r a sh i r o k e m u r i g a a g a cl t e i m a s U (fleurs_jpn_000375-fleurs_jpn_000375) +n o o by o o r i t o k o o d o o n o s o o k a N k a N k e e w a pau k a g a k U sh a t a ch i n o k e N ky u u o u r a z u k e r u m o n o d e s U (fleurs_jpn_000376-fleurs_jpn_000376) +s u i y o o b i n o i b e N t o n o a t o pau k a r u p a n e d o w a s e N sh u k e N d e f U t a ts u n o k o j i N r e e s u n i sh U ts u j o o sh i m a sh I t a (fleurs_jpn_000377-fleurs_jpn_000377) +s e N h a cl py a k u n e N d a i i r a i pau g u N t a i g a t o o ch a k U s u r u m a d e h a i ch i w a k o n o by o o k i n i k a N k e e s u r u m o N d a i n i s o o g u u sh I t a k o t o w a a r i m a s e N d e sh I t a (fleurs_jpn_000378-fleurs_jpn_000378) +sh I k a sh i pau ky a p U t e N n o w i k e cl t o o u sh i n a cl t a a t o pau i N d o w a n a n a ts u n o w i k e cl t o o u sh i n a i pau s a N j u u r o k u r a N sh I k a d e k i m a s e N d e sh I t a (fleurs_jpn_000379-fleurs_jpn_000379) +k a j i n o d e w a ts u u j o o pau t o k u b e ts u n a i N sh o k u y a e N t a a t e i m e N t o o y o o i sh I t e i m a s U pau g e s U t o g a k i b u N y o k U sh i s e ts u n a i n i t o m a r u y o o n i s u r u t a m e d e s U (fleurs_jpn_000380-fleurs_jpn_000380) +s o r e d e m o pau t o o ky o k U k a r a n o a d o b a i s u o u k e pau s u b e t e n o hy o o sh I k i o m a m o r i pau a N z e N j o o n o k e e k o k u n i s a i sh i N n o ch u u i o h a r a i m a sh o o (fleurs_jpn_000381-fleurs_jpn_000381) +o w a k a r e d e w a a r i m a s e N k o r e w a h I t o ts u n o sh o o n o o w a r i d e a r i pau a t a r a sh i i sh o o n o m a k u a k e d e s U (fleurs_jpn_000382-fleurs_jpn_000382) +s a f a r i t o w a pau a f u r i k a n o y a s e e d o o b u ts u pau t o k u n i s a b a N n a n i i r u y a s e e d o o b u ts u n o k a N s a ts u o m o k U t e k I t o sh I t a r i k u r o d e n o ry o k o o o s a sh i m a s U (fleurs_jpn_000383-fleurs_jpn_000383) +f u y u n i k I t a b a r u t o k a i o o o d a N s u r u b a a i w a pau s e N sh I ts u n o i ch i o k a k u n i N sh I t e k u d a s a i pau k o o r i n o n a k a o ts U k i s u s u m u s a i n i m o cl t o m o e e ky o o o u k e r u s e N sh I ts u d e w a o s o r o sh i i h o d o n o s o o o N g a n a r i h i b i k i m a s U (fleurs_jpn_000384-fleurs_jpn_000384) +k o k o w a i g i r i s u n o sh o k u m i N ch I sh i h a i sh a g a j i b u N t a ch i n o ry o o d o t o sh I t a b a sh o n a n o d e pau sh o k u m i N ch i j i d a i n o sh o o k o o s a g a s o o t o s u r u h o o w a pau k o k o k a r a h a j i m e r u n o g a y o i d e sh o o (fleurs_jpn_000385-fleurs_jpn_000385) +e b i s u sh i w a pau s a k u g e N s u r u s u u ch i o s a d a m e m a s e N d e sh I t a g a pau s a k u g e N w a ch u u g o k u n o k e e z a i s a N sh U ts u ry o u n i m o t o z u i t e j i cl sh I s a r e r u d a r o o t o n o b e m a sh I t a (fleurs_jpn_000386-fleurs_jpn_000386) +s a i ny u u k o k U sh o cl k u w a sh i N k o N ry o k o o n o j i k i g a s U k u n a i k a r u ch a a sh o cl k u y o r i m o h a y a k u o t o z u r e pau n a g a b i k i pau y o r i sh o o j o o g a a cl k a s u r u k o t o g a a r i m a s U (fleurs_jpn_000387-fleurs_jpn_000387) +k i n o o n o a s a pau t o r u k o n o g a j i a N t e cl p u n o k e e s a ts U h o N b u d e j i d o o sh a b a k u d a N n o b a k U h a ts u n i y o r i pau k e e k a N f U t a r i g a sh i b o o sh i pau f U sh o o sh a w a n i j u u n i N o k o e m a sh I t a (fleurs_jpn_000388-fleurs_jpn_000388) +sh o k u b u ts u w a n i N g e N g a s u u s a N s o o ts U k u r i pau n i N g e N g a i k I t o sh I t e h a k i d a s u n i s a N k a t a N s o o t o r i k o N d e i m a s U (fleurs_jpn_000389-fleurs_jpn_000389) +s e N p a k u d e b u cl sh i o y u s o o s u r u n o w a pau u m i o k o e t e h I t o y a b u cl sh i o t a i ry o o y u s o o s u r u m o cl t o m o k o o r i ts u t e k i n a h o o h o o d e s U (fleurs_jpn_000390-fleurs_jpn_000390) +k a r i f o r u n i a sh u u n o a a n o r u d o pau sh u w a r u ts e n e cl g a a ch i j i w a pau b o o ry o k u t e k i n a b i d e o g e e m u o m i s e e n e N sh a n i h a N b a i y a r e N t a r u s u r u k o t o o k i N sh I s u r u h o o a N n i sh o m e e sh i m a sh I t a (fleurs_jpn_000391-fleurs_jpn_000391) diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/result.txt b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/result.txt new file mode 100644 index 0000000000000000000000000000000000000000..0942de413b09694ac00685719fe42023db68490d --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/result.txt @@ -0,0 +1,1961 @@ + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,---------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/hyp.trn| +|---------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000800 | 1 64 | 92.2 6.3 1.6 0.0 7.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000801 | 1 103 | 93.2 1.9 4.9 1.0 7.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000802 | 1 34 | 73.5 14.7 11.8 5.9 32.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000803 | 1 96 | 90.6 3.1 6.3 2.1 11.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000804 | 1 32 | 93.8 6.3 0.0 3.1 9.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000805 | 1 20 | 90.0 0.0 10.0 0.0 10.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000806 | 1 62 | 85.5 8.1 6.5 0.0 14.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000807 | 1 14 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000808 | 1 18 | 94.4 0.0 5.6 0.0 5.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000809 | 1 88 | 93.2 5.7 1.1 2.3 9.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000810 | 1 70 | 94.3 2.9 2.9 5.7 11.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000811 | 1 24 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000812 | 1 92 | 95.7 3.3 1.1 0.0 4.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000813 | 1 42 | 88.1 11.9 0.0 7.1 19.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000814 | 1 34 | 88.2 5.9 5.9 0.0 11.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000815 | 1 42 | 88.1 4.8 7.1 2.4 14.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000816 | 1 32 | 96.9 3.1 0.0 9.4 12.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000817 | 1 32 | 96.9 3.1 0.0 6.3 9.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000818 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000819 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000820 | 1 2 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000821 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000822 | 1 4 | 50.0 50.0 0.0 0.0 50.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000823 | 1 64 | 92.2 1.6 6.3 3.1 10.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000824 | 1 54 | 92.6 3.7 3.7 3.7 11.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000825 | 1 73 | 95.9 2.7 1.4 0.0 4.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000826 | 1 75 | 98.7 0.0 1.3 0.0 1.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000827 | 1 55 | 96.4 3.6 0.0 0.0 3.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000828 | 1 32 | 93.8 3.1 3.1 9.4 15.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000829 | 1 20 | 85.0 10.0 5.0 0.0 15.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000830 | 1 23 | 91.3 8.7 0.0 4.3 13.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000831 | 1 8 | 87.5 12.5 0.0 12.5 25.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000832 | 1 32 | 90.6 6.3 3.1 0.0 9.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000833 | 1 28 | 100.0 0.0 0.0 3.6 3.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000834 | 1 26 | 88.5 7.7 3.8 3.8 15.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000835 | 1 20 | 90.0 5.0 5.0 5.0 15.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000836 | 1 23 | 95.7 4.3 0.0 8.7 13.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000837 | 1 9 | 100.0 0.0 0.0 11.1 11.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000838 | 1 38 | 94.7 2.6 2.6 2.6 7.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000839 | 1 54 | 92.6 5.6 1.9 0.0 7.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000840 | 1 90 | 94.4 4.4 1.1 3.3 8.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000841 | 1 39 | 97.4 2.6 0.0 0.0 2.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000842 | 1 37 | 91.9 5.4 2.7 0.0 8.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000843 | 1 40 | 95.0 5.0 0.0 2.5 7.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000844 | 1 82 | 96.3 2.4 1.2 0.0 3.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000845 | 1 36 | 97.2 0.0 2.8 5.6 8.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000846 | 1 101 | 94.1 4.0 2.0 2.0 7.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000847 | 1 57 | 94.7 3.5 1.8 1.8 7.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000848 | 1 36 | 94.4 0.0 5.6 0.0 5.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000849 | 1 13 | 69.2 15.4 15.4 7.7 38.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000850 | 1 33 | 81.8 6.1 12.1 0.0 18.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000851 | 1 35 | 94.3 2.9 2.9 0.0 5.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000852 | 1 25 | 84.0 12.0 4.0 4.0 20.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000853 | 1 33 | 93.9 6.1 0.0 3.0 9.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000854 | 1 77 | 81.8 14.3 3.9 1.3 19.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000855 | 1 43 | 83.7 7.0 9.3 2.3 18.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000856 | 1 40 | 90.0 7.5 2.5 5.0 15.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000857 | 1 45 | 91.1 6.7 2.2 2.2 11.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000858 | 1 33 | 100.0 0.0 0.0 3.0 3.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000859 | 1 42 | 92.9 7.1 0.0 2.4 9.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000860 | 1 44 | 93.2 6.8 0.0 0.0 6.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000861 | 1 36 | 88.9 8.3 2.8 0.0 11.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000862 | 1 61 | 95.1 1.6 3.3 4.9 9.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000863 | 1 75 | 81.3 14.7 4.0 1.3 20.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000864 | 1 72 | 91.7 0.0 8.3 1.4 9.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000865 | 1 50 | 92.0 2.0 6.0 0.0 8.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000866 | 1 82 | 84.1 7.3 8.5 1.2 17.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000867 | 1 51 | 92.2 5.9 2.0 3.9 11.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000868 | 1 38 | 97.4 0.0 2.6 2.6 5.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000869 | 1 20 | 90.0 10.0 0.0 10.0 20.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000870 | 1 34 | 91.2 0.0 8.8 0.0 8.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000871 | 1 76 | 90.8 3.9 5.3 0.0 9.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000872 | 1 85 | 96.5 3.5 0.0 0.0 3.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000873 | 1 103 | 91.3 5.8 2.9 0.0 8.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000874 | 1 57 | 89.5 3.5 7.0 0.0 10.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000875 | 1 82 | 90.2 6.1 3.7 0.0 9.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000876 | 1 30 | 80.0 10.0 10.0 3.3 23.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000877 | 1 50 | 96.0 0.0 4.0 0.0 4.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000878 | 1 27 | 88.9 11.1 0.0 0.0 11.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000879 | 1 93 | 94.6 1.1 4.3 1.1 6.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000880 | 1 29 | 96.6 0.0 3.4 6.9 10.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000881 | 1 33 | 87.9 12.1 0.0 3.0 15.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000882 | 1 41 | 97.6 2.4 0.0 4.9 7.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000883 | 1 35 | 85.7 14.3 0.0 5.7 20.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000884 | 1 29 | 96.6 0.0 3.4 0.0 3.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000885 | 1 36 | 91.7 5.6 2.8 2.8 11.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000886 | 1 22 | 90.9 4.5 4.5 0.0 9.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000887 | 1 90 | 83.3 5.6 11.1 1.1 17.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000888 | 1 17 | 94.1 5.9 0.0 0.0 5.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000889 | 1 28 | 85.7 7.1 7.1 0.0 14.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000890 | 1 28 | 89.3 10.7 0.0 3.6 14.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000891 | 1 33 | 84.8 9.1 6.1 6.1 21.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000892 | 1 8 | 75.0 25.0 0.0 12.5 37.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000893 | 1 69 | 98.6 1.4 0.0 0.0 1.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000894 | 1 17 | 88.2 5.9 5.9 0.0 11.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000895 | 1 58 | 93.1 3.4 3.4 0.0 6.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000896 | 1 27 | 96.3 3.7 0.0 0.0 3.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000897 | 1 26 | 92.3 3.8 3.8 0.0 7.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000898 | 1 4 | 75.0 25.0 0.0 25.0 50.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000899 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000900 | 1 2 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000901 | 1 4 | 75.0 25.0 0.0 0.0 25.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000902 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000903 | 1 4 | 75.0 25.0 0.0 0.0 25.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000904 | 1 3 | 66.7 33.3 0.0 0.0 33.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000905 | 1 2 | 100.0 0.0 0.0 50.0 50.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000906 | 1 2 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000907 | 1 3 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000908 | 1 56 | 98.2 1.8 0.0 1.8 3.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000909 | 1 74 | 98.6 1.4 0.0 1.4 2.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000910 | 1 80 | 98.8 1.3 0.0 2.5 3.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000911 | 1 25 | 96.0 4.0 0.0 4.0 8.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000912 | 1 35 | 100.0 0.0 0.0 0.0 0.0 0.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000913 | 1 45 | 93.3 4.4 2.2 6.7 13.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000346 | 1 62 | 91.9 1.6 6.5 3.2 11.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000347 | 1 117 | 88.9 4.3 6.8 3.4 14.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000348 | 1 128 | 77.3 6.3 16.4 0.0 22.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000349 | 1 81 | 87.7 8.6 3.7 2.5 14.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000350 | 1 114 | 86.8 7.9 5.3 1.8 14.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000351 | 1 97 | 90.7 4.1 5.2 1.0 10.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000352 | 1 116 | 86.2 8.6 5.2 0.0 13.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000353 | 1 121 | 80.2 12.4 7.4 3.3 23.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000354 | 1 77 | 88.3 3.9 7.8 1.3 13.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000355 | 1 91 | 85.7 12.1 2.2 1.1 15.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000356 | 1 50 | 88.0 10.0 2.0 0.0 12.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000357 | 1 140 | 80.7 14.3 5.0 0.7 20.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000358 | 1 143 | 79.7 11.9 8.4 2.1 22.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000359 | 1 147 | 88.4 6.1 5.4 0.7 12.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000360 | 1 98 | 82.7 12.2 5.1 5.1 22.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000361 | 1 59 | 81.4 11.9 6.8 6.8 25.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000362 | 1 81 | 88.9 9.9 1.2 2.5 13.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000363 | 1 112 | 79.5 13.4 7.1 0.9 21.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000364 | 1 155 | 89.7 7.1 3.2 2.6 12.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000365 | 1 91 | 73.6 15.4 11.0 2.2 28.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000366 | 1 100 | 83.0 9.0 8.0 1.0 18.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000367 | 1 111 | 85.6 7.2 7.2 3.6 18.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000368 | 1 138 | 58.7 17.4 23.9 1.4 42.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000369 | 1 117 | 88.9 6.0 5.1 0.0 11.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000370 | 1 171 | 88.3 7.0 4.7 2.3 14.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000371 | 1 111 | 78.4 10.8 10.8 2.7 24.3 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000372 | 1 121 | 80.2 8.3 11.6 0.0 19.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000373 | 1 224 | 79.9 5.4 14.7 1.8 21.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000374 | 1 120 | 82.5 12.5 5.0 1.7 19.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000375 | 1 57 | 77.2 7.0 15.8 5.3 28.1 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000376 | 1 71 | 93.0 5.6 1.4 2.8 9.9 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000377 | 1 79 | 77.2 12.7 10.1 0.0 22.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000378 | 1 110 | 84.5 10.0 5.5 0.0 15.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000379 | 1 97 | 80.4 10.3 9.3 1.0 20.6 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000380 | 1 114 | 90.4 4.4 5.3 1.8 11.4 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000381 | 1 100 | 79.0 6.0 15.0 2.0 23.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000382 | 1 73 | 79.5 8.2 12.3 0.0 20.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000383 | 1 111 | 89.2 8.1 2.7 0.9 11.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000384 | 1 159 | 73.6 10.7 15.7 0.6 27.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000385 | 1 135 | 77.8 14.8 7.4 3.0 25.2 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000386 | 1 120 | 79.2 13.3 7.5 0.8 21.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000387 | 1 117 | 89.7 5.1 5.1 2.6 12.8 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000388 | 1 126 | 83.3 13.5 3.2 2.4 19.0 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000389 | 1 86 | 95.3 3.5 1.2 5.8 10.5 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000390 | 1 95 | 88.4 5.3 6.3 2.1 13.7 100.0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000391 | 1 135 | 91.9 4.4 3.7 3.7 11.9 100.0 | +|===============================================================================================================| +| Sum/Avg | 160 9700 | 87.5 6.9 5.5 1.9 14.4 93.1 | +|===============================================================================================================| +| Mean | 1.0 60.6 | 89.0 6.9 4.1 2.6 13.6 93.1 | +| S.D. | 0.0 42.8 | 8.5 7.0 4.3 5.0 9.7 25.4 | +| Median | 1.0 50.5 | 90.6 5.7 3.1 1.4 11.6 100.0 | +`---------------------------------------------------------------------------------------------------------------' + + + + SYSTEM SUMMARY PERCENTAGES by SPEAKER + +,---------------------------------------------------------------------------------------------------------------. +|test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/hyp.trn| +|---------------------------------------------------------------------------------------------------------------| +| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000800 | 1 64 | 59 4 1 0 5 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000801 | 1 103 | 96 2 5 1 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000802 | 1 34 | 25 5 4 2 11 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000803 | 1 96 | 87 3 6 2 11 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000804 | 1 32 | 30 2 0 1 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000805 | 1 20 | 18 0 2 0 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000806 | 1 62 | 53 5 4 0 9 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000807 | 1 14 | 14 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000808 | 1 18 | 17 0 1 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000809 | 1 88 | 82 5 1 2 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000810 | 1 70 | 66 2 2 4 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000811 | 1 24 | 24 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000812 | 1 92 | 88 3 1 0 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000813 | 1 42 | 37 5 0 3 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000814 | 1 34 | 30 2 2 0 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000815 | 1 42 | 37 2 3 1 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000816 | 1 32 | 31 1 0 3 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000817 | 1 32 | 31 1 0 2 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000818 | 1 3 | 3 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000819 | 1 3 | 3 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000820 | 1 2 | 2 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000821 | 1 3 | 2 1 0 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000822 | 1 4 | 2 2 0 0 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000823 | 1 64 | 59 1 4 2 7 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000824 | 1 54 | 50 2 2 2 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000825 | 1 73 | 70 2 1 0 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000826 | 1 75 | 74 0 1 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000827 | 1 55 | 53 2 0 0 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000828 | 1 32 | 30 1 1 3 5 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000829 | 1 20 | 17 2 1 0 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000830 | 1 23 | 21 2 0 1 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000831 | 1 8 | 7 1 0 1 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000832 | 1 32 | 29 2 1 0 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000833 | 1 28 | 28 0 0 1 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000834 | 1 26 | 23 2 1 1 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000835 | 1 20 | 18 1 1 1 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000836 | 1 23 | 22 1 0 2 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000837 | 1 9 | 9 0 0 1 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000838 | 1 38 | 36 1 1 1 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000839 | 1 54 | 50 3 1 0 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000840 | 1 90 | 85 4 1 3 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000841 | 1 39 | 38 1 0 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000842 | 1 37 | 34 2 1 0 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000843 | 1 40 | 38 2 0 1 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000844 | 1 82 | 79 2 1 0 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000845 | 1 36 | 35 0 1 2 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000846 | 1 101 | 95 4 2 2 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000847 | 1 57 | 54 2 1 1 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000848 | 1 36 | 34 0 2 0 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000849 | 1 13 | 9 2 2 1 5 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000850 | 1 33 | 27 2 4 0 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000851 | 1 35 | 33 1 1 0 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000852 | 1 25 | 21 3 1 1 5 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000853 | 1 33 | 31 2 0 1 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000854 | 1 77 | 63 11 3 1 15 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000855 | 1 43 | 36 3 4 1 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000856 | 1 40 | 36 3 1 2 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000857 | 1 45 | 41 3 1 1 5 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000858 | 1 33 | 33 0 0 1 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000859 | 1 42 | 39 3 0 1 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000860 | 1 44 | 41 3 0 0 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000861 | 1 36 | 32 3 1 0 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000862 | 1 61 | 58 1 2 3 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000863 | 1 75 | 61 11 3 1 15 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000864 | 1 72 | 66 0 6 1 7 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000865 | 1 50 | 46 1 3 0 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000866 | 1 82 | 69 6 7 1 14 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000867 | 1 51 | 47 3 1 2 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000868 | 1 38 | 37 0 1 1 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000869 | 1 20 | 18 2 0 2 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000870 | 1 34 | 31 0 3 0 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000871 | 1 76 | 69 3 4 0 7 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000872 | 1 85 | 82 3 0 0 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000873 | 1 103 | 94 6 3 0 9 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000874 | 1 57 | 51 2 4 0 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000875 | 1 82 | 74 5 3 0 8 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000876 | 1 30 | 24 3 3 1 7 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000877 | 1 50 | 48 0 2 0 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000878 | 1 27 | 24 3 0 0 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000879 | 1 93 | 88 1 4 1 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000880 | 1 29 | 28 0 1 2 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000881 | 1 33 | 29 4 0 1 5 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000882 | 1 41 | 40 1 0 2 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000883 | 1 35 | 30 5 0 2 7 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000884 | 1 29 | 28 0 1 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000885 | 1 36 | 33 2 1 1 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000886 | 1 22 | 20 1 1 0 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000887 | 1 90 | 75 5 10 1 16 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000888 | 1 17 | 16 1 0 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000889 | 1 28 | 24 2 2 0 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000890 | 1 28 | 25 3 0 1 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000891 | 1 33 | 28 3 2 2 7 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000892 | 1 8 | 6 2 0 1 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000893 | 1 69 | 68 1 0 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000894 | 1 17 | 15 1 1 0 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000895 | 1 58 | 54 2 2 0 4 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000896 | 1 27 | 26 1 0 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000897 | 1 26 | 24 1 1 0 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000898 | 1 4 | 3 1 0 1 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000899 | 1 3 | 3 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000900 | 1 2 | 2 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000901 | 1 4 | 3 1 0 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000902 | 1 3 | 3 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000903 | 1 4 | 3 1 0 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000904 | 1 3 | 2 1 0 0 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000905 | 1 2 | 2 0 0 1 1 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000906 | 1 2 | 2 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000907 | 1 3 | 3 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000908 | 1 56 | 55 1 0 1 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000909 | 1 74 | 73 1 0 1 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000910 | 1 80 | 79 1 0 2 3 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000911 | 1 25 | 24 1 0 1 2 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000912 | 1 35 | 35 0 0 0 0 0 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| cv_jpn_000913 | 1 45 | 42 2 1 3 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000346 | 1 62 | 57 1 4 2 7 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000347 | 1 117 | 104 5 8 4 17 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000348 | 1 128 | 99 8 21 0 29 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000349 | 1 81 | 71 7 3 2 12 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000350 | 1 114 | 99 9 6 2 17 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000351 | 1 97 | 88 4 5 1 10 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000352 | 1 116 | 100 10 6 0 16 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000353 | 1 121 | 97 15 9 4 28 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000354 | 1 77 | 68 3 6 1 10 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000355 | 1 91 | 78 11 2 1 14 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000356 | 1 50 | 44 5 1 0 6 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000357 | 1 140 | 113 20 7 1 28 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000358 | 1 143 | 114 17 12 3 32 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000359 | 1 147 | 130 9 8 1 18 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000360 | 1 98 | 81 12 5 5 22 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000361 | 1 59 | 48 7 4 4 15 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000362 | 1 81 | 72 8 1 2 11 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000363 | 1 112 | 89 15 8 1 24 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000364 | 1 155 | 139 11 5 4 20 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000365 | 1 91 | 67 14 10 2 26 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000366 | 1 100 | 83 9 8 1 18 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000367 | 1 111 | 95 8 8 4 20 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000368 | 1 138 | 81 24 33 2 59 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000369 | 1 117 | 104 7 6 0 13 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000370 | 1 171 | 151 12 8 4 24 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000371 | 1 111 | 87 12 12 3 27 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000372 | 1 121 | 97 10 14 0 24 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000373 | 1 224 | 179 12 33 4 49 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000374 | 1 120 | 99 15 6 2 23 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000375 | 1 57 | 44 4 9 3 16 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000376 | 1 71 | 66 4 1 2 7 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000377 | 1 79 | 61 10 8 0 18 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000378 | 1 110 | 93 11 6 0 17 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000379 | 1 97 | 78 10 9 1 20 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000380 | 1 114 | 103 5 6 2 13 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000381 | 1 100 | 79 6 15 2 23 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000382 | 1 73 | 58 6 9 0 15 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000383 | 1 111 | 99 9 3 1 13 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000384 | 1 159 | 117 17 25 1 43 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000385 | 1 135 | 105 20 10 4 34 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000386 | 1 120 | 95 16 9 1 26 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000387 | 1 117 | 105 6 6 3 15 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000388 | 1 126 | 105 17 4 3 24 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000389 | 1 86 | 82 3 1 5 9 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000390 | 1 95 | 84 5 6 2 13 1 | +|-----------------------+---------------------+-----------------------------------------------------------------| +| fleurs_jpn_000391 | 1 135 | 124 6 5 5 16 1 | +|===============================================================================================================| +| Sum | 160 9700 | 8489 674 537 187 1398 149 | +|===============================================================================================================| +| Mean | 1.0 60.6 | 53.1 4.2 3.4 1.2 8.7 0.9 | +| S.D. | 0.0 42.8 | 35.7 4.8 5.2 1.3 9.8 0.3 | +| Median | 1.0 50.5 | 46.5 2.0 1.0 1.0 5.0 1.0 | +`---------------------------------------------------------------------------------------------------------------' + + + DUMP OF SYSTEM ALIGNMENT STRUCTURE + +System name: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/score_wer/hyp.trn + +Speakers: + 0: cv_jpn_000800 + 1: cv_jpn_000801 + 2: cv_jpn_000802 + 3: cv_jpn_000803 + 4: cv_jpn_000804 + 5: cv_jpn_000805 + 6: cv_jpn_000806 + 7: cv_jpn_000807 + 8: cv_jpn_000808 + 9: cv_jpn_000809 + 10: cv_jpn_000810 + 11: cv_jpn_000811 + 12: cv_jpn_000812 + 13: cv_jpn_000813 + 14: cv_jpn_000814 + 15: cv_jpn_000815 + 16: cv_jpn_000816 + 17: cv_jpn_000817 + 18: cv_jpn_000818 + 19: cv_jpn_000819 + 20: cv_jpn_000820 + 21: cv_jpn_000821 + 22: cv_jpn_000822 + 23: cv_jpn_000823 + 24: cv_jpn_000824 + 25: cv_jpn_000825 + 26: cv_jpn_000826 + 27: cv_jpn_000827 + 28: cv_jpn_000828 + 29: cv_jpn_000829 + 30: cv_jpn_000830 + 31: cv_jpn_000831 + 32: cv_jpn_000832 + 33: cv_jpn_000833 + 34: cv_jpn_000834 + 35: cv_jpn_000835 + 36: cv_jpn_000836 + 37: cv_jpn_000837 + 38: cv_jpn_000838 + 39: cv_jpn_000839 + 40: cv_jpn_000840 + 41: cv_jpn_000841 + 42: cv_jpn_000842 + 43: cv_jpn_000843 + 44: cv_jpn_000844 + 45: cv_jpn_000845 + 46: cv_jpn_000846 + 47: cv_jpn_000847 + 48: cv_jpn_000848 + 49: cv_jpn_000849 + 50: cv_jpn_000850 + 51: cv_jpn_000851 + 52: cv_jpn_000852 + 53: cv_jpn_000853 + 54: cv_jpn_000854 + 55: cv_jpn_000855 + 56: cv_jpn_000856 + 57: cv_jpn_000857 + 58: cv_jpn_000858 + 59: cv_jpn_000859 + 60: cv_jpn_000860 + 61: cv_jpn_000861 + 62: cv_jpn_000862 + 63: cv_jpn_000863 + 64: cv_jpn_000864 + 65: cv_jpn_000865 + 66: cv_jpn_000866 + 67: cv_jpn_000867 + 68: cv_jpn_000868 + 69: cv_jpn_000869 + 70: cv_jpn_000870 + 71: cv_jpn_000871 + 72: cv_jpn_000872 + 73: cv_jpn_000873 + 74: cv_jpn_000874 + 75: cv_jpn_000875 + 76: cv_jpn_000876 + 77: cv_jpn_000877 + 78: cv_jpn_000878 + 79: cv_jpn_000879 + 80: cv_jpn_000880 + 81: cv_jpn_000881 + 82: cv_jpn_000882 + 83: cv_jpn_000883 + 84: cv_jpn_000884 + 85: cv_jpn_000885 + 86: cv_jpn_000886 + 87: cv_jpn_000887 + 88: cv_jpn_000888 + 89: cv_jpn_000889 + 90: cv_jpn_000890 + 91: cv_jpn_000891 + 92: cv_jpn_000892 + 93: cv_jpn_000893 + 94: cv_jpn_000894 + 95: cv_jpn_000895 + 96: cv_jpn_000896 + 97: cv_jpn_000897 + 98: cv_jpn_000898 + 99: cv_jpn_000899 + 100: cv_jpn_000900 + 101: cv_jpn_000901 + 102: cv_jpn_000902 + 103: cv_jpn_000903 + 104: cv_jpn_000904 + 105: cv_jpn_000905 + 106: cv_jpn_000906 + 107: cv_jpn_000907 + 108: cv_jpn_000908 + 109: cv_jpn_000909 + 110: cv_jpn_000910 + 111: cv_jpn_000911 + 112: cv_jpn_000912 + 113: cv_jpn_000913 + 114: fleurs_jpn_000346 + 115: fleurs_jpn_000347 + 116: fleurs_jpn_000348 + 117: fleurs_jpn_000349 + 118: fleurs_jpn_000350 + 119: fleurs_jpn_000351 + 120: fleurs_jpn_000352 + 121: fleurs_jpn_000353 + 122: fleurs_jpn_000354 + 123: fleurs_jpn_000355 + 124: fleurs_jpn_000356 + 125: fleurs_jpn_000357 + 126: fleurs_jpn_000358 + 127: fleurs_jpn_000359 + 128: fleurs_jpn_000360 + 129: fleurs_jpn_000361 + 130: fleurs_jpn_000362 + 131: fleurs_jpn_000363 + 132: fleurs_jpn_000364 + 133: fleurs_jpn_000365 + 134: fleurs_jpn_000366 + 135: fleurs_jpn_000367 + 136: fleurs_jpn_000368 + 137: fleurs_jpn_000369 + 138: fleurs_jpn_000370 + 139: fleurs_jpn_000371 + 140: fleurs_jpn_000372 + 141: fleurs_jpn_000373 + 142: fleurs_jpn_000374 + 143: fleurs_jpn_000375 + 144: fleurs_jpn_000376 + 145: fleurs_jpn_000377 + 146: fleurs_jpn_000378 + 147: fleurs_jpn_000379 + 148: fleurs_jpn_000380 + 149: fleurs_jpn_000381 + 150: fleurs_jpn_000382 + 151: fleurs_jpn_000383 + 152: fleurs_jpn_000384 + 153: fleurs_jpn_000385 + 154: fleurs_jpn_000386 + 155: fleurs_jpn_000387 + 156: fleurs_jpn_000388 + 157: fleurs_jpn_000389 + 158: fleurs_jpn_000390 + 159: fleurs_jpn_000391 + +Speaker sentences 0: cv_jpn_000800 #utts: 1 +id: (cv_jpn_000800-cv_jpn_000800) +Scores: (#C #S #D #I) 59 4 1 0 +REF: k a k o t o m i r a I t o N o M u j u n t e k i j i k o d o o i ts u n a r u g a y U e n i PAU i sh i k i t e k i n a n o d e a r u +HYP: k a k o t o m i r a E t o D o G u j u n t e k i j i k o d o o i ts u n a r u g a y O e n i *** i sh i k i t e k i n a n o d e a r u +Eval: S S S S D + +Speaker sentences 1: cv_jpn_000801 #utts: 1 +id: (cv_jpn_000801-cv_jpn_000801) +Scores: (#C #S #D #I) 96 2 5 1 +REF: s e k a I o k e e s e e s u r u t o t o m O n i PAU j i k o j i sh i n * o k e e s E e s U r u s O o Z o o t e k i s e k a i n o s o o z o o t e k i y o o s o t o sh i t e pau k o b u ts u g a k o b u ts u d e a r u +HYP: s e k a Y o k e e s e e s u r u t o t o m * n i *** j i k o j i sh i n Y o k e e s * e s E r u s * o * o o t e k i s e k a i n o s o o z o o t e k i y o o s o t o sh i t e pau k o b u ts u g a k o b u ts u d e a r u +Eval: S D D I D S D D + +Speaker sentences 2: cv_jpn_000802 #utts: 1 +id: (cv_jpn_000802-cv_jpn_000802) +Scores: (#C #S #D #I) 25 5 4 2 +REF: p a S o k o n d e g e e m U Y a r u N i * * n G A f u E t e k i t e R U +HYP: p a Z o k o n d e g e e m * I a r u * i T O n O H f u I t e k i t e * * +Eval: S D S D I I S S S D D + +Speaker sentences 3: cv_jpn_000803 #utts: 1 +id: (cv_jpn_000803-cv_jpn_000803) +Scores: (#C #S #D #I) 87 3 6 2 +REF: k a G a k u n o sh i m e s U a t a r a sh I i j i j i * ts u PAU a t a r a sh i i k a n n e n PAU k a n ky O o sh i h a i n O a t a r a sh i i k a n o o s E e o m o cl t e *** n a n i O h a j i m e r u k a w a +HYP: k a N a k u n o sh i m e s A a t a r a sh * i j i j i U ts u *** a t a r a sh i i k a n n e n *** k a n ky * o sh i h a i n W a t a r a sh i i k a n o o s * e o m o cl t e PAU n a n i * h a j i m e r u k a w a +Eval: S S D I D D D S D I D + +Speaker sentences 4: cv_jpn_000804 #utts: 1 +id: (cv_jpn_000804-cv_jpn_000804) +Scores: (#C #S #D #I) 30 2 0 1 +REF: o m o sh i r o I n o n i *** r o o D o n a g a s u g i t e d a r u i +HYP: o m o sh i r o U n o n i PAU r o o T o n a g a s u g i t e d a r u i +Eval: S I S + +Speaker sentences 5: cv_jpn_000805 #utts: 1 +id: (cv_jpn_000805-cv_jpn_000805) +Scores: (#C #S #D #I) 18 0 2 0 +REF: k o r e j o o sh u u h a n CL p o i n A a +HYP: k o r e j o o sh u u h a n ** p o i n * a +Eval: D D + +Speaker sentences 6: cv_jpn_000806 #utts: 1 +id: (cv_jpn_000806-cv_jpn_000806) +Scores: (#C #S #D #I) 53 5 4 0 +REF: k a g a k u sh a m o s e k a i o h o o k a ts U T e k i n i t o o i TS U t e k i n i s E ts u m E e sh I Y O o t o sh i t e i r u +HYP: k a g a k u sh a m o s e k a i o h o o k a ts * S e k i n i t o o i CH I t e k i n i s A ts u m * e sh * * U o t o sh i t e i r u +Eval: D S S S S D D D S + +Speaker sentences 7: cv_jpn_000807 #utts: 1 +id: (cv_jpn_000807-cv_jpn_000807) +Scores: (#C #S #D #I) 14 0 0 0 +REF: f u ts u u n i ts u m a r a n +HYP: f u ts u u n i ts u m a r a n +Eval: + +Speaker sentences 8: cv_jpn_000808 #utts: 1 +id: (cv_jpn_000808-cv_jpn_000808) +Scores: (#C #S #D #I) 17 0 1 0 +REF: sh i cl k a r i SH i t e k u d a s a i +HYP: sh i cl k a r i ** i t e k u d a s a i +Eval: D + +Speaker sentences 9: cv_jpn_000809 #utts: 1 +id: (cv_jpn_000809-cv_jpn_000809) +Scores: (#C #S #D #I) 82 5 1 2 +REF: w a t a sh i w * a m i g i n o G o t o k i R e k i sh i t e k i s e E m e e n o j i k a k u t o i u g o t o k i m o n O o b e n sh o o h o o t e k i r o n R i t o * I u n o d e a r u +HYP: w a t a sh i w A a m i g i n o N o t o k i D e k i sh i t e k i s e I m e e n o j i k a k u t o i u g o t o k i m o n * o b e n sh o o h o o t e k i r o n B i t o Y U u n o d e a r u +Eval: I S S S D S I S + +Speaker sentences 10: cv_jpn_000810 #utts: 1 +id: (cv_jpn_000810-cv_jpn_000810) +Scores: (#C #S #D #I) 66 2 2 4 +REF: w a t a sh i w a *** sh a k a i k e e s e e n o k o n t e e n i w a *** d * * I o NY u s o s u t e k i n a m o n o g a h a t a r a i t e i r u t O o m o U +HYP: w a t a sh i w a PAU sh a k a i k e e s e e n o k o n t e e n i w a PAU d E Y O o N u s o s u t e k i n a m o n o g a h a t a r a i t e i r u t * o m o * +Eval: I I I I S S D D + +Speaker sentences 11: cv_jpn_000811 #utts: 1 +id: (cv_jpn_000811-cv_jpn_000811) +Scores: (#C #S #D #I) 24 0 0 0 +REF: n a n i o s u r u ts u m o r i d a cl t a n o k a +HYP: n a n i o s u r u ts u m o r i d a cl t a n o k a +Eval: + +Speaker sentences 12: cv_jpn_000812 #utts: 1 +id: (cv_jpn_000812-cv_jpn_000812) +Scores: (#C #S #D #I) 88 3 1 0 +REF: k o b u ts u t e k i t a g a j i k o h i t e e t e k i n i t a n n i t e n sh u u g o o t e k i n i k a n g a e r a r E r u t o k i PAU s o r e g a b u TS u r i t e k i S e k a i d e a r u +HYP: k o b u ts u t e k i t a g a j i k o h i t e e t e k i n i t a n n i t e n sh u u g o o t e k i n i k a n g a e r a r U r u t o k i *** s o r e g a b u Z u r i t e k i T e k a i d e a r u +Eval: S D S S + +Speaker sentences 13: cv_jpn_000813 #utts: 1 +id: (cv_jpn_000813-cv_jpn_000813) +Scores: (#C #S #D #I) 37 5 0 3 +REF: a M e g a F u cl t a n o d e * pau y a KY U U n o sh i * * a i g a a r i m a s e n d e sh i t a +HYP: a N e g a Z u cl t a n o d e A pau y a CL K I n o sh i R A a i g a a r i m a s e n d e sh i t a +Eval: S S I S S S I I + +Speaker sentences 14: cv_jpn_000814 #utts: 1 +id: (cv_jpn_000814-cv_jpn_000814) +Scores: (#C #S #D #I) 30 2 2 0 +REF: k o r e w a N i CL P o n d e u CL t e i n a i t a b e m o n o d e s u +HYP: k o r e w a R i ** H o n d e u ** t e i n a i t a b e m o n o d e s u +Eval: S D S D + +Speaker sentences 15: cv_jpn_000815 #utts: 1 +id: (cv_jpn_000815-cv_jpn_000815) +Scores: (#C #S #D #I) 37 2 3 1 +REF: w a t a sh i w a PAU h e n sh u u i n o PAU y o n e n k u r a I H a y a cl t a ** t o o m o U +HYP: w a t a sh i w a *** h e n sh u u i n o *** y o n e n k u r a E W a y a cl t a CL t o o m o * +Eval: D D S S I D + +Speaker sentences 16: cv_jpn_000816 #utts: 1 +id: (cv_jpn_000816-cv_jpn_000816) +Scores: (#C #S #D #I) 31 1 0 3 +REF: i s a n n i k o n o k o t o b a n o i m i * * * o o sh i E m a sh i t a +HYP: i s a n n i k o n o k o t o b a n o i m i Y O O o o sh i A m a sh i t a +Eval: I I I S + +Speaker sentences 17: cv_jpn_000817 #utts: 1 +id: (cv_jpn_000817-cv_jpn_000817) +Scores: (#C #S #D #I) 31 1 0 2 +REF: k a Z e g a ts * * u y o i h i w a t e n i s u g a d e k i m a s e n +HYP: k a S e g a ts U S u y o i h i w a t e n i s u g a d e k i m a s e n +Eval: S I I + +Speaker sentences 18: cv_jpn_000818 #utts: 1 +id: (cv_jpn_000818-cv_jpn_000818) +Scores: (#C #S #D #I) 3 0 0 0 +REF: i ch i +HYP: i ch i +Eval: + +Speaker sentences 19: cv_jpn_000819 #utts: 1 +id: (cv_jpn_000819-cv_jpn_000819) +Scores: (#C #S #D #I) 3 0 0 0 +REF: h a i +HYP: h a i +Eval: + +Speaker sentences 20: cv_jpn_000820 #utts: 1 +id: (cv_jpn_000820-cv_jpn_000820) +Scores: (#C #S #D #I) 2 0 0 0 +REF: n i +HYP: n i +Eval: + +Speaker sentences 21: cv_jpn_000821 #utts: 1 +id: (cv_jpn_000821-cv_jpn_000821) +Scores: (#C #S #D #I) 2 1 0 0 +REF: R e i +HYP: D e i +Eval: S + +Speaker sentences 22: cv_jpn_000822 #utts: 1 +id: (cv_jpn_000822-cv_jpn_000822) +Scores: (#C #S #D #I) 2 2 0 0 +REF: R o k U +HYP: T o k I +Eval: S S + +Speaker sentences 23: cv_jpn_000823 #utts: 1 +id: (cv_jpn_000823-cv_jpn_000823) +Scores: (#C #S #D #I) 59 1 4 2 +REF: m i r u t o i u k o t o t o *** h a t a r a k u t o i u k O T o t o g a *** F u k a b u n r i t e k i D E n a k e r e b a n a r a n a i +HYP: m i r u t o i u k o t o t o PAU h a t a r a k u t o i u k * * o t o g a PAU S u k a b u n r i t e k i * * n a k e r e b a n a r a n a i +Eval: I D D I S D D + +Speaker sentences 24: cv_jpn_000824 #utts: 1 +id: (cv_jpn_000824-cv_jpn_000824) +Scores: (#C #S #D #I) 50 2 2 2 +REF: w a r e w a r e * o t a m a sh i i n o S O k o k a r a * u g O K a s u m o n o d e n a k e r e b a n a r a n a i +HYP: w a r e w a r e O o t a m a sh i i n o Z U k o k a r a G u g * * a s u m o n o d e n a k e r e b a n a r a n a i +Eval: I S S I D D + +Speaker sentences 25: cv_jpn_000825 #utts: 1 +id: (cv_jpn_000825-cv_jpn_000825) +Scores: (#C #S #D #I) 70 2 1 0 +REF: z e cl t a i b e n sh o o h o o t e k i n a r u g a Y u e n i i d e Y a t e k i ch o cl k a n t e k i k e e k i g a F u k u m a r e r u n o d e a r u +HYP: z e cl t a i b e n sh o o h o o t e k i n a r u g a I u e n i i d e * a t e k i ch o cl k a n t e k i k e e k i g a CL u k u m a r e r u n o d e a r u +Eval: S D S + +Speaker sentences 26: cv_jpn_000826 #utts: 1 +id: (cv_jpn_000826-cv_jpn_000826) +Scores: (#C #S #D #I) 74 0 1 0 +REF: d o k o m a d e m o t a t o i ch i t o n o s o o g o h i t e e t e k i n a z e cl t a i m u j u n t e k i j i k o d O o i ts u n o s e k a i n i sh i t e +HYP: d o k o m a d e m o t a t o i ch i t o n o s o o g o h i t e e t e k i n a z e cl t a i m u j u n t e k i j i k o d * o i ts u n o s e k a i n i sh i t e +Eval: D + +Speaker sentences 27: cv_jpn_000827 #utts: 1 +id: (cv_jpn_000827-cv_jpn_000827) +Scores: (#C #S #D #I) 53 2 0 0 +REF: sh i k a r u n i n i n g e n t o k a n ky o o t o N o k a n k e e w a m o t o k o o i n o k a n K e e d e a r i +HYP: sh i k a r u n i n i n g e n t o k a n ky o o t o R o k a n k e e w a m o t o k o o i n o k a n KY e e d e a r i +Eval: S S + +Speaker sentences 28: cv_jpn_000828 #utts: 1 +id: (cv_jpn_000828-cv_jpn_000828) +Scores: (#C #S #D #I) 30 1 1 3 +REF: * i s a N n i k * o n o k o t o b a n o i m i * o o sh i E m a sh i t a +HYP: I i s a * n i k O o n o k o t o b a n o i m i Y o o sh i A m a sh i t a +Eval: I D I I S + +Speaker sentences 29: cv_jpn_000829 #utts: 1 +id: (cv_jpn_000829-cv_jpn_000829) +Scores: (#C #S #D #I) 17 2 1 0 +REF: K E e k i g a n a n a ts U a r i m a s u +HYP: * G e k i g a n a n a ts S a r i m a s u +Eval: D S S + +Speaker sentences 30: cv_jpn_000830 #utts: 1 +id: (cv_jpn_000830-cv_jpn_000830) +Scores: (#C #S #D #I) 21 2 0 1 +REF: k o ch i r a W a k o b A y a sh * i s a n d e s u +HYP: k o ch i r a B a k o b U y a sh I i s a n d e s u +Eval: S S I + +Speaker sentences 31: cv_jpn_000831 #utts: 1 +id: (cv_jpn_000831-cv_jpn_000831) +Scores: (#C #S #D #I) 7 1 0 1 +REF: m o sh * i m O sh i +HYP: m o sh I i m A sh i +Eval: I S + +Speaker sentences 32: cv_jpn_000832 #utts: 1 +id: (cv_jpn_000832-cv_jpn_000832) +Scores: (#C #S #D #I) 29 2 1 0 +REF: k o k o w a O o k i k u t e n i G I y a k a n a m a ch i d e s u +HYP: k o k o w a * o k i k u t e n i K U y a k a n a m a ch i d e s u +Eval: D S S + +Speaker sentences 33: cv_jpn_000833 #utts: 1 +id: (cv_jpn_000833-cv_jpn_000833) +Scores: (#C #S #D #I) 28 0 0 1 +REF: s o n o u ch i k a i * a k u s a r e r u k a r a i s o g e +HYP: s o n o u ch i k a i Y a k u s a r e r u k a r a i s o g e +Eval: I + +Speaker sentences 34: cv_jpn_000834 #utts: 1 +id: (cv_jpn_000834-cv_jpn_000834) +Scores: (#C #S #D #I) 23 2 1 1 +REF: a m a s a g a * O s a E r a r e t e t e ch o o d o I i +HYP: a m a s a g a F U s a I r a r e t e t e ch o o d o * i +Eval: I S S D + +Speaker sentences 35: cv_jpn_000835 #utts: 1 +id: (cv_jpn_000835-cv_jpn_000835) +Scores: (#C #S #D #I) 18 1 1 1 +REF: h o K e n sh i ts u n o d o A o * a k e t a +HYP: h o G e n sh i ts u n o d o * o A a k e t a +Eval: S D I + +Speaker sentences 36: cv_jpn_000836 #utts: 1 +id: (cv_jpn_000836-cv_jpn_000836) +Scores: (#C #S #D #I) 22 1 0 2 +REF: m U d a * n i * o w a cl t e m o k i n i sh i n a i +HYP: m O d a N n i O o w a cl t e m o k i n i sh i n a i +Eval: S I I + +Speaker sentences 37: cv_jpn_000837 #utts: 1 +id: (cv_jpn_000837-cv_jpn_000837) +Scores: (#C #S #D #I) 9 0 0 1 +REF: a r i g a ** t a y a +HYP: a r i g a CL t a y a +Eval: I + +Speaker sentences 38: cv_jpn_000838 #utts: 1 +id: (cv_jpn_000838-cv_jpn_000838) +Scores: (#C #S #D #I) 36 1 1 1 +REF: i D o o g a r a k u d a t o j i k a n O w a * s u r e t e t a n o sh i m e r u +HYP: i T o o g a r a k u d a t o j i k a n * w a O s u r e t e t a n o sh i m e r u +Eval: S D I + +Speaker sentences 39: cv_jpn_000839 #utts: 1 +id: (cv_jpn_000839-cv_jpn_000839) +Scores: (#C #S #D #I) 50 3 1 0 +REF: k a G a k u w a g i J u ts u k a s a r e r u n i O o j i t e j o o sh i k i n o u ch i n i h a i cl t e Y u k u +HYP: k a K a k u w a g i Z u ts u k a s a r e r u n i * o j i t e j o o sh i k i n o u ch i n i h a i cl t e I u k u +Eval: S S D S + +Speaker sentences 40: cv_jpn_000840 #utts: 1 +id: (cv_jpn_000840-cv_jpn_000840) +Scores: (#C #S #D #I) 85 4 1 3 +REF: sh i k a sh i t o k i g a k a K o n i h a i r u k o t o s o n o k o t o g a pau m i r * a I o U m u k o t o d e a r i PAU a r a t a n a r u * ** SH u t a i g a d e t e k u r u k o t o d e a r u +HYP: sh i k a sh i t o k i g a k a * o n i h a i r u k o t o s o n o k o t o g a pau m i r A a Y o O m u k o t o d e a r i W a r a t a n a r u I CL S u t a i g a d e t e k u r u k o t o d e a r u +Eval: D I S S S I I S + +Speaker sentences 41: cv_jpn_000841 #utts: 1 +id: (cv_jpn_000841-cv_jpn_000841) +Scores: (#C #S #D #I) 38 1 0 0 +REF: t e r e b i o k a i k a E t e pau t e r e b i o m i r u j i k a n g a f u e t a +HYP: t e r e b i o k a i k a I t e pau t e r e b i o m i r u j i k a n g a f u e t a +Eval: S + +Speaker sentences 42: cv_jpn_000842 #utts: 1 +id: (cv_jpn_000842-cv_jpn_000842) +Scores: (#C #S #D #I) 34 2 1 0 +REF: k a k a r U sh U t a i n o m i PAU i ts u m a d e m o i k i r u n o d e a r u +HYP: k a k a r I sh I t a i n o m i *** i ts u m a d e m o i k i r u n o d e a r u +Eval: S S D + +Speaker sentences 43: cv_jpn_000843 #utts: 1 +id: (cv_jpn_000843-cv_jpn_000843) +Scores: (#C #S #D #I) 38 2 0 1 +REF: n i n k i R a a m e n * y a n i N a r a n d a r a n i j i k a n m a ch i d a cl t a +HYP: n i n k i D a a m e n I y a n i G a r a n d a r a n i j i k a n m a ch i d a cl t a +Eval: S I S + +Speaker sentences 44: cv_jpn_000844 #utts: 1 +id: (cv_jpn_000844-cv_jpn_000844) +Scores: (#C #S #D #I) 79 2 1 0 +REF: s o r e o m o ch i i r u n i n g e n n o i y o k u n i i Z o n sh i PAU s o sh i t e k o r e w a k a r e n o m o cl t e i r u k a ch i n o sh a k u d o n i i Z o n s u r u +HYP: s o r e o m o ch i i r u n i n g e n n o i y o k u n i i T o n sh i *** s o sh i t e k o r e w a k a r e n o m o cl t e i r u k a ch i n o sh a k u d o n i i T o n s u r u +Eval: S D S + +Speaker sentences 45: cv_jpn_000845 #utts: 1 +id: (cv_jpn_000845-cv_jpn_000845) +Scores: (#C #S #D #I) 35 0 1 2 +REF: m a w a r * i * w a m i n n a PAU k a n g a e r u k o t o o y a m e t e i t a +HYP: m a w a r U i O w a m i n n a *** k a n g a e r u k o t o o y a m e t e i t a +Eval: I I D + +Speaker sentences 46: cv_jpn_000846 #utts: 1 +id: (cv_jpn_000846-cv_jpn_000846) +Scores: (#C #S #D #I) 95 4 2 2 +REF: k o o i t e k i ch o * cl * k a n t e k i n i s e k a I o m i r u t o i u k o t o w a PAU GY a k u n i k o o i t e k i ch o cl k a n t e k i n i s e k a I o k e e s e e s u r u k o t o o F U k u m u n o d e a r u +HYP: k o o i t e k i ch o K cl U k a n t e k i n i s e k a Y o m i r u t o i u k o t o w a *** J a k u n i k o o i t e k i ch o cl k a n t e k i n i s e k a Y o k e e s e e s u r u k o t o o * H k u m u n o d e a r u +Eval: I I S D S S D S + +Speaker sentences 47: cv_jpn_000847 #utts: 1 +id: (cv_jpn_000847-cv_jpn_000847) +Scores: (#C #S #D #I) 54 2 1 1 +REF: sh i n ** p a i K a k e s a s e m a i t o s u r u k i z u k a i g a PAU y o k e I n i sh i n p a i s a s e t e sh i m a u +HYP: sh i n CL p a i T a k e s a s e m a i t o s u r u k i z u k a i g a *** y o k e E n i sh i n p a i s a s e t e sh i m a u +Eval: I S D S + +Speaker sentences 48: cv_jpn_000848 #utts: 1 +id: (cv_jpn_000848-cv_jpn_000848) +Scores: (#C #S #D #I) 34 0 2 0 +REF: k o n o m i ch i w a t o t e m o s e m a i n o d e PAU a b U n a i d e s u +HYP: k o n o m i ch i w a t o t e m o s e m a i n o d e *** a b * n a i d e s u +Eval: D D + +Speaker sentences 49: cv_jpn_000849 #utts: 1 +id: (cv_jpn_000849-cv_jpn_000849) +Scores: (#C #S #D #I) 9 2 2 1 +REF: * o B o e g a W a r U i n E +HYP: W o H o e g a * a r * i n I +Eval: I S D D S + +Speaker sentences 50: cv_jpn_000850 #utts: 1 +id: (cv_jpn_000850-cv_jpn_000850) +Scores: (#C #S #D #I) 27 2 4 0 +REF: t o i R E w a r o o k a N o H i D a r i g a W a n i a r i m a s u +HYP: t o i * * w a r o o k a M o * i T a r i g a * a n i a r i m a s u +Eval: D D S D S D + +Speaker sentences 51: cv_jpn_000851 #utts: 1 +id: (cv_jpn_000851-cv_jpn_000851) +Scores: (#C #S #D #I) 33 1 1 0 +REF: t a n a k a s a n n o h i D a R i n i k i m u r a s a n g a i m a s u +HYP: t a n a k a s a n n o h i T a * i n i k i m u r a s a n g a i m a s u +Eval: S D + +Speaker sentences 52: cv_jpn_000852 #utts: 1 +id: (cv_jpn_000852-cv_jpn_000852) +Scores: (#C #S #D #I) 21 3 1 1 +REF: m a cl k u r O n A t a m a G o CL t e s u g o i n * e +HYP: m a cl k u r A n O t a m a B o ** t e s u g o i n I e +Eval: S S S D I + +Speaker sentences 53: cv_jpn_000853 #utts: 1 +id: (cv_jpn_000853-cv_jpn_000853) +Scores: (#C #S #D #I) 31 2 0 1 +REF: sh o HY o o m i t a i n a d o k u sh O k a n s o o b u n * o k a i t a +HYP: sh o H o o m i t a i n a d o k u sh U k a n s o o b u n M o k a i t a +Eval: S S I + +Speaker sentences 54: cv_jpn_000854 #utts: 1 +id: (cv_jpn_000854-cv_jpn_000854) +Scores: (#C #S #D #I) 63 11 3 1 +REF: g e n j i ts u n o s e k a i w a t a * N o i ch i t o sh i t e k e cl t E e s E r a R E T a k a t a CH I o M O cl t A s e k a i D E n a k e r e B a n a r a n a i +HYP: g e n j i ts u n o s e k a i w a t a M O o i ch i t o sh i t e k e cl t * e s U r a * I D a k a t a SH U o * A cl t O s e k a i Z U n a k e r e M a n a r a n a i +Eval: I S D S D S S S S D S S S S S + +Speaker sentences 55: cv_jpn_000855 #utts: 1 +id: (cv_jpn_000855-cv_jpn_000855) +Scores: (#C #S #D #I) 36 3 4 1 +REF: sh o o h i n k e n s a k u g a W A k a r I Y a s u i t * o k a u k i N i n a r u n O N i +HYP: sh o o h i n k e n s a k u g a * O k a r * E a s u i t O o k a u k i * i n a r u n * A i +Eval: D S D S I D D S + +Speaker sentences 56: cv_jpn_000856 #utts: 1 +id: (cv_jpn_000856-cv_jpn_000856) +Scores: (#C #S #D #I) 36 3 1 2 +REF: CH i sh i k i w a *** r e k i sh i t e K I k a t e e * d e n a k e r e B a n a r a n a i +HYP: TS i sh i k i w a PAU r e k i sh i t e * CL k a t e e R d e n a k e r e M a n a r a n a i +Eval: S I D S I S + +Speaker sentences 57: cv_jpn_000857 #utts: 1 +id: (cv_jpn_000857-cv_jpn_000857) +Scores: (#C #S #D #I) 41 3 1 1 +REF: m o n o g o t o n O j U n B a n O k a e r u d a k e d e u m a k u i k u k o t o m o * a r u +HYP: m o n o g o t o n N j I n P a n * k a e r u d a k e d e u m a k u i k u k o t o m o W a r u +Eval: S S S D I + +Speaker sentences 58: cv_jpn_000858 #utts: 1 +id: (cv_jpn_000858-cv_jpn_000858) +Scores: (#C #S #D #I) 33 0 0 1 +REF: k o n o k i s * e ts u w a k a ts u o n o s a sh i m i g a z e cl p i n +HYP: k o n o k i s E e ts u w a k a ts u o n o s a sh i m i g a z e cl p i n +Eval: I + +Speaker sentences 59: cv_jpn_000859 #utts: 1 +id: (cv_jpn_000859-cv_jpn_000859) +Scores: (#C #S #D #I) 39 3 0 1 +REF: k a k e n i sh i cl p a i sh i t e m o o ch ** I ts u i t e s O n sh i ts u o u k e i R e r u +HYP: k a k e n i sh i cl p a i sh i t e m o o ch TS U ts u i t e s A n sh i ts u o u k e i D e r u +Eval: I S S S + +Speaker sentences 60: cv_jpn_000860 #utts: 1 +id: (cv_jpn_000860-cv_jpn_000860) +Scores: (#C #S #D #I) 41 3 0 0 +REF: s o r e y u e n i t e ts u g a K u g a z e n t a i n o g a k U d e a r u t o s u r e B a +HYP: s o r e y u e n i t e ts u g a P u g a z e n t a i n o g a k O d e a r u t o s u r e W a +Eval: S S S + +Speaker sentences 61: cv_jpn_000861 #utts: 1 +id: (cv_jpn_000861-cv_jpn_000861) +Scores: (#C #S #D #I) 32 3 1 0 +REF: CH i i S a n a y a o y a d a g a y A s u k u t e h a n j O o sh i t e r u +HYP: K i i Z a n a y a o y a d a g a y E s u k u t e h a n j * o sh i t e r u +Eval: S S S D + +Speaker sentences 62: cv_jpn_000862 #utts: 1 +id: (cv_jpn_000862-cv_jpn_000862) +Scores: (#C #S #D #I) 58 1 2 3 +REF: i n * f u r a g * a k i n O o F u z e n n i * o ch i i cl t e pau k o k u g a i e d a CL sh u ts u s u r u h i t o m o d e t e k i t a +HYP: i n I f u r a g A a k i n * o H u z e n n i Y o ch i i cl t e pau k o k u g a i e d a ** sh u ts u s u r u h i t o m o d e t e k i t a +Eval: I I D S I D + +Speaker sentences 63: cv_jpn_000863 #utts: 1 +id: (cv_jpn_000863-cv_jpn_000863) +Scores: (#C #S #D #I) 61 11 3 1 +REF: ts u g i n i k a g a k u w a s o n z a I o *** sh u j u n o RY o o I k i n i w a k a cl t e s o r e z O r E n O ry o o I k i n I TS U i t E k e n KY U U s u r u +HYP: ts u g i n i k a g a k u w a s o n z a Y o PAU sh u j u n o D o o E k i n i w a k a cl t e s o r e z U r A n * ry o o U k i n * ** Z i t I k e n I K I s u r u +Eval: S I S S S S D S D D S S S S S + +Speaker sentences 64: cv_jpn_000864 #utts: 1 +id: (cv_jpn_000864-cv_jpn_000864) +Scores: (#C #S #D #I) 66 0 6 1 +REF: s o r e d e w a *** t o k I t o i u m o n o n o s E e r i ts u sh i Y O o w a n a k u pau sh u n k a n t o i U m o n o m o n a k u N a r u n o d e a r u +HYP: s o r e d e w a PAU t o k * t o i u m o n o n o s * e r i ts u sh i * * o w a n a k u pau sh u n k a n t o i * m o n o m o n a k u * a r u n o d e a r u +Eval: I D D D D D D + +Speaker sentences 65: cv_jpn_000865 #utts: 1 +id: (cv_jpn_000865-cv_jpn_000865) +Scores: (#C #S #D #I) 46 1 3 0 +REF: a k a i b u r a n k o PAU K o n k u r I i t o s e e n o s u b e r i d a i PAU k a w a i t a s u n a b a +HYP: a k a i b u r a n k o *** H o n k u r * i t o s e e n o s u b e r i d a i *** k a w a i t a s u n a b a +Eval: D S D D + +Speaker sentences 66: cv_jpn_000866 #utts: 1 +id: (cv_jpn_000866-cv_jpn_000866) +Scores: (#C #S #D #I) 69 6 7 1 +REF: sh i k a sh i s o r e w a d o k o m a d e m O k O k o k a r a D E t e *** K o k o e k a e r i k u r u s E e sh i ts U o m o CL t A M o N o d e n a k E R e b a n a r a n a i +HYP: sh i k a sh i s o r e w a d o k o m a d e m N k U k o k a r a R I t e PAU P o k o e k a e r i k u r u s * e sh i ts * o m o ** t * * o M o d e n a k * * e b a n a r a n a i +Eval: S S S S I S D D D D D S D D + +Speaker sentences 67: cv_jpn_000867 #utts: 1 +id: (cv_jpn_000867-cv_jpn_000867) +Scores: (#C #S #D #I) 47 3 1 2 +REF: a r i t o * a r a Y U r u d e m A o m a k i ch i r a sh i t e *** m i n n A k a r a u r a m i o k a cl t e r u +HYP: a r i t o W a r a * I r u d e m O o m a k i ch i r a sh i t e PAU m i n n E k a r a u r a m i o k a cl t e r u +Eval: I D S S I S + +Speaker sentences 68: cv_jpn_000868 #utts: 1 +id: (cv_jpn_000868-cv_jpn_000868) +Scores: (#C #S #D #I) 37 0 1 1 +REF: k o n o ** t e e d o PAU s a w a g i n i n a r u k o t o m o n a i n o d a r o o +HYP: k o n o CL t e e d o *** s a w a g i n i n a r u k o t o m o n a i n o d a r o o +Eval: I D + +Speaker sentences 69: cv_jpn_000869 #utts: 1 +id: (cv_jpn_000869-cv_jpn_000869) +Scores: (#C #S #D #I) 18 2 0 2 +REF: k o n o n e d a n d e * u r E ch a U k a a * +HYP: k o n o n e d a n d e W u r I ch a N k a a N +Eval: I S S I + +Speaker sentences 70: cv_jpn_000870 #utts: 1 +id: (cv_jpn_000870-cv_jpn_000870) +Scores: (#C #S #D #I) 31 0 3 0 +REF: h i n o k a g e n n i ch U u i sh i n a i t o s u g u N I k o g e r u +HYP: h i n o k a g e n n i ch * u i sh i n a i t o s u g u * * k o g e r u +Eval: D D D + +Speaker sentences 71: cv_jpn_000871 #utts: 1 +id: (cv_jpn_000871-cv_jpn_000871) +Scores: (#C #S #D #I) 69 3 4 0 +REF: e n B a n N o U e n i p o ts u r i t o ch i i s a n a a n a g A H I R a i t a s a i sh o w a ts u m a y o o j i t e e d o n o ch i i s a n a a n a d a cl t a +HYP: e n M a n D o W e n i p o ts u r i t o ch i i s a n a a n a g * * * * a i t a s a i sh o w a ts u m a y o o j i t e e d o n o ch i i s a n a a n a d a cl t a +Eval: S S S D D D D + +Speaker sentences 72: cv_jpn_000872 #utts: 1 +id: (cv_jpn_000872-cv_jpn_000872) +Scores: (#C #S #D #I) 82 3 0 0 +REF: s o r e w a W a r e w a r e o i k a sh i n a g a r a w a r e w a r e o D o r e e k a s u r u n o d e a r u pau w a r e W a r e n o t a m a sh i i o k o r o s u n o d e a r u +HYP: s o r e w a PAU a r e w a r e o i k a sh i n a g a r a w a r e w a r e o T o r e e k a s u r u n o d e a r u pau w a r e B a r e n o t a m a sh i i o k o r o s u n o d e a r u +Eval: S S S + +Speaker sentences 73: cv_jpn_000873 #utts: 1 +id: (cv_jpn_000873-cv_jpn_000873) +Scores: (#C #S #D #I) 94 6 3 0 +REF: r e k i sh i t e k i n i a t a e r a r E t a m o n o w a PAU Z e cl t a i m u j u n t e k i j i K o D o o i ts u t e k i g e n z a i n I o i t e s E k a i SH i t e k i n i a t a e r a r e t a m o n o t o sh i t E +HYP: r e k i sh i t e k i n i a t a e r a r * t a m o n o w a *** D e cl t a i m u j u n t e k i j i G o T o o i ts u t e k i g e n z a i n * o i t e s U k a i H i t e k i n i a t a e r a r e t a m o n o t o sh i t I +Eval: D D S S S D S S S + +Speaker sentences 74: cv_jpn_000874 #utts: 1 +id: (cv_jpn_000874-cv_jpn_000874) +Scores: (#C #S #D #I) 51 2 4 0 +REF: m u j u n t e k i j i K o d o o i ts u t o sh i t e pau i ts u m o k o n o s e k a i n i ch o o e TS U t E k i d e a R U +HYP: m u j u n t e k i j i G o d o o i ts u t o sh i t e pau i ts u m o k o n o s e k a i n i ch o o e ** SH t * k i d e a * * +Eval: S D S D D D + +Speaker sentences 75: cv_jpn_000875 #utts: 1 +id: (cv_jpn_000875-cv_jpn_000875) +Scores: (#C #S #D #I) 74 5 3 0 +REF: y u E n i z e cl t a I m u j u n t e k i j i K o d o o i ts u t o sh i t e g e n Z a i k a r a g e n z a I e t o U G o k i i k u s e k a i n o g e n z a i n I o i t e +HYP: y u U n i z e cl t a E m u j u n t e k i j i G o d o o i ts u t o sh i t e g e n S a i k a r a g e n z a * e t o * B o k i i k u s e k a i n o g e n z a i n * o i t e +Eval: S S S S D D S D + +Speaker sentences 76: cv_jpn_000876 #utts: 1 +id: (cv_jpn_000876-cv_jpn_000876) +Scores: (#C #S #D #I) 24 3 3 1 +REF: * a r e pau B o t a n o sh i t E m O d a CL sh U TS u d e k i n a i +HYP: H a r e pau W o t a n o sh i t O m N d a ** sh * ** u d e k i n a i +Eval: I S S S D D D + +Speaker sentences 77: cv_jpn_000877 #utts: 1 +id: (cv_jpn_000877-cv_jpn_000877) +Scores: (#C #S #D #I) 48 0 2 0 +REF: sh i k a sh i w a t a sh i W a s o k o n i s e k a i n o j i k o d o o i ts u O o k u n o d e w a n a i +HYP: sh i k a sh i w a t a sh i * a s o k o n i s e k a i n o j i k o d o o i ts u * o k u n o d e w a n a i +Eval: D D + +Speaker sentences 78: cv_jpn_000878 #utts: 1 +id: (cv_jpn_000878-cv_jpn_000878) +Scores: (#C #S #D #I) 24 3 0 0 +REF: n e M U T a k u n a r u n o g a h a y a k u n a cl t a +HYP: n e B E K a k u n a r u n o g a h a y a k u n a cl t a +Eval: S S S + +Speaker sentences 79: cv_jpn_000879 #utts: 1 +id: (cv_jpn_000879-cv_jpn_000879) +Scores: (#C #S #D #I) 88 1 4 1 +REF: w a t a sh i w a N i n g e n n * o r e k i sh i t e k i k e e s e e n o t a ch i b a k a r a g E e j u ts u o m i r u n o d e a cl t e PAU K o o sh a k a r a Z e n sh a o m i r u n o d e w a n a i +HYP: w a t a sh i w a * i n g e n n O o r e k i sh i t e k i k e e s e e n o t a ch i b a k a r a g * e j u ts u o m i r u n o d e a cl t e *** * o o sh a k a r a D e n sh a o m i r u n o d e w a n a i +Eval: D I D D D S + +Speaker sentences 80: cv_jpn_000880 #utts: 1 +id: (cv_jpn_000880-cv_jpn_000880) +Scores: (#C #S #D #I) 28 0 1 2 +REF: a o i t o m a t o sh i k a n a k u t e k a u k a * m a * y o U +HYP: a o i t o m a t o sh i k a n a k u t e k a u k a B m a I y o * +Eval: I I D + +Speaker sentences 81: cv_jpn_000881 #utts: 1 +id: (cv_jpn_000881-cv_jpn_000881) +Scores: (#C #S #D #I) 29 4 0 1 +REF: SH i n k i * J i gy o o n i o o k i n a k i t a I o y o s E t e i r u +HYP: S i n k i Z U i gy o o n i o o k i n a k i t a Y o y o s U t e i r u +Eval: S I S S S + +Speaker sentences 82: cv_jpn_000882 #utts: 1 +id: (cv_jpn_000882-cv_jpn_000882) +Scores: (#C #S #D #I) 40 1 0 2 +REF: n a n i k a sh i r a n o * * i n s e n t i b u G a n a i t o k i b i sh i i n o d e w a +HYP: n a n i k a sh i r a n o I N i n s e n t i b u W a n a i t o k i b i sh i i n o d e w a +Eval: I I S + +Speaker sentences 83: cv_jpn_000883 #utts: 1 +id: (cv_jpn_000883-cv_jpn_000883) +Scores: (#C #S #D #I) 30 5 0 2 +REF: j i k A n S e e g e n n o i b e n t o d e s u t o r E S U t a m a r * * u +HYP: j i k O n SH e e g e n n o i b e n t o d e s u t o r U SH I t a m a r U B u +Eval: S S S S S I I + +Speaker sentences 84: cv_jpn_000884 #utts: 1 +id: (cv_jpn_000884-cv_jpn_000884) +Scores: (#C #S #D #I) 28 0 1 0 +REF: m a W a r i n o h i t o w a b o o z e n t o sh i t e i t a +HYP: m a * a r i n o h i t o w a b o o z e n t o sh i t e i t a +Eval: D + +Speaker sentences 85: cv_jpn_000885 #utts: 1 +id: (cv_jpn_000885-cv_jpn_000885) +Scores: (#C #S #D #I) 33 2 1 1 +REF: s o n n a n a i y o o n o m e e r U G a PAU n a n k e n m o k * i t e i t a +HYP: s o n n a n a i y o o n o m e e r E W a *** n a n k e n m o k U i t e i t a +Eval: S S D I + +Speaker sentences 86: cv_jpn_000886 #utts: 1 +id: (cv_jpn_000886-cv_jpn_000886) +Scores: (#C #S #D #I) 20 1 1 0 +REF: n I j i k a i d E d e e s u i sh i t e i t a +HYP: n * j i k a i d R d e e s u i sh i t e i t a +Eval: D S + +Speaker sentences 87: cv_jpn_000887 #utts: 1 +id: (cv_jpn_000887-cv_jpn_000887) +Scores: (#C #S #D #I) 75 5 10 1 +REF: t o k i d o k i PAU J i B u n n O K o k o r o G a w a k a r a n a k u n A r u t o k i g a a r u d a k a r a b o k u w a k A a T E n * o H I k i PAU n o o t o n i k a K i H a j i m e r u +HYP: t o k i d o k i *** CH i * u n n * * o k o r o W a w a k a r a n a k u n O r u t o k i g a a r u d a k a r a b o k u w a k * a * * n I o * CL k i *** n o o t o n i k a CH i * a j i m e r u +Eval: D S D D D S S D D D I D S D S D + +Speaker sentences 88: cv_jpn_000888 #utts: 1 +id: (cv_jpn_000888-cv_jpn_000888) +Scores: (#C #S #D #I) 16 1 0 0 +REF: m o o n i g e t e ch a D a m e d a +HYP: m o o n i g e t e ch a T a m e d a +Eval: S + +Speaker sentences 89: cv_jpn_000889 #utts: 1 +id: (cv_jpn_000889-cv_jpn_000889) +Scores: (#C #S #D #I) 24 2 2 0 +REF: k a r e w a PAU B o o CL t o t a CH i ts u k u sh i t e i t a +HYP: k a r e w a *** P o o ** t o t a J i ts u k u sh i t e i t a +Eval: D S D S + +Speaker sentences 90: cv_jpn_000890 #utts: 1 +id: (cv_jpn_000890-cv_jpn_000890) +Scores: (#C #S #D #I) 25 3 0 1 +REF: d a r * E n i m o m e E w a k U w a k a k e t a k u n a i +HYP: d a r A U n i m o m e I w a k O w a k a k e t a k u n a i +Eval: I S S S + +Speaker sentences 91: cv_jpn_000891 #utts: 1 +id: (cv_jpn_000891-cv_jpn_000891) +Scores: (#C #S #D #I) 28 3 2 2 +REF: M a s a k a PAU t o o m o CL t e D o * a n * o t o cl t e o n i g I cl t a +HYP: W a s a k a *** t o o m o ** t e T o W a n O o t o cl t e o n i g E cl t a +Eval: S D D S I I S + +Speaker sentences 92: cv_jpn_000892 #utts: 1 +id: (cv_jpn_000892-cv_jpn_000892) +Scores: (#C #S #D #I) 6 2 0 1 +REF: ** S U i m a s e n +HYP: SH T E i m a s e n +Eval: I S S + +Speaker sentences 93: cv_jpn_000893 #utts: 1 +id: (cv_jpn_000893-cv_jpn_000893) +Scores: (#C #S #D #I) 68 1 0 0 +REF: k a y o U n i sh i t e sh i cl t e i r u t o t o m o n i sh i cl t e i n a i t o k o r o k a r a t a n ky u u w a h a j i m a r u n o d e a r u +HYP: k a y o O n i sh i t e sh i cl t e i r u t o t o m o n i sh i cl t e i n a i t o k o r o k a r a t a n ky u u w a h a j i m a r u n o d e a r u +Eval: S + +Speaker sentences 94: cv_jpn_000894 #utts: 1 +id: (cv_jpn_000894-cv_jpn_000894) +Scores: (#C #S #D #I) 15 1 1 0 +REF: a i s a TS u w a d a i j i d a Y o +HYP: a i s a ** u w a d a i j i d a I o +Eval: D S + +Speaker sentences 95: cv_jpn_000895 #utts: 1 +id: (cv_jpn_000895-cv_jpn_000895) +Scores: (#C #S #D #I) 54 2 2 0 +REF: t o j i t a m o n O o i k a n i h i r o g e t e m o h i r a i t a m o n o n i W a n a r a N U t o i cl t e i r u g a +HYP: t o j i t a m o n * o i k a n i h i r o g e t e m o h i r a i t a m o n o n i * a n a r a R O t o i cl t e i r u g a +Eval: D D S S + +Speaker sentences 96: cv_jpn_000896 #utts: 1 +id: (cv_jpn_000896-cv_jpn_000896) +Scores: (#C #S #D #I) 26 1 0 0 +REF: t a m e sh i n i i k u ts u k a ts u k u cl t e m i y o O +HYP: t a m e sh i n i i k u ts u k a ts u k u cl t e m i y o A +Eval: S + +Speaker sentences 97: cv_jpn_000897 #utts: 1 +id: (cv_jpn_000897-cv_jpn_000897) +Scores: (#C #S #D #I) 24 1 1 0 +REF: z u i b u n a k o g i n a sh o o b a i d a Y o n A a +HYP: z u i b u n a k o g i n a sh o o b a i d a I o n * a +Eval: S D + +Speaker sentences 98: cv_jpn_000898 #utts: 1 +id: (cv_jpn_000898-cv_jpn_000898) +Scores: (#C #S #D #I) 3 1 0 1 +REF: w a * CH i +HYP: w a T E i +Eval: I S + +Speaker sentences 99: cv_jpn_000899 #utts: 1 +id: (cv_jpn_000899-cv_jpn_000899) +Scores: (#C #S #D #I) 3 0 0 0 +REF: i ch i +HYP: i ch i +Eval: + +Speaker sentences 100: cv_jpn_000900 #utts: 1 +id: (cv_jpn_000900-cv_jpn_000900) +Scores: (#C #S #D #I) 2 0 0 0 +REF: g o +HYP: g o +Eval: + +Speaker sentences 101: cv_jpn_000901 #utts: 1 +id: (cv_jpn_000901-cv_jpn_000901) +Scores: (#C #S #D #I) 3 1 0 0 +REF: sh i CH i +HYP: sh i K i +Eval: S + +Speaker sentences 102: cv_jpn_000902 #utts: 1 +id: (cv_jpn_000902-cv_jpn_000902) +Scores: (#C #S #D #I) 3 0 0 0 +REF: i i e +HYP: i i e +Eval: + +Speaker sentences 103: cv_jpn_000903 #utts: 1 +id: (cv_jpn_000903-cv_jpn_000903) +Scores: (#C #S #D #I) 3 1 0 0 +REF: W a ch i +HYP: H a ch i +Eval: S + +Speaker sentences 104: cv_jpn_000904 #utts: 1 +id: (cv_jpn_000904-cv_jpn_000904) +Scores: (#C #S #D #I) 2 1 0 0 +REF: R e i +HYP: N e i +Eval: S + +Speaker sentences 105: cv_jpn_000905 #utts: 1 +id: (cv_jpn_000905-cv_jpn_000905) +Scores: (#C #S #D #I) 2 0 0 1 +REF: sh * i +HYP: sh I i +Eval: I + +Speaker sentences 106: cv_jpn_000906 #utts: 1 +id: (cv_jpn_000906-cv_jpn_000906) +Scores: (#C #S #D #I) 2 0 0 0 +REF: k u +HYP: k u +Eval: + +Speaker sentences 107: cv_jpn_000907 #utts: 1 +id: (cv_jpn_000907-cv_jpn_000907) +Scores: (#C #S #D #I) 3 0 0 0 +REF: i ch i +HYP: i ch i +Eval: + +Speaker sentences 108: cv_jpn_000908 #utts: 1 +id: (cv_jpn_000908-cv_jpn_000908) +Scores: (#C #S #D #I) 55 1 0 1 +REF: k a G a k u g a a k i r a k a n i s u r u *** ky a cl k a n t e k i sh i n r i n i sh i t a g a u k o t o n i y o cl t e +HYP: k a K a k u g a a k i r a k a n i s u r u PAU ky a cl k a n t e k i sh i n r i n i sh i t a g a u k o t o n i y o cl t e +Eval: S I + +Speaker sentences 109: cv_jpn_000909 #utts: 1 +id: (cv_jpn_000909-cv_jpn_000909) +Scores: (#C #S #D #I) 73 1 0 1 +REF: k a k o t o m i r a i t o n o m u j u n t e k i j i k o d o o i ts u t o sh i t e n o g e n z a i g a *** k a t a ch i o m o TS u t o i u k o t o d e a r u +HYP: k a k o t o m i r a i t o n o m u j u n t e k i j i k o d o o i ts u t o sh i t e n o g e n z a i g a PAU k a t a ch i o m o SH u t o i u k o t o d e a r u +Eval: I S + +Speaker sentences 110: cv_jpn_000910 #utts: 1 +id: (cv_jpn_000910-cv_jpn_000910) +Scores: (#C #S #D #I) 79 1 0 2 +REF: b u ts u r i t e k i s e k a i w a *** s u u g a k u t e k i k i g o o n i y o cl t e a r a w a s a r e r u *** s u u g a k u t e k i k a t a ch i n o S e k a i d e a r u +HYP: b u ts u r i t e k i s e k a i w a PAU s u u g a k u t e k i k i g o o n i y o cl t e a r a w a s a r e r u PAU s u u g a k u t e k i k a t a ch i n o SH e k a i d e a r u +Eval: I I S + +Speaker sentences 111: cv_jpn_000911 #utts: 1 +id: (cv_jpn_000911-cv_jpn_000911) +Scores: (#C #S #D #I) 24 1 0 1 +REF: * o n a j i g e n sh o o d e s a n k o o N i n a r u +HYP: W o n a j i g e n sh o o d e s a n k o o G i n a r u +Eval: I S + +Speaker sentences 112: cv_jpn_000912 #utts: 1 +id: (cv_jpn_000912-cv_jpn_000912) +Scores: (#C #S #D #I) 35 0 0 0 +REF: g a i k o k u k a r a k i t a m o n o d a t o sh i cl t e b i cl k u r i +HYP: g a i k o k u k a r a k i t a m o n o d a t o sh i cl t e b i cl k u r i +Eval: + +Speaker sentences 113: cv_jpn_000913 #utts: 1 +id: (cv_jpn_000913-cv_jpn_000913) +Scores: (#C #S #D #I) 42 2 1 3 +REF: i w a y U r u j i cl s e n n i y o cl t e k a k u t o * k u sh i R A i * * cl t a m o n o d e a r u +HYP: i w a y O r u j i cl s e n n i y o cl t e k a k u t o U k u sh i * K i T A cl t a m o n o d e a r u +Eval: S I D S I I + +Speaker sentences 114: fleurs_jpn_000346 #utts: 1 +id: (fleurs_jpn_000346-fleurs_jpn_000346) +Scores: (#C #S #D #I) 57 1 4 2 +REF: o n a j i y O o n i PAU d a n s e e w a h i z a O o o u z u b o n o h a K U k o t o g a *** g i m * u z u k e r a r e t e i m a s u +HYP: o n a j i y * o n i *** d a n s e e w a h i z a * o o u z u b o n o h a * CL k o t o g a PAU g i m U u z u k e r a r e t e i m a s u +Eval: D D D D S I I + +Speaker sentences 115: fleurs_jpn_000347 #utts: 1 +id: (fleurs_jpn_000347-fleurs_jpn_000347) +Scores: (#C #S #D #I) 104 5 8 4 +REF: k o n o s a a b i s U W a PAU G o r a k u s * e N O h a j i m e t o s u r u s e n p a k u y a PAU e n k a k u ch i d e *** d e e t a y a o n s E e o h i TS u Y O o t o s u r u t a n K e n t a i n i *** h i n ** p a N n i r i y o o s a r e t e i m a s u +HYP: k o n o s a a b i s * * a *** K o r a k u s E e W A h a j i m e t o s u r u s e n p a k u y a *** e n k a k u ch i d e PAU d e e t a y a o n s * e o h i S u * * o t o s u r u t a n G e n t a i n i PAU h i n CL p a * n i r i y o o s a r e t e i m a s u +Eval: D D D S I S S D I D S D D S I I D + +Speaker sentences 116: fleurs_jpn_000348 #utts: 1 +id: (fleurs_jpn_000348-fleurs_jpn_000348) +Scores: (#C #S #D #I) 99 8 21 0 +REF: ky o o f u u PAU hy O o PAU k a D o n o k o o s u i RY o U PAU o Y o b i Y a m a k a J i W a PAU r a i u PAU t a ts u m a k i PAU M i z U F u k i PAU O Y o b I s a i k u r O N n A d O n o k i b i sh i i k i sh O o k e e t a I y a s o n o e e ky O o n i y o r u m o n o D e s u +HYP: ky o o f u u *** hy * o *** k a T o n o k o o s u i R o * *** o * o b i * a m a k a SH i B a *** r a i u *** t a ts u m a k i *** * i z * * u k i *** * * o b E s a i k u r * * n O d A n o k i b i sh i i k i sh * o k e e t a * y a s o n o e e ky * o n i y o r u m o n o R e s u +Eval: D D D S S D D D D S S D D D D D D D D D S D D S S D D D S + +Speaker sentences 117: fleurs_jpn_000349 #utts: 1 +id: (fleurs_jpn_000349-fleurs_jpn_000349) +Scores: (#C #S #D #I) 71 7 3 2 +REF: i n t a a n e cl t o w a PAU m a s u k o MY U N i k e e sh O n t o t a i j i n k o * * MY u N i k e e sh o N n o ry o o y O o s o o k a n e s O n a e t a k a n ky o o d e s u +HYP: i n t a a n e cl t o w a *** m a s u k o M I R i k e e sh U n t o t a i j i n k o M I R u * i k e e sh o O n o ry o o y * o s o o k a n e s U n a e t a k a n ky o o d e s u +Eval: D S S S S I I S D S D S + +Speaker sentences 118: fleurs_jpn_000350 #utts: 1 +id: (fleurs_jpn_000350-fleurs_jpn_000350) +Scores: (#C #S #D #I) 99 9 6 2 +REF: k a j i n o D e w a ts u u j o o PAU t o k u b e TS u N a i n sh O k U y a e n t a a t e i m e n t O o y o o I sh i t e i m a s u PAU g e s u T O G a k i b u N y o k u sh i s e TS u n a i n i t o * * m a r u y o o N i s u r u t a m e d e s u +HYP: k a j i n o R e w a ts u u j o o *** t o k u b e S u R a i n sh I k O y a e n t a a t e i m e n t * o y o o * sh i t e i m a s u *** g e s u * * W a k i b u I y o k u sh i s e S u n a i n i t o R A m a r u y o o R i s u r u t a m e d e s u +Eval: S D S S S S D D D D D S S S I I S + +Speaker sentences 119: fleurs_jpn_000351 #utts: 1 +id: (fleurs_jpn_000351-fleurs_jpn_000351) +Scores: (#C #S #D #I) 88 4 5 1 +REF: sh i k a sh i pau KY a P u t e n n o W i k e cl t o o U sh i n a cl t a a * t o PAU i n d o w a n a n a ts u n o W i k e cl t o o U sh i n a i PAU s a n j u u r o k u r a N sh i k a d e k i m a s e n d e sh i t a +HYP: sh i k a sh i pau K a K u t e n n o B i k e cl t o o * sh i n a cl t a a D t o *** i n d o w a n a n a ts u n o B i k e cl t o o * sh i n a i *** s a n j u u r o k u r a * sh i k a d e k i m a s e n d e sh i t a +Eval: S S S D I D S D D D + +Speaker sentences 120: fleurs_jpn_000352 #utts: 1 +id: (fleurs_jpn_000352-fleurs_jpn_000352) +Scores: (#C #S #D #I) 100 10 6 0 +REF: F o o k u r a n d o n o k o o sh i k I ts U u k a w a F o o k u r a n d o sh o t o o P o n d o e f u k e e p i i d e i ch i p o n D o g a i ch I I G I R i S U P o n d o j i i B i i P i i t o t o o k a n i k o t e e s a r e t e i m a s u +HYP: H o o k u r a n d o n o k o o sh i k E ts * u k a w a H o o k u r a n d o sh o t o o K o n d o e f u k e e p i i d e i ch i p o n N o g a i ch * * * * * i E E B o n d o j i i P i i B i i t o t o o k a n i k o t e e s a r e t e i m a s u +Eval: S S D S S S D D D D D S S S S S + +Speaker sentences 121: fleurs_jpn_000353 #utts: 1 +id: (fleurs_jpn_000353-fleurs_jpn_000353) +Scores: (#C #S #D #I) 97 15 9 4 +REF: h a sh i sh i t a * n o K A M I G A T A k U U k a n w * A j u u g o m E e t o r U d e s u PAU n i s e n j U u i ch i n e N h a ch i g a ts u n i SH U N k o o sh i pau n i s e n j u u n A n A n E n * s a n g a ts u m a D e k a i ts u U sh i m a s e n * d e sh i t a +HYP: h a sh i sh i t a N n o * * J O O H O O k * O k a n w O CH j u u g o m * e t o r O d e s u *** n i s e n j * u i ch i n e * h a ch i g a ts u n i ** S E k o o sh i pau n i s e n j u u n O n E n * n I s a n g a ts u m a N e k a i ts u E sh i m a s e n G d e sh i t a +Eval: I D D S S S S S S D S I S D S D D D D S S S S D I S S I + +Speaker sentences 122: fleurs_jpn_000354 #utts: 1 +id: (fleurs_jpn_000354-fleurs_jpn_000354) +Scores: (#C #S #D #I) 68 3 6 1 +REF: i cl p u n k a n d e * f u cl t O o s U R u ch i i k i m O a r e b a PAU f u cl t o o s u r u m a d e N i n a N P U n m o k a k a r u ch i i k i m o a r i m a s u +HYP: i cl p u n k a n d e H f u cl t * o s O G u ch i i k i m * a r e b a *** f u cl t o o s u r u m a d e M i n a * * * n m o k a k a r u ch i i k i m o a r i m a s u +Eval: I D S S D D S D D D + +Speaker sentences 123: fleurs_jpn_000355 #utts: 1 +id: (fleurs_jpn_000355-fleurs_jpn_000355) +Scores: (#C #S #D #I) 78 11 2 1 +REF: p i r a m i cl D O n o o t o t o H i k a R i n o sh o o w a PAU k o n o k a n k o o CH i d e t o k u n i k o D o m o * t a CH i G a t a n o sh i m e r U m o y o o sh I n o h i t o TS u D e s u +HYP: p i r a m i cl T A n o o t o t o SH i k a * i n o sh o o w a *** k o n o k a n k o o SH i d e t o k u n i k o R o m o A t a SH i K a t a n o sh i m e r E m o y o o sh U n o h i t o S u R e s u +Eval: S S S D D S S I S S S S S S + +Speaker sentences 124: fleurs_jpn_000356 #utts: 1 +id: (fleurs_jpn_000356-fleurs_jpn_000356) +Scores: (#C #S #D #I) 44 5 1 0 +REF: s o n o t a M e PAU t a N n i R a B e r u t o sh i t e HY o o k i g a ts u i k a s a r e g a ch i d e s u +HYP: s o n o t a N e *** t a I n i D a D e r u t o sh i t e H o o k i g a ts u i k a s a r e g a ch i d e s u +Eval: S D S S S S + +Speaker sentences 125: fleurs_jpn_000357 #utts: 1 +id: (fleurs_jpn_000357-fleurs_jpn_000357) +Scores: (#C #S #D #I) 113 20 7 1 +REF: g e n S O n s u r u k o t o g a sh i R a r e t e i r u n i j U U g * o m a i n O D a n R a cl P u PAU B u r o o D O s a i d o w a PAU g e n S O n s u r U t o o g a I b u n k e N n o s a i k o n o U TS u sh i d e s u PAU t e g a k i n I y o r u g e n p o N w a g e n S o N sh i t e i m A s e n +HYP: g e n Z U n s u r u k o t o g a sh i T a r e t e i r u n i j I O g O o m a i n A T a n D a cl T u *** P u r o o T U s a i d o w a *** g e n Z U n s u r A t o o g a E b u n k e * n o s a i k o n o * ** u sh i d e s u *** t e g a k i n * y o r u g e n p o O w a g e n Z o O sh i t e i m O s e n +Eval: S S S S S I S S S S D S S S D S S S S D D D D D S S S S + +Speaker sentences 126: fleurs_jpn_000358 #utts: 1 +id: (fleurs_jpn_000358-fleurs_jpn_000358) +Scores: (#C #S #D #I) 114 17 12 3 +REF: k a r e N o s e TS U o t a d a sh I i t o m i t o m e r u H i t o m O i m a sh i t a g a PAU o o k u N O h i t o W A s O n o GY A k U d e pau t a i y o o k E e d e W a t a i Y O o t o s * * o N o T a n o h o sh i g a *** ch i KY u u n o m A W a r I O i d o o sh i T e I r u t o sh i n J i t e i m a sh i t a +HYP: k a r e M o s e S O o t a d a sh * i t o m i t o m e r u SH i t o m * i m a sh i t a g a *** o o k u G U h i t o * O s U n o G E k O d e pau t a i y o o k * e d e * a t a i * * o t o s I N o H o K a n o h o sh i g a PAU ch i K u u n o m * * a r * E i d o o sh i S e * r u t o sh i n CH i t e i m a sh i t a +Eval: S S S D S D D S S D S S S S S D D D D I I S S I S D D D S S D S + +Speaker sentences 127: fleurs_jpn_000359 #utts: 1 +id: (fleurs_jpn_000359-fleurs_jpn_000359) +Scores: (#C #S #D #I) 130 9 8 1 +REF: ch i b e cl t o m e e s O o N o ch U u sh i n w a sh i N s e e Y o g a d e s u PAU s a m a z a M a n a k a m i g a m i * o sh i k a k u k a s u r u k o t o d e PAU e n e r u g i i ch a n e r u g a J o o k a s a r e PAU ch a k u r a g a k a CL s e e k a s a r e PAU s a t o r I n O i sh i k I g A U m a r e m a s u +HYP: ch i b e cl t o m e e s * o * o ch * u sh i n w a sh i I s e e I o g a d e s u *** s a m a z a N a n a k a m i g a m i Y o sh i k a k u k a s u r u k o t o d e *** e n e r u g i i ch a n e r u g a SH o o k a s a r e *** ch a k u r a g a k a ** s e e k a s a r e *** s a t o r U n E i sh i k E g O O m a r e m a s u +Eval: D D D S S D S I D S D D D S S S S S + +Speaker sentences 128: fleurs_jpn_000360 #utts: 1 +id: (fleurs_jpn_000360-fleurs_jpn_000360) +Scores: (#C #S #D #I) 81 12 5 5 +REF: m i n a m i * a F u r I k a n i * a r u s u b e t e n * O k o k u r i TS u k o o e N t o d o o y O o n i PAU k o n O k o o e N n i W a m a i n i CH i * h o g o H i t o * NY u u e n RY O o g A K a k a r i m a s u +HYP: m i n a m i Y a H u r E k a n i Y a r u s u b e t e n R U k o k u r i S u k o o e * t o d o o y * o n i *** k o n U k o o e * n i * a m a i n i J i A h o g o SH i t o N I u u e n G U o g O T a k a r i m a s u +Eval: I S S I I S S D D D S D D S I S I S S S S S + +Speaker sentences 129: fleurs_jpn_000361 #utts: 1 +id: (fleurs_jpn_000361-fleurs_jpn_000361) +Scores: (#C #S #D #I) 48 7 4 4 +REF: R e cl sh * a PAU k u r u * M a PAU s O n * * o T a n O o o k u n O k o o ts U u sh u d a n g a s O k o k a r A u m a r e m a sh i t a +HYP: D e cl sh S a CL k u r u N G a *** s U n U H o K a n * o o k u n U k o o ts * u sh u d a n g a s U k o k a r * u m a r e m a sh i t a +Eval: S I S I S D S I I S D S D S D + +Speaker sentences 130: fleurs_jpn_000362 #utts: 1 +id: (fleurs_jpn_000362-fleurs_jpn_000362) +Scores: (#C #S #D #I) 72 8 1 2 +REF: i n t a a n e cl t o w a PAU m a s u k o MY U n i K e e sh o n t o t a i j i n k o MY U n i K e e sh * o n n o ry o o y o o s o o k a n E s o n a * e t a k a n ky o o D e s u +HYP: i n t a a n e cl t o w a *** m a s u k o M I n i G e e sh o n t o t a i j i n k o M I n i G e e sh A o n n o ry o o y o o s o o k a n I s o n a I e t a k a n ky o o R e s u +Eval: D S S S S S S I S I S + +Speaker sentences 131: fleurs_jpn_000363 #utts: 1 +id: (fleurs_jpn_000363-fleurs_jpn_000363) +Scores: (#C #S #D #I) 89 15 8 1 +REF: BY o o i n d e w a PAU k a n s e n k a n r i t e j u N sh o n i sh i T a g a i pau T a n i N e n O k a n s e n N o k a n o o s E e O F U s e G u t a m e n i k a n j a O k a k u r I s u r U N a D o N O s o ch I o t o cl t e i m * A S u +HYP: KY o o i n d e w a *** k a n s e n k a n r i t e j u U sh o n i sh i K a g a i pau K a n i I e n U k a n s e n G o k a n o o s * e * * * s e K u t a m e n i k a n j a * k a k u r * s u r A D a M o M I s o ch * o t o cl t e i m U SH I u +Eval: S D S S S S S S D D D D S D D S S S S S D I S S + +Speaker sentences 132: fleurs_jpn_000364 #utts: 1 +id: (fleurs_jpn_000364-fleurs_jpn_000364) +Scores: (#C #S #D #I) 139 11 5 4 +REF: r e n p o o G i k a i w a n i s e n g o n e n d o k a r a w a i s e TS u b u TS u t o r I sh I m a r i H o * o e n o sh i k i n t e e ky O o o k a i SH i sh i pau e F u b i i a i w a a D a r u T o p o r u n o n i j u u n i N n o s o o s a * i n * o t o o NY u * u sh I n a k e r e B a n a r a n a i t o k i t E e sh i m a sh i t a +HYP: r e n p o o R i k a i w a n i s e n g o n e n d o k a r a w a i s e S u b u S u t o r E sh U m a r i * o H o e n o sh i k i n t e e ky * o o k a i J i sh i pau e R u b i i a i w a a T a r u Z o p o r u n o n i j u u n i * n o s o o s a N i n Y o t o o ** u N u sh U n a k e r e W a n a r a n a i t o k i t * e sh i m a sh i t a +Eval: S S S S S D I D S S S S D I I D I S S D + +Speaker sentences 133: fleurs_jpn_000365 #utts: 1 +id: (fleurs_jpn_000365-fleurs_jpn_000365) +Scores: (#C #S #D #I) 67 14 10 2 +REF: p i i e I ch i PAU R e B E r U w a PAU k e n s a sh i t A k a G A k u b u CL sh I TS U N i F u k u M a r E R U s u i s o i * O n p i i e i ch i n O e i ch i n o * ry o o d e sh i m E s a r e m A s u +HYP: p i i e * ch i *** D e R I r O w a *** k e n s a sh i t * k a K O k u b u ** sh * ** S E i * u k u G a r * A N s u i s o i Y U n p i i e i ch i n * e i ch i n o R ry o o d e sh i m A s a r e m E s u +Eval: D D S S S S D D S S D D D S S D S D S S I S D I S S + +Speaker sentences 134: fleurs_jpn_000366 #utts: 1 +id: (fleurs_jpn_000366-fleurs_jpn_000366) +Scores: (#C #S #D #I) 83 9 8 1 +REF: s o r e d e m o PAU t o o KY O K u k a r A n O a D O b a i s U o u k e pau s u b e t e n o HY o o sh i k i * o m a M o r i PAU a n z e n j o o n o k e e K o K U n i s a i sh i N n o ch U u i o h a r a i m a sh o o +HYP: s o r e d e m o *** t o o ** RY I u k a r U n * a R U b a i s * o u k e pau s u b e t e n o ** o o sh i k i O o m a * o r i *** a n z e n j o o n o k e e G o G O n i s a i sh i I n o ch * u i o h a r a i m a sh o o +Eval: D D S S S D S S D D I D D S S S S D + +Speaker sentences 135: fleurs_jpn_000367 #utts: 1 +id: (fleurs_jpn_000367-fleurs_jpn_000367) +Scores: (#C #S #D #I) 95 8 8 4 +REF: k o r e r a w a t a m a n i k o n Z a ts U s u r * * * U k a Z o k u m u k e n o b I i ch i D e PAU k a i g a N n i W A s a m a z a m a n a t e n p o G A n a r a n d e i m a s u PAU a n z e N n i o Y o * G u k o t o g a d e k i m a s u +HYP: k o r e r a w a t a m a n i k o n G a ts * s u r O K A CL k a TS o k u m u k e n o b * i ch i T e *** k a i g a I n i * * s a m a z a m a n a t e n p o W O n a r a n d e i m a s u *** a n z e * n i o * o E B u k o t o g a d e k i m a s u +Eval: S D I I I S S D S D S D D S S D D D I S + +Speaker sentences 136: fleurs_jpn_000368 #utts: 1 +id: (fleurs_jpn_000368-fleurs_jpn_000368) +Scores: (#C #S #D #I) 81 24 33 2 +REF: sh i n N o PAU m i e n a i ch I i M U PAU E R u E E a a R U E s U O o E n U pau * a n d o PAU E r U E E E F U E E E s u t I I O o PAU s e n ky U u HY A k U H a ch i j u U KY u U PAU P I i HY a k u ky u u n o s O n z a i m o * M A t a PAU b A a CH A r u ch i I m u n o d o K u J I n O y O o s o d e a r u +HYP: sh i n D o *** m i e n a i ch * i * * *** * * u * D a a * * * s * * o * n O pau N a n d o *** * r * * * * * * A S A s u t * * * o *** s e n ky E u ** * k * * a ch i j u K I u P E E J i KY a k u ky u u n o s U n z a i m o N O D t a *** b * a Z E r u ch i E m u n o d o G u SH U n A y * o s o d e a r u +Eval: S D D D D D D D D S D D D D D D S I D D D D D D D D S S S D D D D S D D D D S S S S S S S S I S S D D S S S S S S S D + +Speaker sentences 137: fleurs_jpn_000369 #utts: 1 +id: (fleurs_jpn_000369-fleurs_jpn_000369) +Scores: (#C #S #D #I) 104 7 6 0 +REF: k o n o s a a b i s u w a PAU g o r a k u s e n o h a j i m e t o s u r u s e n p a k u y a PAU e n K a k u CH i D e d e e t a y A o n s e E o H I TS u y O o t o s u r u t a n k e n t a i N i h i n p a N n i r i y o o s a r e t e i m a s u +HYP: k o n o s a a b i s u w a *** g o r a k u s e n o h a j i m e t o s u r u s e n p a k u y a *** e n G a k u SH i R e d e e t a y * o n s e Y o * * S u y * o t o s u r u t a n k e n t a i R i h i n p a A n i r i y o o s a r e t e i m a s u +Eval: D D S S S D S D D S D S S + +Speaker sentences 138: fleurs_jpn_000370 #utts: 1 +id: (fleurs_jpn_000370-fleurs_jpn_000370) +Scores: (#C #S #D #I) 151 12 8 4 +REF: s a k u b a N PAU b u * e n o s u a i R e s u k a r a G o j u cl k i r o s a n j U u i ch i m a i r u h a n a r e t a r a p u r a t a sh i n a i d e PAU g e n sh O K U j o o i n g I i n d e * a r u k u r i s u t I i n a PAU f e r * u n a n d e s u PAU d e PAU k i r u H i n a a J o sh i g a D a i t o o ry o o s * e n e n o sh U TS u B a o s e n g e n sh i m a sh i t a +HYP: s a k u b a * *** b u E e n o s u a i D e s u k a r a K o j u cl k i r o s a n j * u i ch i m a i r u h a n a r e t a r a p u r a t a sh i n a i d e *** g e n sh U G O j o o i n g * i n d e W a r u k u r i s u t E i n a *** f e r U u n a n d e s u *** d e *** k i r u K i n a a Z o sh i g a N a i t o o ry o o s E e n e n o sh I S u D a o s e n g e n sh i m a sh i t a +Eval: D D I S S D D S S S D I S D I D D S S S I S S S + +Speaker sentences 139: fleurs_jpn_000371 #utts: 1 +id: (fleurs_jpn_000371-fleurs_jpn_000371) +Scores: (#C #S #D #I) 87 12 12 3 +REF: o n A J i TS u k * i n i PAU m a sh U h a D o n o k a CL s o o r O D e b E TS u n o RY O K a K U k i g a k a CL s o o r O o o o b a a r a n sh i pau k a b e n i g e k i t o * ts u sh i t e *** j u u SH I CH I n i n g a sh i B O o sh i m a sh i t a +HYP: o n * O i Z u k U i n i *** m a sh I h a T o n o k a ** s o o r * * e b U S u n o R U G a * CL k i g a k a ** s o o r * o o o b a a r a n sh i pau k a b e n i g e k i t o T ts u sh i t e PAU j u u ** * N A n i n g a sh i * * o sh i m a sh i t a +Eval: D S S I D S S D D D S S S S S D S D D I I D D S S D D + +Speaker sentences 140: fleurs_jpn_000372 #utts: 1 +id: (fleurs_jpn_000372-fleurs_jpn_000372) +Scores: (#C #S #D #I) 97 10 14 0 +REF: h a sh i sh i t a n o K A M I G A T A k U u k a N w a j u u g o m E e t o R U d e s u PAU n i s e n j u u I ch i n e N h a CH i g a ts u n i sh u n k O o sh i pau n i s e n j u u n a n A N e N s a n g a TS u m a D e k a i ts U u sh i m a s e n d e sh i t a +HYP: h a sh i sh i t a n o * * * J O O H O k * u k a * w a j u u g o m * e t o * O d e s u *** n i s e n j u u * ch i n e * h a J i g a ts u n i sh u n k * o sh i pau n i s e n j u u n a n * I e * s a n g a S u m a R e k a i ts * u sh i m a s e n d e sh i t a +Eval: D D D S S S S S D D D D S D D D S D D S D S S D + +Speaker sentences 141: fleurs_jpn_000373 #utts: 1 +id: (fleurs_jpn_000373-fleurs_jpn_000373) +Scores: (#C #S #D #I) 179 12 33 4 +REF: b u n M e e t o i u k o t o B a w a PAU sh i m i N o I m i s u r u * r a t e n g o n o k E e Y o o sh i sh I I A i b U I A i E r U A i E s u k a r a k i t e * o r i PAU sh i m * i n O i * m i s u r u r a t e n G o N o m e e sh i sh I I A i b U I A i E s u PAU t o sh i y a t o sh i k o cl k a o I m i sh i PAU n a n R a k a n o k a t a ch i D e sh a k a i n o k i b o o t e e g i s u r u sh I I A I B u i A i t I I E E E s u t o I u m e e sh i n i k a n k e e sh i t e i m a s u +HYP: b u n N e e t o i u k o t o W a w a *** sh i m i I o * m i s u r u A r a t e n g o n o k I e * o o sh i sh * * * i b * * * i * r * D i * s u k a r a k i t e W o r i *** sh i m E i n * i O m i s u r u r a t e n K o R o m e e sh i sh * * * i b * * * i * s u *** t o sh i y a t o sh i k o cl k a o * m i sh i *** n a n N a k a n o k a t a ch i R e sh a k a i n o k i b o o t e e g i s u r u sh * * * * * u i B i t * * * * A s u t o Y u m e e sh i n i k a n k e e sh i t e i m a s u +Eval: S S D S D I S D D D D D D D D D S D I D I D I S S D D D D D D D D D D S S D D D D D S D D D D S S + +Speaker sentences 142: fleurs_jpn_000374 #utts: 1 +id: (fleurs_jpn_000374-fleurs_jpn_000374) +Scores: (#C #S #D #I) 99 15 6 2 +REF: ts u u j o o PAU k o k o D e w a i TS u m O k a n k O o KY A k u y a GY o o sh a t a CH i G a h a CL s u r U o t o G a K i k o e t e k i m a s u PAU o t o t o ** H i k a r i g a o R i n a s u m O n o g a t a r i W a m a r u d E e h o n n * o y o o D e s u +HYP: ts u u j o o *** k o k o R e w a i S u m A k a n k * o T E k u y a RY o o sh a t a SH i K a h a ** s u r * o t o K a CH i k o e t e k i m a s u W o t o t o SH I i k a r i g a o * i n a s u m U n o g a t a r i U a m a r u d * e h o n n A o y o o R e s u +Eval: D S S S D S S S S S D D S S S I S D S S D I S + +Speaker sentences 143: fleurs_jpn_000375 #utts: 1 +id: (fleurs_jpn_000375-fleurs_jpn_000375) +Scores: (#C #S #D #I) 44 4 9 3 +REF: t e r e b i n o h o o d o o n i Y o R U T o pau g e n p a ts u k a r a SH I R O k E M u R I g A a g a cl t e i m a s u * * * +HYP: t e r e b i n o h o o d o o n i * o * N D o pau g e n p a ts u k a r a ** * * A k * * u * E g * a g a cl t e i m a s u I D E +Eval: D D S S D D D S D D D S D I I I + +Speaker sentences 144: fleurs_jpn_000376 #utts: 1 +id: (fleurs_jpn_000376-fleurs_jpn_000376) +Scores: (#C #S #D #I) 66 4 1 2 +REF: n o o BY o o r i t o k o o d o o n o s o o k a n k a n k e e w a PAU k a G a k u sh a t a CH i n o k e n ky u u * * o u r a z u k e r U m o n o d e s u +HYP: n o o B o o r i t o k o o d o o n o s o o k a n k a n k e e w a *** k a Y a k u sh a t a SH i n o k e n ky u u W A o u r a z u k e r E m o n o d e s u +Eval: S D S S I I S + +Speaker sentences 145: fleurs_jpn_000377 #utts: 1 +id: (fleurs_jpn_000377-fleurs_jpn_000377) +Scores: (#C #S #D #I) 61 10 8 0 +REF: s u i y o o b i n O i b e n t o n O a t o PAU k a r u p a n e D o w a s e n sh U k e n D E F u T a TS u n O k o j i n R E e s U N i SH U TS u j o o sh i m a sh i t a +HYP: s u i y o o b i n * i b e n t o n * a t o *** k a r u p a n e R o w a s e n sh I k e n * G O u K a S u n U k o j i n * D e s * * i ** I S u j o o sh i m a sh i t a +Eval: D D D S S D S S S S S D S D D D S S + +Speaker sentences 146: fleurs_jpn_000378 #utts: 1 +id: (fleurs_jpn_000378-fleurs_jpn_000378) +Scores: (#C #S #D #I) 93 11 6 0 +REF: s e n h a CL PY A k u n e n d a I i r a i PAU g u n t a i g a t o o CH a k U s u r U M a D e h a i ch i w a k o n o BY O o k i n i k a n K e e s u r U M o n d a i n i s o o g U u sh i t a k o t o w A a r i m a s e n d e sh i t a +HYP: s e n h a ** P E k u n e n d a * i r a i *** g u n t a i g a t o o J a k * s u r E W a R e h a i ch i w a k o n o B Y o k i n i k a n KY e e s u r E B o n d a i n i s o o g * u sh i t a k o t o w * a r i m a s e n d e sh i t a +Eval: D S S D D S D S S S S S S S S D D + +Speaker sentences 147: fleurs_jpn_000379 #utts: 1 +id: (fleurs_jpn_000379-fleurs_jpn_000379) +Scores: (#C #S #D #I) 78 10 9 1 +REF: sh i k a sh i ** PAU ky a P u t e N n o W i k e cl T o o U sh I n a cl T A a t o PAU i n d o W a n a n a ts u n O W i k e cl t o o U sh i n a i PAU s a n j U U r o k u r a N sh i k a D e k i m a s e n d e sh i t a +HYP: sh i k a sh i CL K ky a K u t e * n o B i k e cl D o o * sh U n a cl * D a t o *** i n d o * a n a n a ts u n E B i k e cl t o o * sh i n a i *** s a n j * O r o k u r a * sh i k a R e k i m a s e n d e sh i t a +Eval: I S S D S S D S D S D D S S D D D S D S + +Speaker sentences 148: fleurs_jpn_000380 #utts: 1 +id: (fleurs_jpn_000380-fleurs_jpn_000380) +Scores: (#C #S #D #I) 103 5 6 2 +REF: k a j i n o d e w a ts u u j o o PAU t o k u b e ts u n a i n sh O k U y a e n t a a t e i m e n t o o y o o i sh i t e i m a s u PAU g e s U T o g a k i b u N y O k u sh i s e TS u n a i n i t * * o m a r u y O o n i s u r u t a m e D e s u +HYP: k a j i n o d e w a ts u u j o o *** t o k u b e ts u n a i n sh A k O y a e n t a a t e i m e n t o o y o o i sh i t e i m a s u *** g e s * * o g a k i b u * y A k u sh i s e S u n a i n i t O R o m a r u y * o n i s u r u t a m e R e s u +Eval: D S S D D D D S S I I D S + +Speaker sentences 149: fleurs_jpn_000381 #utts: 1 +id: (fleurs_jpn_000381-fleurs_jpn_000381) +Scores: (#C #S #D #I) 79 6 15 2 +REF: s o r e d e M o PAU t o o ky O K u k a r a n O a D O b a i s U o U K e pau s u b e t e n o hy o * O sh i k I O m a M o r i PAU a n z e n j O o n o k E e k o K U n i s a i sh * i n n o ch U u i o h a r a i m a sh O o +HYP: s o r e d e * o *** t o o ky * I u k a r a n A a R A b a i s * o * G e pau s u b e t e n o hy o W A sh i k * * m a * o r i *** a n z e n j * o n o k * e k o * * n i s a i sh I i n n o ch * u i o h a r a i m a sh * o +Eval: D D D S S S S D D S I S D D D D D D D D I D D + +Speaker sentences 150: fleurs_jpn_000382 #utts: 1 +id: (fleurs_jpn_000382-fleurs_jpn_000382) +Scores: (#C #S #D #I) 58 6 9 0 +REF: o w A k a r E D e w A a r i m a s e N k o r E W a H i t o ts u N o sh O O N o o w a r i d e a r i pau a t a r a sh I i sh o o n o m A k U a k e d e s u +HYP: o w O k a r U G e w * a r i m a s e PAU k o r * * a SH i t o ts u * o sh * * * o o w a r i d e a r i pau a t a r a sh * i sh o o n o m O k * a k e d e s u +Eval: S S S D S D D S D D D D D S D + +Speaker sentences 151: fleurs_jpn_000383 #utts: 1 +id: (fleurs_jpn_000383-fleurs_jpn_000383) +Scores: (#C #S #D #I) 99 9 3 1 +REF: s a F a r i t o w a PAU a f u r i k a n o y a s e e d o o B u TS u pau t o k U n i s a B a n n a n I i r u y a s e * e d o o B u ts u n o k a n s a TS u O m o k u t e k i t o sh i t a r i k u r o d e n o RY o k O o o s a sh i m a s u +HYP: s a W a r i t o w a *** a f u r i k a n o y a s e e d o o G u Z u pau t o k O n i s a W a n n a n * i r u y a s e R e d o o G u ts u n o k a n s a S u A m o k u t e k i t o sh i t a r i k u r o d e n o Y o k * o o s a sh i m a s u +Eval: S D S S S S D I S S S S D + +Speaker sentences 152: fleurs_jpn_000384 #utts: 1 +id: (fleurs_jpn_000384-fleurs_jpn_000384) +Scores: (#C #S #D #I) 117 17 25 1 +REF: F u y u n I k i t a b a r u t o k a I o O o d a n s u r U b A a i w a PAU s e N SH i TS u N O i ch i O k a k u n i N sh I T e k u d a s a i * PAU k o o r I n o n a k A o ts U k i S U s u M u s a i n i m o cl t o m O E e ky O o o U k e r u s e N SH i ts u d e W A o s o r O SH I I H O D o n o s O o o N g a n a r i H i b i k i M a s u +HYP: * u y u n E k i t a b a r u t o k a Y o M o d a n s u r O b * a i w a *** s e * ** i S u * * i ch i * k a k u n i * sh * S e k u d a s a i K I k o o r E n o n a k * o ts I k i * TS s u * u s a i n i m o cl t o m * * e ky * o o * k e r u s e * ** i ts u d e * * o s o r * ** U J U U M o n o s * o o * g a n a r i K i b i k i G a s u +Eval: D S S S S D D D D S D D D D D S I S S D S D S D D D D D D D D D D D S S S S S D D S S + +Speaker sentences 153: fleurs_jpn_000385 #utts: 1 +id: (fleurs_jpn_000385-fleurs_jpn_000385) +Scores: (#C #S #D #I) 105 20 10 4 +REF: k o k o w a I G i r i s u n o sh o k u m i n CH i sh I h a i * sh a * g a j i b u n t a CH i n o ry o o D o t o sh I T a b a sh O n a N o * d e pau sh o k U m i n CH i j i D A I n O sh o o k o o s A G a s o o t o s u r * U H O O w a PAU k o k o K a r A h a j i M e r U n O g a y o i D E sh o o +HYP: k o k o w a * * i r i s u n o sh o k u m i n ** i sh E h a i E sh a E g a j i b u n t a SH i n o ry o o R o t o sh * * a b a sh U n a R o U d e pau sh o k O m i n T i j i * R E n I sh o o k o o s E K a s o o t o s u r E K A T A w a *** k o k o * a r * h a j i B e r E n A g a y o i * U sh o o +Eval: D D D S I I S S D D S S I S S D S S S S S I S S S S D D D S S S D S + +Speaker sentences 154: fleurs_jpn_000386 #utts: 1 +id: (fleurs_jpn_000386-fleurs_jpn_000386) +Scores: (#C #S #D #I) 95 16 9 1 +REF: E B I S U sh i w a PAU s a k u g e n s u r u s u u ch I o s a D a M E M a s e n d e sh i t a G a pau s a k u g e n w a ch U U G o k u n o k e e z a i s a n sh U TS u RY o U n i m o t o z u i t e j i CL sh i s a R E r u d a r O o t o * n o B E m a sh i t a +HYP: * * * K O sh i w a *** s a k u g e n s u r u s u u ch O o s a R a Y A W a s e n d e sh i t a K a pau s a k u g e n w a ch I I W o k u n o k e e z a i s a n sh I S u Y o * n i m o t o z u i t e j i ** sh i s a * * r u d a r * o t o N n o O I m a sh i t a +Eval: D D D S S D S S S S S S S S S S S S D D D D D I S S + +Speaker sentences 155: fleurs_jpn_000387 #utts: 1 +id: (fleurs_jpn_000387-fleurs_jpn_000387) +Scores: (#C #S #D #I) 105 6 6 3 +REF: s a * i NY u u k O k u sh o cl k u w a sh i n k o n ry o k o o n o j i k i g a s u k u n a i *** k a r u * ch a a sh o cl k u y o r I m o h a Y A k U o t o z u r e PAU n a G a b i k i PAU y o r i sh o o j O o g A a CL k a s u r u k o t o g a a r i m a s u +HYP: s a U i N u u k U k u sh o cl k u w a sh i n k o n ry o k o o n o j i k i g a s u k u n a i PAU k a r u E ch a a sh o cl k u y o r E m o h a * E k O o t o z u r e *** n a W a b i k i *** y o r i sh o o j * o g * a ** k a s u r u k o t o g a a r i m a s u +Eval: I S S I I S D S S D S D D D D + +Speaker sentences 156: fleurs_jpn_000388 #utts: 1 +id: (fleurs_jpn_000388-fleurs_jpn_000388) +Scores: (#C #S #D #I) 105 17 4 3 +REF: K i n o o n o a s a PAU t o r u k o n o g a j i a n t e cl p u n o k e e s a TS u * h o n B U D e j i d o o sh a b a k u D a N n o b a k U H a TS U N i y o r i pau K e e k a n f u t a r I g a sh i b O o sh i pau F U sh o * O sh a w a n i j u u n i N o k o * E m a sh i t a +HYP: KY i n o o n o a s a *** t o r u k o n o g a j i a n t e cl p u n o k e e s a S u O h o n W O R e j i d o o sh a b a k u R a * n o b a k * A a S O R i y o r i pau KY e e k a n f u t a r E g a sh i b * o sh i pau H O sh o W A sh a w a n i j u u n i Y o k o A I m a sh i t a +Eval: S D S I S S S S D D S S S S S S D S S I S S I S + +Speaker sentences 157: fleurs_jpn_000389 #utts: 1 +id: (fleurs_jpn_000389-fleurs_jpn_000389) +Scores: (#C #S #D #I) 82 3 1 5 +REF: sh o k u b u ts u W a n i n g E n g a s u u s a n S o o ts u k u r i PAU n i n g e n g * * a ** * * i k i t o sh i t e h a k i d a s u n i s a n k a t a n s o o t o r i k o n d e i m a s u +HYP: sh o k u b u ts u D a n i n g I n g a s u u s a n Z o o ts u k u r i *** n i n g e n g A K a CL K O i k i t o sh i t e h a k i d a s u n i s a n k a t a n s o o t o r i k o n d e i m a s u +Eval: S S S D I I I I I + +Speaker sentences 158: fleurs_jpn_000390 #utts: 1 +id: (fleurs_jpn_000390-fleurs_jpn_000390) +Scores: (#C #S #D #I) 84 5 6 2 +REF: s e n p a k u * D e b u CL sh i o Y U s o o s u r u n o w a PAU u m i o k o e t e h i t o y a b u CL sh i o t a * i ry o o y U s o o s u r u m o cl t o m o k o o r i TS U T e k i n a h o o h o o D e s u +HYP: s e n p a k u R E e b u ** sh i o * I s o o s u r u n o w a *** u m i o k o e t e h i t o y a b u ** sh i o t a R i ry o o y I s o o s u r u m o cl t o m o k o o r i ** * S e k i n a h o o h o o R e s u +Eval: I S D D S D D I S D D S S + +Speaker sentences 159: fleurs_jpn_000391 #utts: 1 +id: (fleurs_jpn_000391-fleurs_jpn_000391) +Scores: (#C #S #D #I) 124 6 5 5 +REF: k a r i F o r u n i a sh u u n o a * a n o r u d o PAU sh u w a r u ts E n e cl G a a ch i J i w a PAU b o o * RY o k u t e k i n a b i d e o g e e m u o m i s e e n e n sh a n i h a n b a I y a r e n t a R U s u r * u k o t o o k i n sh i s u r u h o o * a N n i sh * o m e e sh i m a sh i t a +HYP: k a r i H o r u n i a sh u u n o a W a n o r u d o *** sh u w a r u ts U n e cl K a a ch i SH i w a *** b o o R Y o k u t e k i n a b i d e o g e e m u o m i s e e n e n sh a n i h a n b a * y a r e n t a * E s u r E u k o t o o k i n sh i s u r u h o o W a * n i sh A o m e e sh i m a sh i t a +Eval: S I D S S S D I S D D S I I D I + + diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/text b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/text new file mode 100644 index 0000000000000000000000000000000000000000..c7ecb7bfd960ef4982e19b43e3a85b659ee9fb2b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/text @@ -0,0 +1,160 @@ +cv_jpn_000800 k a k o t o m i r a e t o d o g u j u N t e k i j i k o d o o i ts u n a r u g a y o e n i i sh I k i t e k i n a n o d e a r u +cv_jpn_000801 s e k a y o k e e s e e s u r u t o t o m n i j i k o j i sh i N y o k e e s e s e r u s o o o t e k i s e k a i n o s o o z o o t e k i y o o s o t o sh I t e pau k o b u ts u g a k o b u ts u d e a r u +cv_jpn_000802 p a z o k o N d e g e e m i a r u I t o n o h f u i t e k i t e +cv_jpn_000803 k a N a k u n o sh i m e s a a t a r a sh i j i j i u ts u a t a r a sh i i k a N n e N k a N ky o sh i h a i n w a t a r a sh i i k a n o o s e o m o cl t e pau n a n i h a j i m e r u k a w a +cv_jpn_000804 o m o sh i r o u n o n i pau r o o t o n a g a s u g i t e d a r u i +cv_jpn_000805 k o r e j o o sh u u h a N p o i n a +cv_jpn_000806 k a g a k U sh a m o s e k a i o h o o k a ts s e k i n i t o o i ch i t e k i n i s a ts u m e sh u o t o sh I t e i r u +cv_jpn_000807 f U ts u u n i ts u m a r a N +cv_jpn_000808 sh I cl k a r i I t e k u d a s a i +cv_jpn_000809 w a t a sh i w a a m i g i n o n o t o k i d e k I sh I t e k I s e i m e e n o j i k a k u t o i u g o t o k i m o n o b e N sh o o h o o t e k i r o N b i t o y u u n o d e a r u +cv_jpn_000810 w a t a sh i w a pau sh a k a i k e e s e e n o k o N t e e n i w a pau d e y o o n u s o s u t e k i n a m o n o g a h a t a r a i t e i r u t o m o +cv_jpn_000811 n a n i o s u r u ts u m o r i d a cl t a n o k a +cv_jpn_000812 k o b u ts u t e k I t a g a j i k o h I t e e t e k i n i t a N n i t e N sh u u g o o t e k i n i k a N g a e r a r u r u t o k i s o r e g a b u z u r i t e k I t e k a i d e a r u +cv_jpn_000813 a n e g a z u cl t a n o d e a pau y a cl k i n o sh i r a a i g a a r i m a s e N d e sh I t a +cv_jpn_000814 k o r e w a r i h o N d e u t e i n a i t a b e m o n o d e s U +cv_jpn_000815 w a t a sh i w a h e N sh u u i n o y o n e N k u r a e w a y a cl t a cl t o o m o +cv_jpn_000816 i s a N n i k o n o k o t o b a n o i m i y o o o o sh i a m a sh I t a +cv_jpn_000817 k a s e g a ts U s u y o i h i w a t e n i s u g a d e k i m a s e N +cv_jpn_000818 i ch i +cv_jpn_000819 h a i +cv_jpn_000820 n i +cv_jpn_000821 d e i +cv_jpn_000822 t o k i +cv_jpn_000823 m i r u t o i u k o t o t o pau h a t a r a k U t o i u k o t o g a pau s U k a b u N r i t e k i n a k e r e b a n a r a n a i +cv_jpn_000824 w a r e w a r e o o t a m a sh i i n o z u k o k a r a g u g a s u m o n o d e n a k e r e b a n a r a n a i +cv_jpn_000825 z e cl t a i b e N sh o o h o o t e k i n a r u g a i u e n i i d e a t e k I ch o cl k a N t e k I k e e k i g a cl U k u m a r e r u n o d e a r u +cv_jpn_000826 d o k o m a d e m o t a t o i ch i t o n o s o o g o h I t e e t e k i n a z e cl t a i m u j u N t e k i j i k o d o i ts u n o s e k a i n i sh I t e +cv_jpn_000827 sh I k a r u n i n i N g e N t o k a N ky o o t o r o k a N k e e w a m o t o k o o i n o k a N ky e e d e a r i +cv_jpn_000828 i i s a n i k o o n o k o t o b a n o i m i y o o sh i a m a sh I t a +cv_jpn_000829 g e k i g a n a n a ts s a r i m a s U +cv_jpn_000830 k o ch i r a b a k o b u y a sh i i s a N d e s U +cv_jpn_000831 m o sh i i m a sh i +cv_jpn_000832 k o k o w a o k i k U t e n i k u y a k a n a m a ch i d e s U +cv_jpn_000833 s o n o u ch i k a i y a k U s a r e r u k a r a i s o g e +cv_jpn_000834 a m a s a g a f u s a i r a r e t e t e ch o o d o i +cv_jpn_000835 h o g e N sh I ts u n o d o o a a k e t a +cv_jpn_000836 m o d a N n i o o w a cl t e m o k i n i sh i n a i +cv_jpn_000837 a r i g a cl t a y a +cv_jpn_000838 i t o o g a r a k u d a t o j i k a N w a o s u r e t e t a n o sh i m e r u +cv_jpn_000839 k a k a k u w a g i z u ts U k a s a r e r u n i o j i t e j o o sh I k i n o u ch i n i h a i cl t e i u k u +cv_jpn_000840 sh I k a sh I t o k i g a k a o n i h a i r u k o t o s o n o k o t o g a pau m i r a a y o o m u k o t o d e a r i w a r a t a n a r u i cl s u t a i g a d e t e k u r u k o t o d e a r u +cv_jpn_000841 t e r e b i o k a i k a i t e pau t e r e b i o m i r u j i k a N g a f u e t a +cv_jpn_000842 k a k a r i sh I t a i n o m i i ts u m a d e m o i k i r u n o d e a r u +cv_jpn_000843 n i N k i d a a m e N i y a n i g a r a N d a r a n i j i k a N m a ch i d a cl t a +cv_jpn_000844 s o r e o m o ch i i r u n i N g e N n o i y o k u n i i t o N sh i s o sh I t e k o r e w a k a r e n o m o cl t e i r u k a ch i n o sh a k u d o n i i t o N s u r u +cv_jpn_000845 m a w a r u i o w a m i N n a k a N g a e r u k o t o o y a m e t e i t a +cv_jpn_000846 k o o i t e k i ch o k cl U k a N t e k i n i s e k a y o m i r u t o i u k o t o w a j a k u n i k o o i t e k i ch o cl k a N t e k i n i s e k a y o k e e s e e s u r u k o t o o h k u m u n o d e a r u +cv_jpn_000847 sh i N cl p a i t a k e s a s e m a i t o s u r u k i z u k a i g a y o k e e n i sh i N p a i s a s e t e sh i m a u +cv_jpn_000848 k o n o m i ch i w a t o t e m o s e m a i n o d e a b n a i d e s U +cv_jpn_000849 w o h o e g a a r i n i +cv_jpn_000850 t o i w a r o o k a m o i t a r i g a a n i a r i m a s U +cv_jpn_000851 t a n a k a s a N n o h i t a i n i k i m u r a s a N g a i m a s U +cv_jpn_000852 m a cl k u r a n o t a m a b o t e s u g o i n i e +cv_jpn_000853 sh o h o o m i t a i n a d o k U sh u k a N s o o b u N m o k a i t a +cv_jpn_000854 g e N j i ts u n o s e k a i w a t a m o o i ch i t o sh I t e k e cl t e s u r a i d a k a t a sh u o a cl t o s e k a i z u n a k e r e m a n a r a n a i +cv_jpn_000855 sh o o h i N k e N s a k u g a o k a r e a s u i t o o k a u k i i n a r u n a i +cv_jpn_000856 ts i sh I k i w a pau r e k I sh I t e cl k a t e e r d e n a k e r e m a n a r a n a i +cv_jpn_000857 m o n o g o t o n N j i N p a N k a e r u d a k e d e u m a k u i k U k o t o m o w a r u +cv_jpn_000858 k o n o k i s e e ts u w a k a ts u o n o s a sh i m i g a z e cl p i N +cv_jpn_000859 k a k e n i sh i cl p a i sh I t e m o o ch ts U ts u i t e s a N sh I ts u o u k e i d e r u +cv_jpn_000860 s o r e y u e n i t e ts u g a p u g a z e N t a i n o g a k o d e a r u t o s u r e w a +cv_jpn_000861 k i i z a n a y a o y a d a g a y e s U k U t e h a N j o sh I t e r u +cv_jpn_000862 i n i f u r a g a a k i n o h u z e N n i y o ch i i cl t e pau k o k u g a i e d a sh U ts u s u r u h I t o m o d e t e k i t a +cv_jpn_000863 ts u g i n i k a g a k u w a s o N z a y o pau sh u j u n o d o o e k i n i w a k a cl t e s o r e z u r a N ry o o u k i N z i t I k e N i k I s u r u +cv_jpn_000864 s o r e d e w a pau t o k t o i u m o n o n o s e r i ts u sh i o w a n a k u pau sh u N k a N t o i m o n o m o n a k u a r u n o d e a r u +cv_jpn_000865 a k a i b u r a N k o h o N k u r i t o s e e n o s u b e r i d a i k a w a i t a s u n a b a +cv_jpn_000866 sh I k a sh i s o r e w a d o k o m a d e m N k U k o k a r a r i t e pau p o k o e k a e r i k u r u s e sh I ts o m o t o m o d e n a k e b a n a r a n a i +cv_jpn_000867 a r i t o w a r a i r u d e m o o m a k I ch i r a sh I t e pau m i N n e k a r a u r a m i o k a cl t e r u +cv_jpn_000868 k o n o cl t e e d o s a w a g i n i n a r u k o t o m o n a i n o d a r o o +cv_jpn_000869 k o n o n e d a N d e w u r i ch a N k a a N +cv_jpn_000870 h i n o k a g e N n i ch u i sh i n a i t o s u g u k o g e r u +cv_jpn_000871 e N m a N d o w e n i p o ts u r i t o ch i i s a n a a n a g a i t a s a i sh o w a ts u m a y o o j i t e e d o n o ch i i s a n a a n a d a cl t a +cv_jpn_000872 s o r e w a pau a r e w a r e o i k a sh i n a g a r a w a r e w a r e o t o r e e k a s u r u n o d e a r u pau w a r e b a r e n o t a m a sh i i o k o r o s u n o d e a r u +cv_jpn_000873 r e k I sh I t e k i n i a t a e r a r t a m o n o w a d e cl t a i m u j u N t e k i j i g o t o o i ts u t e k i g e N z a i n o i t e s U k a i h I t e k i n i a t a e r a r e t a m o n o t o sh I t i +cv_jpn_000874 m u j u N t e k i j i g o d o o i ts U t o sh I t e pau i ts u m o k o n o s e k a i n i ch o o e sh t k i d e a +cv_jpn_000875 y u u n i z e cl t a e m u j u N t e k i j i g o d o o i ts U t o sh I t e g e N s a i k a r a g e N z a e t o b o k i i k u s e k a i n o g e N z a i n o i t e +cv_jpn_000876 h a r e pau w o t a N o sh I t o m N d a sh u d e k i n a i +cv_jpn_000877 sh I k a sh i w a t a sh i a s o k o n i s e k a i n o j i k o d o o i ts u o k u n o d e w a n a i +cv_jpn_000878 n e b e k a k u n a r u n o g a h a y a k u n a cl t a +cv_jpn_000879 w a t a sh i w a i N g e N n o o r e k I sh I t e k I k e e s e e n o t a ch i b a k a r a g e j u ts u o m i r u n o d e a cl t e o o sh a k a r a d e N sh a o m i r u n o d e w a n a i +cv_jpn_000880 a o i t o m a t o sh I k a n a k U t e k a u k a b m a i y o +cv_jpn_000881 s i N k i z u i gy o o n i o o k i n a k I t a y o y o s U t e i r u +cv_jpn_000882 n a n i k a sh i r a n o i n i N s e N t i b u w a n a i t o k i b i sh i i n o d e w a +cv_jpn_000883 j i k o N sh e e g e N n o i b e N t o d e s U t o r u sh I t a m a r u b u +cv_jpn_000884 m a a r i n o h I t o w a b o o z e N t o sh I t e i t a +cv_jpn_000885 s o N n a n a i y o o n o m e e r e w a n a N k e N m o k U I t e i t a +cv_jpn_000886 n j i k a i d r d e e s u i sh I t e i t a +cv_jpn_000887 t o k i d o k i ch i u N n o k o r o w a w a k a r a n a k u n o r u t o k i g a a r u d a k a r a b o k u w a k a n i o cl k i n o o t o n i k a ch i a j i m e r u +cv_jpn_000888 m o o n i g e t e ch a t a m e d a +cv_jpn_000889 k a r e w a p o o t o t a j i ts U k u sh i t e i t a +cv_jpn_000890 d a r a u n i m o m e i w a k o w a k a k e t a k u n a i +cv_jpn_000891 w a s a k a t o o m o t e t o w a n o o t o cl t e o n i g e cl t a +cv_jpn_000892 sh t e i m a s e N +cv_jpn_000893 k a y o o n i sh I t e sh I cl t e i r u t o t o m o n i sh i cl t e i n a i t o k o r o k a r a t a N ky u u w a h a j i m a r u n o d e a r u +cv_jpn_000894 a i s a u w a d a i j i d a i o +cv_jpn_000895 t o j i t a m o n o i k a n i h i r o g e t e m o h i r a i t a m o n o n i a n a r a r o t o i cl t e i r u g a +cv_jpn_000896 t a m e sh i n i i k u ts U k a ts U k u cl t e m i y o a +cv_jpn_000897 z u i b u N a k o g i n a sh o o b a i d a i o n a +cv_jpn_000898 w a t e i +cv_jpn_000899 i ch i +cv_jpn_000900 g o +cv_jpn_000901 sh i k i +cv_jpn_000902 i i e +cv_jpn_000903 h a ch i +cv_jpn_000904 n e i +cv_jpn_000905 sh i i +cv_jpn_000906 k u +cv_jpn_000907 i ch i +cv_jpn_000908 k a k a k u g a a k i r a k a n i s u r u pau ky a cl k a N t e k I sh i N r i n i sh I t a g a u k o t o n i y o cl t e +cv_jpn_000909 k a k o t o m i r a i t o n o m u j u N t e k i j i k o d o o i ts u t o sh I t e n o g e N z a i g a pau k a t a ch i o m o sh U t o i u k o t o d e a r u +cv_jpn_000910 b u ts u r i t e k I s e k a i w a pau s u u g a k u t e k I k i g o o n i y o cl t e a r a w a s a r e r u pau s u u g a k u t e k I k a t a ch i n o sh e k a i d e a r u +cv_jpn_000911 w o n a j i g e N sh o o d e s a N k o o g i n a r u +cv_jpn_000912 g a i k o k u k a r a k i t a m o n o d a t o sh i cl t e b i cl k u r i +cv_jpn_000913 i w a y o r u j i cl s e N n i y o cl t e k a k U t o U k U sh i k i t a cl t a m o n o d e a r u +fleurs_jpn_000346 o n a j i y o n i d a N s e e w a h i z a o o u z u b o N o h a cl k o t o g a pau g i m u u z u k e r a r e t e i m a s U +fleurs_jpn_000347 k o n o s a a b i s a k o r a k U s e e w a h a j i m e t o s u r u s e N p a k u y a e N k a k U ch i d e pau d e e t a y a o N s e o h I s u o t o s u r u t a N g e N t a i n i pau h i N cl p a n i r i y o o s a r e t e i m a s U +fleurs_jpn_000348 ky o o f u u hy o k a t o n o k o o s u i r o o o b i a m a k a sh i b a r a i u t a ts u m a k i i z u k i o b e s a i k u r n o d a n o k i b i sh i i k I sh o k e e t a y a s o n o e e ky o n i y o r u m o n o r e s U +fleurs_jpn_000349 i N t a a n e cl t o w a m a s U k o m i r i k e e sh u N t o t a i j i N k o m i r u i k e e sh o o n o ry o o y o s o o k a n e s u n a e t a k a N ky o o d e s U +fleurs_jpn_000350 k a j i n o r e w a ts u u j o o t o k u b e s u r a i N sh I k o y a e N t a a t e i m e N t o y o o sh I t e i m a s U g e s u w a k i b u i y o k u sh I s e s u n a i n i t o r a m a r u y o o r i s u r u t a m e d e s U +fleurs_jpn_000351 sh I k a sh i pau k a k u t e N n o b i k e cl t o o sh i n a cl t a a d t o i N d o w a n a n a ts u n o b i k e cl t o o sh i n a i s a N j u u r o k u r a sh I k a d e k i m a s e N d e sh i t a +fleurs_jpn_000352 h o o k u r a N d o n o k o o sh I k e ts u k a w a h o o k u r a N d o sh o t o o k o N d o e f U k e e p i i d e i ch i p o N n o g a i ch i e e b o N d o j i i p i i b i i t o t o o k a n i k o t e e s a r e t e i m a s U +fleurs_jpn_000353 h a sh I sh I t a N n o j o o h o o k o k a N w o ch j u u g o m e t o r o d e s U n i s e N j u i ch i n e h a ch i g a ts u n i s e k o o sh i pau n i s e N j u u n o n e N n i s a N g a ts u m a n e k a i ts u e sh i m a s e N g d e sh I t a +fleurs_jpn_000354 i cl p u N k a N d e h f u cl t o s o g u ch i i k i m a r e b a f u cl t o o s u r u m a d e m i n a N m o k a k a r u ch i i k i m o a r i m a s U +fleurs_jpn_000355 p i r a m i cl t a n o o t o t o sh i k a i n o sh o o w a k o n o k a N k o o sh i d e t o k u n i k o r o m o a t a sh i k a t a n o sh i m e r e m o y o o sh u n o h I t o s u r e s U +fleurs_jpn_000356 s o n o t a n e t a i n i d a d e r u t o sh I t e h o o k i g a ts u i k a s a r e g a ch i d e s U +fleurs_jpn_000357 g e N z u N s u r u k o t o g a sh i t a r e t e i r u n i j i o g o o m a i n a t a N d a cl t u p u r o o t u s a i d o w a g e N z u N s u r a t o o g a e b u N k e n o s a i k o n o u sh i d e s U t e g a k i n y o r u g e N p o o w a g e N z o o sh I t e i m o s e N +fleurs_jpn_000358 k a r e m o s e s o o t a d a sh i t o m i t o m e r u sh I t o m i m a sh I t a g a o o k u g u h I t o o s u n o g e k o d e pau t a i y o o k e d e a t a i o t o s i n o h o k a n o h o sh i g a pau ch I k u u n o m a r e i d o o sh I s e r u t o sh i N ch i t e i m a sh I t a +fleurs_jpn_000359 ch i b e cl t o m e e s o o ch u sh i N w a sh i i s e e i o g a d e s U s a m a z a n a n a k a m i g a m i y o sh I k a k U k a s u r u k o t o d e e n e r u g i i ch a n e r u g a sh o o k a s a r e ch a k u r a g a k a s e e k a s a r e s a t o r u n e i sh I k e g o o m a r e m a s U +fleurs_jpn_000360 m i n a m i y a h u r e k a n i y a r u s u b e t e n r u k o k u r i s U k o o e t o d o o y o n i k o n u k o o e n i a m a i n i j i a h o g o sh I t o n i u u e N g u o g o t a k a r i m a s U +fleurs_jpn_000361 d e cl sh s a cl k u r u n g a s u n u h o k a n o o k u n u k o o ts u sh u d a N g a s u k o k a r u m a r e m a sh I t a +fleurs_jpn_000362 i N t a a n e cl t o w a m a s U k o m i n i g e e sh o N t o t a i j i N k o m i n i g e e sh a o N n o ry o o y o o s o o k a n i s o n a i e t a k a N ky o o r e s U +fleurs_jpn_000363 ky o o i N d e w a k a N s e N k a N r i t e j u u sh o N i sh I k a g a i pau k a n i i e n u k a N s e N g o k a n o o s e s e k u t a m e n i k a N j a k a k u r s u r a d a m o m i s o ch o t o cl t e i m u sh I U +fleurs_jpn_000364 r e N p o o r i k a i w a n i s e N g o n e N d o k a r a w a i s e s u b u s u t o r e sh u m a r i o h o e n o sh I k i N t e e ky o o k a i j i sh i pau e r u b i i a i w a a t a r u z o p o r u n o n i j u u n i n o s o o s a N i N y o t o o u n u sh u n a k e r e w a n a r a n a i t o k I t e sh i m a sh I t a +fleurs_jpn_000365 p i i e ch i d e r i r o w a k e N s a sh I t k a k o k u b u sh s e i u k u g a r a n s u i s o i y u n p i i e i ch i n e i ch i n o r ry o o d e sh i m a s a r e m e s U +fleurs_jpn_000366 s o r e d e m o t o o ry i u k a r u n a r u b a i s o u k e pau s u b e t e n o o o sh I k i o o m a o r i a N z e N j o o n o k e e g o g o n i s a i sh i i n o ch u i o h a r a i m a sh o o +fleurs_jpn_000367 k o r e r a w a t a m a n i k o N g a ts s u r o k a cl k a ts o k u m u k e n o b i ch i t e k a i g a i n i s a m a z a m a n a t e N p o w o n a r a N d e i m a s U a N z e N i o o e b u k o t o g a d e k i m a s U +fleurs_jpn_000368 sh i N d o m i e n a i ch i u d a a s o N o pau n a N d o r a s a s U t o s e N ky e u k a ch i j u k i u p e e j i ky a k U ky u u n o s u N z a i m o n o d t a b a z e r u ch i e m u n o d o g u sh u n a y o s o d e a r u +fleurs_jpn_000369 k o n o s a a b i s u w a g o r a k U s e N o h a j i m e t o s u r u s e N p a k u y a e N g a k U sh i r e d e e t a y o N s e y o s u y o t o s u r u t a N k e N t a i r i h i N p a a n i r i y o o s a r e t e i m a s U +fleurs_jpn_000370 s a k u b a b u e e n o s u a i d e s U k a r a k o j u cl k i r o s a N j u i ch i m a i r u h a n a r e t a r a p u r a t a sh i n a i d e g e N sh u g o j o o i N g i N d e w a r u k u r i s u t e i n a f e r u u n a N d e s u d e k i r u k i n a a z o sh i g a n a i t o o ry o o s e e N e n o sh I s u d a o s e N g e N sh i m a sh I t a +fleurs_jpn_000371 o n o i z u k u i n i m a sh I h a t o n o k a s o o r e b u s u n o r u g a cl k i g a k a s o o r o o o b a a r a N sh i pau k a b e n i g e k i t o t ts u sh I t e pau j u u n a n i N g a sh i o sh i m a sh I t a +fleurs_jpn_000372 h a sh I sh I t a n o j o o h o k u k a w a j u u g o m e t o o d e s U n i s e N j u u ch i n e h a j i g a ts u n i sh u N k o sh i pau n i s e N j u u n a n i e s a N g a s u m a r e k a i ts u sh i m a s e N d e sh I t a +fleurs_jpn_000373 b u N n e e t o i u k o t o w a w a sh i m i i o m i s u r u a r a t e N g o n o k i e o o sh i sh i b i r d i s U k a r a k I t e w o r i sh i m e i N i o m i s u r u r a t e N k o r o m e e sh i sh i b i s U t o sh i y a t o sh I k o cl k a o m i sh i n a N n a k a n o k a t a ch i r e sh a k a i n o k i b o o t e e g i s u r u sh u i b i t a s U t o y u m e e sh i n i k a N k e e sh I t e i m a s U +fleurs_jpn_000374 ts u u j o o k o k o r e w a i s u m a k a N k o t e k u y a ry o o sh a t a sh i k a h a s u r o t o k a ch i k o e t e k i m a s U w o t o t o sh I i k a r i g a o i n a s u m u n o g a t a r i u a m a r u d e h o N n a o y o o r e s U +fleurs_jpn_000375 t e r e b i n o h o o d o o n i o N d o pau g e N p a ts U k a r a a k u e g a g a cl t e i m a s U i d e +fleurs_jpn_000376 n o o b o o r i t o k o o d o o n o s o o k a N k a N k e e w a k a y a k u sh a t a sh i n o k e N ky u u w a o u r a z u k e r e m o n o d e s U +fleurs_jpn_000377 s u i y o o b i n i b e N t o n a t o k a r u p a n e r o w a s e N sh I k e N g o U k a s u n u k o j i N d e s i I s u j o o sh i m a sh I t a +fleurs_jpn_000378 s e N h a p e k u n e N d a i r a i g u N t a i g a t o o j a k s u r e w a r e h a i ch i w a k o n o b y o k i n i k a N ky e e s u r e b o N d a i n i s o o g u sh I t a k o t o w a r i m a s e N d e sh I t a +fleurs_jpn_000379 sh I k a sh i cl k ky a k u t e n o b i k e cl d o o sh u n a cl d a t o i N d o a n a n a ts u n e b i k e cl t o o sh i n a i s a N j o r o k u r a sh I k a r e k i m a s e N d e sh I t a +fleurs_jpn_000380 k a j i n o d e w a ts u u j o o t o k u b e ts u n a i N sh a k o y a e N t a a t e i m e N t o o y o o i sh I t e i m a s U g e s o g a k i b u y a k u sh i s e s u n a i n i t o r o m a r u y o n i s u r u t a m e r e s U +fleurs_jpn_000381 s o r e d e o t o o ky i u k a r a n a a r a b a i s o g e pau s u b e t e n o hy o w a sh I k m a o r i a N z e N j o n o k e k o n i s a i sh i i N n o ch u i o h a r a i m a sh o +fleurs_jpn_000382 o w o k a r u g e w a r i m a s e pau k o r a sh I t o ts u o sh o o w a r i d e a r i pau a t a r a sh i sh o o n o m o k a k e d e s U +fleurs_jpn_000383 s a w a r i t o w a a f u r i k a n o y a s e e d o o g u z u pau t o k o n i s a w a N n a n i r u y a s e r e d o o g u ts u n o k a N s a s u a m o k u t e k i t o sh I t a r i k u r o d e n o y o k o o s a sh i m a s U +fleurs_jpn_000384 u y u n e k i t a b a r u t o k a y o m o d a N s u r o b a i w a s e i s u i ch i k a k u n i sh s e k u d a s a i k I k o o r e n o n a k o ts I k i ts s u u s a i n i m o cl t o m e ky o o k e r u s e i ts u d e o s o r u j u u m o n o s o o g a n a r i k i b i k i g a s U +fleurs_jpn_000385 k o k o w a i r i s u n o sh o k u m i N i sh e h a i e sh a e g a j i b u N t a sh i n o ry o o r o t o sh a b a sh u n a r o u d e pau sh o k o m i N t i j i r e n i sh o o k o o s e k a s o o t o s u r e k a t a w a k o k o a r h a j i b e r e n a g a y o i u sh o o +fleurs_jpn_000386 k o sh i w a s a k u g e N s u r u s u u ch o o s a r a y a w a s e N d e sh I t a k a pau s a k u g e N w a ch i i w o k u n o k e e z a i s a N sh I s u y o n i m o t o z u i t e j i sh i s a r u d a r o t o N n o o i m a sh I t a +fleurs_jpn_000387 s a u i n u u k u k u sh o cl k u w a sh i N k o N ry o k o o n o j i k i g a s U k u n a i pau k a r u e ch a a sh o cl k u y o r e m o h a e k o o t o z u r e n a w a b i k i y o r i sh o o j o g a k a s u r u k o t o g a a r i m a s U +fleurs_jpn_000388 ky i n o o n o a s a t o r u k o n o g a j i a N t e cl p u n o k e e s a s u o h o N w o r e j i d o o sh a b a k u r a n o b a k a a s o r i y o r i pau ky e e k a N f u t a r e g a sh i b o sh i pau h o sh o w a sh a w a n i j u u n i y o k o a i m a sh I t a +fleurs_jpn_000389 sh o k u b u ts u d a n i N g i N g a s u u s a N z o o ts U k u r i n i N g e N g a k a cl k o i k i t o sh I t e h a k i d a s u n i s a N k a t a N s o o t o r i k o N d e i m a s U +fleurs_jpn_000390 s e N p a k u r e e b u sh i o i s o o s u r u n o w a u m i o k o e t e h I t o y a b u sh i o t a r i ry o o y i s o o s u r u m o cl t o m o k o o r i s e k i n a h o o h o o r e s U +fleurs_jpn_000391 k a r i h o r u n i a sh u u n o a w a n o r u d o sh u w a r u ts u n e cl k a a ch i sh i w a b o o r y o k u t e k i n a b i d e o g e e m u o m i s e e n e N sh a n i h a N b a y a r e N t a e s u r e u k o t o o k i N sh i s u r u h o o w a n i sh a o m e e sh i m a sh I t a diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/token b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/token new file mode 100644 index 0000000000000000000000000000000000000000..c7ecb7bfd960ef4982e19b43e3a85b659ee9fb2b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/token @@ -0,0 +1,160 @@ +cv_jpn_000800 k a k o t o m i r a e t o d o g u j u N t e k i j i k o d o o i ts u n a r u g a y o e n i i sh I k i t e k i n a n o d e a r u +cv_jpn_000801 s e k a y o k e e s e e s u r u t o t o m n i j i k o j i sh i N y o k e e s e s e r u s o o o t e k i s e k a i n o s o o z o o t e k i y o o s o t o sh I t e pau k o b u ts u g a k o b u ts u d e a r u +cv_jpn_000802 p a z o k o N d e g e e m i a r u I t o n o h f u i t e k i t e +cv_jpn_000803 k a N a k u n o sh i m e s a a t a r a sh i j i j i u ts u a t a r a sh i i k a N n e N k a N ky o sh i h a i n w a t a r a sh i i k a n o o s e o m o cl t e pau n a n i h a j i m e r u k a w a +cv_jpn_000804 o m o sh i r o u n o n i pau r o o t o n a g a s u g i t e d a r u i +cv_jpn_000805 k o r e j o o sh u u h a N p o i n a +cv_jpn_000806 k a g a k U sh a m o s e k a i o h o o k a ts s e k i n i t o o i ch i t e k i n i s a ts u m e sh u o t o sh I t e i r u +cv_jpn_000807 f U ts u u n i ts u m a r a N +cv_jpn_000808 sh I cl k a r i I t e k u d a s a i +cv_jpn_000809 w a t a sh i w a a m i g i n o n o t o k i d e k I sh I t e k I s e i m e e n o j i k a k u t o i u g o t o k i m o n o b e N sh o o h o o t e k i r o N b i t o y u u n o d e a r u +cv_jpn_000810 w a t a sh i w a pau sh a k a i k e e s e e n o k o N t e e n i w a pau d e y o o n u s o s u t e k i n a m o n o g a h a t a r a i t e i r u t o m o +cv_jpn_000811 n a n i o s u r u ts u m o r i d a cl t a n o k a +cv_jpn_000812 k o b u ts u t e k I t a g a j i k o h I t e e t e k i n i t a N n i t e N sh u u g o o t e k i n i k a N g a e r a r u r u t o k i s o r e g a b u z u r i t e k I t e k a i d e a r u +cv_jpn_000813 a n e g a z u cl t a n o d e a pau y a cl k i n o sh i r a a i g a a r i m a s e N d e sh I t a +cv_jpn_000814 k o r e w a r i h o N d e u t e i n a i t a b e m o n o d e s U +cv_jpn_000815 w a t a sh i w a h e N sh u u i n o y o n e N k u r a e w a y a cl t a cl t o o m o +cv_jpn_000816 i s a N n i k o n o k o t o b a n o i m i y o o o o sh i a m a sh I t a +cv_jpn_000817 k a s e g a ts U s u y o i h i w a t e n i s u g a d e k i m a s e N +cv_jpn_000818 i ch i +cv_jpn_000819 h a i +cv_jpn_000820 n i +cv_jpn_000821 d e i +cv_jpn_000822 t o k i +cv_jpn_000823 m i r u t o i u k o t o t o pau h a t a r a k U t o i u k o t o g a pau s U k a b u N r i t e k i n a k e r e b a n a r a n a i +cv_jpn_000824 w a r e w a r e o o t a m a sh i i n o z u k o k a r a g u g a s u m o n o d e n a k e r e b a n a r a n a i +cv_jpn_000825 z e cl t a i b e N sh o o h o o t e k i n a r u g a i u e n i i d e a t e k I ch o cl k a N t e k I k e e k i g a cl U k u m a r e r u n o d e a r u +cv_jpn_000826 d o k o m a d e m o t a t o i ch i t o n o s o o g o h I t e e t e k i n a z e cl t a i m u j u N t e k i j i k o d o i ts u n o s e k a i n i sh I t e +cv_jpn_000827 sh I k a r u n i n i N g e N t o k a N ky o o t o r o k a N k e e w a m o t o k o o i n o k a N ky e e d e a r i +cv_jpn_000828 i i s a n i k o o n o k o t o b a n o i m i y o o sh i a m a sh I t a +cv_jpn_000829 g e k i g a n a n a ts s a r i m a s U +cv_jpn_000830 k o ch i r a b a k o b u y a sh i i s a N d e s U +cv_jpn_000831 m o sh i i m a sh i +cv_jpn_000832 k o k o w a o k i k U t e n i k u y a k a n a m a ch i d e s U +cv_jpn_000833 s o n o u ch i k a i y a k U s a r e r u k a r a i s o g e +cv_jpn_000834 a m a s a g a f u s a i r a r e t e t e ch o o d o i +cv_jpn_000835 h o g e N sh I ts u n o d o o a a k e t a +cv_jpn_000836 m o d a N n i o o w a cl t e m o k i n i sh i n a i +cv_jpn_000837 a r i g a cl t a y a +cv_jpn_000838 i t o o g a r a k u d a t o j i k a N w a o s u r e t e t a n o sh i m e r u +cv_jpn_000839 k a k a k u w a g i z u ts U k a s a r e r u n i o j i t e j o o sh I k i n o u ch i n i h a i cl t e i u k u +cv_jpn_000840 sh I k a sh I t o k i g a k a o n i h a i r u k o t o s o n o k o t o g a pau m i r a a y o o m u k o t o d e a r i w a r a t a n a r u i cl s u t a i g a d e t e k u r u k o t o d e a r u +cv_jpn_000841 t e r e b i o k a i k a i t e pau t e r e b i o m i r u j i k a N g a f u e t a +cv_jpn_000842 k a k a r i sh I t a i n o m i i ts u m a d e m o i k i r u n o d e a r u +cv_jpn_000843 n i N k i d a a m e N i y a n i g a r a N d a r a n i j i k a N m a ch i d a cl t a +cv_jpn_000844 s o r e o m o ch i i r u n i N g e N n o i y o k u n i i t o N sh i s o sh I t e k o r e w a k a r e n o m o cl t e i r u k a ch i n o sh a k u d o n i i t o N s u r u +cv_jpn_000845 m a w a r u i o w a m i N n a k a N g a e r u k o t o o y a m e t e i t a +cv_jpn_000846 k o o i t e k i ch o k cl U k a N t e k i n i s e k a y o m i r u t o i u k o t o w a j a k u n i k o o i t e k i ch o cl k a N t e k i n i s e k a y o k e e s e e s u r u k o t o o h k u m u n o d e a r u +cv_jpn_000847 sh i N cl p a i t a k e s a s e m a i t o s u r u k i z u k a i g a y o k e e n i sh i N p a i s a s e t e sh i m a u +cv_jpn_000848 k o n o m i ch i w a t o t e m o s e m a i n o d e a b n a i d e s U +cv_jpn_000849 w o h o e g a a r i n i +cv_jpn_000850 t o i w a r o o k a m o i t a r i g a a n i a r i m a s U +cv_jpn_000851 t a n a k a s a N n o h i t a i n i k i m u r a s a N g a i m a s U +cv_jpn_000852 m a cl k u r a n o t a m a b o t e s u g o i n i e +cv_jpn_000853 sh o h o o m i t a i n a d o k U sh u k a N s o o b u N m o k a i t a +cv_jpn_000854 g e N j i ts u n o s e k a i w a t a m o o i ch i t o sh I t e k e cl t e s u r a i d a k a t a sh u o a cl t o s e k a i z u n a k e r e m a n a r a n a i +cv_jpn_000855 sh o o h i N k e N s a k u g a o k a r e a s u i t o o k a u k i i n a r u n a i +cv_jpn_000856 ts i sh I k i w a pau r e k I sh I t e cl k a t e e r d e n a k e r e m a n a r a n a i +cv_jpn_000857 m o n o g o t o n N j i N p a N k a e r u d a k e d e u m a k u i k U k o t o m o w a r u +cv_jpn_000858 k o n o k i s e e ts u w a k a ts u o n o s a sh i m i g a z e cl p i N +cv_jpn_000859 k a k e n i sh i cl p a i sh I t e m o o ch ts U ts u i t e s a N sh I ts u o u k e i d e r u +cv_jpn_000860 s o r e y u e n i t e ts u g a p u g a z e N t a i n o g a k o d e a r u t o s u r e w a +cv_jpn_000861 k i i z a n a y a o y a d a g a y e s U k U t e h a N j o sh I t e r u +cv_jpn_000862 i n i f u r a g a a k i n o h u z e N n i y o ch i i cl t e pau k o k u g a i e d a sh U ts u s u r u h I t o m o d e t e k i t a +cv_jpn_000863 ts u g i n i k a g a k u w a s o N z a y o pau sh u j u n o d o o e k i n i w a k a cl t e s o r e z u r a N ry o o u k i N z i t I k e N i k I s u r u +cv_jpn_000864 s o r e d e w a pau t o k t o i u m o n o n o s e r i ts u sh i o w a n a k u pau sh u N k a N t o i m o n o m o n a k u a r u n o d e a r u +cv_jpn_000865 a k a i b u r a N k o h o N k u r i t o s e e n o s u b e r i d a i k a w a i t a s u n a b a +cv_jpn_000866 sh I k a sh i s o r e w a d o k o m a d e m N k U k o k a r a r i t e pau p o k o e k a e r i k u r u s e sh I ts o m o t o m o d e n a k e b a n a r a n a i +cv_jpn_000867 a r i t o w a r a i r u d e m o o m a k I ch i r a sh I t e pau m i N n e k a r a u r a m i o k a cl t e r u +cv_jpn_000868 k o n o cl t e e d o s a w a g i n i n a r u k o t o m o n a i n o d a r o o +cv_jpn_000869 k o n o n e d a N d e w u r i ch a N k a a N +cv_jpn_000870 h i n o k a g e N n i ch u i sh i n a i t o s u g u k o g e r u +cv_jpn_000871 e N m a N d o w e n i p o ts u r i t o ch i i s a n a a n a g a i t a s a i sh o w a ts u m a y o o j i t e e d o n o ch i i s a n a a n a d a cl t a +cv_jpn_000872 s o r e w a pau a r e w a r e o i k a sh i n a g a r a w a r e w a r e o t o r e e k a s u r u n o d e a r u pau w a r e b a r e n o t a m a sh i i o k o r o s u n o d e a r u +cv_jpn_000873 r e k I sh I t e k i n i a t a e r a r t a m o n o w a d e cl t a i m u j u N t e k i j i g o t o o i ts u t e k i g e N z a i n o i t e s U k a i h I t e k i n i a t a e r a r e t a m o n o t o sh I t i +cv_jpn_000874 m u j u N t e k i j i g o d o o i ts U t o sh I t e pau i ts u m o k o n o s e k a i n i ch o o e sh t k i d e a +cv_jpn_000875 y u u n i z e cl t a e m u j u N t e k i j i g o d o o i ts U t o sh I t e g e N s a i k a r a g e N z a e t o b o k i i k u s e k a i n o g e N z a i n o i t e +cv_jpn_000876 h a r e pau w o t a N o sh I t o m N d a sh u d e k i n a i +cv_jpn_000877 sh I k a sh i w a t a sh i a s o k o n i s e k a i n o j i k o d o o i ts u o k u n o d e w a n a i +cv_jpn_000878 n e b e k a k u n a r u n o g a h a y a k u n a cl t a +cv_jpn_000879 w a t a sh i w a i N g e N n o o r e k I sh I t e k I k e e s e e n o t a ch i b a k a r a g e j u ts u o m i r u n o d e a cl t e o o sh a k a r a d e N sh a o m i r u n o d e w a n a i +cv_jpn_000880 a o i t o m a t o sh I k a n a k U t e k a u k a b m a i y o +cv_jpn_000881 s i N k i z u i gy o o n i o o k i n a k I t a y o y o s U t e i r u +cv_jpn_000882 n a n i k a sh i r a n o i n i N s e N t i b u w a n a i t o k i b i sh i i n o d e w a +cv_jpn_000883 j i k o N sh e e g e N n o i b e N t o d e s U t o r u sh I t a m a r u b u +cv_jpn_000884 m a a r i n o h I t o w a b o o z e N t o sh I t e i t a +cv_jpn_000885 s o N n a n a i y o o n o m e e r e w a n a N k e N m o k U I t e i t a +cv_jpn_000886 n j i k a i d r d e e s u i sh I t e i t a +cv_jpn_000887 t o k i d o k i ch i u N n o k o r o w a w a k a r a n a k u n o r u t o k i g a a r u d a k a r a b o k u w a k a n i o cl k i n o o t o n i k a ch i a j i m e r u +cv_jpn_000888 m o o n i g e t e ch a t a m e d a +cv_jpn_000889 k a r e w a p o o t o t a j i ts U k u sh i t e i t a +cv_jpn_000890 d a r a u n i m o m e i w a k o w a k a k e t a k u n a i +cv_jpn_000891 w a s a k a t o o m o t e t o w a n o o t o cl t e o n i g e cl t a +cv_jpn_000892 sh t e i m a s e N +cv_jpn_000893 k a y o o n i sh I t e sh I cl t e i r u t o t o m o n i sh i cl t e i n a i t o k o r o k a r a t a N ky u u w a h a j i m a r u n o d e a r u +cv_jpn_000894 a i s a u w a d a i j i d a i o +cv_jpn_000895 t o j i t a m o n o i k a n i h i r o g e t e m o h i r a i t a m o n o n i a n a r a r o t o i cl t e i r u g a +cv_jpn_000896 t a m e sh i n i i k u ts U k a ts U k u cl t e m i y o a +cv_jpn_000897 z u i b u N a k o g i n a sh o o b a i d a i o n a +cv_jpn_000898 w a t e i +cv_jpn_000899 i ch i +cv_jpn_000900 g o +cv_jpn_000901 sh i k i +cv_jpn_000902 i i e +cv_jpn_000903 h a ch i +cv_jpn_000904 n e i +cv_jpn_000905 sh i i +cv_jpn_000906 k u +cv_jpn_000907 i ch i +cv_jpn_000908 k a k a k u g a a k i r a k a n i s u r u pau ky a cl k a N t e k I sh i N r i n i sh I t a g a u k o t o n i y o cl t e +cv_jpn_000909 k a k o t o m i r a i t o n o m u j u N t e k i j i k o d o o i ts u t o sh I t e n o g e N z a i g a pau k a t a ch i o m o sh U t o i u k o t o d e a r u +cv_jpn_000910 b u ts u r i t e k I s e k a i w a pau s u u g a k u t e k I k i g o o n i y o cl t e a r a w a s a r e r u pau s u u g a k u t e k I k a t a ch i n o sh e k a i d e a r u +cv_jpn_000911 w o n a j i g e N sh o o d e s a N k o o g i n a r u +cv_jpn_000912 g a i k o k u k a r a k i t a m o n o d a t o sh i cl t e b i cl k u r i +cv_jpn_000913 i w a y o r u j i cl s e N n i y o cl t e k a k U t o U k U sh i k i t a cl t a m o n o d e a r u +fleurs_jpn_000346 o n a j i y o n i d a N s e e w a h i z a o o u z u b o N o h a cl k o t o g a pau g i m u u z u k e r a r e t e i m a s U +fleurs_jpn_000347 k o n o s a a b i s a k o r a k U s e e w a h a j i m e t o s u r u s e N p a k u y a e N k a k U ch i d e pau d e e t a y a o N s e o h I s u o t o s u r u t a N g e N t a i n i pau h i N cl p a n i r i y o o s a r e t e i m a s U +fleurs_jpn_000348 ky o o f u u hy o k a t o n o k o o s u i r o o o b i a m a k a sh i b a r a i u t a ts u m a k i i z u k i o b e s a i k u r n o d a n o k i b i sh i i k I sh o k e e t a y a s o n o e e ky o n i y o r u m o n o r e s U +fleurs_jpn_000349 i N t a a n e cl t o w a m a s U k o m i r i k e e sh u N t o t a i j i N k o m i r u i k e e sh o o n o ry o o y o s o o k a n e s u n a e t a k a N ky o o d e s U +fleurs_jpn_000350 k a j i n o r e w a ts u u j o o t o k u b e s u r a i N sh I k o y a e N t a a t e i m e N t o y o o sh I t e i m a s U g e s u w a k i b u i y o k u sh I s e s u n a i n i t o r a m a r u y o o r i s u r u t a m e d e s U +fleurs_jpn_000351 sh I k a sh i pau k a k u t e N n o b i k e cl t o o sh i n a cl t a a d t o i N d o w a n a n a ts u n o b i k e cl t o o sh i n a i s a N j u u r o k u r a sh I k a d e k i m a s e N d e sh i t a +fleurs_jpn_000352 h o o k u r a N d o n o k o o sh I k e ts u k a w a h o o k u r a N d o sh o t o o k o N d o e f U k e e p i i d e i ch i p o N n o g a i ch i e e b o N d o j i i p i i b i i t o t o o k a n i k o t e e s a r e t e i m a s U +fleurs_jpn_000353 h a sh I sh I t a N n o j o o h o o k o k a N w o ch j u u g o m e t o r o d e s U n i s e N j u i ch i n e h a ch i g a ts u n i s e k o o sh i pau n i s e N j u u n o n e N n i s a N g a ts u m a n e k a i ts u e sh i m a s e N g d e sh I t a +fleurs_jpn_000354 i cl p u N k a N d e h f u cl t o s o g u ch i i k i m a r e b a f u cl t o o s u r u m a d e m i n a N m o k a k a r u ch i i k i m o a r i m a s U +fleurs_jpn_000355 p i r a m i cl t a n o o t o t o sh i k a i n o sh o o w a k o n o k a N k o o sh i d e t o k u n i k o r o m o a t a sh i k a t a n o sh i m e r e m o y o o sh u n o h I t o s u r e s U +fleurs_jpn_000356 s o n o t a n e t a i n i d a d e r u t o sh I t e h o o k i g a ts u i k a s a r e g a ch i d e s U +fleurs_jpn_000357 g e N z u N s u r u k o t o g a sh i t a r e t e i r u n i j i o g o o m a i n a t a N d a cl t u p u r o o t u s a i d o w a g e N z u N s u r a t o o g a e b u N k e n o s a i k o n o u sh i d e s U t e g a k i n y o r u g e N p o o w a g e N z o o sh I t e i m o s e N +fleurs_jpn_000358 k a r e m o s e s o o t a d a sh i t o m i t o m e r u sh I t o m i m a sh I t a g a o o k u g u h I t o o s u n o g e k o d e pau t a i y o o k e d e a t a i o t o s i n o h o k a n o h o sh i g a pau ch I k u u n o m a r e i d o o sh I s e r u t o sh i N ch i t e i m a sh I t a +fleurs_jpn_000359 ch i b e cl t o m e e s o o ch u sh i N w a sh i i s e e i o g a d e s U s a m a z a n a n a k a m i g a m i y o sh I k a k U k a s u r u k o t o d e e n e r u g i i ch a n e r u g a sh o o k a s a r e ch a k u r a g a k a s e e k a s a r e s a t o r u n e i sh I k e g o o m a r e m a s U +fleurs_jpn_000360 m i n a m i y a h u r e k a n i y a r u s u b e t e n r u k o k u r i s U k o o e t o d o o y o n i k o n u k o o e n i a m a i n i j i a h o g o sh I t o n i u u e N g u o g o t a k a r i m a s U +fleurs_jpn_000361 d e cl sh s a cl k u r u n g a s u n u h o k a n o o k u n u k o o ts u sh u d a N g a s u k o k a r u m a r e m a sh I t a +fleurs_jpn_000362 i N t a a n e cl t o w a m a s U k o m i n i g e e sh o N t o t a i j i N k o m i n i g e e sh a o N n o ry o o y o o s o o k a n i s o n a i e t a k a N ky o o r e s U +fleurs_jpn_000363 ky o o i N d e w a k a N s e N k a N r i t e j u u sh o N i sh I k a g a i pau k a n i i e n u k a N s e N g o k a n o o s e s e k u t a m e n i k a N j a k a k u r s u r a d a m o m i s o ch o t o cl t e i m u sh I U +fleurs_jpn_000364 r e N p o o r i k a i w a n i s e N g o n e N d o k a r a w a i s e s u b u s u t o r e sh u m a r i o h o e n o sh I k i N t e e ky o o k a i j i sh i pau e r u b i i a i w a a t a r u z o p o r u n o n i j u u n i n o s o o s a N i N y o t o o u n u sh u n a k e r e w a n a r a n a i t o k I t e sh i m a sh I t a +fleurs_jpn_000365 p i i e ch i d e r i r o w a k e N s a sh I t k a k o k u b u sh s e i u k u g a r a n s u i s o i y u n p i i e i ch i n e i ch i n o r ry o o d e sh i m a s a r e m e s U +fleurs_jpn_000366 s o r e d e m o t o o ry i u k a r u n a r u b a i s o u k e pau s u b e t e n o o o sh I k i o o m a o r i a N z e N j o o n o k e e g o g o n i s a i sh i i n o ch u i o h a r a i m a sh o o +fleurs_jpn_000367 k o r e r a w a t a m a n i k o N g a ts s u r o k a cl k a ts o k u m u k e n o b i ch i t e k a i g a i n i s a m a z a m a n a t e N p o w o n a r a N d e i m a s U a N z e N i o o e b u k o t o g a d e k i m a s U +fleurs_jpn_000368 sh i N d o m i e n a i ch i u d a a s o N o pau n a N d o r a s a s U t o s e N ky e u k a ch i j u k i u p e e j i ky a k U ky u u n o s u N z a i m o n o d t a b a z e r u ch i e m u n o d o g u sh u n a y o s o d e a r u +fleurs_jpn_000369 k o n o s a a b i s u w a g o r a k U s e N o h a j i m e t o s u r u s e N p a k u y a e N g a k U sh i r e d e e t a y o N s e y o s u y o t o s u r u t a N k e N t a i r i h i N p a a n i r i y o o s a r e t e i m a s U +fleurs_jpn_000370 s a k u b a b u e e n o s u a i d e s U k a r a k o j u cl k i r o s a N j u i ch i m a i r u h a n a r e t a r a p u r a t a sh i n a i d e g e N sh u g o j o o i N g i N d e w a r u k u r i s u t e i n a f e r u u n a N d e s u d e k i r u k i n a a z o sh i g a n a i t o o ry o o s e e N e n o sh I s u d a o s e N g e N sh i m a sh I t a +fleurs_jpn_000371 o n o i z u k u i n i m a sh I h a t o n o k a s o o r e b u s u n o r u g a cl k i g a k a s o o r o o o b a a r a N sh i pau k a b e n i g e k i t o t ts u sh I t e pau j u u n a n i N g a sh i o sh i m a sh I t a +fleurs_jpn_000372 h a sh I sh I t a n o j o o h o k u k a w a j u u g o m e t o o d e s U n i s e N j u u ch i n e h a j i g a ts u n i sh u N k o sh i pau n i s e N j u u n a n i e s a N g a s u m a r e k a i ts u sh i m a s e N d e sh I t a +fleurs_jpn_000373 b u N n e e t o i u k o t o w a w a sh i m i i o m i s u r u a r a t e N g o n o k i e o o sh i sh i b i r d i s U k a r a k I t e w o r i sh i m e i N i o m i s u r u r a t e N k o r o m e e sh i sh i b i s U t o sh i y a t o sh I k o cl k a o m i sh i n a N n a k a n o k a t a ch i r e sh a k a i n o k i b o o t e e g i s u r u sh u i b i t a s U t o y u m e e sh i n i k a N k e e sh I t e i m a s U +fleurs_jpn_000374 ts u u j o o k o k o r e w a i s u m a k a N k o t e k u y a ry o o sh a t a sh i k a h a s u r o t o k a ch i k o e t e k i m a s U w o t o t o sh I i k a r i g a o i n a s u m u n o g a t a r i u a m a r u d e h o N n a o y o o r e s U +fleurs_jpn_000375 t e r e b i n o h o o d o o n i o N d o pau g e N p a ts U k a r a a k u e g a g a cl t e i m a s U i d e +fleurs_jpn_000376 n o o b o o r i t o k o o d o o n o s o o k a N k a N k e e w a k a y a k u sh a t a sh i n o k e N ky u u w a o u r a z u k e r e m o n o d e s U +fleurs_jpn_000377 s u i y o o b i n i b e N t o n a t o k a r u p a n e r o w a s e N sh I k e N g o U k a s u n u k o j i N d e s i I s u j o o sh i m a sh I t a +fleurs_jpn_000378 s e N h a p e k u n e N d a i r a i g u N t a i g a t o o j a k s u r e w a r e h a i ch i w a k o n o b y o k i n i k a N ky e e s u r e b o N d a i n i s o o g u sh I t a k o t o w a r i m a s e N d e sh I t a +fleurs_jpn_000379 sh I k a sh i cl k ky a k u t e n o b i k e cl d o o sh u n a cl d a t o i N d o a n a n a ts u n e b i k e cl t o o sh i n a i s a N j o r o k u r a sh I k a r e k i m a s e N d e sh I t a +fleurs_jpn_000380 k a j i n o d e w a ts u u j o o t o k u b e ts u n a i N sh a k o y a e N t a a t e i m e N t o o y o o i sh I t e i m a s U g e s o g a k i b u y a k u sh i s e s u n a i n i t o r o m a r u y o n i s u r u t a m e r e s U +fleurs_jpn_000381 s o r e d e o t o o ky i u k a r a n a a r a b a i s o g e pau s u b e t e n o hy o w a sh I k m a o r i a N z e N j o n o k e k o n i s a i sh i i N n o ch u i o h a r a i m a sh o +fleurs_jpn_000382 o w o k a r u g e w a r i m a s e pau k o r a sh I t o ts u o sh o o w a r i d e a r i pau a t a r a sh i sh o o n o m o k a k e d e s U +fleurs_jpn_000383 s a w a r i t o w a a f u r i k a n o y a s e e d o o g u z u pau t o k o n i s a w a N n a n i r u y a s e r e d o o g u ts u n o k a N s a s u a m o k u t e k i t o sh I t a r i k u r o d e n o y o k o o s a sh i m a s U +fleurs_jpn_000384 u y u n e k i t a b a r u t o k a y o m o d a N s u r o b a i w a s e i s u i ch i k a k u n i sh s e k u d a s a i k I k o o r e n o n a k o ts I k i ts s u u s a i n i m o cl t o m e ky o o k e r u s e i ts u d e o s o r u j u u m o n o s o o g a n a r i k i b i k i g a s U +fleurs_jpn_000385 k o k o w a i r i s u n o sh o k u m i N i sh e h a i e sh a e g a j i b u N t a sh i n o ry o o r o t o sh a b a sh u n a r o u d e pau sh o k o m i N t i j i r e n i sh o o k o o s e k a s o o t o s u r e k a t a w a k o k o a r h a j i b e r e n a g a y o i u sh o o +fleurs_jpn_000386 k o sh i w a s a k u g e N s u r u s u u ch o o s a r a y a w a s e N d e sh I t a k a pau s a k u g e N w a ch i i w o k u n o k e e z a i s a N sh I s u y o n i m o t o z u i t e j i sh i s a r u d a r o t o N n o o i m a sh I t a +fleurs_jpn_000387 s a u i n u u k u k u sh o cl k u w a sh i N k o N ry o k o o n o j i k i g a s U k u n a i pau k a r u e ch a a sh o cl k u y o r e m o h a e k o o t o z u r e n a w a b i k i y o r i sh o o j o g a k a s u r u k o t o g a a r i m a s U +fleurs_jpn_000388 ky i n o o n o a s a t o r u k o n o g a j i a N t e cl p u n o k e e s a s u o h o N w o r e j i d o o sh a b a k u r a n o b a k a a s o r i y o r i pau ky e e k a N f u t a r e g a sh i b o sh i pau h o sh o w a sh a w a n i j u u n i y o k o a i m a sh I t a +fleurs_jpn_000389 sh o k u b u ts u d a n i N g i N g a s u u s a N z o o ts U k u r i n i N g e N g a k a cl k o i k i t o sh I t e h a k i d a s u n i s a N k a t a N s o o t o r i k o N d e i m a s U +fleurs_jpn_000390 s e N p a k u r e e b u sh i o i s o o s u r u n o w a u m i o k o e t e h I t o y a b u sh i o t a r i ry o o y i s o o s u r u m o cl t o m o k o o r i s e k i n a h o o h o o r e s U +fleurs_jpn_000391 k a r i h o r u n i a sh u u n o a w a n o r u d o sh u w a r u ts u n e cl k a a ch i sh i w a b o o r y o k u t e k i n a b i d e o g e e m u o m i s e e n e N sh a n i h a N b a y a r e N t a e s u r e u k o t o o k i N sh i s u r u h o o w a n i sh a o m e e sh i m a sh I t a diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/token_int b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/decode_asr_asr_model_valid.loss.ave/test_10min_jpn/token_int new file mode 100644 index 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a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/run.sh b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/run.sh new file mode 100755 index 0000000000000000000000000000000000000000..728871c3644814f174d2a085eb4e0e9da64264aa --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/run.sh @@ -0,0 +1 @@ +./asr.sh --ngpu 1 --stage 11 --stop_stage 13 --nj 32 --inference_nj 4 --gpu_inference true --lang jpn --inference_asr_model valid.loss.ave.pth --local_data_opts '--duration 1h --lid false --multilingual false --single_lang jpn' --use_lm false --token_type word --feats_type raw --feats_normalize utterance_mvn --asr_config conf/tuning/train_asr_s3prl_houlsby.yaml --inference_config conf/decode_asr.yaml --train_set train_1h_jpn --valid_set dev_10min_jpn --test_sets 'dev_10min_jpn test_10min_jpn' --asr_tag train_asr_s3prl_houlsby_jpn_1h --expdir test_pr --asr_stats_dir test_pr/asr_stats_jpn_1h --local_score_opts 'false false monolingual' --stage 11 "$@"; exit $? diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/tensorboard/train/events.out.tfevents.1705248670.stan.302884.0 b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/tensorboard/train/events.out.tfevents.1705248670.stan.302884.0 new file mode 100644 index 0000000000000000000000000000000000000000..bd4e78e4efc4e0c105c84d5e10b72aa39ab8a0d0 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/tensorboard/train/events.out.tfevents.1705248670.stan.302884.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a70d51456870aef31b9407e71bfde09632a6e9f6100cfb1c97333da360bdda24 +size 49873274 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/tensorboard/train/events.out.tfevents.1705427854.stan.897747.0 b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/tensorboard/train/events.out.tfevents.1705427854.stan.897747.0 new file mode 100644 index 0000000000000000000000000000000000000000..81817c906ea37f97c26727d0e38c66d92c277b3b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/tensorboard/train/events.out.tfevents.1705427854.stan.897747.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75acb02b334c8ce3e94e89e32af41ebfd3383ffe140567b4795fdd7b1f9c5cee +size 49562100 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/tensorboard/valid/events.out.tfevents.1705248670.stan.302884.1 b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/tensorboard/valid/events.out.tfevents.1705248670.stan.302884.1 new file mode 100644 index 0000000000000000000000000000000000000000..a3db33f47aa760794d7c7c857a14bc25e9d8c3e7 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/tensorboard/valid/events.out.tfevents.1705248670.stan.302884.1 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e506d6181b139590bb3555626959e93258a73d0233bae957dd4ccdf1bd6f0171 +size 11190 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/tensorboard/valid/events.out.tfevents.1705427854.stan.897747.1 b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/tensorboard/valid/events.out.tfevents.1705427854.stan.897747.1 new file mode 100644 index 0000000000000000000000000000000000000000..cbbd30880f8bf7c53d7cbc5ea6140bc1cd0b392b --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/tensorboard/valid/events.out.tfevents.1705427854.stan.897747.1 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a06b8e91fa9eb98f6dd5a394772594d8a60b9ccee3a99d2a17bf2e3cd2877a21 +size 11190 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/train.1.log b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/train.1.log new file mode 100644 index 0000000000000000000000000000000000000000..2c4713ff7617fdd39c10017af3a381515021aca9 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/train.1.log @@ -0,0 +1,1406 @@ +# python3 -m espnet2.bin.asr_train --use_preprocessor true --bpemodel none --token_type word --token_list data/jpn_token_list/word/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_jpn/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_jpn_1h/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_jpn/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_jpn_1h/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_1h_jpn/text,text,text --train_shape_file test_pr/asr_stats_jpn_1h/train/text_shape.word --valid_data_path_and_name_and_type dump/raw/dev_10min_jpn/text,text,text --valid_shape_file test_pr/asr_stats_jpn_1h/valid/text_shape.word --ngpu 1 --multiprocessing_distributed True +# Started at Mon Jan 15 00:11:06 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_train.py --use_preprocessor true --bpemodel none --token_type word --token_list data/jpn_token_list/word/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_jpn/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_jpn_1h/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_jpn/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_jpn_1h/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_1h_jpn/text,text,text --train_shape_file test_pr/asr_stats_jpn_1h/train/text_shape.word --valid_data_path_and_name_and_type dump/raw/dev_10min_jpn/text,text,text --valid_shape_file test_pr/asr_stats_jpn_1h/valid/text_shape.word --ngpu 1 --multiprocessing_distributed True +[stan] 2024-01-15 00:11:07,734 (asr:523) INFO: Vocabulary size: 41 +[stan] 2024-01-15 00:11:07,796 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +[stan] 2024-01-15 00:11:07,796 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +[stan] 2024-01-15 00:11:07,907 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +[stan] 2024-01-15 00:11:09,200 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.mask_emb.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.label_embs_concat.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.0.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.1.0.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.2.0.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.3.0.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.4.0.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.5.0.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.6.0.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_g.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_v.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,022 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,023 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.weight.requires_grad = False +[stan] 2024-01-15 00:11:10,024 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.bias.requires_grad = False +[stan] 2024-01-15 00:11:10,423 (abs_task:1275) INFO: pytorch.version=1.12.0+cu113, cuda.available=True, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True +[stan] 2024-01-15 00:11:10,425 (abs_task:1276) INFO: Model structure: +ESPnetASRModel( + (frontend): S3prlFrontend( + (upstream): S3PRLUpstream( + (upstream): UpstreamExpert( + (model): HubertModel( + (feature_extractor): ConvFeatureExtractionModel( + (conv_layers): ModuleList( + (0): Sequential( + (0): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): Fp32GroupNorm(512, 512, eps=1e-05, affine=True) + (3): GELU(approximate=none) + ) + (1): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (2): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (3): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (4): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (5): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (6): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + ) + ) + (post_extract_proj): Linear(in_features=512, out_features=768, bias=True) + (dropout_input): Dropout(p=0.1, inplace=False) + (dropout_features): Dropout(p=0.1, inplace=False) + (encoder): TransformerEncoder( + (pos_conv): Sequential( + (0): Conv1d(768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16) + (1): SamePad() + (2): GELU(approximate=none) + ) + (layers): ModuleList( + (0): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (1): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (2): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (3): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (4): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (5): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (6): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (7): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (8): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (9): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (10): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (11): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + ) + (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (final_proj): Linear(in_features=768, out_features=256, bias=True) + ) + ) + ) + (featurizer): Featurizer() + ) + (specaug): SpecAug( + (time_warp): TimeWarp(window=5, mode=bicubic) + (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq) + (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time) + ) + (normalize): UtteranceMVN(norm_means=True, norm_vars=False) + (preencoder): LinearProjection( + (linear_out): Linear(in_features=768, out_features=80, bias=True) + (dropout): Dropout(p=0.0, inplace=False) + ) + (encoder): TransformerEncoder( + (embed): Conv2dSubsampling2( + (conv): Sequential( + (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU() + (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) + (3): ReLU() + ) + (out): Sequential( + (0): Linear(in_features=9472, out_features=256, bias=True) + (1): PositionalEncoding( + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + ) + (encoders): MultiSequential( + (0): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (1): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + ) + (criterion_att): LabelSmoothingLoss( + (criterion): KLDivLoss() + ) + (ctc): CTC( + (ctc_lo): Linear(in_features=256, out_features=41, bias=True) + (ctc_loss): CTCLoss() + ) +) + +Model summary: + Class Name: ESPnetASRModel + Total Number of model parameters: 99.97 M + Number of trainable parameters: 5.27 M (5.3%) + Size: 21.08 MB + Type: torch.float32 +[stan] 2024-01-15 00:11:10,425 (abs_task:1279) INFO: Optimizer: +Adam ( +Parameter Group 0 + amsgrad: False + betas: (0.9, 0.999) + capturable: False + eps: 1e-08 + foreach: None + lr: 0.0001 + maximize: False + weight_decay: 1e-06 +) +[stan] 2024-01-15 00:11:10,425 (abs_task:1280) INFO: Scheduler: None +[stan] 2024-01-15 00:11:10,426 (abs_task:1289) INFO: Saving the configuration in test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml +[stan] 2024-01-15 00:11:10,577 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-15 00:11:10,619 (abs_task:1665) INFO: [train] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/train_1h_jpn/wav.scp", "type": "sound"} + text: {"path": "dump/raw/train_1h_jpn/text", "type": "text"} + preprocess: ) +[stan] 2024-01-15 00:11:10,619 (abs_task:1666) INFO: [train] Batch sampler: SortedBatchSampler(N-batch=84, batch_size=8, shape_file=test_pr/asr_stats_jpn_1h/train/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-15 00:11:10,619 (abs_task:1667) INFO: [train] mini-batch sizes summary: N-batch=84, mean=8.0, min=8, max=9 +[stan] 2024-01-15 00:11:10,630 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-15 00:11:10,631 (abs_task:1665) INFO: [valid] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_jpn/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_jpn/text", "type": "text"} + preprocess: ) +[stan] 2024-01-15 00:11:10,631 (abs_task:1666) INFO: [valid] Batch sampler: SortedBatchSampler(N-batch=15, batch_size=8, shape_file=test_pr/asr_stats_jpn_1h/valid/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-15 00:11:10,631 (abs_task:1667) INFO: [valid] mini-batch sizes summary: N-batch=15, mean=8.4, min=8, max=9 +[stan] 2024-01-15 00:11:10,631 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-15 00:11:10,642 (abs_task:1665) INFO: [plot_att] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_jpn/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_jpn/text", "type": "text"} + preprocess: ) +[stan] 2024-01-15 00:11:10,642 (abs_task:1666) INFO: [plot_att] Batch sampler: UnsortedBatchSampler(N-batch=126, batch_size=1, key_file=test_pr/asr_stats_jpn_1h/valid/speech_shape, +[stan] 2024-01-15 00:11:10,642 (abs_task:1667) INFO: [plot_att] mini-batch sizes summary: N-batch=3, mean=1.0, min=1, max=1 +[stan] 2024-01-15 00:11:10,674 (trainer:300) INFO: 1/30epoch started +[stan] 2024-01-15 00:11:16,234 (trainer:763) INFO: 1epoch:train:1-40batch: iter_time=0.002, forward_time=0.083, loss_ctc=42.996, loss=42.996, backward_time=0.011, grad_norm=680.921, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.552 +[stan] 2024-01-15 00:11:20,168 (trainer:763) INFO: 1epoch:train:41-80batch: iter_time=4.117e-05, forward_time=0.051, loss_ctc=32.650, loss=32.650, backward_time=0.008, grad_norm=160.536, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-15 00:11:23,494 (trainer:763) INFO: 1epoch:train:81-120batch: iter_time=4.091e-05, forward_time=0.044, loss_ctc=27.139, loss=27.139, backward_time=0.007, grad_norm=127.118, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.332 +[stan] 2024-01-15 00:11:27,817 (trainer:763) INFO: 1epoch:train:121-160batch: iter_time=4.206e-05, forward_time=0.056, loss_ctc=36.296, loss=36.296, backward_time=0.009, grad_norm=113.048, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-15 00:11:31,774 (trainer:763) INFO: 1epoch:train:161-200batch: iter_time=4.339e-05, forward_time=0.052, loss_ctc=33.044, loss=33.044, backward_time=0.008, grad_norm=120.435, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-15 00:11:35,776 (trainer:763) INFO: 1epoch:train:201-240batch: iter_time=4.277e-05, forward_time=0.052, loss_ctc=33.089, loss=33.089, backward_time=0.008, grad_norm=90.040, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-15 00:11:39,684 (trainer:763) INFO: 1epoch:train:241-280batch: iter_time=4.114e-05, forward_time=0.051, loss_ctc=31.415, loss=31.415, backward_time=0.008, grad_norm=88.448, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:11:43,418 (trainer:763) INFO: 1epoch:train:281-320batch: iter_time=4.325e-05, forward_time=0.049, loss_ctc=26.834, loss=26.834, backward_time=0.008, grad_norm=116.279, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.373 +[stan] 2024-01-15 00:11:47,581 (trainer:763) INFO: 1epoch:train:321-360batch: iter_time=4.363e-05, forward_time=0.054, loss_ctc=25.686, loss=25.686, backward_time=0.009, grad_norm=104.742, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-15 00:11:51,526 (trainer:763) INFO: 1epoch:train:361-400batch: iter_time=4.256e-05, forward_time=0.052, loss_ctc=20.438, loss=20.438, backward_time=0.008, grad_norm=89.189, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-15 00:11:55,408 (trainer:763) INFO: 1epoch:train:401-440batch: iter_time=4.162e-05, forward_time=0.051, loss_ctc=18.023, loss=18.023, backward_time=0.008, grad_norm=130.931, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-15 00:11:59,410 (trainer:763) INFO: 1epoch:train:441-480batch: iter_time=4.481e-05, forward_time=0.052, loss_ctc=17.716, loss=17.716, backward_time=0.008, grad_norm=151.986, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-15 00:12:03,283 (trainer:763) INFO: 1epoch:train:481-520batch: iter_time=4.297e-05, forward_time=0.051, loss_ctc=15.845, loss=15.845, backward_time=0.008, grad_norm=123.498, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-15 00:12:07,242 (trainer:763) INFO: 1epoch:train:521-560batch: iter_time=4.286e-05, forward_time=0.052, loss_ctc=14.862, loss=14.862, backward_time=0.008, grad_norm=84.792, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-15 00:12:11,260 (trainer:763) INFO: 1epoch:train:561-600batch: iter_time=4.391e-05, forward_time=0.053, loss_ctc=14.627, loss=14.627, backward_time=0.008, grad_norm=100.552, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:12:15,182 (trainer:763) INFO: 1epoch:train:601-640batch: iter_time=4.561e-05, forward_time=0.051, loss_ctc=13.599, loss=13.599, backward_time=0.008, grad_norm=104.338, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-15 00:12:19,090 (trainer:763) INFO: 1epoch:train:641-680batch: iter_time=4.188e-05, forward_time=0.051, loss_ctc=12.365, loss=12.365, backward_time=0.008, grad_norm=110.701, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:12:23,012 (trainer:763) INFO: 1epoch:train:681-720batch: iter_time=4.387e-05, forward_time=0.051, loss_ctc=11.612, loss=11.612, backward_time=0.008, grad_norm=75.503, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-15 00:12:26,924 (trainer:763) INFO: 1epoch:train:721-760batch: iter_time=4.234e-05, forward_time=0.051, loss_ctc=12.061, loss=12.061, backward_time=0.008, grad_norm=95.908, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:12:31,089 (trainer:763) INFO: 1epoch:train:761-800batch: iter_time=3.999e-05, forward_time=0.055, loss_ctc=12.530, loss=12.530, backward_time=0.008, grad_norm=122.818, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-15 00:12:35,630 (trainer:354) INFO: 1epoch results: [train] iter_time=1.511e-04, forward_time=0.053, loss_ctc=22.642, loss=22.642, backward_time=0.008, grad_norm=139.589, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402, time=1 minute and 20.46 seconds, total_count=800, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=27.145, cer_ctc=0.216, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=27.145, time=1.07 seconds, total_count=15, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.42 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:12:36,655 (trainer:417) INFO: The best model has been updated: valid.loss +[stan] 2024-01-15 00:12:36,655 (trainer:288) INFO: 2/30epoch started. Estimated time to finish: 41 minutes and 33.45 seconds +[stan] 2024-01-15 00:12:40,672 (trainer:763) INFO: 2epoch:train:1-40batch: iter_time=0.003, forward_time=0.049, loss_ctc=10.649, loss=10.649, backward_time=0.009, grad_norm=133.875, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-15 00:12:44,685 (trainer:763) INFO: 2epoch:train:41-80batch: iter_time=4.430e-05, forward_time=0.053, loss_ctc=11.397, loss=11.397, backward_time=0.009, grad_norm=86.226, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-15 00:12:48,503 (trainer:763) INFO: 2epoch:train:81-120batch: iter_time=4.189e-05, forward_time=0.050, loss_ctc=10.257, loss=10.257, backward_time=0.008, grad_norm=80.047, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.382 +[stan] 2024-01-15 00:12:52,298 (trainer:763) INFO: 2epoch:train:121-160batch: iter_time=4.182e-05, forward_time=0.050, loss_ctc=9.716, loss=9.716, backward_time=0.008, grad_norm=97.073, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-15 00:12:56,607 (trainer:763) INFO: 2epoch:train:161-200batch: iter_time=4.288e-05, forward_time=0.056, loss_ctc=11.691, loss=11.691, backward_time=0.009, grad_norm=106.117, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-15 00:13:00,326 (trainer:763) INFO: 2epoch:train:201-240batch: iter_time=4.159e-05, forward_time=0.049, loss_ctc=9.296, loss=9.296, backward_time=0.008, grad_norm=113.073, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.372 +[stan] 2024-01-15 00:13:04,234 (trainer:763) INFO: 2epoch:train:241-280batch: iter_time=4.189e-05, forward_time=0.051, loss_ctc=9.983, loss=9.983, backward_time=0.008, grad_norm=101.765, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:13:08,321 (trainer:763) INFO: 2epoch:train:281-320batch: iter_time=4.243e-05, forward_time=0.053, loss_ctc=10.431, loss=10.431, backward_time=0.009, grad_norm=100.751, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.409 +[stan] 2024-01-15 00:13:12,394 (trainer:763) INFO: 2epoch:train:321-360batch: iter_time=4.466e-05, forward_time=0.053, loss_ctc=10.077, loss=10.077, backward_time=0.009, grad_norm=118.191, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.407 +[stan] 2024-01-15 00:13:16,108 (trainer:763) INFO: 2epoch:train:361-400batch: iter_time=4.684e-05, forward_time=0.049, loss_ctc=8.702, loss=8.702, backward_time=0.008, grad_norm=99.601, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.371 +[stan] 2024-01-15 00:13:20,062 (trainer:763) INFO: 2epoch:train:401-440batch: iter_time=4.236e-05, forward_time=0.052, loss_ctc=9.631, loss=9.631, backward_time=0.008, grad_norm=94.044, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-15 00:13:24,022 (trainer:763) INFO: 2epoch:train:441-480batch: iter_time=4.259e-05, forward_time=0.052, loss_ctc=9.367, loss=9.367, backward_time=0.009, grad_norm=85.715, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-15 00:13:27,953 (trainer:763) INFO: 2epoch:train:481-520batch: iter_time=4.461e-05, forward_time=0.051, loss_ctc=9.275, loss=9.275, backward_time=0.009, grad_norm=72.834, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-15 00:13:32,287 (trainer:763) INFO: 2epoch:train:521-560batch: iter_time=4.348e-05, forward_time=0.057, loss_ctc=10.143, loss=10.143, backward_time=0.009, grad_norm=108.964, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-15 00:13:36,225 (trainer:763) INFO: 2epoch:train:561-600batch: iter_time=4.154e-05, forward_time=0.052, loss_ctc=8.697, loss=8.697, backward_time=0.009, grad_norm=82.391, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-15 00:13:39,955 (trainer:763) INFO: 2epoch:train:601-640batch: iter_time=4.292e-05, forward_time=0.049, loss_ctc=7.722, loss=7.722, backward_time=0.009, grad_norm=81.968, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.373 +[stan] 2024-01-15 00:13:43,985 (trainer:763) INFO: 2epoch:train:641-680batch: iter_time=4.225e-05, forward_time=0.053, loss_ctc=8.754, loss=8.754, backward_time=0.008, grad_norm=88.997, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-15 00:13:48,029 (trainer:763) INFO: 2epoch:train:681-720batch: iter_time=4.286e-05, forward_time=0.053, loss_ctc=9.025, loss=9.025, backward_time=0.009, grad_norm=110.475, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-15 00:13:51,726 (trainer:763) INFO: 2epoch:train:721-760batch: iter_time=4.400e-05, forward_time=0.049, loss_ctc=7.905, loss=7.905, backward_time=0.008, grad_norm=82.876, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.370 +[stan] 2024-01-15 00:13:55,919 (trainer:763) INFO: 2epoch:train:761-800batch: iter_time=4.100e-05, forward_time=0.055, loss_ctc=9.218, loss=9.218, backward_time=0.009, grad_norm=93.061, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.419 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-15 00:14:00,227 (trainer:354) INFO: 2epoch results: [train] iter_time=1.725e-04, forward_time=0.052, loss_ctc=9.596, loss=9.596, backward_time=0.009, grad_norm=96.902, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396, time=1 minute and 19.34 seconds, total_count=1600, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=22.807, cer_ctc=0.183, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=22.807, time=1.07 seconds, total_count=30, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.17 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:14:01,086 (trainer:417) INFO: The best model has been updated: valid.loss +[stan] 2024-01-15 00:14:01,086 (trainer:288) INFO: 3/30epoch started. Estimated time to finish: 39 minutes and 45.76 seconds +[stan] 2024-01-15 00:14:05,617 (trainer:763) INFO: 3epoch:train:1-40batch: iter_time=0.003, forward_time=0.056, loss_ctc=9.322, loss=9.322, backward_time=0.009, grad_norm=95.409, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.453 +[stan] 2024-01-15 00:14:09,286 (trainer:763) INFO: 3epoch:train:41-80batch: iter_time=4.228e-05, forward_time=0.048, loss_ctc=7.104, loss=7.104, backward_time=0.008, grad_norm=74.033, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.367 +[stan] 2024-01-15 00:14:13,309 (trainer:763) INFO: 3epoch:train:81-120batch: iter_time=4.147e-05, forward_time=0.053, loss_ctc=8.217, loss=8.217, backward_time=0.009, grad_norm=102.735, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:14:17,090 (trainer:763) INFO: 3epoch:train:121-160batch: iter_time=4.836e-05, forward_time=0.050, loss_ctc=7.383, loss=7.383, backward_time=0.009, grad_norm=84.027, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-15 00:14:21,612 (trainer:763) INFO: 3epoch:train:161-200batch: iter_time=4.543e-05, forward_time=0.059, loss_ctc=9.842, loss=9.842, backward_time=0.009, grad_norm=128.157, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.452 +[stan] 2024-01-15 00:14:25,259 (trainer:763) INFO: 3epoch:train:201-240batch: iter_time=4.395e-05, forward_time=0.048, loss_ctc=6.816, loss=6.816, backward_time=0.008, grad_norm=73.940, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.365 +[stan] 2024-01-15 00:14:29,049 (trainer:763) INFO: 3epoch:train:241-280batch: iter_time=4.251e-05, forward_time=0.050, loss_ctc=7.300, loss=7.300, backward_time=0.009, grad_norm=84.033, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-15 00:14:32,964 (trainer:763) INFO: 3epoch:train:281-320batch: iter_time=4.424e-05, forward_time=0.051, loss_ctc=7.689, loss=7.689, backward_time=0.008, grad_norm=83.837, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:14:37,228 (trainer:763) INFO: 3epoch:train:321-360batch: iter_time=4.337e-05, forward_time=0.056, loss_ctc=8.749, loss=8.749, backward_time=0.009, grad_norm=94.642, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-15 00:14:41,163 (trainer:763) INFO: 3epoch:train:361-400batch: iter_time=4.175e-05, forward_time=0.052, loss_ctc=7.420, loss=7.420, backward_time=0.008, grad_norm=76.343, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-15 00:14:44,849 (trainer:763) INFO: 3epoch:train:401-440batch: iter_time=4.794e-05, forward_time=0.048, loss_ctc=6.775, loss=6.775, backward_time=0.008, grad_norm=82.348, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.369 +[stan] 2024-01-15 00:14:48,472 (trainer:763) INFO: 3epoch:train:441-480batch: iter_time=4.257e-05, forward_time=0.048, loss_ctc=6.278, loss=6.278, backward_time=0.008, grad_norm=78.791, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.362 +[stan] 2024-01-15 00:14:52,837 (trainer:763) INFO: 3epoch:train:481-520batch: iter_time=4.220e-05, forward_time=0.057, loss_ctc=8.398, loss=8.398, backward_time=0.010, grad_norm=89.901, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-15 00:14:56,821 (trainer:763) INFO: 3epoch:train:521-560batch: iter_time=4.249e-05, forward_time=0.052, loss_ctc=7.572, loss=7.572, backward_time=0.008, grad_norm=78.127, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-15 00:15:00,839 (trainer:763) INFO: 3epoch:train:561-600batch: iter_time=4.274e-05, forward_time=0.053, loss_ctc=7.349, loss=7.349, backward_time=0.009, grad_norm=78.447, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:15:04,996 (trainer:763) INFO: 3epoch:train:601-640batch: iter_time=4.277e-05, forward_time=0.054, loss_ctc=7.966, loss=7.966, backward_time=0.009, grad_norm=87.627, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-15 00:15:08,563 (trainer:763) INFO: 3epoch:train:641-680batch: iter_time=4.478e-05, forward_time=0.047, loss_ctc=5.911, loss=5.911, backward_time=0.008, grad_norm=75.495, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.357 +[stan] 2024-01-15 00:15:12,569 (trainer:763) INFO: 3epoch:train:681-720batch: iter_time=4.236e-05, forward_time=0.052, loss_ctc=7.426, loss=7.426, backward_time=0.009, grad_norm=82.033, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-15 00:15:16,821 (trainer:763) INFO: 3epoch:train:721-760batch: iter_time=4.474e-05, forward_time=0.056, loss_ctc=7.616, loss=7.616, backward_time=0.009, grad_norm=85.901, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-15 00:15:20,506 (trainer:763) INFO: 3epoch:train:761-800batch: iter_time=4.023e-05, forward_time=0.048, loss_ctc=6.077, loss=6.077, backward_time=0.008, grad_norm=80.913, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.368 +[stan] 2024-01-15 00:15:24,830 (trainer:354) INFO: 3epoch results: [train] iter_time=1.865e-04, forward_time=0.052, loss_ctc=7.560, loss=7.560, backward_time=0.009, grad_norm=85.837, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.5 seconds, total_count=2400, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=21.990, cer_ctc=0.171, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=21.990, time=1.09 seconds, total_count=45, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.16 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:15:25,788 (trainer:417) INFO: The best model has been updated: valid.loss +[stan] 2024-01-15 00:15:25,789 (trainer:288) INFO: 4/30epoch started. Estimated time to finish: 38 minutes and 16.03 seconds +[stan] 2024-01-15 00:15:30,133 (trainer:763) INFO: 4epoch:train:1-40batch: iter_time=0.003, forward_time=0.054, loss_ctc=6.999, loss=6.999, backward_time=0.009, grad_norm=89.683, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-15 00:15:34,004 (trainer:763) INFO: 4epoch:train:41-80batch: iter_time=4.347e-05, forward_time=0.051, loss_ctc=6.436, loss=6.436, backward_time=0.009, grad_norm=74.199, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-15 00:15:38,023 (trainer:763) INFO: 4epoch:train:81-120batch: iter_time=4.194e-05, forward_time=0.053, loss_ctc=7.120, loss=7.120, backward_time=0.009, grad_norm=83.950, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:15:41,941 (trainer:763) INFO: 4epoch:train:121-160batch: iter_time=4.055e-05, forward_time=0.051, loss_ctc=6.528, loss=6.528, backward_time=0.008, grad_norm=77.702, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-15 00:15:46,071 (trainer:763) INFO: 4epoch:train:161-200batch: iter_time=4.419e-05, forward_time=0.054, loss_ctc=6.999, loss=6.999, backward_time=0.009, grad_norm=78.205, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.413 +[stan] 2024-01-15 00:15:50,343 (trainer:763) INFO: 4epoch:train:201-240batch: iter_time=4.416e-05, forward_time=0.056, loss_ctc=7.134, loss=7.134, backward_time=0.009, grad_norm=78.383, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-15 00:15:53,838 (trainer:763) INFO: 4epoch:train:241-280batch: iter_time=4.217e-05, forward_time=0.046, loss_ctc=5.231, loss=5.231, backward_time=0.008, grad_norm=77.303, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.349 +[stan] 2024-01-15 00:15:57,877 (trainer:763) INFO: 4epoch:train:281-320batch: iter_time=4.237e-05, forward_time=0.053, loss_ctc=6.628, loss=6.628, backward_time=0.009, grad_norm=85.361, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-15 00:16:01,583 (trainer:763) INFO: 4epoch:train:321-360batch: iter_time=4.275e-05, forward_time=0.049, loss_ctc=5.445, loss=5.445, backward_time=0.008, grad_norm=72.315, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.371 +[stan] 2024-01-15 00:16:05,802 (trainer:763) INFO: 4epoch:train:361-400batch: iter_time=4.306e-05, forward_time=0.055, loss_ctc=6.870, loss=6.870, backward_time=0.009, grad_norm=89.009, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.422 +[stan] 2024-01-15 00:16:09,665 (trainer:763) INFO: 4epoch:train:401-440batch: iter_time=4.634e-05, forward_time=0.051, loss_ctc=6.235, loss=6.235, backward_time=0.009, grad_norm=82.846, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.386 +[stan] 2024-01-15 00:16:13,643 (trainer:763) INFO: 4epoch:train:441-480batch: iter_time=4.530e-05, forward_time=0.052, loss_ctc=5.863, loss=5.863, backward_time=0.009, grad_norm=80.162, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-15 00:16:17,688 (trainer:763) INFO: 4epoch:train:481-520batch: iter_time=4.353e-05, forward_time=0.053, loss_ctc=6.478, loss=6.478, backward_time=0.009, grad_norm=84.708, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-15 00:16:21,586 (trainer:763) INFO: 4epoch:train:521-560batch: iter_time=4.185e-05, forward_time=0.051, loss_ctc=5.672, loss=5.672, backward_time=0.008, grad_norm=84.442, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.390 +[stan] 2024-01-15 00:16:25,775 (trainer:763) INFO: 4epoch:train:561-600batch: iter_time=4.232e-05, forward_time=0.055, loss_ctc=6.410, loss=6.410, backward_time=0.009, grad_norm=81.487, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.419 +[stan] 2024-01-15 00:16:29,264 (trainer:763) INFO: 4epoch:train:601-640batch: iter_time=4.176e-05, forward_time=0.046, loss_ctc=4.951, loss=4.951, backward_time=0.008, grad_norm=77.497, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.349 +[stan] 2024-01-15 00:16:33,365 (trainer:763) INFO: 4epoch:train:641-680batch: iter_time=4.206e-05, forward_time=0.054, loss_ctc=6.240, loss=6.240, backward_time=0.009, grad_norm=82.304, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.410 +[stan] 2024-01-15 00:16:37,171 (trainer:763) INFO: 4epoch:train:681-720batch: iter_time=4.281e-05, forward_time=0.050, loss_ctc=5.473, loss=5.473, backward_time=0.008, grad_norm=73.877, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.381 +[stan] 2024-01-15 00:16:41,055 (trainer:763) INFO: 4epoch:train:721-760batch: iter_time=4.178e-05, forward_time=0.051, loss_ctc=5.774, loss=5.774, backward_time=0.008, grad_norm=78.678, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-15 00:16:45,207 (trainer:763) INFO: 4epoch:train:761-800batch: iter_time=4.132e-05, forward_time=0.054, loss_ctc=6.164, loss=6.164, backward_time=0.009, grad_norm=79.736, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-15 00:16:49,474 (trainer:354) INFO: 4epoch results: [train] iter_time=1.767e-04, forward_time=0.052, loss_ctc=6.233, loss=6.233, backward_time=0.009, grad_norm=80.592, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.49 seconds, total_count=3200, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=22.886, cer_ctc=0.173, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=22.886, time=1.07 seconds, total_count=60, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.13 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:16:50,346 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:16:50,346 (trainer:288) INFO: 5/30epoch started. Estimated time to finish: 36 minutes and 47.87 seconds +[stan] 2024-01-15 00:16:54,540 (trainer:763) INFO: 5epoch:train:1-40batch: iter_time=0.003, forward_time=0.052, loss_ctc=5.405, loss=5.405, backward_time=0.008, grad_norm=79.467, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.419 +[stan] 2024-01-15 00:16:58,459 (trainer:763) INFO: 5epoch:train:41-80batch: iter_time=4.291e-05, forward_time=0.051, loss_ctc=5.451, loss=5.451, backward_time=0.009, grad_norm=86.429, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-15 00:17:02,661 (trainer:763) INFO: 5epoch:train:81-120batch: iter_time=4.355e-05, forward_time=0.055, loss_ctc=6.272, loss=6.272, backward_time=0.009, grad_norm=84.487, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.420 +[stan] 2024-01-15 00:17:06,410 (trainer:763) INFO: 5epoch:train:121-160batch: iter_time=4.233e-05, forward_time=0.049, loss_ctc=5.137, loss=5.137, backward_time=0.008, grad_norm=69.841, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.375 +[stan] 2024-01-15 00:17:10,677 (trainer:763) INFO: 5epoch:train:161-200batch: iter_time=4.504e-05, forward_time=0.056, loss_ctc=6.443, loss=6.443, backward_time=0.009, grad_norm=86.475, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-15 00:17:14,311 (trainer:763) INFO: 5epoch:train:201-240batch: iter_time=4.402e-05, forward_time=0.048, loss_ctc=4.496, loss=4.496, backward_time=0.008, grad_norm=70.629, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.363 +[stan] 2024-01-15 00:17:18,391 (trainer:763) INFO: 5epoch:train:241-280batch: iter_time=4.362e-05, forward_time=0.053, loss_ctc=5.544, loss=5.544, backward_time=0.009, grad_norm=82.167, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-15 00:17:22,291 (trainer:763) INFO: 5epoch:train:281-320batch: iter_time=4.489e-05, forward_time=0.051, loss_ctc=5.147, loss=5.147, backward_time=0.009, grad_norm=75.223, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.390 +[stan] 2024-01-15 00:17:26,260 (trainer:763) INFO: 5epoch:train:321-360batch: iter_time=4.454e-05, forward_time=0.052, loss_ctc=5.088, loss=5.088, backward_time=0.009, grad_norm=75.461, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397 +[stan] 2024-01-15 00:17:30,054 (trainer:763) INFO: 5epoch:train:361-400batch: iter_time=4.909e-05, forward_time=0.050, loss_ctc=5.275, loss=5.275, backward_time=0.008, grad_norm=74.746, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-15 00:17:34,368 (trainer:763) INFO: 5epoch:train:401-440batch: iter_time=4.492e-05, forward_time=0.056, loss_ctc=6.491, loss=6.491, backward_time=0.009, grad_norm=85.690, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-15 00:17:38,244 (trainer:763) INFO: 5epoch:train:441-480batch: iter_time=4.319e-05, forward_time=0.051, loss_ctc=4.999, loss=4.999, backward_time=0.008, grad_norm=73.828, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-15 00:17:41,941 (trainer:763) INFO: 5epoch:train:481-520batch: iter_time=4.591e-05, forward_time=0.049, loss_ctc=4.621, loss=4.621, backward_time=0.008, grad_norm=75.219, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.370 +[stan] 2024-01-15 00:17:45,982 (trainer:763) INFO: 5epoch:train:521-560batch: iter_time=4.958e-05, forward_time=0.053, loss_ctc=5.266, loss=5.266, backward_time=0.009, grad_norm=77.503, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-15 00:17:50,229 (trainer:763) INFO: 5epoch:train:561-600batch: iter_time=4.369e-05, forward_time=0.055, loss_ctc=5.595, loss=5.595, backward_time=0.009, grad_norm=82.309, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-15 00:17:53,853 (trainer:763) INFO: 5epoch:train:601-640batch: iter_time=4.891e-05, forward_time=0.048, loss_ctc=4.413, loss=4.413, backward_time=0.008, grad_norm=76.697, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.362 +[stan] 2024-01-15 00:17:57,804 (trainer:763) INFO: 5epoch:train:641-680batch: iter_time=4.654e-05, forward_time=0.052, loss_ctc=5.079, loss=5.079, backward_time=0.009, grad_norm=85.169, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-15 00:18:01,910 (trainer:763) INFO: 5epoch:train:681-720batch: iter_time=4.329e-05, forward_time=0.054, loss_ctc=5.545, loss=5.545, backward_time=0.009, grad_norm=86.237, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.410 +[stan] 2024-01-15 00:18:05,587 (trainer:763) INFO: 5epoch:train:721-760batch: iter_time=4.145e-05, forward_time=0.048, loss_ctc=4.113, loss=4.113, backward_time=0.008, grad_norm=70.187, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.368 +[stan] 2024-01-15 00:18:09,619 (trainer:763) INFO: 5epoch:train:761-800batch: iter_time=4.471e-05, forward_time=0.053, loss_ctc=5.063, loss=5.063, backward_time=0.009, grad_norm=83.288, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-15 00:18:14,007 (trainer:354) INFO: 5epoch results: [train] iter_time=1.801e-04, forward_time=0.052, loss_ctc=5.272, loss=5.272, backward_time=0.009, grad_norm=79.053, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396, time=1 minute and 19.35 seconds, total_count=4000, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=23.713, cer_ctc=0.166, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=23.713, time=1.09 seconds, total_count=75, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.22 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:18:14,900 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:18:14,900 (trainer:288) INFO: 6/30epoch started. Estimated time to finish: 35 minutes and 21.13 seconds +[stan] 2024-01-15 00:18:19,383 (trainer:763) INFO: 6epoch:train:1-40batch: iter_time=0.003, forward_time=0.055, loss_ctc=5.383, loss=5.383, backward_time=0.009, grad_norm=82.514, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-15 00:18:23,524 (trainer:763) INFO: 6epoch:train:41-80batch: iter_time=4.283e-05, forward_time=0.054, loss_ctc=5.355, loss=5.355, backward_time=0.009, grad_norm=84.414, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.414 +[stan] 2024-01-15 00:18:27,066 (trainer:763) INFO: 6epoch:train:81-120batch: iter_time=4.115e-05, forward_time=0.047, loss_ctc=3.927, loss=3.927, backward_time=0.008, grad_norm=73.160, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.354 +[stan] 2024-01-15 00:18:31,258 (trainer:763) INFO: 6epoch:train:121-160batch: iter_time=4.295e-05, forward_time=0.055, loss_ctc=5.347, loss=5.347, backward_time=0.009, grad_norm=84.525, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.419 +[stan] 2024-01-15 00:18:34,998 (trainer:763) INFO: 6epoch:train:161-200batch: iter_time=4.116e-05, forward_time=0.049, loss_ctc=4.247, loss=4.247, backward_time=0.008, grad_norm=82.856, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.374 +[stan] 2024-01-15 00:18:38,865 (trainer:763) INFO: 6epoch:train:201-240batch: iter_time=4.187e-05, forward_time=0.051, loss_ctc=4.643, loss=4.643, backward_time=0.008, grad_norm=76.493, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-15 00:18:42,795 (trainer:763) INFO: 6epoch:train:241-280batch: iter_time=4.338e-05, forward_time=0.051, loss_ctc=4.531, loss=4.531, backward_time=0.009, grad_norm=82.008, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-15 00:18:46,691 (trainer:763) INFO: 6epoch:train:281-320batch: iter_time=4.317e-05, forward_time=0.051, loss_ctc=4.415, loss=4.415, backward_time=0.009, grad_norm=71.549, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.389 +[stan] 2024-01-15 00:18:50,933 (trainer:763) INFO: 6epoch:train:321-360batch: iter_time=4.306e-05, forward_time=0.055, loss_ctc=5.356, loss=5.356, backward_time=0.009, grad_norm=89.265, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-15 00:18:54,515 (trainer:763) INFO: 6epoch:train:361-400batch: iter_time=4.274e-05, forward_time=0.047, loss_ctc=4.073, loss=4.073, backward_time=0.008, grad_norm=77.491, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.358 +[stan] 2024-01-15 00:18:58,657 (trainer:763) INFO: 6epoch:train:401-440batch: iter_time=4.274e-05, forward_time=0.054, loss_ctc=4.872, loss=4.872, backward_time=0.009, grad_norm=89.414, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.414 +[stan] 2024-01-15 00:19:02,820 (trainer:763) INFO: 6epoch:train:441-480batch: iter_time=4.308e-05, forward_time=0.054, loss_ctc=4.941, loss=4.941, backward_time=0.009, grad_norm=81.874, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-15 00:19:06,651 (trainer:763) INFO: 6epoch:train:481-520batch: iter_time=4.205e-05, forward_time=0.050, loss_ctc=4.278, loss=4.278, backward_time=0.008, grad_norm=79.436, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-15 00:19:10,580 (trainer:763) INFO: 6epoch:train:521-560batch: iter_time=4.347e-05, forward_time=0.051, loss_ctc=4.190, loss=4.190, backward_time=0.008, grad_norm=81.533, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-15 00:19:14,165 (trainer:763) INFO: 6epoch:train:561-600batch: iter_time=4.289e-05, forward_time=0.047, loss_ctc=3.716, loss=3.716, backward_time=0.008, grad_norm=70.562, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.358 +[stan] 2024-01-15 00:19:18,712 (trainer:763) INFO: 6epoch:train:601-640batch: iter_time=4.456e-05, forward_time=0.059, loss_ctc=6.156, loss=6.156, backward_time=0.010, grad_norm=86.209, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.455 +[stan] 2024-01-15 00:19:22,603 (trainer:763) INFO: 6epoch:train:641-680batch: iter_time=4.203e-05, forward_time=0.051, loss_ctc=4.400, loss=4.400, backward_time=0.009, grad_norm=74.611, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.389 +[stan] 2024-01-15 00:19:26,331 (trainer:763) INFO: 6epoch:train:681-720batch: iter_time=4.291e-05, forward_time=0.049, loss_ctc=3.869, loss=3.869, backward_time=0.008, grad_norm=74.512, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.373 +[stan] 2024-01-15 00:19:30,108 (trainer:763) INFO: 6epoch:train:721-760batch: iter_time=4.325e-05, forward_time=0.050, loss_ctc=4.077, loss=4.077, backward_time=0.008, grad_norm=75.296, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-15 00:19:34,253 (trainer:763) INFO: 6epoch:train:761-800batch: iter_time=4.030e-05, forward_time=0.054, loss_ctc=4.812, loss=4.812, backward_time=0.009, grad_norm=86.747, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.414 +[stan] 2024-01-15 00:19:38,554 (trainer:354) INFO: 6epoch results: [train] iter_time=1.855e-04, forward_time=0.052, loss_ctc=4.630, loss=4.630, backward_time=0.009, grad_norm=80.224, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.43 seconds, total_count=4800, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=25.586, cer_ctc=0.171, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=25.586, time=1.08 seconds, total_count=90, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.14 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:19:39,469 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:19:39,470 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/1epoch.pth +[stan] 2024-01-15 00:19:39,470 (trainer:288) INFO: 7/30epoch started. Estimated time to finish: 33 minutes and 55.18 seconds +[stan] 2024-01-15 00:19:43,692 (trainer:763) INFO: 7epoch:train:1-40batch: iter_time=0.003, forward_time=0.052, loss_ctc=4.356, loss=4.356, backward_time=0.009, grad_norm=84.664, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.422 +[stan] 2024-01-15 00:19:47,715 (trainer:763) INFO: 7epoch:train:41-80batch: iter_time=4.884e-05, forward_time=0.053, loss_ctc=4.353, loss=4.353, backward_time=0.009, grad_norm=77.877, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:19:51,772 (trainer:763) INFO: 7epoch:train:81-120batch: iter_time=4.386e-05, forward_time=0.053, loss_ctc=4.631, loss=4.631, backward_time=0.009, grad_norm=86.117, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-15 00:19:55,546 (trainer:763) INFO: 7epoch:train:121-160batch: iter_time=4.300e-05, forward_time=0.049, loss_ctc=3.732, loss=3.732, backward_time=0.008, grad_norm=71.416, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-15 00:19:59,797 (trainer:763) INFO: 7epoch:train:161-200batch: iter_time=4.451e-05, forward_time=0.056, loss_ctc=4.861, loss=4.861, backward_time=0.009, grad_norm=83.833, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-15 00:20:03,536 (trainer:763) INFO: 7epoch:train:201-240batch: iter_time=4.318e-05, forward_time=0.049, loss_ctc=3.729, loss=3.729, backward_time=0.008, grad_norm=75.698, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.374 +[stan] 2024-01-15 00:20:07,754 (trainer:763) INFO: 7epoch:train:241-280batch: iter_time=4.660e-05, forward_time=0.055, loss_ctc=4.729, loss=4.729, backward_time=0.009, grad_norm=75.381, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.422 +[stan] 2024-01-15 00:20:11,507 (trainer:763) INFO: 7epoch:train:281-320batch: iter_time=4.429e-05, forward_time=0.049, loss_ctc=3.729, loss=3.729, backward_time=0.008, grad_norm=74.947, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.375 +[stan] 2024-01-15 00:20:15,727 (trainer:763) INFO: 7epoch:train:321-360batch: iter_time=4.513e-05, forward_time=0.055, loss_ctc=4.611, loss=4.611, backward_time=0.009, grad_norm=74.847, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.422 +[stan] 2024-01-15 00:20:19,440 (trainer:763) INFO: 7epoch:train:361-400batch: iter_time=4.777e-05, forward_time=0.049, loss_ctc=3.729, loss=3.729, backward_time=0.008, grad_norm=86.725, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.371 +[stan] 2024-01-15 00:20:23,223 (trainer:763) INFO: 7epoch:train:401-440batch: iter_time=4.543e-05, forward_time=0.050, loss_ctc=3.540, loss=3.540, backward_time=0.008, grad_norm=82.015, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-15 00:20:27,249 (trainer:763) INFO: 7epoch:train:441-480batch: iter_time=4.457e-05, forward_time=0.053, loss_ctc=4.395, loss=4.395, backward_time=0.009, grad_norm=85.160, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:20:30,961 (trainer:763) INFO: 7epoch:train:481-520batch: iter_time=4.474e-05, forward_time=0.049, loss_ctc=3.564, loss=3.564, backward_time=0.008, grad_norm=76.687, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.371 +[stan] 2024-01-15 00:20:35,117 (trainer:763) INFO: 7epoch:train:521-560batch: iter_time=4.295e-05, forward_time=0.054, loss_ctc=4.325, loss=4.325, backward_time=0.009, grad_norm=77.362, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-15 00:20:39,231 (trainer:763) INFO: 7epoch:train:561-600batch: iter_time=4.285e-05, forward_time=0.054, loss_ctc=4.426, loss=4.426, backward_time=0.009, grad_norm=74.749, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.411 +[stan] 2024-01-15 00:20:43,003 (trainer:763) INFO: 7epoch:train:601-640batch: iter_time=4.266e-05, forward_time=0.049, loss_ctc=3.467, loss=3.467, backward_time=0.008, grad_norm=75.873, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-15 00:20:46,796 (trainer:763) INFO: 7epoch:train:641-680batch: iter_time=4.538e-05, forward_time=0.050, loss_ctc=3.643, loss=3.643, backward_time=0.008, grad_norm=76.570, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-15 00:20:50,828 (trainer:763) INFO: 7epoch:train:681-720batch: iter_time=4.269e-05, forward_time=0.053, loss_ctc=4.147, loss=4.147, backward_time=0.009, grad_norm=77.945, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-15 00:20:54,811 (trainer:763) INFO: 7epoch:train:721-760batch: iter_time=4.181e-05, forward_time=0.052, loss_ctc=4.104, loss=4.104, backward_time=0.009, grad_norm=75.645, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-15 00:20:58,937 (trainer:763) INFO: 7epoch:train:761-800batch: iter_time=4.097e-05, forward_time=0.054, loss_ctc=4.177, loss=4.177, backward_time=0.009, grad_norm=77.492, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.413 +[stan] 2024-01-15 00:21:03,167 (trainer:354) INFO: 7epoch results: [train] iter_time=1.746e-04, forward_time=0.052, loss_ctc=4.113, loss=4.113, backward_time=0.009, grad_norm=78.550, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.55 seconds, total_count=5600, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=27.158, cer_ctc=0.175, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=27.158, time=1.07 seconds, total_count=105, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.08 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:21:04,089 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:21:04,090 (trainer:288) INFO: 8/30epoch started. Estimated time to finish: 32 minutes and 29.79 seconds +[stan] 2024-01-15 00:21:08,614 (trainer:763) INFO: 8epoch:train:1-40batch: iter_time=0.003, forward_time=0.056, loss_ctc=4.443, loss=4.443, backward_time=0.009, grad_norm=80.392, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.452 +[stan] 2024-01-15 00:21:12,664 (trainer:763) INFO: 8epoch:train:41-80batch: iter_time=4.650e-05, forward_time=0.053, loss_ctc=4.090, loss=4.090, backward_time=0.009, grad_norm=84.166, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.405 +[stan] 2024-01-15 00:21:16,074 (trainer:763) INFO: 8epoch:train:81-120batch: iter_time=4.101e-05, forward_time=0.045, loss_ctc=2.659, loss=2.659, backward_time=0.008, grad_norm=67.898, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.341 +[stan] 2024-01-15 00:21:19,959 (trainer:763) INFO: 8epoch:train:121-160batch: iter_time=4.466e-05, forward_time=0.051, loss_ctc=3.550, loss=3.550, backward_time=0.009, grad_norm=70.731, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-15 00:21:24,058 (trainer:763) INFO: 8epoch:train:161-200batch: iter_time=4.200e-05, forward_time=0.054, loss_ctc=4.185, loss=4.185, backward_time=0.009, grad_norm=82.567, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.410 +[stan] 2024-01-15 00:21:27,977 (trainer:763) INFO: 8epoch:train:201-240batch: iter_time=4.387e-05, forward_time=0.051, loss_ctc=3.595, loss=3.595, backward_time=0.009, grad_norm=76.232, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-15 00:21:31,952 (trainer:763) INFO: 8epoch:train:241-280batch: iter_time=4.487e-05, forward_time=0.052, loss_ctc=3.843, loss=3.843, backward_time=0.008, grad_norm=77.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397 +[stan] 2024-01-15 00:21:35,879 (trainer:763) INFO: 8epoch:train:281-320batch: iter_time=4.234e-05, forward_time=0.051, loss_ctc=3.642, loss=3.642, backward_time=0.008, grad_norm=81.999, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-15 00:21:39,934 (trainer:763) INFO: 8epoch:train:321-360batch: iter_time=4.143e-05, forward_time=0.053, loss_ctc=4.144, loss=4.144, backward_time=0.009, grad_norm=88.235, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.405 +[stan] 2024-01-15 00:21:43,765 (trainer:763) INFO: 8epoch:train:361-400batch: iter_time=4.176e-05, forward_time=0.050, loss_ctc=3.271, loss=3.271, backward_time=0.009, grad_norm=71.269, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-15 00:21:47,769 (trainer:763) INFO: 8epoch:train:401-440batch: iter_time=4.411e-05, forward_time=0.052, loss_ctc=3.838, loss=3.838, backward_time=0.009, grad_norm=76.638, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-15 00:21:51,777 (trainer:763) INFO: 8epoch:train:441-480batch: iter_time=4.329e-05, forward_time=0.052, loss_ctc=4.120, loss=4.120, backward_time=0.009, grad_norm=81.106, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-15 00:21:55,652 (trainer:763) INFO: 8epoch:train:481-520batch: iter_time=4.158e-05, forward_time=0.051, loss_ctc=3.601, loss=3.601, backward_time=0.008, grad_norm=81.139, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-15 00:21:59,668 (trainer:763) INFO: 8epoch:train:521-560batch: iter_time=4.258e-05, forward_time=0.053, loss_ctc=3.578, loss=3.578, backward_time=0.008, grad_norm=71.751, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:22:03,541 (trainer:763) INFO: 8epoch:train:561-600batch: iter_time=4.401e-05, forward_time=0.051, loss_ctc=3.600, loss=3.600, backward_time=0.009, grad_norm=69.210, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-15 00:22:07,270 (trainer:763) INFO: 8epoch:train:601-640batch: iter_time=4.371e-05, forward_time=0.049, loss_ctc=3.299, loss=3.299, backward_time=0.008, grad_norm=71.757, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.373 +[stan] 2024-01-15 00:22:11,570 (trainer:763) INFO: 8epoch:train:641-680batch: iter_time=4.349e-05, forward_time=0.056, loss_ctc=4.107, loss=4.107, backward_time=0.009, grad_norm=83.343, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-15 00:22:15,494 (trainer:763) INFO: 8epoch:train:681-720batch: iter_time=4.101e-05, forward_time=0.051, loss_ctc=3.343, loss=3.343, backward_time=0.008, grad_norm=69.208, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-15 00:22:19,434 (trainer:763) INFO: 8epoch:train:721-760batch: iter_time=4.274e-05, forward_time=0.052, loss_ctc=3.524, loss=3.524, backward_time=0.009, grad_norm=68.748, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-15 00:22:23,040 (trainer:763) INFO: 8epoch:train:761-800batch: iter_time=3.954e-05, forward_time=0.047, loss_ctc=3.074, loss=3.074, backward_time=0.008, grad_norm=70.558, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.361 +[stan] 2024-01-15 00:22:27,285 (trainer:354) INFO: 8epoch results: [train] iter_time=1.706e-04, forward_time=0.052, loss_ctc=3.675, loss=3.675, backward_time=0.009, grad_norm=76.210, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395, time=1 minute and 19.02 seconds, total_count=6400, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=28.067, cer_ctc=0.172, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=28.067, time=1.07 seconds, total_count=120, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.1 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:22:28,225 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:22:28,225 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/7epoch.pth +[stan] 2024-01-15 00:22:28,225 (trainer:288) INFO: 9/30epoch started. Estimated time to finish: 31 minutes and 3.26 seconds +[stan] 2024-01-15 00:22:32,490 (trainer:763) INFO: 9epoch:train:1-40batch: iter_time=0.003, forward_time=0.052, loss_ctc=3.714, loss=3.714, backward_time=0.009, grad_norm=71.595, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-15 00:22:36,616 (trainer:763) INFO: 9epoch:train:41-80batch: iter_time=4.358e-05, forward_time=0.054, loss_ctc=4.118, loss=4.118, backward_time=0.009, grad_norm=76.686, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.413 +[stan] 2024-01-15 00:22:40,536 (trainer:763) INFO: 9epoch:train:81-120batch: iter_time=4.299e-05, forward_time=0.051, loss_ctc=3.414, loss=3.414, backward_time=0.009, grad_norm=71.951, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-15 00:22:44,815 (trainer:763) INFO: 9epoch:train:121-160batch: iter_time=4.423e-05, forward_time=0.056, loss_ctc=4.353, loss=4.353, backward_time=0.009, grad_norm=87.119, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-15 00:22:48,688 (trainer:763) INFO: 9epoch:train:161-200batch: iter_time=4.601e-05, forward_time=0.051, loss_ctc=3.233, loss=3.233, backward_time=0.008, grad_norm=66.021, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-15 00:22:52,363 (trainer:763) INFO: 9epoch:train:201-240batch: iter_time=4.287e-05, forward_time=0.048, loss_ctc=2.973, loss=2.973, backward_time=0.008, grad_norm=64.891, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.367 +[stan] 2024-01-15 00:22:56,487 (trainer:763) INFO: 9epoch:train:241-280batch: iter_time=4.374e-05, forward_time=0.054, loss_ctc=3.821, loss=3.821, backward_time=0.009, grad_norm=78.061, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.412 +[stan] 2024-01-15 00:23:00,357 (trainer:763) INFO: 9epoch:train:281-320batch: iter_time=4.640e-05, forward_time=0.051, loss_ctc=3.435, loss=3.435, backward_time=0.009, grad_norm=73.588, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-15 00:23:04,280 (trainer:763) INFO: 9epoch:train:321-360batch: iter_time=4.276e-05, forward_time=0.051, loss_ctc=3.269, loss=3.269, backward_time=0.008, grad_norm=71.332, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-15 00:23:08,352 (trainer:763) INFO: 9epoch:train:361-400batch: iter_time=4.565e-05, forward_time=0.054, loss_ctc=3.689, loss=3.689, backward_time=0.009, grad_norm=82.696, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.407 +[stan] 2024-01-15 00:23:12,365 (trainer:763) INFO: 9epoch:train:401-440batch: iter_time=4.285e-05, forward_time=0.053, loss_ctc=3.431, loss=3.431, backward_time=0.008, grad_norm=74.874, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-15 00:23:16,545 (trainer:763) INFO: 9epoch:train:441-480batch: iter_time=4.226e-05, forward_time=0.055, loss_ctc=3.685, loss=3.685, backward_time=0.009, grad_norm=72.314, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.418 +[stan] 2024-01-15 00:23:20,384 (trainer:763) INFO: 9epoch:train:481-520batch: iter_time=4.405e-05, forward_time=0.050, loss_ctc=3.038, loss=3.038, backward_time=0.008, grad_norm=69.178, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.384 +[stan] 2024-01-15 00:23:24,293 (trainer:763) INFO: 9epoch:train:521-560batch: iter_time=4.259e-05, forward_time=0.051, loss_ctc=3.499, loss=3.499, backward_time=0.009, grad_norm=73.227, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:23:28,118 (trainer:763) INFO: 9epoch:train:561-600batch: iter_time=4.235e-05, forward_time=0.050, loss_ctc=3.082, loss=3.082, backward_time=0.008, grad_norm=73.640, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.382 +[stan] 2024-01-15 00:23:31,894 (trainer:763) INFO: 9epoch:train:601-640batch: iter_time=4.378e-05, forward_time=0.050, loss_ctc=3.120, loss=3.120, backward_time=0.008, grad_norm=72.999, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-15 00:23:36,306 (trainer:763) INFO: 9epoch:train:641-680batch: iter_time=4.577e-05, forward_time=0.058, loss_ctc=4.178, loss=4.178, backward_time=0.009, grad_norm=73.480, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-15 00:23:40,388 (trainer:763) INFO: 9epoch:train:681-720batch: iter_time=4.330e-05, forward_time=0.053, loss_ctc=3.603, loss=3.603, backward_time=0.009, grad_norm=75.126, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-15 00:23:43,821 (trainer:763) INFO: 9epoch:train:721-760batch: iter_time=4.787e-05, forward_time=0.045, loss_ctc=2.484, loss=2.484, backward_time=0.008, grad_norm=59.888, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.343 +[stan] 2024-01-15 00:23:47,949 (trainer:763) INFO: 9epoch:train:761-800batch: iter_time=4.292e-05, forward_time=0.054, loss_ctc=3.553, loss=3.553, backward_time=0.009, grad_norm=74.401, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.413 +[stan] 2024-01-15 00:23:52,176 (trainer:354) INFO: 9epoch results: [train] iter_time=1.899e-04, forward_time=0.052, loss_ctc=3.484, loss=3.484, backward_time=0.009, grad_norm=73.153, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399, time=1 minute and 19.8 seconds, total_count=7200, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=29.651, cer_ctc=0.177, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=29.651, time=1.07 seconds, total_count=135, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.08 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:23:53,142 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:23:53,143 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/8epoch.pth +[stan] 2024-01-15 00:23:53,143 (trainer:288) INFO: 10/30epoch started. Estimated time to finish: 29 minutes and 39.09 seconds +[stan] 2024-01-15 00:23:57,273 (trainer:763) INFO: 10epoch:train:1-40batch: iter_time=0.003, forward_time=0.051, loss_ctc=3.247, loss=3.247, backward_time=0.008, grad_norm=68.711, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.413 +[stan] 2024-01-15 00:24:01,053 (trainer:763) INFO: 10epoch:train:41-80batch: iter_time=4.505e-05, forward_time=0.049, loss_ctc=3.026, loss=3.026, backward_time=0.008, grad_norm=64.594, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-15 00:24:05,160 (trainer:763) INFO: 10epoch:train:81-120batch: iter_time=4.673e-05, forward_time=0.054, loss_ctc=3.465, loss=3.465, backward_time=0.009, grad_norm=74.002, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.411 +[stan] 2024-01-15 00:24:09,358 (trainer:763) INFO: 10epoch:train:121-160batch: iter_time=4.550e-05, forward_time=0.055, loss_ctc=3.764, loss=3.764, backward_time=0.009, grad_norm=79.373, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.420 +[stan] 2024-01-15 00:24:13,360 (trainer:763) INFO: 10epoch:train:161-200batch: iter_time=4.566e-05, forward_time=0.052, loss_ctc=3.311, loss=3.311, backward_time=0.009, grad_norm=67.979, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-15 00:24:16,919 (trainer:763) INFO: 10epoch:train:201-240batch: iter_time=4.424e-05, forward_time=0.047, loss_ctc=2.564, loss=2.564, backward_time=0.008, grad_norm=63.349, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.356 +[stan] 2024-01-15 00:24:21,002 (trainer:763) INFO: 10epoch:train:241-280batch: iter_time=4.298e-05, forward_time=0.053, loss_ctc=3.711, loss=3.711, backward_time=0.009, grad_norm=73.863, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-15 00:24:25,141 (trainer:763) INFO: 10epoch:train:281-320batch: iter_time=4.315e-05, forward_time=0.054, loss_ctc=3.331, loss=3.331, backward_time=0.009, grad_norm=66.896, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.414 +[stan] 2024-01-15 00:24:28,969 (trainer:763) INFO: 10epoch:train:321-360batch: iter_time=4.325e-05, forward_time=0.050, loss_ctc=3.164, loss=3.164, backward_time=0.008, grad_norm=70.744, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-15 00:24:32,526 (trainer:763) INFO: 10epoch:train:361-400batch: iter_time=4.476e-05, forward_time=0.047, loss_ctc=2.697, loss=2.697, backward_time=0.008, grad_norm=63.528, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.356 +[stan] 2024-01-15 00:24:36,906 (trainer:763) INFO: 10epoch:train:401-440batch: iter_time=4.290e-05, forward_time=0.057, loss_ctc=4.016, loss=4.016, backward_time=0.010, grad_norm=80.239, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-15 00:24:40,908 (trainer:763) INFO: 10epoch:train:441-480batch: iter_time=4.404e-05, forward_time=0.052, loss_ctc=3.317, loss=3.317, backward_time=0.008, grad_norm=74.341, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-15 00:24:44,698 (trainer:763) INFO: 10epoch:train:481-520batch: iter_time=4.223e-05, forward_time=0.050, loss_ctc=3.028, loss=3.028, backward_time=0.009, grad_norm=78.010, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-15 00:24:48,734 (trainer:763) INFO: 10epoch:train:521-560batch: iter_time=4.578e-05, forward_time=0.053, loss_ctc=3.225, loss=3.225, backward_time=0.009, grad_norm=69.692, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-15 00:24:52,644 (trainer:763) INFO: 10epoch:train:561-600batch: iter_time=4.396e-05, forward_time=0.051, loss_ctc=2.984, loss=2.984, backward_time=0.009, grad_norm=68.136, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:24:56,817 (trainer:763) INFO: 10epoch:train:601-640batch: iter_time=4.355e-05, forward_time=0.055, loss_ctc=3.463, loss=3.463, backward_time=0.009, grad_norm=72.097, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-15 00:25:00,580 (trainer:763) INFO: 10epoch:train:641-680batch: iter_time=4.431e-05, forward_time=0.049, loss_ctc=2.758, loss=2.758, backward_time=0.008, grad_norm=64.379, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.376 +[stan] 2024-01-15 00:25:04,613 (trainer:763) INFO: 10epoch:train:681-720batch: iter_time=4.354e-05, forward_time=0.053, loss_ctc=3.280, loss=3.280, backward_time=0.008, grad_norm=73.477, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-15 00:25:08,453 (trainer:763) INFO: 10epoch:train:721-760batch: iter_time=4.381e-05, forward_time=0.050, loss_ctc=2.856, loss=2.856, backward_time=0.008, grad_norm=63.639, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.384 +[stan] 2024-01-15 00:25:12,596 (trainer:763) INFO: 10epoch:train:761-800batch: iter_time=4.232e-05, forward_time=0.054, loss_ctc=3.662, loss=3.662, backward_time=0.009, grad_norm=72.080, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.414 +[stan] 2024-01-15 00:25:16,831 (trainer:354) INFO: 10epoch results: [train] iter_time=1.802e-04, forward_time=0.052, loss_ctc=3.243, loss=3.243, backward_time=0.009, grad_norm=70.456, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.53 seconds, total_count=8000, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=30.562, cer_ctc=0.170, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=30.562, time=1.08 seconds, total_count=150, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.08 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:25:17,903 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:25:17,903 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/9epoch.pth +[stan] 2024-01-15 00:25:17,903 (trainer:288) INFO: 11/30epoch started. Estimated time to finish: 28 minutes and 14.46 seconds +[stan] 2024-01-15 00:25:22,077 (trainer:763) INFO: 11epoch:train:1-40batch: iter_time=0.003, forward_time=0.051, loss_ctc=3.058, loss=3.058, backward_time=0.008, grad_norm=71.622, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-15 00:25:26,100 (trainer:763) INFO: 11epoch:train:41-80batch: iter_time=4.367e-05, forward_time=0.053, loss_ctc=3.298, loss=3.298, backward_time=0.009, grad_norm=68.305, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:25:29,968 (trainer:763) INFO: 11epoch:train:81-120batch: iter_time=4.528e-05, forward_time=0.051, loss_ctc=2.945, loss=2.945, backward_time=0.008, grad_norm=61.947, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-15 00:25:33,869 (trainer:763) INFO: 11epoch:train:121-160batch: iter_time=4.354e-05, forward_time=0.051, loss_ctc=3.255, loss=3.255, backward_time=0.008, grad_norm=72.413, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.390 +[stan] 2024-01-15 00:25:38,080 (trainer:763) INFO: 11epoch:train:161-200batch: iter_time=4.397e-05, forward_time=0.055, loss_ctc=3.443, loss=3.443, backward_time=0.009, grad_norm=77.479, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.421 +[stan] 2024-01-15 00:25:41,827 (trainer:763) INFO: 11epoch:train:201-240batch: iter_time=4.593e-05, forward_time=0.049, loss_ctc=2.819, loss=2.819, backward_time=0.008, grad_norm=65.622, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.375 +[stan] 2024-01-15 00:25:45,773 (trainer:763) INFO: 11epoch:train:241-280batch: iter_time=4.233e-05, forward_time=0.052, loss_ctc=2.897, loss=2.897, backward_time=0.009, grad_norm=63.969, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-15 00:25:49,661 (trainer:763) INFO: 11epoch:train:281-320batch: iter_time=4.730e-05, forward_time=0.051, loss_ctc=2.960, loss=2.960, backward_time=0.008, grad_norm=64.564, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.389 +[stan] 2024-01-15 00:25:53,906 (trainer:763) INFO: 11epoch:train:321-360batch: iter_time=4.800e-05, forward_time=0.055, loss_ctc=3.652, loss=3.652, backward_time=0.009, grad_norm=77.120, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-15 00:25:57,608 (trainer:763) INFO: 11epoch:train:361-400batch: iter_time=4.536e-05, forward_time=0.049, loss_ctc=2.818, loss=2.818, backward_time=0.008, grad_norm=64.437, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.370 +[stan] 2024-01-15 00:26:01,483 (trainer:763) INFO: 11epoch:train:401-440batch: iter_time=4.295e-05, forward_time=0.051, loss_ctc=2.699, loss=2.699, backward_time=0.008, grad_norm=64.835, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-15 00:26:05,534 (trainer:763) INFO: 11epoch:train:441-480batch: iter_time=4.218e-05, forward_time=0.053, loss_ctc=3.230, loss=3.230, backward_time=0.009, grad_norm=65.634, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.405 +[stan] 2024-01-15 00:26:09,574 (trainer:763) INFO: 11epoch:train:481-520batch: iter_time=4.319e-05, forward_time=0.053, loss_ctc=3.160, loss=3.160, backward_time=0.009, grad_norm=68.977, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-15 00:26:13,556 (trainer:763) INFO: 11epoch:train:521-560batch: iter_time=4.303e-05, forward_time=0.052, loss_ctc=3.025, loss=3.025, backward_time=0.009, grad_norm=65.971, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-15 00:26:17,521 (trainer:763) INFO: 11epoch:train:561-600batch: iter_time=4.360e-05, forward_time=0.052, loss_ctc=3.061, loss=3.061, backward_time=0.008, grad_norm=68.638, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-15 00:26:21,423 (trainer:763) INFO: 11epoch:train:601-640batch: iter_time=4.313e-05, forward_time=0.051, loss_ctc=2.742, loss=2.742, backward_time=0.009, grad_norm=67.174, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.390 +[stan] 2024-01-15 00:26:25,255 (trainer:763) INFO: 11epoch:train:641-680batch: iter_time=4.359e-05, forward_time=0.050, loss_ctc=2.933, loss=2.933, backward_time=0.009, grad_norm=67.670, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-15 00:26:29,161 (trainer:763) INFO: 11epoch:train:681-720batch: iter_time=4.229e-05, forward_time=0.051, loss_ctc=2.872, loss=2.872, backward_time=0.009, grad_norm=75.653, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.390 +[stan] 2024-01-15 00:26:33,352 (trainer:763) INFO: 11epoch:train:721-760batch: iter_time=4.396e-05, forward_time=0.055, loss_ctc=3.294, loss=3.294, backward_time=0.009, grad_norm=67.369, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.419 +[stan] 2024-01-15 00:26:37,273 (trainer:763) INFO: 11epoch:train:761-800batch: iter_time=4.024e-05, forward_time=0.051, loss_ctc=2.861, loss=2.861, backward_time=0.008, grad_norm=67.208, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-15 00:26:41,479 (trainer:354) INFO: 11epoch results: [train] iter_time=1.822e-04, forward_time=0.052, loss_ctc=3.051, loss=3.051, backward_time=0.009, grad_norm=68.330, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.44 seconds, total_count=8800, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=30.956, cer_ctc=0.176, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=30.956, time=1.07 seconds, total_count=165, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.06 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:26:42,383 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:26:42,384 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/10epoch.pth +[stan] 2024-01-15 00:26:42,384 (trainer:288) INFO: 12/30epoch started. Estimated time to finish: 26 minutes and 49.32 seconds +[stan] 2024-01-15 00:26:46,422 (trainer:763) INFO: 12epoch:train:1-40batch: iter_time=0.003, forward_time=0.050, loss_ctc=2.976, loss=2.976, backward_time=0.009, grad_norm=61.025, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-15 00:26:50,605 (trainer:763) INFO: 12epoch:train:41-80batch: iter_time=4.393e-05, forward_time=0.055, loss_ctc=3.349, loss=3.349, backward_time=0.009, grad_norm=70.374, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.418 +[stan] 2024-01-15 00:26:54,322 (trainer:763) INFO: 12epoch:train:81-120batch: iter_time=4.337e-05, forward_time=0.049, loss_ctc=2.634, loss=2.634, backward_time=0.008, grad_norm=61.746, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.372 +[stan] 2024-01-15 00:26:58,014 (trainer:763) INFO: 12epoch:train:121-160batch: iter_time=4.549e-05, forward_time=0.048, loss_ctc=2.592, loss=2.592, backward_time=0.008, grad_norm=60.733, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.369 +[stan] 2024-01-15 00:27:02,268 (trainer:763) INFO: 12epoch:train:161-200batch: iter_time=4.231e-05, forward_time=0.056, loss_ctc=3.652, loss=3.652, backward_time=0.009, grad_norm=73.917, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-15 00:27:06,431 (trainer:763) INFO: 12epoch:train:201-240batch: iter_time=4.413e-05, forward_time=0.054, loss_ctc=3.278, loss=3.278, backward_time=0.009, grad_norm=65.851, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-15 00:27:10,310 (trainer:763) INFO: 12epoch:train:241-280batch: iter_time=4.407e-05, forward_time=0.051, loss_ctc=2.811, loss=2.811, backward_time=0.009, grad_norm=61.520, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-15 00:27:14,226 (trainer:763) INFO: 12epoch:train:281-320batch: iter_time=4.214e-05, forward_time=0.051, loss_ctc=2.863, loss=2.863, backward_time=0.008, grad_norm=65.367, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-15 00:27:18,183 (trainer:763) INFO: 12epoch:train:321-360batch: iter_time=4.653e-05, forward_time=0.052, loss_ctc=3.146, loss=3.146, backward_time=0.009, grad_norm=69.442, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-15 00:27:22,049 (trainer:763) INFO: 12epoch:train:361-400batch: iter_time=4.417e-05, forward_time=0.051, loss_ctc=2.687, loss=2.687, backward_time=0.008, grad_norm=63.111, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-15 00:27:26,064 (trainer:763) INFO: 12epoch:train:401-440batch: iter_time=4.277e-05, forward_time=0.052, loss_ctc=3.322, loss=3.322, backward_time=0.009, grad_norm=69.046, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-15 00:27:29,558 (trainer:763) INFO: 12epoch:train:441-480batch: iter_time=4.226e-05, forward_time=0.046, loss_ctc=2.224, loss=2.224, backward_time=0.008, grad_norm=58.170, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.349 +[stan] 2024-01-15 00:27:33,893 (trainer:763) INFO: 12epoch:train:481-520batch: iter_time=4.373e-05, forward_time=0.057, loss_ctc=3.379, loss=3.379, backward_time=0.009, grad_norm=64.700, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-15 00:27:38,041 (trainer:763) INFO: 12epoch:train:521-560batch: iter_time=4.351e-05, forward_time=0.054, loss_ctc=3.318, loss=3.318, backward_time=0.009, grad_norm=68.173, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-15 00:27:41,859 (trainer:763) INFO: 12epoch:train:561-600batch: iter_time=4.259e-05, forward_time=0.050, loss_ctc=2.583, loss=2.583, backward_time=0.008, grad_norm=61.484, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.382 +[stan] 2024-01-15 00:27:45,765 (trainer:763) INFO: 12epoch:train:601-640batch: iter_time=4.264e-05, forward_time=0.051, loss_ctc=3.011, loss=3.011, backward_time=0.009, grad_norm=67.612, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:27:49,947 (trainer:763) INFO: 12epoch:train:641-680batch: iter_time=4.567e-05, forward_time=0.055, loss_ctc=3.249, loss=3.249, backward_time=0.009, grad_norm=64.711, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.418 +[stan] 2024-01-15 00:27:53,519 (trainer:763) INFO: 12epoch:train:681-720batch: iter_time=4.302e-05, forward_time=0.047, loss_ctc=2.075, loss=2.075, backward_time=0.008, grad_norm=57.769, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.357 +[stan] 2024-01-15 00:27:57,525 (trainer:763) INFO: 12epoch:train:721-760batch: iter_time=4.283e-05, forward_time=0.052, loss_ctc=3.049, loss=3.049, backward_time=0.009, grad_norm=64.262, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-15 00:28:01,819 (trainer:763) INFO: 12epoch:train:761-800batch: iter_time=4.003e-05, forward_time=0.056, loss_ctc=3.343, loss=3.343, backward_time=0.009, grad_norm=69.268, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-15 00:28:06,048 (trainer:354) INFO: 12epoch results: [train] iter_time=1.750e-04, forward_time=0.052, loss_ctc=2.977, loss=2.977, backward_time=0.009, grad_norm=64.914, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.51 seconds, total_count=9600, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=31.392, cer_ctc=0.168, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=31.392, time=1.09 seconds, total_count=180, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.06 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:28:07,054 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:28:07,055 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/11epoch.pth +[stan] 2024-01-15 00:28:07,055 (trainer:288) INFO: 13/30epoch started. Estimated time to finish: 25 minutes and 24.57 seconds +[stan] 2024-01-15 00:28:11,238 (trainer:763) INFO: 13epoch:train:1-40batch: iter_time=0.002, forward_time=0.052, loss_ctc=2.823, loss=2.823, backward_time=0.009, grad_norm=60.497, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.418 +[stan] 2024-01-15 00:28:14,990 (trainer:763) INFO: 13epoch:train:41-80batch: iter_time=4.190e-05, forward_time=0.049, loss_ctc=2.641, loss=2.641, backward_time=0.008, grad_norm=63.396, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.375 +[stan] 2024-01-15 00:28:18,770 (trainer:763) INFO: 13epoch:train:81-120batch: iter_time=4.276e-05, forward_time=0.050, loss_ctc=2.757, loss=2.757, backward_time=0.008, grad_norm=61.093, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-15 00:28:23,097 (trainer:763) INFO: 13epoch:train:121-160batch: iter_time=4.426e-05, forward_time=0.056, loss_ctc=3.295, loss=3.295, backward_time=0.010, grad_norm=72.727, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-15 00:28:26,717 (trainer:763) INFO: 13epoch:train:161-200batch: iter_time=4.118e-05, forward_time=0.048, loss_ctc=2.415, loss=2.415, backward_time=0.008, grad_norm=60.535, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.362 +[stan] 2024-01-15 00:28:30,821 (trainer:763) INFO: 13epoch:train:201-240batch: iter_time=4.250e-05, forward_time=0.054, loss_ctc=3.110, loss=3.110, backward_time=0.009, grad_norm=70.914, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.410 +[stan] 2024-01-15 00:28:34,939 (trainer:763) INFO: 13epoch:train:241-280batch: iter_time=4.167e-05, forward_time=0.054, loss_ctc=2.956, loss=2.956, backward_time=0.009, grad_norm=68.427, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.412 +[stan] 2024-01-15 00:28:38,739 (trainer:763) INFO: 13epoch:train:281-320batch: iter_time=4.526e-05, forward_time=0.050, loss_ctc=2.670, loss=2.670, backward_time=0.008, grad_norm=63.312, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.380 +[stan] 2024-01-15 00:28:42,789 (trainer:763) INFO: 13epoch:train:321-360batch: iter_time=4.258e-05, forward_time=0.053, loss_ctc=2.947, loss=2.947, backward_time=0.009, grad_norm=64.533, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.405 +[stan] 2024-01-15 00:28:46,773 (trainer:763) INFO: 13epoch:train:361-400batch: iter_time=4.287e-05, forward_time=0.052, loss_ctc=2.925, loss=2.925, backward_time=0.009, grad_norm=65.151, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-15 00:28:50,764 (trainer:763) INFO: 13epoch:train:401-440batch: iter_time=4.483e-05, forward_time=0.052, loss_ctc=2.779, loss=2.779, backward_time=0.009, grad_norm=61.806, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399 +[stan] 2024-01-15 00:28:54,642 (trainer:763) INFO: 13epoch:train:441-480batch: iter_time=4.393e-05, forward_time=0.051, loss_ctc=2.732, loss=2.732, backward_time=0.008, grad_norm=62.536, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-15 00:28:58,317 (trainer:763) INFO: 13epoch:train:481-520batch: iter_time=4.153e-05, forward_time=0.048, loss_ctc=2.484, loss=2.484, backward_time=0.008, grad_norm=61.597, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.367 +[stan] 2024-01-15 00:29:02,399 (trainer:763) INFO: 13epoch:train:521-560batch: iter_time=4.531e-05, forward_time=0.053, loss_ctc=3.016, loss=3.016, backward_time=0.009, grad_norm=65.886, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-15 00:29:06,564 (trainer:763) INFO: 13epoch:train:561-600batch: iter_time=4.576e-05, forward_time=0.054, loss_ctc=3.140, loss=3.140, backward_time=0.009, grad_norm=64.256, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-15 00:29:10,578 (trainer:763) INFO: 13epoch:train:601-640batch: iter_time=4.361e-05, forward_time=0.053, loss_ctc=2.894, loss=2.894, backward_time=0.009, grad_norm=62.914, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-15 00:29:14,442 (trainer:763) INFO: 13epoch:train:641-680batch: iter_time=4.267e-05, forward_time=0.051, loss_ctc=2.665, loss=2.665, backward_time=0.009, grad_norm=58.841, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.386 +[stan] 2024-01-15 00:29:18,480 (trainer:763) INFO: 13epoch:train:681-720batch: iter_time=4.229e-05, forward_time=0.053, loss_ctc=2.817, loss=2.817, backward_time=0.009, grad_norm=63.426, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-15 00:29:22,128 (trainer:763) INFO: 13epoch:train:721-760batch: iter_time=4.522e-05, forward_time=0.048, loss_ctc=2.437, loss=2.437, backward_time=0.008, grad_norm=57.029, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.365 +[stan] 2024-01-15 00:29:26,500 (trainer:763) INFO: 13epoch:train:761-800batch: iter_time=4.268e-05, forward_time=0.057, loss_ctc=3.268, loss=3.268, backward_time=0.009, grad_norm=67.296, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-15 00:29:30,681 (trainer:354) INFO: 13epoch results: [train] iter_time=1.621e-04, forward_time=0.052, loss_ctc=2.839, loss=2.839, backward_time=0.009, grad_norm=63.809, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.52 seconds, total_count=10400, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=32.349, cer_ctc=0.170, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=32.349, time=1.08 seconds, total_count=195, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.03 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:29:31,603 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:29:31,603 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/12epoch.pth +[stan] 2024-01-15 00:29:31,603 (trainer:288) INFO: 14/30epoch started. Estimated time to finish: 23 minutes and 59.68 seconds +[stan] 2024-01-15 00:29:35,697 (trainer:763) INFO: 14epoch:train:1-40batch: iter_time=0.003, forward_time=0.050, loss_ctc=2.709, loss=2.709, backward_time=0.009, grad_norm=62.813, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.409 +[stan] 2024-01-15 00:29:39,782 (trainer:763) INFO: 14epoch:train:41-80batch: iter_time=4.337e-05, forward_time=0.053, loss_ctc=2.973, loss=2.973, backward_time=0.009, grad_norm=66.502, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-15 00:29:43,762 (trainer:763) INFO: 14epoch:train:81-120batch: iter_time=4.354e-05, forward_time=0.052, loss_ctc=2.711, loss=2.711, backward_time=0.009, grad_norm=62.003, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-15 00:29:47,548 (trainer:763) INFO: 14epoch:train:121-160batch: iter_time=4.449e-05, forward_time=0.050, loss_ctc=2.508, loss=2.508, backward_time=0.008, grad_norm=61.591, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-15 00:29:51,435 (trainer:763) INFO: 14epoch:train:161-200batch: iter_time=4.578e-05, forward_time=0.051, loss_ctc=2.509, loss=2.509, backward_time=0.008, grad_norm=56.370, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.389 +[stan] 2024-01-15 00:29:55,491 (trainer:763) INFO: 14epoch:train:201-240batch: iter_time=4.615e-05, forward_time=0.053, loss_ctc=2.983, loss=2.983, backward_time=0.008, grad_norm=66.289, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-15 00:29:59,361 (trainer:763) INFO: 14epoch:train:241-280batch: iter_time=4.411e-05, forward_time=0.051, loss_ctc=2.660, loss=2.660, backward_time=0.009, grad_norm=59.764, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-15 00:30:03,456 (trainer:763) INFO: 14epoch:train:281-320batch: iter_time=4.540e-05, forward_time=0.054, loss_ctc=3.023, loss=3.023, backward_time=0.009, grad_norm=64.841, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.409 +[stan] 2024-01-15 00:30:07,287 (trainer:763) INFO: 14epoch:train:321-360batch: iter_time=4.133e-05, forward_time=0.050, loss_ctc=2.451, loss=2.451, backward_time=0.008, grad_norm=59.904, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-15 00:30:11,136 (trainer:763) INFO: 14epoch:train:361-400batch: iter_time=4.572e-05, forward_time=0.050, loss_ctc=2.604, loss=2.604, backward_time=0.008, grad_norm=58.509, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-15 00:30:15,194 (trainer:763) INFO: 14epoch:train:401-440batch: iter_time=4.355e-05, forward_time=0.053, loss_ctc=3.106, loss=3.106, backward_time=0.009, grad_norm=65.422, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-15 00:30:19,274 (trainer:763) INFO: 14epoch:train:441-480batch: iter_time=4.450e-05, forward_time=0.053, loss_ctc=3.175, loss=3.175, backward_time=0.009, grad_norm=68.004, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-15 00:30:23,076 (trainer:763) INFO: 14epoch:train:481-520batch: iter_time=4.680e-05, forward_time=0.050, loss_ctc=2.319, loss=2.319, backward_time=0.008, grad_norm=59.382, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.380 +[stan] 2024-01-15 00:30:27,017 (trainer:763) INFO: 14epoch:train:521-560batch: iter_time=4.317e-05, forward_time=0.052, loss_ctc=2.667, loss=2.667, backward_time=0.009, grad_norm=59.511, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-15 00:30:30,930 (trainer:763) INFO: 14epoch:train:561-600batch: iter_time=4.335e-05, forward_time=0.051, loss_ctc=2.760, loss=2.760, backward_time=0.009, grad_norm=66.134, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:30:34,706 (trainer:763) INFO: 14epoch:train:601-640batch: iter_time=4.264e-05, forward_time=0.050, loss_ctc=2.433, loss=2.433, backward_time=0.008, grad_norm=61.044, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-15 00:30:38,911 (trainer:763) INFO: 14epoch:train:641-680batch: iter_time=4.385e-05, forward_time=0.055, loss_ctc=3.129, loss=3.129, backward_time=0.009, grad_norm=64.533, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.420 +[stan] 2024-01-15 00:30:43,176 (trainer:763) INFO: 14epoch:train:681-720batch: iter_time=4.355e-05, forward_time=0.056, loss_ctc=3.401, loss=3.401, backward_time=0.009, grad_norm=66.874, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-15 00:30:46,936 (trainer:763) INFO: 14epoch:train:721-760batch: iter_time=4.232e-05, forward_time=0.049, loss_ctc=2.164, loss=2.164, backward_time=0.008, grad_norm=57.660, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.376 +[stan] 2024-01-15 00:30:50,579 (trainer:763) INFO: 14epoch:train:761-800batch: iter_time=3.845e-05, forward_time=0.048, loss_ctc=2.114, loss=2.114, backward_time=0.008, grad_norm=56.579, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.364 +[stan] 2024-01-15 00:30:54,899 (trainer:354) INFO: 14epoch results: [train] iter_time=1.900e-04, forward_time=0.052, loss_ctc=2.720, loss=2.720, backward_time=0.009, grad_norm=62.186, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395, time=1 minute and 19.06 seconds, total_count=11200, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=33.143, cer_ctc=0.171, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=33.143, time=1.1 seconds, total_count=210, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.14 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:30:55,837 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:30:55,837 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/13epoch.pth +[stan] 2024-01-15 00:30:55,837 (trainer:288) INFO: 15/30epoch started. Estimated time to finish: 22 minutes and 34.47 seconds +[stan] 2024-01-15 00:31:00,149 (trainer:763) INFO: 15epoch:train:1-40batch: iter_time=0.003, forward_time=0.053, loss_ctc=2.930, loss=2.930, backward_time=0.009, grad_norm=59.657, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-15 00:31:03,905 (trainer:763) INFO: 15epoch:train:41-80batch: iter_time=4.258e-05, forward_time=0.049, loss_ctc=2.294, loss=2.294, backward_time=0.008, grad_norm=60.522, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.376 +[stan] 2024-01-15 00:31:07,918 (trainer:763) INFO: 15epoch:train:81-120batch: iter_time=4.412e-05, forward_time=0.052, loss_ctc=2.779, loss=2.779, backward_time=0.008, grad_norm=62.583, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-15 00:31:12,218 (trainer:763) INFO: 15epoch:train:121-160batch: iter_time=4.234e-05, forward_time=0.056, loss_ctc=3.311, loss=3.311, backward_time=0.009, grad_norm=69.228, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-15 00:31:15,917 (trainer:763) INFO: 15epoch:train:161-200batch: iter_time=4.211e-05, forward_time=0.049, loss_ctc=2.437, loss=2.437, backward_time=0.008, grad_norm=62.046, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.370 +[stan] 2024-01-15 00:31:19,887 (trainer:763) INFO: 15epoch:train:201-240batch: iter_time=4.499e-05, forward_time=0.052, loss_ctc=2.587, loss=2.587, backward_time=0.009, grad_norm=58.995, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397 +[stan] 2024-01-15 00:31:23,823 (trainer:763) INFO: 15epoch:train:241-280batch: iter_time=4.625e-05, forward_time=0.051, loss_ctc=2.574, loss=2.574, backward_time=0.009, grad_norm=62.360, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-15 00:31:28,134 (trainer:763) INFO: 15epoch:train:281-320batch: iter_time=4.533e-05, forward_time=0.056, loss_ctc=3.144, loss=3.144, backward_time=0.009, grad_norm=68.755, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-15 00:31:32,091 (trainer:763) INFO: 15epoch:train:321-360batch: iter_time=4.472e-05, forward_time=0.052, loss_ctc=2.629, loss=2.629, backward_time=0.009, grad_norm=60.499, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-15 00:31:35,808 (trainer:763) INFO: 15epoch:train:361-400batch: iter_time=4.206e-05, forward_time=0.049, loss_ctc=2.240, loss=2.240, backward_time=0.008, grad_norm=57.677, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.372 +[stan] 2024-01-15 00:31:39,954 (trainer:763) INFO: 15epoch:train:401-440batch: iter_time=4.477e-05, forward_time=0.054, loss_ctc=2.839, loss=2.839, backward_time=0.009, grad_norm=64.532, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.414 +[stan] 2024-01-15 00:31:43,691 (trainer:763) INFO: 15epoch:train:441-480batch: iter_time=4.563e-05, forward_time=0.049, loss_ctc=2.231, loss=2.231, backward_time=0.008, grad_norm=58.717, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.374 +[stan] 2024-01-15 00:31:47,959 (trainer:763) INFO: 15epoch:train:481-520batch: iter_time=4.291e-05, forward_time=0.056, loss_ctc=2.946, loss=2.946, backward_time=0.009, grad_norm=66.559, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-15 00:31:51,586 (trainer:763) INFO: 15epoch:train:521-560batch: iter_time=4.245e-05, forward_time=0.048, loss_ctc=2.047, loss=2.047, backward_time=0.008, grad_norm=58.335, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.363 +[stan] 2024-01-15 00:31:55,590 (trainer:763) INFO: 15epoch:train:561-600batch: iter_time=4.368e-05, forward_time=0.052, loss_ctc=2.833, loss=2.833, backward_time=0.009, grad_norm=62.862, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-15 00:31:59,504 (trainer:763) INFO: 15epoch:train:601-640batch: iter_time=4.594e-05, forward_time=0.051, loss_ctc=2.520, loss=2.520, backward_time=0.008, grad_norm=65.272, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:32:03,312 (trainer:763) INFO: 15epoch:train:641-680batch: iter_time=4.357e-05, forward_time=0.050, loss_ctc=2.419, loss=2.419, backward_time=0.009, grad_norm=59.267, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.381 +[stan] 2024-01-15 00:32:07,480 (trainer:763) INFO: 15epoch:train:681-720batch: iter_time=4.347e-05, forward_time=0.054, loss_ctc=2.719, loss=2.719, backward_time=0.009, grad_norm=60.261, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-15 00:32:11,299 (trainer:763) INFO: 15epoch:train:721-760batch: iter_time=4.347e-05, forward_time=0.050, loss_ctc=2.295, loss=2.295, backward_time=0.009, grad_norm=55.935, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.382 +[stan] 2024-01-15 00:32:15,239 (trainer:763) INFO: 15epoch:train:761-800batch: iter_time=4.315e-05, forward_time=0.052, loss_ctc=2.601, loss=2.601, backward_time=0.008, grad_norm=60.802, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-15 00:32:19,440 (trainer:354) INFO: 15epoch results: [train] iter_time=1.843e-04, forward_time=0.052, loss_ctc=2.618, loss=2.618, backward_time=0.009, grad_norm=61.743, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.48 seconds, total_count=12000, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=33.553, cer_ctc=0.177, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=33.553, time=1.07 seconds, total_count=225, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:32:20,382 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:32:20,382 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/14epoch.pth +[stan] 2024-01-15 00:32:20,382 (trainer:288) INFO: 16/30epoch started. Estimated time to finish: 21 minutes and 9.71 seconds +[stan] 2024-01-15 00:32:24,565 (trainer:763) INFO: 16epoch:train:1-40batch: iter_time=0.003, forward_time=0.051, loss_ctc=2.349, loss=2.349, backward_time=0.008, grad_norm=59.234, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.418 +[stan] 2024-01-15 00:32:28,479 (trainer:763) INFO: 16epoch:train:41-80batch: iter_time=4.494e-05, forward_time=0.051, loss_ctc=2.411, loss=2.411, backward_time=0.009, grad_norm=59.262, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:32:32,477 (trainer:763) INFO: 16epoch:train:81-120batch: iter_time=4.221e-05, forward_time=0.052, loss_ctc=2.594, loss=2.594, backward_time=0.009, grad_norm=62.343, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-15 00:32:36,405 (trainer:763) INFO: 16epoch:train:121-160batch: iter_time=4.576e-05, forward_time=0.051, loss_ctc=2.355, loss=2.355, backward_time=0.009, grad_norm=59.945, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-15 00:32:40,704 (trainer:763) INFO: 16epoch:train:161-200batch: iter_time=4.506e-05, forward_time=0.056, loss_ctc=3.225, loss=3.225, backward_time=0.009, grad_norm=67.264, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-15 00:32:44,394 (trainer:763) INFO: 16epoch:train:201-240batch: iter_time=4.386e-05, forward_time=0.048, loss_ctc=2.178, loss=2.178, backward_time=0.008, grad_norm=58.933, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.369 +[stan] 2024-01-15 00:32:47,985 (trainer:763) INFO: 16epoch:train:241-280batch: iter_time=4.233e-05, forward_time=0.047, loss_ctc=1.970, loss=1.970, backward_time=0.008, grad_norm=56.463, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.359 +[stan] 2024-01-15 00:32:52,133 (trainer:763) INFO: 16epoch:train:281-320batch: iter_time=4.336e-05, forward_time=0.054, loss_ctc=2.609, loss=2.609, backward_time=0.009, grad_norm=65.195, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-15 00:32:56,104 (trainer:763) INFO: 16epoch:train:321-360batch: iter_time=4.570e-05, forward_time=0.052, loss_ctc=2.542, loss=2.542, backward_time=0.008, grad_norm=63.812, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397 +[stan] 2024-01-15 00:33:00,163 (trainer:763) INFO: 16epoch:train:361-400batch: iter_time=4.254e-05, forward_time=0.053, loss_ctc=2.611, loss=2.611, backward_time=0.009, grad_norm=61.062, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-15 00:33:04,250 (trainer:763) INFO: 16epoch:train:401-440batch: iter_time=4.299e-05, forward_time=0.053, loss_ctc=2.819, loss=2.819, backward_time=0.009, grad_norm=64.055, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.409 +[stan] 2024-01-15 00:33:08,252 (trainer:763) INFO: 16epoch:train:441-480batch: iter_time=4.595e-05, forward_time=0.052, loss_ctc=2.547, loss=2.547, backward_time=0.009, grad_norm=60.031, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-15 00:33:12,073 (trainer:763) INFO: 16epoch:train:481-520batch: iter_time=4.236e-05, forward_time=0.050, loss_ctc=2.337, loss=2.337, backward_time=0.008, grad_norm=59.211, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.382 +[stan] 2024-01-15 00:33:16,048 (trainer:763) INFO: 16epoch:train:521-560batch: iter_time=4.281e-05, forward_time=0.052, loss_ctc=2.454, loss=2.454, backward_time=0.009, grad_norm=62.981, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397 +[stan] 2024-01-15 00:33:19,999 (trainer:763) INFO: 16epoch:train:561-600batch: iter_time=4.375e-05, forward_time=0.052, loss_ctc=2.558, loss=2.558, backward_time=0.009, grad_norm=57.440, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-15 00:33:23,839 (trainer:763) INFO: 16epoch:train:601-640batch: iter_time=4.445e-05, forward_time=0.050, loss_ctc=2.186, loss=2.186, backward_time=0.009, grad_norm=57.312, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.384 +[stan] 2024-01-15 00:33:27,943 (trainer:763) INFO: 16epoch:train:641-680batch: iter_time=4.346e-05, forward_time=0.054, loss_ctc=2.545, loss=2.545, backward_time=0.009, grad_norm=58.170, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.410 +[stan] 2024-01-15 00:33:32,020 (trainer:763) INFO: 16epoch:train:681-720batch: iter_time=4.314e-05, forward_time=0.053, loss_ctc=2.659, loss=2.659, backward_time=0.009, grad_norm=61.797, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-15 00:33:35,993 (trainer:763) INFO: 16epoch:train:721-760batch: iter_time=4.174e-05, forward_time=0.052, loss_ctc=2.420, loss=2.420, backward_time=0.009, grad_norm=59.817, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397 +[stan] 2024-01-15 00:33:40,154 (trainer:763) INFO: 16epoch:train:761-800batch: iter_time=4.258e-05, forward_time=0.054, loss_ctc=2.773, loss=2.773, backward_time=0.009, grad_norm=62.823, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-15 00:33:44,333 (trainer:354) INFO: 16epoch results: [train] iter_time=1.920e-04, forward_time=0.052, loss_ctc=2.507, loss=2.507, backward_time=0.009, grad_norm=60.858, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399, time=1 minute and 19.85 seconds, total_count=12800, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=33.985, cer_ctc=0.166, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=33.985, time=1.07 seconds, total_count=240, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.03 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:33:45,290 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:33:45,290 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/15epoch.pth +[stan] 2024-01-15 00:33:45,290 (trainer:288) INFO: 17/30epoch started. Estimated time to finish: 19 minutes and 45.29 seconds +[stan] 2024-01-15 00:33:49,208 (trainer:763) INFO: 17epoch:train:1-40batch: iter_time=0.003, forward_time=0.048, loss_ctc=1.956, loss=1.956, backward_time=0.008, grad_norm=55.293, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:33:52,955 (trainer:763) INFO: 17epoch:train:41-80batch: iter_time=4.227e-05, forward_time=0.049, loss_ctc=2.231, loss=2.231, backward_time=0.008, grad_norm=55.716, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.375 +[stan] 2024-01-15 00:33:57,065 (trainer:763) INFO: 17epoch:train:81-120batch: iter_time=4.181e-05, forward_time=0.054, loss_ctc=2.561, loss=2.561, backward_time=0.009, grad_norm=64.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.411 +[stan] 2024-01-15 00:34:01,432 (trainer:763) INFO: 17epoch:train:121-160batch: iter_time=4.537e-05, forward_time=0.057, loss_ctc=2.950, loss=2.950, backward_time=0.009, grad_norm=66.040, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-15 00:34:05,075 (trainer:763) INFO: 17epoch:train:161-200batch: iter_time=4.162e-05, forward_time=0.048, loss_ctc=2.177, loss=2.177, backward_time=0.008, grad_norm=55.410, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.364 +[stan] 2024-01-15 00:34:09,244 (trainer:763) INFO: 17epoch:train:201-240batch: iter_time=4.302e-05, forward_time=0.055, loss_ctc=2.591, loss=2.591, backward_time=0.009, grad_norm=61.888, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-15 00:34:13,057 (trainer:763) INFO: 17epoch:train:241-280batch: iter_time=4.178e-05, forward_time=0.050, loss_ctc=2.261, loss=2.261, backward_time=0.008, grad_norm=60.033, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.381 +[stan] 2024-01-15 00:34:16,899 (trainer:763) INFO: 17epoch:train:281-320batch: iter_time=4.193e-05, forward_time=0.050, loss_ctc=2.188, loss=2.188, backward_time=0.008, grad_norm=63.914, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.384 +[stan] 2024-01-15 00:34:21,055 (trainer:763) INFO: 17epoch:train:321-360batch: iter_time=4.641e-05, forward_time=0.054, loss_ctc=2.675, loss=2.675, backward_time=0.009, grad_norm=60.939, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-15 00:34:24,633 (trainer:763) INFO: 17epoch:train:361-400batch: iter_time=4.497e-05, forward_time=0.047, loss_ctc=1.943, loss=1.943, backward_time=0.008, grad_norm=55.811, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.358 +[stan] 2024-01-15 00:34:28,696 (trainer:763) INFO: 17epoch:train:401-440batch: iter_time=4.253e-05, forward_time=0.053, loss_ctc=2.392, loss=2.392, backward_time=0.009, grad_norm=57.939, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-15 00:34:32,724 (trainer:763) INFO: 17epoch:train:441-480batch: iter_time=4.585e-05, forward_time=0.053, loss_ctc=2.327, loss=2.327, backward_time=0.009, grad_norm=56.982, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-15 00:34:36,729 (trainer:763) INFO: 17epoch:train:481-520batch: iter_time=4.380e-05, forward_time=0.052, loss_ctc=2.566, loss=2.566, backward_time=0.009, grad_norm=65.007, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-15 00:34:40,627 (trainer:763) INFO: 17epoch:train:521-560batch: iter_time=4.273e-05, forward_time=0.051, loss_ctc=2.237, loss=2.237, backward_time=0.009, grad_norm=56.194, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.390 +[stan] 2024-01-15 00:34:44,745 (trainer:763) INFO: 17epoch:train:561-600batch: iter_time=4.276e-05, forward_time=0.054, loss_ctc=2.518, loss=2.518, backward_time=0.009, grad_norm=58.758, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.412 +[stan] 2024-01-15 00:34:48,524 (trainer:763) INFO: 17epoch:train:601-640batch: iter_time=4.319e-05, forward_time=0.050, loss_ctc=2.205, loss=2.205, backward_time=0.008, grad_norm=58.942, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-15 00:34:52,570 (trainer:763) INFO: 17epoch:train:641-680batch: iter_time=4.473e-05, forward_time=0.053, loss_ctc=2.357, loss=2.357, backward_time=0.008, grad_norm=64.257, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-15 00:34:56,364 (trainer:763) INFO: 17epoch:train:681-720batch: iter_time=4.322e-05, forward_time=0.050, loss_ctc=2.030, loss=2.030, backward_time=0.009, grad_norm=59.060, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-15 00:35:00,220 (trainer:763) INFO: 17epoch:train:721-760batch: iter_time=4.492e-05, forward_time=0.051, loss_ctc=2.120, loss=2.120, backward_time=0.008, grad_norm=58.002, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-15 00:35:04,371 (trainer:763) INFO: 17epoch:train:761-800batch: iter_time=4.153e-05, forward_time=0.054, loss_ctc=2.589, loss=2.589, backward_time=0.009, grad_norm=64.060, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-15 00:35:08,575 (trainer:354) INFO: 17epoch results: [train] iter_time=1.890e-04, forward_time=0.052, loss_ctc=2.343, loss=2.343, backward_time=0.009, grad_norm=59.919, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395, time=1 minute and 19.16 seconds, total_count=13600, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=35.188, cer_ctc=0.168, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=35.188, time=1.1 seconds, total_count=255, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.03 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:35:09,535 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:35:09,535 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/16epoch.pth +[stan] 2024-01-15 00:35:09,535 (trainer:288) INFO: 18/30epoch started. Estimated time to finish: 18 minutes and 20.31 seconds +[stan] 2024-01-15 00:35:13,978 (trainer:763) INFO: 18epoch:train:1-40batch: iter_time=0.003, forward_time=0.055, loss_ctc=2.517, loss=2.517, backward_time=0.009, grad_norm=59.596, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-15 00:35:17,892 (trainer:763) INFO: 18epoch:train:41-80batch: iter_time=4.640e-05, forward_time=0.051, loss_ctc=2.413, loss=2.413, backward_time=0.009, grad_norm=57.674, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:35:21,319 (trainer:763) INFO: 18epoch:train:81-120batch: iter_time=4.310e-05, forward_time=0.045, loss_ctc=1.618, loss=1.618, backward_time=0.008, grad_norm=50.531, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.343 +[stan] 2024-01-15 00:35:25,584 (trainer:763) INFO: 18epoch:train:121-160batch: iter_time=4.493e-05, forward_time=0.056, loss_ctc=2.630, loss=2.630, backward_time=0.009, grad_norm=62.636, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-15 00:35:29,376 (trainer:763) INFO: 18epoch:train:161-200batch: iter_time=4.354e-05, forward_time=0.050, loss_ctc=2.112, loss=2.112, backward_time=0.008, grad_norm=57.301, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-15 00:35:33,547 (trainer:763) INFO: 18epoch:train:201-240batch: iter_time=4.350e-05, forward_time=0.055, loss_ctc=2.423, loss=2.423, backward_time=0.009, grad_norm=61.361, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-15 00:35:37,691 (trainer:763) INFO: 18epoch:train:241-280batch: iter_time=4.377e-05, forward_time=0.054, loss_ctc=2.645, loss=2.645, backward_time=0.009, grad_norm=64.629, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.414 +[stan] 2024-01-15 00:35:41,543 (trainer:763) INFO: 18epoch:train:281-320batch: iter_time=4.238e-05, forward_time=0.050, loss_ctc=2.230, loss=2.230, backward_time=0.008, grad_norm=58.361, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-15 00:35:45,261 (trainer:763) INFO: 18epoch:train:321-360batch: iter_time=4.478e-05, forward_time=0.049, loss_ctc=2.114, loss=2.114, backward_time=0.008, grad_norm=58.970, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.372 +[stan] 2024-01-15 00:35:49,495 (trainer:763) INFO: 18epoch:train:361-400batch: iter_time=4.321e-05, forward_time=0.056, loss_ctc=2.674, loss=2.674, backward_time=0.009, grad_norm=64.323, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.423 +[stan] 2024-01-15 00:35:53,428 (trainer:763) INFO: 18epoch:train:401-440batch: iter_time=4.195e-05, forward_time=0.051, loss_ctc=2.303, loss=2.303, backward_time=0.008, grad_norm=54.555, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-15 00:35:57,376 (trainer:763) INFO: 18epoch:train:441-480batch: iter_time=4.664e-05, forward_time=0.052, loss_ctc=2.235, loss=2.235, backward_time=0.009, grad_norm=57.331, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-15 00:36:01,172 (trainer:763) INFO: 18epoch:train:481-520batch: iter_time=4.298e-05, forward_time=0.050, loss_ctc=2.070, loss=2.070, backward_time=0.009, grad_norm=56.548, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.380 +[stan] 2024-01-15 00:36:05,199 (trainer:763) INFO: 18epoch:train:521-560batch: iter_time=4.446e-05, forward_time=0.053, loss_ctc=2.511, loss=2.511, backward_time=0.009, grad_norm=60.966, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-15 00:36:09,150 (trainer:763) INFO: 18epoch:train:561-600batch: iter_time=4.310e-05, forward_time=0.052, loss_ctc=2.177, loss=2.177, backward_time=0.008, grad_norm=59.503, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-15 00:36:12,976 (trainer:763) INFO: 18epoch:train:601-640batch: iter_time=4.254e-05, forward_time=0.050, loss_ctc=2.231, loss=2.231, backward_time=0.009, grad_norm=53.452, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.382 +[stan] 2024-01-15 00:36:17,051 (trainer:763) INFO: 18epoch:train:641-680batch: iter_time=4.288e-05, forward_time=0.053, loss_ctc=2.513, loss=2.513, backward_time=0.008, grad_norm=61.617, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.407 +[stan] 2024-01-15 00:36:20,717 (trainer:763) INFO: 18epoch:train:681-720batch: iter_time=4.178e-05, forward_time=0.048, loss_ctc=1.862, loss=1.862, backward_time=0.009, grad_norm=51.697, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.366 +[stan] 2024-01-15 00:36:24,920 (trainer:763) INFO: 18epoch:train:721-760batch: iter_time=4.636e-05, forward_time=0.055, loss_ctc=2.647, loss=2.647, backward_time=0.009, grad_norm=60.287, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.420 +[stan] 2024-01-15 00:36:28,772 (trainer:763) INFO: 18epoch:train:761-800batch: iter_time=4.044e-05, forward_time=0.051, loss_ctc=2.155, loss=2.155, backward_time=0.008, grad_norm=53.884, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-15 00:36:32,956 (trainer:354) INFO: 18epoch results: [train] iter_time=1.955e-04, forward_time=0.052, loss_ctc=2.304, loss=2.304, backward_time=0.009, grad_norm=58.261, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396, time=1 minute and 19.31 seconds, total_count=14400, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=36.120, cer_ctc=0.174, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=36.120, time=1.07 seconds, total_count=270, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.04 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:36:33,925 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:36:33,925 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/17epoch.pth +[stan] 2024-01-15 00:36:33,926 (trainer:288) INFO: 19/30epoch started. Estimated time to finish: 16 minutes and 55.5 seconds +[stan] 2024-01-15 00:36:38,236 (trainer:763) INFO: 19epoch:train:1-40batch: iter_time=0.003, forward_time=0.053, loss_ctc=2.167, loss=2.167, backward_time=0.009, grad_norm=56.242, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-15 00:36:42,254 (trainer:763) INFO: 19epoch:train:41-80batch: iter_time=4.201e-05, forward_time=0.053, loss_ctc=2.367, loss=2.367, backward_time=0.009, grad_norm=61.471, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:36:46,408 (trainer:763) INFO: 19epoch:train:81-120batch: iter_time=4.128e-05, forward_time=0.054, loss_ctc=2.577, loss=2.577, backward_time=0.009, grad_norm=60.937, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-15 00:36:50,220 (trainer:763) INFO: 19epoch:train:121-160batch: iter_time=4.191e-05, forward_time=0.050, loss_ctc=2.015, loss=2.015, backward_time=0.008, grad_norm=57.550, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.381 +[stan] 2024-01-15 00:36:54,135 (trainer:763) INFO: 19epoch:train:161-200batch: iter_time=4.197e-05, forward_time=0.051, loss_ctc=1.910, loss=1.910, backward_time=0.008, grad_norm=53.602, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:36:58,218 (trainer:763) INFO: 19epoch:train:201-240batch: iter_time=4.327e-05, forward_time=0.053, loss_ctc=2.435, loss=2.435, backward_time=0.009, grad_norm=60.247, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-15 00:37:02,184 (trainer:763) INFO: 19epoch:train:241-280batch: iter_time=4.285e-05, forward_time=0.052, loss_ctc=2.291, loss=2.291, backward_time=0.009, grad_norm=62.372, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-15 00:37:05,940 (trainer:763) INFO: 19epoch:train:281-320batch: iter_time=4.348e-05, forward_time=0.049, loss_ctc=1.981, loss=1.981, backward_time=0.008, grad_norm=57.056, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.376 +[stan] 2024-01-15 00:37:09,738 (trainer:763) INFO: 19epoch:train:321-360batch: iter_time=4.282e-05, forward_time=0.050, loss_ctc=2.146, loss=2.146, backward_time=0.008, grad_norm=59.636, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.380 +[stan] 2024-01-15 00:37:13,864 (trainer:763) INFO: 19epoch:train:361-400batch: iter_time=4.537e-05, forward_time=0.054, loss_ctc=2.539, loss=2.539, backward_time=0.009, grad_norm=59.698, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.412 +[stan] 2024-01-15 00:37:18,010 (trainer:763) INFO: 19epoch:train:401-440batch: iter_time=4.632e-05, forward_time=0.054, loss_ctc=2.234, loss=2.234, backward_time=0.009, grad_norm=55.461, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-15 00:37:21,689 (trainer:763) INFO: 19epoch:train:441-480batch: iter_time=4.210e-05, forward_time=0.048, loss_ctc=2.029, loss=2.029, backward_time=0.008, grad_norm=54.464, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.368 +[stan] 2024-01-15 00:37:25,728 (trainer:763) INFO: 19epoch:train:481-520batch: iter_time=4.649e-05, forward_time=0.053, loss_ctc=2.259, loss=2.259, backward_time=0.009, grad_norm=55.159, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-15 00:37:29,749 (trainer:763) INFO: 19epoch:train:521-560batch: iter_time=4.177e-05, forward_time=0.053, loss_ctc=2.222, loss=2.222, backward_time=0.009, grad_norm=59.280, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:37:33,322 (trainer:763) INFO: 19epoch:train:561-600batch: iter_time=4.328e-05, forward_time=0.047, loss_ctc=1.739, loss=1.739, backward_time=0.008, grad_norm=52.812, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.357 +[stan] 2024-01-15 00:37:37,494 (trainer:763) INFO: 19epoch:train:601-640batch: iter_time=4.518e-05, forward_time=0.055, loss_ctc=2.640, loss=2.640, backward_time=0.009, grad_norm=63.605, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-15 00:37:41,415 (trainer:763) INFO: 19epoch:train:641-680batch: iter_time=4.494e-05, forward_time=0.051, loss_ctc=2.177, loss=2.177, backward_time=0.008, grad_norm=58.019, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-15 00:37:45,457 (trainer:763) INFO: 19epoch:train:681-720batch: iter_time=4.205e-05, forward_time=0.053, loss_ctc=2.287, loss=2.287, backward_time=0.009, grad_norm=56.298, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-15 00:37:49,506 (trainer:763) INFO: 19epoch:train:721-760batch: iter_time=4.279e-05, forward_time=0.053, loss_ctc=2.430, loss=2.430, backward_time=0.009, grad_norm=58.790, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.405 +[stan] 2024-01-15 00:37:53,220 (trainer:763) INFO: 19epoch:train:761-800batch: iter_time=4.224e-05, forward_time=0.049, loss_ctc=1.940, loss=1.940, backward_time=0.009, grad_norm=56.180, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.371 +[stan] 2024-01-15 00:37:57,419 (trainer:354) INFO: 19epoch results: [train] iter_time=1.880e-04, forward_time=0.052, loss_ctc=2.219, loss=2.219, backward_time=0.009, grad_norm=57.944, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396, time=1 minute and 19.37 seconds, total_count=15200, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=35.460, cer_ctc=0.167, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=35.460, time=1.08 seconds, total_count=285, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.04 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:37:58,483 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:37:58,483 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/18epoch.pth +[stan] 2024-01-15 00:37:58,483 (trainer:288) INFO: 20/30epoch started. Estimated time to finish: 15 minutes and 30.84 seconds +[stan] 2024-01-15 00:38:02,854 (trainer:763) INFO: 20epoch:train:1-40batch: iter_time=0.003, forward_time=0.054, loss_ctc=2.346, loss=2.346, backward_time=0.009, grad_norm=61.005, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-15 00:38:06,851 (trainer:763) INFO: 20epoch:train:41-80batch: iter_time=4.243e-05, forward_time=0.052, loss_ctc=2.253, loss=2.253, backward_time=0.009, grad_norm=54.846, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-15 00:38:10,594 (trainer:763) INFO: 20epoch:train:81-120batch: iter_time=4.336e-05, forward_time=0.049, loss_ctc=2.040, loss=2.040, backward_time=0.008, grad_norm=56.473, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.374 +[stan] 2024-01-15 00:38:14,810 (trainer:763) INFO: 20epoch:train:121-160batch: iter_time=4.308e-05, forward_time=0.055, loss_ctc=2.611, loss=2.611, backward_time=0.009, grad_norm=61.057, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.422 +[stan] 2024-01-15 00:38:18,486 (trainer:763) INFO: 20epoch:train:161-200batch: iter_time=4.468e-05, forward_time=0.048, loss_ctc=1.759, loss=1.759, backward_time=0.008, grad_norm=51.381, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.367 +[stan] 2024-01-15 00:38:22,525 (trainer:763) INFO: 20epoch:train:201-240batch: iter_time=4.523e-05, forward_time=0.053, loss_ctc=2.398, loss=2.398, backward_time=0.009, grad_norm=63.885, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-15 00:38:26,626 (trainer:763) INFO: 20epoch:train:241-280batch: iter_time=4.374e-05, forward_time=0.054, loss_ctc=2.442, loss=2.442, backward_time=0.009, grad_norm=58.999, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.410 +[stan] 2024-01-15 00:38:30,411 (trainer:763) INFO: 20epoch:train:281-320batch: iter_time=4.312e-05, forward_time=0.050, loss_ctc=2.002, loss=2.002, backward_time=0.009, grad_norm=60.324, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-15 00:38:34,435 (trainer:763) INFO: 20epoch:train:321-360batch: iter_time=4.257e-05, forward_time=0.053, loss_ctc=2.111, loss=2.111, backward_time=0.009, grad_norm=59.184, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:38:38,187 (trainer:763) INFO: 20epoch:train:361-400batch: iter_time=4.212e-05, forward_time=0.049, loss_ctc=1.875, loss=1.875, backward_time=0.008, grad_norm=54.371, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.375 +[stan] 2024-01-15 00:38:42,251 (trainer:763) INFO: 20epoch:train:401-440batch: iter_time=4.527e-05, forward_time=0.053, loss_ctc=2.563, loss=2.563, backward_time=0.009, grad_norm=61.620, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-15 00:38:46,130 (trainer:763) INFO: 20epoch:train:441-480batch: iter_time=4.692e-05, forward_time=0.051, loss_ctc=2.028, loss=2.028, backward_time=0.008, grad_norm=55.791, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-15 00:38:50,449 (trainer:763) INFO: 20epoch:train:481-520batch: iter_time=4.245e-05, forward_time=0.056, loss_ctc=2.458, loss=2.458, backward_time=0.009, grad_norm=59.013, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-15 00:38:54,295 (trainer:763) INFO: 20epoch:train:521-560batch: iter_time=4.283e-05, forward_time=0.050, loss_ctc=2.044, loss=2.044, backward_time=0.008, grad_norm=62.139, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-15 00:38:58,259 (trainer:763) INFO: 20epoch:train:561-600batch: iter_time=4.471e-05, forward_time=0.052, loss_ctc=2.296, loss=2.296, backward_time=0.009, grad_norm=57.991, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-15 00:39:02,174 (trainer:763) INFO: 20epoch:train:601-640batch: iter_time=4.309e-05, forward_time=0.051, loss_ctc=2.175, loss=2.175, backward_time=0.009, grad_norm=55.796, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:39:06,294 (trainer:763) INFO: 20epoch:train:641-680batch: iter_time=4.358e-05, forward_time=0.054, loss_ctc=2.327, loss=2.327, backward_time=0.009, grad_norm=58.299, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.412 +[stan] 2024-01-15 00:39:10,159 (trainer:763) INFO: 20epoch:train:681-720batch: iter_time=4.390e-05, forward_time=0.051, loss_ctc=1.930, loss=1.930, backward_time=0.009, grad_norm=53.917, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.386 +[stan] 2024-01-15 00:39:14,110 (trainer:763) INFO: 20epoch:train:721-760batch: iter_time=4.231e-05, forward_time=0.052, loss_ctc=2.287, loss=2.287, backward_time=0.009, grad_norm=58.429, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-15 00:39:18,366 (trainer:763) INFO: 20epoch:train:761-800batch: iter_time=4.333e-05, forward_time=0.056, loss_ctc=2.537, loss=2.537, backward_time=0.009, grad_norm=63.096, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-15 00:39:22,553 (trainer:354) INFO: 20epoch results: [train] iter_time=1.909e-04, forward_time=0.052, loss_ctc=2.224, loss=2.224, backward_time=0.009, grad_norm=58.381, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399, time=1 minute and 19.96 seconds, total_count=16000, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=36.262, cer_ctc=0.170, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=36.262, time=1.07 seconds, total_count=300, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.04 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:39:23,471 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:39:23,472 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/19epoch.pth +[stan] 2024-01-15 00:39:23,472 (trainer:288) INFO: 21/30epoch started. Estimated time to finish: 14 minutes and 6.4 seconds +[stan] 2024-01-15 00:39:27,399 (trainer:763) INFO: 21epoch:train:1-40batch: iter_time=0.003, forward_time=0.048, loss_ctc=1.891, loss=1.891, backward_time=0.008, grad_norm=55.041, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-15 00:39:31,312 (trainer:763) INFO: 21epoch:train:41-80batch: iter_time=4.219e-05, forward_time=0.051, loss_ctc=2.248, loss=2.248, backward_time=0.009, grad_norm=58.547, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:39:35,215 (trainer:763) INFO: 21epoch:train:81-120batch: iter_time=4.182e-05, forward_time=0.051, loss_ctc=1.984, loss=1.984, backward_time=0.009, grad_norm=54.373, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.390 +[stan] 2024-01-15 00:39:39,604 (trainer:763) INFO: 21epoch:train:121-160batch: iter_time=4.300e-05, forward_time=0.057, loss_ctc=2.853, loss=2.853, backward_time=0.010, grad_norm=64.728, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-15 00:39:43,408 (trainer:763) INFO: 21epoch:train:161-200batch: iter_time=4.206e-05, forward_time=0.050, loss_ctc=1.921, loss=1.921, backward_time=0.008, grad_norm=55.465, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.380 +[stan] 2024-01-15 00:39:46,833 (trainer:763) INFO: 21epoch:train:201-240batch: iter_time=4.260e-05, forward_time=0.045, loss_ctc=1.327, loss=1.327, backward_time=0.007, grad_norm=48.581, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.342 +[stan] 2024-01-15 00:39:51,150 (trainer:763) INFO: 21epoch:train:241-280batch: iter_time=4.257e-05, forward_time=0.056, loss_ctc=2.689, loss=2.689, backward_time=0.009, grad_norm=58.366, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-15 00:39:55,239 (trainer:763) INFO: 21epoch:train:281-320batch: iter_time=4.534e-05, forward_time=0.053, loss_ctc=2.432, loss=2.432, backward_time=0.009, grad_norm=57.145, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.409 +[stan] 2024-01-15 00:39:59,118 (trainer:763) INFO: 21epoch:train:321-360batch: iter_time=4.439e-05, forward_time=0.051, loss_ctc=2.041, loss=2.041, backward_time=0.009, grad_norm=55.689, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-15 00:40:02,891 (trainer:763) INFO: 21epoch:train:361-400batch: iter_time=4.329e-05, forward_time=0.049, loss_ctc=1.816, loss=1.816, backward_time=0.008, grad_norm=53.637, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-15 00:40:07,015 (trainer:763) INFO: 21epoch:train:401-440batch: iter_time=4.295e-05, forward_time=0.054, loss_ctc=2.308, loss=2.308, backward_time=0.009, grad_norm=59.085, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.412 +[stan] 2024-01-15 00:40:10,674 (trainer:763) INFO: 21epoch:train:441-480batch: iter_time=4.332e-05, forward_time=0.048, loss_ctc=1.990, loss=1.990, backward_time=0.008, grad_norm=58.308, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.366 +[stan] 2024-01-15 00:40:14,646 (trainer:763) INFO: 21epoch:train:481-520batch: iter_time=4.528e-05, forward_time=0.052, loss_ctc=2.200, loss=2.200, backward_time=0.009, grad_norm=56.714, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397 +[stan] 2024-01-15 00:40:19,048 (trainer:763) INFO: 21epoch:train:521-560batch: iter_time=4.582e-05, forward_time=0.057, loss_ctc=2.758, loss=2.758, backward_time=0.009, grad_norm=67.377, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-15 00:40:22,699 (trainer:763) INFO: 21epoch:train:561-600batch: iter_time=4.319e-05, forward_time=0.048, loss_ctc=1.664, loss=1.664, backward_time=0.008, grad_norm=54.743, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.365 +[stan] 2024-01-15 00:40:26,873 (trainer:763) INFO: 21epoch:train:601-640batch: iter_time=4.372e-05, forward_time=0.055, loss_ctc=2.375, loss=2.375, backward_time=0.009, grad_norm=57.081, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-15 00:40:30,909 (trainer:763) INFO: 21epoch:train:641-680batch: iter_time=4.499e-05, forward_time=0.053, loss_ctc=2.241, loss=2.241, backward_time=0.009, grad_norm=59.152, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-15 00:40:34,599 (trainer:763) INFO: 21epoch:train:681-720batch: iter_time=4.207e-05, forward_time=0.048, loss_ctc=1.760, loss=1.760, backward_time=0.008, grad_norm=51.137, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.369 +[stan] 2024-01-15 00:40:38,248 (trainer:763) INFO: 21epoch:train:721-760batch: iter_time=4.412e-05, forward_time=0.048, loss_ctc=1.627, loss=1.627, backward_time=0.008, grad_norm=50.909, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.365 +[stan] 2024-01-15 00:40:42,440 (trainer:763) INFO: 21epoch:train:761-800batch: iter_time=4.000e-05, forward_time=0.055, loss_ctc=2.138, loss=2.138, backward_time=0.009, grad_norm=58.700, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.419 +[stan] 2024-01-15 00:40:46,648 (trainer:354) INFO: 21epoch results: [train] iter_time=1.782e-04, forward_time=0.052, loss_ctc=2.113, loss=2.113, backward_time=0.009, grad_norm=56.739, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395, time=1 minute and 19.04 seconds, total_count=16800, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=36.634, cer_ctc=0.169, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=36.634, time=1.08 seconds, total_count=315, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:40:47,679 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:40:47,680 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/20epoch.pth +[stan] 2024-01-15 00:40:47,680 (trainer:288) INFO: 22/30epoch started. Estimated time to finish: 12 minutes and 41.57 seconds +[stan] 2024-01-15 00:40:51,748 (trainer:763) INFO: 22epoch:train:1-40batch: iter_time=0.003, forward_time=0.050, loss_ctc=1.783, loss=1.783, backward_time=0.009, grad_norm=55.736, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-15 00:40:55,831 (trainer:763) INFO: 22epoch:train:41-80batch: iter_time=4.339e-05, forward_time=0.053, loss_ctc=2.360, loss=2.360, backward_time=0.009, grad_norm=58.539, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-15 00:40:59,841 (trainer:763) INFO: 22epoch:train:81-120batch: iter_time=4.280e-05, forward_time=0.052, loss_ctc=2.158, loss=2.158, backward_time=0.009, grad_norm=59.431, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-15 00:41:03,574 (trainer:763) INFO: 22epoch:train:121-160batch: iter_time=4.090e-05, forward_time=0.049, loss_ctc=1.662, loss=1.662, backward_time=0.008, grad_norm=53.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.373 +[stan] 2024-01-15 00:41:07,721 (trainer:763) INFO: 22epoch:train:161-200batch: iter_time=4.300e-05, forward_time=0.054, loss_ctc=2.352, loss=2.352, backward_time=0.009, grad_norm=60.315, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-15 00:41:11,572 (trainer:763) INFO: 22epoch:train:201-240batch: iter_time=4.190e-05, forward_time=0.050, loss_ctc=1.949, loss=1.949, backward_time=0.008, grad_norm=54.002, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-15 00:41:15,723 (trainer:763) INFO: 22epoch:train:241-280batch: iter_time=4.430e-05, forward_time=0.054, loss_ctc=2.249, loss=2.249, backward_time=0.009, grad_norm=57.866, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-15 00:41:19,277 (trainer:763) INFO: 22epoch:train:281-320batch: iter_time=4.358e-05, forward_time=0.047, loss_ctc=1.671, loss=1.671, backward_time=0.008, grad_norm=52.710, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.355 +[stan] 2024-01-15 00:41:23,294 (trainer:763) INFO: 22epoch:train:321-360batch: iter_time=4.289e-05, forward_time=0.053, loss_ctc=2.254, loss=2.254, backward_time=0.009, grad_norm=58.840, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:41:27,262 (trainer:763) INFO: 22epoch:train:361-400batch: iter_time=4.329e-05, forward_time=0.052, loss_ctc=1.965, loss=1.965, backward_time=0.008, grad_norm=53.579, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397 +[stan] 2024-01-15 00:41:31,431 (trainer:763) INFO: 22epoch:train:401-440batch: iter_time=4.306e-05, forward_time=0.054, loss_ctc=2.418, loss=2.418, backward_time=0.009, grad_norm=61.063, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-15 00:41:35,494 (trainer:763) INFO: 22epoch:train:441-480batch: iter_time=4.169e-05, forward_time=0.053, loss_ctc=2.137, loss=2.137, backward_time=0.009, grad_norm=56.178, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-15 00:41:39,337 (trainer:763) INFO: 22epoch:train:481-520batch: iter_time=4.272e-05, forward_time=0.050, loss_ctc=1.891, loss=1.891, backward_time=0.008, grad_norm=60.083, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.384 +[stan] 2024-01-15 00:41:43,294 (trainer:763) INFO: 22epoch:train:521-560batch: iter_time=4.177e-05, forward_time=0.052, loss_ctc=2.118, loss=2.118, backward_time=0.009, grad_norm=60.842, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-15 00:41:47,081 (trainer:763) INFO: 22epoch:train:561-600batch: iter_time=4.523e-05, forward_time=0.050, loss_ctc=1.654, loss=1.654, backward_time=0.008, grad_norm=50.142, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-15 00:41:51,394 (trainer:763) INFO: 22epoch:train:601-640batch: iter_time=4.354e-05, forward_time=0.056, loss_ctc=2.456, loss=2.456, backward_time=0.009, grad_norm=62.046, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-15 00:41:55,119 (trainer:763) INFO: 22epoch:train:641-680batch: iter_time=4.266e-05, forward_time=0.049, loss_ctc=1.720, loss=1.720, backward_time=0.008, grad_norm=53.644, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.372 +[stan] 2024-01-15 00:41:59,255 (trainer:763) INFO: 22epoch:train:681-720batch: iter_time=4.421e-05, forward_time=0.054, loss_ctc=2.096, loss=2.096, backward_time=0.009, grad_norm=55.801, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.413 +[stan] 2024-01-15 00:42:03,085 (trainer:763) INFO: 22epoch:train:721-760batch: iter_time=4.256e-05, forward_time=0.050, loss_ctc=2.016, loss=2.016, backward_time=0.008, grad_norm=56.840, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-15 00:42:06,878 (trainer:763) INFO: 22epoch:train:761-800batch: iter_time=3.932e-05, forward_time=0.050, loss_ctc=1.700, loss=1.700, backward_time=0.008, grad_norm=50.715, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-15 00:42:11,114 (trainer:354) INFO: 22epoch results: [train] iter_time=1.681e-04, forward_time=0.052, loss_ctc=2.031, loss=2.031, backward_time=0.009, grad_norm=56.574, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396, time=1 minute and 19.28 seconds, total_count=17600, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=37.201, cer_ctc=0.175, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=37.201, time=1.07 seconds, total_count=330, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.08 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:42:12,060 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:42:12,061 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/21epoch.pth +[stan] 2024-01-15 00:42:12,061 (trainer:288) INFO: 23/30epoch started. Estimated time to finish: 11 minutes and 16.87 seconds +[stan] 2024-01-15 00:42:16,436 (trainer:763) INFO: 23epoch:train:1-40batch: iter_time=0.003, forward_time=0.054, loss_ctc=2.225, loss=2.225, backward_time=0.009, grad_norm=57.515, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-15 00:42:20,019 (trainer:763) INFO: 23epoch:train:41-80batch: iter_time=4.611e-05, forward_time=0.047, loss_ctc=1.588, loss=1.588, backward_time=0.008, grad_norm=53.413, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.358 +[stan] 2024-01-15 00:42:24,179 (trainer:763) INFO: 23epoch:train:81-120batch: iter_time=4.278e-05, forward_time=0.054, loss_ctc=2.240, loss=2.240, backward_time=0.009, grad_norm=60.376, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-15 00:42:28,419 (trainer:763) INFO: 23epoch:train:121-160batch: iter_time=4.688e-05, forward_time=0.055, loss_ctc=2.342, loss=2.342, backward_time=0.009, grad_norm=54.550, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-15 00:42:32,187 (trainer:763) INFO: 23epoch:train:161-200batch: iter_time=4.326e-05, forward_time=0.049, loss_ctc=1.811, loss=1.811, backward_time=0.008, grad_norm=51.694, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-15 00:42:36,284 (trainer:763) INFO: 23epoch:train:201-240batch: iter_time=4.199e-05, forward_time=0.054, loss_ctc=2.228, loss=2.228, backward_time=0.009, grad_norm=57.911, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.410 +[stan] 2024-01-15 00:42:40,346 (trainer:763) INFO: 23epoch:train:241-280batch: iter_time=4.612e-05, forward_time=0.053, loss_ctc=2.254, loss=2.254, backward_time=0.009, grad_norm=58.715, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-15 00:42:44,234 (trainer:763) INFO: 23epoch:train:281-320batch: iter_time=4.259e-05, forward_time=0.051, loss_ctc=1.986, loss=1.986, backward_time=0.009, grad_norm=57.264, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.389 +[stan] 2024-01-15 00:42:48,134 (trainer:763) INFO: 23epoch:train:321-360batch: iter_time=4.580e-05, forward_time=0.051, loss_ctc=2.003, loss=2.003, backward_time=0.008, grad_norm=55.511, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.390 +[stan] 2024-01-15 00:42:52,016 (trainer:763) INFO: 23epoch:train:361-400batch: iter_time=4.312e-05, forward_time=0.051, loss_ctc=2.019, loss=2.019, backward_time=0.009, grad_norm=56.544, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-15 00:42:55,973 (trainer:763) INFO: 23epoch:train:401-440batch: iter_time=4.262e-05, forward_time=0.052, loss_ctc=1.926, loss=1.926, backward_time=0.009, grad_norm=58.141, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-15 00:43:00,066 (trainer:763) INFO: 23epoch:train:441-480batch: iter_time=4.603e-05, forward_time=0.054, loss_ctc=1.931, loss=1.931, backward_time=0.009, grad_norm=54.393, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.409 +[stan] 2024-01-15 00:43:04,162 (trainer:763) INFO: 23epoch:train:481-520batch: iter_time=4.461e-05, forward_time=0.054, loss_ctc=2.406, loss=2.406, backward_time=0.008, grad_norm=57.287, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.409 +[stan] 2024-01-15 00:43:07,824 (trainer:763) INFO: 23epoch:train:521-560batch: iter_time=4.511e-05, forward_time=0.048, loss_ctc=1.470, loss=1.470, backward_time=0.008, grad_norm=46.961, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.366 +[stan] 2024-01-15 00:43:11,997 (trainer:763) INFO: 23epoch:train:561-600batch: iter_time=4.457e-05, forward_time=0.054, loss_ctc=2.331, loss=2.331, backward_time=0.009, grad_norm=55.338, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-15 00:43:15,767 (trainer:763) INFO: 23epoch:train:601-640batch: iter_time=4.391e-05, forward_time=0.049, loss_ctc=1.886, loss=1.886, backward_time=0.008, grad_norm=52.164, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-15 00:43:19,914 (trainer:763) INFO: 23epoch:train:641-680batch: iter_time=4.448e-05, forward_time=0.054, loss_ctc=2.159, loss=2.159, backward_time=0.009, grad_norm=61.472, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-15 00:43:23,564 (trainer:763) INFO: 23epoch:train:681-720batch: iter_time=4.690e-05, forward_time=0.048, loss_ctc=1.663, loss=1.663, backward_time=0.008, grad_norm=49.070, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.365 +[stan] 2024-01-15 00:43:27,576 (trainer:763) INFO: 23epoch:train:721-760batch: iter_time=4.592e-05, forward_time=0.052, loss_ctc=2.034, loss=2.034, backward_time=0.009, grad_norm=53.541, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-15 00:43:31,459 (trainer:763) INFO: 23epoch:train:761-800batch: iter_time=4.360e-05, forward_time=0.051, loss_ctc=2.052, loss=2.052, backward_time=0.009, grad_norm=55.909, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-15 00:43:35,677 (trainer:354) INFO: 23epoch results: [train] iter_time=2.001e-04, forward_time=0.052, loss_ctc=2.027, loss=2.027, backward_time=0.009, grad_norm=55.388, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.48 seconds, total_count=18400, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=37.886, cer_ctc=0.168, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=37.886, time=1.09 seconds, total_count=345, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:43:36,736 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:43:36,736 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/22epoch.pth +[stan] 2024-01-15 00:43:36,736 (trainer:288) INFO: 24/30epoch started. Estimated time to finish: 9 minutes and 52.28 seconds +[stan] 2024-01-15 00:43:41,260 (trainer:763) INFO: 24epoch:train:1-40batch: iter_time=0.003, forward_time=0.056, loss_ctc=2.458, loss=2.458, backward_time=0.009, grad_norm=59.537, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.452 +[stan] 2024-01-15 00:43:44,970 (trainer:763) INFO: 24epoch:train:41-80batch: iter_time=4.192e-05, forward_time=0.049, loss_ctc=1.582, loss=1.582, backward_time=0.008, grad_norm=49.518, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.371 +[stan] 2024-01-15 00:43:48,905 (trainer:763) INFO: 24epoch:train:81-120batch: iter_time=4.240e-05, forward_time=0.051, loss_ctc=2.243, loss=2.243, backward_time=0.009, grad_norm=58.547, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-15 00:43:52,773 (trainer:763) INFO: 24epoch:train:121-160batch: iter_time=4.395e-05, forward_time=0.051, loss_ctc=1.758, loss=1.758, backward_time=0.008, grad_norm=52.151, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-15 00:43:56,646 (trainer:763) INFO: 24epoch:train:161-200batch: iter_time=4.500e-05, forward_time=0.051, loss_ctc=2.089, loss=2.089, backward_time=0.009, grad_norm=54.092, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-15 00:44:00,719 (trainer:763) INFO: 24epoch:train:201-240batch: iter_time=4.420e-05, forward_time=0.053, loss_ctc=2.119, loss=2.119, backward_time=0.009, grad_norm=57.140, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.407 +[stan] 2024-01-15 00:44:04,632 (trainer:763) INFO: 24epoch:train:241-280batch: iter_time=4.370e-05, forward_time=0.051, loss_ctc=1.856, loss=1.856, backward_time=0.009, grad_norm=51.550, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:44:08,571 (trainer:763) INFO: 24epoch:train:281-320batch: iter_time=4.283e-05, forward_time=0.052, loss_ctc=1.914, loss=1.914, backward_time=0.009, grad_norm=55.408, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-15 00:44:12,610 (trainer:763) INFO: 24epoch:train:321-360batch: iter_time=4.245e-05, forward_time=0.053, loss_ctc=2.179, loss=2.179, backward_time=0.009, grad_norm=60.449, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-15 00:44:16,641 (trainer:763) INFO: 24epoch:train:361-400batch: iter_time=4.363e-05, forward_time=0.053, loss_ctc=2.116, loss=2.116, backward_time=0.009, grad_norm=56.249, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-15 00:44:20,684 (trainer:763) INFO: 24epoch:train:401-440batch: iter_time=4.303e-05, forward_time=0.053, loss_ctc=1.991, loss=1.991, backward_time=0.009, grad_norm=55.038, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-15 00:44:24,619 (trainer:763) INFO: 24epoch:train:441-480batch: iter_time=4.272e-05, forward_time=0.052, loss_ctc=1.847, loss=1.847, backward_time=0.008, grad_norm=56.900, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-15 00:44:28,452 (trainer:763) INFO: 24epoch:train:481-520batch: iter_time=4.365e-05, forward_time=0.050, loss_ctc=1.741, loss=1.741, backward_time=0.009, grad_norm=51.531, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-15 00:44:32,229 (trainer:763) INFO: 24epoch:train:521-560batch: iter_time=4.288e-05, forward_time=0.049, loss_ctc=1.883, loss=1.883, backward_time=0.009, grad_norm=58.207, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-15 00:44:36,442 (trainer:763) INFO: 24epoch:train:561-600batch: iter_time=4.533e-05, forward_time=0.055, loss_ctc=2.234, loss=2.234, backward_time=0.009, grad_norm=58.047, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.421 +[stan] 2024-01-15 00:44:40,509 (trainer:763) INFO: 24epoch:train:601-640batch: iter_time=4.560e-05, forward_time=0.053, loss_ctc=1.926, loss=1.926, backward_time=0.009, grad_norm=55.582, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.407 +[stan] 2024-01-15 00:44:44,200 (trainer:763) INFO: 24epoch:train:641-680batch: iter_time=4.271e-05, forward_time=0.048, loss_ctc=1.673, loss=1.673, backward_time=0.008, grad_norm=52.233, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.369 +[stan] 2024-01-15 00:44:48,116 (trainer:763) INFO: 24epoch:train:681-720batch: iter_time=4.205e-05, forward_time=0.051, loss_ctc=1.647, loss=1.647, backward_time=0.009, grad_norm=50.362, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-15 00:44:52,107 (trainer:763) INFO: 24epoch:train:721-760batch: iter_time=4.275e-05, forward_time=0.052, loss_ctc=2.045, loss=2.045, backward_time=0.009, grad_norm=54.127, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399 +[stan] 2024-01-15 00:44:56,034 (trainer:763) INFO: 24epoch:train:761-800batch: iter_time=4.064e-05, forward_time=0.051, loss_ctc=1.867, loss=1.867, backward_time=0.008, grad_norm=55.476, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-15 00:45:00,223 (trainer:354) INFO: 24epoch results: [train] iter_time=1.817e-04, forward_time=0.052, loss_ctc=1.959, loss=1.959, backward_time=0.009, grad_norm=55.107, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396, time=1 minute and 19.37 seconds, total_count=19200, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=37.800, cer_ctc=0.180, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=37.800, time=1.07 seconds, total_count=360, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.04 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:45:01,193 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:45:01,194 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/23epoch.pth +[stan] 2024-01-15 00:45:01,194 (trainer:288) INFO: 25/30epoch started. Estimated time to finish: 8 minutes and 27.63 seconds +[stan] 2024-01-15 00:45:05,494 (trainer:763) INFO: 25epoch:train:1-40batch: iter_time=0.003, forward_time=0.053, loss_ctc=1.873, loss=1.873, backward_time=0.009, grad_norm=56.115, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-15 00:45:09,727 (trainer:763) INFO: 25epoch:train:41-80batch: iter_time=4.834e-05, forward_time=0.055, loss_ctc=2.242, loss=2.242, backward_time=0.009, grad_norm=56.000, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.423 +[stan] 2024-01-15 00:45:13,468 (trainer:763) INFO: 25epoch:train:81-120batch: iter_time=4.232e-05, forward_time=0.049, loss_ctc=1.666, loss=1.666, backward_time=0.008, grad_norm=49.428, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.374 +[stan] 2024-01-15 00:45:17,447 (trainer:763) INFO: 25epoch:train:121-160batch: iter_time=4.251e-05, forward_time=0.052, loss_ctc=2.073, loss=2.073, backward_time=0.009, grad_norm=60.404, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-15 00:45:21,309 (trainer:763) INFO: 25epoch:train:161-200batch: iter_time=4.664e-05, forward_time=0.051, loss_ctc=1.584, loss=1.584, backward_time=0.008, grad_norm=53.404, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.386 +[stan] 2024-01-15 00:45:24,925 (trainer:763) INFO: 25epoch:train:201-240batch: iter_time=4.504e-05, forward_time=0.048, loss_ctc=1.731, loss=1.731, backward_time=0.008, grad_norm=53.760, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.361 +[stan] 2024-01-15 00:45:29,191 (trainer:763) INFO: 25epoch:train:241-280batch: iter_time=4.403e-05, forward_time=0.056, loss_ctc=2.261, loss=2.261, backward_time=0.009, grad_norm=57.678, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-15 00:45:33,282 (trainer:763) INFO: 25epoch:train:281-320batch: iter_time=4.696e-05, forward_time=0.054, loss_ctc=2.174, loss=2.174, backward_time=0.009, grad_norm=56.169, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.409 +[stan] 2024-01-15 00:45:37,172 (trainer:763) INFO: 25epoch:train:321-360batch: iter_time=4.494e-05, forward_time=0.051, loss_ctc=1.869, loss=1.869, backward_time=0.009, grad_norm=52.029, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.389 +[stan] 2024-01-15 00:45:40,757 (trainer:763) INFO: 25epoch:train:361-400batch: iter_time=4.609e-05, forward_time=0.047, loss_ctc=1.579, loss=1.579, backward_time=0.008, grad_norm=52.021, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.358 +[stan] 2024-01-15 00:45:45,064 (trainer:763) INFO: 25epoch:train:401-440batch: iter_time=4.489e-05, forward_time=0.056, loss_ctc=2.198, loss=2.198, backward_time=0.009, grad_norm=56.617, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-15 00:45:49,146 (trainer:763) INFO: 25epoch:train:441-480batch: iter_time=4.493e-05, forward_time=0.053, loss_ctc=1.943, loss=1.943, backward_time=0.009, grad_norm=54.230, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-15 00:45:53,141 (trainer:763) INFO: 25epoch:train:481-520batch: iter_time=4.379e-05, forward_time=0.052, loss_ctc=1.929, loss=1.929, backward_time=0.009, grad_norm=52.017, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399 +[stan] 2024-01-15 00:45:57,281 (trainer:763) INFO: 25epoch:train:521-560batch: iter_time=4.427e-05, forward_time=0.054, loss_ctc=2.188, loss=2.188, backward_time=0.009, grad_norm=54.274, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.414 +[stan] 2024-01-15 00:46:01,038 (trainer:763) INFO: 25epoch:train:561-600batch: iter_time=4.677e-05, forward_time=0.049, loss_ctc=1.517, loss=1.517, backward_time=0.008, grad_norm=48.896, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.376 +[stan] 2024-01-15 00:46:04,883 (trainer:763) INFO: 25epoch:train:601-640batch: iter_time=4.382e-05, forward_time=0.050, loss_ctc=1.816, loss=1.816, backward_time=0.008, grad_norm=51.433, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.384 +[stan] 2024-01-15 00:46:08,599 (trainer:763) INFO: 25epoch:train:641-680batch: iter_time=4.346e-05, forward_time=0.049, loss_ctc=1.622, loss=1.622, backward_time=0.008, grad_norm=48.431, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.371 +[stan] 2024-01-15 00:46:12,925 (trainer:763) INFO: 25epoch:train:681-720batch: iter_time=4.690e-05, forward_time=0.057, loss_ctc=2.253, loss=2.253, backward_time=0.009, grad_norm=56.005, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-15 00:46:16,717 (trainer:763) INFO: 25epoch:train:721-760batch: iter_time=4.709e-05, forward_time=0.050, loss_ctc=1.737, loss=1.737, backward_time=0.008, grad_norm=50.298, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-15 00:46:20,743 (trainer:763) INFO: 25epoch:train:761-800batch: iter_time=4.186e-05, forward_time=0.053, loss_ctc=1.753, loss=1.753, backward_time=0.008, grad_norm=52.793, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-15 00:46:24,946 (trainer:354) INFO: 25epoch results: [train] iter_time=1.793e-04, forward_time=0.052, loss_ctc=1.900, loss=1.900, backward_time=0.009, grad_norm=53.600, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398, time=1 minute and 19.63 seconds, total_count=20000, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=38.596, cer_ctc=0.170, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=38.596, time=1.07 seconds, total_count=375, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:46:25,920 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:46:25,920 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/24epoch.pth +[stan] 2024-01-15 00:46:25,920 (trainer:288) INFO: 26/30epoch started. Estimated time to finish: 7 minutes and 3.05 seconds +[stan] 2024-01-15 00:46:30,399 (trainer:763) INFO: 26epoch:train:1-40batch: iter_time=0.003, forward_time=0.055, loss_ctc=2.135, loss=2.135, backward_time=0.009, grad_norm=59.613, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-15 00:46:34,164 (trainer:763) INFO: 26epoch:train:41-80batch: iter_time=4.374e-05, forward_time=0.049, loss_ctc=1.672, loss=1.672, backward_time=0.008, grad_norm=54.850, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.376 +[stan] 2024-01-15 00:46:38,111 (trainer:763) INFO: 26epoch:train:81-120batch: iter_time=4.307e-05, forward_time=0.052, loss_ctc=1.881, loss=1.881, backward_time=0.009, grad_norm=53.385, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-15 00:46:42,018 (trainer:763) INFO: 26epoch:train:121-160batch: iter_time=4.313e-05, forward_time=0.051, loss_ctc=1.802, loss=1.802, backward_time=0.009, grad_norm=51.147, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-15 00:46:45,766 (trainer:763) INFO: 26epoch:train:161-200batch: iter_time=4.471e-05, forward_time=0.049, loss_ctc=1.635, loss=1.635, backward_time=0.008, grad_norm=51.728, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.375 +[stan] 2024-01-15 00:46:49,882 (trainer:763) INFO: 26epoch:train:201-240batch: iter_time=4.349e-05, forward_time=0.054, loss_ctc=2.117, loss=2.117, backward_time=0.009, grad_norm=58.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.412 +[stan] 2024-01-15 00:46:53,710 (trainer:763) INFO: 26epoch:train:241-280batch: iter_time=4.292e-05, forward_time=0.050, loss_ctc=1.700, loss=1.700, backward_time=0.008, grad_norm=54.238, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-15 00:46:57,745 (trainer:763) INFO: 26epoch:train:281-320batch: iter_time=4.319e-05, forward_time=0.053, loss_ctc=2.090, loss=2.090, backward_time=0.009, grad_norm=59.963, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-15 00:47:01,870 (trainer:763) INFO: 26epoch:train:321-360batch: iter_time=4.529e-05, forward_time=0.054, loss_ctc=2.160, loss=2.160, backward_time=0.009, grad_norm=57.693, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.412 +[stan] 2024-01-15 00:47:05,720 (trainer:763) INFO: 26epoch:train:361-400batch: iter_time=4.507e-05, forward_time=0.050, loss_ctc=1.638, loss=1.638, backward_time=0.008, grad_norm=51.750, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-15 00:47:09,743 (trainer:763) INFO: 26epoch:train:401-440batch: iter_time=4.425e-05, forward_time=0.053, loss_ctc=1.927, loss=1.927, backward_time=0.009, grad_norm=54.699, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:47:13,556 (trainer:763) INFO: 26epoch:train:441-480batch: iter_time=4.363e-05, forward_time=0.050, loss_ctc=1.827, loss=1.827, backward_time=0.008, grad_norm=54.676, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.381 +[stan] 2024-01-15 00:47:17,622 (trainer:763) INFO: 26epoch:train:481-520batch: iter_time=4.377e-05, forward_time=0.053, loss_ctc=1.896, loss=1.896, backward_time=0.009, grad_norm=57.127, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-15 00:47:21,617 (trainer:763) INFO: 26epoch:train:521-560batch: iter_time=4.382e-05, forward_time=0.052, loss_ctc=1.868, loss=1.868, backward_time=0.009, grad_norm=53.829, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399 +[stan] 2024-01-15 00:47:25,599 (trainer:763) INFO: 26epoch:train:561-600batch: iter_time=4.654e-05, forward_time=0.052, loss_ctc=2.081, loss=2.081, backward_time=0.009, grad_norm=57.088, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-15 00:47:29,321 (trainer:763) INFO: 26epoch:train:601-640batch: iter_time=4.372e-05, forward_time=0.049, loss_ctc=1.466, loss=1.466, backward_time=0.008, grad_norm=49.328, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.372 +[stan] 2024-01-15 00:47:33,679 (trainer:763) INFO: 26epoch:train:641-680batch: iter_time=4.445e-05, forward_time=0.057, loss_ctc=2.208, loss=2.208, backward_time=0.009, grad_norm=59.261, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-15 00:47:37,312 (trainer:763) INFO: 26epoch:train:681-720batch: iter_time=4.336e-05, forward_time=0.048, loss_ctc=1.541, loss=1.541, backward_time=0.008, grad_norm=51.117, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.363 +[stan] 2024-01-15 00:47:41,333 (trainer:763) INFO: 26epoch:train:721-760batch: iter_time=4.347e-05, forward_time=0.053, loss_ctc=1.939, loss=1.939, backward_time=0.009, grad_norm=55.027, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:47:45,263 (trainer:763) INFO: 26epoch:train:761-800batch: iter_time=4.523e-05, forward_time=0.051, loss_ctc=1.899, loss=1.899, backward_time=0.008, grad_norm=55.738, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-15 00:47:49,461 (trainer:354) INFO: 26epoch results: [train] iter_time=1.837e-04, forward_time=0.052, loss_ctc=1.874, loss=1.874, backward_time=0.009, grad_norm=55.018, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.42 seconds, total_count=20800, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=39.123, cer_ctc=0.174, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=39.123, time=1.08 seconds, total_count=390, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.04 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:47:50,445 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:47:50,445 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/25epoch.pth +[stan] 2024-01-15 00:47:50,445 (trainer:288) INFO: 27/30epoch started. Estimated time to finish: 5 minutes and 38.43 seconds +[stan] 2024-01-15 00:47:54,768 (trainer:763) INFO: 27epoch:train:1-40batch: iter_time=0.003, forward_time=0.053, loss_ctc=1.957, loss=1.957, backward_time=0.009, grad_norm=54.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-15 00:47:58,925 (trainer:763) INFO: 27epoch:train:41-80batch: iter_time=4.509e-05, forward_time=0.054, loss_ctc=2.237, loss=2.237, backward_time=0.009, grad_norm=55.568, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-15 00:48:02,488 (trainer:763) INFO: 27epoch:train:81-120batch: iter_time=4.473e-05, forward_time=0.047, loss_ctc=1.325, loss=1.325, backward_time=0.008, grad_norm=46.264, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.356 +[stan] 2024-01-15 00:48:06,547 (trainer:763) INFO: 27epoch:train:121-160batch: iter_time=4.580e-05, forward_time=0.053, loss_ctc=1.672, loss=1.672, backward_time=0.009, grad_norm=49.826, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-15 00:48:10,525 (trainer:763) INFO: 27epoch:train:161-200batch: iter_time=4.513e-05, forward_time=0.052, loss_ctc=2.052, loss=2.052, backward_time=0.009, grad_norm=54.061, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-15 00:48:14,021 (trainer:763) INFO: 27epoch:train:201-240batch: iter_time=4.679e-05, forward_time=0.046, loss_ctc=1.246, loss=1.246, backward_time=0.008, grad_norm=44.878, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.349 +[stan] 2024-01-15 00:48:18,491 (trainer:763) INFO: 27epoch:train:241-280batch: iter_time=4.492e-05, forward_time=0.058, loss_ctc=2.322, loss=2.322, backward_time=0.010, grad_norm=61.931, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.447 +[stan] 2024-01-15 00:48:22,359 (trainer:763) INFO: 27epoch:train:281-320batch: iter_time=4.669e-05, forward_time=0.051, loss_ctc=1.858, loss=1.858, backward_time=0.009, grad_norm=59.561, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-15 00:48:26,171 (trainer:763) INFO: 27epoch:train:321-360batch: iter_time=4.689e-05, forward_time=0.050, loss_ctc=1.649, loss=1.649, backward_time=0.008, grad_norm=51.739, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.381 +[stan] 2024-01-15 00:48:30,272 (trainer:763) INFO: 27epoch:train:361-400batch: iter_time=4.347e-05, forward_time=0.054, loss_ctc=1.906, loss=1.906, backward_time=0.008, grad_norm=52.612, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.410 +[stan] 2024-01-15 00:48:34,197 (trainer:763) INFO: 27epoch:train:401-440batch: iter_time=4.481e-05, forward_time=0.051, loss_ctc=1.729, loss=1.729, backward_time=0.009, grad_norm=54.697, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-15 00:48:37,915 (trainer:763) INFO: 27epoch:train:441-480batch: iter_time=4.555e-05, forward_time=0.049, loss_ctc=1.518, loss=1.518, backward_time=0.008, grad_norm=48.710, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.372 +[stan] 2024-01-15 00:48:42,164 (trainer:763) INFO: 27epoch:train:481-520batch: iter_time=4.443e-05, forward_time=0.056, loss_ctc=2.185, loss=2.185, backward_time=0.009, grad_norm=58.129, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-15 00:48:46,190 (trainer:763) INFO: 27epoch:train:521-560batch: iter_time=4.518e-05, forward_time=0.053, loss_ctc=1.834, loss=1.834, backward_time=0.009, grad_norm=55.228, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:48:50,238 (trainer:763) INFO: 27epoch:train:561-600batch: iter_time=4.381e-05, forward_time=0.054, loss_ctc=1.709, loss=1.709, backward_time=0.008, grad_norm=50.384, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.405 +[stan] 2024-01-15 00:48:53,793 (trainer:763) INFO: 27epoch:train:601-640batch: iter_time=4.685e-05, forward_time=0.047, loss_ctc=1.212, loss=1.212, backward_time=0.008, grad_norm=45.627, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.355 +[stan] 2024-01-15 00:48:58,198 (trainer:763) INFO: 27epoch:train:641-680batch: iter_time=4.578e-05, forward_time=0.058, loss_ctc=2.274, loss=2.274, backward_time=0.009, grad_norm=61.493, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.440 +[stan] 2024-01-15 00:49:02,447 (trainer:763) INFO: 27epoch:train:681-720batch: iter_time=4.534e-05, forward_time=0.055, loss_ctc=2.208, loss=2.208, backward_time=0.010, grad_norm=52.596, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-15 00:49:06,208 (trainer:763) INFO: 27epoch:train:721-760batch: iter_time=4.590e-05, forward_time=0.049, loss_ctc=1.445, loss=1.445, backward_time=0.008, grad_norm=51.831, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.376 +[stan] 2024-01-15 00:49:09,983 (trainer:763) INFO: 27epoch:train:761-800batch: iter_time=4.095e-05, forward_time=0.049, loss_ctc=1.661, loss=1.661, backward_time=0.009, grad_norm=53.114, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-15 00:49:14,181 (trainer:354) INFO: 27epoch results: [train] iter_time=1.960e-04, forward_time=0.052, loss_ctc=1.799, loss=1.799, backward_time=0.009, grad_norm=53.119, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398, time=1 minute and 19.62 seconds, total_count=21600, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=38.881, cer_ctc=0.172, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=38.881, time=1.07 seconds, total_count=405, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.04 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:49:15,168 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:49:15,169 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/26epoch.pth +[stan] 2024-01-15 00:49:15,169 (trainer:288) INFO: 28/30epoch started. Estimated time to finish: 4 minutes and 13.83 seconds +[stan] 2024-01-15 00:49:19,593 (trainer:763) INFO: 28epoch:train:1-40batch: iter_time=0.003, forward_time=0.055, loss_ctc=2.109, loss=2.109, backward_time=0.009, grad_norm=56.002, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.442 +[stan] 2024-01-15 00:49:23,144 (trainer:763) INFO: 28epoch:train:41-80batch: iter_time=4.249e-05, forward_time=0.047, loss_ctc=1.302, loss=1.302, backward_time=0.008, grad_norm=49.269, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.355 +[stan] 2024-01-15 00:49:26,690 (trainer:763) INFO: 28epoch:train:81-120batch: iter_time=4.188e-05, forward_time=0.047, loss_ctc=1.223, loss=1.223, backward_time=0.008, grad_norm=46.735, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.355 +[stan] 2024-01-15 00:49:31,153 (trainer:763) INFO: 28epoch:train:121-160batch: iter_time=4.349e-05, forward_time=0.058, loss_ctc=2.321, loss=2.321, backward_time=0.010, grad_norm=63.337, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-15 00:49:35,089 (trainer:763) INFO: 28epoch:train:161-200batch: iter_time=4.288e-05, forward_time=0.051, loss_ctc=1.690, loss=1.690, backward_time=0.009, grad_norm=53.432, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-15 00:49:39,111 (trainer:763) INFO: 28epoch:train:201-240batch: iter_time=4.310e-05, forward_time=0.053, loss_ctc=1.881, loss=1.881, backward_time=0.009, grad_norm=51.881, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:49:43,348 (trainer:763) INFO: 28epoch:train:241-280batch: iter_time=4.461e-05, forward_time=0.055, loss_ctc=2.220, loss=2.220, backward_time=0.009, grad_norm=59.734, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-15 00:49:46,957 (trainer:763) INFO: 28epoch:train:281-320batch: iter_time=4.672e-05, forward_time=0.047, loss_ctc=1.445, loss=1.445, backward_time=0.008, grad_norm=48.978, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.361 +[stan] 2024-01-15 00:49:50,689 (trainer:763) INFO: 28epoch:train:321-360batch: iter_time=4.283e-05, forward_time=0.049, loss_ctc=1.486, loss=1.486, backward_time=0.008, grad_norm=48.724, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.373 +[stan] 2024-01-15 00:49:55,009 (trainer:763) INFO: 28epoch:train:361-400batch: iter_time=4.380e-05, forward_time=0.056, loss_ctc=2.205, loss=2.205, backward_time=0.010, grad_norm=58.028, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-15 00:49:58,827 (trainer:763) INFO: 28epoch:train:401-440batch: iter_time=4.422e-05, forward_time=0.050, loss_ctc=1.471, loss=1.471, backward_time=0.008, grad_norm=49.620, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.382 +[stan] 2024-01-15 00:50:02,864 (trainer:763) INFO: 28epoch:train:441-480batch: iter_time=4.513e-05, forward_time=0.053, loss_ctc=1.764, loss=1.764, backward_time=0.009, grad_norm=52.930, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-15 00:50:06,724 (trainer:763) INFO: 28epoch:train:481-520batch: iter_time=4.226e-05, forward_time=0.051, loss_ctc=1.660, loss=1.660, backward_time=0.009, grad_norm=50.150, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.386 +[stan] 2024-01-15 00:50:10,676 (trainer:763) INFO: 28epoch:train:521-560batch: iter_time=4.539e-05, forward_time=0.052, loss_ctc=1.815, loss=1.815, backward_time=0.009, grad_norm=53.713, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-15 00:50:14,669 (trainer:763) INFO: 28epoch:train:561-600batch: iter_time=4.420e-05, forward_time=0.052, loss_ctc=1.761, loss=1.761, backward_time=0.009, grad_norm=52.421, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399 +[stan] 2024-01-15 00:50:18,704 (trainer:763) INFO: 28epoch:train:601-640batch: iter_time=4.816e-05, forward_time=0.053, loss_ctc=1.785, loss=1.785, backward_time=0.009, grad_norm=56.300, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-15 00:50:22,647 (trainer:763) INFO: 28epoch:train:641-680batch: iter_time=4.627e-05, forward_time=0.052, loss_ctc=1.725, loss=1.725, backward_time=0.009, grad_norm=51.584, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-15 00:50:26,455 (trainer:763) INFO: 28epoch:train:681-720batch: iter_time=4.501e-05, forward_time=0.050, loss_ctc=1.663, loss=1.663, backward_time=0.009, grad_norm=50.057, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.381 +[stan] 2024-01-15 00:50:30,612 (trainer:763) INFO: 28epoch:train:721-760batch: iter_time=4.478e-05, forward_time=0.054, loss_ctc=1.763, loss=1.763, backward_time=0.009, grad_norm=52.911, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-15 00:50:34,573 (trainer:763) INFO: 28epoch:train:761-800batch: iter_time=4.239e-05, forward_time=0.052, loss_ctc=1.667, loss=1.667, backward_time=0.009, grad_norm=49.208, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-15 00:50:38,768 (trainer:354) INFO: 28epoch results: [train] iter_time=1.835e-04, forward_time=0.052, loss_ctc=1.748, loss=1.748, backward_time=0.009, grad_norm=52.751, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.48 seconds, total_count=22400, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=39.351, cer_ctc=0.170, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=39.351, time=1.09 seconds, total_count=420, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.03 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:50:39,760 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:50:39,760 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/27epoch.pth +[stan] 2024-01-15 00:50:39,760 (trainer:288) INFO: 29/30epoch started. Estimated time to finish: 2 minutes and 49.22 seconds +[stan] 2024-01-15 00:50:44,062 (trainer:763) INFO: 29epoch:train:1-40batch: iter_time=0.003, forward_time=0.053, loss_ctc=1.809, loss=1.809, backward_time=0.009, grad_norm=53.589, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-15 00:50:48,095 (trainer:763) INFO: 29epoch:train:41-80batch: iter_time=4.750e-05, forward_time=0.053, loss_ctc=1.798, loss=1.798, backward_time=0.009, grad_norm=53.693, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-15 00:50:51,951 (trainer:763) INFO: 29epoch:train:81-120batch: iter_time=4.383e-05, forward_time=0.052, loss_ctc=1.427, loss=1.427, backward_time=0.009, grad_norm=46.552, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-15 00:50:56,033 (trainer:763) INFO: 29epoch:train:121-160batch: iter_time=4.317e-05, forward_time=0.053, loss_ctc=1.756, loss=1.756, backward_time=0.009, grad_norm=54.003, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-15 00:50:59,987 (trainer:763) INFO: 29epoch:train:161-200batch: iter_time=4.312e-05, forward_time=0.052, loss_ctc=1.711, loss=1.711, backward_time=0.009, grad_norm=53.426, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-15 00:51:03,833 (trainer:763) INFO: 29epoch:train:201-240batch: iter_time=4.451e-05, forward_time=0.050, loss_ctc=1.747, loss=1.747, backward_time=0.009, grad_norm=50.451, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-15 00:51:07,814 (trainer:763) INFO: 29epoch:train:241-280batch: iter_time=4.231e-05, forward_time=0.052, loss_ctc=1.662, loss=1.662, backward_time=0.008, grad_norm=49.460, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-15 00:51:11,734 (trainer:763) INFO: 29epoch:train:281-320batch: iter_time=4.765e-05, forward_time=0.051, loss_ctc=1.682, loss=1.682, backward_time=0.009, grad_norm=54.623, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-15 00:51:15,775 (trainer:763) INFO: 29epoch:train:321-360batch: iter_time=4.581e-05, forward_time=0.053, loss_ctc=2.075, loss=2.075, backward_time=0.009, grad_norm=55.592, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-15 00:51:19,503 (trainer:763) INFO: 29epoch:train:361-400batch: iter_time=4.492e-05, forward_time=0.049, loss_ctc=1.379, loss=1.379, backward_time=0.008, grad_norm=48.649, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.373 +[stan] 2024-01-15 00:51:23,639 (trainer:763) INFO: 29epoch:train:401-440batch: iter_time=4.406e-05, forward_time=0.054, loss_ctc=2.079, loss=2.079, backward_time=0.009, grad_norm=59.433, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.413 +[stan] 2024-01-15 00:51:27,878 (trainer:763) INFO: 29epoch:train:441-480batch: iter_time=4.441e-05, forward_time=0.055, loss_ctc=2.090, loss=2.090, backward_time=0.009, grad_norm=57.194, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-15 00:51:31,626 (trainer:763) INFO: 29epoch:train:481-520batch: iter_time=4.309e-05, forward_time=0.049, loss_ctc=1.440, loss=1.440, backward_time=0.008, grad_norm=52.212, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.375 +[stan] 2024-01-15 00:51:35,393 (trainer:763) INFO: 29epoch:train:521-560batch: iter_time=4.580e-05, forward_time=0.049, loss_ctc=1.575, loss=1.575, backward_time=0.009, grad_norm=48.842, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-15 00:51:39,238 (trainer:763) INFO: 29epoch:train:561-600batch: iter_time=4.349e-05, forward_time=0.050, loss_ctc=1.609, loss=1.609, backward_time=0.008, grad_norm=51.856, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.384 +[stan] 2024-01-15 00:51:43,274 (trainer:763) INFO: 29epoch:train:601-640batch: iter_time=4.302e-05, forward_time=0.053, loss_ctc=1.646, loss=1.646, backward_time=0.008, grad_norm=51.886, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-15 00:51:47,293 (trainer:763) INFO: 29epoch:train:641-680batch: iter_time=4.446e-05, forward_time=0.053, loss_ctc=1.747, loss=1.747, backward_time=0.009, grad_norm=54.253, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:51:51,324 (trainer:763) INFO: 29epoch:train:681-720batch: iter_time=4.759e-05, forward_time=0.053, loss_ctc=1.778, loss=1.778, backward_time=0.009, grad_norm=50.331, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-15 00:51:55,340 (trainer:763) INFO: 29epoch:train:721-760batch: iter_time=4.439e-05, forward_time=0.053, loss_ctc=1.798, loss=1.798, backward_time=0.009, grad_norm=51.921, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-15 00:51:59,284 (trainer:763) INFO: 29epoch:train:761-800batch: iter_time=4.080e-05, forward_time=0.052, loss_ctc=1.658, loss=1.658, backward_time=0.009, grad_norm=52.493, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-15 00:52:03,461 (trainer:354) INFO: 29epoch results: [train] iter_time=1.892e-04, forward_time=0.052, loss_ctc=1.723, loss=1.723, backward_time=0.009, grad_norm=52.523, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398, time=1 minute and 19.6 seconds, total_count=23200, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=39.929, cer_ctc=0.164, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=39.929, time=1.07 seconds, total_count=435, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.03 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:52:04,419 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:52:04,420 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/28epoch.pth +[stan] 2024-01-15 00:52:04,420 (trainer:288) INFO: 30/30epoch started. Estimated time to finish: 1 minute and 24.61 seconds +[stan] 2024-01-15 00:52:08,662 (trainer:763) INFO: 30epoch:train:1-40batch: iter_time=0.003, forward_time=0.052, loss_ctc=1.787, loss=1.787, backward_time=0.009, grad_norm=51.197, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-15 00:52:12,701 (trainer:763) INFO: 30epoch:train:41-80batch: iter_time=4.649e-05, forward_time=0.053, loss_ctc=1.704, loss=1.704, backward_time=0.009, grad_norm=50.406, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-15 00:52:16,766 (trainer:763) INFO: 30epoch:train:81-120batch: iter_time=4.254e-05, forward_time=0.053, loss_ctc=1.863, loss=1.863, backward_time=0.009, grad_norm=53.794, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-15 00:52:20,359 (trainer:763) INFO: 30epoch:train:121-160batch: iter_time=4.222e-05, forward_time=0.047, loss_ctc=1.339, loss=1.339, backward_time=0.008, grad_norm=49.133, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.359 +[stan] 2024-01-15 00:52:24,405 (trainer:763) INFO: 30epoch:train:161-200batch: iter_time=4.308e-05, forward_time=0.053, loss_ctc=1.889, loss=1.889, backward_time=0.009, grad_norm=55.493, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.405 +[stan] 2024-01-15 00:52:28,342 (trainer:763) INFO: 30epoch:train:201-240batch: iter_time=4.285e-05, forward_time=0.052, loss_ctc=1.631, loss=1.631, backward_time=0.008, grad_norm=51.763, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-15 00:52:32,247 (trainer:763) INFO: 30epoch:train:241-280batch: iter_time=4.357e-05, forward_time=0.051, loss_ctc=1.507, loss=1.507, backward_time=0.009, grad_norm=52.268, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.390 +[stan] 2024-01-15 00:52:36,259 (trainer:763) INFO: 30epoch:train:281-320batch: iter_time=4.304e-05, forward_time=0.052, loss_ctc=1.831, loss=1.831, backward_time=0.009, grad_norm=51.800, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-15 00:52:40,108 (trainer:763) INFO: 30epoch:train:321-360batch: iter_time=4.464e-05, forward_time=0.050, loss_ctc=1.462, loss=1.462, backward_time=0.008, grad_norm=49.757, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-15 00:52:44,145 (trainer:763) INFO: 30epoch:train:361-400batch: iter_time=4.319e-05, forward_time=0.053, loss_ctc=1.694, loss=1.694, backward_time=0.009, grad_norm=52.162, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-15 00:52:48,046 (trainer:763) INFO: 30epoch:train:401-440batch: iter_time=4.557e-05, forward_time=0.051, loss_ctc=1.731, loss=1.731, backward_time=0.009, grad_norm=54.192, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.390 +[stan] 2024-01-15 00:52:51,987 (trainer:763) INFO: 30epoch:train:441-480batch: iter_time=4.335e-05, forward_time=0.052, loss_ctc=1.579, loss=1.579, backward_time=0.008, grad_norm=51.035, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-15 00:52:55,859 (trainer:763) INFO: 30epoch:train:481-520batch: iter_time=4.497e-05, forward_time=0.051, loss_ctc=1.661, loss=1.661, backward_time=0.009, grad_norm=54.466, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-15 00:53:00,044 (trainer:763) INFO: 30epoch:train:521-560batch: iter_time=4.285e-05, forward_time=0.055, loss_ctc=1.879, loss=1.879, backward_time=0.009, grad_norm=52.799, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.418 +[stan] 2024-01-15 00:53:04,236 (trainer:763) INFO: 30epoch:train:561-600batch: iter_time=4.438e-05, forward_time=0.055, loss_ctc=1.948, loss=1.948, backward_time=0.009, grad_norm=56.917, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.419 +[stan] 2024-01-15 00:53:08,051 (trainer:763) INFO: 30epoch:train:601-640batch: iter_time=4.584e-05, forward_time=0.050, loss_ctc=1.548, loss=1.548, backward_time=0.009, grad_norm=51.606, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.381 +[stan] 2024-01-15 00:53:11,979 (trainer:763) INFO: 30epoch:train:641-680batch: iter_time=4.781e-05, forward_time=0.051, loss_ctc=1.729, loss=1.729, backward_time=0.008, grad_norm=54.101, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-15 00:53:15,972 (trainer:763) INFO: 30epoch:train:681-720batch: iter_time=4.218e-05, forward_time=0.052, loss_ctc=1.590, loss=1.590, backward_time=0.008, grad_norm=52.927, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399 +[stan] 2024-01-15 00:53:19,664 (trainer:763) INFO: 30epoch:train:721-760batch: iter_time=4.261e-05, forward_time=0.048, loss_ctc=1.330, loss=1.330, backward_time=0.009, grad_norm=44.811, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.369 +[stan] 2024-01-15 00:53:23,667 (trainer:763) INFO: 30epoch:train:761-800batch: iter_time=4.071e-05, forward_time=0.052, loss_ctc=1.644, loss=1.644, backward_time=0.009, grad_norm=51.331, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-15 00:53:27,847 (trainer:354) INFO: 30epoch results: [train] iter_time=1.985e-04, forward_time=0.052, loss_ctc=1.667, loss=1.667, backward_time=0.009, grad_norm=52.098, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396, time=1 minute and 19.32 seconds, total_count=24000, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=39.688, cer_ctc=0.167, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=39.688, time=1.08 seconds, total_count=450, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.02 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-15 00:53:28,897 (trainer:415) INFO: There are no improvements in this epoch +[stan] 2024-01-15 00:53:28,897 (trainer:471) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/29epoch.pth +[stan] 2024-01-15 00:53:28,897 (trainer:489) INFO: The training was finished at 30 epochs +[stan] 2024-01-15 00:53:28,913 (average_nbest_models:69) INFO: Averaging 5best models: criterion="valid.loss": test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave_5best.pth +# Accounting: time=2543 threads=1 +# Ended (code 0) at Mon Jan 15 00:53:29 CST 2024, elapsed time 2543 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/train.log b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/train.log new file mode 100644 index 0000000000000000000000000000000000000000..8dd03e984abceb278a422e0e4b4936c3e72afc6c --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/train.log @@ -0,0 +1,1406 @@ +# python3 -m espnet2.bin.asr_train --use_preprocessor true --bpemodel none --token_type word --token_list data/jpn_token_list/word/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_jpn/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_jpn_1h/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_jpn/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_jpn_1h/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_1h_jpn/text,text,text --train_shape_file test_pr/asr_stats_jpn_1h/train/text_shape.word --valid_data_path_and_name_and_type dump/raw/dev_10min_jpn/text,text,text --valid_shape_file test_pr/asr_stats_jpn_1h/valid/text_shape.word --ngpu 1 --multiprocessing_distributed True +# Started at Wed Jan 17 01:57:30 CST 2024 +# +/home/stan/miniconda3/envs/espnet/bin/python3 /home/stan/Desktop/espnet/espnet2/bin/asr_train.py --use_preprocessor true --bpemodel none --token_type word --token_list data/jpn_token_list/word/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/dev_10min_jpn/wav.scp,speech,sound --valid_shape_file test_pr/asr_stats_jpn_1h/valid/speech_shape --resume true --ignore_init_mismatch false --fold_length 80000 --output_dir test_pr/asr_train_asr_s3prl_houlsby_jpn_1h --config conf/tuning/train_asr_s3prl_houlsby.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_jpn/wav.scp,speech,sound --train_shape_file test_pr/asr_stats_jpn_1h/train/speech_shape --fold_length 150 --train_data_path_and_name_and_type dump/raw/train_1h_jpn/text,text,text --train_shape_file test_pr/asr_stats_jpn_1h/train/text_shape.word --valid_data_path_and_name_and_type dump/raw/dev_10min_jpn/text,text,text --valid_shape_file test_pr/asr_stats_jpn_1h/valid/text_shape.word --ngpu 1 --multiprocessing_distributed True +[stan] 2024-01-17 01:57:32,019 (asr:523) INFO: Vocabulary size: 41 +[stan] 2024-01-17 01:57:32,082 (download:170) INFO: Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/hubert_base_ls960.pt +[stan] 2024-01-17 01:57:32,082 (download:181) INFO: Using URL's local file: hub/cc1d9e25da3db01e710fe51bbcc725322be55e3d6eebc947f0aa4743e81bfc34.hubert_base_ls960.pt +[stan] 2024-01-17 01:57:32,194 (hubert_model:289) INFO: HubertModel Config: HubertConfig(label_rate=50.0, extractor_mode='default', encoder_layers=12, encoder_embed_dim=768, encoder_ffn_embed_dim=3072, encoder_attention_heads=12, activation_fn='gelu', layer_type='transformer', dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, encoder_layerdrop=0.05, dropout_input=0.1, dropout_features=0.1, final_dim=256, untie_final_proj=False, layer_norm_first=False, conv_feature_layers='[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', conv_bias=False, logit_temp=0.1, target_glu=False, feature_grad_mult=0.1, mask_length=10, mask_prob=0.8, mask_selection='static', mask_other=0.0, no_mask_overlap=False, mask_min_space=1, mask_channel_length=10, mask_channel_prob=0.0, mask_channel_selection='static', mask_channel_other=0.0, no_mask_channel_overlap=False, mask_channel_min_space=1, conv_pos=128, conv_pos_groups=16, latent_temp=[2.0, 0.5, 0.999995], skip_masked=False, skip_nomask=False, checkpoint_activations=False, required_seq_len_multiple=2, depthwise_conv_kernel_size=31, attn_type='', pos_enc_type='abs', fp16=True) +[stan] 2024-01-17 01:57:33,500 (espnet_model:169) WARNING: Set decoder to none as ctc_weight==1.0 +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.mask_emb.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.label_embs_concat.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.0.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.0.2.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.1.0.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.2.0.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.3.0.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.4.0.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.5.0.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.feature_extractor.conv_layers.6.0.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.post_extract_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_g.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.pos_conv.0.weight_v.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.0.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.1.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.2.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,336 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.3.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.4.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.5.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.6.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.7.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.8.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,337 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.9.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.10.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.k_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.v_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.q_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn.out_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.self_attn_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc1.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.fc2.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layers.11.final_layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.encoder.layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.layer_norm.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.weight.requires_grad = False +[stan] 2024-01-17 01:57:34,338 (abs_task:1247) INFO: Setting frontend.upstream.upstream.model.final_proj.bias.requires_grad = False +[stan] 2024-01-17 01:57:34,745 (abs_task:1275) INFO: pytorch.version=1.12.0+cu113, cuda.available=True, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True +[stan] 2024-01-17 01:57:34,747 (abs_task:1276) INFO: Model structure: +ESPnetASRModel( + (frontend): S3prlFrontend( + (upstream): S3PRLUpstream( + (upstream): UpstreamExpert( + (model): HubertModel( + (feature_extractor): ConvFeatureExtractionModel( + (conv_layers): ModuleList( + (0): Sequential( + (0): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): Fp32GroupNorm(512, 512, eps=1e-05, affine=True) + (3): GELU(approximate=none) + ) + (1): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (2): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (3): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (4): Sequential( + (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (5): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + (6): Sequential( + (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) + (1): Dropout(p=0.0, inplace=False) + (2): GELU(approximate=none) + ) + ) + ) + (post_extract_proj): Linear(in_features=512, out_features=768, bias=True) + (dropout_input): Dropout(p=0.1, inplace=False) + (dropout_features): Dropout(p=0.1, inplace=False) + (encoder): TransformerEncoder( + (pos_conv): Sequential( + (0): Conv1d(768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16) + (1): SamePad() + (2): GELU(approximate=none) + ) + (layers): ModuleList( + (0): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (1): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (2): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (3): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (4): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (5): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (6): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (7): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (8): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (9): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (10): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + (11): HoulsbyTransformerSentenceEncoderLayer( + (self_attn): MultiheadAttention( + (dropout_module): FairseqDropout() + (k_proj): Linear(in_features=768, out_features=768, bias=True) + (v_proj): Linear(in_features=768, out_features=768, bias=True) + (q_proj): Linear(in_features=768, out_features=768, bias=True) + (out_proj): Linear(in_features=768, out_features=768, bias=True) + ) + (dropout1): Dropout(p=0.1, inplace=False) + (dropout2): Dropout(p=0.0, inplace=False) + (dropout3): Dropout(p=0.1, inplace=False) + (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (fc1): Linear(in_features=768, out_features=3072, bias=True) + (fc2): Linear(in_features=3072, out_features=768, bias=True) + (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + (adapter): Houlsby_Adapter( + (houlsby_adapter): Sequential( + (0): Linear(in_features=768, out_features=32, bias=True) + (1): GELU(approximate=none) + (2): Linear(in_features=32, out_features=768, bias=True) + ) + ) + ) + ) + (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) + ) + (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) + (final_proj): Linear(in_features=768, out_features=256, bias=True) + ) + ) + ) + (featurizer): Featurizer() + ) + (specaug): SpecAug( + (time_warp): TimeWarp(window=5, mode=bicubic) + (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq) + (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time) + ) + (normalize): UtteranceMVN(norm_means=True, norm_vars=False) + (preencoder): LinearProjection( + (linear_out): Linear(in_features=768, out_features=80, bias=True) + (dropout): Dropout(p=0.0, inplace=False) + ) + (encoder): TransformerEncoder( + (embed): Conv2dSubsampling2( + (conv): Sequential( + (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU() + (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) + (3): ReLU() + ) + (out): Sequential( + (0): Linear(in_features=9472, out_features=256, bias=True) + (1): PositionalEncoding( + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + ) + (encoders): MultiSequential( + (0): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (1): EncoderLayer( + (self_attn): MultiHeadedAttention( + (linear_q): Linear(in_features=256, out_features=256, bias=True) + (linear_k): Linear(in_features=256, out_features=256, bias=True) + (linear_v): Linear(in_features=256, out_features=256, bias=True) + (linear_out): Linear(in_features=256, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + (feed_forward): PositionwiseFeedForward( + (w_1): Linear(in_features=256, out_features=1024, bias=True) + (w_2): Linear(in_features=1024, out_features=256, bias=True) + (dropout): Dropout(p=0.1, inplace=False) + (activation): ReLU() + ) + (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + (dropout): Dropout(p=0.1, inplace=False) + ) + ) + (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True) + ) + (criterion_att): LabelSmoothingLoss( + (criterion): KLDivLoss() + ) + (ctc): CTC( + (ctc_lo): Linear(in_features=256, out_features=41, bias=True) + (ctc_loss): CTCLoss() + ) +) + +Model summary: + Class Name: ESPnetASRModel + Total Number of model parameters: 99.97 M + Number of trainable parameters: 5.27 M (5.3%) + Size: 21.08 MB + Type: torch.float32 +[stan] 2024-01-17 01:57:34,747 (abs_task:1279) INFO: Optimizer: +Adam ( +Parameter Group 0 + amsgrad: False + betas: (0.9, 0.999) + capturable: False + eps: 1e-08 + foreach: None + lr: 0.0001 + maximize: False + weight_decay: 1e-06 +) +[stan] 2024-01-17 01:57:34,747 (abs_task:1280) INFO: Scheduler: None +[stan] 2024-01-17 01:57:34,747 (abs_task:1289) INFO: Saving the configuration in test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/config.yaml +[stan] 2024-01-17 01:57:34,900 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-17 01:57:34,943 (abs_task:1665) INFO: [train] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/train_1h_jpn/wav.scp", "type": "sound"} + text: {"path": "dump/raw/train_1h_jpn/text", "type": "text"} + preprocess: ) +[stan] 2024-01-17 01:57:34,943 (abs_task:1666) INFO: [train] Batch sampler: SortedBatchSampler(N-batch=84, batch_size=8, shape_file=test_pr/asr_stats_jpn_1h/train/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-17 01:57:34,943 (abs_task:1667) INFO: [train] mini-batch sizes summary: N-batch=84, mean=8.0, min=8, max=9 +[stan] 2024-01-17 01:57:34,954 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-17 01:57:34,955 (abs_task:1665) INFO: [valid] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_jpn/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_jpn/text", "type": "text"} + preprocess: ) +[stan] 2024-01-17 01:57:34,955 (abs_task:1666) INFO: [valid] Batch sampler: SortedBatchSampler(N-batch=15, batch_size=8, shape_file=test_pr/asr_stats_jpn_1h/valid/speech_shape, sort_in_batch=descending, sort_batch=descending) +[stan] 2024-01-17 01:57:34,955 (abs_task:1667) INFO: [valid] mini-batch sizes summary: N-batch=15, mean=8.4, min=8, max=9 +[stan] 2024-01-17 01:57:34,956 (asr:494) INFO: Optional Data Names: ('text_spk2', 'text_spk3', 'text_spk4', 'prompt') +[stan] 2024-01-17 01:57:34,966 (abs_task:1665) INFO: [plot_att] dataset: +ESPnetDataset( + speech: {"path": "dump/raw/dev_10min_jpn/wav.scp", "type": "sound"} + text: {"path": "dump/raw/dev_10min_jpn/text", "type": "text"} + preprocess: ) +[stan] 2024-01-17 01:57:34,966 (abs_task:1666) INFO: [plot_att] Batch sampler: UnsortedBatchSampler(N-batch=126, batch_size=1, key_file=test_pr/asr_stats_jpn_1h/valid/speech_shape, +[stan] 2024-01-17 01:57:34,966 (abs_task:1667) INFO: [plot_att] mini-batch sizes summary: N-batch=3, mean=1.0, min=1, max=1 +[stan] 2024-01-17 01:57:34,999 (trainer:303) INFO: 1/30epoch started +[stan] 2024-01-17 01:57:40,558 (trainer:762) INFO: 1epoch:train:1-40batch: iter_time=0.002, forward_time=0.083, loss_ctc=42.996, loss=42.996, backward_time=0.011, grad_norm=680.913, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.552 +[stan] 2024-01-17 01:57:44,494 (trainer:762) INFO: 1epoch:train:41-80batch: iter_time=4.475e-05, forward_time=0.051, loss_ctc=32.650, loss=32.650, backward_time=0.008, grad_norm=160.543, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-17 01:57:47,824 (trainer:762) INFO: 1epoch:train:81-120batch: iter_time=4.259e-05, forward_time=0.044, loss_ctc=27.139, loss=27.139, backward_time=0.007, grad_norm=127.116, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.333 +[stan] 2024-01-17 01:57:52,158 (trainer:762) INFO: 1epoch:train:121-160batch: iter_time=4.643e-05, forward_time=0.057, loss_ctc=36.296, loss=36.296, backward_time=0.009, grad_norm=112.859, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-17 01:57:56,119 (trainer:762) INFO: 1epoch:train:161-200batch: iter_time=4.692e-05, forward_time=0.052, loss_ctc=33.045, loss=33.045, backward_time=0.008, grad_norm=120.566, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-17 01:58:00,128 (trainer:762) INFO: 1epoch:train:201-240batch: iter_time=4.189e-05, forward_time=0.052, loss_ctc=33.086, loss=33.086, backward_time=0.008, grad_norm=88.687, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-17 01:58:04,038 (trainer:762) INFO: 1epoch:train:241-280batch: iter_time=4.384e-05, forward_time=0.051, loss_ctc=31.401, loss=31.401, backward_time=0.008, grad_norm=86.943, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-17 01:58:07,776 (trainer:762) INFO: 1epoch:train:281-320batch: iter_time=4.250e-05, forward_time=0.049, loss_ctc=26.744, loss=26.744, backward_time=0.008, grad_norm=114.411, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.374 +[stan] 2024-01-17 01:58:11,945 (trainer:762) INFO: 1epoch:train:321-360batch: iter_time=4.442e-05, forward_time=0.055, loss_ctc=25.590, loss=25.590, backward_time=0.009, grad_norm=106.702, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-17 01:58:15,894 (trainer:762) INFO: 1epoch:train:361-400batch: iter_time=4.877e-05, forward_time=0.052, loss_ctc=20.398, loss=20.398, backward_time=0.008, grad_norm=88.878, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-17 01:58:19,777 (trainer:762) INFO: 1epoch:train:401-440batch: iter_time=4.249e-05, forward_time=0.051, loss_ctc=17.992, loss=17.992, backward_time=0.008, grad_norm=130.126, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-17 01:58:23,783 (trainer:762) INFO: 1epoch:train:441-480batch: iter_time=4.659e-05, forward_time=0.052, loss_ctc=17.739, loss=17.739, backward_time=0.008, grad_norm=162.952, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-17 01:58:27,658 (trainer:762) INFO: 1epoch:train:481-520batch: iter_time=4.257e-05, forward_time=0.051, loss_ctc=15.899, loss=15.899, backward_time=0.008, grad_norm=127.991, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-17 01:58:31,620 (trainer:762) INFO: 1epoch:train:521-560batch: iter_time=4.252e-05, forward_time=0.052, loss_ctc=14.876, loss=14.876, backward_time=0.008, grad_norm=80.731, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-17 01:58:35,639 (trainer:762) INFO: 1epoch:train:561-600batch: iter_time=4.878e-05, forward_time=0.053, loss_ctc=14.626, loss=14.626, backward_time=0.008, grad_norm=106.659, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-17 01:58:39,562 (trainer:762) INFO: 1epoch:train:601-640batch: iter_time=4.290e-05, forward_time=0.051, loss_ctc=13.565, loss=13.565, backward_time=0.008, grad_norm=105.005, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 01:58:43,480 (trainer:762) INFO: 1epoch:train:641-680batch: iter_time=4.380e-05, forward_time=0.051, loss_ctc=12.362, loss=12.362, backward_time=0.008, grad_norm=117.876, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 01:58:47,414 (trainer:762) INFO: 1epoch:train:681-720batch: iter_time=4.504e-05, forward_time=0.051, loss_ctc=11.607, loss=11.607, backward_time=0.008, grad_norm=77.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-17 01:58:51,330 (trainer:762) INFO: 1epoch:train:721-760batch: iter_time=4.341e-05, forward_time=0.051, loss_ctc=12.076, loss=12.076, backward_time=0.008, grad_norm=96.979, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 01:58:55,501 (trainer:762) INFO: 1epoch:train:761-800batch: iter_time=4.341e-05, forward_time=0.055, loss_ctc=12.540, loss=12.540, backward_time=0.008, grad_norm=124.477, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-17 01:59:00,070 (trainer:357) INFO: 1epoch results: [train] iter_time=1.561e-04, forward_time=0.053, loss_ctc=22.632, loss=22.632, backward_time=0.008, grad_norm=140.878, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402, time=1 minute and 20.54 seconds, total_count=800, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=27.207, cer_ctc=0.216, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=27.207, time=1.07 seconds, total_count=15, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.45 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 01:59:01,121 (trainer:416) INFO: The best model has been updated: valid.loss +[stan] 2024-01-17 01:59:01,121 (trainer:291) INFO: 2/30epoch started. Estimated time to finish: 41 minutes and 37.55 seconds +[stan] 2024-01-17 01:59:05,154 (trainer:762) INFO: 2epoch:train:1-40batch: iter_time=0.003, forward_time=0.050, loss_ctc=10.648, loss=10.648, backward_time=0.009, grad_norm=125.522, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-17 01:59:09,169 (trainer:762) INFO: 2epoch:train:41-80batch: iter_time=4.460e-05, forward_time=0.053, loss_ctc=11.380, loss=11.380, backward_time=0.009, grad_norm=83.276, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-17 01:59:12,991 (trainer:762) INFO: 2epoch:train:81-120batch: iter_time=4.322e-05, forward_time=0.050, loss_ctc=10.274, loss=10.274, backward_time=0.008, grad_norm=83.722, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.382 +[stan] 2024-01-17 01:59:16,789 (trainer:762) INFO: 2epoch:train:121-160batch: iter_time=4.381e-05, forward_time=0.050, loss_ctc=9.737, loss=9.737, backward_time=0.008, grad_norm=100.241, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.380 +[stan] 2024-01-17 01:59:21,101 (trainer:762) INFO: 2epoch:train:161-200batch: iter_time=4.333e-05, forward_time=0.056, loss_ctc=11.696, loss=11.696, backward_time=0.010, grad_norm=115.477, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-17 01:59:24,825 (trainer:762) INFO: 2epoch:train:201-240batch: iter_time=4.437e-05, forward_time=0.049, loss_ctc=9.337, loss=9.337, backward_time=0.008, grad_norm=125.147, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.372 +[stan] 2024-01-17 01:59:28,736 (trainer:762) INFO: 2epoch:train:241-280batch: iter_time=4.489e-05, forward_time=0.051, loss_ctc=10.001, loss=10.001, backward_time=0.008, grad_norm=101.130, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-17 01:59:32,825 (trainer:762) INFO: 2epoch:train:281-320batch: iter_time=4.460e-05, forward_time=0.053, loss_ctc=10.463, loss=10.463, backward_time=0.009, grad_norm=99.327, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.409 +[stan] 2024-01-17 01:59:36,900 (trainer:762) INFO: 2epoch:train:321-360batch: iter_time=4.447e-05, forward_time=0.053, loss_ctc=10.082, loss=10.082, backward_time=0.009, grad_norm=115.750, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.407 +[stan] 2024-01-17 01:59:40,618 (trainer:762) INFO: 2epoch:train:361-400batch: iter_time=4.345e-05, forward_time=0.049, loss_ctc=8.726, loss=8.726, backward_time=0.008, grad_norm=98.732, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.372 +[stan] 2024-01-17 01:59:44,569 (trainer:762) INFO: 2epoch:train:401-440batch: iter_time=4.567e-05, forward_time=0.052, loss_ctc=9.639, loss=9.639, backward_time=0.009, grad_norm=88.018, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-17 01:59:48,527 (trainer:762) INFO: 2epoch:train:441-480batch: iter_time=4.530e-05, forward_time=0.052, loss_ctc=9.396, loss=9.396, backward_time=0.009, grad_norm=86.141, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-17 01:59:52,457 (trainer:762) INFO: 2epoch:train:481-520batch: iter_time=4.436e-05, forward_time=0.051, loss_ctc=9.310, loss=9.310, backward_time=0.009, grad_norm=72.767, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-17 01:59:56,791 (trainer:762) INFO: 2epoch:train:521-560batch: iter_time=4.320e-05, forward_time=0.057, loss_ctc=10.175, loss=10.175, backward_time=0.009, grad_norm=114.559, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-17 02:00:00,730 (trainer:762) INFO: 2epoch:train:561-600batch: iter_time=4.599e-05, forward_time=0.052, loss_ctc=8.703, loss=8.703, backward_time=0.009, grad_norm=82.663, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-17 02:00:04,459 (trainer:762) INFO: 2epoch:train:601-640batch: iter_time=4.436e-05, forward_time=0.049, loss_ctc=7.738, loss=7.738, backward_time=0.009, grad_norm=82.846, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.373 +[stan] 2024-01-17 02:00:08,490 (trainer:762) INFO: 2epoch:train:641-680batch: iter_time=4.485e-05, forward_time=0.053, loss_ctc=8.777, loss=8.777, backward_time=0.008, grad_norm=91.077, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-17 02:00:12,539 (trainer:762) INFO: 2epoch:train:681-720batch: iter_time=4.871e-05, forward_time=0.053, loss_ctc=9.039, loss=9.039, backward_time=0.009, grad_norm=111.093, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.405 +[stan] 2024-01-17 02:00:16,238 (trainer:762) INFO: 2epoch:train:721-760batch: iter_time=4.631e-05, forward_time=0.049, loss_ctc=7.934, loss=7.934, backward_time=0.008, grad_norm=88.266, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.370 +[stan] 2024-01-17 02:00:20,435 (trainer:762) INFO: 2epoch:train:761-800batch: iter_time=4.704e-05, forward_time=0.055, loss_ctc=9.225, loss=9.225, backward_time=0.009, grad_norm=88.724, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.420 +/home/stan/Desktop/espnet/espnet2/train/reporter.py:79: UserWarning: No valid stats found + warnings.warn("No valid stats found") +[stan] 2024-01-17 02:00:24,742 (trainer:357) INFO: 2epoch results: [train] iter_time=1.713e-04, forward_time=0.052, loss_ctc=9.614, loss=9.614, backward_time=0.009, grad_norm=97.724, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396, time=1 minute and 19.38 seconds, total_count=1600, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=22.779, cer_ctc=0.183, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=22.779, time=1.06 seconds, total_count=30, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.18 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:00:25,646 (trainer:416) INFO: The best model has been updated: valid.loss +[stan] 2024-01-17 02:00:25,646 (trainer:291) INFO: 3/30epoch started. Estimated time to finish: 39 minutes and 49.06 seconds +[stan] 2024-01-17 02:00:30,171 (trainer:762) INFO: 3epoch:train:1-40batch: iter_time=0.003, forward_time=0.056, loss_ctc=9.331, loss=9.331, backward_time=0.009, grad_norm=91.452, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.452 +[stan] 2024-01-17 02:00:33,823 (trainer:762) INFO: 3epoch:train:41-80batch: iter_time=4.435e-05, forward_time=0.048, loss_ctc=7.121, loss=7.121, backward_time=0.008, grad_norm=75.742, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.365 +[stan] 2024-01-17 02:00:37,838 (trainer:762) INFO: 3epoch:train:81-120batch: iter_time=4.247e-05, forward_time=0.053, loss_ctc=8.235, loss=8.235, backward_time=0.009, grad_norm=102.031, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-17 02:00:41,611 (trainer:762) INFO: 3epoch:train:121-160batch: iter_time=4.391e-05, forward_time=0.049, loss_ctc=7.381, loss=7.381, backward_time=0.008, grad_norm=81.535, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-17 02:00:46,122 (trainer:762) INFO: 3epoch:train:161-200batch: iter_time=4.319e-05, forward_time=0.059, loss_ctc=9.795, loss=9.795, backward_time=0.009, grad_norm=112.234, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.451 +[stan] 2024-01-17 02:00:49,758 (trainer:762) INFO: 3epoch:train:201-240batch: iter_time=4.553e-05, forward_time=0.048, loss_ctc=6.863, loss=6.863, backward_time=0.009, grad_norm=83.780, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.363 +[stan] 2024-01-17 02:00:53,537 (trainer:762) INFO: 3epoch:train:241-280batch: iter_time=4.718e-05, forward_time=0.049, loss_ctc=7.365, loss=7.365, backward_time=0.009, grad_norm=94.327, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-17 02:00:57,441 (trainer:762) INFO: 3epoch:train:281-320batch: iter_time=4.380e-05, forward_time=0.051, loss_ctc=7.752, loss=7.752, backward_time=0.008, grad_norm=94.584, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.390 +[stan] 2024-01-17 02:01:01,702 (trainer:762) INFO: 3epoch:train:321-360batch: iter_time=4.417e-05, forward_time=0.056, loss_ctc=8.767, loss=8.767, backward_time=0.010, grad_norm=89.742, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-17 02:01:05,632 (trainer:762) INFO: 3epoch:train:361-400batch: iter_time=4.514e-05, forward_time=0.051, loss_ctc=7.440, loss=7.440, backward_time=0.008, grad_norm=74.612, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-17 02:01:09,310 (trainer:762) INFO: 3epoch:train:401-440batch: iter_time=4.251e-05, forward_time=0.048, loss_ctc=6.804, loss=6.804, backward_time=0.008, grad_norm=85.043, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.368 +[stan] 2024-01-17 02:01:12,934 (trainer:762) INFO: 3epoch:train:441-480batch: iter_time=4.570e-05, forward_time=0.048, loss_ctc=6.285, loss=6.285, backward_time=0.008, grad_norm=76.698, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.362 +[stan] 2024-01-17 02:01:17,299 (trainer:762) INFO: 3epoch:train:481-520batch: iter_time=4.826e-05, forward_time=0.057, loss_ctc=8.424, loss=8.424, backward_time=0.010, grad_norm=87.608, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-17 02:01:21,282 (trainer:762) INFO: 3epoch:train:521-560batch: iter_time=4.533e-05, forward_time=0.052, loss_ctc=7.594, loss=7.594, backward_time=0.008, grad_norm=77.345, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-17 02:01:25,289 (trainer:762) INFO: 3epoch:train:561-600batch: iter_time=4.622e-05, forward_time=0.052, loss_ctc=7.357, loss=7.357, backward_time=0.009, grad_norm=76.803, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-17 02:01:29,440 (trainer:762) INFO: 3epoch:train:601-640batch: iter_time=4.710e-05, forward_time=0.054, loss_ctc=7.959, loss=7.959, backward_time=0.009, grad_norm=84.342, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-17 02:01:33,006 (trainer:762) INFO: 3epoch:train:641-680batch: iter_time=4.330e-05, forward_time=0.047, loss_ctc=5.902, loss=5.902, backward_time=0.008, grad_norm=73.790, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.357 +[stan] 2024-01-17 02:01:37,013 (trainer:762) INFO: 3epoch:train:681-720batch: iter_time=4.700e-05, forward_time=0.052, loss_ctc=7.430, loss=7.430, backward_time=0.009, grad_norm=80.591, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-17 02:01:41,265 (trainer:762) INFO: 3epoch:train:721-760batch: iter_time=4.520e-05, forward_time=0.056, loss_ctc=7.623, loss=7.623, backward_time=0.009, grad_norm=86.925, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-17 02:01:44,946 (trainer:762) INFO: 3epoch:train:761-800batch: iter_time=4.175e-05, forward_time=0.048, loss_ctc=6.075, loss=6.075, backward_time=0.008, grad_norm=80.433, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.368 +[stan] 2024-01-17 02:01:49,279 (trainer:357) INFO: 3epoch results: [train] iter_time=1.921e-04, forward_time=0.052, loss_ctc=7.574, loss=7.574, backward_time=0.009, grad_norm=85.481, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396, time=1 minute and 19.38 seconds, total_count=2400, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=22.115, cer_ctc=0.173, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=22.115, time=1.08 seconds, total_count=45, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.17 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:01:50,299 (trainer:416) INFO: The best model has been updated: valid.loss +[stan] 2024-01-17 02:01:50,299 (trainer:291) INFO: 4/30epoch started. Estimated time to finish: 38 minutes and 17.7 seconds +[stan] 2024-01-17 02:01:54,628 (trainer:762) INFO: 4epoch:train:1-40batch: iter_time=0.003, forward_time=0.053, loss_ctc=7.007, loss=7.007, backward_time=0.009, grad_norm=87.061, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-17 02:01:58,502 (trainer:762) INFO: 4epoch:train:41-80batch: iter_time=4.338e-05, forward_time=0.051, loss_ctc=6.439, loss=6.439, backward_time=0.009, grad_norm=74.707, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-17 02:02:02,530 (trainer:762) INFO: 4epoch:train:81-120batch: iter_time=4.357e-05, forward_time=0.053, loss_ctc=7.140, loss=7.140, backward_time=0.009, grad_norm=85.255, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-17 02:02:06,449 (trainer:762) INFO: 4epoch:train:121-160batch: iter_time=4.490e-05, forward_time=0.051, loss_ctc=6.535, loss=6.535, backward_time=0.008, grad_norm=76.596, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 02:02:10,575 (trainer:762) INFO: 4epoch:train:161-200batch: iter_time=4.517e-05, forward_time=0.054, loss_ctc=7.012, loss=7.012, backward_time=0.009, grad_norm=78.132, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.412 +[stan] 2024-01-17 02:02:14,848 (trainer:762) INFO: 4epoch:train:201-240batch: iter_time=4.505e-05, forward_time=0.056, loss_ctc=7.159, loss=7.159, backward_time=0.009, grad_norm=80.820, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-17 02:02:18,343 (trainer:762) INFO: 4epoch:train:241-280batch: iter_time=4.525e-05, forward_time=0.046, loss_ctc=5.252, loss=5.252, backward_time=0.008, grad_norm=78.954, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.349 +[stan] 2024-01-17 02:02:22,386 (trainer:762) INFO: 4epoch:train:281-320batch: iter_time=4.386e-05, forward_time=0.053, loss_ctc=6.662, loss=6.662, backward_time=0.009, grad_norm=94.811, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-17 02:02:26,093 (trainer:762) INFO: 4epoch:train:321-360batch: iter_time=4.284e-05, forward_time=0.049, loss_ctc=5.418, loss=5.418, backward_time=0.008, grad_norm=69.914, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.371 +[stan] 2024-01-17 02:02:30,312 (trainer:762) INFO: 4epoch:train:361-400batch: iter_time=4.736e-05, forward_time=0.055, loss_ctc=6.863, loss=6.863, backward_time=0.009, grad_norm=82.002, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.422 +[stan] 2024-01-17 02:02:34,172 (trainer:762) INFO: 4epoch:train:401-440batch: iter_time=4.308e-05, forward_time=0.051, loss_ctc=6.232, loss=6.232, backward_time=0.009, grad_norm=81.994, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.386 +[stan] 2024-01-17 02:02:38,148 (trainer:762) INFO: 4epoch:train:441-480batch: iter_time=4.342e-05, forward_time=0.052, loss_ctc=5.887, loss=5.887, backward_time=0.009, grad_norm=78.461, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-17 02:02:42,192 (trainer:762) INFO: 4epoch:train:481-520batch: iter_time=4.390e-05, forward_time=0.053, loss_ctc=6.472, loss=6.472, backward_time=0.009, grad_norm=86.047, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-17 02:02:46,090 (trainer:762) INFO: 4epoch:train:521-560batch: iter_time=4.259e-05, forward_time=0.051, loss_ctc=5.701, loss=5.701, backward_time=0.008, grad_norm=90.364, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.390 +[stan] 2024-01-17 02:02:50,292 (trainer:762) INFO: 4epoch:train:561-600batch: iter_time=4.644e-05, forward_time=0.055, loss_ctc=6.433, loss=6.433, backward_time=0.009, grad_norm=85.222, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.420 +[stan] 2024-01-17 02:02:53,790 (trainer:762) INFO: 4epoch:train:601-640batch: iter_time=4.369e-05, forward_time=0.046, loss_ctc=4.949, loss=4.949, backward_time=0.008, grad_norm=77.757, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.350 +[stan] 2024-01-17 02:02:57,909 (trainer:762) INFO: 4epoch:train:641-680batch: iter_time=4.629e-05, forward_time=0.054, loss_ctc=6.267, loss=6.267, backward_time=0.009, grad_norm=86.959, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.412 +[stan] 2024-01-17 02:03:01,728 (trainer:762) INFO: 4epoch:train:681-720batch: iter_time=4.521e-05, forward_time=0.050, loss_ctc=5.504, loss=5.504, backward_time=0.008, grad_norm=75.198, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.382 +[stan] 2024-01-17 02:03:05,618 (trainer:762) INFO: 4epoch:train:721-760batch: iter_time=4.721e-05, forward_time=0.051, loss_ctc=5.773, loss=5.773, backward_time=0.008, grad_norm=73.843, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.389 +[stan] 2024-01-17 02:03:09,780 (trainer:762) INFO: 4epoch:train:761-800batch: iter_time=4.167e-05, forward_time=0.054, loss_ctc=6.179, loss=6.179, backward_time=0.009, grad_norm=76.270, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-17 02:03:14,056 (trainer:357) INFO: 4epoch results: [train] iter_time=1.735e-04, forward_time=0.052, loss_ctc=6.244, loss=6.244, backward_time=0.009, grad_norm=81.018, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.55 seconds, total_count=3200, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=23.091, cer_ctc=0.168, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=23.091, time=1.06 seconds, total_count=60, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.14 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:03:14,971 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:03:14,971 (trainer:291) INFO: 5/30epoch started. Estimated time to finish: 36 minutes and 49.82 seconds +[stan] 2024-01-17 02:03:19,168 (trainer:762) INFO: 5epoch:train:1-40batch: iter_time=0.003, forward_time=0.052, loss_ctc=5.420, loss=5.420, backward_time=0.008, grad_norm=78.270, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.419 +[stan] 2024-01-17 02:03:23,095 (trainer:762) INFO: 5epoch:train:41-80batch: iter_time=4.568e-05, forward_time=0.051, loss_ctc=5.453, loss=5.453, backward_time=0.009, grad_norm=82.315, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-17 02:03:27,306 (trainer:762) INFO: 5epoch:train:81-120batch: iter_time=4.541e-05, forward_time=0.055, loss_ctc=6.271, loss=6.271, backward_time=0.009, grad_norm=81.701, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.421 +[stan] 2024-01-17 02:03:31,060 (trainer:762) INFO: 5epoch:train:121-160batch: iter_time=4.308e-05, forward_time=0.049, loss_ctc=5.150, loss=5.150, backward_time=0.008, grad_norm=70.174, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.375 +[stan] 2024-01-17 02:03:35,336 (trainer:762) INFO: 5epoch:train:161-200batch: iter_time=5.040e-05, forward_time=0.056, loss_ctc=6.450, loss=6.450, backward_time=0.009, grad_norm=84.788, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-17 02:03:38,972 (trainer:762) INFO: 5epoch:train:201-240batch: iter_time=4.453e-05, forward_time=0.048, loss_ctc=4.501, loss=4.501, backward_time=0.008, grad_norm=71.063, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.364 +[stan] 2024-01-17 02:03:43,054 (trainer:762) INFO: 5epoch:train:241-280batch: iter_time=4.719e-05, forward_time=0.053, loss_ctc=5.555, loss=5.555, backward_time=0.009, grad_norm=81.450, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-17 02:03:46,959 (trainer:762) INFO: 5epoch:train:281-320batch: iter_time=4.503e-05, forward_time=0.051, loss_ctc=5.157, loss=5.157, backward_time=0.009, grad_norm=75.791, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.390 +[stan] 2024-01-17 02:03:50,929 (trainer:762) INFO: 5epoch:train:321-360batch: iter_time=4.511e-05, forward_time=0.052, loss_ctc=5.110, loss=5.110, backward_time=0.009, grad_norm=77.501, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397 +[stan] 2024-01-17 02:03:54,722 (trainer:762) INFO: 5epoch:train:361-400batch: iter_time=4.628e-05, forward_time=0.050, loss_ctc=5.299, loss=5.299, backward_time=0.008, grad_norm=77.273, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-17 02:03:59,041 (trainer:762) INFO: 5epoch:train:401-440batch: iter_time=4.729e-05, forward_time=0.056, loss_ctc=6.485, loss=6.485, backward_time=0.009, grad_norm=84.752, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-17 02:04:02,977 (trainer:762) INFO: 5epoch:train:441-480batch: iter_time=4.521e-05, forward_time=0.052, loss_ctc=5.004, loss=5.004, backward_time=0.008, grad_norm=74.666, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-17 02:04:06,677 (trainer:762) INFO: 5epoch:train:481-520batch: iter_time=4.722e-05, forward_time=0.049, loss_ctc=4.615, loss=4.615, backward_time=0.008, grad_norm=73.438, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.370 +[stan] 2024-01-17 02:04:10,719 (trainer:762) INFO: 5epoch:train:521-560batch: iter_time=4.346e-05, forward_time=0.053, loss_ctc=5.284, loss=5.284, backward_time=0.009, grad_norm=79.082, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-17 02:04:14,977 (trainer:762) INFO: 5epoch:train:561-600batch: iter_time=4.499e-05, forward_time=0.056, loss_ctc=5.595, loss=5.595, backward_time=0.009, grad_norm=80.178, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-17 02:04:18,604 (trainer:762) INFO: 5epoch:train:601-640batch: iter_time=4.358e-05, forward_time=0.048, loss_ctc=4.382, loss=4.382, backward_time=0.008, grad_norm=69.903, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.363 +[stan] 2024-01-17 02:04:22,556 (trainer:762) INFO: 5epoch:train:641-680batch: iter_time=4.755e-05, forward_time=0.052, loss_ctc=5.043, loss=5.043, backward_time=0.009, grad_norm=78.999, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-17 02:04:26,665 (trainer:762) INFO: 5epoch:train:681-720batch: iter_time=4.375e-05, forward_time=0.054, loss_ctc=5.565, loss=5.565, backward_time=0.009, grad_norm=85.943, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.411 +[stan] 2024-01-17 02:04:30,344 (trainer:762) INFO: 5epoch:train:721-760batch: iter_time=4.721e-05, forward_time=0.048, loss_ctc=4.139, loss=4.139, backward_time=0.008, grad_norm=72.236, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.368 +[stan] 2024-01-17 02:04:34,372 (trainer:762) INFO: 5epoch:train:761-800batch: iter_time=4.402e-05, forward_time=0.053, loss_ctc=5.096, loss=5.096, backward_time=0.009, grad_norm=85.961, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-17 02:04:38,661 (trainer:357) INFO: 5epoch results: [train] iter_time=1.841e-04, forward_time=0.052, loss_ctc=5.278, loss=5.278, backward_time=0.009, grad_norm=78.274, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.48 seconds, total_count=4000, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=23.927, cer_ctc=0.171, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=23.927, time=1.06 seconds, total_count=75, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.15 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:04:39,614 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:04:39,614 (trainer:291) INFO: 6/30epoch started. Estimated time to finish: 35 minutes and 23.07 seconds +[stan] 2024-01-17 02:04:44,104 (trainer:762) INFO: 6epoch:train:1-40batch: iter_time=0.003, forward_time=0.055, loss_ctc=5.354, loss=5.354, backward_time=0.009, grad_norm=78.153, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.449 +[stan] 2024-01-17 02:04:48,263 (trainer:762) INFO: 6epoch:train:41-80batch: iter_time=4.469e-05, forward_time=0.054, loss_ctc=5.360, loss=5.360, backward_time=0.009, grad_norm=82.877, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-17 02:04:51,817 (trainer:762) INFO: 6epoch:train:81-120batch: iter_time=4.323e-05, forward_time=0.047, loss_ctc=3.950, loss=3.950, backward_time=0.008, grad_norm=73.701, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.355 +[stan] 2024-01-17 02:04:56,012 (trainer:762) INFO: 6epoch:train:121-160batch: iter_time=4.663e-05, forward_time=0.055, loss_ctc=5.390, loss=5.390, backward_time=0.009, grad_norm=90.821, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.419 +[stan] 2024-01-17 02:04:59,749 (trainer:762) INFO: 6epoch:train:161-200batch: iter_time=4.307e-05, forward_time=0.049, loss_ctc=4.226, loss=4.226, backward_time=0.008, grad_norm=79.134, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.374 +[stan] 2024-01-17 02:05:03,611 (trainer:762) INFO: 6epoch:train:201-240batch: iter_time=4.700e-05, forward_time=0.051, loss_ctc=4.635, loss=4.635, backward_time=0.008, grad_norm=76.436, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.386 +[stan] 2024-01-17 02:05:07,541 (trainer:762) INFO: 6epoch:train:241-280batch: iter_time=4.539e-05, forward_time=0.051, loss_ctc=4.548, loss=4.548, backward_time=0.009, grad_norm=81.878, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-17 02:05:11,433 (trainer:762) INFO: 6epoch:train:281-320batch: iter_time=4.549e-05, forward_time=0.051, loss_ctc=4.401, loss=4.401, backward_time=0.009, grad_norm=70.199, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.389 +[stan] 2024-01-17 02:05:15,678 (trainer:762) INFO: 6epoch:train:321-360batch: iter_time=4.603e-05, forward_time=0.055, loss_ctc=5.357, loss=5.357, backward_time=0.009, grad_norm=87.279, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-17 02:05:19,261 (trainer:762) INFO: 6epoch:train:361-400batch: iter_time=4.677e-05, forward_time=0.047, loss_ctc=4.068, loss=4.068, backward_time=0.008, grad_norm=76.950, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.358 +[stan] 2024-01-17 02:05:23,405 (trainer:762) INFO: 6epoch:train:401-440batch: iter_time=4.926e-05, forward_time=0.054, loss_ctc=4.888, loss=4.888, backward_time=0.009, grad_norm=89.746, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.414 +[stan] 2024-01-17 02:05:27,567 (trainer:762) INFO: 6epoch:train:441-480batch: iter_time=4.747e-05, forward_time=0.054, loss_ctc=4.943, loss=4.943, backward_time=0.009, grad_norm=83.137, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-17 02:05:31,398 (trainer:762) INFO: 6epoch:train:481-520batch: iter_time=4.304e-05, forward_time=0.050, loss_ctc=4.294, loss=4.294, backward_time=0.008, grad_norm=81.055, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-17 02:05:35,324 (trainer:762) INFO: 6epoch:train:521-560batch: iter_time=4.559e-05, forward_time=0.051, loss_ctc=4.211, loss=4.211, backward_time=0.008, grad_norm=82.580, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 02:05:38,908 (trainer:762) INFO: 6epoch:train:561-600batch: iter_time=4.573e-05, forward_time=0.047, loss_ctc=3.741, loss=3.741, backward_time=0.008, grad_norm=71.195, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.358 +[stan] 2024-01-17 02:05:43,453 (trainer:762) INFO: 6epoch:train:601-640batch: iter_time=4.691e-05, forward_time=0.059, loss_ctc=6.141, loss=6.141, backward_time=0.010, grad_norm=85.427, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.454 +[stan] 2024-01-17 02:05:47,342 (trainer:762) INFO: 6epoch:train:641-680batch: iter_time=4.427e-05, forward_time=0.051, loss_ctc=4.390, loss=4.390, backward_time=0.009, grad_norm=73.192, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.389 +[stan] 2024-01-17 02:05:51,070 (trainer:762) INFO: 6epoch:train:681-720batch: iter_time=4.443e-05, forward_time=0.049, loss_ctc=3.876, loss=3.876, backward_time=0.008, grad_norm=73.866, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.373 +[stan] 2024-01-17 02:05:54,848 (trainer:762) INFO: 6epoch:train:721-760batch: iter_time=4.489e-05, forward_time=0.050, loss_ctc=4.087, loss=4.087, backward_time=0.008, grad_norm=75.924, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-17 02:05:58,995 (trainer:762) INFO: 6epoch:train:761-800batch: iter_time=4.641e-05, forward_time=0.054, loss_ctc=4.804, loss=4.804, backward_time=0.009, grad_norm=80.021, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-17 02:06:03,240 (trainer:357) INFO: 6epoch results: [train] iter_time=1.811e-04, forward_time=0.052, loss_ctc=4.633, loss=4.633, backward_time=0.009, grad_norm=79.679, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.46 seconds, total_count=4800, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=25.536, cer_ctc=0.167, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=25.536, time=1.07 seconds, total_count=90, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.1 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:06:04,277 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:06:04,278 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/1epoch.pth +[stan] 2024-01-17 02:06:04,278 (trainer:291) INFO: 7/30epoch started. Estimated time to finish: 33 minutes and 57.12 seconds +[stan] 2024-01-17 02:06:08,508 (trainer:762) INFO: 7epoch:train:1-40batch: iter_time=0.002, forward_time=0.052, loss_ctc=4.306, loss=4.306, backward_time=0.009, grad_norm=78.119, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.423 +[stan] 2024-01-17 02:06:12,537 (trainer:762) INFO: 7epoch:train:41-80batch: iter_time=4.330e-05, forward_time=0.053, loss_ctc=4.353, loss=4.353, backward_time=0.009, grad_norm=74.874, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-17 02:06:16,595 (trainer:762) INFO: 7epoch:train:81-120batch: iter_time=4.523e-05, forward_time=0.053, loss_ctc=4.613, loss=4.613, backward_time=0.009, grad_norm=79.239, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-17 02:06:20,369 (trainer:762) INFO: 7epoch:train:121-160batch: iter_time=4.236e-05, forward_time=0.050, loss_ctc=3.763, loss=3.763, backward_time=0.008, grad_norm=73.842, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-17 02:06:24,612 (trainer:762) INFO: 7epoch:train:161-200batch: iter_time=4.430e-05, forward_time=0.055, loss_ctc=4.887, loss=4.887, backward_time=0.009, grad_norm=84.535, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-17 02:06:28,347 (trainer:762) INFO: 7epoch:train:201-240batch: iter_time=4.336e-05, forward_time=0.049, loss_ctc=3.713, loss=3.713, backward_time=0.008, grad_norm=74.738, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.373 +[stan] 2024-01-17 02:06:32,558 (trainer:762) INFO: 7epoch:train:241-280batch: iter_time=4.603e-05, forward_time=0.055, loss_ctc=4.757, loss=4.757, backward_time=0.009, grad_norm=76.475, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.421 +[stan] 2024-01-17 02:06:36,307 (trainer:762) INFO: 7epoch:train:281-320batch: iter_time=4.344e-05, forward_time=0.049, loss_ctc=3.757, loss=3.757, backward_time=0.008, grad_norm=75.934, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.375 +[stan] 2024-01-17 02:06:40,522 (trainer:762) INFO: 7epoch:train:321-360batch: iter_time=4.547e-05, forward_time=0.055, loss_ctc=4.610, loss=4.610, backward_time=0.009, grad_norm=78.445, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.421 +[stan] 2024-01-17 02:06:44,238 (trainer:762) INFO: 7epoch:train:361-400batch: iter_time=4.448e-05, forward_time=0.049, loss_ctc=3.716, loss=3.716, backward_time=0.008, grad_norm=87.877, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.372 +[stan] 2024-01-17 02:06:48,024 (trainer:762) INFO: 7epoch:train:401-440batch: iter_time=4.661e-05, forward_time=0.050, loss_ctc=3.524, loss=3.524, backward_time=0.008, grad_norm=78.971, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-17 02:06:52,029 (trainer:762) INFO: 7epoch:train:441-480batch: iter_time=4.456e-05, forward_time=0.052, loss_ctc=4.406, loss=4.406, backward_time=0.009, grad_norm=85.735, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-17 02:06:55,746 (trainer:762) INFO: 7epoch:train:481-520batch: iter_time=4.379e-05, forward_time=0.049, loss_ctc=3.592, loss=3.592, backward_time=0.008, grad_norm=77.431, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.372 +[stan] 2024-01-17 02:06:59,906 (trainer:762) INFO: 7epoch:train:521-560batch: iter_time=4.342e-05, forward_time=0.054, loss_ctc=4.324, loss=4.324, backward_time=0.009, grad_norm=78.016, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-17 02:07:04,021 (trainer:762) INFO: 7epoch:train:561-600batch: iter_time=4.381e-05, forward_time=0.054, loss_ctc=4.422, loss=4.422, backward_time=0.009, grad_norm=76.387, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.411 +[stan] 2024-01-17 02:07:07,794 (trainer:762) INFO: 7epoch:train:601-640batch: iter_time=4.479e-05, forward_time=0.049, loss_ctc=3.454, loss=3.454, backward_time=0.008, grad_norm=74.941, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-17 02:07:11,583 (trainer:762) INFO: 7epoch:train:641-680batch: iter_time=4.688e-05, forward_time=0.050, loss_ctc=3.656, loss=3.656, backward_time=0.008, grad_norm=77.605, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-17 02:07:15,615 (trainer:762) INFO: 7epoch:train:681-720batch: iter_time=4.797e-05, forward_time=0.053, loss_ctc=4.137, loss=4.137, backward_time=0.009, grad_norm=78.069, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-17 02:07:19,598 (trainer:762) INFO: 7epoch:train:721-760batch: iter_time=4.371e-05, forward_time=0.052, loss_ctc=4.125, loss=4.125, backward_time=0.009, grad_norm=79.815, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-17 02:07:23,722 (trainer:762) INFO: 7epoch:train:761-800batch: iter_time=4.138e-05, forward_time=0.054, loss_ctc=4.189, loss=4.189, backward_time=0.009, grad_norm=79.736, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.412 +[stan] 2024-01-17 02:07:27,961 (trainer:357) INFO: 7epoch results: [train] iter_time=1.612e-04, forward_time=0.052, loss_ctc=4.115, loss=4.115, backward_time=0.009, grad_norm=78.539, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.51 seconds, total_count=5600, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=27.133, cer_ctc=0.176, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=27.133, time=1.06 seconds, total_count=105, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.11 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:07:28,923 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:07:28,923 (trainer:291) INFO: 8/30epoch started. Estimated time to finish: 32 minutes and 31.47 seconds +[stan] 2024-01-17 02:07:33,463 (trainer:762) INFO: 8epoch:train:1-40batch: iter_time=0.003, forward_time=0.056, loss_ctc=4.434, loss=4.434, backward_time=0.009, grad_norm=80.642, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.454 +[stan] 2024-01-17 02:07:37,528 (trainer:762) INFO: 8epoch:train:41-80batch: iter_time=4.550e-05, forward_time=0.053, loss_ctc=4.097, loss=4.097, backward_time=0.009, grad_norm=79.588, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-17 02:07:40,958 (trainer:762) INFO: 8epoch:train:81-120batch: iter_time=4.275e-05, forward_time=0.045, loss_ctc=2.674, loss=2.674, backward_time=0.008, grad_norm=66.364, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.343 +[stan] 2024-01-17 02:07:44,867 (trainer:762) INFO: 8epoch:train:121-160batch: iter_time=4.761e-05, forward_time=0.051, loss_ctc=3.542, loss=3.542, backward_time=0.009, grad_norm=69.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-17 02:07:48,989 (trainer:762) INFO: 8epoch:train:161-200batch: iter_time=4.554e-05, forward_time=0.054, loss_ctc=4.167, loss=4.167, backward_time=0.009, grad_norm=80.369, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.412 +[stan] 2024-01-17 02:07:52,928 (trainer:762) INFO: 8epoch:train:201-240batch: iter_time=4.589e-05, forward_time=0.052, loss_ctc=3.589, loss=3.589, backward_time=0.009, grad_norm=75.554, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-17 02:07:56,922 (trainer:762) INFO: 8epoch:train:241-280batch: iter_time=4.657e-05, forward_time=0.052, loss_ctc=3.894, loss=3.894, backward_time=0.009, grad_norm=82.757, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399 +[stan] 2024-01-17 02:08:00,860 (trainer:762) INFO: 8epoch:train:281-320batch: iter_time=4.811e-05, forward_time=0.052, loss_ctc=3.608, loss=3.608, backward_time=0.008, grad_norm=78.881, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-17 02:08:04,926 (trainer:762) INFO: 8epoch:train:321-360batch: iter_time=4.613e-05, forward_time=0.053, loss_ctc=4.155, loss=4.155, backward_time=0.009, grad_norm=87.054, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.407 +[stan] 2024-01-17 02:08:08,761 (trainer:762) INFO: 8epoch:train:361-400batch: iter_time=4.783e-05, forward_time=0.050, loss_ctc=3.281, loss=3.281, backward_time=0.009, grad_norm=72.803, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-17 02:08:12,774 (trainer:762) INFO: 8epoch:train:401-440batch: iter_time=4.563e-05, forward_time=0.052, loss_ctc=3.851, loss=3.851, backward_time=0.009, grad_norm=75.624, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-17 02:08:16,795 (trainer:762) INFO: 8epoch:train:441-480batch: iter_time=4.792e-05, forward_time=0.053, loss_ctc=4.118, loss=4.118, backward_time=0.009, grad_norm=80.044, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-17 02:08:20,678 (trainer:762) INFO: 8epoch:train:481-520batch: iter_time=4.463e-05, forward_time=0.051, loss_ctc=3.577, loss=3.577, backward_time=0.009, grad_norm=77.850, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-17 02:08:24,708 (trainer:762) INFO: 8epoch:train:521-560batch: iter_time=4.481e-05, forward_time=0.053, loss_ctc=3.550, loss=3.550, backward_time=0.009, grad_norm=69.759, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-17 02:08:28,590 (trainer:762) INFO: 8epoch:train:561-600batch: iter_time=4.530e-05, forward_time=0.051, loss_ctc=3.593, loss=3.593, backward_time=0.009, grad_norm=67.804, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-17 02:08:32,327 (trainer:762) INFO: 8epoch:train:601-640batch: iter_time=4.421e-05, forward_time=0.049, loss_ctc=3.285, loss=3.285, backward_time=0.008, grad_norm=71.198, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.374 +[stan] 2024-01-17 02:08:36,635 (trainer:762) INFO: 8epoch:train:641-680batch: iter_time=4.366e-05, forward_time=0.056, loss_ctc=4.159, loss=4.159, backward_time=0.010, grad_norm=83.264, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-17 02:08:40,570 (trainer:762) INFO: 8epoch:train:681-720batch: iter_time=4.584e-05, forward_time=0.052, loss_ctc=3.328, loss=3.328, backward_time=0.008, grad_norm=71.418, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-17 02:08:44,516 (trainer:762) INFO: 8epoch:train:721-760batch: iter_time=4.485e-05, forward_time=0.052, loss_ctc=3.530, loss=3.530, backward_time=0.009, grad_norm=68.090, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-17 02:08:48,126 (trainer:762) INFO: 8epoch:train:761-800batch: iter_time=4.283e-05, forward_time=0.047, loss_ctc=3.063, loss=3.063, backward_time=0.008, grad_norm=72.289, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.361 +[stan] 2024-01-17 02:08:52,399 (trainer:357) INFO: 8epoch results: [train] iter_time=1.894e-04, forward_time=0.052, loss_ctc=3.675, loss=3.675, backward_time=0.009, grad_norm=75.524, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396, time=1 minute and 19.28 seconds, total_count=6400, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=28.364, cer_ctc=0.175, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=28.364, time=1.07 seconds, total_count=120, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.13 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:08:53,461 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:08:53,462 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/7epoch.pth +[stan] 2024-01-17 02:08:53,463 (trainer:291) INFO: 9/30epoch started. Estimated time to finish: 31 minutes and 5.77 seconds +[stan] 2024-01-17 02:08:57,716 (trainer:762) INFO: 9epoch:train:1-40batch: iter_time=0.003, forward_time=0.053, loss_ctc=3.709, loss=3.709, backward_time=0.009, grad_norm=75.098, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-17 02:09:01,848 (trainer:762) INFO: 9epoch:train:41-80batch: iter_time=4.650e-05, forward_time=0.054, loss_ctc=4.143, loss=4.143, backward_time=0.009, grad_norm=82.056, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.413 +[stan] 2024-01-17 02:09:05,767 (trainer:762) INFO: 9epoch:train:81-120batch: iter_time=4.202e-05, forward_time=0.051, loss_ctc=3.422, loss=3.422, backward_time=0.009, grad_norm=74.841, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 02:09:10,057 (trainer:762) INFO: 9epoch:train:121-160batch: iter_time=4.447e-05, forward_time=0.056, loss_ctc=4.301, loss=4.301, backward_time=0.009, grad_norm=76.586, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-17 02:09:13,933 (trainer:762) INFO: 9epoch:train:161-200batch: iter_time=4.788e-05, forward_time=0.051, loss_ctc=3.231, loss=3.231, backward_time=0.008, grad_norm=68.335, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-17 02:09:17,610 (trainer:762) INFO: 9epoch:train:201-240batch: iter_time=4.406e-05, forward_time=0.048, loss_ctc=2.983, loss=2.983, backward_time=0.008, grad_norm=66.412, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.368 +[stan] 2024-01-17 02:09:21,738 (trainer:762) INFO: 9epoch:train:241-280batch: iter_time=4.380e-05, forward_time=0.054, loss_ctc=3.825, loss=3.825, backward_time=0.009, grad_norm=76.063, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.413 +[stan] 2024-01-17 02:09:25,604 (trainer:762) INFO: 9epoch:train:281-320batch: iter_time=4.458e-05, forward_time=0.051, loss_ctc=3.428, loss=3.428, backward_time=0.009, grad_norm=70.630, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-17 02:09:29,522 (trainer:762) INFO: 9epoch:train:321-360batch: iter_time=4.350e-05, forward_time=0.051, loss_ctc=3.281, loss=3.281, backward_time=0.008, grad_norm=70.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 02:09:33,500 (trainer:762) INFO: 9epoch:train:361-400batch: iter_time=4.465e-05, forward_time=0.052, loss_ctc=3.686, loss=3.686, backward_time=0.009, grad_norm=84.627, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-17 02:09:37,503 (trainer:762) INFO: 9epoch:train:401-440batch: iter_time=4.363e-05, forward_time=0.052, loss_ctc=3.430, loss=3.430, backward_time=0.008, grad_norm=76.276, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-17 02:09:41,676 (trainer:762) INFO: 9epoch:train:441-480batch: iter_time=4.288e-05, forward_time=0.054, loss_ctc=3.672, loss=3.672, backward_time=0.009, grad_norm=71.284, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-17 02:09:45,516 (trainer:762) INFO: 9epoch:train:481-520batch: iter_time=4.506e-05, forward_time=0.050, loss_ctc=3.020, loss=3.020, backward_time=0.008, grad_norm=66.435, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.384 +[stan] 2024-01-17 02:09:49,426 (trainer:762) INFO: 9epoch:train:521-560batch: iter_time=4.372e-05, forward_time=0.051, loss_ctc=3.484, loss=3.484, backward_time=0.009, grad_norm=73.088, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-17 02:09:53,248 (trainer:762) INFO: 9epoch:train:561-600batch: iter_time=4.387e-05, forward_time=0.050, loss_ctc=3.110, loss=3.110, backward_time=0.008, grad_norm=77.796, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.382 +[stan] 2024-01-17 02:09:57,020 (trainer:762) INFO: 9epoch:train:601-640batch: iter_time=4.336e-05, forward_time=0.049, loss_ctc=3.087, loss=3.087, backward_time=0.008, grad_norm=70.580, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-17 02:10:01,429 (trainer:762) INFO: 9epoch:train:641-680batch: iter_time=4.449e-05, forward_time=0.058, loss_ctc=4.192, loss=4.192, backward_time=0.009, grad_norm=77.584, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-17 02:10:05,506 (trainer:762) INFO: 9epoch:train:681-720batch: iter_time=4.366e-05, forward_time=0.053, loss_ctc=3.604, loss=3.604, backward_time=0.009, grad_norm=80.969, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-17 02:10:08,935 (trainer:762) INFO: 9epoch:train:721-760batch: iter_time=4.333e-05, forward_time=0.045, loss_ctc=2.471, loss=2.471, backward_time=0.008, grad_norm=60.030, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.343 +[stan] 2024-01-17 02:10:13,066 (trainer:762) INFO: 9epoch:train:761-800batch: iter_time=4.233e-05, forward_time=0.054, loss_ctc=3.577, loss=3.577, backward_time=0.009, grad_norm=77.396, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.413 +[stan] 2024-01-17 02:10:17,294 (trainer:357) INFO: 9epoch results: [train] iter_time=1.713e-04, forward_time=0.052, loss_ctc=3.483, loss=3.483, backward_time=0.009, grad_norm=73.810, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398, time=1 minute and 19.67 seconds, total_count=7200, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=29.920, cer_ctc=0.176, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=29.920, time=1.07 seconds, total_count=135, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.1 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:10:18,299 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:10:18,301 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/8epoch.pth +[stan] 2024-01-17 02:10:18,301 (trainer:291) INFO: 10/30epoch started. Estimated time to finish: 29 minutes and 41.04 seconds +[stan] 2024-01-17 02:10:22,449 (trainer:762) INFO: 10epoch:train:1-40batch: iter_time=0.003, forward_time=0.051, loss_ctc=3.254, loss=3.254, backward_time=0.008, grad_norm=71.078, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.414 +[stan] 2024-01-17 02:10:26,232 (trainer:762) INFO: 10epoch:train:41-80batch: iter_time=4.609e-05, forward_time=0.050, loss_ctc=3.031, loss=3.031, backward_time=0.008, grad_norm=63.479, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-17 02:10:30,335 (trainer:762) INFO: 10epoch:train:81-120batch: iter_time=4.809e-05, forward_time=0.054, loss_ctc=3.468, loss=3.468, backward_time=0.009, grad_norm=75.496, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.410 +[stan] 2024-01-17 02:10:34,534 (trainer:762) INFO: 10epoch:train:121-160batch: iter_time=4.475e-05, forward_time=0.055, loss_ctc=3.781, loss=3.781, backward_time=0.009, grad_norm=85.397, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.420 +[stan] 2024-01-17 02:10:38,544 (trainer:762) INFO: 10epoch:train:161-200batch: iter_time=4.694e-05, forward_time=0.052, loss_ctc=3.314, loss=3.314, backward_time=0.009, grad_norm=70.853, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-17 02:10:42,103 (trainer:762) INFO: 10epoch:train:201-240batch: iter_time=4.508e-05, forward_time=0.047, loss_ctc=2.560, loss=2.560, backward_time=0.008, grad_norm=62.339, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.356 +[stan] 2024-01-17 02:10:46,187 (trainer:762) INFO: 10epoch:train:241-280batch: iter_time=4.612e-05, forward_time=0.053, loss_ctc=3.703, loss=3.703, backward_time=0.009, grad_norm=72.478, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-17 02:10:50,326 (trainer:762) INFO: 10epoch:train:281-320batch: iter_time=4.469e-05, forward_time=0.054, loss_ctc=3.351, loss=3.351, backward_time=0.009, grad_norm=66.689, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.414 +[stan] 2024-01-17 02:10:54,153 (trainer:762) INFO: 10epoch:train:321-360batch: iter_time=4.662e-05, forward_time=0.050, loss_ctc=3.184, loss=3.184, backward_time=0.008, grad_norm=72.821, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-17 02:10:57,712 (trainer:762) INFO: 10epoch:train:361-400batch: iter_time=4.470e-05, forward_time=0.047, loss_ctc=2.692, loss=2.692, backward_time=0.008, grad_norm=61.787, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.356 +[stan] 2024-01-17 02:11:02,097 (trainer:762) INFO: 10epoch:train:401-440batch: iter_time=4.323e-05, forward_time=0.057, loss_ctc=4.032, loss=4.032, backward_time=0.010, grad_norm=81.865, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-17 02:11:06,100 (trainer:762) INFO: 10epoch:train:441-480batch: iter_time=4.587e-05, forward_time=0.052, loss_ctc=3.294, loss=3.294, backward_time=0.008, grad_norm=72.751, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-17 02:11:09,892 (trainer:762) INFO: 10epoch:train:481-520batch: iter_time=4.489e-05, forward_time=0.050, loss_ctc=3.027, loss=3.027, backward_time=0.009, grad_norm=75.658, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-17 02:11:13,979 (trainer:762) INFO: 10epoch:train:521-560batch: iter_time=4.491e-05, forward_time=0.054, loss_ctc=3.204, loss=3.204, backward_time=0.009, grad_norm=66.426, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.409 +[stan] 2024-01-17 02:11:17,898 (trainer:762) INFO: 10epoch:train:561-600batch: iter_time=4.526e-05, forward_time=0.051, loss_ctc=2.987, loss=2.987, backward_time=0.009, grad_norm=67.724, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 02:11:22,072 (trainer:762) INFO: 10epoch:train:601-640batch: iter_time=4.373e-05, forward_time=0.055, loss_ctc=3.444, loss=3.444, backward_time=0.009, grad_norm=69.634, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-17 02:11:25,834 (trainer:762) INFO: 10epoch:train:641-680batch: iter_time=4.624e-05, forward_time=0.049, loss_ctc=2.772, loss=2.772, backward_time=0.009, grad_norm=65.105, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.376 +[stan] 2024-01-17 02:11:29,870 (trainer:762) INFO: 10epoch:train:681-720batch: iter_time=4.288e-05, forward_time=0.053, loss_ctc=3.291, loss=3.291, backward_time=0.008, grad_norm=75.151, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-17 02:11:33,715 (trainer:762) INFO: 10epoch:train:721-760batch: iter_time=4.514e-05, forward_time=0.050, loss_ctc=2.868, loss=2.868, backward_time=0.008, grad_norm=65.304, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.384 +[stan] 2024-01-17 02:11:37,860 (trainer:762) INFO: 10epoch:train:761-800batch: iter_time=4.125e-05, forward_time=0.054, loss_ctc=3.663, loss=3.663, backward_time=0.009, grad_norm=77.135, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.414 +[stan] 2024-01-17 02:11:42,079 (trainer:357) INFO: 10epoch results: [train] iter_time=1.888e-04, forward_time=0.052, loss_ctc=3.246, loss=3.246, backward_time=0.009, grad_norm=70.959, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398, time=1 minute and 19.63 seconds, total_count=8000, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=31.047, cer_ctc=0.170, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=31.047, time=1.08 seconds, total_count=150, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.07 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:11:43,112 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:11:43,113 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/9epoch.pth +[stan] 2024-01-17 02:11:43,114 (trainer:291) INFO: 11/30epoch started. Estimated time to finish: 28 minutes and 16.23 seconds +[stan] 2024-01-17 02:11:47,282 (trainer:762) INFO: 11epoch:train:1-40batch: iter_time=0.003, forward_time=0.051, loss_ctc=3.073, loss=3.073, backward_time=0.008, grad_norm=71.184, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-17 02:11:51,310 (trainer:762) INFO: 11epoch:train:41-80batch: iter_time=4.539e-05, forward_time=0.053, loss_ctc=3.247, loss=3.247, backward_time=0.009, grad_norm=62.932, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-17 02:11:55,186 (trainer:762) INFO: 11epoch:train:81-120batch: iter_time=4.538e-05, forward_time=0.051, loss_ctc=2.950, loss=2.950, backward_time=0.008, grad_norm=62.281, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-17 02:11:59,092 (trainer:762) INFO: 11epoch:train:121-160batch: iter_time=4.403e-05, forward_time=0.051, loss_ctc=3.264, loss=3.264, backward_time=0.008, grad_norm=74.503, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-17 02:12:03,309 (trainer:762) INFO: 11epoch:train:161-200batch: iter_time=4.336e-05, forward_time=0.055, loss_ctc=3.440, loss=3.440, backward_time=0.009, grad_norm=82.167, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.422 +[stan] 2024-01-17 02:12:07,066 (trainer:762) INFO: 11epoch:train:201-240batch: iter_time=4.428e-05, forward_time=0.049, loss_ctc=2.818, loss=2.818, backward_time=0.008, grad_norm=69.162, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.376 +[stan] 2024-01-17 02:12:11,018 (trainer:762) INFO: 11epoch:train:241-280batch: iter_time=4.426e-05, forward_time=0.052, loss_ctc=2.899, loss=2.899, backward_time=0.009, grad_norm=63.546, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-17 02:12:14,912 (trainer:762) INFO: 11epoch:train:281-320batch: iter_time=4.702e-05, forward_time=0.051, loss_ctc=2.944, loss=2.944, backward_time=0.008, grad_norm=63.899, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.389 +[stan] 2024-01-17 02:12:19,166 (trainer:762) INFO: 11epoch:train:321-360batch: iter_time=4.633e-05, forward_time=0.056, loss_ctc=3.627, loss=3.627, backward_time=0.009, grad_norm=74.164, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-17 02:12:22,875 (trainer:762) INFO: 11epoch:train:361-400batch: iter_time=4.347e-05, forward_time=0.049, loss_ctc=2.847, loss=2.847, backward_time=0.008, grad_norm=65.388, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.371 +[stan] 2024-01-17 02:12:26,756 (trainer:762) INFO: 11epoch:train:401-440batch: iter_time=4.380e-05, forward_time=0.051, loss_ctc=2.702, loss=2.702, backward_time=0.008, grad_norm=65.760, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-17 02:12:30,804 (trainer:762) INFO: 11epoch:train:441-480batch: iter_time=4.304e-05, forward_time=0.053, loss_ctc=3.210, loss=3.210, backward_time=0.009, grad_norm=68.804, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.405 +[stan] 2024-01-17 02:12:34,844 (trainer:762) INFO: 11epoch:train:481-520batch: iter_time=4.487e-05, forward_time=0.053, loss_ctc=3.193, loss=3.193, backward_time=0.009, grad_norm=73.031, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-17 02:12:38,828 (trainer:762) INFO: 11epoch:train:521-560batch: iter_time=4.275e-05, forward_time=0.052, loss_ctc=2.984, loss=2.984, backward_time=0.009, grad_norm=62.875, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-17 02:12:42,790 (trainer:762) INFO: 11epoch:train:561-600batch: iter_time=4.364e-05, forward_time=0.052, loss_ctc=3.052, loss=3.052, backward_time=0.008, grad_norm=67.786, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-17 02:12:46,698 (trainer:762) INFO: 11epoch:train:601-640batch: iter_time=4.533e-05, forward_time=0.051, loss_ctc=2.763, loss=2.763, backward_time=0.009, grad_norm=65.096, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-17 02:12:50,533 (trainer:762) INFO: 11epoch:train:641-680batch: iter_time=4.408e-05, forward_time=0.050, loss_ctc=2.938, loss=2.938, backward_time=0.009, grad_norm=65.392, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-17 02:12:54,446 (trainer:762) INFO: 11epoch:train:681-720batch: iter_time=4.358e-05, forward_time=0.051, loss_ctc=2.854, loss=2.854, backward_time=0.009, grad_norm=73.124, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-17 02:12:58,636 (trainer:762) INFO: 11epoch:train:721-760batch: iter_time=4.515e-05, forward_time=0.055, loss_ctc=3.294, loss=3.294, backward_time=0.009, grad_norm=68.193, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.419 +[stan] 2024-01-17 02:13:02,554 (trainer:762) INFO: 11epoch:train:761-800batch: iter_time=4.104e-05, forward_time=0.051, loss_ctc=2.865, loss=2.865, backward_time=0.009, grad_norm=67.014, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 02:13:06,796 (trainer:357) INFO: 11epoch results: [train] iter_time=1.776e-04, forward_time=0.052, loss_ctc=3.048, loss=3.048, backward_time=0.009, grad_norm=68.315, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.51 seconds, total_count=8800, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=31.089, cer_ctc=0.176, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=31.089, time=1.08 seconds, total_count=165, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.09 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:13:07,834 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:13:07,835 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/10epoch.pth +[stan] 2024-01-17 02:13:07,835 (trainer:291) INFO: 12/30epoch started. Estimated time to finish: 26 minutes and 51.26 seconds +[stan] 2024-01-17 02:13:11,863 (trainer:762) INFO: 12epoch:train:1-40batch: iter_time=0.002, forward_time=0.050, loss_ctc=3.022, loss=3.022, backward_time=0.009, grad_norm=63.467, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-17 02:13:16,048 (trainer:762) INFO: 12epoch:train:41-80batch: iter_time=4.419e-05, forward_time=0.055, loss_ctc=3.331, loss=3.331, backward_time=0.009, grad_norm=70.210, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.418 +[stan] 2024-01-17 02:13:19,768 (trainer:762) INFO: 12epoch:train:81-120batch: iter_time=4.530e-05, forward_time=0.049, loss_ctc=2.623, loss=2.623, backward_time=0.008, grad_norm=59.661, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.372 +[stan] 2024-01-17 02:13:23,464 (trainer:762) INFO: 12epoch:train:121-160batch: iter_time=4.467e-05, forward_time=0.048, loss_ctc=2.581, loss=2.581, backward_time=0.008, grad_norm=60.927, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.369 +[stan] 2024-01-17 02:13:27,721 (trainer:762) INFO: 12epoch:train:161-200batch: iter_time=4.444e-05, forward_time=0.056, loss_ctc=3.660, loss=3.660, backward_time=0.009, grad_norm=74.047, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-17 02:13:31,886 (trainer:762) INFO: 12epoch:train:201-240batch: iter_time=4.383e-05, forward_time=0.054, loss_ctc=3.289, loss=3.289, backward_time=0.009, grad_norm=65.082, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-17 02:13:35,772 (trainer:762) INFO: 12epoch:train:241-280batch: iter_time=4.351e-05, forward_time=0.051, loss_ctc=2.820, loss=2.820, backward_time=0.009, grad_norm=62.410, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-17 02:13:39,696 (trainer:762) INFO: 12epoch:train:281-320batch: iter_time=4.342e-05, forward_time=0.051, loss_ctc=2.889, loss=2.889, backward_time=0.008, grad_norm=67.452, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 02:13:43,656 (trainer:762) INFO: 12epoch:train:321-360batch: iter_time=4.425e-05, forward_time=0.052, loss_ctc=3.163, loss=3.163, backward_time=0.009, grad_norm=71.473, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-17 02:13:47,529 (trainer:762) INFO: 12epoch:train:361-400batch: iter_time=4.671e-05, forward_time=0.051, loss_ctc=2.721, loss=2.721, backward_time=0.008, grad_norm=64.513, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-17 02:13:51,548 (trainer:762) INFO: 12epoch:train:401-440batch: iter_time=4.694e-05, forward_time=0.053, loss_ctc=3.328, loss=3.328, backward_time=0.009, grad_norm=68.102, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-17 02:13:55,041 (trainer:762) INFO: 12epoch:train:441-480batch: iter_time=4.294e-05, forward_time=0.046, loss_ctc=2.252, loss=2.252, backward_time=0.008, grad_norm=58.832, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.349 +[stan] 2024-01-17 02:13:59,378 (trainer:762) INFO: 12epoch:train:481-520batch: iter_time=4.474e-05, forward_time=0.057, loss_ctc=3.382, loss=3.382, backward_time=0.009, grad_norm=65.980, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.434 +[stan] 2024-01-17 02:14:03,523 (trainer:762) INFO: 12epoch:train:521-560batch: iter_time=4.326e-05, forward_time=0.054, loss_ctc=3.336, loss=3.336, backward_time=0.009, grad_norm=72.617, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.414 +[stan] 2024-01-17 02:14:07,340 (trainer:762) INFO: 12epoch:train:561-600batch: iter_time=4.803e-05, forward_time=0.050, loss_ctc=2.580, loss=2.580, backward_time=0.008, grad_norm=61.151, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.382 +[stan] 2024-01-17 02:14:11,246 (trainer:762) INFO: 12epoch:train:601-640batch: iter_time=4.505e-05, forward_time=0.051, loss_ctc=3.031, loss=3.031, backward_time=0.009, grad_norm=69.349, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.390 +[stan] 2024-01-17 02:14:15,425 (trainer:762) INFO: 12epoch:train:641-680batch: iter_time=4.399e-05, forward_time=0.055, loss_ctc=3.241, loss=3.241, backward_time=0.009, grad_norm=65.540, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.418 +[stan] 2024-01-17 02:14:18,994 (trainer:762) INFO: 12epoch:train:681-720batch: iter_time=4.369e-05, forward_time=0.047, loss_ctc=2.096, loss=2.096, backward_time=0.008, grad_norm=60.796, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.357 +[stan] 2024-01-17 02:14:22,994 (trainer:762) INFO: 12epoch:train:721-760batch: iter_time=4.508e-05, forward_time=0.052, loss_ctc=3.047, loss=3.047, backward_time=0.009, grad_norm=64.050, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-17 02:14:27,282 (trainer:762) INFO: 12epoch:train:761-800batch: iter_time=4.183e-05, forward_time=0.056, loss_ctc=3.349, loss=3.349, backward_time=0.009, grad_norm=68.234, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.429 +[stan] 2024-01-17 02:14:31,461 (trainer:357) INFO: 12epoch results: [train] iter_time=1.609e-04, forward_time=0.052, loss_ctc=2.987, loss=2.987, backward_time=0.009, grad_norm=65.695, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.51 seconds, total_count=9600, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=31.667, cer_ctc=0.171, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=31.667, time=1.07 seconds, total_count=180, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:14:32,427 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:14:32,428 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/11epoch.pth +[stan] 2024-01-17 02:14:32,428 (trainer:291) INFO: 13/30epoch started. Estimated time to finish: 25 minutes and 26.14 seconds +[stan] 2024-01-17 02:14:36,628 (trainer:762) INFO: 13epoch:train:1-40batch: iter_time=0.003, forward_time=0.052, loss_ctc=2.802, loss=2.802, backward_time=0.009, grad_norm=59.756, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.420 +[stan] 2024-01-17 02:14:40,386 (trainer:762) INFO: 13epoch:train:41-80batch: iter_time=4.523e-05, forward_time=0.049, loss_ctc=2.663, loss=2.663, backward_time=0.008, grad_norm=64.231, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.376 +[stan] 2024-01-17 02:14:44,176 (trainer:762) INFO: 13epoch:train:81-120batch: iter_time=4.507e-05, forward_time=0.050, loss_ctc=2.794, loss=2.794, backward_time=0.008, grad_norm=61.517, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-17 02:14:48,511 (trainer:762) INFO: 13epoch:train:121-160batch: iter_time=4.739e-05, forward_time=0.057, loss_ctc=3.270, loss=3.270, backward_time=0.010, grad_norm=69.263, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-17 02:14:52,134 (trainer:762) INFO: 13epoch:train:161-200batch: iter_time=4.319e-05, forward_time=0.048, loss_ctc=2.412, loss=2.412, backward_time=0.008, grad_norm=59.801, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.362 +[stan] 2024-01-17 02:14:56,242 (trainer:762) INFO: 13epoch:train:201-240batch: iter_time=4.395e-05, forward_time=0.054, loss_ctc=3.113, loss=3.113, backward_time=0.009, grad_norm=71.118, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.411 +[stan] 2024-01-17 02:15:00,363 (trainer:762) INFO: 13epoch:train:241-280batch: iter_time=4.445e-05, forward_time=0.054, loss_ctc=2.915, loss=2.915, backward_time=0.009, grad_norm=64.222, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.412 +[stan] 2024-01-17 02:15:04,166 (trainer:762) INFO: 13epoch:train:281-320batch: iter_time=4.668e-05, forward_time=0.050, loss_ctc=2.692, loss=2.692, backward_time=0.008, grad_norm=62.273, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.380 +[stan] 2024-01-17 02:15:08,219 (trainer:762) INFO: 13epoch:train:321-360batch: iter_time=4.718e-05, forward_time=0.053, loss_ctc=2.942, loss=2.942, backward_time=0.009, grad_norm=63.367, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.405 +[stan] 2024-01-17 02:15:12,200 (trainer:762) INFO: 13epoch:train:361-400batch: iter_time=4.477e-05, forward_time=0.052, loss_ctc=2.909, loss=2.909, backward_time=0.009, grad_norm=63.559, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-17 02:15:16,186 (trainer:762) INFO: 13epoch:train:401-440batch: iter_time=4.572e-05, forward_time=0.052, loss_ctc=2.791, loss=2.791, backward_time=0.009, grad_norm=62.403, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399 +[stan] 2024-01-17 02:15:20,059 (trainer:762) INFO: 13epoch:train:441-480batch: iter_time=4.545e-05, forward_time=0.051, loss_ctc=2.734, loss=2.734, backward_time=0.008, grad_norm=62.223, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-17 02:15:23,727 (trainer:762) INFO: 13epoch:train:481-520batch: iter_time=4.354e-05, forward_time=0.048, loss_ctc=2.471, loss=2.471, backward_time=0.008, grad_norm=58.784, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.367 +[stan] 2024-01-17 02:15:27,811 (trainer:762) INFO: 13epoch:train:521-560batch: iter_time=4.799e-05, forward_time=0.053, loss_ctc=3.009, loss=3.009, backward_time=0.009, grad_norm=61.397, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-17 02:15:31,970 (trainer:762) INFO: 13epoch:train:561-600batch: iter_time=4.801e-05, forward_time=0.054, loss_ctc=3.122, loss=3.122, backward_time=0.009, grad_norm=64.597, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-17 02:15:35,980 (trainer:762) INFO: 13epoch:train:601-640batch: iter_time=4.615e-05, forward_time=0.052, loss_ctc=2.899, loss=2.899, backward_time=0.009, grad_norm=64.744, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-17 02:15:39,840 (trainer:762) INFO: 13epoch:train:641-680batch: iter_time=4.454e-05, forward_time=0.051, loss_ctc=2.657, loss=2.657, backward_time=0.009, grad_norm=58.590, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.386 +[stan] 2024-01-17 02:15:43,860 (trainer:762) INFO: 13epoch:train:681-720batch: iter_time=4.494e-05, forward_time=0.053, loss_ctc=2.818, loss=2.818, backward_time=0.009, grad_norm=62.458, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-17 02:15:47,494 (trainer:762) INFO: 13epoch:train:721-760batch: iter_time=4.760e-05, forward_time=0.048, loss_ctc=2.401, loss=2.401, backward_time=0.008, grad_norm=56.743, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.363 +[stan] 2024-01-17 02:15:51,850 (trainer:762) INFO: 13epoch:train:761-800batch: iter_time=4.466e-05, forward_time=0.057, loss_ctc=3.256, loss=3.256, backward_time=0.009, grad_norm=66.280, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-17 02:15:56,055 (trainer:357) INFO: 13epoch results: [train] iter_time=1.756e-04, forward_time=0.052, loss_ctc=2.834, loss=2.834, backward_time=0.009, grad_norm=62.866, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.5 seconds, total_count=10400, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=32.968, cer_ctc=0.176, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=32.968, time=1.08 seconds, total_count=195, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:15:57,114 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:15:57,115 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/12epoch.pth +[stan] 2024-01-17 02:15:57,115 (trainer:291) INFO: 14/30epoch started. Estimated time to finish: 24 minutes and 1.23 seconds +[stan] 2024-01-17 02:16:01,183 (trainer:762) INFO: 14epoch:train:1-40batch: iter_time=0.002, forward_time=0.050, loss_ctc=2.720, loss=2.720, backward_time=0.009, grad_norm=60.855, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-17 02:16:05,265 (trainer:762) INFO: 14epoch:train:41-80batch: iter_time=4.634e-05, forward_time=0.053, loss_ctc=2.989, loss=2.989, backward_time=0.009, grad_norm=66.436, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-17 02:16:09,242 (trainer:762) INFO: 14epoch:train:81-120batch: iter_time=4.624e-05, forward_time=0.052, loss_ctc=2.654, loss=2.654, backward_time=0.009, grad_norm=61.531, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-17 02:16:13,035 (trainer:762) INFO: 14epoch:train:121-160batch: iter_time=4.351e-05, forward_time=0.050, loss_ctc=2.517, loss=2.517, backward_time=0.008, grad_norm=61.469, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-17 02:16:16,939 (trainer:762) INFO: 14epoch:train:161-200batch: iter_time=4.351e-05, forward_time=0.051, loss_ctc=2.504, loss=2.504, backward_time=0.009, grad_norm=58.437, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.390 +[stan] 2024-01-17 02:16:21,012 (trainer:762) INFO: 14epoch:train:201-240batch: iter_time=4.556e-05, forward_time=0.053, loss_ctc=2.988, loss=2.988, backward_time=0.009, grad_norm=64.882, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.407 +[stan] 2024-01-17 02:16:24,900 (trainer:762) INFO: 14epoch:train:241-280batch: iter_time=4.649e-05, forward_time=0.051, loss_ctc=2.672, loss=2.672, backward_time=0.009, grad_norm=62.709, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.389 +[stan] 2024-01-17 02:16:29,012 (trainer:762) INFO: 14epoch:train:281-320batch: iter_time=4.398e-05, forward_time=0.054, loss_ctc=3.015, loss=3.015, backward_time=0.009, grad_norm=63.325, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.411 +[stan] 2024-01-17 02:16:32,859 (trainer:762) INFO: 14epoch:train:321-360batch: iter_time=4.418e-05, forward_time=0.050, loss_ctc=2.460, loss=2.460, backward_time=0.008, grad_norm=58.669, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-17 02:16:36,724 (trainer:762) INFO: 14epoch:train:361-400batch: iter_time=4.360e-05, forward_time=0.051, loss_ctc=2.590, loss=2.590, backward_time=0.008, grad_norm=56.630, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.386 +[stan] 2024-01-17 02:16:40,793 (trainer:762) INFO: 14epoch:train:401-440batch: iter_time=4.469e-05, forward_time=0.053, loss_ctc=3.057, loss=3.057, backward_time=0.009, grad_norm=66.292, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.407 +[stan] 2024-01-17 02:16:44,878 (trainer:762) INFO: 14epoch:train:441-480batch: iter_time=4.444e-05, forward_time=0.053, loss_ctc=3.180, loss=3.180, backward_time=0.009, grad_norm=66.697, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-17 02:16:48,685 (trainer:762) INFO: 14epoch:train:481-520batch: iter_time=4.617e-05, forward_time=0.050, loss_ctc=2.325, loss=2.325, backward_time=0.008, grad_norm=58.407, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.381 +[stan] 2024-01-17 02:16:52,638 (trainer:762) INFO: 14epoch:train:521-560batch: iter_time=4.489e-05, forward_time=0.052, loss_ctc=2.660, loss=2.660, backward_time=0.009, grad_norm=58.701, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-17 02:16:56,560 (trainer:762) INFO: 14epoch:train:561-600batch: iter_time=4.351e-05, forward_time=0.051, loss_ctc=2.711, loss=2.711, backward_time=0.009, grad_norm=61.423, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 02:17:00,343 (trainer:762) INFO: 14epoch:train:601-640batch: iter_time=4.452e-05, forward_time=0.050, loss_ctc=2.443, loss=2.443, backward_time=0.008, grad_norm=61.232, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-17 02:17:04,557 (trainer:762) INFO: 14epoch:train:641-680batch: iter_time=4.584e-05, forward_time=0.055, loss_ctc=3.164, loss=3.164, backward_time=0.009, grad_norm=66.446, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.421 +[stan] 2024-01-17 02:17:08,828 (trainer:762) INFO: 14epoch:train:681-720batch: iter_time=4.565e-05, forward_time=0.056, loss_ctc=3.377, loss=3.377, backward_time=0.009, grad_norm=66.595, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-17 02:17:12,587 (trainer:762) INFO: 14epoch:train:721-760batch: iter_time=4.825e-05, forward_time=0.049, loss_ctc=2.169, loss=2.169, backward_time=0.008, grad_norm=56.936, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.376 +[stan] 2024-01-17 02:17:16,235 (trainer:762) INFO: 14epoch:train:761-800batch: iter_time=4.117e-05, forward_time=0.048, loss_ctc=2.109, loss=2.109, backward_time=0.008, grad_norm=56.289, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.365 +[stan] 2024-01-17 02:17:20,427 (trainer:357) INFO: 14epoch results: [train] iter_time=1.555e-04, forward_time=0.052, loss_ctc=2.715, loss=2.715, backward_time=0.009, grad_norm=61.698, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395, time=1 minute and 19.19 seconds, total_count=11200, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=33.686, cer_ctc=0.171, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=33.686, time=1.07 seconds, total_count=210, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:17:21,411 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:17:21,412 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/13epoch.pth +[stan] 2024-01-17 02:17:21,412 (trainer:291) INFO: 15/30epoch started. Estimated time to finish: 22 minutes and 35.9 seconds +[stan] 2024-01-17 02:17:25,730 (trainer:762) INFO: 15epoch:train:1-40batch: iter_time=0.003, forward_time=0.053, loss_ctc=2.933, loss=2.933, backward_time=0.010, grad_norm=61.567, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-17 02:17:29,491 (trainer:762) INFO: 15epoch:train:41-80batch: iter_time=4.375e-05, forward_time=0.049, loss_ctc=2.317, loss=2.317, backward_time=0.008, grad_norm=62.153, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.376 +[stan] 2024-01-17 02:17:33,512 (trainer:762) INFO: 15epoch:train:81-120batch: iter_time=4.444e-05, forward_time=0.053, loss_ctc=2.753, loss=2.753, backward_time=0.008, grad_norm=59.831, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-17 02:17:37,820 (trainer:762) INFO: 15epoch:train:121-160batch: iter_time=4.880e-05, forward_time=0.056, loss_ctc=3.317, loss=3.317, backward_time=0.009, grad_norm=72.439, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-17 02:17:41,522 (trainer:762) INFO: 15epoch:train:161-200batch: iter_time=4.475e-05, forward_time=0.049, loss_ctc=2.465, loss=2.465, backward_time=0.008, grad_norm=61.825, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.370 +[stan] 2024-01-17 02:17:45,495 (trainer:762) INFO: 15epoch:train:201-240batch: iter_time=4.338e-05, forward_time=0.052, loss_ctc=2.581, loss=2.581, backward_time=0.009, grad_norm=59.692, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397 +[stan] 2024-01-17 02:17:49,438 (trainer:762) INFO: 15epoch:train:241-280batch: iter_time=4.425e-05, forward_time=0.052, loss_ctc=2.550, loss=2.550, backward_time=0.009, grad_norm=60.386, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-17 02:17:53,755 (trainer:762) INFO: 15epoch:train:281-320batch: iter_time=4.364e-05, forward_time=0.056, loss_ctc=3.130, loss=3.130, backward_time=0.009, grad_norm=68.711, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-17 02:17:57,718 (trainer:762) INFO: 15epoch:train:321-360batch: iter_time=4.704e-05, forward_time=0.052, loss_ctc=2.614, loss=2.614, backward_time=0.009, grad_norm=59.655, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-17 02:18:01,437 (trainer:762) INFO: 15epoch:train:361-400batch: iter_time=4.751e-05, forward_time=0.049, loss_ctc=2.255, loss=2.255, backward_time=0.008, grad_norm=60.519, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.372 +[stan] 2024-01-17 02:18:05,588 (trainer:762) INFO: 15epoch:train:401-440batch: iter_time=4.690e-05, forward_time=0.054, loss_ctc=2.809, loss=2.809, backward_time=0.009, grad_norm=64.306, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-17 02:18:09,330 (trainer:762) INFO: 15epoch:train:441-480batch: iter_time=4.507e-05, forward_time=0.049, loss_ctc=2.221, loss=2.221, backward_time=0.008, grad_norm=56.813, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.374 +[stan] 2024-01-17 02:18:13,607 (trainer:762) INFO: 15epoch:train:481-520batch: iter_time=4.656e-05, forward_time=0.056, loss_ctc=2.928, loss=2.928, backward_time=0.009, grad_norm=67.152, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.428 +[stan] 2024-01-17 02:18:17,239 (trainer:762) INFO: 15epoch:train:521-560batch: iter_time=4.494e-05, forward_time=0.048, loss_ctc=2.029, loss=2.029, backward_time=0.008, grad_norm=57.321, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.363 +[stan] 2024-01-17 02:18:21,255 (trainer:762) INFO: 15epoch:train:561-600batch: iter_time=4.747e-05, forward_time=0.053, loss_ctc=2.818, loss=2.818, backward_time=0.009, grad_norm=62.744, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-17 02:18:25,175 (trainer:762) INFO: 15epoch:train:601-640batch: iter_time=4.354e-05, forward_time=0.051, loss_ctc=2.497, loss=2.497, backward_time=0.008, grad_norm=63.618, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 02:18:28,998 (trainer:762) INFO: 15epoch:train:641-680batch: iter_time=4.278e-05, forward_time=0.050, loss_ctc=2.417, loss=2.417, backward_time=0.009, grad_norm=61.225, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.382 +[stan] 2024-01-17 02:18:33,170 (trainer:762) INFO: 15epoch:train:681-720batch: iter_time=4.680e-05, forward_time=0.055, loss_ctc=2.728, loss=2.728, backward_time=0.009, grad_norm=60.914, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-17 02:18:37,003 (trainer:762) INFO: 15epoch:train:721-760batch: iter_time=4.395e-05, forward_time=0.050, loss_ctc=2.282, loss=2.282, backward_time=0.009, grad_norm=55.029, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-17 02:18:40,950 (trainer:762) INFO: 15epoch:train:761-800batch: iter_time=4.253e-05, forward_time=0.052, loss_ctc=2.589, loss=2.589, backward_time=0.008, grad_norm=61.340, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-17 02:18:45,183 (trainer:357) INFO: 15epoch results: [train] iter_time=1.853e-04, forward_time=0.052, loss_ctc=2.611, loss=2.611, backward_time=0.009, grad_norm=61.862, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398, time=1 minute and 19.61 seconds, total_count=12000, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=33.976, cer_ctc=0.172, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=33.976, time=1.08 seconds, total_count=225, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.08 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:18:46,254 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:18:46,256 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/14epoch.pth +[stan] 2024-01-17 02:18:46,256 (trainer:291) INFO: 16/30epoch started. Estimated time to finish: 21 minutes and 11.26 seconds +[stan] 2024-01-17 02:18:50,432 (trainer:762) INFO: 16epoch:train:1-40batch: iter_time=0.003, forward_time=0.052, loss_ctc=2.350, loss=2.350, backward_time=0.008, grad_norm=59.825, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-17 02:18:54,356 (trainer:762) INFO: 16epoch:train:41-80batch: iter_time=4.482e-05, forward_time=0.051, loss_ctc=2.396, loss=2.396, backward_time=0.009, grad_norm=59.637, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 02:18:58,344 (trainer:762) INFO: 16epoch:train:81-120batch: iter_time=4.798e-05, forward_time=0.052, loss_ctc=2.577, loss=2.577, backward_time=0.009, grad_norm=61.422, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399 +[stan] 2024-01-17 02:19:02,275 (trainer:762) INFO: 16epoch:train:121-160batch: iter_time=4.673e-05, forward_time=0.051, loss_ctc=2.354, loss=2.354, backward_time=0.009, grad_norm=59.485, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-17 02:19:06,581 (trainer:762) INFO: 16epoch:train:161-200batch: iter_time=4.329e-05, forward_time=0.056, loss_ctc=3.210, loss=3.210, backward_time=0.009, grad_norm=66.285, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-17 02:19:10,275 (trainer:762) INFO: 16epoch:train:201-240batch: iter_time=4.379e-05, forward_time=0.048, loss_ctc=2.149, loss=2.149, backward_time=0.008, grad_norm=56.733, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.369 +[stan] 2024-01-17 02:19:13,868 (trainer:762) INFO: 16epoch:train:241-280batch: iter_time=4.502e-05, forward_time=0.047, loss_ctc=1.960, loss=1.960, backward_time=0.008, grad_norm=59.447, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.359 +[stan] 2024-01-17 02:19:18,017 (trainer:762) INFO: 16epoch:train:281-320batch: iter_time=4.400e-05, forward_time=0.054, loss_ctc=2.631, loss=2.631, backward_time=0.009, grad_norm=65.997, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-17 02:19:21,990 (trainer:762) INFO: 16epoch:train:321-360batch: iter_time=4.496e-05, forward_time=0.052, loss_ctc=2.559, loss=2.559, backward_time=0.009, grad_norm=63.401, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397 +[stan] 2024-01-17 02:19:26,048 (trainer:762) INFO: 16epoch:train:361-400batch: iter_time=4.359e-05, forward_time=0.053, loss_ctc=2.614, loss=2.614, backward_time=0.009, grad_norm=61.850, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-17 02:19:30,134 (trainer:762) INFO: 16epoch:train:401-440batch: iter_time=4.487e-05, forward_time=0.053, loss_ctc=2.800, loss=2.800, backward_time=0.009, grad_norm=64.072, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.409 +[stan] 2024-01-17 02:19:34,136 (trainer:762) INFO: 16epoch:train:441-480batch: iter_time=4.703e-05, forward_time=0.052, loss_ctc=2.504, loss=2.504, backward_time=0.009, grad_norm=57.858, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-17 02:19:37,953 (trainer:762) INFO: 16epoch:train:481-520batch: iter_time=4.407e-05, forward_time=0.050, loss_ctc=2.366, loss=2.366, backward_time=0.008, grad_norm=60.850, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.382 +[stan] 2024-01-17 02:19:41,928 (trainer:762) INFO: 16epoch:train:521-560batch: iter_time=4.297e-05, forward_time=0.052, loss_ctc=2.443, loss=2.443, backward_time=0.009, grad_norm=60.130, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397 +[stan] 2024-01-17 02:19:45,878 (trainer:762) INFO: 16epoch:train:561-600batch: iter_time=4.562e-05, forward_time=0.052, loss_ctc=2.545, loss=2.545, backward_time=0.009, grad_norm=56.409, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-17 02:19:49,711 (trainer:762) INFO: 16epoch:train:601-640batch: iter_time=4.694e-05, forward_time=0.050, loss_ctc=2.188, loss=2.188, backward_time=0.009, grad_norm=56.935, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-17 02:19:53,780 (trainer:762) INFO: 16epoch:train:641-680batch: iter_time=4.456e-05, forward_time=0.053, loss_ctc=2.501, loss=2.501, backward_time=0.009, grad_norm=58.566, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.407 +[stan] 2024-01-17 02:19:57,856 (trainer:762) INFO: 16epoch:train:681-720batch: iter_time=4.485e-05, forward_time=0.053, loss_ctc=2.659, loss=2.659, backward_time=0.009, grad_norm=64.426, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-17 02:20:01,822 (trainer:762) INFO: 16epoch:train:721-760batch: iter_time=4.424e-05, forward_time=0.052, loss_ctc=2.397, loss=2.397, backward_time=0.009, grad_norm=61.125, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-17 02:20:05,984 (trainer:762) INFO: 16epoch:train:761-800batch: iter_time=4.498e-05, forward_time=0.054, loss_ctc=2.719, loss=2.719, backward_time=0.009, grad_norm=60.931, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-17 02:20:10,157 (trainer:357) INFO: 16epoch results: [train] iter_time=1.704e-04, forward_time=0.052, loss_ctc=2.496, loss=2.496, backward_time=0.009, grad_norm=60.769, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399, time=1 minute and 19.8 seconds, total_count=12800, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=34.460, cer_ctc=0.168, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=34.460, time=1.06 seconds, total_count=240, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.03 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:20:11,155 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:20:11,157 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/15epoch.pth +[stan] 2024-01-17 02:20:11,157 (trainer:291) INFO: 17/30epoch started. Estimated time to finish: 19 minutes and 46.64 seconds +[stan] 2024-01-17 02:20:15,066 (trainer:762) INFO: 17epoch:train:1-40batch: iter_time=0.003, forward_time=0.048, loss_ctc=1.966, loss=1.966, backward_time=0.008, grad_norm=55.368, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-17 02:20:18,803 (trainer:762) INFO: 17epoch:train:41-80batch: iter_time=4.553e-05, forward_time=0.049, loss_ctc=2.235, loss=2.235, backward_time=0.008, grad_norm=56.803, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.374 +[stan] 2024-01-17 02:20:22,909 (trainer:762) INFO: 17epoch:train:81-120batch: iter_time=4.683e-05, forward_time=0.054, loss_ctc=2.559, loss=2.559, backward_time=0.009, grad_norm=63.070, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.411 +[stan] 2024-01-17 02:20:27,271 (trainer:762) INFO: 17epoch:train:121-160batch: iter_time=4.818e-05, forward_time=0.057, loss_ctc=2.932, loss=2.932, backward_time=0.009, grad_norm=63.829, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-17 02:20:30,910 (trainer:762) INFO: 17epoch:train:161-200batch: iter_time=4.631e-05, forward_time=0.048, loss_ctc=2.176, loss=2.176, backward_time=0.008, grad_norm=56.842, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.364 +[stan] 2024-01-17 02:20:35,072 (trainer:762) INFO: 17epoch:train:201-240batch: iter_time=4.698e-05, forward_time=0.054, loss_ctc=2.572, loss=2.572, backward_time=0.009, grad_norm=60.721, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-17 02:20:38,883 (trainer:762) INFO: 17epoch:train:241-280batch: iter_time=4.677e-05, forward_time=0.050, loss_ctc=2.239, loss=2.239, backward_time=0.008, grad_norm=60.207, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.381 +[stan] 2024-01-17 02:20:42,719 (trainer:762) INFO: 17epoch:train:281-320batch: iter_time=4.652e-05, forward_time=0.050, loss_ctc=2.179, loss=2.179, backward_time=0.008, grad_norm=60.972, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-17 02:20:46,869 (trainer:762) INFO: 17epoch:train:321-360batch: iter_time=4.461e-05, forward_time=0.054, loss_ctc=2.677, loss=2.677, backward_time=0.009, grad_norm=61.750, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-17 02:20:50,441 (trainer:762) INFO: 17epoch:train:361-400batch: iter_time=5.065e-05, forward_time=0.047, loss_ctc=1.925, loss=1.925, backward_time=0.008, grad_norm=55.875, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.357 +[stan] 2024-01-17 02:20:54,498 (trainer:762) INFO: 17epoch:train:401-440batch: iter_time=4.727e-05, forward_time=0.053, loss_ctc=2.373, loss=2.373, backward_time=0.009, grad_norm=59.292, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-17 02:20:58,521 (trainer:762) INFO: 17epoch:train:441-480batch: iter_time=4.946e-05, forward_time=0.053, loss_ctc=2.325, loss=2.325, backward_time=0.009, grad_norm=56.555, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-17 02:21:02,529 (trainer:762) INFO: 17epoch:train:481-520batch: iter_time=5.064e-05, forward_time=0.052, loss_ctc=2.520, loss=2.520, backward_time=0.009, grad_norm=61.278, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-17 02:21:06,423 (trainer:762) INFO: 17epoch:train:521-560batch: iter_time=4.510e-05, forward_time=0.051, loss_ctc=2.239, loss=2.239, backward_time=0.009, grad_norm=57.494, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.389 +[stan] 2024-01-17 02:21:10,533 (trainer:762) INFO: 17epoch:train:561-600batch: iter_time=4.800e-05, forward_time=0.054, loss_ctc=2.539, loss=2.539, backward_time=0.009, grad_norm=59.946, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.411 +[stan] 2024-01-17 02:21:14,308 (trainer:762) INFO: 17epoch:train:601-640batch: iter_time=4.314e-05, forward_time=0.049, loss_ctc=2.235, loss=2.235, backward_time=0.008, grad_norm=59.178, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-17 02:21:18,349 (trainer:762) INFO: 17epoch:train:641-680batch: iter_time=4.601e-05, forward_time=0.053, loss_ctc=2.351, loss=2.351, backward_time=0.008, grad_norm=63.807, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-17 02:21:22,184 (trainer:762) INFO: 17epoch:train:681-720batch: iter_time=4.861e-05, forward_time=0.051, loss_ctc=2.005, loss=2.005, backward_time=0.009, grad_norm=57.629, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-17 02:21:26,033 (trainer:762) INFO: 17epoch:train:721-760batch: iter_time=4.293e-05, forward_time=0.050, loss_ctc=2.068, loss=2.068, backward_time=0.009, grad_norm=58.153, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-17 02:21:30,184 (trainer:762) INFO: 17epoch:train:761-800batch: iter_time=4.433e-05, forward_time=0.054, loss_ctc=2.591, loss=2.591, backward_time=0.009, grad_norm=64.500, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-17 02:21:34,378 (trainer:357) INFO: 17epoch results: [train] iter_time=1.927e-04, forward_time=0.052, loss_ctc=2.335, loss=2.335, backward_time=0.009, grad_norm=59.664, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395, time=1 minute and 19.1 seconds, total_count=13600, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=35.667, cer_ctc=0.171, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=35.667, time=1.07 seconds, total_count=255, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:21:35,378 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:21:35,380 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/16epoch.pth +[stan] 2024-01-17 02:21:35,380 (trainer:291) INFO: 18/30epoch started. Estimated time to finish: 18 minutes and 21.47 seconds +[stan] 2024-01-17 02:21:39,821 (trainer:762) INFO: 18epoch:train:1-40batch: iter_time=0.003, forward_time=0.055, loss_ctc=2.531, loss=2.531, backward_time=0.009, grad_norm=60.836, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.444 +[stan] 2024-01-17 02:21:43,745 (trainer:762) INFO: 18epoch:train:41-80batch: iter_time=5.082e-05, forward_time=0.051, loss_ctc=2.386, loss=2.386, backward_time=0.009, grad_norm=57.072, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 02:21:47,180 (trainer:762) INFO: 18epoch:train:81-120batch: iter_time=4.318e-05, forward_time=0.045, loss_ctc=1.618, loss=1.618, backward_time=0.008, grad_norm=51.248, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.343 +[stan] 2024-01-17 02:21:51,454 (trainer:762) INFO: 18epoch:train:121-160batch: iter_time=4.521e-05, forward_time=0.056, loss_ctc=2.624, loss=2.624, backward_time=0.009, grad_norm=62.946, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.427 +[stan] 2024-01-17 02:21:55,251 (trainer:762) INFO: 18epoch:train:161-200batch: iter_time=4.448e-05, forward_time=0.050, loss_ctc=2.087, loss=2.087, backward_time=0.009, grad_norm=55.857, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.380 +[stan] 2024-01-17 02:21:59,431 (trainer:762) INFO: 18epoch:train:201-240batch: iter_time=4.572e-05, forward_time=0.055, loss_ctc=2.416, loss=2.416, backward_time=0.009, grad_norm=56.916, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.418 +[stan] 2024-01-17 02:22:03,578 (trainer:762) INFO: 18epoch:train:241-280batch: iter_time=4.495e-05, forward_time=0.054, loss_ctc=2.614, loss=2.614, backward_time=0.009, grad_norm=60.530, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-17 02:22:07,432 (trainer:762) INFO: 18epoch:train:281-320batch: iter_time=4.409e-05, forward_time=0.050, loss_ctc=2.199, loss=2.199, backward_time=0.008, grad_norm=57.627, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-17 02:22:11,148 (trainer:762) INFO: 18epoch:train:321-360batch: iter_time=4.475e-05, forward_time=0.049, loss_ctc=2.077, loss=2.077, backward_time=0.008, grad_norm=54.480, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.372 +[stan] 2024-01-17 02:22:15,310 (trainer:762) INFO: 18epoch:train:361-400batch: iter_time=4.489e-05, forward_time=0.054, loss_ctc=2.667, loss=2.667, backward_time=0.009, grad_norm=62.137, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-17 02:22:19,240 (trainer:762) INFO: 18epoch:train:401-440batch: iter_time=4.422e-05, forward_time=0.051, loss_ctc=2.334, loss=2.334, backward_time=0.008, grad_norm=57.649, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-17 02:22:23,185 (trainer:762) INFO: 18epoch:train:441-480batch: iter_time=4.595e-05, forward_time=0.052, loss_ctc=2.263, loss=2.263, backward_time=0.009, grad_norm=62.211, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-17 02:22:26,979 (trainer:762) INFO: 18epoch:train:481-520batch: iter_time=4.425e-05, forward_time=0.050, loss_ctc=2.067, loss=2.067, backward_time=0.009, grad_norm=58.451, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-17 02:22:31,003 (trainer:762) INFO: 18epoch:train:521-560batch: iter_time=4.543e-05, forward_time=0.053, loss_ctc=2.501, loss=2.501, backward_time=0.009, grad_norm=61.906, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-17 02:22:34,952 (trainer:762) INFO: 18epoch:train:561-600batch: iter_time=4.439e-05, forward_time=0.052, loss_ctc=2.168, loss=2.168, backward_time=0.008, grad_norm=58.993, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-17 02:22:38,776 (trainer:762) INFO: 18epoch:train:601-640batch: iter_time=4.530e-05, forward_time=0.050, loss_ctc=2.250, loss=2.250, backward_time=0.009, grad_norm=54.601, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.382 +[stan] 2024-01-17 02:22:42,851 (trainer:762) INFO: 18epoch:train:641-680batch: iter_time=4.444e-05, forward_time=0.053, loss_ctc=2.520, loss=2.520, backward_time=0.008, grad_norm=62.738, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.407 +[stan] 2024-01-17 02:22:46,508 (trainer:762) INFO: 18epoch:train:681-720batch: iter_time=4.456e-05, forward_time=0.048, loss_ctc=1.861, loss=1.861, backward_time=0.008, grad_norm=51.695, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.366 +[stan] 2024-01-17 02:22:50,707 (trainer:762) INFO: 18epoch:train:721-760batch: iter_time=4.582e-05, forward_time=0.055, loss_ctc=2.686, loss=2.686, backward_time=0.009, grad_norm=60.318, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.420 +[stan] 2024-01-17 02:22:54,555 (trainer:762) INFO: 18epoch:train:761-800batch: iter_time=4.193e-05, forward_time=0.050, loss_ctc=2.174, loss=2.174, backward_time=0.008, grad_norm=54.925, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-17 02:22:58,760 (trainer:357) INFO: 18epoch results: [train] iter_time=1.844e-04, forward_time=0.052, loss_ctc=2.302, loss=2.302, backward_time=0.009, grad_norm=58.157, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396, time=1 minute and 19.24 seconds, total_count=14400, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=35.684, cer_ctc=0.168, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=35.684, time=1.08 seconds, total_count=270, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:22:59,860 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:22:59,861 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/17epoch.pth +[stan] 2024-01-17 02:22:59,861 (trainer:291) INFO: 19/30epoch started. Estimated time to finish: 16 minutes and 56.57 seconds +[stan] 2024-01-17 02:23:04,167 (trainer:762) INFO: 19epoch:train:1-40batch: iter_time=0.003, forward_time=0.053, loss_ctc=2.149, loss=2.149, backward_time=0.009, grad_norm=55.894, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-17 02:23:08,193 (trainer:762) INFO: 19epoch:train:41-80batch: iter_time=4.541e-05, forward_time=0.053, loss_ctc=2.315, loss=2.315, backward_time=0.009, grad_norm=58.966, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-17 02:23:12,355 (trainer:762) INFO: 19epoch:train:81-120batch: iter_time=4.318e-05, forward_time=0.054, loss_ctc=2.547, loss=2.547, backward_time=0.009, grad_norm=60.185, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-17 02:23:16,165 (trainer:762) INFO: 19epoch:train:121-160batch: iter_time=4.532e-05, forward_time=0.050, loss_ctc=2.038, loss=2.038, backward_time=0.008, grad_norm=58.168, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.381 +[stan] 2024-01-17 02:23:20,078 (trainer:762) INFO: 19epoch:train:161-200batch: iter_time=4.199e-05, forward_time=0.051, loss_ctc=1.933, loss=1.933, backward_time=0.008, grad_norm=54.987, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-17 02:23:24,162 (trainer:762) INFO: 19epoch:train:201-240batch: iter_time=4.577e-05, forward_time=0.053, loss_ctc=2.407, loss=2.407, backward_time=0.009, grad_norm=58.552, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-17 02:23:28,131 (trainer:762) INFO: 19epoch:train:241-280batch: iter_time=4.673e-05, forward_time=0.052, loss_ctc=2.266, loss=2.266, backward_time=0.009, grad_norm=61.368, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397 +[stan] 2024-01-17 02:23:31,896 (trainer:762) INFO: 19epoch:train:281-320batch: iter_time=4.322e-05, forward_time=0.049, loss_ctc=1.974, loss=1.974, backward_time=0.008, grad_norm=56.857, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.376 +[stan] 2024-01-17 02:23:35,700 (trainer:762) INFO: 19epoch:train:321-360batch: iter_time=4.328e-05, forward_time=0.050, loss_ctc=2.156, loss=2.156, backward_time=0.008, grad_norm=58.331, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.380 +[stan] 2024-01-17 02:23:39,830 (trainer:762) INFO: 19epoch:train:361-400batch: iter_time=4.441e-05, forward_time=0.054, loss_ctc=2.516, loss=2.516, backward_time=0.009, grad_norm=56.206, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.413 +[stan] 2024-01-17 02:23:43,984 (trainer:762) INFO: 19epoch:train:401-440batch: iter_time=4.645e-05, forward_time=0.054, loss_ctc=2.248, loss=2.248, backward_time=0.009, grad_norm=58.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-17 02:23:47,664 (trainer:762) INFO: 19epoch:train:441-480batch: iter_time=4.305e-05, forward_time=0.048, loss_ctc=2.028, loss=2.028, backward_time=0.008, grad_norm=54.361, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.368 +[stan] 2024-01-17 02:23:51,704 (trainer:762) INFO: 19epoch:train:481-520batch: iter_time=4.540e-05, forward_time=0.053, loss_ctc=2.243, loss=2.243, backward_time=0.009, grad_norm=54.952, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-17 02:23:55,730 (trainer:762) INFO: 19epoch:train:521-560batch: iter_time=4.900e-05, forward_time=0.053, loss_ctc=2.193, loss=2.193, backward_time=0.009, grad_norm=57.490, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-17 02:23:59,310 (trainer:762) INFO: 19epoch:train:561-600batch: iter_time=4.546e-05, forward_time=0.047, loss_ctc=1.766, loss=1.766, backward_time=0.008, grad_norm=53.897, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.358 +[stan] 2024-01-17 02:24:03,481 (trainer:762) INFO: 19epoch:train:601-640batch: iter_time=4.398e-05, forward_time=0.055, loss_ctc=2.673, loss=2.673, backward_time=0.009, grad_norm=66.844, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-17 02:24:07,401 (trainer:762) INFO: 19epoch:train:641-680batch: iter_time=4.747e-05, forward_time=0.051, loss_ctc=2.168, loss=2.168, backward_time=0.009, grad_norm=58.178, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 02:24:11,440 (trainer:762) INFO: 19epoch:train:681-720batch: iter_time=4.610e-05, forward_time=0.053, loss_ctc=2.299, loss=2.299, backward_time=0.009, grad_norm=60.526, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-17 02:24:15,486 (trainer:762) INFO: 19epoch:train:721-760batch: iter_time=4.332e-05, forward_time=0.053, loss_ctc=2.437, loss=2.437, backward_time=0.009, grad_norm=58.117, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.405 +[stan] 2024-01-17 02:24:19,197 (trainer:762) INFO: 19epoch:train:761-800batch: iter_time=4.346e-05, forward_time=0.049, loss_ctc=1.970, loss=1.970, backward_time=0.008, grad_norm=57.405, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.371 +[stan] 2024-01-17 02:24:23,358 (trainer:357) INFO: 19epoch results: [train] iter_time=1.789e-04, forward_time=0.052, loss_ctc=2.216, loss=2.216, backward_time=0.009, grad_norm=57.970, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.4 seconds, total_count=15200, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=35.866, cer_ctc=0.166, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=35.866, time=1.07 seconds, total_count=285, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.03 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:24:24,388 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:24:24,389 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/18epoch.pth +[stan] 2024-01-17 02:24:24,389 (trainer:291) INFO: 20/30epoch started. Estimated time to finish: 15 minutes and 31.75 seconds +[stan] 2024-01-17 02:24:28,764 (trainer:762) INFO: 20epoch:train:1-40batch: iter_time=0.003, forward_time=0.054, loss_ctc=2.371, loss=2.371, backward_time=0.009, grad_norm=60.758, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.437 +[stan] 2024-01-17 02:24:32,754 (trainer:762) INFO: 20epoch:train:41-80batch: iter_time=4.451e-05, forward_time=0.052, loss_ctc=2.227, loss=2.227, backward_time=0.009, grad_norm=51.491, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399 +[stan] 2024-01-17 02:24:36,481 (trainer:762) INFO: 20epoch:train:81-120batch: iter_time=4.556e-05, forward_time=0.049, loss_ctc=2.010, loss=2.010, backward_time=0.008, grad_norm=54.944, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.373 +[stan] 2024-01-17 02:24:40,695 (trainer:762) INFO: 20epoch:train:121-160batch: iter_time=4.372e-05, forward_time=0.055, loss_ctc=2.648, loss=2.648, backward_time=0.009, grad_norm=62.294, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.421 +[stan] 2024-01-17 02:24:44,365 (trainer:762) INFO: 20epoch:train:161-200batch: iter_time=4.677e-05, forward_time=0.048, loss_ctc=1.756, loss=1.756, backward_time=0.008, grad_norm=50.910, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.367 +[stan] 2024-01-17 02:24:48,398 (trainer:762) INFO: 20epoch:train:201-240batch: iter_time=4.668e-05, forward_time=0.053, loss_ctc=2.344, loss=2.344, backward_time=0.009, grad_norm=58.172, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-17 02:24:52,504 (trainer:762) INFO: 20epoch:train:241-280batch: iter_time=4.525e-05, forward_time=0.054, loss_ctc=2.467, loss=2.467, backward_time=0.009, grad_norm=58.015, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.410 +[stan] 2024-01-17 02:24:56,296 (trainer:762) INFO: 20epoch:train:281-320batch: iter_time=4.338e-05, forward_time=0.050, loss_ctc=1.994, loss=1.994, backward_time=0.009, grad_norm=56.862, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-17 02:25:00,324 (trainer:762) INFO: 20epoch:train:321-360batch: iter_time=4.710e-05, forward_time=0.053, loss_ctc=2.086, loss=2.086, backward_time=0.009, grad_norm=58.918, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-17 02:25:04,084 (trainer:762) INFO: 20epoch:train:361-400batch: iter_time=4.407e-05, forward_time=0.049, loss_ctc=1.867, loss=1.867, backward_time=0.008, grad_norm=52.213, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.376 +[stan] 2024-01-17 02:25:08,153 (trainer:762) INFO: 20epoch:train:401-440batch: iter_time=4.829e-05, forward_time=0.053, loss_ctc=2.564, loss=2.564, backward_time=0.009, grad_norm=61.996, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.407 +[stan] 2024-01-17 02:25:12,029 (trainer:762) INFO: 20epoch:train:441-480batch: iter_time=4.671e-05, forward_time=0.051, loss_ctc=2.040, loss=2.040, backward_time=0.008, grad_norm=55.925, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-17 02:25:16,348 (trainer:762) INFO: 20epoch:train:481-520batch: iter_time=4.457e-05, forward_time=0.056, loss_ctc=2.449, loss=2.449, backward_time=0.009, grad_norm=58.237, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-17 02:25:20,196 (trainer:762) INFO: 20epoch:train:521-560batch: iter_time=4.626e-05, forward_time=0.050, loss_ctc=2.022, loss=2.022, backward_time=0.008, grad_norm=59.704, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-17 02:25:24,157 (trainer:762) INFO: 20epoch:train:561-600batch: iter_time=4.731e-05, forward_time=0.052, loss_ctc=2.312, loss=2.312, backward_time=0.009, grad_norm=56.296, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-17 02:25:28,068 (trainer:762) INFO: 20epoch:train:601-640batch: iter_time=4.410e-05, forward_time=0.051, loss_ctc=2.157, loss=2.157, backward_time=0.009, grad_norm=53.929, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-17 02:25:32,180 (trainer:762) INFO: 20epoch:train:641-680batch: iter_time=4.452e-05, forward_time=0.054, loss_ctc=2.304, loss=2.304, backward_time=0.009, grad_norm=58.189, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.411 +[stan] 2024-01-17 02:25:36,042 (trainer:762) INFO: 20epoch:train:681-720batch: iter_time=4.607e-05, forward_time=0.051, loss_ctc=1.922, loss=1.922, backward_time=0.008, grad_norm=50.942, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.386 +[stan] 2024-01-17 02:25:39,998 (trainer:762) INFO: 20epoch:train:721-760batch: iter_time=4.567e-05, forward_time=0.052, loss_ctc=2.271, loss=2.271, backward_time=0.009, grad_norm=55.312, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-17 02:25:44,250 (trainer:762) INFO: 20epoch:train:761-800batch: iter_time=4.412e-05, forward_time=0.056, loss_ctc=2.546, loss=2.546, backward_time=0.009, grad_norm=64.393, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-17 02:25:48,435 (trainer:357) INFO: 20epoch results: [train] iter_time=2.011e-04, forward_time=0.052, loss_ctc=2.217, loss=2.217, backward_time=0.009, grad_norm=56.975, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399, time=1 minute and 19.93 seconds, total_count=16000, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=36.456, cer_ctc=0.165, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=36.456, time=1.07 seconds, total_count=300, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.04 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:25:49,491 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:25:49,492 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/19epoch.pth +[stan] 2024-01-17 02:25:49,492 (trainer:291) INFO: 21/30epoch started. Estimated time to finish: 14 minutes and 7.25 seconds +[stan] 2024-01-17 02:25:53,418 (trainer:762) INFO: 21epoch:train:1-40batch: iter_time=0.002, forward_time=0.048, loss_ctc=1.864, loss=1.864, backward_time=0.008, grad_norm=54.343, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 02:25:57,349 (trainer:762) INFO: 21epoch:train:41-80batch: iter_time=4.360e-05, forward_time=0.051, loss_ctc=2.239, loss=2.239, backward_time=0.009, grad_norm=59.068, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-17 02:26:01,251 (trainer:762) INFO: 21epoch:train:81-120batch: iter_time=4.310e-05, forward_time=0.051, loss_ctc=2.016, loss=2.016, backward_time=0.009, grad_norm=55.862, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.390 +[stan] 2024-01-17 02:26:05,646 (trainer:762) INFO: 21epoch:train:121-160batch: iter_time=4.450e-05, forward_time=0.057, loss_ctc=2.847, loss=2.847, backward_time=0.010, grad_norm=65.476, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.439 +[stan] 2024-01-17 02:26:09,453 (trainer:762) INFO: 21epoch:train:161-200batch: iter_time=4.553e-05, forward_time=0.050, loss_ctc=1.893, loss=1.893, backward_time=0.009, grad_norm=52.175, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.381 +[stan] 2024-01-17 02:26:12,878 (trainer:762) INFO: 21epoch:train:201-240batch: iter_time=4.306e-05, forward_time=0.045, loss_ctc=1.277, loss=1.277, backward_time=0.007, grad_norm=47.270, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.342 +[stan] 2024-01-17 02:26:17,203 (trainer:762) INFO: 21epoch:train:241-280batch: iter_time=4.446e-05, forward_time=0.056, loss_ctc=2.668, loss=2.668, backward_time=0.009, grad_norm=59.990, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-17 02:26:21,301 (trainer:762) INFO: 21epoch:train:281-320batch: iter_time=4.497e-05, forward_time=0.054, loss_ctc=2.429, loss=2.429, backward_time=0.009, grad_norm=58.161, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.410 +[stan] 2024-01-17 02:26:25,183 (trainer:762) INFO: 21epoch:train:321-360batch: iter_time=4.822e-05, forward_time=0.051, loss_ctc=2.042, loss=2.042, backward_time=0.009, grad_norm=54.048, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-17 02:26:28,956 (trainer:762) INFO: 21epoch:train:361-400batch: iter_time=4.729e-05, forward_time=0.049, loss_ctc=1.797, loss=1.797, backward_time=0.008, grad_norm=56.137, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-17 02:26:33,088 (trainer:762) INFO: 21epoch:train:401-440batch: iter_time=4.411e-05, forward_time=0.054, loss_ctc=2.289, loss=2.289, backward_time=0.009, grad_norm=61.447, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.413 +[stan] 2024-01-17 02:26:36,752 (trainer:762) INFO: 21epoch:train:441-480batch: iter_time=4.400e-05, forward_time=0.048, loss_ctc=1.989, loss=1.989, backward_time=0.008, grad_norm=58.478, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.366 +[stan] 2024-01-17 02:26:40,733 (trainer:762) INFO: 21epoch:train:481-520batch: iter_time=4.519e-05, forward_time=0.052, loss_ctc=2.209, loss=2.209, backward_time=0.009, grad_norm=56.555, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-17 02:26:45,141 (trainer:762) INFO: 21epoch:train:521-560batch: iter_time=4.407e-05, forward_time=0.058, loss_ctc=2.739, loss=2.739, backward_time=0.009, grad_norm=67.602, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-17 02:26:48,792 (trainer:762) INFO: 21epoch:train:561-600batch: iter_time=4.378e-05, forward_time=0.048, loss_ctc=1.689, loss=1.689, backward_time=0.008, grad_norm=56.129, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.365 +[stan] 2024-01-17 02:26:52,967 (trainer:762) INFO: 21epoch:train:601-640batch: iter_time=4.620e-05, forward_time=0.055, loss_ctc=2.353, loss=2.353, backward_time=0.009, grad_norm=56.532, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-17 02:26:57,004 (trainer:762) INFO: 21epoch:train:641-680batch: iter_time=4.522e-05, forward_time=0.053, loss_ctc=2.253, loss=2.253, backward_time=0.009, grad_norm=61.017, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-17 02:27:00,701 (trainer:762) INFO: 21epoch:train:681-720batch: iter_time=4.374e-05, forward_time=0.049, loss_ctc=1.790, loss=1.790, backward_time=0.008, grad_norm=52.242, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.370 +[stan] 2024-01-17 02:27:04,353 (trainer:762) INFO: 21epoch:train:721-760batch: iter_time=4.462e-05, forward_time=0.048, loss_ctc=1.615, loss=1.615, backward_time=0.008, grad_norm=50.172, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.365 +[stan] 2024-01-17 02:27:08,552 (trainer:762) INFO: 21epoch:train:761-800batch: iter_time=4.332e-05, forward_time=0.055, loss_ctc=2.105, loss=2.105, backward_time=0.009, grad_norm=56.006, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.420 +[stan] 2024-01-17 02:27:12,704 (trainer:357) INFO: 21epoch results: [train] iter_time=1.674e-04, forward_time=0.052, loss_ctc=2.105, loss=2.105, backward_time=0.009, grad_norm=56.936, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395, time=1 minute and 19.13 seconds, total_count=16800, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=36.701, cer_ctc=0.168, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=36.701, time=1.07 seconds, total_count=315, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.01 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:27:13,691 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:27:13,692 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/20epoch.pth +[stan] 2024-01-17 02:27:13,692 (trainer:291) INFO: 22/30epoch started. Estimated time to finish: 12 minutes and 42.3 seconds +[stan] 2024-01-17 02:27:17,779 (trainer:762) INFO: 22epoch:train:1-40batch: iter_time=0.003, forward_time=0.050, loss_ctc=1.790, loss=1.790, backward_time=0.009, grad_norm=58.022, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-17 02:27:21,856 (trainer:762) INFO: 22epoch:train:41-80batch: iter_time=4.531e-05, forward_time=0.053, loss_ctc=2.350, loss=2.350, backward_time=0.009, grad_norm=56.756, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-17 02:27:25,868 (trainer:762) INFO: 22epoch:train:81-120batch: iter_time=4.742e-05, forward_time=0.052, loss_ctc=2.131, loss=2.131, backward_time=0.009, grad_norm=60.653, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-17 02:27:29,601 (trainer:762) INFO: 22epoch:train:121-160batch: iter_time=4.812e-05, forward_time=0.049, loss_ctc=1.668, loss=1.668, backward_time=0.008, grad_norm=51.814, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.373 +[stan] 2024-01-17 02:27:33,748 (trainer:762) INFO: 22epoch:train:161-200batch: iter_time=4.426e-05, forward_time=0.054, loss_ctc=2.330, loss=2.330, backward_time=0.009, grad_norm=58.913, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-17 02:27:37,599 (trainer:762) INFO: 22epoch:train:201-240batch: iter_time=4.430e-05, forward_time=0.050, loss_ctc=1.947, loss=1.947, backward_time=0.008, grad_norm=56.660, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-17 02:27:41,748 (trainer:762) INFO: 22epoch:train:241-280batch: iter_time=4.731e-05, forward_time=0.054, loss_ctc=2.269, loss=2.269, backward_time=0.009, grad_norm=58.686, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-17 02:27:45,303 (trainer:762) INFO: 22epoch:train:281-320batch: iter_time=4.438e-05, forward_time=0.047, loss_ctc=1.650, loss=1.650, backward_time=0.008, grad_norm=54.674, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.355 +[stan] 2024-01-17 02:27:49,319 (trainer:762) INFO: 22epoch:train:321-360batch: iter_time=4.796e-05, forward_time=0.053, loss_ctc=2.234, loss=2.234, backward_time=0.009, grad_norm=59.497, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-17 02:27:53,287 (trainer:762) INFO: 22epoch:train:361-400batch: iter_time=4.523e-05, forward_time=0.052, loss_ctc=1.985, loss=1.985, backward_time=0.008, grad_norm=53.902, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397 +[stan] 2024-01-17 02:27:57,454 (trainer:762) INFO: 22epoch:train:401-440batch: iter_time=4.583e-05, forward_time=0.054, loss_ctc=2.443, loss=2.443, backward_time=0.009, grad_norm=61.371, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-17 02:28:01,515 (trainer:762) INFO: 22epoch:train:441-480batch: iter_time=4.730e-05, forward_time=0.053, loss_ctc=2.106, loss=2.106, backward_time=0.009, grad_norm=53.323, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-17 02:28:05,358 (trainer:762) INFO: 22epoch:train:481-520batch: iter_time=4.466e-05, forward_time=0.050, loss_ctc=1.879, loss=1.879, backward_time=0.008, grad_norm=55.602, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.384 +[stan] 2024-01-17 02:28:09,316 (trainer:762) INFO: 22epoch:train:521-560batch: iter_time=4.683e-05, forward_time=0.052, loss_ctc=2.092, loss=2.092, backward_time=0.009, grad_norm=58.151, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-17 02:28:13,098 (trainer:762) INFO: 22epoch:train:561-600batch: iter_time=4.439e-05, forward_time=0.050, loss_ctc=1.634, loss=1.634, backward_time=0.008, grad_norm=49.460, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-17 02:28:17,404 (trainer:762) INFO: 22epoch:train:601-640batch: iter_time=4.618e-05, forward_time=0.056, loss_ctc=2.453, loss=2.453, backward_time=0.009, grad_norm=62.707, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-17 02:28:21,123 (trainer:762) INFO: 22epoch:train:641-680batch: iter_time=4.366e-05, forward_time=0.049, loss_ctc=1.692, loss=1.692, backward_time=0.008, grad_norm=55.499, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.372 +[stan] 2024-01-17 02:28:25,255 (trainer:762) INFO: 22epoch:train:681-720batch: iter_time=4.609e-05, forward_time=0.054, loss_ctc=2.080, loss=2.080, backward_time=0.009, grad_norm=56.221, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.413 +[stan] 2024-01-17 02:28:29,081 (trainer:762) INFO: 22epoch:train:721-760batch: iter_time=4.442e-05, forward_time=0.050, loss_ctc=1.988, loss=1.988, backward_time=0.008, grad_norm=56.061, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-17 02:28:32,868 (trainer:762) INFO: 22epoch:train:761-800batch: iter_time=4.317e-05, forward_time=0.050, loss_ctc=1.688, loss=1.688, backward_time=0.008, grad_norm=51.333, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-17 02:28:37,077 (trainer:357) INFO: 22epoch results: [train] iter_time=1.858e-04, forward_time=0.052, loss_ctc=2.021, loss=2.021, backward_time=0.009, grad_norm=56.465, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396, time=1 minute and 19.25 seconds, total_count=17600, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=36.547, cer_ctc=0.168, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=36.547, time=1.08 seconds, total_count=330, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:28:38,161 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:28:38,163 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/21epoch.pth +[stan] 2024-01-17 02:28:38,163 (trainer:291) INFO: 23/30epoch started. Estimated time to finish: 11 minutes and 17.51 seconds +[stan] 2024-01-17 02:28:42,521 (trainer:762) INFO: 23epoch:train:1-40batch: iter_time=0.003, forward_time=0.054, loss_ctc=2.236, loss=2.236, backward_time=0.009, grad_norm=57.843, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.435 +[stan] 2024-01-17 02:28:46,115 (trainer:762) INFO: 23epoch:train:41-80batch: iter_time=4.593e-05, forward_time=0.047, loss_ctc=1.559, loss=1.559, backward_time=0.008, grad_norm=53.214, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.359 +[stan] 2024-01-17 02:28:50,285 (trainer:762) INFO: 23epoch:train:81-120batch: iter_time=4.406e-05, forward_time=0.054, loss_ctc=2.244, loss=2.244, backward_time=0.009, grad_norm=60.307, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-17 02:28:54,537 (trainer:762) INFO: 23epoch:train:121-160batch: iter_time=4.416e-05, forward_time=0.055, loss_ctc=2.375, loss=2.375, backward_time=0.009, grad_norm=56.125, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-17 02:28:58,305 (trainer:762) INFO: 23epoch:train:161-200batch: iter_time=4.420e-05, forward_time=0.049, loss_ctc=1.782, loss=1.782, backward_time=0.008, grad_norm=52.168, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-17 02:29:02,410 (trainer:762) INFO: 23epoch:train:201-240batch: iter_time=4.387e-05, forward_time=0.054, loss_ctc=2.204, loss=2.204, backward_time=0.009, grad_norm=58.229, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.410 +[stan] 2024-01-17 02:29:06,480 (trainer:762) INFO: 23epoch:train:241-280batch: iter_time=4.730e-05, forward_time=0.053, loss_ctc=2.236, loss=2.236, backward_time=0.009, grad_norm=57.423, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.407 +[stan] 2024-01-17 02:29:10,384 (trainer:762) INFO: 23epoch:train:281-320batch: iter_time=4.750e-05, forward_time=0.051, loss_ctc=1.980, loss=1.980, backward_time=0.009, grad_norm=55.662, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.390 +[stan] 2024-01-17 02:29:14,297 (trainer:762) INFO: 23epoch:train:321-360batch: iter_time=4.384e-05, forward_time=0.051, loss_ctc=1.997, loss=1.997, backward_time=0.008, grad_norm=56.392, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-17 02:29:18,192 (trainer:762) INFO: 23epoch:train:361-400batch: iter_time=4.464e-05, forward_time=0.051, loss_ctc=2.008, loss=2.008, backward_time=0.009, grad_norm=54.740, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.389 +[stan] 2024-01-17 02:29:22,158 (trainer:762) INFO: 23epoch:train:401-440batch: iter_time=4.343e-05, forward_time=0.052, loss_ctc=1.878, loss=1.878, backward_time=0.009, grad_norm=57.203, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397 +[stan] 2024-01-17 02:29:26,259 (trainer:762) INFO: 23epoch:train:441-480batch: iter_time=4.509e-05, forward_time=0.054, loss_ctc=1.946, loss=1.946, backward_time=0.009, grad_norm=55.444, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.410 +[stan] 2024-01-17 02:29:30,367 (trainer:762) INFO: 23epoch:train:481-520batch: iter_time=4.648e-05, forward_time=0.054, loss_ctc=2.420, loss=2.420, backward_time=0.009, grad_norm=58.718, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.411 +[stan] 2024-01-17 02:29:34,040 (trainer:762) INFO: 23epoch:train:521-560batch: iter_time=4.394e-05, forward_time=0.048, loss_ctc=1.477, loss=1.477, backward_time=0.008, grad_norm=48.196, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.367 +[stan] 2024-01-17 02:29:38,225 (trainer:762) INFO: 23epoch:train:561-600batch: iter_time=4.401e-05, forward_time=0.055, loss_ctc=2.323, loss=2.323, backward_time=0.009, grad_norm=55.699, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.418 +[stan] 2024-01-17 02:29:42,007 (trainer:762) INFO: 23epoch:train:601-640batch: iter_time=4.433e-05, forward_time=0.050, loss_ctc=1.864, loss=1.864, backward_time=0.008, grad_norm=51.460, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-17 02:29:46,166 (trainer:762) INFO: 23epoch:train:641-680batch: iter_time=4.727e-05, forward_time=0.054, loss_ctc=2.135, loss=2.135, backward_time=0.009, grad_norm=59.786, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.416 +[stan] 2024-01-17 02:29:49,821 (trainer:762) INFO: 23epoch:train:681-720batch: iter_time=4.719e-05, forward_time=0.048, loss_ctc=1.646, loss=1.646, backward_time=0.008, grad_norm=50.149, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.365 +[stan] 2024-01-17 02:29:53,845 (trainer:762) INFO: 23epoch:train:721-760batch: iter_time=4.702e-05, forward_time=0.053, loss_ctc=2.048, loss=2.048, backward_time=0.009, grad_norm=57.116, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-17 02:29:57,728 (trainer:762) INFO: 23epoch:train:761-800batch: iter_time=4.597e-05, forward_time=0.051, loss_ctc=2.013, loss=2.013, backward_time=0.009, grad_norm=58.714, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-17 02:30:01,899 (trainer:357) INFO: 23epoch results: [train] iter_time=1.699e-04, forward_time=0.052, loss_ctc=2.018, loss=2.018, backward_time=0.009, grad_norm=55.729, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398, time=1 minute and 19.64 seconds, total_count=18400, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=37.845, cer_ctc=0.171, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=37.845, time=1.07 seconds, total_count=345, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.03 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:30:02,905 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:30:02,906 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/22epoch.pth +[stan] 2024-01-17 02:30:02,906 (trainer:291) INFO: 24/30epoch started. Estimated time to finish: 9 minutes and 52.84 seconds +[stan] 2024-01-17 02:30:07,443 (trainer:762) INFO: 24epoch:train:1-40batch: iter_time=0.003, forward_time=0.056, loss_ctc=2.408, loss=2.408, backward_time=0.009, grad_norm=60.336, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.453 +[stan] 2024-01-17 02:30:11,166 (trainer:762) INFO: 24epoch:train:41-80batch: iter_time=4.633e-05, forward_time=0.049, loss_ctc=1.605, loss=1.605, backward_time=0.008, grad_norm=51.440, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.372 +[stan] 2024-01-17 02:30:15,114 (trainer:762) INFO: 24epoch:train:81-120batch: iter_time=4.492e-05, forward_time=0.052, loss_ctc=2.239, loss=2.239, backward_time=0.009, grad_norm=57.592, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-17 02:30:18,991 (trainer:762) INFO: 24epoch:train:121-160batch: iter_time=4.734e-05, forward_time=0.051, loss_ctc=1.727, loss=1.727, backward_time=0.008, grad_norm=54.110, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-17 02:30:22,865 (trainer:762) INFO: 24epoch:train:161-200batch: iter_time=4.495e-05, forward_time=0.051, loss_ctc=2.089, loss=2.089, backward_time=0.009, grad_norm=56.053, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-17 02:30:26,944 (trainer:762) INFO: 24epoch:train:201-240batch: iter_time=4.918e-05, forward_time=0.053, loss_ctc=2.129, loss=2.129, backward_time=0.009, grad_norm=57.707, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-17 02:30:30,866 (trainer:762) INFO: 24epoch:train:241-280batch: iter_time=4.565e-05, forward_time=0.051, loss_ctc=1.870, loss=1.870, backward_time=0.009, grad_norm=54.886, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 02:30:34,804 (trainer:762) INFO: 24epoch:train:281-320batch: iter_time=4.712e-05, forward_time=0.052, loss_ctc=1.900, loss=1.900, backward_time=0.009, grad_norm=54.109, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-17 02:30:38,849 (trainer:762) INFO: 24epoch:train:321-360batch: iter_time=4.430e-05, forward_time=0.053, loss_ctc=2.146, loss=2.146, backward_time=0.009, grad_norm=60.326, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-17 02:30:42,880 (trainer:762) INFO: 24epoch:train:361-400batch: iter_time=4.767e-05, forward_time=0.053, loss_ctc=2.124, loss=2.124, backward_time=0.009, grad_norm=56.764, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-17 02:30:46,923 (trainer:762) INFO: 24epoch:train:401-440batch: iter_time=4.601e-05, forward_time=0.053, loss_ctc=1.989, loss=1.989, backward_time=0.009, grad_norm=54.092, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-17 02:30:50,861 (trainer:762) INFO: 24epoch:train:441-480batch: iter_time=4.462e-05, forward_time=0.052, loss_ctc=1.839, loss=1.839, backward_time=0.008, grad_norm=55.538, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-17 02:30:54,688 (trainer:762) INFO: 24epoch:train:481-520batch: iter_time=4.656e-05, forward_time=0.050, loss_ctc=1.708, loss=1.708, backward_time=0.009, grad_norm=52.331, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-17 02:30:58,462 (trainer:762) INFO: 24epoch:train:521-560batch: iter_time=4.345e-05, forward_time=0.049, loss_ctc=1.910, loss=1.910, backward_time=0.009, grad_norm=59.343, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-17 02:31:02,726 (trainer:762) INFO: 24epoch:train:561-600batch: iter_time=4.582e-05, forward_time=0.057, loss_ctc=2.261, loss=2.261, backward_time=0.009, grad_norm=61.238, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-17 02:31:06,793 (trainer:762) INFO: 24epoch:train:601-640batch: iter_time=4.598e-05, forward_time=0.053, loss_ctc=1.883, loss=1.883, backward_time=0.009, grad_norm=54.557, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.407 +[stan] 2024-01-17 02:31:10,478 (trainer:762) INFO: 24epoch:train:641-680batch: iter_time=4.477e-05, forward_time=0.048, loss_ctc=1.662, loss=1.662, backward_time=0.008, grad_norm=53.173, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.368 +[stan] 2024-01-17 02:31:14,395 (trainer:762) INFO: 24epoch:train:681-720batch: iter_time=4.566e-05, forward_time=0.051, loss_ctc=1.629, loss=1.629, backward_time=0.009, grad_norm=50.349, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 02:31:18,384 (trainer:762) INFO: 24epoch:train:721-760batch: iter_time=4.434e-05, forward_time=0.052, loss_ctc=2.050, loss=2.050, backward_time=0.009, grad_norm=54.289, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399 +[stan] 2024-01-17 02:31:22,312 (trainer:762) INFO: 24epoch:train:761-800batch: iter_time=4.194e-05, forward_time=0.051, loss_ctc=1.882, loss=1.882, backward_time=0.009, grad_norm=56.568, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-17 02:31:26,408 (trainer:357) INFO: 24epoch results: [train] iter_time=1.899e-04, forward_time=0.052, loss_ctc=1.953, loss=1.953, backward_time=0.009, grad_norm=55.740, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.48 seconds, total_count=19200, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=37.842, cer_ctc=0.172, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=37.842, time=1.06 seconds, total_count=360, gpu_max_cached_mem_GB=6.670, [att_plot] time=2.96 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:31:27,508 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:31:27,509 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/23epoch.pth +[stan] 2024-01-17 02:31:27,509 (trainer:291) INFO: 25/30epoch started. Estimated time to finish: 8 minutes and 28.13 seconds +[stan] 2024-01-17 02:31:31,813 (trainer:762) INFO: 25epoch:train:1-40batch: iter_time=0.003, forward_time=0.053, loss_ctc=1.864, loss=1.864, backward_time=0.009, grad_norm=56.115, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-17 02:31:36,060 (trainer:762) INFO: 25epoch:train:41-80batch: iter_time=4.558e-05, forward_time=0.056, loss_ctc=2.224, loss=2.224, backward_time=0.009, grad_norm=55.946, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-17 02:31:39,810 (trainer:762) INFO: 25epoch:train:81-120batch: iter_time=4.752e-05, forward_time=0.049, loss_ctc=1.659, loss=1.659, backward_time=0.008, grad_norm=51.699, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.375 +[stan] 2024-01-17 02:31:43,794 (trainer:762) INFO: 25epoch:train:121-160batch: iter_time=4.303e-05, forward_time=0.052, loss_ctc=2.071, loss=2.071, backward_time=0.009, grad_norm=58.220, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-17 02:31:47,660 (trainer:762) INFO: 25epoch:train:161-200batch: iter_time=4.539e-05, forward_time=0.051, loss_ctc=1.588, loss=1.588, backward_time=0.008, grad_norm=53.978, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.387 +[stan] 2024-01-17 02:31:51,281 (trainer:762) INFO: 25epoch:train:201-240batch: iter_time=4.371e-05, forward_time=0.048, loss_ctc=1.747, loss=1.747, backward_time=0.008, grad_norm=53.227, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.362 +[stan] 2024-01-17 02:31:55,543 (trainer:762) INFO: 25epoch:train:241-280batch: iter_time=4.498e-05, forward_time=0.056, loss_ctc=2.232, loss=2.232, backward_time=0.009, grad_norm=57.421, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.426 +[stan] 2024-01-17 02:31:59,634 (trainer:762) INFO: 25epoch:train:281-320batch: iter_time=4.657e-05, forward_time=0.053, loss_ctc=2.176, loss=2.176, backward_time=0.009, grad_norm=57.697, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.409 +[stan] 2024-01-17 02:32:03,524 (trainer:762) INFO: 25epoch:train:321-360batch: iter_time=4.592e-05, forward_time=0.051, loss_ctc=1.864, loss=1.864, backward_time=0.009, grad_norm=52.802, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.389 +[stan] 2024-01-17 02:32:07,105 (trainer:762) INFO: 25epoch:train:361-400batch: iter_time=4.889e-05, forward_time=0.047, loss_ctc=1.573, loss=1.573, backward_time=0.008, grad_norm=51.233, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.358 +[stan] 2024-01-17 02:32:11,413 (trainer:762) INFO: 25epoch:train:401-440batch: iter_time=4.674e-05, forward_time=0.056, loss_ctc=2.217, loss=2.217, backward_time=0.009, grad_norm=57.643, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.431 +[stan] 2024-01-17 02:32:15,496 (trainer:762) INFO: 25epoch:train:441-480batch: iter_time=4.535e-05, forward_time=0.053, loss_ctc=1.913, loss=1.913, backward_time=0.009, grad_norm=55.282, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-17 02:32:19,501 (trainer:762) INFO: 25epoch:train:481-520batch: iter_time=4.371e-05, forward_time=0.052, loss_ctc=1.928, loss=1.928, backward_time=0.009, grad_norm=53.887, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-17 02:32:23,642 (trainer:762) INFO: 25epoch:train:521-560batch: iter_time=4.690e-05, forward_time=0.054, loss_ctc=2.186, loss=2.186, backward_time=0.009, grad_norm=53.823, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.414 +[stan] 2024-01-17 02:32:27,397 (trainer:762) INFO: 25epoch:train:561-600batch: iter_time=4.525e-05, forward_time=0.049, loss_ctc=1.504, loss=1.504, backward_time=0.008, grad_norm=48.616, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.375 +[stan] 2024-01-17 02:32:31,236 (trainer:762) INFO: 25epoch:train:601-640batch: iter_time=4.364e-05, forward_time=0.050, loss_ctc=1.804, loss=1.804, backward_time=0.008, grad_norm=51.923, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.384 +[stan] 2024-01-17 02:32:34,949 (trainer:762) INFO: 25epoch:train:641-680batch: iter_time=4.674e-05, forward_time=0.049, loss_ctc=1.609, loss=1.609, backward_time=0.008, grad_norm=49.179, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.371 +[stan] 2024-01-17 02:32:39,276 (trainer:762) INFO: 25epoch:train:681-720batch: iter_time=4.622e-05, forward_time=0.057, loss_ctc=2.255, loss=2.255, backward_time=0.009, grad_norm=57.799, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-17 02:32:43,068 (trainer:762) INFO: 25epoch:train:721-760batch: iter_time=4.648e-05, forward_time=0.050, loss_ctc=1.725, loss=1.725, backward_time=0.008, grad_norm=49.818, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.379 +[stan] 2024-01-17 02:32:47,053 (trainer:762) INFO: 25epoch:train:761-800batch: iter_time=4.308e-05, forward_time=0.052, loss_ctc=1.774, loss=1.774, backward_time=0.008, grad_norm=57.113, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398 +[stan] 2024-01-17 02:32:51,231 (trainer:357) INFO: 25epoch results: [train] iter_time=1.723e-04, forward_time=0.052, loss_ctc=1.896, loss=1.896, backward_time=0.009, grad_norm=54.171, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398, time=1 minute and 19.62 seconds, total_count=20000, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=38.268, cer_ctc=0.171, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=38.268, time=1.07 seconds, total_count=375, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.03 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:32:52,251 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:32:52,253 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/24epoch.pth +[stan] 2024-01-17 02:32:52,253 (trainer:291) INFO: 26/30epoch started. Estimated time to finish: 7 minutes and 3.45 seconds +[stan] 2024-01-17 02:32:56,768 (trainer:762) INFO: 26epoch:train:1-40batch: iter_time=0.004, forward_time=0.055, loss_ctc=2.128, loss=2.128, backward_time=0.009, grad_norm=57.902, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.451 +[stan] 2024-01-17 02:33:00,537 (trainer:762) INFO: 26epoch:train:41-80batch: iter_time=4.678e-05, forward_time=0.049, loss_ctc=1.632, loss=1.632, backward_time=0.008, grad_norm=53.623, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-17 02:33:04,485 (trainer:762) INFO: 26epoch:train:81-120batch: iter_time=4.585e-05, forward_time=0.052, loss_ctc=1.856, loss=1.856, backward_time=0.009, grad_norm=53.992, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-17 02:33:08,495 (trainer:762) INFO: 26epoch:train:121-160batch: iter_time=4.842e-05, forward_time=0.054, loss_ctc=1.783, loss=1.783, backward_time=0.009, grad_norm=50.534, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.401 +[stan] 2024-01-17 02:33:12,254 (trainer:762) INFO: 26epoch:train:161-200batch: iter_time=5.283e-05, forward_time=0.049, loss_ctc=1.644, loss=1.644, backward_time=0.008, grad_norm=51.102, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.376 +[stan] 2024-01-17 02:33:16,372 (trainer:762) INFO: 26epoch:train:201-240batch: iter_time=4.412e-05, forward_time=0.054, loss_ctc=2.112, loss=2.112, backward_time=0.009, grad_norm=59.744, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.412 +[stan] 2024-01-17 02:33:20,204 (trainer:762) INFO: 26epoch:train:241-280batch: iter_time=4.309e-05, forward_time=0.050, loss_ctc=1.709, loss=1.709, backward_time=0.009, grad_norm=53.734, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.383 +[stan] 2024-01-17 02:33:24,246 (trainer:762) INFO: 26epoch:train:281-320batch: iter_time=4.475e-05, forward_time=0.053, loss_ctc=2.073, loss=2.073, backward_time=0.009, grad_norm=59.585, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-17 02:33:28,374 (trainer:762) INFO: 26epoch:train:321-360batch: iter_time=4.516e-05, forward_time=0.054, loss_ctc=2.127, loss=2.127, backward_time=0.009, grad_norm=54.963, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.413 +[stan] 2024-01-17 02:33:32,228 (trainer:762) INFO: 26epoch:train:361-400batch: iter_time=4.563e-05, forward_time=0.050, loss_ctc=1.623, loss=1.623, backward_time=0.008, grad_norm=54.125, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-17 02:33:36,256 (trainer:762) INFO: 26epoch:train:401-440batch: iter_time=4.453e-05, forward_time=0.053, loss_ctc=1.946, loss=1.946, backward_time=0.009, grad_norm=56.181, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-17 02:33:40,071 (trainer:762) INFO: 26epoch:train:441-480batch: iter_time=4.374e-05, forward_time=0.050, loss_ctc=1.838, loss=1.838, backward_time=0.008, grad_norm=53.584, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.381 +[stan] 2024-01-17 02:33:44,141 (trainer:762) INFO: 26epoch:train:481-520batch: iter_time=4.892e-05, forward_time=0.053, loss_ctc=1.884, loss=1.884, backward_time=0.009, grad_norm=56.371, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.407 +[stan] 2024-01-17 02:33:48,139 (trainer:762) INFO: 26epoch:train:521-560batch: iter_time=4.628e-05, forward_time=0.052, loss_ctc=1.886, loss=1.886, backward_time=0.009, grad_norm=53.513, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-17 02:33:52,127 (trainer:762) INFO: 26epoch:train:561-600batch: iter_time=4.400e-05, forward_time=0.052, loss_ctc=2.073, loss=2.073, backward_time=0.009, grad_norm=57.580, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399 +[stan] 2024-01-17 02:33:55,854 (trainer:762) INFO: 26epoch:train:601-640batch: iter_time=4.420e-05, forward_time=0.049, loss_ctc=1.480, loss=1.480, backward_time=0.008, grad_norm=50.735, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.373 +[stan] 2024-01-17 02:34:00,218 (trainer:762) INFO: 26epoch:train:641-680batch: iter_time=4.491e-05, forward_time=0.057, loss_ctc=2.209, loss=2.209, backward_time=0.009, grad_norm=57.822, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.436 +[stan] 2024-01-17 02:34:03,855 (trainer:762) INFO: 26epoch:train:681-720batch: iter_time=4.366e-05, forward_time=0.048, loss_ctc=1.511, loss=1.511, backward_time=0.008, grad_norm=49.932, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.364 +[stan] 2024-01-17 02:34:07,877 (trainer:762) INFO: 26epoch:train:721-760batch: iter_time=4.465e-05, forward_time=0.053, loss_ctc=1.939, loss=1.939, backward_time=0.009, grad_norm=57.461, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-17 02:34:11,805 (trainer:762) INFO: 26epoch:train:761-800batch: iter_time=4.183e-05, forward_time=0.051, loss_ctc=1.876, loss=1.876, backward_time=0.009, grad_norm=55.787, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-17 02:34:15,989 (trainer:357) INFO: 26epoch results: [train] iter_time=2.298e-04, forward_time=0.052, loss_ctc=1.866, loss=1.866, backward_time=0.009, grad_norm=54.914, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398, time=1 minute and 19.62 seconds, total_count=20800, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=38.942, cer_ctc=0.179, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=38.942, time=1.07 seconds, total_count=390, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:34:17,023 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:34:17,025 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/25epoch.pth +[stan] 2024-01-17 02:34:17,025 (trainer:291) INFO: 27/30epoch started. Estimated time to finish: 5 minutes and 38.77 seconds +[stan] 2024-01-17 02:34:21,354 (trainer:762) INFO: 27epoch:train:1-40batch: iter_time=0.003, forward_time=0.053, loss_ctc=1.948, loss=1.948, backward_time=0.009, grad_norm=56.082, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.433 +[stan] 2024-01-17 02:34:25,527 (trainer:762) INFO: 27epoch:train:41-80batch: iter_time=4.360e-05, forward_time=0.054, loss_ctc=2.236, loss=2.236, backward_time=0.009, grad_norm=55.252, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.417 +[stan] 2024-01-17 02:34:29,109 (trainer:762) INFO: 27epoch:train:81-120batch: iter_time=4.630e-05, forward_time=0.047, loss_ctc=1.337, loss=1.337, backward_time=0.008, grad_norm=46.224, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.358 +[stan] 2024-01-17 02:34:33,188 (trainer:762) INFO: 27epoch:train:121-160batch: iter_time=4.367e-05, forward_time=0.053, loss_ctc=1.708, loss=1.708, backward_time=0.009, grad_norm=53.543, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.408 +[stan] 2024-01-17 02:34:37,176 (trainer:762) INFO: 27epoch:train:161-200batch: iter_time=4.405e-05, forward_time=0.052, loss_ctc=2.014, loss=2.014, backward_time=0.009, grad_norm=52.193, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399 +[stan] 2024-01-17 02:34:40,689 (trainer:762) INFO: 27epoch:train:201-240batch: iter_time=4.427e-05, forward_time=0.046, loss_ctc=1.240, loss=1.240, backward_time=0.008, grad_norm=43.392, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.351 +[stan] 2024-01-17 02:34:45,171 (trainer:762) INFO: 27epoch:train:241-280batch: iter_time=4.412e-05, forward_time=0.058, loss_ctc=2.308, loss=2.308, backward_time=0.010, grad_norm=57.501, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.448 +[stan] 2024-01-17 02:34:49,050 (trainer:762) INFO: 27epoch:train:281-320batch: iter_time=4.361e-05, forward_time=0.051, loss_ctc=1.825, loss=1.825, backward_time=0.009, grad_norm=58.476, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-17 02:34:52,871 (trainer:762) INFO: 27epoch:train:321-360batch: iter_time=4.709e-05, forward_time=0.050, loss_ctc=1.648, loss=1.648, backward_time=0.009, grad_norm=52.039, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.382 +[stan] 2024-01-17 02:34:56,979 (trainer:762) INFO: 27epoch:train:361-400batch: iter_time=4.504e-05, forward_time=0.054, loss_ctc=1.893, loss=1.893, backward_time=0.009, grad_norm=50.768, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.411 +[stan] 2024-01-17 02:35:00,918 (trainer:762) INFO: 27epoch:train:401-440batch: iter_time=4.495e-05, forward_time=0.052, loss_ctc=1.731, loss=1.731, backward_time=0.009, grad_norm=53.504, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-17 02:35:04,644 (trainer:762) INFO: 27epoch:train:441-480batch: iter_time=4.467e-05, forward_time=0.049, loss_ctc=1.529, loss=1.529, backward_time=0.008, grad_norm=49.870, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.373 +[stan] 2024-01-17 02:35:08,897 (trainer:762) INFO: 27epoch:train:481-520batch: iter_time=4.578e-05, forward_time=0.056, loss_ctc=2.177, loss=2.177, backward_time=0.009, grad_norm=57.423, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.425 +[stan] 2024-01-17 02:35:12,922 (trainer:762) INFO: 27epoch:train:521-560batch: iter_time=4.697e-05, forward_time=0.053, loss_ctc=1.830, loss=1.830, backward_time=0.009, grad_norm=54.008, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-17 02:35:16,889 (trainer:762) INFO: 27epoch:train:561-600batch: iter_time=4.561e-05, forward_time=0.052, loss_ctc=1.720, loss=1.720, backward_time=0.008, grad_norm=50.438, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397 +[stan] 2024-01-17 02:35:20,432 (trainer:762) INFO: 27epoch:train:601-640batch: iter_time=4.718e-05, forward_time=0.047, loss_ctc=1.209, loss=1.209, backward_time=0.008, grad_norm=46.821, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.354 +[stan] 2024-01-17 02:35:24,812 (trainer:762) INFO: 27epoch:train:641-680batch: iter_time=4.432e-05, forward_time=0.057, loss_ctc=2.318, loss=2.318, backward_time=0.009, grad_norm=64.204, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.438 +[stan] 2024-01-17 02:35:29,051 (trainer:762) INFO: 27epoch:train:681-720batch: iter_time=4.494e-05, forward_time=0.055, loss_ctc=2.204, loss=2.204, backward_time=0.010, grad_norm=54.268, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-17 02:35:32,808 (trainer:762) INFO: 27epoch:train:721-760batch: iter_time=4.742e-05, forward_time=0.049, loss_ctc=1.398, loss=1.398, backward_time=0.008, grad_norm=47.469, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.376 +[stan] 2024-01-17 02:35:36,579 (trainer:762) INFO: 27epoch:train:761-800batch: iter_time=4.190e-05, forward_time=0.049, loss_ctc=1.651, loss=1.651, backward_time=0.009, grad_norm=52.465, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.377 +[stan] 2024-01-17 02:35:40,811 (trainer:357) INFO: 27epoch results: [train] iter_time=1.835e-04, forward_time=0.052, loss_ctc=1.796, loss=1.796, backward_time=0.009, grad_norm=52.797, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.398, time=1 minute and 19.63 seconds, total_count=21600, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=39.623, cer_ctc=0.172, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=39.623, time=1.09 seconds, total_count=405, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.07 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:35:41,948 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:35:41,950 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/26epoch.pth +[stan] 2024-01-17 02:35:41,950 (trainer:291) INFO: 28/30epoch started. Estimated time to finish: 4 minutes and 14.11 seconds +[stan] 2024-01-17 02:35:46,363 (trainer:762) INFO: 28epoch:train:1-40batch: iter_time=0.002, forward_time=0.055, loss_ctc=2.102, loss=2.102, backward_time=0.009, grad_norm=52.222, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.441 +[stan] 2024-01-17 02:35:49,913 (trainer:762) INFO: 28epoch:train:41-80batch: iter_time=4.457e-05, forward_time=0.047, loss_ctc=1.277, loss=1.277, backward_time=0.008, grad_norm=49.159, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.355 +[stan] 2024-01-17 02:35:53,458 (trainer:762) INFO: 28epoch:train:81-120batch: iter_time=4.282e-05, forward_time=0.047, loss_ctc=1.200, loss=1.200, backward_time=0.008, grad_norm=46.040, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.354 +[stan] 2024-01-17 02:35:57,921 (trainer:762) INFO: 28epoch:train:121-160batch: iter_time=4.509e-05, forward_time=0.058, loss_ctc=2.312, loss=2.312, backward_time=0.010, grad_norm=60.163, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.446 +[stan] 2024-01-17 02:36:01,855 (trainer:762) INFO: 28epoch:train:161-200batch: iter_time=4.591e-05, forward_time=0.052, loss_ctc=1.693, loss=1.693, backward_time=0.009, grad_norm=53.879, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-17 02:36:05,880 (trainer:762) INFO: 28epoch:train:201-240batch: iter_time=4.398e-05, forward_time=0.053, loss_ctc=1.844, loss=1.844, backward_time=0.009, grad_norm=53.064, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-17 02:36:10,120 (trainer:762) INFO: 28epoch:train:241-280batch: iter_time=4.913e-05, forward_time=0.055, loss_ctc=2.223, loss=2.223, backward_time=0.009, grad_norm=60.062, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-17 02:36:13,733 (trainer:762) INFO: 28epoch:train:281-320batch: iter_time=4.755e-05, forward_time=0.047, loss_ctc=1.435, loss=1.435, backward_time=0.008, grad_norm=49.483, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.361 +[stan] 2024-01-17 02:36:17,460 (trainer:762) INFO: 28epoch:train:321-360batch: iter_time=4.345e-05, forward_time=0.049, loss_ctc=1.515, loss=1.515, backward_time=0.008, grad_norm=50.942, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.373 +[stan] 2024-01-17 02:36:21,778 (trainer:762) INFO: 28epoch:train:361-400batch: iter_time=4.423e-05, forward_time=0.056, loss_ctc=2.208, loss=2.208, backward_time=0.010, grad_norm=58.900, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.432 +[stan] 2024-01-17 02:36:25,595 (trainer:762) INFO: 28epoch:train:401-440batch: iter_time=4.451e-05, forward_time=0.050, loss_ctc=1.494, loss=1.494, backward_time=0.008, grad_norm=49.468, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.382 +[stan] 2024-01-17 02:36:29,628 (trainer:762) INFO: 28epoch:train:441-480batch: iter_time=4.507e-05, forward_time=0.053, loss_ctc=1.741, loss=1.741, backward_time=0.009, grad_norm=53.633, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-17 02:36:33,483 (trainer:762) INFO: 28epoch:train:481-520batch: iter_time=4.485e-05, forward_time=0.050, loss_ctc=1.650, loss=1.650, backward_time=0.009, grad_norm=50.734, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-17 02:36:37,433 (trainer:762) INFO: 28epoch:train:521-560batch: iter_time=4.467e-05, forward_time=0.052, loss_ctc=1.807, loss=1.807, backward_time=0.008, grad_norm=54.845, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-17 02:36:41,425 (trainer:762) INFO: 28epoch:train:561-600batch: iter_time=4.494e-05, forward_time=0.052, loss_ctc=1.805, loss=1.805, backward_time=0.009, grad_norm=55.040, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399 +[stan] 2024-01-17 02:36:45,456 (trainer:762) INFO: 28epoch:train:601-640batch: iter_time=4.654e-05, forward_time=0.053, loss_ctc=1.784, loss=1.784, backward_time=0.009, grad_norm=52.992, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-17 02:36:49,394 (trainer:762) INFO: 28epoch:train:641-680batch: iter_time=4.639e-05, forward_time=0.051, loss_ctc=1.756, loss=1.756, backward_time=0.009, grad_norm=53.638, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-17 02:36:53,197 (trainer:762) INFO: 28epoch:train:681-720batch: iter_time=4.432e-05, forward_time=0.050, loss_ctc=1.681, loss=1.681, backward_time=0.009, grad_norm=54.895, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.380 +[stan] 2024-01-17 02:36:57,352 (trainer:762) INFO: 28epoch:train:721-760batch: iter_time=4.539e-05, forward_time=0.054, loss_ctc=1.744, loss=1.744, backward_time=0.009, grad_norm=52.193, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.415 +[stan] 2024-01-17 02:37:01,316 (trainer:762) INFO: 28epoch:train:761-800batch: iter_time=4.308e-05, forward_time=0.052, loss_ctc=1.690, loss=1.690, backward_time=0.009, grad_norm=52.334, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.396 +[stan] 2024-01-17 02:37:05,478 (trainer:357) INFO: 28epoch results: [train] iter_time=1.578e-04, forward_time=0.052, loss_ctc=1.748, loss=1.748, backward_time=0.009, grad_norm=53.184, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.44 seconds, total_count=22400, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=39.595, cer_ctc=0.169, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=39.595, time=1.07 seconds, total_count=420, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.02 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:37:06,518 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:37:06,519 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/27epoch.pth +[stan] 2024-01-17 02:37:06,519 (trainer:291) INFO: 29/30epoch started. Estimated time to finish: 2 minutes and 49.39 seconds +[stan] 2024-01-17 02:37:10,821 (trainer:762) INFO: 29epoch:train:1-40batch: iter_time=0.003, forward_time=0.053, loss_ctc=1.789, loss=1.789, backward_time=0.009, grad_norm=53.315, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.430 +[stan] 2024-01-17 02:37:14,873 (trainer:762) INFO: 29epoch:train:41-80batch: iter_time=4.486e-05, forward_time=0.053, loss_ctc=1.786, loss=1.786, backward_time=0.009, grad_norm=55.858, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.405 +[stan] 2024-01-17 02:37:18,625 (trainer:762) INFO: 29epoch:train:81-120batch: iter_time=4.929e-05, forward_time=0.049, loss_ctc=1.424, loss=1.424, backward_time=0.009, grad_norm=48.527, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.375 +[stan] 2024-01-17 02:37:22,700 (trainer:762) INFO: 29epoch:train:121-160batch: iter_time=4.597e-05, forward_time=0.053, loss_ctc=1.758, loss=1.758, backward_time=0.009, grad_norm=52.129, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.407 +[stan] 2024-01-17 02:37:26,645 (trainer:762) INFO: 29epoch:train:161-200batch: iter_time=4.357e-05, forward_time=0.052, loss_ctc=1.738, loss=1.738, backward_time=0.009, grad_norm=52.195, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-17 02:37:30,488 (trainer:762) INFO: 29epoch:train:201-240batch: iter_time=4.511e-05, forward_time=0.050, loss_ctc=1.766, loss=1.766, backward_time=0.009, grad_norm=51.889, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.384 +[stan] 2024-01-17 02:37:34,459 (trainer:762) INFO: 29epoch:train:241-280batch: iter_time=4.335e-05, forward_time=0.052, loss_ctc=1.630, loss=1.630, backward_time=0.008, grad_norm=50.146, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397 +[stan] 2024-01-17 02:37:38,372 (trainer:762) INFO: 29epoch:train:281-320batch: iter_time=4.759e-05, forward_time=0.051, loss_ctc=1.691, loss=1.691, backward_time=0.009, grad_norm=55.111, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-17 02:37:42,415 (trainer:762) INFO: 29epoch:train:321-360batch: iter_time=4.539e-05, forward_time=0.053, loss_ctc=2.065, loss=2.065, backward_time=0.009, grad_norm=56.916, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-17 02:37:46,152 (trainer:762) INFO: 29epoch:train:361-400batch: iter_time=4.420e-05, forward_time=0.049, loss_ctc=1.357, loss=1.357, backward_time=0.008, grad_norm=48.436, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.374 +[stan] 2024-01-17 02:37:50,288 (trainer:762) INFO: 29epoch:train:401-440batch: iter_time=4.737e-05, forward_time=0.054, loss_ctc=2.050, loss=2.050, backward_time=0.009, grad_norm=57.745, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.414 +[stan] 2024-01-17 02:37:54,533 (trainer:762) INFO: 29epoch:train:441-480batch: iter_time=4.738e-05, forward_time=0.055, loss_ctc=2.067, loss=2.067, backward_time=0.009, grad_norm=58.458, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-17 02:37:58,291 (trainer:762) INFO: 29epoch:train:481-520batch: iter_time=4.485e-05, forward_time=0.049, loss_ctc=1.448, loss=1.448, backward_time=0.008, grad_norm=50.489, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.376 +[stan] 2024-01-17 02:38:02,069 (trainer:762) INFO: 29epoch:train:521-560batch: iter_time=4.500e-05, forward_time=0.049, loss_ctc=1.577, loss=1.577, backward_time=0.009, grad_norm=51.122, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.378 +[stan] 2024-01-17 02:38:05,924 (trainer:762) INFO: 29epoch:train:561-600batch: iter_time=4.462e-05, forward_time=0.051, loss_ctc=1.610, loss=1.610, backward_time=0.008, grad_norm=51.846, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-17 02:38:09,971 (trainer:762) INFO: 29epoch:train:601-640batch: iter_time=4.426e-05, forward_time=0.053, loss_ctc=1.672, loss=1.672, backward_time=0.008, grad_norm=53.611, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.405 +[stan] 2024-01-17 02:38:13,999 (trainer:762) INFO: 29epoch:train:641-680batch: iter_time=4.608e-05, forward_time=0.053, loss_ctc=1.760, loss=1.760, backward_time=0.009, grad_norm=54.305, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-17 02:38:18,040 (trainer:762) INFO: 29epoch:train:681-720batch: iter_time=4.337e-05, forward_time=0.053, loss_ctc=1.794, loss=1.794, backward_time=0.009, grad_norm=50.252, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-17 02:38:22,060 (trainer:762) INFO: 29epoch:train:721-760batch: iter_time=4.723e-05, forward_time=0.053, loss_ctc=1.818, loss=1.818, backward_time=0.009, grad_norm=52.079, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.402 +[stan] 2024-01-17 02:38:26,011 (trainer:762) INFO: 29epoch:train:761-800batch: iter_time=4.295e-05, forward_time=0.052, loss_ctc=1.640, loss=1.640, backward_time=0.009, grad_norm=50.609, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-17 02:38:30,217 (trainer:357) INFO: 29epoch results: [train] iter_time=1.828e-04, forward_time=0.052, loss_ctc=1.722, loss=1.722, backward_time=0.009, grad_norm=52.752, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.56 seconds, total_count=23200, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=39.365, cer_ctc=0.167, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=39.365, time=1.08 seconds, total_count=435, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.05 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:38:31,311 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:38:31,313 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/28epoch.pth +[stan] 2024-01-17 02:38:31,313 (trainer:291) INFO: 30/30epoch started. Estimated time to finish: 1 minute and 24.7 seconds +[stan] 2024-01-17 02:38:35,555 (trainer:762) INFO: 30epoch:train:1-40batch: iter_time=0.003, forward_time=0.052, loss_ctc=1.796, loss=1.796, backward_time=0.009, grad_norm=51.382, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.424 +[stan] 2024-01-17 02:38:39,594 (trainer:762) INFO: 30epoch:train:41-80batch: iter_time=4.408e-05, forward_time=0.053, loss_ctc=1.712, loss=1.712, backward_time=0.009, grad_norm=52.210, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-17 02:38:43,665 (trainer:762) INFO: 30epoch:train:81-120batch: iter_time=4.430e-05, forward_time=0.053, loss_ctc=1.849, loss=1.849, backward_time=0.009, grad_norm=54.875, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.407 +[stan] 2024-01-17 02:38:47,267 (trainer:762) INFO: 30epoch:train:121-160batch: iter_time=4.661e-05, forward_time=0.047, loss_ctc=1.348, loss=1.348, backward_time=0.008, grad_norm=48.952, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.360 +[stan] 2024-01-17 02:38:51,323 (trainer:762) INFO: 30epoch:train:161-200batch: iter_time=4.647e-05, forward_time=0.053, loss_ctc=1.899, loss=1.899, backward_time=0.009, grad_norm=57.971, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.406 +[stan] 2024-01-17 02:38:55,278 (trainer:762) INFO: 30epoch:train:201-240batch: iter_time=4.300e-05, forward_time=0.052, loss_ctc=1.621, loss=1.621, backward_time=0.008, grad_norm=51.077, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.395 +[stan] 2024-01-17 02:38:59,201 (trainer:762) INFO: 30epoch:train:241-280batch: iter_time=4.379e-05, forward_time=0.051, loss_ctc=1.534, loss=1.534, backward_time=0.009, grad_norm=52.690, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.392 +[stan] 2024-01-17 02:39:03,229 (trainer:762) INFO: 30epoch:train:281-320batch: iter_time=4.471e-05, forward_time=0.053, loss_ctc=1.811, loss=1.811, backward_time=0.009, grad_norm=50.739, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.403 +[stan] 2024-01-17 02:39:07,081 (trainer:762) INFO: 30epoch:train:321-360batch: iter_time=4.689e-05, forward_time=0.050, loss_ctc=1.478, loss=1.478, backward_time=0.008, grad_norm=50.145, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.385 +[stan] 2024-01-17 02:39:11,126 (trainer:762) INFO: 30epoch:train:361-400batch: iter_time=4.752e-05, forward_time=0.053, loss_ctc=1.668, loss=1.668, backward_time=0.009, grad_norm=52.933, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.404 +[stan] 2024-01-17 02:39:15,035 (trainer:762) INFO: 30epoch:train:401-440batch: iter_time=5.176e-05, forward_time=0.051, loss_ctc=1.744, loss=1.744, backward_time=0.009, grad_norm=54.670, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.391 +[stan] 2024-01-17 02:39:18,978 (trainer:762) INFO: 30epoch:train:441-480batch: iter_time=4.358e-05, forward_time=0.052, loss_ctc=1.569, loss=1.569, backward_time=0.008, grad_norm=50.566, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.394 +[stan] 2024-01-17 02:39:22,855 (trainer:762) INFO: 30epoch:train:481-520batch: iter_time=4.349e-05, forward_time=0.051, loss_ctc=1.654, loss=1.654, backward_time=0.009, grad_norm=48.602, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.388 +[stan] 2024-01-17 02:39:27,048 (trainer:762) INFO: 30epoch:train:521-560batch: iter_time=4.519e-05, forward_time=0.055, loss_ctc=1.885, loss=1.885, backward_time=0.009, grad_norm=54.421, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.419 +[stan] 2024-01-17 02:39:31,243 (trainer:762) INFO: 30epoch:train:561-600batch: iter_time=4.578e-05, forward_time=0.055, loss_ctc=1.938, loss=1.938, backward_time=0.009, grad_norm=55.776, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.419 +[stan] 2024-01-17 02:39:35,059 (trainer:762) INFO: 30epoch:train:601-640batch: iter_time=4.365e-05, forward_time=0.050, loss_ctc=1.555, loss=1.555, backward_time=0.009, grad_norm=51.949, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.382 +[stan] 2024-01-17 02:39:38,988 (trainer:762) INFO: 30epoch:train:641-680batch: iter_time=4.451e-05, forward_time=0.052, loss_ctc=1.762, loss=1.762, backward_time=0.008, grad_norm=54.575, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.393 +[stan] 2024-01-17 02:39:42,983 (trainer:762) INFO: 30epoch:train:681-720batch: iter_time=4.441e-05, forward_time=0.052, loss_ctc=1.611, loss=1.611, backward_time=0.008, grad_norm=50.748, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.399 +[stan] 2024-01-17 02:39:46,683 (trainer:762) INFO: 30epoch:train:721-760batch: iter_time=4.318e-05, forward_time=0.048, loss_ctc=1.320, loss=1.320, backward_time=0.009, grad_norm=43.178, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.370 +[stan] 2024-01-17 02:39:50,688 (trainer:762) INFO: 30epoch:train:761-800batch: iter_time=4.102e-05, forward_time=0.052, loss_ctc=1.637, loss=1.637, backward_time=0.009, grad_norm=51.325, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.400 +[stan] 2024-01-17 02:39:54,853 (trainer:357) INFO: 30epoch results: [train] iter_time=2.023e-04, forward_time=0.052, loss_ctc=1.669, loss=1.669, backward_time=0.009, grad_norm=51.939, clip=100.000, loss_scale=1.000, optim_step_time=0.003, optim0_lr0=1.000e-04, train_time=0.397, time=1 minute and 19.45 seconds, total_count=24000, gpu_max_cached_mem_GB=6.670, [valid] loss_ctc=40.392, cer_ctc=0.166, loss_att=nan, acc=nan, cer=nan, wer=nan, loss=40.392, time=1.07 seconds, total_count=450, gpu_max_cached_mem_GB=6.670, [att_plot] time=3.03 seconds, total_count=0, gpu_max_cached_mem_GB=6.670 +[stan] 2024-01-17 02:39:55,858 (trainer:414) INFO: There are no improvements in this epoch +[stan] 2024-01-17 02:39:55,859 (trainer:470) INFO: The model files were removed: test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/29epoch.pth +[stan] 2024-01-17 02:39:55,859 (trainer:488) INFO: The training was finished at 30 epochs +[stan] 2024-01-17 02:39:55,876 (average_nbest_models:69) INFO: Averaging 5best models: criterion="valid.loss": test_pr/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave_5best.pth +# Accounting: time=2546 threads=1 +# Ended (code 0) at Wed Jan 17 02:39:56 CST 2024, elapsed time 2546 seconds diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth new file mode 120000 index 0000000000000000000000000000000000000000..9c3b80cbae0f484d2ec709e847c877ccb92e43c6 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave.pth @@ -0,0 +1 @@ +valid.loss.ave_5best.pth \ No newline at end of file diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave_5best.pth b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave_5best.pth new file mode 100644 index 0000000000000000000000000000000000000000..901fae535c9f200633164c2e33d22d4dbfdc4b80 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.ave_5best.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:857d058c6ed85f00d494fd00f4b76add9182f3ff697b02dfdc5317e7d1551956 +size 21133838 diff --git a/exp/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.best.pth b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.best.pth new file mode 120000 index 0000000000000000000000000000000000000000..decd4978c25819ef61804bd5e36c67c8f3e28fb2 --- /dev/null +++ b/exp/asr_train_asr_s3prl_houlsby_jpn_1h/valid.loss.best.pth @@ -0,0 +1 @@ +3epoch.pth \ No newline at end of file